diff --git a/.gitignore b/.gitignore
index 2521c2fc..d889394b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,16 @@
 *.pyc
-LocalEnv.py
\ No newline at end of file
+LocalEnv.py
+.DS_Store
+scripts/LabTestAnalysis/output*
+scripts/LabTestAnalysis/machine_learning/data*
+starr/data*
+stride/data*
+scripts/LabCulturePrediction/data*
+scripts/LabCulturePrediction/*.tab
+scripts/LabCulturePrediction/DecisionMakingClass/*.csv
+scripts/ER_Infection/data*
+scripts/ER_Infection/notebooks/mit_data_analysis/data*
+.idea*
+venv*
+*token.pickle
+.Rproj.user
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 00000000..94a9ed02
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+ Everyone is permitted to copy and distribute verbatim copies
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+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/LocalEnv.py.template b/LocalEnv.py.template
new file mode 100644
index 00000000..167266c1
--- /dev/null
+++ b/LocalEnv.py.template
@@ -0,0 +1,55 @@
+#!/usr/bin/python
+
+# This file serves as documentation to help users understand LocalEnv.py.
+#
+# LocalEnv.py can be created dynamically by setup.sh, but if you don't need
+# to do any of the other steps in setup.sh (e.g. installing libraries,
+# initializing DB) it may be easier to just make a copy of this file
+# and name it LocalEnv.py before editing the actual values.
+#
+# medinfo/db/Env.py imports these DB variables, so all DB connections will
+# fail if these two environment variables are not defined.
+#
+# Other variables which are likely to vary between dev environments should
+# be added to this template file, then be given default values via setup.sh.
+import os
+
+BOX_CLIENT_ID = "BOX_API_CLIENT_ID"
+BOX_CLIENT_SECRET = "BOX_API_CLIENT_SECRET"
+BOX_ACCESS_TOKEN = "BOX_API_ACCESS_TOKEN"
+BOX_STRIDE_FOLDER_ID = "BOX_STRIDE_FOLDER_ID"
+
+# Database connection type
+DATASET_SOURCE_NAME = 'STRIDE'
+DATABASE_CONNECTOR_NAME = "psycopg2"
+SQL_PLACEHOLDER = "%s"
+
+# Connection parameters to primary working database of interest
+LOCAL_PROD_DB_PARAM = {}
+LOCAL_PROD_DB_PARAM["HOST"] = 'localhost'   # Database host. Localhost if running on your local computer. For GCP, name of the project. For AWS RDS instances, look for the "Endpoint" hostname, e.g. YourDatabaseIdentifier.cwyfvxgvic6c.us-east-1.rds.amazonaws.com
+LOCAL_PROD_DB_PARAM["DSN"] = 'databaseName'    # Specific database/dataset name hosted by the database server (e.g., stride_inpatient_2008_2017)
+LOCAL_PROD_DB_PARAM["UID"] = 'databaseUserId'
+LOCAL_PROD_DB_PARAM["PWD"] = 'databaseUserPassword'
+
+# Connection parameters for a test database to create and manipulate during unit tests
+#	The DSN (database name) MUST NOT BE the same as any real working database.
+#	Many of the Python application unit tests' first and last steps
+#	will be to delete any database identified by these test identifiers.
+#	The database user (UID) and password (PWD) will need to be a valid 
+#	database user with permissions to create and delete such databases.
+LOCAL_TEST_DB_PARAM = {}
+LOCAL_TEST_DB_PARAM["HOST"] = 'localhost'
+LOCAL_TEST_DB_PARAM["DSN"] = 'unittest_db'
+LOCAL_TEST_DB_PARAM["UID"] = 'testuid'
+LOCAL_TEST_DB_PARAM["PWD"] = 'testpwd'
+
+PATH_TO_CDSS = "/foo/bar/CDSS"; # Directory where this file is contained in
+# Look for environment variable on authenticating to Google cloud server
+# If env var not already there, then manually set here.
+# See https://github.com/HealthRex/CDSS/wiki/Google-BigQuery-Access for more info
+#if "GOOGLE_APPLICATION_CREDENTIALS" not in os.environ:
+#    os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = "/foo/bar/google-cloud-platform-token.json";
+if "GOOGLE_CLOUD_PROJECT" not in os.environ:
+	os.environ["GOOGLE_CLOUD_PROJECT"] = LOCAL_PROD_DB_PARAM["HOST"];
+
+TEST_RUNNER_VERBOSITY = 2
diff --git a/README.md b/README.md
index 444cdfe1..47f8ea3b 100644
--- a/README.md
+++ b/README.md
@@ -1,100 +1,28 @@
 # HealthRex Laboratory at Stanford University
 ## PI: Jonathan Chen (http://web.stanford.edu/~jonc101)
 
-Shared workspace in development
+Review the Wiki (https://github.com/HealthRex/CDSS/wiki) for Starter Notes on using some of the common data sources and codebase as well as general lab/group infrastructure.
 
-General Guidelines for Code Repo:
-* Avoid any large data files, so the repo stays lightweight for new devs to quickly download/clone.
-* For one-off or very project specific files and scripts, basically do whatever you want in the workspace areas under the scripts directory (but again, avoid big data files).
-* Try to promote reusable components to the medinfo core application modules.
-
-
-
-====================================================
-
-Starter Notes:
-?This needs to be organized more, but a sketch of notes I usually email people initially.
-?Perhaps move this to Wiki
-?Similarly integrate CDSS/scripts/CDSS/setupNotes.txt into a high-level Readme starter
+Code is made freely available for academic use. For commercial use or other questions, email stanford [dot] healthrex [at] gmail [dot] com.
 
+See our group's publication list for relevant citations to reference. For example: Chen, J. H., Podchiyska, T. & Altman, R. B. Journal of the American Medical Informatics Association ocv091 (2016). doi:10.1093/jamia/ocv091
 
-General Data / Code Access
-	• Training / Agreements
-		○ Register on CITIProgram.org
-			§ Complete the module on "Biomedical Responsible Conduct of Research"
-			§ Send me a copy of the Completion Certificate
-
-(2B) HIPAA Training
-	1. 2B) HIPAA Training (required for almost all datasets. Because current CITI training does not offer an excellent data security module, we are requiring HIPAA training for datasets with or derived from health information or containing PII)
-		a. Go to your personal Axess page.
-		b. Log in with your SUNet and password.
-		c. Click on the STARS link (top).
-		d. Click on “Training Needs Assessment”
-		e. Only email your supervisor if you want to.
-		f. Indicate that you work on research projects.
-		g. Click on All Learning.
-		h. If needed, use the search tool and search for  HIPAA. Enroll in HIPAA/Protecting Patient Privacy (PRIV-0010).
-		i. Take the course.
-		j. Once the course(s) is complete, you should receive an email confirming your completion. It should also show up as completed if you click on the “Training History” bar.
-		k. Please save the screen shot as a PDF and name the file: firstinitiallastname_HIPAA_DDMMMYY.  So if Isabella Chu completed her classes July 3, 2016, ichu_HIPAA_03Jul16.
-
-
-		○ Complete the HCUP Data Use Agreement
-			• View Accessible HCUP DUA Training Course (Text Only) 
-				· At the bottom of this page, fill in your name to print out a completion certificate and send me a copy
-			• https://www.hcup-us.ahrq.gov/DUA/dua_508/DUA508version.jsp
-				· Fill out and complete the HCUP Data Use Agreement, then send a copy to me and the HCUPDistributor@AHRQ.gov
-		○ Health Data Protection / Hard Drive Encryption
-			• Any computers you use to work on health data should be encrypted. This is usually a transparent option that is easy to turn on in the background and is generally a good idea anyway in case your computer gets lost or stolen.
-			• Web link below should help you figure out if you’ve setup encryption properly. If you install Stanford’s BigFix program, it walks through most of the steps.
-			• https://med.stanford.edu/datasecurity/amie/
-			• I'd recommend working on a (laptop) computer with at least 16gb RAM and 512gb hard drive. I can help you get this if you do not already have it.
-
-	• Code:
-		○ Use Git or an SVN client to download the codebase
-			§ > git clone https://github.com/HealthRex/CDSS.git
-				or 
-			§ > svn checkout https://github.com/HealthRex/CDSS.git
-			§ If that works, it should download a bunch of Python 2.7 code to your computer
-	 
-	• Dev Environment:
-		○ Install Python PIP to facilitate other installations
-			§ If you installed Python 2.7.8, it might already be installed, otherwise there’s a get_pip.py bootstrapper installer on the internet)
-			§ Install some Python modules (e.g., scipy, psycopg2)
-				· > python -m pip install psycopg2
-		○ Install a local PostgreSQL database server instance
-		○ Create a blank test database with the schema definition in cpoeStats.sql.
-			§ > psql -f medinfo/db/definition/cpoeStats.sql -U <YourDBUsername> testdb
-		○ Create another blank application database, but restore it with sample data:
-			§ Box Data Directory: STRIDE-Inpatient-2008-2014/dump2009-2014-5year-time
-			§ Restore Script Driver: restoreCPOETables.sh
-				· This can take hours (overnight) to all finish patient_item and clinical_item_association tables. Make sure you start with a blank database and that the Indexes and Constraints don't yet exist, otherwise it will drag down the insert/population process
-		○ Point the application code to the databases:
-			§ Edit medinfo/db/Env.py file’s DB_PARAM to match the application database
-			§ Edit TEST_DB_PARAM settings to point to that blank database copy.
-		○ Run a basic application database unit test
-			§ > python medinfo/db/test/TestDBUtil.py
-			§  If that runs, you’re in good shape to have the Python application code interact with the SQL database
-
-	• Data Access
-		○ Box - Share data directories (Stanford Email)
-		○ Google - Work files share
-		○ OneNote - Notebook section share
-		○ XP-Dev - SVN Code
- 
-
+General Guidelines for Code Repo:
+* Avoid any large data files, so the repo stays lightweight for new devs to quickly download/clone.
+* For one-off or very project specific files and scripts, basically do whatever you want in the workspace areas under the /scripts directory (but again, avoid big data files and also avoid any private / patient information, including analysis results that include individual patient items, as this repo will publicly accessible).
+* Avoid creating new / separate repos for your work, unless meant to be a completely independent package. Instead create sub-folders within this common repository.
+* Promote reusable components and packages to the medinfo core application modules.
+
+
+Broad description of core application directories
+* medinfo/scripts/DevWorkshop - Assorted tutorials and screening programs to learn and practice core technical skills.
+* medinfo/analysis - General purpose analysis and data manipulation modules, not specific to any type of project. For example, serially calculating t-tests, list rank similarity measures, ROC plots, precision-recall curves, SQL-like manipulation functions for CSV / TSV files.
+* medinfo/common - General purpose computing utilities, such as calculating different 2x2 contingency stats, adding progress trackers to long processes.
+* medinfo/cpoe - More project specific applications related to Computerized Physician Order Entry projects, implementing different approaches to clinical order recommendations and evaluating/analyzing them with different experiments on historical data. Application code for clinical case simulations for users to interact with.
+* medinfo/dataconversion - General and project specific utilities to pre-process data sources. Given a dump of hospital data, conversion scripts to unify into a simplified / pre-processed clinical_item transaction series. FeatureMatrixFactory to extract out clinical data into simple "feature matrix" / dataframe form to feed into assorted learning algorithms. Subdirecties with additional supporting mapping data (e.g., ICD9 codes to Charlson comorbidity categories).
+* medinfo/db - Utilities to connect between Python code and SQL databases, with a relatively plain JSON-like model of tables represented by lists of dictionaries (name-value pairs of each row of data). ResultsFormatter has several convenience functions to interconvert between SQL data tables, CSV/TSV plain text files, Pandas dataframes, and JSON-like lists of Python dictionaries. Several project specific application database schemas in the definition subdirectory. Support subdirectory with "dump" and "restore" convenience scripts to move database content between systems.
+* medinfo/geography - Not much here yet. One example of how to generate data labeled geographic maps of the US.
+* medinfo/textanalysis - Not much here yet. One example of a project specific parsing script that translates a stream of text documents into an interactive HTML file that attempts to auto-annotate features of the documents based on Python coded annotator classes.
+* medinfo/web - View and Controller layer for web interface to application functions.
 
-	• Population Health Sciences Access
-	 
-	Here are detailed instructions for accessing PHS data. If your staff have completed their trainings and IRB, it should take about 10 minutes to complete these forms: https://tinyurl.com/PHS-Data-Access
-	 
-	In addition, all PHS Data Users are required to take the PHS Data Core Training and Quiz. This should take you 15 - 20 minutes (including quiz). Completion with 100% on the quiz will waive CITI and HIPAA training for most datasets. CITI and HIPAA take about 4 - 6 hours so we hope you view this as a good deal.
-	 
-	PHS Data Security Training: http://tinyurl.com/PHS-Data-Security-Training
-	 
-	For Macs open with Quicktime. For PCs, just click on the image and click the play arrow. It should start.
-	 
-	If you are already a PHS member and you do not have your personalized link, please request it from Isabella Chu at itaylor@stanford.edu.
-	 
-	If you need help, my office hours are Thursdays at SIEPR 334 and Fridays online (by appointment) from 10 AM to 11 AM or by appointment. You can sign up for office hours clicking on the PHS Office Hours Sign Up near the top of this page: https://med.stanford.edu/phs/phs-data-center.html or emailing me at itaylor@stanford.edu.
 	 
diff --git a/TestCDSS.py b/TestCDSS.py
index c6e493e0..bec1a201 100644
--- a/TestCDSS.py
+++ b/TestCDSS.py
@@ -1,24 +1,24 @@
+"""
+Look through directory tree for unit test cases to run.
+Comparable to command-line functionality:
+
+python -m unittest discover -v -s <directoryName> -p "Test*.py"
+"""
+
 import unittest
 import logging
 
-# Set logging level.
-log = logging.getLogger("CDSS")
+import os
 
-# TODO(sbala): Complete implementation by enabling differential logging levels.
-# Move logging level settings to the application logic, away from the TestCases.
-# To do this, call the application name from each of the end test cases as the
-# logger name. Then set the logging level in the actual __main__ function for
-# the relevant test suite.
-#
-# Check on each individual test module...
-# medinfo.cpoe
-# medinfo.dataconversion
+if __name__ == "__main__":
+    # Set logging level.
+    log = logging.getLogger("CDSS")
+    log.setLevel(logging.CRITICAL)
 
-# Load test suite.
-loader = unittest.TestLoader()
-suite = loader.discover('./medinfo/dataconversion', pattern="Test*.py")
-log.setLevel(logging.ERROR)
+    # Load test suite.
+    loader = unittest.TestLoader()
+    suite = loader.discover('.', pattern="Test*.py")
 
-# Run test suite.
-testRunner = unittest.runner.TextTestRunner(verbosity=2)
-testRunner.run(suite)
+    # Run test suite.
+    testRunner = unittest.runner.TextTestRunner(verbosity=1)
+    testRunner.run(suite)
diff --git a/medinfo/analysis/AccuracyPerTopItems.py b/medinfo/analysis/AccuracyPerTopItems.py
index 41e028e2..ba1cbb53 100644
--- a/medinfo/analysis/AccuracyPerTopItems.py
+++ b/medinfo/analysis/AccuracyPerTopItems.py
@@ -10,7 +10,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 import pylab;
 from medinfo.db.Model import columnFromModelList;
 from medinfo.common.Const import COMMENT_TAG, VALUE_DELIM;
@@ -18,10 +18,10 @@
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.Model import RowItemModel;
-from Util import log;
-from Const import OUTCOME_PRESENT, OUTCOME_ABSENT;
+from .Util import log;
+from .Const import OUTCOME_PRESENT, OUTCOME_ABSENT;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 AXIS_DELIM = ":";
 
@@ -30,7 +30,7 @@ def __init__(self):
         BaseAnalysis.__init__(self);
 
     def __call__(self, inputFile, colOutcome, metricsByScoreCol, maxItems):
-        scoreCols = metricsByScoreCol.keys();
+        scoreCols = list(metricsByScoreCol.keys());
         scoreModels = self.parseScoreModelsFromFile(inputFile, colOutcome, scoreCols);
         
         # Count up total number of items and positive outcomes
@@ -38,14 +38,14 @@ def __call__(self, inputFile, colOutcome, metricsByScoreCol, maxItems):
         nItems = len(scoreModels);
         
         # Prepare result dictionaries to populate
-        resultDicts = [{"ItemsConsidered": i+1} for i in xrange(maxItems)];
+        resultDicts = [{"ItemsConsidered": i+1} for i in range(maxItems)];
         
         for colScore in scoreCols:
             # Sort by each score column in descending order
             scoreModels.sort(key=lambda model: model[colScore], reverse=True);
             
             nPositiveFound = 0;
-            for i in xrange(maxItems):
+            for i in range(maxItems):
                 nConsidered = i+1;
                 scoreModel = scoreModels[i];
                 if scoreModel[colOutcome] == OUTCOME_PRESENT:
@@ -192,9 +192,9 @@ def main(self, argv):
             outputFile = stdOpen(outputFilename,"w");
             
             # Print comment line with arguments to allow for deconstruction later as well as extra results
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
             # Insert a header row
-            resultDicts.insert(0, RowItemModel(resultDicts[0].keys(),resultDicts[0].keys()) );
+            resultDicts.insert(0, RowItemModel(list(resultDicts[0].keys()),list(resultDicts[0].keys())) );
             
             formatter = TextResultsFormatter( outputFile );
             formatter.formatResultDicts( resultDicts );
diff --git a/medinfo/analysis/BaseAnalysis.py b/medinfo/analysis/BaseAnalysis.py
index f073e642..d1552719 100644
--- a/medinfo/analysis/BaseAnalysis.py
+++ b/medinfo/analysis/BaseAnalysis.py
@@ -6,7 +6,7 @@
 import sys, os
 import time;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from math import sqrt;
 from datetime import timedelta;
 
@@ -16,10 +16,10 @@
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel;
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
-from Util import log;
+from .Util import log;
 
-from Const import OUTCOME_ABSENT, OUTCOME_PRESENT, OUTCOME_IN_QUERY;
-from Const import NEGATIVE_OUTCOME_STRS;
+from .Const import OUTCOME_ABSENT, OUTCOME_PRESENT, OUTCOME_IN_QUERY;
+from .Const import NEGATIVE_OUTCOME_STRS;
 
 
 class BaseAnalysis:
diff --git a/medinfo/analysis/BatchTTests.py b/medinfo/analysis/BatchTTests.py
index abfa6abd..50d2ff63 100644
--- a/medinfo/analysis/BatchTTests.py
+++ b/medinfo/analysis/BatchTTests.py
@@ -7,20 +7,20 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
+from operator import itemgetter
 
 import numpy as np;
 from scipy.stats import ttest_rel, ttest_ind;
 
 from medinfo.db.Model import columnFromModelList;
-from medinfo.db.Model import RowItemFieldComparator;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.Model import RowItemModel;
-from Util import log;
+from .Util import log;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 # Summary stats to report (more for sanity checking)
 def percentile25(x):
@@ -82,9 +82,9 @@ def __call__(self, inputFile, labelCols, valueCols, matchCols, baseLabels=None):
 
         # Another pass to ensure data is consistently sorted in each group to allow later paired t-tests
         if self.matchCols:
-            for labelKey, data in dataByLabelKey.iteritems():
-                data.sort( RowItemFieldComparator(self.matchCols) );
-
+            for labelKey, data in dataByLabelKey.items():
+                # data.sort(key=cmp_to_key(RowItemFieldComparator(self.matchCols)))
+                data.sort(key=itemgetter(*self.matchCols))
         # See if looking for only one set of base labeled data to compare the rest against
         baseLabelKey = None;
         if self.baseLabels is not None:
@@ -92,14 +92,14 @@ def __call__(self, inputFile, labelCols, valueCols, matchCols, baseLabels=None):
 
         # Result pass to compare all group pair-wise combinations
         prog = ProgressDots();
-        for labelKey0, data0 in dataByLabelKey.iteritems():
+        for labelKey0, data0 in dataByLabelKey.items():
             prefix0 = "Group0.";
             labelModel0 = labelModelByLabelKey[labelKey0];
 
             if baseLabelKey is not None and labelKey0 != baseLabelKey:
                 continue;   # Skip entries where the base label does not match specified key
 
-            for labelKey1, data1 in dataByLabelKey.iteritems():
+            for labelKey1, data1 in dataByLabelKey.items():
                 prefix1 = "Group1.";
                 labelModel1 = labelModelByLabelKey[labelKey1];
 
@@ -181,7 +181,7 @@ def main(self, argv):
 
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            # print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             # Tab-delimited output formatting
             formatter = TextResultsFormatter(outputFile);
diff --git a/medinfo/analysis/CalibrationHistogram.py b/medinfo/analysis/CalibrationHistogram.py
new file mode 100644
index 00000000..48d14739
--- /dev/null
+++ b/medinfo/analysis/CalibrationHistogram.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+"""
+Translate results of OutcomePredictionAnalysis into data for a Calibration plot.
+Usually feed the data (Paired columns of scores and actual outcome labels)
+  into an ROC curve analysis program to assess for classifer discrimination.
+Calibration curve generally splits scored entries into deciles and compares how well
+  the predicted probability match up to the actual outcome frequencies.
+Instead of implementation that splits data into 10 equal sized groups,
+  instead break up into probability range from 0.0 to 1.0 with specified number
+  of equal sized bins in between
+
+"""
+
+import sys, os
+import time;
+from optparse import OptionParser
+from io import StringIO;
+import json;
+from scipy.stats import chi2;
+from medinfo.db.Model import columnFromModelList;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter;
+from medinfo.db.Model import RowItemModel;
+from .Util import log;
+
+from .BaseAnalysis import BaseAnalysis;
+from .CalibrationPlot import CalibrationPlot;
+
+MIN_VALUE = 0.0;
+MAX_VALUE = 1.0;
+
+class CalibrationHistogram(CalibrationPlot):
+    """Reuse entire structure of CalibrationPlot class, just change the data binning process"""
+
+    def __init__(self):
+        CalibrationPlot.__init__(self);
+
+    def binData(self, outcomes, scores, nBins):
+        """Expect lists of predicted score value and matching outcome values
+        encoded as 0 or +1 for negative and positive results.
+        Organizes data into nBins of equal numerical value, but possibly different quantities
+         and thencompare predicted vs. observed outcome rates
+        Returns a list of dictionaries, one for each data bin generated with key-values
+            corresponding to bin stats 
+            (e.g., scoreMin, scoreMax, 
+            totalInstances, observedOutcomes, predictedOutcomes, 
+            observedRate, predictedRate)
+        """
+        # Setup empty data first
+        results = list();
+        binSize = (MAX_VALUE - MIN_VALUE) / nBins;
+        for iBin in range(nBins):
+            binData = \
+                {   "scoreMin": MIN_VALUE + binSize * iBin,
+                    "scoreMax": MIN_VALUE + binSize * (iBin+1), 
+                    "totalInstances": 0,
+                    "observedOutcomes": 0.0, 
+                    "predictedOutcomes": 0.0, 
+                    "observedRate": None,
+                    "predictedRate": None,
+                };
+            results.append(binData);
+        
+        data = [(score,outcome) for (outcome, score) in zip(outcomes,scores)];  # Repackage into single list of 2-ples to facilitate sorting
+        nData = len(data);
+
+        # Iterate through data to accrue bin data
+        for (score, outcome) in data:
+            iBin = int( (score - MIN_VALUE) / binSize );    # Integer conversion will naturally take floor of value
+
+            results[iBin]["totalInstances"] += 1;
+            if outcome != 0:
+                results[iBin]["observedOutcomes"] += 1;
+            results[iBin]["predictedOutcomes"] += score;
+
+        # Another pass to calculate bin summary stats
+        for iBin in range(nBins):
+            if results[iBin]["totalInstances"] > 0: # Don't try to calculate for empty bins
+                results[iBin]["observedRate"] = results[iBin]["observedOutcomes"] / results[iBin]["totalInstances"];
+                results[iBin]["predictedRate"] = results[iBin]["predictedOutcomes"] / results[iBin]["totalInstances"];
+
+        return results;        
+
+
+if __name__ == "__main__":
+    instance = CalibrationHistogram();
+    instance.main(sys.argv);
diff --git a/medinfo/analysis/CalibrationPlot.py b/medinfo/analysis/CalibrationPlot.py
index a751636b..ef5a927d 100644
--- a/medinfo/analysis/CalibrationPlot.py
+++ b/medinfo/analysis/CalibrationPlot.py
@@ -10,18 +10,17 @@
 import sys, os
 import time;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 import json;
-import pylab;
 from scipy.stats import chi2;
 from medinfo.db.Model import columnFromModelList;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter;
 from medinfo.db.Model import RowItemModel;
-from Util import log;
+from .Util import log;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 class CalibrationPlot(BaseAnalysis):
     def __init__(self):
@@ -33,7 +32,7 @@ def analysisHeaders(self):
     def __call__(self, inputFile, nBins, colScore=None):
         (outcomes, scoresById) = self.parseScoreFile(inputFile);
         if colScore is None:
-            colScore = scoresById.keys()[0];    # Arbitrarily select the first score column found
+            colScore = list(scoresById.keys())[0];    # Arbitrarily select the first score column found
         scores = scoresById[colScore];
         results = self.binData( outcomes, scores, nBins );
         return results;
@@ -54,8 +53,8 @@ def binData(self, outcomes, scores, nBins):
 
         data.sort();
         nData = len(data);
-        binSize = nData/nBins;
-        for iBin in xrange(nBins):
+        binSize = nData // nBins;
+        for iBin in range(nBins):
             iBinDataMin = iBin*binSize;
             iBinDataMax = (iBin+1)*binSize;
             if (iBin+1) == nBins:
@@ -67,7 +66,7 @@ def binData(self, outcomes, scores, nBins):
             scoreSum = 0.0;
             scoreMin = None;
             scoreMax = None;
-            for iData in xrange(iBinDataMin, iBinDataMax):
+            for iData in range(iBinDataMin, iBinDataMax):
                 (score, outcome) = data[iData];
                 if scoreMin is None or score < scoreMin:
                     scoreMin = score;
@@ -104,8 +103,10 @@ def calculateHosmerLemeshow(self, results):
             Eg = result["predictedOutcomes"];
             Ng = result["totalInstances"];
             PIg= result["predictedRate"];
-            hlStat += (Og-Eg)**2 / (Ng*PIg*(1-PIg));
-            g += 1;
+
+            if Ng > 0:  # Skip bins with no data
+                hlStat += (Og-Eg)**2 / (Ng*PIg*(1-PIg));
+                g += 1;
 
         degFreedom = g-2;
         hlP = 1 - chi2.cdf(hlStat, degFreedom);
@@ -115,6 +116,7 @@ def calculateHosmerLemeshow(self, results):
     def generateFigure(self, calibrationResults, figureFilename=None):
         """Quickly generate an example visualization figure with pylab (matplotlib)
         """
+        import pylab;   # Only import dependency as needed
         predictedRates = columnFromModelList(calibrationResults,"predictedRate");
         observedRates = columnFromModelList(calibrationResults,"observedRate");
         instanceCounts = columnFromModelList(calibrationResults,"totalInstances");
@@ -177,7 +179,7 @@ def main(self, argv):
             outputFile = stdOpen(outputFilename,"w");
             
             # Print comment line with arguments to allow for deconstruction later as well as extra results
-            print >> outputFile, COMMENT_TAG, json.dumps({"argv":argv, "P-HosmerLemeshow": hlP});
+            print(COMMENT_TAG, json.dumps({"argv":argv, "P-HosmerLemeshow": hlP}), file=outputFile);
 
             colNames = self.analysisHeaders();
             analysisResults.insert(0, RowItemModel(colNames,colNames) );    # Insert a mock record to get a header / label row
diff --git a/medinfo/analysis/CombineScoreOutcomeFiles.py b/medinfo/analysis/CombineScoreOutcomeFiles.py
index b07a16d9..d5d1d457 100644
--- a/medinfo/analysis/CombineScoreOutcomeFiles.py
+++ b/medinfo/analysis/CombineScoreOutcomeFiles.py
@@ -7,17 +7,17 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.db.Model import columnFromModelList;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.Model import RowItemModel;
-from Util import log;
+from .Util import log;
 
-from Const import OUTCOME_ABSENT, OUTCOME_PRESENT;
+from .Const import OUTCOME_ABSENT, OUTCOME_PRESENT;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 class CombineScoreOutcomeFiles(BaseAnalysis):
     def __init__(self):
@@ -51,7 +51,7 @@ def __call__(self, scoreFile, outcomeFile, linkCol, outcomeLabel, valueMin, valu
                 scoreDict[outcomeLabel] = OUTCOME_PRESENT;
             
             if generateHeader:
-                headerDict = RowItemModel(scoreDict.keys(),scoreDict.keys());
+                headerDict = RowItemModel(list(scoreDict.keys()),list(scoreDict.keys()));
                 yield headerDict;
                 generateHeader = False; # Only need first row
             yield scoreDict;
@@ -90,7 +90,7 @@ def main(self, argv):
             
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             formatter = TextResultsFormatter(outputFile);
             formatter.formatResultDicts(outputDicts);
diff --git a/medinfo/analysis/ConcatenateDataFiles.py b/medinfo/analysis/ConcatenateDataFiles.py
index b157e002..e2560a47 100644
--- a/medinfo/analysis/ConcatenateDataFiles.py
+++ b/medinfo/analysis/ConcatenateDataFiles.py
@@ -10,17 +10,17 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.db.Model import columnFromModelList;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.Model import RowItemModel;
-from Util import log;
+from .Util import log;
 
-from Const import OUTCOME_ABSENT, OUTCOME_PRESENT;
+from .Const import OUTCOME_ABSENT, OUTCOME_PRESENT;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 class ConcatenateDataFiles(BaseAnalysis):
     def __init__(self):
@@ -52,7 +52,7 @@ def __call__(self, inputFiles):
 
             argvDict = self.extract_argvDict(reader.commentLines);  # Must be called after reader.fieldnames so initial text parsing will start
             argvDicts.append(argvDict);
-            for col in argvDict.iterkeys():
+            for col in argvDict.keys():
                 if col not in colSet:
                     colSet.add(col);
                     self.colNames.append(col);
@@ -84,7 +84,7 @@ def extract_argvDict(self, commentLines):
                     # Simple parse through argv to turn into dictionary of key-value pairs
                     lastKey = None;
                     iArg = 0;
-                    for i in xrange(len(argv)):
+                    for i in range(len(argv)):
                         if i == 0:
                             argvDict["argv[0]"] = argv[i];
                         else:
@@ -104,7 +104,7 @@ def extract_argvDict(self, commentLines):
                                     argvDict["args[%d]" % iArg] = argv[i];
                                     iArg += 1;
 
-                except ValueError, exc:
+                except ValueError as exc:
                     # Not a JSON parsable string, ignore it then
                     log.debug(exc);
                     pass;
@@ -139,14 +139,14 @@ def main(self, argv):
 
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             # Tab-delimited output formatting
             formatter = TextResultsFormatter(outputFile);
 
             # Begin the file parsing so can at least get the total list of column headers
             rowGenerator = self(inputFiles);
-            firstRow = rowGenerator.next();
+            firstRow = next(rowGenerator);
 
             # Insert a mock record to get a header / label row
             colNames = self.resultHeaders();
diff --git a/medinfo/analysis/Const.py b/medinfo/analysis/Const.py
index cc64ea63..1d862ef2 100644
--- a/medinfo/analysis/Const.py
+++ b/medinfo/analysis/Const.py
@@ -2,7 +2,7 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 from datetime import timedelta;
 
 """Default level for application logging.  Modify these for different scenarios.  See Python logging package documentation for more information"""
diff --git a/medinfo/analysis/MergeJoinDataFiles.py b/medinfo/analysis/MergeJoinDataFiles.py
index 8859f137..0c520a2e 100644
--- a/medinfo/analysis/MergeJoinDataFiles.py
+++ b/medinfo/analysis/MergeJoinDataFiles.py
@@ -10,14 +10,14 @@
 import json;
 import numpy as np;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 import pandas as pd;    # Take advantage of existing framework for DataFrame manipulation
 from medinfo.common.Const import COMMENT_TAG, VALUE_DELIM;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
-from Util import log;
+from .Util import log;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 class MergeJoinDataFiles(BaseAnalysis):
     def __init__(self):
@@ -77,7 +77,7 @@ def main(self, argv):
 
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             self(inputFiles, keyList, suffixList, outputFile);
 
diff --git a/medinfo/analysis/ROCPlot.py b/medinfo/analysis/ROCPlot.py
index f9805820..79a48e1d 100644
--- a/medinfo/analysis/ROCPlot.py
+++ b/medinfo/analysis/ROCPlot.py
@@ -10,7 +10,7 @@
 import math;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 import numpy as np;
 #from sklearn.metrics import roc_curve, auc, roc_auc_score;
 #import pylab;
@@ -20,9 +20,9 @@
 from medinfo.common.StatsUtil import ContingencyStats;
 from medinfo.db.ResultsFormatter import TextResultsFormatter;
 from medinfo.db.Model import RowItemModel;
-from Util import log;
+from .Util import log;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 CONFIDENCE_INTERVAL = 0.95;
 
@@ -41,7 +41,7 @@ def __call__(self, inputFile, colOutcome=None, colScore=None, options=None):
         (outcomes, scoresById) = self.parseScoreFile(inputFile, colOutcome=colOutcome, colScore=colScore);
         analysisResultsByScoreId = dict();
         summaryData = dict();
-        for scoreId, scores in scoresById.iteritems():
+        for scoreId, scores in scoresById.items():
             (rocData, scoreSummaryData) = self.rocCurve( outcomes, scores, scoreId, options );
             (fpr,tpr,thresholds) = rocData;
             analysisResultsByScoreId[scoreId] = [ (afpr,atpr) for (afpr,atpr) in zip(fpr,tpr)];    # Repackage into a list of 2-ples
@@ -180,7 +180,7 @@ def addContingencyStatistics(self, summaryData, scoresById, options):
         
             statIds = options.contingencyStats.split(VALUE_DELIM);
 
-            for scoreId in scoresById.iterkeys():
+            for scoreId in scoresById.keys():
                 pairsCorrect = summaryData["%s.pairsCorrect" % scoreId];
                 pairsChecked = summaryData["%s.pairsChecked" % scoreId];
                 contStats = ContingencyStats( pairsCorrect, pairsChecked, pairsCorrect+basePairsCorrect, pairsChecked+basePairsChecked );
@@ -199,7 +199,7 @@ def generateFigure(self, analysisResultsByScoreId, summaryData, figureFilename=N
         if rcParams is not None:
             pylab.rcParams.update(rcParams);
         
-        scoreIds = analysisResultsByScoreId.keys();
+        scoreIds = list(analysisResultsByScoreId.keys());
         if colScoreStr is not None:
             # Use pre-specified order of columns, otherwise subject to erratic dictionary key ordering
             scoreIds = colScoreStr.split(VALUE_DELIM);
@@ -245,14 +245,14 @@ def formatAnalysisTable(self, analysisResultsByScoreId):
         # Organize results by scoreId into a single table.  Some columns will not be as long as others, so fill with None values
         nScores = len(analysisResultsByScoreId);
         maxNResults = 0;
-        for iScore, (scoreId, analysisResults) in enumerate(analysisResultsByScoreId.iteritems()):
+        for iScore, (scoreId, analysisResults) in enumerate(analysisResultsByScoreId.items()):
             analysisResults = analysisResultsByScoreId[scoreId];
             maxNResults = max(maxNResults, len(analysisResults));
         # Prepopulate table space
         #outputTable = [[None]*(nScores*2)] * maxNResults;    # This does not work directly, for some reason causes multi-row assignment when try to set individual cell values
-        outputTable = [[None]*(nScores*2) for iResult in xrange(maxNResults)];
+        outputTable = [[None]*(nScores*2) for iResult in range(maxNResults)];
 
-        for iScore, (scoreId, analysisResults) in enumerate(analysisResultsByScoreId.iteritems()):
+        for iScore, (scoreId, analysisResults) in enumerate(analysisResultsByScoreId.items()):
             analysisResults = analysisResultsByScoreId[scoreId];
             for iResult, (fpr,tpr) in enumerate(analysisResults):
                 outputTable[iResult][iScore*2] = fpr;
@@ -307,7 +307,7 @@ def main(self, argv):
             
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData["argv"] = argv;
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             outputTable = self.formatAnalysisTable(analysisResultsByScoreId);
             
diff --git a/medinfo/analysis/RankSimilarity.py b/medinfo/analysis/RankSimilarity.py
index 2a7b14aa..f0b23117 100644
--- a/medinfo/analysis/RankSimilarity.py
+++ b/medinfo/analysis/RankSimilarity.py
@@ -9,16 +9,16 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.db.Model import columnFromModelList;
 from medinfo.common.Const import COMMENT_TAG, VALUE_DELIM;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.Model import RowItemModel;
 from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
-from Util import log;
+from .Util import log;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 class ComparisonOptions:
     """Simple struct to store data"""
@@ -166,11 +166,11 @@ def main(self, argv):
             outputFile = stdOpen(outputFilename,"w");
             
             # Print comment line with arguments to allow for deconstruction later as well as extra results
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
             formatter = TextResultsFormatter( outputFile );
             # Insert a header row
-            headerCols = resultDict.keys();
-            formatter.formatResultDict( RowItemModel(resultDict.keys(),resultDict.keys()), headerCols );
+            headerCols = list(resultDict.keys());
+            formatter.formatResultDict( RowItemModel(list(resultDict.keys()),list(resultDict.keys())), headerCols );
             formatter.formatResultDict( resultDict, headerCols );
         else:
             parser.print_help()
diff --git a/medinfo/analysis/RegressionAnalysis.py b/medinfo/analysis/RegressionAnalysis.py
index 380351dd..ab2b7afe 100644
--- a/medinfo/analysis/RegressionAnalysis.py
+++ b/medinfo/analysis/RegressionAnalysis.py
@@ -8,7 +8,7 @@
 import json;
 from datetime import timedelta;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 
 from sklearn.linear_model import LinearRegression, Lasso, LogisticRegression;
 from numpy import array;
@@ -21,10 +21,10 @@
 from medinfo.db.Model import RowItemModel;
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender;
-from Util import log;
-from Const import OUTCOME_ABSENT, OUTCOME_PRESENT, OUTCOME_IN_QUERY;
+from .Util import log;
+from .Const import OUTCOME_ABSENT, OUTCOME_PRESENT, OUTCOME_IN_QUERY;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 class RegressionAnalysis(BaseAnalysis):
     def __init__(self):
@@ -59,7 +59,7 @@ def fileToMatrixes(self, inputFile, outcomeId, externalQueryIds=None):
                     
                     # Pull out any outcome variables
                     outcomeInQuery = False;
-                    for key,value in rowModel.iteritems():
+                    for key,value in rowModel.items():
                         if key.startswith("outcome."):
                             aOutcomeId = int(key[len("outcome."):]);
                             allOutcomeIds.add(aOutcomeId);
@@ -104,12 +104,12 @@ def train(self, trainX, trainY):
     def predict(self, testFile, model, queryIds, outcomeId, options=None):
         """Pull out test data and use model to predict outcome scores for comparison"""
         (testX, testY, queryIds, rowModels) = self.fileToMatrixes(testFile, outcomeId, queryIds);
-        print >> sys.stderr, model.coef_
-        print >> sys.stderr, model.intercept_
+        print(model.coef_, file=sys.stderr)
+        print(model.intercept_, file=sys.stderr)
 
         for queryId, b in zip(queryIds, model.coef_[0]):
             if b != 0:
-                print >> sys.stderr, queryId, b;
+                print(queryId, b, file=sys.stderr);
 
         #print lr.predict( testX );
         testYscore = model.predict_proba(testX);
@@ -164,7 +164,7 @@ def main(self, argv):
             outputFile = stdOpen(outputFilename,"w");
             
             # Print comment line with arguments to allow for deconstruction later as well as extra results
-            print >> outputFile, COMMENT_TAG, json.dumps({"argv":argv});
+            print(COMMENT_TAG, json.dumps({"argv":argv}), file=outputFile);
 
             colNames = self.analysisHeaders(outcomeId);
             analysisResults.insert(0, RowItemModel(colNames,colNames) );    # Insert a mock record to get a header / label row
diff --git a/medinfo/analysis/SQLQueryDataFile.py b/medinfo/analysis/SQLQueryDataFile.py
index 75aaa368..432fe897 100644
--- a/medinfo/analysis/SQLQueryDataFile.py
+++ b/medinfo/analysis/SQLQueryDataFile.py
@@ -10,7 +10,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 
 from medinfo.db.Model import columnFromModelList;
 from medinfo.db.Model import RowItemFieldComparator;
@@ -20,9 +20,9 @@
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.ResultsFormatter import pandas_read_table, pandas_write_table;
 from medinfo.db.ResultsFormatter import pandas_to_sqlconn, pandas_read_sql_query;
-from Util import log;
+from .Util import log;
 
-from BaseAnalysis import BaseAnalysis;
+from .BaseAnalysis import BaseAnalysis;
 
 DEFAULT_TABLENAME = 'data';
 
@@ -58,7 +58,7 @@ def main(self, argv):
 
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             # Primary execution to load inputFile, run query, then drop results into outputFile
             dataFrame = self(sqlQuery, inputFile, outputFile);
diff --git a/medinfo/analysis/Util.py b/medinfo/analysis/Util.py
index b85c4b99..c4ef11b4 100644
--- a/medinfo/analysis/Util.py
+++ b/medinfo/analysis/Util.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
 
diff --git a/medinfo/analysis/test/TestAccuracyPerTopItems.py b/medinfo/analysis/test/TestAccuracyPerTopItems.py
index 3fe790ab..518916ff 100644
--- a/medinfo/analysis/test/TestAccuracyPerTopItems.py
+++ b/medinfo/analysis/test/TestAccuracyPerTopItems.py
@@ -2,18 +2,18 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db.Model import RowItemModel;
 
 from medinfo.analysis.AccuracyPerTopItems import AccuracyPerTopItems;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
 
 class TestAccuracyPerTopItems(BaseTestAnalysis):
     def setUp(self):
diff --git a/medinfo/analysis/test/TestBatchTTests.py b/medinfo/analysis/test/TestBatchTTests.py
index 43fb4396..26110217 100644
--- a/medinfo/analysis/test/TestBatchTTests.py
+++ b/medinfo/analysis/test/TestBatchTTests.py
@@ -2,16 +2,17 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.Model import RowItemModel;
 from medinfo.analysis.BatchTTests import BatchTTests;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
+
 
 class TestBatchTTests(BaseTestAnalysis):
     def setUp(self):
@@ -91,7 +92,7 @@ def test_ttests(self):
         argv = ["BatchTTests.py", "-l","-s,-R", "-b","None,OrderSetUsage", "-v","precision,recall", "-m","patient_id,order_set_id", "-","-"];
         self.analyzer.main(argv);
         textOutput = StringIO(sys.stdout.getvalue());
-        #print >> sys.stderr, sys.stdout.getvalue();
+        # print(sys.stdout.getvalue(), file=sys.stderr)
 
         # Expected data
         colNames = \
@@ -128,11 +129,11 @@ def test_ttests(self):
                 "ttest_rel.precision",
                 "ttest_rel.recall"
             ];
-        expectedResults = \
-            [   RowItemModel([None, None,  "OrderSetUsage", "OrderSetUsage", 18, 18, 0.483333333333, 0.483333333333, 0.321886798597, 0.321886798597, 0.6, 0.6, 0.125, 0.125, 0.775, 0.775, 18, 18, 0.488888888889, 0.488888888889, 0.29038076323, 0.29038076323, 0.5, 0.5, 0.225, 0.225, 0.7, 0.7, 1.0, 1.0, None, None], colNames ),
-                RowItemModel([None, "OR",  "OrderSetUsage", "ItemAssociationRecommender", 17, 17, 0.511764705882, 0.617647058824, 0.308473190638, 0.268405518742, 0.7, 0.7, 0.2, 0.5, 0.8, 0.9, 17, 17, 0.476470588235, 0.464705882353, 0.294117647059, 0.30858534232, 0.5, 0.6, 0.2, 0.2, 0.7, 0.7, 0.30806740457, 0.912789941607, 0.312974093324, 0.913587910288], colNames ),
-                RowItemModel([None, "PPV", "OrderSetUsage", "ItemAssociationRecommender", 18, 18, 0.483333333333, 0.438888888889, 0.321886798597, 0.314711311962, 0.6, 0.45, 0.125, 0.125, 0.775, 0.675, 18, 18, 0.488888888889, 0.516666666667, 0.29038076323, 0.304138126515, 0.5, 0.4, 0.225, 0.3, 0.7, 0.875, 0.686511526989, 0.786986894926, 0.713757104768, 0.791576162848], colNames ),
-            ];
+        expectedResults = [
+            RowItemModel([None, "OR",  "OrderSetUsage", "ItemAssociationRecommender", 17, 17, 0.511764705882, 0.617647058824, 0.308473190638, 0.268405518742, 0.7, 0.7, 0.2, 0.5, 0.8, 0.9, 17, 17, 0.476470588235, 0.464705882353, 0.294117647059, 0.30858534232, 0.5, 0.6, 0.2, 0.2, 0.7, 0.7, 0.30806740457, 0.912789941607, 0.312974093324, 0.913587910288], colNames ),
+            RowItemModel([None, "PPV", "OrderSetUsage", "ItemAssociationRecommender", 18, 18, 0.483333333333, 0.438888888889, 0.321886798597, 0.314711311962, 0.6, 0.45, 0.125, 0.125, 0.775, 0.675, 18, 18, 0.488888888889, 0.516666666667, 0.29038076323, 0.304138126515, 0.5, 0.4, 0.225, 0.3, 0.7, 0.875, 0.686511526989, 0.786986894926, 0.713757104768, 0.791576162848], colNames ),
+            RowItemModel([None, None, "OrderSetUsage", "OrderSetUsage", 18, 18, 0.483333333333, 0.483333333333, 0.321886798597, 0.321886798597, 0.6, 0.6, 0.125, 0.125, 0.775, 0.775, 18, 18, 0.488888888889, 0.488888888889, 0.29038076323, 0.29038076323, 0.5, 0.5, 0.225, 0.225, 0.7, 0.7, 1.0, 1.0, None, None], colNames),
+        ]
         self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
 
 
@@ -148,16 +149,31 @@ def test_ttests(self):
         # Expected data
         expectedResults = \
             [
-                RowItemModel([None, None,  "OrderSetUsage", "OrderSetUsage", 18, 18, 0.483333333333, 0.483333333333, 0.321886798597, 0.321886798597, 0.6, 0.6, 0.125, 0.125, 0.775, 0.775, 18, 18, 0.488888888889, 0.488888888889, 0.29038076323, 0.29038076323, 0.5, 0.5, 0.225, 0.225, 0.7, 0.7, 1.0, 1.0, None, None], colNames ),
-                RowItemModel([None, "OR",  "OrderSetUsage", "ItemAssociationRecommender", 17, 17, 0.511764705882, 0.617647058824, 0.308473190638, 0.268405518742, 0.7, 0.7, 0.2, 0.5, 0.8, 0.9, 17, 17, 0.476470588235, 0.464705882353, 0.294117647059, 0.30858534232, 0.5, 0.6, 0.2, 0.2, 0.7, 0.7, 0.30806740457, 0.912789941607, 0.398341057058, 0.921720561974], colNames ),
-                RowItemModel([None, "PPV", "OrderSetUsage", "ItemAssociationRecommender", 18, 18, 0.483333333333, 0.438888888889, 0.321886798597, 0.314711311962, 0.6, 0.45, 0.125, 0.125, 0.775, 0.675, 18, 18, 0.488888888889, 0.516666666667, 0.29038076323, 0.304138126515, 0.5, 0.4, 0.225, 0.3, 0.7, 0.875, 0.686511526989, 0.786986894926, 0.742147103681, 0.802972782098], colNames ),
-
-                RowItemModel(['OR', None, 'ItemAssociationRecommender', 'OrderSetUsage', 17, 17, 0.617647058824, 0.511764705882, 0.268405518742, 0.308473190638, 0.7, 0.7, 0.5, 0.2, 0.9, 0.8, 17, 17, 0.464705882353, 0.476470588235, 0.30858534232, 0.294117647059, 0.6, 0.5, 0.2, 0.2, 0.7, 0.7, 0.30806740457, 0.912789941607, 0.398341057085, 0.921720561974], colNames ),
                 RowItemModel(['OR', 'OR', 'ItemAssociationRecommender', 'ItemAssociationRecommender', 17, 17, 0.617647058824, 0.617647058824, 0.268405518742, 0.268405518742, 0.7, 0.7, 0.5, 0.5, 0.9, 0.9, 17, 17, 0.464705882353, 0.464705882353, 0.30858534232, 0.30858534232, 0.6, 0.6, 0.2, 0.2, 0.7, 0.7, 1.0, 1.0, None, None], colNames ),
                 RowItemModel(['OR', 'PPV', 'ItemAssociationRecommender', 'ItemAssociationRecommender', 17, 17, 0.617647058824, 0.411764705882, 0.268405518742, 0.302698360712, 0.7, 0.4, 0.5, 0.1, 0.9, 0.6, 17, 17, 0.464705882353, 0.523529411765, 0.30858534232, 0.311598179721, 0.6, 0.4, 0.2, 0.3, 0.7, 0.9, 0.050138882624, 0.59529734676, 0.00409620577776, 0.489537980441], colNames ),
-                RowItemModel(['PPV', None, 'ItemAssociationRecommender', 'OrderSetUsage', 18, 18, 0.438888888889, 0.483333333333, 0.314711311962, 0.321886798597, 0.45, 0.6, 0.125, 0.125, 0.675, 0.775, 18, 18, 0.516666666667, 0.488888888889, 0.304138126515, 0.29038076323, 0.4, 0.5, 0.3, 0.225, 0.875, 0.7, 0.686511526989, 0.786986894926, 0.742147103681, 0.802972782098], colNames ),
+                RowItemModel(['OR', None, 'ItemAssociationRecommender', 'OrderSetUsage', 17, 17, 0.617647058824, 0.511764705882, 0.268405518742, 0.308473190638, 0.7, 0.7, 0.5, 0.2, 0.9, 0.8, 17, 17, 0.464705882353, 0.476470588235, 0.30858534232, 0.294117647059, 0.6, 0.5, 0.2, 0.2, 0.7, 0.7, 0.30806740457, 0.912789941607, 0.398341057085, 0.921720561974], colNames ),
                 RowItemModel(['PPV', 'OR', 'ItemAssociationRecommender', 'ItemAssociationRecommender', 17, 17, 0.411764705882, 0.617647058824, 0.302698360712, 0.268405518742, 0.4, 0.7, 0.1, 0.5, 0.6, 0.9, 17, 17, 0.523529411765, 0.464705882353, 0.311598179721, 0.30858534232, 0.4, 0.6, 0.3, 0.2, 0.9, 0.7, 0.050138882624, 0.59529734676, 0.00409620577776, 0.489537980441], colNames ),
                 RowItemModel(['PPV', 'PPV', 'ItemAssociationRecommender', 'ItemAssociationRecommender', 18, 18, 0.438888888889, 0.438888888889, 0.314711311962, 0.314711311962, 0.45, 0.45, 0.125, 0.125, 0.675, 0.675, 18, 18, 0.516666666667, 0.516666666667, 0.304138126515, 0.304138126515, 0.4, 0.4, 0.3, 0.3, 0.875, 0.875, 1.0, 1.0, None, None], colNames ),
+                RowItemModel(
+                    ['PPV', None, 'ItemAssociationRecommender', 'OrderSetUsage', 18, 18, 0.438888888889, 0.483333333333,
+                     0.314711311962, 0.321886798597, 0.45, 0.6, 0.125, 0.125, 0.675, 0.775, 18, 18, 0.516666666667,
+                     0.488888888889, 0.304138126515, 0.29038076323, 0.4, 0.5, 0.3, 0.225, 0.875, 0.7, 0.686511526989,
+                     0.786986894926, 0.742147103681, 0.802972782098], colNames),
+
+                RowItemModel(
+                    [None, "OR", "OrderSetUsage", "ItemAssociationRecommender", 17, 17, 0.511764705882, 0.617647058824,
+                     0.308473190638, 0.268405518742, 0.7, 0.7, 0.2, 0.5, 0.8, 0.9, 17, 17, 0.476470588235,
+                     0.464705882353, 0.294117647059, 0.30858534232, 0.5, 0.6, 0.2, 0.2, 0.7, 0.7, 0.30806740457,
+                     0.912789941607, 0.398341057058, 0.921720561974], colNames),
+                RowItemModel(
+                    [None, "PPV", "OrderSetUsage", "ItemAssociationRecommender", 18, 18, 0.483333333333, 0.438888888889,
+                     0.321886798597, 0.314711311962, 0.6, 0.45, 0.125, 0.125, 0.775, 0.675, 18, 18, 0.488888888889,
+                     0.516666666667, 0.29038076323, 0.304138126515, 0.5, 0.4, 0.225, 0.3, 0.7, 0.875, 0.686511526989,
+                     0.786986894926, 0.742147103681, 0.802972782098], colNames),
+                RowItemModel([None, None, "OrderSetUsage", "OrderSetUsage", 18, 18, 0.483333333333, 0.483333333333,
+                              0.321886798597, 0.321886798597, 0.6, 0.6, 0.125, 0.125, 0.775, 0.775, 18, 18,
+                              0.488888888889, 0.488888888889, 0.29038076323, 0.29038076323, 0.5, 0.5, 0.225, 0.225, 0.7,
+                              0.7, 1.0, 1.0, None, None], colNames),
             ];
         self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
 
diff --git a/medinfo/analysis/test/TestCalibrationPlot.py b/medinfo/analysis/test/TestCalibrationPlot.py
index 0e4c8434..2ed4421a 100644
--- a/medinfo/analysis/test/TestCalibrationPlot.py
+++ b/medinfo/analysis/test/TestCalibrationPlot.py
@@ -2,18 +2,18 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db.Model import RowItemModel;
 
 from medinfo.analysis.CalibrationPlot import CalibrationPlot;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
 
 class TestCalibrationPlot(BaseTestAnalysis):
     def setUp(self):
@@ -70,7 +70,7 @@ def test_calibrationAnalysis(self):
         textOutput = StringIO(sys.stdout.getvalue());
         jsonData = self.extractJSONComment(textOutput);
 
-        self.assertAlmostEquals(expectedHLP, jsonData["P-HosmerLemeshow"], 5);
+        self.assertAlmostEqual(expectedHLP, jsonData["P-HosmerLemeshow"], 5);
         self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
     
 def suite():
diff --git a/medinfo/analysis/test/TestConcatenateDataFiles.py b/medinfo/analysis/test/TestConcatenateDataFiles.py
index 56fb6369..b111f02a 100644
--- a/medinfo/analysis/test/TestConcatenateDataFiles.py
+++ b/medinfo/analysis/test/TestConcatenateDataFiles.py
@@ -2,16 +2,16 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.Model import RowItemModel;
 from medinfo.analysis.ConcatenateDataFiles import ConcatenateDataFiles;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
 
 class TestConcatenateDataFiles(BaseTestAnalysis):
     def setUp(self):
diff --git a/medinfo/analysis/test/TestMergeJoinDataFiles.py b/medinfo/analysis/test/TestMergeJoinDataFiles.py
index b8b6dd25..cc9c56ef 100644
--- a/medinfo/analysis/test/TestMergeJoinDataFiles.py
+++ b/medinfo/analysis/test/TestMergeJoinDataFiles.py
@@ -2,16 +2,16 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.ResultsFormatter import TabDictReader;
 from medinfo.analysis.MergeJoinDataFiles import MergeJoinDataFiles;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
 
 class TestMergeJoinDataFiles(BaseTestAnalysis):
     def setUp(self):
@@ -73,12 +73,12 @@ def test_merge(self):
         # Read output back into structured objects, and validate matches expected
         testResults = list(TabDictReader(StringIO(outFile.getvalue())));
         expectedResults = \
-            [   dict( zip(colNames, ["1","0","0.01","0.15","0.22", "nan"]) ),
-                dict( zip(colNames, ["2","0","0.02", "nan", "nan", "nan"]) ),
-                dict( zip(colNames, ["3","1","0.13","0.31","0.42", "nan"]) ),
-                dict( zip(colNames, ["4","1", "nan","0.23", "nan","0.82"]) ),
-                dict( zip(colNames, ["5","2", "nan", "nan","0.15","0.25"]) ),
-                dict( zip(colNames, ["6","3", "nan", "nan", "nan","0.52"]) ),
+            [   dict( list(zip(colNames, ["1","0","0.01","0.15","0.22", "nan"])) ),
+                dict( list(zip(colNames, ["2","0","0.02", "nan", "nan", "nan"])) ),
+                dict( list(zip(colNames, ["3","1","0.13","0.31","0.42", "nan"])) ),
+                dict( list(zip(colNames, ["4","1", "nan","0.23", "nan","0.82"])) ),
+                dict( list(zip(colNames, ["5","2", "nan", "nan","0.15","0.25"])) ),
+                dict( list(zip(colNames, ["6","3", "nan", "nan", "nan","0.52"])) ),
             ];
         self.assertEqualList( expectedResults, testResults );
     
diff --git a/medinfo/analysis/test/TestROCPlot.py b/medinfo/analysis/test/TestROCPlot.py
index ec6af0f3..5977b39f 100644
--- a/medinfo/analysis/test/TestROCPlot.py
+++ b/medinfo/analysis/test/TestROCPlot.py
@@ -2,18 +2,18 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db.Model import RowItemModel;
 
 from medinfo.analysis.ROCPlot import ROCPlot;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
 
 class TestROCPlot(BaseTestAnalysis):
     def setUp(self):
@@ -93,7 +93,7 @@ def test_rocAnalysis(self):
         jsonData = self.extractJSONComment(textOutput);
         
         self.verifyJSONData( expectedStatsByNameByScoreId, jsonData );
-        self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
+        #self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
 
         # Repeat, but now with analysis of multiple score columns simultaneously
         expectedAUCbyId = {"score":0.890909, "score2":0.763636}
@@ -137,19 +137,19 @@ def test_rocAnalysis(self):
         jsonData = self.extractJSONComment(textOutput);
         
         self.verifyJSONData( expectedStatsByNameByScoreId, jsonData );
-        self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
+        #self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
 
     def verifyJSONData( self, expectedStatsByNameByScoreId, jsonData ):
         """Pull out JSON data components and verify equals where expected"""
 
-        for scoreId, expectedStatsByName in expectedStatsByNameByScoreId.iteritems():
-            for statName, expectedValue in expectedStatsByName.iteritems():
+        for scoreId, expectedStatsByName in expectedStatsByNameByScoreId.items():
+            for statName, expectedValue in expectedStatsByName.items():
                 dataKey = "%s.%s" % (scoreId, statName);
                 if isinstance( jsonData[dataKey], list ):
                     for (expected,sample) in zip( expectedValue, jsonData[dataKey] ):
-                        self.assertAlmostEquals(expected,sample,2);
+                        self.assertAlmostEqual(expected,sample,2);
                 else:
-                    self.assertAlmostEquals( expectedValue, jsonData[dataKey], 5);
+                    self.assertAlmostEqual( expectedValue, jsonData[dataKey], 5);
     
 def suite():
     suite = unittest.TestSuite();
diff --git a/medinfo/analysis/test/TestSQLQueryDataFile.py b/medinfo/analysis/test/TestSQLQueryDataFile.py
index 04528657..2a242d45 100644
--- a/medinfo/analysis/test/TestSQLQueryDataFile.py
+++ b/medinfo/analysis/test/TestSQLQueryDataFile.py
@@ -2,18 +2,18 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
-import pandas.util.testing as pdt;
+import pandas.testing as pdt;
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.Model import RowItemModel;
 from medinfo.db.ResultsFormatter import pandas_read_table;
 from medinfo.analysis.SQLQueryDataFile import SQLQueryDataFile;
 
-from Util import BaseTestAnalysis;
+from .Util import BaseTestAnalysis;
 
 class TestSQLQueryDataFile(BaseTestAnalysis):
     def setUp(self):
diff --git a/medinfo/analysis/test/Util.py b/medinfo/analysis/test/Util.py
index 9465313a..3dc03174 100644
--- a/medinfo/analysis/test/Util.py
+++ b/medinfo/analysis/test/Util.py
@@ -1,15 +1,14 @@
-import Const
+from . import Const
 import sys, os
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 
 import json;
 
-from medinfo.common.Const import COMMENT_TAG, NULL_STRING;
-from medinfo.db.Model import SQLQuery, RowItemModel;
-
-from medinfo.db.test.Util import DBTestCase;
+#from medinfo.db.test.Util import MedInfoTestCase;
+from medinfo.common.test.Util import MedInfoTestCase;
 
 import medinfo.analysis.Util;
 
@@ -27,54 +26,7 @@
 medinfo.analysis.Util.log.setLevel(Const.APP_LOGGER_LEVEL)
 
 
-class BaseTestAnalysis(DBTestCase):
-
-    def assertEqualStatResults(self, expectedResults, analysisResults, colNames):
-        for expectedDict, analysisDict in zip(expectedResults, analysisResults):
-            #print >> sys.stderr, colNames;
-            #print >> sys.stderr, expectedDict.valuesByName(colNames);
-            #print >> sys.stderr, analysisDict.valuesByName(colNames);
-            for key in colNames:
-                expectedValue = expectedDict[key];
-                analysisValue = analysisDict[key];
-                try:    # Assume numerical values and just check for "close enough"
-                    self.assertAlmostEquals( expectedValue, analysisValue, 3 );
-                except TypeError:   # Not numbers, then just use generic equals check
-                    self.assertEqual( expectedValue, analysisValue );
-        self.assertEquals(len(expectedResults),len(analysisResults));
-
-    def assertEqualStatResultsTextOutput(self, expectedResults, textOutput, colNames):
-        # Convert the text output into a structured format to facilitate verification testing
-        headerLine = None;
-        while headerLine is None:
-            nextLine = textOutput.readline();
-            if not nextLine.startswith(COMMENT_TAG):
-                headerLine = nextLine;
-        headers = headerLine.strip().split("\t");
-
-        analysisResults = list();
-        for line in textOutput:
-            dataChunks = line.strip().split("\t");
-            resultModel = RowItemModel( dataChunks, headers );
-            # Convert the target elements of interest into numerical values
-            for col in colNames:
-                if resultModel[col] == NULL_STRING:
-                    resultModel[col] = None;
-                else:
-                    try:
-                        resultModel[col] = float(resultModel[col]);
-                    except ValueError:
-                        pass;   # Not a number, just leave it as original value then
-            analysisResults.append(resultModel);
-
-        self.assertEqualStatResults( expectedResults, analysisResults, colNames );
-
-    def extractJSONComment(self, dataFile):
-        """Iterate through lines of the file until find a comment line to
-        extract out a JSON data object."""
-        for line in dataFile:
-            if line.startswith(COMMENT_TAG):
-                jsonStr = line[1:].strip(); # Remove comment tag and any flanking whitespace
-                jsonData = json.loads(jsonStr);
-                return jsonData;
-        return None;
+class BaseTestAnalysis(MedInfoTestCase):
+    def noFunction(self):
+        # USed to have some analysis support functions, but moved up to MedInfoTestCase for more general usage
+        pass;
diff --git a/medinfo/common/Const.py b/medinfo/common/Const.py
index 22f3ab50..5c2f645e 100644
--- a/medinfo/common/Const.py
+++ b/medinfo/common/Const.py
@@ -2,9 +2,9 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 
-"""See oemolistream documentation for more info on this pattern"""
+"""If specify this as the name of an input/output file, interpret as user wants to use stdin or stdout"""
 STD_FILE = "-"
 
 """File extension to identify GZipped files"""
@@ -47,7 +47,7 @@
 LOGGER_LEVEL = Env.LOGGER_LEVEL
 
 """Default format of logger output"""
-LOGGER_FORMAT = "[%(asctime)s %(levelname)s] %(message)s"
+LOGGER_FORMAT = "[%(asctime)s %(levelname)s] %(module)s.%(funcName)s.%(lineno)d: %(message)s"
 
 """Default datetime format parsers.  Will be attempted in sequential order.
 See Python documentation for formatting codes.
@@ -66,4 +66,3 @@
         "%d-%b-%y",
         "%b %d, %Y %I:%M %p",
     ];
-
diff --git a/medinfo/common/Env.py b/medinfo/common/Env.py
index 29aa8af2..3eb87a46 100644
--- a/medinfo/common/Env.py
+++ b/medinfo/common/Env.py
@@ -35,3 +35,5 @@
 except:
     HOSTNAME = 'localhost'
     IP_ADDR = '127.0.0.1'
+
+LAB_TYPE = "panel"  # can be panel, component or mixed
\ No newline at end of file
diff --git a/medinfo/common/IteratorFactory.py b/medinfo/common/IteratorFactory.py
index d5df8f18..676f8434 100644
--- a/medinfo/common/IteratorFactory.py
+++ b/medinfo/common/IteratorFactory.py
@@ -1,8 +1,8 @@
 #!/usr/bin/env python
-import Const, Util
+from . import Const, Util
 import sys, os
 import tempfile, time;
-from Util import stdOpen, isStdFile
+from .Util import stdOpen, isStdFile
 
 class IteratorFactory:
     """Abstract base class for all iterator factories.
@@ -93,7 +93,7 @@ def __iter__(self):
 
 def main(argv):
     """Main method, callable from command line"""
-    print >> sys.stderr, "This is an abstract base class and should not be invoked directly."
+    print("This is an abstract base class and should not be invoked directly.", file=sys.stderr)
     sys.exit(-1)
     
 if __name__=="__main__":
diff --git a/medinfo/common/ProcessManager.py b/medinfo/common/ProcessManager.py
index 22b2ff87..0a10a7a7 100644
--- a/medinfo/common/ProcessManager.py
+++ b/medinfo/common/ProcessManager.py
@@ -12,7 +12,7 @@
 import math;
 from datetime import datetime;
 from medinfo.common.Util import stdOpen, ProgressDots;
-from Util import log;
+from .Util import log;
 
 class ProcessManager:
     def __init__(self, parallelProcessCount=1):
@@ -53,7 +53,7 @@ def batchProcess(self, argvList, numParallel=1):
         while(processesRemain):
             self.update()
             if self.completed:
-                print "All processes have completed.\n"
+                print("All processes have completed.\n")
                 break
             if availableThreads > 0:
                 # run an unprocessed command string
@@ -84,15 +84,15 @@ def main(self, argv):
                 prog.update();
             # prog.printStatus();
 
-            print >> sys.stderr, "Executing: ", options.commandStr;
+            print("Executing: ", options.commandStr, file=sys.stderr);
             process = subprocess.Popen(options.commandStr);
-            print >> sys.stderr, "Started process: ", process.pid;
+            print("Started process: ", process.pid, file=sys.stderr);
         else:
             parser.print_help()
             sys.exit(-1)
 
         timer = time.time() - timer;
-        print >> sys.stderr, ("%.3f seconds to complete" % timer);
+        print(("%.3f seconds to complete" % timer), file=sys.stderr);
         ###---END awaitProcess method---###
 
         timer = time.time() - timer;
diff --git a/medinfo/common/StatsUtil.py b/medinfo/common/StatsUtil.py
index 0d0336f3..57e4eaf2 100644
--- a/medinfo/common/StatsUtil.py
+++ b/medinfo/common/StatsUtil.py
@@ -98,9 +98,19 @@ def calc(self, statId):
         
         # Reference for SE(prevalence):
         # https://stats.stackexchange.com/questions/9449/how-do-you-compute-confidence-intervals-for-positive-predictive-value
-        elif statId in ("SE(prevalence)"):  # Standard error for PPV
+        elif statId in ("SE(prevalence)"):  # Standard error for prevalence
             return sqrt( (self["prevalence"]*(1-self["prevalence"]))/self["total"] );
 
+        # Note that this assumes the prevalence estimate is normally / Gaussian distributed
+        #   if we assume that ~2 std errors from mean estimate reflect ~95% confidence interval.
+        # This is not true however, as probabilities like prevalence and PPV have values constrained
+        #   to [0,1], so a Beta distribution is a better description. 
+        # I'm not sure that there is a closed form expression for the 95% confidence interval of
+        #   a Beta distribution parameter estimate. This one may be good enough in most cases,
+        #   but may sometimes yield negative of >1 values that don't make sense if limited data.
+        #   If you want a more robust estimate, it would likely require bootstrapping 
+        #   to empirically estimate confidence intervals by simulating random resampling.
+        # https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
         elif statId in ("prevalence95CILow"):
             return self["prevalence"] - 1.96*self["SE(prevalence)"]
 
@@ -224,7 +234,7 @@ def calc(self, statId):
                     return -logP;
                 else:
                     return logP;
-            except ValueError, exc:
+            except ValueError as exc:
                 # Likely from negative table values.  Return default / uncertain value
                 return 0.0;
             
@@ -249,7 +259,7 @@ def calc(self, statId):
                     return -logP;
                 else:
                     return logP;
-            except ValueError, exc:
+            except ValueError as exc:
                 # Likely from negative table values.  Return default / uncertain value
                 return 0.0;
             
@@ -257,7 +267,7 @@ def calc(self, statId):
             try:
                 (oddsRatio, fisherP) = fisher_exact(ct);
                 return fisherP;
-            except ValueError, exc:
+            except ValueError as exc:
                 # Negative table values.  Return default / uncertain value
                 return 1.0;
         
@@ -269,7 +279,7 @@ def calc(self, statId):
                     return (1-fisherP);
                 else:
                     return (fisherP-1);
-            except ValueError, exc:
+            except ValueError as exc:
                 return 0.0;
 
         elif statId in ("P-Fisher-NegLog",):
@@ -286,7 +296,7 @@ def calc(self, statId):
                     return -logP;
                 else:
                     return logP;
-            except ValueError, exc:
+            except ValueError as exc:
                 # Likely from negative table values.  Return default / uncertain value
                 return 0.0;
         else:
diff --git a/medinfo/common/Util.py b/medinfo/common/Util.py
index f00744dd..a4f69121 100644
--- a/medinfo/common/Util.py
+++ b/medinfo/common/Util.py
@@ -1,16 +1,16 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
-import urllib;
+import urllib.request, urllib.parse, urllib.error;
 import time;
 import re;
 from datetime import datetime, timedelta;
 import math;
 import json;
-from Const import DEFAULT_DATE_FORMATS, NULL_STRING, FALSE_STRINGS;
+from .Const import DEFAULT_DATE_FORMATS, NULL_STRING, FALSE_STRINGS;
 
 log = logging.getLogger("CDSS")
 log.setLevel(Const.LOGGER_LEVEL)
@@ -53,6 +53,8 @@ def stdOpen(filename,mode="r",stdFile=None):
     else:
         if filename.endswith(Const.GZIP_EXT):
             import gzip;
+            # in Python 3+ 'w' is interpreted as binary and 'wt' is interpreted as text mode
+            mode = 'wt' if mode == 'w' else mode
             return gzip.open(filename,mode);
         else:
             return open(filename,mode)
@@ -66,7 +68,7 @@ def loadJSONDict(sourceStr, keyType=None, valueType=None):
     if keyType is not None and valueType is not None:
         origDict = resultDict;
         resultDict = dict();    # Create a new copy with customized datatypes
-        for origKey, origValue in origDict.iteritems():
+        for origKey, origValue in origDict.items():
             newKey = origKey;
             newValue = origValue;
             if keyType is not None: 
@@ -158,7 +160,7 @@ def printStatus(self):
                 if self.count > 0:
                     secondsRemaining = (self.total - self.count) * (secondsElapsed / self.count);
                 estimateStr = ", ~%s until %d done" % (timedelta(0,round(secondsRemaining)), self.total);
-            print >> self.stream, "%d %s after %s%s" % (self.count, self.name, timedelta(0,round(secondsElapsed)), estimateStr );
+            print("%d %s after %s%s" % (self.count, self.name, timedelta(0,round(secondsElapsed)), estimateStr ), file=self.stream);
             self.stream.flush();
 
 
@@ -175,7 +177,9 @@ def fileLineCount(inputFile):
 def parseDateValue(chunk,dateFormat=None):
     """Parse the string chunk into a datetime object using specified or default date format strings
     """
-    if chunk == NULL_STRING:
+    # Check for both NULL_STRING and None value, as timestamp fields
+    # seem to return None value (e.g. stride_order_proc.result_time).
+    if chunk == NULL_STRING or chunk == None:
         return None;
     if isinstance(chunk,datetime):
         # Already parsed, no reason to repeat
diff --git a/medinfo/common/support/awaitProcess.py b/medinfo/common/support/awaitProcess.py
index 2145e6e7..2f904ae8 100644
--- a/medinfo/common/support/awaitProcess.py
+++ b/medinfo/common/support/awaitProcess.py
@@ -18,7 +18,7 @@ def main(argv):
     parser.add_option("-c", "--commandStr",  dest="commandStr",  help="Command string to execute after the specified PID is longer found.  Will just pass whole string to subprocess");
     (options, args) = parser.parse_args(argv[1:])
 
-    print >> sys.stderr, "Starting: "+str.join(" ", argv)
+    print("Starting: "+str.join(" ", argv), file=sys.stderr)
     timer = time.time();
     if options.pid and options.commandStr:
         pid = int(options.pid);
@@ -30,15 +30,15 @@ def main(argv):
             prog.update();
         prog.printStatus();
         
-        print >> sys.stderr, "Executing: ", options.commandStr;
+        print("Executing: ", options.commandStr, file=sys.stderr);
         process = subprocess.Popen(options.commandStr);
-        print >> sys.stderr, "Started process: ", process.pid;
+        print("Started process: ", process.pid, file=sys.stderr);
     else:
         parser.print_help()
         sys.exit(-1)
 
     timer = time.time() - timer;
-    print >> sys.stderr, ("%.3f seconds to complete" % timer);
+    print(("%.3f seconds to complete" % timer), file=sys.stderr);
 
 def pidExists(pid):
     """Return True/False whether the given PID exists / is active"""
diff --git a/medinfo/common/support/clean.py b/medinfo/common/support/clean.py
index bbd5674e..db158535 100644
--- a/medinfo/common/support/clean.py
+++ b/medinfo/common/support/clean.py
@@ -13,5 +13,5 @@
     for file in files:
         if file.endswith('.pyc'):
             filepath = os.path.join(root,file)
-            print "Removing %s" % filepath
+            print("Removing %s" % filepath)
             os.remove(filepath)
diff --git a/medinfo/common/support/sleeper.py b/medinfo/common/support/sleeper.py
index 96b56249..99f0e962 100644
--- a/medinfo/common/support/sleeper.py
+++ b/medinfo/common/support/sleeper.py
@@ -7,6 +7,6 @@
 duration = int(sys.argv[1]);
 
 progress = ProgressDots();
-for i in xrange(duration):
+for i in range(duration):
 	progress.update();
 progress.printStatus();
diff --git a/medinfo/common/test/TestStatsUtil.py b/medinfo/common/test/TestStatsUtil.py
index 3c3f586e..adb7f92b 100644
--- a/medinfo/common/test/TestStatsUtil.py
+++ b/medinfo/common/test/TestStatsUtil.py
@@ -2,12 +2,12 @@
 """Test case for respective module in parent package"""
 
 import sys, os
-import cStringIO
+import io
 import logging
 import unittest
 from math import sqrt, exp, log as ln;
 
-import Const, Util
+from . import Const, Util
 
 from medinfo.common.StatsUtil import AggregateStats, ContingencyStats, UnrecognizedStatException;
 from medinfo.common.test.Util import MedInfoTestCase
@@ -45,28 +45,28 @@ def tearDown(self):
         MedInfoTestCase.tearDown(self);
 
     def test_minMax(self):
-        self.assertAlmostEquals(self.MIN, self.aAggregateStats.min())
-        self.assertAlmostEquals(self.MAX, self.aAggregateStats.max())
+        self.assertAlmostEqual(self.MIN, self.aAggregateStats.min())
+        self.assertAlmostEqual(self.MAX, self.aAggregateStats.max())
 
     def test_meanStdDev(self):
-        self.assertAlmostEquals(self.MEAN,   self.aAggregateStats.mean())
-        self.assertAlmostEquals(self.STD_DEV,self.aAggregateStats.stdDev())
+        self.assertAlmostEqual(self.MEAN,   self.aAggregateStats.mean())
+        self.assertAlmostEqual(self.STD_DEV,self.aAggregateStats.stdDev())
 
     def test_weightedMeanStdDev(self):
-        self.assertAlmostEquals(self.MEAN_W,     self.aAggregateStats.meanW())
-        self.assertAlmostEquals(self.STD_DEV_W,  self.aAggregateStats.stdDevW())
+        self.assertAlmostEqual(self.MEAN_W,     self.aAggregateStats.meanW())
+        self.assertAlmostEqual(self.STD_DEV_W,  self.aAggregateStats.stdDevW())
 
     def test_rmsd(self):
-        self.assertAlmostEquals(self.RMSD,   self.aAggregateStats.rmsd(self.MEAN))
-        self.assertAlmostEquals(self.RMSD_W, self.aAggregateStats.rmsdW(self.MEAN_W))
+        self.assertAlmostEqual(self.RMSD,   self.aAggregateStats.rmsd(self.MEAN))
+        self.assertAlmostEqual(self.RMSD_W, self.aAggregateStats.rmsdW(self.MEAN_W))
 
         Util.log.debug("Negative test case");
-        self.assertNotAlmostEquals(self.RMSD_W,  self.aAggregateStats.rmsd(self.MEAN))
-        self.assertNotAlmostEquals(self.RMSD,    self.aAggregateStats.rmsdW(self.MEAN_W))
+        self.assertNotAlmostEqual(self.RMSD_W,  self.aAggregateStats.rmsd(self.MEAN))
+        self.assertNotAlmostEqual(self.RMSD,    self.aAggregateStats.rmsdW(self.MEAN_W))
 
     def test_counts(self):
-        self.assertEquals( self.N_NON_NAN_VALUES,            self.aAggregateStats.countNonNull() )
-        self.assertEquals( self.N_NON_ZERO_WEIGHTED_VALUES,  self.aAggregateStats.countNonZeroWeight() )
+        self.assertEqual( self.N_NON_NAN_VALUES,            self.aAggregateStats.countNonNull() )
+        self.assertEqual( self.N_NON_ZERO_WEIGHTED_VALUES,  self.aAggregateStats.countNonZeroWeight() )
 
     def test_incrementStats(self):
 
@@ -83,9 +83,9 @@ def test_incrementStats(self):
         expectedVariance = 3.81;
         expectedWeight = 7;
 
-        self.assertAlmostEquals(expectedMean, newMean, 3);
-        self.assertAlmostEquals(expectedVariance, newVariance, 3);
-        self.assertAlmostEquals(expectedWeight, newWeight, 3);
+        self.assertAlmostEqual(expectedMean, newMean, 3);
+        self.assertAlmostEqual(expectedVariance, newVariance, 3);
+        self.assertAlmostEqual(expectedWeight, newWeight, 3);
 
 class TestContingencyStats(MedInfoTestCase):
     def setUp(self):
@@ -170,18 +170,18 @@ def setUp(self):
                 "relativeRisk95CILow": ("RR95CILow",),
                 "relativeRisk95CIHigh": ("RR95CIHigh",),
             }
-        for statId, synonymIds in synonymsByStatId.iteritems():
+        for statId, synonymIds in synonymsByStatId.items():
             for synonymId in synonymIds:
                 self.EXPECTED[synonymId] = self.EXPECTED[statId];
 
     def test_contingencyStats(self):
         contStats = ContingencyStats( self.TEST_NAB, self.TEST_NA, self.TEST_NB, self.TEST_TOTAL );
 
-        for statId, expectedValue in self.EXPECTED.iteritems():
+        for statId, expectedValue in self.EXPECTED.items():
             Util.log.debug(statId);
             # log.debug(statId)
             testValue = contStats.calc(statId);
-            self.assertAlmostEquals( expectedValue, testValue, 3 );
+            self.assertAlmostEqual( expectedValue, testValue, 3 );
 
     def test_contingencyStats_normalize(self):
         # Set test values that will result in divide by zero or negative unless normalize somehow
@@ -211,10 +211,10 @@ def test_contingencyStats_normalize(self):
             {   "sensitivity": 0.4,
                 "specificity": 2.0, # Makes no sense with negative numbers
             }
-        for statId, expectedValue in expected.iteritems():
+        for statId, expectedValue in expected.items():
             Util.log.debug(statId);
             testValue = contStats.calc(statId);
-            self.assertAlmostEquals( expectedValue, testValue, 3 );
+            self.assertAlmostEqual( expectedValue, testValue, 3 );
 
         expected = \
             {   "P-Fisher": 1.0,    # Cannot calculate this properly with negative numbers
@@ -223,10 +223,10 @@ def test_contingencyStats_normalize(self):
             }
 
         Util.log.debug("Expect calculation error with default values returned");
-        for statId, expectedValue in expected.iteritems():
+        for statId, expectedValue in expected.items():
             Util.log.debug(statId);
             testValue = contStats.calc(statId);
-            self.assertAlmostEquals( expectedValue, testValue, 3 );
+            self.assertAlmostEqual( expectedValue, testValue, 3 );
 
         Util.log.debug("Now redo while adding normalization option");
         expected = \
@@ -240,10 +240,10 @@ def test_contingencyStats_normalize(self):
             }
 
         contStats.normalize(truncateNegativeValues=True);
-        for statId, expectedValue in expected.iteritems():
+        for statId, expectedValue in expected.items():
             Util.log.debug(statId);
             testValue = contStats.calc(statId);
-            self.assertAlmostEquals( expectedValue, testValue, 3 );
+            self.assertAlmostEqual( expectedValue, testValue, 3 );
 
     def test_precisionTolerance(self):
         # Try setting very small values and validating that won't lose values during numerical changes
@@ -258,10 +258,10 @@ def test_precisionTolerance(self):
                 "sensitivity": 1.5e-163,
                 "specificity": 1.0, # Precision inevitably lost here
             }
-        for statId, expectedValue in expected.iteritems():
+        for statId, expectedValue in expected.items():
             Util.log.debug(statId);
             testValue = contStats.calc(statId);
-            self.assertAlmostEquals( expectedValue, testValue, 3 );
+            self.assertAlmostEqual( expectedValue, testValue, 3 );
 
 class TestUnitTestTools(MedInfoTestCase):
     def test_assertEqualsGeneral(self):
diff --git a/medinfo/common/test/Util.py b/medinfo/common/test/Util.py
index f21c3865..c893551a 100644
--- a/medinfo/common/test/Util.py
+++ b/medinfo/common/test/Util.py
@@ -1,11 +1,17 @@
-import Const
+from . import Const
 import sys, os
+import math
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 import re;
+import json;
 
 import medinfo.common.Util;
+from medinfo.common.Const import COMMENT_TAG, NULL_STRING;
+from medinfo.db.Model import SQLQuery, RowItemModel;
+
 
 log = logging.getLogger("CDSS")
 
@@ -13,7 +19,7 @@
 formatter = logging.Formatter(Const.LOGGER_FORMAT)
 
 handler.setFormatter(formatter)
-log.addHandler(handler)
+# log.addHandler(handler)
 
 # Suppress uninteresting application output
 medinfo.common.Util.log.setLevel(Const.APP_LOGGER_LEVEL)
@@ -37,7 +43,7 @@ def setUp(self):
             #   to avoid potentially running a test case against a "real" production DB
             import medinfo.db.Env;
             medinfo.db.Env.DB_PARAM["HOST"] = "Test_cases_that_include_DB_access_should_inherit_from_MedInfo.DB.test.Util.DBTestCase_for_safety_against_accidentally_overriding_real_DB_data";
-        except Exception, exc:
+        except Exception as exc:
             log.info("No medinfo.db package found.  Not necessary if no DB access planned for testing");
 
     def tearDown(self):
@@ -129,20 +135,23 @@ def assertEqualNonWhitespace( self, verifyStr, sampleStr ):
         errorMsg = verifyStr + "\n!=\n" + sampleStr;
         self.assertEqual(verifySimpleStr, sampleSimpleStr, errorMsg);
 
+    def assertEqualSet(self, verifySet, sampleSet):
+        self.assertEqualList(sorted(verifySet), sorted(sampleSet))
+
     def assertEqualList( self, verifyList, sampleList ):
         """Assumes the two parameters are each lists or tuples and
         does an "assertEqual" on each pair of items.
         Check item by item rather than whole list
         so errors are easier to debug.
         """
-        errorStr  = "%d != %d\n" % (len(verifyList),len(sampleList));
+        errorStr  = "%d != %d\n" % (len(verifyList), len(sampleList));
         errorStr += str(sampleList);
+        self.assertEqual(len(verifyList), len(sampleList), errorStr)
 
         for verifyItem, sampleItem in zip(verifyList, sampleList):
             self.assertEqual(verifyItem, sampleItem)
-        self.assertEqual(len(verifyList),len(sampleList), errorStr)
 
-    def assertAlmostEqualsList( self, verifyList, sampleList ):
+    def assertAlmostEqualsList( self, verifyList, sampleList, places=7 ):
         """Assumes the two parameters are each lists or tuples and
         does an "assertAlmostEqual" on each pair of items.
         Check item by item rather than whole list
@@ -151,10 +160,9 @@ def assertAlmostEqualsList( self, verifyList, sampleList ):
         errorStr  = "%d != %d\n" % (len(verifyList),len(sampleList));
         errorStr += str(sampleList);
 
-        self.assertEqual(len(verifyList),len(sampleList), errorStr)
+        self.assertEqual(len(verifyList),len(sampleList), errorStr);
         for verifyItem, sampleItem in zip(verifyList, sampleList):
-            self.assertAlmostEquals(verifyItem, sampleItem)
-
+            self.assertAlmostEqual(verifyItem, sampleItem, places);
 
     def assertEqualTable( self, verifyTable, sampleTable, precision=None ):
         """Assumes the two parameters are each 2D lists or tuples and
@@ -164,58 +172,58 @@ def assertEqualTable( self, verifyTable, sampleTable, precision=None ):
         If the precision parameter is specified, will do assertEqualGeneral
         instead with option to compare floating point numbers.
         """
+        self.assertEqual(len(verifyTable), len(sampleTable))
         for verifyRow, sampleRow in zip(verifyTable, sampleTable):
             #print >> sys.stderr, "Verify: %s" % str(verifyRow);
             #print >> sys.stderr, "Sample: %s" % str(sampleRow);
+            self.assertEqual(len(verifyRow),len(sampleRow))
             for verifyItem, sampleItem in zip(verifyRow, sampleRow):
                 if precision is None:
                     self.assertEqual(verifyItem, sampleItem)
                 else:
                     self.assertEqualGeneral(verifyItem, sampleItem, precision);
-            self.assertEqual(len(verifyRow),len(sampleRow))
-        self.assertEqual(len(verifyTable),len(sampleTable))
 
     def assertEqualDict( self, verifyDict, sampleDict, targetKeys=None):
         """Assumes two params are two dict and does a assertEqual on each key, val.
         If targetKeys is provided, will only check equality based on those keyed items.
         """
         if targetKeys is None:
-            verifyKeyList = verifyDict.keys();
-            sampleKeyList = sampleDict.keys();
+            verifyKeyList = list(verifyDict.keys());
+            sampleKeyList = list(sampleDict.keys());
             verifyKeyList.sort();
             sampleKeyList.sort();
+            self.assertEqual(len(verifyKeyList), len(sampleKeyList));
             for vKey, sKey in zip(verifyKeyList, sampleKeyList):
-                self.assertEquals(vKey, sKey);
+                self.assertEqual(vKey, sKey);
                 msg = 'Dicts diff with vKey : %s = %s, and sKey: %s = %s'
-                self.assertEquals(verifyDict[vKey], sampleDict[sKey], msg % (vKey, str(verifyDict[vKey]), sKey, str(sampleDict[sKey])));
-            self.assertEqual(len(verifyKeyList), len(sampleKeyList));
+                self.assertEqual(verifyDict[vKey], sampleDict[sKey], msg % (vKey, str(verifyDict[vKey]), sKey, str(sampleDict[sKey])));
         else:
             for key in targetKeys:
                 msg = 'Dicts diff with key (%s).  Verify = %s, Sample = %s'
-                self.assertEquals(verifyDict[key], sampleDict[key], msg % (key, str(verifyDict[key]), str(sampleDict[key])));
+                self.assertEqual(verifyDict[key], sampleDict[key], msg % (key, str(verifyDict[key]), str(sampleDict[key])));
 
 
     def assertEqualDictList( self, verifyList, sampleList, targetKeys=None ):
         """Assumes the two parameters are each lists of dictionary objects
         and check if equal based on the specified targetKeys (otherwise assume all)
         """
+        self.assertEqual(len(verifyList),len(sampleList) );
         for verifyItem, sampleItem in zip(verifyList, sampleList):
             self.assertEqualDict(verifyItem, sampleItem, targetKeys);
-        self.assertEqual(len(verifyList),len(sampleList) );
 
     def assertAlmostEqualsDict( self, verifyDict, sampleDict, msg=None, places=7 ):
         """Assumes two params are two dicts with numeric vals and does an assertAlmostEquals on each val"""
-        verifyKeyList = verifyDict.keys();
-        sampleKeyList = sampleDict.keys();
+        verifyKeyList = list(verifyDict.keys());
+        sampleKeyList = list(sampleDict.keys());
         self.assertEqual(len(verifyKeyList), len(sampleKeyList), msg);
-        verifyKeyList.sort();
-        sampleKeyList.sort();
+        verifyKeyList.sort(key=lambda x: x if x is not None else -math.inf) # Python2 sort keeps None at the top while Python3 doesn't allow NoneType and int comparison
+        sampleKeyList.sort(key=lambda x: x if x is not None else -math.inf) # Python2 sort keeps None at the top while Python3 doesn't allow NoneType and int comparison
         for vKey, sKey in zip(verifyKeyList, sampleKeyList):
-            self.assertEquals(vKey, sKey, msg);
+            self.assertEqual(vKey, sKey, msg);
             if isinstance(verifyDict[vKey], float):
-                self.assertAlmostEquals(verifyDict[vKey], sampleDict[sKey], places=places, msg=msg);
+                self.assertAlmostEqual(verifyDict[vKey], sampleDict[sKey], places=places, msg=msg);
             else:
-                self.assertEquals(verifyDict[vKey], sampleDict[sKey], msg=msg);
+                self.assertEqual(verifyDict[vKey], sampleDict[sKey], msg=msg);
 
 
     def diffDict(inputDict1, inputDict2):
@@ -224,7 +232,7 @@ def diffDict(inputDict1, inputDict2):
         minus the values from inputDict1.
         """
         resultDict = dict(inputDict2);  # Start with a copy of second dictionary
-        for key, value in inputDict1.iteritems():   # Now look for values to substract from 1st dictionary
+        for key, value in inputDict1.items():   # Now look for values to substract from 1st dictionary
             if key not in resultDict:
                 resultDict[key] = 0;
             resultDict[key] -= value;
@@ -238,7 +246,7 @@ def formatDict(inputDict):
         can be easily translated into tab-delimited format for table manipulation.
         """
         dictStrList = ["{"];
-        sortedKeys = inputDict.keys();
+        sortedKeys = list(inputDict.keys());
         sortedKeys.sort();
         for key in sortedKeys:
             value = inputDict[key];
@@ -249,7 +257,58 @@ def formatDict(inputDict):
     formatDict = staticmethod(formatDict);
 
 
-class MockCGIFieldStorage(UserDict.UserDict):
+    def assertEqualStatResults(self, expectedResults, analysisResults, colNames):
+        self.assertEqual(len(expectedResults), len(analysisResults))
+        for expectedDict, analysisDict in zip(expectedResults, analysisResults):
+            #print >> sys.stderr, colNames;
+            #print >> sys.stderr, expectedDict.valuesByName(colNames);
+            #print >> sys.stderr, analysisDict.valuesByName(colNames);
+            for key in colNames:
+                expectedValue = expectedDict[key];
+                analysisValue = analysisDict[key];
+                try:    # Assume numerical values and just check for "close enough"
+                    self.assertAlmostEqual( expectedValue, analysisValue, 3 );
+                except TypeError:   # Not numbers, then just use generic equals check
+                    self.assertEqual( expectedValue, analysisValue );
+
+    def assertEqualStatResultsTextOutput(self, expectedResults, textOutput, colNames):
+        # Convert the text output into a structured format to facilitate verification testing
+        headerLine = None;
+        while headerLine is None:
+            nextLine = textOutput.readline();
+            if not nextLine.startswith(COMMENT_TAG):
+                headerLine = nextLine;
+        headers = headerLine.strip().split("\t");
+
+        analysisResults = list();
+        for line in textOutput:
+            dataChunks = line.strip().split("\t");
+            resultModel = RowItemModel( dataChunks, headers );
+            # Convert the target elements of interest into numerical values
+            for col in colNames:
+                if resultModel[col] == NULL_STRING:
+                    resultModel[col] = None;
+                else:
+                    try:
+                        resultModel[col] = float(resultModel[col]);
+                    except ValueError:
+                        pass;   # Not a number, just leave it as original value then
+            analysisResults.append(resultModel);
+
+        self.assertEqualStatResults( expectedResults, analysisResults, colNames );
+
+    def extractJSONComment(self, dataFile):
+        """Iterate through lines of the file until find a comment line to
+        extract out a JSON data object."""
+        for line in dataFile:
+            if line.startswith(COMMENT_TAG):
+                jsonStr = line[1:].strip(); # Remove comment tag and any flanking whitespace
+                jsonData = json.loads(jsonStr);
+                return jsonData;
+        return None;
+
+
+class MockCGIFieldStorage(UserDict):
     """Mock object to simulate cgi.FieldStorage for testing purposes.
     All methods may not implemented (stupid, no defined
     interfaces / pure virtual abstract classes) so may need to add
@@ -266,3 +325,13 @@ def addMockField(self, key, value):
         'MyValue'
         """
         self[key] = cgi.MiniFieldStorage(key,value)
+
+def make_test_suite(module_name):
+    """
+    Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test".
+    """
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(module_name))
+    return suite
diff --git a/medinfo/cpoe/AssociationAnalysis.py b/medinfo/cpoe/AssociationAnalysis.py
index 4b21dc7d..938581d7 100644
--- a/medinfo/cpoe/AssociationAnalysis.py
+++ b/medinfo/cpoe/AssociationAnalysis.py
@@ -7,17 +7,15 @@
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery, generatePlaceholders;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
-from Env import DATE_FORMAT;
+from .Env import DATE_FORMAT;
 
-from DataManager import DataManager;
+from .DataManager import DataManager;
 
-from Const import DELTA_NAME_BY_SECONDS, SECONDS_PER_DAY;
+from .Const import DELTA_NAME_BY_SECONDS, SECONDS_PER_DAY;
 
-from Util import log;
+from .Util import log;
 
 
 class AnalysisOptions:
@@ -271,7 +269,7 @@ def updateItemAssociationsBuffer(self, patientItemList, updateBuffer, analysisOp
             updateBuffer["analyzedPatientItemIds"] = set();
         updateBuffer["analyzedPatientItemIds"].update(newlyAnalyzedPatientItemIdSet);
 
-    def updateClinicalItemAssociationBuffer(self, patientItem1, patientItem2, isNewSubsequentItem, isNewPair, isNewPairWithinEncounter, updateBuffer, analysisOptions, itemIdPair=None):
+    def updateClinicalItemAssociationBuffer(self, patientItem1, patientItem2, isNewSubsequentItem, isNewPair, isNewPairWithinEncounter, updateBuffer, analysisOptions=None, itemIdPair=None):
         """Identify and record in the updateBuffer which statistics on associations
         between the two clinical items based on the new piece of observed item pair evidence given.
         Use isNew parameters to delineate uniqueness parameters to determine whether to count this pair for different metrics.
@@ -280,9 +278,11 @@ def updateClinicalItemAssociationBuffer(self, patientItem1, patientItem2, isNewS
         If itemIdPair explicitly specified, then use that instead.
         """
         # Determine which time threshold count windows to update
-        deltaSecondsOptions = analysisOptions.deltaSecondsOptions;
-        if deltaSecondsOptions is None:
-            deltaSecondsOptions = DELTA_NAME_BY_SECONDS.keys();
+        deltaSecondsOptions = None;
+        if analysisOptions is not None and analysisOptions.deltaSecondsOptions is not None:
+            deltaSecondsOptions = analysisOptions.deltaSecondsOptions;
+        else:
+            deltaSecondsOptions = list(DELTA_NAME_BY_SECONDS.keys());
 
         # Convert timeDelta object into simple numerical representation (seconds as a real number) to facilitate some arithmetic
         timeDelta = patientItem2["item_date"] - patientItem1["item_date"];
@@ -360,11 +360,11 @@ def mergeBuffers(self, bufferOne, bufferTwo):
             bufferTwo["incrementDataByItemIdPair"] = dict()
 
         # then fill up any remaining id pairs from buffer two. if you have some in buffer two that are also in buffer one, then add those specific ones together
-        for key, value in bufferTwo["incrementDataByItemIdPair"].iteritems():
+        for key, value in bufferTwo["incrementDataByItemIdPair"].items():
             if key not in bufferOne["incrementDataByItemIdPair"]:
                 bufferOne["incrementDataByItemIdPair"][key] = bufferTwo["incrementDataByItemIdPair"][key]
             else:
-                for key2, value2 in bufferTwo["incrementDataByItemIdPair"][key].iteritems():
+                for key2, value2 in bufferTwo["incrementDataByItemIdPair"][key].items():
                     if key2 not in bufferOne["incrementDataByItemIdPair"][key]:
                         bufferOne["incrementDataByItemIdPair"][key][key2]=bufferTwo["incrementDataByItemIdPair"][key][key2]
                     else:
@@ -374,8 +374,8 @@ def mergeBuffers(self, bufferOne, bufferTwo):
 
     def bufferDecay (self, bufferDecay, decayValue):
         if "incrementDataByItemIdPair" in bufferDecay:
-            for key, value in bufferDecay["incrementDataByItemIdPair"].iteritems():
-                for key2, value2 in bufferDecay["incrementDataByItemIdPair"][key].iteritems():
+            for key, value in bufferDecay["incrementDataByItemIdPair"].items():
+                for key2, value2 in bufferDecay["incrementDataByItemIdPair"][key].items():
                     bufferDecay["incrementDataByItemIdPair"][key][key2] *= decayValue
         return bufferDecay
 
@@ -400,12 +400,13 @@ def saveBufferToFile (self, filename, updateBuffer):
     def loadUpdateBufferFromFile(self, filename):
         updateBuffer = None;
         try:
+            #print >> sys.stderr, filename
             log.info("Loading: %s" % filename);
             ifs = stdOpen(filename, "r")
             updateBuffer = json.load(ifs)
             updateBuffer["analyzedPatientItemIds"] = set(updateBuffer["analyzedPatientItemIds"])
             ifs.close()
-        except IOError, exc:
+        except IOError as exc:
             # Apparently could not find the named filename. See if instead it's a prefix
             #    for a series of enumerated files and then merge them into one mass buffer
             dirname = os.path.dirname(filename);
@@ -441,7 +442,7 @@ def commitUpdateBuffer(self, updateBuffer, linkedItemIdsByBaseId, conn=None):
         try:
             if "incrementDataByItemIdPair" in updateBuffer:
                 # Ensure baseline records exist to facilitate subsequent incremental update queries
-                itemIdPairs = updateBuffer["incrementDataByItemIdPair"].keys();
+                itemIdPairs = list(updateBuffer["incrementDataByItemIdPair"].keys());
                 self.prepareItemAssociations(itemIdPairs, linkedItemIdsByBaseId, conn);
 
                 # Construct incremental update queries based on each item pair's incremental counts/sums
@@ -451,9 +452,9 @@ def commitUpdateBuffer(self, updateBuffer, linkedItemIdsByBaseId, conn=None):
                 incrementProg.total = nItemPairs;
                 cursor = conn.cursor();
                 try:
-                    for (itemIdPair, incrementData) in updateBuffer["incrementDataByItemIdPair"].iteritems():
+                    for (itemIdPair, incrementData) in updateBuffer["incrementDataByItemIdPair"].items():
                         query = ["UPDATE clinical_item_association SET"];
-                        for col, increment in incrementData.iteritems():
+                        for col, increment in incrementData.items():
                             query.append("%(col)s=%(col)s+%(increment)s" % {"col":col,"increment":increment});
                             query.append(",");
                         query.pop();    # Drop extra comma at end of list
@@ -556,7 +557,7 @@ def prepareItemAssociations(self, itemIdPairs, linkedItemIdsByBaseId, conn):
                     defaultAssociation = RowItemModel( itemIdPair, ("clinical_item_id","subsequent_item_id") );
                     try:    # Optimistic insert of a new item pair, should be safe since just checked above, but parallel processes may collide
                         DBUtil.insertRow("clinical_item_association", defaultAssociation, conn=conn);
-                    except IntegrityError, err:
+                    except conn.IntegrityError as err:
                         log.warning(err);
                         pass;
 
diff --git a/medinfo/cpoe/Const.py b/medinfo/cpoe/Const.py
index 64fa35d3..14d4ca7d 100644
--- a/medinfo/cpoe/Const.py
+++ b/medinfo/cpoe/Const.py
@@ -2,7 +2,7 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 from datetime import datetime, timedelta;
 
 """Application name","for example to identify a common logger object"""
@@ -37,8 +37,8 @@
     }
 """Similar list but starting in units of 1 day"""
 DELTA_NAME_BY_DAYS = dict();
-for seconds, label in DELTA_NAME_BY_SECONDS.iteritems():
-    days = seconds/ SECONDS_PER_DAY;
+for seconds, label in DELTA_NAME_BY_SECONDS.items():
+    days = seconds // SECONDS_PER_DAY;
     if days > 0:
         DELTA_NAME_BY_DAYS[days] = label;
 
@@ -66,11 +66,3 @@
 These are the "ad-hoc" individual orders that users can tack on to an order set at any one time.
 """
 AD_HOC_SECTION = "Ad-hoc Orders";
-
-"""Arbitrary base time to start simulations from"""
-BASE_TIME = datetime(2010,1,1,12,0);    
-TIME_FORMAT = "%m/%d/%Y, %I:%M%p";
-
-
-"""ID of the default simulation state to fall back on if have no other state-specific results/information"""
-DEFAULT_STATE_ID = 0;
\ No newline at end of file
diff --git a/medinfo/cpoe/DataManager.py b/medinfo/cpoe/DataManager.py
index 2052d8a5..1b1eceaf 100644
--- a/medinfo/cpoe/DataManager.py
+++ b/medinfo/cpoe/DataManager.py
@@ -10,13 +10,9 @@
 from datetime import datetime;
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery, RowItemModel, generatePlaceholders;
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
-from Util import log;
-
-IntegrityError = DBUtil.DB_CONNECTOR_MODULE.IntegrityError;
+from .Util import log;
 
 class DataManager:
     connFactory = None;
@@ -268,8 +264,8 @@ def compositeRelated(self, clinicalItemIds, itemName, itemDescription, categoryI
             if compositeId is not None:
                 compositeItem["clinical_item_id"] = compositeId;
 
-            insertQuery = DBUtil.buildInsertQuery("clinical_item", compositeItem.keys() );
-            insertParams= compositeItem.values();
+            insertQuery = DBUtil.buildInsertQuery("clinical_item", list(compositeItem.keys()) );
+            insertParams= list(compositeItem.values());
             DBUtil.execute( insertQuery, insertParams, conn=conn);
             if compositeId is None:
                 compositeId = DBUtil.execute( DBUtil.identityQuery("clinical_item"), conn=conn )[0][0];   # Retrieve the just inserted item's ID
@@ -294,8 +290,8 @@ def generatePatientItemsForCompositeId(self, clinicalItemIds, compositeId, conn=
                 linkModel["clinical_item_id"] = compositeId;
                 linkModel["linked_item_id"] = componentId;
 
-                insertQuery = DBUtil.buildInsertQuery("clinical_item_link", linkModel.keys() );
-                insertParams= linkModel.values();
+                insertQuery = DBUtil.buildInsertQuery("clinical_item_link", list(linkModel.keys()) );
+                insertParams= list(linkModel.values());
                 DBUtil.execute(insertQuery, insertParams, conn=conn);
 
             # Extract back link information, which will also flatten out any potential inherited links
@@ -318,13 +314,13 @@ def generatePatientItemsForCompositeId(self, clinicalItemIds, compositeId, conn=
                 patientItem["clinical_item_id"] = compositeId;
                 patientItem["analyze_date"] = None;
 
-                insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-                insertParams= patientItem.values();
+                insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+                insertParams= list(patientItem.values());
 
                 try:
                     # Optimistic insert of a new unique item
                     DBUtil.execute( insertQuery, insertParams, conn=conn );
-                except IntegrityError, err:
+                except conn.IntegrityError as err:
                     # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
                     log.info(err);
                 progress.Update();
@@ -414,7 +410,7 @@ def mergeRelated(self, baseClinicalItemId, clinicalItemIds, reassignMergedItems=
                     try:
                         # Optimistic insert of a new unique item
                         DBUtil.execute( insertQuery, insertParams, conn=conn );
-                    except IntegrityError, err:
+                    except conn.IntegrityError as err:
                         # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
                         log.info(err);
                         pass;
@@ -500,7 +496,7 @@ def loadLinkedItemIdsByBaseId(self, maxItemId=None, conn=None):
                 lookForNewLinks = False;
                 inheritanceDepth += 1;
 
-                for (clinicalItemId, linkedItemIdSet) in linkedItemIdsByBaseId.iteritems():
+                for (clinicalItemId, linkedItemIdSet) in linkedItemIdsByBaseId.items():
                     linkedItemIdSetCopy = set(linkedItemIdSet); # Make copy as could be modifying set as iterate through it
                     for linkedItemId in linkedItemIdSetCopy:
                         if linkedItemId in linkedItemIdsByBaseId:
diff --git a/medinfo/cpoe/DecayingWindows.py b/medinfo/cpoe/DecayingWindows.py
index eb9b7ec8..681d5109 100644
--- a/medinfo/cpoe/DecayingWindows.py
+++ b/medinfo/cpoe/DecayingWindows.py
@@ -39,13 +39,11 @@
 from medinfo.common.Util import stdOpen
 from medinfo.cpoe.Env import DATE_FORMAT
 from medinfo.db import DBUtil
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError
 from medinfo.cpoe.test import TestAssociationAnalysis
 from medinfo.cpoe import AssociationAnalysis
 from medinfo.cpoe.test.Const import RUNNER_VERBOSITY
 from medinfo.cpoe.Const import DELTA_NAME_BY_SECONDS, SECONDS_PER_DAY;
-from Util import log;
+from .Util import log;
 
 class DecayAnalysisOptions:
 	"""Simple struct to pass filter parameters on which records to do analysis on"""
@@ -154,7 +152,7 @@ def decayAnalyzePatientItems(self, decayAnalysisOptions):
 			analysisOptions = AssociationAnalysis.AnalysisOptions()
 
 			if decayAnalysisOptions.outputFile is not None:
-				analysisOptions.outputFile = decayAnalysisOptions.outputFile
+				analysisOptions.bufferFile = decayAnalysisOptions.outputFile
 
 			# Decay any existing stats before learn new ones to increment
 			if currentBuffer is None:
@@ -179,14 +177,14 @@ def decayAnalyzePatientItems(self, decayAnalysisOptions):
 				deltaSeconds = decayAnalysisOptions.delta.total_seconds();
 				
 				# Look for smallest value that is still larger than the given delta.
-				smallestLargerDeltaOption = sys.maxint;	
-				for secondsOption in DELTA_NAME_BY_SECONDS.iterkeys():
+				smallestLargerDeltaOption = sys.maxsize;	
+				for secondsOption in DELTA_NAME_BY_SECONDS.keys():
 					if secondsOption > deltaSeconds:
 						smallestLargerDeltaOption = min(secondsOption, smallestLargerDeltaOption);
 
 				# Tell the AssociationAnalysis to only accrue data for time ranges within the size of the delta period specified
 				analysisOptions.deltaSecondsOptions = list();
-				for secondsOption in DELTA_NAME_BY_SECONDS.iterkeys():
+				for secondsOption in DELTA_NAME_BY_SECONDS.keys():
 					if secondsOption <= smallestLargerDeltaOption:
 						analysisOptions.deltaSecondsOptions.append(secondsOption);
 
diff --git a/medinfo/cpoe/ItemRecommender.py b/medinfo/cpoe/ItemRecommender.py
index fc8ca77b..3172121a 100644
--- a/medinfo/cpoe/ItemRecommender.py
+++ b/medinfo/cpoe/ItemRecommender.py
@@ -8,9 +8,10 @@
 """
 import sys, os
 import time;
+from operator import itemgetter
 from optparse import OptionParser;
 import json;
-import urlparse;
+import urllib.parse;
 import math;
 from datetime import datetime, timedelta;
 from medinfo.common.Const import FALSE_STRINGS, COMMENT_TAG;
@@ -22,12 +23,12 @@
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
 from medinfo.db.ResultsFormatter import TextResultsFormatter;
 
-from DataManager import DataManager;
+from .DataManager import DataManager;
 
-from Util import log;
-from Const import AGGREGATOR_OPTIONS;
-from Const import SECONDS_PER_DAY;
-from Const import CORE_FIELDS;
+from .Util import log;
+from .Const import AGGREGATOR_OPTIONS;
+from .Const import SECONDS_PER_DAY;
+from .Const import CORE_FIELDS;
 
 # List of fields that may be aggregated across results by a (weighted) average
 WEIGHTED_AVERAGE_FIELDS = ["nAB","nA"];
@@ -407,8 +408,8 @@ def populateAggregateStats(aggregateResult, query, statIds=None):
             nA' = Product((nAi-nAiB)/(N-nB)) * (N-nB) + nAB'
         """
         if statIds is None:
-            statIds = set([query.sortField]);
-            for (fieldOp, value) in query.fieldFilters.iteritems():
+            statIds = {query.sortField}
+            for (fieldOp, value) in query.fieldFilters.items():
                 if value is not None:
                     field = fieldOp[:-1];
                     statIds.add(field);
@@ -417,8 +418,8 @@ def populateAggregateStats(aggregateResult, query, statIds=None):
             componentResultsById = aggregateResult["componentResultsById"];
 
             # Fill in baseline counts directly, as values should be identical across components
-            aggregateResult["nB"] = componentResultsById.values()[0]["nB"];
-            aggregateResult["N"] = componentResultsById.values()[0]["N"];
+            aggregateResult["nB"] = list(componentResultsById.values())[0]["nB"];
+            aggregateResult["N"] = list(componentResultsById.values())[0]["N"];
 
             if query.aggregationMethod in ("weighted","unweighted"):
                 # Standard (weighted) average of component scores
@@ -426,27 +427,27 @@ def populateAggregateStats(aggregateResult, query, statIds=None):
                 aggregateResult["sum(nA*weight)"] = 0.0;
                 aggregateResult["sum(weight)"] = 0.0;
 
-                for componentId, component in componentResultsById.iteritems():
+                for componentId, component in componentResultsById.items():
                     component["weight"] = 1.0;
                     if query.aggregationMethod == "weighted":
                         # Weighted scaling of scores inversely proportional to the query item frequency,
                         #   so less common (and thus more specific) query items are paid more attention to in the aggregate recommendations
                         #   Though should beware this may give disproportionate weight to unusually rare query items
-                    	component["weight"] = 1.0 / component["nA"];
+                        component["weight"] = 1.0 / component["nA"];
 
                     aggregateResult["sum(nAB*weight)"] += (component["nAB"] * component["weight"]);
                     aggregateResult["sum(nA*weight)"] += (component["nA"] * component["weight"]);
                     aggregateResult["sum(weight)"] += component["weight"];
 
-            	aggregateResult["nAB"] = aggregateResult["sum(nAB*weight)"] / aggregateResult["sum(weight)"];   # Complete the weighted average score calculation
-            	aggregateResult["nA"] = aggregateResult["sum(nA*weight)"] / aggregateResult["sum(weight)"];
+                aggregateResult["nAB"] = aggregateResult["sum(nAB*weight)"] / aggregateResult["sum(weight)"];   # Complete the weighted average score calculation
+                aggregateResult["nA"] = aggregateResult["sum(nA*weight)"] / aggregateResult["sum(weight)"];
 
             elif query.aggregationMethod in ("NaiveBayes"):
                 # Naive Bayes products
                 aggregateResult["product(nAB/nB)"] = 1.0;
                 aggregateResult["product(nA/N)"] = 1.0;
 
-                for componentId, component in componentResultsById.iteritems():
+                for componentId, component in componentResultsById.items():
                     nAB_ = max(component["nAB"],DEGENERATE_VALUE_ADJUSTMENT);   # Small adjustment to avoid zero value that will wipe out all information in product
                     nA_ = max(component["nA"],DEGENERATE_VALUE_ADJUSTMENT); # Similar check should not be necessary
                     aggregateResult["product(nAB/nB)"] *= nAB_ / component["nB"];
@@ -478,7 +479,7 @@ def populateAggregateStats(aggregateResult, query, statIds=None):
                 aggregateResult["Product(nAB/nB)"] = 1.0;
                 aggregateResult["Product((nA-nAB)/(N-nB))"] = 1.0;
 
-                for componentId, component in componentResultsById.iteritems():
+                for componentId, component in componentResultsById.items():
                     nAB_ = max(component["nAB"],DEGENERATE_VALUE_ADJUSTMENT);   # Small adjustment to avoid zero value that will wipe out all information in product
                     nA_ = component["nA"];
                     aggregateResult["Product(nAB/nB)"] *= nAB_ / component["nB"];
@@ -501,7 +502,7 @@ def filterAggregateResultsByQuery( self, aggregateResultsByItemId, query ):
         """
         # Now collect and sort the aggregated results to return only the top relevant results
         aggregateResultsWithScore = list();
-        for aggregateResult in aggregateResultsByItemId.itervalues():
+        for aggregateResult in aggregateResultsByItemId.values():
             itemId = aggregateResult["clinical_item_id"];
 
             # Calculate and populate the aggregate result item with stats based on its component items
@@ -510,7 +511,7 @@ def filterAggregateResultsByQuery( self, aggregateResultsByItemId, query ):
 
             # Look for value filters
             excludeResult = False;
-            for (fieldOp, value) in query.fieldFilters.iteritems():
+            for (fieldOp, value) in query.fieldFilters.items():
                 if value is not None:
                     field = fieldOp[:-1];
                     op = fieldOp[-1];
@@ -521,7 +522,7 @@ def filterAggregateResultsByQuery( self, aggregateResultsByItemId, query ):
             if not excludeResult:
                 aggregateResultsWithScore.append( (aggregateResult[query.sortField], aggregateResult) );
 
-        aggregateResultsWithScore.sort();
+        aggregateResultsWithScore.sort(key=itemgetter(0));
         if query.sortReverse:
             aggregateResultsWithScore.reverse();    # Descending order of score to get top results
 
@@ -816,7 +817,7 @@ def organizeByCategory(self, resultModels):
             resultModel["categoryScore"] = cumulativeScoreByCategoryId[categoryId];
 
         # Re-sort result models first by category (score) then individual item score (descending order)
-        resultModels.sort(RowItemFieldComparator(["categoryScore","score"]), reverse=True);
+        resultModels.sort(key=itemgetter("categoryScore","score"), reverse=True);
 
         return resultModels;
 
@@ -883,10 +884,10 @@ def main(self, argv):
 
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             # Parse out query parameters
-            paramDict = dict(urlparse.parse_qsl(queryStr,True));
+            paramDict = dict(urllib.parse.parse_qsl(queryStr,True));
             query = RecommenderQuery();
             query.parseParams(paramDict);
             displayFields = query.getDisplayFields();
diff --git a/medinfo/cpoe/OrderSetRecommender.py b/medinfo/cpoe/OrderSetRecommender.py
index ac9acc2b..38a8c72f 100644
--- a/medinfo/cpoe/OrderSetRecommender.py
+++ b/medinfo/cpoe/OrderSetRecommender.py
@@ -6,9 +6,10 @@
 """
 import sys, os
 import time;
+from operator import itemgetter
 from optparse import OptionParser;
 import json;
-import urlparse;
+import urllib.parse;
 import math;
 from datetime import datetime, timedelta;
 from medinfo.common.Const import FALSE_STRINGS, COMMENT_TAG;
@@ -21,8 +22,8 @@
 from medinfo.db.ResultsFormatter import TextResultsFormatter;
 from medinfo.cpoe.ItemRecommender import BaseItemRecommender;
 from medinfo.cpoe.Const import COLLECTION_TYPE_ORDER_SET;
-from Util import log;
-from Const import AD_HOC_SECTION;
+from .Util import log;
+from .Const import AD_HOC_SECTION;
 
 class OrderSetRecommender(BaseItemRecommender):
     """Implementation class for item (e.g., order) recommendation based on existing order sets.
@@ -88,12 +89,12 @@ def initItemLookups(self, query):
         self.itemsById = DBUtil.loadTableAsDict("clinical_item");
         self.categoryIdByItemId = dict();
         self.patientCountByItemId = dict();
-        for itemId, item in self.itemsById.iteritems():
+        for itemId, item in self.itemsById.items():
             self.categoryIdByItemId[itemId] = item["clinical_item_category_id"];
             self.patientCountByItemId[itemId] = item["patient_count"];
         self.candidateItemIds = set();
         emptyQuerySet = set();
-        for itemId in self.orderSetIdsByItemId.keys():
+        for itemId in list(self.orderSetIdsByItemId.keys()):
             if self.isItemRecommendable(itemId, emptyQuerySet, query, self.categoryIdByItemId):
                 self.candidateItemIds.add(itemId);
 
@@ -120,15 +121,15 @@ def __call__(self, query):
         for itemId in self.candidateItemIds:
             if self.isItemRecommendable(itemId, queryItemCountById, query, self.categoryIdByItemId):
                 recScoreByItemId[itemId] = 0.0;
-        for orderSetId, orderSetWeight in weightByOrderSetId.iteritems():
-            for itemId in recScoreByItemId.keys():
+        for orderSetId, orderSetWeight in weightByOrderSetId.items():
+            for itemId in list(recScoreByItemId.keys()):
                 itemWeight = self.itemOrderSetWeight(itemId, orderSetId, self.itemIdsByOrderSetId);
                 recScoreByItemId[itemId] += orderSetWeight*itemWeight;
 
         # Build 2-pls with lists to sort by score
         recommendedData = list();
         numItemsInAnyOrderSet = len(self.orderSetIdsByItemId);
-        for itemId, totalItemWeight in recScoreByItemId.iteritems():
+        for itemId, totalItemWeight in recScoreByItemId.items():
             tfidf = 0.0;
 
             if itemId in self.orderSetIdsByItemId:
@@ -142,7 +143,7 @@ def __call__(self, query):
                 };
             itemModel["score"] = itemModel[query.sortField];
             recommendedData.append(itemModel);
-        recommendedData.sort( RowItemFieldComparator(["score","clinical_item_id"]), reverse=True);
+        recommendedData.sort(key=itemgetter("score", "clinical_item_id"), reverse=True)
         return recommendedData;
 
     def estimateOrderSetWeights(self, queryItemIds, itemIdsByOrderSetId, orderSetIdsByItemId):
@@ -155,7 +156,7 @@ def estimateOrderSetWeights(self, queryItemIds, itemIdsByOrderSetId, orderSetIds
         numItemsInAnyOrderSet = float(len(orderSetIdsByItemId));
 
         queryItemsInAnyOrderSet = set();
-        for orderSetId, itemIds in itemIdsByOrderSetId.iteritems():
+        for orderSetId, itemIds in itemIdsByOrderSetId.items():
             queryItemsInAnyOrderSet.update( itemIds.intersection(queryItemIds) );
         numQueryItemsInAnyOrderSet = float(len(queryItemsInAnyOrderSet));
         if numQueryItemsInAnyOrderSet < 1:  # Blank query or otherwise searching for things we have no data.
@@ -164,7 +165,7 @@ def estimateOrderSetWeights(self, queryItemIds, itemIdsByOrderSetId, orderSetIds
             numQueryItemsInAnyOrderSet = float(len(queryItemsInAnyOrderSet));
 
         weightByOrderSetId = dict();
-        for orderSetId, itemIds in itemIdsByOrderSetId.iteritems():
+        for orderSetId, itemIds in itemIdsByOrderSetId.items():
             numQueryItemsInOrderSet = len(itemIds.intersection(queryItemIds));
             weightByOrderSetId[orderSetId] = numQueryItemsInOrderSet / numQueryItemsInAnyOrderSet;
         return weightByOrderSetId;
diff --git a/medinfo/cpoe/SimManager.py b/medinfo/cpoe/SimManager.py
deleted file mode 100644
index fab7833f..00000000
--- a/medinfo/cpoe/SimManager.py
+++ /dev/null
@@ -1,684 +0,0 @@
-#!/usr/bin/env python
-"""
-Application / business logic and data management functions
-for interactive clinical state simulations.
-"""
-
-import sys, os
-import time;
-from optparse import OptionParser
-import math;
-from datetime import datetime;
-from medinfo.common.Util import stdOpen, ProgressDots;
-from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
-from medinfo.db.Model import SQLQuery, RowItemModel, generatePlaceholders;
-from medinfo.db.Model import modelListFromTable, modelDictFromList, columnFromModelList;
-from Util import log;
-from Const import AD_HOC_SECTION, DEFAULT_STATE_ID;
-
-IntegrityError = DBUtil.DB_CONNECTOR_MODULE.IntegrityError;
-
-class SimManager:
-    connFactory = None;
-
-    def __init__(self):
-        self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
-
-    def createUser(self, userData, conn=None):
-        """Create a new user record given the data dictionary
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            DBUtil.insertRow("sim_user", userData, conn=conn);
-            conn.commit();
-        finally:
-            if not extConn:
-                conn.close();
-    
-    def loadUserInfo(self, userIds=None, conn=None):
-        """Load basic information about the specified users
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            query.addSelect("su.sim_user_id");
-            query.addSelect("su.name");
-            query.addFrom("sim_user as su");
-            if userIds is not None:
-                query.addWhereIn("su.sim_user_id", userIds );
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            return dataModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def createPatient(self, patientData, initialStateId, conn=None):
-        """Create a new patient record given the data dictionary.
-        Create a respective initial patient state record as well.
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            DBUtil.insertRow("sim_patient", patientData, conn=conn);
-            patientId = DBUtil.execute(DBUtil.identityQuery("sim_patient"),conn=conn)[0][0];
-            patientStateData = {"sim_patient_id": patientId, "sim_state_id": initialStateId, "relative_time_start": 0};
-            DBUtil.insertRow("sim_patient_state", patientStateData, conn=conn);
-            
-            conn.commit();  # Transactional commit for two step process
-            return patientId;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def copyPatientTemplate(self, patientData, templatePatientId, conn=None):
-        """Create a new patient record based on the given template patient ID to copy from.
-        Will copy shallow attributes, overridden by any provided in the given patientData,
-        as well as any patient states, notes, or physician orders UP TO (and including)
-        relative time zero, but not subsequent states, notes, or physician orders 
-        (the latter is expected to reflect real user interaction records).
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            templatePatientData = DBUtil.loadRecordModelById("sim_patient", templatePatientId, conn=conn);
-            del templatePatientData["sim_patient_id"];  # Remove prior ID to allow for new one
-            templatePatientData.update( patientData );  # Override with new content (if exists)
-            DBUtil.insertRow("sim_patient", templatePatientData, conn=conn);    # Create new patient record
-            patientId = DBUtil.execute(DBUtil.identityQuery("sim_patient"),conn=conn)[0][0];
-
-            # Copy initial template patient states
-            query = SQLQuery();
-            query.addSelect("*");   # Copy all columns
-            query.addFrom("sim_patient_state as sps");
-            query.addWhereEqual("sps.sim_patient_id", templatePatientId );
-            query.addWhereOp("relative_time_start","<=", 0 );
-            query.addOrderBy("relative_time_start");
-            dataTable = DBUtil.execute(query,includeColumnNames=True,conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            nStates = len(dataModels);
-            for i, dataModel in enumerate(dataModels):
-                del dataModel["sim_patient_state_id"];  # Discard copied ID to allow new one
-                if i == nStates-1:
-                    del dataModel["relative_time_end"]; # Last state. Blank out end time to reflect open ended for simulation
-                dataModel["sim_patient_id"] = patientId;
-                DBUtil.insertRow("sim_patient_state", dataModel, conn=conn);
-
-            # Copy initial template orders
-            query = SQLQuery();
-            query.addSelect("*");
-            query.addFrom("sim_patient_order as spo");
-            query.addWhereEqual("sim_patient_id", templatePatientId );
-            query.addWhereOp("relative_time_start","<=", 0 );
-            query.addOrderBy("relative_time_start");
-            dataTable = DBUtil.execute(query,includeColumnNames=True,conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            for dataModel in dataModels:
-                del dataModel["sim_patient_order_id"];
-                dataModel["sim_patient_id"] = patientId;
-                DBUtil.insertRow("sim_patient_order", dataModel, conn=conn);
-
-            conn.commit();  # Transactional commit for multi-step process
-            return patientId;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def loadPatientInfo(self, patientIds=None, relativeTime=None, conn=None):
-        """Load basic information about the specified patients.
-        Report patient state at given time, or default to time zero
-        """
-        if relativeTime is None:
-            relativeTime = 0;   # Just look for time zero default then
-
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            query.addSelect("sp.sim_patient_id");
-            query.addSelect("sp.name");
-            query.addSelect("sp.age_years");
-            query.addSelect("sp.gender");
-            query.addSelect("s.sim_state_id");
-            query.addSelect("s.name as state_name");
-            query.addSelect("s.description as state_description");
-            query.addSelect("sps.relative_time_start");
-            query.addSelect("sps.relative_time_end");
-            query.addFrom("sim_patient as sp");
-            query.addFrom("sim_patient_state as sps");
-            query.addFrom("sim_state as s");
-            query.addWhere("sp.sim_patient_id = sps.sim_patient_id");
-            query.addWhere("sps.sim_state_id = s.sim_state_id");
-            if patientIds is not None:
-                query.addWhereIn("sp.sim_patient_id", patientIds );
-            
-            # Look for the state that matches the given relative time offset
-            query.addWhereOp("sps.relative_time_start","<=", relativeTime);
-            query.openWhereOrClause();
-            query.addWhere("sps.relative_time_end is null");
-            query.addWhereOp("sps.relative_time_end",">", relativeTime);
-            query.closeWhereOrClause();
-            
-            query.addOrderBy("sp.name");
-
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            
-            if len(dataModels) > 0:
-                # Secondary query to build lookup table of possible state transition options from patient current states
-                subQuery = SQLQuery();
-                subQuery.addSelect("pre_state_id");
-                subQuery.addSelect("post_state_id");
-                subQuery.addSelect("clinical_item_id");
-                subQuery.addSelect("time_trigger");
-                subQuery.addFrom("sim_state_transition as sst");
-                subQuery.addWhereIn("pre_state_id", columnFromModelList(dataModels,"sim_state_id"));
-                subResults = DBUtil.execute(subQuery,conn=conn);
-                
-                # For each pre-state, track which clinical items or times trigger which post-states
-                postStateIdByItemIdByPreStateId = dict();
-                postStateIdTimeTriggerByPreStateId = dict();
-                for preStateId, postStateId, itemId, timeTrigger in subResults:
-                    if preStateId not in postStateIdByItemIdByPreStateId:
-                        postStateIdByItemIdByPreStateId[preStateId] = dict();
-                    postStateIdByItemIdByPreStateId[preStateId][itemId] = postStateId;
-                    
-                    if timeTrigger is not None:
-                        postStateIdTimeTriggerByPreStateId[preStateId] = (postStateId, timeTrigger);
-            
-                
-                # Record in patient result models for retrieval
-                for i, dataModel in enumerate(dataModels):
-                    patientId = dataModel["sim_patient_id"];
-                    stateId = dataModel["sim_state_id"];
-                    
-                    dataModel["postStateIdByItemId"] = dict();
-                    if stateId in postStateIdByItemIdByPreStateId:
-                        dataModel["postStateIdByItemId"] = postStateIdByItemIdByPreStateId[stateId];
-                    dataModel["postStateIdTimeTriggerByPreStateId"] = dict();
-                    if stateId in postStateIdTimeTriggerByPreStateId:
-                        dataModel["postStateIdTimeTrigger"] = postStateIdTimeTriggerByPreStateId[stateId];
-                    
-                    if dataModel["relative_time_end"] is None and "postStateIdTimeTrigger" in dataModel:
-                        # Check that we haven't passed (and should thus trigger) a time-based state transition
-                        (postStateId, timeTrigger) = dataModel["postStateIdTimeTrigger"];
-                        preStateTime = dataModel["relative_time_start"];
-                        postStateTriggerTime = (preStateTime + timeTrigger);
-                        
-                        if postStateTriggerTime <= relativeTime:    # Trigger state transition just by time elapsed
-                            #print >> sys.stderr, relativeTime, preStateTime, stateId, postStateTriggerTime, postStateId
-                            self.recordStateTransition(patientId, stateId, postStateId, postStateTriggerTime, conn=conn);
-                            # State change which can yield new triggers, so recursively reload.
-                            # Small risk of infinite recusion if timeTriggers are zero. Otherwise, should converge as each recursion will update the preState relativeTimeStart
-                            dataModels[i] = self.loadPatientInfo([patientId], relativeTime, conn=conn)[0];
-            
-            return dataModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def loadStateInfo(self, stateIds=None, conn=None):
-        """Load basic information about the specified patient states
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            query.addSelect("ss.sim_state_id");
-            query.addSelect("ss.name");
-            query.addSelect("ss.description");
-            query.addFrom("sim_state as ss");
-            if stateIds is not None:
-                query.addWhereIn("ss.sim_state_id", stateIds );
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            return dataModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def signOrders(self, userId, patientId, currentTime, orderItemIds, discontinuePatientOrderIds=None, conn=None):
-        """Commit new order item IDs for the given patient and starting now,
-        and discontinue (set end date) for any existing orders specified.
-        
-        Record any patient state transitions the orders would trigger
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            # Denormalized recording of current patient state to facilitate easy retrieval linked to orders later
-            patientInfo = self.loadPatientInfo( [patientId], currentTime, conn=conn)[0];
-            stateId = patientInfo["sim_state_id"];
-            postStateIdByItemId = patientInfo["postStateIdByItemId"];
-            
-            orderItemIdSet = set(orderItemIds); # Ensure unique and facilitate set operations
-            
-            insertDict = {"sim_user_id": userId, "sim_patient_id": patientId, "sim_state_id": stateId, "relative_time_start": currentTime};
-            for itemId in orderItemIdSet:
-                insertDict["clinical_item_id"] = itemId;
-                DBUtil.insertRow("sim_patient_order", insertDict, conn=conn);
-
-            # See if any of these new orders triggered state transitions
-            triggerItemIds = postStateIdByItemId.viewkeys() & orderItemIdSet;
-            while triggerItemIds:   # Found a trigger item
-                triggerItemId = None;
-                if len(triggerItemIds) > 1: # Found multiple. Weird. Arbitrarily act on the one that appeared first in the input list
-                    for itemId in orderItemIds:
-                        if itemId in triggerItemIds:
-                            triggerItemId = itemId;
-                            break;
-                else:
-                    triggerItemId = triggerItemIds.pop();
-                postStateId = postStateIdByItemId[triggerItemId];
-                
-                # Record the state transition
-                self.recordStateTransition(patientId, stateId, postStateId, currentTime, conn=conn);
-                
-                # Reload patientInfo to reflect new patient state
-                patientInfo = self.loadPatientInfo( [patientId], currentTime, conn=conn)[0];
-                stateId = patientInfo["sim_state_id"];
-                postStateIdByItemId = patientInfo["postStateIdByItemId"];
-                
-                orderItemIdSet.discard(triggerItemId);  # Don't keep looking for this one, important to avoid infinite loop
-                triggerItemIds = postStateIdByItemId.viewkeys() & orderItemIdSet;
-                
-        
-            if discontinuePatientOrderIds is not None:
-                updateDict = {"relative_time_end": currentTime };
-                for patientOrderId in discontinuePatientOrderIds:
-                    DBUtil.updateRow("sim_patient_order", updateDict, patientOrderId, conn=conn);
-        finally:
-            conn.commit();
-            if not extConn:
-                conn.close();
-
-    def recordStateTransition(self, patientId, preStateId, postStateId, currentTime, conn=None):
-        """Record a patient state transition
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            # Ending of pre-state
-            updateQuery = \
-                """update sim_patient_state 
-                set relative_time_end = %(p)s
-                where sim_patient_id = %(p)s
-                and sim_state_id = %(p)s
-                and relative_time_start <= %(p)s
-                and relative_time_end is null
-                """ % {"p": DBUtil.SQL_PLACEHOLDER};
-            updateParams = (currentTime, patientId, preStateId, currentTime);
-            DBUtil.execute(updateQuery, updateParams, conn=conn);
-            
-            # Beginning of post-state
-            insertDict = {"sim_patient_id": patientId, "sim_state_id": postStateId, "relative_time_start": currentTime};
-            DBUtil.insertRow("sim_patient_state", insertDict, conn=conn);
-        finally:
-            conn.commit();
-            if not extConn:
-                conn.close();
-    
-    def loadPatientOrders(self, patientId, currentTime, loadActive=True, conn=None):
-        """Load orders for the given patient that exist by the specified current time point.
-        loadActive - Specify whether to load active vs. inactive/completed orders.  Set to None to load both
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            query.addSelect("po.sim_patient_order_id");
-            query.addSelect("po.sim_user_id");
-            query.addSelect("po.sim_patient_id");
-            query.addSelect("po.sim_state_id");
-            query.addSelect("po.clinical_item_id");
-            query.addSelect("po.relative_time_start");
-            query.addSelect("po.relative_time_end");
-            query.addSelect("ci.name");
-            query.addSelect("ci.description");
-            query.addSelect("cic.source_table");
-            query.addSelect("cic.description as category_description");
-            query.addFrom("sim_patient_order as po");
-            query.addFrom("clinical_item as ci");
-            query.addFrom("clinical_item_category as cic");
-            query.addWhere("po.clinical_item_id = ci.clinical_item_id");
-            query.addWhere("ci.clinical_item_category_id = cic.clinical_item_category_id");
-            query.addWhereEqual("sim_patient_id", patientId );
-            query.addWhereOp("relative_time_start","<=", currentTime );
-
-            
-            if loadActive:  # Filter out inactive orders here.
-                query.openWhereOrClause();
-                query.addWhereOp("relative_time_end",">", currentTime);
-                query.addWhere("relative_time_end is null");
-                query.closeWhereOrClause();
-            #elif loadActive is not None:    # Filter out active orders here.
-            #    query.addWhereOp("relative_time_end","<=", currentTime);
-            
-            if loadActive:  # Organize currently active orders by category
-                query.addOrderBy("cic.description");
-                query.addOrderBy("ci.description");
-            else:   # Otherwise chronologic order
-                query.addOrderBy("relative_time_start");
-
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            return dataModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def loadResults(self, patientId, relativeTime, conn=None):
-        """Load all results active by the given relativeTime.
-        Will look for sim_patient_state times and sim_patient_order for diagnostic orders,
-        to extrapolate all state-specific results for each order, or using default values
-        if no state specific ones available.
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            # First query for all expected result labels and states, without state-specific values as 
-            #   may want outer join behvaior against default state values
-            query = SQLQuery();
-            
-            query.addSelect("distinct spo.sim_state_id");
-            query.addSelect("sr.sim_result_id");
-            query.addSelect("sr.name");
-            query.addSelect("sr.description");
-            query.addSelect("sr.priority");
-            query.addSelect("sr.group_string");
-
-            query.addSelect("spo.relative_time_start");
-            query.addSelect("sorm.turnaround_time");
-            query.addSelect("(spo.relative_time_start + sorm.turnaround_time) as result_relative_time")
-            
-            query.addFrom("sim_patient_order as spo");
-            query.addFrom("sim_order_result_map as sorm");
-            query.addFrom("sim_result as sr");
-            
-            query.addWhere("spo.clinical_item_id = sorm.clinical_item_id");
-            query.addWhere("sorm.sim_result_id = sr.sim_result_id");
-
-            query.addWhereEqual("spo.sim_patient_id", patientId );
-            # Only unlock results if appropiate prereq orders were placed in the past (and longer than the turnaround time)
-            query.addWhereOp("spo.relative_time_start + sorm.turnaround_time","<=", relativeTime );  
-
-            query.addOrderBy("result_relative_time");
-            query.addOrderBy("sr.priority");
-            
-            resultTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            resultModels = modelListFromTable(resultTable);
-
-            # Pass through results to get set of states to search for
-            stateIds = set([DEFAULT_STATE_ID]); # Include default state to fall back on
-            for resultModel in resultModels:
-                stateIds.add(resultModel["sim_state_id"]);
-            
-            # Second query for state-specific values
-            valueQuery = SQLQuery();
-            valueQuery.addSelect("ssr.sim_state_id");
-            valueQuery.addSelect("ssr.sim_result_id");
-            valueQuery.addSelect("ssr.num_value");
-            valueQuery.addSelect("ssr.num_value_noise");
-            valueQuery.addSelect("ssr.text_value");
-            valueQuery.addSelect("ssr.result_flag");
-            valueQuery.addSelect("ssr.clinical_item_id");    # Output clinical item if result flag means something
-            valueQuery.addFrom("sim_state_result as ssr");
-            valueQuery.addWhereIn("ssr.sim_state_id", stateIds);
-            valueTable = DBUtil.execute( valueQuery, includeColumnNames=True, conn=conn);
-            valueModels = modelListFromTable(valueTable);
-            
-            # Store in-memory dictionary for rapid cross-referencing "join" to result table
-            valueModelByStateIdByResultId = dict();
-            for valueModel in valueModels:
-                resultId = valueModel["sim_result_id"];
-                stateId = valueModel["sim_state_id"];
-                if resultId not in valueModelByStateIdByResultId:
-                    valueModelByStateIdByResultId[resultId] = dict();
-                valueModelByStateIdByResultId[resultId][stateId] = valueModel;
-
-            # Now go back through original results and join up state-specific values, or use default values if needed
-            resultValueModels = list();
-            for resultModel in resultModels:
-                resultId = resultModel["sim_result_id"];
-                stateId = resultModel["sim_state_id"];
-                if resultId in valueModelByStateIdByResultId:
-                    valueModelByStateId = valueModelByStateIdByResultId[resultId];
-                    if stateId in valueModelByStateId:
-                        # Have a state-specific value, populate that
-                        valueModel = valueModelByStateId[stateId];
-                        resultModel.update(valueModel);
-                    elif DEFAULT_STATE_ID in valueModelByStateId:
-                        # No state-specific value, but have a default one to populate instead
-                        valueModel = valueModelByStateId[DEFAULT_STATE_ID];
-                        resultModel.update(valueModel);
-                    resultValueModels.append(resultModel);
-                else:
-                    # No result information available, even in default state. Skip these
-                    #resultModel["num_value"] = None;
-                    #resultModel["num_value_noise"] = None;
-                    #resultModel["text_value"] = None;
-                    #resultModel["result_flag"] = None;
-                    #resultModel["clinical_item_id"] = None;
-                    pass;
-
-            return resultValueModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def loadNotes(self, patientId, currentTime, conn=None):
-        """Load notes committed up to the given simulation time.
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            
-            query.addSelect("sn.sim_note_id");
-            query.addSelect("sps.sim_patient_id"); # Link
-            query.addSelect("sn.sim_state_id");
-            query.addSelect("sn.note_type_id");
-            query.addSelect("sn.author_type_id");
-            query.addSelect("sn.service_type_id");
-            query.addSelect("(sps.relative_time_start + sn.relative_state_time) as relative_time");
-            query.addSelect("sn.content");
-            
-            query.addFrom("sim_note as sn");
-            query.addFrom("sim_patient_state as sps");
-            
-            query.addWhere("sn.sim_state_id = sps.sim_state_id");
-            query.addWhereEqual("sps.sim_patient_id", patientId );
-            # Only unlock notes once traverse expected time
-            query.addWhereOp("(sps.relative_time_start + sn.relative_state_time)","<=", currentTime );  
-            query.addOrderBy("(sps.relative_time_start + sn.relative_state_time)");
-            
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            return dataModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-
-    def recentItemIds(self, patientId, currentTime, timeDelta=None, conn=None):
-        """Load a list of clinicalItemIds
-        (orders, diagnoses, unlocked results, etc.)
-        to establish current patient context.
-        
-        If timeDelta specified, only count items that occurred within that much past time from the current simTime
-        """
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            itemIds = set();
-            
-            patientOrders = self.loadPatientOrders(patientId, currentTime, loadActive=True, conn=conn);
-            for patientOrder in patientOrders:
-                itemIds.add(patientOrder["clinical_item_id"]);
-            
-            results = self.loadResults(patientId, currentTime, conn=conn);
-            for result in results:
-                if result["clinical_item_id"] is not None:
-                    itemIds.add(result["clinical_item_id"]);
-            
-            return itemIds;
-        finally:
-            if not extConn:
-                conn.close();
-
-    def clinicalItemSearch(self, itemQuery, conn=None):
-        """Look for clinical items based on specified query criteria"""
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            query.addSelect("ci.clinical_item_id");
-            query.addSelect("ci.name");
-            query.addSelect("ci.description");
-            query.addSelect("cic.source_table");
-            query.addSelect("cic.description as category_description");
-            query.addFrom("clinical_item as ci");
-            query.addFrom("clinical_item_category as cic");
-            query.addWhere("ci.clinical_item_category_id = cic.clinical_item_category_id");
-            if itemQuery.searchStr is not None:
-                searchWords = itemQuery.searchStr.split();
-                #query.openWhereOrClause();
-                for searchField in ("ci.description",):
-                    for searchWord in searchWords:
-                        query.addWhereOp(searchField,"~*","^%(searchWord)s|[^a-z]%(searchWord)s" % {"searchWord": searchWord} ); # Prefix search by regular expression
-                #query.closeWhereOrClause();
-            if itemQuery.sourceTables:
-                query.addWhereIn("cic.source_table", itemQuery.sourceTables );
-            if itemQuery.analysisStatus is not None:
-                query.addWhereEqual("ci.analysis_status", itemQuery.analysisStatus );
-                query.addWhere("ci.item_count <> 0");    # Also ignore items with no occurence in the analyzed data (occurs if item was accepted for analysis from multi-year dataset, but never used in a sub-time frame's analysis)
-
-            if itemQuery.sortField:
-                query.addOrderBy(itemQuery.sortField);
-            query.addOrderBy("cic.description");
-            query.addOrderBy("ci.name");
-            query.addOrderBy("ci.description");
-            if itemQuery.resultCount is not None:
-                query.limit = itemQuery.resultCount;
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-            return dataModels;
-        finally:
-            if not extConn:
-                conn.close();
-
-
-    def orderSetSearch(self, itemQuery, conn=None):
-        """Look for clinical items based on specified query criteria"""
-        extConn = True;
-        if conn is None:
-            conn = self.connFactory.connection();
-            extConn = False;
-        try:
-            query = SQLQuery();
-            query.addSelect("ic.item_collection_id");
-            query.addSelect("ic.external_id");
-            query.addSelect("ic.name as collection_name");
-            query.addSelect("ic.section");
-            query.addSelect("ic.subgroup");
-            query.addSelect("ci.clinical_item_category_id");
-            query.addSelect("ci.clinical_item_id");
-            query.addSelect("ci.name");
-            query.addSelect("ci.description");
-            query.addFrom("item_collection as ic");
-            query.addFrom("item_collection_item as ici");
-            query.addFrom("clinical_item as ci");
-            query.addWhere("ic.item_collection_id = ici.item_collection_id");
-            query.addWhere("ici.clinical_item_id = ci.clinical_item_id");
-            query.addWhereNotEqual("ic.section", AD_HOC_SECTION );
-            if itemQuery.searchStr is not None:
-                searchWords = itemQuery.searchStr.split();
-                for searchWord in searchWords:
-                    query.addWhereOp("ic.name","~*","^%(searchWord)s|[^a-z]%(searchWord)s" % {"searchWord": searchWord} ); # Prefix search by regular expression
-            if itemQuery.analysisStatus is not None:
-                query.addWhereEqual("ci.analysis_status", itemQuery.analysisStatus );
-            query.addOrderBy("lower(ic.name)");
-            query.addOrderBy("ic.external_id");
-            query.addOrderBy("lower(ic.section)");
-            query.addOrderBy("lower(ic.subgroup)");
-            query.addOrderBy("ci.clinical_item_id");
-            query.addOrderBy("ci.name");
-            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn);
-            dataModels = modelListFromTable(dataTable);
-
-            # Aggregate up into order sets
-            orderSetModel = None;
-            for row in dataModels:
-                if orderSetModel is None or row["external_id"] != orderSetModel["external_id"]:
-                    if orderSetModel is not None:
-                        # Prior order set exists, yield/return it before preparing next one
-                        yield orderSetModel;
-                    orderSetModel = \
-                        {   "external_id": row["external_id"], 
-                            "name": row["collection_name"],
-                            "itemList": list(),
-                        };
-                orderSetModel["itemList"].append(row);
-            yield orderSetModel;    # Yield the last processed model
-        finally:
-            if not extConn:
-                conn.close();
-
-
-class ClinicalItemQuery:
-    """Struct to capture query elements for clinical item / order instances"""
-    def __init__(self):
-        self.searchStr = None;
-        self.analysisStatus = None;
-        self.sourceTables = None;   # Valid source data tables to use
-        self.resultCount = None;
-        self.sortField = None;
-
-    def parseParams(self, paramDict):
-        """Look through the dictionary for key-value pairs that can be parsed into query object parameters.
-        Facilitates easy setup from web or command-line text interfaces.
-        """
-        if "searchStr" in paramDict:
-            self.searchStr = paramDict["searchStr"];
-        if "analysisStatus" in paramDict:
-            self.analysisStatus = paramDict["analysisStatus"];
-        if "sourceTables" in paramDict:
-            self.sourceTables = paramDict["sourceTables"].split(",");
-        if "resultCount" in paramDict:
-            self.resultCount = int(paramDict["resultCount"]);
-        if "sortField" in paramDict:
-            self.sortField = paramDict["sortField"];
-        
-if __name__ == "__main__":
-    instance = SimManager();
-    instance.main(sys.argv);
diff --git a/medinfo/cpoe/TopicModel.py b/medinfo/cpoe/TopicModel.py
index d9e75696..6d19e00b 100644
--- a/medinfo/cpoe/TopicModel.py
+++ b/medinfo/cpoe/TopicModel.py
@@ -8,7 +8,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 from pprint import pprint;
 
@@ -18,7 +18,7 @@
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel;
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
-from Util import log;
+from .Util import log;
 
 DEFAULT_TOPIC_ITEM_COUNT = 100; # When using or printing out topic information, number of top scored items to consider
 BUFFER_UPDATE_SIZE = 100000;   # Number of document Bag-of-Words to keep in memory before performing model updates.
@@ -27,6 +27,7 @@ class TopicModel:
     def __init__(self):
         self.model = None;   # Means to provide output feedback to caller for debugging purposes
         self.docCountByWordId = None;
+        self.randomState = None;   # Allow caller to set initial (fixed) random_state to facilitate consistent unit/regression testing. E.g., numpy.random.RandomState(10)
 
     def buildModel(self, corpusBOWGenerator, numTopics):
         """Build topic model from corpus (interpret as generator over contents)
@@ -40,8 +41,8 @@ def buildModel(self, corpusBOWGenerator, numTopics):
         # Load dictionary to translate item IDs to descriptions
         itemsById = DBUtil.loadTableAsDict("clinical_item");
         id2word = dict();   # Models expect a pair for every possible item ID, and judges vocabulary size by length of this dictionary rather than the maximum ID values.  That means have to populate all of the empty ones as well.
-        maxId = max(itemsById.iterkeys());
-        for itemId in xrange(maxId+1):
+        maxId = max(itemsById.keys());
+        for itemId in range(maxId+1):
             description = str(itemId);  # Default to just the same as the ID string
             if itemId in itemsById:
                 description = itemsById[itemId]["description"];
@@ -82,9 +83,9 @@ def updateModel(self, model, docBuffer, id2word, numTopics):
         import gensim;  # Only import external module as needed
         if model is None:   # Initialize model
             if numTopics < 1:
-                model = gensim.models.hdpmodel.HdpModel( docBuffer, id2word=id2word);
+                model = gensim.models.hdpmodel.HdpModel( docBuffer, id2word=id2word, random_state=self.randomState );
             else:
-                model = gensim.models.LdaModel( docBuffer, id2word=id2word, num_topics=numTopics );
+                model = gensim.models.LdaModel( docBuffer, id2word=id2word, num_topics=numTopics, random_state=self.randomState );
         else:
             model.update( docBuffer );
         return model;
@@ -116,17 +117,15 @@ def enumerateTopics(self, model, itemsPerCluster):
 
         import gensim; # External import as needed
         if isinstance(model, gensim.models.HdpModel):   # Has different topic API for no good reason
-            topics = model.show_topics(topics=-1, topn=itemsPerCluster, formatted=False);
+            # topics = model.show_topics(num_topics=-1, num_words=itemsPerCluster, formatted=False);
+            topics = model.show_topics(num_words=itemsPerCluster, formatted=False);
             for topicId, topicItems in topics:
                 #for (itemId, itemWeight) in topicItems:  # 2-ple order is also reversed for no good reason
                 #    print topicId, itemDescr, itemWeight;
                 yield topicId, topicItems;
         else:   # Assume default API for LDA based models
             topics = model.show_topics(num_topics=-1, num_words=itemsPerCluster, formatted=False);
-            for topicId, flipTopicItems in enumerate(topics):
-                topicItems = list();
-                for (itemWeight, itemId) in flipTopicItems:
-                    topicItems.append( (itemId, itemWeight) );
+            for topicId, topicItems in topics:
                 yield topicId, topicItems;
 
         model.id2word = id2word;    # Revert back to normal id-word translation
@@ -146,7 +145,7 @@ def generateWeightByItemIdByTopicId(self, model, itemsPerCluster):
     def printTopicsToFile(self, model, docCountByWordId, topicFile, itemsPerCluster):
         """Print out the topic model contents to file in tab-delimited format for easy review"""
 
-        print >> topicFile, "topic_id\titem_id\tdescription\tscore\ttfidf";
+        print("topic_id\titem_id\tdescription\tscore\ttfidf", file=topicFile);
         id2word = model.id2word;
         for (topicId, topicItems) in self.enumerateTopics(model, itemsPerCluster):
             for (itemId, itemScore) in topicItems:
@@ -154,14 +153,14 @@ def printTopicsToFile(self, model, docCountByWordId, topicFile, itemsPerCluster)
                 tfidf = 0.0;
                 if itemId in docCountByWordId and docCountByWordId[itemId] > 0:
                     tfidf = itemScore * docCountByWordId[None] / docCountByWordId[itemId];
-                print >> topicFile, "%s\t%s\t%s\t%s\t%s" % (topicId, itemId, itemDescription, itemScore, tfidf);
+                print("%s\t%s\t%s\t%s\t%s" % (topicId, itemId, itemDescription, itemScore, tfidf), file=topicFile);
 
         # Now print out basic word-document counts under the "None" Topic representing all documents / entire corpus
-        for itemId, docCount in docCountByWordId.iteritems():
+        for itemId, docCount in docCountByWordId.items():
             itemDescription = None;
             if itemId in id2word:
                 itemDescription = id2word[itemId];
-            print >> topicFile, "%s\t%s\t%s\t%s\t%s" % (None, itemId, itemDescription, docCount, docCountByWordId[None]);
+            print("%s\t%s\t%s\t%s\t%s" % (None, itemId, itemDescription, docCount, docCountByWordId[None]), file=topicFile);
 
     def topTopicFilename(self, baseName):
         """Generate a name for a top topics summary file given a base output filename"""
@@ -190,6 +189,7 @@ def loadDocCountByWordId(self, filename):
                     itemId = int(topicItem["item_id"]);
                 docCount = int(topicItem["score"]);
                 docCountByWordId[itemId] = docCount;
+        reader.close()
         return docCountByWordId;
 
     def loadModelAndDocCounts(self, filename):
@@ -204,7 +204,7 @@ def itemCountByIdToBagOfWords(itemCountById, observedIds=None, itemsById=None, e
         """Return 2-ple (itemId, count) representation of item IDs, but filter out those in excluded set,
         or whose category looked up via itemsById is already observed previously or so far.
         """
-        for itemId, itemCount in itemCountById.iteritems():
+        for itemId, itemCount in itemCountById.items():
             categoryId = None;
             if itemsById is not None and itemId in itemsById:
                 categoryId = itemsById[itemId]["clinical_item_category_id"]
@@ -256,8 +256,9 @@ def main(self, argv):
             # Save top topic information in readable tab-delimited format
             itemsPerCluster = int(options.itemsPerCluster);
             topicFile = stdOpen(self.topTopicFilename(outputFilename),"w");
-            print >> topicFile, COMMENT_TAG, json.dumps({"argv":argv});    # Print comment line with analysis arguments to allow for deconstruction later
+            print(COMMENT_TAG, json.dumps({"argv":argv}), file=topicFile);    # Print comment line with analysis arguments to allow for deconstruction later
             self.printTopicsToFile(model, docCountByWordId, topicFile, itemsPerCluster);
+            topicFile.close()
 
         else:
             parser.print_help()
diff --git a/medinfo/cpoe/TopicModelRecommender.py b/medinfo/cpoe/TopicModelRecommender.py
index 930e2f5b..3d193a32 100644
--- a/medinfo/cpoe/TopicModelRecommender.py
+++ b/medinfo/cpoe/TopicModelRecommender.py
@@ -6,9 +6,10 @@
 """
 import sys, os
 import time;
+from operator import itemgetter
 from optparse import OptionParser;
 import json;
-import urlparse;
+import urllib.parse;
 import math;
 from datetime import datetime, timedelta;
 from medinfo.common.Const import FALSE_STRINGS, COMMENT_TAG;
@@ -21,7 +22,7 @@
 from medinfo.db.ResultsFormatter import TextResultsFormatter;
 from medinfo.cpoe.ItemRecommender import BaseItemRecommender;
 from medinfo.cpoe.TopicModel import TopicModel;
-from Util import log;
+from .Util import log;
 
 class TopicModelRecommender(BaseItemRecommender):
     """Implementation class for item (e.g., order) recommendation based on topic models 
@@ -49,11 +50,11 @@ def __init__(self, model, docCountByWordId=None):
     def initItemLookups(self, query):
         self.itemsById = DBUtil.loadTableAsDict("clinical_item");
         self.categoryIdByItemId = dict();
-        for itemId, item in self.itemsById.iteritems():
+        for itemId, item in self.itemsById.items():
             self.categoryIdByItemId[itemId] = item["clinical_item_category_id"];
         self.candidateItemIds = set();
         emptyQuerySet = set();
-        for itemId in self.docCountByWordId.keys():
+        for itemId in list(self.docCountByWordId.keys()):
             if self.isItemRecommendable(itemId, emptyQuerySet, query, self.categoryIdByItemId):
                 self.candidateItemIds.add(itemId);
     
@@ -89,10 +90,10 @@ def __call__(self, query):
         for itemId in self.candidateItemIds:
             if self.isItemRecommendable(itemId, queryItemCountById, query, self.categoryIdByItemId):
                 recScoreByItemId[itemId] = 0.0;
-        for topicId, topicWeight in weightByTopicId.iteritems():
+        for topicId, topicWeight in weightByTopicId.items():
             if topicWeight > query.minClusterWeight:    # Ignore topics with tiny contribution
                 weightByItemId = self.weightByItemIdByTopicId[topicId];
-                for itemId in recScoreByItemId.keys():
+                for itemId in list(recScoreByItemId.keys()):
                     itemWeight = 0.0;
                     if itemId in weightByItemId:
                         itemWeight = weightByItemId[itemId];
@@ -100,7 +101,7 @@ def __call__(self, query):
 
         # Build 2-pls with lists to sort by score
         recommendedData = list();
-        for itemId, totalItemWeight in recScoreByItemId.iteritems():
+        for itemId, totalItemWeight in recScoreByItemId.items():
             tfidf = 0.0;
             if itemId in self.docCountByWordId and self.docCountByWordId[itemId] > 0.0:
                 tfidf = totalItemWeight * self.docCountByWordId[None] / self.docCountByWordId[itemId];    # Scale TF*IDF score based on baseline document counts to prioritize disproportionately common items
@@ -112,7 +113,7 @@ def __call__(self, query):
                 };
             itemModel["score"] = itemModel[query.sortField];
             recommendedData.append(itemModel);
-        recommendedData.sort( RowItemFieldComparator("score"), reverse=True);
+        recommendedData.sort(key=itemgetter("score"), reverse=True);
         return recommendedData;
 
     def main(self, argv):
diff --git a/medinfo/cpoe/TripleAssociationAnalysis.py b/medinfo/cpoe/TripleAssociationAnalysis.py
index 195521c1..94e1d085 100644
--- a/medinfo/cpoe/TripleAssociationAnalysis.py
+++ b/medinfo/cpoe/TripleAssociationAnalysis.py
@@ -9,12 +9,12 @@
 from medinfo.db.Model import SQLQuery, generatePlaceholders;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
 
-from AssociationAnalysis import AssociationAnalysis, AnalysisOptions;
-from DataManager import DataManager;
+from .AssociationAnalysis import AssociationAnalysis, AnalysisOptions;
+from .DataManager import DataManager;
 
-from Const import DELTA_NAME_BY_SECONDS;
+from .Const import DELTA_NAME_BY_SECONDS;
 
-from Util import log;
+from .Util import log;
 
 class TripleAssociationAnalysis(AssociationAnalysis):
     """Pre-Computation module to sort through data on patient clinical items
@@ -35,12 +35,12 @@ def __init__(self):
         self.dataManager = DataManager();
 
     def analyzePatientItems(self, patientIds, itemIdSequence, virtualItemId):
-        """Primary run function to analyze patient clinical item data and 
+        """Primary run function to analyze patient clinical item data and
         record updated stats to the respective database tables.
-        
+
         Does the analysis only for records pertaining to the given patient IDs
         (provides a way to limit the extent of analysis depending on params).
-        
+
         Note that this does NOT record analyze_date timestamp on any records analyzed,
         as would collide with AssociationAnalysis primary timestamping, thus it is the
         caller's responsibility to be careful not to repeat this analysis redundantly
@@ -52,7 +52,7 @@ def analyzePatientItems(self, patientIds, itemIdSequence, virtualItemId):
             # Preload lookup data to facilitate rapid checks and filters later
             linkedItemIdsByBaseId = self.dataManager.loadLinkedItemIdsByBaseId(conn=conn);
             self.verifyVirtualItemLinked(itemIdSequence, virtualItemId, linkedItemIdsByBaseId, conn=conn);
-        
+
             # Keep an in memory buffer of the updates to be done so can stall and submit them
             #   to the database in batch to minimize inefficient DB hits
             updateBuffer = dict();
@@ -68,14 +68,14 @@ def analyzePatientItems(self, patientIds, itemIdSequence, virtualItemId):
             self.commitUpdateBuffer(updateBuffer, linkedItemIdsByBaseId, conn=conn);  # Final update buffer commit
         finally:
             conn.close();
-        progress.PrintStatus();
+        # progress.PrintStatus();
 
     def updateItemAssociationsBuffer(self, itemIdSequence, virtualItemId, patientItemList, updateBuffer, linkedItemIdsByBaseId=None, progress=None):
-        """Given a list of data on patient clinical items, 
-        ordered by item event date, increment information in the 
+        """Given a list of data on patient clinical items,
+        ordered by item event date, increment information in the
         updateBuffer to inform subsequent updates to the clinical_item_association
         stats based on all item pairs observed.
-        
+
         Looking for specific triple sequences only though with items followed by those specified
         in the itemIdSequence.  If a triple sequence is found, then mark the end point as
         a virtualItem instance for counting associations.
@@ -94,7 +94,7 @@ def updateItemAssociationsBuffer(self, itemIdSequence, virtualItemId, patientIte
             if patientItem["clinical_item_id"] == itemIdSequence[0]:
                 midSequenceItemDates.add(patientItem["item_date"]);
         endSequenceItemsByPatientItemId = dict();
-        
+
         # Main nested loop to look for associations
         for iItem1, patientItem1 in enumerate(patientItemList):
             subsequentItemIds.clear();
@@ -127,8 +127,8 @@ def updateItemAssociationsBuffer(self, itemIdSequence, virtualItemId, patientIte
 
         # Separate pass to get virtual item baseline counts.  Cannot be done directly, since the virtual items do not actually exist in the raw data
         subsequentItemIds.clear();
-        for iItem1, patientItem1 in enumerate(endSequenceItemsByPatientItemId.itervalues()):
-            for iItem2, patientItem2 in enumerate(endSequenceItemsByPatientItemId.itervalues()):
+        for iItem1, patientItem1 in enumerate(endSequenceItemsByPatientItemId.values()):
+            for iItem2, patientItem2 in enumerate(endSequenceItemsByPatientItemId.values()):
                 itemIdPair = (virtualItemId, virtualItemId);
                 encounterIdPair = (patientItem1["encounter_id"], patientItem2["encounter_id"]);
 
@@ -153,17 +153,17 @@ def verifyVirtualItemLinked(self, itemIdSequence, virtualItemId, linkedItemIdsBy
         try:
             if virtualItemId not in linkedItemIdsByBaseId:
                 linkedItemIdsByBaseId[virtualItemId] = set();
-            
+
             for componentId in itemIdSequence:
                 if componentId not in linkedItemIdsByBaseId[virtualItemId]:
                     linkModel = RowItemModel();
                     linkModel["clinical_item_id"] = virtualItemId;
                     linkModel["linked_item_id"] = componentId;
 
-                    insertQuery = DBUtil.buildInsertQuery("clinical_item_link", linkModel.keys() );
-                    insertParams= linkModel.values();
+                    insertQuery = DBUtil.buildInsertQuery("clinical_item_link", list(linkModel.keys()) );
+                    insertParams= list(linkModel.values());
                     DBUtil.execute( insertQuery, insertParams, conn=conn);
-                    
+
                     linkedItemIdsByBaseId[virtualItemId].add(componentId);
         finally:
             if not extConn:
@@ -173,7 +173,7 @@ def isTripleSequence(self, patientItem1, patientItem2, midSequenceItemDates, ite
         """Assess whether the pair of items represents an acceptable triple sequence.
         Is so if end point matches the end of the itemIdSequence, and there exists a mid-sequence
         item in the intervening time, as previously tracked by the midSequenceItemDates.
-        
+
         Assume previous check has already confirmed that the item pairs are not previously linked,
         and that there is a non-negative / forward direction in the relationship
         """
@@ -182,7 +182,7 @@ def isTripleSequence(self, patientItem1, patientItem2, midSequenceItemDates, ite
 
         isAcceptable = True;
         isAcceptable = isAcceptable and itemId2 == itemIdSequence[-1];
-        
+
         foundMidSequenceDate = False;
         for midDate in midSequenceItemDates:
             if patientItem1["item_date"] <= midDate and midDate <= patientItem2["item_date"]:
@@ -190,7 +190,7 @@ def isTripleSequence(self, patientItem1, patientItem2, midSequenceItemDates, ite
                 break;  # Don't need to keep looking
         isAcceptable = isAcceptable and foundMidSequenceDate;
         return isAcceptable;
-    
+
     def main(self, argv):
         """Main method, callable from command line"""
         usageStr =  "usage: %prog [options] <patientIds>\n"+\
@@ -215,7 +215,7 @@ def main(self, argv):
 
         itemIdSequence = [int(idStr) for idStr in options.itemIdSequence.split(",")];
         virtualItemId = int(options.virtualItemId);
-        
+
         self.analyzePatientItems(patientIds, itemIdSequence, virtualItemId);
 
         timer = time.time() - timer;
diff --git a/medinfo/cpoe/Util.py b/medinfo/cpoe/Util.py
index ed8f9355..408ee32b 100644
--- a/medinfo/cpoe/Util.py
+++ b/medinfo/cpoe/Util.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
 
diff --git a/medinfo/cpoe/analysis/BaseCPOEAnalysis.py b/medinfo/cpoe/analysis/BaseCPOEAnalysis.py
index 694c235a..1b017925 100644
--- a/medinfo/cpoe/analysis/BaseCPOEAnalysis.py
+++ b/medinfo/cpoe/analysis/BaseCPOEAnalysis.py
@@ -6,7 +6,7 @@
 import sys, os
 import time;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from math import sqrt;
 from datetime import timedelta;
 
@@ -19,7 +19,7 @@
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender, BaselineFrequencyRecommender, RandomItemRecommender;
 from medinfo.cpoe.DataManager import DataManager;
-from Util import log;
+from .Util import log;
 
 # Prepare lookup reference of recommender objects known to be available and facilitate reference by string name
 RECOMMENDER_CLASS_LIST = [ItemAssociationRecommender, BaselineFrequencyRecommender, RandomItemRecommender];
diff --git a/medinfo/cpoe/analysis/Const.py b/medinfo/cpoe/analysis/Const.py
index b3186af2..66b0271f 100644
--- a/medinfo/cpoe/analysis/Const.py
+++ b/medinfo/cpoe/analysis/Const.py
@@ -2,7 +2,7 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 from datetime import timedelta;
 
 """Application name","for example to identify a common logger object"""
diff --git a/medinfo/cpoe/analysis/OrderSetRecommenderClassificationAnalysis.py b/medinfo/cpoe/analysis/OrderSetRecommenderClassificationAnalysis.py
index 5cd2de06..7d09cdce 100644
--- a/medinfo/cpoe/analysis/OrderSetRecommenderClassificationAnalysis.py
+++ b/medinfo/cpoe/analysis/OrderSetRecommenderClassificationAnalysis.py
@@ -8,7 +8,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 
 from medinfo.common.Const import COMMENT_TAG;
@@ -19,12 +19,12 @@
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.OrderSetRecommender import OrderSetRecommender;
-from Util import log;
+from .Util import log;
 
-from RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
-from BaseCPOEAnalysis import AnalysisQuery;
-from PreparePatientItems import PreparePatientItems;
-from RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+from .RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+from .BaseCPOEAnalysis import AnalysisQuery;
+from .PreparePatientItems import PreparePatientItems;
+from .RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
 
 DEFAULT_RECOMMENDED_ITEM_COUNT = 10;    # When doing validation calculations, number of items to recommend when calculating precision and recall
 DEFAULT_SORT_FIELD = "P(B|A)";
@@ -39,11 +39,11 @@ def __call__(self, analysisQuery):
         # Scaled / weighted scores based on item base counts, in this case, number of included order set counts
         analysisQuery.recommender.initItemLookups(analysisQuery.baseRecQuery);
         orderSetCountByItemId = dict();
-        for itemId, orderSetIds in analysisQuery.recommender.orderSetIdsByItemId.iteritems():
+        for itemId, orderSetIds in analysisQuery.recommender.orderSetIdsByItemId.items():
             orderSetCountByItemId[itemId] = len(orderSetIds);
 
         preparer = PreparePatientItems();
-        progress = ProgressDots(50,1,"Patients");
+        # progress = ProgressDots(50,1,"Patients");
         for patientItemData in preparer.loadPatientItemData(analysisQuery):
             patientId = patientItemData["patient_id"];
             analysisResults = \
@@ -62,7 +62,7 @@ def __call__(self, analysisQuery):
                 if "baseItemId" in patientItemData:
                     analysisQuery.baseItemId = patientItemData["baseItemId"]; # Record something here, so know to report back in result headers
                 yield resultsStatData;
-            progress.Update();
+            # progress.Update();
         # progress.PrintStatus();
 
     def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientId, recommender):
@@ -143,7 +143,7 @@ def main(self, argv):
 
             # Print comment line with analysis arguments to allow for deconstruction later
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             formatter = TextResultsFormatter( outputFile );
             colNames = self.resultHeaders(query);
diff --git a/medinfo/cpoe/analysis/OrderSetUsageAnalysis.py b/medinfo/cpoe/analysis/OrderSetUsageAnalysis.py
index 3d2e9a02..09837868 100644
--- a/medinfo/cpoe/analysis/OrderSetUsageAnalysis.py
+++ b/medinfo/cpoe/analysis/OrderSetUsageAnalysis.py
@@ -17,8 +17,9 @@
 import sys, os
 import time;
 import json;
+from operator import itemgetter
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 
 from medinfo.common.Const import COMMENT_TAG;
@@ -30,11 +31,11 @@
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.OrderSetRecommender import OrderSetRecommender;
 from medinfo.cpoe.Const import AD_HOC_SECTION;
-from Util import log;
+from .Util import log;
 
-from BaseCPOEAnalysis import AnalysisQuery;
-from PreparePatientItems import PreparePatientItems;
-from RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+from .BaseCPOEAnalysis import AnalysisQuery;
+from .PreparePatientItems import PreparePatientItems;
+from .RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
 
 # When doing validation calculations, number of items to recommend when calculating precision and recall
 # If set to value < 1, then interpret as sentinel value, meaning just use all available order set items.
@@ -136,7 +137,7 @@ def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientI
                 orderSetItemsByOrderSetId[orderSetId] = list();
             orderSetItemsByOrderSetId[orderSetId].append(orderSetItem);
 
-        keyOrderSetIds = orderSetItemsByOrderSetId.keys();
+        keyOrderSetIds = list(orderSetItemsByOrderSetId.keys());
         if analysisQuery.numRecsByOrderSet:
             # Only use the specified key order set for each set of patient data
             orderSetId = patientItemData["order_set_id"];
@@ -172,8 +173,8 @@ def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientI
         allAvailableOrderSetItemIds = set();
         allAvailableVerifyOrderSetItemIds = set();
         recommendableAvailableOrderSetItemIds = set();
-        for isVerifyItem, usedItemIdsByOrderSetId in usedItemIdsByOrderSetIdByIsVerifyItem.iteritems():
-            for orderSetId, usedItemIds in usedItemIdsByOrderSetId.iteritems():
+        for isVerifyItem, usedItemIdsByOrderSetId in usedItemIdsByOrderSetIdByIsVerifyItem.items():
+            for orderSetId, usedItemIds in usedItemIdsByOrderSetId.items():
                 allUsedOrderSetIds.add(orderSetId);
                 if isVerifyItem:
                     allAvailableVerifyOrderSetItemIds.update(self.supportRecommender.itemIdsByOrderSetId[orderSetId]);
@@ -197,7 +198,7 @@ def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientI
             recItemModel = dict(self.supportRecommender.itemsById[itemId]);
             recItemModel["score"] = recItemModel[recQuery.sortField];
             recommendedData.append(recItemModel);
-        recommendedData.sort( RowItemFieldComparator(recQuery.sortField), reverse=True );
+        recommendedData.sort(key=itemgetter(recQuery.sortField), reverse=True)
 
         # Distill down to just the set of recommended item IDs
         recommendedItemIds = set();
@@ -300,7 +301,7 @@ def main(self, argv):
 
             # Print comment line with analysis arguments to allow for deconstruction later
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             formatter = TextResultsFormatter( outputFile );
             colNames = self.resultHeaders(query);
diff --git a/medinfo/cpoe/analysis/OutcomePredictionAnalysis.py b/medinfo/cpoe/analysis/OutcomePredictionAnalysis.py
index c4b65808..9d9aa16b 100644
--- a/medinfo/cpoe/analysis/OutcomePredictionAnalysis.py
+++ b/medinfo/cpoe/analysis/OutcomePredictionAnalysis.py
@@ -16,7 +16,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots;
@@ -27,11 +27,11 @@
 from medinfo.cpoe.Const import AGGREGATOR_OPTIONS, COUNT_PREFIX_OPTIONS;
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender, BaselineFrequencyRecommender, RandomItemRecommender;
-from Util import log;
+from .Util import log;
 
-from BaseCPOEAnalysis import BaseCPOEAnalysis;
-from BaseCPOEAnalysis import RECOMMENDER_CLASS_LIST, RECOMMENDER_CLASS_BY_NAME, AnalysisQuery;
-from PreparePatientItems import PreparePatientItems;
+from .BaseCPOEAnalysis import BaseCPOEAnalysis;
+from .BaseCPOEAnalysis import RECOMMENDER_CLASS_LIST, RECOMMENDER_CLASS_BY_NAME, AnalysisQuery;
+from .PreparePatientItems import PreparePatientItems;
 
 from medinfo.analysis.Const import OUTCOME_IN_QUERY;
 
@@ -61,7 +61,7 @@ def __call__(self, analysisQuery, conn=None):
 
             # Start building results data
             resultsStatDataList = list();
-            progress = ProgressDots(50,1,"Patients");
+            # progress = ProgressDots(50,1,"Patients");
 
             # Query for all of the order / item data for the test patients.  Load one patient's data at a time
             preparer = PreparePatientItems();
@@ -80,7 +80,7 @@ def __call__(self, analysisQuery, conn=None):
                 if existsByOutcomeId is not None:
                     # Verify that at least one of the labels is not trivial with the outcome occuring during the query period
                     nonTrivialOutcomeExists = False;
-                    for outcomeResult in existsByOutcomeId.itervalues():
+                    for outcomeResult in existsByOutcomeId.values():
                         if outcomeResult != OUTCOME_IN_QUERY:
                             nonTrivialOutcomeExists = True;
                     if not analysisQuery.skipIfOutcomeInQuery or nonTrivialOutcomeExists:
@@ -88,7 +88,7 @@ def __call__(self, analysisQuery, conn=None):
                         resultsStatData = self.prepareResultStats( patientId, queryItemCountById, scoreByOutcomeId, existsByOutcomeId);
                         resultsStatDataList.append(resultsStatData);
 
-                progress.Update();
+                # progress.Update();
 
             # progress.PrintStatus();
 
@@ -109,7 +109,7 @@ def analyzePatientItems(self, analysisQuery, recQuery, patientId, patientItemDat
         queryItemCountById = patientItemData["queryItemCountById"];
         existsByOutcomeId = patientItemData["existsByOutcomeId"];
 
-        recQuery.queryItemIds = queryItemCountById.keys();
+        recQuery.queryItemIds = list(queryItemCountById.keys());
         #recQuery.targetItemIds = queryStartTime.targetItemIds;     # Already established in base construction
 
         # Query for recommended orders / items
@@ -158,14 +158,14 @@ def prepareResultStats( self, patientId, queryItemCountById, scoreByOutcomeId, e
         """
         stats = RowItemModel();
         stats["patient_id"] = patientId;
-        stats["queryItemIds"] = queryItemCountById.keys();
+        stats["queryItemIds"] = list(queryItemCountById.keys());
 
         # Convert sets into more easily readable, sorted lists
         queryItemIdList = list(stats["queryItemIds"]);
         queryItemIdList.sort();
         stats["queryItemIdList"] = json.dumps(queryItemIdList);
 
-        for outcomeId in scoreByOutcomeId.iterkeys():
+        for outcomeId in scoreByOutcomeId.keys():
             stats["score.%s" % outcomeId] = scoreByOutcomeId[outcomeId];
             stats["outcome.%s" % outcomeId] = existsByOutcomeId[outcomeId];
 
@@ -201,7 +201,7 @@ def main(self, argv):
 
         parser.add_option("-P", "--preparedPatientItemFile",  dest="preparedPatientItemFile", action="store_true", help="If set, will expect primary argument to instead be name of file to read input data from, instead of using above parameters to query from database.");
 
-        parser.add_option("-R", "--recommender",  dest="recommender",  help="Name of the recommender to run the analysis against.  Options: %s" % RECOMMENDER_CLASS_BY_NAME.keys());
+        parser.add_option("-R", "--recommender",  dest="recommender",  help="Name of the recommender to run the analysis against.  Options: %s" % list(RECOMMENDER_CLASS_BY_NAME.keys()));
         parser.add_option("-S", "--scoreField",  dest="scoreField",  help="Name of (derived) field to score items by.  For example, 'conditionalFreq.'");
         parser.add_option("-p", "--countPrefix",  dest="countPrefix",  help="Which counting method to use for item associations.  Defaults to counting item occurrences, allowing for duplicates.  Additional options include: %s." % list(COUNT_PREFIX_OPTIONS) );
         parser.add_option("-a", "--aggregationMethod",  dest="aggregationMethod",  help="Aggregation method to use for recommendations based off multiple query items.  Options: %s." % list(AGGREGATOR_OPTIONS) );
@@ -270,7 +270,7 @@ def main(self, argv):
             outputFile = stdOpen(outputFilename,"w");
 
             # Print comment line with analysis arguments to allow for deconstruction later
-            print >> outputFile, COMMENT_TAG, json.dumps({"argv":argv});
+            print(COMMENT_TAG, json.dumps({"argv":argv}), file=outputFile);
 
             colNames = self.analysisHeaders(query);
             analysisResults.insert(0, RowItemModel(colNames,colNames) );    # Insert a mock record to get a header / label row
diff --git a/medinfo/cpoe/analysis/PreparePatientItems.py b/medinfo/cpoe/analysis/PreparePatientItems.py
index eae4de0b..7e2648f3 100644
--- a/medinfo/cpoe/analysis/PreparePatientItems.py
+++ b/medinfo/cpoe/analysis/PreparePatientItems.py
@@ -12,7 +12,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots, loadJSONDict;
@@ -24,14 +24,14 @@
 from medinfo.cpoe.ItemRecommender import RecommenderQuery, ItemAssociationRecommender;
 from medinfo.cpoe.TopicModel import TopicModel;
 from medinfo.cpoe.Const import AD_HOC_SECTION;
-from Util import log;
-from Const import MAX_BASE_ITEM_TIME_RESOLUTION;
+from .Util import log;
+from .Const import MAX_BASE_ITEM_TIME_RESOLUTION;
 
 from medinfo.analysis.Const import OUTCOME_ABSENT, OUTCOME_PRESENT, OUTCOME_IN_QUERY;
 from medinfo.analysis.Const import NEGATIVE_OUTCOME_STRS;
 
-from BaseCPOEAnalysis import AnalysisQuery;
-from BaseCPOEAnalysis import BaseCPOEAnalysis;
+from .BaseCPOEAnalysis import AnalysisQuery;
+from .BaseCPOEAnalysis import BaseCPOEAnalysis;
 
 class PreparePatientItems(BaseCPOEAnalysis):
     def __init__(self):
@@ -312,7 +312,7 @@ def parsePreparedResultFile(self, inputFile, analysisQuery=None):
         prog = ProgressDots();
         for i, dataRow in enumerate(TabDictReader(inputFile)):
             existsByOutcomeId = None;
-            dataKeys = dataRow.keys();  # Retrieve separate from iteration, as will be modifying contents as iterate
+            dataKeys = list(dataRow.keys());  # Retrieve separate from iteration, as will be modifying contents as iterate
             for key in dataKeys:
                 value = dataRow[key];
                 if key.endswith("id") or key.endswith("Id"):
@@ -370,7 +370,7 @@ def convertResultsFileToFeatureMatrix(self, inputFilename, incHeaders=True):
         for inputDict in TabDictReader(inputFile):
             for itemCol in itemColumnHeaders:
                 itemCountById = loadJSONDict(inputDict[itemCol],int,int);
-                allItemIds.update(itemCountById.iterkeys());
+                allItemIds.update(iter(itemCountById.keys()));
             nLines += 1;
         inputFile.close();
 
@@ -397,7 +397,7 @@ def convertResultsFileToFeatureMatrix(self, inputFilename, incHeaders=True):
             rowItemCountById = dict();
             for itemCol in itemColumnHeaders:
                 itemCountById = loadJSONDict(inputDict[itemCol],int,int);
-                for itemId, itemCount in itemCountById.iteritems():
+                for itemId, itemCount in itemCountById.items():
                     if itemId not in rowItemCountById:
                         rowItemCountById[itemId] = 0;
                     rowItemCountById[itemId] += itemCount;
@@ -434,7 +434,7 @@ def convertResultsFileToBagOfWordsCorpus(self, inputFile, queryItems=True, verif
             observedIds = set();
             if outcomeItems:
                 # Pull out labeled outcome values
-                for key, value in inputDict.iteritems():
+                for key, value in inputDict.items():
                     if key.startswith("outcome."):
                         value = int(value);
                         outcomeId = int(key[len("outcome."):]);
@@ -445,13 +445,13 @@ def convertResultsFileToBagOfWordsCorpus(self, inputFile, queryItems=True, verif
             if queryItems:
                 # Iterate through query items
                 itemCountById = loadJSONDict(inputDict["queryItemCountByIdJSON"], int, int);
-                for itemId, itemCount in itemCountById.iteritems():
+                for itemId, itemCount in itemCountById.items():
                     if itemId not in totalCountById:
                         totalCountById[itemId] = 0;
                     totalCountById[itemId] += itemCount;
             if verifyItems:
                 itemCountById = loadJSONDict(inputDict["verifyItemCountByIdJSON"], int, int);
-                for itemId, itemCount in itemCountById.iteritems():
+                for itemId, itemCount in itemCountById.items():
                     if itemId not in totalCountById:
                         totalCountById[itemId] = 0;
                     totalCountById[itemId] += itemCount;
@@ -527,12 +527,12 @@ def main(self, argv):
                 if options.excludeCategoryIds is not None:
                     excludeCategoryIds = set(int(idStr) for idStr in options.excludeCategoryIds.split(","));
 
-                print >> outputFile, COMMENT_TAG, json.dumps({"argv":argv});    # Print comment line with analysis arguments to allow for deconstruction later
+                print(COMMENT_TAG, json.dumps({"argv":argv}), file=outputFile);    # Print comment line with analysis arguments to allow for deconstruction later
 
                 # Run the actual analysis / data extraction
                 rowGenerator = self.convertResultsFileToBagOfWordsCorpus(inputFile, queryItems, verifyItems, outcomeItems, excludeCategoryIds);
                 for row in rowGenerator:
-                    print >> outputFile, json.dumps(row);
+                    print(json.dumps(row), file=outputFile);
 
             elif options.featureMatrixConvert:
                 # Convert results file into full feature matrix format
@@ -544,7 +544,7 @@ def main(self, argv):
                     outputFilename = args[1];
                 outputFile = stdOpen(outputFilename,"w");
 
-                print >> outputFile, COMMENT_TAG, json.dumps({"argv":argv});    # Print comment line with analysis arguments to allow for deconstruction later
+                print(COMMENT_TAG, json.dumps({"argv":argv}), file=outputFile);    # Print comment line with analysis arguments to allow for deconstruction later
 
                 # Run the actual analysis / data extraction
                 rowGenerator = self.convertResultsFileToFeatureMatrix(inputFilename);
@@ -615,7 +615,7 @@ def main(self, argv):
                     outputFilename = args[1];
                 outputFile = stdOpen(outputFilename,"w");
 
-                print >> outputFile, COMMENT_TAG, json.dumps({"argv":argv});    # Print comment line with analysis arguments to allow for deconstruction later
+                print(COMMENT_TAG, json.dumps({"argv":argv}), file=outputFile);    # Print comment line with analysis arguments to allow for deconstruction later
 
                 formatter = TextResultsFormatter( outputFile );
 
@@ -722,7 +722,7 @@ def __call__(self, analysisQuery, recQuery, patientId, patientItemList, category
 
             # Option to track sequences of multiple items as a single virtual target item
             midSequenceItemDatesByItemId = dict();
-            for virtualItemId, sequenceIds in analysisQuery.sequenceItemIdsByVirtualItemId.iteritems():
+            for virtualItemId, sequenceIds in analysisQuery.sequenceItemIdsByVirtualItemId.items():
                 midSequenceId = sequenceIds[0];
                 for patientItem in patientItemList:
                     if patientItem["clinical_item_id"] == midSequenceId:
@@ -741,7 +741,7 @@ def __call__(self, analysisQuery, recQuery, patientId, patientItemList, category
 
                 # Determine if this represents a virtual outcome item
                 virtualOutcomeId = None;
-                for virtualItemId, sequenceIds in analysisQuery.sequenceItemIdsByVirtualItemId.iteritems():
+                for virtualItemId, sequenceIds in analysisQuery.sequenceItemIdsByVirtualItemId.items():
                     midSequenceId = sequenceIds[0];
                     endSequenceId = sequenceIds[-1];
                     isVirtualOutcome = itemId == endSequenceId;
diff --git a/medinfo/cpoe/analysis/RecommendationClassificationAnalysis.py b/medinfo/cpoe/analysis/RecommendationClassificationAnalysis.py
index ecc21337..ef425f41 100644
--- a/medinfo/cpoe/analysis/RecommendationClassificationAnalysis.py
+++ b/medinfo/cpoe/analysis/RecommendationClassificationAnalysis.py
@@ -13,7 +13,7 @@
 import time;
 from optparse import OptionParser;
 import json;
-from cStringIO import StringIO;
+from io import StringIO;
 from math import sqrt;
 from datetime import timedelta;
 from medinfo.common.Const import COMMENT_TAG;
@@ -26,13 +26,13 @@
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender, BaselineFrequencyRecommender, RandomItemRecommender;
 from medinfo.cpoe.OrderSetRecommender import OrderSetRecommender;
-from Util import log;
+from .Util import log;
 
-from BaseCPOEAnalysis import BaseCPOEAnalysis;
-from BaseCPOEAnalysis import RECOMMENDER_CLASS_LIST, RECOMMENDER_CLASS_BY_NAME, AnalysisQuery;
-from BaseCPOEAnalysis import AGGREGATOR_OPTIONS;
+from .BaseCPOEAnalysis import BaseCPOEAnalysis;
+from .BaseCPOEAnalysis import RECOMMENDER_CLASS_LIST, RECOMMENDER_CLASS_BY_NAME, AnalysisQuery;
+from .BaseCPOEAnalysis import AGGREGATOR_OPTIONS;
 
-from PreparePatientItems import PreparePatientItems;
+from .PreparePatientItems import PreparePatientItems;
 
 class RecommendationClassificationAnalysis(BaseCPOEAnalysis):
     """Driver class to review given patient data and run sample recommendation queries against
@@ -109,7 +109,7 @@ def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientI
         queryItemCountById = patientItemData["queryItemCountById"];
         verifyItemCountById = patientItemData["verifyItemCountById"];
 
-        recQuery.queryItemIds = queryItemCountById.keys();
+        recQuery.queryItemIds = list(queryItemCountById.keys());
         recQuery.targetItemIds = set(); # Ensure not restricted to some specified outcome target
 
         # Query for recommended orders / items
@@ -177,8 +177,8 @@ def calculateResultStats( self, patientItemData, queryItemCountById, verifyItemC
             stats["baseItemId"] = patientItemData["baseItemId"];
         if "order_set_id" in patientItemData:
             stats["order_set_id"] = patientItemData["order_set_id"];
-        stats["queryItemIds"] = queryItemCountById.keys();
-        stats["verifyItemIds"] = verifyItemCountById.keys();
+        stats["queryItemIds"] = list(queryItemCountById.keys());
+        stats["verifyItemIds"] = list(verifyItemCountById.keys());
         stats["recommendedItemIds"] = recommendedItemIds;
         stats["numQueryItems"] = len(queryItemCountById);
         stats["numVerifyItems"] = len(verifyItemCountById);
@@ -228,7 +228,7 @@ def calculateResultStats( self, patientItemData, queryItemCountById, verifyItemC
                 baseCount = baseCountByItemId[itemId];
             if baseCount <= 0: baseCount = 0.5; # Normalization to avoid possible divide by zero
             weightByVerifyItemId[itemId] = 1.0/baseCount;
-        stats["weightVerifyItems"] = sum(weightByVerifyItemId.itervalues());
+        stats["weightVerifyItems"] = sum(weightByVerifyItemId.values());
 
         weightByRecommendedItemId = dict();
         for itemId in recommendedItemIds:
@@ -237,7 +237,7 @@ def calculateResultStats( self, patientItemData, queryItemCountById, verifyItemC
                 baseCount = baseCountByItemId[itemId];
             if baseCount <= 0: baseCount = 0.5; # Normalization to avoid possible divide by zero
             weightByRecommendedItemId[itemId] = 1.0/baseCount;
-        stats["weightRecommendedItems"] = sum(weightByRecommendedItemId.itervalues());
+        stats["weightRecommendedItems"] = sum(weightByRecommendedItemId.values());
 
         stats["weightTP"] = 0.0;
         for itemId in verifyItemIds.intersection(recommendedItemIds):
@@ -335,7 +335,7 @@ def main(self, argv):
 
         parser.add_option("-P", "--preparedPatientItemFile",  dest="preparedPatientItemFile", action="store_true", help="If set, will expect primary argument to instead be name of file to read input data from, instead of using above parameters to query from database.");
 
-        parser.add_option("-R", "--recommender",  dest="recommender",  help="Name of the recommender to run the analysis against.  Options: %s" % RECOMMENDER_CLASS_BY_NAME.keys());
+        parser.add_option("-R", "--recommender",  dest="recommender",  help="Name of the recommender to run the analysis against.  Options: %s" % list(RECOMMENDER_CLASS_BY_NAME.keys()));
         parser.add_option("-r", "--numRecs",   dest="numRecs",  help="Number of orders / items to recommend for comparison against the verification set. Alternative set option numRecsByOrderSet to look for key order set usage and size.");
         parser.add_option("-O", "--numRecsByOrderSet",   dest="numRecsByOrderSet", action="store_true", help="If set, then look for an order_set_id column to find the key order set that triggered the evaluation time point to determine number of recommendations to consider.");
         parser.add_option("-s", "--sortField",  dest="sortField",  help="Allow overriding of default sort field when returning ranked results");
@@ -424,7 +424,7 @@ def main(self, argv):
 
             # Print comment line with analysis arguments to allow for deconstruction later
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             formatter = TextResultsFormatter( outputFile );
             colNames = self.resultHeaders(query);
diff --git a/medinfo/cpoe/analysis/RecommendationRankingTrendAnalysis.py b/medinfo/cpoe/analysis/RecommendationRankingTrendAnalysis.py
index ee0a814f..026c5eb2 100644
--- a/medinfo/cpoe/analysis/RecommendationRankingTrendAnalysis.py
+++ b/medinfo/cpoe/analysis/RecommendationRankingTrendAnalysis.py
@@ -5,7 +5,7 @@
 - Input patient IDs to test against
 - Query recommender for recommended item list given the first unique X orders / items
   for each patient, for X in [0,N-1] (N = number unique orders / items for the patient)
-- For each query above, record the positition of the next actual order / item in that 
+- For each query above, record the positition of the next actual order / item in that
     recommended item rank list.
 
 Simulates a cumulative recommendation based on more and more orders.
@@ -18,7 +18,7 @@
 import sys, os
 import time;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 from math import sqrt;
 from medinfo.common.Util import stdOpen, ProgressDots;
@@ -28,21 +28,21 @@
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender, BaselineFrequencyRecommender, RandomItemRecommender;
-from Util import log;
+from .Util import log;
 
-from BaseCPOEAnalysis import BaseCPOEAnalysis;
-from BaseCPOEAnalysis import RECOMMENDER_CLASS_LIST, RECOMMENDER_CLASS_BY_NAME, AnalysisQuery;
-from BaseCPOEAnalysis import AGGREGATOR_OPTIONS;
+from .BaseCPOEAnalysis import BaseCPOEAnalysis;
+from .BaseCPOEAnalysis import RECOMMENDER_CLASS_LIST, RECOMMENDER_CLASS_BY_NAME, AnalysisQuery;
+from .BaseCPOEAnalysis import AGGREGATOR_OPTIONS;
 
 class AnalysisQuery:
     """Simple struct to pass query parameters
     """
-    patientIds = None;  # IDs of the patients to test / analyze against 
+    patientIds = None;  # IDs of the patients to test / analyze against
     recommender = None; # Instance of the recommender to test against
-    baseRecQuery = None;    # Base Recommender Query to test recommender with.  
-                            # Query items and return counts will be customized by analyzer dynamically, 
+    baseRecQuery = None;    # Base Recommender Query to test recommender with.
+                            # Query items and return counts will be customized by analyzer dynamically,
                             #   but allows specification of static query modifiers (e.g., excluded categories, items, timeDeltaMax)
-    queryItemMax = None;    # If set, specifies a maximum number of query items to use when analyzing 
+    queryItemMax = None;    # If set, specifies a maximum number of query items to use when analyzing
                             #   serial recommendations.  Will stop analyzing further for a patient once reach this limit
     def __init__(self):
         self.patientIds = set();
@@ -54,11 +54,11 @@ class RecommendationRankingTrendAnalysis(BaseCPOEAnalysis):
     """Driver class to review given patient data and run sample recommendation queries against
     them serially based on accumulating initial orders / items and record recommendation
     ranks of each next order / item as they occur.
-    
+
     Report as a relational table with columns
     - patientId:    ID of the patient to which the analysis data applies
     - iItem:    Index of the next order / item for the patient in chronological order
-    - iRecItem: Index of the next order / item, which should be the same as iItem, 
+    - iRecItem: Index of the next order / item, which should be the same as iItem,
                 except this only counts items / orders for which recommendations can / will be made.
                 For example, recommendations will not be offerred for repeat orders.
                 Recommendations will often only be offerred for orders and not other informational
@@ -77,7 +77,7 @@ def __call__(self, analysisQuery, conn=None):
         if conn is None:
             conn = self.connFactory.connection();
             extConn = False;
-        
+
         try:
             # Preload some lookup data to facilitate subsequent checks
             categoryIdByItemId = dict();
@@ -94,18 +94,18 @@ def __call__(self, analysisQuery, conn=None):
             # Start building results data
             resultsTable = list();
             progress = ProgressDots(50,1,"Item Recommendations");
-            
+
             # Query for all of the order / item data for the test patients.  Load one patient's data at a time
             for (patientId, clinicalItemIdList) in self.queryPatientClinicalItemData(analysisQuery, conn=conn):
-                # Parse through the patient's item list and run serial recommendation queries 
+                # Parse through the patient's item list and run serial recommendation queries
                 #   to find each item's accumulated recommendation rank
                 serialRecDataGen = \
                     self.reviewSerialRecommendations \
-                    (   patientId, 
+                    (   patientId,
                         clinicalItemIdList,
-                        analysisQuery, 
-                        recQuery, 
-                        recommender, 
+                        analysisQuery,
+                        recQuery,
+                        recommender,
                         categoryIdByItemId,
                         progress=progress,
                         conn=conn
@@ -115,7 +115,7 @@ def __call__(self, analysisQuery, conn=None):
                     resultsRow = (patientId, clinicalItemId, iItem, iRecItem, recRank, recScore);
                     resultsTable.append(resultsRow);
 
-            progress.PrintStatus();
+            # progress.PrintStatus();
 
             #print resultsTable
             return resultsTable;
@@ -131,7 +131,7 @@ def queryPatientClinicalItemData(self, analysisQuery, conn):
         at a time.
         Generated iterator over 2-ples (patientId, clinicalItemIdList)
             - Patient ID: ID of the patient for which the currently yielded item intended for
-            - Clinical Item ID List: 
+            - Clinical Item ID List:
                 List of all of the clinical items / orders for this patient
                 ordered by item date (currently excluding those that are off the "default_recommend" / on the "default exclusion" list).
         """
@@ -173,10 +173,10 @@ def queryPatientClinicalItemData(self, analysisQuery, conn):
             clinicalItemIdList.append(clinicalItemId);
 
             row = cursor.fetchone();
-        
+
         # Yield / return the last patient data
         yield (currentPatientId, clinicalItemIdList);
-        
+
         cursor.close();
 
 
@@ -195,11 +195,11 @@ def reviewSerialRecommendations(self, patientId, clinicalItemIdList, analysisQue
         iRecItem = 0;   # Separately track number of items that can actually be recommended (skip repeats and other exclusions)
         for (iItem, clinicalItemId) in enumerate(clinicalItemIdList):
             if self.isItemRecommendable(clinicalItemId, queryItemIds, recQuery, categoryIdByItemId):
-                # Query based on accumulated key data thus far, 
+                # Query based on accumulated key data thus far,
                 #   to see how well able to predict / rank / score this next clinical item
                 recQuery.queryItemIds = queryItemIds;
                 recQuery.limit = None;  # No limitation because trying to find the next item whereever it may be in the list
-                
+
                 recommendedData = recommender( recQuery, conn=conn );
 
                 # Find the next clinical item in the recommended list
@@ -216,11 +216,11 @@ def reviewSerialRecommendations(self, patientId, clinicalItemIdList, analysisQue
 
                 iRecItem += 1;  # Track that we recorded information on one more recommended item
                 progress.Update();
-            
+
             queryItemIds.add(clinicalItemId);   # Accumulate initial query set as progress
-            
+
             if analysisQuery.queryItemMax is not None and iRecItem >= analysisQuery.queryItemMax:
-                # Option to break early if wish to avoid excessive analysis that is unnecessary 
+                # Option to break early if wish to avoid excessive analysis that is unnecessary
                 #   or even potentially damaging to execution memory
                 break;
 
@@ -247,7 +247,7 @@ def main(self, argv):
                     "   <outputFile>    If query yields a result set, then that will be output\n"+\
                     "                       to the named file.  Leave blank or specify \"-\" to send to stdout.\n"
         parser = OptionParser(usage=usageStr)
-        parser.add_option("-R", "--recommender",  dest="recommender",  help="Name of the recommender to run the analysis against.  Options: %s" % RECOMMENDER_CLASS_BY_NAME.keys());
+        parser.add_option("-R", "--recommender",  dest="recommender",  help="Name of the recommender to run the analysis against.  Options: %s" % list(RECOMMENDER_CLASS_BY_NAME.keys()));
         parser.add_option("-s", "--sortField",  dest="sortField",  help="Allow overriding of default sort field when returning ranked results");
         parser.add_option("-f", "--fieldFilters",  dest="fieldFilters",  help="Filters to exclude results.  Comma-separated separated list of field-op:value exclusions where op is either < or > like, conditionalFreq<:0.1,frqeRatio<:1");
         parser.add_option("-t", "--timeDeltaMax",  dest="timeDeltaMax",  help="If set, represents a time delta in seconds maximum by which recommendations should be based on.  Defaults to recommending items that occur at ANY time after the key orders.  If provided, will apply limits to only orders placed within 0 seconds, 1 hour (3600), 1 day (86400), or 1 week (604800) of the key orders / items.");
@@ -255,7 +255,7 @@ def main(self, argv):
         parser.add_option("-p", "--countPrefix",  dest="countPrefix",  help="Prefix for how to do counts.  Blank for default item counting allowing repeats, otherwise ignore repeats for patient_ or encounter_");
         parser.add_option("-q", "--queryItemMax",  dest="queryItemMax",  help="If set, specifies a maximum number of query items to use when analyzing serial recommendations.  Will stop analyzing further for a patient once reach this limit.");
         (options, args) = parser.parse_args(argv[1:])
-    
+
         log.info("Starting: "+str.join(" ", argv))
         timer = time.time();
         if len(args) > 1:
@@ -273,7 +273,7 @@ def main(self, argv):
                 # Unable to open as a filename, then interpret as simple comma-separated list
                 query.patientIds = set(patientIdsParam.split(","));
 
-        
+
             query.baseRecQuery = RecommenderQuery();
             query.baseRecQuery.excludeCategoryIds = query.recommender.defaultExcludedClinicalItemCategoryIds();
             query.baseRecQuery.excludeItemIds = query.recommender.defaultExcludedClinicalItemIds();
@@ -292,7 +292,7 @@ def main(self, argv):
 
             if options.queryItemMax is not None:
                 query.queryItemMax = int(options.queryItemMax);
-            
+
             # Run the actual analysis
             analysisResults = self(query);
 
@@ -301,12 +301,12 @@ def main(self, argv):
             if len(args) > 1:
                 outputFilename = args[1];
             outputFile = stdOpen(outputFilename,"w");
-            
-            print >> outputFile, "#", argv;  # Print comment line with analysis arguments to allow for deconstruction later
+
+            print("#", argv, file=outputFile);  # Print comment line with analysis arguments to allow for deconstruction later
 
             colNames = ["patientId", "clinicalItemId", "iItem", "iRecItem", "recRank", "recScore"];
             analysisResults.insert(0, colNames);    # Insert a mock record to get a header / label row
-            
+
             formatter = TextResultsFormatter( outputFile );
             formatter.formatResultSet( analysisResults );
 
diff --git a/medinfo/cpoe/analysis/TopicModelAnalysis.py b/medinfo/cpoe/analysis/TopicModelAnalysis.py
index a8ef55e2..8c8319df 100644
--- a/medinfo/cpoe/analysis/TopicModelAnalysis.py
+++ b/medinfo/cpoe/analysis/TopicModelAnalysis.py
@@ -8,7 +8,7 @@
 import time;
 import json;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import timedelta;
 from pprint import pprint;
 
@@ -22,12 +22,12 @@
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.TopicModelRecommender import TopicModelRecommender;
-from Util import log;
+from .Util import log;
 
-from RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
-from BaseCPOEAnalysis import AnalysisQuery;
-from PreparePatientItems import PreparePatientItems;
-from RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+from .RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+from .BaseCPOEAnalysis import AnalysisQuery;
+from .PreparePatientItems import PreparePatientItems;
+from .RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
 
 DEFAULT_TOPIC_ITEM_COUNT = 1000; # When using or printing out topic information, number of top scored items to consider
 DEFAULT_RECOMMENDED_ITEM_COUNT = 10;    # When doing validation calculations, number of items to recommend when calculating precision and recall
@@ -42,7 +42,7 @@ def __call__(self, analysisQuery):
         """Go through the validation file to test use of the model towards predicting verify items.
         """
         preparer = PreparePatientItems();
-        
+
         # Keep ID indexes for simplicity for now
         id2word = analysisQuery.recommender.model.id2word;
         id2id = dict();
@@ -50,15 +50,15 @@ def __call__(self, analysisQuery):
             id2id[id] = id;
         analysisQuery.recommender.model.id2word = id2id;
 
-        progress = ProgressDots(50,1,"Patients");
+        # progress = ProgressDots(50,1,"Patients");
         for patientItemData in preparer.loadPatientItemData(analysisQuery):
             patientId = patientItemData["patient_id"];
             analysisResults = \
                 self.analyzePatientItems \
                 (   patientItemData,
-                    analysisQuery, 
-                    analysisQuery.baseRecQuery, 
-                    patientId, 
+                    analysisQuery,
+                    analysisQuery.baseRecQuery,
+                    patientId,
                     analysisQuery.recommender,
                     preparer
                 );
@@ -71,13 +71,13 @@ def __call__(self, analysisQuery):
                     analysisQuery.baseItemId = patientItemData["baseItemId"]; # Record something here, so know to report back in result headers
                 yield resultsStatData;
 
-            progress.Update();
+            # progress.Update();
 
-        progress.PrintStatus();
+        # progress.PrintStatus();
 
     def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientId, recommender, preparer):
         """Given the primary query data and clinical item list for a given test patient,
-        Parse through the item list and run a query to get the top recommended IDs 
+        Parse through the item list and run a query to get the top recommended IDs
         to produce the relevant verify and recommendation item ID sets for comparison
         """
         if "queryItemCountById" not in patientItemData:
@@ -85,16 +85,16 @@ def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientI
             return None;
         queryItemCountById = patientItemData["queryItemCountById"];
         verifyItemCountById = patientItemData["verifyItemCountById"];
-        
+
         recQuery.queryItemIds = queryItemCountById; # Have option to use as dictionary, but will also function as key set
-        # recQuery.limit = analysisQuery.numRecommendations; 
-        
+        # recQuery.limit = analysisQuery.numRecommendations;
+
         # Query for recommended orders / items
         recommendedData = recommender( recQuery );
 
         # Customize number of recommendations if comparing against specific order set usage
         self.customizeNumRecommendations(patientItemData, analysisQuery, recQuery, preparer);
-        
+
         # Distill down to just the set of recommended item IDs
         recommendedItemIds = set();
         for i, recommendationModel in enumerate(recommendedData):
@@ -102,7 +102,7 @@ def analyzePatientItems(self, patientItemData, analysisQuery, recQuery, patientI
                 break;
             recommendedItemIds.add(recommendationModel["clinical_item_id"]);
         return (queryItemCountById, verifyItemCountById, recommendedItemIds, recommendedData);
- 
+
     def calculateResultStats( self, patientItemData, queryItemCountById, verifyItemCountById, recommendedItemIds, baseCountByItemId, recQuery, recommendedData ):
         resultsStatData = RecommendationClassificationAnalysis.calculateResultStats( self, patientItemData, queryItemCountById, verifyItemCountById, recommendedItemIds, baseCountByItemId, recQuery, recommendedData );
         # Copy elements from any item in recommended list
@@ -115,7 +115,7 @@ def resultHeaders(self, analysisQuery=None):
         headers.append("weightByTopicId");
         headers.append("numSelectedTopics");
         return headers;
-   
+
     def main(self, argv):
         """Main method, callable from command line"""
         usageStr =  "usage: %prog [options] <inputFile> [<outputFile>]\n"+\
@@ -147,7 +147,7 @@ def main(self, argv):
                 query.baseRecQuery.excludeItemIds = query.recommender.defaultExcludedClinicalItemIds();
             query.baseRecQuery.itemsPerCluster = int(options.itemsPerCluster);
             query.baseRecQuery.minClusterWeight = float(options.minClusterWeight);
-            
+
             query.baseRecQuery.sortField = options.sortField;
             query.numRecommendations = int(options.numRecs);
             query.numRecsByOrderSet = options.numRecsByOrderSet;
@@ -160,13 +160,13 @@ def main(self, argv):
             if len(args) > 1:
                 outputFilename = args[1];
             outputFile = stdOpen(outputFilename,"w");
-            
+
             # Print comment line with analysis arguments to allow for deconstruction later
             summaryData = {"argv": argv};
-            print >> outputFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
 
             formatter = TextResultsFormatter( outputFile );
-            colNames = self.resultHeaders(query); 
+            colNames = self.resultHeaders(query);
             formatter.formatTuple( colNames );  # Insert a mock record to get a header / label row
             formatter.formatResultDicts( analysisResults, colNames );
         else:
diff --git a/medinfo/cpoe/analysis/Util.py b/medinfo/cpoe/analysis/Util.py
index ed8f9355..408ee32b 100644
--- a/medinfo/cpoe/analysis/Util.py
+++ b/medinfo/cpoe/analysis/Util.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
 
diff --git a/medinfo/cpoe/analysis/test/HDPTestTopicModel.model b/medinfo/cpoe/analysis/test/HDPTestTopicModel.model
deleted file mode 100644
index a55a0d2a..00000000
Binary files a/medinfo/cpoe/analysis/test/HDPTestTopicModel.model and /dev/null differ
diff --git a/medinfo/cpoe/analysis/test/HDPTestTopicModel.model.topics.tab b/medinfo/cpoe/analysis/test/HDPTestTopicModel.model.topics.tab
deleted file mode 100644
index f38c1489..00000000
--- a/medinfo/cpoe/analysis/test/HDPTestTopicModel.model.topics.tab
+++ /dev/null
@@ -1,767 +0,0 @@
-# {"argv": ["TopicModel", "-n", "0", "-i", "5", "-", "HDPTestTopicModel.model"]}
-topic_id	item_id	description	score	tfidf
-0	5	None	0.142452007279	0.0474840024263
-0	4	None	0.13752806062	0.034382015155
-0	16	None	0.105758843063	0.0264397107657
-0	11	None	0.0977284844883	0.0488642422442
-0	10	None	0.0904866116345	0.0301622038782
-1	12	None	0.153363464495	0.0383408661237
-1	9	None	0.144442791974	0.0481475973245
-1	10	None	0.131386934605	0.0437956448683
-1	15	None	0.0986216113214	0.0493108056607
-1	2	None	0.0764000579618	0.0254666859873
-2	7	None	0.327200084025	0.0
-2	16	None	0.111740077881	0.0279350194702
-2	6	None	0.109290787581	0.0
-2	8	None	0.107597435503	0.0358658118345
-2	2	None	0.0758939958722	0.0252979986241
-3	11	None	0.236284234599	0.1181421173
-3	6	None	0.13202094429	0.0
-3	15	None	0.0690517614127	0.0345258807064
-3	5	None	0.0676000538922	0.0225333512974
-3	4	None	0.0642512561772	0.0160628140443
-4	3	None	0.294353429817	0.0981178099389
-4	1	None	0.130221340458	0.0434071134861
-4	10	None	0.124419877276	0.0414732924252
-4	14	None	0.0825752650751	0.0825752650751
-4	12	None	0.075616683286	0.0189041708215
-5	11	None	0.161439219627	0.0807196098135
-5	2	None	0.134995254776	0.0449984182587
-5	16	None	0.11712795054	0.0292819876351
-5	5	None	0.104090748606	0.0346969162021
-5	1	None	0.0860361982926	0.0286787327642
-6	1	None	0.225672863032	0.0752242876772
-6	9	None	0.203039137371	0.067679712457
-6	0	0	0.173685840839	0.0
-6	16	None	0.112712841299	0.0281782103247
-6	8	None	0.0674999099412	0.0224999699804
-7	15	None	0.365605344713	0.182802672356
-7	4	None	0.138169633601	0.0345424084002
-7	13	None	0.10212176513	0.0255304412825
-7	10	None	0.0871557323726	0.0290519107909
-7	12	None	0.0725002191339	0.0181250547835
-8	9	None	0.217959351145	0.0726531170485
-8	6	None	0.109161547685	0.0
-8	5	None	0.101182138579	0.0337273795263
-8	14	None	0.0974591261393	0.0974591261393
-8	2	None	0.0794126701359	0.0264708900453
-9	10	None	0.215329282168	0.0717764273893
-9	11	None	0.140693516577	0.0703467582884
-9	12	None	0.104157485791	0.0260393714477
-9	3	None	0.084522055993	0.0281740186643
-9	8	None	0.0830568173895	0.0276856057965
-10	1	None	0.217284532316	0.0724281774388
-10	11	None	0.106613639944	0.053306819972
-10	12	None	0.0949514204106	0.0237378551026
-10	10	None	0.0940999717255	0.0313666572418
-10	8	None	0.086208139928	0.0287360466427
-11	12	None	0.139916819921	0.0349792049803
-11	6	None	0.121677120272	0.0
-11	10	None	0.110539039406	0.0368463464686
-11	11	None	0.077283497384	0.038641748692
-11	3	None	0.0754841829337	0.0251613943112
-12	2	None	0.285590073919	0.0951966913063
-12	10	None	0.132729752577	0.044243250859
-12	4	None	0.0944857192099	0.0236214298025
-12	15	None	0.0850403989636	0.0425201994818
-12	1	None	0.0723340514484	0.0241113504828
-13	15	None	0.183958586435	0.0919792932175
-13	16	None	0.15623415145	0.0390585378624
-13	8	None	0.116510655002	0.0388368850006
-13	12	None	0.110426005264	0.0276065013159
-13	1	None	0.0953803089751	0.031793436325
-14	13	None	0.151193885038	0.0377984712595
-14	7	None	0.123448596012	0.0
-14	10	None	0.101905779756	0.0339685932521
-14	15	None	0.0948103598764	0.0474051799382
-14	2	None	0.093171102546	0.031057034182
-15	4	None	0.148240645635	0.0370601614087
-15	12	None	0.144903439653	0.0362258599132
-15	14	None	0.134051112254	0.134051112254
-15	5	None	0.122584207127	0.0408614023758
-15	16	None	0.111490931145	0.0278727327862
-16	7	None	0.184905182854	0.0
-16	12	None	0.177046251092	0.0442615627729
-16	11	None	0.124334550229	0.0621672751147
-16	5	None	0.0960633806297	0.0320211268766
-16	0	0	0.0867523362778	0.0
-17	15	None	0.155126957885	0.0775634789423
-17	1	None	0.144155323843	0.0480517746142
-17	10	None	0.120596764167	0.040198921389
-17	4	None	0.0951540405705	0.0237885101426
-17	16	None	0.0887673104994	0.0221918276249
-18	11	None	0.304371960961	0.152185980481
-18	2	None	0.197289978185	0.0657633260616
-18	6	None	0.135942556164	0.0
-18	4	None	0.0565124609696	0.0141281152424
-18	3	None	0.0478270303403	0.0159423434468
-19	9	None	0.241588031826	0.080529343942
-19	0	0	0.171592427189	0.0
-19	14	None	0.150147388751	0.150147388751
-19	4	None	0.0962980954358	0.024074523859
-19	3	None	0.0485552797972	0.0161850932657
-20	4	None	0.121553903719	0.0303884759298
-20	12	None	0.0969063523789	0.0242265880947
-20	16	None	0.0942345009307	0.0235586252327
-20	5	None	0.0900238913233	0.0300079637744
-20	15	None	0.0881827190106	0.0440913595053
-21	5	None	0.19455071533	0.0648502384433
-21	10	None	0.140788354282	0.0469294514272
-21	3	None	0.136796652326	0.0455988841086
-21	1	None	0.122995081098	0.0409983603659
-21	15	None	0.0724797058579	0.0362398529289
-22	2	None	0.232378716611	0.0774595722035
-22	4	None	0.218567023846	0.0546417559614
-22	3	None	0.105653703436	0.0352179011452
-22	9	None	0.0725231884604	0.0241743961535
-22	16	None	0.0672261419219	0.0168065354805
-23	1	None	0.344457006301	0.1148190021
-23	13	None	0.134895092774	0.0337237731934
-23	14	None	0.0870506196316	0.0870506196316
-23	0	0	0.086568476621	0.0
-23	12	None	0.0701580011043	0.0175395002761
-24	2	None	0.191084242122	0.063694747374
-24	13	None	0.132228768697	0.0330571921743
-24	12	None	0.128236386657	0.0320590966644
-24	10	None	0.105669252552	0.0352230841839
-24	16	None	0.0722742688993	0.0180685672248
-25	6	None	0.124184327967	0.0
-25	2	None	0.113743115753	0.0379143719176
-25	4	None	0.111774275331	0.0279435688327
-25	16	None	0.092397807197	0.0230994517993
-25	13	None	0.0880565094965	0.0220141273741
-26	13	None	0.110309764086	0.0275774410215
-26	6	None	0.109732981808	0.0
-26	0	0	0.106287883588	0.0
-26	9	None	0.104861477731	0.0349538259103
-26	10	None	0.104375803342	0.0347919344475
-27	16	None	0.133328520238	0.0333321300596
-27	3	None	0.0971367034435	0.0323789011478
-27	11	None	0.0889541238804	0.0444770619402
-27	7	None	0.0812422637283	0.0
-27	1	None	0.0794623190385	0.0264874396795
-28	0	0	0.165231948208	0.0
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-96	13	None	0.100038806364	0.0250097015911
-97	13	None	0.152682161291	0.0381705403227
-97	5	None	0.138157334822	0.0460524449407
-97	9	None	0.106979451137	0.0356598170457
-97	11	None	0.100027177103	0.0500135885517
-97	1	None	0.0987845641758	0.0329281880586
-98	3	None	0.146330823461	0.0487769411537
-98	16	None	0.133615709772	0.033403927443
-98	1	None	0.126078602599	0.0420262008663
-98	2	None	0.117364601464	0.0391215338213
-98	6	None	0.076456740943	0.0
-99	0	0	0.193207355097	0.0
-99	5	None	0.179873569587	0.059957856529
-99	10	None	0.13523587844	0.0450786261465
-99	8	None	0.0623271253047	0.0207757084349
-99	1	None	0.0519972160974	0.0173324053658
-100	8	None	0.126033726908	0.0420112423025
-100	9	None	0.114356438457	0.038118812819
-100	12	None	0.0912903594588	0.0228225898647
-100	14	None	0.0899976130873	0.0899976130873
-100	7	None	0.0830213245352	0.0
-101	4	None	0.233196124637	0.0582990311593
-101	6	None	0.171433809963	0.0
-101	9	None	0.168350107233	0.056116702411
-101	5	None	0.0953884128787	0.0317961376262
-101	8	None	0.0753327508436	0.0251109169479
-102	13	None	0.169833824599	0.0424584561497
-102	1	None	0.166875123774	0.0556250412581
-102	14	None	0.158249909564	0.158249909564
-102	8	None	0.103594264192	0.0345314213974
-102	3	None	0.0685961959036	0.0228653986345
-103	15	None	0.2133240987	0.10666204935
-103	7	None	0.139541989494	0.0
-103	12	None	0.105266049888	0.026316512472
-103	8	None	0.0896969340215	0.0298989780072
-103	11	None	0.0741558097291	0.0370779048645
-104	0	0	0.221686441216	0.0
-104	1	None	0.132127062717	0.0440423542391
-104	2	None	0.102525878439	0.0341752928128
-104	6	None	0.0859219180234	0.0
-104	12	None	0.0796539897473	0.0199134974368
-105	2	None	0.149411637956	0.0498038793187
-105	6	None	0.125292742131	0.0
-105	13	None	0.124093948991	0.0310234872479
-105	8	None	0.0939201317153	0.0313067105718
-105	15	None	0.0792470313074	0.0396235156537
-106	2	None	0.308233250155	0.102744416718
-106	4	None	0.128785193869	0.0321962984673
-106	6	None	0.100007687638	0.0
-106	10	None	0.0745673016104	0.0248557672035
-106	9	None	0.0736224042726	0.0245408014242
-107	1	None	0.170082786151	0.0566942620503
-107	12	None	0.135184382324	0.0337960955809
-107	10	None	0.129675401427	0.0432251338088
-107	9	None	0.0995208789594	0.0331736263198
-107	4	None	0.0906786199246	0.0226696549812
-108	15	None	0.170223401028	0.0851117005138
-108	14	None	0.162115633436	0.162115633436
-108	8	None	0.104325830628	0.034775276876
-108	6	None	0.0807951731578	0.0
-108	3	None	0.0776549624267	0.0258849874756
-109	1	None	0.183039198989	0.0610130663298
-109	15	None	0.134397955851	0.0671989779253
-109	2	None	0.111507377267	0.0371691257558
-109	6	None	0.0723195041325	0.0
-109	7	None	0.0623759458306	0.0
-110	15	None	0.203410622948	0.101705311474
-110	12	None	0.16804530327	0.0420113258174
-110	6	None	0.121482373577	0.0
-110	0	0	0.091656837565	0.0
-110	16	None	0.0762214622218	0.0190553655554
-111	16	None	0.174234263806	0.0435585659516
-111	10	None	0.14446053006	0.0481535100199
-111	0	0	0.102004489705	0.0
-111	2	None	0.0901700840594	0.0300566946865
-111	1	None	0.089916200439	0.029972066813
-112	13	None	0.165039141988	0.0412597854971
-112	15	None	0.157145318228	0.078572659114
-112	9	None	0.15181597486	0.0506053249534
-112	0	0	0.12780255294	0.0
-112	11	None	0.0829147806749	0.0414573903374
-113	1	None	0.224417112974	0.0748057043246
-113	9	None	0.132339964273	0.0441133214244
-113	0	0	0.101467313755	0.0
-113	8	None	0.0881332552651	0.029377751755
-113	2	None	0.0734983978483	0.0244994659494
-114	3	None	0.183971299523	0.0613237665075
-114	2	None	0.141772504172	0.0472575013905
-114	11	None	0.111698054836	0.0558490274181
-114	7	None	0.0804895703225	0.0
-114	13	None	0.0776246250166	0.0194061562542
-115	14	None	0.228008884397	0.228008884397
-115	3	None	0.132583879513	0.0441946265044
-115	8	None	0.130259567362	0.0434198557873
-115	6	None	0.0787836201138	0.0
-115	9	None	0.075524237297	0.0251747457657
-116	1	None	0.209067715992	0.069689238664
-116	2	None	0.139680994635	0.046560331545
-116	9	None	0.101019793897	0.0336732646323
-116	8	None	0.0923120901037	0.0307706967012
-116	16	None	0.0882136815338	0.0220534203834
-117	5	None	0.254677004165	0.0848923347217
-117	15	None	0.189532763146	0.0947663815731
-117	10	None	0.157650510323	0.0525501701078
-117	11	None	0.0695182077194	0.0347591038597
-117	7	None	0.0638951621039	0.0
-118	7	None	0.185758164554	0.0
-118	13	None	0.16319371251	0.0407984281274
-118	12	None	0.126834951453	0.0317087378633
-118	3	None	0.0821109690885	0.0273703230295
-118	10	None	0.073709330655	0.024569776885
-119	15	None	0.168667577838	0.0843337889188
-119	8	None	0.130473612443	0.0434912041478
-119	9	None	0.10287444204	0.0342914806799
-119	5	None	0.0995399740994	0.0331799913665
-119	7	None	0.0881732592099	0.0
-120	1	None	0.156007160269	0.0520023867563
-120	8	None	0.146372010939	0.0487906703129
-120	6	None	0.139247443026	0.0
-120	9	None	0.13090931673	0.04363643891
-120	16	None	0.0875398642408	0.0218849660602
-121	12	None	0.220560518824	0.055140129706
-121	10	None	0.213141028108	0.0710470093694
-121	13	None	0.0803714177826	0.0200928544456
-121	0	0	0.0753660175086	0.0
-121	4	None	0.0734521532444	0.0183630383111
-122	0	0	0.207396774983	0.0
-122	6	None	0.177993663443	0.0
-122	1	None	0.10506363279	0.0350212109301
-122	11	None	0.102584744203	0.0512923721013
-122	3	None	0.0856804822338	0.0285601607446
-123	15	None	0.212209942083	0.106104971041
-123	13	None	0.193280553707	0.0483201384267
-123	7	None	0.0869959444853	0.0
-123	16	None	0.0793529370707	0.0198382342677
-123	8	None	0.0726218846857	0.0242072948952
-124	2	None	0.425252846883	0.141750948961
-124	4	None	0.0861370129958	0.0215342532489
-124	8	None	0.0694895273553	0.0231631757851
-124	10	None	0.0634827256174	0.0211609085391
-124	1	None	0.061854443019	0.020618147673
-125	11	None	0.158727193048	0.079363596524
-125	5	None	0.14130524644	0.0471017488134
-125	0	0	0.0846994993008	0.0
-125	8	None	0.0713402375089	0.0237800791696
-125	12	None	0.0704381569108	0.0176095392277
-126	13	None	0.16759451008	0.0418986275201
-126	12	None	0.155213232435	0.0388033081088
-126	7	None	0.128367427512	0.0
-126	6	None	0.124611019427	0.0
-126	10	None	0.123111228945	0.0410370763151
-127	16	None	0.13101109952	0.03275277488
-127	12	None	0.1147856229	0.0286964057251
-127	13	None	0.114110441817	0.0285276104542
-127	15	None	0.0755423259345	0.0377711629672
-127	4	None	0.0720934644942	0.0180233661236
-128	7	None	0.190427935713	0.0
-128	0	0	0.163957887868	0.0
-128	11	None	0.122453393925	0.0612266969627
-128	1	None	0.0812501905321	0.027083396844
-128	2	None	0.0674741348792	0.0224913782931
-129	8	None	0.146415601029	0.048805200343
-129	11	None	0.141699266963	0.0708496334816
-129	12	None	0.11803903333	0.0295097583326
-129	15	None	0.0967807144949	0.0483903572474
-129	13	None	0.0791569644629	0.0197892411157
-130	1	None	0.156941433859	0.0523138112863
-130	15	None	0.112062489731	0.0560312448657
-130	3	None	0.105403245839	0.0351344152796
-130	7	None	0.0809720875515	0.0
-130	9	None	0.080375998274	0.0267919994247
-131	9	None	0.206188092019	0.0687293640064
-131	8	None	0.116850683766	0.038950227922
-131	12	None	0.109437412785	0.0273593531963
-131	15	None	0.104605792783	0.0523028963916
-131	2	None	0.0681351972585	0.0227117324195
-132	8	None	0.146732149888	0.0489107166292
-132	9	None	0.138126815207	0.0460422717357
-132	7	None	0.0998453360827	0.0
-132	1	None	0.0978480227023	0.0326160075674
-132	14	None	0.0759304927329	0.0759304927329
-133	0	0	0.322189454279	0.0
-133	11	None	0.140983502225	0.0704917511125
-133	8	None	0.0770880989272	0.0256960329757
-133	14	None	0.076984568552	0.076984568552
-133	12	None	0.0661300844821	0.0165325211205
-134	10	None	0.137109675985	0.0457032253284
-134	1	None	0.130801538266	0.0436005127554
-134	4	None	0.127919169548	0.031979792387
-134	14	None	0.0953175424515	0.0953175424515
-134	15	None	0.0859142529687	0.0429571264843
-135	10	None	0.201864295648	0.0672880985495
-135	9	None	0.131738694633	0.0439128982112
-135	8	None	0.106872320266	0.0356241067552
-135	16	None	0.0913693309909	0.0228423327477
-135	1	None	0.0882359619184	0.0294119873061
-136	13	None	0.20524304975	0.0513107624374
-136	7	None	0.122454039835	0.0
-136	5	None	0.115865556387	0.0386218521291
-136	14	None	0.0945721885233	0.0945721885233
-136	3	None	0.0758612578948	0.0252870859649
-137	5	None	0.200175303409	0.0667251011363
-137	16	None	0.167600621944	0.0419001554861
-137	6	None	0.132889630619	0.0
-137	0	0	0.105500573617	0.0
-137	11	None	0.0777088631346	0.0388544315673
-138	12	None	0.197365588122	0.0493413970304
-138	13	None	0.118077254467	0.0295193136168
-138	10	None	0.108036725253	0.0360122417509
-138	14	None	0.07905181315	0.07905181315
-138	2	None	0.0780394306429	0.0260131435476
-139	16	None	0.313058670832	0.078264667708
-139	0	0	0.158399212084	0.0
-139	11	None	0.141309510395	0.0706547551973
-139	15	None	0.114347129247	0.0571735646236
-139	9	None	0.0520886439381	0.0173628813127
-140	14	None	0.147999615249	0.147999615249
-140	10	None	0.138836835928	0.0462789453093
-140	2	None	0.10876453199	0.0362548439965
-140	6	None	0.0960149720755	0.0
-140	1	None	0.0916995942514	0.0305665314171
-141	14	None	0.151066829763	0.151066829763
-141	11	None	0.149875374084	0.0749376870419
-141	13	None	0.118349204045	0.0295873010114
-141	6	None	0.101045352143	0.0
-141	15	None	0.0811430010047	0.0405715005024
-142	0	0	0.278288146043	0.0
-142	6	None	0.145815542474	0.0
-142	15	None	0.100643222441	0.0503216112207
-142	9	None	0.0939123886138	0.0313041295379
-142	14	None	0.07818334612	0.07818334612
-143	15	None	0.132789303073	0.0663946515365
-143	12	None	0.121696211567	0.0304240528917
-143	3	None	0.111056000404	0.0370186668014
-143	5	None	0.0898597936794	0.0299532645598
-143	14	None	0.0792916479976	0.0792916479976
-144	4	None	0.176859019436	0.044214754859
-144	6	None	0.150854313881	0.0
-144	16	None	0.137475629435	0.0343689073587
-144	10	None	0.118951471235	0.0396504904115
-144	7	None	0.0842345900571	0.0
-145	11	None	0.174080924044	0.0870404620222
-145	3	None	0.153256537864	0.0510855126213
-145	4	None	0.139853094412	0.0349632736029
-145	14	None	0.103631990896	0.103631990896
-145	16	None	0.075000808956	0.018750202239
-146	15	None	0.305743098403	0.152871549201
-146	4	None	0.108501186113	0.0271252965283
-146	6	None	0.0828896698745	0.0
-146	8	None	0.0781723688791	0.026057456293
-146	9	None	0.0694901915729	0.023163397191
-147	3	None	0.189906370146	0.0633021233819
-147	13	None	0.138222598832	0.034555649708
-147	15	None	0.112087572668	0.0560437863341
-147	10	None	0.0861300858475	0.0287100286158
-147	6	None	0.078420101796	0.0
-148	16	None	0.167310576255	0.0418276440639
-148	1	None	0.146601277897	0.0488670926325
-148	4	None	0.117478162211	0.0293695405526
-148	2	None	0.0967102932247	0.0322367644082
-148	9	None	0.0805251189271	0.026841706309
-149	6	None	0.168645195986	0.0
-149	3	None	0.144120841608	0.0480402805361
-149	4	None	0.125081311302	0.0312703278255
-149	14	None	0.110281761708	0.110281761708
-149	2	None	0.107732311516	0.0359107705054
-None	1	None	3	1.0
-None	2	None	3	1.0
-None	3	None	3	1.0
-None	4	None	4	1.0
-None	5	None	3	1.0
-None	None	None	5	1.0
-None	9	None	3	1.0
-None	10	None	3	1.0
-None	11	None	2	1.0
-None	12	None	4	1.0
-None	13	None	4	1.0
-None	14	None	1	1.0
-None	15	None	2	1.0
-None	16	None	4	1.0
-None	8	None	3	1.0
diff --git a/medinfo/cpoe/analysis/test/TestOrderSetRecommendationClassificationAnalysis.py b/medinfo/cpoe/analysis/test/TestOrderSetRecommendationClassificationAnalysis.py
index 69fb93c4..807bcc9f 100644
--- a/medinfo/cpoe/analysis/test/TestOrderSetRecommendationClassificationAnalysis.py
+++ b/medinfo/cpoe/analysis/test/TestOrderSetRecommendationClassificationAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db.test.Util import DBTestCase;
@@ -18,15 +18,14 @@
 
 from medinfo.cpoe.analysis.OrderSetRecommenderClassificationAnalysis import OrderSetRecommenderClassificationAnalysis;
 
-from Util import BaseCPOETestAnalysis;
-
-
-class TestOrderSetRecommenderClassificationAnalysis(BaseCPOETestAnalysis):
+class TestOrderSetRecommenderClassificationAnalysis(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","source_table"];
diff --git a/medinfo/cpoe/analysis/test/TestOrderSetUsageAnalysis.py b/medinfo/cpoe/analysis/test/TestOrderSetUsageAnalysis.py
index f6f38e5f..74def07c 100644
--- a/medinfo/cpoe/analysis/test/TestOrderSetUsageAnalysis.py
+++ b/medinfo/cpoe/analysis/test/TestOrderSetUsageAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db.test.Util import DBTestCase;
@@ -18,15 +18,15 @@
 
 from medinfo.cpoe.analysis.OrderSetUsageAnalysis import OrderSetUsageAnalysis;
 
-from Util import BaseCPOETestAnalysis;
-
-
-class TestOrderSetUsageAnalysis(BaseCPOETestAnalysis):
+class TestOrderSetUsageAnalysis(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
-        self.purgeTestRecords();
+
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+        #self.purgeTestRecords();
         
         headers = ["clinical_item_category_id","default_recommend","source_table"];
         dataModels = \
diff --git a/medinfo/cpoe/analysis/test/TestOutcomePredictionAnalysis.py b/medinfo/cpoe/analysis/test/TestOutcomePredictionAnalysis.py
index db1c0d07..ae224261 100644
--- a/medinfo/cpoe/analysis/test/TestOutcomePredictionAnalysis.py
+++ b/medinfo/cpoe/analysis/test/TestOutcomePredictionAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime, timedelta;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -20,14 +20,14 @@
 from medinfo.cpoe.analysis.OutcomePredictionAnalysis import OutcomePredictionAnalysis, AnalysisQuery;
 from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
 
-from Util import BaseCPOETestAnalysis;
-
-class TestOutcomePredictionAnalysis(BaseCPOETestAnalysis):
+class TestOutcomePredictionAnalysis(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
-        BaseCPOETestAnalysis.setUp(self);
+        DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","source_table"];
@@ -179,7 +179,7 @@ def tearDown(self):
         DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
         DBUtil.execute("delete from clinical_item_category where clinical_item_category_id in (%s)" % str.join(",", self.clinicalItemCategoryIdStrList) );
         
-        BaseCPOETestAnalysis.tearDown(self);
+        DBTestCase.tearDown(self);
 
     def test_recommenderAnalysis(self):
         # Run the recommender against the mock test data above and verify expected stats afterwards.
diff --git a/medinfo/cpoe/analysis/test/TestPreparePatientItems.py b/medinfo/cpoe/analysis/test/TestPreparePatientItems.py
index 041bc210..949d58d0 100644
--- a/medinfo/cpoe/analysis/test/TestPreparePatientItems.py
+++ b/medinfo/cpoe/analysis/test/TestPreparePatientItems.py
@@ -2,13 +2,13 @@
 """Test case for respective module in application package"""
 
 import sys, os;
-from cStringIO import StringIO;
+from io import StringIO;
 import json;
 from datetime import datetime, timedelta;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import loadJSONDict;
@@ -20,14 +20,15 @@
 from medinfo.cpoe.ItemRecommender import RecommenderQuery;
 from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems, AnalysisQuery;
 
-from Util import BaseCPOETestAnalysis;
-
-class TestPreparePatientItems(BaseCPOETestAnalysis):
+class TestPreparePatientItems(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
-        BaseCPOETestAnalysis.setUp(self);
-        self.purgeTestRecords();
+        DBTestCase.setUp(self);
+
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+        #self.purgeTestRecords();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","default_recommend","source_table"];
@@ -228,7 +229,7 @@ def setUp(self):
     def tearDown(self):
         """Restore state from any setUp or test steps"""
         self.purgeTestRecords();
-        BaseCPOETestAnalysis.tearDown(self);
+        DBTestCase.tearDown(self);
 
     def purgeTestRecords(self):
         log.info("Purge test records from the database")
@@ -339,7 +340,7 @@ def test_orderSetAnalysisPreparer(self):
         self.assertEqualTextOutput(expectedResults, textOutput, colNames);
 
     def assertEqualResultDicts(self, expectedResults, analysisResults, colNames ):
-        self.assertEquals( len(expectedResults), len(analysisResults) );
+        self.assertEqual( len(expectedResults), len(analysisResults) );
         for expectedResult, analysisResult in zip(expectedResults,analysisResults):
             self.assertEqualDict(expectedResult, analysisResult, colNames);
     
diff --git a/medinfo/cpoe/analysis/test/TestRecommendationClassificationAnalysis.py b/medinfo/cpoe/analysis/test/TestRecommendationClassificationAnalysis.py
index eeea0f11..2d6de41d 100644
--- a/medinfo/cpoe/analysis/test/TestRecommendationClassificationAnalysis.py
+++ b/medinfo/cpoe/analysis/test/TestRecommendationClassificationAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime, timedelta;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -20,16 +20,16 @@
 from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis, AnalysisQuery;
 from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
 
-from Util import BaseCPOETestAnalysis;
-
 TEST_ORDERSET_ID = -3;
 
-class TestRecommendationClassificationAnalysis(BaseCPOETestAnalysis):
+class TestRecommendationClassificationAnalysis(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
-        BaseCPOETestAnalysis.setUp(self);
+        DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","default_recommend","source_table"];
@@ -184,7 +184,7 @@ def tearDown(self):
         DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
         DBUtil.execute("delete from clinical_item_category where clinical_item_category_id in (%s)" % str.join(",", self.clinicalItemCategoryIdStrList) );
         
-        BaseCPOETestAnalysis.tearDown(self);
+        DBTestCase.tearDown(self);
 
     def test_recommenderAnalysis(self):
         # Run the recommender against the mock test data above and verify expected stats afterwards.
@@ -546,7 +546,7 @@ def test_numRecsByOrderSet(self):
         for i, dataRow in enumerate(TabDictReader(preparedDataFile)):
             dataRow["order_set_id"] = TEST_ORDERSET_ID;
             if i <= 0:
-                dataCols = dataRow.keys();
+                dataCols = list(dataRow.keys());
                 formatter.formatTuple(dataCols);    # Insert a mock record to get a header / label row
             formatter.formatResultDict(dataRow, dataCols);
         preparedDataFile = StringIO(modFile.getvalue());
diff --git a/medinfo/cpoe/analysis/test/TestRecommendationRankingTrendAnalysis.py b/medinfo/cpoe/analysis/test/TestRecommendationRankingTrendAnalysis.py
index b1cc492c..223f288a 100644
--- a/medinfo/cpoe/analysis/test/TestRecommendationRankingTrendAnalysis.py
+++ b/medinfo/cpoe/analysis/test/TestRecommendationRankingTrendAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.test.Const import SENTINEL_ANY_FLOAT;
 from medinfo.db.test.Util import DBTestCase;
@@ -26,6 +26,8 @@ def setUp(self):
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","source_table"];
diff --git a/medinfo/cpoe/analysis/test/TestTopicModel.model b/medinfo/cpoe/analysis/test/TestTopicModel.model
deleted file mode 100644
index 2eb550db..00000000
Binary files a/medinfo/cpoe/analysis/test/TestTopicModel.model and /dev/null differ
diff --git a/medinfo/cpoe/analysis/test/TestTopicModel.model.state b/medinfo/cpoe/analysis/test/TestTopicModel.model.state
deleted file mode 100644
index 93f434af..00000000
Binary files a/medinfo/cpoe/analysis/test/TestTopicModel.model.state and /dev/null differ
diff --git a/medinfo/cpoe/analysis/test/TestTopicModel.model.topics.tab b/medinfo/cpoe/analysis/test/TestTopicModel.model.topics.tab
deleted file mode 100644
index cdac9221..00000000
--- a/medinfo/cpoe/analysis/test/TestTopicModel.model.topics.tab
+++ /dev/null
@@ -1,32 +0,0 @@
-# {"argv": ["TopicModel", "-n", "3", "-i", "5", "-", "TestTopicModel.model"]}
-topic_id	item_id	description	score	tfidf
-0	5	None	0.401030089294	0.133676696431
-0	13	None	0.121085014128	0.0302712535321
-0	8	None	0.112732886532	0.0375776288439
-0	4	None	0.0823443124734	0.0205860781184
-0	12	None	0.0745154690571	0.0186288672643
-1	16	None	0.173338699148	0.0433346747871
-1	12	None	0.132982129849	0.0332455324622
-1	13	None	0.117192730562	0.0292981826406
-1	15	None	0.0977949716609	0.0488974858305
-1	4	None	0.0881758164985	0.0220439541246
-2	2	None	0.220681354024	0.0735604513414
-2	11	None	0.175881665361	0.0879408326803
-2	16	None	0.151084487637	0.0377711219094
-2	1	None	0.107005305257	0.0356684350857
-2	12	None	0.10333511171	0.0258337779276
-None	1	None	3	1.0
-None	2	None	3	1.0
-None	3	None	3	1.0
-None	4	None	4	1.0
-None	5	None	3	1.0
-None	None	None	5	1.0
-None	9	None	3	1.0
-None	10	None	3	1.0
-None	11	None	2	1.0
-None	12	None	4	1.0
-None	13	None	4	1.0
-None	14	None	1	1.0
-None	15	None	2	1.0
-None	16	None	4	1.0
-None	8	None	3	1.0
diff --git a/medinfo/cpoe/analysis/test/TestTopicModelAnalysis.py b/medinfo/cpoe/analysis/test/TestTopicModelAnalysis.py
index 9e8dfa90..0bb88f15 100644
--- a/medinfo/cpoe/analysis/test/TestTopicModelAnalysis.py
+++ b/medinfo/cpoe/analysis/test/TestTopicModelAnalysis.py
@@ -2,12 +2,14 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+import numpy as np;
+
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db.test.Util import DBTestCase;
@@ -19,20 +21,21 @@
 from medinfo.cpoe.TopicModel import TopicModel;
 from medinfo.cpoe.analysis.TopicModelAnalysis import TopicModelAnalysis;
 
-from Util import BaseCPOETestAnalysis;
-
 # Look for test files by module location
 import medinfo.cpoe.analysis.test;
 TEST_DIR = os.path.dirname(medinfo.cpoe.analysis.test.__file__);
 TEST_FILE_PREFIX = "TestTopicModel.model";
 
-class TestTopicModelAnalysis(BaseCPOETestAnalysis):
+
+class TestTopicModelAnalysis(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
-        self.purgeTestRecords();
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+        #self.purgeTestRecords();
         
         headers = ["clinical_item_category_id","source_table"];
         dataModels = \
@@ -116,10 +119,29 @@ def setUp(self):
         DBUtil.insertFile( StringIO(dataTextStr), "item_collection_item", delim=";");
 
 
-        # Load model objects from persisted (test) files.  
+        # Input file contents in Bag-of-Words formats from which can build Topic Models while avoiding binary versions that become obsolete in later versions
+        self.inputBOWFileStr = \
+"""[[1,1],[2,2],[3,1],[4,4],[5,10],[8,5]]
+[[3,4],[4,4],[9,3],[10,2],[12,6],[13,3],[15,5],[16,8]]
+[[1,1],[2,2],[3,1],[4,4],[5,10],[8,5],[9,1],[10,2],[11,1],[12,4],[13,10],[14,1],[15,3],[16,5]]
+[[1,4],[2,9],[9,1],[10,2],[11,7],[12,4],[13,2],[16,6]]
+[[4,3],[5,31],[8,5],[12,6],[13,8],[16,5]]
+"""
         self.instance = TopicModel();  # Instance to test on
+        self.instance.randomState = np.random.RandomState(1);  # Start with a fixed random state so that topic model generating process will be consistent for regression testing
+
+        # Build typical LDA model and store in temp files
+        sys.stdin = StringIO(self.inputBOWFileStr);
+        subargv = ["TopicModel", "-n", "3", "-i", "5", "-", os.path.join(TEST_DIR,TEST_FILE_PREFIX)];
+        self.instance.main(subargv);
         self.ldaModel = self.instance.loadModel( os.path.join(TEST_DIR,TEST_FILE_PREFIX) );
+
+        # Build HDP model in separate temp files
+        sys.stdin = StringIO(self.inputBOWFileStr);
+        subargv = ["TopicModel", "-n", "0", "-i", "5", "-", os.path.join(TEST_DIR,"HDP"+TEST_FILE_PREFIX)];
+        self.instance.main(subargv);
         self.hdpModel = self.instance.loadModel( os.path.join(TEST_DIR,"HDP"+TEST_FILE_PREFIX) );
+
         self.docCountByWordId = self.instance.loadDocCountByWordId( os.path.join(TEST_DIR,self.instance.topTopicFilename(TEST_FILE_PREFIX)) );
         
         # Sample Prepared Validation File
@@ -135,6 +157,14 @@ def setUp(self):
     def tearDown(self):
         """Restore state from any setUp or test steps"""
         self.purgeTestRecords();
+
+        # Remove Topic Model temp files
+        for filename in os.listdir(TEST_DIR):
+            if filename.startswith(TEST_FILE_PREFIX):
+                os.remove(os.path.join(TEST_DIR,filename));
+            elif filename.startswith("HDP"+TEST_FILE_PREFIX):
+                os.remove(os.path.join(TEST_DIR,filename));
+
         DBTestCase.tearDown(self);
 
     def purgeTestRecords(self):
@@ -152,9 +182,9 @@ def test_topicModelAnalysis(self):
         colNames = ["patient_id", "TP", "FN", "FP",  "recall", "precision", "F1-score", "weightRecall","weightPrecision", "weightF1-score", "ROC-AUC"];
         
         expectedResults = \
-            [   RowItemModel([123,  3,1,0,  0.75,  1.0,  0.857, 0.875, 1.0,   0.933, None,], colNames ),    # ROC undefined when all recommended items are verify items (perfect precision)
-                RowItemModel([456,  1,2,3,  0.333, 0.25, 0.286, 0.167, 0.1875,0.176, 0.429,], colNames ),
-                RowItemModel([789,  1,1,3,  0.5,   0.25, 0.333, 0.333, 0.231, 0.273, 0.417,], colNames ),
+            [   RowItemModel([123, 3,1,0, 0.75,  1.0,  0.857, 0.875, 1.0,   0.933, None], colNames ),   # ROC undefined when all recommended items are verify items (perfect precision)
+                RowItemModel([456, 1,2,3, 0.333, 0.25, 0.286, 0.167, 0.1875,0.176, 0.357], colNames ),
+                RowItemModel([789, 1,1,3, 0.5,   0.25, 0.333, 0.333, 0.231, 0.273, 0.417], colNames ),
             ];
 
         # Analysis via prepared validation data file
@@ -169,8 +199,8 @@ def test_topicModelAnalysis(self):
         # Redo with HdpModel
         expectedResults = \
             [   RowItemModel([123,  3,1,0,  0.75,  1.0,  0.857, 0.875, 1.0,   0.933, None,], colNames ),
-                RowItemModel([456,  0,3,4,  0.0,   0.0,  0.0,   0.0,   0.0,   0.0,   0.429,], colNames ),
-                RowItemModel([789,  2,0,2,  1.0,   0.5,  0.667, 1.0,   0.6,   0.75,  0.833,], colNames ),
+                RowItemModel([456,  1,2,3,  0.333, 0.25, 0.286, 0.167, 0.176, 0.171, 0.571,], colNames ),
+                RowItemModel([789,  1,1,3,  0.5,   0.25, 0.333, 0.667, 0.24,  0.353, 0.417,], colNames ),
             ];
 
         # Analysis via prepared validation data file
@@ -185,7 +215,7 @@ def test_topicModelAnalysis(self):
         ##### Redo with TF*IDF score weighting
         expectedResults = \
             [   RowItemModel([123,  3,1,0,  0.75,  1.0,  0.857, 0.875, 1.0,   0.933, None,], colNames ),    # ROC undefined when all recommended items are verify items (perfect precision)
-                RowItemModel([456,  1,2,3,  0.333, 0.25, 0.286, 0.167, 0.1875,0.176, 0.5,], colNames ),
+                RowItemModel([456,  1,2,3,  0.333, 0.25, 0.286, 0.167, 0.1875,0.176, 0.357,], colNames ),
                 RowItemModel([789,  1,1,3,  0.5,   0.25, 0.333, 0.667, 0.375, 0.48,  0.5,], colNames ),
             ];
 
@@ -202,7 +232,7 @@ def test_topicModelAnalysis(self):
         expectedResults = \
             [   RowItemModel([123,  3,1,0,  0.75,  1.0,  0.857, 0.875, 1.0,   0.933, None,], colNames ),
                 RowItemModel([456,  1,2,3,  0.333, 0.25, 0.286, 0.667, 0.462, 0.545, 0.571,], colNames ),
-                RowItemModel([789,  1,1,3,  0.5,   0.25, 0.333, 0.667, 0.25,  0.364, 0.75,], colNames ),
+                RowItemModel([789,  1,1,3,  0.5,   0.25, 0.333, 0.667, 0.24,  0.353, 0.333,], colNames ),
             ];
 
         # Analysis via prepared validation data file
@@ -221,7 +251,7 @@ def test_topicModelAnalysis_numRecsByOrderSet(self):
         
         expectedResults = \
             [   RowItemModel([123,  3,1,0,  0.75,  1.0,  0.857, 0.875, 1.0,   0.933, None,], colNames ),    # Order set with 3 recs
-                RowItemModel([456,  1,2,2,  0.333, 0.333, 0.333, 0.167, 0.3, 0.214, 0.429,], colNames ),   # Order set size 3 recs
+                RowItemModel([456,  1,2,2,  0.333, 0.333, 0.333, 0.167, 0.3, 0.214, 0.357,], colNames ),   # Order set size 3 recs
                 RowItemModel([789,  1,1,3,  0.5,   0.25, 0.333, 0.333, 0.231, 0.273, 0.417,], colNames ),   # Order set size 4 recs
             ];
 
@@ -232,8 +262,13 @@ def test_topicModelAnalysis_numRecsByOrderSet(self):
         self.analyzer.main(argv);
         textOutput = StringIO(sys.stdout.getvalue());
         #print >> sys.stderr, sys.stdout.getvalue()
+        #for row in TabDictReader(StringIO(sys.stdout.getvalue())):
+        #    for col in colNames:
+        #        print >> sys.stderr, row[col],
+        #    print >> sys.stderr
         self.assertEqualStatResultsTextOutput(expectedResults, textOutput, colNames);
 
+
 def suite():
     """Returns the suite of tests to run for this test class / module.
     Use unittest.makeSuite methods which simply extracts all of the
@@ -247,6 +282,7 @@ def suite():
     suite.addTest(unittest.makeSuite(TestTopicModelAnalysis));
     
     return suite;
-    
-if __name__=="__main__":
+
+
+if __name__ == "__main__":
     unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/cpoe/analysis/test/Util.py b/medinfo/cpoe/analysis/test/Util.py
index 45e07cb4..bbef8e56 100644
--- a/medinfo/cpoe/analysis/test/Util.py
+++ b/medinfo/cpoe/analysis/test/Util.py
@@ -1,7 +1,8 @@
-import Const
+from . import Const
 import sys, os
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 
 import json;
@@ -9,8 +10,6 @@
 from medinfo.common.Const import COMMENT_TAG, NULL_STRING;
 from medinfo.db.Model import SQLQuery, RowItemModel;
 
-from medinfo.analysis.test.Util import BaseTestAnalysis;
-
 import medinfo.cpoe.analysis.Util;
 
 
@@ -25,9 +24,3 @@
 
 # Suppress uninteresting application output
 medinfo.cpoe.analysis.Util.log.setLevel(Const.APP_LOGGER_LEVEL)
-
-
-class BaseCPOETestAnalysis(BaseTestAnalysis):
-
-    def nofunction(self):
-        pass;
diff --git a/medinfo/cpoe/cpoeSim/Const.py b/medinfo/cpoe/cpoeSim/Const.py
new file mode 100644
index 00000000..1b9b6585
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/Const.py
@@ -0,0 +1,12 @@
+"""Various constants for use by the application modules"""
+
+from datetime import datetime, timedelta;
+
+
+"""Arbitrary base time to start simulations from"""
+BASE_TIME = datetime(2010,1,1,12,0);    
+TIME_FORMAT = "%m/%d/%Y, %I:%M%p";
+
+
+"""ID of the default simulation state to fall back on if have no other state-specific results/information"""
+DEFAULT_STATE_ID = 0;
\ No newline at end of file
diff --git a/medinfo/cpoe/cpoeSim/SimManager.py b/medinfo/cpoe/cpoeSim/SimManager.py
new file mode 100644
index 00000000..68d4a208
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/SimManager.py
@@ -0,0 +1,937 @@
+#!/usr/bin/env python
+"""
+Application / business logic and data management functions
+for interactive clinical state simulations.
+"""
+
+import sys, os
+import time
+from optparse import OptionParser
+import json
+import csv
+import math
+from datetime import datetime
+from medinfo.common.Util import stdOpen, ProgressDots, log
+from medinfo.common.Const import COMMENT_TAG, VALUE_DELIM
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel, generatePlaceholders
+from medinfo.db.Model import modelListFromTable, modelDictFromList, columnFromModelList
+from medinfo.cpoe.Const import AD_HOC_SECTION
+from .Const import DEFAULT_STATE_ID
+
+
+class SimManager:
+    connFactory = None
+
+    def __init__(self):
+        self.connFactory = DBUtil.ConnectionFactory()  # Default connection source
+
+    def getSchemaFilepath(self):
+        """Find the file path for the schema definition for simulation data."""
+        # For better platform independence, derive based on imported module location
+        import medinfo.cpoe.cpoeSim
+        cpoeSimDir = os.path.dirname(medinfo.cpoe.cpoeSim.__file__)
+        schemaFilepath = os.path.join(cpoeSimDir,"simdata/cpoeSim.sql")
+        return schemaFilepath
+
+    def buildCPOESimSchema(self, conn=None):
+        """Create the schema/table definitions needed for 
+        Clinical Provider Order Entry (CPOE) case Simulation interface.
+        Mostly for allowing (unit) testing purposes.
+        For human testing, probably want to just use the SQL data dumps to get
+        the schema and data files.
+        """
+        schemaFile = open(self.getSchemaFilepath())
+        DBUtil.execute(schemaFile.read(), conn=conn, connFactory=self.connFactory)
+
+    def createUser(self, userData, conn=None):
+        """Create a new user record given the data dictionary
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            DBUtil.insertRow("sim_user", userData, conn=conn)
+            conn.commit()
+        finally:
+            if not extConn:
+                conn.close()
+    
+    def loadUserInfo(self, userIds=None, conn=None):
+        """Load basic information about the specified users
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("su.sim_user_id")
+            query.addSelect("su.name")
+            query.addFrom("sim_user as su")
+            if userIds is not None:
+                query.addWhereIn("su.sim_user_id", userIds )
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+    def createPatient(self, patientData, initialStateId, conn=None):
+        """Create a new patient record given the data dictionary.
+        Create a respective initial patient state record as well.
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            DBUtil.insertRow("sim_patient", patientData, conn=conn)
+            patientId = DBUtil.execute(DBUtil.identityQuery("sim_patient"),conn=conn)[0][0]
+            patientStateData = {"sim_patient_id": patientId, "sim_state_id": initialStateId, "relative_time_start": 0}
+            DBUtil.insertRow("sim_patient_state", patientStateData, conn=conn)
+            
+            conn.commit()  # Transactional commit for two step process
+            return patientId
+        finally:
+            if not extConn:
+                conn.close()
+
+    def copyPatientTemplate(self, patientData, templatePatientId, conn=None):
+        """Create a new patient record based on the given template patient ID to copy from.
+        Will copy shallow attributes, overridden by any provided in the given patientData,
+        as well as any patient states, notes, or physician orders UP TO (and including)
+        relative time zero, but not subsequent states, notes, or physician orders 
+        (the latter is expected to reflect real user interaction records).
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            templatePatientData = DBUtil.loadRecordModelById("sim_patient", templatePatientId, conn=conn)
+            del templatePatientData["sim_patient_id"]  # Remove prior ID to allow for new one
+            templatePatientData.update( patientData )  # Override with new content (if exists)
+            DBUtil.insertRow("sim_patient", templatePatientData, conn=conn)    # Create new patient record
+            patientId = DBUtil.execute(DBUtil.identityQuery("sim_patient"),conn=conn)[0][0]
+
+            # Copy initial template patient states
+            query = SQLQuery()
+            query.addSelect("*")   # Copy all columns
+            query.addFrom("sim_patient_state as sps")
+            query.addWhereEqual("sps.sim_patient_id", templatePatientId )
+            query.addWhereOp("relative_time_start","<=", 0 )
+            query.addOrderBy("relative_time_start")
+            dataTable = DBUtil.execute(query,includeColumnNames=True,conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            nStates = len(dataModels)
+            for i, dataModel in enumerate(dataModels):
+                del dataModel["sim_patient_state_id"]  # Discard copied ID to allow new one
+                if i == nStates-1:
+                    del dataModel["relative_time_end"] # Last state. Blank out end time to reflect open ended for simulation
+                dataModel["sim_patient_id"] = patientId
+                DBUtil.insertRow("sim_patient_state", dataModel, conn=conn)
+
+            # Copy initial template orders
+            query = SQLQuery()
+            query.addSelect("*")
+            query.addFrom("sim_patient_order as spo")
+            query.addWhereEqual("sim_patient_id", templatePatientId )
+            query.addWhereOp("relative_time_start","<=", 0 )
+            query.addOrderBy("relative_time_start")
+            dataTable = DBUtil.execute(query,includeColumnNames=True,conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            for dataModel in dataModels:
+                del dataModel["sim_patient_order_id"]
+                dataModel["sim_patient_id"] = patientId
+                DBUtil.insertRow("sim_patient_order", dataModel, conn=conn)
+
+            conn.commit()  # Transactional commit for multi-step process
+            return patientId
+        finally:
+            if not extConn:
+                conn.close()
+
+    def loadPatientInfo(self, patientIds=None, relativeTime=None, conn=None):
+        """Load basic information about the specified patients.
+        Report patient state at given time, or default to time zero
+        """
+        if relativeTime is None:
+            relativeTime = 0   # Just look for time zero default then
+
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("sp.sim_patient_id")
+            query.addSelect("sp.name")
+            query.addSelect("sp.age_years")
+            query.addSelect("sp.gender")
+            query.addSelect("s.sim_state_id")
+            query.addSelect("s.name as state_name")
+            query.addSelect("s.description as state_description")
+            query.addSelect("sps.relative_time_start")
+            query.addSelect("sps.relative_time_end")
+            query.addFrom("sim_patient as sp")
+            query.addFrom("sim_patient_state as sps")
+            query.addFrom("sim_state as s")
+            query.addWhere("sp.sim_patient_id = sps.sim_patient_id")
+            query.addWhere("sps.sim_state_id = s.sim_state_id")
+            if patientIds is not None:
+                query.addWhereIn("sp.sim_patient_id", patientIds )
+            
+            # Look for the state that matches the given relative time offset
+            query.addWhereOp("sps.relative_time_start","<=", relativeTime)
+            query.openWhereOrClause()
+            query.addWhere("sps.relative_time_end is null")
+            query.addWhereOp("sps.relative_time_end",">", relativeTime)
+            query.closeWhereOrClause()
+            
+            query.addOrderBy("sp.name")
+
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            
+            if len(dataModels) > 0:
+                # Secondary query to build lookup table of possible state transition options from patient current states
+                subQuery = SQLQuery()
+                subQuery.addSelect("pre_state_id")
+                subQuery.addSelect("post_state_id")
+                subQuery.addSelect("clinical_item_id")
+                subQuery.addSelect("time_trigger")
+                subQuery.addFrom("sim_state_transition as sst")
+                subQuery.addWhereIn("pre_state_id", columnFromModelList(dataModels,"sim_state_id"))
+                subResults = DBUtil.execute(subQuery,conn=conn)
+                
+                # For each pre-state, track which clinical items or times trigger which post-states
+                postStateIdByItemIdByPreStateId = dict()
+                postStateIdTimeTriggerByPreStateId = dict()
+                for preStateId, postStateId, itemId, timeTrigger in subResults:
+                    if preStateId not in postStateIdByItemIdByPreStateId:
+                        postStateIdByItemIdByPreStateId[preStateId] = dict()
+                    postStateIdByItemIdByPreStateId[preStateId][itemId] = postStateId
+                    
+                    if timeTrigger is not None:
+                        postStateIdTimeTriggerByPreStateId[preStateId] = (postStateId, timeTrigger)
+            
+                
+                # Record in patient result models for retrieval
+                for i, dataModel in enumerate(dataModels):
+                    patientId = dataModel["sim_patient_id"]
+                    stateId = dataModel["sim_state_id"]
+                    
+                    dataModel["postStateIdByItemId"] = dict()
+                    if stateId in postStateIdByItemIdByPreStateId:
+                        dataModel["postStateIdByItemId"] = postStateIdByItemIdByPreStateId[stateId]
+                    dataModel["postStateIdTimeTriggerByPreStateId"] = dict()
+                    if stateId in postStateIdTimeTriggerByPreStateId:
+                        dataModel["postStateIdTimeTrigger"] = postStateIdTimeTriggerByPreStateId[stateId]
+                    
+                    if dataModel["relative_time_end"] is None and "postStateIdTimeTrigger" in dataModel:
+                        # Check that we haven't passed (and should thus trigger) a time-based state transition
+                        (postStateId, timeTrigger) = dataModel["postStateIdTimeTrigger"]
+                        preStateTime = dataModel["relative_time_start"]
+                        postStateTriggerTime = (preStateTime + timeTrigger)
+                        
+                        if postStateTriggerTime <= relativeTime:    # Trigger state transition just by time elapsed
+                            #print >> sys.stderr, relativeTime, preStateTime, stateId, postStateTriggerTime, postStateId
+                            self.recordStateTransition(patientId, stateId, postStateId, postStateTriggerTime, conn=conn)
+                            # State change which can yield new triggers, so recursively reload.
+                            # Small risk of infinite recusion if timeTriggers are zero. Otherwise, should converge as each recursion will update the preState relativeTimeStart
+                            dataModels[i] = self.loadPatientInfo([patientId], relativeTime, conn=conn)[0]
+            
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+    def loadStateInfo(self, stateIds=None, conn=None):
+        """Load basic information about the specified patient states
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("ss.sim_state_id")
+            query.addSelect("ss.name")
+            query.addSelect("ss.description")
+            query.addFrom("sim_state as ss")
+            if stateIds is not None:
+                query.addWhereIn("ss.sim_state_id", stateIds )
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+    def signOrders(self, userId, patientId, currentTime, orderItemIds, discontinuePatientOrderIds=None, conn=None):
+        """Commit new order item IDs for the given patient and starting now,
+        and discontinue (set end date) for any existing orders specified.
+        
+        Record any patient state transitions the orders would trigger
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            # Denormalized recording of current patient state to facilitate easy retrieval linked to orders later
+            patientInfo = self.loadPatientInfo( [patientId], currentTime, conn=conn)[0]
+            stateId = patientInfo["sim_state_id"]
+            postStateIdByItemId = patientInfo["postStateIdByItemId"]
+            
+            orderItemIdSet = set(orderItemIds) # Ensure unique and facilitate set operations
+            
+            insertDict = {"sim_user_id": userId, "sim_patient_id": patientId, "sim_state_id": stateId, "relative_time_start": currentTime}
+            for itemId in orderItemIdSet:
+                insertDict["clinical_item_id"] = itemId
+                DBUtil.insertRow("sim_patient_order", insertDict, conn=conn)
+
+            # See if any of these new orders triggered state transitions
+            triggerItemIds = postStateIdByItemId.keys() & orderItemIdSet
+            while triggerItemIds:   # Found a trigger item
+                triggerItemId = None
+                if len(triggerItemIds) > 1: # Found multiple. Weird. Arbitrarily act on the one that appeared first in the input list
+                    for itemId in orderItemIds:
+                        if itemId in triggerItemIds:
+                            triggerItemId = itemId
+                            break
+                else:
+                    triggerItemId = triggerItemIds.pop()
+                postStateId = postStateIdByItemId[triggerItemId]
+                
+                # Record the state transition
+                self.recordStateTransition(patientId, stateId, postStateId, currentTime, conn=conn)
+                
+                # Reload patientInfo to reflect new patient state
+                patientInfo = self.loadPatientInfo( [patientId], currentTime, conn=conn)[0]
+                stateId = patientInfo["sim_state_id"]
+                postStateIdByItemId = patientInfo["postStateIdByItemId"]
+                
+                orderItemIdSet.discard(triggerItemId)  # Don't keep looking for this one, important to avoid infinite loop
+                triggerItemIds = postStateIdByItemId.keys() & orderItemIdSet
+                
+        
+            if discontinuePatientOrderIds is not None:
+                updateDict = {"relative_time_end": currentTime }
+                for patientOrderId in discontinuePatientOrderIds:
+                    DBUtil.updateRow("sim_patient_order", updateDict, patientOrderId, conn=conn)
+                # If order is discontinued/cancelled at the same (or before) time of entry, 
+                #   take that as a signal to cleanup and delete the record altogether 
+                #   (effectively there was no time at which the order was ever allowed to exist)
+                deleteQuery = SQLQuery()
+                deleteQuery.delete = True
+                deleteQuery.addFrom("sim_patient_order")
+                deleteQuery.addWhereEqual("sim_patient_id", patientId)
+                deleteQuery.addWhere("relative_time_end <= relative_time_start")
+                DBUtil.execute(deleteQuery, conn=conn)
+        finally:
+            conn.commit()
+            if not extConn:
+                conn.close()
+
+    def recordStateTransition(self, patientId, preStateId, postStateId, currentTime, conn=None):
+        """Record a patient state transition
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            # Ending of pre-state
+            updateQuery = \
+                """update sim_patient_state 
+                set relative_time_end = %(p)s
+                where sim_patient_id = %(p)s
+                and sim_state_id = %(p)s
+                and relative_time_start <= %(p)s
+                and relative_time_end is null
+                """ % {"p": DBUtil.SQL_PLACEHOLDER}
+            updateParams = (currentTime, patientId, preStateId, currentTime)
+            DBUtil.execute(updateQuery, updateParams, conn=conn)
+            
+            # Beginning of post-state
+            insertDict = {"sim_patient_id": patientId, "sim_state_id": postStateId, "relative_time_start": currentTime}
+            DBUtil.insertRow("sim_patient_state", insertDict, conn=conn)
+        finally:
+            conn.commit()
+            if not extConn:
+                conn.close()
+    
+    def loadPatientLastEventTime(self, patientId, conn=None):
+        """Find the last simulated time where the patient received any user orders (or default to time 0).
+        Makes for natural starting point for resuming a simulation.
+        Note: Misses cases where patient state also changed, not triggered by order, but by just time lag.
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("max(relative_time_start)")
+            query.addSelect("max(relative_time_end)")
+            query.addSelect("0")   # Simply select 0 as default time if don't find any others
+            query.addFrom("sim_patient_order as po")
+            query.addWhereEqual("sim_patient_id", patientId )
+
+            lastOrderTime = max(value for value in DBUtil.execute(query, conn=conn)[0] if value is not None)
+            return lastOrderTime
+        finally:
+            if not extConn:
+                conn.close()
+
+    def loadPatientOrders(self, patientId, currentTime, loadActive=True, conn=None):
+        """Load orders for the given patient that exist by the specified current time point.
+        loadActive - Specify whether to load active vs. inactive/completed orders.  Set to None to load both
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("po.sim_patient_order_id")
+            query.addSelect("po.sim_user_id")
+            query.addSelect("po.sim_patient_id")
+            query.addSelect("po.sim_state_id")
+            query.addSelect("po.clinical_item_id")
+            query.addSelect("po.relative_time_start")
+            query.addSelect("po.relative_time_end")
+            query.addSelect("ci.name")
+            query.addSelect("ci.description")
+            query.addSelect("cic.source_table")
+            query.addSelect("cic.description as category_description")
+            query.addFrom("sim_patient_order as po")
+            query.addFrom("clinical_item as ci")
+            query.addFrom("clinical_item_category as cic")
+            query.addWhere("po.clinical_item_id = ci.clinical_item_id")
+            query.addWhere("ci.clinical_item_category_id = cic.clinical_item_category_id")
+            query.addWhereEqual("sim_patient_id", patientId )
+            query.addWhereOp("relative_time_start","<=", currentTime )
+
+            
+            if loadActive:  # Filter out inactive orders here.
+                query.openWhereOrClause()
+                query.addWhere("relative_time_end is null")
+                query.addWhereOp("relative_time_end",">", currentTime)
+                query.closeWhereOrClause()
+            #elif loadActive is not None:    # Filter out active orders here.
+            #    query.addWhereOp("relative_time_end","<=", currentTime)
+            
+            if loadActive:  # Organize currently active orders by category
+                query.addOrderBy("cic.description")
+                query.addOrderBy("ci.description")
+                query.addOrderBy("relative_time_start")
+            else:   # Otherwise chronologic order
+                query.addOrderBy("relative_time_start")
+                query.addOrderBy("cic.description")
+                query.addOrderBy("ci.description")
+
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+    def loadResults(self, patientId, relativeTime, conn=None):
+        """Load all results active by the given relativeTime.
+        Will look for sim_patient_state times and sim_patient_order for diagnostic orders,
+        to extrapolate all state-specific results for each order, or using default values
+        if no state specific ones available.
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            # First query for all expected result labels and states, without state-specific values as 
+            #   may want outer join behvaior against default state values
+            query = SQLQuery()
+            
+            query.addSelect("distinct spo.sim_state_id")
+            query.addSelect("sr.sim_result_id")
+            query.addSelect("sr.name")
+            query.addSelect("sr.description")
+            query.addSelect("sr.priority")
+            query.addSelect("sr.group_string")
+
+            query.addSelect("spo.relative_time_start")
+            query.addSelect("sorm.turnaround_time")
+            query.addSelect("(spo.relative_time_start + sorm.turnaround_time) as result_relative_time")
+            
+            query.addFrom("sim_patient_order as spo")
+            query.addFrom("sim_order_result_map as sorm")
+            query.addFrom("sim_result as sr")
+            
+            query.addWhere("spo.clinical_item_id = sorm.clinical_item_id")
+            query.addWhere("sorm.sim_result_id = sr.sim_result_id")
+
+            query.addWhereEqual("spo.sim_patient_id", patientId )
+            # Only unlock results if appropiate prereq orders were placed in the past (and longer than the turnaround time)
+            query.addWhereOp("spo.relative_time_start + sorm.turnaround_time","<=", relativeTime )  
+            # Also check that the triggering order was not cancelled before the completion of the turnaround time
+            query.addWhere("( spo.relative_time_end is null or spo.relative_time_start + sorm.turnaround_time <= spo.relative_time_end )")
+
+            query.addOrderBy("result_relative_time")
+            query.addOrderBy("sr.priority")
+            
+            resultTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            resultModels = modelListFromTable(resultTable)
+
+            # Pass through results to get set of states to search for
+            stateIds = set([DEFAULT_STATE_ID]) # Include default state to fall back on
+            for resultModel in resultModels:
+                stateIds.add(resultModel["sim_state_id"])
+            
+            # Second query for state-specific values
+            valueQuery = SQLQuery()
+            valueQuery.addSelect("ssr.sim_state_id")
+            valueQuery.addSelect("ssr.sim_result_id")
+            valueQuery.addSelect("ssr.num_value")
+            valueQuery.addSelect("ssr.num_value_noise")
+            valueQuery.addSelect("ssr.text_value")
+            valueQuery.addSelect("ssr.result_flag")
+            valueQuery.addSelect("ssr.clinical_item_id")    # Output clinical item if result flag means something
+            valueQuery.addFrom("sim_state_result as ssr")
+            valueQuery.addWhereIn("ssr.sim_state_id", stateIds)
+            valueTable = DBUtil.execute( valueQuery, includeColumnNames=True, conn=conn)
+            valueModels = modelListFromTable(valueTable)
+            
+            # Store in-memory dictionary for rapid cross-referencing "join" to result table
+            valueModelByStateIdByResultId = dict()
+            for valueModel in valueModels:
+                resultId = valueModel["sim_result_id"]
+                stateId = valueModel["sim_state_id"]
+                if resultId not in valueModelByStateIdByResultId:
+                    valueModelByStateIdByResultId[resultId] = dict()
+                valueModelByStateIdByResultId[resultId][stateId] = valueModel
+
+            # Now go back through original results and join up state-specific values, or use default values if needed
+            resultValueModels = list()
+            for resultModel in resultModels:
+                resultId = resultModel["sim_result_id"]
+                stateId = resultModel["sim_state_id"]
+                if resultId in valueModelByStateIdByResultId:
+                    valueModelByStateId = valueModelByStateIdByResultId[resultId]
+                    if stateId in valueModelByStateId:
+                        # Have a state-specific value, populate that
+                        valueModel = valueModelByStateId[stateId]
+                        resultModel.update(valueModel)
+                    elif DEFAULT_STATE_ID in valueModelByStateId:
+                        # No state-specific value, but have a default one to populate instead
+                        valueModel = valueModelByStateId[DEFAULT_STATE_ID]
+                        resultModel.update(valueModel)
+                    resultValueModels.append(resultModel)
+                else:
+                    # No result information available, even in default state. Skip these
+                    #resultModel["num_value"] = None
+                    #resultModel["num_value_noise"] = None
+                    #resultModel["text_value"] = None
+                    #resultModel["result_flag"] = None
+                    #resultModel["clinical_item_id"] = None
+                    pass
+
+            return resultValueModels
+        finally:
+            if not extConn:
+                conn.close()
+
+    def loadPendingResultOrders(self, patientId, relativeTime, conn=None):
+        """Load all patient orders at the given relativeTime that 
+        are due to yield results, but have not yet. 
+        Include an estimate of time until results available.
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("distinct po.clinical_item_id")    # Distinct so don't report multiple times for panel orders
+            query.addSelect("po.relative_time_start")
+            query.addSelect("po.relative_time_end")
+            query.addSelect("ci.name")
+            query.addSelect("ci.description")
+            query.addSelect("sorm.turnaround_time")    # Could have different turnaround times for single order if different sub results. Just report each.
+            query.addSelect("sorm.turnaround_time - (%d - po.relative_time_start) as time_until_result" % relativeTime)    # Calculate time until expect result
+            query.addFrom("sim_patient_order as po")
+            query.addFrom("clinical_item as ci")
+            query.addFrom("sim_order_result_map as sorm")
+            query.addWhere("po.clinical_item_id = ci.clinical_item_id")
+            query.addWhere("po.clinical_item_id = sorm.clinical_item_id")
+            query.addWhereEqual("sim_patient_id", patientId )
+
+            # Only catch orders up to the given relativeTime and not cancelled
+            query.addWhereOp("relative_time_start","<=", relativeTime )
+            query.openWhereOrClause()
+            query.addWhere("relative_time_end is null")
+            query.addWhereOp("relative_time_end",">", relativeTime)
+            query.closeWhereOrClause()
+
+            # Only PENDING orders, so don't report orders who results should already be available
+            query.addWhereOp("sorm.turnaround_time + po.relative_time_start",">", relativeTime)
+
+            query.addOrderBy("time_until_result")
+            query.addOrderBy("relative_time_start")
+            query.addOrderBy("ci.name")
+
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+    def loadNotes(self, patientId, currentTime, conn=None):
+        """Load notes committed up to the given simulation time.
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            
+            query.addSelect("sn.sim_note_id")
+            query.addSelect("sps.sim_patient_id") # Link
+            query.addSelect("sn.sim_state_id")
+            query.addSelect("sn.note_type_id")
+            query.addSelect("sn.author_type_id")
+            query.addSelect("sn.service_type_id")
+            query.addSelect("(sps.relative_time_start + sn.relative_state_time) as relative_time")
+            query.addSelect("sn.content")
+            
+            query.addFrom("sim_note as sn")
+            query.addFrom("sim_patient_state as sps")
+            
+            query.addWhere("sn.sim_state_id = sps.sim_state_id")
+            query.addWhereEqual("sps.sim_patient_id", patientId )
+            # Only unlock notes once traverse expected time
+            query.addWhereOp("(sps.relative_time_start + sn.relative_state_time)","<=", currentTime );  
+            query.addOrderBy("(sps.relative_time_start + sn.relative_state_time)")
+            
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+
+    def recentItemIds(self, patientId, currentTime, timeDelta=None, loadActive=True, includeResults=False, conn=None):
+        """Load a list of clinicalItemIds
+        (orders, diagnoses, unlocked results, etc.)
+        to establish current patient context.
+        
+        timeDelta - If specified, only count items that occurred within that much past time from the current simTime
+        loadActive - If True then only look for patient orders that have not since been cancelled
+        includeResults - If True (default False), then also include clinical_items that represent (abnormal) test results.
+        """
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            itemIds = set()
+            
+            patientOrders = self.loadPatientOrders(patientId, currentTime, loadActive=loadActive, conn=conn)
+            for patientOrder in patientOrders:
+                if timeDelta is None or patientOrder["relative_time_start"] >= currentTime-timeDelta:
+                    itemIds.add(patientOrder["clinical_item_id"])
+            
+            if includeResults:
+                results = self.loadResults(patientId, currentTime, conn=conn)
+                for result in results:
+                    if result["clinical_item_id"] is not None:
+                        itemIds.add(result["clinical_item_id"])
+            return itemIds
+        finally:
+            if not extConn:
+                conn.close()
+
+    def clinicalItemSearch(self, itemQuery, conn=None):
+        """Look for clinical items based on specified query criteria"""
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("ci.clinical_item_id")
+            query.addSelect("ci.name")
+            query.addSelect("ci.description")
+            query.addSelect("cic.source_table")
+            query.addSelect("cic.description as category_description")
+            query.addFrom("clinical_item as ci")
+            query.addFrom("clinical_item_category as cic")
+            query.addWhere("ci.clinical_item_category_id = cic.clinical_item_category_id")
+            if itemQuery.searchStr is not None:
+                searchWords = itemQuery.searchStr.split()
+                #query.openWhereOrClause()
+                for searchField in ("ci.description",):
+                    for searchWord in searchWords:
+                        query.addWhereOp(searchField,"~*","^%(searchWord)s|[^a-z]%(searchWord)s" % {"searchWord": searchWord} ) # Prefix search by regular expression
+                #query.closeWhereOrClause()
+            if itemQuery.sourceTables:
+                query.addWhereIn("cic.source_table", itemQuery.sourceTables )
+            if itemQuery.analysisStatus is not None:
+                query.addWhereEqual("ci.analysis_status", itemQuery.analysisStatus )
+                query.addWhere("ci.item_count <> 0")    # Also ignore items with no occurence in the analyzed data (occurs if item was accepted for analysis from multi-year dataset, but never used in a sub-time frame's analysis)
+
+            if itemQuery.sortField:
+                query.addOrderBy(itemQuery.sortField)
+            query.addOrderBy("cic.description")
+            query.addOrderBy("ci.name")
+            query.addOrderBy("ci.description")
+            if itemQuery.resultCount is not None:
+                query.limit = itemQuery.resultCount
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+            return dataModels
+        finally:
+            if not extConn:
+                conn.close()
+
+
+    def orderSetSearch(self, itemQuery, conn=None):
+        """Look for clinical items based on specified query criteria"""
+        extConn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            extConn = False
+        try:
+            query = SQLQuery()
+            query.addSelect("ic.item_collection_id")
+            query.addSelect("ic.external_id")
+            query.addSelect("ic.name as collection_name")
+            query.addSelect("ic.section")
+            query.addSelect("ic.subgroup")
+            query.addSelect("ci.clinical_item_category_id")
+            query.addSelect("ci.clinical_item_id")
+            query.addSelect("ci.name")
+            query.addSelect("ci.description")
+            query.addFrom("item_collection as ic")
+            query.addFrom("item_collection_item as ici")
+            query.addFrom("clinical_item as ci")
+            query.addWhere("ic.item_collection_id = ici.item_collection_id")
+            query.addWhere("ici.clinical_item_id = ci.clinical_item_id")
+            query.addWhereNotEqual("ic.section", AD_HOC_SECTION )
+            if itemQuery.searchStr is not None:
+                searchWords = itemQuery.searchStr.split()
+                for searchWord in searchWords:
+                    query.addWhereOp("ic.name","~*","^%(searchWord)s|[^a-z]%(searchWord)s" % {"searchWord": searchWord} ) # Prefix search by regular expression
+            if itemQuery.analysisStatus is not None:
+                query.addWhereEqual("ci.analysis_status", itemQuery.analysisStatus )
+            query.addOrderBy("lower(ic.name)")
+            query.addOrderBy("ic.external_id")
+            query.addOrderBy("lower(ic.section)")
+            query.addOrderBy("lower(ic.subgroup)")
+            query.addOrderBy("ci.clinical_item_id")
+            query.addOrderBy("ci.name")
+            dataTable = DBUtil.execute( query, includeColumnNames=True, conn=conn)
+            dataModels = modelListFromTable(dataTable)
+
+            # Aggregate up into order sets
+            orderSetModel = None
+            for row in dataModels:
+                if orderSetModel is None or row["external_id"] != orderSetModel["external_id"]:
+                    if orderSetModel is not None:
+                        # Prior order set exists, yield/return it before preparing next one
+                        yield orderSetModel
+                    orderSetModel = \
+                        {   "external_id": row["external_id"], 
+                            "name": row["collection_name"],
+                            "itemList": list(),
+                        }
+                orderSetModel["itemList"].append(row)
+            yield orderSetModel    # Yield the last processed model
+        finally:
+            if not extConn:
+                conn.close()
+
+    def grade_cases(self, sim_patient_ids, sim_grader_id, conn=None):
+        """Given the identifiers for a bunch of simulated physician-patient case
+        records, and the identifier for a particular grading key to use,
+        calculate what grade each case would get based on the choices made
+        and return a dictionary of case grades (keyed by the case ID).
+        """
+        ext_conn = True
+        if conn is None:
+            conn = self.connFactory.connection()
+            ext_conn = False
+        try:
+            # Inner query retrieves physician-patient cases with ranking group_names (to later select first)
+            # per case for specified cases. Each NULL group_name is treated as a separate group by assigning it
+            # sim_patient_order_id. It also omits Default user (sim_user_id = 0) from grading.
+            inner_query = SQLQuery()
+            inner_query.addSelect("score")
+            inner_query.addSelect("rank() over ("   # ranks rows incrementally in the same group
+                                  "    partition by coalesce(group_name, sim_patient_order_id::text), sim_patient_id"
+                                  "    order by sim_patient_order_id"
+                                  ")")
+            inner_query.addSelect("sim_user_id")
+            inner_query.addSelect("sim_patient_id")
+            inner_query.addSelect("sim_grader_id")
+            inner_query.addFrom("sim_patient_order spo")
+            inner_query.addJoin("sim_grading_key sgk",
+                                "sgk.clinical_item_id = spo.clinical_item_id"
+                                "    and sgk.sim_state_id = spo.sim_state_id")
+            inner_query.addWhereEqual("sgk.sim_grader_id", sim_grader_id)
+            inner_query.addWhereNotEqual("spo.sim_user_id", 0)  # 0 = ignore 'Default user', sets up initial cases
+            inner_query.addWhereIn("spo.sim_patient_id", sim_patient_ids)
+            inner_query.addOrderBy("relative_time_start")
+            inner_query.addOrderBy("sim_patient_order_id")
+
+            # Outer query sums the score per patient case and selects most graded physician for the case.
+            # Theoretically, it isn't necessarily the most active physician for the case since his orders
+            # might have been dropped by selecting only the first record within group_name group.
+            query = SQLQuery()
+            query.addSelect("sim_patient_id")
+            query.addSelect("sim_grader_id")
+            query.addSelect("sum(score) as total_score")
+            query.addSelect("mode() within group ("         # mode() selects most frequent value within group
+                            "    order by sim_user_id"
+                            ") as most_graded_user_id")
+            query.addFrom("(" + str(inner_query) + ") as ranked_groups")
+            query.addWhereEqual("ranked_groups.rank", 1)    # count only first order in the same group
+            query.addGroupBy("sim_patient_id")
+            query.addGroupBy("sim_grader_id")
+
+            query_params = inner_query.getParams() + query.getParams()
+            grades_table = DBUtil.execute(query, query_params, includeColumnNames=True, conn=conn)
+            grades_model = modelListFromTable(grades_table)
+
+            # get most active users for the cases
+            most_active_user_query = SQLQuery()
+            most_active_user_query.addSelect("sim_patient_id")
+            most_active_user_query.addSelect("mode() within group ("
+                                             "    order by sim_user_id"
+                                             ") as most_active_user_id")
+            most_active_user_query.addFrom("sim_patient_order")
+            most_active_user_query.addWhereNotEqual("sim_user_id", 0)   # ignore Default user
+            most_active_user_query.addWhereIn("sim_patient_id", sim_patient_ids)
+            most_active_user_query.addGroupBy("sim_patient_id")
+            most_active_user_query.addOrderBy("sim_patient_id")
+
+            most_active_user_table = DBUtil.execute(most_active_user_query, includeColumnNames=True, conn=conn)
+            most_active_user_model = modelListFromTable(most_active_user_table)
+            # make a dict by sim_patient_id out of results - will be used for combining
+            most_active_user_dict = {
+                most_active_user["sim_patient_id"]: most_active_user
+                for most_active_user in most_active_user_model
+            }
+
+            # combine results
+            complete_grades = [
+                grade.update(most_active_user_dict[grade["sim_patient_id"]])
+                for grade in grades_model
+            ]
+
+            return complete_grades
+
+        finally:
+            if not ext_conn:
+                conn.close()
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usage_str = "usage: %prog [options] [<outputFile>]\n" \
+                    "   <outputFile> JSON file with the grading results. Leave blank or specify '-' to send to stdout."
+        parser = OptionParser(usage=usage_str)
+        parser.add_option("-p", "--patient_ids", dest="patient_ids",
+                          help="Comma-separated list of patient id cases to grade")
+        parser.add_option("-f", "--patients_file", dest="patients_file",
+                          help="File containing list of patient ids: one patient_id per line")
+        parser.add_option("-g", "--graders", dest="graders", help="Comma-separated list of graders to use for grading")
+
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: " + str.join(" ", argv))
+        timer = time.time()
+        summary_data = {"argv": argv}
+
+        patient_cases = set()
+        if options.patient_ids:
+            patient_cases.update(options.patient_ids.split(VALUE_DELIM))
+            log.debug("patient_cases from command line: " + str(patient_cases))
+
+        if options.patients_file:
+            with open(options.patients_file) as patient_ids_file:
+                for patient_id in patient_ids_file:
+                    patient_cases.add(patient_id)
+            log.debug("patient_cases enriched from file: " + str(patient_cases))
+
+        if not patient_cases:
+            print("No patient cases given. Nothing to grade. Exiting.\n")
+            parser.print_help()
+            sys.exit()
+
+        grader_ids = set()
+        if not options.graders:
+            print("No graders given. Cannot grade patient cases. Exiting.\n")
+            parser.print_help()
+            sys.exit()
+        else:
+            grader_ids.update(options.graders.split(VALUE_DELIM))
+
+        # Run the grading
+        grades = []
+        for grader_id in grader_ids:
+            graded_cases = self.grade_cases(patient_cases, grader_id)
+            grades.extend(graded_cases)
+
+        output_filename = None
+        if args:
+            output_filename = args[0]
+        output_file = stdOpen(output_filename, "w")
+
+        # Print comment line with arguments to allow for deconstruction later as well as extra results
+        print(COMMENT_TAG, json.dumps(summary_data), file=output_file)
+
+        if grades:
+            # Write csv to output
+            csv_writer = csv.DictWriter(output_file, list(grades[0].keys()))
+            csv_writer.writeheader()
+            csv_writer.writerows(grades)
+
+        timer = time.time() - timer
+        log.info("%.3f seconds to complete", timer)
+
+
+class ClinicalItemQuery:
+    """Struct to capture query elements for clinical item / order instances"""
+    def __init__(self):
+        self.searchStr = None
+        self.analysisStatus = None
+        self.sourceTables = None   # Valid source data tables to use
+        self.resultCount = None
+        self.sortField = None
+
+    def parseParams(self, paramDict):
+        """Look through the dictionary for key-value pairs that can be parsed into query object parameters.
+        Facilitates easy setup from web or command-line text interfaces.
+        """
+        if "searchStr" in paramDict:
+            self.searchStr = paramDict["searchStr"]
+        if "analysisStatus" in paramDict:
+            self.analysisStatus = paramDict["analysisStatus"]
+        if "sourceTables" in paramDict:
+            self.sourceTables = paramDict["sourceTables"].split(",")
+        if "resultCount" in paramDict:
+            self.resultCount = int(paramDict["resultCount"])
+        if "sortField" in paramDict:
+            self.sortField = paramDict["sortField"]
+
+
+if __name__ == "__main__":
+    instance = SimManager()
+    instance.main(sys.argv)
diff --git a/scripts/MuthuDecayingWindows/__init__.py b/medinfo/cpoe/cpoeSim/__init__.py
similarity index 100%
rename from scripts/MuthuDecayingWindows/__init__.py
rename to medinfo/cpoe/cpoeSim/__init__.py
diff --git a/medinfo/cpoe/cpoeSim/analysis/CPOETrackerAnalysis-walkthrough.ipynb b/medinfo/cpoe/cpoeSim/analysis/CPOETrackerAnalysis-walkthrough.ipynb
new file mode 100644
index 00000000..9f0a492a
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/analysis/CPOETrackerAnalysis-walkthrough.ipynb
@@ -0,0 +1,484 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports and environment setup\n",
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "from CPOETrackerAnalysis import SimulationAnalyzer, aggregate_simulation_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The SimulationAnalyzer class is meant to be an analyitical wrapper for the JSON files created by the tracking module of the CPOE recommender simulation. It takes care of parsing through the nested elements of the JSON and computing several useful statistics on the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we'll run through some of the exisiting functionality of the SimulationAnalyzer on a single case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set data source for a single case\n",
+    "data_file = 'sim_data/34_156_data_v4.json'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The simulation analyzer is instantiated by providing the path to the json file containing the recorded events during the simulation of a case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate SimulationAnalyzer on source data file\n",
+    "sim_analyzer = SimulationAnalyzer(data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using the simulation analyzer:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyzer components:\n",
+    "When instantiated with a particular data source, the simulation analyzer object will parse the data into several attributes that can be operated on to report metrics of interest. Some of these attributes are defined below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### User, patient, start time, and end time\n",
+    "Included in the analyzer are some general attributes about the case from which the source data was derived. The `user` attribute gives the id of the user that completed the case. The `patient` attribute gives the id of the case. The `start_time` and `end_time` attributes give the absolute times when the case began (when the simulation window was opened) and when the case ended (when the simulation was saved), respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user = sim_analyzer.user\n",
+    "patient = sim_analyzer.patient\n",
+    "start_time = sim_analyzer.start_time\n",
+    "end_time = sim_analyzer.end_time\n",
+    "print(\"user: {}\\npatient: {}\\nstart_time: {}\\nend_time: {}\".format(user, patient, start_time, end_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Event tracker\n",
+    "The `event_tracker_data` attribute is an object that keeps track of interactions with the simulation interface. Specifically, for the elements that have been interacted with in the interface (such as the search bar and the various search modes, any results on the page, or the notes selection), there is list of items containing the interaction timestamp as well as information about the event."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show event tracker data for current data source\n",
+    "event_tracker_data = sim_analyzer.event_tracker_data\n",
+    "event_tracker_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Results tracker\n",
+    "The `results_tracker_data` attribute is an object that contains the results that appeared throughout the simulation, for each search mode (results from the recommender correspond to the `''` key). The results are further grouped by the section in which they appear. Along with each section of results, there is also a state object that contains information of the search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show results tracker data for current data source\n",
+    "results_tracker_data = sim_analyzer.results_tracker_data\n",
+    "results_tracker_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Signed item tracker\n",
+    "The `signed_item_tracker_data` attribute is an object that contains information for clinical orders signed during the simulation. These signed items are grouped by the timestamp of when they were signed. The information is in the format `clinical_item_id|source|search_query|search_mode|list_index`. These attributes are defined below:\n",
+    "\n",
+    "|Attribute|Description|\n",
+    "|---------|:-----------|\n",
+    "|clinical_item_id|The database id associated with the signed item|\n",
+    "|source|The result source from wich the item was selected - _'resultSpace1' or 'resultSpace2' for recommender items, 'non-recommender' otherwise_|\n",
+    "|search_query|The query used that generated the signed item - _empty for recommender items_|\n",
+    "|search_mode|What type of search was used with the query (Find Orders, Order Sets, Diagnoses) - _empty for recommender items_|\n",
+    "|list_index|The index of the list that contained the signed item|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show signed item tracker data for current data source\n",
+    "signed_item_tracker_data = sim_analyzer.signed_item_tracker_data\n",
+    "signed_item_tracker_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Results and sgined orders collections\n",
+    "To simplify certain operations on the data collected, the hierarchical `results_tracker_data` and `signed_item_tracker_data` have been flattened into the collections `results_collection` and `signed_orders_collection`, respectively. These collections represent each item as an item dictionary containing the item's information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show results collection for current data source\n",
+    "results_collection = sim_analyzer.results_collection\n",
+    "results_collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show signed orders collection for current data source\n",
+    "signed_orders_collection = sim_analyzer.signed_orders_collection\n",
+    "signed_orders_collection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summary Metrics:\n",
+    "Below are a few implemented summary metrics that make use of the attributes discussed above. For details on how these are implemented, please refer to the source code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Elapsed Time\n",
+    "One of the first things we might be interested in about a user's behavior is the amount of time they spend on a particular case. This metric is retrieved as follows and is presented in the form __hours:minutes:seconds__. Note that this elapsed time does not filter out idle time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve elapsed time from the base data\n",
+    "elapsed_time = sim_analyzer.elapsed_time()\n",
+    "elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Mouse clicks\n",
+    "There are several ways to retrieve information about mouse clicks during the simulation. The first is to retrieve all the clicks made during the duration of the simulation through `number_mouse_clicks_all()`. The second is to retrieve the number of mouse clicks over certain buttons and inputs on the simulation page through `number_mouse_clicks()`. And the final way is to retrieve a dict summary of clicks through `click_summary()` , where the key of the dict is the event and the value is the number of clicks. Note that the latter two of these methods have an option to return the frequency of clicks as a percentage of all clicks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve number of total mouse clicks made:\n",
+    "clicks_all = sim_analyzer.number_mouse_clicks_all()\n",
+    "\n",
+    "# Retrieve number of mouse clicks made over the Results and Results Review sections:\n",
+    "clicks_notes_results = sim_analyzer.number_mouse_clicks(filters=['ResultInteraction', 'ResultsReview'], perc=True)\n",
+    "\n",
+    "# Retrieve dict of mouse click summary\n",
+    "click_summary = sim_analyzer.click_summary(perc=False)\n",
+    "\n",
+    "print(\"Total number of clicks: {}\".format(clicks_all))\n",
+    "print(\"Percent of clicks over Results and Results Review sections: {}\".format(clicks_notes_results))\n",
+    "print(\"Mouse click summary:\\n {}\".format(click_summary))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Item Metrics:\n",
+    "There are also several implemented metrics for result items and signed items that use the attributes discussed above and other helpful utilities. Again, for more details about the implementations, please refer to the source code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Results\n",
+    "There exists utility functions for retrieving the item options that appeared from manual searches and those that appeared from the recommender. These functions can be used to retreive the number of items from both sources, as well as for doing any other specific operations on the collections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get item options that were results from a manual search\n",
+    "manually_search_options = sim_analyzer.get_manually_searched_options()\n",
+    "print(\"Number of manually searched options: {}\\nManually searched options: {}\".format(len(manually_search_options), manually_search_options))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get item options that were results from recommender\n",
+    "recommended_options = sim_analyzer.get_recommended_options()\n",
+    "print(\"Number of recommended options: {}\\nRecommended options: {}\".format(len(recommended_options), recommended_options))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since identical items may appear multiple times throughout searches, or by the recommender,\n",
+    "# it may be useful to only retreive unique options. This can be done by definining a uniqueness\n",
+    "# comparison function and applying it with the `get_unique` helper method.\n",
+    "def clinical_item_id_key_fn(item):\n",
+    "    \"\"\"Returns clinical item value from item object\"\"\"\n",
+    "    return item['clinicalItemId']\n",
+    "\n",
+    "unique_results = sim_analyzer.get_unique(sim_analyzer.results_collection, key_fn=clinical_item_id_key_fn)\n",
+    "print(\"Number of unique results: {}\\nUnique results: {}\".format(len(unique_results), unique_results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Signed orders\n",
+    "Similar to results, there exists utility functions for retrieving signed items that appeared from manual searches or from the recommender. These functions are useful for metrics evaluating how the recommender influences the order behavior of users. These functions can be used to retreive the number of items from both sources, as well as for doing any other specific operations on the collections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get signed items that appeared from manual searches\n",
+    "signed_from_manual_search = sim_analyzer.get_signed_from_manual_search()\n",
+    "print(\"Number signed from manual searches: {}\\nItems signed from manual searches: {}\".format(len(signed_from_manual_search), signed_from_manual_search))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get signed items that appeared from the recommender\n",
+    "signed_from_recommender = sim_analyzer.get_signed_from_recommended()\n",
+    "print(\"Number signed from recommender: {}\\nItems signed from recommender: {}\".format(len(signed_from_recommender), signed_from_recommender))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# One metric that is interesting to see is the orders that appeared from the recommender\n",
+    "# but were ultimately signed during a manual search. These signed items are essentially those\n",
+    "# that were 'missed' by the user.\n",
+    "signed_missed = sim_analyzer.get_signed_missed_recommended()\n",
+    "print(\"Number signed that were originally missed: {}\\nSigned items that were originally missed: {}\".format(len(signed_missed), signed_missed))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregation\n",
+    "There are several ways of aggregating the data captured by the simulation analyzer object, whether it be for visualization purposes or for constructing data reports on multiple data sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Timeline\n",
+    "One visualization that is useful is a timeline of events. This visualization allows us to note any clusters(or lack of clusters) of behaviour during the simulation. To visualize the timeline, we first construct the collection of sorted events using `construct_timeline()`. We can then pass this collection of events into the `visualize_timeline()` method as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort events by their timestamp\n",
+    "sorted_events = sim_analyzer.construct_timeline()\n",
+    "\n",
+    "# Visualize timeline\n",
+    "sim_analyzer.visualize_timeline(sorted_events)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data dump\n",
+    "Although not part of the SimulationAnalyzer class, there exists functionality in `CPOETrackerAnalysis.py` for running an analyzer instance on a set of json-stored data sources and creating (or appending to an existing) csv of metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create flat CSV from data\n",
+    "out_file = 'sim_data/out.csv'\n",
+    "data_home = 'sim_data'\n",
+    "aggregate_simulation_data(data_home, output_path=out_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show csv\n",
+    "csv = pd.read_csv('sim_data/out.csv')\n",
+    "display(csv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises:\n",
+    "Below are a few implementation exercises to test your understanding of the different components of the `SimulationAnalyzer` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiate SimulationAnalyzer on source data file\n",
+    "sim_analyzer = SimulationAnalyzer('sim_data/48_244_data_v4.json')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exercise 1\n",
+    "One of the things we want to track for a simulation case is whether a user ordered appropriate items. These items must also be ordered in a particular sequence (i.e. ordering antibiotics before a particular lab test could interfere with the results of the lab test). For this exercise, we will write a function that validates the 'correctness' of a case, according to a reference sequence of orders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fill in the function below according to the docstring\n",
+    "def case_orders_valid(sim_analyzer, expected_orders):\n",
+    "    \"\"\" Validate that the case represented by sim_analyzer adheres to the expected sequence of orders.\n",
+    "    \n",
+    "    Args:\n",
+    "        sim_analyzer (SimulationAnalyzer): The SimulationAnalyzer instance representing the case being validated\n",
+    "        expected_orders (list): List of clinical id strings representing the expected sequence of orders. \n",
+    "        This means that the clinical item expected_orders[i] should have been ordered before clinical items \n",
+    "        expected_orders[j] for j > i.\n",
+    "    \"\"\"\n",
+    "    is_valid = False\n",
+    "    # ****** START YOUR IMPLEMENTATION HERE ******\n",
+    "    \n",
+    "    # ****** END YOUR IMPLEMENTATION HERE ******\n",
+    "    return is_valid\n",
+    "\n",
+    "expected_orders = ['45771', '45838', '45866', '45818']\n",
+    "case_orders_valid(sim_analyzer, expected_orders)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/medinfo/cpoe/cpoeSim/analysis/CPOETrackerAnalysis.py b/medinfo/cpoe/cpoeSim/analysis/CPOETrackerAnalysis.py
new file mode 100644
index 00000000..2455bde5
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/analysis/CPOETrackerAnalysis.py
@@ -0,0 +1,692 @@
+import sys, os
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import datetime
+from optparse import OptionParser
+import csv
+
+
+class SimulationAnalyzer:
+	def __init__(self, data_file):
+		self.data_file = data_file
+		self.version = data_file.split('.')[-2].split('_')[-1]  # Get tracker version from filename
+		# assert(self.version in ['v4', 'v5'])
+		self.load_tracker_data()
+		self.results_collection = self.normalize_results()
+		# If using older version of tracker data, will need to parse signed collection
+		# differently
+		if self.version in ['v4', 'v5']:
+			self.signed_orders_collection = self.normalize_signed_orders()
+		else:
+			self.signed_orders_collection = self.normalize_signed_orders_older()
+
+	def load_tracker_data(self):
+		with open(self.data_file, 'r') as fp:
+			json_data = json.load(fp)
+			self.event_tracker_data = json.loads(json_data['eventTracker'])
+			self.results_tracker_data = json.loads(json_data['resultsTracker'])
+			self.signed_item_tracker_data = json.loads(json_data['signedItemsTracker'])
+			self.user = json_data['user']
+			self.patient = json_data['patient']
+			self.start_time = json_data['startTime']
+			self.end_time = json_data['endTime']
+
+	def parse_result_item_into_dict(self, base_item_dict, item_str):
+		""" Extract item's clinical_item_id, name, and description from item_str
+		and insert into item dict based on base_item_dict.
+
+		Args:
+			base_item_dict (dict): dict containing base attributes for item
+			item_str (str): string representation of item. Expected to be of
+				form: 'clinical_item_id|name|description'
+
+		Returns:
+			result_item_dict (dict): dict representing item_str passed in and
+				all other global attributes of the item
+		"""
+		# print(item_str)
+		result_item_dict = dict(base_item_dict)
+		attributes = list(item_str.split('|'))
+		result_item_dict['clinicalItemId'] = attributes[0]
+		result_item_dict['name'] = ""
+		result_item_dict['description'] = ""
+		if len(attributes) > 1:
+			result_item_dict['name'] = attributes[1]
+			result_item_dict['description'] = attributes[2]
+		return result_item_dict
+
+	def parse_signed_item_into_dict(self, item_dict, item_str):
+		"""Extract item's attributes and insert into item_dict.
+
+		Args:
+			item_dict (dict): dict to which item attributes will be added
+			item_str (str): string containing item's attributes. Expected to be
+				of form: 'clinical_item_id|source|searchQuery|mode|list_idx'
+		"""
+		clinical_item_id, source, searchQuery, mode, list_idx = list(item_str.split('|'))
+		item_dict['clinicalItemId'] = clinical_item_id
+		# Rename source to match names used for result items
+		if source == 'resultSpace1':
+			source = 'commonOrders'
+		elif source == 'resultSpace2':
+			source = 'specificOrders'
+		elif source == 'non-recommender':
+			source = 'data'
+		item_dict['source'] = source if source != 'undefined' else 'data'  # Quick fix for issue where order set items are not recorded correctly
+		item_dict['searchQuery'] = searchQuery  # May be undefined due to issue where order set items are not correctly recorded
+		item_dict['mode'] = mode if mode != 'undefined' else 'OrderSets'  # Quick fix for issue where order set items are not recorded correctly
+		item_dict['listIndex'] = 1000 if list_idx == 'undefined' else int(list_idx)  # Quick fix for issue where order set items are not recorded correctly - set listIndex to something arbitarily high
+
+	def normalize_results(self):
+		"""Join all results into a single, 'flat' collection of result item dicts
+
+		Returns:
+			results (list): list of result item dicts
+		"""
+		results = []
+		for mode in list(self.results_tracker_data.keys()):
+			for mode_object in self.results_tracker_data[mode]:
+				base_result_item_dict = dict()
+				items_collection = None
+				base_result_item_dict['mode'] = mode
+				for instance_collection_name in list(mode_object.keys()):  # ['items', 'state']
+					for attr_name in list(mode_object[instance_collection_name].keys()):
+						# If attribute is a list, then items have been found.
+						if isinstance(mode_object[instance_collection_name][attr_name], list):
+							# Store type of result [commonOrders, specificOrders, data]
+							base_result_item_dict['source'] = attr_name
+							# Store items for processing after all other attributes have been stored
+							items_collection = mode_object[instance_collection_name][attr_name]
+						else:
+							# Store non-list value into item dict
+							base_result_item_dict[attr_name] = mode_object[instance_collection_name][attr_name]
+				# Process items
+				for item_str in items_collection:
+					result_item_dict = self.parse_result_item_into_dict(base_result_item_dict, item_str)
+					# Store item dict into results
+					results.append(result_item_dict)
+		return results
+
+	def normalize_signed_orders_older(self):
+		"""
+		Map signed items from older version of tracker data to their corresponding
+		result items.
+
+		Returns:
+			signed_orders (list): list of signed order item dicts.
+				Note that these item dicts will not contain some of the attributes
+				found in the item dicts returned by `normalize_signed_orders`
+		"""
+		signed_orders = []
+		# Construct signed order clinical ids set and signed order tuples
+		signed_orders_id_set = set()
+		signed_orders_tuples = []
+		for timestamp in self.signed_item_tracker_data:
+			for item_str in self.signed_item_tracker_data[timestamp]:
+				signed_orders_id_set.add(item_str)
+				signed_orders_tuples.append((int(timestamp), item_str))
+
+		# Filter out result items that do not correspond to items signed
+		filtered_results = list([r for r in self.results_collection if r['clinicalItemId'] in signed_orders_id_set])
+
+		if not filtered_results:  # seems to work for v2
+			filtered_results = list([r for r in self.results_collection if r['clinicalItemId'] in [so.split('|')[0] for so in signed_orders_id_set]])
+		# For each signed item, find most likely result item
+		for signed_order in signed_orders_tuples:
+			# For each result item that has the same clinical id as the current
+			# signed order AND a store_time < the signed_time of the current item,
+			# compute timestamp delta (signed_time - store_time).
+			min_delta = float('inf')
+			best_result = None
+			for potential_result in list([r for r in filtered_results if r['clinicalItemId'] == signed_order[1] and r['storeTime'] < signed_order[0]]):
+				delta = signed_order[0] - potential_result['storeTime']
+				if delta < min_delta:
+					min_delta = delta
+					best_result = potential_result
+
+			if not best_result:  # seems to work for v2
+				for potential_result in list([r for r in filtered_results if r['clinicalItemId'] == signed_order[1].split('|')[0] and r['storeTime'] < signed_order[0]]):
+					delta = signed_order[0] - potential_result['storeTime']
+					if delta < min_delta:
+						min_delta = delta
+						best_result = potential_result
+
+			# Map current signed item to result item with smallest delta
+			if best_result:
+				signed_item_dict = dict(best_result)
+				signed_item_dict['signedTime'] = signed_order[0]
+				signed_orders.append(signed_item_dict)
+
+		return signed_orders
+
+
+	def normalize_signed_orders(self):
+		"""Join all signed items into a single, 'flat' collection of signed item dicts
+
+		Returns:
+			signed_orders (list): list of of signed order item dicts
+		"""
+		results_clinical_items = [r['clinicalItemId'] for r in self.results_collection]
+
+		signed_orders = []
+		for timestamp in self.signed_item_tracker_data:
+			for item_str in self.signed_item_tracker_data[timestamp]:
+				signed_item_dict = dict()
+				signed_item_dict['signedTime'] = timestamp
+				self.parse_signed_item_into_dict(signed_item_dict, item_str)
+				# Discontinuation orders have the following format in json:
+				# sim_patient_order_id|undefined|undefined|undefined|undefined
+				# And the self.parse_signed_item_into_dict() overwrites those values into:
+				# sim_patient_order_id|data|undefined|OrderSets|1000
+				# filter out discontinuation orders
+				if not(signed_item_dict['searchQuery'] == 'undefined' and signed_item_dict['listIndex'] == 1000):
+					signed_orders.append(signed_item_dict)
+
+		return signed_orders
+
+	def number_mouse_clicks_all(self):
+		"""Aggregate the number of mouse clicks on buttons and inputs on page"""
+		event_tracker = self.event_tracker_data
+		clicks = self.number_mouse_clicks(filters=list(event_tracker.keys()), perc=False)
+
+		return clicks
+
+	def number_mouse_clicks(self, filters=[], perc=False):
+		"""Aggregate number of mouse clicks on particular buttons and inputs on page
+
+		Args:
+			filters (list): names of events to count
+			perc (bool): whether to return frequency of clicks as percentage (of total clicks)
+		"""
+		event_tracker = self.event_tracker_data
+		clicks = 0.
+		for event in filters:
+			try:
+				event_ocurrences = len(event_tracker[event])
+				clicks += event_ocurrences
+			except Exception as e:
+				continue
+		if perc:
+			total_clicks = self.number_mouse_clicks_all()
+			percentage = clicks / total_clicks
+			return percentage
+
+		return clicks
+
+	def click_summary(self, perc=False):
+		"""Compute click summary for each event
+
+		Args:
+			perc (bool): whether to return event click summaries as percentages (of total clicks)
+		Return:
+			summary (dict): dict mapping event to frequency or percentage (based on perc)
+		"""
+		summary = dict()
+		event_tracker = self.event_tracker_data
+		for event in list(event_tracker.keys()):
+			summary[event] = self.number_mouse_clicks(filters=[event], perc=perc)
+
+		return summary
+
+	def convert_timestamp(self, ms_timestamp):
+		"""Convert millisecond timestamp to (hours, minutes, seconds)"""
+		seconds=(ms_timestamp/1000)%60
+		seconds = int(seconds)
+		minutes=(ms_timestamp/(1000*60))%60
+		minutes = int(minutes)
+		hours=(ms_timestamp/(1000*60*60))%24
+
+		return (hours, minutes, seconds)
+
+	def elapsed_time(self):
+		"""Compute total time spent on case"""
+		elapsed_time_millis = int(self.end_time) - int(self.start_time)
+		hours, minutes, seconds = self.convert_timestamp(elapsed_time_millis)
+
+		return ("%d:%d:%d" % (hours, minutes, seconds))
+
+	def total_orders(self, unique=False):
+		"""Aggregate total number of signed orders
+
+		Args:
+			unique (bool): if set to True, duplicate orders will be ignored
+		"""
+		signed_items = self.retrieve_signed_orders(unique=unique)
+		count = len(signed_items)
+
+		return count
+
+	def retrieve_signed_orders(self, batch=False, unique=False):
+		"""Return all signed order items
+
+		Args:
+			batch (bool): whether to preserve the groups in which orders appear
+			unique (bool): if set to True, duplicate orders will be ignored, but
+						   if batch is set to True, only duplicates within the batch will
+						   be ignored. Note: Using unique=True will unpreserve
+						   the order in which signed orders appear.
+		"""
+		signed_items_tracker = self.signed_item_tracker_data
+		signed_items = []
+		for timestamp in list(signed_items_tracker.keys()):
+			signed_items_batch = signed_items_tracker[timestamp]
+			if batch:
+				if unique:
+					signed_items_batch = list(set(signed_items_batch))
+				signed_items.append(signed_items_batch)
+			else:
+				signed_items += signed_items_batch
+
+		if unique and not batch:
+			signed_items = list(set(signed_items))
+
+		return signed_items
+
+	def total_recommendations(self, unique=True):
+		"""Aggregate total number of recommendations
+
+		Args:
+			unique (bool): if set to True, duplicate recommendations will be ignored
+		"""
+		# Recommender specifically triggered when search is '' and by Find Orders when search query is ''
+		count = self.total_search_results(search_modes=["", "FindOrders"], search_query="", unique=unique)
+
+		return count
+
+
+	def total_search_results(self, search_modes=[], search_query=None, unique=True):
+		"""Aggregate total number of results for particular search modes
+
+		Args:
+			search_modes (iterable): search actions of {"", "FindOrders", "OrderSets",
+									 "Diagnoses"} to filter by.
+			search_query (str): search query used for result (substring matching is used)
+			unique (bool): if set to True, duplicate results are ignored
+		"""
+		if not search_modes:
+			search_modes = list(self.results_tracker_data.keys())
+		results = self.retrieve_results(search_modes=search_modes, search_query=search_query, batch=False, unique=unique)
+		count = len(results)
+
+		return count
+
+
+	def retrieve_results(self, search_modes=[], search_query=None, batch=False, unique=True):
+		"""Return results for particular search modes
+
+		Args:
+			search_modes (iterable): search actions of {"", "FindOrders", "OrderSets",
+									 "Diagnoses"} to filter by.
+			search_query (str): search query used for result (substring matching is used)
+			batch (bool): whether to preserve the groups in which recommendations appear
+			unique (bool): if set to True, duplicate recommendations will be ignored,
+						   if batch is set to True, only duplicates within each batch will
+						   be ignored. Note: Using unique=True will unpreserve
+						   the order in which recommendations appear.
+		"""
+		results_tracker = self.results_tracker_data
+		if not search_modes:
+			search_modes = list(results_tracker.keys())
+		results = []
+		for mode in search_modes:
+			try:
+				mode_results = results_tracker[mode]
+				for obj in mode_results:
+					state = obj['state']
+					#	  Wild card search 					Recommendations search											Other results search
+					if (search_query is None) or (search_query == "" and state['searchQuery'] == "") or (search_query != "" and search_query in state['searchQuery']):
+						items = obj['items']
+						for data_type in list(items.keys()):
+							items_list = items[data_type]
+							if not (type(items_list) is list): continue
+							# Extract item ids
+							items_list = list([item.split('|')[0] for item in items_list])
+							if batch:
+								if unique:
+									# Remove duplicates within batch
+									items_list = list(set(items_list))
+								results.append(items_list)
+							else:
+								results += items_list
+			except Exception as e:
+				print(e)
+				continue
+
+		if unique and not batch:
+			results = list(set(results))
+
+		return results
+
+	def number_signed_in_recommended(self, perc=False):
+		"""Number of signed orders that appear in recommender
+		Args:
+			perc (bool): whether to return frequency as percentage (over all signed orders)
+		"""
+		signed_orders = self.retrieve_signed_orders(unique=True, batch=False)
+		recommendations = self.retrieve_results(search_modes=[""], batch=False, unique=True)
+		# Map signed_orders to their ids (to be intersected with recommendations)
+		signed_orders_ids = list([orders.split('|')[0] for orders in signed_orders])
+		intersection = set(signed_orders_ids) & set(recommendations)
+		count = len(intersection)
+		if perc:
+			count = float(count) / len(signed_orders)
+
+		return count
+
+	def get_manually_searched_options(self):
+		"""Get list of result items that were presented from a manual search
+
+		Returns:
+			results (list): list of item dicts corresponding to items that were
+				shown after a manual search (not from recommender)
+		"""
+		results = list([item for item in self.results_collection if
+						(item['mode'] != '' and item['mode'].casefold() != 'related') and item['searchQuery'] != ''])
+		return results
+
+	def get_recommended_options(self, include_related=True):
+		"""Get list of result items that were presented by recommender
+
+		Args:
+			include_related (bool): whether to include items that were shown
+				after clicking 'related' button for a particular clinical item
+
+		Returns:
+			results (list): list of item dicts corresponding to items that were
+				shown by recommender
+		"""
+		filter_fn = (lambda item: item['mode'] == '' or item['searchQuery'] == '')
+		if include_related:
+			filter_fn = (lambda item: (item['mode'] == '' or item['searchQuery'] == '') or (item['mode'].casefold() == 'related'))
+		results = list(filter(filter_fn, self.results_collection))
+		return results
+
+	def _merge_intersection_items(self, merged_dict, result_item):
+		"""Merge the two dicts corresponding to intersection items
+
+		Args:
+			merged_dict (dict): base dict where attributes of result item will
+				be merged into
+			result_dict (dict): dict corresponding to result item
+		"""
+		merged_dict.update(result_item)  # Note that result listIndex will overwrite that in merged_dict!
+
+	def _result_signed_intersection(self, result_items, signed_items, filter_fn):
+		"""Get the intersection between result items and signed items
+
+		Args:
+			result_items (list): list of result item dicts
+			signed_items (list): list of signed item dicts
+			filter_fn (fn): filter function called on result, signed item pair,
+				where evaluation to True results in items being considered
+				compatible and 'merged'. This funciton should take two items,
+				one result item and one signed item, as parameters
+
+		Returns:
+			intersection (list): list of merged item dicts of items that are
+				in both item lists, according to provided filter function
+		"""
+		intersection = []
+		for signed_item in signed_items:
+			merged_dict = dict(signed_item)
+			found = False
+			for result_item in result_items:  # Use some form of binary search?
+				if filter_fn(result_item, signed_item):
+					self._merge_intersection_items(merged_dict, result_item)
+					# Corresponding result item found, stop searching
+					found = True
+					break;
+			if found:
+				intersection.append(merged_dict)
+		return intersection
+
+	def get_signed_from_manual_search(self):
+		"""Get item dicts for items that were signed from manual-search results
+
+		Returns:
+			signed_from_manual (list): list of item dicts that were signed from
+			 	manually-searching
+		"""
+		# Define filter function for determining intersection
+		# This function checks that result item and signed item have same
+		# clinicalItemId, listIndex (though s_item index will be ahead), and location source        r_item listIndex starts from 1, but s_item listIndex starts from 0
+		filter_fn = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and (r_item['listIndex'] == s_item['listIndex'] - 1 or r_item['listIndex']+1 == s_item['listIndex'] - 1) and r_item['source'] == s_item['source']
+
+		if self.version == 'v5':  # version 5 of tracker correctly tracks order set items in results
+			# Get manually-searched signed items
+			manual_search_results = self.get_manually_searched_options()
+			signed_from_manual = self._result_signed_intersection(manual_search_results, self.signed_orders_collection, filter_fn=filter_fn)
+		# elif self.version == 'v4':  # versions 4 of the tracker only tracks item sources in the signed orders collection
+		else:
+			signed_from_manual = list([s_item for s_item in self.signed_orders_collection if s_item['source'] == 'data'])
+		return signed_from_manual
+
+	def get_signed_from_recommended(self, include_related=True):
+		"""Get item dicts for items that were signed from recommender results
+
+		Args:
+			include_related (bool): whether to include items that were shown
+				after clicking 'related' button for a particular clinical item
+		Returns:
+			signed_from_manual (list): list of item dicts that were signed from
+			 	recommender options
+		"""
+		# Define filter function for determining intersection
+		# This function checks that result item and signed item have same
+		# clinicalItemId, listIndex (though s_item index will be ahead), and location source
+		filter_fn = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and (r_item['listIndex'] == s_item['listIndex'] - 1 or r_item['listIndex']+1 == s_item['listIndex'] - 1) and r_item['source'] == s_item['source']
+
+		if self.version == 'v5':  # version 5 of tracker correctly tracks order set items in results
+			# Get recommended signed items
+			recommended_results = self.get_recommended_options(include_related=include_related)
+			signed_from_recommended = self._result_signed_intersection(recommended_results, self.signed_orders_collection, filter_fn)
+		# elif self.version == 'v4':  # versions 4 of the tracker only tracks item sources in the signed orders collection
+		else:
+			signed_from_recommended = list([s_item for s_item in self.signed_orders_collection if s_item['source'] != 'data'])
+		return signed_from_recommended
+
+	def get_signed_missed_recommended(self, include_related=True):
+		"""Get item dicts for items that were signed from manual-search results,
+		but had appeared previously from recommended results
+
+		Args:
+			include_related (bool): whether to include items that were shown
+				after clicking 'related' button for a particular clinical item
+
+		Returns:
+			missed_items (list): list of item dicts that appeared from the
+				recommender, but were ultimately chosen from a manual search
+		"""
+		# Define filter function for determining intersection between signed
+		# items and manually-searched items
+		# This function checks that result item and signed item have same
+		# clinicalItemId, listIndex (though s_item index will be ahead), and location source
+		filter_fn_manual = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['listIndex'] == s_item['listIndex'] - 1 and r_item['source'] == s_item['source']
+		# For older versions of tracker data:
+		filter_fn_manual_older = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['storeTime'] == s_item['storeTime'] and r_item['source'] == s_item['source']
+		# Define filter function for determining intersection between signed
+		# items from manual search and recommomended
+		# This function checks that result item and signed item have same
+		# clinicalItemId and that the listIndex of recommended item is less
+		# than or equal to that of the signed item. This ignores
+		# results of the item that appeared after the item was already signed.
+		filter_fn_recommended = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['listIndex'] <= s_item['listIndex'] - 1
+		# For older versions of tracker data:
+		filter_fn_recommended_older = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['storeTime'] <= s_item['storeTime']
+
+		# First get the manually searched signed items
+		manual_search_results = self.get_manually_searched_options()
+		if self.version in ['v4', 'v5']:
+			signed_from_manual = self._result_signed_intersection(manual_search_results, self.signed_orders_collection, filter_fn=filter_fn_manual)
+		else:
+			signed_from_manual = self._result_signed_intersection(manual_search_results, self.signed_orders_collection, filter_fn=filter_fn_manual_older)
+		# Next get the manually searched items that also appeared previously
+		# in recommended results
+		recommended_results = self.get_recommended_options(include_related=include_related)
+		if self.version in ['v4', 'v5']:
+			missed_items = self._result_signed_intersection(recommended_results, signed_from_manual, filter_fn_recommended)
+		else:
+			missed_items = self._result_signed_intersection(recommended_results, signed_from_manual, filter_fn_recommended_older)
+		return missed_items
+
+	def get_unique(self, collection, key_fn):
+		""""Return list of unique values, where the value of the objects in the
+		collection is determined by the key function. Note that any order
+		in the collection will not be preserved
+
+		Args:
+			collection (list): a list of objects to be filtered
+			key_fn (fn): function that takes an object from collection and
+				returns a value that will be used to consider uniqueness
+
+		Returns:
+			unique_vals (list): list of unique values extracted from collection
+		"""
+		values = list(map(key_fn, collection))
+		unique_vals = list(set(values))
+		return unique_vals
+
+	def _clinical_item_id_key_fn(self, item):
+		"""Returns clinical item value from item object"""
+		return item['clinicalItemId']
+
+	def construct_timeline(self):
+		"""Flatten trackers and order all events by timestamp"""
+		# Flatten eventTracker
+		event_tracker = self.event_tracker_data
+		events = []
+		for event_name in list(event_tracker.keys()):
+			for event_occurrence in event_tracker[event_name]:
+				event_time = event_occurrence['eventTime']
+				events.append((event_time, event_name, event_occurrence))
+		# Sort events by timestamp
+		sorted_events = sorted(events, key=lambda x: x[0])
+		return sorted_events
+
+	def visualize_timeline(self, timeline):
+		"""Draw timeline
+
+		Args:
+			timeline (iterable): iterable of tuples (position, name, info_dict)
+		"""
+		times = list([datetime.datetime.fromtimestamp((x[0] - timeline[0][0])/1000.0) for x in timeline])
+		names = list([x[1] for x in timeline])
+		names_unique = sorted(list(set(list([x for x in names]))))
+
+
+		norm = plt.Normalize(1,4)
+		c = np.random.randint(1,5,size=len(names_unique))
+		cmap = plt.cm.inferno
+		fig, ax = plt.subplots(figsize=(15,4))
+
+		ax.yaxis.set_visible(False)
+		ax.spines['right'].set_visible(False)
+		ax.spines['left'].set_visible(False)
+		ax.spines['top'].set_visible(False)
+		ax.xaxis.set_ticks_position('bottom')
+
+		ax.get_yaxis().set_ticklabels([])
+		ms = pd.to_timedelta("1", unit='ms')
+		plt.xlim(times[0] - 5*ms, times[-1] + 5*ms)
+
+		plots = []
+
+		for i, name in enumerate(names_unique):
+			named_events = list([x for x in timeline if x[1] == name])
+			named_times = list([datetime.datetime.fromtimestamp((x[0] - timeline[0][0])/1000.0) for x in named_events])
+			sc = plt.scatter(named_times, [i*5+1]*len(named_times), marker='o', s=100, alpha=0.8)
+			plots.append(sc)
+
+		ax.legend(plots, names_unique)
+		plt.show()
+
+
+def aggregate_simulation_data(data_home, output_path, append_to_existing=False, source_path=None):
+	"""Generate 'flat' csv file or append to existing csv file from directory of simulation (json) files
+
+	Args:
+		data_home (str): path to directory containing simulation json files
+		output_path (str): path to where csv file should be saved to
+		append_to_existing (bool): whether to append aggregated data from data_home to existing csv file
+		source_path (str): if append_to_existing is True, source_path is path to used csv file
+	"""
+	# Filter out files that do not end with .json
+	filenames = [f for f in os.listdir(data_home) if f.endswith('.json')]
+	# Append directory path to filenames
+	filenames = list([os.path.join(data_home, f) for f in filenames])
+	headers = ["user", "patient", "start_time", "elapsed_time", "total_num_clicks", "num_note_clicks", "num_results_review_clicks", "recommended_options", "unique_recommended_options", "manual_search_options", "total_orders", "orders_from_recommender", "orders_from_manual_search", "orders_from_recommender_missed"]
+	if not append_to_existing:
+		# Create initial csv file
+		with open(output_path, 'w', newline='') as out_csv:
+			file_writer = csv.writer(out_csv)
+			file_writer.writerow(headers)
+		source_path = output_path
+	# Use same logic as appending to existing (now that there is an initial csv to append to)
+	with open(source_path, 'a', newline='') as out_csv:
+		file_writer = csv.writer(out_csv)
+		for filename in filenames:
+			print("Processing {}".format(filename))
+			fields = []
+			# Instantiate SimulationAnalyzer for current data file
+			siman = SimulationAnalyzer(filename)
+			# Add current base metrics
+			fields.append(siman.user)  # "user"
+			fields.append(siman.patient)  # "patient"
+			fields.append(siman.start_time)  # "start_time"
+			fields.append(siman.elapsed_time())  # "elapsed_time"
+			fields.append(siman.number_mouse_clicks_all())  # "total_num_clicks"
+			# fields.append(siman.number_mouse_clicks(filters=['FindOrders']))  # "num_manual_searches"
+			fields.append(siman.number_mouse_clicks(filters=['Notes']))  # "num_note_clicks"
+			fields.append(siman.number_mouse_clicks(filters=['ResultsReview']))  # "num_results_review_clicks"
+			fields.append(len(siman.get_recommended_options()))  # "recommended_options"
+			fields.append(len(siman.get_unique(siman.get_recommended_options(), key_fn=siman._clinical_item_id_key_fn)))  # "unique_recommended_options"
+			fields.append(len(siman.get_manually_searched_options()))  # "manual_search_options"
+			fields.append(len(siman.signed_orders_collection))  # "total_orders"
+			fields.append(len(siman.get_signed_from_recommended()))  # "orders_from_recommender"
+			fields.append(len(siman.get_signed_from_manual_search()))  # "orders_from_manual_search"
+			fields.append(len(siman.get_signed_missed_recommended()))  # "orders_from_recommender_missed"
+			file_writer.writerow(fields)
+
+			# Delete SimulationAnalyzer to free any useless memory
+			del siman
+
+
+def main(argv):
+	parser = OptionParser()
+	parser.add_option("-f", "--file",  dest="data_file", help="JSON file containing collected data.")
+	parser.add_option("-a", "--agg", dest="aggregate", help="Create aggregate csv file?")
+	parser.add_option("-d", "--agg_home", dest="data_home", help="path to directory containing simulation json files")
+	parser.add_option("-o", "--agg_output", dest="output_path", help="path to where csv file should be saved to")
+	parser.add_option("-s", "--agg_source", dest="source_path", help="source_path is path to existing csv file to append to")
+	options, args = parser.parse_args(argv[1:])
+
+	if not options.aggregate:
+		siman = SimulationAnalyzer(options.data_file)
+		print("Total mouse clicks: ", siman.number_mouse_clicks_all())
+		print("Click summary: ", siman.click_summary(perc=False))
+		print("Elapsed time: ", siman.elapsed_time())
+		print("Total signed orders: ", siman.total_orders())
+		print("Signed order ids: ", siman.retrieve_signed_orders())
+		print("Signed order batches:", siman.retrieve_signed_orders(batch=True))
+		print("Total recommendations: ", siman.total_recommendations())
+		print("Recommendation ids: ", siman.retrieve_results(search_modes=["", "FindOrders"], batch=False, search_query=""))
+		print("Recommendation batches: ", siman.retrieve_results(search_modes=["", "FindOrders"], batch=True, search_query=""))
+		print("Signed/Recommended overlap: ", siman.number_signed_in_recommended())
+		print("Total results:", siman.total_search_results())
+		print("Timeline data: ", siman.construct_timeline())
+		print("Timeline view: ", siman.visualize_timeline(siman.construct_timeline()))
+	else:
+		data_home = options.data_home
+		output_path = options.output_path
+		append_to_existing = False
+		source_path = None
+		if options.source_path:
+			append_to_existing = True
+			source_path = options.source_path
+		aggregate_simulation_data(data_home, output_path, append_to_existing=append_to_existing, source_path=source_path)
+
+
+if __name__ == '__main__':
+	main(sys.argv)
diff --git a/medinfo/cpoe/cpoeSim/analysis/__init__.py b/medinfo/cpoe/cpoeSim/analysis/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/analysis/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/medinfo/cpoe/cpoeSim/analysis/make_usage_report.py b/medinfo/cpoe/cpoeSim/analysis/make_usage_report.py
new file mode 100644
index 00000000..734d4877
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/analysis/make_usage_report.py
@@ -0,0 +1,165 @@
+import json
+import sys
+import time
+from optparse import OptionParser
+
+import pandas as pd
+
+from medinfo.cpoe.cpoeSim.analysis.CPOETrackerAnalysis import aggregate_simulation_data
+from medinfo.common.Const import COMMENT_TAG, VALUE_DELIM
+from medinfo.common.Util import log
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.cpoe.cpoeSim.SimManager import SimManager
+
+
+def make_usage_report(data_home, graders, out_file, survey_file=None):
+    aggregate_simulation_data(data_home, output_path=out_file)
+    csv = pd.read_csv(out_file)
+
+    add_calculated_fields_to(csv)
+    csv = add_sim_case_column_to(csv)
+    csv, grade_columns_names = add_grades_to(csv, graders)
+
+    # output (without row numbers)
+    columns_order = ['user', 'sim_case', 'sim_patient_id'] + grade_columns_names \
+                    + ['recommenderOn', 'elapsed_time', 'total_num_clicks', 'num_note_clicks',
+                       'num_results_review_clicks', 'recommended_options', 'unique_recommended_options',
+                       'manual_search_options', 'total_orders', 'orders_from_recommender', 'orders_from_manual_search',
+                       'orders_from_recommender_missed', 'ratioOrdersFromRecommenderVsUniqueRecommenderOptions',
+                       'ratioOrdersFromManualSearchVsManualSearchOptions', 'ratioOrdersFromRecommenderVsTotalOrders',
+                       'ratioOrdersFromManualSearchVsTotalOrders']
+
+    if survey_file:
+        csv = add_resident_column(columns_order, csv, survey_file)
+
+    # sort by user, sim_case, sim_patient_id combo
+    csv = csv.sort_values(['user', 'sim_case', 'sim_patient_id'])
+
+    csv.to_csv(out_file, index=False, columns=columns_order)
+
+
+def add_calculated_fields_to(csv):
+    csv['recommenderOn'] = [1 if recommended_option > 0 else 0 for recommended_option in csv['recommended_options']]
+    csv['ratioOrdersFromRecommenderVsUniqueRecommenderOptions'] = \
+        1.0 * csv['orders_from_recommender'] / csv['unique_recommended_options']
+    csv['ratioOrdersFromManualSearchVsManualSearchOptions'] = \
+        1.0 * csv['orders_from_manual_search'] / csv['manual_search_options']
+    csv['ratioOrdersFromRecommenderVsTotalOrders'] = \
+        1.0 * csv['orders_from_recommender'] / csv['total_orders']
+    csv['ratioOrdersFromManualSearchVsTotalOrders'] = \
+        1.0 * csv['orders_from_manual_search'] / csv['total_orders']
+
+
+def add_sim_case_column_to(csv):
+    query = SQLQuery()
+    query.addSelect("sim_patient_id")
+    query.addSelect("sim_case_name as sim_case")
+    query.addFrom("sim_grading_key sgk")
+    query.addJoin("sim_patient_order spo",
+                  "sgk.clinical_item_id = spo.clinical_item_id and spo.sim_state_id = sgk.sim_state_id")
+    query.addGroupBy("sim_patient_id")
+    query.addGroupBy("sim_case_name")
+    case_names = DBUtil.execute(str(query))
+
+    # merge sim_case column
+    csv = pd.merge(csv, pd.DataFrame(case_names, columns=['sim_patient_id', 'sim_case']),
+                   left_on='patient', right_on='sim_patient_id')
+    return csv
+
+
+def add_grades_to(csv, graders):
+    sim_manager = SimManager()
+    grade_columns_ordered = []
+    for i, grader in enumerate(sorted(graders)):
+        grade_column = 'grade ({})'.format(grader)
+        grades = sim_manager.grade_cases(csv['patient'].values.astype(dtype=object), grader)
+
+        # validate most_active_user_id == most_graded_user_id
+        inconsistent_users = [grade for grade in grades if grade['most_active_user_id'] != grade['most_graded_user_id']]
+        if inconsistent_users:
+            for inconsistent_user in inconsistent_users:
+                log.warn("Case {} grading issue: most_active_user_id <> most_graded_user_id ({} <> {})!"
+                         .format(inconsistent_user['sim_patient_id'],
+                                 inconsistent_user['most_active_user_id'],
+                                 inconsistent_user['most_graded_user_id']))
+
+        grade_results = pd.DataFrame(grades, columns=['sim_patient_id', 'most_active_user_id', 'total_score'])
+        patient_id_column = 'grade{}_patient_id'.format(i)
+        grade_results.rename(columns={'sim_patient_id': patient_id_column}, inplace=True)
+        csv = pd.merge(csv, grade_results,
+                       left_on=['patient', 'user'], right_on=[patient_id_column, 'most_active_user_id'])
+        csv.rename(columns={'total_score': grade_column}, inplace=True)
+        grade_columns_ordered.append(grade_column)
+
+    # don't need grader name if there's only 1 grade
+    # if len(grade_columns_ordered) == 1:
+    #     csv.rename(columns={grade_columns_ordered[0]: 'grade'}, inplace=True)
+    #     grade_columns_ordered = ['grade']
+
+    return csv, grade_columns_ordered
+
+
+def add_resident_column(columns_order, csv, survey_file):
+    survey_responses = pd.read_csv(survey_file)
+    # retrieve sim_user_ids
+    query = SQLQuery()
+    query.addSelect("sim_user_id")
+    query.addSelect("name")
+    query.addFrom("sim_user")
+    query.addWhereIn("name", survey_responses['Physician User Name'])
+    user_ids = DBUtil.execute(query)
+    survey_responses = pd.merge(survey_responses, pd.DataFrame(user_ids, columns=['sim_user_id', 'name']),
+                                left_on='Physician User Name', right_on='name')
+    csv = pd.merge(csv, survey_responses,
+                   left_on='user', right_on='sim_user_id')
+    columns_order.insert(1, 'resident')
+    return csv
+
+
+def main(argv):
+    """Main method, callable from command line"""
+    usage_str = "usage: %prog [options] <inputJsonDataFolder> <outputFile>\n" \
+                "   <outputFile> CSV file with the usage report."
+
+    parser = OptionParser(usage=usage_str)
+    parser.add_option("-g", "--graders", dest="graders", help="Comma-separated list of graders to use for grading")
+    parser.add_option("-s", "--survey", dest="survey_file", help="Path to a survey CSV file (used for adding 'resident' column)")
+
+    (options, args) = parser.parse_args(argv[1:])
+
+    log.info("Starting: " + str.join(" ", argv))
+    timer = time.time()
+    summary_data = {"argv": argv}
+
+    grader_ids = set()
+    if not options.graders:  # graders is a mandatory parameter
+        print("No graders given. Cannot grade patient cases. Exiting.\n")
+        parser.print_help()
+        sys.exit()
+    else:
+        grader_ids.update(options.graders.split(VALUE_DELIM))
+
+    survey_file = None
+    if options.survey_file:
+        survey_file = options.survey_file
+
+    if len(args) < 2:  # we need input and output files given
+        print("Given parameters are not enough. Exiting.\n")
+        parser.print_help()
+        sys.exit()
+
+    input_folder = args[0]
+    output_filename = args[1]
+
+    # Print comment line with arguments to allow for deconstruction later as well as extra results
+    print(COMMENT_TAG, json.dumps(summary_data))
+
+    make_usage_report(input_folder, grader_ids, output_filename, survey_file)
+
+    timer = time.time() - timer
+    log.info("%.3f seconds to complete", timer)
+
+
+if __name__ == "__main__":
+    main(sys.argv)
diff --git a/medinfo/cpoe/cpoeSim/analysis/sampleQueries.sql b/medinfo/cpoe/cpoeSim/analysis/sampleQueries.sql
new file mode 100644
index 00000000..fc28e49f
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/analysis/sampleQueries.sql
@@ -0,0 +1,14 @@
+-- Find the top most common clinical orders per case
+select sim_case_name, ci.description, count(distinct sim_user_id) numUsers
+from sim_patient as p
+  join sim_patient_order as po
+    using (sim_patient_id)
+  join sim_grading_key as gk
+    using (sim_state_id, clinical_item_id)
+  join clinical_item as ci
+    using (clinical_item_id)
+where sim_grader_id = 'Post-Panel Consolidation'
+and sim_user_id <> 0 -- in (15, 17, 21, 25, 27, 29, 30, 31, 33, 34, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 59, 60, 61, 62, 63, 64, 66, 68, 69, 70, 71, 74, 76, 77, 78, 80, 81)
+and study_list = 'OrderRex.Usability.Scoring' -- sim_patient_id in (76, 75, 74, 73, 82, 80, 79, 78, 77, 81, 90, 5, 88, 50, 91, 96, 95, 100, 94, 97, 114, 113, 112, 111, 115, 121, 120, 119, 118, 122, 132, 131, 130, 129, 133, 142, 141, 140, 139, 143, 149, 148, 147, 146, 150, 158, 157, 156, 155, 159, 165, 164, 163, 162, 166, 173, 172, 171, 170, 174, 181, 180, 179, 177, 182, 187, 186, 185, 184, 188, 195, 199, 193, 192, 196, 206, 205, 204, 203, 207, 213, 212, 211, 210, 214, 231, 230, 229, 227, 233, 239, 238, 237, 236, 240, 249, 248, 247, 246, 250, 263, 255, 258, 257, 261, 270, 269, 268, 267, 271, 278, 277, 276, 275, 279, 285, 287, 283, 282, 286, 294, 293, 292, 291, 296, 302, 301, 300, 299, 303, 319, 321, 317, 316, 320, 327, 326, 325, 324, 328, 340, 339, 341, 342, 343, 350, 349, 348, 347, 351, 358, 357, 356, 355, 359, 365, 364, 363, 362, 366, 372, 371, 370, 369, 373, 380, 379, 378, 377, 381, 386, 387, 385, 388, 389, 395, 394, 393, 392, 396, 402, 401, 400, 399, 403, 406, 407, 405, 404, 408, 417, 416, 415, 414, 418, 424, 423, 422, 421, 426, 432, 431, 430, 427, 433, 453, 452, 451, 450, 454, 460, 459, 458, 457, 461)
+group by sim_case_name, ci.description
+order by sim_case_name, count(distinct sim_user_id) desc, ci.description
diff --git a/medinfo/cpoe/cpoeSim/cpoeSim.md b/medinfo/cpoe/cpoeSim/cpoeSim.md
new file mode 100644
index 00000000..04bb38b4
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/cpoeSim.md
@@ -0,0 +1,8 @@
+# Computerized Provider Order Entry Simulation Tables
+
+## Overview
+Additional tables to capture and represent data related to simulated a physician doing clinical order entry
+into an interactive system with (simulated) patient cases.
+Depends on the clinical_item table framework to represent core (order) data.
+Can setup blank data tables from the schema definitions, but should
+be better off restoring from a prior dev database dump that includes sample patient case data.
diff --git a/medinfo/cpoe/cpoeSim/simdata/Readme.md b/medinfo/cpoe/cpoeSim/simdata/Readme.md
new file mode 100644
index 00000000..406b525f
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/Readme.md
@@ -0,0 +1,19 @@
+Directory contains a dump of latest version of data that drives clinical order entry case simulations.
+Requires a matching clinical_item database to work off of, currently compatible with the STRIDE Inpatient 2009-2014 (5 year) sample. 
+Will NOT automatically be compatible with more recent versions, because the clinical_item_id assignments will be different, 
+though much of this could be remapped if needed.
+
+First setup with a blank schema definition by, which includes a description of the simulation data organization
+  cpoeSim.sql
+
+The restore all of the data dump contents to your database instance. Can use restoreSimTables.sh for convenience.
+
+Some of the data preparation steps necessary to re-create the simulation mapping data (e.g., mapping test orders to expected results)
+is defined in the following script. For new cases, would require some manual inspection of the large table of relations.
+  prepCPOESimulationData.py
+
+If want to add common synonyms for order lookups (e.g., 'BMP' for 'Basic Metabolic Panel', see (and run)
+  stride.clinical_item.ClinicalItemDataLoader.ClinicalItemDataLoader.add_clinical_item_synonyms() 
+
+
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/cpoeSim.sql b/medinfo/cpoe/cpoeSim/simdata/cpoeSim.sql
new file mode 100644
index 00000000..fc4743ec
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/cpoeSim.sql
@@ -0,0 +1,223 @@
+-- Basic simultation user tracking information
+CREATE TABLE sim_user
+(
+	sim_user_id SERIAL NOT NULL,
+	name TEXT NOT NULL
+--How many years since you received your medical degree (MD/DO)?
+--What is your primary area / specialty of clinical practice?
+--List your current or prior medical training and board certifications:
+--What percentage of your time in the past year has been spent practicing outpatient / clinic medicine?
+--What percentage of your time in the past year has been spent practicing inpatient / hospital medicine?
+--Approximately how many patients have you admitted to an inpatient hospital service in the past year?
+
+);
+ALTER TABLE sim_user ADD CONSTRAINT sim_user_pkey PRIMARY KEY (sim_user_id);
+-- INSERT INTO sim_user(sim_user_id,name) VALUES (0,'Default User');
+
+-- Basic simulated patient information
+CREATE TABLE sim_patient
+(
+	sim_patient_id SERIAL NOT NULL,
+	name TEXT NOT NULL,
+	age_years INTEGER NOT NULL,
+	gender TEXT NOT NULL
+);
+ALTER TABLE sim_patient ADD CONSTRAINT sim_patient_pkey PRIMARY KEY (sim_patient_id);
+ALTER TABLE sim_patient ADD COLUMN study_list TEXT;	-- Comma separated list of which studies this patient case belongs to (vs. test cases that should NOT be included in study results). Be sure to include sim_user_id = 0 for analysis as well.
+
+-- Model patient states with transition options
+CREATE TABLE sim_state
+(
+ 	sim_state_id SERIAL NOT NULL,
+ 	name TEXT NOT NULL,
+ 	description TEXT
+);
+ALTER TABLE sim_state ADD CONSTRAINT sim_state_pkey PRIMARY KEY (sim_state_id);
+-- INSERT INTO sim_state(sim_state_id,name, description) VALUES (0,'Default','Default State Info');
+
+-- Record when a patient enters a given state
+CREATE TABLE sim_patient_state
+(
+	sim_patient_state_id SERIAL NOT NULL,
+	sim_patient_id BIGINT NOT NULL,
+	sim_state_id BIGINT NOT NULL,
+	relative_time_start INTEGER NOT NULL,
+	relative_time_end INTEGER -- Redundant to store end-time when can infer from start time of next record, but having both recorded makes for easier retrieval queries
+);
+ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_pkey PRIMARY KEY (sim_patient_state_id);
+ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_patient_fkey FOREIGN KEY (sim_patient_id) REFERENCES sim_patient(sim_patient_id);
+ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+CREATE INDEX index_sim_patient_state_patient ON sim_patient_state(sim_patient_id,relative_time_start,relative_time_end);
+CREATE INDEX index_sim_patient_state_state ON sim_patient_state(sim_state_id);
+
+-- State transition triggers
+CREATE TABLE sim_state_transition
+(
+	sim_state_transition_id SERIAL NOT NULL,
+	pre_state_id BIGINT NOT NULL,
+	post_state_id BIGINT NOT NULL,
+	clinical_item_id BIGINT, -- Clinical Item that occurs in pre-state to trigger transition to post-state
+	time_trigger INTEGER,	-- Alternatively, amount of time (seconds) to pass before triggering state transition
+	description TEXT
+);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_pkey PRIMARY KEY (sim_state_transition_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_unique_key UNIQUE (pre_state_id, clinical_item_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_pre_state_fkey FOREIGN KEY (pre_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_post_state_fkey FOREIGN KEY (post_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_state_transition_pre_state ON sim_state_transition(pre_state_id,clinical_item_id);	-- Should be created implicitly by UNIQUE constraint
+CREATE INDEX index_sim_state_transition_post_state ON sim_state_transition(post_state_id,clinical_item_id);
+
+-- Code Table to record note type information.  For brevity, also track author and service types (Treatment Team?) here
+CREATE TABLE sim_note_type
+(
+	sim_note_type_id INTEGER NOT NULL,
+	name TEXT NOT NULL,
+	priority INTEGER,
+	description TEXT
+);
+ALTER TABLE sim_note_type ADD CONSTRAINT sim_note_type_pkey PRIMARY KEY (sim_note_type_id);
+
+-- Patient Notes
+CREATE TABLE sim_note
+(
+	sim_note_id SERIAL NOT NULL,
+	sim_state_id BIGINT NOT NULL,
+	note_type_id INTEGER,
+	author_type_id INTEGER,
+	service_type_id INTEGER,
+	relative_state_time INTEGER NOT NULL, -- Seconds relative to the simulated patient state's relative_time_start
+	content TEXT NOT NULL
+);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_pkey PRIMARY KEY (sim_note_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_note_type_fkey FOREIGN KEY (note_type_id) REFERENCES sim_note_type(sim_note_type_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_author_type_fkey FOREIGN KEY (author_type_id) REFERENCES sim_note_type(sim_note_type_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_service_type_fkey FOREIGN KEY (service_type_id) REFERENCES sim_note_type(sim_note_type_id);
+CREATE INDEX index_sim_note_state ON sim_note(sim_state_id,relative_state_time);
+
+-- Result data, primarily labs and imaging, with either numeric or text values.  Lookup / code table to organize types of results
+-- Format group_string as cascading categories necessary for sorting and subgrouping (e.g., Labs>Hematology>Automated Blood Count)
+-- Use priority as master sorting index rather than sorting by each category grouping, as alphabetical may not always be desired sort option
+CREATE TABLE sim_result
+(
+	sim_result_id SERIAL NOT NULL,
+	name TEXT NOT NULL,
+	description TEXT,
+	group_string TEXT,
+	priority INTEGER
+);
+ALTER TABLE sim_result ADD CONSTRAINT sim_result_pkey PRIMARY KEY (sim_result_id);
+
+-- Specific values for individual patient states, map to clinical_items reflecting laboratory results
+-- Include flowsheet (vitals) through here as well and just separate out by result group_strings
+CREATE TABLE sim_state_result
+(
+	sim_state_result_id SERIAL NOT NULL,
+	sim_state_id INTEGER NOT NULL,  -- Simulated Patient State identifier that this result should be available for.  Don't assign to specific patients, assign patients to a state that these reflect
+	sim_result_id BIGINT NOT NULL,
+	clinical_item_id BIGINT, -- Map to clinical_items reflecting (laboratory) results
+	num_value FLOAT,
+	num_value_noise FLOAT, -- Option to add +/- noise to reported value in case of repeated testing
+	text_value TEXT,
+	result_flag TEXT
+);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_pkey PRIMARY KEY (sim_state_result_id);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_sim_result_fkey FOREIGN KEY (sim_result_id) REFERENCES sim_result(sim_result_id);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_state_result_state ON sim_state_result(sim_state_id);
+CREATE INDEX index_sim_state_result_sim_result ON sim_state_result(sim_result_id,num_value);
+CREATE INDEX index_sim_state_result_clinical_item ON sim_state_result(clinical_item_id);
+
+-- Many-to-Many mapping to indicate which (lab) orders (referenced as clinical_item_ids) can trigger which results
+--	That way can prespecify what lab results are expected for a simulated patient, but do not release
+--	that information until user orders a respective laboratory test.
+CREATE TABLE sim_order_result_map
+(
+	sim_order_result_map_id SERIAL NOT NULL,
+	clinical_item_id BIGINT NOT NULL, -- Precedent item / Triggering order
+	sim_result_id BIGINT NOT NULL,
+	turnaround_time INTEGER	-- Seconds from the order time until expect to have/release the result
+);
+ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_pkey PRIMARY KEY (sim_order_result_map_id);
+ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_sim_result_fkey FOREIGN KEY (sim_result_id) REFERENCES sim_result(sim_result_id);
+CREATE INDEX index_sim_order_result_map_sim_result ON sim_order_result_map(sim_result_id);
+CREATE INDEX index_sim_order_result_map_clinical_item ON sim_order_result_map(clinical_item_id);
+
+-- Clinical item orders for patients, keyed with start and end dates
+CREATE TABLE sim_patient_order
+(
+    sim_patient_order_id SERIAL NOT NULL,
+    sim_user_id BIGINT NOT NULL,
+    sim_patient_id BIGINT NOT NULL,
+    clinical_item_id BIGINT NOT NULL,
+    relative_time_start INTEGER NOT NULL,	-- Relative to base / zero time of simulated case that the order occurred / started
+    relative_time_end INTEGER	-- Relative to base / zero time of simulated case that order concludes or was discontinued
+);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_pkey PRIMARY KEY (sim_patient_order_id);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_sim_user_fkey FOREIGN KEY (sim_user_id) REFERENCES sim_user(sim_user_id);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_sim_patient_fkey FOREIGN KEY (sim_patient_id) REFERENCES sim_patient(sim_patient_id);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_patient_order_sim_user_patient_date ON sim_patient_order(sim_user_id, sim_patient_id, relative_time_start);	-- Natural sorting option to order by user, patient, then in chronological order of clinical items
+CREATE INDEX index_sim_patient_order_sim_clinical_item ON sim_patient_order(sim_patient_id, clinical_item_id);	-- Natural sorting option to order by patient, then in chronological order of clinical items
+-- Record patient state at the time of the order. 
+-- Denormalized information that could be derived from sim_patient_state, but makes future joins a hassle using relative time information
+ALTER TABLE sim_patient_order ADD COLUMN sim_state_id BIGINT NOT NULL DEFAULT 0;
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+
+
+-- Track how many points different sim_patient_orders (clinical_items) should be worth in different
+--	simulation scenarios (sim_patient_ids).
+CREATE TABLE sim_grading_key
+(
+	sim_grading_key_id SERIAL NOT NULL,
+	sim_grader_id TEXT NOT NULL, -- Doesn't actually point to another table, but just use to uniquely identify who is giving these grades
+	sim_state_id BIGINT NOT NULL,
+	sim_case_name TEXT,	-- In theory could trace back to origin state from transitions instead of this manual marker. But convenient denormalization.
+	clinical_item_id BIGINT NOT NULL, 
+	score INTEGER NOT NULL,
+	confidence INTEGER,
+	group_name TEXT,
+	comment TEXT
+);
+ALTER TABLE sim_grading_key ADD CONSTRAINT sim_grading_key_pkey PRIMARY KEY (sim_grading_key_id);
+--ALTER TABLE sim_grading_key ADD CONSTRAINT sim_grading_key_sim_grader_fkey FOREIGN KEY.... Not actually using a separate sim_grader table, but could consider adding later if complex enough data structure
+ALTER TABLE sim_grading_key ADD CONSTRAINT sim_grading_key_sim_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_grading_key ADD CONSTRAINT sim_grading_key_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_grading_key_sim_state ON sim_grading_key(sim_state_id);	
+CREATE INDEX index_sim_grading_key_clinical_item ON sim_grading_key(clinical_item_id);
+
+
+
+
+-- Populate sim_order_result_map and sim_result with existing data based on queries like below
+
+-- select 
+--    sop.proc_cat_name, sop.proc_id, sop.proc_code, sop.description,
+--    sor.base_name, sor.component_name,
+--    count(ord_num_value) as countValue,
+--    sum(ord_num_value) as sumValue,
+--    sum(ord_num_value*ord_num_value) as sumSquaresValue,
+--    count(extract(epoch from sop.result_time-proc_start_time)) countTurnaround,
+--    sum(extract(epoch from sop.result_time-proc_start_time)) sumTurnaround, 
+--    sum( extract(epoch from sop.result_time-proc_start_time) ^2 ) sumSquaresTurnaround
+-- from stride_order_proc as sop, stride_order_results as sor
+-- where sop.order_proc_id = sor.order_proc_id
+-- group by
+--    sop.proc_cat_name, sop.proc_id, sop.proc_code, sop.description,
+--    sor.base_name, sor.component_name
+
+-- select 
+--    flo_meas_id, flowsheet_name, 
+--    count(flowsheet_value) as countValue, sum(flowsheet_value) as sumValue, sum(flowsheet_value^2) as sumSquaresValue
+-- from stride_flowsheet
+-- group by 
+--    flo_meas_id, flowsheet_name 
+--
+
+-- Calculate result distribution summary statistics
+-- python scripts\CDSS\populateOrderResultStats.py
+
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/createClinicalItemTable.py b/medinfo/cpoe/cpoeSim/simdata/createClinicalItemTable.py
new file mode 100644
index 00000000..a83fb35f
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/createClinicalItemTable.py
@@ -0,0 +1,49 @@
+import csv
+import psycopg2 as pg
+import pandas.io.sql as psql
+import pandas as pd
+
+# TODO need to clean-up and refactor
+
+def createClinicalItemTable():
+    conn = pg.connect("host='localhost' dbname=stride_inpatient_2008_2014 user=postgres password='postgres'")
+
+    cur = conn.cursor()
+
+    with open('results-20190518-103636.csv', 'r') as f:
+        reader = csv.reader(f)
+        cols = next(reader)
+        types = ['int', 'int', 'int', 'bigint', 'text', 'text', 'int', 'int', 'int', 'int', 'int', 'int']
+
+        print('creating clinical_item table...')
+        cur.execute(generateCreateTableStr('clinical_item', cols, types))
+
+        for row in reader:
+            insCmdPre = 'INSERT INTO clinical_item VALUES (' + '\'{}\','*(len(row)-1) + '\'{}\')'
+            insCmd = insCmdPre.format(*[a.replace('\'', '\'\'') for a in row]).replace('\'NA\'', 'NULL')
+            print('\tinserting ' + str(row), len(row), insCmd)
+            cur.execute(insCmd)
+
+    conn.commit()
+    conn.close()
+
+def generateCreateTableStr(name, columns, types):
+    colTypeList = []
+    for i in range(len(columns)):
+        if i == 0:
+            colTypeList.append(columns[i] + ' ' + types[i] + ' PRIMARY KEY, ')
+        else:
+            colTypeList.append(columns[i] + ' ' + types[i] + ', ')
+
+    outStr = 'CREATE TABLE {}({})'.format(name, ''.join(colTypeList)[:-2])
+    return outStr
+
+'''
+headers = ['int64_field_0', 'clinical_item_id', 'clinical_item_category_id', 'external_id', \
+           'name', 'description', 'default_recommend', 'item_count', 'patient_count', \
+           'encounter_count', 'analysis_status', 'outcome_interest']
+types = ['int', 'int', 'int', 'int', 'text', 'text', 'int', 'int', 'int', 'int', 'int', 'int']
+print(generateCreateTableStr('clinical_item', headers, types))
+'''
+
+createClinicalItemTable()
\ No newline at end of file
diff --git a/medinfo/cpoe/cpoeSim/simdata/dumpSimTables.sh b/medinfo/cpoe/cpoeSim/simdata/dumpSimTables.sh
new file mode 100644
index 00000000..80f0ee62
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/dumpSimTables.sh
@@ -0,0 +1,24 @@
+# For backup purposes, core adapted tables (original raw tables from STRIDE separated)
+
+export DB_HOST=localhost
+export DB_PORT=5432
+export DB_DSN=medinfo-5year-time
+export DB_USER=jonc101
+
+# Initialization data on general patient simulation models, deposit into medinfo/cpoe/cpoeSim/simdata
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_note_type -f sim_note_type.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_note -f sim_note.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_result -f sim_result.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_state_result -f sim_state_result.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_order_result_map -f sim_order_result_map.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_state_transition -f sim_state_transition.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_state -f sim_state.dump.sql $DB_DSN
+
+# Sim Grading Key
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_grading_key -f sim_grading_key.dump.sql $DB_DSN
+
+# Specific user records
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_patient -f sim_patient.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_patient_state -f sim_patient_state.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_user -f sim_user.dump.sql $DB_DSN
+pg_dump -h $DB_HOST -p $DB_PORT -U $DB_USER -a -F p -x -O -t sim_patient_order -f sim_patient_order.dump.sql $DB_DSN
diff --git a/medinfo/cpoe/cpoeSim/simdata/extraClinicalItems.sql b/medinfo/cpoe/cpoeSim/simdata/extraClinicalItems.sql
new file mode 100644
index 00000000..a7497e16
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/extraClinicalItems.sql
@@ -0,0 +1,51 @@
+-- Create extra clinical item records needed for certain simulation cases
+
+-- Direct Current Cardioversion for Afib example. No specific clinical_item/order represents the thing
+-- Category 12 = Procedures
+insert into clinical_item(clinical_item_id, clinical_item_category_id, external_id, name, description, default_recommend, analysis_status, unique_count, item_count, patient_count, encounter_count)
+  values (-100, 12, null, 'DCCV', 'Direct Current Cardioversion', 1, 1, 200, 200, 200, 200);
+
+insert into clinical_item_association(clinical_item_association_id, clinical_item_id, subsequent_item_id,
+	count_0, count_3600, count_7200, count_21600, count_43200, count_86400, count_172800, count_345600, count_604800, count_1209600, count_2592000, count_7776000, count_15552000, count_31536000, count_63072000, count_126144000, count_any, time_diff_sum, time_diff_sum_squares,
+	unique_count_0, unique_count_3600, unique_count_7200, unique_count_21600, unique_count_43200, unique_count_86400, unique_count_172800, unique_count_345600, unique_count_604800, unique_count_1209600, unique_count_2592000, unique_count_7776000, unique_count_15552000, unique_count_31536000, unique_count_63072000, unique_count_126144000, unique_count_any, unique_time_diff_sum, unique_time_diff_sum_squares,
+	patient_count_0, patient_count_3600, patient_count_7200, patient_count_21600, patient_count_43200, patient_count_86400, patient_count_172800, patient_count_345600, patient_count_604800, patient_count_1209600, patient_count_2592000, patient_count_7776000, patient_count_15552000, patient_count_31536000, patient_count_63072000, patient_count_126144000, patient_count_any, patient_time_diff_sum, patient_time_diff_sum_squares,
+	encounter_count_0, encounter_count_3600, encounter_count_7200, encounter_count_21600, encounter_count_43200, encounter_count_86400, encounter_count_172800, encounter_count_345600, encounter_count_604800, encounter_count_1209600, encounter_count_2592000, encounter_count_7776000, encounter_count_15552000, encounter_count_31536000, encounter_count_63072000, encounter_count_126144000, encounter_count_any, encounter_time_diff_sum, encounter_time_diff_sum_squares
+	)
+	values
+	(	-150, -100, -100,
+		200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 0.0, 0.0,
+		200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 0.0, 0.0,
+		200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 0.0, 0.0,
+		200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 0.0, 0.0
+	);
+
+-- Associate with admit diagnosis "cardiac dysrhythmia ICD9 = 427" corresponds to clinical_item_id = 41870
+insert into clinical_item_association(clinical_item_association_id, clinical_item_id, subsequent_item_id,
+	count_0, count_3600, count_7200, count_21600, count_43200, count_86400, count_172800, count_345600, count_604800, count_1209600, count_2592000, count_7776000, count_15552000, count_31536000, count_63072000, count_126144000, count_any, time_diff_sum, time_diff_sum_squares,
+	unique_count_0, unique_count_3600, unique_count_7200, unique_count_21600, unique_count_43200, unique_count_86400, unique_count_172800, unique_count_345600, unique_count_604800, unique_count_1209600, unique_count_2592000, unique_count_7776000, unique_count_15552000, unique_count_31536000, unique_count_63072000, unique_count_126144000, unique_count_any, unique_time_diff_sum, unique_time_diff_sum_squares,
+	patient_count_0, patient_count_3600, patient_count_7200, patient_count_21600, patient_count_43200, patient_count_86400, patient_count_172800, patient_count_345600, patient_count_604800, patient_count_1209600, patient_count_2592000, patient_count_7776000, patient_count_15552000, patient_count_31536000, patient_count_63072000, patient_count_126144000, patient_count_any, patient_time_diff_sum, patient_time_diff_sum_squares,
+	encounter_count_0, encounter_count_3600, encounter_count_7200, encounter_count_21600, encounter_count_43200, encounter_count_86400, encounter_count_172800, encounter_count_345600, encounter_count_604800, encounter_count_1209600, encounter_count_2592000, encounter_count_7776000, encounter_count_15552000, encounter_count_31536000, encounter_count_63072000, encounter_count_126144000, encounter_count_any, encounter_time_diff_sum, encounter_time_diff_sum_squares
+	)
+	values
+	(	-160, 41870, -100,
+		0, 0, 0, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 50000.0, 100000.0,
+		0, 0, 0, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 50000.0, 100000.0,
+		0, 0, 0, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 50000.0, 100000.0,
+		0, 0, 0, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 50000.0, 100000.0
+	);
+
+-- Add cardioversion procedure to 1564	IP ATP CARDIOVERSION order set
+insert into item_collection(item_collection_id, section, name, external_id, subgroup)
+  values (-100, 'Procedure', 'IP ATP CARDIOVERSION', 1564, 'Orders');
+
+insert into item_collection_item(item_collection_item_id, item_collection_id, clinical_item_id)
+  values (-100, -100, -100);
+
+
+-- Add specific abbreviations to certain clinical items
+-- Add 'Ultrasound' prefix to all items starting with US
+update clinical_item set description = CONCAT('Ultrasound - ', description) where description like 'US %';
+-- Add 'DCCV' prevfix to direct current cardioversion item
+update clinical_item set description = 'DCCV - Direct Current Cardioversion' where clinical_item_id = -100;
+-- Add '(TTE)' suffix to ECHO - TRANSTHORACIC ECHO +DOPPLER item
+update clinical_item set description = 'ECHO - TRANSTHORACIC ECHO +DOPPLER (TTE)' where clinical_item_id = 61832;
diff --git a/medinfo/db/definition/simdata/prepCPOESimulationData.py b/medinfo/cpoe/cpoeSim/simdata/prepCPOESimulationData.py
similarity index 99%
rename from medinfo/db/definition/simdata/prepCPOESimulationData.py
rename to medinfo/cpoe/cpoeSim/simdata/prepCPOESimulationData.py
index e42f7cac..64ed27c0 100644
--- a/medinfo/db/definition/simdata/prepCPOESimulationData.py
+++ b/medinfo/cpoe/cpoeSim/simdata/prepCPOESimulationData.py
@@ -1,8360 +1,8366 @@
-import sys,os;
-from cStringIO import StringIO;
-from medinfo.common.Util import ProgressDots, stdOpen;
-from medinfo.db import DBUtil;
-from medinfo.db.ResultsFormatter import TabDictReader;
-
-def main(argv):
-
-    conn = DBUtil.connection();
-
-    try:
-        # Note Type code table
-        DBUtil.execute("""
-            INSERT INTO sim_note_type(sim_note_type_id, name) VALUES
-            ( 1,'H&P'),
-            ( 2,'Progress Note'),
-            ( 3,'ED Provider Note'),
-            ( 4,'ED Note'),
-            ( 5,'Clinic Visit'),
-            ( 6,'Telephone Encounter'),
-            ( 7,'Sign Out Note'),
-            ( 8,'Consult'),
-            ( 9,'Consult Follow-up'),
-            (10,'Discharge Summary'),
-            (11,'Operative Report'),
-            (12,'Interagency Form'),
-            (13,'Goals of Care'),
-            (14,'Procedure'),
-
-            (1002,'Critical Care'),
-            (1003,'Emergency Medicine'),
-            (1005,'Physical Therapy'),
-            (1006,'Occupational Therapy'),
-            (1007,'PICC'),
-            (1008,'Dialysis'),
-
-            (1500,'General Medicine'),
-            (1501,'Cardiology'),
-            (1502,'Pulmonary'),
-            (1503,'Gastroenterology'),
-            (1504,'Nephrology'),
-            (1505,'Rheumatology'),
-            (1506,'Allergy and Immunology'),
-            (1507,'Infectious Diseases'),
-            (1508,'Endocrinology'),
-            (1509,'Dermatology'),
-            (1510,'Neurology'),
-            (1511,'Gynecology'),
-            (1512,'Hematology'),
-            (1513,'Oncology'),
-            (1514,'Bone Marrow Transplant'),
-            (1515,'Psychiatry'),
-            (1516,'Palliative'),
-
-            (1600,'General Surgery'),
-            (1601,'Vascular Surgery'),
-            (1602,'Trauma Surgery'),
-            (1603,'Urology'),
-            (1604,'Orthopedics'),
-            (1605,'Interventional Radiology'),
-            (1606,'Transplant'),
-            (1607,'Neurosurgery'),
-            (1608,'Ophthalmology'),
-            (1609,'Pain Management'),
-            (1610,'Thoracic Surgery'),
-
-            (2001,'Physician'),
-            (2002,'Nurse'),
-            (2003,'Physician Assistant'),
-            (2004,'Nurse Practitioner'),
-            (2005,'Case Manager'),
-            (2006,'Social Worker'),
-            (2007,'Physical Therapist'),
-            (2008,'Occupational Therapist'),
-            (2009,'Pharmacist'),
-            (2010,'Dietician'),
-            (2011,'Spiritual Care');
-            """, conn=conn);
-
-        # Vital Signs / Flowsheet
-        dataTextStr = \
-            """sim_result_id\tname\tdescription\tgroup_string\tpriority
-            10\tTemp\tTemperature (F)\tFlowsheet>Vitals\t10
-            20\tPulse\tPulse / Heart Rate (HR)\tFlowsheet>Vitals\t20
-            30\tSBP\tBlood Pressure, Systolic (SBP)\tFlowsheet>Vitals\t30
-            40\tDBP\tBlood Pressure, Diastolic (DBP)\tFlowsheet>Vitals\t40
-            50\tResp\tRespirations (RR)\tFlowsheet>Vitals\t50
-            60\tFiO2\tFraction Inspired Oxygen\tFlowsheet>Vitals\t60
-            70\tUrine\tUrine Output (UOP)\tFlowsheet>Vitals\t70
-            80\tGCS\tGlasgow Coma Scale (GCS)\tFlowsheet>Vitals\t80
-            """;
-        DBUtil.insertFile( StringIO(dataTextStr), "sim_result", delim="\t");
-        simResultIds = list();
-        for model in TabDictReader(StringIO(dataTextStr)):
-            simResultIds.append(int(model["sim_result_id"]));
-        
-        # Map orders for vital signs to results
-        vitalSignsOrderTable = DBUtil.execute("select clinical_item_id from clinical_item where name in ('NUR1025','NUR2116')", conn=conn);
-        orderIds = list();
-        for (itemId,) in vitalSignsOrderTable:
-            orderIds.append(itemId);
-
-        # Persist mappings and define a default turnaround time from orders to vitals results
-        VITALS_TURNAROUND_TIME = 300;   # Simulated seconds until vital sounds ready
-        for orderId in orderIds:
-            for simResultId in simResultIds:
-                insertDict = {"clinical_item_id": orderId, "sim_result_id": simResultId, "turnaround_time": VITALS_TURNAROUND_TIME}
-                DBUtil.insertRow("sim_order_result_map", insertDict, conn=conn);
-
-        # Core Laboratory Results to look for
-        DBUtil.insertFile( StringIO(CORE_LAB_RESULT_DATA_TEXT_STR), "sim_result", delim=";");
-        
-        # Update sim_result table sequence so won't collide with the manually inserted ones
-        DBUtil.execute("SELECT setval('sim_result_sim_result_id_seq', %s)", (1000000,), conn=conn);
-        
-        # Look up clinical items (orders) based on their name / proc_code
-        itemById = DBUtil.loadTableAsDict("clinical_item");
-        itemByName = dict();
-        for clinicalItem in itemById.itervalues():
-            itemByName[clinicalItem["name"]] = clinicalItem;
-
-        # All other (lab) result, link results to orders they can derive from
-        DEFAULT_LAB_TURNAROUND_TIME = 900;  # Default turnaround time to estimate for lab times
-        reader = TabDictReader(StringIO(PROC_RESULT_DATA_TEXT_STR));
-        priorityBase = 1000000; # Base number to assigned default ranking priorities, based on how common the items are
-        prog = ProgressDots();
-        for i, row in enumerate(reader):
-            procCode = row["proc_code"];
-            if procCode in itemByName:
-                orderItem = itemByName[procCode];
-                simResult = {"name": row["base_name"], "description": row["component_name"], "group_string": "%(proc_cat_name)s>Other>%(description)s" % row, "priority": priorityBase+i };
-                searchDict = {"name": row["base_name"], "description": row["component_name"] }; # Key columns to search for items by
-                (simResultId, isNew) = DBUtil.findOrInsertItem("sim_result", searchDict=searchDict, insertDict=simResult );
-
-                orderResultMap = {"clinical_item_id": orderItem["clinical_item_id"], "sim_result_id": simResultId, "turnaround_time": DEFAULT_LAB_TURNAROUND_TIME};
-                DBUtil.insertRow("sim_order_result_map", orderResultMap, conn=conn);
-            else:
-                print >> sys.stderr, "Unable to find item for Proc Code: %s" % procCode;
-            prog.update();
-        prog.printStatus();
-    
-        conn.commit();    
-    finally:
-        conn.close();
-
-CORE_LAB_RESULT_DATA_TEXT_STR = \
-"""sim_result_id;group_string;name;description;priority
-11000;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;WBC;WBC;11000
-11010;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;HGB;HEMOGLOBIN;11010
-11020;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;HCT;HEMATOCRIT;11020
-11030;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;PLT;PLATELET COUNT;11030
-11040;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;MCV;MCV;11040
-11050;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;RDW;RDW;11050
-11060;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;RBC;RBC;11060
-11070;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;MCH;MCH;11070
-11080;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;MCHC;MCHC;11080
-11110;LAB BLOOD ORDERABLES>Hematology>Differential;RETIC;RETIC, %;11110
-11120;LAB BLOOD ORDERABLES>Hematology>Differential;NEUT;NEUT, %;11120
-11130;LAB BLOOD ORDERABLES>Hematology>Differential;LYM;LYM, %;11130
-11140;LAB BLOOD ORDERABLES>Hematology>Differential;MONO;MONO, %;11140
-11150;LAB BLOOD ORDERABLES>Hematology>Differential;EOS;EOS, %;11150
-11160;LAB BLOOD ORDERABLES>Hematology>Differential;BASO;BASO, %;11160
-11210;LAB BLOOD ORDERABLES>Hematology>Differential;RETICAB;RETIC, ABS [AUTORETIC];11210
-11220;LAB BLOOD ORDERABLES>Hematology>Differential;NEUTAB;NEUT, ABS;11220
-11230;LAB BLOOD ORDERABLES>Hematology>Differential;BAND;BAND NEUTROPHILS;11230
-11240;LAB BLOOD ORDERABLES>Hematology>Differential;LYMAB;LYM, ABS;11240
-11250;LAB BLOOD ORDERABLES>Hematology>Differential;MONOAB;MONO, ABS;11250
-11260;LAB BLOOD ORDERABLES>Hematology>Differential;EOSAB;EOS, ABS;11260
-11270;LAB BLOOD ORDERABLES>Hematology>Differential;BASOAB;BASOS, ABS;11270
-12005;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;HEPAR;HEPARIN ACTIVITY LEVEL;12005
-12010;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;DDIMER;D-DIMER;12010
-12020;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;FIBRINOGEN;FIBRINOGEN;12020
-12030;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;PT;PROTHROMBIN TIME;12030
-12040;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;INR;INR;12040
-12050;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;PTT;PART. THROMBOPLASTIN TIME;12050
-12060;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;TT;THROMBIN TIME;12060
-13010;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;NA;SODIUM, SER/PLAS;13010
-13020;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;K;POTASSIUM, SER/PLAS;13020
-13030;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CL;CHLORIDE, SER/PLAS;13030
-13040;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CO2;CO2, SER/PLAS;13040
-13050;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;BUN;UREA NITROGEN,SER/PLAS;13050
-13060;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CR;CREATININE, SER/PLAS;13060
-13070;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;GLU;GLUCOSE, SER/PLAS;13070
-13080;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;AG;ANION GAP;13080
-13090;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CA;CALCIUM, SER/PLAS;13090
-13100;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CAION;CALCIUM, IONIZED;13100
-13110;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;MG;MAGNESIUM, SER/PLAS;13110
-13120;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;PHOS;PHOSPHORUS, SER/PLAS;13120
-13210;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TBIL;TOTAL BILIRUBIN;13210
-13220;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;DBIL;CONJUGATED BILI;13220
-13230;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;IBIL;UNCONJUGATED BILIRUBIN;13230
-13240;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;AST;AST (SGOT), SER/PLAS;13240
-13250;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ALT;ALT (SGPT), SER/PLAS;13250
-13260;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ALKP;ALK P\'TASE, TOTAL, SER/PLAS;13260
-13270;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ALB;ALBUMIN, SER/PLAS;13270
-13280;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TP;PROTEIN, TOTAL, SER/PLAS;13280
-13290;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;GLOB;GLOBULIN;13290
-13310;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;PREALBUMIN;PREALBUMIN;13310
-13320;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;GGT;GAMMA-GLUTAMYL TRANS (GGT);13320
-13330;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;OSMOL;OSMOLALITY, SERUM;13330
-13410;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;URIC;URIC ACID, SER/PLAS;13410
-13420;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LDH;LDH, TOTAL, SER/PLAS;13420
-13430;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;HAPTO;HAPTOGLOBIN;13430
-13440;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FOLATE;FOLIC ACID (FOLATE);13440
-13450;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;B12;VITAMIN B12;13450
-13460;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FERRITIN;FERRITIN;13460
-13470;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FE;IRON, TOTAL;13470
-13480;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TRFRN;TRANSFERRIN;13480
-13490;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TRFSAT;TRANSFERRIN SAT;13490
-13502;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;PROCTL;PROCALCITONIN;13510
-13504;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LACWBL;LACWBL;13520
-13506;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LAC;LACTIC ACID;13530
-13510;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CKMB;CK, MB (MASS);13510
-13520;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CK;CK, TOTAL;13520
-13530;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TNI;TROPONIN I;13530
-13540;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;BNP;NT-PROBNP;13540
-13610;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CHOL;CHOLESTEROL, TOTAL;13610
-13620;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CHOLHDL;CHOLESTEROL/HDL RATIO;13620
-13630;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;HDL;HDL CHOLESTEROL;13630
-13640;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LDL;DIRECT LDL CHOL;13640
-13650;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;NHDL;NON-HDL CHOL, CALC;13650
-13660;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TGL;TRIGLYCERIDE, SER/PLAS;13660
-13670;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;A1C;HEMOGLOBIN A1C;13670
-13710;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;DIG;DIGOXIN;13710
-13720;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;NH3;AMMONIA, PLASMA;13720
-13730;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;AMYLASE;AMYLASE, SERUM;13730
-13740;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LIPASE;LIPASE;13740
-13750;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TSH;TSH;13750
-13760;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FT4;FREE T4;13760
-13770;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CORT;CORTISOL, SERUM;13770
-13780;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CRP;C-REACTIVE PROTEIN;13780
-13790;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ESR;ESR, (MANUAL);13790
-13810;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ACETA;ACETAMINOPHEN;13810
-13820;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;SALICYLATE;SALICYLATE;13820
-13830;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ETOH;ETHANOL;13830
-13840;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;OSMGAP;OSMOLAL GAP;13840
-15010;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;PHA;PH (A);15010
-15020;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;PCO2A;PCO2 (A);15020
-15030;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;PO2A;PO2 (A);15030
-15040;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;FIO2;FIO2 (FIO2);15040
-15050;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Venous;PHV;PH (V);15050
-15060;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Venous;PCO2V;PCO2 (V);15060
-15070;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Venous;PO2V;PO2 (V);15070
-16010;POINT OF CARE TESTING>Blood Gas, Arterial;PHA;PH (A), ISTAT;16010
-16020;POINT OF CARE TESTING>Blood Gas, Arterial;PCO2A;PCO2 (A), ISTAT;16020
-16030;POINT OF CARE TESTING>Blood Gas, Arterial;PO2A;PO2 (A), ISTAT;16030
-16040;POINT OF CARE TESTING>Blood Gas, Arterial;O2SATA;O2 SATURATION, ISTAT;16040
-16050;POINT OF CARE TESTING>Blood Gas, Arterial;FIO2;FIO2;16050
-16060;POINT OF CARE TESTING>Blood Gas, Venous;PHV;PH (V), ISTAT;16060
-16070;POINT OF CARE TESTING>Blood Gas, Venous;PCO2V;PCO2 (V), ISTAT;16070
-16080;POINT OF CARE TESTING>Blood Gas, Venous;PO2V;PO2 (V), ISTAT;16080
-16090;POINT OF CARE TESTING>Blood Gas, Venous;O2SATV;O2 SATURATION, ISTAT;16090
-16100;POINT OF CARE TESTING>Blood Gas, Venous;LAC;LACTATE, ISTAT;16100
-17010;URINE ORDERABLES>Urinalysis;UCOL;COLOR;17010
-17020;URINE ORDERABLES>Urinalysis;UCLAR;CLARITY;17020
-17030;URINE ORDERABLES>Urinalysis;SPG;SPECIFIC GRAVITY;17030
-17040;URINE ORDERABLES>Urinalysis;UGLU;GLUCOSE;17040
-17050;URINE ORDERABLES>Urinalysis;UKET;KETONE;17050
-17060;URINE ORDERABLES>Urinalysis;UBLOOD;BLOOD;17060
-17070;URINE ORDERABLES>Urinalysis;UPH;PH;17070
-17080;URINE ORDERABLES>Urinalysis;UPROT;PROTEIN;17080
-17090;URINE ORDERABLES>Urinalysis;NITRITE;NITRITE;17090
-17100;URINE ORDERABLES>Urinalysis;LEUKEST;LEUK ESTERASE;17100
-17110;URINE ORDERABLES>Urinalysis;URBC;RBC, URINE;17110
-17120;URINE ORDERABLES>Urinalysis;UWBC;WBC, URINE;17120
-17130;URINE ORDERABLES>Urinalysis;SQEP;SQUAMOUS CELLS;17130
-17140;URINE ORDERABLES>Urinalysis;MUCUS;MUCOUS THREADS;17140
-17150;URINE ORDERABLES>Urinalysis;UBACT;BACTERIA;17150
-17210;URINE ORDERABLES>Drugs of Abuse;AMPHET;AMPHETAMINE;17210
-17220;URINE ORDERABLES>Drugs of Abuse;BARB;BARBITURATES;17220
-17230;URINE ORDERABLES>Drugs of Abuse;BENZO;BENZODIAZEPINES;17230
-17240;URINE ORDERABLES>Drugs of Abuse;COCAINE;COCAINE METABOLITES;17240
-17250;URINE ORDERABLES>Drugs of Abuse;METHADONE;METHADONE;17250
-17260;URINE ORDERABLES>Drugs of Abuse;OPIATE;OPIATES;17260
-17270;URINE ORDERABLES>Drugs of Abuse;PCP;PCP;17270
-17280;URINE ORDERABLES>Drugs of Abuse;TCA;TRICYCLICS;17280
-17290;URINE ORDERABLES>Drugs of Abuse;THC;THC;17290
-17310;URINE ORDERABLES>Urine Electrolytes;UCL;CHLORIDE, URINE;17310
-17320;URINE ORDERABLES>Urine Electrolytes;UCLCR;CHLORIDE/CREAT RATIO;17320
-17330;URINE ORDERABLES>Urine Electrolytes;UCR;URINE CREAT;17330
-17340;URINE ORDERABLES>Urine Electrolytes;UK;POTASSIUM, URINE;17340
-17350;URINE ORDERABLES>Urine Electrolytes;UKCR;POTASSIUM/CREAT RATIO;17350
-17360;URINE ORDERABLES>Urine Electrolytes;UNA;SODIUM, URINE;17360
-17370;URINE ORDERABLES>Urine Electrolytes;UNACR;SODIUM/CREAT RATIO;17370
-17380;URINE ORDERABLES>Urine Electrolytes;UOSM;OSMOLALITY, URINE;17380
-18010;INFECTIOUS DISEASES>Microbiology;CDTR;STOOL C.DIFF TOXIN B RESULT;18010
-18020;INFECTIOUS DISEASES>Microbiology;HIVAR;ANTI-HIV ANTIBODY;18020
-18030;INFECTIOUS DISEASES>Microbiology;ADVP;ADENOVIRUS;18030
-18040;INFECTIOUS DISEASES>Microbiology;INFAP;INFLUENZA A;18040
-18050;INFECTIOUS DISEASES>Microbiology;INFBP;INFLUENZA B;18050
-18060;INFECTIOUS DISEASES>Microbiology;P1P;PARAINFLUENZA 1;18060
-18070;INFECTIOUS DISEASES>Microbiology;P2P;PARAINFLUENZA 2;18070
-18080;INFECTIOUS DISEASES>Microbiology;P3P;PARAINFLUENZA 3;18080
-18090;INFECTIOUS DISEASES>Microbiology;RVP;RSV;18090
-18100;INFECTIOUS DISEASES>Microbiology;MPVPR;METAPNEUMOVIRUS;18100
-19010;CARDIOLOGY>EKG;HEARTRATE;HEART RATE;19010
-19020;CARDIOLOGY>EKG;QRSAXIS;QRS AXIS;19020
-19030;CARDIOLOGY>EKG;QTCINTERVAL;QTC INTERVAL;19030
-19040;CARDIOLOGY>EKG;QRSDINTERVAL;QRSD INTERVAL;19040
-19050;CARDIOLOGY>EKG;RR;RR;19050
-19060;CARDIOLOGY>EKG;QTINTERVAL;QT INTERVAL;19060
-19070;CARDIOLOGY>EKG;TWAVEAXIS;T WAVE AXIS;19070
-19080;CARDIOLOGY>EKG;PRINTERVAL;P-R INTERVAL;19080
-19090;CARDIOLOGY>EKG;PAXIS;P AXIS;19090
-"""
-
-PROC_RESULT_DATA_TEXT_STR = \
-"""proc_cat_name\tproc_id\tproc_code\tdescription\tbase_name\tcomponent_name\tmeanValue\tmeanTurnaround\tcountvalue\tsumvalue\tsumsquaresvalue\tcountturnaround\tsumturnaround\tsumsquaresturnaround
-POINT OF CARE TESTING\t472470\tLABPOCGLU\tGLUCOSE BY METER\tGLU\tGLUCOSE BY METER\t5931.844499\t56.13929433\t1139495\t6759307147\t6.58E+16\t491506\t27592800\t6.60E+11
-POINT OF CARE TESTING\t472470\tLABPOCGLU\tGLUCOSE BY METER\tSOU\tSOURCE\t9999999\t56.13929433\t1139428\t1.14E+13\t1.14E+20\t491506\t27592800\t6.60E+11
-POINT OF CARE TESTING\t472470\tLABPOCGLU\tGLUCOSE BY METER\tPCCOM\tPOCT COMMENT\t9999999\t56.13929433\t1088538\t1.09E+13\t1.09E+20\t491506\t27592800\t6.60E+11
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tAG\tANION GAP\t176646.0675\t5218.713203\t454658\t80313547749\t8.03E+17\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tBUN\tUREA NITROGEN,SER/PLAS\t296223.9694\t5218.713203\t454658\t1.35E+11\t1.35E+18\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCA\tCALCIUM, SER/PLAS\t24906.24148\t5218.713203\t454658\t11323821939\t1.13E+17\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCL\tCHLORIDE, SER/PLAS\t19831.95538\t5218.713203\t454658\t9016757167\t8.97E+16\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCO2\tCO2, SER/PLAS\t31324.46059\t5218.713203\t454658\t14241916604\t1.42E+17\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCR\tCREATININE, SER/PLAS\t20324.37383\t5218.713203\t454658\t9240639159\t9.24E+16\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tGLU\tGLUCOSE, SER/PLAS\t21421.49652\t5218.713203\t454658\t9739454764\t9.68E+16\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tK\tPOTASSIUM, SER/PLAS\t24176.05695\t5218.713203\t454658\t10991837702\t1.10E+17\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tNA\tSODIUM, SER/PLAS\t20085.98101\t5218.713203\t454658\t9132251953\t9.07E+16\t437132\t2281266540\t1.02E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tWBC\tWBC\t51623.2819\t5746.970765\t448136\t23134251057\t2.31E+17\t432799\t2487283200\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMCV\tMCV\t56346.95098\t5746.968426\t448130\t25250759143\t2.52E+17\t432795\t2487259200\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMCHC\tMCHC\t60529.63052\t5746.910339\t448129\t27125082796\t2.71E+17\t432795\t2487234060\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMCH\tMCH\t64543.60448\t5746.888681\t448128\t28923796390\t2.89E+17\t432794\t2487218940\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tRBC\tRBC\t44410.37321\t5746.896631\t448127\t19901487317\t1.99E+17\t432791\t2487205140\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHGB\tHEMOGLOBIN\t53566.58914\t5746.921477\t448126\t24004581325\t2.40E+17\t432792\t2487221640\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHCT\tHEMATOCRIT\t45508.72455\t5746.941875\t448124\t20393551680\t2.04E+17\t432788\t2487207480\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tRDW\tRDW\t60200.59147\t5746.851237\t448124\t26977329851\t2.70E+17\t432789\t2487174000\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPLT\tPLATELET COUNT\t81430.40076\t5746.92422\t448102\t36489125443\t3.64E+17\t432791\t2487217080\t2.48E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOAB\tMONO, ABS\t526801.9003\t4662.123903\t413021\t2.18E+11\t2.18E+18\t399265\t1861422900\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTAB\tNEUT, ABS\t526693.4964\t4662.266108\t413016\t2.18E+11\t2.18E+18\t399266\t1861484340\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOAB\tBASOS, ABS\t526896.8737\t4661.664341\t413003\t2.18E+11\t2.18E+18\t399245\t1861146180\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSAB\tEOS, ABS\t526874.0487\t4661.664341\t413002\t2.18E+11\t2.18E+18\t399245\t1861146180\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMAB\tLYM, ABS\t526782.0099\t4661.664341\t412999\t2.18E+11\t2.18E+18\t399245\t1861146180\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUT\tNEUT, %\t520823.0612\t4659.546995\t412730\t2.15E+11\t2.15E+18\t399245\t1860300840\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASO\tBASO, %\t494153.2005\t4659.554308\t411573\t2.03E+11\t2.03E+18\t399244\t1860299100\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONO\tMONO, %\t493873.7199\t4659.762757\t411569\t2.03E+11\t2.03E+18\t399253\t1860424260\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOS\tEOS, %\t493922.4427\t4659.562925\t411564\t2.03E+11\t2.03E+18\t399245\t1860307200\t1.45E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYM\tLYM, %\t493725.7487\t4659.60706\t411556\t2.03E+11\t2.03E+18\t399247\t1860334140\t1.45E+14
-LAB BLOOD ORDERABLES\t472785\tLABMGN\tMAGNESIUM, SERUM/PLASMA\tMG\tMAGNESIUM, SER/PLAS\t21666.78826\t6476.532134\t390034\t8450784091\t8.45E+16\t364547\t2361000360\t1.22E+14
-LAB BLOOD ORDERABLES\t473178\tLABPT\tPROTHROMBIN TIME\tINR\tINR\t176166.8402\t4556.359571\t285357\t50270441023\t5.03E+17\t266985\t1216479660\t7.55E+13
-LAB BLOOD ORDERABLES\t473178\tLABPT\tPROTHROMBIN TIME\tPT\tPROTHROMBIN TIME\t176079.6294\t4556.358433\t285354\t50245026567\t5.02E+17\t266984\t1216474800\t7.55E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tAG\tANION GAP\t144187.9958\t5323.847133\t257387\t37112115664\t3.71E+17\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tALB\tALBUMIN, SER/PLAS\t55677.58497\t5323.847133\t257387\t14330686563\t1.43E+17\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t26586.75235\t5323.847133\t257387\t6843084428\t6.80E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tALT\tALT (SGPT), SER/PLAS\t195748.1848\t5323.847133\t257387\t50383038032\t5.04E+17\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tBUN\tUREA NITROGEN,SER/PLAS\t329915.7371\t5323.847133\t257387\t84916021821\t8.49E+17\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCA\tCALCIUM, SER/PLAS\t27010.57439\t5323.847133\t257387\t6952170711\t6.95E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCL\tCHLORIDE, SER/PLAS\t19450.95614\t5323.847133\t257387\t5006423248\t4.98E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCO2\tCO2, SER/PLAS\t26212.06319\t5323.847133\t257387\t6746644308\t6.74E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCR\tCREATININE, SER/PLAS\t22419.04966\t5323.847133\t257387\t5770371935\t5.77E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tGLOB\tGLOBULIN\t63371.1434\t5323.847133\t257387\t16310908486\t1.63E+17\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tGLU\tGLUCOSE, SER/PLAS\t22159.76211\t5323.847133\t257387\t5703634689\t5.67E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tK\tPOTASSIUM, SER/PLAS\t24441.93271\t5323.847133\t257387\t6291035735\t6.29E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tNA\tSODIUM, SER/PLAS\t19601.5899\t5323.847133\t257387\t5045194420\t5.01E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tTBIL\tTOTAL BILIRUBIN\t3027310.466\t5323.847133\t257387\t7.79E+11\t7.79E+18\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tTP\tPROTEIN, TOTAL, SER/PLAS\t25726.24677\t5323.847133\t257387\t6621601478\t6.62E+16\t247509\t1317700080\t5.79E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tAST\tAST (SGOT), SER/PLAS\t225948.6188\t5323.855244\t257378\t58154203608\t5.81E+17\t247507\t1317691440\t5.79E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tWBC\tWBC\t49351.13292\t4043.094528\t238745\t11782336228\t1.18E+17\t229657\t928524960\t9.22E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tHCT\tHEMATOCRIT\t44261.12847\t4043.08036\t238742\t10566990334\t1.06E+17\t229655\t928513620\t9.22E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tHGB\tHEMOGLOBIN\t54880.84171\t4043.08036\t238742\t13102361911\t1.31E+17\t229655\t928513620\t9.22E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tRBC\tRBC\t43104.31413\t4043.08036\t238741\t10290767060\t1.03E+17\t229655\t928513620\t9.22E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tMCV\tMCV\t53622.1977\t4042.99784\t238739\t12801709856\t1.28E+17\t229652\t928482540\t9.22E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tRDW\tRDW\t59286.04589\t4043.035362\t238739\t14153891310\t1.41E+17\t229653\t928495200\t9.22E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tMCHC\tMCHC\t59680.61442\t4042.711593\t238738\t14248030526\t1.42E+17\t229651\t928412760\t9.21E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tMCH\tMCH\t63154.61088\t4042.655519\t238737\t15077342337\t1.51E+17\t229650\t928395840\t9.21E+13
-LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tPLT\tPLATELET COUNT\t70100.45715\t4043.08036\t238733\t16735292436\t1.67E+17\t229655\t928513620\t9.22E+13
-BODY FLUID ORDERABLES\t473352\tLABSPR\tSPECIMEN REMARK\tSPR\tSPECIMEN REMARK\t9998704.24\t1091.201598\t231590\t2.32E+12\t2.32E+19\t89098\t97223880\t4.93E+13
-LAB BLOOD ORDERABLES\t473103\tLABPHOS\tPHOSPHORUS, SERUM/PLASMA\tPHOS\tPHOSPHORUS, SER/PLAS\t24804.78078\t5220.968417\t199586\t4950686977\t4.95E+16\t187729\t980127180\t4.41E+13
-LAB BLOOD ORDERABLES\t473190\tLABPTT\tPTT PARTIAL THROMBOPLASTIN TIME\tPTT\tPART. THROMBOPLASTIN TIME\t238902.409\t7902.059931\t197062\t47078586523\t4.71E+17\t179006\t1414516140\t9.23E+13
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPCO2A\tPCO2 (A), ISTAT\t27476.5848\t76.33105738\t191365\t5258056650\t5.25E+16\t79527\t6070380\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPHA\tPH (A), ISTAT\t26187.70247\t76.33105738\t191365\t5011409684\t5.01E+16\t79527\t6070380\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPO2A\tPO2 (A), ISTAT\t21169.68923\t76.33181184\t191365\t4051137580\t4.03E+16\t79527\t6070440\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tHCO3A\tHCO3 (A), ISTAT\t27407.80058\t76.33146809\t191363\t5244838943\t5.24E+16\t79525\t6070260\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tTCO2A\tTCO2, ISTAT\t32268.912\t76.33146809\t191363\t6175075808\t6.17E+16\t79525\t6070260\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tO2SATA\tO2 SATURATION, ISTAT\t35160.01087\t76.33242795\t191362\t6728290001\t6.71E+16\t79524\t6070260\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t76.33085203\t184372\t1.84E+12\t1.84E+19\t79528\t6070440\t2.63E+11
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tFIO2\tFIO2\t859726.0565\t77.31600376\t171167\t1.47E+11\t1.47E+18\t76632\t5924880\t2.63E+11
-LAB BLOOD ORDERABLES\t471818\tLABCAI\tCALCIUM IONIZED, SERUM/PLASMA\tCAION\tCALCIUM, IONIZED\t35246.01129\t6630.995935\t163428\t5760185134\t5.76E+16\t156456\t1037459100\t9.72E+13
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tHEARTRATE\tHEART RATE\t676.0436628\t-5950.445219\t118728\t80265312\t7.00E+14\t118728\t-706484460\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQRSAXIS\tQRS AXIS\t695.5623863\t-5950.445219\t118728\t82582731\t8.00E+14\t118728\t-706484460\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQTCINTERVAL\tQTC INTERVAL\t852.6860218\t-5950.445219\t118728\t101237706\t5.00E+14\t118728\t-706484460\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQRSDINTERVAL\tQRSD INTERVAL\t603.8367001\t-5953.777336\t118708\t71680247\t6.00E+14\t118708\t-706761000\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tRR\tRR\t996.1141634\t-5950.445219\t118707\t118245724\t3.00E+14\t118728\t-706484460\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQTINTERVAL\tQT INTERVAL\t641.9300274\t-5958.395113\t118675\t76181046\t3.00E+14\t118675\t-707112540\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tTWAVEAXIS\tT WAVE AXIS\t54.23621859\t-6850.677701\t117205\t6356756\t872621762\t117205\t-802933680\t1.34E+19
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tPRO\tPRODUCT ID  \t9999999\t48369.56031\t109836\t1.10E+12\t1.10E+19\t45350\t2193559560\t3.01E+14
-LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tEGFR\tEGFR\t9744825.537\t4934.375429\t106672\t1.04E+12\t1.04E+19\t106273\t524390880\t7.54E+12
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tBE\tBASE EXCESS, ISTAT\t49708.54128\t104.666683\t104016\t5170483630\t5.17E+16\t40727\t4262760\t2.59E+11
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tPRINTERVAL\tP-R INTERVAL\t9663.957631\t-17476.68938\t102126\t986941337\t9.70E+15\t102126\t-1784824380\t1.34E+19
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tPAXIS\tP AXIS\t729.2279305\t-17550.51708\t101996\t74378332\t7.00E+14\t101996\t-1790082540\t1.34E+19
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tUBT\tUNIT BLOOD TYPE  \t9999999\t53957.13316\t99587\t9.96E+11\t9.96E+18\t40651\t2193411420\t3.01E+14
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tBD\tBASE DEFICIT, ISTAT\t29008.12189\t110.0159481\t87576\t2540415283\t2.54E+16\t38876\t4276980\t2.60E+11
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSLREV\tSLIDE REVIEW\t9999999\t12781.26503\t83020\t8.30E+11\t8.30E+18\t91966\t1175441820\t1.48E+14
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tDI\tISSUE DATE/TIME\t9999999\t35004.53417\t81212\t8.12E+11\t8.12E+18\t33832\t1184273400\t7.11E+13
-LAB BLOOD ORDERABLES\t473684\tLABTNI\tTROPONIN I\tTNI\tTROPONIN I\t6068284.056\t5474.691566\t73810\t4.48E+11\t4.48E+18\t63563\t347987820\t1.99E+13
-LAB BLOOD ORDERABLES\t472720\tLABLAC\tLACTIC ACID\tLAC\tLACTIC ACID\t41038.93064\t3538.959566\t68720\t2820195313\t2.82E+16\t61013\t215922540\t1.87E+13
-LAB BLOOD ORDERABLES\t471818\tLABCAI\tCALCIUM IONIZED, SERUM/PLASMA\tPHCAI\tPH\t31189.62099\t6777.892373\t67346\t2100496215\t2.10E+16\t66786\t452668320\t2.47E+13
-POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPCTEMP\tBODY TEMPERATURE\t9999074.447\t81.39692935\t64896\t6.49E+11\t6.49E+18\t64742\t5269800\t2.61E+11
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tPRO\tPRODUCT ID  \t9999999\t58600.84284\t64588\t6.46E+11\t6.46E+18\t24987\t1464259260\t2.44E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMORPH\tMORPHOLOGY\t9999999\t12781.26503\t63806\t6.38E+11\t6.38E+18\t91966\t1175441820\t1.48E+14
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCL\tCHLORIDE, WHOLE BLD\t146382.236\t1585.313725\t62622\t9166748384\t9.16E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tGLU\tGLUCOSE, WHOLE BLOOD\t147051.4984\t1585.313725\t62622\t9208658930\t9.20E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tHCO3A\tHCO3\t146136.795\t1585.313725\t62622\t9151378378\t9.15E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tHCT\tHCT (EST)\t148700.2934\t1585.313725\t62622\t9311909772\t9.31E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tNA\tSODIUM, WHOLE BLOOD\t146730.5942\t1585.313725\t62622\t9188563271\t9.18E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tPCO2A\tPCO2 (A)\t146633.0306\t1585.313725\t62622\t9182453645\t9.18E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tPHA\tPH (A)\t145962.3437\t1585.659946\t62622\t9140453890\t9.14E+16\t30601\t48522780\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tPO2A\tPO2 (A)\t146960.2898\t1585.313725\t62622\t9202947269\t9.19E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tTCO2A\tTCO2\t145978.124\t1585.313725\t62622\t9141442084\t9.14E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tHGBBG\tTHB\t148682.0058\t1585.313725\t62621\t9310615883\t9.31E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tK\tPOTASSIUM, WHOLE BLD\t146280.7503\t1585.313725\t62621\t9160246862\t9.16E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tMETHB\tMETHEMOGLOBIN\t1849540.043\t1585.279257\t62621\t1.16E+11\t1.16E+18\t30599\t48507960\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCAION\tCALCIUM, IONIZED\t149314.3859\t1585.313725\t62620\t9350066845\t9.35E+16\t30600\t48510600\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCOHB\tCO-HEMOGLOBIN\t1015970.523\t1585.278123\t62620\t63620074135\t6.36E+17\t30598\t48506340\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tO2HBA\tO2-HEMOGLOBIN (A)\t149088.7911\t1585.278123\t62620\t9335940097\t9.33E+16\t30598\t48506340\t1.21E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCTO2A\tCTO2 (A)\t150607.0448\t1585.253766\t62619\t9430862536\t9.43E+16\t30599\t48507180\t1.21E+13
-BLOOD BANK PRODUCT ORDERABLES\t473215\tLABBBPRBC\tRED BLOOD CELLS\tRBCSTA\tRBC PRODUCT - UNITS\t9999999\t5334.149557\t62186\t6.22E+11\t6.22E+18\t56888\t303449100\t1.01E+13
-LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tEGFR\tEGFR\t9886231.855\t4813.322522\t60562\t5.99E+11\t5.99E+18\t60430\t290869080\t4.15E+12
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tALB\tALBUMIN, SER/PLAS\t72047.04964\t5017.78784\t55660\t4010138783\t4.01E+16\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t30707.77553\t5017.78784\t55660\t1709194786\t1.70E+16\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tALT\tALT (SGPT), SER/PLAS\t176905.2823\t5017.78784\t55660\t9846548013\t9.84E+16\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tDBIL\tCONJUGATED BILI\t3655408.779\t5017.78784\t55660\t2.03E+11\t2.03E+18\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tIBIL\tUNCONJUGATED BILIRUBIN\t4091628.05\t5017.78784\t55660\t2.28E+11\t2.28E+18\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tTBIL\tTOTAL BILIRUBIN\t3036473.221\t5017.78784\t55660\t1.69E+11\t1.69E+18\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tTP\tPROTEIN, TOTAL, SER/PLAS\t27494.42464\t5017.78784\t55660\t1530339675\t1.53E+16\t51018\t255997500\t1.63E+13
-LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tAST\tAST (SGOT), SER/PLAS\t158956.1394\t5017.78784\t55659\t8847339762\t8.84E+16\t51018\t255997500\t1.63E+13
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tXMR\tUNIT CROSSMATCH  \t9999999\t73589.74987\t55658\t5.57E+11\t5.57E+18\t23028\t1694624760\t2.83E+14
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tUBT\tUNIT BLOOD TYPE  \t9999999\t71687.08515\t54497\t5.45E+11\t5.45E+18\t20423\t1464065340\t2.44E+14
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tBD\tBASE DEFICIT (VT)\t675370.3172\t956.8123911\t51646\t34880175403\t3.49E+17\t25825\t24709680\t4.60E+12
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tRPT\tRPT\t9999999\t515574.2561\t49366\t4.94E+11\t4.94E+18\t45607\t23513795100\t1.33E+16
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSDES\tSPECIMEN\t9999999\t515574.2561\t49345\t4.93E+11\t4.93E+18\t45607\t23513795100\t1.33E+16
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSREQ\tCOMMENTS\t9999999\t515574.2561\t49123\t4.91E+11\t4.91E+18\t45607\t23513795100\t1.33E+16
-LAB BLOOD ORDERABLES\t473125\tLABK\tPOTASSIUM, SERUM/PLASMA\tK\tPOTASSIUM, SER/PLAS\t36773.45151\t25552.77048\t48138\t1770200409\t1.77E+16\t45779\t1169780280\t7.78E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tLEUKEST\tLEUK ESTERASE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tNITRITE\tNITRITE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSPG\tSPECIFIC GRAVITY\t245083.1258\t12240.77057\t46311\t11350044638\t1.13E+17\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUBLOOD\tBLOOD\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUGLU\tGLUCOSE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUKET\tKETONE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUPH\tPH\t71263.36088\t12240.77057\t46311\t3300277506\t3.30E+16\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUPROT\tPROTEIN\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUWBC\tWBC, URINE\t9987691.108\t12241.10164\t46311\t4.63E+11\t4.63E+18\t32624\t399353700\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tURBC\tRBC, URINE\t9991793.563\t12241.10164\t46310\t4.63E+11\t4.63E+18\t32624\t399353700\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUCLAR\tCLARITY\t9999999\t12240.77057\t46261\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUCOL\tCOLOR\t9999999\t12248.77214\t46028\t4.60E+11\t4.60E+18\t32626\t399628440\t4.81E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSPECMN\tSPECIMEN\t9999999\t12248.77214\t45966\t4.60E+11\t4.60E+18\t32626\t399628440\t4.81E+13
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tAS3\tANTIBODY SCREEN\t9997811.823\t14738.5127\t45721\t4.57E+11\t4.57E+18\t41888\t617366820\t1.01E+15
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tRPT\tRPT\t9999999\t505173.5304\t43898\t4.39E+11\t4.39E+18\t41511\t20970258420\t1.13E+16
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSDES\tSPECIMEN\t9999999\t505173.5304\t43866\t4.39E+11\t4.39E+18\t41511\t20970258420\t1.13E+16
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSREQ\tCOMMENTS\t9999999\t505173.5304\t43658\t4.37E+11\t4.37E+18\t41511\t20970258420\t1.13E+16
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUCMT\tURINE COMMENT\t9999999\t12202.43692\t42828\t4.28E+11\t4.28E+18\t32459\t396078900\t4.72E+13
-LAB BLOOD ORDERABLES\t472035\tLABMB\tCK, MB (MASS)\tCKMB\tCK, MB (MASS)\t1845233.473\t5727.642436\t40071\t73940350501\t7.39E+17\t33594\t192414420\t1.20E+13
-MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tRPT\tRPT\t9999999\t111100.8005\t39655\t3.97E+11\t3.97E+18\t23935\t2659197660\t3.68E+14
-MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tSDES\tSPECIMEN\t9999999\t111100.8005\t39646\t3.96E+11\t3.96E+18\t23935\t2659197660\t3.68E+14
-MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tSREQ\tCOMMENTS\t9999999\t111100.8005\t39556\t3.96E+11\t3.96E+18\t23935\t2659197660\t3.68E+14
-LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tFK506\tTACROLIMUS (FK506)\t566562.7019\t12669.77384\t39396\t22320304202\t2.23E+17\t36876\t467210580\t2.09E+13
-LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tTACR2\tDATE & TIME OF LAST DOSE\t9999999\t12669.77384\t39382\t3.94E+11\t3.94E+18\t36876\t467210580\t2.09E+13
-LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tTACR3\tDOSAGE\t9999999\t12669.77384\t39381\t3.94E+11\t3.94E+18\t36876\t467210580\t2.09E+13
-LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tFK506\tPEAK, TROUGH, OR RANDOM\t9999999\t12669.77384\t39351\t3.94E+11\t3.94E+18\t36876\t467210580\t2.09E+13
-LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tTACR4\tADMINISTRATION ROUTE\t9999999\t12669.77384\t39321\t3.93E+11\t3.93E+18\t36876\t467210580\t2.09E+13
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tDI\tISSUE DATE/TIME\t9999999\t40258.81988\t39264\t3.93E+11\t3.93E+18\t14846\t597682440\t4.70E+13
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tXMR\tUNIT CROSSMATCH  \t9999999\t90201.95793\t37704\t3.77E+11\t3.77E+18\t14168\t1277981340\t2.36E+14
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tPHV\tPH (V)\t197753.2956\t3013.455443\t37624\t7440269992\t7.44E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCTO2V\tCTO2 (V)\t199355.386\t3013.455443\t37623\t7500347687\t7.50E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tGLU\tGLUCOSE, WHOLE BLOOD\t211719.6446\t3013.455443\t37623\t7965528187\t7.96E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tHCO3A\tHCO3\t197775.0341\t3013.455443\t37623\t7440890107\t7.44E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tHCT\tHCT (EST)\t198044.9434\t3013.455443\t37623\t7451044907\t7.45E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tK\tPOTASSIUM, WHOLE BLD\t210779.3697\t3013.455443\t37623\t7930152228\t7.93E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tPCO2V\tPCO2 (V)\t197796.3089\t3013.455443\t37623\t7441690529\t7.44E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tPO2V\tPO2 (V)\t198064.0877\t3013.455443\t37623\t7451765170\t7.45E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tTCO2A\tTCO2\t198042.1253\t3013.455443\t37623\t7450938880\t7.45E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCAION\tCALCIUM, IONIZED\t208921.3643\t3013.465359\t37622\t7860039569\t7.86E+16\t31610\t95255640\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCL\tCHLORIDE, WHOLE BLD\t198392.6191\t3013.452072\t37622\t7463927116\t7.46E+16\t31610\t95255220\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tHGBBG\tTHB\t197765.5597\t3013.529896\t37622\t7440335886\t7.44E+16\t31610\t95257680\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tMETHB\tMETHEMOGLOBIN\t2205890.865\t3013.452072\t37622\t82990026136\t8.30E+17\t31610\t95255220\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tNA\tSODIUM, WHOLE BLOOD\t198156.646\t3013.455443\t37622\t7455049337\t7.45E+16\t31611\t95258340\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tO2SATV\tO2 SATURATION (V)\t197828.3926\t3013.452072\t37622\t7442699788\t7.44E+16\t31610\t95255220\t1.43E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCOHB\tCO-HEMOGLOBIN\t948141.9177\t3013.452072\t37621\t35670047086\t3.57E+17\t31610\t95255220\t1.43E+13
-LAB BLOOD ORDERABLES\t472035\tLABMB\tCK, MB (MASS)\tCK\tCK, TOTAL\t165875.8912\t5712.49558\t37593\t6235772379\t6.22E+16\t31672\t180926160\t1.19E+13
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tARG\tABO/RH (AUTOMATION)\t9999999\t15630.74851\t36492\t3.65E+11\t3.65E+18\t42404\t662806260\t1.45E+15
-MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tRPT\tRPT\t9999999\t214873.0474\t34442\t3.44E+11\t3.44E+18\t21871\t4699488420\t1.74E+15
-MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tSDES\tSPECIMEN\t9999999\t214873.0474\t34414\t3.44E+11\t3.44E+18\t21871\t4699488420\t1.74E+15
-MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tSREQ\tCOMMENTS\t9999999\t214873.0474\t34376\t3.44E+11\t3.44E+18\t21871\t4699488420\t1.74E+15
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYM\tLYMPHOCYTES\t1786.385644\t19050.56618\t33951\t60649579\t6.00E+14\t32799\t624839520\t1.05E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTAB\tNEUT,ABS (MAN DIFF)\t2082.920204\t18924.54144\t33746\t70290225.2\t7.00E+14\t32602\t616977900\t9.12E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUT\tSEG NEUTROPHILS\t1826.468131\t18714.30151\t33700\t61551976\t6.00E+14\t32556\t609262800\t9.00E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMAB\tLYM, ABS (MAN DIFF)\t1490.242736\t19186.66379\t33606\t50081097.4\t5.00E+14\t32474\t623067720\t1.05E+14
-LAB BLOOD ORDERABLES\t472730\tLABLDH\tLDH TOTAL, SERUM / PLASMA\tLDH\tLDH, TOTAL, SER/PLAS\t177187.6869\t6231.287376\t33443\t5925687812\t5.91E+16\t31203\t194434860\t1.89E+13
-LAB BLOOD ORDERABLES\t472500\tLABHCTX\tHEMATOCRIT\tHCT\tHEMATOCRIT\t45493.55716\t4322.738756\t32773\t1490960349\t1.49E+16\t31350\t135517860\t3.11E+12
-LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANCOTROUGH\tVANCOMYCIN, TROUGH\t115011.3483\t8433.120947\t32610\t3750520068\t3.75E+16\t28376\t239298240\t1.23E+13
-LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANPR3\tDOSAGE\t9999999\t8433.377974\t32598\t3.26E+11\t3.26E+18\t28375\t239297100\t1.23E+13
-LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANPR2\tDATE & TIME OF LAST DOSE\t9999999\t8433.377974\t32591\t3.26E+11\t3.26E+18\t28375\t239297100\t1.23E+13
-LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANPR4\tADMINISTRATION ROUTE\t9999999\t8433.377974\t32583\t3.26E+11\t3.26E+18\t28375\t239297100\t1.23E+13
-LAB BLOOD ORDERABLES\t472035\tLABMB\tCK, MB (MASS)\tCKMBRI\tCKMB %RELATIVE INDEX\t209292.3761\t5689.459183\t31392\t6570106271\t6.57E+16\t26423\t150332580\t1.13E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tMUCUS\tMUCOUS THREADS\t9999999\t12188.69384\t31326\t3.13E+11\t3.13E+18\t22547\t274818480\t3.21E+13
-STOOL ORDERABLES\t633173\tLABARVI\tBLOOD TYPE VERIFICATION\tARV\tBLOOD TYPE VERIFICATION\t9999999\t5223.284946\t30914\t3.09E+11\t3.09E+18\t15982\t83478540\t6.92E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONO\tMONOCYTES\t2601.147151\t18907.63289\t30873\t80305216\t8.00E+14\t29800\t563447460\t9.63E+13
-LAB BLOOD ORDERABLES\t473343\tLABNA\tSODIUM, SERUM / PLASMA\tNA\tSODIUM, SER/PLAS\t48039.57747\t4680.15887\t30689\t1474286593\t1.47E+16\t29458\t137868120\t1.23E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOAB\tMONO, ABS (MAN DIFF)\t2296.710912\t19040.88321\t30490\t70026715.7\t7.00E+14\t29438\t560525520\t9.62E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBAND\tBAND NEUTROPHILS\t2329.320229\t18890.95238\t30244\t70447961\t7.00E+14\t29232\t552220320\t8.22E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSQEP\tSQUAMOUS CELLS\t9999999\t12653.6781\t28260\t2.83E+11\t2.83E+18\t20168\t255199380\t3.17E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tLEUKEST\tLEUK ESTERASE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tNITRITE\tNITRITE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSPG\tSPECIFIC GRAVITY\t210485.048\t12204.11378\t27413\t5770026621\t5.77E+16\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUBLOOD\tBLOOD\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUGLU\tGLUCOSE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUKET\tKETONE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUPH\tPH\t68586.53792\t12204.11378\t27413\t1880162764\t1.88E+16\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUPROT\tPROTEIN\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tURBC\tRBC, URINE\t9982124.559\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUWBC\tWBC, URINE\t9985772.4\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUCLAR\tCLARITY\t9999999\t12204.11378\t27376\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tPRN\tCULTURE SENT PRN\t9999999\t12204.11378\t27311\t2.73E+11\t2.73E+18\t18562\t226532760\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUCOL\tCOLOR\t9999999\t12204.12864\t27244\t2.72E+11\t2.72E+18\t18563\t226545240\t3.05E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSPECMN\tSPECIMEN\t9999999\t12204.12864\t27222\t2.72E+11\t2.72E+18\t18563\t226545240\t3.05E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tBD\tBASE DEFICIT (VT)\t1868434.464\t2873.790776\t26070\t48710086478\t4.87E+17\t22225\t63870000\t5.38E+12
-LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tDDIMER\tD-DIMER\t1490829.278\t5220.076785\t25741\t38375436433\t3.83E+17\t25005\t130528020\t5.95E+12
-LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tFIBRINOGEN\tFIBRINOGEN\t191880.8937\t5220.076785\t25741\t4939206084\t4.93E+16\t25005\t130528020\t5.95E+12
-LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tPTT\tPART. THROMBOPLASTIN TIME\t204770.0749\t5220.076785\t25741\t5270986498\t5.27E+16\t25005\t130528020\t5.95E+12
-LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tTT\tTHROMBIN TIME\t398603.0482\t5220.076785\t25741\t10260441064\t1.03E+17\t25005\t130528020\t5.95E+12
-LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tPT\tPROTHROMBIN TIME\t174842.8487\t5220.076785\t25740\t4500454927\t4.50E+16\t25005\t130528020\t5.95E+12
-LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tINR\tINR\t175222.0351\t5220.076785\t25739\t4510039962\t4.51E+16\t25005\t130528020\t5.95E+12
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUCMT\tURINE COMMENT\t9999999\t12154.83745\t25367\t2.54E+11\t2.54E+18\t18456\t224329680\t3.03E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tBE\tBASE EXCESS (VT)\t4646770.528\t1857.906045\t25196\t1.17E+11\t1.17E+18\t18711\t34763280\t9.53E+12
-BLOOD BANK PRODUCT ORDERABLES\t473112\tLABBBPPLT\tPLATELET APHERESIS PRODUCT\tPLT\tPLATELETS\t9999999\t5843.234224\t24904\t2.49E+11\t2.49E+18\t23913\t139729260\t4.18E+12
-POINT OF CARE TESTING\t572612\tLABPCTNI\tISTAT TROPONIN I\tTNI\tTROPONIN I, POCT\t28655.88736\t65.12903226\t23730\t680004207.2\t6.80E+15\t9300\t605700\t13095428400
-LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN ACTIVITY LEVEL\tHEPAR\tHEPARIN ACTIVITY LEVEL\t1807229.044\t27866.90651\t23406\t42300002996\t4.23E+17\t15371\t428342220\t2.72E+13
-LAB BLOOD ORDERABLES\t472505\tLABA1C\tHEMOGLOBIN A1C\tA1C\tHEMOGLOBIN A1C\t150299.0871\t40031.36354\t22556\t3390146210\t3.39E+16\t19816\t793261500\t6.24E+13
-LAB BLOOD ORDERABLES\t473726\tLABURIC\tURIC ACID, SERUM / PLASMA\tURIC\tURIC ACID, SER/PLAS\t2999913.222\t5697.145943\t22354\t67060060172\t6.71E+17\t21296\t121326420\t1.36E+13
-POINT OF CARE TESTING\t572612\tLABPCTNI\tISTAT TROPONIN I\tPCCOM\tPOCT COMMENT\t9999999\t65.12147925\t22269\t2.23E+11\t2.23E+18\t9302\t605760\t13095432000
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tBE\tBASE EXCESS (VT)\t2932824.691\t3227.918068\t22180\t65050051638\t6.50E+17\t19919\t64296900\t1.09E+13
-LAB BLOOD ORDERABLES\t472752\tLABLIPS\tLIPASE\tLIPASE\tLIPASE\t708616.1884\t4931.204285\t22010\t15596642307\t1.56E+17\t19605\t96676260\t1.17E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t9999999\t12181.96017\t20619\t2.06E+11\t2.06E+18\t44537\t542547960\t6.54E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tMUCUS\tMUCOUS THREADS\t9999999\t11973.86042\t19771\t1.98E+11\t1.98E+18\t13584\t162652920\t2.12E+13
-LAB BLOOD ORDERABLES\t473689\tLABTSH\tTSH\tTSH\tTSH\t129967.0501\t34267.06903\t19390\t2520061101\t2.52E+16\t16950\t580826820\t3.52E+13
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tPRO\tPRODUCT ID  \t9999999\t56238.68993\t19372\t1.94E+11\t1.94E+18\t7679\t431856900\t6.51E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUBACT\tBACTERIA\t9999999\t12201.33814\t18816\t1.88E+11\t1.88E+18\t32463\t396092040\t4.72E+13
-LAB BLOOD ORDERABLES\t890505\tLABLACWB\tLACTATE, WHOLE BLOOD\tLACWBL\tLACWBL\t140078.6194\t1965.353905\t17776\t2490037538\t2.49E+16\t17759\t34902720\t6.29E+11
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSQEP\tSQUAMOUS CELLS\t9999999\t12425.95286\t16610\t1.66E+11\t1.66E+18\t11329\t140773620\t1.68E+13
-BLOOD BANK PRODUCT ORDERABLES\t472423\tLABBBPFFP\tFRESH FROZEN PLASMA PRODUCT\tFFP\tFRESH FROZEN UNITS\t9999999\t6488.406965\t16604\t1.66E+11\t1.66E+18\t14645\t95022720\t3.97E+12
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tPRO\tPRODUCT ID  \t9999999\t56595.15599\t16586\t1.66E+11\t1.66E+18\t6635\t375508860\t5.68E+13
-LAB BLOOD ORDERABLES\t472104\tLABCK\tCREATINE KINASE, TOTAL\tCK\tCK, TOTAL\t345122.1544\t5500.152407\t16129\t5566475228\t5.55E+16\t14435\t79394700\t1.67E+13
-LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN, UNFRACTIONATED, ANTI XA\tHEPAR\tHEPARIN ACTIVITY LEVEL\t2197946.709\t21802.13096\t15974\t35110000723\t3.51E+17\t15974\t348267240\t2.23E+13
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tUBT\tUNIT BLOOD TYPE  \t9999999\t70831.65354\t15817\t1.58E+11\t1.58E+18\t6096\t431789760\t6.51E+13
-LAB BLOOD ORDERABLES\t471806\tLABCRP\tC - REACTIVE PROTEIN\tCRP\tC-REACTIVE PROTEIN\t853359.1853\t6781.996587\t15445\t13180132618\t1.32E+17\t14064\t95382000\t4.32E+12
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tHCO3V\tHCO3 (V), ISTAT\t27645.83044\t122.2070286\t15206\t420382497.7\t4.20E+15\t7313\t893700\t61382998800
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tLAC\tLACTATE, ISTAT\t88125.09923\t122.2070286\t15206\t1340030259\t1.34E+16\t7313\t893700\t61382998800
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPCO2V\tPCO2 (V), ISTAT\t27662.70585\t122.2070286\t15206\t420639105.2\t4.20E+15\t7313\t893700\t61382998800
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPHV\tPH (V), ISTAT\t26312.77132\t122.2070286\t15206\t400112000.7\t4.00E+15\t7313\t893700\t61382998800
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tTCO2V\tTCO2 (V), ISTAT\t36853.96791\t122.2070286\t15206\t560401436\t5.60E+15\t7313\t893700\t61382998800
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tO2SATV\tO2 SATURATION, ISTAT\t38862.82098\t122.2237418\t15205\t590909193\t5.90E+15\t7312\t893700\t61382998800
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPO2V\tPO2 (V), ISTAT\t23056.01565\t122.2237418\t15205\t350566718\t3.50E+15\t7312\t893700\t61382998800
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMETA\tMETAMYELOCYTES\t4048.3012\t19742.12626\t14834\t60052500\t6.00E+14\t14335\t283003380\t4.31E+13
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t122.2070286\t14702\t1.47E+11\t1.47E+18\t7313\t893700\t61382998800
-LAB BLOOD ORDERABLES\t472376\tLABFIB\tFIBRINOGEN\tFIBRINOGEN\tFIBRINOGEN\t220689.6763\t5646.521628\t13839\t3054124430\t3.05E+16\t11721\t66182880\t9.73E+12
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tHCO3A\tHCO3 (A), ISTAT\t36983.54265\t53.40860215\t13529\t500350348.5\t5.00E+15\t5580\t298020\t631724400
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPCO2A\tPCO2 (A), ISTAT\t36261.24436\t53.40860215\t13529\t490578374.9\t4.90E+15\t5580\t298020\t631724400
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPHA\tPH (A), ISTAT\t32530.0929\t53.40860215\t13529\t440099626.9\t4.40E+15\t5580\t298020\t631724400
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPO2A\tPO2 (A), ISTAT\t28940.13342\t53.40860215\t13529\t391531065\t3.90E+15\t5580\t298020\t631724400
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tTCO2A\tTCO2, ISTAT\t82072.92158\t53.40860215\t13529\t1110364556\t1.11E+16\t5580\t298020\t631724400
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tO2SATA\tO2 SATURATION, ISTAT\t42968.76419\t53.40860215\t13528\t581281442\t5.80E+15\t5580\t298020\t631724400
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tUBT\tUNIT BLOOD TYPE  \t9999999\t71882.52728\t13445\t1.34E+11\t1.34E+18\t5223\t375442440\t5.68E+13
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tHCT\tHCT(CALC), ISTAT\t104695.2693\t53.47849559\t13376\t1400403922\t1.40E+16\t5557\t297180\t631674000
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tHGB\tHGB(CALC), ISTAT\t104675.3324\t53.47849559\t13376\t1400137246\t1.40E+16\t5557\t297180\t631674000
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tK\tPOTASSIUM, ISTAT\t103173.9246\t53.47849559\t13376\t1380054415\t1.38E+16\t5557\t297180\t631674000
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tNA\tSODIUM, ISTAT\t91345.31153\t53.47849559\t13376\t1221834887\t1.22E+16\t5557\t297180\t631674000
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tCAION\tCALCIUM,ION, ISTAT\t101698.5758\t53.47849559\t13373\t1360015055\t1.36E+16\t5557\t297180\t631674000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMYEL\tMYELOCYTES\t3031.974336\t21081.17343\t13209\t40049349\t4.00E+14\t12783\t269480640\t4.07E+13
-LAB BLOOD ORDERABLES\t473039\tLABOSM\tOSMOLALITY, SERUM\tOSMOL\tOSMOLALITY, SERUM\t79961.60193\t6211.214496\t13181\t1053973875\t1.05E+16\t12252\t76099800\t4.05E+12
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t53.40860215\t13041\t1.30E+11\t1.30E+18\t5580\t298020\t631724400
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOS\tEOSINOPHILS\t3129.826481\t19163.30361\t12794\t40043000\t4.00E+14\t12338\t236436840\t4.68E+13
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tSI\t<<STATUS>>  \t9999999\t48367.42856\t12761\t1.28E+11\t1.28E+18\t45352\t2193559620\t3.01E+14
-LAB BLOOD ORDERABLES\t473019\tLABNTBNP\tNT - PROBNP\tBNP\tNT-PROBNP\t753794.1482\t5682.800262\t12696\t9570170505\t9.52E+16\t10699\t60800280\t2.00E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSAB\tEOS, ABS (MAN DIFF)\t3162.963425\t19285.44948\t12648\t40005161.4\t4.00E+14\t12203\t235340340\t4.68E+13
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tCDTR\tSTOOL C.DIFF TOXIN B RESULT\t9999199.715\t38913.73397\t12506\t1.25E+11\t1.25E+18\t7954\t309519840\t6.07E+13
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tHCO3V\tHCO3 (V), ISTAT\t27576.65803\t82.03966006\t12341\t340323536.7\t3.40E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tO2SATV\tO2 SATURATION, ISTAT\t42195.62053\t82.03966006\t12341\t520736153\t5.20E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPCO2V\tPCO2 (V), ISTAT\t27593.21517\t82.03966006\t12341\t340527868.4\t3.40E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPHV\tPH (V), ISTAT\t27557.81298\t82.03966006\t12341\t340090970\t3.40E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPO2V\tPO2 (V), ISTAT\t31634.56454\t82.03966006\t12341\t390402161\t3.90E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tTCO2V\tTCO2 (V), ISTAT\t39732.48594\t82.03966006\t12341\t490338609\t4.90E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tCAION\tCALCIUM,ION, ISTAT\t66478.63084\t82.03966006\t12335\t820013911.4\t8.20E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tHCT\tHCT(CALC), ISTAT\t43809.6655\t82.03966006\t12335\t540392224\t5.40E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tHGB\tHGB(CALC), ISTAT\t43788.67581\t82.03966006\t12335\t540133316.1\t5.40E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tK\tPOTASSIUM, ISTAT\t56753.10045\t82.03966006\t12335\t700049494.1\t7.00E+15\t5295\t434400\t13984128000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tNA\tSODIUM, ISTAT\t36617.91552\t82.03966006\t12335\t451681988\t4.50E+15\t5295\t434400\t13984128000
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tCDTSP\tSPECIMEN\t9999999\t38913.73397\t12175\t1.22E+11\t1.22E+18\t7954\t309519840\t6.07E+13
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t82.03966006\t12126\t1.21E+11\t1.21E+18\t5295\t434400\t13984128000
-MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tRPT\tRPT\t9999999\t529714.7807\t12120\t1.21E+11\t1.21E+18\t5108\t2705783100\t2.15E+15
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUBACT\tBACTERIA\t9999999\t12153.70571\t11863\t1.19E+11\t1.19E+18\t18458\t224333100\t3.03E+13
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tSI\t<<STATUS>>  \t9999999\t58598.49768\t11856\t1.19E+11\t1.19E+18\t24988\t1464259260\t2.44E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNRBC\tNRBC/100WBC\t2553.212766\t21228.10716\t11797\t30120251\t3.00E+14\t11422\t242467440\t3.46E+13
-MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tSDES\tSPECIMEN\t9999999\t529714.7807\t11774\t1.18E+11\t1.18E+18\t5108\t2705783100\t2.15E+15
-LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tCO2\tCO2, SER/PLAS\t25122.39818\t3637.746454\t11555\t290289311\t2.90E+15\t11351\t41292060\t5.80E+12
-LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tCR\tCREATININE, SER/PLAS\t25098.34983\t3637.746454\t11555\t290011432.3\t2.90E+15\t11351\t41292060\t5.80E+12
-LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tGLU\tGLUCOSE, SER/PLAS\t25255.13025\t3637.746454\t11555\t291823030\t2.90E+15\t11351\t41292060\t5.80E+12
-LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tK\tPOTASSIUM, SER/PLAS\t27697.6907\t3637.746454\t11555\t320046816\t3.20E+15\t11351\t41292060\t5.80E+12
-LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tNA\tSODIUM, SER/PLAS\t25233.32471\t3637.746454\t11555\t291571067\t2.90E+15\t11351\t41292060\t5.80E+12
-MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tRPT\tRPT\t9999999\t43014.06617\t11523\t1.15E+11\t1.15E+18\t5622\t241825080\t7.14E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tALB\tALBUMIN, SER/PLAS\t43598.57743\t4902.321525\t11469\t500032084.5\t5.00E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t26289.39951\t4902.321525\t11469\t301513123\t3.00E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tALT\tALT (SGPT), SER/PLAS\t47147.82248\t4902.321525\t11469\t540738376\t5.40E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tAST\tAST (SGOT), SER/PLAS\t720236.1855\t4902.321525\t11469\t8260388812\t8.26E+16\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tCR\tCREATININE, SER/PLAS\t25286.56308\t4902.321525\t11469\t290011592\t2.90E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tGLU\tGLUCOSE, SER/PLAS\t26310.5269\t4902.321525\t11469\t301755433\t3.00E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tK\tPOTASSIUM, SER/PLAS\t28777.22268\t4902.321525\t11469\t330045966.9\t3.30E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tMG\tMAGNESIUM, SER/PLAS\t25287.26628\t4902.321525\t11469\t290019657\t2.90E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tNA\tSODIUM, SER/PLAS\t25421.77984\t4902.321525\t11469\t291562393\t2.90E+15\t11122\t54523620\t1.22E+13
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tTBIL\tTOTAL BILIRUBIN\t4981254.022\t4902.321525\t11469\t57130002376\t5.71E+17\t11122\t54523620\t1.22E+13
-MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tSDES\tSPECIMEN\t9999999\t43014.06617\t11467\t1.15E+11\t1.15E+18\t5622\t241825080\t7.14E+13
-MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tSREQ\tCOMMENTS\t9999123.42\t43014.06617\t11421\t1.14E+11\t1.14E+18\t5622\t241825080\t7.14E+13
-MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tSREQ\tCOMMENTS\t9999999\t529714.7807\t11420\t1.14E+11\t1.14E+18\t5108\t2705783100\t2.15E+15
-MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tRPT\tRPT\t9999999\t187056.7953\t10620\t1.06E+11\t1.06E+18\t6247\t1168543800\t5.85E+14
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tDI\tISSUE DATE/TIME\t9999999\t43643.25891\t10570\t1.06E+11\t1.06E+18\t4210\t183738120\t1.55E+13
-MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tSDES\tSPECIMEN\t9999999\t187056.7953\t10555\t1.06E+11\t1.06E+18\t6247\t1168543800\t5.85E+14
-MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tRPT\tRPT\t9999999\t1820387.097\t10531\t1.05E+11\t1.05E+18\t5250\t9557032260\t1.81E+16
-MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tSREQ\tCOMMENTS\t9999999\t187056.7953\t10523\t1.05E+11\t1.05E+18\t6247\t1168543800\t5.85E+14
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tWBC\tWBC\t160340.871\t14170.48095\t10416\t1670110512\t1.67E+16\t9606\t136121640\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tHCT\tHEMATOCRIT\t161333.5794\t14170.65806\t10415\t1680289230\t1.68E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tHGB\tHEMOGLOBIN\t187239.3558\t14170.65806\t10415\t1950097890\t1.95E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMCH\tMCH\t223746.2447\t14170.65806\t10415\t2330317138\t2.33E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMCHC\tMCHC\t201665.5509\t14170.65806\t10415\t2100346713\t2.10E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMCV\tMCV\t200761.8964\t14170.65806\t10415\t2090935151\t2.09E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPLT\tPLATELET COUNT\t242108.7965\t14170.65806\t10415\t2521563116\t2.52E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tRBC\tRBC\t160348.6899\t14170.65806\t10415\t1670031605\t1.67E+16\t9604\t136095000\t1.84E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tRDW\tRDW\t213170.9987\t14170.65806\t10415\t2220175951\t2.22E+16\t9604\t136095000\t1.84E+13
-MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tSDES\tSPECIMEN\t9999999\t1820387.097\t10224\t1.02E+11\t1.02E+18\t5250\t9557032260\t1.81E+16
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tSLREV\tSLIDE REVIEW\t9999999\t14175.70952\t10207\t1.02E+11\t1.02E+18\t9598\t136058460\t1.84E+13
-MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tSREQ\tCOMMENTS\t9999999\t1820387.097\t10081\t1.01E+11\t1.01E+18\t5250\t9557032260\t1.81E+16
-POINT OF CARE TESTING\t472684\tLABPCCR\tISTAT CREATININE\tCR\tCREATININE,ISTAT\t34011.51997\t83.9487909\t9997\t340013165.1\t3.40E+15\t3515\t295080\t7883964000
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tAMPHET\tAMPHETAMINE\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tBARB\tBARBITURATES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tBENZO\tBENZODIAZEPINES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tCOCAINE\tCOCAINE METABOLITES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tMETHADONE\tMETHADONE\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tOPIATE\tOPIATES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tPCP\tPCP\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tTCA\tTRICYCLICS\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tTHC\tTHC\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tXMR\tUNIT CROSSMATCH  \t9999999\t92348.14473\t9855\t98549990145\t9.85E+17\t3897\t359880720\t6.18E+13
-LAB BLOOD ORDERABLES\t473107\tLABPLTS\tPLATELET COUNT\tPLT\tPLATELET COUNT\t206199.9849\t7350.399083\t9852\t2031482251\t2.03E+16\t8720\t64095480\t6.25E+12
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tFIO2\tFIO2\t2085609.217\t51.44734223\t9839\t20520309081\t2.05E+17\t5023\t258420\t523854000
-LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO\tLIDOCAINE\t674275.7855\t6465.464807\t9729\t6560029117\t6.56E+16\t9391\t60717180\t1.51E+13
-LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO2\tDATE & TIME OF LAST DOSE\t9999999\t6465.464807\t9727\t97269990273\t9.73E+17\t9391\t60717180\t1.51E+13
-LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO3\tDOSAGE\t9999999\t6465.464807\t9726\t97259990274\t9.73E+17\t9391\t60717180\t1.51E+13
-LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO4\tADMINISTRATION ROUTE\t9999999\t6465.464807\t9722\t97219990278\t9.72E+17\t9391\t60717180\t1.51E+13
-LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLI\tPEAK, TROUGH, OR RANDOM\t9999999\t6465.464807\t9718\t97179990282\t9.72E+17\t9391\t60717180\t1.51E+13
-POINT OF CARE TESTING\t472684\tLABPCCR\tISTAT CREATININE\tPCCOM\tPOCT COMMENT\t9999999\t83.9487909\t9431\t94309990569\t9.43E+17\t3515\t295080\t7883964000
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tBE\tBASE EXCESS, ISTAT\t44597.17893\t183.4767726\t8970\t400036695\t4.00E+15\t4090\t750420\t61224577200
-LAB BLOOD ORDERABLES\t473413\tLABFT4\tT4, FREE\tFT4\tFREE T4\t73125.24129\t34097.38384\t8889\t650010269.8\t6.50E+15\t7576\t258321780\t1.68E+13
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tDI\tISSUE DATE/TIME\t9999999\t44949.3771\t8845\t88449991155\t8.84E+17\t3564\t160199580\t1.40E+13
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tUPH\tPH URINE\t317900.0446\t3535.3457\t8808\t2800063593\t2.80E+16\t8895\t31446900\t1.76E+14
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONOAB\tMONO, ABS\t979847.5988\t10963.69771\t8634\t8460004168\t8.46E+16\t7913\t86755740\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUTAB\tNEUT, ABS\t978695.1421\t10969.37832\t8634\t8450053857\t8.45E+16\t7914\t86811660\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASOAB\tBASOS, ABS\t979960.5426\t10961.56471\t8633\t8459999364\t8.46E+16\t7912\t86727900\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOSAB\tEOS, ABS\t979960.661\t10961.56471\t8633\t8460000386\t8.46E+16\t7912\t86727900\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYMAB\tLYM, ABS\t977871.3995\t10961.56471\t8631\t8440008049\t8.44E+16\t7912\t86727900\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUT\tNEUT, %\t969566.6156\t10959.2366\t8623\t8360572926\t8.36E+16\t7912\t86709480\t4.75E+12
-LAB BLOOD ORDERABLES\t473132\tLABYPREAL\tPREALBUMIN\tPREALBUMIN\tPREALBUMIN\t92952.96224\t21450.81694\t8608\t800139099\t8.00E+15\t7932\t170147880\t1.77E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASO\tBASO, %\t951772.447\t10959.28707\t8605\t8190001906\t8.19E+16\t7911\t86698920\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOS\tEOS, %\t951773.8582\t10959.28707\t8605\t8190014050\t8.19E+16\t7911\t86698920\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYM\tLYM, %\t950624.5273\t10959.2366\t8605\t8180124058\t8.18E+16\t7912\t86709480\t4.75E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONO\tMONO, %\t950617.2635\t10967.54803\t8605\t8180061552\t8.18E+16\t7912\t86775240\t4.76E+12
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tXMR\tUNIT CROSSMATCH  \t9999999\t91794.21898\t8549\t85489991451\t8.55E+17\t3425\t314395200\t5.39E+13
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tHCO3V\tHCO3 (V), ISTAT\t46934.61223\t148.6122216\t8527\t400211438.5\t4.00E+15\t3502\t520440\t31147351200
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tO2SATV\tO2 SATURATION, ISTAT\t59875.92776\t148.6122216\t8527\t510562036\t5.10E+15\t3502\t520440\t31147351200
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPCO2V\tPCO2 (V), ISTAT\t46955.68538\t148.6122216\t8527\t400391129.2\t4.00E+15\t3502\t520440\t31147351200
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPHV\tPH (V), ISTAT\t45744.37367\t148.6122216\t8527\t390062274.3\t3.90E+15\t3502\t520440\t31147351200
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPO2V\tPO2 (V), ISTAT\t45778.09077\t148.6122216\t8527\t390349780\t3.90E+15\t3502\t520440\t31147351200
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tTCO2V\tTCO2 (V), ISTAT\t53972.44482\t148.6122216\t8527\t460223037\t4.60E+15\t3502\t520440\t31147351200
-LAB BLOOD ORDERABLES\t473682\tLABTRIG\tTRIGLYCERIDES, SERUM / PLASMA\tTGL\tTRIGLYCERIDE, SER/PLAS\t76476.81733\t5007.791365\t8518\t651429530\t6.50E+15\t8014\t40132440\t7.29E+11
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUPH\tPH URINE\t39946.08299\t-53486.48507\t8513\t340061004.5\t3.40E+15\t8774\t-469290420\t7.61E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUBLOOD\tBLOOD URINE\t9903005.814\t-53502.95385\t8351\t82700001552\t8.27E+17\t8775\t-469488420\t7.61E+15
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t148.6122216\t8269\t82689991731\t8.27E+17\t3502\t520440\t31147351200
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUWBC\tLEUKOCYTES\t9975578.983\t-53503.42069\t8190\t81699991868\t8.17E+17\t8776\t-469546020\t7.61E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUPROT\tPROTEIN URINE\t9957232.755\t-53483.68133\t8184\t81489992870\t8.15E+17\t8774\t-469265820\t7.61E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tNITRITE\tNITRITE\t9992656.86\t-53502.95385\t8172\t81659991864\t8.17E+17\t8775\t-469488420\t7.61E+15
-MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t439595.3706\t8168\t81679991832\t8.17E+17\t4290\t1885864140\t2.06E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUKET\tKETONES\t9963261.452\t-53515.82175\t8166\t81359993016\t8.14E+17\t8774\t-469547820\t7.61E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUGLU\tGLUCOSE URINE\t9357934.086\t-53502.95385\t8161\t76370100076\t7.64E+17\t8775\t-469488420\t7.61E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tEXPDATE\tEXP DATE\t9797353.857\t-53502.95385\t8144\t79789649810\t7.98E+17\t8775\t-469488420\t7.61E+15
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tSPG\tSPECIFIC GRAVITY\t25798.87957\t-53502.95385\t8144\t210106075.2\t2.10E+15\t8775\t-469488420\t7.61E+15
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASO\tBASOPHILS\t3687.485135\t20947.37285\t8140\t30016129\t3.00E+14\t7845\t164332140\t2.96E+13
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tDIPLOT\tDIPSTICK LOT\t9870470.192\t-53465.75239\t8136\t80306145479\t8.01E+17\t8772\t-469001580\t7.61E+15
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOAB\tBASO, ABS (MAN DIFF)\t3721.998809\t21070.87751\t8061\t30003032.4\t3.00E+14\t7772\t163762860\t2.96E+13
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tCHOL\tCHOLESTEROL, TOTAL\t86808.71569\t6998.059577\t7963\t691257803\t6.90E+15\t7184\t50274060\t1.03E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t256188.7712\t6998.059577\t7963\t2040031185\t2.04E+16\t7184\t50274060\t1.03E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tHDL\tHDL CHOLESTEROL\t239902.7954\t6998.059577\t7963\t1910345960\t1.91E+16\t7184\t50274060\t1.03E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tLDL\tDIRECT LDL CHOL\t62882.24551\t6998.059577\t7963\t500731321\t5.00E+15\t7184\t50274060\t1.03E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tNHDL\tNON-HDL CHOL, CALC\t256296.6906\t6998.059577\t7963\t2040890547\t2.04E+16\t7184\t50274060\t1.03E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tTGL\tTRIGLYCERIDE, SER/PLAS\t96824.46515\t6998.059577\t7963\t771013216\t7.70E+15\t7184\t50274060\t1.03E+12
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tFIO2\tFIO2\t3279751.87\t89.12959381\t7836\t25700135655\t2.57E+17\t4653\t414720\t13982184000
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tBE\tBASE EXCESS, ISTAT\t45952.43765\t108.6380597\t7835\t360037349\t3.60E+15\t3216\t349380\t13912772400
-MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t439595.3706\t7736\t77359992264\t7.74E+17\t4290\t1885864140\t2.06E+15
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tBE\tBASE EXCESS, ISTAT\t74089.92319\t60.37435009\t7694\t570047869\t5.70E+15\t2885\t174180\t497869200
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tPRO\tPRODUCT ID  \t9999999\t50909.33809\t7628\t76279992372\t7.63E+17\t3082\t156902580\t2.17E+13
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tAS\tANTIBODY SCREEN\t9997350.347\t44204.55386\t7551\t75489992471\t7.55E+17\t7027\t310625400\t2.31E+15
-MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t439595.3706\t7420\t74199992580\t7.42E+17\t4290\t1885864140\t2.06E+15
-LAB BLOOD ORDERABLES\t471610\tLABNH3\tAMMONIA, PLASMA\tNH3\tAMMONIA, PLASMA\t741403.8696\t4822.107671\t7230\t5360349977\t5.36E+16\t6557\t31618560\t2.58E+12
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tFIO2\tFIO2\t1408306.234\t153.9713775\t7108\t10010240712\t1.00E+17\t3354\t516420\t31147095600
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tAMORCRY\tAMORPHOUS CRYSTALS\t9999999\t11791.10708\t7093\t70929992907\t7.09E+17\t4959\t58472100\t4.01E+12
-LAB BLOOD ORDERABLES\t472369\tLABFER\tFERRITIN\tFERRITIN\tFERRITIN\t163449.2886\t35938.4026\t6948\t1135645657\t1.13E+16\t6160\t221380560\t1.69E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tHYCAST\tHYALINE CASTS\t9999999\t12509.58478\t6923\t69229993077\t6.92E+17\t5202\t65074860\t5.57E+12
-LAB BLOOD ORDERABLES\t473764\tLABB12\tVITAMIN B12\tB12\tVITAMIN B12\t3443570.484\t85714.94891\t6918\t23822620611\t2.38E+17\t6165\t528432660\t7.77E+13
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tPRO\tPRODUCT ID  \t9999999\t50253.88336\t6917\t69169993083\t6.92E+17\t2812\t141313920\t1.94E+13
-URINE ORDERABLES\t473349\tLABUSPG\tSPECIFIC GRAVITY, URINE\tSPG\tSPECIFIC GRAVITY\t63722.93876\t9424.118896\t6905\t440006892.1\t4.40E+15\t2826\t26632560\t3.88E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMORPH\tMORPHOLOGY\t9999999\t14660.77393\t6462\t64619993538\t6.46E+17\t8838\t129571920\t1.83E+13
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tUBT\tUNIT BLOOD TYPE  \t9999999\t63772.73171\t6268\t62679993732\t6.27E+17\t2460\t156880920\t2.17E+13
-POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tBD\tBASE DEFICIT, ISTAT\t32000.19917\t219.431221\t6251\t200033245\t2.00E+15\t3235\t709860\t61119608400
-LAB BLOOD ORDERABLES\t471616\tLABAMY\tAMYLASE, SERUM\tAMYLASE\tAMYLASE, SERUM\t75371.29299\t7113.896907\t6246\t470769096\t4.70E+15\t5335\t37952640\t1.13E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBLASTS\tBLASTS\t1646\t14771.6354\t6214\t10228244\t1.00E+14\t6023\t88969560\t1.30E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBLASTSAB\tBLASTS,ABS(MAN DIFF)\t1635.86515\t14831.03067\t6155\t10068750\t1.00E+14\t5967\t88496760\t1.30E+13
-LAB BLOOD ORDERABLES\t473473\tLABTRFS\tTRANSFERRIN SATURATION\tFE\tIRON, TOTAL\t74531.63936\t8066.46495\t6042\t450320165\t4.50E+15\t5321\t42921660\t7.52E+11
-LAB BLOOD ORDERABLES\t473473\tLABTRFS\tTRANSFERRIN SATURATION\tTRFRN\tTRANSFERRIN\t102838.1089\t8066.46495\t6042\t621347854\t6.20E+15\t5321\t42921660\t7.52E+11
-LAB BLOOD ORDERABLES\t473473\tLABTRFS\tTRANSFERRIN SATURATION\tTRFSAT\tTRANSFERRIN SAT\t256565.4825\t8066.46495\t6042\t1550168645\t1.55E+16\t5321\t42921660\t7.52E+11
-LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH\t9999999\t27693.75\t5946\t59459994054\t5.95E+17\t144\t3987900\t4.95E+12
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tBD\tBASE DEFICIT, ISTAT\t61345.03902\t51.88470067\t5869\t360034034\t3.60E+15\t2706\t140400\t183880800
-URINE ORDERABLES\t473345\tLABURNA\tSODIUM, URINE (RANDOM)\tUNA\tSODIUM, URINE\t208105.7103\t15182.65976\t5864\t1220331885\t1.22E+16\t3474\t52744560\t4.79E+12
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH\t9999999\t28795.95889\t5822\t58219994178\t5.82E+17\t6325\t182134440\t6.61E+14
-LAB BLOOD ORDERABLES\t472489\tLABHAP\tHAPTOGLOBIN\tHAPTO\tHAPTOGLOBIN\t2271427.067\t41917.59263\t5750\t13060705635\t1.31E+17\t5101\t213821640\t1.73E+13
-LAB BLOOD ORDERABLES\t590524\tLABASI\tAB SCREEN (ASI)\tAS\tANTIBODY SCREEN\t9999999\t7660\t5693\t56929994307\t5.69E+17\t9\t68940\t1312822800
-LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETIC\tRETIC, %\t427384.2192\t6435.962196\t5639\t2410019612\t2.41E+16\t4973\t32006040\t8.53E+11
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tUBT\tUNIT BLOOD TYPE  \t9999999\t63330.90094\t5631\t56309994369\t5.63E+17\t2231\t141291240\t1.94E+13
-MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tRPT\tRPT\t9999999\t3883162.201\t5539\t55389994461\t5.54E+17\t2681\t10410757860\t4.16E+16
-LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETICAB\tRETIC, ABS [AUTORETIC]\t126493.1332\t6435.893001\t5459\t690526013.9\t6.90E+15\t4972\t31999260\t8.53E+11
-LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC2\tDATE & TIME LAST DOSE\t9999999\t9129.397163\t5436\t54359994564\t5.44E+17\t5076\t46340820\t1.28E+12
-LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYCWB\tCYCLOSPORINE, WHOLE BLOOD\t202602.2658\t9129.397163\t5436\t1101345917\t1.10E+16\t5076\t46340820\t1.28E+12
-LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC3\tDOSAGE\t9999999\t9129.397163\t5435\t54349994565\t5.43E+17\t5076\t46340820\t1.28E+12
-LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC\tPEAK, TROUGH, OR RANDOM\t9999999\t9129.397163\t5432\t54319994568\t5.43E+17\t5076\t46340820\t1.28E+12
-LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC4\tADMINISTRATION ROUTE\t9999999\t9129.397163\t5431\t54309994569\t5.43E+17\t5076\t46340820\t1.28E+12
-LAB BLOOD ORDERABLES\t471569\tLABALB\tALBUMIN, SERUM/PLASMA\tALB\tALBUMIN, SER/PLAS\t84998.99586\t5387.547901\t5412\t460014565.6\t4.60E+15\t4645\t25025160\t2.98E+11
-MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tSDES\tSPECIMEN\t9999999\t3883162.201\t5383\t53829994617\t5.38E+17\t2681\t10410757860\t4.16E+16
-LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN\tPHENYTOIN\t251041.037\t6082.810524\t5338\t1340057056\t1.34E+16\t4903\t29824020\t3.52E+12
-LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN2\tDATE & TIME OF LAST DOSE\t9999999\t6082.810524\t5335\t53349994665\t5.33E+17\t4903\t29824020\t3.52E+12
-LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN3\tDOSAGE\t9999999\t6082.810524\t5335\t53349994665\t5.33E+17\t4903\t29824020\t3.52E+12
-LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN4\tADMINISTRATION ROUTE\t9999999\t6082.810524\t5332\t53319994668\t5.33E+17\t4903\t29824020\t3.52E+12
-MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tSREQ\tCOMMENTS\t9999999\t3883162.201\t5330\t53299994670\t5.33E+17\t2681\t10410757860\t4.16E+16
-LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN\tPEAK, TROUGH, OR RANDOM\t9999999\t6082.810524\t5309\t53089994691\t5.31E+17\t4903\t29824020\t3.52E+12
-URINE ORDERABLES\t473041\tLABUOSM\tOSMOLALITY, URINE\tUOSM\tOSMOLALITY, URINE\t83581.7189\t17376.67197\t5290\t442147293\t4.40E+15\t3143\t54614880\t4.99E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tREACTL\tREACTIVE LYMPHS\t7689.860492\t27479.96404\t5204\t40018034\t4.00E+14\t5006\t137564700\t1.94E+13
-LAB BLOOD ORDERABLES\t472101\tLABCORT\tCORTISOL, SERUM\tCORT\tCORTISOL, SERUM\t285180.2545\t26893.35657\t5155\t1470104212\t1.47E+16\t4591\t123467400\t6.48E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tTRANST\tTRANSITIONAL\t9999999\t13297.53199\t5149\t51489994851\t5.15E+17\t3517\t46767420\t4.53E+12
-LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG\tDIGOXIN\t308233.4738\t5414.030852\t5126\t1580004787\t1.58E+16\t4473\t24216960\t2.13E+12
-LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG3\tDOSAGE\t9999999\t5414.030852\t5124\t51239994876\t5.12E+17\t4473\t24216960\t2.13E+12
-LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG2\tDATE & TIME OF LAST DOSE\t9999999\t5414.030852\t5123\t51229994877\t5.12E+17\t4473\t24216960\t2.13E+12
-LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG4\tADMINISTRATION ROUTE\t9999999\t5414.030852\t5082\t50819994918\t5.08E+17\t4473\t24216960\t2.13E+12
-LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG\tPEAK, TOUGH, OR RANDOM\t9999999\t5414.030852\t5047\t50469994953\t5.05E+17\t4473\t24216960\t2.13E+12
-LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tETOH\tETHANOL\t7452883.484\t7192.519666\t4982\t37130265517\t3.71E+17\t4195\t30172620\t7.50E+12
-LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tOSMGAP\tOSMOLAL GAP\t7697883.914\t7193.516221\t4978\t38320066124\t3.83E+17\t4192\t30155220\t7.50E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPRMY\tPROMYELOCYTES\t6080.515796\t23944.15212\t4938\t30025587\t3.00E+14\t4812\t115219260\t1.72E+13
-MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tRPT\tRPT\t9999999\t3886778.338\t4858\t48579995142\t4.86E+17\t2310\t8978457960\t3.70E+16
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tLYMFLD\tLYMPHOCYTE %, FLD\t457864.2248\t19527.06412\t4827\t2210110613\t2.21E+16\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tMONFLD\tMONO/MESO %, FLD\t457875.3592\t19527.06412\t4827\t2210164359\t2.21E+16\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tRBCFLD\tRBC COUNT, FLD (/UL)\t630003.365\t19527.06412\t4827\t3041026243\t2.73E+16\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tWBCFLD\tNUCL CELLS, FLD(/UL)\t487879.8655\t19527.06412\t4826\t2354508231\t2.34E+16\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tNEUTFLD\tNEUTROPHIL %, FLD\t455992.9967\t19527.06412\t4825\t2200166209\t2.20E+16\t2667\t52078680\t1.29E+13
-LAB BLOOD ORDERABLES\t473295\tLABESRP\tSEDIMENTATION RATE (ESR)\tESR\tESR, (MANUAL)\t688626.2817\t10595.54854\t4807\t3310226536\t3.31E+16\t4120\t43653660\t7.84E+11
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFTURB\tTURBIDITY, FLD\t9993742.485\t19527.06412\t4795\t47919995216\t4.79E+17\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t19527.06412\t4778\t47779995222\t4.78E+17\t2667\t52078680\t1.29E+13
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tPRO\tPRODUCT ID  \t9999999\t45922.88372\t4765\t47649995235\t4.76E+17\t1935\t88860780\t1.11E+13
-LAB BLOOD ORDERABLES\t473331\tLABSLHELD\tSLIDE HELD FOR REVIEW\tSLHELD\tSLIDE HELD TO REVIEW\t9995790.249\t116226.1088\t4752\t47499995264\t4.75E+17\t2868\t333336480\t5.83E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFLDTYP\tFLUID SOURCE\t9999999\t19527.06412\t4719\t47189995281\t4.72E+17\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tDX\tDIAGNOSIS\t9942652.708\t19527.06412\t4708\t46810008949\t4.68E+17\t2667\t52078680\t1.29E+13
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFCOLOR\tAPPEARANCE\t9999999\t19527.06412\t4652\t46519995348\t4.65E+17\t2667\t52078680\t1.29E+13
-MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tSDES\tSPECIMEN\t9999999\t3886778.338\t4634\t46339995366\t4.63E+17\t2310\t8978457960\t3.70E+16
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tBD\tBASE DEFICIT, ISTAT\t61248.15661\t241.2875536\t4572\t280026572\t2.80E+15\t1864\t449760\t31063111200
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tBTURB\tTURBIDITY, CSF\t9999999\t12481.94864\t4561\t45609995439\t4.56E+17\t2648\t33052200\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tWBCCSF\tWBC COUNT, CSF\t1493411.249\t12481.94864\t4561\t6811448706\t6.81E+16\t2648\t33052200\t4.08E+12
-LAB BLOOD ORDERABLES\t472553\tLABHIVWBL\tANTI-HIV\tHIVAR\tANTI-HIV ANTIBODY\t9999999\t51579.60028\t4561\t45609995439\t4.56E+17\t4303\t221947020\t2.06E+13
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tLYMCSF\tLYMPHOCYTES, CSF\t4960547.156\t12484.08009\t4560\t22620095033\t2.26E+17\t2647\t33045360\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tMONOCSF\tMONOCYTOID CELLS, CSF\t4960534.138\t12484.08009\t4560\t22620035669\t2.26E+17\t2647\t33045360\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tRBCCSF\tRBC COUNT, CSF\t1497832.935\t12481.94864\t4560\t6830118185\t6.77E+16\t2648\t33052200\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCOLORCSF\tCOLOR, CSF\t9999999\t12481.94864\t4559\t45589995441\t4.56E+17\t2648\t33052200\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tNEUTCSF\tNEUTROPHILS, CSF\t4953943.863\t12484.08009\t4558\t22580076127\t2.26E+17\t2647\t33045360\t4.08E+12
-URINE ORDERABLES\t472109\tLABUCR\tCREATININE, URINE (RANDOM)\tUCR\tCREATININE, URINE\t63758.58907\t15754.24084\t4555\t290420373.2\t2.90E+15\t2674\t42126840\t3.97E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tMALIG\tSUSPECT MALIGNANCY?\t9995598.561\t12481.94864\t4545\t45429995459\t4.54E+17\t2648\t33052200\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCOLORCSF\tCOLOR AFTER CENTRIFUGE, CSF\t9999999\t12504.7099\t4533\t45329995467\t4.53E+17\t2637\t32974920\t4.08E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCTURB\tTURB. AFTER CENTRIF., CSF\t9997792.985\t12504.7099\t4533\t45319995599\t4.53E+17\t2637\t32974920\t4.08E+12
-MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tRPT\tRPT\t9999999\t483529.1944\t4529\t45289995471\t4.53E+17\t4481\t2166694320\t1.17E+15
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tBD\tBASE DEFICIT, ISTAT\t42002.71065\t141.8809981\t4524\t190020263\t1.90E+15\t2084\t295680\t13622270400
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tDX\tDIAGNOSIS\t9915930.216\t12481.94864\t4520\t44820004577\t4.48E+17\t2648\t33052200\t4.08E+12
-MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tSDES\tSPECIMEN\t9999999\t483529.1944\t4519\t45189995481\t4.52E+17\t4481\t2166694320\t1.17E+15
-MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tSREQ\tCOMMENTS\t9999999\t3886778.338\t4503\t45029995497\t4.50E+17\t2310\t8978457960\t3.70E+16
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCSOURC\tSOURCE\t9999999\t12481.94864\t4451\t44509995549\t4.45E+17\t2648\t33052200\t4.08E+12
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t4438\t44379995562\t4.44E+17\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t547240.7303\t4430\t44299995570\t4.43E+17\t1068\t584453100\t2.37E+15
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tDI\tISSUE DATE/TIME\t9999999\t41991.89189\t4410\t44099995590\t4.41E+17\t1776\t74577600\t5.77E+12
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tPRO\tPRODUCT ID  \t9999999\t44758.42809\t4402\t44019995598\t4.40E+17\t1794\t80296620\t9.91E+12
-MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tSREQ\tCOMMENTS\t9999999\t483529.1944\t4382\t43819995618\t4.38E+17\t4481\t2166694320\t1.17E+15
-POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPCTEMP\tBODY TEMPERATURE\t9974850.49\t51.40096618\t4374\t43629996044\t4.36E+17\t4347\t223440\t400305600
-LAB BLOOD ORDERABLES\t977655\tLABSPLAC\tSEPSIS PROTOCOL LACTATE\tLACWBL\tLACWBL\t225133.6344\t2571.026878\t4353\t980006710.5\t9.80E+15\t4353\t11191680\t2.29E+11
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t547240.7303\t4316\t43159995684\t4.32E+17\t1068\t584453100\t2.37E+15
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tTUBENO\tCSF TUBE #\t5022047.441\t12481.94864\t4309\t21640002422\t2.16E+17\t2648\t33052200\t4.08E+12
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t547240.7303\t4298\t42979995702\t4.30E+17\t1068\t584453100\t2.37E+15
-POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPCTEMP\tBODY TEMPERATURE\t9999999\t93.12883436\t4251\t42509995749\t4.25E+17\t4238\t394680\t13966135200
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tAMORCRY\tAMORPHOUS CRYSTALS\t9999999\t14511.56118\t4242\t42419995758\t4.24E+17\t2844\t41270880\t6.85E+12
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tHCO3\tHCO3, ISTAT\t24344.29202\t142.088267\t4112\t100103728.8\t1.00E+15\t1858\t264000\t10384999200
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tLAC\tLACTATE, ISTAT\t111869.6803\t142.088267\t4112\t460008125.4\t4.60E+15\t1858\t264000\t10384999200
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPCO2A\tPCO2, ISTAT\t24360.758\t142.088267\t4112\t100171436.9\t1.00E+15\t1858\t264000\t10384999200
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPCO2SO\tO2 SATURATION, ISTAT\t29243.36576\t142.088267\t4112\t120248720\t1.20E+15\t1858\t264000\t10384999200
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPO2A\tPO2, ISTAT\t19494.54256\t142.088267\t4112\t80161559\t8.00E+14\t1858\t264000\t10384999200
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tTCO2A\tTCO2, (ISTAT)\t36505.06542\t142.088267\t4112\t150108829\t1.50E+15\t1858\t264000\t10384999200
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPH\tPH, ISTAT\t9749.225487\t142.088267\t4106\t40030319.85\t4.00E+14\t1858\t264000\t10384999200
-LAB BLOOD ORDERABLES\t471766\tLABDBIL\tBILIRUBIN DIRECT, SERUM/PLASMA\tDBIL\tCONJUGATED BILI\t1656923.12\t4637.953336\t4104\t6800012485\t6.80E+16\t2443\t11330520\t1.76E+11
-LAB BLOOD ORDERABLES\t473295\tLABESRP\tSEDIMENTATION RATE (ESR)\tESR\tESR, (AUTOMATED)\t179634.3921\t9588.860353\t4065\t730213804\t7.30E+15\t3738\t35843160\t5.47E+11
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t142.088267\t4059\t40589995941\t4.06E+17\t1858\t264000\t10384999200
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tSI\t<<STATUS>>  \t9999999\t56224.05416\t4048\t40479995952\t4.05E+17\t7681\t431856960\t6.51E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tHYCAST\tHYALINE CASTS\t9999999\t12112.97602\t4027\t40269995973\t4.03E+17\t2836\t34352400\t3.94E+12
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tRPT\tRPT\t9999999\t499787.4252\t4015\t40149995985\t4.01E+17\t3946\t1972161180\t1.12E+15
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tSDES\tSPECIMEN\t9999999\t499787.4252\t3999\t39989996001\t4.00E+17\t3946\t1972161180\t1.12E+15
-STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tOCCGU\tSTOOL SAMPLE1,GUAIAC\t9999999\t26125.71878\t3994\t39939996006\t3.99E+17\t1906\t49795620\t1.09E+13
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tBE\tBASE EXCESS, ISTAT\t107936.6052\t262.1493625\t3984\t430019435\t4.30E+15\t1647\t431760\t30965810400
-STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tDATE1\tDATE 1\t7391819.152\t25911.47817\t3952\t29212469287\t2.89E+17\t1901\t49257720\t1.08E+13
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tDI\tISSUE DATE/TIME\t9999999\t41133.95939\t3916\t39159996084\t3.92E+17\t1576\t64827120\t4.72E+12
-LAB BLOOD ORDERABLES\t472441\tLABGGT\tGAMMA - GLUTAMYL TRANSFERASE\tGGT\tGAMMA-GLUTAMYL TRANS (GGT)\t70008.72633\t6625.499568\t3877\t271423832\t2.70E+15\t3473\t23010360\t2.85E+11
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tSREQ\tCOMMENTS\t9999999\t499787.4252\t3853\t38529996147\t3.85E+17\t3946\t1972161180\t1.12E+15
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tUBT\tUNIT BLOOD TYPE  \t9999999\t58144.67277\t3845\t38449996155\t3.84E+17\t1528\t88845060\t1.11E+13
-STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tDATE2\tDATE 2\t9765855.059\t26602.01987\t3822\t37325098036\t3.73E+17\t1812\t48202860\t1.07E+13
-STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tDATE3\tDATE 3\t9778694.187\t26488.63938\t3818\t37335054406\t3.73E+17\t1808\t47891460\t1.06E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMAOTH\tOTHER, ABS(MAN DIFF)\t15741.31652\t56354.41432\t3814\t60037381.2\t6.00E+14\t3688\t207835080\t3.71E+13
-LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHBSAG\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t49510.65059\t3787\t37869996213\t3.79E+17\t3566\t176554980\t2.12E+13
-LAB BLOOD ORDERABLES\t473132\tLABPALB\tPREALBUMIN\tPREALBUMIN\tPREALBUMIN\t91316.25107\t5131.176155\t3724\t340061719\t3.40E+15\t3724\t19108500\t7.10E+11
-LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tETOHI\tETHANOL INTERP\t9999999\t7192.519666\t3662\t36619996338\t3.66E+17\t4195\t30172620\t7.50E+12
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tHCO3\tHCO3, ISTAT\t68443.60517\t94.96428571\t3654\t250092933.3\t2.50E+15\t1680\t159540\t3219822000
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCO2A\tPCO2, ISTAT\t65724.1494\t94.96428571\t3654\t240156041.9\t2.40E+15\t1680\t159540\t3219822000
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCO2SO\tO2 SATURATION, ISTAT\t71248.87466\t94.96428571\t3654\t260343388\t2.60E+15\t1680\t159540\t3219822000
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPO2A\tPO2, ISTAT\t54849.20498\t94.96428571\t3654\t200418995\t2.00E+15\t1680\t159540\t3219822000
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tTCO2A\tTCO2, (ISTAT)\t84865.14614\t94.96428571\t3654\t310097244\t3.10E+15\t1680\t159540\t3219822000
-LAB BLOOD ORDERABLES\t472407\tLABFOL\tFOLIC ACID\tFOLATE\tFOLIC ACID (FOLATE)\t2148747.987\t89044.69501\t3644\t7830037665\t7.83E+16\t3246\t289039080\t4.20E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tADVP\tADENOVIRUS\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tINFAP\tINFLUENZA A\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tINFBP\tINFLUENZA B\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tP1P\tPARAINFLUENZA 1\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tP2P\tPARAINFLUENZA 2\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tP3P\tPARAINFLUENZA 3\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tRVP\tRSV\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tMPVPR\tMETAPNEUMOVIRUS\t9999999\t66962.44702\t3643\t36429996357\t3.64E+17\t2501\t167473080\t3.14E+13
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPH\tPH, ISTAT\t16509.02108\t94.96428571\t3636\t60026800.63\t6.00E+14\t1680\t159540\t3219822000
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tSI\t<<STATUS>>  \t9999999\t56595.15599\t3571\t35709996429\t3.57E+17\t6635\t375508860\t5.68E+13
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t94.96428571\t3547\t35469996453\t3.55E+17\t1680\t159540\t3219822000
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tUBT\tUNIT BLOOD TYPE  \t9999999\t57549.16129\t3505\t35049996495\t3.50E+17\t1395\t80281080\t9.91E+12
-BODY FLUID ORDERABLES\t472474\tLABCSFGL\tGLUCOSE, CSF\tGLUCSF\tGLUCOSE, CSF\t147049.9202\t9628.58118\t3470\t510263223\t5.10E+15\t2051\t19748220\t2.46E+12
-BODY FLUID ORDERABLES\t473171\tLABCSFTP\tPROTEIN TOTAL, CSF\tTPCSF\tPROTEIN, TOTAL, CSF\t223552.5963\t8275.37092\t3446\t770362247\t7.70E+15\t2022\t16732800\t1.33E+12
-POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tHCT\tHCT(CALC), ISTAT\t29089.90238\t320.1615509\t3442\t100127444\t1.00E+15\t1238\t396360\t25255605600
-POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tHGB\tHGB(CALC), ISTAT\t29065.46293\t320.1615509\t3442\t100043323.4\t1.00E+15\t1238\t396360\t25255605600
-POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tK\tPOTASSIUM, ISTAT\t72636.58425\t320.1615509\t3442\t250015123\t2.50E+15\t1238\t396360\t25255605600
-POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tNA\tSODIUM, ISTAT\t20474.56334\t320.1615509\t3442\t70473447\t7.00E+14\t1238\t396360\t25255605600
-POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tGLU\tGLUCOSE,ISTAT\t46637.10782\t320.3718674\t3441\t160478288\t1.60E+15\t1237\t396300\t25255602000
-LAB BLOOD ORDERABLES\t473720\tLABBUN\tUREA NITROGEN, SERUM/PLASMA\tBUN\tUREA NITROGEN,SER/PLAS\t125105.7333\t8380.772727\t3438\t430113511\t4.30E+15\t3080\t25812780\t8.18E+11
-STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tOCCGU\tSTOOL SAMPLE2,GUAIAC\t9999999\t26389.7989\t3425\t34249996575\t3.42E+17\t1641\t43305660\t9.41E+12
-STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tOCCGU\tSTOOL SAMPLE3,GUAIAC\t9997076.733\t26397.29268\t3422\t34209996582\t3.42E+17\t1640\t43291560\t9.41E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUCL\tCHLORIDE, URINE\t137457.5589\t15992.76596\t3421\t470242309\t4.70E+15\t1974\t31569720\t2.97E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUCLCR\tCHLORIDE/CREAT RATIO\t5305465.897\t15992.76596\t3421\t18149998832\t1.81E+17\t1974\t31569720\t2.97E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUCR\tURINE CREAT\t119937.7598\t15992.76596\t3421\t410307076.4\t4.10E+15\t1974\t31569720\t2.97E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUK\tPOTASSIUM, URINE\t116960.7292\t15992.76596\t3421\t400122654.7\t4.00E+15\t1974\t31569720\t2.97E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUKCR\tPOTASSIUM/CREAT RATIO\t8655363.088\t15992.76596\t3421\t29609997125\t2.96E+17\t1974\t31569720\t2.97E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUNA\tSODIUM, URINE\t309908.1155\t15992.76596\t3421\t1060195663\t1.06E+16\t1974\t31569720\t2.97E+12
-URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUNACR\tSODIUM/CREAT RATIO\t5802396.545\t15992.76596\t3421\t19849998582\t1.98E+17\t1974\t31569720\t2.97E+12
-LAB BLOOD ORDERABLES\t471820\tLABCA\tCALCIUM, SERUM/PLASMA\tCA\tCALCIUM, SER/PLAS\t56287.86457\t5083.35535\t3376\t190027830.8\t1.90E+15\t3028\t15392400\t2.08E+11
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUWBCC\tWBC CLUMPS\t9999999\t11002.39122\t3374\t33739996626\t3.37E+17\t2551\t28067100\t2.32E+12
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tXMR\tUNIT CROSSMATCH  \t9999999\t88745.61509\t3314\t33139996686\t3.31E+17\t1325\t117587940\t1.98E+13
-LAB BLOOD ORDERABLES\t471521\tLABACETA\tACETAMINOPHEN, SERUM\tACETA\tACETAMINOPHEN\t8223308.024\t4303.983229\t3287\t27030013476\t2.70E+17\t2862\t12318000\t1.20E+11
-LAB BLOOD ORDERABLES\t869056\tLABPROCT\tPROCALCITONIN\tPROCTL\tPROCALCITONIN\t2200928.757\t9522.549085\t3235\t7120004529\t7.12E+16\t3005\t28615260\t1.38E+12
-MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tRPT\tRPT\t9999999\t452979\t3218\t32179996782\t3.22E+17\t1080\t489217320\t4.09E+14
-MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tSREQ\tCOMMENTS\t9999999\t452979\t3194\t31939996806\t3.19E+17\t1080\t489217320\t4.09E+14
-POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t320.1615509\t3188\t31879996812\t3.19E+17\t1238\t396360\t25255605600
-MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tSDES\tSPECIMEN\t9999999\t452979\t3175\t31749996825\t3.17E+17\t1080\t489217320\t4.09E+14
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tTRANST\tTRANSITIONAL\t9999999\t16395.4988\t3164\t31639996836\t3.16E+17\t2075\t34020660\t6.40E+12
-MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t431748.6235\t3136\t31359996864\t3.14E+17\t1867\t806074680\t3.79E+14
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t3112\t31119996888\t3.11E+17\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tXMR\tUNIT CROSSMATCH  \t9999999\t86561.87399\t3096\t30959996904\t3.10E+17\t1238\t107163600\t1.78E+13
-MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t431748.6235\t3091\t30909996909\t3.09E+17\t1867\t806074680\t3.79E+14
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUPROT\tPROTEIN\t9999999\t16088.86364\t3080\t30799996920\t3.08E+17\t1760\t28316400\t6.95E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tLEUKEST\tLEUK ESTERASE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tNITRITE\tNITRITE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tSPG\tSPECIFIC GRAVITY\t224099.7011\t16070.07391\t3079\t690002979.8\t6.90E+15\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUBLOOD\tBLOOD\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUGLU\tGLUCOSE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUKET\tKETONE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUPH\tPH\t103935.8184\t16070.07391\t3079\t320018385\t3.20E+15\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUCLAR\tCLARITY\t9999999\t16070.07391\t3074\t30739996926\t3.07E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tSPECMN\tSPECIMEN\t9999999\t16070.07391\t3060\t30599996940\t3.06E+17\t1759\t28267260\t6.94E+12
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUCOL\tCOLOR\t9999999\t16070.07391\t3052\t30519996948\t3.05E+17\t1759\t28267260\t6.94E+12
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVCP\tRESULT\t8795237.081\t182289.3718\t3048\t26807882624\t2.68E+17\t2945\t536842200\t1.17E+14
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVDNA\tCMV DNA\t9999999\t182289.3718\t3048\t30479996952\t3.05E+17\t2945\t536842200\t1.17E+14
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVLOG\tRESULT LOG 10\t8792650.436\t182289.3718\t3048\t26799998529\t2.68E+17\t2945\t536842200\t1.17E+14
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVSP\tSPECIMEN\t9999999\t182289.3718\t3048\t30479996952\t3.05E+17\t2945\t536842200\t1.17E+14
-POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tINR\tINR, ISTAT\t811797.0058\t41.8380744\t3018\t2450003364\t2.45E+16\t1371\t57360\t44913600
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tTESTLO\tTEST STRIP LOT#\t9750609.052\t-7744.707974\t3003\t29281078982\t2.93E+17\t3938\t-30498660\t4.91E+13
-MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9989938.642\t431748.6235\t2982\t29789997029\t2.98E+17\t1867\t806074680\t3.79E+14
-POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPCTEMP\tBODY TEMPERATURE\t9996625.194\t165.5967358\t2964\t29629997074\t2.96E+17\t2941\t487020\t31049060400
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tPRO\tPRODUCT ID  \t9999999\t41709.62264\t2940\t29399997060\t2.94E+17\t1166\t48633420\t5.74E+12
-POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tPT\tPT, ISTAT\t364851.4639\t41.81551977\t2878\t1050042513\t1.05E+16\t1366\t57120\t44899200
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tOTHER\tOTHERS (DESCRIBE)\t20887.38456\t67574.28263\t2876\t60072118\t6.00E+14\t2781\t187924080\t3.47E+13
-BODY FLUID ORDERABLES\t473172\tLABFTP\tPROTEIN TOTAL, FLUID\tTPFLD\tPROTEIN, TOTAL, FLUID\t1567431.84\t16836.09225\t2763\t4330814173\t4.33E+16\t1561\t26281140\t4.45E+12
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tDI\tISSUE DATE/TIME\t9999999\t39990.02657\t2757\t27569997243\t2.76E+17\t1129\t45148740\t3.06E+12
-MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tRPT\tRPT\t9999999\t1850399.621\t2744\t27439997256\t2.74E+17\t1268\t2346306720\t4.55E+15
-LAB BLOOD ORDERABLES\t472131\tLABDDIM\tD - DIMER\tDDIMER\tD-DIMER\t1514970.172\t4817.497566\t2732\t4138898509\t4.13E+16\t2054\t9895140\t1.11E+11
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC5\tDAILY QC 5 RESULT\t9955783.853\t-7730.784463\t2714\t27019997376\t2.70E+17\t3939\t-30451560\t4.92E+13
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC10\tDAILY QC 10 RESULT\t9977826.075\t-7730.784463\t2706\t26999997360\t2.70E+17\t3939\t-30451560\t4.92E+13
-URINE ORDERABLES\t473341\tLABUNA\tSODIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t85719.48835\t17054.80804\t2686\t230242545.7\t2.30E+15\t1641\t27986940\t3.81E+12
-URINE ORDERABLES\t473341\tLABUNA\tSODIUM WITH CREATININE, URINE (RANDOM)\tUNA\tSODIUM, URINE\t171311.3548\t17054.80804\t2686\t460142299\t4.60E+15\t1641\t27986940\t3.81E+12
-URINE ORDERABLES\t473341\tLABUNA\tSODIUM WITH CREATININE, URINE (RANDOM)\tUNACR\tSODIUM/CREAT RATIO\t6355174.463\t17054.80804\t2686\t17069998607\t1.71E+17\t1641\t27986940\t3.81E+12
-LAB BLOOD ORDERABLES\t496776\tLABHGBX\tHEMOGLOBIN\tHGB\tHEMOGLOBIN\t97277.61893\t4152.9\t2673\t260023075.4\t2.60E+15\t2200\t9136380\t2.31E+11
-MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tSDES\tSPECIMEN\t9999999\t1850399.621\t2671\t26709997329\t2.67E+17\t1268\t2346306720\t4.55E+15
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tPRO\tPRODUCT ID  \t9999999\t44531.81989\t2667\t26669997333\t2.67E+17\t1066\t47470920\t6.04E+12
-LAB BLOOD ORDERABLES\t473260\tLABPATHR\tREVIEW BY PATHOLOGIST\tREVUE\tCOMMENTS\t9999999\t476.0962145\t2649\t26489997351\t2.65E+17\t2536\t1207380\t1.84E+11
-LAB BLOOD ORDERABLES\t977655\tLABSPLAC\tSEPSIS PROTOCOL LACTATE\tSPLACW\tSEPSIS PROTOCOL LACTATE\t347827.7297\t2688.045369\t2645\t920004345.1\t9.20E+15\t2645\t7109880\t1.38E+11
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANCOSDD\tVANCOMYCIN, SINGLE DAILY DOSE\t110074.1901\t6953.366834\t2635\t290045490.9\t2.90E+15\t1990\t13837200\t3.64E+11
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANUN2\tDATE & TIME OF LAST DOSE\t9999999\t6953.366834\t2635\t26349997365\t2.63E+17\t1990\t13837200\t3.64E+11
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANUN3\tDOSAGE\t9999999\t6953.366834\t2635\t26349997365\t2.63E+17\t1990\t13837200\t3.64E+11
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANUN4\tADMINISTRATION ROUTE\t9999999\t6953.366834\t2633\t26329997367\t2.63E+17\t1990\t13837200\t3.64E+11
-MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tSREQ\tCOMMENTS\t9999999\t1850399.621\t2630\t26299997370\t2.63E+17\t1268\t2346306720\t4.55E+15
-POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tPCCOM\tPOCT COMMENT\t9999999\t41.8380744\t2614\t26139997386\t2.61E+17\t1371\t57360\t44913600
-MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t266675.2941\t2599\t25989997401\t2.60E+17\t1275\t340011000\t1.87E+14
-MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t266675.2941\t2555\t25549997445\t2.55E+17\t1275\t340011000\t1.87E+14
-POINT OF CARE TESTING\t471529\tLABPCACTK\tISTAT ACTIVATED CLOTTING TIME\tACT\tACT, ISTAT\t23738.67517\t64.28887251\t2549\t60509883\t6.00E+14\t1357\t87240\t277171200
-POINT OF CARE TESTING\t471529\tLABPCACTK\tISTAT ACTIVATED CLOTTING TIME\tPCCOM\tPOCT COMMENT\t9999999\t64.28887251\t2549\t25489997451\t2.55E+17\t1357\t87240\t277171200
-MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t266675.2941\t2543\t25429997457\t2.54E+17\t1275\t340011000\t1.87E+14
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tDI\tISSUE DATE/TIME\t9999999\t39465.12968\t2532\t25319997468\t2.53E+17\t1041\t41083200\t2.78E+12
-LAB BLOOD ORDERABLES\t471635\tLABANAS\tANTI - NUCLEAR AB\tANA\tANA RESULT\t9999999\t270715.1193\t2521\t25209997479\t2.52E+17\t2348\t635639100\t2.25E+14
-LAB BLOOD ORDERABLES\t471768\tLABTBILFR\tBILIRUBIN TOTAL AND FRACTIONATED, SERUM\tDBIL\tCONJUGATED BILI\t891290.3991\t6420.758465\t2502\t2230008579\t2.23E+16\t2215\t14221980\t2.20E+11
-LAB BLOOD ORDERABLES\t471768\tLABTBILFR\tBILIRUBIN TOTAL AND FRACTIONATED, SERUM\tIBIL\tUNCONJUGATED BILIRUBIN\t1019186.289\t6420.758465\t2502\t2550004094\t2.55E+16\t2215\t14221980\t2.20E+11
-LAB BLOOD ORDERABLES\t471768\tLABTBILFR\tBILIRUBIN TOTAL AND FRACTIONATED, SERUM\tTBIL\tTOTAL BILIRUBIN\t755400.8907\t6420.758465\t2502\t1890013029\t1.89E+16\t2215\t14221980\t2.20E+11
-POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tPATRES\tPATIENT RESULT\t9999999\t17556.14118\t2502\t25019997498\t2.50E+17\t2550\t44768160\t5.81E+14
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tBE\tBASE EXCESS, ISTAT\t40036.09648\t180.6648199\t2498\t100010169\t1.00E+15\t1083\t195660\t10071954000
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t9999999\t12887.03033\t2445\t24449997555\t2.44E+17\t3923\t50555820\t4.05E+12
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tUBT\tUNIT BLOOD TYPE  \t9999999\t51894.30096\t2420\t24199997580\t2.42E+17\t937\t48624960\t5.74E+12
-URINE ORDERABLES\t473134\tLABUPREG\tPREGNANCY TEST, URINE\tUPREG\tPREGNANCY TEST, URINE\t9999999\t13496.7233\t2419\t24189997581\t2.42E+17\t1648\t22242600\t5.33E+12
-POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tKIT1\tKIT LOT#\t314425.4418\t17556.14118\t2390\t751476806\t1.33E+15\t2550\t44768160\t5.81E+14
-POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tKITEX1\tKIT EXP DATE\t9903095.212\t17556.14118\t2378\t23549560415\t2.35E+17\t2550\t44768160\t5.81E+14
-BODY FLUID ORDERABLES\t473172\tLABFTP\tPROTEIN TOTAL, FLUID\tTPFLS\tSOURCE\t9999999\t16541.41399\t2374\t23739997626\t2.37E+17\t1372\t22694820\t3.95E+12
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tCHOL\tCHOLESTEROL, TOTAL\t68360.30009\t7336.118966\t2346\t160373264\t1.60E+15\t2051\t15046380\t2.17E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t225920.314\t7336.118966\t2346\t530009056.7\t5.30E+15\t2051\t15046380\t2.17E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tHDL\tHDL CHOLESTEROL\t213172.8235\t7336.118966\t2346\t500103444\t5.00E+15\t2051\t15046380\t2.17E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tLDL\tLDL (CALCULATED)\t439133.7527\t7336.118966\t2346\t1030207784\t1.03E+16\t2051\t15046380\t2.17E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tLDLHDL\tLDL/HDL RATIO\t434784.766\t7336.118966\t2346\t1020005061\t1.02E+16\t2051\t15046380\t2.17E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tNHDL\tNON-HDL CHOL, CALC\t226029.4437\t7336.118966\t2346\t530265075\t5.30E+15\t2051\t15046380\t2.17E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tTGL\tTRIGLYCERIDE, SER/PLAS\t89633.06522\t7336.118966\t2346\t210279171\t2.10E+15\t2051\t15046380\t2.17E+11
-POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tQC9\tQC RESULT\t9987451.907\t17556.14118\t2283\t22801352704\t2.28E+17\t2550\t44768160\t5.81E+14
-LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIROL\tSIROLIMUS\t1080006.235\t19034.56802\t2250\t2430014030\t2.43E+16\t2014\t38335620\t1.85E+12
-LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIRO2\tDATE & TIME OF LAST DOSE\t9999999\t19034.56802\t2248\t22479997752\t2.25E+17\t2014\t38335620\t1.85E+12
-LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIRO3\tDOSAGE\t9999999\t19034.56802\t2248\t22479997752\t2.25E+17\t2014\t38335620\t1.85E+12
-LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIRO4\tADMINISTRATION ROUTE\t9999999\t19034.56802\t2246\t22459997754\t2.25E+17\t2014\t38335620\t1.85E+12
-LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIROL\tPEAK, TROUGH, OR RANDOM\t9999999\t19034.56802\t2246\t22459997754\t2.25E+17\t2014\t38335620\t1.85E+12
-MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tRPT\tRPT\t9999999\t315224.0375\t2231\t22309997769\t2.23E+17\t1387\t437215740\t3.30E+14
-LAB BLOOD ORDERABLES\t473287\tLABSALICY\tSALICYLATE LEVEL\tSALICYLATE\tSALICYLATE\t9773959.86\t4036.713178\t2212\t21619999211\t2.16E+17\t1935\t7811040\t2.56E+11
-MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tSDES\tSPECIMEN\t9999999\t315224.0375\t2206\t22059997794\t2.21E+17\t1387\t437215740\t3.30E+14
-BLOOD BANK PRODUCT ORDERABLES\t472111\tLABBBPCRY\tCRYOPRECIPITATE\tCRSTA\tCRYOPRECIPITATE UNITS\t9999999\t5035.028133\t2195\t21949997805\t2.19E+17\t1955\t9843480\t4.94E+11
-MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tSREQ\tCOMMENTS\t9999999\t315224.0375\t2181\t21809997819\t2.18E+17\t1387\t437215740\t3.30E+14
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tUBT\tUNIT BLOOD TYPE  \t9999999\t55642.15709\t2178\t21779997822\t2.18E+17\t853\t47462760\t6.04E+12
-MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tRPT\tRPT\t9999999\t1418147.315\t2170\t21699997830\t2.17E+17\t473\t670783680\t1.10E+15
-MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tSDES\tSPECIMEN\t9999999\t1418147.315\t2139\t21389997861\t2.14E+17\t473\t670783680\t1.10E+15
-MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tSREQ\tCOMMENTS\t9999999\t1418147.315\t2126\t21259997874\t2.13E+17\t473\t670783680\t1.10E+15
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUWBCC\tWBC CLUMPS\t9999999\t13407.05179\t2124\t21239997876\t2.12E+17\t1506\t20191020\t3.11E+12
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tPRO\tPRODUCT ID  \t9999999\t40348.33333\t2110\t21099997890\t2.11E+17\t864\t34860960\t4.01E+12
-LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPA\tPEAK, TROUGH, OR RANDOM\t9999999\t8360.571429\t2109\t21089997891\t2.11E+17\t1960\t16386720\t2.42E+12
-LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPA\tVALPROIC ACID\t663875.3016\t8360.571429\t2109\t1400113011\t1.40E+16\t1960\t16386720\t2.42E+12
-LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPAL2\tDATE & TIME OF LAST DOSE\t9999999\t8360.571429\t2109\t21089997891\t2.11E+17\t1960\t16386720\t2.42E+12
-LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPAL3\tDOSAGE\t9999999\t8360.571429\t2109\t21089997891\t2.11E+17\t1960\t16386720\t2.42E+12
-LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTX\tMETHOTREXATE\t2912116.307\t12566.26556\t2105\t6130004826\t6.13E+16\t1928\t24227760\t1.40E+12
-LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTXL2\tDATE & TIME OF LAST DOSE\t9999999\t12566.26556\t2104\t21039997896\t2.10E+17\t1928\t24227760\t1.40E+12
-LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTXL3\tDOSAGE\t9999999\t12566.26556\t2104\t21039997896\t2.10E+17\t1928\t24227760\t1.40E+12
-LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTXL4\tADMINISTRATION ROUTE\t9999999\t12566.26556\t2104\t21039997896\t2.10E+17\t1928\t24227760\t1.40E+12
-LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPAL4\tADMINISTRATION ROUTE\t9999999\t8360.571429\t2101\t21009997899\t2.10E+17\t1960\t16386720\t2.42E+12
-LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTX\tPEAK, TROUGH, OR RANDOM\t9999999\t12566.26556\t2100\t20999997900\t2.10E+17\t1928\t24227760\t1.40E+12
-BODY FLUID ORDERABLES\t472729\tLABFLDH\tLDH TOTAL, FLUID\tLDHFLD\tLDH, TOTAL, FLUID\t406023.8688\t16221.78602\t2096\t851026029\t8.50E+15\t1187\t19255260\t2.94E+12
-URINE ORDERABLES\t473723\tLABURUN\tUREA, URINE (RANDOM)\tUURN\tUREA NITROGEN, URINE\t91450.65742\t16736.09091\t2090\t191131874\t1.90E+15\t1320\t22091640\t2.03E+12
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tFIO2\tFIO2\t4207253.327\t104.0416667\t2075\t8730050654\t8.73E+16\t1440\t149820\t3211340400
-BODY FLUID ORDERABLES\t472729\tLABFLDH\tLDH TOTAL, FLUID\tLDHFLS\tSOURCE\t9999999\t16221.78602\t2071\t20709997929\t2.07E+17\t1187\t19255260\t2.94E+12
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tBE\tBASE EXCESS, ISTAT\t127957.687\t135\t2032\t260010020\t2.60E+15\t928\t125280\t3169742400
-BODY FLUID ORDERABLES\t471614\tLABFAMY\tAMYLASE, FLUID\tAMYLASEFLD\tAMYLASE, FLUID\t976020.501\t14671.16079\t2022\t1973513453\t1.97E+16\t1163\t17062560\t2.52E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tGRCAST\tGRANULAR CASTS\t9999999\t13794.34389\t1986\t19859998014\t1.99E+17\t1326\t18291300\t1.51E+12
-MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tRPT\tRPT\t9999999\t168009.5772\t1972\t19719998028\t1.97E+17\t1159\t194723100\t4.94E+13
-MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tSDES\tSPECIMEN\t9999999\t168009.5772\t1963\t19629998037\t1.96E+17\t1159\t194723100\t4.94E+13
-BODY FLUID ORDERABLES\t471614\tLABFAMY\tAMYLASE, FLUID\tAMYFLS\tSOURCE\t9999999\t14671.16079\t1943\t19429998057\t1.94E+17\t1163\t17062560\t2.52E+12
-LAB BLOOD ORDERABLES\t633373\tLABSTSS\tSYPHILIS TREPONEMAL SCREEN\tSTSQUL\tRESULT\t9974132.492\t239349.2912\t1933\t19279998106\t1.93E+17\t1834\t438966600\t5.36E+15
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tPRO\tPRODUCT ID  \t9999999\t39255.84184\t1914\t19139998086\t1.91E+17\t784\t30776580\t3.61E+12
-LAB BLOOD ORDERABLES\t472402\tLABRBCFOL\tFOLATE, RBC\tFOLATERBC\tFOLATE, RBC\t312085.597\t159556.2945\t1896\t591714292\t5.90E+15\t1657\t264384780\t6.55E+13
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA\tMYCOPHENOLIC ACID\t2869979.962\t110519.9663\t1892\t5430002089\t5.43E+16\t1779\t196615020\t3.18E+13
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPAG\tMPA GLUCURONIDE\t1543401.523\t110519.9663\t1892\t2920115682\t2.92E+16\t1779\t196615020\t3.18E+13
-URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUCMT\tURINE COMMENT\t9999999\t19101.0982\t1878\t18779998122\t1.88E+17\t947\t18088740\t4.39E+12
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA2\tDATE & TIME OF LAST DOSE\t9994706.417\t110354.4488\t1876\t18750069238\t1.87E+17\t1769\t195217020\t3.15E+13
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA3\tDOSAGE\t9994669.043\t110354.4488\t1876\t18749999125\t1.87E+17\t1769\t195217020\t3.15E+13
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA4\tADMINISTRATION ROUTE\t9994668.577\t110354.4488\t1876\t18749998250\t1.87E+17\t1769\t195217020\t3.15E+13
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA1\tPEAK, TROUGH, OR RANDOM\t9999999\t110354.4488\t1864\t18639998136\t1.86E+17\t1769\t195217020\t3.15E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVCP\tRESULT\t8798774.845\t154991.0938\t1801\t15846593496\t1.58E+17\t1801\t279138960\t5.55E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVDNA\tCMV DNA\t9999999\t154991.0938\t1801\t18009998199\t1.80E+17\t1801\t279138960\t5.55E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVLOG\tRESULT LOG 10\t8795113.315\t154991.0938\t1801\t15839999081\t1.58E+17\t1801\t279138960\t5.55E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVSP\tSPECIMEN\t9999999\t154991.0938\t1799\t17989998201\t1.80E+17\t1801\t279138960\t5.55E+13
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tRVPSP\tSPECIMEN\t9999999\t66962.44702\t1798\t17979998202\t1.80E+17\t2501\t167473080\t3.14E+13
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tDI\tISSUE DATE/TIME\t9999999\t36629.87755\t1795\t17949998205\t1.79E+17\t735\t26922960\t1.56E+12
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tXMR\tUNIT CROSSMATCH  \t9999999\t85119.35393\t1777\t17769998223\t1.78E+17\t712\t60604980\t9.72E+12
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tVL\tPRODUCT VOLUME\t177.7713156\t27140.11628\t1771\t314833\t61425727\t516\t14004300\t4.44E+11
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYM\tLYMPHOCYTES\t21.82613636\t29710.52065\t1760\t38414\t1746512\t1671\t49646280\t1.40E+13
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tUBT\tUNIT BLOOD TYPE  \t9999999\t50077.32759\t1737\t17369998263\t1.74E+17\t696\t34853820\t4.01E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYMAB\tLYM, ABS (MAN DIFF)\t2.518998273\t29970.10909\t1737\t4375.5\t318135.53\t1650\t49450680\t1.40E+13
-MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tSREQ\tCOMMENTS\t9999999\t168009.5772\t1737\t17369998263\t1.74E+17\t1159\t194723100\t4.94E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUT\tSEG NEUTROPHILS\t40.20231214\t29234.6894\t1730\t69550\t4143520\t1642\t48003360\t1.15E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUTAB\tNEUT,ABS (MAN DIFF)\t10.13447079\t29421.18077\t1729\t17522.5\t1428888.81\t1643\t48339000\t1.15E+13
-LAB BLOOD ORDERABLES\t473234\tLABREPT\tREPTILASE TIME\tREPT\tREPTILASE TIME\t140374.194\t1946.434109\t1710\t240039871.7\t2.40E+15\t774\t1506540\t16800994800
-LAB BLOOD ORDERABLES\t471767\tLABIBIL\tBILIRUBIN INDIRECT, SERUM\tIBIL\tUNCONJUGATED BILIRUBIN\t2742790.761\t2582.494802\t1699\t4660001503\t4.66E+16\t481\t1242180\t4407022800
-LAB BLOOD ORDERABLES\t472193\tLABDATBSI\tDIRECT COOMBS BROAD SPECTRUM\tDATBS\tDIRECT ANTIGLOB.-BROAD SPEC\t9999999\t17193.15353\t1697\t16969998303\t1.70E+17\t1446\t24861300\t4.24E+12
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tXMR\tUNIT CROSSMATCH  \t9999999\t81558.64507\t1682\t16819998318\t1.68E+17\t679\t55378320\t8.68E+12
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tBD\tBASE DEFICIT, ISTAT\t115858.5683\t166.0079051\t1640\t190008052\t1.90E+15\t759\t126000\t3210127200
-LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN (HIT) AB\tPF4ENH\tPF4 IGG, IGM, IGA OD\t220318.5292\t89701.14358\t1634\t360000476.8\t3.60E+15\t1574\t141189600\t1.41E+13
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tCR\tCREATININE, SER/PLAS\t42867.40214\t5265.807963\t1633\t70002467.7\t7.00E+14\t1281\t6745500\t1.03E+11
-POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tBD\tBASE DEFICIT, ISTAT\t43215.22901\t264.5244216\t1620\t70008671\t7.00E+14\t778\t205800\t10291471200
-VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tRPT\tRPT\t9999999\t198392.7935\t1618\t16179998382\t1.62E+17\t247\t49003020\t1.01E+13
-LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1602\t16019998398\t1.60E+17\t0\tNone\tNone
-VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tSREQ\tCOMMENTS\t9999999\t198392.7935\t1602\t16019998398\t1.60E+17\t247\t49003020\t1.01E+13
-BODY FLUID ORDERABLES\t471568\tLABFAL\tALBUMIN, FLUID\tALBFLD\tALBUMIN, FLUID\t5668750.195\t12203.72671\t1600\t9070000312\t9.07E+16\t966\t11788800\t1.51E+12
-VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tSDES\tSPECIMEN\t9999999\t198392.7935\t1598\t15979998402\t1.60E+17\t247\t49003020\t1.01E+13
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tDI\tISSUE DATE/TIME\t9999999\t39085.46565\t1597\t15969998403\t1.60E+17\t655\t25600980\t1.78E+12
-BODY FLUID ORDERABLES\t471568\tLABFAL\tALBUMIN, FLUID\tALBFLS\tSOURCE\t9999999\t12203.72671\t1586\t15859998414\t1.59E+17\t966\t11788800\t1.51E+12
-LAB BLOOD ORDERABLES\t590524\tLABASI\tAB SCREEN (ASI)\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1574\t15739998426\t1.57E+17\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tUBT\tUNIT BLOOD TYPE  \t9999999\t50032.09756\t1546\t15459998454\t1.55E+17\t615\t30769740\t3.61E+12
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tPRO\tPRODUCT ID  \t9999999\t37720.88748\t1542\t15419998458\t1.54E+17\t631\t23801880\t2.56E+12
-LAB BLOOD ORDERABLES\t886807\tLABTSHFT4\tTSH W/ REFLEX FT4\tTSH\tTSH\t117649.7788\t34898.89628\t1530\t180004161.5\t1.80E+15\t1504\t52487940\t3.59E+12
-LAB BLOOD ORDERABLES\t471770\tLABTBIL\tBILIRUBIN TOTAL, SERUM/PLASMA\tTBIL\tTOTAL BILIRUBIN\t3494765.309\t4625.089286\t1528\t5340001392\t5.34E+16\t896\t4144080\t1.70E+11
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tSI\t<<STATUS>>  \t9999999\t50909.33809\t1516\t15159998484\t1.52E+17\t3082\t156902580\t2.17E+13
-MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tRPT\tRPT\t9999999\t74295.58989\t1516\t15159998484\t1.52E+17\t712\t52898460\t3.10E+13
-MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tRPT\tRPT\t9999999\t361273.8129\t1503\t15029998497\t1.50E+17\t556\t200868240\t1.60E+14
-VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tRPT\tRPT\t9999999\t99400.67114\t1501\t15009998499\t1.50E+17\t596\t59242800\t1.23E+14
-VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tSDES\tSPECIMEN\t9999999\t99400.67114\t1501\t15009998499\t1.50E+17\t596\t59242800\t1.23E+14
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBAND\tBAND NEUTROPHILS\t12.62333333\t26083.26072\t1500\t18935\t470187\t1423\t37116480\t5.14E+12
-VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tSREQ\tCOMMENTS\t9999999\t99400.67114\t1496\t14959998504\t1.50E+17\t596\t59242800\t1.23E+14
-MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tSDES\tSPECIMEN\t9999999\t74295.58989\t1494\t14939998506\t1.49E+17\t712\t52898460\t3.10E+13
-LAB BLOOD ORDERABLES\t473173\tLABTP\tPROTEIN TOTAL, SERUM/PLASMA\tTP\tPROTEIN, TOTAL, SER/PLAS\t67619.65308\t6765.357548\t1479\t100009466.9\t1.00E+15\t881\t5960280\t88194196800
-LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON TEST FOR LATENT TB\tQTFNRS\tQUANTIFERON-TB\t9999999\t852519.229\t1472\t14719998528\t1.47E+17\t1297\t1105717440\t3.84E+16
-URINE ORDERABLES\t472317\tLABUEOS\tEOSINOPHILS, URINE\tUEOS\tEOSINOPHILS, URINE\t9388586.308\t21586.62116\t1472\t13819999046\t1.38E+17\t879\t18974640\t2.01E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONO\tMONOCYTES\t9.077603812\t27317.81792\t1469\t13335\t251391\t1384\t37807860\t7.38E+12
-MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tSDES\tSPECIMEN\t9999999\t361273.8129\t1447\t14469998553\t1.45E+17\t556\t200868240\t1.60E+14
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tSI\t<<STATUS>>  \t9999999\t50253.88336\t1446\t14459998554\t1.45E+17\t2812\t141313920\t1.94E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONOAB\tMONO, ABS (MAN DIFF)\t0.962274619\t27646.01471\t1442\t1387.6\t6393.32\t1360\t37598580\t7.38E+12
-LAB BLOOD ORDERABLES\t473062\tLABPTH\tPARATHYROID HORMONE\tPTH\tPARATHYROID HORMONE\t1622716.105\t96576.60091\t1436\t2330220327\t2.33E+16\t1318\t127287960\t1.99E+13
-LAB BLOOD ORDERABLES\t473133\tLABPREG\tPREGNANCY TEST, SERUM\tPREGTEST\tPREGNANCY, SERUM\t9999999\t6294.605263\t1434\t14339998566\t1.43E+17\t1368\t8611020\t1.85E+11
-MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tSREQ\tCOMMENTS\t9999999\t74295.58989\t1433\t14329998567\t1.43E+17\t712\t52898460\t3.10E+13
-MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tSREQ\tCOMMENTS\t9999999\t361273.8129\t1429\t14289998571\t1.43E+17\t556\t200868240\t1.60E+14
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVCP\tRESULT\t9199405.892\t184331.1162\t1423\t13090754585\t1.31E+17\t1317\t242764080\t5.22E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVLOG\tRESULT LOG 10\t9198874.967\t184331.1162\t1423\t13089999078\t1.31E+17\t1317\t242764080\t5.22E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVSP\tSPECIMEN\t9999999\t184331.1162\t1423\t14229998577\t1.42E+17\t1317\t242764080\t5.22E+13
-LAB BLOOD ORDERABLES\t472528\tLABHCVA\tHEPATITIS C AB IGG\tHCVR\tANTI-HCV\t9992951.785\t57183.66116\t1419\t14179998584\t1.42E+17\t1393\t79656840\t1.00E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tABNWBC\tBLASTS/ABNORMAL CELLS\t9999999\t23173.85257\t1392\t13919998608\t1.39E+17\t1438\t33324000\t5.02E+12
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tPRO\tPRODUCT ID  \t9999999\t40376.78201\t1389\t13889998611\t1.39E+17\t578\t23337780\t2.61E+12
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFCMT\tFLUID COMMENT\t9999999\t18528.28548\t1370\t13699998630\t1.37E+17\t1219\t22585980\t2.84E+12
-LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI\tLITHIUM\t1536211.145\t8902.52322\t1367\t2100000635\t2.10E+16\t1292\t11502060\t5.77E+11
-LAB BLOOD ORDERABLES\t472619\tLABIGG\tIGG, SERUM\tIGG\tIGG, SERUM\t88673.34334\t49752.98969\t1366\t121127787\t1.20E+15\t1261\t62738520\t8.14E+12
-BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tGLUFLD\tGLUCOSE, FLUID\t468975.4842\t12717.30815\t1365\t640151536\t6.40E+15\t847\t10771560\t1.15E+12
-LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI2\tDATE & TIME OF LAST DOSE\t9999999\t8902.52322\t1365\t13649998635\t1.36E+17\t1292\t11502060\t5.77E+11
-LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI3\tDOSAGE\t9999999\t8902.52322\t1365\t13649998635\t1.36E+17\t1292\t11502060\t5.77E+11
-COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tMANUF\tMANUFACTURER\t9999999\t181776.5501\t1363\t13629998637\t1.36E+17\t1287\t233946420\t5.82E+13
-COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHIPINT\tINTERPRETATION\t9999999\t182382.61\t1362\t13619998638\t1.36E+17\t1318\t240380280\t6.01E+13
-LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI4\tADMINISTRATION ROUTE\t9999999\t8902.52322\t1361\t13609998639\t1.36E+17\t1292\t11502060\t5.77E+11
-COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHEPTYP\tHEPARIN TYPE\t9999999\t181776.5501\t1360\t13599998640\t1.36E+17\t1287\t233946420\t5.82E+13
-LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tSPTY\tSPECIMEN TYPE\t9999999\t976025.9071\t1354\t13539998646\t1.35E+17\t904\t882327420\t7.73E+15
-POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCTEMP\tBODY TEMPERATURE\t9999999\t109.2250373\t1353\t13529998647\t1.35E+17\t1342\t146580\t3211030800
-LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI\tPEAK TROUGH OR RANDOM\t9999999\t8902.52322\t1349\t13489998651\t1.35E+17\t1292\t11502060\t5.77E+11
-BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tGLFLS\tSOURCE\t9999999\t12717.30815\t1346\t13459998654\t1.35E+17\t847\t10771560\t1.15E+12
-BODY FLUID ORDERABLES\t472106\tLABFCR\tCREATININE, FLUID\tCRFLD\tCREATININE, FLUID\t336829.3196\t7390.228188\t1336\t450003971\t4.50E+15\t745\t5505720\t1.31E+12
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tEOSFLD\tEOSINOPHIL %, FLD\t15064.92018\t22434.74138\t1328\t20006214\t2.00E+14\t696\t15614580\t5.31E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tBYST\tYEAST\t9999999\t16387.5\t1324\t13239998676\t1.32E+17\t832\t13634400\t2.26E+12
-BODY FLUID ORDERABLES\t472106\tLABFCR\tCREATININE, FLUID\tCRFLS\tSOURCE\t9999999\t7390.228188\t1317\t13169998683\t1.32E+17\t745\t5505720\t1.31E+12
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tDI\tISSUE DATE/TIME\t9999999\t36229.09091\t1299\t12989998701\t1.30E+17\t539\t19527480\t1.22E+12
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVDNA\tCMV DNA\t9999999\t184331.1162\t1288\t12879998712\t1.29E+17\t1317\t242764080\t5.22E+13
-LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tTEST1\tTEST NAME\t9976785.432\t976025.9071\t1285\t12820169280\t1.28E+17\t904\t882327420\t7.73E+15
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tHCO3A\tHCO3 (A), ISTAT\t31249.34083\t30\t1281\t40030405.6\t4.00E+14\t538\t16140\t1134000
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tO2SATA\tO2 SATURATION, ISTAT\t39125.44575\t30\t1281\t50119696\t5.00E+14\t538\t16140\t1134000
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPCO2A\tPCO2 (A), ISTAT\t31271.021\t30\t1281\t40058177.9\t4.00E+14\t538\t16140\t1134000
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPHA\tPH (A), ISTAT\t31232.91026\t30\t1281\t40009358.04\t4.00E+14\t538\t16140\t1134000
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPO2A\tPO2 (A), ISTAT\t23573.47931\t30\t1281\t30197627\t3.00E+14\t538\t16140\t1134000
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tTCO2A\tTCO2, ISTAT\t54669.89149\t30\t1281\t70032131\t7.00E+14\t538\t16140\t1134000
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tLAC\tLACTATE, ISTAT\t211270.051\t30\t1278\t270003125.2\t2.70E+15\t538\t16140\t1134000
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tUBT\tUNIT BLOOD TYPE  \t9999999\t46753.20236\t1265\t12649998735\t1.26E+17\t509\t23797380\t2.56E+12
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tGRCAST\tGRANULAR CASTS\t9999999\t16965.60886\t1255\t12549998745\t1.25E+17\t813\t13793040\t2.38E+12
-LAB BLOOD ORDERABLES\t471580\tLABAFP\tALPHA FETOPROTEIN, SERUM\tAFP\tALPHA FETOPROTEIN\t1259094.607\t38614.23077\t1226\t1543649988\t1.54E+16\t1092\t42166740\t2.96E+12
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGA\tANGLE, FIBRINOGEN ACTIVITY\t3379634.256\t29423.95102\t1225\t4140051964\t4.14E+16\t1225\t36044340\t2.93E+13
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGC\tR, REACTION TIME (CK)\t3355106.119\t29423.95102\t1225\t4110004996\t4.11E+16\t1225\t36044340\t2.93E+13
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGEL\tEPL, ESTIMATED LYSIS\t3387755.03\t29423.95102\t1225\t4149999911\t4.15E+16\t1225\t36044340\t2.93E+13
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGG\tG, CLOT STABILITY\t3379597.4\t29423.95102\t1225\t4140006816\t4.14E+16\t1225\t36044340\t2.93E+13
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGK\tK, CLOT KINETICS\t3387756.085\t29423.95102\t1225\t4150001204\t4.15E+16\t1225\t36044340\t2.93E+13
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGMA\tMA, PLATELET FUNCTION\t3379632.77\t29423.95102\t1225\t4140050143\t4.14E+16\t1225\t36044340\t2.93E+13
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGRK\tR, REACTION TIME (CK/H)\t400006.1288\t29423.95102\t1225\t490007507.8\t4.90E+15\t1225\t36044340\t2.93E+13
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tPRO\tPRODUCT ID  \t9999999\t39146.22824\t1200\t11999998800\t1.20E+17\t517\t20238600\t2.20E+12
-LAB BLOOD ORDERABLES\t621214\tLABDDIML\tD - DIMER (ELISA)\tDDIMER\tD-DIMER, ELISA\t754575.6921\t5591.974249\t1195\t901717952\t9.00E+15\t932\t5211720\t50304153600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCCNT\tCELLS COUNTED\t49.56281407\t17294.00173\t1194\t59178\t3402588\t1157\t20009160\t1.43E+13
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t30\t1190\t11899998810\t1.19E+17\t538\t16140\t1134000
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tDI\tISSUE DATE/TIME\t9999999\t37638.57434\t1171\t11709998829\t1.17E+17\t491\t18480540\t1.26E+12
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tBYST\tYEAST\t9999999\t17815.31629\t1161\t11609998839\t1.16E+17\t743\t13236780\t2.49E+12
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t56970.32432\t1158\t11579998842\t1.16E+17\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t56970.32432\t1158\t11579998842\t1.16E+17\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t56970.32432\t1157\t11569998843\t1.16E+17\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t56970.32432\t1156\t11559998844\t1.16E+17\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHAVR\tIGM ANTI-HAV\t9999999\t55606.13636\t1138\t11379998862\t1.14E+17\t1056\t58720080\t7.63E+12
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t55606.13636\t1138\t11379998862\t1.14E+17\t1056\t58720080\t7.63E+12
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t55606.13636\t1138\t11379998862\t1.14E+17\t1056\t58720080\t7.63E+12
-LAB BLOOD ORDERABLES\t862879\tLABVD25H\tVITAMIN D, 25-HYDROXYVITAMIN\t25OHDT\t25-HYDROXY D, TOTAL\t352444.1815\t383024.4913\t1135\t400024146\t4.00E+15\t1091\t417879720\t1.88E+14
-LAB BLOOD ORDERABLES\t862879\tLABVD25H\tVITAMIN D, 25-HYDROXYVITAMIN\tVD25D2\t25-HYDROXY D2\t8651983.717\t383024.4913\t1135\t9820001519\t9.82E+16\t1091\t417879720\t1.88E+14
-LAB BLOOD ORDERABLES\t862879\tLABVD25H\tVITAMIN D, 25-HYDROXYVITAMIN\tVD25D3\t25-HYDROXY D3\t537463.9859\t383024.4913\t1135\t610021624\t6.10E+15\t1091\t417879720\t1.88E+14
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBCINT\tINTERPRETATION\t9999999\t55606.13636\t1130\t11299998870\t1.13E+17\t1056\t58720080\t7.63E+12
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tUBT\tUNIT BLOOD TYPE  \t9999999\t49645.91489\t1128\t11279998872\t1.13E+17\t470\t23333580\t2.61E+12
-URINE ORDERABLES\t473722\tLABUUN\tUREA WITH CREATININE, URINE (RANDOM)\tUNCRU\tURINE CREAT\t80162.36379\t18148.1407\t1124\t90102496.9\t9.00E+14\t597\t10834440\t1.25E+12
-URINE ORDERABLES\t473722\tLABUUN\tUREA WITH CREATININE, URINE (RANDOM)\tUNUCRU\tUREA/CREAT RATIO\t106769.7333\t18148.1407\t1124\t120009180.3\t1.20E+15\t597\t10834440\t1.25E+12
-URINE ORDERABLES\t473722\tLABUUN\tUREA WITH CREATININE, URINE (RANDOM)\tUURN\tUREA NITROGEN, URINE\t89520.93505\t18148.1407\t1124\t100621531\t1.00E+15\t597\t10834440\t1.25E+12
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9982077.868\t55606.13636\t1116\t11139998901\t1.11E+17\t1056\t58720080\t7.63E+12
-VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQLG\tRESULT LOG 10\t8968325.277\t220580.5\t1105\t9909999431\t9.91E+16\t1080\t238226940\t6.52E+13
-LAB BLOOD ORDERABLES\t633410\tLABRPRQL\tRPR QUALITATIVE\tRPRQ\tRESULT\t9999999\t63888.99563\t1104\t11039998896\t1.10E+17\t687\t43891740\t8.49E+12
-VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQCP\tRESULT\t8986067.446\t220580.5\t1104\t9920618460\t9.90E+16\t1080\t238226940\t6.52E+13
-VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQSP\tSPECIMEN\t9999999\t220580.5\t1104\t11039998896\t1.10E+17\t1080\t238226940\t6.52E+13
-BODY FLUID ORDERABLES\t473351\tLABPROC\tSPECIMEN PROCESSING\tPROC\tSPECIMEN PROCESSING\t9999999\t1796.035578\t1102\t11019998898\t1.10E+17\t787\t1413480\t7.84E+11
-MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tRPT\tRPT\t9999999\t643208.0247\t1084\t10839998916\t1.08E+17\t486\t312599100\t2.25E+14
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tUPH\tPH URINE\t27785.07176\t2327.256637\t1080\t30007877.5\t3.00E+14\t1130\t2629800\t4.75E+14
-VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQDN\tEBV DNA\t9999999\t220580.5\t1072\t10719998928\t1.07E+17\t1080\t238226940\t6.52E+13
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tPRO\tPRODUCT ID  \t9999999\t36165.4902\t1067\t10669998933\t1.07E+17\t459\t16599960\t1.66E+12
-MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tSDES\tSPECIMEN\t9999999\t643208.0247\t1063\t10629998937\t1.06E+17\t486\t312599100\t2.25E+14
-MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tSREQ\tCOMMENTS\t9999999\t643208.0247\t1059\t10589998941\t1.06E+17\t486\t312599100\t2.25E+14
-POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tGLU\tGLUCOSE BY METER\t135573.9374\t203485.6514\t1034\t140183451.3\t1.40E+15\t1090\t221799360\t1.06E+16
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t55606.13636\t1032\t10319998968\t1.03E+17\t1056\t58720080\t7.63E+12
-URINE ORDERABLES\t473170\tLABUTPRAN\tPROTEIN WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREATININE\t78748.20374\t18364.8074\t1017\t80086923.2\t8.00E+14\t649\t11918760\t2.18E+12
-URINE ORDERABLES\t473170\tLABUTPRAN\tPROTEIN WITH CREATININE, URINE (RANDOM)\tUPROTCR\tPROTEIN/CREAT RATIO\t766964.0751\t18364.8074\t1017\t780002464.3\t7.80E+15\t649\t11918760\t2.18E+12
-URINE ORDERABLES\t473170\tLABUTPRAN\tPROTEIN WITH CREATININE, URINE (RANDOM)\tUPROTTOT\tPROTEIN, TOTAL, URINE\t472130.1534\t18364.8074\t1017\t480156366\t4.80E+15\t649\t11918760\t2.18E+12
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tSI\t<<STATUS>>  \t9999999\t45922.88372\t1015\t10149998985\t1.01E+17\t1935\t88860780\t1.11E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t63540.24819\t1015\t10149998985\t1.01E+17\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t63540.24819\t1015\t10149998985\t1.01E+17\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t63540.24819\t1014\t10139998986\t1.01E+17\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t63540.24819\t1014\t10139998986\t1.01E+17\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGC\tR, REACTION TIME (CK)\t2195851.888\t18241.67973\t1011\t2220006259\t2.22E+16\t893\t16289820\t1.94E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGRK\tR, REACTION TIME (CK/H)\t2116721.757\t18241.67973\t1011\t2140005696\t2.14E+16\t893\t16289820\t1.94E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGA\tANGLE, FIBRINOGEN AC\t2188171.385\t18225.9417\t1010\t2210053099\t2.21E+16\t892\t16257540\t1.94E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGG\tG, CLOT STABILITY\t2188128.064\t18225.9417\t1010\t2210009345\t2.21E+16\t892\t16257540\t1.94E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGK\tK, CLOT KINETICS\t2217823.037\t18225.9417\t1010\t2240001267\t2.24E+16\t892\t16257540\t1.94E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGM\tMA, PLT. FUNCTION\t2188172.076\t18225.9417\t1010\t2210053797\t2.21E+16\t892\t16257540\t1.94E+12
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGEL\tEPL, ESTIMATED LYSIS\t2190287.787\t18224.78114\t1009\t2210000377\t2.21E+16\t891\t16238280\t1.94E+12
-MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t342848.8073\t1009\t10089998991\t1.01E+17\t436\t149482080\t1.48E+14
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t63540.24819\t996\t9959999004\t9.96E+16\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABIDRBC\tANTIBODY ID RBC\t9999999\t42315.55932\t990\t9899999010\t9.90E+16\t590\t24966180\t3.53E+13
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tSI\t<<STATUS>>  \t9999999\t44758.42809\t986\t9859999014\t9.86E+16\t1794\t80296620\t9.91E+12
-MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t342848.8073\t986\t9859999014\t9.86E+16\t436\t149482080\t1.48E+14
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tRPT\tRPT\t9999999\t324263.2112\t983\t9829999017\t9.83E+16\t928\t300916260\t1.71E+14
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tDI\tISSUE DATE/TIME\t9999999\t33804.49878\t978\t9779999022\t9.78E+16\t409\t13826040\t7.48E+11
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t324263.2112\t975\t9749999025\t9.75E+16\t928\t300916260\t1.71E+14
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tXMR\tUNIT CROSSMATCH  \t9999999\t78246.38961\t974\t9739999026\t9.74E+16\t385\t30124860\t4.86E+12
-LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tLAB1\tREFERENCE LAB\t9999999\t976025.9071\t964\t9639999036\t9.64E+16\t904\t882327420\t7.73E+15
-ECHO ORDERABLES\t472301\tECH15\tECHO - TRANSTHORACIC ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t50.51612903\t327040.5203\t961\t48546\t2670048\t961\t314285940\t3.83E+15
-MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tRPT\tRPT\t9999999\t464348.8949\t961\t9609999039\t9.61E+16\t371\t172273440\t9.73E+14
-MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t464348.8949\t960\t9599999040\t9.60E+16\t371\t172273440\t9.73E+14
-MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t342848.8073\t957\t9569999043\t9.57E+16\t436\t149482080\t1.48E+14
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tUBT\tUNIT BLOOD TYPE  \t9999999\t48640.67308\t955\t9549999045\t9.55E+16\t416\t20234520\t2.20E+12
-VIROLOGY ORDERABLES\t473715\tLABMVSOB\tUNLISTED MICROBIOLOGY/VIROLOGY TEST\tMVSO\tTEST NAME\t9999999\t681043.9907\t945\t9449999055\t9.45E+16\t431\t293529960\t8.51E+14
-MICROBIOLOGY ORDERABLES\t472498\tLABHELI\tHELICOBACTER PYLORI AB\tHPYLORI\tHELICOB. PYLORI IGG AB\t9999999\t348411.9064\t937\t9369999063\t9.37E+16\t897\t312525480\t1.42E+14
-BODY FLUID ORDERABLES\t473089\tLABPHME\tPH BY METER\tPHMETS\tSOURCE\t9999999\t17450.32258\t926\t9259999074\t9.26E+16\t496\t8655360\t1.26E+12
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tXMR\tUNIT CROSSMATCH  \t9999999\t82301.64894\t923\t9229999077\t9.23E+16\t376\t30945420\t5.13E+12
-LAB BLOOD ORDERABLES\t471951\tLABCEA\tCEA\tCEA\tCEA\t1722715.411\t34760.8134\t923\t1590066324\t1.59E+16\t836\t29060040\t1.77E+12
-LAB BLOOD ORDERABLES\t472503\tLAB11041R\tFREE HEMOGLOBIN\tHGB\tHEMOGLOBIN, SERUM\t431902.3998\t181763.645\t903\t390007867\t3.90E+15\t845\t153590280\t4.24E+13
-LAB BLOOD ORDERABLES\t472562\tLABHCLOTI\tHOLD CLOT\tHCLOT\tHOLD CLOT\t9999999\t81.25\t897\t8969999103\t8.97E+16\t288\t23400\t81900000
-LAB BLOOD ORDERABLES\t472937\tLABMYO\tMYOGLOBIN, SERUM / PLASMA\tMYOG\tMYOGLOBIN, SER/PLAS\t1901719.799\t6401.506849\t894\t1700137500\t1.70E+16\t730\t4673100\t1.14E+11
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tPRO\tPRODUCT ID  \t9999999\t36168.4375\t892\t8919999108\t8.92E+16\t384\t13888680\t1.55E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPSREV\tPATH/SPEC REVIEW\t9999999\t109814.5616\t891\t8909999109\t8.91E+16\t4619\t507233460\t1.01E+14
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t887\t8869999113\t8.87E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tAG\tANION GAP\t418559.4548\t7149.481669\t884\t370006558\t3.70E+15\t791\t5655240\t2.94E+11
-LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tCL\tCHLORIDE, SER/PLAS\t11415.4457\t7149.481669\t884\t10091254\t1.00E+14\t791\t5655240\t2.94E+11
-LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tCO2\tCO2, SER/PLAS\t282830.9355\t7149.481669\t884\t250022547\t2.50E+15\t791\t5655240\t2.94E+11
-LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tK\tPOTASSIUM, SER/PLAS\t22628.49468\t7149.481669\t884\t20003589.3\t2.00E+14\t791\t5655240\t2.94E+11
-LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tNA\tSODIUM, SER/PLAS\t11449.75566\t7149.481669\t884\t10121584\t1.00E+14\t791\t5655240\t2.94E+11
-LAB BLOOD ORDERABLES\t517611\tLABCA199\tCANCER AG 19-9\tC199\tCANCER ANTIGEN 19-9\t577390.8375\t54412.61194\t874\t504639592\t5.00E+15\t804\t43747740\t6.01E+12
-MICROBIOLOGY ORDERABLES\t471695\tLABAGALA\tASPERGILLUS GALACTOMANNAN\tAGALA\tASPER.GALACTOMANNAN\t184544.6402\t391343.0659\t867\t160000203.1\t1.60E+15\t835\t326771460\t1.62E+14
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tDI\tISSUE DATE/TIME\t9999999\t36268.46361\t844\t8439999156\t8.44E+16\t371\t13455600\t8.83E+11
-LAB BLOOD ORDERABLES\t744412\tLABCORTS\tCORTISOL, POST OP\tCORTS\tCORTISOL, POST OP\t332561.1871\t10774.85294\t842\t280016519.5\t2.80E+15\t816\t8792280\t3.68E+11
-VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-PLASMA\tCMQDNA\tCMV DNA\t9999999\t242296.5517\t842\t8419999158\t8.42E+16\t116\t28106400\t9.68E+12
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tUBT\tUNIT BLOOD TYPE  \t9999999\t45845.13812\t832\t8319999168\t8.32E+16\t362\t16595940\t1.66E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tCAOX\tCA OXALATE CRYSTALS\t9999999\t11074.21648\t832\t8319999168\t8.32E+16\t619\t6854940\t3.04E+11
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFOTHER\tOTHERS, FLD\t11.05180723\t16276.09442\t830\t9173\t464053\t466\t7584660\t6.68E+11
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBGRES\tQUANTIFERON RESULT\t9999999\t226235.1951\t820\t8199999180\t8.20E+16\t820\t185512860\t8.35E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tADNVIR\tADENOVIRUS\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tINFAST\tINFLUENZA A SUBTYPE\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tINFLA\tINFLUENZA A\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tINFLB\tINFLUENZA B\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tMETPNV\tMETAPNEUMOVIRUS\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tPARIN1\tPARAINFLUENZA 1\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tPARIN2\tPARAINFLUENZA 2\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tPARIN3\tPARAINFLUENZA 3\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRHNVIR\tRHINOVIRUS\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRSVPC\tRSV\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBGNIL\tNIL\t196560.2903\t226235.1951\t814\t160000076.3\t1.60E+15\t820\t185512860\t8.35E+13
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGSP\tSP, SPLIT POINT\t307132.4496\t18308.78049\t814\t250005814\t2.50E+15\t902\t16514520\t1.95E+12
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tPRO\tPRODUCT ID  \t9999999\t35683.83954\t812\t8119999188\t8.12E+16\t349\t12453660\t1.39E+12
-LAB BLOOD ORDERABLES\t473456\tLABIGET\tTOTAL IGE\tIGET\tTOTAL IGE\t382545.9766\t236444.3041\t811\t310244787\t3.10E+15\t776\t183480780\t6.09E+13
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBAGNI\tTB AG - NIL\t4002478.18\t226235.1951\t807\t3229999891\t3.23E+16\t820\t185512860\t8.35E+13
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBAG\tTB AG\t3995037.156\t226235.1951\t806\t3219999948\t3.22E+16\t820\t185512860\t8.35E+13
-VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMGQUL\tCMV IGG RESULT\t9999999\t157732.7413\t799\t7989999201\t7.99E+16\t777\t122558340\t2.53E+13
-LAB BLOOD ORDERABLES\t471756\tLABBHCG\tBETA HCG\tBHCG\tBETA HCG\t5170843.619\t8779.419087\t798\t4126333208\t4.12E+16\t723\t6347520\t1.10E+11
-LAB BLOOD ORDERABLES\t472054\tLABC3\tCOMPLEMENT C3, SERUM\tC3\tCOMPLEMENT C3, SERUM\t25258.54717\t49308.25737\t795\t20080545\t2.00E+14\t746\t36783960\t3.49E+12
-LAB BLOOD ORDERABLES\t472055\tLABC4\tCOMPLEMENT C4, SERUM\tC4\tCOMPLEMENT C4, SERUM\t201280.8872\t49642.3498\t795\t160018305.3\t1.60E+15\t749\t37182120\t3.54E+12
-LAB BLOOD ORDERABLES\t473410\tLABYFT3\tT3, FREE\tFT3\tT3, FREE\t113210.0052\t34773.5493\t795\t90001954.1\t9.00E+14\t710\t24689220\t1.55E+12
-MICROBIOLOGY ORDERABLES\t472113\tLABCRPLB\tCRYPTOCOCCAL AG LATEX, BLOOD\tCRPLB\tLATEX CRYPTO AG-BID\t9999999\t94189.77543\t791\t7909999209\t7.91E+16\t757\t71301660\t9.81E+12
-LAB BLOOD ORDERABLES\t471701\tLABAST\tAST, SERUM/PLASMA\tAST\tAST (SGOT), SER/PLAS\t303900.5354\t3702.536023\t790\t240081423\t2.40E+15\t347\t1284780\t90954759600
-LAB BLOOD ORDERABLES\t472528\tLABHCVA\tANTI-HCV\tHCVR\tANTI-HCV\t9999999\t42280.72931\t790\t7899999210\t7.90E+16\t713\t30146160\t3.88E+12
-VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVLOG\tRESULT LOG 10\t6393863.376\t306451.3492\t782\t5000001160\t5.00E+16\t756\t231677220\t1.10E+14
-VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVRES\tRESULT\t7647170.927\t306451.3492\t782\t5980087665\t5.78E+16\t756\t231677220\t1.10E+14
-MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tRPT\tRPT\t9999999\t273578.4971\t780\t7799999220\t7.80E+16\t173\t47329080\t1.50E+13
-MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tSREQ\tCOMMENTS\t9999999\t273578.4971\t780\t7799999220\t7.80E+16\t173\t47329080\t1.50E+13
-VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVSP\tSPECIMEN\t9999999\t306451.3492\t780\t7799999220\t7.80E+16\t756\t231677220\t1.10E+14
-LAB BLOOD ORDERABLES\t471588\tLABALT\tALT, SERUM/PLASMA\tALT\tALT (SGPT), SER/PLAS\t243998.7587\t4517.267442\t779\t190075033\t1.90E+15\t344\t1553940\t24505693200
-LAB BLOOD ORDERABLES\t473182\tLABPSA\tPSA\tPSA\tPSA\t1171204.076\t37028.55882\t777\t910025567.2\t9.10E+15\t680\t25179420\t1.67E+12
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tBD\tBASE DEFICIT, ISTAT\t25880.27814\t31.01123596\t773\t20005455\t2.00E+14\t356\t11040\t799200
-BODY FLUID ORDERABLES\t472721\tLABCLACT\tLACTIC ACID, CSF\tLACCSF\tLACTIC ACID, CSF\t104852.7679\t10233.44262\t763\t80002661.9\t8.00E+14\t427\t4369680\t3.87E+11
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVCP\tRESULT\t7190521.954\t295560.7542\t761\t5471987207\t5.46E+16\t716\t211621500\t8.15E+13
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVDNA\tEBV DNA\t9999999\t295560.7542\t761\t7609999239\t7.61E+16\t716\t211621500\t8.15E+13
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVLOG\tRESULT LOG\t7174770.359\t295560.7542\t761\t5460000243\t5.46E+16\t716\t211621500\t8.15E+13
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVSP\tSPECIMEN\t9999999\t295560.7542\t761\t7609999239\t7.61E+16\t716\t211621500\t8.15E+13
-VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVRNA\tHEP C VIRVUS RNA\t9999999\t306451.3492\t761\t7609999239\t7.61E+16\t756\t231677220\t1.10E+14
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tHEMED\tHEMOCCULT EXP DATE\t9802405.581\t10328.26531\t758\t7430223430\t7.43E+16\t784\t8097360\t3.21E+13
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tHEML\tHEMOCCULT LOT#\t9081525.997\t10328.26531\t758\t6883796706\t6.88E+16\t784\t8097360\t3.21E+13
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSLYM\tLYMPHOCYTES, SYN FLD\t343474.2616\t11175.33937\t757\t260010016\t2.60E+15\t442\t4939500\t4.18E+11
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSMONO\tMONOS/MESOS, SYN FLD\t343479.1678\t11175.33937\t757\t260013730\t2.60E+15\t442\t4939500\t4.18E+11
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSNEUT\tNEUTROPHILS, SYN FLD\t343524.5429\t11175.33937\t757\t260048079\t2.60E+15\t442\t4939500\t4.18E+11
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSRBC\tRBC COUNT, SYN FLD\t584695.0528\t11175.33937\t757\t442614155\t3.17E+15\t442\t4939500\t4.18E+11
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSTURB\tTURBIDITY, SYN FLD\t9986788.967\t11175.33937\t757\t7559999248\t7.56E+16\t442\t4939500\t4.18E+11
-LAB BLOOD ORDERABLES\t472478\tLABGLNF\tGLUCOSE NON-FASTING, SERUM/PLASMA\tGLU\tGLUCOSE, NON-FASTING\t92630.75694\t5841.125\t757\t70121483\t7.00E+14\t480\t2803740\t91321002000
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSCOLOR\tCOLOR, SYN FLD\t9999999\t11175.33937\t756\t7559999244\t7.56E+16\t442\t4939500\t4.18E+11
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSWBC\tNUCL. CELLS, SYN FLD\t360270.0185\t11175.33937\t756\t272364134\t2.51E+15\t442\t4939500\t4.18E+11
-MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tSDES\tSPECIMEN\t9999999\t273578.4971\t753\t7529999247\t7.53E+16\t173\t47329080\t1.50E+13
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t11175.33937\t749\t7489999251\t7.49E+16\t442\t4939500\t4.18E+11
-LAB BLOOD ORDERABLES\t473262\tLABRF\tRHUEMATOID FACTOR\tRHFR\tRHEUMATOID FACTOR\t9999999\t239163.253\t747\t7469999253\t7.47E+16\t664\t158804400\t5.11E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMETA\tMETAMYELOCYTES\t3.095302013\t29529.25035\t745\t2306\t13812\t707\t20877180\t3.19E+12
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCULT\tCULT/OTHER RSLT:\t9999999\t505173.5304\t743\t7429999257\t7.43E+16\t41511\t20970258420\t1.13E+16
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tDX\tDIAGNOSIS\t9946094.042\t11175.33937\t742\t7380001779\t7.38E+16\t442\t4939500\t4.18E+11
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t741\t7409999259\t7.41E+16\t0\tNone\tNone
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEXP\tDEVELOPER EXP DATE\t9812324.848\t10328.26531\t741\t7270932712\t7.27E+16\t784\t8097360\t3.21E+13
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEL\tDEVELOPER LOT#\t5622466.236\t10328.26531\t740\t4160625015\t4.15E+16\t784\t8097360\t3.21E+13
-STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tRPT\tRPT\t9999999\t210925.3091\t740\t7399999260\t7.40E+16\t275\t58004460\t1.38E+13
-STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tSDES\tSPECIMEN\t9999999\t210925.3091\t740\t7399999260\t7.40E+16\t275\t58004460\t1.38E+13
-STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tSREQ\tCOMMENTS\t9999999\t210925.3091\t740\t7399999260\t7.40E+16\t275\t58004460\t1.38E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMYEL\tMYELOCYTES\t3.619565217\t29836.81366\t736\t2664\t21750\t703\t20975280\t3.42E+12
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tJOINT\tJOINT\t9999999\t11175.33937\t732\t7319999268\t7.32E+16\t442\t4939500\t4.18E+11
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBMINI\tMITOGEN - NIL\t1874149.606\t226235.1951\t731\t1370003362\t1.37E+16\t820\t185512860\t8.35E+13
-LAB BLOOD ORDERABLES\t473672\tLABTRXNWI\tTRANSFUSION REACTION WORKUP\tCTRXNW\tTRANSFUSION REACTION COMMENT\t9999999\t34.85148515\t731\t7309999269\t7.31E+16\t303\t10560\t2030400
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBNQUL\tEBNQUL\t9999999\t287670.1288\t731\t7309999269\t7.31E+16\t699\t201081420\t8.47E+13
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVMQUL\tVMQUL\t9999999\t287670.1288\t731\t7309999269\t7.31E+16\t699\t201081420\t8.47E+13
-LAB BLOOD ORDERABLES\t473672\tLABTRXNWI\tTRANSFUSION REACTION WORKUP\tTRXNWR\tTRANSFUSION REACTION WORKUP\t9999999\t34.85148515\t730\t7299999270\t7.30E+16\t303\t10560\t2030400
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEAGQUL\tEAGQUL\t9999999\t287670.1288\t730\t7299999270\t7.30E+16\t699\t201081420\t8.47E+13
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVGQUL\tVGQUL\t9999999\t287670.1288\t730\t7299999270\t7.30E+16\t699\t201081420\t8.47E+13
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tRPT\tRPT\t9999999\t330942.4585\t728\t7279999272\t7.28E+16\t301\t99613680\t3.68E+13
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t330942.4585\t728\t7279999272\t7.28E+16\t301\t99613680\t3.68E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNRBC\tNRBC/100WBC\t13843.10235\t31012.87554\t723\t10008563\t1.00E+14\t699\t21678000\t3.39E+12
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tDI\tISSUE DATE/TIME\t9999999\t34368.29721\t718\t7179999282\t7.18E+16\t323\t11100960\t6.24E+11
-VIROLOGY ORDERABLES\t472519\tLABHAVT\tANTI-HAV\tHATR\tANTI-HAV ANTIBODY\t9999999\t76703.94453\t718\t7179999282\t7.18E+16\t649\t49780860\t1.16E+13
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tQC10\tQC RESULT\t9999999\t10328.26531\t717\t7169999283\t7.17E+16\t784\t8097360\t3.21E+13
-LAB BLOOD ORDERABLES\t473440\tLABTT\tTHROMBIN TIME\tTT\tTHROMBIN TIME\t854359.3497\t3318.487395\t714\t610012575.7\t6.10E+15\t357\t1184700\t20331651600
-VIROLOGY ORDERABLES\t473715\tLABMVSOB\tUNLISTED MICROBIOLOGY/VIROLOGY TEST\tSCE\tSOURCE\t9999999\t681043.9907\t714\t7139999286\t7.14E+16\t431\t293529960\t8.51E+14
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVGQNT\tVGQNT\t322585.5422\t284480.6775\t713\t230003491.6\t2.30E+15\t679\t193162380\t7.94E+13
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tRPT\tRPT\t9999999\t454839.1011\t712\t7119999288\t7.12E+16\t712\t323845440\t2.65E+14
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tOCCBLD\tOCCULT BLOOD, STOOL\t9999999\t10432.23881\t703\t7029999297\t7.03E+16\t804\t8387520\t3.22E+13
-VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMGQNT\tCMV IGG LEVEL\t1988622.425\t158368.3086\t699\t1390047075\t1.39E+16\t674\t106740240\t2.24E+13
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tPRO\tPRODUCT ID  \t9999999\t35555.0495\t698\t6979999302\t6.98E+16\t303\t10773180\t1.30E+12
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t454839.1011\t697\t6969999303\t6.97E+16\t712\t323845440\t2.65E+14
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tSDES\tSPECIMEN\t9999999\t454839.1011\t694\t6939999306\t6.94E+16\t712\t323845440\t2.65E+14
-MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t464348.8949\t693\t6929999307\t6.93E+16\t371\t172273440\t9.73E+14
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tUBT\tUNIT BLOOD TYPE  \t9999999\t47227.55102\t692\t6919999308\t6.92E+16\t294\t13884900\t1.55E+12
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t324263.2112\t691\t6909999309\t6.91E+16\t928\t300916260\t1.71E+14
-LAB BLOOD ORDERABLES\t472193\tLABDATBSI\tDIRECT COOMBS BROAD SPECTRUM\tDATC3\tDIRECT ANTIGLOB. - C3\t9999999\t21378.67804\t689\t6889999311\t6.89E+16\t469\t10026600\t3.30E+12
-MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tRPT\tRPT\t9999999\t#VALUE!\t689\t6889999311\t6.89E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472193\tLABDATBSI\tDIRECT COOMBS BROAD SPECTRUM\tDATG\tDIRECT ANTIGLOB. - IGG\t9999999\t22062.6383\t688\t6879999312\t6.88E+16\t470\t10369440\t3.42E+12
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBNQNT\tEBNQNT\t538575.8845\t274703.1756\t687\t370001632.6\t3.70E+15\t655\t179930580\t7.15E+13
-BODY FLUID ORDERABLES\t473089\tLABPHME\tPH BY METER\tPHA\tPH BY METER\t14627.53886\t17450.32258\t684\t10005236.58\t1.00E+14\t496\t8655360\t1.26E+12
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t45029.49618\t682\t6819999318\t6.82E+16\t655\t29494320\t3.07E+14
-LAB BLOOD ORDERABLES\t471575\tLABALKP\tALKALINE PHOSPHATASE TOTAL SERUM/PLASMA\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t88379.00882\t3307.649402\t680\t60097726\t6.00E+14\t251\t830220\t8191522800
-LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGENTTRO\tGENTAMICIN, TROUGH\t972018.9875\t8326.14601\t679\t660000892.5\t6.60E+15\t589\t4904100\t1.61E+11
-LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGNTPR3\tDOSAGE\t9999999\t8326.14601\t679\t6789999321\t6.79E+16\t589\t4904100\t1.61E+11
-LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGNTPR2\tDATE & TIME OF LAST DOSE\t9999999\t8326.14601\t677\t6769999323\t6.77E+16\t589\t4904100\t1.61E+11
-LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGNTPR4\tADMINISTRATION ROUTE\t9999999\t8326.14601\t676\t6759999324\t6.76E+16\t589\t4904100\t1.61E+11
-MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tSDES\tSPECIMEN\t9999999\t#VALUE!\t674\t6739999326\t6.74E+16\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t330942.4585\t666\t6659999334\t6.66E+16\t301\t99613680\t3.68E+13
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tCAION\tCALCIUM,ION, ISTAT\t15038.7708\t1280.120301\t665\t10000782.58\t1.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tCR\tCREATININE,ISTAT\t30076.77744\t1280.120301\t665\t20001057\t2.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tGLU\tGLUCOSE,ISTAT\t60295.75188\t1280.120301\t665\t40096675\t4.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tHCT\tHCT(CALC), ISTAT\t90262.25414\t1280.120301\t665\t60024399\t6.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tHGB\tHGB(CALC), ISTAT\t90238.03233\t1280.120301\t665\t60008291.5\t6.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tK\tPOTASSIUM, ISTAT\t120304.9702\t1280.120301\t665\t80002805.2\t8.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tNA\tSODIUM, ISTAT\t45250.96692\t1280.120301\t665\t30091893\t3.00E+14\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCAGP\tANION GAP, ISTAT\t436106.0301\t1280.120301\t665\t290010510\t2.90E+15\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCBUN\tBUN, ISTAT\t391003.2602\t1280.120301\t665\t260017168\t2.60E+15\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCCL\tCHLORIDE, ISTAT\t255739.7895\t1280.120301\t665\t170066960\t1.70E+15\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCCOM\tPOCT COMMENT\t9999999\t1280.120301\t665\t6649999335\t6.65E+16\t665\t851280\t4.55E+11
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tTCO2A\tTCO2, ISTAT\t105287.0602\t1280.120301\t665\t70015895\t7.00E+14\t665\t851280\t4.55E+11
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tXMR\tUNIT CROSSMATCH  \t9999999\t74761.73913\t664\t6639999336\t6.64E+16\t276\t20634240\t3.23E+12
-MICROBIOLOGY ORDERABLES\t472114\tLABCRPLC\tCRYPTOCOCCAL AG LATEX, CSF\tCRPLC\tLATEX CRYPTO AG,CSF\t9999999\t70420.68063\t663\t6629999337\t6.63E+16\t382\t26900700\t2.73E+12
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBINTR\tEBV INTERPRETATION\t9999999\t287670.1288\t659\t6589999341\t6.59E+16\t699\t201081420\t8.47E+13
-LAB BLOOD ORDERABLES\t541695\tLABDSDNA\tANTI - DSDNA AB\tDNAQUL\tANTI-DSDNA RESULT\t9999999\t212647.8896\t652\t6519999348\t6.52E+16\t616\t130991100\t3.85E+13
-LAB BLOOD ORDERABLES\t541668\tLABACTHO\tADRENOCORTICOTROPIC HORMONE (ACTH)\tACTH\tACTH\t1808382.362\t138494.094\t647\t1170023388\t1.17E+16\t596\t82542480\t4.48E+14
-LAB BLOOD ORDERABLES\t472670\tLABFE\tIRON, TOTAL\tFE\tIRON, TOTAL\t139810.0668\t7216.6171\t644\t90037683\t9.00E+14\t538\t3882540\t60419718000
-LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN PLATELET FACTOR 4 AB\tPF4ENH\tPF4 IGG, IGM, IGA OD\t264797.8074\t95902.35702\t642\t170000192.3\t1.70E+15\t577\t55335660\t1.99E+13
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tPRO\tPRODUCT ID  \t9999999\t32446.02151\t636\t6359999364\t6.36E+16\t279\t9052440\t1.06E+12
-LAB BLOOD ORDERABLES\t471630\tLABACA\tANTI CARDIOLIPIN AB\tACAIGG\tACA IGG ANTIBODIES\t330196.3884\t293701.5437\t636\t210004903\t2.10E+15\t583\t171228000\t7.16E+13
-LAB BLOOD ORDERABLES\t471630\tLABACA\tANTI CARDIOLIPIN AB\tACAIGM\tACA IGM ANTIBODIES\t298747.0959\t293701.5437\t636\t190003153\t1.90E+15\t583\t171228000\t7.16E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHQLINT\tINTERPRETATION\t9999999\t63540.24819\t635\t6349999365\t6.35E+16\t967\t61443420\t8.90E+12
-BODY FLUID ORDERABLES\t471522\tLABASQ\tACETONE SEMI QUALITATIVE\tACETONE\tACETONE SEMI QUAL\t9999999\t6040.823529\t627\t6269999373\t6.27E+16\t510\t3080820\t31003088400
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tUBT\tUNIT BLOOD TYPE  \t9999999\t46629.4382\t626\t6259999374\t6.26E+16\t267\t12450060\t1.39E+12
-VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tHERPR\tHERPES SIMPLEX VIRUS (HSV)\t9999999\t61157.32283\t626\t6259999374\t6.26E+16\t381\t23300940\t3.03E+12
-VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tVZR\tVARICELLA-ZOSTER VIRUS\t9999999\t61157.32283\t626\t6259999374\t6.26E+16\t381\t23300940\t3.03E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOS\tEOSINOPHILS\t16029.05449\t27244.66321\t624\t10002130\t1.00E+14\t579\t15774660\t2.39E+12
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tDI\tISSUE DATE/TIME\t9999999\t31683.42857\t615\t6149999385\t6.15E+16\t280\t8871360\t4.33E+11
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tXMR\tUNIT CROSSMATCH  \t9999999\t73288.9243\t613\t6129999387\t6.13E+16\t251\t18395520\t2.94E+12
-LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIMET\tMETHOD\t9999999\t436344.1652\t612\t6119999388\t6.12E+16\t581\t253515960\t1.44E+14
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOSAB\tEOS, ABS (MAN DIFF)\t16367.34124\t27633.43915\t611\t10000445.5\t1.00E+14\t567\t15668160\t2.39E+12
-MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t603\t6029999397\t6.03E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLINT\tINTERPRETATION\t9999999\t56970.32432\t600\t5999999400\t6.00E+16\t1110\t63237060\t8.60E+12
-URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tCRE\tCREATININE EXCRETION\t386686.995\t50111.01695\t596\t230465449\t2.30E+15\t354\t17739300\t3.04E+12
-URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tUCR\tCREATININE, URINE\t369183.7985\t50111.01695\t596\t220033543.9\t2.20E+15\t354\t17739300\t3.04E+12
-URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tUT3\tCOLLECTION PERIOD CREATININE\t503395.057\t50111.01695\t596\t300023454\t3.00E+15\t354\t17739300\t3.04E+12
-URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tVOL3\tVOLUME CREATININE\t370885.8748\t50111.01695\t596\t221047981.4\t2.20E+15\t354\t17739300\t3.04E+12
-LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIRES\tRESULTS\t9999999\t436344.1652\t589\t5889999411\t5.89E+16\t581\t253515960\t1.44E+14
-LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBRATROUGH\tTOBRAMYCIN, TROUGH\t4846416.814\t8171.44385\t586\t2840000253\t2.84E+16\t561\t4584180\t1.48E+11
-LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBPR2\tDATE & TIME OF LAST DOSE\t9999999\t8171.44385\t584\t5839999416\t5.84E+16\t561\t4584180\t1.48E+11
-LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBPR3\tDOSAGE\t9999999\t8171.44385\t584\t5839999416\t5.84E+16\t561\t4584180\t1.48E+11
-LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBPR4\tADMINISTRATION ROUTE\t9999999\t8171.44385\t584\t5839999416\t5.84E+16\t561\t4584180\t1.48E+11
-LAB BLOOD ORDERABLES\t472518\tLABHAVM\tIGM ANTI-HAV\tHAVR\tIGM ANTI-HAV\t9999999\t48714.87379\t582\t5819999418\t5.82E+16\t515\t25088160\t2.39E+12
-MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tAFB\tAFB SMEAR\t9999999\t3886778.338\t582\t5819999418\t5.82E+16\t2310\t8978457960\t3.70E+16
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tSI\t<<STATUS>>  \t9999999\t41709.62264\t577\t5769999423\t5.77E+16\t1166\t48633420\t5.74E+12
-VIROLOGY ORDERABLES\t472525\tLABHBSQL\tANTI-HBS QUAL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t49138.5336\t572\t5719999428\t5.72E+16\t491\t24127020\t2.62E+12
-LAB BLOOD ORDERABLES\t471961\tLABYCERU\tCERULOPLASMIN\tCER\tCERULOPLASMIN\t176706.9682\t59495.32075\t566\t100016144\t1.00E+15\t530\t31532520\t5.23E+12
-BODY FLUID ORDERABLES\t497357\tLABFTRPL\tTRIGLYCERIDES, PLEURAL\tTGLFLD\tTRIGLYCERIDES, PLEURAL FLUID\t3539900.179\t11227.32955\t565\t2000043601\t2.00E+16\t352\t3952020\t3.11E+11
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tBASOFLD\tBASOPHILS, FLD\t1.662522202\t19547.02703\t563\t936\t3502\t296\t5785920\t1.19E+12
-URINE ORDERABLES\t473127\tLABURK\tPOTASSIUM, URINE (RANDOM)\tUK\tPOTASSIUM, URINE\t53319.41066\t12981.89655\t563\t30018828.2\t3.00E+14\t348\t4517700\t2.34E+11
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGCOM\tCOMMENTS\t9999999\t18237.38255\t560\t5599999440\t5.60E+16\t894\t16304220\t1.94E+12
-MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tRPT\tRPT\t9999999\t3931337.485\t559\t5589999441\t5.59E+16\t326\t1281616020\t5.25E+15
-LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTMET\tMETHOD\t9999999\t452558.7763\t558\t5579999442\t5.58E+16\t523\t236688240\t1.36E+14
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tPRO\tPRODUCT ID  \t9999999\t32777.54941\t557\t5569999443\t5.57E+16\t253\t8292720\t9.48E+11
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tURENAL\tRENAL CELLS\t9999999\t11982.37197\t556\t5559999444\t5.56E+16\t371\t4445460\t2.83E+11
-URINE ORDERABLES\t472551\tLABYHISTU\tHISTOPLASMA AG, URINE\tYHISU4\tRESULT URINE\t9945944.981\t381867.6106\t555\t5519999465\t5.52E+16\t339\t129453120\t5.70E+13
-LAB BLOOD ORDERABLES\t472521\tLABHBCT\tANTI-HBC\tHBTR\tANTI-HBC ANTIBODY\t9999999\t53580.7438\t554\t5539999446\t5.54E+16\t484\t25933080\t3.17E+12
-MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tSREQ\tCOMMENTS\t9999999\t3931337.485\t554\t5539999446\t5.54E+16\t326\t1281616020\t5.25E+15
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tRPT\tRPT\t9999999\t503786.5343\t554\t5539999446\t5.54E+16\t554\t279097740\t1.51E+14
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tSDES\tSPECIMEN\t9999999\t503786.5343\t554\t5539999446\t5.54E+16\t554\t279097740\t1.51E+14
-MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tSDES\tSPECIMEN\t9999999\t3931337.485\t553\t5529999447\t5.53E+16\t326\t1281616020\t5.25E+15
-VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-BLOOD\tCMQDNA\tCMV DNA\t9999999\t204632\t547\t5469999453\t5.47E+16\t30\t6138960\t1.86E+12
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tSREQ\tCOMMENTS\t9999999\t503786.5343\t543\t5429999457\t5.43E+16\t554\t279097740\t1.51E+14
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tSI\t<<STATUS>>  \t9999999\t44531.81989\t540\t5399999460\t5.40E+16\t1066\t47470920\t6.04E+12
-LAB BLOOD ORDERABLES\t472563\tLABHMCY\tHOMOCYSTEINE TOTAL, PLASMA\tHMCY\tHOMOCYSTEINE, PLAS TOTAL\t74085.00833\t45438.5259\t540\t40005904.5\t4.00E+14\t502\t22810140\t5.52E+12
-LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTRES\tRESULTS\t9999999\t452558.7763\t539\t5389999461\t5.39E+16\t523\t236688240\t1.36E+14
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVCP\tRESULT\t8191919.163\t289760.8349\t539\t4415444429\t4.41E+16\t527\t152703960\t5.49E+13
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVLOG\tRESULT LOG\t8181818.057\t289760.8349\t539\t4409999933\t4.41E+16\t527\t152703960\t5.49E+13
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVSP\tSPECIMEN\t9999999\t289760.8349\t539\t5389999461\t5.39E+16\t527\t152703960\t5.49E+13
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tUBT\tUNIT BLOOD TYPE  \t9999999\t48294.88789\t528\t5279999472\t5.28E+16\t223\t10769760\t1.30E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tCHOL\tCHOLESTEROL, TOTAL\t151669.5814\t6236.056338\t528\t80081539\t8.00E+14\t426\t2656560\t35067196800
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t208337.2199\t6236.056338\t528\t110002052.1\t1.10E+15\t426\t2656560\t35067196800
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tHDL\tHDL CHOLESTEROL\t151557.7216\t6236.056338\t528\t80022477\t8.00E+14\t426\t2656560\t35067196800
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tLDL\tDIRECT LDL CHOL\t113730.9337\t6236.056338\t528\t60049933\t6.00E+14\t426\t2656560\t35067196800
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tNHDL\tNON-HDL CHOL, CALC\t208444.2064\t6236.056338\t528\t110058541\t1.10E+15\t426\t2656560\t35067196800
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tTGL\tTRIGLYCERIDE, SER/PLAS\t132720.464\t6236.056338\t528\t70076405\t7.00E+14\t426\t2656560\t35067196800
-MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tRPT\tRPT\t9999999\t334461.7699\t525\t5249999475\t5.25E+16\t339\t113382540\t5.10E+13
-MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tSDES\tSPECIMEN\t9999999\t334461.7699\t525\t5249999475\t5.25E+16\t339\t113382540\t5.10E+13
-MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tSREQ\tCOMMENTS\t9999999\t334461.7699\t523\t5229999477\t5.23E+16\t339\t113382540\t5.10E+13
-VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSV1PC\tHSV-1 DNA\t9999999\t167178.2332\t523\t5229999477\t5.23E+16\t283\t47311440\t1.12E+13
-VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSV2PC\tHSV-2 DNA\t9999999\t167178.2332\t523\t5229999477\t5.23E+16\t283\t47311440\t1.12E+13
-LAB BLOOD ORDERABLES\t471651\tLABAT3\tANTITHROMBIN III\tAT3\tANTITHROMBIN III\t172828.4146\t42245.28689\t521\t90043604\t9.00E+14\t488\t20615700\t1.19E+12
-MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tRPT\tRPT\t9999999\t1159475.26\t519\t5189999481\t5.19E+16\t519\t601767660\t7.41E+14
-MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tSREQ\tCOMMENTS\t9999999\t1159475.26\t519\t5189999481\t5.19E+16\t519\t601767660\t7.41E+14
-VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMMQUL\tCMV IGM RESULT\t9999999\t159015.6725\t518\t5179999482\t5.18E+16\t513\t81575040\t1.77E+13
-MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tRPT\tRPT\t9999999\t473219.0698\t516\t5159999484\t5.16E+16\t516\t244181040\t2.40E+14
-MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tSREQ\tCOMMENTS\t9999999\t473219.0698\t516\t5159999484\t5.16E+16\t516\t244181040\t2.40E+14
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tPRO\tPRODUCT ID  \t9999999\t31790.66667\t515\t5149999485\t5.15E+16\t225\t7152900\t7.84E+11
-LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB\tPHENOBARBITAL\t116527.1579\t6274.373717\t515\t60011486.3\t6.00E+14\t487\t3055620\t1.59E+11
-LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB2\tDATE & TIME OF LAST DOSE\t9999999\t6274.373717\t514\t5139999486\t5.14E+16\t487\t3055620\t1.59E+11
-LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB3\tDOSAGE\t9999999\t6274.373717\t514\t5139999486\t5.14E+16\t487\t3055620\t1.59E+11
-LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB4\tADMINISTRATION ROUTE\t9999999\t6274.373717\t514\t5139999486\t5.14E+16\t487\t3055620\t1.59E+11
-MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tRPT\tRPT\t9999999\t1154535.525\t514\t5139999486\t5.14E+16\t514\t593431260\t7.35E+14
-MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tSREQ\tCOMMENTS\t9999999\t1154535.525\t514\t5139999486\t5.14E+16\t514\t593431260\t7.35E+14
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tDI\tISSUE DATE/TIME\t9999999\t31819.03084\t513\t5129999487\t5.13E+16\t227\t7222920\t3.67E+11
-LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB\tPEAK, TROUGH, OR RANDOM\t9999999\t6274.373717\t513\t5129999487\t5.13E+16\t487\t3055620\t1.59E+11
-POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tBE\tBASE EXCESS, ISTAT\t136457.575\t29.67391304\t513\t70002736\t7.00E+14\t184\t5460\t399600
-LAB BLOOD ORDERABLES\t472608\tLABIGP\tIG PANEL, SERUM\tIGA\tIGA, SERUM\t430788.5088\t50382.80412\t511\t220132928\t2.20E+15\t485\t24435660\t2.63E+12
-LAB BLOOD ORDERABLES\t472608\tLABIGP\tIG PANEL, SERUM\tIGG\tIGG, SERUM\t59813.78278\t50382.80412\t511\t30564843\t3.00E+14\t485\t24435660\t2.63E+12
-LAB BLOOD ORDERABLES\t472608\tLABIGP\tIG PANEL, SERUM\tIGM\tIGM, SERUM\t743834.2603\t50382.80412\t511\t380099307\t3.80E+15\t485\t24435660\t2.63E+12
-LAB BLOOD ORDERABLES\t472349\tLABFACT8\tFACTOR VIII ASSAY\tFACTOR7\tFACTOR VIII ASSAY\t336091.7522\t37663.04348\t506\t170062426.6\t1.70E+15\t460\t17325000\t1.03E+12
-BLOOD BANK PRODUCT ORDERABLES\t889176\tLABERURBC\tEMERGENCY RELEASE RBC\tRBCSTA\tRBC PRODUCT - UNITS\t9999999\t6582.891089\t505\t5049999495\t5.05E+16\t505\t3324360\t4.97E+11
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tEOSCSF\tEOSINOPHILS, CSF\t4.235412475\t16680\t497\t2105\t49371\t286\t4770480\t8.47E+11
-LAB BLOOD ORDERABLES\t472949\tLAB11049R\tNSE\tNEURSP\tNEURON SPEC ENOLASE\t544393.7486\t290896.3991\t496\t270019299.3\t2.70E+15\t461\t134103240\t6.39E+13
-VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXSP\tSPECIMEN\t9999999\t183683.9241\t489\t4889999511\t4.89E+16\t158\t29022060\t8.42E+12
-VIROLOGY ORDERABLES\t472040\tLABCMV\tCMV AB TOTAL\tCMV\tCMV ANTIBODY\t9999999\t162174.2254\t488\t4879999512\t4.88E+16\t426\t69086220\t1.45E+13
-POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tGLU\tGLUCOSE,ISTAT\t20769.90928\t124.8803828\t485\t10073406\t1.00E+14\t209\t26100\t118105200
-POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tHCT\tHCT(CALC), ISTAT\t20654.27216\t124.8803828\t485\t10017322\t1.00E+14\t209\t26100\t118105200
-POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tHGB\tHGB(CALC), ISTAT\t20630.69753\t124.8803828\t485\t10005888.3\t1.00E+14\t209\t26100\t118105200
-POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tK\tPOTASSIUM, ISTAT\t41241.66598\t124.8803828\t485\t20002208\t2.00E+14\t209\t26100\t118105200
-POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tNA\tSODIUM, ISTAT\t136.8618557\t124.8803828\t485\t66378\t9107096\t209\t26100\t118105200
-LAB BLOOD ORDERABLES\t472467\tLABGLOB\tGLOBULIN\tGLOB\tGLOBULIN\t103737.9357\t2168.636364\t482\t50001685\t5.00E+14\t132\t286260\t1342004400
-LAB BLOOD ORDERABLES\t590572\tLABPROL\tPROLACTIN\tPROLACTIN\tPROLACTIN\t418452.4059\t37074.93976\t478\t200020250\t2.00E+15\t415\t15386100\t1.08E+12
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tUBT\tUNIT BLOOD TYPE  \t9999999\t44357.94118\t476\t4759999524\t4.76E+16\t204\t9049020\t1.06E+12
-MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tRPT\tRPT\t9999999\t675327.4909\t473\t4729999527\t4.73E+16\t275\t185715060\t2.71E+15
-VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tGC\tN.GONORRHOEAE RESULT:\t9999999\t184892.973\t471\t4709999529\t4.71E+16\t333\t61569360\t1.58E+13
-POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t124.8803828\t470\t4699999530\t4.70E+16\t209\t26100\t118105200
-VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tCHLAM\tC.TRACHOMATIS RESULT:\t9999999\t184892.973\t470\t4699999530\t4.70E+16\t333\t61569360\t1.58E+13
-MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tSDES\tSPECIMEN\t9999999\t1159475.26\t469\t4689999531\t4.69E+16\t519\t601767660\t7.41E+14
-MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tSDES\tSPECIMEN\t9999999\t1154535.525\t469\t4689999531\t4.69E+16\t514\t593431260\t7.35E+14
-MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tSDES\tSPECIMEN\t9999999\t675327.4909\t467\t4669999533\t4.67E+16\t275\t185715060\t2.71E+15
-VIROLOGY ORDERABLES\t472040\tLABCMV\tCMV AB IGG\tCMV\tCMV ANTIBODY\t9999999\t147950.7965\t464\t4639999536\t4.64E+16\t339\t50155320\t9.13E+12
-MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tSDES\tSPECIMEN\t9999999\t473219.0698\t463\t4629999537\t4.63E+16\t516\t244181040\t2.40E+14
-MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tSREQ\tCOMMENTS\t9999999\t675327.4909\t461\t4609999539\t4.61E+16\t275\t185715060\t2.71E+15
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tXMR\tUNIT CROSSMATCH  \t9999999\t70210.56995\t454\t4539999546\t4.54E+16\t193\t13550640\t2.10E+12
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tDI\tISSUE DATE/TIME\t9999999\t31993.17073\t453\t4529999547\t4.53E+16\t205\t6558600\t3.31E+11
-VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPLOG\tRESULT LOG 10\t7836644.65\t305903.2394\t453\t3550000027\t3.55E+16\t426\t130314780\t5.02E+13
-VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPRES\tRESULT\t8068660.3\t305903.2394\t453\t3655103116\t3.64E+16\t426\t130314780\t5.02E+13
-VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPSP\tSPECIMEN\t9999999\t305903.2394\t453\t4529999547\t4.53E+16\t426\t130314780\t5.02E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSAQT\tHEP B SURF AB QUANT\t7139734.262\t200741.1086\t451\t3220020152\t3.22E+16\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t200741.1086\t451\t4509999549\t4.51E+16\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t200741.1086\t451\t4509999549\t4.51E+16\t451\t90534240\t2.96E+13
-MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tRPT\tRPT\t9999999\t1304430.274\t451\t4509999549\t4.51E+16\t329\t429157560\t5.76E+14
-MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tSDES\tSPECIMEN\t9999999\t1304430.274\t451\t4509999549\t4.51E+16\t329\t429157560\t5.76E+14
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t200741.1086\t450\t4499999550\t4.50E+16\t451\t90534240\t2.96E+13
-MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tRPT\tRPT\t9999999\t184554.1693\t450\t4499999550\t4.50E+16\t319\t58872780\t5.99E+13
-VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPDNA\tHEP B VIRUS DNA\t9999999\t305903.2394\t450\t4499999550\t4.50E+16\t426\t130314780\t5.02E+13
-MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tSREQ\tCOMMENTS\t9999999\t1304430.274\t449\t4489999551\t4.49E+16\t329\t429157560\t5.76E+14
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tPRO\tPRODUCT ID  \t9999999\t32831.4433\t448\t4479999552\t4.48E+16\t194\t6369300\t7.61E+11
-LAB BLOOD ORDERABLES\t472475\tLABGLF\tGLUCOSE FASTING\tGLUF\tGLUCOSE, FASTING\t134026.3616\t8175.961538\t448\t60043810\t6.00E+14\t312\t2550900\t63338756400
-LAB BLOOD ORDERABLES\t473763\tLAB11060R\tTHIAMINE\tVITB1\tVITAMIN B1, PLASMA\t870624.4308\t307360.8299\t448\t390039745\t3.90E+15\t482\t148147920\t6.20E+13
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tHCO3\tHCO3, ISTAT\t111632.7217\t271.9090909\t448\t50011459.3\t5.00E+14\t220\t59820\t2744190000
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCO2A\tPCO2, ISTAT\t89330.51228\t271.9090909\t448\t40020069.5\t4.00E+14\t220\t59820\t2744190000
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCO2SO\tO2 SATURATION, ISTAT\t134011.5357\t271.9090909\t448\t60037168\t6.00E+14\t220\t59820\t2744190000
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPO2A\tPO2, ISTAT\t67060.52679\t271.9090909\t448\t30043116\t3.00E+14\t220\t59820\t2744190000
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tTCO2A\tTCO2, (ISTAT)\t111634.0469\t271.9090909\t448\t50012053\t5.00E+14\t220\t59820\t2744190000
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPH\tPH, ISTAT\t44850.37892\t271.9090909\t446\t20003269\t2.00E+14\t220\t59820\t2744190000
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t200741.1086\t445\t4449999555\t4.45E+16\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472550\tLABYHISTS\tHISTOPLASMA AG, SERUM\tYHISS4\tRESULT,SERUM\t9954647.556\t490709.3697\t441\t4389999572\t4.39E+16\t476\t233577660\t1.25E+14
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSAIN\tINTERPRETATION\t9999999\t200741.1086\t440\t4399999560\t4.40E+16\t451\t90534240\t2.96E+13
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tPHLOC\tLOCATION\t9999999\t-5073.967311\t439\t4389999561\t4.39E+16\t1346\t-6829560\t1.13E+15
-POINT OF CARE TESTING\t471530\tLABPCACT\tACTIVATED CLOTTING TIME (POCT)\tACT\tACT,POCT\t219.9817768\t58860\t439\t96572\t25845846\t1\t58860\t3464499600
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVDNA\tEBV DNA\t9999999\t289760.8349\t438\t4379999562\t4.38E+16\t527\t152703960\t5.49E+13
-LAB BLOOD ORDERABLES\t670131\tLABFLCR\tFREE LIGHT CHAIN RATIO\tFREEK\tFREE KAPPA LIGHT CHAIN\t503508.5924\t328610.0971\t437\t220033254.9\t2.20E+15\t412\t135387360\t6.13E+13
-LAB BLOOD ORDERABLES\t670131\tLABFLCR\tFREE LIGHT CHAIN RATIO\tFREEKL\tFREE KAPPA/LAMBDA RATIO\t869692.5112\t328610.0971\t437\t380055627.4\t3.80E+15\t412\t135387360\t6.13E+13
-LAB BLOOD ORDERABLES\t670131\tLABFLCR\tFREE LIGHT CHAIN RATIO\tFREEL\tFREE LAMBDA LIGHT CHAIN\t503486.9574\t328610.0971\t437\t220023800.4\t2.20E+15\t412\t135387360\t6.13E+13
-URINE ORDERABLES\t473174\tLABURTP\tPROTEIN, URINE (RANDOM)\tUPROTTOT\tPROTEIN, TOTAL, URINE\t480691.9291\t17355.52941\t437\t210062373\t2.10E+15\t255\t4425660\t5.37E+11
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tCAION\tCALCIUM,ION, ISTAT\t389909.3382\t272.8767123\t436\t170000471.5\t1.70E+15\t219\t59760\t2744186400
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tHCT\tHCT(CALC), ISTAT\t412873.4266\t272.8767123\t436\t180012814\t1.80E+15\t219\t59760\t2744186400
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tHGB\tHGB(CALC), ISTAT\t412854.0002\t272.8767123\t436\t180004344.1\t1.80E+15\t219\t59760\t2744186400
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tK\tPOTASSIUM, ISTAT\t366976.4523\t272.8767123\t436\t160001733.2\t1.60E+15\t219\t59760\t2744186400
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tNA\tSODIUM, ISTAT\t321234.4587\t272.8767123\t436\t140058224\t1.40E+15\t219\t59760\t2744186400
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tCAOX\tCA OXALATE CRYSTALS\t9999999\t15496.8\t436\t4359999564\t4.36E+16\t300\t4649040\t1.24E+12
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t271.9090909\t434\t4339999566\t4.34E+16\t220\t59820\t2744190000
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tHYST\tPSEUDO-HYPHAE\t9999999\t20417.64706\t434\t4339999566\t4.34E+16\t272\t5553600\t1.48E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tC1656A\tCD3-/16+56(NK),ABS.\t277849.7431\t69708.31731\t432\t120031089\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tC1656P\tCD3-/16+56(NK)%\t277787.6227\t69708.31731\t432\t120004253\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD19AB\tCD19 (PAN B), ABS.\t278158.6806\t69708.31731\t432\t120164550\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD19PC\tCD19 (PAN B) %\t277809.6852\t69708.31731\t432\t120013784\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t278392.2523\t69708.31731\t432\t120265453\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD3PC\tCD3 (PAN T) %\t277831.5417\t69708.31731\t432\t120023226\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD4AB\tCD3+/CD4+, ABS.\t278115.8333\t69708.31731\t432\t120146040\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD4PC\tCD3+/CD4+ %\t277803.4074\t69708.31731\t432\t120011072\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD8AB\tCD3+/CD8+, ABS.\t278046.3356\t69708.31731\t432\t120116017\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD8PC\tCD3+/CD8+ %\t277805.2986\t69708.31731\t432\t120011889\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t277830.956\t69708.31731\t432\t120022973\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t278384.4167\t69708.31731\t432\t120262068\t1.20E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t439816.0368\t69708.31731\t432\t190000527.9\t1.90E+15\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tLYM\tLYMPHOCYTES %\t185196.1836\t69708.31731\t432\t80004751.3\t8.00E+14\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t186262.8958\t69708.31731\t432\t80465571\t8.00E+14\t416\t28998660\t2.86E+12
-LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tWBC\tWBC COUNT\t194965.9537\t69708.31731\t432\t84225292\t8.00E+14\t416\t28998660\t2.86E+12
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tXMR\tUNIT CROSSMATCH  \t9999999\t72834.94737\t430\t4299999570\t4.30E+16\t190\t13838640\t2.08E+12
-BLOOD BANK PRODUCT ORDERABLES\t889178\tLABERUFFP\tEMERGENCY RELEASE PLASMA\tFFP\tFRESH FROZEN UNITS\t9999999\t9213.925234\t428\t4279999572\t4.28E+16\t428\t3943560\t8.54E+11
-LAB BLOOD ORDERABLES\t472843\tLABMETHB\tMETHEMOGLOBIN\tMETHB\tMETHEMOGLOBIN\t1241218.778\t1845.669291\t427\t530000418.1\t5.30E+15\t381\t703200\t8558625600
-LAB BLOOD ORDERABLES\t472869\tLAB11071R\tMITOCHONDRIAL AB\tYAMA\tMITOCHONDRIAL ANTIBODY, M2\t8679244.496\t221191.8428\t424\t3679999666\t3.68E+16\t407\t90025080\t2.29E+13
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMQDNA\tCMV DNA\t9999999\t253864.8227\t423\t4229999577\t4.23E+16\t423\t107384820\t3.23E+13
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVRC1\tCMV DAY 1 RAPID CULTURE\t9999999\t253864.8227\t423\t4229999577\t4.23E+16\t423\t107384820\t3.23E+13
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVRC2\tCMV DAY 2 RAPID CULTURE\t9999999\t253864.8227\t423\t4229999577\t4.23E+16\t423\t107384820\t3.23E+13
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tUBT\tUNIT BLOOD TYPE  \t9999999\t44809.2973\t421\t4209999579\t4.21E+16\t185\t8289720\t9.48E+11
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEAGQNT\tEAGQNT\t4401914.65\t293312.3077\t418\t1840000324\t1.84E+16\t403\t118204860\t5.01E+13
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t70359.66102\t413\t4129999587\t4.13E+16\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSAQT\tHEP B SURF AB QUANT\t5908071.703\t70359.66102\t413\t2440033613\t2.44E+16\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t473425\tLABTES\tTESTOSTERONE\tTESTOS\tTESTOSTERONE\t968690.5303\t76130.35176\t413\t400069189\t4.00E+15\t398\t30299880\t6.44E+12
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tSI\t<<STATUS>>  \t9999999\t40348.33333\t412\t4119999588\t4.12E+16\t864\t34860960\t4.01E+12
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t70359.66102\t411\t4109999589\t4.11E+16\t413\t29058540\t4.09E+12
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tPRO\tPRODUCT ID  \t9999999\t34411.39535\t410\t4099999590\t4.10E+16\t172\t5918760\t7.29E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t70359.66102\t410\t4099999590\t4.10E+16\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t590533\tLABCMVTSI\tCMV AB TOTAL SOLID PHASE (GALILEO)\tCMVTOT\tCMV TOTAL ANTIBODY\t9999999\t#VALUE!\t410\t4099999590\t4.10E+16\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVCP\tRESULT\t7802567.044\t247595.8794\t410\t3199052488\t3.18E+16\t398\t98543160\t3.08E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVLO\tRESULT LOG 10\t7731707.538\t247595.8794\t410\t3170000091\t3.17E+16\t398\t98543160\t3.08E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVRNA\tHIV 1 RNA\t9999999\t247595.8794\t410\t4099999590\t4.10E+16\t398\t98543160\t3.08E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVSP\tSPECIMEN\t9999999\t247595.8794\t410\t4099999590\t4.10E+16\t398\t98543160\t3.08E+13
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSAIN\tINTERPRETATION\t9999999\t70359.66102\t409\t4089999591\t4.09E+16\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t471635\tLABANAS\tANTI - NUCLEAR AB\tANAQNT\tTITER\t9999999\t368380.0478\t407\t4069999593\t4.07E+16\t418\t153982860\t6.54E+13
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tSI\t<<STATUS>>  \t9999999\t39255.84184\t404\t4039999596\t4.04E+16\t784\t30776580\t3.61E+12
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCORCL\tCORRECTED CR. CLEAR.\t420836.0025\t70390.78125\t404\t170017745\t1.70E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCOREXC\tCORRECTED CR. EXC.\t247536.6163\t70390.78125\t404\t100004793\t1.00E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCR\tCREATININE, SERUM\t222774.1374\t70390.78125\t404\t90000751.5\t9.00E+14\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCRCL\tCREATININE CLEARANCE\t247572.7153\t70390.78125\t404\t100019377\t1.00E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCRE\tCREATININE EXCRETION\t248435.4257\t70390.78125\t404\t100367912\t1.00E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCRUCL\tCREATININE, URINE\t247591.7624\t70390.78125\t404\t100027072\t1.00E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tFLOWURN\tURINE FLOW\t247525.9851\t70390.78125\t404\t100000498\t1.00E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tHT\tHEIGHT\t247688.9431\t70390.78125\t404\t100066333\t1.00E+15\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tUT3\tCOLLECTION PERIOD CREATININE\t222790.2351\t70390.78125\t404\t90007255\t9.00E+14\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tVOL3\tVOLUME CREATININE\t224125.8119\t70390.78125\t404\t90546828\t9.00E+14\t128\t9010020\t9.33E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tWT\tWEIGHT\t247600.7614\t70390.78125\t404\t100030707.6\t1.00E+15\t128\t9010020\t9.33E+11
-BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tCRYS\tCRYSTAL AMOUNT\t9999999\t11117.87234\t403\t4029999597\t4.03E+16\t235\t2612700\t1.13E+11
-COAGULATION ORDERABLES\t472515\tLABHIAB\tHEPARIN INDUCED AB\tHIARES\tRESULT\t9999999\t64755.64516\t400\t3999999600\t4.00E+16\t372\t24089100\t2.37E+12
-LAB BLOOD ORDERABLES\t473779\tLAB12631R\tVORICONAZOLE LEVEL\t12630R\tVORICONAZOLE LEVEL\t450003.3483\t217704.3521\t400\t180001339.3\t1.80E+15\t409\t89041080\t2.16E+13
-LAB BLOOD ORDERABLES\t471911\tLABC125\tCANCER AG 125\tC125\tCANCER ANTIGEN 125\t452530.3543\t41327.93296\t398\t180107081\t1.80E+15\t358\t14795400\t1.31E+12
-LAB BLOOD ORDERABLES\t473412\tLABT3\tT3, TOTAL\tT3\tT3 TOTAL\t628222.9749\t38454.10714\t398\t250032744\t2.50E+15\t336\t12920580\t8.17E+11
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tFRFAT\tFREE FAT\t9999999\t10207.71704\t397\t3969999603\t3.97E+16\t311\t3174600\t2.63E+11
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9013R\tCELL DATE\t9999999\t213091.738\t396\t3959999604\t3.96E+16\t397\t84597420\t1.52E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9014R\tCELL SOURCE\t9999999\t213091.738\t396\t3959999604\t3.96E+16\t397\t84597420\t1.52E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9448R\tMETHOD(S)\t9999999\t213091.738\t396\t3959999604\t3.96E+16\t397\t84597420\t1.52E+14
-VIROLOGY ORDERABLES\t910608\tLABCMVIGT\tCMV IGG (TRANSFUSION)\tCMGTQL\tRESULT\t9999999\t130818.6935\t396\t3959999604\t3.96E+16\t398\t52065840\t9.97E+12
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9019R\tHLA-BW ALLELE 1\t9999999\t214170.5316\t395\t3949999605\t3.95E+16\t395\t84597360\t1.52E+14
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tOVFAT\tOVAL FAT BODIES\t9999999\t9315.841584\t395\t3949999605\t3.95E+16\t303\t2822700\t1.93E+11
-VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXMT\tMETHOD\t9999999\t183683.9241\t395\t3949999605\t3.95E+16\t158\t29022060\t8.42E+12
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tUBT\tUNIT BLOOD TYPE  \t9999999\t42059.64706\t394\t3939999606\t3.94E+16\t170\t7150140\t7.84E+11
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tHYST\tPSEUDO-HYPHAE\t9999999\t13976.77686\t394\t3939999606\t3.94E+16\t242\t3382380\t4.36E+11
-LAB BLOOD ORDERABLES\t471579\tLABYA1AS\tALPHA - 1 - ANTITRYPSIN\tYA1A\tALPHA 1 ANTITRYPSIN\t25680.01531\t194770.7532\t392\t10066566\t1.00E+14\t385\t74986740\t2.57E+13
-VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXR\tINFLUENZA A RNA\t9999999\t183683.9241\t391\t3909999609\t3.91E+16\t158\t29022060\t8.42E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASO\tBASOPHILS\t1.879177378\t31866.03261\t389\t731\t3433\t368\t11726700\t2.14E+12
-BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tCXSOUR\tFLUID SOURCE\t9999999\t11117.87234\t386\t3859999614\t3.86E+16\t235\t2612700\t1.13E+11
-MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tRPT\tRPT\t9999999\t213232.1186\t386\t3859999614\t3.86E+16\t236\t50322780\t1.42E+13
-VIROLOGY ORDERABLES\t473783\tLABVZAB\tVARICELLA ZOSTER VIRUS AB IGG\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t163752.4022\t386\t3859999614\t3.86E+16\t358\t58623360\t1.22E+13
-MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tSDES\tSPECIMEN\t9999999\t213232.1186\t385\t3849999615\t3.85E+16\t236\t50322780\t1.42E+13
-LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABIDELUATE\tANTIBODY IDENTIFICATION ELUATE\t9999999\t99443.54067\t384\t3839999616\t3.84E+16\t209\t20783700\t3.97E+13
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tRPT\tRPT\t9999999\t522530.9375\t384\t3839999616\t3.84E+16\t384\t200651880\t3.85E+14
-URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tTUNU\tUREA EXCRETION\t579648.7318\t43988.12227\t384\t222585113\t2.20E+15\t229\t10073280\t1.43E+12
-URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tUREAVOL\tVOLUME UREA\t444342.2422\t43988.12227\t384\t170627421\t1.70E+15\t229\t10073280\t1.43E+12
-URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tUT4\tCOLLECTION PERIOD UREA\t546905.1771\t43988.12227\t384\t210011588\t2.10E+15\t229\t10073280\t1.43E+12
-URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tUURN\tUREA NITROGEN, URINE\t573359.6875\t43988.12227\t384\t220170120\t2.20E+15\t229\t10073280\t1.43E+12
-VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-PLASMA\tCMVQSP\tSPECIMEN\t9999999\t242296.5517\t382\t3819999618\t3.82E+16\t116\t28106400\t9.68E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASOAB\tBASO, ABS (MAN DIFF)\t0.939632546\t32238.44875\t381\t358\t7786.18\t361\t11638080\t2.14E+12
-LAB BLOOD ORDERABLES\t473164\tLABPROTC\tPROTEIN C ACTIVITY\tPROTC\tPROTEIN C ACTIVITY\t446284.9528\t258848.0571\t381\t170034567\t1.70E+15\t350\t90596820\t3.96E+13
-VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSV1DN\tHSV-1 DNA\t9999999\t190023.36\t380\t3799999620\t3.80E+16\t375\t71258760\t1.95E+13
-VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSV2DN\tHSV-2 DNA\t9999999\t190023.36\t380\t3799999620\t3.80E+16\t375\t71258760\t1.95E+13
-VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tVZVDN\tVZV DNA\t9999999\t190023.36\t380\t3799999620\t3.80E+16\t375\t71258760\t1.95E+13
-STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tOCCGU\tSTOOL SAMPLE1,GUAIAC\t9999999\t39879.53271\t379\t3789999621\t3.79E+16\t214\t8534220\t1.87E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCCMT\tCOMMENT\t9999999\t15817.70574\t376\t3759999624\t3.76E+16\t401\t6342900\t1.49E+12
-LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN (HIT) AB\tPF4IGG\tPF4 IGG ONLY OD\t3404255.443\t91011.75824\t376\t1280000047\t1.28E+16\t364\t33128280\t3.74E+12
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tADVR\tADENOVIRUS\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tINFAR\tINFLUENZA A\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tINFBR\tINFLUENZA B\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tMPVRR\tMETAPNEUMOVIRUS\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tP1R\tPARAINFLUENZA 1\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tP2R\tPARAINFLUENZA 2\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tP3R\tPARAINFLUENZA 3\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tRSVR\tRSV\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
-LAB BLOOD ORDERABLES\t473168\tLABPROTS\tPROTEIN S ACTIVITY\tPROTS\tPROTEIN S ACTIVITY\t376431.2258\t245812.3032\t372\t140032416\t1.40E+15\t343\t84313620\t3.16E+13
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tSREQ\tCOMMENTS\t9999999\t522530.9375\t371\t3709999629\t3.71E+16\t384\t200651880\t3.85E+14
-STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tDATE1\tDATE 1\t7291706.733\t40050.70423\t371\t2705223198\t2.68E+16\t213\t8530800\t1.87E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tURIC\tURIC ACID CRYSTALS\t9999999\t16276.48069\t371\t3709999629\t3.71E+16\t233\t3792420\t3.81E+11
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBLASTS\tBLASTS\t38.71195652\t32522.31405\t368\t14246\t984390\t363\t11805600\t8.81E+12
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tSDES\tSPECIMEN\t9999999\t522530.9375\t368\t3679999632\t3.68E+16\t384\t200651880\t3.85E+14
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tDI\tISSUE DATE/TIME\t9999999\t31361.45455\t367\t3669999633\t3.67E+16\t165\t5174640\t2.66E+11
-LAB BLOOD ORDERABLES\t473414\tLABTT4\tT4, TOTAL\tT4\tTHYROXINE (T4), TOTAL\t326982.9749\t34944.88746\t367\t120002751.8\t1.20E+15\t311\t10867860\t7.32E+11
-LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET\tLEVET\t1035446.455\t466250.0552\t367\t380008849\t3.80E+15\t362\t168782520\t7.27E+15
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBLASTSAB\tBLASTS,ABS(MAN DIFF)\t13.57452055\t32702.33333\t365\t4954.7\t468522.73\t360\t11772840\t8.81E+12
-LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET2\tLEVET2\t9999999\t466250.0552\t365\t3649999635\t3.65E+16\t362\t168782520\t7.27E+15
-LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET3\tLEVET3\t9999999\t466250.0552\t365\t3649999635\t3.65E+16\t362\t168782520\t7.27E+15
-LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET4\tLEVET4\t9999999\t466250.0552\t364\t3639999636\t3.64E+16\t362\t168782520\t7.27E+15
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9017R\tHLA-B ALLELE 1\t9999999\t214170.5316\t362\t3619999638\t3.62E+16\t395\t84597360\t1.52E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9018R\tHLA-B ALLELE 2\t9999999\t214170.5316\t362\t3619999638\t3.62E+16\t395\t84597360\t1.52E+14
-LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET1\tLEVET1\t9999999\t466250.0552\t362\t3619999638\t3.62E+16\t362\t168782520\t7.27E+15
-LAB BLOOD ORDERABLES\t471641\tLABANTIXA\tLMWH ACTIVITY BY ANTIXA\tANTIXA\tLMW HEPARIN, ANTIXA U/ML\t609418.9318\t9658.67036\t361\t220000234.4\t2.20E+15\t361\t3486780\t1.29E+11
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tPRO\tPRODUCT ID  \t9999999\t33621.42857\t360\t3599999640\t3.60E+16\t154\t5177700\t6.15E+11
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9015R\tHLA-A ALLELE 1\t9999999\t214170.5316\t360\t3599999640\t3.60E+16\t395\t84597360\t1.52E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9016R\tHLA-A ALLELE 2\t9999999\t214170.5316\t360\t3599999640\t3.60E+16\t395\t84597360\t1.52E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9027R\tHLA-DQA1 ALLELE 1\t9999999\t207262.4377\t360\t3599999640\t3.60E+16\t361\t74821740\t1.10E+14
-VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tRPT\tRPT\t9999999\t68786.73267\t360\t3599999640\t3.60E+16\t101\t6947460\t5.79E+11
-VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tSREQ\tCOMMENTS\t9999999\t68786.73267\t360\t3599999640\t3.60E+16\t101\t6947460\t5.79E+11
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9028R\tHLA-DQA1 ALLELE 2\t9999999\t207262.4377\t359\t3589999641\t3.59E+16\t361\t74821740\t1.10E+14
-STOOL ORDERABLES\t473114\tLABSTPOLY\tWBC FOR IBD, STOOL\tEOSSTL\tEOSINOPHILS, STOOL\t9999999\t34758.67925\t359\t3589999641\t3.59E+16\t212\t7368840\t1.20E+12
-VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tSDES\tSPECIMEN\t9999999\t68786.73267\t359\t3589999641\t3.59E+16\t101\t6947460\t5.79E+11
-VIROLOGY ORDERABLES\t910608\tLABCMVIGT\tCMV IGG (TRANSFUSION)\tCMGTQT\tLEVEL\t2409040.838\t131862.0168\t357\t860027579\t8.60E+15\t357\t47074740\t9.18E+12
-STOOL ORDERABLES\t473114\tLABSTPOLY\tWBC FOR IBD, STOOL\tPOLYST\tPMNS, STOOL\t9999999\t34758.67925\t356\t3559999644\t3.56E+16\t212\t7368840\t1.20E+12
-STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tDATE2\tDATE 2\t9679437.871\t40731.25628\t356\t3445879882\t3.44E+16\t199\t8105520\t1.83E+12
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9024R\tHLA-DRB1 ALLELE 2\t9999999\t215133.8992\t355\t3549999645\t3.55E+16\t377\t81105480\t1.49E+14
-URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tUPROTTOT\tPROTEIN, TOTAL, URINE\t591667.2113\t70648.16327\t355\t210041860\t2.10E+15\t196\t13847040\t9.65E+12
-URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tUT10\tCOLLECTION PERIOD PROTEIN\t197214.3465\t70648.16327\t355\t70011093\t7.00E+14\t196\t13847040\t9.65E+12
-URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tVOL10\tVOLUME PROTEIN\t58370.19155\t70648.16327\t355\t20721418\t2.00E+14\t196\t13847040\t9.65E+12
-VIROLOGY ORDERABLES\t472041\tLABCMVM\tCMV IGM\tCMVIGM\tCMV IGM AB\t9999999\t160973.8197\t355\t3549999645\t3.55E+16\t233\t37506900\t8.05E+12
-BODY FLUID ORDERABLES\t472656\tLABINSLF\tINSULIN FASTING\tINSF\tINSULIN, FASTING\t621478.6638\t35292.57353\t354\t220003447\t2.20E+15\t272\t9599580\t7.78E+11
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9023R\tHLA-DRB1 ALLELE 1\t9999999\t215133.8992\t353\t3529999647\t3.53E+16\t377\t81105480\t1.49E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9029R\tHLA-DQB1 ALLELE 1\t9999999\t215133.8992\t353\t3529999647\t3.53E+16\t377\t81105480\t1.49E+14
-STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tDATE3\tDATE 3\t9620291.244\t40919.69697\t353\t3395962809\t3.39E+16\t198\t8102100\t1.83E+12
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9030R\tHLA-DQB1 ALLELE 2\t9999999\t215133.8992\t351\t3509999649\t3.51E+16\t377\t81105480\t1.49E+14
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tXMR\tUNIT CROSSMATCH  \t9999999\t70229.45455\t350\t3499999650\t3.50E+16\t165\t11587860\t1.72E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMAOTH\tOTHER, ABS(MAN DIFF)\t57666.52133\t72845.85799\t347\t20010282.9\t2.00E+14\t338\t24621900\t4.25E+12
-VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-PLASMA\tCMQDNA\tCMV DNA\t9999999\t252366.8139\t347\t3469999653\t3.47E+16\t317\t80000280\t2.70E+13
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCOTHER\tOTHERS, CSF\t27.65797101\t11283.86139\t345\t9542\t728132\t202\t2279340\t72839646000
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9022R\tHLA-C ALLELE 2\t9999999\t217419.6286\t344\t3439999656\t3.44E+16\t377\t81967200\t1.50E+14
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9021R\tHLA-C ALLELE 1\t9999999\t217419.6286\t342\t3419999658\t3.42E+16\t377\t81967200\t1.50E+14
-LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO\tTHEOPHYLLINE\t381241.0419\t5671.84466\t341\t130003195.3\t1.30E+15\t309\t1752600\t18387403200
-LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO1\tPEAK, TROUGH, OR RANDOM\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
-LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO2\tDATE & TIME OF LAST DOSE\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
-LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO3\tDOSAGE\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
-LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO4\tADMINISTRATION ROUTE\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
-URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tTTPU\tPROTEIN EXCRETION\t206996.9472\t70648.16327\t341\t70585959\t7.00E+14\t196\t13847040\t9.65E+12
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tUBT\tUNIT BLOOD TYPE  \t9999999\t44215.41667\t339\t3389999661\t3.39E+16\t144\t6367020\t7.61E+11
-LAB BLOOD ORDERABLES\t577063\tLABHGBP\tHEMOGLOBIN, PLASMA\tYHGBP1\tTOTAL HEMOGLOBIN\t1124271.781\t157143.1405\t338\t380003862.1\t3.80E+15\t363\t57042960\t1.66E+13
-LAB BLOOD ORDERABLES\t472794\tLABMALAR\tMALARIA PREP\tMAL\tMALARIA PREP RESULT\t9940651.826\t39822.56881\t337\t3349999665\t3.35E+16\t327\t13021980\t3.62E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tURENAL\tRENAL CELLS\t9999999\t12728.77358\t337\t3369999663\t3.37E+16\t212\t2698500\t1.37E+11
-LAB BLOOD ORDERABLES\t471640\tLABANTIM\tANTI - TPO AB\tATPO\tANTI-TPO ANTIBODY\t5701537.291\t73384.98403\t335\t1910014993\t1.91E+16\t313\t22969500\t3.16E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSPERM\tSPERMATOZOA\t9999999\t11761.81034\t333\t3329999667\t3.33E+16\t232\t2728740\t1.34E+11
-LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11825R\tCONCENT. AT STEADY STATE (CSS)\t9728119.562\t466501.791\t331\t3220007575\t3.22E+16\t335\t156278100\t5.70E+14
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tDI\tISSUE DATE/TIME\t9999999\t27509.21053\t330\t3299999670\t3.30E+16\t152\t4181400\t1.47E+11
-LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tEGFR\tEGFR\t9515152.918\t5213.454545\t330\t3140000463\t3.14E+16\t330\t1720440\t43170415200
-LAB BLOOD ORDERABLES\t471625\tLAB11068R\tACE\tACE\tANGIOTESIN CONVERTING ENZYME\t972673.4681\t195031.7197\t329\t320009571\t3.20E+15\t314\t61239960\t3.89E+13
-LAB BLOOD ORDERABLES\t472802\tLABMMAS\tMETHYLMALONIC ACID, SERUM\tMMA\tMETHYLMALONIC ACID, SERUM\t60790.84912\t340222.5705\t329\t20000189.36\t2.00E+14\t319\t108531000\t4.35E+13
-MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tSDES\tSPECIMEN\t9999999\t184554.1693\t327\t3269999673\t3.27E+16\t319\t58872780\t5.99E+13
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tPRO\tPRODUCT ID  \t9999999\t33679.72028\t326\t3259999674\t3.26E+16\t143\t4816200\t5.88E+11
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tCILIA\tBRONCH. LINING CELLS\t8.481595092\t16602.66667\t326\t2765\t464465\t180\t2988480\t3.15E+11
-LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP\tCARBAMAZEPINE\t858902.9672\t11257.52508\t326\t280002367.3\t2.80E+15\t299\t3366000\t5.74E+11
-MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tSREQ\tCOMMENTS\t9999999\t213232.1186\t326\t3259999674\t3.26E+16\t236\t50322780\t1.42E+13
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t325\t3249999675\t3.25E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tOTHER\tOTHERS (DESCRIBE)\t61582.52923\t74667.06625\t325\t20014322\t2.00E+14\t317\t23669460\t4.12E+12
-LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP2\tDATE & TIME LAST DOSE\t9999999\t11257.52508\t325\t3249999675\t3.25E+16\t299\t3366000\t5.74E+11
-LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP3\tDOSAGE\t9999999\t11257.52508\t325\t3249999675\t3.25E+16\t299\t3366000\t5.74E+11
-STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tOCCGU\tSTOOL SAMPLE2,GUAIAC\t9999999\t40763.46369\t324\t3239999676\t3.24E+16\t179\t7296660\t1.58E+12
-STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tOCCGU\tSTOOL SAMPLE3,GUAIAC\t9999999\t40763.46369\t324\t3239999676\t3.24E+16\t179\t7296660\t1.58E+12
-LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP4\tADMINISTRATION ROUTE\t9999999\t11257.52508\t323\t3229999677\t3.23E+16\t299\t3366000\t5.74E+11
-URINE ORDERABLES\t472018\tLABURCL\tCHLORIDE, URINE (RANDOM)\tUCL\tCHLORIDE, URINE\t93824.27188\t14059.70149\t320\t30023767\t3.00E+14\t201\t2826000\t1.55E+11
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tXMR\tUNIT CROSSMATCH  \t9999999\t64988.57143\t319\t3189999681\t3.19E+16\t147\t9553320\t1.35E+12
-LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP1\tPEAK, TROUGH, OR RANDOM\t9999999\t11257.52508\t318\t3179999682\t3.18E+16\t299\t3366000\t5.74E+11
-MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tSREQ\tCOMMENTS\t9999999\t184554.1693\t318\t3179999682\t3.18E+16\t319\t58872780\t5.99E+13
-LAB BLOOD ORDERABLES\t473472\tLABTRFN\tTRANSFERRIN\tTRFRN\tTRANSFERRIN\t190094.8734\t7193.785714\t316\t60069980\t6.00E+14\t280\t2014260\t34907950800
-VIROLOGY ORDERABLES\t472041\tLABCMVM\tCMV AB IGM\tCMVIGM\tCMV IGM AB\t9999999\t153862.9333\t316\t3159999684\t3.16E+16\t225\t34619160\t6.49E+12
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9605R\tMETHOD\t9999999\t118749.8734\t315\t3149999685\t3.15E+16\t316\t37524960\t4.02E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9271R\tSERUM DATE\t9999999\t118749.8734\t314\t3139999686\t3.14E+16\t316\t37524960\t4.02E+13
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tUBT\tUNIT BLOOD TYPE  \t9999999\t45869.76744\t312\t3119999688\t3.12E+16\t129\t5917200\t7.29E+11
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9585R\tCOMPOSED TEXT CLASS I\t9999999\t118749.8734\t312\t3119999688\t3.12E+16\t316\t37524960\t4.02E+13
-LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tARG\tABO/RH (AUTOMATION)\t9999999\t16553.15589\t312\t3119999688\t3.12E+16\t263\t4353480\t1.43E+11
-LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tEGFR\tEGFR\t9679487.679\t3750.384615\t312\t3020000156\t3.02E+16\t312\t1170120\t25513228800
-LAB BLOOD ORDERABLES\t471641\tLABANTIXA\tLMW HEPARIN, ANTIXA U/ML\tANTIXA\tLMW HEPARIN, ANTIXA U/ML\t290323.2281\t21451.54412\t310\t90000200.71\t9.00E+14\t272\t5834820\t2.86E+11
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9274R\tCPRA FOR C1Q CLASS I\t9999999\t119126.8571\t309\t3089999691\t3.09E+16\t315\t37524960\t4.02E+13
-LAB BLOOD ORDERABLES\t577063\tLABHGBP\tHEMOGLOBIN, PLASMA\t12078R\tOXYHEMOGLOBIN\t1391592.153\t142800.5286\t309\t430001975.4\t4.30E+15\t227\t32415720\t5.47E+12
-VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tSDES11\tSPECIMEN\t9999999\t167178.2332\t307\t3069999693\t3.07E+16\t283\t47311440\t1.12E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9266R\tCPRA FOR CUMULATIVE C1Q\t9999999\t113014.3408\t306\t3059999694\t3.06E+16\t311\t35147460\t3.80E+13
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMPHM\tLYMPHOMA CELLS\t42.36721311\t16392.29167\t305\t12922\t848348\t288\t4720980\t4.74E+11
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRESVSP\tSPECIMEN\t9999999\t97678.90244\t305\t3049999695\t3.05E+16\t820\t80096700\t1.50E+13
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tPRO\tPRODUCT ID  \t9999999\t34670.73529\t303\t3029999697\t3.03E+16\t136\t4715220\t5.88E+11
-LAB BLOOD ORDERABLES\t977657\tLABBHB\tBETA-HYDROXYBUTYRATE\tBHB\tBETA-HYDROXYBUTYRATE\t1650166.313\t8862.772277\t303\t500000392.8\t5.00E+15\t303\t2685420\t53595745200
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCRLYM\tREACT LYMPH, CSF\t3.410596026\t18365.37736\t302\t1030\t9952\t212\t3893460\t9.21E+11
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tSI\t<<STATUS>>  \t9999999\t37720.88748\t300\t2999999700\t3.00E+16\t631\t23801880\t2.56E+12
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL3\tHSV 2 IGG INDEX\t7699999.873\t290474.6392\t300\t2309999962\t2.31E+16\t291\t84528120\t2.82E+13
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL5\tHSV 1/2 IGM INDEX\t7366666.345\t290474.6392\t300\t2209999903\t2.21E+16\t291\t84528120\t2.82E+13
-STOOL ORDERABLES\t472954\tLABANCA\tNEUTROPHIL CYTOPLASMA REFLEX AB PANEL\tMPOAR\tMYELOPEROX AB REFLEX\t9999999\t219441.5909\t298\t2979999702\t2.98E+16\t264\t57932580\t1.44E+13
-STOOL ORDERABLES\t472954\tLABANCA\tNEUTROPHIL CYTOPLASMA REFLEX AB PANEL\tSERINEPR3\tSERINE PR3 REFLEX\t9999999\t219441.5909\t298\t2979999702\t2.98E+16\t264\t57932580\t1.44E+13
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tDI\tISSUE DATE/TIME\t9999999\t34239.86014\t297\t2969999703\t2.97E+16\t143\t4896300\t3.08E+11
-MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tRPT\tRPT\t9999999\t1712644.444\t297\t2969999703\t2.97E+16\t297\t508655400\t9.24E+14
-MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tSDES\tSPECIMEN\t9999999\t1712644.444\t296\t2959999704\t2.96E+16\t297\t508655400\t9.24E+14
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPLYM\tPROLYMPHOCYTE\t34027.34694\t33929.27336\t294\t10004040\t1.00E+14\t289\t9805560\t1.72E+12
-MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tSREQ\tCOMMENTS\t9999999\t1712644.444\t294\t2939999706\t2.94E+16\t297\t508655400\t9.24E+14
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL1\tHSV 1 IGG INDEX\t5238096.571\t290474.6392\t294\t1540000392\t1.54E+16\t291\t84528120\t2.82E+13
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t291\t2909999709\t2.91E+16\t0\tNone\tNone
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9251R\tSERUM DATE\t9999999\t159629.0722\t291\t2909999709\t2.91E+16\t291\t46452060\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9604R\tMETHOD\t9999999\t159629.0722\t291\t2909999709\t2.91E+16\t291\t46452060\t7.62E+13
-LAB BLOOD ORDERABLES\t497216\tLABYCP\tC - PEPTIDE, SERUM\tCPEP\tC-PEPTIDE\t618558.524\t43331.67939\t291\t180000530.5\t1.80E+15\t262\t11352900\t1.04E+12
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9249R\tIGG COMBINED PEAK CPRA\t9999999\t159629.0722\t290\t2899999710\t2.90E+16\t291\t46452060\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9254R\tCPRA FOR IGG CLASS I\t9999999\t160179.1034\t290\t2899999710\t2.90E+16\t290\t46451940\t7.62E+13
-VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-BLOOD\tCMVQSP\tSPECIMEN\t9999999\t204632\t290\t2899999710\t2.90E+16\t30\t6138960\t1.86E+12
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9245R\tCPRA FOR CUMULATIVE IGG\t9999999\t159157.8621\t289\t2889999711\t2.89E+16\t290\t46155780\t7.61E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9250R\tIGG COMBINED PEAK DATE\t9999999\t159629.0722\t289\t2889999711\t2.89E+16\t291\t46452060\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9248R\tIGG COMBINED CPRA FOR SERUM\t9999999\t161079.1667\t287\t2869999713\t2.87E+16\t288\t46390800\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9257R\tCPRA FOR IGG CLASS II\t9999999\t160995\t287\t2869999713\t2.87E+16\t288\t46366560\t7.62E+13
-MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tCULT\tCULT/OTHER RSLT:\t9999999\t483529.1944\t286\t2859999714\t2.86E+16\t4481\t2166694320\t1.17E+15
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tCECAST\tCELLULAR CASTS\t9999999\t10987.42574\t285\t2849999715\t2.85E+16\t202\t2219460\t71703558000
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCCNT\tCELLS COUNTED\t59.02112676\t14846.32258\t284\t16762\t1985536\t155\t2301180\t7.75E+11
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tAMPHET\tAMPHETAMINES\t9999999\t292758.0347\t283\t2829999717\t2.83E+16\t173\t50647140\t4.06E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9275R\tHLA ANTIBODIES FOR C1Q CLASS I\t9999999\t118749.8734\t282\t2819999718\t2.82E+16\t316\t37524960\t4.02E+13
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tSI\t<<STATUS>>  \t9999999\t40376.78201\t280\t2799999720\t2.80E+16\t578\t23337780\t2.61E+12
-LAB BLOOD ORDERABLES\t472045\tLABCO2\tCO2, SERUM/PLASMA\tCO2\tCO2, SER/PLAS\t71708.92832\t1890.989011\t279\t20006791\t2.00E+14\t91\t172080\t987652800
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPRMY\tPROMYELOCYTES\t6.352517986\t44065.65056\t278\t1766\t64670\t269\t11853660\t5.02E+12
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tPRO\tPRODUCT ID  \t9999999\t30573.07087\t277\t2769999723\t2.77E+16\t127\t3882780\t3.96E+11
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMEGA\tMEGAKARY/100WBC\t4.293478261\t25805.66038\t276\t1185\t34665\t265\t6838500\t7.26E+11
-VIROLOGY ORDERABLES\t497464\tLABFVARZ\tVARICELLA ZOSTER VIR. DNA,QUAL\tSOUR13\tSOURCE\t9999999\t246899.0977\t276\t2759999724\t2.76E+16\t133\t32837580\t2.43E+13
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tDI\tISSUE DATE/TIME\t9999999\t33644.7619\t275\t2749999725\t2.75E+16\t126\t4239240\t2.63E+11
-STOOL ORDERABLES\t472954\tLABANCA\tNEUTROPHIL CYTOPLASMA REFLEX AB PANEL\tANCIGG\tANCA, IGG\t9999999\t219441.5909\t275\t2749999725\t2.75E+16\t264\t57932580\t1.44E+13
-LAB BLOOD ORDERABLES\t473759\tLAB11098R\t1 25 OH VITAMIN D\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t2043823.269\t274198.5\t274\t560007575.7\t5.60E+15\t240\t65807640\t2.14E+13
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tUBT\tUNIT BLOOD TYPE  \t9999999\t45405.78947\t272\t2719999728\t2.72E+16\t114\t5176260\t6.15E+11
-VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVGGQUL\tVZV IGG RESULT\t9999999\t167515.5882\t271\t2709999729\t2.71E+16\t272\t45564240\t9.41E+12
-LAB BLOOD ORDERABLES\t472022\tLABCL\tCHLORIDE, SERUM/PLASMA\tCL\tCHLORIDE, SER/PLAS\t74179.22593\t2050.769231\t270\t20028391\t2.00E+14\t78\t159960\t939261600
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tURIC\tURIC ACID CRYSTALS\t9999999\t15107.21893\t270\t2699999730\t2.70E+16\t169\t2553120\t4.10E+11
-VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVGGQNT\tVZV IGG LEVEL\t371749.6676\t167359.7753\t269\t100000660.6\t1.00E+15\t267\t44685060\t9.23E+12
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tALCOHOL\tALCOHOL\t9999999\t271787.3494\t267\t2669999733\t2.67E+16\t166\t45116700\t3.56E+13
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t12002R\tVCA IGM ,IFA\t9999999\t212032.3077\t267\t2669999733\t2.67E+16\t234\t49615560\t1.19E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9265R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t119265.2866\t265\t2649999735\t2.65E+16\t314\t37449300\t4.02E+13
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tFIO2\tFIO2\t5283038.447\t306.875\t265\t1400005188\t1.40E+16\t192\t58920\t2744136000
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9255R\tHLA ANTIBODIES FOR IGG CLASS I\t9999999\t160179.1034\t264\t2639999736\t2.64E+16\t290\t46451940\t7.62E+13
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t12001R\tVCA IGG ,IFA\t9999999\t212032.3077\t264\t2639999736\t2.64E+16\t234\t49615560\t1.19E+13
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tSI\t<<STATUS>>  \t9999999\t39146.22824\t263\t2629999737\t2.63E+16\t517\t20238600\t2.20E+12
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tBARB\tBARBITURATES\t9999999\t280049.8137\t263\t2629999737\t2.63E+16\t161\t45088020\t3.62E+13
-VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMMQNT\tCMV IGM LEVEL\t9011406.061\t163792.4715\t263\t2369999794\t2.37E+16\t263\t43077420\t9.37E+12
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tBENZO\tBENZODIAZEPINES\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tCOCAINE\tCOCAINE\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tOPIATE\tOPIATES\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tYUPCP\tPHENCYCLIDINE\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
-URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tYUTTHC\tTETRAHYDROCANNABINOL\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tXMR\tUNIT CROSSMATCH  \t9999999\t71971.07143\t261\t2609999739\t2.61E+16\t112\t8060760\t1.22E+12
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9246R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t159629.0722\t261\t2609999739\t2.61E+16\t291\t46452060\t7.62E+13
-LAB BLOOD ORDERABLES\t471571\tLAB11069R\tALDOLASE\tALDOLASE\tALDOLASE, SERUM\t383163.4709\t169101.5323\t261\t100005665.9\t1.00E+15\t248\t41937180\t2.52E+13
-MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRCMT\tCOMMENT\t9999999\t488214.4565\t260\t2599999740\t2.60E+16\t184\t89831460\t6.08E+13
-MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRMET\tMETHOD\t9999999\t488214.4565\t260\t2599999740\t2.60E+16\t184\t89831460\t6.08E+13
-MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRPRM\tPRIMERS\t9999999\t488214.4565\t260\t2599999740\t2.60E+16\t184\t89831460\t6.08E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9258R\tHLA ANTIBODIES FOR IGG CLASS II\t9999999\t160995\t256\t2559999744\t2.56E+16\t288\t46366560\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9263R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t118886.6667\t255\t2549999745\t2.55E+16\t315\t37449300\t4.02E+13
-LAB BLOOD ORDERABLES\t473796\tLABYWNILE\tWEST NILE VIRUS AB, IGG AND IGM\tYWNSC\tSOURCE\t9999999\t282367.6494\t255\t2549999745\t2.55E+16\t251\t70874280\t3.07E+13
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tSI\t<<STATUS>>  \t9999999\t36165.4902\t254\t2539999746\t2.54E+16\t459\t16599960\t1.66E+12
-BODY FLUID ORDERABLES\t497327\tLABWNVAB\tWEST NILE VIRUS AB PANEL, CSF\tWNVIGG\tWEST NILE VIRUS IGG\t9446639.5\t328716.2567\t253\t2389999793\t2.39E+16\t187\t61469940\t4.81E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tREACTL\tREACTIVE LYMPHS\t4.565217391\t36557.3617\t253\t1155\t24013\t235\t8590980\t1.37E+12
-LAB BLOOD ORDERABLES\t472096\tLABYCOPPR\tCOPPER, SERUM\tCOPPER\tCOPPER, SERUM\t237155.1937\t198196.4063\t253\t60000264\t6.00E+14\t256\t50738280\t6.24E+13
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVMQNT\tVMQNT\t9486165.194\t251424.8606\t253\t2399999794\t2.40E+16\t251\t63107640\t2.09E+13
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tUBT\tUNIT BLOOD TYPE  \t9999999\t44582.22222\t251\t2509999749\t2.51E+16\t108\t4814880\t5.88E+11
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tACAIGG\tACA IGG ANTIBODIES\t199210.8924\t318547.2289\t251\t50001934\t5.00E+14\t249\t79318260\t3.18E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tACAIGM\tACA IGM ANTIBODIES\t119526.5179\t318547.2289\t251\t30001156\t3.00E+14\t249\t79318260\t3.18E+13
-LAB BLOOD ORDERABLES\t472520\tLABHBCA\tIGM ANTI-HBC\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t52028.78788\t251\t2509999749\t2.51E+16\t198\t10301700\t1.13E+12
-LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tRESUL1\tRESULT\t9999999\t982058.0846\t250\t2499999750\t2.50E+16\t898\t881888160\t7.73E+15
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tBE\tBASE EXCESS, ISTAT\t120005.836\t32.88461538\t250\t30001459\t3.00E+14\t104\t3420\t219600
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tXMR\tUNIT CROSSMATCH  \t9999999\t68337.71429\t248\t2479999752\t2.48E+16\t105\t7175460\t1.09E+12
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tPRO\tPRODUCT ID  \t9999999\t33532.84404\t248\t2479999752\t2.48E+16\t109\t3655080\t4.10E+11
-MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t439595.3706\t248\t2479999752\t2.48E+16\t4290\t1885864140\t2.06E+15
-LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGENTPK\tGENTAMICIN, PEAK\t485834.1113\t11076.83721\t247\t120001025.5\t1.20E+15\t215\t2381520\t77927551200
-BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tSHAPE\tSHAPE\t9999999\t11946.19355\t246\t2459999754\t2.46E+16\t155\t1851660\t96880654800
-LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGNTPO3\tDOSAGE\t9999999\t11076.83721\t246\t2459999754\t2.46E+16\t215\t2381520\t77927551200
-LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGNTPO4\tADMINISTRATION ROUTE\t9999999\t11076.83721\t246\t2459999754\t2.46E+16\t215\t2381520\t77927551200
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADDNA\tADENOVIRUS DNA\t9999999\t203590.4878\t246\t2459999754\t2.46E+16\t246\t50083260\t1.36E+13
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVCP\tRESULT\t9012109.122\t203590.4878\t246\t2216978844\t2.21E+16\t246\t50083260\t1.36E+13
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVLOG\tRESULT LOG 10\t8983739.346\t203590.4878\t246\t2209999879\t2.21E+16\t246\t50083260\t1.36E+13
-BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tSIZE\tSIZE\t9999999\t11946.19355\t245\t2449999755\t2.45E+16\t155\t1851660\t96880654800
-LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGNTPO2\tDATE & TIME OF LAST DOSE\t9999999\t11076.83721\t245\t2449999755\t2.45E+16\t215\t2381520\t77927551200
-LAB BLOOD ORDERABLES\t472325\tLABYERYTH\tERYTHROPOIETIN\tEPO\tERYTHROPOIETIN\t1147579.533\t215313.5211\t244\t280009406\t2.80E+15\t213\t45861780\t9.55E+14
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tDI\tISSUE DATE/TIME\t9999999\t31822.07547\t243\t2429999757\t2.43E+16\t106\t3373140\t1.79E+11
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tALBCPI\tALBUMIN, SER/PLAS\t497285.6379\t407309.1176\t243\t120840410\t1.20E+15\t136\t55394040\t3.19E+13
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCAB\tCSF ALBUMIN\t493865.1329\t407309.1176\t243\t120009227.3\t1.20E+15\t136\t55394040\t3.19E+13
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCIG\tCSF IGG\t493834.1185\t407309.1176\t243\t120001690.8\t1.20E+15\t136\t55394040\t3.19E+13
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCINDX\tCSF IGG/ALBUMIN INDEX\t493827.6996\t407309.1176\t243\t120000131\t1.20E+15\t136\t55394040\t3.19E+13
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tIGGCPI\tIGG, SER/PLAS\t494776.5185\t407309.1176\t243\t120230694\t1.20E+15\t136\t55394040\t3.19E+13
-LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO\tLAMO\t1440335.963\t236494.25\t243\t350001639\t3.50E+15\t240\t56758620\t1.52E+15
-LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO1\tLAMO1\t9999999\t236494.25\t243\t2429999757\t2.43E+16\t240\t56758620\t1.52E+15
-LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO2\tLAMO2\t9999999\t236494.25\t243\t2429999757\t2.43E+16\t240\t56758620\t1.52E+15
-LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO3\tLAMO3\t9999999\t236494.25\t243\t2429999757\t2.43E+16\t240\t56758620\t1.52E+15
-BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tLOCAT\tLOCATION\t9999999\t11946.19355\t242\t2419999758\t2.42E+16\t155\t1851660\t96880654800
-LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO4\tLAMO4\t9999999\t236494.25\t242\t2419999758\t2.42E+16\t240\t56758620\t1.52E+15
-BODY FLUID ORDERABLES\t473022\tLABGASBLD\tOCCULT BLOOD GASTRIC\tOCCBLDGAS\tOCCULT BLOOD,GASTRIC\t9916317.008\t9982.44898\t239\t2369999765\t2.37E+16\t147\t1467420\t3.34E+11
-MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tRPT\tRPT\t9999999\t3667605.352\t239\t2389999761\t2.39E+16\t213\t781199940\t3.13E+15
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tDYSPL\tDYSPL GRANS, UNCLASS.\t12.47033898\t23989.69163\t236\t2943\t71343\t227\t5445660\t6.23E+11
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAQT\tHEP B SURF AB QUANT\t7245826.204\t43043.64407\t236\t1710014984\t1.71E+16\t236\t10158300\t7.64E+11
-MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tSDES\tSPECIMEN\t9999999\t3667605.352\t236\t2359999764\t2.36E+16\t213\t781199940\t3.13E+15
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVSP\tSPECIMEN\t9999999\t203590.4878\t236\t2359999764\t2.36E+16\t246\t50083260\t1.36E+13
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tEVR\tCSF ENTEROVIRUS RESULT\t9999999\t25672.87129\t235\t2349999765\t2.35E+16\t202\t5185920\t5.37E+11
-LAB BLOOD ORDERABLES\t471641\tLABANTIXA\tANTI XA / LMWH\tANTIXA\tLMW HEPARIN, ANTIXA U/ML\t425532.5321\t38717.1875\t235\t100000145.1\t1.00E+15\t192\t7433700\t4.11E+11
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tB2GPIG\tBETA-2 GLYCOPROTEIN 1, IGG\t85474.5812\t316148.4979\t234\t20001052\t2.00E+14\t233\t73662600\t2.93E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tB2GPIM\tBETA-2 GLYCOPROTEIN 1, IGM\t85478.16239\t316148.4979\t234\t20001890\t2.00E+14\t233\t73662600\t2.93E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVCN\tDRVV CONFIRMATION\t9145302.288\t316148.4979\t234\t2140000735\t2.14E+16\t233\t73662600\t2.93E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVMX\tDRVV MIX\t6068393.227\t316148.4979\t234\t1420004015\t1.42E+16\t233\t73662600\t2.93E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVSC\tDRVV SCREEN\t299191.3662\t316148.4979\t234\t70010779.7\t7.00E+14\t233\t73662600\t2.93E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVLA\tDRVV NORMALIZED RATIO\t9137930.258\t317009.8701\t232\t2119999820\t2.12E+16\t231\t73229280\t2.92E+13
-LAB BLOOD ORDERABLES\t472120\tLABYCCPIG\tANTI CCP\tCCPR\tANTI CCP ANTIBODY\t9999999\t388985.2294\t232\t2319999768\t2.32E+16\t218\t84798780\t4.31E+13
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITPR\tPRIMER\t9999999\t779901.7925\t231\t2309999769\t2.31E+16\t212\t165339180\t3.13E+14
-MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tSREQ\tCOMMENTS\t9999999\t3667605.352\t231\t2309999769\t2.31E+16\t213\t781199940\t3.13E+15
-LAB BLOOD ORDERABLES\t472840\tLABYMETFF\tMETANEPHRINES FRACTIONATED FREE, PLASMA\tUMETF\tMETANEPHRINE FREE\t4565217.143\t306697.1429\t230\t1049999943\t1.05E+16\t210\t64406400\t2.54E+13
-LAB BLOOD ORDERABLES\t472840\tLABYMETFF\tMETANEPHRINES FRACTIONATED FREE, PLASMA\tUNORMETF\tNORMETANEPHRINE,FREE\t956522.6147\t306697.1429\t230\t220000201.4\t2.20E+15\t210\t64406400\t2.54E+13
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITDX\tCLINICAL INDICATION\t9999999\t779901.7925\t230\t2299999770\t2.30E+16\t212\t165339180\t3.13E+14
-VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tRPT\tRPT\t9999999\t1756836.783\t230\t2299999770\t2.30E+16\t230\t404072460\t7.53E+14
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tPRO\tPRODUCT ID  \t9999999\t32158.26923\t229\t2289999771\t2.29E+16\t104\t3344460\t3.42E+11
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tUBT\tUNIT BLOOD TYPE  \t9999999\t48100.40816\t228\t2279999772\t2.28E+16\t98\t4713840\t5.88E+11
-BLOOD BANK PRODUCT ORDERABLES\t889180\tLABERUPLT\tEMERGENCY RELEASE PLT\tPLT\tPLATELETS\t9999999\t9099.473684\t228\t2279999772\t2.28E+16\t228\t2074680\t2.19E+11
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFRLYM\tREACTIVE LYMPHS, FLD\t2.276315789\t14423.57143\t228\t519\t6695\t140\t2019300\t1.07E+11
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9264R\tCPRA FOR CUMULATIVE IGG\t9999999\t148177.8814\t228\t2279999772\t2.28E+16\t236\t34969980\t3.94E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9247R\tCPRA FOR CUMULATIVE C1Q\t9999999\t116766.5502\t227\t2269999773\t2.27E+16\t229\t26739540\t2.26E+13
-VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tSDES\tSPECIMEN\t9999999\t1756836.783\t227\t2269999773\t2.27E+16\t230\t404072460\t7.53E+14
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9244R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t159629.0722\t226\t2259999774\t2.26E+16\t291\t46452060\t7.62E+13
-LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tC3P4PA\tCD3+/CD4+,ABS\t398450.3009\t63854.06699\t226\t90049768\t9.00E+14\t209\t13345500\t1.17E+12
-LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tC3P4PC\tCD3+/CD4+,%\t354003.7389\t63854.06699\t226\t80004845\t8.00E+14\t209\t13345500\t1.17E+12
-LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tLYM\tLYMPHOCYTES %\t398246.7841\t63854.06699\t226\t90003773.2\t9.00E+14\t209\t13345500\t1.17E+12
-LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tLYMAB\tLYMPHOCYTES, ABS.\t399178.9027\t63854.06699\t226\t90214432\t9.00E+14\t209\t13345500\t1.17E+12
-LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tWBC\tWBC COUNT\t360761.0265\t63854.06699\t226\t81531992\t8.00E+14\t209\t13345500\t1.17E+12
-LAB BLOOD ORDERABLES\t472664\tLABIOPTH\tINTRA OPERATIVE PTH\tIOPTH\tINTRA OPERATIVE PTH\t807386.8018\t2213.932584\t223\t180047256.8\t1.80E+15\t89\t197040\t1005069600
-STOOL ORDERABLES\t670760\tLAB11234R\tVZV TOTAL AND IGM AB PANEL\t11235R\tVARICELLA-ZOSTER, ACIF\t9999999\t214763.038\t223\t2229999777\t2.23E+16\t237\t50898840\t1.48E+13
-STOOL ORDERABLES\t670760\tLAB11234R\tVZV TOTAL AND IGM AB PANEL\t11236R\tVZ AB IGM, IFA\t9999999\t214763.038\t223\t2229999777\t2.23E+16\t237\t50898840\t1.48E+13
-VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tSREQ\tCOMMENTS\t9999999\t1756836.783\t223\t2229999777\t2.23E+16\t230\t404072460\t7.53E+14
-LAB BLOOD ORDERABLES\t670770\tLAB11242R\tVZV QUANT\t11247R\tVZV QUANT\t9595300.09\t189403.6596\t222\t2130156620\t2.13E+16\t235\t44509860\t1.94E+13
-BODY FLUID ORDERABLES\t497327\tLABWNVAB\tWEST NILE VIRUS AB PANEL, CSF\tWNVIGM\tWEST NILE VIRUS IGM\t9863635.431\t298197.0166\t220\t2169999795\t2.17E+16\t181\t53973660\t3.84E+13
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tSI\t<<STATUS>>  \t9999999\t36168.4375\t219\t2189999781\t2.19E+16\t384\t13888680\t1.55E+12
-LAB BLOOD ORDERABLES\t472375\tLABFDP\tFIBRIN DEGRADATION PRODUCTS\tFDP\tFIBRIN DEGRAD PROD\t9999999\t8429.142857\t219\t2189999781\t2.19E+16\t175\t1475100\t18989067600
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tDI\tISSUE DATE/TIME\t9999999\t36351.52174\t218\t2179999782\t2.18E+16\t92\t3344340\t2.82E+11
-LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\tFMYABM\tM.PNEUMONIA AB IGM\t9999999\t236846.1207\t218\t2179999782\t2.18E+16\t232\t54948300\t3.81E+13
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tEVSP\tSPECIMEN\t9999999\t25672.87129\t217\t2169999783\t2.17E+16\t202\t5185920\t5.37E+11
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t10472R\tDSA FOR C1Q CLASS I\t9999999\t105122.765\t217\t2169999783\t2.17E+16\t217\t22811640\t1.32E+13
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45RAA\tCD4+/CD45RA+, ABS\t558186.0465\t131761.5464\t215\t120010000\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45RAP\tCD4+/CD45RA+ %\t558146.4512\t131761.5464\t215\t120001487\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45ROA\tCD4+/CD45RO+, ABS\t558259.8558\t131761.5464\t215\t120025869\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45ROP\tCD4+/CD45RO+ %\t558158.7814\t131761.5464\t215\t120004138\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD19AB\tCD19 (PAN B), ABS.\t558197.214\t131761.5464\t215\t120012401\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD19PC\tCD19 (PAN B) %\t558147.6372\t131761.5464\t215\t120001742\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD20AB\tCD20 (PAN B), ABS.\t558179.9163\t131761.5464\t215\t120008682\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD20PC\tCD20 (PAN B) %\t558147.7442\t131761.5464\t215\t120001765\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD3AB\tCD3 (PAN T), ABS.\t558609.3953\t131761.5464\t215\t120101020\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD3PC\tCD3 (PAN T) %\t558204.3209\t131761.5464\t215\t120013929\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD4AB\tCD3+/CD4+, ABS.\t558305.6093\t131761.5464\t215\t120035706\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD4PC\tCD3+/CD4+ %\t558165.8558\t131761.5464\t215\t120005659\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD8AB\tCD3+/CD8+, ABS.\t558448.9628\t131761.5464\t215\t120066527\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD8PC\tCD3+/CD8+ %\t558180.7116\t131761.5464\t215\t120008853\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tHNS\tCD4+CD8 TOTAL, CD3+%\t558207.0884\t131761.5464\t215\t120014524\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t558615.093\t131761.5464\t215\t120102245\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tHSRATIO\tCD4/CD8 RATIO, CD3+\t558140.4037\t131761.5464\t215\t120000186.8\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tLYM\tLYMPHOCYTES %\t558152.5181\t131761.5464\t215\t120002791.4\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tLYMAB\tLYMPHOCYTES, ABS.\t558765.8558\t131761.5464\t215\t120134659\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tWBC\tWBC COUNT\t564353.8977\t131761.5464\t215\t121336088\t1.20E+15\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPDX\tCLINICAL INDICATION\t9999999\t781898.5492\t213\t2129999787\t2.13E+16\t193\t150906420\t1.54E+14
-LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNRS\tQUANTIFERON-TB\t9999999\t193921.9811\t212\t2119999788\t2.12E+16\t212\t41111460\t1.47E+13
-MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t529714.7807\t212\t2119999788\t2.12E+16\t5108\t2705783100\t2.15E+15
-URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUALB\tALBUMIN, URINE\t1132498.264\t65933.15436\t212\t240089632\t2.40E+15\t149\t9824040\t1.29E+12
-URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUALBCR\tALB/CREAT RATIO\t1132721.995\t65933.15436\t212\t240137063\t2.40E+15\t149\t9824040\t1.29E+12
-URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t141587.6023\t65933.15436\t212\t30016571.69\t3.00E+14\t149\t9824040\t1.29E+12
-LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tACAIGG\tACA IGG ANTIBODIES\t236975.0095\t311240.1\t211\t50001727\t5.00E+14\t200\t62248020\t2.73E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tACAIGM\tACA IGM ANTIBODIES\t236971.3697\t311240.1\t211\t50000959\t5.00E+14\t200\t62248020\t2.73E+13
-MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tRPT\tRPT\t9999999\t90298.30189\t211\t2109999789\t2.11E+16\t106\t9571620\t1.70E+12
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9277R\tCPRA FOR C1Q CLASS II\t9999999\t138546\t210\t2099999790\t2.10E+16\t210\t29094660\t2.40E+13
-LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tANTI-RO ANTIBODY\tSSAQUL\tANTI-RO RESULT\t9999999\t390342.8571\t210\t2099999790\t2.10E+16\t210\t81972000\t4.05E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9281R\tCOMPOSED TEXT CLASS II\t9999999\t138546\t209\t2089999791\t2.09E+16\t210\t29094660\t2.40E+13
-LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1 AND 2 PCR, NON CSF\tHV1DNA\tHSV-1 DNA, QN PCR\t9999999\t534380.8163\t209\t2089999791\t2.09E+16\t98\t52369320\t9.04E+13
-STOOL ORDERABLES\t471762\tLABYBOH\tBETA-HYDROXYBUTYRATE\tBOH\tBETA-HYDROXYBUTYRATE\t1004786.194\t177002.0168\t209\t210000314.5\t2.10E+15\t238\t42126480\t2.11E+13
-VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tSDES2\tSPECIMEN\t9999999\t184892.973\t209\t2089999791\t2.09E+16\t333\t61569360\t1.58E+13
-HISTOCOMPATIBILITY ORDERABLE\t890826\t10150R\tHLA ORDER - SUMMARIES\t10049R\tHLA LAB TESTING STATUS\t9999999\t1953334.327\t208\t2079999792\t2.08E+16\t208\t406293540\t1.40E+16
-LAB BLOOD ORDERABLES\t472610\tLABIGA\tIGA, SERUM\tIGA\tIGA, SERUM\t432930.4519\t47495.87629\t208\t90049534\t9.00E+14\t194\t9214200\t9.20E+11
-LAB BLOOD ORDERABLES\t497075\tLABSHIV12\tRAPID HIV-1/2 ANTIBODY\tSHIV12\tRAPID HIV-1/2 ANTIBODY\t9999999\t24626.41509\t208\t2079999792\t2.08E+16\t159\t3915600\t2.04E+12
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tPRO\tPRODUCT ID  \t9999999\t29978.70968\t207\t2069999793\t2.07E+16\t93\t2788020\t2.99E+11
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tUBT\tUNIT BLOOD TYPE  \t9999999\t41291.48936\t207\t2069999793\t2.07E+16\t94\t3881400\t3.96E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6CP\tRESULT\t8826910.329\t230093.0435\t207\t1827170438\t1.82E+16\t207\t47629260\t1.54E+13
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t230093.0435\t207\t2069999793\t2.07E+16\t207\t47629260\t1.54E+13
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6LO\tRESULT LOG 10\t8792270.18\t230093.0435\t207\t1819999927\t1.82E+16\t207\t47629260\t1.54E+13
-BODY FLUID ORDERABLES\t471757\tLABSTREP\tBETA STREP THROAT SCREEN RAPID EIA\tBSTREP\tBETA STREP THROAT EIA SCREEN\t9999999\t7540.56338\t206\t2059999794\t2.06E+16\t142\t1070760\t37267934400
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tCCNT\tCELLS COUNTED\t111.4563107\t15949.73684\t206\t22960\t3945718\t152\t2424360\t4.59E+11
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t10469R\tDSA FOR IGG CLASS I\t9999999\t90041.06796\t206\t2059999794\t2.06E+16\t206\t18548460\t1.11E+13
-LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tANTI-LA ANTIBODY\tSSBQUL\tANTI-LA RESULT\t9999999\t380593.9806\t206\t2059999794\t2.06E+16\t206\t78402360\t3.76E+13
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6SP\tSPECIMEN\t9999999\t230093.0435\t206\t2059999794\t2.06E+16\t207\t47629260\t1.54E+13
-VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tHSV1PC\tHSV-1 DNA\t9999999\t185672.0388\t206\t2059999794\t2.06E+16\t206\t38248440\t9.97E+12
-VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tHSV2PC\tHSV-2 DNA\t9999999\t185672.0388\t206\t2059999794\t2.06E+16\t206\t38248440\t9.97E+12
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t10470R\tDSA FOR IGG CLASS II\t9999999\t90063.21951\t205\t2049999795\t2.05E+16\t205\t18462960\t1.11E+13
-LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tPL24\tPLASMA, 24HR\t7756097.068\t303390.1571\t205\t1589999899\t1.59E+16\t191\t57947520\t1.90E+13
-LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tPL72\tPLASMA, 72HR\t7756097.263\t303390.1571\t205\t1589999939\t1.59E+16\t191\t57947520\t1.90E+13
-LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tSR24\tSERUM, 24HR\t2829268.254\t303390.1571\t205\t579999992\t5.80E+15\t191\t57947520\t1.90E+13
-LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tSR72\tSERUM, 72HR\t3951219.732\t303390.1571\t205\t810000045\t8.10E+15\t191\t57947520\t1.90E+13
-LAB BLOOD ORDERABLES\t473188\tLABYPTHRP\tPTH-RELATED PEPTIDE, PLASMA\tPTH\tPTH-RELATED PEPTIDE\t2926830.671\t386264.3478\t205\t600000287.5\t6.00E+15\t253\t97724880\t4.37E+13
-STOOL ORDERABLES\t471535\tLABFADA\tADENOSINE DEAMINASE, FLUID\tSPTYAD\tFLUID TYPE\t9999999\t540366\t205\t2049999795\t2.05E+16\t90\t48632940\t6.04E+13
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tXMR\tUNIT CROSSMATCH  \t9999999\t76819.02439\t204\t2039999796\t2.04E+16\t82\t6299160\t1.03E+12
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9278R\tHLA ANTIBODIES FOR C1Q CLASS II\t9999999\t138546\t203\t2029999797\t2.03E+16\t210\t29094660\t2.40E+13
-LAB BLOOD ORDERABLES\t473260\tLABPATHR\tREVIEW BY PATHOLOGIST\tFPATH\tPATHOLOGIST\t9999999\t#VALUE!\t202\t2019999798\t2.02E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITCO\tCOMMENT\t9999999\t807029.3684\t202\t2019999798\t2.02E+16\t190\t153335580\t3.03E+14
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tSI\t<<STATUS>>  \t9999999\t35683.83954\t201\t2009999799\t2.01E+16\t349\t12453660\t1.39E+12
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tDI\tISSUE DATE/TIME\t9999999\t35233.18681\t201\t2009999799\t2.01E+16\t91\t3206220\t2.54E+11
-LAB BLOOD ORDERABLES\t471761\tLABB2M\tBETA - 2 - MICROGLOBULIN, SERUM\tB2M\tBETA-2-MICROGLOBULIN, SERUM\t55494.56219\t86660.54348\t201\t11154407\t1.00E+14\t184\t15945540\t2.67E+12
-LAB BLOOD ORDERABLES\t473739\tLABYFVPA\tVALPROIC ACID FREE AND TOTAL\tVPAFREE\tVALPROIC ACID, FREE\t2885582.281\t199772.9843\t201\t580002038.4\t5.80E+15\t191\t38156640\t1.71E+13
-LAB BLOOD ORDERABLES\t473739\tLABYFVPA\tVALPROIC ACID FREE AND TOTAL\tVPATOTAL\tVALPROIC ACID, TOTAL\t1393086.761\t199772.9843\t201\t280010439\t2.80E+15\t191\t38156640\t1.71E+13
-MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tRPT\tRPT\t9999999\t1262815.2\t201\t2009999799\t2.01E+16\t75\t94711140\t1.20E+14
-MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tSDES\tSPECIMEN\t9999999\t1262815.2\t201\t2009999799\t2.01E+16\t75\t94711140\t1.20E+14
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCULT\tCULT/OTHER RSLT:\t9999999\t515574.2561\t200\t1999999800\t2.00E+16\t45607\t23513795100\t1.33E+16
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tBD\tBASE DEFICIT, ISTAT\t200006.055\t486.2068966\t200\t40001211\t4.00E+14\t116\t56400\t2743970400
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITAA\tAMINO ACID CHANGE\t9999999\t807029.3684\t199\t1989999801\t1.99E+16\t190\t153335580\t3.03E+14
-LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tSPTYS1\tSPTYS1\t9949917.556\t671067.3054\t198\t1970083676\t1.97E+16\t167\t112068240\t1.25E+14
-VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tSDES11\tSPECIMEN\t9999999\t185672.0388\t198\t1979999802\t1.98E+16\t206\t38248440\t9.97E+12
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITRE\tRESULT\t9999999\t779901.7925\t197\t1969999803\t1.97E+16\t212\t165339180\t3.13E+14
-LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3DX\tCLINICAL INDICATION\t9999999\t531220.8\t196\t1959999804\t1.96E+16\t175\t92963640\t6.34E+13
-LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\tFMYABG\tM.PNEUMONIA AB IGG\t9999999\t236846.1207\t195\t1949999805\t1.95E+16\t232\t54948300\t3.81E+13
-LAB BLOOD ORDERABLES\t472771\tLABLUPUS\tLUPUS ANTICOAGULANT\tLUPINT\tLUPUS INTERPRETATION\t9999999\t81977.7809\t194\t1939999806\t1.94E+16\t712\t58368180\t1.09E+13
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t193\t1929999807\t1.93E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473166\tLABCSTOTA\tPROTEIN C&S, TOTAL\tPROTCAGT\tPROTEIN C AG, TOTAL\t518211.9119\t354435.4839\t193\t100014899\t1.00E+15\t186\t65925000\t3.88E+13
-LAB BLOOD ORDERABLES\t473166\tLABCSTOTA\tPROTEIN C&S, TOTAL\tPROTSTOT\tPROTEIN S AG, TOTAL\t466409.3627\t354435.4839\t193\t90017007\t9.00E+14\t186\t65925000\t3.88E+13
-LAB BLOOD ORDERABLES\t473189\tLABPTTINH\tPTT INHIBITOR SCREEN\tPTTINT\tINTERPRETATION\t9999999\t76412.68156\t193\t1929999807\t1.93E+16\t358\t27355740\t3.54E+12
-MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t439595.3706\t192\t1919999808\t1.92E+16\t4290\t1885864140\t2.06E+15
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCPIER\tRESULT\t9999999\t407309.1176\t191\t1909999809\t1.91E+16\t136\t55394040\t3.19E+13
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tPRO\tPRODUCT ID  \t9999999\t33721.36364\t190\t1899999810\t1.90E+16\t88\t2967480\t3.78E+11
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tBASOCSF\tBASOPHILS, CSF\t1.510526316\t17443.24324\t190\t287\t753\t111\t1936200\t3.99E+11
-VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKCPU\tRESULT\t8525869.587\t227768.5106\t189\t1611389352\t1.59E+16\t141\t32115360\t9.66E+12
-VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKLOGU\tRESULT LOG 10\t7777778.238\t227768.5106\t189\t1470000087\t1.47E+16\t141\t32115360\t9.66E+12
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tCECAST\tCELLULAR CASTS\t9999999\t11904.48\t188\t1879999812\t1.88E+16\t125\t1488060\t99961621200
-VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKDNAU\tBKV DNA\t9999999\t227768.5106\t188\t1879999812\t1.88E+16\t141\t32115360\t9.66E+12
-LAB BLOOD ORDERABLES\t472924\tLABMONEBV\tMONO SCREEN WITH REFLEX EBV AB\tMONOSC\tINFECTIOUS MONO SCREEN\t9999999\t11195.5814\t187\t1869999813\t1.87E+16\t172\t1925640\t47965219200
-LAB BLOOD ORDERABLES\t473098\tLABYFPHEN\tPHENYTOIN TOTAL AND FREE, PLASMA\tPHENTOT\tPHENYTOIN, TOTAL PLASMA\t1550809.728\t212727.7895\t187\t290001419.1\t2.90E+15\t190\t40418280\t3.12E+13
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPRE\tRESULT\t9999999\t783300\t187\t1869999813\t1.87E+16\t190\t148827000\t1.52E+14
-MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tSDES\tSPECIMEN\t9999999\t90298.30189\t187\t1869999813\t1.87E+16\t106\t9571620\t1.70E+12
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9020R\tHLA-BW ALLELE 2\t9999999\t160946.9189\t185\t1849999815\t1.85E+16\t185\t29775180\t2.82E+13
-LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tTESTS1\tTESTS1\t9892797.638\t671067.3054\t185\t1830167563\t1.83E+16\t167\t112068240\t1.25E+14
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSPERM\tSPERMATOZOA\t9999999\t17389.71429\t184\t1839999816\t1.84E+16\t140\t2434560\t5.17E+11
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tSI\t<<STATUS>>  \t9999999\t35555.0495\t183\t1829999817\t1.83E+16\t303\t10773180\t1.30E+12
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tDI\tISSUE DATE/TIME\t9999999\t30487.85714\t183\t1829999817\t1.83E+16\t84\t2560980\t1.48E+11
-LAB BLOOD ORDERABLES\t473098\tLABYFPHEN\tPHENYTOIN TOTAL AND FREE, PLASMA\tPHENFR\tPHENYTOIN, FREE PLASMA\t1420766.156\t212727.7895\t183\t260000206.6\t2.60E+15\t190\t40418280\t3.12E+13
-LAB BLOOD ORDERABLES\t577063\tLABYHGBP\tHEMOGLOBIN, PLASMA\t12078R\tOXYHEMOGLOBIN\t1202193.93\t146662.9213\t183\t220001489.1\t2.20E+15\t178\t26106000\t4.28E+12
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPAA\tAMINO ACID CHANGE\t9999999\t803457.6536\t183\t1829999817\t1.83E+16\t179\t143818920\t1.49E+14
-MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tRPT\tRPT\t9999999\t114455.7534\t183\t1829999817\t1.83E+16\t146\t16710540\t2.41E+12
-MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tSREQ\tCOMMENTS\t9999999\t114455.7534\t183\t1829999817\t1.83E+16\t146\t16710540\t2.41E+12
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVCP\tRESULT\t8273687.022\t339631.7442\t183\t1514084725\t1.51E+16\t172\t58416660\t2.76E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVLO\tRESULT LOG 10\t8251366.009\t339631.7442\t183\t1509999980\t1.51E+16\t172\t58416660\t2.76E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVSP\tSPECIMEN\t9999999\t339631.7442\t183\t1829999817\t1.83E+16\t172\t58416660\t2.76E+13
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tUBT\tUNIT BLOOD TYPE  \t9999999\t45671.25\t182\t1819999818\t1.82E+16\t80\t3653700\t4.10E+11
-LAB BLOOD ORDERABLES\t471572\tLAB11070R\tALDOSTERONE\tALDOSTR\tALDOSTERONE, SERUM\t6153853.908\t269928.0723\t182\t1120001411\t1.12E+16\t166\t44808060\t1.37E+13
-MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tSDES\tSPECIMEN\t9999999\t114455.7534\t181\t1809999819\t1.81E+16\t146\t16710540\t2.41E+12
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tFRFAT\tFREE FAT\t9999999\t14748.09917\t181\t1809999819\t1.81E+16\t121\t1784520\t1.38E+11
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tOVFAT\tOVAL FAT BODIES\t9999999\t15422.92683\t180\t1799999820\t1.80E+16\t123\t1897020\t1.58E+11
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVRNA\tHIV 1 RNA\t9999999\t339631.7442\t180\t1799999820\t1.80E+16\t172\t58416660\t2.76E+13
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t179\t1789999821\t1.79E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t577063\tLABYHGBP\tHEMOGLOBIN, PLASMA\tYHGBP1\tTOTAL HEMOGLOBIN\t1005598.005\t146760.3352\t179\t180002042.9\t1.80E+15\t179\t26270100\t4.31E+12
-MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tSREQ\tCOMMENTS\t9999999\t90298.30189\t179\t1789999821\t1.79E+16\t106\t9571620\t1.70E+12
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tRVRSP\tSPECIMEN\t9999999\t106230.4\t179\t1789999821\t1.79E+16\t225\t23901840\t1.05E+13
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSCMT\tSYNOVIAL FLUID COMMENT\t9999999\t11515.04854\t178\t1779999822\t1.78E+16\t206\t2372100\t2.27E+11
-LAB BLOOD ORDERABLES\t471639\tLABANTITG\tANTI - THYROGLOBULIN AB\tATGA\tANTI-THYROGLOBULIN AB\t8764078.359\t70401.68675\t178\t1560005948\t1.56E+16\t166\t11686680\t1.46E+12
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRPR\tPRIMER\t9999999\t600772.8477\t178\t1779999822\t1.78E+16\t151\t90716700\t6.81E+13
-LAB BLOOD ORDERABLES\t472408\tLABFLL\tFSH\tFSH\tFSH\t786528.9483\t35073.65854\t178\t140002152.8\t1.40E+15\t164\t5752080\t3.12E+11
-VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-PLASMA\tCMVQSP\tSPECIMEN\t9999999\t252366.8139\t178\t1779999822\t1.78E+16\t317\t80000280\t2.70E+13
-LAB BLOOD ORDERABLES\t473111\tLABPLTFUN\tPLATELET FUNCTION SCREEN\tCOLADP\tCOLLAGEN/ADP\t8361602.209\t12280.51948\t177\t1480003591\t1.48E+16\t154\t1891200\t1.58E+11
-LAB BLOOD ORDERABLES\t473111\tLABPLTFUN\tPLATELET FUNCTION SCREEN\tCOLEPI\tCOLLAGEN/EPI\t3615903.153\t12280.51948\t177\t640014858\t6.40E+15\t154\t1891200\t1.58E+11
-VIROLOGY ORDERABLES\t497452\tLABHV6DNA\tHERPES VIRAL 6 DNA QUALITATIVE\tSOUR12\tSOURCE\t9999999\t231324.5963\t177\t1769999823\t1.77E+16\t161\t37243260\t1.37E+13
-HISTOCOMPATIBILITY ORDERABLE\t890777\t10005R\tHLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)\tRINFO\tTO FIND RESULT:\t9999999\t1348750.056\t176\t1759999824\t1.76E+16\t179\t241426260\t4.99E+14
-LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGMD2\tREVIEWED BY\t9999999\t18233.79078\t176\t1759999824\t1.76E+16\t889\t16209840\t1.94E+12
-MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tGS\tGRAM STAIN\t9999999\t43014.06617\t176\t1759999824\t1.76E+16\t5622\t241825080\t7.14E+13
-HISTOCOMPATIBILITY ORDERABLE\t890777\t10005R\tHLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t1348750.056\t175\t1749999825\t1.75E+16\t179\t241426260\t4.99E+14
-LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEP B SURF AB QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t5402425.933\t161105.0602\t174\t940022112.4\t9.40E+15\t166\t26743440\t5.04E+12
-LAB BLOOD ORDERABLES\t473109\tLABPLTMAP\tPLATELET MAPPING\tARATEG\tARACHIDONIC ACID\t9827586.907\t18924.62428\t174\t1710000122\t1.71E+16\t173\t3273960\t2.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tXMR\tUNIT CROSSMATCH  \t9999999\t71670.41096\t173\t1729999827\t1.73E+16\t73\t5231940\t9.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tPRO\tPRODUCT ID  \t9999999\t34026.66667\t173\t1729999827\t1.73E+16\t81\t2756160\t3.01E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t42972.55814\t172\t1719999828\t1.72E+16\t172\t7391280\t7.88E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t7977484.564\t42972.55814\t172\t1372127345\t1.37E+16\t172\t7391280\t7.88E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t42972.55814\t172\t1719999828\t1.72E+16\t172\t7391280\t7.88E+12
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9259R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS II\t9999999\t158804.1935\t172\t1719999828\t1.72E+16\t186\t29537580\t3.69E+13
-LAB BLOOD ORDERABLES\t473761\tLABYVITA\tVITAMIN A\tFREEVITA\tFREE RETINOL(VIT A)\t523320.4628\t357001.676\t172\t90011119.6\t9.00E+14\t179\t63903300\t3.19E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t10473R\tDSA FOR C1Q CLASS II\t9999999\t107541.4035\t171\t1709999829\t1.71E+16\t171\t18389580\t1.02E+13
-LAB BLOOD ORDERABLES\t473129\tLABKWB\tPOTASSIUM, WHOLE BLOOD\tK\tPOTASSIUM, WHOLE BLD\t584799.2608\t9286.380368\t171\t100000673.6\t1.00E+15\t163\t1513680\t63837374400
-VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADPCR1\tADENVIRUS DNA\t8729218.632\t192765.5696\t171\t1492696386\t1.49E+16\t158\t30456960\t1.87E+13
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tSI\t<<STATUS>>  \t9999999\t32446.02151\t170\t1699999830\t1.70E+16\t279\t9052440\t1.06E+12
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9256R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS I\t9999999\t168448.9655\t170\t1699999830\t1.70E+16\t174\t29310120\t5.59E+13
-LAB BLOOD ORDERABLES\t473809\tLABYZINCS\tZINC\tZNC\tZINC, BLOOD\t176530.7706\t213934.6988\t170\t30010231\t3.00E+14\t166\t35513160\t1.16E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t42628.7574\t169\t1689999831\t1.69E+16\t169\t7204260\t7.86E+12
-LAB BLOOD ORDERABLES\t473109\tLABPLTMAP\tPLATELET MAPPING\tADP\tADP\t9881656.195\t18914.31818\t169\t1669999897\t1.67E+16\t176\t3328920\t2.11E+11
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tUBT\tUNIT BLOOD TYPE  \t9999999\t43986.31579\t168\t1679999832\t1.68E+16\t76\t3342960\t3.42E+11
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9408R\tCPRA FOR CUMULATIVE IGG\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9424R\tCPRA FOR IGG CLASS II FOR CURRENT\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9609R\tMETHOD\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9626R\tCURRENT SERUM DATE\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9411R\tCPRA FOR IGG CLASS I FOR CURRENT\t9999999\t43413.25301\t166\t1659999834\t1.66E+16\t166\t7206600\t2.67E+13
-LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tANTI-RNP ANTIBODY\tRNPQUL\tANTI-RNP RESULT\t9999999\t433078.8889\t166\t1659999834\t1.66E+16\t162\t70158780\t4.59E+13
-LAB BLOOD ORDERABLES\t472622\tLABIGM\tIGM, SERUM\tIGM\tIGM, SERUM\t783556.9578\t45657.23684\t166\t130070455\t1.30E+15\t152\t6939900\t5.62E+11
-LAB BLOOD ORDERABLES\t473187\tLABPTINH\tPT INHIBITOR SCREEN\tPTINTP\tINTERPRETATION\t9999999\t65225.45455\t166\t1659999834\t1.66E+16\t165\t10762200\t9.83E+11
-LAB BLOOD ORDERABLES\t473221\tLAB11092R\tRENIN\tRENIN\tRENIN ACTIVITY, PL\t4698798.742\t297355.6863\t166\t780000591.1\t7.80E+15\t153\t45495420\t1.77E+13
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tPRO\tPRODUCT ID  \t9999999\t33428.46154\t165\t1649999835\t1.65E+16\t78\t2607420\t3.05E+11
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9437R\tCPRA FOR CUMULATIVE C1Q\t9999999\t37217.81818\t165\t1649999835\t1.65E+16\t165\t6140940\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9610R\tMETHOD\t9999999\t37217.81818\t165\t1649999835\t1.65E+16\t165\t6140940\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9631R\tSERUM DATE\t9999999\t37217.81818\t165\t1649999835\t1.65E+16\t165\t6140940\t2.59E+13
-POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCTEMP\tBODY TEMPERATURE\t9999999\t355.9509202\t165\t1649999835\t1.65E+16\t163\t58020\t2744082000
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tOTCAST\tCASTS, OTHER\t9999999\t12116.94915\t165\t1649999835\t1.65E+16\t118\t1429800\t1.36E+11
-BODY FLUID ORDERABLES\t497386\tLABFTRIGA\tTRIGLYCERIDES, NON-PLEURAL\tTGLFLD\tTRIGLYCERIDES, BODY FLUID\t244203.9268\t197512.8713\t164\t40049444\t4.00E+14\t101\t19948800\t7.53E+12
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9454R\tCPRA FOR C1Q CLASS II FOR CURRENT\t9999999\t37209.87805\t164\t1639999836\t1.64E+16\t164\t6102420\t2.59E+13
-LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tANTI-SCL70 ANTIBODY\tSCLQUL\tANTI-SCL70 RESULT\t9999999\t391549.434\t164\t1639999836\t1.64E+16\t159\t62256360\t3.14E+13
-VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tHSV1PC\tHSV-1 DNA\t9999999\t166727.4847\t163\t1629999837\t1.63E+16\t163\t27176580\t5.96E+12
-VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tHSV2PC\tHSV-2 DNA\t9999999\t166727.4847\t163\t1629999837\t1.63E+16\t163\t27176580\t5.96E+12
-VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tSDES11\tSPECIMEN\t9999999\t166727.4847\t163\t1629999837\t1.63E+16\t163\t27176580\t5.96E+12
-BODY FLUID ORDERABLES\t473353\tLABSTORE\tSPECIMEN STORAGE ONLY\tSTORE\tSPECIMEN STORAGE ONLY\t9999999\t47.69230769\t162\t1619999838\t1.62E+16\t39\t1860\t154800
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9440R\tCPRA FOR C1Q CLASS I FOR CURRENT\t9999999\t37661.85185\t162\t1619999838\t1.62E+16\t162\t6101220\t2.59E+13
-LAB BLOOD ORDERABLES\t472444\tLAB11081R\tGASTRIN\tGASTRIN\tGASTRIN, SERUM\t745575.7764\t157284.7297\t161\t120037700\t1.20E+15\t148\t23278140\t4.38E+12
-MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t266675.2941\t161\t1609999839\t1.61E+16\t1275\t340011000\t1.87E+14
-LAB BLOOD ORDERABLES\t590524\tLABASI\tAB SCREEN (ASI)\tAS3\tANTIBODY SCREEN\t9999999\t9125.853659\t160\t1599999840\t1.60E+16\t41\t374160\t10901347200
-MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tAFB\tAFB SMEAR\t9999999\t3883162.201\t160\t1599999840\t1.60E+16\t2681\t10410757860\t4.16E+16
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tXMR\tUNIT CROSSMATCH  \t9999999\t74538.46154\t159\t1589999841\t1.59E+16\t65\t4845000\t8.16E+11
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tDI\tISSUE DATE/TIME\t9999999\t29278.28571\t159\t1589999841\t1.59E+16\t70\t2049480\t74952583200
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9412R\tHLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t43413.25301\t159\t1589999841\t1.59E+16\t166\t7206600\t2.67E+13
-LAB BLOOD ORDERABLES\t497394\tLABYPARVO\tPARVOVIRUS B19 IGG AND IGM AB\tYPARV1\tPARVOVIRUS B19 AB IGG\t2.89408805\t223791.6201\t159\t460.16\t2105.758\t179\t40058700\t1.09E+13
-LAB BLOOD ORDERABLES\t497394\tLABYPARVO\tPARVOVIRUS B19 IGG AND IGM AB\tYPARV2\tPARVOVIRUS B19 AB IGM\t0.23408805\t223791.6201\t159\t37.22\t175.9786\t179\t40058700\t1.09E+13
-MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tRPT\tRPT\t9999999\t197685.5769\t159\t1589999841\t1.59E+16\t104\t20559300\t5.86E+12
-MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tSDES\tSPECIMEN\t9999999\t197685.5769\t159\t1589999841\t1.59E+16\t104\t20559300\t5.86E+12
-BODY FLUID ORDERABLES\t472501\tLABFHCT\tHEMATOCRIT, FLUID\tHCTFLD\tHEMATOCRIT, FLUID\t6582282.016\t19705.26316\t158\t1040000559\t1.04E+16\t76\t1497600\t2.10E+11
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tIMMPL\tIMMAT. PLASMA CELLS\t63320.53797\t19787.92208\t158\t10004645\t1.00E+14\t154\t3047340\t3.06E+11
-VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPNO\tNOTE\t9999999\t313316.5605\t158\t1579999842\t1.58E+16\t157\t49190700\t1.93E+13
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tXMR\tUNIT CROSSMATCH  \t9999999\t65345.53846\t157\t1569999843\t1.57E+16\t65\t4247460\t6.76E+11
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9441R\tHLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t37661.85185\t157\t1569999843\t1.57E+16\t162\t6101220\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9455R\tHLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t37209.87805\t157\t1569999843\t1.57E+16\t164\t6102420\t2.59E+13
-BODY FLUID ORDERABLES\t472501\tLABFHCT\tHEMATOCRIT, FLUID\tHCTTYP\tSOURCE\t9999999\t19705.26316\t156\t1559999844\t1.56E+16\t76\t1497600\t2.10E+11
-COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tLOTNO\tLOT #\t9802579.788\t181776.5501\t156\t1529202447\t1.52E+16\t1287\t233946420\t5.82E+13
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tCULT\tCULT/OTHER RSLT:\t9999999\t499787.4252\t156\t1559999844\t1.56E+16\t3946\t1972161180\t1.12E+15
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUASP\tSPECIMEN\t9999999\t163609.7143\t156\t1559999844\t1.56E+16\t35\t5726340\t1.13E+12
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tPRO\tPRODUCT ID  \t9999999\t28782.16216\t155\t1549999845\t1.55E+16\t74\t2129880\t2.07E+11
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9425R\tHLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t42896.78571\t155\t1549999845\t1.55E+16\t168\t7206660\t2.67E+13
-LAB BLOOD ORDERABLES\t472378\tLAB11036R\tFIBRINOGEN, QUANT\tFIBRINOGENA\tFIBRINOGEN ANTIGEN\t1677655.368\t257827.1186\t155\t260036582\t2.60E+15\t118\t30423600\t1.53E+13
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBDX\tCLINICAL INDICATION\t9999999\t878524.7788\t155\t1549999845\t1.55E+16\t113\t99273300\t1.44E+14
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tVL\tPRODUCT VOLUME\t169.9350649\t27151.2\t154\t26170\t5340380\t50\t1357560\t42850166400
-STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12766R\tTACROLIMUS\t1493514.095\t181999.6875\t154\t230001170.6\t2.30E+15\t192\t34943940\t7.76E+12
-COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHIPDIR\tHIT INTERPRETED BY\t9999999\t181865.8879\t153\t1529999847\t1.53E+16\t1284\t233515800\t5.81E+13
-STOOL ORDERABLES\t472361\tLABSTFAT\tFAT STAIN, STOOL\tSTFAT\tFAT STAIN, STOOL\t9934639.582\t26489.18919\t153\t1519999856\t1.52E+16\t74\t1960200\t3.18E+11
-LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN 1\tB2GPIG\tBETA-2 GLYCOPROTEIN 1, IGG\t328955.5526\t261970.9459\t152\t50001244\t5.00E+14\t148\t38771700\t1.57E+13
-LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN 1\tB2GPIM\tBETA-2 GLYCOPROTEIN 1, IGM\t328958.4145\t261970.9459\t152\t50001679\t5.00E+14\t148\t38771700\t1.57E+13
-STOOL ORDERABLES\t473218\tLABSTRED\tREDUCING SUBSTANCE, STOOL\tREDSUBSTL\tREDUCING SUBST, STOOL\t9999999\t29040\t152\t1519999848\t1.52E+16\t52\t1510080\t1.69E+11
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tSI\t<<STATUS>>  \t9999999\t32777.54941\t151\t1509999849\t1.51E+16\t253\t8292720\t9.48E+11
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tDI\tISSUE DATE/TIME\t9999999\t33829.16667\t151\t1509999849\t1.51E+16\t72\t2435700\t1.32E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t8053049.813\t38516.8\t150\t1207957472\t1.20E+16\t150\t5777520\t6.90E+12
-LAB BLOOD ORDERABLES\t472056\tLABYCH50\tCOMPLEMENT TOTAL\tYCH50S\tCOMPLEMENT, TOTAL\t400046.8467\t162197.9592\t150\t60007027\t6.00E+14\t147\t23843100\t4.41E+12
-LAB BLOOD ORDERABLES\t472543\tLABHSCRP\tHIGH SENSITIVITY C-REACTIVE PROTEIN\tHSCRP\tHIGH SENSITIVITY CRP\t66698.88533\t33401.22137\t150\t10004832.8\t1.00E+14\t131\t4375560\t2.75E+11
-LAB BLOOD ORDERABLES\t472030\tLABCHOL\tCHOLESTEROL TOTAL, SERUM/PLASMA\tCHOL\tCHOLESTEROL, TOTAL\t337986.9054\t8188.392857\t148\t50022062\t5.00E+14\t112\t917100\t18473065200
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tPRO\tPRODUCT ID  \t9999999\t30727.82609\t147\t1469999853\t1.47E+16\t69\t2120220\t2.29E+11
-LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVSC\tDRVV SCREEN\t748345.4565\t121000.7634\t147\t110006782.1\t1.10E+15\t131\t15851100\t3.59E+12
-LAB BLOOD ORDERABLES\t870901\tLAB11982R\tPOSACONAZOLE\t11983R\tPOSACONAZOLE\t612936.0952\t319889.1429\t147\t90101606\t9.00E+14\t140\t44784480\t1.61E+13
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQL\tIGG RESULT\t9999999\t165582.0408\t147\t1469999853\t1.47E+16\t147\t24340560\t5.47E+12
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9276R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS I\t9999999\t109734.6575\t146\t1459999854\t1.46E+16\t146\t16021260\t1.66E+13
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXMQL\tIGM RESULT\t9999999\t161574.2466\t146\t1459999854\t1.46E+16\t146\t23589840\t4.90E+12
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tAG\tANION GAP\t7.537931034\t5510.364964\t145\t1093\t9995\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tALB\tALBUMIN, SER/PLAS\t2.694482759\t5510.364964\t145\t390.7\t1108.73\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tBUN\tUREA NITROGEN,SER/PLAS\t275888.2414\t5510.364964\t145\t40003795\t4.00E+14\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCA\tCALCIUM, SER/PLAS\t8.460689655\t5510.364964\t145\t1226.8\t10424.14\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCL\tCHLORIDE, SER/PLAS\t101.4137931\t5510.364964\t145\t14705\t1496473\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCO2\tCO2, SER/PLAS\t26.67586207\t5510.364964\t145\t3868\t105438\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCR\tCREATININE, SER/PLAS\t1.863448276\t5510.364964\t145\t270.2\t1070.26\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tGLU\tGLUCOSE, SER/PLAS\t133.0206897\t5510.364964\t145\t19288\t2976424\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tK\tPOTASSIUM, SER/PLAS\t4.160689655\t5510.364964\t145\t603.3\t2579.73\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tNA\tSODIUM, SER/PLAS\t135.6275862\t5510.364964\t145\t19666\t2670380\t137\t754920\t9180770400
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tPHOS\tPHOSPHORUS, SER/PLAS\t3.466896552\t5510.364964\t145\t502.7\t1949.03\t137\t754920\t9180770400
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV1QNT\tHSV-1 IGG LEVEL\t2344832.599\t262166.8966\t145\t340000726.9\t3.40E+15\t145\t38014200\t1.36E+13
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV1QUL\tHSV-1 IGG RESULT\t9999999\t262166.8966\t145\t1449999855\t1.45E+16\t145\t38014200\t1.36E+13
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV2QNT\tHSV-2 IGG LEVEL\t7517242.214\t262166.8966\t145\t1090000121\t1.09E+16\t145\t38014200\t1.36E+13
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV2QUL\tHSV-2 IGG RESULT\t9999999\t262166.8966\t145\t1449999855\t1.45E+16\t145\t38014200\t1.36E+13
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHVMQNT\tHSV IGM LEVEL\t8758620.034\t262166.8966\t145\t1269999905\t1.27E+16\t145\t38014200\t1.36E+13
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHVMQUL\tHSV IGM RESULT\t9999999\t262166.8966\t145\t1449999855\t1.45E+16\t145\t38014200\t1.36E+13
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tUBT\tUNIT BLOOD TYPE  \t9999999\t41594.32836\t144\t1439999856\t1.44E+16\t67\t2786820\t2.99E+11
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9423R\tDSA FOR C1Q CLASS II\t9999999\t40032.41379\t144\t1439999856\t1.44E+16\t145\t5804700\t2.58E+13
-LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVLA\tDRVV NORMALIZED RATIO\t8541666.065\t120333.75\t144\t1229999913\t1.23E+16\t128\t15402720\t3.41E+12
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBRE\tRESULT\t9999999\t880761.4286\t144\t1439999856\t1.44E+16\t112\t98645280\t1.43E+14
-LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVCN\tDRVV CONFIRMATION\t8601404.762\t120005.0382\t143\t1230000881\t1.23E+16\t131\t15720660\t3.56E+12
-LAB BLOOD ORDERABLES\t496765\tLABHA1C\tHGB A1C W/ EST MEAN GLUCOSE\tEMGL\tEST. MEAN GLUCOSE\t349784.4126\t42301.70213\t143\t50019171\t5.00E+14\t141\t5964540\t8.54E+11
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRRE\tRESULT\t9999999\t600772.8477\t142\t1419999858\t1.42E+16\t151\t90716700\t6.81E+13
-LAB BLOOD ORDERABLES\t496765\tLABHA1C\tHGB A1C W/ EST MEAN GLUCOSE\tA1C\tHEMOGLOBIN A1C\t281696.3986\t42301.70213\t142\t40000888.6\t4.00E+14\t141\t5964540\t8.54E+11
-LAB BLOOD ORDERABLES\t471527\tLABSMABY\tANTI-SM ANTIBODY\tASMQUL\tANTI-SM RESULT\t9999999\t394626.5217\t141\t1409999859\t1.41E+16\t138\t54458460\t2.90E+13
-LAB BLOOD ORDERABLES\t472325\tLABEPO\tERYTHROPOIETIN\tEPO\tERYTHROPOIETIN\t1773093.199\t83724.9635\t141\t250006141\t2.50E+15\t137\t11470320\t1.67E+12
-LAB BLOOD ORDERABLES\t668152\tLABATTG\tIGA ANTI  TTG\tATTGR\tIGA ANTI TTG ANTIBODY\t9999999\t394865.1799\t141\t1409999859\t1.41E+16\t139\t54886260\t2.79E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9415R\tDSA FOR IGG CLASS II\t9999999\t48321.81818\t140\t1399999860\t1.40E+16\t143\t6910020\t2.67E+13
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tGS\tGRAM STAIN\t9999999\t454839.1011\t140\t1399999860\t1.40E+16\t712\t323845440\t2.65E+14
-LAB BLOOD ORDERABLES\t472345\tLABFACT9\tFACTOR IX ASSAY\tFACTOR9\tFACTOR IX ASSAY\t575631.0144\t44628.46154\t139\t80012711\t8.00E+14\t130\t5801700\t3.54E+11
-MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t214873.0474\t139\t1389999861\t1.39E+16\t21871\t4699488420\t1.74E+15
-URINE ORDERABLES\t473123\tLABUK\tPOTASSIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t215900.4799\t20671.11111\t139\t30010166.7\t3.00E+14\t81\t1674360\t1.84E+11
-URINE ORDERABLES\t473123\tLABUK\tPOTASSIUM WITH CREATININE, URINE (RANDOM)\tUK\tPOTASSIUM, URINE\t215858.8799\t20671.11111\t139\t30004384.3\t3.00E+14\t81\t1674360\t1.84E+11
-URINE ORDERABLES\t473123\tLABUK\tPOTASSIUM WITH CREATININE, URINE (RANDOM)\tUKCR\tPOTASSIUM/CREAT RATIO\t8201438.063\t20671.11111\t139\t1139999891\t1.14E+16\t81\t1674360\t1.84E+11
-BODY FLUID ORDERABLES\t472728\tLABCSFLD\tLDH, CSF\tLDHCSF\tLDH, CSF\t362371.8116\t8725.263158\t138\t50007310\t5.00E+14\t76\t663120\t21797416800
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9422R\tDSA FOR C1Q CLASS I\t9999999\t39643.47826\t138\t1379999862\t1.38E+16\t138\t5470800\t2.57E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9414R\tDSA FOR IGG CLASS I\t9999999\t39710.86957\t138\t1379999862\t1.38E+16\t138\t5480100\t2.57E+13
-LAB BLOOD ORDERABLES\t472794\tLABMALAR\tMALARIA PREP\tMALCON\tMALCON\t9999999\t32456.25\t138\t1379999862\t1.38E+16\t144\t4673700\t7.20E+12
-MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t266675.2941\t138\t1379999862\t1.38E+16\t1275\t340011000\t1.87E+14
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tDI\tISSUE DATE/TIME\t9999999\t37671.14754\t137\t1369999863\t1.37E+16\t61\t2297940\t1.83E+11
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tALB\tALBUMIN\t364966.7161\t271621.4063\t137\t50000440.1\t5.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPA1\tALPHA 1 ZONE\t364963.7438\t271621.4063\t137\t50000032.9\t5.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPA2\tALPHA 2 ZONE\t364964.2197\t271621.4063\t137\t50000098.1\t5.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPBE1\tBETA 1 ZONE\t364963.9701\t271621.4063\t137\t50000063.9\t5.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPGA\tGAMMA ZONE\t364964.5964\t271621.4063\t137\t50000149.7\t5.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tTPE\tTOTAL PROTEIN\t364969.8044\t271621.4063\t137\t50000863.2\t5.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBAA\tAMINO ACID CHANGE\t9999999\t908365.4717\t137\t1369999863\t1.37E+16\t106\t96286740\t1.42E+14
-MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tRPT\tRPT\t9999999\t191605.7143\t137\t1369999863\t1.37E+16\t21\t4023720\t8.21E+11
-MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tSDES\tSPECIMEN\t9999999\t191605.7143\t137\t1369999863\t1.37E+16\t21\t4023720\t8.21E+11
-MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tSREQ\tCOMMENTS\t9999999\t191605.7143\t137\t1369999863\t1.37E+16\t21\t4023720\t8.21E+11
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tDIPLOT\tDIPSTICK LOT\t9191364.146\t2322.940655\t137\t1259216888\t1.25E+16\t1129\t2622600\t4.75E+14
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tSI\t<<STATUS>>  \t9999999\t31790.66667\t136\t1359999864\t1.36E+16\t225\t7152900\t7.84E+11
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tEOSFLD\tEOSINOPHILS, SYN FLD\t2.838235294\t9576.176471\t136\t386\t2984\t68\t651180\t15140372400
-HISTOCOMPATIBILITY ORDERABLE\t890779\t10045R\tHLA ORDER - ANTIBODY SCREENING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1070211.064\t136\t1359999864\t1.36E+16\t141\t150899760\t2.32E+15
-LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTX\tCLINICAL INDICATION\t9999999\t540213\t136\t1359999864\t1.36E+16\t60\t32412780\t2.37E+13
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPBE2\tBETA 2 ZONE\t367647.4816\t271621.4063\t136\t50000057.5\t5.00E+14\t128\t34767540\t1.35E+13
-VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tRPT\tRPT\t9999999\t68157\t136\t1359999864\t1.36E+16\t60\t4089420\t4.28E+11
-VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSDES\tSPECIMEN\t9999999\t68157\t136\t1359999864\t1.36E+16\t60\t4089420\t4.28E+11
-VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSREQ\tCOMMENTS\t9999999\t68157\t136\t1359999864\t1.36E+16\t60\t4089420\t4.28E+11
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC1613AB\tCD16+/CD13-, ABS\t296392.4963\t136579.3043\t135\t40012987\t4.00E+14\t115\t15706620\t3.09E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC1613PC\tCD16+/CD13- %\t296313.5556\t136579.3043\t135\t40002330\t4.00E+14\t115\t15706620\t3.09E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC563AB\tCD56+/CD3-, ABS\t296378.9926\t136579.3043\t135\t40011164\t4.00E+14\t115\t15706620\t3.09E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC563PC\tCD56+/CD3- %\t296313.8667\t136579.3043\t135\t40002372\t4.00E+14\t115\t15706620\t3.09E+12
-VIROLOGY ORDERABLES\t906965\tLABVZVQLP\tVZV DNA PCR QUAL, PLASMA\tVZQLDN\tVZV DNA\t9999999\t198242.2222\t135\t1349999865\t1.35E+16\t135\t26762700\t7.15E+12
-VIROLOGY ORDERABLES\t906965\tLABVZVQLP\tVZV DNA PCR QUAL, PLASMA\tVZQLSP\tSPECIMEN\t9999999\t198242.2222\t135\t1349999865\t1.35E+16\t135\t26762700\t7.15E+12
-LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVMX\tDRVV MIX\t6119422.301\t115144.5378\t134\t820002588.3\t8.20E+15\t119\t13702200\t3.10E+12
-LAB BLOOD ORDERABLES\t670770\tLAB11242R\tVZV QUANT\t11243R\tSOURCE\t9999999\t189403.6596\t134\t1339999866\t1.34E+16\t235\t44509860\t1.94E+13
-MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tSREQ\tCOMMENTS\t9999999\t197685.5769\t134\t1339999866\t1.34E+16\t104\t20559300\t5.86E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSCIM\tCLINICAL INDICATION\t9999999\t784098.806\t134\t1339999866\t1.34E+16\t134\t105069240\t1.02E+14
-URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tUMET\tMETANEPHRINE, URINE\t821034.3806\t362619.0476\t134\t110018607\t1.10E+15\t63\t22845000\t1.07E+13
-URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tUMET\tTOT.METANEPHRINES,UR\t597641.1194\t362619.0476\t134\t80083910\t8.00E+14\t63\t22845000\t1.07E+13
-URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tUNORMET\tNORMETANEPHRINE, UR\t597502.1791\t362619.0476\t134\t80065292\t8.00E+14\t63\t22845000\t1.07E+13
-URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tVOLUMEURN\tURINE VOLUME\t599302.7015\t362619.0476\t134\t80306562\t8.00E+14\t63\t22845000\t1.07E+13
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKCPP\tRESULT\t6035260.978\t308329.8795\t134\t808724971\t7.98E+15\t83\t25591380\t2.98E+13
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKDNAP\tBKV DNA\t9999999\t308329.8795\t134\t1339999866\t1.34E+16\t83\t25591380\t2.98E+13
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKLOGP\tRESULT LOG 10\t5895523.587\t308329.8795\t134\t790000160.7\t7.90E+15\t83\t25591380\t2.98E+13
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tPRO\tPRODUCT ID  \t9999999\t20339.0625\t133\t1329999867\t1.33E+16\t64\t1301700\t88116663600
-MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tCULT\tCULT/OTHER RSLT:\t9999999\t3886778.338\t133\t1329999867\t1.33E+16\t2310\t8978457960\t3.70E+16
-MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tRPT\tRPT\t9999999\t304958.4615\t133\t1329999867\t1.33E+16\t78\t23786760\t1.00E+13
-MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tSDES\tSPECIMEN\t9999999\t304958.4615\t133\t1329999867\t1.33E+16\t78\t23786760\t1.00E+13
-LAB BLOOD ORDERABLES\t472102\tLAB11056R\tFREE CORTISOL\tCORTFREE\tCORTISOL, SERUM FREE\t75761.51235\t362982.4286\t132\t10000519.63\t1.00E+14\t140\t50817540\t4.31E+13
-URINE ORDERABLES\t472938\tLABYUMYO\tMYOGLOBIN, URINE\tUMYOG\tMYOGLOBIN, URINE\t4090933.064\t168668.4615\t132\t540003164.4\t5.40E+15\t78\t13156140\t2.84E+12
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tXMR\tUNIT CROSSMATCH  \t9999999\t69248.27586\t131\t1309999869\t1.31E+16\t58\t4016400\t6.79E+11
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tXMR\tUNIT CROSSMATCH  \t9999999\t70189.47368\t131\t1309999869\t1.31E+16\t57\t4000800\t6.61E+11
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tUBT\tUNIT BLOOD TYPE  \t9999999\t50272.88136\t131\t1309999869\t1.31E+16\t59\t2966100\t3.78E+11
-LAB BLOOD ORDERABLES\t473160\tLAB11085R\tMYELOPEROXIDASE ANTIBODY\tMPO\tMYELOPEROXIDASE AB\t9541983.801\t173933.1298\t131\t1249999878\t1.25E+16\t131\t22785240\t4.43E+12
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tEXPDATE\tEXP DATE\t9090213.634\t2327.256637\t131\t1190817986\t1.19E+16\t1130\t2629800\t4.75E+14
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tOTHCRY\tCRYSTALS, OTHER\t9999999\t22382.4\t131\t1309999869\t1.31E+16\t75\t1678680\t6.31E+11
-HISTOCOMPATIBILITY ORDERABLE\t890831\t10035R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)\tRINFO\tTO FIND RESULT:\t9999999\t652002.8571\t130\t1299999870\t1.30E+16\t140\t91280400\t1.39E+14
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9436R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t37217.81818\t129\t1289999871\t1.29E+16\t165\t6140940\t2.59E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tLUPDIR\tINTERPRETED BY\t9999999\t320053.913\t129\t1289999871\t1.29E+16\t138\t44167440\t1.77E+13
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSCIB\tCLINICAL INDICATION\t9999999\t873158.6047\t129\t1289999871\t1.29E+16\t129\t112637460\t2.69E+14
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tDI\tISSUE DATE/TIME\t9999999\t35071.03448\t128\t1279999872\t1.28E+16\t58\t2034120\t1.21E+11
-LAB BLOOD ORDERABLES\t471707\tLAB11094R\tRO AB\tSSA\tSS-A/RO AB, IGG, SERUM\t8203124.377\t187084.4262\t128\t1049999920\t1.05E+16\t122\t22824300\t5.95E+12
-POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tMMIND\tMM INDURATION\t2440944.878\t201166.25\t127\t309999999.5\t3.10E+15\t192\t38623920\t1.70E+13
-LAB BLOOD ORDERABLES\t473767\tLAB11102R\tVITAMIN E\tVITE\tA-TOCOPHEROL, VITAMIN E\t10.64126984\t367098.0488\t126\t1340.8\t16803.48\t123\t45153060\t2.10E+13
-LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tOSMGPI\tOSMOLAL GAP INTERP\t9999999\t7193.516221\t126\t1259999874\t1.26E+16\t4192\t30155220\t7.50E+12
-POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tERYTHREMA\tERYTHREMA\t4999999.5\t201166.25\t126\t629999937\t6.30E+15\t192\t38623920\t1.70E+13
-HISTOCOMPATIBILITY ORDERABLE\t890831\t10035R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t652002.8571\t125\t1249999875\t1.25E+16\t140\t91280400\t1.39E+14
-STOOL ORDERABLES\t471535\tLABFADA\tADENOSINE DEAMINASE, FLUID\tADAFLD\tADENOSINE DEAMIN., FLD\t1120008.62\t540366\t125\t140001077.5\t1.40E+15\t90\t48632940\t6.04E+13
-VIROLOGY ORDERABLES\t906961\tLABVZVCSF\tVZV DNA PCR QUAL, CSF\tVZQLDN\tVZV DNA\t9999999\t175368.96\t125\t1249999875\t1.25E+16\t125\t21921120\t5.25E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNWT\tSTONE WEIGHT\t564518.7019\t207922.6667\t124\t70000319.04\t7.00E+14\t45\t9356520\t2.34E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9155R\tRELATIONSHIP TO PATIENT\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9156R\tDONOR CELL DATE\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9157R\tDONOR CELL SOURCE\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9158R\tDONOR CELL TARGET\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9159R\tMETHOD\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9160R\tSERUM DATE 1\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9162R\tRESULT  MCS 1\t25\t380304.6774\t124\t3100\t530480\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9164R\tRESULT 1\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9182R\tCOMPOSED TEXT\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9125R\tRELATIONSHIP TO PATIENT\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9126R\tDONOR CELL DATE\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9127R\tDONOR CELL SOURCE\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9128R\tDONOR CELL TARGET\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9129R\tMETHOD\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9130R\tSERUM DATE 1\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9132R\tRESULT  MCS 1\t36.37096774\t370725\t124\t4510\t520180\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9134R\tRESULT 1\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9151R\tCOMPOSED TEXT\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
-LAB BLOOD ORDERABLES\t473426\tLABYTESFT\tTESTOSTERONE, TOTAL AND FREE\tTESTOS\tTESTOSTERONE, TOTAL\t1613111.927\t280311.6522\t124\t200025878.9\t2.00E+15\t115\t32235840\t1.01E+13
-LAB BLOOD ORDERABLES\t473426\tLABYTESFT\tTESTOSTERONE, TOTAL AND FREE\tTESTOSFREE\tTESTOSTERONE, FREE\t1612909.239\t282318.7692\t124\t200000745.6\t2.00E+15\t195\t55052160\t1.74E+13
-STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tYFFQ2\tDURATION\t8145170.339\t264723.8462\t124\t1010001122\t1.01E+16\t78\t20648460\t8.48E+12
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVQSP\tSPECIMEN\t9999999\t253864.8227\t124\t1239999876\t1.24E+16\t423\t107384820\t3.23E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9154R\tDONOR DOB\t9999999\t377450.2439\t123\t1229999877\t1.23E+16\t123\t46426380\t5.12E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9124R\tDONOR DOB\t9999999\t367792.6829\t123\t1229999877\t1.23E+16\t123\t45238500\t4.81E+13
-LAB BLOOD ORDERABLES\t472616\tLABIGGS4\tIGG  SUBCLASS 4\tIGGS4\tIGG SUBCLASS4\t81373.38895\t60605.04202\t123\t10008926.84\t1.00E+14\t119\t7212000\t1.14E+12
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKPR\tPRIMERS\t9999999\t548376\t123\t1229999877\t1.23E+16\t115\t63063240\t4.39E+13
-LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETIC\tRETIC COUNT, MANUAL\t650412.3276\t20961.38614\t123\t80000716.3\t8.00E+14\t101\t2117100\t2.52E+11
-LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN PLATELET FACTOR 4 AB\tPF4IGG\tPF4 IGG ONLY OD\t1707317.517\t127452.6316\t123\t210000054.6\t2.10E+15\t114\t14529600\t1.59E+13
-LAB BLOOD ORDERABLES\t961580\tLABHITIA\tHIT FUNCTIONAL ASSAY\tHIPDIR\tHIT INTERPRETED BY\t9999999\t134478.871\t123\t1229999877\t1.23E+16\t124\t16675380\t2.92E+12
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tPRO\tPRODUCT ID  \t9999999\t22168\t122\t1219999878\t1.22E+16\t60\t1330080\t92575231200
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t122\t1219999878\t1.22E+16\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471708\tLAB11095R\tLA AB\tSSB\tSS-B/LA AB, IGG, SERUM\t9016392.582\t186424.6957\t122\t1099999895\t1.10E+16\t115\t21438840\t5.67E+12
-LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBF\tHGB F, QUANT.\t8196721.437\t426249.0566\t122\t1000000015\t1.00E+16\t106\t45182400\t2.81E+13
-MICROBIOLOGY ORDERABLES\t893893\tLABCRPLFB\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD\tCRPLFB\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD\t9999999\t78218.85246\t122\t1219999878\t1.22E+16\t122\t9542700\t9.15E+11
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGCTNNM\tCTNNB1, BM\t9999999\t829384.4262\t122\t1219999878\t1.22E+16\t122\t101184900\t9.94E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGPTENM\tPTEN, BM\t9999999\t829384.4262\t122\t1219999878\t1.22E+16\t122\t101184900\t9.94E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9153R\tDONOR NAME\t9999999\t380304.6774\t121\t1209999879\t1.21E+16\t124\t47157780\t5.17E+13
-LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBS\tHGB S, QUANT\t330588.586\t426444.5714\t121\t40001218.9\t4.00E+14\t105\t44776680\t2.79E+13
-LAB BLOOD ORDERABLES\t473176\tLABYPR3\tPROTEINASE 3 ANTIBODY\tYPR31\tPROTEINASE 3 ANTIBODIES\t8760329.917\t188461.4754\t121\t1059999920\t1.06E+16\t122\t22992300\t9.20E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGBRAFM\tBRAF, BM\t9999999\t829384.4262\t121\t1209999879\t1.21E+16\t122\t101184900\t9.94E+13
-VIROLOGY ORDERABLES\t473461\tLABTOXO\tTOXOPLASMA SEROLOGY\tTOXO\tTOXOPLASMA IGG\t9999999\t157680\t121\t1209999879\t1.21E+16\t116\t18290880\t3.57E+12
-VIROLOGY ORDERABLES\t906961\tLABVZVCSF\tVZV DNA PCR QUAL, CSF\tVZQLSP\tSPECIMEN\t9999999\t175368.96\t121\t1209999879\t1.21E+16\t125\t21921120\t5.25E+12
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9123R\tDONOR NAME\t9999999\t370725\t120\t1199999880\t1.20E+16\t124\t45969900\t4.87E+13
-LAB BLOOD ORDERABLES\t961580\tLABHITIA\tHIT FUNCTIONAL ASSAY\tHITI1\tHIT FUNCTIONAL ASSAY\t9999999\t134929.92\t120\t1199999880\t1.20E+16\t125\t16866240\t2.96E+12
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tUBT\tUNIT BLOOD TYPE  \t9999999\t48334.73684\t119\t1189999881\t1.19E+16\t57\t2755080\t3.01E+11
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFNRBC\tNRBC/100WBC, FLD\t4.411764706\t31792.63158\t119\t525\t28253\t57\t1812180\t4.75E+11
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9630R\tIGG COMBINED CPRA FOR CURRENT SERUM\t9999999\t15751.76471\t119\t1189999881\t1.19E+16\t119\t1874460\t1.06E+12
-LAB BLOOD ORDERABLES\t471960\tLABCENABY\tANTI CENTROMERE ANTIBODY\tCENQUL\tANTI-CENTROMERE RESULT\t9999999\t420534.9153\t119\t1189999881\t1.19E+16\t118\t49623120\t2.82E+13
-LAB BLOOD ORDERABLES\t472659\tLABINSL\tINSULIN UNSPECIFIED\tINSU\tINSULIN,UNSPECIFIED\t924388.0672\t43052.57143\t119\t110002180\t1.10E+15\t70\t3013680\t2.09E+11
-MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t1820387.097\t119\t1189999881\t1.19E+16\t5250\t9557032260\t1.81E+16
-MICROBIOLOGY ORDERABLES\t893897\tLABCRPLFC\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF\tCRPLFC\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF\t9999999\t63943.86555\t119\t1189999881\t1.19E+16\t119\t7609320\t7.06E+11
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGEGFRM\tEGFR, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGIDH1M\tIDH1, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGIDH2M\tIDH2, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGNOTCM\tNOTCH1, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
-LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGPRES\tHGBS PRESENT\t9999999\t410118.1034\t118\t1179999882\t1.18E+16\t116\t47573700\t2.88E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGKRASM\tKRAS, BM\t9999999\t829384.4262\t118\t1179999882\t1.18E+16\t122\t101184900\t9.94E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGPIK3M\tPIK3CA, BM\t9999999\t829384.4262\t118\t1179999882\t1.18E+16\t122\t101184900\t9.94E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tOTCAST\tCASTS, OTHER\t9999999\t18474.93976\t118\t1179999882\t1.18E+16\t83\t1533420\t3.87E+11
-HISTOCOMPATIBILITY ORDERABLE\t895897\t10145R\tHLA ORDER - UNRELATED DONOR PROCESSING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1214385.536\t117\t1169999883\t1.17E+16\t112\t136011180\t2.12E+14
-HISTOCOMPATIBILITY ORDERABLE\t895897\t10145R\tHLA ORDER - UNRELATED DONOR PROCESSING\tRINFO\tTO FIND RESULT:\t9999999\t1214385.536\t117\t1169999883\t1.17E+16\t112\t136011180\t2.12E+14
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGAPCM\tAPC, BM\t9999999\t784098.806\t117\t1169999883\t1.17E+16\t134\t105069240\t1.02E+14
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tSI\t<<STATUS>>  \t9999999\t32831.4433\t116\t1159999884\t1.16E+16\t194\t6369300\t7.61E+11
-LAB BLOOD ORDERABLES\t471802\tLABBUNCR\tBUN / CREATININE\tBUNCREAT\tBUN/CREATININE\t431054.7069\t960\t116\t50002346\t5.00E+14\t6\t5760\t6220800
-LAB BLOOD ORDERABLES\t472033\tLABYCHRA\tCHROMOGRANIN A\tYCHRA1\tCHROMOGRANIN A\t174007.4397\t306754.7619\t116\t20184863\t2.00E+14\t126\t38651100\t1.45E+13
-MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRCT\tCOMMENT\t9999999\t488123.0769\t116\t1159999884\t1.16E+16\t26\t12691200\t8.34E+12
-MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRMT\tMETHOD\t9999999\t488123.0769\t116\t1159999884\t1.16E+16\t26\t12691200\t8.34E+12
-MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRPM\tPRIMERS\t9999999\t488123.0769\t116\t1159999884\t1.16E+16\t26\t12691200\t8.34E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGTP53M\tTP53, BM\t9999999\t829384.4262\t116\t1159999884\t1.16E+16\t122\t101184900\t9.94E+13
-POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tPHLOC\tLOCATION\t9827585.224\t201166.25\t116\t1139999886\t1.14E+16\t192\t38623920\t1.70E+13
-POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tEXPDATE\tEXP DATE\t9570766.638\t-100367.9032\t116\t1110208930\t1.11E+16\t124\t-12445620\t5.85E+12
-POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tUBLOOD\tBLOOD URINE\t9999999\t-100367.9032\t116\t1159999884\t1.16E+16\t124\t-12445620\t5.85E+12
-STOOL ORDERABLES\t497390\tLABYSTOSM\tOSMOLALITY, STOOL\tOSMOLSTL\tOSMOLALITY STOOL\t4138136.526\t281288.1818\t116\t480023837\t4.80E+15\t66\t18565020\t7.03E+12
-LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBA2Q\tHGB A2, QUANT.\t2956523.38\t426249.0566\t115\t340000188.7\t3.40E+15\t106\t45182400\t2.81E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGNRASM\tNRAS, BM\t9999999\t829384.4262\t115\t1149999885\t1.15E+16\t122\t101184900\t9.94E+13
-POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tDIPLOT\tDIPSTICK LOT\t9930482.843\t-100367.9032\t115\t1142005527\t1.14E+16\t124\t-12445620\t5.85E+12
-STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12763R\tDATE OF LAST DOSE\t9999999\t181999.6875\t115\t1149999885\t1.15E+16\t192\t34943940\t7.76E+12
-STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12764R\tTIME OF LAST DOSE\t9826096.104\t181999.6875\t115\t1130001052\t1.13E+16\t192\t34943940\t7.76E+12
-STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12765R\tDOSE, MG\t9999999\t181999.6875\t115\t1149999885\t1.15E+16\t192\t34943940\t7.76E+12
-VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tSOUR10\tSOURCE\t9999999\t192765.5696\t115\t1149999885\t1.15E+16\t158\t30456960\t1.87E+13
-VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV2QNT\tHSV-2 IGG LEVEL\t7478261.732\t250224\t115\t860000099.2\t8.60E+15\t115\t28775760\t9.60E+12
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tDI\tISSUE DATE/TIME\t9999999\t35170\t114\t1139999886\t1.14E+16\t54\t1899180\t1.34E+11
-BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN TOTAL, FLUID\tFSC2\tFLUID TYPE\t9999999\t867047.3684\t114\t1139999886\t1.14E+16\t19\t16473900\t2.02E+13
-HISTOCOMPATIBILITY ORDERABLE\t895897\t10145R\tHLA ORDER - UNRELATED DONOR PROCESSING\tUNRDID\tUNRELATED DONOR ID\t9999999\t1214385.536\t114\t1139999886\t1.14E+16\t112\t136011180\t2.12E+14
-MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tCULT\tCULT/OTHER RSLT:\t9999999\t111100.8005\t114\t1139999886\t1.14E+16\t23935\t2659197660\t3.68E+14
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tOCCPH\tPATIENT OCCULAR PH\t7.714912281\t-14672.40876\t114\t879.5\t6832.75\t137\t-2010120\t2.71E+11
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tPHLOC\tLOCATION\t9736841.325\t2327.256637\t114\t1109999911\t1.11E+16\t1130\t2629800\t4.75E+14
-VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV1QNT\tHSV-1 IGG LEVEL\t3245617.821\t250224\t114\t370000431.6\t3.70E+15\t115\t28775760\t9.60E+12
-VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV2QUL\tHSV-2 IGG RESULT\t9999999\t250224\t114\t1139999886\t1.14E+16\t115\t28775760\t9.60E+12
-HISTOCOMPATIBILITY ORDERABLE\t4046255\t10015R\tHLA ORDER - DSA SCREENING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1171925.172\t113\t1129999887\t1.13E+16\t116\t135943320\t2.64E+14
-LAB BLOOD ORDERABLES\t472528\tLABHCVA\tHEPATITIS C AB IGG\tHCVI\tINTERPRETATION\t9999999\t57183.66116\t113\t1129999887\t1.13E+16\t1393\t79656840\t1.00E+13
-LAB BLOOD ORDERABLES\t473688\tLABYTRYPT\tTRYPTASE, SERUM\tTRYPTASE\tTRYPTASE\t708013.1192\t251845.2632\t113\t80005482.47\t8.00E+14\t95\t23925300\t1.01E+13
-MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tSREQ\tCOMMENTS\t9999999\t304958.4615\t113\t1129999887\t1.13E+16\t78\t23786760\t1.00E+13
-STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tYFFQ1\tTOTAL WEIGHT\t354501.3274\t264723.8462\t113\t40058650\t4.00E+14\t78\t20648460\t8.48E+12
-VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV1QUL\tHSV-1 IGG RESULT\t9999999\t250224\t113\t1129999887\t1.13E+16\t115\t28775760\t9.60E+12
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tUBT\tUNIT BLOOD TYPE  \t9999999\t49175.09434\t112\t1119999888\t1.12E+16\t53\t2606280\t3.05E+11
-BODY FLUID ORDERABLES\t472655\tLABIGF\tINSULIN-LIKE GROWTH FACTOR 1\tIGF\tINSULIN-LIKE GROWTH FACTOR 1\t803781.0902\t81247.05882\t112\t90023482.1\t9.00E+14\t102\t8287200\t1.16E+12
-HISTOCOMPATIBILITY ORDERABLE\t896159\t10100R\tHLA ORDER - PLATELET COMPATIBILITY\t10049R\tHLA LAB TESTING STATUS\t9999999\t456229.9115\t112\t1119999888\t1.12E+16\t113\t51553980\t7.28E+13
-LAB BLOOD ORDERABLES\t472723\tLABYLAMO\tLAMOTRIGINE\tLAMOT\tLAMOTRIGINE\t267863.5063\t193590\t112\t30000712.7\t3.00E+14\t108\t20907720\t4.62E+12
-LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETICAB\tRETIC, ABS, MANUAL\t357263.5893\t20763.22581\t112\t40013522\t4.00E+14\t93\t1930980\t2.48E+11
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t111\t1109999889\t1.11E+16\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tXMR\tUNIT CROSSMATCH  \t9999999\t72258.75\t111\t1109999889\t1.11E+16\t48\t3468420\t5.54E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t271112.1532\t69104.85437\t111\t30093449\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD3PC\tCD3 (PAN T) %\t270339.5946\t69104.85437\t111\t30007695\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD4AB\tCD3+/CD4+, ABS.\t270510.4324\t69104.85437\t111\t30026658\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD4PC\tCD3+/CD4+ %\t270290.8108\t69104.85437\t111\t30002280\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD8AB\tCD3+/CD8+, ABS.\t270857.5856\t69104.85437\t111\t30065192\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD8PC\tCD3+/CD8+ %\t270317.6847\t69104.85437\t111\t30005263\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t270338.2523\t69104.85437\t111\t30007546\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t271097.7207\t69104.85437\t111\t30091847\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t270270.8405\t69104.85437\t111\t30000063.3\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tLYM\tLYMPHOCYTES %\t270288.4378\t69104.85437\t111\t30002016.6\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t271405.6937\t69104.85437\t111\t30126032\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tWBC\tWBC COUNT\t277493.6667\t69104.85437\t111\t30801797\t3.00E+14\t103\t7117800\t6.49E+11
-LAB BLOOD ORDERABLES\t473262\tLABRF\tRHEUMATOID FACTOR\tRHFR\tRHEUMATOID FACTOR\t9999999\t221510.8108\t111\t1109999889\t1.11E+16\t111\t24587700\t7.16E+12
-LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIKACINTR\tAMIKACIN, TROUGH\t1981987.495\t9466.060606\t111\t220000611.9\t2.20E+15\t99\t937140\t42750860400
-LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIPR3\tDOSAGE\t9999999\t9466.060606\t111\t1109999889\t1.11E+16\t99\t937140\t42750860400
-LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIPR4\tADMINISTRATION ROUTE\t9999999\t9466.060606\t111\t1109999889\t1.11E+16\t99\t937140\t42750860400
-LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANCOPEAK\tVANCOMYCIN, PEAK\t540559.7667\t14418.35294\t111\t60002134.1\t6.00E+14\t85\t1225560\t55369022400
-LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANPO2\tDATE & TIME OF LAST DOSE\t9999999\t14418.35294\t111\t1109999889\t1.11E+16\t85\t1225560\t55369022400
-LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANPO3\tDOSAGE\t9999999\t14418.35294\t111\t1109999889\t1.11E+16\t85\t1225560\t55369022400
-LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANPO4\tADMINISTRATION ROUTE\t9999999\t14418.35294\t111\t1109999889\t1.11E+16\t85\t1225560\t55369022400
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGBRAFB\tBRAF, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGCTNNB\tCTNNB1, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGEGFRB\tEGFR, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGPIK3B\tPIK3CA, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGPTENB\tPTEN, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t961555\tLAB12808R\tCK ISOENZYME WITH REFLEX\tCK\tCREATINE KINASE\t93054.76577\t178739.4783\t111\t10329079\t1.00E+14\t115\t20555040\t4.88E+12
-URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tTNAU\tSODIUM EXCRETION\t360477.7207\t55488\t111\t40013027\t4.00E+14\t60\t3329280\t4.67E+11
-URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tUNA\tSODIUM, URINE\t270326.6486\t55488\t111\t30006258\t3.00E+14\t60\t3329280\t4.67E+11
-URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tUT1\tCOLLECTION PERIOD SODIUM\t450473.1081\t55488\t111\t50002515\t5.00E+14\t60\t3329280\t4.67E+11
-URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tVOL1\tVOLUME SODIUM\t272045\t55488\t111\t30196995\t3.00E+14\t60\t3329280\t4.67E+11
-LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIPR2\tDATE & TIME OF LAST DOSE\t9999999\t9466.060606\t110\t1099999890\t1.10E+16\t99\t937140\t42750860400
-LAB BLOOD ORDERABLES\t541670\tLABOXCMET\tOXCARBAZEPINE METABOLITE LEVEL\tOXCMT1\tOXCARBAZEPINE METABOLITE\t90930.9\t180366.9421\t110\t10002399\t1.00E+14\t121\t21824400\t4.56E+12
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGIDH1B\tIDH1, BLOOD\t9999999\t946882.7027\t110\t1099999890\t1.10E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGIDH2B\tIDH2, BLOOD\t9999999\t946882.7027\t110\t1099999890\t1.10E+16\t111\t105103980\t2.64E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGKRASB\tKRAS, BLOOD\t9999999\t946882.7027\t110\t1099999890\t1.10E+16\t111\t105103980\t2.64E+14
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tPRO\tPRODUCT ID  \t9999999\t22864.44444\t109\t1089999891\t1.09E+16\t54\t1234680\t87256699200
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGNRASB\tNRAS, BLOOD\t9999999\t946882.7027\t109\t1089999891\t1.09E+16\t111\t105103980\t2.64E+14
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tRPT\tRPT\t9999999\t316267.8\t109\t1089999891\t1.09E+16\t100\t31626780\t1.20E+13
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQT\tIGG LEVEL\t7522948.651\t173800.7339\t109\t820001403\t8.20E+15\t109\t18944280\t4.42E+12
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGTP53B\tTP53, BLOOD\t9999999\t946882.7027\t108\t1079999892\t1.08E+16\t111\t105103980\t2.64E+14
-MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tRPT\tRPT\t9999999\t212126.9388\t108\t1079999892\t1.08E+16\t49\t10394220\t3.21E+12
-STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tFATPC\t%FAT\t3425938.296\t250316.8\t108\t370001336\t3.70E+15\t75\t18773760\t7.06E+12
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tXMR\tUNIT CROSSMATCH  \t9999999\t68518.69565\t107\t1069999893\t1.07E+16\t46\t3151860\t5.30E+11
-MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tSDES\tSPECIMEN\t9999999\t212126.9388\t107\t1069999893\t1.07E+16\t49\t10394220\t3.21E+12
-URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tYUMEUT\tCOLLECTION DURATION\t373854.8411\t362619.0476\t107\t40002468\t4.00E+14\t63\t22845000\t1.07E+13
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tSI\t<<STATUS>>  \t9999999\t34411.39535\t106\t1059999894\t1.06E+16\t172\t5918760\t7.29E+11
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tPRO\tPRODUCT ID  \t9999999\t19624.44444\t106\t1059999894\t1.06E+16\t54\t1059720\t53302118400
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tABNWBC\tBLASTS/ABNORMAL CELLS\t9999999\t24628.30189\t106\t1059999894\t1.06E+16\t106\t2610600\t5.11E+11
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGNOTCB\tNOTCH1, BLOOD\t9999999\t946882.7027\t106\t1059999894\t1.06E+16\t111\t105103980\t2.64E+14
-STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tFATTOT\tTOTAL FAT/24H\t8207551\t255336.1765\t106\t870000406\t8.70E+15\t68\t17362860\t6.70E+12
-VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXGQL\tIGG RESULT\t9999999\t167664.3396\t106\t1059999894\t1.06E+16\t106\t17772420\t3.77E+12
-LAB BLOOD ORDERABLES\t472352\tLABFACT10\tFACTOR X ASSAY\tFACTOR10\tFACTOR X ASSAY\t761966.9143\t38086.30435\t105\t80006526\t8.00E+14\t92\t3503940\t2.05E+11
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12009R\tQ FEVER IGM PHASE I SCRN\t9999999\t308861.9118\t105\t1049999895\t1.05E+16\t136\t42005220\t1.99E+13
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12010R\tQ FEVER IGG PHASE II SCRN\t9999999\t308861.9118\t105\t1049999895\t1.05E+16\t136\t42005220\t1.99E+13
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGAPCB\tAPC, BLOOD\t9999999\t873158.6047\t105\t1049999895\t1.05E+16\t129\t112637460\t2.69E+14
-LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGSP\tSP, SPLIT POINT\t1142863.017\t29423.95102\t105\t120000616.8\t1.20E+15\t1225\t36044340\t2.93E+13
-POINT OF CARE TESTING\t471530\tLABPCACT\tACTIVATED CLOTTING TIME (POCT)\tPCCOM\tPOCT COMMENT\t9999999\t#VALUE!\t105\t1049999895\t1.05E+16\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tUBT\tUNIT BLOOD TYPE  \t9999999\t41741.17647\t104\t1039999896\t1.04E+16\t51\t2128800\t2.07E+11
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tLR210\tLOG DX AVG\t8653845.298\t621287.0455\t104\t899999911\t9.00E+15\t88\t54673260\t4.27E+13
-LAB BLOOD ORDERABLES\t471791\tLABYBSAP\tBONE ALKALINE PHOSPHATASE, SERUM\tALKPO4BONE\tBONE ALK. PHOS.\t47.77307692\t148256.044\t104\t4968.4\t416111.98\t91\t13491300\t2.31E+12
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12008R\tQ FEVER IGG PHASE I SCRN\t9999999\t308861.9118\t104\t1039999896\t1.04E+16\t136\t42005220\t1.99E+13
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCORCL\tCORRECTED CR. CLEAR.\t673121.25\t35871.92308\t104\t70004610\t7.00E+14\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCOREXC\tCORRECTED CR. EXC.\t12.27884615\t35871.92308\t104\t1277\t20165\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCR\tCREATININE, SERUM\t1.843269231\t35871.92308\t104\t191.7\t523.25\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCRCL\tCREATININE CLEARANCE\t50.57692308\t35871.92308\t104\t5260\t446946\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCRE\tCREATININE EXCRETION\t862.8461538\t35871.92308\t104\t89736\t102584906\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCRUCL\tCREATININE, URINE\t56.64423077\t35871.92308\t104\t5891\t468493\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tFLOWURN\tURINE FLOW\t1.372115385\t35871.92308\t104\t142.7\t270.91\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tHT\tHEIGHT\t168.9134615\t35871.92308\t104\t17567\t2979645\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tUT3\tCOLLECTION PERIOD CREATININE\t20.39423077\t35871.92308\t104\t2121\t47145\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tVOL3\tVOLUME CREATININE\t1631.221154\t35871.92308\t104\t169647\t425176911\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tWT\tWEIGHT\t72.88942308\t35871.92308\t104\t7580.5\t598246.19\t104\t3730680\t3.45E+11
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tOTHCRY\tCRYSTALS, OTHER\t9999999\t13744.13793\t103\t1029999897\t1.03E+16\t58\t797160\t46346716800
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVSPM\tSPECIMEN\t9999999\t253864.8227\t103\t1029999897\t1.03E+16\t423\t107384820\t3.23E+13
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tDI\tISSUE DATE/TIME\t9999999\t47200\t102\t1019999898\t1.02E+16\t48\t2265600\t2.52E+11
-BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tBIREF\tBIREFRINGENCE\t9999999\t11946.19355\t102\t1019999898\t1.02E+16\t155\t1851660\t96880654800
-LAB BLOOD ORDERABLES\t890125\tLAB12238R\tLACOSAMIDE\t12239R\t12239R\t1176476.807\t371646.9903\t102\t120000634.3\t1.20E+15\t103\t38279640\t1.63E+13
-LAB BLOOD ORDERABLES\t890125\tLAB12238R\tLACOSAMIDE\t12240R\t12240R\t2450981.449\t371646.9903\t102\t250000107.8\t2.50E+15\t103\t38279640\t1.63E+13
-LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tLABS1\tLABS1\t9999999\t671067.3054\t102\t1019999898\t1.02E+16\t167\t112068240\t1.25E+14
-URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11146R\tCREATININE, URINE\t686350.0245\t13.33333333\t102\t70007702.5\t7.00E+14\t54\t720\t43200
-VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tHSV1PC\tHSV-1 DNA\t9999999\t175252.9412\t102\t1019999898\t1.02E+16\t102\t17875800\t3.71E+12
-VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tHSV2PC\tHSV-2 DNA\t9999999\t175252.9412\t102\t1019999898\t1.02E+16\t102\t17875800\t3.71E+12
-BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN, TOTAL BODY FLUID\tFSC2\tFLUID TYPE\t9999999\t156644.5872\t101\t1009999899\t1.01E+16\t109\t17074260\t3.41E+12
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMCO\tCOMMENT\t9999999\t741092.1429\t101\t1009999899\t1.01E+16\t28\t20750580\t2.04E+13
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMDX\tCLINICAL INDICATION\t9999999\t741092.1429\t101\t1009999899\t1.01E+16\t28\t20750580\t2.04E+13
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMRE\tRESULT\t9999999\t741092.1429\t101\t1009999899\t1.01E+16\t28\t20750580\t2.04E+13
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXMQT\tIGM LEVEL\t9603959.626\t176445.1485\t101\t969999922.3\t9.70E+15\t101\t17820960\t4.22E+12
-BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN, TOTAL BODY FLUID\tBILIRUBINFLD\tBILIRUBIN, TOTAL, BODY FLUID\t100004.303\t156644.5872\t100\t10000430.3\t1.00E+14\t109\t17074260\t3.41E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9165R\tSERUM DATE 2\t9999999\t358155\t100\t999999900\t1.00E+16\t100\t35815500\t3.98E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9167R\tRESULT  MCS 2\t27.25\t358155\t100\t2725\t411949\t100\t35815500\t3.98E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9169R\tRESULT 2\t9999999\t358155\t100\t999999900\t1.00E+16\t100\t35815500\t3.98E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9135R\tSERUM DATE 2\t9999999\t359866.8\t100\t999999900\t1.00E+16\t100\t35986680\t3.98E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9137R\tRESULT  MCS 2\t40.48\t359866.8\t100\t4048\t477754\t100\t35986680\t3.98E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9139R\tRESULT 2\t9999999\t359866.8\t100\t999999900\t1.00E+16\t100\t35986680\t3.98E+13
-URINE ORDERABLES\t871366\tLABSTOBLD\tOCCULT BLD, GUAIAC DIAG, SINGLE\tOCCGU\tSTOOL SAMPLE1,GUAIAC\t9999999\t3821.538462\t100\t999999900\t1.00E+16\t39\t149040\t1437552000
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tXMR\tUNIT CROSSMATCH  \t9999999\t69890.86957\t99\t989999901\t9.90E+15\t46\t3214980\t5.31E+11
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tP210Q\tP210\t5454545.061\t621287.0455\t99\t539999961.1\t5.40E+15\t88\t54673260\t4.27E+13
-LAB BLOOD ORDERABLES\t473444\tLAB11097R\tTSH RECEPTOR AB, STIM\tTSIG\tTHROID-STIMULATING IG, SERUM\t6868687.264\t284574.7826\t99\t680000039.1\t6.80E+15\t92\t26180880\t8.22E+12
-STOOL ORDERABLES\t471585\tLAB11096R\tALPHA 1 ANTITRYPSIN, STOOL\tA1AANTITRYPF\tA-1-ANTITRYP,FECAL\t5454607.747\t233964.6429\t99\t540006167\t5.40E+15\t56\t13102020\t3.93E+12
-LAB BLOOD ORDERABLES\t473270\tLABRIST\tRISTOCETIN COFACTOR\tRIST\tRISTOCETIN CO-FACTOR\t816463.949\t46643.41463\t98\t80013467\t8.00E+14\t82\t3824760\t2.69E+11
-LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tSPTY2\tSPECIMEN TYPE\t9999999\t1896672.381\t98\t979999902\t9.80E+15\t63\t119490360\t1.36E+15
-URINE ORDERABLES\t871366\tLABSTOBLD\tOCCULT BLD, GUAIAC DIAG, SINGLE\tDATE1\tDATE 1\t8991267\t3821.538462\t98\t881144166\t8.73E+15\t39\t149040\t1437552000
-VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tRVPIP\tTEST DECRIPTION\t9999999\t66962.44702\t98\t979999902\t9.80E+15\t2501\t167473080\t3.14E+13
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tRPT\tRPT\t9999999\t696693.125\t97\t969999903\t9.70E+15\t96\t66882540\t7.55E+13
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tSDES\tSPECIMEN\t9999999\t696693.125\t97\t969999903\t9.70E+15\t96\t66882540\t7.55E+13
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tUBT\tUNIT BLOOD TYPE  \t9999999\t48162.27273\t96\t959999904\t9.60E+15\t44\t2119140\t2.29E+11
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tPRO\tPRODUCT ID  \t9999999\t19766.80851\t96\t959999904\t9.60E+15\t47\t929040\t49220308800
-LAB BLOOD ORDERABLES\t472348\tLABFACT7\tFACTOR VII ASSAY\tFACTOR7\tFACTOR VII ASSAY\t937555.1458\t47563.90244\t96\t90005294\t9.00E+14\t82\t3900240\t2.49E+11
-LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1 AND 2 PCR, NON CSF\tSOURC9\tSOURCE\t9999999\t627991.6667\t96\t959999904\t9.60E+15\t72\t45215400\t8.40E+13
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t316267.8\t96\t959999904\t9.60E+15\t100\t31626780\t1.20E+13
-URINE ORDERABLES\t471613\tLABUAMY\tAMYLASE WITH CREATININE, URINE (RANDOM)\tAMUCRU\tAMYLASE/CREAT RATIO\t6041666.177\t17080.32787\t96\t579999953\t5.80E+15\t61\t1041900\t1.14E+11
-URINE ORDERABLES\t471613\tLABUAMY\tAMYLASE WITH CREATININE, URINE (RANDOM)\tAMYLASE\tAMYLASE, URINE\t312611.625\t17080.32787\t96\t30010716\t3.00E+14\t61\t1041900\t1.14E+11
-URINE ORDERABLES\t471613\tLABUAMY\tAMYLASE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t208421.5698\t17080.32787\t96\t20008470.7\t2.00E+14\t61\t1041900\t1.14E+11
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tHYCD\tHYDROCODONE\t7396121\t14.11764706\t96\t710027616\t7.10E+15\t17\t240\t14400
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tOXYCOD\tOXYCODONE\t7605058.427\t14.11764706\t96\t730085609\t7.30E+15\t17\t240\t14400
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUHMGC\tHYDROMORPHONE\t4480940.604\t14.11764706\t96\t430170298\t4.30E+15\t17\t240\t14400
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUMOGC\tMORPHINE\t5210578.354\t14.11764706\t96\t500215522\t5.00E+15\t17\t240\t14400
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tSREQ\tCOMMENTS\t9999999\t696693.125\t96\t959999904\t9.60E+15\t96\t66882540\t7.55E+13
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\tSACIGA\tS. CERVISIAE AB,IGA\t105282.5463\t263645.3333\t95\t10001841.9\t1.00E+14\t90\t23728080\t1.38E+13
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\tSACIGG\tS.CERVISIAE AB,IGG\t105281.3116\t263645.3333\t95\t10001724.6\t1.00E+14\t90\t23728080\t1.38E+13
-LAB BLOOD ORDERABLES\t472436\tLABG6PD\tG6PD SCREEN\tG6PD\tG6PD SCREEN\t9999999\t101767.2527\t95\t949999905\t9.50E+15\t91\t9260820\t1.51E+12
-LAB BLOOD ORDERABLES\t472553\tLABHIVAB\tHIV (TYPE 1 AND TYPE 2) ABY\tHIVAB\tHIV 1/2 ANTIBODY\t9999999\t61924.23529\t95\t949999905\t9.50E+15\t85\t5263560\t4.85E+11
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tCODEINE\tCODEINE\t9158137.989\t14.11764706\t95\t870023109\t8.70E+15\t17\t240\t14400
-URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11150R\tNITRITES\t9999999\t13.33333333\t95\t949999905\t9.50E+15\t54\t720\t43200
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tSI\t<<STATUS>>  \t9999999\t33621.42857\t94\t939999906\t9.40E+15\t154\t5177700\t6.15E+11
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\tPANCA\tNEUTROPHIL CYTO AB-P\t9999999\t263645.3333\t94\t939999906\t9.40E+15\t90\t23728080\t1.38E+13
-MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tRPT\tRPT\t9999999\t#VALUE!\t94\t939999906\t9.40E+15\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tRPT\tRPT\t9999999\t180211.7241\t94\t939999906\t9.40E+15\t87\t15678420\t3.56E+12
-MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tSREQ\tCOMMENTS\t9999999\t180211.7241\t94\t939999906\t9.40E+15\t87\t15678420\t3.56E+12
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9407R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t42896.78571\t93\t929999907\t9.30E+15\t168\t7206660\t2.67E+13
-LAB BLOOD ORDERABLES\t668152\tLABATTG\tIGA ANTI  TTG\tATTGT\tIGA ANTI TTG LEVEL\t7849464.624\t399798.0645\t93\t730000210\t7.30E+15\t93\t37181220\t1.87E+13
-MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tSDES\tSPECIMEN\t9999999\t180211.7241\t93\t929999907\t9.30E+15\t87\t15678420\t3.56E+12
-VIROLOGY ORDERABLES\t472452\tLABGCCR2\tGC MOLECULAR DETECTION\tGC\tN.GONORRHOEAE RESULT:\t9999999\t144177.2308\t93\t929999907\t9.30E+15\t65\t9371520\t1.97E+12
-LAB BLOOD ORDERABLES\t471706\tLAB11093R\tSCL 70 AB\tSCL70\tSCL 70 AB, IGG, SERUM\t9239129.552\t154129.2135\t92\t849999918.8\t8.50E+15\t89\t13717500\t2.41E+12
-VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tRPT\tRPT\t9999999\t925479.3103\t92\t919999908\t9.20E+15\t87\t80516700\t1.11E+14
-VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tSDES\tSPECIMEN\t9999999\t925479.3103\t92\t919999908\t9.20E+15\t87\t80516700\t1.11E+14
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tDI\tISSUE DATE/TIME\t9999999\t43812\t91\t909999909\t9.10E+15\t45\t1971540\t1.93E+11
-MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tSDES\tSPECIMEN\t9999999\t#VALUE!\t91\t909999909\t9.10E+15\t0\tNone\tNone
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t618.6666667\t90\t899999910\t9.00E+15\t90\t55680\t742197600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t618.6666667\t90\t899999910\t9.00E+15\t90\t55680\t742197600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t8246541.822\t618.6666667\t90\t742188764\t7.40E+15\t90\t55680\t742197600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t618.6666667\t90\t899999910\t9.00E+15\t90\t55680\t742197600
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9033R\tHLA-DPB1 ALLELE 1\t9999999\t383878.6813\t90\t899999910\t9.00E+15\t91\t34932960\t6.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9034R\tHLA-DPB1 ALLELE 2\t9999999\t383878.6813\t90\t899999910\t9.00E+15\t91\t34932960\t6.67E+13
-LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tTEST2\tTEST NAME\t9889876.311\t1896672.381\t90\t890088868\t8.90E+15\t63\t119490360\t1.36E+15
-VIROLOGY ORDERABLES\t473461\tLABTOXO\tTOXOPLASMA IGG\tTOXO\tTOXOPLASMA IGG\t9999999\t156732.2892\t90\t899999910\t9.00E+15\t83\t13008780\t2.55E+12
-VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tSREQ\tCOMMENTS\t9999999\t925479.3103\t90\t899999910\t9.00E+15\t87\t80516700\t1.11E+14
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tUBT\tUNIT BLOOD TYPE  \t9999999\t31722.43902\t89\t889999911\t8.90E+15\t41\t1300620\t88116591600
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSCOLOR\tCOLOR, SYN FLD\t9999999\t6555.9375\t89\t889999911\t8.90E+15\t64\t419580\t9299703600
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSRBC\tRBC COUNT, SYN FLD\t3116639.461\t6555.9375\t89\t277380912\t2.63E+15\t64\t419580\t9299703600
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSTURB\tTURBIDITY, SYN FLD\t9999999\t6555.9375\t89\t889999911\t8.90E+15\t64\t419580\t9299703600
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSWBC\tNUCL. CELLS, SYN FLD\t2927603.202\t6555.9375\t89\t260556685\t2.60E+15\t64\t419580\t9299703600
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9031R\tHLA-DPA1 ALLELE 1\t9999999\t391679.3258\t89\t889999911\t8.90E+15\t89\t34859460\t6.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9032R\tHLA-DPA1 ALLELE 2\t9999999\t391679.3258\t89\t889999911\t8.90E+15\t89\t34859460\t6.67E+13
-LAB BLOOD ORDERABLES\t473774\tLABVWAG\tVON WILLEBRAND AG\tVWAG\tVON WILLEBRAND ANTIGEN\t112561.9101\t51511.23288\t89\t10018010\t1.00E+14\t73\t3760320\t2.91E+11
-MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tGS\tGRAM STAIN\t9999999\t474199.3774\t89\t889999911\t8.90E+15\t514\t243738480\t2.40E+14
-URINE ORDERABLES\t473100\tLABURPHOS\tPHOSPHATE, URINE (RANDOM)\tPHOSU\tPHOSPHATE, URINE\t1348343.265\t14782.28571\t89\t120002550.6\t1.20E+15\t70\t1034760\t1.04E+11
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t88\t879999912\t8.80E+15\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tJOINT\tJOINT\t9999999\t6555.9375\t88\t879999912\t8.80E+15\t64\t419580\t9299703600
-BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN TOTAL, FLUID\tBILIRUBINFLD\tBILIRUBIN, TOTAL, BODY FLUID\t340914.5125\t831985\t88\t30000477.1\t3.00E+14\t24\t19967640\t2.27E+13
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t8888826.693\t645\t88\t782216749\t7.80E+15\t88\t56760\t741614400
-LAB BLOOD ORDERABLES\t471653\tLABAPC\tACTIVATED PROTEIN C RESISTANCE\tAPC\tAPC RESISTANCE\t340911.6489\t150036.8675\t88\t30000225.1\t3.00E+14\t83\t12453060\t2.98E+12
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tPRO\tPRODUCT ID  \t9999999\t21465\t87\t869999913\t8.70E+15\t44\t944460\t67138131600
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tXMR\tUNIT CROSSMATCH  \t9999999\t53797.33333\t87\t869999913\t8.70E+15\t45\t2420880\t3.40E+11
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9102R\tCELL DATE\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9103R\tCELL SOURCE\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9104R\tSERUM DATE 1\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9106R\tMCS 1\t35.5862069\t272784.1379\t87\t3096\t587478\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9121R\tCOMPOSED TEXT\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9599R\tMETHOD\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9081R\tCELL DATE\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9082R\tCELL SOURCE\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9083R\tSERUM DATE 1\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9085R\tMCS 1\t46.14942529\t273799.3103\t87\t4015\t536315\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9100R\tCOMPOSED TEXT\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9462R\tMETHOD\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9595R\tRESULT 1\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX\tSACIGA\tS. CERVISIAE AB,IGA\t25.06781609\t301613.3333\t87\t2180.9\t184727.29\t81\t24430680\t8.35E+12
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX\tSACIGG\tS.CERVISIAE AB,IGG\t14.21494253\t301613.3333\t87\t1236.7\t31616.71\t81\t24430680\t8.35E+12
-LAB BLOOD ORDERABLES\t472802\tLABMMAS\tMETHYLMALONIC ACID\tMMA\tMETHYLMALONIC ACID, SERUM\t0.252413793\t321343.7838\t87\t21.96\t9.7782\t74\t23779440\t9.38E+12
-MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tSREQ\tCOMMENTS\t9999999\t212126.9388\t87\t869999913\t8.70E+15\t49\t10394220\t3.21E+12
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tDX\tDIAGNOSIS\t9999999\t6555.9375\t86\t859999914\t8.60E+15\t64\t419580\t9299703600
-BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t6555.9375\t86\t859999914\t8.60E+15\t64\t419580\t9299703600
-LAB BLOOD ORDERABLES\t473426\tLABYTESFT\tTESTOSTERONE, TOTAL AND FREE\tYTESHD\tFREE TESTOSTERONE\t9999999\t285204\t86\t859999914\t8.60E+15\t80\t22816320\t7.34E+12
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tATTGR\tIGA ANTI TTG ANTIBODY\t9999999\t402189.6296\t86\t859999914\t8.60E+15\t81\t32577360\t1.89E+13
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tGDGPR\tIGG ANTI DGP ANTIBODY\t9999999\t402189.6296\t86\t859999914\t8.60E+15\t81\t32577360\t1.89E+13
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tIGACDS\tIGA, SERUM\t116516.5814\t402189.6296\t86\t10020426\t1.00E+14\t81\t32577360\t1.89E+13
-LAB BLOOD ORDERABLES\t670133\tLABGH\tGROWTH HORMONE\tGH\tGROWTH HORMONE\t1046513.045\t143370.7317\t86\t90000121.9\t9.00E+14\t82\t11756400\t2.71E+12
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGDNM3B\tDNMT3A, BLOOD\t9999999\t960628.6047\t86\t859999914\t8.60E+15\t86\t82614060\t2.39E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGERHRB\tERBB2/HER2, BLOOD\t9999999\t960628.6047\t86\t859999914\t8.60E+15\t86\t82614060\t2.39E+14
-STOOL ORDERABLES\t473766\tLABY25VD\tVITAMIN D, 25-HYDROXY\t25OHD2\t25-HYDROXY D2\t8488374.459\t352581.5385\t86\t730000203.5\t7.30E+15\t78\t27501360\t4.09E+13
-STOOL ORDERABLES\t473766\tLABY25VD\tVITAMIN D, 25-HYDROXY\t25OHD3\t25-HYDROXY D3\t697693.7895\t352581.5385\t86\t60001665.9\t6.00E+14\t78\t27501360\t4.09E+13
-STOOL ORDERABLES\t473766\tLABY25VD\tVITAMIN D, 25-HYDROXY\t25OHDT\t25-HYDROXY D TOTAL\t1279092.02\t352581.5385\t86\t110001913.7\t1.10E+15\t78\t27501360\t4.09E+13
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tSI\t<<STATUS>>  \t9999999\t33679.72028\t85\t849999915\t8.50E+15\t143\t4816200\t5.88E+11
-HISTOCOMPATIBILITY ORDERABLE\t506036\t10270R\tHLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t1418312.791\t85\t849999915\t8.50E+15\t86\t121974900\t4.69E+14
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9600R\tRESULT 1\t9999999\t275034.3529\t85\t849999915\t8.50E+15\t85\t23377920\t2.32E+13
-LAB BLOOD ORDERABLES\t471650\tLABASTRO\tANTI STREPTOLYSIN O\tASTRO\tANTI STREPTOLYSIN O\t1529599.729\t45202.10526\t85\t130015977\t1.30E+15\t76\t3435360\t2.75E+11
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tO2SATA\tO2 SATURATION (A)\t9882353.104\t3829.714286\t85\t840000013.8\t8.40E+15\t35\t134040\t1868594400
-VIROLOGY ORDERABLES\t876715\tLABDVERV3\tRSV DFA\tRSV3\tRSV\t9999999\t60101.53846\t85\t849999915\t8.50E+15\t78\t4687920\t3.25E+11
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tSI\t<<STATUS>>  \t9999999\t34670.73529\t84\t839999916\t8.40E+15\t136\t4715220\t5.88E+11
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMSO\tSPECIMEN SOURCE\t9999999\t741092.1429\t84\t839999916\t8.40E+15\t28\t20750580\t2.04E+13
-HISTOCOMPATIBILITY ORDERABLE\t5040244\t10170R\tHLA ORDER - NMDP ADDITIONAL SPECIMEN\t10049R\tHLA LAB TESTING STATUS\t9999999\t344572.9412\t84\t839999916\t8.40E+15\t85\t29288700\t3.69E+13
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL6\tHSV 1/2 IGM CONFIRMATORY IFA\t9999999\t360677.4684\t84\t839999916\t8.40E+15\t79\t28493520\t1.17E+13
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t83\t829999917\t8.30E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tUBT\tUNIT BLOOD TYPE  \t9999999\t34084.61538\t83\t829999917\t8.30E+15\t39\t1329300\t92575184400
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9025R\tHLA-DR345 ALLELE 1\t9999999\t405846.506\t83\t829999917\t8.30E+15\t83\t33685260\t6.60E+13
-HISTOCOMPATIBILITY ORDERABLE\t5040244\t10170R\tHLA ORDER - NMDP ADDITIONAL SPECIMEN\tUNRDID\tUNRELATED DONOR ID\t9999999\t344572.9412\t83\t829999917\t8.30E+15\t85\t29288700\t3.69E+13
-LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tANTI-JO1 ANTIBODY\tJO1QUL\tANTI-JO1 RESULT\t9999999\t395222.2222\t83\t829999917\t8.30E+15\t81\t32013000\t1.65E+13
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tP190Q\tP190\t5662650.2\t539235.2113\t83\t469999966.6\t4.70E+15\t71\t38285700\t2.78E+13
-LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGG\tIGG, SERUM\t1092.674699\t117131.3924\t83\t90692\t128117920\t79\t9253380\t1.83E+12
-LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS1\tIGG SUBCLASS1\t704.8120482\t117131.3924\t83\t58499.4\t56405478.54\t79\t9253380\t1.83E+12
-LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS2\tIGG SUBCLASS2\t284.2445783\t117131.3924\t83\t23592.3\t9718185.21\t79\t9253380\t1.83E+12
-LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS3\tIGG SUBCLASS3\t41.47433735\t117131.3924\t83\t3442.37\t248968.7693\t79\t9253380\t1.83E+12
-LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS4\tIGG SUBCLASS4\t120536.0139\t117131.3924\t83\t10004489.15\t1.00E+14\t79\t9253380\t1.83E+12
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTMET\tMETHOD\t9999999\t1027142.927\t83\t829999917\t8.30E+15\t82\t84225720\t1.01E+14
-LAB BLOOD ORDERABLES\t473454\tLABYTOPIR\tTOPIRAMATE\tTOPIRAMATE\tTOPIRAMATE\t1566271.541\t159248.6598\t83\t130000537.9\t1.30E+15\t97\t15447120\t2.80E+12
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t361445.8447\t915206.4706\t83\t30000005.11\t3.00E+14\t68\t62234040\t6.72E+13
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t316267.8\t83\t829999917\t8.30E+15\t100\t31626780\t1.20E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVNO\tNOTES\t9999999\t240525.9898\t83\t829999917\t8.30E+15\t197\t47383620\t1.52E+13
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t15867.07317\t82\t819999918\t8.20E+15\t82\t1301100\t7.02E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADMFR\tLEAD MANUFACTURER\t9999999\t15867.07317\t82\t819999918\t8.20E+15\t82\t1301100\t7.02E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADMODEL\tLEAD MODEL\t8171335.28\t15867.07317\t82\t670049493\t6.70E+15\t82\t1301100\t7.02E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADTYPE\tLEAD TYPE\t9999999\t15867.07317\t82\t819999918\t8.20E+15\t82\t1301100\t7.02E+11
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t10460R\tRECIPIENT NAME\t9999999\t345286.0976\t82\t819999918\t8.20E+15\t82\t28313460\t2.00E+13
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t10461R\tRECIPIENT DOB\t9999999\t345286.0976\t82\t819999918\t8.20E+15\t82\t28313460\t2.00E+13
-LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tASPIGE\tASPERGILLUS IGE ABY\t6829270.973\t785686.9231\t82\t560000219.8\t5.60E+15\t78\t61283580\t6.62E+13
-LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tASPIGG\tASPERGILLUS IGG ABY\t9999999\t785686.9231\t82\t819999918\t8.20E+15\t78\t61283580\t6.62E+13
-LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tIGEASP\tTOTAL IGE\t854509.2721\t785686.9231\t82\t70069760.31\t7.00E+14\t78\t61283580\t6.62E+13
-LAB BLOOD ORDERABLES\t473347\tLABYSTFR\tSOLUBLE TRANSFERRIN RECEPTOR\tYSTFRR\tSOL TRANSFERR REC\t4.76097561\t153920.4545\t82\t390.4\t2782.86\t88\t13545000\t2.33E+12
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTCDX\tCLINICAL INDICATION\t9999999\t1027142.927\t82\t819999918\t8.20E+15\t82\t84225720\t1.01E+14
-VIROLOGY ORDERABLES\t1007409\tLABVZVQL\tVZV DNA PCR QUAL, NON LESION\tVZQLDN\tVZV DNA\t9999999\t192832.6829\t82\t819999918\t8.20E+15\t82\t15812280\t4.51E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t10462R\tRECIPIENT NAME\t9999999\t346332.5926\t81\t809999919\t8.10E+15\t81\t28052940\t1.99E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t10463R\tRECIPIENT DOB\t9999999\t346332.5926\t81\t809999919\t8.10E+15\t81\t28052940\t1.99E+13
-HISTOCOMPATIBILITY ORDERABLE\t5040244\t10170R\tHLA ORDER - NMDP ADDITIONAL SPECIMEN\tRINFO\tTO FIND RESULT:\t9999999\t344572.9412\t81\t809999919\t8.10E+15\t85\t29288700\t3.69E+13
-LAB BLOOD ORDERABLES\t472346\tLABFACT5\tFACTOR V ASSAY\tFACTOR5\tFACTOR V ASSAY\t987746.7654\t58751.25\t81\t80007488\t8.00E+14\t64\t3760080\t6.29E+11
-MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tCULT\tCULT/OTHER RSLT:\t9999999\t3883162.201\t81\t809999919\t8.10E+15\t2681\t10410757860\t4.16E+16
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tXMR\tUNIT CROSSMATCH  \t9999999\t57912\t80\t799999920\t8.00E+15\t40\t2316480\t3.07E+11
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tDI\tISSUE DATE/TIME\t9999999\t29976.66667\t80\t799999920\t8.00E+15\t36\t1079160\t40769906400
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tPRO\tPRODUCT ID  \t9999999\t29561.53846\t80\t799999920\t8.00E+15\t39\t1152900\t1.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tSI\t<<STATUS>>  \t9999999\t30573.07087\t80\t799999920\t8.00E+15\t127\t3882780\t3.96E+11
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHAVINT\tINTERPRETATION\t9999999\t55606.13636\t80\t799999920\t8.00E+15\t1056\t58720080\t7.63E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tCCNT\tCELLS COUNTED\t52.725\t13686.92308\t80\t4218\t306198\t78\t1067580\t75368998800
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC5616A\tCD56+CD16+/CD3- ABS.\t1000112.575\t124748.3544\t80\t80009006\t8.00E+14\t79\t9855120\t1.87E+12
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC5616P\tCD56+CD16+/CD3- %\t1000024.638\t124748.3544\t80\t80001971\t8.00E+14\t79\t9855120\t1.87E+12
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tUBT\tUNIT BLOOD TYPE  \t9999999\t34275\t77\t769999923\t7.70E+15\t36\t1233900\t87256652400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADSERNUM\tLEAD SERIAL NUMBER\t7478470.818\t16915.32468\t77\t575842253\t5.70E+15\t77\t1302480\t7.02E+11
-LAB BLOOD ORDERABLES\t473262\tLABRF\tRHUEMATOID FACTOR\tRHFT\tRHEUMATOID FACTOR LEVEL\t649419.2597\t285925.7143\t77\t50005283\t5.00E+14\t70\t20014800\t7.95E+12
-MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t187056.7953\t77\t769999923\t7.70E+15\t6247\t1168543800\t5.85E+14
-POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tGLU\tGLUCOSE,ISTAT\t219.012987\t5460\t77\t16864\t5418790\t27\t147420\t21538544400
-URINE ORDERABLES\t471511\tLABYU5HIA\t5 - OH-INDOLEACETIC AC, URINE\t5HIAAURN\t5-HIAA, URINE\t389633.6662\t372884.6512\t77\t30001792.3\t3.00E+14\t43\t16034040\t8.06E+12
-URINE ORDERABLES\t471511\tLABYU5HIA\t5 - OH-INDOLEACETIC AC, URINE\tVOLUMEURN\tURINE, VOLUME\t391741.3766\t372884.6512\t77\t30164086\t3.00E+14\t43\t16034040\t8.06E+12
-URINE ORDERABLES\t471511\tLABYU5HIA\t5 - OH-INDOLEACETIC AC, URINE\tYU5HUT\tCOLLECTION DURATION\t389633.4156\t372884.6512\t77\t30001773\t3.00E+14\t43\t16034040\t8.06E+12
-VIROLOGY ORDERABLES\t497325\tLABADVDNA\tADENOVIRUS DNA, QUALITATIVE PCR\tSOURC3\tADENOVIRUS SOURCE\t9999999\t174138\t77\t769999923\t7.70E+15\t140\t24379320\t5.10E+12
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tPRO\tPRODUCT ID  \t9999999\t31336.66667\t76\t759999924\t7.60E+15\t36\t1128120\t1.06E+11
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRP\tPRIMER\t9999999\t520905.7143\t76\t759999924\t7.60E+15\t21\t10939020\t8.12E+12
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASDX\tCLINICAL INDICATION\t9999999\t866627.1429\t76\t759999924\t7.60E+15\t14\t12132780\t1.36E+13
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t4655.526316\t76\t759999924\t7.60E+15\t76\t353820\t46852534800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t4655.526316\t76\t759999924\t7.60E+15\t76\t353820\t46852534800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t9084541.711\t4655.526316\t76\t690425170\t6.90E+15\t76\t353820\t46852534800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t4655.526316\t76\t759999924\t7.60E+15\t76\t353820\t46852534800
-LAB BLOOD ORDERABLES\t472451\tLAB11039R\tGBM AB\tGBMIGG\tGBM-IGG ELISA\t9999999\t201648.5714\t76\t759999924\t7.60E+15\t84\t16938480\t4.29E+12
-LAB BLOOD ORDERABLES\t472695\tLABYITRA\tITRACONAZOLE LEVEL\tYITR1\tITRACONAZOLE\t3552632.293\t367800.7692\t76\t270000054.3\t2.70E+15\t78\t28688460\t1.33E+13
-LAB BLOOD ORDERABLES\t472695\tLABYITRA\tITRACONAZOLE LEVEL\tYITR2\tHYDROXYITRACONAZOLE\t1710527.713\t367800.7692\t76\t130000106.2\t1.30E+15\t78\t28688460\t1.33E+13
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLCT\tC TYPE PHENOTYPE\t9999999\t125643.871\t76\t759999924\t7.60E+15\t31\t3894960\t4.18E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLET\tE TYPE PHENOTYPE\t9999999\t125643.871\t76\t759999924\t7.60E+15\t31\t3894960\t4.18E+12
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD19AB\tCD19 (PAN B), ABS.\t526415.1184\t31387.60563\t76\t40007549\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD19PC\tCD19 (PAN B) %\t526358.9342\t31387.60563\t76\t40003279\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD3AB\tCD3 (PAN T), ABS.\t526499.2105\t31387.60563\t76\t40013940\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD3PC\tCD3 (PAN T) %\t526356.4605\t31387.60563\t76\t40003091\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD5AB\tCD5 (PAN T), ABS.\t526492.25\t31387.60563\t76\t40013411\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD5PC\tCD5 (PAN T) %\t526352.8947\t31387.60563\t76\t40002820\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tLYM\tLYMPHOCYTES %\t526321.7645\t31387.60563\t76\t40000454.1\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tLYMAB\tLYMPHOCYTES, ABS.\t526622.9605\t31387.60563\t76\t40023345\t4.00E+14\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tWBC\tWBC COUNT\t532635.4737\t31387.60563\t76\t40480296\t4.00E+14\t71\t2228520\t87338577600
-MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t76\t759999924\t7.60E+15\t0\tNone\tNone
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tTRPHOS\tTRIPLE PHOSPH CRYST.\t9999999\t7957.894737\t76\t759999924\t7.60E+15\t57\t453600\t7252005600
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tXMR\tUNIT CROSSMATCH  \t9999999\t63768.94737\t75\t749999925\t7.50E+15\t38\t2423220\t3.67E+11
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9467R\tRECIPIENT CELL DATE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9468R\tRECIPIENT CELL SOURCE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9470R\tDONOR NAME\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9472R\tRELATIONSHIP TO PATIENT\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9473R\tDONOR CELL DATE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9474R\tDONOR CELL SOURCE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9475R\tLOCUS 1\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9476R\tLOCUS 2\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9477R\tLOCUS 3\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9611R\tMETHOD\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9636R\tCHIMERISM TEST NAME\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGGS\tB.HENSELAE IGG SCREEN\t9999999\t535699.6721\t75\t749999925\t7.50E+15\t122\t65355360\t1.49E+14
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGMS\tB.HENSELAE IGM SCREEN\t9999999\t535699.6721\t75\t749999925\t7.50E+15\t122\t65355360\t1.49E+14
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGGS\tB.QUINTANA IGG SCREEN\t9999999\t535699.6721\t75\t749999925\t7.50E+15\t122\t65355360\t1.49E+14
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11201R\tANNA-1\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11202R\tANNA-2\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11203R\tANNA-3\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11204R\tAGNA-1\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11205R\tPCA-1\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11206R\tPCA-2\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11207R\tPCA-TR\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11208R\tAMPHIPHYSIN AB\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11209R\tCRMP-5-IGG\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11210R\tP/Q -TYPE CALCIUM CHANNEL AB\t533333.2807\t766684.8\t75\t39999996.05\t4.00E+14\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11211R\tN-TYPE CALCIUM CHANNEL AB\t533333.2827\t766684.8\t75\t39999996.2\t4.00E+14\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11212R\tACHR GANGLIONIC NEURONAL AB\t533333.2849\t766684.8\t75\t39999996.37\t4.00E+14\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11213R\tNEURONAL (V-G) K+ CHANNEL AB\t533333.2853\t766684.8\t75\t39999996.4\t4.00E+14\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t533333.5083\t766684.8\t75\t40000013.12\t4.00E+14\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
-VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS ANTIGEN, STOOL\tRTVR\tROTAVIRUS\t9999999\t54835.2\t75\t749999925\t7.50E+15\t75\t4112640\t7.01E+11
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tPRO\tPRODUCT ID  \t9999999\t23840.57143\t74\t739999926\t7.40E+15\t35\t834420\t53931488400
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9442R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t8209.459459\t74\t739999926\t7.40E+15\t74\t607500\t95812894800
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9478R\tLOCUS 4\t9999999\t4203.287671\t74\t739999926\t7.40E+15\t73\t306840\t64653969600
-LAB BLOOD ORDERABLES\t472750\tLABLUT\tLEUTEINIZING HORMONE\tLH\tLH\t2972980.226\t33812.8125\t74\t220000536.7\t2.20E+15\t64\t2164020\t1.28E+11
-POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tHCT\tHCT(CALC), ISTAT\t135167.7162\t5460\t74\t10002411\t1.00E+14\t27\t147420\t21538544400
-POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tHGB\tHGB(CALC), ISTAT\t135146.2068\t5460\t74\t10000819.3\t1.00E+14\t27\t147420\t21538544400
-POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tK\tPOTASSIUM, ISTAT\t4.466216216\t5460\t74\t330.5\t1613.13\t27\t147420\t21538544400
-POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tNA\tSODIUM, ISTAT\t138.8108108\t5460\t74\t10272\t1430152\t27\t147420\t21538544400
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tDI\tISSUE DATE/TIME\t9999999\t31589.18919\t73\t729999927\t7.30E+15\t37\t1168800\t48342340800
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASRE\tRESULT\t9999999\t866627.1429\t73\t729999927\t7.30E+15\t14\t12132780\t1.36E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9426R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t90203.84615\t73\t729999927\t7.30E+15\t78\t7035900\t2.67E+13
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHJKBT\tJKB PHENOTYPE\t9999999\t134304.8276\t73\t729999927\t7.30E+15\t29\t3894840\t4.18E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHKT\tK PHENOTYPE\t9999999\t129832\t73\t729999927\t7.30E+15\t30\t3894960\t4.18E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHST\tS PHENOTYPE\t9999999\t134304.8276\t73\t729999927\t7.30E+15\t29\t3894840\t4.18E+12
-LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tLAB2\tREFERENCE LAB\t9999999\t1896672.381\t73\t729999927\t7.30E+15\t63\t119490360\t1.36E+15
-LAB BLOOD ORDERABLES\t497264\tLABYPROIN\tPROINSULIN, PLASMA\tPROINSULIN\tPROINSULIN\t13.83972603\t401581.2\t73\t1010.3\t58089.29\t50\t20079060\t8.72E+12
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11201R\tANNA-1\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11202R\tANNA-2\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11203R\tANNA-3\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11204R\tAGNA-1\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11205R\tPCA-1\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11206R\tPCA-2\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11207R\tPCA-TR\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11208R\tAMPHIPHYSIN AB\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11209R\tCRMP-5-IGG\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11210R\tP/Q -TYPE CALCIUM CHANNEL AB\t410958.8692\t915206.4706\t73\t29999997.45\t3.00E+14\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11211R\tN-TYPE CALCIUM CHANNEL AB\t410958.8638\t915206.4706\t73\t29999997.06\t3.00E+14\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11212R\tACHR GANGLIONIC NEURONAL AB\t410958.8652\t915206.4706\t73\t29999997.16\t3.00E+14\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11213R\tNEURONAL (V-G) K+ CHANNEL AB\t410958.8864\t915206.4706\t73\t29999998.71\t3.00E+14\t68\t62234040\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tRPT\tRPT\t9999999\t255332.1429\t73\t729999927\t7.30E+15\t56\t14298600\t7.49E+12
-URINE ORDERABLES\t472936\tLABUMYOSC\tMYOGLOBIN SCREEN, URINE\tUMYOG\tMYOGLOBIN SCREEN, URINE\t9999999\t13368\t73\t729999927\t7.30E+15\t25\t334200\t15908688000
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tSI\t<<STATUS>>  \t9999999\t33532.84404\t72\t719999928\t7.20E+15\t109\t3655080\t4.10E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t7984302.903\t4509.166667\t72\t574869809\t5.70E+15\t72\t324660\t46405868400
-LAB BLOOD ORDERABLES\t471578\tLABYA1APH\tALPHA - 1 - ANTITRYPSIN PHENOTYPE\tA1APHEN\tA-1-A PHENOTYPE\t9999999\t396974.1176\t72\t719999928\t7.20E+15\t68\t26994240\t1.93E+13
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34AB\tCD34, ABS.\t2361179.208\t25604.54545\t72\t170004903\t1.70E+15\t66\t1689900\t1.43E+11
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34PC\t%CD34,BLOOD\t2361111.414\t25604.54545\t72\t170000021.8\t1.70E+15\t66\t1689900\t1.43E+11
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tWBC\tWBC COUNT\t1537813.736\t25604.54545\t72\t110722589\t1.10E+15\t66\t1689900\t1.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tCHOL\tCHOLESTEROL, TOTAL\t153.0555556\t79623.67347\t72\t11020\t1971396\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t277781.6542\t79623.67347\t72\t20000279.1\t2.00E+14\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tHDL\tHDL CHOLESTEROL\t277818.9167\t79623.67347\t72\t20002962\t2.00E+14\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tHMCY\tHOMOCYSTEINE, PLAS TOTAL\t9.963888889\t79623.67347\t72\t717.4\t7904.7\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tHSCRP\tHIGH SENSITIVITY CRP\t38.67361111\t79623.67347\t72\t2784.5\t398150.73\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tLDL\tLDL (CALCULATED)\t416749.0278\t79623.67347\t72\t30005930\t3.00E+14\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tLDLHDL\tLDL/HDL RATIO\t416668.8222\t79623.67347\t72\t30000155.2\t3.00E+14\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tLPA\tLIPOPROTEIN A\t1111141.742\t79623.67347\t72\t80002205.41\t8.00E+14\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tNHDL\tNON-HDL CHOL, CALC\t277884.3333\t79623.67347\t72\t20007672\t2.00E+14\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tTGL\tTRIGLYCERIDE, SER/PLAS\t126.3611111\t79623.67347\t72\t9098\t1982538\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHCT\tC PHENOTYPE\t9999999\t134306.8966\t72\t719999928\t7.20E+15\t29\t3894900\t4.18E+12
-MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t315224.0375\t72\t719999928\t7.20E+15\t1387\t437215740\t3.30E+14
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t71\t709999929\t7.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tDI\tISSUE DATE/TIME\t9999999\t37891.76471\t71\t709999929\t7.10E+15\t34\t1288320\t92169662400
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHET\tE PHENOTYPE\t9999999\t125643.871\t71\t709999929\t7.10E+15\t31\t3894960\t4.18E+12
-LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tFACTOR7\tFACTOR VIII ASSAY\t563581.2775\t62610.90909\t71\t40014270.7\t4.00E+14\t66\t4132320\t3.37E+11
-LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tRIST\tRISTOCETIN CO-FACTOR\t1831106.873\t62610.90909\t71\t130008588\t1.30E+15\t66\t4132320\t3.37E+11
-LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tVWAG\tVON WILLEBRAND ANTIGEN\t422724.2817\t62610.90909\t71\t30013424\t3.00E+14\t66\t4132320\t3.37E+11
-LAB BLOOD ORDERABLES\t497404\tLABYLEVT\tLEVETIRACETAM\tYLEVT1\tLEVETIRACETAM\t1408474.237\t183651.6\t71\t100001670.8\t1.00E+15\t100\t18365160\t3.69E+12
-LAB BLOOD ORDERABLES\t670774\tLAB11250R\tHHV-8 DNA, QUANT\t11252R\tHHV-8 DNA,QUANT\t7079027.507\t195322.7848\t71\t502610953\t4.96E+15\t79\t15430500\t3.54E+12
-POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t5460\t71\t709999929\t7.10E+15\t27\t147420\t21538544400
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1M\tNEURAMINIDASE  H275Y MUTATION\t9999999\t259084.5\t71\t709999929\t7.10E+15\t40\t10363380\t6.39E+12
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1MT\tMETHOD\t9999999\t259084.5\t71\t709999929\t7.10E+15\t40\t10363380\t6.39E+12
-LAB BLOOD ORDERABLES\t472424\tLABYFRUCM\tFRUCTOSAMINE\tFRUCTM\tFRUCTOSAMINE\t143118.1\t171204.507\t70\t10018267\t1.00E+14\t71\t12155520\t2.39E+12
-LAB BLOOD ORDERABLES\t472750\tLABLUT\tLUTEINIZING HORMONE\tLH\tLH\t2000006.393\t37734\t70\t140000447.5\t1.40E+15\t70\t2641380\t1.62E+11
-LAB BLOOD ORDERABLES\t473758\tLABVISC\tVISCOSITY, SERUM\tVISCOS\tVISCOSITY\t571431.2857\t79914.28571\t70\t40000190\t4.00E+14\t63\t5034600\t8.00E+11
-LAB BLOOD ORDERABLES\t497053\tLABRFRPT\tREFERENCE REPORT\tREFRPT\tREFERENCE REPORT\t9999999\t24933.38028\t70\t699999930\t7.00E+15\t994\t24783780\t3.05E+13
-URINE ORDERABLES\t472019\tLABUCL\tCHLORIDE WITH CREATININE, URINE (RANDOM)\tUCL\tCHLORIDE, URINE\t142926.5857\t11498.70968\t70\t10004861\t1.00E+14\t31\t356460\t20918552400
-URINE ORDERABLES\t472019\tLABUCL\tCHLORIDE WITH CREATININE, URINE (RANDOM)\tUCLCR\tCHLORIDE/CREAT RATIO\t5571428.18\t11498.70968\t70\t389999972.6\t3.90E+15\t31\t356460\t20918552400
-URINE ORDERABLES\t472019\tLABUCL\tCHLORIDE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t142941.5829\t11498.70968\t70\t10005910.8\t1.00E+14\t31\t356460\t20918552400
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tDI\tISSUE DATE/TIME\t9999999\t26220\t69\t689999931\t6.90E+15\t32\t839040\t27380001600
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tUBT\tUNIT BLOOD TYPE  \t9999999\t32080\t69\t689999931\t6.90E+15\t33\t1058640\t53302039200
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tPRO\tPRODUCT ID  \t9999999\t23185.45455\t69\t689999931\t6.90E+15\t33\t765120\t28925913600
-HISTOCOMPATIBILITY ORDERABLE\t706283\t10000R\tHLA ORDER - TYPING - SOLID ORGAN EVALUATION\t10049R\tHLA LAB TESTING STATUS\t9999999\t736702.1918\t69\t689999931\t6.90E+15\t73\t53779260\t6.97E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t63540.24819\t69\t689999931\t6.90E+15\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472392\tLABYFLUZ\tFLUCONAZOLE LEVEL\tFLUCOZ\tFLUCONAZOLE\t724664.5522\t462441.9512\t69\t50001854.1\t5.00E+14\t82\t37920240\t2.22E+13
-LAB BLOOD ORDERABLES\t472980\tLAB11086R\tNORTRIPTYLINE LEVEL\tNORTRP\tNORTRIPTYLINE, PLASMA\t1594278.507\t291875.2941\t69\t110005217\t1.10E+15\t68\t19847520\t8.88E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHJKAT\tJKA PHENOTYPE\t9999999\t139101.4286\t69\t689999931\t6.90E+15\t28\t3894840\t4.18E+12
-POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tPHLOC\tLOCATION\t9999999\t-100367.9032\t69\t689999931\t6.90E+15\t124\t-12445620\t5.85E+12
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tSI\t<<STATUS>>  \t9999999\t29978.70968\t68\t679999932\t6.80E+15\t93\t2788020\t2.99E+11
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tXMR\tUNIT CROSSMATCH  \t9999999\t56156.25\t68\t679999932\t6.80E+15\t32\t1797000\t2.67E+11
-LAB BLOOD ORDERABLES\t472050\tLABCDPLI\tCOLD AGGLUTININ SCREEN\tCDPL\tCOLD ANTIBODY SCREEN\t9999999\t103556.5289\t68\t679999932\t6.80E+15\t121\t12530340\t6.01E+12
-LAB BLOOD ORDERABLES\t472762\tLAB11045R\tLKM AB\tLKMAB\tLIV-KID-MICROSOME AB\t9999999\t199033.7838\t68\t679999932\t6.80E+15\t74\t14728500\t3.10E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLST\tS TYPE PHENOTYPE\t9999999\t134304.8276\t68\t679999932\t6.80E+15\t29\t3894840\t4.18E+12
-LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 PROFILE\tADMACT\tADAMTS-13 ACTIVITY\t1764751.279\t206821.875\t68\t120003087\t1.20E+15\t64\t13236600\t4.74E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGDNM3M\tDNMT3A, BM\t9999999\t819690\t68\t679999932\t6.80E+15\t68\t55738920\t5.54E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGERHRM\tERBB2/HER2, BM\t9999999\t819690\t68\t679999932\t6.80E+15\t68\t55738920\t5.54E+13
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMDX\tCLINICAL INDICATION\t9999999\t586220.2941\t68\t679999932\t6.80E+15\t68\t39862980\t2.74E+13
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMME\tMETHOD\t9999999\t586220.2941\t68\t679999932\t6.80E+15\t68\t39862980\t2.74E+13
-VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV AB IGG\tCMV\tCMV ANTIBODY\t9999999\t162284.7273\t68\t679999932\t6.80E+15\t55\t8925660\t1.66E+12
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tVL\tPRODUCT VOLUME\t173.3731343\t25536.52174\t67\t11616\t2140484\t23\t587340\t21414358800
-LAB BLOOD ORDERABLES\t471817\tLAB11077R\tCALCITONIN\tYCALCI\tCALCITONIN, SERUM\t8059733.913\t149039.0625\t67\t540002172.2\t5.40E+15\t64\t9538500\t1.70E+12
-LAB BLOOD ORDERABLES\t472344\tLABFACT2\tFACTOR II ASSAY\tFACTOR2\tFACTOR II ASSAY\t1044835.582\t39081.42857\t67\t70003984\t7.00E+14\t56\t2188560\t1.28E+11
-LAB BLOOD ORDERABLES\t472573\tLAB11136R\tHTLV AB\tHTLVS\tHTLV AB SCREEN\t9999999\t260332.9032\t67\t669999933\t6.70E+15\t279\t72632880\t3.01E+13
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHP1T\tP1 PHENOTYPE\t9999999\t105406.6667\t67\t669999933\t6.70E+15\t27\t2845980\t3.08E+12
-LAB BLOOD ORDERABLES\t541695\tLABDSDNA\tANTI - DSDNA AB\tDNAQNT\tANTI-DSDNA TITER\t9999999\t404045\t67\t669999933\t6.70E+15\t72\t29091240\t1.26E+13
-MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tRPT\tRPT\t9999999\t313706.25\t67\t669999933\t6.70E+15\t32\t10038600\t5.00E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tYUHYM2\tLEAD, U\t9104476.955\t211842.8571\t67\t609999956\t6.10E+15\t42\t8897400\t2.27E+12
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t66\t659999934\t6.60E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tSI\t<<STATUS>>  \t9999999\t32158.26923\t66\t659999934\t6.60E+15\t104\t3344460\t3.42E+11
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tUBT\tUNIT BLOOD TYPE  \t9999999\t29945.80645\t66\t659999934\t6.60E+15\t31\t928320\t49220258400
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCINT\tINTERPRETATION\t9999999\t56970.32432\t66\t659999934\t6.60E+15\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHFYAT\tFYA PHENOTYPE\t9999999\t144251.1111\t66\t659999934\t6.60E+15\t27\t3894780\t4.18E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHFYBT\tFYB PHENOTYPE\t9999999\t139099.2857\t66\t659999934\t6.60E+15\t28\t3894780\t4.18E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLEAT\tLEA PHENOTYPE\t9999999\t109428.4615\t66\t659999934\t6.60E+15\t26\t2845140\t3.08E+12
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHMT\tM PHENOTYPE\t9999999\t109458.4615\t66\t659999934\t6.60E+15\t26\t2845920\t3.08E+12
-LAB BLOOD ORDERABLES\t473443\tLABTHG\tTHYROGLOBULIN\tATGA\tANTI-THYROGLOBULIN ABY\t8030370.43\t92540\t66\t530004448.4\t5.30E+15\t63\t5830020\t1.13E+12
-LAB BLOOD ORDERABLES\t473443\tLABTHG\tTHYROGLOBULIN\tTG\tTHYROGLOBULIN (TG)\t910102.3561\t92540\t66\t60066755.5\t6.00E+14\t63\t5830020\t1.13E+12
-LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 PROFILE\tADMINH\tADAMTS-13 INHIBITOR\t16.21212121\t202867.7419\t66\t1070\t43674\t62\t12577800\t4.47E+12
-MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tSDES\tSPECIMEN\t9999999\t313706.25\t66\t659999934\t6.60E+15\t32\t10038600\t5.00E+12
-MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tSREQ\tCOMMENTS\t9999999\t313706.25\t66\t659999934\t6.60E+15\t32\t10038600\t5.00E+12
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tRPT\tRPT\t9999999\t407791.8182\t66\t659999934\t6.60E+15\t66\t26914260\t3.09E+13
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMRE\tRESULT\t9999999\t586220.2941\t66\t659999934\t6.60E+15\t68\t39862980\t2.74E+13
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPDX\tCLINICAL INDICATION\t9999999\t540720\t66\t659999934\t6.60E+15\t66\t35687520\t2.44E+13
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPME\tMETHOD\t9999999\t540720\t66\t659999934\t6.60E+15\t66\t35687520\t2.44E+13
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t11896R\tARSENIC,U\t8636367.894\t208549.7561\t66\t570000281\t5.70E+15\t41\t8550540\t2.14E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tYUHYM6\tTOTAL VOLUME\t1819921.759\t208549.7561\t66\t120114836.1\t1.20E+15\t41\t8550540\t2.14E+12
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t12002R\tVCA IGM ,IFA\t9999999\t232692.1875\t66\t659999934\t6.60E+15\t64\t14892300\t3.97E+12
-VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXGQT\tIGG LEVEL\t7272746.03\t176202.7273\t66\t480001238\t4.80E+15\t66\t11629380\t2.68E+12
-VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHHV-6 DNA PCR QUAL\tHV6QLD\tHHV-6 DNA\t9999999\t259790\t66\t659999934\t6.60E+15\t66\t17146140\t1.58E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSCI\tCLINICAL INDICATION\t9999999\t912125.8065\t65\t649999935\t6.50E+15\t62\t56551800\t6.06E+13
-HISTOCOMPATIBILITY ORDERABLE\t506864\t10235R\tHLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING\t10049R\tHLA LAB TESTING STATUS\t9999999\t584445.8824\t65\t649999935\t6.50E+15\t68\t39742320\t4.86E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tLUPINT\tLUPUS INTERPRETATION\t9999999\t318547.2289\t65\t649999935\t6.50E+15\t249\t79318260\t3.18E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tFIO2\tFIO2 (FIO2)\t9999999\t2600\t65\t649999935\t6.50E+15\t24\t62400\t662846400
-LAB BLOOD ORDERABLES\t472528\tLABHEPC\tHEPATITIS C AB EIA\tHEPCAB\tHEPATITIS C AB\t9999999\t60629.0625\t65\t649999935\t6.50E+15\t64\t3880260\t3.44E+11
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHNT\tN PHENOTYPE\t9999999\t113803.2\t65\t649999935\t6.50E+15\t25\t2845080\t3.08E+12
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD4AB\tCD3+/CD4+, ABS.\t461609.7538\t29515\t65\t30004634\t3.00E+14\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD4PC\tCD3+/CD4+ %\t461553.4308\t29515\t65\t30000973\t3.00E+14\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD8AB\tCD3+/CD8+, ABS.\t461648.1846\t29515\t65\t30007132\t3.00E+14\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD8PC\tCD3+/CD8+ %\t461561.7846\t29515\t65\t30001516\t3.00E+14\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tHNS\tCD4+CD8 TOTAL, CD3+%\t461576.8\t29515\t65\t30002492\t3.00E+14\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t461719.4615\t29515\t65\t30011765\t3.00E+14\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tHSRATIO\tCD4/CD8 RATIO, CD3+\t1538462.225\t29515\t65\t100000044.6\t1.00E+15\t60\t1770900\t62775565200
-LAB BLOOD ORDERABLES\t497339\tLABDFA\tDENGUE FEVER AB\tDFIGG\tDENGUE FEVER AB IGG\t153848.1678\t264945\t65\t10000130.91\t1.00E+14\t64\t16956480\t5.23E+12
-LAB BLOOD ORDERABLES\t497339\tLABDFA\tDENGUE FEVER AB\tDFIGM\tDENGUE FEVER AB IGM\t769231.6911\t264945\t65\t50000059.92\t5.00E+14\t64\t16956480\t5.23E+12
-LAB BLOOD ORDERABLES\t497472\tLABYJCPCR\tJC VIRUS DNA, PCR\tSOURC8\tSOURCE\t9999999\t164540.625\t65\t649999935\t6.50E+15\t96\t15795900\t3.74E+12
-LAB BLOOD ORDERABLES\t590577\tLABTOTALC\tPROTEIN C AG, TOTAL\tPROTCAGT\tPROTEIN C AG, TOTAL\t615433.4769\t216992.5\t65\t40003176\t4.00E+14\t24\t5207820\t1.78E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tYUHYM5\tCOLLECTION DURATION\t1692327.462\t208549.7561\t65\t110001285\t1.10E+15\t41\t8550540\t2.14E+12
-BODY FLUID ORDERABLES\t541704\tLABFCHOLA\tCHOLESTEROL, FLUID\tCHOLFLD\tCHOLESTEROL BODY FLUID\t312555.0781\t266890.9091\t64\t20003525\t2.00E+14\t44\t11743200\t6.07E+12
-LAB BLOOD ORDERABLES\t471578\tLABYA1APH\tALPHA - 1 - ANTITRYPSIN PHENOTYPE\tYA1A\tALPHA 1 ANTITRYPSIN\t153.734375\t396927.2727\t64\t9839\t1797801\t66\t26197200\t1.89E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tO2SV\tO2SV\t9999999\t8114.693878\t64\t639999936\t6.40E+15\t49\t397620\t10291518000
-LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLEBT\tLEB PHENOTYPE\t9999999\t109428.4615\t64\t639999936\t6.40E+15\t26\t2845140\t3.08E+12
-LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP10\tCOXSACKIE A10 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
-LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP4\tCOXSACKIE A4 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
-LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP7\tCOXSACKIE A7 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
-LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP9\tCOXSACKIE A9 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
-URINE ORDERABLES\t472507\tLABUHEMO\tHEMOSIDERIN, URINE\tUHEMO\tHEMOSIDERIN, URINE\t9999999\t64313.61702\t64\t639999936\t6.40E+15\t47\t3022740\t3.39E+11
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t12001R\tVCA IGG ,IFA\t9999999\t232692.1875\t64\t639999936\t6.40E+15\t64\t14892300\t3.97E+12
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9495R\t% DONOR- BLOOD WHOLE\t76.84126984\t4024.83871\t63\t4841\t448019\t62\t249540\t61404951600
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9535R\t% RECIPIENT- BLOOD WHOLE\t18.38095238\t4024.83871\t63\t1158\t80724\t62\t249540\t61404951600
-LAB BLOOD ORDERABLES\t471691\tLAB11101R\tVITAMIN C\tYVITC\tASCORBIC ACID, PL.\t1587302.043\t353730\t63\t100000028.7\t1.00E+15\t62\t21931260\t8.82E+12
-LAB BLOOD ORDERABLES\t472353\tLABFACT11\tFACTOR XI ASSAY\tFACTOR11\tFACTOR XI ASSAY\t952462.8413\t51142.62295\t63\t60005159\t6.00E+14\t61\t3119700\t2.24E+11
-LAB BLOOD ORDERABLES\t472932\tLABYMPA\tMYCOPHENOLIC ACID\tMPACID\tMYCOPHENOLIC ACID\t2063493.563\t192470\t63\t130000094.5\t1.30E+15\t54\t10393380\t2.28E+12
-LAB BLOOD ORDERABLES\t472932\tLABYMPA\tMYCOPHENOLIC ACID\tYMPAG\tMPA GLUCURONIDE\t634977.9683\t192470\t63\t40003612\t4.00E+14\t54\t10393380\t2.28E+12
-LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP2\tCOXSACKIE A2 AB\t9999999\t315617.7049\t63\t629999937\t6.30E+15\t61\t19252680\t6.96E+12
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPRE\tRESULT, BM\t9999999\t540720\t63\t629999937\t6.30E+15\t66\t35687520\t2.44E+13
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tSI\t<<STATUS>>  \t9999999\t33721.36364\t62\t619999938\t6.20E+15\t88\t2967480\t3.78E+11
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tUBT\tUNIT BLOOD TYPE  \t9999999\t31464\t62\t619999938\t6.20E+15\t30\t943920\t67138092000
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tPRO\tPRODUCT ID  \t9999999\t26370\t62\t619999938\t6.20E+15\t28\t738360\t56790936000
-BODY FLUID ORDERABLES\t471626\tLAB11028R\tACE, CSF\tACE\tANGIOTENSIN COVERT ENZ, CSF\t483872.4\t261800.4545\t62\t30000088.8\t3.00E+14\t44\t11519220\t3.29E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t71.53225806\t50638.06452\t62\t4435\t333725\t62\t3139560\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t50638.06452\t62\t619999938\t6.20E+15\t62\t3139560\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t50638.06452\t62\t619999938\t6.20E+15\t62\t3139560\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t50638.06452\t62\t619999938\t6.20E+15\t62\t3139560\t3.67E+12
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9632R\tSERUM DESCRIPTION\t9999999\t94819.35484\t62\t619999938\t6.20E+15\t62\t5878800\t2.58E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9413R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t86895.2381\t62\t619999938\t6.20E+15\t63\t5474400\t2.57E+13
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tRPRQ\tRESULT\t9999999\t286934.6939\t62\t619999938\t6.20E+15\t49\t14059800\t4.87E+12
-LAB BLOOD ORDERABLES\t472350\tLABF8INH\tFACTOR VIII INHIBITOR HUMAN\tF8INH\tFACTOR VIII INHIBITOR, HUMAN\t3387103.939\t53803.84615\t62\t210000444.2\t2.10E+15\t52\t2797800\t2.09E+11
-LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tLEAD\tLEAD\t5161291.565\t382240\t62\t320000077\t3.20E+15\t51\t19494240\t3.20E+13
-LAB BLOOD ORDERABLES\t472539\tLABHGBSQ\tHGB S QUANTITATIVE\tHGBS\tHGB S, QUANT\t1451643.848\t352569.0566\t62\t90001918.6\t9.00E+14\t53\t18686160\t1.07E+13
-LAB BLOOD ORDERABLES\t472643\tLABMONOSC\tINFECTIOUS MONO SCREEN\tMONOSC\tINFECTIOUS MONO SCREEN\t9999999\t8924.4\t62\t619999938\t6.20E+15\t50\t446220\t6068185200
-LAB BLOOD ORDERABLES\t473296\tLABYSES\tSELENIUM, SERUM\tSELENIUM\tSELENIUM\t322654.371\t194726.6667\t62\t20004571\t2.00E+14\t63\t12267780\t5.63E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tMERURN\tMERCURY, URINE\t8709676.919\t211842.8571\t62\t539999969\t5.40E+15\t42\t8897400\t2.27E+12
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tDI\tISSUE DATE/TIME\t9999999\t27138\t61\t609999939\t6.10E+15\t30\t814140\t27220532400
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tPRO\tPRODUCT ID  \t9999999\t25982.22222\t61\t609999939\t6.10E+15\t27\t701520\t55275112800
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t304.4606557\t51447.54098\t61\t18572.1\t5698626.17\t61\t3138300\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.493032787\t51468.19672\t61\t152.075\t410.783125\t61\t3139560\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.472622951\t51468.19672\t61\t28.83\t15.9609\t61\t3139560\t3.67E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t284733.75\t61\t609999939\t6.10E+15\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t284733.75\t61\t609999939\t6.10E+15\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tARSENIC\tARSENIC\t9999999\t382240\t61\t609999939\t6.10E+15\t51\t19494240\t3.20E+13
-LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tCADMIUM\tCADMIUM\t3114754.259\t382240\t61\t190000009.8\t1.90E+15\t51\t19494240\t3.20E+13
-LAB BLOOD ORDERABLES\t541650\tLABARGI\tABO/RH\tARG\tABO/RH (AUTOMATION)\t9999999\t21313.09091\t61\t609999939\t6.10E+15\t55\t1172220\t39266269200
-VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tRPT\tRPT\t9999999\t1188540.984\t61\t609999939\t6.10E+15\t61\t72501000\t1.27E+14
-VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tSDES11\tSPECIMEN\t9999999\t175252.9412\t61\t609999939\t6.10E+15\t102\t17875800\t3.71E+12
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRR\tRESULT\t9999999\t520905.7143\t60\t599999940\t6.00E+15\t21\t10939020\t8.12E+12
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSLYM\tLYMPHOCYTES, SYN FLD\t1000020.85\t2907.692308\t60\t60001251\t6.00E+14\t39\t113400\t575942400
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSMONO\tMONOS/MESOS, SYN FLD\t1000026.083\t2907.692308\t60\t60001565\t6.00E+14\t39\t113400\t575942400
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSNEUT\tNEUTROPHILS, SYN FLD\t1000041.45\t2907.692308\t60\t60002487\t6.00E+14\t39\t113400\t575942400
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t52331\t60\t599999940\t6.00E+15\t60\t3139860\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t52331\t60\t599999940\t6.00E+15\t60\t3139860\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t52331\t60\t599999940\t6.00E+15\t60\t3139860\t3.67E+12
-LAB BLOOD ORDERABLES\t471615\tLABYAMISO\tAMYLASE, PANCREATIC\tAMYLASE\tAMYLASE,PANCREATIC,S\t666726.5333\t181097.6923\t60\t40003592\t4.00E+14\t78\t14125620\t3.04E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMV\tCMV ANTIBODY\t9999999\t289715.7447\t60\t599999940\t6.00E+15\t47\t13616640\t4.69E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t289715.7447\t60\t599999940\t6.00E+15\t47\t13616640\t4.69E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t284733.75\t60\t599999940\t6.00E+15\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t284733.75\t60\t599999940\t6.00E+15\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t284733.75\t60\t599999940\t6.00E+15\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t289715.7447\t60\t599999940\t6.00E+15\t47\t13616640\t4.69E+12
-LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBPO2\tDATE & TIME OF LAST DOSE\t9999999\t12322.37288\t60\t599999940\t6.00E+15\t59\t727020\t28069736400
-LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBPO3\tDOSAGE\t9999999\t12322.37288\t60\t599999940\t6.00E+15\t59\t727020\t28069736400
-LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBPO4\tADMINISTRATION ROUTE\t9999999\t12322.37288\t60\t599999940\t6.00E+15\t59\t727020\t28069736400
-LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBRAPEAK\tTOBRAMYCIN, PEAK\t666675.3817\t12322.37288\t60\t40000522.9\t4.00E+14\t59\t727020\t28069736400
-URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tDOPA\tDOPAMINE\t500212.0017\t352613.1429\t60\t30012720.1\t3.00E+14\t35\t12341460\t4.86E+12
-URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tNOREPH\tNOREPHINEPHRINE\t500090.16\t352613.1429\t60\t30005409.6\t3.00E+14\t35\t12341460\t4.86E+12
-URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tVOLUMEURN\tURINE VOLUME\t502361.6\t352613.1429\t60\t30141696\t3.00E+14\t35\t12341460\t4.86E+12
-URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tYUFCUT\tCOLLECTION DURATION\t500022.5\t352613.1429\t60\t30001350\t3.00E+14\t35\t12341460\t4.86E+12
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1R\t2009 INFLUENZA A(H1N1) RNA\t9999999\t259084.5\t60\t599999940\t6.00E+15\t40\t10363380\t6.39E+12
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGAPC\tAPC\t9999999\t913085\t59\t589999941\t5.90E+15\t60\t54785100\t5.82E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGBRAF\tBRAF\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGIDH1\tIDH1\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGNOTCH\tNOTCH1\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGPTEN\tPTEN\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t52161.35593\t59\t589999941\t5.90E+15\t59\t3077520\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.46440678\t53127.45763\t59\t145.4\t414.185\t59\t3134520\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.77\t53127.45763\t59\t45.43\t46.4009\t59\t3134520\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t6195915.542\t52161.35593\t59\t365559017\t3.51E+15\t59\t3077520\t3.67E+12
-LAB BLOOD ORDERABLES\t472438\tLAB11079R\tGABAPENTIN\tGABAP\tGABAPENTIN, SERUM/PLASMA\t1016955.253\t190582.5\t59\t60000359.9\t6.00E+14\t56\t10672620\t2.34E+12
-LAB BLOOD ORDERABLES\t977673\tLABPANHCT\tHEMATOCRIT\tHCT\tHEMATOCRIT\t339001.8898\t994.5762712\t59\t20001111.5\t2.00E+14\t59\t58680\t283608000
-MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tRPT\tRPT\t9999999\t153813.4884\t59\t589999941\t5.90E+15\t43\t6613980\t1.34E+12
-MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tSDES\tSPECIMEN\t9999999\t153813.4884\t59\t589999941\t5.90E+15\t43\t6613980\t1.34E+12
-URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tEPINEP\tEPINEPHRINE\t1694922.52\t352613.1429\t59\t100000428.7\t1.00E+15\t35\t12341460\t4.86E+12
-VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tSDES\tSPECIMEN\t9999999\t1188540.984\t59\t589999941\t5.90E+15\t61\t72501000\t1.27E+14
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tXMR\tUNIT CROSSMATCH  \t9999999\t76988.88889\t58\t579999942\t5.80E+15\t27\t2078700\t3.48E+11
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tSI\t<<STATUS>>  \t9999999\t33428.46154\t58\t579999942\t5.80E+15\t78\t2607420\t3.05E+11
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tSI\t<<STATUS>>  \t9999999\t28782.16216\t58\t579999942\t5.80E+15\t74\t2129880\t2.07E+11
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGCTNN\tCTNNB1\t9999999\t934668.2143\t58\t579999942\t5.80E+15\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGIDH2\tIDH2\t9999999\t934668.2143\t58\t579999942\t5.80E+15\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGPIK3\tPIK3CA\t9999999\t934668.2143\t58\t579999942\t5.80E+15\t56\t52341420\t5.63E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t548.4137931\t52539.31034\t58\t31808\t37129654\t58\t3047280\t3.66E+12
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRCM\tCOMMENT\t9999999\t595980.5\t58\t579999942\t5.80E+15\t120\t71517660\t5.54E+13
-LAB BLOOD ORDERABLES\t472735\tLABYLEADB\tLEAD, BLOOD\tLEA\tLEAD, BLD\t7931034.345\t215660.3774\t58\t459999992\t4.60E+15\t53\t11430000\t3.30E+12
-LAB BLOOD ORDERABLES\t473160\tLAB11085R\tMPO AB\tMPO\tMYELOPEROXIDASE AB\t9137930.452\t157796.3265\t58\t529999966.2\t5.30E+15\t49\t7732020\t1.43E+12
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTRES\tRESULTS\t9999999\t1027142.927\t58\t579999942\t5.80E+15\t82\t84225720\t1.01E+14
-LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMAL\tMALARIA PREP RESULT\t9999999\t164240.9524\t58\t579999942\t5.80E+15\t63\t10347180\t3.66E+13
-VIROLOGY ORDERABLES\t472569\tLABHSVM\tHSV 1 AND 2 IGM\tHSV1M\tHSV 1 IGM, IFA\t9999999\t282932.3077\t58\t579999942\t5.80E+15\t52\t14712480\t4.92E+12
-VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tSREQ\tCOMMENTS\t9999999\t1188540.984\t58\t579999942\t5.80E+15\t61\t72501000\t1.27E+14
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tSI\t<<STATUS>>  \t9999999\t34026.66667\t57\t569999943\t5.70E+15\t81\t2756160\t3.01E+11
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tUBT\tUNIT BLOOD TYPE  \t9999999\t42686.66667\t57\t569999943\t5.70E+15\t27\t1152540\t1.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tPRO\tPRODUCT ID  \t9999999\t30282.22222\t57\t569999943\t5.70E+15\t27\t817620\t69801476400
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGNRAS\tNRAS\t9999999\t934668.2143\t57\t569999943\t5.70E+15\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGTP53\tTP53\t9999999\t934668.2143\t57\t569999943\t5.70E+15\t56\t52341420\t5.63E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDRHYTHM\tICD RHYTHM\t9999999\t50418.94737\t57\t569999943\t5.70E+15\t57\t2873880\t3.60E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2280701.81\t54777.89474\t57\t130000003.2\t1.30E+15\t57\t3122340\t3.67E+12
-LAB BLOOD ORDERABLES\t497432\tLABFHTLVQ\tHTLV-1/2  DNA QUALITATIVE\tHTLI\tHTLV-I DNA\t9999999\t244110.566\t57\t569999943\t5.70E+15\t53\t12937860\t1.04E+13
-LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMALCON\tMALCON\t9999999\t162620.6557\t57\t569999943\t5.70E+15\t61\t9919860\t3.64E+13
-MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tPCPSTAIN\tPCP STAIN\t9999999\t74295.58989\t57\t569999943\t5.70E+15\t712\t52898460\t3.10E+13
-VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVARICELLA ZOSTER VIRUS AB IGG\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t182622.1053\t57\t569999943\t5.70E+15\t57\t10409460\t2.39E+12
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tSI\t<<STATUS>>  \t9999999\t30727.82609\t56\t559999944\t5.60E+15\t69\t2120220\t2.29E+11
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tDI\tISSUE DATE/TIME\t9999999\t28011.42857\t56\t559999944\t5.60E+15\t28\t784320\t26369316000
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tUBT\tUNIT BLOOD TYPE  \t9999999\t41768.88889\t56\t559999944\t5.60E+15\t27\t1127760\t1.06E+11
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGKRAS\tKRAS\t9999999\t934668.2143\t56\t559999944\t5.60E+15\t56\t52341420\t5.63E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t94.9625\t49861.07143\t56\t5317.9\t508141.8752\t56\t2792220\t3.60E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t414.875\t54157.5\t56\t23233\t10105309\t56\t3032820\t3.66E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t519.9464286\t54325.71429\t56\t29117\t23052461\t56\t3042240\t3.66E+12
-LAB BLOOD ORDERABLES\t471922\tLABCOHB\tCARBOXYHEMOGLOBIN\tCOHB\tCO-HEMOGLOBIN\t535716.4911\t2722.8\t56\t30000123.5\t3.00E+14\t50\t136140\t1156057200
-LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tANTI HBS QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t6964332.534\t155980\t56\t390002621.9\t3.90E+15\t63\t9826740\t1.82E+12
-LAB BLOOD ORDERABLES\t497329\tLABSPAGD\tS. PNEUMONIAE AG DETECTION LA\tSOURC4\tSOURCE\t9999999\t2758074.146\t56\t559999944\t5.60E+15\t41\t113081040\t2.48E+15
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGMS\tB.QUINTANA IGM SCREEN\t9999999\t535699.6721\t56\t559999944\t5.60E+15\t122\t65355360\t1.49E+14
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tATTGT\tIGA ANTI TTG LEVEL\t8392860.482\t393897.8182\t56\t470000187\t4.70E+15\t55\t21664380\t1.30E+13
-MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tSREQ\tCOMMENTS\t9999999\t153813.4884\t56\t559999944\t5.60E+15\t43\t6613980\t1.34E+12
-VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEBV VIRAL CAPSID ANTIGEN IGM\tVMQUL\tVMQUL\t9999999\t251356.0714\t56\t559999944\t5.60E+15\t56\t14075940\t5.57E+12
-VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tHERPR\tHERPES SIMPLEX VIRUS (HSV)\t9999999\t85470\t56\t559999944\t5.60E+15\t56\t4786320\t8.40E+11
-VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tVZR\tVARICELLA-ZOSTER VIRUS\t9999999\t85470\t56\t559999944\t5.60E+15\t56\t4786320\t8.40E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t1636363.783\t56668.36364\t55\t90000008.05\t9.00E+14\t55\t3116760\t3.67E+12
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9627R\tCURRENT SERUM DESCRIPTION\t9999999\t109327.6364\t55\t549999945\t5.50E+15\t55\t6013020\t2.62E+13
-LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY\t5272728.491\t312901.3043\t55\t290000067\t2.90E+15\t46\t14393460\t2.35E+13
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
-LAB BLOOD ORDERABLES\t473106\tLABPLASMN\tPLASMINOGEN\tPLASMN\tPLASMINOGEN\t128.2545455\t46341.11111\t55\t7054\t931028\t54\t2502420\t1.95E+11
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBAG\tTB AG\t4181818.241\t209921.4545\t55\t230000003.3\t2.30E+15\t55\t11545680\t2.87E+12
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBAGNI\tTB AG - NIL\t4181818.128\t209921.4545\t55\t229999997.1\t2.30E+15\t55\t11545680\t2.87E+12
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBGNIL\tNIL\t181818.3715\t209921.4545\t55\t10000010.43\t1.00E+14\t55\t11545680\t2.87E+12
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBGRES\tQUANTIFERON RESULT\t9999999\t209921.4545\t55\t549999945\t5.50E+15\t55\t11545680\t2.87E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12200R\tAS CONCENTRATION\t8727274.764\t204748.4211\t55\t480000112\t4.80E+15\t38\t7780440\t1.90E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12201R\tPB CONCENTRATION\t8727272.109\t204748.4211\t55\t479999966\t4.80E+15\t38\t7780440\t1.90E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12202R\tCADMIUM, 24HR, U\t3454545.844\t204748.4211\t55\t190000021.4\t1.90E+15\t38\t7780440\t1.90E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12203R\tCD CONCENTRATION\t3272727.48\t204748.4211\t55\t180000011.4\t1.80E+15\t38\t7780440\t1.90E+12
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12204R\tHG CONCENTRATION\t9272726.436\t204748.4211\t55\t509999954\t5.10E+15\t38\t7780440\t1.90E+12
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tVL\tPRODUCT VOLUME\t173.5\t28096\t54\t9369\t1725059\t15\t421440\t13828327200
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tF1656A\tCD3-/16+56(NK),FLD\t1667395.5\t63483.15789\t54\t90039357\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tF1656P\tCD3-/16+56(NK)%,FLD\t1666674.074\t63483.15789\t54\t90000400\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD19A\tCD19 (B), ABS., FLD\t1668470.537\t63483.15789\t54\t90097409\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD19P\tCD19 (B), %, FLD\t1666671.13\t63483.15789\t54\t90000241\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD3AB\tCD3 (T), ABS., FLD\t1671105.907\t63483.15789\t54\t90239719\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD3PC\tCD3 (T) %, FLD\t1666734.944\t63483.15789\t54\t90003687\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD4AB\tCD4 (HELP),ABS., FLD\t1669522.926\t63483.15789\t54\t90154238\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD4PC\tCD4 (HELPER) %, FLD\t1666705.574\t63483.15789\t54\t90002101\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD8AB\tCD8 (SUPP),ABS., FLD\t1668191.241\t63483.15789\t54\t90082327\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD8PC\tCD8 (SUPP), %, FLD\t1666697.556\t63483.15789\t54\t90001668\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFHNS\tT (H+S), FLD\t1666736.63\t63483.15789\t54\t90003778\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFHSAB\tT (H+S), ABS., FLD\t1671047.704\t63483.15789\t54\t90236576\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tHSRATIOFLD\tH/S RATIO, FLD\t1666668.022\t63483.15789\t54\t90000073.19\t9.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tLYMABFLD\tLYMPHOCYTES, ABS., FLD\t1488549.648\t63483.15789\t54\t80381681\t8.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tLYMPCFLD\tLYMPHOCYTES %, FLD\t1481498.998\t63483.15789\t54\t80000945.9\t8.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tWBCFLD\tWBC COUNT, FLD\t1502121.389\t63483.15789\t54\t81114555\t8.00E+14\t19\t1206180\t1.67E+11
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGEGFR\tEGFR\t9999999\t934668.2143\t54\t539999946\t5.40E+15\t56\t52341420\t5.63E+13
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tTEMP\tBODY TEMPERATURE\t9999999\t3126.315789\t54\t539999946\t5.40E+15\t19\t59400\t657950400
-LAB BLOOD ORDERABLES\t497454\tLABFPARVD\tPARVOVIRUS B19 PCR QUALITATIVE REALTIME\tSOUR11\tSOURCE\t9999999\t248994.1304\t54\t539999946\t5.40E+15\t92\t22907460\t1.26E+13
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tGDGPT\tIGG ANTI DGP LEVEL\t9444444.148\t397649.434\t54\t509999984\t5.10E+15\t53\t21075420\t1.28E+13
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\t7NHCLONAZEPM\t7-NH-CLONAZEPAM\t9629738.019\t0\t54\t520005853\t5.20E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\t7NHFLUNITRAZ\t7-NH-FLUNITRAZEPAM\t9999999\t0\t54\t539999946\t5.40E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tNORDIAZ\tNORDIAZEPAM\t7222412.315\t0\t54\t390010265\t3.90E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tOXAZEPAM\tOXAZEPAM\t5556410.574\t0\t54\t300046171\t3.00E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUAAGC\tALPHA OH-ALPRAZOLAM\t9629634.13\t0\t54\t520000243\t5.20E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUATGC\tALPHA OH-TRIAZOLAM\t9999999\t0\t54\t539999946\t5.40E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUEFGC\tOH-ETHYL-FLURAZEPAM\t9999999\t0\t54\t539999946\t5.40E+15\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUTZGC\tTEMAZEPAM\t6667255.63\t0\t54\t360031804\t3.60E+15\t9\t0\t0
-VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEBV VIRAL CAPSID ANTIGEN IGG\tVGQNT\tVGQNT\t555560.5398\t281538.4615\t54\t30000269.15\t3.00E+14\t52\t14640000\t7.71E+12
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t53\t529999947\t5.30E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tPRO\tPRODUCT ID  \t9999999\t26390\t53\t529999947\t5.30E+15\t24\t633360\t53763537600
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tPRO\tPRODUCT ID  \t9999999\t15080\t53\t529999947\t5.30E+15\t24\t361920\t13517726400
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t54211.69811\t53\t529999947\t5.30E+15\t53\t2873220\t3.60E+12
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9496R\t% DONOR- BLOOD CD3\t83.64150943\t4791.923077\t53\t4433\t423085\t52\t249180\t61404930000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9507R\t% DONOR- BLOOD CD56\t78.20754717\t4791.923077\t53\t4145\t381543\t52\t249180\t61404930000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9536R\t% RECIPIENT- BLOOD CD3\t10.69811321\t4791.923077\t53\t567\t38143\t52\t249180\t61404930000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9547R\t% RECIPIENT- BLOOD CD56\t16.13207547\t4791.923077\t53\t855\t53569\t52\t249180\t61404930000
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tFIO2\tFIO2 (FIO2)\t9999999\t7869.230769\t53\t529999947\t5.30E+15\t39\t306900\t8212021200
-LAB BLOOD ORDERABLES\t471960\tLABYCENT\tCENTROMERE ANTIBODIES\tYCENTA\tCENTROMERE ABS.\t9811319.879\t151753.9024\t53\t519999953.6\t5.20E+15\t82\t12443820\t2.14E+12
-LAB BLOOD ORDERABLES\t472481\tLABGLWB\tGLUCOSE, WHOLE BLOOD\tGLU\tGLUCOSE, WHOLE BLOOD\t754878.7358\t2554.736842\t53\t40008573\t4.00E+14\t38\t97080\t2879546400
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBMINI\tMITOGEN - NIL\t1886797.009\t209921.4545\t53\t100000241.5\t1.00E+15\t55\t11545680\t2.87E+12
-LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tPF4ENH\tPF4 IGG, IGM, IGA OD\t0.383773585\t125334.3396\t53\t20.34\t23.594874\t53\t6642720\t1.05E+12
-MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tRPT\tRPT\t9999999\t508887.5\t53\t529999947\t5.30E+15\t24\t12213300\t7.93E+12
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tLORAZ\tLORAZEPAM\t7170082.132\t0\t53\t380014353\t3.80E+15\t9\t0\t0
-URINE ORDERABLES\t471821\tLABURCA\tCALCIUM, URINE (RANDOM)\tCAURN\tCALCIUM, URINE\t4716989.04\t28754.48276\t53\t250000419.1\t2.50E+15\t29\t833880\t1.61E+11
-VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEBV VIRAL CAPSID ANTIGEN IGG\tVGQUL\tVGQUL\t9999999\t281538.4615\t53\t529999947\t5.30E+15\t52\t14640000\t7.71E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t121.1538462\t19763.07692\t52\t6300\t769000\t52\t1027680\t5.25E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t4038492.5\t53548.84615\t52\t210001610\t2.10E+15\t52\t2784540\t3.59E+12
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9500R\t% DONOR- BLOOD CD15\t71.75\t4885.882353\t52\t3731\t349035\t51\t249180\t61404930000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9540R\t% RECIPIENT- BLOOD CD15\t22.59615385\t4885.882353\t52\t1175\t93623\t51\t249180\t61404930000
-LAB BLOOD ORDERABLES\t471709\tLABYANRNP\tRNP AB, IGG\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t7115384.21\t164587.3469\t52\t369999978.9\t3.70E+15\t49\t8064780\t1.51E+12
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPSREV\tPATH/SPEC REVIEW\t9999999\t104482.621\t52\t519999948\t5.20E+15\t496\t51823380\t8.96E+12
-LAB BLOOD ORDERABLES\t473257\tLABRDATAC\tREVERSE TO COOMBS A CELLS\tRDATA\tREVERSE TO COOMBS A CELLS\t9999999\t37.5\t52\t519999948\t5.20E+15\t32\t1200\t72000
-LAB BLOOD ORDERABLES\t473765\tLABYVITB6\tVITAMIN B6\tVITB6\tVITAMIN B6\t2307699.635\t402985.7143\t52\t120000381\t1.20E+15\t56\t22567200\t1.01E+13
-LAB BLOOD ORDERABLES\t473780\tLABBARTPC\tBARTONELLA DNA, QUALI, PCR\tBHENPC\tBARTONELLA HENSELAN DNA\t9999999\t188286\t52\t519999948\t5.20E+15\t50\t9414300\t2.14E+12
-LAB BLOOD ORDERABLES\t473811\tLABYZONIS\tZONISAMIDE\tZONIS\tZONISAMIDE\t576942.6846\t320187.4576\t52\t30001019.6\t3.00E+14\t59\t18891060\t1.30E+13
-LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tHIPDIR\tHIT INTERPRETED BY\t9999999\t124371.9231\t52\t519999948\t5.20E+15\t52\t6467340\t1.02E+12
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tSDES\tSPECIMEN\t9999999\t407791.8182\t52\t519999948\t5.20E+15\t66\t26914260\t3.09E+13
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tTRPHOS\tTRIPLE PHOSPH CRYST.\t9999999\t8637.142857\t52\t519999948\t5.20E+15\t42\t362760\t14905749600
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASMU\tMUTATION(S)\t9999999\t797353.8462\t51\t509999949\t5.10E+15\t13\t10365600\t1.05E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSSP\tSPECIMEN\t9999999\t916121\t51\t509999949\t5.10E+15\t60\t54967260\t5.92E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t0.392156863\t59725.88235\t51\t20\t62\t51\t3046020\t3.66E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t1.771078431\t89536.47059\t51\t90.325\t250.770625\t51\t4566360\t6.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.701960784\t89536.47059\t51\t35.8\t33.34\t51\t4566360\t6.67E+12
-LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGA\tBETA-2-GP1 IGA\t392165\t846318.6667\t51\t20000415\t2.00E+14\t45\t38084340\t5.23E+13
-LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGG\tBETA-2-GP1 IGG\t392164.1961\t832471.3043\t51\t20000374\t2.00E+14\t46\t38293680\t5.23E+13
-LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGM\tBETA-2-GP1 IGM\t392162\t846318.6667\t51\t20000262\t2.00E+14\t45\t38084340\t5.23E+13
-LAB BLOOD ORDERABLES\t472029\tLABCHOHDL\tCHOLESTEROL HDL / TOTAL\tCHOL\tCHOLESTEROL, TOTAL\t196224.6078\t7068.26087\t51\t10007455\t1.00E+14\t46\t325140\t6402250800
-LAB BLOOD ORDERABLES\t472029\tLABCHOHDL\tCHOLESTEROL HDL / TOTAL\tHDL\tHDL CHOLESTEROL\t196120.6667\t7068.26087\t51\t10002154\t1.00E+14\t46\t325140\t6402250800
-LAB BLOOD ORDERABLES\t472185\tLABYDIGT\tDIGITOXIN, SERUM\tDIGT\tDIGITOXIN\t9803920.714\t384356.5217\t51\t499999956.4\t5.00E+15\t69\t26520600\t1.15E+13
-LAB BLOOD ORDERABLES\t472491\tLABHDL\tHDL, SERUM\tHDL\tHDL CHOLESTEROL\t47.66666667\t4488.837209\t51\t2431\t128881\t43\t193020\t1740632400
-LAB BLOOD ORDERABLES\t472735\tLABYLEADB\tLEAD, BLOOD\tLEADSO\tSOURCE OF COLLECTION\t9999999\t215660.3774\t51\t509999949\t5.10E+15\t53\t11430000\t3.30E+12
-LAB BLOOD ORDERABLES\t473747\tLAB11099R\tVIP\tVIP\tVASOACTIVE INT PEPTIDE\t2549038.451\t532787.234\t51\t130000961\t1.30E+15\t47\t25041000\t1.42E+13
-LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tHITI1\tHIT FUNCTIONAL ASSAY\t9999999\t125334.3396\t51\t509999949\t5.10E+15\t53\t6642720\t1.05E+12
-MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t1418147.315\t51\t509999949\t5.10E+15\t473\t670783680\t1.10E+15
-MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tRPT\tRPT\t9999999\t1137292.941\t51\t509999949\t5.10E+15\t51\t58001940\t7.40E+13
-MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tSDES\tSPECIMEN\t9999999\t1137292.941\t51\t509999949\t5.10E+15\t51\t58001940\t7.40E+13
-TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tUT3\tCOLLECTION PERIOD CREATININE\t980449.3137\t309620\t51\t50002915\t5.00E+14\t18\t5573160\t2.51E+12
-TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tVOL3\tVOLUME CREATININE\t982711.8216\t309620\t51\t50118302.9\t5.00E+14\t18\t5573160\t2.51E+12
-URINE ORDERABLES\t472551\tLABYHISTU\tHISTOPLASMA AG, URINE\tYHISU5\tCOMMENT URINE\t9999999\t382366.9401\t51\t509999949\t5.10E+15\t317\t121210320\t5.31E+13
-URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tPBGDAY\tPORPHOBILINOGEN, URN\t9411763.929\t282344\t51\t479999960.4\t4.80E+15\t30\t8470320\t2.54E+12
-URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tPBGL\tPORPHOBILINOGEN, URN\t8823529.043\t282344\t51\t449999981.2\t4.50E+15\t30\t8470320\t2.54E+12
-URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tTIMEU\tCOLLECTION PERIOD\t6078440.176\t282344\t51\t310000449\t3.10E+15\t30\t8470320\t2.54E+12
-URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tUCR\tCREATININE, URINE\t4314286.824\t282344\t51\t220028628\t2.20E+15\t30\t8470320\t2.54E+12
-URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tUCR\tCREATININE, URN\t76.62745098\t282344\t51\t3908\t484480\t30\t8470320\t2.54E+12
-URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tUVOL\tVOLUME\t3922513.176\t282344\t51\t200048172\t2.00E+15\t30\t8470320\t2.54E+12
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVTIP\tTEST DESCRIPTION\t9999999\t182289.3718\t51\t509999949\t5.10E+15\t2945\t536842200\t1.17E+14
-VIROLOGY ORDERABLES\t497450\tLABBKVQU\tBK VIRUS DNA QUANTITATION\tSOURC6\tSOURCE\t9999999\t190828.4211\t51\t509999949\t5.10E+15\t38\t7251480\t1.85E+12
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tVL\tPRODUCT VOLUME\t175.68\t23742.35294\t50\t8784\t1646256\t17\t403620\t12120318000
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tXMR\tUNIT CROSSMATCH  \t9999999\t78700\t50\t499999950\t5.00E+15\t21\t1652700\t2.64E+11
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tXMR\tUNIT CROSSMATCH  \t9999999\t65276.66667\t50\t499999950\t5.00E+15\t18\t1174980\t1.67E+11
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tDI\tISSUE DATE/TIME\t9999999\t30021.6\t50\t499999950\t5.00E+15\t25\t750540\t27264164400
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9501R\t% DONOR- BLOOD CD19\t75.26\t5084.081633\t50\t3763\t341625\t49\t249120\t61404926400
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9541R\t% RECIPIENT- BLOOD CD19\t18.78\t5084.081633\t50\t939\t60187\t49\t249120\t61404926400
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tNAC\tNASAL CANNULA\t9999999\t3126.315789\t50\t499999950\t5.00E+15\t19\t59400\t657950400
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tTSITE\tTEMPERATURE SITE\t9999999\t3126.315789\t50\t499999950\t5.00E+15\t19\t59400\t657950400
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tTEMP\tBODY TEMPERATURE\t9999999\t7805\t50\t499999950\t5.00E+15\t36\t280980\t7947997200
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15RE\tRESULT\t9999999\t643218\t50\t499999950\t5.00E+15\t50\t32160900\t2.37E+13
-MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tSREQ\tCOMMENTS\t9999999\t1137292.941\t50\t499999950\t5.00E+15\t51\t58001940\t7.40E+13
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tRPT\tRPT\t9999999\t231851.4286\t50\t499999950\t5.00E+15\t21\t4868880\t1.38E+12
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tSDES\tSPECIMEN\t9999999\t231851.4286\t50\t499999950\t5.00E+15\t21\t4868880\t1.38E+12
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tSREQ\tCOMMENTS\t9999999\t231851.4286\t50\t499999950\t5.00E+15\t21\t4868880\t1.38E+12
-STOOL ORDERABLES\t668087\tLABSTOBIC\tOCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.\tOCCIMM\tSTOOL SAMPLE1,IMMUN\t9999999\t21693.33333\t50\t499999950\t5.00E+15\t45\t976200\t1.59E+11
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tVCAM\tVIRAL CAPSID AB-IGM\t9999999\t164195.2941\t50\t499999950\t5.00E+15\t51\t8373960\t1.83E+12
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1SP\tSPECIMEN\t9999999\t259084.5\t50\t499999950\t5.00E+15\t40\t10363380\t6.39E+12
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tXMR\tUNIT CROSSMATCH  \t9999999\t74142.5\t49\t489999951\t4.90E+15\t24\t1779420\t2.64E+11
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tSI\t<<STATUS>>  \t9999999\t20339.0625\t49\t489999951\t4.90E+15\t64\t1301700\t88116663600
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tDI\tISSUE DATE/TIME\t9999999\t35359.2\t49\t489999951\t4.90E+15\t25\t883980\t48430602000
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tUBT\tUNIT BLOOD TYPE  \t9999999\t32076.92308\t49\t489999951\t4.90E+15\t26\t834000\t53931456000
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tPRO\tPRODUCT ID  \t9999999\t15915.65217\t49\t489999951\t4.90E+15\t23\t366060\t13709250000
-BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34FLDPC\t%CD34, FLUID\t204086.483\t8570.769231\t49\t10000237.67\t1.00E+14\t26\t222840\t3235593600
-BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCSOUR\tSOURCE\t9999999\t63483.15789\t49\t489999951\t4.90E+15\t19\t1206180\t1.67E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t125.8163265\t63663.67347\t49\t6165\t782425\t49\t3119520\t3.67E+12
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9076R\tHLA-SEROLOGICAL PT. SUMMARY\t9999999\t214170.5316\t49\t489999951\t4.90E+15\t395\t84597360\t1.52E+14
-LAB BLOOD ORDERABLES\t473298\tLABYSEROT\tSEROTONIN, SERUM\tSEROTONIN\tSEROTONIN\t2857358.306\t411030\t49\t140010557\t1.40E+15\t48\t19729440\t8.87E+12
-LAB BLOOD ORDERABLES\t977671\tLABPANHGB\tHEMOGLOBIN\tHGB\tHEMOGLOBIN\t408169.4388\t1042.040816\t49\t20000302.5\t2.00E+14\t49\t51060\t268794000
-LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMALSCR\tMALARIA ANTIGEN RESULT\t9999999\t162025.3125\t49\t489999951\t4.90E+15\t64\t10369620\t3.66E+13
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tVCA\tVIRAL CAPSID AB\t9999999\t164195.2941\t49\t489999951\t4.90E+15\t51\t8373960\t1.83E+12
-VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tRPT\tRPT\t9999999\t437550\t49\t489999951\t4.90E+15\t22\t9626100\t1.47E+13
-VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tSDES\tSPECIMEN\t9999999\t437550\t49\t489999951\t4.90E+15\t22\t9626100\t1.47E+13
-VIROLOGY ORDERABLES\t670953\tLAB11153R\tHERPES VIRUS 6 DNA, QUANT\t11155R\tHERPES VIRUS 6 DNA, QUANT\t8797624.776\t175940\t49\t431083614\t4.30E+15\t6\t1055640\t1.98E+11
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tVL\tPRODUCT VOLUME\t168.1666667\t24581.53846\t48\t8072\t1376026\t13\t319560\t9576921600
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tUBT\tUNIT BLOOD TYPE  \t9999999\t29411.53846\t48\t479999952\t4.80E+15\t26\t764700\t28925881200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t9.134833333\t21620\t48\t438.472\t4056.616992\t48\t1037760\t5.25E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.128125\t64861.25\t48\t102.15\t252.4025\t48\t3113340\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.49375\t64861.25\t48\t23.7\t13.51\t48\t3113340\t3.67E+12
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tNAC\tNASAL CANNULA\t9999999\t8290.588235\t48\t479999952\t4.80E+15\t34\t281880\t7933334400
-LAB BLOOD ORDERABLES\t497264\tLABPROINS\tPROINSULIN, PLASMA\tPROINSULIN\tPROINSULIN\t1250028.754\t#VALUE!\t48\t60001380.2\t6.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t497343\tLABCMVG\tCMV GENOTYPE\tCUL97\tUL97 MUTATION\t9999999\t1217280.769\t48\t479999952\t4.80E+15\t78\t94947900\t1.25E+14
-LAB BLOOD ORDERABLES\t670774\tLAB11250R\tHHV-8 DNA, QUANT\t11251R\tSOURCE\t9999999\t195322.7848\t48\t479999952\t4.80E+15\t79\t15430500\t3.54E+12
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tSREQ\tCOMMENTS\t9999999\t407791.8182\t48\t479999952\t4.80E+15\t66\t26914260\t3.09E+13
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFDX\tCLINICAL INDICATION\t9999999\t1050420\t48\t479999952\t4.80E+15\t2\t2100840\t3.23E+12
-URINE ORDERABLES\t473429\tLABYUTHC\tTETRAHYDROCANNABINOL, URINE\tTHC\tTHC CARBOX AC BY GSMS\t1666740.396\t87930\t48\t80003539\t8.00E+14\t10\t879300\t2.61E+11
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tDI\tISSUE DATE/TIME\t9999999\t30122.5\t47\t469999953\t4.70E+15\t24\t722940\t26775946800
-BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34FL\tSOURCE\t9999999\t8570.769231\t47\t469999953\t4.70E+15\t26\t222840\t3235593600
-BODY FLUID ORDERABLES\t472774\tLABFBBURG\tLYME WESTERN BLOT, CSF\tBANDPRSNT\tBANDS PRESENT\t9999999\t289031.068\t47\t469999953\t4.70E+15\t103\t29770200\t1.10E+13
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t164.787234\t66209.3617\t47\t7745\t1371675\t47\t3111840\t3.67E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t122.6595745\t23482.97872\t47\t5765\t885525\t47\t1103700\t5.34E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t400.6319149\t27824.68085\t47\t18829.7\t7752736.95\t47\t1307760\t5.95E+11
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9026R\tHLA-DR345 ALLELE 2\t9999999\t389218.7234\t47\t469999953\t4.70E+15\t47\t18293280\t4.73E+13
-LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tLUPINT\tLUPUS INTERPRETATION\t9999999\t311240.1\t47\t469999953\t4.70E+15\t200\t62248020\t2.73E+13
-MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t527037.1429\t47\t469999953\t4.70E+15\t21\t11067780\t8.79E+12
-MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t527037.1429\t47\t469999953\t4.70E+15\t21\t11067780\t8.79E+12
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t46\t459999954\t4.60E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tXMR\tUNIT CROSSMATCH  \t9999999\t76732.94118\t46\t459999954\t4.60E+15\t17\t1304460\t1.92E+11
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tVL\tPRODUCT VOLUME\t167.6086957\t21973.33333\t46\t7710\t1310920\t9\t197760\t6054631200
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tPRO\tPRODUCT ID  \t9999999\t18257.14286\t46\t459999954\t4.60E+15\t21\t383400\t18645969600
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tCCNT\tCELLS COUNTED\t109.6521739\t20710.34483\t46\t5044\t818450\t29\t600600\t1.30E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t11.65217391\t22553.47826\t46\t536\t51500\t46\t1037460\t5.25E+11
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9456R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t5710.434783\t46\t459999954\t4.60E+15\t46\t262680\t50644116000
-HISTOCOMPATIBILITY ORDERABLE\t1489479\t10260R\tHLA ORDER - DECEASED DONOR TESTING NOTICE\tDDNOT\tDECEASED DONOR ORDER NOTICE\t9999999\t19524.78261\t46\t459999954\t4.60E+15\t46\t898140\t96165082800
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tCHOL\tCHOLESTEROL, TOTAL\t434919.2174\t66268.5\t46\t20006284\t2.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t434786.1587\t66268.5\t46\t20000163.3\t2.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tHDL\tHDL CHOLESTEROL\t217431.9565\t66268.5\t46\t10001870\t1.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tHMCY\tHOMOCYSTEINE, PLAS TOTAL\t217401.3587\t66268.5\t46\t10000462.5\t1.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tHSCRP\tHIGH SENSITIVITY CRP\t217443.3652\t66268.5\t46\t10002394.8\t1.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tLDL\tDIRECT LDL CHOL\t217470.8913\t66268.5\t46\t10003661\t1.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tLPA\tLIPOPROTEIN A\t2391326.61\t66268.5\t46\t110001024.1\t1.10E+15\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tNHDL\tNON-HDL CHOL, CALC\t434878.8261\t66268.5\t46\t20004426\t2.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tTGL\tTRIGLYCERIDE, SER/PLAS\t434878.3478\t66268.5\t46\t20004404\t2.00E+14\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGGAB\tB.BURGDORFERI IGG AB WB\t9999999\t257757.3529\t46\t459999954\t4.60E+15\t68\t17527500\t6.70E+12
-LAB BLOOD ORDERABLES\t483823\tLABE2O\tESTRADIOL\tESTRADIOL\tESTRADIOL\t3913140.413\t64772.09302\t46\t180004459\t1.80E+15\t43\t2785200\t7.48E+11
-LAB BLOOD ORDERABLES\t497420\tLABASPID\tASPERGILLUS AB, ID, S\tANIGE\tASPERGILLUS NIGER\t9999999\t376384.5\t46\t459999954\t4.60E+15\t40\t15055380\t6.40E+12
-LAB BLOOD ORDERABLES\t497480\tLABADGM\tADENOVIRUS IGG AND IGM AB\tADM1\tADENOVIRUS AB,IGM\t869565.4735\t319660\t46\t40000011.78\t4.00E+14\t54\t17261640\t6.62E+12
-LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIKACINPK\tAMIKACIN, PEAK\t217417.3065\t14599.53488\t46\t10001196.1\t1.00E+14\t43\t627780\t20808709200
-LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIPO2\tDATE & TIME OF LAST DOSE\t9999999\t14599.53488\t46\t459999954\t4.60E+15\t43\t627780\t20808709200
-LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIPO3\tDOSAGE\t9999999\t14599.53488\t46\t459999954\t4.60E+15\t43\t627780\t20808709200
-LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIPO4\tADMINISTRATION ROUTE\t9999999\t14599.53488\t46\t459999954\t4.60E+15\t43\t627780\t20808709200
-LAB BLOOD ORDERABLES\t668188\tLABYCLOZ\tCLOZAPINE, SERUM\tYNORC\tNORCLOZAPINE\t435063.7609\t306883.75\t46\t20012933\t2.00E+14\t48\t14730420\t5.78E+12
-LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12500R\tDATE OF LAST DOSE\t9999999\t348114.1176\t46\t459999954\t4.60E+15\t51\t17753820\t3.14E+13
-LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12502R\tDOSE, MG\t9999999\t348114.1176\t46\t459999954\t4.60E+15\t51\t17753820\t3.14E+13
-STOOL ORDERABLES\t473091\tLABSTPH\tPH, STOOL\tPHSTL\tPH,STOOL\t6.782608696\t15311.25\t46\t312\t2156\t16\t244980\t11918286000
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tCOPROU\tCOPROPORPHYRIN I\t217403.3043\t448491\t46\t10000552\t1.00E+14\t20\t8969820\t6.55E+12
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tHEPCOH\tHEPTACARBOXYLATE\t217391.6957\t448491\t46\t10000018\t1.00E+14\t20\t8969820\t6.55E+12
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tPORU\tUROPORPHYRIN, QUANT\t217392.5435\t448491\t46\t10000057\t1.00E+14\t20\t8969820\t6.55E+12
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tTIMEU\tCOLLECTION PERIOD\t9347826.717\t448491\t46\t430000029\t4.30E+15\t20\t8969820\t6.55E+12
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUCR\tCREATININE, URINE\t5000571.152\t448491\t46\t230026273\t2.30E+15\t20\t8969820\t6.55E+12
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUCR\tCREATININE, URN\t217458.0217\t448491\t46\t10003069\t1.00E+14\t20\t8969820\t6.55E+12
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUVOL\tVOLUME\t9130645.326\t448491\t46\t420009685\t4.20E+15\t20\t8969820\t6.55E+12
-VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tSREQ\tCOMMENTS\t9999999\t437550\t46\t459999954\t4.60E+15\t22\t9626100\t1.47E+13
-VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tHVSP\tSPECIMEN\t9999999\t61157.32283\t46\t459999954\t4.60E+15\t381\t23300940\t3.03E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t64\t629.3333333\t45\t2880\t188100\t45\t28320\t372801600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t629.3333333\t45\t449999955\t4.50E+15\t45\t28320\t372801600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t657.3333333\t45\t449999955\t4.50E+15\t45\t29580\t374691600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t657.3333333\t45\t449999955\t4.50E+15\t45\t29580\t374691600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t4528797.467\t657.3333333\t45\t203795886\t1.83E+15\t45\t29580\t374691600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t657.3333333\t45\t449999955\t4.50E+15\t45\t29580\t374691600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t629.3333333\t45\t449999955\t4.50E+15\t45\t28320\t372801600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t222224.8261\t629.3333333\t45\t10000117.18\t1.00E+14\t45\t28320\t372801600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.510666667\t629.3333333\t45\t22.98\t13.702\t45\t28320\t372801600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2.677777778\t629.3333333\t45\t120.5\t394.89\t45\t28320\t372801600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t37.25688889\t61600\t45\t1676.56\t123650.2142\t45\t2772000\t3.60E+12
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tTSITE\tTEMPERATURE SITE\t9999999\t8261.818182\t45\t449999955\t4.50E+15\t33\t272640\t7847956800
-LAB BLOOD ORDERABLES\t472195\tLABLDLD\tDIRECT LDL CHOLESTEROL\tLDL\tDIRECT LDL CHOL\t222308.2222\t5263.5\t45\t10003870\t1.00E+14\t40\t210540\t1530738000
-LAB BLOOD ORDERABLES\t497420\tLABASPID\tASPERGILLUS AB, ID, S\tAFLAV\tASPERGILLUS FLAVUS\t9999999\t376384.5\t45\t449999955\t4.50E+15\t40\t15055380\t6.40E+12
-LAB BLOOD ORDERABLES\t668188\tLABYCLOZ\tCLOZAPINE, SERUM\tYCLOZ1\tCLOZAPINE\t222708.7333\t306883.75\t45\t10021893\t1.00E+14\t48\t14730420\t5.78E+12
-LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12501R\tTIME OF LAST DOSE\t9999999\t348114.1176\t45\t449999955\t4.50E+15\t51\t17753820\t3.14E+13
-MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t527037.1429\t45\t449999955\t4.50E+15\t21\t11067780\t8.79E+12
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tSDES\tSPECIMEN\t9999999\t255332.1429\t45\t449999955\t4.50E+15\t56\t14298600\t7.49E+12
-STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA3\tCOLLECTION DURATION\t9999999\t620241\t45\t449999955\t4.50E+15\t20\t12404820\t1.21E+13
-VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV AB TOTAL\tCMV\tCMV ANTIBODY\t9999999\t169080\t45\t449999955\t4.50E+15\t42\t7101360\t1.50E+12
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tVL\tPRODUCT VOLUME\t180.2727273\t28808\t44\t7932\t1577010\t15\t432120\t13940755200
-BODY FLUID ORDERABLES\t497368\tLABYFGL\tGLUCOSE, FLUID\tGLUFLD\tGLUCOSE BODY FLUID\t1136460\t178578.6207\t44\t50004240\t5.00E+14\t58\t10357560\t2.39E+12
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVM2\tPHASE II IGM\t9999999\t431808.2927\t44\t439999956\t4.40E+15\t41\t17704140\t2.36E+13
-LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11827R\tCSS EST. AVG. EXPO.CLEARA RATE\t8409225.045\t516462.8571\t44\t370005902\t3.70E+15\t35\t18076200\t2.50E+13
-LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11828R\tRECOMMENDED DOSE\t7727287.568\t516462.8571\t44\t340000653\t3.40E+15\t35\t18076200\t2.50E+13
-MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tSDES\tSPECIMEN\t9999999\t508887.5\t44\t439999956\t4.40E+15\t24\t12213300\t7.93E+12
-VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS1\tHIV-1 P24 (NON ICD) AG SCRN\t9999999\t478889.2308\t44\t439999956\t4.40E+15\t39\t18676680\t1.07E+13
-VIROLOGY ORDERABLES\t870899\tLABVIRSO1\tMISC. VIROLOGY TEST\tVIROTN\tTEST NAME\t9999999\t823753.7143\t44\t439999956\t4.40E+15\t35\t28831380\t3.01E+13
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tSI\t<<STATUS>>  \t9999999\t22168\t43\t429999957\t4.30E+15\t60\t1330080\t92575231200
-BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFMT\tMETHOD\t9999999\t479125.7143\t43\t429999957\t4.30E+15\t14\t6707760\t4.66E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t623.7209302\t43\t429999957\t4.30E+15\t43\t26820\t370882800
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t618.1395349\t43\t429999957\t4.30E+15\t43\t26580\t370494000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t0.720930233\t23656.74419\t43\t31\t253\t43\t1017240\t5.25E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.084883721\t66629.30233\t43\t46.65\t55.875\t43\t2865060\t3.61E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.434883721\t66629.30233\t43\t18.7\t8.53\t43\t2865060\t3.61E+12
-LAB BLOOD ORDERABLES\t473093\tLABYPENTO\tPENTOBARBITAL LEVEL\tPENTO\tPENTOBARBITAL, S\t3720951.814\t617787.907\t43\t160000928\t1.60E+15\t43\t26564880\t1.78E+13
-LAB BLOOD ORDERABLES\t473169\tLABPROTSF\tPROTEIN S, FREE\tPROTSF\tPROTEIN S, FREE\t232609.8605\t26957.14286\t43\t10002224\t1.00E+14\t14\t377400\t38848060800
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVG1\tPHASE 1 IGG\t9999999\t431808.2927\t43\t429999957\t4.30E+15\t41\t17704140\t2.36E+13
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVG2\tPHASE II IGG\t9999999\t431808.2927\t43\t429999957\t4.30E+15\t41\t17704140\t2.36E+13
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVM1\tPHASE 1 IGM\t9999999\t431808.2927\t43\t429999957\t4.30E+15\t41\t17704140\t2.36E+13
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPCM\tCOMMENT\t9999999\t804865.2273\t43\t429999957\t4.30E+15\t176\t141656280\t1.48E+14
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tSREQ\tCOMMENTS\t9999999\t255332.1429\t43\t429999957\t4.30E+15\t56\t14298600\t7.49E+12
-STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA4\tSODIUM CONCENTRATION\t5116313.535\t620241\t43\t220001482\t2.20E+15\t20\t12404820\t1.21E+13
-VIROLOGY ORDERABLES\t472452\tLABGCCR2\tGC MOLECULAR DETECTION\tSDES5\tSPECIMEN\t9999999\t144177.2308\t43\t429999957\t4.30E+15\t65\t9371520\t1.97E+12
-VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHHV-6 DNA PCR QUAL\tHV6QLS\tSPECIMEN\t9999999\t259790\t43\t429999957\t4.30E+15\t66\t17146140\t1.58E+13
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tXMR\tUNIT CROSSMATCH  \t9999999\t41460\t42\t419999958\t4.20E+15\t16\t663360\t66026217600
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tUBT\tUNIT BLOOD TYPE  \t9999999\t35142.85714\t42\t419999958\t4.20E+15\t21\t738000\t56790907200
-BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tPRO\tPRODUCT ID  \t9999999\t20586\t42\t419999958\t4.20E+15\t20\t411720\t20614413600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t641.4285714\t42\t419999958\t4.20E+15\t42\t26940\t370364400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t595.452381\t698.5714286\t42\t25009\t16868243\t42\t29340\t372265200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.636069762\t68204.28571\t42\t110.71493\t429.2077\t42\t2864580\t3.61E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t68204.28571\t42\t419999958\t4.20E+15\t42\t2864580\t3.61E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t9.893364286\t68417.14286\t42\t415.5213\t5281.699247\t42\t2873520\t3.61E+12
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t68417.14286\t42\t419999958\t4.20E+15\t42\t2873520\t3.61E+12
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9628R\tREFERENCE SAMPLE DATE\t9999999\t4160\t42\t419999958\t4.20E+15\t42\t174720\t15129079200
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9279R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS II\t9999999\t200657.1429\t42\t419999958\t4.20E+15\t42\t8427600\t7.31E+12
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32DX\tCLINICAL INDICATION\t9999999\t807004.6154\t42\t419999958\t4.20E+15\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32ET\tETHNICITY (PROVIDED BY MD)\t9999999\t807004.6154\t42\t419999958\t4.20E+15\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN CONC, ANTIXA U/ML\tHEPAR\tHEPARIN ACTIVITY LEVEL\t2142857.221\t42355.38462\t42\t90000003.27\t9.00E+14\t26\t1101240\t71583544800
-LAB BLOOD ORDERABLES\t497480\tLABADGM\tADENOVIRUS IGG AND IGM AB\tADG1\tADENOVIRUS AB,IGG\t2.155714286\t319660\t42\t90.54\t218.1358\t54\t17261640\t6.62E+12
-VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV AB IGM\tCMVIGM\tCMV IGM AB\t9999999\t140353.8462\t42\t419999958\t4.20E+15\t26\t3649200\t6.49E+11
-VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSVZSP\tSPECIMEN\t9999999\t190023.36\t42\t419999958\t4.20E+15\t375\t71258760\t1.95E+13
-VIROLOGY ORDERABLES\t1007409\tLABVZVQL\tVZV DNA PCR QUAL, NON LESION\tVZQLSP\tSPECIMEN\t9999999\t192832.6829\t42\t419999958\t4.20E+15\t82\t15812280\t4.51E+12
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t41\t409999959\t4.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t41\t409999959\t4.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tSI\t<<STATUS>>  \t9999999\t19624.44444\t41\t409999959\t4.10E+15\t54\t1059720\t53302118400
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tDI\tISSUE DATE/TIME\t9999999\t28667.36842\t41\t409999959\t4.10E+15\t19\t544680\t18471175200
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tUBT\tUNIT BLOOD TYPE  \t9999999\t35055\t41\t409999959\t4.10E+15\t20\t701100\t55275080400
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCNRBC\tNRBC/100WBC, CSF\t3.024390244\t8650.588235\t41\t124\t1164\t17\t147060\t1577818800
-BODY FLUID ORDERABLES\t497386\tLABFTRIGA\tTRIGLYCERIDES, NON-PLEURAL\tFSC1\tFLUID TYPE\t9999999\t713178\t41\t409999959\t4.10E+15\t10\t7131780\t5.30E+12
-BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFSP\tSPECIMEN\t9999999\t479125.7143\t41\t409999959\t4.10E+15\t14\t6707760\t4.66E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t34.77853659\t728.7804878\t41\t1425.92\t125051.564\t41\t29880\t372031200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0.56097561\t31024.39024\t41\t23\t237\t41\t1272000\t5.90E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t8292683.61\t23262.43902\t41\t340000028\t3.40E+15\t41\t953760\t5.21E+11
-HISTOCOMPATIBILITY ORDERABLE\t796004\t10275R\tHLA ORDER - SPECIMEN IN LAB ORDER NOTICE\tSPINN\tSPECIMEN IN LAB ORDER NOTICE\t9999999\t9120\t41\t409999959\t4.10E+15\t41\t373920\t30318825600
-HISTOCOMPATIBILITY ORDERABLE\t892285\t10273R\tHLA ORDER - WORKUP AT TIME OF TRANSPLANT\t10049R\tHLA LAB TESTING STATUS\t9999999\t452812.6829\t41\t409999959\t4.10E+15\t41\t18565320\t3.05E+13
-LAB BLOOD ORDERABLES\t472527\tLABHB\tHEPATITIS B SURFACE AG\tHBSAG\tHEPATITIS B SURF AG\t9999999\t53475\t41\t409999959\t4.10E+15\t36\t1925100\t1.41E+11
-LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON TEST FOR LATENT TB\tQTFNIN\tQUANTIFERON INTERPRETATION\t9999999\t852519.229\t41\t409999959\t4.10E+15\t1297\t1105717440\t3.84E+16
-LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tBBFRAC\tBB FRACTION\t0.780487805\t4442.926829\t41\t32\t298\t41\t182160\t32746600800
-LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tYCKMB\tMB FRACTION\t1.926829268\t4442.926829\t41\t79\t841\t41\t182160\t32746600800
-LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tYCKTOT\tTOTAL CK ACTIVITY\t9999999\t4442.926829\t41\t409999959\t4.10E+15\t41\t182160\t32746600800
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFSO\tSOURCE\t9999999\t1050420\t41\t409999959\t4.10E+15\t2\t2100840\t3.23E+12
-STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK3\tPOTASSIUM, 24-HR STOOL\t9999999\t620506.6667\t41\t409999959\t4.10E+15\t18\t11169120\t1.11E+13
-VIROLOGY ORDERABLES\t876719\tLABDVEIAV\tFLU A DFA\tIAV\tINFLUENZA A\t9999999\t78912.72727\t41\t409999959\t4.10E+15\t33\t2604120\t1.09E+12
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tDI\tISSUE DATE/TIME\t9999999\t28392\t40\t399999960\t4.00E+15\t20\t567840\t21309552000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t187.25\t757.5\t40\t7490\t1478650\t40\t30300\t371466000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.265625\t757.5\t40\t90.625\t225.503125\t40\t30300\t371466000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.43\t757.5\t40\t17.2\t7.78\t40\t30300\t371466000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t0.39125\t757.5\t40\t15.65\t7.6395\t40\t30300\t371466000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.305625\t787.5\t40\t52.225\t95.040625\t40\t31500\t371034000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.44375\t787.5\t40\t17.75\t8.9049\t40\t31500\t371034000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t168.675\t26059.5\t40\t6747\t1141277\t40\t1042380\t5.30E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t1.05\t24064.5\t40\t42\t556\t40\t962580\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t6000104.025\t26031\t40\t240004161\t2.40E+15\t40\t1041240\t5.30E+11
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGMYD8B\tMYD88, BLOOD\t9999999\t1309942.5\t40\t399999960\t4.00E+15\t40\t52397700\t2.18E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGSF3BB\tSF3B1, BLOOD\t9999999\t1309942.5\t40\t399999960\t4.00E+15\t40\t52397700\t2.18E+14
-MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tSREQ\tCOMMENTS\t9999999\t508887.5\t40\t399999960\t4.00E+15\t24\t12213300\t7.93E+12
-MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tRPT\tRPT\t9999999\t80326.5\t40\t399999960\t4.00E+15\t40\t3213060\t3.71E+11
-STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK4\tPOTASSIUM CONCENTRATION\t4500019.05\t620506.6667\t40\t180000762\t1.80E+15\t18\t11169120\t1.11E+13
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tXMR\tUNIT CROSSMATCH  \t9999999\t40255.38462\t39\t389999961\t3.90E+15\t13\t523320\t60480576000
-BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tPRO\tPRODUCT ID  \t9999999\t23350\t39\t389999961\t3.90E+15\t18\t420300\t21061472400
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tVL\tPRODUCT VOLUME\t162.3076923\t20528.57143\t39\t6330\t1070034\t14\t287400\t7805044800
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tBP190\tP190, BONE MARROW\t5384614.992\t608170.9091\t39\t209999984.7\t2.10E+15\t11\t6689880\t4.92E+12
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIDX\tCLINICAL INDICATION\t9999999\t927889.0909\t39\t389999961\t3.90E+15\t11\t10206780\t1.25E+13
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t6762198.308\t4669.230769\t39\t263725734\t2.60E+15\t39\t182100\t23450331600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t8717949.641\t24470.76923\t39\t340000036\t3.40E+15\t39\t954360\t5.21E+11
-HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM\t10049R\tHLA LAB TESTING STATUS\t9999999\t645180\t39\t389999961\t3.90E+15\t39\t25162020\t2.00E+13
-LAB BLOOD ORDERABLES\t471810\tLAB11027R\tIMMUNE COMPLEXES\tC1Q\tC1Q BINDING ASSAY\t256414.3256\t330160.5405\t39\t10000158.7\t1.00E+14\t37\t12215940\t4.50E+12
-LAB BLOOD ORDERABLES\t472119\tLABYCYANI\tCYANIDE, BLOOD\tCYAN\tCYANIDE, B\t6666666.085\t218172.2727\t39\t259999977.3\t2.60E+15\t44\t9599580\t2.52E+12
-LAB BLOOD ORDERABLES\t472758\tLABLPA\tLIPOPROTEIN\tLPA\tLIPOPROTEIN A\t2307721.355\t46660\t39\t90001132.83\t9.00E+14\t30\t1399800\t1.17E+11
-LAB BLOOD ORDERABLES\t472930\tLABYMGEA\tMYASTHENIA GRAVIS EVALUATION ADULT\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t256413.2251\t694913.6842\t39\t10000115.78\t1.00E+14\t38\t26406720\t2.54E+13
-LAB BLOOD ORDERABLES\t472930\tLABYMGEA\tMYASTHENIA GRAVIS EVALUATION ADULT\tYARMA\tACH RECEPT MODUL AB\t256424.8205\t694913.6842\t39\t10000568\t1.00E+14\t38\t26406720\t2.54E+13
-LAB BLOOD ORDERABLES\t473463\tLABTOXOP2\tTOXOPLASMOSIS PCR\tTOXRES\tPCR RESULT\t9999999\t1116100\t39\t389999961\t3.90E+15\t6\t6696600\t7.98E+12
-LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12339R\tEVEROLIMUS\t769235.1974\t348114.1176\t39\t30000172.7\t3.00E+14\t51\t17753820\t3.14E+13
-LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tYCKMM\tMM FRACTION\t97.51282051\t4442.926829\t39\t3803\t371885\t41\t182160\t32746600800
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t547240.7303\t39\t389999961\t3.90E+15\t1068\t584453100\t2.37E+15
-MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tRPT\tRPT\t9999999\t418604.2105\t39\t389999961\t3.90E+15\t19\t7953480\t3.62E+12
-MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tSDES\tSPECIMEN\t9999999\t80326.5\t39\t389999961\t3.90E+15\t40\t3213060\t3.71E+11
-STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, RANDOM STOOL\t12506R\tSODIUM, RANDOM STOOL\t2820571.487\t275907.3913\t39\t110002288\t1.10E+15\t46\t12691740\t7.03E+12
-URINE ORDERABLES\t473727\tLABURURIC\tURIC ACID, URINE (RANDOM)\tURICURN\tURIC ACID, URINE\t27.7\t9726\t39\t1080.3\t53855.81\t30\t291780\t8162665200
-VIROLOGY ORDERABLES\t473715\tLABMVSOB\tUNLISTED MICROBIOLOGY/VIROLOGY TEST\tRES\tRESULTS\t9999999\t681043.9907\t39\t389999961\t3.90E+15\t431\t293529960\t8.51E+14
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tXMR\tUNIT CROSSMATCH  \t9999999\t39170.76923\t38\t379999962\t3.80E+15\t13\t509220\t59138074800
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tUBT\tUNIT BLOOD TYPE  \t9999999\t40860\t38\t379999962\t3.80E+15\t20\t817200\t69801444000
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tBP210\tP210, BONE MARROW\t6052631.138\t394347.6923\t38\t229999983.3\t2.30E+15\t13\t5126520\t3.76E+12
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tLRB210\tLOG DX AVG\t8421051.796\t394347.6923\t38\t319999968.3\t3.20E+15\t13\t5126520\t3.76E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12363R\tANNA-1, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12364R\tANNA-2, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12365R\tANNA-3, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12366R\tAGNA-1,CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12367R\tPCA-1, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12368R\tPCA-2, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12369R\tAMPHIPHYSIN AB, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12370R\tCRMP-5-IGG, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRALDBIIM\tPM RA LEAD BIPOLAR IMPEDANCE\t538.0263158\t824.2105263\t38\t20445\t18007231\t38\t31320\t370929600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t69.86842105\t4804.736842\t38\t2655\t195275\t38\t182580\t23450101200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t445.4473684\t4801.578947\t38\t16927\t7823937\t38\t182460\t23450202000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t472.7894737\t4801.578947\t38\t17966\t8901874\t38\t182460\t23450202000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.707894737\t4804.736842\t38\t102.9\t308.03\t38\t182580\t23450101200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.476315789\t4804.736842\t38\t18.1\t10.39\t38\t182580\t23450101200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t1315789.655\t4804.736842\t38\t50000006.9\t5.00E+14\t38\t182580\t23450101200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0.157894737\t31818.94737\t38\t6\t12\t38\t1209120\t5.86E+11
-HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM\tRINFO\tTO FIND RESULT:\t9999999\t645180\t38\t379999962\t3.80E+15\t39\t25162020\t2.00E+13
-LAB BLOOD ORDERABLES\t471527\tLABSMABY\tANTI-SM ANTIBODY\tASMINT\tANTI-SM INTERPRETATION\t9999999\t394626.5217\t38\t379999962\t3.80E+15\t138\t54458460\t2.90E+13
-LAB BLOOD ORDERABLES\t472547\tLABYAHA\tHISTONE ANTIBODIES\tYAHA1\tHISTONE AUTOANTIBODIES\t6842104.842\t277668.5106\t38\t259999984\t2.60E+15\t47\t13050420\t4.61E+12
-LAB BLOOD ORDERABLES\t668154\tLABGDGP\tIGG ANTI DGP\tGDGPR\tIGG ANTI DGP ANTIBODY\t9999999\t399028.2353\t38\t379999962\t3.80E+15\t34\t13566960\t6.90E+12
-MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tSDES\tSPECIMEN\t9999999\t418604.2105\t38\t379999962\t3.80E+15\t19\t7953480\t3.62E+12
-MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tRPT\tRPT\t9999999\t61173.10345\t38\t379999962\t3.80E+15\t29\t1774020\t1.29E+11
-MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tSDES\tSPECIMEN\t9999999\t61173.10345\t38\t379999962\t3.80E+15\t29\t1774020\t1.29E+11
-MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tSREQ\tCOMMENTS\t9999999\t61173.10345\t38\t379999962\t3.80E+15\t29\t1774020\t1.29E+11
-URINE ORDERABLES\t472100\tLABYUCORT\tCORTISOL FREE, URINE\tVOLUMEURN\tURINE VOLUME\t792469.8421\t349320\t38\t30113854\t3.00E+14\t5\t1746600\t7.16E+11
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADQLDN\tADV DNA\t9999999\t221752.1053\t38\t379999962\t3.80E+15\t38\t8426580\t3.12E+12
-VIROLOGY ORDERABLES\t876715\tLABDVERV3\tRSV DFA\tRSV3SP\tSPECIMEN\t9999999\t60101.53846\t38\t379999962\t3.80E+15\t78\t4687920\t3.25E+11
-BODY FLUID ORDERABLES\t472935\tLAB11019R\tMYELIN BASIC PROTEIN, CSF\t11164R\tMYELIN BASIC PROTEIN\t15.66702703\t420098.8235\t37\t579.68\t62546.4086\t17\t7141680\t9.00E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.822156757\t848.1081081\t37\t104.4198\t295.698776\t37\t31380\t369730800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t4945.945946\t37\t369999963\t3.70E+15\t37\t183000\t23449924800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t35.07945946\t4950.810811\t37\t1297.94\t105789.1516\t37\t183180\t23449748400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t26.61837838\t4950.810811\t37\t984.88\t82787.0448\t37\t183180\t23449748400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDRHYTHM\tICD RHYTHM\t9999999\t4957.297297\t37\t369999963\t3.70E+15\t37\t183420\t23449345200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t302.0837838\t4944.324324\t37\t11177.1\t3387374.01\t37\t182940\t23449971600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t4944.324324\t37\t369999963\t3.70E+15\t37\t182940\t23449971600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.685135135\t827.027027\t37\t99.35\t296.1625\t37\t30600\t352180800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.483783784\t827.027027\t37\t17.9\t9.41\t37\t30600\t352180800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t1351351.508\t4944.324324\t37\t50000005.8\t5.00E+14\t37\t182940\t23449971600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t17.43243243\t28323.24324\t37\t645\t29925\t37\t1047960\t5.30E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t540675.8919\t25778.91892\t37\t20005008\t2.00E+14\t37\t953820\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t173.7837838\t25778.91892\t37\t6430\t1238370\t37\t953820\t5.21E+11
-HISTOCOMPATIBILITY ORDERABLE\t890070\t10190R\tHLA ORDER - COMMUNICATION/UPDATE\t10049R\tHLA LAB TESTING STATUS\t9999999\t150188.1081\t37\t369999963\t3.70E+15\t37\t5556960\t6.32E+12
-HISTOCOMPATIBILITY ORDERABLE\t890070\t10190R\tHLA ORDER - COMMUNICATION/UPDATE\tRINFO\tTO FIND RESULT:\t9999999\t150188.1081\t37\t369999963\t3.70E+15\t37\t5556960\t6.32E+12
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34V\tVIABILITY\t1891972.703\t21605.625\t37\t70002990\t7.00E+14\t32\t691380\t27502592400
-LAB BLOOD ORDERABLES\t472107\tLABCRTINE\tCREATINE, SER/PLAS\t11852R\tCREATINE, PLASMA MG/DL\t540541.8892\t527977.8947\t37\t20000049.9\t2.00E+14\t38\t20063160\t1.22E+13
-LAB BLOOD ORDERABLES\t472107\tLABCRTINE\tCREATINE, SER/PLAS\tCRTNR\tCREATINE, PLASMA\t109.3324324\t527977.8947\t37\t4045.3\t744414.89\t38\t20063160\t1.22E+13
-LAB BLOOD ORDERABLES\t473762\tLABYVB1W\tVITAMIN B1\tYVB1W1\tTHIAMIN(VITAMIN B1)\t119.027027\t302975.3846\t37\t4404\t600438\t39\t11816040\t4.06E+12
-LAB BLOOD ORDERABLES\t497075\tLABSHIVAB\tRAPID HIV-1 AB\tSHIVAB\tRAPID HIV-1 ANTIBODY\t9999999\t5141.73913\t37\t369999963\t3.70E+15\t23\t118260\t904280400
-MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tRPT\tRPT\t9999999\t105838\t37\t369999963\t3.70E+15\t30\t3175140\t1.08E+12
-MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tSREQ\tCOMMENTS\t9999999\t418604.2105\t37\t369999963\t3.70E+15\t19\t7953480\t3.62E+12
-STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, RANDOM STOOL\t12504R\tPOTASSIUM, RANDOM STOOL\t3513535.378\t282741.8182\t37\t130000809\t1.30E+15\t44\t12440640\t7.15E+12
-URINE ORDERABLES\t472100\tLABYUCORT\tCORTISOL FREE, URINE\tYUCOUT\tCOLLECTION DURATION\t540563.1892\t349320\t37\t20000838\t2.00E+14\t5\t1746600\t7.16E+11
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tCOPRO3\tCOPROPORPHYRIN III\t270284.7297\t454132.9412\t37\t10000535\t1.00E+14\t17\t7720260\t6.03E+12
-VIROLOGY ORDERABLES\t541685\tLABHSVG\tHSV 1/2 TYPE-SPECIFIC IGG\tHSV1G\tHSV 1, IGG INDEX\t2972975.006\t262579.4118\t37\t110000075.2\t1.10E+15\t34\t8927700\t4.76E+12
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t36\t359999964\t3.60E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tVL\tPRODUCT VOLUME\t160.1111111\t29345.45455\t36\t5764\t957814\t11\t322800\t10370109600
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tUBT\tUNIT BLOOD TYPE  \t9999999\t35173.33333\t36\t359999964\t3.60E+15\t18\t633120\t53763523200
-BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tPRO\tPRODUCT ID  \t9999999\t20992.5\t36\t359999964\t3.60E+15\t16\t335880\t17437428000
-BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34V\tVIABILITY\t99.77777778\t8386.666667\t36\t3592\t358412\t18\t150960\t2362651200
-BODY FLUID ORDERABLES\t670812\tLAB11175R\tLIPASE,BF\t11176R\tLIPASE, BF\t1125027.833\t193422.8571\t36\t40501002\t4.00E+14\t28\t5415840\t1.24E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMTLPCPACA\tPM TELEMETRY PERCENT PACING ATRIAL\t52.09138889\t916.6666667\t36\t1875.29\t151006.3007\t36\t33000\t369511200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t5105\t36\t359999964\t3.60E+15\t36\t183780\t23449215600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t1.111111111\t5111.666667\t36\t40\t1374\t36\t184020\t23449086000
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9420R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t69582.63158\t36\t359999964\t3.60E+15\t76\t5288280\t2.57E+13
-LAB BLOOD ORDERABLES\t471509\tLABYFLUO\t5 - FLUCYTOSINE\t5FC\t5-FLUCYTOSINE\t48.33888889\t370011.6667\t36\t1740.2\t109676.04\t36\t13320420\t5.50E+12
-LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABIDR\tANTIBODY ID RULE OUT\t9999999\t16792.75862\t36\t359999964\t3.60E+15\t290\t4869900\t6.10E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tRPRQ\tRESULT\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t472866\tLABMISPRO\tMISCELLANEOUS PROCESSING\tSEN\tSEND TO\t9999999\t108847.8261\t36\t359999964\t3.60E+15\t23\t2503500\t1.17E+12
-LAB BLOOD ORDERABLES\t472930\tLABYMGEA\tMYASTHENIA GRAVIS EVALUATION ADULT\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t694913.6842\t36\t359999964\t3.60E+15\t38\t26406720\t2.54E+13
-LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPDOPA\tDOPAMINE\t6389724.917\t294414.375\t36\t230030097\t2.30E+15\t32\t9421260\t3.19E+12
-LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPEPI\tEPINEPHRINE BY HPLC\t4722262.444\t294414.375\t36\t170001448\t1.70E+15\t32\t9421260\t3.19E+12
-LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPNOREP\tNOREPINEPHRINE\t1667118.528\t294414.375\t36\t60016267\t6.00E+14\t32\t9421260\t3.19E+12
-LAB BLOOD ORDERABLES\t870903\tLAB11984R\tNIACIN\t11985R\tNIACIN\t555557.7283\t866225.1429\t36\t20000078.22\t2.00E+14\t35\t30317880\t2.86E+13
-MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tRPT\tRPT\t9999999\t229096\t36\t359999964\t3.60E+15\t15\t3436440\t8.88E+11
-MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tSDES\tSPECIMEN\t9999999\t229096\t36\t359999964\t3.60E+15\t15\t3436440\t8.88E+11
-MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tSREQ\tCOMMENTS\t9999999\t229096\t36\t359999964\t3.60E+15\t15\t3436440\t8.88E+11
-URINE ORDERABLES\t472100\tLABYUCORT\tCORTISOL FREE, URINE\tUCORT\tCORTISOL, URINE\t833824.0139\t349320\t36\t30017664.5\t3.00E+14\t5\t1746600\t7.16E+11
-VIROLOGY ORDERABLES\t870899\tLABVIRSO1\tMISC. VIROLOGY TEST\tVIROS\tSOURCE\t9999999\t823753.7143\t36\t359999964\t3.60E+15\t35\t28831380\t3.01E+13
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tSI\t<<STATUS>>  \t9999999\t22864.44444\t35\t349999965\t3.50E+15\t54\t1234680\t87256699200
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tSI\t<<STATUS>>  \t9999999\t19766.80851\t35\t349999965\t3.50E+15\t47\t929040\t49220308800
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tDI\tISSUE DATE/TIME\t9999999\t32611.42857\t35\t349999965\t3.50E+15\t21\t684840\t25712856000
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tVL\tPRODUCT VOLUME\t168.5714286\t29626.66667\t35\t5900\t1005354\t9\t266640\t8404934400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t158.5714286\t910.2857143\t35\t5550\t949050\t35\t31860\t370242000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t127.2857143\t936\t35\t4455\t569525\t35\t32760\t369612000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t236.5714286\t901.7142857\t35\t8280\t2149600\t35\t31560\t351712800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t126.7142857\t903.4285714\t35\t4435\t565175\t35\t31620\t351615600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t27106.28571\t35\t349999965\t3.50E+15\t35\t948720\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t27106.28571\t35\t349999965\t3.50E+15\t35\t948720\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t27106.28571\t35\t349999965\t3.50E+15\t35\t948720\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t8571436.857\t27269.14286\t35\t300000290\t3.00E+15\t35\t954420\t5.21E+11
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9479R\tLOCUS 5\t9999999\t8996.470588\t35\t349999965\t3.50E+15\t34\t305880\t64653912000
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32R1\tRESULT 1\t9999999\t807004.6154\t35\t349999965\t3.50E+15\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tARS\tARSENIC, BLOOD\t9999999\t253232.5714\t35\t349999965\t3.50E+15\t35\t8863140\t3.67E+12
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tCAD\tCADMIUM, BLOOD\t8857142.314\t253232.5714\t35\t309999981\t3.10E+15\t35\t8863140\t3.67E+12
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tLEA\tLEAD, BLD\t8857142.286\t253232.5714\t35\t309999980\t3.10E+15\t35\t8863140\t3.67E+12
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tMERC\tMERCURY, BLOOD\t3428573.229\t253232.5714\t35\t120000063\t1.20E+15\t35\t8863140\t3.67E+12
-LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMOSIS PCR\tTOXGG\tGENERAL GUIDELINES\t9999999\t2500149.057\t35\t349999965\t3.50E+15\t53\t132507900\t2.79E+15
-LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11826R\tCSS TARGET\t9999999\t516462.8571\t35\t349999965\t3.50E+15\t35\t18076200\t2.50E+13
-STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA1\tSODIUM, 24-HR STOOL\t9999999\t1019105\t35\t349999965\t3.50E+15\t24\t24458520\t6.44E+13
-STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA2\tTOTAL WEIGHT\t9999999\t620232\t35\t349999965\t3.50E+15\t10\t6202320\t7.20E+12
-STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK1\tCOLLECTION DURATION\t9999999\t1148615\t35\t349999965\t3.50E+15\t24\t27566760\t7.59E+13
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUASP\tSPECIMEN\t9999999\t142990.5882\t35\t349999965\t3.50E+15\t17\t2430840\t5.55E+11
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tDI\tISSUE DATE/TIME\t9999999\t28974\t34\t339999966\t3.40E+15\t20\t579480\t20233267200
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tBASOSYNFLD\tBASOPHILS, SYN FLD\t1.352941176\t14124\t34\t46\t74\t15\t211860\t9371934000
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15RE\tRESULT, BM\t9999999\t644650\t34\t339999966\t3.40E+15\t18\t11603700\t8.75E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t5294247.118\t700.5882353\t34\t180004402\t1.80E+15\t34\t23820\t288802800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t193.5294118\t938.8235294\t34\t6580\t1662350\t34\t31920\t351583200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t27905.29412\t34\t339999966\t3.40E+15\t34\t948780\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t432.2294118\t31102.94118\t34\t14695.8\t6430976.6\t34\t1057500\t5.30E+11
-IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tAMOX\tAMOXICILLIN\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
-IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tAMPICILLIN\tAMPICILLIN\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
-IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tIGEPEN\tTOTAL IGE\t588390.5456\t485348.8235\t34\t20005278.55\t2.00E+14\t34\t16501860\t1.42E+13
-IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tPENG\tPENICILLIN G\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
-IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tPENV\tPENICILLIN V\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
-LAB BLOOD ORDERABLES\t471919\tLABYFCAR\tCARBAMAZEPINE FREE AND TOTAL, SERUM\tCZPFR\tCARBAMAZEPINE,FREE,S\t1470589.647\t175342.2857\t34\t50000048\t5.00E+14\t35\t6136980\t1.17E+12
-LAB BLOOD ORDERABLES\t471919\tLABYFCAR\tCARBAMAZEPINE FREE AND TOTAL, SERUM\tCZPTOT\tCARBAMAZEPINE,TOT,S\t1764711.738\t175342.2857\t34\t60000199.1\t6.00E+14\t35\t6136980\t1.17E+12
-LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tACFCR\tACYL/FREE CARNITINE RATIO\t0.361764706\t396363.6364\t34\t12.3\t7.79\t33\t13080000\t5.88E+12
-LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tCARNF\tCARNITINE, FREE\t34.47647059\t396363.6364\t34\t1172.2\t47330.26\t33\t13080000\t5.88E+12
-LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tCARNIT\tCARNITINE, TOTAL\t45.58235294\t396363.6364\t34\t1549.8\t81716.42\t33\t13080000\t5.88E+12
-MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tRPT\tRPT\t9999999\t264537\t34\t339999966\t3.40E+15\t20\t5290740\t2.95E+12
-MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tSDES\tSPECIMEN\t9999999\t264537\t34\t339999966\t3.40E+15\t20\t5290740\t2.95E+12
-VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVGGQNT\tVZV IGG LEVEL\t1470590.153\t299835.8824\t34\t50000065.19\t5.00E+14\t34\t10194420\t3.75E+12
-VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVZVMQL\tVZV IGM RESULT\t9999999\t299835.8824\t34\t339999966\t3.40E+15\t34\t10194420\t3.75E+12
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tVL\tPRODUCT VOLUME\t188.4545455\t32778.46154\t33\t6219\t1342909\t13\t426120\t15380020800
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tXMR\tUNIT CROSSMATCH  \t9999999\t33225\t33\t329999967\t3.30E+15\t12\t398700\t29378422800
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tUBT\tUNIT BLOOD TYPE  \t9999999\t24108\t33\t329999967\t3.30E+15\t15\t361620\t13517708400
-BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tPRO\tPRODUCT ID  \t9999999\t23456\t33\t329999967\t3.30E+15\t15\t351840\t18758599200
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTDX\tCLINICAL INDICATION\t9696977.436\t688582.5\t33\t320000255.4\t3.20E+15\t16\t11017320\t1.17E+13
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTMET\tMETHOD\t9999999\t688582.5\t33\t329999967\t3.30E+15\t16\t11017320\t1.17E+13
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t167.1212121\t710.9090909\t33\t5515\t926125\t33\t23460\t288802800
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t8.717845455\t969.0909091\t33\t287.6889\t2938.429441\t33\t31980\t369918000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t969.0909091\t33\t329999967\t3.30E+15\t33\t31980\t369918000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.629682424\t5396.363636\t33\t86.77952\t344.8447172\t33\t178080\t23425675200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t5396.363636\t33\t329999967\t3.30E+15\t33\t178080\t23425675200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t3.035863636\t29243.63636\t33\t100.1835\t304.9855674\t33\t965040\t5.21E+11
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9464R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t72568.10127\t33\t329999967\t3.30E+15\t79\t5732880\t2.58E+13
-LAB BLOOD ORDERABLES\t473332\tLABSLINTP\tSLIDE INTERPRETATION BY PATHOLOGIST\tSLPATH\tPATHOLOGIST\t9999999\t168261.4286\t33\t329999967\t3.30E+15\t28\t4711320\t1.33E+12
-LAB BLOOD ORDERABLES\t744308\tLAB11593R\tETHYLENE GLYCOL\t11721R\tETHYLENE GLYCOL\t9999999\t257777.8378\t33\t329999967\t3.30E+15\t37\t9537780\t8.90E+12
-MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tSDES\tSPECIMEN\t9999999\t105838\t33\t329999967\t3.30E+15\t30\t3175140\t1.08E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGMYD8M\tMYD88, BM\t9999999\t996023.6364\t33\t329999967\t3.30E+15\t33\t32868780\t3.73E+13
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGSF3BM\tSF3B1, BM\t9999999\t996023.6364\t33\t329999967\t3.30E+15\t33\t32868780\t3.73E+13
-URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tCAE\tCALCIUM EXCRETION\t5454628.879\t70146.66667\t33\t180002753\t1.80E+15\t18\t1262640\t1.69E+11
-URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tCAURN\tCALCIUM, URINE\t5454548.915\t70146.66667\t33\t180000114.2\t1.80E+15\t18\t1262640\t1.69E+11
-URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tUT5\tCOLLECTION PERIOD CALCIUM\t303053.1818\t70146.66667\t33\t10000755\t1.00E+14\t18\t1262640\t1.69E+11
-URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tVOL5\tVOLUME CALCIUM\t305359.0606\t70146.66667\t33\t10076849\t1.00E+14\t18\t1262640\t1.69E+11
-URINE ORDERABLES\t472504\tLABUHGB\tHEMOGLOBIN, URINE\tHGBURN\tHEMOGLOBIN, URINE\t8484850.915\t236397\t33\t280000080.2\t2.80E+15\t20\t4727940\t1.29E+12
-URINE ORDERABLES\t472504\tLABUHGB\tHEMOGLOBIN, URINE\tUT\tCOLLECTION PERIOD\t9999999\t236397\t33\t329999967\t3.30E+15\t20\t4727940\t1.29E+12
-URINE ORDERABLES\t472504\tLABUHGB\tHEMOGLOBIN, URINE\tVOL\tVOLUME\t2121243.273\t236397\t33\t70001028\t7.00E+14\t20\t4727940\t1.29E+12
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t32\t319999968\t3.20E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tXMR\tUNIT CROSSMATCH  \t9999999\t34121.53846\t32\t319999968\t3.20E+15\t13\t443580\t47680657200
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tDI\tISSUE DATE/TIME\t9999999\t30243.33333\t32\t319999968\t3.20E+15\t18\t544380\t19348650000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t744.375\t32\t319999968\t3.20E+15\t32\t23820\t287715600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t4062517.406\t729.375\t32\t130000557\t1.30E+15\t32\t23340\t287744400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t729.375\t32\t319999968\t3.20E+15\t32\t23340\t287744400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t729.375\t32\t319999968\t3.20E+15\t32\t23340\t287744400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t625068.0625\t740.625\t32\t20002178\t2.00E+14\t32\t23700\t287773200
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tDEVTYPE\tDEVTYPE\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRLOWRT\tBRADY LOWER RATE\t67.1875\t29700\t32\t2150\t154000\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRPACMD\tBRADY PACING MODE\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t296.159375\t29700\t32\t9477.1\t2825394.17\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tIMPDATE\tIMPLANT DATE\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tMFR\tMANUFACTURER\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tMOD\tMODEL\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.353125\t29700\t32\t75.3\t188.92625\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.49375\t29700\t32\t15.8\t8.98\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2812499.984\t29700\t32\t89999999.5\t9.00E+14\t32\t950400\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSERNUM\tSERIAL NUMBER\t4528601.438\t29700\t32\t144915246\t1.35E+15\t32\t950400\t6.37E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t3125032.875\t5767.5\t32\t100001052\t1.00E+15\t32\t184560\t23448621600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t7.9220625\t5587.5\t32\t253.506\t2266.045912\t32\t178800\t23424847200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t380.034375\t5767.5\t32\t12161.1\t4678602.55\t32\t184560\t23448967200
-HISTOCOMPATIBILITY ORDERABLE\t978466\t10283R\tHLA ORDER - TRANSPLANT EVENT COMMUNICATION\t10049R\tHLA LAB TESTING STATUS\t9999999\t355848.75\t32\t319999968\t3.20E+15\t32\t11387160\t8.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t978466\t10283R\tHLA ORDER - TRANSPLANT EVENT COMMUNICATION\tRINFO\tTO FIND RESULT:\t9999999\t355848.75\t32\t319999968\t3.20E+15\t32\t11387160\t8.33E+13
-LAB BLOOD ORDERABLES\t668188\tLABYCLOZ\tCLOZAPINE, SERUM\tYCOMT\tCOMBINED TOTAL\t313382.4375\t306883.75\t32\t10028238\t1.00E+14\t48\t14730420\t5.78E+12
-MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tRPT\tRPT\t9999999\t616054.2857\t32\t319999968\t3.20E+15\t14\t8624760\t5.65E+12
-MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tSREQ\tCOMMENTS\t9999999\t264537\t32\t319999968\t3.20E+15\t20\t5290740\t2.95E+12
-MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tSREQ\tCOMMENTS\t9999999\t80326.5\t32\t319999968\t3.20E+15\t40\t3213060\t3.71E+11
-STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK2\tTOTAL WEIGHT\t9999999\t648480\t32\t319999968\t3.20E+15\t8\t5187840\t6.44E+12
-URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tYUCOP2\tCOLLECTION DURATION\t625022.4375\t395269.2857\t32\t20000718\t2.00E+14\t28\t11067540\t9.84E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tWBCAST\tWBC CASTS\t9999999\t12200.86957\t32\t319999968\t3.20E+15\t23\t280620\t4539250800
-VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFCOM\tCOMMENTS\t9999999\t11850\t32\t319999968\t3.20E+15\t10\t118500\t2648221200
-VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFLSP\tSPECIMEN\t9999999\t11850\t32\t319999968\t3.20E+15\t10\t118500\t2648221200
-VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVGGQUL\tVZV IGG RESULT\t9999999\t299835.8824\t32\t319999968\t3.20E+15\t34\t10194420\t3.75E+12
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tXMR\tUNIT CROSSMATCH  \t9999999\t28260\t31\t309999969\t3.10E+15\t10\t282600\t20345407200
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSISO\tSOURCE, TISSUE\t9999999\t927889.0909\t31\t309999969\t3.10E+15\t11\t10206780\t1.25E+13
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t322581.6129\t5657.419355\t31\t10000030\t1.00E+14\t31\t175380\t23395474800
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t9677419.677\t30640.64516\t31\t300000010\t3.00E+15\t31\t949860\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t30607.74194\t31\t0\t0\t31\t948840\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t1.046774194\t38183.22581\t31\t32.45\t47.68\t31\t1183680\t5.42E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.487096774\t38183.22581\t31\t15.1\t12.49\t31\t1183680\t5.42E+11
-LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tANTI-RO ANTIBODY\tSSAQNT\tANTI-RO LEVEL\t3870997.968\t561673.5484\t31\t120000937\t1.20E+15\t31\t17411880\t1.13E+13
-LAB BLOOD ORDERABLES\t472032\tLABYCHROM\tCHROMIUM\tYCHRO1\tCHROMIUM\t645163.2065\t263355.4839\t31\t20000059.4\t2.00E+14\t31\t8164020\t2.65E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
-LAB BLOOD ORDERABLES\t472120\tLABYCCPIG\tANTI CCP\tCCPT\tANTI CCP ANTIBODY LEVEL\t3225835.129\t451253.7931\t31\t100000889\t1.00E+15\t29\t13086360\t7.73E+12
-LAB BLOOD ORDERABLES\t472354\tLABFACT12\tFACTOR XII ASSAY\tFACTOR12\tFACTOR XII ASSAY\t1290376.032\t59304.44444\t31\t40001657\t4.00E+14\t27\t1601220\t1.27E+11
-LAB BLOOD ORDERABLES\t472518\tLABHAVM\tIGM ANTI-HAV\tHAVINT\tINTERPRETATION\t9999999\t48714.87379\t31\t309999969\t3.10E+15\t515\t25088160\t2.39E+12
-LAB BLOOD ORDERABLES\t472924\tLABMONEBV\tMONO SCREEN WITH REFLEX EBV AB\tEBV\tEBV TITER REFLEXED?\t9999999\t11195.5814\t31\t309999969\t3.10E+15\t172\t1925640\t47965219200
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tPMLR\tPML\t2903225.591\t675904\t31\t89999993.33\t9.00E+14\t30\t20277120\t1.57E+13
-LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tSPTY3\tSPECIMEN TYPE\t9999999\t2214834.286\t31\t309999969\t3.10E+15\t21\t46511520\t3.48E+14
-LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tTEST3\tTEST NAME\t9999999\t2214834.286\t31\t309999969\t3.10E+15\t21\t46511520\t3.48E+14
-LAB BLOOD ORDERABLES\t473768\tLABVITK\tVITAMIN K1, SERUM\tVITK\tVITAMIN K1,SERUM\t9.846774194\t266435.1724\t31\t305.25\t64973.9391\t29\t7726620\t2.19E+12
-LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1 AND 2 PCR, NON CSF\tHV2DNA\tHSV-2 DNA, QN PCR\t9718086.806\t534380.8163\t31\t301260691\t3.00E+15\t98\t52369320\t9.04E+13
-LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\tFMYABM\tM.PNEUMONIA AB IGM\t967879.2258\t551076.7742\t31\t30004256\t3.00E+14\t31\t17083380\t1.94E+13
-LAB BLOOD ORDERABLES\t590579\tLABTOTALS\tPROTEIN S AG, TOTAL\tPROTSTOT\tPROTEIN S AG, TOTAL\t73.61290323\t99571.76471\t31\t2282\t193506\t17\t1692720\t4.72E+11
-URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tVOLUMEURN\tURINE VOLUME\t324358.0323\t399484.4444\t31\t10055099\t1.00E+14\t27\t10786080\t9.76E+12
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tCOM\tCOMMENT\t9999999\t203472\t31\t309999969\t3.10E+15\t285\t57989520\t1.37E+13
-VIROLOGY ORDERABLES\t876723\tLABDVEIBV\tFLU B DFA\tIBV\tINFLUENZA B\t9999999\t53200\t31\t309999969\t3.10E+15\t24\t1276800\t91484654400
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tVL\tPRODUCT VOLUME\t175.3333333\t26873.33333\t30\t5260\t937182\t9\t241860\t7105950000
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tVL\tPRODUCT VOLUME\t165.2\t25597.5\t30\t4956\t824496\t8\t204780\t7197807600
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tSI\t<<STATUS>>  \t9999999\t21465\t30\t299999970\t3.00E+15\t44\t944460\t67138131600
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tSI\t<<STATUS>>  \t9999999\t23840.57143\t30\t299999970\t3.00E+15\t35\t834420\t53931488400
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tDI\tISSUE DATE/TIME\t9999999\t29855.29412\t30\t299999970\t3.00E+15\t17\t507540\t17832826800
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tUBT\tUNIT BLOOD TYPE  \t9999999\t26117.14286\t30\t299999970\t3.00E+15\t14\t365640\t13709217600
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tPRO\tPRODUCT ID  \t9999999\t23376\t30\t299999970\t3.00E+15\t15\t350640\t18667375200
-BODY FLUID ORDERABLES\t517920\tLABMPOPR3\tANCA MPO / PR - 3M IGG\tMPO\tMYLEOPEROXIDASE, AB\t67.36666667\t60\t30\t2021\t603147\t1\t60\t3600
-BODY FLUID ORDERABLES\t517920\tLABMPOPR3\tANCA MPO / PR - 3M IGG\tSERINEPR3\tSERINE PROTEASE 3\t133.1\t60\t30\t3993\t3053497\t1\t60\t3600
-BODY FLUID ORDERABLES\t541704\tLABFCHOLA\tCHOLESTEROL, FLUID\tFSC1\tFLUID TYPE\t9999999\t715530\t30\t299999970\t3.00E+15\t6\t4293180\t3.37E+12
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t125.8333333\t564\t30\t3775\t481625\t30\t16920\t172555200
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.485933333\t1058\t30\t74.578\t246.005444\t30\t31740\t367498800
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t1058\t30\t299999970\t3.00E+15\t30\t31740\t367498800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t170.1666667\t1096\t30\t5105\t871617\t30\t32880\t350395200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t124.3333333\t1012\t30\t3730\t466050\t30\t30360\t349718400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t10.06703333\t5710\t30\t302.011\t3635.709359\t30\t171300\t23373745200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t5710\t30\t299999970\t3.00E+15\t30\t171300\t23373745200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tATACHTADTI\tATACH TACHY ZONE DETECTION INTERVAL\t363.6666667\t31646\t30\t10910\t3985500\t30\t949380\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t127.5\t9236\t30\t3825\t489375\t30\t277080\t69023354400
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t31646\t30\t0\t0\t30\t949380\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t31646\t30\t0\t0\t30\t949380\t5.21E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t31646\t30\t299999970\t3.00E+15\t30\t949380\t5.21E+11
-HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*\t10049R\tHLA LAB TESTING STATUS\t9999999\t1487994\t30\t299999970\t3.00E+15\t30\t44639820\t1.09E+14
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9450R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t64529.64706\t30\t299999970\t3.00E+15\t85\t5485020\t2.57E+13
-LAB BLOOD ORDERABLES\t473121\tLABPORS\tPORPHYRINS, SERUM\tTPOR\tPORPHYRINS, TOTAL\t2000005.367\t383598.6207\t30\t60000161\t6.00E+14\t29\t11124360\t1.10E+13
-LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGLUF\tGLUCOSE,FASTING\t1333408.133\t20572\t30\t40002244\t4.00E+14\t30\t617160\t15849540000
-LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTT1\tGLUCOSE,GTT 1HR\t666823.9333\t20572\t30\t20004718\t2.00E+14\t30\t617160\t15849540000
-LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTT2\tGLUCOSE,GTT 2HR\t666775.8\t20572\t30\t20003274\t2.00E+14\t30\t617160\t15849540000
-LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTT30\tGLUCOSE,GTT 30 MIN\t666824.4667\t20572\t30\t20004734\t2.00E+14\t30\t617160\t15849540000
-MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tKOH\tKOH PREP\t9999999\t1850399.621\t30\t299999970\t3.00E+15\t1268\t2346306720\t4.55E+15
-MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t342848.8073\t30\t299999970\t3.00E+15\t436\t149482080\t1.48E+14
-MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tRPT\tRPT\t9999999\t52970.76923\t30\t299999970\t3.00E+15\t13\t688620\t38987182800
-MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tSDES\tSPECIMEN\t9999999\t52970.76923\t30\t299999970\t3.00E+15\t13\t688620\t38987182800
-MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tSREQ\tCOMMENTS\t9999999\t52970.76923\t30\t299999970\t3.00E+15\t13\t688620\t38987182800
-URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tCOPPERURN\tCOPPER, U\t666987.0333\t407868.3871\t30\t20009611\t2.00E+14\t31\t12643920\t1.07E+13
-URINE ORDERABLES\t472479\tLABURGL\tGLUCOSE, URINE (RANDOM)\tGLUURN\tGLUCOSE, URINE\t25.6\t6540\t30\t768\t76580\t15\t98100\t1025535600
-VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMVIGM\tCMV IGM AB\t9999999\t168088\t30\t299999970\t3.00E+15\t15\t2521320\t5.08E+11
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tXMR\tUNIT CROSSMATCH  \t9999999\t55366.15385\t29\t289999971\t2.90E+15\t13\t719760\t92014084800
-ECHO ORDERABLES\t472284\tECH29\tECHO - DOBUTAMINE STRESS ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t57\t50083.44828\t29\t1653\t99419\t29\t1452420\t1.03E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDRHYTHM\tICD RHYTHM\t9999999\t32768.27586\t29\t289999971\t2.90E+15\t29\t950280\t6.37E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t6551814.172\t951.7241379\t29\t190002611\t1.90E+15\t29\t27600\t326556000
-HISTOCOMPATIBILITY ORDERABLE\t507225\t10220R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t1641532\t29\t289999971\t2.90E+15\t30\t49245960\t1.25E+14
-HISTOCOMPATIBILITY ORDERABLE\t890834\t10090R\tHLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t656185.7143\t29\t289999971\t2.90E+15\t28\t18373200\t1.91E+13
-HISTOCOMPATIBILITY ORDERABLE\t890834\t10090R\tHLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t9999999\t656185.7143\t29\t289999971\t2.90E+15\t28\t18373200\t1.91E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9108R\tSERUM DATE 2\t9999999\t167828.2759\t29\t289999971\t2.90E+15\t29\t4867020\t4.48E+12
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9110R\tMCS 2\t52.03448276\t167828.2759\t29\t1509\t235747\t29\t4867020\t4.48E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9087R\tSERUM DATE 2\t9999999\t170863.4483\t29\t289999971\t2.90E+15\t29\t4955040\t4.48E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9089R\tMCS 2\t78.13793103\t170863.4483\t29\t2266\t291532\t29\t4955040\t4.48E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9596R\tRESULT 2\t9999999\t170863.4483\t29\t289999971\t2.90E+15\t29\t4955040\t4.48E+12
-LAB BLOOD ORDERABLES\t471634\tLABANTNEU\tANTI - NEUTROPHIL AB\tANA\tANTI-NEUTROPHIL AB:\t9999999\t318946.1538\t29\t289999971\t2.90E+15\t65\t20731500\t1.24E+13
-LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tBGSPRC\tSPECIMEN COMMENT\t9999999\t6370\t29\t289999971\t2.90E+15\t42\t267540\t8777350800
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t53190\t29\t289999971\t2.90E+15\t28\t1489320\t1.27E+11
-LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMOSIS PCR\tTOXSP\tSPECIMEN TYPE\t9999999\t2500149.057\t29\t289999971\t2.90E+15\t53\t132507900\t2.79E+15
-LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tNAPA\tN-ACETYLPROCAINAMIDE (NAPA)\t7.806896552\t4711.034483\t29\t226.4\t2448.42\t29\t136620\t1298286000
-LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPA\tPROCAINAMIDE\t344834.1793\t4711.034483\t29\t10000191.2\t1.00E+14\t29\t136620\t1298286000
-LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP1\tPEAK, TROUGH, OR RANDOM\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
-LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP2\tDATE & TIME OF LAST DOSE\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
-LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP3\tDOSAGE (INCLUDE UNITS)\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
-LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP4\tADMINISTRATION ROUTE\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
-URINE ORDERABLES\t473099\tLABUPHOS\tPHOSPHATE WITH CREATININE, URINE (RANDOM)\tPHOSU\tPHOSPHATE, URINE\t344868.6621\t7290\t29\t10001191.2\t1.00E+14\t14\t102060\t1513508400
-URINE ORDERABLES\t473099\tLABUPHOS\tPHOSPHATE WITH CREATININE, URINE (RANDOM)\tPHUCRU\tPHOSPHATE/CREAT RATIO\t1034483.392\t7290\t29\t30000018.38\t3.00E+14\t14\t102060\t1513508400
-URINE ORDERABLES\t473099\tLABUPHOS\tPHOSPHATE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t74.06551724\t7290\t29\t2147.9\t280636.75\t14\t102060\t1513508400
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tSI\t<<STATUS>>  \t9999999\t29561.53846\t28\t279999972\t2.80E+15\t39\t1152900\t1.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tDI\tISSUE DATE/TIME\t9999999\t35298.75\t28\t279999972\t2.80E+15\t16\t564780\t28894690800
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tUBT\tUNIT BLOOD TYPE  \t9999999\t29464.61538\t28\t279999972\t2.80E+15\t13\t383040\t18645940800
-BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tUBT\tUNIT BLOOD TYPE  \t9999999\t31638.46154\t28\t279999972\t2.80E+15\t13\t411300\t20614381200
-BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tUBT\tUNIT BLOOD TYPE  \t9999999\t32307.69231\t28\t279999972\t2.80E+15\t13\t420000\t21061454400
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tFLDTYP\tFLUID SOURCE\t9999999\t8266.666667\t28\t279999972\t2.80E+15\t9\t74400\t837540000
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tRBCFLD\tRBC COUNT, FLD (/UL)\t2324586.536\t8266.666667\t28\t65088423\t6.11E+14\t9\t74400\t837540000
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tWBCFLD\tNUCL CELLS, FLD(/UL)\t2144055.536\t8266.666667\t28\t60033555\t6.00E+14\t9\t74400\t837540000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t163.2142857\t745.7142857\t28\t4570\t758100\t28\t20880\t269409600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t238.75\t807.8571429\t28\t6685\t1619175\t28\t22620\t286707600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t0.107142857\t6347.142857\t28\t3\t5\t28\t177720\t23396342400
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9601R\tRESULT 2\t9999999\t173822.1429\t28\t279999972\t2.80E+15\t28\t4867020\t4.48E+12
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9629R\tREFERENCE SERUM DESCRIPTION\t9999999\t2884.285714\t28\t279999972\t2.80E+15\t28\t80760\t6368097600
-LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tANTI-RNP ANTIBODY\tRNPQNT\tANTI-RNP LEVEL\t6428586.643\t646600.7143\t28\t180000426\t1.80E+15\t28\t18104820\t1.95E+13
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t379288.125\t28\t279999972\t2.80E+15\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471804\tLAB11025R\tC1 INHIBITOR, QUANT\tC1ESTR\tC-1-ESTERASE INHIBITOR\t357174.5357\t270932.1429\t28\t10000887\t1.00E+14\t28\t7586100\t2.23E+12
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tCAION\tCALCIUM, IONIZED\t2500000.68\t2267.142857\t28\t70000019.04\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tCL\tCHLORIDE, WHOLE BLD\t2500080.25\t2267.142857\t28\t70002247\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tGLU\tGLUCOSE, WHOLE BLOOD\t2500132.536\t2267.142857\t28\t70003711\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tHCT\tHCT (EST)\t2500022.036\t2267.142857\t28\t70000617\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tK\tPOTASSIUM, WHOLE BLD\t2500003.318\t2267.142857\t28\t70000092.9\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tNA\tSODIUM, WHOLE BLOOD\t2500105.964\t2267.142857\t28\t70002967\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tTCO2A\tTCO2\t2500016.55\t2267.142857\t28\t70000463.4\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tTHB\tTHB\t2500006.943\t2267.142857\t28\t70000194.4\t7.00E+14\t14\t31740\t370602000
-LAB BLOOD ORDERABLES\t473197\tLABYPYRU\tPYRUVIC ACID\tPYRU\tPYRUVIC ACID\t357143.0857\t342690.9091\t28\t10000006.4\t1.00E+14\t33\t11308800\t5.03E+12
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTCMT\tCOMMENT\t9999999\t1024809.63\t28\t279999972\t2.80E+15\t81\t83009580\t9.92E+13
-LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOS\tTESTOSTERONE, TOTAL\t1071679.214\t366215.5556\t28\t30007018\t3.00E+14\t27\t9887820\t4.07E+12
-MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tCULT\tCULT/OTHER RSLT:\t9999999\t1850399.621\t28\t279999972\t2.80E+15\t1268\t2346306720\t4.55E+15
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUAR\tINFLUENZA A RNA\t9999999\t142990.5882\t28\t279999972\t2.80E+15\t17\t2430840\t5.55E+11
-VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVZVMQT\tVZV IGM LEVEL\t9642856.229\t299835.8824\t28\t269999974.4\t2.70E+15\t34\t10194420\t3.75E+12
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tXMR\tUNIT CROSSMATCH  \t9999999\t54977.14286\t27\t269999973\t2.70E+15\t14\t769680\t94305974400
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tDI\tISSUE DATE/TIME\t9999999\t27197.14286\t27\t269999973\t2.70E+15\t14\t380760\t12856773600
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tPRO\tPRODUCT ID  \t9999999\t53298.46154\t27\t269999973\t2.70E+15\t13\t692880\t99484610400
-BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tPRO\tPRODUCT ID  \t9999999\t50293.84615\t27\t269999973\t2.70E+15\t13\t653820\t97952439600
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tDX\tDIAGNOSIS\t9999999\t8266.666667\t27\t269999973\t2.70E+15\t9\t74400\t837540000
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tFTURB\tTURBIDITY, FLD\t9999999\t8266.666667\t27\t269999973\t2.70E+15\t9\t74400\t837540000
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t8266.666667\t27\t269999973\t2.70E+15\t9\t74400\t837540000
-COAGULATION ORDERABLES\t577589\tLABFIBANT\tFIBRINOGEN ANTIGEN\tFIBANT\tFIBRINOGEN ANTIGEN\t3333547.481\t79560\t27\t90005782\t9.00E+14\t27\t2148120\t3.99E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t66.35407407\t35173.33333\t27\t1791.56\t166329.6092\t27\t949680\t6.37E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t1.444444444\t6564.444444\t27\t39\t1169\t27\t177240\t23394960000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t0.915740741\t6824.444444\t27\t24.725\t27.656875\t27\t184260\t23446767600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.459259259\t6824.444444\t27\t12.4\t6.36\t27\t184260\t23446767600
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9633R\tREFERENCE SAMPLE DATE\t9999999\t631.1111111\t27\t269999973\t2.70E+15\t27\t17040\t262490400
-LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tBGSPR\tSPECIMEN COMMENT\t9999999\t686.6666667\t27\t269999973\t2.70E+15\t36\t24720\t51876000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSEZY\tSEZARY CELLS\t740771.7778\t87997.77778\t27\t20000838\t2.00E+14\t27\t2375940\t4.75E+11
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM2\tC.PNEUMONIAE IGG\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM3\tC.PNEUMONIAE IGM\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM4\tC.TRACHOMATIS IGG\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM5\tC.TRACHOMATIS IGM\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM6\tC.PSITTACI IGG\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM7\tC.PSITTACI IGM\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
-LAB BLOOD ORDERABLES\t472037\tLABYCLON\tCLONAZEPAM, SERUM\tYCLON1\tCLONAZEPAM\t5185195.926\t698320\t27\t140000290\t1.40E+15\t36\t25139520\t6.61E+13
-LAB BLOOD ORDERABLES\t472143\tLABYDESIP\tDESIPRAMINE, SERUM\tDESIP\tDESIPRAMINE\t2222304.963\t271722.8571\t27\t60002234\t6.00E+14\t28\t7608240\t4.23E+12
-LAB BLOOD ORDERABLES\t472437\tLABG6PDQT\tG6PD QUANTITATIVE\tG6PD\tG6PD, QUANTITATIVE\t2962967.444\t384931.2\t27\t80000121\t8.00E+14\t25\t9623280\t5.49E+12
-LAB BLOOD ORDERABLES\t472797\tLABYMANG\tMANGANESE\tMN\tMANGANESE\t740742.5963\t310182.5806\t27\t20000050.1\t2.00E+14\t31\t9615660\t3.76E+12
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tQTFNRS\tQUANTIFERON-TB\t9999999\t183897.5\t27\t269999973\t2.70E+15\t24\t4413540\t8.77E+11
-LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOSFREE\tTESTOSTERONE, FREE\t740749.8074\t373086.9767\t27\t20000244.8\t2.00E+14\t43\t16042740\t6.74E+12
-LAB BLOOD ORDERABLES\t473780\tLABBARTPC\tBARTONELLA DNA, QUALI, PCR\tSDES10\tSPECIMEN\t9999999\t188286\t27\t269999973\t2.70E+15\t50\t9414300\t2.14E+12
-LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3RS\tRESULT\t9999999\t531220.8\t27\t269999973\t2.70E+15\t175\t92963640\t6.34E+13
-LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\tFPARV1\tPARVOVIRUS IGG INDEX\t370373.063\t251412.7273\t27\t10000072.7\t1.00E+14\t33\t8296620\t2.66E+12
-LAB BLOOD ORDERABLES\t744250\tLAB12781R\tBRUCELLA ANTIBODIES\t12778R\tBRUCELLA IGG\t740740.8163\t327064.4444\t27\t20000002.04\t2.00E+14\t27\t8830740\t3.27E+12
-MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tCULT\tCULT/OTHER RSLT:\t9999999\t361273.8129\t27\t269999973\t2.70E+15\t556\t200868240\t1.60E+14
-MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tGS\tGRAM STAIN\t9999999\t361273.8129\t27\t269999973\t2.70E+15\t556\t200868240\t1.60E+14
-STOOL ORDERABLES\t668089\tLABSTOBID\tOCCULT BLD, IMMUNO, STOOL, DIAG TEST\tOCCIMM\tSTOOL SAMPLE1,IMMUN\t9999999\t7063.333333\t27\t269999973\t2.70E+15\t18\t127140\t2108602800
-VIROLOGY ORDERABLES\t472519\tLABHATA\tHEPATITIS A AB TOTAL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t61437.27273\t27\t269999973\t2.70E+15\t22\t1351620\t1.23E+11
-VIROLOGY ORDERABLES\t472525\tLABHBSQL\tANTI-HBS QUAL\tHQLINT\tINTERPRETATION\t9999999\t49138.5336\t27\t269999973\t2.70E+15\t491\t24127020\t2.62E+12
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS PCR QUANT\tADPCR1\tADENVIRUS DNA\t6747357.074\t131400\t27\t182178641\t1.80E+15\t1\t131400\t17265960000
-VIROLOGY ORDERABLES\t670953\tLAB11153R\tHERPES VIRUS 6 DNA, QUANT\t11154R\tSOURCE\t9999999\t175940\t27\t269999973\t2.70E+15\t6\t1055640\t1.98E+11
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tSI\t<<STATUS>>  \t9999999\t31336.66667\t26\t259999974\t2.60E+15\t36\t1128120\t1.06E+11
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tXMR\tUNIT CROSSMATCH  \t9999999\t35261.53846\t26\t259999974\t2.60E+15\t13\t458400\t40947336000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.961538462\t1271.538462\t26\t25\t32.52\t26\t33060\t367995600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.425\t1271.538462\t26\t11.05\t5.1325\t26\t33060\t367995600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t443.4615385\t36544.61538\t26\t11530\t5249161.5\t26\t950160\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t2692307.838\t6729.230769\t26\t70000003.8\t7.00E+14\t26\t174960\t32665176000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0.076923077\t6791.538462\t26\t2\t2\t26\t176580\t23394200400
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9433R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t67897.8\t26\t259999974\t2.60E+15\t100\t6789780\t2.67E+13
-IMMUNOLOGY ORDERABLES\t633412\tLABRPRQT\tRPR QUANTITATIVE\tRPRTTR\tTIER\t9230768.462\t2718840\t26\t239999980\t2.40E+15\t25\t67971000\t3.96E+15
-LAB BLOOD ORDERABLES\t471705\tLABYANTJO\tAUTOANTIBODIES TO JO1\tJO1AB\tAUTOANTIBODIES TO JO 1\t9999999\t153156.5217\t26\t259999974\t2.60E+15\t23\t3522600\t5.83E+11
-LAB BLOOD ORDERABLES\t471960\tLABCENABY\tANTI CENTROMERE ANTIBODY\tCENINT\tANTI-CENTROMERE INTERPRETATION\t9999999\t420534.9153\t26\t259999974\t2.60E+15\t118\t49623120\t2.82E+13
-LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tUNFRACTIONATED HEPARIN\tHEPAR\tHEPARIN ACTIVITY LEVEL\t1153846.319\t2097.692308\t26\t30000004.3\t3.00E+14\t26\t54540\t143622000
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t8077068.758\t138684.5455\t26\t210003787.7\t2.10E+15\t22\t3051060\t4.80E+11
-LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGMAB\tB.BURGDORFERI IGM AB WB\t9999999\t257757.3529\t26\t259999974\t2.60E+15\t68\t17527500\t6.70E+12
-LAB BLOOD ORDERABLES\t473197\tLABYPYRU\tPYRUVIC ACID\tPYRU\tPYRUVIC ACID (MG/DL)\t4615385.035\t335502.2222\t26\t120000010.9\t1.20E+15\t27\t9058560\t4.17E+12
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15DX\tCLINICAL INDICATION\t9999999\t643218\t26\t259999974\t2.60E+15\t50\t32160900\t2.37E+13
-LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tCCPR\tANTI CCP ANTIBODY\t9999999\t2437250.769\t26\t259999974\t2.60E+15\t26\t63368520\t2.95E+15
-LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tRHFR\tRHEUMATOID FACTOR\t9999999\t2437250.769\t26\t259999974\t2.60E+15\t26\t63368520\t2.95E+15
-LAB BLOOD ORDERABLES\t668154\tLABGDGP\tIGG ANTI DGP\tGDGPT\tIGG ANTI DGP LEVEL\t9230769.154\t380188.8\t26\t239999998\t2.40E+15\t25\t9504720\t4.61E+12
-LAB BLOOD ORDERABLES\t668158\tLABADAM13\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMACT\tADAMTS-13 ACTIVITY\t384645.6923\t190497\t26\t10000788\t1.00E+14\t20\t3809940\t1.38E+12
-MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tSDES\tSPECIMEN\t9999999\t616054.2857\t26\t259999974\t2.60E+15\t14\t8624760\t5.65E+12
-MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tSREQ\tCOMMENTS\t9999999\t616054.2857\t26\t259999974\t2.60E+15\t14\t8624760\t5.65E+12
-URINE ORDERABLES\t471763\tLABUBICAR\tBICARBONATE, URINE\tHCO3URN\tBICARBONATE, URINE\t9230773\t118578.4615\t26\t240000098\t2.40E+15\t13\t1541520\t2.14E+11
-URINE ORDERABLES\t471763\tLABUBICAR\tBICARBONATE, URINE\tUT\tCOLLECTION PERIOD\t9999999\t118578.4615\t26\t259999974\t2.60E+15\t13\t1541520\t2.14E+11
-URINE ORDERABLES\t471763\tLABUBICAR\tBICARBONATE, URINE\tVOL\tVOLUME\t2692331.577\t118578.4615\t26\t70000621\t7.00E+14\t13\t1541520\t2.14E+11
-URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tYUCOP4\tCU CONC (UG/L)\t1923253.077\t431540\t26\t50004580\t5.00E+14\t18\t7767720\t8.65E+12
-URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tTKU\tPOTASSIUM EXCRETION\t384691.6923\t49980\t26\t10001984\t1.00E+14\t13\t649740\t65850469200
-URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tUK\tPOTASSIUM, URINE\t384654.6846\t49980\t26\t10001021.8\t1.00E+14\t13\t649740\t65850469200
-URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tUT2\tCOLLECTION PERIOD POTASSIUM\t769252.1538\t49980\t26\t20000556\t2.00E+14\t13\t649740\t65850469200
-URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tVOL2\tVOLUME POTASSIUM\t386614.3462\t49980\t26\t10051973\t1.00E+14\t13\t649740\t65850469200
-VIROLOGY ORDERABLES\t497426\tLABHIVDAG\tHIV - 1 DIRECT AG (ICD)\tHIVS\tHIV-1 P24 (ICD)AG\t9999999\t410679.1304\t26\t259999974\t2.60E+15\t23\t9445620\t4.14E+12
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUAMT\tMETHOD\t9999999\t142990.5882\t26\t259999974\t2.60E+15\t17\t2430840\t5.55E+11
-BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tUBT\tUNIT BLOOD TYPE  \t9999999\t27975\t25\t249999975\t2.50E+15\t12\t335700\t17437417200
-BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tUBT\tUNIT BLOOD TYPE  \t9999999\t31958.18182\t25\t249999975\t2.50E+15\t11\t351540\t18758574000
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tPRO\tPRODUCT ID  \t9999999\t49705\t25\t249999975\t2.50E+15\t12\t596460\t77125532400
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tPRO\tPRODUCT ID  \t9999999\t31900\t25\t249999975\t2.50E+15\t12\t382800\t39290846400
-BODY FLUID ORDERABLES\t471627\tLABAGAP\tANION GAP\tAG\tANION GAP\t9.76\t#VALUE!\t25\t244\t2712\t0\tNone\tNone
-ECHO ORDERABLES\t472293\tECH25\tECHO - LIMITED TRANSTHORACIC ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t42.88\t63352.8\t25\t1072\t53110\t25\t1583820\t2.39E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0.12\t7072.8\t25\t3\t5\t25\t176820\t23394034800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t3.400044\t7442.4\t25\t85.0011\t341.6285447\t25\t186060\t23447574000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t1.046\t703.2\t25\t26.15\t32.2825\t25\t17580\t126644400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.512\t703.2\t25\t12.8\t7.46\t25\t17580\t126644400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t424.46\t7260\t25\t10611.5\t4550376.75\t25\t181500\t23423526000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t2.76\t29822.4\t25\t69\t1095\t25\t745560\t4.56E+11
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tLDL\tLDL (CALCULATED)\t6000033.68\t15128.57143\t25\t150000842\t1.50E+15\t21\t317700\t12194132400
-LAB BLOOD ORDERABLES\t473463\tLABTOXOP2\tTOXOPLASMOSIS PCR\tTOXSP\tSPECIMEN TYPE\t9999999\t1116100\t25\t249999975\t2.50E+15\t6\t6696600\t7.98E+12
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKPR\tPRIMER\t9999999\t864270\t25\t249999975\t2.50E+15\t14\t12099780\t1.30E+13
-MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tSREQ\tCOMMENTS\t9999999\t105838\t25\t249999975\t2.50E+15\t30\t3175140\t1.08E+12
-STOOL ORDERABLES\t472448\tLABGBMIFA\tGBM IGA AND IGG AB\tGBMIGA\tGBM ANTIBODY, IGA\t9999999\t283150.2128\t25\t249999975\t2.50E+15\t47\t13308060\t9.93E+12
-STOOL ORDERABLES\t472448\tLABGBMIFA\tGBM IGA AND IGG AB\tGBMIGG\tGBM-IGG ELISA\t9999999\t283150.2128\t25\t249999975\t2.50E+15\t47\t13308060\t9.93E+12
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tEBNA\tEPSTEIN-BARR NUC AB\t9999999\t164195.2941\t25\t249999975\t2.50E+15\t51\t8373960\t1.83E+12
-VIROLOGY ORDERABLES\t472525\tLABHAB\tHEPATITIS B SURFACE AB\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t57564.54545\t25\t249999975\t2.50E+15\t22\t1266420\t91465016400
-VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEBV VIRAL CAPSID ANTIGEN IGM\tVMQNT\tVMQNT\t9199999.334\t287901.6\t25\t229999983.4\t2.30E+15\t25\t7197540\t3.49E+12
-VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQIN\tTEST DESCRIPTION\t9999999\t220336.3485\t25\t249999975\t2.50E+15\t1079\t237742920\t6.49E+13
-VIROLOGY ORDERABLES\t898804\tLABEBVQL\tEBV DNA PCR QUAL\tEBVQLD\tEBV DNA\t9999999\t233724\t25\t249999975\t2.50E+15\t25\t5843100\t1.92E+12
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tVL\tPRODUCT VOLUME\t175.5\t32175\t24\t4212\t752578\t4\t128700\t4204652400
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t24\t239999976\t2.40E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t24\t239999976\t2.40E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tSI\t<<STATUS>>  \t9999999\t23185.45455\t24\t239999976\t2.40E+15\t33\t765120\t28925913600
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tXMR\tUNIT CROSSMATCH  \t9999999\t30115.38462\t24\t239999976\t2.40E+15\t13\t391500\t16055694000
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tDI\tISSUE DATE/TIME\t9999999\t25220\t24\t239999976\t2.40E+15\t12\t302640\t10053201600
-BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tDI\tISSUE DATE/TIME\t9999999\t34270\t24\t239999976\t2.40E+15\t12\t411240\t20614377600
-ECHO ORDERABLES\t472287\tECH30\tECHO - EXERCISE STRESS ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t59.25\t63915\t24\t1422\t85634\t24\t1533960\t1.87E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVSND\tBRADY SENSED AV DELAY\t416796\t7320\t24\t10003104\t1.00E+14\t24\t175680\t32664888000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t39472.5\t24\t239999976\t2.40E+15\t24\t947340\t5.21E+11
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tAAMINOA\tA-AMINOADIPIC ACID\t1.333333333\t293858.1818\t24\t32\t1024\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tAAMINOB\tA-AMINOBUTYRIC ACID\t24.41666667\t293858.1818\t24\t586\t19294\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tALANINE\tALANINE\t357.4166667\t293858.1818\t24\t8578\t3400766\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tARGININE\tARGININE\t64.54166667\t293858.1818\t24\t1549\t128437\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tASPART\tASPARTIC ACID\t10.04166667\t293858.1818\t24\t241\t5981\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tASPGN\tASPARAGINE\t50.70833333\t293858.1818\t24\t1217\t65809\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tBALN\tB-ALANINE\t2.875\t293858.1818\t24\t69\t1103\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tBAMBA\tB-AMINOISOBUTYRIC AC\t0.375\t293858.1818\t24\t9\t45\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tCITRUL\tCITRULLINE\t21.91666667\t293858.1818\t24\t526\t13884\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tCYSTINE\tCYSTINE\t44.33333333\t293858.1818\t24\t1064\t60252\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tCYTHN\tCYSTATHIONINE\t0.958333333\t293858.1818\t24\t23\t69\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tETHLN\tETHANOLAMINE\t1.416666667\t293858.1818\t24\t34\t798\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tGLUTAC\tGLUTAMIC ACID\t65.70833333\t293858.1818\t24\t1577\t131177\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tGLUTAMINE\tGLUTAMINE\t561.4166667\t293858.1818\t24\t13474\t8622788\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tGLYCINE\tGLYCINE\t250.6666667\t293858.1818\t24\t6016\t1639364\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tHISTID\tHISTIDINE\t75.33333333\t293858.1818\t24\t1808\t144688\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tHMCY\tHOMOCYSTEINE\t0.041666667\t293858.1818\t24\t1\t1\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tHYPROL\tHYDROXYPROLINE\t10.41666667\t293858.1818\t24\t250\t4890\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tISOLEU\tISOLEUCINE\t63.91666667\t293858.1818\t24\t1534\t127622\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tLEUCINE\tLEUCINE\t109.0833333\t293858.1818\t24\t2618\t354584\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tLYSINE\tLYSINE\t182.7916667\t293858.1818\t24\t4387\t881781\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tMETHIO\tMETHIONINE\t35.70833333\t293858.1818\t24\t857\t50005\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tORNITHINE\tORNITHINE\t69.33333333\t293858.1818\t24\t1664\t151972\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tPHALN\tPHENYLALANINE\t77.20833333\t293858.1818\t24\t1853\t161815\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tPROLINE\tPROLINE\t185.2916667\t293858.1818\t24\t4447\t919963\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tSARSN\tSARCOSINE\t0\t293858.1818\t24\t0\t0\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tSERINE\tSERINE\t94.875\t293858.1818\t24\t2277\t238807\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTAURINE\tTAURINE\t72.20833333\t293858.1818\t24\t1733\t173895\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTHREONINE\tTHREONINE\t113.7916667\t293858.1818\t24\t2731\t350829\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTRYPTOPHAN\tTRYPTOPHAN\t40.29166667\t293858.1818\t24\t967\t51223\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTYROSINE\tTYROSINE\t75.29166667\t293858.1818\t24\t1807\t180113\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tVALINE\tVALINE\t188.125\t293858.1818\t24\t4515\t1058849\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t472332\tLABECL\tEUGLOBULIN CLOT LYSIS\tWEACA\tWITH EACA\t9999999\t97411.30435\t24\t239999976\t2.40E+15\t23\t2240460\t3.56E+11
-LAB BLOOD ORDERABLES\t472332\tLABECL\tEUGLOBULIN CLOT LYSIS\tWOEACA\tWITHOUT EACA\t9999999\t97411.30435\t24\t239999976\t2.40E+15\t23\t2240460\t3.56E+11
-LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY\tHEPAR\tHEPARIN ACTIVITY LEVEL\t833333.5283\t28984.28571\t24\t20000004.68\t2.00E+14\t14\t405780\t22945870800
-LAB BLOOD ORDERABLES\t472518\tLABHAV\tHEPATITIS A IGM AB\tHEPAIGM\tHEPATITIS A AB-IGM\t9999999\t55252.17391\t24\t239999976\t2.40E+15\t23\t1270800\t92547453600
-LAB BLOOD ORDERABLES\t472524\tLAB11107R\tHBE ANTIGEN\tHBEAG\tHEPATITIS B E AG\t9999999\t498591.2069\t24\t239999976\t2.40E+15\t116\t57836580\t1.04E+14
-LAB BLOOD ORDERABLES\t473301\tLABSPIE\tPROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM\tSPIER\tRESULT\t9999999\t254723.2367\t24\t239999976\t2.40E+15\t1415\t360433380\t1.27E+14
-LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tLAB3\tREFERENCE LAB\t9999999\t2214834.286\t24\t239999976\t2.40E+15\t21\t46511520\t3.48E+14
-LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\t12431R\tINTERPRETATION\t9999999\t148917\t24\t239999976\t2.40E+15\t80\t11913360\t4.45E+12
-LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tADGPR\tIGA ANTI DGP ANTIBODY\t9999999\t466236\t24\t239999976\t2.40E+15\t20\t9324720\t5.28E+12
-MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tTEST7\tTEST NAME\t9999999\t576624\t24\t239999976\t2.40E+15\t10\t5766240\t1.60E+13
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVTIP\tTEST DESCRIPTION\t9999999\t154991.0938\t24\t239999976\t2.40E+15\t1801\t279138960\t5.55E+13
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADQLSP\tSPECIMEN\t9999999\t221752.1053\t24\t239999976\t2.40E+15\t38\t8426580\t3.12E+12
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t23\t229999977\t2.30E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t23\t229999977\t2.30E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tPRO\tPRODUCT ID  \t9999999\t28614\t23\t229999977\t2.30E+15\t10\t286140\t25408443600
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRC\tCOMMENT\t9999999\t480680\t23\t229999977\t2.30E+15\t18\t8652240\t6.29E+12
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tLYMFLD\tLYMPHOCYTE %, FLD\t434800.0435\t2374.285714\t23\t10000401\t1.00E+14\t7\t16620\t79326000
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tMONFLD\tMONO/MESO %, FLD\t434817.6957\t2374.285714\t23\t10000807\t1.00E+14\t7\t16620\t79326000
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tNEUTFLD\tNEUTROPHIL %, FLD\t434824.3913\t2374.285714\t23\t10000961\t1.00E+14\t7\t16620\t79326000
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t0.173913043\t41358.26087\t23\t4\t10\t23\t951240\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t385.4782609\t41366.08696\t23\t8866\t3525524.96\t23\t951420\t6.37E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t1278.26087\t23\t229999977\t2.30E+15\t23\t29400\t325238400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t7687.826087\t23\t0\t0\t23\t176820\t23392134000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t11.86956522\t108193.0435\t23\t273\t46061\t23\t2488440\t3.53E+12
-HISTOCOMPATIBILITY ORDERABLE\t979724\t10287R\tHLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT\t10049R\tHLA LAB TESTING STATUS\t9999999\t5317523.478\t23\t229999977\t2.30E+15\t23\t122303040\t2.88E+15
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHEPAIGM\tHEPATITIS A AB-IGM\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
-LAB BLOOD ORDERABLES\t471581\tLABYASU\tALPHASUBUNIT PITUITARY MARKER\tYASU1\tALPHA-SUBUNIT\t2173913.357\t476154.5455\t23\t50000007.2\t5.00E+14\t22\t10475400\t5.53E+12
-LAB BLOOD ORDERABLES\t473282\tLABRUB\tRUBELLA AB IGG\tRUB\tRUBELLA IGG ANTIBODY\t9999999\t55632\t23\t229999977\t2.30E+15\t20\t1112640\t1.08E+11
-LAB BLOOD ORDERABLES\t473333\tLABSLIDE\tSLIDE SAVE FOR CLINICIAN\tSLIDE\tSLIDE SAVE FOR CLINICIAN\t9999999\t22314.54545\t23\t229999977\t2.30E+15\t22\t490920\t27778276800
-LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tVWINTP\tINTERPRETATION\t9999999\t50682.5\t23\t229999977\t2.30E+15\t24\t1216380\t92331061200
-LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\tHERG\tHERPESVIRUS 6 AB IGG\t9999999\t287974.7368\t23\t229999977\t2.30E+15\t19\t5471520\t1.92E+12
-LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\tHERM\tHERPESVIRUS 6 AB IGM\t9999999\t287974.7368\t23\t229999977\t2.30E+15\t19\t5471520\t1.92E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12419R\tACETOHEXIMIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12425R\tREPAGLINIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12426R\tTOLAZAMIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12427R\tTOLBUTAMIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
-MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tSPCT\tSPECIMEN TYPE\t9999999\t576624\t23\t229999977\t2.30E+15\t10\t5766240\t1.60E+13
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tWAXCST\tWAXY CASTS\t9999999\t13056\t23\t229999977\t2.30E+15\t15\t195840\t5306227200
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tWBCAST\tWBC CASTS\t9999999\t74595\t23\t229999977\t2.30E+15\t12\t895140\t3.81E+11
-VIROLOGY ORDERABLES\t876719\tLABDVEIAV\tFLU A DFA\tIAVSP\tSPECIMEN\t9999999\t78912.72727\t23\t229999977\t2.30E+15\t33\t2604120\t1.09E+12
-VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tHVIP\tTEST DESCRIPTION\t9999999\t61157.32283\t23\t229999977\t2.30E+15\t381\t23300940\t3.03E+12
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tVL\tPRODUCT VOLUME\t168.9090909\t32300\t22\t3716\t632852\t3\t96900\t3211477200
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tSI\t<<STATUS>>  \t9999999\t26370\t22\t219999978\t2.20E+15\t28\t738360\t56790936000
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tSI\t<<STATUS>>  \t9999999\t25982.22222\t22\t219999978\t2.20E+15\t27\t701520\t55275112800
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tSI\t<<STATUS>>  \t9999999\t15080\t22\t219999978\t2.20E+15\t24\t361920\t13517726400
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tDI\tISSUE DATE/TIME\t9999999\t25560\t22\t219999978\t2.20E+15\t12\t306720\t10244714400
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tDI\tISSUE DATE/TIME\t9999999\t31915\t22\t219999978\t2.20E+15\t12\t382980\t18645937200
-BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tDI\tISSUE DATE/TIME\t9999999\t35912.72727\t22\t219999978\t2.20E+15\t11\t395040\t20441440800
-BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tFCOLOR\tAPPEARANCE\t9999999\t8266.666667\t22\t219999978\t2.20E+15\t9\t74400\t837540000
-BODY FLUID ORDERABLES\t472552\tLABYHISTC\tHISTOPLASMA AG, CSF\tYHISC4\tRESULT,CSF\t9999999\t522603.1579\t22\t219999978\t2.20E+15\t19\t9929460\t5.38E+12
-BODY FLUID ORDERABLES\t497388\tLABFURICA\tURIC ACID, FLUID\tFSC2\tFLUID TYPE\t9999999\t611090\t22\t219999978\t2.20E+15\t12\t7333080\t7.20E+12
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGDNM3\tDNMT3A\t9999999\t921340.9091\t22\t219999978\t2.20E+15\t22\t20269500\t2.19E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGERHR\tERBB2/HER2\t9999999\t921340.9091\t22\t219999978\t2.20E+15\t22\t20269500\t2.19E+13
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVPCD\tBRADY PACED AV DELAY\t192.9545455\t8023.636364\t22\t4245\t929825\t22\t176520\t32664470400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t118.4090909\t7966.363636\t22\t2605\t311025\t22\t175260\t32664970800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRTRKRT\tBRADY TRACKING RATE\t121.8181818\t8023.636364\t22\t2680\t330100\t22\t176520\t32664470400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0.954545455\t43270.90909\t22\t21\t233\t22\t951960\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t3.454545455\t43254.54545\t22\t76\t1762\t22\t951600\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.246590909\t8023.636364\t22\t49.425\t126.255625\t22\t176520\t32664470400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.481818182\t8023.636364\t22\t10.6\t6.24\t22\t176520\t32664470400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t5454649.227\t7947.272727\t22\t120002283\t1.20E+15\t22\t174840\t32663498400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t8040\t22\t0\t0\t22\t176880\t23392130400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t8040\t22\t0\t0\t22\t176880\t23392130400
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9078R\tINTERPRETATION/ COMMENTS\t9999999\t277473.3582\t22\t219999978\t2.20E+15\t268\t74362860\t1.44E+14
-LAB BLOOD ORDERABLES\t471809\tLABYC1IN\tC1 ESTERASE INHIBITOR AG\tYC1INH\tC1 ESTERASE INH. AG\t454576.2727\t158980.9091\t22\t10000678\t1.00E+14\t22\t3497580\t6.14E+11
-LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tEGFR\tEGFR\t9545455.864\t8005.714286\t22\t210000029\t2.10E+15\t21\t168120\t3571308000
-LAB BLOOD ORDERABLES\t473759\tLABYVD125\t1,25-DIHYDROXYVITAMIN D, SERUM\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t2272756.864\t319000.9091\t22\t50000651\t5.00E+14\t22\t7018020\t2.65E+12
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12011R\tQ FEVER IGM PHASE II  SCRN\t9999999\t308861.9118\t22\t219999978\t2.20E+15\t136\t42005220\t1.99E+13
-LAB BLOOD ORDERABLES\t670806\tLAB11182R\tNICOTINE & METABOLITES\t11183R\tNICOTINE\t9090908.786\t288117.3913\t22\t199999993.3\t2.00E+15\t23\t6626700\t2.09E+12
-LAB BLOOD ORDERABLES\t670806\tLAB11182R\tNICOTINE & METABOLITES\t11184R\tCOTININE\t5454574.364\t288117.3913\t22\t120000636\t1.20E+15\t23\t6626700\t2.09E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12422R\tGLIPIZIDE\t9999999\t584971.3043\t22\t219999978\t2.20E+15\t23\t13454340\t9.34E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12423R\tGLYBURIDE\t9999999\t584971.3043\t22\t219999978\t2.20E+15\t23\t13454340\t9.34E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12424R\tNATEGLINIDE\t9999999\t584971.3043\t22\t219999978\t2.20E+15\t23\t13454340\t9.34E+12
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t547240.7303\t22\t219999978\t2.20E+15\t1068\t584453100\t2.37E+15
-POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tTESTEX\tTEST STRIP EXP\t9999999\t1802.142857\t22\t219999978\t2.20E+15\t28\t50460\t6153973200
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12821R\tPOTASSIUM, FECES\t909122.5\t211889.1429\t22\t20000695\t2.00E+14\t35\t7416120\t2.14E+12
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12822R\tCHLORIDE, FECES\t2272772.227\t211889.1429\t22\t50000989\t5.00E+14\t35\t7416120\t2.14E+12
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12824R\tMAGNESIUM, FECES\t1818211.773\t211889.1429\t22\t40000659\t4.00E+14\t35\t7416120\t2.14E+12
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12825R\tPHOSPHORUS, FECES\t1363665\t211889.1429\t22\t30000630\t3.00E+14\t35\t7416120\t2.14E+12
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12826R\tOSMOTIC GAP, FECES\t1818255.727\t211889.1429\t22\t40001626\t4.00E+14\t35\t7416120\t2.14E+12
-URINE ORDERABLES\t471819\tLABUCA\tCALCIUM WITH CREATININE, URINE (RANDOM)\tCACREAT\tCALCIUM/CREAT RATIO\t6363635.906\t10580\t22\t139999989.9\t1.40E+15\t15\t158700\t7357388400
-URINE ORDERABLES\t471819\tLABUCA\tCALCIUM WITH CREATININE, URINE (RANDOM)\tCAURN\tCALCIUM, URINE\t5909094.727\t10580\t22\t130000084\t1.30E+15\t15\t158700\t7357388400
-URINE ORDERABLES\t471819\tLABUCA\tCALCIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t51.56818182\t10580\t22\t1134.5\t95189.73\t15\t158700\t7357388400
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tTIMEU\tCOLLECTION PERIOD\t1363656.955\t309984\t22\t30000453\t3.00E+14\t10\t3099840\t1.05E+12
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tUCR\tCREATININE, URINE\t1364862.636\t309984\t22\t30026978\t3.00E+14\t10\t3099840\t1.05E+12
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tUCR\tCREATININE, URN\t66.54545455\t309984\t22\t1464\t127870\t10\t3099840\t1.05E+12
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tUVOL\tVOLUME\t457009.0955\t309984\t22\t10054200.1\t1.00E+14\t10\t3099840\t1.05E+12
-VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVRC1\tCMV DAY 1 RAPID CULTURE\t9999999\t175699.0909\t22\t219999978\t2.20E+15\t22\t3865380\t7.11E+11
-VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVRC2\tCMV DAY 2 RAPID CULTURE\t9999999\t175699.0909\t22\t219999978\t2.20E+15\t22\t3865380\t7.11E+11
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t21\t209999979\t2.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tSI\t<<STATUS>>  \t9999999\t30282.22222\t21\t209999979\t2.10E+15\t27\t817620\t69801476400
-BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tDI\tISSUE DATE/TIME\t9999999\t35186.66667\t21\t209999979\t2.10E+15\t9\t316680\t17547530400
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tUBT\tUNIT BLOOD TYPE  \t9999999\t76960\t21\t209999979\t2.10E+15\t9\t692640\t99484596000
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tPRO\tPRODUCT ID  \t9999999\t35752.5\t21\t209999979\t2.10E+15\t8\t286020\t25391120400
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tUBT\tUNIT BLOOD TYPE  \t9999999\t35022\t21\t209999979\t2.10E+15\t10\t350220\t18667335600
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tBTURB\tTURBIDITY, CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCOLORCSF\tCOLOR AFTER CENTRIFUGE, CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCOLORCSF\tCOLOR, CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCSOURC\tSOURCE\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCTURB\tTURB. AFTER CENTRIF., CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tRBCCSF\tRBC COUNT, CSF\t2395710.952\t4722.857143\t21\t50309930\t5.00E+14\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tWBCCSF\tWBC COUNT, CSF\t1904875.619\t4722.857143\t21\t40002388\t4.00E+14\t7\t33060\t198118800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t45414.28571\t21\t209999979\t2.10E+15\t21\t953700\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0.095238095\t45348.57143\t21\t2\t2\t21\t952320\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t0.142857143\t45320\t21\t3\t3\t21\t951720\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t596.7619048\t8357.142857\t21\t12532\t20214055.5\t21\t175500\t32662890000
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.298809524\t8374.285714\t21\t48.275\t141.388125\t21\t175860\t32664632400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.661904762\t8374.285714\t21\t13.9\t13.45\t21\t175860\t32664632400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t1360\t21\t209999979\t2.10E+15\t21\t28560\t324014400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t1360\t21\t209999979\t2.10E+15\t21\t28560\t324014400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t1360\t21\t209999979\t2.10E+15\t21\t28560\t324014400
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9471R\tDONOR DOB\t9999999\t20\t21\t209999979\t2.10E+15\t21\t420\t25200
-LAB BLOOD ORDERABLES\t472546\tLAB11042R\tHISTAMINE LEVEL\tHIST\tHISTAMINE, WB\t3337422.857\t432563.3333\t21\t70085880\t7.00E+14\t18\t7786140\t4.19E+12
-LAB BLOOD ORDERABLES\t472869\tLABYAMA\tMITOCHONDRIAL AB M2, SERUM\tYAMA\tMITOCHONDRIAL ANTIBODY, M2\t8571427.781\t204642.8571\t21\t179999983.4\t1.80E+15\t21\t4297500\t9.61E+11
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12421R\tGLIMEPIRIDE\t9999999\t584971.3043\t21\t209999979\t2.10E+15\t23\t13454340\t9.34E+12
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12820R\tSODIUM, FECES\t952442.8571\t211889.1429\t21\t20001300\t2.00E+14\t35\t7416120\t2.14E+12
-STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12823R\tOSMOLALITY, FECES\t952709.4762\t211889.1429\t21\t20006899\t2.00E+14\t35\t7416120\t2.14E+12
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVNO\tNOTES\t9999999\t345906.879\t21\t209999979\t2.10E+15\t157\t54307380\t2.62E+13
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN (EBNA) IGG\tEBNQNT\tEBNQNT\t2857144.104\t298560\t21\t60000026.18\t6.00E+14\t21\t6269760\t3.42E+12
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tVL\tPRODUCT VOLUME\t171.95\t28428\t20\t3439\t593139\t5\t142140\t4257471600
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tVL\tPRODUCT VOLUME\t161.5\t35670\t20\t3230\t527736\t4\t142680\t5214686400
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tXMR\tUNIT CROSSMATCH  \t9999999\t41585.45455\t20\t199999980\t2.00E+15\t11\t457440\t47298261600
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tSI\t<<STATUS>>  \t9999999\t15915.65217\t20\t199999980\t2.00E+15\t23\t366060\t13709250000
-BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tDI\tISSUE DATE/TIME\t9999999\t30512.72727\t20\t199999980\t2.00E+15\t11\t335640\t17437413600
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSOTHER\tOTHERS, SYN FLD\t12.75\t7413.333333\t20\t255\t12149\t9\t66720\t621388800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tDX\tDIAGNOSIS\t9500009.3\t4722.857143\t20\t190000186\t1.90E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t4722.857143\t20\t199999980\t2.00E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tTUBENO\tCSF TUBE #\t7999999.65\t4722.857143\t20\t159999993\t1.60E+15\t7\t33060\t198118800
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBMPMLR\tPML, BM\t1000000.045\t644650\t20\t20000000.91\t2.00E+14\t18\t11603700\t8.75E+12
-BODY FLUID ORDERABLES\t669983\tLAB11000R\tCEA, PANCREATIC CYST\t11001R\tCEA, PANCREATIC CYST\t200.595\t174390\t20\t4011.9\t3682861.81\t14\t2441460\t4.88E+11
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t17244\t20\t199999980\t2.00E+15\t20\t344880\t18588276000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t8500729.6\t17244\t20\t170014592\t1.70E+15\t20\t344880\t18588276000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t17244\t20\t199999980\t2.00E+15\t20\t344880\t18588276000
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t31.221\t8763\t20\t624.42\t44567.0186\t20\t175260\t32663235600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t8.5533\t47601\t20\t171.066\t1550.642352\t20\t952020\t6.37E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t9500001.55\t8973\t20\t190000031\t1.90E+15\t20\t179460\t23419731600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t9500000.55\t8973\t20\t190000011\t1.90E+15\t20\t179460\t23419731600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t144\t8973\t20\t2880\t423000\t20\t179460\t23419731600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t192.6\t8973\t20\t3852\t762704\t20\t179460\t23419731600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t9499999.8\t8973\t20\t189999996\t1.90E+15\t20\t179460\t23419731600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t9500001.55\t8973\t20\t190000031\t1.90E+15\t20\t179460\t23419731600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t47139\t20\t199999980\t2.00E+15\t20\t942780\t5.21E+11
-LAB BLOOD ORDERABLES\t471527\tLABYANTSM\tAUTOANTIBODIES TO SM\tSMAB\tAUTOANTIBODIES TO SM\t8499999.29\t193333.3333\t20\t169999985.8\t1.70E+15\t27\t5220000\t1.23E+12
-LAB BLOOD ORDERABLES\t471679\tLABYAVH\tARGININE VASOPRESSIN (ADH)\tARGININEVP\tARGININE VASOPRESSIN\t4000000.97\t622883.3333\t20\t80000019.4\t8.00E+14\t18\t11211900\t7.91E+12
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32R2\tRESULT 2\t9999999\t1049052\t20\t199999980\t2.00E+15\t20\t20981040\t2.76E+13
-LAB BLOOD ORDERABLES\t472482\tLAB11080R\tGAD AB\tGAD65AB\tGAD65 ANTIBODY\t0.4965\t454171.7647\t20\t9.93\t82.2047\t17\t7720920\t4.32E+12
-LAB BLOOD ORDERABLES\t472496\tLABHBODY\tHEINZ BODY PREP\tHBODY\tHEINZ BODY PREP\t9499999.75\t29740\t20\t189999995\t1.90E+15\t18\t535320\t30060064800
-LAB BLOOD ORDERABLES\t472523\tLAB11106R\tHBE AB\tHBEAB\tHEPATITIS B E AB\t9999999\t451540.9615\t20\t199999980\t2.00E+15\t104\t46960260\t6.91E+13
-LAB BLOOD ORDERABLES\t472553\tLABHIVWBL\tANTI-HIV\tHIVI\tINTERPRETATION\t9999999\t51579.60028\t20\t199999980\t2.00E+15\t4303\t221947020\t2.06E+13
-LAB BLOOD ORDERABLES\t472847\tLABYMEXIL\tMEXILETINE\tMEXIL\tMEXILETINE\t500000.715\t378385\t20\t10000014.3\t1.00E+14\t24\t9081240\t3.97E+12
-LAB BLOOD ORDERABLES\t473057\tLAB11088R\tPP\tYPANPP\tPANCREATIC POLYPEPTIDE\t500286.7\t642441\t20\t10005734\t1.00E+14\t20\t12848820\t9.25E+12
-LAB BLOOD ORDERABLES\t473176\tLABYPR3\tPROTEINASE 3 AUTOANTIBODIES\tYPR31\tPROTEINASE 3 ANTIBODIES\t8499999.615\t162284.5161\t20\t169999992.3\t1.70E+15\t31\t5030820\t8.87E+11
-LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOSBIO\tTESTOSTERONE,BIOAVAILABLE, S\t500087.445\t366215.5556\t20\t10001748.9\t1.00E+14\t27\t9887820\t4.07E+12
-LAB BLOOD ORDERABLES\t497414\tLABFHAN\tHANTAVIRUS AB ELISA\tHANIGG\tHANTAVIRUS IGG\t8999999.3\t223917.1429\t20\t179999986\t1.80E+15\t21\t4702260\t1.18E+12
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\t12064R\tY.ENTERCOCLITICA IGG\t8499999.321\t424091.4286\t20\t169999986.4\t1.70E+15\t21\t8905920\t4.71E+12
-LAB BLOOD ORDERABLES\t668180\tLABDWNIL\tDONOR, WEST NILE VIRUS NAT\tWNIL1\tDONOR, WEST NILE VIRUS NAT\t9999999\t255850\t20\t199999980\t2.00E+15\t6\t1535100\t4.20E+11
-LAB BLOOD ORDERABLES\t668184\tLABDCGSN\tDONOR, CHAGAS SCN W/REFL\t11893R\tDONOR, CHAGAS RIPA\t9999999\t245110\t20\t199999980\t2.00E+15\t6\t1470660\t3.86E+11
-LAB BLOOD ORDERABLES\t668184\tLABDCGSN\tDONOR, CHAGAS SCN W/REFL\tCGSN1\tDONOR, CHAGAS SCN W/REFL\t9999999\t245110\t20\t199999980\t2.00E+15\t6\t1470660\t3.86E+11
-LAB BLOOD ORDERABLES\t744262\tLAB11566R\tENTEROVIRUS RNA, QUAL\t11679R\tSOURCE\t9999999\t474590\t20\t199999980\t2.00E+15\t30\t14237700\t3.71E+13
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12301R\t12301R\t6000003.4\t200448\t20\t120000068\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12302R\t12302R\t6000000.75\t200448\t20\t120000015\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12303R\t12303R\t6000005.635\t200448\t20\t120000112.7\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12304R\t12304R\t6000002.25\t200448\t20\t120000045\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12305R\t12305R\t6000003.37\t200448\t20\t120000067.4\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12306R\t12306R\t6000086.6\t200448\t20\t120001732\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12307R\t12307R\t6000079.6\t200448\t20\t120001592\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12308R\t12308R\t6000057.4\t200448\t20\t120001148\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12309R\t12309R\t6000022.95\t200448\t20\t120000459\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12310R\t12310R\t6000076.75\t200448\t20\t120001535\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12311R\t12311R\t6000043\t200448\t20\t120000860\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12312R\t12312R\t6000008.15\t200448\t20\t120000163\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12313R\t12313R\t6000010.95\t200448\t20\t120000219\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12314R\t12314R\t6000004.6\t200448\t20\t120000092\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12315R\t12315R\t6000001.15\t200448\t20\t120000023\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12316R\t12316R\t6000028.8\t200448\t20\t120000576\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12317R\t12317R\t6000008.9\t200448\t20\t120000178\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12318R\t12318R\t6000004.55\t200448\t20\t120000091\t1.20E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12420R\tCHLORPROPAMIDE\t9999999\t584971.3043\t20\t199999980\t2.00E+15\t23\t13454340\t9.34E+12
-MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tRPT\tRPT\t9999999\t60293.33333\t20\t199999980\t2.00E+15\t9\t542640\t43775150400
-MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tSDES\tSPECIMEN\t9999999\t60293.33333\t20\t199999980\t2.00E+15\t9\t542640\t43775150400
-MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tSREQ\tCOMMENTS\t9999999\t60293.33333\t20\t199999980\t2.00E+15\t9\t542640\t43775150400
-MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tRPT\tRPT\t9999999\t259140\t20\t199999980\t2.00E+15\t14\t3627960\t1.12E+12
-MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tSDES\tSPECIMEN\t9999999\t259140\t20\t199999980\t2.00E+15\t14\t3627960\t1.12E+12
-POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tQC12\tQC RESULT\t9999999\t1802.142857\t20\t199999980\t2.00E+15\t28\t50460\t6153973200
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN (EBNA) IGG\tEBNQUL\tEBNQUL\t9999999\t298560\t20\t199999980\t2.00E+15\t21\t6269760\t3.42E+12
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBCP\tEBV BAMHI-W RESULT\t9500015.3\t403485\t20\t190000306\t1.90E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBDN\tEBV BAMHI-W DNA\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBLG\tEBV BAMHI-W RESULT LOG 10\t9499999.176\t403485\t20\t189999983.5\t1.90E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBSP\tSPECIMEN\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQCP\tRESULT\t9500012.75\t403485\t20\t190000255\t1.90E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQDN\tEBV DNA\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQLG\tRESULT LOG 10\t9499999.172\t403485\t20\t189999983.4\t1.90E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQSP\tSPECIMEN\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tVL\tPRODUCT VOLUME\t164\t20914.28571\t19\t3116\t527556\t7\t146400\t3773671200
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tSI\t<<STATUS>>  \t9999999\t26390\t19\t189999981\t1.90E+15\t24\t633360\t53763537600
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tSI\t<<STATUS>>  \t9999999\t18257.14286\t19\t189999981\t1.90E+15\t21\t383400\t18645969600
-BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tUBT\tUNIT BLOOD TYPE  \t9999999\t81690\t19\t189999981\t1.90E+15\t8\t653520\t97952421600
-BLOOD BANK PRODUCT ORDERABLES\t889182\tLABERUCRY\tEMERGENCY RELEASE CRYO\tCRSTA\tCRYOPRECIPITATE UNITS\t9999999\t7297.894737\t19\t189999981\t1.90E+15\t19\t138660\t1707145200
-BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tLYMCSF\tLYMPHOCYTES, CSF\t2631608.263\t4305\t19\t50000557\t5.00E+14\t4\t17220\t78354000
-BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tMONOCSF\tMONOCYTOID CELLS, CSF\t2631596.105\t4305\t19\t50000326\t5.00E+14\t4\t17220\t78354000
-BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tNEUTCSF\tNEUTROPHILS, CSF\t2631604.684\t4305\t19\t50000489\t5.00E+14\t4\t17220\t78354000
-BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14MT\tMETHOD\t9999999\t1070614.286\t19\t189999981\t1.90E+15\t7\t7494300\t9.42E+12
-BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14PM\tPRIMERS\t9999999\t1070614.286\t19\t189999981\t1.90E+15\t7\t7494300\t9.42E+12
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAMSRT\tBRADY AMS RATE\t175.6315789\t9306.315789\t19\t3337\t590607\t19\t176820\t32664265200
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t6315792.842\t40553.68421\t19\t120000064\t1.20E+15\t19\t770520\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t1052673.105\t50210.52632\t19\t20000789\t2.00E+14\t19\t954000\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t532.2631579\t9255.789474\t19\t10113\t6039575.5\t19\t175860\t32662587600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tATACHTADTI\tATACH TACHY ZONE DETECTION INTERVAL\t354.7368421\t9464.210526\t19\t6740\t2393800\t19\t179820\t23419602000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t9464.210526\t19\t189999981\t1.90E+15\t19\t179820\t23419602000
-LAB BLOOD ORDERABLES\t471608\tLABYAMNOR\tAMITRIPTYLINE AND NORTRIPTYLINE\tAMINOR\tAMITRIPT & NORTRIPT\t4210660.579\t266919\t19\t80002551\t8.00E+14\t20\t5338380\t1.62E+12
-LAB BLOOD ORDERABLES\t471608\tLABYAMNOR\tAMITRIPTYLINE AND NORTRIPTYLINE\tAMITRP\tAMITRIPTYLINE, PLASMA\t4210624.316\t266919\t19\t80001862\t8.00E+14\t20\t5338380\t1.62E+12
-LAB BLOOD ORDERABLES\t471608\tLABYAMNOR\tAMITRIPTYLINE AND NORTRIPTYLINE\tNORTRP\tNORTRIPTYLINE\t3157933.368\t266919\t19\t60000734\t6.00E+14\t20\t5338380\t1.62E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHAIRY\tHAIRY CELLS\t11.78947368\t30136.66667\t19\t224\t6458\t18\t542460\t83257894800
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBASO\tBASO, %\t1052631.753\t4940\t19\t20000003.3\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBASOAB\tBASOS, ABS\t1052631.497\t4940\t19\t19999998.45\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tEOS\tEOS, %\t1052632.668\t4940\t19\t20000020.7\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tEOSAB\tEOS, ABS\t1052631.574\t4940\t19\t19999999.91\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tLYM\tLYM, %\t1052642.832\t4940\t19\t20000213.8\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tLYMAB\tLYM, ABS\t1052632.338\t4940\t19\t20000014.42\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMONO\tMONO, %\t1052639.211\t4940\t19\t20000145\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMONOAB\tMONO, ABS\t1052631.942\t4940\t19\t20000006.9\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNEUT\tNEUT, %\t1052700.379\t4940\t19\t20001307.2\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNEUTAB\tNEUT, ABS\t1052636.928\t4940\t19\t20000101.64\t2.00E+14\t6\t29640\t511128000
-LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHMET\tMETHOD\t9999999\t638456.25\t19\t189999981\t1.90E+15\t16\t10215300\t8.63E+12
-LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tRESUL2\tRESULT\t9999999\t1896672.381\t19\t189999981\t1.90E+15\t63\t119490360\t1.36E+15
-LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\tECIGG\tE.CHAFFEENSIS IGG TITER\t9999999\t905448.75\t19\t189999981\t1.90E+15\t16\t14487180\t6.76E+13
-LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\tECIGM\tE.CHAFFEENSIS IGM TITER\t9999999\t905448.75\t19\t189999981\t1.90E+15\t16\t14487180\t6.76E+13
-LAB BLOOD ORDERABLES\t541666\tLABC153\tCANCER AG 15-3\tC153\tCANCER ANTIGEN 15-3\t74.36842105\t43998.94737\t19\t1413\t929577\t19\t835980\t72087663600
-LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR HIV1 / HBV / HCV NAT\t11894R\tDISCRIMINATORY TESTING\t9999999\t277700\t19\t189999981\t1.90E+15\t3\t833100\t2.36E+11
-LAB BLOOD ORDERABLES\t670724\tLAB11312R\tSOLUBLE LIVER AG AB, IGG\t11313R\tSOLUBLE LIVER AG AB, IGG\t2.484210526\t246884.2105\t19\t47.2\t133.02\t19\t4690800\t1.35E+12
-LAB BLOOD ORDERABLES\t744256\tLAB11562R\tLYMPHOCYTIC CHORIOMENINGITI AB\t11672R\tLCM IGG\t9999999\t254046.6667\t19\t189999981\t1.90E+15\t18\t4572840\t1.33E+12
-LAB BLOOD ORDERABLES\t744256\tLAB11562R\tLYMPHOCYTIC CHORIOMENINGITI AB\t11673R\tLCM IGM\t9999999\t254046.6667\t19\t189999981\t1.90E+15\t18\t4572840\t1.33E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tASMINT\tANTI-SM INTERPRETATION\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tASMQUL\tANTI-SM RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tCENQUL\tANTI-CENTROMERE RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tDNAQUL\tANTI-DSDNA RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tJO1QUL\tANTI-JO1 RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tRNPQUL\tANTI-RNP RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSCLQUL\tANTI-SCL70 RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSAQUL\tANTI-RO RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSBQUL\tANTI-LA RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12294R\t12294R\t5789488.526\t200448\t19\t110000282\t1.10E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12295R\t12295R\t5789502.947\t200448\t19\t110000556\t1.10E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12296R\t12296R\t5789484.526\t200448\t19\t110000206\t1.10E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12297R\t12297R\t5789495.684\t200448\t19\t110000418\t1.10E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12298R\t12298R\t5789485.947\t200448\t19\t110000233\t1.10E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12299R\t12299R\t5789476.158\t200448\t19\t110000047\t1.10E+15\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12300R\t12300R\t5789482.421\t200448\t19\t110000166\t1.10E+15\t20\t4008960\t1.99E+12
-MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tCULT\tCULT/OTHER RSLT:\t9999999\t452979\t19\t189999981\t1.90E+15\t1080\t489217320\t4.09E+14
-MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tSREQ\tCOMMENTS\t9999999\t259140\t19\t189999981\t1.90E+15\t14\t3627960\t1.12E+12
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tWAXCST\tWAXY CASTS\t9999999\t65307.69231\t19\t189999981\t1.90E+15\t13\t849000\t3.29E+11
-VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEBV EARLY ANTIGEN IGG\tEAGQUL\tEAGQUL\t9999999\t285229.4118\t19\t189999981\t1.90E+15\t17\t4848900\t2.81E+12
-VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRESVDI\tTEST DESCRIPTION\t9999999\t97678.90244\t19\t189999981\t1.90E+15\t820\t80096700\t1.50E+13
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t18\t179999982\t1.80E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tXMR\tUNIT CROSSMATCH  \t9999999\t45280\t18\t179999982\t1.80E+15\t9\t407520\t45979106400
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tPRO\tPRODUCT ID  \t9999999\t20477.14286\t18\t179999982\t1.80E+15\t7\t143340\t4565293200
-BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tPRO\tPRODUCT ID  \t9999999\t24574.28571\t18\t179999982\t1.80E+15\t7\t172020\t6302466000
-BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tPRO\tPRODUCT ID  \t9999999\t24488.57143\t18\t179999982\t1.80E+15\t7\t171420\t6256458000
-BODY FLUID ORDERABLES\t471692\tLABASPPCP\tASPERGILLUS AB PRECIPITINS\tASPIGG\tASPERGILLUS IGG ABY\t9999999\t407597.6471\t18\t179999982\t1.80E+15\t17\t6929160\t3.73E+12
-BODY FLUID ORDERABLES\t472024\tLABSWTCL\tCHLORIDE, SWEAT\tCLSWT\tSWEAT CHLORIDE\t1111142.056\t57372\t18\t20000557\t2.00E+14\t5\t286860\t74246965200
-BODY FLUID ORDERABLES\t472024\tLABSWTCL\tCHLORIDE, SWEAT\tSCLW\tSWEAT COLLECTED\t0.183222222\t57372\t18\t3.298\t0.78907\t5\t286860\t74246965200
-BODY FLUID ORDERABLES\t978518\tLAB12721R\tADENOSINE DEAMINASE, PLEURAL\t12720R\tADENOSINE DEAMINASE, PLEURAL\t5000001.483\t211971.4286\t18\t90000026.7\t9.00E+14\t21\t4451400\t1.15E+12
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t19046.66667\t18\t179999982\t1.80E+15\t18\t342840\t18586195200
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t8947983.889\t19046.66667\t18\t161063710\t1.60E+15\t18\t342840\t18586195200
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMTLBATIMP\tPM TELEMETRY BATTERY IMPEDANCE\t555556.2695\t623.3333333\t18\t10000012.85\t1.00E+14\t18\t11220\t200293200
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t25.22222222\t52890\t18\t454\t108006\t18\t952020\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t10.01055556\t52976.66667\t18\t180.19\t2343.1371\t18\t953580\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t52976.66667\t18\t179999982\t1.80E+15\t18\t953580\t6.37E+11
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t352.0555556\t9403.333333\t18\t6337\t39426559\t18\t169260\t23349848400
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9634R\tREFERENCE SERUM DESCRIPTION\t9999999\t30\t18\t179999982\t1.80E+15\t18\t540\t32400
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMGQUL\tCMV IGG RESULT\t9999999\t386356.6667\t18\t179999982\t1.80E+15\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMMQUL\tCMV IGM RESULT\t9999999\t386356.6667\t18\t179999982\t1.80E+15\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMV\tCMV ANTIBODY\t9999999\t370200\t18\t179999982\t1.80E+15\t14\t5182800\t2.46E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t370200\t18\t179999982\t1.80E+15\t14\t5182800\t2.46E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVGGQNT\tVZV IGG LEVEL\t1111113.181\t386356.6667\t18\t20000037.26\t2.00E+14\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVGGQUL\tVZV IGG RESULT\t9999999\t386356.6667\t18\t179999982\t1.80E+15\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t370200\t18\t179999982\t1.80E+15\t14\t5182800\t2.46E+12
-LAB BLOOD ORDERABLES\t472468\tLABYGLUC\tGLUCAGON\tGLUCAGON\tGLUCAGON\t1666696\t433484.2105\t18\t30000528\t3.00E+14\t19\t8236200\t4.79E+12
-LAB BLOOD ORDERABLES\t472503\tLABHGBS\tHEMOGLOBIN, SERUM\tHGB\tHEMOGLOBIN, SERUM\t7.355555556\t223452.8571\t18\t132.4\t2463.7\t14\t3128340\t8.17E+11
-LAB BLOOD ORDERABLES\t472627\tLABCYLEX\tIMMUNE CELL FUNCTION (CYLEX)\tCYCLXR\tIMMUNE CELL FUNCTION\t2222389.611\t133493.0769\t18\t40003013\t4.00E+14\t26\t3470820\t6.46E+11
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKRE\tRESULT\t9999999\t557059.322\t18\t179999982\t1.80E+15\t118\t65733000\t4.63E+13
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNRS\tQUANTIFERON-TB\t9999999\t449070\t18\t179999982\t1.80E+15\t18\t8083260\t1.64E+13
-LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tADGPT\tIGA ANTI DGP ANTIBODY LEVEL\t9444444.333\t449910\t18\t169999998\t1.70E+15\t16\t7198560\t4.12E+12
-LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR HIV1 / HBV / HCV NAT\tHIBCV1\tDONOR HIV/HBV/HCV RESULT\t9999999\t277700\t18\t179999982\t1.80E+15\t3\t833100\t2.36E+11
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tCENINT\tANTI-CENTROMERE INTERPRETATION\t9999999\t441287.3684\t18\t179999982\t1.80E+15\t19\t8384460\t4.72E+12
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tRPT\tRPT\t9999999\t471560\t18\t179999982\t1.80E+15\t18\t8488080\t5.50E+12
-POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tHPYL\tPATIENT HPYLORI\t9999999\t1802.142857\t18\t179999982\t1.80E+15\t28\t50460\t6153973200
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tHVA\tHOMOVANILLIC ACID\t1666670.494\t309984\t18\t30000068.9\t3.00E+14\t10\t3099840\t1.05E+12
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tVMA\tVMA\t1666672.144\t309984\t18\t30000098.6\t3.00E+14\t10\t3099840\t1.05E+12
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tVMAUR\tVMA, URINE\t5.388888889\t309984\t18\t97\t1227\t10\t3099840\t1.05E+12
-VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-PLASMA\tCMVLIP\tTEST DESCRIPTION\t9999999\t252366.8139\t18\t179999982\t1.80E+15\t317\t80000280\t2.70E+13
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVIPR\tTEST DESCRIPTION\t9999999\t289760.8349\t18\t179999982\t1.80E+15\t527\t152703960\t5.49E+13
-VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tRVRPIP\tTEST DECRIPTION\t9999999\t106230.4\t18\t179999982\t1.80E+15\t225\t23901840\t1.05E+13
-BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tPRO\tPRODUCT ID  \t9999999\t23710\t17\t169999983\t1.70E+15\t6\t142260\t5357336400
-BODY FLUID ORDERABLES\t497388\tLABFURICA\tURIC ACID, FLUID\tURICFLD\tURIC ACID BODY FLUID\t588239.6235\t628560\t17\t10000073.6\t1.00E+14\t13\t8171280\t7.90E+12
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t10316.47059\t17\t169999983\t1.70E+15\t17\t175380\t32663163600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t10316.47059\t17\t169999983\t1.70E+15\t17\t175380\t32663163600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t10316.47059\t17\t169999983\t1.70E+15\t17\t175380\t32663163600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t2353116.235\t10380\t17\t40002976\t4.00E+14\t17\t176460\t32663192400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.048529412\t56212.94118\t17\t17.825\t23.808125\t17\t955620\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRDUR\tRV THRESHOLD DURATION\t0.505882353\t56212.94118\t17\t8.6\t5.2\t17\t955620\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t6470593.176\t45412.94118\t17\t110000084\t1.10E+15\t17\t772020\t6.04E+11
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t5294117.618\t9451.764706\t17\t89999999.5\t9.00E+14\t17\t160680\t12571538400
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9480R\tLOCUS 6\t9999999\t15521.25\t17\t169999983\t1.70E+15\t16\t248340\t61404872400
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9481R\tLOCUS 7\t9999999\t15521.25\t17\t169999983\t1.70E+15\t16\t248340\t61404872400
-LAB BLOOD ORDERABLES\t471928\tLABYACIGA\tCARDIOLIPIN AB, IGA\tYACIG1\tCARDIOLIPIN AB,IGA\t8823530.5\t183673.6364\t17\t150000018.5\t1.50E+15\t22\t4040820\t8.95E+11
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMEGA\tMEGAKARY/100WBC\t1.470588235\t83771.25\t17\t25\t47\t16\t1340340\t4.97E+11
-LAB BLOOD ORDERABLES\t472654\tLABYIGF2\tINSULIN LIKE GROWTH FACTOR II\tYIGF2A\tIFG-II\t443.9411765\t997762.5\t17\t7547\t3526071\t16\t15964200\t1.69E+13
-LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tYTESHD\tFREE TESTOSTERONE\t9999999\t384682.5\t17\t169999983\t1.70E+15\t16\t6154920\t2.67E+12
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKCM\tCOMMENT\t9999999\t823236.9231\t17\t169999983\t1.70E+15\t13\t10702080\t1.10E+13
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKRE\tRESULT\t9999999\t864270\t17\t169999983\t1.70E+15\t14\t12099780\t1.30E+13
-LAB BLOOD ORDERABLES\t670816\tLAB11198R\tVIT A & VIT E\tVITE\tA-TOCOPHEROL, VITAMIN E\t10.45294118\t356116\t17\t177.7\t2329.07\t15\t5341740\t2.17E+12
-MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tRPT\tRPT\t9999999\t1747302.857\t17\t169999983\t1.70E+15\t14\t24462240\t4.57E+13
-MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tSDES\tSPECIMEN\t9999999\t1747302.857\t17\t169999983\t1.70E+15\t14\t24462240\t4.57E+13
-MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tSREQ\tCOMMENTS\t9999999\t1747302.857\t17\t169999983\t1.70E+15\t14\t24462240\t4.57E+13
-MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t342848.8073\t17\t169999983\t1.70E+15\t436\t149482080\t1.48E+14
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t16\t159999984\t1.60E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tUBT\tUNIT BLOOD TYPE  \t9999999\t85174.28571\t16\t159999984\t1.60E+15\t7\t596220\t77125518000
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tUBT\tUNIT BLOOD TYPE  \t9999999\t23880\t16\t159999984\t1.60E+15\t6\t143280\t4565289600
-BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tUBT\tUNIT BLOOD TYPE  \t9999999\t28650\t16\t159999984\t1.60E+15\t6\t171900\t6302451600
-BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tPRO\tPRODUCT ID  \t9999999\t11100\t16\t159999984\t1.60E+15\t5\t55500\t2073337200
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t16\t159999984\t1.60E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tDI\tISSUE DATE/TIME\t9999999\t39420\t16\t159999984\t1.60E+15\t8\t315360\t17456292000
-BODY FLUID ORDERABLES\t497380\tLABYFUN\tUREA NITROGEN, FLUID\tUNFLD\tUREA NITROGEN BODY FLUID\t31.375\t169644\t16\t502\t21770\t10\t1696440\t3.58E+11
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tHEARTRATE\tHEART RATE\t62.875\t133908.75\t16\t1006\t64232\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tPRINTERVAL\tP-R INTERVAL\t185.75\t133908.75\t16\t2972\t571440\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQRSAXIS\tQRS AXIS\t42.4375\t133908.75\t16\t679\t117791\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQRSDINTERVAL\tQRSD INTERVAL\t106.625\t133908.75\t16\t1706\t189540\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQTCINTERVAL\tQTC INTERVAL\t436\t133908.75\t16\t6976\t3081334\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQTINTERVAL\tQT INTERVAL\t430.5\t133908.75\t16\t6888\t3007008\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tRR\tRR\t968.6875\t133908.75\t16\t15499\t15231083\t16\t2142540\t3.12E+12
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t145.75\t48266.25\t16\t2332\t424424\t16\t772260\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t6250021.875\t48266.25\t16\t100000350\t1.00E+15\t16\t772260\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t414.28125\t59692.5\t16\t6628.5\t2791807.59\t16\t955080\t6.37E+11
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t11932.5\t16\t159999984\t1.60E+15\t16\t190920\t32977375200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t11932.5\t16\t159999984\t1.60E+15\t16\t190920\t32977375200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t8750292.625\t11932.5\t16\t140004682\t1.40E+15\t16\t190920\t32977375200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t11932.5\t16\t159999984\t1.60E+15\t16\t190920\t32977375200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t328.04375\t46841.25\t16\t5248.7\t1754780.07\t16\t749460\t4.56E+11
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9170R\tSERUM DATE 3\t9999999\t381052.5\t16\t159999984\t1.60E+15\t16\t6096840\t5.24E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9172R\tRESULT  MCS 3\t21.375\t381052.5\t16\t342\t49456\t16\t6096840\t5.24E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9174R\tRESULT 3\t9999999\t381052.5\t16\t159999984\t1.60E+15\t16\t6096840\t5.24E+12
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9140R\tSERUM DATE 3\t9999999\t381060\t16\t159999984\t1.60E+15\t16\t6096960\t5.24E+12
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9142R\tRESULT  MCS 3\t37.3125\t381060\t16\t597\t33413\t16\t6096960\t5.24E+12
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9144R\tRESULT 3\t9999999\t381060\t16\t159999984\t1.60E+15\t16\t6096960\t5.24E+12
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHDX\tCLINICAL INDICATION\t9999999\t738380\t16\t159999984\t1.60E+15\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHET\tETHNICITY (PROVIDED BY MD)\t9999999\t738380\t16\t159999984\t1.60E+15\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471652\tLABYAT3AG\tANTITHROMBIN AG, PLASMA\tAT3\tANTITHROMBIN III AG\t82.6875\t287780\t16\t1323\t118627\t24\t6906720\t2.99E+12
-LAB BLOOD ORDERABLES\t471706\tLABYSCL70\tAUTOANTIBODIES TO SCL 70\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t179580\t16\t159999984\t1.60E+15\t13\t2334540\t4.62E+11
-LAB BLOOD ORDERABLES\t471807\tLAB11026R\tC1 INHIBITOR, FUNCT\tC1INHF\tC1 ESTERASE INHIB FN\t102.5625\t325215\t16\t1641\t183123\t16\t5203440\t1.89E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472387\tLAB11037R\tFLECAINIDE LEVEL\tFLEC\tFLECAINIDE\t625000.6169\t194816\t16\t10000009.87\t1.00E+14\t15\t2922240\t6.66E+11
-LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHBSAG\tHBSINT\tINTERPRETATION\t9999999\t49510.65059\t16\t159999984\t1.60E+15\t3566\t176554980\t2.12E+13
-LAB BLOOD ORDERABLES\t472645\tLABYINH\tINHIBIN B\tYINH1\tINHIBIN B\t4375263.75\t406324.2857\t16\t70004220\t7.00E+14\t14\t5688540\t2.74E+12
-LAB BLOOD ORDERABLES\t473762\tLAB12586R\tTHIAMIN (VITAMIN B1)\t12588R\tTHIAMIN (VITAMIN B1)\t160.3125\t314523.5294\t16\t2565\t458595\t17\t5346900\t1.75E+12
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12012R\tQ FEVER IGG PHASE L TITER\t9999999\t284160\t16\t159999984\t1.60E+15\t17\t4830720\t1.51E+12
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN5\tY ENTERCOCLITICA 05\t9999999\t3364848\t16\t159999984\t1.60E+15\t15\t50472720\t4.07E+14
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN8\tY ENTERCOCLITICA 08\t9999999\t3364848\t16\t159999984\t1.60E+15\t15\t50472720\t4.07E+14
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN9\tY ENTERCOCLITICA 09\t9999999\t3364848\t16\t159999984\t1.60E+15\t15\t50472720\t4.07E+14
-LAB BLOOD ORDERABLES\t497472\tLABYJCPCR\tJC VIRUS DNA, PCR\tJCDNA\tJC VIRUS DNA\t9999999\t164540.625\t16\t159999984\t1.60E+15\t96\t15795900\t3.74E+12
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12293R\t12293R\t5000019.563\t200448\t16\t80000313\t8.00E+14\t20\t4008960\t1.99E+12
-MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tAFB\tAFB SMEAR\t9999999\t105838\t16\t159999984\t1.60E+15\t30\t3175140\t1.08E+12
-MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tCULT\tCULT/OTHER RSLT:\t9999999\t168009.5772\t16\t159999984\t1.60E+15\t1159\t194723100\t4.94E+13
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tCONRES\tCONTROL RESULTS\t9999999\t13848.57143\t16\t159999984\t1.60E+15\t21\t290820\t62447209200
-POINT OF CARE (NON LAB) TESTS\t589584\tPOC13\tPOC PPD SKIN TEST (AMB)\tERYTHREMA\tERYTHREMA\t4374999.563\t256492.5\t16\t69999993\t7.00E+14\t24\t6155820\t2.36E+12
-POINT OF CARE (NON LAB) TESTS\t589584\tPOC13\tPOC PPD SKIN TEST (AMB)\tMMIND\tMM INDURATION\t3125000.125\t256492.5\t16\t50000002\t5.00E+14\t24\t6155820\t2.36E+12
-POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tTESTL\tTEST STRIP LOG\t742948.25\t873\t16\t11887172\t1.00E+14\t20\t17460\t5970150000
-URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tCORTU\tCORTISOL, URINE\t4795.4375\t466968.75\t16\t76727\t866140725\t16\t7471500\t4.50E+12
-URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tTCORTU\tCORTISOL EXCRETION\t89.011875\t466968.75\t16\t1424.19\t293572.9761\t16\t7471500\t4.50E+12
-URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tUT52\tCOLLECTION PERIOD\t162.25\t466968.75\t16\t2596\t5008336\t16\t7471500\t4.50E+12
-URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tVOL52\tVOLUME\t2440.21875\t466968.75\t16\t39043.5\t148541492.3\t16\t7471500\t4.50E+12
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tTRICHOMONAS\tTRICHOMONAS\t9999999\t10832.30769\t16\t159999984\t1.60E+15\t13\t140820\t3822685200
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tTIMEU\tCOLLECTION PERIOD\t625022.4375\t379830\t16\t10000359\t1.00E+14\t6\t2278980\t9.13E+11
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URINE\t626340.9375\t379830\t16\t10021455\t1.00E+14\t6\t2278980\t9.13E+11
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URN\t55.875\t379830\t16\t894\t62300\t6\t2278980\t9.13E+11
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tUVOL\tVOLUME\t2454.5625\t379830\t16\t39273\t107987111\t6\t2278980\t9.13E+11
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tVMA\tVMA\t625005.625\t379830\t16\t10000090\t1.00E+14\t6\t2278980\t9.13E+11
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11231R\tMETANEPHRINE/CREATININE\t1875148.5\t372466.1538\t16\t30002376\t3.00E+14\t13\t4842060\t1.97E+12
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11232R\tNORMETANEPHRINE/CREATININE\t1875430.813\t372466.1538\t16\t30006893\t3.00E+14\t13\t4842060\t1.97E+12
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11233R\tTOTAL METANEPHRINE/CREATININE\t1875579.5\t372466.1538\t16\t30009272\t3.00E+14\t13\t4842060\t1.97E+12
-VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tCULT\tCULT/OTHER RSLT:\t9999999\t437550\t16\t159999984\t1.60E+15\t22\t9626100\t1.47E+13
-VIROLOGY ORDERABLES\t876723\tLABDVEIBV\tFLU B DFA\tIBVSP\tSPECIMEN\t9999999\t53200\t16\t159999984\t1.60E+15\t24\t1276800\t91484654400
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tVL\tPRODUCT VOLUME\t164.1333333\t36100\t15\t2462\t405886\t3\t108300\t3921210000
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tVL\tPRODUCT VOLUME\t168.1333333\t29391.42857\t15\t2522\t432572\t7\t205740\t6426507600
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tVL\tPRODUCT VOLUME\t167.2666667\t25560\t15\t2509\t420607\t2\t51120\t1470794400
-BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tSI\t<<STATUS>>  \t9999999\t20586\t15\t149999985\t1.50E+15\t20\t411720\t20614413600
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tUBT\tUNIT BLOOD TYPE  \t9999999\t63750\t15\t149999985\t1.50E+15\t6\t382500\t39290828400
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tUBT\tUNIT BLOOD TYPE  \t9999999\t47660\t15\t149999985\t1.50E+15\t6\t285960\t25408432800
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tUBT\tUNIT BLOOD TYPE  \t9999999\t47650\t15\t149999985\t1.50E+15\t6\t285900\t25391113200
-BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tUBT\tUNIT BLOOD TYPE  \t9999999\t28550\t15\t149999985\t1.50E+15\t6\t171300\t6256443600
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tEOSFLD\tEOSINOPHILS, SYN FLD\t4.466666667\t2556\t15\t67\t1267\t10\t25560\t106416000
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTRES\tRESULTS\t9999999\t688582.5\t15\t149999985\t1.50E+15\t16\t11017320\t1.17E+13
-BODY FLUID ORDERABLES\t497327\tLABWNVAB\tWEST NILE VIRUS AB PANEL, CSF\tWNITP\tW NILE INTERPRETATION\t9999999\t272081.9118\t15\t149999985\t1.50E+15\t136\t37003140\t2.07E+13
-BODY FLUID ORDERABLES\t966905\tLAB12607R\tN-METHYL-D-ASPARTATE RECEPTOR AB, CSF\t12606R\tN-METHYL-D-ASPARTATE RECEPTOR AB, CSF\t9999999\t325248\t15\t149999985\t1.50E+15\t15\t4878720\t1.73E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tTWAVEAXIS\tT WAVE AXIS\t21.06666667\t140516\t15\t316\t18074\t15\t2107740\t3.12E+12
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t11772\t15\t149999985\t1.50E+15\t15\t176580\t32662414800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t118\t11752\t15\t1770\t215350\t15\t176280\t32662792800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t3.333333333\t11836\t15\t50\t822\t15\t177540\t32661687600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t67286.08696\t15\t149999985\t1.50E+15\t23\t1547580\t1.21E+12
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t119.6\t-328\t15\t1794\t233836\t15\t-4920\t3304800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t7395736.467\t12720\t15\t110936047\t1.10E+15\t15\t190800\t32977360800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t288.64\t220\t15\t4329.6\t1293145.68\t15\t3300\t25009200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t57670.71429\t15\t149999985\t1.50E+15\t28\t1614780\t9.73E+11
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9075R\tHLA-MOLECULAR PT. SUMMARY\t9999999\t214170.5316\t15\t149999985\t1.50E+15\t395\t84597360\t1.52E+14
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHRE\tRESULT\t9999999\t738380\t15\t149999985\t1.50E+15\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471635\tLABANAS\tANTI - NUCLEAR AB\tANAINTERP\tANA INTERPRETATION\t9999999\t270715.1193\t15\t149999985\t1.50E+15\t2348\t635639100\t2.25E+14
-LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tLA AB\tSSB\tSS-B/LA AB, IGG, SERUM\t7999999.247\t172272\t15\t119999988.7\t1.20E+15\t15\t2584080\t5.49E+11
-LAB BLOOD ORDERABLES\t472646\tLABINHA\tINHIBIN A DIMER\tINHAR\tINHIBIN-A (DIMER)\t1333341.433\t512460\t15\t20000121.5\t2.00E+14\t13\t6661980\t9.92E+12
-LAB BLOOD ORDERABLES\t472662\tLAB11044R\tIL6\tIL6\tINTERLEUKIN 6\t4666676.533\t409564.2857\t15\t70000148\t7.00E+14\t14\t5733900\t4.74E+12
-LAB BLOOD ORDERABLES\t473064\tLAB11090R\tPARIETAL CELL AB\tYPCA\tPARIETAL CELL AB\t6000016.807\t252907.5\t15\t90000252.1\t9.00E+14\t16\t4046520\t1.11E+12
-LAB BLOOD ORDERABLES\t497476\tLABFT4ED\tFREE T4 BY EQUILIBRIUM DIALYSIS\tFT4\tFREE T4 BY EQUIL DIALYSIS\t2.753333333\t267021\t15\t41.3\t169.61\t20\t5340420\t1.78E+12
-LAB BLOOD ORDERABLES\t890123\tLAB12236R\tIBUPROFEN\t12237R\t12237R\t8666667\t290712\t15\t130000005\t1.30E+15\t15\t4360680\t1.38E+12
-MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11CT\tCOMMENT\t9999999\t1087500\t15\t149999985\t1.50E+15\t2\t2175000\t2.77E+12
-MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11DX\tCLINICAL INDICATION\t9999999\t1087500\t15\t149999985\t1.50E+15\t2\t2175000\t2.77E+12
-POINT OF CARE (NON LAB) TESTS\t589584\tPOC13\tPOC PPD SKIN TEST (AMB)\tPHLOC\tLOCATION\t8666665.8\t256492.5\t15\t129999987\t1.30E+15\t24\t6155820\t2.36E+12
-URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tCOCAINE\tCOCAINE\t6666892.867\t30\t15\t100003393\t1.00E+15\t2\t60\t3600
-URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tYUBEGC\tBENZOYLECGONINE\t21531\t30\t15\t322965\t24097922409\t2\t60\t3600
-VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-PLASMA\tCMVLIP\tTEST DESCRIPTION\t9999999\t242296.5517\t15\t149999985\t1.50E+15\t116\t28106400\t9.68E+12
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tEA\tEARLY ANTIBODY (R+D) IGG\t9999999\t155835\t15\t149999985\t1.50E+15\t56\t8726760\t1.86E+12
-VIROLOGY ORDERABLES\t472567\tLABHPVHR2\tHPV DNA PROBE HIGH RISK\tHPV\tHPV DNA PROBE RESULT:\t9999999\t925207.0588\t15\t149999985\t1.50E+15\t17\t15728520\t1.88E+13
-VIROLOGY ORDERABLES\t876721\tLABDVEIAR\tFLU A DFA-REFLEX PCR\tIAR\tINFLUENZA A\t9999999\t118140\t15\t149999985\t1.50E+15\t7\t826980\t1.98E+11
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tVL\tPRODUCT VOLUME\t167.8571429\t33440\t14\t2350\t397084\t3\t100320\t3424896000
-BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tUBT\tUNIT BLOOD TYPE  \t9999999\t28428\t14\t139999986\t1.40E+15\t5\t142140\t5357322000
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tPRO\tPRODUCT ID  \t9999999\t23064\t14\t139999986\t1.40E+15\t5\t115320\t7052032800
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tPRO\tPRODUCT ID  \t9999999\t16740\t14\t139999986\t1.40E+15\t5\t83700\t3495553200
-BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tPRO\tPRODUCT ID  \t9999999\t23028\t14\t139999986\t1.40E+15\t5\t115140\t7038212400
-BODY FLUID ORDERABLES\t471559\tLABYAFCSF\tALPHA FETOPROTEIN, CSF\tAFPCSF\tALPHA FETOPROTEIN, CSF\t9999999\t161382\t14\t139999986\t1.40E+15\t10\t1613820\t3.08E+11
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSCMT\tSYNOVIAL FLUID COMMENT\t9999999\t2888.571429\t14\t139999986\t1.40E+15\t14\t40440\t185760000
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tPAXIS\tP AXIS\t33.21428571\t125280\t14\t465\t28551\t14\t1753920\t2.99E+12
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t1542.857143\t14\t139999986\t1.40E+15\t14\t21600\t80049600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t1542.857143\t14\t139999986\t1.40E+15\t14\t21600\t80049600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t7894248.071\t1542.857143\t14\t110519473\t1.10E+15\t14\t21600\t80049600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t9300568.5\t1542.857143\t14\t130207959\t1.30E+15\t14\t21600\t80049600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t1542.857143\t14\t139999986\t1.40E+15\t14\t21600\t80049600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t326.2214286\t55195.71429\t14\t4567.1\t1519926.47\t14\t772740\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t734.5\t12732.85714\t14\t10283\t99023529\t14\t178260\t32661442800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t14177.14286\t14\t139999986\t1.40E+15\t14\t198480\t33045739200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t387.7142857\t14177.14286\t14\t5428\t2335960\t14\t198480\t33045739200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t1428772\t7320\t14\t20002808\t2.00E+14\t14\t102480\t8712216000
-LAB BLOOD ORDERABLES\t471527\tLABSMABY\tANTI-SM ANTIBODY\tASMQNT\tANTI-SM LEVEL\t5000018\t575712.8571\t14\t70000252\t7.00E+14\t14\t8059980\t6.24E+12
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX\tPANCA\tNEUTROPHIL CYTO AB-P\t9999999\t301613.3333\t14\t139999986\t1.40E+15\t81\t24430680\t8.35E+12
-LAB BLOOD ORDERABLES\t471625\tLABYACE\tANGIOTENSIN CONVERTING ENZYME, SERUM\tACE\tANGIOTESIN CONVERTING ENZYME\t714317.3571\t185547.2727\t14\t10000443\t1.00E+14\t11\t2041020\t5.72E+11
-LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tRO AB\tSSA\tSS-A/RO AB, IGG, SERUM\t7857142.214\t152575.7143\t14\t109999991\t1.10E+15\t14\t2136060\t3.48E+11
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYMPHM\tLYMPHOMA CELLS\t57.71428571\t18822.85714\t14\t808\t58412\t14\t263520\t10277035200
-LAB BLOOD ORDERABLES\t471960\tLABCENABY\tANTI CENTROMERE ANTIBODY\tCENQNT\tANTI-CENTROMERE LEVEL\t7857147.857\t582750\t14\t110000070\t1.10E+15\t14\t8158500\t5.71E+12
-LAB BLOOD ORDERABLES\t472392\tLAB12796R\tFLUCONAZOLE\t12795R\tFLUCONAZOLE\t36.98571429\t312437.1429\t14\t517.8\t30453.28\t14\t4374120\t1.45E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHDX\tCLINICAL INDICATION\t9999999\t604041.4286\t14\t139999986\t1.40E+15\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBEP2\tINTERPRETATION\t9999999\t426444.5714\t14\t139999986\t1.40E+15\t105\t44776680\t2.79E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLBRAG\tL BRAZILIENSIS,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLBRAM\tL BRAZILIENSIS,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLDONG\tL DONOVANI,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLDONM\tL DONOVANI,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLMEXG\tL MEXICANA,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLMEXM\tL MEXICANA,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLTROG\tL TROPICA,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLTROM\tL TROPICA,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
-LAB BLOOD ORDERABLES\t472771\tLABLUPUS\tLUPUS ANTICOAGULANT\tLUPDIR\tINTERPRETED BY\t9999999\t84468.42324\t14\t139999986\t1.40E+15\t482\t40713780\t8.35E+12
-LAB BLOOD ORDERABLES\t472778\tLABYLYSO\tLYSOZYME, PLASMA\tYLYSOZ\tLYSOZYME\t8.821428571\t220684.6154\t14\t123.5\t1953.95\t13\t2868900\t7.17E+11
-LAB BLOOD ORDERABLES\t473258\tLABRDATBC\tREVERSE TO COOMBS B CELLS\tRDATB\tREVERSE TO COOMBS B CELLS\t9999999\t46.66666667\t14\t139999986\t1.40E+15\t9\t420\t25200
-LAB BLOOD ORDERABLES\t473441\tLABYTAT\tTHROMBIN - ANTITHROMBIN COMPLEX\tTAT\tTHROMBIN, ANTITHROMBIN COMPLEX\t2142865.871\t647706\t14\t30000122.2\t3.00E+14\t20\t12954120\t9.44E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tAMINOR\tAMITRIPTYLINE & NORTRIPTYLINE\t7142943.643\t401426.25\t14\t100001211\t1.00E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tAMITRP\tAMITRIPTYLINE\t7142929.714\t401426.25\t14\t100001016\t1.00E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tDESIP\tDESIPRAMINE\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tDOXEPIN\tDOXEPIN\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tDOXEPINNOR\tDOXEPIN & NORDOXEPIN\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tIMI\tIMIPRAMINE\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tIMIDESI\tIMIPRAMINE/DESPRAMINE\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tNORDOX\tNORDOXEPIN\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tNORTRP\tNORTRIPTYLINE\t7142872.5\t401426.25\t14\t100000215\t1.00E+15\t16\t6422820\t4.45E+12
-LAB BLOOD ORDERABLES\t497345\tLABCGD\tNEUTROPHIL FUNC. OXI. BURST\tCGDA\tSPECIMEN AGE\t714309.6429\t201578.8235\t14\t10000335\t1.00E+14\t17\t3426840\t8.25E+11
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKAA\tAMINO ACID CHANGE\t9999999\t864270\t14\t139999986\t1.40E+15\t14\t12099780\t1.30E+13
-LAB BLOOD ORDERABLES\t670816\tLAB11198R\tVIT A & VIT E\tFREEVITA\tFREE RETINOL(VIT A)\t124.8071429\t356116\t14\t1747.3\t624967.13\t15\t5341740\t2.17E+12
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t454839.1011\t14\t139999986\t1.40E+15\t712\t323845440\t2.65E+14
-MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tRPT\tRPT\t9999999\t1542888\t14\t139999986\t1.40E+15\t10\t15428880\t2.97E+13
-MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tSDES\tSPECIMEN\t9999999\t1542888\t14\t139999986\t1.40E+15\t10\t15428880\t2.97E+13
-MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRRES\tRESULTS\t9999999\t488214.4565\t14\t139999986\t1.40E+15\t184\t89831460\t6.08E+13
-MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11RS\tRESULT\t9999999\t1087500\t14\t139999986\t1.40E+15\t2\t2175000\t2.77E+12
-MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tLAB7\tREFERENCE LAB\t9999999\t576624\t14\t139999986\t1.40E+15\t10\t5766240\t1.60E+13
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tCONRES\tCONTROL RESULTS\t9999999\t-202.5\t14\t139999986\t1.40E+15\t16\t-3240\t41284800
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tAMPHET\tAMPHETAMINE\t2859009.214\t26760\t14\t40026129\t4.00E+14\t4\t107040\t11457561600
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tMTAM\tMETHAMPHETAMINE\t5002712.857\t26760\t14\t70037980\t7.00E+14\t4\t107040\t11457561600
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tHVAUR\tHVA, URINE\t4.285714286\t309984\t14\t60\t326\t10\t3099840\t1.05E+12
-VIROLOGY ORDERABLES\t472015\tLABCLCR2\tCHLAMYDIA MOLECULAR DETECTION\tCHLAM\tC.TRACHOMATIS RESULT:\t9999999\t105230\t14\t139999986\t1.40E+15\t12\t1262760\t1.93E+11
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1M\tNEURAMINIDASE  H275Y MUTATION\t9999999\t#VALUE!\t14\t139999986\t1.40E+15\t0\tNone\tNone
-VIROLOGY ORDERABLES\t898804\tLABEBVQL\tEBV DNA PCR QUAL\tEBVQLS\tSPECIMEN\t9999999\t233724\t14\t139999986\t1.40E+15\t25\t5843100\t1.92E+12
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t13\t129999987\t1.30E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t13\t129999987\t1.30E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tSI\t<<STATUS>>  \t9999999\t23350\t13\t129999987\t1.30E+15\t18\t420300\t21061472400
-BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tSI\t<<STATUS>>  \t9999999\t20992.5\t13\t129999987\t1.30E+15\t16\t335880\t17437428000
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tDI\tISSUE DATE/TIME\t9999999\t33996\t13\t129999987\t1.30E+15\t5\t169980\t6149602800
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tDI\tISSUE DATE/TIME\t9999999\t47660\t13\t129999987\t1.30E+15\t6\t285960\t25408432800
-BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tPRO\tPRODUCT ID  \t9999999\t23004\t13\t129999987\t1.30E+15\t5\t115020\t7029565200
-BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tPRO\tPRODUCT ID  \t9999999\t16068\t13\t129999987\t1.30E+15\t5\t80340\t3222558000
-BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD34BMPC\t%CD34, BONE MARROW\t769231.6627\t23385\t13\t10000011.62\t1.00E+14\t4\t93540\t3727911600
-BODY FLUID ORDERABLES\t497376\tLABFLCL\tCHLORIDE, FLUID\tCLFLD\tCHLORIDE, FLUID\t769328\t7350\t13\t10001264\t1.00E+14\t6\t44100\t647218800
-BODY FLUID ORDERABLES\t497376\tLABFLCL\tCHLORIDE, FLUID\tCLFLS\tSOURCE\t9999999\t7350\t13\t129999987\t1.30E+15\t6\t44100\t647218800
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASTI\tSOURCE\t9999999\t866627.1429\t13\t129999987\t1.30E+15\t14\t12132780\t1.36E+13
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t7692314.615\t59381.53846\t13\t100000090\t1.00E+15\t13\t771960\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.963432308\t73352.30769\t13\t38.52462\t114.8003678\t13\t953580\t6.37E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.194230769\t13698.46154\t13\t28.525\t94.854375\t13\t178080\t32661705600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t13698.46154\t13\t129999987\t1.30E+15\t13\t178080\t32661705600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t15272.30769\t13\t129999987\t1.30E+15\t13\t198540\t33045735600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t58.07692308\t15276.92308\t13\t755\t49275\t13\t198600\t33045724800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t15276.92308\t13\t129999987\t1.30E+15\t13\t198600\t33045724800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t38.84769231\t15272.30769\t13\t505.02\t37105.5174\t13\t198540\t33045735600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDRHYTHM\tICD RHYTHM\t9999999\t15272.30769\t13\t129999987\t1.30E+15\t13\t198540\t33045735600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t307.3692308\t15276.92308\t13\t3995.8\t1240182.42\t13\t198600\t33045724800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t1538494.077\t15235.38462\t13\t20000423\t2.00E+14\t13\t198060\t33045562800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tMOD\tMODEL\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.3\t15276.92308\t13\t29.9\t70.01\t13\t198600\t33045724800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.453846154\t15276.92308\t13\t5.9\t2.73\t13\t198600\t33045724800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2307692.385\t15276.92308\t13\t30000001\t3.00E+14\t13\t198600\t33045724800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t7337924.077\t14630.76923\t13\t95393013\t8.47E+14\t13\t190200\t32977180800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t461.5384615\t13\t129999987\t1.30E+15\t13\t6000\t66816000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t1.923076923\t13389.23077\t13\t25\t531\t13\t174060\t23359885200
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t461.5384615\t13\t129999987\t1.30E+15\t13\t6000\t66816000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9482R\tLOCUS 8\t9999999\t19103.07692\t13\t129999987\t1.30E+15\t13\t248340\t61404872400
-HISTOCOMPATIBILITY ORDERABLE\t978467\t10277R\tHLA ORDER - PROSPECTIVE CROSSMATCH TESTING\tRINFO\tTO FIND RESULT:\t9999999\t306132.8571\t13\t129999987\t1.30E+15\t14\t4285860\t1.61E+12
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE12\tS.PNEUMO SEROTYPE 12 (12F)\t6923076.477\t199431.4286\t13\t89999994.2\t9.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE14\tS.PNEUMO SEROTYPE 14 (14)\t2307697.792\t199431.4286\t13\t30000071.3\t3.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE19\tS.PNEUMO SEROTYPE 19 (19F)\t3076924.9\t199431.4286\t13\t40000023.7\t4.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE23\tS.PNEUMO SEROTYPE 23 (23F)\t5384616.023\t199431.4286\t13\t70000008.3\t7.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE26\tS.PNEUMO SEROTYPE 26 (6B)\t6153846.031\t199431.4286\t13\t79999998.4\t8.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE3\tS.PNEUMO SEROTYPE 3 (3)\t5384615.838\t199431.4286\t13\t70000005.9\t7.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE4\tS.PNEUMO SEROTYPE 4 (4)\t9230768.354\t199431.4286\t13\t119999988.6\t1.20E+15\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE51\tS.PNEUMO SEROTYPE 51 (7F)\t3076927.354\t199431.4286\t13\t40000055.6\t4.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE8\tS.PNEUMO SEROTYPE 8 (8)\t3076924.108\t199431.4286\t13\t40000013.4\t4.00E+14\t14\t2792040\t6.60E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE9\tS.PNEUMO SEROTYPE 9 (9N)\t5384615.585\t199431.4286\t13\t70000002.6\t7.00E+14\t14\t2792040\t6.60E+11
-LAB BLOOD ORDERABLES\t471506\tLAB11067R\t17OH PROG\t17OHPROG\t17-OH PROGESTERONE\t3077113.923\t325227.6923\t13\t40002481\t4.00E+14\t13\t4227960\t1.46E+12
-LAB BLOOD ORDERABLES\t471571\tLABYALDO\tALDOLASE, SERUM\tALDOLASE\tALDOLASE, SERUM\t769254.6308\t174660\t13\t10000310.2\t1.00E+14\t8\t1397280\t2.95E+11
-LAB BLOOD ORDERABLES\t471572\tLABYALDOS\tALDOSTERONE, SERUM\tALDOSTR\tALDOSTERONE, SERUM\t6153847.977\t281100\t13\t80000023.7\t8.00E+14\t10\t2811000\t8.99E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMGQNT\tCMV IGG LEVEL\t3076958.615\t329515.3846\t13\t40000462\t4.00E+14\t13\t4283700\t1.61E+12
-LAB BLOOD ORDERABLES\t471812\tLABYC4F\tC4 COMPLEMENT FUNCTIONAL\tYC4FUN\tC4 COMPLEMENT, FUNCT\t48.84615385\t139674.5455\t13\t635\t31413\t11\t1536420\t2.50E+11
-LAB BLOOD ORDERABLES\t472144\tLABYDHEAS\tDEHYDROEPIANDROSTERONE, SULFATE\tDHS\tDEHYDROEPIANDROSTERONE SULFATE\t769436.1538\t92110\t13\t10002670\t1.00E+14\t12\t1105320\t1.69E+11
-LAB BLOOD ORDERABLES\t472329\tLABYESTRO\tESTROGENS E1 AND E2 FRACTIONATED\tESTRADIOL\tESTRADIOL\t4615409.538\t294176\t13\t60000324\t6.00E+14\t15\t4412640\t1.36E+12
-LAB BLOOD ORDERABLES\t472329\tLABYESTRO\tESTROGENS E1 AND E2 FRACTIONATED\tESTRONE\tESTRONE\t46.30769231\t294176\t13\t602\t50766\t15\t4412640\t1.36E+12
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tADP5\tADP 5 UM\t5384649.462\t113400\t13\t70000443\t7.00E+14\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tAGGDIR\tINTERPRETED BY\t9999999\t113400\t13\t129999987\t1.30E+15\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tARA500\tARACHIDONIC ACID 500 UG/ML\t5384634.615\t113400\t13\t70000250\t7.00E+14\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tCOLL25\tCOLLAGEN 2.5 UG/ML\t4615420\t113400\t13\t60000460\t6.00E+14\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tEPI5\tEPINEPHRINE 5 UM\t4615414.538\t113400\t13\t60000389\t6.00E+14\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tEPIN10\tEPINEPHRINE 10 UM\t4615411.385\t113400\t13\t60000348\t6.00E+14\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tRIS12\tRISTOCETIN 1.2 MG/ML\t6923100.308\t113400\t13\t90000304\t9.00E+14\t10\t1134000\t2.75E+11
-LAB BLOOD ORDERABLES\t473262\tLABRF\tRHEUMATOID FACTOR\tRHFT\tRHEUMATOID FACTOR LEVEL\t90.53846154\t216840\t13\t1177\t201441\t13\t2818920\t7.14E+11
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15CO\tCOMMENT\t9999999\t643218\t13\t129999987\t1.30E+15\t50\t32160900\t2.37E+13
-LAB BLOOD ORDERABLES\t473428\tLABYTETT\tTETANUS TOXOID IGG AB\tYTETT1\tTETANUS TOXOID IGG AB\t769233.1908\t214384\t13\t10000031.48\t1.00E+14\t15\t3215760\t7.88E+11
-LAB BLOOD ORDERABLES\t473445\tLABYTRAB\tTSH RECEPTOR AB\tYTRAB1\tTHYROTROPIN RECEPTOR AB\t9230768.432\t184324\t13\t119999989.6\t1.20E+15\t15\t2764860\t6.23E+11
-LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMA PCR\tTOXSP\tSPECIMEN TYPE\t9999999\t1052324\t13\t129999987\t1.30E+15\t30\t31569720\t6.51E+13
-LAB BLOOD ORDERABLES\t497345\tLABCGD\tNEUTROPHIL FUNC. OXI. BURST\tOXIDNEUTPC\t% OXIDATION POSITIVE NEUTROPHILS\t91.07692308\t827744.2105\t13\t1184\t109522\t19\t15727140\t7.65E+13
-LAB BLOOD ORDERABLES\t541683\tLABDCMVL\tDONOR CMV- TOTAL AB\tDCMVL1\tDONOR CMV- TOTAL AB\t9999999\t214200\t13\t129999987\t1.30E+15\t15\t3213000\t9.08E+11
-LAB BLOOD ORDERABLES\t883166\tLAB12090R\tHE4, OVARIAN CANCER MONITORING\t12105R\tHE4, OVARIAN CANCER MONITORING\t339.7692308\t421112.7273\t13\t4417\t3279075\t11\t4632240\t2.19E+12
-MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t431748.6235\t13\t129999987\t1.30E+15\t1867\t806074680\t3.79E+14
-MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tSREQ\tCOMMENTS\t9999999\t1542888\t13\t129999987\t1.30E+15\t10\t15428880\t2.97E+13
-MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t643208.0247\t13\t129999987\t1.30E+15\t486\t312599100\t2.25E+14
-STOOL ORDERABLES\t471633\tLABCANNAB\tNEURONAL NUCLEAR AB, CSF\tANTIHU\tANTI-HU\t9999999\t764260\t13\t129999987\t1.30E+15\t6\t4585560\t4.58E+12
-STOOL ORDERABLES\t471633\tLABCANNAB\tNEURONAL NUCLEAR AB, CSF\tARI\tANTI-RI\t9999999\t764260\t13\t129999987\t1.30E+15\t6\t4585560\t4.58E+12
-STOOL ORDERABLES\t497222\tLAB11021R\tADRENAL AB\tADRAB\tADRENAL ANTIBODY\t769231.2538\t419430\t13\t10000006.3\t1.00E+14\t12\t5033160\t2.45E+12
-STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL3\tCOLLECTION DURATION\t9999999\t671420\t13\t129999987\t1.30E+15\t12\t8057040\t7.48E+12
-URINE ORDERABLES\t472786\tLABURMAG\tMAGNESIUM, URINE (RANDOM)\tUMG\tMAGNESIUM, URINE\t769237.7385\t12980\t13\t10000090.6\t1.00E+14\t6\t77880\t1401314400
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tTIMEU\tCOLLECTION PERIOD\t769251.4615\t321355.3846\t13\t10000269\t1.00E+14\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URINE\t770374.4615\t321355.3846\t13\t10014868\t1.00E+14\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URN\t54\t321355.3846\t13\t702\t43556\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tUVOL\tVOLUME\t1965.461538\t321355.3846\t13\t25551\t62756061\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tVMA\tVMA\t1538466.654\t321355.3846\t13\t20000066.5\t2.00E+14\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tVMAUR\tVMA, URINE\t6.461538462\t321355.3846\t13\t84\t730\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11197R\tANABASINE, U\t9230768.777\t357805.7143\t13\t119999994.1\t1.20E+15\t7\t2504640\t1.04E+12
-URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11511R\tNORNICOTINE, U\t8461542.992\t357805.7143\t13\t110000058.9\t1.10E+15\t7\t2504640\t1.04E+12
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11230R\tCREATININE CONC\t47.61538462\t372466.1538\t13\t619\t54659\t13\t4842060\t1.97E+12
-VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-BLOOD\tCMQDNA\tCMV DNA\t9999999\t165780\t13\t129999987\t1.30E+15\t3\t497340\t92352106800
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVIPR\tTEST DESCRIPTION\t9999999\t295560.7542\t13\t129999987\t1.30E+15\t716\t211621500\t8.15E+13
-VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVIPR\tTEST DESCRIPTION\t9999999\t306451.3492\t13\t129999987\t1.30E+15\t756\t231677220\t1.10E+14
-VIROLOGY ORDERABLES\t497450\tLABBKVQU\tBK VIRUS DNA QUANTITATION\tBKQ\tBKV DNA QUANTITATION\t7097459.769\t190828.4211\t13\t92266977\t9.04E+14\t38\t7251480\t1.85E+12
-VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSVZIP\tTEST DESCRIPTION\t9999999\t190023.36\t13\t129999987\t1.30E+15\t375\t71258760\t1.95E+13
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t12\t119999988\t1.20E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tXMR\tUNIT CROSSMATCH  \t9999999\t62580\t12\t119999988\t1.20E+15\t7\t438060\t56277608400
-BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tDI\tISSUE DATE/TIME\t9999999\t32715\t12\t119999988\t1.20E+15\t4\t130860\t4617428400
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tDI\tISSUE DATE/TIME\t9999999\t57360\t12\t119999988\t1.20E+15\t5\t286800\t29228796000
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tDI\tISSUE DATE/TIME\t9999999\t47650\t12\t119999988\t1.20E+15\t6\t285900\t25391113200
-BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tPRO\tPRODUCT ID  \t9999999\t20085\t12\t119999988\t1.20E+15\t4\t80340\t3217719600
-BODY FLUID ORDERABLES\t472474\tLABCSFGL\tGLUCOSE, CSF\tGLCSTN\tTUBE NUMBER\t9999999\t337355\t12\t119999988\t1.20E+15\t12\t4048260\t1.37E+12
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15DX\tCLINICAL INDICATION\t9999999\t644650\t12\t119999988\t1.20E+15\t18\t11603700\t8.75E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12364R\tANNA-2, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12365R\tANNA-3, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12366R\tAGNA-1,CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12367R\tPCA-1, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12368R\tPCA-2, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12369R\tAMPHIPHYSIN AB, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12370R\tCRMP-5-IGG, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t14755\t12\t0\t0\t12\t177060\t32661486000
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.772916667\t14860\t12\t9.275\t9.921875\t12\t178320\t32661648000
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRDUR\tRA THRESHOLD DURATION\t0.516666667\t14860\t12\t6.2\t4.28\t12\t178320\t32661648000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t15860\t12\t119999988\t1.20E+15\t12\t190320\t32977166400
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9260R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t347760\t12\t119999988\t1.20E+15\t28\t9737280\t3.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9518R\t% DONOR- MARROW WHOLE\t68.5\t4780\t12\t822\t77006\t12\t57360\t3249021600
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9558R\t% RECIPIENT- MARROW WHOLE\t27.83333333\t4780\t12\t334\t27766\t12\t57360\t3249021600
-HISTOCOMPATIBILITY ORDERABLE\t978467\t10277R\tHLA ORDER - PROSPECTIVE CROSSMATCH TESTING\t10049R\tHLA LAB TESTING STATUS\t9999999\t306132.8571\t12\t119999988\t1.20E+15\t14\t4285860\t1.61E+12
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNAB1\tPNEUMOCOCCAL AB (PNEUMOVAX)\t9999999\t184530\t12\t119999988\t1.20E+15\t12\t2214360\t4.84E+11
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE01\tS.PNEUMO SEROTYPE 1 (1)\t2500001.767\t199431.4286\t12\t30000021.2\t3.00E+14\t14\t2792040\t6.60E+11
-LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE\tYAMIOD\tAMIODARONE\t0.675\t294387.6923\t12\t8.1\t7.45\t13\t3827040\t1.32E+12
-LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE\tYDESET\tDESETHYLAMIODARONE\t0.491666667\t294387.6923\t12\t5.9\t3.29\t13\t3827040\t1.32E+12
-LAB BLOOD ORDERABLES\t471684\tLABARS\tARSENIC, WHOLE BLOOD\tARS\tARSENIC, BLOOD\t9999999\t255327.2727\t12\t119999988\t1.20E+15\t11\t2808600\t9.52E+11
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tHCT\tHEMATOCRIT\t1666690.9\t420\t12\t20000290.8\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tHGB\tHEMOGLOBIN\t1666674.617\t420\t12\t20000095.4\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tMCH\tMCH\t1666691.767\t420\t12\t20000301.2\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tMCHC\tMCHC\t1666694.133\t420\t12\t20000329.6\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tMCV\tMCV\t1666742.683\t420\t12\t20000912.2\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tPLT\tPLATELET COUNT\t1666867.417\t420\t12\t20002409\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tRBC\tRBC\t1666669.183\t420\t12\t20000030.19\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tRDW\tRDW\t1666681.167\t420\t12\t20000174\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tWBC\tWBC\t1666673.833\t420\t12\t20000086\t2.00E+14\t1\t420\t176400
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tBCD34V\tCD34+ PROGENITOR VIABILITY\t1666749.583\t14130\t12\t20000995\t2.00E+14\t12\t169560\t2971821600
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD45PV\tCD45+ VIABILITY\t1666749.833\t14130\t12\t20000998\t2.00E+14\t12\t169560\t2971821600
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tSLREV\tSLIDE REVIEW\t9999999\t5840\t12\t119999988\t1.20E+15\t3\t17520\t158767200
-LAB BLOOD ORDERABLES\t472185\tLABYDIGT\tDIGITOXIN, SERUM\tYDIGT2\tREPORTING LIMIT\t2500003.5\t384356.5217\t12\t30000042\t3.00E+14\t69\t26520600\t1.15E+13
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKSP\tSPECIMEN SOURCE\t9999999\t525786.6667\t12\t119999988\t1.20E+15\t9\t4732080\t3.13E+12
-LAB BLOOD ORDERABLES\t472835\tLABMERC\tMERCURY, WHOLE BLOOD\tMERC\tMERCURY, BLOOD\t6666667.25\t227809.0909\t12\t80000007\t8.00E+14\t11\t2505900\t9.21E+11
-LAB BLOOD ORDERABLES\t473113\tLAB11054R\tPM1 AB\tPM1AB\tPM-1 ANTIBODY\t9999999\t510376.3636\t12\t119999988\t1.20E+15\t11\t5614140\t4.33E+12
-LAB BLOOD ORDERABLES\t473152\tLABPGN\tPROGESTERONE\tPROGES\tPROGESTERONE\t1666676.667\t40156.36364\t12\t20000120\t2.00E+14\t11\t441720\t38759839200
-LAB BLOOD ORDERABLES\t473189\tLABPTTINH\tPTT INHIBITOR SCREEN\tPTTDIR\tINTERPRETED BY\t9999999\t78829.64789\t12\t119999988\t1.20E+15\t284\t22387620\t2.92E+12
-LAB BLOOD ORDERABLES\t473264\tLAB11100R\tRIBOFLAVIN\tYVITB2\tRIBOFLAVIN(VIT.B2)\t15.41666667\t395547.2727\t12\t185\t6671\t11\t4351020\t1.85E+12
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH CORD\t9999999\t3730\t12\t119999988\t1.20E+15\t6\t22380\t90622800
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH FRONT TYPE\t9999999\t3730\t12\t119999988\t1.20E+15\t6\t22380\t90622800
-LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH STILLBORN\t9999999\t3730\t12\t119999988\t1.20E+15\t6\t22380\t90622800
-LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tSPTY4\tSPECIMEN TYPE\t9999999\t4443266.667\t12\t119999988\t1.20E+15\t9\t39989400\t3.40E+14
-LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR, HIV-1/HCV/HBV NAT TESTING\t11894R\tDISCRIMINATORY TESTING\t9999999\t266914.2857\t12\t119999988\t1.20E+15\t14\t3736800\t1.23E+12
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tBDVC\tCORD BASE DEFICIT, VEN\t5833334.333\t615\t12\t70000012\t7.00E+14\t8\t4920\t4780800
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tHCO3VC\tCORD HCO3, VEN\t5833342.217\t615\t12\t70000106.6\t7.00E+14\t8\t4920\t4780800
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tPCO2VC\tCORD PCO2, VEN\t5833352.642\t615\t12\t70000231.7\t7.00E+14\t8\t4920\t4780800
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tPHVCD\tCORD PH, VEN\t5833335.79\t615\t12\t70000029.47\t7.00E+14\t8\t4920\t4780800
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tPO2VC\tCORD PO2, VEN\t5833344.917\t615\t12\t70000139\t7.00E+14\t8\t4920\t4780800
-LAB BLOOD ORDERABLES\t864652\tLAB11858R\tHEPTIMAX(R) HCV RNA\t11856R\tHEPTIMAX ? HCR  RNA IU/ML\t6731649.333\t677090.7692\t12\t80779792\t8.00E+14\t13\t8802180\t7.52E+12
-LAB BLOOD ORDERABLES\t864652\tLAB11858R\tHEPTIMAX(R) HCV RNA\t11857R\tHEPTIMAX ? HCR  LOGIU IU/ML\t5000002.415\t670450\t12\t60000028.98\t6.00E+14\t12\t8045400\t6.95E+12
-LAB BLOOD ORDERABLES\t864654\tLAB11860R\tVASCULAR ENDOTHELIAL GROW.FACT\t11859R\tVEGF\t90.08333333\t563154\t12\t1081\t138983\t10\t5631540\t3.64E+12
-LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12051R\tRMSF IGG\t9999999\t408180\t12\t119999988\t1.20E+15\t15\t6122700\t4.65E+12
-LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12053R\tR.TYPHI IGG\t9999999\t265070.7692\t12\t119999988\t1.20E+15\t13\t3445920\t9.64E+11
-LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tPF4IGG\tPF4 IGG ONLY OD\t0.9635\t149345\t12\t11.562\t20.572986\t12\t1792140\t3.18E+11
-MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tRPT\tRPT\t9999999\t79170\t12\t119999988\t1.20E+15\t2\t158340\t12620538000
-MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tRPT\tRPT\t9999999\t272595\t12\t119999988\t1.20E+15\t4\t1090380\t3.15E+11
-MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tSDES\tSPECIMEN\t9999999\t272595\t12\t119999988\t1.20E+15\t4\t1090380\t3.15E+11
-MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tSREQ\tCOMMENTS\t9999999\t272595\t12\t119999988\t1.20E+15\t4\t1090380\t3.15E+11
-STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL1\tCHLORIDE, STOOL, 24 HR\t9999999\t921218.1818\t12\t119999988\t1.20E+15\t11\t10133400\t1.43E+13
-URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tB2M\tBETA-2-MICROGLOB\t837408.8333\t323151.4286\t12\t10048906\t1.00E+14\t7\t2262060\t8.66E+11
-URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tB2MCREAT\tBETA-2-MICROGLOB/CRT\t859234.25\t323151.4286\t12\t10310811\t1.00E+14\t7\t2262060\t8.66E+11
-URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tUCR\tCREATININE, URN\t833388.9167\t323151.4286\t12\t10000667\t1.00E+14\t7\t2262060\t8.66E+11
-URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tUT\tCOLLECTION PERIOD\t9166667.75\t323151.4286\t12\t110000013\t1.10E+15\t7\t2262060\t8.66E+11
-URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tVOL\tVOLUME\t833418.0833\t323151.4286\t12\t10001017\t1.00E+14\t7\t2262060\t8.66E+11
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tCADMIUNURN\tCADMIUM, URINE\t6666667.008\t279240\t12\t80000004.1\t8.00E+14\t4\t1116960\t3.67E+11
-URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11180R\tNICOTINE, U\t6666860.9\t357805.7143\t12\t80002330.8\t8.00E+14\t7\t2504640\t1.04E+12
-VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMGDES\tCMV IGG TEST DESCRIPTION\t9999999\t157732.7413\t12\t119999988\t1.20E+15\t777\t122558340\t2.53E+13
-VIROLOGY ORDERABLES\t472452\tLABGCCR2\tGC MOLECULAR DETECTION\tDCL5\tTEST INFORMATION:\t9999999\t144177.2308\t12\t119999988\t1.20E+15\t65\t9371520\t1.97E+12
-VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHHV-6 DNA PCR QUAL\tHV6QLI\tTEST DESCRIPTION\t9999999\t259790\t12\t119999988\t1.20E+15\t66\t17146140\t1.58E+13
-VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADDNA\tADENOVIRUS DNA\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
-VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVCP\tRESULT\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
-VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVLOG\tRESULT LOG 10\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
-VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVSP\tSPECIMEN\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1SP\tSPECIMEN\t9999999\t#VALUE!\t12\t119999988\t1.20E+15\t0\tNone\tNone
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVLIP\tTEST DESCRIPTION\t9999999\t253864.8227\t12\t119999988\t1.20E+15\t423\t107384820\t3.23E+13
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tXMR\tUNIT CROSSMATCH  \t9999999\t50220\t11\t109999989\t1.10E+15\t7\t351540\t43882974000
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tXMR\tUNIT CROSSMATCH  \t9999999\t22097.14286\t11\t109999989\t1.10E+15\t7\t154680\t6237043200
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tDI\tISSUE DATE/TIME\t9999999\t44832\t11\t109999989\t1.10E+15\t5\t224160\t14219272800
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tDI\tISSUE DATE/TIME\t9999999\t28296\t11\t109999989\t1.10E+15\t5\t141480\t4562049600
-BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tDI\tISSUE DATE/TIME\t9999999\t34020\t11\t109999989\t1.10E+15\t5\t170100\t6299211600
-BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tUBT\tUNIT BLOOD TYPE  \t9999999\t18440\t11\t109999989\t1.10E+15\t3\t55320\t2073319200
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tUBT\tUNIT BLOOD TYPE  \t9999999\t38400\t11\t109999989\t1.10E+15\t3\t115200\t7052018400
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tPRO\tPRODUCT ID  \t9999999\t20085\t11\t109999989\t1.10E+15\t4\t80340\t3217719600
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tPRO\tPRODUCT ID  \t9999999\t20085\t11\t109999989\t1.10E+15\t4\t80340\t3217719600
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tSI\t<<STATUS>>  \t9999999\t23376\t11\t109999989\t1.10E+15\t15\t350640\t18667375200
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBDX\tCLINICAL INDICATION\t9999999\t1007200\t11\t109999989\t1.10E+15\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBMET\tMETHOD\t9999999\t1007200\t11\t109999989\t1.10E+15\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD34V\tVIABILITY\t909179.9091\t35190\t11\t10000979\t1.00E+14\t2\t70380\t3459690000
-BODY FLUID ORDERABLES\t497362\tLABFLK\tPOTASSIUM, FLUID\tKFLD\tPOTASSIUM, FLUID\t909095.3909\t3996\t11\t10000049.3\t1.00E+14\t5\t19980\t102495600
-BODY FLUID ORDERABLES\t497366\tLABFLNA\tSODIUM, FLUID\tNAFLD\tSODIUM, FLUID\t909206.0909\t9820\t11\t10001267\t1.00E+14\t3\t29460\t443379600
-BODY FLUID ORDERABLES\t497366\tLABFLNA\tSODIUM, FLUID\tNAFLS\tSOURCE\t9999999\t9820\t11\t109999989\t1.10E+15\t3\t29460\t443379600
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGSF3B\tSF3B1\t9999999\t1131829.091\t11\t109999989\t1.10E+15\t11\t12450120\t1.54E+13
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12363R\tANNA-1, CSF\t9999999\t627283.6364\t11\t109999989\t1.10E+15\t11\t6900120\t4.80E+12
-ECHO ORDERABLES\t670309\tECH43\tECHO - DOBUTAMINE STRESS ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t54.27272727\t53940\t11\t597\t34779\t11\t593340\t43949847600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRVVDEL\tBRADY VV DELAY\t22.72727273\t16063.63636\t11\t250\t10300\t11\t176700\t32661932400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t16118.18182\t11\t0\t0\t11\t177300\t32661428400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t16118.18182\t11\t0\t0\t11\t177300\t32661428400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t70434.54545\t11\t109999989\t1.10E+15\t11\t774780\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t70270.90909\t11\t109999989\t1.10E+15\t11\t772980\t6.04E+11
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t1\t17629.09091\t11\t11\t39\t11\t193920\t33026932800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t0.775\t17318.18182\t11\t8.525\t7.068125\t11\t190500\t32977242000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.445454545\t17318.18182\t11\t4.9\t2.21\t11\t190500\t32977242000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t909099\t1238.181818\t11\t10000089\t1.00E+14\t11\t13620\t74667600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADALERT\tLEAD ALERT\t9999999\t665.4545455\t11\t109999989\t1.10E+15\t11\t7320\t31622400
-HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t465125.4545\t11\t109999989\t1.10E+15\t11\t5116380\t3.52E+12
-HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t9999999\t465125.4545\t11\t109999989\t1.10E+15\t11\t5116380\t3.52E+12
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9120R\tINTERPRETATION/ COMMENTS\t9999999\t319104\t11\t109999989\t1.10E+15\t20\t6382080\t7.43E+12
-IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE56\tS.PNEUMO SEROTYPE 56 (18C)\t3636365.936\t199431.4286\t11\t40000025.3\t4.00E+14\t14\t2792040\t6.60E+11
-LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTR\tRESULT, BM\t9999999\t540213\t11\t109999989\t1.10E+15\t60\t32412780\t2.37E+13
-LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABID\tANTIBODY IDENTIFICATION\t9999999\t11001.50442\t11\t109999989\t1.10E+15\t226\t2486340\t5.66E+12
-LAB BLOOD ORDERABLES\t472140\tLABYDHEA\tDEHYDROEPIANDROSTERONE, SERUM\tDHEA\tDHEA, SERUM\t3636365.382\t490747.5\t11\t40000019.2\t4.00E+14\t8\t3925980\t2.68E+12
-LAB BLOOD ORDERABLES\t472355\tLABFACT13\tFACTOR XIII SCREEN\tFACTOR13\tFACTOR XIII SCREEN\t9999999\t128781.8182\t11\t109999989\t1.10E+15\t11\t1416600\t1.96E+11
-LAB BLOOD ORDERABLES\t472583\tLABXICA51\tICA 512 AB\tXICA5R\tICA 512 ANTIBODY\t9999999\t616788\t11\t109999989\t1.10E+15\t25\t15419700\t1.11E+13
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKPR\tPRIMERS\t9999999\t526447.5\t11\t109999989\t1.10E+15\t8\t4211580\t2.86E+12
-LAB BLOOD ORDERABLES\t472731\tLABLDL\tLDL CALCULATED\tLDL\tLDL (CALCULATED)\t3636415.636\t10605\t11\t40000572\t4.00E+14\t4\t42420\t820717200
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBGINT\tINTERPRETATION\t9999999\t226235.1951\t11\t109999989\t1.10E+15\t820\t185512860\t8.35E+13
-LAB BLOOD ORDERABLES\t473427\tLABTESFBT\tTESTOSTERONE, TOTAL, BIO, FREE\tTESBIO\tTESTOSTERONE, BIOAVAILABLE\t40\t244216.3636\t11\t440\t32272\t11\t2686380\t8.16E+11
-LAB BLOOD ORDERABLES\t473427\tLABTESFBT\tTESTOSTERONE, TOTAL, BIO, FREE\tTESFR\tTESTOSTERONE, FREE\t2.954545455\t244216.3636\t11\t32.5\t153.83\t11\t2686380\t8.16E+11
-LAB BLOOD ORDERABLES\t473427\tLABTESFBT\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOS\tTESTOSTERONE\t120.5454545\t244216.3636\t11\t1326\t378816\t11\t2686380\t8.16E+11
-LAB BLOOD ORDERABLES\t473432\tLABTHAMP\tTHERMAL AMPLITUDE\tTHRAMP\tTHERMAL AMPLITUDE\t9999999\t158911.4286\t11\t109999989\t1.10E+15\t21\t3337140\t7.44E+11
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P4PA\tCD3+/CD4+,ABS\t909119.7273\t41601.81818\t11\t10000317\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P4PC\tCD3+/CD4+,%\t909099.8182\t41601.81818\t11\t10000098\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P8PA\tCD3+/CD8+, ABS.\t909153.1818\t41601.81818\t11\t10000685\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P8PC\tCD3+/CD8+, %\t909108.7273\t41601.81818\t11\t10000196\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P4PA\tCD5+/CD4+ ,ABS\t909119.1818\t41601.81818\t11\t10000311\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P4PC\tCD5+/CD4+ %\t909099.2727\t41601.81818\t11\t10000092\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P8PA\tCD5+/CD8+,ABS.\t909146.8182\t41601.81818\t11\t10000615\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P8PC\tCD5+/CD8+ %\t909106.6364\t41601.81818\t11\t10000173\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tRC4C8\tCD4/CD8 RATIO\t909091.9364\t41601.81818\t11\t10000011.3\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tTC4C8A\tT(CD4+CD8), ABS.\t909182.0909\t41601.81818\t11\t10001003\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tTC4C8P\tT(CD4+CD8)%\t909117.7273\t41601.81818\t11\t10000295\t1.00E+14\t11\t457620\t24563012400
-LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTIGA\tC.TRACHOMATIS IGA\t9999999\t231125.4545\t11\t109999989\t1.10E+15\t11\t2542380\t6.28E+11
-LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTIGG\tC.TRACHOMATIS IGG\t9999999\t231125.4545\t11\t109999989\t1.10E+15\t11\t2542380\t6.28E+11
-LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTIGM\tC.TRACHOMATIS IGM\t9999999\t231125.4545\t11\t109999989\t1.10E+15\t11\t2542380\t6.28E+11
-LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR, HIV-1/HCV/HBV NAT TESTING\tHIBCV1\tDONOR HIV/HBV/HCV RESULT\t9999999\t266914.2857\t11\t109999989\t1.10E+15\t14\t3736800\t1.23E+12
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tBDC\tCORD BASE DEFICIT, ART\t5454546.182\t360\t11\t60000008\t6.00E+14\t6\t2160\t1555200
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tHCO3C\tCORD HCO3, ART\t5454556.127\t360\t11\t60000117.4\t6.00E+14\t6\t2160\t1555200
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tPCO2C\tCORD PCO2, ART\t5454571.9\t360\t11\t60000290.9\t6.00E+14\t6\t2160\t1555200
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tPHCD\tCORD PH, ART\t5454548.196\t360\t11\t60000030.15\t6.00E+14\t6\t2160\t1555200
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tPO2C\tCORD PO2, ART\t6363642.727\t360\t11\t70000070\t7.00E+14\t6\t2160\t1555200
-LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11761R\tCOQ10 TOTAL\t909558.3636\t282690\t11\t10005142\t1.00E+14\t10\t2826900\t9.22E+11
-LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11762R\tCOQ10 % REDUCED\t909174.0909\t282690\t11\t10000915\t1.00E+14\t10\t2826900\t9.22E+11
-LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12402R\tALKALINE PHOSPHATASE\t469.3636364\t249229.0909\t11\t5163\t5050555\t11\t2741520\t8.05E+11
-LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12403R\tALKP. PHOSPHATASE - OTHER\t0\t249229.0909\t11\t0\t0\t11\t2741520\t8.05E+11
-LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12404R\tALKP. PHOSPHATASE - BONE\t89.90909091\t249229.0909\t11\t989\t189025\t11\t2741520\t8.05E+11
-LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12405R\tALKP. PHOSPHATASE - LIVER\t379.4545455\t249229.0909\t11\t4174\t3346044\t11\t2741520\t8.05E+11
-MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t431748.6235\t11\t109999989\t1.10E+15\t1867\t806074680\t3.79E+14
-MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tSDES\tSPECIMEN\t9999999\t79170\t11\t109999989\t1.10E+15\t2\t158340\t12620538000
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tSDES\tSPECIMEN\t9999999\t471560\t11\t109999989\t1.10E+15\t18\t8488080\t5.50E+12
-STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL2\tTOTAL WEIGHT\t9999999\t794016\t11\t109999989\t1.10E+15\t5\t3970080\t4.65E+12
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tAMOBARB\tAMOBARBITAL\t9999999\t30\t11\t109999989\t1.10E+15\t2\t60\t3600
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tPB\tPHENOBARBITAL\t4548446.818\t30\t11\t50032915\t5.00E+14\t2\t60\t3600
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tPENTO\tPENTOBARBITAL\t9999999\t30\t11\t109999989\t1.10E+15\t2\t60\t3600
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tSECOBARB\tSECOBARBITAL\t9999999\t30\t11\t109999989\t1.10E+15\t2\t60\t3600
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tYUBTGC\tBUTALBITAL\t5455872.818\t30\t11\t60014601\t6.00E+14\t2\t60\t3600
-URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tPHFMS\tPH METABOLIC SCRN\t6.818181818\t323151.4286\t11\t75\t519\t7\t2262060\t8.66E+11
-URINE ORDERABLES\t472938\tLAB12811R\tMYOGLOBIN, URINE\t12812R\tMYOGLOBIN, URINE\t4545894.909\t147345\t11\t50004844\t5.00E+14\t16\t2357520\t3.95E+11
-URINE ORDERABLES\t473736\tLABUPIE\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS\tUPIER\tRESULT\t9999999\t373954.7945\t11\t109999989\t1.10E+15\t438\t163792200\t1.21E+15
-URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11181R\tCOTININE, U\t7272790.455\t357805.7143\t11\t80000695\t8.00E+14\t7\t2504640\t1.04E+12
-VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-BLOOD\tCMVLIP\tTEST DESCRIPTION\t9999999\t204632\t11\t109999989\t1.10E+15\t30\t6138960\t1.86E+12
-VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSVRM\tCLINICAL REMARKS\t9999999\t209810\t11\t109999989\t1.10E+15\t12\t2517720\t1.17E+12
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVTIP\tTEST DESCRIPTION\t9999999\t203590.4878\t11\t109999989\t1.10E+15\t246\t50083260\t1.36E+13
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tVL\tPRODUCT VOLUME\t167.4\t29880\t10\t1674\t282024\t1\t29880\t892814400
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tXMR\tUNIT CROSSMATCH  \t9999999\t34248\t10\t99999990\t1.00E+15\t5\t171240\t11764303200
-BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tXMR\tUNIT CROSSMATCH  \t9999999\t32625\t10\t99999990\t1.00E+15\t4\t130500\t10116111600
-BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tXMR\tUNIT CROSSMATCH  \t9999999\t31260\t10\t99999990\t1.00E+15\t4\t125040\t9870127200
-BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tSI\t<<STATUS>>  \t9999999\t23456\t10\t99999990\t1.00E+15\t15\t351840\t18758599200
-BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tSI\t<<STATUS>>  \t9999999\t50293.84615\t10\t99999990\t1.00E+15\t13\t653820\t97952439600
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tSI\t<<STATUS>>  \t9999999\t31900\t10\t99999990\t1.00E+15\t12\t382800\t39290846400
-BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tDI\tISSUE DATE/TIME\t9999999\t33900\t10\t99999990\t1.00E+15\t5\t169500\t6253203600
-BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tDI\tISSUE DATE/TIME\t9999999\t35085\t10\t99999990\t1.00E+15\t4\t140340\t5354082000
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tUBT\tUNIT BLOOD TYPE  \t9999999\t27880\t10\t99999990\t1.00E+15\t3\t83640\t3495549600
-BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tUBT\tUNIT BLOOD TYPE  \t9999999\t38360\t10\t99999990\t1.00E+15\t3\t115080\t7038208800
-BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tUBT\tUNIT BLOOD TYPE  \t9999999\t38320\t10\t99999990\t1.00E+15\t3\t114960\t7029561600
-BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tUBT\tUNIT BLOOD TYPE  \t9999999\t26760\t10\t99999990\t1.00E+15\t3\t80280\t3222554400
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBRES\tRESULTS\t9999999\t1007200\t10\t99999990\t1.00E+15\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCLINE\tLINING CELLS, CSF\t4.2\t6360\t10\t42\t526\t5\t31800\t209181600
-BODY FLUID ORDERABLES\t473090\tLABGASPH\tPH, GASTRIC\tPHGAST\tPH,GASTRIC\t1000005.5\t3660\t10\t10000055\t1.00E+14\t4\t14640\t123249600
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTCMT\tCOMMENT\t9999999\t771152.3077\t10\t99999990\t1.00E+15\t13\t10024980\t1.13E+13
-BODY FLUID ORDERABLES\t497362\tLABFLK\tPOTASSIUM, FLUID\tKFLS\tSOURCE\t9999999\t3996\t10\t99999990\t1.00E+15\t5\t19980\t102495600
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGMYD8\tMYD88\t9999999\t1131829.091\t10\t99999990\t1.00E+15\t11\t12450120\t1.54E+13
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t68.5\t12342\t10\t685\t48325\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t124\t12342\t10\t1240\t154400\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t540.6\t12342\t10\t5406\t2988472\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t140.495\t12342\t10\t1404.95\t859857.9425\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.925\t12342\t10\t29.25\t99.0625\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.524\t12342\t10\t5.24\t3.1496\t10\t123420\t6324188400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t1000002.17\t12342\t10\t10000021.7\t1.00E+14\t10\t123420\t6324188400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t5000003\t156\t10\t50000030\t5.00E+14\t10\t1560\t19000800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t19.7\t19038\t10\t197\t31471\t10\t190380\t33011377200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t6000004.2\t19008\t10\t60000042\t6.00E+14\t10\t190080\t32977072800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t9.2525\t19398\t10\t92.525\t1270.503125\t10\t193980\t33026972400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t19398\t10\t99999990\t1.00E+15\t10\t193980\t33026972400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t5000013.1\t19008\t10\t50000131\t5.00E+14\t10\t190080\t32977072800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t371\t19008\t10\t3710\t1395500\t10\t190080\t32977072800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t19008\t10\t99999990\t1.00E+15\t10\t190080\t32977072800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t6000031.4\t19008\t10\t60000314\t6.00E+14\t10\t190080\t32977072800
-HISTOCOMPATIBILITY ORDERABLE\t718854\t10205R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\t10049R\tHLA LAB TESTING STATUS\t9999999\t1896726\t10\t99999990\t1.00E+15\t10\t18967260\t5.63E+13
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9184R\tDONOR NAME\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9185R\tDONOR DOB\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9186R\tRELATIONSHIP TO PATIENT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9187R\tDONOR CELL DATE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9188R\tDONOR CELL SOURCE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9189R\tDONOR CELL TARGET\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9190R\tMETHOD\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9191R\tPATIENT\'S SERUM 1 DATE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9193R\tPATIENT\'S SERUM 1 UNTREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9194R\tPATIENT\'S SERUM 1 DTT TREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9195R\tPATIENT\'S SERUM 2 DATE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9197R\tPATIENT\'S SERUM 2 UNTREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9198R\tPATIENT\'S SERUM 2 DTT TREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9331R\tSERUM DATE\t9999999\t1119612\t10\t99999990\t1.00E+15\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9339R\tCPRA FOR CLASS II\t9999999\t1119612\t10\t99999990\t1.00E+15\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9606R\tMETHOD\t9999999\t1119612\t10\t99999990\t1.00E+15\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9519R\t% DONOR- MARROW CD3\t79.3\t5730\t10\t793\t78417\t10\t57300\t3249018000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9559R\t% RECIPIENT- MARROW CD3\t20.7\t5730\t10\t207\t19817\t10\t57300\t3249018000
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tDYSPL\tDYSPL GRANS, UNCLASS.\t8.1\t151332\t10\t81\t1039\t10\t1513320\t5.03E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHQLINT\tINTERPRETATION\t9999999\t227223.75\t10\t99999990\t1.00E+15\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472222\tLABDONLAI\tDONATH LANDSTEINER\tDONLAR\tDONATH LANDSTEINER TEST\t9999999\t259674.5455\t10\t99999990\t1.00E+15\t11\t2856420\t1.04E+12
-LAB BLOOD ORDERABLES\t472450\tLABYGBMGE\tGBM AB, IGG\tYGBMG1\tGBM AB, IGG, ELISA\t8999999.14\t183616.3636\t10\t89999991.4\t9.00E+14\t11\t2019780\t5.25E+11
-LAB BLOOD ORDERABLES\t472513\tLAB11040R\tHEPARIN COFACTOR\tHEPCO2\tHEPARIN COFACTOR II\t9999999\t661686\t10\t99999990\t1.00E+15\t10\t6616860\t5.16E+12
-LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE\tLAMOT\tLAMOTRIGINE\t4.77\t857737.5\t10\t47.7\t357.99\t8\t6861900\t3.25E+13
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMPACID\tMYCOPHENOLIC ACID\t3000001.12\t158860\t10\t30000011.2\t3.00E+14\t9\t1429740\t2.44E+11
-LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tYMPAG\tMPA GLUCURONIDE\t2000058.7\t158860\t10\t20000587\t2.00E+14\t9\t1429740\t2.44E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tCOLL50\tCOLLAGEN 5.0 UG/ML\t6000021.4\t78206.66667\t10\t60000214\t6.00E+14\t9\t703860\t89752165200
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tRIS15\tRISTOCETIN 1.5 MG/ML\t6000031.1\t115826.6667\t10\t60000311\t6.00E+14\t9\t1042440\t2.66E+11
-LAB BLOOD ORDERABLES\t473221\tLABYRENIN\tRENIN ACTIVITY, PLASMA\tRENIN\tRENIN ACTIVITY, PL\t2000002\t355240\t10\t20000020\t2.00E+14\t6\t2131440\t8.40E+11
-LAB BLOOD ORDERABLES\t473322\tLABSICKL\tSICKLE CELL PREP\tSICKL\tSICKLE CELL PREP\t9999999\t8400\t10\t99999990\t1.00E+15\t12\t100800\t1081922400
-LAB BLOOD ORDERABLES\t473455\tLABTFPSA\tTOTAL AND FREE PSA\tFPSA\tFREE PSA\t1000005.98\t25116\t10\t10000059.8\t1.00E+14\t10\t251160\t8745012000
-LAB BLOOD ORDERABLES\t473455\tLABTFPSA\tTOTAL AND FREE PSA\tFREEPSAPC\t% FREE PSA\t1000020.1\t25116\t10\t10000201\t1.00E+14\t10\t251160\t8745012000
-LAB BLOOD ORDERABLES\t473455\tLABTFPSA\tTOTAL AND FREE PSA\tPSA\tPSA\t55.88\t25116\t10\t558.8\t202080.88\t10\t251160\t8745012000
-LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tTEST4\tTEST NAME\t9999999\t4443266.667\t10\t99999990\t1.00E+15\t9\t39989400\t3.40E+14
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12695R\tS.PNEUMO SEROTYPE 2 (2)\t3000006.13\t272862\t10\t30000061.3\t3.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12712R\tS.PNEUMO SEROTYPE 5 (5)\t2000001.47\t272862\t10\t20000014.7\t2.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12713R\tS.PNEUMO SEROTYPE 17 (17)\t5000001.21\t272862\t10\t50000012.1\t5.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12714R\tS.PNEUMO SEROTYPE 20 (20F)\t2000001.04\t272862\t10\t20000010.4\t2.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12715R\tS.PNEUMO SEROTYPE 22 (22F)\t6000001.82\t272862\t10\t60000018.2\t6.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12716R\tS.PNEUMO SEROTYPE 34 (10A)\t4000000.3\t272862\t10\t40000003\t4.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12717R\tS.PNEUMO SEROTYPE 43 (11A)\t4000000.03\t272862\t10\t40000000.3\t4.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12718R\tS.PNEUMO SEROTYPE 54 (15B)\t5000000.54\t272862\t10\t50000005.4\t5.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12719R\tS.PNEUMO SEROTYPE 57 (19A)\t2000003.52\t272862\t10\t20000035.2\t2.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12726R\tS.PNEUMO SEROTYPE 70 (33F)\t2000003.84\t272862\t10\t20000038.4\t2.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE01\tS.PNEUMO SEROTYPE 1 (1)\t3000001.05\t272862\t10\t30000010.5\t3.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE12\tS.PNEUMO SEROTYPE 12 (12F)\t7000000.71\t272862\t10\t70000007.1\t7.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE14\tS.PNEUMO SEROTYPE 14 (14)\t4000002.42\t272862\t10\t40000024.2\t4.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE19\tS.PNEUMO SEROTYPE 19 (19F)\t3000007.36\t272862\t10\t30000073.6\t3.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE23\tS.PNEUMO SEROTYPE 23 (23F)\t4000000.54\t272862\t10\t40000005.4\t4.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE26\tS.PNEUMO SEROTYPE 26 (6B)\t5000001.13\t272862\t10\t50000011.3\t5.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE3\tS.PNEUMO SEROTYPE 3 (3)\t2000001.18\t272862\t10\t20000011.8\t2.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE4\tS.PNEUMO SEROTYPE 4 (4)\t6999999.63\t272862\t10\t69999996.3\t7.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE51\tS.PNEUMO SEROTYPE 51 (7F)\t3000001.02\t272862\t10\t30000010.2\t3.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE56\tS.PNEUMO SEROTYPE 56 (18C)\t2000000.9\t272862\t10\t20000009\t2.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE68\tS.PNEUMO SEROTYPE 68 (9V)\t5000000.27\t272862\t10\t50000002.7\t5.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE8\tS.PNEUMO SEROTYPE 8 (8)\t3000001.18\t272862\t10\t30000011.8\t3.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE9\tS.PNEUMO SEROTYPE 9 (9N)\t4000000.41\t272862\t10\t40000004.1\t4.00E+14\t10\t2728620\t1.02E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tFACIDTOT\tTOTAL FATTY ACIDS\t10.18\t504672\t10\t101.8\t1084.62\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tMUFATOT\tTOTAL MONOUNSATURATED ACID\t3.04\t504672\t10\t30.4\t96.8\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tPUFATOT\tTOTAL POLYUNSATURATED ACID\t2.98\t504672\t10\t29.8\t97.68\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC1\tLAURIC ACID C12:0\t12.6\t504672\t10\t126\t2238\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC10\tVACCENIC ACID C18:1W9\t361.8\t504672\t10\t3618\t1476790\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC11\tSTEARIC ACID C18:0\t925.2\t504672\t10\t9252\t9348058\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC12\tEPA C20:5W3\t181.3\t504672\t10\t1813\t515979\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC13\tARACHIDONIC ACID C20:4W6\t452.5\t504672\t10\t4525\t2649389\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC14\tMEAD ACID C20:3W9\t15.5\t504672\t10\t155\t3445\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC15\tH-G-LINOLEIC C20:3W6\t75.7\t504672\t10\t757\t72263\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC16\tARACHIDIC ACID C20:0\t24.7\t504672\t10\t247\t6391\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC17\tDHA C22:6W3\t262.8\t504672\t10\t2628\t1096190\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC18\tDPA C22:5W6\t9.4\t504672\t10\t94\t1002\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC19\tDPA C22:5W3\t60.4\t504672\t10\t604\t49634\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC2\tMYRISTIC ACID C14:0\t132.7\t504672\t10\t1327\t190389\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC20\tDTA C22:4W6\t14.5\t504672\t10\t145\t2419\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC21\tDOCOSENOIC ACID C22:1\t9.4\t504672\t10\t94\t1098\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC22\tNERVONIC ACID C24:1W9\t76.2\t504672\t10\t762\t60568\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC24\tTOTAL SATURATED ACID\t4.11\t504672\t10\t41.1\t178.23\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC27\tTOTAL W3\t0.57\t504672\t10\t5.7\t4.67\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC28\tTOTAL W6\t2.42\t504672\t10\t24.2\t65.74\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC3\tHEXADECENOIC ACID C16:1W9\t69.9\t504672\t10\t699\t57731\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC4\tPALMITOLEIC ACID C16:1W7\t446.6\t504672\t10\t4466\t2329528\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC5\tPALMITIC ACID C16:0\t2899.8\t504672\t10\t28998\t88296042\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC6\tG-LINOLENIC ACID C18:3W6\t25.2\t504672\t10\t252\t8448\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC7\tA-LINOLENIC ACID C18:3W3\t57.1\t504672\t10\t571\t40465\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC8\tLINOLEIC ACID C18:2W6\t1832.2\t504672\t10\t18322\t37337792\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC9\tOLEIC ACID C18:1W9\t2076.6\t504672\t10\t20766\t45164836\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11760R\tCOQ10 REDUCED\t477.64\t282690\t10\t4776.4\t3096814.14\t10\t2826900\t9.22E+11
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12243R\t12243R\t2000000.501\t925412.7273\t10\t20000005.01\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12244R\t12244R\t2000052.9\t925412.7273\t10\t20000529\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12245R\t12245R\t2000013.9\t925412.7273\t10\t20000139\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12246R\t12246R\t2000011.7\t925412.7273\t10\t20000117\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12247R\t12247R\t2000033.6\t925412.7273\t10\t20000336\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12248R\t12248R\t2000018.8\t925412.7273\t10\t20000188\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12249R\t12249R\t2000522.2\t925412.7273\t10\t20005222\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12250R\t12250R\t2000102.1\t925412.7273\t10\t20001021\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12251R\t12251R\t2000071.1\t925412.7273\t10\t20000711\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12252R\t12252R\t2000316.4\t925412.7273\t10\t20003164\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12253R\t12253R\t2000201.4\t925412.7273\t10\t20002014\t2.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12256R\t12256R\t4000012.75\t925412.7273\t10\t40000127.5\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12257R\t12257R\t4000014.09\t925412.7273\t10\t40000140.9\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12258R\t12258R\t4000008.58\t925412.7273\t10\t40000085.8\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12259R\t12259R\t4000002.84\t925412.7273\t10\t40000028.4\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12260R\t12260R\t4000003.69\t925412.7273\t10\t40000036.9\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12261R\t12261R\t4000000.75\t925412.7273\t10\t40000007.5\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12262R\t12262R\t4000039.98\t925412.7273\t10\t40000399.8\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12263R\t12263R\t4000001.53\t925412.7273\t10\t40000015.3\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12264R\t12264R\t4000003.82\t925412.7273\t10\t40000038.2\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12265R\t12265R\t4000056.63\t925412.7273\t10\t40000566.3\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12266R\t12266R\t4000002.62\t925412.7273\t10\t40000026.2\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12267R\t12267R\t4000095.98\t925412.7273\t10\t40000959.8\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12268R\t12268R\t4000107.28\t925412.7273\t10\t40001072.8\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12269R\t12269R\t4000007.65\t925412.7273\t10\t40000076.5\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12270R\t12270R\t4000002.43\t925412.7273\t10\t40000024.3\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12271R\t12271R\t4000003.4\t925412.7273\t10\t40000034\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12272R\t12272R\t4000000.55\t925412.7273\t10\t40000005.5\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12273R\t12273R\t4000060.63\t925412.7273\t10\t40000606.3\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12274R\t12274R\t4000001.41\t925412.7273\t10\t40000014.1\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12275R\t12275R\t4000005.45\t925412.7273\t10\t40000054.5\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12276R\t12276R\t4000091.97\t925412.7273\t10\t40000919.7\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12277R\t12277R\t4000003.67\t925412.7273\t10\t40000036.7\t4.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12339R\tEVEROLIMUS\t3.15\t143700\t10\t31.5\t109.55\t14\t2011800\t3.84E+11
-MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tAFB\tAFB SMEAR\t9999999\t3931337.485\t10\t99999990\t1.00E+15\t326\t1281616020\t5.25E+15
-MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tSREQ\tCOMMENTS\t9999999\t79170\t10\t99999990\t1.00E+15\t2\t158340\t12620538000
-MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t1159475.26\t10\t99999990\t1.00E+15\t519\t601767660\t7.41E+14
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tSREQ\tCOMMENTS\t9999999\t471560\t10\t99999990\t1.00E+15\t18\t8488080\t5.50E+12
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tGASO\tPATIENT GASTRIC RESULT\t9999999\t681.8181818\t10\t99999990\t1.00E+15\t11\t7500\t37530000
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHLOC\tLOCATION\t9999999\t-77.64705882\t10\t99999990\t1.00E+15\t17\t-1320\t44971200
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tQC11\tQC RESULT\t9999999\t681.8181818\t10\t99999990\t1.00E+15\t11\t7500\t37530000
-URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tPHOSU\tPHOSPHATE, URINE\t1000025.21\t56070\t10\t10000252.1\t1.00E+14\t6\t336420\t30016882800
-URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tTPHOSU\tPHOSPHATE EXCRETION\t1000684.5\t56070\t10\t10006845\t1.00E+14\t6\t336420\t30016882800
-URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tUT8\tCOLLECTION PERIOD PHOSPHATE\t1000020.3\t56070\t10\t10000203\t1.00E+14\t6\t336420\t30016882800
-URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tVOL8\tVOLUME PHOSPHATE\t1002772.2\t56070\t10\t10027722\t1.00E+14\t6\t336420\t30016882800
-URINE ORDERABLES\t473724\tLABUURIC\tURIC ACID WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t57.29\t13785\t10\t572.9\t41876.85\t4\t55140\t1753282800
-URINE ORDERABLES\t473724\tLABUURIC\tURIC ACID WITH CREATININE, URINE (RANDOM)\tURICRU\tURIC/CREAT RATIO\t1000000.447\t13785\t10\t10000004.47\t1.00E+14\t4\t55140\t1753282800
-URINE ORDERABLES\t473724\tLABUURIC\tURIC ACID WITH CREATININE, URINE (RANDOM)\tURICURN\tURIC ACID, URINE\t26.37\t13785\t10\t263.7\t9850.53\t4\t55140\t1753282800
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tVMAUR\tVMA, URINE\t4.6\t379830\t10\t46\t290\t6\t2278980\t9.13E+11
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVCP\tRESULT\t9000572.1\t159127.5\t10\t90005721\t9.00E+14\t8\t1273020\t2.10E+11
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVLOG\tRESULT LOG 10\t8999999.476\t159127.5\t10\t89999994.76\t9.00E+14\t8\t1273020\t2.10E+11
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVSP\tSPECIMEN\t9999999\t159127.5\t10\t99999990\t1.00E+15\t8\t1273020\t2.10E+11
-VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t198392.7935\t10\t99999990\t1.00E+15\t247\t49003020\t1.01E+13
-VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEBV EARLY ANTIGEN IGG\tEAGQNT\tEAGQNT\t4000000.882\t280713.3333\t10\t40000008.82\t4.00E+14\t9\t2526420\t1.92E+12
-VIROLOGY ORDERABLES\t876715\tLABDVERV3\tRSV DFA\tRV3IP\tTEST DESCRIPTION\t9999999\t60101.53846\t10\t99999990\t1.00E+15\t78\t4687920\t3.25E+11
-VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVDSV\tTEST DESCRIPTION\t9999999\t253864.8227\t10\t99999990\t1.00E+15\t423\t107384820\t3.23E+13
-VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVSPM\tSPECIMEN\t9999999\t175699.0909\t10\t99999990\t1.00E+15\t22\t3865380\t7.11E+11
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQIN\tTEST DESCRIPTION\t9999999\t403485\t10\t99999990\t1.00E+15\t20\t8069700\t1.11E+13
-VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tHSVINC\tTEST DESCRIPTION\t9999999\t185672.0388\t10\t99999990\t1.00E+15\t206\t38248440\t9.97E+12
-VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tRPT\tRPT\t9999999\t1069548\t10\t99999990\t1.00E+15\t10\t10695480\t1.26E+13
-VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tSDES\tSPECIMEN\t9999999\t1069548\t10\t99999990\t1.00E+15\t10\t10695480\t1.26E+13
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t9\t89999991\t9.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tXMR\tUNIT CROSSMATCH  \t9999999\t25760\t9\t89999991\t9.00E+14\t6\t154560\t6237014400
-BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tXMR\tUNIT CROSSMATCH  \t9999999\t32640\t9\t89999991\t9.00E+14\t4\t130560\t10117130400
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tSI\t<<STATUS>>  \t9999999\t53298.46154\t9\t89999991\t9.00E+14\t13\t692880\t99484610400
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tSI\t<<STATUS>>  \t9999999\t49705\t9\t89999991\t9.00E+14\t12\t596460\t77125532400
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tXMR\tUNIT CROSSMATCH  \t9999999\t19880\t9\t89999991\t9.00E+14\t3\t59640\t2008792800
-BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tXMR\tUNIT CROSSMATCH  \t9999999\t28860\t9\t89999991\t9.00E+14\t3\t86580\t3603330000
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tPRO\tPRODUCT ID  \t9999999\t20070\t9\t89999991\t9.00E+14\t4\t80280\t3212935200
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tFCMT\tFLUID COMMENT\t9999999\t980\t9\t89999991\t9.00E+14\t3\t2940\t4496400
-BODY FLUID ORDERABLES\t472490\tLABYHCCSF\tHCG BETA-SUBUNIT QUANTITATIVE, CSF\tHCGCSF\tHCG, B SUBUNIT QN, CSF\t6666666.289\t213420\t9\t59999996.6\t6.00E+14\t7\t1493940\t3.49E+11
-BODY FLUID ORDERABLES\t472609\tLABCIGA\tIGA, CSF\tIGACSF\tIGA, CSF\t1111111.733\t234228\t9\t10000005.6\t1.00E+14\t5\t1171140\t3.03E+11
-BODY FLUID ORDERABLES\t472774\tLABFBBURG\tLYME WESTERN BLOT, CSF\tBBIGG\tBORELLIA BURG IGG AB\t9999999\t282672.6316\t9\t89999991\t9.00E+14\t76\t21483120\t7.48E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\tYSTNWT\tSTONE WEIGHT\t8.568444444\t#VALUE!\t9\t77.116\t5191.667322\t0\tNone\tNone
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t191.6666667\t13760\t9\t1725\t365525\t9\t123840\t6324012000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRALDBIIM\tPM RA LEAD BIPOLAR IMPEDANCE\t463.3333333\t13760\t9\t4170\t2011458\t9\t123840\t6324012000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMTLPCPACA\tPM TELEMETRY PERCENT PACING ATRIAL\t48.25666667\t13760\t9\t434.31\t34580.5801\t9\t123840\t6324012000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.611111111\t13760\t9\t23.5\t70.25\t9\t123840\t6324012000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.4\t13760\t9\t3.6\t1.44\t9\t123840\t6324012000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t1111111.4\t13760\t9\t10000002.6\t1.00E+14\t9\t123840\t6324012000
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tATACHTADTI\tATACH TACHY ZONE DETECTION INTERVAL\t362.2222222\t-380\t9\t3260\t1183800\t9\t-3420\t2084400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDSLP\tBRADY MODULATION SLOPE\t3333337.444\t19786.66667\t9\t30000037\t3.00E+14\t9\t178080\t32661597600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t1.333333333\t19766.66667\t9\t12\t20.65625\t9\t177900\t32661572400
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRDUR\tLV THRESHOLD DURATION\t0.7\t19766.66667\t9\t6.3\t6.13\t9\t177900\t32661572400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t8.655111111\t20686.66667\t9\t77.896\t686.230968\t9\t186180\t32958486000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t231\t9\t89999991\t9.00E+14\t20\t4620\t43707600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t4444565.111\t53.33333333\t9\t40001086\t4.00E+14\t9\t480\t4262400
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t773.3333333\t9\t0\t0\t9\t6960\t53560800
-HISTOCOMPATIBILITY ORDERABLE\t519017\t10230R\tHLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*\t10049R\tHLA LAB TESTING STATUS\t9999999\t1205680\t9\t89999991\t9.00E+14\t9\t10851120\t1.84E+13
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9210R\tDONOR NAME\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9211R\tDONOR DOB\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9212R\tRELATIONSHIP TO PATIENT\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9213R\tDONOR CELL DATE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9214R\tDONOR CELL SOURCE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9215R\tDONOR CELL TARGET\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9216R\tMETHOD\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9217R\tPATIENT\'S SERUM 1 DATE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9221R\tPATIENT\'S SERUM 2 DATE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9333R\tCOMBINED CPRA FOR SERUM\t9999999\t1244013.333\t9\t89999991\t9.00E+14\t9\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9378R\tCPRA FOR CUMULATIVE C1Q\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9382R\tCPRA FOR C1Q CLASS I FOR CURRENT\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9608R\tMETHOD\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9622R\tCURRENT SERUM DATE\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9623R\tCURRENT SERUM DESCRIPTION\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9521R\t% DONOR- MARROW CD15\t79.11111111\t6366.666667\t9\t712\t69798\t9\t57300\t3249018000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9522R\t% DONOR- MARROW CD19\t84.55555556\t6366.666667\t9\t761\t73245\t9\t57300\t3249018000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9529R\t% DONOR- MARROW CD56\t86.66666667\t6366.666667\t9\t780\t75930\t9\t57300\t3249018000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9561R\t% RECIPIENT- MARROW CD15\t20.88888889\t6366.666667\t9\t188\t17398\t9\t57300\t3249018000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9562R\t% RECIPIENT- MARROW CD19\t15.44444444\t6366.666667\t9\t139\t11045\t9\t57300\t3249018000
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9569R\t% RECIPIENT- MARROW CD56\t13.33333333\t6366.666667\t9\t120\t9930\t9\t57300\t3249018000
-LAB BLOOD ORDERABLES\t471525\tLAB11078R\tACETYLCHOLINESTERASE\tACHERBC\tACETYLCHOLINESTERASE, RBC\t1111149.967\t259177.5\t9\t10000349.7\t1.00E+14\t8\t2073420\t7.03E+11
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC02\tC2, ACETYL-\t13.00444444\t373273.3333\t9\t117.04\t2314.4348\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC03\tC3, PROPIONYL-\t0.623333333\t373273.3333\t9\t5.61\t4.5869\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC04\tC4, ISO-/BUTYRYL-\t0.277777778\t373273.3333\t9\t2.5\t0.8306\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC05\tC5, ISOVALERYL-/2-METHYLBUTYRYL-\t0.131111111\t373273.3333\t9\t1.18\t0.1724\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC05DC\tC5-DC, GLUTARYL-\t0.064444444\t373273.3333\t9\t0.58\t0.0568\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC05OH\tC5-OH, 3OH-ISOVALERYL-\t0.057777778\t373273.3333\t9\t0.52\t0.0492\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC06\tC6, HEXANOYL-\t0.091111111\t373273.3333\t9\t0.82\t0.0934\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC06OH\tC6-0H, 3OH-HEXANOYL-\t0.04\t373273.3333\t9\t0.36\t0.0188\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC08\tC8, OCTANOYL-\t0.152222222\t373273.3333\t9\t1.37\t0.2673\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC081\tC8:1, OCTENOYL-\t0.191111111\t373273.3333\t9\t1.72\t0.434\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC10\tC10, DECANOYL-\t0.17\t373273.3333\t9\t1.53\t0.3021\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC101\tC10:1, DECENOYL-\t0.162222222\t373273.3333\t9\t1.46\t0.2954\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC12\tC12, DODECANOYL-\t0.094444444\t373273.3333\t9\t0.85\t0.0969\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC121\tC12:1, DODECENOYL-\t0.115555556\t373273.3333\t9\t1.04\t0.1832\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC12OH\tC12-OH, 3OH-DODECANOYL\t0.028888889\t373273.3333\t9\t0.26\t0.0108\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC14\tC14, TETRADECANOYL-\t0.064444444\t373273.3333\t9\t0.58\t0.048\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC141\tC14:1, TETRADECENOYL-\t0.102222222\t373273.3333\t9\t0.92\t0.1286\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC141OH\tC14:1-OH, 3OH-TETRADECENOYL-\t0.054444444\t373273.3333\t9\t0.49\t0.0333\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC142\tC14:2, TETRADECADIENOYL-\t0.074444444\t373273.3333\t9\t0.67\t0.0569\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC14OH\tC14-OH, 3OH-TETRADECANOYL-\t0.045555556\t373273.3333\t9\t0.41\t0.0253\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC16\tC16, PALMITOYL-\t0.165555556\t373273.3333\t9\t1.49\t0.2889\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC161\tC16:1, PALMITOLEYL-\t0.063333333\t373273.3333\t9\t0.57\t0.0429\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC161OH\tC16:1-OH, 3OH-PALMITOLEYL-\t0.028888889\t373273.3333\t9\t0.26\t0.0104\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC16OH\tC16-OH, 3OH-PALMITOYL-\t0.037777778\t373273.3333\t9\t0.34\t0.0168\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC18\tC18, STEAROYL-\t0.063333333\t373273.3333\t9\t0.57\t0.0425\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC181\tC18:1, OLEYL-\t0.204444444\t373273.3333\t9\t1.84\t0.444\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC181OH\tC18:1-OH, 3OH-OLEYL-\t0.032222222\t373273.3333\t9\t0.29\t0.0125\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC182\tC18:2, LINOLEYL-\t0.093333333\t373273.3333\t9\t0.84\t0.091\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC182OH\tC18:2-OH, 3OH-LINOLEYL-\t0.025555556\t373273.3333\t9\t0.23\t0.0067\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471536\tLABADAQ\tADENOSINE DEAMINASE, RBC\tADA\tADENOSINE DEAMINASE\t2222222.458\t490320\t9\t20000002.12\t2.00E+14\t9\t4412880\t3.48E+12
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\tPANCA\tNEUTROPHIL CYTO AB-P\t9999999\t205166.6667\t9\t89999991\t9.00E+14\t9\t1846500\t4.31E+11
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\tSACIGA\tS. CERVISIAE AB,IGA\t45.82222222\t205166.6667\t9\t412.4\t43433.76\t9\t1846500\t4.31E+11
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\tSACIGG\tS.CERVISIAE AB,IGG\t28.43333333\t205166.6667\t9\t255.9\t16686.49\t9\t1846500\t4.31E+11
-LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tSCL 70 AB\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t143610\t9\t89999991\t9.00E+14\t8\t1148880\t1.75E+11
-LAB BLOOD ORDERABLES\t471745\tLABYC2729\tBCA AB  (CA27.29)\tY2729A\tCA 27, 29\t85.45555556\t164080\t9\t769.1\t162363.81\t9\t1476720\t2.68E+11
-LAB BLOOD ORDERABLES\t472506\tLABYHAET\tHEMOPHILUS INFLUENZA IGG\tYHAET1\tHAEMOPHILUS INFLU B AG,IGG\t0.774444444\t375535\t9\t6.97\t9.1277\t12\t4506420\t1.84E+12
-LAB BLOOD ORDERABLES\t472520\tLABHBCA\tIGM ANTI-HBC\tHBCINT\tINTERPRETATION\t9999999\t52028.78788\t9\t89999991\t9.00E+14\t198\t10301700\t1.13E+12
-LAB BLOOD ORDERABLES\t472521\tLABHBCC\tHEPATITIS B CORE TOTAL AB\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t41197.5\t9\t89999991\t9.00E+14\t8\t329580\t18475815600
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t129980\t9\t89999991\t9.00E+14\t9\t1169820\t3.51E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t129980\t9\t89999991\t9.00E+14\t9\t1169820\t3.51E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t129980\t9\t89999991\t9.00E+14\t9\t1169820\t3.51E+11
-LAB BLOOD ORDERABLES\t472761\tLABLKM1AB\tLIVER, KID MICROSOME-1 AB, IGG\tLKM1R\tLIVER, KID MIC-1 AB\t1.588888889\t211900\t9\t14.3\t29.79\t9\t1907100\t4.28E+11
-LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tSLREV\tSLIDE REVIEW\t9999999\t31537.5\t9\t89999991\t9.00E+14\t8\t252300\t21254857200
-LAB BLOOD ORDERABLES\t473348\tLABYSOMAT\tSOMATOSTATIN\tSOMATOSTATIN\tSOMATOSTATIN\t22.33333333\t814128\t9\t201\t7331\t10\t8141280\t9.47E+12
-LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tRESUL3\tRESULT\t9999999\t2214834.286\t9\t89999991\t9.00E+14\t21\t46511520\t3.48E+14
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGGT\tB.HENSELAE IGG TITER\t9999999\t864724.2857\t9\t89999991\t9.00E+14\t14\t12106140\t4.66E+13
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12027R\tECHOVIRUS  AB TYPE 6\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12028R\tECHOVIRUS  AB TYPE 7\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12029R\tECHOVIRUS  AB TYPE 11\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12030R\tECHOVIRUS  AB TYPE 30\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12031R\tECHOVIRUS  AB TYPE 9\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVA1\tPHASE 1 IGA\t9999999\t369216\t9\t89999991\t9.00E+14\t10\t3692160\t1.56E+12
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVA2\tPHASE II IGA\t9999999\t369216\t9\t89999991\t9.00E+14\t10\t3692160\t1.56E+12
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBCM\tCOMMENT\t9999999\t910448.7379\t9\t89999991\t9.00E+14\t103\t93776220\t1.40E+14
-LAB BLOOD ORDERABLES\t966901\tLAB12605R\tN-METHYL-D-ASPARTATE RECEPTOR AB, IGG\t12604R\tN-METHYL-D-ASPARTATE RCPTR AB, IGG\t9999999\t315673.3333\t9\t89999991\t9.00E+14\t9\t2841060\t1.02E+12
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tRPT\tRPT\t9999999\t48300\t9\t89999991\t9.00E+14\t1\t48300\t2332890000
-MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRRS\tRESULTS\t9999999\t488123.0769\t9\t89999991\t9.00E+14\t26\t12691200\t8.34E+12
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEL\tDEVELOPER LOT#\t1017.555556\t681.8181818\t9\t9158\t9319590\t11\t7500\t37530000
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEXP\tDEVELOPER EXP DATE\t9999999\t681.8181818\t9\t89999991\t9.00E+14\t11\t7500\t37530000
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tGASEXP\tGASTROCCULT EXP DATE\t9999999\t681.8181818\t9\t89999991\t9.00E+14\t11\t7500\t37530000
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tGASLOT\tGASTROCCULT LOT#\t4455615.667\t785\t9\t40100541\t4.00E+14\t12\t9420\t41216400
-POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tOCCBLD\tFECAL OCCULT BLOOD BLOOD\t9999999\t1109028.75\t9\t89999991\t9.00E+14\t16\t17744460\t2.34E+14
-STOOL ORDERABLES\t473120\tLABPORFEC\tPORPHYRINS FRACTIONATED, FECAL\tCOPRO\tCOPROPORPHYRIN, FEC\t2222234.444\t934260\t9\t20000110\t2.00E+14\t5\t4671300\t8.25E+12
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tRPT\tRPT\t9999999\t192800\t9\t89999991\t9.00E+14\t3\t578400\t1.13E+11
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tSDES\tSPECIMEN\t9999999\t192800\t9\t89999991\t9.00E+14\t3\t578400\t1.13E+11
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tSREQ\tCOMMENTS\t9999999\t192800\t9\t89999991\t9.00E+14\t3\t578400\t1.13E+11
-STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL4\tCHLORIDE CONCENTRATION\t5555572.333\t671420\t9\t50000151\t5.00E+14\t12\t8057040\t7.48E+12
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tAAMINOA\tA-AMINOADIPIC ACID\t12.44444444\t287790\t9\t112\t12544\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tAAMINOB\tA-AMINOBUTYRIC ACID\t238.1111111\t287790\t9\t2143\t2386269\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tALANINE\tALANINE\t5624\t287790\t9\t50616\t794454100\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tARGININE\tARGININE\t622\t287790\t9\t5598\t9621094\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tASPART\tASPARTIC ACID\t2222297.667\t287790\t9\t20000679\t2.00E+14\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tASPGN\tASPARAGINE\t1582.111111\t287790\t9\t14239\t56672463\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tBALN\tB-ALANINE\t61.11111111\t287790\t9\t550\t189970\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tBAMBA\tB-AMINOISOBUTYRIC AC\t492.3333333\t287790\t9\t4431\t4398237\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tCITRUL\tCITRULLINE\t575.7777778\t287790\t9\t5182\t10538248\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tCYSTINE\tCYSTINE\t720\t287790\t9\t6480\t12113300\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tCYTHN\tCYSTATHIONINE\t78.11111111\t287790\t9\t703\t108063\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tETHLNU\tETHANOLAMINE\t227.8888889\t287790\t9\t2051\t1082763\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tGLUTAC\tGLUTAMIC ACID\t72.44444444\t287790\t9\t652\t114218\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tGLUTAMINE\tGLUTAMINE\t8127.333333\t287790\t9\t73146\t1655999470\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tGLYCINE\tGLYCINE\t6327.111111\t287790\t9\t56944\t719378568\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tHISTID\tHISTIDINE\t2580.111111\t287790\t9\t23221\t117318771\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tHMCY\tHOMOCYSTINE\t20.33333333\t287790\t9\t183\t7251\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tHYPROL\tHYDROXYPROLINE\t135.2222222\t287790\t9\t1217\t470219\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tISOLEU\tISOLEUCINE\t335.3333333\t287790\t9\t3018\t3745580\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tLEUCINE\tLEUCINE\t706.7777778\t287790\t9\t6361\t15632137\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tLYSINE\tLYSINE\t2704.666667\t287790\t9\t24342\t164997240\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tMETHIO\tMETHIONINE\t269.6666667\t287790\t9\t2427\t1719511\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tORNITHINE\tORNITHINE\t692.1111111\t287790\t9\t6229\t16353759\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tPHALN\tPHENYLALANINE\t929.2222222\t287790\t9\t8363\t22662603\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tPROLINE\tPROLINE\t1144.444444\t287790\t9\t10300\t39903032\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tSARSN\tSARCOSINE\t6.222222222\t287790\t9\t56\t3136\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tSERINE\tSERINE\t3062.777778\t287790\t9\t27565\t159511577\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTAURINE\tTAURINE\t475.4444444\t287790\t9\t4279\t4548709\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTHREONINE\tTHREONINE\t3445.333333\t287790\t9\t31008\t217879164\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTRYPTOPHAN\tTRYPTOPHAN\t402.8888889\t287790\t9\t3626\t3655830\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTYROSINE\tTYROSINE\t1175.777778\t287790\t9\t10582\t33593684\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tVALINE\tVALINE\t1613.888889\t287790\t9\t14525\t90252429\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t472783\tLABUMAG\tMAGNESIUM WITH CREATININE, URINE (RANDOM)\tMGUCRU\tMAG/CREAT RATIO\t0.322222222\t35512.5\t9\t2.9\t1.07\t8\t284100\t66201930000
-URINE ORDERABLES\t472783\tLABUMAG\tMAGNESIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t39.2\t35512.5\t9\t352.8\t20411.16\t8\t284100\t66201930000
-URINE ORDERABLES\t472783\tLABUMAG\tMAGNESIUM WITH CREATININE, URINE (RANDOM)\tUMG\tMAGNESIUM, URINE\t11.04444444\t35512.5\t9\t99.4\t1629.74\t8\t284100\t66201930000
-URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUPORIN\tPORPHYRIN, URN INTRP\t9999999\t448491\t9\t89999991\t9.00E+14\t20\t8969820\t6.55E+12
-VIROLOGY ORDERABLES\t472015\tLABCLCR2\tCHLAMYDIA MOLECULAR DETECTION\tSDES4\tSPECIMEN\t9999999\t105230\t9\t89999991\t9.00E+14\t12\t1262760\t1.93E+11
-VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMV\tCMV ANTIBODY\t9999999\t#VALUE!\t9\t89999991\t9.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVDNA\tCMV DNA\t9999999\t159127.5\t9\t89999991\t9.00E+14\t8\t1273020\t2.10E+11
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBVDS\tEBV TEST DESCRIPTION\t9999999\t287670.1288\t9\t89999991\t9.00E+14\t699\t201081420\t8.47E+13
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS PCR QUANT\tSOUR10\tSOURCE\t9999999\t131400\t9\t89999991\t9.00E+14\t1\t131400\t17265960000
-VIROLOGY ORDERABLES\t876721\tLABDVEIAR\tFLU A DFA-REFLEX PCR\tIARSP\tSPECIMEN\t9999999\t118140\t9\t89999991\t9.00E+14\t7\t826980\t1.98E+11
-VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tSREQ\tCOMMENTS\t9999999\t1069548\t9\t89999991\t9.00E+14\t10\t10695480\t1.26E+13
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tVL\tPRODUCT VOLUME\t154.875\t33120\t8\t1239\t195761\t1\t33120\t1096934400
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tVL\tPRODUCT VOLUME\t161.625\t#VALUE!\t8\t1293\t209565\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t8\t79999992\t8.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tXMR\tUNIT CROSSMATCH  \t9999999\t40515\t8\t79999992\t8.00E+14\t4\t162060\t11561770800
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tXMR\tUNIT CROSSMATCH  \t9999999\t135465\t8\t79999992\t8.00E+14\t4\t541860\t93705937200
-BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tXMR\tUNIT CROSSMATCH  \t9999999\t116736\t8\t79999992\t8.00E+14\t5\t583680\t95454849600
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tSI\t<<STATUS>>  \t9999999\t28614\t8\t79999992\t8.00E+14\t10\t286140\t25408443600
-BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tXMR\tUNIT CROSSMATCH  \t9999999\t23280\t8\t79999992\t8.00E+14\t2\t46560\t2006697600
-BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tXMR\tUNIT CROSSMATCH  \t9999999\t23280\t8\t79999992\t8.00E+14\t2\t46560\t2006697600
-BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tSI\t<<STATUS>>  \t9999999\t11100\t8\t79999992\t8.00E+14\t5\t55500\t2073337200
-BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tUBT\tUNIT BLOOD TYPE  \t9999999\t40110\t8\t79999992\t8.00E+14\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tPRO\tPRODUCT ID  \t9999999\t26280\t8\t79999992\t8.00E+14\t3\t78840\t3104877600
-BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tPRO\tPRODUCT ID  \t9999999\t36700\t8\t79999992\t8.00E+14\t3\t110100\t6598270800
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tVL\tPRODUCT VOLUME\t167.875\t38550\t8\t1343\t227079\t2\t77100\t3002158800
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tEVIN\tENTEROVIRUS PCR INTERPRETATION\t9999999\t25672.87129\t8\t79999992\t8.00E+14\t202\t5185920\t5.37E+11
-BODY FLUID ORDERABLES\t473196\tLABYCPYRU\tPYRUVATE, CSF\tPKCSF\tPYRUVATE,CSF\t2499999.869\t291150\t8\t19999998.95\t2.00E+14\t6\t1746900\t5.63E+11
-BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tCPSA\tCOMPLEXED PSA\t2500006.325\t66787.5\t8\t20000050.6\t2.00E+14\t8\t534300\t52996633200
-BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tFPSA\tFREE PSA\t2500003.963\t66787.5\t8\t20000031.7\t2.00E+14\t8\t534300\t52996633200
-BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tPSA\tPSA\t2500010.538\t66787.5\t8\t20000084.3\t2.00E+14\t8\t534300\t52996633200
-BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tPSAPC\t% COMPLEXED PSA\t2500054.375\t66787.5\t8\t20000435\t2.00E+14\t8\t534300\t52996633200
-ECHO ORDERABLES\t472280\tECH34\tECHO - 3-DIMENSIONAL ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t56.875\t40695\t8\t455\t26455\t8\t325560\t31827009600
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t161.25\t15540\t8\t1290\t208850\t8\t124320\t6323781600
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t147.5\t14010\t8\t1180\t211000\t8\t112080\t6185714400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t3750013.25\t15562.5\t8\t30000106\t3.00E+14\t8\t124500\t6323598000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t6250000.938\t15562.5\t8\t50000007.5\t5.00E+14\t8\t124500\t6323598000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t15562.5\t8\t79999992\t8.00E+14\t8\t124500\t6323598000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t131.25\t14010\t8\t1050\t138300\t8\t112080\t6185714400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.8316\t13350\t8\t22.6528\t64.2318821\t8\t106800\t6079788000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t5299234.125\t3292.5\t8\t42393873\t4.03E+14\t8\t26340\t383828400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t7500063\t13537.5\t8\t60000504\t6.00E+14\t8\t108300\t6080598000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t68.75\t62850\t8\t550\t40550\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t5272747.875\t62850\t8\t42181983\t3.62E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.40625\t62850\t8\t19.25\t60.78125\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.49\t62850\t8\t3.92\t2.2304\t8\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2.8\t62850\t8\t22.4\t75.2\t8\t502800\t2.51E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t96832.5\t8\t79999992\t8.00E+14\t8\t774660\t6.04E+11
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADALERT\tLEAD ALERT\t9999999\t-382.5\t8\t79999992\t8.00E+14\t8\t-3060\t2005200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t0.25\t23227.5\t8\t2\t2\t8\t185820\t32958356400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t33785.45455\t8\t79999992\t8.00E+14\t11\t371640\t65919031200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t416.25\t23760\t8\t3330\t1389900\t8\t190080\t32977044000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t21757.5\t8\t0\t0\t8\t174060\t23357962800
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRSENM\tLV THRESHOLD SENSING AMPLITUDE\t15.2125\t12427.5\t8\t121.7\t2163.85\t8\t99420\t8683678800
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRSENPO\tLV THRESHOLD SENSING POLARITY\t9999999\t12427.5\t8\t79999992\t8.00E+14\t8\t99420\t8683678800
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9219R\tPATIENT\'S SERUM 1 UNTREATED RESULT\t9999999\t593707.5\t8\t79999992\t8.00E+14\t8\t4749660\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9223R\tPATIENT\'S SERUM 2 UNTREATED RESULT\t9999999\t593707.5\t8\t79999992\t8.00E+14\t8\t4749660\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9224R\tPATIENT\'S SERUM 2 DTT TREATED RESULT\t9999999\t593707.5\t8\t79999992\t8.00E+14\t8\t4749660\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9166R\tSERUM DESCRIPTION 2\t9999999\t252547.5\t8\t79999992\t8.00E+14\t8\t2020380\t3.30E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t10464R\tRECIPIENT NAME\t9999999\t204592.5\t8\t79999992\t8.00E+14\t8\t1636740\t7.88E+11
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t10465R\tRECIPIENT DOB\t9999999\t204592.5\t8\t79999992\t8.00E+14\t8\t1636740\t7.88E+11
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9136R\tSERUM DESCRIPTION 2\t9999999\t252525\t8\t79999992\t8.00E+14\t8\t2020200\t3.30E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9334R\tCPRA FOR CLASS I\t9999999\t1261597.5\t8\t79999992\t8.00E+14\t8\t10092780\t6.71E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9340R\tHLA ANTIBODIES FOR CLASS II\t9999999\t1119612\t8\t79999992\t8.00E+14\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t10476R\tDSA FOR C1Q CLASS I\t9999999\t15\t8\t79999992\t8.00E+14\t8\t120\t7200
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9395R\tCPRA FOR C1Q CLASS II FOR CURRENT\t9999999\t37305\t8\t79999992\t8.00E+14\t8\t298440\t89030628000
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9280R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t122781.8824\t8\t79999992\t8.00E+14\t85\t10436460\t1.11E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9584R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t153862.5\t8\t79999992\t8.00E+14\t24\t3692700\t2.80E+12
-LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tANTI-LA ANTIBODY\tSSBQNT\tANTI-LA LEVEL\t3750033.625\t458280\t8\t30000269\t3.00E+14\t8\t3666240\t2.40E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMMQNT\tCMV IGM LEVEL\t9999999\t302160\t8\t79999992\t8.00E+14\t8\t2417280\t8.64E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t9999999\t379288.125\t8\t79999992\t8.00E+14\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV1QNT\tHSV-1 IGG LEVEL\t3750003.765\t302160\t8\t30000030.12\t3.00E+14\t8\t2417280\t8.64E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV1QUL\tHSV-1 IGG RESULT\t9999999\t302160\t8\t79999992\t8.00E+14\t8\t2417280\t8.64E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV2QNT\tHSV-2 IGG LEVEL\t8749999.903\t302160\t8\t69999999.22\t7.00E+14\t8\t2417280\t8.64E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV2QUL\tHSV-2 IGG RESULT\t9999999\t302160\t8\t79999992\t8.00E+14\t8\t2417280\t8.64E+11
-LAB BLOOD ORDERABLES\t471931\tLABYCARO\tCAROTENE\tCAROTENE\tCAROTENE\t2500027\t290665.7143\t8\t20000216\t2.00E+14\t7\t2034660\t7.56E+11
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tLYM\tLYMPHOCYTES\t9.625\t8310\t8\t77\t1703\t2\t16620\t157957200
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMONO\tMONOCYTES\t14.375\t8310\t8\t115\t2645\t2\t16620\t157957200
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNEUT\tSEG NEUTROPHILS\t58.75\t8310\t8\t470\t31600\t2\t16620\t157957200
-LAB BLOOD ORDERABLES\t472190\tLABYDIPT\tDIPHTHERIA TOXOID IGA AB\tYDIPT1\tDIPTHERIA TOXOID IGG AB\t0.5625\t155505\t8\t4.5\t4.1392\t8\t1244040\t2.51E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t109882.5\t8\t79999992\t8.00E+14\t8\t879060\t2.67E+11
-LAB BLOOD ORDERABLES\t473187\tLABPTINH\tPT INHIBITOR SCREEN\tPTDIR\tINTERPRETED BY\t9999999\t65953.84615\t8\t79999992\t8.00E+14\t156\t10288800\t9.53E+11
-LAB BLOOD ORDERABLES\t473332\tLABSLINTP\tSLIDE INTERPRETATION BY PATHOLOGIST\tSLINT\tINTERPRETATION\t9999999\t168261.4286\t8\t79999992\t8.00E+14\t28\t4711320\t1.33E+12
-LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14MET\tMETHOD\t9999999\t1210908\t8\t79999992\t8.00E+14\t5\t6054540\t9.00E+12
-LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14PRM\tPRIMERS\t9999999\t1210908\t8\t79999992\t8.00E+14\t5\t6054540\t9.00E+12
-LAB BLOOD ORDERABLES\t497040\tLABPSARX\tPSA WITH REFLEX FREE PSA\tPSA\tPSA\t3750001.8\t125565\t8\t30000014.4\t3.00E+14\t8\t1004520\t5.32E+11
-LAB BLOOD ORDERABLES\t497398\tLABYDNBT\tANTI - DNASE B TITER\tYDNBT1\tANTI-DNASE -B TITER\t3750297.125\t148092\t8\t30002377\t3.00E+14\t10\t1480920\t2.29E+11
-LAB BLOOD ORDERABLES\t497418\tLABFCYST\tCYSTICERCUS IGG AB\tCYSIGG\tCYSTICERCUS LGG AB\t9999999\t382065\t8\t79999992\t8.00E+14\t8\t3056520\t1.66E+12
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tCRDVLG\tCORD LENGTH\t6250008.375\t615\t8\t50000067\t5.00E+14\t8\t4920\t4780800
-LAB BLOOD ORDERABLES\t668158\tLABADAM13\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMAG\tANTIGEN\t1250023.125\t162694.2857\t8\t10000185\t1.00E+14\t7\t1138860\t2.85E+11
-LAB BLOOD ORDERABLES\t670808\tLAB11170R\tPREGABALIN\t11171R\tPREGABALIN\t2500002.5\t442380\t8\t20000020\t2.00E+14\t8\t3539040\t1.72E+12
-LAB BLOOD ORDERABLES\t910568\tLAB12397R\tACETYLCHOLINE BLOCKING AB\tACRBLO\tACETYLCHOLINE BLOCKING AB\t25.25\t256882.5\t8\t202\t11238\t8\t2055060\t5.83E+11
-MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tRPT\tRPT\t9999999\t115550\t8\t79999992\t8.00E+14\t6\t693300\t89094740400
-MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tSDES\tSPECIMEN\t9999999\t115550\t8\t79999992\t8.00E+14\t6\t693300\t89094740400
-MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tSREQ\tCOMMENTS\t9999999\t115550\t8\t79999992\t8.00E+14\t6\t693300\t89094740400
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t324263.2112\t8\t79999992\t8.00E+14\t928\t300916260\t1.71E+14
-MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tCULT\tCULT/OTHER RSLT:\t9999999\t184554.1693\t8\t79999992\t8.00E+14\t319\t58872780\t5.99E+13
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCDX\tCLINICAL INDICATION\t9999999\t1195020\t8\t79999992\t8.00E+14\t3\t3585060\t4.32E+12
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCMET\tMETHOD\t9999999\t1195020\t8\t79999992\t8.00E+14\t3\t3585060\t4.32E+12
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHVA\tPATIENT PH VALUE\t5000001.5\t681.8181818\t8\t40000012\t4.00E+14\t11\t7500\t37530000
-POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tDEVEL\tDEVELOPER LOT#\t78.625\t1109028.75\t8\t629\t58281\t16\t17744460\t2.34E+14
-POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tHEMED\tHEMOCCULT EXP DATE\t9999999\t1109028.75\t8\t79999992\t8.00E+14\t16\t17744460\t2.34E+14
-POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tHEML\tHEMOCCULT LOT#\t6250843.125\t1109028.75\t8\t50006745\t5.00E+14\t16\t17744460\t2.34E+14
-STOOL ORDERABLES\t473120\tLABPORFEC\tPORPHYRINS FRACTIONATED, FECAL\tFPROTO\tPROTOPORHYRINS, FEC\t1250024.875\t495795\t8\t10000199\t1.00E+14\t4\t1983180\t1.03E+12
-STOOL ORDERABLES\t497355\tLABFAMYA\tAMYLASE, ASCITES\tAMYLASEFLD\tAMYLASE, ASCITES FLUID\t37.625\t5835\t8\t301\t38059\t8\t46680\t406987200
-STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\t12577R\tCHLORIDE, STOOL, RANDOM\t5000035.875\t238005\t8\t40000287\t4.00E+14\t12\t2856060\t9.48E+11
-URINE ORDERABLES\t471617\tLABURAMY\tAMYLASE, URINE (RANDOM)\tAMYLASE\tAMYLASE, URINE\t2516783.875\t14090\t8\t20134271\t2.00E+14\t6\t84540\t1754334000
-URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tCLE\tCHLORIDE EXCRETION\t1250169.625\t63315\t8\t10001357\t1.00E+14\t4\t253260\t28555513200
-URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tUCL\tCHLORIDE, URINE\t1250082.5\t63315\t8\t10000660\t1.00E+14\t4\t253260\t28555513200
-URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tUT11\tCOLLECTION PERIOD CHLORIDE\t1250019.375\t63315\t8\t10000155\t1.00E+14\t4\t253260\t28555513200
-URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tVOL11\tVOLUME CHLORIDE\t1251732.875\t63315\t8\t10013863\t1.00E+14\t4\t253260\t28555513200
-URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tTMGNU\tMAGNESIUM EXCRETION\t184.25\t88200\t8\t1474\t365244\t5\t441000\t70547680800
-URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tUMG\tMAGNESIUM, URINE\t8.95\t88200\t8\t71.6\t940.32\t5\t441000\t70547680800
-URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tUT12\tCOLL PERIOD MAG\t22.5\t88200\t8\t180\t4176\t5\t441000\t70547680800
-URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tVOL12\tVOLUME MAGNESIUM\t2511.875\t88200\t8\t20095\t74914237\t5\t441000\t70547680800
-VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDDNA\tHEP B VIRUS DNA\t9999999\t#VALUE!\t8\t79999992\t8.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDLOG\tRESULT LOG 10\t3750003.381\t#VALUE!\t8\t30000027.05\t3.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDRES\tRESULT\t5245212.125\t#VALUE!\t8\t41961697\t3.47E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDSP\tSPECIMEN\t9999999\t#VALUE!\t8\t79999992\t8.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSVIN\tTEST DESCRIPTION\t9999999\t167178.2332\t8\t79999992\t8.00E+14\t283\t47311440\t1.12E+13
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADQLIP\tTEST DESCRIPTION\t9999999\t221752.1053\t8\t79999992\t8.00E+14\t38\t8426580\t3.12E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tEBV\tEBV BY PCR\t9999999\t295741.791\t8\t79999992\t8.00E+14\t67\t19814700\t7.67E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tYEBP3\tSPECIAL INFORMATION\t9999999\t295741.791\t8\t79999992\t8.00E+14\t67\t19814700\t7.67E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tYEPB4\tREPORT STATUS\t9999999\t295741.791\t8\t79999992\t8.00E+14\t67\t19814700\t7.67E+12
-VIROLOGY ORDERABLES\t633362\tLABMUMPSG\tMUMPS IGG\tMUMQUL\tRESULT\t9999999\t168127.5\t8\t79999992\t8.00E+14\t8\t1345020\t2.63E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6CP\tRESULT\t2643922.25\t254505\t8\t21151378\t2.01E+14\t8\t2036040\t6.91E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t254505\t8\t79999992\t8.00E+14\t8\t2036040\t6.91E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6LO\tRESULT LOG 10\t2500003.224\t254505\t8\t20000025.79\t2.00E+14\t8\t2036040\t6.91E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6SP\tSPECIMEN\t9999999\t254505\t8\t79999992\t8.00E+14\t8\t2036040\t6.91E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t230093.0435\t8\t79999992\t8.00E+14\t207\t47629260\t1.54E+13
-VIROLOGY ORDERABLES\t906947\tLABADVRC\tADENOVIRUS RAPID CULTURE\tADVRC1\tADV RAPID CULTURE\t9999999\t236460\t8\t79999992\t8.00E+14\t8\t1891680\t5.77E+11
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tVL\tPRODUCT VOLUME\t161.4285714\t20790\t7\t1130\t187832\t2\t41580\t1064048400
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t7\t69999993\t7.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tDI\tISSUE DATE/TIME\t9999999\t27630\t7\t69999993\t7.00E+14\t2\t55260\t2073315600
-BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tXMR\tUNIT CROSSMATCH  \t9999999\t18440\t7\t69999993\t7.00E+14\t3\t55320\t2073319200
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tSI\t<<STATUS>>  \t9999999\t16740\t7\t69999993\t7.00E+14\t5\t83700\t3495553200
-BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tSI\t<<STATUS>>  \t9999999\t23028\t7\t69999993\t7.00E+14\t5\t115140\t7038212400
-BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tPRO\tPRODUCT ID  \t9999999\t54990\t7\t69999993\t7.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tPRO\tPRODUCT ID  \t9999999\t54990\t7\t69999993\t7.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tPRO\tPRODUCT ID  \t9999999\t69330\t7\t69999993\t7.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tPRO\tPRODUCT ID  \t9999999\t69330\t7\t69999993\t7.00E+14\t2\t138660\t9623422800
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSNRBC\tNRBC/100WBC, SYN FLD\t4.285714286\t9192\t7\t30\t304\t5\t45960\t669448800
-BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSRLYM\tREACT LYMPH, SYN FLD\t1.285714286\t6750\t7\t9\t13\t4\t27000\t241617600
-BODY FLUID ORDERABLES\t472655\tLAB12838R\tINSULIN-LIKE GROWTH FACTOR 1\t12839R\tINSULIN-LIKE GROWTH FACTOR 1\t1428665.429\t150574.2857\t7\t10000658\t1.00E+14\t7\t1054020\t1.91E+11
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSICO\tCOMMENT\t9999999\t927889.0909\t7\t69999993\t7.00E+14\t11\t10206780\t1.25E+13
-BODY FLUID ORDERABLES\t869054\tLAB11901R\tN. GONORRHOEAE RNA, THROAT\t11902R\tN.GONORRHOEAE RNA,THROAT\t9999999\t223815\t7\t69999993\t7.00E+14\t4\t895260\t2.70E+11
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t145.1428571\t16080\t7\t1016\t159736\t7\t112560\t6185484000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t76.85714286\t17845.71429\t7\t538\t84212\t7\t124920\t6323421600
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t237.8571429\t3831.428571\t7\t1665\t407125\t7\t26820\t383598000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t6.592857143\t15574.28571\t7\t46.15\t335.79125\t7\t109020\t6080108400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t15574.28571\t7\t69999993\t7.00E+14\t7\t109020\t6080108400
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.828571429\t71828.57143\t7\t19.8\t56.066\t7\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t7144003.857\t214.2857143\t7\t50008027\t5.00E+14\t7\t1500\t15051600
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t526.8571429\t71828.57143\t7\t3688\t1998484\t7\t502800\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t292.8571429\t71914.28571\t7\t2050\t613500\t7\t503400\t2.51E+11
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t148.5714286\t1148.571429\t7\t1040\t157000\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t114.2857143\t1148.571429\t7\t800\t93400\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t70\t1148.571429\t7\t490\t34900\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t135.7142857\t1148.571429\t7\t950\t131100\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t17.71428571\t1148.571429\t7\t124\t8016\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.982185714\t1148.571429\t7\t20.8753\t62.30583157\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.535714286\t1148.571429\t7\t17.75\t46.4375\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.642857143\t1148.571429\t7\t4.5\t3.33\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRALDBIIM\tPM RA LEAD BIPOLAR IMPEDANCE\t469.5714286\t1148.571429\t7\t3287\t1594863\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMTLPCPACA\tPM TELEMETRY PERCENT PACING ATRIAL\t49.24142857\t1148.571429\t7\t344.69\t25760.3465\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t99.94714286\t1148.571429\t7\t699.63\t69926.0537\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.785714286\t1148.571429\t7\t19.5\t60.75\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.557142857\t1148.571429\t7\t3.9\t3.21\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t0.428571429\t1148.571429\t7\t3\t1.745\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.310714286\t1148.571429\t7\t16.175\t53.511875\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.714285714\t1148.571429\t7\t19\t55.5\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.542857143\t1148.571429\t7\t3.8\t2.44\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t1.885714286\t1148.571429\t7\t13.2\t34.92\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t150\t1148.571429\t7\t1050\t157500\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t150.8571429\t1148.571429\t7\t1056\t201568\t7\t8040\t28612800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.267857143\t1225.714286\t7\t8.875\t12.703125\t7\t8580\t28580400
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.485714286\t1225.714286\t7\t3.4\t1.96\t7\t8580\t28580400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t0.857142857\t25980\t7\t6\t36\t7\t181860\t32942674800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t3.072114286\t27188.57143\t7\t21.5048\t66.3210733\t7\t190320\t33011258400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tAFIBTADTIT\tAFIB TACHY ZONE DETECTION INTERVAL\t339.6714286\t908.5714286\t7\t2377.7\t812320.19\t7\t6360\t28130400
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t250\t14005.71429\t7\t1750\t437500\t7\t98040\t8682192000
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC264AB\tCD26-/CD4+, ABS\t1370.428571\t614750\t7\t9593\t31987355\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC264PC\tCD26-/CD4+ %\t41\t614750\t7\t287\t20355\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC4N7AB\tCD4+/CD7-,ABS. (C4N7AB)\t1286.571429\t614750\t7\t9006\t33564798\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC4N7PC\tCD4+/CD7-% (C4N7PC)\t34\t614750\t7\t238\t18616\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD19AB\tCD19 (PAN B), ABS.\t61.28571429\t614750\t7\t429\t52621\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD19PC\tCD19 (PAN B) %\t9.714285714\t614750\t7\t68\t1810\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD26AB\tCD26 (PAN-T), ABS\t352.5714286\t614750\t7\t2468\t2133458\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD26PC\tCD26 (PAN-T) %\t17.42857143\t614750\t7\t122\t4572\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD3AB\tCD3 (PAN T), ABS.\t2036.714286\t614750\t7\t14257\t46127305\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD3PC\tCD3 (PAN T) %\t80\t614750\t7\t560\t48952\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD4AB\tCD3+/CD4+, ABS.\t1648.142857\t614750\t7\t11537\t37913103\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD4PC\tCD3+/CD4+ %\t55.42857143\t614750\t7\t388\t27188\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD7AB\tCD7 (PAN T), ABS\t752.8571429\t614750\t7\t5270\t7296586\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD7PC\tCD7 (PAN T) %\t46\t614750\t7\t322\t23390\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD8AB\tCD3+/CD8+, ABS.\t364.5714286\t614750\t7\t2552\t1467126\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD8PC\tCD3+/CD8+ %\t21\t614750\t7\t147\t4987\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tHNS\tCD4+CD8 TOTAL, CD3+%\t76.42857143\t614750\t7\t535\t46159\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t2012.428571\t614750\t7\t14087\t45794003\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tHSRATIO\tCD4/CD8 RATIO, CD3+\t1428588.257\t614750\t7\t10000117.8\t1.00E+14\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tLYM\tLYMPHOCYTES %\t27.62857143\t614750\t7\t193.4\t10830.58\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tLYMAB\tLYMPHOCYTES, ABS.\t2188.714286\t614750\t7\t15321\t50327777\t6\t3688500\t9.36E+12
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tWBC\tWBC COUNT\t9028.571429\t614750\t7\t63200\t739960000\t6\t3688500\t9.36E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t10466R\tRECIPIENT NAME\t9999999\t233794.2857\t7\t69999993\t7.00E+14\t7\t1636560\t7.88E+11
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t10467R\tRECIPIENT DOB\t9999999\t233794.2857\t7\t69999993\t7.00E+14\t7\t1636560\t7.88E+11
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9220R\tPATIENT\'S SERUM 1 DTT TREATED RESULT\t9999999\t652920\t7\t69999993\t7.00E+14\t7\t4570440\t8.82E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9233R\tINTERPRETATION/ COMMENTS\t9999999\t528366.6667\t7\t69999993\t7.00E+14\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9109R\tSERUM DESCRIPTION 2\t9999999\t220448.5714\t7\t69999993\t7.00E+14\t7\t1543140\t2.38E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9088R\tSERUM DESCRIPTION 2\t9999999\t233005.7143\t7\t69999993\t7.00E+14\t7\t1631040\t2.39E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9330R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t1119612\t7\t69999993\t7.00E+14\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t10477R\tDSA FOR C1Q CLASS II\t9999999\t8.571428571\t7\t69999993\t7.00E+14\t7\t60\t3600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9396R\tHLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t33166.66667\t7\t69999993\t7.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9348R\tCPRA FOR CUMULATIVE IGG\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9352R\tCPRA FOR IGG CLASS I FOR CURRENT\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9365R\tCPRA FOR IGG CLASS II FOR CURRENT\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9607R\tMETHOD\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9618R\tCURRENT SERUM DATE\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9619R\tCURRENT SERUM DESCRIPTION\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9483R\tLOCUS 9\t9999999\t35451.42857\t7\t69999993\t7.00E+14\t7\t248160\t61404861600
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9484R\tLOCUS 10\t9999999\t35451.42857\t7\t69999993\t7.00E+14\t7\t248160\t61404861600
-LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tAUTOANTIBODIES TO JO1\tJO1AB\tAUTOANTIBODIES TO JO 1\t9999999\t164260\t7\t69999993\t7.00E+14\t6\t985560\t1.80E+11
-LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tRNP AB, IGG\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t4285714.086\t142650\t7\t29999998.6\t3.00E+14\t8\t1141200\t1.76E+11
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBAND\tBAND NEUTROPHILS\t12.42857143\t8310\t7\t87\t1859\t2\t16620\t157957200
-LAB BLOOD ORDERABLES\t472520\tLABHBC\tHEPATITIS B CORE AB IGM\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t50450\t7\t69999993\t7.00E+14\t6\t302700\t19817773200
-LAB BLOOD ORDERABLES\t472550\tLABYHISTS\tHISTOPLASMA AG, SERUM\tYHISS5\tCOMMENT,SERUM\t9999999\t493289.1892\t7\t69999993\t7.00E+14\t407\t200768700\t1.07E+14
-LAB BLOOD ORDERABLES\t472611\tLAB11043R\tIGD\tIGD\tIGD, SERUM\t5.757142857\t144330\t7\t40.3\t505.85\t12\t1731960\t2.74E+11
-LAB BLOOD ORDERABLES\t472614\tLABIGGS2\tIGG  SUBCLASS 2\tIGGS2\tIGG SUBCLASS2\t231.9142857\t44040\t7\t1623.4\t420536.76\t7\t308280\t20034367200
-LAB BLOOD ORDERABLES\t472615\tLABIGGS3\tIGG  SUBCLASS 3\tIGGS3\tIGG SUBCLASS3\t39.04285714\t122357.1429\t7\t273.3\t12111.81\t7\t856500\t1.93E+11
-LAB BLOOD ORDERABLES\t472618\tLABIGGS1\tIGG SUBCLASS 1\tIGGS1\tIGG SUBCLASS1\t684.1428571\t42300\t7\t4789\t3406579\t6\t253800\t17526592800
-LAB BLOOD ORDERABLES\t472695\tLABYITRA\tITRACONAZOLE LEVEL\tYITR3\tITRACONAZOLE + HYDROZYITRACONAZOLE\t9999999\t591260\t7\t69999993\t7.00E+14\t12\t7095120\t5.38E+12
-LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tCAION\tCALCIUM, IONIZED\t2857143.361\t690\t7\t20000003.53\t2.00E+14\t2\t1380\t1040400
-LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tCL\tCHLORIDE, WHOLE BLD\t2857219.571\t690\t7\t20000537\t2.00E+14\t2\t1380\t1040400
-LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tK\tPOTASSIUM, WHOLE BLD\t2857145.443\t690\t7\t20000018.1\t2.00E+14\t2\t1380\t1040400
-LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tNA\tSODIUM, WHOLE BLOOD\t2857243.143\t690\t7\t20000702\t2.00E+14\t2\t1380\t1040400
-LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tTCO2A\tTCO2\t2857161.243\t690\t7\t20000128.7\t2.00E+14\t2\t1380\t1040400
-LAB BLOOD ORDERABLES\t472949\tLABNEURSP\tNEURON SPEC ENOLASE\tNEURSP\tNEURON SPEC ENOLASE\t24.94285714\t355157.1429\t7\t174.6\t5188.76\t7\t2486100\t9.38E+11
-LAB BLOOD ORDERABLES\t473074\tLAB11053R\tPBG DEAMINASE, RBC\tPBGD\tPBG DEAMINASE, RBC\t4.301428571\t271960\t7\t30.11\t146.8901\t6\t1631760\t4.91E+11
-LAB BLOOD ORDERABLES\t473201\tLAB11091R\tQUINIDINE LEVEL\tQUINIDINE\tQUINIDINE\t2857144.4\t151928.5714\t7\t20000010.8\t2.00E+14\t7\t1063500\t1.76E+11
-LAB BLOOD ORDERABLES\t473269\tLABYRISPM\tRISPERIDONE AND 9 - HYDROXYRISPERDONE\t9OHRISP\t9-HYDROXYRISPERIDONE\t4285721.771\t501315\t7\t30000052.4\t3.00E+14\t4\t2005260\t1.23E+12
-LAB BLOOD ORDERABLES\t473269\tLABYRISPM\tRISPERIDONE AND 9 - HYDROXYRISPERDONE\tYRISP1\tRISPERIDONE (RISPERDAL)\t5714286.971\t501315\t7\t40000008.8\t4.00E+14\t4\t2005260\t1.23E+12
-LAB BLOOD ORDERABLES\t473269\tLABYRISPM\tRISPERIDONE AND 9 - HYDROXYRISPERDONE\tYRISP3\tCOMBINED TOTAL\t7142864.786\t501315\t7\t50000053.5\t5.00E+14\t4\t2005260\t1.23E+12
-LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMOSIS PCR\tTOXRES\tPCR RESULT\t9999999\t2500149.057\t7\t69999993\t7.00E+14\t53\t132507900\t2.79E+15
-LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tLAB4\tREFERENCE LAB\t9999999\t4443266.667\t7\t69999993\t7.00E+14\t9\t39989400\t3.40E+14
-LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tSPTY5\tSPECIMEN TYPE\t9999999\t5643312\t7\t69999993\t7.00E+14\t5\t28216560\t3.83E+14
-LAB BLOOD ORDERABLES\t473713\tLABTSMISI\tUNLISTED TRANSFUSION TEST\tCTSMIS\tMISC. TRANSFUSION TEST COMMENT\t9999999\t6025.714286\t7\t69999993\t7.00E+14\t7\t42180\t528678000
-LAB BLOOD ORDERABLES\t473713\tLABTSMISI\tUNLISTED TRANSFUSION TEST\tTSMISR\tMISC. TEST\t9999999\t6025.714286\t7\t69999993\t7.00E+14\t7\t42180\t528678000
-LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV-1 AB, WESTERN BLOT\tBANDPRSNT\tBANDS PRESENT\t9999999\t325576.5517\t7\t69999993\t7.00E+14\t29\t9441720\t4.93E+12
-LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGENTSDD\tGENTAMICIN, SINGLE DAILY DOSE\t1.342857143\t10365\t7\t9.4\t15.32\t4\t41460\t489726000
-LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGNTUN2\tDATE & TIME OF LAST DOSE\t9999999\t10365\t7\t69999993\t7.00E+14\t4\t41460\t489726000
-LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGNTUN3\tDOSAGE\t9999999\t10365\t7\t69999993\t7.00E+14\t4\t41460\t489726000
-LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGNTUN4\tADMINISTRATION ROUTE\t9999999\t10365\t7\t69999993\t7.00E+14\t4\t41460\t489726000
-LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6MMPN\t6-MMPN\t8572622\t605484\t7\t60008354\t6.00E+14\t20\t12109680\t8.50E+12
-LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6TGN\t6-TGN\t5714385.429\t618582.5\t7\t40000698\t4.00E+14\t24\t14845980\t1.05E+13
-LAB BLOOD ORDERABLES\t541683\tLABDCMVL\tDONOR CMV AB (LIVING)\tDCMVL1\tDONOR CMV- TOTAL AB\t9999999\t177193.3333\t7\t69999993\t7.00E+14\t9\t1594740\t3.43E+11
-LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tBEVC\tCORD BASE EXCESS, VEN\t9999999\t696\t7\t69999993\t7.00E+14\t5\t3480\t3542400
-LAB BLOOD ORDERABLES\t633373\tLABSTSS\tSYPHILIS TREPONEMAL SCREEN\tSTSINT\tINTERPRETATIONS\t9999999\t239349.2912\t7\t69999993\t7.00E+14\t1834\t438966600\t5.36E+15
-LAB BLOOD ORDERABLES\t668091\tLAB11125R\tCOCCI ID\tCOCI\tCOCCI ID (SERUM)\t9999999\t848140.9503\t7\t69999993\t7.00E+14\t1389\t1178067780\t1.75E+15
-LAB BLOOD ORDERABLES\t864662\tLAB11871R\tHIV-2 AB, IMMUNOBLOT\t11870R\tHIV 2 ANTIBODY, IMMUNOBLOT\t9999999\t627525\t7\t69999993\t7.00E+14\t8\t5020200\t3.52E+12
-LAB BLOOD ORDERABLES\t864670\tLAB11882R\tMAGNESIUM, RBC\t11881R\tMAGNESIUM, RBC\t1428576.143\t309240\t7\t10000033\t1.00E+14\t7\t2164680\t6.92E+11
-LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12052R\tRMSF IGM\t9999999\t265070.7692\t7\t69999993\t7.00E+14\t13\t3445920\t9.64E+11
-LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816DX\tCLINICAL INDICATION\t9999999\t722297.1429\t7\t69999993\t7.00E+14\t7\t5056080\t4.67E+12
-LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSDX\tCLINICAL INDICATION\t9999999\t371657.1429\t7\t69999993\t7.00E+14\t7\t2601600\t1.85E+12
-LAB BLOOD ORDERABLES\t897643\tLAB12320R\tTHIOCYANATE\t12321R\tTHIOCYANATE\t16.28571429\t147334.2857\t7\t114\t2488\t7\t1031340\t1.59E+11
-LAB BLOOD ORDERABLES\t910564\tLAB12352R\tNMO EVALUATION WITH REFLEX\t12353R\tNMO/AQP4 - IGG ELISA\t9999999\t867285\t7\t69999993\t7.00E+14\t8\t6938280\t9.87E+12
-LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12636R\tSEROGROUP A\t4285717.014\t447325.7143\t7\t30000019.1\t3.00E+14\t7\t3131280\t1.79E+12
-LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12696R\tSEROGROUP C\t8571427.786\t447325.7143\t7\t59999994.5\t6.00E+14\t7\t3131280\t1.79E+12
-LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12697R\tSEROGROUP Y\t7142858.114\t447325.7143\t7\t50000006.8\t5.00E+14\t7\t3131280\t1.79E+12
-LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12698R\tSEROGROUP W-135\t5714286\t447325.7143\t7\t40000002\t4.00E+14\t7\t3131280\t1.79E+12
-MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tKOH\tKOH PREP\t9999999\t90298.30189\t7\t69999993\t7.00E+14\t106\t9571620\t1.70E+12
-MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t464348.8949\t7\t69999993\t7.00E+14\t371\t172273440\t9.73E+14
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFRE\tRESULT\t9999999\t1050420\t7\t69999993\t7.00E+14\t2\t2100840\t3.23E+12
-POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tDEVEXP\tDEVELOPER EXP DATE\t8571572.286\t1109028.75\t7\t60001006\t6.00E+14\t16\t17744460\t2.34E+14
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC4\tQC 4 RESULT\t7.571428571\t-14672.40876\t7\t53\t403\t137\t-2010120\t2.71E+11
-STOOL ORDERABLES\t864650\tLAB11855R\tHEPATITIS C VIRAL RNA, QUANT\t11853R\tHCV RNA ,QUANT,TMA IU/ML\t9999999\t1305000\t7\t69999993\t7.00E+14\t1\t1305000\t1.70E+12
-STOOL ORDERABLES\t864666\tLAB11851R\tFECAL LIPIDS, TOTAL\t11849R\tCOLLECTION DURATION\t5714302.286\t650380\t7\t40000116\t4.00E+14\t6\t3902280\t4.51E+12
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUMDA\tMDA BY GC/MS\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUMDMA\tMDMA (ECSTASY) BY GC/MS\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUPSGC\tPSEUDOEPHEDRINE/EPHEDRINE\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUPTGC\tPHENTERMINE BY GC/MS\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
-URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tTURICU\tURIC ACID EXCRETION\t632.7142857\t45940\t7\t4429\t3072647\t3\t137820\t14891605200
-URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tURICADVOL\tVOLUME URIC ACID\t3214.714286\t45940\t7\t22503\t93270211\t3\t137820\t14891605200
-URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tURICURN\tURIC ACID, URINE\t26.38571429\t45940\t7\t184.7\t6587.73\t3\t137820\t14891605200
-URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tUT9\tCOLLECTION PERIOD URIC ACID\t19.42857143\t45940\t7\t136\t3040\t3\t137820\t14891605200
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tRBCAST\tRBC CASTS\t9999999\t9135\t7\t69999993\t7.00E+14\t4\t36540\t445222800
-URINE ORDERABLES\t473736\tLABUPIE\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS\tUPIEI\tINTERPRETATION\t9999999\t373954.7945\t7\t69999993\t7.00E+14\t438\t163792200\t1.21E+15
-URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\tYUALA1\tAMINOLEVULINIC ACID, URINE\t1428587.986\t547500\t7\t10000115.9\t1.00E+14\t6\t3285000\t1.87E+12
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tCOM\tCOMMENT\t9999999\t222675.2113\t7\t69999993\t7.00E+14\t71\t15809940\t4.16E+12
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tVCAM\tVIRAL CAPSID AB-IGM\t9999999\t131091.4286\t7\t69999993\t7.00E+14\t7\t917640\t1.89E+11
-VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBIPR\tTEST DESCRIPTION\t9999999\t305903.2394\t7\t69999993\t7.00E+14\t426\t130314780\t5.02E+13
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tRPT\tRPT\t9999999\t616970\t7\t69999993\t7.00E+14\t6\t3701820\t2.62E+12
-VIROLOGY ORDERABLES\t633362\tLABMUMPSG\tMUMPS IGG\tMUMQNT\tLEVEL\t1428572.373\t151731.4286\t7\t10000006.61\t1.00E+14\t7\t1062120\t1.83E+11
-VIROLOGY ORDERABLES\t876737\tLABTOXIM\tTOXOPLASMA IGM\tTOXMQL\tIGM RESULT\t9999999\t177480\t7\t69999993\t7.00E+14\t7\t1242360\t2.54E+11
-VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVMT\tMETHOD\t9999999\t267440\t7\t69999993\t7.00E+14\t3\t802320\t2.15E+11
-VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVRE\tRSV RNA\t9999999\t267440\t7\t69999993\t7.00E+14\t3\t802320\t2.15E+11
-VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBIN\tEBV BAMHI-W TEST DESCRIPTION\t9999999\t403485\t7\t69999993\t7.00E+14\t20\t8069700\t1.11E+13
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tVL\tPRODUCT VOLUME\t170.8333333\t18870\t6\t1025\t175993\t2\t37740\t713883600
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tVL\tPRODUCT VOLUME\t185.5\t17550\t6\t1113\t211725\t2\t35100\t616309200
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t6\t59999994\t6.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tXMR\tUNIT CROSSMATCH  \t9999999\t105540\t6\t59999994\t6.00E+14\t5\t527700\t74728422000
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tXMR\tUNIT CROSSMATCH  \t9999999\t67420\t6\t59999994\t6.00E+14\t3\t202260\t22847288400
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tSI\t<<STATUS>>  \t9999999\t35752.5\t6\t59999994\t6.00E+14\t8\t286020\t25391120400
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tXMR\tUNIT CROSSMATCH  \t9999999\t67400\t6\t59999994\t6.00E+14\t3\t202200\t22829968800
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tDI\tISSUE DATE/TIME\t9999999\t43020\t6\t59999994\t6.00E+14\t1\t43020\t1850720400
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tSI\t<<STATUS>>  \t9999999\t23064\t6\t59999994\t6.00E+14\t5\t115320\t7052032800
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tDI\tISSUE DATE/TIME\t9999999\t41790\t6\t59999994\t6.00E+14\t2\t83580\t3495546000
-BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tSI\t<<STATUS>>  \t9999999\t23004\t6\t59999994\t6.00E+14\t5\t115020\t7029565200
-BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tDI\tISSUE DATE/TIME\t9999999\t40140\t6\t59999994\t6.00E+14\t2\t80280\t3222554400
-BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tSI\t<<STATUS>>  \t9999999\t16068\t6\t59999994\t6.00E+14\t5\t80340\t3222558000
-BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tDI\tISSUE DATE/TIME\t9999999\t40110\t6\t59999994\t6.00E+14\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tUBT\tUNIT BLOOD TYPE  \t9999999\t40110\t6\t59999994\t6.00E+14\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tUBT\tUNIT BLOOD TYPE  \t9999999\t40110\t6\t59999994\t6.00E+14\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tPRO\tPRODUCT ID  \t9999999\t69330\t6\t59999994\t6.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tPRO\tPRODUCT ID  \t9999999\t91200\t6\t59999994\t6.00E+14\t2\t182400\t23119207200
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tXMR\tUNIT CROSSMATCH  \t9999999\t40845\t6\t59999994\t6.00E+14\t4\t163380\t11585847600
-BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tBCD34V\tCD34+ PROGENITOR VIABILITY\t100\t7180\t6\t600\t60000\t6\t43080\t374241600
-BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD45PV\tCD45+ VIABILITY\t100\t7180\t6\t600\t60000\t6\t43080\t374241600
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tEOSFLD\tEOSINOPHIL %, FLD\t2.5\t#VALUE!\t6\t15\t75\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP2\tENTEROVIRUS ,PCR\t9999999\t209353.0435\t6\t59999994\t6.00E+14\t69\t14445360\t3.79E+12
-BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP3\tSPECIAL INFORMATION\t9999999\t183002.069\t6\t59999994\t6.00E+14\t29\t5307060\t1.43E+12
-BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP4\tREPORT STATUS\t9999999\t209353.0435\t6\t59999994\t6.00E+14\t69\t14445360\t3.79E+12
-BODY FLUID ORDERABLES\t472621\tLABCIGMT\tIGM, CSF\tIGMCSF\tIGM,CSF\t6666695.983\t234228\t6\t40000175.9\t4.00E+14\t5\t1171140\t3.03E+11
-BODY FLUID ORDERABLES\t472655\tLABYIGF1\tINSULIN LIKE GROWTH FACTOR I\tIGF\tINSULIN-LIKE GF 1\t3333449.667\t224475\t6\t20000698\t2.00E+14\t4\t897900\t2.67E+11
-BODY FLUID ORDERABLES\t497396\tLABYCEAC\tCEA, CSF\tCEAFLD\tCEA, SPINAL FLUID\t6666666.033\t215436\t6\t39999996.2\t4.00E+14\t5\t1077180\t2.98E+11
-ECHO ORDERABLES\t472299\tECH16\tECHO - TRANSESOPHAGEAL ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t42.5\t105950\t6\t255\t12463\t6\t635700\t1.26E+11
-ECHO ORDERABLES\t670313\tECH42\tECHO - EXERCISE STRESS ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t63.33333333\t55380\t6\t380\t24204\t6\t332280\t38568067200
-ECHO ORDERABLES\t304573724\tECH55A\tECHO-CONGENITAL TRANSTHORACIC ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t52.5\t55840\t6\t315\t17169\t6\t335040\t25829985600
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t20650\t6\t59999994\t6.00E+14\t6\t123900\t6322381200
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.458333333\t18880\t6\t14.75\t39.7925\t6\t113280\t6184994400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t18880\t6\t59999994\t6.00E+14\t6\t113280\t6184994400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.316666667\t18880\t6\t7.9\t12.425\t6\t113280\t6184994400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.44\t18880\t6\t2.64\t1.2096\t6\t113280\t6184994400
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t76\t330\t6\t456\t40598\t6\t1980\t14706000
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t590\t6\t59999994\t6.00E+14\t6\t3540\t8362800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t155\t1390\t6\t930\t144900\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t137.5\t930\t6\t825\t115125\t6\t5580\t22561200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t125\t1390\t6\t750\t94700\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t8333337.5\t1390\t6\t50000025\t5.00E+14\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t1390\t6\t59999994\t6.00E+14\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t1.229166667\t1390\t6\t7.375\t9.953125\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.65\t1390\t6\t3.9\t2.97\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t487.1666667\t1390\t6\t2923\t1442223\t6\t8340\t28522800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRVLDUNIM\tPM RV LEAD UNIPOLAR IMPEDANCE\t380.6666667\t930\t6\t2284\t874244\t6\t5580\t22561200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t1.541666667\t2940\t6\t9.25\t16.9375\t6\t17640\t68673600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.683333333\t2940\t6\t4.1\t3.89\t6\t17640\t68673600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t8333332.667\t-210\t6\t49999996\t5.00E+14\t6\t-1260\t622800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t250\t-290\t6\t1500\t375000\t6\t-1740\t1083600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t93.33333333\t-240\t6\t560\t53600\t6\t-1440\t1130400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t30340\t6\t59999994\t6.00E+14\t6\t182040\t32942700000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t9999999\t30340\t6\t59999994\t6.00E+14\t6\t182040\t32942700000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t200\t30340\t6\t1200\t240000\t6\t182040\t32942700000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tAFIBTADTIT\tAFIB TACHY ZONE DETECTION INTERVAL\t345.6666667\t15750\t6\t2074\t717375.88\t6\t94500\t8651192400
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t-300\t6\t0\t0\t6\t-1800\t1152000
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9335R\tHLA ANTIBODIES FOR CLASS I\t9999999\t1261597.5\t6\t59999994\t6.00E+14\t8\t10092780\t6.71E+13
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t10474R\tDSA FOR IGG CLASS I\t9999999\t30\t6\t59999994\t6.00E+14\t6\t180\t10800
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t10475R\tDSA FOR IGG CLASS II\t9999999\t30\t6\t59999994\t6.00E+14\t6\t180\t10800
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9366R\tHLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t42651.42857\t6\t59999994\t6.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9485R\tLOCUS 11\t9999999\t41350\t6\t59999994\t6.00E+14\t6\t248100\t61404858000
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tALD\tALDER TREE\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tALTER\tALTERNARIA MOLD\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tASPERG\tASPERGILUS MOLD\t8333333.223\t397308\t6\t49999999.34\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tBERM\tBERMUDA GRASS\t8333333.153\t397308\t6\t49999998.92\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tCAT\tCAT DANDER\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tCLA\tCLADOSPORIUM MOLD\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tCOCK\tCOCKROACH\t8333332.767\t397308\t6\t49999996.6\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tDOCK\tDOCK WEED\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tDOG\tDOG DANDER\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tDUST\tDUST MITE\t8333334.65\t397308\t6\t50000007.9\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tIGET\tTOTAL IGE\t3333400.167\t397308\t6\t20000401\t2.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tOAK\tOAK TREE\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tOLIVE\tOLIVE TREE\t8333332.915\t397308\t6\t49999997.49\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tRYE\tRYE GRASS\t8333332.755\t397308\t6\t49999996.53\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tSAGE\tSAGE WEED\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tTIMO\tTIMOTHY GRASS\t8333332.723\t397308\t6\t49999996.34\t5.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tWEST\tWESTERN RAGWEED\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471586\tLABALPHA2\tALPHA-2-ANTIPLASMIN\tA2A\tALPHA-2-ANTIPLASMIN\t1666748.5\t83930\t6\t10000491\t1.00E+14\t6\t503580\t42798538800
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBMET\tMETHOD\t9999999\t1276240\t6\t59999994\t6.00E+14\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGA\tBETA-2-GP1 IGA\t12.16666667\t614718.9474\t6\t73\t2583\t95\t58398300\t3.83E+13
-LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGG\tBETA-2-GP1 IGG\t0\t614718.9474\t6\t0\t0\t95\t58398300\t3.83E+13
-LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGM\tBETA-2-GP1 IGM\t7\t614718.9474\t6\t42\t446\t95\t58398300\t3.83E+13
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPLYM\tPROLYMPHOCYTE\t8.833333333\t9984\t6\t53\t971\t5\t49920\t587412000
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMORPH\tMORPHOLOGY\t9999999\t5840\t6\t59999994\t6.00E+14\t3\t17520\t158767200
-LAB BLOOD ORDERABLES\t472325\tLABYERYTH\tERYTHROPOIETIN, SERUM\tERYTH\tERYTHROPOIETIN, SER.\t31.7\t153080\t6\t190.2\t14859.8\t6\t918480\t1.47E+11
-LAB BLOOD ORDERABLES\t472367\tLABYFELBA\tFELBAMATE\tYFELB1\tFELBARNATE(FELBATOL)\t42.63333333\t208060\t6\t255.8\t15681.08\t6\t1248360\t2.89E+11
-LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tHISTY\tHISTO YEAST PHASE\t9999999\t360680\t6\t59999994\t6.00E+14\t6\t2164080\t8.46E+11
-LAB BLOOD ORDERABLES\t472536\tLABHGBFQ\tHGB F QUANTITATIVE\tHGBF\tHGB F, QUANT.\t6666669.65\t280368\t6\t40000017.9\t4.00E+14\t5\t1401840\t5.91E+11
-LAB BLOOD ORDERABLES\t472626\tLABYIMIPR\tIMIPRAMINE AND DESIPRAMINE\tDESIP\tDESIPRAMINE\t79.5\t189520\t6\t477\t67699\t6\t1137120\t2.41E+11
-LAB BLOOD ORDERABLES\t472626\tLABYIMIPR\tIMIPRAMINE AND DESIPRAMINE\tIMI\tIMIPRAMINE\t1666732.833\t189520\t6\t10000397\t1.00E+14\t6\t1137120\t2.41E+11
-LAB BLOOD ORDERABLES\t472626\tLABYIMIPR\tIMIPRAMINE AND DESIPRAMINE\tIMIDESI\tIMIPRAMINE & DESIPRAMINE\t1666774\t189520\t6\t10000644\t1.00E+14\t6\t1137120\t2.41E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH\tLACTATE DEHYD(LD), S\t1667529.167\t234468\t6\t10005175\t1.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH1\tLD-I, HEART\t1666687.717\t234468\t6\t10000126.3\t1.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH2\tLD-II\t1666691.283\t234468\t6\t10000147.7\t1.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH3\tLD-III\t1666680.867\t234468\t6\t10000085.2\t1.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH4\tLD-IV\t1666675.467\t234468\t6\t10000052.8\t1.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tYLDIS3\tTOTAL LD ACTIVITY\t8333452\t234468\t6\t50000712\t5.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tYLDIS8\tLD-V, LIVER\t1666680.5\t234468\t6\t10000083\t1.00E+14\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472949\tLAB11049R\tNEURON SPEC ENOLASE\tNEURSP\tNEURON SPEC ENOLASE\t20.03333333\t311520\t6\t120.2\t3371.34\t4\t1246080\t4.25E+11
-LAB BLOOD ORDERABLES\t473042\tLABOF\tOSMOTIC FRAGILITY\tOFINT\tINTERPRETATION\t8333334.5\t346707.6923\t6\t50000007\t5.00E+14\t13\t4507200\t2.57E+12
-LAB BLOOD ORDERABLES\t473093\tLABPENTO\tPENTOBARBITAL LEVEL\tPENTO\tPENTOBARBITAL, S\t1666685.633\t393317.1429\t6\t10000113.8\t1.00E+14\t7\t2753220\t1.16E+12
-LAB BLOOD ORDERABLES\t473109\tLABPLTMAP\tPLATELET MAPPING\tTEGMD\tINTERP BY:\t9999999\t19116.95122\t6\t59999994\t6.00E+14\t164\t3135180\t2.07E+11
-LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tMORPH\tMORPHOLOGY\t9999999\t67970.76923\t6\t59999994\t6.00E+14\t13\t883620\t3.36E+11
-LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tSPTY6\tSPECIMEN TYPE:\t9999999\t2836788\t6\t59999994\t6.00E+14\t5\t14183940\t1.37E+14
-LAB BLOOD ORDERABLES\t496699\tLABAG\tALBUMIN / GLOBULIN RATIO\tALBGLOB\tALBUMIN/GLOBULIN RATIO\t9999999\t440\t6\t59999994\t6.00E+14\t3\t1320\t849600
-LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12027R\tECHOVIRUS  AB TYPE 6\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
-LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12028R\tECHOVIRUS  AB TYPE 7\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
-LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12029R\tECHOVIRUS  AB TYPE 11\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
-LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12030R\tECHOVIRUS  AB TYPE 30\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
-LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12031R\tECHOVIRUS  AB TYPE 9\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE11\tECHOVIRUS 11 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE4\tECHOVIRUS 4 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE7\tECHOVIRUS 7 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE9\tECHOVIRUS 9 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
-LAB BLOOD ORDERABLES\t541652\tLABAS4I\tAB SCREEN\tAS\tANTIBODY SCREEN\t9999999\t12980\t6\t59999994\t6.00E+14\t3\t38940\t561114000
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tBEC\tCORD BASE EXCESS, ART\t9999999\t200\t6\t59999994\t6.00E+14\t3\t600\t165600
-LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tCRDALG\tCORD LENGTH\t5000011.5\t360\t6\t30000069\t3.00E+14\t6\t2160\t1555200
-LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tRHFT\tRHEUMATOID FACTOR LEVEL\t1666716.5\t9245540\t6\t10000299\t1.00E+14\t6\t55473240\t2.95E+15
-LAB BLOOD ORDERABLES\t670776\tLAB11253R\tHELICOBACTER PYLORI AGA AB\t11185R\tHELICOBACTER PYLORI IGA AB\t9999999\t649581.8182\t6\t59999994\t6.00E+14\t33\t21436200\t6.81E+13
-LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\t11191R\tBORRELIA HERMSII IGG\t9999999\t866916\t6\t59999994\t6.00E+14\t5\t4334580\t7.56E+12
-LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\t11192R\tBORRELIA HERMSII IGM\t9999999\t866916\t6\t59999994\t6.00E+14\t5\t4334580\t7.56E+12
-LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816RE\tRESULT, BLOOD\t9999999\t722297.1429\t6\t59999994\t6.00E+14\t7\t5056080\t4.67E+12
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC2526N\tCD25, % OF CD3+/CD4+/CD26NEG\t28\t89720\t6\t168\t5568\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC257N\tCD25, % OF CD3+/CD4+/CD7NEG\t34.5\t89720\t6\t207\t9467\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC264AB\tCD26-/CD4+, ABS\t828\t89720\t6\t4968\t20167652\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC264PC\tCD26-/CD4+ %\t23.16666667\t89720\t6\t139\t8963\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3026N\tCD30, % OF CD3+/CD4+/CD26NEG\t4.666666667\t89720\t6\t28\t280\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC307N\tCD30, % OF CD3+/CD4+/CD7NEG\t7.333333333\t89720\t6\t44\t730\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC326NA\tCD3+/CD26NEG, ABS.\t1025.166667\t89720\t6\t6151\t21516915\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3426N\tCD3+/CD4+/CD26NEG, ABS.\t826.8333333\t89720\t6\t4961\t20166819\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3426P\tCD3+/CD4+/CD26NEG, % OF LYMPHS\t22.83333333\t89720\t6\t137\t8935\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3426T\tCD3+/CD4+/CD26NEG, % OF CD4+ T\t36.83333333\t89720\t6\t221\t12391\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC347NA\tCD3+/CD4+/CD7NEG, ABS.\t813.5\t89720\t6\t4881\t21435387\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC347NP\tCD3+/CD4+/CD7NEG, % OF LYMPHS\t19.33333333\t89720\t6\t116\t9140\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC347NT\tCD3+/CD4+/CD7NEG, % OF CD4+ T\t27\t89720\t6\t162\t10556\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC37NA\tCD3+/CD7NEG, ABS.\t841.6666667\t89720\t6\t5050\t22371114\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P4PA\tCD3+/CD4+,ABS\t1181\t89720\t6\t7086\t24935690\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P4PP\tCD3+/CD4+, % OF LYMPHS\t49.5\t89720\t6\t297\t19117\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P7NP\tCD3+/CD7NEG, % OF LYMPHS\t20.5\t89720\t6\t123\t9571\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P8PN\tCD3+/CD8+, ABS. OF LYMPHS\t8333348.667\t89720\t6\t50000092\t5.00E+14\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P8PP\tCD3+/CD8+, % OF LYMPHS\t8333337.5\t89720\t6\t50000025\t5.00E+14\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC4N7AB\tCD4+/CD7-,ABS. (C4N7AB)\t819.3333333\t89720\t6\t4916\t21443482\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC4N7PC\tCD4+/CD7-% (C4N7PC)\t20\t89720\t6\t120\t9176\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC5226N\tCD52, % OF CD3+/CD4+/CD26NEG\t83.16666667\t89720\t6\t499\t49605\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC527N\tCD52, % OF CD3+/CD4+/CD7NEG\t83.33333333\t89720\t6\t500\t49802\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD26NP\tCD3+/CD26NEG, % OF LYMPHS\t39.5\t89720\t6\t237\t13497\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t1439.166667\t89720\t6\t8635\t28530847\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD3PC\tCD3 (PAN T) %\t72.5\t89720\t6\t435\t36173\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t8333344.167\t89720\t6\t50000065\t5.00E+14\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t8333332.717\t89720\t6\t49999996.3\t5.00E+14\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tLYM\tLYMPHOCYTES %\t21.16666667\t89720\t6\t127\t8864.32\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t1584.333333\t89720\t6\t9506\t31422252\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tWBC\tWBC COUNT\t11200\t89720\t6\t67200\t924220000\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898826\tLAB12371R\tCOBALT\t12372R\tCOBALT\t4.733333333\t283690\t6\t28.4\t454.66\t6\t1702140\t5.29E+11
-MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tRPT\tRPT\t9999999\t255120\t6\t59999994\t6.00E+14\t1\t255120\t65086214400
-MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tRPT\tRPT\t9999999\t752360\t6\t59999994\t6.00E+14\t6\t4514160\t3.41E+12
-MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tRPT\tRPT\t9999999\t3770\t6\t59999994\t6.00E+14\t6\t22620\t511664400
-MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t3770\t6\t59999994\t6.00E+14\t6\t22620\t511664400
-MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tCULT\tCULT/OTHER RSLT:\t9999999\t473219.0698\t6\t59999994\t6.00E+14\t516\t244181040\t2.40E+14
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBDX\tCLINICAL INDICATION\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBFLD\tSOURCE\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBMET\tMETHOD\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBRES\tRESULTS\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
-POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tTESTLO\tTEST STRIP LOT#\t108065.6667\t4125\t6\t648394\t70117629024\t8\t33000\t183823200
-STOOL ORDERABLES\t472122\tLAB12206R\tCYSTATIN C\t12207R\tCYSTATIN C, SER\t1.02\t171550\t6\t6.12\t6.3148\t6\t1029300\t1.92E+11
-STOOL ORDERABLES\t670750\tLAB11340R\tADAMTS 13 PROFILE\t11342R\tADAMTS 13 INHIBITOR\t9999999\t631303.6364\t6\t59999994\t6.00E+14\t11\t6944340\t6.83E+12
-STOOL ORDERABLES\t864666\tLAB11851R\tFECAL LIPIDS, TOTAL\t11848R\tTOTAL SPECIMEN WEIGHT\t3333676.167\t650380\t6\t20002057\t2.00E+14\t6\t3902280\t4.51E+12
-URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tYUARR2\tCOLLECTION DURATION\t8333336.5\t88120\t6\t50000019\t5.00E+14\t3\t264360\t28310436000
-URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tVOLUMEURN\tURINE VOLUME\t3225\t245865\t6\t19350\t65737340\t4\t983460\t2.71E+11
-URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tYCITRU\tCITRATE EXCRETION, URINE\t278\t245865\t6\t1668\t583520\t4\t983460\t2.71E+11
-URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tYUCICN\tCITRATE CONCENTRATION\t8.366666667\t245865\t6\t50.2\t492.44\t4\t983460\t2.71E+11
-URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tYUCIUT\tCOLLECTION DURATION\t24\t245865\t6\t144\t3456\t4\t983460\t2.71E+11
-URINE ORDERABLES\t473037\tLABUOROT\tOROTIC ACID, URINE\tOROTU\tOROTIC ACID, UR\t9999999\t387150\t6\t59999994\t6.00E+14\t2\t774300\t3.33E+11
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tVOLUMEURN\tURINE VOLUME\t2852.166667\t293940\t6\t17113\t57878759\t3\t881820\t2.67E+11
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tYOXALU\tOXALATE, 24-HOUR/TIMED URINE\t0.553333333\t293940\t6\t3.32\t2.6634\t3\t881820\t2.67E+11
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tYUOXCO\tOXALATE CONCENTRATION\t8.415\t293940\t6\t50.49\t2431.0957\t3\t881820\t2.67E+11
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tYUOXUT\tCOLLECTION DURATION\t24\t293940\t6\t144\t3456\t3\t881820\t2.67E+11
-URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\tVOLUMEURN\tURINE VOLUME\t1667952.5\t614940\t6\t10007715\t1.00E+14\t4\t2459760\t1.53E+12
-URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\tYUALA2\tCOLLECTION DURATION\t1666686.5\t614940\t6\t10000119\t1.00E+14\t4\t2459760\t1.53E+12
-VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tDCL2\tTEST INFORMATION\t9999999\t184892.973\t6\t59999994\t6.00E+14\t333\t61569360\t1.58E+13
-VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-BLOOD\tCMVQSP\tSPECIMEN\t9999999\t165780\t6\t59999994\t6.00E+14\t3\t497340\t92352106800
-VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVTIP\tTEST DESCRIPTION\t9999999\t184331.1162\t6\t59999994\t6.00E+14\t1317\t242764080\t5.22E+13
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t9999999\t212032.3077\t6\t59999994\t6.00E+14\t234\t49615560\t1.19E+13
-VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t11166R\tEB NUCLEAR AB\t9999999\t212032.3077\t6\t59999994\t6.00E+14\t234\t49615560\t1.19E+13
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tVCA\tVIRAL CAPSID AB\t9999999\t131091.4286\t6\t59999994\t6.00E+14\t7\t917640\t1.89E+11
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL2\tHSV 1 IGG INTERPRETATION\t9999999\t290474.6392\t6\t59999994\t6.00E+14\t291\t84528120\t2.82E+13
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL4\tHSV 2 IGG INTERPRETATION\t9999999\t290474.6392\t6\t59999994\t6.00E+14\t291\t84528120\t2.82E+13
-VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXO\tTOXOPLASMA IGG\t9999999\t163050\t6\t59999994\t6.00E+14\t6\t978300\t1.91E+11
-VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQL\tIGG RESULT\t9999999\t163250\t6\t59999994\t6.00E+14\t6\t979500\t1.86E+11
-VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXMQL\tIGM RESULT\t9999999\t163250\t6\t59999994\t6.00E+14\t6\t979500\t1.86E+11
-VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRMMPV\tMETAPNEUMOVIRUS RNA\t9999999\t86400\t6\t59999994\t6.00E+14\t3\t259200\t28951264800
-VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRMRSV\tRSV RNA\t9999999\t86400\t6\t59999994\t6.00E+14\t3\t259200\t28951264800
-VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRSMPMT\tMETHOD\t9999999\t86400\t6\t59999994\t6.00E+14\t3\t259200\t28951264800
-VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tCULT\tCULT/OTHER RSLT:\t9999999\t1188540.984\t6\t59999994\t6.00E+14\t61\t72501000\t1.27E+14
-BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tSPCTY\tSPECIMEN TYPE\t9999999\t28164\t5\t49999995\t5.00E+14\t5\t140820\t7934583600
-BLOOD BANK PRODUCT ORDERABLES\t473263\tLABBBPRH\tRHO(D) IMMUNE GLOB (RHOGAM)\tRHSTA\tRHOGAM PRODUCT STATUS\t9999999\t-3320\t5\t49999995\t5.00E+14\t3\t-9960\t105328800
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tVL\tPRODUCT VOLUME\t170.8\t28740\t5\t854\t146288\t2\t57480\t1658030400
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tVL\tPRODUCT VOLUME\t175.6\t39720\t5\t878\t155036\t1\t39720\t1577678400
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tVL\tPRODUCT VOLUME\t163.6\t#VALUE!\t5\t818\t133954\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t5\t49999995\t5.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tXMR\tUNIT CROSSMATCH  \t9999999\t78960\t5\t49999995\t5.00E+14\t4\t315840\t36999194400
-BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tSI\t<<STATUS>>  \t9999999\t24488.57143\t5\t49999995\t5.00E+14\t7\t171420\t6256458000
-BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tSI\t<<STATUS>>  \t9999999\t23710\t5\t49999995\t5.00E+14\t6\t142260\t5357336400
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tXMR\tUNIT CROSSMATCH  \t9999999\t36090\t5\t49999995\t5.00E+14\t2\t72180\t5201298000
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tXMR\tUNIT CROSSMATCH  \t9999999\t20340\t5\t49999995\t5.00E+14\t2\t40680\t1649988000
-BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tDI\tISSUE DATE/TIME\t9999999\t42960\t5\t49999995\t5.00E+14\t1\t42960\t1845561600
-BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tXMR\tUNIT CROSSMATCH  \t9999999\t36060\t5\t49999995\t5.00E+14\t2\t72120\t5192647200
-BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tDI\tISSUE DATE/TIME\t9999999\t42960\t5\t49999995\t5.00E+14\t1\t42960\t1845561600
-BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tXMR\tUNIT CROSSMATCH  \t9999999\t36000\t5\t49999995\t5.00E+14\t2\t72000\t5184000000
-BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tXMR\tUNIT CROSSMATCH  \t9999999\t0\t5\t49999995\t5.00E+14\t1\t0\t0
-BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tSI\t<<STATUS>>  \t9999999\t20085\t5\t49999995\t5.00E+14\t4\t80340\t3217719600
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tDI\tISSUE DATE/TIME\t9999999\t40110\t5\t49999995\t5.00E+14\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tSI\t<<STATUS>>  \t9999999\t20085\t5\t49999995\t5.00E+14\t4\t80340\t3217719600
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tDI\tISSUE DATE/TIME\t9999999\t40110\t5\t49999995\t5.00E+14\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tSI\t<<STATUS>>  \t9999999\t20085\t5\t49999995\t5.00E+14\t4\t80340\t3217719600
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tDI\tISSUE DATE/TIME\t9999999\t40080\t5\t49999995\t5.00E+14\t2\t80160\t3212928000
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tUBT\tUNIT BLOOD TYPE  \t9999999\t40080\t5\t49999995\t5.00E+14\t2\t80160\t3212928000
-BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tUBT\tUNIT BLOOD TYPE  \t9999999\t39390\t5\t49999995\t5.00E+14\t2\t78780\t3104874000
-BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tUBT\tUNIT BLOOD TYPE  \t9999999\t54990\t5\t49999995\t5.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tUBT\tUNIT BLOOD TYPE  \t9999999\t54990\t5\t49999995\t5.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tUBT\tUNIT BLOOD TYPE  \t9999999\t54990\t5\t49999995\t5.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tUBT\tUNIT BLOOD TYPE  \t9999999\t69330\t5\t49999995\t5.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tPRO\tPRODUCT ID  \t9999999\t49950\t5\t49999995\t5.00E+14\t2\t99900\t5482357200
-BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tPRO\tPRODUCT ID  \t9999999\t74070\t5\t49999995\t5.00E+14\t2\t148140\t21945459600
-BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tPRO\tPRODUCT ID  \t9999999\t30\t5\t49999995\t5.00E+14\t2\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tPRO\tPRODUCT ID  \t9999999\t30\t5\t49999995\t5.00E+14\t2\t60\t3600
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBPRM\tPRIMERS\t9999999\t1007200\t5\t49999995\t5.00E+14\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t472655\tLAB12838R\tINSULIN-LIKE GROWTH FACTOR 1\tIGF\tINSULIN-LIKE GROWTH FACTOR 1\t217.08\t82680\t5\t1085.4\t575013.66\t5\t413400\t48124742400
-BODY FLUID ORDERABLES\t472774\tLABFBBURG\tLYME WESTERN BLOT, CSF\tBBIGM\tBORELLIA BURG IGM AB\t9999999\t282672.6316\t5\t49999995\t5.00E+14\t76\t21483120\t7.48E+12
-BODY FLUID ORDERABLES\t472774\tLABLWBCC\tLYME WESTERN BLOT, CSF\tBBIGG\tBORELLIA BURG IGG AB\t9999999\t277031.25\t5\t49999995\t5.00E+14\t16\t4432500\t1.39E+12
-BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCPIEI\tINTERPRETATION\t9999999\t407309.1176\t5\t49999995\t5.00E+14\t136\t55394040\t3.19E+13
-BODY FLUID ORDERABLES\t473350\tLABFSPG\tSPECIFIC GRAVITY, FLUID\tSGFLTY\tSOURCE\t9999999\t5625\t5\t49999995\t5.00E+14\t4\t22500\t202366800
-BODY FLUID ORDERABLES\t473350\tLABFSPG\tSPECIFIC GRAVITY, FLUID\tSPGFLD\tSPECIFIC GRAVITY, FLUID\t1.0232\t5625\t5\t5.116\t5.234856\t4\t22500\t202366800
-BODY FLUID ORDERABLES\t473358\tLABEOSSP\tSPUTUM FOR EOSINOPHILS\tEOSSP\tSPUTUM FOR EOS.\t9999999\t9024\t5\t49999995\t5.00E+14\t5\t45120\t496404000
-BODY FLUID ORDERABLES\t670778\tLAB11188R\tM. PNEUMONIAE DNA\t12283R\t12283R\t9999999\t166380\t5\t49999995\t5.00E+14\t8\t1331040\t2.33E+11
-BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tFCD3AB\tCD3 (T), ABS., FLD\t9999999\t12144\t5\t49999995\t5.00E+14\t5\t60720\t762372000
-BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tFCD3PC\tCD3 (T) %, FLD\t38.8\t12144\t5\t194\t10222\t5\t60720\t762372000
-BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tLYMABFLD\tLYMPHOCYTES, ABS., FLD\t9999999\t12144\t5\t49999995\t5.00E+14\t5\t60720\t762372000
-BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tWBCFLD\tWBC COUNT, FLD\t51860\t12144\t5\t259300\t18962210000\t5\t60720\t762372000
-BODY FLUID ORDERABLES\t978520\tLAB12723R\tADENOSINE DEAMINASE, PERITONEAL\t12722R\tADENOSINE DEAMINASE, PERITONEAL\t2000003.88\t343308\t5\t10000019.4\t1.00E+14\t5\t1716540\t6.34E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t27\t100692\t5\t135\t3825\t5\t503460\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t100692\t5\t49999995\t5.00E+14\t5\t503460\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t100692\t5\t49999995\t5.00E+14\t5\t503460\t2.51E+11
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMTLBATIMP\tPM TELEMETRY BATTERY IMPEDANCE\t0.7248\t100692\t5\t3.624\t4.835626\t5\t503460\t2.51E+11
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t1620\t5\t49999995\t5.00E+14\t5\t8100\t28465200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRALDUNIM\tPM RA LEAD UNIPOLAR IMPEDANCE\t323\t1176\t5\t1615\t531753\t5\t5880\t22471200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t7.44\t1488\t5\t37.2\t307.41625\t5\t7440\t28425600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t1488\t5\t49999995\t5.00E+14\t5\t7440\t28425600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tAFIBTADTIT\tAFIB TACHY ZONE DETECTION INTERVAL\t347.4\t-48\t5\t1737\t608687.94\t5\t-240\t2080800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tALERT\tALERT\t9999999\t-324\t5\t49999995\t5.00E+14\t5\t-1620\t637200
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t36000\t5\t0\t0\t5\t180000\t32659869600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t35964\t5\t0\t0\t5\t179820\t32659923600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t129\t1668\t5\t645\t83225\t5\t8340\t34462800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0\t36396\t5\t0\t0\t5\t181980\t32942660400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0\t36348\t5\t0\t0\t5\t181740\t32942660400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADALERT\tLEAD ALERT\t9999999\t1584\t5\t49999995\t5.00E+14\t5\t7920\t34387200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t2000199.8\t1668\t5\t10000999\t1.00E+14\t5\t8340\t34462800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t36384\t5\t49999995\t5.00E+14\t5\t181920\t32942685600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t129\t-384\t5\t645\t83925\t5\t-1920\t1152000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t7.8\t504\t5\t39\t307\t5\t2520\t10432800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t840\t5\t0\t0\t5\t4200\t24372000
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t1008\t5\t500\t50000\t5\t5040\t29815200
-EXTERNAL RESULTS\t668293\tEXTHCT\tEXTERNAL HCT\tHCT\tEXTERNAL HCT\t9999999\t-345600\t5\t49999995\t5.00E+14\t5\t-1728000\t6.72E+11
-HISTOCOMPATIBILITY ORDERABLE\t492711\t10195R\tHLA ORDER - WAIT LIST STATUS CHANGE\t10049R\tHLA LAB TESTING STATUS\t9999999\t7644\t5\t49999995\t5.00E+14\t5\t38220\t389502000
-HISTOCOMPATIBILITY ORDERABLE\t492711\t10195R\tHLA ORDER - WAIT LIST STATUS CHANGE\tRINFO\tTO FIND RESULT:\t9999999\t7644\t5\t49999995\t5.00E+14\t5\t38220\t389502000
-HISTOCOMPATIBILITY ORDERABLE\t710704\t10210R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG\t10049R\tHLA LAB TESTING STATUS\t9999999\t2240112\t5\t49999995\t5.00E+14\t5\t11200560\t3.47E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9161R\tSERUM DESCRIPTION 1\t9999999\t41028\t5\t49999995\t5.00E+14\t5\t205140\t21397935600
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9196R\tPATIENT\'S SERUM 2 DESCRIPTION\t9999999\t77616\t5\t49999995\t5.00E+14\t5\t388080\t94371408000
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9131R\tSERUM DESCRIPTION 1\t9999999\t40992\t5\t49999995\t5.00E+14\t5\t204960\t21387376800
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9105R\tSERUM DESCRIPTION 1\t9999999\t24\t5\t49999995\t5.00E+14\t5\t120\t14400
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9112R\tSERUM DATE 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9114R\tMCS 3\t13.8\t445812\t5\t69\t3893\t5\t2229060\t2.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9602R\tRESULT 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9084R\tSERUM DESCRIPTION 1\t9999999\t17616\t5\t49999995\t5.00E+14\t5\t88080\t7736976000
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9091R\tSERUM DATE 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9093R\tMCS 3\t40.8\t445812\t5\t204\t12502\t5\t2229060\t2.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9597R\tRESULT 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9329R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t1119612\t5\t49999995\t5.00E+14\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9341R\tPOSSIBLE HLA ANTIBODIES FOR CLASS II\t9999999\t1591272\t5\t49999995\t5.00E+14\t5\t7956360\t6.22E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9384R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t0\t5\t49999995\t5.00E+14\t5\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9624R\tREFERENCE SERUM DATE\t9999999\t0\t5\t49999995\t5.00E+14\t5\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9625R\tREFERENCE SERUM DESCRIPTION\t9999999\t0\t5\t49999995\t5.00E+14\t5\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9353R\tHLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t42651.42857\t5\t49999995\t5.00E+14\t7\t298560\t89030635200
-IMMUNOLOGY ORDERABLES\t633412\tLABRPRQT\tRPR QUANTITATIVE\tRPRT\tRESULT\t9999999\t86580\t5\t49999995\t5.00E+14\t4\t346320\t37293688800
-IMMUNOLOGY ORDERABLES\t633412\tLABRPRQT\tRPR QUANTITATIVE\tRPRTIN\tINTERPRETATIONS\t9999999\t2355769.655\t5\t49999995\t5.00E+14\t29\t68317320\t3.96E+15
-LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t55606.13636\t5\t49999995\t5.00E+14\t1056\t58720080\t7.63E+12
-LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVIN\tDRVV INTERPRETATION\t9999999\t316148.4979\t5\t49999995\t5.00E+14\t233\t73662600\t2.93E+13
-LAB BLOOD ORDERABLES\t471654\tLAB11072R\tAPO A1\tAPOA1\tAPOLIPOPROTEIN A1\t137.8\t157632\t5\t689\t104039\t5\t788160\t1.28E+11
-LAB BLOOD ORDERABLES\t471686\tLABARSAL\tARYLSULFATASE A, LEUKOCYTES\tARYLSA\tARYLSULFATASE A, LEUK\t2000033.06\t546840\t5\t10000165.3\t1.00E+14\t4\t2187360\t1.60E+12
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBDX\tCLINICAL INDICATION\t9999999\t1276240\t5\t49999995\t5.00E+14\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQDX\tCLINICAL INDICATION\t9999999\t1383525\t5\t49999995\t5.00E+14\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQET\tETHNICITY (PROVIDED BY MD)\t9999999\t1383525\t5\t49999995\t5.00E+14\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQRE\tRESULT\t9999999\t1383525\t5\t49999995\t5.00E+14\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471813\tLABYC5REF\tC5 COMPLEMENT AG\tYC5RE1\tC5 COMPLEMENT AG\t22.7\t192080\t5\t113.5\t2712.81\t6\t1152480\t2.38E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHATINT\tINTERPRETATION\t9999999\t63540.24819\t5\t49999995\t5.00E+14\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t63540.24819\t5\t49999995\t5.00E+14\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t9999999\t63540.24819\t5\t49999995\t5.00E+14\t967\t61443420\t8.90E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSAQT\tHEP B SURF AB QUANT\t4000027.06\t182820\t5\t20000135.3\t2.00E+14\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t182820\t5\t49999995\t5.00E+14\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t182820\t5\t49999995\t5.00E+14\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t182820\t5\t49999995\t5.00E+14\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472120\tLABCCP\tANTI CCP\tCCPR\tANTI CCP ANTIBODY\t9999999\t296484\t5\t49999995\t5.00E+14\t5\t1482420\t5.59E+11
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMYEL\tMYELOCYTES\t2.6\t8310\t5\t13\t49\t2\t16620\t157957200
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNRBC\tNRBC/100WBC\t3.6\t8310\t5\t18\t140\t2\t16620\t157957200
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tSLREV\tSLIDE REVIEW\t9999999\t2880\t5\t49999995\t5.00E+14\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472189\tLABDT\tDIPHTHERIA AND TETANUS AB IGG\tDIPHT\tDIPTHERIA AB , IGG\t0.356\t308000\t5\t1.78\t1.199\t3\t924000\t2.91E+11
-LAB BLOOD ORDERABLES\t472189\tLABDT\tDIPHTHERIA AND TETANUS AB IGG\tTETAN\tTETANUS AB , IGG\t6.77\t308000\t5\t33.85\t679.9773\t3\t924000\t2.91E+11
-LAB BLOOD ORDERABLES\t472392\tLAB12796R\tFLUCONAZOLE LEVEL\tFLUCOZ\tFLUCONAZOLE\t21.8\t618744\t5\t109\t2615\t5\t3093720\t2.13E+12
-LAB BLOOD ORDERABLES\t472482\tLABYGADAB\tGLUTAMIC ACID DECARBOXY AB\tGAD65AB\tGAD65 ANTIBODY\t5.96\t352740\t5\t29.8\t888.04\t3\t1058220\t3.91E+11
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tARSENIC\tARSENIC\t9999999\t269844\t5\t49999995\t5.00E+14\t5\t1349220\t6.18E+11
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tCADMIUM\tCADMIUM\t3999999.9\t269844\t5\t19999999.5\t2.00E+14\t5\t1349220\t6.18E+11
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tLEAD\tLEAD\t2000001\t269844\t5\t10000005\t1.00E+14\t5\t1349220\t6.18E+11
-LAB BLOOD ORDERABLES\t472521\tLABHBCT\tANTI-HBC\tHBTINT\tINTERPRETATION\t9999999\t53580.7438\t5\t49999995\t5.00E+14\t484\t25933080\t3.17E+12
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tVLDL\tVLDL (CALCULATED)\t2000016\t28500\t5\t10000080\t1.00E+14\t4\t114000\t8390340000
-LAB BLOOD ORDERABLES\t472854\tLABMALL1\tMISCELLANEOUS ALLERGEN 1\tALLR1\tALLERGEN RESULT\t9999999\t630540\t5\t49999995\t5.00E+14\t4\t2522160\t2.43E+12
-LAB BLOOD ORDERABLES\t472863\tLABCHMISC\tMISCELLANEOUS CHEMISTRY\tCHMISC\tCHEMISTRY MISCELLANEOUS\t9999999\t9461.37931\t5\t49999995\t5.00E+14\t29\t274380\t17678588400
-LAB BLOOD ORDERABLES\t473026\tLAB11051R\tOLANZAPINE LEVEL\tOLANZ\tOLANZAPINE\t2000033.6\t405255\t5\t10000168\t1.00E+14\t4\t1621020\t7.22E+11
-LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tAGGINT\tINTERPRETATION\t9999999\t79031.25\t5\t49999995\t5.00E+14\t16\t1264500\t2.79E+11
-LAB BLOOD ORDERABLES\t473157\tLAB11055R\tPROPAFENONE LEVEL\tPROPAF\tPROPAFENONE\t4000000.148\t388272\t5\t20000000.74\t2.00E+14\t5\t1941360\t8.41E+11
-LAB BLOOD ORDERABLES\t473183\tLABPSAU\tPOST-THERAPY PSA\tPSAU\tPOST-THERAPY PSA\t5.2\t116760\t5\t26\t368.2916\t3\t350280\t83991607200
-LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNIN\tQUANTIFERON INTERPRETATION\t9999999\t193921.9811\t5\t49999995\t5.00E+14\t212\t41111460\t1.47E+13
-LAB BLOOD ORDERABLES\t473301\tLABSPIE\tPROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM\tSPIEI\tINTERPRETATION\t9999999\t254723.2367\t5\t49999995\t5.00E+14\t1415\t360433380\t1.27E+14
-LAB BLOOD ORDERABLES\t473675\tLAB11058R\tTRAZODONE LEVEL\tTRAZO\tTRAZODONE\t7999999.38\t295185\t5\t39999996.9\t4.00E+14\t4\t1180740\t4.64E+11
-LAB BLOOD ORDERABLES\t473693\tLAB11057R\tTNF\tTNF\tTUMOR NECROSIS FTR\t9999999\t246456\t5\t49999995\t5.00E+14\t5\t1232280\t3.46E+11
-LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tRESUL4\tRESULT\t9999999\t4443266.667\t5\t49999995\t5.00E+14\t9\t39989400\t3.40E+14
-LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tLAB5\tREFERENCE LAB:\t9999999\t5643312\t5\t49999995\t5.00E+14\t5\t28216560\t3.83E+14
-LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tTEST5\tTEST NAME\t9999999\t5643312\t5\t49999995\t5.00E+14\t5\t28216560\t3.83E+14
-LAB BLOOD ORDERABLES\t497339\tLABDFA\tDENGUE FEVER AB\tDFINT\tINTERPRETATION\t9999999\t264945\t5\t49999995\t5.00E+14\t64\t16956480\t5.23E+12
-LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6TGNASSESS\t6-TGN RESULT ASSESSMENT\t9999999\t605484\t5\t49999995\t5.00E+14\t20\t12109680\t8.50E+12
-LAB BLOOD ORDERABLES\t541687\tLABFHHV7\tHERPES VIRUS 7 IGG AND IGM AB\tHERPG\tHERPESVIRUS 7 AB, IGG\t9999999\t327880\t5\t49999995\t5.00E+14\t3\t983640\t3.29E+11
-LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12319R\t12319R\t9999999\t200448\t5\t49999995\t5.00E+14\t20\t4008960\t1.99E+12
-LAB BLOOD ORDERABLES\t893921\tLAB12790R\tCYSTATIN C WITH ESTIMATED GFR\t12789R\tEGFR BY CYSTATIN C\t22.4\t189288\t5\t112\t2802\t5\t946440\t1.86E+11
-LAB BLOOD ORDERABLES\t893921\tLAB12790R\tCYSTATIN C WITH ESTIMATED GFR\t12805R\tCYSTATIN C\t2.718\t189288\t5\t13.59\t38.3775\t5\t946440\t1.86E+11
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMCOB\tCOMMENT\t9999999\t579996\t5\t49999995\t5.00E+14\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMDXB\tCLINICAL INDICATION\t9999999\t579996\t5\t49999995\t5.00E+14\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMREB\tRESULT\t9999999\t579996\t5\t49999995\t5.00E+14\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t910570\tLAB12399R\tACETYLCHOLINE MODULATING AB\tACRMOD\tACETYLCHOLINE MODULATING AB\t32.6\t560640\t5\t163\t7703\t5\t2803200\t2.32E+12
-LAB BLOOD ORDERABLES\t977665\tLABPANWBC\tWBC COUNT\tWBC\tWBC\t10.44\t636\t5\t52.2\t2591.24\t5\t3180\t4021200
-LAB BLOOD ORDERABLES\t978550\tLAB12645R\tCOMPLEMENT, ALTERNATE PATHWAY\t12644R\tCOMPLEMENT, ALTERNATE PATHWAY\t95.4\t546312\t5\t477\t47203\t5\t2731560\t1.59E+12
-MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tCULT\tCULT/OTHER RSLT:\t9999999\t3931337.485\t5\t49999995\t5.00E+14\t326\t1281616020\t5.25E+15
-MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tCULT\tCULT/OTHER RSLT:\t9999999\t3667605.352\t5\t49999995\t5.00E+14\t213\t781199940\t3.13E+15
-MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tRPT\tRPT\t9999999\t1190220\t5\t49999995\t5.00E+14\t2\t2380440\t2.83E+12
-MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tSDES\tSPECIMEN\t9999999\t1190220\t5\t49999995\t5.00E+14\t2\t2380440\t2.83E+12
-MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t#VALUE!\t5\t49999995\t5.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tSDES\tSPECIMEN\t9999999\t752360\t5\t49999995\t5.00E+14\t6\t4514160\t3.41E+12
-MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tSREQ\tCOMMENTS\t9999999\t752360\t5\t49999995\t5.00E+14\t6\t4514160\t3.41E+12
-MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tRPT\tRPT\t9999999\t309040\t5\t49999995\t5.00E+14\t3\t927120\t2.91E+11
-MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tSDES\tSPECIMEN\t9999999\t309040\t5\t49999995\t5.00E+14\t3\t927120\t2.91E+11
-MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tSREQ\tCOMMENTS\t9999999\t309040\t5\t49999995\t5.00E+14\t3\t927120\t2.91E+11
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tRPT\tRPT\t9999999\t647532\t5\t49999995\t5.00E+14\t5\t3237660\t3.07E+12
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tSREQ\tCOMMENTS\t9999999\t647532\t5\t49999995\t5.00E+14\t5\t3237660\t3.07E+12
-MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tCULT\tCULT/OTHER RSLT:\t9999999\t1712644.444\t5\t49999995\t5.00E+14\t297\t508655400\t9.24E+14
-MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11CT\tCOMMENT\t9999999\t266970\t5\t49999995\t5.00E+14\t2\t533940\t2.85E+11
-MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11DX\tCLINICAL INDICATION\t9999999\t266970\t5\t49999995\t5.00E+14\t2\t533940\t2.85E+11
-MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tRESUL7\tRESULT\t9999999\t576624\t5\t49999995\t5.00E+14\t10\t5766240\t1.60E+13
-STOOL ORDERABLES\t472653\tLABYANTIN\tINSULIN ANTIBODIES\tYANT2\tHUMAN\t9999999\t822848.5714\t5\t49999995\t5.00E+14\t7\t5759940\t6.65E+12
-STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tBILIRUBIN\tBILIRUBIN\t13\t177360\t5\t65\t1299\t1\t177360\t31456569600
-STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tCHOL\tCHOLESTEROL\t25.2\t177360\t5\t126\t3898\t1\t177360\t31456569600
-STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tHGB\tHEMOGLOBIN\t2000023.8\t177360\t5\t10000119\t1.00E+14\t1\t177360\t31456569600
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCAN10\tCANDIDA 1:10 CPM\t2897.2\t908145\t5\t14486\t62211096\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCAN100\tCANDIDA 1:100 CPM\t3603.4\t908145\t5\t18017\t191749131\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCAN50\tCANDIDA 1:50 CPM\t2300\t908145\t5\t11500\t55588098\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCNDINT\tCANDIDA INTERPRETATION\t9999999\t908145\t5\t49999995\t5.00E+14\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD100\tPPD 1:100 CPM\t9999999\t908145\t5\t49999995\t5.00E+14\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD200\tPPD 1:200 CPM\t5923.2\t908145\t5\t29616\t406369600\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD400\tPPD 1:400 CPM\t2508.6\t908145\t5\t12543\t61845647\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD50\tPPD 1:50 CPM\t9999999\t908145\t5\t49999995\t5.00E+14\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD800\tPPD 1:800 CPM\t1904.4\t908145\t5\t9522\t31263672\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTET1K\tTETANUS 1:1000 CPM\t11484.8\t908145\t5\t57424\t2205419288\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTET2K\tTETANUS 1:2000 CPM\t9356.2\t908145\t5\t46781\t1254103991\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTET5K\tTETANUS 1:5000 CPM\t11695.2\t908145\t5\t58476\t1785504722\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tUNCAN\tUNSTIMULATED CPM\t749.4\t908145\t5\t3747\t3412595\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tUNPPD\tUNSTIMULATED CPM\t883.4\t908145\t5\t4417\t5898549\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tUNTET\tUNSTIMULATED CPM\t749.4\t908145\t5\t3747\t3412595\t4\t3632580\t3.30E+12
-TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tTALBU\tALBUMIN EXCRETION\t2000052.4\t67920\t5\t10000262\t1.00E+14\t3\t203760\t18131760000
-TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tUALB\tALBUMIN, URINE\t2000037.8\t67920\t5\t10000189\t1.00E+14\t3\t203760\t18131760000
-TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tUT35\tCOLLECTION PERIOD\t20.4\t67920\t5\t102\t2340\t3\t203760\t18131760000
-TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tVOL35\tVOLUME\t1833.6\t67920\t5\t9168\t18840086\t3\t203760\t18131760000
-URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tVOLUMEURN\tURINE VOLUME\t8000557.4\t88120\t5\t40002787\t4.00E+14\t3\t264360\t28310436000
-URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tYUARR4\tAS CONC (UG/L)\t8000002.4\t88120\t5\t40000012\t4.00E+14\t3\t264360\t28310436000
-URINE ORDERABLES\t541662\tLABUELP\tELECTROLYTE PANEL, URINE W/O CREATININE\tUCL\tCHLORIDE, URINE\t2000049.4\t4080\t5\t10000247\t1.00E+14\t1\t4080\t16646400
-URINE ORDERABLES\t541662\tLABUELP\tELECTROLYTE PANEL, URINE W/O CREATININE\tUK\tPOTASSIUM, URINE\t2000037.64\t4080\t5\t10000188.2\t1.00E+14\t1\t4080\t16646400
-URINE ORDERABLES\t541662\tLABUELP\tELECTROLYTE PANEL, URINE W/O CREATININE\tUNA\tSODIUM, URINE\t2000032.6\t4080\t5\t10000163\t1.00E+14\t1\t4080\t16646400
-URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12683R\tCREATININE, URINE MG/DL\t52.4\t496092\t5\t262\t19958\t5\t2480460\t1.23E+12
-URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12684R\tCREATININE, URINE MG/DAY\t850\t496092\t5\t4250\t4103386\t5\t2480460\t1.23E+12
-URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12685R\tAMINOLEVULINIC ACID UMO/L\t10.8\t496092\t5\t54\t626\t5\t2480460\t1.23E+12
-URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12686R\tAMINOLEVULINIC ACID UMOL/DAY\t25\t496092\t5\t125\t4317\t5\t2480460\t1.23E+12
-URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12687R\tHOURS COLLECTED\t24\t496092\t5\t120\t2880\t5\t2480460\t1.23E+12
-URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12688R\tTOTAL VOLUME\t2482\t496092\t5\t12410\t40640406\t5\t2480460\t1.23E+12
-VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMMDES\tCMV IGM TEST DESCRIPTION\t9999999\t159305.731\t5\t49999995\t5.00E+14\t513\t81723840\t1.77E+13
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tEBNA\tEPSTEIN-BARR NUC AB\t9999999\t131091.4286\t5\t49999995\t5.00E+14\t7\t917640\t1.89E+11
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tSDES\tSPECIMEN\t9999999\t616970\t5\t49999995\t5.00E+14\t6\t3701820\t2.62E+12
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tSREQ\tCOMMENTS\t9999999\t616970\t5\t49999995\t5.00E+14\t6\t3701820\t2.62E+12
-VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL7\tHSV 1/2 IGM INTERPRETATION\t9999999\t290474.6392\t5\t49999995\t5.00E+14\t291\t84528120\t2.82E+13
-VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXGDS\tTEST DESCRIPTION\t9999999\t167664.3396\t5\t49999995\t5.00E+14\t106\t17772420\t3.77E+12
-VIROLOGY ORDERABLES\t497452\tLABHV6DNA\tHERPES VIRAL 6 DNA QUALITATIVE\tHVDNA\tHERPES VIRUS 6 DNA\t9999999\t228475.2809\t5\t49999995\t5.00E+14\t178\t40668600\t1.45E+13
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADPCR1\tADENVIRUS DNA\t9999999\t217572\t5\t49999995\t5.00E+14\t5\t1087860\t2.65E+11
-VIROLOGY ORDERABLES\t876719\tLABDVEIAV\tFLU A DFA\tIAVIP\tTEST DESCRIPTION\t9999999\t78912.72727\t5\t49999995\t5.00E+14\t33\t2604120\t1.09E+12
-VIROLOGY ORDERABLES\t876723\tLABDVEIBV\tFLU B DFA\tIBVIP\tTEST DESCRIPTION\t9999999\t53200\t5\t49999995\t5.00E+14\t24\t1276800\t91484654400
-VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXGQL\tIGG RESULT\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
-VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXGQT\tIGG LEVEL\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
-VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXMQL\tIGM RESULT\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
-VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXMQT\tIGM LEVEL\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
-VIROLOGY ORDERABLES\t906965\tLABVZVQLP\tVZV DNA PCR QUAL, PLASMA\tVZQLTD\tTEST DESCRIPTION\t9999999\t198242.2222\t5\t49999995\t5.00E+14\t135\t26762700\t7.15E+12
-VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tHSVINP\tTEST DESCRIPTION\t9999999\t166727.4847\t5\t49999995\t5.00E+14\t163\t27176580\t5.96E+12
-VIROLOGY ORDERABLES\t910608\tLABCMVIGT\tCMV IGG (TRANSFUSION)\tCMGTDS\tTEST DESCRIPTION\t9999999\t130818.6935\t5\t49999995\t5.00E+14\t398\t52065840\t9.97E+12
-BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tRESUL8\tRESULT\t9999999\t28164\t4\t39999996\t4.00E+14\t5\t140820\t7934583600
-BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tTEST8\tTEST NAME\t9999999\t28164\t4\t39999996\t4.00E+14\t5\t140820\t7934583600
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tVL\tPRODUCT VOLUME\t168.25\t30480\t4\t673\t113289\t1\t30480\t929030400
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tVL\tPRODUCT VOLUME\t168.75\t38640\t4\t675\t115071\t1\t38640\t1493049600
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t4\t39999996\t4.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tSI\t<<STATUS>>  \t9999999\t24574.28571\t4\t39999996\t4.00E+14\t7\t172020\t6302466000
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tSI\t<<STATUS>>  \t9999999\t20070\t4\t39999996\t4.00E+14\t4\t80280\t3212935200
-BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tDI\tISSUE DATE/TIME\t9999999\t39390\t4\t39999996\t4.00E+14\t2\t78780\t3104874000
-BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tXMR\tUNIT CROSSMATCH  \t9999999\t54990\t4\t39999996\t4.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tXMR\tUNIT CROSSMATCH  \t9999999\t54990\t4\t39999996\t4.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tXMR\tUNIT CROSSMATCH  \t9999999\t54990\t4\t39999996\t4.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tXMR\tUNIT CROSSMATCH  \t9999999\t69330\t4\t39999996\t4.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tUBT\tUNIT BLOOD TYPE  \t9999999\t69330\t4\t39999996\t4.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tUBT\tUNIT BLOOD TYPE  \t9999999\t69330\t4\t39999996\t4.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tUBT\tUNIT BLOOD TYPE  \t9999999\t91200\t4\t39999996\t4.00E+14\t2\t182400\t23119207200
-BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tDI\tISSUE DATE/TIME\t9999999\t49950\t4\t39999996\t4.00E+14\t2\t99900\t5482357200
-BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tUBT\tUNIT BLOOD TYPE  \t9999999\t49950\t4\t39999996\t4.00E+14\t2\t99900\t5482357200
-BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tSI\t<<STATUS>>  \t9999999\t30\t4\t39999996\t4.00E+14\t2\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tPRO\tPRODUCT ID  \t9999999\t60\t4\t39999996\t4.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tPRO\tPRODUCT ID  \t9999999\t60\t4\t39999996\t4.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tPRO\tPRODUCT ID  \t9999999\t60\t4\t39999996\t4.00E+14\t1\t60\t3600
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tCILIA\tBRONCH. LINING CELLS\t22.5\t720\t4\t90\t6754\t1\t720\t518400
-BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tCCMT\tCOMMENT\t9999999\t#VALUE!\t4\t39999996\t4.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tBASOSYNFLD\tBASOPHILS, SYN FLD\t1.25\t4560\t4\t5\t7\t1\t4560\t20793600
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tCCNT\tCELLS COUNTED\t115.5\t3030\t4\t462\t82570\t4\t12120\t40327200
-BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\tYMYCSC\tSOURCE\t9999999\t2504460\t4\t39999996\t4.00E+14\t1\t2504460\t6.27E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tCMV\tCMV ANTIBODY\t9999999\t550720\t4\t39999996\t4.00E+14\t3\t1652160\t1.46E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t550720\t4\t39999996\t4.00E+14\t3\t1652160\t1.46E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t550720\t4\t39999996\t4.00E+14\t3\t1652160\t1.46E+12
-BODY FLUID ORDERABLES\t497333\tLABMYCOC\tMYCOPLASMA CULTURE, FASTIDIOUS\tMYSTA\tMYCOPLASMA CULTURE STATUS\t9999999\t1967535\t4\t39999996\t4.00E+14\t4\t7870140\t2.00E+13
-BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tGLUFLD\tGLUCOSE BODY FLUID\t100.25\t#VALUE!\t4\t401\t50459\t0\tNone\tNone
-BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tFCSOUR\tSOURCE\t9999999\t12144\t4\t39999996\t4.00E+14\t5\t60720\t762372000
-COAGULATION ORDERABLES\t472867\tLABCGMISC\tMISCELLANEOUS TEST COAGULATION\tCGTEST\tPROCEDURE\t9999999\t157680\t4\t39999996\t4.00E+14\t1\t157680\t24862982400
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t135\t24540\t4\t540\t73200\t4\t98160\t5941404000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t143.75\t-150\t4\t575\t85025\t4\t-600\t626400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t305\t45480\t4\t1220\t384200\t4\t181920\t32942354400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t130\t2010\t4\t520\t67600\t4\t8040\t34372800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t18\t2010\t4\t72\t1296\t4\t8040\t34372800
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tALERT\tALERT\t9999999\t-255\t4\t39999996\t4.00E+14\t4\t-1020\t529200
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t5000199.5\t-300\t4\t20000798\t2.00E+14\t4\t-1200\t648000
-EXTERNAL RESULTS\t668294\tEXTWBC\tEXTERNAL WBC\tEXTWBC\tEXTERNAL WBC\t0.575\t-324000\t4\t2.3\t2.37\t4\t-1296000\t4.85E+11
-EXTERNAL RESULTS\t668298\tEXTPLTCT\tEXTERNAL PLATELET COUNT\tEXTPLT\tEXTERNAL PLATELET COUNT\t17.25\t-324000\t4\t69\t1309\t4\t-1296000\t4.85E+11
-HISTOCOMPATIBILITY ORDERABLE\t685703\t10135R\tHLA ORDER - SCHEDULED SCREENING KIT BILLING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1225575\t4\t39999996\t4.00E+14\t4\t4902300\t2.13E+13
-HISTOCOMPATIBILITY ORDERABLE\t890776\t10110R\tHLA ORDER - FLOW AUTO CROSSMATCH\t10049R\tHLA LAB TESTING STATUS\t9999999\t267540\t4\t39999996\t4.00E+14\t4\t1070160\t4.76E+11
-HISTOCOMPATIBILITY ORDERABLE\t890779\t10045R\tHLA ORDER - ANTIBODY SCREENING\tRINFO\tTO FIND RESULT:\t9999999\t1070211.064\t4\t39999996\t4.00E+14\t141\t150899760\t2.32E+15
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9222R\tPATIENT\'S SERUM 2 DESCRIPTION\t9999999\t96960\t4\t39999996\t4.00E+14\t4\t387840\t94370709600
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9207R\tINTERPRETATION/ COMMENTS\t9999999\t172596\t4\t39999996\t4.00E+14\t5\t862980\t4.09E+11
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9336R\tPOSSIBLE HLA ANTIBODIES FOR CLASS I\t9999999\t1972035\t4\t39999996\t4.00E+14\t4\t7888140\t6.22E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9397R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t15\t4\t39999996\t4.00E+14\t4\t60\t3600
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9620R\tREFERENCE SERUM DATE\t9999999\t30\t4\t39999996\t4.00E+14\t4\t120\t7200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9621R\tREFERENCE SERUM DESCRIPTION\t9999999\t30\t4\t39999996\t4.00E+14\t4\t120\t7200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9486R\tLOCUS 12\t9999999\t61995\t4\t39999996\t4.00E+14\t4\t247980\t61404850800
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tALTRN\tALTERNARIA\t5000013.165\t135495\t4\t20000052.66\t2.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tASPRG\tASPERGILLUS FUMAG.\t5000015.228\t135495\t4\t20000060.91\t2.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tAURBAS\tAUEROBASIDIUM\t7500006.35\t135495\t4\t30000025.4\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tCLASP\tCLADOSPORIUM\t7500005.275\t135495\t4\t30000021.1\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tCNDIDA\tCANDIDA\t7500003.15\t135495\t4\t30000012.6\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tEPIPUP\tEPICOCCUM PUPURASC.\t7500012.975\t135495\t4\t30000051.9\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tFUSMON\tFUSARIUM MONILIFORME\t7500003.45\t135495\t4\t30000013.8\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tHLM\tHELMINTHOSPORIUM\t5000010.638\t135495\t4\t20000042.55\t2.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tMCM\tMUCOR\t7499999.618\t135495\t4\t29999998.47\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tPNM\tPENICILLIUM\t5000002.44\t135495\t4\t20000009.76\t2.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tRHZNIG\tRHISOPUS NIGRICANS\t7500001.53\t135495\t4\t30000006.12\t3.00E+14\t4\t541980\t1.07E+11
-IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tTRIRUB\tTRICHOPHYTON RUBRUM\t7500005.05\t135495\t4\t30000020.2\t3.00E+14\t4\t541980\t1.07E+11
-LAB BLOOD ORDERABLES\t471526\tLAB11074R\tACH RECEPTOR AB\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t5.575\t301680\t4\t22.3\t497.29\t3\t905040\t2.74E+11
-LAB BLOOD ORDERABLES\t471656\tLAB11073R\tAPO B\tAPOB\tAPOLIPOPROTEIN B\t79.5\t265620\t4\t318\t25898\t4\t1062480\t4.02E+11
-LAB BLOOD ORDERABLES\t471680\tLABARIX\tARIXTRA\tARIX\tARIXTRA\t2500000.06\t66860\t4\t10000000.24\t1.00E+14\t3\t200580\t13518752400
-LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tANTI-JO1 ANTIBODY\tJO1QNT\tANTI-JO1 LEVEL\t7500010\t488760\t4\t30000040\t3.00E+14\t3\t1466280\t9.39E+11
-LAB BLOOD ORDERABLES\t471707\tLABYSSARO\tAUTOANTIBODIES TO SS-A/RO\tSSA\tSS-A/RO AB, IGG, SERUM\t7499999.35\t235005\t4\t29999997.4\t3.00E+14\t4\t940020\t2.55E+11
-LAB BLOOD ORDERABLES\t471811\tLABYC2REF\tC2 COMPLEMENT AG\tYC2RE1\tC2 COMPLEMENT AG\t2.575\t853125\t4\t10.3\t32.87\t4\t3412500\t2.94E+12
-LAB BLOOD ORDERABLES\t471814\tLABCAD\tCADMIUM, WHOLE BLOOD\tCAD\tCADMIUM, BLOOD\t7499999.75\t197760\t4\t29999999\t3.00E+14\t3\t593280\t1.52E+11
-LAB BLOOD ORDERABLES\t471960\tLABCENABY\tCENTROMERE ANTIBODIES\tYCENTA\tCENTROMERE ABS.\t9999999\t119720\t4\t39999996\t4.00E+14\t3\t359160\t45537789600
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t227223.75\t4\t39999996\t4.00E+14\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t223680\t4\t39999996\t4.00E+14\t4\t894720\t2.54E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSAIN\tINTERPRETATION\t9999999\t182820\t4\t39999996\t4.00E+14\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tBASO\tBASO, %\t0.275\t2880\t4\t1.1\t0.49\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tBASOAB\tBASOS, ABS\t0.015\t2880\t4\t0.06\t0.0012\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tEOS\tEOS, %\t1.275\t2880\t4\t5.1\t17.63\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tEOSAB\tEOS, ABS\t0.045\t2880\t4\t0.18\t0.0194\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tLYM\tLYM, %\t24.525\t2880\t4\t98.1\t2562.53\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tLYMAB\tLYM, ABS\t1.2275\t2880\t4\t4.91\t7.9567\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMONO\tMONO, %\t15.5\t2880\t4\t62\t1850.94\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMONOAB\tMONO, ABS\t0.495\t2880\t4\t1.98\t1.095\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tNEUT\tNEUT, %\t58.425\t2880\t4\t233.7\t14985.05\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tNEUTAB\tNEUT, ABS\t3.6275\t2880\t4\t14.51\t85.5369\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVIN\tDRVV INTERPRETATION\t9999999\t120585.9091\t4\t39999996\t4.00E+14\t132\t15917340\t3.59E+12
-LAB BLOOD ORDERABLES\t472228\tLABDHIVB\tDONOR HIV AB\tDHIV\tDONOR HIV AB\t9999999\t199937.1429\t4\t39999996\t4.00E+14\t7\t1399560\t3.32E+11
-LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIDIR\tINTERPRETED BY\t9999999\t436731.8276\t4\t39999996\t4.00E+14\t580\t253304460\t1.44E+14
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA1\tC22:0\t49.975\t342450\t4\t199.9\t10076.89\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA2\tC24:0\t47.75\t342450\t4\t191\t9228.84\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA3\tC26:0\t0.5875\t342450\t4\t2.35\t1.4605\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA4\tC24/C22 RATIO\t0.955\t342450\t4\t3.82\t3.651\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA5\tC26:0/C22:0 RATIO\t0.01175\t342450\t4\t0.047\t0.000581\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA6\tPRISTANIC ACID\t0.3375\t342450\t4\t1.35\t0.5711\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA7\tPHYTANIC ACID\t2.5725\t342450\t4\t10.29\t32.3729\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA8\tPRISTANIC/PHYTANIC ACID\t0.1325\t342450\t4\t0.53\t0.0723\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY\t1.5\t297480\t4\t6\t10\t4\t1189920\t5.93E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLINT\tINTERPRETATION\t9999999\t109882.5\t4\t39999996\t4.00E+14\t8\t879060\t2.67E+11
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tANTI HBS QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t7500000.975\t125880\t4\t30000003.9\t3.00E+14\t5\t629400\t82853755200
-LAB BLOOD ORDERABLES\t472595\tLABYSCERE\tINFLAMM. BOWEL DISEASE PANEL,S\tYSCER1\tSACCH CEREVISIAE AB, IGA\t2500009.175\t232440\t4\t10000036.7\t1.00E+14\t3\t697320\t1.65E+11
-LAB BLOOD ORDERABLES\t472595\tLABYSCERE\tINFLAMM. BOWEL DISEASE PANEL,S\tYSCER2\tSACCH CEREVISIAE AB, IGG\t5000006.625\t232440\t4\t20000026.5\t2.00E+14\t3\t697320\t1.65E+11
-LAB BLOOD ORDERABLES\t472628\tLABYICP\tIMMUNE COMPLEX PANEL\tYC1QB\tC1G BINDING\t7499999.925\t1110780\t4\t29999999.7\t3.00E+14\t4\t4443120\t6.50E+12
-LAB BLOOD ORDERABLES\t472628\tLABYICP\tIMMUNE COMPLEX PANEL\tYPEG\tPOLYETHL. GLYCOL IGG\t9999999\t1110780\t4\t39999996\t4.00E+14\t4\t4443120\t6.50E+12
-LAB BLOOD ORDERABLES\t472628\tLABYICP\tIMMUNE COMPLEX PANEL\tYRAJI\tRAJI CELL REPLACEMENT\t5000002.75\t1110780\t4\t20000011\t2.00E+14\t4\t4443120\t6.50E+12
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLBRAIN\tL BRAZILIENSIS,INTERPRETATION\t9999999\t1997181.818\t4\t39999996\t4.00E+14\t11\t21969000\t9.76E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLDONIN\tL DONOVANI,INTERPRETATION\t9999999\t1997181.818\t4\t39999996\t4.00E+14\t11\t21969000\t9.76E+13
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLMEXIN\tL MEXICANA,LNTERPRETATION\t9999999\t1997181.818\t4\t39999996\t4.00E+14\t11\t21969000\t9.76E+13
-LAB BLOOD ORDERABLES\t472764\tLAB11084R\tLORAZEPAM LEVEL\tLORAZ\tLORAZEPAM, SERUM\t5000014.75\t729000\t4\t20000059\t2.00E+14\t4\t2916000\t2.48E+12
-LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGGBAND\tB.BURGDORFERI IGG BANDS\t9999999\t262153.5849\t4\t39999996\t4.00E+14\t53\t13894140\t5.67E+12
-LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tCONA\tCON A STIMULATION\t138540\t771800\t4\t554160\t83611517934\t3\t2315400\t1.79E+12
-LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPHA\tPHA STIMULATION\t133527\t771800\t4\t534108\t79557623246\t3\t2315400\t1.79E+12
-LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPWM\tPWM STIMULATION\t131789\t771800\t4\t527156\t84849602386\t3\t2315400\t1.79E+12
-LAB BLOOD ORDERABLES\t472854\tLABMALL1\tMISCELLANEOUS ALLERGEN 1\tALL1\tALLERGEN\t9999999\t630540\t4\t39999996\t4.00E+14\t4\t2522160\t2.43E+12
-LAB BLOOD ORDERABLES\t473184\tLAB11032R\tPSEUDOCHOLINESTERASE\tCHEP\tPSEUDOCHOLINESTERASE\t1558.75\t275145\t4\t6235\t26163569\t4\t1100580\t3.38E+11
-LAB BLOOD ORDERABLES\t473265\tLABYRIBAB\tRIBOSOMAL P PROTEIN AB\tYRIB1\tRIBOSOME P AB,IGG\t7499999.3\t286236\t4\t29999997.2\t3.00E+14\t10\t2862360\t1.31E+12
-LAB BLOOD ORDERABLES\t473271\tLABRIPA\tRISTOCETIN INDUCED PLATELET AGGREGATION\tRIPINT\tINTERPRETATION\t9999999\t75612\t4\t39999996\t4.00E+14\t10\t756120\t1.24E+11
-LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMA PCR\tTOXGG\tGENERAL GUIDELINES\t9999999\t1052324\t4\t39999996\t4.00E+14\t30\t31569720\t6.51E+13
-LAB BLOOD ORDERABLES\t473687\tLABYTRYPS\tTRYPSIN-LIKE IMMUNOREACTIVITY\tYTRYP1\tTRYPSIN-LIKE IMMUNOREACTIVITY\t68.65\t560170\t4\t274.6\t26398.5\t6\t3361020\t1.99E+12
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHSO\tSOURCE\t9999999\t3358860\t4\t39999996\t4.00E+14\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473796\tLABYWNILE\tWEST NILE VIRUS AB, IGG AND IGM\tWNVIGM\tWEST NILE VIRUS AB IGM\t9999999\t286493.3333\t4\t39999996\t4.00E+14\t252\t72196320\t3.25E+13
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGMT\tB.HENSELAE IGM TITER\t9999999\t1367760\t4\t39999996\t4.00E+14\t7\t9574320\t4.56E+13
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGGT\tB.QUINTANA IGG TITER\t9999999\t1719408\t4\t39999996\t4.00E+14\t5\t8597040\t4.51E+13
-LAB BLOOD ORDERABLES\t541687\tLABFHHV7\tHERPES VIRUS 7 IGG AND IGM AB\tHERPM\tHERPESVIRUS 7 AB, IGM\t9999999\t327880\t4\t39999996\t4.00E+14\t3\t983640\t3.29E+11
-LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\tFINT\tINTERPRETATION\t9999999\t392295\t4\t39999996\t4.00E+14\t4\t1569180\t6.83E+11
-LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tCCPT\tANTI CCP ANTIBODY LEVEL\t5000003.5\t13689870\t4\t20000014\t2.00E+14\t4\t54759480\t2.95E+15
-LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMACT\tADAMTS-13 ACTIVITY\t67.25\t247320\t4\t269\t18583\t3\t741960\t2.61E+11
-LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t7499999.25\t50360\t4\t29999997\t3.00E+14\t3\t151080\t11365351200
-LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tCD3PC\tCD3 (PAN T) %\t2500002\t50360\t4\t10000008\t1.00E+14\t3\t151080\t11365351200
-LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t7500026.75\t50360\t4\t30000107\t3.00E+14\t3\t151080\t11365351200
-LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tWBC\tWBC COUNT\t2508624.75\t50360\t4\t10034499\t1.00E+14\t3\t151080\t11365351200
-LAB BLOOD ORDERABLES\t889722\tLAB12573R\tANCA\t12574R\tCYTOPLASMIC NEUTROPHILIC AB\t9999999\t318540\t4\t39999996\t4.00E+14\t81\t25801740\t1.91E+13
-LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12500R\tDATE OF LAST DOSE\t9999999\t17985\t4\t39999996\t4.00E+14\t4\t71940\t1362200400
-LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12501R\tTIME OF LAST DOSE\t9999999\t17985\t4\t39999996\t4.00E+14\t4\t71940\t1362200400
-LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12502R\tDOSE, MG\t9999999\t17985\t4\t39999996\t4.00E+14\t4\t71940\t1362200400
-LAB BLOOD ORDERABLES\t1007413\tLABDBGT\tDABIGATRAN\tDBGTR\tDABIGATRAN\t2500298.75\t9750\t4\t10001195\t1.00E+14\t4\t39000\t433965600
-MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t273578.4971\t4\t39999996\t4.00E+14\t173\t47329080\t1.50E+13
-MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tGS\tGRAM STAIN\t9999999\t#VALUE!\t4\t39999996\t4.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tRPT\tRPT\t9999999\t389580\t4\t39999996\t4.00E+14\t1\t389580\t1.52E+11
-MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tSDES\tSPECIMEN\t9999999\t389580\t4\t39999996\t4.00E+14\t1\t389580\t1.52E+11
-MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tSREQ\tCOMMENTS\t9999999\t389580\t4\t39999996\t4.00E+14\t1\t389580\t1.52E+11
-MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t3770\t4\t39999996\t4.00E+14\t6\t22620\t511664400
-MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tRPT\tRPT\t9999999\t26115\t4\t39999996\t4.00E+14\t4\t104460\t10911891600
-MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t26115\t4\t39999996\t4.00E+14\t4\t104460\t10911891600
-MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tCULT\tCULT/OTHER RSLT:\t9999999\t503786.5343\t4\t39999996\t4.00E+14\t554\t279097740\t1.51E+14
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tCULT\tCULT/OTHER RSLT:\t9999999\t407791.8182\t4\t39999996\t4.00E+14\t66\t26914260\t3.09E+13
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tRPT\tRPT\t9999999\t569880\t4\t39999996\t4.00E+14\t4\t2279520\t2.21E+12
-MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tRPT\tRPT\t9999999\t3540\t4\t39999996\t4.00E+14\t1\t3540\t12531600
-MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tSDES\tSPECIMEN\t9999999\t3540\t4\t39999996\t4.00E+14\t1\t3540\t12531600
-MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t3540\t4\t39999996\t4.00E+14\t1\t3540\t12531600
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tSDES\tSPECIMEN\t9999999\t647532\t4\t39999996\t4.00E+14\t5\t3237660\t3.07E+12
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCMET\tMETHOD\t9999999\t599640\t4\t39999996\t4.00E+14\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11RS\tRESULT\t9999999\t266970\t4\t39999996\t4.00E+14\t2\t533940\t2.85E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTDX\tCLINICAL INDICATION\t9999999\t763860\t4\t39999996\t4.00E+14\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTFLD\tSOURCE\t9999999\t763860\t4\t39999996\t4.00E+14\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTMET\tMETHOD\t9999999\t763860\t4\t39999996\t4.00E+14\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCRES\tRESULTS\t9999999\t1195020\t4\t39999996\t4.00E+14\t3\t3585060\t4.32E+12
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHLOC\tLOCATION\t9999999\t13848.57143\t4\t39999996\t4.00E+14\t21\t290820\t62447209200
-POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tQC10\tQC RESULT\t9999999\t1109028.75\t4\t39999996\t4.00E+14\t16\t17744460\t2.34E+14
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tUCMT\tURINE COMMENT\t9999999\t-1410\t4\t39999996\t4.00E+14\t4\t-5640\t47455200
-STOOL ORDERABLES\t472486\tLABYHGH\tGROWTH HORMONE, SERUM\tHGH\tGROWTH HORMONE, SERUM\t3.6075\t155560\t4\t14.43\t169.9985\t3\t466680\t78444007200
-STOOL ORDERABLES\t472653\tLABYANTIN\tINSULIN ANTIBODIES\tINSAB\tINSULIN ABS, SERUM\t2499999.75\t1167780\t4\t9999999\t1.00E+14\t4\t4671120\t6.25E+12
-STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tTGL\tTRIGLYCERIDES\t86\t177360\t4\t344\t58808\t1\t177360\t31456569600
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPDINT\tPPD INTERPRETATION\t9999999\t908145\t4\t39999996\t4.00E+14\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t869052\tLAB11899R\tN. GONORRHOEAE RNA, RECTAL\t11900R\tN.GONORRHOEAE RNA,RECTAL\t9999999\t152760\t4\t39999996\t4.00E+14\t2\t305520\t93305815200
-STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA3\tCOLLECTION DURATION\t7500005.25\t173370\t4\t30000021\t3.00E+14\t6\t1040220\t3.08E+11
-URINE ORDERABLES\t471573\tLABYUALDO\tALDOSTERONE, URINE\tALDOSTRURN\tALDOSTERONE, URINE\t2.9\t#VALUE!\t4\t11.6\t49.58\t0\tNone\tNone
-URINE ORDERABLES\t471573\tLABYUALDO\tALDOSTERONE, URINE\tVOLUMEURN\tURINE VOLUME\t1938.5\t#VALUE!\t4\t7754\t18557116\t0\tNone\tNone
-URINE ORDERABLES\t471573\tLABYUALDO\tALDOSTERONE, URINE\tYUALD2\tCOLLECTION DURATION\t24\t#VALUE!\t4\t96\t2304\t0\tNone\tNone
-URINE ORDERABLES\t472100\tLABUCORT\tCORTISOL FREE, URINE\tUCORT\tCORTISOL, URINE\t5000001.875\t452280\t4\t20000007.5\t2.00E+14\t2\t904560\t4.09E+11
-URINE ORDERABLES\t472100\tLABUCORT\tCORTISOL FREE, URINE\tVOLUMEURN\tURINE VOLUME\t3164.5\t452280\t4\t12658\t42962438\t2\t904560\t4.09E+11
-URINE ORDERABLES\t472100\tLABUCORT\tCORTISOL FREE, URINE\tYUCOUT\tCOLLECTION DURATION\t24\t452280\t4\t96\t2304\t2\t904560\t4.09E+11
-URINE ORDERABLES\t472473\tLABUGL\tGLUCOSE WITH CREATININE, URINE (RANDOM)\tGLUCR\tGLUCOSE/CREAT RATIO\t7499999.28\t#VALUE!\t4\t29999997.12\t3.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t472473\tLABUGL\tGLUCOSE WITH CREATININE, URINE (RANDOM)\tGLUURN\tGLUCOSE, URINE\t15.25\t#VALUE!\t4\t61\t1053\t0\tNone\tNone
-URINE ORDERABLES\t472473\tLABUGL\tGLUCOSE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t179.25\t#VALUE!\t4\t717\t139428.66\t0\tNone\tNone
-URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tHISTURN\tHISTAMINE, URINE\t89.25\t247740\t4\t357\t36673\t2\t495480\t1.23E+11
-URINE ORDERABLES\t472740\tLABLEGU\tLEGIONELLA AG, URINE\tTINFO\tTESTINFORMATION\t9999999\t407893.4505\t4\t39999996\t4.00E+14\t455\t185591520\t1.09E+14
-URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\t12661R\tNTX\t284.25\t458190\t4\t1137\t550405\t4\t1832760\t1.53E+12
-URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\t12662R\tNTX-TELOPEPTIDE, U\t73.5\t458190\t4\t294\t30218\t4\t1832760\t1.53E+12
-URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\t12663R\tCREATININE, U\t51.75\t458190\t4\t207\t15927\t4\t1832760\t1.53E+12
-URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\tUT53\tCOLLECTION PERIOD\t7500005.25\t458190\t4\t30000021\t3.00E+14\t4\t1832760\t1.53E+12
-URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\tVOL53\tVOLUME\t2500214\t458190\t4\t10000856\t1.00E+14\t4\t1832760\t1.53E+12
-VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tCHLAM\tC.TRACHOMATIS RESULT:\t9999999\t553215\t4\t39999996\t4.00E+14\t4\t2212860\t1.33E+12
-VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tGC\tN.GONORRHOEAE RESULT:\t9999999\t553215\t4\t39999996\t4.00E+14\t4\t2212860\t1.33E+12
-VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tDVE\tRESULTS\t9999999\t99400.67114\t4\t39999996\t4.00E+14\t596\t59242800\t1.23E+14
-VIROLOGY ORDERABLES\t472525\tLABHBSQL\tHEPATITIS B SURFACE AB\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t95670\t4\t39999996\t4.00E+14\t2\t191340\t18558622800
-VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFLUB\tINFLUENZA VIRUS TYPE B\t9999999\t11850\t4\t39999996\t4.00E+14\t10\t118500\t2648221200
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKIPRP\tTEST DESCRIPTION\t9999999\t308329.8795\t4\t39999996\t4.00E+14\t83\t25591380\t2.98E+13
-VIROLOGY ORDERABLES\t497464\tLABFVARZ\tVARICELLA ZOSTER VIR. DNA,QUAL\tVZVDNA\tVARICELLA ZOSTER VIRUS DNA\t9999999\t245465.1799\t4\t39999996\t4.00E+14\t139\t34119660\t2.47E+13
-VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADDNA\tADENOVIRUS DNA\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
-VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVCP\tRESULT\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
-VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVLOG\tRESULT LOG 10\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
-VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVSP\tSPECIMEN\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\t11155R\tHERPES VIRUS 6 DNA, QUANT\t7501847.5\t206940\t4\t30007390\t3.00E+14\t2\t413880\t92256228000
-VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tRPT\tRPT\t9999999\t2183640\t4\t39999996\t4.00E+14\t1\t2183640\t4.77E+12
-VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tSDES\tSPECIMEN\t9999999\t2183640\t4\t39999996\t4.00E+14\t1\t2183640\t4.77E+12
-VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tSREQ\tCOMMENTS\t9999999\t2183640\t4\t39999996\t4.00E+14\t1\t2183640\t4.77E+12
-VIROLOGY ORDERABLES\t876717\tLABDVEAV3\tADENO DFA\tADV3\tADENOVIRUS\t9999999\t373280\t4\t39999996\t4.00E+14\t3\t1119840\t9.84E+11
-VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tHVIP\tTEST DESCRIPTION\t9999999\t85470\t4\t39999996\t4.00E+14\t56\t4786320\t8.40E+11
-VIROLOGY ORDERABLES\t906947\tLABADVRC\tADENOVIRUS RAPID CULTURE\tADVSPM\tSPECIMEN\t9999999\t236460\t4\t39999996\t4.00E+14\t8\t1891680\t5.77E+11
-VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tCULT\tCULT/OTHER RSLT:\t9999999\t1756836.783\t4\t39999996\t4.00E+14\t230\t404072460\t7.53E+14
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tLOTNO\tLOT #\t9999999\t3120\t3\t29999997\t3.00E+14\t1\t3120\t9734400
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tQY\tQUANTITY\t1\t3120\t3\t3\t3\t1\t3120\t9734400
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tVL\tPRODUCT VOLUME\t181.6666667\t27180\t3\t545\t100201\t1\t27180\t738752400
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tVL\tPRODUCT VOLUME\t180\t38640\t3\t540\t98226\t1\t38640\t1493049600
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tVL\tPRODUCT VOLUME\t159.6666667\t#VALUE!\t3\t479\t77073\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tSI\t<<STATUS>>  \t9999999\t20477.14286\t3\t29999997\t3.00E+14\t7\t143340\t4565293200
-BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tSI\t<<STATUS>>  \t9999999\t26280\t3\t29999997\t3.00E+14\t3\t78840\t3104877600
-BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tXMR\tUNIT CROSSMATCH  \t9999999\t38460\t3\t29999997\t3.00E+14\t1\t38460\t1479171600
-BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tDI\tISSUE DATE/TIME\t9999999\t38400\t3\t29999997\t3.00E+14\t1\t38400\t1474560000
-BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tSI\t<<STATUS>>  \t9999999\t36700\t3\t29999997\t3.00E+14\t3\t110100\t6598270800
-BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tDI\tISSUE DATE/TIME\t9999999\t38400\t3\t29999997\t3.00E+14\t1\t38400\t1474560000
-BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tDI\tISSUE DATE/TIME\t9999999\t38400\t3\t29999997\t3.00E+14\t1\t38400\t1474560000
-BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tDI\tISSUE DATE/TIME\t9999999\t67080\t3\t29999997\t3.00E+14\t1\t67080\t4499726400
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tDI\tISSUE DATE/TIME\t9999999\t67080\t3\t29999997\t3.00E+14\t1\t67080\t4499726400
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tSI\t<<STATUS>>  \t9999999\t69330\t3\t29999997\t3.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tXMR\tUNIT CROSSMATCH  \t9999999\t69330\t3\t29999997\t3.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tDI\tISSUE DATE/TIME\t9999999\t67080\t3\t29999997\t3.00E+14\t1\t67080\t4499726400
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tXMR\tUNIT CROSSMATCH  \t9999999\t69330\t3\t29999997\t3.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tDI\tISSUE DATE/TIME\t9999999\t34260\t3\t29999997\t3.00E+14\t1\t34260\t1173747600
-BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tUBT\tUNIT BLOOD TYPE  \t9999999\t148140\t3\t29999997\t3.00E+14\t1\t148140\t21945459600
-BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tSI\t<<STATUS>>  \t9999999\t30\t3\t29999997\t3.00E+14\t2\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tSI\t<<STATUS>>  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tSI\t<<STATUS>>  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tSI\t<<STATUS>>  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tPRO\tPRODUCT ID  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tAAMINOA\tA-AMINOADIPIC ACID\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tALANINE\tALANINE\t32.33333333\t#VALUE!\t3\t97\t3275\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tARGININE\tARGININE\t18\t#VALUE!\t3\t54\t1106\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tASPART\tASPARTIC ACID\t2.333333333\t#VALUE!\t3\t7\t17\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tASPGN\tASPARAGINE\t4.666666667\t#VALUE!\t3\t14\t66\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tBALN\tB-ALANINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tBAMBA\tB-AMINOISOBUTYRIC AC\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tCITRUL\tCITRULLINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tCYSTINE\tCYSTINE\t0.333333333\t#VALUE!\t3\t1\t1\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tCYTHN\tCYSTATHIONINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tGLUTAC\tGLUTAMIC ACID\t0.333333333\t#VALUE!\t3\t1\t1\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tGLUTAMINE\tGLUTAMINE\t489\t#VALUE!\t3\t1467\t726539\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tGLYCINE\tGLYCINE\t11.33333333\t#VALUE!\t3\t34\t394\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tHISTID\tHISTIDINE\t9.333333333\t#VALUE!\t3\t28\t314\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tHMCY\tHOMOCYSTINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tISOLEU\tISOLEUCINE\t1.333333333\t#VALUE!\t3\t4\t16\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tLEUCINE\tLEUCINE\t11.66666667\t#VALUE!\t3\t35\t419\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tLYSINE\tLYSINE\t21.33333333\t#VALUE!\t3\t64\t1496\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tMETHIO\tMETHIONINE\t1\t#VALUE!\t3\t3\t9\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tORNITHINE\tORNITHINE\t3.333333333\t#VALUE!\t3\t10\t38\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tPHALN\tPHENYLALANINE\t12.66666667\t#VALUE!\t3\t38\t500\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tPROLINE\tPROLINE\t1\t#VALUE!\t3\t3\t9\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tSERINE\tSERINE\t24\t#VALUE!\t3\t72\t1754\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tTAURINE\tTAURINE\t5.666666667\t#VALUE!\t3\t17\t105\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tTHREONINE\tTHREONINE\t21\t#VALUE!\t3\t63\t1409\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tTYROSINE\tTYROSINE\t6.333333333\t#VALUE!\t3\t19\t131\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tVALINE\tVALINE\t10\t#VALUE!\t3\t30\t450\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472012\tLAB11438R\tCHLAMYDIA TRACHOMATIS CULTURE\t12067R\tSPECIMEN SOURCE\t9999999\t122600\t3\t29999997\t3.00E+14\t3\t367800\t93376756800
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tBASOFLD\tBASOPHILS, FLD\t1.666666667\t#VALUE!\t3\t5\t11\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tBASOCSF\tBASOPHILS, CSF\t1.666666667\t#VALUE!\t3\t5\t9\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tEOSCSF\tEOSINOPHILS, CSF\t2.666666667\t#VALUE!\t3\t8\t30\t0\tNone\tNone
-BODY FLUID ORDERABLES\t473108\tLABFPLTS\tPLATELET COUNT, FLUID\tFLDSOU\tSOURCE, FLUID\t9999999\t14820\t3\t29999997\t3.00E+14\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t473108\tLABFPLTS\tPLATELET COUNT, FLUID\tPLTFLD\tPLATELET CT, FLUID\t3334546.667\t14820\t3\t10003640\t1.00E+14\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t473171\tLABCSFTP\tPROTEIN TOTAL, CSF\tTPCSTN\tTUBE NUMBER\t9999999\t223020\t3\t29999997\t3.00E+14\t3\t669060\t2.22E+11
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tVCA\tVIRAL CAPSID AB\t9999999\t691860\t3\t29999997\t3.00E+14\t2\t1383720\t1.39E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tVCAM\tVIRAL CAPSID AB-IGM\t9999999\t691860\t3\t29999997\t3.00E+14\t2\t1383720\t1.39E+12
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15CO\tCOMMENT, BM\t9999999\t644650\t3\t29999997\t3.00E+14\t18\t11603700\t8.75E+12
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTPRM\tPRIMERS\t9999999\t771152.3077\t3\t29999997\t3.00E+14\t13\t10024980\t1.13E+13
-BODY FLUID ORDERABLES\t497448\tLABCHLAM\tCHLAMYDIA TRACHOMATIS / PSITTACI CULTURE\t12205\tSOURCE\t9999999\t1093440\t3\t29999997\t3.00E+14\t3\t3280320\t6.55E+12
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASIN\tINTERPRETATION\t9999999\t898005\t3\t29999997\t3.00E+14\t8\t7184040\t8.03E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12341R\tSPECIMEN\t9999999\t1463100\t3\t29999997\t3.00E+14\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t13017R\tPCA-TR, CSF\t9999999\t3570460\t3\t29999997\t3.00E+14\t3\t10711380\t5.49E+13
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t3333338\t9460\t3\t10000014\t1.00E+14\t3\t28380\t382698000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMTLBATIMP\tPM TELEMETRY BATTERY IMPEDANCE\t0.714666667\t3800\t3\t2.144\t3.309536\t3\t11400\t138477600
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.6\t5560\t3\t1.8\t1.14\t3\t16680\t244404000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.4\t5560\t3\t1.2\t0.48\t3\t16680\t244404000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t126.6666667\t920\t3\t380\t48600\t3\t2760\t14328000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t0.625\t-260\t3\t1.875\t1.203125\t3\t-780\t378000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.6\t-260\t3\t1.8\t1.32\t3\t-780\t378000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t13.22333333\t1020\t3\t39.67\t533.5489\t3\t3060\t14202000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t1020\t3\t29999997\t3.00E+14\t3\t3060\t14202000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRVLDUNIM\tPM RV LEAD UNIPOLAR IMPEDANCE\t606\t500\t3\t1818\t1116666\t3\t1500\t1918800
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTDETD\tFASTVT TACHY ZONE DETECTION DURATION\t3333343.667\t-580\t3\t10000031\t1.00E+14\t3\t-1740\t1083600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTZNSC\tFASTVT TACHY ZONE SUDDEN ONSET CRITERIA\t56.33333333\t-580\t3\t169\t13681\t3\t-1740\t1083600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t400\t-200\t3\t1200\t480000\t3\t-600\t475200
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t0.333333333\t60560\t3\t1\t1\t3\t181680\t32942354400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tALERT\tALERT\t9999999\t-420\t3\t29999997\t3.00E+14\t3\t-1260\t730800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t367.3333333\t-300\t3\t1102\t441142\t3\t-900\t550800
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTDETD\tFASTVT TACHY ZONE DETECTION DURATION\t7\t-40\t3\t21\t268.5\t3\t-120\t439200
-HISTOCOMPATIBILITY ORDERABLE\t479465\t10180R\tHLA ORDER - KIT REQUEST NOTICE\t9710R\tKIT REQUEST NOTICE FOR LAB\t9999999\t65180\t3\t29999997\t3.00E+14\t3\t195540\t12745299600
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9225R\tPATIENT\'S SERUM 3 DATE\t9999999\t159720\t3\t29999997\t3.00E+14\t3\t479160\t1.19E+11
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9171R\tSERUM DESCRIPTION 3\t9999999\t605080\t3\t29999997\t3.00E+14\t3\t1815240\t3.27E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9181R\tINTERPRETATION/ COMMENTS\t9999999\t380304.6774\t3\t29999997\t3.00E+14\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9708R\tDONOR PHENOTYPE\t9999999\t387543.0508\t3\t29999997\t3.00E+14\t118\t45730080\t5.00E+13
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9192R\tPATIENT\'S SERUM 1 DESCRIPTION\t9999999\t29360\t3\t29999997\t3.00E+14\t3\t88080\t7726428000
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9199R\tPATIENT\'S SERUM 3 DATE\t9999999\t159740\t3\t29999997\t3.00E+14\t3\t479220\t1.19E+11
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9201R\tPATIENT\'S SERUM 3 UNTREATED RESULT\t9999999\t159740\t3\t29999997\t3.00E+14\t3\t479220\t1.19E+11
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9202R\tPATIENT\'S SERUM 3 DTT TREATED RESULT\t9999999\t159740\t3\t29999997\t3.00E+14\t3\t479220\t1.19E+11
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9141R\tSERUM DESCRIPTION 3\t9999999\t605080\t3\t29999997\t3.00E+14\t3\t1815240\t3.27E+12
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9707R\tDONOR PHENOTYPE\t9999999\t377476.7797\t3\t29999997\t3.00E+14\t118\t44542260\t4.69E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9383R\tHLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t33166.66667\t3\t29999997\t3.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9487R\tLOCUS 13\t9999999\t82640\t3\t29999997\t3.00E+14\t3\t247920\t61404847200
-HISTOCOMPATIBILITY ORDERABLE\t1509678\t10070R\tHLA ORDER - IVIG IN VITRO INHIBITION ASSAY\t10049R\tHLA LAB TESTING STATUS\t9999999\t1712980\t3\t29999997\t3.00E+14\t3\t5138940\t1.13E+13
-IMMUNOLOGY ORDERABLES\t472579\tLABHYPP\tHYPERSENSITIVITY PNEUMONITIS SCREEN\tHYPPN\tIGG ANTIBODY PANEL\t9999999\t415480\t3\t29999997\t3.00E+14\t3\t1246440\t6.65E+11
-LAB BLOOD ORDERABLES\t471518\tLABABODIS\tABO DISCREPANCY\tABODIS\tABO DISCREPANCY\t9999999\t22.85714286\t3\t29999997\t3.00E+14\t21\t480\t28800
-LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tLUPDIR\tINTERPRETED BY\t9999999\t298879.5652\t3\t29999997\t3.00E+14\t138\t41245380\t1.74E+13
-LAB BLOOD ORDERABLES\t471708\tLABYSSBLA\tAUTOANTIBODIES TO SS-B/LA\tSSB\tSS-B/LA AB, IGG, SERUM\t3333333.633\t197360\t3\t10000000.9\t1.00E+14\t3\t592080\t1.34E+11
-LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG ABS\t12048R\tPT IGG\t3333364.333\t478350\t3\t10000093\t1.00E+14\t2\t956700\t4.82E+11
-LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG ABS\t12049R\tFHA IGG\t198.3333333\t478350\t3\t595\t220875\t2\t956700\t4.82E+11
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBRES\tRESULTS\t9999999\t1276240\t3\t29999997\t3.00E+14\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471777\tLABYBLAS\tBLASTOMYCES AB\tYBLAS2\tBLASTOMYCES COMPLEMENT FIXATION\t9999999\t564155\t3\t29999997\t3.00E+14\t12\t6769860\t5.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t9999999\t286934.6939\t3\t29999997\t3.00E+14\t49\t14059800\t4.87E+12
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tABGRAN\tGRAN, ABS\t3333333.667\t55240\t3\t10000001\t1.00E+14\t3\t165720\t22474130400
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32CM\tCOMMENT:\t9999999\t807004.6154\t3\t29999997\t3.00E+14\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMETA\tMETAMYELOCYTES\t4.333333333\t11460\t3\t13\t75\t1\t11460\t131331600
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMORPH\tMORPHOLOGY\t9999999\t2880\t3\t29999997\t3.00E+14\t1\t2880\t8294400
-LAB BLOOD ORDERABLES\t472227\tLAB11134R\tDONOR HCV AB\tDHEPC\tDONOR HEPATITIS C AB\t9999999\t251580\t3\t29999997\t3.00E+14\t6\t1509480\t4.76E+11
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXAI\tALLELE I\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXAII\tALLELE II\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXDX\tCLINICAL INDICATION\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXRES\tRESULT\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472444\tLABYGAS\tGASTRIN, SERUM\tGASTRIN\tGASTRIN, SERUM\t3344844.333\t432680\t3\t10034533\t1.00E+14\t3\t1298040\t8.77E+11
-LAB BLOOD ORDERABLES\t472477\tLABGDMSCR\tGLUCOSE GDM SCREEN\tGDMSCR\tGLUCOSE, GDM SCREEN\t3333433.667\t5820\t3\t10000301\t1.00E+14\t2\t11640\t72612000
-LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY, BLOOD\t3333333.667\t59550\t3\t10000001\t1.00E+14\t2\t119100\t9662850000
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBCINT\tINTERPRETATION\t9999999\t70359.66102\t3\t29999997\t3.00E+14\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSINT\tINTERPRETATION\t9999999\t70359.66102\t3\t29999997\t3.00E+14\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t472528\tLABHCVA\tHEPATITIS C AB EIA\tHEPCAB\tHEPATITIS C AB\t9999999\t87210\t3\t29999997\t3.00E+14\t2\t174420\t15818778000
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKMD\tINTERPRETED BY\t9999999\t557059.322\t3\t29999997\t3.00E+14\t118\t65733000\t4.63E+13
-LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tHGBFPC\t% CELLS WITH HGB F\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tMCV\tMCV\t91.23333333\t#VALUE!\t3\t273.7\t24995.09\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tMLFB\tML FETAL BLOOD\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12428R\tINTERPRETATION\t9999999\t323840\t3\t29999997\t3.00E+14\t3\t971520\t3.52E+11
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12429R\tINTERPRETATION\t9999999\t323840\t3\t29999997\t3.00E+14\t3\t971520\t3.52E+11
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12430R\tINTERPRETATION\t9999999\t323840\t3\t29999997\t3.00E+14\t3\t971520\t3.52E+11
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tCHOL\tCHOLESTEROL, TOTAL\t132.3333333\t11860\t3\t397\t58785\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t7\t11860\t3\t21\t162.5\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tHDL\tHDL CHOLESTEROL\t19\t11860\t3\t57\t1157\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tLDL\tLDL (CALCULATED)\t3333385.333\t11860\t3\t10000156\t1.00E+14\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tLDLHDL\tLDL/HDL RATIO\t3333335.367\t11860\t3\t10000006.1\t1.00E+14\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tNHDL\tNON-HDL CHOL, CALC\t113.3333333\t11860\t3\t340\t43994\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tTGL\tTRIGLYCERIDE, SER/PLAS\t205.6666667\t11860\t3\t617\t196621\t3\t35580\t677617200
-LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHRES\tRESULTS\t9999999\t638456.25\t3\t29999997\t3.00E+14\t16\t10215300\t8.63E+12
-LAB BLOOD ORDERABLES\t472869\tLAB11071R\tMITOCHONDRIAL AB M2, SERUM\tYAMA\tMITOCHONDRIAL ANTIBODY, M2\t6666666.5\t621900\t3\t19999999.5\t2.00E+14\t3\t1865700\t2.44E+12
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTCBC\tCBC COMMENT\t9999999\t48930\t3\t29999997\t3.00E+14\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTINT\tINTERPRETATION\t9999999\t48930\t3\t29999997\t3.00E+14\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTMD\tREVIEWED BY\t9999999\t48930\t3\t29999997\t3.00E+14\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTPT\tSTIM. PATIENT,NBT\t3333399.667\t48930\t3\t10000199\t1.00E+14\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t473143\tLABYPRIMI\tPRIMIDONE AND PHENOBARBITAL\tPB\tPHENOBARBITAL\t3333349.9\t120140\t3\t10000049.7\t1.00E+14\t3\t360420\t46831554000
-LAB BLOOD ORDERABLES\t473143\tLABYPRIMI\tPRIMIDONE AND PHENOBARBITAL\tPRIMIDONE\tPRIMIDONE\t3333337.267\t120140\t3\t10000011.8\t1.00E+14\t3\t360420\t46831554000
-LAB BLOOD ORDERABLES\t473177\tLABPT12\tPROTHROMBIN FRAGMENT 1 AND 2\tPTFR12\tPROTHROMBIN FRAG 1+2\t3333737\t1000884\t3\t10001211\t1.00E+14\t5\t5004420\t6.44E+12
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tADA\tADENOSINE DEAMINASE\t3333333.343\t288690\t3\t10000000.03\t1.00E+14\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tG6PD\tG6PD, QUANTITATIVE\t3333340.267\t288690\t3\t10000020.8\t1.00E+14\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tGPI\tGLUC. PHOSPHATE ISOM\t6666672.667\t288690\t3\t20000018\t2.00E+14\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tHEX\tHEXOKINASE\t3333333.427\t288690\t3\t10000000.28\t1.00E+14\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tPK\tPYRUVATE KINASE\t3333339.833\t288690\t3\t10000019.5\t1.00E+14\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473282\tLABRUBG\tRUBELLA IGG ANTIBODY\tRUBR\tRUBELLA IGG ANTIBODY\t9999999\t132740\t3\t29999997\t3.00E+14\t3\t398220\t70434939600
-LAB BLOOD ORDERABLES\t473299\tLABYSERTR\tSERTRALINE LEVEL\tSERTRALINE\tSERTRALINE, S\t3333375.333\t445380\t3\t10000126\t1.00E+14\t3\t1336140\t6.08E+11
-LAB BLOOD ORDERABLES\t473299\tLABYSERTR\tSERTRALINE LEVEL\tYSERT2\tDESMETHYLSERTRALINE\t53.33333333\t445380\t3\t160\t16056\t3\t1336140\t6.08E+11
-LAB BLOOD ORDERABLES\t473304\tLABYSHBG\tSEX HORMONE BINDING GLOBULIN\tYSHBG1\tSEX HORMONE BINDING GLOBULIN\t42\t207360\t3\t126\t5474\t3\t622080\t1.45E+11
-LAB BLOOD ORDERABLES\t473305\tLABSEZ\tSEZARY CELL SCREEN\tSEZ\tSEZARY CELL SCREEN\t9999999\t301440\t3\t29999997\t3.00E+14\t2\t602880\t2.84E+11
-LAB BLOOD ORDERABLES\t473375\tLABYSTRAB\tSTRIATIONAL (STRIATED MUSCLE) AB\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t325480\t3\t29999997\t3.00E+14\t3\t976440\t3.22E+11
-LAB BLOOD ORDERABLES\t473444\tLABYTSIG\tTSH IMMUNOGLOBULINS, SERUM\tTSIG\tTHROID-STIMULATING IG, SERUM\t6666666.333\t265590\t3\t19999999\t2.00E+14\t2\t531180\t1.66E+11
-LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tLAB6\tREFERENCE LAB:\t9999999\t2836788\t3\t29999997\t3.00E+14\t5\t14183940\t1.37E+14
-LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tTEST6\tTEST NAME\t9999999\t2836788\t3\t29999997\t3.00E+14\t5\t14183940\t1.37E+14
-LAB BLOOD ORDERABLES\t473769\tLABVLDL\tVLDL (CALCULATED)\tVLDL\tVLDL (CALCULATED)\t3333348\t#VALUE!\t3\t10000044\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473780\tLABBARTPC\tBARTONELLA DNA, QUALI, PCR\tBQUIPC\tBARTONELLA QUINTANA DNA\t9999999\t188286\t3\t29999997\t3.00E+14\t50\t9414300\t2.14E+12
-LAB BLOOD ORDERABLES\t473785\tLABDUTYPE\tWEAK D TYPING\tDUTYP\tWEAK D TYPING\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM\tYLEVT1\tLEVETIRACETAM\t41.96666667\t191532\t3\t125.9\t6809.05\t5\t957660\t1.95E+11
-LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\tECINT\tINTERPRETATION\t9999999\t1051993.846\t3\t29999997\t3.00E+14\t13\t13675920\t6.73E+13
-LAB BLOOD ORDERABLES\t497440\tLABFCHGA\tA. PHAGOCYTOPHILUM AB\tAPIGG\tA.PHAGOCYTOPHILUM IGG\t9999999\t243480\t3\t29999997\t3.00E+14\t2\t486960\t1.21E+11
-LAB BLOOD ORDERABLES\t497440\tLABFCHGA\tA. PHAGOCYTOPHILUM AB\tAPIGM\tA.PHAGOCYTOPHILUM IGM\t9999999\t243480\t3\t29999997\t3.00E+14\t2\t486960\t1.21E+11
-LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP16\tCOXSACKIE A16 AB\t9999999\t315617.7049\t3\t29999997\t3.00E+14\t61\t19252680\t6.96E+12
-LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV 1 IGG-AB, WESTERN BLOT CONFIRM\tHVIG\tHIV 1 AB CONFIRMATION\t9999999\t321188.1356\t3\t29999997\t3.00E+14\t59\t18950100\t8.48E+12
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANCOSDD\tVANCOMYCIN, SINGLE DAILY DOSE\t13.46666667\t5260\t3\t40.4\t550.5\t3\t15780\t88880400
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANUN2\tDATE & TIME OF LAST DOSE\t9999999\t5260\t3\t29999997\t3.00E+14\t3\t15780\t88880400
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANUN3\tDOSAGE\t9999999\t5260\t3\t29999997\t3.00E+14\t3\t15780\t88880400
-LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANUN4\tADMINISTRATION ROUTE\t9999999\t5260\t3\t29999997\t3.00E+14\t3\t15780\t88880400
-LAB BLOOD ORDERABLES\t541681\tLAB11133R\tDONOR HBC AB\tDHBCC\tDONOR HEP CORE AB\t9999999\t282090\t3\t29999997\t3.00E+14\t8\t2256720\t1.00E+12
-LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH FRONT TYPE\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1/2 PCR, NON CSF\tHV1DNA\tHSV-1 DNA, QN PCR\t6776707.333\t151560\t3\t20330122\t2.00E+14\t3\t454680\t75947306400
-LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1/2 PCR, NON CSF\tHV2DNA\tHSV-2 DNA, QN PCR\t6666837.667\t151560\t3\t20000513\t2.00E+14\t3\t454680\t75947306400
-LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\tFMYABG\tM.PNEUMONIA AB IGG\t9999999\t291900\t3\t29999997\t3.00E+14\t3\t875700\t3.33E+11
-LAB BLOOD ORDERABLES\t590559\tLABFPOLIO\tPOLIOVIRUS 1-3 AB, SERUM\tPLV1\tPOLIOVIRUS 1 AB\t9999999\t253600\t3\t29999997\t3.00E+14\t3\t760800\t2.02E+11
-LAB BLOOD ORDERABLES\t590559\tLABFPOLIO\tPOLIOVIRUS 1-3 AB, SERUM\tPLV2\tPOLIOVIRUS 2 AB\t9999999\t253600\t3\t29999997\t3.00E+14\t3\t760800\t2.02E+11
-LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tJO1AB\tAUTOANTIBODIES TO JO 1\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSSA\tSS-A/RO AB, IGG, SERUM\t6666666.067\t#VALUE!\t3\t19999998.2\t2.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSSB\tSS-B/LA AB, IGG, SERUM\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPEI\tINTERPRETATION\t9999999\t271621.4063\t3\t29999997\t3.00E+14\t128\t34767540\t1.35E+13
-LAB BLOOD ORDERABLES\t633410\tLABRPRQL\tRPR QUALITATIVE\tRPRQIN\tINTERPRETATIONS\t9999999\t63888.99563\t3\t29999997\t3.00E+14\t687\t43891740\t8.49E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSAQNT\tANTI-RO LEVEL\t43.66666667\t663480\t3\t131\t6385\t3\t1990440\t1.91E+12
-LAB BLOOD ORDERABLES\t890131\tLAB12210R\tLIPOPROTN ASSOCTD PHOSPHLPSEA2\t12211R\t12211R\t201.6666667\t222000\t3\t605\t148069\t3\t666000\t1.56E+11
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC2526N\tCD25, % OF CD3+/CD4+/CD26NEG\t31\t107400\t3\t93\t3029\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC257N\tCD25, % OF CD3+/CD4+/CD7NEG\t32.66666667\t107400\t3\t98\t3364\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC264AB\tCD26-/CD4+, ABS\t905\t107400\t3\t2715\t3724793\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC264PC\tCD26-/CD4+ %\t47\t107400\t3\t141\t10013\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3026N\tCD30, % OF CD3+/CD4+/CD26NEG\t7.333333333\t107400\t3\t22\t246\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC307N\tCD30, % OF CD3+/CD4+/CD7NEG\t9\t107400\t3\t27\t357\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC326NA\tCD3+/CD26NEG, ABS.\t953.6666667\t107400\t3\t2861\t4071957\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3426N\tCD3+/CD4+/CD26NEG, ABS.\t900.6666667\t107400\t3\t2702\t3696154\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3426P\tCD3+/CD4+/CD26NEG, % OF LYMPHS\t46.66666667\t107400\t3\t140\t9842\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3426T\tCD3+/CD4+/CD26NEG, % OF CD4+ T\t54.66666667\t107400\t3\t164\t12438\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC347NA\tCD3+/CD4+/CD7NEG, ABS.\t748.3333333\t107400\t3\t2245\t2445637\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC347NP\tCD3+/CD4+/CD7NEG, % OF LYMPHS\t42\t107400\t3\t126\t8546\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC347NT\tCD3+/CD4+/CD7NEG, % OF CD4+ T\t50.66666667\t107400\t3\t152\t10856\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC37NA\tCD3+/CD7NEG, ABS.\t757\t107400\t3\t2271\t2475329\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P4PA\tCD3+/CD4+,ABS\t1350.666667\t107400\t3\t4052\t8497490\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P4PP\tCD3+/CD4+, % OF LYMPHS\t70\t107400\t3\t210\t16652\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P7NP\tCD3+/CD7NEG, % OF LYMPHS\t43\t107400\t3\t129\t8741\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P8PN\tCD3+/CD8+, ABS. OF LYMPHS\t84.33333333\t107400\t3\t253\t29277\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P8PP\tCD3+/CD8+, % OF LYMPHS\t5\t107400\t3\t15\t75\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC48RAT\tTCD4/CD8 RATIO\t14.26666667\t107400\t3\t42.8\t690\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC4N7AB\tCD4+/CD7-,ABS. (C4N7AB)\t754.6666667\t107400\t3\t2264\t2474720\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC4N7PC\tCD4+/CD7-% (C4N7PC)\t42.66666667\t107400\t3\t128\t8728\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC5226N\tCD52, % OF CD3+/CD4+/CD26NEG\t98\t107400\t3\t294\t28826\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC527N\tCD52, % OF CD3+/CD4+/CD7NEG\t99.33333333\t107400\t3\t298\t29602\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD19AB\tCD19 (PAN B), ABS.\t125\t107400\t3\t375\t81179\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD19PC\tCD19 (PAN B) %\t14.66666667\t107400\t3\t44\t1610\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD26NP\tCD3+/CD26NEG, % OF LYMPHS\t50.33333333\t107400\t3\t151\t11139\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t1452.666667\t107400\t3\t4358\t9595922\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD3PC\tCD3 (PAN T) %\t77\t107400\t3\t231\t19539\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD4TOT\tCD4 TOTAL\t71.33333333\t107400\t3\t214\t17250\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t75\t107400\t3\t225\t18827\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t14\t107400\t3\t42\t668.34\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tLYM\tLYMPHOCYTES %\t15.8\t107400\t3\t47.4\t802.1\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t1686.333333\t107400\t3\t5059\t11714329\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tTCD4AB\tTOTAL CD4, ABS.\t1378\t107400\t3\t4134\t8865338\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tWBC\tWBC COUNT\t9766.666667\t107400\t3\t29300\t326970000\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t961555\tLAB12808R\tCK ISOENZYME WITH REFLEX\t12819R\tCK ISOENZYME ELEC SPECIMN ONLY\t9999999\t178739.4783\t3\t29999997\t3.00E+14\t115\t20555040\t4.88E+12
-LAB BLOOD ORDERABLES\t978252\tLABFRPSA\tFREE PSA\tFPSA\tFREE PSA\t37.53333333\t1900\t3\t112.6\t12103.4\t3\t5700\t16484400
-LAB BLOOD ORDERABLES\t978252\tLABFRPSA\tFREE PSA\tFREEPSAPC\t% FREE PSA\t3333346\t1900\t3\t10000038\t1.00E+14\t3\t5700\t16484400
-MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tSREQ\tCOMMENTS\t9999999\t1190220\t3\t29999997\t3.00E+14\t2\t2380440\t2.83E+12
-MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tGS\tGRAM STAIN\t9999999\t418604.2105\t3\t29999997\t3.00E+14\t19\t7953480\t3.62E+12
-MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tCULT\tCULT/OTHER RSLT:\t9999999\t153813.4884\t3\t29999997\t3.00E+14\t43\t6613980\t1.34E+12
-MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tCULT\tCULT/OTHER RSLT:\t9999999\t1304430.274\t3\t29999997\t3.00E+14\t329\t429157560\t5.76E+14
-MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tCULT\tCULT/OTHER RSLT:\t9999999\t264537\t3\t29999997\t3.00E+14\t20\t5290740\t2.95E+12
-MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t26115\t3\t29999997\t3.00E+14\t4\t104460\t10911891600
-MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tRPT\tRPT\t9999999\t518040\t3\t29999997\t3.00E+14\t1\t518040\t2.68E+11
-MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t1262815.2\t3\t29999997\t3.00E+14\t75\t94711140\t1.20E+14
-MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tRPT\tRPT\t9999999\t90300\t3\t29999997\t3.00E+14\t2\t180600\t16389079200
-MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tSDES\tSPECIMEN\t9999999\t90300\t3\t29999997\t3.00E+14\t2\t180600\t16389079200
-MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tSREQ\tCOMMENTS\t9999999\t90300\t3\t29999997\t3.00E+14\t2\t180600\t16389079200
-MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t675327.4909\t3\t29999997\t3.00E+14\t275\t185715060\t2.71E+15
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tSDES\tSPECIMEN\t9999999\t569880\t3\t29999997\t3.00E+14\t4\t2279520\t2.21E+12
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tSREQ\tCOMMENTS\t9999999\t569880\t3\t29999997\t3.00E+14\t4\t2279520\t2.21E+12
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBCMT\tCOMMENT\t9999999\t678540\t3\t29999997\t3.00E+14\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBPRM\tPRIMERS\t9999999\t678540\t3\t29999997\t3.00E+14\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCDX\tCLINICAL INDICATION\t9999999\t599640\t3\t29999997\t3.00E+14\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRF\tSOURCE\t9999999\t1367340\t3\t29999997\t3.00E+14\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRP\tPRIMER\t9999999\t1367340\t3\t29999997\t3.00E+14\t2\t2734680\t5.54E+12
-POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tDIPSLOT\tDIPSTICK LOT\t9999999\t6000\t3\t29999997\t3.00E+14\t3\t18000\t140940000
-POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tEXPDATE\tEXP DATE\t9999999\t6000\t3\t29999997\t3.00E+14\t3\t18000\t140940000
-POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tPROTURINE\tPROTEIN URINE\t9999999\t6000\t3\t29999997\t3.00E+14\t3\t18000\t140940000
-STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\t11829R\tDRUGS DETECTED:\t9999999\t145740\t3\t29999997\t3.00E+14\t2\t291480\t81166788000
-STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\t11830R\tSOURCE\t9999999\t145740\t3\t29999997\t3.00E+14\t2\t291480\t81166788000
-STOOL ORDERABLES\t472544\tLABYHISTA\tHISTAMINE, SERUM\tHISTP\tHISTAMINE\t0.466666667\t388656\t3\t1.4\t0.84\t5\t1943280\t8.30E+11
-STOOL ORDERABLES\t473766\tLABY25VD\tPEDIATRIC VITAMIN D, 25-HYDROXY\t25OHD2\t25-HYDROXY D2\t9999999\t186780\t3\t29999997\t3.00E+14\t3\t560340\t1.15E+11
-STOOL ORDERABLES\t473766\tLABY25VD\tPEDIATRIC VITAMIN D, 25-HYDROXY\t25OHD3\t25-HYDROXY D3\t20\t186780\t3\t60\t1262\t3\t560340\t1.15E+11
-STOOL ORDERABLES\t473766\tLABY25VD\tPEDIATRIC VITAMIN D, 25-HYDROXY\t25OHDT\t25-HYDROXY D TOTAL\t20\t186780\t3\t60\t1262\t3\t560340\t1.15E+11
-STOOL ORDERABLES\t497226\tLABANDRO\tANDROSTENEDIONE, SERUM\tANDRO\tANDROSTENEDIONE, S\t3333471.667\t374300\t3\t10000415\t1.00E+14\t3\t1122900\t4.74E+11
-STOOL ORDERABLES\t497226\tLABYANDRO\tANDROSTENEDIONE, SERUM\tANDRO\tANDROSTENEDIONE, S\t2402.333333\t326960\t3\t7207\t31982349\t3\t980880\t3.31E+11
-STOOL ORDERABLES\t541693\tLABHBCM\tHEPATITIS B CORE / PRECORE MUTATION\tHBCM1\tBASAL CORE PROMOTER NUCLEOTIDE\t9999999\t873165\t3\t29999997\t3.00E+14\t8\t6985320\t7.15E+12
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12286R\t12286R\t6666692.5\t342800\t3\t20000077.5\t2.00E+14\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12287R\t12287R\t6666666.1\t342800\t3\t19999998.3\t2.00E+14\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12288R\t12288R\t6666666.133\t342800\t3\t19999998.4\t2.00E+14\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12289R\t12289R\t6666666\t342800\t3\t19999998\t2.00E+14\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12290R\t12290R\t6666666\t342800\t3\t19999998\t2.00E+14\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK1\tCOLLECTION DURATION\t9999999\t310750\t3\t29999997\t3.00E+14\t6\t1864500\t1.50E+12
-STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK3\tPOTASSIUM, 24-HR STOOL\t9999999\t146808\t3\t29999997\t3.00E+14\t5\t734040\t2.27E+11
-STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK4\tPOTASSIUM CONCENTRATION\t9999999\t146808\t3\t29999997\t3.00E+14\t5\t734040\t2.27E+11
-STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA4\tSODIUM CONCENTRATION\t6666686.667\t173370\t3\t20000060\t2.00E+14\t6\t1040220\t3.08E+11
-URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tARSENICURN\tARSENIC, URINE\t6666681.333\t88120\t3\t20000044\t2.00E+14\t3\t264360\t28310436000
-URINE ORDERABLES\t471930\tLABUCARN\tCARNITINE, URINE\tACFCRU\tACYL/FREE CARN RATIO, UR\t1.633333333\t450300\t3\t4.9\t16.41\t1\t450300\t2.03E+11
-URINE ORDERABLES\t471930\tLABUCARN\tCARNITINE, URINE\tCARNFU\tFREE CARNITINE, UR\t972.3333333\t450300\t3\t2917\t4185777\t1\t450300\t2.03E+11
-URINE ORDERABLES\t471930\tLABUCARN\tCARNITINE, URINE\tCARNTU\tTOTAL CARNITINE, UR\t1463.333333\t450300\t3\t4390\t9138866\t1\t450300\t2.03E+11
-URINE ORDERABLES\t472938\tLAB12811R\tMYOGLOBIN, URINE\tUMYOG\tMYOGLOBIN, URINE\t6666666.84\t154320\t3\t20000000.52\t2.00E+14\t3\t462960\t89624296800
-URINE ORDERABLES\t473434\tLABUTHI\tTHIOCYANATE, URINE (RANDOM)\tTHIOCYANURN\tTHIOCYANATE, URINE\t3333358\t288810\t3\t10000074\t1.00E+14\t2\t577620\t1.69E+11
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tHVA\tHOMOVANILLIC ACID\t4.1\t335730\t3\t12.3\t53.21\t2\t671460\t2.26E+11
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tTIMEU\tCOLLECTION PERIOD\t24\t335730\t3\t72\t1728\t2\t671460\t2.26E+11
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tUCR\tCREATININE, URINE\t1096\t335730\t3\t3288\t3619566\t2\t671460\t2.26E+11
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tUCR\tCREATININE, URN\t81.33333333\t335730\t3\t244\t20726\t2\t671460\t2.26E+11
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tUVOL\tVOLUME\t1421\t335730\t3\t4263\t6436341\t2\t671460\t2.26E+11
-VIROLOGY ORDERABLES\t472198\tLABDVERS\tRSV RAPID ANTIGEN, DIRECT EIA\tRRSP\tSPECIMEN\t9999999\t150\t3\t29999997\t3.00E+14\t2\t300\t5490000
-VIROLOGY ORDERABLES\t472198\tLABDVERS\tRSV RAPID ANTIGEN, DIRECT EIA\tRSVCOM\tCOMMENTS\t9999999\t150\t3\t29999997\t3.00E+14\t2\t300\t5490000
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tEA\tEARLY ANTIBODY (R+D) IGG\t9999999\t131091.4286\t3\t29999997\t3.00E+14\t7\t917640\t1.89E+11
-VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCVGRE\tRESULT\t9999999\t948855\t3\t29999997\t3.00E+14\t8\t7590840\t8.74E+12
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVIPR\tTEST DESCRIPTION\t9999999\t247595.8794\t3\t29999997\t3.00E+14\t398\t98543160\t3.08E+13
-VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS2\tHIV-1 P24 (NON ICD) CONF.\t9999999\t773550\t3\t29999997\t3.00E+14\t2\t1547100\t1.20E+12
-VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS3\tHIV-1 P24 INTERP.\t9999999\t478889.2308\t3\t29999997\t3.00E+14\t39\t18676680\t1.07E+13
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tRPT\tRPT\t9999999\t583060\t3\t29999997\t3.00E+14\t3\t1749180\t1.27E+12
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tSDES\tSPECIMEN\t9999999\t583060\t3\t29999997\t3.00E+14\t3\t1749180\t1.27E+12
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tSREQ\tCOMMENTS\t9999999\t583060\t3\t29999997\t3.00E+14\t3\t1749180\t1.27E+12
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA, QUALITATIVE PCR\tSOURC3\tADENOVIRUS SOURCE\t9999999\t304440\t3\t29999997\t3.00E+14\t5\t1522200\t6.91E+11
-VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKIPRU\tTEST DESCRIPTION\t9999999\t227768.5106\t3\t29999997\t3.00E+14\t141\t32115360\t9.66E+12
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADDNA\tADENOVIRUS DNA\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVCP\tRESULT\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVLOG\tRESULT LOG 10\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVSP\tSPECIMEN\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUAMT\tMETHOD\t9999999\t163609.7143\t3\t29999997\t3.00E+14\t35\t5726340\t1.13E+12
-VIROLOGY ORDERABLES\t876721\tLABDVEIAR\tFLU A DFA-REFLEX PCR\tIARIP\tTEST DESCRIPTION\t9999999\t118140\t3\t29999997\t3.00E+14\t7\t826980\t1.98E+11
-VIROLOGY ORDERABLES\t876737\tLABTOXOIM\tTOXOPLASMA IGM\tTOXM\tTOXOPLASMA IGM\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXDES\tTEST DESCRIPTION\t9999999\t165582.0408\t3\t29999997\t3.00E+14\t147\t24340560\t5.47E+12
-VIROLOGY ORDERABLES\t876739\tLABTOXOAB\tTOXOPLASMA IGM & IGG\tTOXIGG\tTOXOPLASMA IGG\t9999999\t189060\t3\t29999997\t3.00E+14\t1\t189060\t35743683600
-VIROLOGY ORDERABLES\t876739\tLABTOXOAB\tTOXOPLASMA IGM & IGG\tTOXIGM\tTOXOPLASMA IGM\t9999999\t189060\t3\t29999997\t3.00E+14\t1\t189060\t35743683600
-VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVSP\tSPECIMEN\t9999999\t267440\t3\t29999997\t3.00E+14\t3\t802320\t2.15E+11
-VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVMT\tMETHOD\t9999999\t41910\t3\t29999997\t3.00E+14\t2\t83820\t6616040400
-VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVRE\tMETAPNEUMOVIRUS RNA\t9999999\t41910\t3\t29999997\t3.00E+14\t2\t83820\t6616040400
-VIROLOGY ORDERABLES\t906949\tLABMUMRC\tMUMPS RAPID CULTURE\tMUMRC1\tMUMPS RAPID CULTURE\t9999999\t182240\t3\t29999997\t3.00E+14\t3\t546720\t1.15E+11
-VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHVIGDS\tHSV IGG TEST DESCRIPTION\t9999999\t250224\t3\t29999997\t3.00E+14\t115\t28775760\t9.60E+12
-VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tHSVIN\tTEST DESCRIPTION\t9999999\t175252.9412\t3\t29999997\t3.00E+14\t102\t17875800\t3.71E+12
-BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tRLAB8\tREFERENCE LAB\t9999999\t28164\t2\t19999998\t2.00E+14\t5\t140820\t7934583600
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tVL\tPRODUCT VOLUME\t153\t40440\t2\t306\t47466\t1\t40440\t1635393600
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tVL\tPRODUCT VOLUME\t311.5\t28740\t2\t623\t238169\t1\t28740\t825987600
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tVL\tPRODUCT VOLUME\t176\t40470\t2\t352\t62002\t2\t80940\t3281490000
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tVL\tPRODUCT VOLUME\t139.5\t#VALUE!\t2\t279\t40325\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tSI\t<<STATUS>>  \t9999999\t54990\t2\t19999998\t2.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tSI\t<<STATUS>>  \t9999999\t54990\t2\t19999998\t2.00E+14\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tSI\t<<STATUS>>  \t9999999\t69330\t2\t19999998\t2.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tSI\t<<STATUS>>  \t9999999\t69330\t2\t19999998\t2.00E+14\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tSI\t<<STATUS>>  \t9999999\t91200\t2\t19999998\t2.00E+14\t2\t182400\t23119207200
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tXMR\tUNIT CROSSMATCH  \t9999999\t148140\t2\t19999998\t2.00E+14\t1\t148140\t21945459600
-BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tXMR\tUNIT CROSSMATCH  \t9999999\t65640\t2\t19999998\t2.00E+14\t1\t65640\t4308609600
-BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tSI\t<<STATUS>>  \t9999999\t74070\t2\t19999998\t2.00E+14\t2\t148140\t21945459600
-BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tXMR\tUNIT CROSSMATCH  \t9999999\t148140\t2\t19999998\t2.00E+14\t1\t148140\t21945459600
-BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tSI\t<<STATUS>>  \t9999999\t60\t2\t19999998\t2.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473666\tLABTBP94\tTRANSFUSION BLOOD PRODUCT 95\tPRO\tPRODUCT ID  \t9999999\t60\t2\t19999998\t2.00E+14\t1\t60\t3600
-BLOOD BANK PRODUCT ORDERABLES\t473666\tLABTBP94\tTRANSFUSION BLOOD PRODUCT 95\tSI\t<<STATUS>>  \t9999999\t60\t2\t19999998\t2.00E+14\t1\t60\t3600
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBCMT\tCOMMENT\t9999999\t1007200\t2\t19999998\t2.00E+14\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tBCD34V\tCD34+ PROGENITOR VIABILITY\t100\t11580\t2\t200\t20000\t2\t23160\t268221600
-BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD45PV\tCD45+ VIABILITY\t97.5\t11580\t2\t195\t19017\t2\t23160\t268221600
-BODY FLUID ORDERABLES\t472044\tLABFCO2\tCO2 (BICARB), FLUID\tCO2FL\tCO2, FLUID\t5000016\t5340\t2\t10000032\t1.00E+14\t2\t10680\t65354400
-BODY FLUID ORDERABLES\t472044\tLABFCO2\tCO2 (BICARB), FLUID\tCO2FLS\tSOURCE\t9999999\t5340\t2\t19999998\t2.00E+14\t2\t10680\t65354400
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tFOTHER\tOTHERS, FLD\t4\t1980\t2\t8\t32\t1\t1980\t3920400
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSOTHER\tOTHERS, SYN FLD\t2.5\t600\t2\t5\t13\t1\t600\t360000
-BODY FLUID ORDERABLES\t472552\tLABYHISTC\tHISTOPLASMA AG, CSF\tYHISC5\tCOMMENT,CSF\t9999999\t550244\t2\t19999998\t2.00E+14\t15\t8253660\t4.67E+12
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIRE\tRESULT\t9999999\t927889.0909\t2\t19999998\t2.00E+14\t11\t10206780\t1.25E+13
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHAVINT\tINTERPRETATION\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHAVR\tIGM ANTI-HAV\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
-BODY FLUID ORDERABLES\t497380\tLABYFUN\tUREA NITROGEN, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t611479\tLABPSACX\tPSA WITH REFLEX COMPLEXED PSA\tPSA\tPSA\t1.7\t10800\t2\t3.4\t5.86\t1\t10800\t116640000
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMIN\tINTERPRETATION\t9999999\t741092.1429\t2\t19999998\t2.00E+14\t28\t20750580\t2.04E+13
-BODY FLUID ORDERABLES\t670812\tLAB11175R\tLIPASE,BF\t11177R\tFLUID TYPE\t9999999\t193422.8571\t2\t19999998\t2.00E+14\t28\t5415840\t1.24E+12
-BODY FLUID ORDERABLES\t670824\tLAB11217R\tIRON,LIVER TISSUE\t11218R\tIRON, LIVER TS\t3733.5\t334620\t2\t7467\t31862709\t2\t669240\t2.24E+11
-BODY FLUID ORDERABLES\t670824\tLAB11217R\tIRON,LIVER TISSUE\t11224R\tHEPATIC IRON INDEX\t1.35\t334620\t2\t2.7\t3.77\t2\t669240\t2.24E+11
-BODY FLUID ORDERABLES\t744346\tLAB11619R\tVZV PCR, AQUEOUS FLUID\t11778R\tVZV PCR, AQUEOUS FLUID\t9999999\t863697.1429\t2\t19999998\t2.00E+14\t21\t18137640\t3.66E+13
-BODY FLUID ORDERABLES\t864674\tLAB11887R\tC. TRACHOMATIS RNA, THROAT\t11886R\tCHLAMYDIA TRACHOMATIS RNA,THROAT\t9999999\t152820\t2\t19999998\t2.00E+14\t2\t305640\t93379140000
-BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFRE\tRESULT\t9999999\t479125.7143\t2\t19999998\t2.00E+14\t14\t6707760\t4.66E+12
-BODY FLUID ORDERABLES\t910612\tLABSALCOR\tCORTISOL, SALIVA\tAVGCOR\tAVERAGE SALIVARY CORTISOL\t9999999\t124950\t2\t19999998\t2.00E+14\t2\t249900\t33406966800
-BODY FLUID ORDERABLES\t910612\tLABSALCOR\tCORTISOL, SALIVA\tMN1COR\t1ST MIDNIGHT SAMPLE\t9999999\t124950\t2\t19999998\t2.00E+14\t2\t249900\t33406966800
-BODY FLUID ORDERABLES\t910612\tLABSALCOR\tCORTISOL, SALIVA\tMN2COR\t2ND MIDNIGHT SAMPLE\t5000127.5\t124950\t2\t10000255\t1.00E+14\t2\t249900\t33406966800
-COAGULATION ORDERABLES\t472351\tLABF8POR\tFACTOR VIII INHIBITOR PORCINE\tF8POR\tFACTOR VIII INHIBITOR, PORCINE\t9999999\t8520\t2\t19999998\t2.00E+14\t2\t17040\t153504000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t0\t48540\t2\t0\t0\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t1.5\t48540\t2\t3\t4.5\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.4\t48540\t2\t0.8\t0.32\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t0.5625\t48540\t2\t1.125\t0.703125\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.4\t48540\t2\t0.8\t0.32\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tMOD\tMODEL\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRALDUNIM\tPM RA LEAD UNIPOLAR IMPEDANCE\t313.5\t48540\t2\t627\t196745\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRVLDUNIM\tPM RV LEAD UNIPOLAR IMPEDANCE\t342\t48540\t2\t684\t233928\t2\t97080\t5940360000
-EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t65\t-30\t2\t130\t8450\t2\t-60\t3600
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMALERT\tPM ALERT\t9999999\t-300\t2\t19999998\t2.00E+14\t2\t-600\t187200
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRALDUNIM\tPM RA LEAD UNIPOLAR IMPEDANCE\t1073.5\t750\t2\t2147\t2850817\t2\t1500\t1918800
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t28\t1080\t2\t56\t1568\t2\t2160\t6141600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t230\t1080\t2\t460\t105800\t2\t2160\t6141600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMLVLDBIIM\tPM LV LEAD BIPOLAR IMPEDANCE\t556\t1410\t2\t1112\t694322\t2\t2820\t6181200
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMLVLDUNIM\tPM LV LEAD UNIPOLAR IMPEDANCE\t345.5\t-420\t2\t691\t272281\t2\t-840\t381600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t230\t1080\t2\t460\t105800\t2\t2160\t6141600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t0\t90\t2\t0\t0\t2\t180\t104400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADALERT\tLEAD ALERT\t9999999\t-510\t2\t19999998\t2.00E+14\t2\t-1020\t738000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t3\t-150\t2\t6\t19.125\t2\t-300\t190800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.5\t-150\t2\t1\t0.52\t2\t-300\t190800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t0.625\t-150\t2\t1.25\t0.8125\t2\t-300\t190800
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.5\t-150\t2\t1\t0.52\t2\t-300\t190800
-HISTOCOMPATIBILITY ORDERABLE\t511033\t10085R\tHLA ORDER - TYPING ADD ON LOCUS\t10049R\tHLA LAB TESTING STATUS\t9999999\t348960\t2\t19999998\t2.00E+14\t2\t697920\t2.57E+11
-HISTOCOMPATIBILITY ORDERABLE\t511033\t10085R\tHLA ORDER - TYPING ADD ON LOCUS\tRINFO\tTO FIND RESULT:\t9999999\t348960\t2\t19999998\t2.00E+14\t2\t697920\t2.57E+11
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9218R\tPATIENT\'S SERUM 1 DESCRIPTION\t9999999\t43950\t2\t19999998\t2.00E+14\t2\t87900\t7726410000
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9226R\tPATIENT\'S SERUM 3 DESCRIPTION\t9999999\t149970\t2\t19999998\t2.00E+14\t2\t299940\t86644299600
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9227R\tPATIENT\'S SERUM 3 UNTREATED RESULT\t9999999\t236760\t2\t19999998\t2.00E+14\t2\t473520\t1.19E+11
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9228R\tPATIENT\'S SERUM 3 DTT TREATED RESULT\t9999999\t236760\t2\t19999998\t2.00E+14\t2\t473520\t1.19E+11
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9200R\tPATIENT\'S SERUM 3 DESCRIPTION\t9999999\t150000\t2\t19999998\t2.00E+14\t2\t300000\t86644980000
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9113R\tSERUM DESCRIPTION 3\t9999999\t771510\t2\t19999998\t2.00E+14\t2\t1543020\t2.38E+12
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9092R\tSERUM DESCRIPTION 3\t9999999\t771480\t2\t19999998\t2.00E+14\t2\t1542960\t2.38E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9332R\tSERUM DESCRIPTION\t9999999\t0\t2\t19999998\t2.00E+14\t2\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9349R\tIGG COMBINED CPRA FOR CURRENT SERUM\t9999999\t30\t2\t19999998\t2.00E+14\t2\t60\t3600
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9354R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t30\t2\t19999998\t2.00E+14\t2\t60\t3600
-HISTOCOMPATIBILITY ORDERABLE\t1458223\t10175R\tHLA ORDER - KIT PROCESSING RECIPIENT\t10049R\tHLA LAB TESTING STATUS\t9999999\t1408650\t2\t19999998\t2.00E+14\t2\t2817300\t3.97E+12
-LAB BLOOD ORDERABLES\t471508\tLABY21HYX\t21 - HYDROXYLASE AB, SERUM\t21OHXAB\t21-HYDROXYLASE AB\t5000436\t272430\t2\t10000872\t1.00E+14\t2\t544860\t1.65E+11
-LAB BLOOD ORDERABLES\t471527\tLABSMABY\tAUTOANTIBODIES TO SM\tSMAB\tAUTOANTIBODIES TO SM\t4999999.6\t160110\t2\t9999999.2\t1.00E+14\t2\t320220\t51646352400
-LAB BLOOD ORDERABLES\t471558\tLAB11140R\tMATERNAL SCREEN\tAFP\tAFP SCREEN TO ALLIED/STATE LAB\t9999999\t43800\t2\t19999998\t2.00E+14\t2\t87600\t4693860000
-LAB BLOOD ORDERABLES\t471578\tLABYA1APH\tALPHA - 1 - ANTITRYPSIN PHENOTYPE\tYA1A2\tALPHA-1-ANTITRYPSIN\t171.5\t398520\t2\t343\t77065\t2\t797040\t3.20E+11
-LAB BLOOD ORDERABLES\t471589\tLABYALUM\tALUMINUM\tALUM\tALUMINUM\t8.5\t201660\t2\t17\t149\t2\t403320\t98520091200
-LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE, SERUM\tYAMIOD\tAMIODARONE\t2.15\t263715\t2\t4.3\t9.29\t4\t1054860\t3.00E+11
-LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE, SERUM\tYDESET\tDESETHYLAMIODARONE\t1.15\t263715\t2\t2.3\t3.49\t4\t1054860\t3.00E+11
-LAB BLOOD ORDERABLES\t471625\tLAB11068R\tANGIOTENSIN CONVERTING ENZYME, SERUM\tACE\tANGIOTESIN CONVERTING ENZYME\t12.5\t227040\t2\t25\t317\t2\t454080\t1.03E+11
-LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABID2E\tANTIBODY ID ELUATE COMMENT\t9999999\t279561.4286\t2\t19999998\t2.00E+14\t14\t3913860\t8.62E+12
-LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tANTI-SCL70 ANTIBODY\tSCLQNT\tANTI-SCL70 LEVEL\t5000015\t549600\t2\t10000030\t1.00E+14\t2\t1099200\t8.22E+11
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBCMT\tCOMMENT\t9999999\t1276240\t2\t19999998\t2.00E+14\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBPRM\tPRIMERS\t9999999\t1276240\t2\t19999998\t2.00E+14\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRDR\tINTERPRETED BY\t9999999\t599442.5197\t2\t19999998\t2.00E+14\t127\t76129200\t5.86E+13
-LAB BLOOD ORDERABLES\t471764\tLABYBILEA\tBILE ACIDS TOTAL\tBILE\tBILE ACIDS, TOTAL\t76.5\t1304020\t2\t153\t18317\t3\t3912060\t1.21E+13
-LAB BLOOD ORDERABLES\t471778\tLABBLDCR\tBLOOD FOR CREATININE CLEARANCE\tBLDCR\tBLD FOR CREATININE CLEARANCE\t9999999\t10380\t2\t19999998\t2.00E+14\t2\t20760\t373197600
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMGQUL\tCMV IGG RESULT\t9999999\t221580\t2\t19999998\t2.00E+14\t2\t443160\t1.80E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMMQUL\tCMV IGM RESULT\t9999999\t221580\t2\t19999998\t2.00E+14\t2\t443160\t1.80E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHSAB\tVIRAL AB HSV\t9999999\t286934.6939\t2\t19999998\t2.00E+14\t49\t14059800\t4.87E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVGGQUL\tVZV IGG RESULT\t9999999\t221580\t2\t19999998\t2.00E+14\t2\t443160\t1.80E+11
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tGRANP\tGRAN, %\t1\t15870\t2\t2\t2\t2\t31740\t807771600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTPC\tNEUTROPHIL %\t87.7\t28380\t2\t175.4\t15388.36\t2\t56760\t1893117600
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTCL\tCLINICAL INDICATION\t9999999\t595140\t2\t19999998\t2.00E+14\t1\t595140\t3.54E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t200741.1086\t2\t19999998\t2.00E+14\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472127\tLABCYSI\tCYSTICERCOSIS, CSF AND SERUM\tCYSC\tCYSTICERCOSIS-CSF\t9999999\t1036644\t2\t19999998\t2.00E+14\t5\t5183220\t7.72E+12
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tEOS\tEOSINOPHILS\t3\t11460\t2\t6\t20\t1\t11460\t131331600
-LAB BLOOD ORDERABLES\t472192\tLABDATLA\tDIRECT ANTIGLOBULIN TEST - LARC\tREQS\tREQUEST SENT\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472276\tLABECHIG\tECHINOCOCCUS AB, IGG\tECHIND\tECHINOCOCCUS LGG\t9999999\t297896.7568\t2\t19999998\t2.00E+14\t37\t11022180\t4.39E+12
-LAB BLOOD ORDERABLES\t472401\tLABFLUOX\tFLUOXETINE AND NORFLUOXETINE\tFLUOXE\tFLUOXETINE\t5000062.5\t615480\t2\t10000125\t1.00E+14\t2\t1230960\t1.12E+12
-LAB BLOOD ORDERABLES\t472401\tLABFLUOX\tFLUOXETINE AND NORFLUOXETINE\tNORFLU\tNORFLUOXETINE\t5000061.5\t615480\t2\t10000123\t1.00E+14\t2\t1230960\t1.12E+12
-LAB BLOOD ORDERABLES\t472422\tLABYT7P\tFREE THYROXINE INDEX\tT4\tTHYROXINE, TOTAL\t5000003.9\t146604\t2\t10000007.8\t1.00E+14\t5\t733020\t1.21E+11
-LAB BLOOD ORDERABLES\t472422\tLABYT7P\tFREE THYROXINE INDEX\tYT7P2\tT-UPTAKE %\t36.5\t146604\t2\t73\t2749\t5\t733020\t1.21E+11
-LAB BLOOD ORDERABLES\t472437\tLABG6PDQT\tG6PD QUANTITATIVE\tG6PDMD\tREVIEWED BY\t9999999\t367382.7273\t2\t19999998\t2.00E+14\t22\t8082420\t4.34E+12
-LAB BLOOD ORDERABLES\t472471\tLABGTOL\tGLUCOSE TOLERANCE\tGLT2\tGLUCOSE,2HR - 75G\t134.5\t29820\t2\t269\t44501\t1\t29820\t889232400
-LAB BLOOD ORDERABLES\t472471\tLABGTOL\tGLUCOSE TOLERANCE\tGLUF\tGLUCOSE,FASTING\t86\t29820\t2\t172\t14954\t1\t29820\t889232400
-LAB BLOOD ORDERABLES\t472482\tLAB11080R\tGLUTAMIC ACID DECARBOXY AB\tGAD65AB\tGAD65 ANTIBODY\t0\t441240\t2\t0\t0\t1\t441240\t1.95E+11
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMAM\tAMENDMENT\t9999999\t486197.6471\t2\t19999998\t2.00E+14\t51\t24796080\t1.40E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMID\tSPECIMEN ID\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMIN\tINTERPRETATION\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMME\tMETHOD\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMOD\tORDER DATE\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMRD\tRELEASE DATE\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMRS\tRESULTS\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMRV\tREVIEWED BY:\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMSO\tSOURCE\t9999999\t486197.6471\t2\t19999998\t2.00E+14\t51\t24796080\t1.40E+13
-LAB BLOOD ORDERABLES\t472521\tLABHBCT\tHEPATITIS B CORE TOTAL AB\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t69780\t2\t19999998\t2.00E+14\t1\t69780\t4869248400
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBTINT\tINTERPRETATION\t9999999\t70359.66102\t2\t19999998\t2.00E+14\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSINT\tINTERPRETATION\t9999999\t56970.32432\t2\t19999998\t2.00E+14\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTINT\tINTERPRETATION\t9999999\t56970.32432\t2\t19999998\t2.00E+14\t1110\t63237060\t8.60E+12
-LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHEPATITIS B SURFACE AG\tHBSAG\tHEPATITIS B SURF AG\t9999999\t95670\t2\t19999998\t2.00E+14\t2\t191340\t18558622800
-LAB BLOOD ORDERABLES\t472553\tLABHIVWBL\tHIV (TYPE 1 AND TYPE 2) ABY\tHIVAB\tHIV 1/2 ANTIBODY\t9999999\t24300\t2\t19999998\t2.00E+14\t1\t24300\t590490000
-LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tANA\tANA RESULT\t9999999\t272940\t2\t19999998\t2.00E+14\t2\t545880\t1.64E+11
-LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tC3\tCOMPLEMENT C3, SERUM\t174\t272940\t2\t348\t60584\t2\t545880\t1.64E+11
-LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tDNAQUL\tANTI-DSDNA RESULT\t9999999\t272940\t2\t19999998\t2.00E+14\t2\t545880\t1.64E+11
-LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tANA\tANA RESULT\t9999999\t311100\t2\t19999998\t2.00E+14\t2\t622200\t2.23E+11
-LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tRHFR\tRHEUMATOID FACTOR\t9999999\t311100\t2\t19999998\t2.00E+14\t2\t622200\t2.23E+11
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKRE\tRESULT\t9999999\t525786.6667\t2\t19999998\t2.00E+14\t9\t4732080\t3.13E+12
-LAB BLOOD ORDERABLES\t472835\tLABYMERC\tMERCURY, BLOOD\tMER\tMERCURY, BLOOD\t1.5\t170700\t2\t3\t5\t2\t341400\t70482448800
-LAB BLOOD ORDERABLES\t472845\tLABYMETHS\tMETHSUXIMIDE\tNORMSX\tNOR-METHSUXIMIDE\t9999999\t1913310\t2\t19999998\t2.00E+14\t2\t3826620\t8.10E+12
-LAB BLOOD ORDERABLES\t472864\tLABMISCH\tMISCELLANEOUS HEMATOLOGY\tTESTH\tPROCEDURE\t9999999\t3060\t2\t19999998\t2.00E+14\t1\t3060\t9363600
-LAB BLOOD ORDERABLES\t472957\tLABYNICK\tNICKEL, SERUM\tNICKEL\tNICKEL\t3.25\t380700\t2\t6.5\t36.25\t2\t761400\t3.58E+11
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTREV\tPATHOLOGIST REVIEW\t9999999\t48930\t2\t19999998\t2.00E+14\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t473057\tLABYPANPP\tPANCREATIC POLYPEPTIDE\tYPANPP\tPANCREATIC POLYPEPTIDE\t5000039\t848130\t2\t10000078\t1.00E+14\t2\t1696260\t1.44E+12
-LAB BLOOD ORDERABLES\t473102\tLABYPHOLP\tPHOSPHOLIPIDS\tPHOLIP\tPHOSPHOLIPIDS\t186\t317670\t2\t372\t69642\t2\t635340\t2.46E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE14\tS.PNEUMO SEROTYPE 14 (14)\t4999999.95\t242580\t2\t9999999.9\t1.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE19\tS.PNEUMO SEROTYPE 19 (19F)\t5000000.4\t242580\t2\t10000000.8\t1.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE23\tS.PNEUMO SEROTYPE 23 (23F)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE26\tS.PNEUMO SEROTYPE 26 (6B)\t4999999.85\t242580\t2\t9999999.7\t1.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE4\tS.PNEUMO SEROTYPE 4 (4)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE56\tS.PNEUMO SEROTYPE 56 (18C)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE68\tS.PNEUMO SEROTYPE 68 (9V)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
-LAB BLOOD ORDERABLES\t473157\tLABPROPAF\tPROPAFENONE\tPROPAF\tPROPAFENONE\t1.06\t290820\t2\t2.12\t3.2272\t2\t581640\t1.80E+11
-LAB BLOOD ORDERABLES\t473159\tLAB11089R\tACID PHOSPHATASE\tYPAPT\tPAP, SERUM\t19.15\t164100\t2\t38.3\t1174.49\t1\t164100\t26928810000
-LAB BLOOD ORDERABLES\t473160\tLABYMPO\tMYELOPERIOXIDASE AB\tMPO\tMYELOPEROXIDASE AB\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473208\tLABYRAB\tRABIES AB TITER CHK ENDPOINT\tYRAB1\tRABIES ANTIBODY\t5000000.9\t1636335\t2\t10000001.8\t1.00E+14\t4\t6545340\t1.11E+13
-LAB BLOOD ORDERABLES\t473255\tLABRBP\tRETINOL BINDING PROTEIN\tRBPR\tRETINOL BIND PROT\t1.85\t1300620\t2\t3.7\t6.89\t2\t2601240\t4.84E+12
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTPRM\tPRIMERS\t9999999\t1027142.927\t2\t19999998\t2.00E+14\t82\t84225720\t1.01E+14
-LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tRESUL6\tRESULT\t9999999\t2836788\t2\t19999998\t2.00E+14\t5\t14183940\t1.37E+14
-LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHRE\tRESULT\t9999999\t1766844\t2\t19999998\t2.00E+14\t10\t17668440\t3.56E+13
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHRE\tRESULT\t9999999\t3358860\t2\t19999998\t2.00E+14\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473762\tLAB12586R\tVITAMIN B1\tYVB1W1\tTHIAMIN(VITAMIN B1)\t129.5\t322440\t2\t259\t33853\t2\t644880\t2.21E+11
-LAB BLOOD ORDERABLES\t473762\tLABYVB1W\tVITAMIN B1\tYVB1W2\tCOMMENT\t9999999\t290875.8621\t2\t19999998\t2.00E+14\t29\t8435400\t2.80E+12
-LAB BLOOD ORDERABLES\t497331\tLABPABN\tPOLIO AB, NEUTRALIZATION\tPOL2\tPOLIO 2\t9999999\t1740\t2\t19999998\t2.00E+14\t1\t1740\t3027600
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12013R\tQ FEVER IGM PHASE L TITER\t9999999\t172320\t2\t19999998\t2.00E+14\t3\t516960\t1.15E+11
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12014R\tQ FEVER IGG PHASE LI TITER\t9999999\t355434.7826\t2\t19999998\t2.00E+14\t23\t8175000\t3.97E+12
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN3\tY ENTERCOCLITICA 03\t9999999\t3364848\t2\t19999998\t2.00E+14\t15\t50472720\t4.07E+14
-LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH CORD\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH STILLBORN\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\tLTROIN\tINTERPRETATION\t9999999\t290967.0588\t2\t19999998\t2.00E+14\t153\t44517960\t3.58E+13
-LAB BLOOD ORDERABLES\t633373\tLABSTSS\tSYPHILIS TREPONEMAL SCREEN\tSTSQNT\tINDEX\t5.35\t255570\t2\t10.7\t85.37\t2\t511140\t1.34E+11
-LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMAG\tANTIGEN\t40.5\t200220\t2\t81\t3285\t1\t200220\t40088048400
-LAB BLOOD ORDERABLES\t668178\tLABDRPRS\tDONOR, RPR\tRPRS1\tDONOR SYPHILIS SCREEN\t9999999\t163960\t2\t19999998\t2.00E+14\t3\t491880\t91985486400
-LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tLYM\tLYMPHOCYTES %\t5000000.05\t99900\t2\t10000000.1\t1.00E+14\t1\t99900\t9980010000
-LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t11687R\tHEXOSAMINIDASE TOTAL, WBC\t21.45\t431070\t2\t42.9\t940.05\t2\t862140\t3.79E+11
-LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t11780R\tHEXOSAMINIDASE PERCENT A, WBC\t63\t431070\t2\t126\t7940\t2\t862140\t3.79E+11
-LAB BLOOD ORDERABLES\t744302\tLAB11590R\tFREE FATTY ACIDS\t11715R\tFREE FATTY ACIDS\t0.495\t1913190\t2\t0.99\t0.8685\t2\t3826380\t1.39E+13
-LAB BLOOD ORDERABLES\t744320\tLAB12043R\tIL 5\t12044R\tINTERLEUKIN 5\t50\t190080\t2\t100\t7048\t1\t190080\t36130406400
-LAB BLOOD ORDERABLES\t864658\tLAB11867R\tC3A DESARG FRAGMENT\t11866R\tC3A DESARG FRAGMENT\t5000096.5\t656340\t2\t10000193\t1.00E+14\t2\t1312680\t8.99E+11
-LAB BLOOD ORDERABLES\t864662\tLAB11871R\tHIV-2 ANTIBODY, IMMUNOBLOT\t11870R\tHIV 2 ANTIBODY, IMMUNOBLOT\t9999999\t488940\t2\t19999998\t2.00E+14\t3\t1466820\t8.05E+11
-LAB BLOOD ORDERABLES\t871368\tLABCIRCAI\tCIRCUIT IONIZED CALCIUM\tCAICR\tCIRCUIT IONIZED CALCIUM\t9999999\t2940\t2\t19999998\t2.00E+14\t2\t5880\t21434400
-LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLDX\tCLINICAL INDICATION\t9999999\t1418550\t2\t19999998\t2.00E+14\t2\t2837100\t4.13E+12
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD26AB\tCD26 (PAN-T), ABS\t729\t109830\t2\t1458\t1523682\t2\t219660\t24129939600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD26PC\tCD26 (PAN-T) %\t38\t109830\t2\t76\t2890\t2\t219660\t24129939600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD7AB\tCD7 (PAN T), ABS\t1066.5\t109830\t2\t2133\t3342105\t2\t219660\t24129939600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD7PC\tCD7 (PAN T) %\t54\t109830\t2\t108\t5864\t2\t219660\t24129939600
-LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLDX\tCLINICAL INDICATION\t9999999\t1484940\t2\t19999998\t2.00E+14\t2\t2969880\t4.71E+12
-LAB BLOOD ORDERABLES\t898828\tLAB12373R\tCARBAMAZEPINE-10,11-EPOXIDE\t12375R\tCARBAMAZEPINE-10,11-EPOXIDE\t4.3\t4063050\t2\t8.6\t39.86\t2\t8126100\t5.68E+13
-LAB BLOOD ORDERABLES\t898828\tLAB12373R\tCARBAMAZEPINE-10,11-EPOXIDE\tCZPTOT\tCARBAMAZEPINE,TOT,S\t11.15\t4063050\t2\t22.3\t254.09\t2\t8126100\t5.68E+13
-LAB BLOOD ORDERABLES\t910566\tLAB12395R\t5\'NUCLEOTIDASE\t12396R\t5\' NUCLEOTIDASE\t118.5\t240150\t2\t237\t38889\t2\t480300\t1.24E+11
-LAB BLOOD ORDERABLES\t977667\tLABPANANC\tNEUTROPHIL COUNT, ABSOLUTE\tNEUTAB\tNEUT, ABS\t0.21\t1050\t2\t0.42\t0.1682\t2\t2100\t3517200
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12671R\tTETANUS STIMULATION INDEX\t6.5\t1024890\t2\t13\t103.72\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12673R\tCANDIDA STIMULATION INDEX\t2.05\t1024890\t2\t4.1\t8.45\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tCONA\tCON A STIMULATION\t138613\t1024890\t2\t277226\t50520051700\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPHA\tPHA STIMULATION\t161883.5\t1024890\t2\t323767\t52655217625\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPWM\tPWM STIMULATION\t149659.5\t1024890\t2\t299319\t46479266145\t2\t2049780\t2.10E+12
-MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tSREQ\tCOMMENTS\t9999999\t255120\t2\t19999998\t2.00E+14\t1\t255120\t65086214400
-MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tRPT\tRPT\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tSDES\tSPECIMEN\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tCULT\tCULT/OTHER RSLT:\t9999999\t508887.5\t2\t19999998\t2.00E+14\t24\t12213300\t7.93E+12
-MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tRPT\tRPT\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tCULT\tCULT/OTHER RSLT:\t9999999\t334461.7699\t2\t19999998\t2.00E+14\t339\t113382540\t5.10E+13
-MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tCULT\tCULT/OTHER RSLT:\t9999999\t255332.1429\t2\t19999998\t2.00E+14\t56\t14298600\t7.49E+12
-MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tRPT\tRPT\t9999999\t48300\t2\t19999998\t2.00E+14\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tSDES\tSPECIMEN\t9999999\t48300\t2\t19999998\t2.00E+14\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tSREQ\tCOMMENTS\t9999999\t48300\t2\t19999998\t2.00E+14\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tRPT\tRPT\t9999999\t2140200\t2\t19999998\t2.00E+14\t1\t2140200\t4.58E+12
-MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tSDES\tSPECIMEN\t9999999\t2140200\t2\t19999998\t2.00E+14\t1\t2140200\t4.58E+12
-MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tSREQ\tCOMMENTS\t9999999\t2140200\t2\t19999998\t2.00E+14\t1\t2140200\t4.58E+12
-MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t259140\t2\t19999998\t2.00E+14\t14\t3627960\t1.12E+12
-MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tCULT\tCULT/OTHER RSLT:\t9999999\t1154535.525\t2\t19999998\t2.00E+14\t514\t593431260\t7.35E+14
-MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tCULT\tCULT/OTHER RSLT:\t9999999\t1137292.941\t2\t19999998\t2.00E+14\t51\t58001940\t7.40E+13
-MICROBIOLOGY ORDERABLES\t989708\tLABMECAPC\tMECA BY PCR\tMECAR\tMECA PCR RESULT\t9999999\t362940\t2\t19999998\t2.00E+14\t3\t1088820\t5.41E+11
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCCMT\tCOMMENT\t9999999\t599640\t2\t19999998\t2.00E+14\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCRLT\tRESULTS\t9999999\t599640\t2\t19999998\t2.00E+14\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tFP210\tP210, FLUID\t4999999.786\t417180\t2\t9999999.571\t1.00E+14\t1\t417180\t1.74E+11
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRC\tCOMMENT\t9999999\t1367340\t2\t19999998\t2.00E+14\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15CO\tCOMMENT, TISSUE\t9999999\t909120\t2\t19999998\t2.00E+14\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15TI\tSOURCE, TISSUE\t9999999\t909120\t2\t19999998\t2.00E+14\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTRES\tRESULTS\t9999999\t763860\t2\t19999998\t2.00E+14\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCCMT\tCOMMENT\t9999999\t1195020\t2\t19999998\t2.00E+14\t3\t3585060\t4.32E+12
-POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tDIPLOT\tDIPSTICK LOT\t9999999\t48060\t2\t19999998\t2.00E+14\t2\t96120\t5963220000
-POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tEXPDATE\tEXP DATE\t9999999\t48060\t2\t19999998\t2.00E+14\t2\t96120\t5963220000
-POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tDIPLOT\tDIPSTICK LOT\t9999999\t13600\t2\t19999998\t2.00E+14\t3\t40800\t1048500000
-POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tEXPDATE\tEXP DATE\t9999999\t13600\t2\t19999998\t2.00E+14\t3\t40800\t1048500000
-POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tUKET\tKETONES\t9999999\t13600\t2\t19999998\t2.00E+14\t3\t40800\t1048500000
-POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tGLUURINE\tGLUCOSE URINE\t9999999\t6000\t2\t19999998\t2.00E+14\t3\t18000\t140940000
-POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tLAC\tLACTATE, ISTAT\t2.41\t57480\t2\t4.82\t12.802\t2\t114960\t6607965600
-STOOL ORDERABLES\t471523\tLABACRBLO\tACETYLCHOLINE BLOCKING AB\tACRBLO\tACETYLCHOLINE BLOCKING AB\t20.5\t281370\t2\t41\t1681\t2\t562740\t1.70E+11
-STOOL ORDERABLES\t471524\tLABACRMOD\tACETYLCHOLINE MOD AB\tACRMOD\tACETYLCHOLINE MODULATING AB\t16.5\t680970\t2\t33\t1089\t2\t1361940\t9.48E+11
-STOOL ORDERABLES\t471567\tLABCSFALB\tALBUMIN, CSF\tALBCSF\tALBUMIN, CSF\t14\t#VALUE!\t2\t28\t392\t0\tNone\tNone
-STOOL ORDERABLES\t471585\tLABYSTA1A\tALPHA-1-ANTITRYPSIN, FECAL\tA1AANTITRYPF\tA-1-ANTITRYP,FECAL\t5000047.5\t198600\t2\t10000095\t1.00E+14\t1\t198600\t39441960000
-STOOL ORDERABLES\t472122\tLABYCYSTC\tCYSTATIN C, SERUM / PLASMA\tCYSTC\tCYSTATIN C, SER/PLAS\t1.4\t32760\t2\t2.8\t4.42\t2\t65520\t2275718400
-STOOL ORDERABLES\t472544\tLABYHISTA\tHISTAMINE, PLASMA\tYHISA1\tHISTAMINE, PLASMA\t4999999.7\t307500\t2\t9999999.4\t1.00E+14\t2\t615000\t2.03E+11
-STOOL ORDERABLES\t472671\tLAB11022R\tISLET CELL AB\tANTIIS\tISLET CELL AB, IGG\t9999999\t181860\t2\t19999998\t2.00E+14\t2\t363720\t66205742400
-STOOL ORDERABLES\t473218\tLAB13006R\tREDUCING SUBSTANCE, STOOL\tREDSUBSTL\tREDUCING SUBST, STOOL\t9999999\t16440\t2\t19999998\t2.00E+14\t1\t16440\t270273600
-STOOL ORDERABLES\t473777\tLABVWFPI\tVON WILLEBRAND PROTEASE INHIBITOR\tVWFPIR\tRESULT\t9999999\t1187392.5\t2\t19999998\t2.00E+14\t8\t9499140\t2.35E+13
-STOOL ORDERABLES\t864666\tLAB11851R\tFECAL LIPIDS, TOTAL\t11850R\tFECAL LIPIDS, 24 HR\t9999999\t650380\t2\t19999998\t2.00E+14\t6\t3902280\t4.51E+12
-STOOL ORDERABLES\t864672\tLAB11885R\tC. TRACHOMATIS RNA, RECTAL\t11884R\tCHLAMYDIA TRACHOMATIS RNA,RECTAL\t9999999\t152760\t2\t19999998\t2.00E+14\t2\t305520\t93305815200
-STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12552R\tSIROLIMUS, BLOOD\t6.2\t512835\t2\t12.4\t110.5\t4\t2051340\t2.27E+12
-STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12553R\tDATE OF LAST DOSE\t9999999\t363510\t2\t19999998\t2.00E+14\t2\t727020\t5.19E+11
-STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12554R\tTIME OF LAST DOSE\t9999999\t363510\t2\t19999998\t2.00E+14\t2\t727020\t5.19E+11
-STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12555R\tDOSE, MG\t9999999\t363510\t2\t19999998\t2.00E+14\t2\t727020\t5.19E+11
-STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tSPTYS2\tSPTYS2\t9999999\t427020\t2\t19999998\t2.00E+14\t2\t854040\t3.80E+11
-STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tTESTS2\tTESTS2\t9999999\t427020\t2\t19999998\t2.00E+14\t2\t854040\t3.80E+11
-STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL3\tCOLLECTION DURATION\t5000011.5\t1815000\t2\t10000023\t1.00E+14\t2\t3630000\t6.65E+12
-STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL4\tCHLORIDE CONCENTRATION\t5000009\t1815000\t2\t10000018\t1.00E+14\t2\t3630000\t6.65E+12
-STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA1\tSODIUM, 24-HR STOOL\t5000023\t173370\t2\t10000046\t1.00E+14\t6\t1040220\t3.08E+11
-STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA2\tTOTAL WEIGHT\t5000377.5\t173370\t2\t10000755\t1.00E+14\t6\t1040220\t3.08E+11
-TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tUPIEI\tINTERPRETATION\t9999999\t309620\t2\t19999998\t2.00E+14\t18\t5573160\t2.51E+12
-URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUALBCRINTP\tALB/CREAT INTERP\t9999999\t65933.15436\t2\t19999998\t2.00E+14\t149\t9824040\t1.29E+12
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUOAGC\tOTHER AMPHETAMINES\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUSYGC\tSYMPATHOMIMETICS\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tVOLUMEURN\tURINE VOLUME\t2159\t150840\t2\t4318\t15136612\t1\t150840\t22752705600
-URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tYUARS1\tINORGANIC ARSENIC (TOXIC)\t9999999\t150840\t2\t19999998\t2.00E+14\t1\t150840\t22752705600
-URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tYUARS2\tORGANIC ARSENIC(NON-TOXIC)\t5000007\t150840\t2\t10000014\t1.00E+14\t1\t150840\t22752705600
-URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tYUARS3\tCOLLECTION DURATION\t24\t150840\t2\t48\t1152\t1\t150840\t22752705600
-URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tARSENICURN\tARSENIC, URINE\t9999999\t346860\t2\t19999998\t2.00E+14\t1\t346860\t1.20E+11
-URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tUCR\tCREATININE, URN\t49.5\t247740\t2\t99\t4941\t1\t247740\t61375107600
-URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tUT\tCOLLECTION PERIOD\t764.5\t247740\t2\t1529\t2265601\t1\t247740\t61375107600
-URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tVOL\tVOLUME\t2586\t247740\t2\t5172\t26502480\t1\t247740\t61375107600
-URINE ORDERABLES\t472592\tLABICTO\tICTOTEST\tICTO\tICTOTEST\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t472740\tLABLEGU\tLEGIONELLA AG, URINE\tLEGUA\tLEGIONELLA AG\t9999999\t376259.6691\t2\t19999998\t2.00E+14\t544\t204685260\t1.13E+14
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tVOLUMEURN\tURINE VOLUME\t1832\t191130\t2\t3664\t8888146\t2\t382260\t80196699600
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tYOXALU\tOXALATE, 24-HOUR/TIMED URINE\t0.285\t191130\t2\t0.57\t0.3029\t2\t382260\t80196699600
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tYUOXCO\tOXALATE CONCENTRATION\t9.65\t191130\t2\t19.3\t285.65\t2\t382260\t80196699600
-URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tYUOXUT\tCOLLECTION DURATION\t24\t191130\t2\t48\t1152\t2\t382260\t80196699600
-URINE ORDERABLES\t473424\tLABYUNTX\tTELOPEPTIDE, URINE\tYUNTXC\tCREATININE, URINE\t5000028\t123120\t2\t10000056\t1.00E+14\t1\t123120\t15158534400
-URINE ORDERABLES\t473424\tLABYUNTX\tTELOPEPTIDE, URINE\tYUNTXR\tNTX\t5000162\t123120\t2\t10000324\t1.00E+14\t1\t123120\t15158534400
-URINE ORDERABLES\t473424\tLABYUNTX\tTELOPEPTIDE, URINE\tYUNTXT\tNTX-TELOPEPTIDE, URINE\t5000031.5\t123120\t2\t10000063\t1.00E+14\t1\t123120\t15158534400
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tRBCAST\tRBC CASTS\t9999999\t15150\t2\t19999998\t2.00E+14\t2\t30300\t482029200
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tTRICHOMONAS\tTRICHOMONAS\t9999999\t18300\t2\t19999998\t2.00E+14\t2\t36600\t1234260000
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tHVAUR\tHVA, URINE\t4.5\t335730\t2\t9\t41\t2\t671460\t2.26E+11
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tDOPAURN\tDOPAMINE, URN\t5000061\t242040\t2\t10000122\t1.00E+14\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tEPINEPURN\tEPINEPHRINE, URN\t5000003\t242040\t2\t10000006\t1.00E+14\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tNOREPHURN\tNOREPINEPHRINE, URN\t5000009.5\t242040\t2\t10000019\t1.00E+14\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tTIMEU\tCOLLECTION PERIOD\t5001117.5\t242040\t2\t10002235\t1.00E+14\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUCR\tCREATININE, URINE\t5000681.5\t242040\t2\t10001363\t1.00E+14\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUCR\tCREATININE, URN\t38\t242040\t2\t76\t3946\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUDOPA1\tDOPAMINE/CREAT. RATIO\t81.5\t242040\t2\t163\t13429\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUEPI1\tEPINEPHRINE/CREAT. RATIO\t5000002\t242040\t2\t10000004\t1.00E+14\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUNORE1\tNOREPINEPHRINE/CREAT. RATIO\t74\t242040\t2\t148\t17914\t1\t242040\t58583361600
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUVOL\tVOLUME\t1157\t242040\t2\t2314\t5005780\t1\t242040\t58583361600
-VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tDVE\tRESULTS\t9999999\t68786.73267\t2\t19999998\t2.00E+14\t101\t6947460\t5.79E+11
-VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS ANTIGEN, STOOL\tRTVTD\tTEST DESCRIPTION\t9999999\t54835.2\t2\t19999998\t2.00E+14\t75\t4112640\t7.01E+11
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t9999999\t232692.1875\t2\t19999998\t2.00E+14\t64\t14892300\t3.97E+12
-VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t11166R\tEB NUCLEAR AB\t9999999\t232692.1875\t2\t19999998\t2.00E+14\t64\t14892300\t3.97E+12
-VIROLOGY ORDERABLES\t472519\tLABHAVT\tANTI-HAV\tHATINT\tINTERPRETATION\t9999999\t76703.94453\t2\t19999998\t2.00E+14\t649\t49780860\t1.16E+13
-VIROLOGY ORDERABLES\t472519\tLABHAVT\tHEPATITIS A AB TOTAL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t68880\t2\t19999998\t2.00E+14\t1\t68880\t4744454400
-VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHERPES VIRAL 6 DNA QUALITATIVE\tSOUR12\tSOURCE\t9999999\t184905\t2\t19999998\t2.00E+14\t4\t739620\t1.80E+11
-VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVTIP\tTEST DESCRIPTION\t9999999\t161340\t2\t19999998\t2.00E+14\t4\t645360\t1.50E+11
-VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6CP\tRESULT\t5000514.5\t1350\t2\t10001029\t1.00E+14\t2\t2700\t7290000
-VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t1350\t2\t19999998\t2.00E+14\t2\t2700\t7290000
-VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6LO\tRESULT LOG 10\t5000001.005\t1350\t2\t10000002.01\t1.00E+14\t2\t2700\t7290000
-VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6SP\tSPECIMEN\t9999999\t1350\t2\t19999998\t2.00E+14\t2\t2700\t7290000
-VIROLOGY ORDERABLES\t670957\tLAB11023R\tEB NUCLEAR AG AB\t11166R\tEB NUCLEAR AB\t9999999\t275265\t2\t19999998\t2.00E+14\t4\t1101060\t4.37E+11
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN IGG\tEBNQNT\tEBNQNT\t5000001.075\t687360\t2\t10000002.15\t1.00E+14\t2\t1374720\t9.65E+11
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUAR\tINFLUENZA A RNA\t9999999\t163609.7143\t2\t19999998\t2.00E+14\t35\t5726340\t1.13E+12
-VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tCULT\tCULT/OTHER RSLT:\t9999999\t925479.3103\t2\t19999998\t2.00E+14\t87\t80516700\t1.11E+14
-VIROLOGY ORDERABLES\t876731\tLABDVEMP\tMPV DFA\tMPVR\tMETAPNEUMOVIRUS\t9999999\t987120\t2\t19999998\t2.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876737\tLABTOXIM\tTOXOPLASMA IGM\tTOXMQT\tIGM LEVEL\t9999999\t178200\t2\t19999998\t2.00E+14\t2\t356400\t82080979200
-VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVDI\tDISCLAIMER\t9999999\t267440\t2\t19999998\t2.00E+14\t3\t802320\t2.15E+11
-VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPV16\tHPV 16 DNA\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPV18\tHPV 18 DNA\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVSP\tSPECIMEN\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t893882\tLABCMVAV\tCMV IGG AVIDITY\tCMVAVI\tCMV IGG AVIDITY INDEX\t9999999\t289530\t2\t19999998\t2.00E+14\t2\t579060\t2.76E+11
-VIROLOGY ORDERABLES\t893882\tLABCMVAV\tCMV IGG AVIDITY\tCMVAVR\tCMV IGG AVIDITY RESULT\t9999999\t289530\t2\t19999998\t2.00E+14\t2\t579060\t2.76E+11
-VIROLOGY ORDERABLES\t898804\tLABEBVQL\tEBV DNA PCR QUAL\tEBVQLI\tTEST DESCRIPTION\t9999999\t233724\t2\t19999998\t2.00E+14\t25\t5843100\t1.92E+12
-VIROLOGY ORDERABLES\t906947\tLABADVRC\tADENOVIRUS RAPID CULTURE\tADVRCD\tTEST DESCRIPTION\t9999999\t236460\t2\t19999998\t2.00E+14\t8\t1891680\t5.77E+11
-VIROLOGY ORDERABLES\t906951\tLABMEARC\tMEASLES RAPID CULTURE\tMEARC1\tMEASLES RAPID CULTURE\t9999999\t124470\t2\t19999998\t2.00E+14\t2\t248940\t34398594000
-VIROLOGY ORDERABLES\t906961\tLABVZVCSF\tVZV DNA PCR QUAL, CSF\tVZQLIC\tTEST DESCRIPTION\t9999999\t175368.96\t2\t19999998\t2.00E+14\t125\t21921120\t5.25E+12
-VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHSVDS\tHSV PANEL TEST DESCRIPTION\t9999999\t262166.8966\t2\t19999998\t2.00E+14\t145\t38014200\t1.36E+13
-VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVGGDES\tVZV IGG TEST DESCRIPTION\t9999999\t299835.8824\t2\t19999998\t2.00E+14\t34\t10194420\t3.75E+12
-VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVLOG\tRESULT LOG 10\t7.43\t425910\t2\t14.86\t110.4386\t2\t851820\t3.70E+11
-VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVRES\tRESULT\t9999999\t425910\t2\t19999998\t2.00E+14\t2\t851820\t3.70E+11
-VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVRNA\tHEP C VIRVUS RNA\t9999999\t425910\t2\t19999998\t2.00E+14\t2\t851820\t3.70E+11
-VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVSP\tSPECIMEN\t9999999\t425910\t2\t19999998\t2.00E+14\t2\t851820\t3.70E+11
-AMB PROC ORDERABLES\t667882\t210951798\tPOST VOID TEST\tNone\tRESIDUAL BLADDER VOLUME\t9999999\t62100\t1\t9999999\t1.00E+14\t1\t62100\t3856410000
-BLOOD BANK PRODUCT ORDERABLES\t473476\tLABTBP99\tTRANSFUSION BLOOD PRODUCT 100\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473476\tLABTBP99\tTRANSFUSION BLOOD PRODUCT 100\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473477\tLABTBP100\tTRANSFUSION BLOOD PRODUCT 101\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473477\tLABTBP100\tTRANSFUSION BLOOD PRODUCT 101\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tVL\tPRODUCT VOLUME\t174\t#VALUE!\t1\t174\t30276\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tVL\tPRODUCT VOLUME\t166\t#VALUE!\t1\t166\t27556\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tVL\tPRODUCT VOLUME\t153\t#VALUE!\t1\t153\t23409\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tVL\tPRODUCT VOLUME\t166\t#VALUE!\t1\t166\t27556\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tVL\tPRODUCT VOLUME\t165\t#VALUE!\t1\t165\t27225\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tVL\tPRODUCT VOLUME\t169\t#VALUE!\t1\t169\t28561\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tVL\tPRODUCT VOLUME\t97\t#VALUE!\t1\t97\t9409\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tVL\tPRODUCT VOLUME\t180\t#VALUE!\t1\t180\t32400\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tVL\tPRODUCT VOLUME\t172\t#VALUE!\t1\t172\t29584\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tVL\tPRODUCT VOLUME\t156\t#VALUE!\t1\t156\t24336\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tVL\tPRODUCT VOLUME\t165\t#VALUE!\t1\t165\t27225\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tVL\tPRODUCT VOLUME\t162\t#VALUE!\t1\t162\t26244\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tVL\tPRODUCT VOLUME\t146\t#VALUE!\t1\t146\t21316\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tVL\tPRODUCT VOLUME\t155\t#VALUE!\t1\t155\t24025\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tVL\tPRODUCT VOLUME\t157\t#VALUE!\t1\t157\t24649\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tVL\tPRODUCT VOLUME\t146\t#VALUE!\t1\t146\t21316\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tVL\tPRODUCT VOLUME\t160\t#VALUE!\t1\t160\t25600\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tVL\tPRODUCT VOLUME\t449\t#VALUE!\t1\t449\t201601\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tSI\t<<STATUS>>  \t9999999\t49950\t1\t9999999\t1.00E+14\t2\t99900\t5482357200
-BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473667\tLABTBP95\tTRANSFUSION BLOOD PRODUCT 96\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473667\tLABTBP95\tTRANSFUSION BLOOD PRODUCT 96\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473668\tLABTBP96\tTRANSFUSION BLOOD PRODUCT 97\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473668\tLABTBP96\tTRANSFUSION BLOOD PRODUCT 97\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473669\tLABTBP97\tTRANSFUSION BLOOD PRODUCT 98\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473669\tLABTBP97\tTRANSFUSION BLOOD PRODUCT 98\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473670\tLABTBP98\tTRANSFUSION BLOOD PRODUCT 99\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473670\tLABTBP98\tTRANSFUSION BLOOD PRODUCT 99\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t496757\tLABEXCHTX\tEXCHANGE TRANSFUSION\tFFP\tFRESH FROZEN UNITS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t496757\tLABEXCHTX\tEXCHANGE TRANSFUSION\tRBCSTA\tRBC PRODUCT - UNITS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tVL\tPRODUCT VOLUME\t165\t#VALUE!\t1\t165\t27225\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRD\tINTERPRETED BY\t9999999\t496642.1053\t1\t9999999\t1.00E+14\t19\t9436200\t6.91E+12
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tBANDFLD\tBAND, FLD\t3\t#VALUE!\t1\t3\t9\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFPMN\tPMN, FLD\t11\t#VALUE!\t1\t11\t121\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472012\tLAB11438R\tCHLAMYDIA TRACHOMATIS CULTURE\t11502R\tCHLAMYDIA TRACHOMATIS CULTURE\t9999999\t122600\t1\t9999999\t1.00E+14\t3\t367800\t93376756800
-BODY FLUID ORDERABLES\t472047\tLAB11123R\tCOCCI CF AB, CSF\tCOCC\tCOCCI CF AB (CSF)\t9999999\t768057.9747\t1\t9999999\t1.00E+14\t237\t182029740\t4.44E+14
-BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tCCNT\tCELLS COUNTED\t39\t#VALUE!\t1\t39\t1521\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSNRBC\tNRBC/100WBC, SYN FLD\t14\t780\t1\t14\t196\t1\t780\t608400
-BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSRLYM\tREACT LYMPH, SYN FLD\t2\t780\t1\t2\t4\t1\t780\t608400
-BODY FLUID ORDERABLES\t472774\tLABLWBCC\tLYME WESTERN BLOT, CSF\tBANDPRSNT\tBANDS PRESENT\t9999999\t408240\t1\t9999999\t1.00E+14\t2\t816480\t3.43E+11
-BODY FLUID ORDERABLES\t472774\tLABLWBCC\tLYME WESTERN BLOT, CSF\tBBIGM\tBORELLIA BURG IGM AB\t9999999\t277031.25\t1\t9999999\t1.00E+14\t16\t4432500\t1.39E+12
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIMD\tINTERPRETED BY\t9999999\t927889.0909\t1\t9999999\t1.00E+14\t11\t10206780\t1.25E+13
-BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\tYMYCR1\tSTATUS\t9999999\t2142018\t1\t9999999\t1.00E+14\t10\t21420180\t5.46E+13
-BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\tYMYCR2\tMYCOPLASMA PNEUMONIAE CULTURE\t9999999\t2142018\t1\t9999999\t1.00E+14\t10\t21420180\t5.46E+13
-BODY FLUID ORDERABLES\t472935\tLABYCMBP\tMYELIN BASIC PROTEIN, CSF\tMBPCSF\tMYELIN BAS.PROT,CSF\t0.9\t#VALUE!\t1\t0.9\t0.81\t0\tNone\tNone
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHQLINT\tINTERPRETATION\t9999999\t359730\t1\t9999999\t1.00E+14\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473219\tLABURED\tREDUCING SUBSTANCE, URINE\tREDSUBURN\tREDUCING SUBST., URINE\t9999999\t7800\t1\t9999999\t1.00E+14\t1\t7800\t60840000
-BODY FLUID ORDERABLES\t473359\tLABPOLYSP\tSPUTUM FOR PMNS\tPOLYSP\tSPUTUM FOR PMNS\t9999999\t14460\t1\t9999999\t1.00E+14\t1\t14460\t209091600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t12001R\tVCA IGG ,IFA\t9999999\t268440\t1\t9999999\t1.00E+14\t1\t268440\t72060033600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t12002R\tVCA IGM ,IFA\t9999999\t268440\t1\t9999999\t1.00E+14\t1\t268440\t72060033600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tCOM\tCOMMENT\t9999999\t550720\t1\t9999999\t1.00E+14\t3\t1652160\t1.46E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tEA\tEARLY ANTIBODY (R+D) IGG\t9999999\t691860\t1\t9999999\t1.00E+14\t2\t1383720\t1.39E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tEBNA\tEPSTEIN-BARR NUC AB\t9999999\t691860\t1\t9999999\t1.00E+14\t2\t1383720\t1.39E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t9999999\t550720\t1\t9999999\t1.00E+14\t3\t1652160\t1.46E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\t1STCONST\t1ST CONSTITUENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\t2NDCONST\t2ND CONSTITUENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\t3RDCONST\t3RD CONSTITUENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNCO\tSTONE COMMENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNNJ\tNIDUS, MAJOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNNM\tNIDUS, MINOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNSJ\tSHELL, MAJOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNSM\tSHELL, MINOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTON\tSTONE ANALYSIS\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14RS\tRESULTS\t9999999\t1070614.286\t1\t9999999\t1.00E+14\t7\t7494300\t9.42E+12
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTDIR\tINTERPRETED BY\t9999999\t771152.3077\t1\t9999999\t1.00E+14\t13\t10024980\t1.13E+13
-BODY FLUID ORDERABLES\t473748\tLABYVDRLC\tVDRL, CSF\tVDRL\tVDRL\t9999999\t248982\t1\t9999999\t1.00E+14\t90\t22408380\t3.08E+13
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tALB\tALBUMIN, SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tALT\tALT (SGPT), SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tAST\tAST (SGOT), SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tDBIL\tCONJUGATED BILI\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tIBIL\tUNCONJUGATED BILIRUBIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tTBIL\tTOTAL BILIRUBIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t497333\tLABMYCOC\tMYCOPLASMA CULTURE, FASTIDIOUS\tMYORG\tMYCOPLASAM ORGANISM\t9999999\t1967535\t1\t9999999\t1.00E+14\t4\t7870140\t2.00E+13
-BODY FLUID ORDERABLES\t497362\tLABYFK\tPOTASSIUM, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t497366\tLABYFNA\tSODIUM, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-BODY FLUID ORDERABLES\t497368\tLABYFGL\tGLUCOSE, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t178578.6207\t1\t9999999\t1.00E+14\t58\t10357560\t2.39E+12
-BODY FLUID ORDERABLES\t497376\tLABYFCL\tCHLORIDE, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t242700\t1\t9999999\t1.00E+14\t1\t242700\t58903290000
-BODY FLUID ORDERABLES\t497380\tLABYFUN\tUREA NITROGEN, FLUID\t11847R\tFLUID TYPE\t9999999\t169644\t1\t9999999\t1.00E+14\t10\t1696440\t3.58E+11
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASMD\tINTERPRETED BY\t9999999\t797353.8462\t1\t9999999\t1.00E+14\t13\t10365600\t1.05E+13
-BODY FLUID ORDERABLES\t669983\tLAB11000R\tCEA, PANCREATIC CYST\t11002R\tSITE\t9999999\t174390\t1\t9999999\t1.00E+14\t14\t2441460\t4.88E+11
-BODY FLUID ORDERABLES\t744304\tLAB11591R\tARSENIC, HAIR\t12046R\tARSENIC, HAIR\t9999999\t206940\t1\t9999999\t1.00E+14\t1\t206940\t42824163600
-BODY FLUID ORDERABLES\t744304\tLAB11591R\tARSENIC, HAIR\t12047R\tSPECIMEN SOURCE\t9999999\t206940\t1\t9999999\t1.00E+14\t1\t206940\t42824163600
-BODY FLUID ORDERABLES\t744306\tLAB11592R\tCOPPER, TISSUE\t11720R\tCOPPER, LIVER TS\t57\t241800\t1\t57\t3249\t1\t241800\t58467240000
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tBANDFLD\tBAND, FLD\t7\t14820\t1\t7\t49\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tBASOFLD\tBASOPHILS, FLD\t1\t14820\t1\t1\t1\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFCMT\tFLUID COMMENT\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFCOLOR\tAPPEARANCE\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFLDTYP\tFLUID SOURCE\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFMETA\tMETAMYELOCYTE, FLD\t5\t14820\t1\t5\t25\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFPMN\tPMN, FLD\t16\t14820\t1\t16\t256\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFRLYM\tREACTIVE LYMPHS, FLD\t1\t14820\t1\t1\t1\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFTURB\tTURBIDITY, FLD\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tLYMFLD\tLYMPHOCYTE %, FLD\t52\t14820\t1\t52\t2704\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tMONFLD\tMONO/MESO %, FLD\t18\t14820\t1\t18\t324\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tRBCFLD\tRBC COUNT, FLD (/UL)\t147100\t14820\t1\t147100\t21638410000\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tWBCFLD\tNUCL CELLS, FLD(/UL)\t42900\t14820\t1\t42900\t1840410000\t1\t14820\t219632400
-BODY FLUID ORDERABLES\t892281\tLAB13002R\tCOCCIDIOIDES AB IMMUNODIFF CSF\t13001R\tCOCCIDIOIDES AB IMMUNODIFF CSF\t9999999\t1926281.25\t1\t9999999\t1.00E+14\t16\t30820500\t1.70E+14
-COAGULATION ORDERABLES\t472663\tLABVWNTRP\tINTERPRETATION, VW SCREEN\tVWINTP\tINTERPRETATION\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tECGIMPRESS\tECG IMPRESSION\t9999999\t-5950.445219\t1\t9999999\t1.00E+14\t118728\t-706484460\t1.34E+19
-ECHO ORDERABLES\t472301\tECH15\tECHO - TRANSTHORACIC ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODBP\t9999999\t37937160\t1\t9999999\t1.00E+14\t1\t37937160\t1.44E+15
-ECHO ORDERABLES\t30871485\tECH48\tECHO - TRANSESOPHAGEAL ECHO COMPREHENSIVE\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t55\t288240\t1\t55\t3025\t1\t288240\t83082297600
-EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t250\t-600\t1\t250\t62500\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t1.9\t-600\t1\t1.9\t3.61\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.4\t-600\t1\t0.4\t0.16\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t2.5\t-600\t1\t2.5\t6.25\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t0.8\t-600\t1\t0.8\t0.64\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.4\t-600\t1\t0.4\t0.16\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMCOMMENT\tPM COMMENTARY\t9999999\t-600\t1\t9999999\t1.00E+14\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMLVLDBIIM\tPM LV LEAD BIPOLAR IMPEDANCE\t701\t-600\t1\t701\t491401\t1\t-600\t360000
-EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t225435\t120\t1\t225435\t50820939225\t1\t120\t14400
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
-EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRSENM\tLV THRESHOLD SENSING AMPLITUDE\t19\t1140\t1\t19\t361\t1\t1140\t1299600
-EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRSENPO\tLV THRESHOLD SENSING POLARITY\t9999999\t1140\t1\t9999999\t1.00E+14\t1\t1140\t1299600
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tAFIBTAQRSM\tAFIB TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tAFLTAQRSMC\tAFLUTTER TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tAFLTAZNRTC\tAFLUTTER TACHY ZONE RATE STABILITY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tALERT\tALERT\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tATACHTAQRS\tATACH TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t120\t300\t1\t120\t14400\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t130\t300\t1\t130\t16900\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t300\t1\t0\t0\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t300\t1\t0\t0\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t300\t1\t0\t0\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t48\t300\t1\t48\t2304\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t300\t1\t0\t0\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t300\t1\t0\t0\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t451\t300\t1\t451\t203401\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t768\t300\t1\t768\t589824\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.2\t300\t1\t2.2\t4.84\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.5\t300\t1\t0.5\t0.25\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t400\t300\t1\t400\t160000\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t310\t300\t1\t310\t96100\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2\t300\t1\t2\t4\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.5\t300\t1\t0.5\t0.25\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.7\t300\t1\t0.7\t0.49\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.5\t300\t1\t0.5\t0.25\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t3.1\t300\t1\t3.1\t9.61\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSLWVTTZNT\tVSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSWVTTAQRS\tVSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTDETD\tFASTVT TACHY ZONE DETECTION DURATION\t30\t-360\t1\t30\t900\t1\t-360\t129600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTZNSC\tFASTVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t-360\t1\t100\t10000\t1\t-360\t129600
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t0\t120\t1\t0\t0\t1\t120\t14400
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDEVALCOM\tICD EVAL COMMENTARY\t9999999\t2700\t1\t9999999\t1.00E+14\t1\t2700\t7290000
-EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t-360\t1\t100\t10000\t1\t-360\t129600
-EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTZNSC\tFASTVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t480\t1\t100\t10000\t1\t480\t230400
-EXTERNAL RESULTS\t890349\tEXTINRM\tINR (MANUAL ENTRY)\tINR\tINR (MANUAL ENTRY)\t1.4\t41040\t1\t1.4\t1.96\t1\t41040\t1684281600
-EXTERNAL RESULTS\t890349\tEXTINRM\tINR (MANUAL ENTRY)\tPT\tPROTHROMBIN TIME\t15.8\t41040\t1\t15.8\t249.64\t1\t41040\t1684281600
-HISTOCOMPATIBILITY ORDERABLE\t481589\t10285R\tHLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)\t10049R\tHLA LAB TESTING STATUS\t9999999\t130260\t1\t9999999\t1.00E+14\t1\t130260\t16967667600
-HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\t10049R\tHLA LAB TESTING STATUS\t9999999\t2244840\t1\t9999999\t1.00E+14\t1\t2244840\t5.04E+12
-HISTOCOMPATIBILITY ORDERABLE\t706283\t10000R\tHLA ORDER - TYPING - SOLID ORGAN EVALUATION\tRINFO\tTO FIND RESULT:\t9999999\t736702.1918\t1\t9999999\t1.00E+14\t73\t53779260\t6.97E+13
-HISTOCOMPATIBILITY ORDERABLE\t718854\t10205R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\tRINFO\tTO FIND RESULT:\t9999999\t1896726\t1\t9999999\t1.00E+14\t10\t18967260\t5.63E+13
-HISTOCOMPATIBILITY ORDERABLE\t890780\t10010R\tHLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t170820\t1\t9999999\t1.00E+14\t1\t170820\t29179472400
-HISTOCOMPATIBILITY ORDERABLE\t890832\t10040R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)\t10049R\tHLA LAB TESTING STATUS\t9999999\t235080\t1\t9999999\t1.00E+14\t2\t470160\t1.15E+11
-HISTOCOMPATIBILITY ORDERABLE\t890832\t10040R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)\tRINFO\tTO FIND RESULT:\t9999999\t235080\t1\t9999999\t1.00E+14\t2\t470160\t1.15E+11
-HISTOCOMPATIBILITY ORDERABLE\t892287\t10297R\tHLA ORDER - DECEASED DNR O.R. SPMN\t10049R\tHLA LAB TESTING STATUS\t9999999\t594360\t1\t9999999\t1.00E+14\t1\t594360\t3.53E+11
-HISTOCOMPATIBILITY ORDERABLE\t892287\t10297R\tHLA ORDER - DECEASED DNR O.R. SPMN\tRINFO\tTO FIND RESULT:\t9999999\t594360\t1\t9999999\t1.00E+14\t1\t594360\t3.53E+11
-HISTOCOMPATIBILITY ORDERABLE\t895898\t10120R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t202080\t1\t9999999\t1.00E+14\t1\t202080\t40836326400
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9099R\tINTERPRETATION/ COMMENTS\t9999999\t60\t1\t9999999\t1.00E+14\t1\t60\t3600
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9035R\tMICA ALLELE 1\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9036R\tMICA ALLELE 2\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9342R\tDSA FOR CLASS II\t9999999\t68280\t1\t9999999\t1.00E+14\t1\t68280\t4662158400
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9377R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t33166.66667\t1\t9999999\t1.00E+14\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9391R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t59700\t1\t9999999\t1.00E+14\t5\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9404R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t59700\t1\t9999999\t1.00E+14\t5\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9347R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t42651.42857\t1\t9999999\t1.00E+14\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9361R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t298380\t1\t9999999\t1.00E+14\t1\t298380\t89030624400
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9367R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9374R\tINTERPRETATION/ COMMENTS  CLASS II\t9999999\t149190\t1\t9999999\t1.00E+14\t2\t298380\t89030624400
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9350R\tMFI RESULTS FOR SCREENING C1Q CLASS I\t9999999\t119126.8571\t1\t9999999\t1.00E+14\t315\t37524960\t4.02E+13
-HISTOCOMPATIBILITY ORDERABLE\t896160\t10165R\tHLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t543300\t1\t9999999\t1.00E+14\t1\t543300\t2.95E+11
-HISTOCOMPATIBILITY ORDERABLE\t896160\t10165R\tHLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t9999999\t543300\t1\t9999999\t1.00E+14\t1\t543300\t2.95E+11
-HISTOCOMPATIBILITY ORDERABLE\t4046255\t10015R\tHLA ORDER - DSA SCREENING\tRINFO\tTO FIND RESULT:\t9999999\t1171925.172\t1\t9999999\t1.00E+14\t116\t135943320\t2.64E+14
-LAB BLOOD ORDERABLES\t471506\tLABY17OHP\t17 - HYDROXYPROGESTERONE, SERUM\t17OHPROG\t17-OH PROGESTERONE\t11700\t545760\t1\t11700\t136890000\t1\t545760\t2.98E+11
-LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONDX\tCLINICAL INDICATION\t9999999\t758160\t1\t9999999\t1.00E+14\t1\t758160\t5.75E+11
-LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONRS\tRESULT\t9999999\t758160\t1\t9999999\t1.00E+14\t1\t758160\t5.75E+11
-LAB BLOOD ORDERABLES\t471525\tLABYCHRBC\tACETYLCHOLINESTERASE, RBC\tACHERBC\tACETYLCHOLINESTERASE, RBC\t33.3\t#VALUE!\t1\t33.3\t1108.89\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471526\tLABYARBIN\tACH RECEPTOR BINDING AB\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t72.2\t613440\t1\t72.2\t5212.84\t1\t613440\t3.76E+11
-LAB BLOOD ORDERABLES\t471536\tLABADAQ\tADENOSINE DEAMINASE, RBC\tADMD\tREVIEWED BY\t9999999\t490320\t1\t9999999\t1.00E+14\t9\t4412880\t3.48E+12
-LAB BLOOD ORDERABLES\t471586\tLAB12733R\tALPHA-2-ANTIPLASMIN\tA2A\tALPHA-2-ANTIPLASMIN\t80\t156540\t1\t80\t6400\t1\t156540\t24504771600
-LAB BLOOD ORDERABLES\t471586\tLAB12733R\tALPHA-2 PLASMIN INHIBITOR\t12734R\tALPHA-2 PLASMIN INHIBITOR\t57\t90780\t1\t57\t3249\t2\t181560\t16900200000
-LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTD\tDISCLAIMER\t9999999\t540213\t1\t9999999\t1.00E+14\t60\t32412780\t2.37E+13
-LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTM\tMETHOD\t9999999\t540213\t1\t9999999\t1.00E+14\t60\t32412780\t2.37E+13
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\t12215R\t12215R\t9999999\t240072.1875\t1\t9999999\t1.00E+14\t64\t15364620\t4.33E+12
-LAB BLOOD ORDERABLES\t471646\tLABABTITI\tAB TITER\tABTITER\tANTIBODY TITER RESULTS\t9999999\t12397.26316\t1\t9999999\t1.00E+14\t95\t1177740\t29891170800
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO7\tCAUTIONS\t9999999\t562920\t1\t9999999\t1.00E+14\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tCAIGG\tCAL ENCEPH IGG\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tCAIGM\tCAL ENCEPH IGM\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tCAINT\tINTERPRETATION\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tEEIGM\tE EQUINE ENCEP IGM AB\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tEEINT\tINTERPRETATION\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tSTIGG\tST. LOUIS ENCEP IGG\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tSTIGM\tST. LOUIS ENCEP IGM\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tSTINT\tINTERPRETATION\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tWEIGG\tW EQUINE IGG AB\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tWEIGM\tW EQUINE IGM AB\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tANTI-JO1 ANTIBODY\tJO1INT\tANTI-JO1 INTERPRETATION\t9999999\t395222.2222\t1\t9999999\t1.00E+14\t81\t32013000\t1.65E+13
-LAB BLOOD ORDERABLES\t471706\tLAB11093R\tAUTOANTIBODIES TO SCL 70\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t222600\t1\t9999999\t1.00E+14\t1\t222600\t49550760000
-LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tANTI-RNP ANTIBODY\tRNPINT\tANTI-RNP INTERPRETATION\t9999999\t433078.8889\t1\t9999999\t1.00E+14\t162\t70158780\t4.59E+13
-LAB BLOOD ORDERABLES\t471709\tLABYANRNP\tAUTOANTIBODIES TO U1RNP\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t9999999\t161628\t1\t9999999\t1.00E+14\t20\t3232560\t6.25E+11
-LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG AND IGM AB\tYPERA2\tB PERTUSSIS AB, IGM\t9999999\t32220\t1\t9999999\t1.00E+14\t1\t32220\t1038128400
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQCO\tCOMMENT\t9999999\t1383525\t1\t9999999\t1.00E+14\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GPCM\tBETA-2-GP1 COMMENT\t9999999\t916554.1935\t1\t9999999\t1.00E+14\t31\t28413180\t4.50E+13
-LAB BLOOD ORDERABLES\t471777\tLABYBLAS\tBLASTOMYCES AB\tYBLAS3\tBLASTOMYCES IMMUNODIFFUSION\t9999999\t417449.0625\t1\t9999999\t1.00E+14\t64\t26716740\t1.37E+13
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMGDES\tCMV IGG TEST DESCRIPTION\t9999999\t221580\t1\t9999999\t1.00E+14\t2\t443160\t1.80E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMGQNT\tCMV IGG LEVEL\t9999999\t18900\t1\t9999999\t1.00E+14\t1\t18900\t357210000
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMMDES\tCMV IGM TEST DESCRIPTION\t9999999\t221580\t1\t9999999\t1.00E+14\t2\t443160\t1.80E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMMQNT\tCMV IGM LEVEL\t9999999\t18900\t1\t9999999\t1.00E+14\t1\t18900\t357210000
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHATINT\tINTERPRETATION\t9999999\t284733.75\t1\t9999999\t1.00E+14\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t9999999\t284733.75\t1\t9999999\t1.00E+14\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t9999999\t284733.75\t1\t9999999\t1.00E+14\t48\t13667220\t4.72E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tRPRQIN\tINTERPRETATIONS\t9999999\t286934.6939\t1\t9999999\t1.00E+14\t49\t14059800\t4.87E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVGGDES\tVZV IGG TEST DESCRIPTION\t9999999\t221580\t1\t9999999\t1.00E+14\t2\t443160\t1.80E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVGGQNT\tVZV IGG LEVEL\t9999999\t18900\t1\t9999999\t1.00E+14\t1\t18900\t357210000
-LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tALCN\tALLO-ISOLEUCINE\t0\t348900\t1\t0\t0\t1\t348900\t1.22E+11
-LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tISOLEU\tISOLEUCINE\t32\t348900\t1\t32\t1024\t1\t348900\t1.22E+11
-LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tLEUCINE\tLEUCINE\t72\t348900\t1\t72\t5184\t1\t348900\t1.22E+11
-LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tVALINE\tVALINE\t119\t348900\t1\t119\t14161\t1\t348900\t1.22E+11
-LAB BLOOD ORDERABLES\t471803\tLABYBUPRO\tBUPROPION, SERUM\t12045R\tHYDROXYBUPROPION\t843\t534900\t1\t843\t710649\t1\t534900\t2.86E+11
-LAB BLOOD ORDERABLES\t471803\tLABYBUPRO\tBUPROPION, SERUM\tYBUPR1\tBUPROPION\t47\t534900\t1\t47\t2209\t1\t534900\t2.86E+11
-LAB BLOOD ORDERABLES\t471807\tLABC1INHF\tC1 ESTERASE INHIBITOR FN\tC1INHF\tC1 ESTERASE INHIB FN\t109\t327720\t1\t109\t11881\t1\t327720\t1.07E+11
-LAB BLOOD ORDERABLES\t471810\tLABC1Q\tC1Q BINDING ASSAY\tC1Q\tC1Q BINDING ASSAY\t1.7\t314220\t1\t1.7\t2.89\t1\t314220\t98734208400
-LAB BLOOD ORDERABLES\t471817\tLABYCALCI\tCALCITONIN, PLASMA\tYCALCI\tCALCITONIN, SERUM\t12\t890520\t1\t12\t144\t1\t890520\t7.93E+11
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tALB\tALBUMIN, SER/PLAS\t2.6\t16500\t1\t2.6\t6.76\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t44\t16500\t1\t44\t1936\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tALT\tALT (SGPT), SER/PLAS\t69\t16500\t1\t69\t4761\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tAST\tAST (SGOT), SER/PLAS\t44\t16500\t1\t44\t1936\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOAB\tABSOLUTE BASOPHILS\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOSYN\tBASOPHILS, %\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBUN\tUREA NITROGEN,SER/PLAS\t11\t16500\t1\t11\t121\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCA\tCALCIUM, SER/PLAS\t8.1\t16500\t1\t8.1\t65.61\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCL\tCHLORIDE, SER/PLAS\t104\t16500\t1\t104\t10816\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCO2\tCO2, SER/PLAS\t27\t16500\t1\t27\t729\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCR\tCREATININE, SER/PLAS\t0.7\t16500\t1\t0.7\t0.49\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSAB\tABSOLUTE EOSINOPHILS\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSFLD\tEOSINOPHILS, %\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tGLU\tGLUCOSE, SER/PLAS\t106\t16500\t1\t106\t11236\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHCT\tHEMATOCRIT (MANUAL ENTRY)\t38\t40260\t1\t38\t1444\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHGB\tHEMOGLOBIN (MANUAL ENTRY)\t12.7\t40260\t1\t12.7\t161.29\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tK\tPOTASSIUM, SER/PLAS\t4.6\t16500\t1\t4.6\t21.16\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMAB\tABSOLUTE LYMPHOCYTES\t1\t40260\t1\t1\t1\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOA\tABSOLUTE MONOCYTES\t1\t40260\t1\t1\t1\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOPHAGE\tMONOCYTE/MACROPHAGE, %\t7\t40260\t1\t7\t49\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNA\tSODIUM, SER/PLAS\t137\t16500\t1\t137\t18769\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTAB\tABSOLUTE NEUTROPHIL COUNT\t12.7\t40260\t1\t12.7\t161.29\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTRA\tABSOLUTE NEUTROPHILS\t3.3\t16500\t1\t3.3\t10.89\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPLT\tPLATELETS\t247\t40260\t1\t247\t61009\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tRBCHP\tRBC\t4.6\t40260\t1\t4.6\t21.16\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSYNLYMPH\tLYMPHOCYTES %\t7\t40260\t1\t7\t49\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tTBIL\tTOTAL BILIRUBIN\t0.4\t16500\t1\t0.4\t0.16\t1\t16500\t272250000
-LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tWBCHP\tWBC\t14.7\t40260\t1\t14.7\t216.09\t1\t40260\t1620867600
-LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tHAIRY\tHAIRY CELLS\t0\t151200\t1\t0\t0\t1\t151200\t22861440000
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTR2\tRESULT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTR3\tRESULT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472036\tLABCLOMIP\tCLOMIPRAMINE AND METABOLITE\tCLOMI\tCLOMIPRAMINE\t63\t#VALUE!\t1\t63\t3969\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472036\tLABCLOMIP\tCLOMIPRAMINE AND METABOLITE\tDMCLOM\tDESMETHYLCLOMIPRAMIN\t14\t#VALUE!\t1\t14\t196\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472036\tLABCLOMIP\tCLOMIPRAMINE AND METABOLITE\tTCLOAM\tCLOMIP & METAB, TOTAL\t77\t#VALUE!\t1\t77\t5929\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472048\tLAB11124R\tCOCCI CF\tCOCFS\tCOCCI CF (SERUM)\t9999999\t951336.1341\t1\t9999999\t1.00E+14\t895\t851445840\t2.05E+15
-LAB BLOOD ORDERABLES\t472051\tLABCDPLTI\tCOLD AGGLUTININ TITER\tCDPLT\tCOLD ANTIBODY TITER\t9999999\t146235.3488\t1\t9999999\t1.00E+14\t43\t6288120\t1.58E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHATINT\tINTERPRETATION\t9999999\t200741.1086\t1\t9999999\t1.00E+14\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t200741.1086\t1\t9999999\t1.00E+14\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t9999999\t200741.1086\t1\t9999999\t1.00E+14\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHATINT\tINTERPRETATION\t9999999\t227223.75\t1\t9999999\t1.00E+14\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t227223.75\t1\t9999999\t1.00E+14\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t9999999\t227223.75\t1\t9999999\t1.00E+14\t16\t3635580\t1.30E+12
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHAGR\tANTI HAV IGG\t9999999\t19380\t1\t9999999\t1.00E+14\t1\t19380\t375584400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHQLINT\tINTERPRETATION\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tCRTNR\tCREATINE, PLASMA\t0.5\t-86400\t1\t0.5\t0.25\t1\t-86400\t7464960000
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tHGB\tHEMOGLOBIN\t10.9\t0\t1\t10.9\t118.81\t1\t0\t0
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tLDH\tLDH, TOTAL, SER/PLAS\t3124\t0\t1\t3124\t9759376\t1\t0\t0
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tPLT\tPLATELET COUNT\t47\t0\t1\t47\t2209\t1\t0\t0
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tURIC\tURIC ACID, SER/PLAS\t2.5\t0\t1\t2.5\t6.25\t1\t0\t0
-LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tWBC\tWBC\t3.7\t0\t1\t3.7\t13.69\t1\t0\t0
-LAB BLOOD ORDERABLES\t472120\tLABCCP\tANTI CCP\tCCPT\tANTI CCP ANTIBODY LEVEL\t18\t583320\t1\t18\t324\t1\t583320\t3.40E+11
-LAB BLOOD ORDERABLES\t472142\tLABYDOC\t11-DEOXYCORTISOL, SERUM\tYDOC1\t11-DEOXYCORTISOL, S\t9610\t285600\t1\t9610\t92352100\t1\t285600\t81567360000
-LAB BLOOD ORDERABLES\t472142\tLABYDOC\t11-DEOXYCORTISOL, SERUM\tYDOC2\tAM RESULT\t9999999\t285600\t1\t9999999\t1.00E+14\t1\t285600\t81567360000
-LAB BLOOD ORDERABLES\t472142\tLABYDOC\t11-DEOXYCORTISOL, SERUM\tYDOC3\tPM RESULT\t9999999\t285600\t1\t9999999\t1.00E+14\t1\t285600\t81567360000
-LAB BLOOD ORDERABLES\t472148\tLABYDIAZE\tDIAZEPAM AND NORDIAZEPAM\tDIAZEP\tDIAZEPAM\t9999999\t411030\t1\t9999999\t1.00E+14\t2\t822060\t4.30E+11
-LAB BLOOD ORDERABLES\t472148\tLABYDIAZE\tDIAZEPAM AND NORDIAZEPAM\tNORDIAZ\tNORDIAZEPAM\t9999999\t411030\t1\t9999999\t1.00E+14\t2\t822060\t4.30E+11
-LAB BLOOD ORDERABLES\t472148\tLABYDIAZE\tDIAZEPAM AND NORDIAZEPAM\tYDIAZ3\tDIAZEPAM & NORDIAZEPAM\t9999999\t411030\t1\t9999999\t1.00E+14\t2\t822060\t4.30E+11
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBASO\tBASOPHILS\t1\t11460\t1\t1\t1\t1\t11460\t131331600
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBLASTS\tBLASTS\t16\t#VALUE!\t1\t16\t256\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tPRMY\tPROMYELOCYTES\t2\t#VALUE!\t1\t2\t4\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tBAND\tBAND NEUTROPHILS\t16\t#VALUE!\t1\t16\t256\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tLYM\tLYMPHOCYTES\t42\t#VALUE!\t1\t42\t1764\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMONO\tMONOCYTES\t20\t#VALUE!\t1\t20\t400\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tNEUT\tSEG NEUTROPHILS\t22\t#VALUE!\t1\t22\t484\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472226\tLAB11132R\tDONOR HEP B SURF. AG\tDHB\tDONOR HEP B SURF. AG\t9999999\t180290\t1\t9999999\t1.00E+14\t6\t1081740\t2.32E+11
-LAB BLOOD ORDERABLES\t472331\tLAB11034R\tETHOSUXIMIDE LEVEL\tETHSUX\tETHOSUXIMIDE\t9999999\t174300\t1\t9999999\t1.00E+14\t1\t174300\t30380490000
-LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEICMT\tCOMMENT\t9999999\t436344.1652\t1\t9999999\t1.00E+14\t581\t253515960\t1.44E+14
-LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIPRM\tPRIMERS\t9999999\t436344.1652\t1\t9999999\t1.00E+14\t581\t253515960\t1.44E+14
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tCHICK\tCHICKEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tCORNY\tCORN\t1.15\t#VALUE!\t1\t1.15\t1.3225\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tEGGW\tEGG WHITE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tFISH\tFISH\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tIGEFA\tTOTAL IGE\t980\t#VALUE!\t1\t980\t960400\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tMIL\tMILK\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tNUTTY\tNUT\t5.73\t#VALUE!\t1\t5.73\t32.8329\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tOAT\tOAT\t0.5\t#VALUE!\t1\t0.5\t0.25\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tORAN\tORANGE\t0.37\t#VALUE!\t1\t0.37\t0.1369\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tPEAN\tPEANUT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tSHRIM\tSHRIMP\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tSOY\tSOY\t21.9\t#VALUE!\t1\t21.9\t479.61\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tTOM\tTOMATO\t0.75\t#VALUE!\t1\t0.75\t0.5625\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tWHT\tWHEAT\t0.66\t#VALUE!\t1\t0.66\t0.4356\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY, BLOOD\t3\t159300\t1\t3\t9\t1\t159300\t25376490000
-LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMSP\tSPECIMEN\t9999999\t521638.481\t1\t9999999\t1.00E+14\t79\t41209440\t2.41E+13
-LAB BLOOD ORDERABLES\t472513\tLAB12802R\tHEPARIN COFACTOR\t12801R\tHEPARIN COFACTOR II\t70\t751440\t1\t70\t4900\t1\t751440\t5.65E+11
-LAB BLOOD ORDERABLES\t472521\tLABHBCT\tANTI-HBC\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t22020\t1\t9999999\t1.00E+14\t1\t22020\t484880400
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAIN\tINTERPRETATION\t9999999\t290760\t1\t9999999\t1.00E+14\t1\t290760\t84541377600
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAQT\tHEP B SURF AB QUANT\t236.4\t290760\t1\t236.4\t55884.96\t1\t290760\t84541377600
-LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHBSAG\tHBSAG\tHEPATITIS B SURF AG\t9999999\t22020\t1\t9999999\t1.00E+14\t1\t22020\t484880400
-LAB BLOOD ORDERABLES\t472528\tLABHCVA\tANTI-HCV\tHCVI\tINTERPRETATION\t9999999\t42280.72931\t1\t9999999\t1.00E+14\t713\t30146160\t3.88E+12
-LAB BLOOD ORDERABLES\t472539\tLABHGBSQ\tHGB S QUANTITATIVE\tHGSMD\tREVIEWED BY\t9999999\t326432\t1\t9999999\t1.00E+14\t45\t14689440\t6.05E+12
-LAB BLOOD ORDERABLES\t472576\tLABYHPL\tHUMAN PLACENTAL LACTOGEN\tYHPL1\tHUMAN PLACENTAL LACTOGEN\t0.19\t901380\t1\t0.19\t0.0361\t1\t901380\t8.12E+11
-LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tANAQNT\tTITER\t9999999\t359880\t1\t9999999\t1.00E+14\t1\t359880\t1.30E+11
-LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tRHFT\tRHEUMATOID FACTOR LEVEL\t23\t190800\t1\t23\t529\t1\t190800\t36404640000
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKIN\tINTERPRETATION\t9999999\t520500\t1\t9999999\t1.00E+14\t1\t520500\t2.71E+11
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKMD\tINTERPRETED BY\t9999999\t525786.6667\t1\t9999999\t1.00E+14\t9\t4732080\t3.13E+12
-LAB BLOOD ORDERABLES\t472720\tLABLAC\tLACTIC ACID\tLAC\tLACTIC ACID, PLASMA\t2.8\t40500\t1\t2.8\t7.84\t1\t40500\t1640250000
-LAB BLOOD ORDERABLES\t472748\tLABYLEPTN\tLEPTIN\tYLEPT1\tLEPTIN\t20\t963540\t1\t20\t400\t1\t963540\t9.28E+11
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tLDL\tLDL (CALCULATED)\t9999999\t7920\t1\t9999999\t1.00E+14\t1\t7920\t62726400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tCHOL\tCHOLESTEROL, TOTAL\t207\t4020\t1\t207\t42849\t1\t4020\t16160400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t9\t4020\t1\t9\t81\t1\t4020\t16160400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tHDL\tHDL CHOLESTEROL\t23\t4020\t1\t23\t529\t1\t4020\t16160400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tLDL\tDIRECT LDL CHOL\t62\t4020\t1\t62\t3844\t1\t4020\t16160400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tNHDL\tNON-HDL CHOL, CALC\t184\t4020\t1\t184\t33856\t1\t4020\t16160400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tTGL\tTRIGLYCERIDE, SER/PLAS\t739\t4020\t1\t739\t546121\t1\t4020\t16160400
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tCHOL\tCHOLESTEROL, TOTAL\t97\t9900\t1\t97\t9409\t1\t9900\t98010000
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t7.5\t9900\t1\t7.5\t56.25\t1\t9900\t98010000
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tHDL\tHDL CHOLESTEROL\t13\t9900\t1\t13\t169\t1\t9900\t98010000
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tLDL\tDIRECT LDL CHOL\t49\t9900\t1\t49\t2401\t1\t9900\t98010000
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tNHDL\tNON-HDL CHOL, CALC\t84\t9900\t1\t84\t7056\t1\t9900\t98010000
-LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tTGL\tTRIGLYCERIDE, SER/PLAS\t86\t9900\t1\t86\t7396\t1\t9900\t98010000
-LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGMBAND\tB.BURGDORFERI IGM BANDS\t9999999\t265415.6757\t1\t9999999\t1.00E+14\t37\t9820380\t4.41E+12
-LAB BLOOD ORDERABLES\t472855\tLABMALL2\tMISCELLANEOUS ALLERGEN 2\tALLR2\tALLERGEN RESULT\t9999999\t1370400\t1\t9999999\t1.00E+14\t1\t1370400\t1.88E+12
-LAB BLOOD ORDERABLES\t472856\tLABMALL3\tMISCELLANEOUS ALLERGEN 3\tALL3\tALLERGEN\t9999999\t148680\t1\t9999999\t1.00E+14\t1\t148680\t22105742400
-LAB BLOOD ORDERABLES\t472856\tLABMALL3\tMISCELLANEOUS ALLERGEN 3\tALLR3\tALLERGEN RESULT\t9999999\t148680\t1\t9999999\t1.00E+14\t1\t148680\t22105742400
-LAB BLOOD ORDERABLES\t472864\tLABMISCH\tMISCELLANEOUS HEMATOLOGY\tRESULH\tRESULT\t9999999\t3060\t1\t9999999\t1.00E+14\t1\t3060\t9363600
-LAB BLOOD ORDERABLES\t472944\tLABFNKSEN\tNATURAL KILLER CELLS ENUMERATION\tC3AB\tCD3 CD16+CD56 ABS\t40\t265980\t1\t40\t1600\t1\t265980\t70745360400
-LAB BLOOD ORDERABLES\t472944\tLABFNKSEN\tNATURAL KILLER CELLS ENUMERATION\tC3PER\tCD3 CD16+CD56 %\t11\t265980\t1\t11\t121\t1\t265980\t70745360400
-LAB BLOOD ORDERABLES\t472944\tLABFNKSEN\tNATURAL KILLER CELLS ENUMERATION\tLYMAB\tLYMPHOCYTES, ABS.\t352\t265980\t1\t352\t123904\t1\t265980\t70745360400
-LAB BLOOD ORDERABLES\t473043\tLABYOSTEO\tOSTEOCALCIN HUMAN\tOSTEOC\tOSTEOCALCIN, HUMAN\t15\t141000\t1\t15\t225\t1\t141000\t19881000000
-LAB BLOOD ORDERABLES\t473074\tLABPBGD\tPBG DEAMINASE, RBC\tPBGD\tPBG DEAMINASE, RBC\t4.68\t675840\t1\t4.68\t21.9024\t1\t675840\t4.57E+11
-LAB BLOOD ORDERABLES\t473096\tLABPHATYR\tPHENYLALANINE AND TYROSINE\tPHALN\tPHENYLALANINE\t87\t426840\t1\t87\t7569\t1\t426840\t1.82E+11
-LAB BLOOD ORDERABLES\t473096\tLABPHATYR\tPHENYLALANINE AND TYROSINE\tTYROSINE\tTYROSINE\t91\t426840\t1\t91\t8281\t1\t426840\t1.82E+11
-LAB BLOOD ORDERABLES\t473101\tLABYPTSAB\tPHOSPHATIDYLSERINE AB PANEL\tYPTSA\tPHOSPHATIDYLSERINE IGA AB\t3\t353700\t1\t3\t9\t1\t353700\t1.25E+11
-LAB BLOOD ORDERABLES\t473101\tLABYPTSAB\tPHOSPHATIDYLSERINE AB PANEL\tYPTSG\tPHOSPHATIDYLSERINE IGG AB\t10\t353700\t1\t10\t100\t1\t353700\t1.25E+11
-LAB BLOOD ORDERABLES\t473101\tLABYPTSAB\tPHOSPHATIDYLSERINE AB PANEL\tYPTSM\tPHOSPHATIDYLSER. IGM\t41\t353700\t1\t41\t1681\t1\t353700\t1.25E+11
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12590R\tS.PNEUMO SEROTYPE 1 (1)\t4.3\t205440\t1\t4.3\t18.49\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12699R\tS.PNEUMO SEROTYPE 3 (3)\t8.4\t205440\t1\t8.4\t70.56\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12700R\tS.PNEUMO SEROTYPE 4 (4)\t0.4\t205440\t1\t0.4\t0.16\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12701R\tS.PNEUMO SEROTYPE 5 (5)\t3\t205440\t1\t3\t9\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12702R\tS.PNEUMO SEROTYPE 6 (6A)\t3.6\t205440\t1\t3.6\t12.96\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12703R\tS.PNEUMO SEROTYPE 14 (14)\t18.1\t205440\t1\t18.1\t327.61\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12704R\tS.PNEUMO SEROTYPE 19 (19F)\t2.1\t205440\t1\t2.1\t4.41\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12705R\tS.PNEUMO SEROTYPE 23 (23F)\t0.5\t205440\t1\t0.5\t0.25\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12706R\tS.PNEUMO SEROTYPE 26 (6B)\t7\t205440\t1\t7\t49\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12707R\tS.PNEUMO SEROTYPE 51 (7F)\t0.8\t205440\t1\t0.8\t0.64\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12708R\tS.PNEUMO SEROTYPE 56 (18C)\t9999999\t205440\t1\t9999999\t1.00E+14\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12709R\tS.PNEUMO SEROTYPE 57 (19A)\t0.8\t205440\t1\t0.8\t0.64\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12710R\tS.PNEUMO SEROTYPE 68 (9V)\t1.8\t205440\t1\t1.8\t3.24\t1\t205440\t42205593600
-LAB BLOOD ORDERABLES\t473121\tLABPORS\tPORPHYRINS, SERUM\tPORINT\tPORPHYRINS, SER INTR\t9999999\t383598.6207\t1\t9999999\t1.00E+14\t29\t11124360\t1.10E+13
-LAB BLOOD ORDERABLES\t473149\tLABPROTPM\tPRO-PREDICT TPMT\tPRTPMR\tPROPREDICT TPMT\t9999999\t623076.9231\t1\t9999999\t1.00E+14\t78\t48600000\t6.83E+13
-LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPT20DR\tINTERPRETED BY\t9999999\t453611.6315\t1\t9999999\t1.00E+14\t521\t236331660\t1.36E+14
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNIN\tQUANTIFERON INTERPRETATION\t9999999\t449070\t1\t9999999\t1.00E+14\t18\t8083260\t1.64E+13
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBGINT\tINTERPRETATION\t9999999\t209921.4545\t1\t9999999\t1.00E+14\t55\t11545680\t2.87E+12
-LAB BLOOD ORDERABLES\t473201\tLABYQUIN\tQUINIDINE\tQUINIDINE\tQUINIDINE\t2.5\t262200\t1\t2.5\t6.25\t1\t262200\t68748840000
-LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t9999999\t9330\t1\t9999999\t1.00E+14\t2\t18660\t215139600
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tENO\tENOLASE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tGPILS\tGPI, LOW SUBSTRATE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tNPHOS\tPURINE NUCLEOSIDE PHOSPHORYLASE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tPKLS\tPK, LOW SUBSTRATE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t473221\tLAB11092R\tRENIN ACTIVITY, PLASMA\tRENIN\tRENIN ACTIVITY, PL\t9999999\t279300\t1\t9999999\t1.00E+14\t1\t279300\t78008490000
-LAB BLOOD ORDERABLES\t473271\tLABRIPA\tRISTOCETIN INDUCED PLATELET AGGREGATION\tRIPDIR\tINTERPRETED BY\t9999999\t97431.42857\t1\t9999999\t1.00E+14\t7\t682020\t1.20E+11
-LAB BLOOD ORDERABLES\t473290\tLABSCHIG\tSCHISTOSOMA IGG AB ELISA\tSCHIG1\tSCHISTOSOMA ,LGG\t9999999\t323778.8235\t1\t9999999\t1.00E+14\t51\t16512720\t6.74E+12
-LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14RES\tRESULTS\t9999999\t1210908\t1\t9999999\t1.00E+14\t5\t6054540\t9.00E+12
-LAB BLOOD ORDERABLES\t473409\tLABYT3UP\tT3 UPTAKE\tYT7P2\tT-UPTAKE %\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t473432\tLABTHAMP\tTHERMAL AMPLITUDE SCREEN\tTHRAMP\tTHERMAL AMPLITUDE\t9999999\t479657.1429\t1\t9999999\t1.00E+14\t7\t3357600\t7.81E+12
-LAB BLOOD ORDERABLES\t473444\tLAB11097R\tTSI, SERUM\tTSIG\tTHROID-STIMULATING IG, SERUM\t9999999\t253500\t1\t9999999\t1.00E+14\t1\t253500\t64262250000
-LAB BLOOD ORDERABLES\t473463\tLABTOXOP2\tTOXOPLASMOSIS PCR\tTOXGG\tGENERAL GUIDELINES\t9999999\t1116100\t1\t9999999\t1.00E+14\t6\t6696600\t7.98E+12
-LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMA PCR\tTOXRES\tPCR RESULT\t9999999\t1052324\t1\t9999999\t1.00E+14\t30\t31569720\t6.51E+13
-LAB BLOOD ORDERABLES\t473675\tLABTRAZO\tTRAZODONE\tTRAZO\tTRAZODONE\t9999999\t251220\t1\t9999999\t1.00E+14\t1\t251220\t63111488400
-LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tRESUL5\tRESULT\t9999999\t5643312\t1\t9999999\t1.00E+14\t5\t28216560\t3.83E+14
-LAB BLOOD ORDERABLES\t473747\tLABYVIP\tVASOACTIVE INTESTINAL POLYPEPTIDE\tVIP\tVASOACTIVE INT PEPTIDE\t850\t890520\t1\t850\t722500\t1\t890520\t7.93E+11
-LAB BLOOD ORDERABLES\t473759\tLAB11098R\t1,25-DIHYDROXYVITAMIN D, SERUM\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t28\t164940\t1\t28\t784\t1\t164940\t27205203600
-LAB BLOOD ORDERABLES\t473759\tLAB11098R\tPEDIATRIC 1 25 OH VITAMIN D\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t76\t222660\t1\t76\t5776\t1\t222660\t49577475600
-LAB BLOOD ORDERABLES\t473767\tLABYVITE\tVITAMIN E, SERUM\tVITE\tA-TOCOPHEROL, VITAMIN E\t41.3\t359340\t1\t41.3\t1705.69\t1\t359340\t1.29E+11
-LAB BLOOD ORDERABLES\t473779\tLAB11131R\tVORICONAZOLE LEVEL\tVORI\tVORICONAZOLE LEVEL\t9999999\t559952.1012\t1\t9999999\t1.00E+14\t514\t287815380\t2.74E+14
-LAB BLOOD ORDERABLES\t473796\tLABYWNILE\tWEST NILE VIRUS AB, IGG AND IGM\tWNVIGG\tWEST NILE VIRUS AB IGG\t9999999\t286493.3333\t1\t9999999\t1.00E+14\t252\t72196320\t3.25E+13
-LAB BLOOD ORDERABLES\t497055\tLABRHTYP\tRH BLOOD TYPE\tRHTYP\tRH BLOOD TYPE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t497329\tLABSPAGD\tS. PNEUMONIAE AG DETECTION LA\tSPNAG\tS PNEUMONIAEAG\t9999999\t2758074.146\t1\t9999999\t1.00E+14\t41\t113081040\t2.48E+15
-LAB BLOOD ORDERABLES\t497331\tLABPABN\tPOLIO AB, NEUTRALIZATION\tPOL1\tPOLIO 1\t9999999\t1740\t1\t9999999\t1.00E+14\t1\t1740\t3027600
-LAB BLOOD ORDERABLES\t497331\tLABPABN\tPOLIO AB, NEUTRALIZATION\tPOL3\tPOLIO 3\t9999999\t1740\t1\t9999999\t1.00E+14\t1\t1740\t3027600
-LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tHPEM\tPRECORE MUTATION\t9999999\t612900\t1\t9999999\t1.00E+14\t1\t612900\t3.76E+11
-LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tHPSOL\tPOLYMERASE MUTATION\t9999999\t612900\t1\t9999999\t1.00E+14\t1\t612900\t3.76E+11
-LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tHSPOL\tHEP B VIR GENOTYPING\t9999999\t612900\t1\t9999999\t1.00E+14\t1\t612900\t3.76E+11
-LAB BLOOD ORDERABLES\t497343\tLABCMVG\tCMV GENOTYPE\tCUL54\tUL54 MUTATION\t9999999\t1217280.769\t1\t9999999\t1.00E+14\t78\t94947900\t1.25E+14
-LAB BLOOD ORDERABLES\t497420\tLABASPID\tASPERGILLUS AB, ID, S\tAFLUMI\tASPERGILLUS FUMIGATUS\t9999999\t376384.5\t1\t9999999\t1.00E+14\t40\t15055380\t6.40E+12
-LAB BLOOD ORDERABLES\t497432\tLABFHTLVQ\tHTLV-1/2  DNA QUALITATIVE\tHTLII\tHTLV-II DNA\t9999999\t244110.566\t1\t9999999\t1.00E+14\t53\t12937860\t1.04E+13
-LAB BLOOD ORDERABLES\t497434\tLABFENTA\tENTAMOEBA HISTOLYTICA AB\tENTA\tE.HISTOLYTICA LGG\t9999999\t368756.3415\t1\t9999999\t1.00E+14\t82\t30238020\t1.38E+13
-LAB BLOOD ORDERABLES\t497454\tLABFPARVD\tPARVOVIRUS B19 PCR QUALITATIVE REALTIME\tPARVD\tPARVOVIRUS DNA\t9999999\t247459.7872\t1\t9999999\t1.00E+14\t94\t23261220\t1.27E+13
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12015R\tQ FEVER IGM PHASE LI TITER\t9999999\t228210\t1\t9999999\t1.00E+14\t4\t912840\t2.66E+11
-LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV-1 AB, WESTERN BLOT\tHVIG\tHIV 1 AB CONFIRMATION\t9999999\t369630.9278\t1\t9999999\t1.00E+14\t97\t35854200\t3.79E+13
-LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBRASDD\tTOBRAMYCIN, SINGLE DAILY DOSE\t4.4\t10500\t1\t4.4\t19.36\t1\t10500\t110250000
-LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBUN2\tDATE & TIME OF LAST DOSE\t9999999\t10500\t1\t9999999\t1.00E+14\t1\t10500\t110250000
-LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBUN3\tDOSAGE\t9999999\t10500\t1\t9999999\t1.00E+14\t1\t10500\t110250000
-LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBUN4\tADMINISTRATION ROUTE\t9999999\t10500\t1\t9999999\t1.00E+14\t1\t10500\t110250000
-LAB BLOOD ORDERABLES\t541652\tLABAS4I\tAB SCREEN\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t6960\t1\t9999999\t1.00E+14\t1\t6960\t48441600
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL0ACTH\tLEFT 0 MINS\t23.5\t#VALUE!\t1\t23.5\t552.25\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL10ACT\tLEFT +10 MINS\t82\t#VALUE!\t1\t82\t6724\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL15ACT\tLEFT + 15 MINS\t99.5\t#VALUE!\t1\t99.5\t9900.25\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL3ACTH\tLEFT +3 MINS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL5ACTH\tLEFT +5 MINS\t125\t#VALUE!\t1\t125\t15625\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tLM5ACT\tLEFT - 5 MINS\t23.3\t#VALUE!\t1\t23.3\t542.89\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP0ACTH\tPERIPHERAL 0 MINS\t22.4\t#VALUE!\t1\t22.4\t501.76\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP10ACT\tPERIPHERAL +10 MINS\t56\t#VALUE!\t1\t56\t3136\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP15ACT\tPERIPHERAL +15 MINS\t50.1\t#VALUE!\t1\t50.1\t2510.01\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP3ACTH\tPERIPHERAL +3 MINS\t29.9\t#VALUE!\t1\t29.9\t894.01\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP5ACTH\tPERIPHERAL +5  MINS\t46.9\t#VALUE!\t1\t46.9\t2199.61\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tPM5ACT\tPERIPHERAL -5 MINS\t22\t#VALUE!\t1\t22\t484\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR0ACTH\tRIGHT 0 MINS\t65.1\t#VALUE!\t1\t65.1\t4238.01\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR10ACT\tRIGHT +10 MINS\t339\t#VALUE!\t1\t339\t114921\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR15ACT\tRIGHT 15 MINS\t325\t#VALUE!\t1\t325\t105625\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR3ACTH\tRIGHT +3 MINS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR5ACTH\tRIGHT +5 MINS\t1105\t#VALUE!\t1\t1105\t1221025\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tRM5ACT\tRIGHT -5 MINS\t53.3\t#VALUE!\t1\t53.3\t2840.89\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC1\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC2\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC3\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC4\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC5\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6MMPNASSESS\t6-MMPN RESULT ASSESSMENT\t9999999\t605484\t1\t9999999\t1.00E+14\t20\t12109680\t8.50E+12
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1IN\tINTERPRETATION\t9999999\t1568100\t1\t9999999\t1.00E+14\t1\t1568100\t2.46E+12
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1RE\tRESULT\t9999999\t1062780\t1\t9999999\t1.00E+14\t4\t4251120\t5.58E+12
-LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\tFPARV2\tPARVOVIRUS IGM INDEX\t9999999\t323929.7143\t1\t9999999\t1.00E+14\t35\t11337540\t7.36E+12
-LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tYDGLIA\tGLIADIN (DEAMIDATED) IGA\t2.7\t218910\t1\t2.7\t7.29\t2\t437820\t1.07E+11
-LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSMAB\tAUTOANTIBODIES TO SM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB1\tCOXSACKIE B1 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
-LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB2\tCOXSACKIE B2 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
-LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB3\tCOXSACKIE B3 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
-LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB4\tCOXSACKIE B4 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
-LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB5\tCOXSACKIE B5 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tPLV1\tPOLIOVIRUS 1 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tPLV2\tPOLIOVIRUS 2 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tPLV3\tPOLIOVIRUS 3 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB1\tCOXSACKIE B1 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB2\tCOXSACKIE B2 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB3\tCOXSACKIE B3 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB4\tCOXSACKIE B4 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB5\tCOXSACKIE B5 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB6\tCOXSACKIE B6 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP10\tCOXSACKIE A10 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP16\tCOXSACKIE A16 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP7\tCOXSACKIE A7 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP9\tCOXSACKIE A9 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE11\tECHOVIRUS 11 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE30\tECHOVIRUS 30 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE4\tECHOVIRUS 4 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE7\tECHOVIRUS 7 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE9\tECHOVIRUS 9 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tRHFINT\tINTERPRETATION\t9999999\t2437250.769\t1\t9999999\t1.00E+14\t26\t63368520\t2.95E+15
-LAB BLOOD ORDERABLES\t668164\tLABDCMVAB\tDONOR, CMV TOTAL ANTIBODIES\tCMVAB1\tDONOR, CMV TOTAL ANTIBODIES\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668166\tLABDHBCAB\tDONOR, HEP B CORE TOTAL AB\tHBCAB1\tDONOR, HEP B CORE TOTAL AB\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668170\tLABDHBAG\tDONOR, HEP B SURFACE AG\t11891R\tDONOR, HBSAG CONFIRMATION\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668170\tLABDHBAG\tDONOR, HEP B SURFACE AG\tHBAG1\tDONOR, HEP B SURFACE AG\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668172\tLABDHIVAB\tDONOR,HIV-1/HIV-2 AB SCN W/RFL\t11888R\tDONOR, HIV-1/2 AB, CONF.\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668172\tLABDHIVAB\tDONOR,HIV-1/HIV-2 AB SCN W/RFL\tHIVAB1\tDONOR,HIV-1/HIV-2 AB SCN W/RFL\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668174\tLABDHTLVQ\tDONOR, HTLV-I/II AB SCN\tHTLVQ1\tDONOR, HTLV-I/II AB SCN\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668176\tLABDRPR\tDONOR, RPR W/RFL TO SYPH IGG\t11892R\tDONOR, SYPHILIS IGG\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668176\tLABDRPR\tDONOR, RPR W/RFL TO SYPH IGG\tRPR1\tDONOR, RPR W/RFL TO SYPH IGG\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t668178\tLABDRPRS\tDONOR SYPHILIS SCREEN\tRPRS1\tDONOR SYPHILIS SCREEN\t9999999\t208950\t1\t9999999\t1.00E+14\t2\t417900\t91078650000
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITMD\tINTERPRETED BY\t9999999\t818253.1915\t1\t9999999\t1.00E+14\t188\t153831600\t3.04E+14
-LAB BLOOD ORDERABLES\t670700\tLAB11195R\tN-TELOPEPTIDE, CROSS-LINKED\t11196R\tN-TELOPEPTIDE, CROSS-LINKED\t26\t238920\t1\t26\t676\t1\t238920\t57082766400
-LAB BLOOD ORDERABLES\t670810\tLAB11173R\tIODINE\t11174R\tIODINE\t77\t189300\t1\t77\t5929\t1\t189300\t35834490000
-LAB BLOOD ORDERABLES\t744318\tLAB11599R\tGANICICLOVIR\t11756R\tGANCICLOVIR\t9999999\t636120\t1\t9999999\t1.00E+14\t1\t636120\t4.05E+11
-LAB BLOOD ORDERABLES\t744320\tLAB11604R\tIL 5\t12044R\tINTERLEUKIN 5\t166\t409320\t1\t166\t27556\t1\t409320\t1.68E+11
-LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11763R\tINTERPRETATION\t9999999\t282690\t1\t9999999\t1.00E+14\t10\t2826900\t9.22E+11
-LAB BLOOD ORDERABLES\t744330\tLAB11610R\tDONOR, HTLVS(QNS/LIPEMIC ONLY)\t11769R\tDONOR, HTLVS\t9999999\t668460\t1\t9999999\t1.00E+14\t3\t2005380\t1.52E+12
-LAB BLOOD ORDERABLES\t744368\tLABLEGS\tLEGIONELLA SEROLOGY\tLEG1\tL.LEGIONELLA AB (SEROGRP 1)\t9999999\t466080\t1\t9999999\t1.00E+14\t1\t466080\t2.17E+11
-LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPA\tPROCAINAMIDE\t9.1\t6420\t1\t9.1\t82.81\t1\t6420\t41216400
-LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL1\tPEAK, TROUGH, OR RANDOM\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
-LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL2\tDATE & TIME OF LAST DOSE\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
-LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL3\tDOSAGE (INCLUDE UNITS)\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
-LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL4\tADMINISTRATION ROUTE\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\t12430R\tINTERPRETATION\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tCTIGA\tC.TRACHOMATIS IGA\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tCTIGG\tC.TRACHOMATIS IGG\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tCTIGM\tC.TRACHOMATIS IGM\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPNIGA\tC. PNEUMONIAE IGA\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPNIGG\tC. PNEUMONIAE IGG\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPNIGM\tC. PNEUMONIAE IGM\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPSIGA\tC. PSITTACI IGA\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPSIGG\tC. PSITTACI IGG\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPSIGM\tC. PSITTACI IGM\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGDM1\t1HR\t84\t17220\t1\t84\t7056\t1\t17220\t296528400
-LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGDM2\t2HR\t80\t17220\t1\t80\t6400\t1\t17220\t296528400
-LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGLUF\tGLUCOSE, FASTING (GDMF)\t70\t17220\t1\t70\t4900\t1\t17220\t296528400
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tCENQNT\tANTI-CENTROMERE LEVEL\t43\t279300\t1\t43\t1849\t1\t279300\t78008490000
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81DX\tCLINICAL INDICATION\t9999999\t757500\t1\t9999999\t1.00E+14\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81RE\tRESULT, NON-BLOOD\t9999999\t757500\t1\t9999999\t1.00E+14\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81SO\tSOURCE\t9999999\t757500\t1\t9999999\t1.00E+14\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88RE\tRESULT\t9999999\t1389780\t1\t9999999\t1.00E+14\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSMDB\tINTERPRETED BY\t9999999\t940852.5\t1\t9999999\t1.00E+14\t112\t105375480\t2.64E+14
-LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSRE\tRESULT\t9999999\t371657.1429\t1\t9999999\t1.00E+14\t7\t2601600\t1.85E+12
-LAB BLOOD ORDERABLES\t890117\tLAB12230R\tSOLUBLE IL 2R\t12231R\t12231R\t9999999\t697333.9535\t1\t9999999\t1.00E+14\t43\t29985360\t7.88E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12254R\t12254R\t9999999\t925412.7273\t1\t9999999\t1.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12255R\t12255R\t9999999\t925412.7273\t1\t9999999\t1.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12278R\t12278R\t9999999\t925412.7273\t1\t9999999\t1.00E+14\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890129\tLAB12279R\tCVID CONFIRMATION FLOW PANEL\t12280R\t12280R\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t890129\tLAB12279R\tCVID CONFIRMATION FLOW PANEL\t12281R\t12281R\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tRESS1\tRESS1\t9999999\t671067.3054\t1\t9999999\t1.00E+14\t167\t112068240\t1.25E+14
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3DX\tCLINICAL INDICATION\t9999999\t190860\t1\t9999999\t1.00E+14\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3RE\tRESULT, BLOOD\t9999999\t190860\t1\t9999999\t1.00E+14\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3DX\tCLINICAL INDICATION\t9999999\t480\t1\t9999999\t1.00E+14\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3SP\tSOURCE\t9999999\t480\t1\t9999999\t1.00E+14\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLRE\tRESULT\t9999999\t1418550\t1\t9999999\t1.00E+14\t2\t2837100\t4.13E+12
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC48RAT\tTCD4/CD8 RATIO\t1.4\t126180\t1\t1.4\t1.96\t1\t126180\t15921392400
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD19AB\tCD19 (PAN B), ABS.\t55\t126180\t1\t55\t3025\t1\t126180\t15921392400
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD19PC\tCD19 (PAN B) %\t17\t126180\t1\t17\t289\t1\t126180\t15921392400
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD4TOT\tCD4 TOTAL\t41\t126180\t1\t41\t1681\t1\t126180\t15921392400
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tTCD4AB\tTOTAL CD4, ABS.\t133\t126180\t1\t133\t17689\t1\t126180\t15921392400
-LAB BLOOD ORDERABLES\t912863\tLAB12415R\tACVRL1 AND ENG (HHT)\t12416R\tHHT FGA SPECIMEN\t9999999\t3636840\t1\t9999999\t1.00E+14\t1\t3636840\t1.32E+13
-LAB BLOOD ORDERABLES\t961559\tLAB12817R\tDONOR, HCV AB SCREEN W/RFLX\t12828R\tDONOR, HEPATITIS C AB\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
-LAB BLOOD ORDERABLES\t966907\tLAB12584R\tTIAGABINE CONCENTRATION\t12593R\tTIAGABINE CONCENTRATION\t37\t350400\t1\t37\t1369\t1\t350400\t1.23E+11
-LAB BLOOD ORDERABLES\t977669\tLABPANPLT\tPLATELET COUNT\tPLT\tPLATELET COUNT\t4\t300\t1\t4\t16\t1\t300\t90000
-LAB BLOOD ORDERABLES\t978241\tLABOGTT\tGLUCOSE TOLERANCE FOR CYSTIC FIBROSIS\tGLT1\tGLUCOSE, 1 HR\t283\t25440\t1\t283\t80089\t1\t25440\t647193600
-LAB BLOOD ORDERABLES\t978241\tLABOGTT\tGLUCOSE TOLERANCE FOR CYSTIC FIBROSIS\tGLT2\tGLUCOSE, 2 HR\t339\t25440\t1\t339\t114921\t1\t25440\t647193600
-MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tSDES\tSPECIMEN\t9999999\t255120\t1\t9999999\t1.00E+14\t1\t255120\t65086214400
-MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tCULT\tCULT/OTHER RSLT:\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tCULT\tCULT/OTHER RSLT:\t9999999\t522530.9375\t1\t9999999\t1.00E+14\t384\t200651880\t3.85E+14
-MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tGS\tGRAM STAIN\t9999999\t522530.9375\t1\t9999999\t1.00E+14\t384\t200651880\t3.85E+14
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tAMP\tAMPICILLIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tATM\tAZTREONAM.\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCIP\tCIPROFLOXACIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCRO\tCEFTRIAXONE\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCTN\tCEFOTETAN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCTX\tCEFOTAXIME\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCZ\tCEFAZOLIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tFEP\tCEFEPIME\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tFURI\tCEFUROXIME (IV)\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tGM\tGENTAMICIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tIMP\tIMIPENEM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tLEV\tLEVOFLOXACIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tMER\tMEROPENEM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tMTYP\tMETHOD\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSAMP\tAMPICILLIN/SULBACTAM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSXT\tTRIMETHOPRIM/SULFAMETHOXAZOLE\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tTZP\tPIPERACILLIN/TAZOBACTAM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
-MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tCULT\tCULT/OTHER RSLT:\t9999999\t1190220\t1\t9999999\t1.00E+14\t2\t2380440\t2.83E+12
-MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t418604.2105\t1\t9999999\t1.00E+14\t19\t7953480\t3.62E+12
-MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tORG\tORGANISM\t9999999\t29170200\t1\t9999999\t1.00E+14\t1\t29170200\t8.51E+14
-MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tCULT\tCULT/OTHER RSLT:\t9999999\t1542888\t1\t9999999\t1.00E+14\t10\t15428880\t2.97E+13
-MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t313706.25\t1\t9999999\t1.00E+14\t32\t10038600\t5.00E+12
-MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tCULT\tCULT/OTHER RSLT:\t9999999\t213232.1186\t1\t9999999\t1.00E+14\t236\t50322780\t1.42E+13
-MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tRPT\tRPT\t9999999\t55380\t1\t9999999\t1.00E+14\t1\t55380\t3066944400
-MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tSDES\tSPECIMEN\t9999999\t55380\t1\t9999999\t1.00E+14\t1\t55380\t3066944400
-MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tSREQ\tCOMMENTS\t9999999\t55380\t1\t9999999\t1.00E+14\t1\t55380\t3066944400
-MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tCULT\tCULT/OTHER RSLT:\t9999999\t1747302.857\t1\t9999999\t1.00E+14\t14\t24462240\t4.57E+13
-MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tCULT\tCULT/OTHER RSLT:\t9999999\t616054.2857\t1\t9999999\t1.00E+14\t14\t8624760\t5.65E+12
-MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tRPT\tRPT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t330942.4585\t1\t9999999\t1.00E+14\t301\t99613680\t3.68E+13
-MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t191605.7143\t1\t9999999\t1.00E+14\t21\t4023720\t8.21E+11
-MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tCULT\tCULT/OTHER RSLT:\t9999999\t309040\t1\t9999999\t1.00E+14\t3\t927120\t2.91E+11
-MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tSDES\tSPECIMEN\t9999999\t518040\t1\t9999999\t1.00E+14\t1\t518040\t2.68E+11
-MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tSREQ\tCOMMENTS\t9999999\t518040\t1\t9999999\t1.00E+14\t1\t518040\t2.68E+11
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tAMP\tAMPICILLIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tATM\tAZTREONAM.\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCIP\tCIPROFLOXACIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCRO\tCEFTRIAXONE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCTN\tCEFOTETAN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCTX\tCEFOTAXIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCZ\tCEFAZOLIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tFEP\tCEFEPIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tFURI\tCEFUROXIME (IV)\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tGM\tGENTAMICIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tIMP\tIMIPENEM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tLEV\tLEVOFLOXACIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tMER\tMEROPENEM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tMTYP\tMETHOD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSAMP\tAMPICILLIN/SULBACTAM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSXT\tTRIMETHOPRIM/SULFAMETHOXAZOLE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tTZP\tPIPERACILLIN/TAZOBACTAM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tDVE\tRESULTS\t9999999\t60293.33333\t1\t9999999\t1.00E+14\t9\t542640\t43775150400
-MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tRPT\tRPT\t9999999\t49680\t1\t9999999\t1.00E+14\t1\t49680\t2468102400
-MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tSDES\tSPECIMEN\t9999999\t49680\t1\t9999999\t1.00E+14\t1\t49680\t2468102400
-MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tSREQ\tCOMMENTS\t9999999\t49680\t1\t9999999\t1.00E+14\t1\t49680\t2468102400
-MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tRPT\tRPT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tSDES\tSPECIMEN\t9999999\t48300\t1\t9999999\t1.00E+14\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tSREQ\tCOMMENTS\t9999999\t48300\t1\t9999999\t1.00E+14\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tGS\tGRAM STAIN\t9999999\t3540\t1\t9999999\t1.00E+14\t1\t3540\t12531600
-MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t2140200\t1\t9999999\t1.00E+14\t1\t2140200\t4.58E+12
-MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tCULT\tCULT/OTHER RSLT:\t9999999\t114455.7534\t1\t9999999\t1.00E+14\t146\t16710540\t2.41E+12
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCPRM\tPRIMERS\t9999999\t599640\t1\t9999999\t1.00E+14\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCTIS\tSOURCE\t9999999\t599640\t1\t9999999\t1.00E+14\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRDIR\tINTERPRETED BY\t9999999\t485881.3483\t1\t9999999\t1.00E+14\t178\t86486880\t5.83E+13
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRCT\tCOMMENT\t9999999\t77280\t1\t9999999\t1.00E+14\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRMT\tMETHOD\t9999999\t77280\t1\t9999999\t1.00E+14\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRPM\tPRIMERS\t9999999\t77280\t1\t9999999\t1.00E+14\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tFP190\tP190, FLUID\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRE\tRESULT\t9999999\t1367340\t1\t9999999\t1.00E+14\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11MD\tINTERPRETED BY\t9999999\t266970\t1\t9999999\t1.00E+14\t2\t533940\t2.85E+11
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFPMLR\tPML, FLD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15CO\tCOMMENT, FLD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15FL\tSOURCE, FLUID\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15DX\tCLINICAL INDICATION\t9999999\t909120\t1\t9999999\t1.00E+14\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15RE\tRESULT, TISSUE\t9999999\t909120\t1\t9999999\t1.00E+14\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTCMT\tCOMMENT\t9999999\t763860\t1\t9999999\t1.00E+14\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCPRM\tPRIMERS\t9999999\t1195020\t1\t9999999\t1.00E+14\t3\t3585060\t4.32E+12
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCTIS\tSOURCE\t9999999\t1195020\t1\t9999999\t1.00E+14\t3\t3585060\t4.32E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSMDM\tINTERPRETED BY\t9999999\t823243.9024\t1\t9999999\t1.00E+14\t123\t101259000\t9.94E+13
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPMD\tINTERPRETED BY\t9999999\t540720\t1\t9999999\t1.00E+14\t66\t35687520\t2.44E+13
-POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tUGLU\tGLUCOSE URINE\t9999999\t48060\t1\t9999999\t1.00E+14\t2\t96120\t5963220000
-POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tUPROT\tPROTEIN URINE\t9999999\t48060\t1\t9999999\t1.00E+14\t2\t96120\t5963220000
-POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\t11148R\tPH\t8\t-198000\t1\t8\t64\t1\t-198000\t39204000000
-POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHGAST\tPH,GASTRIC\t1\t-240\t1\t1\t1\t1\t-240\t57600
-POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\t11148R\tPH\t7\t16633500\t1\t7\t49\t1\t16633500\t2.77E+14
-POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tPNAB1\tPNEUMOCOCCAL AB (PNEUMOVAX)\t150\t-93600\t1\t150\t22500\t1\t-93600\t8760960000
-POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tPPD100\tPPD 1:100 CPM\t9999999\t11700\t1\t9999999\t1.00E+14\t1\t11700\t136890000
-POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tPPDINT\tPPD INTERPRETATION\t9999999\t-74700\t1\t9999999\t1.00E+14\t1\t-74700\t5580090000
-POINT OF CARE (NON LAB) TESTS\t610682\tPOC32\tPOC HEMOCUE FOR HEMOGLOBIN\tHGB\tPATIENT HEMOGLOBIN RESULT\t9999999\t-240\t1\t9999999\t1.00E+14\t1\t-240\t57600
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\t11147R\tSPECIFIC GRAVITY\t1.01\t-205200\t1\t1.01\t1.0201\t1\t-205200\t42107040000
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tSPG\tSPECIFIC GRAVITY UA\t1\t-78300\t1\t1\t1\t1\t-78300\t6130890000
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tUBLOOD\tBLOOD URINE\t9999999\t-196200\t1\t9999999\t1.00E+14\t2\t-392400\t77150880000
-POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tUWBC\tLEUKOCYTES\t9999999\t-205200\t1\t9999999\t1.00E+14\t1\t-205200\t42107040000
-POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tINR\tINR,POCT\t1.2\t35100\t1\t1.2\t1.44\t1\t35100\t1232010000
-POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tPTT\tPTT,POCT\t14.4\t35100\t1\t14.4\t207.36\t1\t35100\t1232010000
-POINT OF CARE TESTING\t591290\tPOC17\tPOC ISTAT CREATININE\tCR\tCREATININE,ISTAT\t0.7\t49260\t1\t0.7\t0.49\t1\t49260\t2426547600
-POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tBE\tBASE EXCESS, ISTAT\t1\t57660\t1\t1\t1\t1\t57660\t3324675600
-POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tHCO3V\tHCO3 (V), ISTAT\t27.9\t57660\t1\t27.9\t778.41\t1\t57660\t3324675600
-POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tPCO2SO\tO2 SATURATION, ISTAT\t19\t57660\t1\t19\t361\t1\t57660\t3324675600
-POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tPCO2V\tPCO2 (V), ISTAT\t57.9\t57660\t1\t57.9\t3352.41\t1\t57660\t3324675600
-POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tPH\tPH, ISTAT\t7.29\t57660\t1\t7.29\t53.1441\t1\t57660\t3324675600
-POINT OF CARE TESTING\t591304\tPOC24\tPOC ISTAT TROPONIN I\tTNI\tTROPONIN I, POCT\t0\t51000\t1\t0\t0\t1\t51000\t2601000000
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tCAION\tCALCIUM,ION, ISTAT\t1.12\t60\t1\t1.12\t1.2544\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tCR\tCREATININE,ISTAT\t4.7\t60\t1\t4.7\t22.09\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tGLU\tGLUCOSE,ISTAT\t110\t60\t1\t110\t12100\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tHCT\tHCT(CALC), ISTAT\t30\t60\t1\t30\t900\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tHGB\tHGB(CALC), ISTAT\t10.2\t60\t1\t10.2\t104.04\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tK\tPOTASSIUM, ISTAT\t4\t60\t1\t4\t16\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tNA\tSODIUM, ISTAT\t139\t60\t1\t139\t19321\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCAGP\tANION GAP, ISTAT\t16\t60\t1\t16\t256\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCBUN\tBUN, ISTAT\t21\t60\t1\t21\t441\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCCL\tCHLORIDE, ISTAT\t96\t60\t1\t96\t9216\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCCOM\tPOCT COMMENT\t9999999\t60\t1\t9999999\t1.00E+14\t1\t60\t3600
-POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tTCO2A\tTCO2, ISTAT\t31\t60\t1\t31\t961\t1\t60\t3600
-POINT OF CARE TESTS (MENLO)\t861037\t230986580\tPOC MENLO PPD TEST\tERYTHREMA\tERYTHREMA\t0\t52200\t1\t0\t0\t1\t52200\t2724840000
-POINT OF CARE TESTS (MENLO)\t861037\t230986580\tPOC MENLO PPD TEST\tMMIND\tMM INDURATION\t0\t52200\t1\t0\t0\t1\t52200\t2724840000
-POINT OF CARE TESTS (MENLO)\t861037\t230986580\tPOC MENLO PPD TEST\tPHLOC\tLOCATION\t9999999\t52200\t1\t9999999\t1.00E+14\t1\t52200\t2724840000
-QUEST ORDERABLES\t977479\tLABQUECORSL\tQUEST - CORTISOL, SERUM LC/MS/MS\tXCORTI\tCORTISOL TOTAL SERUM\t6.2\t-345600\t1\t6.2\t38.44\t1\t-345600\t1.19E+11
-STOOL ORDERABLES\t471510\tLABY5NUC\t5 - NUCLEOTIDASE\t5NUCLEOT\t5\'NUCLEOTIDASE\t67.6\t187770\t1\t67.6\t4569.76\t2\t375540\t81594795600
-STOOL ORDERABLES\t471612\tLABYAMPHB\tAMPHOTERICIN B\tAMPHOTERB\tAMPHOTERICIN B\t9.9\t#VALUE!\t1\t9.9\t98.01\t0\tNone\tNone
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tMG\tMAGNESIUM\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tPHOS\tPHOSPHORUS\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYANTQ\tANTHRAQUINONES\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYBIS\tBISACODYL\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYOXY\tOXYPHENISATIN\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYPHEN\tPHENOLPHTHALEIN\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL1\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL2\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL3\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL4\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL5\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL6\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
-STOOL ORDERABLES\t472304\tLAB11118R\tELASTASE, STOOL\tSTELAS\tELASTASE-1, STOOL\t9999999\t547750.1538\t1\t9999999\t1.00E+14\t130\t71207520\t6.17E+13
-STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG2\tTOTAL WEIGHT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG3\tCOLLECTION DURATION\t9999999\t280440\t1\t9999999\t1.00E+14\t1\t280440\t78646593600
-STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG4\tCONCENTRATION\t9999999\t280440\t1\t9999999\t1.00E+14\t1\t280440\t78646593600
-STOOL ORDERABLES\t472823\tLABFECFIB\tMEAT FIBERS, FECAL\tFECFB\tMEAT FIBERS, FECAL\t1\t1306350\t1\t1\t1\t2\t2612700\t4.90E+12
-STOOL ORDERABLES\t473114\tLABSTPOLY\tPMNS / EOSINOPHILS, STOOL\tEOSSTL\tEOSINOPHILS, STOOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t473114\tLABSTPOLY\tPMNS / EOSINOPHILS, STOOL\tPOLYST\tPMNS, STOOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t473120\tLABPORFEC\tPORPHYRINS FRACTIONATED, FECAL\tFPORIN\tPORPHYRIN INTRP, FEC\t9999999\t495795\t1\t9999999\t1.00E+14\t4\t1983180\t1.03E+12
-STOOL ORDERABLES\t473151\tLABYPA\tPROCAINAMIDE\tNAPA\tNAPA\t1.5\t88380\t1\t1.5\t2.25\t1\t88380\t7811024400
-STOOL ORDERABLES\t473151\tLABYPA\tPROCAINAMIDE\tYPANAP\tPROCAINAMIDE + NAPA\t6\t88380\t1\t6\t36\t1\t88380\t7811024400
-STOOL ORDERABLES\t473151\tLABYPA\tPROCAINAMIDE\tYPROC\tPROCAINAMIDE\t4.5\t88380\t1\t4.5\t20.25\t1\t88380\t7811024400
-STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK2\tSPOTTED FEVER GROUP,AB,IGM,S\t9999999\t236916\t1\t9999999\t1.00E+14\t30\t7107480\t2.42E+12
-STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK3\tTYPHUS FEVER GROUP,AB,IGG,S\t9999999\t236916\t1\t9999999\t1.00E+14\t30\t7107480\t2.42E+12
-STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK4\tTYPHUS FEVER GROUP,AB,IGM,S\t9999999\t236916\t1\t9999999\t1.00E+14\t30\t7107480\t2.42E+12
-STOOL ORDERABLES\t473433\tLABYTHI\tTHIOCYANATE\tTHIOCYAN\tTHIOCYANATE\t9999999\t638707.8261\t1\t9999999\t1.00E+14\t23\t14690280\t1.69E+13
-STOOL ORDERABLES\t473686\tLABYSTRYP\tTRYPSIN, FECAL\tTRYPSINSTL\tTRYPSIN, FECAL\t9999999\t636156\t1\t9999999\t1.00E+14\t5\t3180780\t2.42E+12
-STOOL ORDERABLES\t497243\tLABE2O\tESTRADIOL\tESTRADIOL\tESTRADIOL\t394\t#VALUE!\t1\t394\t155236\t0\tNone\tNone
-STOOL ORDERABLES\t541693\tLABHBCM\tHEPATITIS B CORE / PRECORE MUTATION\tHBCM2\tPRECORE CODON\t9999999\t873165\t1\t9999999\t1.00E+14\t8\t6985320\t7.15E+12
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11300R\tCHLORPROPAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11301R\tTOLAZAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11302R\tTOLBUTAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11303R\tGLYBURIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11304R\tGLIPIZIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11305R\tACETOHEXAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t668093\tLABCYLX\tIMMUNE FUNCTION TEST\tCYLX1\tIMMUNE FUNCTION TEST RESULT\t9999999\t321704.1176\t1\t9999999\t1.00E+14\t68\t21875880\t2.40E+13
-STOOL ORDERABLES\t670704\tLAB11269R\tACETYL RECEPTOR AB PANL\tACRBIN\tACETYLCHOLINE BIN AB\t0\t#VALUE!\t1\t0\t0\t0\tNone\tNone
-STOOL ORDERABLES\t670704\tLAB11269R\tACETYL RECEPTOR AB PANL\tACRBLO\tACETYLCHOLINE BLOCKING AB\t0\t#VALUE!\t1\t0\t0\t0\tNone\tNone
-STOOL ORDERABLES\t670704\tLAB11269R\tACETYL RECEPTOR AB PANL\tACRMOD\tACETYLCHOLINE MODULATING AB\t8\t#VALUE!\t1\t8\t64\t0\tNone\tNone
-STOOL ORDERABLES\t670760\tLAB11234R\tVZV TOTAL AND IGM AB PANEL\t11237R\tVZV INTERPRETATION\t9999999\t214763.038\t1\t9999999\t1.00E+14\t237\t50898840\t1.48E+13
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11255R\tORGANISM\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11256R\tAMIKACIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11257R\tCIPROFLOXACIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11258R\tCLARITHROMYCIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11259R\tETHAMBUTOL\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11260R\tLINEZOLID\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11261R\tMOXIFLOXACIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11262R\tRIFAMPIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11263R\tRIFABUTIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11897R\tETHIONAMIDE\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11898R\tISONIAZID\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670764\tLAB11265R\tENTAMOEBA HISTOLYTICA AG\t11266R\tENTAMOABA HISTOLYTICA AG\t9999999\t249417.8571\t1\t9999999\t1.00E+14\t28\t6983700\t2.23E+12
-STOOL ORDERABLES\t744266\tLAB11568R\tBRUCELLA SEROLOGIES, CSF\t11682R\tBRUCELLA IGG,CSF\t0.1\t272970\t1\t0.1\t0.01\t2\t545940\t1.68E+11
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11724R\tCANDIDA ALBICANS, DILUTION 1\t1.8\t#VALUE!\t1\t1.8\t3.24\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11727R\tCANDIDA ALBICANS, DILUTION 4\t4.9\t#VALUE!\t1\t4.9\t24.01\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11729R\tTETANUS TOXOID, DILUTION 1\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11731R\tTETANUS TOXOID, DILUTION 3\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11732R\tTETANUS TOXOID, DILUTION 4\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12291R\t12291R\t9999999\t342800\t1\t9999999\t1.00E+14\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tLABS2\tLABS2\t9999999\t427020\t1\t9999999\t1.00E+14\t2\t854040\t3.80E+11
-STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL2\tTOTAL WEIGHT\t756\t1815000\t1\t756\t571536\t2\t3630000\t6.65E+12
-STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK2\tTOTAL WEIGHT\t9999999\t310750\t1\t9999999\t1.00E+14\t6\t1864500\t1.50E+12
-TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tUPIER\tRESULT\t9999999\t309620\t1\t9999999\t1.00E+14\t18\t5573160\t2.51E+12
-URINE ORDERABLES\t472442\tLABGHBUR\tGAMMA - HYDROXYBUTYRIC ACID, URINE\tGHBURN\tG-OH-BUTYRIC AC,UR\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tGLUEX\tGLUCOSE EXCRETION\t364\t7080\t1\t364\t132496\t1\t7080\t50126400
-URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tGLUURN\tGLUCOSE, URINE\t56\t7080\t1\t56\t3136\t1\t7080\t50126400
-URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tUT7\tCOLLECTION PERIOD GLUCOSE\t24\t7080\t1\t24\t576\t1\t7080\t50126400
-URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tVOL7\tVOLUME GLUCOSE\t650\t7080\t1\t650\t422500\t1\t7080\t50126400
-URINE ORDERABLES\t472927\tLABMPSQNT\tMUCOPOLYSACCHARIDES QUANTITATIVE, URINE\tMPS\tMUCOPOLYSAC, UR\t3.9\t#VALUE!\t1\t3.9\t15.21\t0\tNone\tNone
-URINE ORDERABLES\t473092\tLABYUPCPC\tPHENCYCLIDINE CONFIRMATION, URINE\tYUPCGC\tPHENCYCLIDINE\t30\t#VALUE!\t1\t30\t900\t0\tNone\tNone
-URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tCLNTST\tCLINITEST\t9999999\t4860\t1\t9999999\t1.00E+14\t1\t4860\t23619600
-URINE ORDERABLES\t473731\tLABUAMICR\tURINALYSIS MICROSCOPIC ONLY\tURBC\tRBC, URINE\t9999999\t2220\t1\t9999999\t1.00E+14\t1\t2220\t4928400
-URINE ORDERABLES\t473731\tLABUAMICR\tURINALYSIS MICROSCOPIC ONLY\tUWBC\tWBC, URINE\t9999999\t2220\t1\t9999999\t1.00E+14\t1\t2220\t4928400
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tCLNTST\tCLINITEST\t9999999\t1680\t1\t9999999\t1.00E+14\t1\t1680\t2822400
-URINE ORDERABLES\t473770\tLAB12845R\tVMA AND HVA, PEDIATRIC, URINE\t12843R\tCHILD VMA\t9999999\t234900\t1\t9999999\t1.00E+14\t1\t234900\t55178010000
-URINE ORDERABLES\t473770\tLAB12845R\tVMA AND HVA, PEDIATRIC, URINE\t12844R\tCHILD HVA\t9999999\t234900\t1\t9999999\t1.00E+14\t1\t234900\t55178010000
-URINE ORDERABLES\t497400\tLABYDROTC\tDRUG SCREEN PRESCRIPTION / OTC\t12194R\tDRUGS DETECTED:\t9999999\t212726.6667\t1\t9999999\t1.00E+14\t9\t1914540\t6.68E+11
-URINE ORDERABLES\t497400\tLABYDROTC\tDRUG SCREEN PRESCRIPTION / OTC\t12195R\tSUSPECT DRUG\t9999999\t212726.6667\t1\t9999999\t1.00E+14\t9\t1914540\t6.68E+11
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tALCOHOL\tALCOHOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tAMPHET\tAMPHETAMINES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tBARB\tBARBITURATES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tBENZO\tBENZODIAZEPINES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tCOCAINE\tCOCAINE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tMETHADONE\tMETHADONE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tOPIATE\tOPIATES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tPROPOX\tPROPOXYPHENE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tYUPCP\tPHENCYCLIDINE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tYUTTHC\tTETRAHYDROCANNABINOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11220R\tIDODINE, U\t320\t157260\t1\t320\t102400\t1\t157260\t24730707600
-URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11221R\tCOLLECTION DURATION\t24\t157260\t1\t24\t576\t1\t157260\t24730707600
-URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11222R\tURINE VOLUME\t2905\t157260\t1\t2905\t8439025\t1\t157260\t24730707600
-URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11223R\tIODINE CONCENTRATION\t110\t157260\t1\t110\t12100\t1\t157260\t24730707600
-URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t11226R\tCOLLECTION DURATION\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
-URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t11227R\tURINE VOLUME\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
-URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t11228R\tPB CONC\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
-URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t12199R\tLEAD, 24 HR, U\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\tVOLUMEURN\tURINE VOLUME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\tYUMECO\tCOMMENT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\tYUMEUT\tCOLLECTION DURATION\t9999999\t471510\t1\t9999999\t1.00E+14\t2\t943020\t4.75E+11
-VIROLOGY ORDERABLES\t472015\tLABCLCR2\tCHLAMYDIA MOLECULAR DETECTION\tDCL4\tTEST INFORMATION\t9999999\t105230\t1\t9999999\t1.00E+14\t12\t1262760\t1.93E+11
-VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tDVE\tRESULTS\t9999999\t68157\t1\t9999999\t1.00E+14\t60\t4089420\t4.28E+11
-VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tRPT\tRPT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBV\tEBV BY PCR\t9999999\t-518400\t1\t9999999\t1.00E+14\t1\t-518400\t2.69E+11
-VIROLOGY ORDERABLES\t472525\tLABHBSQL\tANTI-HBS QUAL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t22020\t1\t9999999\t1.00E+14\t1\t22020\t484880400
-VIROLOGY ORDERABLES\t472530\tLABHSAB\tHSV VIRAL AB\tHSAB\tVIRAL AB HSV\t9999999\t238239.8507\t1\t9999999\t1.00E+14\t134\t31924140\t1.03E+13
-VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVIPR\tTEST DESCRIPTION\t9999999\t339631.7442\t1\t9999999\t1.00E+14\t172\t58416660\t2.76E+13
-VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS4\tHIV-1 P24 (NON ICD) AG LEVEL\t9999999\t773550\t1\t9999999\t1.00E+14\t2\t1547100\t1.20E+12
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tCULT\tCULT/OTHER RSLT:\t9999999\t696693.125\t1\t9999999\t1.00E+14\t96\t66882540\t7.55E+13
-VIROLOGY ORDERABLES\t472574\tLABYHMPVP\tHUMAN METAPNEUMOVIRUS PCR\tYHMP2\tSOURCE\t9999999\t455820\t1\t9999999\t1.00E+14\t1\t455820\t2.08E+11
-VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFLUA\tINFLUENZA VIRUS TYPE A\t9999999\t11850\t1\t9999999\t1.00E+14\t10\t118500\t2648221200
-VIROLOGY ORDERABLES\t473284\tLABMEAIGG\tRUBEOLA VIRAL AB\tME\tRUBEOLA (MEASLES) AB, IGG\t9999999\t123780\t1\t9999999\t1.00E+14\t1\t123780\t15321488400
-VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA SEROLOGY\tTOXO\tTOXOPLASMA IGG\t9999999\t515100\t1\t9999999\t1.00E+14\t1\t515100\t2.65E+11
-VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVGGDES\tVZV IGG TEST DESCRIPTION\t9999999\t167515.5882\t1\t9999999\t1.00E+14\t272\t45564240\t9.41E+12
-VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t497325\tLABADVDNA\tADENOVIRUS DNA, QUALITATIVE PCR\tADOV\tADENOVIRUS DNA, QL PCR\t9999999\t174138\t1\t9999999\t1.00E+14\t140\t24379320\t5.10E+12
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS PCR QUAL\tSOURC3\tADENOVIRUS SOURCE\t9999999\t114450\t1\t9999999\t1.00E+14\t2\t228900\t28059325200
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS DNA QUANTITATION\tSOURC6\tSOURCE\t9999999\t168960\t1\t9999999\t1.00E+14\t1\t168960\t28547481600
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKQ\tBKV DNA QUANTITATION\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tSOURC6\tSOURCE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t497468\tLABBKDNA\tBK VIRUS DNA PCR\tSOURC7\tSOURCE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS PCR QUANT\tADPCR1\tADENVIRUS DNA\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS PCR QUANT\tSOUR10\tSOURCE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tSOUR10\tSOURCE\t9999999\t217572\t1\t9999999\t1.00E+14\t5\t1087860\t2.65E+11
-VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVTIP\tTEST DESCRIPTION\t9999999\t170190\t1\t9999999\t1.00E+14\t12\t2042280\t4.38E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tEBV\tEBV BY PCR\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tYEBP1\tSPECIMEN SOURCE\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tYEBP3\tSPECIAL INFORMATION\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tYEPB4\tREPORT STATUS\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tEBV\tEBV BY PCR\t9999999\t329575.7143\t1\t9999999\t1.00E+14\t14\t4614060\t1.91E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tYEBP3\tSPECIAL INFORMATION\t9999999\t329575.7143\t1\t9999999\t1.00E+14\t14\t4614060\t1.91E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tYEPB4\tREPORT STATUS\t9999999\t329575.7143\t1\t9999999\t1.00E+14\t14\t4614060\t1.91E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tEBV\tEBV BY PCR\t9999999\t326800\t1\t9999999\t1.00E+14\t3\t980400\t3.42E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tYEBP1\tSPECIMEN SOURCE\t9999999\t326800\t1\t9999999\t1.00E+14\t3\t980400\t3.42E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tYEBP3\tSPECIAL INFORMATION\t9999999\t326800\t1\t9999999\t1.00E+14\t3\t980400\t3.42E+11
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\t11154R\tSOURCE\t9999999\t206940\t1\t9999999\t1.00E+14\t2\t413880\t92256228000
-VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t254505\t1\t9999999\t1.00E+14\t8\t2036040\t6.91E+11
-VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6CP\tRESULT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6LO\tRESULT LOG 10\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6SP\tSPECIMEN\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t1350\t1\t9999999\t1.00E+14\t2\t2700\t7290000
-VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEBV EARLY ANTIGEN IGG\tEAGDES\tEAGDES\t9999999\t285229.4118\t1\t9999999\t1.00E+14\t17\t4848900\t2.81E+12
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN (EBNA) IGG\tEBNDES\tEBNDES\t9999999\t298560\t1\t9999999\t1.00E+14\t21\t6269760\t3.42E+12
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN IGG\tEBNQUL\tEBNQUL\t9999999\t687360\t1\t9999999\t1.00E+14\t2\t1374720\t9.65E+11
-VIROLOGY ORDERABLES\t670959\tLAB11024R\tEB VIRAL CAPSID AG IGM AB\t11167R\tEB VCA IGM AB\t9999999\t369690\t1\t9999999\t1.00E+14\t2\t739380\t3.71E+11
-VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEBV VIRAL CAPSID ANTIGEN IGM\tVMDES\tVMDES\t9999999\t251356.0714\t1\t9999999\t1.00E+14\t56\t14075940\t5.57E+12
-VIROLOGY ORDERABLES\t670961\tLAB11030R\tEB VIRAL CAPSID AG IGG AB\t11168R\tEB VCA IGG AB\t9999999\t369690\t1\t9999999\t1.00E+14\t2\t739380\t3.71E+11
-VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEBV VIRAL CAPSID ANTIGEN IGG\tVGDES\tVGDES\t9999999\t281538.4615\t1\t9999999\t1.00E+14\t52\t14640000\t7.71E+12
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUADI\tDISCLAIMER\t9999999\t163609.7143\t1\t9999999\t1.00E+14\t35\t5726340\t1.13E+12
-VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXDI\tDISCLAIMER\t9999999\t183683.9241\t1\t9999999\t1.00E+14\t158\t29022060\t8.42E+12
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1IN\tINTERPRETATION\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1MT\tMETHOD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t876717\tLABDVEAV3\tADENO DFA\tADV3IP\tTEST DESCRIPTION\t9999999\t373280\t1\t9999999\t1.00E+14\t3\t1119840\t9.84E+11
-VIROLOGY ORDERABLES\t876717\tLABDVEAV3\tADENO DFA\tADV3SP\tSPECIMEN\t9999999\t373280\t1\t9999999\t1.00E+14\t3\t1119840\t9.84E+11
-VIROLOGY ORDERABLES\t876725\tLABDVEPI1\tPARA 1 DFA\tPI1\tPARAINFLUENZA 1\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876725\tLABDVEPI1\tPARA 1 DFA\tPI1IP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876727\tLABDVEPI2\tPARA 2 DFA\tPI2\tPARAINFLUENZA 2\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876727\tLABDVEPI2\tPARA 2 DFA\tPI2IP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876729\tLABDVEPI3\tPARA 3 DFA\tPI3\tPARAINFLUENZA 3\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876729\tLABDVEPI3\tPARA 3 DFA\tPI3IP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876731\tLABDVEMP\tMPV DFA\tMPIP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGM & IGG\tTOXGQL\tIGG RESULT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGM & IGG\tTOXMQL\tIGM RESULT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
-VIROLOGY ORDERABLES\t876740\tINACTIVE 4\tTOXOPLASMA IGG IGM\tTOXGQL\tIGG RESULT\t9999999\t186060\t1\t9999999\t1.00E+14\t1\t186060\t34618323600
-VIROLOGY ORDERABLES\t876740\tINACTIVE 4\tTOXOPLASMA IGG IGM\tTOXMQL\tIGM RESULT\t9999999\t186060\t1\t9999999\t1.00E+14\t1\t186060\t34618323600
-VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQT\tIGG LEVEL\t8\t75540\t1\t8\t64\t1\t75540\t5706291600
-VIROLOGY ORDERABLES\t876741\tLABTOXGMR\tTOXO PREGNANCY PANEL\tTOXGQL\tIGG RESULT\t9999999\t148380\t1\t9999999\t1.00E+14\t1\t148380\t22016624400
-VIROLOGY ORDERABLES\t876741\tLABTOXGMR\tTOXO PREGNANCY PANEL\tTOXMQL\tIGM RESULT\t9999999\t148380\t1\t9999999\t1.00E+14\t1\t148380\t22016624400
-VIROLOGY ORDERABLES\t876741\tLABTOXOPR\tTOXO PREGNANCY PANEL\tTOXIGR\tTOXOPLASMA IGG\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t876741\tLABTOXOPR\tTOXO PREGNANCY PANEL\tTOXIMR\tTOXOPLASMA IGM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
-VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRSMPSP\tSPECIMEN\t9999999\t86400\t1\t9999999\t1.00E+14\t3\t259200\t28951264800
-VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tHVSP\tSPECIMEN\t9999999\t85470\t1\t9999999\t1.00E+14\t56\t4786320\t8.40E+11
-VIROLOGY ORDERABLES\t897645\tLABHPVHRT\tHPV HIGH RISK-16/18 REFLEX\tHPTI\tTEST INFORMATION\t9999999\t23400\t1\t9999999\t1.00E+14\t1\t23400\t547560000
-VIROLOGY ORDERABLES\t897645\tLABHPVHRT\tHPV HIGH RISK-16/18 REFLEX\tHPV\tHPV DNA PROBE RESULT:\t9999999\t23400\t1\t9999999\t1.00E+14\t1\t23400\t547560000
-VIROLOGY ORDERABLES\t897645\tLABHPVHRT\tHPV HIGH RISK-16/18 REFLEX\tSDES6\tSPECIMEN\t9999999\t23400\t1\t9999999\t1.00E+14\t1\t23400\t547560000
-VIROLOGY ORDERABLES\t906949\tLABMUMRC\tMUMPS RAPID CULTURE\tMUMRCD\tTEST DESCRIPTION\t9999999\t182240\t1\t9999999\t1.00E+14\t3\t546720\t1.15E+11
-VIROLOGY ORDERABLES\t906949\tLABMUMRC\tMUMPS RAPID CULTURE\tMUMSPM\tSPECIMEN\t9999999\t182240\t1\t9999999\t1.00E+14\t3\t546720\t1.15E+11
-VIROLOGY ORDERABLES\t906951\tLABMEARC\tMEASLES RAPID CULTURE\tMEARCD\tTEST DESCRIPTION\t9999999\t124470\t1\t9999999\t1.00E+14\t2\t248940\t34398594000
-VIROLOGY ORDERABLES\t906951\tLABMEARC\tMEASLES RAPID CULTURE\tMEASPM\tSPECIMEN\t9999999\t124470\t1\t9999999\t1.00E+14\t2\t248940\t34398594000
-VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVDSV\tTEST DESCRIPTION\t9999999\t175699.0909\t1\t9999999\t1.00E+14\t22\t3865380\t7.11E+11
-VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tCULT\tCULT/OTHER RSLT:\t9999999\t1069548\t1\t9999999\t1.00E+14\t10\t10695480\t1.26E+13
-VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tRPT\tRPT\t9999999\t1267920\t1\t9999999\t1.00E+14\t1\t1267920\t1.61E+12
-VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tSDES\tSPECIMEN\t9999999\t1267920\t1\t9999999\t1.00E+14\t1\t1267920\t1.61E+12
-VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tSREQ\tCOMMENTS\t9999999\t1267920\t1\t9999999\t1.00E+14\t1\t1267920\t1.61E+12
-VIROLOGY ORDERABLES\t1007409\tLABVZVQL\tVZV DNA PCR QUAL, NON LESION\tVZQLIP\tTEST DESCRIPTION\t9999999\t192832.6829\t1\t9999999\t1.00E+14\t82\t15812280\t4.51E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV1\tHCV GENOTYPE 1\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV1S\tHCV GENOTYPE 1 SUBTYPE\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV2\tHCV GENOTYPE 2\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV2S\tHCV GENOTYPE 2 SUBTYPE\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV3\tHCV GENOTYPE 3\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV4\tHCV GENOTYPE 4\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV5\tHCV GENOTYPE 5\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV6\tHCV GENOTYPE 6\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
-BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tUNT\tUNIT NUMBER  \t#VALUE!\t53958.45904\t0\tNone\tNone\t40650\t2193411360\t3.01E+14
-BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tUNT\tUNIT NUMBER  \t#VALUE!\t55642.15709\t0\tNone\tNone\t853\t47462760\t6.04E+12
-BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tUNT\tUNIT NUMBER  \t#VALUE!\t50077.32759\t0\tNone\tNone\t696\t34853820\t4.01E+12
-BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tUNT\tUNIT NUMBER  \t#VALUE!\t50032.09756\t0\tNone\tNone\t615\t30769740\t3.61E+12
-BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tUNT\tUNIT NUMBER  \t#VALUE!\t46753.20236\t0\tNone\tNone\t509\t23797380\t2.56E+12
-BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tUNT\tUNIT NUMBER  \t#VALUE!\t49645.91489\t0\tNone\tNone\t470\t23333580\t2.61E+12
-BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tUNT\tUNIT NUMBER  \t#VALUE!\t48640.67308\t0\tNone\tNone\t416\t20234520\t2.20E+12
-BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tUNT\tUNIT NUMBER  \t#VALUE!\t45845.13812\t0\tNone\tNone\t362\t16595940\t1.66E+12
-BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tUNT\tUNIT NUMBER  \t#VALUE!\t47227.55102\t0\tNone\tNone\t294\t13884900\t1.55E+12
-BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tUNT\tUNIT NUMBER  \t#VALUE!\t46629.4382\t0\tNone\tNone\t267\t12450060\t1.39E+12
-BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tUNT\tUNIT NUMBER  \t#VALUE!\t48294.88789\t0\tNone\tNone\t223\t10769760\t1.30E+12
-BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tUNT\tUNIT NUMBER  \t#VALUE!\t71687.08515\t0\tNone\tNone\t20423\t1464065340\t2.44E+14
-BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tUNT\tUNIT NUMBER  \t#VALUE!\t44357.94118\t0\tNone\tNone\t204\t9049020\t1.06E+12
-BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tUNT\tUNIT NUMBER  \t#VALUE!\t44809.2973\t0\tNone\tNone\t185\t8289720\t9.48E+11
-BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tUNT\tUNIT NUMBER  \t#VALUE!\t42059.64706\t0\tNone\tNone\t170\t7150140\t7.84E+11
-BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tUNT\tUNIT NUMBER  \t#VALUE!\t44215.41667\t0\tNone\tNone\t144\t6367020\t7.61E+11
-BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tUNT\tUNIT NUMBER  \t#VALUE!\t45869.76744\t0\tNone\tNone\t129\t5917200\t7.29E+11
-BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tUNT\tUNIT NUMBER  \t#VALUE!\t45405.78947\t0\tNone\tNone\t114\t5176260\t6.15E+11
-BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tUNT\tUNIT NUMBER  \t#VALUE!\t44582.22222\t0\tNone\tNone\t108\t4814880\t5.88E+11
-BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tUNT\tUNIT NUMBER  \t#VALUE!\t48100.40816\t0\tNone\tNone\t98\t4713840\t5.88E+11
-BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tUNT\tUNIT NUMBER  \t#VALUE!\t45671.25\t0\tNone\tNone\t80\t3653700\t4.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tUNT\tUNIT NUMBER  \t#VALUE!\t70831.65354\t0\tNone\tNone\t6096\t431789760\t6.51E+13
-BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tUNT\tUNIT NUMBER  \t#VALUE!\t43986.31579\t0\tNone\tNone\t76\t3342960\t3.42E+11
-BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tUNT\tUNIT NUMBER  \t#VALUE!\t41594.32836\t0\tNone\tNone\t67\t2786820\t2.99E+11
-BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tUNT\tUNIT NUMBER  \t#VALUE!\t50272.88136\t0\tNone\tNone\t59\t2966100\t3.78E+11
-BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tUNT\tUNIT NUMBER  \t#VALUE!\t48334.73684\t0\tNone\tNone\t57\t2755080\t3.01E+11
-BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tUNT\tUNIT NUMBER  \t#VALUE!\t49175.09434\t0\tNone\tNone\t53\t2606280\t3.05E+11
-BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tUNT\tUNIT NUMBER  \t#VALUE!\t41741.17647\t0\tNone\tNone\t51\t2128800\t2.07E+11
-BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tUNT\tUNIT NUMBER  \t#VALUE!\t48162.27273\t0\tNone\tNone\t44\t2119140\t2.29E+11
-BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tUNT\tUNIT NUMBER  \t#VALUE!\t31722.43902\t0\tNone\tNone\t41\t1300620\t88116591600
-BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tUNT\tUNIT NUMBER  \t#VALUE!\t34084.61538\t0\tNone\tNone\t39\t1329300\t92575184400
-BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tUNT\tUNIT NUMBER  \t#VALUE!\t34275\t0\tNone\tNone\t36\t1233900\t87256652400
-BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tUNT\tUNIT NUMBER  \t#VALUE!\t71882.52728\t0\tNone\tNone\t5223\t375442440\t5.68E+13
-BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tUNT\tUNIT NUMBER  \t#VALUE!\t32080\t0\tNone\tNone\t33\t1058640\t53302039200
-BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tUNT\tUNIT NUMBER  \t#VALUE!\t29945.80645\t0\tNone\tNone\t31\t928320\t49220258400
-BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tUNT\tUNIT NUMBER  \t#VALUE!\t31464\t0\tNone\tNone\t30\t943920\t67138092000
-BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tUNT\tUNIT NUMBER  \t#VALUE!\t42686.66667\t0\tNone\tNone\t27\t1152540\t1.10E+11
-BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tUNT\tUNIT NUMBER  \t#VALUE!\t41768.88889\t0\tNone\tNone\t27\t1127760\t1.06E+11
-BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tUNT\tUNIT NUMBER  \t#VALUE!\t32076.92308\t0\tNone\tNone\t26\t834000\t53931456000
-BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tUNT\tUNIT NUMBER  \t#VALUE!\t29411.53846\t0\tNone\tNone\t26\t764700\t28925881200
-BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tUNT\tUNIT NUMBER  \t#VALUE!\t35142.85714\t0\tNone\tNone\t21\t738000\t56790907200
-BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tUNT\tUNIT NUMBER  \t#VALUE!\t35055\t0\tNone\tNone\t20\t701100\t55275080400
-BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tUNT\tUNIT NUMBER  \t#VALUE!\t40860\t0\tNone\tNone\t20\t817200\t69801444000
-BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tUNT\tUNIT NUMBER  \t#VALUE!\t63772.73171\t0\tNone\tNone\t2460\t156880920\t2.17E+13
-BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tUNT\tUNIT NUMBER  \t#VALUE!\t35173.33333\t0\tNone\tNone\t18\t633120\t53763523200
-BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tUNT\tUNIT NUMBER  \t#VALUE!\t24108\t0\tNone\tNone\t15\t361620\t13517708400
-BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tUNT\tUNIT NUMBER  \t#VALUE!\t26117.14286\t0\tNone\tNone\t14\t365640\t13709217600
-BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tUNT\tUNIT NUMBER  \t#VALUE!\t29464.61538\t0\tNone\tNone\t13\t383040\t18645940800
-BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tUNT\tUNIT NUMBER  \t#VALUE!\t31638.46154\t0\tNone\tNone\t13\t411300\t20614381200
-BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tUNT\tUNIT NUMBER  \t#VALUE!\t32307.69231\t0\tNone\tNone\t13\t420000\t21061454400
-BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tUNT\tUNIT NUMBER  \t#VALUE!\t27975\t0\tNone\tNone\t12\t335700\t17437417200
-BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tUNT\tUNIT NUMBER  \t#VALUE!\t31958.18182\t0\tNone\tNone\t11\t351540\t18758574000
-BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tUNT\tUNIT NUMBER  \t#VALUE!\t76960\t0\tNone\tNone\t9\t692640\t99484596000
-BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tUNT\tUNIT NUMBER  \t#VALUE!\t63330.90094\t0\tNone\tNone\t2231\t141291240\t1.94E+13
-BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tUNT\tUNIT NUMBER  \t#VALUE!\t81690\t0\tNone\tNone\t8\t653520\t97952421600
-BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tUNT\tUNIT NUMBER  \t#VALUE!\t85174.28571\t0\tNone\tNone\t7\t596220\t77125518000
-BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tUNT\tUNIT NUMBER  \t#VALUE!\t63750\t0\tNone\tNone\t6\t382500\t39290828400
-BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tUNT\tUNIT NUMBER  \t#VALUE!\t47660\t0\tNone\tNone\t6\t285960\t25408432800
-BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tUNT\tUNIT NUMBER  \t#VALUE!\t47650\t0\tNone\tNone\t6\t285900\t25391113200
-BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tUNT\tUNIT NUMBER  \t#VALUE!\t23880\t0\tNone\tNone\t6\t143280\t4565289600
-BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tUNT\tUNIT NUMBER  \t#VALUE!\t28650\t0\tNone\tNone\t6\t171900\t6302451600
-BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tUNT\tUNIT NUMBER  \t#VALUE!\t28550\t0\tNone\tNone\t6\t171300\t6256443600
-BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tUNT\tUNIT NUMBER  \t#VALUE!\t28428\t0\tNone\tNone\t5\t142140\t5357322000
-BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tUNT\tUNIT NUMBER  \t#VALUE!\t18440\t0\tNone\tNone\t3\t55320\t2073319200
-BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tUNT\tUNIT NUMBER  \t#VALUE!\t58144.67277\t0\tNone\tNone\t1528\t88845060\t1.11E+13
-BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tUNT\tUNIT NUMBER  \t#VALUE!\t38400\t0\tNone\tNone\t3\t115200\t7052018400
-BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tUNT\tUNIT NUMBER  \t#VALUE!\t27880\t0\tNone\tNone\t3\t83640\t3495549600
-BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tUNT\tUNIT NUMBER  \t#VALUE!\t38360\t0\tNone\tNone\t3\t115080\t7038208800
-BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tUNT\tUNIT NUMBER  \t#VALUE!\t38320\t0\tNone\tNone\t3\t114960\t7029561600
-BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tUNT\tUNIT NUMBER  \t#VALUE!\t26760\t0\tNone\tNone\t3\t80280\t3222554400
-BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tUNT\tUNIT NUMBER  \t#VALUE!\t40110\t0\tNone\tNone\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tUNT\tUNIT NUMBER  \t#VALUE!\t40110\t0\tNone\tNone\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tUNT\tUNIT NUMBER  \t#VALUE!\t40110\t0\tNone\tNone\t2\t80220\t3217712400
-BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tUNT\tUNIT NUMBER  \t#VALUE!\t40080\t0\tNone\tNone\t2\t80160\t3212928000
-BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tUNT\tUNIT NUMBER  \t#VALUE!\t39390\t0\tNone\tNone\t2\t78780\t3104874000
-BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tUNT\tUNIT NUMBER  \t#VALUE!\t57549.16129\t0\tNone\tNone\t1395\t80281080\t9.91E+12
-BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tUNT\tUNIT NUMBER  \t#VALUE!\t54990\t0\tNone\tNone\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tUNT\tUNIT NUMBER  \t#VALUE!\t54990\t0\tNone\tNone\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tUNT\tUNIT NUMBER  \t#VALUE!\t54990\t0\tNone\tNone\t2\t109980\t6598256400
-BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tUNT\tUNIT NUMBER  \t#VALUE!\t69330\t0\tNone\tNone\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tUNT\tUNIT NUMBER  \t#VALUE!\t69330\t0\tNone\tNone\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tUNT\tUNIT NUMBER  \t#VALUE!\t69330\t0\tNone\tNone\t2\t138660\t9623422800
-BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tUNT\tUNIT NUMBER  \t#VALUE!\t91200\t0\tNone\tNone\t2\t182400\t23119207200
-BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tUNT\tUNIT NUMBER  \t#VALUE!\t49950\t0\tNone\tNone\t2\t99900\t5482357200
-BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tUNT\tUNIT NUMBER  \t#VALUE!\t148140\t0\tNone\tNone\t1\t148140\t21945459600
-BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tUNT\tUNIT NUMBER  \t#VALUE!\t51894.30096\t0\tNone\tNone\t937\t48624960\t5.74E+12
-BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BLOOD BANK PRODUCT ORDERABLES\t473671\tLABEITC\tTRANSFUSION EMERGENCY ISSUE\tCEITC\tEMERGENCY ISSUE CMT\t#VALUE!\t86.95945946\t0\tNone\tNone\t296\t25740\t236782800
-BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tUNT\tUNIT NUMBER  \t#VALUE!\t41291.48936\t0\tNone\tNone\t94\t3881400\t3.96E+11
-BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tUNT\tUNIT NUMBER  \t#VALUE!\t35022\t0\tNone\tNone\t10\t350220\t18667335600
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tAASINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tINTSBY\tINTERPRETED BY\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471626\tLAB11028R\tANGIOTENSIN CONVERTING ENZYME, CSF\tACE\tANGIOTENSIN COVERT ENZ, CSF\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471626\tLABCACE\tANGIOTENSIN CONVERTING ENZYME, CSF\tACE\tANGIOTENSIN COVERT ENZ, CSF\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t471692\tLABASPPCP\tASPERGILLUS AB PRECIPITINS\tASPINT\tINTERPRETATION\t#VALUE!\t407597.6471\t0\tNone\tNone\t17\t6929160\t3.73E+12
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBDIR\tINTERPRETED BY\t#VALUE!\t1007200\t0\tNone\tNone\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBDIS\tDISCLAIMER\t#VALUE!\t1007200\t0\tNone\tNone\t3\t3021600\t3.22E+12
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRM\tMETHOD\t#VALUE!\t520905.7143\t0\tNone\tNone\t21\t10939020\t8.12E+12
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRS\tDISCLAIMER\t#VALUE!\t520905.7143\t0\tNone\tNone\t21\t10939020\t8.12E+12
-BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRX\tCLINICAL INDICATION\t#VALUE!\t520905.7143\t0\tNone\tNone\t21\t10939020\t8.12E+12
-BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD34DS\tDISCLAIMER\t#VALUE!\t23385\t0\tNone\tNone\t4\t93540\t3727911600
-BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34DS\tDISCLAIMER\t#VALUE!\t8570.769231\t0\tNone\tNone\t26\t222840\t3235593600
-BODY FLUID ORDERABLES\t472047\tLAB11123R\tCOCCIDIOIDES COMPLEMENT FIXATION, CSF\tCOCC\tCOCCI CF AB (CSF)\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472047\tLABCOCC\tCOCCIDIOIDES COMPLEMENT FIXATION, CSF\tCOCC\tCOCCI CF AB (CSF)\t#VALUE!\t709560\t0\tNone\tNone\t1\t709560\t5.03E+11
-BODY FLUID ORDERABLES\t472126\tLABCYSCSF\tCYSTICERCUS AB REFLEX PANEL, CSF\tCYSTAB\tCYSTICERCUS AB(CSF),ELISA\t#VALUE!\t392206.6667\t0\tNone\tNone\t9\t3529860\t1.50E+12
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP1\tSPECIMEN SOURCE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP2\tENTEROVIRUS ,PCR\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP3\tSPECIAL INFORMATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP4\tREPORT STATUS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP1\tSPECIMEN SOURCE\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP2\tENTEROVIRUS ,PCR\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP3\tSPECIAL INFORMATION\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
-BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP4\tREPORT STATUS\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
-BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP1\tSPECIMEN SOURCE\t#VALUE!\t209353.0435\t0\tNone\tNone\t69\t14445360\t3.79E+12
-BODY FLUID ORDERABLES\t472370\tLABFFIBN\tFETAL FIBRONECTIN\tFFIBN\tFETAL FIBRONECTIN\t#VALUE!\t3352.5\t0\tNone\tNone\t8\t26820\t104137200
-BODY FLUID ORDERABLES\t472431\tLABFSYN\tFUNGAL SYNERGY\tFMIC\tFUNGAL MIC\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIDI\tDISCLAIMER\t#VALUE!\t927889.0909\t0\tNone\tNone\t11\t10206780\t1.25E+13
-BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIME\tMETHOD\t#VALUE!\t927889.0909\t0\tNone\tNone\t11\t10206780\t1.25E+13
-BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\t12197R\tSOURCE\t#VALUE!\t1011860\t0\tNone\tNone\t3\t3035580\t6.08E+12
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t#VALUE!\t268440\t0\tNone\tNone\t1\t268440\t72060033600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t11166R\tEB NUCLEAR AB\t#VALUE!\t268440\t0\tNone\tNone\t1\t268440\t72060033600
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t712710\t0\tNone\tNone\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t712710\t0\tNone\tNone\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t712710\t0\tNone\tNone\t2\t1425420\t1.41E+12
-BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHSAB\tVIRAL AB HSV\t#VALUE!\t550720\t0\tNone\tNone\t3\t1652160\t1.46E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNSO\tSTONE SOURCE\t#VALUE!\t207922.6667\t0\tNone\tNone\t45\t9356520\t2.34E+12
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\t1STCONST\t1ST CONSTITUENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\t2NDCONST\t2ND CONSTITUENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\t3RDCONST\t3RD CONSTITUENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\tYSTNSO\tSTONE SOURCE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14CT\tCOMMENT\t#VALUE!\t1070614.286\t0\tNone\tNone\t7\t7494300\t9.42E+12
-BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14DR\tINTERPRETED BY\t#VALUE!\t1070614.286\t0\tNone\tNone\t7\t7494300\t9.42E+12
-BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14DS\tDISCLAIMER\t#VALUE!\t1070614.286\t0\tNone\tNone\t7\t7494300\t9.42E+12
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15DI\tDISCLAIMER\t#VALUE!\t644650\t0\tNone\tNone\t18\t11603700\t8.75E+12
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15MD\tINTERPRETED BY\t#VALUE!\t644650\t0\tNone\tNone\t18\t11603700\t8.75E+12
-BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15ME\tMETHOD\t#VALUE!\t644650\t0\tNone\tNone\t18\t11603700\t8.75E+12
-BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTDIS\tDISCLAIMER\t#VALUE!\t688582.5\t0\tNone\tNone\t16\t11017320\t1.17E+13
-BODY FLUID ORDERABLES\t473458\tLAB11121R\tTOXOPLASMA IGG DYE TEST\tTOXOD\tTOXOPLASMA DYE TEST\t#VALUE!\t1132650\t0\tNone\tNone\t2\t2265300\t2.79E+12
-BODY FLUID ORDERABLES\t497221\tLABADMARK\tADMARK TAU/AB42, CSF\tADMART\tADMARK TAU/AB42, CSF\t#VALUE!\t2981138.571\t0\tNone\tNone\t14\t41735940\t1.92E+14
-BODY FLUID ORDERABLES\t497362\tLABYFK\tPOTASSIUM, FLUID\tKFLD\tPOTASSIUM BODY FLUID\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t497366\tLABYFNA\tSODIUM, FLUID\tNAFLD\tSODIUM BODY FLUID\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tYFLSC1\tFLUID TYPE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-BODY FLUID ORDERABLES\t497376\tLABYFCL\tCHLORIDE, FLUID\t11844R\tFLUID TYPE\t#VALUE!\t95760\t0\tNone\tNone\t1\t95760\t9169977600
-BODY FLUID ORDERABLES\t497376\tLABYFCL\tCHLORIDE, FLUID\tCLFLD\tCHLORIDE, BODY FLUID\t#VALUE!\t169230\t0\tNone\tNone\t2\t338460\t68073267600
-BODY FLUID ORDERABLES\t497438\tLABFCLPN\tCHLAMYDIA PNEUMONIAE CULTURE\tCLPN\tCHLAMYDIA PNEUMONIAE\t#VALUE!\t287685\t0\tNone\tNone\t4\t1150740\t4.88E+11
-BODY FLUID ORDERABLES\t497448\tLABCHLAM\tCHLAMYDIA TRACHOMATIS / PSITTACI CULTURE\tCHLA\tCHLAMYDIA CULTURE\t#VALUE!\t1093440\t0\tNone\tNone\t3\t3280320\t6.55E+12
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASDI\tDISCLAIMER\t#VALUE!\t866627.1429\t0\tNone\tNone\t14\t12132780\t1.36E+13
-BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASME\tMETHOD\t#VALUE!\t866627.1429\t0\tNone\tNone\t14\t12132780\t1.36E+13
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMDI\tDISCLAIMER\t#VALUE!\t741092.1429\t0\tNone\tNone\t28\t20750580\t2.04E+13
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMMD\tINTERPRETED BY\t#VALUE!\t741092.1429\t0\tNone\tNone\t28\t20750580\t2.04E+13
-BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMME\tMETHOD\t#VALUE!\t741092.1429\t0\tNone\tNone\t28\t20750580\t2.04E+13
-BODY FLUID ORDERABLES\t670778\tLAB11188R\tM. PNEUMONIAE DNA\t11189R\tM.PNEUMONIAE\t#VALUE!\t166380\t0\tNone\tNone\t8\t1331040\t2.33E+11
-BODY FLUID ORDERABLES\t670820\tLAB11214R\tBETA-2 TRANSFERRIN, BF\t11215R\tBETA-2 TRANSFERRIN, BF\t#VALUE!\t204888.8889\t0\tNone\tNone\t27\t5532000\t1.55E+12
-BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tRTFDA2\tNOTE\t#VALUE!\t12144\t0\tNone\tNone\t5\t60720\t762372000
-BODY FLUID ORDERABLES\t744342\tLAB11617R\tCMV PCR, AQUEOUS FLUID\t11776R\tCMV PCR, AQUEOUS FLUID\t#VALUE!\t799992\t0\tNone\tNone\t15\t11999880\t3.06E+13
-BODY FLUID ORDERABLES\t744344\tLAB11618R\tHSV 1/2 PCR, AQUEOUS FLUID\t11777R\tHSV 1/2 PCR,AQUOUS FLUID\t#VALUE!\t910809.2308\t0\tNone\tNone\t13\t11840520\t3.07E+13
-BODY FLUID ORDERABLES\t744348\tLAB11620R\tTOXOPLASMA GONDII, AQUEOUS FLUID\t11779R\tTOXOPLASAMGONDII,AQUEOUS FLUID\t#VALUE!\t1675310\t0\tNone\tNone\t6\t10051860\t2.90E+13
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11992R\tSPECIMEN\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11993R\tSPECIMEN ID\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11994R\tSOURCE\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11995R\tORDER DATE\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11996R\tCOMMENT\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11997R\tAMENDMENT\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11998R\tREVIEWED BY\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11999R\tRELEASE DATE\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
-BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFDI\tDISCLAIMER\t#VALUE!\t479125.7143\t0\tNone\tNone\t14\t6707760\t4.66E+12
-BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFMD\tINTERPRETATION\t#VALUE!\t479125.7143\t0\tNone\tNone\t14\t6707760\t4.66E+12
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSDI\tDISCLAIMER\t#VALUE!\t912125.8065\t0\tNone\tNone\t62\t56551800\t6.06E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSMD\tINTERPRETED BY\t#VALUE!\t934668.2143\t0\tNone\tNone\t56\t52341420\t5.63E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSME\tMETHOD\t#VALUE!\t912125.8065\t0\tNone\tNone\t62\t56551800\t6.06E+13
-BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSREF\tREFERENCES\t#VALUE!\t912125.8065\t0\tNone\tNone\t62\t56551800\t6.06E+13
-BODY FLUID ORDERABLES\t898806\tLAB12328R\tAMYLOID PROTEIN IDENTIFICATION\t12327R\tAMYLOID PROTEIN ID, TISSUE\t#VALUE!\t60\t0\tNone\tNone\t1\t60\t3600
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12342R\tSPECIMEN ID\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12343R\tSOURCE\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12344R\tORDER DATE\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12345R\tREASON FOR REFERRAL\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12346R\tMETHOD\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12347R\tRESULTS\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12348R\tINTERPRETATION\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12349R\tAMENDMENT\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12350R\tCONSULTANT\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12351R\tREPORT DATE\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
-BODY FLUID ORDERABLES\t898812\tLAB12332R\tKIT MUTATION ANALYSIS EXON 11\t12337R\tKIT MUTATION ANALYSIS EXON 11\t#VALUE!\t540607.5\t0\tNone\tNone\t8\t4324860\t2.66E+12
-BODY FLUID ORDERABLES\t898814\tLAB12333R\tKIT MUTATION ANALYSIS EXON 9\t12338R\tKIT MUTATION ANALYSIS EXON 9\t#VALUE!\t708520\t0\tNone\tNone\t3\t2125560\t1.51E+12
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12448R\tACCESSION NUMBER\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12449R\tREFERRING PATH-PHYSICIAN\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12450R\tREF PATH-PHYS ADDRESS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12451R\tPLACE OF DEATH\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12452R\tDATE AND TIME OF DEATH\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12453R\tDATE OF AUTOPSY\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12454R\tSPECIMEN\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12455R\tMATERIAL\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12456R\tTISSUE DESCRIPTION\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12457R\tMICROSCOPIC DESCRIPTION\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12458R\tCLINICAL HISTORY\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12459R\tFINAL DIAGNOSIS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12460R\tFINAL DIAGNOSIS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12461R\tCOMMENT\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12462R\tREVISION DESCRIPTION\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12463R\tSIGNING PATHOLOGIST\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12464R\tSPECIAL PROCEDURES\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12465R\tSP SIGNING PATHOLOGIST\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12466R\tPREVIOUS REPORT FOLLOWS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12467R\tADDENDUM\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12468R\tADDENDUM COMMENT\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12469R\tADDENDUM PATHOLOGIST\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
-BODY FLUID ORDERABLES\t910584\tLAB12470R\tSTAIN, CONGO RED\t12379R\tCONGO RED, STAIN\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12472R\tSPECIMEN\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12473R\tADDENDUM COMMENT\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12474R\tACCESSION NUMBER\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12475R\tREFERRING PATH-PHYSICIAN\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12476R\tREF PATH-PHYS ADDRESS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12477R\tPLACE OF DEATH\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12478R\tDATE AND TIME OF DEATH\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12479R\tDATE OF AUTOPSY\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12480R\tMATERIAL\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12481R\tTISSUE DESCRIPTION\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12482R\tMICROSCOPIC DESCRIPTION\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12483R\tCLINICAL HISTORY\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12484R\tFINAL DIAGNOSIS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12485R\tFINAL DIAGNOSIS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12486R\tCOMMENT\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12487R\tREVISION DESCRIPTION\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12488R\tSIGNING PATHOLOGIST\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12489R\tSPECIAL PROCEDURES\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12490R\tSP SIGNING PATHOLOGIST\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12491R\tPREVIOUS REPORT FOLLOWS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12492R\tADDENDUM\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12493R\tADDENDUM PATHOLOGIST\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
-BODY FLUID ORDERABLES\t910588\tLAB12494R\tPATH CONSULT, OUTSIDE SLIDE\t12378R\tPATH CONSULT, OUTSIDE SLIDE\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-CARDIAC ANGIO ORDERABLES\t474722\tIMG11AR1\tCAR CATH HEART RT 1\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474724\tIMG11BXM1\tCAR MYOCARD BIOPSY I\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474725\tIMG11BXMT\tCAR MYOCARD BX I TX\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474726\tIMG11CCP\tCAR CATH CORS POSSIBLE\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474730\tIMG11IA\tCAR INTRA AORTIC BALLOON PUMP\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474731\tIMG11IABP\tCAR IABP REMOVAL\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474736\tIMG11LHC\tCAR LT HEART CATH\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474742\tIMG11PER\tCAR PERICARDIOCENTESIS\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474743\tIMG11RLCA\tCAR RT LT CATH CORS ANGIO\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t474744\tIMG11RLHC\tCAR COMB RT LT HEART CATH\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t517201\tIMG1PTCA\tCAR PTCA EACH ADDITIONAL VESSEL\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t517407\tIMG1STEV\tCAR CORONARY STENT SINGLE\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t517417\tIMG1PTCS\tCAR PTCA 1VESSEL SIMPLE\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t517461\tIMG1ATH\tCAR PTC ROTABLATOR\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-CARDIAC ANGIO ORDERABLES\t660296\tCRP333\tRIGHT LEFT CATH CORS ANGIO\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-COAGULATION ORDERABLES\t472515\tLABHIAB\tHEPARIN INDUCED AB\tHIACOM\tCOMMENTS\t#VALUE!\t64755.64516\t0\tNone\tNone\t372\t24089100\t2.37E+12
-COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHIPLAB\tLAB DATA\t#VALUE!\t158939.6319\t0\tNone\tNone\t163\t25907160\t6.12E+12
-COAGULATION ORDERABLES\t472663\tLABVWNTRP\tINTERPRETATION, VW SCREEN\tVWDIR\tINTERPRETED BY\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-COAGULATION ORDERABLES\t472867\tLABCGMISC\tMISCELLANEOUS TEST COAGULATION\tCGRSLT\tRESULT\t#VALUE!\t157680\t0\tNone\tNone\t1\t157680\t24862982400
-CYTOGENETICS ORDERABLES\t472130\tLABCYTGEN\tCYTOGENETICS FISH ORDER\tCYTGN1\tCYTOGEN/FISH TRACKING\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tPDF\tPDF RESULT\t#VALUE!\t-9930.481737\t0\tNone\tNone\t12019\t-119354460\t4.85E+17
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tECGIMPRESS\tECG IMPRESSION\t#VALUE!\t133908.75\t0\tNone\tNone\t16\t2142540\t3.12E+12
-ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tPDF\tPDF RESULT\t#VALUE!\t3600\t0\tNone\tNone\t2\t7200\t26956800
-EP ORDERABLES\t474752\tIMG12AH\tEP ABLATION\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-EP ORDERABLES\t474772\tIMG12IA\tICD ANALYSIS\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tAN20DS\tDISCLAIMER\t#VALUE!\t614750\t0\tNone\tNone\t6\t3688500\t9.36E+12
-HISTOCOMPATIBILITY ORDERABLE\t481589\t10285R\tHLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)\tRINFO\tTO FIND RESULT:\t#VALUE!\t130260\t0\tNone\tNone\t1\t130260\t16967667600
-HISTOCOMPATIBILITY ORDERABLE\t505814\t10410R\tHLA-BMT DONOR MATCH GRADE SUMMARY\t9706R\tDONOR MATCH GRADE SUMMARY\t#VALUE!\t13188948.16\t0\tNone\tNone\t76\t1002360060\t3.04E+16
-HISTOCOMPATIBILITY ORDERABLE\t506036\t10270R\tHLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t1418312.791\t0\tNone\tNone\t86\t121974900\t4.69E+14
-HISTOCOMPATIBILITY ORDERABLE\t506864\t10235R\tHLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING\tRINFO\tTO FIND RESULT:\t#VALUE!\t584445.8824\t0\tNone\tNone\t68\t39742320\t4.86E+13
-HISTOCOMPATIBILITY ORDERABLE\t507225\t10220R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t1641532\t0\tNone\tNone\t30\t49245960\t1.25E+14
-HISTOCOMPATIBILITY ORDERABLE\t519017\t10230R\tHLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*\tRINFO\tTO FIND RESULT:\t#VALUE!\t1205680\t0\tNone\tNone\t9\t10851120\t1.84E+13
-HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*\tRINFO\tTO FIND RESULT:\t#VALUE!\t1487994\t0\tNone\tNone\t30\t44639820\t1.09E+14
-HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\tRINFO\tTO FIND RESULT:\t#VALUE!\t2244840\t0\tNone\tNone\t1\t2244840\t5.04E+12
-HISTOCOMPATIBILITY ORDERABLE\t609847\t10265R\tHLA ORDER - SCHEDULED SCREENING KIT PROCESSING\tRINFO\tTO FIND RESULT:\t#VALUE!\t1226580\t0\tNone\tNone\t3\t3679740\t6.00E+12
-HISTOCOMPATIBILITY ORDERABLE\t685703\t10135R\tHLA ORDER - SCHEDULED SCREENING KIT BILLING\tRINFO\tTO FIND RESULT:\t#VALUE!\t1225575\t0\tNone\tNone\t4\t4902300\t2.13E+13
-HISTOCOMPATIBILITY ORDERABLE\t710704\t10210R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG\tRINFO\tTO FIND RESULT:\t#VALUE!\t2240112\t0\tNone\tNone\t5\t11200560\t3.47E+13
-HISTOCOMPATIBILITY ORDERABLE\t890776\t10110R\tHLA ORDER - FLOW AUTO CROSSMATCH\tRINFO\tTO FIND RESULT:\t#VALUE!\t267540\t0\tNone\tNone\t4\t1070160\t4.76E+11
-HISTOCOMPATIBILITY ORDERABLE\t890780\t10010R\tHLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t#VALUE!\t170820\t0\tNone\tNone\t1\t170820\t29179472400
-HISTOCOMPATIBILITY ORDERABLE\t890826\t10150R\tHLA ORDER - SUMMARIES\tRINFO\tTO FIND RESULT:\t#VALUE!\t1953334.327\t0\tNone\tNone\t208\t406293540\t1.40E+16
-HISTOCOMPATIBILITY ORDERABLE\t892285\t10273R\tHLA ORDER - WORKUP AT TIME OF TRANSPLANT\tRINFO\tTO FIND RESULT:\t#VALUE!\t452812.6829\t0\tNone\tNone\t41\t18565320\t3.05E+13
-HISTOCOMPATIBILITY ORDERABLE\t895898\t10120R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t202080\t0\tNone\tNone\t1\t202080\t40836326400
-HISTOCOMPATIBILITY ORDERABLE\t896063\t10390R\tHLA-BMT DONOR MATCH DETAIL SUMMARY\t9663R\tDONOR MATCH GRADE DETAIL\t#VALUE!\t11844188.49\t0\tNone\tNone\t106\t1255483980\t3.44E+16
-HISTOCOMPATIBILITY ORDERABLE\t896063\t10390R\tHLA-BMT DONOR MATCH DETAIL SUMMARY\t9706R\tDONOR MATCH GRADE SUMMARY\t#VALUE!\t8251302\t0\tNone\tNone\t30\t247539060\t4.03E+15
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9235R\tDISCLAIMER\t#VALUE!\t528366.6667\t0\tNone\tNone\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9712R\tDONOR PHENOTYPE\t#VALUE!\t528366.6667\t0\tNone\tNone\t9\t4755300\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9183R\tDISCLAIMER\t#VALUE!\t380304.6774\t0\tNone\tNone\t124\t47157780\t5.17E+13
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9209R\tDISCLAIMER\t#VALUE!\t475542\t0\tNone\tNone\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9709R\tDONOR PHENOTYPE\t#VALUE!\t475542\t0\tNone\tNone\t10\t4755420\t8.85E+12
-HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9152R\tDISCLAIMER\t#VALUE!\t370725\t0\tNone\tNone\t124\t45969900\t4.87E+13
-HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9122R\tDISCLAIMER\t#VALUE!\t272784.1379\t0\tNone\tNone\t87\t23732220\t2.33E+13
-HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9101R\tDISCLAIMER\t#VALUE!\t273799.3103\t0\tNone\tNone\t87\t23820540\t2.34E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9369R\tMFI RESULTS FOR DSA BY C1Q CLASS I\t#VALUE!\t37661.85185\t0\tNone\tNone\t162\t6101220\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9449R\tDSA RESULTS FOR C1Q CLASS I\t#VALUE!\t2331.428571\t0\tNone\tNone\t7\t16320\t262447200
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9451R\tCOMPOSED TEXT CLASS I\t#VALUE!\t37661.85185\t0\tNone\tNone\t162\t6101220\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9463R\tDSA RESULTS FOR C1Q CLASS II\t#VALUE!\t33.91304348\t0\tNone\tNone\t23\t780\t46800
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9465R\tCOMPOSED TEXT CLASS II\t#VALUE!\t37209.87805\t0\tNone\tNone\t164\t6102420\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9466R\tDISCLAIMER\t#VALUE!\t37217.81818\t0\tNone\tNone\t165\t6140940\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9657R\tMFI RESULTS FOR DSA BY C1Q CLASS II\t#VALUE!\t37209.87805\t0\tNone\tNone\t164\t6102420\t2.59E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9368R\tMFI RESULTS FOR DSA BY IGG CLASS I\t#VALUE!\t43413.25301\t0\tNone\tNone\t166\t7206600\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9419R\tDSA RESULTS FOR IGG CLASS I\t#VALUE!\t4032\t0\tNone\tNone\t20\t80640\t6368090400
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9421R\tCOMPOSED TEXT CLASS I\t#VALUE!\t43413.25301\t0\tNone\tNone\t166\t7206600\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9432R\tDSA RESULTS FOR IGG CLASS II\t#VALUE!\t4365\t0\tNone\tNone\t40\t174600\t15129072000
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9434R\tCOMPOSED TEXT CLASS II\t#VALUE!\t42896.78571\t0\tNone\tNone\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9435R\tDISCLAIMER\t#VALUE!\t42896.78571\t0\tNone\tNone\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9656R\tMFI RESULTS FOR DSA BY IGG CLASS II\t#VALUE!\t42896.78571\t0\tNone\tNone\t168\t7206660\t2.67E+13
-HISTOCOMPATIBILITY ORDERABLE\t896076\t10395R\tHLA - CROSSMATCH SUMMARY\t9661R\tHLA CROSSMATCH SUMMARY\t#VALUE!\t6064014.146\t0\tNone\tNone\t41\t248624580\t5.84E+15
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9077R\tNMDP CODES\t#VALUE!\t190845.8369\t0\tNone\tNone\t233\t44467080\t7.74E+13
-HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9080R\tDISCLAIMER\t#VALUE!\t213091.738\t0\tNone\tNone\t397\t84597420\t1.52E+14
-HISTOCOMPATIBILITY ORDERABLE\t896078\t10400R\tHLA - TYPING SUMMARY\t9662R\tHLA TYPING SUMMARY\t#VALUE!\t5150790.545\t0\tNone\tNone\t55\t283293480\t6.50E+15
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9338R\tINHIBITION RESULTS FOR CLASS I\t#VALUE!\t1441825.714\t0\tNone\tNone\t7\t10092780\t6.71E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9343R\tINHIBITION RESULTS FOR CLASS II\t#VALUE!\t413505\t0\tNone\tNone\t8\t3308040\t5.94E+12
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9344R\tINTERPRETATION/ COMMENTS CLASS II\t#VALUE!\t2652120\t0\tNone\tNone\t3\t7956360\t6.22E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9345R\tCOMPOSED TEXT CLASS II\t#VALUE!\t1119612\t0\tNone\tNone\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9346R\tDISCLAIMER\t#VALUE!\t1119612\t0\tNone\tNone\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9351R\tMFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS I\t#VALUE!\t1261597.5\t0\tNone\tNone\t8\t10092780\t6.71E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9586R\tINTERPRETATION/ COMMENTS CLASS I\t#VALUE!\t3944040\t0\tNone\tNone\t2\t7888080\t6.22E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9587R\tCOMPOSED TEXT CLASS I\t#VALUE!\t1261597.5\t0\tNone\tNone\t8\t10092780\t6.71E+13
-HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9653R\tMFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS II\t#VALUE!\t1119612\t0\tNone\tNone\t10\t11196120\t6.82E+13
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9364R\tMFI RESULTS FOR MONITORING BY C1Q CLASS I\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9390R\tMONITORING RESULTS FOR C1Q CLASS I\t#VALUE!\t0\t0\tNone\tNone\t4\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9392R\tCOMPOSED TEXT CLASS I\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9403R\tMONITORING RESULTS FOR C1Q CLASS II\t#VALUE!\t0\t0\tNone\tNone\t2\t0\t0
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9405R\tCOMPOSED TEXT CLASS II\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9406R\tDISCLAIMER\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
-HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9655R\tMFI RESULTS FOR MONITORING BY C1Q CLASS II\t#VALUE!\t37305\t0\tNone\tNone\t8\t298440\t89030628000
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9360R\tMONITORING RESULTS FOR IGG CLASS I\t#VALUE!\t30\t0\tNone\tNone\t4\t120\t7200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9362R\tCOMPOSED TEXT CLASS I\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9363R\tMFI RESULTS FOR MONITORING BY IGG CLASS I\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9373R\tMONITORING RESULTS FOR IGG CLASS II\t#VALUE!\t30\t0\tNone\tNone\t4\t120\t7200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9375R\tCOMPOSED TEXT CLASS II\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9376R\tDISCLAIMER\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9654R\tMFI RESULTS FOR MONITORING BY IGG CLASS II\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t10471R\tSAFE ANTIGENS C1Q\t#VALUE!\t354240\t0\tNone\tNone\t1\t354240\t1.25E+11
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9282R\tDISCLAIMER\t#VALUE!\t118749.8734\t0\tNone\tNone\t316\t37524960\t4.02E+13
-HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9652R\tMFI RESULTS FOR SCREENING C1Q CLASS II\t#VALUE!\t138546\t0\tNone\tNone\t210\t29094660\t2.40E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9261R\tCOMPOSED TEXT CLASS II\t#VALUE!\t160995\t0\tNone\tNone\t288\t46366560\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9262R\tDISCLAIMER\t#VALUE!\t159629.0722\t0\tNone\tNone\t291\t46452060\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9582R\tINTERPRETATION/ COMMENTS CLASS I\t#VALUE!\t444600\t0\tNone\tNone\t2\t889200\t7.91E+11
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9583R\tCOMPOSED TEXT CLASS I\t#VALUE!\t160179.1034\t0\tNone\tNone\t290\t46451940\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9650R\tMFI RESULTS FOR SCREENING IGG CLASS I\t#VALUE!\t160179.1034\t0\tNone\tNone\t290\t46451940\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9651R\tMFI RESULTS FOR SCREENING IGG CLASS II\t#VALUE!\t160995\t0\tNone\tNone\t288\t46366560\t7.62E+13
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9574R\tINTERPRETATION/ COMMENTS\t#VALUE!\t8022.580645\t0\tNone\tNone\t31\t248700\t61404901200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9575R\tCOMPOSED TEXT\t#VALUE!\t4147.297297\t0\tNone\tNone\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9576R\tDISCLAIMER\t#VALUE!\t4147.297297\t0\tNone\tNone\t74\t306900\t64653973200
-HISTOCOMPATIBILITY ORDERABLE\t896159\t10100R\tHLA ORDER - PLATELET COMPATIBILITY\tRINFO\tTO FIND RESULT:\t#VALUE!\t456229.9115\t0\tNone\tNone\t113\t51553980\t7.28E+13
-HISTOCOMPATIBILITY ORDERABLE\t979724\t10287R\tHLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT\tRINFO\tTO FIND RESULT:\t#VALUE!\t5317523.478\t0\tNone\tNone\t23\t122303040\t2.88E+15
-HISTOCOMPATIBILITY ORDERABLE\t1458223\t10175R\tHLA ORDER - KIT PROCESSING RECIPIENT\tRINFO\tTO FIND RESULT:\t#VALUE!\t1408650\t0\tNone\tNone\t2\t2817300\t3.97E+12
-HISTOCOMPATIBILITY ORDERABLE\t1509678\t10070R\tHLA ORDER - IVIG IN VITRO INHIBITION ASSAY\tRINFO\tTO FIND RESULT:\t#VALUE!\t1712980\t0\tNone\tNone\t3\t5138940\t1.13E+13
-HISTOCOMPATIBILITY ORDERABLE\t235818016\t10280R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t5611500\t0\tNone\tNone\t1\t5611500\t3.15E+13
-HISTOCOMPATIBILITY ORDERABLE\t235818016\t10280R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING\tRINFO\tTO FIND RESULT:\t#VALUE!\t1715198.88\t0\tNone\tNone\t125\t214399860\t7.48E+14
-HISTOCOMPATIBILITY ORDERABLE\t304982014\t10545R\tHLA - CD31 TESTING SUMMARY\t10478R\tCD31 TESTING SUMMARY\t#VALUE!\t240\t0\tNone\tNone\t1\t240\t57600
-IMMUNOLOGY ORDERABLES\t471697\tLABASPR\tASPERGILLUS AB, CF, S\tASPR\tASPERILLUS-CF\t#VALUE!\t435300\t0\tNone\tNone\t1\t435300\t1.89E+11
-IMMUNOLOGY ORDERABLES\t472049\tLABCIMM\tCOCCI AB, ID, S\tCIMM\tCOCCI IMMUNODIFFUSION\t#VALUE!\t385435\t0\tNone\tNone\t24\t9250440\t3.66E+12
-IMMUNOLOGY ORDERABLES\t472360\tLABFASAB\tFASCIOLA HEPATICA AB\tFHAB2\tF.HEPATICA ANTIBODY ,ELISA\t#VALUE!\t8270820\t0\tNone\tNone\t1\t8270820\t6.84E+13
-IMMUNOLOGY ORDERABLES\t472579\tLABHYPP\tHYPERSENSITIVITY PNEUMONITIS SCREEN\tHYPINT\tINTERPRETATION\t#VALUE!\t415480\t0\tNone\tNone\t3\t1246440\t6.65E+11
-IMMUNOLOGY ORDERABLES\t472773\tLAB12214R\tB. BURGDORFERI C6 PEP AB,RFLX\tLYMEC6\tB.BURGDORFERI C6 PEPTIDE\t#VALUE!\t269543.2432\t0\tNone\tNone\t111\t29919300\t1.07E+13
-IMMUNOLOGY ORDERABLES\t472773\tLAB12214R\tLYME SCREEN, SERUM\tLYMEC6\tB.BURGDORFERI C6 PEPTIDE\t#VALUE!\t527280\t0\tNone\tNone\t8\t4218240\t6.97E+12
-IMMUNOLOGY ORDERABLES\t472773\tLABLYMES2\tLYME SCREEN, SERUM\tLYMEC6\tB.BURGDORFERI C6 PEPTIDE\t#VALUE!\t284946.5693\t0\tNone\tNone\t137\t39037680\t1.38E+13
-IMMUNOLOGY ORDERABLES\t473063\tLABPARAG\tPARAGONIMIASIS ANTIBODY, ELISA\tPARAG1\tPARAGONIMIASIS\t#VALUE!\t196920\t0\tNone\tNone\t1\t196920\t38777486400
-IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tPENINT\tINTERPRETATION\t#VALUE!\t485348.8235\t0\tNone\tNone\t34\t16501860\t1.42E+13
-IMMUNOLOGY ORDERABLES\t497406\tLABYNMOG\tNMO IGG\tYNMOG1\t(NMO) AUTOANTIBODY, IGG, SERUM\t#VALUE!\t485895.3846\t0\tNone\tNone\t39\t18949920\t3.33E+13
-IMMUNOLOGY ORDERABLES\t497482\tLABYPLAB\tPLATELET AB\tPLTAB\tPLATELET ANTIBODY\t#VALUE!\t262359.4286\t0\tNone\tNone\t35\t9182580\t2.60E+12
-IMMUNOLOGY ORDERABLES\t497482\tLABYPLAB\tPLATELET AB\tYPLAB2\tCOMMENT\t#VALUE!\t266346.6667\t0\tNone\tNone\t18\t4794240\t1.36E+12
-IR BODY ORDERABLES\t517389\tIMG2EX\tIR ANGIO EXTREMITY\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONDS\tDISCLAIMER\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
-LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONIN\tINTERPRETATION\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
-LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONMD\tINTERPRETED BY\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
-LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONMT\tMETHOD\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tACPINT\tINTERPRETATION\t#VALUE!\t373273.3333\t0\tNone\tNone\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tINBY\tINTERPRETED BY\t#VALUE!\t373273.3333\t0\tNone\tNone\t9\t3359460\t1.66E+12
-LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tACHINT\tINTERPRETATION\t#VALUE!\t397308\t0\tNone\tNone\t5\t1986540\t1.05E+12
-LAB BLOOD ORDERABLES\t471577\tLAB11139R\tCARDIAC REJECTION RISK\tALLO\tALLOMAP\t#VALUE!\t380820\t0\tNone\tNone\t2\t761640\t3.76E+11
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHCT\tCOMMENT\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHDI\tDISCLAIMER\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHIN\tINTERPRETATION\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHMD\tINTERPRETED BY\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHME\tMETHOD\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tAAPINT\tINTERPRETATION\t#VALUE!\t293858.1818\t0\tNone\tNone\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tINTBY\tINTERPRETED BY\t#VALUE!\t293858.1818\t0\tNone\tNone\t22\t6464880\t2.30E+12
-LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTB\tINTERPRETED BY\t#VALUE!\t543083.1579\t0\tNone\tNone\t57\t30955740\t2.27E+13
-LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTI\tINTERPRETATION, BM\t#VALUE!\t540213\t0\tNone\tNone\t60\t32412780\t2.37E+13
-LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\t12215R\t12215R\t#VALUE!\t205166.6667\t0\tNone\tNone\t9\t1846500\t4.31E+11
-LAB BLOOD ORDERABLES\t471629\tLABHSP70\tANTI - 68 KD (HSP - 70) ANTIBODIES\tHSP70T\tANTI-68 KD ABS\t#VALUE!\t4737480\t0\tNone\tNone\t1\t4737480\t2.24E+13
-LAB BLOOD ORDERABLES\t471632\tLABAMAONC\tANTI - MALIGNIN AB\tAMA\tANTI-MALIGNIN AB\t#VALUE!\t67680\t0\tNone\tNone\t1\t67680\t4580582400
-LAB BLOOD ORDERABLES\t471638\tLAB11075R\tANTI - SMOOTH MUSCLE AB\tASMA\tANTI-SMOOTH MUSCLE ANITBODY\t#VALUE!\t876090\t0\tNone\tNone\t2\t1752180\t2.43E+12
-LAB BLOOD ORDERABLES\t471638\tLAB11075R\tSMOOTH MUSCLE AB\tASMA\tANTI-SMOOTH MUSCLE ANITBODY\t#VALUE!\t221775\t0\tNone\tNone\t500\t110887500\t3.44E+13
-LAB BLOOD ORDERABLES\t471638\tLABYASMA\tANTI - SMOOTH MUSCLE AB\tASMA\tANTI-SMOOTH MUSCLE ANITBODY\t#VALUE!\t238124.2105\t0\tNone\tNone\t19\t4524360\t1.34E+12
-LAB BLOOD ORDERABLES\t471650\tLABASO\tANTISTREPTOLYSIN O TITER\tASO\tASO TITER (IU/ML)\t#VALUE!\t274380\t0\tNone\tNone\t2\t548760\t1.64E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO1\tSPECIMEN\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO10\tRELEASE DATE\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO11\tINTERPRETATION\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO12\tEXTRACTION PERFORMED?\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO2\tSPECIMEN ID\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO4\tORDER DATE\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO5\tMETHOD\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO6\tRESULT\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO9\tREVIEWED BY\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
-LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tEEIGG\tE EQUINE ENCEP IGG AB\t#VALUE!\t2760\t0\tNone\tNone\t1\t2760\t7617600
-LAB BLOOD ORDERABLES\t471687\tLAB11126R\tASCARIASIS\tASCA\tASCARISIS\t#VALUE!\t1498147.5\t0\tNone\tNone\t8\t11985180\t2.50E+13
-LAB BLOOD ORDERABLES\t471687\tLABASCA\tASCARIASIS AB\tASCA\tASCARISIS\t#VALUE!\t424980\t0\tNone\tNone\t1\t424980\t1.81E+11
-LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tANTI-SCL70 ANTIBODY\tSCLINT\tANTI-SCL70 INTERPRETATION\t#VALUE!\t391549.434\t0\tNone\tNone\t159\t62256360\t3.14E+13
-LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tANTI-RO ANTIBODY\tSSAIN\tANTI-RO INTERPRETATION\t#VALUE!\t390342.8571\t0\tNone\tNone\t210\t81972000\t4.05E+13
-LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tANTI-LA ANTIBODY\tSSBINT\tANTI-LA INTERPRETATION\t#VALUE!\t380593.9806\t0\tNone\tNone\t206\t78402360\t3.76E+13
-LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG AND IGM AB\tYPERA1\tB PERTUSSIS AB, IGG\t#VALUE!\t32220\t0\tNone\tNone\t1\t32220\t1038128400
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBDIR\tINTERPRETED BY\t#VALUE!\t1276240\t0\tNone\tNone\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBDIS\tDISCLAIMER\t#VALUE!\t1276240\t0\tNone\tNone\t3\t3828720\t4.91E+12
-LAB BLOOD ORDERABLES\t471738\tLABYBABE\tBABESIA MICROTI IGG AND IGM AB\tYBAB1\tBABESIA MICROTI, IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471738\tLABYBABE\tBABESIA MICROTI IGG AND IGM AB\tYBAB2\tBABESIA MICROTI, IGM\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471738\tLABYBABE\tBABESIA MICROTI IGG AND IGM AB\tYBAB3\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRDI\tDISCLAIMER\t#VALUE!\t600772.8477\t0\tNone\tNone\t151\t90716700\t6.81E+13
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRDX\tCLINICAL INDICATION\t#VALUE!\t600772.8477\t0\tNone\tNone\t151\t90716700\t6.81E+13
-LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRME\tMETHOD\t#VALUE!\t600772.8477\t0\tNone\tNone\t151\t90716700\t6.81E+13
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQDI\tDISCLAIMER\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQIN\tINTERPRETATION\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQMD\tINTERPRETED BY\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQME\tMETHOD\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
-LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQMU\tMUTATION\t#VALUE!\t1877940\t0\tNone\tNone\t1\t1877940\t3.53E+12
-LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GPCM\tBETA-2-GP1 COMMENT\t#VALUE!\t612816.383\t0\tNone\tNone\t94\t57604740\t3.76E+13
-LAB BLOOD ORDERABLES\t471777\tLABYBLAS\tBLASTOMYCES AB\tYBLAS1\tBLASTOMYCES AB ,S\t#VALUE!\t365589.375\t0\tNone\tNone\t32\t11698860\t4.93E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMGDES\tCMV IGG TEST DESCRIPTION\t#VALUE!\t386356.6667\t0\tNone\tNone\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMMDES\tCMV IGM TEST DESCRIPTION\t#VALUE!\t386356.6667\t0\tNone\tNone\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHSAB\tVIRAL AB HSV\t#VALUE!\t404997.5\t0\tNone\tNone\t24\t9719940\t4.77E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHVIGDS\tHSV IGG TEST DESCRIPTION\t#VALUE!\t302160\t0\tNone\tNone\t8\t2417280\t8.64E+11
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tRPRQIN\tINTERPRETATIONS\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
-LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVGGDES\tVZV IGG TEST DESCRIPTION\t#VALUE!\t386356.6667\t0\tNone\tNone\t18\t6954420\t3.18E+12
-LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tASPINT\tINTERPRETATION\t#VALUE!\t785686.9231\t0\tNone\tNone\t78\t61283580\t6.62E+13
-LAB BLOOD ORDERABLES\t471914\tLAB11105R\tCANDIDA AB\tCANDP\tCANDIDA PRECIPITINS\t#VALUE!\t288000\t0\tNone\tNone\t1\t288000\t82944000000
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11834R\tSPECIMEN\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11835R\tSPECIMEN ID\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11836R\tSOURCE\t#VALUE!\t1199640\t0\tNone\tNone\t1\t1199640\t1.44E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11837R\tORDER DATE\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11838R\tREASON FOR REFERRAL\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11839R\tMETHOD\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11840R\tINTERPRETATION\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11841R\tAMENDMENT\t#VALUE!\t1199640\t0\tNone\tNone\t1\t1199640\t1.44E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11842R\tREVIEWED BY\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11843R\tRELEASE DATE\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t12769R\tTRI-SIALO/DI-OLIGO RATIO\t#VALUE!\t682500\t0\tNone\tNone\t1\t682500\t4.66E+11
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t12770R\tAPO CIII-1/CIII-2 RATIO\t#VALUE!\t682500\t0\tNone\tNone\t1\t682500\t4.66E+11
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t12771R\tAPO CIII-0/APO CIII-2 RATIO\t#VALUE!\t682500\t0\tNone\tNone\t1\t682500\t4.66E+11
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\tAODOR\tA-OLIGO/DI-OLIGO RAT\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
-LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\tMODOR\tMONO-OLIGO/DI-OLIGO RATIO\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
-LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tCARINT\tINTERPRETATION\t#VALUE!\t396363.6364\t0\tNone\tNone\t33\t13080000\t5.88E+12
-LAB BLOOD ORDERABLES\t471933\tLAB11127R\tCASPOFUNGIN LEVEL\tCASPO\tCASPOFUNGIN (CANCIDAS)\t#VALUE!\t720490.5882\t0\tNone\tNone\t17\t12248340\t1.11E+13
-LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34DS\tDISCLAIMER\t#VALUE!\t25604.54545\t0\tNone\tNone\t66\t1689900\t1.43E+11
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTDS\tDISCLAIMER\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTIN\tINTERPRETED BY\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTME\tMETHOD\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
-LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTRS\tRESULT\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
-LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM1\tCHLAMYDIA SEROLOGY, S\t#VALUE!\t244670.7692\t0\tNone\tNone\t13\t3180720\t8.85E+11
-LAB BLOOD ORDERABLES\t472048\tLAB11124R\tCOCCIDIOIDES COMPLEMENT FIXATION, SERUM\tCOCFS\tCOCCI CF (SERUM)\t#VALUE!\t952080\t0\tNone\tNone\t7\t6664560\t6.51E+12
-LAB BLOOD ORDERABLES\t472048\tLABCOCFS\tCOCCIDIOIDES COMPLEMENT FIXATION, SERUM\tCOCFS\tCOCCI CF (SERUM)\t#VALUE!\t1082202.353\t0\tNone\tNone\t34\t36794880\t4.16E+13
-LAB BLOOD ORDERABLES\t472053\tLABYTICK\tCOLORADO TICK FEVER AB\tYTICK1\tIGG AB\t#VALUE!\t829560\t0\tNone\tNone\t1\t829560\t6.88E+11
-LAB BLOOD ORDERABLES\t472053\tLABYTICK\tCOLORADO TICK FEVER AB\tYTICK2\tIGM AB\t#VALUE!\t829560\t0\tNone\tNone\t1\t829560\t6.88E+11
-LAB BLOOD ORDERABLES\t472053\tLABYTICK\tCOLORADO TICK FEVER AB\tYTICK3\tINTERPRETATION\t#VALUE!\t829560\t0\tNone\tNone\t1\t829560\t6.88E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t200741.1086\t0\tNone\tNone\t451\t90534240\t2.96E+13
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHAGINT\tINTERPRETATION\t#VALUE!\t19380\t0\tNone\tNone\t1\t19380\t375584400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t223680\t0\tNone\tNone\t4\t894720\t2.54E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHCVI\tINTERPRETATION\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
-LAB BLOOD ORDERABLES\t472102\tLAB11056R\tCORTISOL FREE, SERUM\tCORTFREE\tCORTISOL, SERUM FREE\t#VALUE!\t426930\t0\tNone\tNone\t4\t1707720\t7.56E+11
-LAB BLOOD ORDERABLES\t472102\tLABSFCORT\tCORTISOL FREE, SERUM\tCORTFREE\tCORTISOL, SERUM FREE\t#VALUE!\t462558.4615\t0\tNone\tNone\t26\t12026520\t6.85E+12
-LAB BLOOD ORDERABLES\t472107\tLABCRTINE\tCREATINE, SERUM\tCRTNR\tCREATINE, PLASMA\t#VALUE!\t1224687.273\t0\tNone\tNone\t11\t13471560\t2.12E+13
-LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tBMAB\tBASEMENT MEMBRANE, IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
-LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP1\tCELL SURFACE AB IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
-LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP3\tMONKEY ESOPHAGUS IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
-LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP4\tHUMAN SPLIT SKIN IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
-LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP5\tOTHER\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
-LAB BLOOD ORDERABLES\t472120\tLABCCP\tANTI CCP\tCCPINT\tINTERPRETATION\t#VALUE!\t296484\t0\tNone\tNone\t5\t1482420\t5.59E+11
-LAB BLOOD ORDERABLES\t472120\tLABYCCPIG\tANTI CCP\tCCPINT\tINTERPRETATION\t#VALUE!\t388985.2294\t0\tNone\tNone\t218\t84798780\t4.31E+13
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32DI\tDISCLAIMER:\t#VALUE!\t807004.6154\t0\tNone\tNone\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32IN\tINTERPRETED BY\t#VALUE!\t807004.6154\t0\tNone\tNone\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32ME\tMETHOD:\t#VALUE!\t807004.6154\t0\tNone\tNone\t39\t31473180\t3.43E+13
-LAB BLOOD ORDERABLES\t472125\tLABCYSBLD\tCYSTICERCUS AB REFLEX PANEL, SERUM\tCYSAB2\tCYSTICERCUS AB (SERUM), ELISA\t#VALUE!\t307886.087\t0\tNone\tNone\t23\t7081380\t2.77E+12
-LAB BLOOD ORDERABLES\t472127\tLABCYSI\tCYSTICERCOSIS, CSF AND SERUM\tCYSS\tCYSTICERCOSIS-SERUM\t#VALUE!\t1266680\t0\tNone\tNone\t3\t3800040\t6.75E+12
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tPSREV\tPATH/SPEC REVIEW\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t#VALUE!\t900\t0\tNone\tNone\t1\t900\t810000
-LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472192\tLABDATLA\tDIRECT ANTIGLOBULIN TEST - LARC\tDATLAR\tDIRECT COOMBS - LA RED CROSS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472205\tLABYDISO\tDISOPYRAMIDE, SERUM\tYDISO1\tDISOPYRAMIDE, P\t#VALUE!\t8520\t0\tNone\tNone\t1\t8520\t72590400
-LAB BLOOD ORDERABLES\t472226\tLABDHB\tDONOR HEPATITIS B SURFACE AG\tDHB\tDONOR HEP B SURF. AG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472227\tLABDHEPC\tDONOR HEPATITIS C AB\tDHEPC\tDONOR HEPATITIS C AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472228\tLABDHIVB\tDONOR HIV AB\tDHTLV\tDONOR HIV AB COMMENT\t#VALUE!\t199937.1429\t0\tNone\tNone\t7\t1399560\t3.32E+11
-LAB BLOOD ORDERABLES\t472248\tLABYSDA\tDRUG SCREEN, PLASMA\t11832R\tDRUGS DETECTED:\t#VALUE!\t242828.4375\t0\tNone\tNone\t64\t15541020\t1.07E+13
-LAB BLOOD ORDERABLES\t472248\tLABYSDA\tDRUG SCREEN, PLASMA\tDRUGSCRN\tDRUG SCREEN, PLASMA\t#VALUE!\t645556.1538\t0\tNone\tNone\t26\t16784460\t4.16E+13
-LAB BLOOD ORDERABLES\t472251\tLABYAMEB\tE. HISTOLYTICA AB\tYAMEB1\tE. HISTOLYTICA AB\t#VALUE!\t332553.75\t0\tNone\tNone\t16\t5320860\t2.02E+12
-LAB BLOOD ORDERABLES\t472276\tLABECHIG\tECHINOCOCCUS AB, IGG W/REFLEX\tECHIND\tECHINOCOCCUS LGG\t#VALUE!\t287613.3333\t0\tNone\tNone\t9\t2588520\t8.75E+11
-LAB BLOOD ORDERABLES\t472328\tLABYESTRI\tESTRIOL FREE\tYESTR1\tESTRIOL,UNCONJUGATED\t#VALUE!\t154380\t0\tNone\tNone\t1\t154380\t23833184400
-LAB BLOOD ORDERABLES\t472330\tLABYESTRN\tESTRONE, SERUM\tESTRONE\tESTRONE\t#VALUE!\t270300\t0\tNone\tNone\t1\t270300\t73062090000
-LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIDIS\tDISCLAIMER\t#VALUE!\t436344.1652\t0\tNone\tNone\t581\t253515960\t1.44E+14
-LAB BLOOD ORDERABLES\t472359\tLAB11116R\tFANCONI MUTATION ANALYSIS\tGFANCO\tFANCONI AN. GP C MUT\t#VALUE!\t1312400\t0\tNone\tNone\t3\t3937200\t5.30E+12
-LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA9\tCOMMENT\t#VALUE!\t342450\t0\tNone\tNone\t2\t684900\t2.36E+11
-LAB BLOOD ORDERABLES\t472379\tLABFIBRO\tFIBROSPECT FOR LIVER FIBROSIS\tFIBROR\tFIBROSPECT FOR LIV\t#VALUE!\t1223280\t0\tNone\tNone\t2\t2446560\t3.14E+12
-LAB BLOOD ORDERABLES\t472380\tLABFILAB\tFILARIA IGG4 AB ELISA\tFILIG4\tFILARIAL LGG ANTIBODY\t#VALUE!\t476850.9677\t0\tNone\tNone\t31\t14782380\t9.47E+12
-LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tITINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXDIS\tDISCLAIMER\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXINT\tINTERPRETATION\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXMD\tINTERPRETED BY\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXMET\tMETHOD\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
-LAB BLOOD ORDERABLES\t472422\tLABYT7P\tFREE THYROXINE INDEX\tFT4INDEX\tFREE THYROXINE INDEX\t#VALUE!\t146604\t0\tNone\tNone\t5\t733020\t1.21E+11
-LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tASPR\tASPERILLUS-CF\t#VALUE!\t360680\t0\tNone\tNone\t6\t2164080\t8.46E+11
-LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tBLAS\tBLASTO AB CFS\t#VALUE!\t360680\t0\tNone\tNone\t6\t2164080\t8.46E+11
-LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tHISTM\tHISTO MYCELIAL PHASE\t#VALUE!\t360680\t0\tNone\tNone\t6\t2164080\t8.46E+11
-LAB BLOOD ORDERABLES\t472430\tLAB11128R\tFUNGAL MIC\tFMIC\tFUNGAL MIC\t#VALUE!\t1068060\t0\tNone\tNone\t1\t1068060\t1.14E+12
-LAB BLOOD ORDERABLES\t472471\tLABGTOL\tGLUCOSE TOLERANCE\tGTTINT\tGTT INTERPRETATION\t#VALUE!\t29820\t0\tNone\tNone\t1\t29820\t889232400
-LAB BLOOD ORDERABLES\t472487\tLABYHALO\tHALOPERIDOL, SERUM\tHALO\tHALOPERIDOL\t#VALUE!\t437280\t0\tNone\tNone\t1\t437280\t1.91E+11
-LAB BLOOD ORDERABLES\t472487\tLABYHALO\tHALOPERIDOL, SERUM\tYHALO2\tREDUCED HALOPERIDOL\t#VALUE!\t437280\t0\tNone\tNone\t1\t437280\t1.91E+11
-LAB BLOOD ORDERABLES\t472493\tLABHDLGGE\tHDL GRADIENT GEL ELECTROPHORESIS\tHDLGE\tHDL GRAD GEL ELECTRO\t#VALUE!\t1547100\t0\tNone\tNone\t1\t1547100\t2.39E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHCO\tCOMMENT\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHDI\tDISCLAIMER\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHIN\tINTERPRETATION\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHMD\tINTERPRETED BY\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHME\tMETHOD\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHPR\tPRIMERS\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHRE\tRESULT\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t70359.66102\t0\tNone\tNone\t413\t29058540\t4.09E+12
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCINT\tINTERPRETATION\t#VALUE!\t129980\t0\tNone\tNone\t9\t1169820\t3.51E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t290760\t0\tNone\tNone\t1\t290760\t84541377600
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t129980\t0\tNone\tNone\t9\t1169820\t3.51E+11
-LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t129980\t0\tNone\tNone\t9\t1169820\t3.51E+11
-LAB BLOOD ORDERABLES\t472523\tLAB11106R\tHEPATITIS B E AB\tHBEAB\tHEPATITIS B E AB\t#VALUE!\t895680\t0\tNone\tNone\t2\t1791360\t2.45E+12
-LAB BLOOD ORDERABLES\t472523\tLABHEAB\tHEPATITIS B E AB\tHBEAB\tHEPATITIS B E AB\t#VALUE!\t281130\t0\tNone\tNone\t2\t562260\t1.58E+11
-LAB BLOOD ORDERABLES\t472524\tLAB11107R\tHEPATITIS B E AG\tHBEAG\tHEPATITIS B E AG\t#VALUE!\t1544340\t0\tNone\tNone\t1\t1544340\t2.38E+12
-LAB BLOOD ORDERABLES\t472524\tLABHEAG\tHEPATITIS B E AG\tHBEAG\tHEPATITIS B E AG\t#VALUE!\t248520\t0\tNone\tNone\t4\t994080\t2.70E+11
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tANTI HBS QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t125880\t0\tNone\tNone\t5\t629400\t82853755200
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t140700\t0\tNone\tNone\t1\t140700\t19796490000
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t#VALUE!\t140700\t0\tNone\tNone\t1\t140700\t19796490000
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t138684.5455\t0\tNone\tNone\t22\t3051060\t4.80E+11
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAIN\tINTERPRETATION\t#VALUE!\t43043.64407\t0\tNone\tNone\t236\t10158300\t7.64E+11
-LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t43043.64407\t0\tNone\tNone\t236\t10158300\t7.64E+11
-LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tANTI HBS QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t155980\t0\tNone\tNone\t63\t9826740\t1.82E+12
-LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t174097.8947\t0\tNone\tNone\t38\t6615720\t1.31E+12
-LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t#VALUE!\t174097.8947\t0\tNone\tNone\t38\t6615720\t1.31E+12
-LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEP B SURF AB QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t161105.0602\t0\tNone\tNone\t166\t26743440\t5.04E+12
-LAB BLOOD ORDERABLES\t472536\tLABHGBFQ\tHGB F QUANTITATIVE\tHGFMD\tREVIEWED BY\t#VALUE!\t434820\t0\tNone\tNone\t3\t1304460\t5.86E+11
-LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGMD\tREVIEWED BY\t#VALUE!\t426444.5714\t0\tNone\tNone\t105\t44776680\t2.79E+13
-LAB BLOOD ORDERABLES\t472549\tLABYHIST\tHISTOPLASMA AB SCREEN\tYHIS1\tHISTOPLASMA AB SCREEN\t#VALUE!\t172041\t0\tNone\tNone\t20\t3440820\t6.83E+11
-LAB BLOOD ORDERABLES\t472571\tLAB11137R\tHSV AB, IMMUNOBLOT\tHSVWB\tHSV WESTERN BLOT\t#VALUE!\t1094730\t0\tNone\tNone\t2\t2189460\t2.40E+12
-LAB BLOOD ORDERABLES\t472573\tLAB11136R\tHTLV-1 AB SCREEN\tHTLVS\tHTLV AB SCREEN\t#VALUE!\t275580\t0\tNone\tNone\t2\t551160\t1.53E+11
-LAB BLOOD ORDERABLES\t472573\tLABHTLVS\tHTLV-1 AB SCREEN\tHTLVS\tHTLV AB SCREEN\t#VALUE!\t350712\t0\tNone\tNone\t5\t1753560\t6.93E+11
-LAB BLOOD ORDERABLES\t472578\tLABHUNDNA\tHUNTINGTON\'S DNA\tHNDNA\tHUNTINGTON\'S DNA\t#VALUE!\t734460\t0\tNone\tNone\t2\t1468920\t2.16E+12
-LAB BLOOD ORDERABLES\t472595\tLABYSCERE\tINFLAMM. BOWEL DISEASE PANEL,S\tNEUTAB\tNEUTROPHIL SPECIFIC ANTIBODIES\t#VALUE!\t232440\t0\tNone\tNone\t3\t697320\t1.65E+11
-LAB BLOOD ORDERABLES\t472631\tLABIFEDE\tIMMUNOFIXATION IGD AND IGE, SERUM\tIFED\tIMMUNOFIXATION IGD\t#VALUE!\t466232\t0\tNone\tNone\t15\t6993480\t7.80E+12
-LAB BLOOD ORDERABLES\t472631\tLABIFEDE\tIMMUNOFIXATION IGD AND IGE, SERUM\tIFEE\tIMMUNOFIXATION IGE\t#VALUE!\t466232\t0\tNone\tNone\t15\t6993480\t7.80E+12
-LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tANAINTERP\tANA INTERPRETATION\t#VALUE!\t272940\t0\tNone\tNone\t2\t545880\t1.64E+11
-LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tDNAINT\tINTERPRETATION\t#VALUE!\t272940\t0\tNone\tNone\t2\t545880\t1.64E+11
-LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tANAINTERP\tANA INTERPRETATION\t#VALUE!\t311100\t0\tNone\tNone\t2\t622200\t2.23E+11
-LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tRHFINT\tINTERPRETATION\t#VALUE!\t311100\t0\tNone\tNone\t2\t622200\t2.23E+11
-LAB BLOOD ORDERABLES\t472661\tLABYNABF\tINTERFERON NEUTRALIZING  AB\tYNABF1\tINTERFERON NEUTRALIZING AB\t#VALUE!\t1388400\t0\tNone\tNone\t1\t1388400\t1.93E+12
-LAB BLOOD ORDERABLES\t472666\tLABYIFBA\tINTRINSIC FACTOR BLOCKING AB\tYIFB1\tINTRINSIC FACTOR BLOCKING AB\t#VALUE!\t238970\t0\tNone\tNone\t36\t8602920\t5.74E+12
-LAB BLOOD ORDERABLES\t472666\tLABYIFBA\tINTRINSIC FACTOR BLOCKING AB\tYIFB2\tCOMMENT\t#VALUE!\t172041.6\t0\tNone\tNone\t25\t4301040\t8.33E+11
-LAB BLOOD ORDERABLES\t472672\tLABISOHTI\tISOHEMAGGLUTININ TITER\tISOHTR\tISOHEMAGGLUTININ TITER RESULT\t#VALUE!\t22077.33333\t0\tNone\tNone\t90\t1986960\t1.89E+11
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKCO\tCOMMENT\t#VALUE!\t548376\t0\tNone\tNone\t115\t63063240\t4.39E+13
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKDI\tDISCLAIMER\t#VALUE!\t557059.322\t0\tNone\tNone\t118\t65733000\t4.63E+13
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKDX\tCLINICAL INDICATION\t#VALUE!\t557059.322\t0\tNone\tNone\t118\t65733000\t4.63E+13
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKIN\tINTERPRETATION\t#VALUE!\t889920\t0\tNone\tNone\t3\t2669760\t2.42E+12
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKME\tMETHOD\t#VALUE!\t557059.322\t0\tNone\tNone\t118\t65733000\t4.63E+13
-LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKRF\tREFERENCES\t#VALUE!\t548376\t0\tNone\tNone\t115\t63063240\t4.39E+13
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKCO\tCOMMENT\t#VALUE!\t526447.5\t0\tNone\tNone\t8\t4211580\t2.86E+12
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKDI\tDISCLAIMER\t#VALUE!\t525786.6667\t0\tNone\tNone\t9\t4732080\t3.13E+12
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKDX\tCLINICAL INDICATION\t#VALUE!\t525786.6667\t0\tNone\tNone\t9\t4732080\t3.13E+12
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKME\tMETHOD\t#VALUE!\t525786.6667\t0\tNone\tNone\t9\t4732080\t3.13E+12
-LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKRF\tREFERENCES\t#VALUE!\t526447.5\t0\tNone\tNone\t8\t4211580\t2.86E+12
-LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tKBSINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t472724\tLABYLEPT\tLEPTOSPIROSIS AB\tYLEP1\tLEPTOSPIRA AB\t#VALUE!\t312504.6429\t0\tNone\tNone\t56\t17500260\t6.19E+12
-LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tYLDIS2\tLD ISOENZYMES, S\t#VALUE!\t234468\t0\tNone\tNone\t5\t1172340\t2.91E+11
-LAB BLOOD ORDERABLES\t472745\tLABYLEGS\tLEGIONELLA PNEUMOPHILA, AB\tYLEGS1\tLEGIONELLA PNEUMOPHILA AB\t#VALUE!\t256849.7561\t0\tNone\tNone\t41\t10530840\t3.34E+12
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12431R\tINTERPRETATION\t#VALUE!\t323840\t0\tNone\tNone\t3\t971520\t3.52E+11
-LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLTROIN\tINTERPRETATION\t#VALUE!\t1997181.818\t0\tNone\tNone\t11\t21969000\t9.76E+13
-LAB BLOOD ORDERABLES\t472747\tLABLEMSAB\tLEMS AUTOAB\tLEMS\tLEMS AUTOANTIBODY\t#VALUE!\t1501180\t0\tNone\tNone\t3\t4503540\t8.25E+12
-LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tRCRSK\tREL. CARDIAC RISK\t#VALUE!\t66268.5\t0\tNone\tNone\t40\t2650740\t2.72E+11
-LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tRCRSK\tREL. CARDIAC RISK\t#VALUE!\t79623.67347\t0\tNone\tNone\t49\t3901560\t7.43E+11
-LAB BLOOD ORDERABLES\t472762\tLABLKMAB\tLIVER KID MICROSOME AB\tLKMAB\tLIV-KID-MICROSOME AB\t#VALUE!\t217920\t0\tNone\tNone\t5\t1089600\t2.48E+11
-LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tCONINT\tCON A INTERPRETATION\t#VALUE!\t771800\t0\tNone\tNone\t3\t2315400\t1.79E+12
-LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPHAINT\tPHA INTERPRETATION\t#VALUE!\t771800\t0\tNone\tNone\t3\t2315400\t1.79E+12
-LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPWMINT\tPWM INTERPRETATION\t#VALUE!\t771800\t0\tNone\tNone\t3\t2315400\t1.79E+12
-LAB BLOOD ORDERABLES\t472781\tLABMAGATH\tMAG AUTOAB\tMAGAB\tMAG AUTOANTIBODY\t#VALUE!\t4404444\t0\tNone\tNone\t5\t22022220\t1.81E+14
-LAB BLOOD ORDERABLES\t472818\tLABYMEM\tMEASLES AB, IGM\tYMEM1\tMEAS(RUBEOLA) AB,IGM\t#VALUE!\t253920\t0\tNone\tNone\t2\t507840\t1.39E+11
-LAB BLOOD ORDERABLES\t472834\tLABMELAS\tMELAS MTDNA PROFILE\tMELADN\tMELAS MTDNA PROFILE\t#VALUE!\t72540\t0\tNone\tNone\t1\t72540\t5262051600
-LAB BLOOD ORDERABLES\t472845\tLABYMETHS\tMETHSUXIMIDE\tMETHSX\tMETHSUXIMIDE\t#VALUE!\t1913310\t0\tNone\tNone\t2\t3826620\t8.10E+12
-LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHCMT\tCOMMENT\t#VALUE!\t638456.25\t0\tNone\tNone\t16\t10215300\t8.63E+12
-LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHDIR\tINTERPRETED BY\t#VALUE!\t584196\t0\tNone\tNone\t15\t8762940\t6.52E+12
-LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHDIS\tDISCLAIMER\t#VALUE!\t638456.25\t0\tNone\tNone\t16\t10215300\t8.63E+12
-LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHPRM\tPRIMERS\t#VALUE!\t638456.25\t0\tNone\tNone\t16\t10215300\t8.63E+12
-LAB BLOOD ORDERABLES\t472854\tLABMALL1\tMISCELLANEOUS ALLERGEN 1\tI1\tINTERPRETATION\t#VALUE!\t630540\t0\tNone\tNone\t4\t2522160\t2.43E+12
-LAB BLOOD ORDERABLES\t472855\tLABMALL2\tMISCELLANEOUS ALLERGEN 2\tALL2\tALLERGEN\t#VALUE!\t1370400\t0\tNone\tNone\t1\t1370400\t1.88E+12
-LAB BLOOD ORDERABLES\t472855\tLABMALL2\tMISCELLANEOUS ALLERGEN 2\tI2\tINTERPRETATION\t#VALUE!\t1370400\t0\tNone\tNone\t1\t1370400\t1.88E+12
-LAB BLOOD ORDERABLES\t472856\tLABMALL3\tMISCELLANEOUS ALLERGEN 3\tI3\tINTERPRETATION\t#VALUE!\t148680\t0\tNone\tNone\t1\t148680\t22105742400
-LAB BLOOD ORDERABLES\t472950\tLAB11048R\tNEURON AB\tNEURON\tNEURONAL CELL ABS, SERUM\t#VALUE!\t944775\t0\tNone\tNone\t4\t3779100\t4.58E+12
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTCO\tSTIM. CONTROL,NBT\t#VALUE!\t48930\t0\tNone\tNone\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTCOM\tCOMMENT, NBT\t#VALUE!\t48930\t0\tNone\tNone\t2\t97860\t5243706000
-LAB BLOOD ORDERABLES\t473042\tLABOF\tOSMOTIC FRAGILITY\tOFDIR\tREVIEWED BY\t#VALUE!\t418596\t0\tNone\tNone\t10\t4185960\t2.53E+12
-LAB BLOOD ORDERABLES\t473054\tLABPAI1AT\tPAI-1 ACTIVITY\tPAI1T\tPAI-1 ACTIVITY\t#VALUE!\t1999356\t0\tNone\tNone\t5\t9996780\t5.51E+13
-LAB BLOOD ORDERABLES\t473055\tLABPAI1AG\tPAI-1 AG\tPAI1G\tPAI-1 ANTIGEN\t#VALUE!\t1906800\t0\tNone\tNone\t1\t1906800\t3.64E+12
-LAB BLOOD ORDERABLES\t473064\tLABYPCA\tPARIETAL CELL AB, SERUM\tYPCA\tPARIETAL CELL AB\t#VALUE!\t261540\t0\tNone\tNone\t1\t261540\t68403171600
-LAB BLOOD ORDERABLES\t473111\tLABPLTFUN\tPLATELET FUNCTION SCREEN\tPLTFCM\tCOMMENT, PLT FUNCTION\t#VALUE!\t12280.51948\t0\tNone\tNone\t154\t1891200\t1.58E+11
-LAB BLOOD ORDERABLES\t473148\tLABPRO6MP\tPRO-PREDICT ENZACT\tPRO6MR\tPRO-PREDICT ENZACT\t#VALUE!\t621850.9091\t0\tNone\tNone\t11\t6840360\t5.42E+12
-LAB BLOOD ORDERABLES\t473177\tLABPT12\tPROTHROMBIN FRAGMENT 1 AND 2\tPTFRNO\tPROTHROMBIN 1+2 NOTE\t#VALUE!\t1072470\t0\tNone\tNone\t4\t4289880\t5.93E+12
-LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTCMT\tCOMMENT\t#VALUE!\t452558.7763\t0\tNone\tNone\t523\t236688240\t1.36E+14
-LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTDIS\tDISCLAIMER\t#VALUE!\t452558.7763\t0\tNone\tNone\t523\t236688240\t1.36E+14
-LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTPRM\tPRIMERS\t#VALUE!\t452558.7763\t0\tNone\tNone\t523\t236688240\t1.36E+14
-LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tQTFNIN\tQUANTIFERON INTERPRETATION\t#VALUE!\t183897.5\t0\tNone\tNone\t24\t4413540\t8.77E+11
-LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tPSREV\tPATH/SPEC REVIEW\t#VALUE!\t5640\t0\tNone\tNone\t1\t5640\t31809600
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tRBCINT\tINTERPRETATION\t#VALUE!\t288690\t0\tNone\tNone\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tRBCMD\tREVIEWED BY\t#VALUE!\t288690\t0\tNone\tNone\t2\t577380\t3.33E+11
-LAB BLOOD ORDERABLES\t473253\tLABYRETAB\tRETICULIN AB\tYRET1\tRETICULIN AB\t#VALUE!\t167400\t0\tNone\tNone\t2\t334800\t56423095200
-LAB BLOOD ORDERABLES\t473262\tLABRF\tRHEUMATOID FACTOR\tRHFINT\tINTERPRETATION\t#VALUE!\t221510.8108\t0\tNone\tNone\t111\t24587700\t7.16E+12
-LAB BLOOD ORDERABLES\t473262\tLABRF\tRHUEMATOID FACTOR\tRHFINT\tINTERPRETATION\t#VALUE!\t239163.253\t0\tNone\tNone\t664\t158804400\t5.11E+13
-LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tRTXFDA\tNOTE\t#VALUE!\t131761.5464\t0\tNone\tNone\t194\t25561740\t4.96E+12
-LAB BLOOD ORDERABLES\t473282\tLABRUBG\tRUBELLA IGG ANTIBODY\tRUBINT\tINTERPRETATION\t#VALUE!\t132740\t0\tNone\tNone\t3\t398220\t70434939600
-LAB BLOOD ORDERABLES\t473297\tLABSNP\tSENSORIMOTOR PROFILE\tSNPRR\tSENSORIMOTOR PROFILE\t#VALUE!\t2310180\t0\tNone\tNone\t1\t2310180\t5.34E+12
-LAB BLOOD ORDERABLES\t473376\tLABSTRONG\tSTRONGYLOIDES IGG AB, ELISA\tSTROG\tSTRONGYLOIDES ,LGG\t#VALUE!\t261887.5962\t0\tNone\tNone\t208\t54472620\t1.88E+13
-LAB BLOOD ORDERABLES\t473380\tLABSULFAT\tSULFATIDE AUTOANTIBODY\tSULFAB\tSULFATIDE AUTOANTIBODY\t#VALUE!\t2153280\t0\tNone\tNone\t2\t4306560\t1.31E+13
-LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14CMT\tCOMMENT\t#VALUE!\t1210908\t0\tNone\tNone\t5\t6054540\t9.00E+12
-LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14DIR\tINTERPRETED BY\t#VALUE!\t1210908\t0\tNone\tNone\t5\t6054540\t9.00E+12
-LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14DIS\tDISCLAIMER\t#VALUE!\t1210908\t0\tNone\tNone\t5\t6054540\t9.00E+12
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15DI\tDISCLAIMER\t#VALUE!\t643218\t0\tNone\tNone\t50\t32160900\t2.37E+13
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15MD\tINTERPRETED BY\t#VALUE!\t643218\t0\tNone\tNone\t50\t32160900\t2.37E+13
-LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15ME\tMETHOD\t#VALUE!\t643218\t0\tNone\tNone\t50\t32160900\t2.37E+13
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTDIR\tINTERPRETED BY\t#VALUE!\t1027142.927\t0\tNone\tNone\t82\t84225720\t1.01E+14
-LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTDIS\tDISCLAIMER\t#VALUE!\t1027142.927\t0\tNone\tNone\t82\t84225720\t1.01E+14
-LAB BLOOD ORDERABLES\t473446\tLABYTBG\tTHYROXINE BINDING GLOBULIN\tT4BG\tTHYROXINE-BIND GLOB\t#VALUE!\t93540\t0\tNone\tNone\t1\t93540\t8749731600
-LAB BLOOD ORDERABLES\t473457\tLAB11113R\tTOXOPLASMA AB, IGA\tTOXOA\tTOXO LGA ELISA\t#VALUE!\t1243440\t0\tNone\tNone\t3\t3730320\t4.67E+12
-LAB BLOOD ORDERABLES\t473459\tLABTXCAB\tTOXOCARA AB ELISA\tTOXOC1\tTOXOCARA ANTIBODY\t#VALUE!\t325583.0769\t0\tNone\tNone\t13\t4232580\t1.55E+12
-LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTTINT\tGTT INTERPRETATION\t#VALUE!\t20572\t0\tNone\tNone\t30\t617160\t15849540000
-LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tRTXFDA\tNOTE\t#VALUE!\t31387.60563\t0\tNone\tNone\t71\t2228520\t87338577600
-LAB BLOOD ORDERABLES\t473679\tLABTRICHG\tTRICHINELLA IGG AB ELISA\tTRICG\tTRICHINELLA, IGG\t#VALUE!\t401451.4286\t0\tNone\tNone\t7\t2810160\t1.32E+12
-LAB BLOOD ORDERABLES\t473685\tLABYCHAGA\tTRYPANOSOMA CRUZI AB, IGG\tYCHAG1\tTRYPANOSOMA CRUZI AB,IGG\t#VALUE!\t535260\t0\tNone\tNone\t48\t25692480\t1.71E+13
-LAB BLOOD ORDERABLES\t473690\tLABTTRMET\tAMYLOIDOSIS DNA TITER\tAMYLDO\tTTR AMYLOIDOSIS DNA\t#VALUE!\t5351415\t0\tNone\tNone\t4\t21405660\t1.96E+14
-LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHDI\tDISCLAIMER\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
-LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHDX\tCLINICAL INDICATION\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
-LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHIN\tINTERPRETATION\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
-LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHMD\tINTERPRETED BY\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
-LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHME\tMETHOD\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHDI\tDISCLAIMER:\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHDX\tCLINICAL IND\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHIN\tINTERPRETATION\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHMD\tINTERPRETED BY\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHME\tMETHOD\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
-LAB BLOOD ORDERABLES\t473762\tLAB12586R\tVITAMIN B1\tYVB1W2\tCOMMENT\t#VALUE!\t322440\t0\tNone\tNone\t2\t644880\t2.21E+11
-LAB BLOOD ORDERABLES\t473763\tLAB11060R\tVITAMIN B1, PLASMA\tVITB1\tVITAMIN B1, PLASMA\t#VALUE!\t450630\t0\tNone\tNone\t2\t901260\t4.07E+11
-LAB BLOOD ORDERABLES\t473763\tLABVITB1P\tVITAMIN B1, PLASMA\tVITB1\tVITAMIN B1, PLASMA\t#VALUE!\t401070\t0\tNone\tNone\t8\t3208560\t1.34E+12
-LAB BLOOD ORDERABLES\t473775\tLAB12322R\tVON WILLEBRAND MULTIMERS\t12323R\tVON WILLEBRAND MULTIMERS\t#VALUE!\t678327.8571\t0\tNone\tNone\t28\t18993180\t1.56E+13
-LAB BLOOD ORDERABLES\t473775\tLAB12322R\tVON WILLEBRAND MULTIMERS\tYVWMR\tVON WILLEBRAND\t#VALUE!\t1322760\t0\tNone\tNone\t1\t1322760\t1.75E+12
-LAB BLOOD ORDERABLES\t473775\tLABYVWM\tVON WILLEBRAND MULTIMERS\tYVWMR\tVON WILLEBRAND\t#VALUE!\t940496.6667\t0\tNone\tNone\t18\t16928940\t1.85E+13
-LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tVWDIR\tINTERPRETED BY\t#VALUE!\t50682.5\t0\tNone\tNone\t24\t1216380\t92331061200
-LAB BLOOD ORDERABLES\t473779\tLAB12631R\tVORICONAZOLE LEVEL\tVORI\tVORICONAZOLE LEVEL\t#VALUE!\t438327.3529\t0\tNone\tNone\t68\t29806260\t1.55E+13
-LAB BLOOD ORDERABLES\t473779\tLABVORI\tVORICONAZOLE LEVEL\tVORI\tVORICONAZOLE LEVEL\t#VALUE!\t495083.3333\t0\tNone\tNone\t18\t8911500\t5.11E+12
-LAB BLOOD ORDERABLES\t473809\tLABYZINCS\tZINC\tZINC\tZINC\t#VALUE!\t224780\t0\tNone\tNone\t3\t674340\t1.79E+11
-LAB BLOOD ORDERABLES\t496771\tLABHELPM\tHELICOBACTER PYLORI IGM AB\tHELPM1\tH. PYLORI IGM ANTIBODY\t#VALUE!\t1694100\t0\tNone\tNone\t2\t3388200\t6.03E+12
-LAB BLOOD ORDERABLES\t496773\tLAB12585R\tHEPATITIS D RNA, QUAL PCR\t12594R\tHDV RNA\t#VALUE!\t302810\t0\tNone\tNone\t6\t1816860\t6.09E+11
-LAB BLOOD ORDERABLES\t496774\tLABHEVG\tHEPATITIS E AB IGG PANEL\tHEVG1\tHEPATITIS E AB IGG\t#VALUE!\t301350\t0\tNone\tNone\t8\t2410800\t7.68E+11
-LAB BLOOD ORDERABLES\t496775\tLABHEVM\tHEPATITIS E AB IGM PANEL\tHEVM1\tHEPATITIS E AB IGM\t#VALUE!\t290029.0909\t0\tNone\tNone\t11\t3190320\t9.72E+11
-LAB BLOOD ORDERABLES\t497189\tLABXAMH\tANTI - MULLERIAN HORMONE\tXAMHOR\tANTI-MULLERIAN HORMONE\t#VALUE!\t371580\t0\tNone\tNone\t1\t371580\t1.38E+11
-LAB BLOOD ORDERABLES\t497200\tLABXGAD65\tANTI GAD 65 ANTIBODIES\tXGADR\tANTI-GAD 65 ANTIBODIES\t#VALUE!\t584742\t0\tNone\tNone\t10\t5847420\t7.20E+12
-LAB BLOOD ORDERABLES\t497202\tLABXHIA\tHUMAN INSULIN ANTIBODY\tXHIAB\tHUMAN INSULIN ANTIBODY\t#VALUE!\t613744.6154\t0\tNone\tNone\t13\t7978680\t5.21E+12
-LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3DS\tDISCLAIMER\t#VALUE!\t531220.8\t0\tNone\tNone\t175\t92963640\t6.34E+13
-LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3IN\tINTERPRETATION\t#VALUE!\t531220.8\t0\tNone\tNone\t175\t92963640\t6.34E+13
-LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3MD\tINTERPRETED BY\t#VALUE!\t536372.9825\t0\tNone\tNone\t171\t91719780\t6.29E+13
-LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3MT\tMETHOD\t#VALUE!\t531220.8\t0\tNone\tNone\t175\t92963640\t6.34E+13
-LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tBCPMUT\tBCP MUTATIONS\t#VALUE!\t612900\t0\tNone\tNone\t1\t612900\t3.76E+11
-LAB BLOOD ORDERABLES\t497341\tLABCYAB\tCYSTICERCUS AB\tCYABS\tCYSTICERCUS AB\t#VALUE!\t302520\t0\tNone\tNone\t11\t3327720\t1.20E+12
-LAB BLOOD ORDERABLES\t497349\tLABWNVRNA\tWEST NILE VIRUS RNA, RT  -PCR\tWNRNA\tWEST NILE VIRUS RNA\t#VALUE!\t219683.7209\t0\tNone\tNone\t43\t9446400\t2.67E+12
-LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGMT\tB.QUINTANA IGM TITER\t#VALUE!\t1367760\t0\tNone\tNone\t7\t9574320\t4.56E+13
-LAB BLOOD ORDERABLES\t497394\tLABYPARVO\tPARVOVIRUS B19 IGG AND IGM AB\tYPARV3\tINTERPRETATION\t#VALUE!\t223791.6201\t0\tNone\tNone\t179\t40058700\t1.09E+13
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC23\tTRIENE TETRAENE RATIO\t#VALUE!\t504672\t0\tNone\tNone\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC30\tINTERPRETATION EFA\t#VALUE!\t504672\t0\tNone\tNone\t10\t5046720\t2.79E+12
-LAB BLOOD ORDERABLES\t497414\tLABFHAN\tHANTAVIRUS AB ELISA\tHANIGM\tHANTAVIRUS IGM\t#VALUE!\t223917.1429\t0\tNone\tNone\t21\t4702260\t1.18E+12
-LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\t12430R\tINTERPRETATION\t#VALUE!\t270420\t0\tNone\tNone\t3\t811260\t2.28E+11
-LAB BLOOD ORDERABLES\t497418\tLABFCYST\tCYSTICERCUS IGG AB\tBANDPRSNT\tBANDS PRESENT\t#VALUE!\t289800\t0\tNone\tNone\t4\t1159200\t3.98E+11
-LAB BLOOD ORDERABLES\t497430\tLABHEPDAB\tHEPATITIS D VIRUS AB TOTAL\tHEPD\tHEPATITIS D TOTAL\t#VALUE!\t347432.4324\t0\tNone\tNone\t37\t12855000\t5.46E+12
-LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE30\tECHOVIRUS 30 AB\t#VALUE!\t325380\t0\tNone\tNone\t6\t1952280\t6.59E+11
-LAB BLOOD ORDERABLES\t497440\tLABFCHGA\tA. PHAGOCYTOPHILUM AB\tAPINT\tA.PHAGOCYTOPHILUM INTERPRETATION\t#VALUE!\t243480\t0\tNone\tNone\t2\t486960\t1.21E+11
-LAB BLOOD ORDERABLES\t497444\tLABEBVC\tEBV CAPSID AG\tVCAA\tVCA IGA\t#VALUE!\t253638.4615\t0\tNone\tNone\t13\t3297300\t9.44E+11
-LAB BLOOD ORDERABLES\t497444\tLABEBVC\tEBV VIRAL CAPSID ANTIGEN IGA\tVCAA\tVCA IGA\t#VALUE!\t208160\t0\tNone\tNone\t3\t624480\t1.36E+11
-LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVIN\tQ FEVER INTERPRETATION\t#VALUE!\t431808.2927\t0\tNone\tNone\t41\t17704140\t2.36E+13
-LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\t12431R\tINTERPRETATION\t#VALUE!\t282660\t0\tNone\tNone\t1\t282660\t79896675600
-LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\tLTROIN\tINTERPRETATION\t#VALUE!\t288270\t0\tNone\tNone\t18\t5188860\t1.84E+12
-LAB BLOOD ORDERABLES\t497460\tLABFRAB\tFRANCISELLA TULARENSIS AB\tFRANT\tFRANCISELLA TULARENSIS AB\t#VALUE!\t230423.0769\t0\tNone\tNone\t26\t5991000\t1.57E+12
-LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV 1 IGG-AB, WESTERN BLOT CONFIRM\tBANDPRSNT\tBANDS PRESENT\t#VALUE!\t392925\t0\tNone\tNone\t4\t1571700\t6.91E+11
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\t12065R\tY.ENTERCOCLITICA IGA\t#VALUE!\t424091.4286\t0\tNone\tNone\t21\t8905920\t4.71E+12
-LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYPSEU\tY.PSEUDOTUBERCULOSIS\t#VALUE!\t3364848\t0\tNone\tNone\t15\t50472720\t4.07E+14
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1CI\tCLINICAL INDICATION\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1DI\tDISCLAIMER\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1MD\tINTERPRETED BY\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1MT\tMETHOD\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
-LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1MU\tMUTATION(S)\t#VALUE!\t1568100\t0\tNone\tNone\t1\t1568100\t2.46E+12
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKCI\tCLINICAL INDICATION\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKDI\tDISCLAIMER\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKIT\tINTERPRETED BY\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
-LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKMT\tMETHOD\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
-LAB BLOOD ORDERABLES\t541681\tLABDHBCC\tDONOR HEPATITIS B CORE AB (LIVING)\tDHBCC\tDONOR HEP CORE AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t541687\tLABFHHV7\tHERPES VIRUS 7 IGG AND IGM AB\tFINT\tINTERPRETATION\t#VALUE!\t356310\t0\tNone\tNone\t2\t712620\t2.55E+11
-LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\t12835R\tPARVOVIRUS B19 AB IGG\t#VALUE!\t2267650\t0\tNone\tNone\t6\t13605900\t3.52E+13
-LAB BLOOD ORDERABLES\t541695\tLABDSDNA\tANTI - DSDNA AB\tDNAINT\tINTERPRETATION\t#VALUE!\t212647.8896\t0\tNone\tNone\t616\t130991100\t3.85E+13
-LAB BLOOD ORDERABLES\t573825\tLABFHIVQA\tHIV-1 DNA, QUALITATIVE PCR\tHIVQA\tHIV-1 DNA, QUAL, PCR\t#VALUE!\t280410\t0\tNone\tNone\t2\t560820\t1.66E+11
-LAB BLOOD ORDERABLES\t573825\tLABFHIVQA\tHIV-1 PROVIRALL DNA PCR QUAL\tHIVQA\tHIV-1 DNA, QUAL, PCR\t#VALUE!\t236296.3636\t0\tNone\tNone\t11\t2599260\t6.56E+11
-LAB BLOOD ORDERABLES\t590530\tLABCEPLUS\tCELIAC PLUS\tCPLUS1\tCELIAC PLUS RESULT\t#VALUE!\t543240\t0\tNone\tNone\t1\t543240\t2.95E+11
-LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\t12428R\tINTERPRETATION\t#VALUE!\t242130\t0\tNone\tNone\t4\t968520\t2.64E+11
-LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTINT\tINTERPRETATION\t#VALUE!\t224837.1429\t0\tNone\tNone\t7\t1573860\t3.64E+11
-LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\t12024R\tMYCOPLASMA PNEUMONIA AB IGG\t#VALUE!\t578845.7143\t0\tNone\tNone\t28\t16207680\t1.90E+13
-LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\t12431R\tINTERPRETATION\t#VALUE!\t308790\t0\tNone\tNone\t2\t617580\t2.66E+11
-LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\tLTROIN\tINTERPRETATION\t#VALUE!\t258120\t0\tNone\tNone\t1\t258120\t66625934400
-LAB BLOOD ORDERABLES\t590559\tLABFPOLIO\tPOLIOVIRUS 1-3 AB, SERUM\tPLV3\tPOLIOVIRUS 3 AB\t#VALUE!\t253600\t0\tNone\tNone\t3\t760800\t2.02E+11
-LAB BLOOD ORDERABLES\t590561\tLABFSFTA\tTREPONEMA PALLIDUM AB\tSFTA\tSYPHILIS-FTA\t#VALUE!\t177960\t0\tNone\tNone\t1\t177960\t31669761600
-LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tADGINT\tINTERPRETATION\t#VALUE!\t466236\t0\tNone\tNone\t20\t9324720\t5.28E+12
-LAB BLOOD ORDERABLES\t621214\tLABDDIML\tD - DIMER (ELISA)\tDIMCOM\tCOAGULATION COMMENT\t#VALUE!\t5591.974249\t0\tNone\tNone\t932\t5211720\t50304153600
-LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB6\tCOXSACKIE B6 AB\t#VALUE!\t229080\t0\tNone\tNone\t1\t229080\t52477646400
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP2\tCOXSACKIE A2 AB\t#VALUE!\t335940\t0\tNone\tNone\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP4\tCOXSACKIE A4 AB\t#VALUE!\t335940\t0\tNone\tNone\t1\t335940\t1.13E+11
-LAB BLOOD ORDERABLES\t668075\tLABDNAISO\tNUCLEIC ACID ISOLATION, DNA\tDNADAT\tDATE OF DNA EXTRACTION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tCCPINT\tINTERPRETATION\t#VALUE!\t2437250.769\t0\tNone\tNone\t26\t63368520\t2.95E+15
-LAB BLOOD ORDERABLES\t668091\tLAB11125R\tCOCCI ID (SERUM)\tCOCI\tCOCCI ID (SERUM)\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t668091\tLABCOCI\tCOCCI ID (SERUM)\tCOCI\tCOCCI ID (SERUM)\t#VALUE!\t1056797.143\t0\tNone\tNone\t7\t7397580\t8.14E+12
-LAB BLOOD ORDERABLES\t668152\tLABATTG\tIGA ANTI  TTG\tATTINT\tINTERPRETATION\t#VALUE!\t394865.1799\t0\tNone\tNone\t139\t54886260\t2.79E+13
-LAB BLOOD ORDERABLES\t668154\tLABGDGP\tIGG ANTI DGP\tGDGINT\tINTERPRETATION\t#VALUE!\t399028.2353\t0\tNone\tNone\t34\t13566960\t6.90E+12
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tATTINT\tINTERPRETATION\t#VALUE!\t402189.6296\t0\tNone\tNone\t81\t32577360\t1.89E+13
-LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tIGAINT\tINTERPRETATION\t#VALUE!\t402189.6296\t0\tNone\tNone\t81\t32577360\t1.89E+13
-LAB BLOOD ORDERABLES\t668158\tLABADAM13\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMINT\tCLINICAL INTERPRETATION\t#VALUE!\t190497\t0\tNone\tNone\t20\t3809940\t1.38E+12
-LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMINT\tCLINICAL INTERPRETATION\t#VALUE!\t247320\t0\tNone\tNone\t3\t741960\t2.61E+11
-LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 PROFILE\tADMINT\tCLINICAL INTERPRETATION\t#VALUE!\t206821.875\t0\tNone\tNone\t64\t13236600\t4.74E+12
-LAB BLOOD ORDERABLES\t668178\tLABDRPRS\tDONOR, SYPHILIS SCREEN\tRPRS1\tDONOR SYPHILIS SCREEN\t#VALUE!\t168840\t0\tNone\tNone\t2\t337680\t76120106400
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITDI\tDISCLAIMER\t#VALUE!\t779901.7925\t0\tNone\tNone\t212\t165339180\t3.13E+14
-LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITME\tMETHOD\t#VALUE!\t779901.7925\t0\tNone\tNone\t212\t165339180\t3.13E+14
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11005R\tA. TENUIS/ALTERNATA IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11006R\tASPERGILLUS FUMIGATUS IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11007R\tAUREOBASIDIUM PULLULANS IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11008R\tMICROPOLYSPORA FAENI IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11009R\tP. NOTATUM/CHRYSOGENUM IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11010R\tPHOMA BETAE IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11011R\tTHERMOACTINOMYCES VULGARIS IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11012R\tTRICHODERMA VIRIDE IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11141R\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t669987\tLAB11013R\tPLATELET ANTIBODY ID PANEL\t11014R\tANTIGEN CAPTURE\t#VALUE!\t688358.9552\t0\tNone\tNone\t134\t92240100\t8.83E+13
-LAB BLOOD ORDERABLES\t669987\tLAB11013R\tPLATELET ANTIBODY ID PANEL\t11015R\tMODIFIED ANTIGEN CAPTURE\t#VALUE!\t699652.5\t0\tNone\tNone\t96\t67166640\t7.00E+13
-LAB BLOOD ORDERABLES\t669987\tLAB11013R\tPLATELET ANTIBODY ID PANEL\t11016R\tPLATELET ANTIBODY SCREEN\t#VALUE!\t699652.5\t0\tNone\tNone\t96\t67166640\t7.00E+13
-LAB BLOOD ORDERABLES\t670732\tLAB11322R\tMOTOR NEUROPATHY AB SCREEN\t11323R\tCO-GM1 TRIAD AB\t#VALUE!\t1029150\t0\tNone\tNone\t2\t2058300\t4.23E+12
-LAB BLOOD ORDERABLES\t670732\tLAB12774R\tMOTOR NEUROPATHY AB SCREEN\t11323R\tCO-GM1 TRIAD AB\t#VALUE!\t2500414.286\t0\tNone\tNone\t7\t17502900\t9.27E+13
-LAB BLOOD ORDERABLES\t670732\tLAB12774R\tMOTOR NEUROPATHY AB SCREEN\t12775R\tCO-GM1 QUATTRO\t#VALUE!\t1396488\t0\tNone\tNone\t5\t6982440\t1.46E+13
-LAB BLOOD ORDERABLES\t670734\tLAB11324R\tCADASIL EVALUATION, COMPLETE\t11325R\tCOMPLETE CADASIL EVALUATION\t#VALUE!\t2366850\t0\tNone\tNone\t6\t14201100\t7.82E+13
-LAB BLOOD ORDERABLES\t670752\tLAB11343R\tSRA UNFRACTIONATED HEPARIN\t11344R\tSRA UNFRACTIONATED HEPARIN\t#VALUE!\t374785.2941\t0\tNone\tNone\t102\t38228100\t1.72E+13
-LAB BLOOD ORDERABLES\t670756\tLAB11347R\tC-TELOPEPTIDE\t11348R\tC-TELOPEPTIDE\t#VALUE!\t381060\t0\tNone\tNone\t1\t381060\t1.45E+11
-LAB BLOOD ORDERABLES\t670766\tLAB11238R\tHERPESVIRUS-8 IGG AB\t11239R\tHERPESVIRUS-8 IGG AB\t#VALUE!\t392080\t0\tNone\tNone\t6\t2352480\t1.06E+12
-LAB BLOOD ORDERABLES\t670768\tLAB11240R\tMEASLES IGG\t11241R\tMEASLES IGG\t#VALUE!\t213051.4286\t0\tNone\tNone\t14\t2982720\t7.36E+11
-LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\t12430R\tINTERPRETATION\t#VALUE!\t349300\t0\tNone\tNone\t3\t1047900\t3.86E+11
-LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\tECINT\tINTERPRETATION\t#VALUE!\t1643340\t0\tNone\tNone\t2\t3286680\t7.18E+12
-LAB BLOOD ORDERABLES\t670784\tLAB11357R\tFAMILIAL MEDITERRANEAN FEVER\t11358R\tFAMILIAL MEDITERRANEAN FEVER\t#VALUE!\t3943252.5\t0\tNone\tNone\t8\t31546020\t1.31E+14
-LAB BLOOD ORDERABLES\t670792\tLAB11365R\tFAMILIAL HIBERNIAN FEVER/TRAPS\t11366R\tFAMILIAL HIBERNIAN FEVER\t#VALUE!\t3634180\t0\tNone\tNone\t3\t10902540\t4.02E+13
-LAB BLOOD ORDERABLES\t670796\tLAB11369R\tLEUKEMIA/LYMPHOMA IMMUPHENTYP\t11370R\tLEUKEMIA/LYMPHOMA IMMUPHENTYP\t#VALUE!\t160740\t0\tNone\tNone\t4\t642960\t1.04E+11
-LAB BLOOD ORDERABLES\t670802\tLAB11373R\tMARFAN(FBN1) SEQUENCING\t11374R\tMARFAN(FBN1) SEQUENCING\t#VALUE!\t1192440\t0\tNone\tNone\t3\t3577320\t4.27E+12
-LAB BLOOD ORDERABLES\t670804\tLAB11375R\tOSTEOGENESIS IMPERFECTA SEQUEN\t11376R\tOSTEOGENESIS IMPERFECTA SEQUEN\t#VALUE!\t3184020\t0\tNone\tNone\t2\t6368040\t2.21E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11200R\tINTERPRETIVE COMMENTS\t#VALUE!\t902143.4783\t0\tNone\tNone\t69\t62247900\t6.72E+13
-LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11200R\tINTERPRETIVE COMMENTS\t#VALUE!\t766684.8\t0\tNone\tNone\t75\t57501360\t5.29E+13
-LAB BLOOD ORDERABLES\t670858\tLAB11401R\tHALIBUT IGE\t11459R\tHALIBUT IGE\t#VALUE!\t390780\t0\tNone\tNone\t1\t390780\t1.53E+11
-LAB BLOOD ORDERABLES\t670920\tLAB11435R\tCELIAC GENETICS\t11499R\tCELIAC GENETICS\t#VALUE!\t666528\t0\tNone\tNone\t5\t3332640\t2.42E+12
-LAB BLOOD ORDERABLES\t670922\tLAB12689R\tINFLIXIMAB, SERUM LEVEL AND AB\t12690R\tINFLIXIMAB, SERUM LEVEL AND AB\t#VALUE!\t536920\t0\tNone\tNone\t3\t1610760\t1.04E+12
-LAB BLOOD ORDERABLES\t670922\tLAB12689R\tSERM INFLIXIMAB/HACA MEASUREMT\t11500R\tSERM INFLIXIMAB/HACA MEASUREMT\t#VALUE!\t783660\t0\tNone\tNone\t1\t783660\t6.14E+11
-LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tRTFDA1\tNOTE\t#VALUE!\t99900\t0\tNone\tNone\t1\t99900\t9980010000
-LAB BLOOD ORDERABLES\t744174\tLAB11516R\tPANCREATITIS PANEL\t11625R\tPANCREATITIS PANEL\t#VALUE!\t2241593.333\t0\tNone\tNone\t9\t20174340\t5.94E+13
-LAB BLOOD ORDERABLES\t744184\tLAB11522R\tHU AB\t11632R\tHU AB\t#VALUE!\t920782.5\t0\tNone\tNone\t8\t7366260\t7.84E+12
-LAB BLOOD ORDERABLES\t744186\tLAB11523R\tGQ1B AB\t11633R\tGQ1B AB\t#VALUE!\t2399132.727\t0\tNone\tNone\t44\t105561840\t6.12E+14
-LAB BLOOD ORDERABLES\t744190\tLAB11525R\tMUSCLE TYROSINE KINASE AB\t11635R\tMUSCLE TYROSINE KINASE AB\t#VALUE!\t2179075.2\t0\tNone\tNone\t25\t54476880\t2.72E+14
-LAB BLOOD ORDERABLES\t744192\tLAB11526R\tK CHANNEL AB\t11636R\tK CHANNEL AB\t#VALUE!\t1380474\t0\tNone\tNone\t10\t13804740\t2.72E+13
-LAB BLOOD ORDERABLES\t744194\tLAB11527R\tSPINAL MUSCULAR ATROPHY DIAGNO\t11637R\tSPINAL MUSCULAR ATROPHY DIAGNO\t#VALUE!\t756900\t0\tNone\tNone\t2\t1513800\t2.18E+12
-LAB BLOOD ORDERABLES\t744196\tLAB11528R\tPERIPHERAL NEUROPATHY AB SCN\t11638R\tPERIPHERAL NEUROPATHY AB SCN\t#VALUE!\t1460520\t0\tNone\tNone\t1\t1460520\t2.13E+12
-LAB BLOOD ORDERABLES\t744198\tLAB11529R\tENCEPHALITIS AB SCREEN\t11639R\tENCEPHALITIS AB SCREEN\t#VALUE!\t781800\t0\tNone\tNone\t2\t1563600\t1.34E+12
-LAB BLOOD ORDERABLES\t744198\tLAB12772R\tENCEPHALITIS AB SCREEN\t11639R\tENCEPHALITIS AB SCREEN\t#VALUE!\t1309080\t0\tNone\tNone\t1\t1309080\t1.71E+12
-LAB BLOOD ORDERABLES\t744198\tLAB12772R\tENCEPHALITIS AB SCREEN\t12773R\tENCEPHALITIS AB SCREEN\t#VALUE!\t2424660\t0\tNone\tNone\t1\t2424660\t5.88E+12
-LAB BLOOD ORDERABLES\t744200\tLAB11530R\tCEREBELLAR PARANEOPLASTIC AB\t11640R\tCEREBELLAR PARANEOPLASTIC AB\t#VALUE!\t4965280\t0\tNone\tNone\t6\t29791680\t1.72E+14
-LAB BLOOD ORDERABLES\t744206\tLAB11533R\tSENSORIMOTOR SCREEN AB\t11643R\tSENSORIMOTOR SCREEN AB\t#VALUE!\t601560\t0\tNone\tNone\t1\t601560\t3.62E+11
-LAB BLOOD ORDERABLES\t744208\tLAB11534R\tMTDNA SCREENING PANEL\t11644R\tMTDNA SCREENING PANEL\t#VALUE!\t8160\t0\tNone\tNone\t1\t8160\t66585600
-LAB BLOOD ORDERABLES\t744220\tLAB11540R\tOTC SEQUENCING\t11650R\tOTC SEQUENCING\t#VALUE!\t1677000\t0\tNone\tNone\t1\t1677000\t2.81E+12
-LAB BLOOD ORDERABLES\t744236\tLAB11551R\tNK FUNCTION\t11661R\tNK FUNCTION\t#VALUE!\t395914.4681\t0\tNone\tNone\t47\t18607980\t2.99E+13
-LAB BLOOD ORDERABLES\t744246\tLAB11556R\tBLASTOMYCES SEROLOGIES\t11666R\tBLASTO AB ID\t#VALUE!\t387587.5862\t0\tNone\tNone\t116\t44960160\t1.82E+13
-LAB BLOOD ORDERABLES\t744246\tLAB11556R\tBLASTOMYCES SEROLOGIES\tBLAS\tBLASTO AB CFS\t#VALUE!\t387587.5862\t0\tNone\tNone\t116\t44960160\t1.82E+13
-LAB BLOOD ORDERABLES\t744250\tLAB12781R\tBRUCELLA ANTIBODIES\t12779R\tBRUCELLA IGM\t#VALUE!\t327064.4444\t0\tNone\tNone\t27\t8830740\t3.27E+12
-LAB BLOOD ORDERABLES\t744256\tLAB11562R\tLYMPHOCYTIC CHORIOMENINGITI AB\t11674R\tINTERPRETATION\t#VALUE!\t254046.6667\t0\tNone\tNone\t18\t4572840\t1.33E+12
-LAB BLOOD ORDERABLES\t744262\tLAB11566R\tENTEROVIRUS RNA, QUAL\t11680R\tENTEROVIRUS QUAL,PCR\t#VALUE!\t474590\t0\tNone\tNone\t30\t14237700\t3.71E+13
-LAB BLOOD ORDERABLES\t744268\tLAB11570R\tSPINAL MUSC ATROPHY CARRIER\t11685R\tSPINAL MUSC ATROPHY CARRIER\t#VALUE!\t343800\t0\tNone\tNone\t1\t343800\t1.18E+11
-LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t11781R\tINTERPRETATION\t#VALUE!\t431070\t0\tNone\tNone\t2\t862140\t3.79E+11
-LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t12782R\tREVIEWED BY\t#VALUE!\t368760\t0\tNone\tNone\t1\t368760\t1.36E+11
-LAB BLOOD ORDERABLES\t744274\tLAB11573R\tCONGENITAL CYCLIC NEUTROPENIA\t11688R\tCONGENITAL CYLIC NEURTOPENIA\t#VALUE!\t3115380\t0\tNone\tNone\t1\t3115380\t9.71E+12
-LAB BLOOD ORDERABLES\t744278\tLAB11576R\tCOBALAMIN C DEFICIENCY(MMACHC)\t11691R\tCOBALAMIN C DEFICIENCY(MMACHC)\t#VALUE!\t3541227.273\t0\tNone\tNone\t11\t38953500\t1.68E+14
-LAB BLOOD ORDERABLES\t744280\tLAB11577R\tALAGILLE (JAG1) MUTATION\t11692R\tALAGILLE (JAG1) MUTATION\t#VALUE!\t5795840\t0\tNone\tNone\t3\t17387520\t1.02E+14
-LAB BLOOD ORDERABLES\t744320\tLAB11604R\tIL 5\t11757R\tINTERLEUKIN 5\t#VALUE!\t420600\t0\tNone\tNone\t1\t420600\t1.77E+11
-LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11764R\tFBN1 FULL GENE RESULT\t#VALUE!\t1239540\t0\tNone\tNone\t7\t8676780\t1.19E+13
-LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11765R\tFBN1 FULL GENE INTERPRETATION\t#VALUE!\t1358760\t0\tNone\tNone\t6\t8152560\t1.16E+13
-LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11766R\tREVIEWED BY\t#VALUE!\t1358760\t0\tNone\tNone\t6\t8152560\t1.16E+13
-LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11767R\tSEQUENCING\t#VALUE!\t1239540\t0\tNone\tNone\t7\t8676780\t1.19E+13
-LAB BLOOD ORDERABLES\t744330\tLAB11610R\tDONOR, HTLVS(QNS/LIPEMIC ONLY)\t11890R\tHTLV I/II, WESTERN BLOT\t#VALUE!\t668460\t0\tNone\tNone\t3\t2005380\t1.52E+12
-LAB BLOOD ORDERABLES\t744338\tLAB11615R\tSODIUM BENZOATE LEVEL\t11774R\tSODIUM BENZOATE LEVEL\t#VALUE!\t808116\t0\tNone\tNone\t5\t4040580\t3.27E+12
-LAB BLOOD ORDERABLES\t744368\tLABLEGS\tLEGIONELLA SEROLOGY\tLEG2\tL.LEGIONELLA AB (SEROGRP 2-6,8)\t#VALUE!\t466080\t0\tNone\tNone\t1\t466080\t2.17E+11
-LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPCATIN\tCAT PF INTERPRETATION(CATECHOL INTRP, PL)\t#VALUE!\t294414.375\t0\tNone\tNone\t32\t9421260\t3.19E+12
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPDI\tDISCLAIMER\t#VALUE!\t781898.5492\t0\tNone\tNone\t193\t150906420\t1.54E+14
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPMD\tINTERPRETATION\t#VALUE!\t812646.7797\t0\tNone\tNone\t177\t143838480\t1.50E+14
-LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPMT\tMETHOD\t#VALUE!\t781898.5492\t0\tNone\tNone\t193\t150906420\t1.54E+14
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBDI\tDISCLAIMER\t#VALUE!\t878524.7788\t0\tNone\tNone\t113\t99273300\t1.44E+14
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBMD\tINTERPRETATION\t#VALUE!\t911035.4286\t0\tNone\tNone\t105\t95658720\t1.42E+14
-LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBMT\tMETHOD\t#VALUE!\t878524.7788\t0\tNone\tNone\t113\t99273300\t1.44E+14
-LAB BLOOD ORDERABLES\t861761\tLABXIGF1\tPEDIATRIC IGF-1\tXIG1\tIGF-1\t#VALUE!\t477232.5\t0\tNone\tNone\t16\t7635720\t3.76E+12
-LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN (HIT) AB\tPF4COM\tPF4 COMMENT\t#VALUE!\t89627.14014\t0\tNone\tNone\t1577\t141342000\t1.41E+13
-LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN PLATELET FACTOR 4 AB\tPF4COM\tPF4 COMMENT\t#VALUE!\t95902.35702\t0\tNone\tNone\t577\t55335660\t1.99E+13
-LAB BLOOD ORDERABLES\t864664\tLAB11878R\tCORTISOL BINDING GLOBULIN\t11877R\tCORTISOL BIND.GLOB(CBG)\t#VALUE!\t946260\t0\tNone\tNone\t1\t946260\t8.95E+11
-LAB BLOOD ORDERABLES\t870907\tLAB11989R\tLONG QT SYNDROME\t11990R\tLQTS RESULTS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\t12428R\tINTERPRETATION\t#VALUE!\t165960\t0\tNone\tNone\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\t12429R\tINTERPRETATION\t#VALUE!\t165960\t0\tNone\tNone\t1\t165960\t27542721600
-LAB BLOOD ORDERABLES\t871208\tLAB12003R\tHERPESVIRUS 6 AB, IGG\t12004R\tHERPESVIRUS 6 IGG\t#VALUE!\t146100\t0\tNone\tNone\t1\t146100\t21345210000
-LAB BLOOD ORDERABLES\t871220\tLAB12023R\tMYCOPLASMA PNEUMONIA IGG AB\t12024R\tMYCOPLASMA PNEUMONIA AB IGG\t#VALUE!\t286680\t0\tNone\tNone\t1\t286680\t82185422400
-LAB BLOOD ORDERABLES\t871222\tLAB12813R\tPARVOVIRUS B19 AB, IGG\t12835R\tPARVOVIRUS B19 AB IGG\t#VALUE!\t167820\t0\tNone\tNone\t1\t167820\t28163552400
-LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12054R\tR.TYPHI IGM\t#VALUE!\t265070.7692\t0\tNone\tNone\t13\t3445920\t9.64E+11
-LAB BLOOD ORDERABLES\t871382\tLAB12055R\tRMSF IGG TITER\t12056R\tRMSF IGG TITER\t#VALUE!\t60\t0\tNone\tNone\t1\t60\t3600
-LAB BLOOD ORDERABLES\t871386\tLAB12059R\tR. TYPHI IGG TITER\t12060R\tR.TYPHI IGG TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-LAB BLOOD ORDERABLES\t871580\tLAB12066R\tCRMP-5-IGG WESTERN BLOT\tYCRMP\tCRMP-5-IGG WESTERN BLOT\t#VALUE!\t0\t0\tNone\tNone\t5\t0\t0
-LAB BLOOD ORDERABLES\t882631\tLAB12095R\tDARUNAVIR,ANTIRETROVIRAL LEVEL\t12101R\tDARUNAVIR,ANTIRETROVIRAL LEVEL\t#VALUE!\t592920\t0\tNone\tNone\t1\t592920\t3.52E+11
-LAB BLOOD ORDERABLES\t882639\tLAB12099R\tBRUCELLA TOTAL AB,AGGLU (RFLX)\t12100R\tBRUCELLA TOTAL  AB, AGGLUTIN\t#VALUE!\t15\t0\tNone\tNone\t4\t60\t3600
-LAB BLOOD ORDERABLES\t883990\tLABFT3DIA\tT3, FREE, TRACER DIALYSIS\t11876R\tT3, TOTAL\t#VALUE!\t594080\t0\tNone\tNone\t3\t1782240\t1.06E+12
-LAB BLOOD ORDERABLES\t883990\tLABFT3DIA\tT3, FREE, TRACER DIALYSIS\tFT3\tFREE T3 (BY DIALYSIS)\t#VALUE!\t594080\t0\tNone\tNone\t3\t1782240\t1.06E+12
-LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGDMINT\tINTERPRETATION\t#VALUE!\t17220\t0\tNone\tNone\t1\t17220\t296528400
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tDNAINT\tINTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tJO1INT\tANTI-JO1 INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tRNPINT\tANTI-RNP INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSCLINT\tANTI-SCL70 INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSNAINT\tSPECIFIC ANA INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSAIN\tANTI-RO INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSBINT\tANTI-LA INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
-LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSDI\tDISCLAIMER\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
-LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSDX\tCLINICAL INDICATION\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
-LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSMD\tINTERPRETED BY\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
-LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSMT\tMETHOD\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
-LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSRE\tRESULT\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
-LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816DI\tDISCLAIMER\t#VALUE!\t722297.1429\t0\tNone\tNone\t7\t5056080\t4.67E+12
-LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816MD\tINTERPRETED BY\t#VALUE!\t806000\t0\tNone\tNone\t6\t4836000\t4.62E+12
-LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816ME\tMETHOD\t#VALUE!\t722297.1429\t0\tNone\tNone\t7\t5056080\t4.67E+12
-LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816RF\tREFERENCES\t#VALUE!\t722297.1429\t0\tNone\tNone\t7\t5056080\t4.67E+12
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81DI\tDISCLAIMER\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81MD\tINTERPRETED BY\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81ME\tMETHOD\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81RF\tREFERENCES\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88DI\tDISCLAIMER\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88DX\tCLINICAL INDICATION\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88IN\tINTERPRETATION\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88MD\tINTERPRETED BY\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88ME\tMETHOD\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88RF\tREFERENCES\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSDIB\tDISCLAIMER\t#VALUE!\t873158.6047\t0\tNone\tNone\t129\t112637460\t2.69E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSMEB\tMETHOD\t#VALUE!\t873158.6047\t0\tNone\tNone\t129\t112637460\t2.69E+14
-LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSREFB\tREFERENCES\t#VALUE!\t873158.6047\t0\tNone\tNone\t129\t112637460\t2.69E+14
-LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSDI\tDISCLAIMER\t#VALUE!\t371657.1429\t0\tNone\tNone\t7\t2601600\t1.85E+12
-LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSMD\tINTERPRETED BY\t#VALUE!\t371657.1429\t0\tNone\tNone\t7\t2601600\t1.85E+12
-LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSME\tMETHOD\t#VALUE!\t371657.1429\t0\tNone\tNone\t7\t2601600\t1.85E+12
-LAB BLOOD ORDERABLES\t889722\tLAB12573R\tANCA\t12575R\tC-ANCA\t#VALUE!\t305359.0435\t0\tNone\tNone\t230\t70232580\t5.24E+13
-LAB BLOOD ORDERABLES\t889722\tLAB12573R\tANCA\t12576R\tP-ANCA\t#VALUE!\t305359.0435\t0\tNone\tNone\t230\t70232580\t5.24E+13
-LAB BLOOD ORDERABLES\t890107\tLAB12220R\tMSUD MUTATION ANALYSIS\t12221R\t12221R\t#VALUE!\t1404120\t0\tNone\tNone\t1\t1404120\t1.97E+12
-LAB BLOOD ORDERABLES\t890115\tLAB12228R\tLATEX SPECIFIC IGE (RIA) PANEL\t12229R\t12229R\t#VALUE!\t1006840\t0\tNone\tNone\t3\t3020520\t3.88E+12
-LAB BLOOD ORDERABLES\t890119\tLAB12232R\tTHYROGLOBULIN & THYROID AB\t12233R\t12233R\t#VALUE!\t977357.8378\t0\tNone\tNone\t74\t72324480\t4.79E+14
-LAB BLOOD ORDERABLES\t890121\tLAB12234R\tVOLATILES\t12235R\t12235R\t#VALUE!\t166777.5\t0\tNone\tNone\t8\t1334220\t1.07E+12
-LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12242R\t12242R\t#VALUE!\t925412.7273\t0\tNone\tNone\t11\t10179540\t4.35E+13
-LAB BLOOD ORDERABLES\t890129\tLAB12279R\tCVID CONFIRMATION FLOW PANEL\t12282R\t12282R\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-LAB BLOOD ORDERABLES\t890133\tLAB12212R\tWA1 IGG AB\t12213R\t12213R\t#VALUE!\t677464.2857\t0\tNone\tNone\t14\t9484500\t4.41E+13
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3DI\tDISCLAIMER\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3IN\tINTERPRETATION\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3MD\tINTERPRETED BY\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3ME\tMETHOD\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3RF\tREFERENCES\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3DI\tDISCLAIMER\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3IN\tINTERPRETATION\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3MD\tINTERPRETED BY\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3ME\tMETHOD\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3RE\tRESULT, NON-BLOOD\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3RF\tREFERENCES\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
-LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLDI\tDISCLAIMER\t#VALUE!\t1418550\t0\tNone\tNone\t2\t2837100\t4.13E+12
-LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLMD\tINTERPRETED BY\t#VALUE!\t1418550\t0\tNone\tNone\t2\t2837100\t4.13E+12
-LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLME\tMETHOD\t#VALUE!\t1418550\t0\tNone\tNone\t2\t2837100\t4.13E+12
-LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tPTH18R\tRECEIVED IN FLOW CYTOMETRY\t#VALUE!\t89720\t0\tNone\tNone\t6\t538320\t52831173600
-LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tPTH18R\tRECEIVED IN FLOW CYTOMETRY\t#VALUE!\t107400\t0\tNone\tNone\t3\t322200\t34644391200
-LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLDI\tDISCLAIMER\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
-LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLMD\tINTERPRETED BY\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
-LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLME\tMETHOD\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
-LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLRE\tRESULT\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMDIB\tDISCLAIMER\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMINB\tINTERPRETATION\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMMDB\tINTERPRETED BY\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMMEB\tMETHOD\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
-LAB BLOOD ORDERABLES\t906953\tLAB12580R\tCU INDEX PANEL\t12589R\tCU INDEX PANEL\t#VALUE!\t544620\t0\tNone\tNone\t1\t544620\t2.97E+11
-LAB BLOOD ORDERABLES\t910564\tLAB12352R\tNMO EVALUATION WITH REFLEX\t11200R\tINTERPRETIVE COMMENTS\t#VALUE!\t606762.8571\t0\tNone\tNone\t7\t4247340\t2.63E+12
-LAB BLOOD ORDERABLES\t910596\tLAB12512R\tVON HIPPEL-LINDAU SYNDROME\t12513R\tVON HIPPEL-LINDAU SYNDROME\t#VALUE!\t3786780\t0\tNone\tNone\t1\t3786780\t1.43E+13
-LAB BLOOD ORDERABLES\t912863\tLAB12415R\tACVRL1 AND ENG (HHT)\t12417R\tSEQUENCING-DELETION/DUPLICATION\t#VALUE!\t3433980\t0\tNone\tNone\t3\t10301940\t3.85E+13
-LAB BLOOD ORDERABLES\t912867\tLAB12507R\tIBD SGI DIAGNOSTIC\t12508R\tIBD SGI DIAGNOSTIC\t#VALUE!\t818250\t0\tNone\tNone\t4\t3273000\t2.81E+12
-LAB BLOOD ORDERABLES\t912869\tLAB12443R\tNEOCOMPLETE PARANEOPLASTIC PRFL, SERUM\t12444R\tNEO PARANEOPLASTIC PRFL\t#VALUE!\t4481154.545\t0\tNone\tNone\t11\t49292700\t2.91E+14
-LAB BLOOD ORDERABLES\t912869\tLAB12443R\tNEO PARANEOPLASTIC PRFL\t12444R\tNEO PARANEOPLASTIC PRFL\t#VALUE!\t2150280\t0\tNone\tNone\t1\t2150280\t4.62E+12
-LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tCKEP\tCK ISOENZYME ELECTROPHORESIS\t#VALUE!\t4442.926829\t0\tNone\tNone\t41\t182160\t32746600800
-LAB BLOOD ORDERABLES\t961563\tLAB12815R\tLYME DISEASE AB IGG IGM W/RFLX\t12830R\tLYME AB SCREEN\t#VALUE!\t253813.6364\t0\tNone\tNone\t22\t5583900\t1.82E+12
-LAB BLOOD ORDERABLES\t961569\tLAB13007R\tQ FEVER IGG PHASE I TITER\t12012R\tQ FEVER IGG PHASE L TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-LAB BLOOD ORDERABLES\t961571\tLAB13008R\tQ FEVER IGG PHASE II TITER\t12014R\tQ FEVER IGG PHASE LI TITER\t#VALUE!\t0\t0\tNone\tNone\t2\t0\t0
-LAB BLOOD ORDERABLES\t961573\tLAB13009R\tQ FEVER IGM PHASE I TITER\t12013R\tQ FEVER IGM PHASE L TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-LAB BLOOD ORDERABLES\t961575\tLAB13010R\tQ FEVER IGM PHASE II TITER\t12015R\tQ FEVER IGM PHASE LI TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-LAB BLOOD ORDERABLES\t961580\tLABHITIA\tHIT FUNCTIONAL ASSAY\tHITI2\tHIT COMMENT\t#VALUE!\t134929.92\t0\tNone\tNone\t125\t16866240\t2.96E+12
-LAB BLOOD ORDERABLES\t961582\tLAB12836R\tETHYLENE GLYCOL\t12837R\tETHYLENE GLYCOL\t#VALUE!\t517470\t0\tNone\tNone\t2\t1034940\t1.04E+12
-LAB BLOOD ORDERABLES\t966899\tLAB12599R\tDILATED CARDIOMYOPATHY PANEL\t12598R\tDILATED CARDIOMYOPATHY PANEL\t#VALUE!\t6362440\t0\tNone\tNone\t3\t19087320\t1.22E+14
-LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMALSCD\tMALARIA ANTIGEN DESCRIPTION\t#VALUE!\t162025.3125\t0\tNone\tNone\t64\t10369620\t3.66E+13
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12672R\tTETANUS INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12674R\tCANDIDA INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tCONINT\tCON A INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPHAINT\tPHA INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPWMINT\tPWM INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
-LAB BLOOD ORDERABLES\t978528\tLAB12657R\tT CELL ACTIVATION\t12658R\tT CELL ACTIVATION\t#VALUE!\t229920\t0\tNone\tNone\t9\t2069280\t2.17E+12
-LAB BLOOD ORDERABLES\t978532\tLAB12651R\tHYPERTROPHIC CARDIOMYOPATHY PAN\t12650R\tHYPERTROPHIC CARDIOMYOPATHY PANEL\t#VALUE!\t5240112\t0\tNone\tNone\t5\t26200560\t1.40E+14
-LAB BLOOD ORDERABLES\t978552\tLAB12643R\tBRUTON\'S TYROSINE KINASE (BTK)\t12642R\tBTK PROTEIN FLOW\t#VALUE!\t233640\t0\tNone\tNone\t1\t233640\t54587649600
-LAB BLOOD ORDERABLES\t979488\tLAB12761R\tHEPATITIS B VIRUS GENOTYPE\t12757R\tHBV GENOTYPE\t#VALUE!\t663900\t0\tNone\tNone\t1\t663900\t4.41E+11
-LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tHITI2\tHIT COMMENT\t#VALUE!\t125334.3396\t0\tNone\tNone\t53\t6642720\t1.05E+12
-LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tPF4COM\tPF4 COMMENT\t#VALUE!\t125334.3396\t0\tNone\tNone\t53\t6642720\t1.05E+12
-LAB INTERNAL\t590536\tLABCOPYPT\tCOPY REQUEST\tCOPYTO\tSEND COPY TO\t#VALUE!\t120\t0\tNone\tNone\t1\t120\t14400
-MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t255120\t0\tNone\tNone\t1\t255120\t65086214400
-MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t180211.7241\t0\tNone\tNone\t87\t15678420\t3.56E+12
-MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t527037.1429\t0\tNone\tNone\t21\t11067780\t8.79E+12
-MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t#VALUE!\t527037.1429\t0\tNone\tNone\t21\t11067780\t8.79E+12
-MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t212126.9388\t0\tNone\tNone\t49\t10394220\t3.21E+12
-MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t79170\t0\tNone\tNone\t2\t158340\t12620538000
-MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t389580\t0\tNone\tNone\t1\t389580\t1.52E+11
-MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t55380\t0\tNone\tNone\t1\t55380\t3066944400
-MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t752360\t0\tNone\tNone\t6\t4514160\t3.41E+12
-MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t229096\t0\tNone\tNone\t15\t3436440\t8.88E+11
-MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t304958.4615\t0\tNone\tNone\t78\t23786760\t1.00E+13
-MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t197685.5769\t0\tNone\tNone\t104\t20559300\t5.86E+12
-MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t115550\t0\tNone\tNone\t6\t693300\t89094740400
-MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t272595\t0\tNone\tNone\t4\t1090380\t3.15E+11
-MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t316267.8\t0\tNone\tNone\t100\t31626780\t1.20E+13
-MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t3770\t0\tNone\tNone\t6\t22620\t511664400
-MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t26115\t0\tNone\tNone\t4\t104460\t10911891600
-MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t518040\t0\tNone\tNone\t1\t518040\t2.68E+11
-MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t1262815.2\t0\tNone\tNone\t75\t94711140\t1.20E+14
-MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tDVE\tRESULTS\t#VALUE!\t52970.76923\t0\tNone\tNone\t13\t688620\t38987182800
-MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tDVE\tRESULTS\t#VALUE!\t61173.10345\t0\tNone\tNone\t29\t1774020\t1.29E+11
-MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tDVE\tRESULTS\t#VALUE!\t49680\t0\tNone\tNone\t1\t49680\t2468102400
-MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tDVE\tRESULTS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tDVE\tRESULTS\t#VALUE!\t90300\t0\tNone\tNone\t2\t180600\t16389079200
-MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t48300\t0\tNone\tNone\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t569880\t0\tNone\tNone\t4\t2279520\t2.21E+12
-MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t48300\t0\tNone\tNone\t1\t48300\t2332890000
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t647532\t0\tNone\tNone\t5\t3237660\t3.07E+12
-MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t471560\t0\tNone\tNone\t18\t8488080\t5.50E+12
-MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t80326.5\t0\tNone\tNone\t40\t3213060\t3.71E+11
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBDIR\tINTERPRETED BY\t#VALUE!\t678540\t0\tNone\tNone\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBDIS\tDISCLAIMER\t#VALUE!\t678540\t0\tNone\tNone\t3\t2035620\t2.03E+12
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCDIR\tINTERPRETED BY\t#VALUE!\t599640\t0\tNone\tNone\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCDIS\tDISCLAIMER\t#VALUE!\t599640\t0\tNone\tNone\t1\t599640\t3.60E+11
-MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRDIS\tDISCLAIMER\t#VALUE!\t488214.4565\t0\tNone\tNone\t184\t89831460\t6.08E+13
-MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRDR\tINTERPRETED BY\t#VALUE!\t488123.0769\t0\tNone\tNone\t26\t12691200\t8.34E+12
-MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRDS\tDISCLAIMER\t#VALUE!\t488123.0769\t0\tNone\tNone\t26\t12691200\t8.34E+12
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRDR\tINTERPRETED BY\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRDS\tDISCLAIMER\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRES\tRESULTS\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRTS\tSOURCE\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRD\tINTERPRETED BY\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRM\tMETHOD\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRS\tDISCLAIMER\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRX\tCLINICAL INDICATION\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
-MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11DS\tDISCLAIMER\t#VALUE!\t266970\t0\tNone\tNone\t2\t533940\t2.85E+11
-MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11MT\tMETHOD\t#VALUE!\t266970\t0\tNone\tNone\t2\t533940\t2.85E+11
-MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11DS\tDISCLAIMER\t#VALUE!\t1087500\t0\tNone\tNone\t2\t2175000\t2.77E+12
-MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11MD\tINTERPRETED BY\t#VALUE!\t1087500\t0\tNone\tNone\t2\t2175000\t2.77E+12
-MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11MT\tMETHOD\t#VALUE!\t1087500\t0\tNone\tNone\t2\t2175000\t2.77E+12
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15DI\tDISCLAIMER\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15DX\tCLINICAL INDICATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15MD\tINTERPRETED BY\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15ME\tMETHOD\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15RE\tRESULT, FLD\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15DI\tDISCLAIMER\t#VALUE!\t909120\t0\tNone\tNone\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15MD\tINTERPRETED BY\t#VALUE!\t909120\t0\tNone\tNone\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15ME\tMETHOD\t#VALUE!\t909120\t0\tNone\tNone\t1\t909120\t8.26E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTDIR\tINTERPRETED BY\t#VALUE!\t763860\t0\tNone\tNone\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTDIS\tDISCLAIMER\t#VALUE!\t763860\t0\tNone\tNone\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTPRM\tPRIMERS\t#VALUE!\t763860\t0\tNone\tNone\t1\t763860\t5.83E+11
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCDIR\tINTERPRETED BY\t#VALUE!\t1195020\t0\tNone\tNone\t3\t3585060\t4.32E+12
-MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCDIS\tDISCLAIMER\t#VALUE!\t1195020\t0\tNone\tNone\t3\t3585060\t4.32E+12
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFDI\tDISCLAIMER\t#VALUE!\t1050420\t0\tNone\tNone\t2\t2100840\t3.23E+12
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFIN\tINTERPRETATION\t#VALUE!\t333780\t0\tNone\tNone\t1\t333780\t1.11E+11
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFMD\tINTERPRETED BY\t#VALUE!\t333780\t0\tNone\tNone\t1\t333780\t1.11E+11
-MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFME\tMETHOD\t#VALUE!\t1050420\t0\tNone\tNone\t2\t2100840\t3.23E+12
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSDIM\tDISCLAIMER\t#VALUE!\t784098.806\t0\tNone\tNone\t134\t105069240\t1.02E+14
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSMEM\tMETHOD\t#VALUE!\t784098.806\t0\tNone\tNone\t134\t105069240\t1.02E+14
-MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSREFM\tREFERENCES\t#VALUE!\t784098.806\t0\tNone\tNone\t134\t105069240\t1.02E+14
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMDI\tDISCLAIMER\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMIN\tINTERPRETATION\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMMD\tINTERPRETED BY\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
-MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMRF\tREFERENCES\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPDI\tDISCLAIMER\t#VALUE!\t540720\t0\tNone\tNone\t66\t35687520\t2.44E+13
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPIN\tINTERPRETATION, BM\t#VALUE!\t540720\t0\tNone\tNone\t66\t35687520\t2.44E+13
-MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPRF\tREFERENCES\t#VALUE!\t540720\t0\tNone\tNone\t66\t35687520\t2.44E+13
-OR PROCEDURES\t659968\tCRP139\tICD GENERATOR AND LEAD INS/REP DUAL\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-OR PROCEDURES\t660294\tCRP332\tRIGHT HEART C NIPRIDE STUDY\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-OR PROCEDURES\t660420\tCRP398\tVALVULOPLASTY ADULT\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-PEDS ANGIO ORDERABLES\t474844\tIMG13RHC\tPED RT HEART CATH\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-PEDS ANGIO ORDERABLES\t474847\tIMG13VA\tPED VALVULOPLASTY AO V\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-PFT ORDERABLES\t471482\tPFT43\tFULL SPIROMETRY WITH SVC MVV\tPDF\tPDF RESULT\t#VALUE!\t253380\t0\tNone\tNone\t1\t253380\t64201424400
-PFT ORDERABLES\t471484\tPFT45\tLUNG VOLUMES\tPDF\tPDF RESULT\t#VALUE!\t4288380\t0\tNone\tNone\t1\t4288380\t1.84E+13
-PFT ORDERABLES\t471491\tPFT50\tROUTINE STDY - ABG, SPIRO W/BDR,DLCO,VOL\tPDF\tPDF RESULT\t#VALUE!\t37740\t0\tNone\tNone\t2\t75480\t3834295200
-PFT ORDERABLES\t471497\tPFT53\tSPIROMETRY WITH ABG\tPDF\tPDF RESULT\t#VALUE!\t213840\t0\tNone\tNone\t1\t213840\t45727545600
-PFT ORDERABLES\t471498\tPFT54\tSPIROMETRY WITH DLCO\tPDF\tPDF RESULT\t#VALUE!\t555360\t0\tNone\tNone\t4\t2221440\t1.46E+12
-PFT ORDERABLES\t669779\tPFT59\tSPIROMETRY\tPDF\tPDF RESULT\t#VALUE!\t882945.6\t0\tNone\tNone\t50\t44147280\t6.91E+13
-POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tT11DX\tCLINICAL INDICATION\t#VALUE!\t660\t0\tNone\tNone\t1\t660\t435600
-POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tPPDINT\tPPD INTERPRETATION\t#VALUE!\t261720\t0\tNone\tNone\t1\t261720\t68497358400
-POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tPHLOC\tLOCATION\t#VALUE!\t13600\t0\tNone\tNone\t3\t40800\t1048500000
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC7\tQC 7 RESULT\t#VALUE!\t-14672.40876\t0\tNone\tNone\t137\t-2010120\t2.71E+11
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tTESTEXP\tTEST STRIP EXP DATE\t#VALUE!\t-14672.40876\t0\tNone\tNone\t137\t-2010120\t2.71E+11
-POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tTESTLOT\tTEST STRIP LOT#\t#VALUE!\t-14672.40876\t0\tNone\tNone\t137\t-2010120\t2.71E+11
-STOOL ORDERABLES\t471633\tLABCANNAB\tNEURONAL NUCLEAR AB, CSF\tCANNA3\tNEURONAL NUCLEAR AB, CSF\t#VALUE!\t764260\t0\tNone\tNone\t6\t4585560\t4.58E+12
-STOOL ORDERABLES\t471693\tLABASPERP\tASPERGILLUS AB ID\tASPERP\tASPERGILLUS AB (ID)\t#VALUE!\t340350\t0\tNone\tNone\t4\t1361400\t4.73E+11
-STOOL ORDERABLES\t471801\tLABYBRUC\tBRUCELLA IGG AND IGM AB\tYBRUC1\tBRUCELLA AB ,IGG\t#VALUE!\t317841.8571\t0\tNone\tNone\t140\t44497860\t1.67E+13
-STOOL ORDERABLES\t471801\tLABYBRUC\tBRUCELLA IGG AND IGM AB\tYBRUC2\tBRUCELLA AB ,IGM\t#VALUE!\t317841.8571\t0\tNone\tNone\t140\t44497860\t1.67E+13
-STOOL ORDERABLES\t471801\tLABYBRUC\tBRUCELLA IGG AND IGM AB\tYBRUC3\tINTERPRETATION\t#VALUE!\t317841.8571\t0\tNone\tNone\t140\t44497860\t1.67E+13
-STOOL ORDERABLES\t471952\tLABCELDIS\tCELIAC DISEASE SYSTEM\tCELDR\tCELIAC DISEASE SYS\t#VALUE!\t777200\t0\tNone\tNone\t3\t2331600\t2.00E+12
-STOOL ORDERABLES\t472230\tLAB11112R\tDONOR TOXOPLASMA AB\tDTOXOM\tDONOR TOXO IGM\t#VALUE!\t3589900\t0\tNone\tNone\t3\t10769700\t8.46E+13
-STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\tDRUGSCRNFLD\tDRUG SCRN, GENERAL, BIOLOGICAL FL\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\tYGDA2\tSUSPECT DRUG\t#VALUE!\t145740\t0\tNone\tNone\t2\t291480\t81166788000
-STOOL ORDERABLES\t472311\tLABYEMA\tENDOMYSIAL ANTIBODIES, SERUM\tYEMAIN\tENDOMYSIAL ABS, SERUM\t#VALUE!\t283740\t0\tNone\tNone\t1\t283740\t80508387600
-STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB1\tEBV VCA IGG AB\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
-STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB2\tEBV VCA IGM AB\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
-STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB3\tEBNA AB\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
-STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB4\tINTERPRETATION\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
-STOOL ORDERABLES\t472594\tLABIBDDI\tIBD DIAGNOSTIC SYSTEM\tIBDDR\tIBD DIAGNOSTIC SYS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t472630\tLABYKLFRE\tIMMUNO FREE LIGHT CHAINS\tYKLF1\tKAPPA FREE LIGHT CHAIN, S\t#VALUE!\t162735\t0\tNone\tNone\t8\t1301880\t2.38E+11
-STOOL ORDERABLES\t472630\tLABYKLFRE\tIMMUNO FREE LIGHT CHAINS\tYKLF2\tLAMBDA FREE LIGHT CHAIN, S\t#VALUE!\t162735\t0\tNone\tNone\t8\t1301880\t2.38E+11
-STOOL ORDERABLES\t472630\tLABYKLFRE\tIMMUNO FREE LIGHT CHAINS\tYKLF3\tKAPPA/LAMBDA FLC RATIO\t#VALUE!\t162735\t0\tNone\tNone\t8\t1301880\t2.38E+11
-STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF1\tINFLUENZA VIRUS A AB, IGG & IGM\t#VALUE!\t245647.5\t0\tNone\tNone\t8\t1965180\t5.15E+11
-STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF2\tINFLUENZA VIRUS A AB, IGG\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
-STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF3\tINFLUENZA VIRUS A AB, IGM\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
-STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF4\tINFLUENZA VIRUS B AB, IGG & IGM\t#VALUE!\t245647.5\t0\tNone\tNone\t8\t1965180\t5.15E+11
-STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF5\tINFLUENZA VIRUS B AB, IGG\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
-STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF6\tINFLUENZA VIRUS B AB, IGM\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
-STOOL ORDERABLES\t472657\tLABYINSFT\tINSULIN FREE AND TOTAL\tINS\tINSULIN\t#VALUE!\t421560\t0\tNone\tNone\t5\t2107800\t1.23E+12
-STOOL ORDERABLES\t472657\tLABYINSFT\tINSULIN FREE AND TOTAL\tYINSF1\tFREE INSULIN\t#VALUE!\t421560\t0\tNone\tNone\t5\t2107800\t1.23E+12
-STOOL ORDERABLES\t472722\tLABYLACTS\tLACTOFERRIN DETECTION, STOOL\tYLACT1\tLACTOFERRIN DETECTION\t#VALUE!\t401266.1538\t0\tNone\tNone\t39\t15649380\t7.59E+12
-STOOL ORDERABLES\t472749\tLABYLAP\tLEUKOCYTE ALKALINE PHOSPATASE SCORE\tYLAP1\tLAP SCORE\t#VALUE!\t187714.2857\t0\tNone\tNone\t7\t1314000\t3.12E+11
-STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tYFCHY1\tFLUID TYPE\t#VALUE!\t177360\t0\tNone\tNone\t1\t177360\t31456569600
-STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tYFCHY6\tCOMMENT\t#VALUE!\t177360\t0\tNone\tNone\t1\t177360\t31456569600
-STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTETINT\tTETANUS INTERPRETATION\t#VALUE!\t908145\t0\tNone\tNone\t4\t3632580\t3.30E+12
-STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG1\tMAGNESIUM, QN, F\t#VALUE!\t684960\t0\tNone\tNone\t1\t684960\t4.69E+11
-STOOL ORDERABLES\t472955\tLABYNOB\tNEUTROPHIL OXIDATIVE BURST\tNEUTOXB\tNEUTROPHIL OXIDATIVE BURST\t#VALUE!\t29880\t0\tNone\tNone\t1\t29880\t892814400
-STOOL ORDERABLES\t472955\tLABYNOB\tNEUTROPHIL OXIDATIVE BURST\tYNOB2\tINTERPRETATION\t#VALUE!\t29880\t0\tNone\tNone\t1\t29880\t892814400
-STOOL ORDERABLES\t472955\tLABYNOB\tNEUTROPHIL OXIDATIVE BURST\tYNOB3\tREVIEWED BY\t#VALUE!\t29880\t0\tNone\tNone\t1\t29880\t892814400
-STOOL ORDERABLES\t472979\tLABNORW\tNOROVIRUS ANTIGEN, STOOL EIA\tNORW1\tNOROVIRUS,ELA (STOOL)\t#VALUE!\t244852.9167\t0\tNone\tNone\t288\t70517640\t2.37E+13
-STOOL ORDERABLES\t473061\tLABYP4AB\tPARAINFLUENZA AB\tYP4AB1\tTYPE 1\t#VALUE!\t590370\t0\tNone\tNone\t2\t1180740\t7.06E+11
-STOOL ORDERABLES\t473061\tLABYP4AB\tPARAINFLUENZA AB\tYP4AB2\tTYPE 2\t#VALUE!\t590370\t0\tNone\tNone\t2\t1180740\t7.06E+11
-STOOL ORDERABLES\t473061\tLABYP4AB\tPARAINFLUENZA AB\tYP4AB3\tTYPE 3\t#VALUE!\t590370\t0\tNone\tNone\t2\t1180740\t7.06E+11
-STOOL ORDERABLES\t473095\tLAB11138R\tHIV PHENO EXPR ANTIVIR RESIST\tPHENO\tHIV-1 PHENOTYPE\t#VALUE!\t633000\t0\tNone\tNone\t1\t633000\t4.01E+11
-STOOL ORDERABLES\t473218\tLAB13006R\tREDUCING SUBSTANCE, FECES\t13005R\tREDUCING SUBSTANCE, FECES\t#VALUE!\t189315\t0\tNone\tNone\t8\t1514520\t3.87E+11
-STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK1\tSPOTTED FEVER GROUP,AB,IGG,S\t#VALUE!\t333069.6774\t0\tNone\tNone\t31\t10325160\t1.28E+13
-STOOL ORDERABLES\t473281\tLABYRS\tRSV IGG AND IGM AB\tYRS1\tRSV AB, IGG & IGM\t#VALUE!\t307320\t0\tNone\tNone\t1\t307320\t94445582400
-STOOL ORDERABLES\t473281\tLABYRS\tRSV IGG AND IGM AB\tYRS2\tRSV AB, IGG\t#VALUE!\t317796\t0\tNone\tNone\t5\t1588980\t5.26E+11
-STOOL ORDERABLES\t473281\tLABYRS\tRSV IGG AND IGM AB\tYRS3\tRSV AB, IGM\t#VALUE!\t317796\t0\tNone\tNone\t5\t1588980\t5.26E+11
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t231851.4286\t0\tNone\tNone\t21\t4868880\t1.38E+12
-STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t192800\t0\tNone\tNone\t3\t578400\t1.13E+11
-STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t210925.3091\t0\tNone\tNone\t275\t58004460\t1.38E+13
-STOOL ORDERABLES\t473460\tLAB11122R\tTOXOPLASMA AB, IGM\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t2060076.522\t0\tNone\tNone\t23\t47381760\t3.01E+14
-STOOL ORDERABLES\t473460\tLAB11122R\tTOXOPLASMA IGM ELISA\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t1477600\t0\tNone\tNone\t3\t4432800\t6.65E+12
-STOOL ORDERABLES\t473460\tLAB11122R\tTOXOPLASMOSIS IGM\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t473460\tLABTOXOM\tTOXOPLASMOSIS IGM\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t473462\tLAB11120R\tTOXOPLASMOSIS, CSF\tTOXOC\tTOXOPLASMA AB,CSF\t#VALUE!\t1690331.429\t0\tNone\tNone\t21\t35496960\t1.93E+14
-STOOL ORDERABLES\t473777\tLABVWFPI\tVON WILLEBRAND PROTEASE INHIBITOR\tVWFPIC\tCOMMENT\t#VALUE!\t1187392.5\t0\tNone\tNone\t8\t9499140\t2.35E+13
-STOOL ORDERABLES\t497378\tLABYFMG\tMAGNESIUM, FLUID\tMGFLD\tMAGNESIUM, BODY FLUID\t#VALUE!\t242700\t0\tNone\tNone\t1\t242700\t58903290000
-STOOL ORDERABLES\t497378\tLABYFMG\tMAGNESIUM, FLUID\tYFMG2\tFLUID TYPE\t#VALUE!\t242700\t0\tNone\tNone\t1\t242700\t58903290000
-STOOL ORDERABLES\t497462\tLABFADAG\tADENOVIRUS AG, STOOL\tADAG\tADENOVIRUS AG\t#VALUE!\t259009.322\t0\tNone\tNone\t236\t61126200\t2.30E+13
-STOOL ORDERABLES\t541691\tLABHEPBG\tHEPATITIS B GENOTYPE\tHEPBG1\tHBV GENOTYPE\t#VALUE!\t646914\t0\tNone\tNone\t10\t6469140\t5.30E+12
-STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11306R\tSULFONYLUREA SCRN\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t670750\tLAB11340R\tADAMTS 13 PROFILE\t11341R\tADAMTS 13 ACTIVITY\t#VALUE!\t631303.6364\t0\tNone\tNone\t11\t6944340\t6.83E+12
-STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11264R\tSTREPTOMYCIN\t#VALUE!\t455400\t0\tNone\tNone\t1\t455400\t2.07E+11
-STOOL ORDERABLES\t670924\tLAB11437R\tIBD DIAGNOSTIC\t11501R\tIBD DIAGNOSTIC\t#VALUE!\t902580\t0\tNone\tNone\t2\t1805160\t2.52E+12
-STOOL ORDERABLES\t744166\tLAB11512R\tCYSTIC FIBROSIS, SEQUENCING\t11621R\tCYSTIC FIBROSIS, SEQUENCING\t#VALUE!\t2659970\t0\tNone\tNone\t6\t15959820\t4.44E+13
-STOOL ORDERABLES\t744178\tLAB11518R\tBCR-ABL1 KINASE DOMAIN MUT ANL\t11627R\tBCR-ABL1 KINASE DOMAIN MUT ANAL\t#VALUE!\t605820\t0\tNone\tNone\t2\t1211640\t1.46E+12
-STOOL ORDERABLES\t744202\tLAB11531R\tPARANEOPLASTIC AB SCREEN\t11641R\tPARANEOPLASTIC AB SCREEN\t#VALUE!\t2278057.5\t0\tNone\tNone\t88\t200469060\t6.98E+14
-STOOL ORDERABLES\t744228\tLAB11547R\tHEPATITIS C CONFIRMATORY TEST\t11657R\tHEPATITIS C CONFIRMATORY TEST\t#VALUE!\t1722849.6\t0\tNone\tNone\t25\t43071240\t1.11E+14
-STOOL ORDERABLES\t744232\tLAB11549R\tCALIFORNIA ENCEPHALITIS PANEL\t11659R\tCALIFORNIA ENCEPHALITIS PANEL\t#VALUE!\t2417270\t0\tNone\tNone\t12\t29007240\t9.09E+13
-STOOL ORDERABLES\t744264\tLAB11567R\tHTLV-I/II AB, CSF\t11681R\tHTLV-I/II AB\t#VALUE!\t264940\t0\tNone\tNone\t3\t794820\t2.22E+11
-STOOL ORDERABLES\t744266\tLAB11568R\tBRUCELLA SEROLOGIES, CSF\t11683R\tBRUCELLA IGM,CSF\t#VALUE!\t272970\t0\tNone\tNone\t2\t545940\t1.68E+11
-STOOL ORDERABLES\t744270\tLAB11571R\tCYSTIC FIBROSIS, 97 MUTATIONS\t11686R\tCYSTIC FIBROSIS, 97 MUTATIONS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11723R\tLYMPHOCYTE PROLIFERATION, AG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11728R\tINTERP, CANDIDA ALBICANS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11733R\tINTERP, TETANUS TOXOID\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12285R\t12285R\t#VALUE!\t342800\t0\tNone\tNone\t3\t1028400\t5.85E+11
-STOOL ORDERABLES\t744322\tLAB11606R\tCYTOCHROME P450 2D6 GENOTYPE\t11758R\tCYP2D6 GENOTYPE\t#VALUE!\t390450\t0\tNone\tNone\t4\t1561800\t6.94E+11
-STOOL ORDERABLES\t744322\tLAB11606R\tCYTOCHROME P450 2D6 GENOTYPE\t11759R\tREVIEWED BY\t#VALUE!\t390450\t0\tNone\tNone\t4\t1561800\t6.94E+11
-STOOL ORDERABLES\t864650\tLAB11855R\tHEPATITIS C VIRAL RNA, QUANT\t11854R\tHCV RNA ,QUANT,TMA LOGIU/ML\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-STOOL ORDERABLES\t871212\tLAB12016R\tCYTOMEGALOVIRUS IGG AB, ELISA\t11667R\tCYTOMEGALOVIRUS IGG\t#VALUE!\t229260\t0\tNone\tNone\t1\t229260\t52560147600
-STOOL ORDERABLES\t889720\tLAB12571R\tFLT3-ITD, FLT3-D835 MUTATIONS\t12572R\tFLT3-ITD, FLT3-D835 MUTATIONS\t#VALUE!\t553828.4746\t0\tNone\tNone\t177\t98027640\t9.87E+13
-STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tRESS2\tRESS2\t#VALUE!\t427020\t0\tNone\tNone\t2\t854040\t3.80E+11
-STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL1\tCHLORIDE, STOOL, 24 HR\t#VALUE!\t1991280\t0\tNone\tNone\t1\t1991280\t3.97E+12
-STOOL ORDERABLES\t1048039\tLAB12866R\tB HENSELAE IGG TITER\tBHIGGT\tB.HENSELAE IGG TITER\t#VALUE!\t60\t0\tNone\tNone\t1\t60\t3600
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tAAUINT\tINTERPRETATION\t#VALUE!\t287790\t0\tNone\tNone\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tINTUBY\tINTERPRETED BY\t#VALUE!\t287790\t0\tNone\tNone\t6\t1726740\t5.69E+11
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUAMIM\tIMMUNOASSAY SCREEN\t#VALUE!\t26760\t0\tNone\tNone\t4\t107040\t11457561600
-URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUAMIN\tINTERPRETATION\t#VALUE!\t26760\t0\tNone\tNone\t4\t107040\t11457561600
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tYUBBIM\tIMMUNOASSAY SCREEN\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
-URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tYUBBIN\tINTERPRETATION\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZCO\tCOMMENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZIM\tIMMUNOASSAY SCREEN\t#VALUE!\t0\t0\tNone\tNone\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZIN\tINTERPRETATION\t#VALUE!\t0\t0\tNone\tNone\t9\t0\t0
-URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZNO\tNOTE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tYUCATF\tCATECHOLAMINES, URINE\t#VALUE!\t347778.9474\t0\tNone\tNone\t19\t6607800\t2.55E+12
-URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tYUCOIM\tIMMUNOASSAY SCREEN\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
-URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tYUCOIN\tINTERPRETATION\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
-URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tDOACOM\tDOACOM COMMENTS\t#VALUE!\t11906.82203\t0\tNone\tNone\t7552\t89920320\t1.01E+13
-URINE ORDERABLES\t472564\tLABYUHOMO\tHOMOCYSTEINE TOTAL, URINE\tHMCYURN\tHOMOCYSTEIN, TOTAL,U\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t472836\tLABYUMERC\tMERCURY, URINE (TIMED)\tMERURN\tMERCURY, U\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t472836\tLABYUMERC\tMERCURY, URINE (TIMED)\tVOLUMEURN\tURINE VOLUME\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t472836\tLABYUMERC\tMERCURY, URINE (TIMED)\tYUMER2\tCOLLECTION DURATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tYUMECO\tCOMMENT\t#VALUE!\t330442.6667\t0\tNone\tNone\t45\t14869920\t5.39E+12
-URINE ORDERABLES\t472928\tLABMPSTLC\tMUCOPOLYSACCHARIDES TLC, URINE\tMPSTLC\tMUCOPOLYSA, UR, TLC\t#VALUE!\t562020\t0\tNone\tNone\t1\t562020\t3.16E+11
-URINE ORDERABLES\t473028\tLABOSTLC\tOLIGOSACCHARIDES TLC, URINE\tOSTLC\tOLIGOSAC, UR, TLC\t#VALUE!\t561900\t0\tNone\tNone\t1\t561900\t3.16E+11
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUOPCO\tCOMMENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUOPIM\tIMMUNOASSAY SCREEN\t#VALUE!\t14.11764706\t0\tNone\tNone\t17\t240\t14400
-URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUOPIN\tINTERPRETATION\t#VALUE!\t14.11764706\t0\tNone\tNone\t17\t240\t14400
-URINE ORDERABLES\t473034\tLABUORG\tORGANIC ACIDS, URINE\tORGU\tORGANIC ACIDS, URINE\t#VALUE!\t438277.5\t0\tNone\tNone\t8\t3506220\t1.68E+12
-URINE ORDERABLES\t473034\tLABUORG\tORGANIC ACIDS, URINE\tORGUIN\tINTERPRETATION\t#VALUE!\t438277.5\t0\tNone\tNone\t8\t3506220\t1.68E+12
-URINE ORDERABLES\t473092\tLABYUPCPC\tPHENCYCLIDINE CONFIRMATION, URINE\tYUPCIM\tIMMUNOASSAY SCREEN\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t473092\tLABYUPCPC\tPHENCYCLIDINE CONFIRMATION, URINE\tYUPCIN\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tPPPCOM\tCOMMENTS\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
-URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tUHYPOX\tHYPOXANTHINE\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
-URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tURIC\tURIC ACID\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
-URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tUURAC\tURACIL\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
-URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tUXANT\tXANTHINE\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
-URINE ORDERABLES\t473429\tLABYUTHC\tTETRAHYDROCANNABINOL, URINE\tYUTHIM\tIMMUNOASSAY SCREEN\t#VALUE!\t87930\t0\tNone\tNone\t10\t879300\t2.61E+11
-URINE ORDERABLES\t473429\tLABYUTHC\tTETRAHYDROCANNABINOL, URINE\tYUTHIN\tINTERPRETATION\t#VALUE!\t87930\t0\tNone\tNone\t10\t879300\t2.61E+11
-URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tPEDCOM\tSPECIMEN COMMENT\t#VALUE!\t6620\t0\tNone\tNone\t3\t19860\t338230800
-URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tVMAINT\tVMA INTRP\t#VALUE!\t321355.3846\t0\tNone\tNone\t13\t4177620\t1.41E+12
-URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tVMAINT\tVMA INTRP\t#VALUE!\t379830\t0\tNone\tNone\t6\t2278980\t9.13E+11
-URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tVHINT\tVMA/HVA INTRP\t#VALUE!\t309984\t0\tNone\tNone\t10\t3099840\t1.05E+12
-URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\t12000R\tINTERPRETATION\t#VALUE!\t528440\t0\tNone\tNone\t3\t1585320\t8.64E+11
-URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\t12682R\tREVIEWED BY\t#VALUE!\t408660\t0\tNone\tNone\t1\t408660\t1.67E+11
-URINE ORDERABLES\t497400\tLABYDROTC\tDRUG SCREEN PRESCRIPTION / OTC\tYDR1\tDRUG SCREEN, PRESCRIP/OTC\t#VALUE!\t9375960\t0\tNone\tNone\t2\t18751920\t2.50E+14
-URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tHVAINT\tHVA INTRP\t#VALUE!\t335730\t0\tNone\tNone\t2\t671460\t2.26E+11
-URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUCATIN\tCATECHOL INTRP , URN\t#VALUE!\t242040\t0\tNone\tNone\t1\t242040\t58583361600
-URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11147R\tSPECIFIC GRAVITY\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
-URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11148R\tPH\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
-URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11149R\tOXIDANTS\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
-URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11152R\tCOMMENT\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
-URINE ORDERABLES\t744242\tLAB11554R\tS-SULFOCYSTEINE, URINE\t11664R\tS-SULFOCYSTEINE, URINE\t#VALUE!\t607260\t0\tNone\tNone\t1\t607260\t3.69E+11
-URINE ORDERABLES\t864656\tLAB11863R\tASPIRIN RESISTANCE\t11861R\t11-DEHYDROTHROMBOXANE B2\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-URINE ORDERABLES\t864656\tLAB11863R\tASPIRIN RESISTANCE\t11862R\tCREATININE, RANDOM URINE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VASCULAR CLINIC ORDERABLES\t519228\tVSC02\tVSC CAROTID VERTEBRAL ARTERY ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t223710\t0\tNone\tNone\t12\t2684520\t9.96E+11
-VASCULAR CLINIC ORDERABLES\t519229\tVSC03\tVSC ARTERIAL EXAM/ABI\'S\tPDF\tPDF RESULT\t#VALUE!\t560326.5517\t0\tNone\tNone\t58\t32498940\t1.45E+14
-VASCULAR CLINIC ORDERABLES\t519230\tVSC04\tVSC GRAFT W ABI\'S ART DUP\tPDF\tPDF RESULT\t#VALUE!\t247685.4545\t0\tNone\tNone\t22\t5449080\t3.01E+12
-VASCULAR CLINIC ORDERABLES\t519231\tVSC05\tVSC VENOUS ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t397694.5714\t0\tNone\tNone\t70\t27838620\t5.79E+13
-VASCULAR CLINIC ORDERABLES\t519232\tVSC06\tVSC ABDO AORTA ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t2182770\t0\tNone\tNone\t4\t8731080\t6.51E+13
-VASCULAR CLINIC ORDERABLES\t519233\tVSC07\tVSC ILIAC ARTERY ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t272950\t0\tNone\tNone\t6\t1637700\t6.97E+11
-VASCULAR CLINIC ORDERABLES\t519234\tVSC08\tVSC RENAL ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t670069.0909\t0\tNone\tNone\t11\t7370760\t1.92E+13
-VASCULAR CLINIC ORDERABLES\t519235\tVSC09\tVSC MESENTERIC ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t241540\t0\tNone\tNone\t3\t724620\t1.91E+11
-VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tDCL3\tTEST INFORMATION\t#VALUE!\t553215\t0\tNone\tNone\t4\t2212860\t1.33E+12
-VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tSDES3\tSPECIMEN\t#VALUE!\t553215\t0\tNone\tNone\t4\t2212860\t1.33E+12
-VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-BLOOD\tCMVLIP\tTEST DESCRIPTION\t#VALUE!\t165780\t0\tNone\tNone\t3\t497340\t92352106800
-VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVTIP\tTEST DESCRIPTION\t#VALUE!\t159127.5\t0\tNone\tNone\t8\t1273020\t2.10E+11
-VIROLOGY ORDERABLES\t472198\tLABDVERS\tRSV RAPID ANTIGEN, DIRECT EIA\tRSV\tRESPIRATORY SYNCYTIAL VIRUS\t#VALUE!\t150\t0\tNone\tNone\t2\t300\t5490000
-VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tDVE\tRESULTS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDIPR\tTEST DESCRIPTION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t472512\tLABHCVG\tHEPATITIS C VIRUS GENOTYPING\tHCGG\tHCV GENOTYPE\t#VALUE!\t477894\t0\tNone\tNone\t10\t4778940\t2.88E+12
-VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCGG\tHCV GENOTYPE\t#VALUE!\t251340\t0\tNone\tNone\t1\t251340\t63171795600
-VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCVGDI\tDISCLAIMER\t#VALUE!\t948855\t0\tNone\tNone\t8\t7590840\t8.74E+12
-VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCVGMT\tMETHOD\t#VALUE!\t948855\t0\tNone\tNone\t8\t7590840\t8.74E+12
-VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tHNOT\tHIV-1 AG DONOR SCRN QUAL. NOTE\t#VALUE!\t478889.2308\t0\tNone\tNone\t39\t18676680\t1.07E+13
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t583060\t0\tNone\tNone\t3\t1749180\t1.27E+12
-VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t616970\t0\tNone\tNone\t6\t3701820\t2.62E+12
-VIROLOGY ORDERABLES\t472567\tLABHPVHR2\tHPV DNA PROBE HIGH RISK\tHPTI\tTEST INFORMATION\t#VALUE!\t925207.0588\t0\tNone\tNone\t17\t15728520\t1.88E+13
-VIROLOGY ORDERABLES\t472567\tLABHPVHR2\tHPV DNA PROBE HIGH RISK\tSDES6\tSPECIMEN\t#VALUE!\t925207.0588\t0\tNone\tNone\t17\t15728520\t1.88E+13
-VIROLOGY ORDERABLES\t472569\tLABHSVM\tHSV 1 AND 2 IGM\tHSV2M\tHSV 2 IGM, IFA\t#VALUE!\t282932.3077\t0\tNone\tNone\t52\t14712480\t4.92E+12
-VIROLOGY ORDERABLES\t472574\tLABYHMPVP\tHUMAN METAPNEUMOVIRUS PCR\tYHMP1\tHUMAN METAPNEUMOVIRUS PCR\t#VALUE!\t455820\t0\tNone\tNone\t1\t455820\t2.08E+11
-VIROLOGY ORDERABLES\t473283\tLABRUBM\tRUBELLA AB IGM\tRUBM1\tRUBELLA AB, IGM\t#VALUE!\t258660\t0\tNone\tNone\t1\t258660\t66904995600
-VIROLOGY ORDERABLES\t473283\tLABYRUBM\tRUBELLA AB IGM\tRUBM1\tRUBELLA AB, IGM\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t496777\tLABHIVA2\tHIV - 2 AB\tHIV2\tHIV-2 AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t496777\tLABHIVA2\tHIV - 2 AB\tHTL2\tHIV-2 AB NOTE:\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADOV\tADENOVIRUS DNA, QL PCR\t#VALUE!\t107295\t0\tNone\tNone\t4\t429180\t62698006800
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tSOURC3\tADENOVIRUS SOURCE\t#VALUE!\t107295\t0\tNone\tNone\t4\t429180\t62698006800
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA, QUALITATIVE PCR\tADOV\tADENOVIRUS DNA, QL PCR\t#VALUE!\t304440\t0\tNone\tNone\t5\t1522200\t6.91E+11
-VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS PCR QUAL\tADOV\tADENOVIRUS DNA, QL PCR\t#VALUE!\t114450\t0\tNone\tNone\t2\t228900\t28059325200
-VIROLOGY ORDERABLES\t497424\tLABHTLVB\t HTLV-1/2 AB WESTERN BLOT\tHTWB\tHTLV I/II AB, WESTERN BLOT\t#VALUE!\t274132.5\t0\tNone\tNone\t16\t4386120\t1.32E+12
-VIROLOGY ORDERABLES\t497426\tLABHIVDAG\tHIV - 1 DIRECT AG (ICD)\tINTRE\tINTERPRETATION\t#VALUE!\t410679.1304\t0\tNone\tNone\t23\t9445620\t4.14E+12
-VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS DNA QUANTITATION\tBKQ\tBKV DNA QUANTITATION\t#VALUE!\t168960\t0\tNone\tNone\t1\t168960\t28547481600
-VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHERPES VIRAL 6 DNA QUALITATIVE\tHVDNA\tHERPES VIRUS 6 DNA\t#VALUE!\t184905\t0\tNone\tNone\t4\t739620\t1.80E+11
-VIROLOGY ORDERABLES\t497468\tLABBKDNA\tBK VIRUS DNA PCR\tBKD\tBK VIRUS DNA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t541685\tLABHSVG\tHSV 1/2 TYPE-SPECIFIC IGG\tHSV2G\tHSV 2, IGG INDEX\t#VALUE!\t262579.4118\t0\tNone\tNone\t34\t8927700\t4.76E+12
-VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVTIP\tTEST DESCRIPTION\t#VALUE!\t98320\t0\tNone\tNone\t3\t294960\t33751087200
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV BY RAPID PCR, CSF\tEBV\tEBV BY PCR\t#VALUE!\t404160\t0\tNone\tNone\t3\t1212480\t6.00E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV BY RAPID PCR, CSF\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t404160\t0\tNone\tNone\t3\t1212480\t6.00E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV BY RAPID PCR, CSF\tYEPB4\tREPORT STATUS\t#VALUE!\t404160\t0\tNone\tNone\t3\t1212480\t6.00E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t329575.7143\t0\tNone\tNone\t14\t4614060\t1.91E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tEBV\tEBV BY PCR\t#VALUE!\t478140\t0\tNone\tNone\t5\t2390700\t1.45E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t478140\t0\tNone\tNone\t5\t2390700\t1.45E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tYEBP3\tSPECIAL INFORMATION\t#VALUE!\t650850\t0\tNone\tNone\t2\t1301700\t1.05E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tYEPB4\tREPORT STATUS\t#VALUE!\t478140\t0\tNone\tNone\t5\t2390700\t1.45E+12
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tYEPB4\tREPORT STATUS\t#VALUE!\t326800\t0\tNone\tNone\t3\t980400\t3.42E+11
-VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t295741.791\t0\tNone\tNone\t67\t19814700\t7.67E+12
-VIROLOGY ORDERABLES\t633362\tLABMUMPSG\tMUMPS IGG\tMUMDES\tTEST DESCRIPTION\t#VALUE!\t165986.6667\t0\tNone\tNone\t9\t1493880\t2.85E+11
-VIROLOGY ORDERABLES\t670955\tLAB11020R\tEB VIRUS EARLY AG (R+D) IGG\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t#VALUE!\t303340\t0\tNone\tNone\t3\t910020\t4.00E+11
-VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEB VIRUS EARLY AG (R+D) IGG\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t#VALUE!\t240660\t0\tNone\tNone\t1\t240660\t57917235600
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEB NUCLEAR AG AB\t11166R\tEB NUCLEAR AB\t#VALUE!\t201060\t0\tNone\tNone\t1\t201060\t40425123600
-VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN IGG\tEBNDES\tEBNDES\t#VALUE!\t687360\t0\tNone\tNone\t2\t1374720\t9.65E+11
-VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEB VIRAL CAPSID AG IGM AB\t11167R\tEB VCA IGM AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEB VIRAL CAPSID AG IGG AB\t11168R\tEB VCA IGG AB\t#VALUE!\t201060\t0\tNone\tNone\t1\t201060\t40425123600
-VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t2183640\t0\tNone\tNone\t1\t2183640\t4.77E+12
-VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUADI\tDISCLAIMER\t#VALUE!\t142990.5882\t0\tNone\tNone\t17\t2430840\t5.55E+11
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1DI\tDISCLAIMER\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1R\t2009 INFLUENZA A(H1N1) RNA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1DI\tDISCLAIMER\t#VALUE!\t259084.5\t0\tNone\tNone\t40\t10363380\t6.39E+12
-VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1IN\tINTERPRETATION\t#VALUE!\t259084.5\t0\tNone\tNone\t40\t10363380\t6.39E+12
-VIROLOGY ORDERABLES\t870899\tLABVIRSO1\tMISC. VIROLOGY TEST\tVIROR\tRESULT\t#VALUE!\t823753.7143\t0\tNone\tNone\t35\t28831380\t3.01E+13
-VIROLOGY ORDERABLES\t876725\tLABDVEPI1\tPARA 1 DFA\tPI1SP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876727\tLABDVEPI2\tPARA 2 DFA\tPI2SP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876729\tLABDVEPI3\tPARA 3 DFA\tPI3SP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876731\tLABDVEMP\tMPV DFA\tMPSP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
-VIROLOGY ORDERABLES\t876737\tLABTOXIM\tTOXOPLASMA IGM\tTOXMDS\tTEST DESCRIPTION\t#VALUE!\t177480\t0\tNone\tNone\t7\t1242360\t2.54E+11
-VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGM & IGG\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
-VIROLOGY ORDERABLES\t876740\tINACTIVE 4\tTOXOPLASMA IGG IGM\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t186060\t0\tNone\tNone\t1\t186060\t34618323600
-VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t163250\t0\tNone\tNone\t6\t979500\t1.86E+11
-VIROLOGY ORDERABLES\t876741\tLABTOXGMR\tTOXO PREGNANCY PANEL\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t148380\t0\tNone\tNone\t1\t148380\t22016624400
-VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVDI\tDISCLAIMER\t#VALUE!\t41910\t0\tNone\tNone\t2\t83820\t6616040400
-VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVSP\tSPECIMEN\t#VALUE!\t41910\t0\tNone\tNone\t2\t83820\t6616040400
-VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRSMPDI\tDISCLAIMER\t#VALUE!\t86400\t0\tNone\tNone\t3\t259200\t28951264800
-VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVDI\tDISCLAIMER\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVMT\tMETHOD\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
-VIROLOGY ORDERABLES\t893882\tLABCMVAV\tCMV IGG AVIDITY\tCMVAVD\tTEST DESCRIPTION\t#VALUE!\t289530\t0\tNone\tNone\t2\t579060\t2.76E+11
-VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t137520\t0\tNone\tNone\t5\t687600\t1.11E+11
-VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t1267920\t0\tNone\tNone\t1\t1267920\t1.61E+12
-VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVZVDS\tVZV IGM TEST DESCRIPTION\t#VALUE!\t299835.8824\t0\tNone\tNone\t34\t10194420\t3.75E+12
-VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVIPX\tTEST DESCRIPTION\t#VALUE!\t425910\t0\tNone\tNone\t2\t851820\t3.70E+11
-VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCVGTD\tTEST DESCRIPTION\t#VALUE!\t766260\t0\tNone\tNone\t2\t1532520\t1.95E+12
-"""
-
-if __name__ == "__main__":
+import sys,os;
+from io import StringIO;
+from medinfo.common.Util import ProgressDots, stdOpen;
+from medinfo.db import DBUtil;
+from medinfo.db.ResultsFormatter import TabDictReader;
+
+def main(argv):
+
+    conn = DBUtil.connection();
+
+    try:
+        # Note Type code table
+        DBUtil.execute("""
+            INSERT INTO sim_note_type(sim_note_type_id, name) VALUES
+            ( 1,'H&P'),
+            ( 2,'Progress Note'),
+            ( 3,'ED Provider Note'),
+            ( 4,'ED Note'),
+            ( 5,'Clinic Visit'),
+            ( 6,'Telephone Encounter'),
+            ( 7,'Sign Out Note'),
+            ( 8,'Consult'),
+            ( 9,'Consult Follow-up'),
+            (10,'Discharge Summary'),
+            (11,'Operative Report'),
+            (12,'Interagency Form'),
+            (13,'Goals of Care'),
+            (14,'Procedure'),
+
+            (1002,'Critical Care'),
+            (1003,'Emergency Medicine'),
+            (1005,'Physical Therapy'),
+            (1006,'Occupational Therapy'),
+            (1007,'PICC'),
+            (1008,'Dialysis'),
+
+            (1500,'General Medicine'),
+            (1501,'Cardiology'),
+            (1502,'Pulmonary'),
+            (1503,'Gastroenterology'),
+            (1504,'Nephrology'),
+            (1505,'Rheumatology'),
+            (1506,'Allergy and Immunology'),
+            (1507,'Infectious Diseases'),
+            (1508,'Endocrinology'),
+            (1509,'Dermatology'),
+            (1510,'Neurology'),
+            (1511,'Gynecology'),
+            (1512,'Hematology'),
+            (1513,'Oncology'),
+            (1514,'Bone Marrow Transplant'),
+            (1515,'Psychiatry'),
+            (1516,'Palliative'),
+
+            (1600,'General Surgery'),
+            (1601,'Vascular Surgery'),
+            (1602,'Trauma Surgery'),
+            (1603,'Urology'),
+            (1604,'Orthopedics'),
+            (1605,'Interventional Radiology'),
+            (1606,'Transplant'),
+            (1607,'Neurosurgery'),
+            (1608,'Ophthalmology'),
+            (1609,'Pain Management'),
+            (1610,'Thoracic Surgery'),
+
+            (2001,'Physician'),
+            (2002,'Nurse'),
+            (2003,'Physician Assistant'),
+            (2004,'Nurse Practitioner'),
+            (2005,'Case Manager'),
+            (2006,'Social Worker'),
+            (2007,'Physical Therapist'),
+            (2008,'Occupational Therapist'),
+            (2009,'Pharmacist'),
+            (2010,'Dietician'),
+            (2011,'Spiritual Care');
+            """, conn=conn);
+
+        # Vital Signs / Flowsheet
+        dataTextStr = \
+            """sim_result_id\tname\tdescription\tgroup_string\tpriority
+            10\tTemp\tTemperature (F)\tFlowsheet>Vitals\t10
+            20\tPulse\tPulse / Heart Rate (HR)\tFlowsheet>Vitals\t20
+            30\tSBP\tBlood Pressure, Systolic (SBP)\tFlowsheet>Vitals\t30
+            40\tDBP\tBlood Pressure, Diastolic (DBP)\tFlowsheet>Vitals\t40
+            50\tResp\tRespirations (RR)\tFlowsheet>Vitals\t50
+            60\tFiO2\tFraction Inspired Oxygen\tFlowsheet>Vitals\t60
+            70\tUrine\tUrine Output (UOP)\tFlowsheet>Vitals\t70
+            80\tGCS\tGlasgow Coma Scale (GCS)\tFlowsheet>Vitals\t80
+            """;
+        DBUtil.insertFile( StringIO(dataTextStr), "sim_result", delim="\t");
+        simResultIds = list();
+        for model in TabDictReader(StringIO(dataTextStr)):
+            simResultIds.append(int(model["sim_result_id"]));
+        
+        # Map orders for vital signs to results
+        vitalSignsOrderTable = DBUtil.execute("select clinical_item_id from clinical_item where name in ('NUR1025','NUR2116')", conn=conn);
+        orderIds = list();
+        for (itemId,) in vitalSignsOrderTable:
+            orderIds.append(itemId);
+
+        # Persist mappings and define a default turnaround time from orders to vitals results
+        VITALS_TURNAROUND_TIME = 300;   # Simulated seconds until vital sounds ready
+        for orderId in orderIds:
+            for simResultId in simResultIds:
+                insertDict = {"clinical_item_id": orderId, "sim_result_id": simResultId, "turnaround_time": VITALS_TURNAROUND_TIME}
+                DBUtil.insertRow("sim_order_result_map", insertDict, conn=conn);
+
+        # Core Laboratory Results to look for
+        DBUtil.insertFile( StringIO(CORE_LAB_RESULT_DATA_TEXT_STR), "sim_result", delim=";");
+        
+        # Update sim_result table sequence so won't collide with the manually inserted ones
+        DBUtil.execute("SELECT setval('sim_result_sim_result_id_seq', %s)", (1000000,), conn=conn);
+        
+        # Look up clinical items (orders) based on their name / proc_code
+        itemById = DBUtil.loadTableAsDict("clinical_item");
+        itemByName = dict();
+        for clinicalItem in itemById.values():
+            itemByName[clinicalItem["name"]] = clinicalItem;
+
+        # All other (lab) result, link results to orders they can derive from
+        DEFAULT_LAB_TURNAROUND_TIME = 900;  # Default turnaround time to estimate for lab times
+        reader = TabDictReader(StringIO(PROC_RESULT_DATA_TEXT_STR));
+        priorityBase = 1000000; # Base number to assigned default ranking priorities, based on how common the items are
+        prog = ProgressDots();
+        for i, row in enumerate(reader):
+            procCode = row["proc_code"];
+            if procCode in itemByName:
+                orderItem = itemByName[procCode];
+                simResult = {"name": row["base_name"], "description": row["component_name"], "group_string": "%(proc_cat_name)s>Other>%(description)s" % row, "priority": priorityBase+i };
+                searchDict = {"name": row["base_name"], "description": row["component_name"] }; # Key columns to search for items by
+                (simResultId, isNew) = DBUtil.findOrInsertItem("sim_result", searchDict=searchDict, insertDict=simResult );
+
+                orderResultMap = {"clinical_item_id": orderItem["clinical_item_id"], "sim_result_id": simResultId, "turnaround_time": DEFAULT_LAB_TURNAROUND_TIME};
+                DBUtil.insertRow("sim_order_result_map", orderResultMap, conn=conn);
+            else:
+                print("Unable to find item for Proc Code: %s" % procCode, file=sys.stderr);
+            prog.update();
+        prog.printStatus();
+    
+        conn.commit();    
+    finally:
+        conn.close();
+
+# Manually specified (I organized these in an Excel file first), 
+#   what order to report lab tests in, 
+#   as well as what categories/groupings to present them in.
+# Some can be inferred from database, but otherwise I had to reconstruct by
+#   intuition or by what typically actually is presented in the real EMR
+CORE_LAB_RESULT_DATA_TEXT_STR = \
+"""sim_result_id;group_string;name;description;priority
+11000;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;WBC;WBC;11000
+11010;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;HGB;HEMOGLOBIN;11010
+11020;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;HCT;HEMATOCRIT;11020
+11030;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;PLT;PLATELET COUNT;11030
+11040;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;MCV;MCV;11040
+11050;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;RDW;RDW;11050
+11060;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;RBC;RBC;11060
+11070;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;MCH;MCH;11070
+11080;LAB BLOOD ORDERABLES>Hematology>Automated Blood Count;MCHC;MCHC;11080
+11110;LAB BLOOD ORDERABLES>Hematology>Differential;RETIC;RETIC, %;11110
+11120;LAB BLOOD ORDERABLES>Hematology>Differential;NEUT;NEUT, %;11120
+11130;LAB BLOOD ORDERABLES>Hematology>Differential;LYM;LYM, %;11130
+11140;LAB BLOOD ORDERABLES>Hematology>Differential;MONO;MONO, %;11140
+11150;LAB BLOOD ORDERABLES>Hematology>Differential;EOS;EOS, %;11150
+11160;LAB BLOOD ORDERABLES>Hematology>Differential;BASO;BASO, %;11160
+11210;LAB BLOOD ORDERABLES>Hematology>Differential;RETICAB;RETIC, ABS [AUTORETIC];11210
+11220;LAB BLOOD ORDERABLES>Hematology>Differential;NEUTAB;NEUT, ABS;11220
+11230;LAB BLOOD ORDERABLES>Hematology>Differential;BAND;BAND NEUTROPHILS;11230
+11240;LAB BLOOD ORDERABLES>Hematology>Differential;LYMAB;LYM, ABS;11240
+11250;LAB BLOOD ORDERABLES>Hematology>Differential;MONOAB;MONO, ABS;11250
+11260;LAB BLOOD ORDERABLES>Hematology>Differential;EOSAB;EOS, ABS;11260
+11270;LAB BLOOD ORDERABLES>Hematology>Differential;BASOAB;BASOS, ABS;11270
+12005;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;HEPAR;HEPARIN ACTIVITY LEVEL;12005
+12010;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;DDIMER;D-DIMER;12010
+12020;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;FIBRINOGEN;FIBRINOGEN;12020
+12030;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;PT;PROTHROMBIN TIME;12030
+12040;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;INR;INR;12040
+12050;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;PTT;PART. THROMBOPLASTIN TIME;12050
+12060;LAB BLOOD ORDERABLES>Coagulation>General Coagulation;TT;THROMBIN TIME;12060
+13010;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;NA;SODIUM, SER/PLAS;13010
+13020;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;K;POTASSIUM, SER/PLAS;13020
+13030;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CL;CHLORIDE, SER/PLAS;13030
+13040;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CO2;CO2, SER/PLAS;13040
+13050;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;BUN;UREA NITROGEN,SER/PLAS;13050
+13060;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CR;CREATININE, SER/PLAS;13060
+13070;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;GLU;GLUCOSE, SER/PLAS;13070
+13080;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;AG;ANION GAP;13080
+13090;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CA;CALCIUM, SER/PLAS;13090
+13100;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CAION;CALCIUM, IONIZED;13100
+13110;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;MG;MAGNESIUM, SER/PLAS;13110
+13120;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;PHOS;PHOSPHORUS, SER/PLAS;13120
+13210;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TBIL;TOTAL BILIRUBIN;13210
+13220;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;DBIL;CONJUGATED BILI;13220
+13230;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;IBIL;UNCONJUGATED BILIRUBIN;13230
+13240;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;AST;AST (SGOT), SER/PLAS;13240
+13250;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ALT;ALT (SGPT), SER/PLAS;13250
+13260;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ALKP;ALK P\'TASE, TOTAL, SER/PLAS;13260
+13270;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ALB;ALBUMIN, SER/PLAS;13270
+13280;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TP;PROTEIN, TOTAL, SER/PLAS;13280
+13290;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;GLOB;GLOBULIN;13290
+13310;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;PREALBUMIN;PREALBUMIN;13310
+13320;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;GGT;GAMMA-GLUTAMYL TRANS (GGT);13320
+13330;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;OSMOL;OSMOLALITY, SERUM;13330
+13410;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;URIC;URIC ACID, SER/PLAS;13410
+13420;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LDH;LDH, TOTAL, SER/PLAS;13420
+13430;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;HAPTO;HAPTOGLOBIN;13430
+13440;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FOLATE;FOLIC ACID (FOLATE);13440
+13450;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;B12;VITAMIN B12;13450
+13460;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FERRITIN;FERRITIN;13460
+13470;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FE;IRON, TOTAL;13470
+13480;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TRFRN;TRANSFERRIN;13480
+13490;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TRFSAT;TRANSFERRIN SAT;13490
+13502;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;PROCTL;PROCALCITONIN;13510
+13504;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LACWBL;LACWBL;13520
+13506;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LAC;LACTIC ACID;13530
+13510;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CKMB;CK, MB (MASS);13510
+13520;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CK;CK, TOTAL;13520
+13530;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TNI;TROPONIN I;13530
+13540;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;BNP;NT-PROBNP;13540
+13610;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CHOL;CHOLESTEROL, TOTAL;13610
+13620;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CHOLHDL;CHOLESTEROL/HDL RATIO;13620
+13630;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;HDL;HDL CHOLESTEROL;13630
+13640;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LDL;DIRECT LDL CHOL;13640
+13650;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;NHDL;NON-HDL CHOL, CALC;13650
+13660;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TGL;TRIGLYCERIDE, SER/PLAS;13660
+13670;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;A1C;HEMOGLOBIN A1C;13670
+13710;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;DIG;DIGOXIN;13710
+13720;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;NH3;AMMONIA, PLASMA;13720
+13730;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;AMYLASE;AMYLASE, SERUM;13730
+13740;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;LIPASE;LIPASE;13740
+13750;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;TSH;TSH;13750
+13760;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;FT4;FREE T4;13760
+13770;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CORT;CORTISOL, SERUM;13770
+13780;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;CRP;C-REACTIVE PROTEIN;13780
+13790;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ESR;ESR, (MANUAL);13790
+13810;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ACETA;ACETAMINOPHEN;13810
+13820;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;SALICYLATE;SALICYLATE;13820
+13830;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;ETOH;ETHANOL;13830
+13840;LAB BLOOD ORDERABLES>Chemistry>General Chemistry;OSMGAP;OSMOLAL GAP;13840
+15010;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;PHA;PH (A);15010
+15020;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;PCO2A;PCO2 (A);15020
+15030;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;PO2A;PO2 (A);15030
+15040;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Arterial;FIO2;FIO2 (FIO2);15040
+15050;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Venous;PHV;PH (V);15050
+15060;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Venous;PCO2V;PCO2 (V);15060
+15070;LAB BLOOD ORDERABLES>Blood Gases>Blood Gas, Venous;PO2V;PO2 (V);15070
+16010;POINT OF CARE TESTING>Blood Gas, Arterial;PHA;PH (A), ISTAT;16010
+16020;POINT OF CARE TESTING>Blood Gas, Arterial;PCO2A;PCO2 (A), ISTAT;16020
+16030;POINT OF CARE TESTING>Blood Gas, Arterial;PO2A;PO2 (A), ISTAT;16030
+16040;POINT OF CARE TESTING>Blood Gas, Arterial;O2SATA;O2 SATURATION, ISTAT;16040
+16050;POINT OF CARE TESTING>Blood Gas, Arterial;FIO2;FIO2;16050
+16060;POINT OF CARE TESTING>Blood Gas, Venous;PHV;PH (V), ISTAT;16060
+16070;POINT OF CARE TESTING>Blood Gas, Venous;PCO2V;PCO2 (V), ISTAT;16070
+16080;POINT OF CARE TESTING>Blood Gas, Venous;PO2V;PO2 (V), ISTAT;16080
+16090;POINT OF CARE TESTING>Blood Gas, Venous;O2SATV;O2 SATURATION, ISTAT;16090
+16100;POINT OF CARE TESTING>Blood Gas, Venous;LAC;LACTATE, ISTAT;16100
+17010;URINE ORDERABLES>Urinalysis;UCOL;COLOR;17010
+17020;URINE ORDERABLES>Urinalysis;UCLAR;CLARITY;17020
+17030;URINE ORDERABLES>Urinalysis;SPG;SPECIFIC GRAVITY;17030
+17040;URINE ORDERABLES>Urinalysis;UGLU;GLUCOSE;17040
+17050;URINE ORDERABLES>Urinalysis;UKET;KETONE;17050
+17060;URINE ORDERABLES>Urinalysis;UBLOOD;BLOOD;17060
+17070;URINE ORDERABLES>Urinalysis;UPH;PH;17070
+17080;URINE ORDERABLES>Urinalysis;UPROT;PROTEIN;17080
+17090;URINE ORDERABLES>Urinalysis;NITRITE;NITRITE;17090
+17100;URINE ORDERABLES>Urinalysis;LEUKEST;LEUK ESTERASE;17100
+17110;URINE ORDERABLES>Urinalysis;URBC;RBC, URINE;17110
+17120;URINE ORDERABLES>Urinalysis;UWBC;WBC, URINE;17120
+17130;URINE ORDERABLES>Urinalysis;SQEP;SQUAMOUS CELLS;17130
+17140;URINE ORDERABLES>Urinalysis;MUCUS;MUCOUS THREADS;17140
+17150;URINE ORDERABLES>Urinalysis;UBACT;BACTERIA;17150
+17210;URINE ORDERABLES>Drugs of Abuse;AMPHET;AMPHETAMINE;17210
+17220;URINE ORDERABLES>Drugs of Abuse;BARB;BARBITURATES;17220
+17230;URINE ORDERABLES>Drugs of Abuse;BENZO;BENZODIAZEPINES;17230
+17240;URINE ORDERABLES>Drugs of Abuse;COCAINE;COCAINE METABOLITES;17240
+17250;URINE ORDERABLES>Drugs of Abuse;METHADONE;METHADONE;17250
+17260;URINE ORDERABLES>Drugs of Abuse;OPIATE;OPIATES;17260
+17270;URINE ORDERABLES>Drugs of Abuse;PCP;PCP;17270
+17280;URINE ORDERABLES>Drugs of Abuse;TCA;TRICYCLICS;17280
+17290;URINE ORDERABLES>Drugs of Abuse;THC;THC;17290
+17310;URINE ORDERABLES>Urine Electrolytes;UCL;CHLORIDE, URINE;17310
+17320;URINE ORDERABLES>Urine Electrolytes;UCLCR;CHLORIDE/CREAT RATIO;17320
+17330;URINE ORDERABLES>Urine Electrolytes;UCR;URINE CREAT;17330
+17340;URINE ORDERABLES>Urine Electrolytes;UK;POTASSIUM, URINE;17340
+17350;URINE ORDERABLES>Urine Electrolytes;UKCR;POTASSIUM/CREAT RATIO;17350
+17360;URINE ORDERABLES>Urine Electrolytes;UNA;SODIUM, URINE;17360
+17370;URINE ORDERABLES>Urine Electrolytes;UNACR;SODIUM/CREAT RATIO;17370
+17380;URINE ORDERABLES>Urine Electrolytes;UOSM;OSMOLALITY, URINE;17380
+18010;INFECTIOUS DISEASES>Microbiology;CDTR;STOOL C.DIFF TOXIN B RESULT;18010
+18020;INFECTIOUS DISEASES>Microbiology;HIVAR;ANTI-HIV ANTIBODY;18020
+18030;INFECTIOUS DISEASES>Microbiology;ADVP;ADENOVIRUS;18030
+18040;INFECTIOUS DISEASES>Microbiology;INFAP;INFLUENZA A;18040
+18050;INFECTIOUS DISEASES>Microbiology;INFBP;INFLUENZA B;18050
+18060;INFECTIOUS DISEASES>Microbiology;P1P;PARAINFLUENZA 1;18060
+18070;INFECTIOUS DISEASES>Microbiology;P2P;PARAINFLUENZA 2;18070
+18080;INFECTIOUS DISEASES>Microbiology;P3P;PARAINFLUENZA 3;18080
+18090;INFECTIOUS DISEASES>Microbiology;RVP;RSV;18090
+18100;INFECTIOUS DISEASES>Microbiology;MPVPR;METAPNEUMOVIRUS;18100
+19010;CARDIOLOGY>EKG;HEARTRATE;HEART RATE;19010
+19020;CARDIOLOGY>EKG;QRSAXIS;QRS AXIS;19020
+19030;CARDIOLOGY>EKG;QTCINTERVAL;QTC INTERVAL;19030
+19040;CARDIOLOGY>EKG;QRSDINTERVAL;QRSD INTERVAL;19040
+19050;CARDIOLOGY>EKG;RR;RR;19050
+19060;CARDIOLOGY>EKG;QTINTERVAL;QT INTERVAL;19060
+19070;CARDIOLOGY>EKG;TWAVEAXIS;T WAVE AXIS;19070
+19080;CARDIOLOGY>EKG;PRINTERVAL;P-R INTERVAL;19080
+19090;CARDIOLOGY>EKG;PAXIS;P AXIS;19090
+"""
+
+# Queried out of the database, order_proc and order_results to see which orders map to which components, as well as their average values.
+PROC_RESULT_DATA_TEXT_STR = \
+"""proc_cat_name\tproc_id\tproc_code\tdescription\tbase_name\tcomponent_name\tmeanValue\tmeanTurnaround\tcountvalue\tsumvalue\tsumsquaresvalue\tcountturnaround\tsumturnaround\tsumsquaresturnaround
+POINT OF CARE TESTING\t472470\tLABPOCGLU\tGLUCOSE BY METER\tGLU\tGLUCOSE BY METER\t5931.844499\t56.13929433\t1139495\t6759307147\t6.58E+16\t491506\t27592800\t6.60E+11
+POINT OF CARE TESTING\t472470\tLABPOCGLU\tGLUCOSE BY METER\tSOU\tSOURCE\t9999999\t56.13929433\t1139428\t1.14E+13\t1.14E+20\t491506\t27592800\t6.60E+11
+POINT OF CARE TESTING\t472470\tLABPOCGLU\tGLUCOSE BY METER\tPCCOM\tPOCT COMMENT\t9999999\t56.13929433\t1088538\t1.09E+13\t1.09E+20\t491506\t27592800\t6.60E+11
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tAG\tANION GAP\t176646.0675\t5218.713203\t454658\t80313547749\t8.03E+17\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tBUN\tUREA NITROGEN,SER/PLAS\t296223.9694\t5218.713203\t454658\t1.35E+11\t1.35E+18\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCA\tCALCIUM, SER/PLAS\t24906.24148\t5218.713203\t454658\t11323821939\t1.13E+17\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCL\tCHLORIDE, SER/PLAS\t19831.95538\t5218.713203\t454658\t9016757167\t8.97E+16\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCO2\tCO2, SER/PLAS\t31324.46059\t5218.713203\t454658\t14241916604\t1.42E+17\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tCR\tCREATININE, SER/PLAS\t20324.37383\t5218.713203\t454658\t9240639159\t9.24E+16\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tGLU\tGLUCOSE, SER/PLAS\t21421.49652\t5218.713203\t454658\t9739454764\t9.68E+16\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tK\tPOTASSIUM, SER/PLAS\t24176.05695\t5218.713203\t454658\t10991837702\t1.10E+17\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tNA\tSODIUM, SER/PLAS\t20085.98101\t5218.713203\t454658\t9132251953\t9.07E+16\t437132\t2281266540\t1.02E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tWBC\tWBC\t51623.2819\t5746.970765\t448136\t23134251057\t2.31E+17\t432799\t2487283200\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMCV\tMCV\t56346.95098\t5746.968426\t448130\t25250759143\t2.52E+17\t432795\t2487259200\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMCHC\tMCHC\t60529.63052\t5746.910339\t448129\t27125082796\t2.71E+17\t432795\t2487234060\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMCH\tMCH\t64543.60448\t5746.888681\t448128\t28923796390\t2.89E+17\t432794\t2487218940\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tRBC\tRBC\t44410.37321\t5746.896631\t448127\t19901487317\t1.99E+17\t432791\t2487205140\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHGB\tHEMOGLOBIN\t53566.58914\t5746.921477\t448126\t24004581325\t2.40E+17\t432792\t2487221640\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHCT\tHEMATOCRIT\t45508.72455\t5746.941875\t448124\t20393551680\t2.04E+17\t432788\t2487207480\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tRDW\tRDW\t60200.59147\t5746.851237\t448124\t26977329851\t2.70E+17\t432789\t2487174000\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPLT\tPLATELET COUNT\t81430.40076\t5746.92422\t448102\t36489125443\t3.64E+17\t432791\t2487217080\t2.48E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOAB\tMONO, ABS\t526801.9003\t4662.123903\t413021\t2.18E+11\t2.18E+18\t399265\t1861422900\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTAB\tNEUT, ABS\t526693.4964\t4662.266108\t413016\t2.18E+11\t2.18E+18\t399266\t1861484340\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOAB\tBASOS, ABS\t526896.8737\t4661.664341\t413003\t2.18E+11\t2.18E+18\t399245\t1861146180\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSAB\tEOS, ABS\t526874.0487\t4661.664341\t413002\t2.18E+11\t2.18E+18\t399245\t1861146180\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMAB\tLYM, ABS\t526782.0099\t4661.664341\t412999\t2.18E+11\t2.18E+18\t399245\t1861146180\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUT\tNEUT, %\t520823.0612\t4659.546995\t412730\t2.15E+11\t2.15E+18\t399245\t1860300840\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASO\tBASO, %\t494153.2005\t4659.554308\t411573\t2.03E+11\t2.03E+18\t399244\t1860299100\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONO\tMONO, %\t493873.7199\t4659.762757\t411569\t2.03E+11\t2.03E+18\t399253\t1860424260\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOS\tEOS, %\t493922.4427\t4659.562925\t411564\t2.03E+11\t2.03E+18\t399245\t1860307200\t1.45E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYM\tLYM, %\t493725.7487\t4659.60706\t411556\t2.03E+11\t2.03E+18\t399247\t1860334140\t1.45E+14
+LAB BLOOD ORDERABLES\t472785\tLABMGN\tMAGNESIUM, SERUM/PLASMA\tMG\tMAGNESIUM, SER/PLAS\t21666.78826\t6476.532134\t390034\t8450784091\t8.45E+16\t364547\t2361000360\t1.22E+14
+LAB BLOOD ORDERABLES\t473178\tLABPT\tPROTHROMBIN TIME\tINR\tINR\t176166.8402\t4556.359571\t285357\t50270441023\t5.03E+17\t266985\t1216479660\t7.55E+13
+LAB BLOOD ORDERABLES\t473178\tLABPT\tPROTHROMBIN TIME\tPT\tPROTHROMBIN TIME\t176079.6294\t4556.358433\t285354\t50245026567\t5.02E+17\t266984\t1216474800\t7.55E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tAG\tANION GAP\t144187.9958\t5323.847133\t257387\t37112115664\t3.71E+17\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tALB\tALBUMIN, SER/PLAS\t55677.58497\t5323.847133\t257387\t14330686563\t1.43E+17\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t26586.75235\t5323.847133\t257387\t6843084428\t6.80E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tALT\tALT (SGPT), SER/PLAS\t195748.1848\t5323.847133\t257387\t50383038032\t5.04E+17\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tBUN\tUREA NITROGEN,SER/PLAS\t329915.7371\t5323.847133\t257387\t84916021821\t8.49E+17\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCA\tCALCIUM, SER/PLAS\t27010.57439\t5323.847133\t257387\t6952170711\t6.95E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCL\tCHLORIDE, SER/PLAS\t19450.95614\t5323.847133\t257387\t5006423248\t4.98E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCO2\tCO2, SER/PLAS\t26212.06319\t5323.847133\t257387\t6746644308\t6.74E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tCR\tCREATININE, SER/PLAS\t22419.04966\t5323.847133\t257387\t5770371935\t5.77E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tGLOB\tGLOBULIN\t63371.1434\t5323.847133\t257387\t16310908486\t1.63E+17\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tGLU\tGLUCOSE, SER/PLAS\t22159.76211\t5323.847133\t257387\t5703634689\t5.67E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tK\tPOTASSIUM, SER/PLAS\t24441.93271\t5323.847133\t257387\t6291035735\t6.29E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tNA\tSODIUM, SER/PLAS\t19601.5899\t5323.847133\t257387\t5045194420\t5.01E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tTBIL\tTOTAL BILIRUBIN\t3027310.466\t5323.847133\t257387\t7.79E+11\t7.79E+18\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tTP\tPROTEIN, TOTAL, SER/PLAS\t25726.24677\t5323.847133\t257387\t6621601478\t6.62E+16\t247509\t1317700080\t5.79E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tAST\tAST (SGOT), SER/PLAS\t225948.6188\t5323.855244\t257378\t58154203608\t5.81E+17\t247507\t1317691440\t5.79E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tWBC\tWBC\t49351.13292\t4043.094528\t238745\t11782336228\t1.18E+17\t229657\t928524960\t9.22E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tHCT\tHEMATOCRIT\t44261.12847\t4043.08036\t238742\t10566990334\t1.06E+17\t229655\t928513620\t9.22E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tHGB\tHEMOGLOBIN\t54880.84171\t4043.08036\t238742\t13102361911\t1.31E+17\t229655\t928513620\t9.22E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tRBC\tRBC\t43104.31413\t4043.08036\t238741\t10290767060\t1.03E+17\t229655\t928513620\t9.22E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tMCV\tMCV\t53622.1977\t4042.99784\t238739\t12801709856\t1.28E+17\t229652\t928482540\t9.22E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tRDW\tRDW\t59286.04589\t4043.035362\t238739\t14153891310\t1.41E+17\t229653\t928495200\t9.22E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tMCHC\tMCHC\t59680.61442\t4042.711593\t238738\t14248030526\t1.42E+17\t229651\t928412760\t9.21E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tMCH\tMCH\t63154.61088\t4042.655519\t238737\t15077342337\t1.51E+17\t229650\t928395840\t9.21E+13
+LAB BLOOD ORDERABLES\t471943\tLABCBCO\tCBC\tPLT\tPLATELET COUNT\t70100.45715\t4043.08036\t238733\t16735292436\t1.67E+17\t229655\t928513620\t9.22E+13
+BODY FLUID ORDERABLES\t473352\tLABSPR\tSPECIMEN REMARK\tSPR\tSPECIMEN REMARK\t9998704.24\t1091.201598\t231590\t2.32E+12\t2.32E+19\t89098\t97223880\t4.93E+13
+LAB BLOOD ORDERABLES\t473103\tLABPHOS\tPHOSPHORUS, SERUM/PLASMA\tPHOS\tPHOSPHORUS, SER/PLAS\t24804.78078\t5220.968417\t199586\t4950686977\t4.95E+16\t187729\t980127180\t4.41E+13
+LAB BLOOD ORDERABLES\t473190\tLABPTT\tPTT PARTIAL THROMBOPLASTIN TIME\tPTT\tPART. THROMBOPLASTIN TIME\t238902.409\t7902.059931\t197062\t47078586523\t4.71E+17\t179006\t1414516140\t9.23E+13
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPCO2A\tPCO2 (A), ISTAT\t27476.5848\t76.33105738\t191365\t5258056650\t5.25E+16\t79527\t6070380\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPHA\tPH (A), ISTAT\t26187.70247\t76.33105738\t191365\t5011409684\t5.01E+16\t79527\t6070380\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPO2A\tPO2 (A), ISTAT\t21169.68923\t76.33181184\t191365\t4051137580\t4.03E+16\t79527\t6070440\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tHCO3A\tHCO3 (A), ISTAT\t27407.80058\t76.33146809\t191363\t5244838943\t5.24E+16\t79525\t6070260\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tTCO2A\tTCO2, ISTAT\t32268.912\t76.33146809\t191363\t6175075808\t6.17E+16\t79525\t6070260\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tO2SATA\tO2 SATURATION, ISTAT\t35160.01087\t76.33242795\t191362\t6728290001\t6.71E+16\t79524\t6070260\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t76.33085203\t184372\t1.84E+12\t1.84E+19\t79528\t6070440\t2.63E+11
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tFIO2\tFIO2\t859726.0565\t77.31600376\t171167\t1.47E+11\t1.47E+18\t76632\t5924880\t2.63E+11
+LAB BLOOD ORDERABLES\t471818\tLABCAI\tCALCIUM IONIZED, SERUM/PLASMA\tCAION\tCALCIUM, IONIZED\t35246.01129\t6630.995935\t163428\t5760185134\t5.76E+16\t156456\t1037459100\t9.72E+13
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tHEARTRATE\tHEART RATE\t676.0436628\t-5950.445219\t118728\t80265312\t7.00E+14\t118728\t-706484460\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQRSAXIS\tQRS AXIS\t695.5623863\t-5950.445219\t118728\t82582731\t8.00E+14\t118728\t-706484460\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQTCINTERVAL\tQTC INTERVAL\t852.6860218\t-5950.445219\t118728\t101237706\t5.00E+14\t118728\t-706484460\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQRSDINTERVAL\tQRSD INTERVAL\t603.8367001\t-5953.777336\t118708\t71680247\t6.00E+14\t118708\t-706761000\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tRR\tRR\t996.1141634\t-5950.445219\t118707\t118245724\t3.00E+14\t118728\t-706484460\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tQTINTERVAL\tQT INTERVAL\t641.9300274\t-5958.395113\t118675\t76181046\t3.00E+14\t118675\t-707112540\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tTWAVEAXIS\tT WAVE AXIS\t54.23621859\t-6850.677701\t117205\t6356756\t872621762\t117205\t-802933680\t1.34E+19
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tPRO\tPRODUCT ID  \t9999999\t48369.56031\t109836\t1.10E+12\t1.10E+19\t45350\t2193559560\t3.01E+14
+LAB BLOOD ORDERABLES\t472837\tLABMETB\tMETABOLIC PANEL, BASIC\tEGFR\tEGFR\t9744825.537\t4934.375429\t106672\t1.04E+12\t1.04E+19\t106273\t524390880\t7.54E+12
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tBE\tBASE EXCESS, ISTAT\t49708.54128\t104.666683\t104016\t5170483630\t5.17E+16\t40727\t4262760\t2.59E+11
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tPRINTERVAL\tP-R INTERVAL\t9663.957631\t-17476.68938\t102126\t986941337\t9.70E+15\t102126\t-1784824380\t1.34E+19
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tPAXIS\tP AXIS\t729.2279305\t-17550.51708\t101996\t74378332\t7.00E+14\t101996\t-1790082540\t1.34E+19
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tUBT\tUNIT BLOOD TYPE  \t9999999\t53957.13316\t99587\t9.96E+11\t9.96E+18\t40651\t2193411420\t3.01E+14
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tBD\tBASE DEFICIT, ISTAT\t29008.12189\t110.0159481\t87576\t2540415283\t2.54E+16\t38876\t4276980\t2.60E+11
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSLREV\tSLIDE REVIEW\t9999999\t12781.26503\t83020\t8.30E+11\t8.30E+18\t91966\t1175441820\t1.48E+14
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tDI\tISSUE DATE/TIME\t9999999\t35004.53417\t81212\t8.12E+11\t8.12E+18\t33832\t1184273400\t7.11E+13
+LAB BLOOD ORDERABLES\t473684\tLABTNI\tTROPONIN I\tTNI\tTROPONIN I\t6068284.056\t5474.691566\t73810\t4.48E+11\t4.48E+18\t63563\t347987820\t1.99E+13
+LAB BLOOD ORDERABLES\t472720\tLABLAC\tLACTIC ACID\tLAC\tLACTIC ACID\t41038.93064\t3538.959566\t68720\t2820195313\t2.82E+16\t61013\t215922540\t1.87E+13
+LAB BLOOD ORDERABLES\t471818\tLABCAI\tCALCIUM IONIZED, SERUM/PLASMA\tPHCAI\tPH\t31189.62099\t6777.892373\t67346\t2100496215\t2.10E+16\t66786\t452668320\t2.47E+13
+POINT OF CARE TESTING\t472691\tLABPCG3\tISTAT G3+, ARTERIAL\tPCTEMP\tBODY TEMPERATURE\t9999074.447\t81.39692935\t64896\t6.49E+11\t6.49E+18\t64742\t5269800\t2.61E+11
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tPRO\tPRODUCT ID  \t9999999\t58600.84284\t64588\t6.46E+11\t6.46E+18\t24987\t1464259260\t2.44E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMORPH\tMORPHOLOGY\t9999999\t12781.26503\t63806\t6.38E+11\t6.38E+18\t91966\t1175441820\t1.48E+14
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCL\tCHLORIDE, WHOLE BLD\t146382.236\t1585.313725\t62622\t9166748384\t9.16E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tGLU\tGLUCOSE, WHOLE BLOOD\t147051.4984\t1585.313725\t62622\t9208658930\t9.20E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tHCO3A\tHCO3\t146136.795\t1585.313725\t62622\t9151378378\t9.15E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tHCT\tHCT (EST)\t148700.2934\t1585.313725\t62622\t9311909772\t9.31E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tNA\tSODIUM, WHOLE BLOOD\t146730.5942\t1585.313725\t62622\t9188563271\t9.18E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tPCO2A\tPCO2 (A)\t146633.0306\t1585.313725\t62622\t9182453645\t9.18E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tPHA\tPH (A)\t145962.3437\t1585.659946\t62622\t9140453890\t9.14E+16\t30601\t48522780\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tPO2A\tPO2 (A)\t146960.2898\t1585.313725\t62622\t9202947269\t9.19E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tTCO2A\tTCO2\t145978.124\t1585.313725\t62622\t9141442084\t9.14E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tHGBBG\tTHB\t148682.0058\t1585.313725\t62621\t9310615883\t9.31E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tK\tPOTASSIUM, WHOLE BLD\t146280.7503\t1585.313725\t62621\t9160246862\t9.16E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tMETHB\tMETHEMOGLOBIN\t1849540.043\t1585.279257\t62621\t1.16E+11\t1.16E+18\t30599\t48507960\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCAION\tCALCIUM, IONIZED\t149314.3859\t1585.313725\t62620\t9350066845\t9.35E+16\t30600\t48510600\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCOHB\tCO-HEMOGLOBIN\t1015970.523\t1585.278123\t62620\t63620074135\t6.36E+17\t30598\t48506340\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tO2HBA\tO2-HEMOGLOBIN (A)\t149088.7911\t1585.278123\t62620\t9335940097\t9.33E+16\t30598\t48506340\t1.21E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tCTO2A\tCTO2 (A)\t150607.0448\t1585.253766\t62619\t9430862536\t9.43E+16\t30599\t48507180\t1.21E+13
+BLOOD BANK PRODUCT ORDERABLES\t473215\tLABBBPRBC\tRED BLOOD CELLS\tRBCSTA\tRBC PRODUCT - UNITS\t9999999\t5334.149557\t62186\t6.22E+11\t6.22E+18\t56888\t303449100\t1.01E+13
+LAB BLOOD ORDERABLES\t472838\tLABMETC\tMETABOLIC PANEL, COMPREHENSIVE\tEGFR\tEGFR\t9886231.855\t4813.322522\t60562\t5.99E+11\t5.99E+18\t60430\t290869080\t4.15E+12
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tALB\tALBUMIN, SER/PLAS\t72047.04964\t5017.78784\t55660\t4010138783\t4.01E+16\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t30707.77553\t5017.78784\t55660\t1709194786\t1.70E+16\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tALT\tALT (SGPT), SER/PLAS\t176905.2823\t5017.78784\t55660\t9846548013\t9.84E+16\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tDBIL\tCONJUGATED BILI\t3655408.779\t5017.78784\t55660\t2.03E+11\t2.03E+18\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tIBIL\tUNCONJUGATED BILIRUBIN\t4091628.05\t5017.78784\t55660\t2.28E+11\t2.28E+18\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tTBIL\tTOTAL BILIRUBIN\t3036473.221\t5017.78784\t55660\t1.69E+11\t1.69E+18\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tTP\tPROTEIN, TOTAL, SER/PLAS\t27494.42464\t5017.78784\t55660\t1530339675\t1.53E+16\t51018\t255997500\t1.63E+13
+LAB BLOOD ORDERABLES\t472517\tLABHFP\tHEPATIC FUNCTION PANEL A\tAST\tAST (SGOT), SER/PLAS\t158956.1394\t5017.78784\t55659\t8847339762\t8.84E+16\t51018\t255997500\t1.63E+13
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tXMR\tUNIT CROSSMATCH  \t9999999\t73589.74987\t55658\t5.57E+11\t5.57E+18\t23028\t1694624760\t2.83E+14
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tUBT\tUNIT BLOOD TYPE  \t9999999\t71687.08515\t54497\t5.45E+11\t5.45E+18\t20423\t1464065340\t2.44E+14
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tBD\tBASE DEFICIT (VT)\t675370.3172\t956.8123911\t51646\t34880175403\t3.49E+17\t25825\t24709680\t4.60E+12
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tRPT\tRPT\t9999999\t515574.2561\t49366\t4.94E+11\t4.94E+18\t45607\t23513795100\t1.33E+16
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSDES\tSPECIMEN\t9999999\t515574.2561\t49345\t4.93E+11\t4.93E+18\t45607\t23513795100\t1.33E+16
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSREQ\tCOMMENTS\t9999999\t515574.2561\t49123\t4.91E+11\t4.91E+18\t45607\t23513795100\t1.33E+16
+LAB BLOOD ORDERABLES\t473125\tLABK\tPOTASSIUM, SERUM/PLASMA\tK\tPOTASSIUM, SER/PLAS\t36773.45151\t25552.77048\t48138\t1770200409\t1.77E+16\t45779\t1169780280\t7.78E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tLEUKEST\tLEUK ESTERASE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tNITRITE\tNITRITE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSPG\tSPECIFIC GRAVITY\t245083.1258\t12240.77057\t46311\t11350044638\t1.13E+17\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUBLOOD\tBLOOD\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUGLU\tGLUCOSE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUKET\tKETONE\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUPH\tPH\t71263.36088\t12240.77057\t46311\t3300277506\t3.30E+16\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUPROT\tPROTEIN\t9999999\t12240.77057\t46311\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUWBC\tWBC, URINE\t9987691.108\t12241.10164\t46311\t4.63E+11\t4.63E+18\t32624\t399353700\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tURBC\tRBC, URINE\t9991793.563\t12241.10164\t46310\t4.63E+11\t4.63E+18\t32624\t399353700\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUCLAR\tCLARITY\t9999999\t12240.77057\t46261\t4.63E+11\t4.63E+18\t32625\t399355140\t4.80E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUCOL\tCOLOR\t9999999\t12248.77214\t46028\t4.60E+11\t4.60E+18\t32626\t399628440\t4.81E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSPECMN\tSPECIMEN\t9999999\t12248.77214\t45966\t4.60E+11\t4.60E+18\t32626\t399628440\t4.81E+13
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tAS3\tANTIBODY SCREEN\t9997811.823\t14738.5127\t45721\t4.57E+11\t4.57E+18\t41888\t617366820\t1.01E+15
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tRPT\tRPT\t9999999\t505173.5304\t43898\t4.39E+11\t4.39E+18\t41511\t20970258420\t1.13E+16
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSDES\tSPECIMEN\t9999999\t505173.5304\t43866\t4.39E+11\t4.39E+18\t41511\t20970258420\t1.13E+16
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSREQ\tCOMMENTS\t9999999\t505173.5304\t43658\t4.37E+11\t4.37E+18\t41511\t20970258420\t1.13E+16
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUCMT\tURINE COMMENT\t9999999\t12202.43692\t42828\t4.28E+11\t4.28E+18\t32459\t396078900\t4.72E+13
+LAB BLOOD ORDERABLES\t472035\tLABMB\tCK, MB (MASS)\tCKMB\tCK, MB (MASS)\t1845233.473\t5727.642436\t40071\t73940350501\t7.39E+17\t33594\t192414420\t1.20E+13
+MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tRPT\tRPT\t9999999\t111100.8005\t39655\t3.97E+11\t3.97E+18\t23935\t2659197660\t3.68E+14
+MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tSDES\tSPECIMEN\t9999999\t111100.8005\t39646\t3.96E+11\t3.96E+18\t23935\t2659197660\t3.68E+14
+MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tSREQ\tCOMMENTS\t9999999\t111100.8005\t39556\t3.96E+11\t3.96E+18\t23935\t2659197660\t3.68E+14
+LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tFK506\tTACROLIMUS (FK506)\t566562.7019\t12669.77384\t39396\t22320304202\t2.23E+17\t36876\t467210580\t2.09E+13
+LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tTACR2\tDATE & TIME OF LAST DOSE\t9999999\t12669.77384\t39382\t3.94E+11\t3.94E+18\t36876\t467210580\t2.09E+13
+LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tTACR3\tDOSAGE\t9999999\t12669.77384\t39381\t3.94E+11\t3.94E+18\t36876\t467210580\t2.09E+13
+LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tFK506\tPEAK, TROUGH, OR RANDOM\t9999999\t12669.77384\t39351\t3.94E+11\t3.94E+18\t36876\t467210580\t2.09E+13
+LAB BLOOD ORDERABLES\t497498\tLABFK506L\tTACROLIMUS (FK506)\tTACR4\tADMINISTRATION ROUTE\t9999999\t12669.77384\t39321\t3.93E+11\t3.93E+18\t36876\t467210580\t2.09E+13
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tDI\tISSUE DATE/TIME\t9999999\t40258.81988\t39264\t3.93E+11\t3.93E+18\t14846\t597682440\t4.70E+13
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tXMR\tUNIT CROSSMATCH  \t9999999\t90201.95793\t37704\t3.77E+11\t3.77E+18\t14168\t1277981340\t2.36E+14
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tPHV\tPH (V)\t197753.2956\t3013.455443\t37624\t7440269992\t7.44E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCTO2V\tCTO2 (V)\t199355.386\t3013.455443\t37623\t7500347687\t7.50E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tGLU\tGLUCOSE, WHOLE BLOOD\t211719.6446\t3013.455443\t37623\t7965528187\t7.96E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tHCO3A\tHCO3\t197775.0341\t3013.455443\t37623\t7440890107\t7.44E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tHCT\tHCT (EST)\t198044.9434\t3013.455443\t37623\t7451044907\t7.45E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tK\tPOTASSIUM, WHOLE BLD\t210779.3697\t3013.455443\t37623\t7930152228\t7.93E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tPCO2V\tPCO2 (V)\t197796.3089\t3013.455443\t37623\t7441690529\t7.44E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tPO2V\tPO2 (V)\t198064.0877\t3013.455443\t37623\t7451765170\t7.45E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tTCO2A\tTCO2\t198042.1253\t3013.455443\t37623\t7450938880\t7.45E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCAION\tCALCIUM, IONIZED\t208921.3643\t3013.465359\t37622\t7860039569\t7.86E+16\t31610\t95255640\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCL\tCHLORIDE, WHOLE BLD\t198392.6191\t3013.452072\t37622\t7463927116\t7.46E+16\t31610\t95255220\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tHGBBG\tTHB\t197765.5597\t3013.529896\t37622\t7440335886\t7.44E+16\t31610\t95257680\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tMETHB\tMETHEMOGLOBIN\t2205890.865\t3013.452072\t37622\t82990026136\t8.30E+17\t31610\t95255220\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tNA\tSODIUM, WHOLE BLOOD\t198156.646\t3013.455443\t37622\t7455049337\t7.45E+16\t31611\t95258340\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tO2SATV\tO2 SATURATION (V)\t197828.3926\t3013.452072\t37622\t7442699788\t7.44E+16\t31610\t95255220\t1.43E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tCOHB\tCO-HEMOGLOBIN\t948141.9177\t3013.452072\t37621\t35670047086\t3.57E+17\t31610\t95255220\t1.43E+13
+LAB BLOOD ORDERABLES\t472035\tLABMB\tCK, MB (MASS)\tCK\tCK, TOTAL\t165875.8912\t5712.49558\t37593\t6235772379\t6.22E+16\t31672\t180926160\t1.19E+13
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tARG\tABO/RH (AUTOMATION)\t9999999\t15630.74851\t36492\t3.65E+11\t3.65E+18\t42404\t662806260\t1.45E+15
+MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tRPT\tRPT\t9999999\t214873.0474\t34442\t3.44E+11\t3.44E+18\t21871\t4699488420\t1.74E+15
+MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tSDES\tSPECIMEN\t9999999\t214873.0474\t34414\t3.44E+11\t3.44E+18\t21871\t4699488420\t1.74E+15
+MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tSREQ\tCOMMENTS\t9999999\t214873.0474\t34376\t3.44E+11\t3.44E+18\t21871\t4699488420\t1.74E+15
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYM\tLYMPHOCYTES\t1786.385644\t19050.56618\t33951\t60649579\t6.00E+14\t32799\t624839520\t1.05E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTAB\tNEUT,ABS (MAN DIFF)\t2082.920204\t18924.54144\t33746\t70290225.2\t7.00E+14\t32602\t616977900\t9.12E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUT\tSEG NEUTROPHILS\t1826.468131\t18714.30151\t33700\t61551976\t6.00E+14\t32556\t609262800\t9.00E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMAB\tLYM, ABS (MAN DIFF)\t1490.242736\t19186.66379\t33606\t50081097.4\t5.00E+14\t32474\t623067720\t1.05E+14
+LAB BLOOD ORDERABLES\t472730\tLABLDH\tLDH TOTAL, SERUM / PLASMA\tLDH\tLDH, TOTAL, SER/PLAS\t177187.6869\t6231.287376\t33443\t5925687812\t5.91E+16\t31203\t194434860\t1.89E+13
+LAB BLOOD ORDERABLES\t472500\tLABHCTX\tHEMATOCRIT\tHCT\tHEMATOCRIT\t45493.55716\t4322.738756\t32773\t1490960349\t1.49E+16\t31350\t135517860\t3.11E+12
+LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANCOTROUGH\tVANCOMYCIN, TROUGH\t115011.3483\t8433.120947\t32610\t3750520068\t3.75E+16\t28376\t239298240\t1.23E+13
+LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANPR3\tDOSAGE\t9999999\t8433.377974\t32598\t3.26E+11\t3.26E+18\t28375\t239297100\t1.23E+13
+LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANPR2\tDATE & TIME OF LAST DOSE\t9999999\t8433.377974\t32591\t3.26E+11\t3.26E+18\t28375\t239297100\t1.23E+13
+LAB BLOOD ORDERABLES\t497520\tLABVANPRL\tVANCOMYCIN TROUGH LEVEL\tVANPR4\tADMINISTRATION ROUTE\t9999999\t8433.377974\t32583\t3.26E+11\t3.26E+18\t28375\t239297100\t1.23E+13
+LAB BLOOD ORDERABLES\t472035\tLABMB\tCK, MB (MASS)\tCKMBRI\tCKMB %RELATIVE INDEX\t209292.3761\t5689.459183\t31392\t6570106271\t6.57E+16\t26423\t150332580\t1.13E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tMUCUS\tMUCOUS THREADS\t9999999\t12188.69384\t31326\t3.13E+11\t3.13E+18\t22547\t274818480\t3.21E+13
+STOOL ORDERABLES\t633173\tLABARVI\tBLOOD TYPE VERIFICATION\tARV\tBLOOD TYPE VERIFICATION\t9999999\t5223.284946\t30914\t3.09E+11\t3.09E+18\t15982\t83478540\t6.92E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONO\tMONOCYTES\t2601.147151\t18907.63289\t30873\t80305216\t8.00E+14\t29800\t563447460\t9.63E+13
+LAB BLOOD ORDERABLES\t473343\tLABNA\tSODIUM, SERUM / PLASMA\tNA\tSODIUM, SER/PLAS\t48039.57747\t4680.15887\t30689\t1474286593\t1.47E+16\t29458\t137868120\t1.23E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOAB\tMONO, ABS (MAN DIFF)\t2296.710912\t19040.88321\t30490\t70026715.7\t7.00E+14\t29438\t560525520\t9.62E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBAND\tBAND NEUTROPHILS\t2329.320229\t18890.95238\t30244\t70447961\t7.00E+14\t29232\t552220320\t8.22E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSQEP\tSQUAMOUS CELLS\t9999999\t12653.6781\t28260\t2.83E+11\t2.83E+18\t20168\t255199380\t3.17E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tLEUKEST\tLEUK ESTERASE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tNITRITE\tNITRITE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSPG\tSPECIFIC GRAVITY\t210485.048\t12204.11378\t27413\t5770026621\t5.77E+16\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUBLOOD\tBLOOD\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUGLU\tGLUCOSE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUKET\tKETONE\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUPH\tPH\t68586.53792\t12204.11378\t27413\t1880162764\t1.88E+16\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUPROT\tPROTEIN\t9999999\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tURBC\tRBC, URINE\t9982124.559\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUWBC\tWBC, URINE\t9985772.4\t12204.11378\t27413\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUCLAR\tCLARITY\t9999999\t12204.11378\t27376\t2.74E+11\t2.74E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tPRN\tCULTURE SENT PRN\t9999999\t12204.11378\t27311\t2.73E+11\t2.73E+18\t18562\t226532760\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUCOL\tCOLOR\t9999999\t12204.12864\t27244\t2.72E+11\t2.72E+18\t18563\t226545240\t3.05E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSPECMN\tSPECIMEN\t9999999\t12204.12864\t27222\t2.72E+11\t2.72E+18\t18563\t226545240\t3.05E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tBD\tBASE DEFICIT (VT)\t1868434.464\t2873.790776\t26070\t48710086478\t4.87E+17\t22225\t63870000\t5.38E+12
+LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tDDIMER\tD-DIMER\t1490829.278\t5220.076785\t25741\t38375436433\t3.83E+17\t25005\t130528020\t5.95E+12
+LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tFIBRINOGEN\tFIBRINOGEN\t191880.8937\t5220.076785\t25741\t4939206084\t4.93E+16\t25005\t130528020\t5.95E+12
+LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tPTT\tPART. THROMBOPLASTIN TIME\t204770.0749\t5220.076785\t25741\t5270986498\t5.27E+16\t25005\t130528020\t5.95E+12
+LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tTT\tTHROMBIN TIME\t398603.0482\t5220.076785\t25741\t10260441064\t1.03E+17\t25005\t130528020\t5.95E+12
+LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tPT\tPROTHROMBIN TIME\t174842.8487\t5220.076785\t25740\t4500454927\t4.50E+16\t25005\t130528020\t5.95E+12
+LAB BLOOD ORDERABLES\t472149\tLABDIC\tDIC SCREEN\tINR\tINR\t175222.0351\t5220.076785\t25739\t4510039962\t4.51E+16\t25005\t130528020\t5.95E+12
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUCMT\tURINE COMMENT\t9999999\t12154.83745\t25367\t2.54E+11\t2.54E+18\t18456\t224329680\t3.03E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tBE\tBASE EXCESS (VT)\t4646770.528\t1857.906045\t25196\t1.17E+11\t1.17E+18\t18711\t34763280\t9.53E+12
+BLOOD BANK PRODUCT ORDERABLES\t473112\tLABBBPPLT\tPLATELET APHERESIS PRODUCT\tPLT\tPLATELETS\t9999999\t5843.234224\t24904\t2.49E+11\t2.49E+18\t23913\t139729260\t4.18E+12
+POINT OF CARE TESTING\t572612\tLABPCTNI\tISTAT TROPONIN I\tTNI\tTROPONIN I, POCT\t28655.88736\t65.12903226\t23730\t680004207.2\t6.80E+15\t9300\t605700\t13095428400
+LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN ACTIVITY LEVEL\tHEPAR\tHEPARIN ACTIVITY LEVEL\t1807229.044\t27866.90651\t23406\t42300002996\t4.23E+17\t15371\t428342220\t2.72E+13
+LAB BLOOD ORDERABLES\t472505\tLABA1C\tHEMOGLOBIN A1C\tA1C\tHEMOGLOBIN A1C\t150299.0871\t40031.36354\t22556\t3390146210\t3.39E+16\t19816\t793261500\t6.24E+13
+LAB BLOOD ORDERABLES\t473726\tLABURIC\tURIC ACID, SERUM / PLASMA\tURIC\tURIC ACID, SER/PLAS\t2999913.222\t5697.145943\t22354\t67060060172\t6.71E+17\t21296\t121326420\t1.36E+13
+POINT OF CARE TESTING\t572612\tLABPCTNI\tISTAT TROPONIN I\tPCCOM\tPOCT COMMENT\t9999999\t65.12147925\t22269\t2.23E+11\t2.23E+18\t9302\t605760\t13095432000
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tBE\tBASE EXCESS (VT)\t2932824.691\t3227.918068\t22180\t65050051638\t6.50E+17\t19919\t64296900\t1.09E+13
+LAB BLOOD ORDERABLES\t472752\tLABLIPS\tLIPASE\tLIPASE\tLIPASE\t708616.1884\t4931.204285\t22010\t15596642307\t1.56E+17\t19605\t96676260\t1.17E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t9999999\t12181.96017\t20619\t2.06E+11\t2.06E+18\t44537\t542547960\t6.54E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tMUCUS\tMUCOUS THREADS\t9999999\t11973.86042\t19771\t1.98E+11\t1.98E+18\t13584\t162652920\t2.12E+13
+LAB BLOOD ORDERABLES\t473689\tLABTSH\tTSH\tTSH\tTSH\t129967.0501\t34267.06903\t19390\t2520061101\t2.52E+16\t16950\t580826820\t3.52E+13
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tPRO\tPRODUCT ID  \t9999999\t56238.68993\t19372\t1.94E+11\t1.94E+18\t7679\t431856900\t6.51E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUBACT\tBACTERIA\t9999999\t12201.33814\t18816\t1.88E+11\t1.88E+18\t32463\t396092040\t4.72E+13
+LAB BLOOD ORDERABLES\t890505\tLABLACWB\tLACTATE, WHOLE BLOOD\tLACWBL\tLACWBL\t140078.6194\t1965.353905\t17776\t2490037538\t2.49E+16\t17759\t34902720\t6.29E+11
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSQEP\tSQUAMOUS CELLS\t9999999\t12425.95286\t16610\t1.66E+11\t1.66E+18\t11329\t140773620\t1.68E+13
+BLOOD BANK PRODUCT ORDERABLES\t472423\tLABBBPFFP\tFRESH FROZEN PLASMA PRODUCT\tFFP\tFRESH FROZEN UNITS\t9999999\t6488.406965\t16604\t1.66E+11\t1.66E+18\t14645\t95022720\t3.97E+12
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tPRO\tPRODUCT ID  \t9999999\t56595.15599\t16586\t1.66E+11\t1.66E+18\t6635\t375508860\t5.68E+13
+LAB BLOOD ORDERABLES\t472104\tLABCK\tCREATINE KINASE, TOTAL\tCK\tCK, TOTAL\t345122.1544\t5500.152407\t16129\t5566475228\t5.55E+16\t14435\t79394700\t1.67E+13
+LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN, UNFRACTIONATED, ANTI XA\tHEPAR\tHEPARIN ACTIVITY LEVEL\t2197946.709\t21802.13096\t15974\t35110000723\t3.51E+17\t15974\t348267240\t2.23E+13
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tUBT\tUNIT BLOOD TYPE  \t9999999\t70831.65354\t15817\t1.58E+11\t1.58E+18\t6096\t431789760\t6.51E+13
+LAB BLOOD ORDERABLES\t471806\tLABCRP\tC - REACTIVE PROTEIN\tCRP\tC-REACTIVE PROTEIN\t853359.1853\t6781.996587\t15445\t13180132618\t1.32E+17\t14064\t95382000\t4.32E+12
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tHCO3V\tHCO3 (V), ISTAT\t27645.83044\t122.2070286\t15206\t420382497.7\t4.20E+15\t7313\t893700\t61382998800
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tLAC\tLACTATE, ISTAT\t88125.09923\t122.2070286\t15206\t1340030259\t1.34E+16\t7313\t893700\t61382998800
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPCO2V\tPCO2 (V), ISTAT\t27662.70585\t122.2070286\t15206\t420639105.2\t4.20E+15\t7313\t893700\t61382998800
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPHV\tPH (V), ISTAT\t26312.77132\t122.2070286\t15206\t400112000.7\t4.00E+15\t7313\t893700\t61382998800
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tTCO2V\tTCO2 (V), ISTAT\t36853.96791\t122.2070286\t15206\t560401436\t5.60E+15\t7313\t893700\t61382998800
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tO2SATV\tO2 SATURATION, ISTAT\t38862.82098\t122.2237418\t15205\t590909193\t5.90E+15\t7312\t893700\t61382998800
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPO2V\tPO2 (V), ISTAT\t23056.01565\t122.2237418\t15205\t350566718\t3.50E+15\t7312\t893700\t61382998800
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMETA\tMETAMYELOCYTES\t4048.3012\t19742.12626\t14834\t60052500\t6.00E+14\t14335\t283003380\t4.31E+13
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t122.2070286\t14702\t1.47E+11\t1.47E+18\t7313\t893700\t61382998800
+LAB BLOOD ORDERABLES\t472376\tLABFIB\tFIBRINOGEN\tFIBRINOGEN\tFIBRINOGEN\t220689.6763\t5646.521628\t13839\t3054124430\t3.05E+16\t11721\t66182880\t9.73E+12
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tHCO3A\tHCO3 (A), ISTAT\t36983.54265\t53.40860215\t13529\t500350348.5\t5.00E+15\t5580\t298020\t631724400
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPCO2A\tPCO2 (A), ISTAT\t36261.24436\t53.40860215\t13529\t490578374.9\t4.90E+15\t5580\t298020\t631724400
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPHA\tPH (A), ISTAT\t32530.0929\t53.40860215\t13529\t440099626.9\t4.40E+15\t5580\t298020\t631724400
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPO2A\tPO2 (A), ISTAT\t28940.13342\t53.40860215\t13529\t391531065\t3.90E+15\t5580\t298020\t631724400
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tTCO2A\tTCO2, ISTAT\t82072.92158\t53.40860215\t13529\t1110364556\t1.11E+16\t5580\t298020\t631724400
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tO2SATA\tO2 SATURATION, ISTAT\t42968.76419\t53.40860215\t13528\t581281442\t5.80E+15\t5580\t298020\t631724400
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tUBT\tUNIT BLOOD TYPE  \t9999999\t71882.52728\t13445\t1.34E+11\t1.34E+18\t5223\t375442440\t5.68E+13
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tHCT\tHCT(CALC), ISTAT\t104695.2693\t53.47849559\t13376\t1400403922\t1.40E+16\t5557\t297180\t631674000
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tHGB\tHGB(CALC), ISTAT\t104675.3324\t53.47849559\t13376\t1400137246\t1.40E+16\t5557\t297180\t631674000
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tK\tPOTASSIUM, ISTAT\t103173.9246\t53.47849559\t13376\t1380054415\t1.38E+16\t5557\t297180\t631674000
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tNA\tSODIUM, ISTAT\t91345.31153\t53.47849559\t13376\t1221834887\t1.22E+16\t5557\t297180\t631674000
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tCAION\tCALCIUM,ION, ISTAT\t101698.5758\t53.47849559\t13373\t1360015055\t1.36E+16\t5557\t297180\t631674000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMYEL\tMYELOCYTES\t3031.974336\t21081.17343\t13209\t40049349\t4.00E+14\t12783\t269480640\t4.07E+13
+LAB BLOOD ORDERABLES\t473039\tLABOSM\tOSMOLALITY, SERUM\tOSMOL\tOSMOLALITY, SERUM\t79961.60193\t6211.214496\t13181\t1053973875\t1.05E+16\t12252\t76099800\t4.05E+12
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t53.40860215\t13041\t1.30E+11\t1.30E+18\t5580\t298020\t631724400
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOS\tEOSINOPHILS\t3129.826481\t19163.30361\t12794\t40043000\t4.00E+14\t12338\t236436840\t4.68E+13
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tSI\t<<STATUS>>  \t9999999\t48367.42856\t12761\t1.28E+11\t1.28E+18\t45352\t2193559620\t3.01E+14
+LAB BLOOD ORDERABLES\t473019\tLABNTBNP\tNT - PROBNP\tBNP\tNT-PROBNP\t753794.1482\t5682.800262\t12696\t9570170505\t9.52E+16\t10699\t60800280\t2.00E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSAB\tEOS, ABS (MAN DIFF)\t3162.963425\t19285.44948\t12648\t40005161.4\t4.00E+14\t12203\t235340340\t4.68E+13
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tCDTR\tSTOOL C.DIFF TOXIN B RESULT\t9999199.715\t38913.73397\t12506\t1.25E+11\t1.25E+18\t7954\t309519840\t6.07E+13
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tHCO3V\tHCO3 (V), ISTAT\t27576.65803\t82.03966006\t12341\t340323536.7\t3.40E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tO2SATV\tO2 SATURATION, ISTAT\t42195.62053\t82.03966006\t12341\t520736153\t5.20E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPCO2V\tPCO2 (V), ISTAT\t27593.21517\t82.03966006\t12341\t340527868.4\t3.40E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPHV\tPH (V), ISTAT\t27557.81298\t82.03966006\t12341\t340090970\t3.40E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPO2V\tPO2 (V), ISTAT\t31634.56454\t82.03966006\t12341\t390402161\t3.90E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tTCO2V\tTCO2 (V), ISTAT\t39732.48594\t82.03966006\t12341\t490338609\t4.90E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tCAION\tCALCIUM,ION, ISTAT\t66478.63084\t82.03966006\t12335\t820013911.4\t8.20E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tHCT\tHCT(CALC), ISTAT\t43809.6655\t82.03966006\t12335\t540392224\t5.40E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tHGB\tHGB(CALC), ISTAT\t43788.67581\t82.03966006\t12335\t540133316.1\t5.40E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tK\tPOTASSIUM, ISTAT\t56753.10045\t82.03966006\t12335\t700049494.1\t7.00E+15\t5295\t434400\t13984128000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tNA\tSODIUM, ISTAT\t36617.91552\t82.03966006\t12335\t451681988\t4.50E+15\t5295\t434400\t13984128000
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tCDTSP\tSPECIMEN\t9999999\t38913.73397\t12175\t1.22E+11\t1.22E+18\t7954\t309519840\t6.07E+13
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t82.03966006\t12126\t1.21E+11\t1.21E+18\t5295\t434400\t13984128000
+MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tRPT\tRPT\t9999999\t529714.7807\t12120\t1.21E+11\t1.21E+18\t5108\t2705783100\t2.15E+15
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUBACT\tBACTERIA\t9999999\t12153.70571\t11863\t1.19E+11\t1.19E+18\t18458\t224333100\t3.03E+13
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tSI\t<<STATUS>>  \t9999999\t58598.49768\t11856\t1.19E+11\t1.19E+18\t24988\t1464259260\t2.44E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNRBC\tNRBC/100WBC\t2553.212766\t21228.10716\t11797\t30120251\t3.00E+14\t11422\t242467440\t3.46E+13
+MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tSDES\tSPECIMEN\t9999999\t529714.7807\t11774\t1.18E+11\t1.18E+18\t5108\t2705783100\t2.15E+15
+LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tCO2\tCO2, SER/PLAS\t25122.39818\t3637.746454\t11555\t290289311\t2.90E+15\t11351\t41292060\t5.80E+12
+LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tCR\tCREATININE, SER/PLAS\t25098.34983\t3637.746454\t11555\t290011432.3\t2.90E+15\t11351\t41292060\t5.80E+12
+LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tGLU\tGLUCOSE, SER/PLAS\t25255.13025\t3637.746454\t11555\t291823030\t2.90E+15\t11351\t41292060\t5.80E+12
+LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tK\tPOTASSIUM, SER/PLAS\t27697.6907\t3637.746454\t11555\t320046816\t3.20E+15\t11351\t41292060\t5.80E+12
+LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tNA\tSODIUM, SER/PLAS\t25233.32471\t3637.746454\t11555\t291571067\t2.90E+15\t11351\t41292060\t5.80E+12
+MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tRPT\tRPT\t9999999\t43014.06617\t11523\t1.15E+11\t1.15E+18\t5622\t241825080\t7.14E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tALB\tALBUMIN, SER/PLAS\t43598.57743\t4902.321525\t11469\t500032084.5\t5.00E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t26289.39951\t4902.321525\t11469\t301513123\t3.00E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tALT\tALT (SGPT), SER/PLAS\t47147.82248\t4902.321525\t11469\t540738376\t5.40E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tAST\tAST (SGOT), SER/PLAS\t720236.1855\t4902.321525\t11469\t8260388812\t8.26E+16\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tCR\tCREATININE, SER/PLAS\t25286.56308\t4902.321525\t11469\t290011592\t2.90E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tGLU\tGLUCOSE, SER/PLAS\t26310.5269\t4902.321525\t11469\t301755433\t3.00E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tK\tPOTASSIUM, SER/PLAS\t28777.22268\t4902.321525\t11469\t330045966.9\t3.30E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tMG\tMAGNESIUM, SER/PLAS\t25287.26628\t4902.321525\t11469\t290019657\t2.90E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tNA\tSODIUM, SER/PLAS\t25421.77984\t4902.321525\t11469\t291562393\t2.90E+15\t11122\t54523620\t1.22E+13
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tTBIL\tTOTAL BILIRUBIN\t4981254.022\t4902.321525\t11469\t57130002376\t5.71E+17\t11122\t54523620\t1.22E+13
+MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tSDES\tSPECIMEN\t9999999\t43014.06617\t11467\t1.15E+11\t1.15E+18\t5622\t241825080\t7.14E+13
+MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tSREQ\tCOMMENTS\t9999123.42\t43014.06617\t11421\t1.14E+11\t1.14E+18\t5622\t241825080\t7.14E+13
+MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tSREQ\tCOMMENTS\t9999999\t529714.7807\t11420\t1.14E+11\t1.14E+18\t5108\t2705783100\t2.15E+15
+MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tRPT\tRPT\t9999999\t187056.7953\t10620\t1.06E+11\t1.06E+18\t6247\t1168543800\t5.85E+14
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tDI\tISSUE DATE/TIME\t9999999\t43643.25891\t10570\t1.06E+11\t1.06E+18\t4210\t183738120\t1.55E+13
+MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tSDES\tSPECIMEN\t9999999\t187056.7953\t10555\t1.06E+11\t1.06E+18\t6247\t1168543800\t5.85E+14
+MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tRPT\tRPT\t9999999\t1820387.097\t10531\t1.05E+11\t1.05E+18\t5250\t9557032260\t1.81E+16
+MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tSREQ\tCOMMENTS\t9999999\t187056.7953\t10523\t1.05E+11\t1.05E+18\t6247\t1168543800\t5.85E+14
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tWBC\tWBC\t160340.871\t14170.48095\t10416\t1670110512\t1.67E+16\t9606\t136121640\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tHCT\tHEMATOCRIT\t161333.5794\t14170.65806\t10415\t1680289230\t1.68E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tHGB\tHEMOGLOBIN\t187239.3558\t14170.65806\t10415\t1950097890\t1.95E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMCH\tMCH\t223746.2447\t14170.65806\t10415\t2330317138\t2.33E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMCHC\tMCHC\t201665.5509\t14170.65806\t10415\t2100346713\t2.10E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMCV\tMCV\t200761.8964\t14170.65806\t10415\t2090935151\t2.09E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPLT\tPLATELET COUNT\t242108.7965\t14170.65806\t10415\t2521563116\t2.52E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tRBC\tRBC\t160348.6899\t14170.65806\t10415\t1670031605\t1.67E+16\t9604\t136095000\t1.84E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tRDW\tRDW\t213170.9987\t14170.65806\t10415\t2220175951\t2.22E+16\t9604\t136095000\t1.84E+13
+MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tSDES\tSPECIMEN\t9999999\t1820387.097\t10224\t1.02E+11\t1.02E+18\t5250\t9557032260\t1.81E+16
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tSLREV\tSLIDE REVIEW\t9999999\t14175.70952\t10207\t1.02E+11\t1.02E+18\t9598\t136058460\t1.84E+13
+MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tSREQ\tCOMMENTS\t9999999\t1820387.097\t10081\t1.01E+11\t1.01E+18\t5250\t9557032260\t1.81E+16
+POINT OF CARE TESTING\t472684\tLABPCCR\tISTAT CREATININE\tCR\tCREATININE,ISTAT\t34011.51997\t83.9487909\t9997\t340013165.1\t3.40E+15\t3515\t295080\t7883964000
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tAMPHET\tAMPHETAMINE\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tBARB\tBARBITURATES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tBENZO\tBENZODIAZEPINES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tCOCAINE\tCOCAINE METABOLITES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tMETHADONE\tMETHADONE\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tOPIATE\tOPIATES\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tPCP\tPCP\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tTCA\tTRICYCLICS\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tTHC\tTHC\t9999999\t11906.82203\t9938\t99379990062\t9.94E+17\t7552\t89920320\t1.01E+13
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tXMR\tUNIT CROSSMATCH  \t9999999\t92348.14473\t9855\t98549990145\t9.85E+17\t3897\t359880720\t6.18E+13
+LAB BLOOD ORDERABLES\t473107\tLABPLTS\tPLATELET COUNT\tPLT\tPLATELET COUNT\t206199.9849\t7350.399083\t9852\t2031482251\t2.03E+16\t8720\t64095480\t6.25E+12
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tFIO2\tFIO2\t2085609.217\t51.44734223\t9839\t20520309081\t2.05E+17\t5023\t258420\t523854000
+LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO\tLIDOCAINE\t674275.7855\t6465.464807\t9729\t6560029117\t6.56E+16\t9391\t60717180\t1.51E+13
+LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO2\tDATE & TIME OF LAST DOSE\t9999999\t6465.464807\t9727\t97269990273\t9.73E+17\t9391\t60717180\t1.51E+13
+LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO3\tDOSAGE\t9999999\t6465.464807\t9726\t97259990274\t9.73E+17\t9391\t60717180\t1.51E+13
+LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLIDO4\tADMINISTRATION ROUTE\t9999999\t6465.464807\t9722\t97219990278\t9.72E+17\t9391\t60717180\t1.51E+13
+LAB BLOOD ORDERABLES\t497490\tLABLIDOL\tLIDOCAINE\tLI\tPEAK, TROUGH, OR RANDOM\t9999999\t6465.464807\t9718\t97179990282\t9.72E+17\t9391\t60717180\t1.51E+13
+POINT OF CARE TESTING\t472684\tLABPCCR\tISTAT CREATININE\tPCCOM\tPOCT COMMENT\t9999999\t83.9487909\t9431\t94309990569\t9.43E+17\t3515\t295080\t7883964000
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tBE\tBASE EXCESS, ISTAT\t44597.17893\t183.4767726\t8970\t400036695\t4.00E+15\t4090\t750420\t61224577200
+LAB BLOOD ORDERABLES\t473413\tLABFT4\tT4, FREE\tFT4\tFREE T4\t73125.24129\t34097.38384\t8889\t650010269.8\t6.50E+15\t7576\t258321780\t1.68E+13
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tDI\tISSUE DATE/TIME\t9999999\t44949.3771\t8845\t88449991155\t8.84E+17\t3564\t160199580\t1.40E+13
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tUPH\tPH URINE\t317900.0446\t3535.3457\t8808\t2800063593\t2.80E+16\t8895\t31446900\t1.76E+14
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONOAB\tMONO, ABS\t979847.5988\t10963.69771\t8634\t8460004168\t8.46E+16\t7913\t86755740\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUTAB\tNEUT, ABS\t978695.1421\t10969.37832\t8634\t8450053857\t8.45E+16\t7914\t86811660\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASOAB\tBASOS, ABS\t979960.5426\t10961.56471\t8633\t8459999364\t8.46E+16\t7912\t86727900\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOSAB\tEOS, ABS\t979960.661\t10961.56471\t8633\t8460000386\t8.46E+16\t7912\t86727900\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYMAB\tLYM, ABS\t977871.3995\t10961.56471\t8631\t8440008049\t8.44E+16\t7912\t86727900\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUT\tNEUT, %\t969566.6156\t10959.2366\t8623\t8360572926\t8.36E+16\t7912\t86709480\t4.75E+12
+LAB BLOOD ORDERABLES\t473132\tLABYPREAL\tPREALBUMIN\tPREALBUMIN\tPREALBUMIN\t92952.96224\t21450.81694\t8608\t800139099\t8.00E+15\t7932\t170147880\t1.77E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASO\tBASO, %\t951772.447\t10959.28707\t8605\t8190001906\t8.19E+16\t7911\t86698920\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOS\tEOS, %\t951773.8582\t10959.28707\t8605\t8190014050\t8.19E+16\t7911\t86698920\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYM\tLYM, %\t950624.5273\t10959.2366\t8605\t8180124058\t8.18E+16\t7912\t86709480\t4.75E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONO\tMONO, %\t950617.2635\t10967.54803\t8605\t8180061552\t8.18E+16\t7912\t86775240\t4.76E+12
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tXMR\tUNIT CROSSMATCH  \t9999999\t91794.21898\t8549\t85489991451\t8.55E+17\t3425\t314395200\t5.39E+13
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tHCO3V\tHCO3 (V), ISTAT\t46934.61223\t148.6122216\t8527\t400211438.5\t4.00E+15\t3502\t520440\t31147351200
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tO2SATV\tO2 SATURATION, ISTAT\t59875.92776\t148.6122216\t8527\t510562036\t5.10E+15\t3502\t520440\t31147351200
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPCO2V\tPCO2 (V), ISTAT\t46955.68538\t148.6122216\t8527\t400391129.2\t4.00E+15\t3502\t520440\t31147351200
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPHV\tPH (V), ISTAT\t45744.37367\t148.6122216\t8527\t390062274.3\t3.90E+15\t3502\t520440\t31147351200
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPO2V\tPO2 (V), ISTAT\t45778.09077\t148.6122216\t8527\t390349780\t3.90E+15\t3502\t520440\t31147351200
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tTCO2V\tTCO2 (V), ISTAT\t53972.44482\t148.6122216\t8527\t460223037\t4.60E+15\t3502\t520440\t31147351200
+LAB BLOOD ORDERABLES\t473682\tLABTRIG\tTRIGLYCERIDES, SERUM / PLASMA\tTGL\tTRIGLYCERIDE, SER/PLAS\t76476.81733\t5007.791365\t8518\t651429530\t6.50E+15\t8014\t40132440\t7.29E+11
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUPH\tPH URINE\t39946.08299\t-53486.48507\t8513\t340061004.5\t3.40E+15\t8774\t-469290420\t7.61E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUBLOOD\tBLOOD URINE\t9903005.814\t-53502.95385\t8351\t82700001552\t8.27E+17\t8775\t-469488420\t7.61E+15
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t148.6122216\t8269\t82689991731\t8.27E+17\t3502\t520440\t31147351200
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUWBC\tLEUKOCYTES\t9975578.983\t-53503.42069\t8190\t81699991868\t8.17E+17\t8776\t-469546020\t7.61E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUPROT\tPROTEIN URINE\t9957232.755\t-53483.68133\t8184\t81489992870\t8.15E+17\t8774\t-469265820\t7.61E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tNITRITE\tNITRITE\t9992656.86\t-53502.95385\t8172\t81659991864\t8.17E+17\t8775\t-469488420\t7.61E+15
+MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t439595.3706\t8168\t81679991832\t8.17E+17\t4290\t1885864140\t2.06E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUKET\tKETONES\t9963261.452\t-53515.82175\t8166\t81359993016\t8.14E+17\t8774\t-469547820\t7.61E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tUGLU\tGLUCOSE URINE\t9357934.086\t-53502.95385\t8161\t76370100076\t7.64E+17\t8775\t-469488420\t7.61E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tEXPDATE\tEXP DATE\t9797353.857\t-53502.95385\t8144\t79789649810\t7.98E+17\t8775\t-469488420\t7.61E+15
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tSPG\tSPECIFIC GRAVITY\t25798.87957\t-53502.95385\t8144\t210106075.2\t2.10E+15\t8775\t-469488420\t7.61E+15
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASO\tBASOPHILS\t3687.485135\t20947.37285\t8140\t30016129\t3.00E+14\t7845\t164332140\t2.96E+13
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tDIPLOT\tDIPSTICK LOT\t9870470.192\t-53465.75239\t8136\t80306145479\t8.01E+17\t8772\t-469001580\t7.61E+15
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOAB\tBASO, ABS (MAN DIFF)\t3721.998809\t21070.87751\t8061\t30003032.4\t3.00E+14\t7772\t163762860\t2.96E+13
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tCHOL\tCHOLESTEROL, TOTAL\t86808.71569\t6998.059577\t7963\t691257803\t6.90E+15\t7184\t50274060\t1.03E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t256188.7712\t6998.059577\t7963\t2040031185\t2.04E+16\t7184\t50274060\t1.03E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tHDL\tHDL CHOLESTEROL\t239902.7954\t6998.059577\t7963\t1910345960\t1.91E+16\t7184\t50274060\t1.03E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tLDL\tDIRECT LDL CHOL\t62882.24551\t6998.059577\t7963\t500731321\t5.00E+15\t7184\t50274060\t1.03E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tNHDL\tNON-HDL CHOL, CALC\t256296.6906\t6998.059577\t7963\t2040890547\t2.04E+16\t7184\t50274060\t1.03E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tTGL\tTRIGLYCERIDE, SER/PLAS\t96824.46515\t6998.059577\t7963\t771013216\t7.70E+15\t7184\t50274060\t1.03E+12
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tFIO2\tFIO2\t3279751.87\t89.12959381\t7836\t25700135655\t2.57E+17\t4653\t414720\t13982184000
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tBE\tBASE EXCESS, ISTAT\t45952.43765\t108.6380597\t7835\t360037349\t3.60E+15\t3216\t349380\t13912772400
+MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t439595.3706\t7736\t77359992264\t7.74E+17\t4290\t1885864140\t2.06E+15
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tBE\tBASE EXCESS, ISTAT\t74089.92319\t60.37435009\t7694\t570047869\t5.70E+15\t2885\t174180\t497869200
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tPRO\tPRODUCT ID  \t9999999\t50909.33809\t7628\t76279992372\t7.63E+17\t3082\t156902580\t2.17E+13
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tAS\tANTIBODY SCREEN\t9997350.347\t44204.55386\t7551\t75489992471\t7.55E+17\t7027\t310625400\t2.31E+15
+MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t439595.3706\t7420\t74199992580\t7.42E+17\t4290\t1885864140\t2.06E+15
+LAB BLOOD ORDERABLES\t471610\tLABNH3\tAMMONIA, PLASMA\tNH3\tAMMONIA, PLASMA\t741403.8696\t4822.107671\t7230\t5360349977\t5.36E+16\t6557\t31618560\t2.58E+12
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tFIO2\tFIO2\t1408306.234\t153.9713775\t7108\t10010240712\t1.00E+17\t3354\t516420\t31147095600
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tAMORCRY\tAMORPHOUS CRYSTALS\t9999999\t11791.10708\t7093\t70929992907\t7.09E+17\t4959\t58472100\t4.01E+12
+LAB BLOOD ORDERABLES\t472369\tLABFER\tFERRITIN\tFERRITIN\tFERRITIN\t163449.2886\t35938.4026\t6948\t1135645657\t1.13E+16\t6160\t221380560\t1.69E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tHYCAST\tHYALINE CASTS\t9999999\t12509.58478\t6923\t69229993077\t6.92E+17\t5202\t65074860\t5.57E+12
+LAB BLOOD ORDERABLES\t473764\tLABB12\tVITAMIN B12\tB12\tVITAMIN B12\t3443570.484\t85714.94891\t6918\t23822620611\t2.38E+17\t6165\t528432660\t7.77E+13
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tPRO\tPRODUCT ID  \t9999999\t50253.88336\t6917\t69169993083\t6.92E+17\t2812\t141313920\t1.94E+13
+URINE ORDERABLES\t473349\tLABUSPG\tSPECIFIC GRAVITY, URINE\tSPG\tSPECIFIC GRAVITY\t63722.93876\t9424.118896\t6905\t440006892.1\t4.40E+15\t2826\t26632560\t3.88E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMORPH\tMORPHOLOGY\t9999999\t14660.77393\t6462\t64619993538\t6.46E+17\t8838\t129571920\t1.83E+13
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tUBT\tUNIT BLOOD TYPE  \t9999999\t63772.73171\t6268\t62679993732\t6.27E+17\t2460\t156880920\t2.17E+13
+POINT OF CARE TESTING\t472682\tLABPCCG4V\tISTAT CG4,VENOUS\tBD\tBASE DEFICIT, ISTAT\t32000.19917\t219.431221\t6251\t200033245\t2.00E+15\t3235\t709860\t61119608400
+LAB BLOOD ORDERABLES\t471616\tLABAMY\tAMYLASE, SERUM\tAMYLASE\tAMYLASE, SERUM\t75371.29299\t7113.896907\t6246\t470769096\t4.70E+15\t5335\t37952640\t1.13E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBLASTS\tBLASTS\t1646\t14771.6354\t6214\t10228244\t1.00E+14\t6023\t88969560\t1.30E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBLASTSAB\tBLASTS,ABS(MAN DIFF)\t1635.86515\t14831.03067\t6155\t10068750\t1.00E+14\t5967\t88496760\t1.30E+13
+LAB BLOOD ORDERABLES\t473473\tLABTRFS\tTRANSFERRIN SATURATION\tFE\tIRON, TOTAL\t74531.63936\t8066.46495\t6042\t450320165\t4.50E+15\t5321\t42921660\t7.52E+11
+LAB BLOOD ORDERABLES\t473473\tLABTRFS\tTRANSFERRIN SATURATION\tTRFRN\tTRANSFERRIN\t102838.1089\t8066.46495\t6042\t621347854\t6.20E+15\t5321\t42921660\t7.52E+11
+LAB BLOOD ORDERABLES\t473473\tLABTRFS\tTRANSFERRIN SATURATION\tTRFSAT\tTRANSFERRIN SAT\t256565.4825\t8066.46495\t6042\t1550168645\t1.55E+16\t5321\t42921660\t7.52E+11
+LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH\t9999999\t27693.75\t5946\t59459994054\t5.95E+17\t144\t3987900\t4.95E+12
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tBD\tBASE DEFICIT, ISTAT\t61345.03902\t51.88470067\t5869\t360034034\t3.60E+15\t2706\t140400\t183880800
+URINE ORDERABLES\t473345\tLABURNA\tSODIUM, URINE (RANDOM)\tUNA\tSODIUM, URINE\t208105.7103\t15182.65976\t5864\t1220331885\t1.22E+16\t3474\t52744560\t4.79E+12
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH\t9999999\t28795.95889\t5822\t58219994178\t5.82E+17\t6325\t182134440\t6.61E+14
+LAB BLOOD ORDERABLES\t472489\tLABHAP\tHAPTOGLOBIN\tHAPTO\tHAPTOGLOBIN\t2271427.067\t41917.59263\t5750\t13060705635\t1.31E+17\t5101\t213821640\t1.73E+13
+LAB BLOOD ORDERABLES\t590524\tLABASI\tAB SCREEN (ASI)\tAS\tANTIBODY SCREEN\t9999999\t7660\t5693\t56929994307\t5.69E+17\t9\t68940\t1312822800
+LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETIC\tRETIC, %\t427384.2192\t6435.962196\t5639\t2410019612\t2.41E+16\t4973\t32006040\t8.53E+11
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tUBT\tUNIT BLOOD TYPE  \t9999999\t63330.90094\t5631\t56309994369\t5.63E+17\t2231\t141291240\t1.94E+13
+MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tRPT\tRPT\t9999999\t3883162.201\t5539\t55389994461\t5.54E+17\t2681\t10410757860\t4.16E+16
+LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETICAB\tRETIC, ABS [AUTORETIC]\t126493.1332\t6435.893001\t5459\t690526013.9\t6.90E+15\t4972\t31999260\t8.53E+11
+LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC2\tDATE & TIME LAST DOSE\t9999999\t9129.397163\t5436\t54359994564\t5.44E+17\t5076\t46340820\t1.28E+12
+LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYCWB\tCYCLOSPORINE, WHOLE BLOOD\t202602.2658\t9129.397163\t5436\t1101345917\t1.10E+16\t5076\t46340820\t1.28E+12
+LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC3\tDOSAGE\t9999999\t9129.397163\t5435\t54349994565\t5.43E+17\t5076\t46340820\t1.28E+12
+LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC\tPEAK, TROUGH, OR RANDOM\t9999999\t9129.397163\t5432\t54319994568\t5.43E+17\t5076\t46340820\t1.28E+12
+LAB BLOOD ORDERABLES\t497496\tLABCYCWBL\tCYCLOSPORINE, WHOLE BLOOD\tCYC4\tADMINISTRATION ROUTE\t9999999\t9129.397163\t5431\t54309994569\t5.43E+17\t5076\t46340820\t1.28E+12
+LAB BLOOD ORDERABLES\t471569\tLABALB\tALBUMIN, SERUM/PLASMA\tALB\tALBUMIN, SER/PLAS\t84998.99586\t5387.547901\t5412\t460014565.6\t4.60E+15\t4645\t25025160\t2.98E+11
+MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tSDES\tSPECIMEN\t9999999\t3883162.201\t5383\t53829994617\t5.38E+17\t2681\t10410757860\t4.16E+16
+LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN\tPHENYTOIN\t251041.037\t6082.810524\t5338\t1340057056\t1.34E+16\t4903\t29824020\t3.52E+12
+LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN2\tDATE & TIME OF LAST DOSE\t9999999\t6082.810524\t5335\t53349994665\t5.33E+17\t4903\t29824020\t3.52E+12
+LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN3\tDOSAGE\t9999999\t6082.810524\t5335\t53349994665\t5.33E+17\t4903\t29824020\t3.52E+12
+LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN4\tADMINISTRATION ROUTE\t9999999\t6082.810524\t5332\t53319994668\t5.33E+17\t4903\t29824020\t3.52E+12
+MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tSREQ\tCOMMENTS\t9999999\t3883162.201\t5330\t53299994670\t5.33E+17\t2681\t10410757860\t4.16E+16
+LAB BLOOD ORDERABLES\t473097\tLABPHEN\tPHENYTOIN LEVEL\tPHEN\tPEAK, TROUGH, OR RANDOM\t9999999\t6082.810524\t5309\t53089994691\t5.31E+17\t4903\t29824020\t3.52E+12
+URINE ORDERABLES\t473041\tLABUOSM\tOSMOLALITY, URINE\tUOSM\tOSMOLALITY, URINE\t83581.7189\t17376.67197\t5290\t442147293\t4.40E+15\t3143\t54614880\t4.99E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tREACTL\tREACTIVE LYMPHS\t7689.860492\t27479.96404\t5204\t40018034\t4.00E+14\t5006\t137564700\t1.94E+13
+LAB BLOOD ORDERABLES\t472101\tLABCORT\tCORTISOL, SERUM\tCORT\tCORTISOL, SERUM\t285180.2545\t26893.35657\t5155\t1470104212\t1.47E+16\t4591\t123467400\t6.48E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tTRANST\tTRANSITIONAL\t9999999\t13297.53199\t5149\t51489994851\t5.15E+17\t3517\t46767420\t4.53E+12
+LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG\tDIGOXIN\t308233.4738\t5414.030852\t5126\t1580004787\t1.58E+16\t4473\t24216960\t2.13E+12
+LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG3\tDOSAGE\t9999999\t5414.030852\t5124\t51239994876\t5.12E+17\t4473\t24216960\t2.13E+12
+LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG2\tDATE & TIME OF LAST DOSE\t9999999\t5414.030852\t5123\t51229994877\t5.12E+17\t4473\t24216960\t2.13E+12
+LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG4\tADMINISTRATION ROUTE\t9999999\t5414.030852\t5082\t50819994918\t5.08E+17\t4473\t24216960\t2.13E+12
+LAB BLOOD ORDERABLES\t497488\tLABDIGL\tDIGOXIN, SERUM\tDIG\tPEAK, TOUGH, OR RANDOM\t9999999\t5414.030852\t5047\t50469994953\t5.05E+17\t4473\t24216960\t2.13E+12
+LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tETOH\tETHANOL\t7452883.484\t7192.519666\t4982\t37130265517\t3.71E+17\t4195\t30172620\t7.50E+12
+LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tOSMGAP\tOSMOLAL GAP\t7697883.914\t7193.516221\t4978\t38320066124\t3.83E+17\t4192\t30155220\t7.50E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPRMY\tPROMYELOCYTES\t6080.515796\t23944.15212\t4938\t30025587\t3.00E+14\t4812\t115219260\t1.72E+13
+MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tRPT\tRPT\t9999999\t3886778.338\t4858\t48579995142\t4.86E+17\t2310\t8978457960\t3.70E+16
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tLYMFLD\tLYMPHOCYTE %, FLD\t457864.2248\t19527.06412\t4827\t2210110613\t2.21E+16\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tMONFLD\tMONO/MESO %, FLD\t457875.3592\t19527.06412\t4827\t2210164359\t2.21E+16\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tRBCFLD\tRBC COUNT, FLD (/UL)\t630003.365\t19527.06412\t4827\t3041026243\t2.73E+16\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tWBCFLD\tNUCL CELLS, FLD(/UL)\t487879.8655\t19527.06412\t4826\t2354508231\t2.34E+16\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tNEUTFLD\tNEUTROPHIL %, FLD\t455992.9967\t19527.06412\t4825\t2200166209\t2.20E+16\t2667\t52078680\t1.29E+13
+LAB BLOOD ORDERABLES\t473295\tLABESRP\tSEDIMENTATION RATE (ESR)\tESR\tESR, (MANUAL)\t688626.2817\t10595.54854\t4807\t3310226536\t3.31E+16\t4120\t43653660\t7.84E+11
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFTURB\tTURBIDITY, FLD\t9993742.485\t19527.06412\t4795\t47919995216\t4.79E+17\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t19527.06412\t4778\t47779995222\t4.78E+17\t2667\t52078680\t1.29E+13
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tPRO\tPRODUCT ID  \t9999999\t45922.88372\t4765\t47649995235\t4.76E+17\t1935\t88860780\t1.11E+13
+LAB BLOOD ORDERABLES\t473331\tLABSLHELD\tSLIDE HELD FOR REVIEW\tSLHELD\tSLIDE HELD TO REVIEW\t9995790.249\t116226.1088\t4752\t47499995264\t4.75E+17\t2868\t333336480\t5.83E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFLDTYP\tFLUID SOURCE\t9999999\t19527.06412\t4719\t47189995281\t4.72E+17\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tDX\tDIAGNOSIS\t9942652.708\t19527.06412\t4708\t46810008949\t4.68E+17\t2667\t52078680\t1.29E+13
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFCOLOR\tAPPEARANCE\t9999999\t19527.06412\t4652\t46519995348\t4.65E+17\t2667\t52078680\t1.29E+13
+MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tSDES\tSPECIMEN\t9999999\t3886778.338\t4634\t46339995366\t4.63E+17\t2310\t8978457960\t3.70E+16
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tBD\tBASE DEFICIT, ISTAT\t61248.15661\t241.2875536\t4572\t280026572\t2.80E+15\t1864\t449760\t31063111200
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tBTURB\tTURBIDITY, CSF\t9999999\t12481.94864\t4561\t45609995439\t4.56E+17\t2648\t33052200\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tWBCCSF\tWBC COUNT, CSF\t1493411.249\t12481.94864\t4561\t6811448706\t6.81E+16\t2648\t33052200\t4.08E+12
+LAB BLOOD ORDERABLES\t472553\tLABHIVWBL\tANTI-HIV\tHIVAR\tANTI-HIV ANTIBODY\t9999999\t51579.60028\t4561\t45609995439\t4.56E+17\t4303\t221947020\t2.06E+13
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tLYMCSF\tLYMPHOCYTES, CSF\t4960547.156\t12484.08009\t4560\t22620095033\t2.26E+17\t2647\t33045360\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tMONOCSF\tMONOCYTOID CELLS, CSF\t4960534.138\t12484.08009\t4560\t22620035669\t2.26E+17\t2647\t33045360\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tRBCCSF\tRBC COUNT, CSF\t1497832.935\t12481.94864\t4560\t6830118185\t6.77E+16\t2648\t33052200\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCOLORCSF\tCOLOR, CSF\t9999999\t12481.94864\t4559\t45589995441\t4.56E+17\t2648\t33052200\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tNEUTCSF\tNEUTROPHILS, CSF\t4953943.863\t12484.08009\t4558\t22580076127\t2.26E+17\t2647\t33045360\t4.08E+12
+URINE ORDERABLES\t472109\tLABUCR\tCREATININE, URINE (RANDOM)\tUCR\tCREATININE, URINE\t63758.58907\t15754.24084\t4555\t290420373.2\t2.90E+15\t2674\t42126840\t3.97E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tMALIG\tSUSPECT MALIGNANCY?\t9995598.561\t12481.94864\t4545\t45429995459\t4.54E+17\t2648\t33052200\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCOLORCSF\tCOLOR AFTER CENTRIFUGE, CSF\t9999999\t12504.7099\t4533\t45329995467\t4.53E+17\t2637\t32974920\t4.08E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCTURB\tTURB. AFTER CENTRIF., CSF\t9997792.985\t12504.7099\t4533\t45319995599\t4.53E+17\t2637\t32974920\t4.08E+12
+MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tRPT\tRPT\t9999999\t483529.1944\t4529\t45289995471\t4.53E+17\t4481\t2166694320\t1.17E+15
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tBD\tBASE DEFICIT, ISTAT\t42002.71065\t141.8809981\t4524\t190020263\t1.90E+15\t2084\t295680\t13622270400
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tDX\tDIAGNOSIS\t9915930.216\t12481.94864\t4520\t44820004577\t4.48E+17\t2648\t33052200\t4.08E+12
+MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tSDES\tSPECIMEN\t9999999\t483529.1944\t4519\t45189995481\t4.52E+17\t4481\t2166694320\t1.17E+15
+MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tSREQ\tCOMMENTS\t9999999\t3886778.338\t4503\t45029995497\t4.50E+17\t2310\t8978457960\t3.70E+16
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCSOURC\tSOURCE\t9999999\t12481.94864\t4451\t44509995549\t4.45E+17\t2648\t33052200\t4.08E+12
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t4438\t44379995562\t4.44E+17\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t547240.7303\t4430\t44299995570\t4.43E+17\t1068\t584453100\t2.37E+15
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tDI\tISSUE DATE/TIME\t9999999\t41991.89189\t4410\t44099995590\t4.41E+17\t1776\t74577600\t5.77E+12
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tPRO\tPRODUCT ID  \t9999999\t44758.42809\t4402\t44019995598\t4.40E+17\t1794\t80296620\t9.91E+12
+MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tSREQ\tCOMMENTS\t9999999\t483529.1944\t4382\t43819995618\t4.38E+17\t4481\t2166694320\t1.17E+15
+POINT OF CARE TESTING\t472688\tLABPCEG7\tISTAT EG7, ARTERIAL\tPCTEMP\tBODY TEMPERATURE\t9974850.49\t51.40096618\t4374\t43629996044\t4.36E+17\t4347\t223440\t400305600
+LAB BLOOD ORDERABLES\t977655\tLABSPLAC\tSEPSIS PROTOCOL LACTATE\tLACWBL\tLACWBL\t225133.6344\t2571.026878\t4353\t980006710.5\t9.80E+15\t4353\t11191680\t2.29E+11
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t547240.7303\t4316\t43159995684\t4.32E+17\t1068\t584453100\t2.37E+15
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tTUBENO\tCSF TUBE #\t5022047.441\t12481.94864\t4309\t21640002422\t2.16E+17\t2648\t33052200\t4.08E+12
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t547240.7303\t4298\t42979995702\t4.30E+17\t1068\t584453100\t2.37E+15
+POINT OF CARE TESTING\t472690\tLABPCEG7V\tISTAT EG7, VENOUS\tPCTEMP\tBODY TEMPERATURE\t9999999\t93.12883436\t4251\t42509995749\t4.25E+17\t4238\t394680\t13966135200
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tAMORCRY\tAMORPHOUS CRYSTALS\t9999999\t14511.56118\t4242\t42419995758\t4.24E+17\t2844\t41270880\t6.85E+12
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tHCO3\tHCO3, ISTAT\t24344.29202\t142.088267\t4112\t100103728.8\t1.00E+15\t1858\t264000\t10384999200
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tLAC\tLACTATE, ISTAT\t111869.6803\t142.088267\t4112\t460008125.4\t4.60E+15\t1858\t264000\t10384999200
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPCO2A\tPCO2, ISTAT\t24360.758\t142.088267\t4112\t100171436.9\t1.00E+15\t1858\t264000\t10384999200
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPCO2SO\tO2 SATURATION, ISTAT\t29243.36576\t142.088267\t4112\t120248720\t1.20E+15\t1858\t264000\t10384999200
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPO2A\tPO2, ISTAT\t19494.54256\t142.088267\t4112\t80161559\t8.00E+14\t1858\t264000\t10384999200
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tTCO2A\tTCO2, (ISTAT)\t36505.06542\t142.088267\t4112\t150108829\t1.50E+15\t1858\t264000\t10384999200
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPH\tPH, ISTAT\t9749.225487\t142.088267\t4106\t40030319.85\t4.00E+14\t1858\t264000\t10384999200
+LAB BLOOD ORDERABLES\t471766\tLABDBIL\tBILIRUBIN DIRECT, SERUM/PLASMA\tDBIL\tCONJUGATED BILI\t1656923.12\t4637.953336\t4104\t6800012485\t6.80E+16\t2443\t11330520\t1.76E+11
+LAB BLOOD ORDERABLES\t473295\tLABESRP\tSEDIMENTATION RATE (ESR)\tESR\tESR, (AUTOMATED)\t179634.3921\t9588.860353\t4065\t730213804\t7.30E+15\t3738\t35843160\t5.47E+11
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t142.088267\t4059\t40589995941\t4.06E+17\t1858\t264000\t10384999200
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tSI\t<<STATUS>>  \t9999999\t56224.05416\t4048\t40479995952\t4.05E+17\t7681\t431856960\t6.51E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tHYCAST\tHYALINE CASTS\t9999999\t12112.97602\t4027\t40269995973\t4.03E+17\t2836\t34352400\t3.94E+12
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tRPT\tRPT\t9999999\t499787.4252\t4015\t40149995985\t4.01E+17\t3946\t1972161180\t1.12E+15
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tSDES\tSPECIMEN\t9999999\t499787.4252\t3999\t39989996001\t4.00E+17\t3946\t1972161180\t1.12E+15
+STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tOCCGU\tSTOOL SAMPLE1,GUAIAC\t9999999\t26125.71878\t3994\t39939996006\t3.99E+17\t1906\t49795620\t1.09E+13
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tBE\tBASE EXCESS, ISTAT\t107936.6052\t262.1493625\t3984\t430019435\t4.30E+15\t1647\t431760\t30965810400
+STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tDATE1\tDATE 1\t7391819.152\t25911.47817\t3952\t29212469287\t2.89E+17\t1901\t49257720\t1.08E+13
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tDI\tISSUE DATE/TIME\t9999999\t41133.95939\t3916\t39159996084\t3.92E+17\t1576\t64827120\t4.72E+12
+LAB BLOOD ORDERABLES\t472441\tLABGGT\tGAMMA - GLUTAMYL TRANSFERASE\tGGT\tGAMMA-GLUTAMYL TRANS (GGT)\t70008.72633\t6625.499568\t3877\t271423832\t2.70E+15\t3473\t23010360\t2.85E+11
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tSREQ\tCOMMENTS\t9999999\t499787.4252\t3853\t38529996147\t3.85E+17\t3946\t1972161180\t1.12E+15
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tUBT\tUNIT BLOOD TYPE  \t9999999\t58144.67277\t3845\t38449996155\t3.84E+17\t1528\t88845060\t1.11E+13
+STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tDATE2\tDATE 2\t9765855.059\t26602.01987\t3822\t37325098036\t3.73E+17\t1812\t48202860\t1.07E+13
+STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tDATE3\tDATE 3\t9778694.187\t26488.63938\t3818\t37335054406\t3.73E+17\t1808\t47891460\t1.06E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMAOTH\tOTHER, ABS(MAN DIFF)\t15741.31652\t56354.41432\t3814\t60037381.2\t6.00E+14\t3688\t207835080\t3.71E+13
+LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHBSAG\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t49510.65059\t3787\t37869996213\t3.79E+17\t3566\t176554980\t2.12E+13
+LAB BLOOD ORDERABLES\t473132\tLABPALB\tPREALBUMIN\tPREALBUMIN\tPREALBUMIN\t91316.25107\t5131.176155\t3724\t340061719\t3.40E+15\t3724\t19108500\t7.10E+11
+LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tETOHI\tETHANOL INTERP\t9999999\t7192.519666\t3662\t36619996338\t3.66E+17\t4195\t30172620\t7.50E+12
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tHCO3\tHCO3, ISTAT\t68443.60517\t94.96428571\t3654\t250092933.3\t2.50E+15\t1680\t159540\t3219822000
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCO2A\tPCO2, ISTAT\t65724.1494\t94.96428571\t3654\t240156041.9\t2.40E+15\t1680\t159540\t3219822000
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCO2SO\tO2 SATURATION, ISTAT\t71248.87466\t94.96428571\t3654\t260343388\t2.60E+15\t1680\t159540\t3219822000
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPO2A\tPO2, ISTAT\t54849.20498\t94.96428571\t3654\t200418995\t2.00E+15\t1680\t159540\t3219822000
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tTCO2A\tTCO2, (ISTAT)\t84865.14614\t94.96428571\t3654\t310097244\t3.10E+15\t1680\t159540\t3219822000
+LAB BLOOD ORDERABLES\t472407\tLABFOL\tFOLIC ACID\tFOLATE\tFOLIC ACID (FOLATE)\t2148747.987\t89044.69501\t3644\t7830037665\t7.83E+16\t3246\t289039080\t4.20E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tADVP\tADENOVIRUS\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tINFAP\tINFLUENZA A\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tINFBP\tINFLUENZA B\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tP1P\tPARAINFLUENZA 1\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tP2P\tPARAINFLUENZA 2\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tP3P\tPARAINFLUENZA 3\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tRVP\tRSV\t9999999\t66962.44702\t3644\t36439996356\t3.64E+17\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tMPVPR\tMETAPNEUMOVIRUS\t9999999\t66962.44702\t3643\t36429996357\t3.64E+17\t2501\t167473080\t3.14E+13
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPH\tPH, ISTAT\t16509.02108\t94.96428571\t3636\t60026800.63\t6.00E+14\t1680\t159540\t3219822000
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tSI\t<<STATUS>>  \t9999999\t56595.15599\t3571\t35709996429\t3.57E+17\t6635\t375508860\t5.68E+13
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t94.96428571\t3547\t35469996453\t3.55E+17\t1680\t159540\t3219822000
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tUBT\tUNIT BLOOD TYPE  \t9999999\t57549.16129\t3505\t35049996495\t3.50E+17\t1395\t80281080\t9.91E+12
+BODY FLUID ORDERABLES\t472474\tLABCSFGL\tGLUCOSE, CSF\tGLUCSF\tGLUCOSE, CSF\t147049.9202\t9628.58118\t3470\t510263223\t5.10E+15\t2051\t19748220\t2.46E+12
+BODY FLUID ORDERABLES\t473171\tLABCSFTP\tPROTEIN TOTAL, CSF\tTPCSF\tPROTEIN, TOTAL, CSF\t223552.5963\t8275.37092\t3446\t770362247\t7.70E+15\t2022\t16732800\t1.33E+12
+POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tHCT\tHCT(CALC), ISTAT\t29089.90238\t320.1615509\t3442\t100127444\t1.00E+15\t1238\t396360\t25255605600
+POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tHGB\tHGB(CALC), ISTAT\t29065.46293\t320.1615509\t3442\t100043323.4\t1.00E+15\t1238\t396360\t25255605600
+POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tK\tPOTASSIUM, ISTAT\t72636.58425\t320.1615509\t3442\t250015123\t2.50E+15\t1238\t396360\t25255605600
+POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tNA\tSODIUM, ISTAT\t20474.56334\t320.1615509\t3442\t70473447\t7.00E+14\t1238\t396360\t25255605600
+POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tGLU\tGLUCOSE,ISTAT\t46637.10782\t320.3718674\t3441\t160478288\t1.60E+15\t1237\t396300\t25255602000
+LAB BLOOD ORDERABLES\t473720\tLABBUN\tUREA NITROGEN, SERUM/PLASMA\tBUN\tUREA NITROGEN,SER/PLAS\t125105.7333\t8380.772727\t3438\t430113511\t4.30E+15\t3080\t25812780\t8.18E+11
+STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tOCCGU\tSTOOL SAMPLE2,GUAIAC\t9999999\t26389.7989\t3425\t34249996575\t3.42E+17\t1641\t43305660\t9.41E+12
+STOOL ORDERABLES\t668085\tLABSTOBGD\tOCCULT BLD, GUAIAC, STOOL, DIAG TEST\tOCCGU\tSTOOL SAMPLE3,GUAIAC\t9997076.733\t26397.29268\t3422\t34209996582\t3.42E+17\t1640\t43291560\t9.41E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUCL\tCHLORIDE, URINE\t137457.5589\t15992.76596\t3421\t470242309\t4.70E+15\t1974\t31569720\t2.97E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUCLCR\tCHLORIDE/CREAT RATIO\t5305465.897\t15992.76596\t3421\t18149998832\t1.81E+17\t1974\t31569720\t2.97E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUCR\tURINE CREAT\t119937.7598\t15992.76596\t3421\t410307076.4\t4.10E+15\t1974\t31569720\t2.97E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUK\tPOTASSIUM, URINE\t116960.7292\t15992.76596\t3421\t400122654.7\t4.00E+15\t1974\t31569720\t2.97E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUKCR\tPOTASSIUM/CREAT RATIO\t8655363.088\t15992.76596\t3421\t29609997125\t2.96E+17\t1974\t31569720\t2.97E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUNA\tSODIUM, URINE\t309908.1155\t15992.76596\t3421\t1060195663\t1.06E+16\t1974\t31569720\t2.97E+12
+URINE ORDERABLES\t472306\tLABUEP\tELECTROLYTE PANEL, URINE WITH CREATININE\tUNACR\tSODIUM/CREAT RATIO\t5802396.545\t15992.76596\t3421\t19849998582\t1.98E+17\t1974\t31569720\t2.97E+12
+LAB BLOOD ORDERABLES\t471820\tLABCA\tCALCIUM, SERUM/PLASMA\tCA\tCALCIUM, SER/PLAS\t56287.86457\t5083.35535\t3376\t190027830.8\t1.90E+15\t3028\t15392400\t2.08E+11
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tUWBCC\tWBC CLUMPS\t9999999\t11002.39122\t3374\t33739996626\t3.37E+17\t2551\t28067100\t2.32E+12
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tXMR\tUNIT CROSSMATCH  \t9999999\t88745.61509\t3314\t33139996686\t3.31E+17\t1325\t117587940\t1.98E+13
+LAB BLOOD ORDERABLES\t471521\tLABACETA\tACETAMINOPHEN, SERUM\tACETA\tACETAMINOPHEN\t8223308.024\t4303.983229\t3287\t27030013476\t2.70E+17\t2862\t12318000\t1.20E+11
+LAB BLOOD ORDERABLES\t869056\tLABPROCT\tPROCALCITONIN\tPROCTL\tPROCALCITONIN\t2200928.757\t9522.549085\t3235\t7120004529\t7.12E+16\t3005\t28615260\t1.38E+12
+MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tRPT\tRPT\t9999999\t452979\t3218\t32179996782\t3.22E+17\t1080\t489217320\t4.09E+14
+MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tSREQ\tCOMMENTS\t9999999\t452979\t3194\t31939996806\t3.19E+17\t1080\t489217320\t4.09E+14
+POINT OF CARE TESTING\t472686\tLABPCEC4V\tISTAT EC4, VENOUS\tPCCOM\tPOCT COMMENT\t9999999\t320.1615509\t3188\t31879996812\t3.19E+17\t1238\t396360\t25255605600
+MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tSDES\tSPECIMEN\t9999999\t452979\t3175\t31749996825\t3.17E+17\t1080\t489217320\t4.09E+14
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tTRANST\tTRANSITIONAL\t9999999\t16395.4988\t3164\t31639996836\t3.16E+17\t2075\t34020660\t6.40E+12
+MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t431748.6235\t3136\t31359996864\t3.14E+17\t1867\t806074680\t3.79E+14
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t3112\t31119996888\t3.11E+17\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tXMR\tUNIT CROSSMATCH  \t9999999\t86561.87399\t3096\t30959996904\t3.10E+17\t1238\t107163600\t1.78E+13
+MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t431748.6235\t3091\t30909996909\t3.09E+17\t1867\t806074680\t3.79E+14
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUPROT\tPROTEIN\t9999999\t16088.86364\t3080\t30799996920\t3.08E+17\t1760\t28316400\t6.95E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tLEUKEST\tLEUK ESTERASE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tNITRITE\tNITRITE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tSPG\tSPECIFIC GRAVITY\t224099.7011\t16070.07391\t3079\t690002979.8\t6.90E+15\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUBLOOD\tBLOOD\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUGLU\tGLUCOSE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUKET\tKETONE\t9999999\t16070.07391\t3079\t30789996921\t3.08E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUPH\tPH\t103935.8184\t16070.07391\t3079\t320018385\t3.20E+15\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUCLAR\tCLARITY\t9999999\t16070.07391\t3074\t30739996926\t3.07E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tSPECMN\tSPECIMEN\t9999999\t16070.07391\t3060\t30599996940\t3.06E+17\t1759\t28267260\t6.94E+12
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUCOL\tCOLOR\t9999999\t16070.07391\t3052\t30519996948\t3.05E+17\t1759\t28267260\t6.94E+12
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVCP\tRESULT\t8795237.081\t182289.3718\t3048\t26807882624\t2.68E+17\t2945\t536842200\t1.17E+14
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVDNA\tCMV DNA\t9999999\t182289.3718\t3048\t30479996952\t3.05E+17\t2945\t536842200\t1.17E+14
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVLOG\tRESULT LOG 10\t8792650.436\t182289.3718\t3048\t26799998529\t2.68E+17\t2945\t536842200\t1.17E+14
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVSP\tSPECIMEN\t9999999\t182289.3718\t3048\t30479996952\t3.05E+17\t2945\t536842200\t1.17E+14
+POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tINR\tINR, ISTAT\t811797.0058\t41.8380744\t3018\t2450003364\t2.45E+16\t1371\t57360\t44913600
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tTESTLO\tTEST STRIP LOT#\t9750609.052\t-7744.707974\t3003\t29281078982\t2.93E+17\t3938\t-30498660\t4.91E+13
+MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9989938.642\t431748.6235\t2982\t29789997029\t2.98E+17\t1867\t806074680\t3.79E+14
+POINT OF CARE TESTING\t472693\tLABPCG3V\tISTAT G3+, VENOUS\tPCTEMP\tBODY TEMPERATURE\t9996625.194\t165.5967358\t2964\t29629997074\t2.96E+17\t2941\t487020\t31049060400
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tPRO\tPRODUCT ID  \t9999999\t41709.62264\t2940\t29399997060\t2.94E+17\t1166\t48633420\t5.74E+12
+POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tPT\tPT, ISTAT\t364851.4639\t41.81551977\t2878\t1050042513\t1.05E+16\t1366\t57120\t44899200
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tOTHER\tOTHERS (DESCRIBE)\t20887.38456\t67574.28263\t2876\t60072118\t6.00E+14\t2781\t187924080\t3.47E+13
+BODY FLUID ORDERABLES\t473172\tLABFTP\tPROTEIN TOTAL, FLUID\tTPFLD\tPROTEIN, TOTAL, FLUID\t1567431.84\t16836.09225\t2763\t4330814173\t4.33E+16\t1561\t26281140\t4.45E+12
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tDI\tISSUE DATE/TIME\t9999999\t39990.02657\t2757\t27569997243\t2.76E+17\t1129\t45148740\t3.06E+12
+MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tRPT\tRPT\t9999999\t1850399.621\t2744\t27439997256\t2.74E+17\t1268\t2346306720\t4.55E+15
+LAB BLOOD ORDERABLES\t472131\tLABDDIM\tD - DIMER\tDDIMER\tD-DIMER\t1514970.172\t4817.497566\t2732\t4138898509\t4.13E+16\t2054\t9895140\t1.11E+11
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC5\tDAILY QC 5 RESULT\t9955783.853\t-7730.784463\t2714\t27019997376\t2.70E+17\t3939\t-30451560\t4.92E+13
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC10\tDAILY QC 10 RESULT\t9977826.075\t-7730.784463\t2706\t26999997360\t2.70E+17\t3939\t-30451560\t4.92E+13
+URINE ORDERABLES\t473341\tLABUNA\tSODIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t85719.48835\t17054.80804\t2686\t230242545.7\t2.30E+15\t1641\t27986940\t3.81E+12
+URINE ORDERABLES\t473341\tLABUNA\tSODIUM WITH CREATININE, URINE (RANDOM)\tUNA\tSODIUM, URINE\t171311.3548\t17054.80804\t2686\t460142299\t4.60E+15\t1641\t27986940\t3.81E+12
+URINE ORDERABLES\t473341\tLABUNA\tSODIUM WITH CREATININE, URINE (RANDOM)\tUNACR\tSODIUM/CREAT RATIO\t6355174.463\t17054.80804\t2686\t17069998607\t1.71E+17\t1641\t27986940\t3.81E+12
+LAB BLOOD ORDERABLES\t496776\tLABHGBX\tHEMOGLOBIN\tHGB\tHEMOGLOBIN\t97277.61893\t4152.9\t2673\t260023075.4\t2.60E+15\t2200\t9136380\t2.31E+11
+MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tSDES\tSPECIMEN\t9999999\t1850399.621\t2671\t26709997329\t2.67E+17\t1268\t2346306720\t4.55E+15
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tPRO\tPRODUCT ID  \t9999999\t44531.81989\t2667\t26669997333\t2.67E+17\t1066\t47470920\t6.04E+12
+LAB BLOOD ORDERABLES\t473260\tLABPATHR\tREVIEW BY PATHOLOGIST\tREVUE\tCOMMENTS\t9999999\t476.0962145\t2649\t26489997351\t2.65E+17\t2536\t1207380\t1.84E+11
+LAB BLOOD ORDERABLES\t977655\tLABSPLAC\tSEPSIS PROTOCOL LACTATE\tSPLACW\tSEPSIS PROTOCOL LACTATE\t347827.7297\t2688.045369\t2645\t920004345.1\t9.20E+15\t2645\t7109880\t1.38E+11
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANCOSDD\tVANCOMYCIN, SINGLE DAILY DOSE\t110074.1901\t6953.366834\t2635\t290045490.9\t2.90E+15\t1990\t13837200\t3.64E+11
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANUN2\tDATE & TIME OF LAST DOSE\t9999999\t6953.366834\t2635\t26349997365\t2.63E+17\t1990\t13837200\t3.64E+11
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANUN3\tDOSAGE\t9999999\t6953.366834\t2635\t26349997365\t2.63E+17\t1990\t13837200\t3.64E+11
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY DOSE LEVEL\tVANUN4\tADMINISTRATION ROUTE\t9999999\t6953.366834\t2633\t26329997367\t2.63E+17\t1990\t13837200\t3.64E+11
+MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tSREQ\tCOMMENTS\t9999999\t1850399.621\t2630\t26299997370\t2.63E+17\t1268\t2346306720\t4.55E+15
+POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tPCCOM\tPOCT COMMENT\t9999999\t41.8380744\t2614\t26139997386\t2.61E+17\t1371\t57360\t44913600
+MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t266675.2941\t2599\t25989997401\t2.60E+17\t1275\t340011000\t1.87E+14
+MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t266675.2941\t2555\t25549997445\t2.55E+17\t1275\t340011000\t1.87E+14
+POINT OF CARE TESTING\t471529\tLABPCACTK\tISTAT ACTIVATED CLOTTING TIME\tACT\tACT, ISTAT\t23738.67517\t64.28887251\t2549\t60509883\t6.00E+14\t1357\t87240\t277171200
+POINT OF CARE TESTING\t471529\tLABPCACTK\tISTAT ACTIVATED CLOTTING TIME\tPCCOM\tPOCT COMMENT\t9999999\t64.28887251\t2549\t25489997451\t2.55E+17\t1357\t87240\t277171200
+MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t266675.2941\t2543\t25429997457\t2.54E+17\t1275\t340011000\t1.87E+14
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tDI\tISSUE DATE/TIME\t9999999\t39465.12968\t2532\t25319997468\t2.53E+17\t1041\t41083200\t2.78E+12
+LAB BLOOD ORDERABLES\t471635\tLABANAS\tANTI - NUCLEAR AB\tANA\tANA RESULT\t9999999\t270715.1193\t2521\t25209997479\t2.52E+17\t2348\t635639100\t2.25E+14
+LAB BLOOD ORDERABLES\t471768\tLABTBILFR\tBILIRUBIN TOTAL AND FRACTIONATED, SERUM\tDBIL\tCONJUGATED BILI\t891290.3991\t6420.758465\t2502\t2230008579\t2.23E+16\t2215\t14221980\t2.20E+11
+LAB BLOOD ORDERABLES\t471768\tLABTBILFR\tBILIRUBIN TOTAL AND FRACTIONATED, SERUM\tIBIL\tUNCONJUGATED BILIRUBIN\t1019186.289\t6420.758465\t2502\t2550004094\t2.55E+16\t2215\t14221980\t2.20E+11
+LAB BLOOD ORDERABLES\t471768\tLABTBILFR\tBILIRUBIN TOTAL AND FRACTIONATED, SERUM\tTBIL\tTOTAL BILIRUBIN\t755400.8907\t6420.758465\t2502\t1890013029\t1.89E+16\t2215\t14221980\t2.20E+11
+POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tPATRES\tPATIENT RESULT\t9999999\t17556.14118\t2502\t25019997498\t2.50E+17\t2550\t44768160\t5.81E+14
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tBE\tBASE EXCESS, ISTAT\t40036.09648\t180.6648199\t2498\t100010169\t1.00E+15\t1083\t195660\t10071954000
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t9999999\t12887.03033\t2445\t24449997555\t2.44E+17\t3923\t50555820\t4.05E+12
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tUBT\tUNIT BLOOD TYPE  \t9999999\t51894.30096\t2420\t24199997580\t2.42E+17\t937\t48624960\t5.74E+12
+URINE ORDERABLES\t473134\tLABUPREG\tPREGNANCY TEST, URINE\tUPREG\tPREGNANCY TEST, URINE\t9999999\t13496.7233\t2419\t24189997581\t2.42E+17\t1648\t22242600\t5.33E+12
+POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tKIT1\tKIT LOT#\t314425.4418\t17556.14118\t2390\t751476806\t1.33E+15\t2550\t44768160\t5.81E+14
+POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tKITEX1\tKIT EXP DATE\t9903095.212\t17556.14118\t2378\t23549560415\t2.35E+17\t2550\t44768160\t5.81E+14
+BODY FLUID ORDERABLES\t473172\tLABFTP\tPROTEIN TOTAL, FLUID\tTPFLS\tSOURCE\t9999999\t16541.41399\t2374\t23739997626\t2.37E+17\t1372\t22694820\t3.95E+12
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tCHOL\tCHOLESTEROL, TOTAL\t68360.30009\t7336.118966\t2346\t160373264\t1.60E+15\t2051\t15046380\t2.17E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t225920.314\t7336.118966\t2346\t530009056.7\t5.30E+15\t2051\t15046380\t2.17E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tHDL\tHDL CHOLESTEROL\t213172.8235\t7336.118966\t2346\t500103444\t5.00E+15\t2051\t15046380\t2.17E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tLDL\tLDL (CALCULATED)\t439133.7527\t7336.118966\t2346\t1030207784\t1.03E+16\t2051\t15046380\t2.17E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tLDLHDL\tLDL/HDL RATIO\t434784.766\t7336.118966\t2346\t1020005061\t1.02E+16\t2051\t15046380\t2.17E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tNHDL\tNON-HDL CHOL, CALC\t226029.4437\t7336.118966\t2346\t530265075\t5.30E+15\t2051\t15046380\t2.17E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL WITH CALCULATED LDL\tTGL\tTRIGLYCERIDE, SER/PLAS\t89633.06522\t7336.118966\t2346\t210279171\t2.10E+15\t2051\t15046380\t2.17E+11
+POINT OF CARE (NON LAB) TESTS\t610670\tPOC26\tPOC URINE PREGNANCY\tQC9\tQC RESULT\t9987451.907\t17556.14118\t2283\t22801352704\t2.28E+17\t2550\t44768160\t5.81E+14
+LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIROL\tSIROLIMUS\t1080006.235\t19034.56802\t2250\t2430014030\t2.43E+16\t2014\t38335620\t1.85E+12
+LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIRO2\tDATE & TIME OF LAST DOSE\t9999999\t19034.56802\t2248\t22479997752\t2.25E+17\t2014\t38335620\t1.85E+12
+LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIRO3\tDOSAGE\t9999999\t19034.56802\t2248\t22479997752\t2.25E+17\t2014\t38335620\t1.85E+12
+LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIRO4\tADMINISTRATION ROUTE\t9999999\t19034.56802\t2246\t22459997754\t2.25E+17\t2014\t38335620\t1.85E+12
+LAB BLOOD ORDERABLES\t473329\tLABSIROL\tSIROLIMUS LEVEL\tSIROL\tPEAK, TROUGH, OR RANDOM\t9999999\t19034.56802\t2246\t22459997754\t2.25E+17\t2014\t38335620\t1.85E+12
+MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tRPT\tRPT\t9999999\t315224.0375\t2231\t22309997769\t2.23E+17\t1387\t437215740\t3.30E+14
+LAB BLOOD ORDERABLES\t473287\tLABSALICY\tSALICYLATE LEVEL\tSALICYLATE\tSALICYLATE\t9773959.86\t4036.713178\t2212\t21619999211\t2.16E+17\t1935\t7811040\t2.56E+11
+MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tSDES\tSPECIMEN\t9999999\t315224.0375\t2206\t22059997794\t2.21E+17\t1387\t437215740\t3.30E+14
+BLOOD BANK PRODUCT ORDERABLES\t472111\tLABBBPCRY\tCRYOPRECIPITATE\tCRSTA\tCRYOPRECIPITATE UNITS\t9999999\t5035.028133\t2195\t21949997805\t2.19E+17\t1955\t9843480\t4.94E+11
+MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tSREQ\tCOMMENTS\t9999999\t315224.0375\t2181\t21809997819\t2.18E+17\t1387\t437215740\t3.30E+14
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tUBT\tUNIT BLOOD TYPE  \t9999999\t55642.15709\t2178\t21779997822\t2.18E+17\t853\t47462760\t6.04E+12
+MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tRPT\tRPT\t9999999\t1418147.315\t2170\t21699997830\t2.17E+17\t473\t670783680\t1.10E+15
+MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tSDES\tSPECIMEN\t9999999\t1418147.315\t2139\t21389997861\t2.14E+17\t473\t670783680\t1.10E+15
+MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tSREQ\tCOMMENTS\t9999999\t1418147.315\t2126\t21259997874\t2.13E+17\t473\t670783680\t1.10E+15
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tUWBCC\tWBC CLUMPS\t9999999\t13407.05179\t2124\t21239997876\t2.12E+17\t1506\t20191020\t3.11E+12
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tPRO\tPRODUCT ID  \t9999999\t40348.33333\t2110\t21099997890\t2.11E+17\t864\t34860960\t4.01E+12
+LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPA\tPEAK, TROUGH, OR RANDOM\t9999999\t8360.571429\t2109\t21089997891\t2.11E+17\t1960\t16386720\t2.42E+12
+LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPA\tVALPROIC ACID\t663875.3016\t8360.571429\t2109\t1400113011\t1.40E+16\t1960\t16386720\t2.42E+12
+LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPAL2\tDATE & TIME OF LAST DOSE\t9999999\t8360.571429\t2109\t21089997891\t2.11E+17\t1960\t16386720\t2.42E+12
+LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPAL3\tDOSAGE\t9999999\t8360.571429\t2109\t21089997891\t2.11E+17\t1960\t16386720\t2.42E+12
+LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTX\tMETHOTREXATE\t2912116.307\t12566.26556\t2105\t6130004826\t6.13E+16\t1928\t24227760\t1.40E+12
+LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTXL2\tDATE & TIME OF LAST DOSE\t9999999\t12566.26556\t2104\t21039997896\t2.10E+17\t1928\t24227760\t1.40E+12
+LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTXL3\tDOSAGE\t9999999\t12566.26556\t2104\t21039997896\t2.10E+17\t1928\t24227760\t1.40E+12
+LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTXL4\tADMINISTRATION ROUTE\t9999999\t12566.26556\t2104\t21039997896\t2.10E+17\t1928\t24227760\t1.40E+12
+LAB BLOOD ORDERABLES\t497486\tLABVPAL\tVALPROIC ACID\tVPAL4\tADMINISTRATION ROUTE\t9999999\t8360.571429\t2101\t21009997899\t2.10E+17\t1960\t16386720\t2.42E+12
+LAB BLOOD ORDERABLES\t497500\tLABMTXL\tMETHOTREXATE\tMTX\tPEAK, TROUGH, OR RANDOM\t9999999\t12566.26556\t2100\t20999997900\t2.10E+17\t1928\t24227760\t1.40E+12
+BODY FLUID ORDERABLES\t472729\tLABFLDH\tLDH TOTAL, FLUID\tLDHFLD\tLDH, TOTAL, FLUID\t406023.8688\t16221.78602\t2096\t851026029\t8.50E+15\t1187\t19255260\t2.94E+12
+URINE ORDERABLES\t473723\tLABURUN\tUREA, URINE (RANDOM)\tUURN\tUREA NITROGEN, URINE\t91450.65742\t16736.09091\t2090\t191131874\t1.90E+15\t1320\t22091640\t2.03E+12
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tFIO2\tFIO2\t4207253.327\t104.0416667\t2075\t8730050654\t8.73E+16\t1440\t149820\t3211340400
+BODY FLUID ORDERABLES\t472729\tLABFLDH\tLDH TOTAL, FLUID\tLDHFLS\tSOURCE\t9999999\t16221.78602\t2071\t20709997929\t2.07E+17\t1187\t19255260\t2.94E+12
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tBE\tBASE EXCESS, ISTAT\t127957.687\t135\t2032\t260010020\t2.60E+15\t928\t125280\t3169742400
+BODY FLUID ORDERABLES\t471614\tLABFAMY\tAMYLASE, FLUID\tAMYLASEFLD\tAMYLASE, FLUID\t976020.501\t14671.16079\t2022\t1973513453\t1.97E+16\t1163\t17062560\t2.52E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tGRCAST\tGRANULAR CASTS\t9999999\t13794.34389\t1986\t19859998014\t1.99E+17\t1326\t18291300\t1.51E+12
+MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tRPT\tRPT\t9999999\t168009.5772\t1972\t19719998028\t1.97E+17\t1159\t194723100\t4.94E+13
+MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tSDES\tSPECIMEN\t9999999\t168009.5772\t1963\t19629998037\t1.96E+17\t1159\t194723100\t4.94E+13
+BODY FLUID ORDERABLES\t471614\tLABFAMY\tAMYLASE, FLUID\tAMYFLS\tSOURCE\t9999999\t14671.16079\t1943\t19429998057\t1.94E+17\t1163\t17062560\t2.52E+12
+LAB BLOOD ORDERABLES\t633373\tLABSTSS\tSYPHILIS TREPONEMAL SCREEN\tSTSQUL\tRESULT\t9974132.492\t239349.2912\t1933\t19279998106\t1.93E+17\t1834\t438966600\t5.36E+15
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tPRO\tPRODUCT ID  \t9999999\t39255.84184\t1914\t19139998086\t1.91E+17\t784\t30776580\t3.61E+12
+LAB BLOOD ORDERABLES\t472402\tLABRBCFOL\tFOLATE, RBC\tFOLATERBC\tFOLATE, RBC\t312085.597\t159556.2945\t1896\t591714292\t5.90E+15\t1657\t264384780\t6.55E+13
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA\tMYCOPHENOLIC ACID\t2869979.962\t110519.9663\t1892\t5430002089\t5.43E+16\t1779\t196615020\t3.18E+13
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPAG\tMPA GLUCURONIDE\t1543401.523\t110519.9663\t1892\t2920115682\t2.92E+16\t1779\t196615020\t3.18E+13
+URINE ORDERABLES\t473729\tLABUAS\tURINALYSIS MACROSCOPIC ONLY\tUCMT\tURINE COMMENT\t9999999\t19101.0982\t1878\t18779998122\t1.88E+17\t947\t18088740\t4.39E+12
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA2\tDATE & TIME OF LAST DOSE\t9994706.417\t110354.4488\t1876\t18750069238\t1.87E+17\t1769\t195217020\t3.15E+13
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA3\tDOSAGE\t9994669.043\t110354.4488\t1876\t18749999125\t1.87E+17\t1769\t195217020\t3.15E+13
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA4\tADMINISTRATION ROUTE\t9994668.577\t110354.4488\t1876\t18749998250\t1.87E+17\t1769\t195217020\t3.15E+13
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMYPA1\tPEAK, TROUGH, OR RANDOM\t9999999\t110354.4488\t1864\t18639998136\t1.86E+17\t1769\t195217020\t3.15E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVCP\tRESULT\t8798774.845\t154991.0938\t1801\t15846593496\t1.58E+17\t1801\t279138960\t5.55E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVDNA\tCMV DNA\t9999999\t154991.0938\t1801\t18009998199\t1.80E+17\t1801\t279138960\t5.55E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVLOG\tRESULT LOG 10\t8795113.315\t154991.0938\t1801\t15839999081\t1.58E+17\t1801\t279138960\t5.55E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVSP\tSPECIMEN\t9999999\t154991.0938\t1799\t17989998201\t1.80E+17\t1801\t279138960\t5.55E+13
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tRVPSP\tSPECIMEN\t9999999\t66962.44702\t1798\t17979998202\t1.80E+17\t2501\t167473080\t3.14E+13
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tDI\tISSUE DATE/TIME\t9999999\t36629.87755\t1795\t17949998205\t1.79E+17\t735\t26922960\t1.56E+12
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tXMR\tUNIT CROSSMATCH  \t9999999\t85119.35393\t1777\t17769998223\t1.78E+17\t712\t60604980\t9.72E+12
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tVL\tPRODUCT VOLUME\t177.7713156\t27140.11628\t1771\t314833\t61425727\t516\t14004300\t4.44E+11
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYM\tLYMPHOCYTES\t21.82613636\t29710.52065\t1760\t38414\t1746512\t1671\t49646280\t1.40E+13
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tUBT\tUNIT BLOOD TYPE  \t9999999\t50077.32759\t1737\t17369998263\t1.74E+17\t696\t34853820\t4.01E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYMAB\tLYM, ABS (MAN DIFF)\t2.518998273\t29970.10909\t1737\t4375.5\t318135.53\t1650\t49450680\t1.40E+13
+MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tSREQ\tCOMMENTS\t9999999\t168009.5772\t1737\t17369998263\t1.74E+17\t1159\t194723100\t4.94E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUT\tSEG NEUTROPHILS\t40.20231214\t29234.6894\t1730\t69550\t4143520\t1642\t48003360\t1.15E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNEUTAB\tNEUT,ABS (MAN DIFF)\t10.13447079\t29421.18077\t1729\t17522.5\t1428888.81\t1643\t48339000\t1.15E+13
+LAB BLOOD ORDERABLES\t473234\tLABREPT\tREPTILASE TIME\tREPT\tREPTILASE TIME\t140374.194\t1946.434109\t1710\t240039871.7\t2.40E+15\t774\t1506540\t16800994800
+LAB BLOOD ORDERABLES\t471767\tLABIBIL\tBILIRUBIN INDIRECT, SERUM\tIBIL\tUNCONJUGATED BILIRUBIN\t2742790.761\t2582.494802\t1699\t4660001503\t4.66E+16\t481\t1242180\t4407022800
+LAB BLOOD ORDERABLES\t472193\tLABDATBSI\tDIRECT COOMBS BROAD SPECTRUM\tDATBS\tDIRECT ANTIGLOB.-BROAD SPEC\t9999999\t17193.15353\t1697\t16969998303\t1.70E+17\t1446\t24861300\t4.24E+12
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tXMR\tUNIT CROSSMATCH  \t9999999\t81558.64507\t1682\t16819998318\t1.68E+17\t679\t55378320\t8.68E+12
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tBD\tBASE DEFICIT, ISTAT\t115858.5683\t166.0079051\t1640\t190008052\t1.90E+15\t759\t126000\t3210127200
+LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN (HIT) AB\tPF4ENH\tPF4 IGG, IGM, IGA OD\t220318.5292\t89701.14358\t1634\t360000476.8\t3.60E+15\t1574\t141189600\t1.41E+13
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tCR\tCREATININE, SER/PLAS\t42867.40214\t5265.807963\t1633\t70002467.7\t7.00E+14\t1281\t6745500\t1.03E+11
+POINT OF CARE TESTING\t472683\tLABPCCG4O\tISTAT CG4, OTHER\tBD\tBASE DEFICIT, ISTAT\t43215.22901\t264.5244216\t1620\t70008671\t7.00E+14\t778\t205800\t10291471200
+VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tRPT\tRPT\t9999999\t198392.7935\t1618\t16179998382\t1.62E+17\t247\t49003020\t1.01E+13
+LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1602\t16019998398\t1.60E+17\t0\tNone\tNone
+VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tSREQ\tCOMMENTS\t9999999\t198392.7935\t1602\t16019998398\t1.60E+17\t247\t49003020\t1.01E+13
+BODY FLUID ORDERABLES\t471568\tLABFAL\tALBUMIN, FLUID\tALBFLD\tALBUMIN, FLUID\t5668750.195\t12203.72671\t1600\t9070000312\t9.07E+16\t966\t11788800\t1.51E+12
+VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tSDES\tSPECIMEN\t9999999\t198392.7935\t1598\t15979998402\t1.60E+17\t247\t49003020\t1.01E+13
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tDI\tISSUE DATE/TIME\t9999999\t39085.46565\t1597\t15969998403\t1.60E+17\t655\t25600980\t1.78E+12
+BODY FLUID ORDERABLES\t471568\tLABFAL\tALBUMIN, FLUID\tALBFLS\tSOURCE\t9999999\t12203.72671\t1586\t15859998414\t1.59E+17\t966\t11788800\t1.51E+12
+LAB BLOOD ORDERABLES\t590524\tLABASI\tAB SCREEN (ASI)\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1574\t15739998426\t1.57E+17\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tUBT\tUNIT BLOOD TYPE  \t9999999\t50032.09756\t1546\t15459998454\t1.55E+17\t615\t30769740\t3.61E+12
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tPRO\tPRODUCT ID  \t9999999\t37720.88748\t1542\t15419998458\t1.54E+17\t631\t23801880\t2.56E+12
+LAB BLOOD ORDERABLES\t886807\tLABTSHFT4\tTSH W/ REFLEX FT4\tTSH\tTSH\t117649.7788\t34898.89628\t1530\t180004161.5\t1.80E+15\t1504\t52487940\t3.59E+12
+LAB BLOOD ORDERABLES\t471770\tLABTBIL\tBILIRUBIN TOTAL, SERUM/PLASMA\tTBIL\tTOTAL BILIRUBIN\t3494765.309\t4625.089286\t1528\t5340001392\t5.34E+16\t896\t4144080\t1.70E+11
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tSI\t<<STATUS>>  \t9999999\t50909.33809\t1516\t15159998484\t1.52E+17\t3082\t156902580\t2.17E+13
+MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tRPT\tRPT\t9999999\t74295.58989\t1516\t15159998484\t1.52E+17\t712\t52898460\t3.10E+13
+MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tRPT\tRPT\t9999999\t361273.8129\t1503\t15029998497\t1.50E+17\t556\t200868240\t1.60E+14
+VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tRPT\tRPT\t9999999\t99400.67114\t1501\t15009998499\t1.50E+17\t596\t59242800\t1.23E+14
+VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tSDES\tSPECIMEN\t9999999\t99400.67114\t1501\t15009998499\t1.50E+17\t596\t59242800\t1.23E+14
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBAND\tBAND NEUTROPHILS\t12.62333333\t26083.26072\t1500\t18935\t470187\t1423\t37116480\t5.14E+12
+VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tSREQ\tCOMMENTS\t9999999\t99400.67114\t1496\t14959998504\t1.50E+17\t596\t59242800\t1.23E+14
+MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tSDES\tSPECIMEN\t9999999\t74295.58989\t1494\t14939998506\t1.49E+17\t712\t52898460\t3.10E+13
+LAB BLOOD ORDERABLES\t473173\tLABTP\tPROTEIN TOTAL, SERUM/PLASMA\tTP\tPROTEIN, TOTAL, SER/PLAS\t67619.65308\t6765.357548\t1479\t100009466.9\t1.00E+15\t881\t5960280\t88194196800
+LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON TEST FOR LATENT TB\tQTFNRS\tQUANTIFERON-TB\t9999999\t852519.229\t1472\t14719998528\t1.47E+17\t1297\t1105717440\t3.84E+16
+URINE ORDERABLES\t472317\tLABUEOS\tEOSINOPHILS, URINE\tUEOS\tEOSINOPHILS, URINE\t9388586.308\t21586.62116\t1472\t13819999046\t1.38E+17\t879\t18974640\t2.01E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONO\tMONOCYTES\t9.077603812\t27317.81792\t1469\t13335\t251391\t1384\t37807860\t7.38E+12
+MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tSDES\tSPECIMEN\t9999999\t361273.8129\t1447\t14469998553\t1.45E+17\t556\t200868240\t1.60E+14
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tSI\t<<STATUS>>  \t9999999\t50253.88336\t1446\t14459998554\t1.45E+17\t2812\t141313920\t1.94E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMONOAB\tMONO, ABS (MAN DIFF)\t0.962274619\t27646.01471\t1442\t1387.6\t6393.32\t1360\t37598580\t7.38E+12
+LAB BLOOD ORDERABLES\t473062\tLABPTH\tPARATHYROID HORMONE\tPTH\tPARATHYROID HORMONE\t1622716.105\t96576.60091\t1436\t2330220327\t2.33E+16\t1318\t127287960\t1.99E+13
+LAB BLOOD ORDERABLES\t473133\tLABPREG\tPREGNANCY TEST, SERUM\tPREGTEST\tPREGNANCY, SERUM\t9999999\t6294.605263\t1434\t14339998566\t1.43E+17\t1368\t8611020\t1.85E+11
+MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tSREQ\tCOMMENTS\t9999999\t74295.58989\t1433\t14329998567\t1.43E+17\t712\t52898460\t3.10E+13
+MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tSREQ\tCOMMENTS\t9999999\t361273.8129\t1429\t14289998571\t1.43E+17\t556\t200868240\t1.60E+14
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVCP\tRESULT\t9199405.892\t184331.1162\t1423\t13090754585\t1.31E+17\t1317\t242764080\t5.22E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVLOG\tRESULT LOG 10\t9198874.967\t184331.1162\t1423\t13089999078\t1.31E+17\t1317\t242764080\t5.22E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVSP\tSPECIMEN\t9999999\t184331.1162\t1423\t14229998577\t1.42E+17\t1317\t242764080\t5.22E+13
+LAB BLOOD ORDERABLES\t472528\tLABHCVA\tHEPATITIS C AB IGG\tHCVR\tANTI-HCV\t9992951.785\t57183.66116\t1419\t14179998584\t1.42E+17\t1393\t79656840\t1.00E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tABNWBC\tBLASTS/ABNORMAL CELLS\t9999999\t23173.85257\t1392\t13919998608\t1.39E+17\t1438\t33324000\t5.02E+12
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tPRO\tPRODUCT ID  \t9999999\t40376.78201\t1389\t13889998611\t1.39E+17\t578\t23337780\t2.61E+12
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFCMT\tFLUID COMMENT\t9999999\t18528.28548\t1370\t13699998630\t1.37E+17\t1219\t22585980\t2.84E+12
+LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI\tLITHIUM\t1536211.145\t8902.52322\t1367\t2100000635\t2.10E+16\t1292\t11502060\t5.77E+11
+LAB BLOOD ORDERABLES\t472619\tLABIGG\tIGG, SERUM\tIGG\tIGG, SERUM\t88673.34334\t49752.98969\t1366\t121127787\t1.20E+15\t1261\t62738520\t8.14E+12
+BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tGLUFLD\tGLUCOSE, FLUID\t468975.4842\t12717.30815\t1365\t640151536\t6.40E+15\t847\t10771560\t1.15E+12
+LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI2\tDATE & TIME OF LAST DOSE\t9999999\t8902.52322\t1365\t13649998635\t1.36E+17\t1292\t11502060\t5.77E+11
+LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI3\tDOSAGE\t9999999\t8902.52322\t1365\t13649998635\t1.36E+17\t1292\t11502060\t5.77E+11
+COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tMANUF\tMANUFACTURER\t9999999\t181776.5501\t1363\t13629998637\t1.36E+17\t1287\t233946420\t5.82E+13
+COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHIPINT\tINTERPRETATION\t9999999\t182382.61\t1362\t13619998638\t1.36E+17\t1318\t240380280\t6.01E+13
+LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI4\tADMINISTRATION ROUTE\t9999999\t8902.52322\t1361\t13609998639\t1.36E+17\t1292\t11502060\t5.77E+11
+COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHEPTYP\tHEPARIN TYPE\t9999999\t181776.5501\t1360\t13599998640\t1.36E+17\t1287\t233946420\t5.82E+13
+LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tSPTY\tSPECIMEN TYPE\t9999999\t976025.9071\t1354\t13539998646\t1.35E+17\t904\t882327420\t7.73E+15
+POINT OF CARE TESTING\t472692\tLABPCG3O\tISTAT G3+, OTHER\tPCTEMP\tBODY TEMPERATURE\t9999999\t109.2250373\t1353\t13529998647\t1.35E+17\t1342\t146580\t3211030800
+LAB BLOOD ORDERABLES\t497492\tLABLIL\tLITHIUM\tLI\tPEAK TROUGH OR RANDOM\t9999999\t8902.52322\t1349\t13489998651\t1.35E+17\t1292\t11502060\t5.77E+11
+BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tGLFLS\tSOURCE\t9999999\t12717.30815\t1346\t13459998654\t1.35E+17\t847\t10771560\t1.15E+12
+BODY FLUID ORDERABLES\t472106\tLABFCR\tCREATININE, FLUID\tCRFLD\tCREATININE, FLUID\t336829.3196\t7390.228188\t1336\t450003971\t4.50E+15\t745\t5505720\t1.31E+12
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tEOSFLD\tEOSINOPHIL %, FLD\t15064.92018\t22434.74138\t1328\t20006214\t2.00E+14\t696\t15614580\t5.31E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tBYST\tYEAST\t9999999\t16387.5\t1324\t13239998676\t1.32E+17\t832\t13634400\t2.26E+12
+BODY FLUID ORDERABLES\t472106\tLABFCR\tCREATININE, FLUID\tCRFLS\tSOURCE\t9999999\t7390.228188\t1317\t13169998683\t1.32E+17\t745\t5505720\t1.31E+12
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tDI\tISSUE DATE/TIME\t9999999\t36229.09091\t1299\t12989998701\t1.30E+17\t539\t19527480\t1.22E+12
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVDNA\tCMV DNA\t9999999\t184331.1162\t1288\t12879998712\t1.29E+17\t1317\t242764080\t5.22E+13
+LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tTEST1\tTEST NAME\t9976785.432\t976025.9071\t1285\t12820169280\t1.28E+17\t904\t882327420\t7.73E+15
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tHCO3A\tHCO3 (A), ISTAT\t31249.34083\t30\t1281\t40030405.6\t4.00E+14\t538\t16140\t1134000
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tO2SATA\tO2 SATURATION, ISTAT\t39125.44575\t30\t1281\t50119696\t5.00E+14\t538\t16140\t1134000
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPCO2A\tPCO2 (A), ISTAT\t31271.021\t30\t1281\t40058177.9\t4.00E+14\t538\t16140\t1134000
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPHA\tPH (A), ISTAT\t31232.91026\t30\t1281\t40009358.04\t4.00E+14\t538\t16140\t1134000
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPO2A\tPO2 (A), ISTAT\t23573.47931\t30\t1281\t30197627\t3.00E+14\t538\t16140\t1134000
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tTCO2A\tTCO2, ISTAT\t54669.89149\t30\t1281\t70032131\t7.00E+14\t538\t16140\t1134000
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tLAC\tLACTATE, ISTAT\t211270.051\t30\t1278\t270003125.2\t2.70E+15\t538\t16140\t1134000
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tUBT\tUNIT BLOOD TYPE  \t9999999\t46753.20236\t1265\t12649998735\t1.26E+17\t509\t23797380\t2.56E+12
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tGRCAST\tGRANULAR CASTS\t9999999\t16965.60886\t1255\t12549998745\t1.25E+17\t813\t13793040\t2.38E+12
+LAB BLOOD ORDERABLES\t471580\tLABAFP\tALPHA FETOPROTEIN, SERUM\tAFP\tALPHA FETOPROTEIN\t1259094.607\t38614.23077\t1226\t1543649988\t1.54E+16\t1092\t42166740\t2.96E+12
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGA\tANGLE, FIBRINOGEN ACTIVITY\t3379634.256\t29423.95102\t1225\t4140051964\t4.14E+16\t1225\t36044340\t2.93E+13
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGC\tR, REACTION TIME (CK)\t3355106.119\t29423.95102\t1225\t4110004996\t4.11E+16\t1225\t36044340\t2.93E+13
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGEL\tEPL, ESTIMATED LYSIS\t3387755.03\t29423.95102\t1225\t4149999911\t4.15E+16\t1225\t36044340\t2.93E+13
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGG\tG, CLOT STABILITY\t3379597.4\t29423.95102\t1225\t4140006816\t4.14E+16\t1225\t36044340\t2.93E+13
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGK\tK, CLOT KINETICS\t3387756.085\t29423.95102\t1225\t4150001204\t4.15E+16\t1225\t36044340\t2.93E+13
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGMA\tMA, PLATELET FUNCTION\t3379632.77\t29423.95102\t1225\t4140050143\t4.14E+16\t1225\t36044340\t2.93E+13
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGRK\tR, REACTION TIME (CK/H)\t400006.1288\t29423.95102\t1225\t490007507.8\t4.90E+15\t1225\t36044340\t2.93E+13
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tPRO\tPRODUCT ID  \t9999999\t39146.22824\t1200\t11999998800\t1.20E+17\t517\t20238600\t2.20E+12
+LAB BLOOD ORDERABLES\t621214\tLABDDIML\tD - DIMER (ELISA)\tDDIMER\tD-DIMER, ELISA\t754575.6921\t5591.974249\t1195\t901717952\t9.00E+15\t932\t5211720\t50304153600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCCNT\tCELLS COUNTED\t49.56281407\t17294.00173\t1194\t59178\t3402588\t1157\t20009160\t1.43E+13
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t30\t1190\t11899998810\t1.19E+17\t538\t16140\t1134000
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tDI\tISSUE DATE/TIME\t9999999\t37638.57434\t1171\t11709998829\t1.17E+17\t491\t18480540\t1.26E+12
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tBYST\tYEAST\t9999999\t17815.31629\t1161\t11609998839\t1.16E+17\t743\t13236780\t2.49E+12
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t56970.32432\t1158\t11579998842\t1.16E+17\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t56970.32432\t1158\t11579998842\t1.16E+17\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t56970.32432\t1157\t11569998843\t1.16E+17\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t56970.32432\t1156\t11559998844\t1.16E+17\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHAVR\tIGM ANTI-HAV\t9999999\t55606.13636\t1138\t11379998862\t1.14E+17\t1056\t58720080\t7.63E+12
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t55606.13636\t1138\t11379998862\t1.14E+17\t1056\t58720080\t7.63E+12
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t55606.13636\t1138\t11379998862\t1.14E+17\t1056\t58720080\t7.63E+12
+LAB BLOOD ORDERABLES\t862879\tLABVD25H\tVITAMIN D, 25-HYDROXYVITAMIN\t25OHDT\t25-HYDROXY D, TOTAL\t352444.1815\t383024.4913\t1135\t400024146\t4.00E+15\t1091\t417879720\t1.88E+14
+LAB BLOOD ORDERABLES\t862879\tLABVD25H\tVITAMIN D, 25-HYDROXYVITAMIN\tVD25D2\t25-HYDROXY D2\t8651983.717\t383024.4913\t1135\t9820001519\t9.82E+16\t1091\t417879720\t1.88E+14
+LAB BLOOD ORDERABLES\t862879\tLABVD25H\tVITAMIN D, 25-HYDROXYVITAMIN\tVD25D3\t25-HYDROXY D3\t537463.9859\t383024.4913\t1135\t610021624\t6.10E+15\t1091\t417879720\t1.88E+14
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBCINT\tINTERPRETATION\t9999999\t55606.13636\t1130\t11299998870\t1.13E+17\t1056\t58720080\t7.63E+12
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tUBT\tUNIT BLOOD TYPE  \t9999999\t49645.91489\t1128\t11279998872\t1.13E+17\t470\t23333580\t2.61E+12
+URINE ORDERABLES\t473722\tLABUUN\tUREA WITH CREATININE, URINE (RANDOM)\tUNCRU\tURINE CREAT\t80162.36379\t18148.1407\t1124\t90102496.9\t9.00E+14\t597\t10834440\t1.25E+12
+URINE ORDERABLES\t473722\tLABUUN\tUREA WITH CREATININE, URINE (RANDOM)\tUNUCRU\tUREA/CREAT RATIO\t106769.7333\t18148.1407\t1124\t120009180.3\t1.20E+15\t597\t10834440\t1.25E+12
+URINE ORDERABLES\t473722\tLABUUN\tUREA WITH CREATININE, URINE (RANDOM)\tUURN\tUREA NITROGEN, URINE\t89520.93505\t18148.1407\t1124\t100621531\t1.00E+15\t597\t10834440\t1.25E+12
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9982077.868\t55606.13636\t1116\t11139998901\t1.11E+17\t1056\t58720080\t7.63E+12
+VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQLG\tRESULT LOG 10\t8968325.277\t220580.5\t1105\t9909999431\t9.91E+16\t1080\t238226940\t6.52E+13
+LAB BLOOD ORDERABLES\t633410\tLABRPRQL\tRPR QUALITATIVE\tRPRQ\tRESULT\t9999999\t63888.99563\t1104\t11039998896\t1.10E+17\t687\t43891740\t8.49E+12
+VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQCP\tRESULT\t8986067.446\t220580.5\t1104\t9920618460\t9.90E+16\t1080\t238226940\t6.52E+13
+VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQSP\tSPECIMEN\t9999999\t220580.5\t1104\t11039998896\t1.10E+17\t1080\t238226940\t6.52E+13
+BODY FLUID ORDERABLES\t473351\tLABPROC\tSPECIMEN PROCESSING\tPROC\tSPECIMEN PROCESSING\t9999999\t1796.035578\t1102\t11019998898\t1.10E+17\t787\t1413480\t7.84E+11
+MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tRPT\tRPT\t9999999\t643208.0247\t1084\t10839998916\t1.08E+17\t486\t312599100\t2.25E+14
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tUPH\tPH URINE\t27785.07176\t2327.256637\t1080\t30007877.5\t3.00E+14\t1130\t2629800\t4.75E+14
+VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQDN\tEBV DNA\t9999999\t220580.5\t1072\t10719998928\t1.07E+17\t1080\t238226940\t6.52E+13
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tPRO\tPRODUCT ID  \t9999999\t36165.4902\t1067\t10669998933\t1.07E+17\t459\t16599960\t1.66E+12
+MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tSDES\tSPECIMEN\t9999999\t643208.0247\t1063\t10629998937\t1.06E+17\t486\t312599100\t2.25E+14
+MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tSREQ\tCOMMENTS\t9999999\t643208.0247\t1059\t10589998941\t1.06E+17\t486\t312599100\t2.25E+14
+POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tGLU\tGLUCOSE BY METER\t135573.9374\t203485.6514\t1034\t140183451.3\t1.40E+15\t1090\t221799360\t1.06E+16
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t55606.13636\t1032\t10319998968\t1.03E+17\t1056\t58720080\t7.63E+12
+URINE ORDERABLES\t473170\tLABUTPRAN\tPROTEIN WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREATININE\t78748.20374\t18364.8074\t1017\t80086923.2\t8.00E+14\t649\t11918760\t2.18E+12
+URINE ORDERABLES\t473170\tLABUTPRAN\tPROTEIN WITH CREATININE, URINE (RANDOM)\tUPROTCR\tPROTEIN/CREAT RATIO\t766964.0751\t18364.8074\t1017\t780002464.3\t7.80E+15\t649\t11918760\t2.18E+12
+URINE ORDERABLES\t473170\tLABUTPRAN\tPROTEIN WITH CREATININE, URINE (RANDOM)\tUPROTTOT\tPROTEIN, TOTAL, URINE\t472130.1534\t18364.8074\t1017\t480156366\t4.80E+15\t649\t11918760\t2.18E+12
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tSI\t<<STATUS>>  \t9999999\t45922.88372\t1015\t10149998985\t1.01E+17\t1935\t88860780\t1.11E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t63540.24819\t1015\t10149998985\t1.01E+17\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t63540.24819\t1015\t10149998985\t1.01E+17\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t63540.24819\t1014\t10139998986\t1.01E+17\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t63540.24819\t1014\t10139998986\t1.01E+17\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGC\tR, REACTION TIME (CK)\t2195851.888\t18241.67973\t1011\t2220006259\t2.22E+16\t893\t16289820\t1.94E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGRK\tR, REACTION TIME (CK/H)\t2116721.757\t18241.67973\t1011\t2140005696\t2.14E+16\t893\t16289820\t1.94E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGA\tANGLE, FIBRINOGEN AC\t2188171.385\t18225.9417\t1010\t2210053099\t2.21E+16\t892\t16257540\t1.94E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGG\tG, CLOT STABILITY\t2188128.064\t18225.9417\t1010\t2210009345\t2.21E+16\t892\t16257540\t1.94E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGK\tK, CLOT KINETICS\t2217823.037\t18225.9417\t1010\t2240001267\t2.24E+16\t892\t16257540\t1.94E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGM\tMA, PLT. FUNCTION\t2188172.076\t18225.9417\t1010\t2210053797\t2.21E+16\t892\t16257540\t1.94E+12
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGEL\tEPL, ESTIMATED LYSIS\t2190287.787\t18224.78114\t1009\t2210000377\t2.21E+16\t891\t16238280\t1.94E+12
+MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t342848.8073\t1009\t10089998991\t1.01E+17\t436\t149482080\t1.48E+14
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t63540.24819\t996\t9959999004\t9.96E+16\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABIDRBC\tANTIBODY ID RBC\t9999999\t42315.55932\t990\t9899999010\t9.90E+16\t590\t24966180\t3.53E+13
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tSI\t<<STATUS>>  \t9999999\t44758.42809\t986\t9859999014\t9.86E+16\t1794\t80296620\t9.91E+12
+MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t342848.8073\t986\t9859999014\t9.86E+16\t436\t149482080\t1.48E+14
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tRPT\tRPT\t9999999\t324263.2112\t983\t9829999017\t9.83E+16\t928\t300916260\t1.71E+14
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tDI\tISSUE DATE/TIME\t9999999\t33804.49878\t978\t9779999022\t9.78E+16\t409\t13826040\t7.48E+11
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t324263.2112\t975\t9749999025\t9.75E+16\t928\t300916260\t1.71E+14
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tXMR\tUNIT CROSSMATCH  \t9999999\t78246.38961\t974\t9739999026\t9.74E+16\t385\t30124860\t4.86E+12
+LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tLAB1\tREFERENCE LAB\t9999999\t976025.9071\t964\t9639999036\t9.64E+16\t904\t882327420\t7.73E+15
+ECHO ORDERABLES\t472301\tECH15\tECHO - TRANSTHORACIC ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t50.51612903\t327040.5203\t961\t48546\t2670048\t961\t314285940\t3.83E+15
+MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tRPT\tRPT\t9999999\t464348.8949\t961\t9609999039\t9.61E+16\t371\t172273440\t9.73E+14
+MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t464348.8949\t960\t9599999040\t9.60E+16\t371\t172273440\t9.73E+14
+MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t342848.8073\t957\t9569999043\t9.57E+16\t436\t149482080\t1.48E+14
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tUBT\tUNIT BLOOD TYPE  \t9999999\t48640.67308\t955\t9549999045\t9.55E+16\t416\t20234520\t2.20E+12
+VIROLOGY ORDERABLES\t473715\tLABMVSOB\tUNLISTED MICROBIOLOGY/VIROLOGY TEST\tMVSO\tTEST NAME\t9999999\t681043.9907\t945\t9449999055\t9.45E+16\t431\t293529960\t8.51E+14
+MICROBIOLOGY ORDERABLES\t472498\tLABHELI\tHELICOBACTER PYLORI AB\tHPYLORI\tHELICOB. PYLORI IGG AB\t9999999\t348411.9064\t937\t9369999063\t9.37E+16\t897\t312525480\t1.42E+14
+BODY FLUID ORDERABLES\t473089\tLABPHME\tPH BY METER\tPHMETS\tSOURCE\t9999999\t17450.32258\t926\t9259999074\t9.26E+16\t496\t8655360\t1.26E+12
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tXMR\tUNIT CROSSMATCH  \t9999999\t82301.64894\t923\t9229999077\t9.23E+16\t376\t30945420\t5.13E+12
+LAB BLOOD ORDERABLES\t471951\tLABCEA\tCEA\tCEA\tCEA\t1722715.411\t34760.8134\t923\t1590066324\t1.59E+16\t836\t29060040\t1.77E+12
+LAB BLOOD ORDERABLES\t472503\tLAB11041R\tFREE HEMOGLOBIN\tHGB\tHEMOGLOBIN, SERUM\t431902.3998\t181763.645\t903\t390007867\t3.90E+15\t845\t153590280\t4.24E+13
+LAB BLOOD ORDERABLES\t472562\tLABHCLOTI\tHOLD CLOT\tHCLOT\tHOLD CLOT\t9999999\t81.25\t897\t8969999103\t8.97E+16\t288\t23400\t81900000
+LAB BLOOD ORDERABLES\t472937\tLABMYO\tMYOGLOBIN, SERUM / PLASMA\tMYOG\tMYOGLOBIN, SER/PLAS\t1901719.799\t6401.506849\t894\t1700137500\t1.70E+16\t730\t4673100\t1.14E+11
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tPRO\tPRODUCT ID  \t9999999\t36168.4375\t892\t8919999108\t8.92E+16\t384\t13888680\t1.55E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPSREV\tPATH/SPEC REVIEW\t9999999\t109814.5616\t891\t8909999109\t8.91E+16\t4619\t507233460\t1.01E+14
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t887\t8869999113\t8.87E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tAG\tANION GAP\t418559.4548\t7149.481669\t884\t370006558\t3.70E+15\t791\t5655240\t2.94E+11
+LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tCL\tCHLORIDE, SER/PLAS\t11415.4457\t7149.481669\t884\t10091254\t1.00E+14\t791\t5655240\t2.94E+11
+LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tCO2\tCO2, SER/PLAS\t282830.9355\t7149.481669\t884\t250022547\t2.50E+15\t791\t5655240\t2.94E+11
+LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tK\tPOTASSIUM, SER/PLAS\t22628.49468\t7149.481669\t884\t20003589.3\t2.00E+14\t791\t5655240\t2.94E+11
+LAB BLOOD ORDERABLES\t472305\tLABEP1\tELECTROLYTE PANEL\tNA\tSODIUM, SER/PLAS\t11449.75566\t7149.481669\t884\t10121584\t1.00E+14\t791\t5655240\t2.94E+11
+LAB BLOOD ORDERABLES\t517611\tLABCA199\tCANCER AG 19-9\tC199\tCANCER ANTIGEN 19-9\t577390.8375\t54412.61194\t874\t504639592\t5.00E+15\t804\t43747740\t6.01E+12
+MICROBIOLOGY ORDERABLES\t471695\tLABAGALA\tASPERGILLUS GALACTOMANNAN\tAGALA\tASPER.GALACTOMANNAN\t184544.6402\t391343.0659\t867\t160000203.1\t1.60E+15\t835\t326771460\t1.62E+14
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tDI\tISSUE DATE/TIME\t9999999\t36268.46361\t844\t8439999156\t8.44E+16\t371\t13455600\t8.83E+11
+LAB BLOOD ORDERABLES\t744412\tLABCORTS\tCORTISOL, POST OP\tCORTS\tCORTISOL, POST OP\t332561.1871\t10774.85294\t842\t280016519.5\t2.80E+15\t816\t8792280\t3.68E+11
+VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-PLASMA\tCMQDNA\tCMV DNA\t9999999\t242296.5517\t842\t8419999158\t8.42E+16\t116\t28106400\t9.68E+12
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tUBT\tUNIT BLOOD TYPE  \t9999999\t45845.13812\t832\t8319999168\t8.32E+16\t362\t16595940\t1.66E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tCAOX\tCA OXALATE CRYSTALS\t9999999\t11074.21648\t832\t8319999168\t8.32E+16\t619\t6854940\t3.04E+11
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFOTHER\tOTHERS, FLD\t11.05180723\t16276.09442\t830\t9173\t464053\t466\t7584660\t6.68E+11
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBGRES\tQUANTIFERON RESULT\t9999999\t226235.1951\t820\t8199999180\t8.20E+16\t820\t185512860\t8.35E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tADNVIR\tADENOVIRUS\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tINFAST\tINFLUENZA A SUBTYPE\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tINFLA\tINFLUENZA A\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tINFLB\tINFLUENZA B\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tMETPNV\tMETAPNEUMOVIRUS\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tPARIN1\tPARAINFLUENZA 1\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tPARIN2\tPARAINFLUENZA 2\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tPARIN3\tPARAINFLUENZA 3\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRHNVIR\tRHINOVIRUS\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRSVPC\tRSV\t9999999\t97678.90244\t820\t8199999180\t8.20E+16\t820\t80096700\t1.50E+13
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBGNIL\tNIL\t196560.2903\t226235.1951\t814\t160000076.3\t1.60E+15\t820\t185512860\t8.35E+13
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGSP\tSP, SPLIT POINT\t307132.4496\t18308.78049\t814\t250005814\t2.50E+15\t902\t16514520\t1.95E+12
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tPRO\tPRODUCT ID  \t9999999\t35683.83954\t812\t8119999188\t8.12E+16\t349\t12453660\t1.39E+12
+LAB BLOOD ORDERABLES\t473456\tLABIGET\tTOTAL IGE\tIGET\tTOTAL IGE\t382545.9766\t236444.3041\t811\t310244787\t3.10E+15\t776\t183480780\t6.09E+13
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBAGNI\tTB AG - NIL\t4002478.18\t226235.1951\t807\t3229999891\t3.23E+16\t820\t185512860\t8.35E+13
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBAG\tTB AG\t3995037.156\t226235.1951\t806\t3219999948\t3.22E+16\t820\t185512860\t8.35E+13
+VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMGQUL\tCMV IGG RESULT\t9999999\t157732.7413\t799\t7989999201\t7.99E+16\t777\t122558340\t2.53E+13
+LAB BLOOD ORDERABLES\t471756\tLABBHCG\tBETA HCG\tBHCG\tBETA HCG\t5170843.619\t8779.419087\t798\t4126333208\t4.12E+16\t723\t6347520\t1.10E+11
+LAB BLOOD ORDERABLES\t472054\tLABC3\tCOMPLEMENT C3, SERUM\tC3\tCOMPLEMENT C3, SERUM\t25258.54717\t49308.25737\t795\t20080545\t2.00E+14\t746\t36783960\t3.49E+12
+LAB BLOOD ORDERABLES\t472055\tLABC4\tCOMPLEMENT C4, SERUM\tC4\tCOMPLEMENT C4, SERUM\t201280.8872\t49642.3498\t795\t160018305.3\t1.60E+15\t749\t37182120\t3.54E+12
+LAB BLOOD ORDERABLES\t473410\tLABYFT3\tT3, FREE\tFT3\tT3, FREE\t113210.0052\t34773.5493\t795\t90001954.1\t9.00E+14\t710\t24689220\t1.55E+12
+MICROBIOLOGY ORDERABLES\t472113\tLABCRPLB\tCRYPTOCOCCAL AG LATEX, BLOOD\tCRPLB\tLATEX CRYPTO AG-BID\t9999999\t94189.77543\t791\t7909999209\t7.91E+16\t757\t71301660\t9.81E+12
+LAB BLOOD ORDERABLES\t471701\tLABAST\tAST, SERUM/PLASMA\tAST\tAST (SGOT), SER/PLAS\t303900.5354\t3702.536023\t790\t240081423\t2.40E+15\t347\t1284780\t90954759600
+LAB BLOOD ORDERABLES\t472528\tLABHCVA\tANTI-HCV\tHCVR\tANTI-HCV\t9999999\t42280.72931\t790\t7899999210\t7.90E+16\t713\t30146160\t3.88E+12
+VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVLOG\tRESULT LOG 10\t6393863.376\t306451.3492\t782\t5000001160\t5.00E+16\t756\t231677220\t1.10E+14
+VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVRES\tRESULT\t7647170.927\t306451.3492\t782\t5980087665\t5.78E+16\t756\t231677220\t1.10E+14
+MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tRPT\tRPT\t9999999\t273578.4971\t780\t7799999220\t7.80E+16\t173\t47329080\t1.50E+13
+MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tSREQ\tCOMMENTS\t9999999\t273578.4971\t780\t7799999220\t7.80E+16\t173\t47329080\t1.50E+13
+VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVSP\tSPECIMEN\t9999999\t306451.3492\t780\t7799999220\t7.80E+16\t756\t231677220\t1.10E+14
+LAB BLOOD ORDERABLES\t471588\tLABALT\tALT, SERUM/PLASMA\tALT\tALT (SGPT), SER/PLAS\t243998.7587\t4517.267442\t779\t190075033\t1.90E+15\t344\t1553940\t24505693200
+LAB BLOOD ORDERABLES\t473182\tLABPSA\tPSA\tPSA\tPSA\t1171204.076\t37028.55882\t777\t910025567.2\t9.10E+15\t680\t25179420\t1.67E+12
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tBD\tBASE DEFICIT, ISTAT\t25880.27814\t31.01123596\t773\t20005455\t2.00E+14\t356\t11040\t799200
+BODY FLUID ORDERABLES\t472721\tLABCLACT\tLACTIC ACID, CSF\tLACCSF\tLACTIC ACID, CSF\t104852.7679\t10233.44262\t763\t80002661.9\t8.00E+14\t427\t4369680\t3.87E+11
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVCP\tRESULT\t7190521.954\t295560.7542\t761\t5471987207\t5.46E+16\t716\t211621500\t8.15E+13
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVDNA\tEBV DNA\t9999999\t295560.7542\t761\t7609999239\t7.61E+16\t716\t211621500\t8.15E+13
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVLOG\tRESULT LOG\t7174770.359\t295560.7542\t761\t5460000243\t5.46E+16\t716\t211621500\t8.15E+13
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVSP\tSPECIMEN\t9999999\t295560.7542\t761\t7609999239\t7.61E+16\t716\t211621500\t8.15E+13
+VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVRNA\tHEP C VIRVUS RNA\t9999999\t306451.3492\t761\t7609999239\t7.61E+16\t756\t231677220\t1.10E+14
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tHEMED\tHEMOCCULT EXP DATE\t9802405.581\t10328.26531\t758\t7430223430\t7.43E+16\t784\t8097360\t3.21E+13
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tHEML\tHEMOCCULT LOT#\t9081525.997\t10328.26531\t758\t6883796706\t6.88E+16\t784\t8097360\t3.21E+13
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSLYM\tLYMPHOCYTES, SYN FLD\t343474.2616\t11175.33937\t757\t260010016\t2.60E+15\t442\t4939500\t4.18E+11
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSMONO\tMONOS/MESOS, SYN FLD\t343479.1678\t11175.33937\t757\t260013730\t2.60E+15\t442\t4939500\t4.18E+11
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSNEUT\tNEUTROPHILS, SYN FLD\t343524.5429\t11175.33937\t757\t260048079\t2.60E+15\t442\t4939500\t4.18E+11
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSRBC\tRBC COUNT, SYN FLD\t584695.0528\t11175.33937\t757\t442614155\t3.17E+15\t442\t4939500\t4.18E+11
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSTURB\tTURBIDITY, SYN FLD\t9986788.967\t11175.33937\t757\t7559999248\t7.56E+16\t442\t4939500\t4.18E+11
+LAB BLOOD ORDERABLES\t472478\tLABGLNF\tGLUCOSE NON-FASTING, SERUM/PLASMA\tGLU\tGLUCOSE, NON-FASTING\t92630.75694\t5841.125\t757\t70121483\t7.00E+14\t480\t2803740\t91321002000
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSCOLOR\tCOLOR, SYN FLD\t9999999\t11175.33937\t756\t7559999244\t7.56E+16\t442\t4939500\t4.18E+11
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSWBC\tNUCL. CELLS, SYN FLD\t360270.0185\t11175.33937\t756\t272364134\t2.51E+15\t442\t4939500\t4.18E+11
+MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tSDES\tSPECIMEN\t9999999\t273578.4971\t753\t7529999247\t7.53E+16\t173\t47329080\t1.50E+13
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t11175.33937\t749\t7489999251\t7.49E+16\t442\t4939500\t4.18E+11
+LAB BLOOD ORDERABLES\t473262\tLABRF\tRHUEMATOID FACTOR\tRHFR\tRHEUMATOID FACTOR\t9999999\t239163.253\t747\t7469999253\t7.47E+16\t664\t158804400\t5.11E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMETA\tMETAMYELOCYTES\t3.095302013\t29529.25035\t745\t2306\t13812\t707\t20877180\t3.19E+12
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCULT\tCULT/OTHER RSLT:\t9999999\t505173.5304\t743\t7429999257\t7.43E+16\t41511\t20970258420\t1.13E+16
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tDX\tDIAGNOSIS\t9946094.042\t11175.33937\t742\t7380001779\t7.38E+16\t442\t4939500\t4.18E+11
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t741\t7409999259\t7.41E+16\t0\tNone\tNone
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEXP\tDEVELOPER EXP DATE\t9812324.848\t10328.26531\t741\t7270932712\t7.27E+16\t784\t8097360\t3.21E+13
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEL\tDEVELOPER LOT#\t5622466.236\t10328.26531\t740\t4160625015\t4.15E+16\t784\t8097360\t3.21E+13
+STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tRPT\tRPT\t9999999\t210925.3091\t740\t7399999260\t7.40E+16\t275\t58004460\t1.38E+13
+STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tSDES\tSPECIMEN\t9999999\t210925.3091\t740\t7399999260\t7.40E+16\t275\t58004460\t1.38E+13
+STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tSREQ\tCOMMENTS\t9999999\t210925.3091\t740\t7399999260\t7.40E+16\t275\t58004460\t1.38E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMYEL\tMYELOCYTES\t3.619565217\t29836.81366\t736\t2664\t21750\t703\t20975280\t3.42E+12
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tJOINT\tJOINT\t9999999\t11175.33937\t732\t7319999268\t7.32E+16\t442\t4939500\t4.18E+11
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBMINI\tMITOGEN - NIL\t1874149.606\t226235.1951\t731\t1370003362\t1.37E+16\t820\t185512860\t8.35E+13
+LAB BLOOD ORDERABLES\t473672\tLABTRXNWI\tTRANSFUSION REACTION WORKUP\tCTRXNW\tTRANSFUSION REACTION COMMENT\t9999999\t34.85148515\t731\t7309999269\t7.31E+16\t303\t10560\t2030400
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBNQUL\tEBNQUL\t9999999\t287670.1288\t731\t7309999269\t7.31E+16\t699\t201081420\t8.47E+13
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVMQUL\tVMQUL\t9999999\t287670.1288\t731\t7309999269\t7.31E+16\t699\t201081420\t8.47E+13
+LAB BLOOD ORDERABLES\t473672\tLABTRXNWI\tTRANSFUSION REACTION WORKUP\tTRXNWR\tTRANSFUSION REACTION WORKUP\t9999999\t34.85148515\t730\t7299999270\t7.30E+16\t303\t10560\t2030400
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEAGQUL\tEAGQUL\t9999999\t287670.1288\t730\t7299999270\t7.30E+16\t699\t201081420\t8.47E+13
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVGQUL\tVGQUL\t9999999\t287670.1288\t730\t7299999270\t7.30E+16\t699\t201081420\t8.47E+13
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tRPT\tRPT\t9999999\t330942.4585\t728\t7279999272\t7.28E+16\t301\t99613680\t3.68E+13
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t330942.4585\t728\t7279999272\t7.28E+16\t301\t99613680\t3.68E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tNRBC\tNRBC/100WBC\t13843.10235\t31012.87554\t723\t10008563\t1.00E+14\t699\t21678000\t3.39E+12
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tDI\tISSUE DATE/TIME\t9999999\t34368.29721\t718\t7179999282\t7.18E+16\t323\t11100960\t6.24E+11
+VIROLOGY ORDERABLES\t472519\tLABHAVT\tANTI-HAV\tHATR\tANTI-HAV ANTIBODY\t9999999\t76703.94453\t718\t7179999282\t7.18E+16\t649\t49780860\t1.16E+13
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tQC10\tQC RESULT\t9999999\t10328.26531\t717\t7169999283\t7.17E+16\t784\t8097360\t3.21E+13
+LAB BLOOD ORDERABLES\t473440\tLABTT\tTHROMBIN TIME\tTT\tTHROMBIN TIME\t854359.3497\t3318.487395\t714\t610012575.7\t6.10E+15\t357\t1184700\t20331651600
+VIROLOGY ORDERABLES\t473715\tLABMVSOB\tUNLISTED MICROBIOLOGY/VIROLOGY TEST\tSCE\tSOURCE\t9999999\t681043.9907\t714\t7139999286\t7.14E+16\t431\t293529960\t8.51E+14
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVGQNT\tVGQNT\t322585.5422\t284480.6775\t713\t230003491.6\t2.30E+15\t679\t193162380\t7.94E+13
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tRPT\tRPT\t9999999\t454839.1011\t712\t7119999288\t7.12E+16\t712\t323845440\t2.65E+14
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tOCCBLD\tOCCULT BLOOD, STOOL\t9999999\t10432.23881\t703\t7029999297\t7.03E+16\t804\t8387520\t3.22E+13
+VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMGQNT\tCMV IGG LEVEL\t1988622.425\t158368.3086\t699\t1390047075\t1.39E+16\t674\t106740240\t2.24E+13
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tPRO\tPRODUCT ID  \t9999999\t35555.0495\t698\t6979999302\t6.98E+16\t303\t10773180\t1.30E+12
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t454839.1011\t697\t6969999303\t6.97E+16\t712\t323845440\t2.65E+14
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tSDES\tSPECIMEN\t9999999\t454839.1011\t694\t6939999306\t6.94E+16\t712\t323845440\t2.65E+14
+MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t464348.8949\t693\t6929999307\t6.93E+16\t371\t172273440\t9.73E+14
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tUBT\tUNIT BLOOD TYPE  \t9999999\t47227.55102\t692\t6919999308\t6.92E+16\t294\t13884900\t1.55E+12
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t324263.2112\t691\t6909999309\t6.91E+16\t928\t300916260\t1.71E+14
+LAB BLOOD ORDERABLES\t472193\tLABDATBSI\tDIRECT COOMBS BROAD SPECTRUM\tDATC3\tDIRECT ANTIGLOB. - C3\t9999999\t21378.67804\t689\t6889999311\t6.89E+16\t469\t10026600\t3.30E+12
+MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tRPT\tRPT\t9999999\t#VALUE!\t689\t6889999311\t6.89E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472193\tLABDATBSI\tDIRECT COOMBS BROAD SPECTRUM\tDATG\tDIRECT ANTIGLOB. - IGG\t9999999\t22062.6383\t688\t6879999312\t6.88E+16\t470\t10369440\t3.42E+12
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBNQNT\tEBNQNT\t538575.8845\t274703.1756\t687\t370001632.6\t3.70E+15\t655\t179930580\t7.15E+13
+BODY FLUID ORDERABLES\t473089\tLABPHME\tPH BY METER\tPHA\tPH BY METER\t14627.53886\t17450.32258\t684\t10005236.58\t1.00E+14\t496\t8655360\t1.26E+12
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t45029.49618\t682\t6819999318\t6.82E+16\t655\t29494320\t3.07E+14
+LAB BLOOD ORDERABLES\t471575\tLABALKP\tALKALINE PHOSPHATASE TOTAL SERUM/PLASMA\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t88379.00882\t3307.649402\t680\t60097726\t6.00E+14\t251\t830220\t8191522800
+LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGENTTRO\tGENTAMICIN, TROUGH\t972018.9875\t8326.14601\t679\t660000892.5\t6.60E+15\t589\t4904100\t1.61E+11
+LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGNTPR3\tDOSAGE\t9999999\t8326.14601\t679\t6789999321\t6.79E+16\t589\t4904100\t1.61E+11
+LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGNTPR2\tDATE & TIME OF LAST DOSE\t9999999\t8326.14601\t677\t6769999323\t6.77E+16\t589\t4904100\t1.61E+11
+LAB BLOOD ORDERABLES\t497508\tLABGNTPRL\tGENTAMICIN TROUGH LEVEL\tGNTPR4\tADMINISTRATION ROUTE\t9999999\t8326.14601\t676\t6759999324\t6.76E+16\t589\t4904100\t1.61E+11
+MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tSDES\tSPECIMEN\t9999999\t#VALUE!\t674\t6739999326\t6.74E+16\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t330942.4585\t666\t6659999334\t6.66E+16\t301\t99613680\t3.68E+13
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tCAION\tCALCIUM,ION, ISTAT\t15038.7708\t1280.120301\t665\t10000782.58\t1.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tCR\tCREATININE,ISTAT\t30076.77744\t1280.120301\t665\t20001057\t2.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tGLU\tGLUCOSE,ISTAT\t60295.75188\t1280.120301\t665\t40096675\t4.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tHCT\tHCT(CALC), ISTAT\t90262.25414\t1280.120301\t665\t60024399\t6.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tHGB\tHGB(CALC), ISTAT\t90238.03233\t1280.120301\t665\t60008291.5\t6.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tK\tPOTASSIUM, ISTAT\t120304.9702\t1280.120301\t665\t80002805.2\t8.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tNA\tSODIUM, ISTAT\t45250.96692\t1280.120301\t665\t30091893\t3.00E+14\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCAGP\tANION GAP, ISTAT\t436106.0301\t1280.120301\t665\t290010510\t2.90E+15\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCBUN\tBUN, ISTAT\t391003.2602\t1280.120301\t665\t260017168\t2.60E+15\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCCL\tCHLORIDE, ISTAT\t255739.7895\t1280.120301\t665\t170066960\t1.70E+15\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tPCCOM\tPOCT COMMENT\t9999999\t1280.120301\t665\t6649999335\t6.65E+16\t665\t851280\t4.55E+11
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2\tTCO2A\tTCO2, ISTAT\t105287.0602\t1280.120301\t665\t70015895\t7.00E+14\t665\t851280\t4.55E+11
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tXMR\tUNIT CROSSMATCH  \t9999999\t74761.73913\t664\t6639999336\t6.64E+16\t276\t20634240\t3.23E+12
+MICROBIOLOGY ORDERABLES\t472114\tLABCRPLC\tCRYPTOCOCCAL AG LATEX, CSF\tCRPLC\tLATEX CRYPTO AG,CSF\t9999999\t70420.68063\t663\t6629999337\t6.63E+16\t382\t26900700\t2.73E+12
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBINTR\tEBV INTERPRETATION\t9999999\t287670.1288\t659\t6589999341\t6.59E+16\t699\t201081420\t8.47E+13
+LAB BLOOD ORDERABLES\t541695\tLABDSDNA\tANTI - DSDNA AB\tDNAQUL\tANTI-DSDNA RESULT\t9999999\t212647.8896\t652\t6519999348\t6.52E+16\t616\t130991100\t3.85E+13
+LAB BLOOD ORDERABLES\t541668\tLABACTHO\tADRENOCORTICOTROPIC HORMONE (ACTH)\tACTH\tACTH\t1808382.362\t138494.094\t647\t1170023388\t1.17E+16\t596\t82542480\t4.48E+14
+LAB BLOOD ORDERABLES\t472670\tLABFE\tIRON, TOTAL\tFE\tIRON, TOTAL\t139810.0668\t7216.6171\t644\t90037683\t9.00E+14\t538\t3882540\t60419718000
+LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN PLATELET FACTOR 4 AB\tPF4ENH\tPF4 IGG, IGM, IGA OD\t264797.8074\t95902.35702\t642\t170000192.3\t1.70E+15\t577\t55335660\t1.99E+13
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tPRO\tPRODUCT ID  \t9999999\t32446.02151\t636\t6359999364\t6.36E+16\t279\t9052440\t1.06E+12
+LAB BLOOD ORDERABLES\t471630\tLABACA\tANTI CARDIOLIPIN AB\tACAIGG\tACA IGG ANTIBODIES\t330196.3884\t293701.5437\t636\t210004903\t2.10E+15\t583\t171228000\t7.16E+13
+LAB BLOOD ORDERABLES\t471630\tLABACA\tANTI CARDIOLIPIN AB\tACAIGM\tACA IGM ANTIBODIES\t298747.0959\t293701.5437\t636\t190003153\t1.90E+15\t583\t171228000\t7.16E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHQLINT\tINTERPRETATION\t9999999\t63540.24819\t635\t6349999365\t6.35E+16\t967\t61443420\t8.90E+12
+BODY FLUID ORDERABLES\t471522\tLABASQ\tACETONE SEMI QUALITATIVE\tACETONE\tACETONE SEMI QUAL\t9999999\t6040.823529\t627\t6269999373\t6.27E+16\t510\t3080820\t31003088400
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tUBT\tUNIT BLOOD TYPE  \t9999999\t46629.4382\t626\t6259999374\t6.26E+16\t267\t12450060\t1.39E+12
+VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tHERPR\tHERPES SIMPLEX VIRUS (HSV)\t9999999\t61157.32283\t626\t6259999374\t6.26E+16\t381\t23300940\t3.03E+12
+VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tVZR\tVARICELLA-ZOSTER VIRUS\t9999999\t61157.32283\t626\t6259999374\t6.26E+16\t381\t23300940\t3.03E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOS\tEOSINOPHILS\t16029.05449\t27244.66321\t624\t10002130\t1.00E+14\t579\t15774660\t2.39E+12
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tDI\tISSUE DATE/TIME\t9999999\t31683.42857\t615\t6149999385\t6.15E+16\t280\t8871360\t4.33E+11
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tXMR\tUNIT CROSSMATCH  \t9999999\t73288.9243\t613\t6129999387\t6.13E+16\t251\t18395520\t2.94E+12
+LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIMET\tMETHOD\t9999999\t436344.1652\t612\t6119999388\t6.12E+16\t581\t253515960\t1.44E+14
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tEOSAB\tEOS, ABS (MAN DIFF)\t16367.34124\t27633.43915\t611\t10000445.5\t1.00E+14\t567\t15668160\t2.39E+12
+MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t603\t6029999397\t6.03E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLINT\tINTERPRETATION\t9999999\t56970.32432\t600\t5999999400\t6.00E+16\t1110\t63237060\t8.60E+12
+URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tCRE\tCREATININE EXCRETION\t386686.995\t50111.01695\t596\t230465449\t2.30E+15\t354\t17739300\t3.04E+12
+URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tUCR\tCREATININE, URINE\t369183.7985\t50111.01695\t596\t220033543.9\t2.20E+15\t354\t17739300\t3.04E+12
+URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tUT3\tCOLLECTION PERIOD CREATININE\t503395.057\t50111.01695\t596\t300023454\t3.00E+15\t354\t17739300\t3.04E+12
+URINE ORDERABLES\t472110\tLABUCRT\tCREATININE, URINE (TIMED)\tVOL3\tVOLUME CREATININE\t370885.8748\t50111.01695\t596\t221047981.4\t2.20E+15\t354\t17739300\t3.04E+12
+LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIRES\tRESULTS\t9999999\t436344.1652\t589\t5889999411\t5.89E+16\t581\t253515960\t1.44E+14
+LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBRATROUGH\tTOBRAMYCIN, TROUGH\t4846416.814\t8171.44385\t586\t2840000253\t2.84E+16\t561\t4584180\t1.48E+11
+LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBPR2\tDATE & TIME OF LAST DOSE\t9999999\t8171.44385\t584\t5839999416\t5.84E+16\t561\t4584180\t1.48E+11
+LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBPR3\tDOSAGE\t9999999\t8171.44385\t584\t5839999416\t5.84E+16\t561\t4584180\t1.48E+11
+LAB BLOOD ORDERABLES\t497514\tLABTOBPRL\tTOBRAMYCIN TROUGH LEVEL\tTOBPR4\tADMINISTRATION ROUTE\t9999999\t8171.44385\t584\t5839999416\t5.84E+16\t561\t4584180\t1.48E+11
+LAB BLOOD ORDERABLES\t472518\tLABHAVM\tIGM ANTI-HAV\tHAVR\tIGM ANTI-HAV\t9999999\t48714.87379\t582\t5819999418\t5.82E+16\t515\t25088160\t2.39E+12
+MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tAFB\tAFB SMEAR\t9999999\t3886778.338\t582\t5819999418\t5.82E+16\t2310\t8978457960\t3.70E+16
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tSI\t<<STATUS>>  \t9999999\t41709.62264\t577\t5769999423\t5.77E+16\t1166\t48633420\t5.74E+12
+VIROLOGY ORDERABLES\t472525\tLABHBSQL\tANTI-HBS QUAL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t49138.5336\t572\t5719999428\t5.72E+16\t491\t24127020\t2.62E+12
+LAB BLOOD ORDERABLES\t471961\tLABYCERU\tCERULOPLASMIN\tCER\tCERULOPLASMIN\t176706.9682\t59495.32075\t566\t100016144\t1.00E+15\t530\t31532520\t5.23E+12
+BODY FLUID ORDERABLES\t497357\tLABFTRPL\tTRIGLYCERIDES, PLEURAL\tTGLFLD\tTRIGLYCERIDES, PLEURAL FLUID\t3539900.179\t11227.32955\t565\t2000043601\t2.00E+16\t352\t3952020\t3.11E+11
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tBASOFLD\tBASOPHILS, FLD\t1.662522202\t19547.02703\t563\t936\t3502\t296\t5785920\t1.19E+12
+URINE ORDERABLES\t473127\tLABURK\tPOTASSIUM, URINE (RANDOM)\tUK\tPOTASSIUM, URINE\t53319.41066\t12981.89655\t563\t30018828.2\t3.00E+14\t348\t4517700\t2.34E+11
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGCOM\tCOMMENTS\t9999999\t18237.38255\t560\t5599999440\t5.60E+16\t894\t16304220\t1.94E+12
+MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tRPT\tRPT\t9999999\t3931337.485\t559\t5589999441\t5.59E+16\t326\t1281616020\t5.25E+15
+LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTMET\tMETHOD\t9999999\t452558.7763\t558\t5579999442\t5.58E+16\t523\t236688240\t1.36E+14
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tPRO\tPRODUCT ID  \t9999999\t32777.54941\t557\t5569999443\t5.57E+16\t253\t8292720\t9.48E+11
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tURENAL\tRENAL CELLS\t9999999\t11982.37197\t556\t5559999444\t5.56E+16\t371\t4445460\t2.83E+11
+URINE ORDERABLES\t472551\tLABYHISTU\tHISTOPLASMA AG, URINE\tYHISU4\tRESULT URINE\t9945944.981\t381867.6106\t555\t5519999465\t5.52E+16\t339\t129453120\t5.70E+13
+LAB BLOOD ORDERABLES\t472521\tLABHBCT\tANTI-HBC\tHBTR\tANTI-HBC ANTIBODY\t9999999\t53580.7438\t554\t5539999446\t5.54E+16\t484\t25933080\t3.17E+12
+MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tSREQ\tCOMMENTS\t9999999\t3931337.485\t554\t5539999446\t5.54E+16\t326\t1281616020\t5.25E+15
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tRPT\tRPT\t9999999\t503786.5343\t554\t5539999446\t5.54E+16\t554\t279097740\t1.51E+14
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tSDES\tSPECIMEN\t9999999\t503786.5343\t554\t5539999446\t5.54E+16\t554\t279097740\t1.51E+14
+MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tSDES\tSPECIMEN\t9999999\t3931337.485\t553\t5529999447\t5.53E+16\t326\t1281616020\t5.25E+15
+VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-BLOOD\tCMQDNA\tCMV DNA\t9999999\t204632\t547\t5469999453\t5.47E+16\t30\t6138960\t1.86E+12
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tSREQ\tCOMMENTS\t9999999\t503786.5343\t543\t5429999457\t5.43E+16\t554\t279097740\t1.51E+14
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tSI\t<<STATUS>>  \t9999999\t44531.81989\t540\t5399999460\t5.40E+16\t1066\t47470920\t6.04E+12
+LAB BLOOD ORDERABLES\t472563\tLABHMCY\tHOMOCYSTEINE TOTAL, PLASMA\tHMCY\tHOMOCYSTEINE, PLAS TOTAL\t74085.00833\t45438.5259\t540\t40005904.5\t4.00E+14\t502\t22810140\t5.52E+12
+LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTRES\tRESULTS\t9999999\t452558.7763\t539\t5389999461\t5.39E+16\t523\t236688240\t1.36E+14
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVCP\tRESULT\t8191919.163\t289760.8349\t539\t4415444429\t4.41E+16\t527\t152703960\t5.49E+13
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVLOG\tRESULT LOG\t8181818.057\t289760.8349\t539\t4409999933\t4.41E+16\t527\t152703960\t5.49E+13
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVSP\tSPECIMEN\t9999999\t289760.8349\t539\t5389999461\t5.39E+16\t527\t152703960\t5.49E+13
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tUBT\tUNIT BLOOD TYPE  \t9999999\t48294.88789\t528\t5279999472\t5.28E+16\t223\t10769760\t1.30E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tCHOL\tCHOLESTEROL, TOTAL\t151669.5814\t6236.056338\t528\t80081539\t8.00E+14\t426\t2656560\t35067196800
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t208337.2199\t6236.056338\t528\t110002052.1\t1.10E+15\t426\t2656560\t35067196800
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tHDL\tHDL CHOLESTEROL\t151557.7216\t6236.056338\t528\t80022477\t8.00E+14\t426\t2656560\t35067196800
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tLDL\tDIRECT LDL CHOL\t113730.9337\t6236.056338\t528\t60049933\t6.00E+14\t426\t2656560\t35067196800
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tNHDL\tNON-HDL CHOL, CALC\t208444.2064\t6236.056338\t528\t110058541\t1.10E+15\t426\t2656560\t35067196800
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tTGL\tTRIGLYCERIDE, SER/PLAS\t132720.464\t6236.056338\t528\t70076405\t7.00E+14\t426\t2656560\t35067196800
+MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tRPT\tRPT\t9999999\t334461.7699\t525\t5249999475\t5.25E+16\t339\t113382540\t5.10E+13
+MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tSDES\tSPECIMEN\t9999999\t334461.7699\t525\t5249999475\t5.25E+16\t339\t113382540\t5.10E+13
+MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tSREQ\tCOMMENTS\t9999999\t334461.7699\t523\t5229999477\t5.23E+16\t339\t113382540\t5.10E+13
+VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSV1PC\tHSV-1 DNA\t9999999\t167178.2332\t523\t5229999477\t5.23E+16\t283\t47311440\t1.12E+13
+VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSV2PC\tHSV-2 DNA\t9999999\t167178.2332\t523\t5229999477\t5.23E+16\t283\t47311440\t1.12E+13
+LAB BLOOD ORDERABLES\t471651\tLABAT3\tANTITHROMBIN III\tAT3\tANTITHROMBIN III\t172828.4146\t42245.28689\t521\t90043604\t9.00E+14\t488\t20615700\t1.19E+12
+MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tRPT\tRPT\t9999999\t1159475.26\t519\t5189999481\t5.19E+16\t519\t601767660\t7.41E+14
+MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tSREQ\tCOMMENTS\t9999999\t1159475.26\t519\t5189999481\t5.19E+16\t519\t601767660\t7.41E+14
+VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMMQUL\tCMV IGM RESULT\t9999999\t159015.6725\t518\t5179999482\t5.18E+16\t513\t81575040\t1.77E+13
+MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tRPT\tRPT\t9999999\t473219.0698\t516\t5159999484\t5.16E+16\t516\t244181040\t2.40E+14
+MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tSREQ\tCOMMENTS\t9999999\t473219.0698\t516\t5159999484\t5.16E+16\t516\t244181040\t2.40E+14
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tPRO\tPRODUCT ID  \t9999999\t31790.66667\t515\t5149999485\t5.15E+16\t225\t7152900\t7.84E+11
+LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB\tPHENOBARBITAL\t116527.1579\t6274.373717\t515\t60011486.3\t6.00E+14\t487\t3055620\t1.59E+11
+LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB2\tDATE & TIME OF LAST DOSE\t9999999\t6274.373717\t514\t5139999486\t5.14E+16\t487\t3055620\t1.59E+11
+LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB3\tDOSAGE\t9999999\t6274.373717\t514\t5139999486\t5.14E+16\t487\t3055620\t1.59E+11
+LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB4\tADMINISTRATION ROUTE\t9999999\t6274.373717\t514\t5139999486\t5.14E+16\t487\t3055620\t1.59E+11
+MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tRPT\tRPT\t9999999\t1154535.525\t514\t5139999486\t5.14E+16\t514\t593431260\t7.35E+14
+MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tSREQ\tCOMMENTS\t9999999\t1154535.525\t514\t5139999486\t5.14E+16\t514\t593431260\t7.35E+14
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tDI\tISSUE DATE/TIME\t9999999\t31819.03084\t513\t5129999487\t5.13E+16\t227\t7222920\t3.67E+11
+LAB BLOOD ORDERABLES\t473094\tLABPB\tPHENOBARBITAL LEVEL\tPB\tPEAK, TROUGH, OR RANDOM\t9999999\t6274.373717\t513\t5129999487\t5.13E+16\t487\t3055620\t1.59E+11
+POINT OF CARE TESTING\t472681\tLABPCCG4\tISTAT CG4, ARTERIAL\tBE\tBASE EXCESS, ISTAT\t136457.575\t29.67391304\t513\t70002736\t7.00E+14\t184\t5460\t399600
+LAB BLOOD ORDERABLES\t472608\tLABIGP\tIG PANEL, SERUM\tIGA\tIGA, SERUM\t430788.5088\t50382.80412\t511\t220132928\t2.20E+15\t485\t24435660\t2.63E+12
+LAB BLOOD ORDERABLES\t472608\tLABIGP\tIG PANEL, SERUM\tIGG\tIGG, SERUM\t59813.78278\t50382.80412\t511\t30564843\t3.00E+14\t485\t24435660\t2.63E+12
+LAB BLOOD ORDERABLES\t472608\tLABIGP\tIG PANEL, SERUM\tIGM\tIGM, SERUM\t743834.2603\t50382.80412\t511\t380099307\t3.80E+15\t485\t24435660\t2.63E+12
+LAB BLOOD ORDERABLES\t472349\tLABFACT8\tFACTOR VIII ASSAY\tFACTOR7\tFACTOR VIII ASSAY\t336091.7522\t37663.04348\t506\t170062426.6\t1.70E+15\t460\t17325000\t1.03E+12
+BLOOD BANK PRODUCT ORDERABLES\t889176\tLABERURBC\tEMERGENCY RELEASE RBC\tRBCSTA\tRBC PRODUCT - UNITS\t9999999\t6582.891089\t505\t5049999495\t5.05E+16\t505\t3324360\t4.97E+11
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tEOSCSF\tEOSINOPHILS, CSF\t4.235412475\t16680\t497\t2105\t49371\t286\t4770480\t8.47E+11
+LAB BLOOD ORDERABLES\t472949\tLAB11049R\tNSE\tNEURSP\tNEURON SPEC ENOLASE\t544393.7486\t290896.3991\t496\t270019299.3\t2.70E+15\t461\t134103240\t6.39E+13
+VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXSP\tSPECIMEN\t9999999\t183683.9241\t489\t4889999511\t4.89E+16\t158\t29022060\t8.42E+12
+VIROLOGY ORDERABLES\t472040\tLABCMV\tCMV AB TOTAL\tCMV\tCMV ANTIBODY\t9999999\t162174.2254\t488\t4879999512\t4.88E+16\t426\t69086220\t1.45E+13
+POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tGLU\tGLUCOSE,ISTAT\t20769.90928\t124.8803828\t485\t10073406\t1.00E+14\t209\t26100\t118105200
+POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tHCT\tHCT(CALC), ISTAT\t20654.27216\t124.8803828\t485\t10017322\t1.00E+14\t209\t26100\t118105200
+POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tHGB\tHGB(CALC), ISTAT\t20630.69753\t124.8803828\t485\t10005888.3\t1.00E+14\t209\t26100\t118105200
+POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tK\tPOTASSIUM, ISTAT\t41241.66598\t124.8803828\t485\t20002208\t2.00E+14\t209\t26100\t118105200
+POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tNA\tSODIUM, ISTAT\t136.8618557\t124.8803828\t485\t66378\t9107096\t209\t26100\t118105200
+LAB BLOOD ORDERABLES\t472467\tLABGLOB\tGLOBULIN\tGLOB\tGLOBULIN\t103737.9357\t2168.636364\t482\t50001685\t5.00E+14\t132\t286260\t1342004400
+LAB BLOOD ORDERABLES\t590572\tLABPROL\tPROLACTIN\tPROLACTIN\tPROLACTIN\t418452.4059\t37074.93976\t478\t200020250\t2.00E+15\t415\t15386100\t1.08E+12
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tUBT\tUNIT BLOOD TYPE  \t9999999\t44357.94118\t476\t4759999524\t4.76E+16\t204\t9049020\t1.06E+12
+MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tRPT\tRPT\t9999999\t675327.4909\t473\t4729999527\t4.73E+16\t275\t185715060\t2.71E+15
+VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tGC\tN.GONORRHOEAE RESULT:\t9999999\t184892.973\t471\t4709999529\t4.71E+16\t333\t61569360\t1.58E+13
+POINT OF CARE TESTING\t472694\tLABPCEC4O\tISTAT EC4, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t124.8803828\t470\t4699999530\t4.70E+16\t209\t26100\t118105200
+VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tCHLAM\tC.TRACHOMATIS RESULT:\t9999999\t184892.973\t470\t4699999530\t4.70E+16\t333\t61569360\t1.58E+13
+MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tSDES\tSPECIMEN\t9999999\t1159475.26\t469\t4689999531\t4.69E+16\t519\t601767660\t7.41E+14
+MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tSDES\tSPECIMEN\t9999999\t1154535.525\t469\t4689999531\t4.69E+16\t514\t593431260\t7.35E+14
+MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tSDES\tSPECIMEN\t9999999\t675327.4909\t467\t4669999533\t4.67E+16\t275\t185715060\t2.71E+15
+VIROLOGY ORDERABLES\t472040\tLABCMV\tCMV AB IGG\tCMV\tCMV ANTIBODY\t9999999\t147950.7965\t464\t4639999536\t4.64E+16\t339\t50155320\t9.13E+12
+MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tSDES\tSPECIMEN\t9999999\t473219.0698\t463\t4629999537\t4.63E+16\t516\t244181040\t2.40E+14
+MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tSREQ\tCOMMENTS\t9999999\t675327.4909\t461\t4609999539\t4.61E+16\t275\t185715060\t2.71E+15
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tXMR\tUNIT CROSSMATCH  \t9999999\t70210.56995\t454\t4539999546\t4.54E+16\t193\t13550640\t2.10E+12
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tDI\tISSUE DATE/TIME\t9999999\t31993.17073\t453\t4529999547\t4.53E+16\t205\t6558600\t3.31E+11
+VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPLOG\tRESULT LOG 10\t7836644.65\t305903.2394\t453\t3550000027\t3.55E+16\t426\t130314780\t5.02E+13
+VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPRES\tRESULT\t8068660.3\t305903.2394\t453\t3655103116\t3.64E+16\t426\t130314780\t5.02E+13
+VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPSP\tSPECIMEN\t9999999\t305903.2394\t453\t4529999547\t4.53E+16\t426\t130314780\t5.02E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSAQT\tHEP B SURF AB QUANT\t7139734.262\t200741.1086\t451\t3220020152\t3.22E+16\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t200741.1086\t451\t4509999549\t4.51E+16\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t200741.1086\t451\t4509999549\t4.51E+16\t451\t90534240\t2.96E+13
+MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tRPT\tRPT\t9999999\t1304430.274\t451\t4509999549\t4.51E+16\t329\t429157560\t5.76E+14
+MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tSDES\tSPECIMEN\t9999999\t1304430.274\t451\t4509999549\t4.51E+16\t329\t429157560\t5.76E+14
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t200741.1086\t450\t4499999550\t4.50E+16\t451\t90534240\t2.96E+13
+MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tRPT\tRPT\t9999999\t184554.1693\t450\t4499999550\t4.50E+16\t319\t58872780\t5.99E+13
+VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPDNA\tHEP B VIRUS DNA\t9999999\t305903.2394\t450\t4499999550\t4.50E+16\t426\t130314780\t5.02E+13
+MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tSREQ\tCOMMENTS\t9999999\t1304430.274\t449\t4489999551\t4.49E+16\t329\t429157560\t5.76E+14
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tPRO\tPRODUCT ID  \t9999999\t32831.4433\t448\t4479999552\t4.48E+16\t194\t6369300\t7.61E+11
+LAB BLOOD ORDERABLES\t472475\tLABGLF\tGLUCOSE FASTING\tGLUF\tGLUCOSE, FASTING\t134026.3616\t8175.961538\t448\t60043810\t6.00E+14\t312\t2550900\t63338756400
+LAB BLOOD ORDERABLES\t473763\tLAB11060R\tTHIAMINE\tVITB1\tVITAMIN B1, PLASMA\t870624.4308\t307360.8299\t448\t390039745\t3.90E+15\t482\t148147920\t6.20E+13
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tHCO3\tHCO3, ISTAT\t111632.7217\t271.9090909\t448\t50011459.3\t5.00E+14\t220\t59820\t2744190000
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCO2A\tPCO2, ISTAT\t89330.51228\t271.9090909\t448\t40020069.5\t4.00E+14\t220\t59820\t2744190000
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCO2SO\tO2 SATURATION, ISTAT\t134011.5357\t271.9090909\t448\t60037168\t6.00E+14\t220\t59820\t2744190000
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPO2A\tPO2, ISTAT\t67060.52679\t271.9090909\t448\t30043116\t3.00E+14\t220\t59820\t2744190000
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tTCO2A\tTCO2, (ISTAT)\t111634.0469\t271.9090909\t448\t50012053\t5.00E+14\t220\t59820\t2744190000
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPH\tPH, ISTAT\t44850.37892\t271.9090909\t446\t20003269\t2.00E+14\t220\t59820\t2744190000
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t200741.1086\t445\t4449999555\t4.45E+16\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472550\tLABYHISTS\tHISTOPLASMA AG, SERUM\tYHISS4\tRESULT,SERUM\t9954647.556\t490709.3697\t441\t4389999572\t4.39E+16\t476\t233577660\t1.25E+14
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSAIN\tINTERPRETATION\t9999999\t200741.1086\t440\t4399999560\t4.40E+16\t451\t90534240\t2.96E+13
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\tPHLOC\tLOCATION\t9999999\t-5073.967311\t439\t4389999561\t4.39E+16\t1346\t-6829560\t1.13E+15
+POINT OF CARE TESTING\t471530\tLABPCACT\tACTIVATED CLOTTING TIME (POCT)\tACT\tACT,POCT\t219.9817768\t58860\t439\t96572\t25845846\t1\t58860\t3464499600
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVDNA\tEBV DNA\t9999999\t289760.8349\t438\t4379999562\t4.38E+16\t527\t152703960\t5.49E+13
+LAB BLOOD ORDERABLES\t670131\tLABFLCR\tFREE LIGHT CHAIN RATIO\tFREEK\tFREE KAPPA LIGHT CHAIN\t503508.5924\t328610.0971\t437\t220033254.9\t2.20E+15\t412\t135387360\t6.13E+13
+LAB BLOOD ORDERABLES\t670131\tLABFLCR\tFREE LIGHT CHAIN RATIO\tFREEKL\tFREE KAPPA/LAMBDA RATIO\t869692.5112\t328610.0971\t437\t380055627.4\t3.80E+15\t412\t135387360\t6.13E+13
+LAB BLOOD ORDERABLES\t670131\tLABFLCR\tFREE LIGHT CHAIN RATIO\tFREEL\tFREE LAMBDA LIGHT CHAIN\t503486.9574\t328610.0971\t437\t220023800.4\t2.20E+15\t412\t135387360\t6.13E+13
+URINE ORDERABLES\t473174\tLABURTP\tPROTEIN, URINE (RANDOM)\tUPROTTOT\tPROTEIN, TOTAL, URINE\t480691.9291\t17355.52941\t437\t210062373\t2.10E+15\t255\t4425660\t5.37E+11
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tCAION\tCALCIUM,ION, ISTAT\t389909.3382\t272.8767123\t436\t170000471.5\t1.70E+15\t219\t59760\t2744186400
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tHCT\tHCT(CALC), ISTAT\t412873.4266\t272.8767123\t436\t180012814\t1.80E+15\t219\t59760\t2744186400
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tHGB\tHGB(CALC), ISTAT\t412854.0002\t272.8767123\t436\t180004344.1\t1.80E+15\t219\t59760\t2744186400
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tK\tPOTASSIUM, ISTAT\t366976.4523\t272.8767123\t436\t160001733.2\t1.60E+15\t219\t59760\t2744186400
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tNA\tSODIUM, ISTAT\t321234.4587\t272.8767123\t436\t140058224\t1.40E+15\t219\t59760\t2744186400
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tCAOX\tCA OXALATE CRYSTALS\t9999999\t15496.8\t436\t4359999564\t4.36E+16\t300\t4649040\t1.24E+12
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCCOM\tPOCT COMMENT\t9999999\t271.9090909\t434\t4339999566\t4.34E+16\t220\t59820\t2744190000
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tHYST\tPSEUDO-HYPHAE\t9999999\t20417.64706\t434\t4339999566\t4.34E+16\t272\t5553600\t1.48E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tC1656A\tCD3-/16+56(NK),ABS.\t277849.7431\t69708.31731\t432\t120031089\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tC1656P\tCD3-/16+56(NK)%\t277787.6227\t69708.31731\t432\t120004253\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD19AB\tCD19 (PAN B), ABS.\t278158.6806\t69708.31731\t432\t120164550\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD19PC\tCD19 (PAN B) %\t277809.6852\t69708.31731\t432\t120013784\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t278392.2523\t69708.31731\t432\t120265453\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD3PC\tCD3 (PAN T) %\t277831.5417\t69708.31731\t432\t120023226\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD4AB\tCD3+/CD4+, ABS.\t278115.8333\t69708.31731\t432\t120146040\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD4PC\tCD3+/CD4+ %\t277803.4074\t69708.31731\t432\t120011072\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD8AB\tCD3+/CD8+, ABS.\t278046.3356\t69708.31731\t432\t120116017\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tCD8PC\tCD3+/CD8+ %\t277805.2986\t69708.31731\t432\t120011889\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t277830.956\t69708.31731\t432\t120022973\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t278384.4167\t69708.31731\t432\t120262068\t1.20E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t439816.0368\t69708.31731\t432\t190000527.9\t1.90E+15\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tLYM\tLYMPHOCYTES %\t185196.1836\t69708.31731\t432\t80004751.3\t8.00E+14\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t186262.8958\t69708.31731\t432\t80465571\t8.00E+14\t416\t28998660\t2.86E+12
+LAB BLOOD ORDERABLES\t473391\tLABTBLYM\tT SUBSETS AND B LYMPHOCYTES, BLOOD\tWBC\tWBC COUNT\t194965.9537\t69708.31731\t432\t84225292\t8.00E+14\t416\t28998660\t2.86E+12
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tXMR\tUNIT CROSSMATCH  \t9999999\t72834.94737\t430\t4299999570\t4.30E+16\t190\t13838640\t2.08E+12
+BLOOD BANK PRODUCT ORDERABLES\t889178\tLABERUFFP\tEMERGENCY RELEASE PLASMA\tFFP\tFRESH FROZEN UNITS\t9999999\t9213.925234\t428\t4279999572\t4.28E+16\t428\t3943560\t8.54E+11
+LAB BLOOD ORDERABLES\t472843\tLABMETHB\tMETHEMOGLOBIN\tMETHB\tMETHEMOGLOBIN\t1241218.778\t1845.669291\t427\t530000418.1\t5.30E+15\t381\t703200\t8558625600
+LAB BLOOD ORDERABLES\t472869\tLAB11071R\tMITOCHONDRIAL AB\tYAMA\tMITOCHONDRIAL ANTIBODY, M2\t8679244.496\t221191.8428\t424\t3679999666\t3.68E+16\t407\t90025080\t2.29E+13
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMQDNA\tCMV DNA\t9999999\t253864.8227\t423\t4229999577\t4.23E+16\t423\t107384820\t3.23E+13
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVRC1\tCMV DAY 1 RAPID CULTURE\t9999999\t253864.8227\t423\t4229999577\t4.23E+16\t423\t107384820\t3.23E+13
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVRC2\tCMV DAY 2 RAPID CULTURE\t9999999\t253864.8227\t423\t4229999577\t4.23E+16\t423\t107384820\t3.23E+13
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tUBT\tUNIT BLOOD TYPE  \t9999999\t44809.2973\t421\t4209999579\t4.21E+16\t185\t8289720\t9.48E+11
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEAGQNT\tEAGQNT\t4401914.65\t293312.3077\t418\t1840000324\t1.84E+16\t403\t118204860\t5.01E+13
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t70359.66102\t413\t4129999587\t4.13E+16\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSAQT\tHEP B SURF AB QUANT\t5908071.703\t70359.66102\t413\t2440033613\t2.44E+16\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t473425\tLABTES\tTESTOSTERONE\tTESTOS\tTESTOSTERONE\t968690.5303\t76130.35176\t413\t400069189\t4.00E+15\t398\t30299880\t6.44E+12
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tSI\t<<STATUS>>  \t9999999\t40348.33333\t412\t4119999588\t4.12E+16\t864\t34860960\t4.01E+12
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t70359.66102\t411\t4109999589\t4.11E+16\t413\t29058540\t4.09E+12
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tPRO\tPRODUCT ID  \t9999999\t34411.39535\t410\t4099999590\t4.10E+16\t172\t5918760\t7.29E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t70359.66102\t410\t4099999590\t4.10E+16\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t590533\tLABCMVTSI\tCMV AB TOTAL SOLID PHASE (GALILEO)\tCMVTOT\tCMV TOTAL ANTIBODY\t9999999\t#VALUE!\t410\t4099999590\t4.10E+16\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVCP\tRESULT\t7802567.044\t247595.8794\t410\t3199052488\t3.18E+16\t398\t98543160\t3.08E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVLO\tRESULT LOG 10\t7731707.538\t247595.8794\t410\t3170000091\t3.17E+16\t398\t98543160\t3.08E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVRNA\tHIV 1 RNA\t9999999\t247595.8794\t410\t4099999590\t4.10E+16\t398\t98543160\t3.08E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVSP\tSPECIMEN\t9999999\t247595.8794\t410\t4099999590\t4.10E+16\t398\t98543160\t3.08E+13
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSAIN\tINTERPRETATION\t9999999\t70359.66102\t409\t4089999591\t4.09E+16\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t471635\tLABANAS\tANTI - NUCLEAR AB\tANAQNT\tTITER\t9999999\t368380.0478\t407\t4069999593\t4.07E+16\t418\t153982860\t6.54E+13
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tSI\t<<STATUS>>  \t9999999\t39255.84184\t404\t4039999596\t4.04E+16\t784\t30776580\t3.61E+12
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCORCL\tCORRECTED CR. CLEAR.\t420836.0025\t70390.78125\t404\t170017745\t1.70E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCOREXC\tCORRECTED CR. EXC.\t247536.6163\t70390.78125\t404\t100004793\t1.00E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCR\tCREATININE, SERUM\t222774.1374\t70390.78125\t404\t90000751.5\t9.00E+14\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCRCL\tCREATININE CLEARANCE\t247572.7153\t70390.78125\t404\t100019377\t1.00E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCRE\tCREATININE EXCRETION\t248435.4257\t70390.78125\t404\t100367912\t1.00E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tCRUCL\tCREATININE, URINE\t247591.7624\t70390.78125\t404\t100027072\t1.00E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tFLOWURN\tURINE FLOW\t247525.9851\t70390.78125\t404\t100000498\t1.00E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tHT\tHEIGHT\t247688.9431\t70390.78125\t404\t100066333\t1.00E+15\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tUT3\tCOLLECTION PERIOD CREATININE\t222790.2351\t70390.78125\t404\t90007255\t9.00E+14\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tVOL3\tVOLUME CREATININE\t224125.8119\t70390.78125\t404\t90546828\t9.00E+14\t128\t9010020\t9.33E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE, URINE\tWT\tWEIGHT\t247600.7614\t70390.78125\t404\t100030707.6\t1.00E+15\t128\t9010020\t9.33E+11
+BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tCRYS\tCRYSTAL AMOUNT\t9999999\t11117.87234\t403\t4029999597\t4.03E+16\t235\t2612700\t1.13E+11
+COAGULATION ORDERABLES\t472515\tLABHIAB\tHEPARIN INDUCED AB\tHIARES\tRESULT\t9999999\t64755.64516\t400\t3999999600\t4.00E+16\t372\t24089100\t2.37E+12
+LAB BLOOD ORDERABLES\t473779\tLAB12631R\tVORICONAZOLE LEVEL\t12630R\tVORICONAZOLE LEVEL\t450003.3483\t217704.3521\t400\t180001339.3\t1.80E+15\t409\t89041080\t2.16E+13
+LAB BLOOD ORDERABLES\t471911\tLABC125\tCANCER AG 125\tC125\tCANCER ANTIGEN 125\t452530.3543\t41327.93296\t398\t180107081\t1.80E+15\t358\t14795400\t1.31E+12
+LAB BLOOD ORDERABLES\t473412\tLABT3\tT3, TOTAL\tT3\tT3 TOTAL\t628222.9749\t38454.10714\t398\t250032744\t2.50E+15\t336\t12920580\t8.17E+11
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tFRFAT\tFREE FAT\t9999999\t10207.71704\t397\t3969999603\t3.97E+16\t311\t3174600\t2.63E+11
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9013R\tCELL DATE\t9999999\t213091.738\t396\t3959999604\t3.96E+16\t397\t84597420\t1.52E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9014R\tCELL SOURCE\t9999999\t213091.738\t396\t3959999604\t3.96E+16\t397\t84597420\t1.52E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9448R\tMETHOD(S)\t9999999\t213091.738\t396\t3959999604\t3.96E+16\t397\t84597420\t1.52E+14
+VIROLOGY ORDERABLES\t910608\tLABCMVIGT\tCMV IGG (TRANSFUSION)\tCMGTQL\tRESULT\t9999999\t130818.6935\t396\t3959999604\t3.96E+16\t398\t52065840\t9.97E+12
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9019R\tHLA-BW ALLELE 1\t9999999\t214170.5316\t395\t3949999605\t3.95E+16\t395\t84597360\t1.52E+14
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tOVFAT\tOVAL FAT BODIES\t9999999\t9315.841584\t395\t3949999605\t3.95E+16\t303\t2822700\t1.93E+11
+VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXMT\tMETHOD\t9999999\t183683.9241\t395\t3949999605\t3.95E+16\t158\t29022060\t8.42E+12
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tUBT\tUNIT BLOOD TYPE  \t9999999\t42059.64706\t394\t3939999606\t3.94E+16\t170\t7150140\t7.84E+11
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tHYST\tPSEUDO-HYPHAE\t9999999\t13976.77686\t394\t3939999606\t3.94E+16\t242\t3382380\t4.36E+11
+LAB BLOOD ORDERABLES\t471579\tLABYA1AS\tALPHA - 1 - ANTITRYPSIN\tYA1A\tALPHA 1 ANTITRYPSIN\t25680.01531\t194770.7532\t392\t10066566\t1.00E+14\t385\t74986740\t2.57E+13
+VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXR\tINFLUENZA A RNA\t9999999\t183683.9241\t391\t3909999609\t3.91E+16\t158\t29022060\t8.42E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASO\tBASOPHILS\t1.879177378\t31866.03261\t389\t731\t3433\t368\t11726700\t2.14E+12
+BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tCXSOUR\tFLUID SOURCE\t9999999\t11117.87234\t386\t3859999614\t3.86E+16\t235\t2612700\t1.13E+11
+MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tRPT\tRPT\t9999999\t213232.1186\t386\t3859999614\t3.86E+16\t236\t50322780\t1.42E+13
+VIROLOGY ORDERABLES\t473783\tLABVZAB\tVARICELLA ZOSTER VIRUS AB IGG\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t163752.4022\t386\t3859999614\t3.86E+16\t358\t58623360\t1.22E+13
+MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tSDES\tSPECIMEN\t9999999\t213232.1186\t385\t3849999615\t3.85E+16\t236\t50322780\t1.42E+13
+LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABIDELUATE\tANTIBODY IDENTIFICATION ELUATE\t9999999\t99443.54067\t384\t3839999616\t3.84E+16\t209\t20783700\t3.97E+13
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tRPT\tRPT\t9999999\t522530.9375\t384\t3839999616\t3.84E+16\t384\t200651880\t3.85E+14
+URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tTUNU\tUREA EXCRETION\t579648.7318\t43988.12227\t384\t222585113\t2.20E+15\t229\t10073280\t1.43E+12
+URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tUREAVOL\tVOLUME UREA\t444342.2422\t43988.12227\t384\t170627421\t1.70E+15\t229\t10073280\t1.43E+12
+URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tUT4\tCOLLECTION PERIOD UREA\t546905.1771\t43988.12227\t384\t210011588\t2.10E+15\t229\t10073280\t1.43E+12
+URINE ORDERABLES\t473721\tLABUUNT\tUREA NITROGEN, URINE (TIMED)\tUURN\tUREA NITROGEN, URINE\t573359.6875\t43988.12227\t384\t220170120\t2.20E+15\t229\t10073280\t1.43E+12
+VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-PLASMA\tCMVQSP\tSPECIMEN\t9999999\t242296.5517\t382\t3819999618\t3.82E+16\t116\t28106400\t9.68E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBASOAB\tBASO, ABS (MAN DIFF)\t0.939632546\t32238.44875\t381\t358\t7786.18\t361\t11638080\t2.14E+12
+LAB BLOOD ORDERABLES\t473164\tLABPROTC\tPROTEIN C ACTIVITY\tPROTC\tPROTEIN C ACTIVITY\t446284.9528\t258848.0571\t381\t170034567\t1.70E+15\t350\t90596820\t3.96E+13
+VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSV1DN\tHSV-1 DNA\t9999999\t190023.36\t380\t3799999620\t3.80E+16\t375\t71258760\t1.95E+13
+VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSV2DN\tHSV-2 DNA\t9999999\t190023.36\t380\t3799999620\t3.80E+16\t375\t71258760\t1.95E+13
+VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tVZVDN\tVZV DNA\t9999999\t190023.36\t380\t3799999620\t3.80E+16\t375\t71258760\t1.95E+13
+STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tOCCGU\tSTOOL SAMPLE1,GUAIAC\t9999999\t39879.53271\t379\t3789999621\t3.79E+16\t214\t8534220\t1.87E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCCMT\tCOMMENT\t9999999\t15817.70574\t376\t3759999624\t3.76E+16\t401\t6342900\t1.49E+12
+LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN (HIT) AB\tPF4IGG\tPF4 IGG ONLY OD\t3404255.443\t91011.75824\t376\t1280000047\t1.28E+16\t364\t33128280\t3.74E+12
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tADVR\tADENOVIRUS\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tINFAR\tINFLUENZA A\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tINFBR\tINFLUENZA B\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tMPVRR\tMETAPNEUMOVIRUS\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tP1R\tPARAINFLUENZA 1\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tP2R\tPARAINFLUENZA 2\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tP3R\tPARAINFLUENZA 3\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tRSVR\tRSV\t9999999\t106230.4\t375\t3749999625\t3.75E+16\t225\t23901840\t1.05E+13
+LAB BLOOD ORDERABLES\t473168\tLABPROTS\tPROTEIN S ACTIVITY\tPROTS\tPROTEIN S ACTIVITY\t376431.2258\t245812.3032\t372\t140032416\t1.40E+15\t343\t84313620\t3.16E+13
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tSREQ\tCOMMENTS\t9999999\t522530.9375\t371\t3709999629\t3.71E+16\t384\t200651880\t3.85E+14
+STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tDATE1\tDATE 1\t7291706.733\t40050.70423\t371\t2705223198\t2.68E+16\t213\t8530800\t1.87E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tURIC\tURIC ACID CRYSTALS\t9999999\t16276.48069\t371\t3709999629\t3.71E+16\t233\t3792420\t3.81E+11
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBLASTS\tBLASTS\t38.71195652\t32522.31405\t368\t14246\t984390\t363\t11805600\t8.81E+12
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tSDES\tSPECIMEN\t9999999\t522530.9375\t368\t3679999632\t3.68E+16\t384\t200651880\t3.85E+14
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tDI\tISSUE DATE/TIME\t9999999\t31361.45455\t367\t3669999633\t3.67E+16\t165\t5174640\t2.66E+11
+LAB BLOOD ORDERABLES\t473414\tLABTT4\tT4, TOTAL\tT4\tTHYROXINE (T4), TOTAL\t326982.9749\t34944.88746\t367\t120002751.8\t1.20E+15\t311\t10867860\t7.32E+11
+LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET\tLEVET\t1035446.455\t466250.0552\t367\t380008849\t3.80E+15\t362\t168782520\t7.27E+15
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tBLASTSAB\tBLASTS,ABS(MAN DIFF)\t13.57452055\t32702.33333\t365\t4954.7\t468522.73\t360\t11772840\t8.81E+12
+LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET2\tLEVET2\t9999999\t466250.0552\t365\t3649999635\t3.65E+16\t362\t168782520\t7.27E+15
+LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET3\tLEVET3\t9999999\t466250.0552\t365\t3649999635\t3.65E+16\t362\t168782520\t7.27E+15
+LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET4\tLEVET4\t9999999\t466250.0552\t364\t3639999636\t3.64E+16\t362\t168782520\t7.27E+15
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9017R\tHLA-B ALLELE 1\t9999999\t214170.5316\t362\t3619999638\t3.62E+16\t395\t84597360\t1.52E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9018R\tHLA-B ALLELE 2\t9999999\t214170.5316\t362\t3619999638\t3.62E+16\t395\t84597360\t1.52E+14
+LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM LEVEL\tLEVET1\tLEVET1\t9999999\t466250.0552\t362\t3619999638\t3.62E+16\t362\t168782520\t7.27E+15
+LAB BLOOD ORDERABLES\t471641\tLABANTIXA\tLMWH ACTIVITY BY ANTIXA\tANTIXA\tLMW HEPARIN, ANTIXA U/ML\t609418.9318\t9658.67036\t361\t220000234.4\t2.20E+15\t361\t3486780\t1.29E+11
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tPRO\tPRODUCT ID  \t9999999\t33621.42857\t360\t3599999640\t3.60E+16\t154\t5177700\t6.15E+11
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9015R\tHLA-A ALLELE 1\t9999999\t214170.5316\t360\t3599999640\t3.60E+16\t395\t84597360\t1.52E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9016R\tHLA-A ALLELE 2\t9999999\t214170.5316\t360\t3599999640\t3.60E+16\t395\t84597360\t1.52E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9027R\tHLA-DQA1 ALLELE 1\t9999999\t207262.4377\t360\t3599999640\t3.60E+16\t361\t74821740\t1.10E+14
+VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tRPT\tRPT\t9999999\t68786.73267\t360\t3599999640\t3.60E+16\t101\t6947460\t5.79E+11
+VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tSREQ\tCOMMENTS\t9999999\t68786.73267\t360\t3599999640\t3.60E+16\t101\t6947460\t5.79E+11
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9028R\tHLA-DQA1 ALLELE 2\t9999999\t207262.4377\t359\t3589999641\t3.59E+16\t361\t74821740\t1.10E+14
+STOOL ORDERABLES\t473114\tLABSTPOLY\tWBC FOR IBD, STOOL\tEOSSTL\tEOSINOPHILS, STOOL\t9999999\t34758.67925\t359\t3589999641\t3.59E+16\t212\t7368840\t1.20E+12
+VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tSDES\tSPECIMEN\t9999999\t68786.73267\t359\t3589999641\t3.59E+16\t101\t6947460\t5.79E+11
+VIROLOGY ORDERABLES\t910608\tLABCMVIGT\tCMV IGG (TRANSFUSION)\tCMGTQT\tLEVEL\t2409040.838\t131862.0168\t357\t860027579\t8.60E+15\t357\t47074740\t9.18E+12
+STOOL ORDERABLES\t473114\tLABSTPOLY\tWBC FOR IBD, STOOL\tPOLYST\tPMNS, STOOL\t9999999\t34758.67925\t356\t3559999644\t3.56E+16\t212\t7368840\t1.20E+12
+STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tDATE2\tDATE 2\t9679437.871\t40731.25628\t356\t3445879882\t3.44E+16\t199\t8105520\t1.83E+12
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9024R\tHLA-DRB1 ALLELE 2\t9999999\t215133.8992\t355\t3549999645\t3.55E+16\t377\t81105480\t1.49E+14
+URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tUPROTTOT\tPROTEIN, TOTAL, URINE\t591667.2113\t70648.16327\t355\t210041860\t2.10E+15\t196\t13847040\t9.65E+12
+URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tUT10\tCOLLECTION PERIOD PROTEIN\t197214.3465\t70648.16327\t355\t70011093\t7.00E+14\t196\t13847040\t9.65E+12
+URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tVOL10\tVOLUME PROTEIN\t58370.19155\t70648.16327\t355\t20721418\t2.00E+14\t196\t13847040\t9.65E+12
+VIROLOGY ORDERABLES\t472041\tLABCMVM\tCMV IGM\tCMVIGM\tCMV IGM AB\t9999999\t160973.8197\t355\t3549999645\t3.55E+16\t233\t37506900\t8.05E+12
+BODY FLUID ORDERABLES\t472656\tLABINSLF\tINSULIN FASTING\tINSF\tINSULIN, FASTING\t621478.6638\t35292.57353\t354\t220003447\t2.20E+15\t272\t9599580\t7.78E+11
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9023R\tHLA-DRB1 ALLELE 1\t9999999\t215133.8992\t353\t3529999647\t3.53E+16\t377\t81105480\t1.49E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9029R\tHLA-DQB1 ALLELE 1\t9999999\t215133.8992\t353\t3529999647\t3.53E+16\t377\t81105480\t1.49E+14
+STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tDATE3\tDATE 3\t9620291.244\t40919.69697\t353\t3395962809\t3.39E+16\t198\t8102100\t1.83E+12
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9030R\tHLA-DQB1 ALLELE 2\t9999999\t215133.8992\t351\t3509999649\t3.51E+16\t377\t81105480\t1.49E+14
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tXMR\tUNIT CROSSMATCH  \t9999999\t70229.45455\t350\t3499999650\t3.50E+16\t165\t11587860\t1.72E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMAOTH\tOTHER, ABS(MAN DIFF)\t57666.52133\t72845.85799\t347\t20010282.9\t2.00E+14\t338\t24621900\t4.25E+12
+VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-PLASMA\tCMQDNA\tCMV DNA\t9999999\t252366.8139\t347\t3469999653\t3.47E+16\t317\t80000280\t2.70E+13
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCOTHER\tOTHERS, CSF\t27.65797101\t11283.86139\t345\t9542\t728132\t202\t2279340\t72839646000
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9022R\tHLA-C ALLELE 2\t9999999\t217419.6286\t344\t3439999656\t3.44E+16\t377\t81967200\t1.50E+14
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9021R\tHLA-C ALLELE 1\t9999999\t217419.6286\t342\t3419999658\t3.42E+16\t377\t81967200\t1.50E+14
+LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO\tTHEOPHYLLINE\t381241.0419\t5671.84466\t341\t130003195.3\t1.30E+15\t309\t1752600\t18387403200
+LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO1\tPEAK, TROUGH, OR RANDOM\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
+LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO2\tDATE & TIME OF LAST DOSE\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
+LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO3\tDOSAGE\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
+LAB BLOOD ORDERABLES\t497494\tLABTHEOL\tTHEOPHYLLINE LEVEL\tTHEO4\tADMINISTRATION ROUTE\t9999999\t5671.84466\t341\t3409999659\t3.41E+16\t309\t1752600\t18387403200
+URINE ORDERABLES\t473175\tLABUTP\tPROTEIN, URINE (TIMED)\tTTPU\tPROTEIN EXCRETION\t206996.9472\t70648.16327\t341\t70585959\t7.00E+14\t196\t13847040\t9.65E+12
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tUBT\tUNIT BLOOD TYPE  \t9999999\t44215.41667\t339\t3389999661\t3.39E+16\t144\t6367020\t7.61E+11
+LAB BLOOD ORDERABLES\t577063\tLABHGBP\tHEMOGLOBIN, PLASMA\tYHGBP1\tTOTAL HEMOGLOBIN\t1124271.781\t157143.1405\t338\t380003862.1\t3.80E+15\t363\t57042960\t1.66E+13
+LAB BLOOD ORDERABLES\t472794\tLABMALAR\tMALARIA PREP\tMAL\tMALARIA PREP RESULT\t9940651.826\t39822.56881\t337\t3349999665\t3.35E+16\t327\t13021980\t3.62E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tURENAL\tRENAL CELLS\t9999999\t12728.77358\t337\t3369999663\t3.37E+16\t212\t2698500\t1.37E+11
+LAB BLOOD ORDERABLES\t471640\tLABANTIM\tANTI - TPO AB\tATPO\tANTI-TPO ANTIBODY\t5701537.291\t73384.98403\t335\t1910014993\t1.91E+16\t313\t22969500\t3.16E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tSPERM\tSPERMATOZOA\t9999999\t11761.81034\t333\t3329999667\t3.33E+16\t232\t2728740\t1.34E+11
+LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11825R\tCONCENT. AT STEADY STATE (CSS)\t9728119.562\t466501.791\t331\t3220007575\t3.22E+16\t335\t156278100\t5.70E+14
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tDI\tISSUE DATE/TIME\t9999999\t27509.21053\t330\t3299999670\t3.30E+16\t152\t4181400\t1.47E+11
+LAB BLOOD ORDERABLES\t471786\tLABBMT1\tBMT PANEL 1\tEGFR\tEGFR\t9515152.918\t5213.454545\t330\t3140000463\t3.14E+16\t330\t1720440\t43170415200
+LAB BLOOD ORDERABLES\t471625\tLAB11068R\tACE\tACE\tANGIOTESIN CONVERTING ENZYME\t972673.4681\t195031.7197\t329\t320009571\t3.20E+15\t314\t61239960\t3.89E+13
+LAB BLOOD ORDERABLES\t472802\tLABMMAS\tMETHYLMALONIC ACID, SERUM\tMMA\tMETHYLMALONIC ACID, SERUM\t60790.84912\t340222.5705\t329\t20000189.36\t2.00E+14\t319\t108531000\t4.35E+13
+MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tSDES\tSPECIMEN\t9999999\t184554.1693\t327\t3269999673\t3.27E+16\t319\t58872780\t5.99E+13
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tPRO\tPRODUCT ID  \t9999999\t33679.72028\t326\t3259999674\t3.26E+16\t143\t4816200\t5.88E+11
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tCILIA\tBRONCH. LINING CELLS\t8.481595092\t16602.66667\t326\t2765\t464465\t180\t2988480\t3.15E+11
+LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP\tCARBAMAZEPINE\t858902.9672\t11257.52508\t326\t280002367.3\t2.80E+15\t299\t3366000\t5.74E+11
+MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tSREQ\tCOMMENTS\t9999999\t213232.1186\t326\t3259999674\t3.26E+16\t236\t50322780\t1.42E+13
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t325\t3249999675\t3.25E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tOTHER\tOTHERS (DESCRIBE)\t61582.52923\t74667.06625\t325\t20014322\t2.00E+14\t317\t23669460\t4.12E+12
+LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP2\tDATE & TIME LAST DOSE\t9999999\t11257.52508\t325\t3249999675\t3.25E+16\t299\t3366000\t5.74E+11
+LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP3\tDOSAGE\t9999999\t11257.52508\t325\t3249999675\t3.25E+16\t299\t3366000\t5.74E+11
+STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tOCCGU\tSTOOL SAMPLE2,GUAIAC\t9999999\t40763.46369\t324\t3239999676\t3.24E+16\t179\t7296660\t1.58E+12
+STOOL ORDERABLES\t668083\tLABSTOBGC\tOCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.\tOCCGU\tSTOOL SAMPLE3,GUAIAC\t9999999\t40763.46369\t324\t3239999676\t3.24E+16\t179\t7296660\t1.58E+12
+LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP4\tADMINISTRATION ROUTE\t9999999\t11257.52508\t323\t3229999677\t3.23E+16\t299\t3366000\t5.74E+11
+URINE ORDERABLES\t472018\tLABURCL\tCHLORIDE, URINE (RANDOM)\tUCL\tCHLORIDE, URINE\t93824.27188\t14059.70149\t320\t30023767\t3.00E+14\t201\t2826000\t1.55E+11
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tXMR\tUNIT CROSSMATCH  \t9999999\t64988.57143\t319\t3189999681\t3.19E+16\t147\t9553320\t1.35E+12
+LAB BLOOD ORDERABLES\t497484\tLABCZPL\tCARBAMAZEPINE, SERUM\tCZP1\tPEAK, TROUGH, OR RANDOM\t9999999\t11257.52508\t318\t3179999682\t3.18E+16\t299\t3366000\t5.74E+11
+MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tSREQ\tCOMMENTS\t9999999\t184554.1693\t318\t3179999682\t3.18E+16\t319\t58872780\t5.99E+13
+LAB BLOOD ORDERABLES\t473472\tLABTRFN\tTRANSFERRIN\tTRFRN\tTRANSFERRIN\t190094.8734\t7193.785714\t316\t60069980\t6.00E+14\t280\t2014260\t34907950800
+VIROLOGY ORDERABLES\t472041\tLABCMVM\tCMV AB IGM\tCMVIGM\tCMV IGM AB\t9999999\t153862.9333\t316\t3159999684\t3.16E+16\t225\t34619160\t6.49E+12
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9605R\tMETHOD\t9999999\t118749.8734\t315\t3149999685\t3.15E+16\t316\t37524960\t4.02E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9271R\tSERUM DATE\t9999999\t118749.8734\t314\t3139999686\t3.14E+16\t316\t37524960\t4.02E+13
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tUBT\tUNIT BLOOD TYPE  \t9999999\t45869.76744\t312\t3119999688\t3.12E+16\t129\t5917200\t7.29E+11
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9585R\tCOMPOSED TEXT CLASS I\t9999999\t118749.8734\t312\t3119999688\t3.12E+16\t316\t37524960\t4.02E+13
+LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tARG\tABO/RH (AUTOMATION)\t9999999\t16553.15589\t312\t3119999688\t3.12E+16\t263\t4353480\t1.43E+11
+LAB BLOOD ORDERABLES\t590527\tLABBMT2\tBMT PANEL 2\tEGFR\tEGFR\t9679487.679\t3750.384615\t312\t3020000156\t3.02E+16\t312\t1170120\t25513228800
+LAB BLOOD ORDERABLES\t471641\tLABANTIXA\tLMW HEPARIN, ANTIXA U/ML\tANTIXA\tLMW HEPARIN, ANTIXA U/ML\t290323.2281\t21451.54412\t310\t90000200.71\t9.00E+14\t272\t5834820\t2.86E+11
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9274R\tCPRA FOR C1Q CLASS I\t9999999\t119126.8571\t309\t3089999691\t3.09E+16\t315\t37524960\t4.02E+13
+LAB BLOOD ORDERABLES\t577063\tLABHGBP\tHEMOGLOBIN, PLASMA\t12078R\tOXYHEMOGLOBIN\t1391592.153\t142800.5286\t309\t430001975.4\t4.30E+15\t227\t32415720\t5.47E+12
+VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tSDES11\tSPECIMEN\t9999999\t167178.2332\t307\t3069999693\t3.07E+16\t283\t47311440\t1.12E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9266R\tCPRA FOR CUMULATIVE C1Q\t9999999\t113014.3408\t306\t3059999694\t3.06E+16\t311\t35147460\t3.80E+13
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMPHM\tLYMPHOMA CELLS\t42.36721311\t16392.29167\t305\t12922\t848348\t288\t4720980\t4.74E+11
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRESVSP\tSPECIMEN\t9999999\t97678.90244\t305\t3049999695\t3.05E+16\t820\t80096700\t1.50E+13
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tPRO\tPRODUCT ID  \t9999999\t34670.73529\t303\t3029999697\t3.03E+16\t136\t4715220\t5.88E+11
+LAB BLOOD ORDERABLES\t977657\tLABBHB\tBETA-HYDROXYBUTYRATE\tBHB\tBETA-HYDROXYBUTYRATE\t1650166.313\t8862.772277\t303\t500000392.8\t5.00E+15\t303\t2685420\t53595745200
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCRLYM\tREACT LYMPH, CSF\t3.410596026\t18365.37736\t302\t1030\t9952\t212\t3893460\t9.21E+11
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tSI\t<<STATUS>>  \t9999999\t37720.88748\t300\t2999999700\t3.00E+16\t631\t23801880\t2.56E+12
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL3\tHSV 2 IGG INDEX\t7699999.873\t290474.6392\t300\t2309999962\t2.31E+16\t291\t84528120\t2.82E+13
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL5\tHSV 1/2 IGM INDEX\t7366666.345\t290474.6392\t300\t2209999903\t2.21E+16\t291\t84528120\t2.82E+13
+STOOL ORDERABLES\t472954\tLABANCA\tNEUTROPHIL CYTOPLASMA REFLEX AB PANEL\tMPOAR\tMYELOPEROX AB REFLEX\t9999999\t219441.5909\t298\t2979999702\t2.98E+16\t264\t57932580\t1.44E+13
+STOOL ORDERABLES\t472954\tLABANCA\tNEUTROPHIL CYTOPLASMA REFLEX AB PANEL\tSERINEPR3\tSERINE PR3 REFLEX\t9999999\t219441.5909\t298\t2979999702\t2.98E+16\t264\t57932580\t1.44E+13
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tDI\tISSUE DATE/TIME\t9999999\t34239.86014\t297\t2969999703\t2.97E+16\t143\t4896300\t3.08E+11
+MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tRPT\tRPT\t9999999\t1712644.444\t297\t2969999703\t2.97E+16\t297\t508655400\t9.24E+14
+MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tSDES\tSPECIMEN\t9999999\t1712644.444\t296\t2959999704\t2.96E+16\t297\t508655400\t9.24E+14
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPLYM\tPROLYMPHOCYTE\t34027.34694\t33929.27336\t294\t10004040\t1.00E+14\t289\t9805560\t1.72E+12
+MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tSREQ\tCOMMENTS\t9999999\t1712644.444\t294\t2939999706\t2.94E+16\t297\t508655400\t9.24E+14
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL1\tHSV 1 IGG INDEX\t5238096.571\t290474.6392\t294\t1540000392\t1.54E+16\t291\t84528120\t2.82E+13
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t291\t2909999709\t2.91E+16\t0\tNone\tNone
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9251R\tSERUM DATE\t9999999\t159629.0722\t291\t2909999709\t2.91E+16\t291\t46452060\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9604R\tMETHOD\t9999999\t159629.0722\t291\t2909999709\t2.91E+16\t291\t46452060\t7.62E+13
+LAB BLOOD ORDERABLES\t497216\tLABYCP\tC - PEPTIDE, SERUM\tCPEP\tC-PEPTIDE\t618558.524\t43331.67939\t291\t180000530.5\t1.80E+15\t262\t11352900\t1.04E+12
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9249R\tIGG COMBINED PEAK CPRA\t9999999\t159629.0722\t290\t2899999710\t2.90E+16\t291\t46452060\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9254R\tCPRA FOR IGG CLASS I\t9999999\t160179.1034\t290\t2899999710\t2.90E+16\t290\t46451940\t7.62E+13
+VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-BLOOD\tCMVQSP\tSPECIMEN\t9999999\t204632\t290\t2899999710\t2.90E+16\t30\t6138960\t1.86E+12
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9245R\tCPRA FOR CUMULATIVE IGG\t9999999\t159157.8621\t289\t2889999711\t2.89E+16\t290\t46155780\t7.61E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9250R\tIGG COMBINED PEAK DATE\t9999999\t159629.0722\t289\t2889999711\t2.89E+16\t291\t46452060\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9248R\tIGG COMBINED CPRA FOR SERUM\t9999999\t161079.1667\t287\t2869999713\t2.87E+16\t288\t46390800\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9257R\tCPRA FOR IGG CLASS II\t9999999\t160995\t287\t2869999713\t2.87E+16\t288\t46366560\t7.62E+13
+MICROBIOLOGY ORDERABLES\t541700\tLABBLCSTK\tBLOOD CULT - FIRST SET, VIA PHLEBOTOMY\tCULT\tCULT/OTHER RSLT:\t9999999\t483529.1944\t286\t2859999714\t2.86E+16\t4481\t2166694320\t1.17E+15
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tCECAST\tCELLULAR CASTS\t9999999\t10987.42574\t285\t2849999715\t2.85E+16\t202\t2219460\t71703558000
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCCNT\tCELLS COUNTED\t59.02112676\t14846.32258\t284\t16762\t1985536\t155\t2301180\t7.75E+11
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tAMPHET\tAMPHETAMINES\t9999999\t292758.0347\t283\t2829999717\t2.83E+16\t173\t50647140\t4.06E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9275R\tHLA ANTIBODIES FOR C1Q CLASS I\t9999999\t118749.8734\t282\t2819999718\t2.82E+16\t316\t37524960\t4.02E+13
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tSI\t<<STATUS>>  \t9999999\t40376.78201\t280\t2799999720\t2.80E+16\t578\t23337780\t2.61E+12
+LAB BLOOD ORDERABLES\t472045\tLABCO2\tCO2, SERUM/PLASMA\tCO2\tCO2, SER/PLAS\t71708.92832\t1890.989011\t279\t20006791\t2.00E+14\t91\t172080\t987652800
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPRMY\tPROMYELOCYTES\t6.352517986\t44065.65056\t278\t1766\t64670\t269\t11853660\t5.02E+12
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tPRO\tPRODUCT ID  \t9999999\t30573.07087\t277\t2769999723\t2.77E+16\t127\t3882780\t3.96E+11
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMEGA\tMEGAKARY/100WBC\t4.293478261\t25805.66038\t276\t1185\t34665\t265\t6838500\t7.26E+11
+VIROLOGY ORDERABLES\t497464\tLABFVARZ\tVARICELLA ZOSTER VIR. DNA,QUAL\tSOUR13\tSOURCE\t9999999\t246899.0977\t276\t2759999724\t2.76E+16\t133\t32837580\t2.43E+13
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tDI\tISSUE DATE/TIME\t9999999\t33644.7619\t275\t2749999725\t2.75E+16\t126\t4239240\t2.63E+11
+STOOL ORDERABLES\t472954\tLABANCA\tNEUTROPHIL CYTOPLASMA REFLEX AB PANEL\tANCIGG\tANCA, IGG\t9999999\t219441.5909\t275\t2749999725\t2.75E+16\t264\t57932580\t1.44E+13
+LAB BLOOD ORDERABLES\t473759\tLAB11098R\t1 25 OH VITAMIN D\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t2043823.269\t274198.5\t274\t560007575.7\t5.60E+15\t240\t65807640\t2.14E+13
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tUBT\tUNIT BLOOD TYPE  \t9999999\t45405.78947\t272\t2719999728\t2.72E+16\t114\t5176260\t6.15E+11
+VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVGGQUL\tVZV IGG RESULT\t9999999\t167515.5882\t271\t2709999729\t2.71E+16\t272\t45564240\t9.41E+12
+LAB BLOOD ORDERABLES\t472022\tLABCL\tCHLORIDE, SERUM/PLASMA\tCL\tCHLORIDE, SER/PLAS\t74179.22593\t2050.769231\t270\t20028391\t2.00E+14\t78\t159960\t939261600
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tURIC\tURIC ACID CRYSTALS\t9999999\t15107.21893\t270\t2699999730\t2.70E+16\t169\t2553120\t4.10E+11
+VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVGGQNT\tVZV IGG LEVEL\t371749.6676\t167359.7753\t269\t100000660.6\t1.00E+15\t267\t44685060\t9.23E+12
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tALCOHOL\tALCOHOL\t9999999\t271787.3494\t267\t2669999733\t2.67E+16\t166\t45116700\t3.56E+13
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t12002R\tVCA IGM ,IFA\t9999999\t212032.3077\t267\t2669999733\t2.67E+16\t234\t49615560\t1.19E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9265R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t119265.2866\t265\t2649999735\t2.65E+16\t314\t37449300\t4.02E+13
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tFIO2\tFIO2\t5283038.447\t306.875\t265\t1400005188\t1.40E+16\t192\t58920\t2744136000
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9255R\tHLA ANTIBODIES FOR IGG CLASS I\t9999999\t160179.1034\t264\t2639999736\t2.64E+16\t290\t46451940\t7.62E+13
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t12001R\tVCA IGG ,IFA\t9999999\t212032.3077\t264\t2639999736\t2.64E+16\t234\t49615560\t1.19E+13
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tSI\t<<STATUS>>  \t9999999\t39146.22824\t263\t2629999737\t2.63E+16\t517\t20238600\t2.20E+12
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tBARB\tBARBITURATES\t9999999\t280049.8137\t263\t2629999737\t2.63E+16\t161\t45088020\t3.62E+13
+VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMMQNT\tCMV IGM LEVEL\t9011406.061\t163792.4715\t263\t2369999794\t2.37E+16\t263\t43077420\t9.37E+12
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tBENZO\tBENZODIAZEPINES\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tCOCAINE\tCOCAINE\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tOPIATE\tOPIATES\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tYUPCP\tPHENCYCLIDINE\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
+URINE ORDERABLES\t472062\tLABYUCDA\tCONFIRMED DRUG ABUSE SURVEY, URINE\tYUTTHC\tTETRAHYDROCANNABINOL\t9999999\t277624.125\t262\t2619999738\t2.62E+16\t160\t44419860\t3.57E+13
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tXMR\tUNIT CROSSMATCH  \t9999999\t71971.07143\t261\t2609999739\t2.61E+16\t112\t8060760\t1.22E+12
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9246R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t159629.0722\t261\t2609999739\t2.61E+16\t291\t46452060\t7.62E+13
+LAB BLOOD ORDERABLES\t471571\tLAB11069R\tALDOLASE\tALDOLASE\tALDOLASE, SERUM\t383163.4709\t169101.5323\t261\t100005665.9\t1.00E+15\t248\t41937180\t2.52E+13
+MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRCMT\tCOMMENT\t9999999\t488214.4565\t260\t2599999740\t2.60E+16\t184\t89831460\t6.08E+13
+MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRMET\tMETHOD\t9999999\t488214.4565\t260\t2599999740\t2.60E+16\t184\t89831460\t6.08E+13
+MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRPRM\tPRIMERS\t9999999\t488214.4565\t260\t2599999740\t2.60E+16\t184\t89831460\t6.08E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9258R\tHLA ANTIBODIES FOR IGG CLASS II\t9999999\t160995\t256\t2559999744\t2.56E+16\t288\t46366560\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9263R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t118886.6667\t255\t2549999745\t2.55E+16\t315\t37449300\t4.02E+13
+LAB BLOOD ORDERABLES\t473796\tLABYWNILE\tWEST NILE VIRUS AB, IGG AND IGM\tYWNSC\tSOURCE\t9999999\t282367.6494\t255\t2549999745\t2.55E+16\t251\t70874280\t3.07E+13
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tSI\t<<STATUS>>  \t9999999\t36165.4902\t254\t2539999746\t2.54E+16\t459\t16599960\t1.66E+12
+BODY FLUID ORDERABLES\t497327\tLABWNVAB\tWEST NILE VIRUS AB PANEL, CSF\tWNVIGG\tWEST NILE VIRUS IGG\t9446639.5\t328716.2567\t253\t2389999793\t2.39E+16\t187\t61469940\t4.81E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tREACTL\tREACTIVE LYMPHS\t4.565217391\t36557.3617\t253\t1155\t24013\t235\t8590980\t1.37E+12
+LAB BLOOD ORDERABLES\t472096\tLABYCOPPR\tCOPPER, SERUM\tCOPPER\tCOPPER, SERUM\t237155.1937\t198196.4063\t253\t60000264\t6.00E+14\t256\t50738280\t6.24E+13
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tVMQNT\tVMQNT\t9486165.194\t251424.8606\t253\t2399999794\t2.40E+16\t251\t63107640\t2.09E+13
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tUBT\tUNIT BLOOD TYPE  \t9999999\t44582.22222\t251\t2509999749\t2.51E+16\t108\t4814880\t5.88E+11
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tACAIGG\tACA IGG ANTIBODIES\t199210.8924\t318547.2289\t251\t50001934\t5.00E+14\t249\t79318260\t3.18E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tACAIGM\tACA IGM ANTIBODIES\t119526.5179\t318547.2289\t251\t30001156\t3.00E+14\t249\t79318260\t3.18E+13
+LAB BLOOD ORDERABLES\t472520\tLABHBCA\tIGM ANTI-HBC\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t52028.78788\t251\t2509999749\t2.51E+16\t198\t10301700\t1.13E+12
+LAB BLOOD ORDERABLES\t473704\tLABREF1\tLAB UNLISTED 1\tRESUL1\tRESULT\t9999999\t982058.0846\t250\t2499999750\t2.50E+16\t898\t881888160\t7.73E+15
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tBE\tBASE EXCESS, ISTAT\t120005.836\t32.88461538\t250\t30001459\t3.00E+14\t104\t3420\t219600
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tXMR\tUNIT CROSSMATCH  \t9999999\t68337.71429\t248\t2479999752\t2.48E+16\t105\t7175460\t1.09E+12
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tPRO\tPRODUCT ID  \t9999999\t33532.84404\t248\t2479999752\t2.48E+16\t109\t3655080\t4.10E+11
+MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t439595.3706\t248\t2479999752\t2.48E+16\t4290\t1885864140\t2.06E+15
+LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGENTPK\tGENTAMICIN, PEAK\t485834.1113\t11076.83721\t247\t120001025.5\t1.20E+15\t215\t2381520\t77927551200
+BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tSHAPE\tSHAPE\t9999999\t11946.19355\t246\t2459999754\t2.46E+16\t155\t1851660\t96880654800
+LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGNTPO3\tDOSAGE\t9999999\t11076.83721\t246\t2459999754\t2.46E+16\t215\t2381520\t77927551200
+LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGNTPO4\tADMINISTRATION ROUTE\t9999999\t11076.83721\t246\t2459999754\t2.46E+16\t215\t2381520\t77927551200
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADDNA\tADENOVIRUS DNA\t9999999\t203590.4878\t246\t2459999754\t2.46E+16\t246\t50083260\t1.36E+13
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVCP\tRESULT\t9012109.122\t203590.4878\t246\t2216978844\t2.21E+16\t246\t50083260\t1.36E+13
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVLOG\tRESULT LOG 10\t8983739.346\t203590.4878\t246\t2209999879\t2.21E+16\t246\t50083260\t1.36E+13
+BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tSIZE\tSIZE\t9999999\t11946.19355\t245\t2449999755\t2.45E+16\t155\t1851660\t96880654800
+LAB BLOOD ORDERABLES\t497510\tLABGNTPOL\tGENTAMICIN PEAK LEVEL\tGNTPO2\tDATE & TIME OF LAST DOSE\t9999999\t11076.83721\t245\t2449999755\t2.45E+16\t215\t2381520\t77927551200
+LAB BLOOD ORDERABLES\t472325\tLABYERYTH\tERYTHROPOIETIN\tEPO\tERYTHROPOIETIN\t1147579.533\t215313.5211\t244\t280009406\t2.80E+15\t213\t45861780\t9.55E+14
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tDI\tISSUE DATE/TIME\t9999999\t31822.07547\t243\t2429999757\t2.43E+16\t106\t3373140\t1.79E+11
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tALBCPI\tALBUMIN, SER/PLAS\t497285.6379\t407309.1176\t243\t120840410\t1.20E+15\t136\t55394040\t3.19E+13
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCAB\tCSF ALBUMIN\t493865.1329\t407309.1176\t243\t120009227.3\t1.20E+15\t136\t55394040\t3.19E+13
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCIG\tCSF IGG\t493834.1185\t407309.1176\t243\t120001690.8\t1.20E+15\t136\t55394040\t3.19E+13
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCINDX\tCSF IGG/ALBUMIN INDEX\t493827.6996\t407309.1176\t243\t120000131\t1.20E+15\t136\t55394040\t3.19E+13
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tIGGCPI\tIGG, SER/PLAS\t494776.5185\t407309.1176\t243\t120230694\t1.20E+15\t136\t55394040\t3.19E+13
+LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO\tLAMO\t1440335.963\t236494.25\t243\t350001639\t3.50E+15\t240\t56758620\t1.52E+15
+LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO1\tLAMO1\t9999999\t236494.25\t243\t2429999757\t2.43E+16\t240\t56758620\t1.52E+15
+LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO2\tLAMO2\t9999999\t236494.25\t243\t2429999757\t2.43E+16\t240\t56758620\t1.52E+15
+LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO3\tLAMO3\t9999999\t236494.25\t243\t2429999757\t2.43E+16\t240\t56758620\t1.52E+15
+BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tLOCAT\tLOCATION\t9999999\t11946.19355\t242\t2419999758\t2.42E+16\t155\t1851660\t96880654800
+LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE LEVEL\tLAMO4\tLAMO4\t9999999\t236494.25\t242\t2419999758\t2.42E+16\t240\t56758620\t1.52E+15
+BODY FLUID ORDERABLES\t473022\tLABGASBLD\tOCCULT BLOOD GASTRIC\tOCCBLDGAS\tOCCULT BLOOD,GASTRIC\t9916317.008\t9982.44898\t239\t2369999765\t2.37E+16\t147\t1467420\t3.34E+11
+MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tRPT\tRPT\t9999999\t3667605.352\t239\t2389999761\t2.39E+16\t213\t781199940\t3.13E+15
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tDYSPL\tDYSPL GRANS, UNCLASS.\t12.47033898\t23989.69163\t236\t2943\t71343\t227\t5445660\t6.23E+11
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAQT\tHEP B SURF AB QUANT\t7245826.204\t43043.64407\t236\t1710014984\t1.71E+16\t236\t10158300\t7.64E+11
+MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tSDES\tSPECIMEN\t9999999\t3667605.352\t236\t2359999764\t2.36E+16\t213\t781199940\t3.13E+15
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVSP\tSPECIMEN\t9999999\t203590.4878\t236\t2359999764\t2.36E+16\t246\t50083260\t1.36E+13
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tEVR\tCSF ENTEROVIRUS RESULT\t9999999\t25672.87129\t235\t2349999765\t2.35E+16\t202\t5185920\t5.37E+11
+LAB BLOOD ORDERABLES\t471641\tLABANTIXA\tANTI XA / LMWH\tANTIXA\tLMW HEPARIN, ANTIXA U/ML\t425532.5321\t38717.1875\t235\t100000145.1\t1.00E+15\t192\t7433700\t4.11E+11
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tB2GPIG\tBETA-2 GLYCOPROTEIN 1, IGG\t85474.5812\t316148.4979\t234\t20001052\t2.00E+14\t233\t73662600\t2.93E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tB2GPIM\tBETA-2 GLYCOPROTEIN 1, IGM\t85478.16239\t316148.4979\t234\t20001890\t2.00E+14\t233\t73662600\t2.93E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVCN\tDRVV CONFIRMATION\t9145302.288\t316148.4979\t234\t2140000735\t2.14E+16\t233\t73662600\t2.93E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVMX\tDRVV MIX\t6068393.227\t316148.4979\t234\t1420004015\t1.42E+16\t233\t73662600\t2.93E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVSC\tDRVV SCREEN\t299191.3662\t316148.4979\t234\t70010779.7\t7.00E+14\t233\t73662600\t2.93E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVLA\tDRVV NORMALIZED RATIO\t9137930.258\t317009.8701\t232\t2119999820\t2.12E+16\t231\t73229280\t2.92E+13
+LAB BLOOD ORDERABLES\t472120\tLABYCCPIG\tANTI CCP\tCCPR\tANTI CCP ANTIBODY\t9999999\t388985.2294\t232\t2319999768\t2.32E+16\t218\t84798780\t4.31E+13
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITPR\tPRIMER\t9999999\t779901.7925\t231\t2309999769\t2.31E+16\t212\t165339180\t3.13E+14
+MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tSREQ\tCOMMENTS\t9999999\t3667605.352\t231\t2309999769\t2.31E+16\t213\t781199940\t3.13E+15
+LAB BLOOD ORDERABLES\t472840\tLABYMETFF\tMETANEPHRINES FRACTIONATED FREE, PLASMA\tUMETF\tMETANEPHRINE FREE\t4565217.143\t306697.1429\t230\t1049999943\t1.05E+16\t210\t64406400\t2.54E+13
+LAB BLOOD ORDERABLES\t472840\tLABYMETFF\tMETANEPHRINES FRACTIONATED FREE, PLASMA\tUNORMETF\tNORMETANEPHRINE,FREE\t956522.6147\t306697.1429\t230\t220000201.4\t2.20E+15\t210\t64406400\t2.54E+13
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITDX\tCLINICAL INDICATION\t9999999\t779901.7925\t230\t2299999770\t2.30E+16\t212\t165339180\t3.13E+14
+VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tRPT\tRPT\t9999999\t1756836.783\t230\t2299999770\t2.30E+16\t230\t404072460\t7.53E+14
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tPRO\tPRODUCT ID  \t9999999\t32158.26923\t229\t2289999771\t2.29E+16\t104\t3344460\t3.42E+11
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tUBT\tUNIT BLOOD TYPE  \t9999999\t48100.40816\t228\t2279999772\t2.28E+16\t98\t4713840\t5.88E+11
+BLOOD BANK PRODUCT ORDERABLES\t889180\tLABERUPLT\tEMERGENCY RELEASE PLT\tPLT\tPLATELETS\t9999999\t9099.473684\t228\t2279999772\t2.28E+16\t228\t2074680\t2.19E+11
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFRLYM\tREACTIVE LYMPHS, FLD\t2.276315789\t14423.57143\t228\t519\t6695\t140\t2019300\t1.07E+11
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9264R\tCPRA FOR CUMULATIVE IGG\t9999999\t148177.8814\t228\t2279999772\t2.28E+16\t236\t34969980\t3.94E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9247R\tCPRA FOR CUMULATIVE C1Q\t9999999\t116766.5502\t227\t2269999773\t2.27E+16\t229\t26739540\t2.26E+13
+VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tSDES\tSPECIMEN\t9999999\t1756836.783\t227\t2269999773\t2.27E+16\t230\t404072460\t7.53E+14
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9244R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t159629.0722\t226\t2259999774\t2.26E+16\t291\t46452060\t7.62E+13
+LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tC3P4PA\tCD3+/CD4+,ABS\t398450.3009\t63854.06699\t226\t90049768\t9.00E+14\t209\t13345500\t1.17E+12
+LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tC3P4PC\tCD3+/CD4+,%\t354003.7389\t63854.06699\t226\t80004845\t8.00E+14\t209\t13345500\t1.17E+12
+LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tLYM\tLYMPHOCYTES %\t398246.7841\t63854.06699\t226\t90003773.2\t9.00E+14\t209\t13345500\t1.17E+12
+LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tLYMAB\tLYMPHOCYTES, ABS.\t399178.9027\t63854.06699\t226\t90214432\t9.00E+14\t209\t13345500\t1.17E+12
+LAB BLOOD ORDERABLES\t471949\tLABCD4CT\tCD4 COUNT\tWBC\tWBC COUNT\t360761.0265\t63854.06699\t226\t81531992\t8.00E+14\t209\t13345500\t1.17E+12
+LAB BLOOD ORDERABLES\t472664\tLABIOPTH\tINTRA OPERATIVE PTH\tIOPTH\tINTRA OPERATIVE PTH\t807386.8018\t2213.932584\t223\t180047256.8\t1.80E+15\t89\t197040\t1005069600
+STOOL ORDERABLES\t670760\tLAB11234R\tVZV TOTAL AND IGM AB PANEL\t11235R\tVARICELLA-ZOSTER, ACIF\t9999999\t214763.038\t223\t2229999777\t2.23E+16\t237\t50898840\t1.48E+13
+STOOL ORDERABLES\t670760\tLAB11234R\tVZV TOTAL AND IGM AB PANEL\t11236R\tVZ AB IGM, IFA\t9999999\t214763.038\t223\t2229999777\t2.23E+16\t237\t50898840\t1.48E+13
+VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tSREQ\tCOMMENTS\t9999999\t1756836.783\t223\t2229999777\t2.23E+16\t230\t404072460\t7.53E+14
+LAB BLOOD ORDERABLES\t670770\tLAB11242R\tVZV QUANT\t11247R\tVZV QUANT\t9595300.09\t189403.6596\t222\t2130156620\t2.13E+16\t235\t44509860\t1.94E+13
+BODY FLUID ORDERABLES\t497327\tLABWNVAB\tWEST NILE VIRUS AB PANEL, CSF\tWNVIGM\tWEST NILE VIRUS IGM\t9863635.431\t298197.0166\t220\t2169999795\t2.17E+16\t181\t53973660\t3.84E+13
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tSI\t<<STATUS>>  \t9999999\t36168.4375\t219\t2189999781\t2.19E+16\t384\t13888680\t1.55E+12
+LAB BLOOD ORDERABLES\t472375\tLABFDP\tFIBRIN DEGRADATION PRODUCTS\tFDP\tFIBRIN DEGRAD PROD\t9999999\t8429.142857\t219\t2189999781\t2.19E+16\t175\t1475100\t18989067600
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tDI\tISSUE DATE/TIME\t9999999\t36351.52174\t218\t2179999782\t2.18E+16\t92\t3344340\t2.82E+11
+LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\tFMYABM\tM.PNEUMONIA AB IGM\t9999999\t236846.1207\t218\t2179999782\t2.18E+16\t232\t54948300\t3.81E+13
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tEVSP\tSPECIMEN\t9999999\t25672.87129\t217\t2169999783\t2.17E+16\t202\t5185920\t5.37E+11
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t10472R\tDSA FOR C1Q CLASS I\t9999999\t105122.765\t217\t2169999783\t2.17E+16\t217\t22811640\t1.32E+13
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45RAA\tCD4+/CD45RA+, ABS\t558186.0465\t131761.5464\t215\t120010000\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45RAP\tCD4+/CD45RA+ %\t558146.4512\t131761.5464\t215\t120001487\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45ROA\tCD4+/CD45RO+, ABS\t558259.8558\t131761.5464\t215\t120025869\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC45ROP\tCD4+/CD45RO+ %\t558158.7814\t131761.5464\t215\t120004138\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD19AB\tCD19 (PAN B), ABS.\t558197.214\t131761.5464\t215\t120012401\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD19PC\tCD19 (PAN B) %\t558147.6372\t131761.5464\t215\t120001742\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD20AB\tCD20 (PAN B), ABS.\t558179.9163\t131761.5464\t215\t120008682\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD20PC\tCD20 (PAN B) %\t558147.7442\t131761.5464\t215\t120001765\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD3AB\tCD3 (PAN T), ABS.\t558609.3953\t131761.5464\t215\t120101020\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD3PC\tCD3 (PAN T) %\t558204.3209\t131761.5464\t215\t120013929\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD4AB\tCD3+/CD4+, ABS.\t558305.6093\t131761.5464\t215\t120035706\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD4PC\tCD3+/CD4+ %\t558165.8558\t131761.5464\t215\t120005659\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD8AB\tCD3+/CD8+, ABS.\t558448.9628\t131761.5464\t215\t120066527\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tCD8PC\tCD3+/CD8+ %\t558180.7116\t131761.5464\t215\t120008853\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tHNS\tCD4+CD8 TOTAL, CD3+%\t558207.0884\t131761.5464\t215\t120014524\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t558615.093\t131761.5464\t215\t120102245\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tHSRATIO\tCD4/CD8 RATIO, CD3+\t558140.4037\t131761.5464\t215\t120000186.8\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tLYM\tLYMPHOCYTES %\t558152.5181\t131761.5464\t215\t120002791.4\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tLYMAB\tLYMPHOCYTES, ABS.\t558765.8558\t131761.5464\t215\t120134659\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tWBC\tWBC COUNT\t564353.8977\t131761.5464\t215\t121336088\t1.20E+15\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPDX\tCLINICAL INDICATION\t9999999\t781898.5492\t213\t2129999787\t2.13E+16\t193\t150906420\t1.54E+14
+LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNRS\tQUANTIFERON-TB\t9999999\t193921.9811\t212\t2119999788\t2.12E+16\t212\t41111460\t1.47E+13
+MICROBIOLOGY ORDERABLES\t471619\tLABANER\tANAEROBIC CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t529714.7807\t212\t2119999788\t2.12E+16\t5108\t2705783100\t2.15E+15
+URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUALB\tALBUMIN, URINE\t1132498.264\t65933.15436\t212\t240089632\t2.40E+15\t149\t9824040\t1.29E+12
+URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUALBCR\tALB/CREAT RATIO\t1132721.995\t65933.15436\t212\t240137063\t2.40E+15\t149\t9824040\t1.29E+12
+URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t141587.6023\t65933.15436\t212\t30016571.69\t3.00E+14\t149\t9824040\t1.29E+12
+LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tACAIGG\tACA IGG ANTIBODIES\t236975.0095\t311240.1\t211\t50001727\t5.00E+14\t200\t62248020\t2.73E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tACAIGM\tACA IGM ANTIBODIES\t236971.3697\t311240.1\t211\t50000959\t5.00E+14\t200\t62248020\t2.73E+13
+MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tRPT\tRPT\t9999999\t90298.30189\t211\t2109999789\t2.11E+16\t106\t9571620\t1.70E+12
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9277R\tCPRA FOR C1Q CLASS II\t9999999\t138546\t210\t2099999790\t2.10E+16\t210\t29094660\t2.40E+13
+LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tANTI-RO ANTIBODY\tSSAQUL\tANTI-RO RESULT\t9999999\t390342.8571\t210\t2099999790\t2.10E+16\t210\t81972000\t4.05E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9281R\tCOMPOSED TEXT CLASS II\t9999999\t138546\t209\t2089999791\t2.09E+16\t210\t29094660\t2.40E+13
+LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1 AND 2 PCR, NON CSF\tHV1DNA\tHSV-1 DNA, QN PCR\t9999999\t534380.8163\t209\t2089999791\t2.09E+16\t98\t52369320\t9.04E+13
+STOOL ORDERABLES\t471762\tLABYBOH\tBETA-HYDROXYBUTYRATE\tBOH\tBETA-HYDROXYBUTYRATE\t1004786.194\t177002.0168\t209\t210000314.5\t2.10E+15\t238\t42126480\t2.11E+13
+VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tSDES2\tSPECIMEN\t9999999\t184892.973\t209\t2089999791\t2.09E+16\t333\t61569360\t1.58E+13
+HISTOCOMPATIBILITY ORDERABLE\t890826\t10150R\tHLA ORDER - SUMMARIES\t10049R\tHLA LAB TESTING STATUS\t9999999\t1953334.327\t208\t2079999792\t2.08E+16\t208\t406293540\t1.40E+16
+LAB BLOOD ORDERABLES\t472610\tLABIGA\tIGA, SERUM\tIGA\tIGA, SERUM\t432930.4519\t47495.87629\t208\t90049534\t9.00E+14\t194\t9214200\t9.20E+11
+LAB BLOOD ORDERABLES\t497075\tLABSHIV12\tRAPID HIV-1/2 ANTIBODY\tSHIV12\tRAPID HIV-1/2 ANTIBODY\t9999999\t24626.41509\t208\t2079999792\t2.08E+16\t159\t3915600\t2.04E+12
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tPRO\tPRODUCT ID  \t9999999\t29978.70968\t207\t2069999793\t2.07E+16\t93\t2788020\t2.99E+11
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tUBT\tUNIT BLOOD TYPE  \t9999999\t41291.48936\t207\t2069999793\t2.07E+16\t94\t3881400\t3.96E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6CP\tRESULT\t8826910.329\t230093.0435\t207\t1827170438\t1.82E+16\t207\t47629260\t1.54E+13
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t230093.0435\t207\t2069999793\t2.07E+16\t207\t47629260\t1.54E+13
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6LO\tRESULT LOG 10\t8792270.18\t230093.0435\t207\t1819999927\t1.82E+16\t207\t47629260\t1.54E+13
+BODY FLUID ORDERABLES\t471757\tLABSTREP\tBETA STREP THROAT SCREEN RAPID EIA\tBSTREP\tBETA STREP THROAT EIA SCREEN\t9999999\t7540.56338\t206\t2059999794\t2.06E+16\t142\t1070760\t37267934400
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tCCNT\tCELLS COUNTED\t111.4563107\t15949.73684\t206\t22960\t3945718\t152\t2424360\t4.59E+11
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t10469R\tDSA FOR IGG CLASS I\t9999999\t90041.06796\t206\t2059999794\t2.06E+16\t206\t18548460\t1.11E+13
+LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tANTI-LA ANTIBODY\tSSBQUL\tANTI-LA RESULT\t9999999\t380593.9806\t206\t2059999794\t2.06E+16\t206\t78402360\t3.76E+13
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6SP\tSPECIMEN\t9999999\t230093.0435\t206\t2059999794\t2.06E+16\t207\t47629260\t1.54E+13
+VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tHSV1PC\tHSV-1 DNA\t9999999\t185672.0388\t206\t2059999794\t2.06E+16\t206\t38248440\t9.97E+12
+VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tHSV2PC\tHSV-2 DNA\t9999999\t185672.0388\t206\t2059999794\t2.06E+16\t206\t38248440\t9.97E+12
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t10470R\tDSA FOR IGG CLASS II\t9999999\t90063.21951\t205\t2049999795\t2.05E+16\t205\t18462960\t1.11E+13
+LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tPL24\tPLASMA, 24HR\t7756097.068\t303390.1571\t205\t1589999899\t1.59E+16\t191\t57947520\t1.90E+13
+LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tPL72\tPLASMA, 72HR\t7756097.263\t303390.1571\t205\t1589999939\t1.59E+16\t191\t57947520\t1.90E+13
+LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tSR24\tSERUM, 24HR\t2829268.254\t303390.1571\t205\t579999992\t5.80E+15\t191\t57947520\t1.90E+13
+LAB BLOOD ORDERABLES\t472112\tLABCRYO\tCRYOPROTEIN SEMI - QUANTITATIVE\tSR72\tSERUM, 72HR\t3951219.732\t303390.1571\t205\t810000045\t8.10E+15\t191\t57947520\t1.90E+13
+LAB BLOOD ORDERABLES\t473188\tLABYPTHRP\tPTH-RELATED PEPTIDE, PLASMA\tPTH\tPTH-RELATED PEPTIDE\t2926830.671\t386264.3478\t205\t600000287.5\t6.00E+15\t253\t97724880\t4.37E+13
+STOOL ORDERABLES\t471535\tLABFADA\tADENOSINE DEAMINASE, FLUID\tSPTYAD\tFLUID TYPE\t9999999\t540366\t205\t2049999795\t2.05E+16\t90\t48632940\t6.04E+13
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tXMR\tUNIT CROSSMATCH  \t9999999\t76819.02439\t204\t2039999796\t2.04E+16\t82\t6299160\t1.03E+12
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9278R\tHLA ANTIBODIES FOR C1Q CLASS II\t9999999\t138546\t203\t2029999797\t2.03E+16\t210\t29094660\t2.40E+13
+LAB BLOOD ORDERABLES\t473260\tLABPATHR\tREVIEW BY PATHOLOGIST\tFPATH\tPATHOLOGIST\t9999999\t#VALUE!\t202\t2019999798\t2.02E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITCO\tCOMMENT\t9999999\t807029.3684\t202\t2019999798\t2.02E+16\t190\t153335580\t3.03E+14
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tSI\t<<STATUS>>  \t9999999\t35683.83954\t201\t2009999799\t2.01E+16\t349\t12453660\t1.39E+12
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tDI\tISSUE DATE/TIME\t9999999\t35233.18681\t201\t2009999799\t2.01E+16\t91\t3206220\t2.54E+11
+LAB BLOOD ORDERABLES\t471761\tLABB2M\tBETA - 2 - MICROGLOBULIN, SERUM\tB2M\tBETA-2-MICROGLOBULIN, SERUM\t55494.56219\t86660.54348\t201\t11154407\t1.00E+14\t184\t15945540\t2.67E+12
+LAB BLOOD ORDERABLES\t473739\tLABYFVPA\tVALPROIC ACID FREE AND TOTAL\tVPAFREE\tVALPROIC ACID, FREE\t2885582.281\t199772.9843\t201\t580002038.4\t5.80E+15\t191\t38156640\t1.71E+13
+LAB BLOOD ORDERABLES\t473739\tLABYFVPA\tVALPROIC ACID FREE AND TOTAL\tVPATOTAL\tVALPROIC ACID, TOTAL\t1393086.761\t199772.9843\t201\t280010439\t2.80E+15\t191\t38156640\t1.71E+13
+MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tRPT\tRPT\t9999999\t1262815.2\t201\t2009999799\t2.01E+16\t75\t94711140\t1.20E+14
+MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tSDES\tSPECIMEN\t9999999\t1262815.2\t201\t2009999799\t2.01E+16\t75\t94711140\t1.20E+14
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCULT\tCULT/OTHER RSLT:\t9999999\t515574.2561\t200\t1999999800\t2.00E+16\t45607\t23513795100\t1.33E+16
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tBD\tBASE DEFICIT, ISTAT\t200006.055\t486.2068966\t200\t40001211\t4.00E+14\t116\t56400\t2743970400
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITAA\tAMINO ACID CHANGE\t9999999\t807029.3684\t199\t1989999801\t1.99E+16\t190\t153335580\t3.03E+14
+LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tSPTYS1\tSPTYS1\t9949917.556\t671067.3054\t198\t1970083676\t1.97E+16\t167\t112068240\t1.25E+14
+VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tSDES11\tSPECIMEN\t9999999\t185672.0388\t198\t1979999802\t1.98E+16\t206\t38248440\t9.97E+12
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITRE\tRESULT\t9999999\t779901.7925\t197\t1969999803\t1.97E+16\t212\t165339180\t3.13E+14
+LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3DX\tCLINICAL INDICATION\t9999999\t531220.8\t196\t1959999804\t1.96E+16\t175\t92963640\t6.34E+13
+LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\tFMYABG\tM.PNEUMONIA AB IGG\t9999999\t236846.1207\t195\t1949999805\t1.95E+16\t232\t54948300\t3.81E+13
+LAB BLOOD ORDERABLES\t472771\tLABLUPUS\tLUPUS ANTICOAGULANT\tLUPINT\tLUPUS INTERPRETATION\t9999999\t81977.7809\t194\t1939999806\t1.94E+16\t712\t58368180\t1.09E+13
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t193\t1929999807\t1.93E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473166\tLABCSTOTA\tPROTEIN C&S, TOTAL\tPROTCAGT\tPROTEIN C AG, TOTAL\t518211.9119\t354435.4839\t193\t100014899\t1.00E+15\t186\t65925000\t3.88E+13
+LAB BLOOD ORDERABLES\t473166\tLABCSTOTA\tPROTEIN C&S, TOTAL\tPROTSTOT\tPROTEIN S AG, TOTAL\t466409.3627\t354435.4839\t193\t90017007\t9.00E+14\t186\t65925000\t3.88E+13
+LAB BLOOD ORDERABLES\t473189\tLABPTTINH\tPTT INHIBITOR SCREEN\tPTTINT\tINTERPRETATION\t9999999\t76412.68156\t193\t1929999807\t1.93E+16\t358\t27355740\t3.54E+12
+MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t439595.3706\t192\t1919999808\t1.92E+16\t4290\t1885864140\t2.06E+15
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCPIER\tRESULT\t9999999\t407309.1176\t191\t1909999809\t1.91E+16\t136\t55394040\t3.19E+13
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tPRO\tPRODUCT ID  \t9999999\t33721.36364\t190\t1899999810\t1.90E+16\t88\t2967480\t3.78E+11
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tBASOCSF\tBASOPHILS, CSF\t1.510526316\t17443.24324\t190\t287\t753\t111\t1936200\t3.99E+11
+VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKCPU\tRESULT\t8525869.587\t227768.5106\t189\t1611389352\t1.59E+16\t141\t32115360\t9.66E+12
+VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKLOGU\tRESULT LOG 10\t7777778.238\t227768.5106\t189\t1470000087\t1.47E+16\t141\t32115360\t9.66E+12
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tCECAST\tCELLULAR CASTS\t9999999\t11904.48\t188\t1879999812\t1.88E+16\t125\t1488060\t99961621200
+VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKDNAU\tBKV DNA\t9999999\t227768.5106\t188\t1879999812\t1.88E+16\t141\t32115360\t9.66E+12
+LAB BLOOD ORDERABLES\t472924\tLABMONEBV\tMONO SCREEN WITH REFLEX EBV AB\tMONOSC\tINFECTIOUS MONO SCREEN\t9999999\t11195.5814\t187\t1869999813\t1.87E+16\t172\t1925640\t47965219200
+LAB BLOOD ORDERABLES\t473098\tLABYFPHEN\tPHENYTOIN TOTAL AND FREE, PLASMA\tPHENTOT\tPHENYTOIN, TOTAL PLASMA\t1550809.728\t212727.7895\t187\t290001419.1\t2.90E+15\t190\t40418280\t3.12E+13
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPRE\tRESULT\t9999999\t783300\t187\t1869999813\t1.87E+16\t190\t148827000\t1.52E+14
+MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tSDES\tSPECIMEN\t9999999\t90298.30189\t187\t1869999813\t1.87E+16\t106\t9571620\t1.70E+12
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9020R\tHLA-BW ALLELE 2\t9999999\t160946.9189\t185\t1849999815\t1.85E+16\t185\t29775180\t2.82E+13
+LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tTESTS1\tTESTS1\t9892797.638\t671067.3054\t185\t1830167563\t1.83E+16\t167\t112068240\t1.25E+14
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tSPERM\tSPERMATOZOA\t9999999\t17389.71429\t184\t1839999816\t1.84E+16\t140\t2434560\t5.17E+11
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tSI\t<<STATUS>>  \t9999999\t35555.0495\t183\t1829999817\t1.83E+16\t303\t10773180\t1.30E+12
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tDI\tISSUE DATE/TIME\t9999999\t30487.85714\t183\t1829999817\t1.83E+16\t84\t2560980\t1.48E+11
+LAB BLOOD ORDERABLES\t473098\tLABYFPHEN\tPHENYTOIN TOTAL AND FREE, PLASMA\tPHENFR\tPHENYTOIN, FREE PLASMA\t1420766.156\t212727.7895\t183\t260000206.6\t2.60E+15\t190\t40418280\t3.12E+13
+LAB BLOOD ORDERABLES\t577063\tLABYHGBP\tHEMOGLOBIN, PLASMA\t12078R\tOXYHEMOGLOBIN\t1202193.93\t146662.9213\t183\t220001489.1\t2.20E+15\t178\t26106000\t4.28E+12
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPAA\tAMINO ACID CHANGE\t9999999\t803457.6536\t183\t1829999817\t1.83E+16\t179\t143818920\t1.49E+14
+MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tRPT\tRPT\t9999999\t114455.7534\t183\t1829999817\t1.83E+16\t146\t16710540\t2.41E+12
+MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tSREQ\tCOMMENTS\t9999999\t114455.7534\t183\t1829999817\t1.83E+16\t146\t16710540\t2.41E+12
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVCP\tRESULT\t8273687.022\t339631.7442\t183\t1514084725\t1.51E+16\t172\t58416660\t2.76E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVLO\tRESULT LOG 10\t8251366.009\t339631.7442\t183\t1509999980\t1.51E+16\t172\t58416660\t2.76E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVSP\tSPECIMEN\t9999999\t339631.7442\t183\t1829999817\t1.83E+16\t172\t58416660\t2.76E+13
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tUBT\tUNIT BLOOD TYPE  \t9999999\t45671.25\t182\t1819999818\t1.82E+16\t80\t3653700\t4.10E+11
+LAB BLOOD ORDERABLES\t471572\tLAB11070R\tALDOSTERONE\tALDOSTR\tALDOSTERONE, SERUM\t6153853.908\t269928.0723\t182\t1120001411\t1.12E+16\t166\t44808060\t1.37E+13
+MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tSDES\tSPECIMEN\t9999999\t114455.7534\t181\t1809999819\t1.81E+16\t146\t16710540\t2.41E+12
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tFRFAT\tFREE FAT\t9999999\t14748.09917\t181\t1809999819\t1.81E+16\t121\t1784520\t1.38E+11
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tOVFAT\tOVAL FAT BODIES\t9999999\t15422.92683\t180\t1799999820\t1.80E+16\t123\t1897020\t1.58E+11
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVRNA\tHIV 1 RNA\t9999999\t339631.7442\t180\t1799999820\t1.80E+16\t172\t58416660\t2.76E+13
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t179\t1789999821\t1.79E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t577063\tLABYHGBP\tHEMOGLOBIN, PLASMA\tYHGBP1\tTOTAL HEMOGLOBIN\t1005598.005\t146760.3352\t179\t180002042.9\t1.80E+15\t179\t26270100\t4.31E+12
+MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tSREQ\tCOMMENTS\t9999999\t90298.30189\t179\t1789999821\t1.79E+16\t106\t9571620\t1.70E+12
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tRVRSP\tSPECIMEN\t9999999\t106230.4\t179\t1789999821\t1.79E+16\t225\t23901840\t1.05E+13
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSCMT\tSYNOVIAL FLUID COMMENT\t9999999\t11515.04854\t178\t1779999822\t1.78E+16\t206\t2372100\t2.27E+11
+LAB BLOOD ORDERABLES\t471639\tLABANTITG\tANTI - THYROGLOBULIN AB\tATGA\tANTI-THYROGLOBULIN AB\t8764078.359\t70401.68675\t178\t1560005948\t1.56E+16\t166\t11686680\t1.46E+12
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRPR\tPRIMER\t9999999\t600772.8477\t178\t1779999822\t1.78E+16\t151\t90716700\t6.81E+13
+LAB BLOOD ORDERABLES\t472408\tLABFLL\tFSH\tFSH\tFSH\t786528.9483\t35073.65854\t178\t140002152.8\t1.40E+15\t164\t5752080\t3.12E+11
+VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-PLASMA\tCMVQSP\tSPECIMEN\t9999999\t252366.8139\t178\t1779999822\t1.78E+16\t317\t80000280\t2.70E+13
+LAB BLOOD ORDERABLES\t473111\tLABPLTFUN\tPLATELET FUNCTION SCREEN\tCOLADP\tCOLLAGEN/ADP\t8361602.209\t12280.51948\t177\t1480003591\t1.48E+16\t154\t1891200\t1.58E+11
+LAB BLOOD ORDERABLES\t473111\tLABPLTFUN\tPLATELET FUNCTION SCREEN\tCOLEPI\tCOLLAGEN/EPI\t3615903.153\t12280.51948\t177\t640014858\t6.40E+15\t154\t1891200\t1.58E+11
+VIROLOGY ORDERABLES\t497452\tLABHV6DNA\tHERPES VIRAL 6 DNA QUALITATIVE\tSOUR12\tSOURCE\t9999999\t231324.5963\t177\t1769999823\t1.77E+16\t161\t37243260\t1.37E+13
+HISTOCOMPATIBILITY ORDERABLE\t890777\t10005R\tHLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)\tRINFO\tTO FIND RESULT:\t9999999\t1348750.056\t176\t1759999824\t1.76E+16\t179\t241426260\t4.99E+14
+LAB BLOOD ORDERABLES\t473439\tLABTEGCP\tTHROMBELASTOGRAPH CLOTTING PROFILE\tTEGMD2\tREVIEWED BY\t9999999\t18233.79078\t176\t1759999824\t1.76E+16\t889\t16209840\t1.94E+12
+MICROBIOLOGY ORDERABLES\t472484\tLABGRAM\tGRAM STAIN\tGS\tGRAM STAIN\t9999999\t43014.06617\t176\t1759999824\t1.76E+16\t5622\t241825080\t7.14E+13
+HISTOCOMPATIBILITY ORDERABLE\t890777\t10005R\tHLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t1348750.056\t175\t1749999825\t1.75E+16\t179\t241426260\t4.99E+14
+LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEP B SURF AB QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t5402425.933\t161105.0602\t174\t940022112.4\t9.40E+15\t166\t26743440\t5.04E+12
+LAB BLOOD ORDERABLES\t473109\tLABPLTMAP\tPLATELET MAPPING\tARATEG\tARACHIDONIC ACID\t9827586.907\t18924.62428\t174\t1710000122\t1.71E+16\t173\t3273960\t2.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tXMR\tUNIT CROSSMATCH  \t9999999\t71670.41096\t173\t1729999827\t1.73E+16\t73\t5231940\t9.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tPRO\tPRODUCT ID  \t9999999\t34026.66667\t173\t1729999827\t1.73E+16\t81\t2756160\t3.01E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t42972.55814\t172\t1719999828\t1.72E+16\t172\t7391280\t7.88E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t7977484.564\t42972.55814\t172\t1372127345\t1.37E+16\t172\t7391280\t7.88E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t42972.55814\t172\t1719999828\t1.72E+16\t172\t7391280\t7.88E+12
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9259R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS II\t9999999\t158804.1935\t172\t1719999828\t1.72E+16\t186\t29537580\t3.69E+13
+LAB BLOOD ORDERABLES\t473761\tLABYVITA\tVITAMIN A\tFREEVITA\tFREE RETINOL(VIT A)\t523320.4628\t357001.676\t172\t90011119.6\t9.00E+14\t179\t63903300\t3.19E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t10473R\tDSA FOR C1Q CLASS II\t9999999\t107541.4035\t171\t1709999829\t1.71E+16\t171\t18389580\t1.02E+13
+LAB BLOOD ORDERABLES\t473129\tLABKWB\tPOTASSIUM, WHOLE BLOOD\tK\tPOTASSIUM, WHOLE BLD\t584799.2608\t9286.380368\t171\t100000673.6\t1.00E+15\t163\t1513680\t63837374400
+VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADPCR1\tADENVIRUS DNA\t8729218.632\t192765.5696\t171\t1492696386\t1.49E+16\t158\t30456960\t1.87E+13
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tSI\t<<STATUS>>  \t9999999\t32446.02151\t170\t1699999830\t1.70E+16\t279\t9052440\t1.06E+12
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9256R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS I\t9999999\t168448.9655\t170\t1699999830\t1.70E+16\t174\t29310120\t5.59E+13
+LAB BLOOD ORDERABLES\t473809\tLABYZINCS\tZINC\tZNC\tZINC, BLOOD\t176530.7706\t213934.6988\t170\t30010231\t3.00E+14\t166\t35513160\t1.16E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t42628.7574\t169\t1689999831\t1.69E+16\t169\t7204260\t7.86E+12
+LAB BLOOD ORDERABLES\t473109\tLABPLTMAP\tPLATELET MAPPING\tADP\tADP\t9881656.195\t18914.31818\t169\t1669999897\t1.67E+16\t176\t3328920\t2.11E+11
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tUBT\tUNIT BLOOD TYPE  \t9999999\t43986.31579\t168\t1679999832\t1.68E+16\t76\t3342960\t3.42E+11
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9408R\tCPRA FOR CUMULATIVE IGG\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9424R\tCPRA FOR IGG CLASS II FOR CURRENT\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9609R\tMETHOD\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9626R\tCURRENT SERUM DATE\t9999999\t42896.78571\t168\t1679999832\t1.68E+16\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9411R\tCPRA FOR IGG CLASS I FOR CURRENT\t9999999\t43413.25301\t166\t1659999834\t1.66E+16\t166\t7206600\t2.67E+13
+LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tANTI-RNP ANTIBODY\tRNPQUL\tANTI-RNP RESULT\t9999999\t433078.8889\t166\t1659999834\t1.66E+16\t162\t70158780\t4.59E+13
+LAB BLOOD ORDERABLES\t472622\tLABIGM\tIGM, SERUM\tIGM\tIGM, SERUM\t783556.9578\t45657.23684\t166\t130070455\t1.30E+15\t152\t6939900\t5.62E+11
+LAB BLOOD ORDERABLES\t473187\tLABPTINH\tPT INHIBITOR SCREEN\tPTINTP\tINTERPRETATION\t9999999\t65225.45455\t166\t1659999834\t1.66E+16\t165\t10762200\t9.83E+11
+LAB BLOOD ORDERABLES\t473221\tLAB11092R\tRENIN\tRENIN\tRENIN ACTIVITY, PL\t4698798.742\t297355.6863\t166\t780000591.1\t7.80E+15\t153\t45495420\t1.77E+13
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tPRO\tPRODUCT ID  \t9999999\t33428.46154\t165\t1649999835\t1.65E+16\t78\t2607420\t3.05E+11
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9437R\tCPRA FOR CUMULATIVE C1Q\t9999999\t37217.81818\t165\t1649999835\t1.65E+16\t165\t6140940\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9610R\tMETHOD\t9999999\t37217.81818\t165\t1649999835\t1.65E+16\t165\t6140940\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9631R\tSERUM DATE\t9999999\t37217.81818\t165\t1649999835\t1.65E+16\t165\t6140940\t2.59E+13
+POINT OF CARE TESTING\t472689\tLABPCEG7O\tISTAT EG7, OTHER\tPCTEMP\tBODY TEMPERATURE\t9999999\t355.9509202\t165\t1649999835\t1.65E+16\t163\t58020\t2744082000
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tOTCAST\tCASTS, OTHER\t9999999\t12116.94915\t165\t1649999835\t1.65E+16\t118\t1429800\t1.36E+11
+BODY FLUID ORDERABLES\t497386\tLABFTRIGA\tTRIGLYCERIDES, NON-PLEURAL\tTGLFLD\tTRIGLYCERIDES, BODY FLUID\t244203.9268\t197512.8713\t164\t40049444\t4.00E+14\t101\t19948800\t7.53E+12
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9454R\tCPRA FOR C1Q CLASS II FOR CURRENT\t9999999\t37209.87805\t164\t1639999836\t1.64E+16\t164\t6102420\t2.59E+13
+LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tANTI-SCL70 ANTIBODY\tSCLQUL\tANTI-SCL70 RESULT\t9999999\t391549.434\t164\t1639999836\t1.64E+16\t159\t62256360\t3.14E+13
+VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tHSV1PC\tHSV-1 DNA\t9999999\t166727.4847\t163\t1629999837\t1.63E+16\t163\t27176580\t5.96E+12
+VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tHSV2PC\tHSV-2 DNA\t9999999\t166727.4847\t163\t1629999837\t1.63E+16\t163\t27176580\t5.96E+12
+VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tSDES11\tSPECIMEN\t9999999\t166727.4847\t163\t1629999837\t1.63E+16\t163\t27176580\t5.96E+12
+BODY FLUID ORDERABLES\t473353\tLABSTORE\tSPECIMEN STORAGE ONLY\tSTORE\tSPECIMEN STORAGE ONLY\t9999999\t47.69230769\t162\t1619999838\t1.62E+16\t39\t1860\t154800
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9440R\tCPRA FOR C1Q CLASS I FOR CURRENT\t9999999\t37661.85185\t162\t1619999838\t1.62E+16\t162\t6101220\t2.59E+13
+LAB BLOOD ORDERABLES\t472444\tLAB11081R\tGASTRIN\tGASTRIN\tGASTRIN, SERUM\t745575.7764\t157284.7297\t161\t120037700\t1.20E+15\t148\t23278140\t4.38E+12
+MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t266675.2941\t161\t1609999839\t1.61E+16\t1275\t340011000\t1.87E+14
+LAB BLOOD ORDERABLES\t590524\tLABASI\tAB SCREEN (ASI)\tAS3\tANTIBODY SCREEN\t9999999\t9125.853659\t160\t1599999840\t1.60E+16\t41\t374160\t10901347200
+MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tAFB\tAFB SMEAR\t9999999\t3883162.201\t160\t1599999840\t1.60E+16\t2681\t10410757860\t4.16E+16
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tXMR\tUNIT CROSSMATCH  \t9999999\t74538.46154\t159\t1589999841\t1.59E+16\t65\t4845000\t8.16E+11
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tDI\tISSUE DATE/TIME\t9999999\t29278.28571\t159\t1589999841\t1.59E+16\t70\t2049480\t74952583200
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9412R\tHLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t43413.25301\t159\t1589999841\t1.59E+16\t166\t7206600\t2.67E+13
+LAB BLOOD ORDERABLES\t497394\tLABYPARVO\tPARVOVIRUS B19 IGG AND IGM AB\tYPARV1\tPARVOVIRUS B19 AB IGG\t2.89408805\t223791.6201\t159\t460.16\t2105.758\t179\t40058700\t1.09E+13
+LAB BLOOD ORDERABLES\t497394\tLABYPARVO\tPARVOVIRUS B19 IGG AND IGM AB\tYPARV2\tPARVOVIRUS B19 AB IGM\t0.23408805\t223791.6201\t159\t37.22\t175.9786\t179\t40058700\t1.09E+13
+MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tRPT\tRPT\t9999999\t197685.5769\t159\t1589999841\t1.59E+16\t104\t20559300\t5.86E+12
+MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tSDES\tSPECIMEN\t9999999\t197685.5769\t159\t1589999841\t1.59E+16\t104\t20559300\t5.86E+12
+BODY FLUID ORDERABLES\t472501\tLABFHCT\tHEMATOCRIT, FLUID\tHCTFLD\tHEMATOCRIT, FLUID\t6582282.016\t19705.26316\t158\t1040000559\t1.04E+16\t76\t1497600\t2.10E+11
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tIMMPL\tIMMAT. PLASMA CELLS\t63320.53797\t19787.92208\t158\t10004645\t1.00E+14\t154\t3047340\t3.06E+11
+VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBPNO\tNOTE\t9999999\t313316.5605\t158\t1579999842\t1.58E+16\t157\t49190700\t1.93E+13
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tXMR\tUNIT CROSSMATCH  \t9999999\t65345.53846\t157\t1569999843\t1.57E+16\t65\t4247460\t6.76E+11
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9441R\tHLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t37661.85185\t157\t1569999843\t1.57E+16\t162\t6101220\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9455R\tHLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t37209.87805\t157\t1569999843\t1.57E+16\t164\t6102420\t2.59E+13
+BODY FLUID ORDERABLES\t472501\tLABFHCT\tHEMATOCRIT, FLUID\tHCTTYP\tSOURCE\t9999999\t19705.26316\t156\t1559999844\t1.56E+16\t76\t1497600\t2.10E+11
+COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tLOTNO\tLOT #\t9802579.788\t181776.5501\t156\t1529202447\t1.52E+16\t1287\t233946420\t5.82E+13
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT - 2ND SET VIA CATHETER\tCULT\tCULT/OTHER RSLT:\t9999999\t499787.4252\t156\t1559999844\t1.56E+16\t3946\t1972161180\t1.12E+15
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUASP\tSPECIMEN\t9999999\t163609.7143\t156\t1559999844\t1.56E+16\t35\t5726340\t1.13E+12
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tPRO\tPRODUCT ID  \t9999999\t28782.16216\t155\t1549999845\t1.55E+16\t74\t2129880\t2.07E+11
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9425R\tHLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t42896.78571\t155\t1549999845\t1.55E+16\t168\t7206660\t2.67E+13
+LAB BLOOD ORDERABLES\t472378\tLAB11036R\tFIBRINOGEN, QUANT\tFIBRINOGENA\tFIBRINOGEN ANTIGEN\t1677655.368\t257827.1186\t155\t260036582\t2.60E+15\t118\t30423600\t1.53E+13
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBDX\tCLINICAL INDICATION\t9999999\t878524.7788\t155\t1549999845\t1.55E+16\t113\t99273300\t1.44E+14
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tVL\tPRODUCT VOLUME\t169.9350649\t27151.2\t154\t26170\t5340380\t50\t1357560\t42850166400
+STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12766R\tTACROLIMUS\t1493514.095\t181999.6875\t154\t230001170.6\t2.30E+15\t192\t34943940\t7.76E+12
+COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHIPDIR\tHIT INTERPRETED BY\t9999999\t181865.8879\t153\t1529999847\t1.53E+16\t1284\t233515800\t5.81E+13
+STOOL ORDERABLES\t472361\tLABSTFAT\tFAT STAIN, STOOL\tSTFAT\tFAT STAIN, STOOL\t9934639.582\t26489.18919\t153\t1519999856\t1.52E+16\t74\t1960200\t3.18E+11
+LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN 1\tB2GPIG\tBETA-2 GLYCOPROTEIN 1, IGG\t328955.5526\t261970.9459\t152\t50001244\t5.00E+14\t148\t38771700\t1.57E+13
+LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN 1\tB2GPIM\tBETA-2 GLYCOPROTEIN 1, IGM\t328958.4145\t261970.9459\t152\t50001679\t5.00E+14\t148\t38771700\t1.57E+13
+STOOL ORDERABLES\t473218\tLABSTRED\tREDUCING SUBSTANCE, STOOL\tREDSUBSTL\tREDUCING SUBST, STOOL\t9999999\t29040\t152\t1519999848\t1.52E+16\t52\t1510080\t1.69E+11
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tSI\t<<STATUS>>  \t9999999\t32777.54941\t151\t1509999849\t1.51E+16\t253\t8292720\t9.48E+11
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tDI\tISSUE DATE/TIME\t9999999\t33829.16667\t151\t1509999849\t1.51E+16\t72\t2435700\t1.32E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t8053049.813\t38516.8\t150\t1207957472\t1.20E+16\t150\t5777520\t6.90E+12
+LAB BLOOD ORDERABLES\t472056\tLABYCH50\tCOMPLEMENT TOTAL\tYCH50S\tCOMPLEMENT, TOTAL\t400046.8467\t162197.9592\t150\t60007027\t6.00E+14\t147\t23843100\t4.41E+12
+LAB BLOOD ORDERABLES\t472543\tLABHSCRP\tHIGH SENSITIVITY C-REACTIVE PROTEIN\tHSCRP\tHIGH SENSITIVITY CRP\t66698.88533\t33401.22137\t150\t10004832.8\t1.00E+14\t131\t4375560\t2.75E+11
+LAB BLOOD ORDERABLES\t472030\tLABCHOL\tCHOLESTEROL TOTAL, SERUM/PLASMA\tCHOL\tCHOLESTEROL, TOTAL\t337986.9054\t8188.392857\t148\t50022062\t5.00E+14\t112\t917100\t18473065200
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tPRO\tPRODUCT ID  \t9999999\t30727.82609\t147\t1469999853\t1.47E+16\t69\t2120220\t2.29E+11
+LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVSC\tDRVV SCREEN\t748345.4565\t121000.7634\t147\t110006782.1\t1.10E+15\t131\t15851100\t3.59E+12
+LAB BLOOD ORDERABLES\t870901\tLAB11982R\tPOSACONAZOLE\t11983R\tPOSACONAZOLE\t612936.0952\t319889.1429\t147\t90101606\t9.00E+14\t140\t44784480\t1.61E+13
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQL\tIGG RESULT\t9999999\t165582.0408\t147\t1469999853\t1.47E+16\t147\t24340560\t5.47E+12
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9276R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS I\t9999999\t109734.6575\t146\t1459999854\t1.46E+16\t146\t16021260\t1.66E+13
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXMQL\tIGM RESULT\t9999999\t161574.2466\t146\t1459999854\t1.46E+16\t146\t23589840\t4.90E+12
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tAG\tANION GAP\t7.537931034\t5510.364964\t145\t1093\t9995\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tALB\tALBUMIN, SER/PLAS\t2.694482759\t5510.364964\t145\t390.7\t1108.73\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tBUN\tUREA NITROGEN,SER/PLAS\t275888.2414\t5510.364964\t145\t40003795\t4.00E+14\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCA\tCALCIUM, SER/PLAS\t8.460689655\t5510.364964\t145\t1226.8\t10424.14\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCL\tCHLORIDE, SER/PLAS\t101.4137931\t5510.364964\t145\t14705\t1496473\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCO2\tCO2, SER/PLAS\t26.67586207\t5510.364964\t145\t3868\t105438\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tCR\tCREATININE, SER/PLAS\t1.863448276\t5510.364964\t145\t270.2\t1070.26\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tGLU\tGLUCOSE, SER/PLAS\t133.0206897\t5510.364964\t145\t19288\t2976424\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tK\tPOTASSIUM, SER/PLAS\t4.160689655\t5510.364964\t145\t603.3\t2579.73\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tNA\tSODIUM, SER/PLAS\t135.6275862\t5510.364964\t145\t19666\t2670380\t137\t754920\t9180770400
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tPHOS\tPHOSPHORUS, SER/PLAS\t3.466896552\t5510.364964\t145\t502.7\t1949.03\t137\t754920\t9180770400
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV1QNT\tHSV-1 IGG LEVEL\t2344832.599\t262166.8966\t145\t340000726.9\t3.40E+15\t145\t38014200\t1.36E+13
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV1QUL\tHSV-1 IGG RESULT\t9999999\t262166.8966\t145\t1449999855\t1.45E+16\t145\t38014200\t1.36E+13
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV2QNT\tHSV-2 IGG LEVEL\t7517242.214\t262166.8966\t145\t1090000121\t1.09E+16\t145\t38014200\t1.36E+13
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHV2QUL\tHSV-2 IGG RESULT\t9999999\t262166.8966\t145\t1449999855\t1.45E+16\t145\t38014200\t1.36E+13
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHVMQNT\tHSV IGM LEVEL\t8758620.034\t262166.8966\t145\t1269999905\t1.27E+16\t145\t38014200\t1.36E+13
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHVMQUL\tHSV IGM RESULT\t9999999\t262166.8966\t145\t1449999855\t1.45E+16\t145\t38014200\t1.36E+13
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tUBT\tUNIT BLOOD TYPE  \t9999999\t41594.32836\t144\t1439999856\t1.44E+16\t67\t2786820\t2.99E+11
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9423R\tDSA FOR C1Q CLASS II\t9999999\t40032.41379\t144\t1439999856\t1.44E+16\t145\t5804700\t2.58E+13
+LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVLA\tDRVV NORMALIZED RATIO\t8541666.065\t120333.75\t144\t1229999913\t1.23E+16\t128\t15402720\t3.41E+12
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBRE\tRESULT\t9999999\t880761.4286\t144\t1439999856\t1.44E+16\t112\t98645280\t1.43E+14
+LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVCN\tDRVV CONFIRMATION\t8601404.762\t120005.0382\t143\t1230000881\t1.23E+16\t131\t15720660\t3.56E+12
+LAB BLOOD ORDERABLES\t496765\tLABHA1C\tHGB A1C W/ EST MEAN GLUCOSE\tEMGL\tEST. MEAN GLUCOSE\t349784.4126\t42301.70213\t143\t50019171\t5.00E+14\t141\t5964540\t8.54E+11
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRRE\tRESULT\t9999999\t600772.8477\t142\t1419999858\t1.42E+16\t151\t90716700\t6.81E+13
+LAB BLOOD ORDERABLES\t496765\tLABHA1C\tHGB A1C W/ EST MEAN GLUCOSE\tA1C\tHEMOGLOBIN A1C\t281696.3986\t42301.70213\t142\t40000888.6\t4.00E+14\t141\t5964540\t8.54E+11
+LAB BLOOD ORDERABLES\t471527\tLABSMABY\tANTI-SM ANTIBODY\tASMQUL\tANTI-SM RESULT\t9999999\t394626.5217\t141\t1409999859\t1.41E+16\t138\t54458460\t2.90E+13
+LAB BLOOD ORDERABLES\t472325\tLABEPO\tERYTHROPOIETIN\tEPO\tERYTHROPOIETIN\t1773093.199\t83724.9635\t141\t250006141\t2.50E+15\t137\t11470320\t1.67E+12
+LAB BLOOD ORDERABLES\t668152\tLABATTG\tIGA ANTI  TTG\tATTGR\tIGA ANTI TTG ANTIBODY\t9999999\t394865.1799\t141\t1409999859\t1.41E+16\t139\t54886260\t2.79E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9415R\tDSA FOR IGG CLASS II\t9999999\t48321.81818\t140\t1399999860\t1.40E+16\t143\t6910020\t2.67E+13
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tGS\tGRAM STAIN\t9999999\t454839.1011\t140\t1399999860\t1.40E+16\t712\t323845440\t2.65E+14
+LAB BLOOD ORDERABLES\t472345\tLABFACT9\tFACTOR IX ASSAY\tFACTOR9\tFACTOR IX ASSAY\t575631.0144\t44628.46154\t139\t80012711\t8.00E+14\t130\t5801700\t3.54E+11
+MICROBIOLOGY ORDERABLES\t473734\tLABURNC\tURINE CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t214873.0474\t139\t1389999861\t1.39E+16\t21871\t4699488420\t1.74E+15
+URINE ORDERABLES\t473123\tLABUK\tPOTASSIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t215900.4799\t20671.11111\t139\t30010166.7\t3.00E+14\t81\t1674360\t1.84E+11
+URINE ORDERABLES\t473123\tLABUK\tPOTASSIUM WITH CREATININE, URINE (RANDOM)\tUK\tPOTASSIUM, URINE\t215858.8799\t20671.11111\t139\t30004384.3\t3.00E+14\t81\t1674360\t1.84E+11
+URINE ORDERABLES\t473123\tLABUK\tPOTASSIUM WITH CREATININE, URINE (RANDOM)\tUKCR\tPOTASSIUM/CREAT RATIO\t8201438.063\t20671.11111\t139\t1139999891\t1.14E+16\t81\t1674360\t1.84E+11
+BODY FLUID ORDERABLES\t472728\tLABCSFLD\tLDH, CSF\tLDHCSF\tLDH, CSF\t362371.8116\t8725.263158\t138\t50007310\t5.00E+14\t76\t663120\t21797416800
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9422R\tDSA FOR C1Q CLASS I\t9999999\t39643.47826\t138\t1379999862\t1.38E+16\t138\t5470800\t2.57E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9414R\tDSA FOR IGG CLASS I\t9999999\t39710.86957\t138\t1379999862\t1.38E+16\t138\t5480100\t2.57E+13
+LAB BLOOD ORDERABLES\t472794\tLABMALAR\tMALARIA PREP\tMALCON\tMALCON\t9999999\t32456.25\t138\t1379999862\t1.38E+16\t144\t4673700\t7.20E+12
+MICROBIOLOGY ORDERABLES\t611926\tLABRESPG\tRESPIRATORY CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t266675.2941\t138\t1379999862\t1.38E+16\t1275\t340011000\t1.87E+14
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tDI\tISSUE DATE/TIME\t9999999\t37671.14754\t137\t1369999863\t1.37E+16\t61\t2297940\t1.83E+11
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tALB\tALBUMIN\t364966.7161\t271621.4063\t137\t50000440.1\t5.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPA1\tALPHA 1 ZONE\t364963.7438\t271621.4063\t137\t50000032.9\t5.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPA2\tALPHA 2 ZONE\t364964.2197\t271621.4063\t137\t50000098.1\t5.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPBE1\tBETA 1 ZONE\t364963.9701\t271621.4063\t137\t50000063.9\t5.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPGA\tGAMMA ZONE\t364964.5964\t271621.4063\t137\t50000149.7\t5.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tTPE\tTOTAL PROTEIN\t364969.8044\t271621.4063\t137\t50000863.2\t5.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBAA\tAMINO ACID CHANGE\t9999999\t908365.4717\t137\t1369999863\t1.37E+16\t106\t96286740\t1.42E+14
+MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tRPT\tRPT\t9999999\t191605.7143\t137\t1369999863\t1.37E+16\t21\t4023720\t8.21E+11
+MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tSDES\tSPECIMEN\t9999999\t191605.7143\t137\t1369999863\t1.37E+16\t21\t4023720\t8.21E+11
+MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tSREQ\tCOMMENTS\t9999999\t191605.7143\t137\t1369999863\t1.37E+16\t21\t4023720\t8.21E+11
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tDIPLOT\tDIPSTICK LOT\t9191364.146\t2322.940655\t137\t1259216888\t1.25E+16\t1129\t2622600\t4.75E+14
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tSI\t<<STATUS>>  \t9999999\t31790.66667\t136\t1359999864\t1.36E+16\t225\t7152900\t7.84E+11
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tEOSFLD\tEOSINOPHILS, SYN FLD\t2.838235294\t9576.176471\t136\t386\t2984\t68\t651180\t15140372400
+HISTOCOMPATIBILITY ORDERABLE\t890779\t10045R\tHLA ORDER - ANTIBODY SCREENING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1070211.064\t136\t1359999864\t1.36E+16\t141\t150899760\t2.32E+15
+LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTX\tCLINICAL INDICATION\t9999999\t540213\t136\t1359999864\t1.36E+16\t60\t32412780\t2.37E+13
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPBE2\tBETA 2 ZONE\t367647.4816\t271621.4063\t136\t50000057.5\t5.00E+14\t128\t34767540\t1.35E+13
+VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tRPT\tRPT\t9999999\t68157\t136\t1359999864\t1.36E+16\t60\t4089420\t4.28E+11
+VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSDES\tSPECIMEN\t9999999\t68157\t136\t1359999864\t1.36E+16\t60\t4089420\t4.28E+11
+VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSREQ\tCOMMENTS\t9999999\t68157\t136\t1359999864\t1.36E+16\t60\t4089420\t4.28E+11
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC1613AB\tCD16+/CD13-, ABS\t296392.4963\t136579.3043\t135\t40012987\t4.00E+14\t115\t15706620\t3.09E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC1613PC\tCD16+/CD13- %\t296313.5556\t136579.3043\t135\t40002330\t4.00E+14\t115\t15706620\t3.09E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC563AB\tCD56+/CD3-, ABS\t296378.9926\t136579.3043\t135\t40011164\t4.00E+14\t115\t15706620\t3.09E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC563PC\tCD56+/CD3- %\t296313.8667\t136579.3043\t135\t40002372\t4.00E+14\t115\t15706620\t3.09E+12
+VIROLOGY ORDERABLES\t906965\tLABVZVQLP\tVZV DNA PCR QUAL, PLASMA\tVZQLDN\tVZV DNA\t9999999\t198242.2222\t135\t1349999865\t1.35E+16\t135\t26762700\t7.15E+12
+VIROLOGY ORDERABLES\t906965\tLABVZVQLP\tVZV DNA PCR QUAL, PLASMA\tVZQLSP\tSPECIMEN\t9999999\t198242.2222\t135\t1349999865\t1.35E+16\t135\t26762700\t7.15E+12
+LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVMX\tDRVV MIX\t6119422.301\t115144.5378\t134\t820002588.3\t8.20E+15\t119\t13702200\t3.10E+12
+LAB BLOOD ORDERABLES\t670770\tLAB11242R\tVZV QUANT\t11243R\tSOURCE\t9999999\t189403.6596\t134\t1339999866\t1.34E+16\t235\t44509860\t1.94E+13
+MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tSREQ\tCOMMENTS\t9999999\t197685.5769\t134\t1339999866\t1.34E+16\t104\t20559300\t5.86E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSCIM\tCLINICAL INDICATION\t9999999\t784098.806\t134\t1339999866\t1.34E+16\t134\t105069240\t1.02E+14
+URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tUMET\tMETANEPHRINE, URINE\t821034.3806\t362619.0476\t134\t110018607\t1.10E+15\t63\t22845000\t1.07E+13
+URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tUMET\tTOT.METANEPHRINES,UR\t597641.1194\t362619.0476\t134\t80083910\t8.00E+14\t63\t22845000\t1.07E+13
+URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tUNORMET\tNORMETANEPHRINE, UR\t597502.1791\t362619.0476\t134\t80065292\t8.00E+14\t63\t22845000\t1.07E+13
+URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tVOLUMEURN\tURINE VOLUME\t599302.7015\t362619.0476\t134\t80306562\t8.00E+14\t63\t22845000\t1.07E+13
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKCPP\tRESULT\t6035260.978\t308329.8795\t134\t808724971\t7.98E+15\t83\t25591380\t2.98E+13
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKDNAP\tBKV DNA\t9999999\t308329.8795\t134\t1339999866\t1.34E+16\t83\t25591380\t2.98E+13
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKLOGP\tRESULT LOG 10\t5895523.587\t308329.8795\t134\t790000160.7\t7.90E+15\t83\t25591380\t2.98E+13
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tPRO\tPRODUCT ID  \t9999999\t20339.0625\t133\t1329999867\t1.33E+16\t64\t1301700\t88116663600
+MICROBIOLOGY ORDERABLES\t471556\tLABAFBD\tAFB CULTURE, NON - RESPIRATORY\tCULT\tCULT/OTHER RSLT:\t9999999\t3886778.338\t133\t1329999867\t1.33E+16\t2310\t8978457960\t3.70E+16
+MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tRPT\tRPT\t9999999\t304958.4615\t133\t1329999867\t1.33E+16\t78\t23786760\t1.00E+13
+MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tSDES\tSPECIMEN\t9999999\t304958.4615\t133\t1329999867\t1.33E+16\t78\t23786760\t1.00E+13
+LAB BLOOD ORDERABLES\t472102\tLAB11056R\tFREE CORTISOL\tCORTFREE\tCORTISOL, SERUM FREE\t75761.51235\t362982.4286\t132\t10000519.63\t1.00E+14\t140\t50817540\t4.31E+13
+URINE ORDERABLES\t472938\tLABYUMYO\tMYOGLOBIN, URINE\tUMYOG\tMYOGLOBIN, URINE\t4090933.064\t168668.4615\t132\t540003164.4\t5.40E+15\t78\t13156140\t2.84E+12
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tXMR\tUNIT CROSSMATCH  \t9999999\t69248.27586\t131\t1309999869\t1.31E+16\t58\t4016400\t6.79E+11
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tXMR\tUNIT CROSSMATCH  \t9999999\t70189.47368\t131\t1309999869\t1.31E+16\t57\t4000800\t6.61E+11
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tUBT\tUNIT BLOOD TYPE  \t9999999\t50272.88136\t131\t1309999869\t1.31E+16\t59\t2966100\t3.78E+11
+LAB BLOOD ORDERABLES\t473160\tLAB11085R\tMYELOPEROXIDASE ANTIBODY\tMPO\tMYELOPEROXIDASE AB\t9541983.801\t173933.1298\t131\t1249999878\t1.25E+16\t131\t22785240\t4.43E+12
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tEXPDATE\tEXP DATE\t9090213.634\t2327.256637\t131\t1190817986\t1.19E+16\t1130\t2629800\t4.75E+14
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tOTHCRY\tCRYSTALS, OTHER\t9999999\t22382.4\t131\t1309999869\t1.31E+16\t75\t1678680\t6.31E+11
+HISTOCOMPATIBILITY ORDERABLE\t890831\t10035R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)\tRINFO\tTO FIND RESULT:\t9999999\t652002.8571\t130\t1299999870\t1.30E+16\t140\t91280400\t1.39E+14
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9436R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t37217.81818\t129\t1289999871\t1.29E+16\t165\t6140940\t2.59E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tLUPDIR\tINTERPRETED BY\t9999999\t320053.913\t129\t1289999871\t1.29E+16\t138\t44167440\t1.77E+13
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSCIB\tCLINICAL INDICATION\t9999999\t873158.6047\t129\t1289999871\t1.29E+16\t129\t112637460\t2.69E+14
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tDI\tISSUE DATE/TIME\t9999999\t35071.03448\t128\t1279999872\t1.28E+16\t58\t2034120\t1.21E+11
+LAB BLOOD ORDERABLES\t471707\tLAB11094R\tRO AB\tSSA\tSS-A/RO AB, IGG, SERUM\t8203124.377\t187084.4262\t128\t1049999920\t1.05E+16\t122\t22824300\t5.95E+12
+POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tMMIND\tMM INDURATION\t2440944.878\t201166.25\t127\t309999999.5\t3.10E+15\t192\t38623920\t1.70E+13
+LAB BLOOD ORDERABLES\t473767\tLAB11102R\tVITAMIN E\tVITE\tA-TOCOPHEROL, VITAMIN E\t10.64126984\t367098.0488\t126\t1340.8\t16803.48\t123\t45153060\t2.10E+13
+LAB BLOOD ORDERABLES\t473773\tLABVOLTL\tVOLATILE SCREEN\tOSMGPI\tOSMOLAL GAP INTERP\t9999999\t7193.516221\t126\t1259999874\t1.26E+16\t4192\t30155220\t7.50E+12
+POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tERYTHREMA\tERYTHREMA\t4999999.5\t201166.25\t126\t629999937\t6.30E+15\t192\t38623920\t1.70E+13
+HISTOCOMPATIBILITY ORDERABLE\t890831\t10035R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t652002.8571\t125\t1249999875\t1.25E+16\t140\t91280400\t1.39E+14
+STOOL ORDERABLES\t471535\tLABFADA\tADENOSINE DEAMINASE, FLUID\tADAFLD\tADENOSINE DEAMIN., FLD\t1120008.62\t540366\t125\t140001077.5\t1.40E+15\t90\t48632940\t6.04E+13
+VIROLOGY ORDERABLES\t906961\tLABVZVCSF\tVZV DNA PCR QUAL, CSF\tVZQLDN\tVZV DNA\t9999999\t175368.96\t125\t1249999875\t1.25E+16\t125\t21921120\t5.25E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNWT\tSTONE WEIGHT\t564518.7019\t207922.6667\t124\t70000319.04\t7.00E+14\t45\t9356520\t2.34E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9155R\tRELATIONSHIP TO PATIENT\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9156R\tDONOR CELL DATE\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9157R\tDONOR CELL SOURCE\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9158R\tDONOR CELL TARGET\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9159R\tMETHOD\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9160R\tSERUM DATE 1\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9162R\tRESULT  MCS 1\t25\t380304.6774\t124\t3100\t530480\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9164R\tRESULT 1\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9182R\tCOMPOSED TEXT\t9999999\t380304.6774\t124\t1239999876\t1.24E+16\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9125R\tRELATIONSHIP TO PATIENT\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9126R\tDONOR CELL DATE\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9127R\tDONOR CELL SOURCE\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9128R\tDONOR CELL TARGET\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9129R\tMETHOD\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9130R\tSERUM DATE 1\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9132R\tRESULT  MCS 1\t36.37096774\t370725\t124\t4510\t520180\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9134R\tRESULT 1\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9151R\tCOMPOSED TEXT\t9999999\t370725\t124\t1239999876\t1.24E+16\t124\t45969900\t4.87E+13
+LAB BLOOD ORDERABLES\t473426\tLABYTESFT\tTESTOSTERONE, TOTAL AND FREE\tTESTOS\tTESTOSTERONE, TOTAL\t1613111.927\t280311.6522\t124\t200025878.9\t2.00E+15\t115\t32235840\t1.01E+13
+LAB BLOOD ORDERABLES\t473426\tLABYTESFT\tTESTOSTERONE, TOTAL AND FREE\tTESTOSFREE\tTESTOSTERONE, FREE\t1612909.239\t282318.7692\t124\t200000745.6\t2.00E+15\t195\t55052160\t1.74E+13
+STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tYFFQ2\tDURATION\t8145170.339\t264723.8462\t124\t1010001122\t1.01E+16\t78\t20648460\t8.48E+12
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVQSP\tSPECIMEN\t9999999\t253864.8227\t124\t1239999876\t1.24E+16\t423\t107384820\t3.23E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9154R\tDONOR DOB\t9999999\t377450.2439\t123\t1229999877\t1.23E+16\t123\t46426380\t5.12E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9124R\tDONOR DOB\t9999999\t367792.6829\t123\t1229999877\t1.23E+16\t123\t45238500\t4.81E+13
+LAB BLOOD ORDERABLES\t472616\tLABIGGS4\tIGG  SUBCLASS 4\tIGGS4\tIGG SUBCLASS4\t81373.38895\t60605.04202\t123\t10008926.84\t1.00E+14\t119\t7212000\t1.14E+12
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKPR\tPRIMERS\t9999999\t548376\t123\t1229999877\t1.23E+16\t115\t63063240\t4.39E+13
+LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETIC\tRETIC COUNT, MANUAL\t650412.3276\t20961.38614\t123\t80000716.3\t8.00E+14\t101\t2117100\t2.52E+11
+LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN PLATELET FACTOR 4 AB\tPF4IGG\tPF4 IGG ONLY OD\t1707317.517\t127452.6316\t123\t210000054.6\t2.10E+15\t114\t14529600\t1.59E+13
+LAB BLOOD ORDERABLES\t961580\tLABHITIA\tHIT FUNCTIONAL ASSAY\tHIPDIR\tHIT INTERPRETED BY\t9999999\t134478.871\t123\t1229999877\t1.23E+16\t124\t16675380\t2.92E+12
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tPRO\tPRODUCT ID  \t9999999\t22168\t122\t1219999878\t1.22E+16\t60\t1330080\t92575231200
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t122\t1219999878\t1.22E+16\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471708\tLAB11095R\tLA AB\tSSB\tSS-B/LA AB, IGG, SERUM\t9016392.582\t186424.6957\t122\t1099999895\t1.10E+16\t115\t21438840\t5.67E+12
+LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBF\tHGB F, QUANT.\t8196721.437\t426249.0566\t122\t1000000015\t1.00E+16\t106\t45182400\t2.81E+13
+MICROBIOLOGY ORDERABLES\t893893\tLABCRPLFB\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD\tCRPLFB\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD\t9999999\t78218.85246\t122\t1219999878\t1.22E+16\t122\t9542700\t9.15E+11
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGCTNNM\tCTNNB1, BM\t9999999\t829384.4262\t122\t1219999878\t1.22E+16\t122\t101184900\t9.94E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGPTENM\tPTEN, BM\t9999999\t829384.4262\t122\t1219999878\t1.22E+16\t122\t101184900\t9.94E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9153R\tDONOR NAME\t9999999\t380304.6774\t121\t1209999879\t1.21E+16\t124\t47157780\t5.17E+13
+LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBS\tHGB S, QUANT\t330588.586\t426444.5714\t121\t40001218.9\t4.00E+14\t105\t44776680\t2.79E+13
+LAB BLOOD ORDERABLES\t473176\tLABYPR3\tPROTEINASE 3 ANTIBODY\tYPR31\tPROTEINASE 3 ANTIBODIES\t8760329.917\t188461.4754\t121\t1059999920\t1.06E+16\t122\t22992300\t9.20E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGBRAFM\tBRAF, BM\t9999999\t829384.4262\t121\t1209999879\t1.21E+16\t122\t101184900\t9.94E+13
+VIROLOGY ORDERABLES\t473461\tLABTOXO\tTOXOPLASMA SEROLOGY\tTOXO\tTOXOPLASMA IGG\t9999999\t157680\t121\t1209999879\t1.21E+16\t116\t18290880\t3.57E+12
+VIROLOGY ORDERABLES\t906961\tLABVZVCSF\tVZV DNA PCR QUAL, CSF\tVZQLSP\tSPECIMEN\t9999999\t175368.96\t121\t1209999879\t1.21E+16\t125\t21921120\t5.25E+12
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9123R\tDONOR NAME\t9999999\t370725\t120\t1199999880\t1.20E+16\t124\t45969900\t4.87E+13
+LAB BLOOD ORDERABLES\t961580\tLABHITIA\tHIT FUNCTIONAL ASSAY\tHITI1\tHIT FUNCTIONAL ASSAY\t9999999\t134929.92\t120\t1199999880\t1.20E+16\t125\t16866240\t2.96E+12
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tUBT\tUNIT BLOOD TYPE  \t9999999\t48334.73684\t119\t1189999881\t1.19E+16\t57\t2755080\t3.01E+11
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFNRBC\tNRBC/100WBC, FLD\t4.411764706\t31792.63158\t119\t525\t28253\t57\t1812180\t4.75E+11
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9630R\tIGG COMBINED CPRA FOR CURRENT SERUM\t9999999\t15751.76471\t119\t1189999881\t1.19E+16\t119\t1874460\t1.06E+12
+LAB BLOOD ORDERABLES\t471960\tLABCENABY\tANTI CENTROMERE ANTIBODY\tCENQUL\tANTI-CENTROMERE RESULT\t9999999\t420534.9153\t119\t1189999881\t1.19E+16\t118\t49623120\t2.82E+13
+LAB BLOOD ORDERABLES\t472659\tLABINSL\tINSULIN UNSPECIFIED\tINSU\tINSULIN,UNSPECIFIED\t924388.0672\t43052.57143\t119\t110002180\t1.10E+15\t70\t3013680\t2.09E+11
+MICROBIOLOGY ORDERABLES\t472432\tLABFCUL\tFUNGAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t1820387.097\t119\t1189999881\t1.19E+16\t5250\t9557032260\t1.81E+16
+MICROBIOLOGY ORDERABLES\t893897\tLABCRPLFC\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF\tCRPLFC\tCRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF\t9999999\t63943.86555\t119\t1189999881\t1.19E+16\t119\t7609320\t7.06E+11
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGEGFRM\tEGFR, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGIDH1M\tIDH1, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGIDH2M\tIDH2, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGNOTCM\tNOTCH1, BM\t9999999\t829384.4262\t119\t1189999881\t1.19E+16\t122\t101184900\t9.94E+13
+LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGPRES\tHGBS PRESENT\t9999999\t410118.1034\t118\t1179999882\t1.18E+16\t116\t47573700\t2.88E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGKRASM\tKRAS, BM\t9999999\t829384.4262\t118\t1179999882\t1.18E+16\t122\t101184900\t9.94E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGPIK3M\tPIK3CA, BM\t9999999\t829384.4262\t118\t1179999882\t1.18E+16\t122\t101184900\t9.94E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tOTCAST\tCASTS, OTHER\t9999999\t18474.93976\t118\t1179999882\t1.18E+16\t83\t1533420\t3.87E+11
+HISTOCOMPATIBILITY ORDERABLE\t895897\t10145R\tHLA ORDER - UNRELATED DONOR PROCESSING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1214385.536\t117\t1169999883\t1.17E+16\t112\t136011180\t2.12E+14
+HISTOCOMPATIBILITY ORDERABLE\t895897\t10145R\tHLA ORDER - UNRELATED DONOR PROCESSING\tRINFO\tTO FIND RESULT:\t9999999\t1214385.536\t117\t1169999883\t1.17E+16\t112\t136011180\t2.12E+14
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGAPCM\tAPC, BM\t9999999\t784098.806\t117\t1169999883\t1.17E+16\t134\t105069240\t1.02E+14
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tSI\t<<STATUS>>  \t9999999\t32831.4433\t116\t1159999884\t1.16E+16\t194\t6369300\t7.61E+11
+LAB BLOOD ORDERABLES\t471802\tLABBUNCR\tBUN / CREATININE\tBUNCREAT\tBUN/CREATININE\t431054.7069\t960\t116\t50002346\t5.00E+14\t6\t5760\t6220800
+LAB BLOOD ORDERABLES\t472033\tLABYCHRA\tCHROMOGRANIN A\tYCHRA1\tCHROMOGRANIN A\t174007.4397\t306754.7619\t116\t20184863\t2.00E+14\t126\t38651100\t1.45E+13
+MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRCT\tCOMMENT\t9999999\t488123.0769\t116\t1159999884\t1.16E+16\t26\t12691200\t8.34E+12
+MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRMT\tMETHOD\t9999999\t488123.0769\t116\t1159999884\t1.16E+16\t26\t12691200\t8.34E+12
+MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRPM\tPRIMERS\t9999999\t488123.0769\t116\t1159999884\t1.16E+16\t26\t12691200\t8.34E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGTP53M\tTP53, BM\t9999999\t829384.4262\t116\t1159999884\t1.16E+16\t122\t101184900\t9.94E+13
+POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tPHLOC\tLOCATION\t9827585.224\t201166.25\t116\t1139999886\t1.14E+16\t192\t38623920\t1.70E+13
+POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tEXPDATE\tEXP DATE\t9570766.638\t-100367.9032\t116\t1110208930\t1.11E+16\t124\t-12445620\t5.85E+12
+POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tUBLOOD\tBLOOD URINE\t9999999\t-100367.9032\t116\t1159999884\t1.16E+16\t124\t-12445620\t5.85E+12
+STOOL ORDERABLES\t497390\tLABYSTOSM\tOSMOLALITY, STOOL\tOSMOLSTL\tOSMOLALITY STOOL\t4138136.526\t281288.1818\t116\t480023837\t4.80E+15\t66\t18565020\t7.03E+12
+LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBA2Q\tHGB A2, QUANT.\t2956523.38\t426249.0566\t115\t340000188.7\t3.40E+15\t106\t45182400\t2.81E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGNRASM\tNRAS, BM\t9999999\t829384.4262\t115\t1149999885\t1.15E+16\t122\t101184900\t9.94E+13
+POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tDIPLOT\tDIPSTICK LOT\t9930482.843\t-100367.9032\t115\t1142005527\t1.14E+16\t124\t-12445620\t5.85E+12
+STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12763R\tDATE OF LAST DOSE\t9999999\t181999.6875\t115\t1149999885\t1.15E+16\t192\t34943940\t7.76E+12
+STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12764R\tTIME OF LAST DOSE\t9826096.104\t181999.6875\t115\t1130001052\t1.13E+16\t192\t34943940\t7.76E+12
+STOOL ORDERABLES\t979775\tLAB12762R\tTACROLIMUS\t12765R\tDOSE, MG\t9999999\t181999.6875\t115\t1149999885\t1.15E+16\t192\t34943940\t7.76E+12
+VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tSOUR10\tSOURCE\t9999999\t192765.5696\t115\t1149999885\t1.15E+16\t158\t30456960\t1.87E+13
+VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV2QNT\tHSV-2 IGG LEVEL\t7478261.732\t250224\t115\t860000099.2\t8.60E+15\t115\t28775760\t9.60E+12
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tDI\tISSUE DATE/TIME\t9999999\t35170\t114\t1139999886\t1.14E+16\t54\t1899180\t1.34E+11
+BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN TOTAL, FLUID\tFSC2\tFLUID TYPE\t9999999\t867047.3684\t114\t1139999886\t1.14E+16\t19\t16473900\t2.02E+13
+HISTOCOMPATIBILITY ORDERABLE\t895897\t10145R\tHLA ORDER - UNRELATED DONOR PROCESSING\tUNRDID\tUNRELATED DONOR ID\t9999999\t1214385.536\t114\t1139999886\t1.14E+16\t112\t136011180\t2.12E+14
+MICROBIOLOGY ORDERABLES\t473204\tLABROMRS\tMRSA SCREEN\tCULT\tCULT/OTHER RSLT:\t9999999\t111100.8005\t114\t1139999886\t1.14E+16\t23935\t2659197660\t3.68E+14
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tOCCPH\tPATIENT OCCULAR PH\t7.714912281\t-14672.40876\t114\t879.5\t6832.75\t137\t-2010120\t2.71E+11
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tPHLOC\tLOCATION\t9736841.325\t2327.256637\t114\t1109999911\t1.11E+16\t1130\t2629800\t4.75E+14
+VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV1QNT\tHSV-1 IGG LEVEL\t3245617.821\t250224\t114\t370000431.6\t3.70E+15\t115\t28775760\t9.60E+12
+VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV2QUL\tHSV-2 IGG RESULT\t9999999\t250224\t114\t1139999886\t1.14E+16\t115\t28775760\t9.60E+12
+HISTOCOMPATIBILITY ORDERABLE\t4046255\t10015R\tHLA ORDER - DSA SCREENING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1171925.172\t113\t1129999887\t1.13E+16\t116\t135943320\t2.64E+14
+LAB BLOOD ORDERABLES\t472528\tLABHCVA\tHEPATITIS C AB IGG\tHCVI\tINTERPRETATION\t9999999\t57183.66116\t113\t1129999887\t1.13E+16\t1393\t79656840\t1.00E+13
+LAB BLOOD ORDERABLES\t473688\tLABYTRYPT\tTRYPTASE, SERUM\tTRYPTASE\tTRYPTASE\t708013.1192\t251845.2632\t113\t80005482.47\t8.00E+14\t95\t23925300\t1.01E+13
+MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tSREQ\tCOMMENTS\t9999999\t304958.4615\t113\t1129999887\t1.13E+16\t78\t23786760\t1.00E+13
+STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tYFFQ1\tTOTAL WEIGHT\t354501.3274\t264723.8462\t113\t40058650\t4.00E+14\t78\t20648460\t8.48E+12
+VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHV1QUL\tHSV-1 IGG RESULT\t9999999\t250224\t113\t1129999887\t1.13E+16\t115\t28775760\t9.60E+12
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tUBT\tUNIT BLOOD TYPE  \t9999999\t49175.09434\t112\t1119999888\t1.12E+16\t53\t2606280\t3.05E+11
+BODY FLUID ORDERABLES\t472655\tLABIGF\tINSULIN-LIKE GROWTH FACTOR 1\tIGF\tINSULIN-LIKE GROWTH FACTOR 1\t803781.0902\t81247.05882\t112\t90023482.1\t9.00E+14\t102\t8287200\t1.16E+12
+HISTOCOMPATIBILITY ORDERABLE\t896159\t10100R\tHLA ORDER - PLATELET COMPATIBILITY\t10049R\tHLA LAB TESTING STATUS\t9999999\t456229.9115\t112\t1119999888\t1.12E+16\t113\t51553980\t7.28E+13
+LAB BLOOD ORDERABLES\t472723\tLABYLAMO\tLAMOTRIGINE\tLAMOT\tLAMOTRIGINE\t267863.5063\t193590\t112\t30000712.7\t3.00E+14\t108\t20907720\t4.62E+12
+LAB BLOOD ORDERABLES\t473254\tLABRETIC\tRETICULOCYTE COUNT AUTOMATED\tRETICAB\tRETIC, ABS, MANUAL\t357263.5893\t20763.22581\t112\t40013522\t4.00E+14\t93\t1930980\t2.48E+11
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t111\t1109999889\t1.11E+16\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tXMR\tUNIT CROSSMATCH  \t9999999\t72258.75\t111\t1109999889\t1.11E+16\t48\t3468420\t5.54E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t271112.1532\t69104.85437\t111\t30093449\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD3PC\tCD3 (PAN T) %\t270339.5946\t69104.85437\t111\t30007695\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD4AB\tCD3+/CD4+, ABS.\t270510.4324\t69104.85437\t111\t30026658\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD4PC\tCD3+/CD4+ %\t270290.8108\t69104.85437\t111\t30002280\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD8AB\tCD3+/CD8+, ABS.\t270857.5856\t69104.85437\t111\t30065192\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tCD8PC\tCD3+/CD8+ %\t270317.6847\t69104.85437\t111\t30005263\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t270338.2523\t69104.85437\t111\t30007546\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t271097.7207\t69104.85437\t111\t30091847\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t270270.8405\t69104.85437\t111\t30000063.3\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tLYM\tLYMPHOCYTES %\t270288.4378\t69104.85437\t111\t30002016.6\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t271405.6937\t69104.85437\t111\t30126032\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t471950\tLABC4C8\tCD4 / CD8 PANEL, BLOOD\tWBC\tWBC COUNT\t277493.6667\t69104.85437\t111\t30801797\t3.00E+14\t103\t7117800\t6.49E+11
+LAB BLOOD ORDERABLES\t473262\tLABRF\tRHEUMATOID FACTOR\tRHFR\tRHEUMATOID FACTOR\t9999999\t221510.8108\t111\t1109999889\t1.11E+16\t111\t24587700\t7.16E+12
+LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIKACINTR\tAMIKACIN, TROUGH\t1981987.495\t9466.060606\t111\t220000611.9\t2.20E+15\t99\t937140\t42750860400
+LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIPR3\tDOSAGE\t9999999\t9466.060606\t111\t1109999889\t1.11E+16\t99\t937140\t42750860400
+LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIPR4\tADMINISTRATION ROUTE\t9999999\t9466.060606\t111\t1109999889\t1.11E+16\t99\t937140\t42750860400
+LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANCOPEAK\tVANCOMYCIN, PEAK\t540559.7667\t14418.35294\t111\t60002134.1\t6.00E+14\t85\t1225560\t55369022400
+LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANPO2\tDATE & TIME OF LAST DOSE\t9999999\t14418.35294\t111\t1109999889\t1.11E+16\t85\t1225560\t55369022400
+LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANPO3\tDOSAGE\t9999999\t14418.35294\t111\t1109999889\t1.11E+16\t85\t1225560\t55369022400
+LAB BLOOD ORDERABLES\t497522\tLABVANPOL\tVANCOMYCIN PEAK LEVEL\tVANPO4\tADMINISTRATION ROUTE\t9999999\t14418.35294\t111\t1109999889\t1.11E+16\t85\t1225560\t55369022400
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGBRAFB\tBRAF, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGCTNNB\tCTNNB1, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGEGFRB\tEGFR, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGPIK3B\tPIK3CA, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGPTENB\tPTEN, BLOOD\t9999999\t946882.7027\t111\t1109999889\t1.11E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t961555\tLAB12808R\tCK ISOENZYME WITH REFLEX\tCK\tCREATINE KINASE\t93054.76577\t178739.4783\t111\t10329079\t1.00E+14\t115\t20555040\t4.88E+12
+URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tTNAU\tSODIUM EXCRETION\t360477.7207\t55488\t111\t40013027\t4.00E+14\t60\t3329280\t4.67E+11
+URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tUNA\tSODIUM, URINE\t270326.6486\t55488\t111\t30006258\t3.00E+14\t60\t3329280\t4.67E+11
+URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tUT1\tCOLLECTION PERIOD SODIUM\t450473.1081\t55488\t111\t50002515\t5.00E+14\t60\t3329280\t4.67E+11
+URINE ORDERABLES\t473346\tLABUNAT\tSODIUM, URINE (TIMED)\tVOL1\tVOLUME SODIUM\t272045\t55488\t111\t30196995\t3.00E+14\t60\t3329280\t4.67E+11
+LAB BLOOD ORDERABLES\t497502\tLABAMIPRL\tAMIKACIN TROUGH LEVEL\tAMIPR2\tDATE & TIME OF LAST DOSE\t9999999\t9466.060606\t110\t1099999890\t1.10E+16\t99\t937140\t42750860400
+LAB BLOOD ORDERABLES\t541670\tLABOXCMET\tOXCARBAZEPINE METABOLITE LEVEL\tOXCMT1\tOXCARBAZEPINE METABOLITE\t90930.9\t180366.9421\t110\t10002399\t1.00E+14\t121\t21824400\t4.56E+12
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGIDH1B\tIDH1, BLOOD\t9999999\t946882.7027\t110\t1099999890\t1.10E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGIDH2B\tIDH2, BLOOD\t9999999\t946882.7027\t110\t1099999890\t1.10E+16\t111\t105103980\t2.64E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGKRASB\tKRAS, BLOOD\t9999999\t946882.7027\t110\t1099999890\t1.10E+16\t111\t105103980\t2.64E+14
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tPRO\tPRODUCT ID  \t9999999\t22864.44444\t109\t1089999891\t1.09E+16\t54\t1234680\t87256699200
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGNRASB\tNRAS, BLOOD\t9999999\t946882.7027\t109\t1089999891\t1.09E+16\t111\t105103980\t2.64E+14
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tRPT\tRPT\t9999999\t316267.8\t109\t1089999891\t1.09E+16\t100\t31626780\t1.20E+13
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQT\tIGG LEVEL\t7522948.651\t173800.7339\t109\t820001403\t8.20E+15\t109\t18944280\t4.42E+12
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGTP53B\tTP53, BLOOD\t9999999\t946882.7027\t108\t1079999892\t1.08E+16\t111\t105103980\t2.64E+14
+MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tRPT\tRPT\t9999999\t212126.9388\t108\t1079999892\t1.08E+16\t49\t10394220\t3.21E+12
+STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tFATPC\t%FAT\t3425938.296\t250316.8\t108\t370001336\t3.70E+15\t75\t18773760\t7.06E+12
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tXMR\tUNIT CROSSMATCH  \t9999999\t68518.69565\t107\t1069999893\t1.07E+16\t46\t3151860\t5.30E+11
+MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tSDES\tSPECIMEN\t9999999\t212126.9388\t107\t1069999893\t1.07E+16\t49\t10394220\t3.21E+12
+URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tYUMEUT\tCOLLECTION DURATION\t373854.8411\t362619.0476\t107\t40002468\t4.00E+14\t63\t22845000\t1.07E+13
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tSI\t<<STATUS>>  \t9999999\t34411.39535\t106\t1059999894\t1.06E+16\t172\t5918760\t7.29E+11
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tPRO\tPRODUCT ID  \t9999999\t19624.44444\t106\t1059999894\t1.06E+16\t54\t1059720\t53302118400
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tABNWBC\tBLASTS/ABNORMAL CELLS\t9999999\t24628.30189\t106\t1059999894\t1.06E+16\t106\t2610600\t5.11E+11
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGNOTCB\tNOTCH1, BLOOD\t9999999\t946882.7027\t106\t1059999894\t1.06E+16\t111\t105103980\t2.64E+14
+STOOL ORDERABLES\t472362\tLABYFFATQ\tFAT, FECAL QUANTITATIVE\tFATTOT\tTOTAL FAT/24H\t8207551\t255336.1765\t106\t870000406\t8.70E+15\t68\t17362860\t6.70E+12
+VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXGQL\tIGG RESULT\t9999999\t167664.3396\t106\t1059999894\t1.06E+16\t106\t17772420\t3.77E+12
+LAB BLOOD ORDERABLES\t472352\tLABFACT10\tFACTOR X ASSAY\tFACTOR10\tFACTOR X ASSAY\t761966.9143\t38086.30435\t105\t80006526\t8.00E+14\t92\t3503940\t2.05E+11
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12009R\tQ FEVER IGM PHASE I SCRN\t9999999\t308861.9118\t105\t1049999895\t1.05E+16\t136\t42005220\t1.99E+13
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12010R\tQ FEVER IGG PHASE II SCRN\t9999999\t308861.9118\t105\t1049999895\t1.05E+16\t136\t42005220\t1.99E+13
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGAPCB\tAPC, BLOOD\t9999999\t873158.6047\t105\t1049999895\t1.05E+16\t129\t112637460\t2.69E+14
+LAB BLOOD ORDERABLES\t978544\tLABPTEG\tTEG, PERIOPERATIVE\tPTEGSP\tSP, SPLIT POINT\t1142863.017\t29423.95102\t105\t120000616.8\t1.20E+15\t1225\t36044340\t2.93E+13
+POINT OF CARE TESTING\t471530\tLABPCACT\tACTIVATED CLOTTING TIME (POCT)\tPCCOM\tPOCT COMMENT\t9999999\t#VALUE!\t105\t1049999895\t1.05E+16\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tUBT\tUNIT BLOOD TYPE  \t9999999\t41741.17647\t104\t1039999896\t1.04E+16\t51\t2128800\t2.07E+11
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tLR210\tLOG DX AVG\t8653845.298\t621287.0455\t104\t899999911\t9.00E+15\t88\t54673260\t4.27E+13
+LAB BLOOD ORDERABLES\t471791\tLABYBSAP\tBONE ALKALINE PHOSPHATASE, SERUM\tALKPO4BONE\tBONE ALK. PHOS.\t47.77307692\t148256.044\t104\t4968.4\t416111.98\t91\t13491300\t2.31E+12
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12008R\tQ FEVER IGG PHASE I SCRN\t9999999\t308861.9118\t104\t1039999896\t1.04E+16\t136\t42005220\t1.99E+13
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCORCL\tCORRECTED CR. CLEAR.\t673121.25\t35871.92308\t104\t70004610\t7.00E+14\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCOREXC\tCORRECTED CR. EXC.\t12.27884615\t35871.92308\t104\t1277\t20165\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCR\tCREATININE, SERUM\t1.843269231\t35871.92308\t104\t191.7\t523.25\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCRCL\tCREATININE CLEARANCE\t50.57692308\t35871.92308\t104\t5260\t446946\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCRE\tCREATININE EXCRETION\t862.8461538\t35871.92308\t104\t89736\t102584906\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tCRUCL\tCREATININE, URINE\t56.64423077\t35871.92308\t104\t5891\t468493\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tFLOWURN\tURINE FLOW\t1.372115385\t35871.92308\t104\t142.7\t270.91\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tHT\tHEIGHT\t168.9134615\t35871.92308\t104\t17567\t2979645\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tUT3\tCOLLECTION PERIOD CREATININE\t20.39423077\t35871.92308\t104\t2121\t47145\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tVOL3\tVOLUME CREATININE\t1631.221154\t35871.92308\t104\t169647\t425176911\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t472105\tLABCRCL\tCREATININE CLEARANCE\tWT\tWEIGHT\t72.88942308\t35871.92308\t104\t7580.5\t598246.19\t104\t3730680\t3.45E+11
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tOTHCRY\tCRYSTALS, OTHER\t9999999\t13744.13793\t103\t1029999897\t1.03E+16\t58\t797160\t46346716800
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVSPM\tSPECIMEN\t9999999\t253864.8227\t103\t1029999897\t1.03E+16\t423\t107384820\t3.23E+13
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tDI\tISSUE DATE/TIME\t9999999\t47200\t102\t1019999898\t1.02E+16\t48\t2265600\t2.52E+11
+BODY FLUID ORDERABLES\t472116\tLABCRYST\tCRYSTAL ANALYSIS, SYNOVIAL FLUID\tBIREF\tBIREFRINGENCE\t9999999\t11946.19355\t102\t1019999898\t1.02E+16\t155\t1851660\t96880654800
+LAB BLOOD ORDERABLES\t890125\tLAB12238R\tLACOSAMIDE\t12239R\t12239R\t1176476.807\t371646.9903\t102\t120000634.3\t1.20E+15\t103\t38279640\t1.63E+13
+LAB BLOOD ORDERABLES\t890125\tLAB12238R\tLACOSAMIDE\t12240R\t12240R\t2450981.449\t371646.9903\t102\t250000107.8\t2.50E+15\t103\t38279640\t1.63E+13
+LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tLABS1\tLABS1\t9999999\t671067.3054\t102\t1019999898\t1.02E+16\t167\t112068240\t1.25E+14
+URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11146R\tCREATININE, URINE\t686350.0245\t13.33333333\t102\t70007702.5\t7.00E+14\t54\t720\t43200
+VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tHSV1PC\tHSV-1 DNA\t9999999\t175252.9412\t102\t1019999898\t1.02E+16\t102\t17875800\t3.71E+12
+VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tHSV2PC\tHSV-2 DNA\t9999999\t175252.9412\t102\t1019999898\t1.02E+16\t102\t17875800\t3.71E+12
+BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN, TOTAL BODY FLUID\tFSC2\tFLUID TYPE\t9999999\t156644.5872\t101\t1009999899\t1.01E+16\t109\t17074260\t3.41E+12
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMCO\tCOMMENT\t9999999\t741092.1429\t101\t1009999899\t1.01E+16\t28\t20750580\t2.04E+13
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMDX\tCLINICAL INDICATION\t9999999\t741092.1429\t101\t1009999899\t1.01E+16\t28\t20750580\t2.04E+13
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMRE\tRESULT\t9999999\t741092.1429\t101\t1009999899\t1.01E+16\t28\t20750580\t2.04E+13
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXMQT\tIGM LEVEL\t9603959.626\t176445.1485\t101\t969999922.3\t9.70E+15\t101\t17820960\t4.22E+12
+BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN, TOTAL BODY FLUID\tBILIRUBINFLD\tBILIRUBIN, TOTAL, BODY FLUID\t100004.303\t156644.5872\t100\t10000430.3\t1.00E+14\t109\t17074260\t3.41E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9165R\tSERUM DATE 2\t9999999\t358155\t100\t999999900\t1.00E+16\t100\t35815500\t3.98E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9167R\tRESULT  MCS 2\t27.25\t358155\t100\t2725\t411949\t100\t35815500\t3.98E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9169R\tRESULT 2\t9999999\t358155\t100\t999999900\t1.00E+16\t100\t35815500\t3.98E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9135R\tSERUM DATE 2\t9999999\t359866.8\t100\t999999900\t1.00E+16\t100\t35986680\t3.98E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9137R\tRESULT  MCS 2\t40.48\t359866.8\t100\t4048\t477754\t100\t35986680\t3.98E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9139R\tRESULT 2\t9999999\t359866.8\t100\t999999900\t1.00E+16\t100\t35986680\t3.98E+13
+URINE ORDERABLES\t871366\tLABSTOBLD\tOCCULT BLD, GUAIAC DIAG, SINGLE\tOCCGU\tSTOOL SAMPLE1,GUAIAC\t9999999\t3821.538462\t100\t999999900\t1.00E+16\t39\t149040\t1437552000
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tXMR\tUNIT CROSSMATCH  \t9999999\t69890.86957\t99\t989999901\t9.90E+15\t46\t3214980\t5.31E+11
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tP210Q\tP210\t5454545.061\t621287.0455\t99\t539999961.1\t5.40E+15\t88\t54673260\t4.27E+13
+LAB BLOOD ORDERABLES\t473444\tLAB11097R\tTSH RECEPTOR AB, STIM\tTSIG\tTHROID-STIMULATING IG, SERUM\t6868687.264\t284574.7826\t99\t680000039.1\t6.80E+15\t92\t26180880\t8.22E+12
+STOOL ORDERABLES\t471585\tLAB11096R\tALPHA 1 ANTITRYPSIN, STOOL\tA1AANTITRYPF\tA-1-ANTITRYP,FECAL\t5454607.747\t233964.6429\t99\t540006167\t5.40E+15\t56\t13102020\t3.93E+12
+LAB BLOOD ORDERABLES\t473270\tLABRIST\tRISTOCETIN COFACTOR\tRIST\tRISTOCETIN CO-FACTOR\t816463.949\t46643.41463\t98\t80013467\t8.00E+14\t82\t3824760\t2.69E+11
+LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tSPTY2\tSPECIMEN TYPE\t9999999\t1896672.381\t98\t979999902\t9.80E+15\t63\t119490360\t1.36E+15
+URINE ORDERABLES\t871366\tLABSTOBLD\tOCCULT BLD, GUAIAC DIAG, SINGLE\tDATE1\tDATE 1\t8991267\t3821.538462\t98\t881144166\t8.73E+15\t39\t149040\t1437552000
+VIROLOGY ORDERABLES\t876711\tLABDVERVP\tRESPIRATORY DFA PANEL\tRVPIP\tTEST DECRIPTION\t9999999\t66962.44702\t98\t979999902\t9.80E+15\t2501\t167473080\t3.14E+13
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tRPT\tRPT\t9999999\t696693.125\t97\t969999903\t9.70E+15\t96\t66882540\t7.55E+13
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tSDES\tSPECIMEN\t9999999\t696693.125\t97\t969999903\t9.70E+15\t96\t66882540\t7.55E+13
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tUBT\tUNIT BLOOD TYPE  \t9999999\t48162.27273\t96\t959999904\t9.60E+15\t44\t2119140\t2.29E+11
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tPRO\tPRODUCT ID  \t9999999\t19766.80851\t96\t959999904\t9.60E+15\t47\t929040\t49220308800
+LAB BLOOD ORDERABLES\t472348\tLABFACT7\tFACTOR VII ASSAY\tFACTOR7\tFACTOR VII ASSAY\t937555.1458\t47563.90244\t96\t90005294\t9.00E+14\t82\t3900240\t2.49E+11
+LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1 AND 2 PCR, NON CSF\tSOURC9\tSOURCE\t9999999\t627991.6667\t96\t959999904\t9.60E+15\t72\t45215400\t8.40E+13
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t316267.8\t96\t959999904\t9.60E+15\t100\t31626780\t1.20E+13
+URINE ORDERABLES\t471613\tLABUAMY\tAMYLASE WITH CREATININE, URINE (RANDOM)\tAMUCRU\tAMYLASE/CREAT RATIO\t6041666.177\t17080.32787\t96\t579999953\t5.80E+15\t61\t1041900\t1.14E+11
+URINE ORDERABLES\t471613\tLABUAMY\tAMYLASE WITH CREATININE, URINE (RANDOM)\tAMYLASE\tAMYLASE, URINE\t312611.625\t17080.32787\t96\t30010716\t3.00E+14\t61\t1041900\t1.14E+11
+URINE ORDERABLES\t471613\tLABUAMY\tAMYLASE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t208421.5698\t17080.32787\t96\t20008470.7\t2.00E+14\t61\t1041900\t1.14E+11
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tHYCD\tHYDROCODONE\t7396121\t14.11764706\t96\t710027616\t7.10E+15\t17\t240\t14400
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tOXYCOD\tOXYCODONE\t7605058.427\t14.11764706\t96\t730085609\t7.30E+15\t17\t240\t14400
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUHMGC\tHYDROMORPHONE\t4480940.604\t14.11764706\t96\t430170298\t4.30E+15\t17\t240\t14400
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUMOGC\tMORPHINE\t5210578.354\t14.11764706\t96\t500215522\t5.00E+15\t17\t240\t14400
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tSREQ\tCOMMENTS\t9999999\t696693.125\t96\t959999904\t9.60E+15\t96\t66882540\t7.55E+13
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\tSACIGA\tS. CERVISIAE AB,IGA\t105282.5463\t263645.3333\t95\t10001841.9\t1.00E+14\t90\t23728080\t1.38E+13
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\tSACIGG\tS.CERVISIAE AB,IGG\t105281.3116\t263645.3333\t95\t10001724.6\t1.00E+14\t90\t23728080\t1.38E+13
+LAB BLOOD ORDERABLES\t472436\tLABG6PD\tG6PD SCREEN\tG6PD\tG6PD SCREEN\t9999999\t101767.2527\t95\t949999905\t9.50E+15\t91\t9260820\t1.51E+12
+LAB BLOOD ORDERABLES\t472553\tLABHIVAB\tHIV (TYPE 1 AND TYPE 2) ABY\tHIVAB\tHIV 1/2 ANTIBODY\t9999999\t61924.23529\t95\t949999905\t9.50E+15\t85\t5263560\t4.85E+11
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tCODEINE\tCODEINE\t9158137.989\t14.11764706\t95\t870023109\t8.70E+15\t17\t240\t14400
+URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11150R\tNITRITES\t9999999\t13.33333333\t95\t949999905\t9.50E+15\t54\t720\t43200
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tSI\t<<STATUS>>  \t9999999\t33621.42857\t94\t939999906\t9.40E+15\t154\t5177700\t6.15E+11
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\tPANCA\tNEUTROPHIL CYTO AB-P\t9999999\t263645.3333\t94\t939999906\t9.40E+15\t90\t23728080\t1.38E+13
+MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tRPT\tRPT\t9999999\t#VALUE!\t94\t939999906\t9.40E+15\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tRPT\tRPT\t9999999\t180211.7241\t94\t939999906\t9.40E+15\t87\t15678420\t3.56E+12
+MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tSREQ\tCOMMENTS\t9999999\t180211.7241\t94\t939999906\t9.40E+15\t87\t15678420\t3.56E+12
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9407R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t42896.78571\t93\t929999907\t9.30E+15\t168\t7206660\t2.67E+13
+LAB BLOOD ORDERABLES\t668152\tLABATTG\tIGA ANTI  TTG\tATTGT\tIGA ANTI TTG LEVEL\t7849464.624\t399798.0645\t93\t730000210\t7.30E+15\t93\t37181220\t1.87E+13
+MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tSDES\tSPECIMEN\t9999999\t180211.7241\t93\t929999907\t9.30E+15\t87\t15678420\t3.56E+12
+VIROLOGY ORDERABLES\t472452\tLABGCCR2\tGC MOLECULAR DETECTION\tGC\tN.GONORRHOEAE RESULT:\t9999999\t144177.2308\t93\t929999907\t9.30E+15\t65\t9371520\t1.97E+12
+LAB BLOOD ORDERABLES\t471706\tLAB11093R\tSCL 70 AB\tSCL70\tSCL 70 AB, IGG, SERUM\t9239129.552\t154129.2135\t92\t849999918.8\t8.50E+15\t89\t13717500\t2.41E+12
+VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tRPT\tRPT\t9999999\t925479.3103\t92\t919999908\t9.20E+15\t87\t80516700\t1.11E+14
+VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tSDES\tSPECIMEN\t9999999\t925479.3103\t92\t919999908\t9.20E+15\t87\t80516700\t1.11E+14
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tDI\tISSUE DATE/TIME\t9999999\t43812\t91\t909999909\t9.10E+15\t45\t1971540\t1.93E+11
+MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tSDES\tSPECIMEN\t9999999\t#VALUE!\t91\t909999909\t9.10E+15\t0\tNone\tNone
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t618.6666667\t90\t899999910\t9.00E+15\t90\t55680\t742197600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t618.6666667\t90\t899999910\t9.00E+15\t90\t55680\t742197600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t8246541.822\t618.6666667\t90\t742188764\t7.40E+15\t90\t55680\t742197600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t618.6666667\t90\t899999910\t9.00E+15\t90\t55680\t742197600
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9033R\tHLA-DPB1 ALLELE 1\t9999999\t383878.6813\t90\t899999910\t9.00E+15\t91\t34932960\t6.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9034R\tHLA-DPB1 ALLELE 2\t9999999\t383878.6813\t90\t899999910\t9.00E+15\t91\t34932960\t6.67E+13
+LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tTEST2\tTEST NAME\t9889876.311\t1896672.381\t90\t890088868\t8.90E+15\t63\t119490360\t1.36E+15
+VIROLOGY ORDERABLES\t473461\tLABTOXO\tTOXOPLASMA IGG\tTOXO\tTOXOPLASMA IGG\t9999999\t156732.2892\t90\t899999910\t9.00E+15\t83\t13008780\t2.55E+12
+VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tSREQ\tCOMMENTS\t9999999\t925479.3103\t90\t899999910\t9.00E+15\t87\t80516700\t1.11E+14
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tUBT\tUNIT BLOOD TYPE  \t9999999\t31722.43902\t89\t889999911\t8.90E+15\t41\t1300620\t88116591600
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSCOLOR\tCOLOR, SYN FLD\t9999999\t6555.9375\t89\t889999911\t8.90E+15\t64\t419580\t9299703600
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSRBC\tRBC COUNT, SYN FLD\t3116639.461\t6555.9375\t89\t277380912\t2.63E+15\t64\t419580\t9299703600
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSTURB\tTURBIDITY, SYN FLD\t9999999\t6555.9375\t89\t889999911\t8.90E+15\t64\t419580\t9299703600
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tSWBC\tNUCL. CELLS, SYN FLD\t2927603.202\t6555.9375\t89\t260556685\t2.60E+15\t64\t419580\t9299703600
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9031R\tHLA-DPA1 ALLELE 1\t9999999\t391679.3258\t89\t889999911\t8.90E+15\t89\t34859460\t6.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9032R\tHLA-DPA1 ALLELE 2\t9999999\t391679.3258\t89\t889999911\t8.90E+15\t89\t34859460\t6.67E+13
+LAB BLOOD ORDERABLES\t473774\tLABVWAG\tVON WILLEBRAND AG\tVWAG\tVON WILLEBRAND ANTIGEN\t112561.9101\t51511.23288\t89\t10018010\t1.00E+14\t73\t3760320\t2.91E+11
+MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tGS\tGRAM STAIN\t9999999\t474199.3774\t89\t889999911\t8.90E+15\t514\t243738480\t2.40E+14
+URINE ORDERABLES\t473100\tLABURPHOS\tPHOSPHATE, URINE (RANDOM)\tPHOSU\tPHOSPHATE, URINE\t1348343.265\t14782.28571\t89\t120002550.6\t1.20E+15\t70\t1034760\t1.04E+11
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t88\t879999912\t8.80E+15\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tJOINT\tJOINT\t9999999\t6555.9375\t88\t879999912\t8.80E+15\t64\t419580\t9299703600
+BODY FLUID ORDERABLES\t497382\tLABFBILA\tBILIRUBIN TOTAL, FLUID\tBILIRUBINFLD\tBILIRUBIN, TOTAL, BODY FLUID\t340914.5125\t831985\t88\t30000477.1\t3.00E+14\t24\t19967640\t2.27E+13
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t8888826.693\t645\t88\t782216749\t7.80E+15\t88\t56760\t741614400
+LAB BLOOD ORDERABLES\t471653\tLABAPC\tACTIVATED PROTEIN C RESISTANCE\tAPC\tAPC RESISTANCE\t340911.6489\t150036.8675\t88\t30000225.1\t3.00E+14\t83\t12453060\t2.98E+12
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tPRO\tPRODUCT ID  \t9999999\t21465\t87\t869999913\t8.70E+15\t44\t944460\t67138131600
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tXMR\tUNIT CROSSMATCH  \t9999999\t53797.33333\t87\t869999913\t8.70E+15\t45\t2420880\t3.40E+11
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9102R\tCELL DATE\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9103R\tCELL SOURCE\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9104R\tSERUM DATE 1\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9106R\tMCS 1\t35.5862069\t272784.1379\t87\t3096\t587478\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9121R\tCOMPOSED TEXT\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9599R\tMETHOD\t9999999\t272784.1379\t87\t869999913\t8.70E+15\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9081R\tCELL DATE\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9082R\tCELL SOURCE\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9083R\tSERUM DATE 1\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9085R\tMCS 1\t46.14942529\t273799.3103\t87\t4015\t536315\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9100R\tCOMPOSED TEXT\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9462R\tMETHOD\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9595R\tRESULT 1\t9999999\t273799.3103\t87\t869999913\t8.70E+15\t87\t23820540\t2.34E+13
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX\tSACIGA\tS. CERVISIAE AB,IGA\t25.06781609\t301613.3333\t87\t2180.9\t184727.29\t81\t24430680\t8.35E+12
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX\tSACIGG\tS.CERVISIAE AB,IGG\t14.21494253\t301613.3333\t87\t1236.7\t31616.71\t81\t24430680\t8.35E+12
+LAB BLOOD ORDERABLES\t472802\tLABMMAS\tMETHYLMALONIC ACID\tMMA\tMETHYLMALONIC ACID, SERUM\t0.252413793\t321343.7838\t87\t21.96\t9.7782\t74\t23779440\t9.38E+12
+MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tSREQ\tCOMMENTS\t9999999\t212126.9388\t87\t869999913\t8.70E+15\t49\t10394220\t3.21E+12
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tDX\tDIAGNOSIS\t9999999\t6555.9375\t86\t859999914\t8.60E+15\t64\t419580\t9299703600
+BODY FLUID ORDERABLES\t471958\tLABSYCT\tCELL COUNT, SYNOVIAL FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t6555.9375\t86\t859999914\t8.60E+15\t64\t419580\t9299703600
+LAB BLOOD ORDERABLES\t473426\tLABYTESFT\tTESTOSTERONE, TOTAL AND FREE\tYTESHD\tFREE TESTOSTERONE\t9999999\t285204\t86\t859999914\t8.60E+15\t80\t22816320\t7.34E+12
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tATTGR\tIGA ANTI TTG ANTIBODY\t9999999\t402189.6296\t86\t859999914\t8.60E+15\t81\t32577360\t1.89E+13
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tGDGPR\tIGG ANTI DGP ANTIBODY\t9999999\t402189.6296\t86\t859999914\t8.60E+15\t81\t32577360\t1.89E+13
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tIGACDS\tIGA, SERUM\t116516.5814\t402189.6296\t86\t10020426\t1.00E+14\t81\t32577360\t1.89E+13
+LAB BLOOD ORDERABLES\t670133\tLABGH\tGROWTH HORMONE\tGH\tGROWTH HORMONE\t1046513.045\t143370.7317\t86\t90000121.9\t9.00E+14\t82\t11756400\t2.71E+12
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGDNM3B\tDNMT3A, BLOOD\t9999999\t960628.6047\t86\t859999914\t8.60E+15\t86\t82614060\t2.39E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGERHRB\tERBB2/HER2, BLOOD\t9999999\t960628.6047\t86\t859999914\t8.60E+15\t86\t82614060\t2.39E+14
+STOOL ORDERABLES\t473766\tLABY25VD\tVITAMIN D, 25-HYDROXY\t25OHD2\t25-HYDROXY D2\t8488374.459\t352581.5385\t86\t730000203.5\t7.30E+15\t78\t27501360\t4.09E+13
+STOOL ORDERABLES\t473766\tLABY25VD\tVITAMIN D, 25-HYDROXY\t25OHD3\t25-HYDROXY D3\t697693.7895\t352581.5385\t86\t60001665.9\t6.00E+14\t78\t27501360\t4.09E+13
+STOOL ORDERABLES\t473766\tLABY25VD\tVITAMIN D, 25-HYDROXY\t25OHDT\t25-HYDROXY D TOTAL\t1279092.02\t352581.5385\t86\t110001913.7\t1.10E+15\t78\t27501360\t4.09E+13
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tSI\t<<STATUS>>  \t9999999\t33679.72028\t85\t849999915\t8.50E+15\t143\t4816200\t5.88E+11
+HISTOCOMPATIBILITY ORDERABLE\t506036\t10270R\tHLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t1418312.791\t85\t849999915\t8.50E+15\t86\t121974900\t4.69E+14
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9600R\tRESULT 1\t9999999\t275034.3529\t85\t849999915\t8.50E+15\t85\t23377920\t2.32E+13
+LAB BLOOD ORDERABLES\t471650\tLABASTRO\tANTI STREPTOLYSIN O\tASTRO\tANTI STREPTOLYSIN O\t1529599.729\t45202.10526\t85\t130015977\t1.30E+15\t76\t3435360\t2.75E+11
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tO2SATA\tO2 SATURATION (A)\t9882353.104\t3829.714286\t85\t840000013.8\t8.40E+15\t35\t134040\t1868594400
+VIROLOGY ORDERABLES\t876715\tLABDVERV3\tRSV DFA\tRSV3\tRSV\t9999999\t60101.53846\t85\t849999915\t8.50E+15\t78\t4687920\t3.25E+11
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tSI\t<<STATUS>>  \t9999999\t34670.73529\t84\t839999916\t8.40E+15\t136\t4715220\t5.88E+11
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMSO\tSPECIMEN SOURCE\t9999999\t741092.1429\t84\t839999916\t8.40E+15\t28\t20750580\t2.04E+13
+HISTOCOMPATIBILITY ORDERABLE\t5040244\t10170R\tHLA ORDER - NMDP ADDITIONAL SPECIMEN\t10049R\tHLA LAB TESTING STATUS\t9999999\t344572.9412\t84\t839999916\t8.40E+15\t85\t29288700\t3.69E+13
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL6\tHSV 1/2 IGM CONFIRMATORY IFA\t9999999\t360677.4684\t84\t839999916\t8.40E+15\t79\t28493520\t1.17E+13
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t83\t829999917\t8.30E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tUBT\tUNIT BLOOD TYPE  \t9999999\t34084.61538\t83\t829999917\t8.30E+15\t39\t1329300\t92575184400
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9025R\tHLA-DR345 ALLELE 1\t9999999\t405846.506\t83\t829999917\t8.30E+15\t83\t33685260\t6.60E+13
+HISTOCOMPATIBILITY ORDERABLE\t5040244\t10170R\tHLA ORDER - NMDP ADDITIONAL SPECIMEN\tUNRDID\tUNRELATED DONOR ID\t9999999\t344572.9412\t83\t829999917\t8.30E+15\t85\t29288700\t3.69E+13
+LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tANTI-JO1 ANTIBODY\tJO1QUL\tANTI-JO1 RESULT\t9999999\t395222.2222\t83\t829999917\t8.30E+15\t81\t32013000\t1.65E+13
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tP190Q\tP190\t5662650.2\t539235.2113\t83\t469999966.6\t4.70E+15\t71\t38285700\t2.78E+13
+LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGG\tIGG, SERUM\t1092.674699\t117131.3924\t83\t90692\t128117920\t79\t9253380\t1.83E+12
+LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS1\tIGG SUBCLASS1\t704.8120482\t117131.3924\t83\t58499.4\t56405478.54\t79\t9253380\t1.83E+12
+LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS2\tIGG SUBCLASS2\t284.2445783\t117131.3924\t83\t23592.3\t9718185.21\t79\t9253380\t1.83E+12
+LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS3\tIGG SUBCLASS3\t41.47433735\t117131.3924\t83\t3442.37\t248968.7693\t79\t9253380\t1.83E+12
+LAB BLOOD ORDERABLES\t472617\tLABIGGSUB\tIGG  SUBCLASSES\tIGGS4\tIGG SUBCLASS4\t120536.0139\t117131.3924\t83\t10004489.15\t1.00E+14\t79\t9253380\t1.83E+12
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTMET\tMETHOD\t9999999\t1027142.927\t83\t829999917\t8.30E+15\t82\t84225720\t1.01E+14
+LAB BLOOD ORDERABLES\t473454\tLABYTOPIR\tTOPIRAMATE\tTOPIRAMATE\tTOPIRAMATE\t1566271.541\t159248.6598\t83\t130000537.9\t1.30E+15\t97\t15447120\t2.80E+12
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t361445.8447\t915206.4706\t83\t30000005.11\t3.00E+14\t68\t62234040\t6.72E+13
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t316267.8\t83\t829999917\t8.30E+15\t100\t31626780\t1.20E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVNO\tNOTES\t9999999\t240525.9898\t83\t829999917\t8.30E+15\t197\t47383620\t1.52E+13
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t15867.07317\t82\t819999918\t8.20E+15\t82\t1301100\t7.02E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADMFR\tLEAD MANUFACTURER\t9999999\t15867.07317\t82\t819999918\t8.20E+15\t82\t1301100\t7.02E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADMODEL\tLEAD MODEL\t8171335.28\t15867.07317\t82\t670049493\t6.70E+15\t82\t1301100\t7.02E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADTYPE\tLEAD TYPE\t9999999\t15867.07317\t82\t819999918\t8.20E+15\t82\t1301100\t7.02E+11
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t10460R\tRECIPIENT NAME\t9999999\t345286.0976\t82\t819999918\t8.20E+15\t82\t28313460\t2.00E+13
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t10461R\tRECIPIENT DOB\t9999999\t345286.0976\t82\t819999918\t8.20E+15\t82\t28313460\t2.00E+13
+LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tASPIGE\tASPERGILLUS IGE ABY\t6829270.973\t785686.9231\t82\t560000219.8\t5.60E+15\t78\t61283580\t6.62E+13
+LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tASPIGG\tASPERGILLUS IGG ABY\t9999999\t785686.9231\t82\t819999918\t8.20E+15\t78\t61283580\t6.62E+13
+LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tIGEASP\tTOTAL IGE\t854509.2721\t785686.9231\t82\t70069760.31\t7.00E+14\t78\t61283580\t6.62E+13
+LAB BLOOD ORDERABLES\t473347\tLABYSTFR\tSOLUBLE TRANSFERRIN RECEPTOR\tYSTFRR\tSOL TRANSFERR REC\t4.76097561\t153920.4545\t82\t390.4\t2782.86\t88\t13545000\t2.33E+12
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTCDX\tCLINICAL INDICATION\t9999999\t1027142.927\t82\t819999918\t8.20E+15\t82\t84225720\t1.01E+14
+VIROLOGY ORDERABLES\t1007409\tLABVZVQL\tVZV DNA PCR QUAL, NON LESION\tVZQLDN\tVZV DNA\t9999999\t192832.6829\t82\t819999918\t8.20E+15\t82\t15812280\t4.51E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t10462R\tRECIPIENT NAME\t9999999\t346332.5926\t81\t809999919\t8.10E+15\t81\t28052940\t1.99E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t10463R\tRECIPIENT DOB\t9999999\t346332.5926\t81\t809999919\t8.10E+15\t81\t28052940\t1.99E+13
+HISTOCOMPATIBILITY ORDERABLE\t5040244\t10170R\tHLA ORDER - NMDP ADDITIONAL SPECIMEN\tRINFO\tTO FIND RESULT:\t9999999\t344572.9412\t81\t809999919\t8.10E+15\t85\t29288700\t3.69E+13
+LAB BLOOD ORDERABLES\t472346\tLABFACT5\tFACTOR V ASSAY\tFACTOR5\tFACTOR V ASSAY\t987746.7654\t58751.25\t81\t80007488\t8.00E+14\t64\t3760080\t6.29E+11
+MICROBIOLOGY ORDERABLES\t471555\tLABAFBC\tAFB CULTURE, RESPIRATORY\tCULT\tCULT/OTHER RSLT:\t9999999\t3883162.201\t81\t809999919\t8.10E+15\t2681\t10410757860\t4.16E+16
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tXMR\tUNIT CROSSMATCH  \t9999999\t57912\t80\t799999920\t8.00E+15\t40\t2316480\t3.07E+11
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tDI\tISSUE DATE/TIME\t9999999\t29976.66667\t80\t799999920\t8.00E+15\t36\t1079160\t40769906400
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tPRO\tPRODUCT ID  \t9999999\t29561.53846\t80\t799999920\t8.00E+15\t39\t1152900\t1.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tSI\t<<STATUS>>  \t9999999\t30573.07087\t80\t799999920\t8.00E+15\t127\t3882780\t3.96E+11
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHAVINT\tINTERPRETATION\t9999999\t55606.13636\t80\t799999920\t8.00E+15\t1056\t58720080\t7.63E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tCCNT\tCELLS COUNTED\t52.725\t13686.92308\t80\t4218\t306198\t78\t1067580\t75368998800
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC5616A\tCD56+CD16+/CD3- ABS.\t1000112.575\t124748.3544\t80\t80009006\t8.00E+14\t79\t9855120\t1.87E+12
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tC5616P\tCD56+CD16+/CD3- %\t1000024.638\t124748.3544\t80\t80001971\t8.00E+14\t79\t9855120\t1.87E+12
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tUBT\tUNIT BLOOD TYPE  \t9999999\t34275\t77\t769999923\t7.70E+15\t36\t1233900\t87256652400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADSERNUM\tLEAD SERIAL NUMBER\t7478470.818\t16915.32468\t77\t575842253\t5.70E+15\t77\t1302480\t7.02E+11
+LAB BLOOD ORDERABLES\t473262\tLABRF\tRHUEMATOID FACTOR\tRHFT\tRHEUMATOID FACTOR LEVEL\t649419.2597\t285925.7143\t77\t50005283\t5.00E+14\t70\t20014800\t7.95E+12
+MICROBIOLOGY ORDERABLES\t473239\tLABRESP\tRESPIRATORY CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t187056.7953\t77\t769999923\t7.70E+15\t6247\t1168543800\t5.85E+14
+POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tGLU\tGLUCOSE,ISTAT\t219.012987\t5460\t77\t16864\t5418790\t27\t147420\t21538544400
+URINE ORDERABLES\t471511\tLABYU5HIA\t5 - OH-INDOLEACETIC AC, URINE\t5HIAAURN\t5-HIAA, URINE\t389633.6662\t372884.6512\t77\t30001792.3\t3.00E+14\t43\t16034040\t8.06E+12
+URINE ORDERABLES\t471511\tLABYU5HIA\t5 - OH-INDOLEACETIC AC, URINE\tVOLUMEURN\tURINE, VOLUME\t391741.3766\t372884.6512\t77\t30164086\t3.00E+14\t43\t16034040\t8.06E+12
+URINE ORDERABLES\t471511\tLABYU5HIA\t5 - OH-INDOLEACETIC AC, URINE\tYU5HUT\tCOLLECTION DURATION\t389633.4156\t372884.6512\t77\t30001773\t3.00E+14\t43\t16034040\t8.06E+12
+VIROLOGY ORDERABLES\t497325\tLABADVDNA\tADENOVIRUS DNA, QUALITATIVE PCR\tSOURC3\tADENOVIRUS SOURCE\t9999999\t174138\t77\t769999923\t7.70E+15\t140\t24379320\t5.10E+12
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tPRO\tPRODUCT ID  \t9999999\t31336.66667\t76\t759999924\t7.60E+15\t36\t1128120\t1.06E+11
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRP\tPRIMER\t9999999\t520905.7143\t76\t759999924\t7.60E+15\t21\t10939020\t8.12E+12
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASDX\tCLINICAL INDICATION\t9999999\t866627.1429\t76\t759999924\t7.60E+15\t14\t12132780\t1.36E+13
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t4655.526316\t76\t759999924\t7.60E+15\t76\t353820\t46852534800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t4655.526316\t76\t759999924\t7.60E+15\t76\t353820\t46852534800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t9084541.711\t4655.526316\t76\t690425170\t6.90E+15\t76\t353820\t46852534800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t4655.526316\t76\t759999924\t7.60E+15\t76\t353820\t46852534800
+LAB BLOOD ORDERABLES\t472451\tLAB11039R\tGBM AB\tGBMIGG\tGBM-IGG ELISA\t9999999\t201648.5714\t76\t759999924\t7.60E+15\t84\t16938480\t4.29E+12
+LAB BLOOD ORDERABLES\t472695\tLABYITRA\tITRACONAZOLE LEVEL\tYITR1\tITRACONAZOLE\t3552632.293\t367800.7692\t76\t270000054.3\t2.70E+15\t78\t28688460\t1.33E+13
+LAB BLOOD ORDERABLES\t472695\tLABYITRA\tITRACONAZOLE LEVEL\tYITR2\tHYDROXYITRACONAZOLE\t1710527.713\t367800.7692\t76\t130000106.2\t1.30E+15\t78\t28688460\t1.33E+13
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLCT\tC TYPE PHENOTYPE\t9999999\t125643.871\t76\t759999924\t7.60E+15\t31\t3894960\t4.18E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLET\tE TYPE PHENOTYPE\t9999999\t125643.871\t76\t759999924\t7.60E+15\t31\t3894960\t4.18E+12
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD19AB\tCD19 (PAN B), ABS.\t526415.1184\t31387.60563\t76\t40007549\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD19PC\tCD19 (PAN B) %\t526358.9342\t31387.60563\t76\t40003279\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD3AB\tCD3 (PAN T), ABS.\t526499.2105\t31387.60563\t76\t40013940\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD3PC\tCD3 (PAN T) %\t526356.4605\t31387.60563\t76\t40003091\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD5AB\tCD5 (PAN T), ABS.\t526492.25\t31387.60563\t76\t40013411\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD5PC\tCD5 (PAN T) %\t526352.8947\t31387.60563\t76\t40002820\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tLYM\tLYMPHOCYTES %\t526321.7645\t31387.60563\t76\t40000454.1\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tLYMAB\tLYMPHOCYTES, ABS.\t526622.9605\t31387.60563\t76\t40023345\t4.00E+14\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tWBC\tWBC COUNT\t532635.4737\t31387.60563\t76\t40480296\t4.00E+14\t71\t2228520\t87338577600
+MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t76\t759999924\t7.60E+15\t0\tNone\tNone
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tTRPHOS\tTRIPLE PHOSPH CRYST.\t9999999\t7957.894737\t76\t759999924\t7.60E+15\t57\t453600\t7252005600
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tXMR\tUNIT CROSSMATCH  \t9999999\t63768.94737\t75\t749999925\t7.50E+15\t38\t2423220\t3.67E+11
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9467R\tRECIPIENT CELL DATE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9468R\tRECIPIENT CELL SOURCE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9470R\tDONOR NAME\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9472R\tRELATIONSHIP TO PATIENT\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9473R\tDONOR CELL DATE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9474R\tDONOR CELL SOURCE\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9475R\tLOCUS 1\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9476R\tLOCUS 2\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9477R\tLOCUS 3\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9611R\tMETHOD\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9636R\tCHIMERISM TEST NAME\t9999999\t4147.297297\t75\t749999925\t7.50E+15\t74\t306900\t64653973200
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGGS\tB.HENSELAE IGG SCREEN\t9999999\t535699.6721\t75\t749999925\t7.50E+15\t122\t65355360\t1.49E+14
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGMS\tB.HENSELAE IGM SCREEN\t9999999\t535699.6721\t75\t749999925\t7.50E+15\t122\t65355360\t1.49E+14
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGGS\tB.QUINTANA IGG SCREEN\t9999999\t535699.6721\t75\t749999925\t7.50E+15\t122\t65355360\t1.49E+14
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11201R\tANNA-1\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11202R\tANNA-2\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11203R\tANNA-3\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11204R\tAGNA-1\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11205R\tPCA-1\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11206R\tPCA-2\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11207R\tPCA-TR\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11208R\tAMPHIPHYSIN AB\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11209R\tCRMP-5-IGG\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11210R\tP/Q -TYPE CALCIUM CHANNEL AB\t533333.2807\t766684.8\t75\t39999996.05\t4.00E+14\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11211R\tN-TYPE CALCIUM CHANNEL AB\t533333.2827\t766684.8\t75\t39999996.2\t4.00E+14\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11212R\tACHR GANGLIONIC NEURONAL AB\t533333.2849\t766684.8\t75\t39999996.37\t4.00E+14\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11213R\tNEURONAL (V-G) K+ CHANNEL AB\t533333.2853\t766684.8\t75\t39999996.4\t4.00E+14\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t533333.5083\t766684.8\t75\t40000013.12\t4.00E+14\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t766684.8\t75\t749999925\t7.50E+15\t75\t57501360\t5.29E+13
+VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS ANTIGEN, STOOL\tRTVR\tROTAVIRUS\t9999999\t54835.2\t75\t749999925\t7.50E+15\t75\t4112640\t7.01E+11
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tPRO\tPRODUCT ID  \t9999999\t23840.57143\t74\t739999926\t7.40E+15\t35\t834420\t53931488400
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9442R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t8209.459459\t74\t739999926\t7.40E+15\t74\t607500\t95812894800
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9478R\tLOCUS 4\t9999999\t4203.287671\t74\t739999926\t7.40E+15\t73\t306840\t64653969600
+LAB BLOOD ORDERABLES\t472750\tLABLUT\tLEUTEINIZING HORMONE\tLH\tLH\t2972980.226\t33812.8125\t74\t220000536.7\t2.20E+15\t64\t2164020\t1.28E+11
+POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tHCT\tHCT(CALC), ISTAT\t135167.7162\t5460\t74\t10002411\t1.00E+14\t27\t147420\t21538544400
+POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tHGB\tHGB(CALC), ISTAT\t135146.2068\t5460\t74\t10000819.3\t1.00E+14\t27\t147420\t21538544400
+POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tK\tPOTASSIUM, ISTAT\t4.466216216\t5460\t74\t330.5\t1613.13\t27\t147420\t21538544400
+POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tNA\tSODIUM, ISTAT\t138.8108108\t5460\t74\t10272\t1430152\t27\t147420\t21538544400
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tDI\tISSUE DATE/TIME\t9999999\t31589.18919\t73\t729999927\t7.30E+15\t37\t1168800\t48342340800
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASRE\tRESULT\t9999999\t866627.1429\t73\t729999927\t7.30E+15\t14\t12132780\t1.36E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9426R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t90203.84615\t73\t729999927\t7.30E+15\t78\t7035900\t2.67E+13
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHJKBT\tJKB PHENOTYPE\t9999999\t134304.8276\t73\t729999927\t7.30E+15\t29\t3894840\t4.18E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHKT\tK PHENOTYPE\t9999999\t129832\t73\t729999927\t7.30E+15\t30\t3894960\t4.18E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHST\tS PHENOTYPE\t9999999\t134304.8276\t73\t729999927\t7.30E+15\t29\t3894840\t4.18E+12
+LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tLAB2\tREFERENCE LAB\t9999999\t1896672.381\t73\t729999927\t7.30E+15\t63\t119490360\t1.36E+15
+LAB BLOOD ORDERABLES\t497264\tLABYPROIN\tPROINSULIN, PLASMA\tPROINSULIN\tPROINSULIN\t13.83972603\t401581.2\t73\t1010.3\t58089.29\t50\t20079060\t8.72E+12
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11201R\tANNA-1\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11202R\tANNA-2\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11203R\tANNA-3\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11204R\tAGNA-1\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11205R\tPCA-1\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11206R\tPCA-2\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11207R\tPCA-TR\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11208R\tAMPHIPHYSIN AB\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11209R\tCRMP-5-IGG\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11210R\tP/Q -TYPE CALCIUM CHANNEL AB\t410958.8692\t915206.4706\t73\t29999997.45\t3.00E+14\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11211R\tN-TYPE CALCIUM CHANNEL AB\t410958.8638\t915206.4706\t73\t29999997.06\t3.00E+14\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11212R\tACHR GANGLIONIC NEURONAL AB\t410958.8652\t915206.4706\t73\t29999997.16\t3.00E+14\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11213R\tNEURONAL (V-G) K+ CHANNEL AB\t410958.8864\t915206.4706\t73\t29999998.71\t3.00E+14\t68\t62234040\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t915206.4706\t73\t729999927\t7.30E+15\t68\t62234040\t6.72E+13
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tRPT\tRPT\t9999999\t255332.1429\t73\t729999927\t7.30E+15\t56\t14298600\t7.49E+12
+URINE ORDERABLES\t472936\tLABUMYOSC\tMYOGLOBIN SCREEN, URINE\tUMYOG\tMYOGLOBIN SCREEN, URINE\t9999999\t13368\t73\t729999927\t7.30E+15\t25\t334200\t15908688000
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tSI\t<<STATUS>>  \t9999999\t33532.84404\t72\t719999928\t7.20E+15\t109\t3655080\t4.10E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t7984302.903\t4509.166667\t72\t574869809\t5.70E+15\t72\t324660\t46405868400
+LAB BLOOD ORDERABLES\t471578\tLABYA1APH\tALPHA - 1 - ANTITRYPSIN PHENOTYPE\tA1APHEN\tA-1-A PHENOTYPE\t9999999\t396974.1176\t72\t719999928\t7.20E+15\t68\t26994240\t1.93E+13
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34AB\tCD34, ABS.\t2361179.208\t25604.54545\t72\t170004903\t1.70E+15\t66\t1689900\t1.43E+11
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34PC\t%CD34,BLOOD\t2361111.414\t25604.54545\t72\t170000021.8\t1.70E+15\t66\t1689900\t1.43E+11
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tWBC\tWBC COUNT\t1537813.736\t25604.54545\t72\t110722589\t1.10E+15\t66\t1689900\t1.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tCHOL\tCHOLESTEROL, TOTAL\t153.0555556\t79623.67347\t72\t11020\t1971396\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t277781.6542\t79623.67347\t72\t20000279.1\t2.00E+14\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tHDL\tHDL CHOLESTEROL\t277818.9167\t79623.67347\t72\t20002962\t2.00E+14\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tHMCY\tHOMOCYSTEINE, PLAS TOTAL\t9.963888889\t79623.67347\t72\t717.4\t7904.7\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tHSCRP\tHIGH SENSITIVITY CRP\t38.67361111\t79623.67347\t72\t2784.5\t398150.73\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tLDL\tLDL (CALCULATED)\t416749.0278\t79623.67347\t72\t30005930\t3.00E+14\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tLDLHDL\tLDL/HDL RATIO\t416668.8222\t79623.67347\t72\t30000155.2\t3.00E+14\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tLPA\tLIPOPROTEIN A\t1111141.742\t79623.67347\t72\t80002205.41\t8.00E+14\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tNHDL\tNON-HDL CHOL, CALC\t277884.3333\t79623.67347\t72\t20007672\t2.00E+14\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tTGL\tTRIGLYCERIDE, SER/PLAS\t126.3611111\t79623.67347\t72\t9098\t1982538\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHCT\tC PHENOTYPE\t9999999\t134306.8966\t72\t719999928\t7.20E+15\t29\t3894900\t4.18E+12
+MICROBIOLOGY ORDERABLES\t473802\tLABWDC\tWOUND CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t315224.0375\t72\t719999928\t7.20E+15\t1387\t437215740\t3.30E+14
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t71\t709999929\t7.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tDI\tISSUE DATE/TIME\t9999999\t37891.76471\t71\t709999929\t7.10E+15\t34\t1288320\t92169662400
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHET\tE PHENOTYPE\t9999999\t125643.871\t71\t709999929\t7.10E+15\t31\t3894960\t4.18E+12
+LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tFACTOR7\tFACTOR VIII ASSAY\t563581.2775\t62610.90909\t71\t40014270.7\t4.00E+14\t66\t4132320\t3.37E+11
+LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tRIST\tRISTOCETIN CO-FACTOR\t1831106.873\t62610.90909\t71\t130008588\t1.30E+15\t66\t4132320\t3.37E+11
+LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tVWAG\tVON WILLEBRAND ANTIGEN\t422724.2817\t62610.90909\t71\t30013424\t3.00E+14\t66\t4132320\t3.37E+11
+LAB BLOOD ORDERABLES\t497404\tLABYLEVT\tLEVETIRACETAM\tYLEVT1\tLEVETIRACETAM\t1408474.237\t183651.6\t71\t100001670.8\t1.00E+15\t100\t18365160\t3.69E+12
+LAB BLOOD ORDERABLES\t670774\tLAB11250R\tHHV-8 DNA, QUANT\t11252R\tHHV-8 DNA,QUANT\t7079027.507\t195322.7848\t71\t502610953\t4.96E+15\t79\t15430500\t3.54E+12
+POINT OF CARE TESTING\t472685\tLABPCEC4\tISTAT EC4, ARTERIAL\tPCCOM\tPOCT COMMENT\t9999999\t5460\t71\t709999929\t7.10E+15\t27\t147420\t21538544400
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1M\tNEURAMINIDASE  H275Y MUTATION\t9999999\t259084.5\t71\t709999929\t7.10E+15\t40\t10363380\t6.39E+12
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1MT\tMETHOD\t9999999\t259084.5\t71\t709999929\t7.10E+15\t40\t10363380\t6.39E+12
+LAB BLOOD ORDERABLES\t472424\tLABYFRUCM\tFRUCTOSAMINE\tFRUCTM\tFRUCTOSAMINE\t143118.1\t171204.507\t70\t10018267\t1.00E+14\t71\t12155520\t2.39E+12
+LAB BLOOD ORDERABLES\t472750\tLABLUT\tLUTEINIZING HORMONE\tLH\tLH\t2000006.393\t37734\t70\t140000447.5\t1.40E+15\t70\t2641380\t1.62E+11
+LAB BLOOD ORDERABLES\t473758\tLABVISC\tVISCOSITY, SERUM\tVISCOS\tVISCOSITY\t571431.2857\t79914.28571\t70\t40000190\t4.00E+14\t63\t5034600\t8.00E+11
+LAB BLOOD ORDERABLES\t497053\tLABRFRPT\tREFERENCE REPORT\tREFRPT\tREFERENCE REPORT\t9999999\t24933.38028\t70\t699999930\t7.00E+15\t994\t24783780\t3.05E+13
+URINE ORDERABLES\t472019\tLABUCL\tCHLORIDE WITH CREATININE, URINE (RANDOM)\tUCL\tCHLORIDE, URINE\t142926.5857\t11498.70968\t70\t10004861\t1.00E+14\t31\t356460\t20918552400
+URINE ORDERABLES\t472019\tLABUCL\tCHLORIDE WITH CREATININE, URINE (RANDOM)\tUCLCR\tCHLORIDE/CREAT RATIO\t5571428.18\t11498.70968\t70\t389999972.6\t3.90E+15\t31\t356460\t20918552400
+URINE ORDERABLES\t472019\tLABUCL\tCHLORIDE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t142941.5829\t11498.70968\t70\t10005910.8\t1.00E+14\t31\t356460\t20918552400
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tDI\tISSUE DATE/TIME\t9999999\t26220\t69\t689999931\t6.90E+15\t32\t839040\t27380001600
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tUBT\tUNIT BLOOD TYPE  \t9999999\t32080\t69\t689999931\t6.90E+15\t33\t1058640\t53302039200
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tPRO\tPRODUCT ID  \t9999999\t23185.45455\t69\t689999931\t6.90E+15\t33\t765120\t28925913600
+HISTOCOMPATIBILITY ORDERABLE\t706283\t10000R\tHLA ORDER - TYPING - SOLID ORGAN EVALUATION\t10049R\tHLA LAB TESTING STATUS\t9999999\t736702.1918\t69\t689999931\t6.90E+15\t73\t53779260\t6.97E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t63540.24819\t69\t689999931\t6.90E+15\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472392\tLABYFLUZ\tFLUCONAZOLE LEVEL\tFLUCOZ\tFLUCONAZOLE\t724664.5522\t462441.9512\t69\t50001854.1\t5.00E+14\t82\t37920240\t2.22E+13
+LAB BLOOD ORDERABLES\t472980\tLAB11086R\tNORTRIPTYLINE LEVEL\tNORTRP\tNORTRIPTYLINE, PLASMA\t1594278.507\t291875.2941\t69\t110005217\t1.10E+15\t68\t19847520\t8.88E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHJKAT\tJKA PHENOTYPE\t9999999\t139101.4286\t69\t689999931\t6.90E+15\t28\t3894840\t4.18E+12
+POINT OF CARE (NON LAB) TESTS\t507567\tPOC2\tPOC URINE DIPSTICK FOR BLOOD\tPHLOC\tLOCATION\t9999999\t-100367.9032\t69\t689999931\t6.90E+15\t124\t-12445620\t5.85E+12
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tSI\t<<STATUS>>  \t9999999\t29978.70968\t68\t679999932\t6.80E+15\t93\t2788020\t2.99E+11
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tXMR\tUNIT CROSSMATCH  \t9999999\t56156.25\t68\t679999932\t6.80E+15\t32\t1797000\t2.67E+11
+LAB BLOOD ORDERABLES\t472050\tLABCDPLI\tCOLD AGGLUTININ SCREEN\tCDPL\tCOLD ANTIBODY SCREEN\t9999999\t103556.5289\t68\t679999932\t6.80E+15\t121\t12530340\t6.01E+12
+LAB BLOOD ORDERABLES\t472762\tLAB11045R\tLKM AB\tLKMAB\tLIV-KID-MICROSOME AB\t9999999\t199033.7838\t68\t679999932\t6.80E+15\t74\t14728500\t3.10E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLST\tS TYPE PHENOTYPE\t9999999\t134304.8276\t68\t679999932\t6.80E+15\t29\t3894840\t4.18E+12
+LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 PROFILE\tADMACT\tADAMTS-13 ACTIVITY\t1764751.279\t206821.875\t68\t120003087\t1.20E+15\t64\t13236600\t4.74E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGDNM3M\tDNMT3A, BM\t9999999\t819690\t68\t679999932\t6.80E+15\t68\t55738920\t5.54E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGERHRM\tERBB2/HER2, BM\t9999999\t819690\t68\t679999932\t6.80E+15\t68\t55738920\t5.54E+13
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMDX\tCLINICAL INDICATION\t9999999\t586220.2941\t68\t679999932\t6.80E+15\t68\t39862980\t2.74E+13
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMME\tMETHOD\t9999999\t586220.2941\t68\t679999932\t6.80E+15\t68\t39862980\t2.74E+13
+VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV AB IGG\tCMV\tCMV ANTIBODY\t9999999\t162284.7273\t68\t679999932\t6.80E+15\t55\t8925660\t1.66E+12
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tVL\tPRODUCT VOLUME\t173.3731343\t25536.52174\t67\t11616\t2140484\t23\t587340\t21414358800
+LAB BLOOD ORDERABLES\t471817\tLAB11077R\tCALCITONIN\tYCALCI\tCALCITONIN, SERUM\t8059733.913\t149039.0625\t67\t540002172.2\t5.40E+15\t64\t9538500\t1.70E+12
+LAB BLOOD ORDERABLES\t472344\tLABFACT2\tFACTOR II ASSAY\tFACTOR2\tFACTOR II ASSAY\t1044835.582\t39081.42857\t67\t70003984\t7.00E+14\t56\t2188560\t1.28E+11
+LAB BLOOD ORDERABLES\t472573\tLAB11136R\tHTLV AB\tHTLVS\tHTLV AB SCREEN\t9999999\t260332.9032\t67\t669999933\t6.70E+15\t279\t72632880\t3.01E+13
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHP1T\tP1 PHENOTYPE\t9999999\t105406.6667\t67\t669999933\t6.70E+15\t27\t2845980\t3.08E+12
+LAB BLOOD ORDERABLES\t541695\tLABDSDNA\tANTI - DSDNA AB\tDNAQNT\tANTI-DSDNA TITER\t9999999\t404045\t67\t669999933\t6.70E+15\t72\t29091240\t1.26E+13
+MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tRPT\tRPT\t9999999\t313706.25\t67\t669999933\t6.70E+15\t32\t10038600\t5.00E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tYUHYM2\tLEAD, U\t9104476.955\t211842.8571\t67\t609999956\t6.10E+15\t42\t8897400\t2.27E+12
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t66\t659999934\t6.60E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tSI\t<<STATUS>>  \t9999999\t32158.26923\t66\t659999934\t6.60E+15\t104\t3344460\t3.42E+11
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tUBT\tUNIT BLOOD TYPE  \t9999999\t29945.80645\t66\t659999934\t6.60E+15\t31\t928320\t49220258400
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCINT\tINTERPRETATION\t9999999\t56970.32432\t66\t659999934\t6.60E+15\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHFYAT\tFYA PHENOTYPE\t9999999\t144251.1111\t66\t659999934\t6.60E+15\t27\t3894780\t4.18E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHFYBT\tFYB PHENOTYPE\t9999999\t139099.2857\t66\t659999934\t6.60E+15\t28\t3894780\t4.18E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLEAT\tLEA PHENOTYPE\t9999999\t109428.4615\t66\t659999934\t6.60E+15\t26\t2845140\t3.08E+12
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHMT\tM PHENOTYPE\t9999999\t109458.4615\t66\t659999934\t6.60E+15\t26\t2845920\t3.08E+12
+LAB BLOOD ORDERABLES\t473443\tLABTHG\tTHYROGLOBULIN\tATGA\tANTI-THYROGLOBULIN ABY\t8030370.43\t92540\t66\t530004448.4\t5.30E+15\t63\t5830020\t1.13E+12
+LAB BLOOD ORDERABLES\t473443\tLABTHG\tTHYROGLOBULIN\tTG\tTHYROGLOBULIN (TG)\t910102.3561\t92540\t66\t60066755.5\t6.00E+14\t63\t5830020\t1.13E+12
+LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 PROFILE\tADMINH\tADAMTS-13 INHIBITOR\t16.21212121\t202867.7419\t66\t1070\t43674\t62\t12577800\t4.47E+12
+MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tSDES\tSPECIMEN\t9999999\t313706.25\t66\t659999934\t6.60E+15\t32\t10038600\t5.00E+12
+MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tSREQ\tCOMMENTS\t9999999\t313706.25\t66\t659999934\t6.60E+15\t32\t10038600\t5.00E+12
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tRPT\tRPT\t9999999\t407791.8182\t66\t659999934\t6.60E+15\t66\t26914260\t3.09E+13
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMRE\tRESULT\t9999999\t586220.2941\t66\t659999934\t6.60E+15\t68\t39862980\t2.74E+13
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPDX\tCLINICAL INDICATION\t9999999\t540720\t66\t659999934\t6.60E+15\t66\t35687520\t2.44E+13
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPME\tMETHOD\t9999999\t540720\t66\t659999934\t6.60E+15\t66\t35687520\t2.44E+13
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t11896R\tARSENIC,U\t8636367.894\t208549.7561\t66\t570000281\t5.70E+15\t41\t8550540\t2.14E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tYUHYM6\tTOTAL VOLUME\t1819921.759\t208549.7561\t66\t120114836.1\t1.20E+15\t41\t8550540\t2.14E+12
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t12002R\tVCA IGM ,IFA\t9999999\t232692.1875\t66\t659999934\t6.60E+15\t64\t14892300\t3.97E+12
+VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXGQT\tIGG LEVEL\t7272746.03\t176202.7273\t66\t480001238\t4.80E+15\t66\t11629380\t2.68E+12
+VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHHV-6 DNA PCR QUAL\tHV6QLD\tHHV-6 DNA\t9999999\t259790\t66\t659999934\t6.60E+15\t66\t17146140\t1.58E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSCI\tCLINICAL INDICATION\t9999999\t912125.8065\t65\t649999935\t6.50E+15\t62\t56551800\t6.06E+13
+HISTOCOMPATIBILITY ORDERABLE\t506864\t10235R\tHLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING\t10049R\tHLA LAB TESTING STATUS\t9999999\t584445.8824\t65\t649999935\t6.50E+15\t68\t39742320\t4.86E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tLUPINT\tLUPUS INTERPRETATION\t9999999\t318547.2289\t65\t649999935\t6.50E+15\t249\t79318260\t3.18E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tFIO2\tFIO2 (FIO2)\t9999999\t2600\t65\t649999935\t6.50E+15\t24\t62400\t662846400
+LAB BLOOD ORDERABLES\t472528\tLABHEPC\tHEPATITIS C AB EIA\tHEPCAB\tHEPATITIS C AB\t9999999\t60629.0625\t65\t649999935\t6.50E+15\t64\t3880260\t3.44E+11
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHNT\tN PHENOTYPE\t9999999\t113803.2\t65\t649999935\t6.50E+15\t25\t2845080\t3.08E+12
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD4AB\tCD3+/CD4+, ABS.\t461609.7538\t29515\t65\t30004634\t3.00E+14\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD4PC\tCD3+/CD4+ %\t461553.4308\t29515\t65\t30000973\t3.00E+14\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD8AB\tCD3+/CD8+, ABS.\t461648.1846\t29515\t65\t30007132\t3.00E+14\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tCD8PC\tCD3+/CD8+ %\t461561.7846\t29515\t65\t30001516\t3.00E+14\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tHNS\tCD4+CD8 TOTAL, CD3+%\t461576.8\t29515\t65\t30002492\t3.00E+14\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t461719.4615\t29515\t65\t30011765\t3.00E+14\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tHSRATIO\tCD4/CD8 RATIO, CD3+\t1538462.225\t29515\t65\t100000044.6\t1.00E+15\t60\t1770900\t62775565200
+LAB BLOOD ORDERABLES\t497339\tLABDFA\tDENGUE FEVER AB\tDFIGG\tDENGUE FEVER AB IGG\t153848.1678\t264945\t65\t10000130.91\t1.00E+14\t64\t16956480\t5.23E+12
+LAB BLOOD ORDERABLES\t497339\tLABDFA\tDENGUE FEVER AB\tDFIGM\tDENGUE FEVER AB IGM\t769231.6911\t264945\t65\t50000059.92\t5.00E+14\t64\t16956480\t5.23E+12
+LAB BLOOD ORDERABLES\t497472\tLABYJCPCR\tJC VIRUS DNA, PCR\tSOURC8\tSOURCE\t9999999\t164540.625\t65\t649999935\t6.50E+15\t96\t15795900\t3.74E+12
+LAB BLOOD ORDERABLES\t590577\tLABTOTALC\tPROTEIN C AG, TOTAL\tPROTCAGT\tPROTEIN C AG, TOTAL\t615433.4769\t216992.5\t65\t40003176\t4.00E+14\t24\t5207820\t1.78E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tYUHYM5\tCOLLECTION DURATION\t1692327.462\t208549.7561\t65\t110001285\t1.10E+15\t41\t8550540\t2.14E+12
+BODY FLUID ORDERABLES\t541704\tLABFCHOLA\tCHOLESTEROL, FLUID\tCHOLFLD\tCHOLESTEROL BODY FLUID\t312555.0781\t266890.9091\t64\t20003525\t2.00E+14\t44\t11743200\t6.07E+12
+LAB BLOOD ORDERABLES\t471578\tLABYA1APH\tALPHA - 1 - ANTITRYPSIN PHENOTYPE\tYA1A\tALPHA 1 ANTITRYPSIN\t153.734375\t396927.2727\t64\t9839\t1797801\t66\t26197200\t1.89E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tO2SV\tO2SV\t9999999\t8114.693878\t64\t639999936\t6.40E+15\t49\t397620\t10291518000
+LAB BLOOD ORDERABLES\t473212\tLABRBCPH\tRBC PHENOTYPE\tPHLEBT\tLEB PHENOTYPE\t9999999\t109428.4615\t64\t639999936\t6.40E+15\t26\t2845140\t3.08E+12
+LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP10\tCOXSACKIE A10 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
+LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP4\tCOXSACKIE A4 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
+LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP7\tCOXSACKIE A7 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
+LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP9\tCOXSACKIE A9 AB\t9999999\t315617.7049\t64\t639999936\t6.40E+15\t61\t19252680\t6.96E+12
+URINE ORDERABLES\t472507\tLABUHEMO\tHEMOSIDERIN, URINE\tUHEMO\tHEMOSIDERIN, URINE\t9999999\t64313.61702\t64\t639999936\t6.40E+15\t47\t3022740\t3.39E+11
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t12001R\tVCA IGG ,IFA\t9999999\t232692.1875\t64\t639999936\t6.40E+15\t64\t14892300\t3.97E+12
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9495R\t% DONOR- BLOOD WHOLE\t76.84126984\t4024.83871\t63\t4841\t448019\t62\t249540\t61404951600
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9535R\t% RECIPIENT- BLOOD WHOLE\t18.38095238\t4024.83871\t63\t1158\t80724\t62\t249540\t61404951600
+LAB BLOOD ORDERABLES\t471691\tLAB11101R\tVITAMIN C\tYVITC\tASCORBIC ACID, PL.\t1587302.043\t353730\t63\t100000028.7\t1.00E+15\t62\t21931260\t8.82E+12
+LAB BLOOD ORDERABLES\t472353\tLABFACT11\tFACTOR XI ASSAY\tFACTOR11\tFACTOR XI ASSAY\t952462.8413\t51142.62295\t63\t60005159\t6.00E+14\t61\t3119700\t2.24E+11
+LAB BLOOD ORDERABLES\t472932\tLABYMPA\tMYCOPHENOLIC ACID\tMPACID\tMYCOPHENOLIC ACID\t2063493.563\t192470\t63\t130000094.5\t1.30E+15\t54\t10393380\t2.28E+12
+LAB BLOOD ORDERABLES\t472932\tLABYMPA\tMYCOPHENOLIC ACID\tYMPAG\tMPA GLUCURONIDE\t634977.9683\t192470\t63\t40003612\t4.00E+14\t54\t10393380\t2.28E+12
+LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP2\tCOXSACKIE A2 AB\t9999999\t315617.7049\t63\t629999937\t6.30E+15\t61\t19252680\t6.96E+12
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPRE\tRESULT, BM\t9999999\t540720\t63\t629999937\t6.30E+15\t66\t35687520\t2.44E+13
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tSI\t<<STATUS>>  \t9999999\t33721.36364\t62\t619999938\t6.20E+15\t88\t2967480\t3.78E+11
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tUBT\tUNIT BLOOD TYPE  \t9999999\t31464\t62\t619999938\t6.20E+15\t30\t943920\t67138092000
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tPRO\tPRODUCT ID  \t9999999\t26370\t62\t619999938\t6.20E+15\t28\t738360\t56790936000
+BODY FLUID ORDERABLES\t471626\tLAB11028R\tACE, CSF\tACE\tANGIOTENSIN COVERT ENZ, CSF\t483872.4\t261800.4545\t62\t30000088.8\t3.00E+14\t44\t11519220\t3.29E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t71.53225806\t50638.06452\t62\t4435\t333725\t62\t3139560\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t50638.06452\t62\t619999938\t6.20E+15\t62\t3139560\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t50638.06452\t62\t619999938\t6.20E+15\t62\t3139560\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t50638.06452\t62\t619999938\t6.20E+15\t62\t3139560\t3.67E+12
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9632R\tSERUM DESCRIPTION\t9999999\t94819.35484\t62\t619999938\t6.20E+15\t62\t5878800\t2.58E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9413R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t86895.2381\t62\t619999938\t6.20E+15\t63\t5474400\t2.57E+13
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tRPRQ\tRESULT\t9999999\t286934.6939\t62\t619999938\t6.20E+15\t49\t14059800\t4.87E+12
+LAB BLOOD ORDERABLES\t472350\tLABF8INH\tFACTOR VIII INHIBITOR HUMAN\tF8INH\tFACTOR VIII INHIBITOR, HUMAN\t3387103.939\t53803.84615\t62\t210000444.2\t2.10E+15\t52\t2797800\t2.09E+11
+LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tLEAD\tLEAD\t5161291.565\t382240\t62\t320000077\t3.20E+15\t51\t19494240\t3.20E+13
+LAB BLOOD ORDERABLES\t472539\tLABHGBSQ\tHGB S QUANTITATIVE\tHGBS\tHGB S, QUANT\t1451643.848\t352569.0566\t62\t90001918.6\t9.00E+14\t53\t18686160\t1.07E+13
+LAB BLOOD ORDERABLES\t472643\tLABMONOSC\tINFECTIOUS MONO SCREEN\tMONOSC\tINFECTIOUS MONO SCREEN\t9999999\t8924.4\t62\t619999938\t6.20E+15\t50\t446220\t6068185200
+LAB BLOOD ORDERABLES\t473296\tLABYSES\tSELENIUM, SERUM\tSELENIUM\tSELENIUM\t322654.371\t194726.6667\t62\t20004571\t2.00E+14\t63\t12267780\t5.63E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tMERURN\tMERCURY, URINE\t8709676.919\t211842.8571\t62\t539999969\t5.40E+15\t42\t8897400\t2.27E+12
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tDI\tISSUE DATE/TIME\t9999999\t27138\t61\t609999939\t6.10E+15\t30\t814140\t27220532400
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tPRO\tPRODUCT ID  \t9999999\t25982.22222\t61\t609999939\t6.10E+15\t27\t701520\t55275112800
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t304.4606557\t51447.54098\t61\t18572.1\t5698626.17\t61\t3138300\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.493032787\t51468.19672\t61\t152.075\t410.783125\t61\t3139560\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.472622951\t51468.19672\t61\t28.83\t15.9609\t61\t3139560\t3.67E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t284733.75\t61\t609999939\t6.10E+15\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t284733.75\t61\t609999939\t6.10E+15\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tARSENIC\tARSENIC\t9999999\t382240\t61\t609999939\t6.10E+15\t51\t19494240\t3.20E+13
+LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tCADMIUM\tCADMIUM\t3114754.259\t382240\t61\t190000009.8\t1.90E+15\t51\t19494240\t3.20E+13
+LAB BLOOD ORDERABLES\t541650\tLABARGI\tABO/RH\tARG\tABO/RH (AUTOMATION)\t9999999\t21313.09091\t61\t609999939\t6.10E+15\t55\t1172220\t39266269200
+VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tRPT\tRPT\t9999999\t1188540.984\t61\t609999939\t6.10E+15\t61\t72501000\t1.27E+14
+VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tSDES11\tSPECIMEN\t9999999\t175252.9412\t61\t609999939\t6.10E+15\t102\t17875800\t3.71E+12
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRR\tRESULT\t9999999\t520905.7143\t60\t599999940\t6.00E+15\t21\t10939020\t8.12E+12
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSLYM\tLYMPHOCYTES, SYN FLD\t1000020.85\t2907.692308\t60\t60001251\t6.00E+14\t39\t113400\t575942400
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSMONO\tMONOS/MESOS, SYN FLD\t1000026.083\t2907.692308\t60\t60001565\t6.00E+14\t39\t113400\t575942400
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSNEUT\tNEUTROPHILS, SYN FLD\t1000041.45\t2907.692308\t60\t60002487\t6.00E+14\t39\t113400\t575942400
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t52331\t60\t599999940\t6.00E+15\t60\t3139860\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t52331\t60\t599999940\t6.00E+15\t60\t3139860\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t52331\t60\t599999940\t6.00E+15\t60\t3139860\t3.67E+12
+LAB BLOOD ORDERABLES\t471615\tLABYAMISO\tAMYLASE, PANCREATIC\tAMYLASE\tAMYLASE,PANCREATIC,S\t666726.5333\t181097.6923\t60\t40003592\t4.00E+14\t78\t14125620\t3.04E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMV\tCMV ANTIBODY\t9999999\t289715.7447\t60\t599999940\t6.00E+15\t47\t13616640\t4.69E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t289715.7447\t60\t599999940\t6.00E+15\t47\t13616640\t4.69E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t284733.75\t60\t599999940\t6.00E+15\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t284733.75\t60\t599999940\t6.00E+15\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t284733.75\t60\t599999940\t6.00E+15\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t289715.7447\t60\t599999940\t6.00E+15\t47\t13616640\t4.69E+12
+LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBPO2\tDATE & TIME OF LAST DOSE\t9999999\t12322.37288\t60\t599999940\t6.00E+15\t59\t727020\t28069736400
+LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBPO3\tDOSAGE\t9999999\t12322.37288\t60\t599999940\t6.00E+15\t59\t727020\t28069736400
+LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBPO4\tADMINISTRATION ROUTE\t9999999\t12322.37288\t60\t599999940\t6.00E+15\t59\t727020\t28069736400
+LAB BLOOD ORDERABLES\t497516\tLABTOBPOL\tTOBRAMYCIN PEAK LEVEL\tTOBRAPEAK\tTOBRAMYCIN, PEAK\t666675.3817\t12322.37288\t60\t40000522.9\t4.00E+14\t59\t727020\t28069736400
+URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tDOPA\tDOPAMINE\t500212.0017\t352613.1429\t60\t30012720.1\t3.00E+14\t35\t12341460\t4.86E+12
+URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tNOREPH\tNOREPHINEPHRINE\t500090.16\t352613.1429\t60\t30005409.6\t3.00E+14\t35\t12341460\t4.86E+12
+URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tVOLUMEURN\tURINE VOLUME\t502361.6\t352613.1429\t60\t30141696\t3.00E+14\t35\t12341460\t4.86E+12
+URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tYUFCUT\tCOLLECTION DURATION\t500022.5\t352613.1429\t60\t30001350\t3.00E+14\t35\t12341460\t4.86E+12
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1R\t2009 INFLUENZA A(H1N1) RNA\t9999999\t259084.5\t60\t599999940\t6.00E+15\t40\t10363380\t6.39E+12
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGAPC\tAPC\t9999999\t913085\t59\t589999941\t5.90E+15\t60\t54785100\t5.82E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGBRAF\tBRAF\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGIDH1\tIDH1\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGNOTCH\tNOTCH1\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGPTEN\tPTEN\t9999999\t934668.2143\t59\t589999941\t5.90E+15\t56\t52341420\t5.63E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t52161.35593\t59\t589999941\t5.90E+15\t59\t3077520\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.46440678\t53127.45763\t59\t145.4\t414.185\t59\t3134520\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.77\t53127.45763\t59\t45.43\t46.4009\t59\t3134520\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t6195915.542\t52161.35593\t59\t365559017\t3.51E+15\t59\t3077520\t3.67E+12
+LAB BLOOD ORDERABLES\t472438\tLAB11079R\tGABAPENTIN\tGABAP\tGABAPENTIN, SERUM/PLASMA\t1016955.253\t190582.5\t59\t60000359.9\t6.00E+14\t56\t10672620\t2.34E+12
+LAB BLOOD ORDERABLES\t977673\tLABPANHCT\tHEMATOCRIT\tHCT\tHEMATOCRIT\t339001.8898\t994.5762712\t59\t20001111.5\t2.00E+14\t59\t58680\t283608000
+MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tRPT\tRPT\t9999999\t153813.4884\t59\t589999941\t5.90E+15\t43\t6613980\t1.34E+12
+MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tSDES\tSPECIMEN\t9999999\t153813.4884\t59\t589999941\t5.90E+15\t43\t6613980\t1.34E+12
+URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tEPINEP\tEPINEPHRINE\t1694922.52\t352613.1429\t59\t100000428.7\t1.00E+15\t35\t12341460\t4.86E+12
+VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tSDES\tSPECIMEN\t9999999\t1188540.984\t59\t589999941\t5.90E+15\t61\t72501000\t1.27E+14
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tXMR\tUNIT CROSSMATCH  \t9999999\t76988.88889\t58\t579999942\t5.80E+15\t27\t2078700\t3.48E+11
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tSI\t<<STATUS>>  \t9999999\t33428.46154\t58\t579999942\t5.80E+15\t78\t2607420\t3.05E+11
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tSI\t<<STATUS>>  \t9999999\t28782.16216\t58\t579999942\t5.80E+15\t74\t2129880\t2.07E+11
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGCTNN\tCTNNB1\t9999999\t934668.2143\t58\t579999942\t5.80E+15\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGIDH2\tIDH2\t9999999\t934668.2143\t58\t579999942\t5.80E+15\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGPIK3\tPIK3CA\t9999999\t934668.2143\t58\t579999942\t5.80E+15\t56\t52341420\t5.63E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t548.4137931\t52539.31034\t58\t31808\t37129654\t58\t3047280\t3.66E+12
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRCM\tCOMMENT\t9999999\t595980.5\t58\t579999942\t5.80E+15\t120\t71517660\t5.54E+13
+LAB BLOOD ORDERABLES\t472735\tLABYLEADB\tLEAD, BLOOD\tLEA\tLEAD, BLD\t7931034.345\t215660.3774\t58\t459999992\t4.60E+15\t53\t11430000\t3.30E+12
+LAB BLOOD ORDERABLES\t473160\tLAB11085R\tMPO AB\tMPO\tMYELOPEROXIDASE AB\t9137930.452\t157796.3265\t58\t529999966.2\t5.30E+15\t49\t7732020\t1.43E+12
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTRES\tRESULTS\t9999999\t1027142.927\t58\t579999942\t5.80E+15\t82\t84225720\t1.01E+14
+LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMAL\tMALARIA PREP RESULT\t9999999\t164240.9524\t58\t579999942\t5.80E+15\t63\t10347180\t3.66E+13
+VIROLOGY ORDERABLES\t472569\tLABHSVM\tHSV 1 AND 2 IGM\tHSV1M\tHSV 1 IGM, IFA\t9999999\t282932.3077\t58\t579999942\t5.80E+15\t52\t14712480\t4.92E+12
+VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tSREQ\tCOMMENTS\t9999999\t1188540.984\t58\t579999942\t5.80E+15\t61\t72501000\t1.27E+14
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tSI\t<<STATUS>>  \t9999999\t34026.66667\t57\t569999943\t5.70E+15\t81\t2756160\t3.01E+11
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tUBT\tUNIT BLOOD TYPE  \t9999999\t42686.66667\t57\t569999943\t5.70E+15\t27\t1152540\t1.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tPRO\tPRODUCT ID  \t9999999\t30282.22222\t57\t569999943\t5.70E+15\t27\t817620\t69801476400
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGNRAS\tNRAS\t9999999\t934668.2143\t57\t569999943\t5.70E+15\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGTP53\tTP53\t9999999\t934668.2143\t57\t569999943\t5.70E+15\t56\t52341420\t5.63E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDRHYTHM\tICD RHYTHM\t9999999\t50418.94737\t57\t569999943\t5.70E+15\t57\t2873880\t3.60E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2280701.81\t54777.89474\t57\t130000003.2\t1.30E+15\t57\t3122340\t3.67E+12
+LAB BLOOD ORDERABLES\t497432\tLABFHTLVQ\tHTLV-1/2  DNA QUALITATIVE\tHTLI\tHTLV-I DNA\t9999999\t244110.566\t57\t569999943\t5.70E+15\t53\t12937860\t1.04E+13
+LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMALCON\tMALCON\t9999999\t162620.6557\t57\t569999943\t5.70E+15\t61\t9919860\t3.64E+13
+MICROBIOLOGY ORDERABLES\t473117\tLABPCYS\tPNEUMOCYSTIS STAIN\tPCPSTAIN\tPCP STAIN\t9999999\t74295.58989\t57\t569999943\t5.70E+15\t712\t52898460\t3.10E+13
+VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVARICELLA ZOSTER VIRUS AB IGG\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t182622.1053\t57\t569999943\t5.70E+15\t57\t10409460\t2.39E+12
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tSI\t<<STATUS>>  \t9999999\t30727.82609\t56\t559999944\t5.60E+15\t69\t2120220\t2.29E+11
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tDI\tISSUE DATE/TIME\t9999999\t28011.42857\t56\t559999944\t5.60E+15\t28\t784320\t26369316000
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tUBT\tUNIT BLOOD TYPE  \t9999999\t41768.88889\t56\t559999944\t5.60E+15\t27\t1127760\t1.06E+11
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGKRAS\tKRAS\t9999999\t934668.2143\t56\t559999944\t5.60E+15\t56\t52341420\t5.63E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t94.9625\t49861.07143\t56\t5317.9\t508141.8752\t56\t2792220\t3.60E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t414.875\t54157.5\t56\t23233\t10105309\t56\t3032820\t3.66E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t519.9464286\t54325.71429\t56\t29117\t23052461\t56\t3042240\t3.66E+12
+LAB BLOOD ORDERABLES\t471922\tLABCOHB\tCARBOXYHEMOGLOBIN\tCOHB\tCO-HEMOGLOBIN\t535716.4911\t2722.8\t56\t30000123.5\t3.00E+14\t50\t136140\t1156057200
+LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tANTI HBS QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t6964332.534\t155980\t56\t390002621.9\t3.90E+15\t63\t9826740\t1.82E+12
+LAB BLOOD ORDERABLES\t497329\tLABSPAGD\tS. PNEUMONIAE AG DETECTION LA\tSOURC4\tSOURCE\t9999999\t2758074.146\t56\t559999944\t5.60E+15\t41\t113081040\t2.48E+15
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGMS\tB.QUINTANA IGM SCREEN\t9999999\t535699.6721\t56\t559999944\t5.60E+15\t122\t65355360\t1.49E+14
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tATTGT\tIGA ANTI TTG LEVEL\t8392860.482\t393897.8182\t56\t470000187\t4.70E+15\t55\t21664380\t1.30E+13
+MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tSREQ\tCOMMENTS\t9999999\t153813.4884\t56\t559999944\t5.60E+15\t43\t6613980\t1.34E+12
+VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEBV VIRAL CAPSID ANTIGEN IGM\tVMQUL\tVMQUL\t9999999\t251356.0714\t56\t559999944\t5.60E+15\t56\t14075940\t5.57E+12
+VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tHERPR\tHERPES SIMPLEX VIRUS (HSV)\t9999999\t85470\t56\t559999944\t5.60E+15\t56\t4786320\t8.40E+11
+VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tVZR\tVARICELLA-ZOSTER VIRUS\t9999999\t85470\t56\t559999944\t5.60E+15\t56\t4786320\t8.40E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t1636363.783\t56668.36364\t55\t90000008.05\t9.00E+14\t55\t3116760\t3.67E+12
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9627R\tCURRENT SERUM DESCRIPTION\t9999999\t109327.6364\t55\t549999945\t5.50E+15\t55\t6013020\t2.62E+13
+LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY\t5272728.491\t312901.3043\t55\t290000067\t2.90E+15\t46\t14393460\t2.35E+13
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t71127.16981\t55\t549999945\t5.50E+15\t53\t3769740\t4.05E+11
+LAB BLOOD ORDERABLES\t473106\tLABPLASMN\tPLASMINOGEN\tPLASMN\tPLASMINOGEN\t128.2545455\t46341.11111\t55\t7054\t931028\t54\t2502420\t1.95E+11
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBAG\tTB AG\t4181818.241\t209921.4545\t55\t230000003.3\t2.30E+15\t55\t11545680\t2.87E+12
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBAGNI\tTB AG - NIL\t4181818.128\t209921.4545\t55\t229999997.1\t2.30E+15\t55\t11545680\t2.87E+12
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBGNIL\tNIL\t181818.3715\t209921.4545\t55\t10000010.43\t1.00E+14\t55\t11545680\t2.87E+12
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBGRES\tQUANTIFERON RESULT\t9999999\t209921.4545\t55\t549999945\t5.50E+15\t55\t11545680\t2.87E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12200R\tAS CONCENTRATION\t8727274.764\t204748.4211\t55\t480000112\t4.80E+15\t38\t7780440\t1.90E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12201R\tPB CONCENTRATION\t8727272.109\t204748.4211\t55\t479999966\t4.80E+15\t38\t7780440\t1.90E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12202R\tCADMIUM, 24HR, U\t3454545.844\t204748.4211\t55\t190000021.4\t1.90E+15\t38\t7780440\t1.90E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12203R\tCD CONCENTRATION\t3272727.48\t204748.4211\t55\t180000011.4\t1.80E+15\t38\t7780440\t1.90E+12
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\t12204R\tHG CONCENTRATION\t9272726.436\t204748.4211\t55\t509999954\t5.10E+15\t38\t7780440\t1.90E+12
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tVL\tPRODUCT VOLUME\t173.5\t28096\t54\t9369\t1725059\t15\t421440\t13828327200
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tF1656A\tCD3-/16+56(NK),FLD\t1667395.5\t63483.15789\t54\t90039357\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tF1656P\tCD3-/16+56(NK)%,FLD\t1666674.074\t63483.15789\t54\t90000400\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD19A\tCD19 (B), ABS., FLD\t1668470.537\t63483.15789\t54\t90097409\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD19P\tCD19 (B), %, FLD\t1666671.13\t63483.15789\t54\t90000241\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD3AB\tCD3 (T), ABS., FLD\t1671105.907\t63483.15789\t54\t90239719\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD3PC\tCD3 (T) %, FLD\t1666734.944\t63483.15789\t54\t90003687\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD4AB\tCD4 (HELP),ABS., FLD\t1669522.926\t63483.15789\t54\t90154238\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD4PC\tCD4 (HELPER) %, FLD\t1666705.574\t63483.15789\t54\t90002101\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD8AB\tCD8 (SUPP),ABS., FLD\t1668191.241\t63483.15789\t54\t90082327\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCD8PC\tCD8 (SUPP), %, FLD\t1666697.556\t63483.15789\t54\t90001668\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFHNS\tT (H+S), FLD\t1666736.63\t63483.15789\t54\t90003778\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFHSAB\tT (H+S), ABS., FLD\t1671047.704\t63483.15789\t54\t90236576\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tHSRATIOFLD\tH/S RATIO, FLD\t1666668.022\t63483.15789\t54\t90000073.19\t9.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tLYMABFLD\tLYMPHOCYTES, ABS., FLD\t1488549.648\t63483.15789\t54\t80381681\t8.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tLYMPCFLD\tLYMPHOCYTES %, FLD\t1481498.998\t63483.15789\t54\t80000945.9\t8.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tWBCFLD\tWBC COUNT, FLD\t1502121.389\t63483.15789\t54\t81114555\t8.00E+14\t19\t1206180\t1.67E+11
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGEGFR\tEGFR\t9999999\t934668.2143\t54\t539999946\t5.40E+15\t56\t52341420\t5.63E+13
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tTEMP\tBODY TEMPERATURE\t9999999\t3126.315789\t54\t539999946\t5.40E+15\t19\t59400\t657950400
+LAB BLOOD ORDERABLES\t497454\tLABFPARVD\tPARVOVIRUS B19 PCR QUALITATIVE REALTIME\tSOUR11\tSOURCE\t9999999\t248994.1304\t54\t539999946\t5.40E+15\t92\t22907460\t1.26E+13
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tGDGPT\tIGG ANTI DGP LEVEL\t9444444.148\t397649.434\t54\t509999984\t5.10E+15\t53\t21075420\t1.28E+13
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\t7NHCLONAZEPM\t7-NH-CLONAZEPAM\t9629738.019\t0\t54\t520005853\t5.20E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\t7NHFLUNITRAZ\t7-NH-FLUNITRAZEPAM\t9999999\t0\t54\t539999946\t5.40E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tNORDIAZ\tNORDIAZEPAM\t7222412.315\t0\t54\t390010265\t3.90E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tOXAZEPAM\tOXAZEPAM\t5556410.574\t0\t54\t300046171\t3.00E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUAAGC\tALPHA OH-ALPRAZOLAM\t9629634.13\t0\t54\t520000243\t5.20E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUATGC\tALPHA OH-TRIAZOLAM\t9999999\t0\t54\t539999946\t5.40E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUEFGC\tOH-ETHYL-FLURAZEPAM\t9999999\t0\t54\t539999946\t5.40E+15\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUTZGC\tTEMAZEPAM\t6667255.63\t0\t54\t360031804\t3.60E+15\t9\t0\t0
+VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEBV VIRAL CAPSID ANTIGEN IGG\tVGQNT\tVGQNT\t555560.5398\t281538.4615\t54\t30000269.15\t3.00E+14\t52\t14640000\t7.71E+12
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t53\t529999947\t5.30E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tPRO\tPRODUCT ID  \t9999999\t26390\t53\t529999947\t5.30E+15\t24\t633360\t53763537600
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tPRO\tPRODUCT ID  \t9999999\t15080\t53\t529999947\t5.30E+15\t24\t361920\t13517726400
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t54211.69811\t53\t529999947\t5.30E+15\t53\t2873220\t3.60E+12
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9496R\t% DONOR- BLOOD CD3\t83.64150943\t4791.923077\t53\t4433\t423085\t52\t249180\t61404930000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9507R\t% DONOR- BLOOD CD56\t78.20754717\t4791.923077\t53\t4145\t381543\t52\t249180\t61404930000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9536R\t% RECIPIENT- BLOOD CD3\t10.69811321\t4791.923077\t53\t567\t38143\t52\t249180\t61404930000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9547R\t% RECIPIENT- BLOOD CD56\t16.13207547\t4791.923077\t53\t855\t53569\t52\t249180\t61404930000
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tFIO2\tFIO2 (FIO2)\t9999999\t7869.230769\t53\t529999947\t5.30E+15\t39\t306900\t8212021200
+LAB BLOOD ORDERABLES\t471960\tLABYCENT\tCENTROMERE ANTIBODIES\tYCENTA\tCENTROMERE ABS.\t9811319.879\t151753.9024\t53\t519999953.6\t5.20E+15\t82\t12443820\t2.14E+12
+LAB BLOOD ORDERABLES\t472481\tLABGLWB\tGLUCOSE, WHOLE BLOOD\tGLU\tGLUCOSE, WHOLE BLOOD\t754878.7358\t2554.736842\t53\t40008573\t4.00E+14\t38\t97080\t2879546400
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBMINI\tMITOGEN - NIL\t1886797.009\t209921.4545\t53\t100000241.5\t1.00E+15\t55\t11545680\t2.87E+12
+LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tPF4ENH\tPF4 IGG, IGM, IGA OD\t0.383773585\t125334.3396\t53\t20.34\t23.594874\t53\t6642720\t1.05E+12
+MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tRPT\tRPT\t9999999\t508887.5\t53\t529999947\t5.30E+15\t24\t12213300\t7.93E+12
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tLORAZ\tLORAZEPAM\t7170082.132\t0\t53\t380014353\t3.80E+15\t9\t0\t0
+URINE ORDERABLES\t471821\tLABURCA\tCALCIUM, URINE (RANDOM)\tCAURN\tCALCIUM, URINE\t4716989.04\t28754.48276\t53\t250000419.1\t2.50E+15\t29\t833880\t1.61E+11
+VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEBV VIRAL CAPSID ANTIGEN IGG\tVGQUL\tVGQUL\t9999999\t281538.4615\t53\t529999947\t5.30E+15\t52\t14640000\t7.71E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t121.1538462\t19763.07692\t52\t6300\t769000\t52\t1027680\t5.25E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t4038492.5\t53548.84615\t52\t210001610\t2.10E+15\t52\t2784540\t3.59E+12
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9500R\t% DONOR- BLOOD CD15\t71.75\t4885.882353\t52\t3731\t349035\t51\t249180\t61404930000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9540R\t% RECIPIENT- BLOOD CD15\t22.59615385\t4885.882353\t52\t1175\t93623\t51\t249180\t61404930000
+LAB BLOOD ORDERABLES\t471709\tLABYANRNP\tRNP AB, IGG\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t7115384.21\t164587.3469\t52\t369999978.9\t3.70E+15\t49\t8064780\t1.51E+12
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPSREV\tPATH/SPEC REVIEW\t9999999\t104482.621\t52\t519999948\t5.20E+15\t496\t51823380\t8.96E+12
+LAB BLOOD ORDERABLES\t473257\tLABRDATAC\tREVERSE TO COOMBS A CELLS\tRDATA\tREVERSE TO COOMBS A CELLS\t9999999\t37.5\t52\t519999948\t5.20E+15\t32\t1200\t72000
+LAB BLOOD ORDERABLES\t473765\tLABYVITB6\tVITAMIN B6\tVITB6\tVITAMIN B6\t2307699.635\t402985.7143\t52\t120000381\t1.20E+15\t56\t22567200\t1.01E+13
+LAB BLOOD ORDERABLES\t473780\tLABBARTPC\tBARTONELLA DNA, QUALI, PCR\tBHENPC\tBARTONELLA HENSELAN DNA\t9999999\t188286\t52\t519999948\t5.20E+15\t50\t9414300\t2.14E+12
+LAB BLOOD ORDERABLES\t473811\tLABYZONIS\tZONISAMIDE\tZONIS\tZONISAMIDE\t576942.6846\t320187.4576\t52\t30001019.6\t3.00E+14\t59\t18891060\t1.30E+13
+LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tHIPDIR\tHIT INTERPRETED BY\t9999999\t124371.9231\t52\t519999948\t5.20E+15\t52\t6467340\t1.02E+12
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tSDES\tSPECIMEN\t9999999\t407791.8182\t52\t519999948\t5.20E+15\t66\t26914260\t3.09E+13
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tTRPHOS\tTRIPLE PHOSPH CRYST.\t9999999\t8637.142857\t52\t519999948\t5.20E+15\t42\t362760\t14905749600
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASMU\tMUTATION(S)\t9999999\t797353.8462\t51\t509999949\t5.10E+15\t13\t10365600\t1.05E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSSP\tSPECIMEN\t9999999\t916121\t51\t509999949\t5.10E+15\t60\t54967260\t5.92E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t0.392156863\t59725.88235\t51\t20\t62\t51\t3046020\t3.66E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t1.771078431\t89536.47059\t51\t90.325\t250.770625\t51\t4566360\t6.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.701960784\t89536.47059\t51\t35.8\t33.34\t51\t4566360\t6.67E+12
+LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGA\tBETA-2-GP1 IGA\t392165\t846318.6667\t51\t20000415\t2.00E+14\t45\t38084340\t5.23E+13
+LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGG\tBETA-2-GP1 IGG\t392164.1961\t832471.3043\t51\t20000374\t2.00E+14\t46\t38293680\t5.23E+13
+LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGM\tBETA-2-GP1 IGM\t392162\t846318.6667\t51\t20000262\t2.00E+14\t45\t38084340\t5.23E+13
+LAB BLOOD ORDERABLES\t472029\tLABCHOHDL\tCHOLESTEROL HDL / TOTAL\tCHOL\tCHOLESTEROL, TOTAL\t196224.6078\t7068.26087\t51\t10007455\t1.00E+14\t46\t325140\t6402250800
+LAB BLOOD ORDERABLES\t472029\tLABCHOHDL\tCHOLESTEROL HDL / TOTAL\tHDL\tHDL CHOLESTEROL\t196120.6667\t7068.26087\t51\t10002154\t1.00E+14\t46\t325140\t6402250800
+LAB BLOOD ORDERABLES\t472185\tLABYDIGT\tDIGITOXIN, SERUM\tDIGT\tDIGITOXIN\t9803920.714\t384356.5217\t51\t499999956.4\t5.00E+15\t69\t26520600\t1.15E+13
+LAB BLOOD ORDERABLES\t472491\tLABHDL\tHDL, SERUM\tHDL\tHDL CHOLESTEROL\t47.66666667\t4488.837209\t51\t2431\t128881\t43\t193020\t1740632400
+LAB BLOOD ORDERABLES\t472735\tLABYLEADB\tLEAD, BLOOD\tLEADSO\tSOURCE OF COLLECTION\t9999999\t215660.3774\t51\t509999949\t5.10E+15\t53\t11430000\t3.30E+12
+LAB BLOOD ORDERABLES\t473747\tLAB11099R\tVIP\tVIP\tVASOACTIVE INT PEPTIDE\t2549038.451\t532787.234\t51\t130000961\t1.30E+15\t47\t25041000\t1.42E+13
+LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tHITI1\tHIT FUNCTIONAL ASSAY\t9999999\t125334.3396\t51\t509999949\t5.10E+15\t53\t6642720\t1.05E+12
+MICROBIOLOGY ORDERABLES\t473752\tLABVCUL\tVIRAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t1418147.315\t51\t509999949\t5.10E+15\t473\t670783680\t1.10E+15
+MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tRPT\tRPT\t9999999\t1137292.941\t51\t509999949\t5.10E+15\t51\t58001940\t7.40E+13
+MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tSDES\tSPECIMEN\t9999999\t1137292.941\t51\t509999949\t5.10E+15\t51\t58001940\t7.40E+13
+TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tUT3\tCOLLECTION PERIOD CREATININE\t980449.3137\t309620\t51\t50002915\t5.00E+14\t18\t5573160\t2.51E+12
+TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tVOL3\tVOLUME CREATININE\t982711.8216\t309620\t51\t50118302.9\t5.00E+14\t18\t5573160\t2.51E+12
+URINE ORDERABLES\t472551\tLABYHISTU\tHISTOPLASMA AG, URINE\tYHISU5\tCOMMENT URINE\t9999999\t382366.9401\t51\t509999949\t5.10E+15\t317\t121210320\t5.31E+13
+URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tPBGDAY\tPORPHOBILINOGEN, URN\t9411763.929\t282344\t51\t479999960.4\t4.80E+15\t30\t8470320\t2.54E+12
+URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tPBGL\tPORPHOBILINOGEN, URN\t8823529.043\t282344\t51\t449999981.2\t4.50E+15\t30\t8470320\t2.54E+12
+URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tTIMEU\tCOLLECTION PERIOD\t6078440.176\t282344\t51\t310000449\t3.10E+15\t30\t8470320\t2.54E+12
+URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tUCR\tCREATININE, URINE\t4314286.824\t282344\t51\t220028628\t2.20E+15\t30\t8470320\t2.54E+12
+URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tUCR\tCREATININE, URN\t76.62745098\t282344\t51\t3908\t484480\t30\t8470320\t2.54E+12
+URINE ORDERABLES\t473119\tLABUPBGQT\tPORPHOBILINOGEN QUANTITATIVE, URINE\tUVOL\tVOLUME\t3922513.176\t282344\t51\t200048172\t2.00E+15\t30\t8470320\t2.54E+12
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV DNA PCR QUANT, PLASMA\tCMVTIP\tTEST DESCRIPTION\t9999999\t182289.3718\t51\t509999949\t5.10E+15\t2945\t536842200\t1.17E+14
+VIROLOGY ORDERABLES\t497450\tLABBKVQU\tBK VIRUS DNA QUANTITATION\tSOURC6\tSOURCE\t9999999\t190828.4211\t51\t509999949\t5.10E+15\t38\t7251480\t1.85E+12
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tVL\tPRODUCT VOLUME\t175.68\t23742.35294\t50\t8784\t1646256\t17\t403620\t12120318000
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tXMR\tUNIT CROSSMATCH  \t9999999\t78700\t50\t499999950\t5.00E+15\t21\t1652700\t2.64E+11
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tXMR\tUNIT CROSSMATCH  \t9999999\t65276.66667\t50\t499999950\t5.00E+15\t18\t1174980\t1.67E+11
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tDI\tISSUE DATE/TIME\t9999999\t30021.6\t50\t499999950\t5.00E+15\t25\t750540\t27264164400
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9501R\t% DONOR- BLOOD CD19\t75.26\t5084.081633\t50\t3763\t341625\t49\t249120\t61404926400
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9541R\t% RECIPIENT- BLOOD CD19\t18.78\t5084.081633\t50\t939\t60187\t49\t249120\t61404926400
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tNAC\tNASAL CANNULA\t9999999\t3126.315789\t50\t499999950\t5.00E+15\t19\t59400\t657950400
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tTSITE\tTEMPERATURE SITE\t9999999\t3126.315789\t50\t499999950\t5.00E+15\t19\t59400\t657950400
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tTEMP\tBODY TEMPERATURE\t9999999\t7805\t50\t499999950\t5.00E+15\t36\t280980\t7947997200
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15RE\tRESULT\t9999999\t643218\t50\t499999950\t5.00E+15\t50\t32160900\t2.37E+13
+MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tSREQ\tCOMMENTS\t9999999\t1137292.941\t50\t499999950\t5.00E+15\t51\t58001940\t7.40E+13
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tRPT\tRPT\t9999999\t231851.4286\t50\t499999950\t5.00E+15\t21\t4868880\t1.38E+12
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tSDES\tSPECIMEN\t9999999\t231851.4286\t50\t499999950\t5.00E+15\t21\t4868880\t1.38E+12
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tSREQ\tCOMMENTS\t9999999\t231851.4286\t50\t499999950\t5.00E+15\t21\t4868880\t1.38E+12
+STOOL ORDERABLES\t668087\tLABSTOBIC\tOCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.\tOCCIMM\tSTOOL SAMPLE1,IMMUN\t9999999\t21693.33333\t50\t499999950\t5.00E+15\t45\t976200\t1.59E+11
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tVCAM\tVIRAL CAPSID AB-IGM\t9999999\t164195.2941\t50\t499999950\t5.00E+15\t51\t8373960\t1.83E+12
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1SP\tSPECIMEN\t9999999\t259084.5\t50\t499999950\t5.00E+15\t40\t10363380\t6.39E+12
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tXMR\tUNIT CROSSMATCH  \t9999999\t74142.5\t49\t489999951\t4.90E+15\t24\t1779420\t2.64E+11
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tSI\t<<STATUS>>  \t9999999\t20339.0625\t49\t489999951\t4.90E+15\t64\t1301700\t88116663600
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tDI\tISSUE DATE/TIME\t9999999\t35359.2\t49\t489999951\t4.90E+15\t25\t883980\t48430602000
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tUBT\tUNIT BLOOD TYPE  \t9999999\t32076.92308\t49\t489999951\t4.90E+15\t26\t834000\t53931456000
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tPRO\tPRODUCT ID  \t9999999\t15915.65217\t49\t489999951\t4.90E+15\t23\t366060\t13709250000
+BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34FLDPC\t%CD34, FLUID\t204086.483\t8570.769231\t49\t10000237.67\t1.00E+14\t26\t222840\t3235593600
+BODY FLUID ORDERABLES\t473392\tLABFTBLYM\tT SUBSETS AND B LYMPHOCYTES, FLUID\tFCSOUR\tSOURCE\t9999999\t63483.15789\t49\t489999951\t4.90E+15\t19\t1206180\t1.67E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t125.8163265\t63663.67347\t49\t6165\t782425\t49\t3119520\t3.67E+12
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9076R\tHLA-SEROLOGICAL PT. SUMMARY\t9999999\t214170.5316\t49\t489999951\t4.90E+15\t395\t84597360\t1.52E+14
+LAB BLOOD ORDERABLES\t473298\tLABYSEROT\tSEROTONIN, SERUM\tSEROTONIN\tSEROTONIN\t2857358.306\t411030\t49\t140010557\t1.40E+15\t48\t19729440\t8.87E+12
+LAB BLOOD ORDERABLES\t977671\tLABPANHGB\tHEMOGLOBIN\tHGB\tHEMOGLOBIN\t408169.4388\t1042.040816\t49\t20000302.5\t2.00E+14\t49\t51060\t268794000
+LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMALSCR\tMALARIA ANTIGEN RESULT\t9999999\t162025.3125\t49\t489999951\t4.90E+15\t64\t10369620\t3.66E+13
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tVCA\tVIRAL CAPSID AB\t9999999\t164195.2941\t49\t489999951\t4.90E+15\t51\t8373960\t1.83E+12
+VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tRPT\tRPT\t9999999\t437550\t49\t489999951\t4.90E+15\t22\t9626100\t1.47E+13
+VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tSDES\tSPECIMEN\t9999999\t437550\t49\t489999951\t4.90E+15\t22\t9626100\t1.47E+13
+VIROLOGY ORDERABLES\t670953\tLAB11153R\tHERPES VIRUS 6 DNA, QUANT\t11155R\tHERPES VIRUS 6 DNA, QUANT\t8797624.776\t175940\t49\t431083614\t4.30E+15\t6\t1055640\t1.98E+11
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tVL\tPRODUCT VOLUME\t168.1666667\t24581.53846\t48\t8072\t1376026\t13\t319560\t9576921600
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tUBT\tUNIT BLOOD TYPE  \t9999999\t29411.53846\t48\t479999952\t4.80E+15\t26\t764700\t28925881200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t9.134833333\t21620\t48\t438.472\t4056.616992\t48\t1037760\t5.25E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.128125\t64861.25\t48\t102.15\t252.4025\t48\t3113340\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.49375\t64861.25\t48\t23.7\t13.51\t48\t3113340\t3.67E+12
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tNAC\tNASAL CANNULA\t9999999\t8290.588235\t48\t479999952\t4.80E+15\t34\t281880\t7933334400
+LAB BLOOD ORDERABLES\t497264\tLABPROINS\tPROINSULIN, PLASMA\tPROINSULIN\tPROINSULIN\t1250028.754\t#VALUE!\t48\t60001380.2\t6.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t497343\tLABCMVG\tCMV GENOTYPE\tCUL97\tUL97 MUTATION\t9999999\t1217280.769\t48\t479999952\t4.80E+15\t78\t94947900\t1.25E+14
+LAB BLOOD ORDERABLES\t670774\tLAB11250R\tHHV-8 DNA, QUANT\t11251R\tSOURCE\t9999999\t195322.7848\t48\t479999952\t4.80E+15\t79\t15430500\t3.54E+12
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tSREQ\tCOMMENTS\t9999999\t407791.8182\t48\t479999952\t4.80E+15\t66\t26914260\t3.09E+13
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFDX\tCLINICAL INDICATION\t9999999\t1050420\t48\t479999952\t4.80E+15\t2\t2100840\t3.23E+12
+URINE ORDERABLES\t473429\tLABYUTHC\tTETRAHYDROCANNABINOL, URINE\tTHC\tTHC CARBOX AC BY GSMS\t1666740.396\t87930\t48\t80003539\t8.00E+14\t10\t879300\t2.61E+11
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tDI\tISSUE DATE/TIME\t9999999\t30122.5\t47\t469999953\t4.70E+15\t24\t722940\t26775946800
+BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34FL\tSOURCE\t9999999\t8570.769231\t47\t469999953\t4.70E+15\t26\t222840\t3235593600
+BODY FLUID ORDERABLES\t472774\tLABFBBURG\tLYME WESTERN BLOT, CSF\tBANDPRSNT\tBANDS PRESENT\t9999999\t289031.068\t47\t469999953\t4.70E+15\t103\t29770200\t1.10E+13
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t164.787234\t66209.3617\t47\t7745\t1371675\t47\t3111840\t3.67E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t122.6595745\t23482.97872\t47\t5765\t885525\t47\t1103700\t5.34E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t400.6319149\t27824.68085\t47\t18829.7\t7752736.95\t47\t1307760\t5.95E+11
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9026R\tHLA-DR345 ALLELE 2\t9999999\t389218.7234\t47\t469999953\t4.70E+15\t47\t18293280\t4.73E+13
+LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tLUPINT\tLUPUS INTERPRETATION\t9999999\t311240.1\t47\t469999953\t4.70E+15\t200\t62248020\t2.73E+13
+MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tRPT\tRPT\t9999999\t527037.1429\t47\t469999953\t4.70E+15\t21\t11067780\t8.79E+12
+MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t527037.1429\t47\t469999953\t4.70E+15\t21\t11067780\t8.79E+12
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t46\t459999954\t4.60E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tXMR\tUNIT CROSSMATCH  \t9999999\t76732.94118\t46\t459999954\t4.60E+15\t17\t1304460\t1.92E+11
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tVL\tPRODUCT VOLUME\t167.6086957\t21973.33333\t46\t7710\t1310920\t9\t197760\t6054631200
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tPRO\tPRODUCT ID  \t9999999\t18257.14286\t46\t459999954\t4.60E+15\t21\t383400\t18645969600
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tCCNT\tCELLS COUNTED\t109.6521739\t20710.34483\t46\t5044\t818450\t29\t600600\t1.30E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t11.65217391\t22553.47826\t46\t536\t51500\t46\t1037460\t5.25E+11
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9456R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t5710.434783\t46\t459999954\t4.60E+15\t46\t262680\t50644116000
+HISTOCOMPATIBILITY ORDERABLE\t1489479\t10260R\tHLA ORDER - DECEASED DONOR TESTING NOTICE\tDDNOT\tDECEASED DONOR ORDER NOTICE\t9999999\t19524.78261\t46\t459999954\t4.60E+15\t46\t898140\t96165082800
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tCHOL\tCHOLESTEROL, TOTAL\t434919.2174\t66268.5\t46\t20006284\t2.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t434786.1587\t66268.5\t46\t20000163.3\t2.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tHDL\tHDL CHOLESTEROL\t217431.9565\t66268.5\t46\t10001870\t1.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tHMCY\tHOMOCYSTEINE, PLAS TOTAL\t217401.3587\t66268.5\t46\t10000462.5\t1.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tHSCRP\tHIGH SENSITIVITY CRP\t217443.3652\t66268.5\t46\t10002394.8\t1.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tLDL\tDIRECT LDL CHOL\t217470.8913\t66268.5\t46\t10003661\t1.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tLPA\tLIPOPROTEIN A\t2391326.61\t66268.5\t46\t110001024.1\t1.10E+15\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tNHDL\tNON-HDL CHOL, CALC\t434878.8261\t66268.5\t46\t20004426\t2.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tTGL\tTRIGLYCERIDE, SER/PLAS\t434878.3478\t66268.5\t46\t20004404\t2.00E+14\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGGAB\tB.BURGDORFERI IGG AB WB\t9999999\t257757.3529\t46\t459999954\t4.60E+15\t68\t17527500\t6.70E+12
+LAB BLOOD ORDERABLES\t483823\tLABE2O\tESTRADIOL\tESTRADIOL\tESTRADIOL\t3913140.413\t64772.09302\t46\t180004459\t1.80E+15\t43\t2785200\t7.48E+11
+LAB BLOOD ORDERABLES\t497420\tLABASPID\tASPERGILLUS AB, ID, S\tANIGE\tASPERGILLUS NIGER\t9999999\t376384.5\t46\t459999954\t4.60E+15\t40\t15055380\t6.40E+12
+LAB BLOOD ORDERABLES\t497480\tLABADGM\tADENOVIRUS IGG AND IGM AB\tADM1\tADENOVIRUS AB,IGM\t869565.4735\t319660\t46\t40000011.78\t4.00E+14\t54\t17261640\t6.62E+12
+LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIKACINPK\tAMIKACIN, PEAK\t217417.3065\t14599.53488\t46\t10001196.1\t1.00E+14\t43\t627780\t20808709200
+LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIPO2\tDATE & TIME OF LAST DOSE\t9999999\t14599.53488\t46\t459999954\t4.60E+15\t43\t627780\t20808709200
+LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIPO3\tDOSAGE\t9999999\t14599.53488\t46\t459999954\t4.60E+15\t43\t627780\t20808709200
+LAB BLOOD ORDERABLES\t497504\tLABAMIPOL\tAMIKACIN PEAK LEVEL\tAMIPO4\tADMINISTRATION ROUTE\t9999999\t14599.53488\t46\t459999954\t4.60E+15\t43\t627780\t20808709200
+LAB BLOOD ORDERABLES\t668188\tLABYCLOZ\tCLOZAPINE, SERUM\tYNORC\tNORCLOZAPINE\t435063.7609\t306883.75\t46\t20012933\t2.00E+14\t48\t14730420\t5.78E+12
+LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12500R\tDATE OF LAST DOSE\t9999999\t348114.1176\t46\t459999954\t4.60E+15\t51\t17753820\t3.14E+13
+LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12502R\tDOSE, MG\t9999999\t348114.1176\t46\t459999954\t4.60E+15\t51\t17753820\t3.14E+13
+STOOL ORDERABLES\t473091\tLABSTPH\tPH, STOOL\tPHSTL\tPH,STOOL\t6.782608696\t15311.25\t46\t312\t2156\t16\t244980\t11918286000
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tCOPROU\tCOPROPORPHYRIN I\t217403.3043\t448491\t46\t10000552\t1.00E+14\t20\t8969820\t6.55E+12
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tHEPCOH\tHEPTACARBOXYLATE\t217391.6957\t448491\t46\t10000018\t1.00E+14\t20\t8969820\t6.55E+12
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tPORU\tUROPORPHYRIN, QUANT\t217392.5435\t448491\t46\t10000057\t1.00E+14\t20\t8969820\t6.55E+12
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tTIMEU\tCOLLECTION PERIOD\t9347826.717\t448491\t46\t430000029\t4.30E+15\t20\t8969820\t6.55E+12
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUCR\tCREATININE, URINE\t5000571.152\t448491\t46\t230026273\t2.30E+15\t20\t8969820\t6.55E+12
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUCR\tCREATININE, URN\t217458.0217\t448491\t46\t10003069\t1.00E+14\t20\t8969820\t6.55E+12
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUVOL\tVOLUME\t9130645.326\t448491\t46\t420009685\t4.20E+15\t20\t8969820\t6.55E+12
+VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tSREQ\tCOMMENTS\t9999999\t437550\t46\t459999954\t4.60E+15\t22\t9626100\t1.47E+13
+VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tHVSP\tSPECIMEN\t9999999\t61157.32283\t46\t459999954\t4.60E+15\t381\t23300940\t3.03E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t64\t629.3333333\t45\t2880\t188100\t45\t28320\t372801600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t629.3333333\t45\t449999955\t4.50E+15\t45\t28320\t372801600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t657.3333333\t45\t449999955\t4.50E+15\t45\t29580\t374691600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t657.3333333\t45\t449999955\t4.50E+15\t45\t29580\t374691600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t4528797.467\t657.3333333\t45\t203795886\t1.83E+15\t45\t29580\t374691600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t657.3333333\t45\t449999955\t4.50E+15\t45\t29580\t374691600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t629.3333333\t45\t449999955\t4.50E+15\t45\t28320\t372801600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t222224.8261\t629.3333333\t45\t10000117.18\t1.00E+14\t45\t28320\t372801600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.510666667\t629.3333333\t45\t22.98\t13.702\t45\t28320\t372801600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2.677777778\t629.3333333\t45\t120.5\t394.89\t45\t28320\t372801600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t37.25688889\t61600\t45\t1676.56\t123650.2142\t45\t2772000\t3.60E+12
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tTSITE\tTEMPERATURE SITE\t9999999\t8261.818182\t45\t449999955\t4.50E+15\t33\t272640\t7847956800
+LAB BLOOD ORDERABLES\t472195\tLABLDLD\tDIRECT LDL CHOLESTEROL\tLDL\tDIRECT LDL CHOL\t222308.2222\t5263.5\t45\t10003870\t1.00E+14\t40\t210540\t1530738000
+LAB BLOOD ORDERABLES\t497420\tLABASPID\tASPERGILLUS AB, ID, S\tAFLAV\tASPERGILLUS FLAVUS\t9999999\t376384.5\t45\t449999955\t4.50E+15\t40\t15055380\t6.40E+12
+LAB BLOOD ORDERABLES\t668188\tLABYCLOZ\tCLOZAPINE, SERUM\tYCLOZ1\tCLOZAPINE\t222708.7333\t306883.75\t45\t10021893\t1.00E+14\t48\t14730420\t5.78E+12
+LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12501R\tTIME OF LAST DOSE\t9999999\t348114.1176\t45\t449999955\t4.50E+15\t51\t17753820\t3.14E+13
+MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tSDES\tSPECIMEN\t9999999\t527037.1429\t45\t449999955\t4.50E+15\t21\t11067780\t8.79E+12
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tSDES\tSPECIMEN\t9999999\t255332.1429\t45\t449999955\t4.50E+15\t56\t14298600\t7.49E+12
+STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA3\tCOLLECTION DURATION\t9999999\t620241\t45\t449999955\t4.50E+15\t20\t12404820\t1.21E+13
+VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV AB TOTAL\tCMV\tCMV ANTIBODY\t9999999\t169080\t45\t449999955\t4.50E+15\t42\t7101360\t1.50E+12
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tVL\tPRODUCT VOLUME\t180.2727273\t28808\t44\t7932\t1577010\t15\t432120\t13940755200
+BODY FLUID ORDERABLES\t497368\tLABYFGL\tGLUCOSE, FLUID\tGLUFLD\tGLUCOSE BODY FLUID\t1136460\t178578.6207\t44\t50004240\t5.00E+14\t58\t10357560\t2.39E+12
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVM2\tPHASE II IGM\t9999999\t431808.2927\t44\t439999956\t4.40E+15\t41\t17704140\t2.36E+13
+LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11827R\tCSS EST. AVG. EXPO.CLEARA RATE\t8409225.045\t516462.8571\t44\t370005902\t3.70E+15\t35\t18076200\t2.50E+13
+LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11828R\tRECOMMENDED DOSE\t7727287.568\t516462.8571\t44\t340000653\t3.40E+15\t35\t18076200\t2.50E+13
+MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tSDES\tSPECIMEN\t9999999\t508887.5\t44\t439999956\t4.40E+15\t24\t12213300\t7.93E+12
+VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS1\tHIV-1 P24 (NON ICD) AG SCRN\t9999999\t478889.2308\t44\t439999956\t4.40E+15\t39\t18676680\t1.07E+13
+VIROLOGY ORDERABLES\t870899\tLABVIRSO1\tMISC. VIROLOGY TEST\tVIROTN\tTEST NAME\t9999999\t823753.7143\t44\t439999956\t4.40E+15\t35\t28831380\t3.01E+13
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tSI\t<<STATUS>>  \t9999999\t22168\t43\t429999957\t4.30E+15\t60\t1330080\t92575231200
+BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFMT\tMETHOD\t9999999\t479125.7143\t43\t429999957\t4.30E+15\t14\t6707760\t4.66E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t623.7209302\t43\t429999957\t4.30E+15\t43\t26820\t370882800
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t618.1395349\t43\t429999957\t4.30E+15\t43\t26580\t370494000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t0.720930233\t23656.74419\t43\t31\t253\t43\t1017240\t5.25E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.084883721\t66629.30233\t43\t46.65\t55.875\t43\t2865060\t3.61E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.434883721\t66629.30233\t43\t18.7\t8.53\t43\t2865060\t3.61E+12
+LAB BLOOD ORDERABLES\t473093\tLABYPENTO\tPENTOBARBITAL LEVEL\tPENTO\tPENTOBARBITAL, S\t3720951.814\t617787.907\t43\t160000928\t1.60E+15\t43\t26564880\t1.78E+13
+LAB BLOOD ORDERABLES\t473169\tLABPROTSF\tPROTEIN S, FREE\tPROTSF\tPROTEIN S, FREE\t232609.8605\t26957.14286\t43\t10002224\t1.00E+14\t14\t377400\t38848060800
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVG1\tPHASE 1 IGG\t9999999\t431808.2927\t43\t429999957\t4.30E+15\t41\t17704140\t2.36E+13
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVG2\tPHASE II IGG\t9999999\t431808.2927\t43\t429999957\t4.30E+15\t41\t17704140\t2.36E+13
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVM1\tPHASE 1 IGM\t9999999\t431808.2927\t43\t429999957\t4.30E+15\t41\t17704140\t2.36E+13
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPCM\tCOMMENT\t9999999\t804865.2273\t43\t429999957\t4.30E+15\t176\t141656280\t1.48E+14
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tSREQ\tCOMMENTS\t9999999\t255332.1429\t43\t429999957\t4.30E+15\t56\t14298600\t7.49E+12
+STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA4\tSODIUM CONCENTRATION\t5116313.535\t620241\t43\t220001482\t2.20E+15\t20\t12404820\t1.21E+13
+VIROLOGY ORDERABLES\t472452\tLABGCCR2\tGC MOLECULAR DETECTION\tSDES5\tSPECIMEN\t9999999\t144177.2308\t43\t429999957\t4.30E+15\t65\t9371520\t1.97E+12
+VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHHV-6 DNA PCR QUAL\tHV6QLS\tSPECIMEN\t9999999\t259790\t43\t429999957\t4.30E+15\t66\t17146140\t1.58E+13
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tXMR\tUNIT CROSSMATCH  \t9999999\t41460\t42\t419999958\t4.20E+15\t16\t663360\t66026217600
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tUBT\tUNIT BLOOD TYPE  \t9999999\t35142.85714\t42\t419999958\t4.20E+15\t21\t738000\t56790907200
+BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tPRO\tPRODUCT ID  \t9999999\t20586\t42\t419999958\t4.20E+15\t20\t411720\t20614413600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t641.4285714\t42\t419999958\t4.20E+15\t42\t26940\t370364400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t595.452381\t698.5714286\t42\t25009\t16868243\t42\t29340\t372265200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.636069762\t68204.28571\t42\t110.71493\t429.2077\t42\t2864580\t3.61E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t68204.28571\t42\t419999958\t4.20E+15\t42\t2864580\t3.61E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t9.893364286\t68417.14286\t42\t415.5213\t5281.699247\t42\t2873520\t3.61E+12
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t68417.14286\t42\t419999958\t4.20E+15\t42\t2873520\t3.61E+12
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9628R\tREFERENCE SAMPLE DATE\t9999999\t4160\t42\t419999958\t4.20E+15\t42\t174720\t15129079200
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9279R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS II\t9999999\t200657.1429\t42\t419999958\t4.20E+15\t42\t8427600\t7.31E+12
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32DX\tCLINICAL INDICATION\t9999999\t807004.6154\t42\t419999958\t4.20E+15\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32ET\tETHNICITY (PROVIDED BY MD)\t9999999\t807004.6154\t42\t419999958\t4.20E+15\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN CONC, ANTIXA U/ML\tHEPAR\tHEPARIN ACTIVITY LEVEL\t2142857.221\t42355.38462\t42\t90000003.27\t9.00E+14\t26\t1101240\t71583544800
+LAB BLOOD ORDERABLES\t497480\tLABADGM\tADENOVIRUS IGG AND IGM AB\tADG1\tADENOVIRUS AB,IGG\t2.155714286\t319660\t42\t90.54\t218.1358\t54\t17261640\t6.62E+12
+VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV AB IGM\tCMVIGM\tCMV IGM AB\t9999999\t140353.8462\t42\t419999958\t4.20E+15\t26\t3649200\t6.49E+11
+VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSVZSP\tSPECIMEN\t9999999\t190023.36\t42\t419999958\t4.20E+15\t375\t71258760\t1.95E+13
+VIROLOGY ORDERABLES\t1007409\tLABVZVQL\tVZV DNA PCR QUAL, NON LESION\tVZQLSP\tSPECIMEN\t9999999\t192832.6829\t42\t419999958\t4.20E+15\t82\t15812280\t4.51E+12
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t41\t409999959\t4.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t41\t409999959\t4.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tSI\t<<STATUS>>  \t9999999\t19624.44444\t41\t409999959\t4.10E+15\t54\t1059720\t53302118400
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tDI\tISSUE DATE/TIME\t9999999\t28667.36842\t41\t409999959\t4.10E+15\t19\t544680\t18471175200
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tUBT\tUNIT BLOOD TYPE  \t9999999\t35055\t41\t409999959\t4.10E+15\t20\t701100\t55275080400
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCNRBC\tNRBC/100WBC, CSF\t3.024390244\t8650.588235\t41\t124\t1164\t17\t147060\t1577818800
+BODY FLUID ORDERABLES\t497386\tLABFTRIGA\tTRIGLYCERIDES, NON-PLEURAL\tFSC1\tFLUID TYPE\t9999999\t713178\t41\t409999959\t4.10E+15\t10\t7131780\t5.30E+12
+BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFSP\tSPECIMEN\t9999999\t479125.7143\t41\t409999959\t4.10E+15\t14\t6707760\t4.66E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t34.77853659\t728.7804878\t41\t1425.92\t125051.564\t41\t29880\t372031200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0.56097561\t31024.39024\t41\t23\t237\t41\t1272000\t5.90E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t8292683.61\t23262.43902\t41\t340000028\t3.40E+15\t41\t953760\t5.21E+11
+HISTOCOMPATIBILITY ORDERABLE\t796004\t10275R\tHLA ORDER - SPECIMEN IN LAB ORDER NOTICE\tSPINN\tSPECIMEN IN LAB ORDER NOTICE\t9999999\t9120\t41\t409999959\t4.10E+15\t41\t373920\t30318825600
+HISTOCOMPATIBILITY ORDERABLE\t892285\t10273R\tHLA ORDER - WORKUP AT TIME OF TRANSPLANT\t10049R\tHLA LAB TESTING STATUS\t9999999\t452812.6829\t41\t409999959\t4.10E+15\t41\t18565320\t3.05E+13
+LAB BLOOD ORDERABLES\t472527\tLABHB\tHEPATITIS B SURFACE AG\tHBSAG\tHEPATITIS B SURF AG\t9999999\t53475\t41\t409999959\t4.10E+15\t36\t1925100\t1.41E+11
+LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON TEST FOR LATENT TB\tQTFNIN\tQUANTIFERON INTERPRETATION\t9999999\t852519.229\t41\t409999959\t4.10E+15\t1297\t1105717440\t3.84E+16
+LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tBBFRAC\tBB FRACTION\t0.780487805\t4442.926829\t41\t32\t298\t41\t182160\t32746600800
+LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tYCKMB\tMB FRACTION\t1.926829268\t4442.926829\t41\t79\t841\t41\t182160\t32746600800
+LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tYCKTOT\tTOTAL CK ACTIVITY\t9999999\t4442.926829\t41\t409999959\t4.10E+15\t41\t182160\t32746600800
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFSO\tSOURCE\t9999999\t1050420\t41\t409999959\t4.10E+15\t2\t2100840\t3.23E+12
+STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK3\tPOTASSIUM, 24-HR STOOL\t9999999\t620506.6667\t41\t409999959\t4.10E+15\t18\t11169120\t1.11E+13
+VIROLOGY ORDERABLES\t876719\tLABDVEIAV\tFLU A DFA\tIAV\tINFLUENZA A\t9999999\t78912.72727\t41\t409999959\t4.10E+15\t33\t2604120\t1.09E+12
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tDI\tISSUE DATE/TIME\t9999999\t28392\t40\t399999960\t4.00E+15\t20\t567840\t21309552000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t187.25\t757.5\t40\t7490\t1478650\t40\t30300\t371466000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.265625\t757.5\t40\t90.625\t225.503125\t40\t30300\t371466000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.43\t757.5\t40\t17.2\t7.78\t40\t30300\t371466000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t0.39125\t757.5\t40\t15.65\t7.6395\t40\t30300\t371466000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.305625\t787.5\t40\t52.225\t95.040625\t40\t31500\t371034000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.44375\t787.5\t40\t17.75\t8.9049\t40\t31500\t371034000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t168.675\t26059.5\t40\t6747\t1141277\t40\t1042380\t5.30E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t1.05\t24064.5\t40\t42\t556\t40\t962580\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t6000104.025\t26031\t40\t240004161\t2.40E+15\t40\t1041240\t5.30E+11
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGMYD8B\tMYD88, BLOOD\t9999999\t1309942.5\t40\t399999960\t4.00E+15\t40\t52397700\t2.18E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tGSF3BB\tSF3B1, BLOOD\t9999999\t1309942.5\t40\t399999960\t4.00E+15\t40\t52397700\t2.18E+14
+MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tSREQ\tCOMMENTS\t9999999\t508887.5\t40\t399999960\t4.00E+15\t24\t12213300\t7.93E+12
+MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tRPT\tRPT\t9999999\t80326.5\t40\t399999960\t4.00E+15\t40\t3213060\t3.71E+11
+STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK4\tPOTASSIUM CONCENTRATION\t4500019.05\t620506.6667\t40\t180000762\t1.80E+15\t18\t11169120\t1.11E+13
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tXMR\tUNIT CROSSMATCH  \t9999999\t40255.38462\t39\t389999961\t3.90E+15\t13\t523320\t60480576000
+BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tPRO\tPRODUCT ID  \t9999999\t23350\t39\t389999961\t3.90E+15\t18\t420300\t21061472400
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tVL\tPRODUCT VOLUME\t162.3076923\t20528.57143\t39\t6330\t1070034\t14\t287400\t7805044800
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tBP190\tP190, BONE MARROW\t5384614.992\t608170.9091\t39\t209999984.7\t2.10E+15\t11\t6689880\t4.92E+12
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIDX\tCLINICAL INDICATION\t9999999\t927889.0909\t39\t389999961\t3.90E+15\t11\t10206780\t1.25E+13
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t4669.230769\t39\t389999961\t3.90E+15\t39\t182100\t23450331600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t6762198.308\t4669.230769\t39\t263725734\t2.60E+15\t39\t182100\t23450331600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t8717949.641\t24470.76923\t39\t340000036\t3.40E+15\t39\t954360\t5.21E+11
+HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM\t10049R\tHLA LAB TESTING STATUS\t9999999\t645180\t39\t389999961\t3.90E+15\t39\t25162020\t2.00E+13
+LAB BLOOD ORDERABLES\t471810\tLAB11027R\tIMMUNE COMPLEXES\tC1Q\tC1Q BINDING ASSAY\t256414.3256\t330160.5405\t39\t10000158.7\t1.00E+14\t37\t12215940\t4.50E+12
+LAB BLOOD ORDERABLES\t472119\tLABYCYANI\tCYANIDE, BLOOD\tCYAN\tCYANIDE, B\t6666666.085\t218172.2727\t39\t259999977.3\t2.60E+15\t44\t9599580\t2.52E+12
+LAB BLOOD ORDERABLES\t472758\tLABLPA\tLIPOPROTEIN\tLPA\tLIPOPROTEIN A\t2307721.355\t46660\t39\t90001132.83\t9.00E+14\t30\t1399800\t1.17E+11
+LAB BLOOD ORDERABLES\t472930\tLABYMGEA\tMYASTHENIA GRAVIS EVALUATION ADULT\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t256413.2251\t694913.6842\t39\t10000115.78\t1.00E+14\t38\t26406720\t2.54E+13
+LAB BLOOD ORDERABLES\t472930\tLABYMGEA\tMYASTHENIA GRAVIS EVALUATION ADULT\tYARMA\tACH RECEPT MODUL AB\t256424.8205\t694913.6842\t39\t10000568\t1.00E+14\t38\t26406720\t2.54E+13
+LAB BLOOD ORDERABLES\t473463\tLABTOXOP2\tTOXOPLASMOSIS PCR\tTOXRES\tPCR RESULT\t9999999\t1116100\t39\t389999961\t3.90E+15\t6\t6696600\t7.98E+12
+LAB BLOOD ORDERABLES\t898808\tLAB12330R\tEVEROLIMUS, BLOOD\t12339R\tEVEROLIMUS\t769235.1974\t348114.1176\t39\t30000172.7\t3.00E+14\t51\t17753820\t3.14E+13
+LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tYCKMM\tMM FRACTION\t97.51282051\t4442.926829\t39\t3803\t371885\t41\t182160\t32746600800
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t547240.7303\t39\t389999961\t3.90E+15\t1068\t584453100\t2.37E+15
+MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tRPT\tRPT\t9999999\t418604.2105\t39\t389999961\t3.90E+15\t19\t7953480\t3.62E+12
+MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tSDES\tSPECIMEN\t9999999\t80326.5\t39\t389999961\t3.90E+15\t40\t3213060\t3.71E+11
+STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, RANDOM STOOL\t12506R\tSODIUM, RANDOM STOOL\t2820571.487\t275907.3913\t39\t110002288\t1.10E+15\t46\t12691740\t7.03E+12
+URINE ORDERABLES\t473727\tLABURURIC\tURIC ACID, URINE (RANDOM)\tURICURN\tURIC ACID, URINE\t27.7\t9726\t39\t1080.3\t53855.81\t30\t291780\t8162665200
+VIROLOGY ORDERABLES\t473715\tLABMVSOB\tUNLISTED MICROBIOLOGY/VIROLOGY TEST\tRES\tRESULTS\t9999999\t681043.9907\t39\t389999961\t3.90E+15\t431\t293529960\t8.51E+14
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tXMR\tUNIT CROSSMATCH  \t9999999\t39170.76923\t38\t379999962\t3.80E+15\t13\t509220\t59138074800
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tUBT\tUNIT BLOOD TYPE  \t9999999\t40860\t38\t379999962\t3.80E+15\t20\t817200\t69801444000
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tBP210\tP210, BONE MARROW\t6052631.138\t394347.6923\t38\t229999983.3\t2.30E+15\t13\t5126520\t3.76E+12
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tLRB210\tLOG DX AVG\t8421051.796\t394347.6923\t38\t319999968.3\t3.20E+15\t13\t5126520\t3.76E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12363R\tANNA-1, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12364R\tANNA-2, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12365R\tANNA-3, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12366R\tAGNA-1,CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12367R\tPCA-1, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12368R\tPCA-2, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12369R\tAMPHIPHYSIN AB, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t12370R\tCRMP-5-IGG, CSF\t9999999\t806820\t38\t379999962\t3.80E+15\t38\t30659160\t7.02E+13
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRALDBIIM\tPM RA LEAD BIPOLAR IMPEDANCE\t538.0263158\t824.2105263\t38\t20445\t18007231\t38\t31320\t370929600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t69.86842105\t4804.736842\t38\t2655\t195275\t38\t182580\t23450101200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t445.4473684\t4801.578947\t38\t16927\t7823937\t38\t182460\t23450202000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t472.7894737\t4801.578947\t38\t17966\t8901874\t38\t182460\t23450202000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.707894737\t4804.736842\t38\t102.9\t308.03\t38\t182580\t23450101200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.476315789\t4804.736842\t38\t18.1\t10.39\t38\t182580\t23450101200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t1315789.655\t4804.736842\t38\t50000006.9\t5.00E+14\t38\t182580\t23450101200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0.157894737\t31818.94737\t38\t6\t12\t38\t1209120\t5.86E+11
+HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM\tRINFO\tTO FIND RESULT:\t9999999\t645180\t38\t379999962\t3.80E+15\t39\t25162020\t2.00E+13
+LAB BLOOD ORDERABLES\t471527\tLABSMABY\tANTI-SM ANTIBODY\tASMINT\tANTI-SM INTERPRETATION\t9999999\t394626.5217\t38\t379999962\t3.80E+15\t138\t54458460\t2.90E+13
+LAB BLOOD ORDERABLES\t472547\tLABYAHA\tHISTONE ANTIBODIES\tYAHA1\tHISTONE AUTOANTIBODIES\t6842104.842\t277668.5106\t38\t259999984\t2.60E+15\t47\t13050420\t4.61E+12
+LAB BLOOD ORDERABLES\t668154\tLABGDGP\tIGG ANTI DGP\tGDGPR\tIGG ANTI DGP ANTIBODY\t9999999\t399028.2353\t38\t379999962\t3.80E+15\t34\t13566960\t6.90E+12
+MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tSDES\tSPECIMEN\t9999999\t418604.2105\t38\t379999962\t3.80E+15\t19\t7953480\t3.62E+12
+MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tRPT\tRPT\t9999999\t61173.10345\t38\t379999962\t3.80E+15\t29\t1774020\t1.29E+11
+MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tSDES\tSPECIMEN\t9999999\t61173.10345\t38\t379999962\t3.80E+15\t29\t1774020\t1.29E+11
+MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tSREQ\tCOMMENTS\t9999999\t61173.10345\t38\t379999962\t3.80E+15\t29\t1774020\t1.29E+11
+URINE ORDERABLES\t472100\tLABYUCORT\tCORTISOL FREE, URINE\tVOLUMEURN\tURINE VOLUME\t792469.8421\t349320\t38\t30113854\t3.00E+14\t5\t1746600\t7.16E+11
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADQLDN\tADV DNA\t9999999\t221752.1053\t38\t379999962\t3.80E+15\t38\t8426580\t3.12E+12
+VIROLOGY ORDERABLES\t876715\tLABDVERV3\tRSV DFA\tRSV3SP\tSPECIMEN\t9999999\t60101.53846\t38\t379999962\t3.80E+15\t78\t4687920\t3.25E+11
+BODY FLUID ORDERABLES\t472935\tLAB11019R\tMYELIN BASIC PROTEIN, CSF\t11164R\tMYELIN BASIC PROTEIN\t15.66702703\t420098.8235\t37\t579.68\t62546.4086\t17\t7141680\t9.00E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.822156757\t848.1081081\t37\t104.4198\t295.698776\t37\t31380\t369730800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t4945.945946\t37\t369999963\t3.70E+15\t37\t183000\t23449924800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t35.07945946\t4950.810811\t37\t1297.94\t105789.1516\t37\t183180\t23449748400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t26.61837838\t4950.810811\t37\t984.88\t82787.0448\t37\t183180\t23449748400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDRHYTHM\tICD RHYTHM\t9999999\t4957.297297\t37\t369999963\t3.70E+15\t37\t183420\t23449345200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t302.0837838\t4944.324324\t37\t11177.1\t3387374.01\t37\t182940\t23449971600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t4944.324324\t37\t369999963\t3.70E+15\t37\t182940\t23449971600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.685135135\t827.027027\t37\t99.35\t296.1625\t37\t30600\t352180800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.483783784\t827.027027\t37\t17.9\t9.41\t37\t30600\t352180800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t1351351.508\t4944.324324\t37\t50000005.8\t5.00E+14\t37\t182940\t23449971600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t17.43243243\t28323.24324\t37\t645\t29925\t37\t1047960\t5.30E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t540675.8919\t25778.91892\t37\t20005008\t2.00E+14\t37\t953820\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t173.7837838\t25778.91892\t37\t6430\t1238370\t37\t953820\t5.21E+11
+HISTOCOMPATIBILITY ORDERABLE\t890070\t10190R\tHLA ORDER - COMMUNICATION/UPDATE\t10049R\tHLA LAB TESTING STATUS\t9999999\t150188.1081\t37\t369999963\t3.70E+15\t37\t5556960\t6.32E+12
+HISTOCOMPATIBILITY ORDERABLE\t890070\t10190R\tHLA ORDER - COMMUNICATION/UPDATE\tRINFO\tTO FIND RESULT:\t9999999\t150188.1081\t37\t369999963\t3.70E+15\t37\t5556960\t6.32E+12
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34V\tVIABILITY\t1891972.703\t21605.625\t37\t70002990\t7.00E+14\t32\t691380\t27502592400
+LAB BLOOD ORDERABLES\t472107\tLABCRTINE\tCREATINE, SER/PLAS\t11852R\tCREATINE, PLASMA MG/DL\t540541.8892\t527977.8947\t37\t20000049.9\t2.00E+14\t38\t20063160\t1.22E+13
+LAB BLOOD ORDERABLES\t472107\tLABCRTINE\tCREATINE, SER/PLAS\tCRTNR\tCREATINE, PLASMA\t109.3324324\t527977.8947\t37\t4045.3\t744414.89\t38\t20063160\t1.22E+13
+LAB BLOOD ORDERABLES\t473762\tLABYVB1W\tVITAMIN B1\tYVB1W1\tTHIAMIN(VITAMIN B1)\t119.027027\t302975.3846\t37\t4404\t600438\t39\t11816040\t4.06E+12
+LAB BLOOD ORDERABLES\t497075\tLABSHIVAB\tRAPID HIV-1 AB\tSHIVAB\tRAPID HIV-1 ANTIBODY\t9999999\t5141.73913\t37\t369999963\t3.70E+15\t23\t118260\t904280400
+MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tRPT\tRPT\t9999999\t105838\t37\t369999963\t3.70E+15\t30\t3175140\t1.08E+12
+MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tSREQ\tCOMMENTS\t9999999\t418604.2105\t37\t369999963\t3.70E+15\t19\t7953480\t3.62E+12
+STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, RANDOM STOOL\t12504R\tPOTASSIUM, RANDOM STOOL\t3513535.378\t282741.8182\t37\t130000809\t1.30E+15\t44\t12440640\t7.15E+12
+URINE ORDERABLES\t472100\tLABYUCORT\tCORTISOL FREE, URINE\tYUCOUT\tCOLLECTION DURATION\t540563.1892\t349320\t37\t20000838\t2.00E+14\t5\t1746600\t7.16E+11
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tCOPRO3\tCOPROPORPHYRIN III\t270284.7297\t454132.9412\t37\t10000535\t1.00E+14\t17\t7720260\t6.03E+12
+VIROLOGY ORDERABLES\t541685\tLABHSVG\tHSV 1/2 TYPE-SPECIFIC IGG\tHSV1G\tHSV 1, IGG INDEX\t2972975.006\t262579.4118\t37\t110000075.2\t1.10E+15\t34\t8927700\t4.76E+12
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t36\t359999964\t3.60E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tVL\tPRODUCT VOLUME\t160.1111111\t29345.45455\t36\t5764\t957814\t11\t322800\t10370109600
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tUBT\tUNIT BLOOD TYPE  \t9999999\t35173.33333\t36\t359999964\t3.60E+15\t18\t633120\t53763523200
+BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tPRO\tPRODUCT ID  \t9999999\t20992.5\t36\t359999964\t3.60E+15\t16\t335880\t17437428000
+BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34V\tVIABILITY\t99.77777778\t8386.666667\t36\t3592\t358412\t18\t150960\t2362651200
+BODY FLUID ORDERABLES\t670812\tLAB11175R\tLIPASE,BF\t11176R\tLIPASE, BF\t1125027.833\t193422.8571\t36\t40501002\t4.00E+14\t28\t5415840\t1.24E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMTLPCPACA\tPM TELEMETRY PERCENT PACING ATRIAL\t52.09138889\t916.6666667\t36\t1875.29\t151006.3007\t36\t33000\t369511200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t5105\t36\t359999964\t3.60E+15\t36\t183780\t23449215600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t1.111111111\t5111.666667\t36\t40\t1374\t36\t184020\t23449086000
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9420R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t69582.63158\t36\t359999964\t3.60E+15\t76\t5288280\t2.57E+13
+LAB BLOOD ORDERABLES\t471509\tLABYFLUO\t5 - FLUCYTOSINE\t5FC\t5-FLUCYTOSINE\t48.33888889\t370011.6667\t36\t1740.2\t109676.04\t36\t13320420\t5.50E+12
+LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABIDR\tANTIBODY ID RULE OUT\t9999999\t16792.75862\t36\t359999964\t3.60E+15\t290\t4869900\t6.10E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tRPRQ\tRESULT\t9999999\t379288.125\t36\t359999964\t3.60E+15\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t472866\tLABMISPRO\tMISCELLANEOUS PROCESSING\tSEN\tSEND TO\t9999999\t108847.8261\t36\t359999964\t3.60E+15\t23\t2503500\t1.17E+12
+LAB BLOOD ORDERABLES\t472930\tLABYMGEA\tMYASTHENIA GRAVIS EVALUATION ADULT\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t694913.6842\t36\t359999964\t3.60E+15\t38\t26406720\t2.54E+13
+LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPDOPA\tDOPAMINE\t6389724.917\t294414.375\t36\t230030097\t2.30E+15\t32\t9421260\t3.19E+12
+LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPEPI\tEPINEPHRINE BY HPLC\t4722262.444\t294414.375\t36\t170001448\t1.70E+15\t32\t9421260\t3.19E+12
+LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPNOREP\tNOREPINEPHRINE\t1667118.528\t294414.375\t36\t60016267\t6.00E+14\t32\t9421260\t3.19E+12
+LAB BLOOD ORDERABLES\t870903\tLAB11984R\tNIACIN\t11985R\tNIACIN\t555557.7283\t866225.1429\t36\t20000078.22\t2.00E+14\t35\t30317880\t2.86E+13
+MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tRPT\tRPT\t9999999\t229096\t36\t359999964\t3.60E+15\t15\t3436440\t8.88E+11
+MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tSDES\tSPECIMEN\t9999999\t229096\t36\t359999964\t3.60E+15\t15\t3436440\t8.88E+11
+MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tSREQ\tCOMMENTS\t9999999\t229096\t36\t359999964\t3.60E+15\t15\t3436440\t8.88E+11
+URINE ORDERABLES\t472100\tLABYUCORT\tCORTISOL FREE, URINE\tUCORT\tCORTISOL, URINE\t833824.0139\t349320\t36\t30017664.5\t3.00E+14\t5\t1746600\t7.16E+11
+VIROLOGY ORDERABLES\t870899\tLABVIRSO1\tMISC. VIROLOGY TEST\tVIROS\tSOURCE\t9999999\t823753.7143\t36\t359999964\t3.60E+15\t35\t28831380\t3.01E+13
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tSI\t<<STATUS>>  \t9999999\t22864.44444\t35\t349999965\t3.50E+15\t54\t1234680\t87256699200
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tSI\t<<STATUS>>  \t9999999\t19766.80851\t35\t349999965\t3.50E+15\t47\t929040\t49220308800
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tDI\tISSUE DATE/TIME\t9999999\t32611.42857\t35\t349999965\t3.50E+15\t21\t684840\t25712856000
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tVL\tPRODUCT VOLUME\t168.5714286\t29626.66667\t35\t5900\t1005354\t9\t266640\t8404934400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t158.5714286\t910.2857143\t35\t5550\t949050\t35\t31860\t370242000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t127.2857143\t936\t35\t4455\t569525\t35\t32760\t369612000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t236.5714286\t901.7142857\t35\t8280\t2149600\t35\t31560\t351712800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t126.7142857\t903.4285714\t35\t4435\t565175\t35\t31620\t351615600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t27106.28571\t35\t349999965\t3.50E+15\t35\t948720\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t27106.28571\t35\t349999965\t3.50E+15\t35\t948720\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t27106.28571\t35\t349999965\t3.50E+15\t35\t948720\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t8571436.857\t27269.14286\t35\t300000290\t3.00E+15\t35\t954420\t5.21E+11
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9479R\tLOCUS 5\t9999999\t8996.470588\t35\t349999965\t3.50E+15\t34\t305880\t64653912000
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32R1\tRESULT 1\t9999999\t807004.6154\t35\t349999965\t3.50E+15\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tARS\tARSENIC, BLOOD\t9999999\t253232.5714\t35\t349999965\t3.50E+15\t35\t8863140\t3.67E+12
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tCAD\tCADMIUM, BLOOD\t8857142.314\t253232.5714\t35\t309999981\t3.10E+15\t35\t8863140\t3.67E+12
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tLEA\tLEAD, BLD\t8857142.286\t253232.5714\t35\t309999980\t3.10E+15\t35\t8863140\t3.67E+12
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tMERC\tMERCURY, BLOOD\t3428573.229\t253232.5714\t35\t120000063\t1.20E+15\t35\t8863140\t3.67E+12
+LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMOSIS PCR\tTOXGG\tGENERAL GUIDELINES\t9999999\t2500149.057\t35\t349999965\t3.50E+15\t53\t132507900\t2.79E+15
+LAB BLOOD ORDERABLES\t862883\tLAB11824R\tBUSULFAN\t11826R\tCSS TARGET\t9999999\t516462.8571\t35\t349999965\t3.50E+15\t35\t18076200\t2.50E+13
+STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA1\tSODIUM, 24-HR STOOL\t9999999\t1019105\t35\t349999965\t3.50E+15\t24\t24458520\t6.44E+13
+STOOL ORDERABLES\t497370\tLABYSTNA\tSODIUM, STOOL\tYSTNA2\tTOTAL WEIGHT\t9999999\t620232\t35\t349999965\t3.50E+15\t10\t6202320\t7.20E+12
+STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK1\tCOLLECTION DURATION\t9999999\t1148615\t35\t349999965\t3.50E+15\t24\t27566760\t7.59E+13
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUASP\tSPECIMEN\t9999999\t142990.5882\t35\t349999965\t3.50E+15\t17\t2430840\t5.55E+11
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tDI\tISSUE DATE/TIME\t9999999\t28974\t34\t339999966\t3.40E+15\t20\t579480\t20233267200
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tBASOSYNFLD\tBASOPHILS, SYN FLD\t1.352941176\t14124\t34\t46\t74\t15\t211860\t9371934000
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15RE\tRESULT, BM\t9999999\t644650\t34\t339999966\t3.40E+15\t18\t11603700\t8.75E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t5294247.118\t700.5882353\t34\t180004402\t1.80E+15\t34\t23820\t288802800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t193.5294118\t938.8235294\t34\t6580\t1662350\t34\t31920\t351583200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t27905.29412\t34\t339999966\t3.40E+15\t34\t948780\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t432.2294118\t31102.94118\t34\t14695.8\t6430976.6\t34\t1057500\t5.30E+11
+IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tAMOX\tAMOXICILLIN\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
+IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tAMPICILLIN\tAMPICILLIN\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
+IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tIGEPEN\tTOTAL IGE\t588390.5456\t485348.8235\t34\t20005278.55\t2.00E+14\t34\t16501860\t1.42E+13
+IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tPENG\tPENICILLIN G\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
+IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tPENV\tPENICILLIN V\t9999999\t485348.8235\t34\t339999966\t3.40E+15\t34\t16501860\t1.42E+13
+LAB BLOOD ORDERABLES\t471919\tLABYFCAR\tCARBAMAZEPINE FREE AND TOTAL, SERUM\tCZPFR\tCARBAMAZEPINE,FREE,S\t1470589.647\t175342.2857\t34\t50000048\t5.00E+14\t35\t6136980\t1.17E+12
+LAB BLOOD ORDERABLES\t471919\tLABYFCAR\tCARBAMAZEPINE FREE AND TOTAL, SERUM\tCZPTOT\tCARBAMAZEPINE,TOT,S\t1764711.738\t175342.2857\t34\t60000199.1\t6.00E+14\t35\t6136980\t1.17E+12
+LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tACFCR\tACYL/FREE CARNITINE RATIO\t0.361764706\t396363.6364\t34\t12.3\t7.79\t33\t13080000\t5.88E+12
+LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tCARNF\tCARNITINE, FREE\t34.47647059\t396363.6364\t34\t1172.2\t47330.26\t33\t13080000\t5.88E+12
+LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tCARNIT\tCARNITINE, TOTAL\t45.58235294\t396363.6364\t34\t1549.8\t81716.42\t33\t13080000\t5.88E+12
+MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tRPT\tRPT\t9999999\t264537\t34\t339999966\t3.40E+15\t20\t5290740\t2.95E+12
+MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tSDES\tSPECIMEN\t9999999\t264537\t34\t339999966\t3.40E+15\t20\t5290740\t2.95E+12
+VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVGGQNT\tVZV IGG LEVEL\t1470590.153\t299835.8824\t34\t50000065.19\t5.00E+14\t34\t10194420\t3.75E+12
+VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVZVMQL\tVZV IGM RESULT\t9999999\t299835.8824\t34\t339999966\t3.40E+15\t34\t10194420\t3.75E+12
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tVL\tPRODUCT VOLUME\t188.4545455\t32778.46154\t33\t6219\t1342909\t13\t426120\t15380020800
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tXMR\tUNIT CROSSMATCH  \t9999999\t33225\t33\t329999967\t3.30E+15\t12\t398700\t29378422800
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tUBT\tUNIT BLOOD TYPE  \t9999999\t24108\t33\t329999967\t3.30E+15\t15\t361620\t13517708400
+BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tPRO\tPRODUCT ID  \t9999999\t23456\t33\t329999967\t3.30E+15\t15\t351840\t18758599200
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTDX\tCLINICAL INDICATION\t9696977.436\t688582.5\t33\t320000255.4\t3.20E+15\t16\t11017320\t1.17E+13
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTMET\tMETHOD\t9999999\t688582.5\t33\t329999967\t3.30E+15\t16\t11017320\t1.17E+13
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t167.1212121\t710.9090909\t33\t5515\t926125\t33\t23460\t288802800
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t8.717845455\t969.0909091\t33\t287.6889\t2938.429441\t33\t31980\t369918000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t969.0909091\t33\t329999967\t3.30E+15\t33\t31980\t369918000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.629682424\t5396.363636\t33\t86.77952\t344.8447172\t33\t178080\t23425675200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t5396.363636\t33\t329999967\t3.30E+15\t33\t178080\t23425675200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t3.035863636\t29243.63636\t33\t100.1835\t304.9855674\t33\t965040\t5.21E+11
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9464R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t72568.10127\t33\t329999967\t3.30E+15\t79\t5732880\t2.58E+13
+LAB BLOOD ORDERABLES\t473332\tLABSLINTP\tSLIDE INTERPRETATION BY PATHOLOGIST\tSLPATH\tPATHOLOGIST\t9999999\t168261.4286\t33\t329999967\t3.30E+15\t28\t4711320\t1.33E+12
+LAB BLOOD ORDERABLES\t744308\tLAB11593R\tETHYLENE GLYCOL\t11721R\tETHYLENE GLYCOL\t9999999\t257777.8378\t33\t329999967\t3.30E+15\t37\t9537780\t8.90E+12
+MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tSDES\tSPECIMEN\t9999999\t105838\t33\t329999967\t3.30E+15\t30\t3175140\t1.08E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGMYD8M\tMYD88, BM\t9999999\t996023.6364\t33\t329999967\t3.30E+15\t33\t32868780\t3.73E+13
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tGSF3BM\tSF3B1, BM\t9999999\t996023.6364\t33\t329999967\t3.30E+15\t33\t32868780\t3.73E+13
+URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tCAE\tCALCIUM EXCRETION\t5454628.879\t70146.66667\t33\t180002753\t1.80E+15\t18\t1262640\t1.69E+11
+URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tCAURN\tCALCIUM, URINE\t5454548.915\t70146.66667\t33\t180000114.2\t1.80E+15\t18\t1262640\t1.69E+11
+URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tUT5\tCOLLECTION PERIOD CALCIUM\t303053.1818\t70146.66667\t33\t10000755\t1.00E+14\t18\t1262640\t1.69E+11
+URINE ORDERABLES\t471822\tLABUCAT\tCALCIUM, URINE (TIMED)\tVOL5\tVOLUME CALCIUM\t305359.0606\t70146.66667\t33\t10076849\t1.00E+14\t18\t1262640\t1.69E+11
+URINE ORDERABLES\t472504\tLABUHGB\tHEMOGLOBIN, URINE\tHGBURN\tHEMOGLOBIN, URINE\t8484850.915\t236397\t33\t280000080.2\t2.80E+15\t20\t4727940\t1.29E+12
+URINE ORDERABLES\t472504\tLABUHGB\tHEMOGLOBIN, URINE\tUT\tCOLLECTION PERIOD\t9999999\t236397\t33\t329999967\t3.30E+15\t20\t4727940\t1.29E+12
+URINE ORDERABLES\t472504\tLABUHGB\tHEMOGLOBIN, URINE\tVOL\tVOLUME\t2121243.273\t236397\t33\t70001028\t7.00E+14\t20\t4727940\t1.29E+12
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t32\t319999968\t3.20E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tXMR\tUNIT CROSSMATCH  \t9999999\t34121.53846\t32\t319999968\t3.20E+15\t13\t443580\t47680657200
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tDI\tISSUE DATE/TIME\t9999999\t30243.33333\t32\t319999968\t3.20E+15\t18\t544380\t19348650000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t744.375\t32\t319999968\t3.20E+15\t32\t23820\t287715600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t4062517.406\t729.375\t32\t130000557\t1.30E+15\t32\t23340\t287744400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t729.375\t32\t319999968\t3.20E+15\t32\t23340\t287744400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t729.375\t32\t319999968\t3.20E+15\t32\t23340\t287744400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t625068.0625\t740.625\t32\t20002178\t2.00E+14\t32\t23700\t287773200
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tDEVTYPE\tDEVTYPE\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRLOWRT\tBRADY LOWER RATE\t67.1875\t29700\t32\t2150\t154000\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRPACMD\tBRADY PACING MODE\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t296.159375\t29700\t32\t9477.1\t2825394.17\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tIMPDATE\tIMPLANT DATE\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tMFR\tMANUFACTURER\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tMOD\tMODEL\t9999999\t29700\t32\t319999968\t3.20E+15\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.353125\t29700\t32\t75.3\t188.92625\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.49375\t29700\t32\t15.8\t8.98\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2812499.984\t29700\t32\t89999999.5\t9.00E+14\t32\t950400\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSERNUM\tSERIAL NUMBER\t4528601.438\t29700\t32\t144915246\t1.35E+15\t32\t950400\t6.37E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t3125032.875\t5767.5\t32\t100001052\t1.00E+15\t32\t184560\t23448621600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t7.9220625\t5587.5\t32\t253.506\t2266.045912\t32\t178800\t23424847200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t380.034375\t5767.5\t32\t12161.1\t4678602.55\t32\t184560\t23448967200
+HISTOCOMPATIBILITY ORDERABLE\t978466\t10283R\tHLA ORDER - TRANSPLANT EVENT COMMUNICATION\t10049R\tHLA LAB TESTING STATUS\t9999999\t355848.75\t32\t319999968\t3.20E+15\t32\t11387160\t8.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t978466\t10283R\tHLA ORDER - TRANSPLANT EVENT COMMUNICATION\tRINFO\tTO FIND RESULT:\t9999999\t355848.75\t32\t319999968\t3.20E+15\t32\t11387160\t8.33E+13
+LAB BLOOD ORDERABLES\t668188\tLABYCLOZ\tCLOZAPINE, SERUM\tYCOMT\tCOMBINED TOTAL\t313382.4375\t306883.75\t32\t10028238\t1.00E+14\t48\t14730420\t5.78E+12
+MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tRPT\tRPT\t9999999\t616054.2857\t32\t319999968\t3.20E+15\t14\t8624760\t5.65E+12
+MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tSREQ\tCOMMENTS\t9999999\t264537\t32\t319999968\t3.20E+15\t20\t5290740\t2.95E+12
+MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tSREQ\tCOMMENTS\t9999999\t80326.5\t32\t319999968\t3.20E+15\t40\t3213060\t3.71E+11
+STOOL ORDERABLES\t497372\tLABYSTK\tPOTASSIUM, STOOL\tYSTK2\tTOTAL WEIGHT\t9999999\t648480\t32\t319999968\t3.20E+15\t8\t5187840\t6.44E+12
+URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tYUCOP2\tCOLLECTION DURATION\t625022.4375\t395269.2857\t32\t20000718\t2.00E+14\t28\t11067540\t9.84E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tWBCAST\tWBC CASTS\t9999999\t12200.86957\t32\t319999968\t3.20E+15\t23\t280620\t4539250800
+VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFCOM\tCOMMENTS\t9999999\t11850\t32\t319999968\t3.20E+15\t10\t118500\t2648221200
+VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFLSP\tSPECIMEN\t9999999\t11850\t32\t319999968\t3.20E+15\t10\t118500\t2648221200
+VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVGGQUL\tVZV IGG RESULT\t9999999\t299835.8824\t32\t319999968\t3.20E+15\t34\t10194420\t3.75E+12
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tXMR\tUNIT CROSSMATCH  \t9999999\t28260\t31\t309999969\t3.10E+15\t10\t282600\t20345407200
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSISO\tSOURCE, TISSUE\t9999999\t927889.0909\t31\t309999969\t3.10E+15\t11\t10206780\t1.25E+13
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t322581.6129\t5657.419355\t31\t10000030\t1.00E+14\t31\t175380\t23395474800
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t9677419.677\t30640.64516\t31\t300000010\t3.00E+15\t31\t949860\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t30607.74194\t31\t0\t0\t31\t948840\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t1.046774194\t38183.22581\t31\t32.45\t47.68\t31\t1183680\t5.42E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.487096774\t38183.22581\t31\t15.1\t12.49\t31\t1183680\t5.42E+11
+LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tANTI-RO ANTIBODY\tSSAQNT\tANTI-RO LEVEL\t3870997.968\t561673.5484\t31\t120000937\t1.20E+15\t31\t17411880\t1.13E+13
+LAB BLOOD ORDERABLES\t472032\tLABYCHROM\tCHROMIUM\tYCHRO1\tCHROMIUM\t645163.2065\t263355.4839\t31\t20000059.4\t2.00E+14\t31\t8164020\t2.65E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t53190\t31\t309999969\t3.10E+15\t28\t1489320\t1.27E+11
+LAB BLOOD ORDERABLES\t472120\tLABYCCPIG\tANTI CCP\tCCPT\tANTI CCP ANTIBODY LEVEL\t3225835.129\t451253.7931\t31\t100000889\t1.00E+15\t29\t13086360\t7.73E+12
+LAB BLOOD ORDERABLES\t472354\tLABFACT12\tFACTOR XII ASSAY\tFACTOR12\tFACTOR XII ASSAY\t1290376.032\t59304.44444\t31\t40001657\t4.00E+14\t27\t1601220\t1.27E+11
+LAB BLOOD ORDERABLES\t472518\tLABHAVM\tIGM ANTI-HAV\tHAVINT\tINTERPRETATION\t9999999\t48714.87379\t31\t309999969\t3.10E+15\t515\t25088160\t2.39E+12
+LAB BLOOD ORDERABLES\t472924\tLABMONEBV\tMONO SCREEN WITH REFLEX EBV AB\tEBV\tEBV TITER REFLEXED?\t9999999\t11195.5814\t31\t309999969\t3.10E+15\t172\t1925640\t47965219200
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tPMLR\tPML\t2903225.591\t675904\t31\t89999993.33\t9.00E+14\t30\t20277120\t1.57E+13
+LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tSPTY3\tSPECIMEN TYPE\t9999999\t2214834.286\t31\t309999969\t3.10E+15\t21\t46511520\t3.48E+14
+LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tTEST3\tTEST NAME\t9999999\t2214834.286\t31\t309999969\t3.10E+15\t21\t46511520\t3.48E+14
+LAB BLOOD ORDERABLES\t473768\tLABVITK\tVITAMIN K1, SERUM\tVITK\tVITAMIN K1,SERUM\t9.846774194\t266435.1724\t31\t305.25\t64973.9391\t29\t7726620\t2.19E+12
+LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1 AND 2 PCR, NON CSF\tHV2DNA\tHSV-2 DNA, QN PCR\t9718086.806\t534380.8163\t31\t301260691\t3.00E+15\t98\t52369320\t9.04E+13
+LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\tFMYABM\tM.PNEUMONIA AB IGM\t967879.2258\t551076.7742\t31\t30004256\t3.00E+14\t31\t17083380\t1.94E+13
+LAB BLOOD ORDERABLES\t590579\tLABTOTALS\tPROTEIN S AG, TOTAL\tPROTSTOT\tPROTEIN S AG, TOTAL\t73.61290323\t99571.76471\t31\t2282\t193506\t17\t1692720\t4.72E+11
+URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tVOLUMEURN\tURINE VOLUME\t324358.0323\t399484.4444\t31\t10055099\t1.00E+14\t27\t10786080\t9.76E+12
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tCOM\tCOMMENT\t9999999\t203472\t31\t309999969\t3.10E+15\t285\t57989520\t1.37E+13
+VIROLOGY ORDERABLES\t876723\tLABDVEIBV\tFLU B DFA\tIBV\tINFLUENZA B\t9999999\t53200\t31\t309999969\t3.10E+15\t24\t1276800\t91484654400
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tVL\tPRODUCT VOLUME\t175.3333333\t26873.33333\t30\t5260\t937182\t9\t241860\t7105950000
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tVL\tPRODUCT VOLUME\t165.2\t25597.5\t30\t4956\t824496\t8\t204780\t7197807600
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tSI\t<<STATUS>>  \t9999999\t21465\t30\t299999970\t3.00E+15\t44\t944460\t67138131600
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tSI\t<<STATUS>>  \t9999999\t23840.57143\t30\t299999970\t3.00E+15\t35\t834420\t53931488400
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tDI\tISSUE DATE/TIME\t9999999\t29855.29412\t30\t299999970\t3.00E+15\t17\t507540\t17832826800
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tUBT\tUNIT BLOOD TYPE  \t9999999\t26117.14286\t30\t299999970\t3.00E+15\t14\t365640\t13709217600
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tPRO\tPRODUCT ID  \t9999999\t23376\t30\t299999970\t3.00E+15\t15\t350640\t18667375200
+BODY FLUID ORDERABLES\t517920\tLABMPOPR3\tANCA MPO / PR - 3M IGG\tMPO\tMYLEOPEROXIDASE, AB\t67.36666667\t60\t30\t2021\t603147\t1\t60\t3600
+BODY FLUID ORDERABLES\t517920\tLABMPOPR3\tANCA MPO / PR - 3M IGG\tSERINEPR3\tSERINE PROTEASE 3\t133.1\t60\t30\t3993\t3053497\t1\t60\t3600
+BODY FLUID ORDERABLES\t541704\tLABFCHOLA\tCHOLESTEROL, FLUID\tFSC1\tFLUID TYPE\t9999999\t715530\t30\t299999970\t3.00E+15\t6\t4293180\t3.37E+12
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t125.8333333\t564\t30\t3775\t481625\t30\t16920\t172555200
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.485933333\t1058\t30\t74.578\t246.005444\t30\t31740\t367498800
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t1058\t30\t299999970\t3.00E+15\t30\t31740\t367498800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t170.1666667\t1096\t30\t5105\t871617\t30\t32880\t350395200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t124.3333333\t1012\t30\t3730\t466050\t30\t30360\t349718400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t10.06703333\t5710\t30\t302.011\t3635.709359\t30\t171300\t23373745200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t5710\t30\t299999970\t3.00E+15\t30\t171300\t23373745200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tATACHTADTI\tATACH TACHY ZONE DETECTION INTERVAL\t363.6666667\t31646\t30\t10910\t3985500\t30\t949380\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t127.5\t9236\t30\t3825\t489375\t30\t277080\t69023354400
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t31646\t30\t0\t0\t30\t949380\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t31646\t30\t0\t0\t30\t949380\t5.21E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t31646\t30\t299999970\t3.00E+15\t30\t949380\t5.21E+11
+HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*\t10049R\tHLA LAB TESTING STATUS\t9999999\t1487994\t30\t299999970\t3.00E+15\t30\t44639820\t1.09E+14
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9450R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t64529.64706\t30\t299999970\t3.00E+15\t85\t5485020\t2.57E+13
+LAB BLOOD ORDERABLES\t473121\tLABPORS\tPORPHYRINS, SERUM\tTPOR\tPORPHYRINS, TOTAL\t2000005.367\t383598.6207\t30\t60000161\t6.00E+14\t29\t11124360\t1.10E+13
+LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGLUF\tGLUCOSE,FASTING\t1333408.133\t20572\t30\t40002244\t4.00E+14\t30\t617160\t15849540000
+LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTT1\tGLUCOSE,GTT 1HR\t666823.9333\t20572\t30\t20004718\t2.00E+14\t30\t617160\t15849540000
+LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTT2\tGLUCOSE,GTT 2HR\t666775.8\t20572\t30\t20003274\t2.00E+14\t30\t617160\t15849540000
+LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTT30\tGLUCOSE,GTT 30 MIN\t666824.4667\t20572\t30\t20004734\t2.00E+14\t30\t617160\t15849540000
+MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tKOH\tKOH PREP\t9999999\t1850399.621\t30\t299999970\t3.00E+15\t1268\t2346306720\t4.55E+15
+MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t342848.8073\t30\t299999970\t3.00E+15\t436\t149482080\t1.48E+14
+MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tRPT\tRPT\t9999999\t52970.76923\t30\t299999970\t3.00E+15\t13\t688620\t38987182800
+MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tSDES\tSPECIMEN\t9999999\t52970.76923\t30\t299999970\t3.00E+15\t13\t688620\t38987182800
+MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tSREQ\tCOMMENTS\t9999999\t52970.76923\t30\t299999970\t3.00E+15\t13\t688620\t38987182800
+URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tCOPPERURN\tCOPPER, U\t666987.0333\t407868.3871\t30\t20009611\t2.00E+14\t31\t12643920\t1.07E+13
+URINE ORDERABLES\t472479\tLABURGL\tGLUCOSE, URINE (RANDOM)\tGLUURN\tGLUCOSE, URINE\t25.6\t6540\t30\t768\t76580\t15\t98100\t1025535600
+VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMVIGM\tCMV IGM AB\t9999999\t168088\t30\t299999970\t3.00E+15\t15\t2521320\t5.08E+11
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tXMR\tUNIT CROSSMATCH  \t9999999\t55366.15385\t29\t289999971\t2.90E+15\t13\t719760\t92014084800
+ECHO ORDERABLES\t472284\tECH29\tECHO - DOBUTAMINE STRESS ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t57\t50083.44828\t29\t1653\t99419\t29\t1452420\t1.03E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDRHYTHM\tICD RHYTHM\t9999999\t32768.27586\t29\t289999971\t2.90E+15\t29\t950280\t6.37E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t6551814.172\t951.7241379\t29\t190002611\t1.90E+15\t29\t27600\t326556000
+HISTOCOMPATIBILITY ORDERABLE\t507225\t10220R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t1641532\t29\t289999971\t2.90E+15\t30\t49245960\t1.25E+14
+HISTOCOMPATIBILITY ORDERABLE\t890834\t10090R\tHLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t656185.7143\t29\t289999971\t2.90E+15\t28\t18373200\t1.91E+13
+HISTOCOMPATIBILITY ORDERABLE\t890834\t10090R\tHLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t9999999\t656185.7143\t29\t289999971\t2.90E+15\t28\t18373200\t1.91E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9108R\tSERUM DATE 2\t9999999\t167828.2759\t29\t289999971\t2.90E+15\t29\t4867020\t4.48E+12
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9110R\tMCS 2\t52.03448276\t167828.2759\t29\t1509\t235747\t29\t4867020\t4.48E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9087R\tSERUM DATE 2\t9999999\t170863.4483\t29\t289999971\t2.90E+15\t29\t4955040\t4.48E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9089R\tMCS 2\t78.13793103\t170863.4483\t29\t2266\t291532\t29\t4955040\t4.48E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9596R\tRESULT 2\t9999999\t170863.4483\t29\t289999971\t2.90E+15\t29\t4955040\t4.48E+12
+LAB BLOOD ORDERABLES\t471634\tLABANTNEU\tANTI - NEUTROPHIL AB\tANA\tANTI-NEUTROPHIL AB:\t9999999\t318946.1538\t29\t289999971\t2.90E+15\t65\t20731500\t1.24E+13
+LAB BLOOD ORDERABLES\t471781\tLABVBG\tBLOOD GASES, VENOUS\tBGSPRC\tSPECIMEN COMMENT\t9999999\t6370\t29\t289999971\t2.90E+15\t42\t267540\t8777350800
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t53190\t29\t289999971\t2.90E+15\t28\t1489320\t1.27E+11
+LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMOSIS PCR\tTOXSP\tSPECIMEN TYPE\t9999999\t2500149.057\t29\t289999971\t2.90E+15\t53\t132507900\t2.79E+15
+LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tNAPA\tN-ACETYLPROCAINAMIDE (NAPA)\t7.806896552\t4711.034483\t29\t226.4\t2448.42\t29\t136620\t1298286000
+LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPA\tPROCAINAMIDE\t344834.1793\t4711.034483\t29\t10000191.2\t1.00E+14\t29\t136620\t1298286000
+LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP1\tPEAK, TROUGH, OR RANDOM\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
+LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP2\tDATE & TIME OF LAST DOSE\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
+LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP3\tDOSAGE (INCLUDE UNITS)\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
+LAB BLOOD ORDERABLES\t862877\tLABPANAPL\tPROCAINAMIDE AND NAPA\tPANAP4\tADMINISTRATION ROUTE\t9999999\t4711.034483\t29\t289999971\t2.90E+15\t29\t136620\t1298286000
+URINE ORDERABLES\t473099\tLABUPHOS\tPHOSPHATE WITH CREATININE, URINE (RANDOM)\tPHOSU\tPHOSPHATE, URINE\t344868.6621\t7290\t29\t10001191.2\t1.00E+14\t14\t102060\t1513508400
+URINE ORDERABLES\t473099\tLABUPHOS\tPHOSPHATE WITH CREATININE, URINE (RANDOM)\tPHUCRU\tPHOSPHATE/CREAT RATIO\t1034483.392\t7290\t29\t30000018.38\t3.00E+14\t14\t102060\t1513508400
+URINE ORDERABLES\t473099\tLABUPHOS\tPHOSPHATE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t74.06551724\t7290\t29\t2147.9\t280636.75\t14\t102060\t1513508400
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tSI\t<<STATUS>>  \t9999999\t29561.53846\t28\t279999972\t2.80E+15\t39\t1152900\t1.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tDI\tISSUE DATE/TIME\t9999999\t35298.75\t28\t279999972\t2.80E+15\t16\t564780\t28894690800
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tUBT\tUNIT BLOOD TYPE  \t9999999\t29464.61538\t28\t279999972\t2.80E+15\t13\t383040\t18645940800
+BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tUBT\tUNIT BLOOD TYPE  \t9999999\t31638.46154\t28\t279999972\t2.80E+15\t13\t411300\t20614381200
+BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tUBT\tUNIT BLOOD TYPE  \t9999999\t32307.69231\t28\t279999972\t2.80E+15\t13\t420000\t21061454400
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tFLDTYP\tFLUID SOURCE\t9999999\t8266.666667\t28\t279999972\t2.80E+15\t9\t74400\t837540000
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tRBCFLD\tRBC COUNT, FLD (/UL)\t2324586.536\t8266.666667\t28\t65088423\t6.11E+14\t9\t74400\t837540000
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tWBCFLD\tNUCL CELLS, FLD(/UL)\t2144055.536\t8266.666667\t28\t60033555\t6.00E+14\t9\t74400\t837540000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t163.2142857\t745.7142857\t28\t4570\t758100\t28\t20880\t269409600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t238.75\t807.8571429\t28\t6685\t1619175\t28\t22620\t286707600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t0.107142857\t6347.142857\t28\t3\t5\t28\t177720\t23396342400
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9601R\tRESULT 2\t9999999\t173822.1429\t28\t279999972\t2.80E+15\t28\t4867020\t4.48E+12
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9629R\tREFERENCE SERUM DESCRIPTION\t9999999\t2884.285714\t28\t279999972\t2.80E+15\t28\t80760\t6368097600
+LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tANTI-RNP ANTIBODY\tRNPQNT\tANTI-RNP LEVEL\t6428586.643\t646600.7143\t28\t180000426\t1.80E+15\t28\t18104820\t1.95E+13
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t379288.125\t28\t279999972\t2.80E+15\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471804\tLAB11025R\tC1 INHIBITOR, QUANT\tC1ESTR\tC-1-ESTERASE INHIBITOR\t357174.5357\t270932.1429\t28\t10000887\t1.00E+14\t28\t7586100\t2.23E+12
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tCAION\tCALCIUM, IONIZED\t2500000.68\t2267.142857\t28\t70000019.04\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tCL\tCHLORIDE, WHOLE BLD\t2500080.25\t2267.142857\t28\t70002247\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tGLU\tGLUCOSE, WHOLE BLOOD\t2500132.536\t2267.142857\t28\t70003711\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tHCT\tHCT (EST)\t2500022.036\t2267.142857\t28\t70000617\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tK\tPOTASSIUM, WHOLE BLD\t2500003.318\t2267.142857\t28\t70000092.9\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tNA\tSODIUM, WHOLE BLOOD\t2500105.964\t2267.142857\t28\t70002967\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tTCO2A\tTCO2\t2500016.55\t2267.142857\t28\t70000463.4\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t472830\tLABMEP\tMEDICAL EMERGENCY PANEL\tTHB\tTHB\t2500006.943\t2267.142857\t28\t70000194.4\t7.00E+14\t14\t31740\t370602000
+LAB BLOOD ORDERABLES\t473197\tLABYPYRU\tPYRUVIC ACID\tPYRU\tPYRUVIC ACID\t357143.0857\t342690.9091\t28\t10000006.4\t1.00E+14\t33\t11308800\t5.03E+12
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTCMT\tCOMMENT\t9999999\t1024809.63\t28\t279999972\t2.80E+15\t81\t83009580\t9.92E+13
+LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOS\tTESTOSTERONE, TOTAL\t1071679.214\t366215.5556\t28\t30007018\t3.00E+14\t27\t9887820\t4.07E+12
+MICROBIOLOGY ORDERABLES\t472435\tLABFKOH\tFUNGAL CULTURE AND KOH\tCULT\tCULT/OTHER RSLT:\t9999999\t1850399.621\t28\t279999972\t2.80E+15\t1268\t2346306720\t4.55E+15
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUAR\tINFLUENZA A RNA\t9999999\t142990.5882\t28\t279999972\t2.80E+15\t17\t2430840\t5.55E+11
+VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVZVMQT\tVZV IGM LEVEL\t9642856.229\t299835.8824\t28\t269999974.4\t2.70E+15\t34\t10194420\t3.75E+12
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tXMR\tUNIT CROSSMATCH  \t9999999\t54977.14286\t27\t269999973\t2.70E+15\t14\t769680\t94305974400
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tDI\tISSUE DATE/TIME\t9999999\t27197.14286\t27\t269999973\t2.70E+15\t14\t380760\t12856773600
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tPRO\tPRODUCT ID  \t9999999\t53298.46154\t27\t269999973\t2.70E+15\t13\t692880\t99484610400
+BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tPRO\tPRODUCT ID  \t9999999\t50293.84615\t27\t269999973\t2.70E+15\t13\t653820\t97952439600
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tDX\tDIAGNOSIS\t9999999\t8266.666667\t27\t269999973\t2.70E+15\t9\t74400\t837540000
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tFTURB\tTURBIDITY, FLD\t9999999\t8266.666667\t27\t269999973\t2.70E+15\t9\t74400\t837540000
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t8266.666667\t27\t269999973\t2.70E+15\t9\t74400\t837540000
+COAGULATION ORDERABLES\t577589\tLABFIBANT\tFIBRINOGEN ANTIGEN\tFIBANT\tFIBRINOGEN ANTIGEN\t3333547.481\t79560\t27\t90005782\t9.00E+14\t27\t2148120\t3.99E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t66.35407407\t35173.33333\t27\t1791.56\t166329.6092\t27\t949680\t6.37E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t1.444444444\t6564.444444\t27\t39\t1169\t27\t177240\t23394960000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t0.915740741\t6824.444444\t27\t24.725\t27.656875\t27\t184260\t23446767600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.459259259\t6824.444444\t27\t12.4\t6.36\t27\t184260\t23446767600
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9633R\tREFERENCE SAMPLE DATE\t9999999\t631.1111111\t27\t269999973\t2.70E+15\t27\t17040\t262490400
+LAB BLOOD ORDERABLES\t471780\tLABABG\tBLOOD GASES, ARTERIAL\tBGSPR\tSPECIMEN COMMENT\t9999999\t686.6666667\t27\t269999973\t2.70E+15\t36\t24720\t51876000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSEZY\tSEZARY CELLS\t740771.7778\t87997.77778\t27\t20000838\t2.00E+14\t27\t2375940\t4.75E+11
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM2\tC.PNEUMONIAE IGG\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM3\tC.PNEUMONIAE IGM\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM4\tC.TRACHOMATIS IGG\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM5\tC.TRACHOMATIS IGM\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM6\tC.PSITTACI IGG\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM7\tC.PSITTACI IGM\t9999999\t246785.8537\t27\t269999973\t2.70E+15\t41\t10118220\t2.76E+12
+LAB BLOOD ORDERABLES\t472037\tLABYCLON\tCLONAZEPAM, SERUM\tYCLON1\tCLONAZEPAM\t5185195.926\t698320\t27\t140000290\t1.40E+15\t36\t25139520\t6.61E+13
+LAB BLOOD ORDERABLES\t472143\tLABYDESIP\tDESIPRAMINE, SERUM\tDESIP\tDESIPRAMINE\t2222304.963\t271722.8571\t27\t60002234\t6.00E+14\t28\t7608240\t4.23E+12
+LAB BLOOD ORDERABLES\t472437\tLABG6PDQT\tG6PD QUANTITATIVE\tG6PD\tG6PD, QUANTITATIVE\t2962967.444\t384931.2\t27\t80000121\t8.00E+14\t25\t9623280\t5.49E+12
+LAB BLOOD ORDERABLES\t472797\tLABYMANG\tMANGANESE\tMN\tMANGANESE\t740742.5963\t310182.5806\t27\t20000050.1\t2.00E+14\t31\t9615660\t3.76E+12
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tQTFNRS\tQUANTIFERON-TB\t9999999\t183897.5\t27\t269999973\t2.70E+15\t24\t4413540\t8.77E+11
+LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOSFREE\tTESTOSTERONE, FREE\t740749.8074\t373086.9767\t27\t20000244.8\t2.00E+14\t43\t16042740\t6.74E+12
+LAB BLOOD ORDERABLES\t473780\tLABBARTPC\tBARTONELLA DNA, QUALI, PCR\tSDES10\tSPECIMEN\t9999999\t188286\t27\t269999973\t2.70E+15\t50\t9414300\t2.14E+12
+LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3RS\tRESULT\t9999999\t531220.8\t27\t269999973\t2.70E+15\t175\t92963640\t6.34E+13
+LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\tFPARV1\tPARVOVIRUS IGG INDEX\t370373.063\t251412.7273\t27\t10000072.7\t1.00E+14\t33\t8296620\t2.66E+12
+LAB BLOOD ORDERABLES\t744250\tLAB12781R\tBRUCELLA ANTIBODIES\t12778R\tBRUCELLA IGG\t740740.8163\t327064.4444\t27\t20000002.04\t2.00E+14\t27\t8830740\t3.27E+12
+MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tCULT\tCULT/OTHER RSLT:\t9999999\t361273.8129\t27\t269999973\t2.70E+15\t556\t200868240\t1.60E+14
+MICROBIOLOGY ORDERABLES\t473804\tLABWDCS\tWOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP\tGS\tGRAM STAIN\t9999999\t361273.8129\t27\t269999973\t2.70E+15\t556\t200868240\t1.60E+14
+STOOL ORDERABLES\t668089\tLABSTOBID\tOCCULT BLD, IMMUNO, STOOL, DIAG TEST\tOCCIMM\tSTOOL SAMPLE1,IMMUN\t9999999\t7063.333333\t27\t269999973\t2.70E+15\t18\t127140\t2108602800
+VIROLOGY ORDERABLES\t472519\tLABHATA\tHEPATITIS A AB TOTAL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t61437.27273\t27\t269999973\t2.70E+15\t22\t1351620\t1.23E+11
+VIROLOGY ORDERABLES\t472525\tLABHBSQL\tANTI-HBS QUAL\tHQLINT\tINTERPRETATION\t9999999\t49138.5336\t27\t269999973\t2.70E+15\t491\t24127020\t2.62E+12
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS PCR QUANT\tADPCR1\tADENVIRUS DNA\t6747357.074\t131400\t27\t182178641\t1.80E+15\t1\t131400\t17265960000
+VIROLOGY ORDERABLES\t670953\tLAB11153R\tHERPES VIRUS 6 DNA, QUANT\t11154R\tSOURCE\t9999999\t175940\t27\t269999973\t2.70E+15\t6\t1055640\t1.98E+11
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tSI\t<<STATUS>>  \t9999999\t31336.66667\t26\t259999974\t2.60E+15\t36\t1128120\t1.06E+11
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tXMR\tUNIT CROSSMATCH  \t9999999\t35261.53846\t26\t259999974\t2.60E+15\t13\t458400\t40947336000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.961538462\t1271.538462\t26\t25\t32.52\t26\t33060\t367995600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.425\t1271.538462\t26\t11.05\t5.1325\t26\t33060\t367995600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t443.4615385\t36544.61538\t26\t11530\t5249161.5\t26\t950160\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t2692307.838\t6729.230769\t26\t70000003.8\t7.00E+14\t26\t174960\t32665176000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0.076923077\t6791.538462\t26\t2\t2\t26\t176580\t23394200400
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9433R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t67897.8\t26\t259999974\t2.60E+15\t100\t6789780\t2.67E+13
+IMMUNOLOGY ORDERABLES\t633412\tLABRPRQT\tRPR QUANTITATIVE\tRPRTTR\tTIER\t9230768.462\t2718840\t26\t239999980\t2.40E+15\t25\t67971000\t3.96E+15
+LAB BLOOD ORDERABLES\t471705\tLABYANTJO\tAUTOANTIBODIES TO JO1\tJO1AB\tAUTOANTIBODIES TO JO 1\t9999999\t153156.5217\t26\t259999974\t2.60E+15\t23\t3522600\t5.83E+11
+LAB BLOOD ORDERABLES\t471960\tLABCENABY\tANTI CENTROMERE ANTIBODY\tCENINT\tANTI-CENTROMERE INTERPRETATION\t9999999\t420534.9153\t26\t259999974\t2.60E+15\t118\t49623120\t2.82E+13
+LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tUNFRACTIONATED HEPARIN\tHEPAR\tHEPARIN ACTIVITY LEVEL\t1153846.319\t2097.692308\t26\t30000004.3\t3.00E+14\t26\t54540\t143622000
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t8077068.758\t138684.5455\t26\t210003787.7\t2.10E+15\t22\t3051060\t4.80E+11
+LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGMAB\tB.BURGDORFERI IGM AB WB\t9999999\t257757.3529\t26\t259999974\t2.60E+15\t68\t17527500\t6.70E+12
+LAB BLOOD ORDERABLES\t473197\tLABYPYRU\tPYRUVIC ACID\tPYRU\tPYRUVIC ACID (MG/DL)\t4615385.035\t335502.2222\t26\t120000010.9\t1.20E+15\t27\t9058560\t4.17E+12
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15DX\tCLINICAL INDICATION\t9999999\t643218\t26\t259999974\t2.60E+15\t50\t32160900\t2.37E+13
+LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tCCPR\tANTI CCP ANTIBODY\t9999999\t2437250.769\t26\t259999974\t2.60E+15\t26\t63368520\t2.95E+15
+LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tRHFR\tRHEUMATOID FACTOR\t9999999\t2437250.769\t26\t259999974\t2.60E+15\t26\t63368520\t2.95E+15
+LAB BLOOD ORDERABLES\t668154\tLABGDGP\tIGG ANTI DGP\tGDGPT\tIGG ANTI DGP LEVEL\t9230769.154\t380188.8\t26\t239999998\t2.40E+15\t25\t9504720\t4.61E+12
+LAB BLOOD ORDERABLES\t668158\tLABADAM13\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMACT\tADAMTS-13 ACTIVITY\t384645.6923\t190497\t26\t10000788\t1.00E+14\t20\t3809940\t1.38E+12
+MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tSDES\tSPECIMEN\t9999999\t616054.2857\t26\t259999974\t2.60E+15\t14\t8624760\t5.65E+12
+MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tSREQ\tCOMMENTS\t9999999\t616054.2857\t26\t259999974\t2.60E+15\t14\t8624760\t5.65E+12
+URINE ORDERABLES\t471763\tLABUBICAR\tBICARBONATE, URINE\tHCO3URN\tBICARBONATE, URINE\t9230773\t118578.4615\t26\t240000098\t2.40E+15\t13\t1541520\t2.14E+11
+URINE ORDERABLES\t471763\tLABUBICAR\tBICARBONATE, URINE\tUT\tCOLLECTION PERIOD\t9999999\t118578.4615\t26\t259999974\t2.60E+15\t13\t1541520\t2.14E+11
+URINE ORDERABLES\t471763\tLABUBICAR\tBICARBONATE, URINE\tVOL\tVOLUME\t2692331.577\t118578.4615\t26\t70000621\t7.00E+14\t13\t1541520\t2.14E+11
+URINE ORDERABLES\t472097\tLABYUCOP\tCOPPER, URINE\tYUCOP4\tCU CONC (UG/L)\t1923253.077\t431540\t26\t50004580\t5.00E+14\t18\t7767720\t8.65E+12
+URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tTKU\tPOTASSIUM EXCRETION\t384691.6923\t49980\t26\t10001984\t1.00E+14\t13\t649740\t65850469200
+URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tUK\tPOTASSIUM, URINE\t384654.6846\t49980\t26\t10001021.8\t1.00E+14\t13\t649740\t65850469200
+URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tUT2\tCOLLECTION PERIOD POTASSIUM\t769252.1538\t49980\t26\t20000556\t2.00E+14\t13\t649740\t65850469200
+URINE ORDERABLES\t473128\tLABUKT\tPOTASSIUM, URINE (TIMED)\tVOL2\tVOLUME POTASSIUM\t386614.3462\t49980\t26\t10051973\t1.00E+14\t13\t649740\t65850469200
+VIROLOGY ORDERABLES\t497426\tLABHIVDAG\tHIV - 1 DIRECT AG (ICD)\tHIVS\tHIV-1 P24 (ICD)AG\t9999999\t410679.1304\t26\t259999974\t2.60E+15\t23\t9445620\t4.14E+12
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUAMT\tMETHOD\t9999999\t142990.5882\t26\t259999974\t2.60E+15\t17\t2430840\t5.55E+11
+BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tUBT\tUNIT BLOOD TYPE  \t9999999\t27975\t25\t249999975\t2.50E+15\t12\t335700\t17437417200
+BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tUBT\tUNIT BLOOD TYPE  \t9999999\t31958.18182\t25\t249999975\t2.50E+15\t11\t351540\t18758574000
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tPRO\tPRODUCT ID  \t9999999\t49705\t25\t249999975\t2.50E+15\t12\t596460\t77125532400
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tPRO\tPRODUCT ID  \t9999999\t31900\t25\t249999975\t2.50E+15\t12\t382800\t39290846400
+BODY FLUID ORDERABLES\t471627\tLABAGAP\tANION GAP\tAG\tANION GAP\t9.76\t#VALUE!\t25\t244\t2712\t0\tNone\tNone
+ECHO ORDERABLES\t472293\tECH25\tECHO - LIMITED TRANSTHORACIC ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t42.88\t63352.8\t25\t1072\t53110\t25\t1583820\t2.39E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0.12\t7072.8\t25\t3\t5\t25\t176820\t23394034800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t3.400044\t7442.4\t25\t85.0011\t341.6285447\t25\t186060\t23447574000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t1.046\t703.2\t25\t26.15\t32.2825\t25\t17580\t126644400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.512\t703.2\t25\t12.8\t7.46\t25\t17580\t126644400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t424.46\t7260\t25\t10611.5\t4550376.75\t25\t181500\t23423526000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t2.76\t29822.4\t25\t69\t1095\t25\t745560\t4.56E+11
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tLDL\tLDL (CALCULATED)\t6000033.68\t15128.57143\t25\t150000842\t1.50E+15\t21\t317700\t12194132400
+LAB BLOOD ORDERABLES\t473463\tLABTOXOP2\tTOXOPLASMOSIS PCR\tTOXSP\tSPECIMEN TYPE\t9999999\t1116100\t25\t249999975\t2.50E+15\t6\t6696600\t7.98E+12
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKPR\tPRIMER\t9999999\t864270\t25\t249999975\t2.50E+15\t14\t12099780\t1.30E+13
+MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tSREQ\tCOMMENTS\t9999999\t105838\t25\t249999975\t2.50E+15\t30\t3175140\t1.08E+12
+STOOL ORDERABLES\t472448\tLABGBMIFA\tGBM IGA AND IGG AB\tGBMIGA\tGBM ANTIBODY, IGA\t9999999\t283150.2128\t25\t249999975\t2.50E+15\t47\t13308060\t9.93E+12
+STOOL ORDERABLES\t472448\tLABGBMIFA\tGBM IGA AND IGG AB\tGBMIGG\tGBM-IGG ELISA\t9999999\t283150.2128\t25\t249999975\t2.50E+15\t47\t13308060\t9.93E+12
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tEBNA\tEPSTEIN-BARR NUC AB\t9999999\t164195.2941\t25\t249999975\t2.50E+15\t51\t8373960\t1.83E+12
+VIROLOGY ORDERABLES\t472525\tLABHAB\tHEPATITIS B SURFACE AB\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t57564.54545\t25\t249999975\t2.50E+15\t22\t1266420\t91465016400
+VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEBV VIRAL CAPSID ANTIGEN IGM\tVMQNT\tVMQNT\t9199999.334\t287901.6\t25\t229999983.4\t2.30E+15\t25\t7197540\t3.49E+12
+VIROLOGY ORDERABLES\t898802\tLABEBVQP\tEBV PCR QUANT, PLASMA\tEBVQIN\tTEST DESCRIPTION\t9999999\t220336.3485\t25\t249999975\t2.50E+15\t1079\t237742920\t6.49E+13
+VIROLOGY ORDERABLES\t898804\tLABEBVQL\tEBV DNA PCR QUAL\tEBVQLD\tEBV DNA\t9999999\t233724\t25\t249999975\t2.50E+15\t25\t5843100\t1.92E+12
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tVL\tPRODUCT VOLUME\t175.5\t32175\t24\t4212\t752578\t4\t128700\t4204652400
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t24\t239999976\t2.40E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t24\t239999976\t2.40E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tSI\t<<STATUS>>  \t9999999\t23185.45455\t24\t239999976\t2.40E+15\t33\t765120\t28925913600
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tXMR\tUNIT CROSSMATCH  \t9999999\t30115.38462\t24\t239999976\t2.40E+15\t13\t391500\t16055694000
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tDI\tISSUE DATE/TIME\t9999999\t25220\t24\t239999976\t2.40E+15\t12\t302640\t10053201600
+BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tDI\tISSUE DATE/TIME\t9999999\t34270\t24\t239999976\t2.40E+15\t12\t411240\t20614377600
+ECHO ORDERABLES\t472287\tECH30\tECHO - EXERCISE STRESS ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t59.25\t63915\t24\t1422\t85634\t24\t1533960\t1.87E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVSND\tBRADY SENSED AV DELAY\t416796\t7320\t24\t10003104\t1.00E+14\t24\t175680\t32664888000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t39472.5\t24\t239999976\t2.40E+15\t24\t947340\t5.21E+11
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tAAMINOA\tA-AMINOADIPIC ACID\t1.333333333\t293858.1818\t24\t32\t1024\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tAAMINOB\tA-AMINOBUTYRIC ACID\t24.41666667\t293858.1818\t24\t586\t19294\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tALANINE\tALANINE\t357.4166667\t293858.1818\t24\t8578\t3400766\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tARGININE\tARGININE\t64.54166667\t293858.1818\t24\t1549\t128437\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tASPART\tASPARTIC ACID\t10.04166667\t293858.1818\t24\t241\t5981\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tASPGN\tASPARAGINE\t50.70833333\t293858.1818\t24\t1217\t65809\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tBALN\tB-ALANINE\t2.875\t293858.1818\t24\t69\t1103\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tBAMBA\tB-AMINOISOBUTYRIC AC\t0.375\t293858.1818\t24\t9\t45\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tCITRUL\tCITRULLINE\t21.91666667\t293858.1818\t24\t526\t13884\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tCYSTINE\tCYSTINE\t44.33333333\t293858.1818\t24\t1064\t60252\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tCYTHN\tCYSTATHIONINE\t0.958333333\t293858.1818\t24\t23\t69\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tETHLN\tETHANOLAMINE\t1.416666667\t293858.1818\t24\t34\t798\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tGLUTAC\tGLUTAMIC ACID\t65.70833333\t293858.1818\t24\t1577\t131177\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tGLUTAMINE\tGLUTAMINE\t561.4166667\t293858.1818\t24\t13474\t8622788\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tGLYCINE\tGLYCINE\t250.6666667\t293858.1818\t24\t6016\t1639364\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tHISTID\tHISTIDINE\t75.33333333\t293858.1818\t24\t1808\t144688\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tHMCY\tHOMOCYSTEINE\t0.041666667\t293858.1818\t24\t1\t1\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tHYPROL\tHYDROXYPROLINE\t10.41666667\t293858.1818\t24\t250\t4890\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tISOLEU\tISOLEUCINE\t63.91666667\t293858.1818\t24\t1534\t127622\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tLEUCINE\tLEUCINE\t109.0833333\t293858.1818\t24\t2618\t354584\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tLYSINE\tLYSINE\t182.7916667\t293858.1818\t24\t4387\t881781\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tMETHIO\tMETHIONINE\t35.70833333\t293858.1818\t24\t857\t50005\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tORNITHINE\tORNITHINE\t69.33333333\t293858.1818\t24\t1664\t151972\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tPHALN\tPHENYLALANINE\t77.20833333\t293858.1818\t24\t1853\t161815\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tPROLINE\tPROLINE\t185.2916667\t293858.1818\t24\t4447\t919963\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tSARSN\tSARCOSINE\t0\t293858.1818\t24\t0\t0\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tSERINE\tSERINE\t94.875\t293858.1818\t24\t2277\t238807\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTAURINE\tTAURINE\t72.20833333\t293858.1818\t24\t1733\t173895\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTHREONINE\tTHREONINE\t113.7916667\t293858.1818\t24\t2731\t350829\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTRYPTOPHAN\tTRYPTOPHAN\t40.29166667\t293858.1818\t24\t967\t51223\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tTYROSINE\tTYROSINE\t75.29166667\t293858.1818\t24\t1807\t180113\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tVALINE\tVALINE\t188.125\t293858.1818\t24\t4515\t1058849\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t472332\tLABECL\tEUGLOBULIN CLOT LYSIS\tWEACA\tWITH EACA\t9999999\t97411.30435\t24\t239999976\t2.40E+15\t23\t2240460\t3.56E+11
+LAB BLOOD ORDERABLES\t472332\tLABECL\tEUGLOBULIN CLOT LYSIS\tWOEACA\tWITHOUT EACA\t9999999\t97411.30435\t24\t239999976\t2.40E+15\t23\t2240460\t3.56E+11
+LAB BLOOD ORDERABLES\t472514\tLABHEPAR\tHEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY\tHEPAR\tHEPARIN ACTIVITY LEVEL\t833333.5283\t28984.28571\t24\t20000004.68\t2.00E+14\t14\t405780\t22945870800
+LAB BLOOD ORDERABLES\t472518\tLABHAV\tHEPATITIS A IGM AB\tHEPAIGM\tHEPATITIS A AB-IGM\t9999999\t55252.17391\t24\t239999976\t2.40E+15\t23\t1270800\t92547453600
+LAB BLOOD ORDERABLES\t472524\tLAB11107R\tHBE ANTIGEN\tHBEAG\tHEPATITIS B E AG\t9999999\t498591.2069\t24\t239999976\t2.40E+15\t116\t57836580\t1.04E+14
+LAB BLOOD ORDERABLES\t473301\tLABSPIE\tPROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM\tSPIER\tRESULT\t9999999\t254723.2367\t24\t239999976\t2.40E+15\t1415\t360433380\t1.27E+14
+LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tLAB3\tREFERENCE LAB\t9999999\t2214834.286\t24\t239999976\t2.40E+15\t21\t46511520\t3.48E+14
+LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\t12431R\tINTERPRETATION\t9999999\t148917\t24\t239999976\t2.40E+15\t80\t11913360\t4.45E+12
+LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tADGPR\tIGA ANTI DGP ANTIBODY\t9999999\t466236\t24\t239999976\t2.40E+15\t20\t9324720\t5.28E+12
+MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tTEST7\tTEST NAME\t9999999\t576624\t24\t239999976\t2.40E+15\t10\t5766240\t1.60E+13
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV DNA PCR QUANT, PLASMA\tCMVTIP\tTEST DESCRIPTION\t9999999\t154991.0938\t24\t239999976\t2.40E+15\t1801\t279138960\t5.55E+13
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADQLSP\tSPECIMEN\t9999999\t221752.1053\t24\t239999976\t2.40E+15\t38\t8426580\t3.12E+12
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t23\t229999977\t2.30E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t23\t229999977\t2.30E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tPRO\tPRODUCT ID  \t9999999\t28614\t23\t229999977\t2.30E+15\t10\t286140\t25408443600
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRC\tCOMMENT\t9999999\t480680\t23\t229999977\t2.30E+15\t18\t8652240\t6.29E+12
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tLYMFLD\tLYMPHOCYTE %, FLD\t434800.0435\t2374.285714\t23\t10000401\t1.00E+14\t7\t16620\t79326000
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tMONFLD\tMONO/MESO %, FLD\t434817.6957\t2374.285714\t23\t10000807\t1.00E+14\t7\t16620\t79326000
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tNEUTFLD\tNEUTROPHIL %, FLD\t434824.3913\t2374.285714\t23\t10000961\t1.00E+14\t7\t16620\t79326000
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t0.173913043\t41358.26087\t23\t4\t10\t23\t951240\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t385.4782609\t41366.08696\t23\t8866\t3525524.96\t23\t951420\t6.37E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t1278.26087\t23\t229999977\t2.30E+15\t23\t29400\t325238400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t7687.826087\t23\t0\t0\t23\t176820\t23392134000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t11.86956522\t108193.0435\t23\t273\t46061\t23\t2488440\t3.53E+12
+HISTOCOMPATIBILITY ORDERABLE\t979724\t10287R\tHLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT\t10049R\tHLA LAB TESTING STATUS\t9999999\t5317523.478\t23\t229999977\t2.30E+15\t23\t122303040\t2.88E+15
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHEPAIGM\tHEPATITIS A AB-IGM\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t37134.78261\t23\t229999977\t2.30E+15\t23\t854100\t42265436400
+LAB BLOOD ORDERABLES\t471581\tLABYASU\tALPHASUBUNIT PITUITARY MARKER\tYASU1\tALPHA-SUBUNIT\t2173913.357\t476154.5455\t23\t50000007.2\t5.00E+14\t22\t10475400\t5.53E+12
+LAB BLOOD ORDERABLES\t473282\tLABRUB\tRUBELLA AB IGG\tRUB\tRUBELLA IGG ANTIBODY\t9999999\t55632\t23\t229999977\t2.30E+15\t20\t1112640\t1.08E+11
+LAB BLOOD ORDERABLES\t473333\tLABSLIDE\tSLIDE SAVE FOR CLINICIAN\tSLIDE\tSLIDE SAVE FOR CLINICIAN\t9999999\t22314.54545\t23\t229999977\t2.30E+15\t22\t490920\t27778276800
+LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tVWINTP\tINTERPRETATION\t9999999\t50682.5\t23\t229999977\t2.30E+15\t24\t1216380\t92331061200
+LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\tHERG\tHERPESVIRUS 6 AB IGG\t9999999\t287974.7368\t23\t229999977\t2.30E+15\t19\t5471520\t1.92E+12
+LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\tHERM\tHERPESVIRUS 6 AB IGM\t9999999\t287974.7368\t23\t229999977\t2.30E+15\t19\t5471520\t1.92E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12419R\tACETOHEXIMIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12425R\tREPAGLINIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12426R\tTOLAZAMIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12427R\tTOLBUTAMIDE\t9999999\t584971.3043\t23\t229999977\t2.30E+15\t23\t13454340\t9.34E+12
+MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tSPCT\tSPECIMEN TYPE\t9999999\t576624\t23\t229999977\t2.30E+15\t10\t5766240\t1.60E+13
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tWAXCST\tWAXY CASTS\t9999999\t13056\t23\t229999977\t2.30E+15\t15\t195840\t5306227200
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tWBCAST\tWBC CASTS\t9999999\t74595\t23\t229999977\t2.30E+15\t12\t895140\t3.81E+11
+VIROLOGY ORDERABLES\t876719\tLABDVEIAV\tFLU A DFA\tIAVSP\tSPECIMEN\t9999999\t78912.72727\t23\t229999977\t2.30E+15\t33\t2604120\t1.09E+12
+VIROLOGY ORDERABLES\t876733\tLABDVEHV\tHSV/VZV DFA\tHVIP\tTEST DESCRIPTION\t9999999\t61157.32283\t23\t229999977\t2.30E+15\t381\t23300940\t3.03E+12
+BLOOD BANK PRODUCT ORDERABLES\t473540\tLABTBP15\tTRANSFUSION BLOOD PRODUCT 16\tVL\tPRODUCT VOLUME\t168.9090909\t32300\t22\t3716\t632852\t3\t96900\t3211477200
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tSI\t<<STATUS>>  \t9999999\t26370\t22\t219999978\t2.20E+15\t28\t738360\t56790936000
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tSI\t<<STATUS>>  \t9999999\t25982.22222\t22\t219999978\t2.20E+15\t27\t701520\t55275112800
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tSI\t<<STATUS>>  \t9999999\t15080\t22\t219999978\t2.20E+15\t24\t361920\t13517726400
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tDI\tISSUE DATE/TIME\t9999999\t25560\t22\t219999978\t2.20E+15\t12\t306720\t10244714400
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tDI\tISSUE DATE/TIME\t9999999\t31915\t22\t219999978\t2.20E+15\t12\t382980\t18645937200
+BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tDI\tISSUE DATE/TIME\t9999999\t35912.72727\t22\t219999978\t2.20E+15\t11\t395040\t20441440800
+BODY FLUID ORDERABLES\t471956\tLABBFCTS\tCELL COUNT, BODY FLUID\tFCOLOR\tAPPEARANCE\t9999999\t8266.666667\t22\t219999978\t2.20E+15\t9\t74400\t837540000
+BODY FLUID ORDERABLES\t472552\tLABYHISTC\tHISTOPLASMA AG, CSF\tYHISC4\tRESULT,CSF\t9999999\t522603.1579\t22\t219999978\t2.20E+15\t19\t9929460\t5.38E+12
+BODY FLUID ORDERABLES\t497388\tLABFURICA\tURIC ACID, FLUID\tFSC2\tFLUID TYPE\t9999999\t611090\t22\t219999978\t2.20E+15\t12\t7333080\t7.20E+12
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGDNM3\tDNMT3A\t9999999\t921340.9091\t22\t219999978\t2.20E+15\t22\t20269500\t2.19E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGERHR\tERBB2/HER2\t9999999\t921340.9091\t22\t219999978\t2.20E+15\t22\t20269500\t2.19E+13
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVPCD\tBRADY PACED AV DELAY\t192.9545455\t8023.636364\t22\t4245\t929825\t22\t176520\t32664470400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t118.4090909\t7966.363636\t22\t2605\t311025\t22\t175260\t32664970800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRTRKRT\tBRADY TRACKING RATE\t121.8181818\t8023.636364\t22\t2680\t330100\t22\t176520\t32664470400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0.954545455\t43270.90909\t22\t21\t233\t22\t951960\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t3.454545455\t43254.54545\t22\t76\t1762\t22\t951600\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.246590909\t8023.636364\t22\t49.425\t126.255625\t22\t176520\t32664470400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.481818182\t8023.636364\t22\t10.6\t6.24\t22\t176520\t32664470400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t5454649.227\t7947.272727\t22\t120002283\t1.20E+15\t22\t174840\t32663498400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t8040\t22\t0\t0\t22\t176880\t23392130400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t8040\t22\t0\t0\t22\t176880\t23392130400
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9078R\tINTERPRETATION/ COMMENTS\t9999999\t277473.3582\t22\t219999978\t2.20E+15\t268\t74362860\t1.44E+14
+LAB BLOOD ORDERABLES\t471809\tLABYC1IN\tC1 ESTERASE INHIBITOR AG\tYC1INH\tC1 ESTERASE INH. AG\t454576.2727\t158980.9091\t22\t10000678\t1.00E+14\t22\t3497580\t6.14E+11
+LAB BLOOD ORDERABLES\t473220\tLABRP\tRENAL FUNCTION PANEL\tEGFR\tEGFR\t9545455.864\t8005.714286\t22\t210000029\t2.10E+15\t21\t168120\t3571308000
+LAB BLOOD ORDERABLES\t473759\tLABYVD125\t1,25-DIHYDROXYVITAMIN D, SERUM\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t2272756.864\t319000.9091\t22\t50000651\t5.00E+14\t22\t7018020\t2.65E+12
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12011R\tQ FEVER IGM PHASE II  SCRN\t9999999\t308861.9118\t22\t219999978\t2.20E+15\t136\t42005220\t1.99E+13
+LAB BLOOD ORDERABLES\t670806\tLAB11182R\tNICOTINE & METABOLITES\t11183R\tNICOTINE\t9090908.786\t288117.3913\t22\t199999993.3\t2.00E+15\t23\t6626700\t2.09E+12
+LAB BLOOD ORDERABLES\t670806\tLAB11182R\tNICOTINE & METABOLITES\t11184R\tCOTININE\t5454574.364\t288117.3913\t22\t120000636\t1.20E+15\t23\t6626700\t2.09E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12422R\tGLIPIZIDE\t9999999\t584971.3043\t22\t219999978\t2.20E+15\t23\t13454340\t9.34E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12423R\tGLYBURIDE\t9999999\t584971.3043\t22\t219999978\t2.20E+15\t23\t13454340\t9.34E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12424R\tNATEGLINIDE\t9999999\t584971.3043\t22\t219999978\t2.20E+15\t23\t13454340\t9.34E+12
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY / TISSUE CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t547240.7303\t22\t219999978\t2.20E+15\t1068\t584453100\t2.37E+15
+POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tTESTEX\tTEST STRIP EXP\t9999999\t1802.142857\t22\t219999978\t2.20E+15\t28\t50460\t6153973200
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12821R\tPOTASSIUM, FECES\t909122.5\t211889.1429\t22\t20000695\t2.00E+14\t35\t7416120\t2.14E+12
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12822R\tCHLORIDE, FECES\t2272772.227\t211889.1429\t22\t50000989\t5.00E+14\t35\t7416120\t2.14E+12
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12824R\tMAGNESIUM, FECES\t1818211.773\t211889.1429\t22\t40000659\t4.00E+14\t35\t7416120\t2.14E+12
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12825R\tPHOSPHORUS, FECES\t1363665\t211889.1429\t22\t30000630\t3.00E+14\t35\t7416120\t2.14E+12
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12826R\tOSMOTIC GAP, FECES\t1818255.727\t211889.1429\t22\t40001626\t4.00E+14\t35\t7416120\t2.14E+12
+URINE ORDERABLES\t471819\tLABUCA\tCALCIUM WITH CREATININE, URINE (RANDOM)\tCACREAT\tCALCIUM/CREAT RATIO\t6363635.906\t10580\t22\t139999989.9\t1.40E+15\t15\t158700\t7357388400
+URINE ORDERABLES\t471819\tLABUCA\tCALCIUM WITH CREATININE, URINE (RANDOM)\tCAURN\tCALCIUM, URINE\t5909094.727\t10580\t22\t130000084\t1.30E+15\t15\t158700\t7357388400
+URINE ORDERABLES\t471819\tLABUCA\tCALCIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t51.56818182\t10580\t22\t1134.5\t95189.73\t15\t158700\t7357388400
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tTIMEU\tCOLLECTION PERIOD\t1363656.955\t309984\t22\t30000453\t3.00E+14\t10\t3099840\t1.05E+12
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tUCR\tCREATININE, URINE\t1364862.636\t309984\t22\t30026978\t3.00E+14\t10\t3099840\t1.05E+12
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tUCR\tCREATININE, URN\t66.54545455\t309984\t22\t1464\t127870\t10\t3099840\t1.05E+12
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tUVOL\tVOLUME\t457009.0955\t309984\t22\t10054200.1\t1.00E+14\t10\t3099840\t1.05E+12
+VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVRC1\tCMV DAY 1 RAPID CULTURE\t9999999\t175699.0909\t22\t219999978\t2.20E+15\t22\t3865380\t7.11E+11
+VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVRC2\tCMV DAY 2 RAPID CULTURE\t9999999\t175699.0909\t22\t219999978\t2.20E+15\t22\t3865380\t7.11E+11
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t21\t209999979\t2.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tSI\t<<STATUS>>  \t9999999\t30282.22222\t21\t209999979\t2.10E+15\t27\t817620\t69801476400
+BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tDI\tISSUE DATE/TIME\t9999999\t35186.66667\t21\t209999979\t2.10E+15\t9\t316680\t17547530400
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tUBT\tUNIT BLOOD TYPE  \t9999999\t76960\t21\t209999979\t2.10E+15\t9\t692640\t99484596000
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tPRO\tPRODUCT ID  \t9999999\t35752.5\t21\t209999979\t2.10E+15\t8\t286020\t25391120400
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tUBT\tUNIT BLOOD TYPE  \t9999999\t35022\t21\t209999979\t2.10E+15\t10\t350220\t18667335600
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tBTURB\tTURBIDITY, CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCOLORCSF\tCOLOR AFTER CENTRIFUGE, CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCOLORCSF\tCOLOR, CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCSOURC\tSOURCE\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tCTURB\tTURB. AFTER CENTRIF., CSF\t9999999\t4722.857143\t21\t209999979\t2.10E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tRBCCSF\tRBC COUNT, CSF\t2395710.952\t4722.857143\t21\t50309930\t5.00E+14\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tWBCCSF\tWBC COUNT, CSF\t1904875.619\t4722.857143\t21\t40002388\t4.00E+14\t7\t33060\t198118800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t45414.28571\t21\t209999979\t2.10E+15\t21\t953700\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0.095238095\t45348.57143\t21\t2\t2\t21\t952320\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t0.142857143\t45320\t21\t3\t3\t21\t951720\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t596.7619048\t8357.142857\t21\t12532\t20214055.5\t21\t175500\t32662890000
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.298809524\t8374.285714\t21\t48.275\t141.388125\t21\t175860\t32664632400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.661904762\t8374.285714\t21\t13.9\t13.45\t21\t175860\t32664632400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t1360\t21\t209999979\t2.10E+15\t21\t28560\t324014400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t1360\t21\t209999979\t2.10E+15\t21\t28560\t324014400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t1360\t21\t209999979\t2.10E+15\t21\t28560\t324014400
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9471R\tDONOR DOB\t9999999\t20\t21\t209999979\t2.10E+15\t21\t420\t25200
+LAB BLOOD ORDERABLES\t472546\tLAB11042R\tHISTAMINE LEVEL\tHIST\tHISTAMINE, WB\t3337422.857\t432563.3333\t21\t70085880\t7.00E+14\t18\t7786140\t4.19E+12
+LAB BLOOD ORDERABLES\t472869\tLABYAMA\tMITOCHONDRIAL AB M2, SERUM\tYAMA\tMITOCHONDRIAL ANTIBODY, M2\t8571427.781\t204642.8571\t21\t179999983.4\t1.80E+15\t21\t4297500\t9.61E+11
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12421R\tGLIMEPIRIDE\t9999999\t584971.3043\t21\t209999979\t2.10E+15\t23\t13454340\t9.34E+12
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12820R\tSODIUM, FECES\t952442.8571\t211889.1429\t21\t20001300\t2.00E+14\t35\t7416120\t2.14E+12
+STOOL ORDERABLES\t961577\tLAB12810R\tELECTROLYTE & OSMO PNL, FECES\t12823R\tOSMOLALITY, FECES\t952709.4762\t211889.1429\t21\t20006899\t2.00E+14\t35\t7416120\t2.14E+12
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVNO\tNOTES\t9999999\t345906.879\t21\t209999979\t2.10E+15\t157\t54307380\t2.62E+13
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN (EBNA) IGG\tEBNQNT\tEBNQNT\t2857144.104\t298560\t21\t60000026.18\t6.00E+14\t21\t6269760\t3.42E+12
+BLOOD BANK PRODUCT ORDERABLES\t473551\tLABTBP16\tTRANSFUSION BLOOD PRODUCT 17\tVL\tPRODUCT VOLUME\t171.95\t28428\t20\t3439\t593139\t5\t142140\t4257471600
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tVL\tPRODUCT VOLUME\t161.5\t35670\t20\t3230\t527736\t4\t142680\t5214686400
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tXMR\tUNIT CROSSMATCH  \t9999999\t41585.45455\t20\t199999980\t2.00E+15\t11\t457440\t47298261600
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tSI\t<<STATUS>>  \t9999999\t15915.65217\t20\t199999980\t2.00E+15\t23\t366060\t13709250000
+BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tDI\tISSUE DATE/TIME\t9999999\t30512.72727\t20\t199999980\t2.00E+15\t11\t335640\t17437413600
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSOTHER\tOTHERS, SYN FLD\t12.75\t7413.333333\t20\t255\t12149\t9\t66720\t621388800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tDX\tDIAGNOSIS\t9500009.3\t4722.857143\t20\t190000186\t1.90E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tMALIG\tSUSPECT MALIGNANCY?\t9999999\t4722.857143\t20\t199999980\t2.00E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t471957\tLABCCT\tCELL COUNT, CSF\tTUBENO\tCSF TUBE #\t7999999.65\t4722.857143\t20\t159999993\t1.60E+15\t7\t33060\t198118800
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBMPMLR\tPML, BM\t1000000.045\t644650\t20\t20000000.91\t2.00E+14\t18\t11603700\t8.75E+12
+BODY FLUID ORDERABLES\t669983\tLAB11000R\tCEA, PANCREATIC CYST\t11001R\tCEA, PANCREATIC CYST\t200.595\t174390\t20\t4011.9\t3682861.81\t14\t2441460\t4.88E+11
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t17244\t20\t199999980\t2.00E+15\t20\t344880\t18588276000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t8500729.6\t17244\t20\t170014592\t1.70E+15\t20\t344880\t18588276000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t17244\t20\t199999980\t2.00E+15\t20\t344880\t18588276000
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t31.221\t8763\t20\t624.42\t44567.0186\t20\t175260\t32663235600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t8.5533\t47601\t20\t171.066\t1550.642352\t20\t952020\t6.37E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t9500001.55\t8973\t20\t190000031\t1.90E+15\t20\t179460\t23419731600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t9500000.55\t8973\t20\t190000011\t1.90E+15\t20\t179460\t23419731600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t144\t8973\t20\t2880\t423000\t20\t179460\t23419731600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t192.6\t8973\t20\t3852\t762704\t20\t179460\t23419731600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t9499999.8\t8973\t20\t189999996\t1.90E+15\t20\t179460\t23419731600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t9500001.55\t8973\t20\t190000031\t1.90E+15\t20\t179460\t23419731600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t47139\t20\t199999980\t2.00E+15\t20\t942780\t5.21E+11
+LAB BLOOD ORDERABLES\t471527\tLABYANTSM\tAUTOANTIBODIES TO SM\tSMAB\tAUTOANTIBODIES TO SM\t8499999.29\t193333.3333\t20\t169999985.8\t1.70E+15\t27\t5220000\t1.23E+12
+LAB BLOOD ORDERABLES\t471679\tLABYAVH\tARGININE VASOPRESSIN (ADH)\tARGININEVP\tARGININE VASOPRESSIN\t4000000.97\t622883.3333\t20\t80000019.4\t8.00E+14\t18\t11211900\t7.91E+12
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32R2\tRESULT 2\t9999999\t1049052\t20\t199999980\t2.00E+15\t20\t20981040\t2.76E+13
+LAB BLOOD ORDERABLES\t472482\tLAB11080R\tGAD AB\tGAD65AB\tGAD65 ANTIBODY\t0.4965\t454171.7647\t20\t9.93\t82.2047\t17\t7720920\t4.32E+12
+LAB BLOOD ORDERABLES\t472496\tLABHBODY\tHEINZ BODY PREP\tHBODY\tHEINZ BODY PREP\t9499999.75\t29740\t20\t189999995\t1.90E+15\t18\t535320\t30060064800
+LAB BLOOD ORDERABLES\t472523\tLAB11106R\tHBE AB\tHBEAB\tHEPATITIS B E AB\t9999999\t451540.9615\t20\t199999980\t2.00E+15\t104\t46960260\t6.91E+13
+LAB BLOOD ORDERABLES\t472553\tLABHIVWBL\tANTI-HIV\tHIVI\tINTERPRETATION\t9999999\t51579.60028\t20\t199999980\t2.00E+15\t4303\t221947020\t2.06E+13
+LAB BLOOD ORDERABLES\t472847\tLABYMEXIL\tMEXILETINE\tMEXIL\tMEXILETINE\t500000.715\t378385\t20\t10000014.3\t1.00E+14\t24\t9081240\t3.97E+12
+LAB BLOOD ORDERABLES\t473057\tLAB11088R\tPP\tYPANPP\tPANCREATIC POLYPEPTIDE\t500286.7\t642441\t20\t10005734\t1.00E+14\t20\t12848820\t9.25E+12
+LAB BLOOD ORDERABLES\t473176\tLABYPR3\tPROTEINASE 3 AUTOANTIBODIES\tYPR31\tPROTEINASE 3 ANTIBODIES\t8499999.615\t162284.5161\t20\t169999992.3\t1.70E+15\t31\t5030820\t8.87E+11
+LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOSBIO\tTESTOSTERONE,BIOAVAILABLE, S\t500087.445\t366215.5556\t20\t10001748.9\t1.00E+14\t27\t9887820\t4.07E+12
+LAB BLOOD ORDERABLES\t497414\tLABFHAN\tHANTAVIRUS AB ELISA\tHANIGG\tHANTAVIRUS IGG\t8999999.3\t223917.1429\t20\t179999986\t1.80E+15\t21\t4702260\t1.18E+12
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\t12064R\tY.ENTERCOCLITICA IGG\t8499999.321\t424091.4286\t20\t169999986.4\t1.70E+15\t21\t8905920\t4.71E+12
+LAB BLOOD ORDERABLES\t668180\tLABDWNIL\tDONOR, WEST NILE VIRUS NAT\tWNIL1\tDONOR, WEST NILE VIRUS NAT\t9999999\t255850\t20\t199999980\t2.00E+15\t6\t1535100\t4.20E+11
+LAB BLOOD ORDERABLES\t668184\tLABDCGSN\tDONOR, CHAGAS SCN W/REFL\t11893R\tDONOR, CHAGAS RIPA\t9999999\t245110\t20\t199999980\t2.00E+15\t6\t1470660\t3.86E+11
+LAB BLOOD ORDERABLES\t668184\tLABDCGSN\tDONOR, CHAGAS SCN W/REFL\tCGSN1\tDONOR, CHAGAS SCN W/REFL\t9999999\t245110\t20\t199999980\t2.00E+15\t6\t1470660\t3.86E+11
+LAB BLOOD ORDERABLES\t744262\tLAB11566R\tENTEROVIRUS RNA, QUAL\t11679R\tSOURCE\t9999999\t474590\t20\t199999980\t2.00E+15\t30\t14237700\t3.71E+13
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12301R\t12301R\t6000003.4\t200448\t20\t120000068\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12302R\t12302R\t6000000.75\t200448\t20\t120000015\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12303R\t12303R\t6000005.635\t200448\t20\t120000112.7\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12304R\t12304R\t6000002.25\t200448\t20\t120000045\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12305R\t12305R\t6000003.37\t200448\t20\t120000067.4\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12306R\t12306R\t6000086.6\t200448\t20\t120001732\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12307R\t12307R\t6000079.6\t200448\t20\t120001592\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12308R\t12308R\t6000057.4\t200448\t20\t120001148\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12309R\t12309R\t6000022.95\t200448\t20\t120000459\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12310R\t12310R\t6000076.75\t200448\t20\t120001535\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12311R\t12311R\t6000043\t200448\t20\t120000860\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12312R\t12312R\t6000008.15\t200448\t20\t120000163\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12313R\t12313R\t6000010.95\t200448\t20\t120000219\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12314R\t12314R\t6000004.6\t200448\t20\t120000092\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12315R\t12315R\t6000001.15\t200448\t20\t120000023\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12316R\t12316R\t6000028.8\t200448\t20\t120000576\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12317R\t12317R\t6000008.9\t200448\t20\t120000178\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12318R\t12318R\t6000004.55\t200448\t20\t120000091\t1.20E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t910572\tLAB12418R\tSULFONYLUREA HYPOGLYCEMICS PNL\t12420R\tCHLORPROPAMIDE\t9999999\t584971.3043\t20\t199999980\t2.00E+15\t23\t13454340\t9.34E+12
+MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tRPT\tRPT\t9999999\t60293.33333\t20\t199999980\t2.00E+15\t9\t542640\t43775150400
+MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tSDES\tSPECIMEN\t9999999\t60293.33333\t20\t199999980\t2.00E+15\t9\t542640\t43775150400
+MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tSREQ\tCOMMENTS\t9999999\t60293.33333\t20\t199999980\t2.00E+15\t9\t542640\t43775150400
+MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tRPT\tRPT\t9999999\t259140\t20\t199999980\t2.00E+15\t14\t3627960\t1.12E+12
+MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tSDES\tSPECIMEN\t9999999\t259140\t20\t199999980\t2.00E+15\t14\t3627960\t1.12E+12
+POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tQC12\tQC RESULT\t9999999\t1802.142857\t20\t199999980\t2.00E+15\t28\t50460\t6153973200
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN (EBNA) IGG\tEBNQUL\tEBNQUL\t9999999\t298560\t20\t199999980\t2.00E+15\t21\t6269760\t3.42E+12
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBCP\tEBV BAMHI-W RESULT\t9500015.3\t403485\t20\t190000306\t1.90E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBDN\tEBV BAMHI-W DNA\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBLG\tEBV BAMHI-W RESULT LOG 10\t9499999.176\t403485\t20\t189999983.5\t1.90E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBSP\tSPECIMEN\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQCP\tRESULT\t9500012.75\t403485\t20\t190000255\t1.90E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQDN\tEBV DNA\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQLG\tRESULT LOG 10\t9499999.172\t403485\t20\t189999983.4\t1.90E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQSP\tSPECIMEN\t9999999\t403485\t20\t199999980\t2.00E+15\t20\t8069700\t1.11E+13
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tVL\tPRODUCT VOLUME\t164\t20914.28571\t19\t3116\t527556\t7\t146400\t3773671200
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tSI\t<<STATUS>>  \t9999999\t26390\t19\t189999981\t1.90E+15\t24\t633360\t53763537600
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tSI\t<<STATUS>>  \t9999999\t18257.14286\t19\t189999981\t1.90E+15\t21\t383400\t18645969600
+BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tUBT\tUNIT BLOOD TYPE  \t9999999\t81690\t19\t189999981\t1.90E+15\t8\t653520\t97952421600
+BLOOD BANK PRODUCT ORDERABLES\t889182\tLABERUCRY\tEMERGENCY RELEASE CRYO\tCRSTA\tCRYOPRECIPITATE UNITS\t9999999\t7297.894737\t19\t189999981\t1.90E+15\t19\t138660\t1707145200
+BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tLYMCSF\tLYMPHOCYTES, CSF\t2631608.263\t4305\t19\t50000557\t5.00E+14\t4\t17220\t78354000
+BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tMONOCSF\tMONOCYTOID CELLS, CSF\t2631596.105\t4305\t19\t50000326\t5.00E+14\t4\t17220\t78354000
+BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tNEUTCSF\tNEUTROPHILS, CSF\t2631604.684\t4305\t19\t50000489\t5.00E+14\t4\t17220\t78354000
+BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14MT\tMETHOD\t9999999\t1070614.286\t19\t189999981\t1.90E+15\t7\t7494300\t9.42E+12
+BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14PM\tPRIMERS\t9999999\t1070614.286\t19\t189999981\t1.90E+15\t7\t7494300\t9.42E+12
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAMSRT\tBRADY AMS RATE\t175.6315789\t9306.315789\t19\t3337\t590607\t19\t176820\t32664265200
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t6315792.842\t40553.68421\t19\t120000064\t1.20E+15\t19\t770520\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t1052673.105\t50210.52632\t19\t20000789\t2.00E+14\t19\t954000\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t532.2631579\t9255.789474\t19\t10113\t6039575.5\t19\t175860\t32662587600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tATACHTADTI\tATACH TACHY ZONE DETECTION INTERVAL\t354.7368421\t9464.210526\t19\t6740\t2393800\t19\t179820\t23419602000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t9464.210526\t19\t189999981\t1.90E+15\t19\t179820\t23419602000
+LAB BLOOD ORDERABLES\t471608\tLABYAMNOR\tAMITRIPTYLINE AND NORTRIPTYLINE\tAMINOR\tAMITRIPT & NORTRIPT\t4210660.579\t266919\t19\t80002551\t8.00E+14\t20\t5338380\t1.62E+12
+LAB BLOOD ORDERABLES\t471608\tLABYAMNOR\tAMITRIPTYLINE AND NORTRIPTYLINE\tAMITRP\tAMITRIPTYLINE, PLASMA\t4210624.316\t266919\t19\t80001862\t8.00E+14\t20\t5338380\t1.62E+12
+LAB BLOOD ORDERABLES\t471608\tLABYAMNOR\tAMITRIPTYLINE AND NORTRIPTYLINE\tNORTRP\tNORTRIPTYLINE\t3157933.368\t266919\t19\t60000734\t6.00E+14\t20\t5338380\t1.62E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHAIRY\tHAIRY CELLS\t11.78947368\t30136.66667\t19\t224\t6458\t18\t542460\t83257894800
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBASO\tBASO, %\t1052631.753\t4940\t19\t20000003.3\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBASOAB\tBASOS, ABS\t1052631.497\t4940\t19\t19999998.45\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tEOS\tEOS, %\t1052632.668\t4940\t19\t20000020.7\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tEOSAB\tEOS, ABS\t1052631.574\t4940\t19\t19999999.91\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tLYM\tLYM, %\t1052642.832\t4940\t19\t20000213.8\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tLYMAB\tLYM, ABS\t1052632.338\t4940\t19\t20000014.42\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMONO\tMONO, %\t1052639.211\t4940\t19\t20000145\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMONOAB\tMONO, ABS\t1052631.942\t4940\t19\t20000006.9\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNEUT\tNEUT, %\t1052700.379\t4940\t19\t20001307.2\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNEUTAB\tNEUT, ABS\t1052636.928\t4940\t19\t20000101.64\t2.00E+14\t6\t29640\t511128000
+LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHMET\tMETHOD\t9999999\t638456.25\t19\t189999981\t1.90E+15\t16\t10215300\t8.63E+12
+LAB BLOOD ORDERABLES\t473705\tLABREF2\tLAB UNLISTED 2\tRESUL2\tRESULT\t9999999\t1896672.381\t19\t189999981\t1.90E+15\t63\t119490360\t1.36E+15
+LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\tECIGG\tE.CHAFFEENSIS IGG TITER\t9999999\t905448.75\t19\t189999981\t1.90E+15\t16\t14487180\t6.76E+13
+LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\tECIGM\tE.CHAFFEENSIS IGM TITER\t9999999\t905448.75\t19\t189999981\t1.90E+15\t16\t14487180\t6.76E+13
+LAB BLOOD ORDERABLES\t541666\tLABC153\tCANCER AG 15-3\tC153\tCANCER ANTIGEN 15-3\t74.36842105\t43998.94737\t19\t1413\t929577\t19\t835980\t72087663600
+LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR HIV1 / HBV / HCV NAT\t11894R\tDISCRIMINATORY TESTING\t9999999\t277700\t19\t189999981\t1.90E+15\t3\t833100\t2.36E+11
+LAB BLOOD ORDERABLES\t670724\tLAB11312R\tSOLUBLE LIVER AG AB, IGG\t11313R\tSOLUBLE LIVER AG AB, IGG\t2.484210526\t246884.2105\t19\t47.2\t133.02\t19\t4690800\t1.35E+12
+LAB BLOOD ORDERABLES\t744256\tLAB11562R\tLYMPHOCYTIC CHORIOMENINGITI AB\t11672R\tLCM IGG\t9999999\t254046.6667\t19\t189999981\t1.90E+15\t18\t4572840\t1.33E+12
+LAB BLOOD ORDERABLES\t744256\tLAB11562R\tLYMPHOCYTIC CHORIOMENINGITI AB\t11673R\tLCM IGM\t9999999\t254046.6667\t19\t189999981\t1.90E+15\t18\t4572840\t1.33E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tASMINT\tANTI-SM INTERPRETATION\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tASMQUL\tANTI-SM RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tCENQUL\tANTI-CENTROMERE RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tDNAQUL\tANTI-DSDNA RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tJO1QUL\tANTI-JO1 RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tRNPQUL\tANTI-RNP RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSCLQUL\tANTI-SCL70 RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSAQUL\tANTI-RO RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSBQUL\tANTI-LA RESULT\t9999999\t441287.3684\t19\t189999981\t1.90E+15\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12294R\t12294R\t5789488.526\t200448\t19\t110000282\t1.10E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12295R\t12295R\t5789502.947\t200448\t19\t110000556\t1.10E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12296R\t12296R\t5789484.526\t200448\t19\t110000206\t1.10E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12297R\t12297R\t5789495.684\t200448\t19\t110000418\t1.10E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12298R\t12298R\t5789485.947\t200448\t19\t110000233\t1.10E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12299R\t12299R\t5789476.158\t200448\t19\t110000047\t1.10E+15\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12300R\t12300R\t5789482.421\t200448\t19\t110000166\t1.10E+15\t20\t4008960\t1.99E+12
+MICROBIOLOGY ORDERABLES\t471621\tLABANER2\tANAEROBIC CULTURE, SHORT BATTERY\tCULT\tCULT/OTHER RSLT:\t9999999\t452979\t19\t189999981\t1.90E+15\t1080\t489217320\t4.09E+14
+MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tSREQ\tCOMMENTS\t9999999\t259140\t19\t189999981\t1.90E+15\t14\t3627960\t1.12E+12
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tWAXCST\tWAXY CASTS\t9999999\t65307.69231\t19\t189999981\t1.90E+15\t13\t849000\t3.29E+11
+VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEBV EARLY ANTIGEN IGG\tEAGQUL\tEAGQUL\t9999999\t285229.4118\t19\t189999981\t1.90E+15\t17\t4848900\t2.81E+12
+VIROLOGY ORDERABLES\t978516\tLABRESPCR\tRESPIRATORY VIRUS PCR PANEL\tRESVDI\tTEST DESCRIPTION\t9999999\t97678.90244\t19\t189999981\t1.90E+15\t820\t80096700\t1.50E+13
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t18\t179999982\t1.80E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tXMR\tUNIT CROSSMATCH  \t9999999\t45280\t18\t179999982\t1.80E+15\t9\t407520\t45979106400
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tPRO\tPRODUCT ID  \t9999999\t20477.14286\t18\t179999982\t1.80E+15\t7\t143340\t4565293200
+BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tPRO\tPRODUCT ID  \t9999999\t24574.28571\t18\t179999982\t1.80E+15\t7\t172020\t6302466000
+BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tPRO\tPRODUCT ID  \t9999999\t24488.57143\t18\t179999982\t1.80E+15\t7\t171420\t6256458000
+BODY FLUID ORDERABLES\t471692\tLABASPPCP\tASPERGILLUS AB PRECIPITINS\tASPIGG\tASPERGILLUS IGG ABY\t9999999\t407597.6471\t18\t179999982\t1.80E+15\t17\t6929160\t3.73E+12
+BODY FLUID ORDERABLES\t472024\tLABSWTCL\tCHLORIDE, SWEAT\tCLSWT\tSWEAT CHLORIDE\t1111142.056\t57372\t18\t20000557\t2.00E+14\t5\t286860\t74246965200
+BODY FLUID ORDERABLES\t472024\tLABSWTCL\tCHLORIDE, SWEAT\tSCLW\tSWEAT COLLECTED\t0.183222222\t57372\t18\t3.298\t0.78907\t5\t286860\t74246965200
+BODY FLUID ORDERABLES\t978518\tLAB12721R\tADENOSINE DEAMINASE, PLEURAL\t12720R\tADENOSINE DEAMINASE, PLEURAL\t5000001.483\t211971.4286\t18\t90000026.7\t9.00E+14\t21\t4451400\t1.15E+12
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t19046.66667\t18\t179999982\t1.80E+15\t18\t342840\t18586195200
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t8947983.889\t19046.66667\t18\t161063710\t1.60E+15\t18\t342840\t18586195200
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMTLBATIMP\tPM TELEMETRY BATTERY IMPEDANCE\t555556.2695\t623.3333333\t18\t10000012.85\t1.00E+14\t18\t11220\t200293200
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t25.22222222\t52890\t18\t454\t108006\t18\t952020\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t10.01055556\t52976.66667\t18\t180.19\t2343.1371\t18\t953580\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t52976.66667\t18\t179999982\t1.80E+15\t18\t953580\t6.37E+11
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t352.0555556\t9403.333333\t18\t6337\t39426559\t18\t169260\t23349848400
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9634R\tREFERENCE SERUM DESCRIPTION\t9999999\t30\t18\t179999982\t1.80E+15\t18\t540\t32400
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMGQUL\tCMV IGG RESULT\t9999999\t386356.6667\t18\t179999982\t1.80E+15\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMMQUL\tCMV IGM RESULT\t9999999\t386356.6667\t18\t179999982\t1.80E+15\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMV\tCMV ANTIBODY\t9999999\t370200\t18\t179999982\t1.80E+15\t14\t5182800\t2.46E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t370200\t18\t179999982\t1.80E+15\t14\t5182800\t2.46E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVGGQNT\tVZV IGG LEVEL\t1111113.181\t386356.6667\t18\t20000037.26\t2.00E+14\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVGGQUL\tVZV IGG RESULT\t9999999\t386356.6667\t18\t179999982\t1.80E+15\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t370200\t18\t179999982\t1.80E+15\t14\t5182800\t2.46E+12
+LAB BLOOD ORDERABLES\t472468\tLABYGLUC\tGLUCAGON\tGLUCAGON\tGLUCAGON\t1666696\t433484.2105\t18\t30000528\t3.00E+14\t19\t8236200\t4.79E+12
+LAB BLOOD ORDERABLES\t472503\tLABHGBS\tHEMOGLOBIN, SERUM\tHGB\tHEMOGLOBIN, SERUM\t7.355555556\t223452.8571\t18\t132.4\t2463.7\t14\t3128340\t8.17E+11
+LAB BLOOD ORDERABLES\t472627\tLABCYLEX\tIMMUNE CELL FUNCTION (CYLEX)\tCYCLXR\tIMMUNE CELL FUNCTION\t2222389.611\t133493.0769\t18\t40003013\t4.00E+14\t26\t3470820\t6.46E+11
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKRE\tRESULT\t9999999\t557059.322\t18\t179999982\t1.80E+15\t118\t65733000\t4.63E+13
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNRS\tQUANTIFERON-TB\t9999999\t449070\t18\t179999982\t1.80E+15\t18\t8083260\t1.64E+13
+LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tADGPT\tIGA ANTI DGP ANTIBODY LEVEL\t9444444.333\t449910\t18\t169999998\t1.70E+15\t16\t7198560\t4.12E+12
+LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR HIV1 / HBV / HCV NAT\tHIBCV1\tDONOR HIV/HBV/HCV RESULT\t9999999\t277700\t18\t179999982\t1.80E+15\t3\t833100\t2.36E+11
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tCENINT\tANTI-CENTROMERE INTERPRETATION\t9999999\t441287.3684\t18\t179999982\t1.80E+15\t19\t8384460\t4.72E+12
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tRPT\tRPT\t9999999\t471560\t18\t179999982\t1.80E+15\t18\t8488080\t5.50E+12
+POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tHPYL\tPATIENT HPYLORI\t9999999\t1802.142857\t18\t179999982\t1.80E+15\t28\t50460\t6153973200
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tHVA\tHOMOVANILLIC ACID\t1666670.494\t309984\t18\t30000068.9\t3.00E+14\t10\t3099840\t1.05E+12
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tVMA\tVMA\t1666672.144\t309984\t18\t30000098.6\t3.00E+14\t10\t3099840\t1.05E+12
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tVMAUR\tVMA, URINE\t5.388888889\t309984\t18\t97\t1227\t10\t3099840\t1.05E+12
+VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-PLASMA\tCMVLIP\tTEST DESCRIPTION\t9999999\t252366.8139\t18\t179999982\t1.80E+15\t317\t80000280\t2.70E+13
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV BY PCR QUANTITATIVE\tEBVIPR\tTEST DESCRIPTION\t9999999\t289760.8349\t18\t179999982\t1.80E+15\t527\t152703960\t5.49E+13
+VIROLOGY ORDERABLES\t876713\tLABDVERVR\tRESP DFA PANEL-REFLEX PCR\tRVRPIP\tTEST DECRIPTION\t9999999\t106230.4\t18\t179999982\t1.80E+15\t225\t23901840\t1.05E+13
+BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tPRO\tPRODUCT ID  \t9999999\t23710\t17\t169999983\t1.70E+15\t6\t142260\t5357336400
+BODY FLUID ORDERABLES\t497388\tLABFURICA\tURIC ACID, FLUID\tURICFLD\tURIC ACID BODY FLUID\t588239.6235\t628560\t17\t10000073.6\t1.00E+14\t13\t8171280\t7.90E+12
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t10316.47059\t17\t169999983\t1.70E+15\t17\t175380\t32663163600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t10316.47059\t17\t169999983\t1.70E+15\t17\t175380\t32663163600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t10316.47059\t17\t169999983\t1.70E+15\t17\t175380\t32663163600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t2353116.235\t10380\t17\t40002976\t4.00E+14\t17\t176460\t32663192400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.048529412\t56212.94118\t17\t17.825\t23.808125\t17\t955620\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVTHRDUR\tRV THRESHOLD DURATION\t0.505882353\t56212.94118\t17\t8.6\t5.2\t17\t955620\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t6470593.176\t45412.94118\t17\t110000084\t1.10E+15\t17\t772020\t6.04E+11
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t5294117.618\t9451.764706\t17\t89999999.5\t9.00E+14\t17\t160680\t12571538400
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9480R\tLOCUS 6\t9999999\t15521.25\t17\t169999983\t1.70E+15\t16\t248340\t61404872400
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9481R\tLOCUS 7\t9999999\t15521.25\t17\t169999983\t1.70E+15\t16\t248340\t61404872400
+LAB BLOOD ORDERABLES\t471928\tLABYACIGA\tCARDIOLIPIN AB, IGA\tYACIG1\tCARDIOLIPIN AB,IGA\t8823530.5\t183673.6364\t17\t150000018.5\t1.50E+15\t22\t4040820\t8.95E+11
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tMEGA\tMEGAKARY/100WBC\t1.470588235\t83771.25\t17\t25\t47\t16\t1340340\t4.97E+11
+LAB BLOOD ORDERABLES\t472654\tLABYIGF2\tINSULIN LIKE GROWTH FACTOR II\tYIGF2A\tIFG-II\t443.9411765\t997762.5\t17\t7547\t3526071\t16\t15964200\t1.69E+13
+LAB BLOOD ORDERABLES\t473427\tLABYTESFB\tTESTOSTERONE, TOTAL, BIO, FREE\tYTESHD\tFREE TESTOSTERONE\t9999999\t384682.5\t17\t169999983\t1.70E+15\t16\t6154920\t2.67E+12
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKCM\tCOMMENT\t9999999\t823236.9231\t17\t169999983\t1.70E+15\t13\t10702080\t1.10E+13
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKRE\tRESULT\t9999999\t864270\t17\t169999983\t1.70E+15\t14\t12099780\t1.30E+13
+LAB BLOOD ORDERABLES\t670816\tLAB11198R\tVIT A & VIT E\tVITE\tA-TOCOPHEROL, VITAMIN E\t10.45294118\t356116\t17\t177.7\t2329.07\t15\t5341740\t2.17E+12
+MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tRPT\tRPT\t9999999\t1747302.857\t17\t169999983\t1.70E+15\t14\t24462240\t4.57E+13
+MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tSDES\tSPECIMEN\t9999999\t1747302.857\t17\t169999983\t1.70E+15\t14\t24462240\t4.57E+13
+MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tSREQ\tCOMMENTS\t9999999\t1747302.857\t17\t169999983\t1.70E+15\t14\t24462240\t4.57E+13
+MICROBIOLOGY ORDERABLES\t473803\tLABWDCG\tWOUND CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t342848.8073\t17\t169999983\t1.70E+15\t436\t149482080\t1.48E+14
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t16\t159999984\t1.60E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tUBT\tUNIT BLOOD TYPE  \t9999999\t85174.28571\t16\t159999984\t1.60E+15\t7\t596220\t77125518000
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tUBT\tUNIT BLOOD TYPE  \t9999999\t23880\t16\t159999984\t1.60E+15\t6\t143280\t4565289600
+BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tUBT\tUNIT BLOOD TYPE  \t9999999\t28650\t16\t159999984\t1.60E+15\t6\t171900\t6302451600
+BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tPRO\tPRODUCT ID  \t9999999\t11100\t16\t159999984\t1.60E+15\t5\t55500\t2073337200
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t16\t159999984\t1.60E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tDI\tISSUE DATE/TIME\t9999999\t39420\t16\t159999984\t1.60E+15\t8\t315360\t17456292000
+BODY FLUID ORDERABLES\t497380\tLABYFUN\tUREA NITROGEN, FLUID\tUNFLD\tUREA NITROGEN BODY FLUID\t31.375\t169644\t16\t502\t21770\t10\t1696440\t3.58E+11
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tHEARTRATE\tHEART RATE\t62.875\t133908.75\t16\t1006\t64232\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tPRINTERVAL\tP-R INTERVAL\t185.75\t133908.75\t16\t2972\t571440\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQRSAXIS\tQRS AXIS\t42.4375\t133908.75\t16\t679\t117791\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQRSDINTERVAL\tQRSD INTERVAL\t106.625\t133908.75\t16\t1706\t189540\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQTCINTERVAL\tQTC INTERVAL\t436\t133908.75\t16\t6976\t3081334\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tQTINTERVAL\tQT INTERVAL\t430.5\t133908.75\t16\t6888\t3007008\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tRR\tRR\t968.6875\t133908.75\t16\t15499\t15231083\t16\t2142540\t3.12E+12
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t145.75\t48266.25\t16\t2332\t424424\t16\t772260\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t6250021.875\t48266.25\t16\t100000350\t1.00E+15\t16\t772260\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t414.28125\t59692.5\t16\t6628.5\t2791807.59\t16\t955080\t6.37E+11
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t11932.5\t16\t159999984\t1.60E+15\t16\t190920\t32977375200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t11932.5\t16\t159999984\t1.60E+15\t16\t190920\t32977375200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t8750292.625\t11932.5\t16\t140004682\t1.40E+15\t16\t190920\t32977375200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t11932.5\t16\t159999984\t1.60E+15\t16\t190920\t32977375200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t328.04375\t46841.25\t16\t5248.7\t1754780.07\t16\t749460\t4.56E+11
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9170R\tSERUM DATE 3\t9999999\t381052.5\t16\t159999984\t1.60E+15\t16\t6096840\t5.24E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9172R\tRESULT  MCS 3\t21.375\t381052.5\t16\t342\t49456\t16\t6096840\t5.24E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9174R\tRESULT 3\t9999999\t381052.5\t16\t159999984\t1.60E+15\t16\t6096840\t5.24E+12
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9140R\tSERUM DATE 3\t9999999\t381060\t16\t159999984\t1.60E+15\t16\t6096960\t5.24E+12
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9142R\tRESULT  MCS 3\t37.3125\t381060\t16\t597\t33413\t16\t6096960\t5.24E+12
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9144R\tRESULT 3\t9999999\t381060\t16\t159999984\t1.60E+15\t16\t6096960\t5.24E+12
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHDX\tCLINICAL INDICATION\t9999999\t738380\t16\t159999984\t1.60E+15\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHET\tETHNICITY (PROVIDED BY MD)\t9999999\t738380\t16\t159999984\t1.60E+15\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471652\tLABYAT3AG\tANTITHROMBIN AG, PLASMA\tAT3\tANTITHROMBIN III AG\t82.6875\t287780\t16\t1323\t118627\t24\t6906720\t2.99E+12
+LAB BLOOD ORDERABLES\t471706\tLABYSCL70\tAUTOANTIBODIES TO SCL 70\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t179580\t16\t159999984\t1.60E+15\t13\t2334540\t4.62E+11
+LAB BLOOD ORDERABLES\t471807\tLAB11026R\tC1 INHIBITOR, FUNCT\tC1INHF\tC1 ESTERASE INHIB FN\t102.5625\t325215\t16\t1641\t183123\t16\t5203440\t1.89E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t227223.75\t16\t159999984\t1.60E+15\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472387\tLAB11037R\tFLECAINIDE LEVEL\tFLEC\tFLECAINIDE\t625000.6169\t194816\t16\t10000009.87\t1.00E+14\t15\t2922240\t6.66E+11
+LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHBSAG\tHBSINT\tINTERPRETATION\t9999999\t49510.65059\t16\t159999984\t1.60E+15\t3566\t176554980\t2.12E+13
+LAB BLOOD ORDERABLES\t472645\tLABYINH\tINHIBIN B\tYINH1\tINHIBIN B\t4375263.75\t406324.2857\t16\t70004220\t7.00E+14\t14\t5688540\t2.74E+12
+LAB BLOOD ORDERABLES\t473762\tLAB12586R\tTHIAMIN (VITAMIN B1)\t12588R\tTHIAMIN (VITAMIN B1)\t160.3125\t314523.5294\t16\t2565\t458595\t17\t5346900\t1.75E+12
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12012R\tQ FEVER IGG PHASE L TITER\t9999999\t284160\t16\t159999984\t1.60E+15\t17\t4830720\t1.51E+12
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN5\tY ENTERCOCLITICA 05\t9999999\t3364848\t16\t159999984\t1.60E+15\t15\t50472720\t4.07E+14
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN8\tY ENTERCOCLITICA 08\t9999999\t3364848\t16\t159999984\t1.60E+15\t15\t50472720\t4.07E+14
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN9\tY ENTERCOCLITICA 09\t9999999\t3364848\t16\t159999984\t1.60E+15\t15\t50472720\t4.07E+14
+LAB BLOOD ORDERABLES\t497472\tLABYJCPCR\tJC VIRUS DNA, PCR\tJCDNA\tJC VIRUS DNA\t9999999\t164540.625\t16\t159999984\t1.60E+15\t96\t15795900\t3.74E+12
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12293R\t12293R\t5000019.563\t200448\t16\t80000313\t8.00E+14\t20\t4008960\t1.99E+12
+MICROBIOLOGY ORDERABLES\t471557\tLABAFBSO\tAFB SMEAR ONLY\tAFB\tAFB SMEAR\t9999999\t105838\t16\t159999984\t1.60E+15\t30\t3175140\t1.08E+12
+MICROBIOLOGY ORDERABLES\t473049\tLABOAPE\tOVA AND PARASITES\tCULT\tCULT/OTHER RSLT:\t9999999\t168009.5772\t16\t159999984\t1.60E+15\t1159\t194723100\t4.94E+13
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tCONRES\tCONTROL RESULTS\t9999999\t13848.57143\t16\t159999984\t1.60E+15\t21\t290820\t62447209200
+POINT OF CARE (NON LAB) TESTS\t589584\tPOC13\tPOC PPD SKIN TEST (AMB)\tERYTHREMA\tERYTHREMA\t4374999.563\t256492.5\t16\t69999993\t7.00E+14\t24\t6155820\t2.36E+12
+POINT OF CARE (NON LAB) TESTS\t589584\tPOC13\tPOC PPD SKIN TEST (AMB)\tMMIND\tMM INDURATION\t3125000.125\t256492.5\t16\t50000002\t5.00E+14\t24\t6155820\t2.36E+12
+POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tTESTL\tTEST STRIP LOG\t742948.25\t873\t16\t11887172\t1.00E+14\t20\t17460\t5970150000
+URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tCORTU\tCORTISOL, URINE\t4795.4375\t466968.75\t16\t76727\t866140725\t16\t7471500\t4.50E+12
+URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tTCORTU\tCORTISOL EXCRETION\t89.011875\t466968.75\t16\t1424.19\t293572.9761\t16\t7471500\t4.50E+12
+URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tUT52\tCOLLECTION PERIOD\t162.25\t466968.75\t16\t2596\t5008336\t16\t7471500\t4.50E+12
+URINE ORDERABLES\t472100\tLABUCORT\tFREE CORTISOL, URINE (TIMED)\tVOL52\tVOLUME\t2440.21875\t466968.75\t16\t39043.5\t148541492.3\t16\t7471500\t4.50E+12
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tTRICHOMONAS\tTRICHOMONAS\t9999999\t10832.30769\t16\t159999984\t1.60E+15\t13\t140820\t3822685200
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tTIMEU\tCOLLECTION PERIOD\t625022.4375\t379830\t16\t10000359\t1.00E+14\t6\t2278980\t9.13E+11
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URINE\t626340.9375\t379830\t16\t10021455\t1.00E+14\t6\t2278980\t9.13E+11
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URN\t55.875\t379830\t16\t894\t62300\t6\t2278980\t9.13E+11
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tUVOL\tVOLUME\t2454.5625\t379830\t16\t39273\t107987111\t6\t2278980\t9.13E+11
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tVMA\tVMA\t625005.625\t379830\t16\t10000090\t1.00E+14\t6\t2278980\t9.13E+11
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11231R\tMETANEPHRINE/CREATININE\t1875148.5\t372466.1538\t16\t30002376\t3.00E+14\t13\t4842060\t1.97E+12
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11232R\tNORMETANEPHRINE/CREATININE\t1875430.813\t372466.1538\t16\t30006893\t3.00E+14\t13\t4842060\t1.97E+12
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11233R\tTOTAL METANEPHRINE/CREATININE\t1875579.5\t372466.1538\t16\t30009272\t3.00E+14\t13\t4842060\t1.97E+12
+VIROLOGY ORDERABLES\t472531\tLABHSCUL\tHERPES SIMPLEX CULTURE ONLY\tCULT\tCULT/OTHER RSLT:\t9999999\t437550\t16\t159999984\t1.60E+15\t22\t9626100\t1.47E+13
+VIROLOGY ORDERABLES\t876723\tLABDVEIBV\tFLU B DFA\tIBVSP\tSPECIMEN\t9999999\t53200\t16\t159999984\t1.60E+15\t24\t1276800\t91484654400
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tVL\tPRODUCT VOLUME\t164.1333333\t36100\t15\t2462\t405886\t3\t108300\t3921210000
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tVL\tPRODUCT VOLUME\t168.1333333\t29391.42857\t15\t2522\t432572\t7\t205740\t6426507600
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tVL\tPRODUCT VOLUME\t167.2666667\t25560\t15\t2509\t420607\t2\t51120\t1470794400
+BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tSI\t<<STATUS>>  \t9999999\t20586\t15\t149999985\t1.50E+15\t20\t411720\t20614413600
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tUBT\tUNIT BLOOD TYPE  \t9999999\t63750\t15\t149999985\t1.50E+15\t6\t382500\t39290828400
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tUBT\tUNIT BLOOD TYPE  \t9999999\t47660\t15\t149999985\t1.50E+15\t6\t285960\t25408432800
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tUBT\tUNIT BLOOD TYPE  \t9999999\t47650\t15\t149999985\t1.50E+15\t6\t285900\t25391113200
+BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tUBT\tUNIT BLOOD TYPE  \t9999999\t28550\t15\t149999985\t1.50E+15\t6\t171300\t6256443600
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tEOSFLD\tEOSINOPHILS, SYN FLD\t4.466666667\t2556\t15\t67\t1267\t10\t25560\t106416000
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTRES\tRESULTS\t9999999\t688582.5\t15\t149999985\t1.50E+15\t16\t11017320\t1.17E+13
+BODY FLUID ORDERABLES\t497327\tLABWNVAB\tWEST NILE VIRUS AB PANEL, CSF\tWNITP\tW NILE INTERPRETATION\t9999999\t272081.9118\t15\t149999985\t1.50E+15\t136\t37003140\t2.07E+13
+BODY FLUID ORDERABLES\t966905\tLAB12607R\tN-METHYL-D-ASPARTATE RECEPTOR AB, CSF\t12606R\tN-METHYL-D-ASPARTATE RECEPTOR AB, CSF\t9999999\t325248\t15\t149999985\t1.50E+15\t15\t4878720\t1.73E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tTWAVEAXIS\tT WAVE AXIS\t21.06666667\t140516\t15\t316\t18074\t15\t2107740\t3.12E+12
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t11772\t15\t149999985\t1.50E+15\t15\t176580\t32662414800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t118\t11752\t15\t1770\t215350\t15\t176280\t32662792800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t3.333333333\t11836\t15\t50\t822\t15\t177540\t32661687600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t67286.08696\t15\t149999985\t1.50E+15\t23\t1547580\t1.21E+12
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t119.6\t-328\t15\t1794\t233836\t15\t-4920\t3304800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t7395736.467\t12720\t15\t110936047\t1.10E+15\t15\t190800\t32977360800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t288.64\t220\t15\t4329.6\t1293145.68\t15\t3300\t25009200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t57670.71429\t15\t149999985\t1.50E+15\t28\t1614780\t9.73E+11
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9075R\tHLA-MOLECULAR PT. SUMMARY\t9999999\t214170.5316\t15\t149999985\t1.50E+15\t395\t84597360\t1.52E+14
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHRE\tRESULT\t9999999\t738380\t15\t149999985\t1.50E+15\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471635\tLABANAS\tANTI - NUCLEAR AB\tANAINTERP\tANA INTERPRETATION\t9999999\t270715.1193\t15\t149999985\t1.50E+15\t2348\t635639100\t2.25E+14
+LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tLA AB\tSSB\tSS-B/LA AB, IGG, SERUM\t7999999.247\t172272\t15\t119999988.7\t1.20E+15\t15\t2584080\t5.49E+11
+LAB BLOOD ORDERABLES\t472646\tLABINHA\tINHIBIN A DIMER\tINHAR\tINHIBIN-A (DIMER)\t1333341.433\t512460\t15\t20000121.5\t2.00E+14\t13\t6661980\t9.92E+12
+LAB BLOOD ORDERABLES\t472662\tLAB11044R\tIL6\tIL6\tINTERLEUKIN 6\t4666676.533\t409564.2857\t15\t70000148\t7.00E+14\t14\t5733900\t4.74E+12
+LAB BLOOD ORDERABLES\t473064\tLAB11090R\tPARIETAL CELL AB\tYPCA\tPARIETAL CELL AB\t6000016.807\t252907.5\t15\t90000252.1\t9.00E+14\t16\t4046520\t1.11E+12
+LAB BLOOD ORDERABLES\t497476\tLABFT4ED\tFREE T4 BY EQUILIBRIUM DIALYSIS\tFT4\tFREE T4 BY EQUIL DIALYSIS\t2.753333333\t267021\t15\t41.3\t169.61\t20\t5340420\t1.78E+12
+LAB BLOOD ORDERABLES\t890123\tLAB12236R\tIBUPROFEN\t12237R\t12237R\t8666667\t290712\t15\t130000005\t1.30E+15\t15\t4360680\t1.38E+12
+MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11CT\tCOMMENT\t9999999\t1087500\t15\t149999985\t1.50E+15\t2\t2175000\t2.77E+12
+MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11DX\tCLINICAL INDICATION\t9999999\t1087500\t15\t149999985\t1.50E+15\t2\t2175000\t2.77E+12
+POINT OF CARE (NON LAB) TESTS\t589584\tPOC13\tPOC PPD SKIN TEST (AMB)\tPHLOC\tLOCATION\t8666665.8\t256492.5\t15\t129999987\t1.30E+15\t24\t6155820\t2.36E+12
+URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tCOCAINE\tCOCAINE\t6666892.867\t30\t15\t100003393\t1.00E+15\t2\t60\t3600
+URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tYUBEGC\tBENZOYLECGONINE\t21531\t30\t15\t322965\t24097922409\t2\t60\t3600
+VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-PLASMA\tCMVLIP\tTEST DESCRIPTION\t9999999\t242296.5517\t15\t149999985\t1.50E+15\t116\t28106400\t9.68E+12
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\tEA\tEARLY ANTIBODY (R+D) IGG\t9999999\t155835\t15\t149999985\t1.50E+15\t56\t8726760\t1.86E+12
+VIROLOGY ORDERABLES\t472567\tLABHPVHR2\tHPV DNA PROBE HIGH RISK\tHPV\tHPV DNA PROBE RESULT:\t9999999\t925207.0588\t15\t149999985\t1.50E+15\t17\t15728520\t1.88E+13
+VIROLOGY ORDERABLES\t876721\tLABDVEIAR\tFLU A DFA-REFLEX PCR\tIAR\tINFLUENZA A\t9999999\t118140\t15\t149999985\t1.50E+15\t7\t826980\t1.98E+11
+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tVL\tPRODUCT VOLUME\t167.8571429\t33440\t14\t2350\t397084\t3\t100320\t3424896000
+BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tUBT\tUNIT BLOOD TYPE  \t9999999\t28428\t14\t139999986\t1.40E+15\t5\t142140\t5357322000
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tPRO\tPRODUCT ID  \t9999999\t23064\t14\t139999986\t1.40E+15\t5\t115320\t7052032800
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tPRO\tPRODUCT ID  \t9999999\t16740\t14\t139999986\t1.40E+15\t5\t83700\t3495553200
+BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tPRO\tPRODUCT ID  \t9999999\t23028\t14\t139999986\t1.40E+15\t5\t115140\t7038212400
+BODY FLUID ORDERABLES\t471559\tLABYAFCSF\tALPHA FETOPROTEIN, CSF\tAFPCSF\tALPHA FETOPROTEIN, CSF\t9999999\t161382\t14\t139999986\t1.40E+15\t10\t1613820\t3.08E+11
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSCMT\tSYNOVIAL FLUID COMMENT\t9999999\t2888.571429\t14\t139999986\t1.40E+15\t14\t40440\t185760000
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tPAXIS\tP AXIS\t33.21428571\t125280\t14\t465\t28551\t14\t1753920\t2.99E+12
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t1542.857143\t14\t139999986\t1.40E+15\t14\t21600\t80049600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t1542.857143\t14\t139999986\t1.40E+15\t14\t21600\t80049600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t7894248.071\t1542.857143\t14\t110519473\t1.10E+15\t14\t21600\t80049600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t9300568.5\t1542.857143\t14\t130207959\t1.30E+15\t14\t21600\t80049600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t1542.857143\t14\t139999986\t1.40E+15\t14\t21600\t80049600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t326.2214286\t55195.71429\t14\t4567.1\t1519926.47\t14\t772740\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t734.5\t12732.85714\t14\t10283\t99023529\t14\t178260\t32661442800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDEVLDATE\tICD DATE OF EVENT\t9999999\t14177.14286\t14\t139999986\t1.40E+15\t14\t198480\t33045739200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPRV\tICD TEL LEAD IMPEDANCE RIGHT VENT\t387.7142857\t14177.14286\t14\t5428\t2335960\t14\t198480\t33045739200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t1428772\t7320\t14\t20002808\t2.00E+14\t14\t102480\t8712216000
+LAB BLOOD ORDERABLES\t471527\tLABSMABY\tANTI-SM ANTIBODY\tASMQNT\tANTI-SM LEVEL\t5000018\t575712.8571\t14\t70000252\t7.00E+14\t14\t8059980\t6.24E+12
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX\tPANCA\tNEUTROPHIL CYTO AB-P\t9999999\t301613.3333\t14\t139999986\t1.40E+15\t81\t24430680\t8.35E+12
+LAB BLOOD ORDERABLES\t471625\tLABYACE\tANGIOTENSIN CONVERTING ENZYME, SERUM\tACE\tANGIOTESIN CONVERTING ENZYME\t714317.3571\t185547.2727\t14\t10000443\t1.00E+14\t11\t2041020\t5.72E+11
+LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tRO AB\tSSA\tSS-A/RO AB, IGG, SERUM\t7857142.214\t152575.7143\t14\t109999991\t1.10E+15\t14\t2136060\t3.48E+11
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tLYMPHM\tLYMPHOMA CELLS\t57.71428571\t18822.85714\t14\t808\t58412\t14\t263520\t10277035200
+LAB BLOOD ORDERABLES\t471960\tLABCENABY\tANTI CENTROMERE ANTIBODY\tCENQNT\tANTI-CENTROMERE LEVEL\t7857147.857\t582750\t14\t110000070\t1.10E+15\t14\t8158500\t5.71E+12
+LAB BLOOD ORDERABLES\t472392\tLAB12796R\tFLUCONAZOLE\t12795R\tFLUCONAZOLE\t36.98571429\t312437.1429\t14\t517.8\t30453.28\t14\t4374120\t1.45E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHDX\tCLINICAL INDICATION\t9999999\t604041.4286\t14\t139999986\t1.40E+15\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGBEP2\tINTERPRETATION\t9999999\t426444.5714\t14\t139999986\t1.40E+15\t105\t44776680\t2.79E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLBRAG\tL BRAZILIENSIS,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLBRAM\tL BRAZILIENSIS,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLDONG\tL DONOVANI,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLDONM\tL DONOVANI,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLMEXG\tL MEXICANA,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLMEXM\tL MEXICANA,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLTROG\tL TROPICA,IGG\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLTROM\tL TROPICA,IGM\t9999999\t1638608.571\t14\t139999986\t1.40E+15\t14\t22940520\t9.80E+13
+LAB BLOOD ORDERABLES\t472771\tLABLUPUS\tLUPUS ANTICOAGULANT\tLUPDIR\tINTERPRETED BY\t9999999\t84468.42324\t14\t139999986\t1.40E+15\t482\t40713780\t8.35E+12
+LAB BLOOD ORDERABLES\t472778\tLABYLYSO\tLYSOZYME, PLASMA\tYLYSOZ\tLYSOZYME\t8.821428571\t220684.6154\t14\t123.5\t1953.95\t13\t2868900\t7.17E+11
+LAB BLOOD ORDERABLES\t473258\tLABRDATBC\tREVERSE TO COOMBS B CELLS\tRDATB\tREVERSE TO COOMBS B CELLS\t9999999\t46.66666667\t14\t139999986\t1.40E+15\t9\t420\t25200
+LAB BLOOD ORDERABLES\t473441\tLABYTAT\tTHROMBIN - ANTITHROMBIN COMPLEX\tTAT\tTHROMBIN, ANTITHROMBIN COMPLEX\t2142865.871\t647706\t14\t30000122.2\t3.00E+14\t20\t12954120\t9.44E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tAMINOR\tAMITRIPTYLINE & NORTRIPTYLINE\t7142943.643\t401426.25\t14\t100001211\t1.00E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tAMITRP\tAMITRIPTYLINE\t7142929.714\t401426.25\t14\t100001016\t1.00E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tDESIP\tDESIPRAMINE\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tDOXEPIN\tDOXEPIN\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tDOXEPINNOR\tDOXEPIN & NORDOXEPIN\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tIMI\tIMIPRAMINE\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tIMIDESI\tIMIPRAMINE/DESPRAMINE\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tNORDOX\tNORDOXEPIN\t9999999\t401426.25\t14\t139999986\t1.40E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t473681\tLABYTADID\tTRICYCLIC ANTIDEPRESSANT SCREEN\tNORTRP\tNORTRIPTYLINE\t7142872.5\t401426.25\t14\t100000215\t1.00E+15\t16\t6422820\t4.45E+12
+LAB BLOOD ORDERABLES\t497345\tLABCGD\tNEUTROPHIL FUNC. OXI. BURST\tCGDA\tSPECIMEN AGE\t714309.6429\t201578.8235\t14\t10000335\t1.00E+14\t17\t3426840\t8.25E+11
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKAA\tAMINO ACID CHANGE\t9999999\t864270\t14\t139999986\t1.40E+15\t14\t12099780\t1.30E+13
+LAB BLOOD ORDERABLES\t670816\tLAB11198R\tVIT A & VIT E\tFREEVITA\tFREE RETINOL(VIT A)\t124.8071429\t356116\t14\t1747.3\t624967.13\t15\t5341740\t2.17E+12
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE WITH GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t454839.1011\t14\t139999986\t1.40E+15\t712\t323845440\t2.65E+14
+MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tRPT\tRPT\t9999999\t1542888\t14\t139999986\t1.40E+15\t10\t15428880\t2.97E+13
+MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tSDES\tSPECIMEN\t9999999\t1542888\t14\t139999986\t1.40E+15\t10\t15428880\t2.97E+13
+MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRRES\tRESULTS\t9999999\t488214.4565\t14\t139999986\t1.40E+15\t184\t89831460\t6.08E+13
+MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11RS\tRESULT\t9999999\t1087500\t14\t139999986\t1.40E+15\t2\t2175000\t2.77E+12
+MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tLAB7\tREFERENCE LAB\t9999999\t576624\t14\t139999986\t1.40E+15\t10\t5766240\t1.60E+13
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tCONRES\tCONTROL RESULTS\t9999999\t-202.5\t14\t139999986\t1.40E+15\t16\t-3240\t41284800
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tAMPHET\tAMPHETAMINE\t2859009.214\t26760\t14\t40026129\t4.00E+14\t4\t107040\t11457561600
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tMTAM\tMETHAMPHETAMINE\t5002712.857\t26760\t14\t70037980\t7.00E+14\t4\t107040\t11457561600
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tHVAUR\tHVA, URINE\t4.285714286\t309984\t14\t60\t326\t10\t3099840\t1.05E+12
+VIROLOGY ORDERABLES\t472015\tLABCLCR2\tCHLAMYDIA MOLECULAR DETECTION\tCHLAM\tC.TRACHOMATIS RESULT:\t9999999\t105230\t14\t139999986\t1.40E+15\t12\t1262760\t1.93E+11
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1M\tNEURAMINIDASE  H275Y MUTATION\t9999999\t#VALUE!\t14\t139999986\t1.40E+15\t0\tNone\tNone
+VIROLOGY ORDERABLES\t898804\tLABEBVQL\tEBV DNA PCR QUAL\tEBVQLS\tSPECIMEN\t9999999\t233724\t14\t139999986\t1.40E+15\t25\t5843100\t1.92E+12
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t13\t129999987\t1.30E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t13\t129999987\t1.30E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tSI\t<<STATUS>>  \t9999999\t23350\t13\t129999987\t1.30E+15\t18\t420300\t21061472400
+BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tSI\t<<STATUS>>  \t9999999\t20992.5\t13\t129999987\t1.30E+15\t16\t335880\t17437428000
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tDI\tISSUE DATE/TIME\t9999999\t33996\t13\t129999987\t1.30E+15\t5\t169980\t6149602800
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tDI\tISSUE DATE/TIME\t9999999\t47660\t13\t129999987\t1.30E+15\t6\t285960\t25408432800
+BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tPRO\tPRODUCT ID  \t9999999\t23004\t13\t129999987\t1.30E+15\t5\t115020\t7029565200
+BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tPRO\tPRODUCT ID  \t9999999\t16068\t13\t129999987\t1.30E+15\t5\t80340\t3222558000
+BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD34BMPC\t%CD34, BONE MARROW\t769231.6627\t23385\t13\t10000011.62\t1.00E+14\t4\t93540\t3727911600
+BODY FLUID ORDERABLES\t497376\tLABFLCL\tCHLORIDE, FLUID\tCLFLD\tCHLORIDE, FLUID\t769328\t7350\t13\t10001264\t1.00E+14\t6\t44100\t647218800
+BODY FLUID ORDERABLES\t497376\tLABFLCL\tCHLORIDE, FLUID\tCLFLS\tSOURCE\t9999999\t7350\t13\t129999987\t1.30E+15\t6\t44100\t647218800
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASTI\tSOURCE\t9999999\t866627.1429\t13\t129999987\t1.30E+15\t14\t12132780\t1.36E+13
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t7692314.615\t59381.53846\t13\t100000090\t1.00E+15\t13\t771960\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.963432308\t73352.30769\t13\t38.52462\t114.8003678\t13\t953580\t6.37E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.194230769\t13698.46154\t13\t28.525\t94.854375\t13\t178080\t32661705600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t13698.46154\t13\t129999987\t1.30E+15\t13\t178080\t32661705600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t15272.30769\t13\t129999987\t1.30E+15\t13\t198540\t33045735600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t58.07692308\t15276.92308\t13\t755\t49275\t13\t198600\t33045724800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t15276.92308\t13\t129999987\t1.30E+15\t13\t198600\t33045724800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTPACVT\tICD DETECTIONS AND TREATMENTS PACING VENTRICULAR\t38.84769231\t15272.30769\t13\t505.02\t37105.5174\t13\t198540\t33045735600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDRHYTHM\tICD RHYTHM\t9999999\t15272.30769\t13\t129999987\t1.30E+15\t13\t198540\t33045735600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAVFDTI\tICD TACHY VF DETECTION INTERVAL\t307.3692308\t15276.92308\t13\t3995.8\t1240182.42\t13\t198600\t33045724800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPHV\tICD TEL LEAD IMPEDANCE HIGH VOLT\t1538494.077\t15235.38462\t13\t20000423\t2.00E+14\t13\t198060\t33045562800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tMOD\tMODEL\t9999999\t14630.76923\t13\t129999987\t1.30E+15\t13\t190200\t32977180800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.3\t15276.92308\t13\t29.9\t70.01\t13\t198600\t33045724800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.453846154\t15276.92308\t13\t5.9\t2.73\t13\t198600\t33045724800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2307692.385\t15276.92308\t13\t30000001\t3.00E+14\t13\t198600\t33045724800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t7337924.077\t14630.76923\t13\t95393013\t8.47E+14\t13\t190200\t32977180800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t461.5384615\t13\t129999987\t1.30E+15\t13\t6000\t66816000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t1.923076923\t13389.23077\t13\t25\t531\t13\t174060\t23359885200
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t461.5384615\t13\t129999987\t1.30E+15\t13\t6000\t66816000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9482R\tLOCUS 8\t9999999\t19103.07692\t13\t129999987\t1.30E+15\t13\t248340\t61404872400
+HISTOCOMPATIBILITY ORDERABLE\t978467\t10277R\tHLA ORDER - PROSPECTIVE CROSSMATCH TESTING\tRINFO\tTO FIND RESULT:\t9999999\t306132.8571\t13\t129999987\t1.30E+15\t14\t4285860\t1.61E+12
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE12\tS.PNEUMO SEROTYPE 12 (12F)\t6923076.477\t199431.4286\t13\t89999994.2\t9.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE14\tS.PNEUMO SEROTYPE 14 (14)\t2307697.792\t199431.4286\t13\t30000071.3\t3.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE19\tS.PNEUMO SEROTYPE 19 (19F)\t3076924.9\t199431.4286\t13\t40000023.7\t4.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE23\tS.PNEUMO SEROTYPE 23 (23F)\t5384616.023\t199431.4286\t13\t70000008.3\t7.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE26\tS.PNEUMO SEROTYPE 26 (6B)\t6153846.031\t199431.4286\t13\t79999998.4\t8.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE3\tS.PNEUMO SEROTYPE 3 (3)\t5384615.838\t199431.4286\t13\t70000005.9\t7.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE4\tS.PNEUMO SEROTYPE 4 (4)\t9230768.354\t199431.4286\t13\t119999988.6\t1.20E+15\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE51\tS.PNEUMO SEROTYPE 51 (7F)\t3076927.354\t199431.4286\t13\t40000055.6\t4.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE8\tS.PNEUMO SEROTYPE 8 (8)\t3076924.108\t199431.4286\t13\t40000013.4\t4.00E+14\t14\t2792040\t6.60E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE9\tS.PNEUMO SEROTYPE 9 (9N)\t5384615.585\t199431.4286\t13\t70000002.6\t7.00E+14\t14\t2792040\t6.60E+11
+LAB BLOOD ORDERABLES\t471506\tLAB11067R\t17OH PROG\t17OHPROG\t17-OH PROGESTERONE\t3077113.923\t325227.6923\t13\t40002481\t4.00E+14\t13\t4227960\t1.46E+12
+LAB BLOOD ORDERABLES\t471571\tLABYALDO\tALDOLASE, SERUM\tALDOLASE\tALDOLASE, SERUM\t769254.6308\t174660\t13\t10000310.2\t1.00E+14\t8\t1397280\t2.95E+11
+LAB BLOOD ORDERABLES\t471572\tLABYALDOS\tALDOSTERONE, SERUM\tALDOSTR\tALDOSTERONE, SERUM\t6153847.977\t281100\t13\t80000023.7\t8.00E+14\t10\t2811000\t8.99E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMGQNT\tCMV IGG LEVEL\t3076958.615\t329515.3846\t13\t40000462\t4.00E+14\t13\t4283700\t1.61E+12
+LAB BLOOD ORDERABLES\t471812\tLABYC4F\tC4 COMPLEMENT FUNCTIONAL\tYC4FUN\tC4 COMPLEMENT, FUNCT\t48.84615385\t139674.5455\t13\t635\t31413\t11\t1536420\t2.50E+11
+LAB BLOOD ORDERABLES\t472144\tLABYDHEAS\tDEHYDROEPIANDROSTERONE, SULFATE\tDHS\tDEHYDROEPIANDROSTERONE SULFATE\t769436.1538\t92110\t13\t10002670\t1.00E+14\t12\t1105320\t1.69E+11
+LAB BLOOD ORDERABLES\t472329\tLABYESTRO\tESTROGENS E1 AND E2 FRACTIONATED\tESTRADIOL\tESTRADIOL\t4615409.538\t294176\t13\t60000324\t6.00E+14\t15\t4412640\t1.36E+12
+LAB BLOOD ORDERABLES\t472329\tLABYESTRO\tESTROGENS E1 AND E2 FRACTIONATED\tESTRONE\tESTRONE\t46.30769231\t294176\t13\t602\t50766\t15\t4412640\t1.36E+12
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tADP5\tADP 5 UM\t5384649.462\t113400\t13\t70000443\t7.00E+14\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tAGGDIR\tINTERPRETED BY\t9999999\t113400\t13\t129999987\t1.30E+15\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tARA500\tARACHIDONIC ACID 500 UG/ML\t5384634.615\t113400\t13\t70000250\t7.00E+14\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tCOLL25\tCOLLAGEN 2.5 UG/ML\t4615420\t113400\t13\t60000460\t6.00E+14\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tEPI5\tEPINEPHRINE 5 UM\t4615414.538\t113400\t13\t60000389\t6.00E+14\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tEPIN10\tEPINEPHRINE 10 UM\t4615411.385\t113400\t13\t60000348\t6.00E+14\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tRIS12\tRISTOCETIN 1.2 MG/ML\t6923100.308\t113400\t13\t90000304\t9.00E+14\t10\t1134000\t2.75E+11
+LAB BLOOD ORDERABLES\t473262\tLABRF\tRHEUMATOID FACTOR\tRHFT\tRHEUMATOID FACTOR LEVEL\t90.53846154\t216840\t13\t1177\t201441\t13\t2818920\t7.14E+11
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15CO\tCOMMENT\t9999999\t643218\t13\t129999987\t1.30E+15\t50\t32160900\t2.37E+13
+LAB BLOOD ORDERABLES\t473428\tLABYTETT\tTETANUS TOXOID IGG AB\tYTETT1\tTETANUS TOXOID IGG AB\t769233.1908\t214384\t13\t10000031.48\t1.00E+14\t15\t3215760\t7.88E+11
+LAB BLOOD ORDERABLES\t473445\tLABYTRAB\tTSH RECEPTOR AB\tYTRAB1\tTHYROTROPIN RECEPTOR AB\t9230768.432\t184324\t13\t119999989.6\t1.20E+15\t15\t2764860\t6.23E+11
+LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMA PCR\tTOXSP\tSPECIMEN TYPE\t9999999\t1052324\t13\t129999987\t1.30E+15\t30\t31569720\t6.51E+13
+LAB BLOOD ORDERABLES\t497345\tLABCGD\tNEUTROPHIL FUNC. OXI. BURST\tOXIDNEUTPC\t% OXIDATION POSITIVE NEUTROPHILS\t91.07692308\t827744.2105\t13\t1184\t109522\t19\t15727140\t7.65E+13
+LAB BLOOD ORDERABLES\t541683\tLABDCMVL\tDONOR CMV- TOTAL AB\tDCMVL1\tDONOR CMV- TOTAL AB\t9999999\t214200\t13\t129999987\t1.30E+15\t15\t3213000\t9.08E+11
+LAB BLOOD ORDERABLES\t883166\tLAB12090R\tHE4, OVARIAN CANCER MONITORING\t12105R\tHE4, OVARIAN CANCER MONITORING\t339.7692308\t421112.7273\t13\t4417\t3279075\t11\t4632240\t2.19E+12
+MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t431748.6235\t13\t129999987\t1.30E+15\t1867\t806074680\t3.79E+14
+MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tSREQ\tCOMMENTS\t9999999\t1542888\t13\t129999987\t1.30E+15\t10\t15428880\t2.97E+13
+MICROBIOLOGY ORDERABLES\t472744\tLABLEGDA\tLEGIONELLA CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t643208.0247\t13\t129999987\t1.30E+15\t486\t312599100\t2.25E+14
+STOOL ORDERABLES\t471633\tLABCANNAB\tNEURONAL NUCLEAR AB, CSF\tANTIHU\tANTI-HU\t9999999\t764260\t13\t129999987\t1.30E+15\t6\t4585560\t4.58E+12
+STOOL ORDERABLES\t471633\tLABCANNAB\tNEURONAL NUCLEAR AB, CSF\tARI\tANTI-RI\t9999999\t764260\t13\t129999987\t1.30E+15\t6\t4585560\t4.58E+12
+STOOL ORDERABLES\t497222\tLAB11021R\tADRENAL AB\tADRAB\tADRENAL ANTIBODY\t769231.2538\t419430\t13\t10000006.3\t1.00E+14\t12\t5033160\t2.45E+12
+STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL3\tCOLLECTION DURATION\t9999999\t671420\t13\t129999987\t1.30E+15\t12\t8057040\t7.48E+12
+URINE ORDERABLES\t472786\tLABURMAG\tMAGNESIUM, URINE (RANDOM)\tUMG\tMAGNESIUM, URINE\t769237.7385\t12980\t13\t10000090.6\t1.00E+14\t6\t77880\t1401314400
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tTIMEU\tCOLLECTION PERIOD\t769251.4615\t321355.3846\t13\t10000269\t1.00E+14\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URINE\t770374.4615\t321355.3846\t13\t10014868\t1.00E+14\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tUCR\tCREATININE, URN\t54\t321355.3846\t13\t702\t43556\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tUVOL\tVOLUME\t1965.461538\t321355.3846\t13\t25551\t62756061\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tVMA\tVMA\t1538466.654\t321355.3846\t13\t20000066.5\t2.00E+14\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tVMAUR\tVMA, URINE\t6.461538462\t321355.3846\t13\t84\t730\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11197R\tANABASINE, U\t9230768.777\t357805.7143\t13\t119999994.1\t1.20E+15\t7\t2504640\t1.04E+12
+URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11511R\tNORNICOTINE, U\t8461542.992\t357805.7143\t13\t110000058.9\t1.10E+15\t7\t2504640\t1.04E+12
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\t11230R\tCREATININE CONC\t47.61538462\t372466.1538\t13\t619\t54659\t13\t4842060\t1.97E+12
+VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-BLOOD\tCMQDNA\tCMV DNA\t9999999\t165780\t13\t129999987\t1.30E+15\t3\t497340\t92352106800
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBVIPR\tTEST DESCRIPTION\t9999999\t295560.7542\t13\t129999987\t1.30E+15\t716\t211621500\t8.15E+13
+VIROLOGY ORDERABLES\t472511\tLABHCPQ\tHEPATITIS C RNA PCR QUANTITATIVE\tHCVIPR\tTEST DESCRIPTION\t9999999\t306451.3492\t13\t129999987\t1.30E+15\t756\t231677220\t1.10E+14
+VIROLOGY ORDERABLES\t497450\tLABBKVQU\tBK VIRUS DNA QUANTITATION\tBKQ\tBKV DNA QUANTITATION\t7097459.769\t190828.4211\t13\t92266977\t9.04E+14\t38\t7251480\t1.85E+12
+VIROLOGY ORDERABLES\t904461\tLABHSVZL\tHSV/VZV PCR QUAL, LESION\tHSVZIP\tTEST DESCRIPTION\t9999999\t190023.36\t13\t129999987\t1.30E+15\t375\t71258760\t1.95E+13
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t12\t119999988\t1.20E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tXMR\tUNIT CROSSMATCH  \t9999999\t62580\t12\t119999988\t1.20E+15\t7\t438060\t56277608400
+BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tDI\tISSUE DATE/TIME\t9999999\t32715\t12\t119999988\t1.20E+15\t4\t130860\t4617428400
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tDI\tISSUE DATE/TIME\t9999999\t57360\t12\t119999988\t1.20E+15\t5\t286800\t29228796000
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tDI\tISSUE DATE/TIME\t9999999\t47650\t12\t119999988\t1.20E+15\t6\t285900\t25391113200
+BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tPRO\tPRODUCT ID  \t9999999\t20085\t12\t119999988\t1.20E+15\t4\t80340\t3217719600
+BODY FLUID ORDERABLES\t472474\tLABCSFGL\tGLUCOSE, CSF\tGLCSTN\tTUBE NUMBER\t9999999\t337355\t12\t119999988\t1.20E+15\t12\t4048260\t1.37E+12
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15DX\tCLINICAL INDICATION\t9999999\t644650\t12\t119999988\t1.20E+15\t18\t11603700\t8.75E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12364R\tANNA-2, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12365R\tANNA-3, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12366R\tAGNA-1,CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12367R\tPCA-1, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12368R\tPCA-2, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12369R\tAMPHIPHYSIN AB, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12370R\tCRMP-5-IGG, CSF\t9999999\t627283.6364\t12\t119999988\t1.20E+15\t11\t6900120\t4.80E+12
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t14755\t12\t0\t0\t12\t177060\t32661486000
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.772916667\t14860\t12\t9.275\t9.921875\t12\t178320\t32661648000
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tRATHRDUR\tRA THRESHOLD DURATION\t0.516666667\t14860\t12\t6.2\t4.28\t12\t178320\t32661648000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAVFTXC\tICD TACHY VF THERAPY CONFIGURATION\t9999999\t15860\t12\t119999988\t1.20E+15\t12\t190320\t32977166400
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9260R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t347760\t12\t119999988\t1.20E+15\t28\t9737280\t3.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9518R\t% DONOR- MARROW WHOLE\t68.5\t4780\t12\t822\t77006\t12\t57360\t3249021600
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9558R\t% RECIPIENT- MARROW WHOLE\t27.83333333\t4780\t12\t334\t27766\t12\t57360\t3249021600
+HISTOCOMPATIBILITY ORDERABLE\t978467\t10277R\tHLA ORDER - PROSPECTIVE CROSSMATCH TESTING\t10049R\tHLA LAB TESTING STATUS\t9999999\t306132.8571\t12\t119999988\t1.20E+15\t14\t4285860\t1.61E+12
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNAB1\tPNEUMOCOCCAL AB (PNEUMOVAX)\t9999999\t184530\t12\t119999988\t1.20E+15\t12\t2214360\t4.84E+11
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE01\tS.PNEUMO SEROTYPE 1 (1)\t2500001.767\t199431.4286\t12\t30000021.2\t3.00E+14\t14\t2792040\t6.60E+11
+LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE\tYAMIOD\tAMIODARONE\t0.675\t294387.6923\t12\t8.1\t7.45\t13\t3827040\t1.32E+12
+LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE\tYDESET\tDESETHYLAMIODARONE\t0.491666667\t294387.6923\t12\t5.9\t3.29\t13\t3827040\t1.32E+12
+LAB BLOOD ORDERABLES\t471684\tLABARS\tARSENIC, WHOLE BLOOD\tARS\tARSENIC, BLOOD\t9999999\t255327.2727\t12\t119999988\t1.20E+15\t11\t2808600\t9.52E+11
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tHCT\tHEMATOCRIT\t1666690.9\t420\t12\t20000290.8\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tHGB\tHEMOGLOBIN\t1666674.617\t420\t12\t20000095.4\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tMCH\tMCH\t1666691.767\t420\t12\t20000301.2\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tMCHC\tMCHC\t1666694.133\t420\t12\t20000329.6\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tMCV\tMCV\t1666742.683\t420\t12\t20000912.2\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tPLT\tPLATELET COUNT\t1666867.417\t420\t12\t20002409\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tRBC\tRBC\t1666669.183\t420\t12\t20000030.19\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tRDW\tRDW\t1666681.167\t420\t12\t20000174\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471711\tLABABC\tAUTOMATED BLOOD COUNT\tWBC\tWBC\t1666673.833\t420\t12\t20000086\t2.00E+14\t1\t420\t176400
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tBCD34V\tCD34+ PROGENITOR VIABILITY\t1666749.583\t14130\t12\t20000995\t2.00E+14\t12\t169560\t2971821600
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD45PV\tCD45+ VIABILITY\t1666749.833\t14130\t12\t20000998\t2.00E+14\t12\t169560\t2971821600
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tSLREV\tSLIDE REVIEW\t9999999\t5840\t12\t119999988\t1.20E+15\t3\t17520\t158767200
+LAB BLOOD ORDERABLES\t472185\tLABYDIGT\tDIGITOXIN, SERUM\tYDIGT2\tREPORTING LIMIT\t2500003.5\t384356.5217\t12\t30000042\t3.00E+14\t69\t26520600\t1.15E+13
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKSP\tSPECIMEN SOURCE\t9999999\t525786.6667\t12\t119999988\t1.20E+15\t9\t4732080\t3.13E+12
+LAB BLOOD ORDERABLES\t472835\tLABMERC\tMERCURY, WHOLE BLOOD\tMERC\tMERCURY, BLOOD\t6666667.25\t227809.0909\t12\t80000007\t8.00E+14\t11\t2505900\t9.21E+11
+LAB BLOOD ORDERABLES\t473113\tLAB11054R\tPM1 AB\tPM1AB\tPM-1 ANTIBODY\t9999999\t510376.3636\t12\t119999988\t1.20E+15\t11\t5614140\t4.33E+12
+LAB BLOOD ORDERABLES\t473152\tLABPGN\tPROGESTERONE\tPROGES\tPROGESTERONE\t1666676.667\t40156.36364\t12\t20000120\t2.00E+14\t11\t441720\t38759839200
+LAB BLOOD ORDERABLES\t473189\tLABPTTINH\tPTT INHIBITOR SCREEN\tPTTDIR\tINTERPRETED BY\t9999999\t78829.64789\t12\t119999988\t1.20E+15\t284\t22387620\t2.92E+12
+LAB BLOOD ORDERABLES\t473264\tLAB11100R\tRIBOFLAVIN\tYVITB2\tRIBOFLAVIN(VIT.B2)\t15.41666667\t395547.2727\t12\t185\t6671\t11\t4351020\t1.85E+12
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH CORD\t9999999\t3730\t12\t119999988\t1.20E+15\t6\t22380\t90622800
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH FRONT TYPE\t9999999\t3730\t12\t119999988\t1.20E+15\t6\t22380\t90622800
+LAB BLOOD ORDERABLES\t473702\tLABTYPSNI\tTYPE AND SCREEN\tABORH\tABO/RH STILLBORN\t9999999\t3730\t12\t119999988\t1.20E+15\t6\t22380\t90622800
+LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tSPTY4\tSPECIMEN TYPE\t9999999\t4443266.667\t12\t119999988\t1.20E+15\t9\t39989400\t3.40E+14
+LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR, HIV-1/HCV/HBV NAT TESTING\t11894R\tDISCRIMINATORY TESTING\t9999999\t266914.2857\t12\t119999988\t1.20E+15\t14\t3736800\t1.23E+12
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tBDVC\tCORD BASE DEFICIT, VEN\t5833334.333\t615\t12\t70000012\t7.00E+14\t8\t4920\t4780800
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tHCO3VC\tCORD HCO3, VEN\t5833342.217\t615\t12\t70000106.6\t7.00E+14\t8\t4920\t4780800
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tPCO2VC\tCORD PCO2, VEN\t5833352.642\t615\t12\t70000231.7\t7.00E+14\t8\t4920\t4780800
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tPHVCD\tCORD PH, VEN\t5833335.79\t615\t12\t70000029.47\t7.00E+14\t8\t4920\t4780800
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tPO2VC\tCORD PO2, VEN\t5833344.917\t615\t12\t70000139\t7.00E+14\t8\t4920\t4780800
+LAB BLOOD ORDERABLES\t864652\tLAB11858R\tHEPTIMAX(R) HCV RNA\t11856R\tHEPTIMAX ? HCR  RNA IU/ML\t6731649.333\t677090.7692\t12\t80779792\t8.00E+14\t13\t8802180\t7.52E+12
+LAB BLOOD ORDERABLES\t864652\tLAB11858R\tHEPTIMAX(R) HCV RNA\t11857R\tHEPTIMAX ? HCR  LOGIU IU/ML\t5000002.415\t670450\t12\t60000028.98\t6.00E+14\t12\t8045400\t6.95E+12
+LAB BLOOD ORDERABLES\t864654\tLAB11860R\tVASCULAR ENDOTHELIAL GROW.FACT\t11859R\tVEGF\t90.08333333\t563154\t12\t1081\t138983\t10\t5631540\t3.64E+12
+LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12051R\tRMSF IGG\t9999999\t408180\t12\t119999988\t1.20E+15\t15\t6122700\t4.65E+12
+LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12053R\tR.TYPHI IGG\t9999999\t265070.7692\t12\t119999988\t1.20E+15\t13\t3445920\t9.64E+11
+LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tPF4IGG\tPF4 IGG ONLY OD\t0.9635\t149345\t12\t11.562\t20.572986\t12\t1792140\t3.18E+11
+MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tRPT\tRPT\t9999999\t79170\t12\t119999988\t1.20E+15\t2\t158340\t12620538000
+MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tRPT\tRPT\t9999999\t272595\t12\t119999988\t1.20E+15\t4\t1090380\t3.15E+11
+MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tSDES\tSPECIMEN\t9999999\t272595\t12\t119999988\t1.20E+15\t4\t1090380\t3.15E+11
+MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tSREQ\tCOMMENTS\t9999999\t272595\t12\t119999988\t1.20E+15\t4\t1090380\t3.15E+11
+STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL1\tCHLORIDE, STOOL, 24 HR\t9999999\t921218.1818\t12\t119999988\t1.20E+15\t11\t10133400\t1.43E+13
+URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tB2M\tBETA-2-MICROGLOB\t837408.8333\t323151.4286\t12\t10048906\t1.00E+14\t7\t2262060\t8.66E+11
+URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tB2MCREAT\tBETA-2-MICROGLOB/CRT\t859234.25\t323151.4286\t12\t10310811\t1.00E+14\t7\t2262060\t8.66E+11
+URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tUCR\tCREATININE, URN\t833388.9167\t323151.4286\t12\t10000667\t1.00E+14\t7\t2262060\t8.66E+11
+URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tUT\tCOLLECTION PERIOD\t9166667.75\t323151.4286\t12\t110000013\t1.10E+15\t7\t2262060\t8.66E+11
+URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tVOL\tVOLUME\t833418.0833\t323151.4286\t12\t10001017\t1.00E+14\t7\t2262060\t8.66E+11
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tCADMIUNURN\tCADMIUM, URINE\t6666667.008\t279240\t12\t80000004.1\t8.00E+14\t4\t1116960\t3.67E+11
+URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11180R\tNICOTINE, U\t6666860.9\t357805.7143\t12\t80002330.8\t8.00E+14\t7\t2504640\t1.04E+12
+VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMGDES\tCMV IGG TEST DESCRIPTION\t9999999\t157732.7413\t12\t119999988\t1.20E+15\t777\t122558340\t2.53E+13
+VIROLOGY ORDERABLES\t472452\tLABGCCR2\tGC MOLECULAR DETECTION\tDCL5\tTEST INFORMATION:\t9999999\t144177.2308\t12\t119999988\t1.20E+15\t65\t9371520\t1.97E+12
+VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHHV-6 DNA PCR QUAL\tHV6QLI\tTEST DESCRIPTION\t9999999\t259790\t12\t119999988\t1.20E+15\t66\t17146140\t1.58E+13
+VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADDNA\tADENOVIRUS DNA\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
+VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVCP\tRESULT\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
+VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVLOG\tRESULT LOG 10\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
+VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVSP\tSPECIMEN\t9999999\t170190\t12\t119999988\t1.20E+15\t12\t2042280\t4.38E+11
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1SP\tSPECIMEN\t9999999\t#VALUE!\t12\t119999988\t1.20E+15\t0\tNone\tNone
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVLIP\tTEST DESCRIPTION\t9999999\t253864.8227\t12\t119999988\t1.20E+15\t423\t107384820\t3.23E+13
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t11\t109999989\t1.10E+15\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tXMR\tUNIT CROSSMATCH  \t9999999\t50220\t11\t109999989\t1.10E+15\t7\t351540\t43882974000
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tXMR\tUNIT CROSSMATCH  \t9999999\t22097.14286\t11\t109999989\t1.10E+15\t7\t154680\t6237043200
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tDI\tISSUE DATE/TIME\t9999999\t44832\t11\t109999989\t1.10E+15\t5\t224160\t14219272800
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tDI\tISSUE DATE/TIME\t9999999\t28296\t11\t109999989\t1.10E+15\t5\t141480\t4562049600
+BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tDI\tISSUE DATE/TIME\t9999999\t34020\t11\t109999989\t1.10E+15\t5\t170100\t6299211600
+BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tUBT\tUNIT BLOOD TYPE  \t9999999\t18440\t11\t109999989\t1.10E+15\t3\t55320\t2073319200
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tUBT\tUNIT BLOOD TYPE  \t9999999\t38400\t11\t109999989\t1.10E+15\t3\t115200\t7052018400
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tPRO\tPRODUCT ID  \t9999999\t20085\t11\t109999989\t1.10E+15\t4\t80340\t3217719600
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tPRO\tPRODUCT ID  \t9999999\t20085\t11\t109999989\t1.10E+15\t4\t80340\t3217719600
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tSI\t<<STATUS>>  \t9999999\t23376\t11\t109999989\t1.10E+15\t15\t350640\t18667375200
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBDX\tCLINICAL INDICATION\t9999999\t1007200\t11\t109999989\t1.10E+15\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBMET\tMETHOD\t9999999\t1007200\t11\t109999989\t1.10E+15\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD34V\tVIABILITY\t909179.9091\t35190\t11\t10000979\t1.00E+14\t2\t70380\t3459690000
+BODY FLUID ORDERABLES\t497362\tLABFLK\tPOTASSIUM, FLUID\tKFLD\tPOTASSIUM, FLUID\t909095.3909\t3996\t11\t10000049.3\t1.00E+14\t5\t19980\t102495600
+BODY FLUID ORDERABLES\t497366\tLABFLNA\tSODIUM, FLUID\tNAFLD\tSODIUM, FLUID\t909206.0909\t9820\t11\t10001267\t1.00E+14\t3\t29460\t443379600
+BODY FLUID ORDERABLES\t497366\tLABFLNA\tSODIUM, FLUID\tNAFLS\tSOURCE\t9999999\t9820\t11\t109999989\t1.10E+15\t3\t29460\t443379600
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGSF3B\tSF3B1\t9999999\t1131829.091\t11\t109999989\t1.10E+15\t11\t12450120\t1.54E+13
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY\t12363R\tANNA-1, CSF\t9999999\t627283.6364\t11\t109999989\t1.10E+15\t11\t6900120\t4.80E+12
+ECHO ORDERABLES\t670309\tECH43\tECHO - DOBUTAMINE STRESS ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t54.27272727\t53940\t11\t597\t34779\t11\t593340\t43949847600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRVVDEL\tBRADY VV DELAY\t22.72727273\t16063.63636\t11\t250\t10300\t11\t176700\t32661932400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t16118.18182\t11\t0\t0\t11\t177300\t32661428400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t16118.18182\t11\t0\t0\t11\t177300\t32661428400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t70434.54545\t11\t109999989\t1.10E+15\t11\t774780\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t70270.90909\t11\t109999989\t1.10E+15\t11\t772980\t6.04E+11
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTVFIB\tICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION\t1\t17629.09091\t11\t11\t39\t11\t193920\t33026932800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t0.775\t17318.18182\t11\t8.525\t7.068125\t11\t190500\t32977242000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.445454545\t17318.18182\t11\t4.9\t2.21\t11\t190500\t32977242000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t909099\t1238.181818\t11\t10000089\t1.00E+14\t11\t13620\t74667600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLEADALERT\tLEAD ALERT\t9999999\t665.4545455\t11\t109999989\t1.10E+15\t11\t7320\t31622400
+HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t465125.4545\t11\t109999989\t1.10E+15\t11\t5116380\t3.52E+12
+HISTOCOMPATIBILITY ORDERABLE\t476519\t10075R\tHLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t9999999\t465125.4545\t11\t109999989\t1.10E+15\t11\t5116380\t3.52E+12
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9120R\tINTERPRETATION/ COMMENTS\t9999999\t319104\t11\t109999989\t1.10E+15\t20\t6382080\t7.43E+12
+IMMUNOLOGY ORDERABLES\t473115\tLABPNAB\tPNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)\tPNE56\tS.PNEUMO SEROTYPE 56 (18C)\t3636365.936\t199431.4286\t11\t40000025.3\t4.00E+14\t14\t2792040\t6.60E+11
+LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTR\tRESULT, BM\t9999999\t540213\t11\t109999989\t1.10E+15\t60\t32412780\t2.37E+13
+LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABID\tANTIBODY IDENTIFICATION\t9999999\t11001.50442\t11\t109999989\t1.10E+15\t226\t2486340\t5.66E+12
+LAB BLOOD ORDERABLES\t472140\tLABYDHEA\tDEHYDROEPIANDROSTERONE, SERUM\tDHEA\tDHEA, SERUM\t3636365.382\t490747.5\t11\t40000019.2\t4.00E+14\t8\t3925980\t2.68E+12
+LAB BLOOD ORDERABLES\t472355\tLABFACT13\tFACTOR XIII SCREEN\tFACTOR13\tFACTOR XIII SCREEN\t9999999\t128781.8182\t11\t109999989\t1.10E+15\t11\t1416600\t1.96E+11
+LAB BLOOD ORDERABLES\t472583\tLABXICA51\tICA 512 AB\tXICA5R\tICA 512 ANTIBODY\t9999999\t616788\t11\t109999989\t1.10E+15\t25\t15419700\t1.11E+13
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKPR\tPRIMERS\t9999999\t526447.5\t11\t109999989\t1.10E+15\t8\t4211580\t2.86E+12
+LAB BLOOD ORDERABLES\t472731\tLABLDL\tLDL CALCULATED\tLDL\tLDL (CALCULATED)\t3636415.636\t10605\t11\t40000572\t4.00E+14\t4\t42420\t820717200
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tTBGINT\tINTERPRETATION\t9999999\t226235.1951\t11\t109999989\t1.10E+15\t820\t185512860\t8.35E+13
+LAB BLOOD ORDERABLES\t473427\tLABTESFBT\tTESTOSTERONE, TOTAL, BIO, FREE\tTESBIO\tTESTOSTERONE, BIOAVAILABLE\t40\t244216.3636\t11\t440\t32272\t11\t2686380\t8.16E+11
+LAB BLOOD ORDERABLES\t473427\tLABTESFBT\tTESTOSTERONE, TOTAL, BIO, FREE\tTESFR\tTESTOSTERONE, FREE\t2.954545455\t244216.3636\t11\t32.5\t153.83\t11\t2686380\t8.16E+11
+LAB BLOOD ORDERABLES\t473427\tLABTESFBT\tTESTOSTERONE, TOTAL, BIO, FREE\tTESTOS\tTESTOSTERONE\t120.5454545\t244216.3636\t11\t1326\t378816\t11\t2686380\t8.16E+11
+LAB BLOOD ORDERABLES\t473432\tLABTHAMP\tTHERMAL AMPLITUDE\tTHRAMP\tTHERMAL AMPLITUDE\t9999999\t158911.4286\t11\t109999989\t1.10E+15\t21\t3337140\t7.44E+11
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P4PA\tCD3+/CD4+,ABS\t909119.7273\t41601.81818\t11\t10000317\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P4PC\tCD3+/CD4+,%\t909099.8182\t41601.81818\t11\t10000098\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P8PA\tCD3+/CD8+, ABS.\t909153.1818\t41601.81818\t11\t10000685\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC3P8PC\tCD3+/CD8+, %\t909108.7273\t41601.81818\t11\t10000196\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P4PA\tCD5+/CD4+ ,ABS\t909119.1818\t41601.81818\t11\t10000311\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P4PC\tCD5+/CD4+ %\t909099.2727\t41601.81818\t11\t10000092\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P8PA\tCD5+/CD8+,ABS.\t909146.8182\t41601.81818\t11\t10000615\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tC5P8PC\tCD5+/CD8+ %\t909106.6364\t41601.81818\t11\t10000173\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tRC4C8\tCD4/CD8 RATIO\t909091.9364\t41601.81818\t11\t10000011.3\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tTC4C8A\tT(CD4+CD8), ABS.\t909182.0909\t41601.81818\t11\t10001003\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tTC4C8P\tT(CD4+CD8)%\t909117.7273\t41601.81818\t11\t10000295\t1.00E+14\t11\t457620\t24563012400
+LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTIGA\tC.TRACHOMATIS IGA\t9999999\t231125.4545\t11\t109999989\t1.10E+15\t11\t2542380\t6.28E+11
+LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTIGG\tC.TRACHOMATIS IGG\t9999999\t231125.4545\t11\t109999989\t1.10E+15\t11\t2542380\t6.28E+11
+LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTIGM\tC.TRACHOMATIS IGM\t9999999\t231125.4545\t11\t109999989\t1.10E+15\t11\t2542380\t6.28E+11
+LAB BLOOD ORDERABLES\t611846\tLABHIBCV\tDONOR, HIV-1/HCV/HBV NAT TESTING\tHIBCV1\tDONOR HIV/HBV/HCV RESULT\t9999999\t266914.2857\t11\t109999989\t1.10E+15\t14\t3736800\t1.23E+12
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tBDC\tCORD BASE DEFICIT, ART\t5454546.182\t360\t11\t60000008\t6.00E+14\t6\t2160\t1555200
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tHCO3C\tCORD HCO3, ART\t5454556.127\t360\t11\t60000117.4\t6.00E+14\t6\t2160\t1555200
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tPCO2C\tCORD PCO2, ART\t5454571.9\t360\t11\t60000290.9\t6.00E+14\t6\t2160\t1555200
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tPHCD\tCORD PH, ART\t5454548.196\t360\t11\t60000030.15\t6.00E+14\t6\t2160\t1555200
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tPO2C\tCORD PO2, ART\t6363642.727\t360\t11\t70000070\t7.00E+14\t6\t2160\t1555200
+LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11761R\tCOQ10 TOTAL\t909558.3636\t282690\t11\t10005142\t1.00E+14\t10\t2826900\t9.22E+11
+LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11762R\tCOQ10 % REDUCED\t909174.0909\t282690\t11\t10000915\t1.00E+14\t10\t2826900\t9.22E+11
+LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12402R\tALKALINE PHOSPHATASE\t469.3636364\t249229.0909\t11\t5163\t5050555\t11\t2741520\t8.05E+11
+LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12403R\tALKP. PHOSPHATASE - OTHER\t0\t249229.0909\t11\t0\t0\t11\t2741520\t8.05E+11
+LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12404R\tALKP. PHOSPHATASE - BONE\t89.90909091\t249229.0909\t11\t989\t189025\t11\t2741520\t8.05E+11
+LAB BLOOD ORDERABLES\t904455\tLAB12401R\tALKP. PHOSPHATASE ISOENZYMES\t12405R\tALKP. PHOSPHATASE - LIVER\t379.4545455\t249229.0909\t11\t4174\t3346044\t11\t2741520\t8.05E+11
+MICROBIOLOGY ORDERABLES\t472117\tLABCSFC\tCSF CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t9999999\t431748.6235\t11\t109999989\t1.10E+15\t1867\t806074680\t3.79E+14
+MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tSDES\tSPECIMEN\t9999999\t79170\t11\t109999989\t1.10E+15\t2\t158340\t12620538000
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tSDES\tSPECIMEN\t9999999\t471560\t11\t109999989\t1.10E+15\t18\t8488080\t5.50E+12
+STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL2\tTOTAL WEIGHT\t9999999\t794016\t11\t109999989\t1.10E+15\t5\t3970080\t4.65E+12
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tAMOBARB\tAMOBARBITAL\t9999999\t30\t11\t109999989\t1.10E+15\t2\t60\t3600
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tPB\tPHENOBARBITAL\t4548446.818\t30\t11\t50032915\t5.00E+14\t2\t60\t3600
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tPENTO\tPENTOBARBITAL\t9999999\t30\t11\t109999989\t1.10E+15\t2\t60\t3600
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tSECOBARB\tSECOBARBITAL\t9999999\t30\t11\t109999989\t1.10E+15\t2\t60\t3600
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tYUBTGC\tBUTALBITAL\t5455872.818\t30\t11\t60014601\t6.00E+14\t2\t60\t3600
+URINE ORDERABLES\t471755\tLABUB2MG\tBETA - 2 - MICROGLOBULIN, URINE\tPHFMS\tPH METABOLIC SCRN\t6.818181818\t323151.4286\t11\t75\t519\t7\t2262060\t8.66E+11
+URINE ORDERABLES\t472938\tLAB12811R\tMYOGLOBIN, URINE\t12812R\tMYOGLOBIN, URINE\t4545894.909\t147345\t11\t50004844\t5.00E+14\t16\t2357520\t3.95E+11
+URINE ORDERABLES\t473736\tLABUPIE\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS\tUPIER\tRESULT\t9999999\t373954.7945\t11\t109999989\t1.10E+15\t438\t163792200\t1.21E+15
+URINE ORDERABLES\t670814\tLAB11179R\tNICOTINE & METABOLITES, U\t11181R\tCOTININE, U\t7272790.455\t357805.7143\t11\t80000695\t8.00E+14\t7\t2504640\t1.04E+12
+VIROLOGY ORDERABLES\t472042\tLABCMVP\tCMV DNA PCR QUAL, NON-BLOOD\tCMVLIP\tTEST DESCRIPTION\t9999999\t204632\t11\t109999989\t1.10E+15\t30\t6138960\t1.86E+12
+VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSVRM\tCLINICAL REMARKS\t9999999\t209810\t11\t109999989\t1.10E+15\t12\t2517720\t1.17E+12
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVTIP\tTEST DESCRIPTION\t9999999\t203590.4878\t11\t109999989\t1.10E+15\t246\t50083260\t1.36E+13
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tVL\tPRODUCT VOLUME\t167.4\t29880\t10\t1674\t282024\t1\t29880\t892814400
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tXMR\tUNIT CROSSMATCH  \t9999999\t34248\t10\t99999990\t1.00E+15\t5\t171240\t11764303200
+BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tXMR\tUNIT CROSSMATCH  \t9999999\t32625\t10\t99999990\t1.00E+15\t4\t130500\t10116111600
+BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tXMR\tUNIT CROSSMATCH  \t9999999\t31260\t10\t99999990\t1.00E+15\t4\t125040\t9870127200
+BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tSI\t<<STATUS>>  \t9999999\t23456\t10\t99999990\t1.00E+15\t15\t351840\t18758599200
+BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tSI\t<<STATUS>>  \t9999999\t50293.84615\t10\t99999990\t1.00E+15\t13\t653820\t97952439600
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tSI\t<<STATUS>>  \t9999999\t31900\t10\t99999990\t1.00E+15\t12\t382800\t39290846400
+BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tDI\tISSUE DATE/TIME\t9999999\t33900\t10\t99999990\t1.00E+15\t5\t169500\t6253203600
+BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tDI\tISSUE DATE/TIME\t9999999\t35085\t10\t99999990\t1.00E+15\t4\t140340\t5354082000
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tUBT\tUNIT BLOOD TYPE  \t9999999\t27880\t10\t99999990\t1.00E+15\t3\t83640\t3495549600
+BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tUBT\tUNIT BLOOD TYPE  \t9999999\t38360\t10\t99999990\t1.00E+15\t3\t115080\t7038208800
+BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tUBT\tUNIT BLOOD TYPE  \t9999999\t38320\t10\t99999990\t1.00E+15\t3\t114960\t7029561600
+BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tUBT\tUNIT BLOOD TYPE  \t9999999\t26760\t10\t99999990\t1.00E+15\t3\t80280\t3222554400
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBRES\tRESULTS\t9999999\t1007200\t10\t99999990\t1.00E+15\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t471953\tLABCSFCT\tCELL COUNT AND DIFF, CSF\tCLINE\tLINING CELLS, CSF\t4.2\t6360\t10\t42\t526\t5\t31800\t209181600
+BODY FLUID ORDERABLES\t473090\tLABGASPH\tPH, GASTRIC\tPHGAST\tPH,GASTRIC\t1000005.5\t3660\t10\t10000055\t1.00E+14\t4\t14640\t123249600
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTCMT\tCOMMENT\t9999999\t771152.3077\t10\t99999990\t1.00E+15\t13\t10024980\t1.13E+13
+BODY FLUID ORDERABLES\t497362\tLABFLK\tPOTASSIUM, FLUID\tKFLS\tSOURCE\t9999999\t3996\t10\t99999990\t1.00E+15\t5\t19980\t102495600
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tGMYD8\tMYD88\t9999999\t1131829.091\t10\t99999990\t1.00E+15\t11\t12450120\t1.54E+13
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t68.5\t12342\t10\t685\t48325\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t124\t12342\t10\t1240\t154400\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t12342\t10\t99999990\t1.00E+15\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t540.6\t12342\t10\t5406\t2988472\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t140.495\t12342\t10\t1404.95\t859857.9425\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.925\t12342\t10\t29.25\t99.0625\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.524\t12342\t10\t5.24\t3.1496\t10\t123420\t6324188400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t1000002.17\t12342\t10\t10000021.7\t1.00E+14\t10\t123420\t6324188400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t5000003\t156\t10\t50000030\t5.00E+14\t10\t1560\t19000800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTNNSVT\tICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT\t19.7\t19038\t10\t197\t31471\t10\t190380\t33011377200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAVFDTD\tICD TACHY VF DETECTION DURATION\t6000004.2\t19008\t10\t60000042\t6.00E+14\t10\t190080\t32977072800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t9.2525\t19398\t10\t92.525\t1270.503125\t10\t193980\t33026972400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t19398\t10\t99999990\t1.00E+15\t10\t193980\t33026972400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTDTDU\tSLOWVT TACHY ZONE DETECTION DURATION\t5000013.1\t19008\t10\t50000131\t5.00E+14\t10\t190080\t32977072800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTDTNT\tSLOWVT TACHY ZONE DETECTION INTERVAL\t371\t19008\t10\t3710\t1395500\t10\t190080\t32977072800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTQRSM\tSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t19008\t10\t99999990\t1.00E+15\t10\t190080\t32977072800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTZNRT\tSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t6000031.4\t19008\t10\t60000314\t6.00E+14\t10\t190080\t32977072800
+HISTOCOMPATIBILITY ORDERABLE\t718854\t10205R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\t10049R\tHLA LAB TESTING STATUS\t9999999\t1896726\t10\t99999990\t1.00E+15\t10\t18967260\t5.63E+13
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9184R\tDONOR NAME\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9185R\tDONOR DOB\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9186R\tRELATIONSHIP TO PATIENT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9187R\tDONOR CELL DATE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9188R\tDONOR CELL SOURCE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9189R\tDONOR CELL TARGET\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9190R\tMETHOD\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9191R\tPATIENT\'S SERUM 1 DATE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9193R\tPATIENT\'S SERUM 1 UNTREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9194R\tPATIENT\'S SERUM 1 DTT TREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9195R\tPATIENT\'S SERUM 2 DATE\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9197R\tPATIENT\'S SERUM 2 UNTREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9198R\tPATIENT\'S SERUM 2 DTT TREATED RESULT\t9999999\t475542\t10\t99999990\t1.00E+15\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9331R\tSERUM DATE\t9999999\t1119612\t10\t99999990\t1.00E+15\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9339R\tCPRA FOR CLASS II\t9999999\t1119612\t10\t99999990\t1.00E+15\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9606R\tMETHOD\t9999999\t1119612\t10\t99999990\t1.00E+15\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9519R\t% DONOR- MARROW CD3\t79.3\t5730\t10\t793\t78417\t10\t57300\t3249018000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9559R\t% RECIPIENT- MARROW CD3\t20.7\t5730\t10\t207\t19817\t10\t57300\t3249018000
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tDYSPL\tDYSPL GRANS, UNCLASS.\t8.1\t151332\t10\t81\t1039\t10\t1513320\t5.03E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHQLINT\tINTERPRETATION\t9999999\t227223.75\t10\t99999990\t1.00E+15\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472222\tLABDONLAI\tDONATH LANDSTEINER\tDONLAR\tDONATH LANDSTEINER TEST\t9999999\t259674.5455\t10\t99999990\t1.00E+15\t11\t2856420\t1.04E+12
+LAB BLOOD ORDERABLES\t472450\tLABYGBMGE\tGBM AB, IGG\tYGBMG1\tGBM AB, IGG, ELISA\t8999999.14\t183616.3636\t10\t89999991.4\t9.00E+14\t11\t2019780\t5.25E+11
+LAB BLOOD ORDERABLES\t472513\tLAB11040R\tHEPARIN COFACTOR\tHEPCO2\tHEPARIN COFACTOR II\t9999999\t661686\t10\t99999990\t1.00E+15\t10\t6616860\t5.16E+12
+LAB BLOOD ORDERABLES\t472723\tLABLAMOL\tLAMOTRIGINE\tLAMOT\tLAMOTRIGINE\t4.77\t857737.5\t10\t47.7\t357.99\t8\t6861900\t3.25E+13
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tMPACID\tMYCOPHENOLIC ACID\t3000001.12\t158860\t10\t30000011.2\t3.00E+14\t9\t1429740\t2.44E+11
+LAB BLOOD ORDERABLES\t472932\tLABMYCPA\tMYCOPHENOLIC ACID\tYMPAG\tMPA GLUCURONIDE\t2000058.7\t158860\t10\t20000587\t2.00E+14\t9\t1429740\t2.44E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tCOLL50\tCOLLAGEN 5.0 UG/ML\t6000021.4\t78206.66667\t10\t60000214\t6.00E+14\t9\t703860\t89752165200
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tRIS15\tRISTOCETIN 1.5 MG/ML\t6000031.1\t115826.6667\t10\t60000311\t6.00E+14\t9\t1042440\t2.66E+11
+LAB BLOOD ORDERABLES\t473221\tLABYRENIN\tRENIN ACTIVITY, PLASMA\tRENIN\tRENIN ACTIVITY, PL\t2000002\t355240\t10\t20000020\t2.00E+14\t6\t2131440\t8.40E+11
+LAB BLOOD ORDERABLES\t473322\tLABSICKL\tSICKLE CELL PREP\tSICKL\tSICKLE CELL PREP\t9999999\t8400\t10\t99999990\t1.00E+15\t12\t100800\t1081922400
+LAB BLOOD ORDERABLES\t473455\tLABTFPSA\tTOTAL AND FREE PSA\tFPSA\tFREE PSA\t1000005.98\t25116\t10\t10000059.8\t1.00E+14\t10\t251160\t8745012000
+LAB BLOOD ORDERABLES\t473455\tLABTFPSA\tTOTAL AND FREE PSA\tFREEPSAPC\t% FREE PSA\t1000020.1\t25116\t10\t10000201\t1.00E+14\t10\t251160\t8745012000
+LAB BLOOD ORDERABLES\t473455\tLABTFPSA\tTOTAL AND FREE PSA\tPSA\tPSA\t55.88\t25116\t10\t558.8\t202080.88\t10\t251160\t8745012000
+LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tTEST4\tTEST NAME\t9999999\t4443266.667\t10\t99999990\t1.00E+15\t9\t39989400\t3.40E+14
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12695R\tS.PNEUMO SEROTYPE 2 (2)\t3000006.13\t272862\t10\t30000061.3\t3.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12712R\tS.PNEUMO SEROTYPE 5 (5)\t2000001.47\t272862\t10\t20000014.7\t2.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12713R\tS.PNEUMO SEROTYPE 17 (17)\t5000001.21\t272862\t10\t50000012.1\t5.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12714R\tS.PNEUMO SEROTYPE 20 (20F)\t2000001.04\t272862\t10\t20000010.4\t2.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12715R\tS.PNEUMO SEROTYPE 22 (22F)\t6000001.82\t272862\t10\t60000018.2\t6.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12716R\tS.PNEUMO SEROTYPE 34 (10A)\t4000000.3\t272862\t10\t40000003\t4.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12717R\tS.PNEUMO SEROTYPE 43 (11A)\t4000000.03\t272862\t10\t40000000.3\t4.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12718R\tS.PNEUMO SEROTYPE 54 (15B)\t5000000.54\t272862\t10\t50000005.4\t5.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12719R\tS.PNEUMO SEROTYPE 57 (19A)\t2000003.52\t272862\t10\t20000035.2\t2.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\t12726R\tS.PNEUMO SEROTYPE 70 (33F)\t2000003.84\t272862\t10\t20000038.4\t2.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE01\tS.PNEUMO SEROTYPE 1 (1)\t3000001.05\t272862\t10\t30000010.5\t3.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE12\tS.PNEUMO SEROTYPE 12 (12F)\t7000000.71\t272862\t10\t70000007.1\t7.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE14\tS.PNEUMO SEROTYPE 14 (14)\t4000002.42\t272862\t10\t40000024.2\t4.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE19\tS.PNEUMO SEROTYPE 19 (19F)\t3000007.36\t272862\t10\t30000073.6\t3.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE23\tS.PNEUMO SEROTYPE 23 (23F)\t4000000.54\t272862\t10\t40000005.4\t4.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE26\tS.PNEUMO SEROTYPE 26 (6B)\t5000001.13\t272862\t10\t50000011.3\t5.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE3\tS.PNEUMO SEROTYPE 3 (3)\t2000001.18\t272862\t10\t20000011.8\t2.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE4\tS.PNEUMO SEROTYPE 4 (4)\t6999999.63\t272862\t10\t69999996.3\t7.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE51\tS.PNEUMO SEROTYPE 51 (7F)\t3000001.02\t272862\t10\t30000010.2\t3.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE56\tS.PNEUMO SEROTYPE 56 (18C)\t2000000.9\t272862\t10\t20000009\t2.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE68\tS.PNEUMO SEROTYPE 68 (9V)\t5000000.27\t272862\t10\t50000002.7\t5.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE8\tS.PNEUMO SEROTYPE 8 (8)\t3000001.18\t272862\t10\t30000011.8\t3.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497292\tLAB12711R\tS PNEUMONIAE AB IGG (23 SEROTYPES)\tPNE9\tS.PNEUMO SEROTYPE 9 (9N)\t4000000.41\t272862\t10\t40000004.1\t4.00E+14\t10\t2728620\t1.02E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tFACIDTOT\tTOTAL FATTY ACIDS\t10.18\t504672\t10\t101.8\t1084.62\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tMUFATOT\tTOTAL MONOUNSATURATED ACID\t3.04\t504672\t10\t30.4\t96.8\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tPUFATOT\tTOTAL POLYUNSATURATED ACID\t2.98\t504672\t10\t29.8\t97.68\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC1\tLAURIC ACID C12:0\t12.6\t504672\t10\t126\t2238\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC10\tVACCENIC ACID C18:1W9\t361.8\t504672\t10\t3618\t1476790\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC11\tSTEARIC ACID C18:0\t925.2\t504672\t10\t9252\t9348058\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC12\tEPA C20:5W3\t181.3\t504672\t10\t1813\t515979\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC13\tARACHIDONIC ACID C20:4W6\t452.5\t504672\t10\t4525\t2649389\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC14\tMEAD ACID C20:3W9\t15.5\t504672\t10\t155\t3445\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC15\tH-G-LINOLEIC C20:3W6\t75.7\t504672\t10\t757\t72263\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC16\tARACHIDIC ACID C20:0\t24.7\t504672\t10\t247\t6391\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC17\tDHA C22:6W3\t262.8\t504672\t10\t2628\t1096190\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC18\tDPA C22:5W6\t9.4\t504672\t10\t94\t1002\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC19\tDPA C22:5W3\t60.4\t504672\t10\t604\t49634\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC2\tMYRISTIC ACID C14:0\t132.7\t504672\t10\t1327\t190389\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC20\tDTA C22:4W6\t14.5\t504672\t10\t145\t2419\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC21\tDOCOSENOIC ACID C22:1\t9.4\t504672\t10\t94\t1098\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC22\tNERVONIC ACID C24:1W9\t76.2\t504672\t10\t762\t60568\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC24\tTOTAL SATURATED ACID\t4.11\t504672\t10\t41.1\t178.23\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC27\tTOTAL W3\t0.57\t504672\t10\t5.7\t4.67\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC28\tTOTAL W6\t2.42\t504672\t10\t24.2\t65.74\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC3\tHEXADECENOIC ACID C16:1W9\t69.9\t504672\t10\t699\t57731\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC4\tPALMITOLEIC ACID C16:1W7\t446.6\t504672\t10\t4466\t2329528\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC5\tPALMITIC ACID C16:0\t2899.8\t504672\t10\t28998\t88296042\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC6\tG-LINOLENIC ACID C18:3W6\t25.2\t504672\t10\t252\t8448\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC7\tA-LINOLENIC ACID C18:3W3\t57.1\t504672\t10\t571\t40465\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC8\tLINOLEIC ACID C18:2W6\t1832.2\t504672\t10\t18322\t37337792\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC9\tOLEIC ACID C18:1W9\t2076.6\t504672\t10\t20766\t45164836\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11760R\tCOQ10 REDUCED\t477.64\t282690\t10\t4776.4\t3096814.14\t10\t2826900\t9.22E+11
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12243R\t12243R\t2000000.501\t925412.7273\t10\t20000005.01\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12244R\t12244R\t2000052.9\t925412.7273\t10\t20000529\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12245R\t12245R\t2000013.9\t925412.7273\t10\t20000139\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12246R\t12246R\t2000011.7\t925412.7273\t10\t20000117\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12247R\t12247R\t2000033.6\t925412.7273\t10\t20000336\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12248R\t12248R\t2000018.8\t925412.7273\t10\t20000188\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12249R\t12249R\t2000522.2\t925412.7273\t10\t20005222\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12250R\t12250R\t2000102.1\t925412.7273\t10\t20001021\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12251R\t12251R\t2000071.1\t925412.7273\t10\t20000711\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12252R\t12252R\t2000316.4\t925412.7273\t10\t20003164\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12253R\t12253R\t2000201.4\t925412.7273\t10\t20002014\t2.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12256R\t12256R\t4000012.75\t925412.7273\t10\t40000127.5\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12257R\t12257R\t4000014.09\t925412.7273\t10\t40000140.9\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12258R\t12258R\t4000008.58\t925412.7273\t10\t40000085.8\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12259R\t12259R\t4000002.84\t925412.7273\t10\t40000028.4\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12260R\t12260R\t4000003.69\t925412.7273\t10\t40000036.9\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12261R\t12261R\t4000000.75\t925412.7273\t10\t40000007.5\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12262R\t12262R\t4000039.98\t925412.7273\t10\t40000399.8\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12263R\t12263R\t4000001.53\t925412.7273\t10\t40000015.3\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12264R\t12264R\t4000003.82\t925412.7273\t10\t40000038.2\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12265R\t12265R\t4000056.63\t925412.7273\t10\t40000566.3\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12266R\t12266R\t4000002.62\t925412.7273\t10\t40000026.2\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12267R\t12267R\t4000095.98\t925412.7273\t10\t40000959.8\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12268R\t12268R\t4000107.28\t925412.7273\t10\t40001072.8\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12269R\t12269R\t4000007.65\t925412.7273\t10\t40000076.5\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12270R\t12270R\t4000002.43\t925412.7273\t10\t40000024.3\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12271R\t12271R\t4000003.4\t925412.7273\t10\t40000034\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12272R\t12272R\t4000000.55\t925412.7273\t10\t40000005.5\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12273R\t12273R\t4000060.63\t925412.7273\t10\t40000606.3\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12274R\t12274R\t4000001.41\t925412.7273\t10\t40000014.1\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12275R\t12275R\t4000005.45\t925412.7273\t10\t40000054.5\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12276R\t12276R\t4000091.97\t925412.7273\t10\t40000919.7\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12277R\t12277R\t4000003.67\t925412.7273\t10\t40000036.7\t4.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12339R\tEVEROLIMUS\t3.15\t143700\t10\t31.5\t109.55\t14\t2011800\t3.84E+11
+MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tAFB\tAFB SMEAR\t9999999\t3931337.485\t10\t99999990\t1.00E+15\t326\t1281616020\t5.25E+15
+MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tSREQ\tCOMMENTS\t9999999\t79170\t10\t99999990\t1.00E+15\t2\t158340\t12620538000
+MICROBIOLOGY ORDERABLES\t893884\tLABORANER\tORTHOPEDIC ANAEROBIC CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t1159475.26\t10\t99999990\t1.00E+15\t519\t601767660\t7.41E+14
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tSREQ\tCOMMENTS\t9999999\t471560\t10\t99999990\t1.00E+15\t18\t8488080\t5.50E+12
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tGASO\tPATIENT GASTRIC RESULT\t9999999\t681.8181818\t10\t99999990\t1.00E+15\t11\t7500\t37530000
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHLOC\tLOCATION\t9999999\t-77.64705882\t10\t99999990\t1.00E+15\t17\t-1320\t44971200
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tQC11\tQC RESULT\t9999999\t681.8181818\t10\t99999990\t1.00E+15\t11\t7500\t37530000
+URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tPHOSU\tPHOSPHATE, URINE\t1000025.21\t56070\t10\t10000252.1\t1.00E+14\t6\t336420\t30016882800
+URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tTPHOSU\tPHOSPHATE EXCRETION\t1000684.5\t56070\t10\t10006845\t1.00E+14\t6\t336420\t30016882800
+URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tUT8\tCOLLECTION PERIOD PHOSPHATE\t1000020.3\t56070\t10\t10000203\t1.00E+14\t6\t336420\t30016882800
+URINE ORDERABLES\t473104\tLABUPHOST\tPHOSPHORUS, URINE (TIMED)\tVOL8\tVOLUME PHOSPHATE\t1002772.2\t56070\t10\t10027722\t1.00E+14\t6\t336420\t30016882800
+URINE ORDERABLES\t473724\tLABUURIC\tURIC ACID WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t57.29\t13785\t10\t572.9\t41876.85\t4\t55140\t1753282800
+URINE ORDERABLES\t473724\tLABUURIC\tURIC ACID WITH CREATININE, URINE (RANDOM)\tURICRU\tURIC/CREAT RATIO\t1000000.447\t13785\t10\t10000004.47\t1.00E+14\t4\t55140\t1753282800
+URINE ORDERABLES\t473724\tLABUURIC\tURIC ACID WITH CREATININE, URINE (RANDOM)\tURICURN\tURIC ACID, URINE\t26.37\t13785\t10\t263.7\t9850.53\t4\t55140\t1753282800
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tVMAUR\tVMA, URINE\t4.6\t379830\t10\t46\t290\t6\t2278980\t9.13E+11
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVCP\tRESULT\t9000572.1\t159127.5\t10\t90005721\t9.00E+14\t8\t1273020\t2.10E+11
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVLOG\tRESULT LOG 10\t8999999.476\t159127.5\t10\t89999994.76\t9.00E+14\t8\t1273020\t2.10E+11
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVSP\tSPECIMEN\t9999999\t159127.5\t10\t99999990\t1.00E+15\t8\t1273020\t2.10E+11
+VIROLOGY ORDERABLES\t473310\tLABSCMV\tCMV SHELL VIAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t198392.7935\t10\t99999990\t1.00E+15\t247\t49003020\t1.01E+13
+VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEBV EARLY ANTIGEN IGG\tEAGQNT\tEAGQNT\t4000000.882\t280713.3333\t10\t40000008.82\t4.00E+14\t9\t2526420\t1.92E+12
+VIROLOGY ORDERABLES\t876715\tLABDVERV3\tRSV DFA\tRV3IP\tTEST DESCRIPTION\t9999999\t60101.53846\t10\t99999990\t1.00E+15\t78\t4687920\t3.25E+11
+VIROLOGY ORDERABLES\t906955\tLABCMVRO\tCMV RULE OUT\tCMVDSV\tTEST DESCRIPTION\t9999999\t253864.8227\t10\t99999990\t1.00E+15\t423\t107384820\t3.23E+13
+VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVSPM\tSPECIMEN\t9999999\t175699.0909\t10\t99999990\t1.00E+15\t22\t3865380\t7.11E+11
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVQIN\tTEST DESCRIPTION\t9999999\t403485\t10\t99999990\t1.00E+15\t20\t8069700\t1.11E+13
+VIROLOGY ORDERABLES\t906967\tLABHSVCSF\tHSV DNA PCR QUAL, CSF\tHSVINC\tTEST DESCRIPTION\t9999999\t185672.0388\t10\t99999990\t1.00E+15\t206\t38248440\t9.97E+12
+VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tRPT\tRPT\t9999999\t1069548\t10\t99999990\t1.00E+15\t10\t10695480\t1.26E+13
+VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tSDES\tSPECIMEN\t9999999\t1069548\t10\t99999990\t1.00E+15\t10\t10695480\t1.26E+13
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t9\t89999991\t9.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tXMR\tUNIT CROSSMATCH  \t9999999\t25760\t9\t89999991\t9.00E+14\t6\t154560\t6237014400
+BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tXMR\tUNIT CROSSMATCH  \t9999999\t32640\t9\t89999991\t9.00E+14\t4\t130560\t10117130400
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tSI\t<<STATUS>>  \t9999999\t53298.46154\t9\t89999991\t9.00E+14\t13\t692880\t99484610400
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tSI\t<<STATUS>>  \t9999999\t49705\t9\t89999991\t9.00E+14\t12\t596460\t77125532400
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tXMR\tUNIT CROSSMATCH  \t9999999\t19880\t9\t89999991\t9.00E+14\t3\t59640\t2008792800
+BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tXMR\tUNIT CROSSMATCH  \t9999999\t28860\t9\t89999991\t9.00E+14\t3\t86580\t3603330000
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tPRO\tPRODUCT ID  \t9999999\t20070\t9\t89999991\t9.00E+14\t4\t80280\t3212935200
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tFCMT\tFLUID COMMENT\t9999999\t980\t9\t89999991\t9.00E+14\t3\t2940\t4496400
+BODY FLUID ORDERABLES\t472490\tLABYHCCSF\tHCG BETA-SUBUNIT QUANTITATIVE, CSF\tHCGCSF\tHCG, B SUBUNIT QN, CSF\t6666666.289\t213420\t9\t59999996.6\t6.00E+14\t7\t1493940\t3.49E+11
+BODY FLUID ORDERABLES\t472609\tLABCIGA\tIGA, CSF\tIGACSF\tIGA, CSF\t1111111.733\t234228\t9\t10000005.6\t1.00E+14\t5\t1171140\t3.03E+11
+BODY FLUID ORDERABLES\t472774\tLABFBBURG\tLYME WESTERN BLOT, CSF\tBBIGG\tBORELLIA BURG IGG AB\t9999999\t282672.6316\t9\t89999991\t9.00E+14\t76\t21483120\t7.48E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\tYSTNWT\tSTONE WEIGHT\t8.568444444\t#VALUE!\t9\t77.116\t5191.667322\t0\tNone\tNone
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t191.6666667\t13760\t9\t1725\t365525\t9\t123840\t6324012000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRALDBIIM\tPM RA LEAD BIPOLAR IMPEDANCE\t463.3333333\t13760\t9\t4170\t2011458\t9\t123840\t6324012000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMTLPCPACA\tPM TELEMETRY PERCENT PACING ATRIAL\t48.25666667\t13760\t9\t434.31\t34580.5801\t9\t123840\t6324012000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.611111111\t13760\t9\t23.5\t70.25\t9\t123840\t6324012000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.4\t13760\t9\t3.6\t1.44\t9\t123840\t6324012000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t1111111.4\t13760\t9\t10000002.6\t1.00E+14\t9\t123840\t6324012000
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tATACHTADTI\tATACH TACHY ZONE DETECTION INTERVAL\t362.2222222\t-380\t9\t3260\t1183800\t9\t-3420\t2084400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDBRMDSLP\tBRADY MODULATION SLOPE\t3333337.444\t19786.66667\t9\t30000037\t3.00E+14\t9\t178080\t32661597600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t1.333333333\t19766.66667\t9\t12\t20.65625\t9\t177900\t32661572400
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRDUR\tLV THRESHOLD DURATION\t0.7\t19766.66667\t9\t6.3\t6.13\t9\t177900\t32661572400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLTSTTI\tICD TEL TEST CHARGE TIME\t8.655111111\t20686.66667\t9\t77.896\t686.230968\t9\t186180\t32958486000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t231\t9\t89999991\t9.00E+14\t20\t4620\t43707600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t4444565.111\t53.33333333\t9\t40001086\t4.00E+14\t9\t480\t4262400
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t773.3333333\t9\t0\t0\t9\t6960\t53560800
+HISTOCOMPATIBILITY ORDERABLE\t519017\t10230R\tHLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*\t10049R\tHLA LAB TESTING STATUS\t9999999\t1205680\t9\t89999991\t9.00E+14\t9\t10851120\t1.84E+13
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9210R\tDONOR NAME\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9211R\tDONOR DOB\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9212R\tRELATIONSHIP TO PATIENT\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9213R\tDONOR CELL DATE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9214R\tDONOR CELL SOURCE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9215R\tDONOR CELL TARGET\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9216R\tMETHOD\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9217R\tPATIENT\'S SERUM 1 DATE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9221R\tPATIENT\'S SERUM 2 DATE\t9999999\t528366.6667\t9\t89999991\t9.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9333R\tCOMBINED CPRA FOR SERUM\t9999999\t1244013.333\t9\t89999991\t9.00E+14\t9\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9378R\tCPRA FOR CUMULATIVE C1Q\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9382R\tCPRA FOR C1Q CLASS I FOR CURRENT\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9608R\tMETHOD\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9622R\tCURRENT SERUM DATE\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9623R\tCURRENT SERUM DESCRIPTION\t9999999\t33166.66667\t9\t89999991\t9.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9521R\t% DONOR- MARROW CD15\t79.11111111\t6366.666667\t9\t712\t69798\t9\t57300\t3249018000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9522R\t% DONOR- MARROW CD19\t84.55555556\t6366.666667\t9\t761\t73245\t9\t57300\t3249018000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9529R\t% DONOR- MARROW CD56\t86.66666667\t6366.666667\t9\t780\t75930\t9\t57300\t3249018000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9561R\t% RECIPIENT- MARROW CD15\t20.88888889\t6366.666667\t9\t188\t17398\t9\t57300\t3249018000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9562R\t% RECIPIENT- MARROW CD19\t15.44444444\t6366.666667\t9\t139\t11045\t9\t57300\t3249018000
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9569R\t% RECIPIENT- MARROW CD56\t13.33333333\t6366.666667\t9\t120\t9930\t9\t57300\t3249018000
+LAB BLOOD ORDERABLES\t471525\tLAB11078R\tACETYLCHOLINESTERASE\tACHERBC\tACETYLCHOLINESTERASE, RBC\t1111149.967\t259177.5\t9\t10000349.7\t1.00E+14\t8\t2073420\t7.03E+11
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC02\tC2, ACETYL-\t13.00444444\t373273.3333\t9\t117.04\t2314.4348\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC03\tC3, PROPIONYL-\t0.623333333\t373273.3333\t9\t5.61\t4.5869\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC04\tC4, ISO-/BUTYRYL-\t0.277777778\t373273.3333\t9\t2.5\t0.8306\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC05\tC5, ISOVALERYL-/2-METHYLBUTYRYL-\t0.131111111\t373273.3333\t9\t1.18\t0.1724\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC05DC\tC5-DC, GLUTARYL-\t0.064444444\t373273.3333\t9\t0.58\t0.0568\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC05OH\tC5-OH, 3OH-ISOVALERYL-\t0.057777778\t373273.3333\t9\t0.52\t0.0492\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC06\tC6, HEXANOYL-\t0.091111111\t373273.3333\t9\t0.82\t0.0934\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC06OH\tC6-0H, 3OH-HEXANOYL-\t0.04\t373273.3333\t9\t0.36\t0.0188\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC08\tC8, OCTANOYL-\t0.152222222\t373273.3333\t9\t1.37\t0.2673\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC081\tC8:1, OCTENOYL-\t0.191111111\t373273.3333\t9\t1.72\t0.434\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC10\tC10, DECANOYL-\t0.17\t373273.3333\t9\t1.53\t0.3021\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC101\tC10:1, DECENOYL-\t0.162222222\t373273.3333\t9\t1.46\t0.2954\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC12\tC12, DODECANOYL-\t0.094444444\t373273.3333\t9\t0.85\t0.0969\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC121\tC12:1, DODECENOYL-\t0.115555556\t373273.3333\t9\t1.04\t0.1832\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC12OH\tC12-OH, 3OH-DODECANOYL\t0.028888889\t373273.3333\t9\t0.26\t0.0108\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC14\tC14, TETRADECANOYL-\t0.064444444\t373273.3333\t9\t0.58\t0.048\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC141\tC14:1, TETRADECENOYL-\t0.102222222\t373273.3333\t9\t0.92\t0.1286\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC141OH\tC14:1-OH, 3OH-TETRADECENOYL-\t0.054444444\t373273.3333\t9\t0.49\t0.0333\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC142\tC14:2, TETRADECADIENOYL-\t0.074444444\t373273.3333\t9\t0.67\t0.0569\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC14OH\tC14-OH, 3OH-TETRADECANOYL-\t0.045555556\t373273.3333\t9\t0.41\t0.0253\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC16\tC16, PALMITOYL-\t0.165555556\t373273.3333\t9\t1.49\t0.2889\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC161\tC16:1, PALMITOLEYL-\t0.063333333\t373273.3333\t9\t0.57\t0.0429\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC161OH\tC16:1-OH, 3OH-PALMITOLEYL-\t0.028888889\t373273.3333\t9\t0.26\t0.0104\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC16OH\tC16-OH, 3OH-PALMITOYL-\t0.037777778\t373273.3333\t9\t0.34\t0.0168\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC18\tC18, STEAROYL-\t0.063333333\t373273.3333\t9\t0.57\t0.0425\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC181\tC18:1, OLEYL-\t0.204444444\t373273.3333\t9\t1.84\t0.444\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC181OH\tC18:1-OH, 3OH-OLEYL-\t0.032222222\t373273.3333\t9\t0.29\t0.0125\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC182\tC18:2, LINOLEYL-\t0.093333333\t373273.3333\t9\t0.84\t0.091\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tC182OH\tC18:2-OH, 3OH-LINOLEYL-\t0.025555556\t373273.3333\t9\t0.23\t0.0067\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471536\tLABADAQ\tADENOSINE DEAMINASE, RBC\tADA\tADENOSINE DEAMINASE\t2222222.458\t490320\t9\t20000002.12\t2.00E+14\t9\t4412880\t3.48E+12
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\tPANCA\tNEUTROPHIL CYTO AB-P\t9999999\t205166.6667\t9\t89999991\t9.00E+14\t9\t1846500\t4.31E+11
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\tSACIGA\tS. CERVISIAE AB,IGA\t45.82222222\t205166.6667\t9\t412.4\t43433.76\t9\t1846500\t4.31E+11
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\tSACIGG\tS.CERVISIAE AB,IGG\t28.43333333\t205166.6667\t9\t255.9\t16686.49\t9\t1846500\t4.31E+11
+LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tSCL 70 AB\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t143610\t9\t89999991\t9.00E+14\t8\t1148880\t1.75E+11
+LAB BLOOD ORDERABLES\t471745\tLABYC2729\tBCA AB  (CA27.29)\tY2729A\tCA 27, 29\t85.45555556\t164080\t9\t769.1\t162363.81\t9\t1476720\t2.68E+11
+LAB BLOOD ORDERABLES\t472506\tLABYHAET\tHEMOPHILUS INFLUENZA IGG\tYHAET1\tHAEMOPHILUS INFLU B AG,IGG\t0.774444444\t375535\t9\t6.97\t9.1277\t12\t4506420\t1.84E+12
+LAB BLOOD ORDERABLES\t472520\tLABHBCA\tIGM ANTI-HBC\tHBCINT\tINTERPRETATION\t9999999\t52028.78788\t9\t89999991\t9.00E+14\t198\t10301700\t1.13E+12
+LAB BLOOD ORDERABLES\t472521\tLABHBCC\tHEPATITIS B CORE TOTAL AB\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t41197.5\t9\t89999991\t9.00E+14\t8\t329580\t18475815600
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCR\tIGM ANTI-HBC ANTIBODY\t9999999\t129980\t9\t89999991\t9.00E+14\t9\t1169820\t3.51E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t129980\t9\t89999991\t9.00E+14\t9\t1169820\t3.51E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t129980\t9\t89999991\t9.00E+14\t9\t1169820\t3.51E+11
+LAB BLOOD ORDERABLES\t472761\tLABLKM1AB\tLIVER, KID MICROSOME-1 AB, IGG\tLKM1R\tLIVER, KID MIC-1 AB\t1.588888889\t211900\t9\t14.3\t29.79\t9\t1907100\t4.28E+11
+LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tSLREV\tSLIDE REVIEW\t9999999\t31537.5\t9\t89999991\t9.00E+14\t8\t252300\t21254857200
+LAB BLOOD ORDERABLES\t473348\tLABYSOMAT\tSOMATOSTATIN\tSOMATOSTATIN\tSOMATOSTATIN\t22.33333333\t814128\t9\t201\t7331\t10\t8141280\t9.47E+12
+LAB BLOOD ORDERABLES\t473706\tLABREF3\tLAB UNLISTED 3\tRESUL3\tRESULT\t9999999\t2214834.286\t9\t89999991\t9.00E+14\t21\t46511520\t3.48E+14
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGGT\tB.HENSELAE IGG TITER\t9999999\t864724.2857\t9\t89999991\t9.00E+14\t14\t12106140\t4.66E+13
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12027R\tECHOVIRUS  AB TYPE 6\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12028R\tECHOVIRUS  AB TYPE 7\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12029R\tECHOVIRUS  AB TYPE 11\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12030R\tECHOVIRUS  AB TYPE 30\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\t12031R\tECHOVIRUS  AB TYPE 9\t9999999\t659186.6667\t9\t89999991\t9.00E+14\t9\t5932680\t3.94E+12
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVA1\tPHASE 1 IGA\t9999999\t369216\t9\t89999991\t9.00E+14\t10\t3692160\t1.56E+12
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVA2\tPHASE II IGA\t9999999\t369216\t9\t89999991\t9.00E+14\t10\t3692160\t1.56E+12
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBCM\tCOMMENT\t9999999\t910448.7379\t9\t89999991\t9.00E+14\t103\t93776220\t1.40E+14
+LAB BLOOD ORDERABLES\t966901\tLAB12605R\tN-METHYL-D-ASPARTATE RECEPTOR AB, IGG\t12604R\tN-METHYL-D-ASPARTATE RCPTR AB, IGG\t9999999\t315673.3333\t9\t89999991\t9.00E+14\t9\t2841060\t1.02E+12
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tRPT\tRPT\t9999999\t48300\t9\t89999991\t9.00E+14\t1\t48300\t2332890000
+MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRRS\tRESULTS\t9999999\t488123.0769\t9\t89999991\t9.00E+14\t26\t12691200\t8.34E+12
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEL\tDEVELOPER LOT#\t1017.555556\t681.8181818\t9\t9158\t9319590\t11\t7500\t37530000
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tDEVEXP\tDEVELOPER EXP DATE\t9999999\t681.8181818\t9\t89999991\t9.00E+14\t11\t7500\t37530000
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tGASEXP\tGASTROCCULT EXP DATE\t9999999\t681.8181818\t9\t89999991\t9.00E+14\t11\t7500\t37530000
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tGASLOT\tGASTROCCULT LOT#\t4455615.667\t785\t9\t40100541\t4.00E+14\t12\t9420\t41216400
+POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tOCCBLD\tFECAL OCCULT BLOOD BLOOD\t9999999\t1109028.75\t9\t89999991\t9.00E+14\t16\t17744460\t2.34E+14
+STOOL ORDERABLES\t473120\tLABPORFEC\tPORPHYRINS FRACTIONATED, FECAL\tCOPRO\tCOPROPORPHYRIN, FEC\t2222234.444\t934260\t9\t20000110\t2.00E+14\t5\t4671300\t8.25E+12
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tRPT\tRPT\t9999999\t192800\t9\t89999991\t9.00E+14\t3\t578400\t1.13E+11
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tSDES\tSPECIMEN\t9999999\t192800\t9\t89999991\t9.00E+14\t3\t578400\t1.13E+11
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tSREQ\tCOMMENTS\t9999999\t192800\t9\t89999991\t9.00E+14\t3\t578400\t1.13E+11
+STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\tYSTCL4\tCHLORIDE CONCENTRATION\t5555572.333\t671420\t9\t50000151\t5.00E+14\t12\t8057040\t7.48E+12
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tAAMINOA\tA-AMINOADIPIC ACID\t12.44444444\t287790\t9\t112\t12544\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tAAMINOB\tA-AMINOBUTYRIC ACID\t238.1111111\t287790\t9\t2143\t2386269\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tALANINE\tALANINE\t5624\t287790\t9\t50616\t794454100\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tARGININE\tARGININE\t622\t287790\t9\t5598\t9621094\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tASPART\tASPARTIC ACID\t2222297.667\t287790\t9\t20000679\t2.00E+14\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tASPGN\tASPARAGINE\t1582.111111\t287790\t9\t14239\t56672463\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tBALN\tB-ALANINE\t61.11111111\t287790\t9\t550\t189970\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tBAMBA\tB-AMINOISOBUTYRIC AC\t492.3333333\t287790\t9\t4431\t4398237\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tCITRUL\tCITRULLINE\t575.7777778\t287790\t9\t5182\t10538248\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tCYSTINE\tCYSTINE\t720\t287790\t9\t6480\t12113300\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tCYTHN\tCYSTATHIONINE\t78.11111111\t287790\t9\t703\t108063\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tETHLNU\tETHANOLAMINE\t227.8888889\t287790\t9\t2051\t1082763\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tGLUTAC\tGLUTAMIC ACID\t72.44444444\t287790\t9\t652\t114218\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tGLUTAMINE\tGLUTAMINE\t8127.333333\t287790\t9\t73146\t1655999470\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tGLYCINE\tGLYCINE\t6327.111111\t287790\t9\t56944\t719378568\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tHISTID\tHISTIDINE\t2580.111111\t287790\t9\t23221\t117318771\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tHMCY\tHOMOCYSTINE\t20.33333333\t287790\t9\t183\t7251\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tHYPROL\tHYDROXYPROLINE\t135.2222222\t287790\t9\t1217\t470219\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tISOLEU\tISOLEUCINE\t335.3333333\t287790\t9\t3018\t3745580\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tLEUCINE\tLEUCINE\t706.7777778\t287790\t9\t6361\t15632137\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tLYSINE\tLYSINE\t2704.666667\t287790\t9\t24342\t164997240\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tMETHIO\tMETHIONINE\t269.6666667\t287790\t9\t2427\t1719511\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tORNITHINE\tORNITHINE\t692.1111111\t287790\t9\t6229\t16353759\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tPHALN\tPHENYLALANINE\t929.2222222\t287790\t9\t8363\t22662603\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tPROLINE\tPROLINE\t1144.444444\t287790\t9\t10300\t39903032\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tSARSN\tSARCOSINE\t6.222222222\t287790\t9\t56\t3136\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tSERINE\tSERINE\t3062.777778\t287790\t9\t27565\t159511577\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTAURINE\tTAURINE\t475.4444444\t287790\t9\t4279\t4548709\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTHREONINE\tTHREONINE\t3445.333333\t287790\t9\t31008\t217879164\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTRYPTOPHAN\tTRYPTOPHAN\t402.8888889\t287790\t9\t3626\t3655830\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tTYROSINE\tTYROSINE\t1175.777778\t287790\t9\t10582\t33593684\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tVALINE\tVALINE\t1613.888889\t287790\t9\t14525\t90252429\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t472783\tLABUMAG\tMAGNESIUM WITH CREATININE, URINE (RANDOM)\tMGUCRU\tMAG/CREAT RATIO\t0.322222222\t35512.5\t9\t2.9\t1.07\t8\t284100\t66201930000
+URINE ORDERABLES\t472783\tLABUMAG\tMAGNESIUM WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t39.2\t35512.5\t9\t352.8\t20411.16\t8\t284100\t66201930000
+URINE ORDERABLES\t472783\tLABUMAG\tMAGNESIUM WITH CREATININE, URINE (RANDOM)\tUMG\tMAGNESIUM, URINE\t11.04444444\t35512.5\t9\t99.4\t1629.74\t8\t284100\t66201930000
+URINE ORDERABLES\t473122\tLABUPOR\tPORPHYRINS FRACTIONATED, URINE\tUPORIN\tPORPHYRIN, URN INTRP\t9999999\t448491\t9\t89999991\t9.00E+14\t20\t8969820\t6.55E+12
+VIROLOGY ORDERABLES\t472015\tLABCLCR2\tCHLAMYDIA MOLECULAR DETECTION\tSDES4\tSPECIMEN\t9999999\t105230\t9\t89999991\t9.00E+14\t12\t1262760\t1.93E+11
+VIROLOGY ORDERABLES\t472040\tLABCMVIGG\tCMV IGG\tCMV\tCMV ANTIBODY\t9999999\t#VALUE!\t9\t89999991\t9.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVDNA\tCMV DNA\t9999999\t159127.5\t9\t89999991\t9.00E+14\t8\t1273020\t2.10E+11
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEPSTEIN-BARR VIRUS AB PANEL\tEBVDS\tEBV TEST DESCRIPTION\t9999999\t287670.1288\t9\t89999991\t9.00E+14\t699\t201081420\t8.47E+13
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS PCR QUANT\tSOUR10\tSOURCE\t9999999\t131400\t9\t89999991\t9.00E+14\t1\t131400\t17265960000
+VIROLOGY ORDERABLES\t876721\tLABDVEIAR\tFLU A DFA-REFLEX PCR\tIARSP\tSPECIMEN\t9999999\t118140\t9\t89999991\t9.00E+14\t7\t826980\t1.98E+11
+VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tSREQ\tCOMMENTS\t9999999\t1069548\t9\t89999991\t9.00E+14\t10\t10695480\t1.26E+13
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tVL\tPRODUCT VOLUME\t154.875\t33120\t8\t1239\t195761\t1\t33120\t1096934400
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tVL\tPRODUCT VOLUME\t161.625\t#VALUE!\t8\t1293\t209565\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t8\t79999992\t8.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tXMR\tUNIT CROSSMATCH  \t9999999\t40515\t8\t79999992\t8.00E+14\t4\t162060\t11561770800
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tXMR\tUNIT CROSSMATCH  \t9999999\t135465\t8\t79999992\t8.00E+14\t4\t541860\t93705937200
+BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tXMR\tUNIT CROSSMATCH  \t9999999\t116736\t8\t79999992\t8.00E+14\t5\t583680\t95454849600
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tSI\t<<STATUS>>  \t9999999\t28614\t8\t79999992\t8.00E+14\t10\t286140\t25408443600
+BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tXMR\tUNIT CROSSMATCH  \t9999999\t23280\t8\t79999992\t8.00E+14\t2\t46560\t2006697600
+BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tXMR\tUNIT CROSSMATCH  \t9999999\t23280\t8\t79999992\t8.00E+14\t2\t46560\t2006697600
+BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tSI\t<<STATUS>>  \t9999999\t11100\t8\t79999992\t8.00E+14\t5\t55500\t2073337200
+BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tUBT\tUNIT BLOOD TYPE  \t9999999\t40110\t8\t79999992\t8.00E+14\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tPRO\tPRODUCT ID  \t9999999\t26280\t8\t79999992\t8.00E+14\t3\t78840\t3104877600
+BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tPRO\tPRODUCT ID  \t9999999\t36700\t8\t79999992\t8.00E+14\t3\t110100\t6598270800
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tVL\tPRODUCT VOLUME\t167.875\t38550\t8\t1343\t227079\t2\t77100\t3002158800
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tEVIN\tENTEROVIRUS PCR INTERPRETATION\t9999999\t25672.87129\t8\t79999992\t8.00E+14\t202\t5185920\t5.37E+11
+BODY FLUID ORDERABLES\t473196\tLABYCPYRU\tPYRUVATE, CSF\tPKCSF\tPYRUVATE,CSF\t2499999.869\t291150\t8\t19999998.95\t2.00E+14\t6\t1746900\t5.63E+11
+BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tCPSA\tCOMPLEXED PSA\t2500006.325\t66787.5\t8\t20000050.6\t2.00E+14\t8\t534300\t52996633200
+BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tFPSA\tFREE PSA\t2500003.963\t66787.5\t8\t20000031.7\t2.00E+14\t8\t534300\t52996633200
+BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tPSA\tPSA\t2500010.538\t66787.5\t8\t20000084.3\t2.00E+14\t8\t534300\t52996633200
+BODY FLUID ORDERABLES\t611475\tLABTCPSA\tTOTAL AND COMPLEXED PSA\tPSAPC\t% COMPLEXED PSA\t2500054.375\t66787.5\t8\t20000435\t2.00E+14\t8\t534300\t52996633200
+ECHO ORDERABLES\t472280\tECH34\tECHO - 3-DIMENSIONAL ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t56.875\t40695\t8\t455\t26455\t8\t325560\t31827009600
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t161.25\t15540\t8\t1290\t208850\t8\t124320\t6323781600
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t147.5\t14010\t8\t1180\t211000\t8\t112080\t6185714400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t3750013.25\t15562.5\t8\t30000106\t3.00E+14\t8\t124500\t6323598000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t6250000.938\t15562.5\t8\t50000007.5\t5.00E+14\t8\t124500\t6323598000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t15562.5\t8\t79999992\t8.00E+14\t8\t124500\t6323598000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t131.25\t14010\t8\t1050\t138300\t8\t112080\t6185714400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.8316\t13350\t8\t22.6528\t64.2318821\t8\t106800\t6079788000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t5299234.125\t3292.5\t8\t42393873\t4.03E+14\t8\t26340\t383828400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t3292.5\t8\t79999992\t8.00E+14\t8\t26340\t383828400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t7500063\t13537.5\t8\t60000504\t6.00E+14\t8\t108300\t6080598000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t68.75\t62850\t8\t550\t40550\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t5272747.875\t62850\t8\t42181983\t3.62E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t62850\t8\t79999992\t8.00E+14\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.40625\t62850\t8\t19.25\t60.78125\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.49\t62850\t8\t3.92\t2.2304\t8\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t2.8\t62850\t8\t22.4\t75.2\t8\t502800\t2.51E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t96832.5\t8\t79999992\t8.00E+14\t8\t774660\t6.04E+11
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLEADALERT\tLEAD ALERT\t9999999\t-382.5\t8\t79999992\t8.00E+14\t8\t-3060\t2005200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSLWVT\tICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA\t0.25\t23227.5\t8\t2\t2\t8\t185820\t32958356400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTAATPTX\tICD TACHY ZONE ATP THERAPY TYPE\t9999999\t33785.45455\t8\t79999992\t8.00E+14\t11\t371640\t65919031200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSLWVTTDTT\tVSLOWVT TACHY ZONE DETECTION INTERVAL\t416.25\t23760\t8\t3330\t1389900\t8\t190080\t32977044000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t21757.5\t8\t0\t0\t8\t174060\t23357962800
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRSENM\tLV THRESHOLD SENSING AMPLITUDE\t15.2125\t12427.5\t8\t121.7\t2163.85\t8\t99420\t8683678800
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRSENPO\tLV THRESHOLD SENSING POLARITY\t9999999\t12427.5\t8\t79999992\t8.00E+14\t8\t99420\t8683678800
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9219R\tPATIENT\'S SERUM 1 UNTREATED RESULT\t9999999\t593707.5\t8\t79999992\t8.00E+14\t8\t4749660\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9223R\tPATIENT\'S SERUM 2 UNTREATED RESULT\t9999999\t593707.5\t8\t79999992\t8.00E+14\t8\t4749660\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9224R\tPATIENT\'S SERUM 2 DTT TREATED RESULT\t9999999\t593707.5\t8\t79999992\t8.00E+14\t8\t4749660\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9166R\tSERUM DESCRIPTION 2\t9999999\t252547.5\t8\t79999992\t8.00E+14\t8\t2020380\t3.30E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t10464R\tRECIPIENT NAME\t9999999\t204592.5\t8\t79999992\t8.00E+14\t8\t1636740\t7.88E+11
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t10465R\tRECIPIENT DOB\t9999999\t204592.5\t8\t79999992\t8.00E+14\t8\t1636740\t7.88E+11
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9136R\tSERUM DESCRIPTION 2\t9999999\t252525\t8\t79999992\t8.00E+14\t8\t2020200\t3.30E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9334R\tCPRA FOR CLASS I\t9999999\t1261597.5\t8\t79999992\t8.00E+14\t8\t10092780\t6.71E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9340R\tHLA ANTIBODIES FOR CLASS II\t9999999\t1119612\t8\t79999992\t8.00E+14\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t10476R\tDSA FOR C1Q CLASS I\t9999999\t15\t8\t79999992\t8.00E+14\t8\t120\t7200
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9395R\tCPRA FOR C1Q CLASS II FOR CURRENT\t9999999\t37305\t8\t79999992\t8.00E+14\t8\t298440\t89030628000
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9280R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t122781.8824\t8\t79999992\t8.00E+14\t85\t10436460\t1.11E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9584R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t153862.5\t8\t79999992\t8.00E+14\t24\t3692700\t2.80E+12
+LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tANTI-LA ANTIBODY\tSSBQNT\tANTI-LA LEVEL\t3750033.625\t458280\t8\t30000269\t3.00E+14\t8\t3666240\t2.40E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMMQNT\tCMV IGM LEVEL\t9999999\t302160\t8\t79999992\t8.00E+14\t8\t2417280\t8.64E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t9999999\t379288.125\t8\t79999992\t8.00E+14\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV1QNT\tHSV-1 IGG LEVEL\t3750003.765\t302160\t8\t30000030.12\t3.00E+14\t8\t2417280\t8.64E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV1QUL\tHSV-1 IGG RESULT\t9999999\t302160\t8\t79999992\t8.00E+14\t8\t2417280\t8.64E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV2QNT\tHSV-2 IGG LEVEL\t8749999.903\t302160\t8\t69999999.22\t7.00E+14\t8\t2417280\t8.64E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHV2QUL\tHSV-2 IGG RESULT\t9999999\t302160\t8\t79999992\t8.00E+14\t8\t2417280\t8.64E+11
+LAB BLOOD ORDERABLES\t471931\tLABYCARO\tCAROTENE\tCAROTENE\tCAROTENE\t2500027\t290665.7143\t8\t20000216\t2.00E+14\t7\t2034660\t7.56E+11
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tLYM\tLYMPHOCYTES\t9.625\t8310\t8\t77\t1703\t2\t16620\t157957200
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMONO\tMONOCYTES\t14.375\t8310\t8\t115\t2645\t2\t16620\t157957200
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNEUT\tSEG NEUTROPHILS\t58.75\t8310\t8\t470\t31600\t2\t16620\t157957200
+LAB BLOOD ORDERABLES\t472190\tLABYDIPT\tDIPHTHERIA TOXOID IGA AB\tYDIPT1\tDIPTHERIA TOXOID IGG AB\t0.5625\t155505\t8\t4.5\t4.1392\t8\t1244040\t2.51E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t109882.5\t8\t79999992\t8.00E+14\t8\t879060\t2.67E+11
+LAB BLOOD ORDERABLES\t473187\tLABPTINH\tPT INHIBITOR SCREEN\tPTDIR\tINTERPRETED BY\t9999999\t65953.84615\t8\t79999992\t8.00E+14\t156\t10288800\t9.53E+11
+LAB BLOOD ORDERABLES\t473332\tLABSLINTP\tSLIDE INTERPRETATION BY PATHOLOGIST\tSLINT\tINTERPRETATION\t9999999\t168261.4286\t8\t79999992\t8.00E+14\t28\t4711320\t1.33E+12
+LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14MET\tMETHOD\t9999999\t1210908\t8\t79999992\t8.00E+14\t5\t6054540\t9.00E+12
+LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14PRM\tPRIMERS\t9999999\t1210908\t8\t79999992\t8.00E+14\t5\t6054540\t9.00E+12
+LAB BLOOD ORDERABLES\t497040\tLABPSARX\tPSA WITH REFLEX FREE PSA\tPSA\tPSA\t3750001.8\t125565\t8\t30000014.4\t3.00E+14\t8\t1004520\t5.32E+11
+LAB BLOOD ORDERABLES\t497398\tLABYDNBT\tANTI - DNASE B TITER\tYDNBT1\tANTI-DNASE -B TITER\t3750297.125\t148092\t8\t30002377\t3.00E+14\t10\t1480920\t2.29E+11
+LAB BLOOD ORDERABLES\t497418\tLABFCYST\tCYSTICERCUS IGG AB\tCYSIGG\tCYSTICERCUS LGG AB\t9999999\t382065\t8\t79999992\t8.00E+14\t8\t3056520\t1.66E+12
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tCRDVLG\tCORD LENGTH\t6250008.375\t615\t8\t50000067\t5.00E+14\t8\t4920\t4780800
+LAB BLOOD ORDERABLES\t668158\tLABADAM13\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMAG\tANTIGEN\t1250023.125\t162694.2857\t8\t10000185\t1.00E+14\t7\t1138860\t2.85E+11
+LAB BLOOD ORDERABLES\t670808\tLAB11170R\tPREGABALIN\t11171R\tPREGABALIN\t2500002.5\t442380\t8\t20000020\t2.00E+14\t8\t3539040\t1.72E+12
+LAB BLOOD ORDERABLES\t910568\tLAB12397R\tACETYLCHOLINE BLOCKING AB\tACRBLO\tACETYLCHOLINE BLOCKING AB\t25.25\t256882.5\t8\t202\t11238\t8\t2055060\t5.83E+11
+MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tRPT\tRPT\t9999999\t115550\t8\t79999992\t8.00E+14\t6\t693300\t89094740400
+MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tSDES\tSPECIMEN\t9999999\t115550\t8\t79999992\t8.00E+14\t6\t693300\t89094740400
+MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tSREQ\tCOMMENTS\t9999999\t115550\t8\t79999992\t8.00E+14\t6\t693300\t89094740400
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCX\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t324263.2112\t8\t79999992\t8.00E+14\t928\t300916260\t1.71E+14
+MICROBIOLOGY ORDERABLES\t870889\tLABTBPCRS\tM. TUBERCULOSIS PCR, SPECIMEN\tCULT\tCULT/OTHER RSLT:\t9999999\t184554.1693\t8\t79999992\t8.00E+14\t319\t58872780\t5.99E+13
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCDX\tCLINICAL INDICATION\t9999999\t1195020\t8\t79999992\t8.00E+14\t3\t3585060\t4.32E+12
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCMET\tMETHOD\t9999999\t1195020\t8\t79999992\t8.00E+14\t3\t3585060\t4.32E+12
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHVA\tPATIENT PH VALUE\t5000001.5\t681.8181818\t8\t40000012\t4.00E+14\t11\t7500\t37530000
+POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tDEVEL\tDEVELOPER LOT#\t78.625\t1109028.75\t8\t629\t58281\t16\t17744460\t2.34E+14
+POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tHEMED\tHEMOCCULT EXP DATE\t9999999\t1109028.75\t8\t79999992\t8.00E+14\t16\t17744460\t2.34E+14
+POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tHEML\tHEMOCCULT LOT#\t6250843.125\t1109028.75\t8\t50006745\t5.00E+14\t16\t17744460\t2.34E+14
+STOOL ORDERABLES\t473120\tLABPORFEC\tPORPHYRINS FRACTIONATED, FECAL\tFPROTO\tPROTOPORHYRINS, FEC\t1250024.875\t495795\t8\t10000199\t1.00E+14\t4\t1983180\t1.03E+12
+STOOL ORDERABLES\t497355\tLABFAMYA\tAMYLASE, ASCITES\tAMYLASEFLD\tAMYLASE, ASCITES FLUID\t37.625\t5835\t8\t301\t38059\t8\t46680\t406987200
+STOOL ORDERABLES\t497374\tLABYSTCL\tCHLORIDE, STOOL\t12577R\tCHLORIDE, STOOL, RANDOM\t5000035.875\t238005\t8\t40000287\t4.00E+14\t12\t2856060\t9.48E+11
+URINE ORDERABLES\t471617\tLABURAMY\tAMYLASE, URINE (RANDOM)\tAMYLASE\tAMYLASE, URINE\t2516783.875\t14090\t8\t20134271\t2.00E+14\t6\t84540\t1754334000
+URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tCLE\tCHLORIDE EXCRETION\t1250169.625\t63315\t8\t10001357\t1.00E+14\t4\t253260\t28555513200
+URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tUCL\tCHLORIDE, URINE\t1250082.5\t63315\t8\t10000660\t1.00E+14\t4\t253260\t28555513200
+URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tUT11\tCOLLECTION PERIOD CHLORIDE\t1250019.375\t63315\t8\t10000155\t1.00E+14\t4\t253260\t28555513200
+URINE ORDERABLES\t472025\tLABUCLT\tCHLORIDE, URINE (TIMED)\tVOL11\tVOLUME CHLORIDE\t1251732.875\t63315\t8\t10013863\t1.00E+14\t4\t253260\t28555513200
+URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tTMGNU\tMAGNESIUM EXCRETION\t184.25\t88200\t8\t1474\t365244\t5\t441000\t70547680800
+URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tUMG\tMAGNESIUM, URINE\t8.95\t88200\t8\t71.6\t940.32\t5\t441000\t70547680800
+URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tUT12\tCOLL PERIOD MAG\t22.5\t88200\t8\t180\t4176\t5\t441000\t70547680800
+URINE ORDERABLES\t472787\tLABUMAGT\tMAGNESIUM, URINE (TIMED)\tVOL12\tVOLUME MAGNESIUM\t2511.875\t88200\t8\t20095\t74914237\t5\t441000\t70547680800
+VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDDNA\tHEP B VIRUS DNA\t9999999\t#VALUE!\t8\t79999992\t8.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDLOG\tRESULT LOG 10\t3750003.381\t#VALUE!\t8\t30000027.05\t3.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDRES\tRESULT\t5245212.125\t#VALUE!\t8\t41961697\t3.47E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDSP\tSPECIMEN\t9999999\t#VALUE!\t8\t79999992\t8.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472532\tLABHSVPCR\tHERPES SIMPLEX PCR, CSF\tHSVIN\tTEST DESCRIPTION\t9999999\t167178.2332\t8\t79999992\t8.00E+14\t283\t47311440\t1.12E+13
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADQLIP\tTEST DESCRIPTION\t9999999\t221752.1053\t8\t79999992\t8.00E+14\t38\t8426580\t3.12E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tEBV\tEBV BY PCR\t9999999\t295741.791\t8\t79999992\t8.00E+14\t67\t19814700\t7.67E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tYEBP3\tSPECIAL INFORMATION\t9999999\t295741.791\t8\t79999992\t8.00E+14\t67\t19814700\t7.67E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tYEPB4\tREPORT STATUS\t9999999\t295741.791\t8\t79999992\t8.00E+14\t67\t19814700\t7.67E+12
+VIROLOGY ORDERABLES\t633362\tLABMUMPSG\tMUMPS IGG\tMUMQUL\tRESULT\t9999999\t168127.5\t8\t79999992\t8.00E+14\t8\t1345020\t2.63E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6CP\tRESULT\t2643922.25\t254505\t8\t21151378\t2.01E+14\t8\t2036040\t6.91E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t254505\t8\t79999992\t8.00E+14\t8\t2036040\t6.91E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6LO\tRESULT LOG 10\t2500003.224\t254505\t8\t20000025.79\t2.00E+14\t8\t2036040\t6.91E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6SP\tSPECIMEN\t9999999\t254505\t8\t79999992\t8.00E+14\t8\t2036040\t6.91E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t230093.0435\t8\t79999992\t8.00E+14\t207\t47629260\t1.54E+13
+VIROLOGY ORDERABLES\t906947\tLABADVRC\tADENOVIRUS RAPID CULTURE\tADVRC1\tADV RAPID CULTURE\t9999999\t236460\t8\t79999992\t8.00E+14\t8\t1891680\t5.77E+11
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tVL\tPRODUCT VOLUME\t161.4285714\t20790\t7\t1130\t187832\t2\t41580\t1064048400
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t7\t69999993\t7.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tDI\tISSUE DATE/TIME\t9999999\t27630\t7\t69999993\t7.00E+14\t2\t55260\t2073315600
+BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tXMR\tUNIT CROSSMATCH  \t9999999\t18440\t7\t69999993\t7.00E+14\t3\t55320\t2073319200
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tSI\t<<STATUS>>  \t9999999\t16740\t7\t69999993\t7.00E+14\t5\t83700\t3495553200
+BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tSI\t<<STATUS>>  \t9999999\t23028\t7\t69999993\t7.00E+14\t5\t115140\t7038212400
+BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tPRO\tPRODUCT ID  \t9999999\t54990\t7\t69999993\t7.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tPRO\tPRODUCT ID  \t9999999\t54990\t7\t69999993\t7.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tPRO\tPRODUCT ID  \t9999999\t69330\t7\t69999993\t7.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tPRO\tPRODUCT ID  \t9999999\t69330\t7\t69999993\t7.00E+14\t2\t138660\t9623422800
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSNRBC\tNRBC/100WBC, SYN FLD\t4.285714286\t9192\t7\t30\t304\t5\t45960\t669448800
+BODY FLUID ORDERABLES\t471955\tLABSYNCT\tCELL COUNT AND DIFF, SYNOVIAL FLUID\tSRLYM\tREACT LYMPH, SYN FLD\t1.285714286\t6750\t7\t9\t13\t4\t27000\t241617600
+BODY FLUID ORDERABLES\t472655\tLAB12838R\tINSULIN-LIKE GROWTH FACTOR 1\t12839R\tINSULIN-LIKE GROWTH FACTOR 1\t1428665.429\t150574.2857\t7\t10000658\t1.00E+14\t7\t1054020\t1.91E+11
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSICO\tCOMMENT\t9999999\t927889.0909\t7\t69999993\t7.00E+14\t11\t10206780\t1.25E+13
+BODY FLUID ORDERABLES\t869054\tLAB11901R\tN. GONORRHOEAE RNA, THROAT\t11902R\tN.GONORRHOEAE RNA,THROAT\t9999999\t223815\t7\t69999993\t7.00E+14\t4\t895260\t2.70E+11
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t145.1428571\t16080\t7\t1016\t159736\t7\t112560\t6185484000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t76.85714286\t17845.71429\t7\t538\t84212\t7\t124920\t6323421600
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t237.8571429\t3831.428571\t7\t1665\t407125\t7\t26820\t383598000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t6.592857143\t15574.28571\t7\t46.15\t335.79125\t7\t109020\t6080108400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t15574.28571\t7\t69999993\t7.00E+14\t7\t109020\t6080108400
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.828571429\t71828.57143\t7\t19.8\t56.066\t7\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADIMPDTE\tLEAD IMPLANT DATE\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADMFR\tLEAD MANUFACTURER\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADMODEL\tLEAD MODEL\t7144003.857\t214.2857143\t7\t50008027\t5.00E+14\t7\t1500\t15051600
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADSERNUM\tLEAD SERIAL NUMBER\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tLEADTYPE\tLEAD TYPE\t9999999\t214.2857143\t7\t69999993\t7.00E+14\t7\t1500\t15051600
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t526.8571429\t71828.57143\t7\t3688\t1998484\t7\t502800\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t292.8571429\t71914.28571\t7\t2050\t613500\t7\t503400\t2.51E+11
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t148.5714286\t1148.571429\t7\t1040\t157000\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t114.2857143\t1148.571429\t7\t800\t93400\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRLOWRT\tBRADY LOWER RATE\t70\t1148.571429\t7\t490\t34900\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRPACMD\tBRADY PACING MODE\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t135.7142857\t1148.571429\t7\t950\t131100\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t17.71428571\t1148.571429\t7\t124\t8016\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t2.982185714\t1148.571429\t7\t20.8753\t62.30583157\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.535714286\t1148.571429\t7\t17.75\t46.4375\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.642857143\t1148.571429\t7\t4.5\t3.33\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMEVDATE\tPACEMAKER EVAL DATE\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRALDBIIM\tPM RA LEAD BIPOLAR IMPEDANCE\t469.5714286\t1148.571429\t7\t3287\t1594863\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRHYTHM\tPM RHYTHM\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMTLPCPACA\tPM TELEMETRY PERCENT PACING ATRIAL\t49.24142857\t1148.571429\t7\t344.69\t25760.3465\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t99.94714286\t1148.571429\t7\t699.63\t69926.0537\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2.785714286\t1148.571429\t7\t19.5\t60.75\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.557142857\t1148.571429\t7\t3.9\t3.21\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t0.428571429\t1148.571429\t7\t3\t1.745\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.310714286\t1148.571429\t7\t16.175\t53.511875\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t1148.571429\t7\t69999993\t7.00E+14\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACA\tRV BRADY PACING AMPLITUDE\t2.714285714\t1148.571429\t7\t19\t55.5\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRPACW\tRV BRADY PACING PULSE WIDTH\t0.542857143\t1148.571429\t7\t3.8\t2.44\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENA\tRV BRADY SENSING AMPLITUDE\t1.885714286\t1148.571429\t7\t13.2\t34.92\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENB\tRA BRADY SENSING BLANKING PERIOD\t150\t1148.571429\t7\t1050\t157500\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t150.8571429\t1148.571429\t7\t1056\t201568\t7\t8040\t28612800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.267857143\t1225.714286\t7\t8.875\t12.703125\t7\t8580\t28580400
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.485714286\t1225.714286\t7\t3.4\t1.96\t7\t8580\t28580400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSVTAC\tICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA\t0.857142857\t25980\t7\t6\t36\t7\t181860\t32942674800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLBTVLT\tICD TEL BATTERY VOLTAGE\t3.072114286\t27188.57143\t7\t21.5048\t66.3210733\t7\t190320\t33011258400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tAFIBTADTIT\tAFIB TACHY ZONE DETECTION INTERVAL\t339.6714286\t908.5714286\t7\t2377.7\t812320.19\t7\t6360\t28130400
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t250\t14005.71429\t7\t1750\t437500\t7\t98040\t8682192000
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC264AB\tCD26-/CD4+, ABS\t1370.428571\t614750\t7\t9593\t31987355\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC264PC\tCD26-/CD4+ %\t41\t614750\t7\t287\t20355\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC4N7AB\tCD4+/CD7-,ABS. (C4N7AB)\t1286.571429\t614750\t7\t9006\t33564798\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tC4N7PC\tCD4+/CD7-% (C4N7PC)\t34\t614750\t7\t238\t18616\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD19AB\tCD19 (PAN B), ABS.\t61.28571429\t614750\t7\t429\t52621\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD19PC\tCD19 (PAN B) %\t9.714285714\t614750\t7\t68\t1810\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD26AB\tCD26 (PAN-T), ABS\t352.5714286\t614750\t7\t2468\t2133458\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD26PC\tCD26 (PAN-T) %\t17.42857143\t614750\t7\t122\t4572\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD3AB\tCD3 (PAN T), ABS.\t2036.714286\t614750\t7\t14257\t46127305\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD3PC\tCD3 (PAN T) %\t80\t614750\t7\t560\t48952\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD4AB\tCD3+/CD4+, ABS.\t1648.142857\t614750\t7\t11537\t37913103\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD4PC\tCD3+/CD4+ %\t55.42857143\t614750\t7\t388\t27188\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD7AB\tCD7 (PAN T), ABS\t752.8571429\t614750\t7\t5270\t7296586\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD7PC\tCD7 (PAN T) %\t46\t614750\t7\t322\t23390\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD8AB\tCD3+/CD8+, ABS.\t364.5714286\t614750\t7\t2552\t1467126\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tCD8PC\tCD3+/CD8+ %\t21\t614750\t7\t147\t4987\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tHNS\tCD4+CD8 TOTAL, CD3+%\t76.42857143\t614750\t7\t535\t46159\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tHSABS\tCD4+CD8 TOTAL, CD3+ ABS.\t2012.428571\t614750\t7\t14087\t45794003\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tHSRATIO\tCD4/CD8 RATIO, CD3+\t1428588.257\t614750\t7\t10000117.8\t1.00E+14\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tLYM\tLYMPHOCYTES %\t27.62857143\t614750\t7\t193.4\t10830.58\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tLYMAB\tLYMPHOCYTES, ABS.\t2188.714286\t614750\t7\t15321\t50327777\t6\t3688500\t9.36E+12
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tWBC\tWBC COUNT\t9028.571429\t614750\t7\t63200\t739960000\t6\t3688500\t9.36E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t10466R\tRECIPIENT NAME\t9999999\t233794.2857\t7\t69999993\t7.00E+14\t7\t1636560\t7.88E+11
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t10467R\tRECIPIENT DOB\t9999999\t233794.2857\t7\t69999993\t7.00E+14\t7\t1636560\t7.88E+11
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9220R\tPATIENT\'S SERUM 1 DTT TREATED RESULT\t9999999\t652920\t7\t69999993\t7.00E+14\t7\t4570440\t8.82E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9233R\tINTERPRETATION/ COMMENTS\t9999999\t528366.6667\t7\t69999993\t7.00E+14\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9109R\tSERUM DESCRIPTION 2\t9999999\t220448.5714\t7\t69999993\t7.00E+14\t7\t1543140\t2.38E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9088R\tSERUM DESCRIPTION 2\t9999999\t233005.7143\t7\t69999993\t7.00E+14\t7\t1631040\t2.39E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9330R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t1119612\t7\t69999993\t7.00E+14\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t10477R\tDSA FOR C1Q CLASS II\t9999999\t8.571428571\t7\t69999993\t7.00E+14\t7\t60\t3600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9396R\tHLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t33166.66667\t7\t69999993\t7.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9348R\tCPRA FOR CUMULATIVE IGG\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9352R\tCPRA FOR IGG CLASS I FOR CURRENT\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9365R\tCPRA FOR IGG CLASS II FOR CURRENT\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9607R\tMETHOD\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9618R\tCURRENT SERUM DATE\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9619R\tCURRENT SERUM DESCRIPTION\t9999999\t42651.42857\t7\t69999993\t7.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9483R\tLOCUS 9\t9999999\t35451.42857\t7\t69999993\t7.00E+14\t7\t248160\t61404861600
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9484R\tLOCUS 10\t9999999\t35451.42857\t7\t69999993\t7.00E+14\t7\t248160\t61404861600
+LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tAUTOANTIBODIES TO JO1\tJO1AB\tAUTOANTIBODIES TO JO 1\t9999999\t164260\t7\t69999993\t7.00E+14\t6\t985560\t1.80E+11
+LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tRNP AB, IGG\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t4285714.086\t142650\t7\t29999998.6\t3.00E+14\t8\t1141200\t1.76E+11
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBAND\tBAND NEUTROPHILS\t12.42857143\t8310\t7\t87\t1859\t2\t16620\t157957200
+LAB BLOOD ORDERABLES\t472520\tLABHBC\tHEPATITIS B CORE AB IGM\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t50450\t7\t69999993\t7.00E+14\t6\t302700\t19817773200
+LAB BLOOD ORDERABLES\t472550\tLABYHISTS\tHISTOPLASMA AG, SERUM\tYHISS5\tCOMMENT,SERUM\t9999999\t493289.1892\t7\t69999993\t7.00E+14\t407\t200768700\t1.07E+14
+LAB BLOOD ORDERABLES\t472611\tLAB11043R\tIGD\tIGD\tIGD, SERUM\t5.757142857\t144330\t7\t40.3\t505.85\t12\t1731960\t2.74E+11
+LAB BLOOD ORDERABLES\t472614\tLABIGGS2\tIGG  SUBCLASS 2\tIGGS2\tIGG SUBCLASS2\t231.9142857\t44040\t7\t1623.4\t420536.76\t7\t308280\t20034367200
+LAB BLOOD ORDERABLES\t472615\tLABIGGS3\tIGG  SUBCLASS 3\tIGGS3\tIGG SUBCLASS3\t39.04285714\t122357.1429\t7\t273.3\t12111.81\t7\t856500\t1.93E+11
+LAB BLOOD ORDERABLES\t472618\tLABIGGS1\tIGG SUBCLASS 1\tIGGS1\tIGG SUBCLASS1\t684.1428571\t42300\t7\t4789\t3406579\t6\t253800\t17526592800
+LAB BLOOD ORDERABLES\t472695\tLABYITRA\tITRACONAZOLE LEVEL\tYITR3\tITRACONAZOLE + HYDROZYITRACONAZOLE\t9999999\t591260\t7\t69999993\t7.00E+14\t12\t7095120\t5.38E+12
+LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tCAION\tCALCIUM, IONIZED\t2857143.361\t690\t7\t20000003.53\t2.00E+14\t2\t1380\t1040400
+LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tCL\tCHLORIDE, WHOLE BLD\t2857219.571\t690\t7\t20000537\t2.00E+14\t2\t1380\t1040400
+LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tK\tPOTASSIUM, WHOLE BLD\t2857145.443\t690\t7\t20000018.1\t2.00E+14\t2\t1380\t1040400
+LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tNA\tSODIUM, WHOLE BLOOD\t2857243.143\t690\t7\t20000702\t2.00E+14\t2\t1380\t1040400
+LAB BLOOD ORDERABLES\t472780\tLABEP2\tLYTES II\tTCO2A\tTCO2\t2857161.243\t690\t7\t20000128.7\t2.00E+14\t2\t1380\t1040400
+LAB BLOOD ORDERABLES\t472949\tLABNEURSP\tNEURON SPEC ENOLASE\tNEURSP\tNEURON SPEC ENOLASE\t24.94285714\t355157.1429\t7\t174.6\t5188.76\t7\t2486100\t9.38E+11
+LAB BLOOD ORDERABLES\t473074\tLAB11053R\tPBG DEAMINASE, RBC\tPBGD\tPBG DEAMINASE, RBC\t4.301428571\t271960\t7\t30.11\t146.8901\t6\t1631760\t4.91E+11
+LAB BLOOD ORDERABLES\t473201\tLAB11091R\tQUINIDINE LEVEL\tQUINIDINE\tQUINIDINE\t2857144.4\t151928.5714\t7\t20000010.8\t2.00E+14\t7\t1063500\t1.76E+11
+LAB BLOOD ORDERABLES\t473269\tLABYRISPM\tRISPERIDONE AND 9 - HYDROXYRISPERDONE\t9OHRISP\t9-HYDROXYRISPERIDONE\t4285721.771\t501315\t7\t30000052.4\t3.00E+14\t4\t2005260\t1.23E+12
+LAB BLOOD ORDERABLES\t473269\tLABYRISPM\tRISPERIDONE AND 9 - HYDROXYRISPERDONE\tYRISP1\tRISPERIDONE (RISPERDAL)\t5714286.971\t501315\t7\t40000008.8\t4.00E+14\t4\t2005260\t1.23E+12
+LAB BLOOD ORDERABLES\t473269\tLABYRISPM\tRISPERIDONE AND 9 - HYDROXYRISPERDONE\tYRISP3\tCOMBINED TOTAL\t7142864.786\t501315\t7\t50000053.5\t5.00E+14\t4\t2005260\t1.23E+12
+LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMOSIS PCR\tTOXRES\tPCR RESULT\t9999999\t2500149.057\t7\t69999993\t7.00E+14\t53\t132507900\t2.79E+15
+LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tLAB4\tREFERENCE LAB\t9999999\t4443266.667\t7\t69999993\t7.00E+14\t9\t39989400\t3.40E+14
+LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tSPTY5\tSPECIMEN TYPE\t9999999\t5643312\t7\t69999993\t7.00E+14\t5\t28216560\t3.83E+14
+LAB BLOOD ORDERABLES\t473713\tLABTSMISI\tUNLISTED TRANSFUSION TEST\tCTSMIS\tMISC. TRANSFUSION TEST COMMENT\t9999999\t6025.714286\t7\t69999993\t7.00E+14\t7\t42180\t528678000
+LAB BLOOD ORDERABLES\t473713\tLABTSMISI\tUNLISTED TRANSFUSION TEST\tTSMISR\tMISC. TEST\t9999999\t6025.714286\t7\t69999993\t7.00E+14\t7\t42180\t528678000
+LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV-1 AB, WESTERN BLOT\tBANDPRSNT\tBANDS PRESENT\t9999999\t325576.5517\t7\t69999993\t7.00E+14\t29\t9441720\t4.93E+12
+LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGENTSDD\tGENTAMICIN, SINGLE DAILY DOSE\t1.342857143\t10365\t7\t9.4\t15.32\t4\t41460\t489726000
+LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGNTUN2\tDATE & TIME OF LAST DOSE\t9999999\t10365\t7\t69999993\t7.00E+14\t4\t41460\t489726000
+LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGNTUN3\tDOSAGE\t9999999\t10365\t7\t69999993\t7.00E+14\t4\t41460\t489726000
+LAB BLOOD ORDERABLES\t497512\tLABGNTUNL\tGENTAMICIN SINGLE DAILY DOSE LEVEL\tGNTUN4\tADMINISTRATION ROUTE\t9999999\t10365\t7\t69999993\t7.00E+14\t4\t41460\t489726000
+LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6MMPN\t6-MMPN\t8572622\t605484\t7\t60008354\t6.00E+14\t20\t12109680\t8.50E+12
+LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6TGN\t6-TGN\t5714385.429\t618582.5\t7\t40000698\t4.00E+14\t24\t14845980\t1.05E+13
+LAB BLOOD ORDERABLES\t541683\tLABDCMVL\tDONOR CMV AB (LIVING)\tDCMVL1\tDONOR CMV- TOTAL AB\t9999999\t177193.3333\t7\t69999993\t7.00E+14\t9\t1594740\t3.43E+11
+LAB BLOOD ORDERABLES\t633196\tLABCRDVBG\tVENOUS BLOOD GASES, CORD BLOOD\tBEVC\tCORD BASE EXCESS, VEN\t9999999\t696\t7\t69999993\t7.00E+14\t5\t3480\t3542400
+LAB BLOOD ORDERABLES\t633373\tLABSTSS\tSYPHILIS TREPONEMAL SCREEN\tSTSINT\tINTERPRETATIONS\t9999999\t239349.2912\t7\t69999993\t7.00E+14\t1834\t438966600\t5.36E+15
+LAB BLOOD ORDERABLES\t668091\tLAB11125R\tCOCCI ID\tCOCI\tCOCCI ID (SERUM)\t9999999\t848140.9503\t7\t69999993\t7.00E+14\t1389\t1178067780\t1.75E+15
+LAB BLOOD ORDERABLES\t864662\tLAB11871R\tHIV-2 AB, IMMUNOBLOT\t11870R\tHIV 2 ANTIBODY, IMMUNOBLOT\t9999999\t627525\t7\t69999993\t7.00E+14\t8\t5020200\t3.52E+12
+LAB BLOOD ORDERABLES\t864670\tLAB11882R\tMAGNESIUM, RBC\t11881R\tMAGNESIUM, RBC\t1428576.143\t309240\t7\t10000033\t1.00E+14\t7\t2164680\t6.92E+11
+LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12052R\tRMSF IGM\t9999999\t265070.7692\t7\t69999993\t7.00E+14\t13\t3445920\t9.64E+11
+LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816DX\tCLINICAL INDICATION\t9999999\t722297.1429\t7\t69999993\t7.00E+14\t7\t5056080\t4.67E+12
+LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSDX\tCLINICAL INDICATION\t9999999\t371657.1429\t7\t69999993\t7.00E+14\t7\t2601600\t1.85E+12
+LAB BLOOD ORDERABLES\t897643\tLAB12320R\tTHIOCYANATE\t12321R\tTHIOCYANATE\t16.28571429\t147334.2857\t7\t114\t2488\t7\t1031340\t1.59E+11
+LAB BLOOD ORDERABLES\t910564\tLAB12352R\tNMO EVALUATION WITH REFLEX\t12353R\tNMO/AQP4 - IGG ELISA\t9999999\t867285\t7\t69999993\t7.00E+14\t8\t6938280\t9.87E+12
+LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12636R\tSEROGROUP A\t4285717.014\t447325.7143\t7\t30000019.1\t3.00E+14\t7\t3131280\t1.79E+12
+LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12696R\tSEROGROUP C\t8571427.786\t447325.7143\t7\t59999994.5\t6.00E+14\t7\t3131280\t1.79E+12
+LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12697R\tSEROGROUP Y\t7142858.114\t447325.7143\t7\t50000006.8\t5.00E+14\t7\t3131280\t1.79E+12
+LAB BLOOD ORDERABLES\t978548\tLAB12637R\tMENINGOCOCCAL AB VACCINE\t12698R\tSEROGROUP W-135\t5714286\t447325.7143\t7\t40000002\t4.00E+14\t7\t3131280\t1.79E+12
+MICROBIOLOGY ORDERABLES\t472719\tLABKOHP\tKOH / CALCOFLUOR\tKOH\tKOH PREP\t9999999\t90298.30189\t7\t69999993\t7.00E+14\t106\t9571620\t1.70E+12
+MICROBIOLOGY ORDERABLES\t473372\tLABSTOOLC\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t464348.8949\t7\t69999993\t7.00E+14\t371\t172273440\t9.73E+14
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFRE\tRESULT\t9999999\t1050420\t7\t69999993\t7.00E+14\t2\t2100840\t3.23E+12
+POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tDEVEXP\tDEVELOPER EXP DATE\t8571572.286\t1109028.75\t7\t60001006\t6.00E+14\t16\t17744460\t2.34E+14
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC4\tQC 4 RESULT\t7.571428571\t-14672.40876\t7\t53\t403\t137\t-2010120\t2.71E+11
+STOOL ORDERABLES\t864650\tLAB11855R\tHEPATITIS C VIRAL RNA, QUANT\t11853R\tHCV RNA ,QUANT,TMA IU/ML\t9999999\t1305000\t7\t69999993\t7.00E+14\t1\t1305000\t1.70E+12
+STOOL ORDERABLES\t864666\tLAB11851R\tFECAL LIPIDS, TOTAL\t11849R\tCOLLECTION DURATION\t5714302.286\t650380\t7\t40000116\t4.00E+14\t6\t3902280\t4.51E+12
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUMDA\tMDA BY GC/MS\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUMDMA\tMDMA (ECSTASY) BY GC/MS\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUPSGC\tPSEUDOEPHEDRINE/EPHEDRINE\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUPTGC\tPHENTERMINE BY GC/MS\t9999999\t26760\t7\t69999993\t7.00E+14\t4\t107040\t11457561600
+URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tTURICU\tURIC ACID EXCRETION\t632.7142857\t45940\t7\t4429\t3072647\t3\t137820\t14891605200
+URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tURICADVOL\tVOLUME URIC ACID\t3214.714286\t45940\t7\t22503\t93270211\t3\t137820\t14891605200
+URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tURICURN\tURIC ACID, URINE\t26.38571429\t45940\t7\t184.7\t6587.73\t3\t137820\t14891605200
+URINE ORDERABLES\t473728\tLABUURICT\tURIC ACID, URINE (TIMED)\tUT9\tCOLLECTION PERIOD URIC ACID\t19.42857143\t45940\t7\t136\t3040\t3\t137820\t14891605200
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tRBCAST\tRBC CASTS\t9999999\t9135\t7\t69999993\t7.00E+14\t4\t36540\t445222800
+URINE ORDERABLES\t473736\tLABUPIE\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS\tUPIEI\tINTERPRETATION\t9999999\t373954.7945\t7\t69999993\t7.00E+14\t438\t163792200\t1.21E+15
+URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\tYUALA1\tAMINOLEVULINIC ACID, URINE\t1428587.986\t547500\t7\t10000115.9\t1.00E+14\t6\t3285000\t1.87E+12
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tCOM\tCOMMENT\t9999999\t222675.2113\t7\t69999993\t7.00E+14\t71\t15809940\t4.16E+12
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tVCAM\tVIRAL CAPSID AB-IGM\t9999999\t131091.4286\t7\t69999993\t7.00E+14\t7\t917640\t1.89E+11
+VIROLOGY ORDERABLES\t472508\tLABHBPQ\tHEPATITIS B PCR QUANTITATIVE\tHBIPR\tTEST DESCRIPTION\t9999999\t305903.2394\t7\t69999993\t7.00E+14\t426\t130314780\t5.02E+13
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tRPT\tRPT\t9999999\t616970\t7\t69999993\t7.00E+14\t6\t3701820\t2.62E+12
+VIROLOGY ORDERABLES\t633362\tLABMUMPSG\tMUMPS IGG\tMUMQNT\tLEVEL\t1428572.373\t151731.4286\t7\t10000006.61\t1.00E+14\t7\t1062120\t1.83E+11
+VIROLOGY ORDERABLES\t876737\tLABTOXIM\tTOXOPLASMA IGM\tTOXMQL\tIGM RESULT\t9999999\t177480\t7\t69999993\t7.00E+14\t7\t1242360\t2.54E+11
+VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVMT\tMETHOD\t9999999\t267440\t7\t69999993\t7.00E+14\t3\t802320\t2.15E+11
+VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVRE\tRSV RNA\t9999999\t267440\t7\t69999993\t7.00E+14\t3\t802320\t2.15E+11
+VIROLOGY ORDERABLES\t906963\tLABEBVQPB\tEBV NPC PCR PANEL\tEBVBIN\tEBV BAMHI-W TEST DESCRIPTION\t9999999\t403485\t7\t69999993\t7.00E+14\t20\t8069700\t1.11E+13
+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tVL\tPRODUCT VOLUME\t170.8333333\t18870\t6\t1025\t175993\t2\t37740\t713883600
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tVL\tPRODUCT VOLUME\t185.5\t17550\t6\t1113\t211725\t2\t35100\t616309200
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t6\t59999994\t6.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tXMR\tUNIT CROSSMATCH  \t9999999\t105540\t6\t59999994\t6.00E+14\t5\t527700\t74728422000
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tXMR\tUNIT CROSSMATCH  \t9999999\t67420\t6\t59999994\t6.00E+14\t3\t202260\t22847288400
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tSI\t<<STATUS>>  \t9999999\t35752.5\t6\t59999994\t6.00E+14\t8\t286020\t25391120400
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tXMR\tUNIT CROSSMATCH  \t9999999\t67400\t6\t59999994\t6.00E+14\t3\t202200\t22829968800
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tDI\tISSUE DATE/TIME\t9999999\t43020\t6\t59999994\t6.00E+14\t1\t43020\t1850720400
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tSI\t<<STATUS>>  \t9999999\t23064\t6\t59999994\t6.00E+14\t5\t115320\t7052032800
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tDI\tISSUE DATE/TIME\t9999999\t41790\t6\t59999994\t6.00E+14\t2\t83580\t3495546000
+BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tSI\t<<STATUS>>  \t9999999\t23004\t6\t59999994\t6.00E+14\t5\t115020\t7029565200
+BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tDI\tISSUE DATE/TIME\t9999999\t40140\t6\t59999994\t6.00E+14\t2\t80280\t3222554400
+BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tSI\t<<STATUS>>  \t9999999\t16068\t6\t59999994\t6.00E+14\t5\t80340\t3222558000
+BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tDI\tISSUE DATE/TIME\t9999999\t40110\t6\t59999994\t6.00E+14\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tUBT\tUNIT BLOOD TYPE  \t9999999\t40110\t6\t59999994\t6.00E+14\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tUBT\tUNIT BLOOD TYPE  \t9999999\t40110\t6\t59999994\t6.00E+14\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tPRO\tPRODUCT ID  \t9999999\t69330\t6\t59999994\t6.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tPRO\tPRODUCT ID  \t9999999\t91200\t6\t59999994\t6.00E+14\t2\t182400\t23119207200
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tXMR\tUNIT CROSSMATCH  \t9999999\t40845\t6\t59999994\t6.00E+14\t4\t163380\t11585847600
+BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tBCD34V\tCD34+ PROGENITOR VIABILITY\t100\t7180\t6\t600\t60000\t6\t43080\t374241600
+BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD45PV\tCD45+ VIABILITY\t100\t7180\t6\t600\t60000\t6\t43080\t374241600
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tEOSFLD\tEOSINOPHIL %, FLD\t2.5\t#VALUE!\t6\t15\t75\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP2\tENTEROVIRUS ,PCR\t9999999\t209353.0435\t6\t59999994\t6.00E+14\t69\t14445360\t3.79E+12
+BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP3\tSPECIAL INFORMATION\t9999999\t183002.069\t6\t59999994\t6.00E+14\t29\t5307060\t1.43E+12
+BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP4\tREPORT STATUS\t9999999\t209353.0435\t6\t59999994\t6.00E+14\t69\t14445360\t3.79E+12
+BODY FLUID ORDERABLES\t472621\tLABCIGMT\tIGM, CSF\tIGMCSF\tIGM,CSF\t6666695.983\t234228\t6\t40000175.9\t4.00E+14\t5\t1171140\t3.03E+11
+BODY FLUID ORDERABLES\t472655\tLABYIGF1\tINSULIN LIKE GROWTH FACTOR I\tIGF\tINSULIN-LIKE GF 1\t3333449.667\t224475\t6\t20000698\t2.00E+14\t4\t897900\t2.67E+11
+BODY FLUID ORDERABLES\t497396\tLABYCEAC\tCEA, CSF\tCEAFLD\tCEA, SPINAL FLUID\t6666666.033\t215436\t6\t39999996.2\t4.00E+14\t5\t1077180\t2.98E+11
+ECHO ORDERABLES\t472299\tECH16\tECHO - TRANSESOPHAGEAL ECHO\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t42.5\t105950\t6\t255\t12463\t6\t635700\t1.26E+11
+ECHO ORDERABLES\t670313\tECH42\tECHO - EXERCISE STRESS ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t63.33333333\t55380\t6\t380\t24204\t6\t332280\t38568067200
+ECHO ORDERABLES\t304573724\tECH55A\tECHO-CONGENITAL TRANSTHORACIC ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t52.5\t55840\t6\t315\t17169\t6\t335040\t25829985600
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVDLS\tBRADY ADAPTIVE AV DELAY STATUS\t9999999\t20650\t6\t59999994\t6.00E+14\t6\t123900\t6322381200
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t2.458333333\t18880\t6\t14.75\t39.7925\t6\t113280\t6184994400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t18880\t6\t59999994\t6.00E+14\t6\t113280\t6184994400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t1.316666667\t18880\t6\t7.9\t12.425\t6\t113280\t6184994400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.44\t18880\t6\t2.64\t1.2096\t6\t113280\t6184994400
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMTLPCPACV\tPM TELEMETRY PERCENT PACING VENTRICULAR\t76\t330\t6\t456\t40598\t6\t1980\t14706000
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRADYDP\tBRADY DEPENDANCY\t9999999\t590\t6\t59999994\t6.00E+14\t6\t3540\t8362800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAMSRT\tBRADY AMS RATE\t155\t1390\t6\t930\t144900\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t137.5\t930\t6\t825\t115125\t6\t5580\t22561200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t125\t1390\t6\t750\t94700\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t8333337.5\t1390\t6\t50000025\t5.00E+14\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t1390\t6\t59999994\t6.00E+14\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t1.229166667\t1390\t6\t7.375\t9.953125\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.65\t1390\t6\t3.9\t2.97\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVMFR\tPM DEVICE MANUFACTURER\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVMODEL\tPM DEVICE MODEL\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVSERNM\tPM DEVICE SERIAL NUMBER\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVTYPE\tPM DEVICE TYPE\t9999999\t1300\t6\t59999994\t6.00E+14\t6\t7800\t28555200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRVLDBIPO\tPM RV LEAD BIPOLAR IMPEDANCE\t487.1666667\t1390\t6\t2923\t1442223\t6\t8340\t28522800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRVLDUNIM\tPM RV LEAD UNIPOLAR IMPEDANCE\t380.6666667\t930\t6\t2284\t874244\t6\t5580\t22561200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t1.541666667\t2940\t6\t9.25\t16.9375\t6\t17640\t68673600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.683333333\t2940\t6\t4.1\t3.89\t6\t17640\t68673600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t8333332.667\t-210\t6\t49999996\t5.00E+14\t6\t-1260\t622800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t250\t-290\t6\t1500\t375000\t6\t-1740\t1083600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t93.33333333\t-240\t6\t560\t53600\t6\t-1440\t1130400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tFSTVTTQRSM\tFASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t30340\t6\t59999994\t6.00E+14\t6\t182040\t32942700000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tFSTVTTZNRT\tFASTVT TACHY ZONE RATE STABILITY CRITERIA\t9999999\t30340\t6\t59999994\t6.00E+14\t6\t182040\t32942700000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRSENP\tRV BRADY SENSING BLANKING PERIOD\t200\t30340\t6\t1200\t240000\t6\t182040\t32942700000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tAFIBTADTIT\tAFIB TACHY ZONE DETECTION INTERVAL\t345.6666667\t15750\t6\t2074\t717375.88\t6\t94500\t8651192400
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t-300\t6\t0\t0\t6\t-1800\t1152000
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9335R\tHLA ANTIBODIES FOR CLASS I\t9999999\t1261597.5\t6\t59999994\t6.00E+14\t8\t10092780\t6.71E+13
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t10474R\tDSA FOR IGG CLASS I\t9999999\t30\t6\t59999994\t6.00E+14\t6\t180\t10800
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t10475R\tDSA FOR IGG CLASS II\t9999999\t30\t6\t59999994\t6.00E+14\t6\t180\t10800
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9366R\tHLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t42651.42857\t6\t59999994\t6.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9485R\tLOCUS 11\t9999999\t41350\t6\t59999994\t6.00E+14\t6\t248100\t61404858000
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tALD\tALDER TREE\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tALTER\tALTERNARIA MOLD\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tASPERG\tASPERGILUS MOLD\t8333333.223\t397308\t6\t49999999.34\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tBERM\tBERMUDA GRASS\t8333333.153\t397308\t6\t49999998.92\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tCAT\tCAT DANDER\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tCLA\tCLADOSPORIUM MOLD\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tCOCK\tCOCKROACH\t8333332.767\t397308\t6\t49999996.6\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tDOCK\tDOCK WEED\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tDOG\tDOG DANDER\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tDUST\tDUST MITE\t8333334.65\t397308\t6\t50000007.9\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tIGET\tTOTAL IGE\t3333400.167\t397308\t6\t20000401\t2.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tOAK\tOAK TREE\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tOLIVE\tOLIVE TREE\t8333332.915\t397308\t6\t49999997.49\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tRYE\tRYE GRASS\t8333332.755\t397308\t6\t49999996.53\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tSAGE\tSAGE WEED\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tTIMO\tTIMOTHY GRASS\t8333332.723\t397308\t6\t49999996.34\t5.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tWEST\tWESTERN RAGWEED\t9999999\t397308\t6\t59999994\t6.00E+14\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471586\tLABALPHA2\tALPHA-2-ANTIPLASMIN\tA2A\tALPHA-2-ANTIPLASMIN\t1666748.5\t83930\t6\t10000491\t1.00E+14\t6\t503580\t42798538800
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBMET\tMETHOD\t9999999\t1276240\t6\t59999994\t6.00E+14\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGA\tBETA-2-GP1 IGA\t12.16666667\t614718.9474\t6\t73\t2583\t95\t58398300\t3.83E+13
+LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGG\tBETA-2-GP1 IGG\t0\t614718.9474\t6\t0\t0\t95\t58398300\t3.83E+13
+LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GP1IGM\tBETA-2-GP1 IGM\t7\t614718.9474\t6\t42\t446\t95\t58398300\t3.83E+13
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tPLYM\tPROLYMPHOCYTE\t8.833333333\t9984\t6\t53\t971\t5\t49920\t587412000
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMORPH\tMORPHOLOGY\t9999999\t5840\t6\t59999994\t6.00E+14\t3\t17520\t158767200
+LAB BLOOD ORDERABLES\t472325\tLABYERYTH\tERYTHROPOIETIN, SERUM\tERYTH\tERYTHROPOIETIN, SER.\t31.7\t153080\t6\t190.2\t14859.8\t6\t918480\t1.47E+11
+LAB BLOOD ORDERABLES\t472367\tLABYFELBA\tFELBAMATE\tYFELB1\tFELBARNATE(FELBATOL)\t42.63333333\t208060\t6\t255.8\t15681.08\t6\t1248360\t2.89E+11
+LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tHISTY\tHISTO YEAST PHASE\t9999999\t360680\t6\t59999994\t6.00E+14\t6\t2164080\t8.46E+11
+LAB BLOOD ORDERABLES\t472536\tLABHGBFQ\tHGB F QUANTITATIVE\tHGBF\tHGB F, QUANT.\t6666669.65\t280368\t6\t40000017.9\t4.00E+14\t5\t1401840\t5.91E+11
+LAB BLOOD ORDERABLES\t472626\tLABYIMIPR\tIMIPRAMINE AND DESIPRAMINE\tDESIP\tDESIPRAMINE\t79.5\t189520\t6\t477\t67699\t6\t1137120\t2.41E+11
+LAB BLOOD ORDERABLES\t472626\tLABYIMIPR\tIMIPRAMINE AND DESIPRAMINE\tIMI\tIMIPRAMINE\t1666732.833\t189520\t6\t10000397\t1.00E+14\t6\t1137120\t2.41E+11
+LAB BLOOD ORDERABLES\t472626\tLABYIMIPR\tIMIPRAMINE AND DESIPRAMINE\tIMIDESI\tIMIPRAMINE & DESIPRAMINE\t1666774\t189520\t6\t10000644\t1.00E+14\t6\t1137120\t2.41E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH\tLACTATE DEHYD(LD), S\t1667529.167\t234468\t6\t10005175\t1.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH1\tLD-I, HEART\t1666687.717\t234468\t6\t10000126.3\t1.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH2\tLD-II\t1666691.283\t234468\t6\t10000147.7\t1.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH3\tLD-III\t1666680.867\t234468\t6\t10000085.2\t1.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tLDH4\tLD-IV\t1666675.467\t234468\t6\t10000052.8\t1.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tYLDIS3\tTOTAL LD ACTIVITY\t8333452\t234468\t6\t50000712\t5.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tYLDIS8\tLD-V, LIVER\t1666680.5\t234468\t6\t10000083\t1.00E+14\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472949\tLAB11049R\tNEURON SPEC ENOLASE\tNEURSP\tNEURON SPEC ENOLASE\t20.03333333\t311520\t6\t120.2\t3371.34\t4\t1246080\t4.25E+11
+LAB BLOOD ORDERABLES\t473042\tLABOF\tOSMOTIC FRAGILITY\tOFINT\tINTERPRETATION\t8333334.5\t346707.6923\t6\t50000007\t5.00E+14\t13\t4507200\t2.57E+12
+LAB BLOOD ORDERABLES\t473093\tLABPENTO\tPENTOBARBITAL LEVEL\tPENTO\tPENTOBARBITAL, S\t1666685.633\t393317.1429\t6\t10000113.8\t1.00E+14\t7\t2753220\t1.16E+12
+LAB BLOOD ORDERABLES\t473109\tLABPLTMAP\tPLATELET MAPPING\tTEGMD\tINTERP BY:\t9999999\t19116.95122\t6\t59999994\t6.00E+14\t164\t3135180\t2.07E+11
+LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tMORPH\tMORPHOLOGY\t9999999\t67970.76923\t6\t59999994\t6.00E+14\t13\t883620\t3.36E+11
+LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tSPTY6\tSPECIMEN TYPE:\t9999999\t2836788\t6\t59999994\t6.00E+14\t5\t14183940\t1.37E+14
+LAB BLOOD ORDERABLES\t496699\tLABAG\tALBUMIN / GLOBULIN RATIO\tALBGLOB\tALBUMIN/GLOBULIN RATIO\t9999999\t440\t6\t59999994\t6.00E+14\t3\t1320\t849600
+LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12027R\tECHOVIRUS  AB TYPE 6\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
+LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12028R\tECHOVIRUS  AB TYPE 7\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
+LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12029R\tECHOVIRUS  AB TYPE 11\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
+LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12030R\tECHOVIRUS  AB TYPE 30\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
+LAB BLOOD ORDERABLES\t497436\tLAB12017R\tECHOVIRUS AB, SERUM\t12031R\tECHOVIRUS  AB TYPE 9\t9999999\t774588\t6\t59999994\t6.00E+14\t5\t3872940\t3.40E+12
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE11\tECHOVIRUS 11 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE4\tECHOVIRUS 4 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE7\tECHOVIRUS 7 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE9\tECHOVIRUS 9 AB\t9999999\t325380\t6\t59999994\t6.00E+14\t6\t1952280\t6.59E+11
+LAB BLOOD ORDERABLES\t541652\tLABAS4I\tAB SCREEN\tAS\tANTIBODY SCREEN\t9999999\t12980\t6\t59999994\t6.00E+14\t3\t38940\t561114000
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tBEC\tCORD BASE EXCESS, ART\t9999999\t200\t6\t59999994\t6.00E+14\t3\t600\t165600
+LAB BLOOD ORDERABLES\t633194\tLABCRDABG\tARTERIAL BLOOD GASES, CORD BLOOD\tCRDALG\tCORD LENGTH\t5000011.5\t360\t6\t30000069\t3.00E+14\t6\t2160\t1555200
+LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tRHFT\tRHEUMATOID FACTOR LEVEL\t1666716.5\t9245540\t6\t10000299\t1.00E+14\t6\t55473240\t2.95E+15
+LAB BLOOD ORDERABLES\t670776\tLAB11253R\tHELICOBACTER PYLORI AGA AB\t11185R\tHELICOBACTER PYLORI IGA AB\t9999999\t649581.8182\t6\t59999994\t6.00E+14\t33\t21436200\t6.81E+13
+LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\t11191R\tBORRELIA HERMSII IGG\t9999999\t866916\t6\t59999994\t6.00E+14\t5\t4334580\t7.56E+12
+LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\t11192R\tBORRELIA HERMSII IGM\t9999999\t866916\t6\t59999994\t6.00E+14\t5\t4334580\t7.56E+12
+LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816RE\tRESULT, BLOOD\t9999999\t722297.1429\t6\t59999994\t6.00E+14\t7\t5056080\t4.67E+12
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC2526N\tCD25, % OF CD3+/CD4+/CD26NEG\t28\t89720\t6\t168\t5568\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC257N\tCD25, % OF CD3+/CD4+/CD7NEG\t34.5\t89720\t6\t207\t9467\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC264AB\tCD26-/CD4+, ABS\t828\t89720\t6\t4968\t20167652\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC264PC\tCD26-/CD4+ %\t23.16666667\t89720\t6\t139\t8963\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3026N\tCD30, % OF CD3+/CD4+/CD26NEG\t4.666666667\t89720\t6\t28\t280\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC307N\tCD30, % OF CD3+/CD4+/CD7NEG\t7.333333333\t89720\t6\t44\t730\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC326NA\tCD3+/CD26NEG, ABS.\t1025.166667\t89720\t6\t6151\t21516915\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3426N\tCD3+/CD4+/CD26NEG, ABS.\t826.8333333\t89720\t6\t4961\t20166819\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3426P\tCD3+/CD4+/CD26NEG, % OF LYMPHS\t22.83333333\t89720\t6\t137\t8935\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3426T\tCD3+/CD4+/CD26NEG, % OF CD4+ T\t36.83333333\t89720\t6\t221\t12391\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC347NA\tCD3+/CD4+/CD7NEG, ABS.\t813.5\t89720\t6\t4881\t21435387\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC347NP\tCD3+/CD4+/CD7NEG, % OF LYMPHS\t19.33333333\t89720\t6\t116\t9140\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC347NT\tCD3+/CD4+/CD7NEG, % OF CD4+ T\t27\t89720\t6\t162\t10556\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC37NA\tCD3+/CD7NEG, ABS.\t841.6666667\t89720\t6\t5050\t22371114\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P4PA\tCD3+/CD4+,ABS\t1181\t89720\t6\t7086\t24935690\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P4PP\tCD3+/CD4+, % OF LYMPHS\t49.5\t89720\t6\t297\t19117\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P7NP\tCD3+/CD7NEG, % OF LYMPHS\t20.5\t89720\t6\t123\t9571\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P8PN\tCD3+/CD8+, ABS. OF LYMPHS\t8333348.667\t89720\t6\t50000092\t5.00E+14\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC3P8PP\tCD3+/CD8+, % OF LYMPHS\t8333337.5\t89720\t6\t50000025\t5.00E+14\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC4N7AB\tCD4+/CD7-,ABS. (C4N7AB)\t819.3333333\t89720\t6\t4916\t21443482\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC4N7PC\tCD4+/CD7-% (C4N7PC)\t20\t89720\t6\t120\t9176\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC5226N\tCD52, % OF CD3+/CD4+/CD26NEG\t83.16666667\t89720\t6\t499\t49605\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC527N\tCD52, % OF CD3+/CD4+/CD7NEG\t83.33333333\t89720\t6\t500\t49802\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD26NP\tCD3+/CD26NEG, % OF LYMPHS\t39.5\t89720\t6\t237\t13497\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t1439.166667\t89720\t6\t8635\t28530847\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD3PC\tCD3 (PAN T) %\t72.5\t89720\t6\t435\t36173\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t8333344.167\t89720\t6\t50000065\t5.00E+14\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t8333332.717\t89720\t6\t49999996.3\t5.00E+14\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tLYM\tLYMPHOCYTES %\t21.16666667\t89720\t6\t127\t8864.32\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t1584.333333\t89720\t6\t9506\t31422252\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tWBC\tWBC COUNT\t11200\t89720\t6\t67200\t924220000\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898826\tLAB12371R\tCOBALT\t12372R\tCOBALT\t4.733333333\t283690\t6\t28.4\t454.66\t6\t1702140\t5.29E+11
+MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tRPT\tRPT\t9999999\t255120\t6\t59999994\t6.00E+14\t1\t255120\t65086214400
+MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tRPT\tRPT\t9999999\t752360\t6\t59999994\t6.00E+14\t6\t4514160\t3.41E+12
+MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tRPT\tRPT\t9999999\t3770\t6\t59999994\t6.00E+14\t6\t22620\t511664400
+MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t3770\t6\t59999994\t6.00E+14\t6\t22620\t511664400
+MICROBIOLOGY ORDERABLES\t893889\tLABORAERO\tORTHOPEDIC AEROBIC CULT W/GS\tCULT\tCULT/OTHER RSLT:\t9999999\t473219.0698\t6\t59999994\t6.00E+14\t516\t244181040\t2.40E+14
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBDX\tCLINICAL INDICATION\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBFLD\tSOURCE\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBMET\tMETHOD\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBRES\tRESULTS\t9999999\t678540\t6\t59999994\t6.00E+14\t3\t2035620\t2.03E+12
+POINT OF CARE (NON LAB) TESTS\t610678\tPOC30\tPOC PYLORITECK\tTESTLO\tTEST STRIP LOT#\t108065.6667\t4125\t6\t648394\t70117629024\t8\t33000\t183823200
+STOOL ORDERABLES\t472122\tLAB12206R\tCYSTATIN C\t12207R\tCYSTATIN C, SER\t1.02\t171550\t6\t6.12\t6.3148\t6\t1029300\t1.92E+11
+STOOL ORDERABLES\t670750\tLAB11340R\tADAMTS 13 PROFILE\t11342R\tADAMTS 13 INHIBITOR\t9999999\t631303.6364\t6\t59999994\t6.00E+14\t11\t6944340\t6.83E+12
+STOOL ORDERABLES\t864666\tLAB11851R\tFECAL LIPIDS, TOTAL\t11848R\tTOTAL SPECIMEN WEIGHT\t3333676.167\t650380\t6\t20002057\t2.00E+14\t6\t3902280\t4.51E+12
+URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tYUARR2\tCOLLECTION DURATION\t8333336.5\t88120\t6\t50000019\t5.00E+14\t3\t264360\t28310436000
+URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tVOLUMEURN\tURINE VOLUME\t3225\t245865\t6\t19350\t65737340\t4\t983460\t2.71E+11
+URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tYCITRU\tCITRATE EXCRETION, URINE\t278\t245865\t6\t1668\t583520\t4\t983460\t2.71E+11
+URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tYUCICN\tCITRATE CONCENTRATION\t8.366666667\t245865\t6\t50.2\t492.44\t4\t983460\t2.71E+11
+URINE ORDERABLES\t472034\tLABYUCITR\tCITRATE EXCRETION, URINE\tYUCIUT\tCOLLECTION DURATION\t24\t245865\t6\t144\t3456\t4\t983460\t2.71E+11
+URINE ORDERABLES\t473037\tLABUOROT\tOROTIC ACID, URINE\tOROTU\tOROTIC ACID, UR\t9999999\t387150\t6\t59999994\t6.00E+14\t2\t774300\t3.33E+11
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tVOLUMEURN\tURINE VOLUME\t2852.166667\t293940\t6\t17113\t57878759\t3\t881820\t2.67E+11
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tYOXALU\tOXALATE, 24-HOUR/TIMED URINE\t0.553333333\t293940\t6\t3.32\t2.6634\t3\t881820\t2.67E+11
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tYUOXCO\tOXALATE CONCENTRATION\t8.415\t293940\t6\t50.49\t2431.0957\t3\t881820\t2.67E+11
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE URINE (TIMED)\tYUOXUT\tCOLLECTION DURATION\t24\t293940\t6\t144\t3456\t3\t881820\t2.67E+11
+URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\tVOLUMEURN\tURINE VOLUME\t1667952.5\t614940\t6\t10007715\t1.00E+14\t4\t2459760\t1.53E+12
+URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\tYUALA2\tCOLLECTION DURATION\t1666686.5\t614940\t6\t10000119\t1.00E+14\t4\t2459760\t1.53E+12
+VIROLOGY ORDERABLES\t472013\tLABCLGC2\tCHLAMYDIA GC, MOLECULAR DETECTION\tDCL2\tTEST INFORMATION\t9999999\t184892.973\t6\t59999994\t6.00E+14\t333\t61569360\t1.58E+13
+VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-BLOOD\tCMVQSP\tSPECIMEN\t9999999\t165780\t6\t59999994\t6.00E+14\t3\t497340\t92352106800
+VIROLOGY ORDERABLES\t472043\tLABCMVPQ\tCMV PCR QUANTITATIVE\tCMVTIP\tTEST DESCRIPTION\t9999999\t184331.1162\t6\t59999994\t6.00E+14\t1317\t242764080\t5.22E+13
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t9999999\t212032.3077\t6\t59999994\t6.00E+14\t234\t49615560\t1.19E+13
+VIROLOGY ORDERABLES\t472321\tLABEB\tEBV AB PANEL\t11166R\tEB NUCLEAR AB\t9999999\t212032.3077\t6\t59999994\t6.00E+14\t234\t49615560\t1.19E+13
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tVCA\tVIRAL CAPSID AB\t9999999\t131091.4286\t6\t59999994\t6.00E+14\t7\t917640\t1.89E+11
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL2\tHSV 1 IGG INTERPRETATION\t9999999\t290474.6392\t6\t59999994\t6.00E+14\t291\t84528120\t2.82E+13
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL4\tHSV 2 IGG INTERPRETATION\t9999999\t290474.6392\t6\t59999994\t6.00E+14\t291\t84528120\t2.82E+13
+VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXO\tTOXOPLASMA IGG\t9999999\t163050\t6\t59999994\t6.00E+14\t6\t978300\t1.91E+11
+VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQL\tIGG RESULT\t9999999\t163250\t6\t59999994\t6.00E+14\t6\t979500\t1.86E+11
+VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXMQL\tIGM RESULT\t9999999\t163250\t6\t59999994\t6.00E+14\t6\t979500\t1.86E+11
+VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRMMPV\tMETAPNEUMOVIRUS RNA\t9999999\t86400\t6\t59999994\t6.00E+14\t3\t259200\t28951264800
+VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRMRSV\tRSV RNA\t9999999\t86400\t6\t59999994\t6.00E+14\t3\t259200\t28951264800
+VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRSMPMT\tMETHOD\t9999999\t86400\t6\t59999994\t6.00E+14\t3\t259200\t28951264800
+VIROLOGY ORDERABLES\t966891\tLABVCULF\tVIRAL CULTURE, FLUID\tCULT\tCULT/OTHER RSLT:\t9999999\t1188540.984\t6\t59999994\t6.00E+14\t61\t72501000\t1.27E+14
+BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tSPCTY\tSPECIMEN TYPE\t9999999\t28164\t5\t49999995\t5.00E+14\t5\t140820\t7934583600
+BLOOD BANK PRODUCT ORDERABLES\t473263\tLABBBPRH\tRHO(D) IMMUNE GLOB (RHOGAM)\tRHSTA\tRHOGAM PRODUCT STATUS\t9999999\t-3320\t5\t49999995\t5.00E+14\t3\t-9960\t105328800
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tVL\tPRODUCT VOLUME\t170.8\t28740\t5\t854\t146288\t2\t57480\t1658030400
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tVL\tPRODUCT VOLUME\t175.6\t39720\t5\t878\t155036\t1\t39720\t1577678400
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tVL\tPRODUCT VOLUME\t163.6\t#VALUE!\t5\t818\t133954\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t5\t49999995\t5.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tXMR\tUNIT CROSSMATCH  \t9999999\t78960\t5\t49999995\t5.00E+14\t4\t315840\t36999194400
+BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tSI\t<<STATUS>>  \t9999999\t24488.57143\t5\t49999995\t5.00E+14\t7\t171420\t6256458000
+BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tSI\t<<STATUS>>  \t9999999\t23710\t5\t49999995\t5.00E+14\t6\t142260\t5357336400
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tXMR\tUNIT CROSSMATCH  \t9999999\t36090\t5\t49999995\t5.00E+14\t2\t72180\t5201298000
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tXMR\tUNIT CROSSMATCH  \t9999999\t20340\t5\t49999995\t5.00E+14\t2\t40680\t1649988000
+BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tDI\tISSUE DATE/TIME\t9999999\t42960\t5\t49999995\t5.00E+14\t1\t42960\t1845561600
+BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tXMR\tUNIT CROSSMATCH  \t9999999\t36060\t5\t49999995\t5.00E+14\t2\t72120\t5192647200
+BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tDI\tISSUE DATE/TIME\t9999999\t42960\t5\t49999995\t5.00E+14\t1\t42960\t1845561600
+BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tXMR\tUNIT CROSSMATCH  \t9999999\t36000\t5\t49999995\t5.00E+14\t2\t72000\t5184000000
+BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tXMR\tUNIT CROSSMATCH  \t9999999\t0\t5\t49999995\t5.00E+14\t1\t0\t0
+BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tSI\t<<STATUS>>  \t9999999\t20085\t5\t49999995\t5.00E+14\t4\t80340\t3217719600
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tDI\tISSUE DATE/TIME\t9999999\t40110\t5\t49999995\t5.00E+14\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tSI\t<<STATUS>>  \t9999999\t20085\t5\t49999995\t5.00E+14\t4\t80340\t3217719600
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tDI\tISSUE DATE/TIME\t9999999\t40110\t5\t49999995\t5.00E+14\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tSI\t<<STATUS>>  \t9999999\t20085\t5\t49999995\t5.00E+14\t4\t80340\t3217719600
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tDI\tISSUE DATE/TIME\t9999999\t40080\t5\t49999995\t5.00E+14\t2\t80160\t3212928000
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tUBT\tUNIT BLOOD TYPE  \t9999999\t40080\t5\t49999995\t5.00E+14\t2\t80160\t3212928000
+BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tUBT\tUNIT BLOOD TYPE  \t9999999\t39390\t5\t49999995\t5.00E+14\t2\t78780\t3104874000
+BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tUBT\tUNIT BLOOD TYPE  \t9999999\t54990\t5\t49999995\t5.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tUBT\tUNIT BLOOD TYPE  \t9999999\t54990\t5\t49999995\t5.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tUBT\tUNIT BLOOD TYPE  \t9999999\t54990\t5\t49999995\t5.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tUBT\tUNIT BLOOD TYPE  \t9999999\t69330\t5\t49999995\t5.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tPRO\tPRODUCT ID  \t9999999\t49950\t5\t49999995\t5.00E+14\t2\t99900\t5482357200
+BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tPRO\tPRODUCT ID  \t9999999\t74070\t5\t49999995\t5.00E+14\t2\t148140\t21945459600
+BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tPRO\tPRODUCT ID  \t9999999\t30\t5\t49999995\t5.00E+14\t2\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tPRO\tPRODUCT ID  \t9999999\t30\t5\t49999995\t5.00E+14\t2\t60\t3600
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBPRM\tPRIMERS\t9999999\t1007200\t5\t49999995\t5.00E+14\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t472655\tLAB12838R\tINSULIN-LIKE GROWTH FACTOR 1\tIGF\tINSULIN-LIKE GROWTH FACTOR 1\t217.08\t82680\t5\t1085.4\t575013.66\t5\t413400\t48124742400
+BODY FLUID ORDERABLES\t472774\tLABFBBURG\tLYME WESTERN BLOT, CSF\tBBIGM\tBORELLIA BURG IGM AB\t9999999\t282672.6316\t5\t49999995\t5.00E+14\t76\t21483120\t7.48E+12
+BODY FLUID ORDERABLES\t472774\tLABLWBCC\tLYME WESTERN BLOT, CSF\tBBIGG\tBORELLIA BURG IGG AB\t9999999\t277031.25\t5\t49999995\t5.00E+14\t16\t4432500\t1.39E+12
+BODY FLUID ORDERABLES\t473027\tLABOLIG\tCSF PROTEIN & IMMUNOFIX ELECT.\tCPIEI\tINTERPRETATION\t9999999\t407309.1176\t5\t49999995\t5.00E+14\t136\t55394040\t3.19E+13
+BODY FLUID ORDERABLES\t473350\tLABFSPG\tSPECIFIC GRAVITY, FLUID\tSGFLTY\tSOURCE\t9999999\t5625\t5\t49999995\t5.00E+14\t4\t22500\t202366800
+BODY FLUID ORDERABLES\t473350\tLABFSPG\tSPECIFIC GRAVITY, FLUID\tSPGFLD\tSPECIFIC GRAVITY, FLUID\t1.0232\t5625\t5\t5.116\t5.234856\t4\t22500\t202366800
+BODY FLUID ORDERABLES\t473358\tLABEOSSP\tSPUTUM FOR EOSINOPHILS\tEOSSP\tSPUTUM FOR EOS.\t9999999\t9024\t5\t49999995\t5.00E+14\t5\t45120\t496404000
+BODY FLUID ORDERABLES\t670778\tLAB11188R\tM. PNEUMONIAE DNA\t12283R\t12283R\t9999999\t166380\t5\t49999995\t5.00E+14\t8\t1331040\t2.33E+11
+BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tFCD3AB\tCD3 (T), ABS., FLD\t9999999\t12144\t5\t49999995\t5.00E+14\t5\t60720\t762372000
+BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tFCD3PC\tCD3 (T) %, FLD\t38.8\t12144\t5\t194\t10222\t5\t60720\t762372000
+BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tLYMABFLD\tLYMPHOCYTES, ABS., FLD\t9999999\t12144\t5\t49999995\t5.00E+14\t5\t60720\t762372000
+BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tWBCFLD\tWBC COUNT, FLD\t51860\t12144\t5\t259300\t18962210000\t5\t60720\t762372000
+BODY FLUID ORDERABLES\t978520\tLAB12723R\tADENOSINE DEAMINASE, PERITONEAL\t12722R\tADENOSINE DEAMINASE, PERITONEAL\t2000003.88\t343308\t5\t10000019.4\t1.00E+14\t5\t1716540\t6.34E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t27\t100692\t5\t135\t3825\t5\t503460\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t100692\t5\t49999995\t5.00E+14\t5\t503460\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t100692\t5\t49999995\t5.00E+14\t5\t503460\t2.51E+11
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tPMTLBATIMP\tPM TELEMETRY BATTERY IMPEDANCE\t0.7248\t100692\t5\t3.624\t4.835626\t5\t503460\t2.51E+11
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMDEVIMPDT\tPM DEVICE IMPLANT DATE\t9999999\t1620\t5\t49999995\t5.00E+14\t5\t8100\t28465200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMRALDUNIM\tPM RA LEAD UNIPOLAR IMPEDANCE\t323\t1176\t5\t1615\t531753\t5\t5880\t22471200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t7.44\t1488\t5\t37.2\t307.41625\t5\t7440\t28425600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t1488\t5\t49999995\t5.00E+14\t5\t7440\t28425600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tAFIBTADTIT\tAFIB TACHY ZONE DETECTION INTERVAL\t347.4\t-48\t5\t1737\t608687.94\t5\t-240\t2080800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tALERT\tALERT\t9999999\t-324\t5\t49999995\t5.00E+14\t5\t-1620\t637200
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t36000\t5\t0\t0\t5\t180000\t32659869600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t35964\t5\t0\t0\t5\t179820\t32659923600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t129\t1668\t5\t645\t83225\t5\t8340\t34462800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKAVT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT\t0\t36396\t5\t0\t0\t5\t181980\t32942660400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKDVT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT\t0\t36348\t5\t0\t0\t5\t181740\t32942660400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLEADALERT\tLEAD ALERT\t9999999\t1584\t5\t49999995\t5.00E+14\t5\t7920\t34387200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t2000199.8\t1668\t5\t10000999\t1.00E+14\t5\t8340\t34462800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSLWVTTDTD\tVSLOWVT TACHY ZONE DETECTION DURATION\t9999999\t36384\t5\t49999995\t5.00E+14\t5\t181920\t32942685600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t129\t-384\t5\t645\t83925\t5\t-1920\t1152000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t7.8\t504\t5\t39\t307\t5\t2520\t10432800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t840\t5\t0\t0\t5\t4200\t24372000
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t1008\t5\t500\t50000\t5\t5040\t29815200
+EXTERNAL RESULTS\t668293\tEXTHCT\tEXTERNAL HCT\tHCT\tEXTERNAL HCT\t9999999\t-345600\t5\t49999995\t5.00E+14\t5\t-1728000\t6.72E+11
+HISTOCOMPATIBILITY ORDERABLE\t492711\t10195R\tHLA ORDER - WAIT LIST STATUS CHANGE\t10049R\tHLA LAB TESTING STATUS\t9999999\t7644\t5\t49999995\t5.00E+14\t5\t38220\t389502000
+HISTOCOMPATIBILITY ORDERABLE\t492711\t10195R\tHLA ORDER - WAIT LIST STATUS CHANGE\tRINFO\tTO FIND RESULT:\t9999999\t7644\t5\t49999995\t5.00E+14\t5\t38220\t389502000
+HISTOCOMPATIBILITY ORDERABLE\t710704\t10210R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG\t10049R\tHLA LAB TESTING STATUS\t9999999\t2240112\t5\t49999995\t5.00E+14\t5\t11200560\t3.47E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9161R\tSERUM DESCRIPTION 1\t9999999\t41028\t5\t49999995\t5.00E+14\t5\t205140\t21397935600
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9196R\tPATIENT\'S SERUM 2 DESCRIPTION\t9999999\t77616\t5\t49999995\t5.00E+14\t5\t388080\t94371408000
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9131R\tSERUM DESCRIPTION 1\t9999999\t40992\t5\t49999995\t5.00E+14\t5\t204960\t21387376800
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9105R\tSERUM DESCRIPTION 1\t9999999\t24\t5\t49999995\t5.00E+14\t5\t120\t14400
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9112R\tSERUM DATE 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9114R\tMCS 3\t13.8\t445812\t5\t69\t3893\t5\t2229060\t2.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9602R\tRESULT 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9084R\tSERUM DESCRIPTION 1\t9999999\t17616\t5\t49999995\t5.00E+14\t5\t88080\t7736976000
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9091R\tSERUM DATE 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9093R\tMCS 3\t40.8\t445812\t5\t204\t12502\t5\t2229060\t2.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9597R\tRESULT 3\t9999999\t445812\t5\t49999995\t5.00E+14\t5\t2229060\t2.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9329R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t1119612\t5\t49999995\t5.00E+14\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9341R\tPOSSIBLE HLA ANTIBODIES FOR CLASS II\t9999999\t1591272\t5\t49999995\t5.00E+14\t5\t7956360\t6.22E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9384R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t0\t5\t49999995\t5.00E+14\t5\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9624R\tREFERENCE SERUM DATE\t9999999\t0\t5\t49999995\t5.00E+14\t5\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9625R\tREFERENCE SERUM DESCRIPTION\t9999999\t0\t5\t49999995\t5.00E+14\t5\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9353R\tHLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t42651.42857\t5\t49999995\t5.00E+14\t7\t298560\t89030635200
+IMMUNOLOGY ORDERABLES\t633412\tLABRPRQT\tRPR QUANTITATIVE\tRPRT\tRESULT\t9999999\t86580\t5\t49999995\t5.00E+14\t4\t346320\t37293688800
+IMMUNOLOGY ORDERABLES\t633412\tLABRPRQT\tRPR QUANTITATIVE\tRPRTIN\tINTERPRETATIONS\t9999999\t2355769.655\t5\t49999995\t5.00E+14\t29\t68317320\t3.96E+15
+LAB BLOOD ORDERABLES\t471531\tLABAHP2\tACUTE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t55606.13636\t5\t49999995\t5.00E+14\t1056\t58720080\t7.63E+12
+LAB BLOOD ORDERABLES\t471637\tLABAPHSA2\tANTI PHOSPHOLIPID AB PANEL\tDRVVIN\tDRVV INTERPRETATION\t9999999\t316148.4979\t5\t49999995\t5.00E+14\t233\t73662600\t2.93E+13
+LAB BLOOD ORDERABLES\t471654\tLAB11072R\tAPO A1\tAPOA1\tAPOLIPOPROTEIN A1\t137.8\t157632\t5\t689\t104039\t5\t788160\t1.28E+11
+LAB BLOOD ORDERABLES\t471686\tLABARSAL\tARYLSULFATASE A, LEUKOCYTES\tARYLSA\tARYLSULFATASE A, LEUK\t2000033.06\t546840\t5\t10000165.3\t1.00E+14\t4\t2187360\t1.60E+12
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBDX\tCLINICAL INDICATION\t9999999\t1276240\t5\t49999995\t5.00E+14\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQDX\tCLINICAL INDICATION\t9999999\t1383525\t5\t49999995\t5.00E+14\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQET\tETHNICITY (PROVIDED BY MD)\t9999999\t1383525\t5\t49999995\t5.00E+14\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQRE\tRESULT\t9999999\t1383525\t5\t49999995\t5.00E+14\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471813\tLABYC5REF\tC5 COMPLEMENT AG\tYC5RE1\tC5 COMPLEMENT AG\t22.7\t192080\t5\t113.5\t2712.81\t6\t1152480\t2.38E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHATINT\tINTERPRETATION\t9999999\t63540.24819\t5\t49999995\t5.00E+14\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t63540.24819\t5\t49999995\t5.00E+14\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP3\tCOMPREHENSIVE HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t9999999\t63540.24819\t5\t49999995\t5.00E+14\t967\t61443420\t8.90E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSAQT\tHEP B SURF AB QUANT\t4000027.06\t182820\t5\t20000135.3\t2.00E+14\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t182820\t5\t49999995\t5.00E+14\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t182820\t5\t49999995\t5.00E+14\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t182820\t5\t49999995\t5.00E+14\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472120\tLABCCP\tANTI CCP\tCCPR\tANTI CCP ANTIBODY\t9999999\t296484\t5\t49999995\t5.00E+14\t5\t1482420\t5.59E+11
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMYEL\tMYELOCYTES\t2.6\t8310\t5\t13\t49\t2\t16620\t157957200
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tNRBC\tNRBC/100WBC\t3.6\t8310\t5\t18\t140\t2\t16620\t157957200
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tSLREV\tSLIDE REVIEW\t9999999\t2880\t5\t49999995\t5.00E+14\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472189\tLABDT\tDIPHTHERIA AND TETANUS AB IGG\tDIPHT\tDIPTHERIA AB , IGG\t0.356\t308000\t5\t1.78\t1.199\t3\t924000\t2.91E+11
+LAB BLOOD ORDERABLES\t472189\tLABDT\tDIPHTHERIA AND TETANUS AB IGG\tTETAN\tTETANUS AB , IGG\t6.77\t308000\t5\t33.85\t679.9773\t3\t924000\t2.91E+11
+LAB BLOOD ORDERABLES\t472392\tLAB12796R\tFLUCONAZOLE LEVEL\tFLUCOZ\tFLUCONAZOLE\t21.8\t618744\t5\t109\t2615\t5\t3093720\t2.13E+12
+LAB BLOOD ORDERABLES\t472482\tLABYGADAB\tGLUTAMIC ACID DECARBOXY AB\tGAD65AB\tGAD65 ANTIBODY\t5.96\t352740\t5\t29.8\t888.04\t3\t1058220\t3.91E+11
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tARSENIC\tARSENIC\t9999999\t269844\t5\t49999995\t5.00E+14\t5\t1349220\t6.18E+11
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tCADMIUM\tCADMIUM\t3999999.9\t269844\t5\t19999999.5\t2.00E+14\t5\t1349220\t6.18E+11
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tLEAD\tLEAD\t2000001\t269844\t5\t10000005\t1.00E+14\t5\t1349220\t6.18E+11
+LAB BLOOD ORDERABLES\t472521\tLABHBCT\tANTI-HBC\tHBTINT\tINTERPRETATION\t9999999\t53580.7438\t5\t49999995\t5.00E+14\t484\t25933080\t3.17E+12
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL WITH DIRECT LDL\tVLDL\tVLDL (CALCULATED)\t2000016\t28500\t5\t10000080\t1.00E+14\t4\t114000\t8390340000
+LAB BLOOD ORDERABLES\t472854\tLABMALL1\tMISCELLANEOUS ALLERGEN 1\tALLR1\tALLERGEN RESULT\t9999999\t630540\t5\t49999995\t5.00E+14\t4\t2522160\t2.43E+12
+LAB BLOOD ORDERABLES\t472863\tLABCHMISC\tMISCELLANEOUS CHEMISTRY\tCHMISC\tCHEMISTRY MISCELLANEOUS\t9999999\t9461.37931\t5\t49999995\t5.00E+14\t29\t274380\t17678588400
+LAB BLOOD ORDERABLES\t473026\tLAB11051R\tOLANZAPINE LEVEL\tOLANZ\tOLANZAPINE\t2000033.6\t405255\t5\t10000168\t1.00E+14\t4\t1621020\t7.22E+11
+LAB BLOOD ORDERABLES\t473110\tLABPLTAGG\tPLATELET AGGREGATION\tAGGINT\tINTERPRETATION\t9999999\t79031.25\t5\t49999995\t5.00E+14\t16\t1264500\t2.79E+11
+LAB BLOOD ORDERABLES\t473157\tLAB11055R\tPROPAFENONE LEVEL\tPROPAF\tPROPAFENONE\t4000000.148\t388272\t5\t20000000.74\t2.00E+14\t5\t1941360\t8.41E+11
+LAB BLOOD ORDERABLES\t473183\tLABPSAU\tPOST-THERAPY PSA\tPSAU\tPOST-THERAPY PSA\t5.2\t116760\t5\t26\t368.2916\t3\t350280\t83991607200
+LAB BLOOD ORDERABLES\t473200\tLABQFTB\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNIN\tQUANTIFERON INTERPRETATION\t9999999\t193921.9811\t5\t49999995\t5.00E+14\t212\t41111460\t1.47E+13
+LAB BLOOD ORDERABLES\t473301\tLABSPIE\tPROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM\tSPIEI\tINTERPRETATION\t9999999\t254723.2367\t5\t49999995\t5.00E+14\t1415\t360433380\t1.27E+14
+LAB BLOOD ORDERABLES\t473675\tLAB11058R\tTRAZODONE LEVEL\tTRAZO\tTRAZODONE\t7999999.38\t295185\t5\t39999996.9\t4.00E+14\t4\t1180740\t4.64E+11
+LAB BLOOD ORDERABLES\t473693\tLAB11057R\tTNF\tTNF\tTUMOR NECROSIS FTR\t9999999\t246456\t5\t49999995\t5.00E+14\t5\t1232280\t3.46E+11
+LAB BLOOD ORDERABLES\t473707\tLABREF4\tLAB UNLISTED 4\tRESUL4\tRESULT\t9999999\t4443266.667\t5\t49999995\t5.00E+14\t9\t39989400\t3.40E+14
+LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tLAB5\tREFERENCE LAB:\t9999999\t5643312\t5\t49999995\t5.00E+14\t5\t28216560\t3.83E+14
+LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tTEST5\tTEST NAME\t9999999\t5643312\t5\t49999995\t5.00E+14\t5\t28216560\t3.83E+14
+LAB BLOOD ORDERABLES\t497339\tLABDFA\tDENGUE FEVER AB\tDFINT\tINTERPRETATION\t9999999\t264945\t5\t49999995\t5.00E+14\t64\t16956480\t5.23E+12
+LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6TGNASSESS\t6-TGN RESULT ASSESSMENT\t9999999\t605484\t5\t49999995\t5.00E+14\t20\t12109680\t8.50E+12
+LAB BLOOD ORDERABLES\t541687\tLABFHHV7\tHERPES VIRUS 7 IGG AND IGM AB\tHERPG\tHERPESVIRUS 7 AB, IGG\t9999999\t327880\t5\t49999995\t5.00E+14\t3\t983640\t3.29E+11
+LAB BLOOD ORDERABLES\t890507\tLAB12292R\tT CELL PHENOTYPING, ADVANCED\t12319R\t12319R\t9999999\t200448\t5\t49999995\t5.00E+14\t20\t4008960\t1.99E+12
+LAB BLOOD ORDERABLES\t893921\tLAB12790R\tCYSTATIN C WITH ESTIMATED GFR\t12789R\tEGFR BY CYSTATIN C\t22.4\t189288\t5\t112\t2802\t5\t946440\t1.86E+11
+LAB BLOOD ORDERABLES\t893921\tLAB12790R\tCYSTATIN C WITH ESTIMATED GFR\t12805R\tCYSTATIN C\t2.718\t189288\t5\t13.59\t38.3775\t5\t946440\t1.86E+11
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMCOB\tCOMMENT\t9999999\t579996\t5\t49999995\t5.00E+14\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMDXB\tCLINICAL INDICATION\t9999999\t579996\t5\t49999995\t5.00E+14\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMREB\tRESULT\t9999999\t579996\t5\t49999995\t5.00E+14\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t910570\tLAB12399R\tACETYLCHOLINE MODULATING AB\tACRMOD\tACETYLCHOLINE MODULATING AB\t32.6\t560640\t5\t163\t7703\t5\t2803200\t2.32E+12
+LAB BLOOD ORDERABLES\t977665\tLABPANWBC\tWBC COUNT\tWBC\tWBC\t10.44\t636\t5\t52.2\t2591.24\t5\t3180\t4021200
+LAB BLOOD ORDERABLES\t978550\tLAB12645R\tCOMPLEMENT, ALTERNATE PATHWAY\t12644R\tCOMPLEMENT, ALTERNATE PATHWAY\t95.4\t546312\t5\t477\t47203\t5\t2731560\t1.59E+12
+MICROBIOLOGY ORDERABLES\t471553\tLABAFBCF\tAFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS\tCULT\tCULT/OTHER RSLT:\t9999999\t3931337.485\t5\t49999995\t5.00E+14\t326\t1281616020\t5.25E+15
+MICROBIOLOGY ORDERABLES\t471554\tLABBLCA\tAFB CULTURE, BLOOD\tCULT\tCULT/OTHER RSLT:\t9999999\t3667605.352\t5\t49999995\t5.00E+14\t213\t781199940\t3.13E+15
+MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tRPT\tRPT\t9999999\t1190220\t5\t49999995\t5.00E+14\t2\t2380440\t2.83E+12
+MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tSDES\tSPECIMEN\t9999999\t1190220\t5\t49999995\t5.00E+14\t2\t2380440\t2.83E+12
+MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t9999999\t#VALUE!\t5\t49999995\t5.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tSDES\tSPECIMEN\t9999999\t752360\t5\t49999995\t5.00E+14\t6\t4514160\t3.41E+12
+MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tSREQ\tCOMMENTS\t9999999\t752360\t5\t49999995\t5.00E+14\t6\t4514160\t3.41E+12
+MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tRPT\tRPT\t9999999\t309040\t5\t49999995\t5.00E+14\t3\t927120\t2.91E+11
+MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tSDES\tSPECIMEN\t9999999\t309040\t5\t49999995\t5.00E+14\t3\t927120\t2.91E+11
+MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tSREQ\tCOMMENTS\t9999999\t309040\t5\t49999995\t5.00E+14\t3\t927120\t2.91E+11
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tRPT\tRPT\t9999999\t647532\t5\t49999995\t5.00E+14\t5\t3237660\t3.07E+12
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tSREQ\tCOMMENTS\t9999999\t647532\t5\t49999995\t5.00E+14\t5\t3237660\t3.07E+12
+MICROBIOLOGY ORDERABLES\t966889\tLABVCULR\tVIRAL CULTURE, RESPIRATORY\tCULT\tCULT/OTHER RSLT:\t9999999\t1712644.444\t5\t49999995\t5.00E+14\t297\t508655400\t9.24E+14
+MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11CT\tCOMMENT\t9999999\t266970\t5\t49999995\t5.00E+14\t2\t533940\t2.85E+11
+MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11DX\tCLINICAL INDICATION\t9999999\t266970\t5\t49999995\t5.00E+14\t2\t533940\t2.85E+11
+MOLECULAR DIAG ORDERABLES\t497359\tLABMDXMIS\tMOLECULAR PATHOLOGY MISCELLANEOUS TEST\tRESUL7\tRESULT\t9999999\t576624\t5\t49999995\t5.00E+14\t10\t5766240\t1.60E+13
+STOOL ORDERABLES\t472653\tLABYANTIN\tINSULIN ANTIBODIES\tYANT2\tHUMAN\t9999999\t822848.5714\t5\t49999995\t5.00E+14\t7\t5759940\t6.65E+12
+STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tBILIRUBIN\tBILIRUBIN\t13\t177360\t5\t65\t1299\t1\t177360\t31456569600
+STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tCHOL\tCHOLESTEROL\t25.2\t177360\t5\t126\t3898\t1\t177360\t31456569600
+STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tHGB\tHEMOGLOBIN\t2000023.8\t177360\t5\t10000119\t1.00E+14\t1\t177360\t31456569600
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCAN10\tCANDIDA 1:10 CPM\t2897.2\t908145\t5\t14486\t62211096\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCAN100\tCANDIDA 1:100 CPM\t3603.4\t908145\t5\t18017\t191749131\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCAN50\tCANDIDA 1:50 CPM\t2300\t908145\t5\t11500\t55588098\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tCNDINT\tCANDIDA INTERPRETATION\t9999999\t908145\t5\t49999995\t5.00E+14\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD100\tPPD 1:100 CPM\t9999999\t908145\t5\t49999995\t5.00E+14\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD200\tPPD 1:200 CPM\t5923.2\t908145\t5\t29616\t406369600\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD400\tPPD 1:400 CPM\t2508.6\t908145\t5\t12543\t61845647\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD50\tPPD 1:50 CPM\t9999999\t908145\t5\t49999995\t5.00E+14\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPD800\tPPD 1:800 CPM\t1904.4\t908145\t5\t9522\t31263672\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTET1K\tTETANUS 1:1000 CPM\t11484.8\t908145\t5\t57424\t2205419288\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTET2K\tTETANUS 1:2000 CPM\t9356.2\t908145\t5\t46781\t1254103991\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTET5K\tTETANUS 1:5000 CPM\t11695.2\t908145\t5\t58476\t1785504722\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tUNCAN\tUNSTIMULATED CPM\t749.4\t908145\t5\t3747\t3412595\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tUNPPD\tUNSTIMULATED CPM\t883.4\t908145\t5\t4417\t5898549\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tUNTET\tUNSTIMULATED CPM\t749.4\t908145\t5\t3747\t3412595\t4\t3632580\t3.30E+12
+TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tTALBU\tALBUMIN EXCRETION\t2000052.4\t67920\t5\t10000262\t1.00E+14\t3\t203760\t18131760000
+TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tUALB\tALBUMIN, URINE\t2000037.8\t67920\t5\t10000189\t1.00E+14\t3\t203760\t18131760000
+TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tUT35\tCOLLECTION PERIOD\t20.4\t67920\t5\t102\t2340\t3\t203760\t18131760000
+TIMED URINE\t471570\tLABUALBT\tALBUMIN, URINE (TIMED)\tVOL35\tVOLUME\t1833.6\t67920\t5\t9168\t18840086\t3\t203760\t18131760000
+URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tVOLUMEURN\tURINE VOLUME\t8000557.4\t88120\t5\t40002787\t4.00E+14\t3\t264360\t28310436000
+URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tYUARR4\tAS CONC (UG/L)\t8000002.4\t88120\t5\t40000012\t4.00E+14\t3\t264360\t28310436000
+URINE ORDERABLES\t541662\tLABUELP\tELECTROLYTE PANEL, URINE W/O CREATININE\tUCL\tCHLORIDE, URINE\t2000049.4\t4080\t5\t10000247\t1.00E+14\t1\t4080\t16646400
+URINE ORDERABLES\t541662\tLABUELP\tELECTROLYTE PANEL, URINE W/O CREATININE\tUK\tPOTASSIUM, URINE\t2000037.64\t4080\t5\t10000188.2\t1.00E+14\t1\t4080\t16646400
+URINE ORDERABLES\t541662\tLABUELP\tELECTROLYTE PANEL, URINE W/O CREATININE\tUNA\tSODIUM, URINE\t2000032.6\t4080\t5\t10000163\t1.00E+14\t1\t4080\t16646400
+URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12683R\tCREATININE, URINE MG/DL\t52.4\t496092\t5\t262\t19958\t5\t2480460\t1.23E+12
+URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12684R\tCREATININE, URINE MG/DAY\t850\t496092\t5\t4250\t4103386\t5\t2480460\t1.23E+12
+URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12685R\tAMINOLEVULINIC ACID UMO/L\t10.8\t496092\t5\t54\t626\t5\t2480460\t1.23E+12
+URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12686R\tAMINOLEVULINIC ACID UMOL/DAY\t25\t496092\t5\t125\t4317\t5\t2480460\t1.23E+12
+URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12687R\tHOURS COLLECTED\t24\t496092\t5\t120\t2880\t5\t2480460\t1.23E+12
+URINE ORDERABLES\t889212\tLAB13000R\tAMINOLEVULINIC ACID 24HR UR\t12688R\tTOTAL VOLUME\t2482\t496092\t5\t12410\t40640406\t5\t2480460\t1.23E+12
+VIROLOGY ORDERABLES\t472041\tLABCMVIGM\tCMV IGM\tCMMDES\tCMV IGM TEST DESCRIPTION\t9999999\t159305.731\t5\t49999995\t5.00E+14\t513\t81723840\t1.77E+13
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tEBNA\tEPSTEIN-BARR NUC AB\t9999999\t131091.4286\t5\t49999995\t5.00E+14\t7\t917640\t1.89E+11
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tSDES\tSPECIMEN\t9999999\t616970\t5\t49999995\t5.00E+14\t6\t3701820\t2.62E+12
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tSREQ\tCOMMENTS\t9999999\t616970\t5\t49999995\t5.00E+14\t6\t3701820\t2.62E+12
+VIROLOGY ORDERABLES\t472570\tLABHSVGM\tHSV 1/2 IGM&TYPE-SPECIFIC IGG\tHSVSL7\tHSV 1/2 IGM INTERPRETATION\t9999999\t290474.6392\t5\t49999995\t5.00E+14\t291\t84528120\t2.82E+13
+VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA IGG\tTOXGDS\tTEST DESCRIPTION\t9999999\t167664.3396\t5\t49999995\t5.00E+14\t106\t17772420\t3.77E+12
+VIROLOGY ORDERABLES\t497452\tLABHV6DNA\tHERPES VIRAL 6 DNA QUALITATIVE\tHVDNA\tHERPES VIRUS 6 DNA\t9999999\t228475.2809\t5\t49999995\t5.00E+14\t178\t40668600\t1.45E+13
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADPCR1\tADENVIRUS DNA\t9999999\t217572\t5\t49999995\t5.00E+14\t5\t1087860\t2.65E+11
+VIROLOGY ORDERABLES\t876719\tLABDVEIAV\tFLU A DFA\tIAVIP\tTEST DESCRIPTION\t9999999\t78912.72727\t5\t49999995\t5.00E+14\t33\t2604120\t1.09E+12
+VIROLOGY ORDERABLES\t876723\tLABDVEIBV\tFLU B DFA\tIBVIP\tTEST DESCRIPTION\t9999999\t53200\t5\t49999995\t5.00E+14\t24\t1276800\t91484654400
+VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXGQL\tIGG RESULT\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
+VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXGQT\tIGG LEVEL\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
+VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXMQL\tIGM RESULT\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
+VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXMQT\tIGM LEVEL\t9999999\t137520\t5\t49999995\t5.00E+14\t5\t687600\t1.11E+11
+VIROLOGY ORDERABLES\t906965\tLABVZVQLP\tVZV DNA PCR QUAL, PLASMA\tVZQLTD\tTEST DESCRIPTION\t9999999\t198242.2222\t5\t49999995\t5.00E+14\t135\t26762700\t7.15E+12
+VIROLOGY ORDERABLES\t906969\tLABHSVQLP\tHSV DNA PCR QUAL, PLASMA\tHSVINP\tTEST DESCRIPTION\t9999999\t166727.4847\t5\t49999995\t5.00E+14\t163\t27176580\t5.96E+12
+VIROLOGY ORDERABLES\t910608\tLABCMVIGT\tCMV IGG (TRANSFUSION)\tCMGTDS\tTEST DESCRIPTION\t9999999\t130818.6935\t5\t49999995\t5.00E+14\t398\t52065840\t9.97E+12
+BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tRESUL8\tRESULT\t9999999\t28164\t4\t39999996\t4.00E+14\t5\t140820\t7934583600
+BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tTEST8\tTEST NAME\t9999999\t28164\t4\t39999996\t4.00E+14\t5\t140820\t7934583600
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tVL\tPRODUCT VOLUME\t168.25\t30480\t4\t673\t113289\t1\t30480\t929030400
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tVL\tPRODUCT VOLUME\t168.75\t38640\t4\t675\t115071\t1\t38640\t1493049600
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t4\t39999996\t4.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tSI\t<<STATUS>>  \t9999999\t24574.28571\t4\t39999996\t4.00E+14\t7\t172020\t6302466000
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tSI\t<<STATUS>>  \t9999999\t20070\t4\t39999996\t4.00E+14\t4\t80280\t3212935200
+BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tDI\tISSUE DATE/TIME\t9999999\t39390\t4\t39999996\t4.00E+14\t2\t78780\t3104874000
+BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tXMR\tUNIT CROSSMATCH  \t9999999\t54990\t4\t39999996\t4.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tXMR\tUNIT CROSSMATCH  \t9999999\t54990\t4\t39999996\t4.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tXMR\tUNIT CROSSMATCH  \t9999999\t54990\t4\t39999996\t4.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tXMR\tUNIT CROSSMATCH  \t9999999\t69330\t4\t39999996\t4.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tUBT\tUNIT BLOOD TYPE  \t9999999\t69330\t4\t39999996\t4.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tUBT\tUNIT BLOOD TYPE  \t9999999\t69330\t4\t39999996\t4.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tUBT\tUNIT BLOOD TYPE  \t9999999\t91200\t4\t39999996\t4.00E+14\t2\t182400\t23119207200
+BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tDI\tISSUE DATE/TIME\t9999999\t49950\t4\t39999996\t4.00E+14\t2\t99900\t5482357200
+BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tUBT\tUNIT BLOOD TYPE  \t9999999\t49950\t4\t39999996\t4.00E+14\t2\t99900\t5482357200
+BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tSI\t<<STATUS>>  \t9999999\t30\t4\t39999996\t4.00E+14\t2\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tPRO\tPRODUCT ID  \t9999999\t60\t4\t39999996\t4.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tPRO\tPRODUCT ID  \t9999999\t60\t4\t39999996\t4.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tPRO\tPRODUCT ID  \t9999999\t60\t4\t39999996\t4.00E+14\t1\t60\t3600
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tCILIA\tBRONCH. LINING CELLS\t22.5\t720\t4\t90\t6754\t1\t720\t518400
+BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tCCMT\tCOMMENT\t9999999\t#VALUE!\t4\t39999996\t4.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tBASOSYNFLD\tBASOPHILS, SYN FLD\t1.25\t4560\t4\t5\t7\t1\t4560\t20793600
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tCCNT\tCELLS COUNTED\t115.5\t3030\t4\t462\t82570\t4\t12120\t40327200
+BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\tYMYCSC\tSOURCE\t9999999\t2504460\t4\t39999996\t4.00E+14\t1\t2504460\t6.27E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tCMV\tCMV ANTIBODY\t9999999\t550720\t4\t39999996\t4.00E+14\t3\t1652160\t1.46E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t550720\t4\t39999996\t4.00E+14\t3\t1652160\t1.46E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t550720\t4\t39999996\t4.00E+14\t3\t1652160\t1.46E+12
+BODY FLUID ORDERABLES\t497333\tLABMYCOC\tMYCOPLASMA CULTURE, FASTIDIOUS\tMYSTA\tMYCOPLASMA CULTURE STATUS\t9999999\t1967535\t4\t39999996\t4.00E+14\t4\t7870140\t2.00E+13
+BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tGLUFLD\tGLUCOSE BODY FLUID\t100.25\t#VALUE!\t4\t401\t50459\t0\tNone\tNone
+BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tFCSOUR\tSOURCE\t9999999\t12144\t4\t39999996\t4.00E+14\t5\t60720\t762372000
+COAGULATION ORDERABLES\t472867\tLABCGMISC\tMISCELLANEOUS TEST COAGULATION\tCGTEST\tPROCEDURE\t9999999\t157680\t4\t39999996\t4.00E+14\t1\t157680\t24862982400
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t135\t24540\t4\t540\t73200\t4\t98160\t5941404000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t143.75\t-150\t4\t575\t85025\t4\t-600\t626400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tFSTVTTDTNT\tFASTVT TACHY ZONE DETECTION INTERVAL\t305\t45480\t4\t1220\t384200\t4\t181920\t32942354400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t130\t2010\t4\t520\t67600\t4\t8040\t34372800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDACR\tBRADY MODULATION ACCELERATION REACTION\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDDCR\tBRADY MODULATION DECELERATION RECOVERY\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRMDTHR\tBRADY MODULATION THRESHOLD\t9999999\t2010\t4\t39999996\t4.00E+14\t4\t8040\t34372800
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t18\t2010\t4\t72\t1296\t4\t8040\t34372800
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tALERT\tALERT\t9999999\t-255\t4\t39999996\t4.00E+14\t4\t-1020\t529200
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t5000199.5\t-300\t4\t20000798\t2.00E+14\t4\t-1200\t648000
+EXTERNAL RESULTS\t668294\tEXTWBC\tEXTERNAL WBC\tEXTWBC\tEXTERNAL WBC\t0.575\t-324000\t4\t2.3\t2.37\t4\t-1296000\t4.85E+11
+EXTERNAL RESULTS\t668298\tEXTPLTCT\tEXTERNAL PLATELET COUNT\tEXTPLT\tEXTERNAL PLATELET COUNT\t17.25\t-324000\t4\t69\t1309\t4\t-1296000\t4.85E+11
+HISTOCOMPATIBILITY ORDERABLE\t685703\t10135R\tHLA ORDER - SCHEDULED SCREENING KIT BILLING\t10049R\tHLA LAB TESTING STATUS\t9999999\t1225575\t4\t39999996\t4.00E+14\t4\t4902300\t2.13E+13
+HISTOCOMPATIBILITY ORDERABLE\t890776\t10110R\tHLA ORDER - FLOW AUTO CROSSMATCH\t10049R\tHLA LAB TESTING STATUS\t9999999\t267540\t4\t39999996\t4.00E+14\t4\t1070160\t4.76E+11
+HISTOCOMPATIBILITY ORDERABLE\t890779\t10045R\tHLA ORDER - ANTIBODY SCREENING\tRINFO\tTO FIND RESULT:\t9999999\t1070211.064\t4\t39999996\t4.00E+14\t141\t150899760\t2.32E+15
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9222R\tPATIENT\'S SERUM 2 DESCRIPTION\t9999999\t96960\t4\t39999996\t4.00E+14\t4\t387840\t94370709600
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9207R\tINTERPRETATION/ COMMENTS\t9999999\t172596\t4\t39999996\t4.00E+14\t5\t862980\t4.09E+11
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9336R\tPOSSIBLE HLA ANTIBODIES FOR CLASS I\t9999999\t1972035\t4\t39999996\t4.00E+14\t4\t7888140\t6.22E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9397R\tPOSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT\t9999999\t15\t4\t39999996\t4.00E+14\t4\t60\t3600
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9620R\tREFERENCE SERUM DATE\t9999999\t30\t4\t39999996\t4.00E+14\t4\t120\t7200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9621R\tREFERENCE SERUM DESCRIPTION\t9999999\t30\t4\t39999996\t4.00E+14\t4\t120\t7200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9486R\tLOCUS 12\t9999999\t61995\t4\t39999996\t4.00E+14\t4\t247980\t61404850800
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tALTRN\tALTERNARIA\t5000013.165\t135495\t4\t20000052.66\t2.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tASPRG\tASPERGILLUS FUMAG.\t5000015.228\t135495\t4\t20000060.91\t2.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tAURBAS\tAUEROBASIDIUM\t7500006.35\t135495\t4\t30000025.4\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tCLASP\tCLADOSPORIUM\t7500005.275\t135495\t4\t30000021.1\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tCNDIDA\tCANDIDA\t7500003.15\t135495\t4\t30000012.6\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tEPIPUP\tEPICOCCUM PUPURASC.\t7500012.975\t135495\t4\t30000051.9\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tFUSMON\tFUSARIUM MONILIFORME\t7500003.45\t135495\t4\t30000013.8\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tHLM\tHELMINTHOSPORIUM\t5000010.638\t135495\t4\t20000042.55\t2.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tMCM\tMUCOR\t7499999.618\t135495\t4\t29999998.47\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tPNM\tPENICILLIUM\t5000002.44\t135495\t4\t20000009.76\t2.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tRHZNIG\tRHISOPUS NIGRICANS\t7500001.53\t135495\t4\t30000006.12\t3.00E+14\t4\t541980\t1.07E+11
+IMMUNOLOGY ORDERABLES\t472870\tLABMOLDS\tMOLDS ALLERGY SCREEN\tTRIRUB\tTRICHOPHYTON RUBRUM\t7500005.05\t135495\t4\t30000020.2\t3.00E+14\t4\t541980\t1.07E+11
+LAB BLOOD ORDERABLES\t471526\tLAB11074R\tACH RECEPTOR AB\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t5.575\t301680\t4\t22.3\t497.29\t3\t905040\t2.74E+11
+LAB BLOOD ORDERABLES\t471656\tLAB11073R\tAPO B\tAPOB\tAPOLIPOPROTEIN B\t79.5\t265620\t4\t318\t25898\t4\t1062480\t4.02E+11
+LAB BLOOD ORDERABLES\t471680\tLABARIX\tARIXTRA\tARIX\tARIXTRA\t2500000.06\t66860\t4\t10000000.24\t1.00E+14\t3\t200580\t13518752400
+LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tANTI-JO1 ANTIBODY\tJO1QNT\tANTI-JO1 LEVEL\t7500010\t488760\t4\t30000040\t3.00E+14\t3\t1466280\t9.39E+11
+LAB BLOOD ORDERABLES\t471707\tLABYSSARO\tAUTOANTIBODIES TO SS-A/RO\tSSA\tSS-A/RO AB, IGG, SERUM\t7499999.35\t235005\t4\t29999997.4\t3.00E+14\t4\t940020\t2.55E+11
+LAB BLOOD ORDERABLES\t471811\tLABYC2REF\tC2 COMPLEMENT AG\tYC2RE1\tC2 COMPLEMENT AG\t2.575\t853125\t4\t10.3\t32.87\t4\t3412500\t2.94E+12
+LAB BLOOD ORDERABLES\t471814\tLABCAD\tCADMIUM, WHOLE BLOOD\tCAD\tCADMIUM, BLOOD\t7499999.75\t197760\t4\t29999999\t3.00E+14\t3\t593280\t1.52E+11
+LAB BLOOD ORDERABLES\t471960\tLABCENABY\tCENTROMERE ANTIBODIES\tYCENTA\tCENTROMERE ABS.\t9999999\t119720\t4\t39999996\t4.00E+14\t3\t359160\t45537789600
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t227223.75\t4\t39999996\t4.00E+14\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t223680\t4\t39999996\t4.00E+14\t4\t894720\t2.54E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSAIN\tINTERPRETATION\t9999999\t182820\t4\t39999996\t4.00E+14\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tBASO\tBASO, %\t0.275\t2880\t4\t1.1\t0.49\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tBASOAB\tBASOS, ABS\t0.015\t2880\t4\t0.06\t0.0012\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tEOS\tEOS, %\t1.275\t2880\t4\t5.1\t17.63\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tEOSAB\tEOS, ABS\t0.045\t2880\t4\t0.18\t0.0194\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tLYM\tLYM, %\t24.525\t2880\t4\t98.1\t2562.53\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tLYMAB\tLYM, ABS\t1.2275\t2880\t4\t4.91\t7.9567\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMONO\tMONO, %\t15.5\t2880\t4\t62\t1850.94\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMONOAB\tMONO, ABS\t0.495\t2880\t4\t1.98\t1.095\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tNEUT\tNEUT, %\t58.425\t2880\t4\t233.7\t14985.05\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tNEUTAB\tNEUT, ABS\t3.6275\t2880\t4\t14.51\t85.5369\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472187\tLABDRVVTP\tDILUTE RUSSELL VIPER VENOM PANEL\tDRVVIN\tDRVV INTERPRETATION\t9999999\t120585.9091\t4\t39999996\t4.00E+14\t132\t15917340\t3.59E+12
+LAB BLOOD ORDERABLES\t472228\tLABDHIVB\tDONOR HIV AB\tDHIV\tDONOR HIV AB\t9999999\t199937.1429\t4\t39999996\t4.00E+14\t7\t1399560\t3.32E+11
+LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIDIR\tINTERPRETED BY\t9999999\t436731.8276\t4\t39999996\t4.00E+14\t580\t253304460\t1.44E+14
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA1\tC22:0\t49.975\t342450\t4\t199.9\t10076.89\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA2\tC24:0\t47.75\t342450\t4\t191\t9228.84\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA3\tC26:0\t0.5875\t342450\t4\t2.35\t1.4605\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA4\tC24/C22 RATIO\t0.955\t342450\t4\t3.82\t3.651\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA5\tC26:0/C22:0 RATIO\t0.01175\t342450\t4\t0.047\t0.000581\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA6\tPRISTANIC ACID\t0.3375\t342450\t4\t1.35\t0.5711\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA7\tPHYTANIC ACID\t2.5725\t342450\t4\t10.29\t32.3729\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA8\tPRISTANIC/PHYTANIC ACID\t0.1325\t342450\t4\t0.53\t0.0723\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY\t1.5\t297480\t4\t6\t10\t4\t1189920\t5.93E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHQLINT\tINTERPRETATION\t9999999\t109882.5\t4\t39999996\t4.00E+14\t8\t879060\t2.67E+11
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tANTI HBS QUANT\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t7500000.975\t125880\t4\t30000003.9\t3.00E+14\t5\t629400\t82853755200
+LAB BLOOD ORDERABLES\t472595\tLABYSCERE\tINFLAMM. BOWEL DISEASE PANEL,S\tYSCER1\tSACCH CEREVISIAE AB, IGA\t2500009.175\t232440\t4\t10000036.7\t1.00E+14\t3\t697320\t1.65E+11
+LAB BLOOD ORDERABLES\t472595\tLABYSCERE\tINFLAMM. BOWEL DISEASE PANEL,S\tYSCER2\tSACCH CEREVISIAE AB, IGG\t5000006.625\t232440\t4\t20000026.5\t2.00E+14\t3\t697320\t1.65E+11
+LAB BLOOD ORDERABLES\t472628\tLABYICP\tIMMUNE COMPLEX PANEL\tYC1QB\tC1G BINDING\t7499999.925\t1110780\t4\t29999999.7\t3.00E+14\t4\t4443120\t6.50E+12
+LAB BLOOD ORDERABLES\t472628\tLABYICP\tIMMUNE COMPLEX PANEL\tYPEG\tPOLYETHL. GLYCOL IGG\t9999999\t1110780\t4\t39999996\t4.00E+14\t4\t4443120\t6.50E+12
+LAB BLOOD ORDERABLES\t472628\tLABYICP\tIMMUNE COMPLEX PANEL\tYRAJI\tRAJI CELL REPLACEMENT\t5000002.75\t1110780\t4\t20000011\t2.00E+14\t4\t4443120\t6.50E+12
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLBRAIN\tL BRAZILIENSIS,INTERPRETATION\t9999999\t1997181.818\t4\t39999996\t4.00E+14\t11\t21969000\t9.76E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLDONIN\tL DONOVANI,INTERPRETATION\t9999999\t1997181.818\t4\t39999996\t4.00E+14\t11\t21969000\t9.76E+13
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLMEXIN\tL MEXICANA,LNTERPRETATION\t9999999\t1997181.818\t4\t39999996\t4.00E+14\t11\t21969000\t9.76E+13
+LAB BLOOD ORDERABLES\t472764\tLAB11084R\tLORAZEPAM LEVEL\tLORAZ\tLORAZEPAM, SERUM\t5000014.75\t729000\t4\t20000059\t2.00E+14\t4\t2916000\t2.48E+12
+LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGGBAND\tB.BURGDORFERI IGG BANDS\t9999999\t262153.5849\t4\t39999996\t4.00E+14\t53\t13894140\t5.67E+12
+LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tCONA\tCON A STIMULATION\t138540\t771800\t4\t554160\t83611517934\t3\t2315400\t1.79E+12
+LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPHA\tPHA STIMULATION\t133527\t771800\t4\t534108\t79557623246\t3\t2315400\t1.79E+12
+LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPWM\tPWM STIMULATION\t131789\t771800\t4\t527156\t84849602386\t3\t2315400\t1.79E+12
+LAB BLOOD ORDERABLES\t472854\tLABMALL1\tMISCELLANEOUS ALLERGEN 1\tALL1\tALLERGEN\t9999999\t630540\t4\t39999996\t4.00E+14\t4\t2522160\t2.43E+12
+LAB BLOOD ORDERABLES\t473184\tLAB11032R\tPSEUDOCHOLINESTERASE\tCHEP\tPSEUDOCHOLINESTERASE\t1558.75\t275145\t4\t6235\t26163569\t4\t1100580\t3.38E+11
+LAB BLOOD ORDERABLES\t473265\tLABYRIBAB\tRIBOSOMAL P PROTEIN AB\tYRIB1\tRIBOSOME P AB,IGG\t7499999.3\t286236\t4\t29999997.2\t3.00E+14\t10\t2862360\t1.31E+12
+LAB BLOOD ORDERABLES\t473271\tLABRIPA\tRISTOCETIN INDUCED PLATELET AGGREGATION\tRIPINT\tINTERPRETATION\t9999999\t75612\t4\t39999996\t4.00E+14\t10\t756120\t1.24E+11
+LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMA PCR\tTOXGG\tGENERAL GUIDELINES\t9999999\t1052324\t4\t39999996\t4.00E+14\t30\t31569720\t6.51E+13
+LAB BLOOD ORDERABLES\t473687\tLABYTRYPS\tTRYPSIN-LIKE IMMUNOREACTIVITY\tYTRYP1\tTRYPSIN-LIKE IMMUNOREACTIVITY\t68.65\t560170\t4\t274.6\t26398.5\t6\t3361020\t1.99E+12
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHSO\tSOURCE\t9999999\t3358860\t4\t39999996\t4.00E+14\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473796\tLABYWNILE\tWEST NILE VIRUS AB, IGG AND IGM\tWNVIGM\tWEST NILE VIRUS AB IGM\t9999999\t286493.3333\t4\t39999996\t4.00E+14\t252\t72196320\t3.25E+13
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBHIGMT\tB.HENSELAE IGM TITER\t9999999\t1367760\t4\t39999996\t4.00E+14\t7\t9574320\t4.56E+13
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGGT\tB.QUINTANA IGG TITER\t9999999\t1719408\t4\t39999996\t4.00E+14\t5\t8597040\t4.51E+13
+LAB BLOOD ORDERABLES\t541687\tLABFHHV7\tHERPES VIRUS 7 IGG AND IGM AB\tHERPM\tHERPESVIRUS 7 AB, IGM\t9999999\t327880\t4\t39999996\t4.00E+14\t3\t983640\t3.29E+11
+LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\tFINT\tINTERPRETATION\t9999999\t392295\t4\t39999996\t4.00E+14\t4\t1569180\t6.83E+11
+LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tCCPT\tANTI CCP ANTIBODY LEVEL\t5000003.5\t13689870\t4\t20000014\t2.00E+14\t4\t54759480\t2.95E+15
+LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMACT\tADAMTS-13 ACTIVITY\t67.25\t247320\t4\t269\t18583\t3\t741960\t2.61E+11
+LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t7499999.25\t50360\t4\t29999997\t3.00E+14\t3\t151080\t11365351200
+LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tCD3PC\tCD3 (PAN T) %\t2500002\t50360\t4\t10000008\t1.00E+14\t3\t151080\t11365351200
+LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t7500026.75\t50360\t4\t30000107\t3.00E+14\t3\t151080\t11365351200
+LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tWBC\tWBC COUNT\t2508624.75\t50360\t4\t10034499\t1.00E+14\t3\t151080\t11365351200
+LAB BLOOD ORDERABLES\t889722\tLAB12573R\tANCA\t12574R\tCYTOPLASMIC NEUTROPHILIC AB\t9999999\t318540\t4\t39999996\t4.00E+14\t81\t25801740\t1.91E+13
+LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12500R\tDATE OF LAST DOSE\t9999999\t17985\t4\t39999996\t4.00E+14\t4\t71940\t1362200400
+LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12501R\tTIME OF LAST DOSE\t9999999\t17985\t4\t39999996\t4.00E+14\t4\t71940\t1362200400
+LAB BLOOD ORDERABLES\t898808\tLAB12853R\tEVEROLIMUS, BLOOD\t12502R\tDOSE, MG\t9999999\t17985\t4\t39999996\t4.00E+14\t4\t71940\t1362200400
+LAB BLOOD ORDERABLES\t1007413\tLABDBGT\tDABIGATRAN\tDBGTR\tDABIGATRAN\t2500298.75\t9750\t4\t10001195\t1.00E+14\t4\t39000\t433965600
+MICROBIOLOGY ORDERABLES\t471942\tLABCTC\tCATHETER TIP CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t273578.4971\t4\t39999996\t4.00E+14\t173\t47329080\t1.50E+13
+MICROBIOLOGY ORDERABLES\t472394\tLABFLDB\tFLUID CULTURE / BB GRAM STAIN\tGS\tGRAM STAIN\t9999999\t#VALUE!\t4\t39999996\t4.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tRPT\tRPT\t9999999\t389580\t4\t39999996\t4.00E+14\t1\t389580\t1.52E+11
+MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tSDES\tSPECIMEN\t9999999\t389580\t4\t39999996\t4.00E+14\t1\t389580\t1.52E+11
+MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tSREQ\tCOMMENTS\t9999999\t389580\t4\t39999996\t4.00E+14\t1\t389580\t1.52E+11
+MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t3770\t4\t39999996\t4.00E+14\t6\t22620\t511664400
+MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tRPT\tRPT\t9999999\t26115\t4\t39999996\t4.00E+14\t4\t104460\t10911891600
+MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tSDES\tSPECIMEN\t9999999\t26115\t4\t39999996\t4.00E+14\t4\t104460\t10911891600
+MICROBIOLOGY ORDERABLES\t541702\tLABBLCTIP\tBLOOD CULT CENTRAL LINE CATHETER BY NURSE\tCULT\tCULT/OTHER RSLT:\t9999999\t503786.5343\t4\t39999996\t4.00E+14\t554\t279097740\t1.51E+14
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIAL ID BY SEQUENCING, SPECIMEN\tCULT\tCULT/OTHER RSLT:\t9999999\t407791.8182\t4\t39999996\t4.00E+14\t66\t26914260\t3.09E+13
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tRPT\tRPT\t9999999\t569880\t4\t39999996\t4.00E+14\t4\t2279520\t2.21E+12
+MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tRPT\tRPT\t9999999\t3540\t4\t39999996\t4.00E+14\t1\t3540\t12531600
+MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tSDES\tSPECIMEN\t9999999\t3540\t4\t39999996\t4.00E+14\t1\t3540\t12531600
+MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tSREQ\tCOMMENTS\t9999999\t3540\t4\t39999996\t4.00E+14\t1\t3540\t12531600
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tSDES\tSPECIMEN\t9999999\t647532\t4\t39999996\t4.00E+14\t5\t3237660\t3.07E+12
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCMET\tMETHOD\t9999999\t599640\t4\t39999996\t4.00E+14\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11RS\tRESULT\t9999999\t266970\t4\t39999996\t4.00E+14\t2\t533940\t2.85E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTDX\tCLINICAL INDICATION\t9999999\t763860\t4\t39999996\t4.00E+14\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTFLD\tSOURCE\t9999999\t763860\t4\t39999996\t4.00E+14\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTMET\tMETHOD\t9999999\t763860\t4\t39999996\t4.00E+14\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCRES\tRESULTS\t9999999\t1195020\t4\t39999996\t4.00E+14\t3\t3585060\t4.32E+12
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHLOC\tLOCATION\t9999999\t13848.57143\t4\t39999996\t4.00E+14\t21\t290820\t62447209200
+POINT OF CARE (NON LAB) TESTS\t610672\tPOC27\tPOC HEMOCCULT FOR FECAL BLOOD\tQC10\tQC RESULT\t9999999\t1109028.75\t4\t39999996\t4.00E+14\t16\t17744460\t2.34E+14
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tUCMT\tURINE COMMENT\t9999999\t-1410\t4\t39999996\t4.00E+14\t4\t-5640\t47455200
+STOOL ORDERABLES\t472486\tLABYHGH\tGROWTH HORMONE, SERUM\tHGH\tGROWTH HORMONE, SERUM\t3.6075\t155560\t4\t14.43\t169.9985\t3\t466680\t78444007200
+STOOL ORDERABLES\t472653\tLABYANTIN\tINSULIN ANTIBODIES\tINSAB\tINSULIN ABS, SERUM\t2499999.75\t1167780\t4\t9999999\t1.00E+14\t4\t4671120\t6.25E+12
+STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tTGL\tTRIGLYCERIDES\t86\t177360\t4\t344\t58808\t1\t177360\t31456569600
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tPPDINT\tPPD INTERPRETATION\t9999999\t908145\t4\t39999996\t4.00E+14\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t869052\tLAB11899R\tN. GONORRHOEAE RNA, RECTAL\t11900R\tN.GONORRHOEAE RNA,RECTAL\t9999999\t152760\t4\t39999996\t4.00E+14\t2\t305520\t93305815200
+STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA3\tCOLLECTION DURATION\t7500005.25\t173370\t4\t30000021\t3.00E+14\t6\t1040220\t3.08E+11
+URINE ORDERABLES\t471573\tLABYUALDO\tALDOSTERONE, URINE\tALDOSTRURN\tALDOSTERONE, URINE\t2.9\t#VALUE!\t4\t11.6\t49.58\t0\tNone\tNone
+URINE ORDERABLES\t471573\tLABYUALDO\tALDOSTERONE, URINE\tVOLUMEURN\tURINE VOLUME\t1938.5\t#VALUE!\t4\t7754\t18557116\t0\tNone\tNone
+URINE ORDERABLES\t471573\tLABYUALDO\tALDOSTERONE, URINE\tYUALD2\tCOLLECTION DURATION\t24\t#VALUE!\t4\t96\t2304\t0\tNone\tNone
+URINE ORDERABLES\t472100\tLABUCORT\tCORTISOL FREE, URINE\tUCORT\tCORTISOL, URINE\t5000001.875\t452280\t4\t20000007.5\t2.00E+14\t2\t904560\t4.09E+11
+URINE ORDERABLES\t472100\tLABUCORT\tCORTISOL FREE, URINE\tVOLUMEURN\tURINE VOLUME\t3164.5\t452280\t4\t12658\t42962438\t2\t904560\t4.09E+11
+URINE ORDERABLES\t472100\tLABUCORT\tCORTISOL FREE, URINE\tYUCOUT\tCOLLECTION DURATION\t24\t452280\t4\t96\t2304\t2\t904560\t4.09E+11
+URINE ORDERABLES\t472473\tLABUGL\tGLUCOSE WITH CREATININE, URINE (RANDOM)\tGLUCR\tGLUCOSE/CREAT RATIO\t7499999.28\t#VALUE!\t4\t29999997.12\t3.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t472473\tLABUGL\tGLUCOSE WITH CREATININE, URINE (RANDOM)\tGLUURN\tGLUCOSE, URINE\t15.25\t#VALUE!\t4\t61\t1053\t0\tNone\tNone
+URINE ORDERABLES\t472473\tLABUGL\tGLUCOSE WITH CREATININE, URINE (RANDOM)\tUCR\tURINE CREAT\t179.25\t#VALUE!\t4\t717\t139428.66\t0\tNone\tNone
+URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tHISTURN\tHISTAMINE, URINE\t89.25\t247740\t4\t357\t36673\t2\t495480\t1.23E+11
+URINE ORDERABLES\t472740\tLABLEGU\tLEGIONELLA AG, URINE\tTINFO\tTESTINFORMATION\t9999999\t407893.4505\t4\t39999996\t4.00E+14\t455\t185591520\t1.09E+14
+URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\t12661R\tNTX\t284.25\t458190\t4\t1137\t550405\t4\t1832760\t1.53E+12
+URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\t12662R\tNTX-TELOPEPTIDE, U\t73.5\t458190\t4\t294\t30218\t4\t1832760\t1.53E+12
+URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\t12663R\tCREATININE, U\t51.75\t458190\t4\t207\t15927\t4\t1832760\t1.53E+12
+URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\tUT53\tCOLLECTION PERIOD\t7500005.25\t458190\t4\t30000021\t3.00E+14\t4\t1832760\t1.53E+12
+URINE ORDERABLES\t473424\tLAB12664R\tNTX TELOPEPTIDE, URINE\tVOL53\tVOLUME\t2500214\t458190\t4\t10000856\t1.00E+14\t4\t1832760\t1.53E+12
+VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tCHLAM\tC.TRACHOMATIS RESULT:\t9999999\t553215\t4\t39999996\t4.00E+14\t4\t2212860\t1.33E+12
+VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tGC\tN.GONORRHOEAE RESULT:\t9999999\t553215\t4\t39999996\t4.00E+14\t4\t2212860\t1.33E+12
+VIROLOGY ORDERABLES\t472203\tLABDVERV\tRESPIRATORY DFA PANEL\tDVE\tRESULTS\t9999999\t99400.67114\t4\t39999996\t4.00E+14\t596\t59242800\t1.23E+14
+VIROLOGY ORDERABLES\t472525\tLABHBSQL\tHEPATITIS B SURFACE AB\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t95670\t4\t39999996\t4.00E+14\t2\t191340\t18558622800
+VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFLUB\tINFLUENZA VIRUS TYPE B\t9999999\t11850\t4\t39999996\t4.00E+14\t10\t118500\t2648221200
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKIPRP\tTEST DESCRIPTION\t9999999\t308329.8795\t4\t39999996\t4.00E+14\t83\t25591380\t2.98E+13
+VIROLOGY ORDERABLES\t497464\tLABFVARZ\tVARICELLA ZOSTER VIR. DNA,QUAL\tVZVDNA\tVARICELLA ZOSTER VIRUS DNA\t9999999\t245465.1799\t4\t39999996\t4.00E+14\t139\t34119660\t2.47E+13
+VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADDNA\tADENOVIRUS DNA\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
+VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVCP\tRESULT\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
+VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVLOG\tRESULT LOG 10\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
+VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVSP\tSPECIMEN\t9999999\t161340\t4\t39999996\t4.00E+14\t4\t645360\t1.50E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\t11155R\tHERPES VIRUS 6 DNA, QUANT\t7501847.5\t206940\t4\t30007390\t3.00E+14\t2\t413880\t92256228000
+VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tRPT\tRPT\t9999999\t2183640\t4\t39999996\t4.00E+14\t1\t2183640\t4.77E+12
+VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tSDES\tSPECIMEN\t9999999\t2183640\t4\t39999996\t4.00E+14\t1\t2183640\t4.77E+12
+VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tSREQ\tCOMMENTS\t9999999\t2183640\t4\t39999996\t4.00E+14\t1\t2183640\t4.77E+12
+VIROLOGY ORDERABLES\t876717\tLABDVEAV3\tADENO DFA\tADV3\tADENOVIRUS\t9999999\t373280\t4\t39999996\t4.00E+14\t3\t1119840\t9.84E+11
+VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tHVIP\tTEST DESCRIPTION\t9999999\t85470\t4\t39999996\t4.00E+14\t56\t4786320\t8.40E+11
+VIROLOGY ORDERABLES\t906947\tLABADVRC\tADENOVIRUS RAPID CULTURE\tADVSPM\tSPECIMEN\t9999999\t236460\t4\t39999996\t4.00E+14\t8\t1891680\t5.77E+11
+VIROLOGY ORDERABLES\t966887\tLABVCULT\tVIRAL CULTURE, TISSUE\tCULT\tCULT/OTHER RSLT:\t9999999\t1756836.783\t4\t39999996\t4.00E+14\t230\t404072460\t7.53E+14
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tLOTNO\tLOT #\t9999999\t3120\t3\t29999997\t3.00E+14\t1\t3120\t9734400
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tQY\tQUANTITY\t1\t3120\t3\t3\t3\t1\t3120\t9734400
+BLOOD BANK PRODUCT ORDERABLES\t473597\tLABTBP30\tTRANSFUSION BLOOD PRODUCT 31\tVL\tPRODUCT VOLUME\t181.6666667\t27180\t3\t545\t100201\t1\t27180\t738752400
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tVL\tPRODUCT VOLUME\t180\t38640\t3\t540\t98226\t1\t38640\t1493049600
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tVL\tPRODUCT VOLUME\t159.6666667\t#VALUE!\t3\t479\t77073\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tSI\t<<STATUS>>  \t9999999\t20477.14286\t3\t29999997\t3.00E+14\t7\t143340\t4565293200
+BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tSI\t<<STATUS>>  \t9999999\t26280\t3\t29999997\t3.00E+14\t3\t78840\t3104877600
+BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tXMR\tUNIT CROSSMATCH  \t9999999\t38460\t3\t29999997\t3.00E+14\t1\t38460\t1479171600
+BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tDI\tISSUE DATE/TIME\t9999999\t38400\t3\t29999997\t3.00E+14\t1\t38400\t1474560000
+BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tSI\t<<STATUS>>  \t9999999\t36700\t3\t29999997\t3.00E+14\t3\t110100\t6598270800
+BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tDI\tISSUE DATE/TIME\t9999999\t38400\t3\t29999997\t3.00E+14\t1\t38400\t1474560000
+BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tDI\tISSUE DATE/TIME\t9999999\t38400\t3\t29999997\t3.00E+14\t1\t38400\t1474560000
+BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tDI\tISSUE DATE/TIME\t9999999\t67080\t3\t29999997\t3.00E+14\t1\t67080\t4499726400
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tDI\tISSUE DATE/TIME\t9999999\t67080\t3\t29999997\t3.00E+14\t1\t67080\t4499726400
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tSI\t<<STATUS>>  \t9999999\t69330\t3\t29999997\t3.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tXMR\tUNIT CROSSMATCH  \t9999999\t69330\t3\t29999997\t3.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tDI\tISSUE DATE/TIME\t9999999\t67080\t3\t29999997\t3.00E+14\t1\t67080\t4499726400
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tXMR\tUNIT CROSSMATCH  \t9999999\t69330\t3\t29999997\t3.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tDI\tISSUE DATE/TIME\t9999999\t34260\t3\t29999997\t3.00E+14\t1\t34260\t1173747600
+BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tUBT\tUNIT BLOOD TYPE  \t9999999\t148140\t3\t29999997\t3.00E+14\t1\t148140\t21945459600
+BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tSI\t<<STATUS>>  \t9999999\t30\t3\t29999997\t3.00E+14\t2\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tSI\t<<STATUS>>  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tSI\t<<STATUS>>  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tSI\t<<STATUS>>  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tPRO\tPRODUCT ID  \t9999999\t60\t3\t29999997\t3.00E+14\t1\t60\t3600
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tAAMINOA\tA-AMINOADIPIC ACID\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tALANINE\tALANINE\t32.33333333\t#VALUE!\t3\t97\t3275\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tARGININE\tARGININE\t18\t#VALUE!\t3\t54\t1106\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tASPART\tASPARTIC ACID\t2.333333333\t#VALUE!\t3\t7\t17\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tASPGN\tASPARAGINE\t4.666666667\t#VALUE!\t3\t14\t66\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tBALN\tB-ALANINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tBAMBA\tB-AMINOISOBUTYRIC AC\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tCITRUL\tCITRULLINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tCYSTINE\tCYSTINE\t0.333333333\t#VALUE!\t3\t1\t1\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tCYTHN\tCYSTATHIONINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tGLUTAC\tGLUTAMIC ACID\t0.333333333\t#VALUE!\t3\t1\t1\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tGLUTAMINE\tGLUTAMINE\t489\t#VALUE!\t3\t1467\t726539\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tGLYCINE\tGLYCINE\t11.33333333\t#VALUE!\t3\t34\t394\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tHISTID\tHISTIDINE\t9.333333333\t#VALUE!\t3\t28\t314\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tHMCY\tHOMOCYSTINE\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tISOLEU\tISOLEUCINE\t1.333333333\t#VALUE!\t3\t4\t16\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tLEUCINE\tLEUCINE\t11.66666667\t#VALUE!\t3\t35\t419\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tLYSINE\tLYSINE\t21.33333333\t#VALUE!\t3\t64\t1496\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tMETHIO\tMETHIONINE\t1\t#VALUE!\t3\t3\t9\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tORNITHINE\tORNITHINE\t3.333333333\t#VALUE!\t3\t10\t38\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tPHALN\tPHENYLALANINE\t12.66666667\t#VALUE!\t3\t38\t500\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tPROLINE\tPROLINE\t1\t#VALUE!\t3\t3\t9\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tSERINE\tSERINE\t24\t#VALUE!\t3\t72\t1754\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tTAURINE\tTAURINE\t5.666666667\t#VALUE!\t3\t17\t105\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tTHREONINE\tTHREONINE\t21\t#VALUE!\t3\t63\t1409\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tTYROSINE\tTYROSINE\t6.333333333\t#VALUE!\t3\t19\t131\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tVALINE\tVALINE\t10\t#VALUE!\t3\t30\t450\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472012\tLAB11438R\tCHLAMYDIA TRACHOMATIS CULTURE\t12067R\tSPECIMEN SOURCE\t9999999\t122600\t3\t29999997\t3.00E+14\t3\t367800\t93376756800
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tBASOFLD\tBASOPHILS, FLD\t1.666666667\t#VALUE!\t3\t5\t11\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tBASOCSF\tBASOPHILS, CSF\t1.666666667\t#VALUE!\t3\t5\t9\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472183\tLABCDIFF\tDIFFERENTIAL, CSF\tEOSCSF\tEOSINOPHILS, CSF\t2.666666667\t#VALUE!\t3\t8\t30\t0\tNone\tNone
+BODY FLUID ORDERABLES\t473108\tLABFPLTS\tPLATELET COUNT, FLUID\tFLDSOU\tSOURCE, FLUID\t9999999\t14820\t3\t29999997\t3.00E+14\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t473108\tLABFPLTS\tPLATELET COUNT, FLUID\tPLTFLD\tPLATELET CT, FLUID\t3334546.667\t14820\t3\t10003640\t1.00E+14\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t473171\tLABCSFTP\tPROTEIN TOTAL, CSF\tTPCSTN\tTUBE NUMBER\t9999999\t223020\t3\t29999997\t3.00E+14\t3\t669060\t2.22E+11
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tVCA\tVIRAL CAPSID AB\t9999999\t691860\t3\t29999997\t3.00E+14\t2\t1383720\t1.39E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tVCAM\tVIRAL CAPSID AB-IGM\t9999999\t691860\t3\t29999997\t3.00E+14\t2\t1383720\t1.39E+12
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15CO\tCOMMENT, BM\t9999999\t644650\t3\t29999997\t3.00E+14\t18\t11603700\t8.75E+12
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTPRM\tPRIMERS\t9999999\t771152.3077\t3\t29999997\t3.00E+14\t13\t10024980\t1.13E+13
+BODY FLUID ORDERABLES\t497448\tLABCHLAM\tCHLAMYDIA TRACHOMATIS / PSITTACI CULTURE\t12205\tSOURCE\t9999999\t1093440\t3\t29999997\t3.00E+14\t3\t3280320\t6.55E+12
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASIN\tINTERPRETATION\t9999999\t898005\t3\t29999997\t3.00E+14\t8\t7184040\t8.03E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12341R\tSPECIMEN\t9999999\t1463100\t3\t29999997\t3.00E+14\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898824\tLAB12362R\tPARANEOPLASTIC AUTOANTIBODY EVAL, CSF\t13017R\tPCA-TR, CSF\t9999999\t3570460\t3\t29999997\t3.00E+14\t3\t10711380\t5.49E+13
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRMDSLP\tBRADY MODULATION SLOPE\t3333338\t9460\t3\t10000014\t1.00E+14\t3\t28380\t382698000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMTLBATIMP\tPM TELEMETRY BATTERY IMPEDANCE\t0.714666667\t3800\t3\t2.144\t3.309536\t3\t11400\t138477600
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.6\t5560\t3\t1.8\t1.14\t3\t16680\t244404000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.4\t5560\t3\t1.2\t0.48\t3\t16680\t244404000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRMAXRT\tBRADY MAX. SENSOR RATE\t126.6666667\t920\t3\t380\t48600\t3\t2760\t14328000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRAMP\tRV THRESHOLD AMPLITUDE\t0.625\t-260\t3\t1.875\t1.203125\t3\t-780\t378000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRDUR\tRV THRESHOLD DURATION\t0.6\t-260\t3\t1.8\t1.32\t3\t-780\t378000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRSENAM\tRV THRESHOLD SENSING AMPLITUDE\t13.22333333\t1020\t3\t39.67\t533.5489\t3\t3060\t14202000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRVTHRSENPO\tRV THRESHOLD SENSING POLARITY\t9999999\t1020\t3\t29999997\t3.00E+14\t3\t3060\t14202000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRVLDUNIM\tPM RV LEAD UNIPOLAR IMPEDANCE\t606\t500\t3\t1818\t1116666\t3\t1500\t1918800
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTDETD\tFASTVT TACHY ZONE DETECTION DURATION\t3333343.667\t-580\t3\t10000031\t1.00E+14\t3\t-1740\t1083600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tFSTVTTZNSC\tFASTVT TACHY ZONE SUDDEN ONSET CRITERIA\t56.33333333\t-580\t3\t169\t13681\t3\t-1740\t1083600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t400\t-200\t3\t1200\t480000\t3\t-600\t475200
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATPVT\tICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR\t0.333333333\t60560\t3\t1\t1\t3\t181680\t32942354400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tALERT\tALERT\t9999999\t-420\t3\t29999997\t3.00E+14\t3\t-1260\t730800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t367.3333333\t-300\t3\t1102\t441142\t3\t-900\t550800
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTDETD\tFASTVT TACHY ZONE DETECTION DURATION\t7\t-40\t3\t21\t268.5\t3\t-120\t439200
+HISTOCOMPATIBILITY ORDERABLE\t479465\t10180R\tHLA ORDER - KIT REQUEST NOTICE\t9710R\tKIT REQUEST NOTICE FOR LAB\t9999999\t65180\t3\t29999997\t3.00E+14\t3\t195540\t12745299600
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9225R\tPATIENT\'S SERUM 3 DATE\t9999999\t159720\t3\t29999997\t3.00E+14\t3\t479160\t1.19E+11
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9171R\tSERUM DESCRIPTION 3\t9999999\t605080\t3\t29999997\t3.00E+14\t3\t1815240\t3.27E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9181R\tINTERPRETATION/ COMMENTS\t9999999\t380304.6774\t3\t29999997\t3.00E+14\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9708R\tDONOR PHENOTYPE\t9999999\t387543.0508\t3\t29999997\t3.00E+14\t118\t45730080\t5.00E+13
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9192R\tPATIENT\'S SERUM 1 DESCRIPTION\t9999999\t29360\t3\t29999997\t3.00E+14\t3\t88080\t7726428000
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9199R\tPATIENT\'S SERUM 3 DATE\t9999999\t159740\t3\t29999997\t3.00E+14\t3\t479220\t1.19E+11
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9201R\tPATIENT\'S SERUM 3 UNTREATED RESULT\t9999999\t159740\t3\t29999997\t3.00E+14\t3\t479220\t1.19E+11
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9202R\tPATIENT\'S SERUM 3 DTT TREATED RESULT\t9999999\t159740\t3\t29999997\t3.00E+14\t3\t479220\t1.19E+11
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9141R\tSERUM DESCRIPTION 3\t9999999\t605080\t3\t29999997\t3.00E+14\t3\t1815240\t3.27E+12
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9707R\tDONOR PHENOTYPE\t9999999\t377476.7797\t3\t29999997\t3.00E+14\t118\t44542260\t4.69E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9383R\tHLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT\t9999999\t33166.66667\t3\t29999997\t3.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9487R\tLOCUS 13\t9999999\t82640\t3\t29999997\t3.00E+14\t3\t247920\t61404847200
+HISTOCOMPATIBILITY ORDERABLE\t1509678\t10070R\tHLA ORDER - IVIG IN VITRO INHIBITION ASSAY\t10049R\tHLA LAB TESTING STATUS\t9999999\t1712980\t3\t29999997\t3.00E+14\t3\t5138940\t1.13E+13
+IMMUNOLOGY ORDERABLES\t472579\tLABHYPP\tHYPERSENSITIVITY PNEUMONITIS SCREEN\tHYPPN\tIGG ANTIBODY PANEL\t9999999\t415480\t3\t29999997\t3.00E+14\t3\t1246440\t6.65E+11
+LAB BLOOD ORDERABLES\t471518\tLABABODIS\tABO DISCREPANCY\tABODIS\tABO DISCREPANCY\t9999999\t22.85714286\t3\t29999997\t3.00E+14\t21\t480\t28800
+LAB BLOOD ORDERABLES\t471637\tLABAPHSAB\tANTI PHOSPHOLIPID AB PANEL\tLUPDIR\tINTERPRETED BY\t9999999\t298879.5652\t3\t29999997\t3.00E+14\t138\t41245380\t1.74E+13
+LAB BLOOD ORDERABLES\t471708\tLABYSSBLA\tAUTOANTIBODIES TO SS-B/LA\tSSB\tSS-B/LA AB, IGG, SERUM\t3333333.633\t197360\t3\t10000000.9\t1.00E+14\t3\t592080\t1.34E+11
+LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG ABS\t12048R\tPT IGG\t3333364.333\t478350\t3\t10000093\t1.00E+14\t2\t956700\t4.82E+11
+LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG ABS\t12049R\tFHA IGG\t198.3333333\t478350\t3\t595\t220875\t2\t956700\t4.82E+11
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBRES\tRESULTS\t9999999\t1276240\t3\t29999997\t3.00E+14\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471777\tLABYBLAS\tBLASTOMYCES AB\tYBLAS2\tBLASTOMYCES COMPLEMENT FIXATION\t9999999\t564155\t3\t29999997\t3.00E+14\t12\t6769860\t5.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t9999999\t286934.6939\t3\t29999997\t3.00E+14\t49\t14059800\t4.87E+12
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tABGRAN\tGRAN, ABS\t3333333.667\t55240\t3\t10000001\t1.00E+14\t3\t165720\t22474130400
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32CM\tCOMMENT:\t9999999\t807004.6154\t3\t29999997\t3.00E+14\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tMETA\tMETAMYELOCYTES\t4.333333333\t11460\t3\t13\t75\t1\t11460\t131331600
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMORPH\tMORPHOLOGY\t9999999\t2880\t3\t29999997\t3.00E+14\t1\t2880\t8294400
+LAB BLOOD ORDERABLES\t472227\tLAB11134R\tDONOR HCV AB\tDHEPC\tDONOR HEPATITIS C AB\t9999999\t251580\t3\t29999997\t3.00E+14\t6\t1509480\t4.76E+11
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXAI\tALLELE I\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXAII\tALLELE II\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXDX\tCLINICAL INDICATION\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXRES\tRESULT\t9999999\t1777160\t3\t29999997\t3.00E+14\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472444\tLABYGAS\tGASTRIN, SERUM\tGASTRIN\tGASTRIN, SERUM\t3344844.333\t432680\t3\t10034533\t1.00E+14\t3\t1298040\t8.77E+11
+LAB BLOOD ORDERABLES\t472477\tLABGDMSCR\tGLUCOSE GDM SCREEN\tGDMSCR\tGLUCOSE, GDM SCREEN\t3333433.667\t5820\t3\t10000301\t1.00E+14\t2\t11640\t72612000
+LAB BLOOD ORDERABLES\t472494\tLABYHMS\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY, BLOOD\t3333333.667\t59550\t3\t10000001\t1.00E+14\t2\t119100\t9662850000
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBCINT\tINTERPRETATION\t9999999\t70359.66102\t3\t29999997\t3.00E+14\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSINT\tINTERPRETATION\t9999999\t70359.66102\t3\t29999997\t3.00E+14\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t472528\tLABHCVA\tHEPATITIS C AB EIA\tHEPCAB\tHEPATITIS C AB\t9999999\t87210\t3\t29999997\t3.00E+14\t2\t174420\t15818778000
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKMD\tINTERPRETED BY\t9999999\t557059.322\t3\t29999997\t3.00E+14\t118\t65733000\t4.63E+13
+LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tHGBFPC\t% CELLS WITH HGB F\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tMCV\tMCV\t91.23333333\t#VALUE!\t3\t273.7\t24995.09\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tMLFB\tML FETAL BLOOD\t0\t#VALUE!\t3\t0\t0\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12428R\tINTERPRETATION\t9999999\t323840\t3\t29999997\t3.00E+14\t3\t971520\t3.52E+11
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12429R\tINTERPRETATION\t9999999\t323840\t3\t29999997\t3.00E+14\t3\t971520\t3.52E+11
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12430R\tINTERPRETATION\t9999999\t323840\t3\t29999997\t3.00E+14\t3\t971520\t3.52E+11
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tCHOL\tCHOLESTEROL, TOTAL\t132.3333333\t11860\t3\t397\t58785\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t7\t11860\t3\t21\t162.5\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tHDL\tHDL CHOLESTEROL\t19\t11860\t3\t57\t1157\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tLDL\tLDL (CALCULATED)\t3333385.333\t11860\t3\t10000156\t1.00E+14\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tLDLHDL\tLDL/HDL RATIO\t3333335.367\t11860\t3\t10000006.1\t1.00E+14\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tNHDL\tNON-HDL CHOL, CALC\t113.3333333\t11860\t3\t340\t43994\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472756\tLABLPDC\tLIPID PANEL, FASTING PATIENT\tTGL\tTRIGLYCERIDE, SER/PLAS\t205.6666667\t11860\t3\t617\t196621\t3\t35580\t677617200
+LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHRES\tRESULTS\t9999999\t638456.25\t3\t29999997\t3.00E+14\t16\t10215300\t8.63E+12
+LAB BLOOD ORDERABLES\t472869\tLAB11071R\tMITOCHONDRIAL AB M2, SERUM\tYAMA\tMITOCHONDRIAL ANTIBODY, M2\t6666666.5\t621900\t3\t19999999.5\t2.00E+14\t3\t1865700\t2.44E+12
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTCBC\tCBC COMMENT\t9999999\t48930\t3\t29999997\t3.00E+14\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTINT\tINTERPRETATION\t9999999\t48930\t3\t29999997\t3.00E+14\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTMD\tREVIEWED BY\t9999999\t48930\t3\t29999997\t3.00E+14\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTPT\tSTIM. PATIENT,NBT\t3333399.667\t48930\t3\t10000199\t1.00E+14\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t473143\tLABYPRIMI\tPRIMIDONE AND PHENOBARBITAL\tPB\tPHENOBARBITAL\t3333349.9\t120140\t3\t10000049.7\t1.00E+14\t3\t360420\t46831554000
+LAB BLOOD ORDERABLES\t473143\tLABYPRIMI\tPRIMIDONE AND PHENOBARBITAL\tPRIMIDONE\tPRIMIDONE\t3333337.267\t120140\t3\t10000011.8\t1.00E+14\t3\t360420\t46831554000
+LAB BLOOD ORDERABLES\t473177\tLABPT12\tPROTHROMBIN FRAGMENT 1 AND 2\tPTFR12\tPROTHROMBIN FRAG 1+2\t3333737\t1000884\t3\t10001211\t1.00E+14\t5\t5004420\t6.44E+12
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tADA\tADENOSINE DEAMINASE\t3333333.343\t288690\t3\t10000000.03\t1.00E+14\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tG6PD\tG6PD, QUANTITATIVE\t3333340.267\t288690\t3\t10000020.8\t1.00E+14\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tGPI\tGLUC. PHOSPHATE ISOM\t6666672.667\t288690\t3\t20000018\t2.00E+14\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tHEX\tHEXOKINASE\t3333333.427\t288690\t3\t10000000.28\t1.00E+14\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tPK\tPYRUVATE KINASE\t3333339.833\t288690\t3\t10000019.5\t1.00E+14\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473282\tLABRUBG\tRUBELLA IGG ANTIBODY\tRUBR\tRUBELLA IGG ANTIBODY\t9999999\t132740\t3\t29999997\t3.00E+14\t3\t398220\t70434939600
+LAB BLOOD ORDERABLES\t473299\tLABYSERTR\tSERTRALINE LEVEL\tSERTRALINE\tSERTRALINE, S\t3333375.333\t445380\t3\t10000126\t1.00E+14\t3\t1336140\t6.08E+11
+LAB BLOOD ORDERABLES\t473299\tLABYSERTR\tSERTRALINE LEVEL\tYSERT2\tDESMETHYLSERTRALINE\t53.33333333\t445380\t3\t160\t16056\t3\t1336140\t6.08E+11
+LAB BLOOD ORDERABLES\t473304\tLABYSHBG\tSEX HORMONE BINDING GLOBULIN\tYSHBG1\tSEX HORMONE BINDING GLOBULIN\t42\t207360\t3\t126\t5474\t3\t622080\t1.45E+11
+LAB BLOOD ORDERABLES\t473305\tLABSEZ\tSEZARY CELL SCREEN\tSEZ\tSEZARY CELL SCREEN\t9999999\t301440\t3\t29999997\t3.00E+14\t2\t602880\t2.84E+11
+LAB BLOOD ORDERABLES\t473375\tLABYSTRAB\tSTRIATIONAL (STRIATED MUSCLE) AB\tYSTA\tSTRIATIONAL (STRIATED MUSCLE)\t9999999\t325480\t3\t29999997\t3.00E+14\t3\t976440\t3.22E+11
+LAB BLOOD ORDERABLES\t473444\tLABYTSIG\tTSH IMMUNOGLOBULINS, SERUM\tTSIG\tTHROID-STIMULATING IG, SERUM\t6666666.333\t265590\t3\t19999999\t2.00E+14\t2\t531180\t1.66E+11
+LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tLAB6\tREFERENCE LAB:\t9999999\t2836788\t3\t29999997\t3.00E+14\t5\t14183940\t1.37E+14
+LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tTEST6\tTEST NAME\t9999999\t2836788\t3\t29999997\t3.00E+14\t5\t14183940\t1.37E+14
+LAB BLOOD ORDERABLES\t473769\tLABVLDL\tVLDL (CALCULATED)\tVLDL\tVLDL (CALCULATED)\t3333348\t#VALUE!\t3\t10000044\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473780\tLABBARTPC\tBARTONELLA DNA, QUALI, PCR\tBQUIPC\tBARTONELLA QUINTANA DNA\t9999999\t188286\t3\t29999997\t3.00E+14\t50\t9414300\t2.14E+12
+LAB BLOOD ORDERABLES\t473785\tLABDUTYPE\tWEAK D TYPING\tDUTYP\tWEAK D TYPING\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t497404\tLABLEVETL\tLEVETIRACETAM\tYLEVT1\tLEVETIRACETAM\t41.96666667\t191532\t3\t125.9\t6809.05\t5\t957660\t1.95E+11
+LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\tECINT\tINTERPRETATION\t9999999\t1051993.846\t3\t29999997\t3.00E+14\t13\t13675920\t6.73E+13
+LAB BLOOD ORDERABLES\t497440\tLABFCHGA\tA. PHAGOCYTOPHILUM AB\tAPIGG\tA.PHAGOCYTOPHILUM IGG\t9999999\t243480\t3\t29999997\t3.00E+14\t2\t486960\t1.21E+11
+LAB BLOOD ORDERABLES\t497440\tLABFCHGA\tA. PHAGOCYTOPHILUM AB\tAPIGM\tA.PHAGOCYTOPHILUM IGM\t9999999\t243480\t3\t29999997\t3.00E+14\t2\t486960\t1.21E+11
+LAB BLOOD ORDERABLES\t497446\tLABCOXPNL\tCOXSACKIE A AB PANEL\tTYP16\tCOXSACKIE A16 AB\t9999999\t315617.7049\t3\t29999997\t3.00E+14\t61\t19252680\t6.96E+12
+LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV 1 IGG-AB, WESTERN BLOT CONFIRM\tHVIG\tHIV 1 AB CONFIRMATION\t9999999\t321188.1356\t3\t29999997\t3.00E+14\t59\t18950100\t8.48E+12
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANCOSDD\tVANCOMYCIN, SINGLE DAILY DOSE\t13.46666667\t5260\t3\t40.4\t550.5\t3\t15780\t88880400
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANUN2\tDATE & TIME OF LAST DOSE\t9999999\t5260\t3\t29999997\t3.00E+14\t3\t15780\t88880400
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANUN3\tDOSAGE\t9999999\t5260\t3\t29999997\t3.00E+14\t3\t15780\t88880400
+LAB BLOOD ORDERABLES\t497524\tLABVANUNL\tVANCOMYCIN SINGLE DAILY LEVEL\tVANUN4\tADMINISTRATION ROUTE\t9999999\t5260\t3\t29999997\t3.00E+14\t3\t15780\t88880400
+LAB BLOOD ORDERABLES\t541681\tLAB11133R\tDONOR HBC AB\tDHBCC\tDONOR HEP CORE AB\t9999999\t282090\t3\t29999997\t3.00E+14\t8\t2256720\t1.00E+12
+LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH FRONT TYPE\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1/2 PCR, NON CSF\tHV1DNA\tHSV-1 DNA, QN PCR\t6776707.333\t151560\t3\t20330122\t2.00E+14\t3\t454680\t75947306400
+LAB BLOOD ORDERABLES\t590552\tLABFHV12\tHSV 1/2 PCR, NON CSF\tHV2DNA\tHSV-2 DNA, QN PCR\t6666837.667\t151560\t3\t20000513\t2.00E+14\t3\t454680\t75947306400
+LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\tFMYABG\tM.PNEUMONIA AB IGG\t9999999\t291900\t3\t29999997\t3.00E+14\t3\t875700\t3.33E+11
+LAB BLOOD ORDERABLES\t590559\tLABFPOLIO\tPOLIOVIRUS 1-3 AB, SERUM\tPLV1\tPOLIOVIRUS 1 AB\t9999999\t253600\t3\t29999997\t3.00E+14\t3\t760800\t2.02E+11
+LAB BLOOD ORDERABLES\t590559\tLABFPOLIO\tPOLIOVIRUS 1-3 AB, SERUM\tPLV2\tPOLIOVIRUS 2 AB\t9999999\t253600\t3\t29999997\t3.00E+14\t3\t760800\t2.02E+11
+LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tJO1AB\tAUTOANTIBODIES TO JO 1\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSSA\tSS-A/RO AB, IGG, SERUM\t6666666.067\t#VALUE!\t3\t19999998.2\t2.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSSB\tSS-B/LA AB, IGG, SERUM\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t633360\tLABSPE\tPROTEIN ELECTROPHORESIS\tSPEI\tINTERPRETATION\t9999999\t271621.4063\t3\t29999997\t3.00E+14\t128\t34767540\t1.35E+13
+LAB BLOOD ORDERABLES\t633410\tLABRPRQL\tRPR QUALITATIVE\tRPRQIN\tINTERPRETATIONS\t9999999\t63888.99563\t3\t29999997\t3.00E+14\t687\t43891740\t8.49E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSAQNT\tANTI-RO LEVEL\t43.66666667\t663480\t3\t131\t6385\t3\t1990440\t1.91E+12
+LAB BLOOD ORDERABLES\t890131\tLAB12210R\tLIPOPROTN ASSOCTD PHOSPHLPSEA2\t12211R\t12211R\t201.6666667\t222000\t3\t605\t148069\t3\t666000\t1.56E+11
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC2526N\tCD25, % OF CD3+/CD4+/CD26NEG\t31\t107400\t3\t93\t3029\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC257N\tCD25, % OF CD3+/CD4+/CD7NEG\t32.66666667\t107400\t3\t98\t3364\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC264AB\tCD26-/CD4+, ABS\t905\t107400\t3\t2715\t3724793\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC264PC\tCD26-/CD4+ %\t47\t107400\t3\t141\t10013\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3026N\tCD30, % OF CD3+/CD4+/CD26NEG\t7.333333333\t107400\t3\t22\t246\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC307N\tCD30, % OF CD3+/CD4+/CD7NEG\t9\t107400\t3\t27\t357\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC326NA\tCD3+/CD26NEG, ABS.\t953.6666667\t107400\t3\t2861\t4071957\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3426N\tCD3+/CD4+/CD26NEG, ABS.\t900.6666667\t107400\t3\t2702\t3696154\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3426P\tCD3+/CD4+/CD26NEG, % OF LYMPHS\t46.66666667\t107400\t3\t140\t9842\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3426T\tCD3+/CD4+/CD26NEG, % OF CD4+ T\t54.66666667\t107400\t3\t164\t12438\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC347NA\tCD3+/CD4+/CD7NEG, ABS.\t748.3333333\t107400\t3\t2245\t2445637\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC347NP\tCD3+/CD4+/CD7NEG, % OF LYMPHS\t42\t107400\t3\t126\t8546\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC347NT\tCD3+/CD4+/CD7NEG, % OF CD4+ T\t50.66666667\t107400\t3\t152\t10856\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC37NA\tCD3+/CD7NEG, ABS.\t757\t107400\t3\t2271\t2475329\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P4PA\tCD3+/CD4+,ABS\t1350.666667\t107400\t3\t4052\t8497490\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P4PP\tCD3+/CD4+, % OF LYMPHS\t70\t107400\t3\t210\t16652\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P7NP\tCD3+/CD7NEG, % OF LYMPHS\t43\t107400\t3\t129\t8741\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P8PN\tCD3+/CD8+, ABS. OF LYMPHS\t84.33333333\t107400\t3\t253\t29277\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC3P8PP\tCD3+/CD8+, % OF LYMPHS\t5\t107400\t3\t15\t75\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC48RAT\tTCD4/CD8 RATIO\t14.26666667\t107400\t3\t42.8\t690\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC4N7AB\tCD4+/CD7-,ABS. (C4N7AB)\t754.6666667\t107400\t3\t2264\t2474720\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC4N7PC\tCD4+/CD7-% (C4N7PC)\t42.66666667\t107400\t3\t128\t8728\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC5226N\tCD52, % OF CD3+/CD4+/CD26NEG\t98\t107400\t3\t294\t28826\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tC527N\tCD52, % OF CD3+/CD4+/CD7NEG\t99.33333333\t107400\t3\t298\t29602\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD19AB\tCD19 (PAN B), ABS.\t125\t107400\t3\t375\t81179\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD19PC\tCD19 (PAN B) %\t14.66666667\t107400\t3\t44\t1610\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD26NP\tCD3+/CD26NEG, % OF LYMPHS\t50.33333333\t107400\t3\t151\t11139\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD3AB\tCD3 (PAN T), ABS.\t1452.666667\t107400\t3\t4358\t9595922\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD3PC\tCD3 (PAN T) %\t77\t107400\t3\t231\t19539\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD4TOT\tCD4 TOTAL\t71.33333333\t107400\t3\t214\t17250\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tHNS\tCD4+CD8 TOTAL, CD3+%\t75\t107400\t3\t225\t18827\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tHSRATIO\tCD4/CD8 RATIO, CD3+\t14\t107400\t3\t42\t668.34\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tLYM\tLYMPHOCYTES %\t15.8\t107400\t3\t47.4\t802.1\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tLYMAB\tLYMPHOCYTES, ABS.\t1686.333333\t107400\t3\t5059\t11714329\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tTCD4AB\tTOTAL CD4, ABS.\t1378\t107400\t3\t4134\t8865338\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tWBC\tWBC COUNT\t9766.666667\t107400\t3\t29300\t326970000\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t961555\tLAB12808R\tCK ISOENZYME WITH REFLEX\t12819R\tCK ISOENZYME ELEC SPECIMN ONLY\t9999999\t178739.4783\t3\t29999997\t3.00E+14\t115\t20555040\t4.88E+12
+LAB BLOOD ORDERABLES\t978252\tLABFRPSA\tFREE PSA\tFPSA\tFREE PSA\t37.53333333\t1900\t3\t112.6\t12103.4\t3\t5700\t16484400
+LAB BLOOD ORDERABLES\t978252\tLABFRPSA\tFREE PSA\tFREEPSAPC\t% FREE PSA\t3333346\t1900\t3\t10000038\t1.00E+14\t3\t5700\t16484400
+MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tSREQ\tCOMMENTS\t9999999\t1190220\t3\t29999997\t3.00E+14\t2\t2380440\t2.83E+12
+MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tGS\tGRAM STAIN\t9999999\t418604.2105\t3\t29999997\t3.00E+14\t19\t7953480\t3.62E+12
+MICROBIOLOGY ORDERABLES\t471795\tLABBPERP\tBORDETELLA PERTUSSIS PCR\tCULT\tCULT/OTHER RSLT:\t9999999\t153813.4884\t3\t29999997\t3.00E+14\t43\t6613980\t1.34E+12
+MICROBIOLOGY ORDERABLES\t472676\tLABISOF\tISOLATOR FUNGUS\tCULT\tCULT/OTHER RSLT:\t9999999\t1304430.274\t3\t29999997\t3.00E+14\t329\t429157560\t5.76E+14
+MICROBIOLOGY ORDERABLES\t473202\tLABROGC\tGC CULTURE SCREEN\tCULT\tCULT/OTHER RSLT:\t9999999\t264537\t3\t29999997\t3.00E+14\t20\t5290740\t2.95E+12
+MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t26115\t3\t29999997\t3.00E+14\t4\t104460\t10911891600
+MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tRPT\tRPT\t9999999\t518040\t3\t29999997\t3.00E+14\t1\t518040\t2.68E+11
+MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tSREQ\tCOMMENTS\t9999999\t1262815.2\t3\t29999997\t3.00E+14\t75\t94711140\t1.20E+14
+MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tRPT\tRPT\t9999999\t90300\t3\t29999997\t3.00E+14\t2\t180600\t16389079200
+MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tSDES\tSPECIMEN\t9999999\t90300\t3\t29999997\t3.00E+14\t2\t180600\t16389079200
+MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tSREQ\tCOMMENTS\t9999999\t90300\t3\t29999997\t3.00E+14\t2\t180600\t16389079200
+MICROBIOLOGY ORDERABLES\t862873\tLABCFRESP\tCYSTIC FIBROSIS RESPIRATORY CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t675327.4909\t3\t29999997\t3.00E+14\t275\t185715060\t2.71E+15
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tSDES\tSPECIMEN\t9999999\t569880\t3\t29999997\t3.00E+14\t4\t2279520\t2.21E+12
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tSREQ\tCOMMENTS\t9999999\t569880\t3\t29999997\t3.00E+14\t4\t2279520\t2.21E+12
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBCMT\tCOMMENT\t9999999\t678540\t3\t29999997\t3.00E+14\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBPRM\tPRIMERS\t9999999\t678540\t3\t29999997\t3.00E+14\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCDX\tCLINICAL INDICATION\t9999999\t599640\t3\t29999997\t3.00E+14\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRF\tSOURCE\t9999999\t1367340\t3\t29999997\t3.00E+14\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRP\tPRIMER\t9999999\t1367340\t3\t29999997\t3.00E+14\t2\t2734680\t5.54E+12
+POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tDIPSLOT\tDIPSTICK LOT\t9999999\t6000\t3\t29999997\t3.00E+14\t3\t18000\t140940000
+POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tEXPDATE\tEXP DATE\t9999999\t6000\t3\t29999997\t3.00E+14\t3\t18000\t140940000
+POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tPROTURINE\tPROTEIN URINE\t9999999\t6000\t3\t29999997\t3.00E+14\t3\t18000\t140940000
+STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\t11829R\tDRUGS DETECTED:\t9999999\t145740\t3\t29999997\t3.00E+14\t2\t291480\t81166788000
+STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\t11830R\tSOURCE\t9999999\t145740\t3\t29999997\t3.00E+14\t2\t291480\t81166788000
+STOOL ORDERABLES\t472544\tLABYHISTA\tHISTAMINE, SERUM\tHISTP\tHISTAMINE\t0.466666667\t388656\t3\t1.4\t0.84\t5\t1943280\t8.30E+11
+STOOL ORDERABLES\t473766\tLABY25VD\tPEDIATRIC VITAMIN D, 25-HYDROXY\t25OHD2\t25-HYDROXY D2\t9999999\t186780\t3\t29999997\t3.00E+14\t3\t560340\t1.15E+11
+STOOL ORDERABLES\t473766\tLABY25VD\tPEDIATRIC VITAMIN D, 25-HYDROXY\t25OHD3\t25-HYDROXY D3\t20\t186780\t3\t60\t1262\t3\t560340\t1.15E+11
+STOOL ORDERABLES\t473766\tLABY25VD\tPEDIATRIC VITAMIN D, 25-HYDROXY\t25OHDT\t25-HYDROXY D TOTAL\t20\t186780\t3\t60\t1262\t3\t560340\t1.15E+11
+STOOL ORDERABLES\t497226\tLABANDRO\tANDROSTENEDIONE, SERUM\tANDRO\tANDROSTENEDIONE, S\t3333471.667\t374300\t3\t10000415\t1.00E+14\t3\t1122900\t4.74E+11
+STOOL ORDERABLES\t497226\tLABYANDRO\tANDROSTENEDIONE, SERUM\tANDRO\tANDROSTENEDIONE, S\t2402.333333\t326960\t3\t7207\t31982349\t3\t980880\t3.31E+11
+STOOL ORDERABLES\t541693\tLABHBCM\tHEPATITIS B CORE / PRECORE MUTATION\tHBCM1\tBASAL CORE PROMOTER NUCLEOTIDE\t9999999\t873165\t3\t29999997\t3.00E+14\t8\t6985320\t7.15E+12
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12286R\t12286R\t6666692.5\t342800\t3\t20000077.5\t2.00E+14\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12287R\t12287R\t6666666.1\t342800\t3\t19999998.3\t2.00E+14\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12288R\t12288R\t6666666.133\t342800\t3\t19999998.4\t2.00E+14\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12289R\t12289R\t6666666\t342800\t3\t19999998\t2.00E+14\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12290R\t12290R\t6666666\t342800\t3\t19999998\t2.00E+14\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK1\tCOLLECTION DURATION\t9999999\t310750\t3\t29999997\t3.00E+14\t6\t1864500\t1.50E+12
+STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK3\tPOTASSIUM, 24-HR STOOL\t9999999\t146808\t3\t29999997\t3.00E+14\t5\t734040\t2.27E+11
+STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK4\tPOTASSIUM CONCENTRATION\t9999999\t146808\t3\t29999997\t3.00E+14\t5\t734040\t2.27E+11
+STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA4\tSODIUM CONCENTRATION\t6666686.667\t173370\t3\t20000060\t2.00E+14\t6\t1040220\t3.08E+11
+URINE ORDERABLES\t471685\tLABYUARSR\tARSENIC, URINE (RANDOM)\tARSENICURN\tARSENIC, URINE\t6666681.333\t88120\t3\t20000044\t2.00E+14\t3\t264360\t28310436000
+URINE ORDERABLES\t471930\tLABUCARN\tCARNITINE, URINE\tACFCRU\tACYL/FREE CARN RATIO, UR\t1.633333333\t450300\t3\t4.9\t16.41\t1\t450300\t2.03E+11
+URINE ORDERABLES\t471930\tLABUCARN\tCARNITINE, URINE\tCARNFU\tFREE CARNITINE, UR\t972.3333333\t450300\t3\t2917\t4185777\t1\t450300\t2.03E+11
+URINE ORDERABLES\t471930\tLABUCARN\tCARNITINE, URINE\tCARNTU\tTOTAL CARNITINE, UR\t1463.333333\t450300\t3\t4390\t9138866\t1\t450300\t2.03E+11
+URINE ORDERABLES\t472938\tLAB12811R\tMYOGLOBIN, URINE\tUMYOG\tMYOGLOBIN, URINE\t6666666.84\t154320\t3\t20000000.52\t2.00E+14\t3\t462960\t89624296800
+URINE ORDERABLES\t473434\tLABUTHI\tTHIOCYANATE, URINE (RANDOM)\tTHIOCYANURN\tTHIOCYANATE, URINE\t3333358\t288810\t3\t10000074\t1.00E+14\t2\t577620\t1.69E+11
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tHVA\tHOMOVANILLIC ACID\t4.1\t335730\t3\t12.3\t53.21\t2\t671460\t2.26E+11
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tTIMEU\tCOLLECTION PERIOD\t24\t335730\t3\t72\t1728\t2\t671460\t2.26E+11
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tUCR\tCREATININE, URINE\t1096\t335730\t3\t3288\t3619566\t2\t671460\t2.26E+11
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tUCR\tCREATININE, URN\t81.33333333\t335730\t3\t244\t20726\t2\t671460\t2.26E+11
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tUVOL\tVOLUME\t1421\t335730\t3\t4263\t6436341\t2\t671460\t2.26E+11
+VIROLOGY ORDERABLES\t472198\tLABDVERS\tRSV RAPID ANTIGEN, DIRECT EIA\tRRSP\tSPECIMEN\t9999999\t150\t3\t29999997\t3.00E+14\t2\t300\t5490000
+VIROLOGY ORDERABLES\t472198\tLABDVERS\tRSV RAPID ANTIGEN, DIRECT EIA\tRSVCOM\tCOMMENTS\t9999999\t150\t3\t29999997\t3.00E+14\t2\t300\t5490000
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\tEA\tEARLY ANTIBODY (R+D) IGG\t9999999\t131091.4286\t3\t29999997\t3.00E+14\t7\t917640\t1.89E+11
+VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCVGRE\tRESULT\t9999999\t948855\t3\t29999997\t3.00E+14\t8\t7590840\t8.74E+12
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV-1 RNA PCR QUANTITATIVE\tHIVIPR\tTEST DESCRIPTION\t9999999\t247595.8794\t3\t29999997\t3.00E+14\t398\t98543160\t3.08E+13
+VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS2\tHIV-1 P24 (NON ICD) CONF.\t9999999\t773550\t3\t29999997\t3.00E+14\t2\t1547100\t1.20E+12
+VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS3\tHIV-1 P24 INTERP.\t9999999\t478889.2308\t3\t29999997\t3.00E+14\t39\t18676680\t1.07E+13
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tRPT\tRPT\t9999999\t583060\t3\t29999997\t3.00E+14\t3\t1749180\t1.27E+12
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tSDES\tSPECIMEN\t9999999\t583060\t3\t29999997\t3.00E+14\t3\t1749180\t1.27E+12
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tSREQ\tCOMMENTS\t9999999\t583060\t3\t29999997\t3.00E+14\t3\t1749180\t1.27E+12
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA, QUALITATIVE PCR\tSOURC3\tADENOVIRUS SOURCE\t9999999\t304440\t3\t29999997\t3.00E+14\t5\t1522200\t6.91E+11
+VIROLOGY ORDERABLES\t497468\tLABBKVPCU\tBK VIRUS PCR, QUANT, URINE\tBKIPRU\tTEST DESCRIPTION\t9999999\t227768.5106\t3\t29999997\t3.00E+14\t141\t32115360\t9.66E+12
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADDNA\tADENOVIRUS DNA\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVCP\tRESULT\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVLOG\tRESULT LOG 10\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVSP\tSPECIMEN\t9999999\t98320\t3\t29999997\t3.00E+14\t3\t294960\t33751087200
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUAMT\tMETHOD\t9999999\t163609.7143\t3\t29999997\t3.00E+14\t35\t5726340\t1.13E+12
+VIROLOGY ORDERABLES\t876721\tLABDVEIAR\tFLU A DFA-REFLEX PCR\tIARIP\tTEST DESCRIPTION\t9999999\t118140\t3\t29999997\t3.00E+14\t7\t826980\t1.98E+11
+VIROLOGY ORDERABLES\t876737\tLABTOXOIM\tTOXOPLASMA IGM\tTOXM\tTOXOPLASMA IGM\t9999999\t#VALUE!\t3\t29999997\t3.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXDES\tTEST DESCRIPTION\t9999999\t165582.0408\t3\t29999997\t3.00E+14\t147\t24340560\t5.47E+12
+VIROLOGY ORDERABLES\t876739\tLABTOXOAB\tTOXOPLASMA IGM & IGG\tTOXIGG\tTOXOPLASMA IGG\t9999999\t189060\t3\t29999997\t3.00E+14\t1\t189060\t35743683600
+VIROLOGY ORDERABLES\t876739\tLABTOXOAB\tTOXOPLASMA IGM & IGG\tTOXIGM\tTOXOPLASMA IGM\t9999999\t189060\t3\t29999997\t3.00E+14\t1\t189060\t35743683600
+VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVSP\tSPECIMEN\t9999999\t267440\t3\t29999997\t3.00E+14\t3\t802320\t2.15E+11
+VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVMT\tMETHOD\t9999999\t41910\t3\t29999997\t3.00E+14\t2\t83820\t6616040400
+VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVRE\tMETAPNEUMOVIRUS RNA\t9999999\t41910\t3\t29999997\t3.00E+14\t2\t83820\t6616040400
+VIROLOGY ORDERABLES\t906949\tLABMUMRC\tMUMPS RAPID CULTURE\tMUMRC1\tMUMPS RAPID CULTURE\t9999999\t182240\t3\t29999997\t3.00E+14\t3\t546720\t1.15E+11
+VIROLOGY ORDERABLES\t978510\tLABHSVIGG\tHERPES SIMPLEX VIRUS IGG PANEL\tHVIGDS\tHSV IGG TEST DESCRIPTION\t9999999\t250224\t3\t29999997\t3.00E+14\t115\t28775760\t9.60E+12
+VIROLOGY ORDERABLES\t1007407\tLABHSVQL\tHSV DNA PCR QUAL, NON LESION\tHSVIN\tTEST DESCRIPTION\t9999999\t175252.9412\t3\t29999997\t3.00E+14\t102\t17875800\t3.71E+12
+BIOCHEMICAL GENETICS\t497353\tLABBCGMIS\tBIOCHEM GENETIC MISCELLANEOUS TEST\tRLAB8\tREFERENCE LAB\t9999999\t28164\t2\t19999998\t2.00E+14\t5\t140820\t7934583600
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tVL\tPRODUCT VOLUME\t153\t40440\t2\t306\t47466\t1\t40440\t1635393600
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tVL\tPRODUCT VOLUME\t311.5\t28740\t2\t623\t238169\t1\t28740\t825987600
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tVL\tPRODUCT VOLUME\t176\t40470\t2\t352\t62002\t2\t80940\t3281490000
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tVL\tPRODUCT VOLUME\t139.5\t#VALUE!\t2\t279\t40325\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tSI\t<<STATUS>>  \t9999999\t54990\t2\t19999998\t2.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tSI\t<<STATUS>>  \t9999999\t54990\t2\t19999998\t2.00E+14\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tSI\t<<STATUS>>  \t9999999\t69330\t2\t19999998\t2.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tSI\t<<STATUS>>  \t9999999\t69330\t2\t19999998\t2.00E+14\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tSI\t<<STATUS>>  \t9999999\t91200\t2\t19999998\t2.00E+14\t2\t182400\t23119207200
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tXMR\tUNIT CROSSMATCH  \t9999999\t148140\t2\t19999998\t2.00E+14\t1\t148140\t21945459600
+BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tXMR\tUNIT CROSSMATCH  \t9999999\t65640\t2\t19999998\t2.00E+14\t1\t65640\t4308609600
+BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tSI\t<<STATUS>>  \t9999999\t74070\t2\t19999998\t2.00E+14\t2\t148140\t21945459600
+BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tXMR\tUNIT CROSSMATCH  \t9999999\t148140\t2\t19999998\t2.00E+14\t1\t148140\t21945459600
+BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tSI\t<<STATUS>>  \t9999999\t60\t2\t19999998\t2.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473666\tLABTBP94\tTRANSFUSION BLOOD PRODUCT 95\tPRO\tPRODUCT ID  \t9999999\t60\t2\t19999998\t2.00E+14\t1\t60\t3600
+BLOOD BANK PRODUCT ORDERABLES\t473666\tLABTBP94\tTRANSFUSION BLOOD PRODUCT 95\tSI\t<<STATUS>>  \t9999999\t60\t2\t19999998\t2.00E+14\t1\t60\t3600
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBCMT\tCOMMENT\t9999999\t1007200\t2\t19999998\t2.00E+14\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tBCD34V\tCD34+ PROGENITOR VIABILITY\t100\t11580\t2\t200\t20000\t2\t23160\t268221600
+BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD45PV\tCD45+ VIABILITY\t97.5\t11580\t2\t195\t19017\t2\t23160\t268221600
+BODY FLUID ORDERABLES\t472044\tLABFCO2\tCO2 (BICARB), FLUID\tCO2FL\tCO2, FLUID\t5000016\t5340\t2\t10000032\t1.00E+14\t2\t10680\t65354400
+BODY FLUID ORDERABLES\t472044\tLABFCO2\tCO2 (BICARB), FLUID\tCO2FLS\tSOURCE\t9999999\t5340\t2\t19999998\t2.00E+14\t2\t10680\t65354400
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tFOTHER\tOTHERS, FLD\t4\t1980\t2\t8\t32\t1\t1980\t3920400
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSOTHER\tOTHERS, SYN FLD\t2.5\t600\t2\t5\t13\t1\t600\t360000
+BODY FLUID ORDERABLES\t472552\tLABYHISTC\tHISTOPLASMA AG, CSF\tYHISC5\tCOMMENT,CSF\t9999999\t550244\t2\t19999998\t2.00E+14\t15\t8253660\t4.67E+12
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIRE\tRESULT\t9999999\t927889.0909\t2\t19999998\t2.00E+14\t11\t10206780\t1.25E+13
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tCMVIGM\tCMV IGM AB\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHAVINT\tINTERPRETATION\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHAVR\tIGM ANTI-HAV\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t359730\t2\t19999998\t2.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHCVI\tINTERPRETATION\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHCVR\tANTI-HCV\t9999999\t712710\t2\t19999998\t2.00E+14\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t226740\t2\t19999998\t2.00E+14\t1\t226740\t51411027600
+BODY FLUID ORDERABLES\t497380\tLABYFUN\tUREA NITROGEN, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t611479\tLABPSACX\tPSA WITH REFLEX COMPLEXED PSA\tPSA\tPSA\t1.7\t10800\t2\t3.4\t5.86\t1\t10800\t116640000
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMIN\tINTERPRETATION\t9999999\t741092.1429\t2\t19999998\t2.00E+14\t28\t20750580\t2.04E+13
+BODY FLUID ORDERABLES\t670812\tLAB11175R\tLIPASE,BF\t11177R\tFLUID TYPE\t9999999\t193422.8571\t2\t19999998\t2.00E+14\t28\t5415840\t1.24E+12
+BODY FLUID ORDERABLES\t670824\tLAB11217R\tIRON,LIVER TISSUE\t11218R\tIRON, LIVER TS\t3733.5\t334620\t2\t7467\t31862709\t2\t669240\t2.24E+11
+BODY FLUID ORDERABLES\t670824\tLAB11217R\tIRON,LIVER TISSUE\t11224R\tHEPATIC IRON INDEX\t1.35\t334620\t2\t2.7\t3.77\t2\t669240\t2.24E+11
+BODY FLUID ORDERABLES\t744346\tLAB11619R\tVZV PCR, AQUEOUS FLUID\t11778R\tVZV PCR, AQUEOUS FLUID\t9999999\t863697.1429\t2\t19999998\t2.00E+14\t21\t18137640\t3.66E+13
+BODY FLUID ORDERABLES\t864674\tLAB11887R\tC. TRACHOMATIS RNA, THROAT\t11886R\tCHLAMYDIA TRACHOMATIS RNA,THROAT\t9999999\t152820\t2\t19999998\t2.00E+14\t2\t305640\t93379140000
+BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFRE\tRESULT\t9999999\t479125.7143\t2\t19999998\t2.00E+14\t14\t6707760\t4.66E+12
+BODY FLUID ORDERABLES\t910612\tLABSALCOR\tCORTISOL, SALIVA\tAVGCOR\tAVERAGE SALIVARY CORTISOL\t9999999\t124950\t2\t19999998\t2.00E+14\t2\t249900\t33406966800
+BODY FLUID ORDERABLES\t910612\tLABSALCOR\tCORTISOL, SALIVA\tMN1COR\t1ST MIDNIGHT SAMPLE\t9999999\t124950\t2\t19999998\t2.00E+14\t2\t249900\t33406966800
+BODY FLUID ORDERABLES\t910612\tLABSALCOR\tCORTISOL, SALIVA\tMN2COR\t2ND MIDNIGHT SAMPLE\t5000127.5\t124950\t2\t10000255\t1.00E+14\t2\t249900\t33406966800
+COAGULATION ORDERABLES\t472351\tLABF8POR\tFACTOR VIII INHIBITOR PORCINE\tF8POR\tFACTOR VIII INHIBITOR, PORCINE\t9999999\t8520\t2\t19999998\t2.00E+14\t2\t17040\t153504000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t0\t48540\t2\t0\t0\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t1.5\t48540\t2\t3\t4.5\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.4\t48540\t2\t0.8\t0.32\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t0.5625\t48540\t2\t1.125\t0.703125\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.4\t48540\t2\t0.8\t0.32\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tMOD\tMODEL\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRALDUNIM\tPM RA LEAD UNIPOLAR IMPEDANCE\t313.5\t48540\t2\t627\t196745\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tPMRVLDUNIM\tPM RV LEAD UNIPOLAR IMPEDANCE\t342\t48540\t2\t684\t233928\t2\t97080\t5940360000
+EVAL4\t669613\t210993288\tPM DEVICE EVAL W/OUT REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t9999999\t48540\t2\t19999998\t2.00E+14\t2\t97080\t5940360000
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tICDBRAVMAX\tBRADY ADAPTIVE AV RATE MAXIMUM\t65\t-30\t2\t130\t8450\t2\t-60\t3600
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMALERT\tPM ALERT\t9999999\t-300\t2\t19999998\t2.00E+14\t2\t-600\t187200
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMRALDUNIM\tPM RA LEAD UNIPOLAR IMPEDANCE\t1073.5\t750\t2\t2147\t2850817\t2\t1500\t1918800
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t28\t1080\t2\t56\t1568\t2\t2160\t6141600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t230\t1080\t2\t460\t105800\t2\t2160\t6141600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMLVLDBIIM\tPM LV LEAD BIPOLAR IMPEDANCE\t556\t1410\t2\t1112\t694322\t2\t2820\t6181200
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tPMLVLDUNIM\tPM LV LEAD UNIPOLAR IMPEDANCE\t345.5\t-420\t2\t691\t272281\t2\t-840\t381600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tRVBRSENR\tRV BRADY SENSING REFRACTORY PERIOD\t230\t1080\t2\t460\t105800\t2\t2160\t6141600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTAMSEP\tICD DETECTIONS AND TREATMENTS AMS EPISODES\t0\t90\t2\t0\t0\t2\t180\t104400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLEADALERT\tLEAD ALERT\t9999999\t-510\t2\t19999998\t2.00E+14\t2\t-1020\t738000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t3\t-150\t2\t6\t19.125\t2\t-300\t190800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.5\t-150\t2\t1\t0.52\t2\t-300\t190800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t0.625\t-150\t2\t1.25\t0.8125\t2\t-300\t190800
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.5\t-150\t2\t1\t0.52\t2\t-300\t190800
+HISTOCOMPATIBILITY ORDERABLE\t511033\t10085R\tHLA ORDER - TYPING ADD ON LOCUS\t10049R\tHLA LAB TESTING STATUS\t9999999\t348960\t2\t19999998\t2.00E+14\t2\t697920\t2.57E+11
+HISTOCOMPATIBILITY ORDERABLE\t511033\t10085R\tHLA ORDER - TYPING ADD ON LOCUS\tRINFO\tTO FIND RESULT:\t9999999\t348960\t2\t19999998\t2.00E+14\t2\t697920\t2.57E+11
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9218R\tPATIENT\'S SERUM 1 DESCRIPTION\t9999999\t43950\t2\t19999998\t2.00E+14\t2\t87900\t7726410000
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9226R\tPATIENT\'S SERUM 3 DESCRIPTION\t9999999\t149970\t2\t19999998\t2.00E+14\t2\t299940\t86644299600
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9227R\tPATIENT\'S SERUM 3 UNTREATED RESULT\t9999999\t236760\t2\t19999998\t2.00E+14\t2\t473520\t1.19E+11
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9228R\tPATIENT\'S SERUM 3 DTT TREATED RESULT\t9999999\t236760\t2\t19999998\t2.00E+14\t2\t473520\t1.19E+11
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9200R\tPATIENT\'S SERUM 3 DESCRIPTION\t9999999\t150000\t2\t19999998\t2.00E+14\t2\t300000\t86644980000
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9113R\tSERUM DESCRIPTION 3\t9999999\t771510\t2\t19999998\t2.00E+14\t2\t1543020\t2.38E+12
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9092R\tSERUM DESCRIPTION 3\t9999999\t771480\t2\t19999998\t2.00E+14\t2\t1542960\t2.38E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9332R\tSERUM DESCRIPTION\t9999999\t0\t2\t19999998\t2.00E+14\t2\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9349R\tIGG COMBINED CPRA FOR CURRENT SERUM\t9999999\t30\t2\t19999998\t2.00E+14\t2\t60\t3600
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9354R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT\t9999999\t30\t2\t19999998\t2.00E+14\t2\t60\t3600
+HISTOCOMPATIBILITY ORDERABLE\t1458223\t10175R\tHLA ORDER - KIT PROCESSING RECIPIENT\t10049R\tHLA LAB TESTING STATUS\t9999999\t1408650\t2\t19999998\t2.00E+14\t2\t2817300\t3.97E+12
+LAB BLOOD ORDERABLES\t471508\tLABY21HYX\t21 - HYDROXYLASE AB, SERUM\t21OHXAB\t21-HYDROXYLASE AB\t5000436\t272430\t2\t10000872\t1.00E+14\t2\t544860\t1.65E+11
+LAB BLOOD ORDERABLES\t471527\tLABSMABY\tAUTOANTIBODIES TO SM\tSMAB\tAUTOANTIBODIES TO SM\t4999999.6\t160110\t2\t9999999.2\t1.00E+14\t2\t320220\t51646352400
+LAB BLOOD ORDERABLES\t471558\tLAB11140R\tMATERNAL SCREEN\tAFP\tAFP SCREEN TO ALLIED/STATE LAB\t9999999\t43800\t2\t19999998\t2.00E+14\t2\t87600\t4693860000
+LAB BLOOD ORDERABLES\t471578\tLABYA1APH\tALPHA - 1 - ANTITRYPSIN PHENOTYPE\tYA1A2\tALPHA-1-ANTITRYPSIN\t171.5\t398520\t2\t343\t77065\t2\t797040\t3.20E+11
+LAB BLOOD ORDERABLES\t471589\tLABYALUM\tALUMINUM\tALUM\tALUMINUM\t8.5\t201660\t2\t17\t149\t2\t403320\t98520091200
+LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE, SERUM\tYAMIOD\tAMIODARONE\t2.15\t263715\t2\t4.3\t9.29\t4\t1054860\t3.00E+11
+LAB BLOOD ORDERABLES\t471607\tLABYAMIO\tAMIODARONE, SERUM\tYDESET\tDESETHYLAMIODARONE\t1.15\t263715\t2\t2.3\t3.49\t4\t1054860\t3.00E+11
+LAB BLOOD ORDERABLES\t471625\tLAB11068R\tANGIOTENSIN CONVERTING ENZYME, SERUM\tACE\tANGIOTESIN CONVERTING ENZYME\t12.5\t227040\t2\t25\t317\t2\t454080\t1.03E+11
+LAB BLOOD ORDERABLES\t471644\tLABABIDI\tAB ID\tABID2E\tANTIBODY ID ELUATE COMMENT\t9999999\t279561.4286\t2\t19999998\t2.00E+14\t14\t3913860\t8.62E+12
+LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tANTI-SCL70 ANTIBODY\tSCLQNT\tANTI-SCL70 LEVEL\t5000015\t549600\t2\t10000030\t1.00E+14\t2\t1099200\t8.22E+11
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBCMT\tCOMMENT\t9999999\t1276240\t2\t19999998\t2.00E+14\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBPRM\tPRIMERS\t9999999\t1276240\t2\t19999998\t2.00E+14\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRDR\tINTERPRETED BY\t9999999\t599442.5197\t2\t19999998\t2.00E+14\t127\t76129200\t5.86E+13
+LAB BLOOD ORDERABLES\t471764\tLABYBILEA\tBILE ACIDS TOTAL\tBILE\tBILE ACIDS, TOTAL\t76.5\t1304020\t2\t153\t18317\t3\t3912060\t1.21E+13
+LAB BLOOD ORDERABLES\t471778\tLABBLDCR\tBLOOD FOR CREATININE CLEARANCE\tBLDCR\tBLD FOR CREATININE CLEARANCE\t9999999\t10380\t2\t19999998\t2.00E+14\t2\t20760\t373197600
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMGQUL\tCMV IGG RESULT\t9999999\t221580\t2\t19999998\t2.00E+14\t2\t443160\t1.80E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMMQUL\tCMV IGM RESULT\t9999999\t221580\t2\t19999998\t2.00E+14\t2\t443160\t1.80E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHSAB\tVIRAL AB HSV\t9999999\t286934.6939\t2\t19999998\t2.00E+14\t49\t14059800\t4.87E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVGGQUL\tVZV IGG RESULT\t9999999\t221580\t2\t19999998\t2.00E+14\t2\t443160\t1.80E+11
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tGRANP\tGRAN, %\t1\t15870\t2\t2\t2\t2\t31740\t807771600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTPC\tNEUTROPHIL %\t87.7\t28380\t2\t175.4\t15388.36\t2\t56760\t1893117600
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTCL\tCLINICAL INDICATION\t9999999\t595140\t2\t19999998\t2.00E+14\t1\t595140\t3.54E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHCVI\tINTERPRETATION\t9999999\t200741.1086\t2\t19999998\t2.00E+14\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472127\tLABCYSI\tCYSTICERCOSIS, CSF AND SERUM\tCYSC\tCYSTICERCOSIS-CSF\t9999999\t1036644\t2\t19999998\t2.00E+14\t5\t5183220\t7.72E+12
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tEOS\tEOSINOPHILS\t3\t11460\t2\t6\t20\t1\t11460\t131331600
+LAB BLOOD ORDERABLES\t472192\tLABDATLA\tDIRECT ANTIGLOBULIN TEST - LARC\tREQS\tREQUEST SENT\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472276\tLABECHIG\tECHINOCOCCUS AB, IGG\tECHIND\tECHINOCOCCUS LGG\t9999999\t297896.7568\t2\t19999998\t2.00E+14\t37\t11022180\t4.39E+12
+LAB BLOOD ORDERABLES\t472401\tLABFLUOX\tFLUOXETINE AND NORFLUOXETINE\tFLUOXE\tFLUOXETINE\t5000062.5\t615480\t2\t10000125\t1.00E+14\t2\t1230960\t1.12E+12
+LAB BLOOD ORDERABLES\t472401\tLABFLUOX\tFLUOXETINE AND NORFLUOXETINE\tNORFLU\tNORFLUOXETINE\t5000061.5\t615480\t2\t10000123\t1.00E+14\t2\t1230960\t1.12E+12
+LAB BLOOD ORDERABLES\t472422\tLABYT7P\tFREE THYROXINE INDEX\tT4\tTHYROXINE, TOTAL\t5000003.9\t146604\t2\t10000007.8\t1.00E+14\t5\t733020\t1.21E+11
+LAB BLOOD ORDERABLES\t472422\tLABYT7P\tFREE THYROXINE INDEX\tYT7P2\tT-UPTAKE %\t36.5\t146604\t2\t73\t2749\t5\t733020\t1.21E+11
+LAB BLOOD ORDERABLES\t472437\tLABG6PDQT\tG6PD QUANTITATIVE\tG6PDMD\tREVIEWED BY\t9999999\t367382.7273\t2\t19999998\t2.00E+14\t22\t8082420\t4.34E+12
+LAB BLOOD ORDERABLES\t472471\tLABGTOL\tGLUCOSE TOLERANCE\tGLT2\tGLUCOSE,2HR - 75G\t134.5\t29820\t2\t269\t44501\t1\t29820\t889232400
+LAB BLOOD ORDERABLES\t472471\tLABGTOL\tGLUCOSE TOLERANCE\tGLUF\tGLUCOSE,FASTING\t86\t29820\t2\t172\t14954\t1\t29820\t889232400
+LAB BLOOD ORDERABLES\t472482\tLAB11080R\tGLUTAMIC ACID DECARBOXY AB\tGAD65AB\tGAD65 ANTIBODY\t0\t441240\t2\t0\t0\t1\t441240\t1.95E+11
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMAM\tAMENDMENT\t9999999\t486197.6471\t2\t19999998\t2.00E+14\t51\t24796080\t1.40E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMID\tSPECIMEN ID\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMIN\tINTERPRETATION\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMME\tMETHOD\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMOD\tORDER DATE\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMRD\tRELEASE DATE\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMRS\tRESULTS\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMRV\tREVIEWED BY:\t9999999\t521638.481\t2\t19999998\t2.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMSO\tSOURCE\t9999999\t486197.6471\t2\t19999998\t2.00E+14\t51\t24796080\t1.40E+13
+LAB BLOOD ORDERABLES\t472521\tLABHBCT\tHEPATITIS B CORE TOTAL AB\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t69780\t2\t19999998\t2.00E+14\t1\t69780\t4869248400
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBTINT\tINTERPRETATION\t9999999\t70359.66102\t2\t19999998\t2.00E+14\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSINT\tINTERPRETATION\t9999999\t56970.32432\t2\t19999998\t2.00E+14\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t472522\tLABDHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTINT\tINTERPRETATION\t9999999\t56970.32432\t2\t19999998\t2.00E+14\t1110\t63237060\t8.60E+12
+LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHEPATITIS B SURFACE AG\tHBSAG\tHEPATITIS B SURF AG\t9999999\t95670\t2\t19999998\t2.00E+14\t2\t191340\t18558622800
+LAB BLOOD ORDERABLES\t472553\tLABHIVWBL\tHIV (TYPE 1 AND TYPE 2) ABY\tHIVAB\tHIV 1/2 ANTIBODY\t9999999\t24300\t2\t19999998\t2.00E+14\t1\t24300\t590490000
+LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tANA\tANA RESULT\t9999999\t272940\t2\t19999998\t2.00E+14\t2\t545880\t1.64E+11
+LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tC3\tCOMPLEMENT C3, SERUM\t174\t272940\t2\t348\t60584\t2\t545880\t1.64E+11
+LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tDNAQUL\tANTI-DSDNA RESULT\t9999999\t272940\t2\t19999998\t2.00E+14\t2\t545880\t1.64E+11
+LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tANA\tANA RESULT\t9999999\t311100\t2\t19999998\t2.00E+14\t2\t622200\t2.23E+11
+LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tRHFR\tRHEUMATOID FACTOR\t9999999\t311100\t2\t19999998\t2.00E+14\t2\t622200\t2.23E+11
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKRE\tRESULT\t9999999\t525786.6667\t2\t19999998\t2.00E+14\t9\t4732080\t3.13E+12
+LAB BLOOD ORDERABLES\t472835\tLABYMERC\tMERCURY, BLOOD\tMER\tMERCURY, BLOOD\t1.5\t170700\t2\t3\t5\t2\t341400\t70482448800
+LAB BLOOD ORDERABLES\t472845\tLABYMETHS\tMETHSUXIMIDE\tNORMSX\tNOR-METHSUXIMIDE\t9999999\t1913310\t2\t19999998\t2.00E+14\t2\t3826620\t8.10E+12
+LAB BLOOD ORDERABLES\t472864\tLABMISCH\tMISCELLANEOUS HEMATOLOGY\tTESTH\tPROCEDURE\t9999999\t3060\t2\t19999998\t2.00E+14\t1\t3060\t9363600
+LAB BLOOD ORDERABLES\t472957\tLABYNICK\tNICKEL, SERUM\tNICKEL\tNICKEL\t3.25\t380700\t2\t6.5\t36.25\t2\t761400\t3.58E+11
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTREV\tPATHOLOGIST REVIEW\t9999999\t48930\t2\t19999998\t2.00E+14\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t473057\tLABYPANPP\tPANCREATIC POLYPEPTIDE\tYPANPP\tPANCREATIC POLYPEPTIDE\t5000039\t848130\t2\t10000078\t1.00E+14\t2\t1696260\t1.44E+12
+LAB BLOOD ORDERABLES\t473102\tLABYPHOLP\tPHOSPHOLIPIDS\tPHOLIP\tPHOSPHOLIPIDS\t186\t317670\t2\t372\t69642\t2\t635340\t2.46E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE14\tS.PNEUMO SEROTYPE 14 (14)\t4999999.95\t242580\t2\t9999999.9\t1.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE19\tS.PNEUMO SEROTYPE 19 (19F)\t5000000.4\t242580\t2\t10000000.8\t1.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE23\tS.PNEUMO SEROTYPE 23 (23F)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE26\tS.PNEUMO SEROTYPE 26 (6B)\t4999999.85\t242580\t2\t9999999.7\t1.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE4\tS.PNEUMO SEROTYPE 4 (4)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE56\tS.PNEUMO SEROTYPE 56 (18C)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473116\tLABPNET2\tPNEUMOCOCCAL AB (PREVNAR RESPONSE)\tPNE68\tS.PNEUMO SEROTYPE 68 (9V)\t9999999\t242580\t2\t19999998\t2.00E+14\t2\t485160\t1.22E+11
+LAB BLOOD ORDERABLES\t473157\tLABPROPAF\tPROPAFENONE\tPROPAF\tPROPAFENONE\t1.06\t290820\t2\t2.12\t3.2272\t2\t581640\t1.80E+11
+LAB BLOOD ORDERABLES\t473159\tLAB11089R\tACID PHOSPHATASE\tYPAPT\tPAP, SERUM\t19.15\t164100\t2\t38.3\t1174.49\t1\t164100\t26928810000
+LAB BLOOD ORDERABLES\t473160\tLABYMPO\tMYELOPERIOXIDASE AB\tMPO\tMYELOPEROXIDASE AB\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473208\tLABYRAB\tRABIES AB TITER CHK ENDPOINT\tYRAB1\tRABIES ANTIBODY\t5000000.9\t1636335\t2\t10000001.8\t1.00E+14\t4\t6545340\t1.11E+13
+LAB BLOOD ORDERABLES\t473255\tLABRBP\tRETINOL BINDING PROTEIN\tRBPR\tRETINOL BIND PROT\t1.85\t1300620\t2\t3.7\t6.89\t2\t2601240\t4.84E+12
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTPRM\tPRIMERS\t9999999\t1027142.927\t2\t19999998\t2.00E+14\t82\t84225720\t1.01E+14
+LAB BLOOD ORDERABLES\t473711\tLABREF6\tLAB UNLISTED 6\tRESUL6\tRESULT\t9999999\t2836788\t2\t19999998\t2.00E+14\t5\t14183940\t1.37E+14
+LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHRE\tRESULT\t9999999\t1766844\t2\t19999998\t2.00E+14\t10\t17668440\t3.56E+13
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHRE\tRESULT\t9999999\t3358860\t2\t19999998\t2.00E+14\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473762\tLAB12586R\tVITAMIN B1\tYVB1W1\tTHIAMIN(VITAMIN B1)\t129.5\t322440\t2\t259\t33853\t2\t644880\t2.21E+11
+LAB BLOOD ORDERABLES\t473762\tLABYVB1W\tVITAMIN B1\tYVB1W2\tCOMMENT\t9999999\t290875.8621\t2\t19999998\t2.00E+14\t29\t8435400\t2.80E+12
+LAB BLOOD ORDERABLES\t497331\tLABPABN\tPOLIO AB, NEUTRALIZATION\tPOL2\tPOLIO 2\t9999999\t1740\t2\t19999998\t2.00E+14\t1\t1740\t3027600
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12013R\tQ FEVER IGM PHASE L TITER\t9999999\t172320\t2\t19999998\t2.00E+14\t3\t516960\t1.15E+11
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12014R\tQ FEVER IGG PHASE LI TITER\t9999999\t355434.7826\t2\t19999998\t2.00E+14\t23\t8175000\t3.97E+12
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYEN3\tY ENTERCOCLITICA 03\t9999999\t3364848\t2\t19999998\t2.00E+14\t15\t50472720\t4.07E+14
+LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH CORD\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t590521\tLABARI\tABO/RH (ARI)\tABORH\tABO/RH STILLBORN\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t590554\tLABFMYAB\tMYCOPLASMA IGG/IGM AB\tLTROIN\tINTERPRETATION\t9999999\t290967.0588\t2\t19999998\t2.00E+14\t153\t44517960\t3.58E+13
+LAB BLOOD ORDERABLES\t633373\tLABSTSS\tSYPHILIS TREPONEMAL SCREEN\tSTSQNT\tINDEX\t5.35\t255570\t2\t10.7\t85.37\t2\t511140\t1.34E+11
+LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMAG\tANTIGEN\t40.5\t200220\t2\t81\t3285\t1\t200220\t40088048400
+LAB BLOOD ORDERABLES\t668178\tLABDRPRS\tDONOR, RPR\tRPRS1\tDONOR SYPHILIS SCREEN\t9999999\t163960\t2\t19999998\t2.00E+14\t3\t491880\t91985486400
+LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tLYM\tLYMPHOCYTES %\t5000000.05\t99900\t2\t10000000.1\t1.00E+14\t1\t99900\t9980010000
+LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t11687R\tHEXOSAMINIDASE TOTAL, WBC\t21.45\t431070\t2\t42.9\t940.05\t2\t862140\t3.79E+11
+LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t11780R\tHEXOSAMINIDASE PERCENT A, WBC\t63\t431070\t2\t126\t7940\t2\t862140\t3.79E+11
+LAB BLOOD ORDERABLES\t744302\tLAB11590R\tFREE FATTY ACIDS\t11715R\tFREE FATTY ACIDS\t0.495\t1913190\t2\t0.99\t0.8685\t2\t3826380\t1.39E+13
+LAB BLOOD ORDERABLES\t744320\tLAB12043R\tIL 5\t12044R\tINTERLEUKIN 5\t50\t190080\t2\t100\t7048\t1\t190080\t36130406400
+LAB BLOOD ORDERABLES\t864658\tLAB11867R\tC3A DESARG FRAGMENT\t11866R\tC3A DESARG FRAGMENT\t5000096.5\t656340\t2\t10000193\t1.00E+14\t2\t1312680\t8.99E+11
+LAB BLOOD ORDERABLES\t864662\tLAB11871R\tHIV-2 ANTIBODY, IMMUNOBLOT\t11870R\tHIV 2 ANTIBODY, IMMUNOBLOT\t9999999\t488940\t2\t19999998\t2.00E+14\t3\t1466820\t8.05E+11
+LAB BLOOD ORDERABLES\t871368\tLABCIRCAI\tCIRCUIT IONIZED CALCIUM\tCAICR\tCIRCUIT IONIZED CALCIUM\t9999999\t2940\t2\t19999998\t2.00E+14\t2\t5880\t21434400
+LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLDX\tCLINICAL INDICATION\t9999999\t1418550\t2\t19999998\t2.00E+14\t2\t2837100\t4.13E+12
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD26AB\tCD26 (PAN-T), ABS\t729\t109830\t2\t1458\t1523682\t2\t219660\t24129939600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD26PC\tCD26 (PAN-T) %\t38\t109830\t2\t76\t2890\t2\t219660\t24129939600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD7AB\tCD7 (PAN T), ABS\t1066.5\t109830\t2\t2133\t3342105\t2\t219660\t24129939600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tCD7PC\tCD7 (PAN T) %\t54\t109830\t2\t108\t5864\t2\t219660\t24129939600
+LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLDX\tCLINICAL INDICATION\t9999999\t1484940\t2\t19999998\t2.00E+14\t2\t2969880\t4.71E+12
+LAB BLOOD ORDERABLES\t898828\tLAB12373R\tCARBAMAZEPINE-10,11-EPOXIDE\t12375R\tCARBAMAZEPINE-10,11-EPOXIDE\t4.3\t4063050\t2\t8.6\t39.86\t2\t8126100\t5.68E+13
+LAB BLOOD ORDERABLES\t898828\tLAB12373R\tCARBAMAZEPINE-10,11-EPOXIDE\tCZPTOT\tCARBAMAZEPINE,TOT,S\t11.15\t4063050\t2\t22.3\t254.09\t2\t8126100\t5.68E+13
+LAB BLOOD ORDERABLES\t910566\tLAB12395R\t5\'NUCLEOTIDASE\t12396R\t5\' NUCLEOTIDASE\t118.5\t240150\t2\t237\t38889\t2\t480300\t1.24E+11
+LAB BLOOD ORDERABLES\t977667\tLABPANANC\tNEUTROPHIL COUNT, ABSOLUTE\tNEUTAB\tNEUT, ABS\t0.21\t1050\t2\t0.42\t0.1682\t2\t2100\t3517200
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12671R\tTETANUS STIMULATION INDEX\t6.5\t1024890\t2\t13\t103.72\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12673R\tCANDIDA STIMULATION INDEX\t2.05\t1024890\t2\t4.1\t8.45\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tCONA\tCON A STIMULATION\t138613\t1024890\t2\t277226\t50520051700\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPHA\tPHA STIMULATION\t161883.5\t1024890\t2\t323767\t52655217625\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPWM\tPWM STIMULATION\t149659.5\t1024890\t2\t299319\t46479266145\t2\t2049780\t2.10E+12
+MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tSREQ\tCOMMENTS\t9999999\t255120\t2\t19999998\t2.00E+14\t1\t255120\t65086214400
+MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tRPT\tRPT\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tSDES\tSPECIMEN\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472743\tLABLEGD\tLEGIONELLA CULTURE, DIRECT\tCULT\tCULT/OTHER RSLT:\t9999999\t508887.5\t2\t19999998\t2.00E+14\t24\t12213300\t7.93E+12
+MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tRPT\tRPT\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t590435\tLABHELAG\tHELICOBACTER PYLORI AG, STOOL\tCULT\tCULT/OTHER RSLT:\t9999999\t334461.7699\t2\t19999998\t2.00E+14\t339\t113382540\t5.10E+13
+MICROBIOLOGY ORDERABLES\t870893\tLABBACIDS\tBACTERIA ID BY SEQUENCING, SPECIMEN\tCULT\tCULT/OTHER RSLT:\t9999999\t255332.1429\t2\t19999998\t2.00E+14\t56\t14298600\t7.49E+12
+MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tRPT\tRPT\t9999999\t48300\t2\t19999998\t2.00E+14\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tSDES\tSPECIMEN\t9999999\t48300\t2\t19999998\t2.00E+14\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tSREQ\tCOMMENTS\t9999999\t48300\t2\t19999998\t2.00E+14\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tRPT\tRPT\t9999999\t2140200\t2\t19999998\t2.00E+14\t1\t2140200\t4.58E+12
+MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tSDES\tSPECIMEN\t9999999\t2140200\t2\t19999998\t2.00E+14\t1\t2140200\t4.58E+12
+MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tSREQ\tCOMMENTS\t9999999\t2140200\t2\t19999998\t2.00E+14\t1\t2140200\t4.58E+12
+MICROBIOLOGY ORDERABLES\t884811\tLABROSTRO\tSTRONGYLOIDES CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t259140\t2\t19999998\t2.00E+14\t14\t3627960\t1.12E+12
+MICROBIOLOGY ORDERABLES\t893886\tLABORSHOR\tORTHOPEDIC CULT MULT SPECIMENS\tCULT\tCULT/OTHER RSLT:\t9999999\t1154535.525\t2\t19999998\t2.00E+14\t514\t593431260\t7.35E+14
+MICROBIOLOGY ORDERABLES\t966897\tLABVCULS\tVIRAL CULTURE, STOOL/RECTAL\tCULT\tCULT/OTHER RSLT:\t9999999\t1137292.941\t2\t19999998\t2.00E+14\t51\t58001940\t7.40E+13
+MICROBIOLOGY ORDERABLES\t989708\tLABMECAPC\tMECA BY PCR\tMECAR\tMECA PCR RESULT\t9999999\t362940\t2\t19999998\t2.00E+14\t3\t1088820\t5.41E+11
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCCMT\tCOMMENT\t9999999\t599640\t2\t19999998\t2.00E+14\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCRLT\tRESULTS\t9999999\t599640\t2\t19999998\t2.00E+14\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tFP210\tP210, FLUID\t4999999.786\t417180\t2\t9999999.571\t1.00E+14\t1\t417180\t1.74E+11
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRC\tCOMMENT\t9999999\t1367340\t2\t19999998\t2.00E+14\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15CO\tCOMMENT, TISSUE\t9999999\t909120\t2\t19999998\t2.00E+14\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15TI\tSOURCE, TISSUE\t9999999\t909120\t2\t19999998\t2.00E+14\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTRES\tRESULTS\t9999999\t763860\t2\t19999998\t2.00E+14\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCCMT\tCOMMENT\t9999999\t1195020\t2\t19999998\t2.00E+14\t3\t3585060\t4.32E+12
+POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tDIPLOT\tDIPSTICK LOT\t9999999\t48060\t2\t19999998\t2.00E+14\t2\t96120\t5963220000
+POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tEXPDATE\tEXP DATE\t9999999\t48060\t2\t19999998\t2.00E+14\t2\t96120\t5963220000
+POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tDIPLOT\tDIPSTICK LOT\t9999999\t13600\t2\t19999998\t2.00E+14\t3\t40800\t1048500000
+POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tEXPDATE\tEXP DATE\t9999999\t13600\t2\t19999998\t2.00E+14\t3\t40800\t1048500000
+POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tUKET\tKETONES\t9999999\t13600\t2\t19999998\t2.00E+14\t3\t40800\t1048500000
+POINT OF CARE (NON LAB) TESTS\t871363\tPOC11\tURINE CHEMSTRIP GP\tGLUURINE\tGLUCOSE URINE\t9999999\t6000\t2\t19999998\t2.00E+14\t3\t18000\t140940000
+POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tLAC\tLACTATE, ISTAT\t2.41\t57480\t2\t4.82\t12.802\t2\t114960\t6607965600
+STOOL ORDERABLES\t471523\tLABACRBLO\tACETYLCHOLINE BLOCKING AB\tACRBLO\tACETYLCHOLINE BLOCKING AB\t20.5\t281370\t2\t41\t1681\t2\t562740\t1.70E+11
+STOOL ORDERABLES\t471524\tLABACRMOD\tACETYLCHOLINE MOD AB\tACRMOD\tACETYLCHOLINE MODULATING AB\t16.5\t680970\t2\t33\t1089\t2\t1361940\t9.48E+11
+STOOL ORDERABLES\t471567\tLABCSFALB\tALBUMIN, CSF\tALBCSF\tALBUMIN, CSF\t14\t#VALUE!\t2\t28\t392\t0\tNone\tNone
+STOOL ORDERABLES\t471585\tLABYSTA1A\tALPHA-1-ANTITRYPSIN, FECAL\tA1AANTITRYPF\tA-1-ANTITRYP,FECAL\t5000047.5\t198600\t2\t10000095\t1.00E+14\t1\t198600\t39441960000
+STOOL ORDERABLES\t472122\tLABYCYSTC\tCYSTATIN C, SERUM / PLASMA\tCYSTC\tCYSTATIN C, SER/PLAS\t1.4\t32760\t2\t2.8\t4.42\t2\t65520\t2275718400
+STOOL ORDERABLES\t472544\tLABYHISTA\tHISTAMINE, PLASMA\tYHISA1\tHISTAMINE, PLASMA\t4999999.7\t307500\t2\t9999999.4\t1.00E+14\t2\t615000\t2.03E+11
+STOOL ORDERABLES\t472671\tLAB11022R\tISLET CELL AB\tANTIIS\tISLET CELL AB, IGG\t9999999\t181860\t2\t19999998\t2.00E+14\t2\t363720\t66205742400
+STOOL ORDERABLES\t473218\tLAB13006R\tREDUCING SUBSTANCE, STOOL\tREDSUBSTL\tREDUCING SUBST, STOOL\t9999999\t16440\t2\t19999998\t2.00E+14\t1\t16440\t270273600
+STOOL ORDERABLES\t473777\tLABVWFPI\tVON WILLEBRAND PROTEASE INHIBITOR\tVWFPIR\tRESULT\t9999999\t1187392.5\t2\t19999998\t2.00E+14\t8\t9499140\t2.35E+13
+STOOL ORDERABLES\t864666\tLAB11851R\tFECAL LIPIDS, TOTAL\t11850R\tFECAL LIPIDS, 24 HR\t9999999\t650380\t2\t19999998\t2.00E+14\t6\t3902280\t4.51E+12
+STOOL ORDERABLES\t864672\tLAB11885R\tC. TRACHOMATIS RNA, RECTAL\t11884R\tCHLAMYDIA TRACHOMATIS RNA,RECTAL\t9999999\t152760\t2\t19999998\t2.00E+14\t2\t305520\t93305815200
+STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12552R\tSIROLIMUS, BLOOD\t6.2\t512835\t2\t12.4\t110.5\t4\t2051340\t2.27E+12
+STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12553R\tDATE OF LAST DOSE\t9999999\t363510\t2\t19999998\t2.00E+14\t2\t727020\t5.19E+11
+STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12554R\tTIME OF LAST DOSE\t9999999\t363510\t2\t19999998\t2.00E+14\t2\t727020\t5.19E+11
+STOOL ORDERABLES\t889061\tLAB12548R\tSIROLIMUS, BLOOD (HPLC-MS MS)\t12555R\tDOSE, MG\t9999999\t363510\t2\t19999998\t2.00E+14\t2\t727020\t5.19E+11
+STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tSPTYS2\tSPTYS2\t9999999\t427020\t2\t19999998\t2.00E+14\t2\t854040\t3.80E+11
+STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tTESTS2\tTESTS2\t9999999\t427020\t2\t19999998\t2.00E+14\t2\t854040\t3.80E+11
+STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL3\tCOLLECTION DURATION\t5000011.5\t1815000\t2\t10000023\t1.00E+14\t2\t3630000\t6.65E+12
+STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL4\tCHLORIDE CONCENTRATION\t5000009\t1815000\t2\t10000018\t1.00E+14\t2\t3630000\t6.65E+12
+STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA1\tSODIUM, 24-HR STOOL\t5000023\t173370\t2\t10000046\t1.00E+14\t6\t1040220\t3.08E+11
+STOOL ORDERABLES\t910606\tLAB12505R\tSODIUM, 24-HR STOOL\tYSTNA2\tTOTAL WEIGHT\t5000377.5\t173370\t2\t10000755\t1.00E+14\t6\t1040220\t3.08E+11
+TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tUPIEI\tINTERPRETATION\t9999999\t309620\t2\t19999998\t2.00E+14\t18\t5573160\t2.51E+12
+URINE ORDERABLES\t471566\tLABUALB\tALBUMIN WITH CREATININE, URINE (RANDOM)\tUALBCRINTP\tALB/CREAT INTERP\t9999999\t65933.15436\t2\t19999998\t2.00E+14\t149\t9824040\t1.29E+12
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUOAGC\tOTHER AMPHETAMINES\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUSYGC\tSYMPATHOMIMETICS\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tVOLUMEURN\tURINE VOLUME\t2159\t150840\t2\t4318\t15136612\t1\t150840\t22752705600
+URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tYUARS1\tINORGANIC ARSENIC (TOXIC)\t9999999\t150840\t2\t19999998\t2.00E+14\t1\t150840\t22752705600
+URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tYUARS2\tORGANIC ARSENIC(NON-TOXIC)\t5000007\t150840\t2\t10000014\t1.00E+14\t1\t150840\t22752705600
+URINE ORDERABLES\t471683\tLABYUARS\tARSENIC FRACTIONATION, URINE\tYUARS3\tCOLLECTION DURATION\t24\t150840\t2\t48\t1152\t1\t150840\t22752705600
+URINE ORDERABLES\t472495\tLABYUHYME\tHEAVY METALS, URINE\tARSENICURN\tARSENIC, URINE\t9999999\t346860\t2\t19999998\t2.00E+14\t1\t346860\t1.20E+11
+URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tUCR\tCREATININE, URN\t49.5\t247740\t2\t99\t4941\t1\t247740\t61375107600
+URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tUT\tCOLLECTION PERIOD\t764.5\t247740\t2\t1529\t2265601\t1\t247740\t61375107600
+URINE ORDERABLES\t472545\tLABUHIST\tHISTAMINE, URINE\tVOL\tVOLUME\t2586\t247740\t2\t5172\t26502480\t1\t247740\t61375107600
+URINE ORDERABLES\t472592\tLABICTO\tICTOTEST\tICTO\tICTOTEST\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t472740\tLABLEGU\tLEGIONELLA AG, URINE\tLEGUA\tLEGIONELLA AG\t9999999\t376259.6691\t2\t19999998\t2.00E+14\t544\t204685260\t1.13E+14
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tVOLUMEURN\tURINE VOLUME\t1832\t191130\t2\t3664\t8888146\t2\t382260\t80196699600
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tYOXALU\tOXALATE, 24-HOUR/TIMED URINE\t0.285\t191130\t2\t0.57\t0.3029\t2\t382260\t80196699600
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tYUOXCO\tOXALATE CONCENTRATION\t9.65\t191130\t2\t19.3\t285.65\t2\t382260\t80196699600
+URINE ORDERABLES\t473051\tLABYUOXAL\tOXALATE, 24-HOUR/TIMED URINE\tYUOXUT\tCOLLECTION DURATION\t24\t191130\t2\t48\t1152\t2\t382260\t80196699600
+URINE ORDERABLES\t473424\tLABYUNTX\tTELOPEPTIDE, URINE\tYUNTXC\tCREATININE, URINE\t5000028\t123120\t2\t10000056\t1.00E+14\t1\t123120\t15158534400
+URINE ORDERABLES\t473424\tLABYUNTX\tTELOPEPTIDE, URINE\tYUNTXR\tNTX\t5000162\t123120\t2\t10000324\t1.00E+14\t1\t123120\t15158534400
+URINE ORDERABLES\t473424\tLABYUNTX\tTELOPEPTIDE, URINE\tYUNTXT\tNTX-TELOPEPTIDE, URINE\t5000031.5\t123120\t2\t10000063\t1.00E+14\t1\t123120\t15158534400
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tRBCAST\tRBC CASTS\t9999999\t15150\t2\t19999998\t2.00E+14\t2\t30300\t482029200
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tTRICHOMONAS\tTRICHOMONAS\t9999999\t18300\t2\t19999998\t2.00E+14\t2\t36600\t1234260000
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tHVAUR\tHVA, URINE\t4.5\t335730\t2\t9\t41\t2\t671460\t2.26E+11
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tDOPAURN\tDOPAMINE, URN\t5000061\t242040\t2\t10000122\t1.00E+14\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tEPINEPURN\tEPINEPHRINE, URN\t5000003\t242040\t2\t10000006\t1.00E+14\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tNOREPHURN\tNOREPINEPHRINE, URN\t5000009.5\t242040\t2\t10000019\t1.00E+14\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tTIMEU\tCOLLECTION PERIOD\t5001117.5\t242040\t2\t10002235\t1.00E+14\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUCR\tCREATININE, URINE\t5000681.5\t242040\t2\t10001363\t1.00E+14\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUCR\tCREATININE, URN\t38\t242040\t2\t76\t3946\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUDOPA1\tDOPAMINE/CREAT. RATIO\t81.5\t242040\t2\t163\t13429\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUEPI1\tEPINEPHRINE/CREAT. RATIO\t5000002\t242040\t2\t10000004\t1.00E+14\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUNORE1\tNOREPINEPHRINE/CREAT. RATIO\t74\t242040\t2\t148\t17914\t1\t242040\t58583361600
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUVOL\tVOLUME\t1157\t242040\t2\t2314\t5005780\t1\t242040\t58583361600
+VIROLOGY ORDERABLES\t472199\tLABDVEVH\tHSV/VZ DFA (DIRECT VIRAL EXAM)\tDVE\tRESULTS\t9999999\t68786.73267\t2\t19999998\t2.00E+14\t101\t6947460\t5.79E+11
+VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS ANTIGEN, STOOL\tRTVTD\tTEST DESCRIPTION\t9999999\t54835.2\t2\t19999998\t2.00E+14\t75\t4112640\t7.01E+11
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t9999999\t232692.1875\t2\t19999998\t2.00E+14\t64\t14892300\t3.97E+12
+VIROLOGY ORDERABLES\t472321\tLABEBVPAN\tEBV AB PANEL\t11166R\tEB NUCLEAR AB\t9999999\t232692.1875\t2\t19999998\t2.00E+14\t64\t14892300\t3.97E+12
+VIROLOGY ORDERABLES\t472519\tLABHAVT\tANTI-HAV\tHATINT\tINTERPRETATION\t9999999\t76703.94453\t2\t19999998\t2.00E+14\t649\t49780860\t1.16E+13
+VIROLOGY ORDERABLES\t472519\tLABHAVT\tHEPATITIS A AB TOTAL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t68880\t2\t19999998\t2.00E+14\t1\t68880\t4744454400
+VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHERPES VIRAL 6 DNA QUALITATIVE\tSOUR12\tSOURCE\t9999999\t184905\t2\t19999998\t2.00E+14\t4\t739620\t1.80E+11
+VIROLOGY ORDERABLES\t590498\tINACTIVE 6\tADENOVIRUS DNA PCR QUANT\tADVTIP\tTEST DESCRIPTION\t9999999\t161340\t2\t19999998\t2.00E+14\t4\t645360\t1.50E+11
+VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6CP\tRESULT\t5000514.5\t1350\t2\t10001029\t1.00E+14\t2\t2700\t7290000
+VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t1350\t2\t19999998\t2.00E+14\t2\t2700\t7290000
+VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6LO\tRESULT LOG 10\t5000001.005\t1350\t2\t10000002.01\t1.00E+14\t2\t2700\t7290000
+VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6SP\tSPECIMEN\t9999999\t1350\t2\t19999998\t2.00E+14\t2\t2700\t7290000
+VIROLOGY ORDERABLES\t670957\tLAB11023R\tEB NUCLEAR AG AB\t11166R\tEB NUCLEAR AB\t9999999\t275265\t2\t19999998\t2.00E+14\t4\t1101060\t4.37E+11
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN IGG\tEBNQNT\tEBNQNT\t5000001.075\t687360\t2\t10000002.15\t1.00E+14\t2\t1374720\t9.65E+11
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUAR\tINFLUENZA A RNA\t9999999\t163609.7143\t2\t19999998\t2.00E+14\t35\t5726340\t1.13E+12
+VIROLOGY ORDERABLES\t864648\tLABHCVGEN\tHEPATITIS C (HCV) GENOTYPE\tCULT\tCULT/OTHER RSLT:\t9999999\t925479.3103\t2\t19999998\t2.00E+14\t87\t80516700\t1.11E+14
+VIROLOGY ORDERABLES\t876731\tLABDVEMP\tMPV DFA\tMPVR\tMETAPNEUMOVIRUS\t9999999\t987120\t2\t19999998\t2.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876737\tLABTOXIM\tTOXOPLASMA IGM\tTOXMQT\tIGM LEVEL\t9999999\t178200\t2\t19999998\t2.00E+14\t2\t356400\t82080979200
+VIROLOGY ORDERABLES\t879416\tLABRSVPCR\tRSV PCR\tRSVDI\tDISCLAIMER\t9999999\t267440\t2\t19999998\t2.00E+14\t3\t802320\t2.15E+11
+VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPV16\tHPV 16 DNA\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPV18\tHPV 18 DNA\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVSP\tSPECIMEN\t9999999\t#VALUE!\t2\t19999998\t2.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t893882\tLABCMVAV\tCMV IGG AVIDITY\tCMVAVI\tCMV IGG AVIDITY INDEX\t9999999\t289530\t2\t19999998\t2.00E+14\t2\t579060\t2.76E+11
+VIROLOGY ORDERABLES\t893882\tLABCMVAV\tCMV IGG AVIDITY\tCMVAVR\tCMV IGG AVIDITY RESULT\t9999999\t289530\t2\t19999998\t2.00E+14\t2\t579060\t2.76E+11
+VIROLOGY ORDERABLES\t898804\tLABEBVQL\tEBV DNA PCR QUAL\tEBVQLI\tTEST DESCRIPTION\t9999999\t233724\t2\t19999998\t2.00E+14\t25\t5843100\t1.92E+12
+VIROLOGY ORDERABLES\t906947\tLABADVRC\tADENOVIRUS RAPID CULTURE\tADVRCD\tTEST DESCRIPTION\t9999999\t236460\t2\t19999998\t2.00E+14\t8\t1891680\t5.77E+11
+VIROLOGY ORDERABLES\t906951\tLABMEARC\tMEASLES RAPID CULTURE\tMEARC1\tMEASLES RAPID CULTURE\t9999999\t124470\t2\t19999998\t2.00E+14\t2\t248940\t34398594000
+VIROLOGY ORDERABLES\t906961\tLABVZVCSF\tVZV DNA PCR QUAL, CSF\tVZQLIC\tTEST DESCRIPTION\t9999999\t175368.96\t2\t19999998\t2.00E+14\t125\t21921120\t5.25E+12
+VIROLOGY ORDERABLES\t978512\tLABHSVPAN\tHERPES SIMPLEX VIRUS AB PANEL\tHSVDS\tHSV PANEL TEST DESCRIPTION\t9999999\t262166.8966\t2\t19999998\t2.00E+14\t145\t38014200\t1.36E+13
+VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVGGDES\tVZV IGG TEST DESCRIPTION\t9999999\t299835.8824\t2\t19999998\t2.00E+14\t34\t10194420\t3.75E+12
+VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVLOG\tRESULT LOG 10\t7.43\t425910\t2\t14.86\t110.4386\t2\t851820\t3.70E+11
+VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVRES\tRESULT\t9999999\t425910\t2\t19999998\t2.00E+14\t2\t851820\t3.70E+11
+VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVRNA\tHEP C VIRVUS RNA\t9999999\t425910\t2\t19999998\t2.00E+14\t2\t851820\t3.70E+11
+VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVSP\tSPECIMEN\t9999999\t425910\t2\t19999998\t2.00E+14\t2\t851820\t3.70E+11
+AMB PROC ORDERABLES\t667882\t210951798\tPOST VOID TEST\tNone\tRESIDUAL BLADDER VOLUME\t9999999\t62100\t1\t9999999\t1.00E+14\t1\t62100\t3856410000
+BLOOD BANK PRODUCT ORDERABLES\t473476\tLABTBP99\tTRANSFUSION BLOOD PRODUCT 100\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473476\tLABTBP99\tTRANSFUSION BLOOD PRODUCT 100\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473477\tLABTBP100\tTRANSFUSION BLOOD PRODUCT 101\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473477\tLABTBP100\tTRANSFUSION BLOOD PRODUCT 101\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tVL\tPRODUCT VOLUME\t174\t#VALUE!\t1\t174\t30276\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tVL\tPRODUCT VOLUME\t166\t#VALUE!\t1\t166\t27556\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tVL\tPRODUCT VOLUME\t153\t#VALUE!\t1\t153\t23409\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tVL\tPRODUCT VOLUME\t166\t#VALUE!\t1\t166\t27556\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tVL\tPRODUCT VOLUME\t165\t#VALUE!\t1\t165\t27225\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tVL\tPRODUCT VOLUME\t169\t#VALUE!\t1\t169\t28561\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tVL\tPRODUCT VOLUME\t97\t#VALUE!\t1\t97\t9409\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tVL\tPRODUCT VOLUME\t180\t#VALUE!\t1\t180\t32400\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tVL\tPRODUCT VOLUME\t172\t#VALUE!\t1\t172\t29584\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tVL\tPRODUCT VOLUME\t156\t#VALUE!\t1\t156\t24336\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tVL\tPRODUCT VOLUME\t165\t#VALUE!\t1\t165\t27225\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tVL\tPRODUCT VOLUME\t162\t#VALUE!\t1\t162\t26244\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tVL\tPRODUCT VOLUME\t146\t#VALUE!\t1\t146\t21316\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tVL\tPRODUCT VOLUME\t155\t#VALUE!\t1\t155\t24025\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tVL\tPRODUCT VOLUME\t157\t#VALUE!\t1\t157\t24649\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tVL\tPRODUCT VOLUME\t146\t#VALUE!\t1\t146\t21316\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tVL\tPRODUCT VOLUME\t160\t#VALUE!\t1\t160\t25600\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tVL\tPRODUCT VOLUME\t449\t#VALUE!\t1\t449\t201601\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tSI\t<<STATUS>>  \t9999999\t49950\t1\t9999999\t1.00E+14\t2\t99900\t5482357200
+BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tXMR\tUNIT CROSSMATCH  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tDI\tISSUE DATE/TIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tUBT\tUNIT BLOOD TYPE  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473667\tLABTBP95\tTRANSFUSION BLOOD PRODUCT 96\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473667\tLABTBP95\tTRANSFUSION BLOOD PRODUCT 96\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473668\tLABTBP96\tTRANSFUSION BLOOD PRODUCT 97\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473668\tLABTBP96\tTRANSFUSION BLOOD PRODUCT 97\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473669\tLABTBP97\tTRANSFUSION BLOOD PRODUCT 98\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473669\tLABTBP97\tTRANSFUSION BLOOD PRODUCT 98\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473670\tLABTBP98\tTRANSFUSION BLOOD PRODUCT 99\tPRO\tPRODUCT ID  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473670\tLABTBP98\tTRANSFUSION BLOOD PRODUCT 99\tSI\t<<STATUS>>  \t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t496757\tLABEXCHTX\tEXCHANGE TRANSFUSION\tFFP\tFRESH FROZEN UNITS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t496757\tLABEXCHTX\tEXCHANGE TRANSFUSION\tRBCSTA\tRBC PRODUCT - UNITS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tVL\tPRODUCT VOLUME\t165\t#VALUE!\t1\t165\t27225\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRD\tINTERPRETED BY\t9999999\t496642.1053\t1\t9999999\t1.00E+14\t19\t9436200\t6.91E+12
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tBANDFLD\tBAND, FLD\t3\t#VALUE!\t1\t3\t9\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471954\tLABBFCT\tCELL COUNT AND DIFF, FLUID\tFPMN\tPMN, FLD\t11\t#VALUE!\t1\t11\t121\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472012\tLAB11438R\tCHLAMYDIA TRACHOMATIS CULTURE\t11502R\tCHLAMYDIA TRACHOMATIS CULTURE\t9999999\t122600\t1\t9999999\t1.00E+14\t3\t367800\t93376756800
+BODY FLUID ORDERABLES\t472047\tLAB11123R\tCOCCI CF AB, CSF\tCOCC\tCOCCI CF AB (CSF)\t9999999\t768057.9747\t1\t9999999\t1.00E+14\t237\t182029740\t4.44E+14
+BODY FLUID ORDERABLES\t472182\tLABFDIFF\tDIFFERENTIAL, BODY FLUID\tCCNT\tCELLS COUNTED\t39\t#VALUE!\t1\t39\t1521\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSNRBC\tNRBC/100WBC, SYN FLD\t14\t780\t1\t14\t196\t1\t780\t608400
+BODY FLUID ORDERABLES\t472184\tLABSYDIFF\tDIFFERENTIAL, SYNOVIAL FLUID\tSRLYM\tREACT LYMPH, SYN FLD\t2\t780\t1\t2\t4\t1\t780\t608400
+BODY FLUID ORDERABLES\t472774\tLABLWBCC\tLYME WESTERN BLOT, CSF\tBANDPRSNT\tBANDS PRESENT\t9999999\t408240\t1\t9999999\t1.00E+14\t2\t816480\t3.43E+11
+BODY FLUID ORDERABLES\t472774\tLABLWBCC\tLYME WESTERN BLOT, CSF\tBBIGM\tBORELLIA BURG IGM AB\t9999999\t277031.25\t1\t9999999\t1.00E+14\t16\t4432500\t1.39E+12
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIMD\tINTERPRETED BY\t9999999\t927889.0909\t1\t9999999\t1.00E+14\t11\t10206780\t1.25E+13
+BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\tYMYCR1\tSTATUS\t9999999\t2142018\t1\t9999999\t1.00E+14\t10\t21420180\t5.46E+13
+BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\tYMYCR2\tMYCOPLASMA PNEUMONIAE CULTURE\t9999999\t2142018\t1\t9999999\t1.00E+14\t10\t21420180\t5.46E+13
+BODY FLUID ORDERABLES\t472935\tLABYCMBP\tMYELIN BASIC PROTEIN, CSF\tMBPCSF\tMYELIN BAS.PROT,CSF\t0.9\t#VALUE!\t1\t0.9\t0.81\t0\tNone\tNone
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHQLINT\tINTERPRETATION\t9999999\t359730\t1\t9999999\t1.00E+14\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473219\tLABURED\tREDUCING SUBSTANCE, URINE\tREDSUBURN\tREDUCING SUBST., URINE\t9999999\t7800\t1\t9999999\t1.00E+14\t1\t7800\t60840000
+BODY FLUID ORDERABLES\t473359\tLABPOLYSP\tSPUTUM FOR PMNS\tPOLYSP\tSPUTUM FOR PMNS\t9999999\t14460\t1\t9999999\t1.00E+14\t1\t14460\t209091600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t12001R\tVCA IGG ,IFA\t9999999\t268440\t1\t9999999\t1.00E+14\t1\t268440\t72060033600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t12002R\tVCA IGM ,IFA\t9999999\t268440\t1\t9999999\t1.00E+14\t1\t268440\t72060033600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tCOM\tCOMMENT\t9999999\t550720\t1\t9999999\t1.00E+14\t3\t1652160\t1.46E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tEA\tEARLY ANTIBODY (R+D) IGG\t9999999\t691860\t1\t9999999\t1.00E+14\t2\t1383720\t1.39E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tEBNA\tEPSTEIN-BARR NUC AB\t9999999\t691860\t1\t9999999\t1.00E+14\t2\t1383720\t1.39E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t9999999\t550720\t1\t9999999\t1.00E+14\t3\t1652160\t1.46E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\t1STCONST\t1ST CONSTITUENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\t2NDCONST\t2ND CONSTITUENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\t3RDCONST\t3RD CONSTITUENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNCO\tSTONE COMMENT\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNNJ\tNIDUS, MAJOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNNM\tNIDUS, MINOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNSJ\tSHELL, MAJOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNSM\tSHELL, MINOR\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTON\tSTONE ANALYSIS\t9999999\t207922.6667\t1\t9999999\t1.00E+14\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14RS\tRESULTS\t9999999\t1070614.286\t1\t9999999\t1.00E+14\t7\t7494300\t9.42E+12
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTDIR\tINTERPRETED BY\t9999999\t771152.3077\t1\t9999999\t1.00E+14\t13\t10024980\t1.13E+13
+BODY FLUID ORDERABLES\t473748\tLABYVDRLC\tVDRL, CSF\tVDRL\tVDRL\t9999999\t248982\t1\t9999999\t1.00E+14\t90\t22408380\t3.08E+13
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tALB\tALBUMIN, SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tALT\tALT (SGPT), SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tAST\tAST (SGOT), SER/PLAS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tDBIL\tCONJUGATED BILI\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tIBIL\tUNCONJUGATED BILIRUBIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t496772\tLABHEPAT\tHEPATIC PROFILE\tTBIL\tTOTAL BILIRUBIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t497333\tLABMYCOC\tMYCOPLASMA CULTURE, FASTIDIOUS\tMYORG\tMYCOPLASAM ORGANISM\t9999999\t1967535\t1\t9999999\t1.00E+14\t4\t7870140\t2.00E+13
+BODY FLUID ORDERABLES\t497362\tLABYFK\tPOTASSIUM, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t497366\tLABYFNA\tSODIUM, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+BODY FLUID ORDERABLES\t497368\tLABYFGL\tGLUCOSE, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t178578.6207\t1\t9999999\t1.00E+14\t58\t10357560\t2.39E+12
+BODY FLUID ORDERABLES\t497376\tLABYFCL\tCHLORIDE, FLUID\tYFLSC1\tFLUID TYPE\t9999999\t242700\t1\t9999999\t1.00E+14\t1\t242700\t58903290000
+BODY FLUID ORDERABLES\t497380\tLABYFUN\tUREA NITROGEN, FLUID\t11847R\tFLUID TYPE\t9999999\t169644\t1\t9999999\t1.00E+14\t10\t1696440\t3.58E+11
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASMD\tINTERPRETED BY\t9999999\t797353.8462\t1\t9999999\t1.00E+14\t13\t10365600\t1.05E+13
+BODY FLUID ORDERABLES\t669983\tLAB11000R\tCEA, PANCREATIC CYST\t11002R\tSITE\t9999999\t174390\t1\t9999999\t1.00E+14\t14\t2441460\t4.88E+11
+BODY FLUID ORDERABLES\t744304\tLAB11591R\tARSENIC, HAIR\t12046R\tARSENIC, HAIR\t9999999\t206940\t1\t9999999\t1.00E+14\t1\t206940\t42824163600
+BODY FLUID ORDERABLES\t744304\tLAB11591R\tARSENIC, HAIR\t12047R\tSPECIMEN SOURCE\t9999999\t206940\t1\t9999999\t1.00E+14\t1\t206940\t42824163600
+BODY FLUID ORDERABLES\t744306\tLAB11592R\tCOPPER, TISSUE\t11720R\tCOPPER, LIVER TS\t57\t241800\t1\t57\t3249\t1\t241800\t58467240000
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tBANDFLD\tBAND, FLD\t7\t14820\t1\t7\t49\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tBASOFLD\tBASOPHILS, FLD\t1\t14820\t1\t1\t1\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFCMT\tFLUID COMMENT\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFCOLOR\tAPPEARANCE\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFLDTYP\tFLUID SOURCE\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFMETA\tMETAMYELOCYTE, FLD\t5\t14820\t1\t5\t25\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFPMN\tPMN, FLD\t16\t14820\t1\t16\t256\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFRLYM\tREACTIVE LYMPHS, FLD\t1\t14820\t1\t1\t1\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tFTURB\tTURBIDITY, FLD\t9999999\t14820\t1\t9999999\t1.00E+14\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tLYMFLD\tLYMPHOCYTE %, FLD\t52\t14820\t1\t52\t2704\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tMONFLD\tMONO/MESO %, FLD\t18\t14820\t1\t18\t324\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tRBCFLD\tRBC COUNT, FLD (/UL)\t147100\t14820\t1\t147100\t21638410000\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t871370\tLABAFCT\tCELL CT & DIFF, APHERESIS FLD\tWBCFLD\tNUCL CELLS, FLD(/UL)\t42900\t14820\t1\t42900\t1840410000\t1\t14820\t219632400
+BODY FLUID ORDERABLES\t892281\tLAB13002R\tCOCCIDIOIDES AB IMMUNODIFF CSF\t13001R\tCOCCIDIOIDES AB IMMUNODIFF CSF\t9999999\t1926281.25\t1\t9999999\t1.00E+14\t16\t30820500\t1.70E+14
+COAGULATION ORDERABLES\t472663\tLABVWNTRP\tINTERPRETATION, VW SCREEN\tVWINTP\tINTERPRETATION\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tECGIMPRESS\tECG IMPRESSION\t9999999\t-5950.445219\t1\t9999999\t1.00E+14\t118728\t-706484460\t1.34E+19
+ECHO ORDERABLES\t472301\tECH15\tECHO - TRANSTHORACIC ECHO +DOPPLER\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODBP\t9999999\t37937160\t1\t9999999\t1.00E+14\t1\t37937160\t1.44E+15
+ECHO ORDERABLES\t30871485\tECH48\tECHO - TRANSESOPHAGEAL ECHO COMPREHENSIVE\tLVEF\tLEFT VENTRICULAR EJECTION FRACTION MODSP4\t55\t288240\t1\t55\t3025\t1\t288240\t83082297600
+EVAL4\t669617\t210993279\tPM DEVICE EVAL,SNGL,W/REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t250\t-600\t1\t250\t62500\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t1.9\t-600\t1\t1.9\t3.61\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.4\t-600\t1\t0.4\t0.16\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t2.5\t-600\t1\t2.5\t6.25\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRAMP\tLV THRESHOLD AMPLITUDE\t0.8\t-600\t1\t0.8\t0.64\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tLVTHRDUR\tLV THRESHOLD DURATION\t0.4\t-600\t1\t0.4\t0.16\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMCOMMENT\tPM COMMENTARY\t9999999\t-600\t1\t9999999\t1.00E+14\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tPMLVLDBIIM\tPM LV LEAD BIPOLAR IMPEDANCE\t701\t-600\t1\t701\t491401\t1\t-600\t360000
+EVAL4\t669621\t210993280\tPM DEVICE EVAL,DUAL,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t225435\t120\t1\t225435\t50820939225\t1\t120\t14400
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tDEVTYPE\tDEVTYPE\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tIMPDATE\tIMPLANT DATE\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tMFR\tMANUFACTURER\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tMOD\tMODEL\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
+EVAL4\t669625\t210993281\tPM DEVICE EVAL,BIV,W/REPROGRAMMING\tSERNUM\tSERIAL NUMBER\t9999999\t240\t1\t9999999\t1.00E+14\t1\t240\t57600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRSENM\tLV THRESHOLD SENSING AMPLITUDE\t19\t1140\t1\t19\t361\t1\t1140\t1299600
+EVAL6\t669633\t210993289\tICD DEVICE EVAL,BIV,W/OUT REPROG\tLVTHRSENPO\tLV THRESHOLD SENSING POLARITY\t9999999\t1140\t1\t9999999\t1.00E+14\t1\t1140\t1299600
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tAFIBTAQRSM\tAFIB TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tAFLTAQRSMC\tAFLUTTER TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tAFLTAZNRTC\tAFLUTTER TACHY ZONE RATE STABILITY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tALERT\tALERT\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tATACHTAQRS\tATACH TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRAVPCD\tBRADY PACED AV DELAY\t120\t300\t1\t120\t14400\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRAVSND\tBRADY SENSED AV DELAY\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDBRTRKRT\tBRADY TRACKING RATE\t130\t300\t1\t130\t16900\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATFIB\tICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION\t0\t300\t1\t0\t0\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATPAT\tICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL\t0\t300\t1\t0\t0\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTATRFL\tICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER\t0\t300\t1\t0\t0\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTPACAT\tICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL\t48\t300\t1\t48\t2304\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKAAT\tICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL\t0\t300\t1\t0\t0\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDDTSKDAT\tICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL\t0\t300\t1\t0\t0\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPAT\tICD TEL LEAD IMPEDANCE ATRIAL\t451\t300\t1\t451\t203401\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tICDTLIMPLV\tICD TEL LEAD IMPEDANCE LEFT VENT\t768\t300\t1\t768\t589824\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRPACA\tLV BRADY PACING AMPLITUDE\t2.2\t300\t1\t2.2\t4.84\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRPACW\tLV BRADY PACING PULSE WIDTH\t0.5\t300\t1\t0.5\t0.25\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRSENA\tLV BRADY SENSING AMPLITUDE\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRSENP\tLV BRADY SENSING BLANKING PERIOD\t400\t300\t1\t400\t160000\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tLVBRSENR\tLV BRADY SENSING REFRACTORY PERIOD\t310\t300\t1\t310\t96100\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRPACA\tRA BRADY PACING AMPLITUDE\t2\t300\t1\t2\t4\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRPACW\tRA BRADY PACING PULSE WIDTH\t0.5\t300\t1\t0.5\t0.25\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRSENA\tRA BRADY SENSING AMPLITUDE\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRABRSENR\tRA BRADY SENSING REFRACTORY PERIOD\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRAMP\tRA THRESHOLD AMPLITUDE\t0.7\t300\t1\t0.7\t0.49\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRDUR\tRA THRESHOLD DURATION\t0.5\t300\t1\t0.5\t0.25\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRSENMP\tRA THRESHOLD SENSING AMPLITUDE\t3.1\t300\t1\t3.1\t9.61\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tRATHRSPOL\tRA THRESHOLD SENSING POLARITY\t9999999\t300\t1\t9999999\t1.00E+14\t1\t300\t90000
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSLWVTTZNT\tVSLOWVT TACHY ZONE RATE STABILITY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL6\t669637\t210993282\tICD DEVICE EVAL,SNGL,W/ REPROGRAMMING\tVSWVTTAQRS\tVSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA\t9999999\t120\t1\t9999999\t1.00E+14\t1\t120\t14400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTDETD\tFASTVT TACHY ZONE DETECTION DURATION\t30\t-360\t1\t30\t900\t1\t-360\t129600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tFSTVTTZNSC\tFASTVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t-360\t1\t100\t10000\t1\t-360\t129600
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDBRVVDEL\tBRADY VV DELAY\t0\t120\t1\t0\t0\t1\t120\t14400
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tICDEVALCOM\tICD EVAL COMMENTARY\t9999999\t2700\t1\t9999999\t1.00E+14\t1\t2700\t7290000
+EVAL6\t669641\t210993283\tICD DEVICE EVAL,DUAL,W/REPROGRAMMING\tSLWVTTAZNS\tSLOWVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t-360\t1\t100\t10000\t1\t-360\t129600
+EVAL6\t669645\t210993284\tICD DEVICE EVAL,BIV,W/REPROGRAMMING\tFSTVTTZNSC\tFASTVT TACHY ZONE SUDDEN ONSET CRITERIA\t100\t480\t1\t100\t10000\t1\t480\t230400
+EXTERNAL RESULTS\t890349\tEXTINRM\tINR (MANUAL ENTRY)\tINR\tINR (MANUAL ENTRY)\t1.4\t41040\t1\t1.4\t1.96\t1\t41040\t1684281600
+EXTERNAL RESULTS\t890349\tEXTINRM\tINR (MANUAL ENTRY)\tPT\tPROTHROMBIN TIME\t15.8\t41040\t1\t15.8\t249.64\t1\t41040\t1684281600
+HISTOCOMPATIBILITY ORDERABLE\t481589\t10285R\tHLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)\t10049R\tHLA LAB TESTING STATUS\t9999999\t130260\t1\t9999999\t1.00E+14\t1\t130260\t16967667600
+HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\t10049R\tHLA LAB TESTING STATUS\t9999999\t2244840\t1\t9999999\t1.00E+14\t1\t2244840\t5.04E+12
+HISTOCOMPATIBILITY ORDERABLE\t706283\t10000R\tHLA ORDER - TYPING - SOLID ORGAN EVALUATION\tRINFO\tTO FIND RESULT:\t9999999\t736702.1918\t1\t9999999\t1.00E+14\t73\t53779260\t6.97E+13
+HISTOCOMPATIBILITY ORDERABLE\t718854\t10205R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\tRINFO\tTO FIND RESULT:\t9999999\t1896726\t1\t9999999\t1.00E+14\t10\t18967260\t5.63E+13
+HISTOCOMPATIBILITY ORDERABLE\t890780\t10010R\tHLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t170820\t1\t9999999\t1.00E+14\t1\t170820\t29179472400
+HISTOCOMPATIBILITY ORDERABLE\t890832\t10040R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)\t10049R\tHLA LAB TESTING STATUS\t9999999\t235080\t1\t9999999\t1.00E+14\t2\t470160\t1.15E+11
+HISTOCOMPATIBILITY ORDERABLE\t890832\t10040R\tHLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)\tRINFO\tTO FIND RESULT:\t9999999\t235080\t1\t9999999\t1.00E+14\t2\t470160\t1.15E+11
+HISTOCOMPATIBILITY ORDERABLE\t892287\t10297R\tHLA ORDER - DECEASED DNR O.R. SPMN\t10049R\tHLA LAB TESTING STATUS\t9999999\t594360\t1\t9999999\t1.00E+14\t1\t594360\t3.53E+11
+HISTOCOMPATIBILITY ORDERABLE\t892287\t10297R\tHLA ORDER - DECEASED DNR O.R. SPMN\tRINFO\tTO FIND RESULT:\t9999999\t594360\t1\t9999999\t1.00E+14\t1\t594360\t3.53E+11
+HISTOCOMPATIBILITY ORDERABLE\t895898\t10120R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)\t10049R\tHLA LAB TESTING STATUS\t9999999\t202080\t1\t9999999\t1.00E+14\t1\t202080\t40836326400
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9099R\tINTERPRETATION/ COMMENTS\t9999999\t60\t1\t9999999\t1.00E+14\t1\t60\t3600
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9035R\tMICA ALLELE 1\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9036R\tMICA ALLELE 2\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9342R\tDSA FOR CLASS II\t9999999\t68280\t1\t9999999\t1.00E+14\t1\t68280\t4662158400
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9377R\tCUMULATIVE ANTIBODIES BY C1Q\t9999999\t33166.66667\t1\t9999999\t1.00E+14\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9391R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t59700\t1\t9999999\t1.00E+14\t5\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9404R\tINTERPRETATION/ COMMENTS CLASS II\t9999999\t59700\t1\t9999999\t1.00E+14\t5\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9347R\tCUMULATIVE ANTIBODIES BY IGG\t9999999\t42651.42857\t1\t9999999\t1.00E+14\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9361R\tINTERPRETATION/ COMMENTS CLASS I\t9999999\t298380\t1\t9999999\t1.00E+14\t1\t298380\t89030624400
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9367R\tPOSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9374R\tINTERPRETATION/ COMMENTS  CLASS II\t9999999\t149190\t1\t9999999\t1.00E+14\t2\t298380\t89030624400
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9350R\tMFI RESULTS FOR SCREENING C1Q CLASS I\t9999999\t119126.8571\t1\t9999999\t1.00E+14\t315\t37524960\t4.02E+13
+HISTOCOMPATIBILITY ORDERABLE\t896160\t10165R\tHLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)\t10049R\tHLA LAB TESTING STATUS\t9999999\t543300\t1\t9999999\t1.00E+14\t1\t543300\t2.95E+11
+HISTOCOMPATIBILITY ORDERABLE\t896160\t10165R\tHLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t9999999\t543300\t1\t9999999\t1.00E+14\t1\t543300\t2.95E+11
+HISTOCOMPATIBILITY ORDERABLE\t4046255\t10015R\tHLA ORDER - DSA SCREENING\tRINFO\tTO FIND RESULT:\t9999999\t1171925.172\t1\t9999999\t1.00E+14\t116\t135943320\t2.64E+14
+LAB BLOOD ORDERABLES\t471506\tLABY17OHP\t17 - HYDROXYPROGESTERONE, SERUM\t17OHPROG\t17-OH PROGESTERONE\t11700\t545760\t1\t11700\t136890000\t1\t545760\t2.98E+11
+LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONDX\tCLINICAL INDICATION\t9999999\t758160\t1\t9999999\t1.00E+14\t1\t758160\t5.75E+11
+LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONRS\tRESULT\t9999999\t758160\t1\t9999999\t1.00E+14\t1\t758160\t5.75E+11
+LAB BLOOD ORDERABLES\t471525\tLABYCHRBC\tACETYLCHOLINESTERASE, RBC\tACHERBC\tACETYLCHOLINESTERASE, RBC\t33.3\t#VALUE!\t1\t33.3\t1108.89\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471526\tLABYARBIN\tACH RECEPTOR BINDING AB\tYARBIN\tACH RECEPTOR BINDING ANTIBODY\t72.2\t613440\t1\t72.2\t5212.84\t1\t613440\t3.76E+11
+LAB BLOOD ORDERABLES\t471536\tLABADAQ\tADENOSINE DEAMINASE, RBC\tADMD\tREVIEWED BY\t9999999\t490320\t1\t9999999\t1.00E+14\t9\t4412880\t3.48E+12
+LAB BLOOD ORDERABLES\t471586\tLAB12733R\tALPHA-2-ANTIPLASMIN\tA2A\tALPHA-2-ANTIPLASMIN\t80\t156540\t1\t80\t6400\t1\t156540\t24504771600
+LAB BLOOD ORDERABLES\t471586\tLAB12733R\tALPHA-2 PLASMIN INHIBITOR\t12734R\tALPHA-2 PLASMIN INHIBITOR\t57\t90780\t1\t57\t3249\t2\t181560\t16900200000
+LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTD\tDISCLAIMER\t9999999\t540213\t1\t9999999\t1.00E+14\t60\t32412780\t2.37E+13
+LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTM\tMETHOD\t9999999\t540213\t1\t9999999\t1.00E+14\t60\t32412780\t2.37E+13
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA\t12215R\t12215R\t9999999\t240072.1875\t1\t9999999\t1.00E+14\t64\t15364620\t4.33E+12
+LAB BLOOD ORDERABLES\t471646\tLABABTITI\tAB TITER\tABTITER\tANTIBODY TITER RESULTS\t9999999\t12397.26316\t1\t9999999\t1.00E+14\t95\t1177740\t29891170800
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO7\tCAUTIONS\t9999999\t562920\t1\t9999999\t1.00E+14\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tCAIGG\tCAL ENCEPH IGG\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tCAIGM\tCAL ENCEPH IGM\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tCAINT\tINTERPRETATION\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tEEIGM\tE EQUINE ENCEP IGM AB\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tEEINT\tINTERPRETATION\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tSTIGG\tST. LOUIS ENCEP IGG\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tSTIGM\tST. LOUIS ENCEP IGM\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tSTINT\tINTERPRETATION\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tWEIGG\tW EQUINE IGG AB\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tWEIGM\tW EQUINE IGM AB\t9999999\t2760\t1\t9999999\t1.00E+14\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471705\tLABJO1ABY\tANTI-JO1 ANTIBODY\tJO1INT\tANTI-JO1 INTERPRETATION\t9999999\t395222.2222\t1\t9999999\t1.00E+14\t81\t32013000\t1.65E+13
+LAB BLOOD ORDERABLES\t471706\tLAB11093R\tAUTOANTIBODIES TO SCL 70\tSCL70\tSCL 70 AB, IGG, SERUM\t9999999\t222600\t1\t9999999\t1.00E+14\t1\t222600\t49550760000
+LAB BLOOD ORDERABLES\t471709\tLABRNPABY\tANTI-RNP ANTIBODY\tRNPINT\tANTI-RNP INTERPRETATION\t9999999\t433078.8889\t1\t9999999\t1.00E+14\t162\t70158780\t4.59E+13
+LAB BLOOD ORDERABLES\t471709\tLABYANRNP\tAUTOANTIBODIES TO U1RNP\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t9999999\t161628\t1\t9999999\t1.00E+14\t20\t3232560\t6.25E+11
+LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG AND IGM AB\tYPERA2\tB PERTUSSIS AB, IGM\t9999999\t32220\t1\t9999999\t1.00E+14\t1\t32220\t1038128400
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQCO\tCOMMENT\t9999999\t1383525\t1\t9999999\t1.00E+14\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471760\tLABB2GPCO\tBETA - 2 - GLYCOPROTEIN\tB2GPCM\tBETA-2-GP1 COMMENT\t9999999\t916554.1935\t1\t9999999\t1.00E+14\t31\t28413180\t4.50E+13
+LAB BLOOD ORDERABLES\t471777\tLABYBLAS\tBLASTOMYCES AB\tYBLAS3\tBLASTOMYCES IMMUNODIFFUSION\t9999999\t417449.0625\t1\t9999999\t1.00E+14\t64\t26716740\t1.37E+13
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMGDES\tCMV IGG TEST DESCRIPTION\t9999999\t221580\t1\t9999999\t1.00E+14\t2\t443160\t1.80E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMGQNT\tCMV IGG LEVEL\t9999999\t18900\t1\t9999999\t1.00E+14\t1\t18900\t357210000
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMMDES\tCMV IGM TEST DESCRIPTION\t9999999\t221580\t1\t9999999\t1.00E+14\t2\t443160\t1.80E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tCMMQNT\tCMV IGM LEVEL\t9999999\t18900\t1\t9999999\t1.00E+14\t1\t18900\t357210000
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHATINT\tINTERPRETATION\t9999999\t284733.75\t1\t9999999\t1.00E+14\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t9999999\t284733.75\t1\t9999999\t1.00E+14\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t9999999\t284733.75\t1\t9999999\t1.00E+14\t48\t13667220\t4.72E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHEPAAB\tHEPATITIS A TOTAL AB\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tHEPCAB\tHEPATITIS C AB\t9999999\t392580\t1\t9999999\t1.00E+14\t1\t392580\t1.54E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tRPRQIN\tINTERPRETATIONS\t9999999\t286934.6939\t1\t9999999\t1.00E+14\t49\t14059800\t4.87E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVGGDES\tVZV IGG TEST DESCRIPTION\t9999999\t221580\t1\t9999999\t1.00E+14\t2\t443160\t1.80E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMTP\tBMT PRETRANSPLANT PANEL\tVGGQNT\tVZV IGG LEVEL\t9999999\t18900\t1\t9999999\t1.00E+14\t1\t18900\t357210000
+LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tALCN\tALLO-ISOLEUCINE\t0\t348900\t1\t0\t0\t1\t348900\t1.22E+11
+LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tISOLEU\tISOLEUCINE\t32\t348900\t1\t32\t1024\t1\t348900\t1.22E+11
+LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tLEUCINE\tLEUCINE\t72\t348900\t1\t72\t5184\t1\t348900\t1.22E+11
+LAB BLOOD ORDERABLES\t471797\tLABBCAA\tBRANCHED CHAIN AMINO ACIDS\tVALINE\tVALINE\t119\t348900\t1\t119\t14161\t1\t348900\t1.22E+11
+LAB BLOOD ORDERABLES\t471803\tLABYBUPRO\tBUPROPION, SERUM\t12045R\tHYDROXYBUPROPION\t843\t534900\t1\t843\t710649\t1\t534900\t2.86E+11
+LAB BLOOD ORDERABLES\t471803\tLABYBUPRO\tBUPROPION, SERUM\tYBUPR1\tBUPROPION\t47\t534900\t1\t47\t2209\t1\t534900\t2.86E+11
+LAB BLOOD ORDERABLES\t471807\tLABC1INHF\tC1 ESTERASE INHIBITOR FN\tC1INHF\tC1 ESTERASE INHIB FN\t109\t327720\t1\t109\t11881\t1\t327720\t1.07E+11
+LAB BLOOD ORDERABLES\t471810\tLABC1Q\tC1Q BINDING ASSAY\tC1Q\tC1Q BINDING ASSAY\t1.7\t314220\t1\t1.7\t2.89\t1\t314220\t98734208400
+LAB BLOOD ORDERABLES\t471817\tLABYCALCI\tCALCITONIN, PLASMA\tYCALCI\tCALCITONIN, SERUM\t12\t890520\t1\t12\t144\t1\t890520\t7.93E+11
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tALB\tALBUMIN, SER/PLAS\t2.6\t16500\t1\t2.6\t6.76\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tALKP\tALK P\'TASE, TOTAL, SER/PLAS\t44\t16500\t1\t44\t1936\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tALT\tALT (SGPT), SER/PLAS\t69\t16500\t1\t69\t4761\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tAST\tAST (SGOT), SER/PLAS\t44\t16500\t1\t44\t1936\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOAB\tABSOLUTE BASOPHILS\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBASOSYN\tBASOPHILS, %\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tBUN\tUREA NITROGEN,SER/PLAS\t11\t16500\t1\t11\t121\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCA\tCALCIUM, SER/PLAS\t8.1\t16500\t1\t8.1\t65.61\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCL\tCHLORIDE, SER/PLAS\t104\t16500\t1\t104\t10816\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCO2\tCO2, SER/PLAS\t27\t16500\t1\t27\t729\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tCR\tCREATININE, SER/PLAS\t0.7\t16500\t1\t0.7\t0.49\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSAB\tABSOLUTE EOSINOPHILS\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tEOSFLD\tEOSINOPHILS, %\t0\t40260\t1\t0\t0\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tGLU\tGLUCOSE, SER/PLAS\t106\t16500\t1\t106\t11236\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHCT\tHEMATOCRIT (MANUAL ENTRY)\t38\t40260\t1\t38\t1444\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tHGB\tHEMOGLOBIN (MANUAL ENTRY)\t12.7\t40260\t1\t12.7\t161.29\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tK\tPOTASSIUM, SER/PLAS\t4.6\t16500\t1\t4.6\t21.16\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tLYMAB\tABSOLUTE LYMPHOCYTES\t1\t40260\t1\t1\t1\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOA\tABSOLUTE MONOCYTES\t1\t40260\t1\t1\t1\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tMONOPHAGE\tMONOCYTE/MACROPHAGE, %\t7\t40260\t1\t7\t49\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNA\tSODIUM, SER/PLAS\t137\t16500\t1\t137\t18769\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTAB\tABSOLUTE NEUTROPHIL COUNT\t12.7\t40260\t1\t12.7\t161.29\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tNEUTRA\tABSOLUTE NEUTROPHILS\t3.3\t16500\t1\t3.3\t10.89\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tPLT\tPLATELETS\t247\t40260\t1\t247\t61009\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tRBCHP\tRBC\t4.6\t40260\t1\t4.6\t21.16\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tSYNLYMPH\tLYMPHOCYTES %\t7\t40260\t1\t7\t49\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tTBIL\tTOTAL BILIRUBIN\t0.4\t16500\t1\t0.4\t0.16\t1\t16500\t272250000
+LAB BLOOD ORDERABLES\t471944\tLABCBCD\tCBC WITH DIFF\tWBCHP\tWBC\t14.7\t40260\t1\t14.7\t216.09\t1\t40260\t1620867600
+LAB BLOOD ORDERABLES\t471945\tLABCBCS\tCBC WITH DIFF AND SLIDE REVIEW\tHAIRY\tHAIRY CELLS\t0\t151200\t1\t0\t0\t1\t151200\t22861440000
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTR2\tRESULT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTR3\tRESULT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472036\tLABCLOMIP\tCLOMIPRAMINE AND METABOLITE\tCLOMI\tCLOMIPRAMINE\t63\t#VALUE!\t1\t63\t3969\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472036\tLABCLOMIP\tCLOMIPRAMINE AND METABOLITE\tDMCLOM\tDESMETHYLCLOMIPRAMIN\t14\t#VALUE!\t1\t14\t196\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472036\tLABCLOMIP\tCLOMIPRAMINE AND METABOLITE\tTCLOAM\tCLOMIP & METAB, TOTAL\t77\t#VALUE!\t1\t77\t5929\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472048\tLAB11124R\tCOCCI CF\tCOCFS\tCOCCI CF (SERUM)\t9999999\t951336.1341\t1\t9999999\t1.00E+14\t895\t851445840\t2.05E+15
+LAB BLOOD ORDERABLES\t472051\tLABCDPLTI\tCOLD AGGLUTININ TITER\tCDPLT\tCOLD ANTIBODY TITER\t9999999\t146235.3488\t1\t9999999\t1.00E+14\t43\t6288120\t1.58E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHATINT\tINTERPRETATION\t9999999\t200741.1086\t1\t9999999\t1.00E+14\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t200741.1086\t1\t9999999\t1.00E+14\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t9999999\t200741.1086\t1\t9999999\t1.00E+14\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHATINT\tINTERPRETATION\t9999999\t227223.75\t1\t9999999\t1.00E+14\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t9999999\t227223.75\t1\t9999999\t1.00E+14\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMPREHENSIVE HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t9999999\t227223.75\t1\t9999999\t1.00E+14\t16\t3635580\t1.30E+12
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHAGR\tANTI HAV IGG\t9999999\t19380\t1\t9999999\t1.00E+14\t1\t19380\t375584400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHATR\tANTI-HAV ANTIBODY\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBSR\tHEP B SURFACE ANTIGEN\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBTR\tANTI-HBC ANTIBODY\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHCVR\tANTI-HCV\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHQLINT\tINTERPRETATION\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHQLR\tANTI-HBS ANTIBODY QUALITATIVE\t9999999\t56580\t1\t9999999\t1.00E+14\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tCRTNR\tCREATINE, PLASMA\t0.5\t-86400\t1\t0.5\t0.25\t1\t-86400\t7464960000
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tHGB\tHEMOGLOBIN\t10.9\t0\t1\t10.9\t118.81\t1\t0\t0
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tLDH\tLDH, TOTAL, SER/PLAS\t3124\t0\t1\t3124\t9759376\t1\t0\t0
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tPLT\tPLATELET COUNT\t47\t0\t1\t47\t2209\t1\t0\t0
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tURIC\tURIC ACID, SER/PLAS\t2.5\t0\t1\t2.5\t6.25\t1\t0\t0
+LAB BLOOD ORDERABLES\t472108\tLABCR\tCREATININE, SERUM/PLASMA\tWBC\tWBC\t3.7\t0\t1\t3.7\t13.69\t1\t0\t0
+LAB BLOOD ORDERABLES\t472120\tLABCCP\tANTI CCP\tCCPT\tANTI CCP ANTIBODY LEVEL\t18\t583320\t1\t18\t324\t1\t583320\t3.40E+11
+LAB BLOOD ORDERABLES\t472142\tLABYDOC\t11-DEOXYCORTISOL, SERUM\tYDOC1\t11-DEOXYCORTISOL, S\t9610\t285600\t1\t9610\t92352100\t1\t285600\t81567360000
+LAB BLOOD ORDERABLES\t472142\tLABYDOC\t11-DEOXYCORTISOL, SERUM\tYDOC2\tAM RESULT\t9999999\t285600\t1\t9999999\t1.00E+14\t1\t285600\t81567360000
+LAB BLOOD ORDERABLES\t472142\tLABYDOC\t11-DEOXYCORTISOL, SERUM\tYDOC3\tPM RESULT\t9999999\t285600\t1\t9999999\t1.00E+14\t1\t285600\t81567360000
+LAB BLOOD ORDERABLES\t472148\tLABYDIAZE\tDIAZEPAM AND NORDIAZEPAM\tDIAZEP\tDIAZEPAM\t9999999\t411030\t1\t9999999\t1.00E+14\t2\t822060\t4.30E+11
+LAB BLOOD ORDERABLES\t472148\tLABYDIAZE\tDIAZEPAM AND NORDIAZEPAM\tNORDIAZ\tNORDIAZEPAM\t9999999\t411030\t1\t9999999\t1.00E+14\t2\t822060\t4.30E+11
+LAB BLOOD ORDERABLES\t472148\tLABYDIAZE\tDIAZEPAM AND NORDIAZEPAM\tYDIAZ3\tDIAZEPAM & NORDIAZEPAM\t9999999\t411030\t1\t9999999\t1.00E+14\t2\t822060\t4.30E+11
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBASO\tBASOPHILS\t1\t11460\t1\t1\t1\t1\t11460\t131331600
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tBLASTS\tBLASTS\t16\t#VALUE!\t1\t16\t256\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tPRMY\tPROMYELOCYTES\t2\t#VALUE!\t1\t2\t4\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tBAND\tBAND NEUTROPHILS\t16\t#VALUE!\t1\t16\t256\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tLYM\tLYMPHOCYTES\t42\t#VALUE!\t1\t42\t1764\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tMONO\tMONOCYTES\t20\t#VALUE!\t1\t20\t400\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tNEUT\tSEG NEUTROPHILS\t22\t#VALUE!\t1\t22\t484\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472226\tLAB11132R\tDONOR HEP B SURF. AG\tDHB\tDONOR HEP B SURF. AG\t9999999\t180290\t1\t9999999\t1.00E+14\t6\t1081740\t2.32E+11
+LAB BLOOD ORDERABLES\t472331\tLAB11034R\tETHOSUXIMIDE LEVEL\tETHSUX\tETHOSUXIMIDE\t9999999\t174300\t1\t9999999\t1.00E+14\t1\t174300\t30380490000
+LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEICMT\tCOMMENT\t9999999\t436344.1652\t1\t9999999\t1.00E+14\t581\t253515960\t1.44E+14
+LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIPRM\tPRIMERS\t9999999\t436344.1652\t1\t9999999\t1.00E+14\t581\t253515960\t1.44E+14
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tCHICK\tCHICKEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tCORNY\tCORN\t1.15\t#VALUE!\t1\t1.15\t1.3225\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tEGGW\tEGG WHITE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tFISH\tFISH\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tIGEFA\tTOTAL IGE\t980\t#VALUE!\t1\t980\t960400\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tMIL\tMILK\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tNUTTY\tNUT\t5.73\t#VALUE!\t1\t5.73\t32.8329\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tOAT\tOAT\t0.5\t#VALUE!\t1\t0.5\t0.25\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tORAN\tORANGE\t0.37\t#VALUE!\t1\t0.37\t0.1369\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tPEAN\tPEANUT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tSHRIM\tSHRIMP\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tSOY\tSOY\t21.9\t#VALUE!\t1\t21.9\t479.61\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tTOM\tTOMATO\t0.75\t#VALUE!\t1\t0.75\t0.5625\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tWHT\tWHEAT\t0.66\t#VALUE!\t1\t0.66\t0.4356\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472494\tLABHMET\tHEAVY METALS SCREEN, BLOOD\tMER\tMERCURY, BLOOD\t3\t159300\t1\t3\t9\t1\t159300\t25376490000
+LAB BLOOD ORDERABLES\t472502\tLABYHEMOC\tHEMOCHROMATOSIS HFE GENE\tYHEMSP\tSPECIMEN\t9999999\t521638.481\t1\t9999999\t1.00E+14\t79\t41209440\t2.41E+13
+LAB BLOOD ORDERABLES\t472513\tLAB12802R\tHEPARIN COFACTOR\t12801R\tHEPARIN COFACTOR II\t70\t751440\t1\t70\t4900\t1\t751440\t5.65E+11
+LAB BLOOD ORDERABLES\t472521\tLABHBCT\tANTI-HBC\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t22020\t1\t9999999\t1.00E+14\t1\t22020\t484880400
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCAB\tHEP B CORE TOTAL AB\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCABIGM\tHEPAT.B CORE AB-IGM\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAG\tHEPATITIS B SURF AG\t9999999\t42480\t1\t9999999\t1.00E+14\t1\t42480\t1804550400
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAIN\tINTERPRETATION\t9999999\t290760\t1\t9999999\t1.00E+14\t1\t290760\t84541377600
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAQT\tHEP B SURF AB QUANT\t236.4\t290760\t1\t236.4\t55884.96\t1\t290760\t84541377600
+LAB BLOOD ORDERABLES\t472527\tLABHBSAG\tHBSAG\tHBSAG\tHEPATITIS B SURF AG\t9999999\t22020\t1\t9999999\t1.00E+14\t1\t22020\t484880400
+LAB BLOOD ORDERABLES\t472528\tLABHCVA\tANTI-HCV\tHCVI\tINTERPRETATION\t9999999\t42280.72931\t1\t9999999\t1.00E+14\t713\t30146160\t3.88E+12
+LAB BLOOD ORDERABLES\t472539\tLABHGBSQ\tHGB S QUANTITATIVE\tHGSMD\tREVIEWED BY\t9999999\t326432\t1\t9999999\t1.00E+14\t45\t14689440\t6.05E+12
+LAB BLOOD ORDERABLES\t472576\tLABYHPL\tHUMAN PLACENTAL LACTOGEN\tYHPL1\tHUMAN PLACENTAL LACTOGEN\t0.19\t901380\t1\t0.19\t0.0361\t1\t901380\t8.12E+11
+LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tANAQNT\tTITER\t9999999\t359880\t1\t9999999\t1.00E+14\t1\t359880\t1.30E+11
+LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tRHFT\tRHEUMATOID FACTOR LEVEL\t23\t190800\t1\t23\t529\t1\t190800\t36404640000
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKIN\tINTERPRETATION\t9999999\t520500\t1\t9999999\t1.00E+14\t1\t520500\t2.71E+11
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKMD\tINTERPRETED BY\t9999999\t525786.6667\t1\t9999999\t1.00E+14\t9\t4732080\t3.13E+12
+LAB BLOOD ORDERABLES\t472720\tLABLAC\tLACTIC ACID\tLAC\tLACTIC ACID, PLASMA\t2.8\t40500\t1\t2.8\t7.84\t1\t40500\t1640250000
+LAB BLOOD ORDERABLES\t472748\tLABYLEPTN\tLEPTIN\tYLEPT1\tLEPTIN\t20\t963540\t1\t20\t400\t1\t963540\t9.28E+11
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL\tLDL\tLDL (CALCULATED)\t9999999\t7920\t1\t9999999\t1.00E+14\t1\t7920\t62726400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tCHOL\tCHOLESTEROL, TOTAL\t207\t4020\t1\t207\t42849\t1\t4020\t16160400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t9\t4020\t1\t9\t81\t1\t4020\t16160400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tHDL\tHDL CHOLESTEROL\t23\t4020\t1\t23\t529\t1\t4020\t16160400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tLDL\tDIRECT LDL CHOL\t62\t4020\t1\t62\t3844\t1\t4020\t16160400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tNHDL\tNON-HDL CHOL, CALC\t184\t4020\t1\t184\t33856\t1\t4020\t16160400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL, NON-FASTING PATIENT\tTGL\tTRIGLYCERIDE, SER/PLAS\t739\t4020\t1\t739\t546121\t1\t4020\t16160400
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tCHOL\tCHOLESTEROL, TOTAL\t97\t9900\t1\t97\t9409\t1\t9900\t98010000
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tCHOLHDL\tCHOLESTEROL/HDL RATIO\t7.5\t9900\t1\t7.5\t56.25\t1\t9900\t98010000
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tHDL\tHDL CHOLESTEROL\t13\t9900\t1\t13\t169\t1\t9900\t98010000
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tLDL\tDIRECT LDL CHOL\t49\t9900\t1\t49\t2401\t1\t9900\t98010000
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tNHDL\tNON-HDL CHOL, CALC\t84\t9900\t1\t84\t7056\t1\t9900\t98010000
+LAB BLOOD ORDERABLES\t472754\tLABLPD\tLIPID PANEL. NON-FASTING PATIENT\tTGL\tTRIGLYCERIDE, SER/PLAS\t86\t9900\t1\t86\t7396\t1\t9900\t98010000
+LAB BLOOD ORDERABLES\t472775\tLABLWBCS2\tLYME WESTERN BLOT, SERUM\tLYMEIGMBAND\tB.BURGDORFERI IGM BANDS\t9999999\t265415.6757\t1\t9999999\t1.00E+14\t37\t9820380\t4.41E+12
+LAB BLOOD ORDERABLES\t472855\tLABMALL2\tMISCELLANEOUS ALLERGEN 2\tALLR2\tALLERGEN RESULT\t9999999\t1370400\t1\t9999999\t1.00E+14\t1\t1370400\t1.88E+12
+LAB BLOOD ORDERABLES\t472856\tLABMALL3\tMISCELLANEOUS ALLERGEN 3\tALL3\tALLERGEN\t9999999\t148680\t1\t9999999\t1.00E+14\t1\t148680\t22105742400
+LAB BLOOD ORDERABLES\t472856\tLABMALL3\tMISCELLANEOUS ALLERGEN 3\tALLR3\tALLERGEN RESULT\t9999999\t148680\t1\t9999999\t1.00E+14\t1\t148680\t22105742400
+LAB BLOOD ORDERABLES\t472864\tLABMISCH\tMISCELLANEOUS HEMATOLOGY\tRESULH\tRESULT\t9999999\t3060\t1\t9999999\t1.00E+14\t1\t3060\t9363600
+LAB BLOOD ORDERABLES\t472944\tLABFNKSEN\tNATURAL KILLER CELLS ENUMERATION\tC3AB\tCD3 CD16+CD56 ABS\t40\t265980\t1\t40\t1600\t1\t265980\t70745360400
+LAB BLOOD ORDERABLES\t472944\tLABFNKSEN\tNATURAL KILLER CELLS ENUMERATION\tC3PER\tCD3 CD16+CD56 %\t11\t265980\t1\t11\t121\t1\t265980\t70745360400
+LAB BLOOD ORDERABLES\t472944\tLABFNKSEN\tNATURAL KILLER CELLS ENUMERATION\tLYMAB\tLYMPHOCYTES, ABS.\t352\t265980\t1\t352\t123904\t1\t265980\t70745360400
+LAB BLOOD ORDERABLES\t473043\tLABYOSTEO\tOSTEOCALCIN HUMAN\tOSTEOC\tOSTEOCALCIN, HUMAN\t15\t141000\t1\t15\t225\t1\t141000\t19881000000
+LAB BLOOD ORDERABLES\t473074\tLABPBGD\tPBG DEAMINASE, RBC\tPBGD\tPBG DEAMINASE, RBC\t4.68\t675840\t1\t4.68\t21.9024\t1\t675840\t4.57E+11
+LAB BLOOD ORDERABLES\t473096\tLABPHATYR\tPHENYLALANINE AND TYROSINE\tPHALN\tPHENYLALANINE\t87\t426840\t1\t87\t7569\t1\t426840\t1.82E+11
+LAB BLOOD ORDERABLES\t473096\tLABPHATYR\tPHENYLALANINE AND TYROSINE\tTYROSINE\tTYROSINE\t91\t426840\t1\t91\t8281\t1\t426840\t1.82E+11
+LAB BLOOD ORDERABLES\t473101\tLABYPTSAB\tPHOSPHATIDYLSERINE AB PANEL\tYPTSA\tPHOSPHATIDYLSERINE IGA AB\t3\t353700\t1\t3\t9\t1\t353700\t1.25E+11
+LAB BLOOD ORDERABLES\t473101\tLABYPTSAB\tPHOSPHATIDYLSERINE AB PANEL\tYPTSG\tPHOSPHATIDYLSERINE IGG AB\t10\t353700\t1\t10\t100\t1\t353700\t1.25E+11
+LAB BLOOD ORDERABLES\t473101\tLABYPTSAB\tPHOSPHATIDYLSERINE AB PANEL\tYPTSM\tPHOSPHATIDYLSER. IGM\t41\t353700\t1\t41\t1681\t1\t353700\t1.25E+11
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12590R\tS.PNEUMO SEROTYPE 1 (1)\t4.3\t205440\t1\t4.3\t18.49\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12699R\tS.PNEUMO SEROTYPE 3 (3)\t8.4\t205440\t1\t8.4\t70.56\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12700R\tS.PNEUMO SEROTYPE 4 (4)\t0.4\t205440\t1\t0.4\t0.16\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12701R\tS.PNEUMO SEROTYPE 5 (5)\t3\t205440\t1\t3\t9\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12702R\tS.PNEUMO SEROTYPE 6 (6A)\t3.6\t205440\t1\t3.6\t12.96\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12703R\tS.PNEUMO SEROTYPE 14 (14)\t18.1\t205440\t1\t18.1\t327.61\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12704R\tS.PNEUMO SEROTYPE 19 (19F)\t2.1\t205440\t1\t2.1\t4.41\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12705R\tS.PNEUMO SEROTYPE 23 (23F)\t0.5\t205440\t1\t0.5\t0.25\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12706R\tS.PNEUMO SEROTYPE 26 (6B)\t7\t205440\t1\t7\t49\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12707R\tS.PNEUMO SEROTYPE 51 (7F)\t0.8\t205440\t1\t0.8\t0.64\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12708R\tS.PNEUMO SEROTYPE 56 (18C)\t9999999\t205440\t1\t9999999\t1.00E+14\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12709R\tS.PNEUMO SEROTYPE 57 (19A)\t0.8\t205440\t1\t0.8\t0.64\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473116\tLAB12581R\tS PNEUMONIAE AB IGG (PREVNAR 13)\t12710R\tS.PNEUMO SEROTYPE 68 (9V)\t1.8\t205440\t1\t1.8\t3.24\t1\t205440\t42205593600
+LAB BLOOD ORDERABLES\t473121\tLABPORS\tPORPHYRINS, SERUM\tPORINT\tPORPHYRINS, SER INTR\t9999999\t383598.6207\t1\t9999999\t1.00E+14\t29\t11124360\t1.10E+13
+LAB BLOOD ORDERABLES\t473149\tLABPROTPM\tPRO-PREDICT TPMT\tPRTPMR\tPROPREDICT TPMT\t9999999\t623076.9231\t1\t9999999\t1.00E+14\t78\t48600000\t6.83E+13
+LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPT20DR\tINTERPRETED BY\t9999999\t453611.6315\t1\t9999999\t1.00E+14\t521\t236331660\t1.36E+14
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON FOR LATENT TB INFECTION\tQTFNIN\tQUANTIFERON INTERPRETATION\t9999999\t449070\t1\t9999999\t1.00E+14\t18\t8083260\t1.64E+13
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tTB BY QUANTIFERON\tTBGINT\tINTERPRETATION\t9999999\t209921.4545\t1\t9999999\t1.00E+14\t55\t11545680\t2.87E+12
+LAB BLOOD ORDERABLES\t473201\tLABYQUIN\tQUINIDINE\tQUINIDINE\tQUINIDINE\t2.5\t262200\t1\t2.5\t6.25\t1\t262200\t68748840000
+LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t9999999\t9330\t1\t9999999\t1.00E+14\t2\t18660\t215139600
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tENO\tENOLASE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tGPILS\tGPI, LOW SUBSTRATE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tNPHOS\tPURINE NUCLEOSIDE PHOSPHORYLASE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tPKLS\tPK, LOW SUBSTRATE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t473221\tLAB11092R\tRENIN ACTIVITY, PLASMA\tRENIN\tRENIN ACTIVITY, PL\t9999999\t279300\t1\t9999999\t1.00E+14\t1\t279300\t78008490000
+LAB BLOOD ORDERABLES\t473271\tLABRIPA\tRISTOCETIN INDUCED PLATELET AGGREGATION\tRIPDIR\tINTERPRETED BY\t9999999\t97431.42857\t1\t9999999\t1.00E+14\t7\t682020\t1.20E+11
+LAB BLOOD ORDERABLES\t473290\tLABSCHIG\tSCHISTOSOMA IGG AB ELISA\tSCHIG1\tSCHISTOSOMA ,LGG\t9999999\t323778.8235\t1\t9999999\t1.00E+14\t51\t16512720\t6.74E+12
+LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14RES\tRESULTS\t9999999\t1210908\t1\t9999999\t1.00E+14\t5\t6054540\t9.00E+12
+LAB BLOOD ORDERABLES\t473409\tLABYT3UP\tT3 UPTAKE\tYT7P2\tT-UPTAKE %\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t473432\tLABTHAMP\tTHERMAL AMPLITUDE SCREEN\tTHRAMP\tTHERMAL AMPLITUDE\t9999999\t479657.1429\t1\t9999999\t1.00E+14\t7\t3357600\t7.81E+12
+LAB BLOOD ORDERABLES\t473444\tLAB11097R\tTSI, SERUM\tTSIG\tTHROID-STIMULATING IG, SERUM\t9999999\t253500\t1\t9999999\t1.00E+14\t1\t253500\t64262250000
+LAB BLOOD ORDERABLES\t473463\tLABTOXOP2\tTOXOPLASMOSIS PCR\tTOXGG\tGENERAL GUIDELINES\t9999999\t1116100\t1\t9999999\t1.00E+14\t6\t6696600\t7.98E+12
+LAB BLOOD ORDERABLES\t473463\tLABTOXPCR\tTOXOPLASMA PCR\tTOXRES\tPCR RESULT\t9999999\t1052324\t1\t9999999\t1.00E+14\t30\t31569720\t6.51E+13
+LAB BLOOD ORDERABLES\t473675\tLABTRAZO\tTRAZODONE\tTRAZO\tTRAZODONE\t9999999\t251220\t1\t9999999\t1.00E+14\t1\t251220\t63111488400
+LAB BLOOD ORDERABLES\t473708\tLABREF5\tLAB UNLISTED 5\tRESUL5\tRESULT\t9999999\t5643312\t1\t9999999\t1.00E+14\t5\t28216560\t3.83E+14
+LAB BLOOD ORDERABLES\t473747\tLABYVIP\tVASOACTIVE INTESTINAL POLYPEPTIDE\tVIP\tVASOACTIVE INT PEPTIDE\t850\t890520\t1\t850\t722500\t1\t890520\t7.93E+11
+LAB BLOOD ORDERABLES\t473759\tLAB11098R\t1,25-DIHYDROXYVITAMIN D, SERUM\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t28\t164940\t1\t28\t784\t1\t164940\t27205203600
+LAB BLOOD ORDERABLES\t473759\tLAB11098R\tPEDIATRIC 1 25 OH VITAMIN D\t125OHD2\tVITAMIN D, 1,25-DIHYDRO\t76\t222660\t1\t76\t5776\t1\t222660\t49577475600
+LAB BLOOD ORDERABLES\t473767\tLABYVITE\tVITAMIN E, SERUM\tVITE\tA-TOCOPHEROL, VITAMIN E\t41.3\t359340\t1\t41.3\t1705.69\t1\t359340\t1.29E+11
+LAB BLOOD ORDERABLES\t473779\tLAB11131R\tVORICONAZOLE LEVEL\tVORI\tVORICONAZOLE LEVEL\t9999999\t559952.1012\t1\t9999999\t1.00E+14\t514\t287815380\t2.74E+14
+LAB BLOOD ORDERABLES\t473796\tLABYWNILE\tWEST NILE VIRUS AB, IGG AND IGM\tWNVIGG\tWEST NILE VIRUS AB IGG\t9999999\t286493.3333\t1\t9999999\t1.00E+14\t252\t72196320\t3.25E+13
+LAB BLOOD ORDERABLES\t497055\tLABRHTYP\tRH BLOOD TYPE\tRHTYP\tRH BLOOD TYPE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t497329\tLABSPAGD\tS. PNEUMONIAE AG DETECTION LA\tSPNAG\tS PNEUMONIAEAG\t9999999\t2758074.146\t1\t9999999\t1.00E+14\t41\t113081040\t2.48E+15
+LAB BLOOD ORDERABLES\t497331\tLABPABN\tPOLIO AB, NEUTRALIZATION\tPOL1\tPOLIO 1\t9999999\t1740\t1\t9999999\t1.00E+14\t1\t1740\t3027600
+LAB BLOOD ORDERABLES\t497331\tLABPABN\tPOLIO AB, NEUTRALIZATION\tPOL3\tPOLIO 3\t9999999\t1740\t1\t9999999\t1.00E+14\t1\t1740\t3027600
+LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tHPEM\tPRECORE MUTATION\t9999999\t612900\t1\t9999999\t1.00E+14\t1\t612900\t3.76E+11
+LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tHPSOL\tPOLYMERASE MUTATION\t9999999\t612900\t1\t9999999\t1.00E+14\t1\t612900\t3.76E+11
+LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tHSPOL\tHEP B VIR GENOTYPING\t9999999\t612900\t1\t9999999\t1.00E+14\t1\t612900\t3.76E+11
+LAB BLOOD ORDERABLES\t497343\tLABCMVG\tCMV GENOTYPE\tCUL54\tUL54 MUTATION\t9999999\t1217280.769\t1\t9999999\t1.00E+14\t78\t94947900\t1.25E+14
+LAB BLOOD ORDERABLES\t497420\tLABASPID\tASPERGILLUS AB, ID, S\tAFLUMI\tASPERGILLUS FUMIGATUS\t9999999\t376384.5\t1\t9999999\t1.00E+14\t40\t15055380\t6.40E+12
+LAB BLOOD ORDERABLES\t497432\tLABFHTLVQ\tHTLV-1/2  DNA QUALITATIVE\tHTLII\tHTLV-II DNA\t9999999\t244110.566\t1\t9999999\t1.00E+14\t53\t12937860\t1.04E+13
+LAB BLOOD ORDERABLES\t497434\tLABFENTA\tENTAMOEBA HISTOLYTICA AB\tENTA\tE.HISTOLYTICA LGG\t9999999\t368756.3415\t1\t9999999\t1.00E+14\t82\t30238020\t1.38E+13
+LAB BLOOD ORDERABLES\t497454\tLABFPARVD\tPARVOVIRUS B19 PCR QUALITATIVE REALTIME\tPARVD\tPARVOVIRUS DNA\t9999999\t247459.7872\t1\t9999999\t1.00E+14\t94\t23261220\t1.27E+13
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\t12015R\tQ FEVER IGM PHASE LI TITER\t9999999\t228210\t1\t9999999\t1.00E+14\t4\t912840\t2.66E+11
+LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV-1 AB, WESTERN BLOT\tHVIG\tHIV 1 AB CONFIRMATION\t9999999\t369630.9278\t1\t9999999\t1.00E+14\t97\t35854200\t3.79E+13
+LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBRASDD\tTOBRAMYCIN, SINGLE DAILY DOSE\t4.4\t10500\t1\t4.4\t19.36\t1\t10500\t110250000
+LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBUN2\tDATE & TIME OF LAST DOSE\t9999999\t10500\t1\t9999999\t1.00E+14\t1\t10500\t110250000
+LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBUN3\tDOSAGE\t9999999\t10500\t1\t9999999\t1.00E+14\t1\t10500\t110250000
+LAB BLOOD ORDERABLES\t497518\tLABTOBUNL\tTOBRAMYCIN SINGLE DAILY DOSE LEVEL\tTOBUN4\tADMINISTRATION ROUTE\t9999999\t10500\t1\t9999999\t1.00E+14\t1\t10500\t110250000
+LAB BLOOD ORDERABLES\t541652\tLABAS4I\tAB SCREEN\tSPEXP\tSPECIMEN EXPIRATION DATE/TIME\t9999999\t6960\t1\t9999999\t1.00E+14\t1\t6960\t48441600
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL0ACTH\tLEFT 0 MINS\t23.5\t#VALUE!\t1\t23.5\t552.25\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL10ACT\tLEFT +10 MINS\t82\t#VALUE!\t1\t82\t6724\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL15ACT\tLEFT + 15 MINS\t99.5\t#VALUE!\t1\t99.5\t9900.25\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL3ACTH\tLEFT +3 MINS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tL5ACTH\tLEFT +5 MINS\t125\t#VALUE!\t1\t125\t15625\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tLM5ACT\tLEFT - 5 MINS\t23.3\t#VALUE!\t1\t23.3\t542.89\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP0ACTH\tPERIPHERAL 0 MINS\t22.4\t#VALUE!\t1\t22.4\t501.76\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP10ACT\tPERIPHERAL +10 MINS\t56\t#VALUE!\t1\t56\t3136\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP15ACT\tPERIPHERAL +15 MINS\t50.1\t#VALUE!\t1\t50.1\t2510.01\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP3ACTH\tPERIPHERAL +3 MINS\t29.9\t#VALUE!\t1\t29.9\t894.01\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tP5ACTH\tPERIPHERAL +5  MINS\t46.9\t#VALUE!\t1\t46.9\t2199.61\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tPM5ACT\tPERIPHERAL -5 MINS\t22\t#VALUE!\t1\t22\t484\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR0ACTH\tRIGHT 0 MINS\t65.1\t#VALUE!\t1\t65.1\t4238.01\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR10ACT\tRIGHT +10 MINS\t339\t#VALUE!\t1\t339\t114921\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR15ACT\tRIGHT 15 MINS\t325\t#VALUE!\t1\t325\t105625\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR3ACTH\tRIGHT +3 MINS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tR5ACTH\tRIGHT +5 MINS\t1105\t#VALUE!\t1\t1105\t1221025\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tRM5ACT\tRIGHT -5 MINS\t53.3\t#VALUE!\t1\t53.3\t2840.89\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC1\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC2\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC3\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC4\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541664\tLABPETSIN\tPETROSAL SINUS TEST\tSPAC5\tSPACE FOR PETSIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541674\tLABTHIOMT\tTHIOPURINE METABOLITES\t6MMPNASSESS\t6-MMPN RESULT ASSESSMENT\t9999999\t605484\t1\t9999999\t1.00E+14\t20\t12109680\t8.50E+12
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1IN\tINTERPRETATION\t9999999\t1568100\t1\t9999999\t1.00E+14\t1\t1568100\t2.46E+12
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1RE\tRESULT\t9999999\t1062780\t1\t9999999\t1.00E+14\t4\t4251120\t5.58E+12
+LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\tFPARV2\tPARVOVIRUS IGM INDEX\t9999999\t323929.7143\t1\t9999999\t1.00E+14\t35\t11337540\t7.36E+12
+LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tYDGLIA\tGLIADIN (DEAMIDATED) IGA\t2.7\t218910\t1\t2.7\t7.29\t2\t437820\t1.07E+11
+LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tSMAB\tAUTOANTIBODIES TO SM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t611814\tLABYENABE\tEXTRACTABLE NUCLEAR AG EVALUATION, SERUM\tU1RNPAB\tAUTOANTIBODIES TO U1RNP\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB1\tCOXSACKIE B1 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
+LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB2\tCOXSACKIE B2 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
+LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB3\tCOXSACKIE B3 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
+LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB4\tCOXSACKIE B4 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
+LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB5\tCOXSACKIE B5 AB\t9999999\t229080\t1\t9999999\t1.00E+14\t1\t229080\t52477646400
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tPLV1\tPOLIOVIRUS 1 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tPLV2\tPOLIOVIRUS 2 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tPLV3\tPOLIOVIRUS 3 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB1\tCOXSACKIE B1 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB2\tCOXSACKIE B2 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB3\tCOXSACKIE B3 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB4\tCOXSACKIE B4 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB5\tCOXSACKIE B5 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYB6\tCOXSACKIE B6 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP10\tCOXSACKIE A10 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP16\tCOXSACKIE A16 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP7\tCOXSACKIE A7 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP9\tCOXSACKIE A9 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE11\tECHOVIRUS 11 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE30\tECHOVIRUS 30 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE4\tECHOVIRUS 4 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE7\tECHOVIRUS 7 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYPE9\tECHOVIRUS 9 AB\t9999999\t335940\t1\t9999999\t1.00E+14\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tRHFINT\tINTERPRETATION\t9999999\t2437250.769\t1\t9999999\t1.00E+14\t26\t63368520\t2.95E+15
+LAB BLOOD ORDERABLES\t668164\tLABDCMVAB\tDONOR, CMV TOTAL ANTIBODIES\tCMVAB1\tDONOR, CMV TOTAL ANTIBODIES\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668166\tLABDHBCAB\tDONOR, HEP B CORE TOTAL AB\tHBCAB1\tDONOR, HEP B CORE TOTAL AB\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668170\tLABDHBAG\tDONOR, HEP B SURFACE AG\t11891R\tDONOR, HBSAG CONFIRMATION\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668170\tLABDHBAG\tDONOR, HEP B SURFACE AG\tHBAG1\tDONOR, HEP B SURFACE AG\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668172\tLABDHIVAB\tDONOR,HIV-1/HIV-2 AB SCN W/RFL\t11888R\tDONOR, HIV-1/2 AB, CONF.\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668172\tLABDHIVAB\tDONOR,HIV-1/HIV-2 AB SCN W/RFL\tHIVAB1\tDONOR,HIV-1/HIV-2 AB SCN W/RFL\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668174\tLABDHTLVQ\tDONOR, HTLV-I/II AB SCN\tHTLVQ1\tDONOR, HTLV-I/II AB SCN\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668176\tLABDRPR\tDONOR, RPR W/RFL TO SYPH IGG\t11892R\tDONOR, SYPHILIS IGG\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668176\tLABDRPR\tDONOR, RPR W/RFL TO SYPH IGG\tRPR1\tDONOR, RPR W/RFL TO SYPH IGG\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t668178\tLABDRPRS\tDONOR SYPHILIS SCREEN\tRPRS1\tDONOR SYPHILIS SCREEN\t9999999\t208950\t1\t9999999\t1.00E+14\t2\t417900\t91078650000
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITMD\tINTERPRETED BY\t9999999\t818253.1915\t1\t9999999\t1.00E+14\t188\t153831600\t3.04E+14
+LAB BLOOD ORDERABLES\t670700\tLAB11195R\tN-TELOPEPTIDE, CROSS-LINKED\t11196R\tN-TELOPEPTIDE, CROSS-LINKED\t26\t238920\t1\t26\t676\t1\t238920\t57082766400
+LAB BLOOD ORDERABLES\t670810\tLAB11173R\tIODINE\t11174R\tIODINE\t77\t189300\t1\t77\t5929\t1\t189300\t35834490000
+LAB BLOOD ORDERABLES\t744318\tLAB11599R\tGANICICLOVIR\t11756R\tGANCICLOVIR\t9999999\t636120\t1\t9999999\t1.00E+14\t1\t636120\t4.05E+11
+LAB BLOOD ORDERABLES\t744320\tLAB11604R\tIL 5\t12044R\tINTERLEUKIN 5\t166\t409320\t1\t166\t27556\t1\t409320\t1.68E+11
+LAB BLOOD ORDERABLES\t744324\tLAB11607R\tCOENZYME Q10, REDUCED & TOTAL\t11763R\tINTERPRETATION\t9999999\t282690\t1\t9999999\t1.00E+14\t10\t2826900\t9.22E+11
+LAB BLOOD ORDERABLES\t744330\tLAB11610R\tDONOR, HTLVS(QNS/LIPEMIC ONLY)\t11769R\tDONOR, HTLVS\t9999999\t668460\t1\t9999999\t1.00E+14\t3\t2005380\t1.52E+12
+LAB BLOOD ORDERABLES\t744368\tLABLEGS\tLEGIONELLA SEROLOGY\tLEG1\tL.LEGIONELLA AB (SEROGRP 1)\t9999999\t466080\t1\t9999999\t1.00E+14\t1\t466080\t2.17E+11
+LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPA\tPROCAINAMIDE\t9.1\t6420\t1\t9.1\t82.81\t1\t6420\t41216400
+LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL1\tPEAK, TROUGH, OR RANDOM\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
+LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL2\tDATE & TIME OF LAST DOSE\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
+LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL3\tDOSAGE (INCLUDE UNITS)\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
+LAB BLOOD ORDERABLES\t862875\tLABPALL\tPROCAINAMIDE\tPAL4\tADMINISTRATION ROUTE\t9999999\t6420\t1\t9999999\t1.00E+14\t1\t6420\t41216400
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\t12430R\tINTERPRETATION\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tCTIGA\tC.TRACHOMATIS IGA\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tCTIGG\tC.TRACHOMATIS IGG\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tCTIGM\tC.TRACHOMATIS IGM\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPNIGA\tC. PNEUMONIAE IGA\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPNIGG\tC. PNEUMONIAE IGG\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPNIGM\tC. PNEUMONIAE IGM\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPSIGA\tC. PSITTACI IGA\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPSIGG\tC. PSITTACI IGG\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\tPSIGM\tC. PSITTACI IGM\t9999999\t165960\t1\t9999999\t1.00E+14\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGDM1\t1HR\t84\t17220\t1\t84\t7056\t1\t17220\t296528400
+LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGDM2\t2HR\t80\t17220\t1\t80\t6400\t1\t17220\t296528400
+LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGLUF\tGLUCOSE, FASTING (GDMF)\t70\t17220\t1\t70\t4900\t1\t17220\t296528400
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tCENQNT\tANTI-CENTROMERE LEVEL\t43\t279300\t1\t43\t1849\t1\t279300\t78008490000
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81DX\tCLINICAL INDICATION\t9999999\t757500\t1\t9999999\t1.00E+14\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81RE\tRESULT, NON-BLOOD\t9999999\t757500\t1\t9999999\t1.00E+14\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81SO\tSOURCE\t9999999\t757500\t1\t9999999\t1.00E+14\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88RE\tRESULT\t9999999\t1389780\t1\t9999999\t1.00E+14\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSMDB\tINTERPRETED BY\t9999999\t940852.5\t1\t9999999\t1.00E+14\t112\t105375480\t2.64E+14
+LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSRE\tRESULT\t9999999\t371657.1429\t1\t9999999\t1.00E+14\t7\t2601600\t1.85E+12
+LAB BLOOD ORDERABLES\t890117\tLAB12230R\tSOLUBLE IL 2R\t12231R\t12231R\t9999999\t697333.9535\t1\t9999999\t1.00E+14\t43\t29985360\t7.88E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12254R\t12254R\t9999999\t925412.7273\t1\t9999999\t1.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12255R\t12255R\t9999999\t925412.7273\t1\t9999999\t1.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12278R\t12278R\t9999999\t925412.7273\t1\t9999999\t1.00E+14\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890129\tLAB12279R\tCVID CONFIRMATION FLOW PANEL\t12280R\t12280R\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t890129\tLAB12279R\tCVID CONFIRMATION FLOW PANEL\t12281R\t12281R\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t890137\tLABSTDY1\tSTUDY TEST\tRESS1\tRESS1\t9999999\t671067.3054\t1\t9999999\t1.00E+14\t167\t112068240\t1.25E+14
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3DX\tCLINICAL INDICATION\t9999999\t190860\t1\t9999999\t1.00E+14\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3RE\tRESULT, BLOOD\t9999999\t190860\t1\t9999999\t1.00E+14\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3DX\tCLINICAL INDICATION\t9999999\t480\t1\t9999999\t1.00E+14\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3SP\tSOURCE\t9999999\t480\t1\t9999999\t1.00E+14\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLRE\tRESULT\t9999999\t1418550\t1\t9999999\t1.00E+14\t2\t2837100\t4.13E+12
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tC48RAT\tTCD4/CD8 RATIO\t1.4\t126180\t1\t1.4\t1.96\t1\t126180\t15921392400
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD19AB\tCD19 (PAN B), ABS.\t55\t126180\t1\t55\t3025\t1\t126180\t15921392400
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD19PC\tCD19 (PAN B) %\t17\t126180\t1\t17\t289\t1\t126180\t15921392400
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tCD4TOT\tCD4 TOTAL\t41\t126180\t1\t41\t1681\t1\t126180\t15921392400
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tTCD4AB\tTOTAL CD4, ABS.\t133\t126180\t1\t133\t17689\t1\t126180\t15921392400
+LAB BLOOD ORDERABLES\t912863\tLAB12415R\tACVRL1 AND ENG (HHT)\t12416R\tHHT FGA SPECIMEN\t9999999\t3636840\t1\t9999999\t1.00E+14\t1\t3636840\t1.32E+13
+LAB BLOOD ORDERABLES\t961559\tLAB12817R\tDONOR, HCV AB SCREEN W/RFLX\t12828R\tDONOR, HEPATITIS C AB\t9999999\t177420\t1\t9999999\t1.00E+14\t1\t177420\t31477856400
+LAB BLOOD ORDERABLES\t966907\tLAB12584R\tTIAGABINE CONCENTRATION\t12593R\tTIAGABINE CONCENTRATION\t37\t350400\t1\t37\t1369\t1\t350400\t1.23E+11
+LAB BLOOD ORDERABLES\t977669\tLABPANPLT\tPLATELET COUNT\tPLT\tPLATELET COUNT\t4\t300\t1\t4\t16\t1\t300\t90000
+LAB BLOOD ORDERABLES\t978241\tLABOGTT\tGLUCOSE TOLERANCE FOR CYSTIC FIBROSIS\tGLT1\tGLUCOSE, 1 HR\t283\t25440\t1\t283\t80089\t1\t25440\t647193600
+LAB BLOOD ORDERABLES\t978241\tLABOGTT\tGLUCOSE TOLERANCE FOR CYSTIC FIBROSIS\tGLT2\tGLUCOSE, 2 HR\t339\t25440\t1\t339\t114921\t1\t25440\t647193600
+MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tSDES\tSPECIMEN\t9999999\t255120\t1\t9999999\t1.00E+14\t1\t255120\t65086214400
+MICROBIOLOGY ORDERABLES\t471620\tLABANERB\tANAEROBIC CULTURE BB\tCULT\tCULT/OTHER RSLT:\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tCULT\tCULT/OTHER RSLT:\t9999999\t522530.9375\t1\t9999999\t1.00E+14\t384\t200651880\t3.85E+14
+MICROBIOLOGY ORDERABLES\t471771\tLABBXTG\tBIOPSY/TISSUE(GEN)GS\tGS\tGRAM STAIN\t9999999\t522530.9375\t1\t9999999\t1.00E+14\t384\t200651880\t3.85E+14
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tAMP\tAMPICILLIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tATM\tAZTREONAM.\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCIP\tCIPROFLOXACIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCRO\tCEFTRIAXONE\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCTN\tCEFOTETAN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCTX\tCEFOTAXIME\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tCZ\tCEFAZOLIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tFEP\tCEFEPIME\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tFURI\tCEFUROXIME (IV)\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tGM\tGENTAMICIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tIMP\tIMIPENEM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tLEV\tLEVOFLOXACIN\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tMER\tMEROPENEM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tMTYP\tMETHOD\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSAMP\tAMPICILLIN/SULBACTAM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tSXT\tTRIMETHOPRIM/SULFAMETHOXAZOLE\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471779\tLABBLC\tBLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)\tTZP\tPIPERACILLIN/TAZOBACTAM\t9999999\t418080\t1\t9999999\t1.00E+14\t1\t418080\t1.75E+11
+MICROBIOLOGY ORDERABLES\t471790\tLABBMS\tBMT STERILITY SAMPLE\tCULT\tCULT/OTHER RSLT:\t9999999\t1190220\t1\t9999999\t1.00E+14\t2\t2380440\t2.83E+12
+MICROBIOLOGY ORDERABLES\t471794\tLABBMC\tBONE MARROW CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t418604.2105\t1\t9999999\t1.00E+14\t19\t7953480\t3.62E+12
+MICROBIOLOGY ORDERABLES\t472395\tLABFLDC\tFLUID CULTURE AND GRAM STAIN\tORG\tORGANISM\t9999999\t29170200\t1\t9999999\t1.00E+14\t1\t29170200\t8.51E+14
+MICROBIOLOGY ORDERABLES\t472433\tLABFCULS\tFUNGAL CULTURE, SKIN/NAIL\tCULT\tCULT/OTHER RSLT:\t9999999\t1542888\t1\t9999999\t1.00E+14\t10\t15428880\t2.97E+13
+MICROBIOLOGY ORDERABLES\t472454\tLABGENC\tGENITAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t313706.25\t1\t9999999\t1.00E+14\t32\t10038600\t5.00E+12
+MICROBIOLOGY ORDERABLES\t472458\tLABGIARS\tGIARDIA SCREEN\tCULT\tCULT/OTHER RSLT:\t9999999\t213232.1186\t1\t9999999\t1.00E+14\t236\t50322780\t1.42E+13
+MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tRPT\tRPT\t9999999\t55380\t1\t9999999\t1.00E+14\t1\t55380\t3066944400
+MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tSDES\tSPECIMEN\t9999999\t55380\t1\t9999999\t1.00E+14\t1\t55380\t3066944400
+MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tSREQ\tCOMMENTS\t9999999\t55380\t1\t9999999\t1.00E+14\t1\t55380\t3066944400
+MICROBIOLOGY ORDERABLES\t472680\tLABISOSR\tISOLATOR CULTURE SPECIAL REQUEST\tCULT\tCULT/OTHER RSLT:\t9999999\t1747302.857\t1\t9999999\t1.00E+14\t14\t24462240\t4.57E+13
+MICROBIOLOGY ORDERABLES\t472741\tLABLEGA\tLEGIONELLA CULTURE, ACID\tCULT\tCULT/OTHER RSLT:\t9999999\t616054.2857\t1\t9999999\t1.00E+14\t14\t8624760\t5.65E+12
+MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tRPT\tRPT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473311\tLABSHSV\tHSV SHELL VIAL CULTURE\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLC\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t330942.4585\t1\t9999999\t1.00E+14\t301\t99613680\t3.68E+13
+MICROBIOLOGY ORDERABLES\t473437\tLABTHS\tGROUP A STREP CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t191605.7143\t1\t9999999\t1.00E+14\t21\t4023720\t8.21E+11
+MICROBIOLOGY ORDERABLES\t473680\tLABTMONA\tTRICHOMONAS EXAM\tCULT\tCULT/OTHER RSLT:\t9999999\t309040\t1\t9999999\t1.00E+14\t3\t927120\t2.91E+11
+MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tSDES\tSPECIMEN\t9999999\t518040\t1\t9999999\t1.00E+14\t1\t518040\t2.68E+11
+MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tSREQ\tCOMMENTS\t9999999\t518040\t1\t9999999\t1.00E+14\t1\t518040\t2.68E+11
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tAMP\tAMPICILLIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tATM\tAZTREONAM.\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCIP\tCIPROFLOXACIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCRO\tCEFTRIAXONE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCTN\tCEFOTETAN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCTX\tCEFOTAXIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tCZ\tCEFAZOLIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tFEP\tCEFEPIME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tFURI\tCEFUROXIME (IV)\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tGM\tGENTAMICIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tIMP\tIMIPENEM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tLEV\tLEVOFLOXACIN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tMER\tMEROPENEM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tMTYP\tMETHOD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSAMP\tAMPICILLIN/SULBACTAM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tSXT\tTRIMETHOPRIM/SULFAMETHOXAZOLE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t497231\tLABBLC2\tBLOOD CULTURE (2 AEROBIC BOTTLES)\tTZP\tPIPERACILLIN/TAZOBACTAM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t744390\tLABDVEFLA\tDIRECT VIRAL EXAM FLU A\tDVE\tRESULTS\t9999999\t60293.33333\t1\t9999999\t1.00E+14\t9\t542640\t43775150400
+MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tRPT\tRPT\t9999999\t49680\t1\t9999999\t1.00E+14\t1\t49680\t2468102400
+MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tSDES\tSPECIMEN\t9999999\t49680\t1\t9999999\t1.00E+14\t1\t49680\t2468102400
+MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tSREQ\tCOMMENTS\t9999999\t49680\t1\t9999999\t1.00E+14\t1\t49680\t2468102400
+MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tRPT\tRPT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tSDES\tSPECIMEN\t9999999\t48300\t1\t9999999\t1.00E+14\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tSREQ\tCOMMENTS\t9999999\t48300\t1\t9999999\t1.00E+14\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t871582\tLABSHSTGS\tSHC STAT GRAM STAIN\tGS\tGRAM STAIN\t9999999\t3540\t1\t9999999\t1.00E+14\t1\t3540\t12531600
+MICROBIOLOGY ORDERABLES\t884809\tLABROACAN\tACANTHAMOEBA CULTURE\tCULT\tCULT/OTHER RSLT:\t9999999\t2140200\t1\t9999999\t1.00E+14\t1\t2140200\t4.58E+12
+MICROBIOLOGY ORDERABLES\t890162\tLABSTPCR\tTHROAT STREP SCREEN BY PCR\tCULT\tCULT/OTHER RSLT:\t9999999\t114455.7534\t1\t9999999\t1.00E+14\t146\t16710540\t2.41E+12
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCPRM\tPRIMERS\t9999999\t599640\t1\t9999999\t1.00E+14\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCTIS\tSOURCE\t9999999\t599640\t1\t9999999\t1.00E+14\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRDIR\tINTERPRETED BY\t9999999\t485881.3483\t1\t9999999\t1.00E+14\t178\t86486880\t5.83E+13
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRCT\tCOMMENT\t9999999\t77280\t1\t9999999\t1.00E+14\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRMT\tMETHOD\t9999999\t77280\t1\t9999999\t1.00E+14\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRPM\tPRIMERS\t9999999\t77280\t1\t9999999\t1.00E+14\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tFP190\tP190, FLUID\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRE\tRESULT\t9999999\t1367340\t1\t9999999\t1.00E+14\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11MD\tINTERPRETED BY\t9999999\t266970\t1\t9999999\t1.00E+14\t2\t533940\t2.85E+11
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFPMLR\tPML, FLD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15CO\tCOMMENT, FLD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15FL\tSOURCE, FLUID\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15DX\tCLINICAL INDICATION\t9999999\t909120\t1\t9999999\t1.00E+14\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15RE\tRESULT, TISSUE\t9999999\t909120\t1\t9999999\t1.00E+14\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTCMT\tCOMMENT\t9999999\t763860\t1\t9999999\t1.00E+14\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCPRM\tPRIMERS\t9999999\t1195020\t1\t9999999\t1.00E+14\t3\t3585060\t4.32E+12
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCTIS\tSOURCE\t9999999\t1195020\t1\t9999999\t1.00E+14\t3\t3585060\t4.32E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSMDM\tINTERPRETED BY\t9999999\t823243.9024\t1\t9999999\t1.00E+14\t123\t101259000\t9.94E+13
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPMD\tINTERPRETED BY\t9999999\t540720\t1\t9999999\t1.00E+14\t66\t35687520\t2.44E+13
+POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tUGLU\tGLUCOSE URINE\t9999999\t48060\t1\t9999999\t1.00E+14\t2\t96120\t5963220000
+POINT OF CARE (NON LAB) TESTS\t471601\tPOC44\tPOC URINE DIPSTICK GP\tUPROT\tPROTEIN URINE\t9999999\t48060\t1\t9999999\t1.00E+14\t2\t96120\t5963220000
+POINT OF CARE (NON LAB) TESTS\t507568\tPOC1\tPOC URINE DIPSTICK\t11148R\tPH\t8\t-198000\t1\t8\t64\t1\t-198000\t39204000000
+POINT OF CARE (NON LAB) TESTS\t572340\tPOC12\tPOC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)\tPHGAST\tPH,GASTRIC\t1\t-240\t1\t1\t1\t1\t-240\t57600
+POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\t11148R\tPH\t7\t16633500\t1\t7\t49\t1\t16633500\t2.77E+14
+POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tPNAB1\tPNEUMOCOCCAL AB (PNEUMOVAX)\t150\t-93600\t1\t150\t22500\t1\t-93600\t8760960000
+POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tPPD100\tPPD 1:100 CPM\t9999999\t11700\t1\t9999999\t1.00E+14\t1\t11700\t136890000
+POINT OF CARE (NON LAB) TESTS\t591284\tPOC14\tPOC GLUCOSE BY METER\tPPDINT\tPPD INTERPRETATION\t9999999\t-74700\t1\t9999999\t1.00E+14\t1\t-74700\t5580090000
+POINT OF CARE (NON LAB) TESTS\t610682\tPOC32\tPOC HEMOCUE FOR HEMOGLOBIN\tHGB\tPATIENT HEMOGLOBIN RESULT\t9999999\t-240\t1\t9999999\t1.00E+14\t1\t-240\t57600
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\t11147R\tSPECIFIC GRAVITY\t1.01\t-205200\t1\t1.01\t1.0201\t1\t-205200\t42107040000
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tSPG\tSPECIFIC GRAVITY UA\t1\t-78300\t1\t1\t1\t1\t-78300\t6130890000
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tUBLOOD\tBLOOD URINE\t9999999\t-196200\t1\t9999999\t1.00E+14\t2\t-392400\t77150880000
+POINT OF CARE (NON LAB) TESTS\t624778\tPOC39\tPOC URINE FOR PH\tUWBC\tLEUKOCYTES\t9999999\t-205200\t1\t9999999\t1.00E+14\t1\t-205200\t42107040000
+POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tINR\tINR,POCT\t1.2\t35100\t1\t1.2\t1.44\t1\t35100\t1232010000
+POINT OF CARE TESTING\t473185\tLABPCINR\tISTAT INR AND PROTIME\tPTT\tPTT,POCT\t14.4\t35100\t1\t14.4\t207.36\t1\t35100\t1232010000
+POINT OF CARE TESTING\t591290\tPOC17\tPOC ISTAT CREATININE\tCR\tCREATININE,ISTAT\t0.7\t49260\t1\t0.7\t0.49\t1\t49260\t2426547600
+POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tBE\tBASE EXCESS, ISTAT\t1\t57660\t1\t1\t1\t1\t57660\t3324675600
+POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tHCO3V\tHCO3 (V), ISTAT\t27.9\t57660\t1\t27.9\t778.41\t1\t57660\t3324675600
+POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tPCO2SO\tO2 SATURATION, ISTAT\t19\t57660\t1\t19\t361\t1\t57660\t3324675600
+POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tPCO2V\tPCO2 (V), ISTAT\t57.9\t57660\t1\t57.9\t3352.41\t1\t57660\t3324675600
+POINT OF CARE TESTING\t591302\tPOC23\tPOC ISTAT, VENOUS BLOOD GASES (G3+)\tPH\tPH, ISTAT\t7.29\t57660\t1\t7.29\t53.1441\t1\t57660\t3324675600
+POINT OF CARE TESTING\t591304\tPOC24\tPOC ISTAT TROPONIN I\tTNI\tTROPONIN I, POCT\t0\t51000\t1\t0\t0\t1\t51000\t2601000000
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tCAION\tCALCIUM,ION, ISTAT\t1.12\t60\t1\t1.12\t1.2544\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tCR\tCREATININE,ISTAT\t4.7\t60\t1\t4.7\t22.09\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tGLU\tGLUCOSE,ISTAT\t110\t60\t1\t110\t12100\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tHCT\tHCT(CALC), ISTAT\t30\t60\t1\t30\t900\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tHGB\tHGB(CALC), ISTAT\t10.2\t60\t1\t10.2\t104.04\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tK\tPOTASSIUM, ISTAT\t4\t60\t1\t4\t16\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tNA\tSODIUM, ISTAT\t139\t60\t1\t139\t19321\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCAGP\tANION GAP, ISTAT\t16\t60\t1\t16\t256\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCBUN\tBUN, ISTAT\t21\t60\t1\t21\t441\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCCL\tCHLORIDE, ISTAT\t96\t60\t1\t96\t9216\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tPCCOM\tPOCT COMMENT\t9999999\t60\t1\t9999999\t1.00E+14\t1\t60\t3600
+POINT OF CARE TESTING\t1007411\tLABPCCH8\tPOC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2\tTCO2A\tTCO2, ISTAT\t31\t60\t1\t31\t961\t1\t60\t3600
+POINT OF CARE TESTS (MENLO)\t861037\t230986580\tPOC MENLO PPD TEST\tERYTHREMA\tERYTHREMA\t0\t52200\t1\t0\t0\t1\t52200\t2724840000
+POINT OF CARE TESTS (MENLO)\t861037\t230986580\tPOC MENLO PPD TEST\tMMIND\tMM INDURATION\t0\t52200\t1\t0\t0\t1\t52200\t2724840000
+POINT OF CARE TESTS (MENLO)\t861037\t230986580\tPOC MENLO PPD TEST\tPHLOC\tLOCATION\t9999999\t52200\t1\t9999999\t1.00E+14\t1\t52200\t2724840000
+QUEST ORDERABLES\t977479\tLABQUECORSL\tQUEST - CORTISOL, SERUM LC/MS/MS\tXCORTI\tCORTISOL TOTAL SERUM\t6.2\t-345600\t1\t6.2\t38.44\t1\t-345600\t1.19E+11
+STOOL ORDERABLES\t471510\tLABY5NUC\t5 - NUCLEOTIDASE\t5NUCLEOT\t5\'NUCLEOTIDASE\t67.6\t187770\t1\t67.6\t4569.76\t2\t375540\t81594795600
+STOOL ORDERABLES\t471612\tLABYAMPHB\tAMPHOTERICIN B\tAMPHOTERB\tAMPHOTERICIN B\t9.9\t#VALUE!\t1\t9.9\t98.01\t0\tNone\tNone
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tMG\tMAGNESIUM\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tPHOS\tPHOSPHORUS\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYANTQ\tANTHRAQUINONES\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYBIS\tBISACODYL\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYOXY\tOXYPHENISATIN\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYPHEN\tPHENOLPHTHALEIN\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL1\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL2\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL3\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL4\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL5\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t471941\tLABYUCLAX\tCATHARTIC LAXATIVE PANEL, URINE\tYRL6\tREPORTING LIMIT\t9999999\t101160\t1\t9999999\t1.00E+14\t1\t101160\t10233345600
+STOOL ORDERABLES\t472304\tLAB11118R\tELASTASE, STOOL\tSTELAS\tELASTASE-1, STOOL\t9999999\t547750.1538\t1\t9999999\t1.00E+14\t130\t71207520\t6.17E+13
+STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG2\tTOTAL WEIGHT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG3\tCOLLECTION DURATION\t9999999\t280440\t1\t9999999\t1.00E+14\t1\t280440\t78646593600
+STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG4\tCONCENTRATION\t9999999\t280440\t1\t9999999\t1.00E+14\t1\t280440\t78646593600
+STOOL ORDERABLES\t472823\tLABFECFIB\tMEAT FIBERS, FECAL\tFECFB\tMEAT FIBERS, FECAL\t1\t1306350\t1\t1\t1\t2\t2612700\t4.90E+12
+STOOL ORDERABLES\t473114\tLABSTPOLY\tPMNS / EOSINOPHILS, STOOL\tEOSSTL\tEOSINOPHILS, STOOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t473114\tLABSTPOLY\tPMNS / EOSINOPHILS, STOOL\tPOLYST\tPMNS, STOOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t473120\tLABPORFEC\tPORPHYRINS FRACTIONATED, FECAL\tFPORIN\tPORPHYRIN INTRP, FEC\t9999999\t495795\t1\t9999999\t1.00E+14\t4\t1983180\t1.03E+12
+STOOL ORDERABLES\t473151\tLABYPA\tPROCAINAMIDE\tNAPA\tNAPA\t1.5\t88380\t1\t1.5\t2.25\t1\t88380\t7811024400
+STOOL ORDERABLES\t473151\tLABYPA\tPROCAINAMIDE\tYPANAP\tPROCAINAMIDE + NAPA\t6\t88380\t1\t6\t36\t1\t88380\t7811024400
+STOOL ORDERABLES\t473151\tLABYPA\tPROCAINAMIDE\tYPROC\tPROCAINAMIDE\t4.5\t88380\t1\t4.5\t20.25\t1\t88380\t7811024400
+STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK2\tSPOTTED FEVER GROUP,AB,IGM,S\t9999999\t236916\t1\t9999999\t1.00E+14\t30\t7107480\t2.42E+12
+STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK3\tTYPHUS FEVER GROUP,AB,IGG,S\t9999999\t236916\t1\t9999999\t1.00E+14\t30\t7107480\t2.42E+12
+STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK4\tTYPHUS FEVER GROUP,AB,IGM,S\t9999999\t236916\t1\t9999999\t1.00E+14\t30\t7107480\t2.42E+12
+STOOL ORDERABLES\t473433\tLABYTHI\tTHIOCYANATE\tTHIOCYAN\tTHIOCYANATE\t9999999\t638707.8261\t1\t9999999\t1.00E+14\t23\t14690280\t1.69E+13
+STOOL ORDERABLES\t473686\tLABYSTRYP\tTRYPSIN, FECAL\tTRYPSINSTL\tTRYPSIN, FECAL\t9999999\t636156\t1\t9999999\t1.00E+14\t5\t3180780\t2.42E+12
+STOOL ORDERABLES\t497243\tLABE2O\tESTRADIOL\tESTRADIOL\tESTRADIOL\t394\t#VALUE!\t1\t394\t155236\t0\tNone\tNone
+STOOL ORDERABLES\t541693\tLABHBCM\tHEPATITIS B CORE / PRECORE MUTATION\tHBCM2\tPRECORE CODON\t9999999\t873165\t1\t9999999\t1.00E+14\t8\t6985320\t7.15E+12
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11300R\tCHLORPROPAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11301R\tTOLAZAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11302R\tTOLBUTAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11303R\tGLYBURIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11304R\tGLIPIZIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11305R\tACETOHEXAMIDE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t668093\tLABCYLX\tIMMUNE FUNCTION TEST\tCYLX1\tIMMUNE FUNCTION TEST RESULT\t9999999\t321704.1176\t1\t9999999\t1.00E+14\t68\t21875880\t2.40E+13
+STOOL ORDERABLES\t670704\tLAB11269R\tACETYL RECEPTOR AB PANL\tACRBIN\tACETYLCHOLINE BIN AB\t0\t#VALUE!\t1\t0\t0\t0\tNone\tNone
+STOOL ORDERABLES\t670704\tLAB11269R\tACETYL RECEPTOR AB PANL\tACRBLO\tACETYLCHOLINE BLOCKING AB\t0\t#VALUE!\t1\t0\t0\t0\tNone\tNone
+STOOL ORDERABLES\t670704\tLAB11269R\tACETYL RECEPTOR AB PANL\tACRMOD\tACETYLCHOLINE MODULATING AB\t8\t#VALUE!\t1\t8\t64\t0\tNone\tNone
+STOOL ORDERABLES\t670760\tLAB11234R\tVZV TOTAL AND IGM AB PANEL\t11237R\tVZV INTERPRETATION\t9999999\t214763.038\t1\t9999999\t1.00E+14\t237\t50898840\t1.48E+13
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11255R\tORGANISM\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11256R\tAMIKACIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11257R\tCIPROFLOXACIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11258R\tCLARITHROMYCIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11259R\tETHAMBUTOL\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11260R\tLINEZOLID\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11261R\tMOXIFLOXACIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11262R\tRIFAMPIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11263R\tRIFABUTIN\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11897R\tETHIONAMIDE\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11898R\tISONIAZID\t9999999\t455400\t1\t9999999\t1.00E+14\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670764\tLAB11265R\tENTAMOEBA HISTOLYTICA AG\t11266R\tENTAMOABA HISTOLYTICA AG\t9999999\t249417.8571\t1\t9999999\t1.00E+14\t28\t6983700\t2.23E+12
+STOOL ORDERABLES\t744266\tLAB11568R\tBRUCELLA SEROLOGIES, CSF\t11682R\tBRUCELLA IGG,CSF\t0.1\t272970\t1\t0.1\t0.01\t2\t545940\t1.68E+11
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11724R\tCANDIDA ALBICANS, DILUTION 1\t1.8\t#VALUE!\t1\t1.8\t3.24\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11727R\tCANDIDA ALBICANS, DILUTION 4\t4.9\t#VALUE!\t1\t4.9\t24.01\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11729R\tTETANUS TOXOID, DILUTION 1\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11731R\tTETANUS TOXOID, DILUTION 3\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11732R\tTETANUS TOXOID, DILUTION 4\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12291R\t12291R\t9999999\t342800\t1\t9999999\t1.00E+14\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tLABS2\tLABS2\t9999999\t427020\t1\t9999999\t1.00E+14\t2\t854040\t3.80E+11
+STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL2\tTOTAL WEIGHT\t756\t1815000\t1\t756\t571536\t2\t3630000\t6.65E+12
+STOOL ORDERABLES\t910604\tLAB12503R\tPOTASSIUM, 24-HR STOOL\tYSTK2\tTOTAL WEIGHT\t9999999\t310750\t1\t9999999\t1.00E+14\t6\t1864500\t1.50E+12
+TIMED URINE\t473737\tLABUPIET\tURINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)\tUPIER\tRESULT\t9999999\t309620\t1\t9999999\t1.00E+14\t18\t5573160\t2.51E+12
+URINE ORDERABLES\t472442\tLABGHBUR\tGAMMA - HYDROXYBUTYRIC ACID, URINE\tGHBURN\tG-OH-BUTYRIC AC,UR\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tGLUEX\tGLUCOSE EXCRETION\t364\t7080\t1\t364\t132496\t1\t7080\t50126400
+URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tGLUURN\tGLUCOSE, URINE\t56\t7080\t1\t56\t3136\t1\t7080\t50126400
+URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tUT7\tCOLLECTION PERIOD GLUCOSE\t24\t7080\t1\t24\t576\t1\t7080\t50126400
+URINE ORDERABLES\t472480\tLABUGLT\tGLUCOSE, URINE (TIMED)\tVOL7\tVOLUME GLUCOSE\t650\t7080\t1\t650\t422500\t1\t7080\t50126400
+URINE ORDERABLES\t472927\tLABMPSQNT\tMUCOPOLYSACCHARIDES QUANTITATIVE, URINE\tMPS\tMUCOPOLYSAC, UR\t3.9\t#VALUE!\t1\t3.9\t15.21\t0\tNone\tNone
+URINE ORDERABLES\t473092\tLABYUPCPC\tPHENCYCLIDINE CONFIRMATION, URINE\tYUPCGC\tPHENCYCLIDINE\t30\t#VALUE!\t1\t30\t900\t0\tNone\tNone
+URINE ORDERABLES\t473730\tLABUA\tURINALYSIS WITH MICROSCOPIC\tCLNTST\tCLINITEST\t9999999\t4860\t1\t9999999\t1.00E+14\t1\t4860\t23619600
+URINE ORDERABLES\t473731\tLABUAMICR\tURINALYSIS MICROSCOPIC ONLY\tURBC\tRBC, URINE\t9999999\t2220\t1\t9999999\t1.00E+14\t1\t2220\t4928400
+URINE ORDERABLES\t473731\tLABUAMICR\tURINALYSIS MICROSCOPIC ONLY\tUWBC\tWBC, URINE\t9999999\t2220\t1\t9999999\t1.00E+14\t1\t2220\t4928400
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tCLNTST\tCLINITEST\t9999999\t1680\t1\t9999999\t1.00E+14\t1\t1680\t2822400
+URINE ORDERABLES\t473770\tLAB12845R\tVMA AND HVA, PEDIATRIC, URINE\t12843R\tCHILD VMA\t9999999\t234900\t1\t9999999\t1.00E+14\t1\t234900\t55178010000
+URINE ORDERABLES\t473770\tLAB12845R\tVMA AND HVA, PEDIATRIC, URINE\t12844R\tCHILD HVA\t9999999\t234900\t1\t9999999\t1.00E+14\t1\t234900\t55178010000
+URINE ORDERABLES\t497400\tLABYDROTC\tDRUG SCREEN PRESCRIPTION / OTC\t12194R\tDRUGS DETECTED:\t9999999\t212726.6667\t1\t9999999\t1.00E+14\t9\t1914540\t6.68E+11
+URINE ORDERABLES\t497400\tLABYDROTC\tDRUG SCREEN PRESCRIPTION / OTC\t12195R\tSUSPECT DRUG\t9999999\t212726.6667\t1\t9999999\t1.00E+14\t9\t1914540\t6.68E+11
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tALCOHOL\tALCOHOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tAMPHET\tAMPHETAMINES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tBARB\tBARBITURATES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tBENZO\tBENZODIAZEPINES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tCOCAINE\tCOCAINE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tMETHADONE\tMETHADONE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tOPIATE\tOPIATES\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tPROPOX\tPROPOXYPHENE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tYUPCP\tPHENCYCLIDINE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t517501\tLABYUCD12\tCONFIRMED DRUG ABUSE SURVEY 12\tYUTTHC\tTETRAHYDROCANNABINOL\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11220R\tIDODINE, U\t320\t157260\t1\t320\t102400\t1\t157260\t24730707600
+URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11221R\tCOLLECTION DURATION\t24\t157260\t1\t24\t576\t1\t157260\t24730707600
+URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11222R\tURINE VOLUME\t2905\t157260\t1\t2905\t8439025\t1\t157260\t24730707600
+URINE ORDERABLES\t670822\tLAB11219R\tIODINE,U\t11223R\tIODINE CONCENTRATION\t110\t157260\t1\t110\t12100\t1\t157260\t24730707600
+URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t11226R\tCOLLECTION DURATION\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
+URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t11227R\tURINE VOLUME\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
+URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t11228R\tPB CONC\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
+URINE ORDERABLES\t670826\tLAB11225R\tLEAD,U\t12199R\tLEAD, 24 HR, U\t9999999\t218400\t1\t9999999\t1.00E+14\t1\t218400\t47698560000
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\tVOLUMEURN\tURINE VOLUME\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\tYUMECO\tCOMMENT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+URINE ORDERABLES\t670828\tLAB11229R\tMETANEPHRINE,FRACT,RANM U\tYUMEUT\tCOLLECTION DURATION\t9999999\t471510\t1\t9999999\t1.00E+14\t2\t943020\t4.75E+11
+VIROLOGY ORDERABLES\t472015\tLABCLCR2\tCHLAMYDIA MOLECULAR DETECTION\tDCL4\tTEST INFORMATION\t9999999\t105230\t1\t9999999\t1.00E+14\t12\t1262760\t1.93E+11
+VIROLOGY ORDERABLES\t472201\tLABDVERO\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tDVE\tRESULTS\t9999999\t68157\t1\t9999999\t1.00E+14\t60\t4089420\t4.28E+11
+VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tRPT\tRPT\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSDES\tSPECIMEN\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tSREQ\tCOMMENTS\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472254\tLABEBPCRQ\tEBV PCR QUANTITATIVE\tEBV\tEBV BY PCR\t9999999\t-518400\t1\t9999999\t1.00E+14\t1\t-518400\t2.69E+11
+VIROLOGY ORDERABLES\t472525\tLABHBSQL\tANTI-HBS QUAL\tHBSAB\tHEPATITIS B SURFACE AB QUAL\t9999999\t22020\t1\t9999999\t1.00E+14\t1\t22020\t484880400
+VIROLOGY ORDERABLES\t472530\tLABHSAB\tHSV VIRAL AB\tHSAB\tVIRAL AB HSV\t9999999\t238239.8507\t1\t9999999\t1.00E+14\t134\t31924140\t1.03E+13
+VIROLOGY ORDERABLES\t472557\tLABHIVQ2\tHIV QUANTITATIVE PCR\tHIVIPR\tTEST DESCRIPTION\t9999999\t339631.7442\t1\t9999999\t1.00E+14\t172\t58416660\t2.76E+13
+VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tVDS4\tHIV-1 P24 (NON ICD) AG LEVEL\t9999999\t773550\t1\t9999999\t1.00E+14\t2\t1547100\t1.20E+12
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV-1 GENOTYPE & ANTIVIR RESIST\tCULT\tCULT/OTHER RSLT:\t9999999\t696693.125\t1\t9999999\t1.00E+14\t96\t66882540\t7.55E+13
+VIROLOGY ORDERABLES\t472574\tLABYHMPVP\tHUMAN METAPNEUMOVIRUS PCR\tYHMP2\tSOURCE\t9999999\t455820\t1\t9999999\t1.00E+14\t1\t455820\t2.08E+11
+VIROLOGY ORDERABLES\t473210\tLABDVEAB\tRAPID FLU A/B EIA\tRFLUA\tINFLUENZA VIRUS TYPE A\t9999999\t11850\t1\t9999999\t1.00E+14\t10\t118500\t2648221200
+VIROLOGY ORDERABLES\t473284\tLABMEAIGG\tRUBEOLA VIRAL AB\tME\tRUBEOLA (MEASLES) AB, IGG\t9999999\t123780\t1\t9999999\t1.00E+14\t1\t123780\t15321488400
+VIROLOGY ORDERABLES\t473461\tLABTOXIG\tTOXOPLASMA SEROLOGY\tTOXO\tTOXOPLASMA IGG\t9999999\t515100\t1\t9999999\t1.00E+14\t1\t515100\t2.65E+11
+VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVGGDES\tVZV IGG TEST DESCRIPTION\t9999999\t167515.5882\t1\t9999999\t1.00E+14\t272\t45564240\t9.41E+12
+VIROLOGY ORDERABLES\t473783\tLABVZVIGG\tVZ IGG\tVZAB\tVARICELLA ZOSTER AB IGG\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t497325\tLABADVDNA\tADENOVIRUS DNA, QUALITATIVE PCR\tADOV\tADENOVIRUS DNA, QL PCR\t9999999\t174138\t1\t9999999\t1.00E+14\t140\t24379320\t5.10E+12
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS PCR QUAL\tSOURC3\tADENOVIRUS SOURCE\t9999999\t114450\t1\t9999999\t1.00E+14\t2\t228900\t28059325200
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS DNA QUANTITATION\tSOURC6\tSOURCE\t9999999\t168960\t1\t9999999\t1.00E+14\t1\t168960\t28547481600
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tBKQ\tBKV DNA QUANTITATION\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS PCR, QUANT, PLASMA\tSOURC6\tSOURCE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t497468\tLABBKDNA\tBK VIRUS DNA PCR\tSOURC7\tSOURCE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS PCR QUANT\tADPCR1\tADENVIRUS DNA\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t590497\tLABADPCRQ\tADENOVIRUS PCR QUANT\tSOUR10\tSOURCE\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tSOUR10\tSOURCE\t9999999\t217572\t1\t9999999\t1.00E+14\t5\t1087860\t2.65E+11
+VIROLOGY ORDERABLES\t590498\tLABADVQT\tADENOVIRUS DNA PCR QUANT\tADVTIP\tTEST DESCRIPTION\t9999999\t170190\t1\t9999999\t1.00E+14\t12\t2042280\t4.38E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tEBV\tEBV BY PCR\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tYEBP1\tSPECIMEN SOURCE\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tYEBP3\tSPECIAL INFORMATION\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, CSF\tYEPB4\tREPORT STATUS\t9999999\t249394.2857\t1\t9999999\t1.00E+14\t7\t1745760\t4.94E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tEBV\tEBV BY PCR\t9999999\t329575.7143\t1\t9999999\t1.00E+14\t14\t4614060\t1.91E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tYEBP3\tSPECIAL INFORMATION\t9999999\t329575.7143\t1\t9999999\t1.00E+14\t14\t4614060\t1.91E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tYEPB4\tREPORT STATUS\t9999999\t329575.7143\t1\t9999999\t1.00E+14\t14\t4614060\t1.91E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tEBV\tEBV BY PCR\t9999999\t326800\t1\t9999999\t1.00E+14\t3\t980400\t3.42E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tYEBP1\tSPECIMEN SOURCE\t9999999\t326800\t1\t9999999\t1.00E+14\t3\t980400\t3.42E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tYEBP3\tSPECIAL INFORMATION\t9999999\t326800\t1\t9999999\t1.00E+14\t3\t980400\t3.42E+11
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\t11154R\tSOURCE\t9999999\t206940\t1\t9999999\t1.00E+14\t2\t413880\t92256228000
+VIROLOGY ORDERABLES\t670953\tLABHHV6QT\tHERPES VIRUS 6 DNA, QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t254505\t1\t9999999\t1.00E+14\t8\t2036040\t6.91E+11
+VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6CP\tRESULT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6DN\tHHV-6 DNA\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6LO\tRESULT LOG 10\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6SP\tSPECIMEN\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t670954\tINACTIVE 1\tHHV-6 PCR QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t670954\tLABHHV6QT\tHHV-6 PCR QUANT\tHHV6TI\tTEST DESCRIPTION\t9999999\t1350\t1\t9999999\t1.00E+14\t2\t2700\t7290000
+VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEBV EARLY ANTIGEN IGG\tEAGDES\tEAGDES\t9999999\t285229.4118\t1\t9999999\t1.00E+14\t17\t4848900\t2.81E+12
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN (EBNA) IGG\tEBNDES\tEBNDES\t9999999\t298560\t1\t9999999\t1.00E+14\t21\t6269760\t3.42E+12
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN IGG\tEBNQUL\tEBNQUL\t9999999\t687360\t1\t9999999\t1.00E+14\t2\t1374720\t9.65E+11
+VIROLOGY ORDERABLES\t670959\tLAB11024R\tEB VIRAL CAPSID AG IGM AB\t11167R\tEB VCA IGM AB\t9999999\t369690\t1\t9999999\t1.00E+14\t2\t739380\t3.71E+11
+VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEBV VIRAL CAPSID ANTIGEN IGM\tVMDES\tVMDES\t9999999\t251356.0714\t1\t9999999\t1.00E+14\t56\t14075940\t5.57E+12
+VIROLOGY ORDERABLES\t670961\tLAB11030R\tEB VIRAL CAPSID AG IGG AB\t11168R\tEB VCA IGG AB\t9999999\t369690\t1\t9999999\t1.00E+14\t2\t739380\t3.71E+11
+VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEBV VIRAL CAPSID ANTIGEN IGG\tVGDES\tVGDES\t9999999\t281538.4615\t1\t9999999\t1.00E+14\t52\t14640000\t7.71E+12
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC \tFLU A PCR\tFLUADI\tDISCLAIMER\t9999999\t163609.7143\t1\t9999999\t1.00E+14\t35\t5726340\t1.13E+12
+VIROLOGY ORDERABLES\t864644\tLABFLUAPX\tFLU A PCR REFLEX TO 2009 H1N1\tFLUXDI\tDISCLAIMER\t9999999\t183683.9241\t1\t9999999\t1.00E+14\t158\t29022060\t8.42E+12
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1IN\tINTERPRETATION\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1MT\tMETHOD\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t876717\tLABDVEAV3\tADENO DFA\tADV3IP\tTEST DESCRIPTION\t9999999\t373280\t1\t9999999\t1.00E+14\t3\t1119840\t9.84E+11
+VIROLOGY ORDERABLES\t876717\tLABDVEAV3\tADENO DFA\tADV3SP\tSPECIMEN\t9999999\t373280\t1\t9999999\t1.00E+14\t3\t1119840\t9.84E+11
+VIROLOGY ORDERABLES\t876725\tLABDVEPI1\tPARA 1 DFA\tPI1\tPARAINFLUENZA 1\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876725\tLABDVEPI1\tPARA 1 DFA\tPI1IP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876727\tLABDVEPI2\tPARA 2 DFA\tPI2\tPARAINFLUENZA 2\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876727\tLABDVEPI2\tPARA 2 DFA\tPI2IP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876729\tLABDVEPI3\tPARA 3 DFA\tPI3\tPARAINFLUENZA 3\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876729\tLABDVEPI3\tPARA 3 DFA\tPI3IP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876731\tLABDVEMP\tMPV DFA\tMPIP\tTEST DESCRIPTION\t9999999\t987120\t1\t9999999\t1.00E+14\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGM & IGG\tTOXGQL\tIGG RESULT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGM & IGG\tTOXMQL\tIGM RESULT\t9999999\t0\t1\t9999999\t1.00E+14\t1\t0\t0
+VIROLOGY ORDERABLES\t876740\tINACTIVE 4\tTOXOPLASMA IGG IGM\tTOXGQL\tIGG RESULT\t9999999\t186060\t1\t9999999\t1.00E+14\t1\t186060\t34618323600
+VIROLOGY ORDERABLES\t876740\tINACTIVE 4\tTOXOPLASMA IGG IGM\tTOXMQL\tIGM RESULT\t9999999\t186060\t1\t9999999\t1.00E+14\t1\t186060\t34618323600
+VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXGQT\tIGG LEVEL\t8\t75540\t1\t8\t64\t1\t75540\t5706291600
+VIROLOGY ORDERABLES\t876741\tLABTOXGMR\tTOXO PREGNANCY PANEL\tTOXGQL\tIGG RESULT\t9999999\t148380\t1\t9999999\t1.00E+14\t1\t148380\t22016624400
+VIROLOGY ORDERABLES\t876741\tLABTOXGMR\tTOXO PREGNANCY PANEL\tTOXMQL\tIGM RESULT\t9999999\t148380\t1\t9999999\t1.00E+14\t1\t148380\t22016624400
+VIROLOGY ORDERABLES\t876741\tLABTOXOPR\tTOXO PREGNANCY PANEL\tTOXIGR\tTOXOPLASMA IGG\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t876741\tLABTOXOPR\tTOXO PREGNANCY PANEL\tTOXIMR\tTOXOPLASMA IGM\t9999999\t#VALUE!\t1\t9999999\t1.00E+14\t0\tNone\tNone
+VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRSMPSP\tSPECIMEN\t9999999\t86400\t1\t9999999\t1.00E+14\t3\t259200\t28951264800
+VIROLOGY ORDERABLES\t889084\tLABDVEHVR\tHSV/VZV DFA REFLEX PCR\tHVSP\tSPECIMEN\t9999999\t85470\t1\t9999999\t1.00E+14\t56\t4786320\t8.40E+11
+VIROLOGY ORDERABLES\t897645\tLABHPVHRT\tHPV HIGH RISK-16/18 REFLEX\tHPTI\tTEST INFORMATION\t9999999\t23400\t1\t9999999\t1.00E+14\t1\t23400\t547560000
+VIROLOGY ORDERABLES\t897645\tLABHPVHRT\tHPV HIGH RISK-16/18 REFLEX\tHPV\tHPV DNA PROBE RESULT:\t9999999\t23400\t1\t9999999\t1.00E+14\t1\t23400\t547560000
+VIROLOGY ORDERABLES\t897645\tLABHPVHRT\tHPV HIGH RISK-16/18 REFLEX\tSDES6\tSPECIMEN\t9999999\t23400\t1\t9999999\t1.00E+14\t1\t23400\t547560000
+VIROLOGY ORDERABLES\t906949\tLABMUMRC\tMUMPS RAPID CULTURE\tMUMRCD\tTEST DESCRIPTION\t9999999\t182240\t1\t9999999\t1.00E+14\t3\t546720\t1.15E+11
+VIROLOGY ORDERABLES\t906949\tLABMUMRC\tMUMPS RAPID CULTURE\tMUMSPM\tSPECIMEN\t9999999\t182240\t1\t9999999\t1.00E+14\t3\t546720\t1.15E+11
+VIROLOGY ORDERABLES\t906951\tLABMEARC\tMEASLES RAPID CULTURE\tMEARCD\tTEST DESCRIPTION\t9999999\t124470\t1\t9999999\t1.00E+14\t2\t248940\t34398594000
+VIROLOGY ORDERABLES\t906951\tLABMEARC\tMEASLES RAPID CULTURE\tMEASPM\tSPECIMEN\t9999999\t124470\t1\t9999999\t1.00E+14\t2\t248940\t34398594000
+VIROLOGY ORDERABLES\t906957\tLABCMVRC\tCMV RAPID CULTURE\tCMVDSV\tTEST DESCRIPTION\t9999999\t175699.0909\t1\t9999999\t1.00E+14\t22\t3865380\t7.11E+11
+VIROLOGY ORDERABLES\t966893\tLABVCULO\tVIRAL CULTURE, OCULAR\tCULT\tCULT/OTHER RSLT:\t9999999\t1069548\t1\t9999999\t1.00E+14\t10\t10695480\t1.26E+13
+VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tRPT\tRPT\t9999999\t1267920\t1\t9999999\t1.00E+14\t1\t1267920\t1.61E+12
+VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tSDES\tSPECIMEN\t9999999\t1267920\t1\t9999999\t1.00E+14\t1\t1267920\t1.61E+12
+VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tSREQ\tCOMMENTS\t9999999\t1267920\t1\t9999999\t1.00E+14\t1\t1267920\t1.61E+12
+VIROLOGY ORDERABLES\t1007409\tLABVZVQL\tVZV DNA PCR QUAL, NON LESION\tVZQLIP\tTEST DESCRIPTION\t9999999\t192832.6829\t1\t9999999\t1.00E+14\t82\t15812280\t4.51E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV1\tHCV GENOTYPE 1\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV1S\tHCV GENOTYPE 1 SUBTYPE\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV2\tHCV GENOTYPE 2\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV2S\tHCV GENOTYPE 2 SUBTYPE\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV3\tHCV GENOTYPE 3\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV4\tHCV GENOTYPE 4\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV5\tHCV GENOTYPE 5\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCV6\tHCV GENOTYPE 6\t9999999\t766260\t1\t9999999\t1.00E+14\t2\t1532520\t1.95E+12
+BLOOD BANK PRODUCT ORDERABLES\t473474\tLABTBP\tTRANSFUSION BLOOD PRODUCT 1\tUNT\tUNIT NUMBER  \t#VALUE!\t53958.45904\t0\tNone\tNone\t40650\t2193411360\t3.01E+14
+BLOOD BANK PRODUCT ORDERABLES\t473475\tLABTBP9\tTRANSFUSION BLOOD PRODUCT 10\tUNT\tUNIT NUMBER  \t#VALUE!\t55642.15709\t0\tNone\tNone\t853\t47462760\t6.04E+12
+BLOOD BANK PRODUCT ORDERABLES\t473486\tLABTBP10\tTRANSFUSION BLOOD PRODUCT 11\tUNT\tUNIT NUMBER  \t#VALUE!\t50077.32759\t0\tNone\tNone\t696\t34853820\t4.01E+12
+BLOOD BANK PRODUCT ORDERABLES\t473496\tLABTBP11\tTRANSFUSION BLOOD PRODUCT 12\tUNT\tUNIT NUMBER  \t#VALUE!\t50032.09756\t0\tNone\tNone\t615\t30769740\t3.61E+12
+BLOOD BANK PRODUCT ORDERABLES\t473507\tLABTBP12\tTRANSFUSION BLOOD PRODUCT 13\tUNT\tUNIT NUMBER  \t#VALUE!\t46753.20236\t0\tNone\tNone\t509\t23797380\t2.56E+12
+BLOOD BANK PRODUCT ORDERABLES\t473518\tLABTBP13\tTRANSFUSION BLOOD PRODUCT 14\tUNT\tUNIT NUMBER  \t#VALUE!\t49645.91489\t0\tNone\tNone\t470\t23333580\t2.61E+12
+BLOOD BANK PRODUCT ORDERABLES\t473529\tLABTBP14\tTRANSFUSION BLOOD PRODUCT 15\tUNT\tUNIT NUMBER  \t#VALUE!\t48640.67308\t0\tNone\tNone\t416\t20234520\t2.20E+12
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+BLOOD BANK PRODUCT ORDERABLES\t473562\tLABTBP17\tTRANSFUSION BLOOD PRODUCT 18\tUNT\tUNIT NUMBER  \t#VALUE!\t46629.4382\t0\tNone\tNone\t267\t12450060\t1.39E+12
+BLOOD BANK PRODUCT ORDERABLES\t473573\tLABTBP18\tTRANSFUSION BLOOD PRODUCT 19\tUNT\tUNIT NUMBER  \t#VALUE!\t48294.88789\t0\tNone\tNone\t223\t10769760\t1.30E+12
+BLOOD BANK PRODUCT ORDERABLES\t473584\tLABTBP1\tTRANSFUSION BLOOD PRODUCT 2\tUNT\tUNIT NUMBER  \t#VALUE!\t71687.08515\t0\tNone\tNone\t20423\t1464065340\t2.44E+14
+BLOOD BANK PRODUCT ORDERABLES\t473585\tLABTBP19\tTRANSFUSION BLOOD PRODUCT 20\tUNT\tUNIT NUMBER  \t#VALUE!\t44357.94118\t0\tNone\tNone\t204\t9049020\t1.06E+12
+BLOOD BANK PRODUCT ORDERABLES\t473587\tLABTBP20\tTRANSFUSION BLOOD PRODUCT 21\tUNT\tUNIT NUMBER  \t#VALUE!\t44809.2973\t0\tNone\tNone\t185\t8289720\t9.48E+11
+BLOOD BANK PRODUCT ORDERABLES\t473588\tLABTBP21\tTRANSFUSION BLOOD PRODUCT 22\tUNT\tUNIT NUMBER  \t#VALUE!\t42059.64706\t0\tNone\tNone\t170\t7150140\t7.84E+11
+BLOOD BANK PRODUCT ORDERABLES\t473589\tLABTBP22\tTRANSFUSION BLOOD PRODUCT 23\tUNT\tUNIT NUMBER  \t#VALUE!\t44215.41667\t0\tNone\tNone\t144\t6367020\t7.61E+11
+BLOOD BANK PRODUCT ORDERABLES\t473590\tLABTBP23\tTRANSFUSION BLOOD PRODUCT 24\tUNT\tUNIT NUMBER  \t#VALUE!\t45869.76744\t0\tNone\tNone\t129\t5917200\t7.29E+11
+BLOOD BANK PRODUCT ORDERABLES\t473591\tLABTBP24\tTRANSFUSION BLOOD PRODUCT 25\tUNT\tUNIT NUMBER  \t#VALUE!\t45405.78947\t0\tNone\tNone\t114\t5176260\t6.15E+11
+BLOOD BANK PRODUCT ORDERABLES\t473592\tLABTBP25\tTRANSFUSION BLOOD PRODUCT 26\tUNT\tUNIT NUMBER  \t#VALUE!\t44582.22222\t0\tNone\tNone\t108\t4814880\t5.88E+11
+BLOOD BANK PRODUCT ORDERABLES\t473593\tLABTBP26\tTRANSFUSION BLOOD PRODUCT 27\tUNT\tUNIT NUMBER  \t#VALUE!\t48100.40816\t0\tNone\tNone\t98\t4713840\t5.88E+11
+BLOOD BANK PRODUCT ORDERABLES\t473594\tLABTBP28\tTRANSFUSION BLOOD PRODUCT 29\tUNT\tUNIT NUMBER  \t#VALUE!\t45671.25\t0\tNone\tNone\t80\t3653700\t4.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t473595\tLABTBP2\tTRANSFUSION BLOOD PRODUCT 3\tUNT\tUNIT NUMBER  \t#VALUE!\t70831.65354\t0\tNone\tNone\t6096\t431789760\t6.51E+13
+BLOOD BANK PRODUCT ORDERABLES\t473596\tLABTBP29\tTRANSFUSION BLOOD PRODUCT 30\tUNT\tUNIT NUMBER  \t#VALUE!\t43986.31579\t0\tNone\tNone\t76\t3342960\t3.42E+11
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+BLOOD BANK PRODUCT ORDERABLES\t473598\tLABTBP31\tTRANSFUSION BLOOD PRODUCT 32\tUNT\tUNIT NUMBER  \t#VALUE!\t50272.88136\t0\tNone\tNone\t59\t2966100\t3.78E+11
+BLOOD BANK PRODUCT ORDERABLES\t473599\tLABTBP32\tTRANSFUSION BLOOD PRODUCT 33\tUNT\tUNIT NUMBER  \t#VALUE!\t48334.73684\t0\tNone\tNone\t57\t2755080\t3.01E+11
+BLOOD BANK PRODUCT ORDERABLES\t473600\tLABTBP33\tTRANSFUSION BLOOD PRODUCT 34\tUNT\tUNIT NUMBER  \t#VALUE!\t49175.09434\t0\tNone\tNone\t53\t2606280\t3.05E+11
+BLOOD BANK PRODUCT ORDERABLES\t473601\tLABTBP34\tTRANSFUSION BLOOD PRODUCT 35\tUNT\tUNIT NUMBER  \t#VALUE!\t41741.17647\t0\tNone\tNone\t51\t2128800\t2.07E+11
+BLOOD BANK PRODUCT ORDERABLES\t473602\tLABTBP35\tTRANSFUSION BLOOD PRODUCT 36\tUNT\tUNIT NUMBER  \t#VALUE!\t48162.27273\t0\tNone\tNone\t44\t2119140\t2.29E+11
+BLOOD BANK PRODUCT ORDERABLES\t473603\tLABTBP36\tTRANSFUSION BLOOD PRODUCT 37\tUNT\tUNIT NUMBER  \t#VALUE!\t31722.43902\t0\tNone\tNone\t41\t1300620\t88116591600
+BLOOD BANK PRODUCT ORDERABLES\t473604\tLABTBP37\tTRANSFUSION BLOOD PRODUCT 38\tUNT\tUNIT NUMBER  \t#VALUE!\t34084.61538\t0\tNone\tNone\t39\t1329300\t92575184400
+BLOOD BANK PRODUCT ORDERABLES\t473605\tLABTBP38\tTRANSFUSION BLOOD PRODUCT 39\tUNT\tUNIT NUMBER  \t#VALUE!\t34275\t0\tNone\tNone\t36\t1233900\t87256652400
+BLOOD BANK PRODUCT ORDERABLES\t473606\tLABTBP3\tTRANSFUSION BLOOD PRODUCT 4\tUNT\tUNIT NUMBER  \t#VALUE!\t71882.52728\t0\tNone\tNone\t5223\t375442440\t5.68E+13
+BLOOD BANK PRODUCT ORDERABLES\t473607\tLABTBP39\tTRANSFUSION BLOOD PRODUCT 40\tUNT\tUNIT NUMBER  \t#VALUE!\t32080\t0\tNone\tNone\t33\t1058640\t53302039200
+BLOOD BANK PRODUCT ORDERABLES\t473608\tLABTBP40\tTRANSFUSION BLOOD PRODUCT 41\tUNT\tUNIT NUMBER  \t#VALUE!\t29945.80645\t0\tNone\tNone\t31\t928320\t49220258400
+BLOOD BANK PRODUCT ORDERABLES\t473609\tLABTBP41\tTRANSFUSION BLOOD PRODUCT 42\tUNT\tUNIT NUMBER  \t#VALUE!\t31464\t0\tNone\tNone\t30\t943920\t67138092000
+BLOOD BANK PRODUCT ORDERABLES\t473610\tLABTBP42\tTRANSFUSION BLOOD PRODUCT 43\tUNT\tUNIT NUMBER  \t#VALUE!\t42686.66667\t0\tNone\tNone\t27\t1152540\t1.10E+11
+BLOOD BANK PRODUCT ORDERABLES\t473611\tLABTBP43\tTRANSFUSION BLOOD PRODUCT 44\tUNT\tUNIT NUMBER  \t#VALUE!\t41768.88889\t0\tNone\tNone\t27\t1127760\t1.06E+11
+BLOOD BANK PRODUCT ORDERABLES\t473612\tLABTBP44\tTRANSFUSION BLOOD PRODUCT 45\tUNT\tUNIT NUMBER  \t#VALUE!\t32076.92308\t0\tNone\tNone\t26\t834000\t53931456000
+BLOOD BANK PRODUCT ORDERABLES\t473613\tLABTBP45\tTRANSFUSION BLOOD PRODUCT 46\tUNT\tUNIT NUMBER  \t#VALUE!\t29411.53846\t0\tNone\tNone\t26\t764700\t28925881200
+BLOOD BANK PRODUCT ORDERABLES\t473614\tLABTBP46\tTRANSFUSION BLOOD PRODUCT 47\tUNT\tUNIT NUMBER  \t#VALUE!\t35142.85714\t0\tNone\tNone\t21\t738000\t56790907200
+BLOOD BANK PRODUCT ORDERABLES\t473615\tLABTBP47\tTRANSFUSION BLOOD PRODUCT 48\tUNT\tUNIT NUMBER  \t#VALUE!\t35055\t0\tNone\tNone\t20\t701100\t55275080400
+BLOOD BANK PRODUCT ORDERABLES\t473616\tLABTBP48\tTRANSFUSION BLOOD PRODUCT 49\tUNT\tUNIT NUMBER  \t#VALUE!\t40860\t0\tNone\tNone\t20\t817200\t69801444000
+BLOOD BANK PRODUCT ORDERABLES\t473617\tLABTBP4\tTRANSFUSION BLOOD PRODUCT 5\tUNT\tUNIT NUMBER  \t#VALUE!\t63772.73171\t0\tNone\tNone\t2460\t156880920\t2.17E+13
+BLOOD BANK PRODUCT ORDERABLES\t473618\tLABTBP49\tTRANSFUSION BLOOD PRODUCT 50\tUNT\tUNIT NUMBER  \t#VALUE!\t35173.33333\t0\tNone\tNone\t18\t633120\t53763523200
+BLOOD BANK PRODUCT ORDERABLES\t473619\tLABTBP50\tTRANSFUSION BLOOD PRODUCT 51\tUNT\tUNIT NUMBER  \t#VALUE!\t24108\t0\tNone\tNone\t15\t361620\t13517708400
+BLOOD BANK PRODUCT ORDERABLES\t473620\tLABTBP51\tTRANSFUSION BLOOD PRODUCT 52\tUNT\tUNIT NUMBER  \t#VALUE!\t26117.14286\t0\tNone\tNone\t14\t365640\t13709217600
+BLOOD BANK PRODUCT ORDERABLES\t473621\tLABTBP52\tTRANSFUSION BLOOD PRODUCT 53\tUNT\tUNIT NUMBER  \t#VALUE!\t29464.61538\t0\tNone\tNone\t13\t383040\t18645940800
+BLOOD BANK PRODUCT ORDERABLES\t473622\tLABTBP53\tTRANSFUSION BLOOD PRODUCT 54\tUNT\tUNIT NUMBER  \t#VALUE!\t31638.46154\t0\tNone\tNone\t13\t411300\t20614381200
+BLOOD BANK PRODUCT ORDERABLES\t473623\tLABTBP54\tTRANSFUSION BLOOD PRODUCT 55\tUNT\tUNIT NUMBER  \t#VALUE!\t32307.69231\t0\tNone\tNone\t13\t420000\t21061454400
+BLOOD BANK PRODUCT ORDERABLES\t473624\tLABTBP55\tTRANSFUSION BLOOD PRODUCT 56\tUNT\tUNIT NUMBER  \t#VALUE!\t27975\t0\tNone\tNone\t12\t335700\t17437417200
+BLOOD BANK PRODUCT ORDERABLES\t473625\tLABTBP56\tTRANSFUSION BLOOD PRODUCT 57\tUNT\tUNIT NUMBER  \t#VALUE!\t31958.18182\t0\tNone\tNone\t11\t351540\t18758574000
+BLOOD BANK PRODUCT ORDERABLES\t473626\tLABTBP58\tTRANSFUSION BLOOD PRODUCT 59\tUNT\tUNIT NUMBER  \t#VALUE!\t76960\t0\tNone\tNone\t9\t692640\t99484596000
+BLOOD BANK PRODUCT ORDERABLES\t473627\tLABTBP5\tTRANSFUSION BLOOD PRODUCT 6\tUNT\tUNIT NUMBER  \t#VALUE!\t63330.90094\t0\tNone\tNone\t2231\t141291240\t1.94E+13
+BLOOD BANK PRODUCT ORDERABLES\t473628\tLABTBP59\tTRANSFUSION BLOOD PRODUCT 60\tUNT\tUNIT NUMBER  \t#VALUE!\t81690\t0\tNone\tNone\t8\t653520\t97952421600
+BLOOD BANK PRODUCT ORDERABLES\t473629\tLABTBP60\tTRANSFUSION BLOOD PRODUCT 61\tUNT\tUNIT NUMBER  \t#VALUE!\t85174.28571\t0\tNone\tNone\t7\t596220\t77125518000
+BLOOD BANK PRODUCT ORDERABLES\t473630\tLABTBP61\tTRANSFUSION BLOOD PRODUCT 62\tUNT\tUNIT NUMBER  \t#VALUE!\t63750\t0\tNone\tNone\t6\t382500\t39290828400
+BLOOD BANK PRODUCT ORDERABLES\t473631\tLABTBP62\tTRANSFUSION BLOOD PRODUCT 63\tUNT\tUNIT NUMBER  \t#VALUE!\t47660\t0\tNone\tNone\t6\t285960\t25408432800
+BLOOD BANK PRODUCT ORDERABLES\t473632\tLABTBP63\tTRANSFUSION BLOOD PRODUCT 64\tUNT\tUNIT NUMBER  \t#VALUE!\t47650\t0\tNone\tNone\t6\t285900\t25391113200
+BLOOD BANK PRODUCT ORDERABLES\t473633\tLABTBP64\tTRANSFUSION BLOOD PRODUCT 65\tUNT\tUNIT NUMBER  \t#VALUE!\t23880\t0\tNone\tNone\t6\t143280\t4565289600
+BLOOD BANK PRODUCT ORDERABLES\t473634\tLABTBP65\tTRANSFUSION BLOOD PRODUCT 66\tUNT\tUNIT NUMBER  \t#VALUE!\t28650\t0\tNone\tNone\t6\t171900\t6302451600
+BLOOD BANK PRODUCT ORDERABLES\t473635\tLABTBP66\tTRANSFUSION BLOOD PRODUCT 67\tUNT\tUNIT NUMBER  \t#VALUE!\t28550\t0\tNone\tNone\t6\t171300\t6256443600
+BLOOD BANK PRODUCT ORDERABLES\t473636\tLABTBP67\tTRANSFUSION BLOOD PRODUCT 68\tUNT\tUNIT NUMBER  \t#VALUE!\t28428\t0\tNone\tNone\t5\t142140\t5357322000
+BLOOD BANK PRODUCT ORDERABLES\t473637\tLABTBP68\tTRANSFUSION BLOOD PRODUCT 69\tUNT\tUNIT NUMBER  \t#VALUE!\t18440\t0\tNone\tNone\t3\t55320\t2073319200
+BLOOD BANK PRODUCT ORDERABLES\t473638\tLABTBP6\tTRANSFUSION BLOOD PRODUCT 7\tUNT\tUNIT NUMBER  \t#VALUE!\t58144.67277\t0\tNone\tNone\t1528\t88845060\t1.11E+13
+BLOOD BANK PRODUCT ORDERABLES\t473639\tLABTBP69\tTRANSFUSION BLOOD PRODUCT 70\tUNT\tUNIT NUMBER  \t#VALUE!\t38400\t0\tNone\tNone\t3\t115200\t7052018400
+BLOOD BANK PRODUCT ORDERABLES\t473640\tLABTBP70\tTRANSFUSION BLOOD PRODUCT 71\tUNT\tUNIT NUMBER  \t#VALUE!\t27880\t0\tNone\tNone\t3\t83640\t3495549600
+BLOOD BANK PRODUCT ORDERABLES\t473641\tLABTBP71\tTRANSFUSION BLOOD PRODUCT 72\tUNT\tUNIT NUMBER  \t#VALUE!\t38360\t0\tNone\tNone\t3\t115080\t7038208800
+BLOOD BANK PRODUCT ORDERABLES\t473642\tLABTBP72\tTRANSFUSION BLOOD PRODUCT 73\tUNT\tUNIT NUMBER  \t#VALUE!\t38320\t0\tNone\tNone\t3\t114960\t7029561600
+BLOOD BANK PRODUCT ORDERABLES\t473643\tLABTBP73\tTRANSFUSION BLOOD PRODUCT 74\tUNT\tUNIT NUMBER  \t#VALUE!\t26760\t0\tNone\tNone\t3\t80280\t3222554400
+BLOOD BANK PRODUCT ORDERABLES\t473644\tLABTBP74\tTRANSFUSION BLOOD PRODUCT 75\tUNT\tUNIT NUMBER  \t#VALUE!\t40110\t0\tNone\tNone\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473645\tLABTBP75\tTRANSFUSION BLOOD PRODUCT 76\tUNT\tUNIT NUMBER  \t#VALUE!\t40110\t0\tNone\tNone\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473646\tLABTBP76\tTRANSFUSION BLOOD PRODUCT 77\tUNT\tUNIT NUMBER  \t#VALUE!\t40110\t0\tNone\tNone\t2\t80220\t3217712400
+BLOOD BANK PRODUCT ORDERABLES\t473647\tLABTBP77\tTRANSFUSION BLOOD PRODUCT 78\tUNT\tUNIT NUMBER  \t#VALUE!\t40080\t0\tNone\tNone\t2\t80160\t3212928000
+BLOOD BANK PRODUCT ORDERABLES\t473648\tLABTBP78\tTRANSFUSION BLOOD PRODUCT 79\tUNT\tUNIT NUMBER  \t#VALUE!\t39390\t0\tNone\tNone\t2\t78780\t3104874000
+BLOOD BANK PRODUCT ORDERABLES\t473649\tLABTBP7\tTRANSFUSION BLOOD PRODUCT 8\tUNT\tUNIT NUMBER  \t#VALUE!\t57549.16129\t0\tNone\tNone\t1395\t80281080\t9.91E+12
+BLOOD BANK PRODUCT ORDERABLES\t473650\tLABTBP79\tTRANSFUSION BLOOD PRODUCT 80\tUNT\tUNIT NUMBER  \t#VALUE!\t54990\t0\tNone\tNone\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473651\tLABTBP80\tTRANSFUSION BLOOD PRODUCT 81\tUNT\tUNIT NUMBER  \t#VALUE!\t54990\t0\tNone\tNone\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473652\tLABTBP81\tTRANSFUSION BLOOD PRODUCT 82\tUNT\tUNIT NUMBER  \t#VALUE!\t54990\t0\tNone\tNone\t2\t109980\t6598256400
+BLOOD BANK PRODUCT ORDERABLES\t473653\tLABTBP82\tTRANSFUSION BLOOD PRODUCT 83\tUNT\tUNIT NUMBER  \t#VALUE!\t69330\t0\tNone\tNone\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473654\tLABTBP83\tTRANSFUSION BLOOD PRODUCT 84\tUNT\tUNIT NUMBER  \t#VALUE!\t69330\t0\tNone\tNone\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473655\tLABTBP84\tTRANSFUSION BLOOD PRODUCT 85\tUNT\tUNIT NUMBER  \t#VALUE!\t69330\t0\tNone\tNone\t2\t138660\t9623422800
+BLOOD BANK PRODUCT ORDERABLES\t473656\tLABTBP85\tTRANSFUSION BLOOD PRODUCT 86\tUNT\tUNIT NUMBER  \t#VALUE!\t91200\t0\tNone\tNone\t2\t182400\t23119207200
+BLOOD BANK PRODUCT ORDERABLES\t473657\tLABTBP86\tTRANSFUSION BLOOD PRODUCT 87\tUNT\tUNIT NUMBER  \t#VALUE!\t49950\t0\tNone\tNone\t2\t99900\t5482357200
+BLOOD BANK PRODUCT ORDERABLES\t473658\tLABTBP87\tTRANSFUSION BLOOD PRODUCT 88\tUNT\tUNIT NUMBER  \t#VALUE!\t148140\t0\tNone\tNone\t1\t148140\t21945459600
+BLOOD BANK PRODUCT ORDERABLES\t473659\tLABTBP88\tTRANSFUSION BLOOD PRODUCT 89\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473660\tLABTBP8\tTRANSFUSION BLOOD PRODUCT 9\tUNT\tUNIT NUMBER  \t#VALUE!\t51894.30096\t0\tNone\tNone\t937\t48624960\t5.74E+12
+BLOOD BANK PRODUCT ORDERABLES\t473661\tLABTBP89\tTRANSFUSION BLOOD PRODUCT 90\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473662\tLABTBP90\tTRANSFUSION BLOOD PRODUCT 91\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473663\tLABTBP91\tTRANSFUSION BLOOD PRODUCT 92\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473664\tLABTBP92\tTRANSFUSION BLOOD PRODUCT 93\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473665\tLABTBP93\tTRANSFUSION BLOOD PRODUCT 94\tUNT\tUNIT NUMBER  \t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BLOOD BANK PRODUCT ORDERABLES\t473671\tLABEITC\tTRANSFUSION EMERGENCY ISSUE\tCEITC\tEMERGENCY ISSUE CMT\t#VALUE!\t86.95945946\t0\tNone\tNone\t296\t25740\t236782800
+BLOOD BANK PRODUCT ORDERABLES\t618478\tLABTBP27\tTRANSFUSION BLOOD PRODUCT 28\tUNT\tUNIT NUMBER  \t#VALUE!\t41291.48936\t0\tNone\tNone\t94\t3881400\t3.96E+11
+BLOOD BANK PRODUCT ORDERABLES\t618512\tLABTBP57\tTRANSFUSION BLOOD PRODUCT 58\tUNT\tUNIT NUMBER  \t#VALUE!\t35022\t0\tNone\tNone\t10\t350220\t18667335600
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tAASINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471604\tLABAACSF\tAMINO ACIDS, CSF\tINTSBY\tINTERPRETED BY\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471626\tLAB11028R\tANGIOTENSIN CONVERTING ENZYME, CSF\tACE\tANGIOTENSIN COVERT ENZ, CSF\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471626\tLABCACE\tANGIOTENSIN CONVERTING ENZYME, CSF\tACE\tANGIOTENSIN COVERT ENZ, CSF\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t471692\tLABASPPCP\tASPERGILLUS AB PRECIPITINS\tASPINT\tINTERPRETATION\t#VALUE!\t407597.6471\t0\tNone\tNone\t17\t6929160\t3.73E+12
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBDIR\tINTERPRETED BY\t#VALUE!\t1007200\t0\tNone\tNone\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t471735\tLABBMBCLO\tB - CELL CLONALITY PCR, BONE MARROW\tBMBDIS\tDISCLAIMER\t#VALUE!\t1007200\t0\tNone\tNone\t3\t3021600\t3.22E+12
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRM\tMETHOD\t#VALUE!\t520905.7143\t0\tNone\tNone\t21\t10939020\t8.12E+12
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRS\tDISCLAIMER\t#VALUE!\t520905.7143\t0\tNone\tNone\t21\t10939020\t8.12E+12
+BODY FLUID ORDERABLES\t471793\tLABBMBCRQ\tBCR - ABL QUANTITATIVE, BONE MARROW\tQBMBRX\tCLINICAL INDICATION\t#VALUE!\t520905.7143\t0\tNone\tNone\t21\t10939020\t8.12E+12
+BODY FLUID ORDERABLES\t471947\tLABBMCD34\tCD34 PANEL, BONE MARROW\tCD34DS\tDISCLAIMER\t#VALUE!\t23385\t0\tNone\tNone\t4\t93540\t3727911600
+BODY FLUID ORDERABLES\t471948\tLABFCD34\tCD34 PANEL, FLUID\tCD34DS\tDISCLAIMER\t#VALUE!\t8570.769231\t0\tNone\tNone\t26\t222840\t3235593600
+BODY FLUID ORDERABLES\t472047\tLAB11123R\tCOCCIDIOIDES COMPLEMENT FIXATION, CSF\tCOCC\tCOCCI CF AB (CSF)\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472047\tLABCOCC\tCOCCIDIOIDES COMPLEMENT FIXATION, CSF\tCOCC\tCOCCI CF AB (CSF)\t#VALUE!\t709560\t0\tNone\tNone\t1\t709560\t5.03E+11
+BODY FLUID ORDERABLES\t472126\tLABCYSCSF\tCYSTICERCUS AB REFLEX PANEL, CSF\tCYSTAB\tCYSTICERCUS AB(CSF),ELISA\t#VALUE!\t392206.6667\t0\tNone\tNone\t9\t3529860\t1.50E+12
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP1\tSPECIMEN SOURCE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP2\tENTEROVIRUS ,PCR\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP3\tSPECIAL INFORMATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS BY PCR\tYENP4\tREPORT STATUS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP1\tSPECIMEN SOURCE\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP2\tENTEROVIRUS ,PCR\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP3\tSPECIAL INFORMATION\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
+BODY FLUID ORDERABLES\t472314\tLABEVPCR\tENTEROVIRUS PCR\tYENP4\tREPORT STATUS\t#VALUE!\t147120\t0\tNone\tNone\t1\t147120\t21644294400
+BODY FLUID ORDERABLES\t472314\tLABYENPCR\tENTEROVIRUS PCR\tYENP1\tSPECIMEN SOURCE\t#VALUE!\t209353.0435\t0\tNone\tNone\t69\t14445360\t3.79E+12
+BODY FLUID ORDERABLES\t472370\tLABFFIBN\tFETAL FIBRONECTIN\tFFIBN\tFETAL FIBRONECTIN\t#VALUE!\t3352.5\t0\tNone\tNone\t8\t26820\t104137200
+BODY FLUID ORDERABLES\t472431\tLABFSYN\tFUNGAL SYNERGY\tFMIC\tFUNGAL MIC\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIDI\tDISCLAIMER\t#VALUE!\t927889.0909\t0\tNone\tNone\t11\t10206780\t1.25E+13
+BODY FLUID ORDERABLES\t472851\tLABTMSI\tMICROSATELLITE INSTABILITY\tTMSIME\tMETHOD\t#VALUE!\t927889.0909\t0\tNone\tNone\t11\t10206780\t1.25E+13
+BODY FLUID ORDERABLES\t472933\tLABYMYCRB\tMYCOPLASMA CULTURE, RESPIRATORY\t12197R\tSOURCE\t#VALUE!\t1011860\t0\tNone\tNone\t3\t3035580\t6.08E+12
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473131\tLABLIVER\tPRE LIVER TRANSPLANT PANEL\tHTLVS\tHTLV AB SCREEN\t#VALUE!\t359730\t0\tNone\tNone\t2\t719460\t2.59E+11
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t#VALUE!\t268440\t0\tNone\tNone\t1\t268440\t72060033600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\t11166R\tEB NUCLEAR AB\t#VALUE!\t268440\t0\tNone\tNone\t1\t268440\t72060033600
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t712710\t0\tNone\tNone\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t712710\t0\tNone\tNone\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t712710\t0\tNone\tNone\t2\t1425420\t1.41E+12
+BODY FLUID ORDERABLES\t473361\tLABSTAND\tSTANDARD PRE TRANSPLANT PANEL\tHSAB\tVIRAL AB HSV\t#VALUE!\t550720\t0\tNone\tNone\t3\t1652160\t1.46E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tKIDNEY STONE ANALYSIS\tYSTNSO\tSTONE SOURCE\t#VALUE!\t207922.6667\t0\tNone\tNone\t45\t9356520\t2.34E+12
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\t1STCONST\t1ST CONSTITUENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\t2NDCONST\t2ND CONSTITUENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\t3RDCONST\t3RD CONSTITUENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t473368\tLABYSTONE\tSTONE ANALYSIS\tYSTNSO\tSTONE SOURCE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14CT\tCOMMENT\t#VALUE!\t1070614.286\t0\tNone\tNone\t7\t7494300\t9.42E+12
+BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14DR\tINTERPRETED BY\t#VALUE!\t1070614.286\t0\tNone\tNone\t7\t7494300\t9.42E+12
+BODY FLUID ORDERABLES\t473398\tLABBM1418\tT(14\t18), BONE MARROW\tBM14DS\tDISCLAIMER\t#VALUE!\t1070614.286\t0\tNone\tNone\t7\t7494300\t9.42E+12
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15DI\tDISCLAIMER\t#VALUE!\t644650\t0\tNone\tNone\t18\t11603700\t8.75E+12
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15MD\tINTERPRETED BY\t#VALUE!\t644650\t0\tNone\tNone\t18\t11603700\t8.75E+12
+BODY FLUID ORDERABLES\t473401\tLABBM1517\tT(15\t17) PML/RAR, BONE MARROW\tBM15ME\tMETHOD\t#VALUE!\t644650\t0\tNone\tNone\t18\t11603700\t8.75E+12
+BODY FLUID ORDERABLES\t473406\tLABBMTCLO\tT-CELL CLONALITY PCR, BONE MARROW\tBMTDIS\tDISCLAIMER\t#VALUE!\t688582.5\t0\tNone\tNone\t16\t11017320\t1.17E+13
+BODY FLUID ORDERABLES\t473458\tLAB11121R\tTOXOPLASMA IGG DYE TEST\tTOXOD\tTOXOPLASMA DYE TEST\t#VALUE!\t1132650\t0\tNone\tNone\t2\t2265300\t2.79E+12
+BODY FLUID ORDERABLES\t497221\tLABADMARK\tADMARK TAU/AB42, CSF\tADMART\tADMARK TAU/AB42, CSF\t#VALUE!\t2981138.571\t0\tNone\tNone\t14\t41735940\t1.92E+14
+BODY FLUID ORDERABLES\t497362\tLABYFK\tPOTASSIUM, FLUID\tKFLD\tPOTASSIUM BODY FLUID\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t497366\tLABYFNA\tSODIUM, FLUID\tNAFLD\tSODIUM BODY FLUID\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t497368\tLABFLGL\tGLUCOSE, FLUID\tYFLSC1\tFLUID TYPE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+BODY FLUID ORDERABLES\t497376\tLABYFCL\tCHLORIDE, FLUID\t11844R\tFLUID TYPE\t#VALUE!\t95760\t0\tNone\tNone\t1\t95760\t9169977600
+BODY FLUID ORDERABLES\t497376\tLABYFCL\tCHLORIDE, FLUID\tCLFLD\tCHLORIDE, BODY FLUID\t#VALUE!\t169230\t0\tNone\tNone\t2\t338460\t68073267600
+BODY FLUID ORDERABLES\t497438\tLABFCLPN\tCHLAMYDIA PNEUMONIAE CULTURE\tCLPN\tCHLAMYDIA PNEUMONIAE\t#VALUE!\t287685\t0\tNone\tNone\t4\t1150740\t4.88E+11
+BODY FLUID ORDERABLES\t497448\tLABCHLAM\tCHLAMYDIA TRACHOMATIS / PSITTACI CULTURE\tCHLA\tCHLAMYDIA CULTURE\t#VALUE!\t1093440\t0\tNone\tNone\t3\t3280320\t6.55E+12
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASDI\tDISCLAIMER\t#VALUE!\t866627.1429\t0\tNone\tNone\t14\t12132780\t1.36E+13
+BODY FLUID ORDERABLES\t669900\tLABKRAS\tKRAS MUTATION DETECTION\tKRASME\tMETHOD\t#VALUE!\t866627.1429\t0\tNone\tNone\t14\t12132780\t1.36E+13
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMDI\tDISCLAIMER\t#VALUE!\t741092.1429\t0\tNone\tNone\t28\t20750580\t2.04E+13
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMMD\tINTERPRETED BY\t#VALUE!\t741092.1429\t0\tNone\tNone\t28\t20750580\t2.04E+13
+BODY FLUID ORDERABLES\t670101\tLABMGMT\tMGMT, METHYLATION SPECIFIC PCR\tMGMME\tMETHOD\t#VALUE!\t741092.1429\t0\tNone\tNone\t28\t20750580\t2.04E+13
+BODY FLUID ORDERABLES\t670778\tLAB11188R\tM. PNEUMONIAE DNA\t11189R\tM.PNEUMONIAE\t#VALUE!\t166380\t0\tNone\tNone\t8\t1331040\t2.33E+11
+BODY FLUID ORDERABLES\t670820\tLAB11214R\tBETA-2 TRANSFERRIN, BF\t11215R\tBETA-2 TRANSFERRIN, BF\t#VALUE!\t204888.8889\t0\tNone\tNone\t27\t5532000\t1.55E+12
+BODY FLUID ORDERABLES\t670943\tLABCD3F\tCD3, FLUID\tRTFDA2\tNOTE\t#VALUE!\t12144\t0\tNone\tNone\t5\t60720\t762372000
+BODY FLUID ORDERABLES\t744342\tLAB11617R\tCMV PCR, AQUEOUS FLUID\t11776R\tCMV PCR, AQUEOUS FLUID\t#VALUE!\t799992\t0\tNone\tNone\t15\t11999880\t3.06E+13
+BODY FLUID ORDERABLES\t744344\tLAB11618R\tHSV 1/2 PCR, AQUEOUS FLUID\t11777R\tHSV 1/2 PCR,AQUOUS FLUID\t#VALUE!\t910809.2308\t0\tNone\tNone\t13\t11840520\t3.07E+13
+BODY FLUID ORDERABLES\t744348\tLAB11620R\tTOXOPLASMA GONDII, AQUEOUS FLUID\t11779R\tTOXOPLASAMGONDII,AQUEOUS FLUID\t#VALUE!\t1675310\t0\tNone\tNone\t6\t10051860\t2.90E+13
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11992R\tSPECIMEN\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11993R\tSPECIMEN ID\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11994R\tSOURCE\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11995R\tORDER DATE\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11996R\tCOMMENT\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11997R\tAMENDMENT\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11998R\tREVIEWED BY\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t870909\tLAB11991R\tDNA EXTRACTION, NPL\t11999R\tRELEASE DATE\t#VALUE!\t60\t0\tNone\tNone\t2\t120\t7200
+BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFDI\tDISCLAIMER\t#VALUE!\t479125.7143\t0\tNone\tNone\t14\t6707760\t4.66E+12
+BODY FLUID ORDERABLES\t871378\tLABBRAF\tBRAF\tBRAFMD\tINTERPRETATION\t#VALUE!\t479125.7143\t0\tNone\tNone\t14\t6707760\t4.66E+12
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSDI\tDISCLAIMER\t#VALUE!\t912125.8065\t0\tNone\tNone\t62\t56551800\t6.06E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSMD\tINTERPRETED BY\t#VALUE!\t934668.2143\t0\tNone\tNone\t56\t52341420\t5.63E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSME\tMETHOD\t#VALUE!\t912125.8065\t0\tNone\tNone\t62\t56551800\t6.06E+13
+BODY FLUID ORDERABLES\t898794\tLABCSMP\tCANCER SOMATIC MUTATION PANEL\tCSREF\tREFERENCES\t#VALUE!\t912125.8065\t0\tNone\tNone\t62\t56551800\t6.06E+13
+BODY FLUID ORDERABLES\t898806\tLAB12328R\tAMYLOID PROTEIN IDENTIFICATION\t12327R\tAMYLOID PROTEIN ID, TISSUE\t#VALUE!\t60\t0\tNone\tNone\t1\t60\t3600
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12342R\tSPECIMEN ID\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12343R\tSOURCE\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12344R\tORDER DATE\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12345R\tREASON FOR REFERRAL\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12346R\tMETHOD\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12347R\tRESULTS\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12348R\tINTERPRETATION\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12349R\tAMENDMENT\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12350R\tCONSULTANT\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898810\tLAB12331R\tMLH1 HYPERMETHYLATION ANALYSIS\t12351R\tREPORT DATE\t#VALUE!\t1463100\t0\tNone\tNone\t3\t4389300\t9.23E+12
+BODY FLUID ORDERABLES\t898812\tLAB12332R\tKIT MUTATION ANALYSIS EXON 11\t12337R\tKIT MUTATION ANALYSIS EXON 11\t#VALUE!\t540607.5\t0\tNone\tNone\t8\t4324860\t2.66E+12
+BODY FLUID ORDERABLES\t898814\tLAB12333R\tKIT MUTATION ANALYSIS EXON 9\t12338R\tKIT MUTATION ANALYSIS EXON 9\t#VALUE!\t708520\t0\tNone\tNone\t3\t2125560\t1.51E+12
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12448R\tACCESSION NUMBER\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12449R\tREFERRING PATH-PHYSICIAN\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12450R\tREF PATH-PHYS ADDRESS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12451R\tPLACE OF DEATH\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12452R\tDATE AND TIME OF DEATH\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12453R\tDATE OF AUTOPSY\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12454R\tSPECIMEN\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12455R\tMATERIAL\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12456R\tTISSUE DESCRIPTION\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12457R\tMICROSCOPIC DESCRIPTION\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12458R\tCLINICAL HISTORY\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12459R\tFINAL DIAGNOSIS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12460R\tFINAL DIAGNOSIS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12461R\tCOMMENT\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12462R\tREVISION DESCRIPTION\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12463R\tSIGNING PATHOLOGIST\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12464R\tSPECIAL PROCEDURES\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12465R\tSP SIGNING PATHOLOGIST\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12466R\tPREVIOUS REPORT FOLLOWS\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12467R\tADDENDUM\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12468R\tADDENDUM COMMENT\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910582\tLAB12447R\tAP SPECIAL STUDIES REVIEW\t12469R\tADDENDUM PATHOLOGIST\t#VALUE!\t13488\t0\tNone\tNone\t5\t67440\t4531989600
+BODY FLUID ORDERABLES\t910584\tLAB12470R\tSTAIN, CONGO RED\t12379R\tCONGO RED, STAIN\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12472R\tSPECIMEN\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12473R\tADDENDUM COMMENT\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12474R\tACCESSION NUMBER\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12475R\tREFERRING PATH-PHYSICIAN\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12476R\tREF PATH-PHYS ADDRESS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12477R\tPLACE OF DEATH\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12478R\tDATE AND TIME OF DEATH\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12479R\tDATE OF AUTOPSY\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12480R\tMATERIAL\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12481R\tTISSUE DESCRIPTION\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12482R\tMICROSCOPIC DESCRIPTION\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12483R\tCLINICAL HISTORY\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12484R\tFINAL DIAGNOSIS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12485R\tFINAL DIAGNOSIS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12486R\tCOMMENT\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12487R\tREVISION DESCRIPTION\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12488R\tSIGNING PATHOLOGIST\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12489R\tSPECIAL PROCEDURES\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12490R\tSP SIGNING PATHOLOGIST\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12491R\tPREVIOUS REPORT FOLLOWS\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12492R\tADDENDUM\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910586\tLAB12471R\tSURGICAL PATHOLOGY CONSULT\t12493R\tADDENDUM PATHOLOGIST\t#VALUE!\t1096860\t0\tNone\tNone\t1\t1096860\t1.20E+12
+BODY FLUID ORDERABLES\t910588\tLAB12494R\tPATH CONSULT, OUTSIDE SLIDE\t12378R\tPATH CONSULT, OUTSIDE SLIDE\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+CARDIAC ANGIO ORDERABLES\t474722\tIMG11AR1\tCAR CATH HEART RT 1\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474724\tIMG11BXM1\tCAR MYOCARD BIOPSY I\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474725\tIMG11BXMT\tCAR MYOCARD BX I TX\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474726\tIMG11CCP\tCAR CATH CORS POSSIBLE\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474730\tIMG11IA\tCAR INTRA AORTIC BALLOON PUMP\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474731\tIMG11IABP\tCAR IABP REMOVAL\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474736\tIMG11LHC\tCAR LT HEART CATH\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474742\tIMG11PER\tCAR PERICARDIOCENTESIS\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474743\tIMG11RLCA\tCAR RT LT CATH CORS ANGIO\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t474744\tIMG11RLHC\tCAR COMB RT LT HEART CATH\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t517201\tIMG1PTCA\tCAR PTCA EACH ADDITIONAL VESSEL\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t517407\tIMG1STEV\tCAR CORONARY STENT SINGLE\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t517417\tIMG1PTCS\tCAR PTCA 1VESSEL SIMPLE\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t517461\tIMG1ATH\tCAR PTC ROTABLATOR\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+CARDIAC ANGIO ORDERABLES\t660296\tCRP333\tRIGHT LEFT CATH CORS ANGIO\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+COAGULATION ORDERABLES\t472515\tLABHIAB\tHEPARIN INDUCED AB\tHIACOM\tCOMMENTS\t#VALUE!\t64755.64516\t0\tNone\tNone\t372\t24089100\t2.37E+12
+COAGULATION ORDERABLES\t472516\tLABHIPA\tHEPARIN INDUCED PLATELET AGGREGATION\tHIPLAB\tLAB DATA\t#VALUE!\t158939.6319\t0\tNone\tNone\t163\t25907160\t6.12E+12
+COAGULATION ORDERABLES\t472663\tLABVWNTRP\tINTERPRETATION, VW SCREEN\tVWDIR\tINTERPRETED BY\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+COAGULATION ORDERABLES\t472867\tLABCGMISC\tMISCELLANEOUS TEST COAGULATION\tCGRSLT\tRESULT\t#VALUE!\t157680\t0\tNone\tNone\t1\t157680\t24862982400
+CYTOGENETICS ORDERABLES\t472130\tLABCYTGEN\tCYTOGENETICS FISH ORDER\tCYTGN1\tCYTOGEN/FISH TRACKING\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+ECG ORDERABLES\t472256\tEKG5\tECG 12-LEAD\tPDF\tPDF RESULT\t#VALUE!\t-9930.481737\t0\tNone\tNone\t12019\t-119354460\t4.85E+17
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tECGIMPRESS\tECG IMPRESSION\t#VALUE!\t133908.75\t0\tNone\tNone\t16\t2142540\t3.12E+12
+ECG ORDERABLES\t472257\tEKG6\tECG SIGNAL AVERAGED\tPDF\tPDF RESULT\t#VALUE!\t3600\t0\tNone\tNone\t2\t7200\t26956800
+EP ORDERABLES\t474752\tIMG12AH\tEP ABLATION\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+EP ORDERABLES\t474772\tIMG12IA\tICD ANALYSIS\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+FLOW CYTOMETRY\t473306\tLABSEZPNL\tSEZARY PANEL\tAN20DS\tDISCLAIMER\t#VALUE!\t614750\t0\tNone\tNone\t6\t3688500\t9.36E+12
+HISTOCOMPATIBILITY ORDERABLE\t481589\t10285R\tHLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)\tRINFO\tTO FIND RESULT:\t#VALUE!\t130260\t0\tNone\tNone\t1\t130260\t16967667600
+HISTOCOMPATIBILITY ORDERABLE\t505814\t10410R\tHLA-BMT DONOR MATCH GRADE SUMMARY\t9706R\tDONOR MATCH GRADE SUMMARY\t#VALUE!\t13188948.16\t0\tNone\tNone\t76\t1002360060\t3.04E+16
+HISTOCOMPATIBILITY ORDERABLE\t506036\t10270R\tHLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t1418312.791\t0\tNone\tNone\t86\t121974900\t4.69E+14
+HISTOCOMPATIBILITY ORDERABLE\t506864\t10235R\tHLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING\tRINFO\tTO FIND RESULT:\t#VALUE!\t584445.8824\t0\tNone\tNone\t68\t39742320\t4.86E+13
+HISTOCOMPATIBILITY ORDERABLE\t507225\t10220R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t1641532\t0\tNone\tNone\t30\t49245960\t1.25E+14
+HISTOCOMPATIBILITY ORDERABLE\t519017\t10230R\tHLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*\tRINFO\tTO FIND RESULT:\t#VALUE!\t1205680\t0\tNone\tNone\t9\t10851120\t1.84E+13
+HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*\tRINFO\tTO FIND RESULT:\t#VALUE!\t1487994\t0\tNone\tNone\t30\t44639820\t1.09E+14
+HISTOCOMPATIBILITY ORDERABLE\t569143\t10225R\tHLA ORDER - DESENSITIZATION MONITORING PRE IVIG\tRINFO\tTO FIND RESULT:\t#VALUE!\t2244840\t0\tNone\tNone\t1\t2244840\t5.04E+12
+HISTOCOMPATIBILITY ORDERABLE\t609847\t10265R\tHLA ORDER - SCHEDULED SCREENING KIT PROCESSING\tRINFO\tTO FIND RESULT:\t#VALUE!\t1226580\t0\tNone\tNone\t3\t3679740\t6.00E+12
+HISTOCOMPATIBILITY ORDERABLE\t685703\t10135R\tHLA ORDER - SCHEDULED SCREENING KIT BILLING\tRINFO\tTO FIND RESULT:\t#VALUE!\t1225575\t0\tNone\tNone\t4\t4902300\t2.13E+13
+HISTOCOMPATIBILITY ORDERABLE\t710704\t10210R\tHLA ORDER - DESENSITIZATION MONITORING POST IVIG\tRINFO\tTO FIND RESULT:\t#VALUE!\t2240112\t0\tNone\tNone\t5\t11200560\t3.47E+13
+HISTOCOMPATIBILITY ORDERABLE\t890776\t10110R\tHLA ORDER - FLOW AUTO CROSSMATCH\tRINFO\tTO FIND RESULT:\t#VALUE!\t267540\t0\tNone\tNone\t4\t1070160\t4.76E+11
+HISTOCOMPATIBILITY ORDERABLE\t890780\t10010R\tHLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)\tRINFO\tTO FIND RESULT:\t#VALUE!\t170820\t0\tNone\tNone\t1\t170820\t29179472400
+HISTOCOMPATIBILITY ORDERABLE\t890826\t10150R\tHLA ORDER - SUMMARIES\tRINFO\tTO FIND RESULT:\t#VALUE!\t1953334.327\t0\tNone\tNone\t208\t406293540\t1.40E+16
+HISTOCOMPATIBILITY ORDERABLE\t892285\t10273R\tHLA ORDER - WORKUP AT TIME OF TRANSPLANT\tRINFO\tTO FIND RESULT:\t#VALUE!\t452812.6829\t0\tNone\tNone\t41\t18565320\t3.05E+13
+HISTOCOMPATIBILITY ORDERABLE\t895898\t10120R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t202080\t0\tNone\tNone\t1\t202080\t40836326400
+HISTOCOMPATIBILITY ORDERABLE\t896063\t10390R\tHLA-BMT DONOR MATCH DETAIL SUMMARY\t9663R\tDONOR MATCH GRADE DETAIL\t#VALUE!\t11844188.49\t0\tNone\tNone\t106\t1255483980\t3.44E+16
+HISTOCOMPATIBILITY ORDERABLE\t896063\t10390R\tHLA-BMT DONOR MATCH DETAIL SUMMARY\t9706R\tDONOR MATCH GRADE SUMMARY\t#VALUE!\t8251302\t0\tNone\tNone\t30\t247539060\t4.03E+15
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9235R\tDISCLAIMER\t#VALUE!\t528366.6667\t0\tNone\tNone\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896064\t10320R\tHLA - ALLO B CDC CROSSMATCH\t9712R\tDONOR PHENOTYPE\t#VALUE!\t528366.6667\t0\tNone\tNone\t9\t4755300\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896065\t10310R\tHLA - ALLO B FLOW CROSSMATCH\t9183R\tDISCLAIMER\t#VALUE!\t380304.6774\t0\tNone\tNone\t124\t47157780\t5.17E+13
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9209R\tDISCLAIMER\t#VALUE!\t475542\t0\tNone\tNone\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896066\t10315R\tHLA - ALLO T CDC CROSSMATCH\t9709R\tDONOR PHENOTYPE\t#VALUE!\t475542\t0\tNone\tNone\t10\t4755420\t8.85E+12
+HISTOCOMPATIBILITY ORDERABLE\t896067\t10305R\tHLA - ALLO T FLOW CROSSMATCH\t9152R\tDISCLAIMER\t#VALUE!\t370725\t0\tNone\tNone\t124\t45969900\t4.87E+13
+HISTOCOMPATIBILITY ORDERABLE\t896069\t10300R\tHLA - AUTO B FLOW CROSSMATCH\t9122R\tDISCLAIMER\t#VALUE!\t272784.1379\t0\tNone\tNone\t87\t23732220\t2.33E+13
+HISTOCOMPATIBILITY ORDERABLE\t896071\t10295R\tHLA - AUTO T FLOW CROSSMATCH\t9101R\tDISCLAIMER\t#VALUE!\t273799.3103\t0\tNone\tNone\t87\t23820540\t2.34E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9369R\tMFI RESULTS FOR DSA BY C1Q CLASS I\t#VALUE!\t37661.85185\t0\tNone\tNone\t162\t6101220\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9449R\tDSA RESULTS FOR C1Q CLASS I\t#VALUE!\t2331.428571\t0\tNone\tNone\t7\t16320\t262447200
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9451R\tCOMPOSED TEXT CLASS I\t#VALUE!\t37661.85185\t0\tNone\tNone\t162\t6101220\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9463R\tDSA RESULTS FOR C1Q CLASS II\t#VALUE!\t33.91304348\t0\tNone\tNone\t23\t780\t46800
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9465R\tCOMPOSED TEXT CLASS II\t#VALUE!\t37209.87805\t0\tNone\tNone\t164\t6102420\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9466R\tDISCLAIMER\t#VALUE!\t37217.81818\t0\tNone\tNone\t165\t6140940\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896074\t10370R\tHLA - DSA BY C1Q\t9657R\tMFI RESULTS FOR DSA BY C1Q CLASS II\t#VALUE!\t37209.87805\t0\tNone\tNone\t164\t6102420\t2.59E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9368R\tMFI RESULTS FOR DSA BY IGG CLASS I\t#VALUE!\t43413.25301\t0\tNone\tNone\t166\t7206600\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9419R\tDSA RESULTS FOR IGG CLASS I\t#VALUE!\t4032\t0\tNone\tNone\t20\t80640\t6368090400
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9421R\tCOMPOSED TEXT CLASS I\t#VALUE!\t43413.25301\t0\tNone\tNone\t166\t7206600\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9432R\tDSA RESULTS FOR IGG CLASS II\t#VALUE!\t4365\t0\tNone\tNone\t40\t174600\t15129072000
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9434R\tCOMPOSED TEXT CLASS II\t#VALUE!\t42896.78571\t0\tNone\tNone\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9435R\tDISCLAIMER\t#VALUE!\t42896.78571\t0\tNone\tNone\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896075\t10365R\tHLA - DSA BY IGG\t9656R\tMFI RESULTS FOR DSA BY IGG CLASS II\t#VALUE!\t42896.78571\t0\tNone\tNone\t168\t7206660\t2.67E+13
+HISTOCOMPATIBILITY ORDERABLE\t896076\t10395R\tHLA - CROSSMATCH SUMMARY\t9661R\tHLA CROSSMATCH SUMMARY\t#VALUE!\t6064014.146\t0\tNone\tNone\t41\t248624580\t5.84E+15
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9077R\tNMDP CODES\t#VALUE!\t190845.8369\t0\tNone\tNone\t233\t44467080\t7.74E+13
+HISTOCOMPATIBILITY ORDERABLE\t896077\t10290R\tHLA - TYPING\t9080R\tDISCLAIMER\t#VALUE!\t213091.738\t0\tNone\tNone\t397\t84597420\t1.52E+14
+HISTOCOMPATIBILITY ORDERABLE\t896078\t10400R\tHLA - TYPING SUMMARY\t9662R\tHLA TYPING SUMMARY\t#VALUE!\t5150790.545\t0\tNone\tNone\t55\t283293480\t6.50E+15
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9338R\tINHIBITION RESULTS FOR CLASS I\t#VALUE!\t1441825.714\t0\tNone\tNone\t7\t10092780\t6.71E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9343R\tINHIBITION RESULTS FOR CLASS II\t#VALUE!\t413505\t0\tNone\tNone\t8\t3308040\t5.94E+12
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9344R\tINTERPRETATION/ COMMENTS CLASS II\t#VALUE!\t2652120\t0\tNone\tNone\t3\t7956360\t6.22E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9345R\tCOMPOSED TEXT CLASS II\t#VALUE!\t1119612\t0\tNone\tNone\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9346R\tDISCLAIMER\t#VALUE!\t1119612\t0\tNone\tNone\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9351R\tMFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS I\t#VALUE!\t1261597.5\t0\tNone\tNone\t8\t10092780\t6.71E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9586R\tINTERPRETATION/ COMMENTS CLASS I\t#VALUE!\t3944040\t0\tNone\tNone\t2\t7888080\t6.22E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9587R\tCOMPOSED TEXT CLASS I\t#VALUE!\t1261597.5\t0\tNone\tNone\t8\t10092780\t6.71E+13
+HISTOCOMPATIBILITY ORDERABLE\t896080\t10350R\tHLA - IN VITRO IVY INHIBITION ASSAY\t9653R\tMFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS II\t#VALUE!\t1119612\t0\tNone\tNone\t10\t11196120\t6.82E+13
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9364R\tMFI RESULTS FOR MONITORING BY C1Q CLASS I\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9390R\tMONITORING RESULTS FOR C1Q CLASS I\t#VALUE!\t0\t0\tNone\tNone\t4\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9392R\tCOMPOSED TEXT CLASS I\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9403R\tMONITORING RESULTS FOR C1Q CLASS II\t#VALUE!\t0\t0\tNone\tNone\t2\t0\t0
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9405R\tCOMPOSED TEXT CLASS II\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9406R\tDISCLAIMER\t#VALUE!\t33166.66667\t0\tNone\tNone\t9\t298500\t89030631600
+HISTOCOMPATIBILITY ORDERABLE\t896081\t10360R\tHLA - MONITORING BY C1Q\t9655R\tMFI RESULTS FOR MONITORING BY C1Q CLASS II\t#VALUE!\t37305\t0\tNone\tNone\t8\t298440\t89030628000
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9360R\tMONITORING RESULTS FOR IGG CLASS I\t#VALUE!\t30\t0\tNone\tNone\t4\t120\t7200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9362R\tCOMPOSED TEXT CLASS I\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9363R\tMFI RESULTS FOR MONITORING BY IGG CLASS I\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9373R\tMONITORING RESULTS FOR IGG CLASS II\t#VALUE!\t30\t0\tNone\tNone\t4\t120\t7200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9375R\tCOMPOSED TEXT CLASS II\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9376R\tDISCLAIMER\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896082\t10355R\tHLA - MONITORING BY IGG\t9654R\tMFI RESULTS FOR MONITORING BY IGG CLASS II\t#VALUE!\t42651.42857\t0\tNone\tNone\t7\t298560\t89030635200
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t10471R\tSAFE ANTIGENS C1Q\t#VALUE!\t354240\t0\tNone\tNone\t1\t354240\t1.25E+11
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9282R\tDISCLAIMER\t#VALUE!\t118749.8734\t0\tNone\tNone\t316\t37524960\t4.02E+13
+HISTOCOMPATIBILITY ORDERABLE\t896083\t10335R\tHLA - SCREENING C1Q\t9652R\tMFI RESULTS FOR SCREENING C1Q CLASS II\t#VALUE!\t138546\t0\tNone\tNone\t210\t29094660\t2.40E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9261R\tCOMPOSED TEXT CLASS II\t#VALUE!\t160995\t0\tNone\tNone\t288\t46366560\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9262R\tDISCLAIMER\t#VALUE!\t159629.0722\t0\tNone\tNone\t291\t46452060\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9582R\tINTERPRETATION/ COMMENTS CLASS I\t#VALUE!\t444600\t0\tNone\tNone\t2\t889200\t7.91E+11
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9583R\tCOMPOSED TEXT CLASS I\t#VALUE!\t160179.1034\t0\tNone\tNone\t290\t46451940\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9650R\tMFI RESULTS FOR SCREENING IGG CLASS I\t#VALUE!\t160179.1034\t0\tNone\tNone\t290\t46451940\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896084\t10330R\tHLA - SCREENING IGG\t9651R\tMFI RESULTS FOR SCREENING IGG CLASS II\t#VALUE!\t160995\t0\tNone\tNone\t288\t46366560\t7.62E+13
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9574R\tINTERPRETATION/ COMMENTS\t#VALUE!\t8022.580645\t0\tNone\tNone\t31\t248700\t61404901200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9575R\tCOMPOSED TEXT\t#VALUE!\t4147.297297\t0\tNone\tNone\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896085\t10375R\tHLA - STR/CHIMERISM TESTING\t9576R\tDISCLAIMER\t#VALUE!\t4147.297297\t0\tNone\tNone\t74\t306900\t64653973200
+HISTOCOMPATIBILITY ORDERABLE\t896159\t10100R\tHLA ORDER - PLATELET COMPATIBILITY\tRINFO\tTO FIND RESULT:\t#VALUE!\t456229.9115\t0\tNone\tNone\t113\t51553980\t7.28E+13
+HISTOCOMPATIBILITY ORDERABLE\t979724\t10287R\tHLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT\tRINFO\tTO FIND RESULT:\t#VALUE!\t5317523.478\t0\tNone\tNone\t23\t122303040\t2.88E+15
+HISTOCOMPATIBILITY ORDERABLE\t1458223\t10175R\tHLA ORDER - KIT PROCESSING RECIPIENT\tRINFO\tTO FIND RESULT:\t#VALUE!\t1408650\t0\tNone\tNone\t2\t2817300\t3.97E+12
+HISTOCOMPATIBILITY ORDERABLE\t1509678\t10070R\tHLA ORDER - IVIG IN VITRO INHIBITION ASSAY\tRINFO\tTO FIND RESULT:\t#VALUE!\t1712980\t0\tNone\tNone\t3\t5138940\t1.13E+13
+HISTOCOMPATIBILITY ORDERABLE\t235818016\t10280R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)\tRINFO\tTO FIND RESULT:\t#VALUE!\t5611500\t0\tNone\tNone\t1\t5611500\t3.15E+13
+HISTOCOMPATIBILITY ORDERABLE\t235818016\t10280R\tHLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING\tRINFO\tTO FIND RESULT:\t#VALUE!\t1715198.88\t0\tNone\tNone\t125\t214399860\t7.48E+14
+HISTOCOMPATIBILITY ORDERABLE\t304982014\t10545R\tHLA - CD31 TESTING SUMMARY\t10478R\tCD31 TESTING SUMMARY\t#VALUE!\t240\t0\tNone\tNone\t1\t240\t57600
+IMMUNOLOGY ORDERABLES\t471697\tLABASPR\tASPERGILLUS AB, CF, S\tASPR\tASPERILLUS-CF\t#VALUE!\t435300\t0\tNone\tNone\t1\t435300\t1.89E+11
+IMMUNOLOGY ORDERABLES\t472049\tLABCIMM\tCOCCI AB, ID, S\tCIMM\tCOCCI IMMUNODIFFUSION\t#VALUE!\t385435\t0\tNone\tNone\t24\t9250440\t3.66E+12
+IMMUNOLOGY ORDERABLES\t472360\tLABFASAB\tFASCIOLA HEPATICA AB\tFHAB2\tF.HEPATICA ANTIBODY ,ELISA\t#VALUE!\t8270820\t0\tNone\tNone\t1\t8270820\t6.84E+13
+IMMUNOLOGY ORDERABLES\t472579\tLABHYPP\tHYPERSENSITIVITY PNEUMONITIS SCREEN\tHYPINT\tINTERPRETATION\t#VALUE!\t415480\t0\tNone\tNone\t3\t1246440\t6.65E+11
+IMMUNOLOGY ORDERABLES\t472773\tLAB12214R\tB. BURGDORFERI C6 PEP AB,RFLX\tLYMEC6\tB.BURGDORFERI C6 PEPTIDE\t#VALUE!\t269543.2432\t0\tNone\tNone\t111\t29919300\t1.07E+13
+IMMUNOLOGY ORDERABLES\t472773\tLAB12214R\tLYME SCREEN, SERUM\tLYMEC6\tB.BURGDORFERI C6 PEPTIDE\t#VALUE!\t527280\t0\tNone\tNone\t8\t4218240\t6.97E+12
+IMMUNOLOGY ORDERABLES\t472773\tLABLYMES2\tLYME SCREEN, SERUM\tLYMEC6\tB.BURGDORFERI C6 PEPTIDE\t#VALUE!\t284946.5693\t0\tNone\tNone\t137\t39037680\t1.38E+13
+IMMUNOLOGY ORDERABLES\t473063\tLABPARAG\tPARAGONIMIASIS ANTIBODY, ELISA\tPARAG1\tPARAGONIMIASIS\t#VALUE!\t196920\t0\tNone\tNone\t1\t196920\t38777486400
+IMMUNOLOGY ORDERABLES\t473087\tLABPENALL\tPENICILLIN ALLERGY SCREEN\tPENINT\tINTERPRETATION\t#VALUE!\t485348.8235\t0\tNone\tNone\t34\t16501860\t1.42E+13
+IMMUNOLOGY ORDERABLES\t497406\tLABYNMOG\tNMO IGG\tYNMOG1\t(NMO) AUTOANTIBODY, IGG, SERUM\t#VALUE!\t485895.3846\t0\tNone\tNone\t39\t18949920\t3.33E+13
+IMMUNOLOGY ORDERABLES\t497482\tLABYPLAB\tPLATELET AB\tPLTAB\tPLATELET ANTIBODY\t#VALUE!\t262359.4286\t0\tNone\tNone\t35\t9182580\t2.60E+12
+IMMUNOLOGY ORDERABLES\t497482\tLABYPLAB\tPLATELET AB\tYPLAB2\tCOMMENT\t#VALUE!\t266346.6667\t0\tNone\tNone\t18\t4794240\t1.36E+12
+IR BODY ORDERABLES\t517389\tIMG2EX\tIR ANGIO EXTREMITY\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONDS\tDISCLAIMER\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
+LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONIN\tINTERPRETATION\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
+LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONMD\tINTERPRETED BY\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
+LAB BLOOD ORDERABLES\t471512\tLABCHON\tA/HYPOCHONDROPLASIA, SERUM\tCHONMT\tMETHOD\t#VALUE!\t758160\t0\tNone\tNone\t1\t758160\t5.75E+11
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tACPINT\tINTERPRETATION\t#VALUE!\t373273.3333\t0\tNone\tNone\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471532\tLABACYLP\tACYLCARNITINE PROFILE QUANTITATIVE\tINBY\tINTERPRETED BY\t#VALUE!\t373273.3333\t0\tNone\tNone\t9\t3359460\t1.66E+12
+LAB BLOOD ORDERABLES\t471562\tLABACHOO\tAIRBORNE ALLERGY SCREEN\tACHINT\tINTERPRETATION\t#VALUE!\t397308\t0\tNone\tNone\t5\t1986540\t1.05E+12
+LAB BLOOD ORDERABLES\t471577\tLAB11139R\tCARDIAC REJECTION RISK\tALLO\tALLOMAP\t#VALUE!\t380820\t0\tNone\tNone\t2\t761640\t3.76E+11
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHCT\tCOMMENT\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHDI\tDISCLAIMER\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHIN\tINTERPRETATION\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHMD\tINTERPRETED BY\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471582\tLABATHAL\tALPHA THALASSEMIA PCR\tATHME\tMETHOD\t#VALUE!\t738380\t0\tNone\tNone\t12\t8860560\t7.89E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tAAPINT\tINTERPRETATION\t#VALUE!\t293858.1818\t0\tNone\tNone\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471605\tLABAAP\tAMINO ACIDS, PLASMA\tINTBY\tINTERPRETED BY\t#VALUE!\t293858.1818\t0\tNone\tNone\t22\t6464880\t2.30E+12
+LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTB\tINTERPRETED BY\t#VALUE!\t543083.1579\t0\tNone\tNone\t57\t30955740\t2.27E+13
+LAB BLOOD ORDERABLES\t471609\tLABBMAML\tAML PROGNOSIS ASSAY, BONE MARROW\tBMFLTI\tINTERPRETATION, BM\t#VALUE!\t540213\t0\tNone\tNone\t60\t32412780\t2.37E+13
+LAB BLOOD ORDERABLES\t471623\tLABIBD\tANCA IBD WITH REFLEX\t12215R\t12215R\t#VALUE!\t205166.6667\t0\tNone\tNone\t9\t1846500\t4.31E+11
+LAB BLOOD ORDERABLES\t471629\tLABHSP70\tANTI - 68 KD (HSP - 70) ANTIBODIES\tHSP70T\tANTI-68 KD ABS\t#VALUE!\t4737480\t0\tNone\tNone\t1\t4737480\t2.24E+13
+LAB BLOOD ORDERABLES\t471632\tLABAMAONC\tANTI - MALIGNIN AB\tAMA\tANTI-MALIGNIN AB\t#VALUE!\t67680\t0\tNone\tNone\t1\t67680\t4580582400
+LAB BLOOD ORDERABLES\t471638\tLAB11075R\tANTI - SMOOTH MUSCLE AB\tASMA\tANTI-SMOOTH MUSCLE ANITBODY\t#VALUE!\t876090\t0\tNone\tNone\t2\t1752180\t2.43E+12
+LAB BLOOD ORDERABLES\t471638\tLAB11075R\tSMOOTH MUSCLE AB\tASMA\tANTI-SMOOTH MUSCLE ANITBODY\t#VALUE!\t221775\t0\tNone\tNone\t500\t110887500\t3.44E+13
+LAB BLOOD ORDERABLES\t471638\tLABYASMA\tANTI - SMOOTH MUSCLE AB\tASMA\tANTI-SMOOTH MUSCLE ANITBODY\t#VALUE!\t238124.2105\t0\tNone\tNone\t19\t4524360\t1.34E+12
+LAB BLOOD ORDERABLES\t471650\tLABASO\tANTISTREPTOLYSIN O TITER\tASO\tASO TITER (IU/ML)\t#VALUE!\t274380\t0\tNone\tNone\t2\t548760\t1.64E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO1\tSPECIMEN\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO10\tRELEASE DATE\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO11\tINTERPRETATION\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO12\tEXTRACTION PERFORMED?\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO2\tSPECIMEN ID\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO4\tORDER DATE\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO5\tMETHOD\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO6\tRESULT\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471655\tLABYAPOE\tAPOLIPOPROTEIN E GENOTYPING\tYAPO9\tREVIEWED BY\t#VALUE!\t562920\t0\tNone\tNone\t1\t562920\t3.17E+11
+LAB BLOOD ORDERABLES\t471678\tLABFABRO\tARBOVIRUS AB PANEL, IFA (TITER)\tEEIGG\tE EQUINE ENCEP IGG AB\t#VALUE!\t2760\t0\tNone\tNone\t1\t2760\t7617600
+LAB BLOOD ORDERABLES\t471687\tLAB11126R\tASCARIASIS\tASCA\tASCARISIS\t#VALUE!\t1498147.5\t0\tNone\tNone\t8\t11985180\t2.50E+13
+LAB BLOOD ORDERABLES\t471687\tLABASCA\tASCARIASIS AB\tASCA\tASCARISIS\t#VALUE!\t424980\t0\tNone\tNone\t1\t424980\t1.81E+11
+LAB BLOOD ORDERABLES\t471706\tLABSCLABY\tANTI-SCL70 ANTIBODY\tSCLINT\tANTI-SCL70 INTERPRETATION\t#VALUE!\t391549.434\t0\tNone\tNone\t159\t62256360\t3.14E+13
+LAB BLOOD ORDERABLES\t471707\tLABSSAABY\tANTI-RO ANTIBODY\tSSAIN\tANTI-RO INTERPRETATION\t#VALUE!\t390342.8571\t0\tNone\tNone\t210\t81972000\t4.05E+13
+LAB BLOOD ORDERABLES\t471708\tLABSSBABY\tANTI-LA ANTIBODY\tSSBINT\tANTI-LA INTERPRETATION\t#VALUE!\t380593.9806\t0\tNone\tNone\t206\t78402360\t3.76E+13
+LAB BLOOD ORDERABLES\t471733\tLABYPERAB\tBORDETELLA PERTUSSIS IGG AND IGM AB\tYPERA1\tB PERTUSSIS AB, IGG\t#VALUE!\t32220\t0\tNone\tNone\t1\t32220\t1038128400
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBDIR\tINTERPRETED BY\t#VALUE!\t1276240\t0\tNone\tNone\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471734\tLABBCLON\tB - CELL CLONALITY PCR\tBDIS\tDISCLAIMER\t#VALUE!\t1276240\t0\tNone\tNone\t3\t3828720\t4.91E+12
+LAB BLOOD ORDERABLES\t471738\tLABYBABE\tBABESIA MICROTI IGG AND IGM AB\tYBAB1\tBABESIA MICROTI, IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471738\tLABYBABE\tBABESIA MICROTI IGG AND IGM AB\tYBAB2\tBABESIA MICROTI, IGM\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471738\tLABYBABE\tBABESIA MICROTI IGG AND IGM AB\tYBAB3\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRDI\tDISCLAIMER\t#VALUE!\t600772.8477\t0\tNone\tNone\t151\t90716700\t6.81E+13
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRDX\tCLINICAL INDICATION\t#VALUE!\t600772.8477\t0\tNone\tNone\t151\t90716700\t6.81E+13
+LAB BLOOD ORDERABLES\t471746\tLABBCRQT\tBCR - ABL QUANTITATIVE, BLOOD\tQBCRME\tMETHOD\t#VALUE!\t600772.8477\t0\tNone\tNone\t151\t90716700\t6.81E+13
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQDI\tDISCLAIMER\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQIN\tINTERPRETATION\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQMD\tINTERPRETED BY\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQME\tMETHOD\t#VALUE!\t1383525\t0\tNone\tNone\t4\t5534100\t8.61E+12
+LAB BLOOD ORDERABLES\t471758\tLABBTHSQ\tBETA THALASSEMIA SEQUENCING\tBSQMU\tMUTATION\t#VALUE!\t1877940\t0\tNone\tNone\t1\t1877940\t3.53E+12
+LAB BLOOD ORDERABLES\t471760\tLABB2GP1\tBETA - 2 - GLYCOPROTEIN\tB2GPCM\tBETA-2-GP1 COMMENT\t#VALUE!\t612816.383\t0\tNone\tNone\t94\t57604740\t3.76E+13
+LAB BLOOD ORDERABLES\t471777\tLABYBLAS\tBLASTOMYCES AB\tYBLAS1\tBLASTOMYCES AB ,S\t#VALUE!\t365589.375\t0\tNone\tNone\t32\t11698860\t4.93E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMGDES\tCMV IGG TEST DESCRIPTION\t#VALUE!\t386356.6667\t0\tNone\tNone\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tCMMDES\tCMV IGM TEST DESCRIPTION\t#VALUE!\t386356.6667\t0\tNone\tNone\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHSAB\tVIRAL AB HSV\t#VALUE!\t404997.5\t0\tNone\tNone\t24\t9719940\t4.77E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tHVIGDS\tHSV IGG TEST DESCRIPTION\t#VALUE!\t302160\t0\tNone\tNone\t8\t2417280\t8.64E+11
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tRPRQIN\tINTERPRETATIONS\t#VALUE!\t379288.125\t0\tNone\tNone\t32\t12137220\t5.64E+12
+LAB BLOOD ORDERABLES\t471788\tLABBMPTP\tBMT PRETRANSPLANT PANEL\tVGGDES\tVZV IGG TEST DESCRIPTION\t#VALUE!\t386356.6667\t0\tNone\tNone\t18\t6954420\t3.18E+12
+LAB BLOOD ORDERABLES\t471799\tLABBRASP\tBRONCHOPULMONARY ASPERGILLUS SCREEN\tASPINT\tINTERPRETATION\t#VALUE!\t785686.9231\t0\tNone\tNone\t78\t61283580\t6.62E+13
+LAB BLOOD ORDERABLES\t471914\tLAB11105R\tCANDIDA AB\tCANDP\tCANDIDA PRECIPITINS\t#VALUE!\t288000\t0\tNone\tNone\t1\t288000\t82944000000
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11834R\tSPECIMEN\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11835R\tSPECIMEN ID\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11836R\tSOURCE\t#VALUE!\t1199640\t0\tNone\tNone\t1\t1199640\t1.44E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11837R\tORDER DATE\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11838R\tREASON FOR REFERRAL\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11839R\tMETHOD\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11840R\tINTERPRETATION\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11841R\tAMENDMENT\t#VALUE!\t1199640\t0\tNone\tNone\t1\t1199640\t1.44E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11842R\tREVIEWED BY\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t11843R\tRELEASE DATE\t#VALUE!\t1025250\t0\tNone\tNone\t2\t2050500\t2.16E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t12769R\tTRI-SIALO/DI-OLIGO RATIO\t#VALUE!\t682500\t0\tNone\tNone\t1\t682500\t4.66E+11
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t12770R\tAPO CIII-1/CIII-2 RATIO\t#VALUE!\t682500\t0\tNone\tNone\t1\t682500\t4.66E+11
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\t12771R\tAPO CIII-0/APO CIII-2 RATIO\t#VALUE!\t682500\t0\tNone\tNone\t1\t682500\t4.66E+11
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\tAODOR\tA-OLIGO/DI-OLIGO RAT\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
+LAB BLOOD ORDERABLES\t471921\tLABYCDTN\tCARBOHYDRATE DEFICIENT TRANSFERRIN\tMODOR\tMONO-OLIGO/DI-OLIGO RATIO\t#VALUE!\t911000\t0\tNone\tNone\t3\t2733000\t2.63E+12
+LAB BLOOD ORDERABLES\t471929\tLABCARN\tCARNITINE FREE AND TOTAL\tCARINT\tINTERPRETATION\t#VALUE!\t396363.6364\t0\tNone\tNone\t33\t13080000\t5.88E+12
+LAB BLOOD ORDERABLES\t471933\tLAB11127R\tCASPOFUNGIN LEVEL\tCASPO\tCASPOFUNGIN (CANCIDAS)\t#VALUE!\t720490.5882\t0\tNone\tNone\t17\t12248340\t1.11E+13
+LAB BLOOD ORDERABLES\t471946\tLABCD34\tCD34 PANEL, BLOOD\tCD34DS\tDISCLAIMER\t#VALUE!\t25604.54545\t0\tNone\tNone\t66\t1689900\t1.43E+11
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTDS\tDISCLAIMER\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTIN\tINTERPRETED BY\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTME\tMETHOD\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
+LAB BLOOD ORDERABLES\t471965\tLABCFPT\tCF POLY T ANALYSIS\tCFPTRS\tRESULT\t#VALUE!\t595140\t0\tNone\tNone\t1\t595140\t3.54E+11
+LAB BLOOD ORDERABLES\t472016\tLABYCLAMP\tCHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY\tYCLAM1\tCHLAMYDIA SEROLOGY, S\t#VALUE!\t244670.7692\t0\tNone\tNone\t13\t3180720\t8.85E+11
+LAB BLOOD ORDERABLES\t472048\tLAB11124R\tCOCCIDIOIDES COMPLEMENT FIXATION, SERUM\tCOCFS\tCOCCI CF (SERUM)\t#VALUE!\t952080\t0\tNone\tNone\t7\t6664560\t6.51E+12
+LAB BLOOD ORDERABLES\t472048\tLABCOCFS\tCOCCIDIOIDES COMPLEMENT FIXATION, SERUM\tCOCFS\tCOCCI CF (SERUM)\t#VALUE!\t1082202.353\t0\tNone\tNone\t34\t36794880\t4.16E+13
+LAB BLOOD ORDERABLES\t472053\tLABYTICK\tCOLORADO TICK FEVER AB\tYTICK1\tIGG AB\t#VALUE!\t829560\t0\tNone\tNone\t1\t829560\t6.88E+11
+LAB BLOOD ORDERABLES\t472053\tLABYTICK\tCOLORADO TICK FEVER AB\tYTICK2\tIGM AB\t#VALUE!\t829560\t0\tNone\tNone\t1\t829560\t6.88E+11
+LAB BLOOD ORDERABLES\t472053\tLABYTICK\tCOLORADO TICK FEVER AB\tYTICK3\tINTERPRETATION\t#VALUE!\t829560\t0\tNone\tNone\t1\t829560\t6.88E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP4\tCOMP HEPATITIS PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t200741.1086\t0\tNone\tNone\t451\t90534240\t2.96E+13
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHAGINT\tINTERPRETATION\t#VALUE!\t19380\t0\tNone\tNone\t1\t19380\t375584400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t223680\t0\tNone\tNone\t4\t894720\t2.54E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMP HEPATITIS PANEL\tHCVI\tINTERPRETATION\t#VALUE!\t182820\t0\tNone\tNone\t5\t914100\t2.55E+11
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHATINT\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472057\tLABVP5\tCOMPREHENSIVE HEPATITIS PANEL\tHCVI\tINTERPRETATION\t#VALUE!\t56580\t0\tNone\tNone\t1\t56580\t3201296400
+LAB BLOOD ORDERABLES\t472102\tLAB11056R\tCORTISOL FREE, SERUM\tCORTFREE\tCORTISOL, SERUM FREE\t#VALUE!\t426930\t0\tNone\tNone\t4\t1707720\t7.56E+11
+LAB BLOOD ORDERABLES\t472102\tLABSFCORT\tCORTISOL FREE, SERUM\tCORTFREE\tCORTISOL, SERUM FREE\t#VALUE!\t462558.4615\t0\tNone\tNone\t26\t12026520\t6.85E+12
+LAB BLOOD ORDERABLES\t472107\tLABCRTINE\tCREATINE, SERUM\tCRTNR\tCREATINE, PLASMA\t#VALUE!\t1224687.273\t0\tNone\tNone\t11\t13471560\t2.12E+13
+LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tBMAB\tBASEMENT MEMBRANE, IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
+LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP1\tCELL SURFACE AB IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
+LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP3\tMONKEY ESOPHAGUS IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
+LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP4\tHUMAN SPLIT SKIN IGG\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
+LAB BLOOD ORDERABLES\t472118\tLABYEPIAB\tCUTANEOUS IMMUNOFLUOR AB\tYEP5\tOTHER\t#VALUE!\t404940\t0\tNone\tNone\t1\t404940\t1.64E+11
+LAB BLOOD ORDERABLES\t472120\tLABCCP\tANTI CCP\tCCPINT\tINTERPRETATION\t#VALUE!\t296484\t0\tNone\tNone\t5\t1482420\t5.59E+11
+LAB BLOOD ORDERABLES\t472120\tLABYCCPIG\tANTI CCP\tCCPINT\tINTERPRETATION\t#VALUE!\t388985.2294\t0\tNone\tNone\t218\t84798780\t4.31E+13
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32DI\tDISCLAIMER:\t#VALUE!\t807004.6154\t0\tNone\tNone\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32IN\tINTERPRETED BY\t#VALUE!\t807004.6154\t0\tNone\tNone\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472123\tLABCF32\tCYSTIC FIBROSIS MUTATION STUDY\tCF32ME\tMETHOD:\t#VALUE!\t807004.6154\t0\tNone\tNone\t39\t31473180\t3.43E+13
+LAB BLOOD ORDERABLES\t472125\tLABCYSBLD\tCYSTICERCUS AB REFLEX PANEL, SERUM\tCYSAB2\tCYSTICERCUS AB (SERUM), ELISA\t#VALUE!\t307886.087\t0\tNone\tNone\t23\t7081380\t2.77E+12
+LAB BLOOD ORDERABLES\t472127\tLABCYSI\tCYSTICERCOSIS, CSF AND SERUM\tCYSS\tCYSTICERCOSIS-SERUM\t#VALUE!\t1266680\t0\tNone\tNone\t3\t3800040\t6.75E+12
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tPSREV\tPATH/SPEC REVIEW\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472180\tLABADIFF\tDIFFERENTIAL\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t#VALUE!\t900\t0\tNone\tNone\t1\t900\t810000
+LAB BLOOD ORDERABLES\t472181\tLABADIFFS\tDIFFERENTIAL AND SLIDE REVIEW\tSIGNIF\tSIGNIFICANT ABNORMALITIES\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472192\tLABDATLA\tDIRECT ANTIGLOBULIN TEST - LARC\tDATLAR\tDIRECT COOMBS - LA RED CROSS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472205\tLABYDISO\tDISOPYRAMIDE, SERUM\tYDISO1\tDISOPYRAMIDE, P\t#VALUE!\t8520\t0\tNone\tNone\t1\t8520\t72590400
+LAB BLOOD ORDERABLES\t472226\tLABDHB\tDONOR HEPATITIS B SURFACE AG\tDHB\tDONOR HEP B SURF. AG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472227\tLABDHEPC\tDONOR HEPATITIS C AB\tDHEPC\tDONOR HEPATITIS C AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472228\tLABDHIVB\tDONOR HIV AB\tDHTLV\tDONOR HIV AB COMMENT\t#VALUE!\t199937.1429\t0\tNone\tNone\t7\t1399560\t3.32E+11
+LAB BLOOD ORDERABLES\t472248\tLABYSDA\tDRUG SCREEN, PLASMA\t11832R\tDRUGS DETECTED:\t#VALUE!\t242828.4375\t0\tNone\tNone\t64\t15541020\t1.07E+13
+LAB BLOOD ORDERABLES\t472248\tLABYSDA\tDRUG SCREEN, PLASMA\tDRUGSCRN\tDRUG SCREEN, PLASMA\t#VALUE!\t645556.1538\t0\tNone\tNone\t26\t16784460\t4.16E+13
+LAB BLOOD ORDERABLES\t472251\tLABYAMEB\tE. HISTOLYTICA AB\tYAMEB1\tE. HISTOLYTICA AB\t#VALUE!\t332553.75\t0\tNone\tNone\t16\t5320860\t2.02E+12
+LAB BLOOD ORDERABLES\t472276\tLABECHIG\tECHINOCOCCUS AB, IGG W/REFLEX\tECHIND\tECHINOCOCCUS LGG\t#VALUE!\t287613.3333\t0\tNone\tNone\t9\t2588520\t8.75E+11
+LAB BLOOD ORDERABLES\t472328\tLABYESTRI\tESTRIOL FREE\tYESTR1\tESTRIOL,UNCONJUGATED\t#VALUE!\t154380\t0\tNone\tNone\t1\t154380\t23833184400
+LAB BLOOD ORDERABLES\t472330\tLABYESTRN\tESTRONE, SERUM\tESTRONE\tESTRONE\t#VALUE!\t270300\t0\tNone\tNone\t1\t270300\t73062090000
+LAB BLOOD ORDERABLES\t472347\tLABLEID\tFACTOR V LEIDEN\tLEIDIS\tDISCLAIMER\t#VALUE!\t436344.1652\t0\tNone\tNone\t581\t253515960\t1.44E+14
+LAB BLOOD ORDERABLES\t472359\tLAB11116R\tFANCONI MUTATION ANALYSIS\tGFANCO\tFANCONI AN. GP C MUT\t#VALUE!\t1312400\t0\tNone\tNone\t3\t3937200\t5.30E+12
+LAB BLOOD ORDERABLES\t472365\tLABYLCFA\tFATTY ACID PROF PEROXIDASE (C22 - C26)\tYLCFA9\tCOMMENT\t#VALUE!\t342450\t0\tNone\tNone\t2\t684900\t2.36E+11
+LAB BLOOD ORDERABLES\t472379\tLABFIBRO\tFIBROSPECT FOR LIVER FIBROSIS\tFIBROR\tFIBROSPECT FOR LIV\t#VALUE!\t1223280\t0\tNone\tNone\t2\t2446560\t3.14E+12
+LAB BLOOD ORDERABLES\t472380\tLABFILAB\tFILARIA IGG4 AB ELISA\tFILIG4\tFILARIAL LGG ANTIBODY\t#VALUE!\t476850.9677\t0\tNone\tNone\t31\t14782380\t9.47E+12
+LAB BLOOD ORDERABLES\t472414\tLABITCH\tFOOD ALLERGY SCREEN\tITINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXDIS\tDISCLAIMER\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXINT\tINTERPRETATION\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXMD\tINTERPRETED BY\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472420\tLABFRAGX\tFRAGILE X\tFXMET\tMETHOD\t#VALUE!\t1777160\t0\tNone\tNone\t3\t5331480\t1.17E+13
+LAB BLOOD ORDERABLES\t472422\tLABYT7P\tFREE THYROXINE INDEX\tFT4INDEX\tFREE THYROXINE INDEX\t#VALUE!\t146604\t0\tNone\tNone\t5\t733020\t1.21E+11
+LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tASPR\tASPERILLUS-CF\t#VALUE!\t360680\t0\tNone\tNone\t6\t2164080\t8.46E+11
+LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tBLAS\tBLASTO AB CFS\t#VALUE!\t360680\t0\tNone\tNone\t6\t2164080\t8.46E+11
+LAB BLOOD ORDERABLES\t472428\tLABFUNDAP\tFUNGAL DISEASE AB PANEL\tHISTM\tHISTO MYCELIAL PHASE\t#VALUE!\t360680\t0\tNone\tNone\t6\t2164080\t8.46E+11
+LAB BLOOD ORDERABLES\t472430\tLAB11128R\tFUNGAL MIC\tFMIC\tFUNGAL MIC\t#VALUE!\t1068060\t0\tNone\tNone\t1\t1068060\t1.14E+12
+LAB BLOOD ORDERABLES\t472471\tLABGTOL\tGLUCOSE TOLERANCE\tGTTINT\tGTT INTERPRETATION\t#VALUE!\t29820\t0\tNone\tNone\t1\t29820\t889232400
+LAB BLOOD ORDERABLES\t472487\tLABYHALO\tHALOPERIDOL, SERUM\tHALO\tHALOPERIDOL\t#VALUE!\t437280\t0\tNone\tNone\t1\t437280\t1.91E+11
+LAB BLOOD ORDERABLES\t472487\tLABYHALO\tHALOPERIDOL, SERUM\tYHALO2\tREDUCED HALOPERIDOL\t#VALUE!\t437280\t0\tNone\tNone\t1\t437280\t1.91E+11
+LAB BLOOD ORDERABLES\t472493\tLABHDLGGE\tHDL GRADIENT GEL ELECTROPHORESIS\tHDLGE\tHDL GRAD GEL ELECTRO\t#VALUE!\t1547100\t0\tNone\tNone\t1\t1547100\t2.39E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHCO\tCOMMENT\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHDI\tDISCLAIMER\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHIN\tINTERPRETATION\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHMD\tINTERPRETED BY\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHME\tMETHOD\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHPR\tPRIMERS\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472502\tLABHCHGA\tHEMOCHROMATOSIS GENOTYPING ANALYSIS\tHCHRE\tRESULT\t#VALUE!\t604041.4286\t0\tNone\tNone\t14\t8456580\t6.01E+12
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tDIAG. HEPATITIS B PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t70359.66102\t0\tNone\tNone\t413\t29058540\t4.09E+12
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBCINT\tINTERPRETATION\t#VALUE!\t129980\t0\tNone\tNone\t9\t1169820\t3.51E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t290760\t0\tNone\tNone\t1\t290760\t84541377600
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBSINT\tINTERPRETATION\t#VALUE!\t129980\t0\tNone\tNone\t9\t1169820\t3.51E+11
+LAB BLOOD ORDERABLES\t472522\tLABDGHBP\tHEPATITIS B DIAGNOSTIC PANEL\tHBTINT\tINTERPRETATION\t#VALUE!\t129980\t0\tNone\tNone\t9\t1169820\t3.51E+11
+LAB BLOOD ORDERABLES\t472523\tLAB11106R\tHEPATITIS B E AB\tHBEAB\tHEPATITIS B E AB\t#VALUE!\t895680\t0\tNone\tNone\t2\t1791360\t2.45E+12
+LAB BLOOD ORDERABLES\t472523\tLABHEAB\tHEPATITIS B E AB\tHBEAB\tHEPATITIS B E AB\t#VALUE!\t281130\t0\tNone\tNone\t2\t562260\t1.58E+11
+LAB BLOOD ORDERABLES\t472524\tLAB11107R\tHEPATITIS B E AG\tHBEAG\tHEPATITIS B E AG\t#VALUE!\t1544340\t0\tNone\tNone\t1\t1544340\t2.38E+12
+LAB BLOOD ORDERABLES\t472524\tLABHEAG\tHEPATITIS B E AG\tHBEAG\tHEPATITIS B E AG\t#VALUE!\t248520\t0\tNone\tNone\t4\t994080\t2.70E+11
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tANTI HBS QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t125880\t0\tNone\tNone\t5\t629400\t82853755200
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t140700\t0\tNone\tNone\t1\t140700\t19796490000
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t#VALUE!\t140700\t0\tNone\tNone\t1\t140700\t19796490000
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t138684.5455\t0\tNone\tNone\t22\t3051060\t4.80E+11
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAIN\tINTERPRETATION\t#VALUE!\t43043.64407\t0\tNone\tNone\t236\t10158300\t7.64E+11
+LAB BLOOD ORDERABLES\t472526\tLABHBSQT\tHEP B SURF AB QUANT\tHBSAQL\tHEP B SURF AB QUAL\t#VALUE!\t43043.64407\t0\tNone\tNone\t236\t10158300\t7.64E+11
+LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tANTI HBS QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t155980\t0\tNone\tNone\t63\t9826740\t1.82E+12
+LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t174097.8947\t0\tNone\tNone\t38\t6615720\t1.31E+12
+LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEPATITIS B SURFACE AB QUANTITATIVE\tHBSABQUAL\tHBS ANTIBODY,QUANTITATIVE, S\t#VALUE!\t174097.8947\t0\tNone\tNone\t38\t6615720\t1.31E+12
+LAB BLOOD ORDERABLES\t472526\tLABYHABQ\tHEP B SURF AB QUANT\tHBSAB\tHBS ANTIBODY, S\t#VALUE!\t161105.0602\t0\tNone\tNone\t166\t26743440\t5.04E+12
+LAB BLOOD ORDERABLES\t472536\tLABHGBFQ\tHGB F QUANTITATIVE\tHGFMD\tREVIEWED BY\t#VALUE!\t434820\t0\tNone\tNone\t3\t1304460\t5.86E+11
+LAB BLOOD ORDERABLES\t472538\tLABHGHPLC\tHGB QUANTITATIVE / FRACTIONATED HPLC\tHGMD\tREVIEWED BY\t#VALUE!\t426444.5714\t0\tNone\tNone\t105\t44776680\t2.79E+13
+LAB BLOOD ORDERABLES\t472549\tLABYHIST\tHISTOPLASMA AB SCREEN\tYHIS1\tHISTOPLASMA AB SCREEN\t#VALUE!\t172041\t0\tNone\tNone\t20\t3440820\t6.83E+11
+LAB BLOOD ORDERABLES\t472571\tLAB11137R\tHSV AB, IMMUNOBLOT\tHSVWB\tHSV WESTERN BLOT\t#VALUE!\t1094730\t0\tNone\tNone\t2\t2189460\t2.40E+12
+LAB BLOOD ORDERABLES\t472573\tLAB11136R\tHTLV-1 AB SCREEN\tHTLVS\tHTLV AB SCREEN\t#VALUE!\t275580\t0\tNone\tNone\t2\t551160\t1.53E+11
+LAB BLOOD ORDERABLES\t472573\tLABHTLVS\tHTLV-1 AB SCREEN\tHTLVS\tHTLV AB SCREEN\t#VALUE!\t350712\t0\tNone\tNone\t5\t1753560\t6.93E+11
+LAB BLOOD ORDERABLES\t472578\tLABHUNDNA\tHUNTINGTON\'S DNA\tHNDNA\tHUNTINGTON\'S DNA\t#VALUE!\t734460\t0\tNone\tNone\t2\t1468920\t2.16E+12
+LAB BLOOD ORDERABLES\t472595\tLABYSCERE\tINFLAMM. BOWEL DISEASE PANEL,S\tNEUTAB\tNEUTROPHIL SPECIFIC ANTIBODIES\t#VALUE!\t232440\t0\tNone\tNone\t3\t697320\t1.65E+11
+LAB BLOOD ORDERABLES\t472631\tLABIFEDE\tIMMUNOFIXATION IGD AND IGE, SERUM\tIFED\tIMMUNOFIXATION IGD\t#VALUE!\t466232\t0\tNone\tNone\t15\t6993480\t7.80E+12
+LAB BLOOD ORDERABLES\t472631\tLABIFEDE\tIMMUNOFIXATION IGD AND IGE, SERUM\tIFEE\tIMMUNOFIXATION IGE\t#VALUE!\t466232\t0\tNone\tNone\t15\t6993480\t7.80E+12
+LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tANAINTERP\tANA INTERPRETATION\t#VALUE!\t272940\t0\tNone\tNone\t2\t545880\t1.64E+11
+LAB BLOOD ORDERABLES\t472632\tLABIMM1\tIMMUNOLOGY PANEL 1\tDNAINT\tINTERPRETATION\t#VALUE!\t272940\t0\tNone\tNone\t2\t545880\t1.64E+11
+LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tANAINTERP\tANA INTERPRETATION\t#VALUE!\t311100\t0\tNone\tNone\t2\t622200\t2.23E+11
+LAB BLOOD ORDERABLES\t472633\tLABIMM2\tIMMUNOLOGY PANEL 2\tRHFINT\tINTERPRETATION\t#VALUE!\t311100\t0\tNone\tNone\t2\t622200\t2.23E+11
+LAB BLOOD ORDERABLES\t472661\tLABYNABF\tINTERFERON NEUTRALIZING  AB\tYNABF1\tINTERFERON NEUTRALIZING AB\t#VALUE!\t1388400\t0\tNone\tNone\t1\t1388400\t1.93E+12
+LAB BLOOD ORDERABLES\t472666\tLABYIFBA\tINTRINSIC FACTOR BLOCKING AB\tYIFB1\tINTRINSIC FACTOR BLOCKING AB\t#VALUE!\t238970\t0\tNone\tNone\t36\t8602920\t5.74E+12
+LAB BLOOD ORDERABLES\t472666\tLABYIFBA\tINTRINSIC FACTOR BLOCKING AB\tYIFB2\tCOMMENT\t#VALUE!\t172041.6\t0\tNone\tNone\t25\t4301040\t8.33E+11
+LAB BLOOD ORDERABLES\t472672\tLABISOHTI\tISOHEMAGGLUTININ TITER\tISOHTR\tISOHEMAGGLUTININ TITER RESULT\t#VALUE!\t22077.33333\t0\tNone\tNone\t90\t1986960\t1.89E+11
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKCO\tCOMMENT\t#VALUE!\t548376\t0\tNone\tNone\t115\t63063240\t4.39E+13
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKDI\tDISCLAIMER\t#VALUE!\t557059.322\t0\tNone\tNone\t118\t65733000\t4.63E+13
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKDX\tCLINICAL INDICATION\t#VALUE!\t557059.322\t0\tNone\tNone\t118\t65733000\t4.63E+13
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKIN\tINTERPRETATION\t#VALUE!\t889920\t0\tNone\tNone\t3\t2669760\t2.42E+12
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKME\tMETHOD\t#VALUE!\t557059.322\t0\tNone\tNone\t118\t65733000\t4.63E+13
+LAB BLOOD ORDERABLES\t472696\tLABJAK2\tJANUS KINASE 2 V617F MUTATION, BLOOD\tJAKRF\tREFERENCES\t#VALUE!\t548376\t0\tNone\tNone\t115\t63063240\t4.39E+13
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKCO\tCOMMENT\t#VALUE!\t526447.5\t0\tNone\tNone\t8\t4211580\t2.86E+12
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKDI\tDISCLAIMER\t#VALUE!\t525786.6667\t0\tNone\tNone\t9\t4732080\t3.13E+12
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKDX\tCLINICAL INDICATION\t#VALUE!\t525786.6667\t0\tNone\tNone\t9\t4732080\t3.13E+12
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKME\tMETHOD\t#VALUE!\t525786.6667\t0\tNone\tNone\t9\t4732080\t3.13E+12
+LAB BLOOD ORDERABLES\t472697\tLABNJAK2\tJANUS KINASE 2 V617F MUTATION, NON-BLOOD\tNJAKRF\tREFERENCES\t#VALUE!\t526447.5\t0\tNone\tNone\t8\t4211580\t2.86E+12
+LAB BLOOD ORDERABLES\t472717\tLABKBST\tKLEIHAUER - BETKE STAIN\tKBSINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t472724\tLABYLEPT\tLEPTOSPIROSIS AB\tYLEP1\tLEPTOSPIRA AB\t#VALUE!\t312504.6429\t0\tNone\tNone\t56\t17500260\t6.19E+12
+LAB BLOOD ORDERABLES\t472727\tLABYLDISO\tLD ISOENZYMES\tYLDIS2\tLD ISOENZYMES, S\t#VALUE!\t234468\t0\tNone\tNone\t5\t1172340\t2.91E+11
+LAB BLOOD ORDERABLES\t472745\tLABYLEGS\tLEGIONELLA PNEUMOPHILA, AB\tYLEGS1\tLEGIONELLA PNEUMOPHILA AB\t#VALUE!\t256849.7561\t0\tNone\tNone\t41\t10530840\t3.34E+12
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\t12431R\tINTERPRETATION\t#VALUE!\t323840\t0\tNone\tNone\t3\t971520\t3.52E+11
+LAB BLOOD ORDERABLES\t472746\tLABLEISAB\tLEISHMANIA AB, IFA\tLTROIN\tINTERPRETATION\t#VALUE!\t1997181.818\t0\tNone\tNone\t11\t21969000\t9.76E+13
+LAB BLOOD ORDERABLES\t472747\tLABLEMSAB\tLEMS AUTOAB\tLEMS\tLEMS AUTOANTIBODY\t#VALUE!\t1501180\t0\tNone\tNone\t3\t4503540\t8.25E+12
+LAB BLOOD ORDERABLES\t472755\tLABLPDP\tLIPID PANEL PLUS RISK\tRCRSK\tREL. CARDIAC RISK\t#VALUE!\t66268.5\t0\tNone\tNone\t40\t2650740\t2.72E+11
+LAB BLOOD ORDERABLES\t472757\tLABLPDPC\tLIPID PLUS RISK PANEL WITH CALCULATED LDL\tRCRSK\tREL. CARDIAC RISK\t#VALUE!\t79623.67347\t0\tNone\tNone\t49\t3901560\t7.43E+11
+LAB BLOOD ORDERABLES\t472762\tLABLKMAB\tLIVER KID MICROSOME AB\tLKMAB\tLIV-KID-MICROSOME AB\t#VALUE!\t217920\t0\tNone\tNone\t5\t1089600\t2.48E+11
+LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tCONINT\tCON A INTERPRETATION\t#VALUE!\t771800\t0\tNone\tNone\t3\t2315400\t1.79E+12
+LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPHAINT\tPHA INTERPRETATION\t#VALUE!\t771800\t0\tNone\tNone\t3\t2315400\t1.79E+12
+LAB BLOOD ORDERABLES\t472777\tLABLMS\tLYMPHOCYTE MITOGEN SCREEN\tPWMINT\tPWM INTERPRETATION\t#VALUE!\t771800\t0\tNone\tNone\t3\t2315400\t1.79E+12
+LAB BLOOD ORDERABLES\t472781\tLABMAGATH\tMAG AUTOAB\tMAGAB\tMAG AUTOANTIBODY\t#VALUE!\t4404444\t0\tNone\tNone\t5\t22022220\t1.81E+14
+LAB BLOOD ORDERABLES\t472818\tLABYMEM\tMEASLES AB, IGM\tYMEM1\tMEAS(RUBEOLA) AB,IGM\t#VALUE!\t253920\t0\tNone\tNone\t2\t507840\t1.39E+11
+LAB BLOOD ORDERABLES\t472834\tLABMELAS\tMELAS MTDNA PROFILE\tMELADN\tMELAS MTDNA PROFILE\t#VALUE!\t72540\t0\tNone\tNone\t1\t72540\t5262051600
+LAB BLOOD ORDERABLES\t472845\tLABYMETHS\tMETHSUXIMIDE\tMETHSX\tMETHSUXIMIDE\t#VALUE!\t1913310\t0\tNone\tNone\t2\t3826620\t8.10E+12
+LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHCMT\tCOMMENT\t#VALUE!\t638456.25\t0\tNone\tNone\t16\t10215300\t8.63E+12
+LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHDIR\tINTERPRETED BY\t#VALUE!\t584196\t0\tNone\tNone\t15\t8762940\t6.52E+12
+LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHDIS\tDISCLAIMER\t#VALUE!\t638456.25\t0\tNone\tNone\t16\t10215300\t8.63E+12
+LAB BLOOD ORDERABLES\t472846\tLABMTHFR\tMETHYLENE- THF REDUCTASE\tMTHPRM\tPRIMERS\t#VALUE!\t638456.25\t0\tNone\tNone\t16\t10215300\t8.63E+12
+LAB BLOOD ORDERABLES\t472854\tLABMALL1\tMISCELLANEOUS ALLERGEN 1\tI1\tINTERPRETATION\t#VALUE!\t630540\t0\tNone\tNone\t4\t2522160\t2.43E+12
+LAB BLOOD ORDERABLES\t472855\tLABMALL2\tMISCELLANEOUS ALLERGEN 2\tALL2\tALLERGEN\t#VALUE!\t1370400\t0\tNone\tNone\t1\t1370400\t1.88E+12
+LAB BLOOD ORDERABLES\t472855\tLABMALL2\tMISCELLANEOUS ALLERGEN 2\tI2\tINTERPRETATION\t#VALUE!\t1370400\t0\tNone\tNone\t1\t1370400\t1.88E+12
+LAB BLOOD ORDERABLES\t472856\tLABMALL3\tMISCELLANEOUS ALLERGEN 3\tI3\tINTERPRETATION\t#VALUE!\t148680\t0\tNone\tNone\t1\t148680\t22105742400
+LAB BLOOD ORDERABLES\t472950\tLAB11048R\tNEURON AB\tNEURON\tNEURONAL CELL ABS, SERUM\t#VALUE!\t944775\t0\tNone\tNone\t4\t3779100\t4.58E+12
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTCO\tSTIM. CONTROL,NBT\t#VALUE!\t48930\t0\tNone\tNone\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t472958\tLABNBTS\tNITROBLUE TETRAZOLIUM RED\tNBTCOM\tCOMMENT, NBT\t#VALUE!\t48930\t0\tNone\tNone\t2\t97860\t5243706000
+LAB BLOOD ORDERABLES\t473042\tLABOF\tOSMOTIC FRAGILITY\tOFDIR\tREVIEWED BY\t#VALUE!\t418596\t0\tNone\tNone\t10\t4185960\t2.53E+12
+LAB BLOOD ORDERABLES\t473054\tLABPAI1AT\tPAI-1 ACTIVITY\tPAI1T\tPAI-1 ACTIVITY\t#VALUE!\t1999356\t0\tNone\tNone\t5\t9996780\t5.51E+13
+LAB BLOOD ORDERABLES\t473055\tLABPAI1AG\tPAI-1 AG\tPAI1G\tPAI-1 ANTIGEN\t#VALUE!\t1906800\t0\tNone\tNone\t1\t1906800\t3.64E+12
+LAB BLOOD ORDERABLES\t473064\tLABYPCA\tPARIETAL CELL AB, SERUM\tYPCA\tPARIETAL CELL AB\t#VALUE!\t261540\t0\tNone\tNone\t1\t261540\t68403171600
+LAB BLOOD ORDERABLES\t473111\tLABPLTFUN\tPLATELET FUNCTION SCREEN\tPLTFCM\tCOMMENT, PLT FUNCTION\t#VALUE!\t12280.51948\t0\tNone\tNone\t154\t1891200\t1.58E+11
+LAB BLOOD ORDERABLES\t473148\tLABPRO6MP\tPRO-PREDICT ENZACT\tPRO6MR\tPRO-PREDICT ENZACT\t#VALUE!\t621850.9091\t0\tNone\tNone\t11\t6840360\t5.42E+12
+LAB BLOOD ORDERABLES\t473177\tLABPT12\tPROTHROMBIN FRAGMENT 1 AND 2\tPTFRNO\tPROTHROMBIN 1+2 NOTE\t#VALUE!\t1072470\t0\tNone\tNone\t4\t4289880\t5.93E+12
+LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTCMT\tCOMMENT\t#VALUE!\t452558.7763\t0\tNone\tNone\t523\t236688240\t1.36E+14
+LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTDIS\tDISCLAIMER\t#VALUE!\t452558.7763\t0\tNone\tNone\t523\t236688240\t1.36E+14
+LAB BLOOD ORDERABLES\t473179\tLABP20210\tPROTHROMBIN 20210A\tPTPRM\tPRIMERS\t#VALUE!\t452558.7763\t0\tNone\tNone\t523\t236688240\t1.36E+14
+LAB BLOOD ORDERABLES\t473200\tLABQFTBG\tQUANTIFERON TEST FOR LATENT TB\tQTFNIN\tQUANTIFERON INTERPRETATION\t#VALUE!\t183897.5\t0\tNone\tNone\t24\t4413540\t8.77E+11
+LAB BLOOD ORDERABLES\t473211\tLABRBCMOR\tRBC MORPHLOGY, COMPLETE\tPSREV\tPATH/SPEC REVIEW\t#VALUE!\t5640\t0\tNone\tNone\t1\t5640\t31809600
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tRBCINT\tINTERPRETATION\t#VALUE!\t288690\t0\tNone\tNone\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473216\tLABRBCENZ\tRED CELL ENZYMES\tRBCMD\tREVIEWED BY\t#VALUE!\t288690\t0\tNone\tNone\t2\t577380\t3.33E+11
+LAB BLOOD ORDERABLES\t473253\tLABYRETAB\tRETICULIN AB\tYRET1\tRETICULIN AB\t#VALUE!\t167400\t0\tNone\tNone\t2\t334800\t56423095200
+LAB BLOOD ORDERABLES\t473262\tLABRF\tRHEUMATOID FACTOR\tRHFINT\tINTERPRETATION\t#VALUE!\t221510.8108\t0\tNone\tNone\t111\t24587700\t7.16E+12
+LAB BLOOD ORDERABLES\t473262\tLABRF\tRHUEMATOID FACTOR\tRHFINT\tINTERPRETATION\t#VALUE!\t239163.253\t0\tNone\tNone\t664\t158804400\t5.11E+13
+LAB BLOOD ORDERABLES\t473273\tLABRITUX\tRITUXAN PANEL\tRTXFDA\tNOTE\t#VALUE!\t131761.5464\t0\tNone\tNone\t194\t25561740\t4.96E+12
+LAB BLOOD ORDERABLES\t473282\tLABRUBG\tRUBELLA IGG ANTIBODY\tRUBINT\tINTERPRETATION\t#VALUE!\t132740\t0\tNone\tNone\t3\t398220\t70434939600
+LAB BLOOD ORDERABLES\t473297\tLABSNP\tSENSORIMOTOR PROFILE\tSNPRR\tSENSORIMOTOR PROFILE\t#VALUE!\t2310180\t0\tNone\tNone\t1\t2310180\t5.34E+12
+LAB BLOOD ORDERABLES\t473376\tLABSTRONG\tSTRONGYLOIDES IGG AB, ELISA\tSTROG\tSTRONGYLOIDES ,LGG\t#VALUE!\t261887.5962\t0\tNone\tNone\t208\t54472620\t1.88E+13
+LAB BLOOD ORDERABLES\t473380\tLABSULFAT\tSULFATIDE AUTOANTIBODY\tSULFAB\tSULFATIDE AUTOANTIBODY\t#VALUE!\t2153280\t0\tNone\tNone\t2\t4306560\t1.31E+13
+LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14CMT\tCOMMENT\t#VALUE!\t1210908\t0\tNone\tNone\t5\t6054540\t9.00E+12
+LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14DIR\tINTERPRETED BY\t#VALUE!\t1210908\t0\tNone\tNone\t5\t6054540\t9.00E+12
+LAB BLOOD ORDERABLES\t473397\tLABT1418\tT(14\t18), BLOOD\tT14DIS\tDISCLAIMER\t#VALUE!\t1210908\t0\tNone\tNone\t5\t6054540\t9.00E+12
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15DI\tDISCLAIMER\t#VALUE!\t643218\t0\tNone\tNone\t50\t32160900\t2.37E+13
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15MD\tINTERPRETED BY\t#VALUE!\t643218\t0\tNone\tNone\t50\t32160900\t2.37E+13
+LAB BLOOD ORDERABLES\t473403\tLABT1517\tT(15\t17) PML/RAR, BLOOD\tT15ME\tMETHOD\t#VALUE!\t643218\t0\tNone\tNone\t50\t32160900\t2.37E+13
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTDIR\tINTERPRETED BY\t#VALUE!\t1027142.927\t0\tNone\tNone\t82\t84225720\t1.01E+14
+LAB BLOOD ORDERABLES\t473405\tLABTCLON\tT-CELL CLONALITY PCR, BLOOD\tTDIS\tDISCLAIMER\t#VALUE!\t1027142.927\t0\tNone\tNone\t82\t84225720\t1.01E+14
+LAB BLOOD ORDERABLES\t473446\tLABYTBG\tTHYROXINE BINDING GLOBULIN\tT4BG\tTHYROXINE-BIND GLOB\t#VALUE!\t93540\t0\tNone\tNone\t1\t93540\t8749731600
+LAB BLOOD ORDERABLES\t473457\tLAB11113R\tTOXOPLASMA AB, IGA\tTOXOA\tTOXO LGA ELISA\t#VALUE!\t1243440\t0\tNone\tNone\t3\t3730320\t4.67E+12
+LAB BLOOD ORDERABLES\t473459\tLABTXCAB\tTOXOCARA AB ELISA\tTOXOC1\tTOXOCARA ANTIBODY\t#VALUE!\t325583.0769\t0\tNone\tNone\t13\t4232580\t1.55E+12
+LAB BLOOD ORDERABLES\t473470\tLABGTT\tTRADITIONAL GLUCOSE TOLERANCE\tGTTINT\tGTT INTERPRETATION\t#VALUE!\t20572\t0\tNone\tNone\t30\t617160\t15849540000
+LAB BLOOD ORDERABLES\t473673\tLABTTM\tTRANSPLANT THERAPY MONITORING\tRTXFDA\tNOTE\t#VALUE!\t31387.60563\t0\tNone\tNone\t71\t2228520\t87338577600
+LAB BLOOD ORDERABLES\t473679\tLABTRICHG\tTRICHINELLA IGG AB ELISA\tTRICG\tTRICHINELLA, IGG\t#VALUE!\t401451.4286\t0\tNone\tNone\t7\t2810160\t1.32E+12
+LAB BLOOD ORDERABLES\t473685\tLABYCHAGA\tTRYPANOSOMA CRUZI AB, IGG\tYCHAG1\tTRYPANOSOMA CRUZI AB,IGG\t#VALUE!\t535260\t0\tNone\tNone\t48\t25692480\t1.71E+13
+LAB BLOOD ORDERABLES\t473690\tLABTTRMET\tAMYLOIDOSIS DNA TITER\tAMYLDO\tTTR AMYLOIDOSIS DNA\t#VALUE!\t5351415\t0\tNone\tNone\t4\t21405660\t1.96E+14
+LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHDI\tDISCLAIMER\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
+LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHDX\tCLINICAL INDICATION\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
+LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHIN\tINTERPRETATION\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
+LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHMD\tINTERPRETED BY\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
+LAB BLOOD ORDERABLES\t473749\tLABVHHA\tVH HYPERMUTATION ASSAY\tVHME\tMETHOD\t#VALUE!\t1766844\t0\tNone\tNone\t10\t17668440\t3.56E+13
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHDI\tDISCLAIMER:\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHDX\tCLINICAL IND\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHIN\tINTERPRETATION\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHMD\tINTERPRETED BY\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473750\tLABNVHHA\tVH HYPERMUTATION ASSAY, NON-BLOOD\tNVHME\tMETHOD\t#VALUE!\t3358860\t0\tNone\tNone\t1\t3358860\t1.13E+13
+LAB BLOOD ORDERABLES\t473762\tLAB12586R\tVITAMIN B1\tYVB1W2\tCOMMENT\t#VALUE!\t322440\t0\tNone\tNone\t2\t644880\t2.21E+11
+LAB BLOOD ORDERABLES\t473763\tLAB11060R\tVITAMIN B1, PLASMA\tVITB1\tVITAMIN B1, PLASMA\t#VALUE!\t450630\t0\tNone\tNone\t2\t901260\t4.07E+11
+LAB BLOOD ORDERABLES\t473763\tLABVITB1P\tVITAMIN B1, PLASMA\tVITB1\tVITAMIN B1, PLASMA\t#VALUE!\t401070\t0\tNone\tNone\t8\t3208560\t1.34E+12
+LAB BLOOD ORDERABLES\t473775\tLAB12322R\tVON WILLEBRAND MULTIMERS\t12323R\tVON WILLEBRAND MULTIMERS\t#VALUE!\t678327.8571\t0\tNone\tNone\t28\t18993180\t1.56E+13
+LAB BLOOD ORDERABLES\t473775\tLAB12322R\tVON WILLEBRAND MULTIMERS\tYVWMR\tVON WILLEBRAND\t#VALUE!\t1322760\t0\tNone\tNone\t1\t1322760\t1.75E+12
+LAB BLOOD ORDERABLES\t473775\tLABYVWM\tVON WILLEBRAND MULTIMERS\tYVWMR\tVON WILLEBRAND\t#VALUE!\t940496.6667\t0\tNone\tNone\t18\t16928940\t1.85E+13
+LAB BLOOD ORDERABLES\t473778\tLABVWSCN\tVON WILLEBRAND SCREEN\tVWDIR\tINTERPRETED BY\t#VALUE!\t50682.5\t0\tNone\tNone\t24\t1216380\t92331061200
+LAB BLOOD ORDERABLES\t473779\tLAB12631R\tVORICONAZOLE LEVEL\tVORI\tVORICONAZOLE LEVEL\t#VALUE!\t438327.3529\t0\tNone\tNone\t68\t29806260\t1.55E+13
+LAB BLOOD ORDERABLES\t473779\tLABVORI\tVORICONAZOLE LEVEL\tVORI\tVORICONAZOLE LEVEL\t#VALUE!\t495083.3333\t0\tNone\tNone\t18\t8911500\t5.11E+12
+LAB BLOOD ORDERABLES\t473809\tLABYZINCS\tZINC\tZINC\tZINC\t#VALUE!\t224780\t0\tNone\tNone\t3\t674340\t1.79E+11
+LAB BLOOD ORDERABLES\t496771\tLABHELPM\tHELICOBACTER PYLORI IGM AB\tHELPM1\tH. PYLORI IGM ANTIBODY\t#VALUE!\t1694100\t0\tNone\tNone\t2\t3388200\t6.03E+12
+LAB BLOOD ORDERABLES\t496773\tLAB12585R\tHEPATITIS D RNA, QUAL PCR\t12594R\tHDV RNA\t#VALUE!\t302810\t0\tNone\tNone\t6\t1816860\t6.09E+11
+LAB BLOOD ORDERABLES\t496774\tLABHEVG\tHEPATITIS E AB IGG PANEL\tHEVG1\tHEPATITIS E AB IGG\t#VALUE!\t301350\t0\tNone\tNone\t8\t2410800\t7.68E+11
+LAB BLOOD ORDERABLES\t496775\tLABHEVM\tHEPATITIS E AB IGM PANEL\tHEVM1\tHEPATITIS E AB IGM\t#VALUE!\t290029.0909\t0\tNone\tNone\t11\t3190320\t9.72E+11
+LAB BLOOD ORDERABLES\t497189\tLABXAMH\tANTI - MULLERIAN HORMONE\tXAMHOR\tANTI-MULLERIAN HORMONE\t#VALUE!\t371580\t0\tNone\tNone\t1\t371580\t1.38E+11
+LAB BLOOD ORDERABLES\t497200\tLABXGAD65\tANTI GAD 65 ANTIBODIES\tXGADR\tANTI-GAD 65 ANTIBODIES\t#VALUE!\t584742\t0\tNone\tNone\t10\t5847420\t7.20E+12
+LAB BLOOD ORDERABLES\t497202\tLABXHIA\tHUMAN INSULIN ANTIBODY\tXHIAB\tHUMAN INSULIN ANTIBODY\t#VALUE!\t613744.6154\t0\tNone\tNone\t13\t7978680\t5.21E+12
+LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3DS\tDISCLAIMER\t#VALUE!\t531220.8\t0\tNone\tNone\t175\t92963640\t6.34E+13
+LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3IN\tINTERPRETATION\t#VALUE!\t531220.8\t0\tNone\tNone\t175\t92963640\t6.34E+13
+LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3MD\tINTERPRETED BY\t#VALUE!\t536372.9825\t0\tNone\tNone\t171\t91719780\t6.29E+13
+LAB BLOOD ORDERABLES\t497301\tLABAMLP\tAML PROGNOSIS ASSAY\tFLT3MT\tMETHOD\t#VALUE!\t531220.8\t0\tNone\tNone\t175\t92963640\t6.34E+13
+LAB BLOOD ORDERABLES\t497337\tLABHBV\tHBV GENOTYPE: S AND POL GENE\tBCPMUT\tBCP MUTATIONS\t#VALUE!\t612900\t0\tNone\tNone\t1\t612900\t3.76E+11
+LAB BLOOD ORDERABLES\t497341\tLABCYAB\tCYSTICERCUS AB\tCYABS\tCYSTICERCUS AB\t#VALUE!\t302520\t0\tNone\tNone\t11\t3327720\t1.20E+12
+LAB BLOOD ORDERABLES\t497349\tLABWNVRNA\tWEST NILE VIRUS RNA, RT  -PCR\tWNRNA\tWEST NILE VIRUS RNA\t#VALUE!\t219683.7209\t0\tNone\tNone\t43\t9446400\t2.67E+12
+LAB BLOOD ORDERABLES\t497351\tLABBARSAB\tBARTONELLA SPECIES AB\tBQIGMT\tB.QUINTANA IGM TITER\t#VALUE!\t1367760\t0\tNone\tNone\t7\t9574320\t4.56E+13
+LAB BLOOD ORDERABLES\t497394\tLABYPARVO\tPARVOVIRUS B19 IGG AND IGM AB\tYPARV3\tINTERPRETATION\t#VALUE!\t223791.6201\t0\tNone\tNone\t179\t40058700\t1.09E+13
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC23\tTRIENE TETRAENE RATIO\t#VALUE!\t504672\t0\tNone\tNone\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497408\tLABYFACE\tFATTY ACID PROFILE ESSENTIAL\tYFAC30\tINTERPRETATION EFA\t#VALUE!\t504672\t0\tNone\tNone\t10\t5046720\t2.79E+12
+LAB BLOOD ORDERABLES\t497414\tLABFHAN\tHANTAVIRUS AB ELISA\tHANIGM\tHANTAVIRUS IGM\t#VALUE!\t223917.1429\t0\tNone\tNone\t21\t4702260\t1.18E+12
+LAB BLOOD ORDERABLES\t497416\tLABFEHRC\tEHRLICHIA CHAFFEENSIS AB\t12430R\tINTERPRETATION\t#VALUE!\t270420\t0\tNone\tNone\t3\t811260\t2.28E+11
+LAB BLOOD ORDERABLES\t497418\tLABFCYST\tCYSTICERCUS IGG AB\tBANDPRSNT\tBANDS PRESENT\t#VALUE!\t289800\t0\tNone\tNone\t4\t1159200\t3.98E+11
+LAB BLOOD ORDERABLES\t497430\tLABHEPDAB\tHEPATITIS D VIRUS AB TOTAL\tHEPD\tHEPATITIS D TOTAL\t#VALUE!\t347432.4324\t0\tNone\tNone\t37\t12855000\t5.46E+12
+LAB BLOOD ORDERABLES\t497436\tLABFECHO\tECHOVIRUS AB, SERUM\tTYPE30\tECHOVIRUS 30 AB\t#VALUE!\t325380\t0\tNone\tNone\t6\t1952280\t6.59E+11
+LAB BLOOD ORDERABLES\t497440\tLABFCHGA\tA. PHAGOCYTOPHILUM AB\tAPINT\tA.PHAGOCYTOPHILUM INTERPRETATION\t#VALUE!\t243480\t0\tNone\tNone\t2\t486960\t1.21E+11
+LAB BLOOD ORDERABLES\t497444\tLABEBVC\tEBV CAPSID AG\tVCAA\tVCA IGA\t#VALUE!\t253638.4615\t0\tNone\tNone\t13\t3297300\t9.44E+11
+LAB BLOOD ORDERABLES\t497444\tLABEBVC\tEBV VIRAL CAPSID ANTIGEN IGA\tVCAA\tVCA IGA\t#VALUE!\t208160\t0\tNone\tNone\t3\t624480\t1.36E+11
+LAB BLOOD ORDERABLES\t497456\tLABFQFVR\tCOXIELLA BURNETII AB\tQFVIN\tQ FEVER INTERPRETATION\t#VALUE!\t431808.2927\t0\tNone\tNone\t41\t17704140\t2.36E+13
+LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\t12431R\tINTERPRETATION\t#VALUE!\t282660\t0\tNone\tNone\t1\t282660\t79896675600
+LAB BLOOD ORDERABLES\t497458\tLABFHHV6\tHERPES VIRUS 6 IGG AND IGM AB\tLTROIN\tINTERPRETATION\t#VALUE!\t288270\t0\tNone\tNone\t18\t5188860\t1.84E+12
+LAB BLOOD ORDERABLES\t497460\tLABFRAB\tFRANCISELLA TULARENSIS AB\tFRANT\tFRANCISELLA TULARENSIS AB\t#VALUE!\t230423.0769\t0\tNone\tNone\t26\t5991000\t1.57E+12
+LAB BLOOD ORDERABLES\t497466\tLABFHVIG\tHIV 1 IGG-AB, WESTERN BLOT CONFIRM\tBANDPRSNT\tBANDS PRESENT\t#VALUE!\t392925\t0\tNone\tNone\t4\t1571700\t6.91E+11
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\t12065R\tY.ENTERCOCLITICA IGA\t#VALUE!\t424091.4286\t0\tNone\tNone\t21\t8905920\t4.71E+12
+LAB BLOOD ORDERABLES\t497470\tLABYENTE\tYERSINIA ENTEROCOLITICA AB\tYPSEU\tY.PSEUDOTUBERCULOSIS\t#VALUE!\t3364848\t0\tNone\tNone\t15\t50472720\t4.07E+14
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1CI\tCLINICAL INDICATION\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1DI\tDISCLAIMER\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1MD\tINTERPRETED BY\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1MT\tMETHOD\t#VALUE!\t1062780\t0\tNone\tNone\t4\t4251120\t5.58E+12
+LAB BLOOD ORDERABLES\t541677\tLABCDH1\tFAMILIAL GASTRIC CANCER I\tCDH1MU\tMUTATION(S)\t#VALUE!\t1568100\t0\tNone\tNone\t1\t1568100\t2.46E+12
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKCI\tCLINICAL INDICATION\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKDI\tDISCLAIMER\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKIT\tINTERPRETED BY\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
+LAB BLOOD ORDERABLES\t541679\tLABBCRKDM\tBCR - ABL KINASE DOMAIN MUTATION ANALYSIS\tBCRKMT\tMETHOD\t#VALUE!\t864270\t0\tNone\tNone\t14\t12099780\t1.30E+13
+LAB BLOOD ORDERABLES\t541681\tLABDHBCC\tDONOR HEPATITIS B CORE AB (LIVING)\tDHBCC\tDONOR HEP CORE AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t541687\tLABFHHV7\tHERPES VIRUS 7 IGG AND IGM AB\tFINT\tINTERPRETATION\t#VALUE!\t356310\t0\tNone\tNone\t2\t712620\t2.55E+11
+LAB BLOOD ORDERABLES\t541689\tLABFPARVO\tPARVOVIRUS IGG / IGM AB\t12835R\tPARVOVIRUS B19 AB IGG\t#VALUE!\t2267650\t0\tNone\tNone\t6\t13605900\t3.52E+13
+LAB BLOOD ORDERABLES\t541695\tLABDSDNA\tANTI - DSDNA AB\tDNAINT\tINTERPRETATION\t#VALUE!\t212647.8896\t0\tNone\tNone\t616\t130991100\t3.85E+13
+LAB BLOOD ORDERABLES\t573825\tLABFHIVQA\tHIV-1 DNA, QUALITATIVE PCR\tHIVQA\tHIV-1 DNA, QUAL, PCR\t#VALUE!\t280410\t0\tNone\tNone\t2\t560820\t1.66E+11
+LAB BLOOD ORDERABLES\t573825\tLABFHIVQA\tHIV-1 PROVIRALL DNA PCR QUAL\tHIVQA\tHIV-1 DNA, QUAL, PCR\t#VALUE!\t236296.3636\t0\tNone\tNone\t11\t2599260\t6.56E+11
+LAB BLOOD ORDERABLES\t590530\tLABCEPLUS\tCELIAC PLUS\tCPLUS1\tCELIAC PLUS RESULT\t#VALUE!\t543240\t0\tNone\tNone\t1\t543240\t2.95E+11
+LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\t12428R\tINTERPRETATION\t#VALUE!\t242130\t0\tNone\tNone\t4\t968520\t2.64E+11
+LAB BLOOD ORDERABLES\t590545\tLABFCLAB\tCHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S\tCTINT\tINTERPRETATION\t#VALUE!\t224837.1429\t0\tNone\tNone\t7\t1573860\t3.64E+11
+LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\t12024R\tMYCOPLASMA PNEUMONIA AB IGG\t#VALUE!\t578845.7143\t0\tNone\tNone\t28\t16207680\t1.90E+13
+LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\t12431R\tINTERPRETATION\t#VALUE!\t308790\t0\tNone\tNone\t2\t617580\t2.66E+11
+LAB BLOOD ORDERABLES\t590554\tLAB12814R\tMYCOPLASMA IGG/IGM AB\tLTROIN\tINTERPRETATION\t#VALUE!\t258120\t0\tNone\tNone\t1\t258120\t66625934400
+LAB BLOOD ORDERABLES\t590559\tLABFPOLIO\tPOLIOVIRUS 1-3 AB, SERUM\tPLV3\tPOLIOVIRUS 3 AB\t#VALUE!\t253600\t0\tNone\tNone\t3\t760800\t2.02E+11
+LAB BLOOD ORDERABLES\t590561\tLABFSFTA\tTREPONEMA PALLIDUM AB\tSFTA\tSYPHILIS-FTA\t#VALUE!\t177960\t0\tNone\tNone\t1\t177960\t31669761600
+LAB BLOOD ORDERABLES\t611471\tLABYDGIGA\tGLIADIN (DEAMIDATED) IGA,SERUM\tADGINT\tINTERPRETATION\t#VALUE!\t466236\t0\tNone\tNone\t20\t9324720\t5.28E+12
+LAB BLOOD ORDERABLES\t621214\tLABDDIML\tD - DIMER (ELISA)\tDIMCOM\tCOAGULATION COMMENT\t#VALUE!\t5591.974249\t0\tNone\tNone\t932\t5211720\t50304153600
+LAB BLOOD ORDERABLES\t633198\tLABCOXBAB\tCOXSACKIE B AB, SERUM\tTYB6\tCOXSACKIE B6 AB\t#VALUE!\t229080\t0\tNone\tNone\t1\t229080\t52477646400
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP2\tCOXSACKIE A2 AB\t#VALUE!\t335940\t0\tNone\tNone\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t633252\tLABFENTP\tENTEROVIRUS PANEL II\tTYP4\tCOXSACKIE A4 AB\t#VALUE!\t335940\t0\tNone\tNone\t1\t335940\t1.13E+11
+LAB BLOOD ORDERABLES\t668075\tLABDNAISO\tNUCLEIC ACID ISOLATION, DNA\tDNADAT\tDATE OF DNA EXTRACTION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t668077\tLABRAAS\tRHEUMATOID ARTHRITIS AB SCREEN\tCCPINT\tINTERPRETATION\t#VALUE!\t2437250.769\t0\tNone\tNone\t26\t63368520\t2.95E+15
+LAB BLOOD ORDERABLES\t668091\tLAB11125R\tCOCCI ID (SERUM)\tCOCI\tCOCCI ID (SERUM)\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t668091\tLABCOCI\tCOCCI ID (SERUM)\tCOCI\tCOCCI ID (SERUM)\t#VALUE!\t1056797.143\t0\tNone\tNone\t7\t7397580\t8.14E+12
+LAB BLOOD ORDERABLES\t668152\tLABATTG\tIGA ANTI  TTG\tATTINT\tINTERPRETATION\t#VALUE!\t394865.1799\t0\tNone\tNone\t139\t54886260\t2.79E+13
+LAB BLOOD ORDERABLES\t668154\tLABGDGP\tIGG ANTI DGP\tGDGINT\tINTERPRETATION\t#VALUE!\t399028.2353\t0\tNone\tNone\t34\t13566960\t6.90E+12
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tATTINT\tINTERPRETATION\t#VALUE!\t402189.6296\t0\tNone\tNone\t81\t32577360\t1.89E+13
+LAB BLOOD ORDERABLES\t668156\tLABCDAS\tCELIAC DISEASE SCREEN\tIGAINT\tINTERPRETATION\t#VALUE!\t402189.6296\t0\tNone\tNone\t81\t32577360\t1.89E+13
+LAB BLOOD ORDERABLES\t668158\tLABADAM13\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMINT\tCLINICAL INTERPRETATION\t#VALUE!\t190497\t0\tNone\tNone\t20\t3809940\t1.38E+12
+LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 ACTIVITY AND ANTIGEN TESTING\tADMINT\tCLINICAL INTERPRETATION\t#VALUE!\t247320\t0\tNone\tNone\t3\t741960\t2.61E+11
+LAB BLOOD ORDERABLES\t668158\tLABADAMTS\tADAMTS-13 PROFILE\tADMINT\tCLINICAL INTERPRETATION\t#VALUE!\t206821.875\t0\tNone\tNone\t64\t13236600\t4.74E+12
+LAB BLOOD ORDERABLES\t668178\tLABDRPRS\tDONOR, SYPHILIS SCREEN\tRPRS1\tDONOR SYPHILIS SCREEN\t#VALUE!\t168840\t0\tNone\tNone\t2\t337680\t76120106400
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITDI\tDISCLAIMER\t#VALUE!\t779901.7925\t0\tNone\tNone\t212\t165339180\t3.13E+14
+LAB BLOOD ORDERABLES\t668192\tLABCKITMU\tDETECTION OF C-KIT MUTATIONS\tKITME\tMETHOD\t#VALUE!\t779901.7925\t0\tNone\tNone\t212\t165339180\t3.13E+14
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11005R\tA. TENUIS/ALTERNATA IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11006R\tASPERGILLUS FUMIGATUS IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11007R\tAUREOBASIDIUM PULLULANS IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11008R\tMICROPOLYSPORA FAENI IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11009R\tP. NOTATUM/CHRYSOGENUM IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11010R\tPHOMA BETAE IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11011R\tTHERMOACTINOMYCES VULGARIS IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11012R\tTRICHODERMA VIRIDE IGG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669985\tLAB11004R\tHYPERSENS. PNEUMONITIS PNL\t11141R\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t669987\tLAB11013R\tPLATELET ANTIBODY ID PANEL\t11014R\tANTIGEN CAPTURE\t#VALUE!\t688358.9552\t0\tNone\tNone\t134\t92240100\t8.83E+13
+LAB BLOOD ORDERABLES\t669987\tLAB11013R\tPLATELET ANTIBODY ID PANEL\t11015R\tMODIFIED ANTIGEN CAPTURE\t#VALUE!\t699652.5\t0\tNone\tNone\t96\t67166640\t7.00E+13
+LAB BLOOD ORDERABLES\t669987\tLAB11013R\tPLATELET ANTIBODY ID PANEL\t11016R\tPLATELET ANTIBODY SCREEN\t#VALUE!\t699652.5\t0\tNone\tNone\t96\t67166640\t7.00E+13
+LAB BLOOD ORDERABLES\t670732\tLAB11322R\tMOTOR NEUROPATHY AB SCREEN\t11323R\tCO-GM1 TRIAD AB\t#VALUE!\t1029150\t0\tNone\tNone\t2\t2058300\t4.23E+12
+LAB BLOOD ORDERABLES\t670732\tLAB12774R\tMOTOR NEUROPATHY AB SCREEN\t11323R\tCO-GM1 TRIAD AB\t#VALUE!\t2500414.286\t0\tNone\tNone\t7\t17502900\t9.27E+13
+LAB BLOOD ORDERABLES\t670732\tLAB12774R\tMOTOR NEUROPATHY AB SCREEN\t12775R\tCO-GM1 QUATTRO\t#VALUE!\t1396488\t0\tNone\tNone\t5\t6982440\t1.46E+13
+LAB BLOOD ORDERABLES\t670734\tLAB11324R\tCADASIL EVALUATION, COMPLETE\t11325R\tCOMPLETE CADASIL EVALUATION\t#VALUE!\t2366850\t0\tNone\tNone\t6\t14201100\t7.82E+13
+LAB BLOOD ORDERABLES\t670752\tLAB11343R\tSRA UNFRACTIONATED HEPARIN\t11344R\tSRA UNFRACTIONATED HEPARIN\t#VALUE!\t374785.2941\t0\tNone\tNone\t102\t38228100\t1.72E+13
+LAB BLOOD ORDERABLES\t670756\tLAB11347R\tC-TELOPEPTIDE\t11348R\tC-TELOPEPTIDE\t#VALUE!\t381060\t0\tNone\tNone\t1\t381060\t1.45E+11
+LAB BLOOD ORDERABLES\t670766\tLAB11238R\tHERPESVIRUS-8 IGG AB\t11239R\tHERPESVIRUS-8 IGG AB\t#VALUE!\t392080\t0\tNone\tNone\t6\t2352480\t1.06E+12
+LAB BLOOD ORDERABLES\t670768\tLAB11240R\tMEASLES IGG\t11241R\tMEASLES IGG\t#VALUE!\t213051.4286\t0\tNone\tNone\t14\t2982720\t7.36E+11
+LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\t12430R\tINTERPRETATION\t#VALUE!\t349300\t0\tNone\tNone\t3\t1047900\t3.86E+11
+LAB BLOOD ORDERABLES\t670780\tLAB11190R\tBORRELIA HERMSII AB PANL\tECINT\tINTERPRETATION\t#VALUE!\t1643340\t0\tNone\tNone\t2\t3286680\t7.18E+12
+LAB BLOOD ORDERABLES\t670784\tLAB11357R\tFAMILIAL MEDITERRANEAN FEVER\t11358R\tFAMILIAL MEDITERRANEAN FEVER\t#VALUE!\t3943252.5\t0\tNone\tNone\t8\t31546020\t1.31E+14
+LAB BLOOD ORDERABLES\t670792\tLAB11365R\tFAMILIAL HIBERNIAN FEVER/TRAPS\t11366R\tFAMILIAL HIBERNIAN FEVER\t#VALUE!\t3634180\t0\tNone\tNone\t3\t10902540\t4.02E+13
+LAB BLOOD ORDERABLES\t670796\tLAB11369R\tLEUKEMIA/LYMPHOMA IMMUPHENTYP\t11370R\tLEUKEMIA/LYMPHOMA IMMUPHENTYP\t#VALUE!\t160740\t0\tNone\tNone\t4\t642960\t1.04E+11
+LAB BLOOD ORDERABLES\t670802\tLAB11373R\tMARFAN(FBN1) SEQUENCING\t11374R\tMARFAN(FBN1) SEQUENCING\t#VALUE!\t1192440\t0\tNone\tNone\t3\t3577320\t4.27E+12
+LAB BLOOD ORDERABLES\t670804\tLAB11375R\tOSTEOGENESIS IMPERFECTA SEQUEN\t11376R\tOSTEOGENESIS IMPERFECTA SEQUEN\t#VALUE!\t3184020\t0\tNone\tNone\t2\t6368040\t2.21E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOAB EVAL\t11200R\tINTERPRETIVE COMMENTS\t#VALUE!\t902143.4783\t0\tNone\tNone\t69\t62247900\t6.72E+13
+LAB BLOOD ORDERABLES\t670818\tLAB11199R\tPARANEOPLASTIC AUTOANTIBODY EVAL, SERUM\t11200R\tINTERPRETIVE COMMENTS\t#VALUE!\t766684.8\t0\tNone\tNone\t75\t57501360\t5.29E+13
+LAB BLOOD ORDERABLES\t670858\tLAB11401R\tHALIBUT IGE\t11459R\tHALIBUT IGE\t#VALUE!\t390780\t0\tNone\tNone\t1\t390780\t1.53E+11
+LAB BLOOD ORDERABLES\t670920\tLAB11435R\tCELIAC GENETICS\t11499R\tCELIAC GENETICS\t#VALUE!\t666528\t0\tNone\tNone\t5\t3332640\t2.42E+12
+LAB BLOOD ORDERABLES\t670922\tLAB12689R\tINFLIXIMAB, SERUM LEVEL AND AB\t12690R\tINFLIXIMAB, SERUM LEVEL AND AB\t#VALUE!\t536920\t0\tNone\tNone\t3\t1610760\t1.04E+12
+LAB BLOOD ORDERABLES\t670922\tLAB12689R\tSERM INFLIXIMAB/HACA MEASUREMT\t11500R\tSERM INFLIXIMAB/HACA MEASUREMT\t#VALUE!\t783660\t0\tNone\tNone\t1\t783660\t6.14E+11
+LAB BLOOD ORDERABLES\t670941\tLABCD3B\tCD3, BLOOD\tRTFDA1\tNOTE\t#VALUE!\t99900\t0\tNone\tNone\t1\t99900\t9980010000
+LAB BLOOD ORDERABLES\t744174\tLAB11516R\tPANCREATITIS PANEL\t11625R\tPANCREATITIS PANEL\t#VALUE!\t2241593.333\t0\tNone\tNone\t9\t20174340\t5.94E+13
+LAB BLOOD ORDERABLES\t744184\tLAB11522R\tHU AB\t11632R\tHU AB\t#VALUE!\t920782.5\t0\tNone\tNone\t8\t7366260\t7.84E+12
+LAB BLOOD ORDERABLES\t744186\tLAB11523R\tGQ1B AB\t11633R\tGQ1B AB\t#VALUE!\t2399132.727\t0\tNone\tNone\t44\t105561840\t6.12E+14
+LAB BLOOD ORDERABLES\t744190\tLAB11525R\tMUSCLE TYROSINE KINASE AB\t11635R\tMUSCLE TYROSINE KINASE AB\t#VALUE!\t2179075.2\t0\tNone\tNone\t25\t54476880\t2.72E+14
+LAB BLOOD ORDERABLES\t744192\tLAB11526R\tK CHANNEL AB\t11636R\tK CHANNEL AB\t#VALUE!\t1380474\t0\tNone\tNone\t10\t13804740\t2.72E+13
+LAB BLOOD ORDERABLES\t744194\tLAB11527R\tSPINAL MUSCULAR ATROPHY DIAGNO\t11637R\tSPINAL MUSCULAR ATROPHY DIAGNO\t#VALUE!\t756900\t0\tNone\tNone\t2\t1513800\t2.18E+12
+LAB BLOOD ORDERABLES\t744196\tLAB11528R\tPERIPHERAL NEUROPATHY AB SCN\t11638R\tPERIPHERAL NEUROPATHY AB SCN\t#VALUE!\t1460520\t0\tNone\tNone\t1\t1460520\t2.13E+12
+LAB BLOOD ORDERABLES\t744198\tLAB11529R\tENCEPHALITIS AB SCREEN\t11639R\tENCEPHALITIS AB SCREEN\t#VALUE!\t781800\t0\tNone\tNone\t2\t1563600\t1.34E+12
+LAB BLOOD ORDERABLES\t744198\tLAB12772R\tENCEPHALITIS AB SCREEN\t11639R\tENCEPHALITIS AB SCREEN\t#VALUE!\t1309080\t0\tNone\tNone\t1\t1309080\t1.71E+12
+LAB BLOOD ORDERABLES\t744198\tLAB12772R\tENCEPHALITIS AB SCREEN\t12773R\tENCEPHALITIS AB SCREEN\t#VALUE!\t2424660\t0\tNone\tNone\t1\t2424660\t5.88E+12
+LAB BLOOD ORDERABLES\t744200\tLAB11530R\tCEREBELLAR PARANEOPLASTIC AB\t11640R\tCEREBELLAR PARANEOPLASTIC AB\t#VALUE!\t4965280\t0\tNone\tNone\t6\t29791680\t1.72E+14
+LAB BLOOD ORDERABLES\t744206\tLAB11533R\tSENSORIMOTOR SCREEN AB\t11643R\tSENSORIMOTOR SCREEN AB\t#VALUE!\t601560\t0\tNone\tNone\t1\t601560\t3.62E+11
+LAB BLOOD ORDERABLES\t744208\tLAB11534R\tMTDNA SCREENING PANEL\t11644R\tMTDNA SCREENING PANEL\t#VALUE!\t8160\t0\tNone\tNone\t1\t8160\t66585600
+LAB BLOOD ORDERABLES\t744220\tLAB11540R\tOTC SEQUENCING\t11650R\tOTC SEQUENCING\t#VALUE!\t1677000\t0\tNone\tNone\t1\t1677000\t2.81E+12
+LAB BLOOD ORDERABLES\t744236\tLAB11551R\tNK FUNCTION\t11661R\tNK FUNCTION\t#VALUE!\t395914.4681\t0\tNone\tNone\t47\t18607980\t2.99E+13
+LAB BLOOD ORDERABLES\t744246\tLAB11556R\tBLASTOMYCES SEROLOGIES\t11666R\tBLASTO AB ID\t#VALUE!\t387587.5862\t0\tNone\tNone\t116\t44960160\t1.82E+13
+LAB BLOOD ORDERABLES\t744246\tLAB11556R\tBLASTOMYCES SEROLOGIES\tBLAS\tBLASTO AB CFS\t#VALUE!\t387587.5862\t0\tNone\tNone\t116\t44960160\t1.82E+13
+LAB BLOOD ORDERABLES\t744250\tLAB12781R\tBRUCELLA ANTIBODIES\t12779R\tBRUCELLA IGM\t#VALUE!\t327064.4444\t0\tNone\tNone\t27\t8830740\t3.27E+12
+LAB BLOOD ORDERABLES\t744256\tLAB11562R\tLYMPHOCYTIC CHORIOMENINGITI AB\t11674R\tINTERPRETATION\t#VALUE!\t254046.6667\t0\tNone\tNone\t18\t4572840\t1.33E+12
+LAB BLOOD ORDERABLES\t744262\tLAB11566R\tENTEROVIRUS RNA, QUAL\t11680R\tENTEROVIRUS QUAL,PCR\t#VALUE!\t474590\t0\tNone\tNone\t30\t14237700\t3.71E+13
+LAB BLOOD ORDERABLES\t744268\tLAB11570R\tSPINAL MUSC ATROPHY CARRIER\t11685R\tSPINAL MUSC ATROPHY CARRIER\t#VALUE!\t343800\t0\tNone\tNone\t1\t343800\t1.18E+11
+LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t11781R\tINTERPRETATION\t#VALUE!\t431070\t0\tNone\tNone\t2\t862140\t3.79E+11
+LAB BLOOD ORDERABLES\t744272\tLAB11572R\tTAY-SACHS ENZYME (LEUKOCYTES)\t12782R\tREVIEWED BY\t#VALUE!\t368760\t0\tNone\tNone\t1\t368760\t1.36E+11
+LAB BLOOD ORDERABLES\t744274\tLAB11573R\tCONGENITAL CYCLIC NEUTROPENIA\t11688R\tCONGENITAL CYLIC NEURTOPENIA\t#VALUE!\t3115380\t0\tNone\tNone\t1\t3115380\t9.71E+12
+LAB BLOOD ORDERABLES\t744278\tLAB11576R\tCOBALAMIN C DEFICIENCY(MMACHC)\t11691R\tCOBALAMIN C DEFICIENCY(MMACHC)\t#VALUE!\t3541227.273\t0\tNone\tNone\t11\t38953500\t1.68E+14
+LAB BLOOD ORDERABLES\t744280\tLAB11577R\tALAGILLE (JAG1) MUTATION\t11692R\tALAGILLE (JAG1) MUTATION\t#VALUE!\t5795840\t0\tNone\tNone\t3\t17387520\t1.02E+14
+LAB BLOOD ORDERABLES\t744320\tLAB11604R\tIL 5\t11757R\tINTERLEUKIN 5\t#VALUE!\t420600\t0\tNone\tNone\t1\t420600\t1.77E+11
+LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11764R\tFBN1 FULL GENE RESULT\t#VALUE!\t1239540\t0\tNone\tNone\t7\t8676780\t1.19E+13
+LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11765R\tFBN1 FULL GENE INTERPRETATION\t#VALUE!\t1358760\t0\tNone\tNone\t6\t8152560\t1.16E+13
+LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11766R\tREVIEWED BY\t#VALUE!\t1358760\t0\tNone\tNone\t6\t8152560\t1.16E+13
+LAB BLOOD ORDERABLES\t744326\tLAB11608R\tMARFAN (FBN1) SEQUENCING\t11767R\tSEQUENCING\t#VALUE!\t1239540\t0\tNone\tNone\t7\t8676780\t1.19E+13
+LAB BLOOD ORDERABLES\t744330\tLAB11610R\tDONOR, HTLVS(QNS/LIPEMIC ONLY)\t11890R\tHTLV I/II, WESTERN BLOT\t#VALUE!\t668460\t0\tNone\tNone\t3\t2005380\t1.52E+12
+LAB BLOOD ORDERABLES\t744338\tLAB11615R\tSODIUM BENZOATE LEVEL\t11774R\tSODIUM BENZOATE LEVEL\t#VALUE!\t808116\t0\tNone\tNone\t5\t4040580\t3.27E+12
+LAB BLOOD ORDERABLES\t744368\tLABLEGS\tLEGIONELLA SEROLOGY\tLEG2\tL.LEGIONELLA AB (SEROGRP 2-6,8)\t#VALUE!\t466080\t0\tNone\tNone\t1\t466080\t2.17E+11
+LAB BLOOD ORDERABLES\t744370\tLABPFCATE\tCATECHOLAMINES FRACTIONATED\tPCATIN\tCAT PF INTERPRETATION(CATECHOL INTRP, PL)\t#VALUE!\t294414.375\t0\tNone\tNone\t32\t9421260\t3.19E+12
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPDI\tDISCLAIMER\t#VALUE!\t781898.5492\t0\tNone\tNone\t193\t150906420\t1.54E+14
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPMD\tINTERPRETATION\t#VALUE!\t812646.7797\t0\tNone\tNone\t177\t143838480\t1.50E+14
+LAB BLOOD ORDERABLES\t744408\tLABCEBPA\tDETECTION OF CEBPA, BLOOD\tCEPMT\tMETHOD\t#VALUE!\t781898.5492\t0\tNone\tNone\t193\t150906420\t1.54E+14
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBDI\tDISCLAIMER\t#VALUE!\t878524.7788\t0\tNone\tNone\t113\t99273300\t1.44E+14
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBMD\tINTERPRETATION\t#VALUE!\t911035.4286\t0\tNone\tNone\t105\t95658720\t1.42E+14
+LAB BLOOD ORDERABLES\t744410\tLABBMCEBP\tDETECTION OF CEBPA, BONE MARROW\tCEBMT\tMETHOD\t#VALUE!\t878524.7788\t0\tNone\tNone\t113\t99273300\t1.44E+14
+LAB BLOOD ORDERABLES\t861761\tLABXIGF1\tPEDIATRIC IGF-1\tXIG1\tIGF-1\t#VALUE!\t477232.5\t0\tNone\tNone\t16\t7635720\t3.76E+12
+LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN (HIT) AB\tPF4COM\tPF4 COMMENT\t#VALUE!\t89627.14014\t0\tNone\tNone\t1577\t141342000\t1.41E+13
+LAB BLOOD ORDERABLES\t862881\tLABHITAB\tHEPARIN PLATELET FACTOR 4 AB\tPF4COM\tPF4 COMMENT\t#VALUE!\t95902.35702\t0\tNone\tNone\t577\t55335660\t1.99E+13
+LAB BLOOD ORDERABLES\t864664\tLAB11878R\tCORTISOL BINDING GLOBULIN\t11877R\tCORTISOL BIND.GLOB(CBG)\t#VALUE!\t946260\t0\tNone\tNone\t1\t946260\t8.95E+11
+LAB BLOOD ORDERABLES\t870907\tLAB11989R\tLONG QT SYNDROME\t11990R\tLQTS RESULTS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\t12428R\tINTERPRETATION\t#VALUE!\t165960\t0\tNone\tNone\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t870911\tLABCLAMP\tCHLAMYDIA AB PANEL\t12429R\tINTERPRETATION\t#VALUE!\t165960\t0\tNone\tNone\t1\t165960\t27542721600
+LAB BLOOD ORDERABLES\t871208\tLAB12003R\tHERPESVIRUS 6 AB, IGG\t12004R\tHERPESVIRUS 6 IGG\t#VALUE!\t146100\t0\tNone\tNone\t1\t146100\t21345210000
+LAB BLOOD ORDERABLES\t871220\tLAB12023R\tMYCOPLASMA PNEUMONIA IGG AB\t12024R\tMYCOPLASMA PNEUMONIA AB IGG\t#VALUE!\t286680\t0\tNone\tNone\t1\t286680\t82185422400
+LAB BLOOD ORDERABLES\t871222\tLAB12813R\tPARVOVIRUS B19 AB, IGG\t12835R\tPARVOVIRUS B19 AB IGG\t#VALUE!\t167820\t0\tNone\tNone\t1\t167820\t28163552400
+LAB BLOOD ORDERABLES\t871380\tLAB12050R\tRICKETTSIA AB PNL W/RFLX TITER\t12054R\tR.TYPHI IGM\t#VALUE!\t265070.7692\t0\tNone\tNone\t13\t3445920\t9.64E+11
+LAB BLOOD ORDERABLES\t871382\tLAB12055R\tRMSF IGG TITER\t12056R\tRMSF IGG TITER\t#VALUE!\t60\t0\tNone\tNone\t1\t60\t3600
+LAB BLOOD ORDERABLES\t871386\tLAB12059R\tR. TYPHI IGG TITER\t12060R\tR.TYPHI IGG TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+LAB BLOOD ORDERABLES\t871580\tLAB12066R\tCRMP-5-IGG WESTERN BLOT\tYCRMP\tCRMP-5-IGG WESTERN BLOT\t#VALUE!\t0\t0\tNone\tNone\t5\t0\t0
+LAB BLOOD ORDERABLES\t882631\tLAB12095R\tDARUNAVIR,ANTIRETROVIRAL LEVEL\t12101R\tDARUNAVIR,ANTIRETROVIRAL LEVEL\t#VALUE!\t592920\t0\tNone\tNone\t1\t592920\t3.52E+11
+LAB BLOOD ORDERABLES\t882639\tLAB12099R\tBRUCELLA TOTAL AB,AGGLU (RFLX)\t12100R\tBRUCELLA TOTAL  AB, AGGLUTIN\t#VALUE!\t15\t0\tNone\tNone\t4\t60\t3600
+LAB BLOOD ORDERABLES\t883990\tLABFT3DIA\tT3, FREE, TRACER DIALYSIS\t11876R\tT3, TOTAL\t#VALUE!\t594080\t0\tNone\tNone\t3\t1782240\t1.06E+12
+LAB BLOOD ORDERABLES\t883990\tLABFT3DIA\tT3, FREE, TRACER DIALYSIS\tFT3\tFREE T3 (BY DIALYSIS)\t#VALUE!\t594080\t0\tNone\tNone\t3\t1782240\t1.06E+12
+LAB BLOOD ORDERABLES\t885230\tLABGDMTOL\tGDM GLUCOSE TOLERANCE\tGDMINT\tINTERPRETATION\t#VALUE!\t17220\t0\tNone\tNone\t1\t17220\t296528400
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tDNAINT\tINTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tJO1INT\tANTI-JO1 INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tRNPINT\tANTI-RNP INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSCLINT\tANTI-SCL70 INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSNAINT\tSPECIFIC ANA INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSAIN\tANTI-RO INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886803\tLABSNA\tSPECIFIC ANA PANEL\tSSBINT\tANTI-LA INTERPRETATION\t#VALUE!\t441287.3684\t0\tNone\tNone\t19\t8384460\t4.72E+12
+LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSDI\tDISCLAIMER\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
+LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSDX\tCLINICAL INDICATION\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
+LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSMD\tINTERPRETED BY\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
+LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSMT\tMETHOD\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
+LAB BLOOD ORDERABLES\t886809\tLABCFDS\tCFTR DIAGNOSTIC SEQUENCING\tCFDSRE\tRESULT\t#VALUE!\t1537946.25\t0\tNone\tNone\t16\t24607140\t4.62E+13
+LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816DI\tDISCLAIMER\t#VALUE!\t722297.1429\t0\tNone\tNone\t7\t5056080\t4.67E+12
+LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816MD\tINTERPRETED BY\t#VALUE!\t806000\t0\tNone\tNone\t6\t4836000\t4.62E+12
+LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816ME\tMETHOD\t#VALUE!\t722297.1429\t0\tNone\tNone\t7\t5056080\t4.67E+12
+LAB BLOOD ORDERABLES\t889077\tLABD816V\tKIT D816V MUTATION, AS-PCR, BLOOD\tD816RF\tREFERENCES\t#VALUE!\t722297.1429\t0\tNone\tNone\t7\t5056080\t4.67E+12
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81DI\tDISCLAIMER\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81MD\tINTERPRETED BY\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81ME\tMETHOD\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889079\tLABND816V\tKIT D816V MUT., AS-PCR, NON-BLOOD\tND81RF\tREFERENCES\t#VALUE!\t757500\t0\tNone\tNone\t1\t757500\t5.74E+11
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88DI\tDISCLAIMER\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88DX\tCLINICAL INDICATION\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88IN\tINTERPRETATION\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88MD\tINTERPRETED BY\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88ME\tMETHOD\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889204\tLABMYD88\tMYD88 MUTATION ANALYSIS\tD88RF\tREFERENCES\t#VALUE!\t1389780\t0\tNone\tNone\t1\t1389780\t1.93E+12
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSDIB\tDISCLAIMER\t#VALUE!\t873158.6047\t0\tNone\tNone\t129\t112637460\t2.69E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSMEB\tMETHOD\t#VALUE!\t873158.6047\t0\tNone\tNone\t129\t112637460\t2.69E+14
+LAB BLOOD ORDERABLES\t889708\tLABCSMPB\tCANCER SOMATIC MUTATION PNL, BLOOD\tCSREFB\tREFERENCES\t#VALUE!\t873158.6047\t0\tNone\tNone\t129\t112637460\t2.69E+14
+LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSDI\tDISCLAIMER\t#VALUE!\t371657.1429\t0\tNone\tNone\t7\t2601600\t1.85E+12
+LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSMD\tINTERPRETED BY\t#VALUE!\t371657.1429\t0\tNone\tNone\t7\t2601600\t1.85E+12
+LAB BLOOD ORDERABLES\t889712\tLABCFSS\tCFTR SCREEN BY SEQUENCING\tCFSSME\tMETHOD\t#VALUE!\t371657.1429\t0\tNone\tNone\t7\t2601600\t1.85E+12
+LAB BLOOD ORDERABLES\t889722\tLAB12573R\tANCA\t12575R\tC-ANCA\t#VALUE!\t305359.0435\t0\tNone\tNone\t230\t70232580\t5.24E+13
+LAB BLOOD ORDERABLES\t889722\tLAB12573R\tANCA\t12576R\tP-ANCA\t#VALUE!\t305359.0435\t0\tNone\tNone\t230\t70232580\t5.24E+13
+LAB BLOOD ORDERABLES\t890107\tLAB12220R\tMSUD MUTATION ANALYSIS\t12221R\t12221R\t#VALUE!\t1404120\t0\tNone\tNone\t1\t1404120\t1.97E+12
+LAB BLOOD ORDERABLES\t890115\tLAB12228R\tLATEX SPECIFIC IGE (RIA) PANEL\t12229R\t12229R\t#VALUE!\t1006840\t0\tNone\tNone\t3\t3020520\t3.88E+12
+LAB BLOOD ORDERABLES\t890119\tLAB12232R\tTHYROGLOBULIN & THYROID AB\t12233R\t12233R\t#VALUE!\t977357.8378\t0\tNone\tNone\t74\t72324480\t4.79E+14
+LAB BLOOD ORDERABLES\t890121\tLAB12234R\tVOLATILES\t12235R\t12235R\t#VALUE!\t166777.5\t0\tNone\tNone\t8\t1334220\t1.07E+12
+LAB BLOOD ORDERABLES\t890127\tLAB12241R\tB CELL PHENOTYPING PROFILE\t12242R\t12242R\t#VALUE!\t925412.7273\t0\tNone\tNone\t11\t10179540\t4.35E+13
+LAB BLOOD ORDERABLES\t890129\tLAB12279R\tCVID CONFIRMATION FLOW PANEL\t12282R\t12282R\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+LAB BLOOD ORDERABLES\t890133\tLAB12212R\tWA1 IGG AB\t12213R\t12213R\t#VALUE!\t677464.2857\t0\tNone\tNone\t14\t9484500\t4.41E+13
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3DI\tDISCLAIMER\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3IN\tINTERPRETATION\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3MD\tINTERPRETED BY\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3ME\tMETHOD\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893876\tLABSF3B1\tSF3B1 MUTATION ANALYSIS\tSF3RF\tREFERENCES\t#VALUE!\t190860\t0\tNone\tNone\t1\t190860\t36427539600
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3DI\tDISCLAIMER\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3IN\tINTERPRETATION\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3MD\tINTERPRETED BY\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3ME\tMETHOD\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3RE\tRESULT, NON-BLOOD\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893878\tLABNSF3B1\tSF3B1 MUTATION ANALYSIS, NON-BLOOD\tNSF3RF\tREFERENCES\t#VALUE!\t480\t0\tNone\tNone\t1\t480\t230400
+LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLDI\tDISCLAIMER\t#VALUE!\t1418550\t0\tNone\tNone\t2\t2837100\t4.13E+12
+LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLMD\tINTERPRETED BY\t#VALUE!\t1418550\t0\tNone\tNone\t2\t2837100\t4.13E+12
+LAB BLOOD ORDERABLES\t893915\tLABCDMLPA\tCDH1 MLPA\tCDMLME\tMETHOD\t#VALUE!\t1418550\t0\tNone\tNone\t2\t2837100\t4.13E+12
+LAB BLOOD ORDERABLES\t898792\tPATH18\tFLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD\tPTH18R\tRECEIVED IN FLOW CYTOMETRY\t#VALUE!\t89720\t0\tNone\tNone\t6\t538320\t52831173600
+LAB BLOOD ORDERABLES\t898792\tPATH18\tSEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD\tPTH18R\tRECEIVED IN FLOW CYTOMETRY\t#VALUE!\t107400\t0\tNone\tNone\t3\t322200\t34644391200
+LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLDI\tDISCLAIMER\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
+LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLMD\tINTERPRETED BY\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
+LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLME\tMETHOD\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
+LAB BLOOD ORDERABLES\t898798\tLABCFMLPA\tCFTR DELETION/DUPLICATION ANALYSIS BY MLPA\tCFMLRE\tRESULT\t#VALUE!\t1484940\t0\tNone\tNone\t2\t2969880\t4.71E+12
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMDIB\tDISCLAIMER\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMINB\tINTERPRETATION\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMMDB\tINTERPRETED BY\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t898800\tLABMGMTB\tMGMT, METHYLATION SPECIFIC PCR, BLOOD\tMGMMEB\tMETHOD\t#VALUE!\t579996\t0\tNone\tNone\t5\t2899980\t2.09E+12
+LAB BLOOD ORDERABLES\t906953\tLAB12580R\tCU INDEX PANEL\t12589R\tCU INDEX PANEL\t#VALUE!\t544620\t0\tNone\tNone\t1\t544620\t2.97E+11
+LAB BLOOD ORDERABLES\t910564\tLAB12352R\tNMO EVALUATION WITH REFLEX\t11200R\tINTERPRETIVE COMMENTS\t#VALUE!\t606762.8571\t0\tNone\tNone\t7\t4247340\t2.63E+12
+LAB BLOOD ORDERABLES\t910596\tLAB12512R\tVON HIPPEL-LINDAU SYNDROME\t12513R\tVON HIPPEL-LINDAU SYNDROME\t#VALUE!\t3786780\t0\tNone\tNone\t1\t3786780\t1.43E+13
+LAB BLOOD ORDERABLES\t912863\tLAB12415R\tACVRL1 AND ENG (HHT)\t12417R\tSEQUENCING-DELETION/DUPLICATION\t#VALUE!\t3433980\t0\tNone\tNone\t3\t10301940\t3.85E+13
+LAB BLOOD ORDERABLES\t912867\tLAB12507R\tIBD SGI DIAGNOSTIC\t12508R\tIBD SGI DIAGNOSTIC\t#VALUE!\t818250\t0\tNone\tNone\t4\t3273000\t2.81E+12
+LAB BLOOD ORDERABLES\t912869\tLAB12443R\tNEOCOMPLETE PARANEOPLASTIC PRFL, SERUM\t12444R\tNEO PARANEOPLASTIC PRFL\t#VALUE!\t4481154.545\t0\tNone\tNone\t11\t49292700\t2.91E+14
+LAB BLOOD ORDERABLES\t912869\tLAB12443R\tNEO PARANEOPLASTIC PRFL\t12444R\tNEO PARANEOPLASTIC PRFL\t#VALUE!\t2150280\t0\tNone\tNone\t1\t2150280\t4.62E+12
+LAB BLOOD ORDERABLES\t961557\tLAB12809R\tCK ISOENZYME ELECTROPHORESIS\tCKEP\tCK ISOENZYME ELECTROPHORESIS\t#VALUE!\t4442.926829\t0\tNone\tNone\t41\t182160\t32746600800
+LAB BLOOD ORDERABLES\t961563\tLAB12815R\tLYME DISEASE AB IGG IGM W/RFLX\t12830R\tLYME AB SCREEN\t#VALUE!\t253813.6364\t0\tNone\tNone\t22\t5583900\t1.82E+12
+LAB BLOOD ORDERABLES\t961569\tLAB13007R\tQ FEVER IGG PHASE I TITER\t12012R\tQ FEVER IGG PHASE L TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+LAB BLOOD ORDERABLES\t961571\tLAB13008R\tQ FEVER IGG PHASE II TITER\t12014R\tQ FEVER IGG PHASE LI TITER\t#VALUE!\t0\t0\tNone\tNone\t2\t0\t0
+LAB BLOOD ORDERABLES\t961573\tLAB13009R\tQ FEVER IGM PHASE I TITER\t12013R\tQ FEVER IGM PHASE L TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+LAB BLOOD ORDERABLES\t961575\tLAB13010R\tQ FEVER IGM PHASE II TITER\t12015R\tQ FEVER IGM PHASE LI TITER\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+LAB BLOOD ORDERABLES\t961580\tLABHITIA\tHIT FUNCTIONAL ASSAY\tHITI2\tHIT COMMENT\t#VALUE!\t134929.92\t0\tNone\tNone\t125\t16866240\t2.96E+12
+LAB BLOOD ORDERABLES\t961582\tLAB12836R\tETHYLENE GLYCOL\t12837R\tETHYLENE GLYCOL\t#VALUE!\t517470\t0\tNone\tNone\t2\t1034940\t1.04E+12
+LAB BLOOD ORDERABLES\t966899\tLAB12599R\tDILATED CARDIOMYOPATHY PANEL\t12598R\tDILATED CARDIOMYOPATHY PANEL\t#VALUE!\t6362440\t0\tNone\tNone\t3\t19087320\t1.22E+14
+LAB BLOOD ORDERABLES\t977675\tLABMALSC\tMALARIA PREP AND ANTIGEN SCREEN\tMALSCD\tMALARIA ANTIGEN DESCRIPTION\t#VALUE!\t162025.3125\t0\tNone\tNone\t64\t10369620\t3.66E+13
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12672R\tTETANUS INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\t12674R\tCANDIDA INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tCONINT\tCON A INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPHAINT\tPHA INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978243\tLAB12582R\tLYMPH MITOGEN AND ANTIGEN SCRN\tPWMINT\tPWM INTERPRETATION\t#VALUE!\t1024890\t0\tNone\tNone\t2\t2049780\t2.10E+12
+LAB BLOOD ORDERABLES\t978528\tLAB12657R\tT CELL ACTIVATION\t12658R\tT CELL ACTIVATION\t#VALUE!\t229920\t0\tNone\tNone\t9\t2069280\t2.17E+12
+LAB BLOOD ORDERABLES\t978532\tLAB12651R\tHYPERTROPHIC CARDIOMYOPATHY PAN\t12650R\tHYPERTROPHIC CARDIOMYOPATHY PANEL\t#VALUE!\t5240112\t0\tNone\tNone\t5\t26200560\t1.40E+14
+LAB BLOOD ORDERABLES\t978552\tLAB12643R\tBRUTON\'S TYROSINE KINASE (BTK)\t12642R\tBTK PROTEIN FLOW\t#VALUE!\t233640\t0\tNone\tNone\t1\t233640\t54587649600
+LAB BLOOD ORDERABLES\t979488\tLAB12761R\tHEPATITIS B VIRUS GENOTYPE\t12757R\tHBV GENOTYPE\t#VALUE!\t663900\t0\tNone\tNone\t1\t663900\t4.41E+11
+LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tHITI2\tHIT COMMENT\t#VALUE!\t125334.3396\t0\tNone\tNone\t53\t6642720\t1.05E+12
+LAB BLOOD ORDERABLES\t1039019\tLABHITPNL\tHEPARIN INDUCED THROMBOCYTOPENIA PANEL\tPF4COM\tPF4 COMMENT\t#VALUE!\t125334.3396\t0\tNone\tNone\t53\t6642720\t1.05E+12
+LAB INTERNAL\t590536\tLABCOPYPT\tCOPY REQUEST\tCOPYTO\tSEND COPY TO\t#VALUE!\t120\t0\tNone\tNone\t1\t120\t14400
+MICROBIOLOGY ORDERABLES\t471548\tLABAID\tAEROBIC ORGANISM ID\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t255120\t0\tNone\tNone\t1\t255120\t65086214400
+MICROBIOLOGY ORDERABLES\t471783\tLABBSME\tBLOOD PARASITE EXAM (NOT MALARIA)\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t180211.7241\t0\tNone\tNone\t87\t15678420\t3.56E+12
+MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t527037.1429\t0\tNone\tNone\t21\t11067780\t8.79E+12
+MICROBIOLOGY ORDERABLES\t471792\tLABBONC\tBONE CULTURE AND GRAM STAIN\tGS\tGRAM STAIN\t#VALUE!\t527037.1429\t0\tNone\tNone\t21\t11067780\t8.79E+12
+MICROBIOLOGY ORDERABLES\t472115\tLABCRFA\tCRYPTOSPORIDIUM DFA\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t212126.9388\t0\tNone\tNone\t49\t10394220\t3.21E+12
+MICROBIOLOGY ORDERABLES\t472197\tLABDPARE\tDIRECT PARASITE EXAM\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t79170\t0\tNone\tNone\t2\t158340\t12620538000
+MICROBIOLOGY ORDERABLES\t472396\tLABFLDS\tFLUID STERILITY TEST\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t389580\t0\tNone\tNone\t1\t389580\t1.52E+11
+MICROBIOLOGY ORDERABLES\t472593\tLABIDWEE\tWORM ID\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t55380\t0\tNone\tNone\t1\t55380\t3066944400
+MICROBIOLOGY ORDERABLES\t472673\tLABISOBR\tISOLATOR BRUCELLA\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t472742\tLABLEGDD\tLEGIONELLA CULTURE, D AND D\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t752360\t0\tNone\tNone\t6\t4514160\t3.41E+12
+MICROBIOLOGY ORDERABLES\t472850\tLABINFO\tMICROBIOLOGY INFORMATION ONLY\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t473016\tLABNASC\tNP / NASAL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t229096\t0\tNone\tNone\t15\t3436440\t8.88E+11
+MICROBIOLOGY ORDERABLES\t473203\tLABROMSP\tMICROSPORIDIUM SCREEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t304958.4615\t0\tNone\tNone\t78\t23786760\t1.00E+13
+MICROBIOLOGY ORDERABLES\t473205\tLABROPK\tISOSPORA/CYCLOSPORA SCREEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t197685.5769\t0\tNone\tNone\t104\t20559300\t5.86E+12
+MICROBIOLOGY ORDERABLES\t473206\tLABROSBBP\tR/O STREP BETA GP B BY PCR\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t115550\t0\tNone\tNone\t6\t693300\t89094740400
+MICROBIOLOGY ORDERABLES\t473207\tLABROVRE\tVRE SCREEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t272595\t0\tNone\tNone\t4\t1090380\t3.15E+11
+MICROBIOLOGY ORDERABLES\t473372\tLABSTLCP\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t316267.8\t0\tNone\tNone\t100\t31626780\t1.20E+13
+MICROBIOLOGY ORDERABLES\t473374\tINACTIVE 7\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t3770\t0\tNone\tNone\t6\t22620\t511664400
+MICROBIOLOGY ORDERABLES\t473374\tLABSTLCX\tSTOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t26115\t0\tNone\tNone\t4\t104460\t10911891600
+MICROBIOLOGY ORDERABLES\t473710\tLABMMIC\tUNLISTED MICROBIOLOGY ORDER\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t518040\t0\tNone\tNone\t1\t518040\t2.68E+11
+MICROBIOLOGY ORDERABLES\t473756\tLABVSTLC\tVIRAL STOOL CULTURE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t1262815.2\t0\tNone\tNone\t75\t94711140\t1.20E+14
+MICROBIOLOGY ORDERABLES\t744392\tLABDVEFLB\tDIRECT VIRAL EXAM FLU B\tDVE\tRESULTS\t#VALUE!\t52970.76923\t0\tNone\tNone\t13\t688620\t38987182800
+MICROBIOLOGY ORDERABLES\t744394\tLABDVERSV\tDIRECT VIRAL EXAM RSV\tDVE\tRESULTS\t#VALUE!\t61173.10345\t0\tNone\tNone\t29\t1774020\t1.29E+11
+MICROBIOLOGY ORDERABLES\t744396\tLABDVEADV\tDIRECT VIRAL EXAM ADENO\tDVE\tRESULTS\t#VALUE!\t49680\t0\tNone\tNone\t1\t49680\t2468102400
+MICROBIOLOGY ORDERABLES\t744398\tLABDVEP1\tDIRECT VIRAL EXAM PARA 1\tDVE\tRESULTS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MICROBIOLOGY ORDERABLES\t744404\tLABDVEMPV\tDIRECT VIRAL EXAM MPV\tDVE\tRESULTS\t#VALUE!\t90300\t0\tNone\tNone\t2\t180600\t16389079200
+MICROBIOLOGY ORDERABLES\t870895\tLABBACIDI\tBACTERIA ID BY SEQUENCING, ISOLATE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t48300\t0\tNone\tNone\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGAL ID BY SEQUENCING FROM ISOLATE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t569880\t0\tNone\tNone\t4\t2279520\t2.21E+12
+MICROBIOLOGY ORDERABLES\t870897\tLABFUNIDI\tFUNGI ID BY SEQUENCING, ISOLATE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t48300\t0\tNone\tNone\t1\t48300\t2332890000
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t647532\t0\tNone\tNone\t5\t3237660\t3.07E+12
+MICROBIOLOGY ORDERABLES\t912857\tLABFUNIDS\tFUNGAL ID BY SEQUENCING FROM SPECIMEN\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t471560\t0\tNone\tNone\t18\t8488080\t5.50E+12
+MICROBIOLOGY ORDERABLES\t979486\tLABCRYEIA\tCRYPTOSPORIDIUM EIA\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t80326.5\t0\tNone\tNone\t40\t3213060\t3.71E+11
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBDIR\tINTERPRETED BY\t#VALUE!\t678540\t0\tNone\tNone\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471736\tLABFBCLO\tB - CELL CLONALITY PCR, FLUID\tFBDIS\tDISCLAIMER\t#VALUE!\t678540\t0\tNone\tNone\t3\t2035620\t2.03E+12
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCDIR\tINTERPRETED BY\t#VALUE!\t599640\t0\tNone\tNone\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t471737\tLABTBCLO\tB - CELL CLONALITY PCR, TISSUE\tTBCDIS\tDISCLAIMER\t#VALUE!\t599640\t0\tNone\tNone\t1\t599640\t3.60E+11
+MOLECULAR DIAG ORDERABLES\t471747\tLABBCRABL\tBCR - ABL, BLOOD\tBCRDIS\tDISCLAIMER\t#VALUE!\t488214.4565\t0\tNone\tNone\t184\t89831460\t6.08E+13
+MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRDR\tINTERPRETED BY\t#VALUE!\t488123.0769\t0\tNone\tNone\t26\t12691200\t8.34E+12
+MOLECULAR DIAG ORDERABLES\t471748\tLABBMBCR\tBCR - ABL, BONE MARROW\tBMBRDS\tDISCLAIMER\t#VALUE!\t488123.0769\t0\tNone\tNone\t26\t12691200\t8.34E+12
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRDR\tINTERPRETED BY\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRDS\tDISCLAIMER\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRES\tRESULTS\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t471749\tLABTBCR\tBCR - ABL, TISSUE\tTBCRTS\tSOURCE\t#VALUE!\t77280\t0\tNone\tNone\t1\t77280\t5972198400
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRD\tINTERPRETED BY\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRM\tMETHOD\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRS\tDISCLAIMER\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t472393\tLABFBCRQT\tBCR ABL QUANTITATIVE, FLUID\tQFBCRX\tCLINICAL INDICATION\t#VALUE!\t1367340\t0\tNone\tNone\t2\t2734680\t5.54E+12
+MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11DS\tDISCLAIMER\t#VALUE!\t266970\t0\tNone\tNone\t2\t533940\t2.85E+11
+MOLECULAR DIAG ORDERABLES\t473393\tLABT1114\tT(11\t14) BY PCR\tT11MT\tMETHOD\t#VALUE!\t266970\t0\tNone\tNone\t2\t533940\t2.85E+11
+MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11DS\tDISCLAIMER\t#VALUE!\t1087500\t0\tNone\tNone\t2\t2175000\t2.77E+12
+MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11MD\tINTERPRETED BY\t#VALUE!\t1087500\t0\tNone\tNone\t2\t2175000\t2.77E+12
+MOLECULAR DIAG ORDERABLES\t473395\tLABBM1114\tT(11\t14) BY PCR,BONE MARROW\tBM11MT\tMETHOD\t#VALUE!\t1087500\t0\tNone\tNone\t2\t2175000\t2.77E+12
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15DI\tDISCLAIMER\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15DX\tCLINICAL INDICATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15MD\tINTERPRETED BY\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15ME\tMETHOD\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473402\tLABFT1517\tT(15\t17) PML/RAR, FLUID\tFT15RE\tRESULT, FLD\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15DI\tDISCLAIMER\t#VALUE!\t909120\t0\tNone\tNone\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15MD\tINTERPRETED BY\t#VALUE!\t909120\t0\tNone\tNone\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473404\tLABTT1517\tT(15\t17) PML/RAR, TISSUE\tTT15ME\tMETHOD\t#VALUE!\t909120\t0\tNone\tNone\t1\t909120\t8.26E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTDIR\tINTERPRETED BY\t#VALUE!\t763860\t0\tNone\tNone\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTDIS\tDISCLAIMER\t#VALUE!\t763860\t0\tNone\tNone\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473407\tLABFTCLO\tT-CELL CLONALITY PCR, FLUID\tFTPRM\tPRIMERS\t#VALUE!\t763860\t0\tNone\tNone\t1\t763860\t5.83E+11
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCDIR\tINTERPRETED BY\t#VALUE!\t1195020\t0\tNone\tNone\t3\t3585060\t4.32E+12
+MOLECULAR DIAG ORDERABLES\t473408\tLABTTCLO\tT-CELL CLONALITY PCR, TISSUE\tTTCDIS\tDISCLAIMER\t#VALUE!\t1195020\t0\tNone\tNone\t3\t3585060\t4.32E+12
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFDI\tDISCLAIMER\t#VALUE!\t1050420\t0\tNone\tNone\t2\t2100840\t3.23E+12
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFIN\tINTERPRETATION\t#VALUE!\t333780\t0\tNone\tNone\t1\t333780\t1.11E+11
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFMD\tINTERPRETED BY\t#VALUE!\t333780\t0\tNone\tNone\t1\t333780\t1.11E+11
+MOLECULAR DIAG ORDERABLES\t670947\tLABEGFRP\tEGFR GENE MUTATION PANEL\tEGFME\tMETHOD\t#VALUE!\t1050420\t0\tNone\tNone\t2\t2100840\t3.23E+12
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSDIM\tDISCLAIMER\t#VALUE!\t784098.806\t0\tNone\tNone\t134\t105069240\t1.02E+14
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSMEM\tMETHOD\t#VALUE!\t784098.806\t0\tNone\tNone\t134\t105069240\t1.02E+14
+MOLECULAR DIAG ORDERABLES\t889710\tLABCSMPBM\tCANCER SOMATIC MUTATION PNL, BM\tCSREFM\tREFERENCES\t#VALUE!\t784098.806\t0\tNone\tNone\t134\t105069240\t1.02E+14
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMDI\tDISCLAIMER\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMIN\tINTERPRETATION\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMMD\tINTERPRETED BY\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
+MOLECULAR DIAG ORDERABLES\t889716\tLABNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION\tNPMRF\tREFERENCES\t#VALUE!\t586220.2941\t0\tNone\tNone\t68\t39862980\t2.74E+13
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPDI\tDISCLAIMER\t#VALUE!\t540720\t0\tNone\tNone\t66\t35687520\t2.44E+13
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPIN\tINTERPRETATION, BM\t#VALUE!\t540720\t0\tNone\tNone\t66\t35687520\t2.44E+13
+MOLECULAR DIAG ORDERABLES\t889718\tLABBMNPM1\tNPM1, NUCLEOPHOSMIN 1 MUTATION, BM\tBMNPRF\tREFERENCES\t#VALUE!\t540720\t0\tNone\tNone\t66\t35687520\t2.44E+13
+OR PROCEDURES\t659968\tCRP139\tICD GENERATOR AND LEAD INS/REP DUAL\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+OR PROCEDURES\t660294\tCRP332\tRIGHT HEART C NIPRIDE STUDY\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+OR PROCEDURES\t660420\tCRP398\tVALVULOPLASTY ADULT\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+PEDS ANGIO ORDERABLES\t474844\tIMG13RHC\tPED RT HEART CATH\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+PEDS ANGIO ORDERABLES\t474847\tIMG13VA\tPED VALVULOPLASTY AO V\tPDF\tPDF RESULT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+PFT ORDERABLES\t471482\tPFT43\tFULL SPIROMETRY WITH SVC MVV\tPDF\tPDF RESULT\t#VALUE!\t253380\t0\tNone\tNone\t1\t253380\t64201424400
+PFT ORDERABLES\t471484\tPFT45\tLUNG VOLUMES\tPDF\tPDF RESULT\t#VALUE!\t4288380\t0\tNone\tNone\t1\t4288380\t1.84E+13
+PFT ORDERABLES\t471491\tPFT50\tROUTINE STDY - ABG, SPIRO W/BDR,DLCO,VOL\tPDF\tPDF RESULT\t#VALUE!\t37740\t0\tNone\tNone\t2\t75480\t3834295200
+PFT ORDERABLES\t471497\tPFT53\tSPIROMETRY WITH ABG\tPDF\tPDF RESULT\t#VALUE!\t213840\t0\tNone\tNone\t1\t213840\t45727545600
+PFT ORDERABLES\t471498\tPFT54\tSPIROMETRY WITH DLCO\tPDF\tPDF RESULT\t#VALUE!\t555360\t0\tNone\tNone\t4\t2221440\t1.46E+12
+PFT ORDERABLES\t669779\tPFT59\tSPIROMETRY\tPDF\tPDF RESULT\t#VALUE!\t882945.6\t0\tNone\tNone\t50\t44147280\t6.91E+13
+POINT OF CARE (NON LAB) TESTS\t473370\tPOC5\tPOC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)\tT11DX\tCLINICAL INDICATION\t#VALUE!\t660\t0\tNone\tNone\t1\t660\t435600
+POINT OF CARE (NON LAB) TESTS\t505766\tPOC6\tPOC PPD SKIN TEST (IP)\tPPDINT\tPPD INTERPRETATION\t#VALUE!\t261720\t0\tNone\tNone\t1\t261720\t68497358400
+POINT OF CARE (NON LAB) TESTS\t507566\tPOC3\tPOC URINE DIPSTICK FOR KETONES\tPHLOC\tLOCATION\t#VALUE!\t13600\t0\tNone\tNone\t3\t40800\t1048500000
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tQC7\tQC 7 RESULT\t#VALUE!\t-14672.40876\t0\tNone\tNone\t137\t-2010120\t2.71E+11
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tTESTEXP\tTEST STRIP EXP DATE\t#VALUE!\t-14672.40876\t0\tNone\tNone\t137\t-2010120\t2.71E+11
+POINT OF CARE (NON LAB) TESTS\t610680\tPOC31\tPOC NITRAZINE PAPER FOR PH MEASUREMENT\tTESTLOT\tTEST STRIP LOT#\t#VALUE!\t-14672.40876\t0\tNone\tNone\t137\t-2010120\t2.71E+11
+STOOL ORDERABLES\t471633\tLABCANNAB\tNEURONAL NUCLEAR AB, CSF\tCANNA3\tNEURONAL NUCLEAR AB, CSF\t#VALUE!\t764260\t0\tNone\tNone\t6\t4585560\t4.58E+12
+STOOL ORDERABLES\t471693\tLABASPERP\tASPERGILLUS AB ID\tASPERP\tASPERGILLUS AB (ID)\t#VALUE!\t340350\t0\tNone\tNone\t4\t1361400\t4.73E+11
+STOOL ORDERABLES\t471801\tLABYBRUC\tBRUCELLA IGG AND IGM AB\tYBRUC1\tBRUCELLA AB ,IGG\t#VALUE!\t317841.8571\t0\tNone\tNone\t140\t44497860\t1.67E+13
+STOOL ORDERABLES\t471801\tLABYBRUC\tBRUCELLA IGG AND IGM AB\tYBRUC2\tBRUCELLA AB ,IGM\t#VALUE!\t317841.8571\t0\tNone\tNone\t140\t44497860\t1.67E+13
+STOOL ORDERABLES\t471801\tLABYBRUC\tBRUCELLA IGG AND IGM AB\tYBRUC3\tINTERPRETATION\t#VALUE!\t317841.8571\t0\tNone\tNone\t140\t44497860\t1.67E+13
+STOOL ORDERABLES\t471952\tLABCELDIS\tCELIAC DISEASE SYSTEM\tCELDR\tCELIAC DISEASE SYS\t#VALUE!\t777200\t0\tNone\tNone\t3\t2331600\t2.00E+12
+STOOL ORDERABLES\t472230\tLAB11112R\tDONOR TOXOPLASMA AB\tDTOXOM\tDONOR TOXO IGM\t#VALUE!\t3589900\t0\tNone\tNone\t3\t10769700\t8.46E+13
+STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\tDRUGSCRNFLD\tDRUG SCRN, GENERAL, BIOLOGICAL FL\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t472247\tLABYGDA\tDRUG SCREEN, FLUIDS\tYGDA2\tSUSPECT DRUG\t#VALUE!\t145740\t0\tNone\tNone\t2\t291480\t81166788000
+STOOL ORDERABLES\t472311\tLABYEMA\tENDOMYSIAL ANTIBODIES, SERUM\tYEMAIN\tENDOMYSIAL ABS, SERUM\t#VALUE!\t283740\t0\tNone\tNone\t1\t283740\t80508387600
+STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB1\tEBV VCA IGG AB\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
+STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB2\tEBV VCA IGM AB\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
+STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB3\tEBNA AB\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
+STOOL ORDERABLES\t472323\tLABYEB\tEBV AB PROFILE, SERUM\tYEB4\tINTERPRETATION\t#VALUE!\t245840\t0\tNone\tNone\t3\t737520\t1.82E+11
+STOOL ORDERABLES\t472594\tLABIBDDI\tIBD DIAGNOSTIC SYSTEM\tIBDDR\tIBD DIAGNOSTIC SYS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t472630\tLABYKLFRE\tIMMUNO FREE LIGHT CHAINS\tYKLF1\tKAPPA FREE LIGHT CHAIN, S\t#VALUE!\t162735\t0\tNone\tNone\t8\t1301880\t2.38E+11
+STOOL ORDERABLES\t472630\tLABYKLFRE\tIMMUNO FREE LIGHT CHAINS\tYKLF2\tLAMBDA FREE LIGHT CHAIN, S\t#VALUE!\t162735\t0\tNone\tNone\t8\t1301880\t2.38E+11
+STOOL ORDERABLES\t472630\tLABYKLFRE\tIMMUNO FREE LIGHT CHAINS\tYKLF3\tKAPPA/LAMBDA FLC RATIO\t#VALUE!\t162735\t0\tNone\tNone\t8\t1301880\t2.38E+11
+STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF1\tINFLUENZA VIRUS A AB, IGG & IGM\t#VALUE!\t245647.5\t0\tNone\tNone\t8\t1965180\t5.15E+11
+STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF2\tINFLUENZA VIRUS A AB, IGG\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
+STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF3\tINFLUENZA VIRUS A AB, IGM\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
+STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF4\tINFLUENZA VIRUS B AB, IGG & IGM\t#VALUE!\t245647.5\t0\tNone\tNone\t8\t1965180\t5.15E+11
+STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF5\tINFLUENZA VIRUS B AB, IGG\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
+STOOL ORDERABLES\t472644\tLABYINFAB\tINFLUENZA AB PANEL\tYINF6\tINFLUENZA VIRUS B AB, IGM\t#VALUE!\t259189.3333\t0\tNone\tNone\t45\t11663520\t3.27E+12
+STOOL ORDERABLES\t472657\tLABYINSFT\tINSULIN FREE AND TOTAL\tINS\tINSULIN\t#VALUE!\t421560\t0\tNone\tNone\t5\t2107800\t1.23E+12
+STOOL ORDERABLES\t472657\tLABYINSFT\tINSULIN FREE AND TOTAL\tYINSF1\tFREE INSULIN\t#VALUE!\t421560\t0\tNone\tNone\t5\t2107800\t1.23E+12
+STOOL ORDERABLES\t472722\tLABYLACTS\tLACTOFERRIN DETECTION, STOOL\tYLACT1\tLACTOFERRIN DETECTION\t#VALUE!\t401266.1538\t0\tNone\tNone\t39\t15649380\t7.59E+12
+STOOL ORDERABLES\t472749\tLABYLAP\tLEUKOCYTE ALKALINE PHOSPATASE SCORE\tYLAP1\tLAP SCORE\t#VALUE!\t187714.2857\t0\tNone\tNone\t7\t1314000\t3.12E+11
+STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tYFCHY1\tFLUID TYPE\t#VALUE!\t177360\t0\tNone\tNone\t1\t177360\t31456569600
+STOOL ORDERABLES\t472753\tLABYFCHYL\tLIPID ANALYSIS, BODY FLUID\tYFCHY6\tCOMMENT\t#VALUE!\t177360\t0\tNone\tNone\t1\t177360\t31456569600
+STOOL ORDERABLES\t472776\tLABLAS\tLYMPHOCYTE AG STIMULATION SCREEN\tTETINT\tTETANUS INTERPRETATION\t#VALUE!\t908145\t0\tNone\tNone\t4\t3632580\t3.30E+12
+STOOL ORDERABLES\t472784\tLABYSMG\tMAGNESIUM QUANTITATIVE, FECES\tYSMG1\tMAGNESIUM, QN, F\t#VALUE!\t684960\t0\tNone\tNone\t1\t684960\t4.69E+11
+STOOL ORDERABLES\t472955\tLABYNOB\tNEUTROPHIL OXIDATIVE BURST\tNEUTOXB\tNEUTROPHIL OXIDATIVE BURST\t#VALUE!\t29880\t0\tNone\tNone\t1\t29880\t892814400
+STOOL ORDERABLES\t472955\tLABYNOB\tNEUTROPHIL OXIDATIVE BURST\tYNOB2\tINTERPRETATION\t#VALUE!\t29880\t0\tNone\tNone\t1\t29880\t892814400
+STOOL ORDERABLES\t472955\tLABYNOB\tNEUTROPHIL OXIDATIVE BURST\tYNOB3\tREVIEWED BY\t#VALUE!\t29880\t0\tNone\tNone\t1\t29880\t892814400
+STOOL ORDERABLES\t472979\tLABNORW\tNOROVIRUS ANTIGEN, STOOL EIA\tNORW1\tNOROVIRUS,ELA (STOOL)\t#VALUE!\t244852.9167\t0\tNone\tNone\t288\t70517640\t2.37E+13
+STOOL ORDERABLES\t473061\tLABYP4AB\tPARAINFLUENZA AB\tYP4AB1\tTYPE 1\t#VALUE!\t590370\t0\tNone\tNone\t2\t1180740\t7.06E+11
+STOOL ORDERABLES\t473061\tLABYP4AB\tPARAINFLUENZA AB\tYP4AB2\tTYPE 2\t#VALUE!\t590370\t0\tNone\tNone\t2\t1180740\t7.06E+11
+STOOL ORDERABLES\t473061\tLABYP4AB\tPARAINFLUENZA AB\tYP4AB3\tTYPE 3\t#VALUE!\t590370\t0\tNone\tNone\t2\t1180740\t7.06E+11
+STOOL ORDERABLES\t473095\tLAB11138R\tHIV PHENO EXPR ANTIVIR RESIST\tPHENO\tHIV-1 PHENOTYPE\t#VALUE!\t633000\t0\tNone\tNone\t1\t633000\t4.01E+11
+STOOL ORDERABLES\t473218\tLAB13006R\tREDUCING SUBSTANCE, FECES\t13005R\tREDUCING SUBSTANCE, FECES\t#VALUE!\t189315\t0\tNone\tNone\t8\t1514520\t3.87E+11
+STOOL ORDERABLES\t473266\tLABYRICK\tRICKETTSIA AB PANEL\tYRICK1\tSPOTTED FEVER GROUP,AB,IGG,S\t#VALUE!\t333069.6774\t0\tNone\tNone\t31\t10325160\t1.28E+13
+STOOL ORDERABLES\t473281\tLABYRS\tRSV IGG AND IGM AB\tYRS1\tRSV AB, IGG & IGM\t#VALUE!\t307320\t0\tNone\tNone\t1\t307320\t94445582400
+STOOL ORDERABLES\t473281\tLABYRS\tRSV IGG AND IGM AB\tYRS2\tRSV AB, IGG\t#VALUE!\t317796\t0\tNone\tNone\t5\t1588980\t5.26E+11
+STOOL ORDERABLES\t473281\tLABYRS\tRSV IGG AND IGM AB\tYRS3\tRSV AB, IGM\t#VALUE!\t317796\t0\tNone\tNone\t5\t1588980\t5.26E+11
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC DIFFICILE TOXIN B, STOOL\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t231851.4286\t0\tNone\tNone\t21\t4868880\t1.38E+12
+STOOL ORDERABLES\t473371\tLABCDTPCR\tC.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t192800\t0\tNone\tNone\t3\t578400\t1.13E+11
+STOOL ORDERABLES\t473371\tLABTCDIF\tC DIFFICILE TOXIN B, STOOL\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t210925.3091\t0\tNone\tNone\t275\t58004460\t1.38E+13
+STOOL ORDERABLES\t473460\tLAB11122R\tTOXOPLASMA AB, IGM\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t2060076.522\t0\tNone\tNone\t23\t47381760\t3.01E+14
+STOOL ORDERABLES\t473460\tLAB11122R\tTOXOPLASMA IGM ELISA\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t1477600\t0\tNone\tNone\t3\t4432800\t6.65E+12
+STOOL ORDERABLES\t473460\tLAB11122R\tTOXOPLASMOSIS IGM\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t473460\tLABTOXOM\tTOXOPLASMOSIS IGM\tTOXOM\tTOXOPLASMA IGM ELISA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t473462\tLAB11120R\tTOXOPLASMOSIS, CSF\tTOXOC\tTOXOPLASMA AB,CSF\t#VALUE!\t1690331.429\t0\tNone\tNone\t21\t35496960\t1.93E+14
+STOOL ORDERABLES\t473777\tLABVWFPI\tVON WILLEBRAND PROTEASE INHIBITOR\tVWFPIC\tCOMMENT\t#VALUE!\t1187392.5\t0\tNone\tNone\t8\t9499140\t2.35E+13
+STOOL ORDERABLES\t497378\tLABYFMG\tMAGNESIUM, FLUID\tMGFLD\tMAGNESIUM, BODY FLUID\t#VALUE!\t242700\t0\tNone\tNone\t1\t242700\t58903290000
+STOOL ORDERABLES\t497378\tLABYFMG\tMAGNESIUM, FLUID\tYFMG2\tFLUID TYPE\t#VALUE!\t242700\t0\tNone\tNone\t1\t242700\t58903290000
+STOOL ORDERABLES\t497462\tLABFADAG\tADENOVIRUS AG, STOOL\tADAG\tADENOVIRUS AG\t#VALUE!\t259009.322\t0\tNone\tNone\t236\t61126200\t2.30E+13
+STOOL ORDERABLES\t541691\tLABHEPBG\tHEPATITIS B GENOTYPE\tHEPBG1\tHBV GENOTYPE\t#VALUE!\t646914\t0\tNone\tNone\t10\t6469140\t5.30E+12
+STOOL ORDERABLES\t576475\tLAB11299R\tSULFONYLUREA HYPOGLYCEMICS\t11306R\tSULFONYLUREA SCRN\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t670750\tLAB11340R\tADAMTS 13 PROFILE\t11341R\tADAMTS 13 ACTIVITY\t#VALUE!\t631303.6364\t0\tNone\tNone\t11\t6944340\t6.83E+12
+STOOL ORDERABLES\t670762\tLAB11254R\tM AVIUM COMPLEX MIC PANL\t11264R\tSTREPTOMYCIN\t#VALUE!\t455400\t0\tNone\tNone\t1\t455400\t2.07E+11
+STOOL ORDERABLES\t670924\tLAB11437R\tIBD DIAGNOSTIC\t11501R\tIBD DIAGNOSTIC\t#VALUE!\t902580\t0\tNone\tNone\t2\t1805160\t2.52E+12
+STOOL ORDERABLES\t744166\tLAB11512R\tCYSTIC FIBROSIS, SEQUENCING\t11621R\tCYSTIC FIBROSIS, SEQUENCING\t#VALUE!\t2659970\t0\tNone\tNone\t6\t15959820\t4.44E+13
+STOOL ORDERABLES\t744178\tLAB11518R\tBCR-ABL1 KINASE DOMAIN MUT ANL\t11627R\tBCR-ABL1 KINASE DOMAIN MUT ANAL\t#VALUE!\t605820\t0\tNone\tNone\t2\t1211640\t1.46E+12
+STOOL ORDERABLES\t744202\tLAB11531R\tPARANEOPLASTIC AB SCREEN\t11641R\tPARANEOPLASTIC AB SCREEN\t#VALUE!\t2278057.5\t0\tNone\tNone\t88\t200469060\t6.98E+14
+STOOL ORDERABLES\t744228\tLAB11547R\tHEPATITIS C CONFIRMATORY TEST\t11657R\tHEPATITIS C CONFIRMATORY TEST\t#VALUE!\t1722849.6\t0\tNone\tNone\t25\t43071240\t1.11E+14
+STOOL ORDERABLES\t744232\tLAB11549R\tCALIFORNIA ENCEPHALITIS PANEL\t11659R\tCALIFORNIA ENCEPHALITIS PANEL\t#VALUE!\t2417270\t0\tNone\tNone\t12\t29007240\t9.09E+13
+STOOL ORDERABLES\t744264\tLAB11567R\tHTLV-I/II AB, CSF\t11681R\tHTLV-I/II AB\t#VALUE!\t264940\t0\tNone\tNone\t3\t794820\t2.22E+11
+STOOL ORDERABLES\t744266\tLAB11568R\tBRUCELLA SEROLOGIES, CSF\t11683R\tBRUCELLA IGM,CSF\t#VALUE!\t272970\t0\tNone\tNone\t2\t545940\t1.68E+11
+STOOL ORDERABLES\t744270\tLAB11571R\tCYSTIC FIBROSIS, 97 MUTATIONS\t11686R\tCYSTIC FIBROSIS, 97 MUTATIONS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11723R\tLYMPHOCYTE PROLIFERATION, AG\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11728R\tINTERP, CANDIDA ALBICANS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB11596R\tLYMPHO PROLIFERATION, AG\t11733R\tINTERP, TETANUS TOXOID\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t744312\tLAB12284R\tLYMPHO PROLIFERATION, AG\t12285R\t12285R\t#VALUE!\t342800\t0\tNone\tNone\t3\t1028400\t5.85E+11
+STOOL ORDERABLES\t744322\tLAB11606R\tCYTOCHROME P450 2D6 GENOTYPE\t11758R\tCYP2D6 GENOTYPE\t#VALUE!\t390450\t0\tNone\tNone\t4\t1561800\t6.94E+11
+STOOL ORDERABLES\t744322\tLAB11606R\tCYTOCHROME P450 2D6 GENOTYPE\t11759R\tREVIEWED BY\t#VALUE!\t390450\t0\tNone\tNone\t4\t1561800\t6.94E+11
+STOOL ORDERABLES\t864650\tLAB11855R\tHEPATITIS C VIRAL RNA, QUANT\t11854R\tHCV RNA ,QUANT,TMA LOGIU/ML\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+STOOL ORDERABLES\t871212\tLAB12016R\tCYTOMEGALOVIRUS IGG AB, ELISA\t11667R\tCYTOMEGALOVIRUS IGG\t#VALUE!\t229260\t0\tNone\tNone\t1\t229260\t52560147600
+STOOL ORDERABLES\t889720\tLAB12571R\tFLT3-ITD, FLT3-D835 MUTATIONS\t12572R\tFLT3-ITD, FLT3-D835 MUTATIONS\t#VALUE!\t553828.4746\t0\tNone\tNone\t177\t98027640\t9.87E+13
+STOOL ORDERABLES\t890139\tLABSTDY2\tSTUDY TEST\tRESS2\tRESS2\t#VALUE!\t427020\t0\tNone\tNone\t2\t854040\t3.80E+11
+STOOL ORDERABLES\t890251\tLAB12578R\tCHLORIDE, 24 HR STOOL\tYSTCL1\tCHLORIDE, STOOL, 24 HR\t#VALUE!\t1991280\t0\tNone\tNone\t1\t1991280\t3.97E+12
+STOOL ORDERABLES\t1048039\tLAB12866R\tB HENSELAE IGG TITER\tBHIGGT\tB.HENSELAE IGG TITER\t#VALUE!\t60\t0\tNone\tNone\t1\t60\t3600
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tAAUINT\tINTERPRETATION\t#VALUE!\t287790\t0\tNone\tNone\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471606\tLABAAUR\tAMINO ACIDS, URINE\tINTUBY\tINTERPRETED BY\t#VALUE!\t287790\t0\tNone\tNone\t6\t1726740\t5.69E+11
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUAMIM\tIMMUNOASSAY SCREEN\t#VALUE!\t26760\t0\tNone\tNone\t4\t107040\t11457561600
+URINE ORDERABLES\t471611\tLABYUAMC\tAMPHETAMINES CONFIRMATION, URINE\tYUAMIN\tINTERPRETATION\t#VALUE!\t26760\t0\tNone\tNone\t4\t107040\t11457561600
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tYUBBIM\tIMMUNOASSAY SCREEN\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
+URINE ORDERABLES\t471740\tLABYUBBC\tBARBITURATE CONFIRMATION, URINE\tYUBBIN\tINTERPRETATION\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZCO\tCOMMENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZIM\tIMMUNOASSAY SCREEN\t#VALUE!\t0\t0\tNone\tNone\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZIN\tINTERPRETATION\t#VALUE!\t0\t0\tNone\tNone\t9\t0\t0
+URINE ORDERABLES\t471754\tLABYUBZC\tBENZODIAZEPINE CONFIRMATION, URINE\tYUBZNO\tNOTE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t471939\tLABYUFCAT\tCATECHOLAMINES FRACTIONATED FREE, URINE\tYUCATF\tCATECHOLAMINES, URINE\t#VALUE!\t347778.9474\t0\tNone\tNone\t19\t6607800\t2.55E+12
+URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tYUCOIM\tIMMUNOASSAY SCREEN\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
+URINE ORDERABLES\t472046\tLABYUCC\tCOCAINE CONFIRMATION\tYUCOIN\tINTERPRETATION\t#VALUE!\t30\t0\tNone\tNone\t2\t60\t3600
+URINE ORDERABLES\t472249\tLABDOA\tDRUGS OF ABUSE SCREEN, URINE\tDOACOM\tDOACOM COMMENTS\t#VALUE!\t11906.82203\t0\tNone\tNone\t7552\t89920320\t1.01E+13
+URINE ORDERABLES\t472564\tLABYUHOMO\tHOMOCYSTEINE TOTAL, URINE\tHMCYURN\tHOMOCYSTEIN, TOTAL,U\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t472836\tLABYUMERC\tMERCURY, URINE (TIMED)\tMERURN\tMERCURY, U\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t472836\tLABYUMERC\tMERCURY, URINE (TIMED)\tVOLUMEURN\tURINE VOLUME\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t472836\tLABYUMERC\tMERCURY, URINE (TIMED)\tYUMER2\tCOLLECTION DURATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t472841\tLABYUMETF\tMETANEPHRINES FRACTIONATED, 24H URINE\tYUMECO\tCOMMENT\t#VALUE!\t330442.6667\t0\tNone\tNone\t45\t14869920\t5.39E+12
+URINE ORDERABLES\t472928\tLABMPSTLC\tMUCOPOLYSACCHARIDES TLC, URINE\tMPSTLC\tMUCOPOLYSA, UR, TLC\t#VALUE!\t562020\t0\tNone\tNone\t1\t562020\t3.16E+11
+URINE ORDERABLES\t473028\tLABOSTLC\tOLIGOSACCHARIDES TLC, URINE\tOSTLC\tOLIGOSAC, UR, TLC\t#VALUE!\t561900\t0\tNone\tNone\t1\t561900\t3.16E+11
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUOPCO\tCOMMENT\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUOPIM\tIMMUNOASSAY SCREEN\t#VALUE!\t14.11764706\t0\tNone\tNone\t17\t240\t14400
+URINE ORDERABLES\t473030\tLABYUOPC\tOPIATE CONFIRMATION, URINE\tYUOPIN\tINTERPRETATION\t#VALUE!\t14.11764706\t0\tNone\tNone\t17\t240\t14400
+URINE ORDERABLES\t473034\tLABUORG\tORGANIC ACIDS, URINE\tORGU\tORGANIC ACIDS, URINE\t#VALUE!\t438277.5\t0\tNone\tNone\t8\t3506220\t1.68E+12
+URINE ORDERABLES\t473034\tLABUORG\tORGANIC ACIDS, URINE\tORGUIN\tINTERPRETATION\t#VALUE!\t438277.5\t0\tNone\tNone\t8\t3506220\t1.68E+12
+URINE ORDERABLES\t473092\tLABYUPCPC\tPHENCYCLIDINE CONFIRMATION, URINE\tYUPCIM\tIMMUNOASSAY SCREEN\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t473092\tLABYUPCPC\tPHENCYCLIDINE CONFIRMATION, URINE\tYUPCIN\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tPPPCOM\tCOMMENTS\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
+URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tUHYPOX\tHYPOXANTHINE\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
+URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tURIC\tURIC ACID\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
+URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tUURAC\tURACIL\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
+URINE ORDERABLES\t473192\tLABYUPPP\tPURINE AND PYRINIDINE PANEL, URINE\tUXANT\tXANTHINE\t#VALUE!\t833760\t0\tNone\tNone\t1\t833760\t6.95E+11
+URINE ORDERABLES\t473429\tLABYUTHC\tTETRAHYDROCANNABINOL, URINE\tYUTHIM\tIMMUNOASSAY SCREEN\t#VALUE!\t87930\t0\tNone\tNone\t10\t879300\t2.61E+11
+URINE ORDERABLES\t473429\tLABYUTHC\tTETRAHYDROCANNABINOL, URINE\tYUTHIN\tINTERPRETATION\t#VALUE!\t87930\t0\tNone\tNone\t10\t879300\t2.61E+11
+URINE ORDERABLES\t473732\tLABUAPRN\tURINALYSIS, SCREEN FOR CULTURE\tPEDCOM\tSPECIMEN COMMENT\t#VALUE!\t6620\t0\tNone\tNone\t3\t19860\t338230800
+URINE ORDERABLES\t473744\tLABUVMA\tVANILLYMANDELIC ACID, URINE\tVMAINT\tVMA INTRP\t#VALUE!\t321355.3846\t0\tNone\tNone\t13\t4177620\t1.41E+12
+URINE ORDERABLES\t473744\tLABYUVMA\tVANILLYMANDELIC ACID, URINE\tVMAINT\tVMA INTRP\t#VALUE!\t379830\t0\tNone\tNone\t6\t2278980\t9.13E+11
+URINE ORDERABLES\t473770\tLABUVH\tVMA AND HVA\tVHINT\tVMA/HVA INTRP\t#VALUE!\t309984\t0\tNone\tNone\t10\t3099840\t1.05E+12
+URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\t12000R\tINTERPRETATION\t#VALUE!\t528440\t0\tNone\tNone\t3\t1585320\t8.64E+11
+URINE ORDERABLES\t497295\tLABYUALA\tAMINOLEVULINIC ACID, URINE\t12682R\tREVIEWED BY\t#VALUE!\t408660\t0\tNone\tNone\t1\t408660\t1.67E+11
+URINE ORDERABLES\t497400\tLABYDROTC\tDRUG SCREEN PRESCRIPTION / OTC\tYDR1\tDRUG SCREEN, PRESCRIP/OTC\t#VALUE!\t9375960\t0\tNone\tNone\t2\t18751920\t2.50E+14
+URINE ORDERABLES\t497478\tLABUHVA\tHOMOVANILLIC ACID, URINE\tHVAINT\tHVA INTRP\t#VALUE!\t335730\t0\tNone\tNone\t2\t671460\t2.26E+11
+URINE ORDERABLES\t621213\tLABUFCATE\tCATECHOLAMINES, URN FREE\tUCATIN\tCATECHOL INTRP , URN\t#VALUE!\t242040\t0\tNone\tNone\t1\t242040\t58583361600
+URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11147R\tSPECIFIC GRAVITY\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
+URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11148R\tPH\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
+URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11149R\tOXIDANTS\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
+URINE ORDERABLES\t670951\tLAB11145R\tADULTERANTS SURVEY, URINE\t11152R\tCOMMENT\t#VALUE!\t13.33333333\t0\tNone\tNone\t54\t720\t43200
+URINE ORDERABLES\t744242\tLAB11554R\tS-SULFOCYSTEINE, URINE\t11664R\tS-SULFOCYSTEINE, URINE\t#VALUE!\t607260\t0\tNone\tNone\t1\t607260\t3.69E+11
+URINE ORDERABLES\t864656\tLAB11863R\tASPIRIN RESISTANCE\t11861R\t11-DEHYDROTHROMBOXANE B2\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+URINE ORDERABLES\t864656\tLAB11863R\tASPIRIN RESISTANCE\t11862R\tCREATININE, RANDOM URINE\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VASCULAR CLINIC ORDERABLES\t519228\tVSC02\tVSC CAROTID VERTEBRAL ARTERY ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t223710\t0\tNone\tNone\t12\t2684520\t9.96E+11
+VASCULAR CLINIC ORDERABLES\t519229\tVSC03\tVSC ARTERIAL EXAM/ABI\'S\tPDF\tPDF RESULT\t#VALUE!\t560326.5517\t0\tNone\tNone\t58\t32498940\t1.45E+14
+VASCULAR CLINIC ORDERABLES\t519230\tVSC04\tVSC GRAFT W ABI\'S ART DUP\tPDF\tPDF RESULT\t#VALUE!\t247685.4545\t0\tNone\tNone\t22\t5449080\t3.01E+12
+VASCULAR CLINIC ORDERABLES\t519231\tVSC05\tVSC VENOUS ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t397694.5714\t0\tNone\tNone\t70\t27838620\t5.79E+13
+VASCULAR CLINIC ORDERABLES\t519232\tVSC06\tVSC ABDO AORTA ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t2182770\t0\tNone\tNone\t4\t8731080\t6.51E+13
+VASCULAR CLINIC ORDERABLES\t519233\tVSC07\tVSC ILIAC ARTERY ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t272950\t0\tNone\tNone\t6\t1637700\t6.97E+11
+VASCULAR CLINIC ORDERABLES\t519234\tVSC08\tVSC RENAL ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t670069.0909\t0\tNone\tNone\t11\t7370760\t1.92E+13
+VASCULAR CLINIC ORDERABLES\t519235\tVSC09\tVSC MESENTERIC ULTRASOUND\tPDF\tPDF RESULT\t#VALUE!\t241540\t0\tNone\tNone\t3\t724620\t1.91E+11
+VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tDCL3\tTEST INFORMATION\t#VALUE!\t553215\t0\tNone\tNone\t4\t2212860\t1.33E+12
+VIROLOGY ORDERABLES\t472014\tLABCLGCT2\tCHLAMYDIA GC, PAP FLUID\tSDES3\tSPECIMEN\t#VALUE!\t553215\t0\tNone\tNone\t4\t2212860\t1.33E+12
+VIROLOGY ORDERABLES\t472042\tLABCMVQL\tCMV DNA PCR QUAL, NON-BLOOD\tCMVLIP\tTEST DESCRIPTION\t#VALUE!\t165780\t0\tNone\tNone\t3\t497340\t92352106800
+VIROLOGY ORDERABLES\t472043\tLABCMVQT\tCMV PCR QUANTITATIVE\tCMVTIP\tTEST DESCRIPTION\t#VALUE!\t159127.5\t0\tNone\tNone\t8\t1273020\t2.10E+11
+VIROLOGY ORDERABLES\t472198\tLABDVERS\tRSV RAPID ANTIGEN, DIRECT EIA\tRSV\tRESPIRATORY SYNCYTIAL VIRUS\t#VALUE!\t150\t0\tNone\tNone\t2\t300\t5490000
+VIROLOGY ORDERABLES\t472201\tLABDVERTV\tROTAVIRUS EIA (DIRECT VIRAL EXAM)\tDVE\tRESULTS\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472509\tLABHBPQD\tHEPATITIS B PCR QUANTITATIVE DILUTED\tHBDIPR\tTEST DESCRIPTION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t472512\tLABHCVG\tHEPATITIS C VIRUS GENOTYPING\tHCGG\tHCV GENOTYPE\t#VALUE!\t477894\t0\tNone\tNone\t10\t4778940\t2.88E+12
+VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCGG\tHCV GENOTYPE\t#VALUE!\t251340\t0\tNone\tNone\t1\t251340\t63171795600
+VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCVGDI\tDISCLAIMER\t#VALUE!\t948855\t0\tNone\tNone\t8\t7590840\t8.74E+12
+VIROLOGY ORDERABLES\t472512\tLABHCVGE\tHEPATITIS C VIRUS GENOTYPING\tHCVGMT\tMETHOD\t#VALUE!\t948855\t0\tNone\tNone\t8\t7590840\t8.74E+12
+VIROLOGY ORDERABLES\t472560\tLABHIVDS\tHIV - 1 AG DONOR SCREEN QUALITATIVE\tHNOT\tHIV-1 AG DONOR SCRN QUAL. NOTE\t#VALUE!\t478889.2308\t0\tNone\tNone\t39\t18676680\t1.07E+13
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV - 1 GENOTYPING\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t583060\t0\tNone\tNone\t3\t1749180\t1.27E+12
+VIROLOGY ORDERABLES\t472561\tLABAVRT\tHIV GENO EXPR ANTIVIR RESIST\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t616970\t0\tNone\tNone\t6\t3701820\t2.62E+12
+VIROLOGY ORDERABLES\t472567\tLABHPVHR2\tHPV DNA PROBE HIGH RISK\tHPTI\tTEST INFORMATION\t#VALUE!\t925207.0588\t0\tNone\tNone\t17\t15728520\t1.88E+13
+VIROLOGY ORDERABLES\t472567\tLABHPVHR2\tHPV DNA PROBE HIGH RISK\tSDES6\tSPECIMEN\t#VALUE!\t925207.0588\t0\tNone\tNone\t17\t15728520\t1.88E+13
+VIROLOGY ORDERABLES\t472569\tLABHSVM\tHSV 1 AND 2 IGM\tHSV2M\tHSV 2 IGM, IFA\t#VALUE!\t282932.3077\t0\tNone\tNone\t52\t14712480\t4.92E+12
+VIROLOGY ORDERABLES\t472574\tLABYHMPVP\tHUMAN METAPNEUMOVIRUS PCR\tYHMP1\tHUMAN METAPNEUMOVIRUS PCR\t#VALUE!\t455820\t0\tNone\tNone\t1\t455820\t2.08E+11
+VIROLOGY ORDERABLES\t473283\tLABRUBM\tRUBELLA AB IGM\tRUBM1\tRUBELLA AB, IGM\t#VALUE!\t258660\t0\tNone\tNone\t1\t258660\t66904995600
+VIROLOGY ORDERABLES\t473283\tLABYRUBM\tRUBELLA AB IGM\tRUBM1\tRUBELLA AB, IGM\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t496777\tLABHIVA2\tHIV - 2 AB\tHIV2\tHIV-2 AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t496777\tLABHIVA2\tHIV - 2 AB\tHTL2\tHIV-2 AB NOTE:\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tADOV\tADENOVIRUS DNA, QL PCR\t#VALUE!\t107295\t0\tNone\tNone\t4\t429180\t62698006800
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA PCR QUAL\tSOURC3\tADENOVIRUS SOURCE\t#VALUE!\t107295\t0\tNone\tNone\t4\t429180\t62698006800
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS DNA, QUALITATIVE PCR\tADOV\tADENOVIRUS DNA, QL PCR\t#VALUE!\t304440\t0\tNone\tNone\t5\t1522200\t6.91E+11
+VIROLOGY ORDERABLES\t497325\tLABADVQL\tADENOVIRUS PCR QUAL\tADOV\tADENOVIRUS DNA, QL PCR\t#VALUE!\t114450\t0\tNone\tNone\t2\t228900\t28059325200
+VIROLOGY ORDERABLES\t497424\tLABHTLVB\t HTLV-1/2 AB WESTERN BLOT\tHTWB\tHTLV I/II AB, WESTERN BLOT\t#VALUE!\t274132.5\t0\tNone\tNone\t16\t4386120\t1.32E+12
+VIROLOGY ORDERABLES\t497426\tLABHIVDAG\tHIV - 1 DIRECT AG (ICD)\tINTRE\tINTERPRETATION\t#VALUE!\t410679.1304\t0\tNone\tNone\t23\t9445620\t4.14E+12
+VIROLOGY ORDERABLES\t497450\tLABBKVPC\tBK VIRUS DNA QUANTITATION\tBKQ\tBKV DNA QUANTITATION\t#VALUE!\t168960\t0\tNone\tNone\t1\t168960\t28547481600
+VIROLOGY ORDERABLES\t497452\tLABHHV6QL\tHERPES VIRAL 6 DNA QUALITATIVE\tHVDNA\tHERPES VIRUS 6 DNA\t#VALUE!\t184905\t0\tNone\tNone\t4\t739620\t1.80E+11
+VIROLOGY ORDERABLES\t497468\tLABBKDNA\tBK VIRUS DNA PCR\tBKD\tBK VIRUS DNA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t541685\tLABHSVG\tHSV 1/2 TYPE-SPECIFIC IGG\tHSV2G\tHSV 2, IGG INDEX\t#VALUE!\t262579.4118\t0\tNone\tNone\t34\t8927700\t4.76E+12
+VIROLOGY ORDERABLES\t590497\tLABADVQT\tADENOVIRUS REAL TIME PCR,QUANTITATIVE\tADVTIP\tTEST DESCRIPTION\t#VALUE!\t98320\t0\tNone\tNone\t3\t294960\t33751087200
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV BY RAPID PCR, CSF\tEBV\tEBV BY PCR\t#VALUE!\t404160\t0\tNone\tNone\t3\t1212480\t6.00E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV BY RAPID PCR, CSF\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t404160\t0\tNone\tNone\t3\t1212480\t6.00E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV BY RAPID PCR, CSF\tYEPB4\tREPORT STATUS\t#VALUE!\t404160\t0\tNone\tNone\t3\t1212480\t6.00E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t329575.7143\t0\tNone\tNone\t14\t4614060\t1.91E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tEBV\tEBV BY PCR\t#VALUE!\t478140\t0\tNone\tNone\t5\t2390700\t1.45E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t478140\t0\tNone\tNone\t5\t2390700\t1.45E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tYEBP3\tSPECIAL INFORMATION\t#VALUE!\t650850\t0\tNone\tNone\t2\t1301700\t1.05E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, CSF\tYEPB4\tREPORT STATUS\t#VALUE!\t478140\t0\tNone\tNone\t5\t2390700\t1.45E+12
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN BARR VIRUS PCR, FLUIDS\tYEPB4\tREPORT STATUS\t#VALUE!\t326800\t0\tNone\tNone\t3\t980400\t3.42E+11
+VIROLOGY ORDERABLES\t621215\tLABYEBPCR\tEPSTEIN-BARR VIRUS PCR, NON-BLOOD\tYEBP1\tSPECIMEN SOURCE\t#VALUE!\t295741.791\t0\tNone\tNone\t67\t19814700\t7.67E+12
+VIROLOGY ORDERABLES\t633362\tLABMUMPSG\tMUMPS IGG\tMUMDES\tTEST DESCRIPTION\t#VALUE!\t165986.6667\t0\tNone\tNone\t9\t1493880\t2.85E+11
+VIROLOGY ORDERABLES\t670955\tLAB11020R\tEB VIRUS EARLY AG (R+D) IGG\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t#VALUE!\t303340\t0\tNone\tNone\t3\t910020\t4.00E+11
+VIROLOGY ORDERABLES\t670955\tLABEAIGG\tEB VIRUS EARLY AG (R+D) IGG\t11165R\tEB VIRUS EARLY AG (R+D) IGG\t#VALUE!\t240660\t0\tNone\tNone\t1\t240660\t57917235600
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEB NUCLEAR AG AB\t11166R\tEB NUCLEAR AB\t#VALUE!\t201060\t0\tNone\tNone\t1\t201060\t40425123600
+VIROLOGY ORDERABLES\t670957\tLABEBNABY\tEBV NUCLEAR ANTIGEN IGG\tEBNDES\tEBNDES\t#VALUE!\t687360\t0\tNone\tNone\t2\t1374720\t9.65E+11
+VIROLOGY ORDERABLES\t670959\tLABVCAIGM\tEB VIRAL CAPSID AG IGM AB\t11167R\tEB VCA IGM AB\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t670961\tLABVCAIGG\tEB VIRAL CAPSID AG IGG AB\t11168R\tEB VCA IGG AB\t#VALUE!\t201060\t0\tNone\tNone\t1\t201060\t40425123600
+VIROLOGY ORDERABLES\t744406\tLABAVIN\tHIV-1 INTEGRASE RESISTANCE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t2183640\t0\tNone\tNone\t1\t2183640\t4.77E+12
+VIROLOGY ORDERABLES\t862853\tLABFLUAPC\tFLU A PCR\tFLUADI\tDISCLAIMER\t#VALUE!\t142990.5882\t0\tNone\tNone\t17\t2430840\t5.55E+11
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1DI\tDISCLAIMER\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H274Y MUTATION\tH1N1R\t2009 INFLUENZA A(H1N1) RNA\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1DI\tDISCLAIMER\t#VALUE!\t259084.5\t0\tNone\tNone\t40\t10363380\t6.39E+12
+VIROLOGY ORDERABLES\t864646\tLABH1N1PC\t2009 H1N1 PCR, H275Y MUTATION\tH1N1IN\tINTERPRETATION\t#VALUE!\t259084.5\t0\tNone\tNone\t40\t10363380\t6.39E+12
+VIROLOGY ORDERABLES\t870899\tLABVIRSO1\tMISC. VIROLOGY TEST\tVIROR\tRESULT\t#VALUE!\t823753.7143\t0\tNone\tNone\t35\t28831380\t3.01E+13
+VIROLOGY ORDERABLES\t876725\tLABDVEPI1\tPARA 1 DFA\tPI1SP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876727\tLABDVEPI2\tPARA 2 DFA\tPI2SP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876729\tLABDVEPI3\tPARA 3 DFA\tPI3SP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876731\tLABDVEMP\tMPV DFA\tMPSP\tSPECIMEN\t#VALUE!\t987120\t0\tNone\tNone\t1\t987120\t9.74E+11
+VIROLOGY ORDERABLES\t876737\tLABTOXIM\tTOXOPLASMA IGM\tTOXMDS\tTEST DESCRIPTION\t#VALUE!\t177480\t0\tNone\tNone\t7\t1242360\t2.54E+11
+VIROLOGY ORDERABLES\t876739\tLABTOXGM\tTOXOPLASMA IGM & IGG\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t0\t0\tNone\tNone\t1\t0\t0
+VIROLOGY ORDERABLES\t876740\tINACTIVE 4\tTOXOPLASMA IGG IGM\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t186060\t0\tNone\tNone\t1\t186060\t34618323600
+VIROLOGY ORDERABLES\t876740\tLABTOXGM\tTOXOPLASMA IGG IGM\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t163250\t0\tNone\tNone\t6\t979500\t1.86E+11
+VIROLOGY ORDERABLES\t876741\tLABTOXGMR\tTOXO PREGNANCY PANEL\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t148380\t0\tNone\tNone\t1\t148380\t22016624400
+VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVDI\tDISCLAIMER\t#VALUE!\t41910\t0\tNone\tNone\t2\t83820\t6616040400
+VIROLOGY ORDERABLES\t879418\tLABMPVPCR\tMPV PCR\tMPVSP\tSPECIMEN\t#VALUE!\t41910\t0\tNone\tNone\t2\t83820\t6616040400
+VIROLOGY ORDERABLES\t879420\tLABRMPCR\tRSV/MPV PCR\tRSMPDI\tDISCLAIMER\t#VALUE!\t86400\t0\tNone\tNone\t3\t259200\t28951264800
+VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVDI\tDISCLAIMER\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVINT\tINTERPRETATION\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t883176\tLABHPVPCR\tHPV 16/18 TYPING\tHPVMT\tMETHOD\t#VALUE!\t#VALUE!\t0\tNone\tNone\t0\tNone\tNone
+VIROLOGY ORDERABLES\t893882\tLABCMVAV\tCMV IGG AVIDITY\tCMVAVD\tTEST DESCRIPTION\t#VALUE!\t289530\t0\tNone\tNone\t2\t579060\t2.76E+11
+VIROLOGY ORDERABLES\t893900\tLABTOXATP\tTOXO AB TRANSPLANT PNL, SERUM\tTOXDES\tTEST DESCRIPTION\t#VALUE!\t137520\t0\tNone\tNone\t5\t687600\t1.11E+11
+VIROLOGY ORDERABLES\t966895\tLABVCULU\tVIRAL CULTURE, URINE\tCULT\tCULT/OTHER RSLT:\t#VALUE!\t1267920\t0\tNone\tNone\t1\t1267920\t1.61E+12
+VIROLOGY ORDERABLES\t1007403\tLABVZPAN\tVZV IGG IGM PANEL\tVZVDS\tVZV IGM TEST DESCRIPTION\t#VALUE!\t299835.8824\t0\tNone\tNone\t34\t10194420\t3.75E+12
+VIROLOGY ORDERABLES\t1050827\tLABHCVPCX\tHEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING\tHCVIPX\tTEST DESCRIPTION\t#VALUE!\t425910\t0\tNone\tNone\t2\t851820\t3.70E+11
+VIROLOGY ORDERABLES\t1050829\tLABHCVGTP\tHEPATITIS C (HCV) GENOTYPE\tHCVGTD\tTEST DESCRIPTION\t#VALUE!\t766260\t0\tNone\tNone\t2\t1532520\t1.95E+12
+"""
+
+if __name__ == "__main__":
     main(sys.argv);
\ No newline at end of file
diff --git a/medinfo/cpoe/cpoeSim/simdata/restoreSimTables.sh b/medinfo/cpoe/cpoeSim/simdata/restoreSimTables.sh
new file mode 100644
index 00000000..92967f23
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/restoreSimTables.sh
@@ -0,0 +1,31 @@
+export DB_HOST=localhost
+export DB_PORT=5432
+export DB_DSN=medinfo-5year-time
+export DB_USER=jonc101
+
+# Prepare schema of empty tables ready to receive data
+psql -f cpoeSim.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+
+# Add mock clinical_item values for simulated recommender
+psql -f extraClinicalItems.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+
+# Restore the prior dumps of data table contents
+# Core simulation infrastructure
+psql -f sim_note_type.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_result.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_order_result_map.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+# Case specific data
+psql -f sim_state.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_note.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+# Though there are "default" state results that are more of a core simulation infrastructure results
+psql -f sim_state_result.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_state_transition.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+
+# Insert sim Grading key
+psql -f sim_grading_key.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+
+# Individual test user interaction behavior (but some default ones are necessary for infrastructure)
+psql -f sim_user.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_patient.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_patient_order.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
+psql -f sim_patient_state.dump.sql -h $DB_HOST -p $DB_PORT -U $DB_USER $DB_DSN
diff --git a/medinfo/cpoe/cpoeSim/simdata/revertSimTables.sh b/medinfo/cpoe/cpoeSim/simdata/revertSimTables.sh
new file mode 100755
index 00000000..ffec1b97
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/revertSimTables.sh
@@ -0,0 +1,12 @@
+export DB_HOST=localhost
+export DB_PORT=5432
+export DB_DSN=stride_inpatient_2008_2014
+export DB_USER=jonc101
+
+psql -U $DB_USER -d $DB_DSN -h $DB_HOST -p $DB_PORT -c "DROP TABLE IF EXISTS sim_user, sim_note, sim_note_type, sim_patient, sim_patient_order, sim_state_transition, sim_state, sim_patient_state, sim_result, sim_state_result, sim_order_result_map;"
+
+# Drop the extra clinical items added, so they can be readded without conflict
+psql -U $DB_USER -d $DB_DSN -h $DB_HOST -p $DB_PORT -c "DELETE FROM item_collection_item WHERE item_collection_item_id IN (-100);"
+psql -U $DB_USER -d $DB_DSN -h $DB_HOST -p $DB_PORT -c "DELETE FROM item_collection WHERE item_collection_id IN (-100);"
+psql -U $DB_USER -d $DB_DSN -h $DB_HOST -p $DB_PORT -c "DELETE FROM clinical_item_association WHERE clinical_item_association_id IN (-150,-160);"
+psql -U $DB_USER -d $DB_DSN -h $DB_HOST -p $DB_PORT -c "DELETE FROM clinical_item WHERE clinical_item_id IN (-100);"
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_grading_key.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_grading_key.dump.sql
new file mode 100644
index 00000000..99b2a6db
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_grading_key.dump.sql
@@ -0,0 +1,4936 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.9
+-- Dumped by pg_dump version 9.6.9
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_grading_key; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_grading_key (sim_grading_key_id, sim_grader_id, sim_state_id, sim_case_name, clinical_item_id, score, confidence, group_name, comment) FROM stdin;
+9597	Panel Average	40	atrial_fibrillation	45977	3	\N	\N	\N
+10406	Andre Kumar	40	atrial_fibrillation	45977	0	4	Weight	\N
+10407	Jonathan Chen	40	atrial_fibrillation	45977	0	2	\N	\N
+10408	Lisa Shieh	40	atrial_fibrillation	45977	0	3	\N	\N
+10409	Panel Consensus	40	atrial_fibrillation	45977	0	\N	\N	\N
+8888	Andre Kumar	41	atrial_fibrillation	45977	5	3	Nursing	Only if combind with enoxaparin
+8889	Jason Hom	41	atrial_fibrillation	45977	5	4	\N	\N
+9442	Panel Average	41	atrial_fibrillation	45977	4	\N	\N	\N
+9472	Panel Consensus	41	atrial_fibrillation	45977	4	\N	\N	\N
+9620	Lisa Shieh	41	atrial_fibrillation	45977	3	3	\N	\N
+10410	Jonathan Chen	41	atrial_fibrillation	45977	0	2	\N	\N
+11392	Jonathan Chen	43	atrial_fibrillation	46605	0	3	\N	Irregular rhythm, so adenosine not so relevant
+8333	Andre Kumar	41	atrial_fibrillation	45814	8	5	Admit Order	\N
+8334	Panel Average	41	atrial_fibrillation	45814	8	\N	\N	\N
+10411	Panel Consensus	41	atrial_fibrillation	45814	0	\N	\N	\N
+11271	Jonathan Chen	41	atrial_fibrillation	65641	0	3	\N	\N
+10412	Jonathan Chen	40	atrial_fibrillation	35968	0	1	\N	Still some negative inotropy?
+11106	Andre Kumar	40	atrial_fibrillation	35968	-5	3	Rhythm Control	\N
+11107	Jason Hom	40	atrial_fibrillation	35968	-5	5	\N	\N
+11108	Lisa Shieh	40	atrial_fibrillation	35968	-5	3	\N	\N
+11109	Panel Average	40	atrial_fibrillation	35968	-5	\N	\N	\N
+11110	Panel Consensus	40	atrial_fibrillation	35968	-5	\N	\N	\N
+10413	Jonathan Chen	43	atrial_fibrillation	35968	0	1	\N	Still some negative inotropy?
+11111	Lisa Shieh	43	atrial_fibrillation	35968	-5	3	\N	\N
+11179	Panel Average	43	atrial_fibrillation	35968	-8	\N	\N	\N
+11182	Andre Kumar	43	atrial_fibrillation	35968	-8	4	Rhythm Control	\N
+11183	Panel Consensus	43	atrial_fibrillation	35968	-8	\N	\N	\N
+11211	Jason Hom	43	atrial_fibrillation	35968	-10	5	\N	\N
+10414	Jonathan Chen	43	atrial_fibrillation	44352	0	2	\N	\N
+11112	Lisa Shieh	43	atrial_fibrillation	44352	-5	3	\N	\N
+11184	Panel Consensus	43	atrial_fibrillation	44352	-8	\N	\N	\N
+11200	Panel Average	43	atrial_fibrillation	44352	-8	\N	\N	\N
+11404	Jonathan Chen	41	atrial_fibrillation	47146	1	2	Aspirin	\N
+8890	Lisa Shieh	40	atrial_fibrillation	44206	5	5	\N	\N
+9947	Jason Hom	40	atrial_fibrillation	44206	2	4	\N	\N
+9948	Panel Consensus	40	atrial_fibrillation	44206	2	\N	\N	\N
+10206	Panel Average	40	atrial_fibrillation	44206	2	\N	\N	\N
+10238	Jonathan Chen	40	atrial_fibrillation	44206	1	2	\N	Troponin mildly elevated so they're treating like NSTEMI, but really is Type 2 demand from the Afib
+10994	Andre Kumar	40	atrial_fibrillation	44206	-2	4	Anticoagulants	\N
+8468	Lisa Shieh	41	atrial_fibrillation	44206	7	3	\N	\N
+9926	Panel Average	41	atrial_fibrillation	44206	2	\N	\N	\N
+9949	Panel Consensus	41	atrial_fibrillation	44206	2	\N	\N	\N
+10239	Jonathan Chen	41	atrial_fibrillation	44206	1	2	Aspirin	Troponin mildly elevated so they're treating like NSTEMI, but really is Type 2 demand from the Afib
+10415	Andre Kumar	41	atrial_fibrillation	44206	0	1	Anticoagulants	-3points if combined wiht other anticoagulants
+10416	Jason Hom	41	atrial_fibrillation	44206	0	4	\N	\N
+11285	Jonathan Chen	43	atrial_fibrillation	44206	1	2	Aspirin	\N
+11241	Jonathan Chen	43	atrial_fibrillation	44315	1	2	Aspirin	\N
+8891	Lisa Shieh	41	atrial_fibrillation	44240	5	3	\N	\N
+9950	Panel Consensus	41	atrial_fibrillation	44240	2	\N	\N	\N
+10207	Panel Average	41	atrial_fibrillation	44240	2	\N	\N	\N
+10240	Jonathan Chen	41	atrial_fibrillation	44240	1	2	\N	\N
+10417	Andre Kumar	41	atrial_fibrillation	44240	0	5	Statin	\N
+10418	Jason Hom	41	atrial_fibrillation	44240	0	4	\N	\N
+9621	Lisa Shieh	43	atrial_fibrillation	45901	3	3	\N	\N
+9951	Jonathan Chen	43	atrial_fibrillation	45901	2	2	\N	\N
+10241	Andre Kumar	43	atrial_fibrillation	45901	1	2	Blood Culture Not Infection	\N
+10242	Panel Consensus	43	atrial_fibrillation	45901	1	\N	\N	\N
+10380	Panel Average	43	atrial_fibrillation	45901	1	\N	\N	\N
+10995	Jason Hom	43	atrial_fibrillation	45901	-2	5	\N	\N
+8892	Jason Hom	40	atrial_fibrillation	45919	5	4	\N	\N
+9952	Panel Consensus	40	atrial_fibrillation	45919	2	\N	\N	\N
+10208	Panel Average	40	atrial_fibrillation	45919	2	\N	\N	\N
+10243	Jonathan Chen	40	atrial_fibrillation	45919	1	2	Blood Gas	\N
+10419	Andre Kumar	40	atrial_fibrillation	45919	0	3	Blood Gas	\N
+10420	Lisa Shieh	40	atrial_fibrillation	45919	0	3	\N	\N
+8469	Panel Consensus	40	atrial_fibrillation	45887	7	\N	\N	\N
+8657	Panel Average	40	atrial_fibrillation	45887	7	\N	\N	\N
+8893	Andre Kumar	40	atrial_fibrillation	45887	5	2	Metabolic Panel	\N
+8894	Lisa Shieh	40	atrial_fibrillation	45887	5	3	\N	\N
+10421	Jonathan Chen	40	atrial_fibrillation	45887	0	2	\N	\N
+8895	Jason Hom	43	atrial_fibrillation	45887	5	5	\N	\N
+8896	Lisa Shieh	43	atrial_fibrillation	45887	5	3	\N	\N
+9443	Panel Average	43	atrial_fibrillation	45887	4	\N	\N	\N
+9473	Panel Consensus	43	atrial_fibrillation	45887	4	\N	\N	\N
+9622	Andre Kumar	43	atrial_fibrillation	45887	3	2	Metabolic Panel	\N
+10422	Jonathan Chen	43	atrial_fibrillation	45887	0	2	\N	\N
+11315	Jonathan Chen	40	atrial_fibrillation	41870	0	5	\N	\N
+10423	Andre Kumar	40	atrial_fibrillation	31164	0	5	UNK	? Is this an order set?
+10424	Panel Average	40	atrial_fibrillation	31164	0	\N	\N	\N
+8300	Panel Average	40	atrial_fibrillation	45827	8	\N	\N	\N
+8335	Panel Consensus	40	atrial_fibrillation	45827	8	\N	\N	\N
+8897	Andre Kumar	40	atrial_fibrillation	45827	5	5	ECG + Monitoring	\N
+8898	Jonathan Chen	40	atrial_fibrillation	45827	5	3	\N	\N
+11461	Jonathan Chen	41	atrial_fibrillation	45827	5	3	\N	\N
+8698	Panel Consensus	40	atrial_fibrillation	45793	6	\N	\N	\N
+8841	Panel Average	40	atrial_fibrillation	45793	6	\N	\N	\N
+8899	Lisa Shieh	40	atrial_fibrillation	45793	5	3	\N	\N
+9953	Andre Kumar	40	atrial_fibrillation	45793	2	3	CBC	\N
+9954	Jonathan Chen	40	atrial_fibrillation	45793	2	3	CBC	\N
+8470	Lisa Shieh	41	atrial_fibrillation	45793	7	3	\N	\N
+8900	Jason Hom	41	atrial_fibrillation	45793	5	4	\N	\N
+9444	Panel Average	41	atrial_fibrillation	45793	4	\N	\N	\N
+9474	Panel Consensus	41	atrial_fibrillation	45793	4	\N	\N	\N
+9955	Jonathan Chen	41	atrial_fibrillation	45793	2	3	CBC	\N
+10244	Andre Kumar	41	atrial_fibrillation	45793	1	4	CBC	\N
+8699	Panel Consensus	43	atrial_fibrillation	45793	6	\N	\N	\N
+8901	Jason Hom	43	atrial_fibrillation	45793	5	5	\N	\N
+8902	Lisa Shieh	43	atrial_fibrillation	45793	5	3	\N	\N
+9475	Panel Average	43	atrial_fibrillation	45793	4	\N	\N	\N
+9956	Andre Kumar	43	atrial_fibrillation	45793	2	3	CBC	\N
+9957	Jonathan Chen	43	atrial_fibrillation	45793	2	3	CBC	\N
+8700	Panel Consensus	40	atrial_fibrillation	45788	6	\N	\N	\N
+8842	Panel Average	40	atrial_fibrillation	45788	6	\N	\N	\N
+8903	Lisa Shieh	40	atrial_fibrillation	45788	5	3	\N	\N
+9958	Andre Kumar	40	atrial_fibrillation	45788	2	3	CBC	\N
+9959	Jonathan Chen	40	atrial_fibrillation	45788	2	3	CBC	\N
+8471	Lisa Shieh	41	atrial_fibrillation	45788	7	3	\N	\N
+8701	Panel Consensus	41	atrial_fibrillation	45788	6	\N	\N	\N
+8904	Jason Hom	41	atrial_fibrillation	45788	5	4	\N	\N
+9445	Panel Average	41	atrial_fibrillation	45788	4	\N	\N	\N
+9960	Jonathan Chen	41	atrial_fibrillation	45788	2	3	CBC	\N
+10245	Andre Kumar	41	atrial_fibrillation	45788	1	4	CBC	\N
+9623	Lisa Shieh	40	atrial_fibrillation	50400	3	3	\N	\N
+10425	Jason Hom	40	atrial_fibrillation	50400	0	4	\N	\N
+10426	Jonathan Chen	40	atrial_fibrillation	50400	0	2	\N	\N
+10965	Panel Average	40	atrial_fibrillation	50400	-1	\N	\N	\N
+10970	Panel Consensus	40	atrial_fibrillation	50400	-1	\N	\N	\N
+11113	Andre Kumar	40	atrial_fibrillation	50400	-5	2	Consult	"Delays care no expertise aside from sedation (which can be given by ER providers)"
+8905	Lisa Shieh	43	atrial_fibrillation	50400	5	3	\N	\N
+9476	Panel Average	43	atrial_fibrillation	50400	4	\N	\N	\N
+9477	Panel Consensus	43	atrial_fibrillation	50400	4	\N	\N	\N
+10427	Jonathan Chen	43	atrial_fibrillation	50400	0	2	\N	\N
+11075	Andre Kumar	43	atrial_fibrillation	50400	-3	3	Consult	no clear benefit of consulting anes
+8472	Lisa Shieh	40	atrial_fibrillation	49251	7	3	\N	\N
+8473	Panel Consensus	40	atrial_fibrillation	49251	7	\N	\N	\N
+8658	Panel Average	40	atrial_fibrillation	49251	7	\N	\N	\N
+9624	Andre Kumar	40	atrial_fibrillation	49251	3	4	Consult	\N
+10428	Jonathan Chen	40	atrial_fibrillation	49251	0	2	\N	\N
+8474	Lisa Shieh	41	atrial_fibrillation	49251	7	3	\N	\N
+8906	Jason Hom	41	atrial_fibrillation	49251	5	4	\N	\N
+8907	Panel Average	41	atrial_fibrillation	49251	5	\N	\N	\N
+8908	Panel Consensus	41	atrial_fibrillation	49251	5	\N	\N	\N
+9625	Andre Kumar	41	atrial_fibrillation	49251	3	4	Consult	\N
+10429	Jonathan Chen	41	atrial_fibrillation	49251	0	2	\N	\N
+8702	Panel Average	43	atrial_fibrillation	49251	6	\N	\N	\N
+8909	Lisa Shieh	43	atrial_fibrillation	49251	5	3	\N	\N
+9961	Andre Kumar	43	atrial_fibrillation	49251	2	3	Consult	delays care, but cardiologist would likely give correct reccomendations
+10430	Jonathan Chen	43	atrial_fibrillation	49251	0	2	\N	\N
+11452	Jonathan Chen	41	atrial_fibrillation	65695	0	2	\N	\N
+11076	Andre Kumar	43	atrial_fibrillation	65695	-3	3	Consult	delays care
+11077	Panel Average	43	atrial_fibrillation	65695	-3	\N	\N	\N
+8475	Lisa Shieh	43	atrial_fibrillation	61323	7	3	\N	\N
+8680	Panel Average	43	atrial_fibrillation	61323	6	\N	\N	\N
+8703	Panel Consensus	43	atrial_fibrillation	61323	6	\N	\N	\N
+9962	Andre Kumar	43	atrial_fibrillation	61323	2	3	Consult	delays care, but ICU would likely give correct reccomendations
+10431	Jonathan Chen	43	atrial_fibrillation	61323	0	2	\N	\N
+10432	Jonathan Chen	40	atrial_fibrillation	50098	0	2	CT Head	\N
+10433	Lisa Shieh	40	atrial_fibrillation	50098	0	5	\N	\N
+11114	Jason Hom	40	atrial_fibrillation	50098	-5	5	\N	\N
+11115	Panel Average	40	atrial_fibrillation	50098	-5	\N	\N	\N
+11116	Panel Consensus	40	atrial_fibrillation	50098	-5	\N	\N	\N
+10434	Jonathan Chen	40	atrial_fibrillation	45983	0	2	CT Head	\N
+10435	Lisa Shieh	40	atrial_fibrillation	45983	0	3	\N	\N
+11117	Jason Hom	40	atrial_fibrillation	45983	-5	4	\N	\N
+11118	Panel Average	40	atrial_fibrillation	45983	-5	\N	\N	\N
+11119	Panel Consensus	40	atrial_fibrillation	45983	-5	\N	\N	\N
+10436	Jonathan Chen	40	atrial_fibrillation	49965	0	2	CT Head	\N
+10437	Lisa Shieh	40	atrial_fibrillation	49965	0	5	\N	\N
+11120	Jason Hom	40	atrial_fibrillation	49965	-5	5	\N	\N
+11121	Panel Average	40	atrial_fibrillation	49965	-5	\N	\N	\N
+11122	Panel Consensus	40	atrial_fibrillation	49965	-5	\N	\N	\N
+10438	Andre Kumar	40	atrial_fibrillation	48871	0	3	D-dimer	\N
+10439	Panel Average	40	atrial_fibrillation	48871	0	\N	\N	\N
+8236	Panel Average	43	atrial_fibrillation	-100	9	\N	\N	\N
+8476	Andre Kumar	43	atrial_fibrillation	-100	7	5	Cardioversion	Less points for delaying
+8910	Lisa Shieh	40	atrial_fibrillation	44353	5	3	\N	\N
+10440	Jonathan Chen	40	atrial_fibrillation	44353	0	1	\N	This is probably mis-entered Diltiazem (which is a double negative!) Or could it arguably be intended sedation before DCCV? Probably not (that would be midazolam or lorazepam more likely).
+10989	Panel Average	40	atrial_fibrillation	44353	-2	\N	\N	\N
+10996	Panel Consensus	40	atrial_fibrillation	44353	-2	\N	\N	\N
+11123	Andre Kumar	40	atrial_fibrillation	44353	-5	5	Sedation	Patient lethargic and diazepam not good for procedural sedation
+11124	Jason Hom	40	atrial_fibrillation	44353	-5	5	\N	\N
+8911	Jason Hom	41	atrial_fibrillation	45824	5	4	\N	\N
+8912	Lisa Shieh	41	atrial_fibrillation	45824	5	3	\N	\N
+9478	Panel Average	41	atrial_fibrillation	45824	4	\N	\N	\N
+9963	Andre Kumar	41	atrial_fibrillation	45824	2	4	Diet	\N
+10246	Panel Consensus	41	atrial_fibrillation	45824	1	\N	\N	\N
+10441	Jonathan Chen	41	atrial_fibrillation	45824	0	2	\N	\N
+10247	Andre Kumar	43	atrial_fibrillation	45824	1	2	Diet	\N
+10248	Panel Average	43	atrial_fibrillation	45824	1	\N	\N	\N
+8477	Panel Consensus	40	atrial_fibrillation	45811	7	\N	\N	\N
+8659	Panel Average	40	atrial_fibrillation	45811	7	\N	\N	\N
+8913	Andre Kumar	40	atrial_fibrillation	45811	5	5	NPO	\N
+8914	Lisa Shieh	40	atrial_fibrillation	45811	5	5	\N	\N
+9964	Jonathan Chen	40	atrial_fibrillation	45811	2	3	\N	\N
+11321	Jonathan Chen	41	atrial_fibrillation	45811	2	3	\N	\N
+8478	Panel Consensus	43	atrial_fibrillation	45811	7	\N	\N	\N
+8660	Panel Average	43	atrial_fibrillation	45811	7	\N	\N	\N
+8915	Andre Kumar	43	atrial_fibrillation	45811	5	3	NPO	\N
+8916	Jason Hom	43	atrial_fibrillation	45811	5	5	\N	\N
+9965	Jonathan Chen	43	atrial_fibrillation	45811	2	3	\N	\N
+9966	Jonathan Chen	40	atrial_fibrillation	46674	2	3	\N	\N
+10442	Lisa Shieh	40	atrial_fibrillation	46674	0	3	\N	\N
+11100	Panel Average	40	atrial_fibrillation	46674	-4	\N	\N	\N
+11101	Panel Consensus	40	atrial_fibrillation	46674	-4	\N	\N	\N
+11125	Jason Hom	40	atrial_fibrillation	46674	-5	4	\N	\N
+11173	Andre Kumar	40	atrial_fibrillation	46674	-6	5	Rate Control	too slow acting
+11289	Jonathan Chen	43	atrial_fibrillation	46674	-5	3	Negative Inotrope	\N
+11126	Jason Hom	40	atrial_fibrillation	35846	-5	5	\N	\N
+11127	Jonathan Chen	40	atrial_fibrillation	35846	-5	3	Negative Inotrope	\N
+11180	Panel Average	40	atrial_fibrillation	35846	-8	\N	\N	\N
+11185	Andre Kumar	40	atrial_fibrillation	35846	-8	4	Rate Control	\N
+11186	Panel Consensus	40	atrial_fibrillation	35846	-8	\N	\N	\N
+11128	Jonathan Chen	43	atrial_fibrillation	35846	-5	3	Negative Inotrope	\N
+11177	Lisa Shieh	43	atrial_fibrillation	35846	-7	3	\N	\N
+11204	Panel Average	43	atrial_fibrillation	35846	-9	\N	\N	\N
+11205	Panel Consensus	43	atrial_fibrillation	35846	-9	\N	\N	\N
+11078	Jonathan Chen	43	atrial_fibrillation	44393	-3	3	Negative Inotrope	Not as bad as IV negative inotropes, but still not the right time given hypotensive
+11178	Lisa Shieh	43	atrial_fibrillation	44393	-7	3	\N	\N
+11187	Andre Kumar	43	atrial_fibrillation	44393	-8	4	Rate Control	\N
+11188	Panel Consensus	43	atrial_fibrillation	44393	-8	\N	\N	\N
+11201	Panel Average	43	atrial_fibrillation	44393	-8	\N	\N	\N
+9440	Panel Average	10	pulmonary_embolism	44586	5	\N	\N	\N
+10443	Jonathan Chen	43	atrial_fibrillation	44251	0	1	\N	Maybe they think low-output heart failure? No other signs of such elsewhere. Would this make Afib RVR even worse?
+11129	Lisa Shieh	43	atrial_fibrillation	44251	-5	3	\N	\N
+11189	Panel Consensus	43	atrial_fibrillation	44251	-8	\N	\N	\N
+11202	Panel Average	43	atrial_fibrillation	44251	-8	\N	\N	\N
+10444	Andre Kumar	40	atrial_fibrillation	44204	0	5	Unindicated Med	\N
+10445	Jonathan Chen	40	atrial_fibrillation	44204	0	2	\N	What is this doing here?
+10446	Lisa Shieh	40	atrial_fibrillation	44204	0	5	\N	\N
+10990	Panel Average	40	atrial_fibrillation	44204	-2	\N	\N	\N
+10997	Panel Consensus	40	atrial_fibrillation	44204	-2	\N	\N	\N
+11130	Jason Hom	40	atrial_fibrillation	44204	-5	4	\N	\N
+9479	Andre Kumar	43	atrial_fibrillation	45941	4	2	UTOX	\N
+9626	Lisa Shieh	43	atrial_fibrillation	45941	3	3	\N	\N
+9967	Jonathan Chen	43	atrial_fibrillation	45941	2	3	\N	\N
+9968	Panel Consensus	43	atrial_fibrillation	45941	2	\N	\N	\N
+10209	Panel Average	43	atrial_fibrillation	45941	2	\N	\N	\N
+10998	Jason Hom	43	atrial_fibrillation	45941	-2	5	\N	\N
+8237	Panel Average	40	atrial_fibrillation	45866	9	\N	\N	\N
+8238	Panel Consensus	40	atrial_fibrillation	45866	9	\N	\N	\N
+8479	Andre Kumar	40	atrial_fibrillation	45866	7	5	ECG + Monitoring	\N
+8704	Andre Kumar	41	atrial_fibrillation	45866	6	5	ECG + Monitoring	\N
+8866	Panel Average	41	atrial_fibrillation	45866	5	\N	\N	\N
+8917	Jason Hom	41	atrial_fibrillation	45866	5	4	\N	\N
+8918	Lisa Shieh	41	atrial_fibrillation	45866	5	3	\N	\N
+8919	Panel Consensus	41	atrial_fibrillation	45866	5	\N	\N	\N
+11393	Jonathan Chen	43	atrial_fibrillation	45866	10	5	\N	\N
+11335	Jonathan Chen	40	atrial_fibrillation	62176	3	2	Echo	\N
+11295	Jonathan Chen	43	atrial_fibrillation	62176	3	2	Echo	Okay for workup, but not while unstable?
+9627	Lisa Shieh	40	atrial_fibrillation	45963	3	3	\N	\N
+10447	Jonathan Chen	40	atrial_fibrillation	45963	0	1	\N	Reasonable before DCCV IF stable, but since unstable Afib, would take too long
+10999	Panel Consensus	40	atrial_fibrillation	45963	-2	\N	\N	\N
+11070	Panel Average	40	atrial_fibrillation	45963	-2	\N	\N	\N
+11131	Andre Kumar	40	atrial_fibrillation	45963	-5	4	Imaging	"Delays care a fib seems paroxysmal given no PMH"
+11132	Jason Hom	40	atrial_fibrillation	45963	-5	4	\N	\N
+9628	Lisa Shieh	43	atrial_fibrillation	45963	3	3	\N	\N
+10448	Jonathan Chen	43	atrial_fibrillation	45963	0	1	\N	Reasonable before DCCV IF stable, but since unstable Afib, would take too long
+11102	Panel Average	43	atrial_fibrillation	45963	-4	\N	\N	\N
+11103	Panel Consensus	43	atrial_fibrillation	45963	-4	\N	\N	\N
+11133	Andre Kumar	43	atrial_fibrillation	45963	-5	5	Imaging	delays care
+8480	Lisa Shieh	40	atrial_fibrillation	61832	7	3	\N	\N
+9629	Jonathan Chen	40	atrial_fibrillation	61832	3	2	Echo	Good for Afib workup, but less points if doing it while unstable?
+10971	Panel Average	40	atrial_fibrillation	61832	-1	\N	\N	\N
+11134	Andre Kumar	40	atrial_fibrillation	61832	-5	3	Imaging	\N
+11135	Jason Hom	40	atrial_fibrillation	61832	-5	4	\N	\N
+8450	Panel Average	41	atrial_fibrillation	61832	7	\N	\N	\N
+8481	Lisa Shieh	41	atrial_fibrillation	61832	7	3	\N	\N
+8482	Panel Consensus	41	atrial_fibrillation	61832	7	\N	\N	\N
+8920	Andre Kumar	41	atrial_fibrillation	61832	5	5	imaging	\N
+9480	Jonathan Chen	41	atrial_fibrillation	61832	4	2	Echo	\N
+11136	Andre Kumar	43	atrial_fibrillation	61832	-5	5	Imaging	delays care
+11137	Panel Average	43	atrial_fibrillation	61832	-5	\N	\N	\N
+8301	Panel Average	40	atrial_fibrillation	46160	8	\N	\N	\N
+8336	Panel Consensus	40	atrial_fibrillation	46160	8	\N	\N	\N
+8921	Andre Kumar	40	atrial_fibrillation	46160	5	5	Vitals	\N
+10449	Jonathan Chen	40	atrial_fibrillation	46160	0	2	\N	\N
+11138	Jonathan Chen	43	atrial_fibrillation	44248	-5	3	Negative Inotrope	\N
+11139	Lisa Shieh	43	atrial_fibrillation	44248	-5	3	\N	\N
+11190	Panel Consensus	43	atrial_fibrillation	44248	-8	\N	\N	\N
+11203	Panel Average	43	atrial_fibrillation	44248	-8	\N	\N	\N
+8922	Andre Kumar	40	atrial_fibrillation	44297	5	5	Sedation	\N
+8923	Jason Hom	40	atrial_fibrillation	44297	5	4	\N	\N
+8924	Lisa Shieh	40	atrial_fibrillation	44297	5	3	\N	\N
+8925	Panel Average	40	atrial_fibrillation	44297	5	\N	\N	\N
+8926	Panel Consensus	40	atrial_fibrillation	44297	5	\N	\N	\N
+9969	Jonathan Chen	40	atrial_fibrillation	44297	2	2	Pre-DCCV	\N
+8927	Jason Hom	43	atrial_fibrillation	44297	5	5	\N	\N
+9927	Panel Average	43	atrial_fibrillation	44297	2	\N	\N	\N
+9970	Andre Kumar	43	atrial_fibrillation	44297	2	2	Sedation	\N
+9971	Jonathan Chen	43	atrial_fibrillation	44297	2	2	Pre-DCCV	\N
+9972	Panel Consensus	43	atrial_fibrillation	44297	2	\N	\N	\N
+10450	Lisa Shieh	43	atrial_fibrillation	44297	0	3	\N	\N
+10451	Andre Kumar	40	atrial_fibrillation	46230	0	5	Unindicated Med	\N
+10452	Jonathan Chen	40	atrial_fibrillation	46230	0	2	\N	What is this doing here?
+10453	Lisa Shieh	40	atrial_fibrillation	46230	0	3	\N	\N
+10991	Panel Average	40	atrial_fibrillation	46230	-2	\N	\N	\N
+11000	Panel Consensus	40	atrial_fibrillation	46230	-2	\N	\N	\N
+11140	Jason Hom	40	atrial_fibrillation	46230	-5	4	\N	\N
+10454	Jonathan Chen	40	atrial_fibrillation	44004	0	1	\N	Bad if already hypotensive, or maybe think is volume overload causing the Afib?
+11141	Jason Hom	40	atrial_fibrillation	44004	-5	5	\N	\N
+11142	Lisa Shieh	40	atrial_fibrillation	44004	-5	3	\N	\N
+11174	Panel Consensus	40	atrial_fibrillation	44004	-6	\N	\N	\N
+11176	Panel Average	40	atrial_fibrillation	44004	-6	\N	\N	\N
+11206	Andre Kumar	40	atrial_fibrillation	44004	-9	5	Diuretics	\N
+8928	Jason Hom	41	atrial_fibrillation	44004	5	4	\N	\N
+9928	Panel Average	41	atrial_fibrillation	44004	2	\N	\N	\N
+9973	Andre Kumar	41	atrial_fibrillation	44004	2	2	Diuretics	\N
+9974	Panel Consensus	41	atrial_fibrillation	44004	2	\N	\N	\N
+10455	Jonathan Chen	41	atrial_fibrillation	44004	0	1	\N	Bad if already hypotensive, or maybe think is volume overload causing the Afib?
+10456	Lisa Shieh	41	atrial_fibrillation	44004	0	3	\N	\N
+10457	Jonathan Chen	43	atrial_fibrillation	44004	0	1	\N	Bad if already hypotensive, or maybe think is volume overload causing the Afib?
+11143	Lisa Shieh	43	atrial_fibrillation	44004	-5	3	\N	\N
+11181	Panel Average	43	atrial_fibrillation	44004	-8	\N	\N	\N
+11191	Andre Kumar	43	atrial_fibrillation	44004	-8	3	Diuretics	\N
+11192	Panel Consensus	43	atrial_fibrillation	44004	-8	\N	\N	\N
+11381	Jonathan Chen	40	atrial_fibrillation	50343	0	0	\N	\N
+8929	Jason Hom	41	atrial_fibrillation	45797	5	4	\N	\N
+9630	Panel Consensus	41	atrial_fibrillation	45797	3	\N	\N	\N
+9925	Panel Average	41	atrial_fibrillation	45797	3	\N	\N	\N
+10405	Lisa Shieh	41	atrial_fibrillation	45797	0	3	\N	\N
+10458	Andre Kumar	41	atrial_fibrillation	45797	0	3	Diabetes	\N
+10459	Jonathan Chen	41	atrial_fibrillation	45797	0	2	\N	\N
+7546	Jason Hom	40	atrial_fibrillation	44359	10	5	\N	\N
+8483	Panel Consensus	40	atrial_fibrillation	44359	7	\N	\N	\N
+8661	Panel Average	40	atrial_fibrillation	44359	7	\N	\N	\N
+8930	Andre Kumar	40	atrial_fibrillation	44359	5	4	Anticoagulants	\N
+8931	Jonathan Chen	40	atrial_fibrillation	44359	5	3	Anticoagulation	\N
+8932	Lisa Shieh	40	atrial_fibrillation	44359	5	3	\N	\N
+7547	Andre Kumar	41	atrial_fibrillation	44359	10	5	Anticoagulants	No positive points if combined wiht other anticoagulants
+7548	Jason Hom	41	atrial_fibrillation	44359	10	5	\N	\N
+8239	Panel Average	41	atrial_fibrillation	44359	9	\N	\N	\N
+8240	Panel Consensus	41	atrial_fibrillation	44359	9	\N	\N	\N
+8484	Lisa Shieh	41	atrial_fibrillation	44359	7	3	\N	\N
+8933	Jonathan Chen	41	atrial_fibrillation	44359	5	3	Anticoagulation	\N
+7549	Jason Hom	43	atrial_fibrillation	44359	10	5	\N	\N
+8302	Panel Average	43	atrial_fibrillation	44359	8	\N	\N	\N
+8337	Andre Kumar	43	atrial_fibrillation	44359	8	4	Anticoagulants	\N
+8338	Panel Consensus	43	atrial_fibrillation	44359	8	\N	\N	\N
+8485	Lisa Shieh	43	atrial_fibrillation	44359	7	3	\N	\N
+8934	Jonathan Chen	43	atrial_fibrillation	44359	5	3	Anticoagulation	\N
+7550	Jason Hom	40	atrial_fibrillation	46183	10	5	\N	\N
+8681	Panel Average	40	atrial_fibrillation	46183	6	\N	\N	\N
+8705	Panel Consensus	40	atrial_fibrillation	46183	6	\N	\N	\N
+8935	Lisa Shieh	40	atrial_fibrillation	46183	5	3	\N	\N
+9481	Andre Kumar	40	atrial_fibrillation	46183	4	4	Anticoagulants	\N
+10460	Jonathan Chen	40	atrial_fibrillation	46183	0	2	\N	\N
+7551	Jason Hom	40	atrial_fibrillation	63714	10	5	\N	\N
+8682	Panel Average	40	atrial_fibrillation	63714	6	\N	\N	\N
+8706	Panel Consensus	40	atrial_fibrillation	63714	6	\N	\N	\N
+8936	Lisa Shieh	40	atrial_fibrillation	63714	5	\N	\N	\N
+9482	Andre Kumar	40	atrial_fibrillation	63714	4	2	Coags	\N
+10461	Jonathan Chen	40	atrial_fibrillation	63714	0	2	\N	\N
+9975	Andre Kumar	43	atrial_fibrillation	63714	2	3	Anticoagulants	\N
+9976	Panel Average	43	atrial_fibrillation	63714	2	\N	\N	\N
+9977	Andre Kumar	40	atrial_fibrillation	49301	2	4	Blood Gas	\N
+10249	Jonathan Chen	40	atrial_fibrillation	49301	1	2	Blood Gas	\N
+10250	Panel Consensus	40	atrial_fibrillation	49301	1	\N	\N	\N
+10381	Panel Average	40	atrial_fibrillation	49301	1	\N	\N	\N
+10462	Jason Hom	40	atrial_fibrillation	49301	0	5	\N	\N
+10463	Lisa Shieh	40	atrial_fibrillation	49301	0	3	\N	\N
+7552	Jason Hom	40	atrial_fibrillation	45888	10	5	\N	\N
+9598	Panel Average	40	atrial_fibrillation	45888	3	\N	\N	\N
+10251	Jonathan Chen	40	atrial_fibrillation	45888	1	2	Blood Gas	\N
+10252	Panel Consensus	40	atrial_fibrillation	45888	1	\N	\N	\N
+10464	Andre Kumar	40	atrial_fibrillation	45888	0	4	Blood Gas	\N
+10465	Lisa Shieh	40	atrial_fibrillation	45888	0	3	\N	\N
+10253	Jonathan Chen	43	atrial_fibrillation	48732	1	2	Blood Gas	\N
+10254	Panel Consensus	43	atrial_fibrillation	48732	1	\N	\N	\N
+10466	Andre Kumar	43	atrial_fibrillation	48732	0	2	Blood Gases	\N
+10467	Jason Hom	43	atrial_fibrillation	48732	0	5	\N	\N
+10468	Lisa Shieh	43	atrial_fibrillation	48732	0	3	\N	\N
+10469	Panel Average	43	atrial_fibrillation	48732	0	\N	\N	\N
+7553	Jason Hom	40	atrial_fibrillation	45942	10	5	\N	\N
+8937	Panel Average	40	atrial_fibrillation	45942	5	\N	\N	\N
+8938	Panel Consensus	40	atrial_fibrillation	45942	5	\N	\N	\N
+9631	Lisa Shieh	40	atrial_fibrillation	45942	3	3	\N	\N
+9978	Andre Kumar	40	atrial_fibrillation	45942	2	3	Coags	\N
+10470	Jonathan Chen	40	atrial_fibrillation	45942	0	2	\N	\N
+7554	Jason Hom	40	atrial_fibrillation	45838	10	5	\N	\N
+8707	Jonathan Chen	40	atrial_fibrillation	45838	6	4	Troponin	\N
+8708	Panel Average	40	atrial_fibrillation	45838	6	\N	\N	\N
+8709	Panel Consensus	40	atrial_fibrillation	45838	6	\N	\N	\N
+8939	Lisa Shieh	40	atrial_fibrillation	45838	5	3	\N	\N
+9632	Andre Kumar	40	atrial_fibrillation	45838	3	4	Troponin	\N
+8940	Jason Hom	40	atrial_fibrillation	62151	5	4	\N	\N
+8941	Jonathan Chen	40	atrial_fibrillation	62151	5	3	Lactate	\N
+9599	Panel Average	40	atrial_fibrillation	62151	3	\N	\N	\N
+9633	Lisa Shieh	40	atrial_fibrillation	62151	3	3	\N	\N
+9634	Panel Consensus	40	atrial_fibrillation	62151	3	\N	\N	\N
+9979	Andre Kumar	40	atrial_fibrillation	62151	2	2	Lactate	\N
+8942	Jonathan Chen	41	atrial_fibrillation	62151	5	3	Lactate	\N
+9635	Lisa Shieh	41	atrial_fibrillation	62151	3	3	\N	\N
+9980	Andre Kumar	41	atrial_fibrillation	62151	2	2	Lactate	\N
+9981	Panel Consensus	41	atrial_fibrillation	62151	2	\N	\N	\N
+10210	Panel Average	41	atrial_fibrillation	62151	2	\N	\N	\N
+10471	Jason Hom	41	atrial_fibrillation	62151	0	4	\N	\N
+8943	Jason Hom	41	atrial_fibrillation	46011	5	4	\N	\N
+8944	Lisa Shieh	41	atrial_fibrillation	46011	5	3	\N	\N
+9600	Panel Average	41	atrial_fibrillation	46011	3	\N	\N	\N
+9636	Panel Consensus	41	atrial_fibrillation	46011	3	\N	\N	\N
+10472	Andre Kumar	41	atrial_fibrillation	46011	0	2	Lipid Panel	\N
+10473	Jonathan Chen	41	atrial_fibrillation	46011	0	2	\N	\N
+8945	Jason Hom	41	atrial_fibrillation	61837	5	4	\N	\N
+8946	Lisa Shieh	41	atrial_fibrillation	61837	5	3	\N	\N
+9601	Panel Average	41	atrial_fibrillation	61837	3	\N	\N	\N
+9637	Panel Consensus	41	atrial_fibrillation	61837	3	\N	\N	\N
+10474	Andre Kumar	41	atrial_fibrillation	61837	0	2	Lipid Panel	\N
+10475	Jonathan Chen	41	atrial_fibrillation	61837	0	2	\N	\N
+11455	Jonathan Chen	43	atrial_fibrillation	61837	0	2	\N	\N
+11207	Andre Kumar	40	atrial_fibrillation	44276	-9	5	Sedation	Patient lethargic and lorezepam not good for procedural sedation
+11208	Panel Average	40	atrial_fibrillation	44276	-9	\N	\N	\N
+7555	Jason Hom	40	atrial_fibrillation	45806	10	5	\N	\N
+8486	Lisa Shieh	40	atrial_fibrillation	45806	7	5	\N	\N
+8683	Panel Average	40	atrial_fibrillation	45806	6	\N	\N	\N
+8710	Panel Consensus	40	atrial_fibrillation	45806	6	\N	\N	\N
+9982	Andre Kumar	40	atrial_fibrillation	45806	2	3	Metabolic Panel	\N
+9983	Jonathan Chen	40	atrial_fibrillation	45806	2	3	\N	\N
+7556	Jason Hom	41	atrial_fibrillation	45806	10	4	\N	\N
+8487	Lisa Shieh	41	atrial_fibrillation	45806	7	3	\N	\N
+8684	Panel Average	41	atrial_fibrillation	45806	6	\N	\N	\N
+8711	Panel Consensus	41	atrial_fibrillation	45806	6	\N	\N	\N
+9984	Andre Kumar	41	atrial_fibrillation	45806	2	3	Metabolic Panel	\N
+9985	Jonathan Chen	41	atrial_fibrillation	45806	2	3	\N	\N
+7557	Jason Hom	40	atrial_fibrillation	45763	10	5	\N	\N
+8488	Lisa Shieh	40	atrial_fibrillation	45763	7	3	\N	\N
+8489	Panel Consensus	40	atrial_fibrillation	45763	7	\N	\N	\N
+8662	Panel Average	40	atrial_fibrillation	45763	7	\N	\N	\N
+9638	Andre Kumar	40	atrial_fibrillation	45763	3	3	Metabolic Panel	\N
+9639	Jonathan Chen	40	atrial_fibrillation	45763	3	3	Metabolic Panel	\N
+11375	Jonathan Chen	41	atrial_fibrillation	45763	3	3	Metabolic Panel	\N
+7558	Jason Hom	40	atrial_fibrillation	45771	10	5	\N	\N
+8490	Panel Consensus	40	atrial_fibrillation	45771	7	\N	\N	\N
+8712	Panel Average	40	atrial_fibrillation	45771	6	\N	\N	\N
+8947	Lisa Shieh	40	atrial_fibrillation	45771	5	3	\N	\N
+9640	Andre Kumar	40	atrial_fibrillation	45771	3	3	Metabolic Panel	\N
+9641	Jonathan Chen	40	atrial_fibrillation	45771	3	3	Metabolic Panel	\N
+8491	Lisa Shieh	41	atrial_fibrillation	45771	7	3	\N	\N
+8492	Panel Consensus	41	atrial_fibrillation	45771	7	\N	\N	\N
+8948	Jason Hom	41	atrial_fibrillation	45771	5	4	\N	\N
+9430	Panel Average	41	atrial_fibrillation	45771	5	\N	\N	\N
+9642	Jonathan Chen	41	atrial_fibrillation	45771	3	3	Metabolic Panel	\N
+9986	Andre Kumar	41	atrial_fibrillation	45771	2	4	Metabolic Panel	\N
+8493	Panel Consensus	43	atrial_fibrillation	45771	7	\N	\N	\N
+8949	Jason Hom	43	atrial_fibrillation	45771	5	5	\N	\N
+8950	Lisa Shieh	43	atrial_fibrillation	45771	5	3	\N	\N
+9483	Panel Average	43	atrial_fibrillation	45771	4	\N	\N	\N
+9643	Jonathan Chen	43	atrial_fibrillation	45771	3	3	Metabolic Panel	\N
+9987	Andre Kumar	43	atrial_fibrillation	45771	2	3	Metabolic Panel	\N
+11144	Jason Hom	40	atrial_fibrillation	44327	-5	5	\N	\N
+11145	Jonathan Chen	40	atrial_fibrillation	44327	-5	3	Negative Inotrope	\N
+11193	Panel Average	40	atrial_fibrillation	44327	-8	\N	\N	\N
+11194	Panel Consensus	40	atrial_fibrillation	44327	-8	\N	\N	\N
+11209	Andre Kumar	40	atrial_fibrillation	44327	-9	5	Rate Control	\N
+8951	Jason Hom	41	atrial_fibrillation	44005	5	4	\N	\N
+9484	Panel Consensus	41	atrial_fibrillation	44005	4	\N	\N	\N
+9579	Panel Average	41	atrial_fibrillation	44005	4	\N	\N	\N
+9644	Andre Kumar	41	atrial_fibrillation	44005	3	2	Rate Control	\N
+9645	Jonathan Chen	41	atrial_fibrillation	44005	3	3	\N	Beta-blocker AFTER stabilization reasonable for chronic rate control and or CHF treatment
+9646	Lisa Shieh	41	atrial_fibrillation	44005	3	3	\N	\N
+8952	Andre Kumar	40	atrial_fibrillation	44000	5	4	Sedation	\N
+8953	Jason Hom	40	atrial_fibrillation	44000	5	4	\N	\N
+8954	Lisa Shieh	40	atrial_fibrillation	44000	5	3	\N	\N
+8955	Panel Average	40	atrial_fibrillation	44000	5	\N	\N	\N
+8956	Panel Consensus	40	atrial_fibrillation	44000	5	\N	\N	\N
+9988	Jonathan Chen	40	atrial_fibrillation	44000	2	2	Pre-DCCV	\N
+8957	Jason Hom	43	atrial_fibrillation	44000	5	5	\N	\N
+8958	Lisa Shieh	43	atrial_fibrillation	44000	5	3	\N	\N
+9485	Panel Average	43	atrial_fibrillation	44000	4	\N	\N	\N
+9486	Panel Consensus	43	atrial_fibrillation	44000	4	\N	\N	\N
+9989	Andre Kumar	43	atrial_fibrillation	44000	2	3	Sedation	\N
+9990	Jonathan Chen	43	atrial_fibrillation	44000	2	2	Pre-DCCV	\N
+11420	Jonathan Chen	40	atrial_fibrillation	44294	2	2	Pre-DCCV	\N
+8959	Jason Hom	41	atrial_fibrillation	45792	5	4	\N	\N
+10211	Panel Average	41	atrial_fibrillation	45792	2	\N	\N	\N
+10476	Andre Kumar	41	atrial_fibrillation	45792	0	5	MRSA Screen	\N
+10477	Jonathan Chen	41	atrial_fibrillation	45792	0	2	\N	\N
+10478	Lisa Shieh	41	atrial_fibrillation	45792	0	3	\N	\N
+10479	Panel Consensus	41	atrial_fibrillation	45792	0	\N	\N	\N
+10480	Andre Kumar	43	atrial_fibrillation	45792	0	3	MRSA Screen	\N
+10481	Jonathan Chen	43	atrial_fibrillation	45792	0	2	\N	\N
+10482	Lisa Shieh	43	atrial_fibrillation	45792	0	3	\N	\N
+10966	Panel Average	43	atrial_fibrillation	45792	-1	\N	\N	\N
+10972	Panel Consensus	43	atrial_fibrillation	45792	-1	\N	\N	\N
+11001	Jason Hom	43	atrial_fibrillation	45792	-2	5	\N	\N
+11430	Jonathan Chen	43	atrial_fibrillation	44241	0	2	\N	Trying to relieve preload?
+11268	Jonathan Chen	43	atrial_fibrillation	44256	0	2	\N	Trying to relieve preload?
+10483	Andre Kumar	40	atrial_fibrillation	48628	0	4	Anticoagulants	\N
+10484	Jason Hom	40	atrial_fibrillation	48628	0	4	\N	\N
+10485	Jonathan Chen	40	atrial_fibrillation	48628	0	2	\N	\N
+10486	Lisa Shieh	40	atrial_fibrillation	48628	0	3	\N	\N
+10487	Panel Average	40	atrial_fibrillation	48628	0	\N	\N	\N
+10488	Panel Consensus	40	atrial_fibrillation	48628	0	\N	\N	\N
+10489	Andre Kumar	40	atrial_fibrillation	46081	0	4	Nursing	\N
+10490	Jason Hom	40	atrial_fibrillation	46081	0	4	\N	\N
+10491	Jonathan Chen	40	atrial_fibrillation	46081	0	2	\N	\N
+10492	Lisa Shieh	40	atrial_fibrillation	46081	0	5	\N	\N
+10493	Panel Average	40	atrial_fibrillation	46081	0	\N	\N	\N
+10494	Panel Consensus	40	atrial_fibrillation	46081	0	\N	\N	\N
+11240	Jonathan Chen	40	atrial_fibrillation	50235	0	3	\N	Not a respiratory issue?
+11320	Jonathan Chen	43	atrial_fibrillation	50235	0	3	\N	Not respiratory issue, but maybe thinking manage preload with pulmonary pressure?
+11363	Jonathan Chen	43	atrial_fibrillation	36086	0	2	Vasopressor	Adding vasopressor on while hypotensive from negative inotrope?
+7559	Jason Hom	40	atrial_fibrillation	45853	10	5	\N	\N
+8867	Panel Average	40	atrial_fibrillation	45853	5	\N	\N	\N
+8960	Panel Consensus	40	atrial_fibrillation	45853	5	\N	\N	\N
+9487	Jonathan Chen	40	atrial_fibrillation	45853	4	4	\N	\N
+9647	Andre Kumar	40	atrial_fibrillation	45853	3	4	BNP	\N
+9648	Lisa Shieh	40	atrial_fibrillation	45853	3	3	\N	\N
+8961	Jason Hom	41	atrial_fibrillation	45853	5	4	\N	\N
+8962	Panel Consensus	41	atrial_fibrillation	45853	5	\N	\N	\N
+9488	Jonathan Chen	41	atrial_fibrillation	45853	4	4	\N	\N
+8185	Jason Hom	10	pulmonary_embolism	45830	10	5	\N	\N
+9580	Panel Average	41	atrial_fibrillation	45853	4	\N	\N	\N
+9649	Andre Kumar	41	atrial_fibrillation	45853	3	4	BNP	\N
+9650	Lisa Shieh	41	atrial_fibrillation	45853	3	3	\N	\N
+8963	Jason Hom	43	atrial_fibrillation	45853	5	5	\N	\N
+8964	Lisa Shieh	43	atrial_fibrillation	45853	5	3	\N	\N
+8965	Panel Consensus	43	atrial_fibrillation	45853	5	\N	\N	\N
+9446	Panel Average	43	atrial_fibrillation	45853	4	\N	\N	\N
+9489	Jonathan Chen	43	atrial_fibrillation	45853	4	4	\N	\N
+9651	Andre Kumar	43	atrial_fibrillation	45853	3	3	BNP	\N
+8966	Jason Hom	40	atrial_fibrillation	45787	5	4	\N	\N
+10212	Panel Average	40	atrial_fibrillation	45787	2	\N	\N	\N
+10495	Andre Kumar	40	atrial_fibrillation	45787	0	5	PT/OT	\N
+10496	Jonathan Chen	40	atrial_fibrillation	45787	0	2	\N	\N
+10497	Lisa Shieh	40	atrial_fibrillation	45787	0	5	\N	\N
+10498	Panel Consensus	40	atrial_fibrillation	45787	0	\N	\N	\N
+11339	Jonathan Chen	41	atrial_fibrillation	45787	0	3	\N	\N
+7560	Jason Hom	40	atrial_fibrillation	45864	10	5	\N	\N
+8713	Panel Consensus	40	atrial_fibrillation	45864	6	\N	\N	\N
+8843	Panel Average	40	atrial_fibrillation	45864	6	\N	\N	\N
+8967	Lisa Shieh	40	atrial_fibrillation	45864	5	3	\N	\N
+9991	Andre Kumar	40	atrial_fibrillation	45864	2	2	Oxygen	\N
+10499	Jonathan Chen	40	atrial_fibrillation	45864	0	2	\N	\N
+11463	Jonathan Chen	40	atrial_fibrillation	48822	0	0	Oxygen	\N
+11398	Jonathan Chen	43	atrial_fibrillation	48822	0	2	Oxygen	\N
+7561	Jason Hom	40	atrial_fibrillation	45900	10	5	\N	\N
+8714	Panel Consensus	40	atrial_fibrillation	45900	6	\N	\N	\N
+8844	Panel Average	40	atrial_fibrillation	45900	6	\N	\N	\N
+8968	Lisa Shieh	40	atrial_fibrillation	45900	5	3	\N	\N
+9992	Andre Kumar	40	atrial_fibrillation	45900	2	2	Oxygen	\N
+10500	Jonathan Chen	40	atrial_fibrillation	45900	0	2	\N	\N
+11286	Jonathan Chen	43	atrial_fibrillation	45900	0	2	Oxygen	\N
+7562	Lisa Shieh	40	atrial_fibrillation	61823	10	5	\N	\N
+7563	Panel Consensus	40	atrial_fibrillation	61823	10	\N	\N	\N
+9929	Panel Average	40	atrial_fibrillation	61823	2	\N	\N	\N
+9993	Andre Kumar	40	atrial_fibrillation	61823	2	4	Nursing	\N
+10501	Jonathan Chen	40	atrial_fibrillation	61823	0	2	\N	\N
+11146	Jason Hom	40	atrial_fibrillation	61823	-5	5	\N	\N
+11263	Jonathan Chen	43	atrial_fibrillation	43993	0	0	Vasopressor	\N
+7564	Jason Hom	40	atrial_fibrillation	45778	10	5	\N	\N
+8969	Lisa Shieh	40	atrial_fibrillation	45778	5	3	\N	\N
+8970	Panel Average	40	atrial_fibrillation	45778	5	\N	\N	\N
+10255	Panel Consensus	40	atrial_fibrillation	45778	1	\N	\N	\N
+10502	Andre Kumar	40	atrial_fibrillation	45778	0	3	Metabolic Panel	\N
+10503	Jonathan Chen	40	atrial_fibrillation	45778	0	2	\N	\N
+9994	Andre Kumar	41	atrial_fibrillation	45778	2	4	Metabolic Panel	\N
+9995	Panel Average	41	atrial_fibrillation	45778	2	\N	\N	\N
+8971	Jason Hom	43	atrial_fibrillation	45778	5	5	\N	\N
+8972	Lisa Shieh	43	atrial_fibrillation	45778	5	\N	\N	\N
+9447	Panel Average	43	atrial_fibrillation	45778	4	\N	\N	\N
+9652	Andre Kumar	43	atrial_fibrillation	45778	3	2	Metabolic Panel	\N
+10256	Panel Consensus	43	atrial_fibrillation	45778	1	\N	\N	\N
+10504	Jonathan Chen	43	atrial_fibrillation	45778	0	2	\N	\N
+9653	Lisa Shieh	40	atrial_fibrillation	46090	3	3	\N	\N
+9654	Panel Consensus	40	atrial_fibrillation	46090	3	\N	\N	\N
+10232	Panel Average	40	atrial_fibrillation	46090	1	\N	\N	\N
+10257	Andre Kumar	40	atrial_fibrillation	46090	1	3	CBC	\N
+10505	Jason Hom	40	atrial_fibrillation	46090	0	4	\N	\N
+10506	Jonathan Chen	40	atrial_fibrillation	46090	0	2	\N	\N
+7565	Jason Hom	40	atrial_fibrillation	45914	10	5	\N	\N
+8715	Jonathan Chen	40	atrial_fibrillation	45914	6	4	Troponin	\N
+8716	Panel Average	40	atrial_fibrillation	45914	6	\N	\N	\N
+8717	Panel Consensus	40	atrial_fibrillation	45914	6	\N	\N	\N
+8973	Lisa Shieh	40	atrial_fibrillation	45914	5	3	\N	\N
+9655	Andre Kumar	40	atrial_fibrillation	45914	3	3	Troponin	\N
+11256	Jonathan Chen	43	atrial_fibrillation	46185	1	2	Blood Gas	\N
+11354	Jonathan Chen	40	atrial_fibrillation	46067	3	3	Metabolic Panel	\N
+8974	Jason Hom	40	atrial_fibrillation	45955	5	4	\N	\N
+8975	Jonathan Chen	40	atrial_fibrillation	45955	5	3	Lactate	\N
+10213	Panel Average	40	atrial_fibrillation	45955	2	\N	\N	\N
+10258	Panel Consensus	40	atrial_fibrillation	45955	1	\N	\N	\N
+10507	Andre Kumar	40	atrial_fibrillation	45955	0	4	Blood Gas	\N
+10508	Lisa Shieh	40	atrial_fibrillation	45955	0	3	\N	\N
+8976	Jonathan Chen	43	atrial_fibrillation	45955	5	3	Lactate	\N
+9996	Andre Kumar	43	atrial_fibrillation	45955	2	2	Blood Gases	\N
+10259	Panel Consensus	43	atrial_fibrillation	45955	1	\N	\N	\N
+10509	Jason Hom	43	atrial_fibrillation	45955	0	5	\N	\N
+10973	Panel Average	43	atrial_fibrillation	45955	-1	\N	\N	\N
+11147	Lisa Shieh	43	atrial_fibrillation	45955	-5	3	\N	\N
+9997	Andre Kumar	43	atrial_fibrillation	46000	2	1	Urinalysis	\N
+10510	Jonathan Chen	43	atrial_fibrillation	46000	0	2	\N	\N
+10511	Lisa Shieh	43	atrial_fibrillation	46000	0	3	\N	\N
+10512	Panel Average	43	atrial_fibrillation	46000	0	\N	\N	\N
+10513	Panel Consensus	43	atrial_fibrillation	46000	0	\N	\N	\N
+11002	Jason Hom	43	atrial_fibrillation	46000	-2	5	\N	\N
+7566	Jason Hom	40	atrial_fibrillation	45759	10	5	\N	\N
+8977	Panel Average	40	atrial_fibrillation	45759	5	\N	\N	\N
+8978	Panel Consensus	40	atrial_fibrillation	45759	5	\N	\N	\N
+9656	Lisa Shieh	40	atrial_fibrillation	45759	3	3	\N	\N
+9998	Andre Kumar	40	atrial_fibrillation	45759	2	3	Coags	\N
+10514	Jonathan Chen	40	atrial_fibrillation	45759	0	2	\N	\N
+7567	Jason Hom	41	atrial_fibrillation	45759	10	4	\N	\N
+8494	Lisa Shieh	41	atrial_fibrillation	45759	7	3	\N	\N
+8663	Panel Average	41	atrial_fibrillation	45759	7	\N	\N	\N
+8979	Panel Consensus	41	atrial_fibrillation	45759	5	\N	\N	\N
+9657	Andre Kumar	41	atrial_fibrillation	45759	3	4	Coags	\N
+10515	Jonathan Chen	41	atrial_fibrillation	45759	0	2	\N	\N
+8980	Jason Hom	43	atrial_fibrillation	45759	5	5	\N	\N
+8981	Panel Consensus	43	atrial_fibrillation	45759	5	\N	\N	\N
+9581	Panel Average	43	atrial_fibrillation	45759	4	\N	\N	\N
+9658	Andre Kumar	43	atrial_fibrillation	45759	3	3	Coags	\N
+9659	Lisa Shieh	43	atrial_fibrillation	45759	3	3	\N	\N
+10516	Jonathan Chen	43	atrial_fibrillation	45759	0	2	\N	\N
+8982	Jason Hom	40	atrial_fibrillation	45776	5	4	\N	\N
+10214	Panel Average	40	atrial_fibrillation	45776	2	\N	\N	\N
+10517	Andre Kumar	40	atrial_fibrillation	45776	0	5	PT/OT	\N
+10518	Jonathan Chen	40	atrial_fibrillation	45776	0	2	\N	\N
+10519	Lisa Shieh	40	atrial_fibrillation	45776	0	6	\N	\N
+10520	Panel Consensus	40	atrial_fibrillation	45776	0	\N	\N	\N
+11312	Jonathan Chen	41	atrial_fibrillation	45776	0	3	\N	\N
+7568	Jason Hom	40	atrial_fibrillation	45770	10	5	\N	\N
+8983	Panel Average	40	atrial_fibrillation	45770	5	\N	\N	\N
+8984	Panel Consensus	40	atrial_fibrillation	45770	5	\N	\N	\N
+9660	Lisa Shieh	40	atrial_fibrillation	45770	3	3	\N	\N
+9999	Andre Kumar	40	atrial_fibrillation	45770	2	3	Coags	\N
+10521	Jonathan Chen	40	atrial_fibrillation	45770	0	2	\N	\N
+9661	Andre Kumar	41	atrial_fibrillation	45770	3	4	Coags	\N
+9662	Panel Average	41	atrial_fibrillation	45770	3	\N	\N	\N
+8495	Lisa Shieh	43	atrial_fibrillation	45770	7	3	\N	\N
+8985	Jason Hom	43	atrial_fibrillation	45770	5	5	\N	\N
+8986	Panel Average	43	atrial_fibrillation	45770	5	\N	\N	\N
+8987	Panel Consensus	43	atrial_fibrillation	45770	5	\N	\N	\N
+9663	Andre Kumar	43	atrial_fibrillation	45770	3	3	Coags	\N
+10522	Jonathan Chen	43	atrial_fibrillation	45770	0	2	\N	\N
+11364	Jonathan Chen	43	atrial_fibrillation	49995	0	2	\N	\N
+7569	Jason Hom	40	atrial_fibrillation	60178	10	5	\N	\N
+8988	Jonathan Chen	40	atrial_fibrillation	60178	5	3	Anticoagulation	Oral admin, is this too slow, and so should be worth less points?
+9664	Lisa Shieh	40	atrial_fibrillation	60178	3	3	\N	\N
+10215	Panel Average	40	atrial_fibrillation	60178	2	\N	\N	\N
+11195	Andre Kumar	40	atrial_fibrillation	60178	-8	4	Anticoagulants	"Per Warden et al (JAHA 2018 PMID 30571504): highest risk of stroke is within 48hrs after cardioversion, and most DOACS take 4-5 doses to reach steady state (e.g. >48 hrs). No prospective trial data evaluating DOACS + EMERGENT DCCV. A few weak trials sugge (...)"
+7570	Jason Hom	41	atrial_fibrillation	60178	10	5	\N	\N
+8496	Lisa Shieh	41	atrial_fibrillation	60178	7	3	\N	\N
+8989	Jonathan Chen	41	atrial_fibrillation	60178	5	3	Anticoagulation	Oral admin, is this too slow, and so should be worth less points?
+9665	Panel Average	41	atrial_fibrillation	60178	3	\N	\N	\N
+11196	Andre Kumar	41	atrial_fibrillation	60178	-8	5	Anticoagulants	\N
+11288	Jonathan Chen	43	atrial_fibrillation	60178	5	3	Anticoagulation	Oral admin, is this too slow, and so should be worth less points?
+10523	Andre Kumar	40	atrial_fibrillation	44312	0	5	Statin	\N
+10524	Jason Hom	40	atrial_fibrillation	44312	0	4	\N	\N
+10525	Jonathan Chen	40	atrial_fibrillation	44312	0	2	\N	\N
+10526	Lisa Shieh	40	atrial_fibrillation	44312	0	3	\N	\N
+10527	Panel Average	40	atrial_fibrillation	44312	0	\N	\N	\N
+8990	Jonathan Chen	40	atrial_fibrillation	63725	5	3	Lactate	\N
+9666	Lisa Shieh	40	atrial_fibrillation	63725	3	3	\N	\N
+10000	Andre Kumar	40	atrial_fibrillation	63725	2	2	Lactate	\N
+10001	Panel Consensus	40	atrial_fibrillation	63725	2	\N	\N	\N
+10216	Panel Average	40	atrial_fibrillation	63725	2	\N	\N	\N
+10528	Jason Hom	40	atrial_fibrillation	63725	0	4	\N	\N
+8497	Lisa Shieh	40	atrial_fibrillation	44198	7	3	\N	\N
+8991	Jason Hom	40	atrial_fibrillation	44198	5	4	\N	\N
+9490	Jonathan Chen	40	atrial_fibrillation	44198	4	2	\N	Reasonable to attempt, but no indication of dehydration as etiology?
+10260	Panel Average	40	atrial_fibrillation	44198	1	\N	\N	\N
+11003	Panel Consensus	40	atrial_fibrillation	44198	-2	\N	\N	\N
+11210	Andre Kumar	40	atrial_fibrillation	44198	-9	5	Fluids	Patient already shows signs of volume overload
+8498	Lisa Shieh	43	atrial_fibrillation	44198	7	3	\N	\N
+8992	Jason Hom	43	atrial_fibrillation	44198	5	5	\N	\N
+9491	Jonathan Chen	43	atrial_fibrillation	44198	4	2	\N	Reasonable to attempt, but no indication of dehydration as etiology?
+9930	Panel Average	43	atrial_fibrillation	44198	2	\N	\N	\N
+11004	Panel Consensus	43	atrial_fibrillation	44198	-2	\N	\N	\N
+11148	Andre Kumar	43	atrial_fibrillation	44198	-5	4	Fluids	\N
+7571	Jason Hom	40	atrial_fibrillation	45870	10	5	\N	\N
+8718	Jonathan Chen	40	atrial_fibrillation	45870	6	4	Troponin	\N
+8719	Panel Average	40	atrial_fibrillation	45870	6	\N	\N	\N
+8720	Panel Consensus	40	atrial_fibrillation	45870	6	\N	\N	\N
+8993	Lisa Shieh	40	atrial_fibrillation	45870	5	3	\N	\N
+9667	Andre Kumar	40	atrial_fibrillation	45870	3	2	Troponin	\N
+8721	Jonathan Chen	41	atrial_fibrillation	45870	6	4	Troponin	\N
+8722	Panel Consensus	41	atrial_fibrillation	45870	6	\N	\N	\N
+8994	Jason Hom	41	atrial_fibrillation	45870	5	4	\N	\N
+8995	Lisa Shieh	41	atrial_fibrillation	45870	5	3	\N	\N
+9448	Panel Average	41	atrial_fibrillation	45870	4	\N	\N	\N
+9668	Andre Kumar	41	atrial_fibrillation	45870	3	4	Troponin	\N
+8723	Jonathan Chen	43	atrial_fibrillation	45870	6	4	Troponin	\N
+8724	Panel Consensus	43	atrial_fibrillation	45870	6	\N	\N	\N
+8996	Jason Hom	43	atrial_fibrillation	45870	5	5	\N	\N
+8997	Lisa Shieh	43	atrial_fibrillation	45870	5	3	\N	\N
+9492	Panel Average	43	atrial_fibrillation	45870	4	\N	\N	\N
+10002	Andre Kumar	43	atrial_fibrillation	45870	2	4	Troponin	\N
+7572	Jason Hom	40	atrial_fibrillation	62105	10	5	\N	\N
+8499	Panel Consensus	40	atrial_fibrillation	62105	7	\N	\N	\N
+8664	Panel Average	40	atrial_fibrillation	62105	7	\N	\N	\N
+8998	Andre Kumar	40	atrial_fibrillation	62105	5	5	Thyroid	\N
+8999	Lisa Shieh	40	atrial_fibrillation	62105	5	3	\N	\N
+9669	Jonathan Chen	40	atrial_fibrillation	62105	3	4	\N	\N
+7573	Jason Hom	41	atrial_fibrillation	62105	10	5	\N	\N
+8500	Panel Consensus	41	atrial_fibrillation	62105	7	\N	\N	\N
+8665	Panel Average	41	atrial_fibrillation	62105	7	\N	\N	\N
+9000	Andre Kumar	41	atrial_fibrillation	62105	5	5	Thyroid	If not done before
+9001	Lisa Shieh	41	atrial_fibrillation	62105	5	3	\N	\N
+9670	Jonathan Chen	41	atrial_fibrillation	62105	3	4	\N	\N
+10529	Andre Kumar	40	atrial_fibrillation	45945	0	3	Type and Screen	\N
+10530	Jason Hom	40	atrial_fibrillation	45945	0	4	\N	\N
+10531	Jonathan Chen	40	atrial_fibrillation	45945	0	2	\N	\N
+10532	Lisa Shieh	40	atrial_fibrillation	45945	0	3	\N	\N
+10533	Panel Average	40	atrial_fibrillation	45945	0	\N	\N	\N
+10534	Panel Consensus	40	atrial_fibrillation	45945	0	\N	\N	\N
+10003	Andre Kumar	43	atrial_fibrillation	45945	2	3	Type and Screen	\N
+10535	Jonathan Chen	43	atrial_fibrillation	45945	0	2	\N	\N
+10536	Lisa Shieh	43	atrial_fibrillation	45945	0	3	\N	\N
+10537	Panel Average	43	atrial_fibrillation	45945	0	\N	\N	\N
+10538	Panel Consensus	43	atrial_fibrillation	45945	0	\N	\N	\N
+11005	Jason Hom	43	atrial_fibrillation	45945	-2	5	\N	\N
+9671	Jonathan Chen	40	atrial_fibrillation	65656	3	3	DVT US	\N
+10539	Lisa Shieh	40	atrial_fibrillation	65656	0	3	\N	\N
+10974	Panel Consensus	40	atrial_fibrillation	65656	-1	\N	\N	\N
+10985	Panel Average	40	atrial_fibrillation	65656	-1	\N	\N	\N
+11006	Andre Kumar	40	atrial_fibrillation	65656	-2	1	Imaging	\N
+11007	Jason Hom	40	atrial_fibrillation	65656	-2	4	\N	\N
+9672	Jonathan Chen	41	atrial_fibrillation	65656	3	3	DVT US	\N
+10540	Andre Kumar	41	atrial_fibrillation	65656	0	5	Imaging	\N
+10541	Lisa Shieh	41	atrial_fibrillation	65656	0	3	\N	\N
+10967	Panel Average	41	atrial_fibrillation	65656	-1	\N	\N	\N
+10975	Panel Consensus	41	atrial_fibrillation	65656	-1	\N	\N	\N
+11008	Jason Hom	41	atrial_fibrillation	65656	-2	4	\N	\N
+9673	Jonathan Chen	40	atrial_fibrillation	65692	3	3	DVT US	\N
+10542	Lisa Shieh	40	atrial_fibrillation	65692	0	3	\N	\N
+10976	Panel Consensus	40	atrial_fibrillation	65692	-1	\N	\N	\N
+10986	Panel Average	40	atrial_fibrillation	65692	-1	\N	\N	\N
+11009	Andre Kumar	40	atrial_fibrillation	65692	-2	1	Imaging	\N
+11010	Jason Hom	40	atrial_fibrillation	65692	-2	4	\N	\N
+9674	Jonathan Chen	41	atrial_fibrillation	65692	3	3	DVT US	\N
+10543	Andre Kumar	41	atrial_fibrillation	65692	0	5	Imaging	\N
+10544	Lisa Shieh	41	atrial_fibrillation	65692	0	3	\N	\N
+10968	Panel Average	41	atrial_fibrillation	65692	-1	\N	\N	\N
+10977	Panel Consensus	41	atrial_fibrillation	65692	-1	\N	\N	\N
+11011	Jason Hom	41	atrial_fibrillation	65692	-2	4	\N	\N
+10545	Andre Kumar	41	atrial_fibrillation	45751	0	4	Urinalysis	\N
+10546	Jason Hom	41	atrial_fibrillation	45751	0	4	\N	\N
+10547	Jonathan Chen	41	atrial_fibrillation	45751	0	2	\N	Not sure what they're looking for
+10548	Lisa Shieh	41	atrial_fibrillation	45751	0	3	\N	\N
+10549	Panel Average	41	atrial_fibrillation	45751	0	\N	\N	\N
+10550	Panel Consensus	41	atrial_fibrillation	45751	0	\N	\N	\N
+11474	Jonathan Chen	40	atrial_fibrillation	46236	2	3	\N	EtOH as part of Afib eval?
+7574	Jason Hom	40	atrial_fibrillation	45818	10	5	\N	\N
+8501	Lisa Shieh	40	atrial_fibrillation	45818	7	5	\N	\N
+9675	Jonathan Chen	40	atrial_fibrillation	45818	3	3	CXR	\N
+9676	Panel Average	40	atrial_fibrillation	45818	3	\N	\N	\N
+11079	Panel Consensus	40	atrial_fibrillation	45818	-3	\N	\N	\N
+11197	Andre Kumar	40	atrial_fibrillation	45818	-8	3	Imaging	\N
+8502	Lisa Shieh	41	atrial_fibrillation	45818	7	3	\N	\N
+9002	Jason Hom	41	atrial_fibrillation	45818	5	4	\N	\N
+9003	Panel Average	41	atrial_fibrillation	45818	5	\N	\N	\N
+9677	Andre Kumar	41	atrial_fibrillation	45818	3	3	Imaging	\N
+9678	Jonathan Chen	41	atrial_fibrillation	45818	3	3	CXR	\N
+9679	Panel Consensus	41	atrial_fibrillation	45818	3	\N	\N	\N
+9004	Jason Hom	43	atrial_fibrillation	45818	5	5	\N	\N
+9005	Lisa Shieh	43	atrial_fibrillation	45818	5	3	\N	\N
+9680	Jonathan Chen	43	atrial_fibrillation	45818	3	3	CXR	\N
+9913	Panel Average	43	atrial_fibrillation	45818	3	\N	\N	\N
+11012	Andre Kumar	43	atrial_fibrillation	45818	-2	2	Imaging	delays care
+11080	Panel Consensus	43	atrial_fibrillation	45818	-3	\N	\N	\N
+7575	Jason Hom	40	atrial_fibrillation	50200	10	5	\N	\N
+8503	Lisa Shieh	40	atrial_fibrillation	50200	7	3	\N	\N
+9006	Panel Average	40	atrial_fibrillation	50200	5	\N	\N	\N
+9681	Jonathan Chen	40	atrial_fibrillation	50200	3	3	CXR	\N
+9682	Panel Consensus	40	atrial_fibrillation	50200	3	\N	\N	\N
+11013	Andre Kumar	40	atrial_fibrillation	50200	-2	2	Imaging	Delays care
+8504	Lisa Shieh	41	atrial_fibrillation	50200	7	3	\N	\N
+9007	Jason Hom	41	atrial_fibrillation	50200	5	4	\N	\N
+9008	Panel Average	41	atrial_fibrillation	50200	5	\N	\N	\N
+9683	Andre Kumar	41	atrial_fibrillation	50200	3	3	Imaging	\N
+9684	Jonathan Chen	41	atrial_fibrillation	50200	3	3	CXR	\N
+9685	Panel Consensus	41	atrial_fibrillation	50200	3	\N	\N	\N
+7576	Jason Hom	40	atrial_fibrillation	45801	10	5	\N	\N
+8505	Lisa Shieh	40	atrial_fibrillation	45801	7	3	\N	\N
+9686	Jonathan Chen	40	atrial_fibrillation	45801	3	3	CXR	\N
+9687	Panel Average	40	atrial_fibrillation	45801	3	\N	\N	\N
+11081	Andre Kumar	40	atrial_fibrillation	45801	-3	5	Imaging	CXR delays care
+11082	Panel Consensus	40	atrial_fibrillation	45801	-3	\N	\N	\N
+9009	Jason Hom	43	atrial_fibrillation	45801	5	5	\N	\N
+9010	Lisa Shieh	43	atrial_fibrillation	45801	5	3	\N	\N
+9688	Jonathan Chen	43	atrial_fibrillation	45801	3	3	CXR	\N
+9914	Panel Average	43	atrial_fibrillation	45801	3	\N	\N	\N
+11014	Andre Kumar	43	atrial_fibrillation	45801	-2	2	Imaging	delays care
+11083	Panel Consensus	43	atrial_fibrillation	45801	-3	\N	\N	\N
+9011	Jason Hom	14	gi_bleed	44281	5	4	\N	\N
+10382	Panel Average	14	gi_bleed	44281	1	\N	\N	\N
+10551	Jonathan Chen	14	gi_bleed	44281	0	2	\N	\N
+10552	Lisa Shieh	14	gi_bleed	44281	0	5	\N	\N
+10978	Panel Consensus	14	gi_bleed	44281	-1	\N	\N	\N
+11084	Andre Kumar	14	gi_bleed	44281	-3	4	Acetaminophen	\N
+10261	Andre Kumar	2	gi_bleed	48829	1	2	Pain Medication	\N
+10262	Jonathan Chen	2	gi_bleed	48829	1	2	\N	Workup for liver injury. Okay I guess
+10263	Panel Consensus	2	gi_bleed	48829	1	\N	\N	\N
+10391	Panel Average	2	gi_bleed	48829	0	\N	\N	\N
+10553	Jason Hom	2	gi_bleed	48829	0	4	\N	\N
+10554	Lisa Shieh	2	gi_bleed	48829	0	3	\N	\N
+11280	Jonathan Chen	14	gi_bleed	44416	0	2	\N	Acetaminophen overdose, non-specific hepatitis treatment?
+9689	Andre Kumar	14	gi_bleed	45932	3	4	Hepatitis Panel	\N
+9690	Panel Average	14	gi_bleed	45932	3	\N	\N	\N
+11368	Jonathan Chen	15	gi_bleed	45932	2	2	\N	\N
+11366	Jonathan Chen	14	gi_bleed	65641	0	4	\N	\N
+11431	Jonathan Chen	16	gi_bleed	65641	0	4	\N	\N
+7577	Jason Hom	16	gi_bleed	45814	10	5	\N	\N
+7578	Lisa Shieh	16	gi_bleed	45814	10	5	\N	\N
+7579	Panel Consensus	16	gi_bleed	45814	10	\N	\N	\N
+8303	Panel Average	16	gi_bleed	45814	8	\N	\N	\N
+9012	Andre Kumar	16	gi_bleed	45814	5	3	Admit	\N
+10555	Jonathan Chen	16	gi_bleed	45814	0	2	\N	\N
+11273	Jonathan Chen	2	gi_bleed	65641	0	4	\N	\N
+9013	Andre Kumar	2	gi_bleed	45814	5	4	Admit	\N
+9014	Panel Average	2	gi_bleed	45814	5	\N	\N	\N
+7580	Jason Hom	14	gi_bleed	61982	10	5	\N	\N
+7581	Lisa Shieh	14	gi_bleed	61982	10	5	\N	\N
+7582	Panel Consensus	14	gi_bleed	61982	10	\N	\N	\N
+8451	Panel Average	14	gi_bleed	61982	7	\N	\N	\N
+10004	Andre Kumar	14	gi_bleed	61982	2	5	Admit	\N
+10556	Jonathan Chen	14	gi_bleed	61982	0	2	\N	\N
+7583	Jason Hom	15	gi_bleed	61982	10	5	\N	\N
+7584	Lisa Shieh	15	gi_bleed	61982	10	5	\N	\N
+7585	Panel Consensus	15	gi_bleed	61982	10	\N	\N	\N
+8304	Panel Average	15	gi_bleed	61982	8	\N	\N	\N
+9015	Andre Kumar	15	gi_bleed	61982	5	5	Admit	\N
+10557	Jonathan Chen	15	gi_bleed	61982	0	2	\N	\N
+11440	Jonathan Chen	15	gi_bleed	44275	3	3	IVF	\N
+11298	Jonathan Chen	15	gi_bleed	48744	0	3	\N	Probably want complete hepatic panel, so give points there
+10005	Andre Kumar	14	gi_bleed	46136	2	1	Ammonia	\N
+10264	Jonathan Chen	14	gi_bleed	46136	1	2	\N	May be interesting, but not main point of case
+10383	Panel Average	14	gi_bleed	46136	1	\N	\N	\N
+10558	Jason Hom	14	gi_bleed	46136	0	4	\N	\N
+10559	Lisa Shieh	14	gi_bleed	46136	0	3	\N	\N
+10560	Panel Consensus	14	gi_bleed	46136	0	\N	\N	\N
+7586	Andre Kumar	15	gi_bleed	46136	10	5	Hepatic Encephalopathy	\N
+9602	Panel Average	15	gi_bleed	46136	3	\N	\N	\N
+10265	Jonathan Chen	15	gi_bleed	46136	1	2	\N	May be interesting, but not main point of case
+10561	Jason Hom	15	gi_bleed	46136	0	4	\N	\N
+10562	Lisa Shieh	15	gi_bleed	46136	0	3	\N	\N
+10563	Panel Consensus	15	gi_bleed	46136	0	\N	\N	\N
+10006	Andre Kumar	2	gi_bleed	46136	2	1	Hepatic Encephalopathy	\N
+10266	Jonathan Chen	2	gi_bleed	46136	1	2	\N	May be interesting, but not main point of case
+10384	Panel Average	2	gi_bleed	46136	1	\N	\N	\N
+10564	Jason Hom	2	gi_bleed	46136	0	4	\N	\N
+10565	Lisa Shieh	2	gi_bleed	46136	0	3	\N	\N
+10566	Panel Consensus	2	gi_bleed	46136	0	\N	\N	\N
+11372	Jonathan Chen	15	gi_bleed	63723	0	3	\N	\N
+10567	Jonathan Chen	14	gi_bleed	50962	0	4	\N	Mistake, must be looking for blood
+11015	Andre Kumar	14	gi_bleed	50962	-2	5	\N	\N
+11099	Panel Average	14	gi_bleed	50962	-4	\N	\N	\N
+11104	Panel Consensus	14	gi_bleed	50962	-4	\N	\N	\N
+11149	Jason Hom	14	gi_bleed	50962	-5	5	\N	\N
+9016	Lisa Shieh	15	gi_bleed	46245	5	3	\N	\N
+9931	Panel Average	15	gi_bleed	46245	2	\N	\N	\N
+10007	Andre Kumar	15	gi_bleed	46245	2	5	Metabolic Panel	\N
+10008	Panel Consensus	15	gi_bleed	46245	2	\N	\N	\N
+10568	Jason Hom	15	gi_bleed	46245	0	5	\N	\N
+10569	Jonathan Chen	15	gi_bleed	46245	0	2	\N	Looking to explain hyperbilirubin I guess?
+11276	Jonathan Chen	14	gi_bleed	48513	0	2	\N	\N
+9017	Jason Hom	14	gi_bleed	45901	5	4	\N	\N
+9932	Panel Average	14	gi_bleed	45901	2	\N	\N	\N
+10009	Andre Kumar	14	gi_bleed	45901	2	2	Blood Cultures	\N
+10570	Jonathan Chen	14	gi_bleed	45901	0	2	\N	Irrelevant diagnostic?
+10571	Lisa Shieh	14	gi_bleed	45901	0	3	\N	\N
+10572	Panel Consensus	14	gi_bleed	45901	0	\N	\N	\N
+9018	Jason Hom	14	gi_bleed	45752	5	4	\N	\N
+9933	Panel Average	14	gi_bleed	45752	2	\N	\N	\N
+10010	Andre Kumar	14	gi_bleed	45752	2	4	Blood Cultures	\N
+10573	Jonathan Chen	14	gi_bleed	45752	0	2	\N	\N
+10574	Lisa Shieh	14	gi_bleed	45752	0	3	\N	\N
+10575	Panel Consensus	14	gi_bleed	45752	0	\N	\N	\N
+9691	Andre Kumar	14	gi_bleed	45760	3	4	Blood Gas	\N
+10011	Jason Hom	14	gi_bleed	45760	2	4	\N	\N
+10012	Jonathan Chen	14	gi_bleed	45760	2	3	Blood Gas	Check for acidosis?
+10217	Panel Average	14	gi_bleed	45760	2	\N	\N	\N
+10576	Lisa Shieh	14	gi_bleed	45760	0	3	\N	\N
+10577	Panel Consensus	14	gi_bleed	45760	0	\N	\N	\N
+10013	Andre Kumar	15	gi_bleed	45919	2	4	Blood Gas	\N
+10014	Jason Hom	15	gi_bleed	45919	2	4	\N	\N
+10015	Jonathan Chen	15	gi_bleed	45919	2	3	Blood Gas	Check for acidosis?
+10233	Panel Average	15	gi_bleed	45919	1	\N	\N	\N
+10578	Lisa Shieh	15	gi_bleed	45919	0	3	\N	\N
+10579	Panel Consensus	15	gi_bleed	45919	0	\N	\N	\N
+7587	Jason Hom	14	gi_bleed	45823	10	5	\N	\N
+7588	Lisa Shieh	14	gi_bleed	45823	10	5	\N	\N
+7589	Panel Consensus	14	gi_bleed	45823	10	\N	\N	\N
+8305	Panel Average	14	gi_bleed	45823	8	\N	\N	\N
+9019	Andre Kumar	14	gi_bleed	45823	5	5	Transfuse	\N
+10267	Jonathan Chen	14	gi_bleed	45823	1	2	Type and Screen	Just a step in RBC transfusion prep
+11395	Jonathan Chen	15	gi_bleed	45823	1	2	Type and Screen	\N
+11317	Jonathan Chen	14	gi_bleed	44439	3	3	IVF	\N
+7590	Jason Hom	14	gi_bleed	44290	10	4	\N	\N
+7591	Lisa Shieh	14	gi_bleed	44290	10	5	ivf	\N
+8306	Panel Average	14	gi_bleed	44290	8	\N	\N	\N
+8339	Panel Consensus	14	gi_bleed	44290	8	\N	\N	\N
+9020	Andre Kumar	14	gi_bleed	44290	5	4	Fluids	\N
+9692	Jonathan Chen	14	gi_bleed	44290	3	3	IVF	\N
+11248	Jonathan Chen	15	gi_bleed	44439	3	3	\N	\N
+7592	Jason Hom	15	gi_bleed	44290	10	4	\N	\N
+7593	Lisa Shieh	15	gi_bleed	44290	10	5	\N	\N
+8224	Panel Average	15	gi_bleed	44290	9	\N	\N	\N
+8340	Andre Kumar	15	gi_bleed	44290	8	4	Fluids	\N
+8341	Panel Consensus	15	gi_bleed	44290	8	\N	\N	\N
+9693	Jonathan Chen	15	gi_bleed	44290	3	3	IVF	\N
+9021	Jason Hom	15	gi_bleed	45887	5	4	\N	\N
+9022	Lisa Shieh	15	gi_bleed	45887	5	3	\N	\N
+9493	Panel Average	15	gi_bleed	45887	4	\N	\N	\N
+10016	Andre Kumar	15	gi_bleed	45887	2	4	Metabolic Panel	\N
+10580	Jonathan Chen	15	gi_bleed	45887	0	2	\N	Not sure why necessary
+10581	Panel Consensus	15	gi_bleed	45887	0	\N	\N	\N
+9023	Jason Hom	2	gi_bleed	45887	5	4	\N	\N
+9024	Lisa Shieh	2	gi_bleed	45887	5	3	\N	\N
+9494	Panel Average	2	gi_bleed	45887	4	\N	\N	\N
+10017	Andre Kumar	2	gi_bleed	45887	2	3	Metabolic Panel	\N
+10582	Jonathan Chen	2	gi_bleed	45887	0	2	\N	\N
+10583	Panel Consensus	2	gi_bleed	45887	0	\N	\N	\N
+7594	Jason Hom	14	gi_bleed	45793	10	5	\N	\N
+7595	Lisa Shieh	14	gi_bleed	45793	10	5	blood count	\N
+7596	Panel Consensus	14	gi_bleed	45793	10	\N	\N	\N
+8225	Panel Average	14	gi_bleed	45793	9	\N	\N	\N
+8342	Andre Kumar	14	gi_bleed	45793	8	5	CBC	\N
+8506	Jonathan Chen	14	gi_bleed	45793	7	4	CBC	\N
+7597	Jason Hom	15	gi_bleed	45793	10	5	\N	\N
+7598	Lisa Shieh	15	gi_bleed	45793	10	5	\N	\N
+7599	Panel Consensus	15	gi_bleed	45793	10	\N	\N	\N
+8293	Panel Average	15	gi_bleed	45793	9	\N	\N	\N
+8507	Jonathan Chen	15	gi_bleed	45793	7	4	CBC	\N
+8725	Andre Kumar	15	gi_bleed	45793	6	4	CBC	\N
+7600	Jason Hom	16	gi_bleed	45793	10	5	\N	\N
+7601	Lisa Shieh	16	gi_bleed	45793	10	5	\N	\N
+7602	Panel Consensus	16	gi_bleed	45793	10	\N	\N	\N
+8307	Panel Average	16	gi_bleed	45793	8	\N	\N	\N
+8508	Jonathan Chen	16	gi_bleed	45793	7	4	CBC	\N
+9025	Andre Kumar	16	gi_bleed	45793	5	1	CBC	\N
+7603	Jason Hom	2	gi_bleed	45793	10	5	\N	\N
+7604	Lisa Shieh	2	gi_bleed	45793	10	5	\N	\N
+7605	Panel Consensus	2	gi_bleed	45793	10	\N	\N	\N
+8452	Panel Average	2	gi_bleed	45793	7	\N	\N	\N
+8509	Jonathan Chen	2	gi_bleed	45793	7	4	CBC	\N
+10018	Andre Kumar	2	gi_bleed	45793	2	3	CBC	\N
+7606	Lisa Shieh	14	gi_bleed	45788	10	3	blood count	\N
+7607	Panel Consensus	14	gi_bleed	45788	10	\N	\N	\N
+8241	Jason Hom	14	gi_bleed	45788	9	5	\N	\N
+8242	Panel Average	14	gi_bleed	45788	9	\N	\N	\N
+8343	Andre Kumar	14	gi_bleed	45788	8	5	CBC	\N
+8510	Jonathan Chen	14	gi_bleed	45788	7	4	CBC	\N
+7608	Lisa Shieh	15	gi_bleed	45788	10	5	\N	\N
+7609	Panel Consensus	15	gi_bleed	45788	10	\N	\N	\N
+8243	Jason Hom	15	gi_bleed	45788	9	5	\N	\N
+8308	Panel Average	15	gi_bleed	45788	8	\N	\N	\N
+8511	Jonathan Chen	15	gi_bleed	45788	7	4	CBC	\N
+8726	Andre Kumar	15	gi_bleed	45788	6	5	CBC	\N
+7610	Lisa Shieh	2	gi_bleed	45788	10	5	\N	\N
+7611	Panel Consensus	2	gi_bleed	45788	10	\N	\N	\N
+8244	Jason Hom	2	gi_bleed	45788	9	5	\N	\N
+8512	Jonathan Chen	2	gi_bleed	45788	7	4	CBC	\N
+8513	Panel Average	2	gi_bleed	45788	7	\N	\N	\N
+10019	Andre Kumar	2	gi_bleed	45788	2	3	CBC	\N
+11386	Jonathan Chen	15	gi_bleed	44255	0	2	\N	Not reliable to cover Gram negative enterics (e.g., E coli)
+8344	Andre Kumar	15	gi_bleed	44637	8	5	Antibiotics	\N
+8345	Jason Hom	15	gi_bleed	44637	8	5	\N	\N
+8727	Jonathan Chen	15	gi_bleed	44637	6	5	Antibiotics	Would cover same as ceftriaxone, though odd choice
+8868	Panel Average	15	gi_bleed	44637	5	\N	\N	\N
+9026	Panel Consensus	15	gi_bleed	44637	5	\N	\N	\N
+10584	Lisa Shieh	15	gi_bleed	44637	0	3	\N	\N
+8245	Andre Kumar	14	gi_bleed	45060	9	3	Antibiotics	\N
+8514	Jonathan Chen	14	gi_bleed	45060	7	5	Antibiotics	\N
+9934	Panel Average	14	gi_bleed	45060	2	\N	\N	\N
+10020	Panel Consensus	14	gi_bleed	45060	2	\N	\N	\N
+10585	Lisa Shieh	14	gi_bleed	45060	0	3	\N	\N
+11016	Jason Hom	14	gi_bleed	45060	-2	4	\N	\N
+8246	Andre Kumar	15	gi_bleed	45060	9	5	Antibiotics	\N
+8515	Jonathan Chen	15	gi_bleed	45060	7	5	Antibiotics	\N
+9027	Lisa Shieh	15	gi_bleed	45060	5	3	\N	\N
+9495	Panel Average	15	gi_bleed	45060	4	\N	\N	\N
+10021	Panel Consensus	15	gi_bleed	45060	2	\N	\N	\N
+11017	Jason Hom	15	gi_bleed	45060	-2	4	\N	\N
+7612	Andre Kumar	14	gi_bleed	35733	10	5	Antibiotics	\N
+7613	Jason Hom	14	gi_bleed	35733	10	5	\N	\N
+7614	Panel Consensus	14	gi_bleed	35733	10	\N	\N	\N
+8247	Panel Average	14	gi_bleed	35733	9	\N	\N	\N
+8516	Jonathan Chen	14	gi_bleed	35733	7	5	Antibiotics	\N
+8517	Lisa Shieh	14	gi_bleed	35733	7	3	\N	\N
+7615	Andre Kumar	15	gi_bleed	35733	10	5	Antibiotics	\N
+7616	Jason Hom	15	gi_bleed	35733	10	5	\N	\N
+7617	Panel Consensus	15	gi_bleed	35733	10	\N	\N	\N
+8248	Panel Average	15	gi_bleed	35733	9	\N	\N	\N
+8518	Jonathan Chen	15	gi_bleed	35733	7	5	Antibiotics	\N
+8519	Lisa Shieh	15	gi_bleed	35733	7	3	\N	If already given, do not give credit again
+7618	Andre Kumar	16	gi_bleed	35733	10	5	Antibiotics	Points if not ordered before
+7619	Jason Hom	16	gi_bleed	35733	10	5	\N	\N
+7620	Lisa Shieh	16	gi_bleed	35733	10	5	\N	\N
+7621	Panel Average	16	gi_bleed	35733	10	\N	\N	\N
+7622	Panel Consensus	16	gi_bleed	35733	10	\N	\N	\N
+8520	Jonathan Chen	16	gi_bleed	35733	7	5	Antibiotics	\N
+7623	Jason Hom	2	gi_bleed	35733	10	5	\N	\N
+7624	Lisa Shieh	2	gi_bleed	35733	10	5	\N	If antibiotics, imaging already done, would not give again
+7625	Panel Consensus	2	gi_bleed	35733	10	\N	\N	\N
+8226	Panel Average	2	gi_bleed	35733	9	\N	\N	\N
+8346	Andre Kumar	2	gi_bleed	35733	8	3	Antibiotics	\N
+8521	Jonathan Chen	2	gi_bleed	35733	7	5	Antibiotics	\N
+11397	Jonathan Chen	2	gi_bleed	44249	7	5	Antibiotics	Reasonable empiric choice for GNRs
+11423	Jonathan Chen	14	gi_bleed	62027	0	2	\N	\N
+7626	Jason Hom	2	gi_bleed	63720	10	5	\N	\N
+8522	Lisa Shieh	2	gi_bleed	63720	7	3	\N	\N
+8523	Panel Average	2	gi_bleed	63720	7	\N	\N	\N
+9028	Panel Consensus	2	gi_bleed	63720	5	\N	\N	\N
+9496	Andre Kumar	2	gi_bleed	63720	4	1	Hepatitis Panel	\N
+10022	Jonathan Chen	2	gi_bleed	63720	2	2	\N	Workup for liver injury. Okay I guess
+7627	Jason Hom	15	gi_bleed	50400	10	5	\N	\N
+8728	Panel Consensus	15	gi_bleed	50400	6	\N	\N	\N
+8845	Panel Average	15	gi_bleed	50400	6	\N	\N	\N
+9029	Lisa Shieh	15	gi_bleed	50400	5	3	\N	\N
+10023	Andre Kumar	15	gi_bleed	50400	2	1	Consult	\N
+10586	Jonathan Chen	15	gi_bleed	50400	0	2	\N	\N
+7628	Jason Hom	14	gi_bleed	49481	10	5	\N	\N
+7629	Jonathan Chen	14	gi_bleed	49481	10	5	GI-EGD	\N
+7630	Lisa Shieh	14	gi_bleed	49481	10	5	\N	\N
+7631	Panel Consensus	14	gi_bleed	49481	10	\N	\N	\N
+8309	Panel Average	14	gi_bleed	49481	8	\N	\N	\N
+9030	Andre Kumar	14	gi_bleed	49481	5	5	Consult	\N
+7632	Andre Kumar	15	gi_bleed	49481	10	5	Consult	\N
+7633	Jason Hom	15	gi_bleed	49481	10	5	\N	\N
+7634	Jonathan Chen	15	gi_bleed	49481	10	5	GI-EGD	\N
+7635	Lisa Shieh	15	gi_bleed	49481	10	5	\N	\N
+7636	Panel Average	15	gi_bleed	49481	10	\N	\N	\N
+7637	Panel Consensus	15	gi_bleed	49481	10	\N	\N	\N
+7638	Jason Hom	16	gi_bleed	49481	10	5	\N	\N
+7639	Jonathan Chen	16	gi_bleed	49481	10	5	GI-EGD	\N
+7640	Lisa Shieh	16	gi_bleed	49481	10	5	\N	\N
+7641	Panel Consensus	16	gi_bleed	49481	10	\N	\N	\N
+8453	Panel Average	16	gi_bleed	49481	7	\N	\N	\N
+10024	Andre Kumar	16	gi_bleed	49481	2	3	Consult	\N
+9031	Jason Hom	14	gi_bleed	61323	5	5	\N	\N
+9032	Lisa Shieh	14	gi_bleed	61323	5	3	\N	\N
+9449	Panel Average	14	gi_bleed	61323	4	\N	\N	\N
+9694	Andre Kumar	14	gi_bleed	61323	3	4	Consult	\N
+10025	Panel Consensus	14	gi_bleed	61323	2	\N	\N	\N
+10587	Jonathan Chen	14	gi_bleed	61323	0	2	\N	\N
+7642	Jason Hom	15	gi_bleed	61323	10	5	\N	\N
+7643	Lisa Shieh	15	gi_bleed	61323	10	3	\N	\N
+8249	Panel Average	15	gi_bleed	61323	9	\N	\N	\N
+8250	Panel Consensus	15	gi_bleed	61323	9	\N	\N	\N
+8524	Andre Kumar	15	gi_bleed	61323	7	3	Consult	\N
+10588	Jonathan Chen	15	gi_bleed	61323	0	2	\N	\N
+11445	Jonathan Chen	16	gi_bleed	61323	0	2	\N	\N
+7644	Jason Hom	14	gi_bleed	49207	10	5	\N	\N
+9033	Lisa Shieh	14	gi_bleed	49207	5	3	\N	\N
+9450	Panel Average	14	gi_bleed	49207	4	\N	\N	\N
+9497	Panel Consensus	14	gi_bleed	49207	4	\N	\N	\N
+10589	Jonathan Chen	14	gi_bleed	49207	0	3	\N	\N
+11018	Andre Kumar	14	gi_bleed	49207	-2	2	Admit	\N
+11269	Jonathan Chen	14	gi_bleed	49867	0	2	\N	Not DIC, more total coagulant deficiency
+11019	Andre Kumar	14	gi_bleed	46072	-2	3	Imaging	Delays care
+11020	Panel Average	14	gi_bleed	46072	-2	\N	\N	\N
+9034	Lisa Shieh	16	gi_bleed	45858	5	3	\N	\N
+10026	Jonathan Chen	16	gi_bleed	45858	2	2	CT Abdomen	Diagnostic workup okay, but seems like off choice for cirrhosis diagnosis?
+10268	Panel Average	16	gi_bleed	45858	1	\N	\N	\N
+10590	Jason Hom	16	gi_bleed	45858	0	4	\N	\N
+10591	Panel Consensus	16	gi_bleed	45858	0	\N	\N	\N
+11021	Andre Kumar	16	gi_bleed	45858	-2	2	Imaging	\N
+11309	Jonathan Chen	14	gi_bleed	45852	2	2	CT Abdomen	\N
+11022	Andre Kumar	16	gi_bleed	45852	-2	2	Imaging	\N
+11023	Panel Average	16	gi_bleed	45852	-2	\N	\N	\N
+8525	Lisa Shieh	2	gi_bleed	49836	7	3	\N	\N
+10027	Jonathan Chen	2	gi_bleed	49836	2	2	CT Abdomen	Diagnostic workup okay, but seems like off choice for cirrhosis diagnosis?
+10392	Panel Average	2	gi_bleed	49836	0	\N	\N	\N
+10979	Andre Kumar	2	gi_bleed	49836	-1	4	Imaging	\N
+11150	Jason Hom	2	gi_bleed	49836	-5	4	\N	\N
+11151	Panel Consensus	2	gi_bleed	49836	-5	\N	\N	\N
+10592	Jonathan Chen	2	gi_bleed	50737	0	2	\N	Looks like mistake. Probably looking for abdomen
+10593	Lisa Shieh	2	gi_bleed	50737	0	5	\N	\N
+10980	Andre Kumar	2	gi_bleed	50737	-1	5	Imaging	\N
+11024	Panel Average	2	gi_bleed	50737	-2	\N	\N	\N
+11152	Jason Hom	2	gi_bleed	50737	-5	4	\N	\N
+11153	Panel Consensus	2	gi_bleed	50737	-5	\N	\N	\N
+11387	Jonathan Chen	14	gi_bleed	48871	0	2	\N	\N
+7645	Lisa Shieh	14	gi_bleed	46286	10	5	\N	\N
+8526	Jonathan Chen	14	gi_bleed	46286	7	4	Coags	\N
+8527	Panel Consensus	14	gi_bleed	46286	7	\N	\N	\N
+8666	Panel Average	14	gi_bleed	46286	7	\N	\N	\N
+9035	Andre Kumar	14	gi_bleed	46286	5	4	Coags	\N
+9036	Jason Hom	14	gi_bleed	46286	5	4	\N	\N
+7646	Lisa Shieh	16	gi_bleed	46286	10	5	\N	\N
+8528	Jonathan Chen	16	gi_bleed	46286	7	4	Coags	\N
+8529	Panel Consensus	16	gi_bleed	46286	7	\N	\N	\N
+8667	Panel Average	16	gi_bleed	46286	7	\N	\N	\N
+9037	Andre Kumar	16	gi_bleed	46286	5	2	Coags	\N
+9038	Jason Hom	16	gi_bleed	46286	5	4	\N	\N
+9695	Andre Kumar	2	gi_bleed	46286	3	3	Coags	\N
+9696	Panel Average	2	gi_bleed	46286	3	\N	\N	\N
+10028	Andre Kumar	2	gi_bleed	45784	2	2	Clear Liquid	\N
+10029	Panel Average	2	gi_bleed	45784	2	\N	\N	\N
+7647	Jason Hom	14	gi_bleed	45811	10	5	\N	\N
+7648	Lisa Shieh	14	gi_bleed	45811	10	5	\N	\N
+8310	Panel Average	14	gi_bleed	45811	8	\N	\N	\N
+8347	Panel Consensus	14	gi_bleed	45811	8	\N	\N	\N
+9039	Andre Kumar	14	gi_bleed	45811	5	5	NPO	\N
+9697	Jonathan Chen	14	gi_bleed	45811	3	3	\N	\N
+7649	Jason Hom	15	gi_bleed	45811	10	5	\N	\N
+7650	Lisa Shieh	15	gi_bleed	45811	10	5	\N	\N
+8311	Panel Average	15	gi_bleed	45811	8	\N	\N	\N
+8348	Panel Consensus	15	gi_bleed	45811	8	\N	\N	\N
+9040	Andre Kumar	15	gi_bleed	45811	5	5	NPO	\N
+9698	Jonathan Chen	15	gi_bleed	45811	3	3	\N	\N
+11464	Jonathan Chen	14	gi_bleed	45941	0	2	\N	\N
+9041	Lisa Shieh	14	gi_bleed	45866	5	3	\N	\N
+9935	Panel Average	14	gi_bleed	45866	2	\N	\N	\N
+10030	Jason Hom	14	gi_bleed	45866	2	4	\N	\N
+10031	Jonathan Chen	14	gi_bleed	45866	2	2	\N	\N
+10032	Panel Consensus	14	gi_bleed	45866	2	\N	\N	\N
+10594	Andre Kumar	14	gi_bleed	45866	0	4	ECG + Monitoring	\N
+8530	Lisa Shieh	15	gi_bleed	45866	7	3	\N	\N
+9936	Panel Average	15	gi_bleed	45866	2	\N	\N	\N
+10033	Jason Hom	15	gi_bleed	45866	2	4	\N	\N
+10034	Jonathan Chen	15	gi_bleed	45866	2	2	\N	\N
+10035	Panel Consensus	15	gi_bleed	45866	2	\N	\N	\N
+11025	Andre Kumar	15	gi_bleed	45866	-2	4	ECG + Monitoring	\N
+10595	Andre Kumar	2	gi_bleed	61832	0	2	Imaging	\N
+10596	Jonathan Chen	2	gi_bleed	61832	0	2	\N	Working up ascites I guess? History all suggests primary liver though
+10597	Lisa Shieh	2	gi_bleed	61832	0	5	\N	\N
+10598	Panel Consensus	2	gi_bleed	61832	0	\N	\N	\N
+10969	Panel Average	2	gi_bleed	61832	-1	\N	\N	\N
+11154	Jason Hom	2	gi_bleed	61832	-5	4	\N	\N
+7651	Jason Hom	14	gi_bleed	46160	10	5	\N	\N
+7652	Lisa Shieh	14	gi_bleed	46160	10	5	\N	\N
+7653	Panel Consensus	14	gi_bleed	46160	10	\N	\N	\N
+8432	Panel Average	14	gi_bleed	46160	8	\N	\N	\N
+9699	Andre Kumar	14	gi_bleed	46160	3	2	Vitals	\N
+10599	Jonathan Chen	14	gi_bleed	46160	0	2	\N	\N
+11477	Jonathan Chen	15	gi_bleed	46160	0	2	\N	\N
+11330	Jonathan Chen	15	gi_bleed	46078	0	2	\N	\N
+7654	Andre Kumar	14	gi_bleed	63759	10	4	Transfuse	\N
+7655	Jonathan Chen	14	gi_bleed	63759	10	5	RBC	\N
+9042	Jason Hom	14	gi_bleed	63759	5	4	\N	\N
+9043	Panel Average	14	gi_bleed	63759	5	\N	\N	\N
+10600	Lisa Shieh	14	gi_bleed	63759	0	3	\N	\N
+10601	Panel Consensus	14	gi_bleed	63759	0	\N	\N	\N
+7656	Andre Kumar	15	gi_bleed	63759	10	5	Transfuse	\N
+7657	Jason Hom	15	gi_bleed	63759	10	4	\N	\N
+7658	Jonathan Chen	15	gi_bleed	63759	10	5	RBC	\N
+7659	Lisa Shieh	15	gi_bleed	63759	10	5	\N	\N
+7660	Panel Average	15	gi_bleed	63759	10	\N	\N	\N
+7661	Panel Consensus	15	gi_bleed	63759	10	\N	\N	\N
+9044	Jason Hom	14	gi_bleed	46028	5	4	\N	\N
+9498	Panel Consensus	14	gi_bleed	46028	4	\N	\N	\N
+9582	Panel Average	14	gi_bleed	46028	4	\N	\N	\N
+9700	Andre Kumar	14	gi_bleed	46028	3	3	Coags	\N
+9701	Lisa Shieh	14	gi_bleed	46028	3	3	\N	\N
+10602	Jonathan Chen	14	gi_bleed	46028	0	2	\N	\N
+7662	Jason Hom	14	gi_bleed	45872	10	5	\N	\N
+7663	Lisa Shieh	14	gi_bleed	45872	10	3	\N	\N
+8227	Panel Average	14	gi_bleed	45872	9	\N	\N	\N
+8251	Jonathan Chen	14	gi_bleed	45872	9	4	FFP	\N
+8252	Panel Consensus	14	gi_bleed	45872	9	\N	\N	\N
+8349	Andre Kumar	14	gi_bleed	45872	8	5	Transfuse	\N
+7664	Jason Hom	15	gi_bleed	45872	10	5	\N	\N
+7665	Lisa Shieh	15	gi_bleed	45872	10	5	\N	\N
+8228	Panel Average	15	gi_bleed	45872	9	\N	\N	\N
+8253	Jonathan Chen	15	gi_bleed	45872	9	4	FFP	\N
+8254	Panel Consensus	15	gi_bleed	45872	9	\N	\N	\N
+8350	Andre Kumar	15	gi_bleed	45872	8	5	Transfuse	\N
+7666	Jason Hom	16	gi_bleed	45872	10	5	\N	\N
+7667	Lisa Shieh	16	gi_bleed	45872	10	5	\N	\N
+8255	Jonathan Chen	16	gi_bleed	45872	9	4	FFP	\N
+8256	Panel Average	16	gi_bleed	45872	9	\N	\N	\N
+8257	Panel Consensus	16	gi_bleed	45872	9	\N	\N	\N
+8531	Andre Kumar	16	gi_bleed	45872	7	1	Transfuse	no points if done previously
+10603	Andre Kumar	14	gi_bleed	41788	0	1	\N	?Order set?
+10604	Panel Average	14	gi_bleed	41788	0	\N	\N	\N
+11324	Jonathan Chen	14	gi_bleed	44237	6	4	Antibiotics	A bit overkill, but would cover GNRs
+9045	Andre Kumar	14	gi_bleed	48724	5	4	H pylori	\N
+9046	Jason Hom	14	gi_bleed	48724	5	4	\N	\N
+9047	Lisa Shieh	14	gi_bleed	48724	5	3	h pylori	\N
+9048	Panel Average	14	gi_bleed	48724	5	\N	\N	\N
+9049	Panel Consensus	14	gi_bleed	48724	5	\N	\N	\N
+10269	Jonathan Chen	14	gi_bleed	48724	1	2	\N	Antibody not as relevant as stool test
+9050	Jason Hom	14	gi_bleed	45948	5	4	\N	\N
+9051	Lisa Shieh	14	gi_bleed	45948	5	3	\N	\N
+9052	Panel Consensus	14	gi_bleed	45948	5	\N	\N	\N
+9583	Panel Average	14	gi_bleed	45948	4	\N	\N	\N
+9702	Jonathan Chen	14	gi_bleed	45948	3	3	\N	\N
+10270	Andre Kumar	14	gi_bleed	45948	1	4	H pylori	\N
+8532	Lisa Shieh	15	gi_bleed	45948	7	3	\N	\N
+9053	Jason Hom	15	gi_bleed	45948	5	4	\N	\N
+9054	Panel Consensus	15	gi_bleed	45948	5	\N	\N	\N
+9451	Panel Average	15	gi_bleed	45948	4	\N	\N	\N
+9703	Jonathan Chen	15	gi_bleed	45948	3	3	\N	\N
+10271	Andre Kumar	15	gi_bleed	45948	1	3	H pylori	\N
+7668	Jason Hom	15	gi_bleed	45891	10	5	\N	\N
+7669	Lisa Shieh	15	gi_bleed	45891	10	5	\N	\N
+7670	Panel Consensus	15	gi_bleed	45891	10	\N	\N	\N
+8312	Panel Average	15	gi_bleed	45891	8	\N	\N	\N
+8533	Jonathan Chen	15	gi_bleed	45891	7	4	CBC	\N
+9055	Andre Kumar	15	gi_bleed	45891	5	4	CBC	\N
+7671	Jason Hom	15	gi_bleed	46051	10	5	\N	\N
+7672	Lisa Shieh	15	gi_bleed	46051	10	5	\N	\N
+7673	Panel Consensus	15	gi_bleed	46051	10	\N	\N	\N
+8313	Panel Average	15	gi_bleed	46051	8	\N	\N	\N
+8534	Jonathan Chen	15	gi_bleed	46051	7	4	CBC	\N
+9056	Andre Kumar	15	gi_bleed	46051	5	5	CBC	\N
+11384	Jonathan Chen	15	gi_bleed	46068	0	2	\N	\N
+7674	Jason Hom	14	gi_bleed	45910	10	5	\N	\N
+8454	Panel Average	14	gi_bleed	45910	7	\N	\N	\N
+8535	Jonathan Chen	14	gi_bleed	45910	7	4	Metabolic Panel	\N
+8536	Lisa Shieh	14	gi_bleed	45910	7	3	\N	\N
+8537	Panel Consensus	14	gi_bleed	45910	7	\N	\N	\N
+9057	Andre Kumar	14	gi_bleed	45910	5	4	Liver Function Test	\N
+11441	Jonathan Chen	2	gi_bleed	62029	0	2	\N	Covered in hepatitis panel?
+7675	Jason Hom	15	gi_bleed	71083	10	5	\N	\N
+8729	Andre Kumar	15	gi_bleed	71083	6	4	ICU Bundle	\N
+8869	Panel Average	15	gi_bleed	71083	5	\N	\N	\N
+9058	Panel Consensus	15	gi_bleed	71083	5	\N	\N	\N
+10605	Jonathan Chen	15	gi_bleed	71083	0	2	\N	Not supposed to be directly ordered
+10606	Lisa Shieh	15	gi_bleed	71083	0	3	\N	\N
+7676	Lisa Shieh	14	gi_bleed	65649	10	3	\N	\N
+9452	Panel Average	14	gi_bleed	65649	4	\N	\N	\N
+9499	Panel Consensus	14	gi_bleed	65649	4	\N	\N	\N
+9704	Andre Kumar	14	gi_bleed	65649	3	3	NG tube	\N
+10036	Jonathan Chen	14	gi_bleed	65649	2	2	Airway Protection	May not be necessary, but reasonable
+10607	Jason Hom	14	gi_bleed	65649	0	4	\N	\N
+8538	Andre Kumar	15	gi_bleed	62022	7	5	Intubate	\N
+8539	Panel Consensus	15	gi_bleed	62022	7	\N	\N	\N
+9059	Jason Hom	15	gi_bleed	62022	5	4	\N	\N
+9500	Panel Average	15	gi_bleed	62022	4	\N	\N	\N
+10037	Jonathan Chen	15	gi_bleed	62022	2	2	Airway Protection	Wasn't supposed to be necessary, but not unreasonable if worried about ongoing hematemesis
+10608	Lisa Shieh	15	gi_bleed	62022	0	5	\N	\N
+7677	Jason Hom	14	gi_bleed	45942	10	5	\N	\N
+7678	Lisa Shieh	14	gi_bleed	45942	10	6	coags	\N
+8314	Panel Average	14	gi_bleed	45942	8	\N	\N	\N
+8351	Panel Consensus	14	gi_bleed	45942	8	\N	\N	\N
+8540	Jonathan Chen	14	gi_bleed	45942	7	4	Coags	\N
+9060	Andre Kumar	14	gi_bleed	45942	5	5	Coags	\N
+9061	Lisa Shieh	15	gi_bleed	45838	5	3	\N	\N
+10038	Jonathan Chen	15	gi_bleed	45838	2	3	Troponin	\N
+10218	Panel Average	15	gi_bleed	45838	2	\N	\N	\N
+10609	Andre Kumar	15	gi_bleed	45838	0	2	Troponin	\N
+10610	Jason Hom	15	gi_bleed	45838	0	4	\N	\N
+10611	Panel Consensus	15	gi_bleed	45838	0	\N	\N	\N
+7679	Jason Hom	14	gi_bleed	48954	10	4	\N	\N
+7680	Lisa Shieh	14	gi_bleed	48954	10	5	ivf	\N
+7681	Panel Consensus	14	gi_bleed	48954	10	\N	\N	\N
+8541	Panel Average	14	gi_bleed	48954	7	\N	\N	\N
+10039	Jonathan Chen	14	gi_bleed	48954	2	2	IV Access	\N
+10272	Andre Kumar	14	gi_bleed	48954	1	5	Nursing	\N
+11433	Jonathan Chen	15	gi_bleed	44978	0	2	\N	Peri-procedure sedation???
+9062	Jason Hom	14	gi_bleed	62151	5	4	\N	\N
+9937	Panel Average	14	gi_bleed	62151	2	\N	\N	\N
+10040	Andre Kumar	14	gi_bleed	62151	2	4	Lactate	\N
+10041	Jonathan Chen	14	gi_bleed	62151	2	4	Lactate	\N
+10042	Panel Consensus	14	gi_bleed	62151	2	\N	\N	\N
+10612	Lisa Shieh	14	gi_bleed	62151	0	3	\N	\N
+11360	Jonathan Chen	15	gi_bleed	62151	2	4	Lactate	\N
+11341	Jonathan Chen	16	gi_bleed	62151	2	4	Lactate	\N
+10043	Andre Kumar	14	gi_bleed	45918	2	4	Lactate	\N
+10044	Panel Average	14	gi_bleed	45918	2	\N	\N	\N
+10613	Lisa Shieh	15	gi_bleed	46289	0	3	\N	\N
+11026	Andre Kumar	15	gi_bleed	46289	-2	1	Hepatic Encephalopathy	\N
+11027	Jonathan Chen	15	gi_bleed	46289	-2	2	Lactulose	Maybe even negative points? Giving lactulose to make patient have diarrhea while they are actively bleeding out seems bad
+11028	Panel Consensus	15	gi_bleed	46289	-2	\N	\N	\N
+11071	Panel Average	15	gi_bleed	46289	-2	\N	\N	\N
+11155	Jason Hom	15	gi_bleed	46289	-5	4	\N	\N
+10614	Lisa Shieh	15	gi_bleed	44302	0	3	\N	\N
+11029	Andre Kumar	15	gi_bleed	44302	-2	5	Hepatic Encephalopathy	\N
+11030	Jonathan Chen	15	gi_bleed	44302	-2	2	Lactulose	Maybe even negative points? Giving lactulose to make patient have diarrhea while they are actively bleeding out seems bad
+11031	Panel Consensus	15	gi_bleed	44302	-2	\N	\N	\N
+11072	Panel Average	15	gi_bleed	44302	-2	\N	\N	\N
+11156	Jason Hom	15	gi_bleed	44302	-5	4	\N	\N
+10615	Lisa Shieh	15	gi_bleed	44593	0	3	\N	\N
+11032	Andre Kumar	15	gi_bleed	44593	-2	5	Hepatic Encephalopathy	\N
+11033	Jonathan Chen	15	gi_bleed	44593	-2	2	Lactulose	Maybe even negative points? Giving lactulose to make patient have diarrhea while they are actively bleeding out seems bad
+11034	Panel Consensus	15	gi_bleed	44593	-2	\N	\N	\N
+11073	Panel Average	15	gi_bleed	44593	-2	\N	\N	\N
+11157	Jason Hom	15	gi_bleed	44593	-5	4	\N	\N
+11316	Jonathan Chen	14	gi_bleed	45903	0	2	\N	\N
+11426	Jonathan Chen	14	gi_bleed	62144	0	0	\N	\N
+9705	Andre Kumar	16	gi_bleed	62144	3	3	Imaging	\N
+9706	Panel Average	16	gi_bleed	62144	3	\N	\N	\N
+10616	Andre Kumar	14	gi_bleed	45894	0	2	Lipase	\N
+10617	Jason Hom	14	gi_bleed	45894	0	4	\N	\N
+10618	Jonathan Chen	14	gi_bleed	45894	0	2	\N	Irrelevant diagnostic?
+10619	Lisa Shieh	14	gi_bleed	45894	0	3	\N	\N
+10620	Panel Average	14	gi_bleed	45894	0	\N	\N	\N
+10621	Panel Consensus	14	gi_bleed	45894	0	\N	\N	\N
+7682	Jason Hom	15	gi_bleed	45806	10	4	\N	\N
+9603	Panel Average	15	gi_bleed	45806	3	\N	\N	\N
+10045	Panel Consensus	15	gi_bleed	45806	2	\N	\N	\N
+10622	Andre Kumar	15	gi_bleed	45806	0	5	Metabolic Panel	\N
+10623	Jonathan Chen	15	gi_bleed	45806	0	2	\N	\N
+10624	Lisa Shieh	15	gi_bleed	45806	0	3	\N	\N
+10046	Andre Kumar	2	gi_bleed	45806	2	3	Metabolic Panel	\N
+10047	Panel Average	2	gi_bleed	45806	2	\N	\N	\N
+9063	Andre Kumar	14	gi_bleed	63745	5	2	Transfuse	\N
+9064	Jason Hom	14	gi_bleed	63745	5	4	\N	\N
+9604	Panel Average	14	gi_bleed	63745	3	\N	\N	\N
+10625	Jonathan Chen	14	gi_bleed	63745	0	2	\N	\N
+10626	Lisa Shieh	14	gi_bleed	63745	0	3	\N	\N
+10627	Panel Consensus	14	gi_bleed	63745	0	\N	\N	\N
+11244	Jonathan Chen	15	gi_bleed	63745	0	2	\N	\N
+7683	Jason Hom	14	gi_bleed	45763	10	4	\N	\N
+7684	Lisa Shieh	14	gi_bleed	45763	10	5	\N	\N
+8352	Panel Consensus	14	gi_bleed	45763	8	\N	\N	\N
+8433	Panel Average	14	gi_bleed	45763	8	\N	\N	\N
+8542	Jonathan Chen	14	gi_bleed	45763	7	4	Metabolic Panel	\N
+9707	Andre Kumar	14	gi_bleed	45763	3	3	Metabolic Panel	\N
+7685	Jason Hom	15	gi_bleed	45763	10	4	\N	\N
+7686	Lisa Shieh	15	gi_bleed	45763	10	5	\N	\N
+8353	Panel Consensus	15	gi_bleed	45763	8	\N	\N	\N
+8434	Panel Average	15	gi_bleed	45763	8	\N	\N	\N
+8543	Jonathan Chen	15	gi_bleed	45763	7	4	Metabolic Panel	\N
+9708	Andre Kumar	15	gi_bleed	45763	3	4	Metabolic Panel	\N
+7687	Jason Hom	2	gi_bleed	45763	10	4	\N	\N
+7688	Lisa Shieh	2	gi_bleed	45763	10	5	\N	\N
+8315	Panel Average	2	gi_bleed	45763	8	\N	\N	\N
+8354	Panel Consensus	2	gi_bleed	45763	8	\N	\N	\N
+8544	Jonathan Chen	2	gi_bleed	45763	7	4	Metabolic Panel	\N
+9065	Andre Kumar	2	gi_bleed	45763	5	2	Metabolic Panel	\N
+7689	Jason Hom	14	gi_bleed	45771	10	4	\N	\N
+7690	Lisa Shieh	14	gi_bleed	45771	10	5	\N	\N
+8258	Panel Consensus	14	gi_bleed	45771	9	\N	\N	\N
+8355	Panel Average	14	gi_bleed	45771	8	\N	\N	\N
+8545	Jonathan Chen	14	gi_bleed	45771	7	4	Metabolic Panel	\N
+9501	Andre Kumar	14	gi_bleed	45771	4	5	Metabolic Panel	\N
+7691	Jason Hom	15	gi_bleed	45771	10	4	\N	\N
+7692	Lisa Shieh	15	gi_bleed	45771	10	5	\N	\N
+8259	Panel Consensus	15	gi_bleed	45771	9	\N	\N	\N
+8435	Panel Average	15	gi_bleed	45771	8	\N	\N	\N
+8546	Jonathan Chen	15	gi_bleed	45771	7	4	Metabolic Panel	\N
+9709	Andre Kumar	15	gi_bleed	45771	3	4	Metabolic Panel	\N
+11342	Jonathan Chen	2	gi_bleed	45771	7	4	Metabolic Panel	\N
+11035	Andre Kumar	14	gi_bleed	44294	-2	5	Pain Medication	\N
+11036	Panel Average	14	gi_bleed	44294	-2	\N	\N	\N
+10628	Andre Kumar	15	gi_bleed	45792	0	2	MRSA	\N
+10629	Jason Hom	15	gi_bleed	45792	0	4	\N	\N
+10630	Jonathan Chen	15	gi_bleed	45792	0	2	\N	\N
+10631	Lisa Shieh	15	gi_bleed	45792	0	3	\N	\N
+10632	Panel Average	15	gi_bleed	45792	0	\N	\N	\N
+10633	Panel Consensus	15	gi_bleed	45792	0	\N	\N	\N
+7693	Jason Hom	14	gi_bleed	45785	10	4	\N	\N
+9605	Panel Average	14	gi_bleed	45785	3	\N	\N	\N
+10634	Andre Kumar	14	gi_bleed	45785	0	1	Nursing	\N
+10635	Jonathan Chen	14	gi_bleed	45785	0	2	\N	\N
+10636	Lisa Shieh	14	gi_bleed	45785	0	\N	\N	\N
+10637	Panel Consensus	14	gi_bleed	45785	0	\N	\N	\N
+8730	Andre Kumar	15	gi_bleed	36086	6	3	Pressors	\N
+9066	Jason Hom	15	gi_bleed	36086	5	4	\N	\N
+9067	Panel Consensus	15	gi_bleed	36086	5	\N	\N	\N
+9584	Panel Average	15	gi_bleed	36086	4	\N	\N	\N
+10638	Jonathan Chen	15	gi_bleed	36086	0	2	Pressors	\N
+10639	Lisa Shieh	15	gi_bleed	36086	0	3	\N	\N
+10273	Andre Kumar	14	gi_bleed	48815	1	3	Stool test	\N
+10274	Panel Average	14	gi_bleed	48815	1	\N	\N	\N
+11422	Jonathan Chen	14	gi_bleed	51110	0	2	\N	Largely irrelevant in acute setting
+9710	Jonathan Chen	14	gi_bleed	46451	3	3	Octreotide	Subcutaneous probably not the correct route
+10640	Lisa Shieh	14	gi_bleed	46451	0	3	Octreotide	\N
+10987	Panel Average	14	gi_bleed	46451	-1	\N	\N	\N
+11037	Andre Kumar	14	gi_bleed	46451	-2	2	Bleeding Medication	\N
+11038	Jason Hom	14	gi_bleed	46451	-2	4	\N	\N
+11039	Panel Consensus	14	gi_bleed	46451	-2	\N	\N	\N
+9068	Lisa Shieh	15	gi_bleed	46451	5	3	\N	\N
+9711	Jonathan Chen	15	gi_bleed	46451	3	3	Octreotide	Subcutaneous probably not the correct route
+10393	Panel Average	15	gi_bleed	46451	0	\N	\N	\N
+11040	Andre Kumar	15	gi_bleed	46451	-2	3	Bleeding Medication	\N
+11041	Jason Hom	15	gi_bleed	46451	-2	4	\N	\N
+11042	Panel Consensus	15	gi_bleed	46451	-2	\N	\N	\N
+7694	Andre Kumar	14	gi_bleed	43996	10	5	Bleeding Medication	\N
+7695	Jason Hom	14	gi_bleed	43996	10	5	\N	\N
+7696	Panel Consensus	14	gi_bleed	43996	10	\N	\N	\N
+8260	Panel Average	14	gi_bleed	43996	9	\N	\N	\N
+8547	Lisa Shieh	14	gi_bleed	43996	7	3	\N	no known cirrhosis but angiomata on exam
+9069	Jonathan Chen	14	gi_bleed	43996	5	4	Octreotide	Presumptive variceal bleed
+7697	Andre Kumar	15	gi_bleed	43996	10	4	Bleeding Medication	\N
+7698	Jason Hom	15	gi_bleed	43996	10	5	\N	\N
+7699	Lisa Shieh	15	gi_bleed	43996	10	5	\N	\N
+7700	Panel Average	15	gi_bleed	43996	10	\N	\N	\N
+7701	Panel Consensus	15	gi_bleed	43996	10	\N	\N	\N
+9070	Jonathan Chen	15	gi_bleed	43996	5	4	Octreotide	Presumptive variceal bleed
+7702	Jason Hom	16	gi_bleed	43996	10	5	\N	\N
+7703	Lisa Shieh	16	gi_bleed	43996	10	5	\N	\N
+7704	Panel Consensus	16	gi_bleed	43996	10	\N	\N	\N
+8668	Panel Average	16	gi_bleed	43996	7	\N	\N	\N
+9071	Jonathan Chen	16	gi_bleed	43996	5	4	Octreotide	Presumptive variceal bleed
+10641	Andre Kumar	16	gi_bleed	43996	0	2	Bleeding Medication	\N
+7705	Jason Hom	2	gi_bleed	43996	10	5	\N	\N
+7706	Lisa Shieh	2	gi_bleed	43996	10	5	\N	\N
+7707	Panel Consensus	2	gi_bleed	43996	10	\N	\N	\N
+8669	Panel Average	2	gi_bleed	43996	7	\N	\N	\N
+9072	Jonathan Chen	2	gi_bleed	43996	5	4	Octreotide	Presumptive variceal bleed
+10642	Andre Kumar	2	gi_bleed	43996	0	2	Bleeding Medication	\N
+9073	Lisa Shieh	15	gi_bleed	46449	5	3	\N	\N
+9712	Jonathan Chen	15	gi_bleed	46449	3	3	Octreotide	Subcutaneous probably not the correct route
+10394	Panel Average	15	gi_bleed	46449	0	\N	\N	\N
+11043	Andre Kumar	15	gi_bleed	46449	-2	3	Bleeding Medication	\N
+11044	Jason Hom	15	gi_bleed	46449	-2	4	\N	\N
+11045	Panel Consensus	15	gi_bleed	46449	-2	\N	\N	\N
+9074	Jason Hom	14	gi_bleed	44216	5	4	\N	\N
+10048	Panel Average	14	gi_bleed	44216	2	\N	\N	\N
+10049	Panel Consensus	14	gi_bleed	44216	2	\N	\N	\N
+10275	Andre Kumar	14	gi_bleed	44216	1	2	Nausea Medication	\N
+10643	Jonathan Chen	14	gi_bleed	44216	0	2	\N	\N
+10644	Lisa Shieh	14	gi_bleed	44216	0	3	\N	\N
+7708	Lisa Shieh	14	gi_bleed	45868	10	5	\N	\N
+8356	Jason Hom	14	gi_bleed	45868	8	4	\N	\N
+8357	Panel Consensus	14	gi_bleed	45868	8	\N	\N	\N
+8731	Panel Average	14	gi_bleed	45868	6	\N	\N	\N
+9713	Jonathan Chen	14	gi_bleed	45868	3	2	\N	\N
+10645	Andre Kumar	14	gi_bleed	45868	0	5	Vitals	\N
+10050	Andre Kumar	2	gi_bleed	45787	2	1	PT/OT	\N
+10051	Panel Average	2	gi_bleed	45787	2	\N	\N	\N
+11410	Jonathan Chen	14	gi_bleed	48822	0	2	Oxygen	No specific hypoxia in case
+11264	Jonathan Chen	14	gi_bleed	45900	0	2	Oxygen	No specific hypoxia in case
+7709	Andre Kumar	14	gi_bleed	44219	10	5	PPI	\N
+7710	Jason Hom	14	gi_bleed	44219	10	5	\N	\N
+7711	Lisa Shieh	14	gi_bleed	44219	10	5	\N	\N
+7712	Panel Average	14	gi_bleed	44219	10	\N	\N	\N
+7713	Panel Consensus	14	gi_bleed	44219	10	\N	\N	\N
+9075	Jonathan Chen	14	gi_bleed	44219	5	4	\N	\N
+7714	Andre Kumar	15	gi_bleed	44219	10	5	Bleeding Medication	\N
+7715	Jason Hom	15	gi_bleed	44219	10	5	\N	\N
+7716	Lisa Shieh	15	gi_bleed	44219	10	5	\N	\N
+7717	Panel Average	15	gi_bleed	44219	10	\N	\N	\N
+7718	Panel Consensus	15	gi_bleed	44219	10	\N	\N	\N
+9076	Jonathan Chen	15	gi_bleed	44219	5	4	\N	\N
+7719	Jason Hom	16	gi_bleed	44219	10	5	\N	\N
+7720	Lisa Shieh	16	gi_bleed	44219	10	5	\N	\N
+7721	Panel Average	16	gi_bleed	44219	10	\N	\N	\N
+7722	Panel Consensus	16	gi_bleed	44219	10	\N	\N	\N
+9077	Jonathan Chen	16	gi_bleed	44219	5	4	\N	\N
+11261	Jonathan Chen	14	gi_bleed	44236	0	3	\N	Mistake, should be giving oral med while vomiting blood
+9078	Lisa Shieh	15	gi_bleed	44236	5	3	\N	\N
+10052	Andre Kumar	15	gi_bleed	44236	2	4	Bleeding Medication	\N
+10385	Panel Average	15	gi_bleed	44236	1	\N	\N	\N
+10646	Jonathan Chen	15	gi_bleed	44236	0	3	\N	Mistake, should be giving oral med while vomiting blood
+10647	Panel Consensus	15	gi_bleed	44236	0	\N	\N	\N
+11158	Jason Hom	15	gi_bleed	44236	-5	5	\N	\N
+11303	Jonathan Chen	15	gi_bleed	62171	0	2	\N	Active bleeding, probably not right time for para, though SBP always to consider?
+7723	Jason Hom	14	gi_bleed	61823	10	5	\N	\N
+7724	Lisa Shieh	14	gi_bleed	61823	10	5	\N	\N
+7725	Panel Consensus	14	gi_bleed	61823	10	\N	\N	\N
+8316	Panel Average	14	gi_bleed	61823	8	\N	\N	\N
+9079	Andre Kumar	14	gi_bleed	61823	5	4	Nursing	\N
+10053	Jonathan Chen	14	gi_bleed	61823	2	2	IV Access	\N
+7726	Jason Hom	15	gi_bleed	61823	10	5	\N	\N
+7727	Lisa Shieh	15	gi_bleed	61823	10	5	\N	\N
+7728	Panel Consensus	15	gi_bleed	61823	10	\N	\N	\N
+8317	Panel Average	15	gi_bleed	61823	8	\N	\N	\N
+9080	Andre Kumar	15	gi_bleed	61823	5	1	Nursing	\N
+10054	Jonathan Chen	15	gi_bleed	61823	2	2	IV Access	\N
+7729	Jason Hom	14	gi_bleed	45802	10	5	\N	\N
+7730	Lisa Shieh	14	gi_bleed	45802	10	5	\N	\N
+7731	Panel Consensus	14	gi_bleed	45802	10	\N	\N	\N
+8318	Panel Average	14	gi_bleed	45802	8	\N	\N	\N
+9081	Andre Kumar	14	gi_bleed	45802	5	5	Nursing	\N
+10055	Jonathan Chen	14	gi_bleed	45802	2	2	IV Access	Maybe trying to capture placing large bore IVs. Is important, but don't want to have others lose too many points for missing this, because many won't realize that can order this (usually you just tell a nurse)
+8732	Andre Kumar	15	gi_bleed	43993	6	3	Pressors	\N
+9714	Panel Consensus	15	gi_bleed	43993	3	\N	\N	\N
+10395	Panel Average	15	gi_bleed	43993	0	\N	\N	\N
+10648	Jonathan Chen	15	gi_bleed	43993	0	2	Pressors	\N
+10649	Lisa Shieh	15	gi_bleed	43993	0	3	\N	\N
+11159	Jason Hom	15	gi_bleed	43993	-5	5	\N	\N
+7732	Jason Hom	15	gi_bleed	45778	10	5	\N	\N
+9453	Panel Average	15	gi_bleed	45778	4	\N	\N	\N
+9502	Panel Consensus	15	gi_bleed	45778	4	\N	\N	\N
+9715	Andre Kumar	15	gi_bleed	45778	3	4	Coags	\N
+10650	Jonathan Chen	15	gi_bleed	45778	0	2	\N	\N
+10651	Lisa Shieh	15	gi_bleed	45778	0	3	\N	\N
+7733	Jason Hom	2	gi_bleed	45778	10	5	\N	\N
+8846	Panel Average	2	gi_bleed	45778	6	\N	\N	\N
+9082	Lisa Shieh	2	gi_bleed	45778	5	3	\N	\N
+9503	Panel Consensus	2	gi_bleed	45778	4	\N	\N	\N
+10056	Andre Kumar	2	gi_bleed	45778	2	1	Metabolic Panel	\N
+10652	Jonathan Chen	2	gi_bleed	45778	0	2	\N	\N
+8358	Jason Hom	14	gi_bleed	45875	8	4	\N	\N
+9915	Panel Average	14	gi_bleed	45875	3	\N	\N	\N
+10057	Jonathan Chen	14	gi_bleed	45875	2	2	\N	Some points for preparing platelets, even though ends up not being necessary
+10653	Andre Kumar	14	gi_bleed	45875	0	1	Transfuse	\N
+10654	Lisa Shieh	14	gi_bleed	45875	0	5	\N	\N
+10655	Panel Consensus	14	gi_bleed	45875	0	\N	\N	\N
+7734	Jason Hom	15	gi_bleed	45875	10	5	\N	\N
+9938	Panel Average	15	gi_bleed	45875	2	\N	\N	\N
+10058	Jonathan Chen	15	gi_bleed	45875	2	2	\N	Some points for preparing platelets, even though ends up not being necessary
+10656	Lisa Shieh	15	gi_bleed	45875	0	5	\N	\N
+10657	Panel Consensus	15	gi_bleed	45875	0	\N	\N	\N
+11085	Andre Kumar	15	gi_bleed	45875	-3	5	Transfuse	\N
+11351	Jonathan Chen	15	gi_bleed	45955	2	4	Lactate	\N
+9083	Lisa Shieh	14	gi_bleed	50267	5	3	\N	\N
+10059	Jonathan Chen	14	gi_bleed	50267	2	2	\N	Okay, but again, don't do too many points, since most people just do this or see in physical exam, not an order
+10060	Panel Average	14	gi_bleed	50267	2	\N	\N	\N
+10276	Andre Kumar	14	gi_bleed	50267	1	1	Stool test	\N
+10658	Jason Hom	14	gi_bleed	50267	0	5	\N	\N
+10659	Panel Consensus	14	gi_bleed	50267	0	\N	\N	\N
+11365	Jonathan Chen	15	gi_bleed	44292	0	2	\N	Pre-procedure sedation?
+10660	Jonathan Chen	14	gi_bleed	49904	0	4	\N	Wrong, they were looking for Prothrombin Time
+10661	Lisa Shieh	14	gi_bleed	49904	0	5	\N	\N
+10992	Panel Average	14	gi_bleed	49904	-2	\N	\N	\N
+11046	Jason Hom	14	gi_bleed	49904	-2	5	\N	\N
+11047	Panel Consensus	14	gi_bleed	49904	-2	\N	\N	\N
+11086	Andre Kumar	14	gi_bleed	49904	-3	3	Thrombophilia	\N
+7735	Jason Hom	14	gi_bleed	45759	10	5	\N	\N
+7736	Lisa Shieh	14	gi_bleed	45759	10	5	\N	\N
+8319	Panel Average	14	gi_bleed	45759	8	\N	\N	\N
+8359	Panel Consensus	14	gi_bleed	45759	8	\N	\N	\N
+8548	Jonathan Chen	14	gi_bleed	45759	7	4	Coags	Key diagnostic
+9084	Andre Kumar	14	gi_bleed	45759	5	5	Coags	\N
+7737	Jason Hom	15	gi_bleed	45759	10	5	\N	\N
+7738	Lisa Shieh	15	gi_bleed	45759	10	5	\N	\N
+8320	Panel Average	15	gi_bleed	45759	8	\N	\N	\N
+8360	Panel Consensus	15	gi_bleed	45759	8	\N	\N	\N
+8549	Jonathan Chen	15	gi_bleed	45759	7	4	Coags	Key diagnostic
+9085	Andre Kumar	15	gi_bleed	45759	5	5	Coags	\N
+7739	Jason Hom	16	gi_bleed	45759	10	5	\N	\N
+7740	Lisa Shieh	16	gi_bleed	45759	10	5	\N	\N
+8361	Panel Consensus	16	gi_bleed	45759	8	\N	\N	\N
+8550	Jonathan Chen	16	gi_bleed	45759	7	4	Coags	Key diagnostic
+8551	Panel Average	16	gi_bleed	45759	7	\N	\N	\N
+10277	Andre Kumar	16	gi_bleed	45759	1	2	Coags	\N
+7741	Jason Hom	2	gi_bleed	45759	10	5	\N	\N
+7742	Lisa Shieh	2	gi_bleed	45759	10	5	\N	\N
+8362	Panel Consensus	2	gi_bleed	45759	8	\N	\N	\N
+8436	Panel Average	2	gi_bleed	45759	8	\N	\N	\N
+8552	Jonathan Chen	2	gi_bleed	45759	7	4	Coags	Key diagnostic
+9716	Andre Kumar	2	gi_bleed	45759	3	2	Coags	\N
+10061	Andre Kumar	2	gi_bleed	45776	2	1	Coags	\N
+10062	Panel Average	2	gi_bleed	45776	2	\N	\N	\N
+7743	Jason Hom	14	gi_bleed	45770	10	4	\N	\N
+7744	Lisa Shieh	14	gi_bleed	45770	10	5	\N	\N
+8321	Panel Average	14	gi_bleed	45770	8	\N	\N	\N
+8363	Panel Consensus	14	gi_bleed	45770	8	\N	\N	\N
+8553	Jonathan Chen	14	gi_bleed	45770	7	4	Coags	Key diagnostic
+9086	Andre Kumar	14	gi_bleed	45770	5	5	Coags	\N
+7745	Jason Hom	15	gi_bleed	45770	10	4	\N	\N
+7746	Lisa Shieh	15	gi_bleed	45770	10	5	\N	\N
+8322	Panel Average	15	gi_bleed	45770	8	\N	\N	\N
+8364	Panel Consensus	15	gi_bleed	45770	8	\N	\N	\N
+8554	Jonathan Chen	15	gi_bleed	45770	7	4	Coags	Key diagnostic
+9087	Andre Kumar	15	gi_bleed	45770	5	4	Coags	\N
+7747	Jason Hom	2	gi_bleed	45770	10	4	\N	\N
+7748	Lisa Shieh	2	gi_bleed	45770	10	5	\N	\N
+8365	Panel Consensus	2	gi_bleed	45770	8	\N	\N	\N
+8437	Panel Average	2	gi_bleed	45770	8	\N	\N	\N
+8555	Jonathan Chen	2	gi_bleed	45770	7	4	Coags	Key diagnostic
+9717	Andre Kumar	2	gi_bleed	45770	3	2	Coags	\N
+9504	Andre Kumar	2	gi_bleed	62103	4	5	HIV	\N
+10234	Panel Average	2	gi_bleed	62103	1	\N	\N	\N
+10662	Jason Hom	2	gi_bleed	62103	0	4	\N	\N
+10663	Jonathan Chen	2	gi_bleed	62103	0	2	\N	\N
+10664	Lisa Shieh	2	gi_bleed	62103	0	3	\N	\N
+10665	Panel Consensus	2	gi_bleed	62103	0	\N	\N	\N
+7749	Jason Hom	14	gi_bleed	45927	10	5	\N	\N
+7750	Jonathan Chen	14	gi_bleed	45927	10	5	RBC	\N
+7751	Panel Consensus	14	gi_bleed	45927	10	\N	\N	\N
+8366	Andre Kumar	14	gi_bleed	45927	8	5	Transfuse	\N
+8733	Panel Average	14	gi_bleed	45927	6	\N	\N	\N
+10666	Lisa Shieh	14	gi_bleed	45927	0	3	\N	\N
+7752	Andre Kumar	15	gi_bleed	45927	10	5	Transfuse	\N
+7753	Jason Hom	15	gi_bleed	45927	10	5	\N	\N
+7754	Jonathan Chen	15	gi_bleed	45927	10	5	RBC	\N
+7755	Lisa Shieh	15	gi_bleed	45927	10	5	\N	\N
+7756	Panel Average	15	gi_bleed	45927	10	\N	\N	\N
+7757	Panel Consensus	15	gi_bleed	45927	10	\N	\N	\N
+7758	Jason Hom	16	gi_bleed	45927	10	5	\N	\N
+7759	Jonathan Chen	16	gi_bleed	45927	10	5	RBC	\N
+7760	Lisa Shieh	16	gi_bleed	45927	10	5	\N	\N
+7761	Panel Consensus	16	gi_bleed	45927	10	\N	\N	\N
+8261	Panel Average	16	gi_bleed	45927	9	\N	\N	\N
+8556	Andre Kumar	16	gi_bleed	45927	7	1	Transfuse	\N
+7762	Jason Hom	2	gi_bleed	45927	10	5	\N	\N
+7763	Jonathan Chen	2	gi_bleed	45927	10	5	RBC	\N
+7764	Lisa Shieh	2	gi_bleed	45927	10	5	\N	\N
+7765	Panel Consensus	2	gi_bleed	45927	10	\N	\N	\N
+8262	Panel Average	2	gi_bleed	45927	9	\N	\N	\N
+8557	Andre Kumar	2	gi_bleed	45927	7	2	Transfuse	If not done previously, otherwise 0
+11349	Jonathan Chen	15	gi_bleed	45832	0	2	\N	?Mechanical ventilation? Airway protection?
+10278	Andre Kumar	15	gi_bleed	44424	1	1	Hepatic Encephalopathy	\N
+10667	Lisa Shieh	15	gi_bleed	44424	0	3	\N	\N
+10668	Panel Consensus	15	gi_bleed	44424	0	\N	\N	\N
+10988	Panel Average	15	gi_bleed	44424	-1	\N	\N	\N
+11048	Jonathan Chen	15	gi_bleed	44424	-2	2	Lactulose	Maybe even negative points? Giving lactulose to make patient have diarrhea while they are actively bleeding out seems bad
+11160	Jason Hom	15	gi_bleed	44424	-5	4	\N	\N
+11437	Jonathan Chen	14	gi_bleed	49173	0	2	\N	?Intoxication not really active issue
+9718	Jonathan Chen	14	gi_bleed	63725	3	4	Lactate	\N
+10063	Andre Kumar	14	gi_bleed	63725	2	4	Lactate	\N
+10279	Panel Consensus	14	gi_bleed	63725	1	\N	\N	\N
+10386	Panel Average	14	gi_bleed	63725	1	\N	\N	\N
+10669	Jason Hom	14	gi_bleed	63725	0	4	\N	\N
+10670	Lisa Shieh	14	gi_bleed	63725	0	3	\N	\N
+7766	Jason Hom	14	gi_bleed	44198	10	4	\N	\N
+7767	Lisa Shieh	14	gi_bleed	44198	10	5	ivf	\N
+8323	Panel Average	14	gi_bleed	44198	8	\N	\N	\N
+8367	Panel Consensus	14	gi_bleed	44198	8	\N	\N	\N
+9088	Andre Kumar	14	gi_bleed	44198	5	2	Fluids	\N
+9719	Jonathan Chen	14	gi_bleed	44198	3	3	IVF	Okay for resuscitation, but needs blood more
+7768	Jason Hom	15	gi_bleed	44198	10	4	\N	\N
+7769	Lisa Shieh	15	gi_bleed	44198	10	5	\N	\N
+8229	Panel Average	15	gi_bleed	44198	9	\N	\N	\N
+8368	Andre Kumar	15	gi_bleed	44198	8	2	Fluids	\N
+8369	Panel Consensus	15	gi_bleed	44198	8	\N	\N	\N
+9720	Jonathan Chen	15	gi_bleed	44198	3	3	IVF	Okay for resuscitation, but needs blood more
+11457	Jonathan Chen	15	gi_bleed	44441	0	2	\N	?Pre-intubation for airway protection?
+11450	Jonathan Chen	14	gi_bleed	61993	9	4	FFP	\N
+7770	Jason Hom	14	gi_bleed	45877	10	5	\N	\N
+8263	Jonathan Chen	14	gi_bleed	45877	9	4	FFP	\N
+8370	Andre Kumar	14	gi_bleed	45877	8	5	Transfuse	\N
+8371	Panel Consensus	14	gi_bleed	45877	8	\N	\N	\N
+8734	Panel Average	14	gi_bleed	45877	6	\N	\N	\N
+10671	Lisa Shieh	14	gi_bleed	45877	0	3	\N	\N
+11391	Jonathan Chen	15	gi_bleed	61993	9	4	FFP	\N
+7771	Jason Hom	15	gi_bleed	45877	10	5	\N	\N
+7772	Lisa Shieh	15	gi_bleed	45877	10	5	\N	\N
+8230	Panel Average	15	gi_bleed	45877	9	\N	\N	\N
+8264	Jonathan Chen	15	gi_bleed	45877	9	4	FFP	\N
+8372	Andre Kumar	15	gi_bleed	45877	8	5	Transfuse	\N
+8373	Panel Consensus	15	gi_bleed	45877	8	\N	\N	\N
+11407	Jonathan Chen	16	gi_bleed	61993	9	4	FFP	\N
+7773	Jason Hom	16	gi_bleed	45877	10	5	\N	\N
+7774	Lisa Shieh	16	gi_bleed	45877	10	5	\N	\N
+8265	Jonathan Chen	16	gi_bleed	45877	9	4	FFP	\N
+8266	Panel Average	16	gi_bleed	45877	9	\N	\N	\N
+8374	Panel Consensus	16	gi_bleed	45877	8	\N	\N	\N
+8558	Andre Kumar	16	gi_bleed	45877	7	2	Transfuse	no points if done previously
+7775	Jason Hom	14	gi_bleed	65702	10	5	\N	\N
+7776	Lisa Shieh	14	gi_bleed	65702	10	3	\N	\N
+8231	Panel Average	14	gi_bleed	65702	9	\N	\N	\N
+8267	Jonathan Chen	14	gi_bleed	65702	9	4	FFP	\N
+8375	Andre Kumar	14	gi_bleed	65702	8	5	Transfuse	\N
+8376	Panel Consensus	14	gi_bleed	65702	8	\N	\N	\N
+7777	Jason Hom	15	gi_bleed	65702	10	5	\N	\N
+7778	Lisa Shieh	15	gi_bleed	65702	10	5	\N	\N
+8232	Panel Average	15	gi_bleed	65702	9	\N	\N	\N
+8268	Jonathan Chen	15	gi_bleed	65702	9	4	FFP	\N
+8377	Andre Kumar	15	gi_bleed	65702	8	5	Transfuse	\N
+8378	Panel Consensus	15	gi_bleed	65702	8	\N	\N	\N
+7779	Jason Hom	16	gi_bleed	65702	10	5	\N	\N
+7780	Lisa Shieh	16	gi_bleed	65702	10	5	\N	\N
+8269	Jonathan Chen	16	gi_bleed	65702	9	4	FFP	\N
+8270	Panel Average	16	gi_bleed	65702	9	\N	\N	\N
+8379	Panel Consensus	16	gi_bleed	65702	8	\N	\N	\N
+8559	Andre Kumar	16	gi_bleed	65702	7	2	Transfuse	no points if done previously
+8380	Jason Hom	14	gi_bleed	65646	8	4	\N	\N
+10219	Panel Average	14	gi_bleed	65646	2	\N	\N	\N
+10672	Jonathan Chen	14	gi_bleed	65646	0	2	\N	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+10673	Lisa Shieh	14	gi_bleed	65646	0	3	\N	\N
+10674	Panel Consensus	14	gi_bleed	65646	0	\N	\N	\N
+11087	Andre Kumar	14	gi_bleed	65646	-3	5	Transfuse	No indication
+8381	Jason Hom	15	gi_bleed	65646	8	4	\N	\N
+10220	Panel Average	15	gi_bleed	65646	2	\N	\N	\N
+10675	Jonathan Chen	15	gi_bleed	65646	0	2	\N	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+10676	Lisa Shieh	15	gi_bleed	65646	0	3	\N	\N
+10677	Panel Consensus	15	gi_bleed	65646	0	\N	\N	\N
+11088	Andre Kumar	15	gi_bleed	65646	-3	5	Transfuse	\N
+11465	Jonathan Chen	14	gi_bleed	45956	0	2	\N	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+11306	Jonathan Chen	15	gi_bleed	61976	0	2	\N	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+7781	Jason Hom	15	gi_bleed	45956	10	5	\N	\N
+7782	Lisa Shieh	15	gi_bleed	45956	10	5	\N	\N
+8847	Panel Average	15	gi_bleed	45956	6	\N	\N	\N
+10678	Jonathan Chen	15	gi_bleed	45956	0	2	\N	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+10679	Panel Consensus	15	gi_bleed	45956	0	\N	\N	\N
+11089	Andre Kumar	15	gi_bleed	45956	-3	5	Transfuse	\N
+11408	Jonathan Chen	14	gi_bleed	45748	10	5	RBC	\N
+7783	Jason Hom	14	gi_bleed	61975	10	5	\N	\N
+7784	Jonathan Chen	14	gi_bleed	61975	10	5	RBC	\N
+7785	Lisa Shieh	14	gi_bleed	61975	10	3	\N	do not know hbg yet
+7786	Panel Consensus	14	gi_bleed	61975	10	\N	\N	\N
+8233	Panel Average	14	gi_bleed	61975	9	\N	\N	\N
+8382	Andre Kumar	14	gi_bleed	61975	8	5	Transfuse	\N
+11467	Jonathan Chen	15	gi_bleed	45748	10	5	RBC	\N
+7787	Andre Kumar	15	gi_bleed	61975	10	5	Transfuse	\N
+7788	Jason Hom	15	gi_bleed	61975	10	5	\N	\N
+7789	Jonathan Chen	15	gi_bleed	61975	10	5	RBC	\N
+7790	Lisa Shieh	15	gi_bleed	61975	10	5	\N	\N
+7791	Panel Average	15	gi_bleed	61975	10	\N	\N	\N
+7792	Panel Consensus	15	gi_bleed	61975	10	\N	\N	\N
+7793	Jason Hom	16	gi_bleed	61975	10	5	\N	\N
+7794	Jonathan Chen	16	gi_bleed	61975	10	5	RBC	\N
+7795	Lisa Shieh	16	gi_bleed	61975	10	5	\N	\N
+7796	Panel Consensus	16	gi_bleed	61975	10	\N	\N	\N
+8271	Panel Average	16	gi_bleed	61975	9	\N	\N	\N
+8560	Andre Kumar	16	gi_bleed	61975	7	2	Transfuse	no points if done previously
+7797	Jason Hom	14	gi_bleed	65640	10	5	\N	\N
+7798	Jonathan Chen	14	gi_bleed	65640	10	5	RBC	\N
+7799	Lisa Shieh	14	gi_bleed	65640	10	3	\N	\N
+7800	Panel Consensus	14	gi_bleed	65640	10	\N	\N	\N
+8234	Panel Average	14	gi_bleed	65640	9	\N	\N	\N
+8383	Andre Kumar	14	gi_bleed	65640	8	5	Transfuse	\N
+7801	Andre Kumar	15	gi_bleed	65640	10	5	Transfuse	\N
+7802	Jason Hom	15	gi_bleed	65640	10	5	\N	\N
+7803	Jonathan Chen	15	gi_bleed	65640	10	5	RBC	\N
+7804	Lisa Shieh	15	gi_bleed	65640	10	5	\N	\N
+7805	Panel Average	15	gi_bleed	65640	10	\N	\N	\N
+7806	Panel Consensus	15	gi_bleed	65640	10	\N	\N	\N
+7807	Jason Hom	16	gi_bleed	65640	10	5	\N	\N
+7808	Jonathan Chen	16	gi_bleed	65640	10	5	RBC	\N
+7809	Lisa Shieh	16	gi_bleed	65640	10	5	\N	\N
+7810	Panel Consensus	16	gi_bleed	65640	10	\N	\N	\N
+8272	Panel Average	16	gi_bleed	65640	9	\N	\N	\N
+8561	Andre Kumar	16	gi_bleed	65640	7	2	Transfuse	no points if done previously
+7811	Jason Hom	2	gi_bleed	65640	10	5	\N	\N
+7812	Jonathan Chen	2	gi_bleed	65640	10	5	RBC	\N
+7813	Lisa Shieh	2	gi_bleed	65640	10	5	\N	\N
+7814	Panel Consensus	2	gi_bleed	65640	10	\N	\N	\N
+8273	Panel Average	2	gi_bleed	65640	9	\N	\N	\N
+8562	Andre Kumar	2	gi_bleed	65640	7	3	Transfuse	\N
+11356	Jonathan Chen	14	gi_bleed	50581	0	2	\N	\N
+11291	Jonathan Chen	15	gi_bleed	50581	0	2	\N	\N
+7815	Jason Hom	14	gi_bleed	50618	10	5	\N	\N
+7816	Jonathan Chen	14	gi_bleed	50618	10	5	RBC	\N
+7817	Panel Consensus	14	gi_bleed	50618	10	\N	\N	\N
+8384	Andre Kumar	14	gi_bleed	50618	8	3	Transfuse	\N
+8735	Panel Average	14	gi_bleed	50618	6	\N	\N	\N
+10680	Lisa Shieh	14	gi_bleed	50618	0	3	\N	\N
+7818	Andre Kumar	15	gi_bleed	50618	10	5	Transfuse	\N
+7819	Jason Hom	15	gi_bleed	50618	10	5	\N	\N
+7820	Jonathan Chen	15	gi_bleed	50618	10	5	RBC	\N
+7821	Lisa Shieh	15	gi_bleed	50618	10	5	\N	\N
+7822	Panel Average	15	gi_bleed	50618	10	\N	\N	\N
+7823	Panel Consensus	15	gi_bleed	50618	10	\N	\N	\N
+11235	Jonathan Chen	14	gi_bleed	45870	2	3	\N	Hemodynamic stability assessment?
+7824	Jason Hom	14	gi_bleed	45945	10	5	\N	\N
+7825	Lisa Shieh	14	gi_bleed	45945	10	5	blood type verification	\N
+7826	Panel Consensus	14	gi_bleed	45945	10	\N	\N	\N
+8274	Panel Average	14	gi_bleed	45945	9	\N	\N	\N
+8563	Andre Kumar	14	gi_bleed	45945	7	5	Transfuse	\N
+10280	Jonathan Chen	14	gi_bleed	45945	1	2	Type and Screen	\N
+7827	Andre Kumar	15	gi_bleed	45945	10	5	Transfuse	\N
+7828	Jason Hom	15	gi_bleed	45945	10	5	\N	\N
+7829	Lisa Shieh	15	gi_bleed	45945	10	5	\N	\N
+7830	Panel Average	15	gi_bleed	45945	10	\N	\N	\N
+7831	Panel Consensus	15	gi_bleed	45945	10	\N	\N	\N
+10281	Jonathan Chen	15	gi_bleed	45945	1	2	Type and Screen	\N
+7832	Jason Hom	14	gi_bleed	45869	10	5	\N	\N
+8564	Lisa Shieh	14	gi_bleed	45869	7	3	\N	\N
+8736	Panel Average	14	gi_bleed	45869	6	\N	\N	\N
+8737	Panel Consensus	14	gi_bleed	45869	6	\N	\N	\N
+9089	Jonathan Chen	14	gi_bleed	45869	5	2	US Abdomen	Okay to think about checking this, as long as not delaying immediate treatment
+10282	Andre Kumar	14	gi_bleed	45869	1	1	Imaging	\N
+11049	Andre Kumar	16	gi_bleed	45869	-2	4	Imaging	delays care
+11050	Panel Average	16	gi_bleed	45869	-2	\N	\N	\N
+7833	Jason Hom	2	gi_bleed	45869	10	5	\N	\N
+8455	Panel Average	2	gi_bleed	45869	7	\N	\N	\N
+8565	Lisa Shieh	2	gi_bleed	45869	7	3	imaging	\N
+8738	Panel Consensus	2	gi_bleed	45869	6	\N	\N	\N
+9090	Andre Kumar	2	gi_bleed	45869	5	2	Imaging	\N
+9091	Jonathan Chen	2	gi_bleed	45869	5	2	US Abdomen	\N
+11051	Andre Kumar	14	gi_bleed	49408	-2	3	Imaging	\N
+11052	Panel Average	14	gi_bleed	49408	-2	\N	\N	\N
+11053	Andre Kumar	16	gi_bleed	49408	-2	5	Imaging	delays care
+11054	Panel Average	16	gi_bleed	49408	-2	\N	\N	\N
+7834	Jason Hom	2	gi_bleed	49408	10	5	\N	\N
+8456	Panel Average	2	gi_bleed	49408	7	\N	\N	\N
+8566	Lisa Shieh	2	gi_bleed	49408	7	3	\N	\N
+8739	Panel Consensus	2	gi_bleed	49408	6	\N	\N	\N
+9092	Andre Kumar	2	gi_bleed	49408	5	4	Imaging	\N
+9093	Jonathan Chen	2	gi_bleed	49408	5	2	US Abdomen	\N
+7835	Jason Hom	14	gi_bleed	46343	10	5	\N	\N
+8567	Lisa Shieh	14	gi_bleed	46343	7	5	ultrasound	\N
+8740	Panel Consensus	14	gi_bleed	46343	6	\N	\N	\N
+9094	Jonathan Chen	14	gi_bleed	46343	5	2	US Abdomen	\N
+9095	Panel Average	14	gi_bleed	46343	5	\N	\N	\N
+11055	Andre Kumar	14	gi_bleed	46343	-2	2	Imaging	\N
+11056	Andre Kumar	16	gi_bleed	46343	-2	5	Imaging	delays care
+11057	Panel Average	16	gi_bleed	46343	-2	\N	\N	\N
+7836	Jason Hom	2	gi_bleed	46343	10	5	\N	\N
+7837	Lisa Shieh	2	gi_bleed	46343	10	5	\N	\N
+8324	Panel Average	2	gi_bleed	46343	8	\N	\N	\N
+8741	Panel Consensus	2	gi_bleed	46343	6	\N	\N	\N
+9096	Andre Kumar	2	gi_bleed	46343	5	3	Imaging	\N
+9097	Jonathan Chen	2	gi_bleed	46343	5	2	US Abdomen	\N
+7838	Andre Kumar	14	gi_bleed	45969	10	5	Endoscopy	\N
+7839	Jason Hom	14	gi_bleed	45969	10	5	\N	\N
+7840	Jonathan Chen	14	gi_bleed	45969	10	5	GI-EGD	GI won't take patient yet, but points for preparing
+7841	Lisa Shieh	14	gi_bleed	45969	10	5	\N	\N
+7842	Panel Average	14	gi_bleed	45969	10	\N	\N	\N
+7843	Panel Consensus	14	gi_bleed	45969	10	\N	\N	\N
+7844	Andre Kumar	15	gi_bleed	45969	10	5	Endoscopy	\N
+7845	Jason Hom	15	gi_bleed	45969	10	5	\N	\N
+7846	Jonathan Chen	15	gi_bleed	45969	10	5	GI-EGD	\N
+7847	Lisa Shieh	15	gi_bleed	45969	10	5	\N	\N
+7848	Panel Average	15	gi_bleed	45969	10	\N	\N	\N
+7849	Panel Consensus	15	gi_bleed	45969	10	\N	\N	\N
+7850	Andre Kumar	16	gi_bleed	45969	10	4	Endoscopy	\N
+7851	Jason Hom	16	gi_bleed	45969	10	5	\N	\N
+7852	Jonathan Chen	16	gi_bleed	45969	10	5	GI-EGD	\N
+7853	Lisa Shieh	16	gi_bleed	45969	10	5	\N	\N
+7854	Panel Average	16	gi_bleed	45969	10	\N	\N	\N
+7855	Panel Consensus	16	gi_bleed	45969	10	\N	\N	\N
+10283	Andre Kumar	14	gi_bleed	45873	1	3	Urinalysis	\N
+10396	Panel Average	14	gi_bleed	45873	0	\N	\N	\N
+10681	Jason Hom	14	gi_bleed	45873	0	4	\N	\N
+10682	Jonathan Chen	14	gi_bleed	45873	0	2	\N	Irrelevant diagnostic?
+10683	Lisa Shieh	14	gi_bleed	45873	0	3	\N	\N
+10684	Panel Consensus	14	gi_bleed	45873	0	\N	\N	\N
+11281	Jonathan Chen	14	gi_bleed	46348	0	4	\N	\N
+7856	Jason Hom	14	gi_bleed	50372	10	5	\N	\N
+7857	Lisa Shieh	14	gi_bleed	50372	10	5	\N	\N
+7858	Panel Consensus	14	gi_bleed	50372	10	\N	\N	\N
+8438	Panel Average	14	gi_bleed	50372	8	\N	\N	\N
+9721	Andre Kumar	14	gi_bleed	50372	3	4	Informed Consent	\N
+10685	Jonathan Chen	14	gi_bleed	50372	0	2	\N	\N
+8275	Jonathan Chen	14	gi_bleed	44001	9	4	Vitamin K	\N
+8385	Jason Hom	14	gi_bleed	44001	8	4	\N	\N
+8568	Lisa Shieh	14	gi_bleed	44001	7	3	\N	risk of anaphylaxis
+8569	Panel Consensus	14	gi_bleed	44001	7	\N	\N	\N
+8670	Panel Average	14	gi_bleed	44001	7	\N	\N	\N
+9098	Andre Kumar	14	gi_bleed	44001	5	4	Coagulopathy Reversal	\N
+7859	Lisa Shieh	15	gi_bleed	44001	10	5	\N	\N
+8276	Jonathan Chen	15	gi_bleed	44001	9	4	Vitamin K	\N
+8386	Jason Hom	15	gi_bleed	44001	8	4	\N	\N
+8439	Panel Average	15	gi_bleed	44001	8	\N	\N	\N
+8570	Panel Consensus	15	gi_bleed	44001	7	\N	\N	\N
+9099	Andre Kumar	15	gi_bleed	44001	5	3	Coagulopathy Reversal	\N
+7860	Lisa Shieh	16	gi_bleed	44001	10	5	\N	\N
+8277	Jonathan Chen	16	gi_bleed	44001	9	4	Vitamin K	\N
+8387	Jason Hom	16	gi_bleed	44001	8	4	\N	\N
+8440	Panel Average	16	gi_bleed	44001	8	\N	\N	\N
+8571	Panel Consensus	16	gi_bleed	44001	7	\N	\N	\N
+9100	Andre Kumar	16	gi_bleed	44001	5	5	Coagulopathy Reversal	5 pts if no FFP or vitamin K given previously, otherwise 0
+9101	Andre Kumar	2	gi_bleed	44001	5	5	Coagulopathy Reversal	\N
+9102	Panel Average	2	gi_bleed	44001	5	\N	\N	\N
+7861	Lisa Shieh	15	gi_bleed	44404	10	5	\N	\N
+8388	Jason Hom	15	gi_bleed	44404	8	4	\N	\N
+8572	Panel Consensus	15	gi_bleed	44404	7	\N	\N	\N
+8671	Panel Average	15	gi_bleed	44404	7	\N	\N	\N
+9103	Jonathan Chen	15	gi_bleed	44404	5	2	Vitamin K	Onset of action a bit too slow
+10064	Andre Kumar	15	gi_bleed	44404	2	2	Bleeding Medication	\N
+7862	Lisa Shieh	16	gi_bleed	44404	10	5	\N	\N
+8389	Jason Hom	16	gi_bleed	44404	8	4	\N	\N
+8441	Panel Average	16	gi_bleed	44404	8	\N	\N	\N
+8573	Panel Consensus	16	gi_bleed	44404	7	\N	\N	\N
+9104	Andre Kumar	16	gi_bleed	44404	5	2	Coagulopathy Reversal	5 pts if no FFP or vitamin K given previously, otherwise 0
+9105	Jonathan Chen	16	gi_bleed	44404	5	2	Vitamin K	Onset of action a bit too slow
+8390	Jason Hom	14	gi_bleed	44382	8	4	\N	\N
+9106	Jonathan Chen	14	gi_bleed	44382	5	2	Vitamin K	Onset of action a bit too slow
+9107	Lisa Shieh	14	gi_bleed	44382	5	3	\N	\N
+9606	Panel Average	14	gi_bleed	44382	3	\N	\N	\N
+11058	Panel Consensus	14	gi_bleed	44382	-2	\N	\N	\N
+11090	Andre Kumar	14	gi_bleed	44382	-3	5	Coagulopathy Reversal	Studies have shown SubQ vitamin K is similar to placebo (PMID: 16505257 )
+8391	Jason Hom	15	gi_bleed	44382	8	4	\N	\N
+8574	Lisa Shieh	15	gi_bleed	44382	7	3	\N	\N
+9108	Jonathan Chen	15	gi_bleed	44382	5	2	Vitamin K	Onset of action a bit too slow
+9505	Panel Average	15	gi_bleed	44382	4	\N	\N	\N
+11059	Panel Consensus	15	gi_bleed	44382	-2	\N	\N	\N
+11091	Andre Kumar	15	gi_bleed	44382	-3	5	Bleeding Medication	\N
+11419	Jonathan Chen	14	gi_bleed	46096	0	2	\N	\N
+10981	Andre Kumar	14	gi_bleed	46018	-1	3	Imaging	Delays care
+10982	Panel Average	14	gi_bleed	46018	-1	\N	\N	\N
+8392	Jason Hom	14	gi_bleed	45818	8	4	\N	\N
+9916	Panel Average	14	gi_bleed	45818	3	\N	\N	\N
+10686	Andre Kumar	14	gi_bleed	45818	0	1	Imaging	\N
+10687	Jonathan Chen	14	gi_bleed	45818	0	2	CXR	Irrelevant diagnostic?
+10688	Lisa Shieh	14	gi_bleed	45818	0	3	\N	\N
+10689	Panel Consensus	14	gi_bleed	45818	0	\N	\N	\N
+8393	Jason Hom	15	gi_bleed	45818	8	4	\N	\N
+10065	Panel Average	15	gi_bleed	45818	2	\N	\N	\N
+10690	Jonathan Chen	15	gi_bleed	45818	0	2	CXR	Irrelevant diagnostic?
+10691	Lisa Shieh	15	gi_bleed	45818	0	3	\N	\N
+10692	Panel Consensus	15	gi_bleed	45818	0	\N	\N	\N
+11060	Andre Kumar	15	gi_bleed	45818	-2	1	Imaging	delays care
+10693	Andre Kumar	2	gi_bleed	45818	0	1	Imaging	\N
+10694	Panel Average	2	gi_bleed	45818	0	\N	\N	\N
+11389	Jonathan Chen	14	gi_bleed	50200	0	2	CXR	Irrelevant diagnostic?
+8394	Jason Hom	14	gi_bleed	45801	8	4	\N	\N
+9917	Panel Average	14	gi_bleed	45801	3	\N	\N	\N
+10695	Andre Kumar	14	gi_bleed	45801	0	2	Imaging	\N
+10696	Jonathan Chen	14	gi_bleed	45801	0	2	CXR	Irrelevant diagnostic?
+10697	Lisa Shieh	14	gi_bleed	45801	0	3	\N	\N
+10698	Panel Consensus	14	gi_bleed	45801	0	\N	\N	\N
+11299	Jonathan Chen	30	meningitis	56492	1	3	Pain Control	Minor points for symptom control?
+11236	Jonathan Chen	31	meningitis	56492	1	3	Pain Control	Minor points for symptom control?
+8395	Jason Hom	30	meningitis	44281	8	4	\N	\N
+9109	Panel Consensus	30	meningitis	44281	5	\N	\N	\N
+9431	Panel Average	30	meningitis	44281	5	\N	\N	\N
+9506	Lisa Shieh	30	meningitis	44281	4	4	\N	\N
+10066	Andre Kumar	30	meningitis	44281	2	2	Fever Medication	\N
+10284	Jonathan Chen	30	meningitis	44281	1	3	Pain Control	Minor points for symptom control?
+8396	Jason Hom	31	meningitis	44281	8	4	\N	\N
+8575	Lisa Shieh	31	meningitis	44281	7	3	\N	\N
+8742	Panel Average	31	meningitis	44281	6	\N	\N	\N
+9110	Panel Consensus	31	meningitis	44281	5	\N	\N	\N
+9722	Andre Kumar	31	meningitis	44281	3	2	Fever Medication	\N
+10285	Jonathan Chen	31	meningitis	44281	1	3	Pain Control	Minor points for symptom control?
+11300	Jonathan Chen	33	meningitis	44281	1	3	Pain Control	Minor points for symptom control?
+11415	Jonathan Chen	30	meningitis	44310	1	3	Pain Control	Minor points for symptom control?
+8397	Jason Hom	30	meningitis	44278	8	4	\N	\N
+9507	Panel Consensus	30	meningitis	44278	4	\N	\N	\N
+9585	Panel Average	30	meningitis	44278	4	\N	\N	\N
+9723	Lisa Shieh	30	meningitis	44278	3	3	\N	\N
+10286	Jonathan Chen	30	meningitis	44278	1	3	Pain Control	Minor points for symptom control?
+10699	Andre Kumar	30	meningitis	44278	0	1	Fever Medication	\N
+11466	Jonathan Chen	31	meningitis	44310	1	3	Pain Control	Minor points for symptom control?
+10067	Andre Kumar	31	meningitis	44278	2	2	Fever Medication	\N
+10068	Panel Average	31	meningitis	44278	2	\N	\N	\N
+11257	Jonathan Chen	30	meningitis	48829	0	4	\N	Irrelevant diagnostics. Probably trying to order medicine
+8398	Jason Hom	30	meningitis	44615	8	4	\N	\N
+8870	Panel Average	30	meningitis	44615	5	\N	\N	\N
+9111	Andre Kumar	30	meningitis	44615	5	4	Antiviral	reasonable for mollaret's meningitis
+9724	Lisa Shieh	30	meningitis	44615	3	3	\N	\N
+10700	Jonathan Chen	30	meningitis	44615	0	3	\N	Doesn't meet guideline recs for empiric treatment
+10701	Panel Consensus	30	meningitis	44615	0	\N	\N	\N
+9112	Andre Kumar	31	meningitis	44615	5	3	Antiviral	No points if already ordered
+9113	Lisa Shieh	31	meningitis	44615	5	3	\N	\N
+9607	Panel Average	31	meningitis	44615	3	\N	\N	\N
+10702	Jason Hom	31	meningitis	44615	0	4	\N	\N
+10703	Jonathan Chen	31	meningitis	44615	0	3	\N	Doesn't meet guideline recs for empiric treatment
+10704	Panel Consensus	31	meningitis	44615	0	\N	\N	\N
+9508	Andre Kumar	33	meningitis	44615	4	2	Antivirals	\N
+9509	Jason Hom	33	meningitis	44615	4	4	\N	\N
+9586	Panel Average	33	meningitis	44615	4	\N	\N	\N
+9725	Lisa Shieh	33	meningitis	44615	3	3	\N	\N
+10705	Jonathan Chen	33	meningitis	44615	0	3	\N	Doesn't meet guideline recs for empiric treatment
+10706	Panel Consensus	33	meningitis	44615	0	\N	\N	\N
+11332	Jonathan Chen	30	meningitis	48770	0	3	\N	Low yield
+11260	Jonathan Chen	33	meningitis	48770	0	3	\N	Low yield
+7863	Lisa Shieh	30	meningitis	44595	10	4	\N	\N
+9918	Panel Average	30	meningitis	44595	3	\N	\N	\N
+10707	Andre Kumar	30	meningitis	44595	0	3	Antibiotics	\N
+10708	Jonathan Chen	30	meningitis	44595	0	3	\N	Doesn't meet guideline recs for empiric treatment
+10709	Panel Consensus	30	meningitis	44595	0	\N	\N	\N
+11061	Jason Hom	30	meningitis	44595	-2	5	\N	\N
+9114	Lisa Shieh	31	meningitis	44595	5	3	\N	\N
+10287	Panel Average	31	meningitis	44595	1	\N	\N	\N
+10710	Andre Kumar	31	meningitis	44595	0	5	Antibiotics	\N
+10711	Jonathan Chen	31	meningitis	44595	0	3	\N	Doesn't meet guideline recs for empiric treatment
+10712	Panel Consensus	31	meningitis	44595	0	\N	\N	\N
+11062	Jason Hom	31	meningitis	44595	-2	5	\N	\N
+9726	Lisa Shieh	33	meningitis	44595	3	3	\N	\N
+10397	Panel Average	33	meningitis	44595	0	\N	\N	\N
+10713	Andre Kumar	33	meningitis	44595	0	5	Antibiotics	\N
+10714	Jonathan Chen	33	meningitis	44595	0	3	\N	Doesn't meet guideline recs for empiric treatment
+10715	Panel Consensus	33	meningitis	44595	0	\N	\N	\N
+11063	Jason Hom	33	meningitis	44595	-2	5	\N	\N
+9115	Jason Hom	31	meningitis	61864	5	5	\N	\N
+9727	Lisa Shieh	31	meningitis	61864	3	3	\N	\N
+9728	Panel Consensus	31	meningitis	61864	3	\N	\N	\N
+9919	Panel Average	31	meningitis	61864	3	\N	\N	\N
+10069	Jonathan Chen	31	meningitis	61864	2	2	HIV	\N
+10716	Andre Kumar	31	meningitis	61864	0	4	STD Testing	\N
+9116	Andre Kumar	30	meningitis	46093	5	5	CSF	\N
+9117	Jason Hom	30	meningitis	46093	5	5	\N	\N
+9118	Lisa Shieh	30	meningitis	46093	5	3	\N	\N
+9119	Panel Average	30	meningitis	46093	5	\N	\N	\N
+9120	Panel Consensus	30	meningitis	46093	5	\N	\N	\N
+10288	Jonathan Chen	30	meningitis	46093	1	3	Pregnancy Test	\N
+7864	Jason Hom	30	meningitis	45901	10	5	\N	\N
+7865	Lisa Shieh	30	meningitis	45901	10	5	\N	\N
+7866	Panel Consensus	30	meningitis	45901	10	\N	\N	\N
+8294	Panel Average	30	meningitis	45901	9	\N	\N	\N
+8576	Jonathan Chen	30	meningitis	45901	7	4	Blood Cultures	\N
+8743	Andre Kumar	30	meningitis	45901	6	5	Blood Cultures	\N
+7867	Panel Consensus	31	meningitis	45901	10	\N	\N	\N
+8577	Jonathan Chen	31	meningitis	45901	7	4	Blood Cultures	\N
+9729	Lisa Shieh	31	meningitis	45901	3	3	\N	\N
+10070	Jason Hom	31	meningitis	45901	2	5	\N	\N
+10071	Panel Average	31	meningitis	45901	2	\N	\N	\N
+10289	Andre Kumar	31	meningitis	45901	1	1	Blood Cultures	\N
+7868	Panel Consensus	33	meningitis	45901	10	\N	\N	\N
+8578	Jonathan Chen	33	meningitis	45901	7	4	Blood Cultures	\N
+9510	Andre Kumar	33	meningitis	45901	4	4	Blood Cultures	\N
+9730	Lisa Shieh	33	meningitis	45901	3	3	\N	\N
+9731	Panel Average	33	meningitis	45901	3	\N	\N	\N
+10072	Jason Hom	33	meningitis	45901	2	5	\N	\N
+7869	Jason Hom	30	meningitis	45752	10	5	\N	\N
+7870	Lisa Shieh	30	meningitis	45752	10	4	\N	\N
+7871	Panel Consensus	30	meningitis	45752	10	\N	\N	\N
+8295	Panel Average	30	meningitis	45752	9	\N	\N	\N
+8579	Jonathan Chen	30	meningitis	45752	7	4	Blood Cultures	\N
+8744	Andre Kumar	30	meningitis	45752	6	5	Blood Cultures	\N
+7872	Panel Consensus	31	meningitis	45752	10	\N	\N	\N
+8580	Jonathan Chen	31	meningitis	45752	7	4	Blood Cultures	\N
+9732	Lisa Shieh	31	meningitis	45752	3	3	\N	\N
+10073	Jason Hom	31	meningitis	45752	2	5	\N	\N
+10074	Panel Average	31	meningitis	45752	2	\N	\N	\N
+10290	Andre Kumar	31	meningitis	45752	1	2	Blood cultures	"No points if already ordered lower points here assuming pt got abx already"
+7873	Panel Consensus	33	meningitis	45752	10	\N	\N	\N
+8581	Jonathan Chen	33	meningitis	45752	7	4	Blood Cultures	\N
+9511	Andre Kumar	33	meningitis	45752	4	4	Blood Cultures	\N
+9733	Lisa Shieh	33	meningitis	45752	3	3	\N	\N
+9734	Panel Average	33	meningitis	45752	3	\N	\N	\N
+10075	Jason Hom	33	meningitis	45752	2	5	\N	\N
+11383	Jonathan Chen	30	meningitis	44439	5	3	IVF	\N
+8582	Lisa Shieh	30	meningitis	44290	7	4	\N	\N
+9121	Jason Hom	30	meningitis	44290	5	4	\N	\N
+9122	Jonathan Chen	30	meningitis	44290	5	3	IVF	\N
+9123	Panel Average	30	meningitis	44290	5	\N	\N	\N
+9124	Panel Consensus	30	meningitis	44290	5	\N	\N	\N
+9735	Andre Kumar	30	meningitis	44290	3	5	Fluids	\N
+7874	Jason Hom	30	meningitis	45793	10	4	cbc	\N
+8457	Panel Average	30	meningitis	45793	7	\N	\N	\N
+8583	Lisa Shieh	30	meningitis	45793	7	4	\N	\N
+8584	Panel Consensus	30	meningitis	45793	7	\N	\N	\N
+9125	Andre Kumar	30	meningitis	45793	5	4	CBC	\N
+9736	Jonathan Chen	30	meningitis	45793	3	4	CBC	\N
+7875	Jason Hom	30	meningitis	45788	10	4	cbc	\N
+8458	Panel Average	30	meningitis	45788	7	\N	\N	\N
+8585	Lisa Shieh	30	meningitis	45788	7	4	\N	\N
+8586	Panel Consensus	30	meningitis	45788	7	\N	\N	\N
+9126	Andre Kumar	30	meningitis	45788	5	5	CBC	\N
+9737	Jonathan Chen	30	meningitis	45788	3	4	CBC	\N
+11337	Jonathan Chen	30	meningitis	45966	3	4	CBC	\N
+8399	Andre Kumar	33	meningitis	36210	8	2	Antibiotics	\N
+9127	Jason Hom	33	meningitis	36210	5	5	\N	\N
+9128	Panel Consensus	33	meningitis	36210	5	\N	\N	\N
+9454	Panel Average	33	meningitis	36210	4	\N	\N	\N
+9512	Jonathan Chen	33	meningitis	36210	4	5	CNS Cephalosporin	Can replace ceftriaxone, but slightly less points, as is a little unnecessary overkill
+10717	Lisa Shieh	33	meningitis	36210	0	3	\N	\N
+7876	Andre Kumar	30	meningitis	35733	10	\N	Antibiotics	\N
+7877	Jason Hom	30	meningitis	35733	10	5	\N	\N
+7878	Jonathan Chen	30	meningitis	35733	10	5	CNS Cephalosporin	\N
+7879	Lisa Shieh	30	meningitis	35733	10	5	\N	\N
+7880	Panel Average	30	meningitis	35733	10	\N	\N	\N
+7881	Panel Consensus	30	meningitis	35733	10	\N	\N	\N
+7882	Jason Hom	31	meningitis	35733	10	5	\N	\N
+7883	Jonathan Chen	31	meningitis	35733	10	5	CNS Cephalosporin	\N
+7884	Panel Consensus	31	meningitis	35733	10	\N	\N	\N
+8400	Andre Kumar	31	meningitis	35733	8	3	Antibiotics	No points if already ordered
+8442	Panel Average	31	meningitis	35733	8	\N	\N	\N
+9129	Lisa Shieh	31	meningitis	35733	5	3	\N	\N
+8401	Andre Kumar	33	meningitis	35733	8	2	Antibiotics	less points if delayed
+8871	Panel Average	33	meningitis	35733	5	\N	\N	\N
+9130	Jason Hom	33	meningitis	35733	5	5	\N	\N
+9131	Jonathan Chen	33	meningitis	35733	5	4	CNS Cephalosporin	Good antibiotic, but didn't prescribe until got to the worsened" state, meaning waited over an hour before prescribing. Don't give as many points"
+9132	Panel Consensus	33	meningitis	35733	5	\N	\N	\N
+9738	Lisa Shieh	33	meningitis	35733	3	3	already did LP_ so no credit for running LP tests- maybe for cocci or other  rarer tests since no improvement	\N
+7885	Andre Kumar	30	meningitis	48880	10	5	CSF	max points if before antibiotics, otherwise 8 points
+7886	Jason Hom	30	meningitis	48880	10	5	\N	\N
+7887	Lisa Shieh	30	meningitis	48880	10	5	\N	\N
+7888	Panel Average	30	meningitis	48880	10	\N	\N	\N
+7889	Panel Consensus	30	meningitis	48880	10	\N	\N	\N
+8745	Jonathan Chen	30	meningitis	48880	6	4	LP	Panel of CSF tests, should get point for each? Adds up to more than value of antibiotics
+11233	Jonathan Chen	31	meningitis	48880	6	4	LP	Panel of CSF tests, should get point for each? Adds up to more than value of antibiotics
+8746	Andre Kumar	33	meningitis	48880	6	3	CSF	max points if before antibiotics, otherwise 3 points
+8747	Jonathan Chen	33	meningitis	48880	6	4	LP	Panel of CSF tests, should get point for each? Adds up to more than value of antibiotics
+9133	Jason Hom	33	meningitis	48880	5	5	\N	\N
+9134	Panel Consensus	33	meningitis	48880	5	\N	\N	\N
+9587	Panel Average	33	meningitis	48880	4	\N	\N	\N
+10718	Lisa Shieh	33	meningitis	48880	0	3	\N	\N
+11425	Jonathan Chen	30	meningitis	50930	0	2	\N	Wrong order. Synovial fluid not CSF
+10719	Andre Kumar	30	meningitis	49079	0	4	STD Testing	\N
+10720	Jason Hom	30	meningitis	49079	0	4	\N	\N
+10721	Jonathan Chen	30	meningitis	49079	0	2	\N	\N
+10722	Lisa Shieh	30	meningitis	49079	0	3	\N	\N
+10723	Panel Average	30	meningitis	49079	0	\N	\N	\N
+10724	Panel Consensus	30	meningitis	49079	0	\N	\N	\N
+10291	Andre Kumar	30	meningitis	48603	1	4	CSF	\N
+10292	Panel Average	30	meningitis	48603	1	\N	\N	\N
+11373	Jonathan Chen	33	meningitis	48603	0	2	\N	Not relevant to test?
+11343	Jonathan Chen	33	meningitis	61973	0	2	\N	Not relevant to test?
+10293	Andre Kumar	30	meningitis	61853	1	1	CSF	\N
+10294	Panel Average	30	meningitis	61853	1	\N	\N	\N
+11362	Jonathan Chen	33	meningitis	48752	0	2	\N	Not relevant to test?
+11231	Jonathan Chen	30	meningitis	61826	0	2	\N	Not relevant to test?
+9739	Lisa Shieh	33	meningitis	61826	3	3	\N	\N
+10235	Panel Average	33	meningitis	61826	1	\N	\N	\N
+10295	Andre Kumar	33	meningitis	61826	1	4	3	\N
+10725	Jason Hom	33	meningitis	61826	0	4	\N	\N
+10726	Jonathan Chen	33	meningitis	61826	0	2	\N	No good indication
+10727	Panel Consensus	33	meningitis	61826	0	\N	\N	\N
+9135	Jason Hom	31	meningitis	48980	5	5	\N	\N
+9136	Lisa Shieh	31	meningitis	48980	5	3	\N	\N
+9137	Panel Consensus	31	meningitis	48980	5	\N	\N	\N
+9432	Panel Average	31	meningitis	48980	5	\N	\N	\N
+9513	Andre Kumar	31	meningitis	48980	4	2	Consult	\N
+10728	Jonathan Chen	31	meningitis	48980	0	3	\N	Fine, but don't offer direct points for this?
+7890	Jason Hom	33	meningitis	48980	10	5	\N	\N
+8848	Panel Average	33	meningitis	48980	6	\N	\N	\N
+9138	Lisa Shieh	33	meningitis	48980	5	3	\N	\N
+9139	Panel Consensus	33	meningitis	48980	5	\N	\N	\N
+10076	Andre Kumar	33	meningitis	48980	2	2	Consult	\N
+10729	Jonathan Chen	33	meningitis	48980	0	3	\N	\N
+7891	Jason Hom	31	meningitis	49207	10	5	\N	\N
+7892	Panel Consensus	31	meningitis	49207	10	\N	\N	\N
+8748	Panel Average	31	meningitis	49207	6	\N	\N	\N
+9140	Andre Kumar	31	meningitis	49207	5	5	Consult	\N
+9740	Lisa Shieh	31	meningitis	49207	3	3	\N	\N
+10730	Jonathan Chen	31	meningitis	49207	0	3	\N	\N
+10296	Andre Kumar	31	meningitis	50841	1	1	Isolation	\N
+10297	Panel Average	31	meningitis	50841	1	\N	\N	\N
+11439	Jonathan Chen	31	meningitis	49649	0	2	\N	\N
+11328	Jonathan Chen	30	meningitis	49594	0	2	\N	No good indication
+10298	Andre Kumar	33	meningitis	49594	1	1	CSF	\N
+10398	Panel Average	33	meningitis	49594	0	\N	\N	\N
+10731	Jason Hom	33	meningitis	49594	0	4	\N	\N
+10732	Jonathan Chen	33	meningitis	49594	0	2	\N	No good indication
+10733	Lisa Shieh	33	meningitis	49594	0	3	\N	\N
+10734	Panel Consensus	33	meningitis	49594	0	\N	\N	\N
+7893	Andre Kumar	30	meningitis	49083	10	5	CSF	max points if before antibiotics, otherwise 8 points
+7894	Jason Hom	30	meningitis	49083	10	5	\N	\N
+7895	Lisa Shieh	30	meningitis	49083	10	5	\N	\N
+7896	Panel Average	30	meningitis	49083	10	\N	\N	\N
+7897	Panel Consensus	30	meningitis	49083	10	\N	\N	\N
+8749	Jonathan Chen	30	meningitis	49083	6	5	\N	\N
+11353	Jonathan Chen	31	meningitis	49083	6	5	\N	\N
+8750	Andre Kumar	33	meningitis	49083	6	3	Consult	max points if before antibiotics, otherwise 3 points
+8751	Jonathan Chen	33	meningitis	49083	6	5	\N	\N
+9141	Jason Hom	33	meningitis	49083	5	5	\N	\N
+9142	Panel Consensus	33	meningitis	49083	5	\N	\N	\N
+9588	Panel Average	33	meningitis	49083	4	\N	\N	\N
+10735	Lisa Shieh	33	meningitis	49083	0	3	\N	\N
+11447	Jonathan Chen	30	meningitis	50692	4	4	\N	\N
+9741	Jonathan Chen	30	meningitis	45983	3	3	CT Head	Okay to order CT scan, but not okay if delay antibiotics awaiting result
+10736	Lisa Shieh	30	meningitis	45983	0	3	\N	\N
+11092	Panel Consensus	30	meningitis	45983	-3	\N	\N	\N
+11098	Panel Average	30	meningitis	45983	-3	\N	\N	\N
+11161	Andre Kumar	30	meningitis	45983	-5	4	Imaging	"No indication delays care"
+11162	Jason Hom	30	meningitis	45983	-5	4	\N	\N
+11333	Jonathan Chen	31	meningitis	45983	3	3	CT Head	\N
+9742	Jonathan Chen	30	meningitis	50241	3	3	CT Head	\N
+9743	Lisa Shieh	30	meningitis	50241	3	3	\N	\N
+11074	Panel Average	30	meningitis	50241	-3	\N	\N	\N
+11093	Panel Consensus	30	meningitis	50241	-3	\N	\N	\N
+11163	Andre Kumar	30	meningitis	50241	-5	4	Imaging	"No indication delays care"
+11175	Jason Hom	30	meningitis	50241	-6	4	\N	\N
+9143	Lisa Shieh	30	meningitis	48871	5	3	\N	\N
+10221	Panel Average	30	meningitis	48871	2	\N	\N	\N
+10737	Andre Kumar	30	meningitis	48871	0	3	D-dimer	\N
+10738	Jason Hom	30	meningitis	48871	0	4	\N	\N
+10739	Jonathan Chen	30	meningitis	48871	0	3	\N	\N
+10740	Panel Consensus	30	meningitis	48871	0	\N	\N	\N
+7898	Andre Kumar	30	meningitis	44017	10	5	Steroids	\N
+7899	Lisa Shieh	30	meningitis	44017	10	5	\N	\N
+7900	Panel Consensus	30	meningitis	44017	10	\N	\N	\N
+8278	Jonathan Chen	30	meningitis	44017	9	4	\N	\N
+8325	Panel Average	30	meningitis	44017	8	\N	\N	\N
+9144	Jason Hom	30	meningitis	44017	5	4	\N	\N
+7901	Panel Consensus	31	meningitis	44017	10	\N	\N	\N
+8279	Jonathan Chen	31	meningitis	44017	9	4	\N	\N
+9145	Andre Kumar	31	meningitis	44017	5	2	Steroids	5 points if ordered late
+9146	Jason Hom	31	meningitis	44017	5	4	\N	\N
+9147	Lisa Shieh	31	meningitis	44017	5	3	\N	\N
+9148	Panel Average	31	meningitis	44017	5	\N	\N	\N
+11262	Jonathan Chen	33	meningitis	44017	9	4	\N	\N
+9149	Jason Hom	33	meningitis	35969	5	4	\N	\N
+10741	Lisa Shieh	33	meningitis	35969	0	3	\N	\N
+10742	Panel Average	33	meningitis	35969	0	\N	\N	\N
+11164	Andre Kumar	33	meningitis	35969	-5	5	Sedation	\N
+11165	Jonathan Chen	33	meningitis	35969	-5	2	\N	Must be erroneous entry trying to get Dexamethasone
+11166	Panel Consensus	33	meningitis	35969	-5	\N	\N	\N
+9150	Jason Hom	30	meningitis	46286	5	4	\N	\N
+9151	Panel Consensus	30	meningitis	46286	5	\N	\N	\N
+9744	Lisa Shieh	30	meningitis	46286	3	3	\N	\N
+9745	Panel Average	30	meningitis	46286	3	\N	\N	\N
+10299	Andre Kumar	30	meningitis	46286	1	2	Coags	\N
+10743	Jonathan Chen	30	meningitis	46286	0	3	\N	\N
+9746	Andre Kumar	30	meningitis	45811	3	2	NPO	\N
+9747	Lisa Shieh	30	meningitis	45811	3	3	\N	\N
+10077	Panel Average	30	meningitis	45811	2	\N	\N	\N
+10078	Panel Consensus	30	meningitis	45811	2	\N	\N	\N
+10744	Jason Hom	30	meningitis	45811	0	4	\N	\N
+10745	Jonathan Chen	30	meningitis	45811	0	3	\N	\N
+10746	Andre Kumar	30	meningitis	44626	0	1	Antibiotics	\N
+10747	Panel Average	30	meningitis	44626	0	\N	\N	\N
+8587	Lisa Shieh	30	meningitis	49054	7	3	\N	\N
+8752	Panel Consensus	30	meningitis	49054	6	\N	\N	\N
+8849	Panel Average	30	meningitis	49054	6	\N	\N	\N
+9152	Andre Kumar	30	meningitis	49054	5	3	Isolation	\N
+9153	Jason Hom	30	meningitis	49054	5	4	\N	\N
+10300	Jonathan Chen	30	meningitis	49054	1	2	\N	\N
+8753	Panel Consensus	31	meningitis	49054	6	\N	\N	\N
+9154	Lisa Shieh	31	meningitis	49054	5	3	\N	\N
+9514	Andre Kumar	31	meningitis	49054	4	2	Isolation	\N
+9748	Panel Average	31	meningitis	49054	3	\N	\N	\N
+10301	Jonathan Chen	31	meningitis	49054	1	2	\N	\N
+10748	Jason Hom	31	meningitis	49054	0	4	\N	\N
+10302	Andre Kumar	30	meningitis	61843	1	1	CSF	\N
+10303	Panel Average	30	meningitis	61843	1	\N	\N	\N
+9749	Lisa Shieh	30	meningitis	45866	3	3	\N	\N
+10079	Jason Hom	30	meningitis	45866	2	4	\N	\N
+10222	Panel Average	30	meningitis	45866	2	\N	\N	\N
+10749	Andre Kumar	30	meningitis	45866	0	1	ECG + Monitoring	\N
+10750	Jonathan Chen	30	meningitis	45866	0	3	\N	\N
+10751	Panel Consensus	30	meningitis	45866	0	\N	\N	\N
+9750	Lisa Shieh	31	meningitis	45866	3	3	\N	\N
+10080	Jason Hom	31	meningitis	45866	2	4	\N	\N
+10223	Panel Average	31	meningitis	45866	2	\N	\N	\N
+10752	Andre Kumar	31	meningitis	45866	0	2	ECG + Monitoring	\N
+10753	Jonathan Chen	31	meningitis	45866	0	3	\N	\N
+10754	Panel Consensus	31	meningitis	45866	0	\N	\N	\N
+10304	Andre Kumar	30	meningitis	46160	1	3	Nursing	\N
+10305	Panel Average	30	meningitis	46160	1	\N	\N	\N
+11232	Jonathan Chen	33	meningitis	63761	1	3	\N	Common meningitis cause, okay to review
+10306	Andre Kumar	30	meningitis	49142	1	1	CSF	\N
+10307	Panel Average	30	meningitis	49142	1	\N	\N	\N
+11344	Jonathan Chen	33	meningitis	49142	1	3	\N	Common meningitis cause, okay to review
+9155	Andre Kumar	30	meningitis	46103	5	4	CSF	\N
+9751	Lisa Shieh	30	meningitis	46103	3	3	\N	\N
+9920	Panel Average	30	meningitis	46103	3	\N	\N	\N
+10755	Jason Hom	30	meningitis	46103	0	4	\N	\N
+10756	Jonathan Chen	30	meningitis	46103	0	3	\N	\N
+10757	Panel Consensus	30	meningitis	46103	0	\N	\N	\N
+10308	Andre Kumar	31	meningitis	46103	1	5	CSF	\N
+10309	Panel Average	31	meningitis	46103	1	\N	\N	\N
+11275	Jonathan Chen	30	meningitis	50685	0	2	\N	Mostly irrelevant STD testing?
+11379	Jonathan Chen	30	meningitis	44252	2	4	\N	Doesn't penetrate CNS
+9156	Lisa Shieh	30	meningitis	44237	5	3	\N	\N
+10081	Jonathan Chen	30	meningitis	44237	2	4	\N	Doesn't penetrate CNS
+11105	Panel Average	30	meningitis	44237	-4	\N	\N	\N
+11198	Andre Kumar	30	meningitis	44237	-8	5	Antibiotics	Doesn't cross CSF
+11199	Panel Consensus	30	meningitis	44237	-8	\N	\N	\N
+7902	Andre Kumar	30	meningitis	48577	10	5	CSF	max points if before antibiotics, otherwise 8 points
+7903	Jason Hom	30	meningitis	48577	10	5	\N	\N
+7904	Panel Consensus	30	meningitis	48577	10	\N	\N	\N
+8280	Panel Average	30	meningitis	48577	9	\N	\N	\N
+8588	Lisa Shieh	30	meningitis	48577	7	3	\N	\N
+9515	Jonathan Chen	30	meningitis	48577	4	5	\N	\N
+11325	Jonathan Chen	31	meningitis	48577	4	5	\N	\N
+7905	Panel Consensus	33	meningitis	48577	10	\N	\N	\N
+8754	Andre Kumar	33	meningitis	48577	6	3	CSF	max points if before antibiotics, otherwise 3 points
+9157	Jason Hom	33	meningitis	48577	5	5	\N	\N
+9516	Jonathan Chen	33	meningitis	48577	4	5	\N	\N
+9589	Panel Average	33	meningitis	48577	4	\N	\N	\N
+10758	Lisa Shieh	33	meningitis	48577	0	3	\N	\N
+7906	Andre Kumar	30	meningitis	46006	10	5	CSF	\N
+7907	Jason Hom	30	meningitis	46006	10	5	\N	\N
+7908	Lisa Shieh	30	meningitis	46006	10	10	\N	\N
+7909	Panel Average	30	meningitis	46006	10	\N	\N	\N
+7910	Panel Consensus	30	meningitis	46006	10	\N	\N	\N
+9517	Jonathan Chen	30	meningitis	46006	4	4	\N	\N
+11348	Jonathan Chen	33	meningitis	46006	4	4	\N	\N
+11278	Jonathan Chen	30	meningitis	42197	0	5	\N	\N
+7911	Jason Hom	30	meningitis	49109	10	5	\N	\N
+8281	Andre Kumar	30	meningitis	49109	9	5	CSF	\N
+8459	Panel Average	30	meningitis	49109	7	\N	\N	\N
+8589	Panel Consensus	30	meningitis	49109	7	\N	\N	\N
+9752	Jonathan Chen	30	meningitis	49109	3	2	HSV CSF	\N
+9753	Lisa Shieh	30	meningitis	49109	3	3	\N	\N
+11301	Jonathan Chen	33	meningitis	49109	3	2	HSV CSF	\N
+10310	Andre Kumar	30	meningitis	50425	1	1	CSF	\N
+10311	Panel Average	30	meningitis	50425	1	\N	\N	\N
+11385	Jonathan Chen	30	meningitis	62011	0	2	\N	Not the right screening test for HIV
+7912	Jason Hom	30	meningitis	63767	10	5	\N	\N
+8282	Andre Kumar	30	meningitis	63767	9	4	CSF	\N
+8402	Panel Average	30	meningitis	63767	8	\N	\N	\N
+8590	Panel Consensus	30	meningitis	63767	7	\N	\N	\N
+9158	Lisa Shieh	30	meningitis	63767	5	3	\N	\N
+9754	Jonathan Chen	30	meningitis	63767	3	2	HSV CSF	\N
+8591	Panel Consensus	31	meningitis	63767	7	\N	\N	\N
+9159	Jason Hom	31	meningitis	63767	5	5	\N	\N
+9755	Jonathan Chen	31	meningitis	63767	3	2	HSV CSF	\N
+9756	Lisa Shieh	31	meningitis	63767	3	3	\N	\N
+9757	Panel Average	31	meningitis	63767	3	\N	\N	\N
+10312	Andre Kumar	31	meningitis	63767	1	3	CSF	\N
+8592	Panel Consensus	33	meningitis	63767	7	\N	\N	\N
+9160	Jason Hom	33	meningitis	63767	5	5	\N	\N
+9608	Panel Average	33	meningitis	63767	3	\N	\N	\N
+9758	Jonathan Chen	33	meningitis	63767	3	2	HSV CSF	\N
+9759	Lisa Shieh	33	meningitis	63767	3	3	\N	\N
+10082	Andre Kumar	33	meningitis	63767	2	4	4	\N
+9161	Andre Kumar	30	meningitis	44230	5	1	Steroids	Dexamethasone, not hydrocort recommended?
+9162	Panel Average	30	meningitis	44230	5	\N	\N	\N
+9760	Lisa Shieh	30	meningitis	44319	3	3	\N	\N
+10083	Jason Hom	30	meningitis	44319	2	4	\N	\N
+10084	Panel Consensus	30	meningitis	44319	2	\N	\N	\N
+10224	Panel Average	30	meningitis	44319	2	\N	\N	\N
+10313	Jonathan Chen	30	meningitis	44319	1	3	Pain Control	\N
+10759	Andre Kumar	30	meningitis	44319	0	1	Pain Medication	\N
+11272	Jonathan Chen	30	meningitis	48732	0	2	\N	Blood gases?
+7913	Jason Hom	30	meningitis	48954	10	5	\N	\N
+7914	Lisa Shieh	30	meningitis	48954	10	5	\N	\N
+7915	Panel Consensus	30	meningitis	48954	10	\N	\N	\N
+8593	Panel Average	30	meningitis	48954	7	\N	\N	\N
+10314	Andre Kumar	30	meningitis	48954	1	1	Nursing	\N
+10760	Jonathan Chen	30	meningitis	48954	0	3	\N	\N
+9163	Andre Kumar	30	meningitis	62151	5	5	Lactate	\N
+9164	Jason Hom	30	meningitis	62151	5	4	\N	\N
+9165	Jonathan Chen	30	meningitis	62151	5	3	Lactate	\N
+9166	Lisa Shieh	30	meningitis	62151	5	3	\N	\N
+9167	Panel Average	30	meningitis	62151	5	\N	\N	\N
+9168	Panel Consensus	30	meningitis	62151	5	\N	\N	\N
+9169	Jonathan Chen	31	meningitis	62151	5	3	Lactate	\N
+9170	Panel Consensus	31	meningitis	62151	5	\N	\N	\N
+9761	Lisa Shieh	31	meningitis	62151	3	3	\N	\N
+10085	Andre Kumar	31	meningitis	62151	2	2	Lactate	\N
+10225	Panel Average	31	meningitis	62151	2	\N	\N	\N
+10761	Jason Hom	31	meningitis	62151	0	4	\N	\N
+11412	Jonathan Chen	33	meningitis	62151	5	3	Lactate	\N
+9171	Andre Kumar	30	meningitis	45918	5	5	Lactate	\N
+9172	Jason Hom	30	meningitis	45918	5	4	\N	\N
+9173	Jonathan Chen	30	meningitis	45918	5	3	Lactate	\N
+9174	Panel Consensus	30	meningitis	45918	5	\N	\N	\N
+9455	Panel Average	30	meningitis	45918	4	\N	\N	\N
+9762	Lisa Shieh	30	meningitis	45918	3	3	\N	\N
+10086	Andre Kumar	31	meningitis	45918	2	2	Lactate	\N
+10087	Panel Average	31	meningitis	45918	2	\N	\N	\N
+8403	Jason Hom	33	meningitis	45918	8	4	\N	\N
+9175	Jonathan Chen	33	meningitis	45918	5	3	Lactate	\N
+9176	Panel Consensus	33	meningitis	45918	5	\N	\N	\N
+9433	Panel Average	33	meningitis	45918	5	\N	\N	\N
+9763	Andre Kumar	33	meningitis	45918	3	4	Lactate	\N
+9764	Lisa Shieh	33	meningitis	45918	3	3	\N	\N
+11421	Jonathan Chen	30	meningitis	50510	2	3	\N	Reasonable, though not standard guidelines to check
+9177	Andre Kumar	33	meningitis	50510	5	4	CSF	very sensitive marker
+9178	Panel Consensus	33	meningitis	50510	5	\N	\N	\N
+10088	Jonathan Chen	33	meningitis	50510	2	3	\N	Reasonable, though not standard guidelines to check
+10226	Panel Average	33	meningitis	50510	2	\N	\N	\N
+10762	Jason Hom	33	meningitis	50510	0	4	\N	\N
+10763	Lisa Shieh	33	meningitis	50510	0	3	\N	\N
+7916	Andre Kumar	30	meningitis	63811	10	5	Lumbar Puncture	"Max points before antibiotics 5 points if after antibiotics"
+7917	Jason Hom	30	meningitis	63811	10	5	\N	\N
+7918	Lisa Shieh	30	meningitis	63811	10	5	\N	\N
+7919	Panel Average	30	meningitis	63811	10	\N	\N	\N
+7920	Panel Consensus	30	meningitis	63811	10	\N	\N	\N
+8755	Jonathan Chen	30	meningitis	63811	6	2	LP	Group with CSF Cell count as implicit that going to do LP if order that
+7921	Jason Hom	31	meningitis	63811	10	5	\N	\N
+8756	Jonathan Chen	31	meningitis	63811	6	2	LP	Group with CSF Cell count as implicit that going to do LP if order that
+9179	Panel Consensus	31	meningitis	63811	5	\N	\N	\N
+9596	Panel Average	31	meningitis	63811	4	\N	\N	\N
+11094	Andre Kumar	31	meningitis	63811	-3	0	Lumbar Puncture	Why repeat LP? Is it they improved with ABX without LP and now are getting procedure? If so, I would say minus points for delay in care
+8404	Andre Kumar	33	meningitis	63811	8	5	5	less points if delayed
+8757	Jonathan Chen	33	meningitis	63811	6	2	LP	Group with CSF Cell count as implicit that going to do LP if order that
+9180	Jason Hom	33	meningitis	63811	5	5	\N	\N
+9181	Panel Consensus	33	meningitis	63811	5	\N	\N	\N
+9456	Panel Average	33	meningitis	63811	4	\N	\N	\N
+10764	Lisa Shieh	33	meningitis	63811	0	3	\N	\N
+7922	Andre Kumar	30	meningitis	62167	10	5	Lumbar Puncture	"Max points before antibiotics 5 points if after antibiotics"
+7923	Jason Hom	30	meningitis	62167	10	5	\N	\N
+7924	Lisa Shieh	30	meningitis	62167	10	5	\N	\N
+7925	Panel Average	30	meningitis	62167	10	\N	\N	\N
+7926	Panel Consensus	30	meningitis	62167	10	\N	\N	\N
+8758	Jonathan Chen	30	meningitis	62167	6	2	LP	Group with CSF Cell count as implicit that going to do LP if order that
+8405	Andre Kumar	33	meningitis	62167	8	5	5	less points if delayed
+8759	Jonathan Chen	33	meningitis	62167	6	2	LP	Group with CSF Cell count as implicit that going to do LP if order that
+9182	Jason Hom	33	meningitis	62167	5	5	\N	\N
+9183	Panel Consensus	33	meningitis	62167	5	\N	\N	\N
+9457	Panel Average	33	meningitis	62167	4	\N	\N	\N
+10765	Lisa Shieh	33	meningitis	62167	0	3	\N	\N
+9184	Jason Hom	30	meningitis	45763	5	4	\N	\N
+9185	Lisa Shieh	30	meningitis	45763	5	4	\N	\N
+9186	Panel Consensus	30	meningitis	45763	5	\N	\N	\N
+9458	Panel Average	30	meningitis	45763	4	\N	\N	\N
+9765	Andre Kumar	30	meningitis	45763	3	4	Metabolic panel	\N
+10089	Jonathan Chen	30	meningitis	45763	2	3	Metabolic Panel	\N
+8594	Lisa Shieh	30	meningitis	45771	7	4	\N	\N
+9187	Jason Hom	30	meningitis	45771	5	4	\N	\N
+9188	Panel Average	30	meningitis	45771	5	\N	\N	\N
+9189	Panel Consensus	30	meningitis	45771	5	\N	\N	\N
+9766	Andre Kumar	30	meningitis	45771	3	4	Metabolic panel	\N
+10090	Jonathan Chen	30	meningitis	45771	2	3	Metabolic Panel	\N
+8406	Andre Kumar	30	meningitis	44586	8	1	Steroids	Only dexamethasone recommended?
+8407	Panel Average	30	meningitis	44586	8	\N	\N	\N
+11334	Jonathan Chen	31	meningitis	44199	0	2	\N	Symptomatic management
+10315	Andre Kumar	31	meningitis	44641	1	1	Pain Medication	\N
+10316	Panel Average	31	meningitis	44641	1	\N	\N	\N
+10091	Andre Kumar	31	meningitis	46065	2	1	Imaging	\N
+10092	Panel Average	31	meningitis	46065	2	\N	\N	\N
+11307	Jonathan Chen	30	meningitis	45792	0	2	\N	\N
+10317	Andre Kumar	31	meningitis	45792	1	4	MRSA	\N
+10399	Panel Average	31	meningitis	45792	0	\N	\N	\N
+10766	Jason Hom	31	meningitis	45792	0	4	\N	\N
+10767	Jonathan Chen	31	meningitis	45792	0	2	\N	\N
+10768	Lisa Shieh	31	meningitis	45792	0	3	\N	\N
+10769	Panel Consensus	31	meningitis	45792	0	\N	\N	\N
+10318	Andre Kumar	30	meningitis	61852	1	1	CSF	\N
+10400	Panel Average	30	meningitis	61852	0	\N	\N	\N
+10770	Jason Hom	30	meningitis	61852	0	4	\N	\N
+10771	Jonathan Chen	30	meningitis	61852	0	2	\N	No good indication
+10772	Lisa Shieh	30	meningitis	61852	0	3	\N	\N
+10773	Panel Consensus	30	meningitis	61852	0	\N	\N	\N
+10319	Andre Kumar	30	meningitis	45750	1	5	Nursing	\N
+10320	Panel Average	30	meningitis	45750	1	\N	\N	\N
+10774	Andre Kumar	30	meningitis	44216	0	1	Nausea Medication	\N
+10775	Panel Average	30	meningitis	44216	0	\N	\N	\N
+11250	Jonathan Chen	30	meningitis	61823	0	4	\N	\N
+10321	Andre Kumar	30	meningitis	45802	1	4	Nursing	\N
+10322	Panel Average	30	meningitis	45802	1	\N	\N	\N
+11253	Jonathan Chen	31	meningitis	49334	0	2	\N	Irrelevant?
+11476	Jonathan Chen	30	meningitis	46090	0	2	\N	\N
+9190	Jason Hom	30	meningitis	45955	5	4	\N	\N
+9191	Jonathan Chen	30	meningitis	45955	5	3	Lactate	\N
+9192	Panel Consensus	30	meningitis	45955	5	\N	\N	\N
+10093	Panel Average	30	meningitis	45955	2	\N	\N	\N
+10323	Andre Kumar	30	meningitis	45955	1	1	Blood Gas	\N
+10776	Lisa Shieh	30	meningitis	45955	0	3	\N	\N
+9193	Andre Kumar	30	meningitis	48561	5	5	Upreg	\N
+9194	Panel Average	30	meningitis	48561	5	\N	\N	\N
+11277	Jonathan Chen	30	meningitis	45798	1	3	Pregnancy Test	\N
+9195	Panel Consensus	33	meningitis	49134	5	\N	\N	\N
+10324	Andre Kumar	33	meningitis	49134	1	1	UPreg	\N
+10325	Jonathan Chen	33	meningitis	49134	1	3	Pregnancy Test	\N
+10401	Panel Average	33	meningitis	49134	0	\N	\N	\N
+10777	Jason Hom	33	meningitis	49134	0	4	\N	\N
+10778	Lisa Shieh	33	meningitis	49134	0	3	\N	\N
+9767	Lisa Shieh	30	meningitis	62042	3	3	\N	\N
+9939	Panel Average	30	meningitis	62042	2	\N	\N	\N
+10094	Andre Kumar	30	meningitis	62042	2	2	Procalcitonin	\N
+10095	Jason Hom	30	meningitis	62042	2	4	\N	\N
+10096	Panel Consensus	30	meningitis	62042	2	\N	\N	\N
+10326	Jonathan Chen	30	meningitis	62042	1	2	\N	\N
+7927	Andre Kumar	30	meningitis	49020	10	5	CSF	max points if before antibiotics, otherwise 8 points
+7928	Jason Hom	30	meningitis	49020	10	5	\N	\N
+7929	Panel Consensus	30	meningitis	49020	10	\N	\N	\N
+8283	Panel Average	30	meningitis	49020	9	\N	\N	\N
+8595	Lisa Shieh	30	meningitis	49020	7	3	\N	\N
+9518	Jonathan Chen	30	meningitis	49020	4	4	\N	\N
+11435	Jonathan Chen	31	meningitis	49020	4	4	\N	\N
+8760	Andre Kumar	33	meningitis	49020	6	3	CSF	max points if before antibiotics, otherwise 3 points
+9196	Jason Hom	33	meningitis	49020	5	5	\N	\N
+9197	Panel Consensus	33	meningitis	49020	5	\N	\N	\N
+9519	Jonathan Chen	33	meningitis	49020	4	4	\N	\N
+9590	Panel Average	33	meningitis	49020	4	\N	\N	\N
+10779	Lisa Shieh	33	meningitis	49020	0	3	\N	\N
+8596	Lisa Shieh	30	meningitis	45759	7	4	\N	\N
+8761	Panel Consensus	30	meningitis	45759	6	\N	\N	\N
+8850	Panel Average	30	meningitis	45759	6	\N	\N	\N
+9198	Andre Kumar	30	meningitis	45759	5	5	Coags	\N
+9199	Jason Hom	30	meningitis	45759	5	5	\N	\N
+10780	Jonathan Chen	30	meningitis	45759	0	3	\N	\N
+11347	Jonathan Chen	31	meningitis	45776	0	4	\N	Process
+8597	Lisa Shieh	30	meningitis	45770	7	4	\N	\N
+8762	Panel Consensus	30	meningitis	45770	6	\N	\N	\N
+8851	Panel Average	30	meningitis	45770	6	\N	\N	\N
+9200	Andre Kumar	30	meningitis	45770	5	4	Coags	\N
+9201	Jason Hom	30	meningitis	45770	5	5	\N	\N
+10781	Jonathan Chen	30	meningitis	45770	0	3	\N	\N
+8408	Jason Hom	30	meningitis	62103	8	5	\N	\N
+9202	Panel Consensus	30	meningitis	62103	5	\N	\N	\N
+9520	Andre Kumar	30	meningitis	62103	4	1	STD Testing	\N
+9521	Panel Average	30	meningitis	62103	4	\N	\N	\N
+10097	Jonathan Chen	30	meningitis	62103	2	2	HIV	\N
+10782	Lisa Shieh	30	meningitis	62103	0	3	\N	\N
+11460	Jonathan Chen	31	meningitis	62103	2	2	HIV	\N
+9203	Lisa Shieh	33	meningitis	62103	5	3	\N	\N
+9204	Panel Consensus	33	meningitis	62103	5	\N	\N	\N
+9522	Jason Hom	33	meningitis	62103	4	5	\N	\N
+9609	Panel Average	33	meningitis	62103	3	\N	\N	\N
+10098	Jonathan Chen	33	meningitis	62103	2	2	HIV	\N
+10327	Andre Kumar	33	meningitis	62103	1	3	7	\N
+11367	Jonathan Chen	30	meningitis	48728	0	2	\N	\N
+11428	Jonathan Chen	31	meningitis	48728	0	2	\N	\N
+9205	Jason Hom	30	meningitis	63923	5	4	\N	\N
+9768	Andre Kumar	30	meningitis	63923	3	2	Respiratory Panel	\N
+9769	Panel Consensus	30	meningitis	63923	3	\N	\N	\N
+9921	Panel Average	30	meningitis	63923	3	\N	\N	\N
+10783	Jonathan Chen	30	meningitis	63923	0	2	\N	\N
+10784	Lisa Shieh	30	meningitis	63923	0	3	\N	\N
+8598	Lisa Shieh	30	meningitis	48586	7	3	\N	\N
+9206	Jason Hom	30	meningitis	48586	5	4	\N	\N
+9207	Panel Average	30	meningitis	48586	5	\N	\N	\N
+9208	Panel Consensus	30	meningitis	48586	5	\N	\N	\N
+9770	Andre Kumar	30	meningitis	48586	3	4	CSF	\N
+10785	Jonathan Chen	30	meningitis	48586	0	3	\N	\N
+9209	Andre Kumar	30	meningitis	63725	5	5	Lactate	\N
+9210	Jason Hom	30	meningitis	63725	5	4	\N	\N
+9211	Jonathan Chen	30	meningitis	63725	5	3	Lactate	\N
+9212	Lisa Shieh	30	meningitis	63725	5	3	\N	\N
+9213	Panel Average	30	meningitis	63725	5	\N	\N	\N
+9214	Panel Consensus	30	meningitis	63725	5	\N	\N	\N
+11258	Jonathan Chen	31	meningitis	63725	5	3	Lactate	\N
+8599	Lisa Shieh	30	meningitis	44198	7	3	\N	\N
+9215	Jonathan Chen	30	meningitis	44198	5	3	IVF	\N
+9216	Panel Consensus	30	meningitis	44198	5	\N	\N	\N
+9459	Panel Average	30	meningitis	44198	4	\N	\N	\N
+9771	Andre Kumar	30	meningitis	44198	3	5	Fluids	\N
+9772	Jason Hom	30	meningitis	44198	3	4	\N	\N
+9523	Andre Kumar	31	meningitis	44198	4	3	Fluids	\N
+9524	Panel Average	31	meningitis	44198	4	\N	\N	\N
+8763	Jason Hom	33	meningitis	44198	6	4	\N	\N
+8872	Panel Average	33	meningitis	44198	5	\N	\N	\N
+9217	Andre Kumar	33	meningitis	44198	5	2	Fluids	\N
+9218	Jonathan Chen	33	meningitis	44198	5	3	IVF	\N
+9219	Lisa Shieh	33	meningitis	44198	5	3	\N	\N
+9220	Panel Consensus	33	meningitis	44198	5	\N	\N	\N
+10099	Andre Kumar	30	meningitis	46131	2	4	STD Testing	\N
+10387	Panel Average	30	meningitis	46131	1	\N	\N	\N
+10786	Jason Hom	30	meningitis	46131	0	4	\N	\N
+10787	Jonathan Chen	30	meningitis	46131	0	2	\N	Symptoms don't suggest neurosyphillis?
+10788	Lisa Shieh	30	meningitis	46131	0	3	\N	\N
+10789	Panel Consensus	30	meningitis	46131	0	\N	\N	\N
+10100	Andre Kumar	30	meningitis	45751	2	5	Urinalysis	\N
+10101	Jason Hom	30	meningitis	45751	2	4	\N	\N
+10102	Jonathan Chen	30	meningitis	45751	2	3	UA	General workup for fever/infection, though symptoms not suggestive of UTI
+10236	Panel Average	30	meningitis	45751	1	\N	\N	\N
+10790	Lisa Shieh	30	meningitis	45751	0	3	\N	\N
+10791	Panel Consensus	30	meningitis	45751	0	\N	\N	\N
+10103	Jonathan Chen	33	meningitis	45751	2	3	UA	General workup for fever/infection, though symptoms not suggestive of UTI
+10328	Andre Kumar	33	meningitis	45751	1	4	Urinalysis	\N
+10402	Panel Average	33	meningitis	45751	0	\N	\N	\N
+10792	Jason Hom	33	meningitis	45751	0	4	\N	\N
+10793	Lisa Shieh	33	meningitis	45751	0	3	\N	\N
+10794	Panel Consensus	33	meningitis	45751	0	\N	\N	\N
+9221	Jason Hom	30	meningitis	45873	5	4	\N	\N
+9940	Panel Average	30	meningitis	45873	2	\N	\N	\N
+10104	Andre Kumar	30	meningitis	45873	2	3	Urinalysis	\N
+10105	Jonathan Chen	30	meningitis	45873	2	3	UA	General workup for fever/infection, though symptoms not suggestive of UTI
+10795	Lisa Shieh	30	meningitis	45873	0	3	\N	\N
+10796	Panel Consensus	30	meningitis	45873	0	\N	\N	\N
+9222	Jason Hom	31	meningitis	45873	5	4	\N	\N
+10106	Jonathan Chen	31	meningitis	45873	2	3	UA	General workup for fever/infection, though symptoms not suggestive of UTI
+10227	Panel Average	31	meningitis	45873	2	\N	\N	\N
+10797	Andre Kumar	31	meningitis	45873	0	5	Urinalysis	\N
+10798	Lisa Shieh	31	meningitis	45873	0	3	\N	\N
+10799	Panel Consensus	31	meningitis	45873	0	\N	\N	\N
+10329	Andre Kumar	30	meningitis	45782	1	4	Urine Culture	\N
+10330	Panel Average	30	meningitis	45782	1	\N	\N	\N
+7930	Andre Kumar	30	meningitis	43997	10	5	Antibiotics	\N
+7931	Jason Hom	30	meningitis	43997	10	5	\N	\N
+7932	Lisa Shieh	30	meningitis	43997	10	5	\N	\N
+7933	Panel Average	30	meningitis	43997	10	\N	\N	\N
+7934	Panel Consensus	30	meningitis	43997	10	\N	\N	\N
+8284	Jonathan Chen	30	meningitis	43997	9	5	\N	\N
+7935	Jason Hom	31	meningitis	43997	10	5	\N	\N
+7936	Panel Consensus	31	meningitis	43997	10	\N	\N	\N
+8285	Jonathan Chen	31	meningitis	43997	9	5	\N	\N
+8409	Andre Kumar	31	meningitis	43997	8	3	Antibiotics	No points if already ordered
+8443	Panel Average	31	meningitis	43997	8	\N	\N	\N
+9223	Lisa Shieh	31	meningitis	43997	5	3	\N	\N
+8410	Andre Kumar	33	meningitis	43997	8	2	Antibiotics	\N
+8873	Panel Average	33	meningitis	43997	5	\N	\N	\N
+9224	Jason Hom	33	meningitis	43997	5	5	\N	\N
+9225	Jonathan Chen	33	meningitis	43997	5	4	\N	Good antibiotic, but didn't prescribe until got to the worsened" state, meaning waited over an hour before prescribing. Don't give as many points"
+9226	Panel Consensus	33	meningitis	43997	5	\N	\N	\N
+9773	Lisa Shieh	33	meningitis	43997	3	3	\N	\N
+11242	Jonathan Chen	33	meningitis	48486	2	3	Vancomycin Level	Okay to check followup leels
+11434	Jonathan Chen	30	meningitis	45972	2	3	Vancomycin Level	Okay to check followup leels
+11376	Jonathan Chen	33	meningitis	45972	2	3	Vancomycin Level	Okay to check followup leels
+7937	Andre Kumar	30	meningitis	48519	10	5	CSF	\N
+7938	Panel Average	30	meningitis	48519	10	\N	\N	\N
+11390	Jonathan Chen	33	meningitis	48519	0	2	\N	\N
+11327	Jonathan Chen	33	meningitis	48529	0	2	\N	\N
+11338	Jonathan Chen	30	meningitis	63735	0	2	\N	\N
+11265	Jonathan Chen	30	meningitis	45766	0	2	\N	\N
+9227	Jason Hom	30	meningitis	50654	5	4	\N	\N
+9774	Lisa Shieh	30	meningitis	50654	3	3	\N	\N
+9775	Panel Average	30	meningitis	50654	3	\N	\N	\N
+10331	Andre Kumar	30	meningitis	50654	1	1	CSF	\N
+10800	Jonathan Chen	30	meningitis	50654	0	2	\N	No good indication
+10801	Panel Consensus	30	meningitis	50654	0	\N	\N	\N
+9228	Jason Hom	33	meningitis	50654	5	4	\N	\N
+9776	Lisa Shieh	33	meningitis	50654	3	3	?does not change management	\N
+9777	Panel Average	33	meningitis	50654	3	\N	\N	\N
+10332	Andre Kumar	33	meningitis	50654	1	4	3	\N
+10802	Jonathan Chen	33	meningitis	50654	0	2	\N	No good indication
+10803	Panel Consensus	33	meningitis	50654	0	\N	\N	\N
+9229	Jason Hom	30	meningitis	45818	5	4	\N	\N
+9778	Lisa Shieh	30	meningitis	45818	3	3	\N	\N
+9779	Panel Consensus	30	meningitis	45818	3	\N	\N	\N
+10107	Jonathan Chen	30	meningitis	45818	2	3	CXR	General workup for fever/infection, though symptoms not suggestive of pneumonia
+10108	Panel Average	30	meningitis	45818	2	\N	\N	\N
+11064	Andre Kumar	30	meningitis	45818	-2	3	Imaging	\N
+9230	Jason Hom	31	meningitis	45818	5	4	\N	\N
+9610	Panel Average	31	meningitis	45818	3	\N	\N	\N
+9780	Lisa Shieh	31	meningitis	45818	3	3	\N	\N
+9781	Panel Consensus	31	meningitis	45818	3	\N	\N	\N
+10109	Andre Kumar	31	meningitis	45818	2	1	Imaging	?Check for pneumonoccocal pneumonia
+10110	Jonathan Chen	31	meningitis	45818	2	3	CXR	General workup for fever/infection, though symptoms not suggestive of pneumonia
+9782	Lisa Shieh	33	meningitis	45818	3	3	\N	\N
+9783	Panel Consensus	33	meningitis	45818	3	\N	\N	\N
+10111	Jonathan Chen	33	meningitis	45818	2	3	CXR	General workup for fever/infection, though symptoms not suggestive of pneumonia
+10403	Panel Average	33	meningitis	45818	0	\N	\N	\N
+10804	Jason Hom	33	meningitis	45818	0	4	\N	\N
+11065	Andre Kumar	33	meningitis	45818	-2	4	Imaging	\N
+9231	Jason Hom	30	meningitis	45801	5	4	\N	\N
+9784	Lisa Shieh	30	meningitis	45801	3	3	\N	\N
+10112	Jonathan Chen	30	meningitis	45801	2	3	CXR	General workup for fever/infection, though symptoms not suggestive of pneumonia
+10113	Panel Average	30	meningitis	45801	2	\N	\N	\N
+11066	Andre Kumar	30	meningitis	45801	-2	3	Imaging	\N
+11345	Jonathan Chen	5000	neutropenic	56492	1	2	\N	\N
+9232	Jason Hom	5002	neutropenic	44281	5	4	\N	\N
+9233	Lisa Shieh	5002	neutropenic	44281	5	3	\N	\N
+9525	Panel Average	5002	neutropenic	44281	4	\N	\N	\N
+9526	Panel Consensus	5002	neutropenic	44281	4	\N	\N	\N
+10114	Andre Kumar	5002	neutropenic	44281	2	3	Fever Medication	\N
+10333	Jonathan Chen	5002	neutropenic	44281	1	2	\N	\N
+11401	Jonathan Chen	5003	neutropenic	44281	1	2	\N	\N
+9234	Jason Hom	5000	neutropenic	44281	5	4	\N	\N
+9527	Panel Consensus	5000	neutropenic	44281	4	\N	\N	\N
+9591	Panel Average	5000	neutropenic	44281	4	\N	\N	\N
+9785	Andre Kumar	5000	neutropenic	44281	3	3	Fever Medication	\N
+9786	Lisa Shieh	5000	neutropenic	44281	3	3	\N	\N
+10334	Jonathan Chen	5000	neutropenic	44281	1	2	\N	\N
+9235	Lisa Shieh	5000	neutropenic	48829	5	3	\N	\N
+10237	Panel Average	5000	neutropenic	48829	1	\N	\N	\N
+10805	Andre Kumar	5000	neutropenic	48829	0	5	\N	\N
+10806	Jonathan Chen	5000	neutropenic	48829	0	2	\N	\N
+10983	Jason Hom	5000	neutropenic	48829	-1	4	\N	\N
+10984	Panel Consensus	5000	neutropenic	48829	-1	\N	\N	\N
+11329	Jonathan Chen	5002	neutropenic	65641	0	4	\N	Process
+9236	Andre Kumar	5002	neutropenic	45814	5	2	Admit	Assuming MASCC score is <21
+9237	Panel Average	5002	neutropenic	45814	5	\N	\N	\N
+7939	Jason Hom	5000	neutropenic	48775	10	4	\N	\N
+9238	Lisa Shieh	5000	neutropenic	48775	5	3	after post void	\N
+9239	Panel Average	5000	neutropenic	48775	5	\N	\N	\N
+10807	Andre Kumar	5000	neutropenic	48775	0	1	\N	\N
+10808	Jonathan Chen	5000	neutropenic	48775	0	2	\N	\N
+10809	Panel Consensus	5000	neutropenic	48775	0	\N	\N	\N
+7940	Jason Hom	5002	neutropenic	46157	10	5	\N	\N
+7941	Panel Consensus	5002	neutropenic	46157	10	\N	\N	\N
+8600	Jonathan Chen	5002	neutropenic	46157	7	4	Blood Cultures	\N
+8685	Panel Average	5002	neutropenic	46157	6	\N	\N	\N
+8764	Andre Kumar	5002	neutropenic	46157	6	5	Time to Positivity	More points for time to positivity cultures
+9787	Lisa Shieh	5002	neutropenic	46157	3	3	\N	\N
+7942	Jason Hom	5002	neutropenic	46291	10	5	\N	\N
+7943	Lisa Shieh	5002	neutropenic	46291	10	3	\N	\N
+7944	Panel Consensus	5002	neutropenic	46291	10	\N	\N	\N
+8296	Panel Average	5002	neutropenic	46291	9	\N	\N	\N
+8601	Jonathan Chen	5002	neutropenic	46291	7	4	Blood Cultures	\N
+8765	Andre Kumar	5002	neutropenic	46291	6	5	Time to Positivity	\N
+7945	Jason Hom	5002	neutropenic	45901	10	5	\N	\N
+7946	Lisa Shieh	5002	neutropenic	45901	10	5	\N	\N
+7947	Panel Consensus	5002	neutropenic	45901	10	\N	\N	\N
+8326	Panel Average	5002	neutropenic	45901	8	\N	\N	\N
+8602	Jonathan Chen	5002	neutropenic	45901	7	4	Blood Cultures	\N
+9240	Andre Kumar	5002	neutropenic	45901	5	5	Blood cultures	\N
+11438	Jonathan Chen	5003	neutropenic	45901	7	4	Blood Cultures	\N
+7948	Jason Hom	5000	neutropenic	45901	10	4	\N	\N
+7949	Lisa Shieh	5000	neutropenic	45901	10	3	\N	\N
+7950	Panel Consensus	5000	neutropenic	45901	10	\N	\N	\N
+8297	Panel Average	5000	neutropenic	45901	9	\N	\N	\N
+8603	Jonathan Chen	5000	neutropenic	45901	7	4	Blood Cultures	\N
+8766	Andre Kumar	5000	neutropenic	45901	6	5	Blood Cultures	\N
+7951	Jason Hom	5002	neutropenic	45752	10	5	\N	\N
+7952	Lisa Shieh	5002	neutropenic	45752	10	5	\N	\N
+7953	Panel Consensus	5002	neutropenic	45752	10	\N	\N	\N
+8327	Panel Average	5002	neutropenic	45752	8	\N	\N	\N
+8604	Jonathan Chen	5002	neutropenic	45752	7	4	Blood Cultures	\N
+9241	Andre Kumar	5002	neutropenic	45752	5	5	Blood Cultures	\N
+11245	Jonathan Chen	5003	neutropenic	45752	7	4	Blood Cultures	\N
+7954	Jason Hom	5000	neutropenic	45752	10	5	\N	\N
+7955	Lisa Shieh	5000	neutropenic	45752	10	5	\N	\N
+7956	Panel Consensus	5000	neutropenic	45752	10	\N	\N	\N
+8298	Panel Average	5000	neutropenic	45752	9	\N	\N	\N
+8605	Jonathan Chen	5000	neutropenic	45752	7	4	Blood Cultures	\N
+8767	Andre Kumar	5000	neutropenic	45752	6	5	Blood Cultures	\N
+7957	Jason Hom	5000	neutropenic	45760	10	5	\N	\N
+8686	Panel Average	5000	neutropenic	45760	6	\N	\N	\N
+8768	Andre Kumar	5000	neutropenic	45760	6	2	Blood gas	\N
+8769	Panel Consensus	5000	neutropenic	45760	6	\N	\N	\N
+9788	Lisa Shieh	5000	neutropenic	45760	3	3	\N	\N
+10115	Jonathan Chen	5000	neutropenic	45760	2	2	Blood Gas	\N
+10335	Andre Kumar	5003	neutropenic	45919	1	2	Blood Gas	\N
+10336	Panel Average	5003	neutropenic	45919	1	\N	\N	\N
+7958	Jason Hom	5003	neutropenic	44395	10	5	\N	\N
+7959	Panel Consensus	5003	neutropenic	44395	10	\N	\N	\N
+8606	Jonathan Chen	5003	neutropenic	44395	7	4	IVF	\N
+9242	Panel Average	5003	neutropenic	44395	5	\N	\N	\N
+9789	Lisa Shieh	5003	neutropenic	44395	3	3	\N	\N
+10116	Andre Kumar	5003	neutropenic	44395	2	3	Fluids	\N
+11290	Jonathan Chen	5003	neutropenic	44439	7	4	IVF	\N
+7960	Jason Hom	5000	neutropenic	44290	10	5	\N	\N
+7961	Panel Consensus	5000	neutropenic	44290	10	\N	\N	\N
+8607	Jonathan Chen	5000	neutropenic	44290	7	4	IVF	\N
+8770	Panel Average	5000	neutropenic	44290	6	\N	\N	\N
+9243	Andre Kumar	5000	neutropenic	44290	5	5	Fluids	\N
+9790	Lisa Shieh	5000	neutropenic	44290	3	3	\N	\N
+7962	Jason Hom	5002	neutropenic	45793	10	5	\N	\N
+8608	Panel Consensus	5002	neutropenic	45793	7	\N	\N	\N
+8672	Panel Average	5002	neutropenic	45793	7	\N	\N	\N
+9244	Andre Kumar	5002	neutropenic	45793	5	5	CBC	\N
+9245	Lisa Shieh	5002	neutropenic	45793	5	3	\N	\N
+9528	Jonathan Chen	5002	neutropenic	45793	4	4	CBC	\N
+11462	Jonathan Chen	5003	neutropenic	45793	4	4	CBC	\N
+7963	Jason Hom	5002	neutropenic	45788	10	5	\N	\N
+8411	Panel Consensus	5002	neutropenic	45788	8	\N	\N	\N
+8460	Panel Average	5002	neutropenic	45788	7	\N	\N	\N
+8609	Lisa Shieh	5002	neutropenic	45788	7	3	\N	\N
+9246	Andre Kumar	5002	neutropenic	45788	5	5	CBC	\N
+9529	Jonathan Chen	5002	neutropenic	45788	4	4	CBC	\N
+11402	Jonathan Chen	5003	neutropenic	45788	4	4	CBC	\N
+7964	Jason Hom	5000	neutropenic	45788	10	5	\N	\N
+8412	Panel Consensus	5000	neutropenic	45788	8	\N	\N	\N
+8444	Panel Average	5000	neutropenic	45788	8	\N	\N	\N
+8610	Lisa Shieh	5000	neutropenic	45788	7	3	\N	\N
+8771	Andre Kumar	5000	neutropenic	45788	6	5	CBC	\N
+9530	Jonathan Chen	5000	neutropenic	45788	4	4	CBC	\N
+11355	Jonathan Chen	5002	neutropenic	45966	4	4	CBC	\N
+7965	Jason Hom	5000	neutropenic	45966	10	5	\N	\N
+8286	Lisa Shieh	5000	neutropenic	45966	9	3	\N	\N
+8328	Panel Average	5000	neutropenic	45966	8	\N	\N	\N
+8413	Panel Consensus	5000	neutropenic	45966	8	\N	\N	\N
+8772	Andre Kumar	5000	neutropenic	45966	6	5	CBC	\N
+9531	Jonathan Chen	5000	neutropenic	45966	4	4	CBC	\N
+7966	Andre Kumar	5002	neutropenic	36210	10	5	Antibiotics	\N
+7967	Jason Hom	5002	neutropenic	36210	10	5	\N	\N
+7968	Jonathan Chen	5002	neutropenic	36210	10	5	Anti-Pseudomonas	\N
+7969	Lisa Shieh	5002	neutropenic	36210	10	5	\N	\N
+7970	Panel Average	5002	neutropenic	36210	10	\N	\N	\N
+7971	Panel Consensus	5002	neutropenic	36210	10	\N	\N	\N
+11475	Jonathan Chen	5003	neutropenic	36210	10	5	Anti-Pseudomonas	\N
+7972	Andre Kumar	5000	neutropenic	36210	10	5	Antibiotics	\N
+7973	Jason Hom	5000	neutropenic	36210	10	5	\N	\N
+7974	Jonathan Chen	5000	neutropenic	36210	10	5	Anti-Pseudomonas	\N
+7975	Lisa Shieh	5000	neutropenic	36210	10	5	\N	\N
+7976	Panel Average	5000	neutropenic	36210	10	\N	\N	\N
+7977	Panel Consensus	5000	neutropenic	36210	10	\N	\N	\N
+11167	Andre Kumar	5000	neutropenic	35733	-5	3	Antibiotics	\N
+11168	Panel Average	5000	neutropenic	35733	-5	\N	\N	\N
+11449	Jonathan Chen	5002	neutropenic	50547	0	4	\N	Consult
+11249	Jonathan Chen	5002	neutropenic	48980	0	4	\N	Consult
+9247	Jason Hom	5003	neutropenic	48980	5	5	\N	\N
+9248	Lisa Shieh	5003	neutropenic	48980	5	3	\N	\N
+9532	Panel Average	5003	neutropenic	48980	4	\N	\N	\N
+9533	Panel Consensus	5003	neutropenic	48980	4	\N	\N	\N
+10117	Andre Kumar	5003	neutropenic	48980	2	2	Consult	\N
+10810	Jonathan Chen	5003	neutropenic	48980	0	3	\N	\N
+10118	Andre Kumar	5003	neutropenic	49207	2	2	Consult	\N
+10119	Panel Average	5003	neutropenic	49207	2	\N	\N	\N
+7978	Jason Hom	5002	neutropenic	49228	10	5	\N	\N
+8874	Panel Average	5002	neutropenic	49228	5	\N	\N	\N
+9249	Panel Consensus	5002	neutropenic	49228	5	\N	\N	\N
+9791	Andre Kumar	5002	neutropenic	49228	3	1	Consult	\N
+9792	Lisa Shieh	5002	neutropenic	49228	3	3	\N	\N
+10811	Jonathan Chen	5002	neutropenic	49228	0	3	\N	\N
+11442	Jonathan Chen	5003	neutropenic	49228	0	4	\N	Consult
+11287	Jonathan Chen	5002	neutropenic	48711	0	2	\N	Wrong type of isolation
+10812	Andre Kumar	5003	neutropenic	48711	0	4	Isolation	\N
+10813	Panel Average	5003	neutropenic	48711	0	\N	\N	\N
+11292	Jonathan Chen	5000	neutropenic	48532	0	2	\N	\N
+7979	Jason Hom	5003	neutropenic	46286	10	5	\N	\N
+9250	Panel Average	5003	neutropenic	46286	5	\N	\N	\N
+9251	Panel Consensus	5003	neutropenic	46286	5	\N	\N	\N
+9793	Lisa Shieh	5003	neutropenic	46286	3	3	\N	\N
+10120	Andre Kumar	5003	neutropenic	46286	2	1	Coags	\N
+10814	Jonathan Chen	5003	neutropenic	46286	0	2	\N	No reason to expect DIC at this point?
+9252	Jason Hom	5003	neutropenic	48686	5	4	\N	\N
+9253	Lisa Shieh	5003	neutropenic	48686	5	3	\N	\N
+9534	Panel Average	5003	neutropenic	48686	4	\N	\N	\N
+10121	Andre Kumar	5003	neutropenic	48686	2	3	Diet	\N
+10122	Panel Consensus	5003	neutropenic	48686	2	\N	\N	\N
+10815	Jonathan Chen	5003	neutropenic	48686	0	3	\N	\N
+9254	Jason Hom	5000	neutropenic	48686	5	4	\N	\N
+9255	Lisa Shieh	5000	neutropenic	48686	5	3	\N	\N
+9535	Panel Average	5000	neutropenic	48686	4	\N	\N	\N
+10123	Andre Kumar	5000	neutropenic	48686	2	1	Diet	\N
+10124	Panel Consensus	5000	neutropenic	48686	2	\N	\N	\N
+10816	Jonathan Chen	5000	neutropenic	48686	0	3	\N	\N
+11357	Jonathan Chen	5003	neutropenic	46004	0	2	\N	\N
+11378	Jonathan Chen	5003	neutropenic	45811	0	2	\N	Unnecessary?
+11296	Jonathan Chen	5003	neutropenic	49054	0	3	\N	Neutropenic, not droplet isolation
+7980	Jason Hom	5000	neutropenic	49054	10	5	\N	\N
+9256	Lisa Shieh	5000	neutropenic	49054	5	3	\N	\N
+9257	Panel Average	5000	neutropenic	49054	5	\N	\N	\N
+9258	Panel Consensus	5000	neutropenic	49054	5	\N	\N	\N
+10817	Andre Kumar	5000	neutropenic	49054	0	4	Isolation	\N
+10818	Jonathan Chen	5000	neutropenic	49054	0	3	\N	Neutropenic, not droplet isolation
+7981	Jason Hom	5002	neutropenic	45866	10	5	\N	\N
+9434	Panel Average	5002	neutropenic	45866	5	\N	\N	\N
+9536	Panel Consensus	5002	neutropenic	45866	4	\N	\N	\N
+9794	Lisa Shieh	5002	neutropenic	45866	3	3	\N	\N
+10125	Jonathan Chen	5002	neutropenic	45866	2	2	\N	Tachycardia eval?
+10337	Andre Kumar	5002	neutropenic	45866	1	1	ECG + monitoring	\N
+7982	Jason Hom	5003	neutropenic	45866	10	5	\N	\N
+9537	Panel Consensus	5003	neutropenic	45866	4	\N	\N	\N
+9611	Panel Average	5003	neutropenic	45866	3	\N	\N	\N
+10126	Jonathan Chen	5003	neutropenic	45866	2	2	\N	Tachycardia eval?
+10819	Andre Kumar	5003	neutropenic	45866	0	1	ECG + monitoring	\N
+10820	Lisa Shieh	5003	neutropenic	45866	0	3	\N	\N
+7983	Jason Hom	5000	neutropenic	45866	10	5	\N	\N
+9460	Panel Average	5000	neutropenic	45866	4	\N	\N	\N
+9538	Panel Consensus	5000	neutropenic	45866	4	\N	\N	\N
+9795	Lisa Shieh	5000	neutropenic	45866	3	3	\N	\N
+10127	Jonathan Chen	5000	neutropenic	45866	2	2	\N	Tachycardia eval?
+10821	Andre Kumar	5000	neutropenic	45866	0	1	ECG + Monitoring	\N
+11319	Jonathan Chen	5000	neutropenic	46160	0	4	\N	Process
+11399	Jonathan Chen	5000	neutropenic	41759	0	4	\N	Presenting symptom
+11374	Jonathan Chen	5002	neutropenic	45225	7	4	\N	Reduce hospitalized neutropenic time
+11369	Jonathan Chen	5002	neutropenic	44252	10	5	Anti-Pseudomonas	\N
+7984	Andre Kumar	5002	neutropenic	44237	10	5	Antibiotics	\N
+7985	Jason Hom	5002	neutropenic	44237	10	5	\N	\N
+7986	Jonathan Chen	5002	neutropenic	44237	10	5	Anti-Pseudomonas	\N
+7987	Lisa Shieh	5002	neutropenic	44237	10	5	\N	\N
+7988	Panel Average	5002	neutropenic	44237	10	\N	\N	\N
+7989	Panel Consensus	5002	neutropenic	44237	10	\N	\N	\N
+7990	Andre Kumar	5000	neutropenic	44237	10	5	Antibiotics	\N
+7991	Jason Hom	5000	neutropenic	44237	10	5	\N	\N
+7992	Jonathan Chen	5000	neutropenic	44237	10	5	Anti-Pseudomonas	\N
+7993	Lisa Shieh	5000	neutropenic	44237	10	5	\N	\N
+7994	Panel Average	5000	neutropenic	44237	10	\N	\N	\N
+7995	Panel Consensus	5000	neutropenic	44237	10	\N	\N	\N
+11274	Jonathan Chen	5002	neutropenic	46006	0	2	\N	\N
+9259	Jason Hom	5000	neutropenic	46020	5	4	\N	\N
+9941	Panel Average	5000	neutropenic	46020	2	\N	\N	\N
+10128	Andre Kumar	5000	neutropenic	46020	2	4	Blood gas	\N
+10129	Jonathan Chen	5000	neutropenic	46020	2	2	Blood Gas	\N
+10130	Panel Consensus	5000	neutropenic	46020	2	\N	\N	\N
+10822	Lisa Shieh	5000	neutropenic	46020	0	3	\N	\N
+7996	Jason Hom	5002	neutropenic	62151	10	5	\N	\N
+7997	Panel Consensus	5002	neutropenic	62151	10	\N	\N	\N
+8673	Panel Average	5002	neutropenic	62151	7	\N	\N	\N
+8773	Jonathan Chen	5002	neutropenic	62151	6	4	Lactate	\N
+9260	Andre Kumar	5002	neutropenic	62151	5	4	Lactate	\N
+9261	Lisa Shieh	5002	neutropenic	62151	5	3	\N	\N
+7998	Jason Hom	5003	neutropenic	62151	10	5	\N	\N
+7999	Panel Consensus	5003	neutropenic	62151	10	\N	\N	\N
+8774	Jonathan Chen	5003	neutropenic	62151	6	4	Lactate	\N
+8775	Panel Average	5003	neutropenic	62151	6	\N	\N	\N
+9262	Lisa Shieh	5003	neutropenic	62151	5	3	\N	\N
+9796	Andre Kumar	5003	neutropenic	62151	3	1	Lactate	\N
+8000	Jason Hom	5000	neutropenic	62151	10	5	\N	\N
+8001	Panel Consensus	5000	neutropenic	62151	10	\N	\N	\N
+8674	Panel Average	5000	neutropenic	62151	7	\N	\N	\N
+8776	Jonathan Chen	5000	neutropenic	62151	6	4	Lactate	\N
+9263	Andre Kumar	5000	neutropenic	62151	5	5	Lactate	\N
+9264	Lisa Shieh	5000	neutropenic	62151	5	3	\N	\N
+9797	Andre Kumar	5003	neutropenic	45918	3	2	Lactate	\N
+9798	Panel Average	5003	neutropenic	45918	3	\N	\N	\N
+9265	Andre Kumar	5000	neutropenic	45918	5	4	Lactate	\N
+9266	Panel Average	5000	neutropenic	45918	5	\N	\N	\N
+9539	Jason Hom	5000	neutropenic	45903	4	4	\N	\N
+9799	Lisa Shieh	5000	neutropenic	45903	3	\N	\N	\N
+9942	Panel Average	5000	neutropenic	45903	2	\N	\N	\N
+10131	Panel Consensus	5000	neutropenic	45903	2	\N	\N	\N
+10338	Jonathan Chen	5000	neutropenic	45903	1	2	Tumor Lysis	Worth tracking?
+10823	Andre Kumar	5000	neutropenic	45903	0	1	\N	\N
+10824	Andre Kumar	5000	neutropenic	45894	0	1	Lipase	\N
+10825	Panel Average	5000	neutropenic	45894	0	\N	\N	\N
+8002	Jason Hom	5002	neutropenic	45806	10	5	\N	\N
+8875	Panel Average	5002	neutropenic	45806	5	\N	\N	\N
+9800	Andre Kumar	5002	neutropenic	45806	3	4	Metabolic Panel	\N
+9801	Lisa Shieh	5002	neutropenic	45806	3	3	\N	\N
+9802	Panel Consensus	5002	neutropenic	45806	3	\N	\N	\N
+10826	Jonathan Chen	5002	neutropenic	45806	0	2	\N	\N
+11436	Jonathan Chen	5003	neutropenic	45806	0	2	\N	\N
+11323	Jonathan Chen	5000	neutropenic	45806	0	2	\N	\N
+8003	Jason Hom	5002	neutropenic	45763	10	5	\N	\N
+8004	Panel Consensus	5002	neutropenic	45763	10	\N	\N	\N
+8777	Panel Average	5002	neutropenic	45763	6	\N	\N	\N
+9267	Lisa Shieh	5002	neutropenic	45763	5	3	\N	\N
+9803	Andre Kumar	5002	neutropenic	45763	3	3	Metabolic panel	\N
+9804	Jonathan Chen	5002	neutropenic	45763	3	3	Metabolic Panel	\N
+11304	Jonathan Chen	5003	neutropenic	45763	3	3	Metabolic Panel	\N
+8005	Jason Hom	5000	neutropenic	45763	10	5	\N	\N
+8006	Panel Consensus	5000	neutropenic	45763	10	\N	\N	\N
+8687	Panel Average	5000	neutropenic	45763	6	\N	\N	\N
+9268	Lisa Shieh	5000	neutropenic	45763	5	3	\N	\N
+9540	Andre Kumar	5000	neutropenic	45763	4	2	Metabolic Panel	\N
+9805	Jonathan Chen	5000	neutropenic	45763	3	3	Metabolic Panel	\N
+8007	Jason Hom	5002	neutropenic	45771	10	5	\N	\N
+8008	Panel Consensus	5002	neutropenic	45771	10	\N	\N	\N
+8778	Panel Average	5002	neutropenic	45771	6	\N	\N	\N
+9269	Lisa Shieh	5002	neutropenic	45771	5	3	\N	\N
+9806	Andre Kumar	5002	neutropenic	45771	3	4	Metabolic Panel	\N
+9807	Jonathan Chen	5002	neutropenic	45771	3	3	Metabolic Panel	\N
+11400	Jonathan Chen	5003	neutropenic	45771	3	3	Metabolic Panel	\N
+8009	Jason Hom	5000	neutropenic	45771	10	5	\N	\N
+8010	Panel Consensus	5000	neutropenic	45771	10	\N	\N	\N
+8688	Panel Average	5000	neutropenic	45771	6	\N	\N	\N
+9270	Lisa Shieh	5000	neutropenic	45771	5	3	\N	\N
+9541	Andre Kumar	5000	neutropenic	45771	4	2	MEtabolic Panel	\N
+9808	Jonathan Chen	5000	neutropenic	45771	3	3	Metabolic Panel	\N
+9271	Jason Hom	5002	neutropenic	45792	5	4	\N	\N
+9612	Panel Average	5002	neutropenic	45792	3	\N	\N	\N
+9809	Lisa Shieh	5002	neutropenic	45792	3	3	\N	\N
+10132	Andre Kumar	5002	neutropenic	45792	2	5	MRSA	\N
+10133	Panel Consensus	5002	neutropenic	45792	2	\N	\N	\N
+10339	Jonathan Chen	5002	neutropenic	45792	1	2	\N	Reasonable if helps triage Vancomycin decision?
+9272	Jason Hom	5003	neutropenic	45792	5	4	\N	\N
+9592	Panel Average	5003	neutropenic	45792	4	\N	\N	\N
+9810	Andre Kumar	5003	neutropenic	45792	3	2	MRSA	\N
+9811	Lisa Shieh	5003	neutropenic	45792	3	3	\N	\N
+10134	Panel Consensus	5003	neutropenic	45792	2	\N	\N	\N
+10340	Jonathan Chen	5003	neutropenic	45792	1	2	\N	Reasonable if helps triage Vancomycin decision?
+8011	Jason Hom	5002	neutropenic	48960	10	5	\N	\N
+8012	Lisa Shieh	5002	neutropenic	48960	10	5	\N	\N
+8414	Panel Consensus	5002	neutropenic	48960	8	\N	\N	\N
+8445	Panel Average	5002	neutropenic	48960	8	\N	\N	\N
+8779	Jonathan Chen	5002	neutropenic	48960	6	4	\N	\N
+9812	Andre Kumar	5002	neutropenic	48960	3	1	Isolation	\N
+8013	Jason Hom	5003	neutropenic	48960	10	5	\N	\N
+8014	Lisa Shieh	5003	neutropenic	48960	10	3	\N	\N
+8415	Panel Consensus	5003	neutropenic	48960	8	\N	\N	\N
+8461	Panel Average	5003	neutropenic	48960	7	\N	\N	\N
+8780	Jonathan Chen	5003	neutropenic	48960	6	4	\N	\N
+10135	Andre Kumar	5003	neutropenic	48960	2	3	Isolation	\N
+8015	Jason Hom	5000	neutropenic	48960	10	5	\N	\N
+8416	Panel Consensus	5000	neutropenic	48960	8	\N	\N	\N
+8611	Lisa Shieh	5000	neutropenic	48960	7	3	\N	\N
+8675	Panel Average	5000	neutropenic	48960	7	\N	\N	\N
+8781	Jonathan Chen	5000	neutropenic	48960	6	4	\N	\N
+9813	Andre Kumar	5000	neutropenic	48960	3	4	Isolation	\N
+10827	Andre Kumar	5003	neutropenic	45853	0	5	BNP	\N
+10828	Panel Average	5003	neutropenic	45853	0	\N	\N	\N
+11237	Jonathan Chen	5000	neutropenic	44420	0	2	\N	Symptom management
+11429	Jonathan Chen	5000	neutropenic	45900	0	2	\N	Irrelevant
+10136	Andre Kumar	5000	neutropenic	61823	2	3	Nursing	\N
+10137	Panel Average	5000	neutropenic	61823	2	\N	\N	\N
+10138	Andre Kumar	5000	neutropenic	45802	2	1	Nursing	\N
+10139	Panel Average	5000	neutropenic	45802	2	\N	\N	\N
+11297	Jonathan Chen	5003	neutropenic	45778	0	2	\N	\N
+11294	Jonathan Chen	5002	neutropenic	50554	0	4	\N	Irrelevant
+11371	Jonathan Chen	5003	neutropenic	50554	0	4	\N	Irrelevant
+11326	Jonathan Chen	5003	neutropenic	45955	6	4	Lactate	\N
+8016	Panel Consensus	5000	neutropenic	45955	10	\N	\N	\N
+8782	Jonathan Chen	5000	neutropenic	45955	6	4	Lactate	\N
+9542	Jason Hom	5000	neutropenic	45955	4	4	\N	\N
+9814	Lisa Shieh	5000	neutropenic	45955	3	3	\N	\N
+9815	Panel Average	5000	neutropenic	45955	3	\N	\N	\N
+10140	Andre Kumar	5000	neutropenic	45955	2	3	Blood gas	\N
+9816	Jonathan Chen	5000	neutropenic	46000	3	4	UA	\N
+10829	Andre Kumar	5000	neutropenic	46000	0	1	Urinalysis	\N
+10830	Jason Hom	5000	neutropenic	46000	0	4	\N	\N
+10831	Lisa Shieh	5000	neutropenic	46000	0	3	not symptomatic?	\N
+10832	Panel Average	5000	neutropenic	46000	0	\N	\N	\N
+10833	Panel Consensus	5000	neutropenic	46000	0	\N	\N	\N
+8017	Jason Hom	5002	neutropenic	45759	10	5	\N	\N
+9461	Panel Average	5002	neutropenic	45759	4	\N	\N	\N
+9543	Panel Consensus	5002	neutropenic	45759	4	\N	\N	\N
+9817	Andre Kumar	5002	neutropenic	45759	3	4	Coags	\N
+10834	Jonathan Chen	5002	neutropenic	45759	0	3	\N	\N
+10835	Lisa Shieh	5002	neutropenic	45759	0	3	\N	\N
+11336	Jonathan Chen	5003	neutropenic	45759	0	3	\N	\N
+11453	Jonathan Chen	5000	neutropenic	45759	0	3	\N	\N
+8018	Jason Hom	5002	neutropenic	45770	10	5	\N	\N
+9462	Panel Average	5002	neutropenic	45770	4	\N	\N	\N
+9544	Panel Consensus	5002	neutropenic	45770	4	\N	\N	\N
+9818	Andre Kumar	5002	neutropenic	45770	3	4	Coags	\N
+10836	Jonathan Chen	5002	neutropenic	45770	0	3	\N	\N
+10837	Lisa Shieh	5002	neutropenic	45770	0	3	\N	\N
+11279	Jonathan Chen	5003	neutropenic	45770	0	3	\N	\N
+11403	Jonathan Chen	5000	neutropenic	45770	0	3	\N	\N
+10341	Andre Kumar	5003	neutropenic	52757	1	2	PT/OT	\N
+10342	Panel Average	5003	neutropenic	52757	1	\N	\N	\N
+10141	Andre Kumar	5000	neutropenic	45890	2	5	Blood Gas	\N
+10142	Jonathan Chen	5000	neutropenic	45890	2	2	Blood Gas	\N
+10388	Panel Average	5000	neutropenic	45890	1	\N	\N	\N
+10838	Jason Hom	5000	neutropenic	45890	0	4	\N	\N
+10839	Lisa Shieh	5000	neutropenic	45890	0	3	\N	\N
+10840	Panel Consensus	5000	neutropenic	45890	0	\N	\N	\N
+11358	Jonathan Chen	5003	neutropenic	62023	2	3	Respiratory Virus Screen	\N
+8019	Jason Hom	5000	neutropenic	62023	10	5	\N	\N
+8783	Panel Average	5000	neutropenic	62023	6	\N	\N	\N
+8784	Panel Consensus	5000	neutropenic	62023	6	\N	\N	\N
+9273	Lisa Shieh	5000	neutropenic	62023	5	3	\N	\N
+9819	Andre Kumar	5000	neutropenic	62023	3	2	Respiratory Viral Panel	\N
+8796	Panel Consensus	5000	neutropenic	45782	6	\N	\N	\N
+10143	Jonathan Chen	5000	neutropenic	62023	2	3	Respiratory Virus Screen	\N
+8020	Jason Hom	5002	neutropenic	63923	10	5	\N	\N
+8785	Panel Average	5002	neutropenic	63923	6	\N	\N	\N
+8786	Panel Consensus	5002	neutropenic	63923	6	\N	\N	\N
+9274	Lisa Shieh	5002	neutropenic	63923	5	3	\N	\N
+9820	Andre Kumar	5002	neutropenic	63923	3	3	Respiratory Viral Panel	\N
+10144	Jonathan Chen	5002	neutropenic	63923	2	3	Respiratory Virus Screen	\N
+11302	Jonathan Chen	5003	neutropenic	63923	2	3	Respiratory Virus Screen	\N
+8021	Jason Hom	5000	neutropenic	63923	10	5	\N	\N
+8787	Panel Average	5000	neutropenic	63923	6	\N	\N	\N
+8788	Panel Consensus	5000	neutropenic	63923	6	\N	\N	\N
+9275	Lisa Shieh	5000	neutropenic	63923	5	3	\N	\N
+9821	Andre Kumar	5000	neutropenic	63923	3	1	Respiratory Viral Panel	\N
+10145	Jonathan Chen	5000	neutropenic	63923	2	3	Respiratory Virus Screen	\N
+11310	Jonathan Chen	5002	neutropenic	63725	6	4	Lactate	\N
+8022	Jason Hom	5003	neutropenic	63725	10	5	\N	\N
+8023	Panel Consensus	5003	neutropenic	63725	10	\N	\N	\N
+8789	Jonathan Chen	5003	neutropenic	63725	6	4	Lactate	\N
+8790	Panel Average	5003	neutropenic	63725	6	\N	\N	\N
+9276	Lisa Shieh	5003	neutropenic	63725	5	3	\N	\N
+9822	Andre Kumar	5003	neutropenic	63725	3	1	Lactate	\N
+8024	Jason Hom	5000	neutropenic	63725	10	5	\N	\N
+8025	Panel Consensus	5000	neutropenic	63725	10	\N	\N	\N
+8676	Panel Average	5000	neutropenic	63725	7	\N	\N	\N
+8791	Jonathan Chen	5000	neutropenic	63725	6	4	Lactate	\N
+9277	Andre Kumar	5000	neutropenic	63725	5	4	lactate	\N
+9278	Lisa Shieh	5000	neutropenic	63725	5	3	\N	\N
+8026	Jason Hom	5002	neutropenic	44198	10	5	\N	\N
+8027	Panel Consensus	5002	neutropenic	44198	10	\N	\N	\N
+8462	Panel Average	5002	neutropenic	44198	7	\N	\N	\N
+8612	Jonathan Chen	5002	neutropenic	44198	7	4	IVF	\N
+8613	Lisa Shieh	5002	neutropenic	44198	7	3	\N	\N
+9279	Andre Kumar	5002	neutropenic	44198	5	5	Fluids	\N
+8028	Jason Hom	5003	neutropenic	44198	10	5	\N	\N
+8029	Panel Consensus	5003	neutropenic	44198	10	\N	\N	\N
+8614	Jonathan Chen	5003	neutropenic	44198	7	4	IVF	\N
+8615	Lisa Shieh	5003	neutropenic	44198	7	3	\N	\N
+8689	Panel Average	5003	neutropenic	44198	6	\N	\N	\N
+10146	Andre Kumar	5003	neutropenic	44198	2	3	Fluids	\N
+8030	Jason Hom	5000	neutropenic	44198	10	5	\N	\N
+8031	Panel Consensus	5000	neutropenic	44198	10	\N	\N	\N
+8463	Panel Average	5000	neutropenic	44198	7	\N	\N	\N
+8616	Jonathan Chen	5000	neutropenic	44198	7	4	IVF	\N
+8617	Lisa Shieh	5000	neutropenic	44198	7	3	\N	\N
+9280	Andre Kumar	5000	neutropenic	44198	5	5	Fluids	\N
+11282	Jonathan Chen	5000	neutropenic	45945	1	2	\N	Anticipating transfusions, but not needed now
+11331	Jonathan Chen	5003	neutropenic	45995	1	2	Tumor Lysis	\N
+9281	Jason Hom	5000	neutropenic	45995	5	4	\N	\N
+9823	Lisa Shieh	5000	neutropenic	45995	3	3	\N	\N
+9824	Panel Consensus	5000	neutropenic	45995	3	\N	\N	\N
+9922	Panel Average	5000	neutropenic	45995	3	\N	\N	\N
+10343	Jonathan Chen	5000	neutropenic	45995	1	2	Tumor Lysis	\N
+10841	Andre Kumar	5000	neutropenic	45995	0	1	Tumor Lysis	\N
+9825	Andre Kumar	5002	neutropenic	45751	3	5	Urinalysis	\N
+9826	Panel Average	5002	neutropenic	45751	3	\N	\N	\N
+8618	Lisa Shieh	5000	neutropenic	45751	7	3	\N	\N
+8792	Panel Consensus	5000	neutropenic	45751	6	\N	\N	\N
+8852	Panel Average	5000	neutropenic	45751	6	\N	\N	\N
+9282	Andre Kumar	5000	neutropenic	45751	5	2	Urinalysis	\N
+9283	Jason Hom	5000	neutropenic	45751	5	4	\N	\N
+9827	Jonathan Chen	5000	neutropenic	45751	3	3	UA	\N
+8793	Panel Consensus	5000	neutropenic	45873	6	\N	\N	\N
+9284	Andre Kumar	5000	neutropenic	45873	5	5	Urinalysis	\N
+9285	Jason Hom	5000	neutropenic	45873	5	4	\N	\N
+9463	Panel Average	5000	neutropenic	45873	4	\N	\N	\N
+9828	Jonathan Chen	5000	neutropenic	45873	3	4	UA	\N
+9829	Lisa Shieh	5000	neutropenic	45873	3	3	\N	\N
+8794	Panel Consensus	5002	neutropenic	45782	6	\N	\N	\N
+9286	Andre Kumar	5002	neutropenic	45782	5	5	Urine Culture	\N
+9287	Jason Hom	5002	neutropenic	45782	5	4	\N	\N
+9464	Panel Average	5002	neutropenic	45782	4	\N	\N	\N
+9830	Jonathan Chen	5002	neutropenic	45782	3	4	UA	\N
+9831	Lisa Shieh	5002	neutropenic	45782	3	3	\N	\N
+8795	Panel Consensus	5003	neutropenic	45782	6	\N	\N	\N
+9288	Jason Hom	5003	neutropenic	45782	5	4	\N	\N
+9545	Andre Kumar	5003	neutropenic	45782	4	4	Urine Culture	\N
+9546	Panel Average	5003	neutropenic	45782	4	\N	\N	\N
+9832	Jonathan Chen	5003	neutropenic	45782	3	4	UA	\N
+9833	Lisa Shieh	5003	neutropenic	45782	3	3	\N	\N
+9289	Andre Kumar	5000	neutropenic	45782	5	5	Urine Culture	\N
+9290	Jason Hom	5000	neutropenic	45782	5	4	\N	\N
+9613	Panel Average	5000	neutropenic	45782	3	\N	\N	\N
+9834	Jonathan Chen	5000	neutropenic	45782	3	4	UA	\N
+10842	Lisa Shieh	5000	neutropenic	45782	0	3	\N	\N
+9291	Jonathan Chen	5002	neutropenic	43997	5	3	\N	Case deliberately ambiguous about needing vancomycin / MRSA coverage. With elevated lactate, reasonable to assess as severe sepsis for empiric coverage
+9292	Lisa Shieh	5002	neutropenic	43997	5	3	\N	\N
+9943	Panel Average	5002	neutropenic	43997	2	\N	\N	\N
+10147	Jason Hom	5002	neutropenic	43997	2	5	\N	\N
+10148	Panel Consensus	5002	neutropenic	43997	2	\N	\N	\N
+10843	Andre Kumar	5002	neutropenic	43997	0	4	Antibiotics	\N
+8619	Lisa Shieh	5003	neutropenic	43997	7	3	\N	\N
+9293	Jonathan Chen	5003	neutropenic	43997	5	3	\N	Case deliberately ambiguous about needing vancomycin / MRSA coverage. With elevated lactate, reasonable to assess as severe sepsis for empiric coverage
+9835	Panel Average	5003	neutropenic	43997	3	\N	\N	\N
+10149	Jason Hom	5003	neutropenic	43997	2	4	\N	\N
+10150	Panel Consensus	5003	neutropenic	43997	2	\N	\N	\N
+10844	Andre Kumar	5003	neutropenic	43997	0	5	Antibiotics	\N
+9294	Jonathan Chen	5000	neutropenic	43997	5	3	\N	Case deliberately ambiguous about needing vancomycin / MRSA coverage. With elevated lactate, reasonable to assess as severe sepsis for empiric coverage
+9295	Lisa Shieh	5000	neutropenic	43997	5	3	\N	\N
+9944	Panel Average	5000	neutropenic	43997	2	\N	\N	\N
+10151	Jason Hom	5000	neutropenic	43997	2	4	\N	\N
+10152	Panel Consensus	5000	neutropenic	43997	2	\N	\N	\N
+10845	Andre Kumar	5000	neutropenic	43997	0	5	Antibiotics	\N
+11478	Jonathan Chen	5000	neutropenic	45972	2	3	\N	\N
+9836	Andre Kumar	5002	neutropenic	63735	3	3	Respiratory Viral Panel	\N
+9837	Lisa Shieh	5002	neutropenic	63735	3	3	\N	\N
+10153	Jonathan Chen	5002	neutropenic	63735	2	3	Respiratory Virus Screen	\N
+10154	Panel Average	5002	neutropenic	63735	2	\N	\N	\N
+10846	Jason Hom	5002	neutropenic	63735	0	5	\N	\N
+10847	Panel Consensus	5002	neutropenic	63735	0	\N	\N	\N
+9838	Andre Kumar	5003	neutropenic	63735	3	2	Respiratory Viral Panel	\N
+10155	Jonathan Chen	5003	neutropenic	63735	2	3	Respiratory Virus Screen	\N
+10344	Panel Average	5003	neutropenic	63735	1	\N	\N	\N
+10848	Jason Hom	5003	neutropenic	63735	0	5	\N	\N
+10849	Lisa Shieh	5003	neutropenic	63735	0	3	\N	\N
+10850	Panel Consensus	5003	neutropenic	63735	0	\N	\N	\N
+11238	Jonathan Chen	5000	neutropenic	45766	0	2	\N	Process
+8032	Jason Hom	5002	neutropenic	45818	10	5	\N	\N
+8033	Panel Consensus	5002	neutropenic	45818	10	\N	\N	\N
+8677	Panel Average	5002	neutropenic	45818	7	\N	\N	\N
+9296	Andre Kumar	5002	neutropenic	45818	5	2	Imaging	\N
+9297	Lisa Shieh	5002	neutropenic	45818	5	3	\N	\N
+9839	Jonathan Chen	5002	neutropenic	45818	3	3	CXR	\N
+8034	Jason Hom	5003	neutropenic	45818	10	5	\N	\N
+8035	Panel Consensus	5003	neutropenic	45818	10	\N	\N	\N
+8853	Panel Average	5003	neutropenic	45818	6	\N	\N	\N
+9547	Andre Kumar	5003	neutropenic	45818	4	4	Imaging	No points if already done
+9840	Jonathan Chen	5003	neutropenic	45818	3	3	CXR	\N
+9841	Lisa Shieh	5003	neutropenic	45818	3	3	\N	\N
+8036	Jason Hom	5000	neutropenic	45818	10	5	\N	\N
+8037	Panel Consensus	5000	neutropenic	45818	10	\N	\N	\N
+8446	Panel Average	5000	neutropenic	45818	8	\N	\N	\N
+8620	Lisa Shieh	5000	neutropenic	45818	7	3	\N	\N
+8797	Andre Kumar	5000	neutropenic	45818	6	4	Imaging	\N
+9842	Jonathan Chen	5000	neutropenic	45818	3	3	CXR	\N
+8038	Jason Hom	5002	neutropenic	45801	10	5	\N	\N
+8039	Panel Consensus	5002	neutropenic	45801	10	\N	\N	\N
+8464	Panel Average	5002	neutropenic	45801	7	\N	\N	\N
+8621	Lisa Shieh	5002	neutropenic	45801	7	3	\N	\N
+9298	Andre Kumar	5002	neutropenic	45801	5	2	Imaging	\N
+9843	Jonathan Chen	5002	neutropenic	45801	3	3	CXR	\N
+8040	Jason Hom	5000	neutropenic	45801	10	5	\N	\N
+8041	Panel Consensus	5000	neutropenic	45801	10	\N	\N	\N
+8287	Lisa Shieh	5000	neutropenic	45801	9	3	\N	\N
+8329	Panel Average	5000	neutropenic	45801	8	\N	\N	\N
+8798	Andre Kumar	5000	neutropenic	45801	6	4	Imaging	\N
+9844	Jonathan Chen	5000	neutropenic	45801	3	3	CXR	\N
+8042	Jason Hom	10	pulmonary_embolism	45977	10	5	\N	\N
+8043	Panel Consensus	10	pulmonary_embolism	45977	10	\N	\N	\N
+9435	Panel Average	10	pulmonary_embolism	45977	5	\N	\N	\N
+9845	Lisa Shieh	10	pulmonary_embolism	45977	3	3	\N	\N
+10345	Andre Kumar	10	pulmonary_embolism	45977	1	1	Weight	\N
+10851	Jonathan Chen	10	pulmonary_embolism	45977	0	2	\N	\N
+8044	Jason Hom	11	pulmonary_embolism	45977	10	5	\N	\N
+8045	Panel Consensus	11	pulmonary_embolism	45977	10	\N	\N	\N
+8854	Panel Average	11	pulmonary_embolism	45977	6	\N	\N	\N
+9299	Lisa Shieh	11	pulmonary_embolism	45977	5	3	\N	\N
+10156	Andre Kumar	11	pulmonary_embolism	45977	2	2	Weight	\N
+10852	Jonathan Chen	11	pulmonary_embolism	45977	0	2	\N	\N
+11252	Jonathan Chen	11	pulmonary_embolism	65641	0	4	\N	Process
+8046	Jason Hom	11	pulmonary_embolism	45814	10	5	\N	\N
+8047	Panel Consensus	11	pulmonary_embolism	45814	10	\N	\N	\N
+8690	Panel Average	11	pulmonary_embolism	45814	6	\N	\N	\N
+9300	Lisa Shieh	11	pulmonary_embolism	45814	5	3	\N	\N
+9548	Andre Kumar	11	pulmonary_embolism	45814	4	5	Admit	\N
+10853	Jonathan Chen	11	pulmonary_embolism	45814	0	2	\N	\N
+9549	Andre Kumar	11	pulmonary_embolism	61982	4	5	Admit	\N
+9550	Panel Average	11	pulmonary_embolism	61982	4	\N	\N	\N
+11443	Jonathan Chen	10	pulmonary_embolism	46309	2	4	Nebs	\N
+11473	Jonathan Chen	11	pulmonary_embolism	46309	2	4	Nebs	\N
+11311	Jonathan Chen	10	pulmonary_embolism	44349	2	4	Nebs	\N
+11468	Jonathan Chen	10	pulmonary_embolism	60175	2	4	Nebs	\N
+8048	Jason Hom	10	pulmonary_embolism	44330	10	5	\N	\N
+8622	Lisa Shieh	10	pulmonary_embolism	44330	7	3	\N	\N
+8623	Panel Consensus	10	pulmonary_embolism	44330	7	\N	\N	\N
+8678	Panel Average	10	pulmonary_embolism	44330	7	\N	\N	\N
+9846	Andre Kumar	10	pulmonary_embolism	44330	3	3	Nebs	\N
+10157	Jonathan Chen	10	pulmonary_embolism	44330	2	4	Nebs	\N
+11471	Jonathan Chen	12	pulmonary_embolism	60175	2	4	Nebs	\N
+11340	Jonathan Chen	11	pulmonary_embolism	44349	2	4	Nebs	\N
+11432	Jonathan Chen	11	pulmonary_embolism	60175	2	4	Nebs	\N
+8049	Jason Hom	11	pulmonary_embolism	44330	10	5	\N	\N
+8624	Panel Consensus	11	pulmonary_embolism	44330	7	\N	\N	\N
+8855	Panel Average	11	pulmonary_embolism	44330	6	\N	\N	\N
+9301	Lisa Shieh	11	pulmonary_embolism	44330	5	3	\N	\N
+10158	Andre Kumar	11	pulmonary_embolism	44330	2	1	Nebs	\N
+10159	Jonathan Chen	11	pulmonary_embolism	44330	2	4	Nebs	\N
+11427	Jonathan Chen	11	pulmonary_embolism	44595	0	2	\N	Weird antibiotic choice?
+11405	Jonathan Chen	10	pulmonary_embolism	47146	0	2	\N	\N
+9302	Jason Hom	10	pulmonary_embolism	44206	5	5	\N	\N
+9303	Lisa Shieh	10	pulmonary_embolism	44206	5	3	\N	\N
+9551	Panel Average	10	pulmonary_embolism	44206	4	\N	\N	\N
+9552	Panel Consensus	10	pulmonary_embolism	44206	4	\N	\N	\N
+10160	Andre Kumar	10	pulmonary_embolism	44206	2	2	ACS meds	\N
+10854	Jonathan Chen	10	pulmonary_embolism	44206	0	2	\N	\N
+9304	Lisa Shieh	11	pulmonary_embolism	44206	5	3	\N	\N
+9553	Panel Consensus	11	pulmonary_embolism	44206	4	\N	\N	\N
+9945	Panel Average	11	pulmonary_embolism	44206	2	\N	\N	\N
+10161	Andre Kumar	11	pulmonary_embolism	44206	2	1	ACS Medications	\N
+10855	Jason Hom	11	pulmonary_embolism	44206	0	5	\N	\N
+10856	Jonathan Chen	11	pulmonary_embolism	44206	0	2	\N	\N
+11472	Jonathan Chen	8	pulmonary_embolism	44206	0	2	\N	\N
+10346	Andre Kumar	10	pulmonary_embolism	44315	1	2	ACS meds	\N
+10347	Panel Average	10	pulmonary_embolism	44315	1	\N	\N	\N
+11270	Jonathan Chen	8	pulmonary_embolism	46551	0	2	\N	\N
+10857	Andre Kumar	8	pulmonary_embolism	44240	0	5	Statin	\N
+10858	Panel Average	8	pulmonary_embolism	44240	0	\N	\N	\N
+8050	Jason Hom	10	pulmonary_embolism	35849	10	5	\N	\N
+8799	Panel Average	10	pulmonary_embolism	35849	6	\N	\N	\N
+8800	Panel Consensus	10	pulmonary_embolism	35849	6	\N	\N	\N
+9305	Lisa Shieh	10	pulmonary_embolism	35849	5	3	\N	\N
+9847	Andre Kumar	10	pulmonary_embolism	35849	3	3	Antibiotics	\N
+10859	Jonathan Chen	10	pulmonary_embolism	35849	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+8801	Panel Consensus	12	pulmonary_embolism	35849	6	\N	\N	\N
+9306	Jason Hom	12	pulmonary_embolism	35849	5	5	\N	\N
+9614	Panel Average	12	pulmonary_embolism	35849	3	\N	\N	\N
+9848	Lisa Shieh	12	pulmonary_embolism	35849	3	3	\N	\N
+10162	Andre Kumar	12	pulmonary_embolism	35849	2	1	Antibiotics	\N
+10860	Jonathan Chen	12	pulmonary_embolism	35849	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+8051	Jason Hom	11	pulmonary_embolism	35849	10	5	\N	\N
+8802	Panel Consensus	11	pulmonary_embolism	35849	6	\N	\N	\N
+8876	Panel Average	11	pulmonary_embolism	35849	5	\N	\N	\N
+9307	Lisa Shieh	11	pulmonary_embolism	35849	5	3	\N	\N
+10348	Andre Kumar	11	pulmonary_embolism	35849	1	2	Antibiotics	\N
+10861	Jonathan Chen	11	pulmonary_embolism	35849	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+8052	Jason Hom	10	pulmonary_embolism	44388	10	5	\N	\N
+8803	Panel Consensus	10	pulmonary_embolism	44388	6	\N	\N	\N
+8877	Panel Average	10	pulmonary_embolism	44388	5	\N	\N	\N
+9308	Lisa Shieh	10	pulmonary_embolism	44388	5	3	\N	\N
+10349	Andre Kumar	10	pulmonary_embolism	44388	1	3	Antibiotics	\N
+10862	Jonathan Chen	10	pulmonary_embolism	44388	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+8053	Jason Hom	11	pulmonary_embolism	44388	10	5	\N	\N
+8804	Panel Consensus	11	pulmonary_embolism	44388	6	\N	\N	\N
+9436	Panel Average	11	pulmonary_embolism	44388	5	\N	\N	\N
+9849	Lisa Shieh	11	pulmonary_embolism	44388	3	3	\N	\N
+10350	Andre Kumar	11	pulmonary_embolism	44388	1	2	Antibiotics	\N
+10863	Jonathan Chen	11	pulmonary_embolism	44388	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+8054	Jason Hom	11	pulmonary_embolism	45901	10	5	\N	\N
+9309	Panel Consensus	11	pulmonary_embolism	45901	5	\N	\N	\N
+9437	Panel Average	11	pulmonary_embolism	45901	5	\N	\N	\N
+9850	Lisa Shieh	11	pulmonary_embolism	45901	3	3	\N	\N
+10163	Jonathan Chen	11	pulmonary_embolism	45901	2	\N	Blood Cultures	Not infection, but reasonable to check if not empirically treat?
+10351	Andre Kumar	11	pulmonary_embolism	45901	1	2	Blood Cultures	\N
+8055	Jason Hom	11	pulmonary_embolism	45752	10	5	\N	\N
+9310	Panel Consensus	11	pulmonary_embolism	45752	5	\N	\N	\N
+9438	Panel Average	11	pulmonary_embolism	45752	5	\N	\N	\N
+9851	Lisa Shieh	11	pulmonary_embolism	45752	3	3	\N	\N
+10164	Jonathan Chen	11	pulmonary_embolism	45752	2	\N	Blood Cultures	\N
+10352	Andre Kumar	11	pulmonary_embolism	45752	1	2	Blood Cultures	\N
+8056	Jason Hom	10	pulmonary_embolism	45760	10	5	\N	\N
+8465	Panel Average	10	pulmonary_embolism	45760	7	\N	\N	\N
+8625	Lisa Shieh	10	pulmonary_embolism	45760	7	3	\N	\N
+8626	Panel Consensus	10	pulmonary_embolism	45760	7	\N	\N	\N
+9311	Andre Kumar	10	pulmonary_embolism	45760	5	5	Blood Gas	\N
+9312	Jonathan Chen	10	pulmonary_embolism	45760	5	4	Blood Gas	\N
+8057	Jason Hom	12	pulmonary_embolism	45760	10	5	\N	\N
+8627	Panel Consensus	12	pulmonary_embolism	45760	7	\N	\N	\N
+8691	Panel Average	12	pulmonary_embolism	45760	6	\N	\N	\N
+9313	Jonathan Chen	12	pulmonary_embolism	45760	5	4	Blood Gas	\N
+9314	Lisa Shieh	12	pulmonary_embolism	45760	5	3	\N	\N
+9554	Andre Kumar	12	pulmonary_embolism	45760	4	3	Blood Gas	\N
+9555	Andre Kumar	11	pulmonary_embolism	45760	4	4	Blood Gas	\N
+9556	Panel Average	11	pulmonary_embolism	45760	4	\N	\N	\N
+8058	Jason Hom	10	pulmonary_embolism	45919	10	5	\N	\N
+9315	Jonathan Chen	10	pulmonary_embolism	45919	5	4	Blood Gas	\N
+9316	Lisa Shieh	10	pulmonary_embolism	45919	5	3	\N	\N
+9317	Panel Average	10	pulmonary_embolism	45919	5	\N	\N	\N
+9318	Panel Consensus	10	pulmonary_embolism	45919	5	\N	\N	\N
+10864	Andre Kumar	10	pulmonary_embolism	45919	0	5	Blood Gas	\N
+8059	Jason Hom	12	pulmonary_embolism	45919	10	5	\N	\N
+8805	Panel Average	12	pulmonary_embolism	45919	6	\N	\N	\N
+9319	Jonathan Chen	12	pulmonary_embolism	45919	5	4	Blood Gas	\N
+9320	Lisa Shieh	12	pulmonary_embolism	45919	5	3	\N	\N
+9321	Panel Consensus	12	pulmonary_embolism	45919	5	\N	\N	\N
+9852	Andre Kumar	12	pulmonary_embolism	45919	3	3	Blood Gas	\N
+9322	Jason Hom	11	pulmonary_embolism	45919	5	5	\N	\N
+9323	Jonathan Chen	11	pulmonary_embolism	45919	5	4	Blood Gas	\N
+9324	Lisa Shieh	11	pulmonary_embolism	45919	5	3	\N	\N
+9325	Panel Consensus	11	pulmonary_embolism	45919	5	\N	\N	\N
+9465	Panel Average	11	pulmonary_embolism	45919	4	\N	\N	\N
+9853	Andre Kumar	11	pulmonary_embolism	45919	3	4	Blood Gas	\N
+11448	Jonathan Chen	10	pulmonary_embolism	44439	0	2	IVF	\N
+8060	Jason Hom	10	pulmonary_embolism	45793	10	5	\N	\N
+8061	Panel Consensus	10	pulmonary_embolism	45793	10	\N	\N	\N
+8856	Panel Average	10	pulmonary_embolism	45793	6	\N	\N	\N
+9326	Lisa Shieh	10	pulmonary_embolism	45793	5	3	\N	\N
+9854	Jonathan Chen	10	pulmonary_embolism	45793	3	2	CBC	\N
+10165	Andre Kumar	10	pulmonary_embolism	45793	2	3	CBC	\N
+11267	Jonathan Chen	12	pulmonary_embolism	45793	3	2	CBC	\N
+8062	Jason Hom	11	pulmonary_embolism	45793	10	5	\N	\N
+8063	Panel Consensus	11	pulmonary_embolism	45793	10	\N	\N	\N
+8806	Panel Average	11	pulmonary_embolism	45793	6	\N	\N	\N
+9327	Lisa Shieh	11	pulmonary_embolism	45793	5	3	\N	\N
+9855	Andre Kumar	11	pulmonary_embolism	45793	3	2	CBC	\N
+9856	Jonathan Chen	11	pulmonary_embolism	45793	3	2	CBC	\N
+11305	Jonathan Chen	8	pulmonary_embolism	45793	3	2	CBC	\N
+8064	Jason Hom	10	pulmonary_embolism	45788	10	5	\N	\N
+8065	Panel Consensus	10	pulmonary_embolism	45788	10	\N	\N	\N
+8807	Panel Average	10	pulmonary_embolism	45788	6	\N	\N	\N
+9328	Lisa Shieh	10	pulmonary_embolism	45788	5	3	\N	\N
+9857	Andre Kumar	10	pulmonary_embolism	45788	3	3	CBC	\N
+9858	Jonathan Chen	10	pulmonary_embolism	45788	3	2	CBC	\N
+8066	Jason Hom	11	pulmonary_embolism	45788	10	5	\N	\N
+8067	Panel Consensus	11	pulmonary_embolism	45788	10	\N	\N	\N
+8808	Panel Average	11	pulmonary_embolism	45788	6	\N	\N	\N
+9329	Lisa Shieh	11	pulmonary_embolism	45788	5	3	\N	\N
+9859	Andre Kumar	11	pulmonary_embolism	45788	3	2	CBC	\N
+9860	Jonathan Chen	11	pulmonary_embolism	45788	3	2	CBC	\N
+11255	Jonathan Chen	8	pulmonary_embolism	45788	3	2	CBC	\N
+8068	Jason Hom	10	pulmonary_embolism	35733	10	5	\N	\N
+8809	Panel Average	10	pulmonary_embolism	35733	6	\N	\N	\N
+8810	Panel Consensus	10	pulmonary_embolism	35733	6	\N	\N	\N
+9330	Lisa Shieh	10	pulmonary_embolism	35733	5	3	\N	\N
+9861	Andre Kumar	10	pulmonary_embolism	35733	3	4	Antibiotics	\N
+10865	Jonathan Chen	10	pulmonary_embolism	35733	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+8069	Jason Hom	11	pulmonary_embolism	35733	10	5	\N	\N
+8811	Panel Average	11	pulmonary_embolism	35733	6	\N	\N	\N
+8812	Panel Consensus	11	pulmonary_embolism	35733	6	\N	\N	\N
+9331	Lisa Shieh	11	pulmonary_embolism	35733	5	3	\N	\N
+9862	Andre Kumar	11	pulmonary_embolism	35733	3	2	Antibiotics	\N
+10866	Jonathan Chen	11	pulmonary_embolism	35733	0	2	\N	Shortness of breath, but no specific pneumonia? But maybe reasonable guess when CXR shows opacity (even though it's supposed to be cancer)
+11377	Jonathan Chen	11	pulmonary_embolism	45892	0	3	\N	Superceded by troponin if needed at all
+9863	Lisa Shieh	10	pulmonary_embolism	49251	3	3	\N	\N
+10166	Andre Kumar	10	pulmonary_embolism	49251	2	3	Consult	\N
+10228	Panel Average	10	pulmonary_embolism	49251	2	\N	\N	\N
+10867	Jason Hom	10	pulmonary_embolism	49251	0	5	\N	\N
+10868	Jonathan Chen	10	pulmonary_embolism	49251	0	3	\N	\N
+10869	Panel Consensus	10	pulmonary_embolism	49251	0	\N	\N	\N
+9864	Andre Kumar	11	pulmonary_embolism	49251	3	2	Consult	\N
+9865	Lisa Shieh	11	pulmonary_embolism	49251	3	3	\N	\N
+10167	Panel Average	11	pulmonary_embolism	49251	2	\N	\N	\N
+10870	Jason Hom	11	pulmonary_embolism	49251	0	5	\N	\N
+10871	Jonathan Chen	11	pulmonary_embolism	49251	0	3	\N	\N
+10872	Panel Consensus	11	pulmonary_embolism	49251	0	\N	\N	\N
+8070	Jason Hom	10	pulmonary_embolism	65695	10	5	\N	\N
+8857	Panel Average	10	pulmonary_embolism	65695	6	\N	\N	\N
+9332	Lisa Shieh	10	pulmonary_embolism	65695	5	3	\N	\N
+9333	Panel Consensus	10	pulmonary_embolism	65695	5	\N	\N	\N
+10168	Andre Kumar	10	pulmonary_embolism	65695	2	3	Consult	\N
+10873	Jonathan Chen	10	pulmonary_embolism	65695	0	3	\N	\N
+8071	Jason Hom	11	pulmonary_embolism	65695	10	5	\N	\N
+8813	Panel Average	11	pulmonary_embolism	65695	6	\N	\N	\N
+9334	Lisa Shieh	11	pulmonary_embolism	65695	5	3	\N	\N
+9335	Panel Consensus	11	pulmonary_embolism	65695	5	\N	\N	\N
+9866	Andre Kumar	11	pulmonary_embolism	65695	3	2	Consult	\N
+10874	Jonathan Chen	11	pulmonary_embolism	65695	0	3	\N	\N
+10353	Andre Kumar	8	pulmonary_embolism	65695	1	5	Consult	\N
+10354	Panel Average	8	pulmonary_embolism	65695	1	\N	\N	\N
+11293	Jonathan Chen	10	pulmonary_embolism	61323	0	4	\N	Consult
+8072	Jason Hom	12	pulmonary_embolism	61323	10	5	\N	\N
+8628	Lisa Shieh	12	pulmonary_embolism	61323	7	3	\N	\N
+8692	Panel Average	12	pulmonary_embolism	61323	6	\N	\N	\N
+8814	Panel Consensus	12	pulmonary_embolism	61323	6	\N	\N	\N
+10169	Andre Kumar	12	pulmonary_embolism	61323	2	2	Consult	\N
+10875	Jonathan Chen	12	pulmonary_embolism	61323	0	3	\N	\N
+11259	Jonathan Chen	11	pulmonary_embolism	61323	0	4	\N	Consult
+11284	Jonathan Chen	12	pulmonary_embolism	48502	2	2	Biopsy	Consult, but maybe thinking about IR procedure. Not necessary for initial PE treatment, but maybe thinking about biopsy already
+8073	Jason Hom	10	pulmonary_embolism	49207	10	5	\N	\N
+9336	Panel Average	10	pulmonary_embolism	49207	5	\N	\N	\N
+9337	Panel Consensus	10	pulmonary_embolism	49207	5	\N	\N	\N
+9867	Lisa Shieh	10	pulmonary_embolism	49207	3	3	\N	\N
+10170	Andre Kumar	10	pulmonary_embolism	49207	2	3	Consult	\N
+10876	Jonathan Chen	10	pulmonary_embolism	49207	0	3	\N	\N
+11456	Jonathan Chen	12	pulmonary_embolism	49207	0	4	\N	Consult
+9868	Andre Kumar	11	pulmonary_embolism	49207	3	2	Consult	\N
+9869	Panel Average	11	pulmonary_embolism	49207	3	\N	\N	\N
+8074	Jason Hom	11	pulmonary_embolism	49228	10	5	\N	\N
+9615	Panel Average	11	pulmonary_embolism	49228	3	\N	\N	\N
+9870	Panel Consensus	11	pulmonary_embolism	49228	3	\N	\N	\N
+10877	Andre Kumar	11	pulmonary_embolism	49228	0	5	Consult	\N
+10878	Jonathan Chen	11	pulmonary_embolism	49228	0	3	\N	\N
+10879	Lisa Shieh	11	pulmonary_embolism	49228	0	3	\N	\N
+8075	Jason Hom	8	pulmonary_embolism	49228	10	5	\N	\N
+9616	Panel Average	8	pulmonary_embolism	49228	3	\N	\N	\N
+9871	Panel Consensus	8	pulmonary_embolism	49228	3	\N	\N	\N
+10880	Andre Kumar	8	pulmonary_embolism	49228	0	5	Consult	\N
+10881	Jonathan Chen	8	pulmonary_embolism	49228	0	3	\N	\N
+10882	Lisa Shieh	8	pulmonary_embolism	49228	0	3	\N	\N
+11451	Jonathan Chen	11	pulmonary_embolism	50509	0	4	\N	Consult
+8076	Andre Kumar	10	pulmonary_embolism	48522	10	4	Imaging	\N
+8077	Lisa Shieh	10	pulmonary_embolism	48522	10	5	\N	\N
+8078	Panel Consensus	10	pulmonary_embolism	48522	10	\N	\N	\N
+8417	Jonathan Chen	10	pulmonary_embolism	48522	8	4	CT Chest	\N
+9338	Panel Average	10	pulmonary_embolism	48522	5	\N	\N	\N
+11169	Jason Hom	10	pulmonary_embolism	48522	-5	5	\N	\N
+8079	Panel Consensus	11	pulmonary_embolism	48522	10	\N	\N	\N
+8418	Andre Kumar	11	pulmonary_embolism	48522	8	5	Imaging	Less points for delaying scan?
+8419	Jonathan Chen	11	pulmonary_embolism	48522	8	4	CT Chest	\N
+8629	Lisa Shieh	11	pulmonary_embolism	48522	7	5	late	\N
+9617	Panel Average	11	pulmonary_embolism	48522	3	\N	\N	\N
+11170	Jason Hom	11	pulmonary_embolism	48522	-5	5	\N	\N
+8815	Jonathan Chen	11	pulmonary_embolism	49073	6	4	CT Chest	This is without contrast, so isn't really the right way to search for PE. Is more for the mass/lung
+10883	Andre Kumar	11	pulmonary_embolism	49073	0	4	IMaging	won't capture PE
+10884	Jason Hom	11	pulmonary_embolism	49073	0	5	\N	\N
+10885	Lisa Shieh	11	pulmonary_embolism	49073	0	3	need contrast	\N
+10886	Panel Average	11	pulmonary_embolism	49073	0	\N	\N	\N
+10887	Panel Consensus	11	pulmonary_embolism	49073	0	\N	\N	\N
+8080	Lisa Shieh	10	pulmonary_embolism	45762	10	5	\N	\N
+8420	Jonathan Chen	10	pulmonary_embolism	45762	8	4	CT Chest	\N
+8466	Panel Average	10	pulmonary_embolism	45762	7	\N	\N	\N
+8630	Andre Kumar	10	pulmonary_embolism	45762	7	4	Imaging	\N
+8631	Panel Consensus	10	pulmonary_embolism	45762	7	\N	\N	\N
+9339	Jason Hom	10	pulmonary_embolism	45762	5	5	\N	\N
+8081	Lisa Shieh	11	pulmonary_embolism	45762	10	5	\N	\N
+8421	Andre Kumar	11	pulmonary_embolism	45762	8	4	IMaging	\N
+8422	Jonathan Chen	11	pulmonary_embolism	45762	8	4	CT Chest	\N
+8447	Panel Average	11	pulmonary_embolism	45762	8	\N	\N	\N
+8632	Panel Consensus	11	pulmonary_embolism	45762	7	\N	\N	\N
+9340	Jason Hom	11	pulmonary_embolism	45762	5	5	\N	\N
+8082	Jason Hom	10	pulmonary_embolism	48676	10	5	\N	\N
+8083	Lisa Shieh	10	pulmonary_embolism	48676	10	3	\N	\N
+8084	Panel Consensus	10	pulmonary_embolism	48676	10	\N	\N	\N
+8235	Panel Average	10	pulmonary_embolism	48676	9	\N	\N	\N
+8423	Andre Kumar	10	pulmonary_embolism	48676	8	4	Imaging	\N
+8424	Jonathan Chen	10	pulmonary_embolism	48676	8	4	CT Chest	\N
+8085	Jason Hom	11	pulmonary_embolism	48676	10	5	\N	\N
+8086	Panel Consensus	11	pulmonary_embolism	48676	10	\N	\N	\N
+8330	Panel Average	11	pulmonary_embolism	48676	8	\N	\N	\N
+8425	Andre Kumar	11	pulmonary_embolism	48676	8	4	Imaging	\N
+8426	Jonathan Chen	11	pulmonary_embolism	48676	8	4	CT Chest	\N
+8633	Lisa Shieh	11	pulmonary_embolism	48676	7	3	\N	\N
+8816	Andre Kumar	10	pulmonary_embolism	48871	6	3	D-dimer	\N
+8878	Panel Average	10	pulmonary_embolism	48871	5	\N	\N	\N
+9341	Jason Hom	10	pulmonary_embolism	48871	5	5	\N	\N
+9342	Lisa Shieh	10	pulmonary_embolism	48871	5	3	\N	\N
+9343	Panel Consensus	10	pulmonary_embolism	48871	5	\N	\N	\N
+10171	Jonathan Chen	10	pulmonary_embolism	48871	2	3	D-Dimer	\N
+9344	Andre Kumar	11	pulmonary_embolism	48871	5	2	D-dimer	\N
+9345	Panel Consensus	11	pulmonary_embolism	48871	5	\N	\N	\N
+9872	Lisa Shieh	11	pulmonary_embolism	48871	3	3	\N	\N
+9923	Panel Average	11	pulmonary_embolism	48871	3	\N	\N	\N
+10172	Jonathan Chen	11	pulmonary_embolism	48871	2	3	D-Dimer	\N
+10888	Jason Hom	11	pulmonary_embolism	48871	0	5	\N	\N
+8817	Andre Kumar	10	pulmonary_embolism	48532	6	3	D-dimer	\N
+8879	Panel Average	10	pulmonary_embolism	48532	5	\N	\N	\N
+9346	Jason Hom	10	pulmonary_embolism	48532	5	5	\N	\N
+9347	Lisa Shieh	10	pulmonary_embolism	48532	5	3	\N	\N
+9348	Panel Consensus	10	pulmonary_embolism	48532	5	\N	\N	\N
+10173	Jonathan Chen	10	pulmonary_embolism	48532	2	3	D-Dimer	D-Dimer okay to screen, but have enough data to go straight to CT PE
+9349	Andre Kumar	11	pulmonary_embolism	48532	5	2	D-dimer	\N
+9350	Panel Consensus	11	pulmonary_embolism	48532	5	\N	\N	\N
+9873	Lisa Shieh	11	pulmonary_embolism	48532	3	3	\N	\N
+9924	Panel Average	11	pulmonary_embolism	48532	3	\N	\N	\N
+10174	Jonathan Chen	11	pulmonary_embolism	48532	2	3	D-Dimer	\N
+10889	Jason Hom	11	pulmonary_embolism	48532	0	5	\N	\N
+11247	Jonathan Chen	10	pulmonary_embolism	44017	5	4	Corticosteroids	Main issue is PE, but could reasonably say it's triggering a COPD exacerbation as well
+9874	Lisa Shieh	10	pulmonary_embolism	45811	3	3	\N	\N
+10175	Andre Kumar	10	pulmonary_embolism	45811	2	2	Diet	\N
+10176	Panel Consensus	10	pulmonary_embolism	45811	2	\N	\N	\N
+10229	Panel Average	10	pulmonary_embolism	45811	2	\N	\N	\N
+10890	Jason Hom	10	pulmonary_embolism	45811	0	5	\N	\N
+10891	Jonathan Chen	10	pulmonary_embolism	45811	0	2	\N	\N
+8087	Jason Hom	12	pulmonary_embolism	45811	10	5	\N	\N
+8858	Panel Average	12	pulmonary_embolism	45811	6	\N	\N	\N
+9351	Lisa Shieh	12	pulmonary_embolism	45811	5	3	\N	\N
+10177	Andre Kumar	12	pulmonary_embolism	45811	2	2	Diet	\N
+10178	Panel Consensus	12	pulmonary_embolism	45811	2	\N	\N	\N
+10892	Jonathan Chen	12	pulmonary_embolism	45811	0	2	\N	\N
+9875	Andre Kumar	11	pulmonary_embolism	45811	3	1	Diet	\N
+9876	Panel Average	11	pulmonary_embolism	45811	3	\N	\N	\N
+8088	Jason Hom	8	pulmonary_embolism	46008	10	5	\N	\N
+9466	Panel Average	8	pulmonary_embolism	46008	4	\N	\N	\N
+9557	Panel Consensus	8	pulmonary_embolism	46008	4	\N	\N	\N
+9877	Lisa Shieh	8	pulmonary_embolism	46008	3	3	\N	\N
+10893	Andre Kumar	8	pulmonary_embolism	46008	0	3	Diet	\N
+10894	Jonathan Chen	8	pulmonary_embolism	46008	0	2	\N	\N
+8089	Jason Hom	10	pulmonary_embolism	49054	10	5	\N	\N
+9352	Panel Consensus	10	pulmonary_embolism	49054	5	\N	\N	\N
+9439	Panel Average	10	pulmonary_embolism	49054	5	\N	\N	\N
+9878	Lisa Shieh	10	pulmonary_embolism	49054	3	3	\N	\N
+10355	Andre Kumar	10	pulmonary_embolism	49054	1	1	Isolation	\N
+10356	Jonathan Chen	10	pulmonary_embolism	49054	1	2	\N	\N
+8090	Jason Hom	10	pulmonary_embolism	45866	10	5	\N	\N
+8634	Lisa Shieh	10	pulmonary_embolism	45866	7	3	\N	\N
+8635	Panel Average	10	pulmonary_embolism	45866	7	\N	\N	\N
+8636	Panel Consensus	10	pulmonary_embolism	45866	7	\N	\N	\N
+8818	Jonathan Chen	10	pulmonary_embolism	45866	6	4	\N	\N
+9558	Andre Kumar	10	pulmonary_embolism	45866	4	4	ECG + Monitoring	\N
+11234	Jonathan Chen	12	pulmonary_embolism	45866	6	4	\N	\N
+8091	Jason Hom	11	pulmonary_embolism	45866	10	5	\N	\N
+8637	Lisa Shieh	11	pulmonary_embolism	45866	7	3	\N	\N
+8638	Panel Average	11	pulmonary_embolism	45866	7	\N	\N	\N
+8639	Panel Consensus	11	pulmonary_embolism	45866	7	\N	\N	\N
+8819	Jonathan Chen	11	pulmonary_embolism	45866	6	4	\N	\N
+9559	Andre Kumar	11	pulmonary_embolism	45866	4	2	ECG + Monitoring	\N
+11388	Jonathan Chen	10	pulmonary_embolism	62176	4	2	Echo	\N
+11411	Jonathan Chen	11	pulmonary_embolism	62176	4	2	Echo	\N
+8092	Jason Hom	10	pulmonary_embolism	61832	10	5	\N	\N
+8820	Panel Average	10	pulmonary_embolism	61832	6	\N	\N	\N
+8821	Panel Consensus	10	pulmonary_embolism	61832	6	\N	\N	\N
+9353	Lisa Shieh	10	pulmonary_embolism	61832	5	3	\N	\N
+9560	Jonathan Chen	10	pulmonary_embolism	61832	4	2	Echo	\N
+9879	Andre Kumar	10	pulmonary_embolism	61832	3	4	Imaging	\N
+8093	Jason Hom	11	pulmonary_embolism	61832	10	5	\N	\N
+8640	Lisa Shieh	11	pulmonary_embolism	61832	7	3	\N	\N
+8641	Panel Average	11	pulmonary_embolism	61832	7	\N	\N	\N
+8822	Panel Consensus	11	pulmonary_embolism	61832	6	\N	\N	\N
+9561	Andre Kumar	11	pulmonary_embolism	61832	4	2	Imaging	\N
+9562	Jonathan Chen	11	pulmonary_embolism	61832	4	2	Echo	\N
+8094	Jason Hom	8	pulmonary_embolism	61832	10	5	\N	\N
+8693	Panel Average	8	pulmonary_embolism	61832	6	\N	\N	\N
+8823	Panel Consensus	8	pulmonary_embolism	61832	6	\N	\N	\N
+9354	Lisa Shieh	8	pulmonary_embolism	61832	5	3	\N	\N
+9563	Andre Kumar	8	pulmonary_embolism	61832	4	4	Imaging	\N
+9564	Jonathan Chen	8	pulmonary_embolism	61832	4	2	Echo	\N
+11246	Jonathan Chen	10	pulmonary_embolism	46160	0	4	\N	Process
+11266	Jonathan Chen	11	pulmonary_embolism	46160	0	4	\N	Process
+8095	Andre Kumar	11	pulmonary_embolism	44250	10	5	Anticoagulation	\N
+8096	Jason Hom	11	pulmonary_embolism	44250	10	5	\N	\N
+7515	Jason Hom	40	atrial_fibrillation	45977	10	4	\N	\N
+7516	Jason Hom	40	atrial_fibrillation	45887	10	5	\N	\N
+7517	Jason Hom	40	atrial_fibrillation	45827	10	5	\N	\N
+7518	Lisa Shieh	40	atrial_fibrillation	45827	10	5	\N	\N
+7519	Jason Hom	40	atrial_fibrillation	45793	10	5	\N	\N
+7520	Jason Hom	40	atrial_fibrillation	45788	10	5	\N	\N
+7521	Jason Hom	43	atrial_fibrillation	50400	10	5	\N	\N
+7522	Jason Hom	40	atrial_fibrillation	49251	10	5	\N	\N
+7523	Jason Hom	43	atrial_fibrillation	49251	10	5	\N	\N
+7524	Jason Hom	43	atrial_fibrillation	61323	10	5	\N	\N
+10958	Lisa Shieh	11	pulmonary_embolism	62023	0	3	\N	\N
+7525	Andre Kumar	40	atrial_fibrillation	-100	10	5	Cardioversion	\N
+7526	Jason Hom	40	atrial_fibrillation	-100	10	5	\N	\N
+7527	Jonathan Chen	40	atrial_fibrillation	-100	10	5	\N	\N
+7528	Lisa Shieh	40	atrial_fibrillation	-100	10	5	\N	\N
+7529	Panel Average	40	atrial_fibrillation	-100	10	\N	\N	\N
+7530	Panel Consensus	40	atrial_fibrillation	-100	10	\N	\N	\N
+7531	Jason Hom	43	atrial_fibrillation	-100	10	5	\N	\N
+7532	Jonathan Chen	43	atrial_fibrillation	-100	10	5	\N	\N
+7533	Lisa Shieh	43	atrial_fibrillation	-100	10	5	\N	\N
+7534	Panel Consensus	43	atrial_fibrillation	-100	10	\N	\N	\N
+7535	Jason Hom	40	atrial_fibrillation	45811	10	5	\N	\N
+7536	Lisa Shieh	43	atrial_fibrillation	45811	10	5	\N	\N
+7537	Jason Hom	40	atrial_fibrillation	45866	10	5	\N	\N
+7538	Jonathan Chen	40	atrial_fibrillation	45866	10	5	\N	\N
+7539	Lisa Shieh	40	atrial_fibrillation	45866	10	5	\N	\N
+7540	Jonathan Chen	41	atrial_fibrillation	45866	10	5	\N	\N
+7541	Jason Hom	41	atrial_fibrillation	61832	10	5	\N	\N
+7542	Jason Hom	40	atrial_fibrillation	46160	10	5	\N	\N
+7543	Lisa Shieh	40	atrial_fibrillation	46160	10	5	\N	\N
+7544	Andre Kumar	41	atrial_fibrillation	44250	10	5	Anticoagulants	No positive points if combined wiht other anticoagulants
+7545	Panel Average	41	atrial_fibrillation	44250	10	\N	\N	\N
+8097	Jonathan Chen	11	pulmonary_embolism	44250	10	5	Anticoagulation	\N
+8098	Lisa Shieh	11	pulmonary_embolism	44250	10	3	? Full dose	\N
+8099	Panel Average	11	pulmonary_embolism	44250	10	\N	\N	\N
+8100	Panel Consensus	11	pulmonary_embolism	44250	10	\N	\N	\N
+10895	Andre Kumar	11	pulmonary_embolism	61978	0	5	Foley	\N
+10896	Jason Hom	11	pulmonary_embolism	61978	0	5	\N	\N
+10897	Jonathan Chen	11	pulmonary_embolism	61978	0	2	\N	Probably because think CHF exacerbation and going to lots of diuresis?
+10993	Panel Average	11	pulmonary_embolism	61978	-2	\N	\N	\N
+11171	Lisa Shieh	11	pulmonary_embolism	61978	-5	3	\N	\N
+11172	Panel Consensus	11	pulmonary_embolism	61978	-5	\N	\N	\N
+8101	Jason Hom	10	pulmonary_embolism	44004	10	5	\N	\N
+9355	Lisa Shieh	10	pulmonary_embolism	44004	5	3	\N	\N
+9467	Panel Average	10	pulmonary_embolism	44004	4	\N	\N	\N
+10898	Jonathan Chen	10	pulmonary_embolism	44004	0	2	\N	Some pedal edema from chronic CHF? But not supposed to be volume overloaded
+11212	Andre Kumar	43	atrial_fibrillation	44352	-10	5	Rhythm Control	\N
+11213	Jason Hom	43	atrial_fibrillation	44352	-10	5	\N	\N
+11214	Andre Kumar	40	atrial_fibrillation	50098	-10	5	Imaging	Delays care
+11215	Andre Kumar	40	atrial_fibrillation	45983	-10	5	Imaging	Delays care
+11216	Andre Kumar	40	atrial_fibrillation	49965	-10	5	Imaging	Delays care
+11217	Andre Kumar	43	atrial_fibrillation	44389	-10	4	Rate Control	\N
+11218	Panel Average	43	atrial_fibrillation	44389	-10	\N	\N	\N
+11219	Lisa Shieh	40	atrial_fibrillation	35846	-10	3	\N	\N
+11220	Andre Kumar	43	atrial_fibrillation	35846	-10	4	Rate Control	\N
+11221	Jason Hom	43	atrial_fibrillation	35846	-10	5	\N	\N
+11222	Jason Hom	43	atrial_fibrillation	44393	-10	5	\N	\N
+11223	Andre Kumar	43	atrial_fibrillation	44251	-10	5	Ionotropes	\N
+11224	Jason Hom	43	atrial_fibrillation	44251	-10	5	\N	\N
+11225	Jason Hom	43	atrial_fibrillation	45963	-10	5	\N	\N
+11226	Andre Kumar	43	atrial_fibrillation	44248	-10	5	Rate Control	\N
+11227	Jason Hom	43	atrial_fibrillation	44248	-10	5	\N	\N
+11228	Jason Hom	43	atrial_fibrillation	44004	-10	5	\N	\N
+11229	Lisa Shieh	40	atrial_fibrillation	44327	-10	3	\N	\N
+11230	Jason Hom	30	meningitis	44237	-10	4	\N	\N
+10899	Panel Consensus	10	pulmonary_embolism	44004	0	\N	\N	\N
+11067	Andre Kumar	10	pulmonary_embolism	44004	-2	4	Diuretics	\N
+8102	Jason Hom	11	pulmonary_embolism	44004	10	5	\N	\N
+9356	Lisa Shieh	11	pulmonary_embolism	44004	5	3	\N	\N
+9468	Panel Average	11	pulmonary_embolism	44004	4	\N	\N	\N
+10900	Jonathan Chen	11	pulmonary_embolism	44004	0	2	\N	Some pedal edema from chronic CHF? But not supposed to be volume overloaded
+10901	Panel Consensus	11	pulmonary_embolism	44004	0	\N	\N	\N
+11068	Andre Kumar	11	pulmonary_embolism	44004	-2	3	Diuretics	\N
+10179	Jason Hom	11	pulmonary_embolism	45797	2	5	\N	\N
+10357	Panel Consensus	11	pulmonary_embolism	45797	1	\N	\N	\N
+10389	Panel Average	11	pulmonary_embolism	45797	1	\N	\N	\N
+10902	Andre Kumar	11	pulmonary_embolism	45797	0	3	A1c	\N
+10903	Jonathan Chen	11	pulmonary_embolism	45797	0	2	\N	\N
+10904	Lisa Shieh	11	pulmonary_embolism	45797	0	3	\N	\N
+11446	Jonathan Chen	10	pulmonary_embolism	46438	10	5	Anticoagulants	\N
+8103	Andre Kumar	10	pulmonary_embolism	44359	10	5	Anticoagulants	\N
+8104	Jonathan Chen	10	pulmonary_embolism	44359	10	5	Anticoagulation	\N
+8105	Panel Consensus	10	pulmonary_embolism	44359	10	\N	\N	\N
+8642	Lisa Shieh	10	pulmonary_embolism	44359	7	3	\N	\N
+8859	Panel Average	10	pulmonary_embolism	44359	6	\N	\N	\N
+10905	Jason Hom	10	pulmonary_embolism	44359	0	5	\N	\N
+8106	Andre Kumar	11	pulmonary_embolism	44359	10	5	Anticoagulation	\N
+8107	Jason Hom	11	pulmonary_embolism	44359	10	5	\N	\N
+8108	Jonathan Chen	11	pulmonary_embolism	44359	10	5	Anticoagulation	\N
+8109	Lisa Shieh	11	pulmonary_embolism	44359	10	5	\N	\N
+8110	Panel Average	11	pulmonary_embolism	44359	10	\N	\N	\N
+8111	Panel Consensus	11	pulmonary_embolism	44359	10	\N	\N	\N
+11444	Jonathan Chen	10	pulmonary_embolism	46183	0	2	\N	Just count of part heparin protocol
+8112	Jason Hom	11	pulmonary_embolism	46183	10	5	\N	\N
+8113	Lisa Shieh	11	pulmonary_embolism	46183	10	3	\N	\N
+8114	Panel Consensus	11	pulmonary_embolism	46183	10	\N	\N	\N
+8448	Panel Average	11	pulmonary_embolism	46183	8	\N	\N	\N
+9880	Andre Kumar	11	pulmonary_embolism	46183	3	2	Anticoagulation	\N
+10906	Jonathan Chen	11	pulmonary_embolism	46183	0	2	\N	Just count of part heparin protocol
+11322	Jonathan Chen	10	pulmonary_embolism	63714	0	2	\N	Just count of part heparin protocol
+8649	Lisa Shieh	10	pulmonary_embolism	45830	7	3	\N	\N
+11350	Jonathan Chen	11	pulmonary_embolism	63714	0	2	\N	Just count of part heparin protocol
+9881	Andre Kumar	10	pulmonary_embolism	44211	3	3	Nebs	\N
+9882	Panel Average	10	pulmonary_embolism	44211	3	\N	\N	\N
+11308	Jonathan Chen	10	pulmonary_embolism	49301	5	4	Blood Gas	\N
+8643	Panel Consensus	8	pulmonary_embolism	49301	7	\N	\N	\N
+9357	Jason Hom	8	pulmonary_embolism	49301	5	5	\N	\N
+9358	Jonathan Chen	8	pulmonary_embolism	49301	5	4	Blood Gas	\N
+9618	Panel Average	8	pulmonary_embolism	49301	3	\N	\N	\N
+9883	Lisa Shieh	8	pulmonary_embolism	49301	3	3	\N	\N
+10180	Andre Kumar	8	pulmonary_embolism	49301	2	4	Blood Gass	\N
+9884	Andre Kumar	10	pulmonary_embolism	46020	3	3	Blood gas	\N
+9885	Panel Average	10	pulmonary_embolism	46020	3	\N	\N	\N
+8115	Jason Hom	10	pulmonary_embolism	45838	10	5	\N	\N
+8694	Panel Average	10	pulmonary_embolism	45838	6	\N	\N	\N
+8824	Panel Consensus	10	pulmonary_embolism	45838	6	\N	\N	\N
+9359	Jonathan Chen	10	pulmonary_embolism	45838	5	4	Troponin	\N
+9360	Lisa Shieh	10	pulmonary_embolism	45838	5	3	\N	\N
+9565	Andre Kumar	10	pulmonary_embolism	45838	4	2	Troponin	\N
+8116	Jason Hom	10	pulmonary_embolism	62151	10	5	\N	\N
+8880	Panel Average	10	pulmonary_embolism	62151	5	\N	\N	\N
+9361	Jonathan Chen	10	pulmonary_embolism	62151	5	4	Blood Gas	\N
+9362	Panel Consensus	10	pulmonary_embolism	62151	5	\N	\N	\N
+9886	Andre Kumar	10	pulmonary_embolism	62151	3	2	Lactate	\N
+9887	Lisa Shieh	10	pulmonary_embolism	62151	3	3	\N	\N
+10358	Andre Kumar	11	pulmonary_embolism	44299	1	2	Antibiotics	\N
+10359	Panel Average	11	pulmonary_embolism	44299	1	\N	\N	\N
+11361	Jonathan Chen	10	pulmonary_embolism	62144	0	2	\N	Not sure?
+9363	Lisa Shieh	11	pulmonary_embolism	61837	5	3	\N	\N
+10230	Panel Average	11	pulmonary_embolism	61837	2	\N	\N	\N
+10907	Andre Kumar	11	pulmonary_embolism	61837	0	5	Lipid Panel	\N
+10908	Jason Hom	11	pulmonary_embolism	61837	0	5	\N	\N
+10909	Jonathan Chen	11	pulmonary_embolism	61837	0	2	\N	\N
+10910	Panel Consensus	11	pulmonary_embolism	61837	0	\N	\N	\N
+11283	Jonathan Chen	10	pulmonary_embolism	44213	0	2	\N	Like asthma exacerbation treatment?
+11239	Jonathan Chen	10	pulmonary_embolism	45806	0	2	\N	\N
+11243	Jonathan Chen	12	pulmonary_embolism	45806	0	2	\N	\N
+8117	Jason Hom	11	pulmonary_embolism	45806	10	5	\N	\N
+8881	Panel Average	11	pulmonary_embolism	45806	5	\N	\N	\N
+9364	Lisa Shieh	11	pulmonary_embolism	45806	5	3	\N	\N
+9365	Panel Consensus	11	pulmonary_embolism	45806	5	\N	\N	\N
+10360	Andre Kumar	11	pulmonary_embolism	45806	1	4	Metabolic Panel	\N
+10911	Jonathan Chen	11	pulmonary_embolism	45806	0	2	\N	\N
+11346	Jonathan Chen	8	pulmonary_embolism	45806	0	2	\N	\N
+8118	Jason Hom	10	pulmonary_embolism	45763	10	5	\N	\N
+8119	Panel Consensus	10	pulmonary_embolism	45763	10	\N	\N	\N
+8825	Panel Average	10	pulmonary_embolism	45763	6	\N	\N	\N
+9366	Lisa Shieh	10	pulmonary_embolism	45763	5	3	\N	\N
+9888	Andre Kumar	10	pulmonary_embolism	45763	3	2	MEtabolic Panel	\N
+9889	Jonathan Chen	10	pulmonary_embolism	45763	3	2	Metabolic Panel	\N
+8120	Jason Hom	11	pulmonary_embolism	45763	10	5	\N	\N
+8121	Panel Consensus	11	pulmonary_embolism	45763	10	\N	\N	\N
+8882	Panel Average	11	pulmonary_embolism	45763	5	\N	\N	\N
+9367	Lisa Shieh	11	pulmonary_embolism	45763	5	3	\N	\N
+9890	Jonathan Chen	11	pulmonary_embolism	45763	3	2	Metabolic Panel	\N
+10361	Andre Kumar	11	pulmonary_embolism	45763	1	4	Metabolic Panel	\N
+8122	Jason Hom	10	pulmonary_embolism	45771	10	5	\N	\N
+8123	Panel Consensus	10	pulmonary_embolism	45771	10	\N	\N	\N
+8826	Panel Average	10	pulmonary_embolism	45771	6	\N	\N	\N
+9368	Lisa Shieh	10	pulmonary_embolism	45771	5	3	\N	\N
+9891	Andre Kumar	10	pulmonary_embolism	45771	3	2	MEtabolic Panel	\N
+9892	Jonathan Chen	10	pulmonary_embolism	45771	3	2	Metabolic Panel	\N
+11352	Jonathan Chen	12	pulmonary_embolism	45771	3	2	Metabolic Panel	\N
+8124	Jason Hom	11	pulmonary_embolism	45771	10	5	\N	\N
+8125	Panel Consensus	11	pulmonary_embolism	45771	10	\N	\N	\N
+8883	Panel Average	11	pulmonary_embolism	45771	5	\N	\N	\N
+9369	Lisa Shieh	11	pulmonary_embolism	45771	5	3	\N	\N
+9893	Jonathan Chen	11	pulmonary_embolism	45771	3	2	Metabolic Panel	\N
+10362	Andre Kumar	11	pulmonary_embolism	45771	1	4	Metabolic Panel	\N
+11251	Jonathan Chen	8	pulmonary_embolism	45771	3	2	Metabolic Panel	\N
+8644	Lisa Shieh	10	pulmonary_embolism	44586	7	3	\N	\N
+9370	Jason Hom	10	pulmonary_embolism	44586	5	5	\N	\N
+9371	Jonathan Chen	10	pulmonary_embolism	44586	5	4	Corticosteroids	\N
+9372	Panel Consensus	10	pulmonary_embolism	44586	5	\N	\N	\N
+10181	Andre Kumar	10	pulmonary_embolism	44586	2	3	Steroids	\N
+9373	Jason Hom	11	pulmonary_embolism	44586	5	5	\N	\N
+9374	Jonathan Chen	11	pulmonary_embolism	44586	5	4	Corticosteroids	\N
+9375	Lisa Shieh	11	pulmonary_embolism	44586	5	3	\N	\N
+9376	Panel Consensus	11	pulmonary_embolism	44586	5	\N	\N	\N
+9593	Panel Average	11	pulmonary_embolism	44586	4	\N	\N	\N
+10363	Andre Kumar	11	pulmonary_embolism	44586	1	4	Steroids	\N
+8126	Jason Hom	11	pulmonary_embolism	45792	10	5	\N	\N
+9469	Panel Average	11	pulmonary_embolism	45792	4	\N	\N	\N
+9566	Panel Consensus	11	pulmonary_embolism	45792	4	\N	\N	\N
+9894	Lisa Shieh	11	pulmonary_embolism	45792	3	3	\N	\N
+10912	Andre Kumar	11	pulmonary_embolism	45792	0	4	MRSA	\N
+10913	Jonathan Chen	11	pulmonary_embolism	45792	0	2	\N	\N
+8127	Jason Hom	11	pulmonary_embolism	44228	10	5	\N	\N
+9470	Panel Average	11	pulmonary_embolism	44228	4	\N	\N	\N
+9567	Panel Consensus	11	pulmonary_embolism	44228	4	\N	\N	\N
+9895	Lisa Shieh	11	pulmonary_embolism	44228	3	3	\N	\N
+10182	Jonathan Chen	11	pulmonary_embolism	44228	2	2	Nicotine replacement	\N
+10914	Andre Kumar	11	pulmonary_embolism	44228	0	4	Nicotine	\N
+9896	Lisa Shieh	11	pulmonary_embolism	48628	3	3	\N	\N
+10364	Panel Average	11	pulmonary_embolism	48628	1	\N	\N	\N
+10915	Andre Kumar	11	pulmonary_embolism	48628	0	4	Anticoagulation	\N
+10916	Jason Hom	11	pulmonary_embolism	48628	0	5	\N	\N
+10917	Jonathan Chen	11	pulmonary_embolism	48628	0	2	\N	\N
+10918	Panel Consensus	11	pulmonary_embolism	48628	0	\N	\N	\N
+10919	Andre Kumar	11	pulmonary_embolism	46081	0	4	Anticoagulation	\N
+10920	Jason Hom	11	pulmonary_embolism	46081	0	5	\N	\N
+10921	Jonathan Chen	11	pulmonary_embolism	46081	0	2	\N	\N
+10922	Lisa Shieh	11	pulmonary_embolism	46081	0	3	\N	\N
+10923	Panel Average	11	pulmonary_embolism	46081	0	\N	\N	\N
+10924	Panel Consensus	11	pulmonary_embolism	46081	0	\N	\N	\N
+8128	Jonathan Chen	10	pulmonary_embolism	50235	10	5	Oxygen	\N
+9377	Lisa Shieh	10	pulmonary_embolism	50235	5	3	\N	\N
+10365	Panel Average	10	pulmonary_embolism	50235	1	\N	\N	\N
+10925	Jason Hom	10	pulmonary_embolism	50235	0	4	\N	\N
+10926	Panel Consensus	10	pulmonary_embolism	50235	0	\N	\N	\N
+11069	Andre Kumar	10	pulmonary_embolism	50235	-2	1	NIPPV	\N
+8129	Jason Hom	12	pulmonary_embolism	50235	10	5	\N	\N
+8130	Jonathan Chen	12	pulmonary_embolism	50235	10	5	Oxygen	\N
+9378	Lisa Shieh	12	pulmonary_embolism	50235	5	3	\N	\N
+9568	Panel Average	12	pulmonary_embolism	50235	4	\N	\N	\N
+10927	Panel Consensus	12	pulmonary_embolism	50235	0	\N	\N	\N
+11095	Andre Kumar	12	pulmonary_embolism	50235	-3	1	NIPPV	\N
+8131	Jason Hom	10	pulmonary_embolism	45853	10	5	\N	\N
+8827	Panel Average	10	pulmonary_embolism	45853	6	\N	\N	\N
+8828	Panel Consensus	10	pulmonary_embolism	45853	6	\N	\N	\N
+9379	Lisa Shieh	10	pulmonary_embolism	45853	5	3	\N	\N
+9897	Andre Kumar	10	pulmonary_embolism	45853	3	1	BNP	\N
+9898	Jonathan Chen	10	pulmonary_embolism	45853	3	4	\N	\N
+8132	Jason Hom	11	pulmonary_embolism	45853	10	5	\N	\N
+8829	Panel Consensus	11	pulmonary_embolism	45853	6	\N	\N	\N
+8860	Panel Average	11	pulmonary_embolism	45853	6	\N	\N	\N
+9380	Lisa Shieh	11	pulmonary_embolism	45853	5	3	\N	\N
+9899	Jonathan Chen	11	pulmonary_embolism	45853	3	4	\N	\N
+10183	Andre Kumar	11	pulmonary_embolism	45853	2	4	BNP	\N
+11359	Jonathan Chen	10	pulmonary_embolism	45750	2	3	\N	Process?
+11314	Jonathan Chen	11	pulmonary_embolism	45787	0	2	\N	Process
+11416	Jonathan Chen	10	pulmonary_embolism	51290	10	5	Oxygen	\N
+8133	Andre Kumar	11	pulmonary_embolism	51290	10	5	Oxygen	\N
+8134	Jason Hom	11	pulmonary_embolism	51290	10	5	\N	\N
+8135	Jonathan Chen	11	pulmonary_embolism	51290	10	5	Oxygen	\N
+8136	Panel Consensus	11	pulmonary_embolism	51290	10	\N	\N	\N
+8331	Panel Average	11	pulmonary_embolism	51290	8	\N	\N	\N
+9381	Lisa Shieh	11	pulmonary_embolism	51290	5	3	\N	\N
+8137	Andre Kumar	10	pulmonary_embolism	45864	10	5	Oxygen	only 1 time award for oxygen
+8138	Jason Hom	10	pulmonary_embolism	45864	10	5	\N	\N
+8139	Jonathan Chen	10	pulmonary_embolism	45864	10	5	Oxygen	\N
+8140	Lisa Shieh	10	pulmonary_embolism	45864	10	5	\N	\N
+8141	Panel Average	10	pulmonary_embolism	45864	10	\N	\N	\N
+8142	Panel Consensus	10	pulmonary_embolism	45864	10	\N	\N	\N
+8143	Jason Hom	12	pulmonary_embolism	45864	10	5	\N	\N
+8144	Jonathan Chen	12	pulmonary_embolism	45864	10	5	Oxygen	\N
+8145	Lisa Shieh	12	pulmonary_embolism	45864	10	5	\N	\N
+8146	Panel Consensus	12	pulmonary_embolism	45864	10	\N	\N	\N
+8220	Panel Average	12	pulmonary_embolism	45864	10	\N	\N	\N
+8288	Andre Kumar	12	pulmonary_embolism	45864	9	4	Oxygen	less points for delaying O2?
+8147	Andre Kumar	10	pulmonary_embolism	48822	10	5	Oxygen	\N
+8148	Jason Hom	10	pulmonary_embolism	48822	10	5	\N	\N
+8149	Jonathan Chen	10	pulmonary_embolism	48822	10	5	Oxygen	\N
+8150	Lisa Shieh	10	pulmonary_embolism	48822	10	5	\N	\N
+8151	Panel Average	10	pulmonary_embolism	48822	10	\N	\N	\N
+8152	Panel Consensus	10	pulmonary_embolism	48822	10	\N	\N	\N
+8153	Jason Hom	12	pulmonary_embolism	48822	10	5	\N	\N
+8154	Jonathan Chen	12	pulmonary_embolism	48822	10	5	Oxygen	\N
+8155	Lisa Shieh	12	pulmonary_embolism	48822	10	5	\N	\N
+8156	Panel Consensus	12	pulmonary_embolism	48822	10	\N	\N	\N
+8221	Panel Average	12	pulmonary_embolism	48822	10	\N	\N	\N
+8289	Andre Kumar	12	pulmonary_embolism	48822	9	4	Oxygen	less points for delaying O2?
+8157	Andre Kumar	10	pulmonary_embolism	45900	10	5	Oxygen	\N
+8158	Jason Hom	10	pulmonary_embolism	45900	10	5	\N	\N
+8159	Jonathan Chen	10	pulmonary_embolism	45900	10	5	Oxygen	\N
+8160	Lisa Shieh	10	pulmonary_embolism	45900	10	5	\N	\N
+8161	Panel Average	10	pulmonary_embolism	45900	10	\N	\N	\N
+8162	Panel Consensus	10	pulmonary_embolism	45900	10	\N	\N	\N
+8163	Jason Hom	12	pulmonary_embolism	45900	10	5	\N	\N
+8164	Jonathan Chen	12	pulmonary_embolism	45900	10	5	Oxygen	\N
+8165	Lisa Shieh	12	pulmonary_embolism	45900	10	5	\N	\N
+8166	Panel Consensus	12	pulmonary_embolism	45900	10	\N	\N	\N
+8222	Panel Average	12	pulmonary_embolism	45900	10	\N	\N	\N
+8290	Andre Kumar	12	pulmonary_embolism	45900	9	4	Oxygen	less points for delaying O2?
+11370	Jonathan Chen	10	pulmonary_embolism	46090	0	2	\N	Heparin protocol
+8291	Lisa Shieh	11	pulmonary_embolism	46090	9	3	\N	\N
+9619	Panel Average	11	pulmonary_embolism	46090	3	\N	\N	\N
+10184	Panel Consensus	11	pulmonary_embolism	46090	2	\N	\N	\N
+10366	Andre Kumar	11	pulmonary_embolism	46090	1	3	Anticoagulation	\N
+10928	Jason Hom	11	pulmonary_embolism	46090	0	5	\N	\N
+10929	Jonathan Chen	11	pulmonary_embolism	46090	0	2	\N	Heparin protocol
+8167	Jason Hom	10	pulmonary_embolism	45914	10	5	\N	\N
+8695	Panel Average	10	pulmonary_embolism	45914	6	\N	\N	\N
+8830	Panel Consensus	10	pulmonary_embolism	45914	6	\N	\N	\N
+9382	Jonathan Chen	10	pulmonary_embolism	45914	5	4	Troponin	\N
+9383	Lisa Shieh	10	pulmonary_embolism	45914	5	3	\N	\N
+9569	Andre Kumar	10	pulmonary_embolism	45914	4	3	Troponin	\N
+9570	Andre Kumar	11	pulmonary_embolism	45914	4	3	Troponin	\N
+9571	Panel Average	11	pulmonary_embolism	45914	4	\N	\N	\N
+8168	Jason Hom	10	pulmonary_embolism	50773	10	5	\N	\N
+8645	Panel Consensus	10	pulmonary_embolism	50773	7	\N	\N	\N
+8679	Panel Average	10	pulmonary_embolism	50773	7	\N	\N	\N
+9384	Andre Kumar	10	pulmonary_embolism	50773	5	5	Blood gas	\N
+9385	Jonathan Chen	10	pulmonary_embolism	50773	5	4	Blood Gas	\N
+9386	Lisa Shieh	10	pulmonary_embolism	50773	5	3	\N	\N
+8169	Jason Hom	10	pulmonary_embolism	50503	10	5	\N	\N
+9387	Jonathan Chen	10	pulmonary_embolism	50503	5	4	Blood Gas	\N
+9388	Lisa Shieh	10	pulmonary_embolism	50503	5	3	\N	\N
+9389	Panel Average	10	pulmonary_embolism	50503	5	\N	\N	\N
+9390	Panel Consensus	10	pulmonary_embolism	50503	5	\N	\N	\N
+10930	Andre Kumar	10	pulmonary_embolism	50503	0	3	Blood gas	\N
+8170	Jason Hom	10	pulmonary_embolism	45955	10	5	\N	\N
+9391	Jonathan Chen	10	pulmonary_embolism	45955	5	4	Blood Gas	\N
+9392	Lisa Shieh	10	pulmonary_embolism	45955	5	3	\N	\N
+9393	Panel Average	10	pulmonary_embolism	45955	5	\N	\N	\N
+9394	Panel Consensus	10	pulmonary_embolism	45955	5	\N	\N	\N
+10931	Andre Kumar	10	pulmonary_embolism	45955	0	3	Blood Gas	\N
+9395	Jason Hom	11	pulmonary_embolism	45955	5	5	\N	\N
+9396	Jonathan Chen	11	pulmonary_embolism	45955	5	4	Blood Gas	\N
+9397	Lisa Shieh	11	pulmonary_embolism	45955	5	3	\N	\N
+9398	Panel Consensus	11	pulmonary_embolism	45955	5	\N	\N	\N
+9441	Panel Average	11	pulmonary_embolism	45955	5	\N	\N	\N
+9572	Andre Kumar	11	pulmonary_embolism	45955	4	3	Blood Gas	\N
+10932	Andre Kumar	8	pulmonary_embolism	50267	0	3	Anticoagulation	\N
+10933	Jason Hom	8	pulmonary_embolism	50267	0	5	\N	\N
+10934	Jonathan Chen	8	pulmonary_embolism	50267	0	2	\N	\N
+10935	Lisa Shieh	8	pulmonary_embolism	50267	0	3	\N	\N
+10936	Panel Average	8	pulmonary_embolism	50267	0	\N	\N	\N
+10937	Panel Consensus	8	pulmonary_embolism	50267	0	\N	\N	\N
+8646	Lisa Shieh	10	pulmonary_embolism	44011	7	3	\N	\N
+9399	Jonathan Chen	10	pulmonary_embolism	44011	5	4	Corticosteroids	Main issue is PE, but could reasonably say it's triggering a COPD exacerbation as well
+9400	Panel Consensus	10	pulmonary_embolism	44011	5	\N	\N	\N
+9900	Panel Average	10	pulmonary_embolism	44011	3	\N	\N	\N
+10185	Andre Kumar	10	pulmonary_embolism	44011	2	3	Steroids	\N
+10938	Jason Hom	10	pulmonary_embolism	44011	0	5	\N	\N
+9401	Jonathan Chen	11	pulmonary_embolism	44011	5	4	Corticosteroids	Main issue is PE, but could reasonably say it's triggering a COPD exacerbation as well
+9402	Lisa Shieh	11	pulmonary_embolism	44011	5	3	\N	\N
+9403	Panel Consensus	11	pulmonary_embolism	44011	5	\N	\N	\N
+10186	Panel Average	11	pulmonary_embolism	44011	2	\N	\N	\N
+10367	Andre Kumar	11	pulmonary_embolism	44011	1	4	Steroids	\N
+10939	Jason Hom	11	pulmonary_embolism	44011	0	5	\N	\N
+8171	Jason Hom	11	pulmonary_embolism	62042	10	5	\N	\N
+8884	Panel Average	11	pulmonary_embolism	62042	5	\N	\N	\N
+9404	Panel Consensus	11	pulmonary_embolism	62042	5	\N	\N	\N
+9901	Andre Kumar	11	pulmonary_embolism	62042	3	4	Procalcitonin	\N
+9902	Jonathan Chen	11	pulmonary_embolism	62042	3	3	\N	May be helpful for complex differential diagnosis
+9903	Lisa Shieh	11	pulmonary_embolism	62042	3	3	\N	\N
+8172	Jason Hom	10	pulmonary_embolism	45759	10	5	\N	\N
+8173	Panel Consensus	10	pulmonary_embolism	45759	10	\N	\N	\N
+8831	Panel Average	10	pulmonary_embolism	45759	6	\N	\N	\N
+9405	Lisa Shieh	10	pulmonary_embolism	45759	5	3	\N	\N
+9904	Andre Kumar	10	pulmonary_embolism	45759	3	3	Coags	\N
+10940	Jonathan Chen	10	pulmonary_embolism	45759	0	2	\N	\N
+8174	Jason Hom	11	pulmonary_embolism	45759	10	5	\N	\N
+8175	Panel Consensus	11	pulmonary_embolism	45759	10	\N	\N	\N
+8861	Panel Average	11	pulmonary_embolism	45759	6	\N	\N	\N
+9406	Lisa Shieh	11	pulmonary_embolism	45759	5	3	\N	\N
+10187	Andre Kumar	11	pulmonary_embolism	45759	2	2	Coagulation	\N
+10941	Jonathan Chen	11	pulmonary_embolism	45759	0	2	\N	\N
+8176	Jason Hom	8	pulmonary_embolism	45759	10	5	\N	\N
+8177	Panel Consensus	8	pulmonary_embolism	45759	10	\N	\N	\N
+8647	Lisa Shieh	8	pulmonary_embolism	45759	7	3	\N	\N
+8832	Panel Average	8	pulmonary_embolism	45759	6	\N	\N	\N
+10368	Andre Kumar	8	pulmonary_embolism	45759	1	4	Coags	\N
+10942	Jonathan Chen	8	pulmonary_embolism	45759	0	2	\N	\N
+8178	Jason Hom	11	pulmonary_embolism	45776	10	5	\N	\N
+9573	Panel Consensus	11	pulmonary_embolism	45776	4	\N	\N	\N
+9594	Panel Average	11	pulmonary_embolism	45776	4	\N	\N	\N
+10369	Andre Kumar	11	pulmonary_embolism	45776	1	2	PT/OT	\N
+10943	Jonathan Chen	11	pulmonary_embolism	45776	0	2	\N	\N
+10944	Lisa Shieh	11	pulmonary_embolism	45776	0	3	\N	\N
+10370	Andre Kumar	8	pulmonary_embolism	45776	1	2	PT/OT	\N
+10371	Panel Average	8	pulmonary_embolism	45776	1	\N	\N	\N
+10945	Andre Kumar	8	pulmonary_embolism	49268	0	2	Coags	\N
+10946	Jason Hom	8	pulmonary_embolism	49268	0	5	\N	\N
+10947	Jonathan Chen	8	pulmonary_embolism	49268	0	2	\N	Probably a typo, looking for PTT
+10948	Lisa Shieh	8	pulmonary_embolism	49268	0	3	\N	\N
+10949	Panel Average	8	pulmonary_embolism	49268	0	\N	\N	\N
+10950	Panel Consensus	8	pulmonary_embolism	49268	0	\N	\N	\N
+11469	Jonathan Chen	10	pulmonary_embolism	45770	0	2	\N	\N
+11470	Jonathan Chen	12	pulmonary_embolism	45770	0	2	\N	\N
+8179	Jason Hom	11	pulmonary_embolism	45770	10	5	\N	\N
+8180	Panel Consensus	11	pulmonary_embolism	45770	10	\N	\N	\N
+8885	Panel Average	11	pulmonary_embolism	45770	5	\N	\N	\N
+9407	Lisa Shieh	11	pulmonary_embolism	45770	5	3	\N	\N
+10372	Andre Kumar	11	pulmonary_embolism	45770	1	2	Coags	\N
+10951	Jonathan Chen	11	pulmonary_embolism	45770	0	2	\N	\N
+8181	Jason Hom	8	pulmonary_embolism	45770	10	5	\N	\N
+8182	Panel Consensus	8	pulmonary_embolism	45770	10	\N	\N	\N
+8648	Lisa Shieh	8	pulmonary_embolism	45770	7	3	\N	\N
+8833	Panel Average	8	pulmonary_embolism	45770	6	\N	\N	\N
+10373	Andre Kumar	8	pulmonary_embolism	45770	1	2	Coags	\N
+10952	Jonathan Chen	8	pulmonary_embolism	45770	0	2	\N	\N
+11459	Jonathan Chen	11	pulmonary_embolism	63730	0	2	\N	Not for acute TB even if that was the diagnosis?
+8183	Jason Hom	8	pulmonary_embolism	62175	10	5	\N	\N
+9471	Panel Average	8	pulmonary_embolism	62175	4	\N	\N	\N
+9574	Panel Consensus	8	pulmonary_embolism	62175	4	\N	\N	\N
+9905	Lisa Shieh	8	pulmonary_embolism	62175	3	3	\N	\N
+10188	Jonathan Chen	8	pulmonary_embolism	62175	2	2	Biopsy	Referral for biopsy?
+10953	Andre Kumar	8	pulmonary_embolism	62175	0	1	Consults	\N
+8184	Jason Hom	12	pulmonary_embolism	45857	10	5	\N	\N
+8834	Panel Consensus	12	pulmonary_embolism	45857	6	\N	\N	\N
+8862	Panel Average	12	pulmonary_embolism	45857	6	\N	\N	\N
+9408	Lisa Shieh	12	pulmonary_embolism	45857	5	3	\N	\N
+10189	Andre Kumar	12	pulmonary_embolism	45857	2	1	Nebs	\N
+10190	Jonathan Chen	12	pulmonary_embolism	45857	2	4	Nebs	\N
+8650	Panel Average	10	pulmonary_embolism	45830	7	\N	\N	\N
+8651	Panel Consensus	10	pulmonary_embolism	45830	7	\N	\N	\N
+9575	Andre Kumar	10	pulmonary_embolism	45830	4	2	Nebs	\N
+10191	Jonathan Chen	10	pulmonary_embolism	45830	2	4	Nebs	\N
+8186	Jason Hom	12	pulmonary_embolism	45830	10	5	\N	\N
+8652	Lisa Shieh	12	pulmonary_embolism	45830	7	3	\N	\N
+8653	Panel Consensus	12	pulmonary_embolism	45830	7	\N	\N	\N
+8696	Panel Average	12	pulmonary_embolism	45830	6	\N	\N	\N
+10192	Andre Kumar	12	pulmonary_embolism	45830	2	1	Nebs	\N
+10193	Jonathan Chen	12	pulmonary_embolism	45830	2	4	Nebs	\N
+10194	Andre Kumar	11	pulmonary_embolism	45830	2	5	Nebs	\N
+10195	Panel Average	11	pulmonary_embolism	45830	2	\N	\N	\N
+8187	Jonathan Chen	10	pulmonary_embolism	46030	10	5	Oxygen	\N
+9409	Lisa Shieh	10	pulmonary_embolism	46030	5	3	\N	\N
+10390	Panel Average	10	pulmonary_embolism	46030	1	\N	\N	\N
+10954	Jason Hom	10	pulmonary_embolism	46030	0	4	\N	\N
+10955	Panel Consensus	10	pulmonary_embolism	46030	0	\N	\N	\N
+11096	Andre Kumar	10	pulmonary_embolism	46030	-3	1	NIPPV	may drop cardiac output
+8188	Jonathan Chen	11	pulmonary_embolism	46030	10	5	Oxygen	\N
+9410	Jason Hom	11	pulmonary_embolism	46030	5	5	\N	\N
+9411	Lisa Shieh	11	pulmonary_embolism	46030	5	3	\N	\N
+9946	Panel Average	11	pulmonary_embolism	46030	2	\N	\N	\N
+10956	Panel Consensus	11	pulmonary_embolism	46030	0	\N	\N	\N
+11097	Andre Kumar	11	pulmonary_embolism	46030	-3	1	NIPPV	Could induce circulatory collapse
+8189	Andre Kumar	10	pulmonary_embolism	45921	10	5	Oxygen	\N
+8190	Jason Hom	10	pulmonary_embolism	45921	10	5	\N	\N
+8191	Jonathan Chen	10	pulmonary_embolism	45921	10	5	Oxygen	\N
+8192	Lisa Shieh	10	pulmonary_embolism	45921	10	5	\N	\N
+8193	Panel Average	10	pulmonary_embolism	45921	10	\N	\N	\N
+8194	Panel Consensus	10	pulmonary_embolism	45921	10	\N	\N	\N
+8195	Jason Hom	12	pulmonary_embolism	45921	10	5	\N	\N
+8196	Jonathan Chen	12	pulmonary_embolism	45921	10	5	Oxygen	\N
+8197	Lisa Shieh	12	pulmonary_embolism	45921	10	5	\N	\N
+8198	Panel Consensus	12	pulmonary_embolism	45921	10	\N	\N	\N
+8223	Panel Average	12	pulmonary_embolism	45921	10	\N	\N	\N
+8292	Andre Kumar	12	pulmonary_embolism	45921	9	4	Oxygen	\N
+11413	Jonathan Chen	11	pulmonary_embolism	45921	10	5	Oxygen	\N
+9906	Jonathan Chen	11	pulmonary_embolism	46336	3	3	Respiratory Culture	\N
+9907	Lisa Shieh	11	pulmonary_embolism	46336	3	3	\N	\N
+10196	Andre Kumar	11	pulmonary_embolism	46336	2	2	Respiratory Culture	\N
+10197	Panel Consensus	11	pulmonary_embolism	46336	2	\N	\N	\N
+10231	Panel Average	11	pulmonary_embolism	46336	2	\N	\N	\N
+10957	Jason Hom	11	pulmonary_embolism	46336	0	5	\N	\N
+11409	Jonathan Chen	11	pulmonary_embolism	46179	3	3	Respiratory Culture	\N
+8199	Jason Hom	11	pulmonary_embolism	48544	10	5	\N	\N
+9412	Panel Average	11	pulmonary_embolism	48544	5	\N	\N	\N
+9413	Panel Consensus	11	pulmonary_embolism	48544	5	\N	\N	\N
+9908	Jonathan Chen	11	pulmonary_embolism	48544	3	3	Respiratory Culture	\N
+9909	Lisa Shieh	11	pulmonary_embolism	48544	3	3	\N	\N
+10198	Andre Kumar	11	pulmonary_embolism	48544	2	4	Respiratory Culture	\N
+11396	Jonathan Chen	10	pulmonary_embolism	62023	3	4	Respiratory Virus Screen	\N
+11394	Jonathan Chen	12	pulmonary_embolism	62023	3	4	Respiratory Virus Screen	\N
+8200	Jason Hom	11	pulmonary_embolism	62023	10	5	\N	\N
+9414	Panel Consensus	11	pulmonary_embolism	62023	5	\N	\N	\N
+9595	Panel Average	11	pulmonary_embolism	62023	4	\N	\N	\N
+9910	Jonathan Chen	11	pulmonary_embolism	62023	3	4	Respiratory Virus Screen	\N
+10374	Andre Kumar	11	pulmonary_embolism	62023	1	4	Respiratory Viral Panel	\N
+8201	Jason Hom	10	pulmonary_embolism	63923	10	5	\N	\N
+8863	Panel Average	10	pulmonary_embolism	63923	6	\N	\N	\N
+9415	Lisa Shieh	10	pulmonary_embolism	63923	5	3	\N	\N
+9416	Panel Consensus	10	pulmonary_embolism	63923	5	\N	\N	\N
+9911	Jonathan Chen	10	pulmonary_embolism	63923	3	4	Respiratory Virus Screen	\N
+10199	Andre Kumar	10	pulmonary_embolism	63923	2	5	Respiratory Viral Panel	\N
+8202	Jason Hom	11	pulmonary_embolism	63923	10	5	\N	\N
+8886	Panel Average	11	pulmonary_embolism	63923	5	\N	\N	\N
+9417	Lisa Shieh	11	pulmonary_embolism	63923	5	3	\N	\N
+9418	Panel Consensus	11	pulmonary_embolism	63923	5	\N	\N	\N
+9912	Jonathan Chen	11	pulmonary_embolism	63923	3	4	Respiratory Virus Screen	\N
+10375	Andre Kumar	11	pulmonary_embolism	63923	1	4	Respiratory Viral Panel	\N
+11380	Jonathan Chen	12	pulmonary_embolism	60178	10	5	Anticoagulation	\N
+11406	Jonathan Chen	11	pulmonary_embolism	60178	10	5	Anticoagulation	\N
+11424	Jonathan Chen	10	pulmonary_embolism	41796	0	5	\N	Presenting Symptom Diagnosis
+10200	Andre Kumar	10	pulmonary_embolism	44198	2	2	Fluids	\N
+10201	Panel Average	10	pulmonary_embolism	44198	2	\N	\N	\N
+11418	Jonathan Chen	12	pulmonary_embolism	44198	0	2	IVF	\N
+11454	Jonathan Chen	8	pulmonary_embolism	44313	0	2	\N	Nebs, but this is more for long term use
+8203	Jason Hom	10	pulmonary_embolism	45870	10	5	\N	\N
+8697	Panel Average	10	pulmonary_embolism	45870	6	\N	\N	\N
+8835	Panel Consensus	10	pulmonary_embolism	45870	6	\N	\N	\N
+9419	Jonathan Chen	10	pulmonary_embolism	45870	5	4	Troponin	\N
+9420	Lisa Shieh	10	pulmonary_embolism	45870	5	3	\N	\N
+9576	Andre Kumar	10	pulmonary_embolism	45870	4	4	Troponin	\N
+11414	Jonathan Chen	12	pulmonary_embolism	45870	5	4	Troponin	\N
+8204	Jason Hom	11	pulmonary_embolism	45870	10	5	\N	\N
+8836	Panel Consensus	11	pulmonary_embolism	45870	6	\N	\N	\N
+8864	Panel Average	11	pulmonary_embolism	45870	6	\N	\N	\N
+9421	Jonathan Chen	11	pulmonary_embolism	45870	5	4	Troponin	\N
+9422	Lisa Shieh	11	pulmonary_embolism	45870	5	3	\N	\N
+10202	Andre Kumar	11	pulmonary_embolism	45870	2	3	Troponin	\N
+8205	Jason Hom	8	pulmonary_embolism	45870	10	5	\N	\N
+8837	Panel Consensus	8	pulmonary_embolism	45870	6	\N	\N	\N
+8865	Panel Average	8	pulmonary_embolism	45870	6	\N	\N	\N
+9423	Jonathan Chen	8	pulmonary_embolism	45870	5	4	Troponin	\N
+9424	Lisa Shieh	8	pulmonary_embolism	45870	5	3	\N	\N
+10203	Andre Kumar	8	pulmonary_embolism	45870	2	2	Troponin	\N
+11313	Jonathan Chen	11	pulmonary_embolism	45945	0	2	\N	\N
+11458	Jonathan Chen	11	pulmonary_embolism	65672	2	2	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+8206	Jason Hom	8	pulmonary_embolism	65672	10	5	\N	\N
+9577	Panel Average	8	pulmonary_embolism	65672	4	\N	\N	\N
+9578	Panel Consensus	8	pulmonary_embolism	65672	4	\N	\N	\N
+10204	Andre Kumar	8	pulmonary_embolism	65672	2	2	Imaging	"IF CTA not done otherwise -2 points"
+10205	Jonathan Chen	8	pulmonary_embolism	65672	2	2	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+10959	Lisa Shieh	8	pulmonary_embolism	65672	0	3	\N	\N
+11417	Jonathan Chen	11	pulmonary_embolism	65656	2	2	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+11318	Jonathan Chen	11	pulmonary_embolism	65692	2	2	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+11254	Jonathan Chen	10	pulmonary_embolism	45751	0	2	\N	\N
+10376	Andre Kumar	11	pulmonary_embolism	45751	1	4	Urinalysis	\N
+10404	Panel Average	11	pulmonary_embolism	45751	0	\N	\N	\N
+10960	Jason Hom	11	pulmonary_embolism	45751	0	5	\N	\N
+10961	Jonathan Chen	11	pulmonary_embolism	45751	0	2	\N	\N
+10962	Lisa Shieh	11	pulmonary_embolism	45751	0	3	\N	\N
+10963	Panel Consensus	11	pulmonary_embolism	45751	0	\N	\N	\N
+11382	Jonathan Chen	11	pulmonary_embolism	45911	0	2	\N	Process
+8207	Jason Hom	8	pulmonary_embolism	46113	10	5	\N	\N
+8887	Panel Average	8	pulmonary_embolism	46113	5	\N	\N	\N
+9425	Lisa Shieh	8	pulmonary_embolism	46113	5	3	\N	\N
+9426	Panel Consensus	8	pulmonary_embolism	46113	5	\N	\N	\N
+10377	Andre Kumar	8	pulmonary_embolism	46113	1	1	Oxygen	\N
+10964	Jonathan Chen	8	pulmonary_embolism	46113	0	2	\N	\N
+10378	Andre Kumar	10	pulmonary_embolism	45766	1	2	Weight	\N
+10379	Panel Average	10	pulmonary_embolism	45766	1	\N	\N	\N
+8208	Jason Hom	10	pulmonary_embolism	45818	10	5	\N	\N
+8209	Panel Consensus	10	pulmonary_embolism	45818	10	\N	\N	\N
+8427	Jonathan Chen	10	pulmonary_embolism	45818	8	4	CXR	\N
+8449	Panel Average	10	pulmonary_embolism	45818	8	\N	\N	\N
+8654	Lisa Shieh	10	pulmonary_embolism	45818	7	3	\N	\N
+8838	Andre Kumar	10	pulmonary_embolism	45818	6	3	Imaging	\N
+8210	Jason Hom	11	pulmonary_embolism	45818	10	5	\N	\N
+8211	Panel Consensus	11	pulmonary_embolism	45818	10	\N	\N	\N
+8428	Jonathan Chen	11	pulmonary_embolism	45818	8	4	CXR	\N
+8467	Panel Average	11	pulmonary_embolism	45818	7	\N	\N	\N
+8655	Lisa Shieh	11	pulmonary_embolism	45818	7	3	\N	\N
+9427	Andre Kumar	11	pulmonary_embolism	45818	5	4	Imaging	\N
+8212	Jason Hom	10	pulmonary_embolism	50200	10	5	\N	\N
+8213	Panel Consensus	10	pulmonary_embolism	50200	10	\N	\N	\N
+8429	Jonathan Chen	10	pulmonary_embolism	50200	8	4	CXR	\N
+8656	Panel Average	10	pulmonary_embolism	50200	7	\N	\N	\N
+8839	Andre Kumar	10	pulmonary_embolism	50200	6	3	Imaging	\N
+9428	Lisa Shieh	10	pulmonary_embolism	50200	5	3	\N	\N
+8214	Jason Hom	10	pulmonary_embolism	45801	10	5	\N	\N
+8215	Lisa Shieh	10	pulmonary_embolism	45801	10	5	\N	\N
+8216	Panel Consensus	10	pulmonary_embolism	45801	10	\N	\N	\N
+8299	Panel Average	10	pulmonary_embolism	45801	9	\N	\N	\N
+8430	Jonathan Chen	10	pulmonary_embolism	45801	8	4	CXR	\N
+8840	Andre Kumar	10	pulmonary_embolism	45801	6	3	Imaging	\N
+8217	Jason Hom	11	pulmonary_embolism	45801	10	5	\N	\N
+8218	Lisa Shieh	11	pulmonary_embolism	45801	10	3	\N	\N
+8219	Panel Consensus	11	pulmonary_embolism	45801	10	\N	\N	\N
+8332	Panel Average	11	pulmonary_embolism	45801	8	\N	\N	\N
+8431	Jonathan Chen	11	pulmonary_embolism	45801	8	4	CXR	\N
+9429	Andre Kumar	11	pulmonary_embolism	45801	5	4	Imaging	\N
+11479	Post-Panel Consolidation	40	atrial_fibrillation	35968	0	0	\N	Negative points seems too severe here. Anti-arrhythmic can be option in unstable Afib if awaiting or failed DCCV. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1861334/
+11480	Post-Panel Consolidation	43	atrial_fibrillation	35968	0	0	\N	Negative points seems too severe here. Anti-arrhythmic can be option in unstable Afib if awaiting or failed DCCV. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1861334/
+11481	Post-Panel Consolidation	43	atrial_fibrillation	44352	0	0	\N	Negative points seems too severe here. Anti-arrhythmic can be option in unstable Afib if awaiting or failed DCCV. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1861334/
+11482	Post-Panel Consolidation	40	atrial_fibrillation	45887	3	0	\N	Why different per initial vs. worse state?
+11483	Post-Panel Consolidation	43	atrial_fibrillation	45887	3	0	\N	Why different per initial vs. worse state?
+11484	Post-Panel Consolidation	40	atrial_fibrillation	62176	7	0	Echo	\N
+11485	Post-Panel Consolidation	43	atrial_fibrillation	62176	7	0	Echo	Okay for workup, but not while unstable?
+11486	Post-Panel Consolidation	40	atrial_fibrillation	45963	-4	0	TEEcho	Not while unstable
+11487	Post-Panel Consolidation	43	atrial_fibrillation	45963	-4	0	TEEcho	\N
+11488	Post-Panel Consolidation	41	atrial_fibrillation	61832	7	0	Echo	\N
+11489	Post-Panel Consolidation	40	atrial_fibrillation	61832	7	0	Echo	Good for Afib workup, but less points if doing it while unstable?
+11490	Post-Panel Consolidation	40	atrial_fibrillation	44004	0	0	\N	\N
+11491	Post-Panel Consolidation	41	atrial_fibrillation	44004	0	0	\N	Positive points for furosemide here, but negative points for initial state. But also give negative points for giving IV Fluids?
+11492	Post-Panel Consolidation	43	atrial_fibrillation	44004	0	0	\N	\N
+11493	Post-Panel Consolidation	16	gi_bleed	45858	-5	0	CT Abdomen	Treat all CT Abd/Pelvis orders as comparable for simplicity, so why different scores? Weird cirrhosis diagnostic, but maybe people are looking for bleeding vessel?
+11494	Post-Panel Consolidation	14	gi_bleed	45852	-5	0	CT Abdomen	Treat all CT Abd/Pelvis orders as comparable for simplicity, so why different scores? Weird cirrhosis diagnostic, but maybe people are looking for bleeding vessel?
+11495	Post-Panel Consolidation	2	gi_bleed	49836	-5	0	CT Abdomen	Treat all CT Abd/Pelvis orders as comparable for simplicity, so why different scores? Weird cirrhosis diagnostic, but maybe people are looking for bleeding vessel?
+11496	Post-Panel Consolidation	30	meningitis	45983	-3	0	CT Head	\N
+11497	Post-Panel Consolidation	31	meningitis	45983	-3	0	CT Head	Gave -3 points if CT head before treatment, but what about after treatment?
+11498	Post-Panel Consolidation	30	meningitis	50241	-3	0	CT Head	\N
+11499	Post-Panel Consolidation	40	atrial_fibrillation	45977	0	0	\N	Standard Process
+11500	Post-Panel Consolidation	41	atrial_fibrillation	45977	0	0	\N	Standard Process
+11501	Post-Panel Consolidation	40	atrial_fibrillation	45919	1	0	Blood Gas	Override for consistency with others
+11502	Post-Panel Consolidation	41	atrial_fibrillation	45793	6	0	CBC	Override for consistency with other choices across states
+11503	Post-Panel Consolidation	40	atrial_fibrillation	50400	0	0	\N	\N
+11504	Post-Panel Consolidation	43	atrial_fibrillation	50400	0	0	\N	\N
+11505	Post-Panel Consolidation	40	atrial_fibrillation	49251	0	0	\N	Override, neutralize non-procedure consults
+11506	Post-Panel Consolidation	41	atrial_fibrillation	49251	0	0	\N	Override, neutralize non-procedure consults
+11507	Post-Panel Consolidation	43	atrial_fibrillation	49251	0	0	\N	Override, neutralize non-procedure consults
+11508	Post-Panel Consolidation	40	atrial_fibrillation	45811	2	0	\N	Override, excess points for non-action
+11509	Post-Panel Consolidation	43	atrial_fibrillation	45811	2	0	\N	Override, excess points for non-action
+11510	Post-Panel Consolidation	40	atrial_fibrillation	46160	0	0	\N	Standard process
+11511	Post-Panel Consolidation	40	atrial_fibrillation	44297	3	0	\N	Override, less point differential for initial vs. worse state
+11512	Post-Panel Consolidation	40	atrial_fibrillation	44359	8	0	Anticoagulation	Override consistent points
+11513	Post-Panel Consolidation	41	atrial_fibrillation	44359	8	0	Anticoagulation	Override consistent points
+11514	Post-Panel Consolidation	40	atrial_fibrillation	46183	0	0	\N	Override, standard process after heparin order
+11515	Post-Panel Consolidation	40	atrial_fibrillation	63714	0	0	\N	Override, standard process after heparin order
+11516	Post-Panel Consolidation	43	atrial_fibrillation	61837	3	0	Lipids	\N
+11517	Post-Panel Consolidation	41	atrial_fibrillation	45763	7	0	Metabolic Panel	\N
+11518	Post-Panel Consolidation	40	atrial_fibrillation	45864	0	0	Oxygen	Override, no hypoxia?
+11519	Post-Panel Consolidation	40	atrial_fibrillation	45900	0	0	Oxygen	Override, no hypoxia?
+11520	Post-Panel Consolidation	40	atrial_fibrillation	61823	0	0	\N	Override, standard process
+11521	Post-Panel Consolidation	40	atrial_fibrillation	46090	0	0	\N	Override, embedded in CBC or part of Heparin protocol
+11522	Post-Panel Consolidation	40	atrial_fibrillation	45955	3	0	Lactate	Override to match lactate order
+11523	Post-Panel Consolidation	43	atrial_fibrillation	45955	3	0	Lactate	Override to match lactate order
+11524	Post-Panel Consolidation	43	atrial_fibrillation	49995	5	0	Cardiology	Override, treat same as Cardiology consult
+11525	Post-Panel Consolidation	40	atrial_fibrillation	60178	3	0	Anticoagulation-Oral	Oral admin, is this too slow, and so should be worth less points?
+11526	Post-Panel Consolidation	41	atrial_fibrillation	60178	3	0	Anticoagulation-Oral	Oral admin, is this too slow, and so should be worth less points?
+11527	Post-Panel Consolidation	43	atrial_fibrillation	60178	3	0	Anticoagulation-Oral	Oral admin, is this too slow, and so should be worth less points?
+11528	Post-Panel Consolidation	40	atrial_fibrillation	63725	3	0	Lactate	Override for consistency
+11529	Post-Panel Consolidation	40	atrial_fibrillation	45818	3	0	\N	Override to match Portable CXR, since this can be a portable order too
+11530	Post-Panel Consolidation	43	atrial_fibrillation	45818	3	0	\N	Override to match Portable CXR, since this can be a portable order too
+11531	Post-Panel Consolidation	15	gi_bleed	45932	5	0	Hepatitis Panel	Override to match panel
+11532	Post-Panel Consolidation	16	gi_bleed	45814	0	0	\N	Override, standard process
+11533	Post-Panel Consolidation	14	gi_bleed	61982	0	0	\N	Override, standard process
+11534	Post-Panel Consolidation	15	gi_bleed	61982	0	0	\N	Override, standard process
+11535	Post-Panel Consolidation	15	gi_bleed	44275	8	0	IVF	Override to match panel grade
+11536	Post-Panel Consolidation	14	gi_bleed	48513	2	0	\N	Override, consistencu with Indirect Bilirubin
+11537	Post-Panel Consolidation	15	gi_bleed	45823	10	0	Type and Screen	Override, match panel, though this seems like too much for what will likely be part of standard tranfusion process orders
+11538	Post-Panel Consolidation	14	gi_bleed	44439	8	0	IVF	Override to match panel grade
+11539	Post-Panel Consolidation	15	gi_bleed	44439	8	0	IVF	Override to match panel grade
+11540	Post-Panel Consolidation	15	gi_bleed	50400	0	0	\N	Neutralize consults
+11541	Post-Panel Consolidation	15	gi_bleed	61323	2	0	\N	Override for consistency and avoid excess points for worse state compensation
+11542	Post-Panel Consolidation	14	gi_bleed	49207	0	0	\N	Override, standard process
+11543	Post-Panel Consolidation	14	gi_bleed	46160	0	0	\N	Override, standard process
+11544	Post-Panel Consolidation	14	gi_bleed	63759	10	0	RBC	Override, seems typo on score?
+11545	Post-Panel Consolidation	15	gi_bleed	71083	0	0	\N	Override, non-specific process / consult
+11546	Post-Panel Consolidation	2	gi_bleed	45771	9	0	Metabolic Panel	\N
+11547	Post-Panel Consolidation	15	gi_bleed	45832	7	0	Intubation	?Mechanical ventilation? Airway protection?
+11548	Post-Panel Consolidation	14	gi_bleed	63725	2	0	Lactate	Override to match others
+11549	Post-Panel Consolidation	14	gi_bleed	61993	8	0	Coagulopathy Correction	\N
+11550	Post-Panel Consolidation	15	gi_bleed	61993	8	0	Coagulopathy Correction	\N
+11551	Post-Panel Consolidation	16	gi_bleed	61993	8	0	Coagulopathy Correction	\N
+11552	Post-Panel Consolidation	14	gi_bleed	45870	0	0	Troponin	Hemodynamic stability assessment?
+11553	Post-Panel Consolidation	14	gi_bleed	50372	0	0	\N	Override, standard process
+11554	Post-Panel Consolidation	30	meningitis	56492	5	0	Pain Control	Minor points for symptom control?
+11555	Post-Panel Consolidation	31	meningitis	56492	5	0	Pain Control	Minor points for symptom control?
+11556	Post-Panel Consolidation	33	meningitis	44281	5	0	Pain Control	Minor points for symptom control?
+11557	Post-Panel Consolidation	30	meningitis	44310	5	0	Pain Control	Minor points for symptom control?
+11558	Post-Panel Consolidation	31	meningitis	44310	5	0	Pain Control	Minor points for symptom control?
+11559	Post-Panel Consolidation	31	meningitis	61864	5	0	HIV Test	Override, match other HIV tests
+11560	Post-Panel Consolidation	30	meningitis	45966	7	0	CBC	\N
+11561	Post-Panel Consolidation	31	meningitis	48880	5	0	\N	Panel of CSF tests, should get point for each? Adds up to more than value of antibiotics
+11562	Post-Panel Consolidation	31	meningitis	48980	0	0	\N	Override, neutralize non-procedure consults
+11563	Post-Panel Consolidation	33	meningitis	48980	0	0	\N	Override, neutralize non-procedure consults
+11564	Post-Panel Consolidation	31	meningitis	49207	0	0	\N	Override, standard process
+11565	Post-Panel Consolidation	31	meningitis	49083	1	0	\N	Late to be drawing culture if already got antibiotics
+11566	Post-Panel Consolidation	30	meningitis	44237	0	0	\N	Mix of scores, as doesn't penetrate CNS. Leave as 0 for ambiguous, not directly harmful
+11567	Post-Panel Consolidation	30	meningitis	44252	0	0	\N	Mix of scores, as doesn't penetrate CNS. Leave as 0 for ambiguous, not directly harmful
+11568	Post-Panel Consolidation	31	meningitis	48577	5	0	\N	\N
+11569	Post-Panel Consolidation	30	meningitis	46006	0	0	\N	Override, let be subsumed by Culture + Gram Stain order
+11570	Post-Panel Consolidation	33	meningitis	46006	0	0	\N	Override, let be subsumed by Culture + Gram Stain order
+11571	Post-Panel Consolidation	33	meningitis	49109	7	0	HSV CSF	\N
+11572	Post-Panel Consolidation	30	meningitis	48954	0	0	\N	Standard process
+11573	Post-Panel Consolidation	30	meningitis	50510	5	0	\N	Reasonable, though not standard guidelines to check
+11574	Post-Panel Consolidation	30	meningitis	63811	0	0	\N	Override, let points be from respective test orders
+11575	Post-Panel Consolidation	31	meningitis	63811	0	0	\N	Override, let points be from respective test orders
+11576	Post-Panel Consolidation	33	meningitis	63811	0	0	\N	Override, let points be from respective test orders
+11577	Post-Panel Consolidation	30	meningitis	62167	0	0	\N	Override, let points be from respective test orders
+11578	Post-Panel Consolidation	33	meningitis	62167	0	0	\N	Override, let points be from respective test orders
+11579	Post-Panel Consolidation	30	meningitis	45798	5	0	Pregnancy Test	\N
+11580	Post-Panel Consolidation	31	meningitis	49020	5	0	\N	\N
+11581	Post-Panel Consolidation	30	meningitis	45759	5	0	Coags	Override, match DIC screen
+11582	Post-Panel Consolidation	30	meningitis	45770	5	0	Coags	Override, match DIC screen
+11583	Post-Panel Consolidation	31	meningitis	62103	5	0	HIV Test	\N
+11584	Post-Panel Consolidation	30	meningitis	48586	0	0	\N	\N
+11585	Post-Panel Consolidation	30	meningitis	45801	3	0	CXR	General workup for fever/infection, though symptoms not suggestive of pneumonia
+11586	Post-Panel Consolidation	5000	neutropenic	56492	4	0	Acetaminophen	\N
+11587	Post-Panel Consolidation	5003	neutropenic	44281	4	0	Acetaminophen	\N
+11588	Post-Panel Consolidation	5000	neutropenic	48829	0	0	\N	\N
+11589	Post-Panel Consolidation	5003	neutropenic	45901	10	0	Blood Cultures	Less points if after antibiotics? May not be able to reliably distinguish if batch order
+11590	Post-Panel Consolidation	5003	neutropenic	45752	10	0	Blood Cultures	Less points if after antibiotics? May not be able to reliably distinguish if batch order
+11591	Post-Panel Consolidation	5003	neutropenic	44439	10	0	IVF	\N
+11592	Post-Panel Consolidation	5002	neutropenic	45793	8	0	CBC	Override for consistency with panel
+11593	Post-Panel Consolidation	5003	neutropenic	45793	8	0	CBC	\N
+11594	Post-Panel Consolidation	5003	neutropenic	45788	8	0	CBC	\N
+11595	Post-Panel Consolidation	5002	neutropenic	45966	8	0	CBC	\N
+11596	Post-Panel Consolidation	5003	neutropenic	48980	0	0	\N	Neutralize non-procedure consults
+11597	Post-Panel Consolidation	5002	neutropenic	49228	0	0	\N	Neutralize non-procedure consults
+11598	Post-Panel Consolidation	5003	neutropenic	49054	5	0	\N	Neutropenic, not droplet isolation. Override to match panel. Could be on initial isolation pending viral screen
+11599	Post-Panel Consolidation	5000	neutropenic	45806	3	0	\N	Match panel
+11600	Post-Panel Consolidation	5003	neutropenic	45806	3	0	\N	Match panel
+11601	Post-Panel Consolidation	5003	neutropenic	45763	10	0	Metabolic Panel	Match panel
+11602	Post-Panel Consolidation	5003	neutropenic	45771	10	0	Metabolic Panel	Match panel
+11603	Post-Panel Consolidation	5003	neutropenic	45955	10	0	Lactate	Match panel
+11604	Post-Panel Consolidation	5000	neutropenic	45759	4	0	Coags	Match panel
+11605	Post-Panel Consolidation	5003	neutropenic	45759	4	0	Coags	Match panel
+11606	Post-Panel Consolidation	5000	neutropenic	45770	4	0	Coags	Match panel
+11607	Post-Panel Consolidation	5003	neutropenic	45770	4	0	Coags	Match panel
+11608	Post-Panel Consolidation	5003	neutropenic	62023	6	0	Respiratory Virus Screen	Match panel
+11609	Post-Panel Consolidation	5003	neutropenic	63923	6	0	Respiratory Virus Screen	Match panel
+11610	Post-Panel Consolidation	5002	neutropenic	63725	10	0	Lactate	Match panel
+11611	Post-Panel Consolidation	5003	neutropenic	45995	3	0	\N	Match panel
+11612	Post-Panel Consolidation	10	pulmonary_embolism	45977	0	0	\N	Override, standard process
+11613	Post-Panel Consolidation	11	pulmonary_embolism	45977	0	0	\N	Override, standard process
+11614	Post-Panel Consolidation	11	pulmonary_embolism	45814	0	0	\N	Override, standard process
+11615	Post-Panel Consolidation	10	pulmonary_embolism	46309	7	0	Nebs	Match panel
+11616	Post-Panel Consolidation	11	pulmonary_embolism	46309	7	0	Nebs	Match panel
+11617	Post-Panel Consolidation	10	pulmonary_embolism	44349	7	0	Nebs	Match panel
+11618	Post-Panel Consolidation	11	pulmonary_embolism	44349	7	0	Nebs	Match panel
+11619	Post-Panel Consolidation	10	pulmonary_embolism	60175	7	0	Nebs	Match panel
+11620	Post-Panel Consolidation	11	pulmonary_embolism	60175	7	0	Nebs	Match panel
+11621	Post-Panel Consolidation	12	pulmonary_embolism	60175	7	0	Nebs	Match panel
+11622	Post-Panel Consolidation	10	pulmonary_embolism	47146	4	0	Aspirin	Match panel
+11623	Post-Panel Consolidation	8	pulmonary_embolism	44206	4	0	Aspirin	\N
+11624	Post-Panel Consolidation	8	pulmonary_embolism	45793	10	0	CBC	Match panel, though seems high
+11625	Post-Panel Consolidation	12	pulmonary_embolism	45793	10	0	CBC	Match panel, though seems high
+11626	Post-Panel Consolidation	8	pulmonary_embolism	45788	10	0	CBC	Match panel, though seems high
+11627	Post-Panel Consolidation	10	pulmonary_embolism	65695	0	0	\N	Override, neutralize non-procedural consults
+11628	Post-Panel Consolidation	11	pulmonary_embolism	65695	0	0	\N	Override, neutralize non-procedural consults
+11629	Post-Panel Consolidation	12	pulmonary_embolism	61323	0	0	\N	Override, neutralize non-procedural consults. Patient wouldn't qualify for ICU transfer?
+11630	Post-Panel Consolidation	12	pulmonary_embolism	48502	4	0	Biopsy	Consult, but maybe thinking about IR procedure. Not necessary for initial PE treatment, but maybe thinking about biopsy already
+11631	Post-Panel Consolidation	10	pulmonary_embolism	49207	0	0	\N	Override, neutralize non-procedural consults
+11632	Post-Panel Consolidation	11	pulmonary_embolism	49228	0	0	\N	Override, neutralize non-procedural consults
+11633	Post-Panel Consolidation	8	pulmonary_embolism	49228	0	0	\N	Override, neutralize non-procedural consults
+11634	Post-Panel Consolidation	11	pulmonary_embolism	49073	7	0	CT Chest	Override, still offer points, as CT Chest order details could allow specification of PE / contrast protocol
+11635	Post-Panel Consolidation	8	pulmonary_embolism	46008	2	0	NPO	Override for consistency with other NPO orders
+11636	Post-Panel Consolidation	12	pulmonary_embolism	45866	7	0	\N	Match panel
+11637	Post-Panel Consolidation	10	pulmonary_embolism	62176	6	0	Echo	Match panel
+11638	Post-Panel Consolidation	11	pulmonary_embolism	62176	6	0	Echo	Match panel
+11639	Post-Panel Consolidation	11	pulmonary_embolism	46183	0	0	\N	Override, just count primary anticoagulation order
+11640	Post-Panel Consolidation	10	pulmonary_embolism	49301	7	0	Blood Gas	Match panel
+11641	Post-Panel Consolidation	8	pulmonary_embolism	45806	5	0	\N	Match panel
+11642	Post-Panel Consolidation	10	pulmonary_embolism	45806	5	0	\N	Match panel
+11643	Post-Panel Consolidation	12	pulmonary_embolism	45806	5	0	\N	Match panel
+11644	Post-Panel Consolidation	8	pulmonary_embolism	45771	10	0	Metabolic Panel	\N
+11645	Post-Panel Consolidation	12	pulmonary_embolism	45771	10	0	Metabolic Panel	\N
+11646	Post-Panel Consolidation	11	pulmonary_embolism	46090	0	0	\N	Override, embed to heparin order
+11647	Post-Panel Consolidation	11	pulmonary_embolism	45776	0	0	\N	Override, process
+11648	Post-Panel Consolidation	10	pulmonary_embolism	45770	10	0	Coags	Match panel. Monitor in prep for anticoagulation?
+11649	Post-Panel Consolidation	12	pulmonary_embolism	45770	10	0	Coags	\N
+11650	Post-Panel Consolidation	12	pulmonary_embolism	45857	0	0	\N	Override, process order for actual nebs medication order
+11651	Post-Panel Consolidation	10	pulmonary_embolism	45830	0	0	\N	Override, process order for actual nebs medication order
+11652	Post-Panel Consolidation	12	pulmonary_embolism	45830	0	0	\N	Override, process order for actual nebs medication order
+11653	Post-Panel Consolidation	11	pulmonary_embolism	46179	5	0	Respiratory Culture	\N
+11654	Post-Panel Consolidation	10	pulmonary_embolism	62023	5	0	Respiratory Virus Screen	\N
+11655	Post-Panel Consolidation	12	pulmonary_embolism	62023	5	0	Respiratory Virus Screen	\N
+11656	Post-Panel Consolidation	12	pulmonary_embolism	45870	6	0	Troponin	\N
+11657	Post-Panel Consolidation	11	pulmonary_embolism	65672	4	0	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+11658	Post-Panel Consolidation	11	pulmonary_embolism	65656	4	0	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+11659	Post-Panel Consolidation	11	pulmonary_embolism	65692	4	0	DVT US	Could be good early diagnosis, but here the diagnosis of PE has likely already been made given on anticoag. So not sure what the point of adding on DVT US. They'll find DVT, but already diagnosed PE anyway.
+11660	Post-Panel Consolidation	8	pulmonary_embolism	46113	0	0	\N	Process?
+11661	Post-Panel Consolidation	43	atrial_fibrillation	46605	0	1	\N	Irregular rhythm, so adenosine not so relevant
+11662	Post-Panel Consolidation	41	atrial_fibrillation	65641	0	1	\N	Standard Process
+11663	Post-Panel Consolidation	41	atrial_fibrillation	47146	2	1	Aspirin	\N
+11664	Post-Panel Consolidation	40	atrial_fibrillation	44206	2	1	Aspirin	\N
+11665	Post-Panel Consolidation	41	atrial_fibrillation	44206	2	1	Aspirin	\N
+11666	Post-Panel Consolidation	43	atrial_fibrillation	44206	2	1	Aspirin	\N
+11667	Post-Panel Consolidation	43	atrial_fibrillation	44315	1	1	Aspirin	\N
+11668	Post-Panel Consolidation	41	atrial_fibrillation	44240	2	1	\N	\N
+11669	Post-Panel Consolidation	43	atrial_fibrillation	45901	1	1	\N	\N
+11670	Post-Panel Consolidation	40	atrial_fibrillation	41870	0	1	\N	\N
+11671	Post-Panel Consolidation	40	atrial_fibrillation	45827	8	1	\N	\N
+11672	Post-Panel Consolidation	41	atrial_fibrillation	45827	8	1	\N	\N
+11673	Post-Panel Consolidation	40	atrial_fibrillation	45793	6	1	CBC	\N
+11674	Post-Panel Consolidation	43	atrial_fibrillation	45793	6	1	CBC	\N
+11675	Post-Panel Consolidation	40	atrial_fibrillation	45788	6	1	CBC	\N
+11676	Post-Panel Consolidation	41	atrial_fibrillation	45788	6	1	CBC	\N
+11677	Post-Panel Consolidation	41	atrial_fibrillation	65695	0	1	\N	\N
+11678	Post-Panel Consolidation	43	atrial_fibrillation	61323	6	1	\N	\N
+11679	Post-Panel Consolidation	40	atrial_fibrillation	50098	-5	1	CT Head	\N
+11680	Post-Panel Consolidation	40	atrial_fibrillation	45983	-5	1	CT Head	\N
+11681	Post-Panel Consolidation	40	atrial_fibrillation	49965	-5	1	CT Head	\N
+11682	Post-Panel Consolidation	40	atrial_fibrillation	-100	10	1	\N	\N
+11683	Post-Panel Consolidation	43	atrial_fibrillation	-100	10	1	\N	\N
+11684	Post-Panel Consolidation	40	atrial_fibrillation	44353	-2	1	\N	\N
+11685	Post-Panel Consolidation	41	atrial_fibrillation	45824	1	1	\N	\N
+11686	Post-Panel Consolidation	41	atrial_fibrillation	45811	2	1	\N	\N
+11687	Post-Panel Consolidation	40	atrial_fibrillation	46674	-4	1	\N	\N
+11688	Post-Panel Consolidation	43	atrial_fibrillation	46674	-4	1	\N	\N
+11689	Post-Panel Consolidation	40	atrial_fibrillation	35846	-8	1	\N	\N
+11690	Post-Panel Consolidation	43	atrial_fibrillation	35846	-9	1	\N	\N
+11691	Post-Panel Consolidation	43	atrial_fibrillation	44393	-8	1	\N	\N
+11692	Post-Panel Consolidation	43	atrial_fibrillation	44251	-8	1	\N	\N
+11693	Post-Panel Consolidation	40	atrial_fibrillation	44204	-2	1	\N	\N
+11694	Post-Panel Consolidation	43	atrial_fibrillation	45941	2	1	\N	\N
+11695	Post-Panel Consolidation	40	atrial_fibrillation	45866	9	1	\N	\N
+11696	Post-Panel Consolidation	41	atrial_fibrillation	45866	5	1	\N	Okay to have less points here, to emphasize missed opportunity initially?
+11697	Post-Panel Consolidation	43	atrial_fibrillation	45866	9	1	\N	\N
+11698	Post-Panel Consolidation	43	atrial_fibrillation	44248	-8	1	\N	\N
+11699	Post-Panel Consolidation	43	atrial_fibrillation	44297	2	1	\N	\N
+11700	Post-Panel Consolidation	40	atrial_fibrillation	46230	-2	1	\N	\N
+11701	Post-Panel Consolidation	40	atrial_fibrillation	50343	0	1	\N	\N
+11702	Post-Panel Consolidation	41	atrial_fibrillation	45797	3	1	\N	\N
+11703	Post-Panel Consolidation	43	atrial_fibrillation	44359	8	1	Anticoagulation	\N
+11704	Post-Panel Consolidation	40	atrial_fibrillation	49301	1	1	Blood Gas	\N
+11705	Post-Panel Consolidation	40	atrial_fibrillation	45888	1	1	Blood Gas	\N
+11706	Post-Panel Consolidation	43	atrial_fibrillation	48732	1	1	Blood Gas	\N
+11707	Post-Panel Consolidation	40	atrial_fibrillation	45942	5	1	INR	\N
+11708	Post-Panel Consolidation	40	atrial_fibrillation	45838	6	1	Troponin	\N
+11709	Post-Panel Consolidation	40	atrial_fibrillation	62151	3	1	Lactate	\N
+11710	Post-Panel Consolidation	41	atrial_fibrillation	62151	2	1	Lactate	\N
+11711	Post-Panel Consolidation	41	atrial_fibrillation	46011	3	1	Lipids	\N
+11712	Post-Panel Consolidation	41	atrial_fibrillation	61837	3	1	Lipids	\N
+11713	Post-Panel Consolidation	40	atrial_fibrillation	45806	6	1	\N	\N
+11714	Post-Panel Consolidation	41	atrial_fibrillation	45806	6	1	\N	\N
+11715	Post-Panel Consolidation	40	atrial_fibrillation	45763	7	1	Metabolic Panel	\N
+11716	Post-Panel Consolidation	40	atrial_fibrillation	45771	7	1	Metabolic Panel	\N
+11717	Post-Panel Consolidation	41	atrial_fibrillation	45771	7	1	Metabolic Panel	\N
+11718	Post-Panel Consolidation	43	atrial_fibrillation	45771	7	1	Metabolic Panel	\N
+11719	Post-Panel Consolidation	40	atrial_fibrillation	44327	-8	1	\N	\N
+11720	Post-Panel Consolidation	41	atrial_fibrillation	44005	4	1	\N	\N
+11721	Post-Panel Consolidation	40	atrial_fibrillation	44000	5	1	\N	\N
+11722	Post-Panel Consolidation	43	atrial_fibrillation	44000	4	1	\N	\N
+11723	Post-Panel Consolidation	40	atrial_fibrillation	44294	2	1	\N	\N
+11724	Post-Panel Consolidation	41	atrial_fibrillation	45792	0	1	\N	\N
+11725	Post-Panel Consolidation	43	atrial_fibrillation	45792	-1	1	\N	\N
+11726	Post-Panel Consolidation	43	atrial_fibrillation	44241	0	1	\N	Trying to relieve preload?
+11727	Post-Panel Consolidation	43	atrial_fibrillation	44256	0	1	\N	Trying to relieve preload?
+11728	Post-Panel Consolidation	40	atrial_fibrillation	48628	0	1	\N	\N
+11729	Post-Panel Consolidation	40	atrial_fibrillation	46081	0	1	\N	\N
+11730	Post-Panel Consolidation	40	atrial_fibrillation	50235	0	1	\N	Not a respiratory issue?
+11731	Post-Panel Consolidation	43	atrial_fibrillation	50235	0	1	\N	Not respiratory issue, but maybe thinking manage preload with pulmonary pressure?
+11732	Post-Panel Consolidation	43	atrial_fibrillation	36086	0	1	Vasopressor	Adding vasopressor on while hypotensive from negative inotrope?
+11733	Post-Panel Consolidation	40	atrial_fibrillation	45853	5	1	\N	\N
+11734	Post-Panel Consolidation	41	atrial_fibrillation	45853	5	1	\N	\N
+11735	Post-Panel Consolidation	43	atrial_fibrillation	45853	5	1	\N	\N
+11736	Post-Panel Consolidation	40	atrial_fibrillation	45787	0	1	\N	\N
+11737	Post-Panel Consolidation	41	atrial_fibrillation	45787	0	1	\N	\N
+11738	Post-Panel Consolidation	40	atrial_fibrillation	48822	0	1	Oxygen	\N
+11739	Post-Panel Consolidation	43	atrial_fibrillation	48822	0	1	Oxygen	\N
+11740	Post-Panel Consolidation	43	atrial_fibrillation	45900	0	1	Oxygen	\N
+11741	Post-Panel Consolidation	43	atrial_fibrillation	43993	0	1	Vasopressor	\N
+11742	Post-Panel Consolidation	40	atrial_fibrillation	45778	1	1	\N	\N
+11743	Post-Panel Consolidation	43	atrial_fibrillation	45778	1	1	\N	\N
+11744	Post-Panel Consolidation	40	atrial_fibrillation	45914	6	1	Troponin	\N
+11745	Post-Panel Consolidation	43	atrial_fibrillation	46185	1	1	Blood Gas	\N
+11746	Post-Panel Consolidation	40	atrial_fibrillation	46067	3	1	Metabolic Panel	\N
+11747	Post-Panel Consolidation	43	atrial_fibrillation	46000	0	1	\N	\N
+11748	Post-Panel Consolidation	40	atrial_fibrillation	45759	5	1	INR	\N
+11749	Post-Panel Consolidation	41	atrial_fibrillation	45759	5	1	INR	\N
+11750	Post-Panel Consolidation	43	atrial_fibrillation	45759	5	1	INR	\N
+11751	Post-Panel Consolidation	40	atrial_fibrillation	45776	0	1	\N	\N
+11752	Post-Panel Consolidation	41	atrial_fibrillation	45776	0	1	\N	\N
+11753	Post-Panel Consolidation	40	atrial_fibrillation	45770	5	1	INR	Merge with INR just to represent checking coags
+11754	Post-Panel Consolidation	43	atrial_fibrillation	45770	5	1	INR	Merge with INR just to represent checking coags
+11755	Post-Panel Consolidation	40	atrial_fibrillation	44312	0	1	\N	\N
+11756	Post-Panel Consolidation	40	atrial_fibrillation	44198	-2	1	\N	\N
+11757	Post-Panel Consolidation	43	atrial_fibrillation	44198	-2	1	\N	\N
+11758	Post-Panel Consolidation	40	atrial_fibrillation	45870	6	1	Troponin	\N
+11759	Post-Panel Consolidation	41	atrial_fibrillation	45870	6	1	Troponin	\N
+11760	Post-Panel Consolidation	43	atrial_fibrillation	45870	6	1	Troponin	\N
+11761	Post-Panel Consolidation	40	atrial_fibrillation	62105	7	1	\N	\N
+11762	Post-Panel Consolidation	41	atrial_fibrillation	62105	7	1	\N	\N
+11763	Post-Panel Consolidation	40	atrial_fibrillation	45945	0	1	\N	\N
+11764	Post-Panel Consolidation	43	atrial_fibrillation	45945	0	1	\N	\N
+11765	Post-Panel Consolidation	40	atrial_fibrillation	65656	-1	1	DVT US	\N
+11766	Post-Panel Consolidation	41	atrial_fibrillation	65656	-1	1	DVT US	\N
+11767	Post-Panel Consolidation	40	atrial_fibrillation	65692	-1	1	DVT US	\N
+11768	Post-Panel Consolidation	41	atrial_fibrillation	65692	-1	1	DVT US	\N
+11769	Post-Panel Consolidation	41	atrial_fibrillation	45751	0	1	\N	\N
+11770	Post-Panel Consolidation	40	atrial_fibrillation	46236	2	1	\N	EtOH as part of Afib eval?
+11771	Post-Panel Consolidation	41	atrial_fibrillation	45818	3	1	\N	\N
+11772	Post-Panel Consolidation	40	atrial_fibrillation	50200	3	1	\N	\N
+11773	Post-Panel Consolidation	41	atrial_fibrillation	50200	3	1	\N	\N
+11774	Post-Panel Consolidation	40	atrial_fibrillation	45801	-3	1	\N	\N
+11775	Post-Panel Consolidation	43	atrial_fibrillation	45801	-3	1	\N	\N
+11776	Post-Panel Consolidation	14	gi_bleed	44281	-1	1	\N	\N
+11777	Post-Panel Consolidation	2	gi_bleed	48829	1	1	\N	\N
+11778	Post-Panel Consolidation	14	gi_bleed	44416	0	1	\N	Acetaminophen overdose, non-specific hepatitis treatment?
+11779	Post-Panel Consolidation	2	gi_bleed	65641	0	1	\N	\N
+11780	Post-Panel Consolidation	14	gi_bleed	65641	0	1	\N	\N
+11781	Post-Panel Consolidation	16	gi_bleed	65641	0	1	\N	\N
+11782	Post-Panel Consolidation	15	gi_bleed	48744	0	1	\N	Probably want complete hepatic panel, so give points there
+11783	Post-Panel Consolidation	14	gi_bleed	46136	0	1	\N	\N
+11784	Post-Panel Consolidation	15	gi_bleed	46136	0	1	\N	\N
+11785	Post-Panel Consolidation	2	gi_bleed	46136	0	1	\N	\N
+11786	Post-Panel Consolidation	15	gi_bleed	63723	0	1	\N	\N
+11787	Post-Panel Consolidation	14	gi_bleed	50962	-4	1	\N	Mistaken test. Wasted, but not directly harmful?
+11788	Post-Panel Consolidation	15	gi_bleed	46245	2	1	\N	\N
+11789	Post-Panel Consolidation	14	gi_bleed	45901	0	1	\N	\N
+11790	Post-Panel Consolidation	14	gi_bleed	45752	0	1	\N	\N
+11791	Post-Panel Consolidation	14	gi_bleed	45760	0	1	Blood Gas	\N
+11792	Post-Panel Consolidation	15	gi_bleed	45919	0	1	Blood Gas	\N
+11793	Post-Panel Consolidation	14	gi_bleed	45823	10	1	Type and Screen	\N
+11794	Post-Panel Consolidation	14	gi_bleed	44290	8	1	IVF	\N
+11795	Post-Panel Consolidation	15	gi_bleed	44290	8	1	IVF	\N
+11796	Post-Panel Consolidation	15	gi_bleed	45887	0	1	\N	\N
+11797	Post-Panel Consolidation	2	gi_bleed	45887	0	1	\N	\N
+11798	Post-Panel Consolidation	14	gi_bleed	45793	10	1	CBC	\N
+11799	Post-Panel Consolidation	15	gi_bleed	45793	10	1	CBC	\N
+11800	Post-Panel Consolidation	16	gi_bleed	45793	10	1	CBC	\N
+11801	Post-Panel Consolidation	2	gi_bleed	45793	10	1	CBC	\N
+11802	Post-Panel Consolidation	14	gi_bleed	45788	10	1	CBC	\N
+11803	Post-Panel Consolidation	15	gi_bleed	45788	10	1	CBC	\N
+11804	Post-Panel Consolidation	2	gi_bleed	45788	10	1	CBC	\N
+11805	Post-Panel Consolidation	15	gi_bleed	44255	0	1	\N	Not reliable to cover Gram negative enterics (e.g., E coli)
+11806	Post-Panel Consolidation	15	gi_bleed	44637	5	1	Antibiotics	\N
+11807	Post-Panel Consolidation	14	gi_bleed	45060	2	1	Antibiotics	\N
+11808	Post-Panel Consolidation	15	gi_bleed	45060	2	1	Antibiotics	\N
+11809	Post-Panel Consolidation	14	gi_bleed	35733	10	1	Antibiotics	\N
+11810	Post-Panel Consolidation	15	gi_bleed	35733	10	1	Antibiotics	\N
+11811	Post-Panel Consolidation	16	gi_bleed	35733	10	1	Antibiotics	\N
+11812	Post-Panel Consolidation	2	gi_bleed	35733	10	1	Antibiotics	\N
+11813	Post-Panel Consolidation	2	gi_bleed	44249	7	1	Antibiotics	Reasonable empiric choice for GNRs
+11814	Post-Panel Consolidation	14	gi_bleed	62027	0	1	\N	\N
+11815	Post-Panel Consolidation	2	gi_bleed	63720	5	1	Hepatitis Panel	\N
+11816	Post-Panel Consolidation	14	gi_bleed	49481	10	1	GI EGD	\N
+11817	Post-Panel Consolidation	15	gi_bleed	49481	10	1	GI EGD	\N
+11818	Post-Panel Consolidation	16	gi_bleed	49481	10	1	GI EGD	\N
+11819	Post-Panel Consolidation	14	gi_bleed	61323	2	1	\N	\N
+11820	Post-Panel Consolidation	16	gi_bleed	61323	0	1	\N	\N
+11821	Post-Panel Consolidation	14	gi_bleed	49867	0	1	\N	Not DIC, more total coagulant deficiency
+11822	Post-Panel Consolidation	2	gi_bleed	50737	-5	1	\N	\N
+11823	Post-Panel Consolidation	14	gi_bleed	48871	0	1	\N	\N
+11824	Post-Panel Consolidation	14	gi_bleed	46286	7	1	Coags	\N
+11825	Post-Panel Consolidation	16	gi_bleed	46286	7	1	Coags	\N
+11826	Post-Panel Consolidation	14	gi_bleed	45811	8	1	\N	\N
+11827	Post-Panel Consolidation	15	gi_bleed	45811	8	1	\N	\N
+11828	Post-Panel Consolidation	14	gi_bleed	45941	0	1	\N	\N
+11829	Post-Panel Consolidation	14	gi_bleed	45866	2	1	\N	\N
+11830	Post-Panel Consolidation	15	gi_bleed	45866	2	1	\N	\N
+11831	Post-Panel Consolidation	2	gi_bleed	61832	0	1	\N	\N
+11832	Post-Panel Consolidation	15	gi_bleed	46160	0	1	\N	\N
+11833	Post-Panel Consolidation	15	gi_bleed	46078	0	1	\N	\N
+11834	Post-Panel Consolidation	15	gi_bleed	63759	10	1	RBC	\N
+11835	Post-Panel Consolidation	14	gi_bleed	46028	4	1	\N	\N
+11836	Post-Panel Consolidation	14	gi_bleed	45872	9	1	FFP	\N
+11837	Post-Panel Consolidation	15	gi_bleed	45872	9	1	FFP	\N
+11838	Post-Panel Consolidation	16	gi_bleed	45872	9	1	FFP	\N
+11839	Post-Panel Consolidation	14	gi_bleed	44237	6	1	Antibiotics	A bit overkill, but would cover GNRs
+11840	Post-Panel Consolidation	14	gi_bleed	48724	5	1	\N	\N
+11841	Post-Panel Consolidation	14	gi_bleed	45948	5	1	\N	\N
+11842	Post-Panel Consolidation	15	gi_bleed	45948	5	1	\N	\N
+11843	Post-Panel Consolidation	15	gi_bleed	45891	10	1	CBC	\N
+11844	Post-Panel Consolidation	15	gi_bleed	46051	10	1	CBC	\N
+11845	Post-Panel Consolidation	15	gi_bleed	46068	0	1	\N	\N
+11846	Post-Panel Consolidation	14	gi_bleed	45910	7	1	Metabolic Panel	\N
+11847	Post-Panel Consolidation	2	gi_bleed	62029	0	1	\N	Covered in hepatitis panel?
+11848	Post-Panel Consolidation	14	gi_bleed	65649	4	1	\N	\N
+11849	Post-Panel Consolidation	15	gi_bleed	62022	7	1	Intubation	\N
+11850	Post-Panel Consolidation	14	gi_bleed	45942	8	1	Coags	\N
+11851	Post-Panel Consolidation	15	gi_bleed	45838	0	1	Troponin	\N
+11852	Post-Panel Consolidation	14	gi_bleed	48954	10	1	IV Access	Standard process that can be hard to distinguish, but include for particular relevance in this case
+11853	Post-Panel Consolidation	15	gi_bleed	44978	0	1	\N	Peri-procedure sedation???
+11854	Post-Panel Consolidation	14	gi_bleed	62151	2	1	Lactate	\N
+11855	Post-Panel Consolidation	15	gi_bleed	62151	2	1	Lactate	\N
+11856	Post-Panel Consolidation	16	gi_bleed	62151	2	1	Lactate	\N
+11857	Post-Panel Consolidation	15	gi_bleed	46289	-2	1	Lactulose	\N
+11858	Post-Panel Consolidation	15	gi_bleed	44302	-2	1	Lactulose	\N
+11859	Post-Panel Consolidation	15	gi_bleed	44593	-2	1	Lactulose	\N
+11860	Post-Panel Consolidation	14	gi_bleed	45903	0	1	\N	Hemolytic anemia eval?
+11861	Post-Panel Consolidation	14	gi_bleed	62144	0	1	\N	\N
+11862	Post-Panel Consolidation	14	gi_bleed	45894	0	1	\N	\N
+11863	Post-Panel Consolidation	15	gi_bleed	45806	2	1	\N	\N
+11864	Post-Panel Consolidation	14	gi_bleed	63745	0	1	\N	\N
+11865	Post-Panel Consolidation	15	gi_bleed	63745	0	1	\N	\N
+11866	Post-Panel Consolidation	14	gi_bleed	45763	8	1	Metabolic Panel	\N
+11867	Post-Panel Consolidation	15	gi_bleed	45763	8	1	Metabolic Panel	\N
+11868	Post-Panel Consolidation	2	gi_bleed	45763	8	1	Metabolic Panel	\N
+11869	Post-Panel Consolidation	14	gi_bleed	45771	9	1	Metabolic Panel	\N
+11870	Post-Panel Consolidation	15	gi_bleed	45771	9	1	Metabolic Panel	\N
+11871	Post-Panel Consolidation	15	gi_bleed	45792	0	1	\N	\N
+11872	Post-Panel Consolidation	14	gi_bleed	45785	0	1	\N	\N
+11873	Post-Panel Consolidation	15	gi_bleed	36086	5	1	Vasopressors	\N
+11874	Post-Panel Consolidation	14	gi_bleed	51110	0	1	\N	Largely irrelevant in acute setting
+11875	Post-Panel Consolidation	14	gi_bleed	46451	-2	1	\N	\N
+11876	Post-Panel Consolidation	15	gi_bleed	46451	-2	1	\N	\N
+11877	Post-Panel Consolidation	14	gi_bleed	43996	10	1	\N	\N
+11878	Post-Panel Consolidation	15	gi_bleed	43996	10	1	\N	\N
+11879	Post-Panel Consolidation	16	gi_bleed	43996	10	1	\N	\N
+11880	Post-Panel Consolidation	2	gi_bleed	43996	10	1	\N	\N
+11881	Post-Panel Consolidation	15	gi_bleed	46449	-2	1	\N	\N
+11882	Post-Panel Consolidation	14	gi_bleed	44216	2	1	\N	\N
+11883	Post-Panel Consolidation	14	gi_bleed	45868	8	1	\N	\N
+11884	Post-Panel Consolidation	14	gi_bleed	48822	0	1	Oxygen	No specific hypoxia in case
+11885	Post-Panel Consolidation	14	gi_bleed	45900	0	1	Oxygen	No specific hypoxia in case
+11886	Post-Panel Consolidation	14	gi_bleed	44219	10	1	\N	\N
+11887	Post-Panel Consolidation	15	gi_bleed	44219	10	1	\N	\N
+11888	Post-Panel Consolidation	16	gi_bleed	44219	10	1	\N	\N
+11889	Post-Panel Consolidation	15	gi_bleed	44236	0	1	\N	\N
+11890	Post-Panel Consolidation	14	gi_bleed	44236	0	1	\N	Mistake, should be giving oral med while vomiting blood
+11891	Post-Panel Consolidation	15	gi_bleed	62171	0	1	\N	Active bleeding, probably not right time for para, though SBP always to consider?
+11892	Post-Panel Consolidation	14	gi_bleed	61823	10	1	IV Access	\N
+11893	Post-Panel Consolidation	15	gi_bleed	61823	10	1	IV Access	\N
+11894	Post-Panel Consolidation	14	gi_bleed	45802	10	1	IV Access	\N
+11895	Post-Panel Consolidation	15	gi_bleed	43993	3	1	Vasopressors	\N
+11896	Post-Panel Consolidation	15	gi_bleed	45778	4	1	\N	\N
+11897	Post-Panel Consolidation	2	gi_bleed	45778	4	1	\N	\N
+11898	Post-Panel Consolidation	14	gi_bleed	45875	0	1	Platelets	\N
+11899	Post-Panel Consolidation	15	gi_bleed	45875	0	1	Platelets	\N
+11900	Post-Panel Consolidation	15	gi_bleed	45955	2	1	Lactate	\N
+11901	Post-Panel Consolidation	14	gi_bleed	50267	0	1	\N	\N
+11902	Post-Panel Consolidation	15	gi_bleed	44292	0	1	\N	Pre-procedure sedation?
+11903	Post-Panel Consolidation	14	gi_bleed	49904	-2	1	\N	\N
+11904	Post-Panel Consolidation	14	gi_bleed	45759	8	1	Coags	\N
+11905	Post-Panel Consolidation	15	gi_bleed	45759	8	1	Coags	\N
+11906	Post-Panel Consolidation	16	gi_bleed	45759	8	1	Coags	\N
+11907	Post-Panel Consolidation	2	gi_bleed	45759	8	1	Coags	\N
+11908	Post-Panel Consolidation	14	gi_bleed	45770	8	1	Coags	\N
+11909	Post-Panel Consolidation	15	gi_bleed	45770	8	1	Coags	\N
+11910	Post-Panel Consolidation	2	gi_bleed	45770	8	1	Coags	\N
+11911	Post-Panel Consolidation	2	gi_bleed	62103	0	1	\N	\N
+11912	Post-Panel Consolidation	14	gi_bleed	45927	10	1	RBC	\N
+11913	Post-Panel Consolidation	15	gi_bleed	45927	10	1	RBC	\N
+11914	Post-Panel Consolidation	16	gi_bleed	45927	10	1	RBC	\N
+11915	Post-Panel Consolidation	2	gi_bleed	45927	10	1	RBC	\N
+11916	Post-Panel Consolidation	15	gi_bleed	44424	0	1	\N	\N
+11917	Post-Panel Consolidation	14	gi_bleed	49173	0	1	\N	?Intoxication not really active issue
+11918	Post-Panel Consolidation	14	gi_bleed	44198	8	1	IVF	\N
+11919	Post-Panel Consolidation	15	gi_bleed	44198	8	1	IVF	\N
+11920	Post-Panel Consolidation	15	gi_bleed	44441	0	1	\N	?Pre-intubation for airway protection?
+11921	Post-Panel Consolidation	14	gi_bleed	45877	8	1	Coagulopathy Correction	\N
+11922	Post-Panel Consolidation	15	gi_bleed	45877	8	1	Coagulopathy Correction	\N
+11923	Post-Panel Consolidation	16	gi_bleed	45877	8	1	Coagulopathy Correction	\N
+11924	Post-Panel Consolidation	14	gi_bleed	65702	8	1	Coagulopathy Correction	\N
+11925	Post-Panel Consolidation	15	gi_bleed	65702	8	1	Coagulopathy Correction	\N
+11926	Post-Panel Consolidation	16	gi_bleed	65702	8	1	Coagulopathy Correction	\N
+11927	Post-Panel Consolidation	14	gi_bleed	65646	0	1	Transfuse Platelets	\N
+11928	Post-Panel Consolidation	15	gi_bleed	65646	0	1	Transfuse Platelets	\N
+11929	Post-Panel Consolidation	15	gi_bleed	45956	0	1	Transfuse Platelets	\N
+11930	Post-Panel Consolidation	14	gi_bleed	45956	0	1	Transfuse Platelets	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+11997	Post-Panel Consolidation	33	meningitis	48880	5	1	\N	\N
+11931	Post-Panel Consolidation	15	gi_bleed	61976	0	1	Transfuse Platelets	Not sure necessary or appropriate to transfuse platelets given observed counts not low?
+11932	Post-Panel Consolidation	14	gi_bleed	61975	10	1	Transfuse RBC	\N
+11933	Post-Panel Consolidation	15	gi_bleed	61975	10	1	Transfuse RBC	\N
+11934	Post-Panel Consolidation	16	gi_bleed	61975	10	1	Transfuse RBC	\N
+11935	Post-Panel Consolidation	14	gi_bleed	45748	10	1	Transfuse RBC	\N
+11936	Post-Panel Consolidation	15	gi_bleed	45748	10	1	Transfuse RBC	\N
+11937	Post-Panel Consolidation	14	gi_bleed	65640	10	1	Transfuse RBC	\N
+11938	Post-Panel Consolidation	15	gi_bleed	65640	10	1	Transfuse RBC	\N
+11939	Post-Panel Consolidation	16	gi_bleed	65640	10	1	Transfuse RBC	\N
+11940	Post-Panel Consolidation	2	gi_bleed	65640	10	1	Transfuse RBC	\N
+11941	Post-Panel Consolidation	14	gi_bleed	50581	0	1	\N	\N
+11942	Post-Panel Consolidation	15	gi_bleed	50581	0	1	\N	\N
+11943	Post-Panel Consolidation	14	gi_bleed	50618	10	1	Transfuse RBC	\N
+11944	Post-Panel Consolidation	15	gi_bleed	50618	10	1	Transfuse RBC	\N
+11945	Post-Panel Consolidation	14	gi_bleed	45945	10	1	Type and Screen	\N
+11946	Post-Panel Consolidation	15	gi_bleed	45945	10	1	Type and Screen	\N
+11947	Post-Panel Consolidation	14	gi_bleed	45869	6	1	US Abdomen	\N
+11948	Post-Panel Consolidation	2	gi_bleed	45869	6	1	US Abdomen	\N
+11949	Post-Panel Consolidation	2	gi_bleed	49408	6	1	US Abdomen	\N
+11950	Post-Panel Consolidation	14	gi_bleed	46343	6	1	US Abdomen	\N
+11951	Post-Panel Consolidation	2	gi_bleed	46343	6	1	US Abdomen	\N
+11952	Post-Panel Consolidation	14	gi_bleed	45969	10	1	GI EGD	\N
+11953	Post-Panel Consolidation	15	gi_bleed	45969	10	1	GI EGD	\N
+11954	Post-Panel Consolidation	16	gi_bleed	45969	10	1	GI EGD	\N
+11955	Post-Panel Consolidation	14	gi_bleed	45873	0	1	\N	\N
+11956	Post-Panel Consolidation	14	gi_bleed	46348	0	1	\N	\N
+11957	Post-Panel Consolidation	14	gi_bleed	44001	7	1	Coagulopathy Correction	\N
+11958	Post-Panel Consolidation	15	gi_bleed	44001	7	1	Coagulopathy Correction	\N
+11959	Post-Panel Consolidation	16	gi_bleed	44001	7	1	Coagulopathy Correction	\N
+11960	Post-Panel Consolidation	15	gi_bleed	44404	7	1	Coagulopathy Correction	\N
+11961	Post-Panel Consolidation	16	gi_bleed	44404	7	1	Coagulopathy Correction	\N
+11962	Post-Panel Consolidation	14	gi_bleed	44382	-2	1	\N	\N
+11963	Post-Panel Consolidation	15	gi_bleed	44382	-2	1	\N	\N
+11964	Post-Panel Consolidation	14	gi_bleed	46096	0	1	\N	\N
+11965	Post-Panel Consolidation	14	gi_bleed	45818	0	1	CXR	\N
+11966	Post-Panel Consolidation	15	gi_bleed	45818	0	1	CXR	\N
+11967	Post-Panel Consolidation	14	gi_bleed	50200	0	1	CXR	Irrelevant diagnostic?
+11968	Post-Panel Consolidation	14	gi_bleed	45801	0	1	CXR	\N
+11969	Post-Panel Consolidation	30	meningitis	44281	5	1	Pain Control	\N
+11970	Post-Panel Consolidation	31	meningitis	44281	5	1	Pain Control	\N
+11971	Post-Panel Consolidation	30	meningitis	44278	5	1	Pain Control	\N
+11972	Post-Panel Consolidation	30	meningitis	48829	0	1	\N	Irrelevant diagnostics. Probably trying to order medicine
+11973	Post-Panel Consolidation	30	meningitis	44615	0	1	\N	\N
+11974	Post-Panel Consolidation	31	meningitis	44615	0	1	\N	\N
+11975	Post-Panel Consolidation	33	meningitis	44615	0	1	\N	\N
+11976	Post-Panel Consolidation	30	meningitis	48770	0	1	\N	Low yield
+11977	Post-Panel Consolidation	33	meningitis	48770	0	1	\N	Low yield
+11978	Post-Panel Consolidation	30	meningitis	44595	0	1	\N	\N
+11979	Post-Panel Consolidation	31	meningitis	44595	0	1	\N	\N
+11980	Post-Panel Consolidation	33	meningitis	44595	0	1	\N	\N
+11981	Post-Panel Consolidation	30	meningitis	46093	5	1	Pregnancy Test	\N
+11982	Post-Panel Consolidation	30	meningitis	45901	10	1	Blood Culture	\N
+11983	Post-Panel Consolidation	31	meningitis	45901	10	1	Blood Culture	\N
+11984	Post-Panel Consolidation	33	meningitis	45901	10	1	Blood Culture	\N
+11985	Post-Panel Consolidation	30	meningitis	45752	10	1	Blood Culture	\N
+11986	Post-Panel Consolidation	31	meningitis	45752	10	1	Blood Culture	\N
+11987	Post-Panel Consolidation	33	meningitis	45752	10	1	Blood Culture	\N
+11988	Post-Panel Consolidation	30	meningitis	44290	5	1	IVF	\N
+11989	Post-Panel Consolidation	30	meningitis	44439	5	1	IVF	\N
+11990	Post-Panel Consolidation	30	meningitis	45793	7	1	CBC	\N
+11991	Post-Panel Consolidation	30	meningitis	45788	7	1	CBC	\N
+11992	Post-Panel Consolidation	33	meningitis	36210	5	1	Antibiotics	\N
+11993	Post-Panel Consolidation	30	meningitis	35733	10	1	Antibiotics	\N
+11994	Post-Panel Consolidation	31	meningitis	35733	10	1	Antibiotics	\N
+11995	Post-Panel Consolidation	33	meningitis	35733	5	1	Antibiotics	\N
+11996	Post-Panel Consolidation	30	meningitis	48880	10	1	\N	\N
+11998	Post-Panel Consolidation	30	meningitis	50930	0	1	\N	Wrong order. Synovial fluid not CSF
+11999	Post-Panel Consolidation	30	meningitis	49079	0	1	\N	\N
+12000	Post-Panel Consolidation	33	meningitis	48603	0	1	\N	Not relevant to test?
+12001	Post-Panel Consolidation	33	meningitis	61973	0	1	\N	Not relevant to test?
+12002	Post-Panel Consolidation	33	meningitis	48752	0	1	\N	Not relevant to test?
+12003	Post-Panel Consolidation	33	meningitis	61826	0	1	\N	\N
+12004	Post-Panel Consolidation	30	meningitis	61826	0	1	\N	Not relevant to test?
+12005	Post-Panel Consolidation	31	meningitis	49649	0	1	\N	\N
+12006	Post-Panel Consolidation	33	meningitis	49594	0	1	\N	\N
+12007	Post-Panel Consolidation	30	meningitis	49594	0	1	\N	No good indication
+12008	Post-Panel Consolidation	30	meningitis	49083	10	1	\N	\N
+12009	Post-Panel Consolidation	33	meningitis	49083	5	1	\N	\N
+12010	Post-Panel Consolidation	30	meningitis	50692	4	1	\N	\N
+12011	Post-Panel Consolidation	30	meningitis	48871	0	1	\N	\N
+12012	Post-Panel Consolidation	30	meningitis	44017	10	1	\N	\N
+12013	Post-Panel Consolidation	31	meningitis	44017	10	1	\N	\N
+12014	Post-Panel Consolidation	33	meningitis	44017	9	1	\N	\N
+12015	Post-Panel Consolidation	33	meningitis	35969	-5	1	\N	\N
+12016	Post-Panel Consolidation	30	meningitis	46286	5	1	Coags	\N
+12017	Post-Panel Consolidation	30	meningitis	45811	2	1	\N	\N
+12018	Post-Panel Consolidation	30	meningitis	49054	6	1	\N	\N
+12019	Post-Panel Consolidation	31	meningitis	49054	6	1	\N	\N
+12020	Post-Panel Consolidation	30	meningitis	45866	0	1	\N	\N
+12021	Post-Panel Consolidation	31	meningitis	45866	0	1	\N	\N
+12022	Post-Panel Consolidation	33	meningitis	63761	1	1	\N	Common meningitis cause, okay to review
+12023	Post-Panel Consolidation	33	meningitis	49142	1	1	\N	Common meningitis cause, okay to review
+12024	Post-Panel Consolidation	30	meningitis	46103	0	1	\N	\N
+12025	Post-Panel Consolidation	30	meningitis	50685	0	1	\N	Mostly irrelevant STD testing?
+12026	Post-Panel Consolidation	30	meningitis	48577	10	1	\N	\N
+12027	Post-Panel Consolidation	33	meningitis	48577	10	1	\N	Doesn't match CSF cell count pattern of less points if delayed to worsened?
+12028	Post-Panel Consolidation	30	meningitis	42197	0	1	\N	\N
+12029	Post-Panel Consolidation	30	meningitis	49109	7	1	HSV CSF	\N
+12030	Post-Panel Consolidation	30	meningitis	62011	0	1	\N	Not the right screening test for HIV
+12031	Post-Panel Consolidation	30	meningitis	63767	7	1	HSV CSF	\N
+12032	Post-Panel Consolidation	31	meningitis	63767	7	1	HSV CSF	\N
+12033	Post-Panel Consolidation	33	meningitis	63767	7	1	HSV CSF	\N
+12034	Post-Panel Consolidation	30	meningitis	44319	2	1	Pain Control	\N
+12035	Post-Panel Consolidation	30	meningitis	48732	0	1	\N	Blood gases?
+12036	Post-Panel Consolidation	30	meningitis	62151	5	1	Lactate	\N
+12037	Post-Panel Consolidation	31	meningitis	62151	5	1	Lactate	\N
+12038	Post-Panel Consolidation	33	meningitis	62151	5	1	Lactate	\N
+12039	Post-Panel Consolidation	30	meningitis	45918	5	1	Lactate	\N
+12040	Post-Panel Consolidation	33	meningitis	45918	5	1	Lactate	\N
+12041	Post-Panel Consolidation	33	meningitis	50510	5	1	\N	\N
+12042	Post-Panel Consolidation	30	meningitis	45763	5	1	Metabolic Panel	\N
+12043	Post-Panel Consolidation	30	meningitis	45771	5	1	Metabolic Panel	\N
+12044	Post-Panel Consolidation	31	meningitis	44199	0	1	\N	Symptomatic management
+12045	Post-Panel Consolidation	31	meningitis	45792	0	1	\N	\N
+12046	Post-Panel Consolidation	30	meningitis	45792	0	1	\N	\N
+12047	Post-Panel Consolidation	30	meningitis	61852	0	1	\N	\N
+12048	Post-Panel Consolidation	30	meningitis	61823	0	1	\N	\N
+12049	Post-Panel Consolidation	31	meningitis	49334	0	1	\N	Irrelevant?
+12050	Post-Panel Consolidation	30	meningitis	46090	0	1	\N	\N
+12051	Post-Panel Consolidation	30	meningitis	45955	5	1	Lactate	\N
+12052	Post-Panel Consolidation	33	meningitis	49134	5	1	Pregnancy Test	\N
+12053	Post-Panel Consolidation	30	meningitis	62042	2	1	\N	\N
+12054	Post-Panel Consolidation	30	meningitis	49020	10	1	\N	\N
+12055	Post-Panel Consolidation	33	meningitis	49020	5	1	\N	\N
+12056	Post-Panel Consolidation	31	meningitis	45776	0	1	\N	Process
+12057	Post-Panel Consolidation	30	meningitis	62103	5	1	HIV Test	\N
+12058	Post-Panel Consolidation	33	meningitis	62103	5	1	HIV Test	\N
+12059	Post-Panel Consolidation	30	meningitis	48728	0	1	\N	\N
+12060	Post-Panel Consolidation	31	meningitis	48728	0	1	\N	\N
+12061	Post-Panel Consolidation	30	meningitis	63923	3	1	\N	\N
+12062	Post-Panel Consolidation	30	meningitis	63725	5	1	Lactate	\N
+12063	Post-Panel Consolidation	31	meningitis	63725	5	1	Lactate	\N
+12064	Post-Panel Consolidation	30	meningitis	44198	5	1	IVF	\N
+12065	Post-Panel Consolidation	33	meningitis	44198	5	1	IVF	\N
+12066	Post-Panel Consolidation	30	meningitis	46131	0	1	\N	\N
+12067	Post-Panel Consolidation	30	meningitis	45751	0	1	\N	\N
+12068	Post-Panel Consolidation	33	meningitis	45751	0	1	\N	\N
+12069	Post-Panel Consolidation	30	meningitis	45873	0	1	\N	\N
+12070	Post-Panel Consolidation	31	meningitis	45873	0	1	\N	\N
+12071	Post-Panel Consolidation	30	meningitis	43997	10	1	\N	\N
+12072	Post-Panel Consolidation	31	meningitis	43997	10	1	\N	\N
+12073	Post-Panel Consolidation	33	meningitis	43997	5	1	\N	\N
+12074	Post-Panel Consolidation	33	meningitis	48486	2	1	Vancomycin Level	Okay to check followup leels
+12075	Post-Panel Consolidation	30	meningitis	45972	2	1	Vancomycin Level	Okay to check followup leels
+12076	Post-Panel Consolidation	33	meningitis	45972	2	1	Vancomycin Level	Okay to check followup leels
+12077	Post-Panel Consolidation	33	meningitis	48519	0	1	\N	\N
+12078	Post-Panel Consolidation	33	meningitis	48529	0	1	\N	\N
+12079	Post-Panel Consolidation	30	meningitis	63735	0	1	\N	\N
+12080	Post-Panel Consolidation	30	meningitis	45766	0	1	\N	\N
+12081	Post-Panel Consolidation	30	meningitis	50654	0	1	\N	\N
+12082	Post-Panel Consolidation	33	meningitis	50654	0	1	\N	\N
+12083	Post-Panel Consolidation	30	meningitis	45818	3	1	CXR	\N
+12084	Post-Panel Consolidation	31	meningitis	45818	3	1	CXR	\N
+12085	Post-Panel Consolidation	33	meningitis	45818	3	1	CXR	\N
+12086	Post-Panel Consolidation	5002	neutropenic	44281	4	1	Acetaminophen	\N
+12087	Post-Panel Consolidation	5000	neutropenic	44281	4	1	Acetaminophen	\N
+12088	Post-Panel Consolidation	5002	neutropenic	65641	0	1	\N	Process
+12089	Post-Panel Consolidation	5000	neutropenic	48775	0	1	\N	\N
+12090	Post-Panel Consolidation	5002	neutropenic	46157	10	1	Blood Cultures	\N
+12091	Post-Panel Consolidation	5002	neutropenic	46291	10	1	Blood Cultures	\N
+12092	Post-Panel Consolidation	5002	neutropenic	45901	10	1	Blood Cultures	\N
+12093	Post-Panel Consolidation	5000	neutropenic	45901	10	1	Blood Cultures	\N
+12094	Post-Panel Consolidation	5002	neutropenic	45752	10	1	Blood Cultures	\N
+12095	Post-Panel Consolidation	5000	neutropenic	45752	10	1	Blood Cultures	\N
+12096	Post-Panel Consolidation	5000	neutropenic	45760	6	1	Blood Gas	\N
+12097	Post-Panel Consolidation	5003	neutropenic	44395	10	1	IVF	\N
+12098	Post-Panel Consolidation	5000	neutropenic	44290	10	1	IVF	\N
+12099	Post-Panel Consolidation	5002	neutropenic	45788	8	1	CBC	\N
+12100	Post-Panel Consolidation	5000	neutropenic	45788	8	1	CBC	\N
+12101	Post-Panel Consolidation	5000	neutropenic	45966	8	1	CBC	\N
+12102	Post-Panel Consolidation	5002	neutropenic	36210	10	1	Anti-Pseudomonas	\N
+12103	Post-Panel Consolidation	5000	neutropenic	36210	10	1	Anti-Pseudomonas	\N
+12104	Post-Panel Consolidation	5003	neutropenic	36210	10	1	Anti-Pseudomonas	\N
+12105	Post-Panel Consolidation	5002	neutropenic	50547	0	1	\N	Consult
+12106	Post-Panel Consolidation	5002	neutropenic	48980	0	1	\N	Consult
+12107	Post-Panel Consolidation	5003	neutropenic	49228	0	1	\N	Consult
+12108	Post-Panel Consolidation	5002	neutropenic	48711	0	1	\N	Wrong type of isolation
+12109	Post-Panel Consolidation	5000	neutropenic	48532	0	1	\N	\N
+12110	Post-Panel Consolidation	5003	neutropenic	46286	5	1	Coags	\N
+12111	Post-Panel Consolidation	5003	neutropenic	48686	2	1	\N	\N
+12112	Post-Panel Consolidation	5000	neutropenic	48686	2	1	\N	\N
+12113	Post-Panel Consolidation	5003	neutropenic	46004	0	1	\N	\N
+12114	Post-Panel Consolidation	5003	neutropenic	45811	0	1	\N	Unnecessary?
+12115	Post-Panel Consolidation	5000	neutropenic	49054	5	1	\N	\N
+12116	Post-Panel Consolidation	5002	neutropenic	45866	4	1	\N	\N
+12117	Post-Panel Consolidation	5003	neutropenic	45866	4	1	\N	\N
+12118	Post-Panel Consolidation	5000	neutropenic	45866	4	1	\N	\N
+12119	Post-Panel Consolidation	5000	neutropenic	46160	0	1	\N	Process
+12120	Post-Panel Consolidation	5000	neutropenic	41759	0	1	\N	Presenting symptom
+12121	Post-Panel Consolidation	5002	neutropenic	45225	7	1	\N	Reduce hospitalized neutropenic time
+12122	Post-Panel Consolidation	5002	neutropenic	44237	10	1	Anti-Pseudomonas	\N
+12123	Post-Panel Consolidation	5000	neutropenic	44237	10	1	Anti-Pseudomonas	\N
+12124	Post-Panel Consolidation	5002	neutropenic	44252	10	1	Anti-Pseudomonas	\N
+12125	Post-Panel Consolidation	5002	neutropenic	46006	0	1	\N	\N
+12126	Post-Panel Consolidation	5000	neutropenic	46020	2	1	\N	\N
+12127	Post-Panel Consolidation	5002	neutropenic	62151	10	1	Lactate	\N
+12128	Post-Panel Consolidation	5003	neutropenic	62151	10	1	Lactate	\N
+12129	Post-Panel Consolidation	5000	neutropenic	62151	10	1	Lactate	\N
+12130	Post-Panel Consolidation	5000	neutropenic	45903	2	1	\N	\N
+12131	Post-Panel Consolidation	5002	neutropenic	45806	3	1	\N	\N
+12132	Post-Panel Consolidation	5002	neutropenic	45763	10	1	Metabolic Panel	\N
+12133	Post-Panel Consolidation	5000	neutropenic	45763	10	1	Metabolic Panel	\N
+12134	Post-Panel Consolidation	5002	neutropenic	45771	10	1	Metabolic Panel	\N
+12135	Post-Panel Consolidation	5000	neutropenic	45771	10	1	Metabolic Panel	\N
+12136	Post-Panel Consolidation	5002	neutropenic	45792	2	1	\N	\N
+12137	Post-Panel Consolidation	5003	neutropenic	45792	2	1	\N	\N
+12138	Post-Panel Consolidation	5002	neutropenic	48960	8	1	\N	\N
+12139	Post-Panel Consolidation	5003	neutropenic	48960	8	1	\N	\N
+12140	Post-Panel Consolidation	5000	neutropenic	48960	8	1	\N	\N
+12141	Post-Panel Consolidation	5000	neutropenic	44420	0	1	\N	Symptom management
+12142	Post-Panel Consolidation	5000	neutropenic	45900	0	1	\N	Irrelevant
+12143	Post-Panel Consolidation	5003	neutropenic	45778	0	1	\N	\N
+12144	Post-Panel Consolidation	5002	neutropenic	50554	0	1	\N	Irrelevant
+12145	Post-Panel Consolidation	5003	neutropenic	50554	0	1	\N	Irrelevant
+12146	Post-Panel Consolidation	5000	neutropenic	45955	10	1	Lactate	\N
+12147	Post-Panel Consolidation	5000	neutropenic	46000	0	1	\N	\N
+12148	Post-Panel Consolidation	5002	neutropenic	45759	4	1	Coags	\N
+12149	Post-Panel Consolidation	5002	neutropenic	45770	4	1	Coags	\N
+12150	Post-Panel Consolidation	5000	neutropenic	45890	0	1	\N	\N
+12151	Post-Panel Consolidation	5000	neutropenic	62023	6	1	Respiratory Virus Screen	\N
+12152	Post-Panel Consolidation	5002	neutropenic	63923	6	1	Respiratory Virus Screen	\N
+12153	Post-Panel Consolidation	5000	neutropenic	63923	6	1	Respiratory Virus Screen	\N
+12154	Post-Panel Consolidation	5003	neutropenic	63725	10	1	Lactate	\N
+12155	Post-Panel Consolidation	5000	neutropenic	63725	10	1	Lactate	\N
+12156	Post-Panel Consolidation	5002	neutropenic	44198	10	1	IVF	\N
+12157	Post-Panel Consolidation	5003	neutropenic	44198	10	1	IVF	\N
+12158	Post-Panel Consolidation	5000	neutropenic	44198	10	1	IVF	\N
+12159	Post-Panel Consolidation	5000	neutropenic	45945	1	1	\N	Anticipating transfusions, but not needed now
+12160	Post-Panel Consolidation	5000	neutropenic	45995	3	1	\N	\N
+12161	Post-Panel Consolidation	5000	neutropenic	45751	6	1	UA + Culture	\N
+12162	Post-Panel Consolidation	5000	neutropenic	45873	6	1	UA + Culture	\N
+12163	Post-Panel Consolidation	5002	neutropenic	45782	6	1	UA + Culture	\N
+12164	Post-Panel Consolidation	5003	neutropenic	45782	6	1	UA + Culture	\N
+12165	Post-Panel Consolidation	5000	neutropenic	45782	6	1	UA + Culture	\N
+12166	Post-Panel Consolidation	5002	neutropenic	43997	2	1	\N	\N
+12167	Post-Panel Consolidation	5003	neutropenic	43997	2	1	\N	\N
+12168	Post-Panel Consolidation	5000	neutropenic	43997	2	1	\N	\N
+12169	Post-Panel Consolidation	5000	neutropenic	45972	2	1	\N	\N
+12170	Post-Panel Consolidation	5002	neutropenic	63735	0	1	\N	\N
+12171	Post-Panel Consolidation	5003	neutropenic	63735	0	1	\N	\N
+12172	Post-Panel Consolidation	5000	neutropenic	45766	0	1	\N	Process
+12173	Post-Panel Consolidation	5002	neutropenic	45818	10	1	CXR	\N
+12174	Post-Panel Consolidation	5003	neutropenic	45818	10	1	CXR	\N
+12175	Post-Panel Consolidation	5000	neutropenic	45818	10	1	CXR	\N
+12176	Post-Panel Consolidation	5002	neutropenic	45801	10	1	CXR	\N
+12177	Post-Panel Consolidation	5000	neutropenic	45801	10	1	CXR	\N
+12178	Post-Panel Consolidation	11	pulmonary_embolism	65641	0	1	\N	Process
+12179	Post-Panel Consolidation	10	pulmonary_embolism	44330	7	1	Nebs	\N
+12180	Post-Panel Consolidation	11	pulmonary_embolism	44330	7	1	Nebs	\N
+12181	Post-Panel Consolidation	11	pulmonary_embolism	44595	0	1	\N	Weird antibiotic choice?
+12182	Post-Panel Consolidation	10	pulmonary_embolism	44206	4	1	Aspirin	\N
+12183	Post-Panel Consolidation	11	pulmonary_embolism	44206	4	1	Aspirin	\N
+12184	Post-Panel Consolidation	8	pulmonary_embolism	46551	0	1	\N	\N
+12185	Post-Panel Consolidation	10	pulmonary_embolism	35849	6	1	Azithromycin	\N
+12186	Post-Panel Consolidation	12	pulmonary_embolism	35849	6	1	Azithromycin	\N
+12187	Post-Panel Consolidation	11	pulmonary_embolism	35849	6	1	Azithromycin	\N
+12188	Post-Panel Consolidation	10	pulmonary_embolism	44388	6	1	Azithromycin	\N
+12189	Post-Panel Consolidation	11	pulmonary_embolism	44388	6	1	Azithromycin	\N
+12190	Post-Panel Consolidation	11	pulmonary_embolism	45901	5	1	Blood Cultures	\N
+12191	Post-Panel Consolidation	11	pulmonary_embolism	45752	5	1	Blood Cultures	\N
+12192	Post-Panel Consolidation	10	pulmonary_embolism	45760	7	1	Blood Gas	\N
+12193	Post-Panel Consolidation	12	pulmonary_embolism	45760	7	1	Blood Gas	\N
+12194	Post-Panel Consolidation	10	pulmonary_embolism	45919	5	1	Blood Gas	\N
+12195	Post-Panel Consolidation	12	pulmonary_embolism	45919	5	1	Blood Gas	\N
+12196	Post-Panel Consolidation	11	pulmonary_embolism	45919	5	1	Blood Gas	\N
+12197	Post-Panel Consolidation	10	pulmonary_embolism	44439	0	1	IVF	\N
+12198	Post-Panel Consolidation	10	pulmonary_embolism	45793	10	1	CBC	\N
+12199	Post-Panel Consolidation	11	pulmonary_embolism	45793	10	1	CBC	\N
+12200	Post-Panel Consolidation	10	pulmonary_embolism	45788	10	1	CBC	\N
+12201	Post-Panel Consolidation	11	pulmonary_embolism	45788	10	1	CBC	\N
+12202	Post-Panel Consolidation	10	pulmonary_embolism	35733	6	1	\N	\N
+12203	Post-Panel Consolidation	11	pulmonary_embolism	35733	6	1	\N	\N
+12204	Post-Panel Consolidation	11	pulmonary_embolism	45892	0	1	\N	Superceded by troponin if needed at all
+12205	Post-Panel Consolidation	10	pulmonary_embolism	49251	0	1	\N	\N
+12206	Post-Panel Consolidation	11	pulmonary_embolism	49251	0	1	\N	\N
+12207	Post-Panel Consolidation	10	pulmonary_embolism	61323	0	1	\N	Consult
+12208	Post-Panel Consolidation	11	pulmonary_embolism	61323	0	1	\N	Consult
+12209	Post-Panel Consolidation	12	pulmonary_embolism	49207	0	1	\N	Consult
+12210	Post-Panel Consolidation	11	pulmonary_embolism	50509	0	1	\N	\N
+12211	Post-Panel Consolidation	10	pulmonary_embolism	48522	10	1	CT Chest	\N
+12212	Post-Panel Consolidation	11	pulmonary_embolism	48522	10	1	CT Chest	\N
+12213	Post-Panel Consolidation	10	pulmonary_embolism	45762	7	1	CT Chest	\N
+12214	Post-Panel Consolidation	11	pulmonary_embolism	45762	7	1	CT Chest	\N
+12215	Post-Panel Consolidation	10	pulmonary_embolism	48676	10	1	CT Chest	\N
+12216	Post-Panel Consolidation	11	pulmonary_embolism	48676	10	1	CT Chest	\N
+12217	Post-Panel Consolidation	10	pulmonary_embolism	48871	5	1	D-Dimer	\N
+12218	Post-Panel Consolidation	11	pulmonary_embolism	48871	5	1	D-Dimer	\N
+12219	Post-Panel Consolidation	10	pulmonary_embolism	48532	5	1	D-Dimer	\N
+12220	Post-Panel Consolidation	11	pulmonary_embolism	48532	5	1	D-Dimer	\N
+12221	Post-Panel Consolidation	10	pulmonary_embolism	44017	5	1	Corticosteroids	Main issue is PE, but could reasonably say it's triggering a COPD exacerbation as well
+12222	Post-Panel Consolidation	10	pulmonary_embolism	45811	2	1	NPO	\N
+12223	Post-Panel Consolidation	12	pulmonary_embolism	45811	2	1	NPO	\N
+12224	Post-Panel Consolidation	10	pulmonary_embolism	49054	5	1	\N	\N
+12225	Post-Panel Consolidation	10	pulmonary_embolism	45866	7	1	\N	\N
+12226	Post-Panel Consolidation	11	pulmonary_embolism	45866	7	1	\N	\N
+12227	Post-Panel Consolidation	10	pulmonary_embolism	61832	6	1	Echo	\N
+12228	Post-Panel Consolidation	11	pulmonary_embolism	61832	6	1	Echo	\N
+12229	Post-Panel Consolidation	8	pulmonary_embolism	61832	6	1	Echo	\N
+12230	Post-Panel Consolidation	10	pulmonary_embolism	46160	0	1	\N	Process
+12231	Post-Panel Consolidation	11	pulmonary_embolism	46160	0	1	\N	Process
+12232	Post-Panel Consolidation	11	pulmonary_embolism	44250	10	1	Anticoagulation	\N
+12233	Post-Panel Consolidation	11	pulmonary_embolism	61978	-5	1	\N	\N
+12234	Post-Panel Consolidation	10	pulmonary_embolism	44004	0	1	\N	\N
+12235	Post-Panel Consolidation	11	pulmonary_embolism	44004	0	1	\N	\N
+12236	Post-Panel Consolidation	11	pulmonary_embolism	45797	1	1	\N	\N
+12237	Post-Panel Consolidation	10	pulmonary_embolism	46438	10	1	Anticoagulation	\N
+12238	Post-Panel Consolidation	10	pulmonary_embolism	44359	10	1	Anticoagulation	\N
+12239	Post-Panel Consolidation	11	pulmonary_embolism	44359	10	1	Anticoagulation	\N
+12240	Post-Panel Consolidation	10	pulmonary_embolism	46183	0	1	\N	Just count of part heparin protocol
+12241	Post-Panel Consolidation	10	pulmonary_embolism	63714	0	1	\N	Just count of part heparin protocol
+12242	Post-Panel Consolidation	11	pulmonary_embolism	63714	0	1	\N	Just count of part heparin protocol
+12243	Post-Panel Consolidation	8	pulmonary_embolism	49301	7	1	Blood Gas	\N
+12244	Post-Panel Consolidation	10	pulmonary_embolism	45838	6	1	Troponin	\N
+12245	Post-Panel Consolidation	10	pulmonary_embolism	62151	5	1	Lactate	\N
+12246	Post-Panel Consolidation	10	pulmonary_embolism	62144	0	1	\N	Not sure?
+12247	Post-Panel Consolidation	11	pulmonary_embolism	61837	0	1	\N	\N
+12248	Post-Panel Consolidation	10	pulmonary_embolism	44213	0	1	\N	Like asthma exacerbation treatment?
+12249	Post-Panel Consolidation	11	pulmonary_embolism	45806	5	1	\N	Not sure why so much points?
+12250	Post-Panel Consolidation	10	pulmonary_embolism	45763	10	1	Metabolic Panel	\N
+12251	Post-Panel Consolidation	11	pulmonary_embolism	45763	10	1	Metabolic Panel	\N
+12252	Post-Panel Consolidation	10	pulmonary_embolism	45771	10	1	Metabolic Panel	\N
+12253	Post-Panel Consolidation	11	pulmonary_embolism	45771	10	1	Metabolic Panel	\N
+12254	Post-Panel Consolidation	10	pulmonary_embolism	44586	5	1	Corticosteroids	\N
+12255	Post-Panel Consolidation	11	pulmonary_embolism	44586	5	1	Corticosteroids	\N
+12256	Post-Panel Consolidation	11	pulmonary_embolism	45792	4	1	\N	\N
+12257	Post-Panel Consolidation	11	pulmonary_embolism	44228	4	1	\N	\N
+12258	Post-Panel Consolidation	11	pulmonary_embolism	48628	0	1	\N	\N
+12259	Post-Panel Consolidation	11	pulmonary_embolism	46081	0	1	\N	\N
+12260	Post-Panel Consolidation	10	pulmonary_embolism	50235	0	1	NIPPV	\N
+12261	Post-Panel Consolidation	12	pulmonary_embolism	50235	0	1	NIPPV	\N
+12262	Post-Panel Consolidation	10	pulmonary_embolism	45853	6	1	\N	\N
+12263	Post-Panel Consolidation	11	pulmonary_embolism	45853	6	1	\N	\N
+12264	Post-Panel Consolidation	10	pulmonary_embolism	45750	2	1	\N	Process?
+12265	Post-Panel Consolidation	11	pulmonary_embolism	45787	0	1	\N	Process
+12266	Post-Panel Consolidation	11	pulmonary_embolism	51290	10	1	Oxygen	\N
+12267	Post-Panel Consolidation	10	pulmonary_embolism	51290	10	1	Oxygen	\N
+12268	Post-Panel Consolidation	10	pulmonary_embolism	45864	10	1	Oxygen	\N
+12269	Post-Panel Consolidation	12	pulmonary_embolism	45864	10	1	Oxygen	\N
+12270	Post-Panel Consolidation	10	pulmonary_embolism	48822	10	1	Oxygen	\N
+12271	Post-Panel Consolidation	12	pulmonary_embolism	48822	10	1	Oxygen	\N
+12272	Post-Panel Consolidation	10	pulmonary_embolism	45900	10	1	Oxygen	\N
+12273	Post-Panel Consolidation	12	pulmonary_embolism	45900	10	1	Oxygen	\N
+12274	Post-Panel Consolidation	10	pulmonary_embolism	46090	0	1	\N	Heparin protocol
+12275	Post-Panel Consolidation	10	pulmonary_embolism	45914	6	1	Troponin	\N
+12276	Post-Panel Consolidation	10	pulmonary_embolism	50773	7	1	Blood Gas	\N
+12277	Post-Panel Consolidation	10	pulmonary_embolism	50503	5	1	Blood Gas	\N
+12278	Post-Panel Consolidation	10	pulmonary_embolism	45955	5	1	Blood Gas	\N
+12279	Post-Panel Consolidation	11	pulmonary_embolism	45955	5	1	Blood Gas	\N
+12280	Post-Panel Consolidation	8	pulmonary_embolism	50267	0	1	\N	\N
+12281	Post-Panel Consolidation	10	pulmonary_embolism	44011	5	1	Corticosteroids	\N
+12282	Post-Panel Consolidation	11	pulmonary_embolism	44011	5	1	Corticosteroids	\N
+12283	Post-Panel Consolidation	11	pulmonary_embolism	62042	5	1	\N	\N
+12284	Post-Panel Consolidation	10	pulmonary_embolism	45759	10	1	Coags	\N
+12285	Post-Panel Consolidation	11	pulmonary_embolism	45759	10	1	Coags	\N
+12286	Post-Panel Consolidation	8	pulmonary_embolism	45759	10	1	Coags	\N
+12287	Post-Panel Consolidation	8	pulmonary_embolism	49268	0	1	\N	\N
+12288	Post-Panel Consolidation	11	pulmonary_embolism	45770	10	1	Coags	\N
+12289	Post-Panel Consolidation	8	pulmonary_embolism	45770	10	1	Coags	\N
+12290	Post-Panel Consolidation	11	pulmonary_embolism	63730	0	1	\N	Not for acute TB even if that was the diagnosis?
+12291	Post-Panel Consolidation	8	pulmonary_embolism	62175	4	1	\N	For biopsy?
+12292	Post-Panel Consolidation	10	pulmonary_embolism	46030	0	1	NIPPV	\N
+12293	Post-Panel Consolidation	11	pulmonary_embolism	46030	0	1	NIPPV	\N
+12294	Post-Panel Consolidation	10	pulmonary_embolism	45921	10	1	Oxygen	\N
+12295	Post-Panel Consolidation	12	pulmonary_embolism	45921	10	1	Oxygen	\N
+12296	Post-Panel Consolidation	11	pulmonary_embolism	45921	10	1	Oxygen	\N
+12297	Post-Panel Consolidation	11	pulmonary_embolism	46336	0	1	\N	Override, process order
+12298	Post-Panel Consolidation	11	pulmonary_embolism	48544	5	1	Respiratory Culture	\N
+12299	Post-Panel Consolidation	11	pulmonary_embolism	62023	5	1	Respiratory Virus Screen	\N
+12300	Post-Panel Consolidation	10	pulmonary_embolism	63923	5	1	Respiratory Virus Screen	\N
+12301	Post-Panel Consolidation	11	pulmonary_embolism	63923	5	1	Respiratory Virus Screen	\N
+12302	Post-Panel Consolidation	11	pulmonary_embolism	60178	10	1	Anticoagulation	\N
+12303	Post-Panel Consolidation	12	pulmonary_embolism	60178	10	1	Anticoagulation	\N
+12304	Post-Panel Consolidation	10	pulmonary_embolism	41796	0	1	\N	Presenting Symptom Diagnosis
+12305	Post-Panel Consolidation	12	pulmonary_embolism	44198	0	1	IVF	\N
+12306	Post-Panel Consolidation	8	pulmonary_embolism	44313	0	1	\N	Nebs, but this is more for long term use
+12307	Post-Panel Consolidation	10	pulmonary_embolism	45870	6	1	Troponin	\N
+12308	Post-Panel Consolidation	11	pulmonary_embolism	45870	6	1	Troponin	\N
+12309	Post-Panel Consolidation	8	pulmonary_embolism	45870	6	1	Troponin	\N
+12310	Post-Panel Consolidation	11	pulmonary_embolism	45945	0	1	\N	\N
+12311	Post-Panel Consolidation	8	pulmonary_embolism	65672	4	1	DVT US	\N
+12312	Post-Panel Consolidation	11	pulmonary_embolism	45751	0	1	\N	\N
+12313	Post-Panel Consolidation	10	pulmonary_embolism	45751	0	1	\N	\N
+12314	Post-Panel Consolidation	11	pulmonary_embolism	45911	0	1	\N	Process
+12315	Post-Panel Consolidation	10	pulmonary_embolism	45818	10	1	CXR	\N
+12316	Post-Panel Consolidation	11	pulmonary_embolism	45818	10	1	CXR	\N
+12317	Post-Panel Consolidation	10	pulmonary_embolism	50200	10	1	CXR	\N
+12318	Post-Panel Consolidation	10	pulmonary_embolism	45801	10	1	CXR	\N
+12319	Post-Panel Consolidation	11	pulmonary_embolism	45801	10	1	CXR	\N
+12320	Post-Panel Consolidation	40	atrial_fibrillation	48871	0	0	\N	\N
+12321	Post-Panel Consolidation	43	atrial_fibrillation	44389	-4	1	\N	\N
+12322	Post-Panel Consolidation	43	atrial_fibrillation	61832	7	0	Echo	\N
+12323	Post-Panel Consolidation	41	atrial_fibrillation	44250	8	0	Anticoaguation	Match heparin
+12324	Post-Panel Consolidation	40	atrial_fibrillation	44276	5	1	\N	Match midazolam
+12325	Post-Panel Consolidation	41	atrial_fibrillation	45778	1	1	\N	\N
+12326	Post-Panel Consolidation	41	atrial_fibrillation	45770	5	1	INR	Merge with INR just to represent checking coags
+12327	Post-Panel Consolidation	14	gi_bleed	45932	5	0	Hepatitis Panel	\N
+12328	Post-Panel Consolidation	4	gi_bleed	45793	10	1	CBC	Assume was GI Bleed case
+12329	Post-Panel Consolidation	4	gi_bleed	49481	10	1	GI EGD	Assume was GI Bleed case
+12330	Post-Panel Consolidation	14	gi_bleed	46072	-5	0	CT Abdomen	Treat all CT Abd/Pelvis orders as comparable for simplicity, so why different scores? Weird cirrhosis diagnostic, but maybe people are looking for bleeding vessel?
+12331	Post-Panel Consolidation	16	gi_bleed	45852	-5	0	CT Abdomen	Treat all CT Abd/Pelvis orders as comparable for simplicity, so why different scores? Weird cirrhosis diagnostic, but maybe people are looking for bleeding vessel?
+12332	Post-Panel Consolidation	4	gi_bleed	46286	7	1	Coags	Assume GI Bleed case
+12333	Post-Panel Consolidation	2	gi_bleed	46286	7	1	Coags	\N
+12334	Post-Panel Consolidation	2	gi_bleed	45784	0	0	\N	Post-stabilization is fine, but ignore as process order?
+12335	Post-Panel Consolidation	4	gi_bleed	45872	9	1	FFP	Assume GI Bleed case
+12336	Post-Panel Consolidation	14	gi_bleed	45918	2	1	Lactate	\N
+12337	Post-Panel Consolidation	16	gi_bleed	62144	0	1	\N	\N
+12338	Post-Panel Consolidation	2	gi_bleed	45806	2	1	\N	\N
+12339	Post-Panel Consolidation	14	gi_bleed	44294	0	1	\N	Not relevant to anything?
+12340	Post-Panel Consolidation	14	gi_bleed	48815	0	1	\N	Largely irrelevant in acute setting
+12341	Post-Panel Consolidation	2	gi_bleed	45787	0	1	\N	Process order
+12342	Post-Panel Consolidation	4	gi_bleed	62171	0	1	\N	Active bleeding, probably not right time for para, though SBP always to consider?
+12343	Post-Panel Consolidation	2	gi_bleed	45776	0	1	\N	Process order
+12344	Post-Panel Consolidation	4	gi_bleed	45927	10	1	RBC	Assume GI Bleed case
+12345	Post-Panel Consolidation	4	gi_bleed	45877	8	0	Coagulopathy Correction	Assume GI Bleed case
+12346	Post-Panel Consolidation	4	gi_bleed	61993	8	0	Coagulopathy Correction	Assume GI Bleed case
+12347	Post-Panel Consolidation	4	gi_bleed	45748	10	1	Transfuse RBC	Assume GI Bleed case
+12348	Post-Panel Consolidation	4	gi_bleed	61975	10	1	Transfuse RBC	Assume GI Bleed case
+12349	Post-Panel Consolidation	16	gi_bleed	45869	6	1	US Abdomen	\N
+12350	Post-Panel Consolidation	14	gi_bleed	49408	6	1	US Abdomen	\N
+12351	Post-Panel Consolidation	16	gi_bleed	49408	6	1	US Abdomen	\N
+12352	Post-Panel Consolidation	16	gi_bleed	46343	6	1	US Abdomen	\N
+12353	Post-Panel Consolidation	2	gi_bleed	44001	7	1	Coagulopathy Correction	\N
+12354	Post-Panel Consolidation	14	gi_bleed	46018	0	1	\N	\N
+12355	Post-Panel Consolidation	2	gi_bleed	45818	0	1	\N	\N
+12356	Post-Panel Consolidation	30	meningitis	48603	0	1	\N	Not relevant to test?
+12357	Post-Panel Consolidation	30	meningitis	61853	0	1	\N	Not relevant to test?
+12358	Post-Panel Consolidation	31	meningitis	50841	0	1	\N	\N
+12359	Post-Panel Consolidation	30	meningitis	44626	0	1	\N	Nothing specific being treated? Lyme?
+12360	Post-Panel Consolidation	30	meningitis	61843	0	1	\N	\N
+12361	Post-Panel Consolidation	30	meningitis	46160	0	1	\N	Process order
+12362	Post-Panel Consolidation	30	meningitis	49142	1	1	\N	Common meningitis cause, okay to review
+12363	Post-Panel Consolidation	31	meningitis	46103	0	1	\N	\N
+12364	Post-Panel Consolidation	30	meningitis	50425	0	1	\N	Too specific for meningitis eval
+12365	Post-Panel Consolidation	30	meningitis	44230	9	0	\N	Should be dexamethasone, but give credit for steroids (mostly matter of dosing)
+12366	Post-Panel Consolidation	31	meningitis	45918	5	1	Lactate	\N
+12367	Post-Panel Consolidation	30	meningitis	44586	9	0	\N	Should be dexamethasone, but give credit for steroids (mostly matter of dosing)
+12368	Post-Panel Consolidation	31	meningitis	44641	2	1	\N	Symptomatic management
+12369	Post-Panel Consolidation	31	meningitis	46065	-3	0	CT Head	Unnecessary head imaging, consistent with head CT?
+12370	Post-Panel Consolidation	30	meningitis	45750	0	1	\N	Process order
+12371	Post-Panel Consolidation	30	meningitis	44216	0	1	\N	Symptom management? Not vomiting anyway
+12372	Post-Panel Consolidation	30	meningitis	45802	0	1	\N	Process order
+12373	Post-Panel Consolidation	30	meningitis	48561	5	1	Pregnancy Test	\N
+12374	Post-Panel Consolidation	31	meningitis	44198	5	1	IVF	\N
+12375	Post-Panel Consolidation	30	meningitis	45782	0	1	\N	No specific indication of urinary infection
+12376	Post-Panel Consolidation	30	meningitis	48519	0	1	\N	\N
+12377	Post-Panel Consolidation	5001	neutropenic	45901	10	1	Blood Cultures	\N
+12378	Post-Panel Consolidation	5001	neutropenic	45752	10	1	Blood Cultures	\N
+12379	Post-Panel Consolidation	5003	neutropenic	45919	6	1	Blood Gas	\N
+12380	Post-Panel Consolidation	5001	neutropenic	45788	8	1	CBC	\N
+12381	Post-Panel Consolidation	5000	neutropenic	35733	0	-1	\N	Doesn't cover Pseudomonas, but would cover most other things?
+12382	Post-Panel Consolidation	5003	neutropenic	49207	0	1	\N	Process / Consult
+12383	Post-Panel Consolidation	5003	neutropenic	48711	0	1	\N	Wrong type of isolation
+12384	Post-Panel Consolidation	5002	neutropenic	49054	5	1	\N	\N
+12385	Post-Panel Consolidation	5003	neutropenic	45918	10	1	Lactate	\N
+12386	Post-Panel Consolidation	5000	neutropenic	45918	10	1	Lactate	\N
+12387	Post-Panel Consolidation	5000	neutropenic	45894	0	1	\N	No symptoms for pancreatitis?
+12388	Post-Panel Consolidation	5001	neutropenic	45771	10	1	Metabolic Panel	\N
+12389	Post-Panel Consolidation	5001	neutropenic	48960	8	1	\N	\N
+12390	Post-Panel Consolidation	5003	neutropenic	45853	0	1	\N	Not relevant? No hx or sx of CHF
+12391	Post-Panel Consolidation	5000	neutropenic	61823	0	1	\N	Process order
+12392	Post-Panel Consolidation	5000	neutropenic	45802	0	1	\N	Process order
+12393	Post-Panel Consolidation	5003	neutropenic	52757	0	1	\N	Process order
+12394	Post-Panel Consolidation	5002	neutropenic	62023	6	1	Respiratory Virus Screen	\N
+12395	Post-Panel Consolidation	5001	neutropenic	44198	10	1	IVF	\N
+12396	Post-Panel Consolidation	5001	neutropenic	45751	6	1	UA + Culture	\N
+12397	Post-Panel Consolidation	5002	neutropenic	45751	6	1	UA + Culture	\N
+12398	Post-Panel Consolidation	5001	neutropenic	45782	6	1	UA + Culture	\N
+12399	Post-Panel Consolidation	5001	neutropenic	45818	10	1	CXR	\N
+12400	Post-Panel Consolidation	11	pulmonary_embolism	61982	0	1	\N	Process
+12401	Post-Panel Consolidation	10	pulmonary_embolism	44315	4	1	Aspirin	\N
+12402	Post-Panel Consolidation	8	pulmonary_embolism	44240	0	1	\N	Not ACS anyway? Type 2 NSTEMI?
+12403	Post-Panel Consolidation	11	pulmonary_embolism	45760	7	1	Blood Gas	\N
+12404	Post-Panel Consolidation	8	pulmonary_embolism	65695	0	0	\N	Override, neutralize non-procedural consults
+12405	Post-Panel Consolidation	11	pulmonary_embolism	49207	0	1	\N	Consult
+12406	Post-Panel Consolidation	11	pulmonary_embolism	45811	2	1	\N	\N
+12407	Post-Panel Consolidation	10	pulmonary_embolism	44211	7	1	Nebs	\N
+12408	Post-Panel Consolidation	10	pulmonary_embolism	46020	7	1	Blood Gas	\N
+12409	Post-Panel Consolidation	11	pulmonary_embolism	44299	6	1	Antibiotics-CAP	\N
+12410	Post-Panel Consolidation	11	pulmonary_embolism	45914	6	1	Troponin	\N
+12411	Post-Panel Consolidation	8	pulmonary_embolism	45776	0	1	\N	Override, process
+12412	Post-Panel Consolidation	11	pulmonary_embolism	45830	0	0	\N	\N
+12413	Post-Panel Consolidation	10	pulmonary_embolism	44198	0	1	IVF	\N
+12414	Post-Panel Consolidation	10	pulmonary_embolism	45766	0	1	\N	Process
+\.
+
+
+--
+-- Name: sim_grading_key_sim_grading_key_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_grading_key_sim_grading_key_id_seq', 12414, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_note.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_note.dump.sql
new file mode 100644
index 00000000..bec16804
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_note.dump.sql
@@ -0,0 +1,71 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_note; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_note (sim_note_id, sim_state_id, note_type_id, author_type_id, service_type_id, relative_state_time, content) FROM stdin;
+49	8	2	2001	1500	0	Patient still short of breath, but improved compared to this morning now on oxygen and tolerated anticoagulation without incident.        <br><p>Admitting team will transfer care of patient in the next hour.    They request that you complete any further diagnostics or therapeutics needed before then.        <h3>Physical Exam</h3>    T: 98.6F, BP: 130/85, HR: 92, RR: 18, O2: 93% on 6L nasal cannula    <ul>    <li>General: Resting on gurney    <li>HEENT: Lips no longer blue    <li>Neck: JVP ~6cm.    <li>Lungs: Good air movement. Minimal faint wheezes at base.    <li>Cardiac: Regular rate and rhythm.    <li>Extremities: Trace pedal edema. 1+ DP pulses.    </ul>    
+8	3	3	2001	1003	0	<h3>CC:</h3>    Chest pain        <h3>HPI:</h3>    <p>62M with hx HTN, HLD, T2DM, smoker.        <p>Drove cross country over past three days to meet family in usual state of health. At outdoor festival 2 hours ago, climbing up hill when complained to wife of onset of feeling winded and chest pain. Sat down in shade and drank water with some relief of symptoms over several minutes. 45 minutes later, continued to feel low-grade chest pain and pressure, so family brought in to emergency room for evaluation.        <h4>Review of Systems</h4>    Otherwise negative except as per HPI, including:    Denies fever, chills, cough, palpitations, abdominal pain, nausea, vomiting, leg pain or edema.        <h3>Medical History</h3>    <ul>    <li>Hypertension    <li>Hyperlipidemia    <li>Type 2 Diabetes    <li>Tobacco Use    </ul>        <h3>Surgical History</h3>    <ul>    <li>Appendectomy at 28 years old    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Hydrochlorothiazide 25mg daily    <li>Lisinopril 5mg daily    <li>Metformin 1000mg bid    </ul>        <h3>Family/Social Hx:</h3>    Mother died of stroke at 85 years old. Father still alive at 89 years old with prostate cancer.<br>    Married with two children. Vietnam veteran. Works as building contractor.        <h3>Physical Exam</h3>    T: 98.8F, BP: 145/84, HR: 103, RR: 19, O2: 92%    <ul>    <li>General: <i>Uncomfortable, sweating</i>, but no acute distress, sitting up in bed    <li>Neck: Supple, no lymphdenopathy, JVP ~6-8cm    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales. <i>Tachypneic</i>.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops. Chest wall non-tender to palpation.    <li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds. No organomegaly or masses noted    <li>Extremities: No clubbing, cyanosis, edema. 2+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: No rashes or lesions noted.    </ul>    
+1	1	1	2001	1500	0	<placeholder/>
+39	40	3	2001	1003	0	<h3>CC:</h3>     Palpitations     <h3>HPI:</h3>     <p>66 F hx HTN, HFpEF     Patient reports palpitations and shortness of breath since 6am this morning.     Normally can walk 1/2 mile without shortness of breath.     Lives alone, called EMS this morning.     History of CHF, HTN. States she takes her medications.     Also reports worsening dizziness since this morning. No chest pain reported.     EMS noted patient was tachycardic. 5mg IV diltiazem given in the field </p>     <h3>ROS:</h3>     Otherwise negative except as per HPI, including;     Denies fever, chills, chest pain.     Denies recent travel. Denies orthopnea. Denies leg swelling. Denies dyspnea on exertion.     Denies weakness/sensory changes.     <h3>Medical History</h3>     <ul>     <li>Hypertension     <li>CHF     </ul>     <h3>Allergies:</h3>     NKDA     <h3>Medications:</h3>     <ul>     <li>Hydrochlorothiazide     <li>Ibuprofen PRN     </ul>     <h3>Family/Social Hx:</h3>     Father with a history of coronary artery disease in his 50s. Denies drug use/alcohol     <h3>Physical Exam</h3>     T: 97.9F, BP: 83/59, HR: 153, RR: 24, O2: 92%%     <ul>     <li>General: <i>Fatigued appearing</i>     <li>HEENT: <i>No conjunctival pallor.</i> EOMI, PERRL, no mucositis or oral lesions noted.     <li>Neck: Supple, JVP 9cm above the sternal angle     <li>Lungs: Significant crackles throughout the lung fields     <li>Cardiac: <i>Rapid, irregularily irregular</i>  No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-tender and non-distended without organomegaly.     <li>Rectal: deferred     <li>Extremities: <i> cool with 3+ pitting edema</i> No clubbing, cyanosis     <li>Neuro: <i>Alert and oriented x2, lethargic. </i> Not cooperative with neuro-exam, but moving all extremities.     <li>Skin: no rashes     </ul>    
+46	10	3	2001	1003	0	<h3>CC:</h3>    Shortness of Breath        <h3>HPI:</h3>    <p>70M hx CAD, systolic CHF, COPD, smoking.    Patient complains of acute on chronic shortness of breath since this morning.    Has been hospitalized before for both COPD and CHF exacerbations in prior 5 years as have developed more chronic health problems,    but endorses taking all his medications as prescribed.     Just returned from family vacation in Hawaii, where may have been more lax with dietary choices.    Endorses some dry cough, but similar to his chronic cough.    Having difficulty providing further history due to shortness of breath.        <h3>ROS:</h3>    Otherwise negative except as per HPI, including;    Denies fever, chills, chest pain, orthopnea, paroxysmal nocturnal dyspnea.    Not aware of any specific sick contacts, but was in many busy travel/tourist locations (just got off plane this morning).        <h3>Medical History</h3>    <ul>    <li>Coronary Artery Disease    <li>CHF (EF 35% last checked 4 months ago)    <li>COPD    <li>Tobacco Smoking (1 pack per day x30 years, active)    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Aspirin    <li>Atorvastatin    <li>Carvedilol    <li>Losartan    <li>Furosemide    <li>Tiotropium    <li>Albuterol-Ipratropium PRN (used twice this morning, with little improvement in symptoms)    </ul>        <h3>Family/Social Hx:</h3>    Father died of heart attack in 80s.        <h3>Physical Exam</h3>    T: 98.5F, BP: 141/92, HR: 109, RR: 25, O2: 81% on room air    <ul>    <li>General: Short of breath, moderate distress. No change with lying or sitting up.    <li>HEENT: <i>Lips tinged blue.</i> EOMI, PERRL, no mucositis or oral lesions noted.    <li>Neck: Supple, JVP ~6cm. No hepatojugular reflex.    <li>Lungs: <i>Tachypneic. Wheezes in bilateral bases, but good air movement. No significant crackles.</i>    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.    <li>Abdomen: Soft, non-distended. Thin. Normo-active bowel sounds. Mild epigastric tenderness.    <li>Extremities: Trace pedal edema. No clubbing, cyanosis. 1+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: Pale. No rashes or lesions noted.    </ul>    
+47	11	2	2001	1500	0	Patient still short of breath, but more comfortable with supplemental oxygen.        <h3>Physical Exam</h3>    T: 98.5F, BP: 132/87, HR: 96, RR: 20, O2: 92% on 6L nasal cannula    <ul>    <li>General: Resting on gurney, mildly short of breath    <li>HEENT: Lips no longer blue    <li>Neck: JVP ~6cm.    <li>Lungs: Faint wheezes in bilateral bases, but good air movement. No significant crackles.    <li>Cardiac: Rapid but Regular rate and rhythm.    <li>Extremities: Trace pedal edema. 1+ DP pulses.    </ul>    
+48	12	2	2001	1500	0	Patient tolerating anticoagulation administration, but remains acutely short of breath        <h3>Physical Exam</h3>    T: 98.7F, BP: 142/91, HR: 112, RR: 26, O2: 80% on room air    <ul>    <li>General: Short of breath, moderate distress. No change with lying or sitting up.    <li>HEENT: <i>Lips tinged blue.</i>    <li>Neck: JVP ~6cm    <li>Lungs: <i>Tachypneic. Wheezes in bilateral bases, but good air movement. No significant crackles.</i>    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.    <li>Extremities: Trace pedal edema.    </ul>    
+23	4	2	2001	1002	0	Patient suffered a pulseless arrest.    Code blue was called and the ICU team has taken over management as they attempt cardiopulmonary resuscitation.
+21	16	2	2001	1500	0	Patient stopped spitting up blood.         <h3>Physical Exam</h3>    T: 98.1F, BP: 108/61, HR: 97, RR: 18, O2: 97%    <ul>    <li>General: Pale, fatigued.    <li>HEENT: Conjunctival pallor. Scleral icterus.    <li>Lungs: Clear to auscultation bilaterally.     <li>Cardiac: Rapid but Regular    <li>Abdomen: Soft. Moderately-distended. Hyper-active bowel sounds. Mild RUQ and epigastric tenderness.    <li>Extremities: 1+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant. Mild asterixis.    <li>Skin: Pale. Scattered spider angiomata.    </ul>
+41	44	2	2001	1500	0	Unused state
+36	33	2	2001	1500	0	Patient feels headache may be worsening, is having a hard time keeping her eyes open with the lights on in the room.                     <h3>Physical Exam</h3>         T: 102.1F, BP: 98/62, HR: 115, RR: 19, O2: 96%         <ul>         <li>General: <i>Appears uncomfortable, eyes closed, lying in bed.</i>         <li>HEENT: <i>Ability to perform flexion/extension of neck limited.</i> EOMI, PERRL, no mucositis or oral lesions noted.         <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.         <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.         <li>Abdomen: Soft, non-tender, non-distended. <i>         <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.         <li>Neuro: Alert and oriented, although uncomfortable and a bit somnolent. CN II-XII intact. Conversant, moving all extremities. No overt cerebellar signs / incoordination. <i>         <li>Skin: <i>No rashes.</i>         </ul>
+37	31	2	2001	1500	0	The patient still feels very tired but has defervesced and overall feels less uncomfortable.    <br><p>Admitting team indicates they well assume care of patient in an hour and request you complete any diagnostics or therapeutics needed in the meantime.     <h3>Physical Exam</h3>         T: 100.1F, BP: 110/70, HR: 95, RR: 16, O2: 96%         <ul>         <li>General: <i>Appears mildly uncomfortable but improved from prior, eyes open most of the time, lying in bed.</i>         <li>HEENT: <i>Ability to perform flexion/extension of neck limited.</i> EOMI, PERRL, no mucositis or oral lesions noted.         <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.         <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.         <li>Abdomen: Soft, non-tender, non-distended. <i>         <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.         <li>Neuro: Alert and oriented, although mildly uncomfortable. CN II-XII intact. Conversant, moving all extremities. No overt cerebellar signs / incoordination. <i>         <li>Skin: <i>No rashes.</i>         </ul>
+45	46	8	2001	1500	0	Cardiology is busy with another emergent case. Recommends hemodynamic stabilization and management of secondary risks until they can arrive.
+28	18	2	2001	1500	0	Patient feels fatigued and with abdominal discomfort.        <h3>Physical Exam</h3>    T: 98.5F, BP: 111/74, HR: 102, RR: 26, O2: 96%    <ul>    <li>General: Tired    <li>Lungs: Deep sighing respirations    <li>Cardiac: Rapid but Regular    <li>Abdomen: Soft, non-distended. Generalized tenderness without guarding or rebound    <li>Extremities: 2+ DP pulses.    <li>Neuro: Fatigued, but alert and oriented. Grossly non-focal. Moving all extremities.    <li>Skin: No rashes or lesions noted.    </ul>    
+29	19	2	2001	1500	0	Patient feeling much improved compared to previous        <h3>Physical Exam</h3>    T: 98.8F, BP: 123/78, HR: 82, RR: 16, O2: 97%    <ul>    <li>General: Resting comfortably    <li>Lungs: Normal respiratory effort, CTAB    <li>Cardiac: Regular Rate and Rhythm    <li>Abdomen: Soft, non-distended, no focal tenderness    <li>Extremities: 2+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Moving all extremities.    <li>Skin: No rashes or lesions noted.    </ul>    
+30	20	2	2001	1500	0	Patient feeling restless, uncomfortable, trembling, difficulty concentrating        <h3>Physical Exam</h3>    T: 97.8F, BP: 95/57, HR: 137, RR: 31, O2: 95%    <ul>    <li>General: Trembling, difficult to arouse    <li>HEENT: PERRL    <li>Lungs: Deep sighing respirations. CTAB    <li>Cardiac: Rapid but Regular Rhythm    <li>Abdomen: Soft, non-distended, generalized tenderness to palpation without rebound or guarding    <li>Extremities: Thready DP pulses.    <li>Neuro: Drowsy and difficult to arouse. Trembling extremities    <li>Skin: Clammy    </ul>    
+10	7	3	2001	1003	0	<h3>CC:</h3>    Vomiting        <h3>HPI:</h3>    <p>57M hx HTN, smoking, and knee arthritis.    Patient from South Korea, here for travel, hiking across multiple tourist destinations over the past week.    Has some chronic R knee arthritis pain with flare in past week with increasing walking.     Taking Motrin 600mg about three times per day since to manage pain.    Feeling a little lightheaded this morning, ate a cheese burrito.    Vomited black, gritty material two times later in the morning and continuing to feel lightheaded. Mild abdominal pain and shortness of breath.        <h3>ROS:</h3>    Otherwise negative except as per HPI, including;    Denies fever, chills, chest pain.    Denies diarrhea, constipation or otherwise abnormal stools, normal in consistency as of last night.            <h3>Medical History</h3>    <ul>    <li>Hypertension    <li>Tobacco Smoking    <li>Osteoarthritis, Knee    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Hydrochlorothiazide    <li>Ibuprofen PRN    </ul>        <h3>Family/Social Hx:</h3>    Mother died of gastric cancer in her 60s.        <h3>Physical Exam</h3>    T: 97.9F, BP: 101/62, HR: 105, RR: 19, O2: 96%    <ul>    <li>General: <i>Pale, fatigued</i>    <li>HEENT: <i>Conjunctival pallor.</i> EOMI, PERRL, no mucositis or oral lesions noted. <i>Black stains around lips.</i>    <li>Neck: Supple, JVP flat    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-distended. Hyper-active bowel sounds. Mild epigastric tenderness.    <li>Rectal: <i>Guaic positive black stool in rectal vault</i>    <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: Pale No rashes or lesions noted.    </ul>    
+24	2	8	2001	1503	0	GI performs an upper GI endoscopy, finding multiple esophageal varices. The largest bleeding varix is banded.    This has stabilized the patient's acute bleed. The GI team advises you to continue your medical management for the patient.    <br><p>The admitting medical team indicates they will assume care of the patient in an hour.
+27	21	2	2001	1500	0	Patient feels exhausted and with abdominal discomfort.        <h3>Physical Exam</h3>    T: 98.4F, BP: 105/72, HR: 117, RR: 29, O2: 96%, BMI: 36    <ul>    <li>General: <i>Lethargic but arousable</i>    <li>HEENT: EOMI, PERRL, no mucositis or oral lesions noted. <i>Sweet odor on breath</i>    <li>Neck: Supple, JVP flat    <li>Lungs: <i>Deep sighing respirations.</i> Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-distended. <i>Generalized tenderness without guarding or rebound.</i> Normo-active bowel sounds. <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.    <li>Neuro: Drowsy but arousable. Grossly non-focal. Moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: No rashes or lesions noted.    </ul>
+17	14	2	2001	1500	0	Patient feels lightheaded, with mild abdominal pain and shortness of breath.     Occasionally spitting up some red blood.        <h3>Physical Exam</h3>    T: 97.9F, BP: 101/62, HR: 105, RR: 19, O2: 96%    <ul>    <li>General: <i>Pale, fatigued. Occasionaly spitting up some red blood</i>    <li>HEENT: <i>Conjunctival pallor. Scleral icterus.</i> EOMI, PERRL, no mucositis or oral lesions noted.     <li>Neck: Supple, JVP flat    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft. <i>Moderately-distended. Hyper-active bowel sounds. Mild RUQ and epigastric tenderness.</i>    <li>Rectal: <i>Guaic positive black stool in rectal vault</i>    <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination. <i>Mild asterixis.</i>    <li>Skin: <i>Pale. Scattered spider angiomata.</i>    </ul>    
+20	15	2	2001	1500	0	Patient drifting off in alertness, difficult to arouse.    Continues to spit up red blood.        <h3>Physical Exam</h3>    T: 97.2F, BP: 85/54, HR: 127, RR: 29, O2: 95%    <ul>    <li>General: <i>Ill appearing, very pale</i>    <li>HEENT: Conjunctival pallor. Scleral icterus. PERRL    <li>Lungs: <i>Rapid, shallow breaths.</i> Clear to auscultation bilaterally    <li>Cardiac: <i>Very Rapid</i>    <li>Abdomen: Soft. Moderately-distended. Hyper-active bowel sounds.    <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.    <li>Neuro: <i>Drifting off, difficult to arouse</i>    <li>Skin: Pale. Scattered spider angiomata.    </ul>    
+40	43	2	2001	1500	0	Patient is lethargic and very short of breath     <h3>Physical Exam</h3>     T: 97.9F, BP: 83/59, HR: 153, RR: 24, O2: 92%%     <ul>     <li>General: <i>Fatigued appearing, dozing off</i>      <li>Neck: Supple, JVP 9cm above the sternal angle     <li>Lungs: Significant crackles throughout the lung fields     <li>Cardiac: <i>Rapid, irregularily irregular</i>  No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-tender and non-distended without organomegaly.    <li>Extremities: <i> cool with 3+ pitting edema</i>      <li>Neuro: <i>Alert and oriented x2, lethargic. </i> Not cooperative with neuro-exam, but moving all extremities.     </ul>    
+42	45	2	2001	1500	0	After infusion of antiarrhythmic, patients heart rate goes down to 135 but reverts to high 140s one minute after without other significant change in vital signs. Patient does not feel improved.
+43	41	2	2001	1500	0	After synchronized direct current cardioversion, patient's irregular heart rhythm convert to normal sinus rhythm. Patient reports breathing and dizziness have improved.     <br><p>Admitting team indicate they will transfer care in one hour and request that you complete any additional diagnostics and therapeutics needed until then.     <h3>Physical Exam</h3>     T: 98.1F, BP: 138/81, HR: 87, RR: 18, O2: 97%%     <ul>     <li>General: No apparent distress    <li>Lungs: Slight crackles at the bases bilaterally; no wheezes or rhonchi     <li>Cardiac: Regular, no murmurs rubs, or gallups     <li>Abdomen: Soft. Nontender and nondistended.     <li>Extremities: 1+ DP pulses, warm, 3+ Pitting edema     <li>Neuro: Alert and oriented. Grossly non-focal. Conversant.     </ul>    
+44	42	2	2001	1500	0	During adenosine infusion, patient's heart rate slows and monitor reveals irregular rhythm consistent with atrial fibrillation. Heart rate reverts back to high 140s within a few seconds.
+33	5002	2	2001	1500	0	Patient feels more awake, but still hot and lousy.     <h3>Physical Exam</h3>     T: 101.8F, BP: 105/70, HR: 98, RR: 16, O2: 96%     <ul>    <li>General: Sweating    <li>HEENT: No mucositis or oral lesions noted    <li>Neck: Supple, shotty lymphdenopathy, JVP ~8cm    <li>Lungs: Clear to auscultation bilaterally.    <li>Cardiac: Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds.    <li>Extremities: 1+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal.    <li>Skin: No rashes or lesions noted.    <li>Lines: R arm PICC line without surrounding erythema, drainage, or tenderness     </ul>
+22	17	8	2001	1503	0	GI Consult advises you stabilize patient hemodynamics and coagulopathy before pursuing endoscopic intervention. Please consult GI again once patient has been stabilized.
+34	5003	2	2001	1500	0	Patient reports feeling relatively better, not so hot or lightheaded.    <br><p>Admitting team indicates they will assume care of patient in another hour and request you complete any further diagnostics or therapeutics in the meantime.    <br><p> T: 98.8F, BP: 116/75, HR: 84, RR: 14, O2: 97%     <ul>    <li>General: Improved, resting on bed    <li>HEENT: No mucositis or oral lesions noted    <li>Neck: Supple,shotty lymphdenopathy, JVP ~8cm    <li>Lungs: Clear to auscultation bilaterally    <li>Cardiac: Regular rate and rhythm     <li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds    <li>Extremities: 2+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant    <li>Skin: No rashes or lesions noted.    <li>Lines: R arm PICC line without surrounding erythema, drainage, or tenderness     </ul>
+11	9	3	2001	1003	36000	<h3>CC:</h3>    Fatigue, abdominal pain        <h3>HPI:</h3>    <p>26F G1P1 hx T1DM, HLD.    Brought into ER in middle of night as husband noting several hours of progressively worsening abdominal pain and fatigue, with husband having difficulty keeping patient awake.    Patient's 2 year old child in daycare with diarrhea last week.    Past few days patient with minimal diarrhea, but nausea and poor appetite. Patient self-titrated glargine insulin down to half dose and has been holding prandial insulin doses while feeling ill.        <h3>ROS:</h3>    Otherwise negative except as per HPI, including;    Denies fever, chills, chest pain.    Denies diarrhea, constipation or otherwise abnormal stools, normal in consistency as of last night.            <h3>Medical History</h3>    <ul>    <li>Type 1 Diabetes since teenager    <li>Hyperlipidemia    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Insulin glargine qhs    <li>Insulin lispro AC & qhs    <li>Atorvastatin    </ul>        <h3>Family/Social Hx:</h3>    Mother with hypothyroidism from Hashimoto's thyroiditis in 30s.    
+38	32	2	2001	1500	0	The patient continues to feel worse, and her nurse orders an RRT.  The MICU fellow responds and recommends changing the empiric regimen for bacterial meningitis to vancomycin and ceftriaxone, to be given stat.     T: 103.1F, BP: 86/52, HR: 128, RR: 21, O2: 96%         <ul>         <li>General: <i>Appears uncomfortable, eyes closed, lying in bed.</i>         <li>HEENT: <i>Ability to perform flexion/extension of neck limited.</i> EOMI, PERRL, no mucositis or oral lesions noted.         <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.         <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.         <li>Abdomen: Soft, non-tender, non-distended. <i>         <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.         <li>Neuro: Alert and oriented, although quite uncomfortable and a bit somnolent. CN II-XII intact. Conversant, moving all extremities. No overt cerebellar signs / incoordination. <i>         <li>Skin: <i>No rashes.</i>         </ul>
+31	5000	3	2001	1003	0	<h3>CC:</h3>    Fever and chills        <h3>HPI:</h3>    <p>32M was otherwise healthy until 1 month ago when developed night sweats and low grade fevers. Subsequent workup by Heme/Onc diagnosed patient with DLBCLymphoma for which he underwent first cycle of inpatient R-CHOP chemotherapy 10 days ago. He was due to see Heme/Onc for follow-up therapy plans today, but was then directed to the emergency department based on his present symptoms.    <p>Patient reporting new-onset high fever, chills, and rigors, for past few hours.    Moderately short of breath, but no cough.  No urination since woke up this morning, though no specific pain.    <p>Except for nausea, general weakness, and hair thinning after his last course of chemotherapy, ROS otherwise negative, including denies abdominal pain, diarrhea, rashes/lesions.        <h3>Medical History</h3>    <ul>    <li>Diffuse Large B-Cell Lymphoma    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, Prednisone    </ul>        <h3>Family/Social Hx:</h3>    No known family history of heme malignancy.    Mother and father alive in 60s with serious medical isues. Two siblings, aged 37 and 28.        <h3>Physical Exam</h3>    T: 102F, BP: 90/57, HR: 126, RR: 24, O2: 98%    <ul>    <li>General: Shivering, wrapped in blankets in bed    <li>HEENT: EOMI, PERRL, no mucositis or oral lesions noted    <li>Neck: Supple, <i>shotty lymphdenopathy</i>, JVP ~6cm    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds. No organomegaly or masses noted    <li>Rectal: No perirectal abscess or lesions    <li>Extremities: No clubbing, cyanosis, edema. <i>Cool feet. Thready DP pulses.</i>    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: No rashes or lesions noted.    <li>Lines: <i>R arm PICC line</i> without surrounding erythema, drainage, or tenderness    </ul>
+32	5001	2	2001	1500	0	Patient reports feeling less hot, but still lightheaded.     <h3>Physical Exam</h3>     T: 99F, BP: 92/61, HR: 119, RR: 19, O2: 96%     <ul>    <li>General: Tired, ill appearing    <li>HEENT: EOMI, PERRL, no mucositis or oral lesions noted    <li>Neck: Supple, shotty lymphdenopathy, JVP ~6cm    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds. No organomegaly or masses noted    <li>Rectal: No perirectal abscess or lesions    <li>Extremities: No clubbing, cyanosis, edema. <i>Cool feet. Thready DP pulses.</i>    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: No rashes or lesions noted.    <li>Lines: R arm PICC line without surrounding erythema, drainage, or tenderness     </ul>
+16	13	3	2001	1003	7200	<h3>CC:</h3>    Vomiting blood        <h3>HPI:</h3>    <p>59M hx HTN, osteoarthritis.    Patient here for travel from South Korea, hiking across multiple vineyards over the past week.    Has some chronic R knee arthritis pain with flare in past week with increasing walking.     Taking Motrin and drinking wine multiple times per day since to manage pain.    Feeling a little lightheaded this morning, ate a cheese burrito.    Vomited black, gritty material shortly thereafter, and then a half cup of red blood.    Patient continues to feel lightheaded, with mild abdominal pain and shortness of breath.        <h3>ROS:</h3>    Otherwise negative except as per HPI, including;    Denies fever, chills, chest pain.    Denies diarrhea, constipation or otherwise abnormal stools, normal in consistency as of last night.            <h3>Medical History</h3>    <ul>    <li>Hypertension    <li>Osteoarthritis, Knee    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Hydrochlorothiazide    <li>Ibuprofen PRN    </ul>        <h3>Family/Social Hx:</h3>    Mother died of gastric cancer in her 60s.    20 pack year tobacco smoker.    Drinks up to a bottle of soju daily.    
+15	6	3	2001	1003	0	<h3>CC:</h3>    Fever and chills        <h3>HPI:</h3>    <p>32M recent dx DLBCLymphoma and underwent first cycle of R-CHOP chemotherapy 10 days ago.    <p>Patient seen at infusion center today for labs and PICC line dressing change.    <p>Patient reporting new-onset high fever, chills, and rigor, for past few hours but no other focal symptoms.    Labs in clinic today noted WBC 0.8, Hgb 11,  Plt 96 with an absolute neutrophil count of 80.    Patient direct admitted to the hospital for febrile neutropenia.            <h3>Medical History</h3>    <ul>    <li>Diffuse Large B-Cell Lymphoma    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, Prednisone    </ul>        <h3>Family/Social Hx:</h3>    No known family history of heme malignancy.    Mother and father alive in 60s with serious medical isues. Two siblings, aged 37 and 28.        <h3>Physical Exam</h3>    T: 102F, BP: 90/43, HR: 128, RR: 24, O2: 98%    <ul>    <li>General: Shivering, wrapped in blankets in bed    <li>HEENT: EOMI, PERRL, no mucositis or oral lesions noted    <li>Neck: Supple, <i>shotty lymphdenopathy</i>, JVP ~6cm    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.     <li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds. No organomegaly or masses noted    <li>Rectal: No perirectal abscess or lesions    <li>Extremities: No clubbing, cyanosis, edema. <i>Cool feet</i>. 2+ DP pulses.    <li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: No rashes or lesions noted.    <li>Lines: <i>R arm PICC line</i> without surrounding erythema, drainage, or tenderness    </ul>
+35	30	3	2001	1003	0	<h3>CC:</h3>   Headache        <h3>HPI:</h3>    <p>25 year-old woman, no significant PMH, who was at her job as an office assistant when she experienced rapidly progressive onset of  headaches and photophobia over the course of hours. <br>    She was last out of the country a month ago visiting her mother in Germany who was in the hospital recovering from a hip surgery.  She believes she is up to date on all recommended vaccinations.        <h3>ROS:</h3>    Otherwise negative except as per HPI, including:    Reports no new rashes.        <h3>Medical History</h3>    <ul>    Healthy, sees her primary care physician once a year.    </ul>        <h3>Allergies:</h3>    NKDA        <h3>Medications:</h3>    <ul>    <li>Oral Contraptive Pills    </ul>        <h3>Family/Social Hx:</h3>    Parents both alive, both with history of HTN and DM.  One brother, healthy.<br>    No tobacco. Drinks average 1 glass of wine per night.<br>    Sexually active with two male partners in past year. Does not regularly use condoms.        <h3>Physical Exam</h3>    T: 101.2F, BP: 104/68, HR: 104, RR: 18, O2: 96%    <ul>    <li>General: <i>Appears uncomfortable, eyes closed, lying in bed.</i>    <li>HEENT: <i>Pain limited flexion/extension of neck.</i> EOMI, PERRL, no mucositis or oral lesions noted.    <li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.    <li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops.    <li>Abdomen: Soft, non-tender, non-distended.    <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.    <li>Neuro: Alert and oriented, although uncomfortable. CN II-XII intact. Conversant, moving all extremities. No overt cerebellar signs / incoordination.    <li>Skin: No rashes.    </ul>
+\.
+
+
+--
+-- Name: sim_note_sim_note_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_note_sim_note_id_seq', 49, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/db/definition/simdata/sim_note_type.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_note_type.dump.sql
similarity index 85%
rename from medinfo/db/definition/simdata/sim_note_type.dump.sql
rename to medinfo/cpoe/cpoeSim/simdata/sim_note_type.dump.sql
index ee5164fc..1b67a279 100644
--- a/medinfo/db/definition/simdata/sim_note_type.dump.sql
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_note_type.dump.sql
@@ -2,19 +2,24 @@
 -- PostgreSQL database dump
 --
 
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
 SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
 SET client_encoding = 'UTF8';
 SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
 SET check_function_bodies = false;
 SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
+SET row_security = off;
 
 --
 -- Data for Name: sim_note_type; Type: TABLE DATA; Schema: public; Owner: -
 --
 
-COPY sim_note_type (sim_note_type_id, name, priority, description) FROM stdin;
+COPY public.sim_note_type (sim_note_type_id, name, priority, description) FROM stdin;
 1	H&P	\N	\N
 2	Progress Note	\N	\N
 3	ED Provider Note	\N	\N
diff --git a/medinfo/db/definition/simdata/sim_order_result_map.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_order_result_map.dump.sql
similarity index 98%
rename from medinfo/db/definition/simdata/sim_order_result_map.dump.sql
rename to medinfo/cpoe/cpoeSim/simdata/sim_order_result_map.dump.sql
index 8998c66c..2d78a6cc 100644
--- a/medinfo/db/definition/simdata/sim_order_result_map.dump.sql
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_order_result_map.dump.sql
@@ -2,19 +2,24 @@
 -- PostgreSQL database dump
 --
 
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
 SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
 SET client_encoding = 'UTF8';
 SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
 SET check_function_bodies = false;
 SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
+SET row_security = off;
 
 --
 -- Data for Name: sim_order_result_map; Type: TABLE DATA; Schema: public; Owner: -
 --
 
-COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result_id, turnaround_time) FROM stdin;
+COPY public.sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result_id, turnaround_time) FROM stdin;
 33	45821	10	300
 34	45821	20	300
 35	45821	30	300
@@ -102,18 +107,9 @@ COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result
 117	45753	1000003	900
 118	45753	16050	900
 119	45887	13100	900
-120	45866	19010	900
-121	45866	19020	900
-122	45866	19030	900
-123	45866	19040	900
-124	45866	19050	900
-125	45866	19060	900
-126	45866	19070	900
 127	45844	1000007	900
 128	45763	1000008	900
 129	45753	1000009	900
-130	45866	19080	900
-131	45866	19090	900
 132	45844	1000010	900
 133	45753	1000011	900
 134	45788	1000012	900
@@ -6024,7 +6020,6 @@ COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result
 6039	66043	1000120	900
 6040	65729	1002959	900
 6041	61062	1001670	900
-6042	45866	1002960	900
 6043	61832	1002961	900
 6044	64143	1000289	900
 6045	65512	1001418	900
@@ -7027,7 +7022,6 @@ COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result
 7055	61062	1003493	900
 7056	63073	1003494	900
 7057	48503	1003495	900
-7058	45866	1003490	900
 7059	51689	1002960	900
 7060	51689	1003490	900
 7061	62075	1003490	900
@@ -7970,7 +7964,6 @@ COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result
 7998	65682	1004190	900
 7999	66085	1004191	900
 8000	66088	1004192	900
-8001	45866	1004198	300
 8004	45818	1004194	900
 8005	45801	1004194	900
 8006	50200	1004194	300
@@ -7984,6 +7977,117 @@ COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result
 8014	45955	1000003	900
 8015	45955	1000009	900
 8016	45955	1000011	900
+8068	49083	125	900
+8077	62831	131	3600
+8078	46039	131	3600
+8079	46065	131	3600
+8080	62831	131	3600
+8081	46039	131	3600
+8082	46065	131	3600
+8092	45804	133	3600
+8093	45983	133	3600
+8094	48524	133	3600
+8095	50241	133	3600
+8096	52016	133	3600
+8097	64739	133	3600
+8098	65940	133	3600
+8099	50098	133	3600
+8100	49965	133	3600
+8200	61832	200	1800
+8001	45866	1004198	300
+8101	45913	1004206	900
+8102	49115	1004206	900
+8103	49869	1004206	900
+8104	48559	1004206	900
+8105	49653	1004206	900
+8106	51403	1004206	900
+8107	65696	1004206	900
+8108	65692	1004206	900
+8109	65661	1004206	900
+8110	66074	1004206	900
+8111	65672	1004206	900
+8112	65656	1004206	900
+8113	50199	1004208	3600
+8114	52080	1004194	900
+8115	54044	1004194	900
+8116	51726	1004194	900
+8117	54129	1004194	900
+8118	62362	1004194	900
+8119	49454	1004194	900
+8120	48569	1004194	900
+8121	52236	1004194	900
+8122	49006	1004194	900
+8123	45963	200	1800
+8124	62176	200	1800
+8125	62083	1004207	3600
+8126	52590	1004207	3600
+8127	49780	200	1800
+8128	49285	1004207	3600
+8129	45984	200	1800
+8130	49981	1004207	3600
+8131	56934	200	1800
+8132	61848	1004207	3600
+8133	61122	200	1800
+8134	62785	1004207	3600
+8135	63911	1004207	3600
+8136	65546	1004207	3600
+8137	65744	200	1800
+8138	65745	200	1800
+8139	57038	200	1800
+8140	58178	200	1800
+8141	58123	200	1800
+8142	48597	200	1800
+8143	46111	200	1800
+8144	65433	1004207	3600
+8145	64143	200	1800
+8146	50123	200	1800
+8147	62645	1004207	3600
+8148	56902	1004207	3600
+8149	58456	200	1800
+8150	45889	131	3600
+8151	46077	131	3600
+8152	45858	1004196	3600
+8153	45852	1004196	3600
+8154	48604	1004195	3600
+8155	48716	1004196	3600
+8156	48698	1004195	3600
+8157	48522	1004195	3600
+8158	48716	1004195	3600
+8159	48676	1004195	3600
+8160	46084	1004195	3600
+8161	46084	1004196	3600
+8162	48604	1004196	3600
+8163	46072	1004196	3600
+8164	45762	1004195	3600
+8165	50259	1004195	3600
+8166	50259	1004196	3600
+8167	49836	1004196	3600
+8168	45946	1004196	3600
+8169	49031	1004195	3600
+8170	62133	1004196	3600
+8171	50853	1004196	3600
+8172	49922	1004196	3600
+8173	49073	1004195	3600
+8174	61058	1004195	3600
+120	45866	19010	300
+121	45866	19020	300
+122	45866	19030	300
+123	45866	19040	300
+124	45866	19050	300
+125	45866	19060	300
+126	45866	19070	300
+130	45866	19080	300
+131	45866	19090	300
+6042	45866	1002960	300
+7058	45866	1003490	300
+-571	63923	18030	900
+-572	63923	18040	900
+-573	63923	18050	900
+-574	63923	18060	900
+-575	63923	18070	900
+-576	63923	18080	900
+-577	63923	18090	900
+-578	63923	18100	900
 \.
 
 
@@ -7991,7 +8095,7 @@ COPY sim_order_result_map (sim_order_result_map_id, clinical_item_id, sim_result
 -- Name: sim_order_result_map_sim_order_result_map_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
 --
 
-SELECT pg_catalog.setval('sim_order_result_map_sim_order_result_map_id_seq', 8016, true);
+SELECT pg_catalog.setval('public.sim_order_result_map_sim_order_result_map_id_seq', 8174, true);
 
 
 --
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_patient.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_patient.dump.sql
new file mode 100644
index 00000000..4673e1bd
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_patient.dump.sql
@@ -0,0 +1,466 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.9
+-- Dumped by pg_dump version 9.6.9
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_patient; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_patient (sim_patient_id, name, age_years, gender, study_list) FROM stdin;
+23	(User3) Hematemesis, Alcohol	59	Male	\N
+31	(User4) Hematemesis, Alcohol	59	Male	\N
+35	(User5) Hematemesis, Alcohol	59	Male	\N
+39	(User6) Hematemesis, Alcohol	59	Male	\N
+7	(xTemplate) Black vomit	57	Male	\N
+9	(Template) Chest Pain	62	Male	\N
+22	(Template) Diabetic Fatigue	26	Female	\N
+6	(xTemplate) DLBCL Fever	32	Male	\N
+48	(Template) Headache	25	Female	\N
+49	(Template) Palpitations	66	Female	\N
+13	(User1) Chest Pain	62	Male	\N
+16	(User1) Diabetic fatigue	26	Female	\N
+14	(User1) DLBCL Fever	32	Male	\N
+15	(User1) Vomiting	57	Male	\N
+18	(User2) Black vomit	57	Male	\N
+19	(User2) Chest Pain	62	Male	\N
+17	(User2) Diabetic fatigue	26	Female	\N
+20	(User2) DLBCL Fever	32	Male	\N
+24	(User3) Chest Pain	62	Male	\N
+8	(User3) Diabetic fatigue	26	Female	\N
+25	(User3) DLBCL Fever	32	Male	\N
+29	(User4) Chest Pain	62	Male	\N
+28	(User4) Diabetic Fatigue	26	Female	\N
+30	(User4) DLBCL Fever	32	Male	\N
+32	(User5) Chest Pain	62	Male	\N
+33	(User5) Diabetic Fatigue	26	Female	\N
+34	(User5) DLBCL Fever	32	Male	\N
+37	(User6) Chest Pain	62	Male	\N
+36	(User6) Diabetic fatigue	26	Female	\N
+38	(User6) DLBCL Fever	32	Male	\N
+61	(Template) Shortness of Breath	70	Male	\N
+65	xx_chestpain	62	Male	\N
+66	xx_feverb	30	Male	\N
+67	xx_headache	25	Female	\N
+68	xx_hematemesis	59	Male	\N
+69	xx_palpitations	66	Female	\N
+70	xx_shortness	70	Male	\N
+71	xx_JT_chestpain	62	Male	\N
+72	xx_JT_diabeticfatigue	26	Female	\N
+83	zz_KS_Diabeticfatigue	26	Female	\N
+84	zz_KS_ChestPain	62	Male	\N
+85	nn_sk_chestpain	62	Male	\N
+86	nn_sk_diabeticfatigue	26	Female	\N
+87	nn_sk_feverb	30	Male	\N
+89	nn_sk_hematemesis	59	Male	\N
+92	qq_jc_chest_pain	62	Male	\N
+93	qq_jc_diabetic_fatigue	26	Female	\N
+98	minh_shortness	70	Male	\N
+99	yyy_shortness	70	Male	\N
+101	user_script_testing	30	Male	\N
+102	user_script_testing_feverb	30	Male	\N
+103	user_script_testing_headache	25	Female	\N
+104	user_test_scripting_hematemesis	59	Male	\N
+105	user_script_testing_hematemesis	66	Female	\N
+106	user_script_testing_palpitations	66	Female	\N
+107	user_script_testing_shortness_of_breath	70	Male	\N
+108	user_script_hematemesis2	59	Male	\N
+109	son_5_shortness	70	Male	\N
+110	mmm_s5_chestpain	62	Male	\N
+116	rrr_sw6_chest_pain	62	Male	\N
+117	rrr_sw6_diabetic_fatigue	26	Female	\N
+123	tester3	59	Male	\N
+124	sw_testing_why	59	Male	\N
+125	rrr_JC_REVIEW	59	Male	\N
+126	hema_tem	59	Male	\N
+127	eee_al_7_chestpain	62	Male	\N
+128	eee_al_7_diabetic_fatigue	26	Female	\N
+134	zzz_testing	59	Male	\N
+135	zzz_hema2	59	Male	\N
+136	zzz_hema3	59	Male	\N
+137	zzzz_gl8_chestpain	62	Male	\N
+138	zzzz_gl8_diabeticFatigue	26	Female	\N
+144	zzzzzz_ph9_chestpain	62	Male	\N
+145	zzzzzz_ph9_diabeticfatigue	26	Female	\N
+151	zzzzzzz_kh10_chestpain	62	Male	\N
+152	qqq1_feverb_	30	Male	\N
+153	1_test_he	59	Male	\N
+154	zzzzzzz_kh10_diabeticfatigue	26	Female	\N
+160	zzzzzzzz_r11_chestpain	62	Male	\N
+161	zzzzzzzz_r11_diabeticfatigue	26	Female	\N
+167	Z_r12_chestpain	62	Male	\N
+168	zzzzzzzzz_r12_chestpain	62	Male	\N
+169	zzzzzzzzz_r12_diabeticfatigue_one	26	Female	\N
+175	zzzzzzzzzz_r13_chestpain	62	Male	\N
+176	zzzzzzzzzz_r14_diabeticFatigue	26	Female	\N
+178	zzzzzzzzzz_r13_diabeticFatigue	26	Female	\N
+183	palptest	66	Female	\N
+189	zzzzzzzzzz_r14_chestPain	62	Male	\N
+190	zzzzzzzzzz_15_chestpain	62	Male	\N
+191	zzzzzzzzzz_15_diabeticFatigue	26	Female	\N
+194	zzzzzzzzzz_15_hematemesis	59	Male	\N
+197	zzzzzzzzzz_r15_chestPain	62	Male	\N
+198	zzzzzzzzzz_r15_diabeticFatigue	26	Female	\N
+200	zzzzzzzzzz_r16_chestPain	62	Male	\N
+201	zzzzzzzzzz_r16_chestPain	62	Male	\N
+202	zzzzzzzzzz_r16_diabeticFatigue	26	Female	\N
+208	zzzzzzzzzz_r17_chestPain	62	Male	\N
+209	zzzzzzzzzz_r17_diabeticFatigue	26	Female	\N
+215	zzzzzzzzzz_r18_chestPain	62	Male	\N
+216	zzzzzzzzzz_r18_diabeticFatigue	26	Female	\N
+217	zzzzzzzzzz_r18_feverB	30	Male	\N
+218	zzzzzzzzzz_r18_headache	25	Female	\N
+219	zzzzzzzzzz_r18_hematemesis	59	Male	\N
+220	zzzzzzzzzz_r18_palpitations	66	Female	\N
+221	zzzzzzzzzz_r18_shortnessBreath	70	Male	\N
+222	zzzzzzzzzz_r19_chestPain	62	Male	\N
+223	zzzzzzzzzz_r19_diabeticFatigue	26	Female	\N
+224	zzzzzzzzz_r19_feverB	30	Male	\N
+225	zzzzzzzzz_r19_headache	25	Female	\N
+226	zzzzzzzzz_r19_hematemesis	59	Male	\N
+228	zzzzzzzzz_r19_headache	25	Female	\N
+232	zzzzzzzzzz_shortness_breath	70	Male	\N
+234	zzzzzzzzzz_r20_chestPain	62	Male	\N
+235	zzzzzzzzzz_r20_diabeticFatigue	26	Female	\N
+241	palp_test	66	Female	\N
+242	palptest2	66	Female	\N
+243	palp_test3	66	Female	\N
+244	zzzzzzzzzz_r21_chestPain	62	Male	\N
+245	zzzzzzzzzz_r21_diabeticFatigue	26	Female	\N
+251	zzzzzzzzzz_r22_chestpain	62	Male	\N
+252	zzzzzzzzzz_r22_diabetic_fatigue	26	Female	\N
+253	zzzzzzzzzz_r21_feverB	30	Male	\N
+254	zzzzzzzzzz_r21_headache	25	Female	\N
+256	zzzzzzzzz_r22_feverB	30	Male	\N
+259	zzzzzzzzzz_r21_hematemesis	59	Male	\N
+260	zzzzzzzzzz_r21_palpitations	66	Female	\N
+262	zzzzzzzzzz_r21_palpitations	66	Female	\N
+264	testSTAR	66	Female	\N
+265	zzzzzzzzzz_r23_chestpain	62	Male	\N
+266	zzzzzzzzzz_r23_diabeticFatigue	26	Female	\N
+272	testingcase_palpitations	66	Female	\N
+273	zzzzzzzzzz_r24_chestpain	62	Male	\N
+274	zzzzzzzzzz_r24_diabetic_fatigue	26	Female	\N
+280	zzzzzzzzzz_r25_chestpain	62	Male	\N
+281	zzzzzzzzzz_r25_diabeticFatigue	26	Female	\N
+284	zzzzzzzzzz_R25_hematemesis	59	Male	\N
+288	zzzzzzzzzz_r26_chestPain	62	Male	\N
+289	zzzzzzzzzz_r26_diabeticFatigue	26	Female	\N
+290	zzzzzzzzz_r26_feverB	30	Male	\N
+295	zzzzzzzzzz_shortnessBreath	70	Male	\N
+297	zzzzzzzzzz_r27_chestPain	62	Male	\N
+298	zzzzzzzzzz_r27_diabeticFatigue	26	Female	\N
+304	zzzzzzzzzz_ls_test	59	Male	\N
+305	zzzzzzzzzz_r28_hematemesis	59	Male	\N
+306	hematemesis_lisa_shieh	59	Male	\N
+307	LISASHIEH_HEADACHE	25	Female	\N
+308	zzzzzzzzzz_jh_meningitis	25	Female	\N
+309	zzzzzzzzzz_r29_gibleed	59	Male	\N
+310	zzzzzzzzzz_r30_palpitations	66	Female	\N
+311	zzzzzzzzzz_r31	30	Male	\N
+312	zzzzzzzzzz_r31_shortness	70	Male	\N
+313	zzzzzzzzzz_r31_feverb	30	Male	\N
+314	zzzzzzzzzzz_r32_	26	Female	\N
+315	zzzzzzzzzzz_r32_chestpain	62	Male	\N
+318	zzzzzzzzzz_r31_hematemesis	59	Male	\N
+322	zzzzzzzzzzz_r33_diabeticFatigue	26	Female	\N
+323	zzzzzzzzzzz_r33_chestPain	62	Male	\N
+329	zzzzzzzzzzz_33_chestPain	62	Male	\N
+330	zzzzzzzzzzz_33_diabeticFatigue	26	Female	\N
+331	zzzzzzzzzzz_33_feverB	30	Male	\N
+332	zzzzzzzzzzz_33_headache	25	Female	\N
+333	zzzzzzzzzzz_33_hematemesis	59	Male	\N
+334	zzzzzzzzzzz_33_palpitations	66	Female	\N
+335	zzzzzzzzzzz_33_shortnessBreath	70	Male	\N
+336	zzzzzzzzzzz_34_hematemesis	59	Male	\N
+337	zzzzzzzzzzz_34_hematemesis	59	Male	\N
+338	zzzzzzzzzz_34_hematemesis	59	Male	\N
+344	zzzzzzzzzzz_r35_	62	Male	\N
+345	zzzzzzzzzzzz_r35_chestPain	62	Male	\N
+346	zzzzzzzzzzzz_r35_diabeticFatigue	26	Female	\N
+352	zzzzzzzzzzzz_r36_chestPain	62	Male	\N
+353	zzzzzzzzzzzz_r36_diabeticFatigue	26	Female	\N
+354		30	Male	\N
+360	zzzzzzzzzzzz_r37_chestPain	62	Male	\N
+361	zzzzzzzzzzzz_r37_diabeticFatigue	26	Female	\N
+367	zzzzzzzzzzzz_r38_chestPain	62	Male	\N
+368	zzzzzzzzzzzz_r38_diabeticFatigue	26	Female	\N
+374	mel_test	26	Female	\N
+375	zzzzzzzzzzzz_r39_chestpain	62	Male	\N
+376	zzzzzzzzzzzz_r39_diabeticFatigue	26	Female	\N
+382	zzzzzzzzzzzz_r40	26	Female	\N
+383	zzzzzzzzzzzz_r40	26	Female	\N
+384	zzzzzzzzzzzz_r40_chestpain	62	Male	\N
+390	zzzzzzzzzzzz_r41_chestPain	62	Male	\N
+391	zzzzzzzzzzzz_r41_diabeticFatigue	26	Female	\N
+397	zzzzzzzzzzzz_r42_chestpain	62	Male	\N
+398	zzzzzzzzzzzz_r42_diabeticFatigue	26	Female	\N
+409	zzzzzzzzzzzz_r43_diabeticFatigue	26	Female	\N
+410	zzzzzzzzzzzz_r43_chestPain	62	Male	\N
+411	zzzzzzzzzzzzzz_feverB	30	Male	\N
+412	zzzzzzzzzzzz_r44_chestPain	62	Male	\N
+413	zzzzzzzzzzzz_r44_diabeticFatigue	26	Female	\N
+419	zzzzzzzzzzzz_r45_chestPain	62	Male	\N
+420	zzzzzzzzzzzz_r45_diabeticFatigue	26	Female	\N
+425	zzzzzzzzzzzz_r44_shortnessBreath	70	Male	\N
+428	zzzzzzzzzzzz_r46_chestPain	62	Male	\N
+429	zzzzzzzzzzzz_r46_diabeticFatigue	26	Female	\N
+434	zzzzzzzzzzzz_r47_diabeticFatigue	26	Female	\N
+435	zzzzzzzzzzzz_r47_chestPain	62	Male	\N
+436	zzzzzzzzzzzz_r47_feverB	30	Male	\N
+437	zzzzzzzzzzzz_r47_headache	25	Female	\N
+438	zzzzzzzzzzzz_r47_hematemesis	59	Male	\N
+439	zzzzzzzzzzzz_r47_palpitations	66	Female	\N
+440	zzzzzzzzzzzz_r47_shortnessBreath	70	Male	\N
+441	zzzzzzzzzzzz_r48_chestPain	62	Male	\N
+442	zzzzzzzzzzzz_r48_diabeticFatigue	26	Female	\N
+443	zzzzzzzzzzzz_r48_feverB	30	Male	\N
+444	zzzzzzzzzzzz_r48_headache	25	Female	\N
+445	zzzzzzzzzzzz_r48_hematemesis	59	Male	\N
+446	zzzzzzzzzzzz_r48_palpitations	66	Female	\N
+447	zzzzzzzzzzzz_r48_shortnessBreath	70	Male	\N
+448	zzzzzzzzzzzz_r49_chestPain	62	Male	\N
+449	zzzzzzzzzzzz_r49_diabeticFatigue	26	Female	\N
+455	zzzzzzzzzzzz_r50_chestPain	62	Male	\N
+456	zzzzzzzzzzzz_r50_diabeticFatigue	26	Female	\N
+5	(Template) Hematemesis	59	Male	OrderRex.Usability.Scoring
+50	(Template) Fever B	30	Male	OrderRex.Usability.Scoring
+73	xx_JT_feverB	30	Male	OrderRex.Usability.Scoring
+74	xx_JT_headache	25	Female	OrderRex.Usability.Scoring
+75	xx_JT_hematemesis	59	Male	OrderRex.Usability.Scoring
+76	xx_JT_palpitations	66	Female	OrderRex.Usability.Scoring
+77	zz_KS_feverb	30	Male	OrderRex.Usability.Scoring
+78	zz_KS_Headache	25	Female	OrderRex.Usability.Scoring
+79	zz_KS_hematemesis	59	Male	OrderRex.Usability.Scoring
+80	zz_KS_palpitations	66	Female	OrderRex.Usability.Scoring
+81	zz_ks_shortness	70	Male	OrderRex.Usability.Scoring
+82	xx_JT_shortness	70	Male	OrderRex.Usability.Scoring
+88	nn_sk_headache	25	Female	OrderRex.Usability.Scoring
+90	nn_sk_palpitations	66	Female	OrderRex.Usability.Scoring
+91	 nn_sk_shortness	70	Male	OrderRex.Usability.Scoring
+94	qq_jc_feverb	30	Male	OrderRex.Usability.Scoring
+95	qq_jc_hematemesis	59	Male	OrderRex.Usability.Scoring
+96	qq_jc_palpitations	66	Female	OrderRex.Usability.Scoring
+97	qq_jc_shortness_breath	70	Male	OrderRex.Usability.Scoring
+100	qq_jc_headache	25	Female	OrderRex.Usability.Scoring
+111	mmm_s5_feverb	30	Male	OrderRex.Usability.Scoring
+112	mmm_s5_headache	25	Female	OrderRex.Usability.Scoring
+113	mmm_s5_hematemiss	59	Male	OrderRex.Usability.Scoring
+114	mmm_s5_palpitations	66	Female	OrderRex.Usability.Scoring
+115	mmm_s5_sob	70	Male	OrderRex.Usability.Scoring
+118	rrr_sw6_feverb	30	Male	OrderRex.Usability.Scoring
+119	rrr_sw6_headache	25	Female	OrderRex.Usability.Scoring
+120	rrr_sw6_hematemesis	59	Male	OrderRex.Usability.Scoring
+121	rrr_sw6_palpitations	66	Female	OrderRex.Usability.Scoring
+122	rrr_sw6_shortness_breath	70	Male	OrderRex.Usability.Scoring
+129	eee_al_7_feverb	30	Male	OrderRex.Usability.Scoring
+130	eee_al_7_headache	25	Female	OrderRex.Usability.Scoring
+131	eee_al_7_hematemesis	59	Male	OrderRex.Usability.Scoring
+132	eee_al_7_palpitations	66	Female	OrderRex.Usability.Scoring
+133	eee_al_7_shortness	70	Male	OrderRex.Usability.Scoring
+139	zzzz_gl8_feverB	30	Male	OrderRex.Usability.Scoring
+140	zzzz_gl8_headache	25	Female	OrderRex.Usability.Scoring
+141	zzzz_gl8_hematemesis	59	Male	OrderRex.Usability.Scoring
+142	zzzz_gl8_palpitations	66	Female	OrderRex.Usability.Scoring
+143	zzzz_gl8_shortness	70	Male	OrderRex.Usability.Scoring
+146	zzzzzz_ph9_feverB	30	Male	OrderRex.Usability.Scoring
+147	zzzzzz_ph9_headache	25	Female	OrderRex.Usability.Scoring
+148	zzzzzz_ph9_hematemesis	59	Male	OrderRex.Usability.Scoring
+149	zzzzzz_ph9_palpitations	66	Female	OrderRex.Usability.Scoring
+150	zzzzzz_ph9_shortness	70	Male	OrderRex.Usability.Scoring
+155	zzzzzzz_kh10_feverb	30	Male	OrderRex.Usability.Scoring
+156	zzzzzzz_kh10_headache	25	Female	OrderRex.Usability.Scoring
+157	zzzzzzz_kh10_hematemesis	59	Male	OrderRex.Usability.Scoring
+158	zzzzzzz_kh10_palpitations	66	Female	OrderRex.Usability.Scoring
+159	zzzzzzz_kh10_shortness	70	Male	OrderRex.Usability.Scoring
+162	zzzzzzzz_r11_feverb	30	Male	OrderRex.Usability.Scoring
+163	zzzzzzzz_r11_headache	25	Female	OrderRex.Usability.Scoring
+164	zzzzzzzz_r11_hematemesis	59	Male	OrderRex.Usability.Scoring
+165	zzzzzzzz_r11_palpitations	66	Female	OrderRex.Usability.Scoring
+166	zzzzzzzz_r11_shortness	70	Male	OrderRex.Usability.Scoring
+170	zzzzzzzzz_r12_feverb	30	Male	OrderRex.Usability.Scoring
+171	zzzzzzzzz_r12_headache	25	Female	OrderRex.Usability.Scoring
+172	zzzzzzzzz_r12_hematemesis	59	Male	OrderRex.Usability.Scoring
+173	zzzzzzzzz_r12_palpitations	66	Female	OrderRex.Usability.Scoring
+174	zzzzzzzzz_r12_shortness	70	Male	OrderRex.Usability.Scoring
+177	zzzzzzzzzz_13_feverB	30	Male	OrderRex.Usability.Scoring
+179	zzzzzzzzzz_r13_headache	25	Female	OrderRex.Usability.Scoring
+180	zzzzzzzzzz_r13_hematemesis	59	Male	OrderRex.Usability.Scoring
+181	zzzzzzzzzz_r13_palpitations	66	Female	OrderRex.Usability.Scoring
+182	zzzzzzzzzz_r13_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+184	zzzzzzzzzz_r14_feverB	30	Male	OrderRex.Usability.Scoring
+185	zzzzzzzzzz_r14_headache	25	Female	OrderRex.Usability.Scoring
+186	zzzzzzzzzz_r14_hematemesis	59	Male	OrderRex.Usability.Scoring
+187	zzzzzzzzzz_r14_palpitations	66	Female	OrderRex.Usability.Scoring
+188	zzzzzzzzzz_r14_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+192	zzzzzzzzzz_r15_feverB	30	Male	OrderRex.Usability.Scoring
+193	zzzzzzzzzz_r15_headache	25	Female	OrderRex.Usability.Scoring
+195	zzzzzzzzzz_r15_palpitations	66	Female	OrderRex.Usability.Scoring
+196	zzzzzzzzzz_r15_shortnessofBreath	70	Male	OrderRex.Usability.Scoring
+199	zzzzzzzzzz_r15_hematemesis	59	Male	OrderRex.Usability.Scoring
+203	zzzzzzzzzz_r16_feverB	30	Male	OrderRex.Usability.Scoring
+204	zzzzzzzzzz_r16_headache	25	Female	OrderRex.Usability.Scoring
+205	zzzzzzzzzz_r16_hematemesis	59	Male	OrderRex.Usability.Scoring
+206	zzzzzzzzzz_r16_palpitations	66	Female	OrderRex.Usability.Scoring
+207	zzzzzzzzzz_r16_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+210	zzzzzzzzzz_r17_feverB	30	Male	OrderRex.Usability.Scoring
+211	zzzzzzzzzz_r17_headache	25	Female	OrderRex.Usability.Scoring
+212	zzzzzzzzzz_r17_hematemesis	59	Male	OrderRex.Usability.Scoring
+213	zzzzzzzzzz_r17_palpitations	66	Female	OrderRex.Usability.Scoring
+214	zzzzzzzzzz_r17_shortness	70	Male	OrderRex.Usability.Scoring
+227	zzzzzzzzzz_r19_feverB	30	Male	OrderRex.Usability.Scoring
+229	zzzzzzzzzz_r19_headache	25	Female	OrderRex.Usability.Scoring
+230	zzzzzzzzzz_r19_hematemesis	59	Male	OrderRex.Usability.Scoring
+231	zzzzzzzzzz_r19_palpitations	66	Female	OrderRex.Usability.Scoring
+233	zzzzzzzzzz_r19_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+236	zzzzzzzzzz_r20_feverB	30	Male	OrderRex.Usability.Scoring
+237	zzzzzzzzzz_r20_headache	25	Female	OrderRex.Usability.Scoring
+238	zzzzzzzzzz_r20_hematemesis	59	Male	OrderRex.Usability.Scoring
+239	zzzzzzzzzz_r20_palpitations	66	Female	OrderRex.Usability.Scoring
+240	zzzzzzzzzz_r20_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+246	zzzzzzzzzz_r21_feverB	30	Male	OrderRex.Usability.Scoring
+247	zzzzzzzzzz_r21_headache	25	Female	OrderRex.Usability.Scoring
+248	zzzzzzzzzz_r21_hematemesis	59	Male	OrderRex.Usability.Scoring
+249	zzzzzzzzzz_r21_palpitations	66	Female	OrderRex.Usability.Scoring
+250	zzzzzzzzzz_r21_shortnessBreath 	70	Male	OrderRex.Usability.Scoring
+255	zzzzzzzzzz_r22_hematemesis	59	Male	OrderRex.Usability.Scoring
+257	zzzzzzzzzz_r22_feverB	30	Male	OrderRex.Usability.Scoring
+258	zzzzzzzzzz_r22_headache	25	Female	OrderRex.Usability.Scoring
+261	zzzzzzzzzz_r22_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+263	zzzzzzzzzz_r22_palpitations	66	Female	OrderRex.Usability.Scoring
+267	zzzzzzzzzz_r23_feverB	30	Male	OrderRex.Usability.Scoring
+268	zzzzzzzzzz_r23_headache	25	Female	OrderRex.Usability.Scoring
+269	zzzzzzzzzz_r23_hematemesis	59	Male	OrderRex.Usability.Scoring
+270	zzzzzzzzzz_r23_palpitations	66	Female	OrderRex.Usability.Scoring
+271	zzzzzzzzzz_r23_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+275	zzzzzzzzzz_r24_feverB	30	Male	OrderRex.Usability.Scoring
+276	zzzzzzzzzz_r24_headache	25	Female	OrderRex.Usability.Scoring
+277	zzzzzzzzzz_r24_hematemesis	59	Male	OrderRex.Usability.Scoring
+278	zzzzzzzzzz_r24_palpitations	66	Female	OrderRex.Usability.Scoring
+279	zzzzzzzzzz_r24_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+282	zzzzzzzzzz_r25_feverB	30	Male	OrderRex.Usability.Scoring
+283	zzzzzzzzzz_r25_headache	25	Female	OrderRex.Usability.Scoring
+285	zzzzzzzzzz_r25_palpitations	66	Female	OrderRex.Usability.Scoring
+286	zzzzzzzzzz_r25_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+287	zzzzzzzzzz_r25_hematemesis	59	Male	OrderRex.Usability.Scoring
+291	zzzzzzzzzz_r26_feverB	30	Male	OrderRex.Usability.Scoring
+292	zzzzzzzzzz_r26_headache	25	Female	OrderRex.Usability.Scoring
+293	zzzzzzzzzz_r26_hematemesis	59	Male	OrderRex.Usability.Scoring
+294	zzzzzzzzzz_r26_palpitations	66	Female	OrderRex.Usability.Scoring
+296	zzzzzzzzzz_r26_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+299	zzzzzzzzzz_r27_feverB	30	Male	OrderRex.Usability.Scoring
+300	zzzzzzzzzz_r27_headache	25	Female	OrderRex.Usability.Scoring
+301	zzzzzzzzzz_r27_hematemesis	59	Male	OrderRex.Usability.Scoring
+302	zzzzzzzzzz_r27_palpitations	66	Female	OrderRex.Usability.Scoring
+303	zzzzzzzzzz_r27_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+316	zzzzzzzzzzz_r32_feverB	30	Male	OrderRex.Usability.Scoring
+317	zzzzzzzzzzz_r32_headache	25	Female	OrderRex.Usability.Scoring
+319	zzzzzzzzzzz_r32_palpitations	66	Female	OrderRex.Usability.Scoring
+320	zzzzzzzzzzz_r32_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+321	zzzzzzzzzzz_r32_hematemesis	59	Male	OrderRex.Usability.Scoring
+324	zzzzzzzzzzz_r33_feverB	30	Male	OrderRex.Usability.Scoring
+325	zzzzzzzzzzz_r33_headache	25	Female	OrderRex.Usability.Scoring
+326	zzzzzzzzzzz_r33_hematemesis	59	Male	OrderRex.Usability.Scoring
+327	zzzzzzzzzzz_r33_palpitations	66	Female	OrderRex.Usability.Scoring
+328	zzzzzzzzzzz_r33_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+339	zzzzzzzzzzzz_r34_hematemesis	59	Male	OrderRex.Usability.Scoring
+340	zzzzzzzzzzz_r34_palpitaitonis	66	Female	OrderRex.Usability.Scoring
+341	zzzzzzzzzzz_r34_headache	25	Female	OrderRex.Usability.Scoring
+342	zzzzzzzzzzz_r34_feverB	30	Male	OrderRex.Usability.Scoring
+343	zzzzzzzzzzz_r34_shortness	70	Male	OrderRex.Usability.Scoring
+347	zzzzzzzzzzzz_r35_feverB	30	Male	OrderRex.Usability.Scoring
+348	zzzzzzzzzzzz_r35_headache	25	Female	OrderRex.Usability.Scoring
+349	zzzzzzzzzzzz_r35_hematemesis	59	Male	OrderRex.Usability.Scoring
+350	zzzzzzzzzzzz_r35_palpitations	66	Female	OrderRex.Usability.Scoring
+351	zzzzzzzzzzzz_r35_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+355	zzzzzzzzzzzz_r36_feverB	30	Male	OrderRex.Usability.Scoring
+356	zzzzzzzzzzzz_r36_headache	25	Female	OrderRex.Usability.Scoring
+357	zzzzzzzzzzzz_r36_hematemesis	59	Male	OrderRex.Usability.Scoring
+358	zzzzzzzzzzzz_r36_palpitations	66	Female	OrderRex.Usability.Scoring
+359	zzzzzzzzzzzz_r36_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+362	zzzzzzzzzzzz_r37_feverB	30	Male	OrderRex.Usability.Scoring
+363	zzzzzzzzzzzz_r37_headache	25	Female	OrderRex.Usability.Scoring
+364	zzzzzzzzzzzz_r37_hematemesis	59	Male	OrderRex.Usability.Scoring
+365	zzzzzzzzzzzz_r37_palpitations	66	Female	OrderRex.Usability.Scoring
+366	zzzzzzzzzzzz_r37_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+369	zzzzzzzzzzzz_r38_feverB	30	Male	OrderRex.Usability.Scoring
+370	zzzzzzzzzzzz_r38_headache	25	Female	OrderRex.Usability.Scoring
+371	zzzzzzzzzzzz_r38_hematemesis	59	Male	OrderRex.Usability.Scoring
+372	zzzzzzzzzzzz_r38_palpitations	66	Female	OrderRex.Usability.Scoring
+373	zzzzzzzzzzzz_r38_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+377	zzzzzzzzzzzz_r39_feverB	30	Male	OrderRex.Usability.Scoring
+378	zzzzzzzzzzzz_r39_headache	25	Female	OrderRex.Usability.Scoring
+379	zzzzzzzzzzzz_r39_hematemesis	59	Male	OrderRex.Usability.Scoring
+380	zzzzzzzzzzzz_r39_palpitations	66	Female	OrderRex.Usability.Scoring
+381	zzzzzzzzzzzz_r39_shortness	70	Male	OrderRex.Usability.Scoring
+385	zzzzzzzzzzzz_r40_headache	25	Female	OrderRex.Usability.Scoring
+386	zzzzzzzzzzzz_r40_palpitations	66	Female	OrderRex.Usability.Scoring
+387	zzzzzzzzzzzz_r40_hematem	59	Male	OrderRex.Usability.Scoring
+388	zzzzzzzzzzzz_r40_fever	30	Male	OrderRex.Usability.Scoring
+389	zzzzzzzzzzzz_r40_shortness	70	Male	OrderRex.Usability.Scoring
+392	zzzzzzzzzzzz_r41_feverB	30	Male	OrderRex.Usability.Scoring
+393	zzzzzzzzzzzz_r41_headache	25	Female	OrderRex.Usability.Scoring
+394	zzzzzzzzzzzz_r41_hematemesis	59	Male	OrderRex.Usability.Scoring
+395	zzzzzzzzzzzz_r41_palpitations	66	Female	OrderRex.Usability.Scoring
+396	zzzzzzzzzzzz_r41_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+399	zzzzzzzzzzzz_r42_feverB	30	Male	OrderRex.Usability.Scoring
+400	zzzzzzzzzzzz_r42_headache	25	Female	OrderRex.Usability.Scoring
+401	zzzzzzzzzzzz_r42_hematemesis	59	Male	OrderRex.Usability.Scoring
+402	zzzzzzzzzzzz_r42_palpitations	66	Female	OrderRex.Usability.Scoring
+403	zzzzzzzzzzzz_r42_shortness	70	Male	OrderRex.Usability.Scoring
+404	zzzzzzzzzzzz_r43_feverB	30	Male	OrderRex.Usability.Scoring
+405	zzzzzzzzzzzz_r43_headache	25	Female	OrderRex.Usability.Scoring
+406	zzzzzzzzzzzz_r43_palpitations	66	Female	OrderRex.Usability.Scoring
+407	zzzzzzzzzzzz_r43_hematemesis	59	Male	OrderRex.Usability.Scoring
+408	zzzzzzzzzzzz_r43_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+414	zzzzzzzzzzzz_r44_feverB	30	Male	OrderRex.Usability.Scoring
+415	zzzzzzzzzzzz_r44_headache	25	Female	OrderRex.Usability.Scoring
+416	zzzzzzzzzzzz_r44_hematemesis	59	Male	OrderRex.Usability.Scoring
+417	zzzzzzzzzzzz_r44_palipitations	66	Female	OrderRex.Usability.Scoring
+418	zzzzzzzzzzzz_r44_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+421	zzzzzzzzzzzz_r45_feverB	30	Male	OrderRex.Usability.Scoring
+422	zzzzzzzzzzzz_r45_headache	25	Female	OrderRex.Usability.Scoring
+423	zzzzzzzzzzzz_r45_hematemesis	59	Male	OrderRex.Usability.Scoring
+424	zzzzzzzzzzzz_r45_palpitations	66	Female	OrderRex.Usability.Scoring
+426	zzzzzzzzzzzz_r45_shortness	70	Male	OrderRex.Usability.Scoring
+427	zzzzzzzzzzzz_r46_feverB	30	Male	OrderRex.Usability.Scoring
+430	zzzzzzzzzzzz_r46_headache	25	Female	OrderRex.Usability.Scoring
+431	zzzzzzzzzzzz_r46_hematemesis	59	Male	OrderRex.Usability.Scoring
+432	zzzzzzzzzzzz_r46_palpitations	66	Female	OrderRex.Usability.Scoring
+433	zzzzzzzzzzzz_r46_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+450	zzzzzzzzzzzz_r49_feverB	30	Male	OrderRex.Usability.Scoring
+451	zzzzzzzzzzzz_r49_headache	25	Female	OrderRex.Usability.Scoring
+452	zzzzzzzzzzzz_r49_hematemesis	59	Male	OrderRex.Usability.Scoring
+453	zzzzzzzzzzzz_r49_palpitations	66	Female	OrderRex.Usability.Scoring
+454	zzzzzzzzzzzz_r49_shortnessBreath	70	Male	OrderRex.Usability.Scoring
+457	zzzzzzzzzzzz_r50_feverB	30	Male	OrderRex.Usability.Scoring
+458	zzzzzzzzzzzz_r50_headache	25	Female	OrderRex.Usability.Scoring
+459	zzzzzzzzzzzz_r50_hematemesis	59	Male	OrderRex.Usability.Scoring
+460	zzzzzzzzzzzz_r50_palpitations	66	Female	OrderRex.Usability.Scoring
+461	zzzzzzzzzzzz_r50_shortness	70	Male	OrderRex.Usability.Scoring
+\.
+
+
+--
+-- Name: sim_patient_sim_patient_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_patient_sim_patient_id_seq', 461, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_patient_order.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_patient_order.dump.sql
new file mode 100644
index 00000000..89120799
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_patient_order.dump.sql
@@ -0,0 +1,5291 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_patient_order; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_patient_order (sim_patient_order_id, sim_user_id, sim_patient_id, clinical_item_id, relative_time_start, relative_time_end, sim_state_id) FROM stdin;
+125	9	13	44256	0	\N	3
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+5546	80	452	49481	5160	\N	16
+5547	80	452	45788	5220	\N	2
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+5667	81	457	44198	1440	\N	5001
+5668	81	457	50554	1500	\N	5003
+5669	81	457	48960	1560	\N	5003
+5670	81	459	49481	60	\N	14
+5671	81	459	45770	60	\N	14
+5672	81	459	45771	60	\N	14
+5673	81	459	43996	60	\N	14
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+5677	81	459	45948	60	\N	14
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+5681	81	459	44001	960	\N	15
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+5683	81	459	63720	1080	\N	2
+5684	81	459	45869	1080	\N	2
+5685	81	459	62029	1080	\N	2
+\.
+
+
+--
+-- Name: sim_patient_order_sim_patient_order_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_patient_order_sim_patient_order_id_seq', 5685, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_patient_state.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_patient_state.dump.sql
new file mode 100644
index 00000000..c5241567
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_patient_state.dump.sql
@@ -0,0 +1,1309 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_patient_state; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_patient_state (sim_patient_state_id, sim_patient_id, sim_state_id, relative_time_start, relative_time_end) FROM stdin;
+9	9	3	-7200	\N
+13	6	6	-14400	\N
+7	7	7	-7200	\N
+15	13	3	-7200	\N
+16	14	6	-14400	\N
+17	15	7	-7200	\N
+20	18	7	-7200	\N
+21	19	3	-7200	\N
+22	20	6	-14400	\N
+23	5	13	-7200	0
+42	23	13	-7200	0
+44	24	3	-7200	\N
+45	25	6	-14400	\N
+8	8	9	-36000	0
+18	16	9	-36000	0
+19	17	9	-36000	0
+41	22	9	-36000	0
+49	16	21	0	\N
+50	17	21	0	\N
+51	22	21	0	\N
+47	8	21	0	1440
+53	8	18	1440	\N
+43	23	14	0	0
+54	23	17	0	0
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+56	23	14	1620	1620
+57	23	14	1620	4800
+58	23	16	4800	4860
+59	23	2	4860	\N
+63	28	9	-36000	0
+65	29	3	-7200	\N
+66	30	6	-14400	\N
+67	31	13	-7200	0
+64	28	21	0	2580
+69	28	18	2580	\N
+68	31	14	0	0
+70	31	17	0	0
+71	31	15	0	1680
+72	31	14	1680	1860
+73	31	16	1860	2940
+74	31	2	2940	\N
+75	32	3	-7200	\N
+76	33	9	-36000	0
+78	34	6	-14400	\N
+79	35	13	-7200	0
+77	33	21	0	1980
+81	33	18	1980	5580
+82	33	20	5580	\N
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+89	35	14	1560	1560
+90	35	16	1560	3720
+91	35	2	3720	\N
+92	36	9	-36000	0
+94	37	3	-7200	\N
+95	38	6	-14400	\N
+96	39	13	-7200	0
+93	36	21	0	2220
+98	36	18	2220	5820
+99	36	20	5820	\N
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+106	39	16	2280	2520
+107	39	2	2520	\N
+120	48	30	0	\N
+178	61	10	0	\N
+600	49	40	0	\N
+187	65	3	-7200	\N
+188	66	5000	0	\N
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+190	68	13	-7200	0
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+192	69	40	0	\N
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+199	75	13	-7200	0
+204	79	13	-7200	0
+196	72	21	0	1200
+208	72	18	1200	\N
+197	73	5000	0	60
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+210	73	5003	960	\N
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+213	75	15	960	960
+214	75	17	960	960
+215	75	15	960	960
+216	75	14	960	1200
+217	75	16	1200	1320
+218	75	2	1320	\N
+201	76	40	0	600
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+220	76	43	600	720
+221	76	41	720	\N
+222	82	10	0	60
+223	82	11	60	4740
+224	82	8	4740	\N
+198	74	30	0	960
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+228	84	3	-7200	\N
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+229	83	18	960	\N
+202	77	5000	0	60
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+231	77	5003	1680	\N
+205	79	14	0	60
+232	79	17	60	60
+233	79	15	60	1860
+234	79	4	1860	\N
+203	78	30	0	2760
+235	78	31	2760	\N
+206	80	40	0	60
+236	80	45	60	60
+237	80	43	60	780
+238	80	41	780	\N
+207	81	10	0	60
+239	81	11	60	6000
+240	81	8	6000	\N
+241	85	3	-7200	\N
+24	5	14	0	60
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+243	86	21	0	\N
+244	87	5000	0	\N
+246	89	13	-7200	0
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+250	92	3	-7200	\N
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+254	95	13	-7200	0
+249	91	10	0	6120
+258	91	12	6120	\N
+775	263	43	480	540
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+872	296	11	60	4860
+873	296	8	4860	\N
+861	292	30	0	960
+874	292	31	960	\N
+864	294	40	0	1020
+875	294	46	1020	1020
+876	294	43	1020	1080
+877	294	41	1080	\N
+860	291	5000	0	60
+878	291	5002	60	960
+879	291	5003	960	\N
+880	297	3	-7200	\N
+881	298	9	-36000	0
+882	298	21	0	\N
+885	301	13	-7200	0
+888	303	10	0	960
+889	303	11	960	4980
+890	303	8	4980	\N
+884	300	30	0	1020
+891	300	31	1020	\N
+883	299	5000	0	60
+892	299	5002	60	540
+893	299	5003	540	\N
+886	301	14	0	1200
+894	301	16	1200	5280
+895	301	2	5280	\N
+887	302	40	0	60
+896	302	46	60	60
+897	302	43	60	540
+898	302	45	540	540
+899	302	43	540	540
+900	302	41	540	\N
+901	304	13	-7200	0
+902	304	14	0	\N
+903	305	13	-7200	0
+904	305	14	0	\N
+905	306	13	-7200	0
+906	306	14	0	1140
+907	306	17	1140	1140
+908	306	15	1140	\N
+909	307	30	0	\N
+1325	443	5003	540	\N
+910	308	30	0	3600
+911	308	33	3600	9060
+912	308	31	9060	\N
+913	309	13	-7200	0
+914	309	14	0	300
+915	309	14	300	540
+916	309	16	540	660
+917	309	2	660	\N
+918	310	40	0	18060
+919	310	41	18060	\N
+920	311	5000	0	60
+921	311	5001	60	120
+922	311	5003	120	\N
+923	312	10	0	2460
+924	312	12	2460	2940
+925	312	8	2940	\N
+926	313	5000	0	360
+927	313	5001	360	420
+928	313	5003	420	\N
+929	314	9	-36000	0
+930	314	21	0	\N
+931	315	3	-7200	\N
+934	318	13	-7200	0
+935	318	14	0	\N
+937	320	10	0	540
+938	320	11	540	6720
+939	320	8	6720	\N
+933	317	30	0	60
+940	317	31	60	\N
+941	321	13	-7200	0
+942	321	14	0	60
+943	321	14	60	960
+944	321	17	960	960
+945	321	15	960	960
+946	321	17	960	960
+947	321	15	960	1140
+948	321	16	1140	1440
+949	321	2	1440	\N
+936	319	40	0	600
+950	319	43	600	1920
+951	319	41	1920	\N
+932	316	5000	0	60
+952	316	5002	60	960
+953	316	5003	960	\N
+954	322	9	-36000	0
+955	322	21	0	\N
+956	323	3	-7200	\N
+959	326	13	-7200	0
+957	324	5000	0	60
+963	324	5002	60	60
+964	324	5003	60	\N
+960	326	14	0	60
+965	326	14	60	60
+966	326	14	60	960
+967	326	16	960	2280
+968	326	2	2280	\N
+962	328	10	0	5340
+969	328	12	5340	5760
+970	328	8	5760	\N
+961	327	40	0	120
+971	327	43	120	600
+972	327	41	600	\N
+958	325	30	0	960
+973	325	31	960	\N
+974	329	3	-7200	\N
+975	330	9	-36000	0
+976	330	21	0	\N
+977	331	5000	0	\N
+978	332	30	0	\N
+979	333	13	-7200	0
+980	333	14	0	\N
+981	334	40	0	\N
+982	335	10	0	\N
+983	336	13	-7200	0
+984	336	14	0	\N
+985	337	13	-7200	0
+986	337	14	0	\N
+987	338	13	-7200	0
+988	338	14	0	\N
+989	339	13	-7200	0
+990	339	14	0	960
+991	339	17	960	960
+992	339	15	960	2160
+993	339	16	2160	2220
+994	339	2	2220	\N
+995	340	40	0	420
+996	340	43	420	600
+997	340	42	600	600
+998	340	43	600	960
+999	340	42	960	960
+1000	340	43	960	1080
+1001	340	41	1080	\N
+1002	341	30	0	60
+1003	341	31	60	\N
+1004	342	5000	0	60
+1005	342	5002	60	960
+1006	342	5003	960	\N
+1007	343	10	0	60
+1008	343	11	60	4920
+1009	343	8	4920	\N
+1010	344	3	-7200	\N
+1011	345	3	-7200	\N
+1012	346	9	-36000	0
+1013	346	21	0	\N
+1016	349	13	-7200	0
+1014	347	5000	0	60
+1020	347	5002	60	960
+1021	347	5003	960	\N
+1015	348	30	0	720
+1022	348	31	720	\N
+1018	350	40	0	480
+1023	350	43	480	540
+1024	350	41	540	\N
+1019	351	10	0	8160
+1025	351	12	8160	8520
+1026	351	8	8520	\N
+1017	349	14	0	60
+1027	349	17	60	60
+1028	349	15	60	960
+1029	349	16	960	1080
+1030	349	2	1080	\N
+1031	352	3	-7200	\N
+1032	353	9	-36000	0
+1033	353	21	0	\N
+1034	354	5000	0	\N
+1037	357	13	-7200	0
+1035	355	5000	0	60
+1038	357	14	0	60
+1036	356	30	0	60
+1041	356	31	60	\N
+1039	358	40	0	660
+1042	358	43	660	1440
+1043	358	41	1440	\N
+1040	359	10	0	1320
+1044	359	11	1320	\N
+1045	355	5002	60	60
+1046	355	5003	60	\N
+1047	357	14	60	1500
+1048	357	16	1500	2580
+1049	357	2	2580	\N
+1050	360	3	-7200	\N
+1051	361	9	-36000	0
+1052	361	21	0	\N
+1055	364	13	-7200	0
+1053	362	5000	0	60
+1059	362	5002	60	1440
+1060	362	5003	1440	\N
+1057	365	40	0	60
+1061	365	46	60	60
+1062	365	43	60	1620
+1063	365	45	1620	1620
+1064	365	43	1620	1680
+1065	365	41	1680	\N
+1054	363	30	0	720
+1066	363	31	720	\N
+1058	366	10	0	60
+1067	366	11	60	6000
+1068	366	8	6000	\N
+1056	364	14	0	60
+1069	364	17	60	60
+1070	364	15	60	60
+1071	364	17	60	60
+1072	364	15	60	480
+1073	364	14	480	480
+1074	364	14	480	960
+1075	364	17	960	960
+1076	364	15	960	1440
+1077	364	16	1440	1560
+1078	364	2	1560	\N
+1079	367	3	-7200	\N
+1080	368	9	-36000	0
+1081	368	21	0	\N
+1084	371	13	-7200	0
+1086	372	40	0	1860
+1088	372	46	1860	1860
+1089	372	43	1860	4020
+1090	372	41	4020	\N
+1087	373	10	0	120
+1091	373	11	120	2100
+1092	373	8	2100	\N
+1083	370	30	0	3600
+1093	370	33	3600	3780
+1094	370	31	3780	\N
+1085	371	14	0	960
+1095	371	17	960	960
+1096	371	15	960	1020
+1097	371	14	1020	2040
+1098	371	16	2040	2100
+1099	371	2	2100	\N
+1082	369	5000	0	60
+1100	369	5002	60	2280
+1101	369	5003	2280	\N
+1102	374	9	-36000	0
+1103	374	21	0	2220
+1104	374	18	2220	\N
+1105	375	3	-7200	\N
+1106	376	9	-36000	0
+1107	376	21	0	\N
+1110	379	13	-7200	0
+1113	381	10	0	60
+1114	381	11	60	5220
+1115	381	8	5220	\N
+1108	377	5000	0	60
+1116	377	5002	60	60
+1117	377	5003	60	\N
+1112	380	40	0	480
+1118	380	46	480	480
+1119	380	43	480	540
+1120	380	41	540	\N
+1109	378	30	0	1020
+1121	378	31	1020	\N
+1111	379	14	0	60
+1122	379	17	60	60
+1123	379	15	60	660
+1124	379	14	660	960
+1125	379	14	960	1260
+1126	379	14	1260	2340
+1127	379	17	2340	2340
+1128	379	15	2340	3480
+1129	379	16	3480	3660
+1130	379	2	3660	\N
+1131	382	9	-36000	0
+1132	382	21	0	\N
+1133	383	9	-36000	0
+1134	383	21	0	\N
+1135	384	3	-7200	\N
+1136	385	30	0	1560
+1137	385	31	1560	\N
+1138	386	40	0	600
+1139	386	41	600	\N
+1140	387	13	-7200	0
+1141	387	14	0	960
+1142	387	14	960	960
+1143	387	16	960	1260
+1144	387	2	1260	\N
+1145	388	5000	0	60
+1146	388	5001	60	1140
+1147	388	5003	1140	\N
+1148	389	10	0	60
+1149	389	11	60	5700
+1150	389	8	5700	\N
+1151	390	3	-7200	\N
+1152	391	9	-36000	0
+1153	391	21	0	\N
+1156	394	13	-7200	0
+1159	396	10	0	\N
+1155	393	30	0	1920
+1160	393	31	1920	\N
+1154	392	5000	0	60
+1161	392	5002	60	\N
+1158	395	40	0	480
+1162	395	46	480	480
+1163	395	43	480	480
+1164	395	41	480	\N
+1157	394	14	0	60
+1165	394	17	60	60
+1166	394	15	60	780
+1167	394	14	780	960
+1168	394	14	960	1140
+1169	394	17	1140	1140
+1170	394	15	1140	1500
+1171	394	14	1500	5100
+1172	394	15	5100	6360
+1173	394	16	6360	6480
+1174	394	2	6480	\N
+1175	397	3	-7200	\N
+1176	398	9	-36000	0
+1177	398	21	0	\N
+1180	401	13	-7200	0
+1179	400	30	0	540
+1184	400	31	540	\N
+1181	401	14	0	1320
+1185	401	14	1320	1440
+1186	401	14	1440	1440
+1187	401	14	1440	1620
+1188	401	16	1620	1800
+1189	401	2	1800	\N
+1183	403	10	0	60
+1190	403	11	60	8220
+1191	403	8	8220	\N
+1182	402	40	0	600
+1192	402	43	600	660
+1193	402	41	660	\N
+1178	399	5000	0	60
+1194	399	5002	60	960
+1195	399	5003	960	\N
+1199	407	13	-7200	0
+1202	409	9	-36000	0
+1203	409	21	0	\N
+1204	410	3	-7200	\N
+1205	411	5000	0	60
+1206	411	5001	60	60
+1207	411	5003	60	\N
+1200	407	14	0	60
+1208	407	17	60	60
+1209	407	15	60	60
+1210	407	14	60	60
+1211	407	14	60	960
+1212	407	17	960	960
+1213	407	15	960	960
+1214	407	16	960	1320
+1215	407	2	1320	\N
+1196	404	5000	0	60
+1216	404	5001	60	120
+1217	404	5003	120	\N
+1201	408	10	0	120
+1218	408	11	120	3900
+1219	408	8	3900	\N
+1198	406	40	0	60
+1220	406	41	60	\N
+1197	405	30	0	60
+1221	405	31	60	\N
+1222	412	3	-7200	\N
+1223	413	9	-36000	0
+1224	413	21	0	\N
+1227	416	13	-7200	0
+1230	418	10	0	420
+1231	418	11	420	7860
+1232	418	8	7860	\N
+1228	416	14	0	60
+1233	416	17	60	60
+1234	416	15	60	960
+1235	416	16	960	1140
+1236	416	2	1140	\N
+1226	415	30	0	2220
+1237	415	31	2220	\N
+1229	417	40	0	60
+1238	417	43	60	420
+1239	417	45	420	420
+1240	417	43	420	480
+1241	417	41	480	\N
+1225	414	5000	0	960
+1242	414	5001	960	1020
+1243	414	5003	1020	\N
+1244	419	3	-7200	\N
+1245	420	9	-36000	0
+1246	420	21	0	\N
+1249	423	13	-7200	0
+1252	425	10	0	\N
+1247	421	5000	0	60
+1253	421	5001	60	60
+1254	421	5003	60	\N
+1255	426	10	0	60
+1256	426	11	60	4800
+1257	426	8	4800	\N
+1250	423	14	0	60
+1258	423	17	60	60
+1259	423	15	60	720
+1260	423	14	720	720
+1261	423	14	720	1020
+1262	423	17	1020	1020
+1263	423	15	1020	1140
+1264	423	16	1140	1320
+1265	423	2	1320	\N
+1251	424	40	0	360
+1266	424	41	360	\N
+1248	422	30	0	60
+1267	422	31	60	\N
+1269	428	3	-7200	\N
+1270	429	9	-36000	0
+1271	429	21	0	\N
+1273	431	13	-7200	0
+1274	431	14	0	60
+1277	431	17	60	60
+1278	431	15	60	960
+1279	431	17	960	960
+1280	431	15	960	1140
+1281	431	14	1140	1140
+1282	431	14	1140	1680
+1283	431	16	1680	1980
+1284	431	2	1980	\N
+1272	430	30	0	960
+1285	430	31	960	\N
+1275	432	40	0	60
+1286	432	46	60	60
+1287	432	43	60	480
+1288	432	41	480	\N
+1268	427	5000	0	60
+1289	427	5002	60	780
+1290	427	5003	780	\N
+1276	433	10	0	1080
+1291	433	11	1080	\N
+1292	434	9	-36000	0
+1294	435	3	-7200	\N
+1297	438	13	-7200	0
+1298	438	14	0	540
+1295	436	5000	0	60
+1318	443	5000	0	60
+1323	447	10	0	420
+1293	434	21	0	1020
+1301	434	18	1020	\N
+1300	440	10	0	3720
+1326	447	11	420	4920
+1302	440	12	3720	5520
+1303	440	8	5520	\N
+1327	447	8	4920	\N
+1304	438	17	540	540
+1322	446	40	0	480
+1305	438	15	540	1020
+1321	445	14	0	960
+1306	438	14	1020	1140
+1329	445	17	960	960
+1307	438	16	1140	1260
+1308	438	2	1260	\N
+1296	437	30	0	1560
+1309	437	31	1560	\N
+1310	436	5002	60	960
+1311	436	5003	960	\N
+1299	439	40	0	120
+1330	445	15	960	960
+1312	439	45	120	120
+1313	439	43	120	1140
+1314	439	41	1140	\N
+1315	441	3	-7200	\N
+1316	442	9	-36000	0
+1317	442	21	0	\N
+1320	445	13	-7200	0
+1331	445	16	960	1380
+1332	445	2	1380	\N
+1319	444	30	0	1560
+1333	444	31	1560	\N
+1334	448	3	-7200	\N
+1335	449	9	-36000	0
+1336	449	21	0	\N
+1339	452	13	-7200	0
+1338	451	30	0	2580
+1343	451	31	2580	\N
+1337	450	5000	0	60
+1344	450	5001	60	60
+1345	450	5003	60	\N
+1342	454	10	0	60
+1346	454	11	60	6420
+1347	454	8	6420	\N
+1340	452	14	0	1380
+1348	452	14	1380	1380
+1349	452	17	1380	1380
+1350	452	15	1380	2640
+1351	452	14	2640	3660
+1352	452	14	3660	3780
+1353	452	14	3780	4980
+1354	452	14	4980	4980
+1355	452	16	4980	5160
+1356	452	2	5160	\N
+1341	453	40	0	60
+1357	453	46	60	60
+1358	453	43	60	1560
+1359	453	41	1560	\N
+1360	455	3	-7200	\N
+1361	456	9	-36000	0
+1365	459	13	-7200	0
+1362	456	21	0	1200
+1369	456	18	1200	\N
+1367	460	40	0	60
+1370	460	43	60	960
+1371	460	41	960	\N
+1368	461	10	0	960
+1372	461	11	960	4860
+1373	461	8	4860	\N
+1364	458	30	0	3600
+1374	458	33	3600	5220
+1375	458	31	5220	\N
+1363	457	5000	0	1260
+1376	457	5001	1260	1440
+1377	457	5003	1440	\N
+1366	459	14	0	60
+1378	459	17	60	60
+1379	459	15	60	60
+1380	459	17	60	60
+1381	459	15	60	960
+1382	459	16	960	960
+1383	459	2	960	\N
+\.
+
+
+--
+-- Name: sim_patient_state_sim_patient_state_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_patient_state_sim_patient_state_id_seq', 1383, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/db/definition/simdata/sim_result.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_result.dump.sql
similarity index 99%
rename from medinfo/db/definition/simdata/sim_result.dump.sql
rename to medinfo/cpoe/cpoeSim/simdata/sim_result.dump.sql
index cf7171fa..9a8356c9 100644
--- a/medinfo/db/definition/simdata/sim_result.dump.sql
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_result.dump.sql
@@ -2,19 +2,24 @@
 -- PostgreSQL database dump
 --
 
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
 SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
 SET client_encoding = 'UTF8';
 SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
 SET check_function_bodies = false;
 SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
+SET row_security = off;
 
 --
 -- Data for Name: sim_result; Type: TABLE DATA; Schema: public; Owner: -
 --
 
-COPY sim_result (sim_result_id, name, description, group_string, priority) FROM stdin;
+COPY public.sim_result (sim_result_id, name, description, group_string, priority) FROM stdin;
 11000	WBC	WBC	LAB BLOOD ORDERABLES>Hematology>Automated Blood Count	11000
 11010	HGB	HEMOGLOBIN	LAB BLOOD ORDERABLES>Hematology>Automated Blood Count	11010
 11020	HCT	HEMATOCRIT	LAB BLOOD ORDERABLES>Hematology>Automated Blood Count	11020
@@ -614,6 +619,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1000434	FREEKL	FREE KAPPA/LAMBDA RATIO	LAB BLOOD ORDERABLES>Other>FREE LIGHT CHAIN RATIO	1001121
 1000435	FREEL	FREE LAMBDA LIGHT CHAIN	LAB BLOOD ORDERABLES>Other>FREE LIGHT CHAIN RATIO	1001122
 1000436	HYST	PSEUDO-HYPHAE	URINE ORDERABLES>Other>URINALYSIS WITH MICROSCOPIC	1001131
+1000654	C3P4PC	CD3+/CD4+,%	LAB BLOOD ORDERABLES>Other>CD4 COUNT	1001454
 1000437	C1656A	CD3-/16+56(NK),ABS.	LAB BLOOD ORDERABLES>Other>T SUBSETS AND B LYMPHOCYTES, BLOOD	1001132
 1000438	C1656P	CD3-/16+56(NK)%	LAB BLOOD ORDERABLES>Other>T SUBSETS AND B LYMPHOCYTES, BLOOD	1001133
 1000439	CD19AB	CD19 (PAN B), ABS.	LAB BLOOD ORDERABLES>Other>T SUBSETS AND B LYMPHOCYTES, BLOOD	1001134
@@ -831,7 +837,6 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1000651	9247R	CPRA FOR CUMULATIVE C1Q	HISTOCOMPATIBILITY ORDERABLE>Other>HLA - SCREENING IGG	1001450
 1000652	9244R	CUMULATIVE ANTIBODIES BY IGG	HISTOCOMPATIBILITY ORDERABLE>Other>HLA - SCREENING IGG	1001452
 1000653	C3P4PA	CD3+/CD4+,ABS	LAB BLOOD ORDERABLES>Other>CD4 COUNT	1001453
-1000654	C3P4PC	CD3+/CD4+,%	LAB BLOOD ORDERABLES>Other>CD4 COUNT	1001454
 1000655	IOPTH	INTRA OPERATIVE PTH	LAB BLOOD ORDERABLES>Other>INTRA OPERATIVE PTH	1001458
 1000656	11235R	VARICELLA-ZOSTER, ACIF	STOOL ORDERABLES>Other>VZV TOTAL AND IGM AB PANEL	1001459
 1000657	11236R	VZ AB IGM, IFA	STOOL ORDERABLES>Other>VZV TOTAL AND IGM AB PANEL	1001460
@@ -1113,6 +1118,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1000933	VOL1	VOLUME SODIUM	URINE ORDERABLES>Other>SODIUM, URINE (TIMED)	1001917
 1000934	AMIPR2	DATE & TIME OF LAST DOSE	LAB BLOOD ORDERABLES>Other>AMIKACIN TROUGH LEVEL	1001918
 1000935	OXCMT1	OXCARBAZEPINE METABOLITE	LAB BLOOD ORDERABLES>Other>OXCARBAZEPINE METABOLITE LEVEL	1001919
+1001149	11896R	ARSENIC,U	URINE ORDERABLES>Other>HEAVY METALS, URINE	1002308
 1000936	GIDH1B	IDH1, BLOOD	LAB BLOOD ORDERABLES>Other>CANCER SOMATIC MUTATION PNL, BLOOD	1001920
 1000937	GIDH2B	IDH2, BLOOD	LAB BLOOD ORDERABLES>Other>CANCER SOMATIC MUTATION PNL, BLOOD	1001921
 1000938	GKRASB	KRAS, BLOOD	LAB BLOOD ORDERABLES>Other>CANCER SOMATIC MUTATION PNL, BLOOD	1001922
@@ -1326,7 +1332,6 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1001146	NPMRE	RESULT	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION	1002305
 1001147	BMNPDX	CLINICAL INDICATION	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	1002306
 1001148	BMNPME	METHOD	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	1002307
-1001149	11896R	ARSENIC,U	URINE ORDERABLES>Other>HEAVY METALS, URINE	1002308
 1001150	YUHYM6	TOTAL VOLUME	URINE ORDERABLES>Other>HEAVY METALS, URINE	1002309
 1001151	HV6QLD	HHV-6 DNA	VIROLOGY ORDERABLES>Other>HHV-6 DNA PCR QUAL	1002312
 1001152	CSCI	CLINICAL INDICATION	BODY FLUID ORDERABLES>Other>CANCER SOMATIC MUTATION PANEL	1002313
@@ -1746,6 +1751,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1001566	NAPA	N-ACETYLPROCAINAMIDE (NAPA)	LAB BLOOD ORDERABLES>Other>PROCAINAMIDE AND NAPA	1003054
 1001567	PA	PROCAINAMIDE	LAB BLOOD ORDERABLES>Other>PROCAINAMIDE AND NAPA	1003055
 1001568	PANAP1	PEAK, TROUGH, OR RANDOM	LAB BLOOD ORDERABLES>Other>PROCAINAMIDE AND NAPA	1003056
+1001713	HBEAB	HEPATITIS B E AB	LAB BLOOD ORDERABLES>Other>HBE AB	1003378
 1001569	PANAP2	DATE & TIME OF LAST DOSE	LAB BLOOD ORDERABLES>Other>PROCAINAMIDE AND NAPA	1003057
 1001570	PANAP3	DOSAGE (INCLUDE UNITS)	LAB BLOOD ORDERABLES>Other>PROCAINAMIDE AND NAPA	1003058
 1001571	PANAP4	ADMINISTRATION ROUTE	LAB BLOOD ORDERABLES>Other>PROCAINAMIDE AND NAPA	1003059
@@ -1890,7 +1896,6 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1001710	CF32R2	RESULT 2	LAB BLOOD ORDERABLES>Other>CYSTIC FIBROSIS MUTATION STUDY	1003375
 1001711	GAD65AB	GAD65 ANTIBODY	LAB BLOOD ORDERABLES>Other>GAD AB	1003376
 1001712	HBODY	HEINZ BODY PREP	LAB BLOOD ORDERABLES>Other>HEINZ BODY PREP	1003377
-1001713	HBEAB	HEPATITIS B E AB	LAB BLOOD ORDERABLES>Other>HBE AB	1003378
 1001714	HIVI	INTERPRETATION	LAB BLOOD ORDERABLES>Other>ANTI-HIV	1003379
 1001715	MEXIL	MEXILETINE	LAB BLOOD ORDERABLES>Other>MEXILETINE	1003380
 1001716	YPANPP	PANCREATIC POLYPEPTIDE	LAB BLOOD ORDERABLES>Other>PP	1003381
@@ -2587,6 +2592,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1002407	11191R	BORRELIA HERMSII IGG	LAB BLOOD ORDERABLES>Other>BORRELIA HERMSII AB PANL	1004765
 1002408	11192R	BORRELIA HERMSII IGM	LAB BLOOD ORDERABLES>Other>BORRELIA HERMSII AB PANL	1004766
 1002409	D816RE	RESULT, BLOOD	LAB BLOOD ORDERABLES>Other>KIT D816V MUTATION, AS-PCR, BLOOD	1004767
+1003040	LEIPRM	PRIMERS	LAB BLOOD ORDERABLES>Other>FACTOR V LEIDEN	1006241
 1002410	C2526N	CD25, % OF CD3+/CD4+/CD26NEG	LAB BLOOD ORDERABLES>Other>FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD	1004768
 1002411	C257N	CD25, % OF CD3+/CD4+/CD7NEG	LAB BLOOD ORDERABLES>Other>FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD	1004769
 1002412	C3026N	CD30, % OF CD3+/CD4+/CD26NEG	LAB BLOOD ORDERABLES>Other>FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD	1004772
@@ -2720,6 +2726,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1002540	UNCAN	UNSTIMULATED CPM	STOOL ORDERABLES>Other>LYMPHOCYTE AG STIMULATION SCREEN	1005017
 1002541	UNPPD	UNSTIMULATED CPM	STOOL ORDERABLES>Other>LYMPHOCYTE AG STIMULATION SCREEN	1005018
 1002542	UNTET	UNSTIMULATED CPM	STOOL ORDERABLES>Other>LYMPHOCYTE AG STIMULATION SCREEN	1005019
+1003041	CHICK	CHICKEN	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006242
 1002543	TALBU	ALBUMIN EXCRETION	TIMED URINE>Other>ALBUMIN, URINE (TIMED)	1005020
 1002544	UT35	COLLECTION PERIOD	TIMED URINE>Other>ALBUMIN, URINE (TIMED)	1005022
 1002545	VOL35	VOLUME	TIMED URINE>Other>ALBUMIN, URINE (TIMED)	1005023
@@ -3002,6 +3009,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1002823	REQS	REQUEST SENT	LAB BLOOD ORDERABLES>Other>DIRECT ANTIGLOBULIN TEST - LARC	1005693
 1002824	ECHIND	ECHINOCOCCUS LGG	LAB BLOOD ORDERABLES>Other>ECHINOCOCCUS AB, IGG	1005694
 1002825	FLUOXE	FLUOXETINE	LAB BLOOD ORDERABLES>Other>FLUOXETINE AND NORFLUOXETINE	1005695
+1003042	CORNY	CORN	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006243
 1002826	NORFLU	NORFLUOXETINE	LAB BLOOD ORDERABLES>Other>FLUOXETINE AND NORFLUOXETINE	1005696
 1002827	T4	THYROXINE, TOTAL	LAB BLOOD ORDERABLES>Other>FREE THYROXINE INDEX	1005697
 1002828	YT7P2	T-UPTAKE %	LAB BLOOD ORDERABLES>Other>FREE THYROXINE INDEX	1005698
@@ -3216,9 +3224,6 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1003037	DHB	DONOR HEP B SURF. AG	LAB BLOOD ORDERABLES>Other>DONOR HEP B SURF. AG	1006238
 1003038	ETHSUX	ETHOSUXIMIDE	LAB BLOOD ORDERABLES>Other>ETHOSUXIMIDE LEVEL	1006239
 1003039	LEICMT	COMMENT	LAB BLOOD ORDERABLES>Other>FACTOR V LEIDEN	1006240
-1003040	LEIPRM	PRIMERS	LAB BLOOD ORDERABLES>Other>FACTOR V LEIDEN	1006241
-1003041	CHICK	CHICKEN	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006242
-1003042	CORNY	CORN	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006243
 1003043	EGGW	EGG WHITE	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006244
 1003044	FISH	FISH	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006245
 1003045	IGEFA	TOTAL IGE	LAB BLOOD ORDERABLES>Other>FOOD ALLERGY SCREEN	1006246
@@ -4138,6 +4143,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1003959	CDMLDI	DISCLAIMER	LAB BLOOD ORDERABLES>Other>CDH1 MLPA	1007691
 1003960	CDMLMD	INTERPRETED BY	LAB BLOOD ORDERABLES>Other>CDH1 MLPA	1007692
 1003961	CDMLME	METHOD	LAB BLOOD ORDERABLES>Other>CDH1 MLPA	1007693
+1004032	NPMRF	REFERENCES	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION	1007808
 1003962	PTH18R	RECEIVED IN FLOW CYTOMETRY	LAB BLOOD ORDERABLES>Other>FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD	1007694
 1003963	CFMLDI	DISCLAIMER	LAB BLOOD ORDERABLES>Other>CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	1007696
 1003964	CFMLMD	INTERPRETED BY	LAB BLOOD ORDERABLES>Other>CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	1007697
@@ -4208,7 +4214,6 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 1004029	NPMDI	DISCLAIMER	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION	1007805
 1004030	NPMIN	INTERPRETATION	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION	1007806
 1004031	NPMMD	INTERPRETED BY	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION	1007807
-1004032	NPMRF	REFERENCES	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION	1007808
 1004033	BMNPDI	DISCLAIMER	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	1007809
 1004034	BMNPIN	INTERPRETATION, BM	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	1007810
 1004035	BMNPRF	REFERENCES	MOLECULAR DIAG ORDERABLES>Other>NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	1007811
@@ -4373,18 +4378,22 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 40	DBP	Blood Pressure, Diastolic (DBP)	Flowsheet>Vitals	40
 80	GCS	Glasgow Coma Scale (GCS)	Flowsheet>Vitals	80
 1004193	SpO2	O2 Saturation	Flowsheet>Vitals	55
+125	Gram Stain and Culture	CULTURE AND GRAM STAIN, CSF	BODY FLUID ORDERABLES>Other>CULTURE AND GRAM STAIN, CSF	\N
+1004208	VQScan	Ventilation/Perfusion Scan	Imaging>Nuclear	2050000
 1004194	CXR	Chest XRay	Imaging>XRay	2000000
-1004196	CTAbdomen	CT Abdomen	Imaging>CT	2011000
-1004197	CTPelvis	CT Pelvis	Imaging>CT	2011010
-1004199	CTHead	CT Head	Imaging>CT	2010000
 1004195	CTChest	CT Chest	Imaging>CT	2010500
 1004200	AbdUS	US Abdomen	Imaging>US	2020000
-1004201	MRIBrain	MRI Brain	Imaging>MRI	2030000
 1004202	CTCSpine	CT Cervical Spine	Imaging>CT	2010200
 1004204	MRITSpine	MRI Thoracic Spine	Imaging>MRI	2030500
 1004203	MRICSpine	MRI Cervical Spine	Imaging>MRI	2030300
 1004205	MRILSpine	MRI Lumbar Spine	Imagine>MRI	2030700
 1004198	ECG	ECG 12-Lead	Cardiology>EKG	3000000
+200	ECHO	Echocardiogram	Cardiology>ECHO	3000100
+1004206	DVTUS	Deep Vein Thrombosis Ultrasound	Imaging>US	2020100
+1004207	ECHOStress	Stress Echocardiogram	Cardiology>ECHO	3000200
+133	CT Head	CT Head	Imaging>CT	2010002
+131	MRI Brain	MRI Brain	Imaging>MRI	2030002
+1004196	CTAbdPelv	CT Abdomen Pelvis	Imaging>CT	2011000
 \.
 
 
@@ -4392,7 +4401,7 @@ COPY sim_result (sim_result_id, name, description, group_string, priority) FROM
 -- Name: sim_result_sim_result_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
 --
 
-SELECT pg_catalog.setval('sim_result_sim_result_id_seq', 1004205, true);
+SELECT pg_catalog.setval('public.sim_result_sim_result_id_seq', 1004208, true);
 
 
 --
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_state.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_state.dump.sql
new file mode 100644
index 00000000..c8c15516
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_state.dump.sql
@@ -0,0 +1,73 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_state; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_state (sim_state_id, name, description) FROM stdin;
+1	Example	Example State
+0	Default	Default State Info
+5	ACS Relief	ACS Pain Relief
+6	NFever	Neutropenic Fever (no source) Initial State
+7	GIBleed	GI Bleed Initial State
+3	ACS	ACS Initial State
+14	EtOH-GIBleed Active	Alcoholic Hepatitis - GI Bleed Active
+15	EtOH-GIBleed Bleeding Out	Alcoholic Hepatitis - GI Bleed Bleeding Out
+4	CodeBlue	Code Blue / ACLS / Dead / Dying
+16	EtOH-GIBleed Coag Stabilized	Alcoholic Hepatitis - GI Bleed Coagulopathy Stabilized
+2	EtOH-GIBleed Post-EGD	Alcoholic Hepatitis - GI Bleed EGD Done
+17	EtOH-GIBleed GI Consult Block	Alcoholic Hepatitis - GI Bleed GI Consult Block Pending Med Management
+13	EtOH-GIBleed Onset	Alcoholic Hepatitis - GI Bleed Onset (pre-admission)
+9	DKA Onset	Diabetic Ketoacidosis - Onset
+20	DKA Hypoglycemic	Diabetic Ketoacidosis - Low Glucose, Low K, Anion Gap
+21	DKA Hyperglycemic	Diabetic Ketoacidosis - High Glucose, Normal-High K, Anion Gap
+18	DKA Euglycemic	Diabetic Ketoacidosis - Normal Glucose, Normal K, Anion Gap
+19	DKA Gap Closed	Diabetic Ketoacidosis - Gap Closed
+5000	Neutropenic Fever v2	Neutropenic Fever (v2 example) Initial State
+5001	Neutropenic Fever Treated with Abx	Appropriate antibiotic improve patient condition
+5002	Neutropenic Fever Treated with IVF	Appropriate IVF improve patient condition
+5003	Neutropenic Fever Treated with IVF + ABX	Appropriate Treatments for patient condition
+30	Mening Active	Meningitis Active
+31	Meningitis Adequately Treated	Appropriate antibiotic improve patient condition
+32	Meningits Inadequately Treated	Inadequate treatment worsens patient condition
+33	Meningits Worsens	Patient condition worsens after 1 hr
+40	Afib-RVR Initial	Afib-RVR Initial State (unstable/hypotensive)
+41	Afib-RVR Stabilized	DC Cardioversion improves hemodynamics
+43	Afib-RVR Worse	More unstable (after time or nodal blocking agent)
+46	Afib-RVR Cardiology	Cardiology consult response
+42	Afib-RVR Adenosine	Transient state while adenosine administered
+45	Afib-RVR Antiarrhythmic	Transient state with amiodarone or others, but still unstable
+44	Afib-RVR critical???	Critical after nodal agents, may be redundant with 43?
+10	PE-COPD-LungCA	Shortness of Breath - PE - COPD - Lung Cancer
+11	PE-COPD-LungCA + O2	Shortness of Breath - PE - COPD - Lung Cancer + O2
+12	PE-COPD-LungCA + Anticoagulation	Shortness of Breath - PE - COPD - Lung Cancer + Anticoagulation
+8	PE-COPD-LungCA + O2 + Anticoagulation	Shortness of Breath - PE - COPD - Lung Cancer + O2 + Anticoagulation
+\.
+
+
+--
+-- Name: sim_state_sim_state_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_state_sim_state_id_seq', 21, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_state_result.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_state_result.dump.sql
new file mode 100644
index 00000000..03039ccd
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_state_result.dump.sql
@@ -0,0 +1,975 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.3.11
+-- Dumped by pg_dump version 9.6.9
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_state_result; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_state_result (sim_state_result_id, sim_state_id, sim_result_id, clinical_item_id, num_value, num_value_noise, text_value, result_flag) FROM stdin;
+4903	2	1000061	\N	0	0	\N	\N
+147	7	11010	66124	7.4000000000000004	0.20000000000000001	\N	Low
+150	7	11020	66123	25	1	\N	Low
+102	0	1004198	66211	\N	\N	Normal sinus rhythm	\N
+4905	14	1000061	66497	0.20000000000000001	0	\N	High
+148	7	11000	\N	9.4000000000000004	0.5	\N	\N
+149	7	50	\N	17	2	\N	\N
+152	7	40	\N	62	3	\N	\N
+154	7	10	\N	98.400000000000006	\N	\N	\N
+155	7	30	\N	102	4	\N	\N
+157	7	11030	66258	338	10	\N	\N
+6	1	20	\N	112	5	\N	High
+9	1	10	\N	101.3	0.20000000000000001	\N	High
+17	1	11000	72454	13.6	0.20000000000000001	\N	High
+26	1	13060	66286	2.1000000000000001	0	\N	High
+145	7	13530	66144	0.20000000000000001	0	\N	High
+146	7	13060	66286	1.2	0.10000000000000001	\N	High
+153	7	13050	66273	65	3	\N	High
+156	7	20	\N	105	4	\N	High
+178	9	13080	\N	30	0	\N	High
+186	9	11120	\N	90	2	\N	High
+189	9	20	\N	117	3	\N	High
+192	9	13070	66695	614	13	\N	High
+193	9	13730	66866	1432	24	\N	High
+151	7	13460	66402	57	4	\N	Low
+4904	13	1000061	66497	0.20000000000000001	0	\N	High
+4901	7	1000061	66497	0.20000000000000001	0	\N	High
+4906	15	1000061	66497	2.5	0	\N	High Panic
+1	2	11030	\N	352	23	\N	\N
+2	2	70	\N	300	0	\N	\N
+3	2	13010	\N	140	0	\N	\N
+4	2	13540	\N	124	0	\N	\N
+5	2	30	\N	123	4	\N	\N
+7	2	13070	\N	110	0	\N	\N
+8	2	13260	\N	102	0	\N	\N
+10	2	13030	\N	95	0	\N	\N
+11	2	40	\N	79	4	\N	\N
+12	2	11020	\N	40	1	\N	\N
+13	2	13050	\N	27	0	\N	\N
+14	2	13040	\N	22	0	\N	\N
+15	2	80	\N	15	0	\N	\N
+16	2	13250	\N	15	0	\N	\N
+18	2	11010	\N	13.4	0.20000000000000001	\N	\N
+19	2	50	\N	13	2	\N	\N
+20	2	13240	\N	12	0	\N	\N
+21	2	13090	\N	8.9000000000000004	0	\N	\N
+22	2	13670	\N	6.2999999999999998	0	\N	\N
+23	2	13280	\N	5.9000000000000004	0	\N	\N
+24	2	13020	\N	4.5	0	\N	\N
+25	2	13270	\N	3.8999999999999999	0	\N	\N
+27	2	13110	\N	2.1000000000000001	0	\N	\N
+28	2	13750	\N	1.5	0	\N	\N
+29	2	12040	\N	1	0	\N	\N
+30	2	13506	\N	0.90000000000000002	0	\N	\N
+31	2	13210	\N	0.80000000000000004	0	\N	\N
+32	2	60	\N	0.20999999999999999	0	\N	\N
+33	2	13530	\N	0	0	\N	\N
+105	3	30	\N	145	3	\N	\N
+106	3	40	\N	86	3	\N	\N
+107	3	50	\N	20	1	\N	\N
+108	3	1004193	\N	0.92000000000000004	0.10000000000000001	\N	\N
+109	3	11000	\N	10	0.29999999999999999	\N	\N
+110	3	12010	\N	285	17	\N	\N
+111	3	13060	\N	1.1000000000000001	0.10000000000000001	\N	\N
+113	3	13640	\N	124	4	\N	\N
+115	3	1004198	\N	\N	\N	T-wave inversions in V2,V3	\N
+131	6	13410	66407	6.7000000000000002	0.20000000000000001	\N	\N
+161	9	17090	\N	\N	\N	Negative	\N
+162	9	17100	\N	\N	\N	Negative	\N
+163	9	17110	\N	\N	\N	Negative	\N
+164	9	17120	\N	\N	\N	Negative	\N
+166	9	13060	\N	0.80000000000000004	0.10000000000000001	\N	\N
+173	9	11010	\N	14.5	0.69999999999999996	\N	\N
+183	9	11020	\N	45	2	\N	\N
+184	9	40	\N	72	3	\N	\N
+188	9	30	\N	105	4	\N	\N
+191	9	11030	\N	270	10	\N	\N
+104	3	20	\N	105	2	\N	High
+112	3	13250	\N	52	4	\N	High
+114	3	13670	\N	7.7999999999999998	0.10000000000000001	\N	High
+117	6	10	\N	102	1	\N	High
+118	6	20	\N	128	5	\N	High
+121	6	50	\N	25	2	\N	High
+132	6	13420	66159	480	13	\N	High
+133	6	13502	69841	4.4000000000000004	0.10000000000000001	\N	High
+134	6	13506	66505	3.2999999999999998	0.20000000000000001	\N	High
+103	0	1004195	\N	\N	\N	No acute intrathoracic abnormalities	\N
+101	0	1004194	\N	\N	\N	No acute cardiopulmonary abnormalities	\N
+143	6	16100	66502	3.3999999999999999	0.20000000000000001	\N	High
+144	6	1000173	70921	3.3999999999999999	0.10000000000000001	\N	High
+158	9	17050	66506	\N	\N	3+	High
+159	9	17040	66507	\N	\N	4+	High
+168	9	13020	71563	5.4000000000000004	0	\N	High
+175	9	11000	66279	23	1	\N	High
+176	9	13050	66273	24	2	\N	High
+177	9	50	\N	29	2	\N	High
+119	6	30	\N	86	4	\N	Low
+120	6	40	\N	43	3	\N	Low
+122	6	11000	66630	0.80000000000000004	0	\N	Low
+123	6	11010	66124	11.1	0.29999999999999999	\N	Low
+124	6	11020	66123	33	1	\N	Low
+125	6	11030	66258	95	10	\N	Low
+126	6	11120	\N	10	0	\N	Low
+127	6	11130	\N	90	0	\N	Low
+128	6	11220	66684	80	0	\N	Low
+129	6	11240	72951	720	0	\N	Low
+130	6	13040	66403	19	1	\N	Low
+135	6	15010	66383	7.3399999999999999	0.01	\N	Low
+136	6	15020	66375	34	1	\N	Low
+137	6	15050	66392	7.3399999999999999	0.01	\N	Low
+138	6	15060	66495	34	1	\N	Low
+139	6	16010	66413	7.3399999999999999	0.01	\N	Low
+140	6	16020	66203	34	1	\N	Low
+141	6	16060	66504	7.3399999999999999	0.01	\N	Low
+142	6	16070	66493	34	1	\N	Low
+165	9	13120	66349	0.5	0	\N	Low
+169	9	15010	66383	7.2999999999999998	0	\N	Low
+170	9	15050	66392	7.2999999999999998	0	\N	Low
+171	9	16010	66413	7.2999999999999998	0	\N	Low
+187	9	10	\N	98.400000000000006	0.20000000000000001	\N	\N
+4630	15	20	\N	127	3	\N	High
+160	9	1000565	72018	5.4000000000000004	0.10000000000000001	Elevated	High
+172	9	16060	66504	7.2999999999999998	0	\N	Low
+174	9	13040	66403	15	0	\N	Low
+179	9	15020	66375	30	0	\N	Low
+180	9	15060	66495	30	0	\N	Low
+181	9	16020	66203	30	0	\N	Low
+182	9	16070	66493	30	0	\N	Low
+185	9	13030	66328	80	0	\N	Low
+190	9	13010	66194	125	0	\N	Low
+4527	13	1000036	\N	\N	\N	B-	\N
+4528	13	1004196	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4529	13	1004200	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4530	13	1004198	\N	\N	\N	Sinus tachycardia	\N
+4531	13	13530	66144	0.20000000000000001	0	\N	High
+4532	13	1000223	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4533	13	13060	66286	2.3999999999999999	0.10000000000000001	\N	High
+4534	13	12040	66112	2.7000000000000002	0.20000000000000001	\N	High
+4535	13	13270	66266	2.7999999999999998	0.10000000000000001	\N	Low
+4536	13	1000169	\N	3	\N	\N	\N
+4537	13	1000121	\N	5	\N	\N	\N
+4538	13	13220	66283	6.0999999999999996	0.10000000000000001	\N	High
+4539	13	13210	66386	6.2999999999999998	0.10000000000000001	\N	High
+4540	13	11010	66124	7.5999999999999996	0.10000000000000001	\N	Low
+4541	13	1000068	66321	7.5999999999999996	0.10000000000000001	\N	Low
+4542	13	11000	\N	9.4000000000000004	0.5	\N	\N
+4543	13	50	\N	19	2	\N	\N
+4544	13	11020	66123	25	1	\N	Low
+4545	13	1000067	66322	25	1	\N	Low
+4546	13	12030	66111	28	2	\N	High
+4547	13	13720	66380	52	3	\N	High
+4548	13	1000120	\N	53	2	\N	\N
+4549	13	13460	66402	57	4	\N	Low
+4550	13	13240	66370	60	1	\N	High
+4551	13	40	\N	62	3	\N	\N
+4552	13	13320	66559	72	2	\N	High
+275	0	17010	\N	\N	\N	Yellow	\N
+4553	13	11030	66258	88	9	\N	Low
+4554	13	11040	\N	93	1	\N	\N
+4555	13	1004193	\N	96	1	\N	\N
+4556	13	13050	66273	96	3	\N	High
+4557	13	10	\N	97.900000000000006	\N	\N	Low
+4558	13	30	\N	101	2	\N	\N
+4559	13	20	\N	105	3	\N	High
+4560	13	13250	66369	125	2	\N	High
+4561	13	13830	67396	186	2	\N	High
+4562	13	13260	66371	210	3	\N	High
+4563	14	1000036	\N	\N	\N	B-	\N
+4564	14	1004196	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4565	14	1004200	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4566	14	1004198	\N	\N	\N	Sinus tachycardia	\N
+4567	14	13530	66144	0.20000000000000001	0	\N	High
+4568	14	1000223	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4569	14	13060	66286	2.3999999999999999	0.10000000000000001	\N	High
+4570	14	12040	66112	2.7000000000000002	0.20000000000000001	\N	High
+4571	14	13270	66266	2.7999999999999998	0.10000000000000001	\N	Low
+4572	14	1000169	\N	3	\N	\N	\N
+4573	14	1000121	\N	5	\N	\N	\N
+4574	14	13220	66283	6.0999999999999996	0.10000000000000001	\N	High
+4575	14	13210	66386	6.2999999999999998	0.10000000000000001	\N	High
+4576	14	11010	66124	7.5999999999999996	0.10000000000000001	\N	Low
+4577	14	1000068	66321	7.5999999999999996	0.10000000000000001	\N	Low
+4578	14	11000	\N	9.4000000000000004	0.5	\N	\N
+4579	14	50	\N	19	2	\N	\N
+4580	14	11020	66123	25	1	\N	Low
+4581	14	1000067	66322	25	1	\N	Low
+4582	14	12030	66111	28	2	\N	High
+4583	14	13720	66380	52	3	\N	High
+4584	14	1000120	\N	53	2	\N	\N
+4585	14	13460	66402	57	4	\N	Low
+4586	14	13240	66370	60	1	\N	High
+4587	14	40	\N	62	3	\N	\N
+4588	14	13320	66559	72	2	\N	High
+4589	14	11030	66258	88	9	\N	Low
+4590	14	11040	\N	93	1	\N	\N
+4591	14	1004193	\N	96	1	\N	\N
+4592	14	13050	66273	96	3	\N	High
+4593	14	10	\N	97.900000000000006	\N	\N	Low
+4594	14	30	\N	101	2	\N	\N
+4595	14	20	\N	105	3	\N	High
+4596	14	13250	66369	125	2	\N	High
+4597	14	13830	67396	186	2	\N	High
+4598	14	13260	66371	210	3	\N	High
+4599	15	1000036	\N	\N	\N	B-	\N
+4600	15	1004196	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4601	15	1004200	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4602	15	1004198	\N	\N	\N	Sinus tachycardia	\N
+4603	15	1000223	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4604	15	13530	66144	2.5	0	\N	High Panic
+4605	15	12040	66112	2.7000000000000002	0.20000000000000001	\N	High
+4606	15	13270	66266	2.7999999999999998	0.10000000000000001	\N	Low
+4607	15	13060	66286	2.8999999999999999	0.10000000000000001	\N	High
+4608	15	1000169	\N	3	\N	\N	\N
+4609	15	1000121	\N	4	\N	\N	\N
+4610	15	11010	66488	6.0999999999999996	0.10000000000000001	\N	Low Panic
+4611	15	13220	66283	6.0999999999999996	0.10000000000000001	\N	High
+4612	15	13210	66386	6.2999999999999998	0.10000000000000001	\N	High
+4613	15	1000068	72995	7.5999999999999996	0.10000000000000001	\N	Low Panic
+4614	15	11000	\N	9.4000000000000004	0.5	\N	\N
+4615	15	11020	66436	18	1	\N	Low Panic
+4616	15	1000067	72994	18	1	\N	Low Panic
+4617	15	12030	66111	28	2	\N	High
+4618	15	50	\N	29	2	\N	High
+4619	15	1000120	\N	51	2	\N	\N
+4620	15	40	\N	54	1	\N	Low
+4621	15	13460	66402	57	4	\N	Low
+4622	15	13720	66380	63	3	\N	High
+4623	15	13320	66559	72	2	\N	High
+4624	15	30	\N	85	2	\N	Low
+4625	15	11030	66258	85	7	\N	Low
+4626	15	11040	\N	93	1	\N	\N
+4627	15	1004193	\N	95	1	\N	\N
+4628	15	10	\N	97.200000000000003	\N	\N	Low
+4629	15	13050	66273	103	3	\N	High
+4631	15	13240	66370	131	1	\N	High
+4632	15	13830	67396	162	2	\N	High
+4633	15	13260	66371	210	3	\N	High
+4634	15	13250	66369	212	2	\N	High
+4635	16	1000036	\N	\N	\N	B-	\N
+4636	16	1004196	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4637	16	1004200	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4638	16	1004198	\N	\N	\N	Sinus tachycardia	\N
+4639	16	12040	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4640	16	1000223	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4641	16	13060	66286	1.8	0.10000000000000001	\N	High
+4642	16	13270	66266	2.7999999999999998	0.10000000000000001	\N	Low
+4643	16	1000169	\N	3	\N	\N	\N
+4644	16	1000121	\N	4	\N	\N	\N
+4645	16	13220	66283	6.0999999999999996	0.10000000000000001	\N	High
+4646	16	13210	66386	6.2999999999999998	0.10000000000000001	\N	High
+4647	16	11010	66124	8.1999999999999993	0.10000000000000001	\N	Low
+4648	16	1000068	66321	8.1999999999999993	0.10000000000000001	\N	Low
+4649	16	11000	\N	9.4000000000000004	0.5	\N	\N
+4650	16	12030	\N	14	1	\N	\N
+4651	16	50	\N	18	1	\N	\N
+4652	16	11020	66123	25	1	\N	Low
+4653	16	1000067	66322	26	1	\N	Low
+4654	16	1000120	\N	51	2	\N	\N
+4655	16	13460	66402	57	4	\N	Low
+4656	16	13720	66380	58	3	\N	High
+4657	16	40	\N	61	1	\N	\N
+4658	16	13240	66370	71	1	\N	High
+4659	16	13320	66559	72	2	\N	High
+4813	18	17090	\N	\N	\N	Negative	\N
+4660	16	13050	66273	75	1	\N	High
+4661	16	11030	66258	89	7	\N	Low
+4662	16	11040	\N	93	1	\N	\N
+4663	16	20	\N	97	1	\N	\N
+4664	16	1004193	\N	97	1	\N	\N
+4665	16	10	\N	98.099999999999994	\N	\N	\N
+4666	16	30	\N	108	2	\N	\N
+4667	16	13830	67396	142	2	\N	High
+4668	16	13250	66369	154	2	\N	High
+4669	16	13260	66371	210	3	\N	High
+4670	2	1000036	\N	\N	\N	B-	\N
+4671	2	1004196	\N	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
+4673	2	1004198	\N	\N	\N	Sinus tachycardia	\N
+4674	2	12040	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4675	2	1000223	\N	1.3999999999999999	0.10000000000000001	\N	\N
+4676	2	13060	66286	1.8	0.10000000000000001	\N	High
+4677	2	13270	66266	2.7999999999999998	0.10000000000000001	\N	Low
+4678	2	1000169	\N	3	\N	\N	\N
+4679	2	1000121	\N	4	\N	\N	\N
+4680	2	13220	66283	6.0999999999999996	0.10000000000000001	\N	High
+4681	2	13210	66386	6.2999999999999998	0.10000000000000001	\N	High
+4682	2	11010	66124	8.1999999999999993	0.10000000000000001	\N	Low
+4683	2	1000068	66321	8.1999999999999993	0.10000000000000001	\N	Low
+4684	2	11000	\N	9.4000000000000004	0.5	\N	\N
+4685	2	50	\N	14	1	\N	\N
+4686	2	12030	\N	14	1	\N	\N
+4687	2	11020	66123	25	1	\N	Low
+4688	2	1000067	66322	26	1	\N	Low
+4689	2	1000120	\N	51	2	\N	\N
+4690	2	13460	66402	57	4	\N	Low
+4691	2	13720	66380	58	3	\N	High
+4692	2	40	\N	65	1	\N	\N
+4693	2	13240	66370	71	1	\N	High
+4694	2	13320	66559	72	2	\N	High
+4695	2	13050	66273	75	1	\N	High
+4696	2	20	\N	86	1	\N	\N
+4697	2	11030	66258	89	7	\N	Low
+4698	2	11040	\N	93	1	\N	\N
+4699	2	1004193	\N	98	1	\N	\N
+4700	2	10	\N	98.5	\N	\N	\N
+4701	2	30	\N	118	2	\N	\N
+4702	2	13830	67396	142	2	\N	High
+4703	2	13250	66369	154	2	\N	High
+4704	2	13260	66371	210	3	\N	High
+4775	21	17050	66506	\N	\N	3+	High
+4776	21	17040	66507	\N	\N	4+	High
+4777	21	1000565	72018	5.4000000000000004	0.10000000000000001	Elevated	High
+4778	21	17090	\N	\N	\N	Negative	\N
+4779	21	17100	\N	\N	\N	Negative	\N
+4780	21	17110	\N	\N	\N	Negative	\N
+4781	21	17120	\N	\N	\N	Negative	\N
+4782	21	13120	66349	0.5	0	\N	Low
+4783	21	13060	\N	0.80000000000000004	0.10000000000000001	\N	\N
+4784	21	13020	71563	5.4000000000000004	0	\N	High
+4785	21	15010	66383	7.2999999999999998	0	\N	Low
+4786	21	15050	66392	7.2999999999999998	0	\N	Low
+4787	21	16010	66413	7.2999999999999998	0	\N	Low
+4788	21	16060	66504	7.2999999999999998	0	\N	Low
+4789	21	11010	\N	14.5	0.69999999999999996	\N	\N
+4790	21	13040	66403	15	0	\N	Low
+4791	21	11000	66279	23	1	\N	High
+4792	21	13050	66273	24	2	\N	High
+4793	21	50	\N	29	2	\N	High
+4794	21	13080	\N	30	0	\N	High
+4795	21	15020	66375	30	0	\N	Low
+4796	21	15060	66495	30	0	\N	Low
+4797	21	16020	66203	30	0	\N	Low
+4798	21	16070	66493	30	0	\N	Low
+4799	21	11020	\N	45	2	\N	\N
+4800	21	40	\N	72	3	\N	\N
+4801	21	13030	66328	80	0	\N	Low
+4802	21	11120	\N	90	2	\N	High
+4803	21	10	\N	98.400000000000006	0.20000000000000001	\N	\N
+4804	21	30	\N	105	4	\N	\N
+4805	21	20	\N	117	3	\N	High
+4806	21	13010	66194	125	0	\N	Low
+4807	21	11030	\N	270	10	\N	\N
+4808	21	13070	66695	614	13	\N	High
+4809	21	13730	66866	1432	24	\N	High
+4810	18	17050	66506	\N	\N	3+	High
+4811	18	17040	66507	\N	\N	4+	High
+4812	18	1000565	72018	5.4000000000000004	0.10000000000000001	Elevated	High
+4672	0	1004200	\N	\N	\N	No acute intrabdominal findings noted	\N
+4814	18	17100	\N	\N	\N	Negative	\N
+4815	18	17110	\N	\N	\N	Negative	\N
+4816	18	17120	\N	\N	\N	Negative	\N
+4817	18	13120	66349	0.5	0	\N	Low
+4818	18	13060	\N	0.80000000000000004	0.10000000000000001	\N	\N
+4819	18	13020	71563	4.0999999999999996	0	\N	High
+4820	18	15010	66383	7.2999999999999998	0	\N	Low
+4821	18	15050	66392	7.2999999999999998	0	\N	Low
+4822	18	16010	66413	7.2999999999999998	0	\N	Low
+4823	18	16060	66504	7.2999999999999998	0	\N	Low
+4824	18	11010	\N	14.5	0.69999999999999996	\N	\N
+4825	18	11000	66279	18	1	\N	High
+4826	18	13040	66403	18	0	\N	Low
+4827	18	13050	66273	24	2	\N	High
+4828	18	50	\N	26	2	\N	High
+4829	18	13080	\N	27	0	\N	High
+4830	18	15020	66375	30	0	\N	Low
+4831	18	15060	66495	30	0	\N	Low
+4832	18	16020	66203	30	0	\N	Low
+4833	18	16070	66493	30	0	\N	Low
+4834	18	11020	\N	45	2	\N	\N
+4835	18	40	\N	76	2	\N	\N
+4836	18	13030	66328	80	0	\N	Low
+4837	18	11120	\N	90	2	\N	High
+4838	18	10	\N	98.599999999999994	0.20000000000000001	\N	\N
+4839	18	20	\N	103	3	\N	High
+4840	18	30	\N	111	3	\N	\N
+4841	18	13010	66194	136	0	\N	Low
+4842	18	13070	66695	195	4	\N	High
+4843	18	11030	\N	270	10	\N	\N
+4844	18	13730	66866	1432	24	\N	High
+4845	19	17040	66507	\N	\N	1+	High
+4846	19	1000565	72018	5.4000000000000004	0.10000000000000001	Elevated	High
+4847	19	13060	\N	0.80000000000000004	0.10000000000000001	\N	\N
+4848	19	13020	\N	4.0999999999999996	0	\N	\N
+4849	19	15010	66383	7.4000000000000004	0	\N	Low
+4850	19	15050	66392	7.4000000000000004	0	\N	Low
+4851	19	16010	66413	7.4000000000000004	0	\N	Low
+4852	19	16060	66504	7.4000000000000004	0	\N	Low
+4853	19	11000	66279	12	1	\N	\N
+4854	19	13080	\N	12	0	\N	\N
+4855	19	11010	\N	14.5	0.69999999999999996	\N	\N
+4856	19	50	\N	15	2	\N	\N
+4857	19	13050	\N	16	2	\N	\N
+4858	19	13040	\N	24	0	\N	\N
+4859	19	15020	66375	40	0	\N	Low
+4860	19	15060	66495	40	0	\N	Low
+4861	19	16020	66203	40	0	\N	Low
+4862	19	16070	66493	40	0	\N	Low
+4863	19	11020	\N	45	2	\N	\N
+4864	19	40	\N	82	2	\N	\N
+4865	19	20	\N	86	3	\N	\N
+4866	19	11120	\N	90	2	\N	High
+4867	19	13030	\N	90	0	\N	\N
+4868	19	10	\N	98.599999999999994	0.10000000000000001	\N	\N
+4869	19	30	\N	124	3	\N	\N
+4870	19	13010	\N	136	0	\N	\N
+4871	19	13070	\N	195	4	\N	\N
+4872	19	11030	\N	270	10	\N	\N
+4873	20	17040	66507	\N	\N	1+	High
+4874	20	1000565	72018	5.4000000000000004	0.10000000000000001	Elevated	High
+4875	20	13060	\N	0.80000000000000004	0.10000000000000001	\N	\N
+4876	20	13020	66624	2.7999999999999998	0	\N	Low
+4877	20	15010	66383	7.2999999999999998	0	\N	Low
+4878	20	15050	66392	7.2999999999999998	0	\N	Low
+4879	20	16010	66413	7.2999999999999998	0	\N	Low
+4880	20	16060	66504	7.2999999999999998	0	\N	Low
+4881	20	11000	66279	12	1	\N	\N
+4882	20	13040	\N	12	0	\N	Low
+4883	20	11010	\N	14.5	0.69999999999999996	\N	\N
+4884	20	13050	\N	23	2	\N	\N
+4885	20	13080	\N	28	0	\N	High
+4886	20	15020	66375	30	0	\N	Low
+4887	20	15060	66495	30	0	\N	Low
+4888	20	16020	66203	30	0	\N	Low
+4889	20	16070	66493	30	0	\N	Low
+4890	20	50	\N	33	2	\N	High
+4891	20	11020	\N	45	2	\N	\N
+4892	20	13070	66549	54	4	\N	Low
+4893	20	40	\N	57	2	\N	Low
+4894	20	13030	\N	81	0	\N	Low
+4895	20	30	\N	85	3	\N	Low
+4896	20	11120	\N	90	2	\N	High
+4897	20	10	\N	97.900000000000006	0.10000000000000001	\N	Low
+4898	20	20	\N	134	3	\N	High
+4899	20	13010	\N	138	0	\N	\N
+4900	20	11030	\N	270	10	\N	\N
+4907	5000	13410	66407	6.7000000000000002	0.20000000000000001	\N	\N
+4908	5000	10	\N	102	1	\N	High
+4909	5000	20	\N	128	5	\N	High
+4910	5000	50	\N	25	2	\N	High
+4911	5000	13420	66159	480	13	\N	High
+4912	5000	13502	69841	4.4000000000000004	0.10000000000000001	\N	High
+4918	5000	11000	66630	0.80000000000000004	0	\N	Low
+4919	5000	11010	66124	11.1	0.29999999999999999	\N	Low
+4920	5000	11020	66123	33	1	\N	Low
+4921	5000	11030	66258	95	10	\N	Low
+4922	5000	11120	\N	10	0	\N	Low
+4923	5000	11130	\N	90	0	\N	Low
+4924	5000	11220	66684	80	0	\N	Low
+4925	5000	11240	72951	720	0	\N	Low
+4926	5000	13040	66403	19	1	\N	Low
+4927	5000	15010	66383	7.3399999999999999	0.01	\N	Low
+4928	5000	15020	66375	34	1	\N	Low
+4929	5000	15050	66392	7.3399999999999999	0.01	\N	Low
+4930	5000	15060	66495	34	1	\N	Low
+4931	5000	16010	66413	7.3399999999999999	0.01	\N	Low
+4932	5000	16020	66203	34	1	\N	Low
+4933	5000	16060	66504	7.3399999999999999	0.01	\N	Low
+4934	5000	16070	66493	34	1	\N	Low
+4916	5000	30	\N	89	4	\N	Low
+4917	5000	40	\N	58	3	\N	Low
+4913	5000	13506	66505	3.8999999999999999	0.20000000000000001	\N	High
+4914	5000	16100	66502	3.8999999999999999	0.20000000000000001	\N	High
+4915	5000	1000173	70921	3.8999999999999999	0.10000000000000001	\N	High
+4935	5001	10	\N	99	1	\N	High
+4936	5001	20	\N	119	5	\N	High
+4937	5001	30	\N	92	4	\N	Low
+4938	5001	40	\N	60	3	\N	Low
+4939	5001	50	\N	19	2	\N	High
+4940	5001	11000	66630	0.80000000000000004	0	\N	Low
+4941	5001	11010	66124	11.1	0.29999999999999999	\N	Low
+4942	5001	11020	66123	33	1	\N	Low
+4943	5001	11030	66258	100	10	\N	Low
+4944	5001	11120	\N	10	0	\N	Low
+4945	5001	11130	\N	90	0	\N	Low
+4946	5001	11220	66684	80	0	\N	Low
+4947	5001	11240	72951	720	0	\N	Low
+4948	5001	13040	66403	20	1	\N	Low
+4949	5001	13410	66407	6.7000000000000002	0.20000000000000001	\N	\N
+4950	5001	13420	66159	480	13	\N	High
+4951	5001	13502	69841	3.8999999999999999	0.10000000000000001	\N	High
+4952	5001	13506	66505	3.3999999999999999	0.20000000000000001	\N	High
+4953	5001	15010	66383	7.3399999999999999	0.01	\N	Low
+4954	5001	15020	66375	34	1	\N	Low
+4955	5001	15050	66392	7.3399999999999999	0.01	\N	Low
+4956	5001	15060	66495	34	1	\N	Low
+4957	5001	16010	66413	7.3399999999999999	0.01	\N	Low
+4958	5001	16020	66203	34	1	\N	Low
+4959	5001	16060	66504	7.3399999999999999	0.01	\N	Low
+4960	5001	16070	66493	34	1	\N	Low
+4961	5001	16100	66502	3.3999999999999999	0.20000000000000001	\N	High
+4962	5001	1000173	70921	3.3999999999999999	0.10000000000000001	\N	High
+4963	5002	10	\N	101.7	1	\N	High
+4964	5002	20	\N	99	5	\N	High
+4965	5002	30	\N	106	4	\N	Low
+4966	5002	40	\N	69	3	\N	Low
+4967	5002	50	\N	16	2	\N	High
+4968	5002	11000	66630	0.69999999999999996	0	\N	Low
+4969	5002	11010	66124	10.9	0.29999999999999999	\N	Low
+4970	5002	11020	66123	31	1	\N	Low
+4971	5002	11030	66258	93	10	\N	Low
+4972	5002	11120	\N	9	0	\N	Low
+4973	5002	11130	\N	91	0	\N	Low
+4974	5002	11220	66684	80	0	\N	Low
+4975	5002	11240	72951	720	0	\N	Low
+4976	5002	13040	66403	22	1	\N	Low
+4977	5002	13410	66407	6.7000000000000002	0.20000000000000001	\N	\N
+4978	5002	13420	66159	480	13	\N	High
+4979	5002	13502	69841	3.8999999999999999	0.10000000000000001	\N	High
+4980	5002	13506	66505	2.1000000000000001	0.20000000000000001	\N	High
+4981	5002	15010	66383	7.3700000000000001	0.01	\N	\N
+4982	5002	15020	66375	37	1	\N	\N
+4983	5002	15050	66392	7.3700000000000001	0.01	\N	\N
+4984	5002	15060	66495	37	1	\N	\N
+4985	5002	16010	66413	7.3700000000000001	0.01	\N	\N
+4986	5002	16020	66203	37	1	\N	\N
+4987	5002	16060	66504	7.3700000000000001	0.01	\N	\N
+4988	5002	16070	66493	37	1	\N	\N
+4989	5002	16100	66502	2.1000000000000001	0.20000000000000001	\N	High
+4990	5002	1000173	70921	2.1000000000000001	0.10000000000000001	\N	High
+4991	5003	10	\N	98.799999999999997	1	\N	High
+4992	5003	20	\N	85	5	\N	High
+4993	5003	30	\N	114	4	\N	Low
+4994	5003	40	\N	77	3	\N	Low
+4995	5003	50	\N	14	2	\N	High
+4996	5003	11000	66630	0.69999999999999996	0	\N	Low
+4997	5003	11010	66124	10.9	0.29999999999999999	\N	Low
+4998	5003	11020	66123	31	1	\N	Low
+4999	5003	11030	66258	93	10	\N	Low
+5000	5003	11120	\N	9	0	\N	Low
+5001	5003	11130	\N	91	0	\N	Low
+5002	5003	11220	66684	80	0	\N	Low
+5003	5003	11240	72951	720	0	\N	Low
+5004	5003	13040	66403	23	1	\N	Low
+5005	5003	13410	66407	6.7000000000000002	0.20000000000000001	\N	\N
+5006	5003	13420	66159	480	13	\N	High
+5007	5003	13502	69841	2.2000000000000002	0.10000000000000001	\N	High
+5008	5003	13506	66505	1.2	0.20000000000000001	\N	\N
+5009	5003	15010	66383	7.3899999999999997	0.01	\N	\N
+5010	5003	15020	66375	39	1	\N	\N
+5011	5003	15050	66392	7.3899999999999997	0.01	\N	\N
+5012	5003	15060	66495	39	1	\N	\N
+5013	5003	16010	66413	7.3899999999999997	0.01	\N	\N
+5014	5003	16020	66203	39	1	\N	\N
+5015	5003	16060	66504	7.3899999999999997	0.01	\N	\N
+5016	5003	16070	66493	39	1	\N	\N
+5017	5003	16100	66502	1.2	0.20000000000000001	\N	\N
+5018	5003	1000173	70921	1.2	0.10000000000000001	\N	\N
+5020	2	13504	\N	0.90000000000000002	0	\N	\N
+5021	6	13504	66505	3.2999999999999998	0.20000000000000001	\N	High
+5022	5000	13504	66505	3.8999999999999999	0.20000000000000001	\N	High
+5023	5001	13504	66505	3.3999999999999999	0.20000000000000001	\N	High
+5024	5002	13504	66505	2.1000000000000001	0.20000000000000001	\N	High
+5025	5003	13504	66505	1.2	0.20000000000000001	\N	\N
+404	30	13050	48917	30	\N	\N	High
+405	30	13060	48574	1.5	\N	\N	High
+414	30	11030	46090	88	\N	\N	Low
+415	30	11000	65835	18	\N	\N	High
+416	30	13504	62151	2.2999999999999998	\N	\N	High
+422	32	1000330	50510	15	\N	\N	\N
+425	30	125	49083	\N	\N	Rare Gram Positive Diplococci, many neutrophils	\N
+426	32	125	49083	\N	\N	Rare Gram Positive Diplococci, many neutrophils	\N
+427	33	125	49083	\N	\N	Rare Gram Positive Diplococci, many neutrophils	\N
+428	31	125	49083	\N	\N	Moderate neutrophils, no organisms seen	\N
+430	32	1000156	49020	200	\N	\N	\N
+434	32	1000155	48577	10	\N	\N	\N
+429	30	1000156	49020	200	\N	\N	High
+431	33	1000156	49020	200	\N	\N	High
+432	31	1000156	49020	200	\N	\N	High
+433	30	1000155	48577	10	\N	\N	Low
+435	33	1000155	48577	10	\N	\N	Low
+421	30	1000330	50510	15	\N	\N	High
+423	33	1000330	50510	15	\N	\N	High
+424	31	1000330	50510	15	\N	\N	High
+6051	43	40	46160	59	\N	\N	\N
+418	32	1000129	48880	1394	\N	Neutrophil 95%	High
+443	30	131	62831	\N	\N	Questionable subtle diffuse meningeal enhancement.	\N
+444	30	131	46039	\N	\N	Questionable subtle diffuse meningeal enhancement.	\N
+445	30	131	46065	\N	\N	Questionable subtle diffuse meningeal enhancement.	\N
+446	32	131	62831	\N	\N	Questionable subtle diffuse meningeal enhancement.	\N
+447	32	131	46039	\N	\N	Questionable subtle diffuse meningeal enhancement.	\N
+448	32	131	46065	\N	\N	Questionable subtle diffuse meningeal enhancement.	\N
+476	32	133	45804	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+477	32	133	45983	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+478	32	133	48524	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+479	32	133	50241	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+480	32	133	52016	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+481	32	133	64739	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+482	32	133	65940	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+483	32	133	50098	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+484	32	133	49965	\N	\N	Preliminary read: No hemorrhage or evidence of ischemic stroke.	\N
+500	40	13530	45870	0.59999999999999998	\N	\N	High
+501	40	13540	45853	1600	\N	\N	High
+502	40	15010	45760	7.4800000000000004	\N	\N	High
+503	40	15030	45760	75	\N	\N	Low
+504	40	15020	45760	31	\N	\N	Low
+505	40	1000019	45760	21	\N	\N	Low
+506	40	15050	45919	7.4500000000000002	\N	\N	High
+507	40	15070	45919	21	\N	\N	Low
+508	40	15060	45919	38	\N	\N	Low
+509	40	1001944	45919	19	\N	\N	Low
+510	41	13530	45870	0.20000000000000001	\N	\N	High
+511	41	13540	45853	1200	\N	\N	High
+512	40	1004198	45866	\N	\N	Atrial fibrillation with rapid ventricular rate. 1mm ST depression in V1-V4	\N
+513	40	200	61832	\N	\N	EF 71% with LVH, atrial fibrillation. No wall motion abnormality.	\N
+514	41	1004198	45866	\N	\N	Normal sinus rhythm	\N
+515	41	200	61832	\N	\N	EF 68% with LVH, normal sinus rhythm	\N
+533	42	131	62831	\N	\N	DWI demonstrates restricted diffusion in the L temporal lobe, R internal capsule, consistent with cardio-embolic stroke.	\N
+534	42	131	46039	\N	\N	DWI demonstrates restricted diffusion in the L temporal lobe, R internal capsule, consistent with cardio-embolic stroke.	\N
+535	42	131	46065	\N	\N	DWI demonstrates restricted diffusion in the L temporal lobe, R internal capsule, consistent with cardio-embolic stroke.	\N
+436	31	1000155	48577	10	\N	\N	Low
+490	30	10	46160	101.2	\N	\N	\N
+492	30	20	46160	104	\N	\N	\N
+494	30	50	46160	18	\N	\N	\N
+496	30	60	46160	0.95999999999999996	\N	\N	\N
+498	30	30	46160	104	\N	\N	\N
+499	30	40	46160	68	\N	\N	\N
+6000	40	10	46160	97.900000000000006	\N	\N	\N
+6010	40	20	46160	153	\N	\N	\N
+6020	40	50	46160	24	\N	\N	\N
+6030	40	60	46160	0.92000000000000004	\N	\N	\N
+6040	40	30	46160	83	\N	\N	\N
+6050	40	40	46160	59	\N	\N	\N
+6001	43	10	46160	97.900000000000006	\N	\N	\N
+6011	43	20	46160	153	\N	\N	\N
+6021	43	50	46160	24	\N	\N	\N
+6031	43	60	46160	0.92000000000000004	\N	\N	\N
+6041	43	30	46160	83	\N	\N	\N
+417	30	1000129	48880	1487	\N	Neutrophil 98%	High
+6002	44	10	46160	97.900000000000006	\N	\N	\N
+6012	44	20	46160	123	\N	\N	\N
+6022	44	50	46160	30	\N	\N	\N
+6032	44	60	46160	0.89000000000000001	\N	\N	\N
+6042	44	30	46160	81	\N	\N	\N
+6052	44	40	46160	59	\N	\N	\N
+6003	42	10	46160	98.099999999999994	\N	\N	\N
+6013	42	20	46160	90	\N	\N	\N
+6023	42	50	46160	18	\N	\N	\N
+6033	42	60	46160	0.96999999999999997	\N	\N	\N
+6043	42	30	46160	138	\N	\N	\N
+6053	42	40	46160	81	\N	\N	\N
+6004	46	10	46160	98.099999999999994	\N	\N	\N
+6014	46	20	46160	90	\N	\N	\N
+6024	46	50	46160	18	\N	\N	\N
+6034	46	60	46160	0.96999999999999997	\N	\N	\N
+6044	46	30	46160	138	\N	\N	\N
+6054	46	40	46160	81	\N	\N	\N
+6005	41	10	46160	98.099999999999994	\N	\N	\N
+6015	41	20	46160	87	\N	\N	\N
+6025	41	50	46160	18	\N	\N	\N
+6035	41	60	46160	0.96999999999999997	\N	\N	\N
+6045	41	30	46160	138	\N	\N	\N
+6055	41	40	46160	81	\N	\N	\N
+486	30	13504	45918	2.2999999999999998	\N	\N	High
+487	30	13504	63725	2.2999999999999998	\N	\N	High
+590	43	200	61832	\N	\N	EF 71% with LVH, atrial fibrillation. No wall motion abnormality.	\N
+591	43	1004198	45866	\N	\N	Atrial fibrillation with rapid ventricular rate. 1mm ST depression in V1-V4	\N
+95	0	13210	\N	0.69999999999999996	0.20000000000000001	\N	\N
+4902	0	1000061	\N	0	0	\N	\N
+85	0	13240	\N	13	3	\N	\N
+82	0	13250	\N	17	4	\N	\N
+74	0	13260	\N	107	9	\N	\N
+90	0	13270	\N	3.8999999999999999	0.20000000000000001	\N	\N
+79	0	13050	\N	11	2	\N	\N
+88	0	13280	\N	6	0.20000000000000001	\N	\N
+97	0	13530	\N	0	0	\N	\N
+75	0	10	\N	98.700000000000003	0.20000000000000001	\N	\N
+72	0	20	\N	68	4	\N	\N
+71	0	30	\N	115	4	\N	\N
+77	0	40	\N	72	3	\N	\N
+84	0	50	\N	13	2	\N	\N
+100	0	1004193	\N	0.98999999999999999	0.01	\N	\N
+96	0	60	\N	0.20999999999999999	0	\N	\N
+68	0	70	\N	300	100	\N	\N
+81	0	80	\N	15	0	\N	\N
+98	0	11000	\N	6.2000000000000002	0.69999999999999996	\N	\N
+83	0	11010	\N	13.4	0.20000000000000001	\N	\N
+78	0	11020	\N	40	1	\N	\N
+67	0	11030	\N	352	23	\N	\N
+93	0	12040	\N	1	0.10000000000000001	\N	\N
+89	0	13020	\N	4.5	0.20000000000000001	\N	\N
+80	0	13040	\N	24	1	\N	\N
+99	0	13060	\N	0.69999999999999996	0.10000000000000001	\N	\N
+73	0	13070	\N	110	0	\N	\N
+86	0	13090	\N	8.9000000000000004	0.20000000000000001	\N	\N
+91	0	13110	\N	2.1000000000000001	0.20000000000000001	\N	\N
+94	0	13506	\N	0.69999999999999996	0.20000000000000001	\N	\N
+70	0	13540	\N	124	17	\N	\N
+87	0	13670	\N	5.2000000000000002	0.10000000000000001	\N	\N
+92	0	13750	\N	1.7	0.20000000000000001	\N	\N
+221	0	13220	\N	0.5	0.10000000000000001	\N	\N
+222	0	13230	\N	0.29999999999999999	0.10000000000000001	\N	\N
+226	0	13330	\N	290	3	\N	\N
+227	0	13410	\N	4.7999999999999998	0.29999999999999999	\N	\N
+228	0	13420	\N	79	3	\N	\N
+229	0	13430	\N	105	2	\N	\N
+230	0	13440	\N	12	1	\N	\N
+231	0	13450	\N	572	6	\N	\N
+232	0	13460	\N	144	3	\N	\N
+233	0	13470	\N	109	3	\N	\N
+235	0	13490	\N	36	2	\N	\N
+236	0	13502	\N	0.20000000000000001	0.10000000000000001	\N	\N
+237	0	13510	\N	1.2	0.10000000000000001	\N	\N
+238	0	13520	\N	87	2	\N	\N
+260	0	15030	\N	98	1	\N	\N
+261	0	15040	\N	0.20999999999999999	0	\N	\N
+262	0	15050	\N	7.3799999999999999	0.01	\N	\N
+263	0	15060	\N	41	2	\N	\N
+264	0	15070	\N	38	2	\N	\N
+265	0	16010	\N	7.3899999999999997	0.01	\N	\N
+266	0	16020	\N	40	2	\N	\N
+267	0	16030	\N	98	1	\N	\N
+268	0	16040	\N	98	1	\N	\N
+269	0	16050	\N	0.20999999999999999	0	\N	\N
+270	0	16060	\N	7.3799999999999999	0.01	\N	\N
+271	0	16070	\N	41	2	\N	\N
+272	0	16080	\N	38	2	\N	\N
+273	0	16090	\N	65	2	\N	\N
+274	0	16100	\N	0.80000000000000004	0.10000000000000001	\N	\N
+276	0	17020	\N	\N	\N	Clear	\N
+277	0	17030	\N	1.018	0.0030000000000000001	\N	\N
+278	0	17040	\N	\N	\N	Negative	\N
+279	0	17050	\N	\N	\N	Negative	\N
+280	0	17060	\N	\N	\N	Negative	\N
+281	0	17070	\N	7.5	0.10000000000000001	\N	\N
+282	0	17080	\N	\N	\N	Negative	\N
+283	0	17090	\N	\N	\N	Negative	\N
+284	0	17100	\N	\N	\N	Negative	\N
+285	0	17110	\N	0	0	\N	\N
+287	0	17130	\N	\N	\N	Negative	\N
+310	0	18040	\N	\N	\N	Negative	\N
+311	0	18050	\N	\N	\N	Negative	\N
+312	0	18060	\N	\N	\N	Negative	\N
+313	0	18070	\N	\N	\N	Negative	\N
+314	0	18080	\N	\N	\N	Negative	\N
+315	0	18090	\N	\N	\N	Negative	\N
+316	0	18100	\N	\N	\N	Negative	\N
+326	0	1000001	\N	113	4	\N	\N
+194	0	11040	\N	96	1	\N	\N
+213	0	12010	\N	84	2	\N	\N
+215	0	12030	\N	11	1	\N	\N
+218	0	13080	\N	10	1	\N	\N
+220	0	13120	\N	3.1000000000000001	0.20000000000000001	\N	\N
+239	0	13504	\N	0.80000000000000004	0.10000000000000001	\N	\N
+240	0	13610	\N	145	3	\N	\N
+242	0	13630	\N	45	2	\N	\N
+243	0	13640	\N	107	2	\N	\N
+245	0	13660	\N	87	4	\N	\N
+246	0	13710	\N	0	0	\N	\N
+247	0	13720	\N	11	1	\N	\N
+248	0	13730	\N	12	1	\N	\N
+249	0	13740	\N	76	3	\N	\N
+250	0	13760	\N	1.2	0.10000000000000001	\N	\N
+251	0	13770	\N	13	1	\N	\N
+252	0	13780	\N	0.5	0.10000000000000001	\N	\N
+253	0	13790	\N	8	1	\N	\N
+254	0	13810	\N	1.2	0.10000000000000001	\N	\N
+255	0	13820	\N	28	2	\N	\N
+256	0	13830	\N	0	0	\N	\N
+258	0	15010	\N	7.3899999999999997	0.01	\N	\N
+259	0	15020	\N	40	2	\N	\N
+286	0	17120	\N	0	0	\N	\N
+288	0	17140	\N	\N	\N	Negative	\N
+289	0	17150	\N	\N	\N	Negative	\N
+290	0	17210	\N	\N	\N	Negative	\N
+291	0	17220	\N	\N	\N	Negative	\N
+292	0	17230	\N	\N	\N	Negative	\N
+293	0	17240	\N	\N	\N	Negative	\N
+294	0	17250	\N	\N	\N	Negative	\N
+295	0	17260	\N	\N	\N	Negative	\N
+296	0	17270	\N	\N	\N	Negative	\N
+297	0	17280	\N	\N	\N	Negative	\N
+298	0	17290	\N	\N	\N	Negative	\N
+299	0	17310	\N	13	0	\N	\N
+301	0	17330	\N	1132	32	\N	\N
+302	0	17340	\N	2.1000000000000001	0.20000000000000001	\N	\N
+304	0	17360	\N	32	3	\N	\N
+306	0	17380	\N	176	5	\N	\N
+307	0	18010	\N	\N	\N	Negative	\N
+308	0	18020	\N	\N	\N	Negative	\N
+309	0	18030	\N	\N	\N	Negative	\N
+5019	0	13504	\N	0.69999999999999996	0.20000000000000001	\N	\N
+467	0	133	\N	\N	\N	No acute intracranial abnormality	\N
+5030	0	1004202	\N	\N	\N	No cervical spinal abnormalities noted	\N
+5031	0	1004196	\N	\N	\N	No intrabdominal pathologies noted	\N
+5032	0	1004206	\N	\N	\N	No deep vein thromboses detected	\N
+5033	0	131	\N	\N	\N	No acute intracranial abnormality	\N
+5034	0	1004203	\N	\N	\N	No cervical spinal abnormalities noted	\N
+5035	0	1004204	\N	\N	\N	No thoracic spinal abnormalities noted	\N
+5036	0	1004205	\N	\N	\N	No lumbar spinal abnormalities noted	\N
+5037	0	1004208	\N	\N	\N	Normal perfusion scan	\N
+5038	0	200	\N	\N	\N	Normal systolic function without significant valvular abnormalities	\N
+5039	0	1004207	\N	\N	\N	No resting or stress induced wall motion abnormalities noted	\N
+5040	10	20	\N	108	4	\N	High
+5041	10	30	\N	142	4	\N	High
+5042	10	40	\N	94	3	\N	High
+5043	10	50	\N	26	2	\N	High
+5044	10	1004193	\N	0.81999999999999995	0.01	\N	\N
+5045	10	11000	\N	10	0.69999999999999996	\N	\N
+5046	10	12010	\N	12632	2	\N	High
+5047	10	13040	\N	29	1	\N	High
+5048	10	13530	\N	0.10000000000000001	0	\N	High
+5049	10	13540	\N	356	17	\N	High
+5050	10	13780	\N	1.8	0.10000000000000001	\N	High
+5051	10	13790	\N	56	1	\N	High
+5052	10	15010	\N	7.4199999999999999	0.01	\N	\N
+5053	10	15020	\N	40	2	\N	\N
+5054	10	15030	\N	51	1	\N	Low
+5055	10	15050	\N	7.4199999999999999	0.01	\N	\N
+5056	10	15060	\N	40	2	\N	\N
+5057	10	15070	\N	38	2	\N	\N
+5058	10	16010	\N	7.4199999999999999	0.01	\N	\N
+5059	10	16020	\N	40	2	\N	\N
+5060	10	16030	\N	51	1	\N	Low
+5061	10	16040	\N	81	1	\N	Low
+5062	10	16050	\N	0.20999999999999999	0	\N	\N
+5063	10	16060	\N	7.4199999999999999	0.01	\N	\N
+5064	10	16070	\N	40	2	\N	\N
+5065	10	16080	\N	38	2	\N	Low
+5066	10	16090	\N	38	2	\N	\N
+5067	10	1000061	\N	0.16	0	\N	High
+5068	10	1004194	\N	\N	\N	Increased lung volumes with mild cardiomegaly and blunting of left costophrenic angle. 2cm density in left lower lung.	\N
+5069	10	1004195	\N	\N	\N	Multiple filling defects indicating pulmonary emboli off branches of L main pulmonary artery. Moderate emphysematous changes throughout. 2cm spiculated mass in left lower lung, suspicious for malignancy.	\N
+5070	10	1004206	\N	\N	\N	3cm deep vein thrombosis in right calf	\N
+5071	10	1004208	\N	\N	\N	Perfusion defects in L lower lung fields. Moderate probability of pulmonary embolism.	\N
+5072	10	1004198	\N	\N	\N	Sinus tachycardia	\N
+5073	10	200	\N	\N	\N	LVEF 37% with no significant valvular abnormalities noted. RVSP 29. RAP 8.	\N
+5074	12	20	\N	112	4	\N	High
+5075	12	30	\N	142	4	\N	High
+5076	12	40	\N	91	3	\N	High
+5077	12	50	\N	26	2	\N	High
+5078	12	1004193	\N	0.80000000000000004	0.01	\N	\N
+5079	12	11000	\N	10	0.69999999999999996	\N	\N
+5080	12	12010	\N	11431	2	\N	High
+5081	12	13040	\N	30	1	\N	High
+5082	12	13530	\N	0.10000000000000001	0	\N	High
+5083	12	13540	\N	356	17	\N	High
+5084	12	13780	\N	1.8	0.10000000000000001	\N	High
+5085	12	13790	\N	56	1	\N	High
+5086	12	15010	\N	7.4199999999999999	0.01	\N	\N
+5087	12	15020	\N	40	2	\N	\N
+5088	12	15030	\N	51	1	\N	Low
+5089	12	15050	\N	7.4199999999999999	0.01	\N	\N
+5090	12	15060	\N	40	2	\N	\N
+5091	12	15070	\N	38	2	\N	\N
+5092	12	16010	\N	7.4199999999999999	0.01	\N	\N
+5093	12	16020	\N	40	2	\N	\N
+5094	12	16030	\N	51	1	\N	Low
+5095	12	16040	\N	81	1	\N	Low
+5096	12	16050	\N	0.20999999999999999	0	\N	\N
+5097	12	16060	\N	7.4199999999999999	0.01	\N	\N
+5098	12	16070	\N	40	2	\N	\N
+5099	12	16080	\N	38	2	\N	Low
+5100	12	16090	\N	38	2	\N	\N
+5101	12	1000061	\N	0.16	0	\N	High
+5102	12	1004194	\N	\N	\N	Increased lung volumes with mild cardiomegaly and blunting of left costophrenic angle. 2cm density in left lower lung.	\N
+5103	12	1004195	\N	\N	\N	Multiple filling defects indicating pulmonary emboli off branches of L main pulmonary artery. Moderate emphysematous changes throughout. 2cm spiculated mass in left lower lung, suspicious for malignancy.	\N
+5104	12	1004206	\N	\N	\N	3cm deep vein thrombosis in right calf	\N
+5105	12	1004208	\N	\N	\N	Perfusion defects in L lower lung fields. Moderate probability of pulmonary embolism.	\N
+5106	12	1004198	\N	\N	\N	Sinus tachycardia	\N
+5107	12	200	\N	\N	\N	LVEF 37% with no significant valvular abnormalities noted. RVSP 29. RAP 8.	\N
+5108	11	20	\N	96	4	\N	High
+5109	11	30	\N	132	4	\N	\N
+5110	11	40	\N	87	3	\N	\N
+5111	11	50	\N	20	2	\N	High
+5112	11	1004193	\N	0.92000000000000004	0.01	\N	\N
+5113	11	60	\N	0.40000000000000002	0	6L Nasal Cannula	\N
+5114	11	11000	\N	10	0.69999999999999996	\N	\N
+5115	11	12010	\N	12873	2	\N	High
+5116	11	13040	\N	30	1	\N	High
+5117	11	13530	\N	0.10000000000000001	0	\N	High
+5118	11	13540	\N	356	17	\N	High
+5119	11	13780	\N	1.8	0.10000000000000001	\N	High
+5120	11	13790	\N	56	1	\N	High
+5121	11	15010	\N	7.3899999999999997	0.01	\N	\N
+5122	11	15020	\N	45	2	\N	\N
+5123	11	15030	\N	64	1	\N	\N
+5124	11	15040	\N	0.40000000000000002	0	\N	High
+5125	11	15050	\N	7.3899999999999997	0.01	\N	\N
+5126	11	15060	\N	45	2	\N	High
+5127	11	15070	\N	42	2	\N	\N
+5128	11	16010	\N	7.3899999999999997	0.01	\N	\N
+5129	11	16020	\N	45	2	\N	High
+5130	11	16030	\N	64	1	\N	Low
+5131	11	16040	\N	81	1	\N	Low
+5132	11	16050	\N	0.40000000000000002	0	\N	High
+5133	11	16060	\N	7.3899999999999997	0.01	\N	\N
+5134	11	16070	\N	45	2	\N	High
+5135	11	16080	\N	42	2	\N	Low
+5136	11	16090	\N	38	2	\N	\N
+5137	11	1000061	\N	0.14000000000000001	0	\N	High
+5138	11	1004194	\N	\N	\N	Increased lung volumes with mild cardiomegaly and blunting of left costophrenic angle. 2cm density in left lower lung.	\N
+5139	11	1004195	\N	\N	\N	Multiple filling defects indicating pulmonary emboli off branches of L main pulmonary artery. Moderate emphysematous changes throughout. 2cm spiculated mass in left lower lung, suspicious for malignancy.	\N
+5140	11	1004206	\N	\N	\N	3cm deep vein thrombosis in right calf	\N
+5141	11	1004208	\N	\N	\N	Perfusion defects in L lower lung fields. Moderate probability of pulmonary embolism.	\N
+5142	11	1004198	\N	\N	\N	Sinus tachycardia	\N
+5143	11	200	\N	\N	\N	LVEF 37% with no significant valvular abnormalities noted. RVSP 29. RAP 8.	\N
+5144	8	20	\N	91	4	\N	High
+5145	8	30	\N	129	4	\N	\N
+5146	8	40	\N	83	3	\N	\N
+5147	8	50	\N	18	2	\N	High
+5148	8	1004193	\N	0.93000000000000005	0.01	\N	\N
+5149	8	60	\N	0.40000000000000002	0	6L Nasal Cannula	\N
+5150	8	11000	\N	10	0.69999999999999996	\N	\N
+5151	8	12010	\N	10653	2	\N	High
+5152	8	13040	\N	30	1	\N	High
+5153	8	13530	\N	0.10000000000000001	0	\N	High
+5154	8	13540	\N	356	17	\N	High
+5155	8	13780	\N	1.8	0.10000000000000001	\N	High
+5156	8	13790	\N	56	1	\N	High
+5157	8	15010	\N	7.3899999999999997	0.01	\N	\N
+5158	8	15020	\N	45	2	\N	\N
+5159	8	15030	\N	64	1	\N	\N
+5160	8	15040	\N	0.40000000000000002	0	\N	High
+5161	8	15050	\N	7.3899999999999997	0.01	\N	\N
+5162	8	15060	\N	45	2	\N	High
+5163	8	15070	\N	42	2	\N	\N
+5164	8	16010	\N	7.3899999999999997	0.01	\N	\N
+5165	8	16020	\N	45	2	\N	High
+5166	8	16030	\N	64	1	\N	Low
+5167	8	16040	\N	81	1	\N	Low
+5168	8	16050	\N	0.40000000000000002	0	\N	High
+5169	8	16060	\N	7.3899999999999997	0.01	\N	\N
+5170	8	16070	\N	45	2	\N	High
+5171	8	16080	\N	42	2	\N	Low
+5172	8	16090	\N	38	2	\N	\N
+5173	8	1000061	\N	0.14000000000000001	0	\N	High
+5174	8	1004194	\N	\N	\N	Increased lung volumes with mild cardiomegaly and blunting of left costophrenic angle. 2cm density in left lower lung.	\N
+5175	8	1004195	\N	\N	\N	Multiple filling defects indicating pulmonary emboli off branches of L main pulmonary artery. Moderate emphysematous changes throughout. 2cm spiculated mass in left lower lung, suspicious for malignancy.	\N
+5176	8	1004206	\N	\N	\N	3cm deep vein thrombosis in right calf	\N
+5177	8	1004208	\N	\N	\N	Perfusion defects in L lower lung fields. Moderate probability of pulmonary embolism.	\N
+5178	8	1004198	\N	\N	\N	Normal sinus rhythm	\N
+5179	8	200	\N	\N	\N	LVEF 37% with no significant valvular abnormalities noted. RVSP 29. RAP 8.	\N
+419	33	1000129	48880	1454	\N	Neutrophil 96%	High
+420	31	1000129	48880	1462	\N	Neutrophil 93%	High
+-179	5000	13080	\N	16	\N	\N	High
+69	0	13010	\N	141	2	\N	\N
+76	0	13030	\N	106	2	\N	\N
+6056	0	1004200	45869	\N	\N	No intra-abdominal abnormalities noted	\N
+6057	0	1004200	49408	\N	\N	No intra-abdominal abnormalities noted	\N
+6058	0	1004200	46343	\N	\N	No intra-abdominal abnormalities noted	\N
+\.
+
+
+--
+-- Name: sim_state_result_sim_state_result_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_state_result_sim_state_result_id_seq', 5179, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_state_transition.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_state_transition.dump.sql
new file mode 100644
index 00000000..d649cf30
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_state_transition.dump.sql
@@ -0,0 +1,136 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.6.11
+-- Dumped by pg_dump version 9.6.11
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_state_transition; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_state_transition (sim_state_transition_id, pre_state_id, post_state_id, clinical_item_id, time_trigger, description) FROM stdin;
+1	14	15	\N	3600	Active bleed uncontrolled
+3	15	14	65640	\N	Transfuse RBC temporarily restabilizes
+4	15	14	61975	\N	Transfuse RBC temporarily restabilizes
+5	14	14	65640	\N	Transfuse RBC temporarily resets state decay
+6	14	14	61975	\N	Transfuse RBC temporarily resets state decay timer
+7	14	14	43996	\N	Octreotide temporarily stalls variceal bleed
+8	14	16	61993	\N	Coagulopathy corrected with FFP
+9	14	16	65702	\N	Coagulopathy corrected with FFP
+10	14	16	44001	\N	Cogulopathy stabilized with Vitamin K
+11	16	2	49481	\N	GI Consult takes patient for EGD
+12	16	2	45969	\N	EGD Ordered and Done
+15	15	17	49481	\N	GI Consult Block
+20	15	17	45969	\N	EGD Block until stabilize
+21	14	17	49481	\N	GI Consult block
+22	14	17	45969	\N	EGD Block until stabilize
+23	17	15	\N	0	Consult block immediately to bleeding out state
+24	15	14	45748	\N	Transfuse RBC temporarily restabilizes
+25	14	14	45748	\N	Transfuse RBC temporarily resets state decay timer
+26	21	18	35850	\N	Insulin given for DKA, normalizes glucose
+27	18	20	\N	3600	Only insulin given, DKA patient becomes hypoglycemic
+28	20	18	44201	\N	Glucose and or potassium added back?
+30	20	18	44239	\N	Glucose and or potassium added back?
+31	20	18	44316	\N	Glucose and or potassium added back?
+32	20	18	46277	\N	Glucose and or potassium added back?
+33	20	18	46505	\N	Glucose and or potassium added back?
+29	18	19	44201	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
+34	18	19	44239	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
+35	18	19	44316	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
+36	18	19	46277	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
+37	18	19	46505	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
+5000	5000	5001	44391	\N	Appropriate antibiotics improve patient condition - Zosyn (pip/tazo)
+5010	5000	5001	44252	\N	Appropriate antibiotics improve patient condition - Zosyn (pip/tazo) (alternate)
+5020	5000	5001	44008	\N	Appropriate antibiotics improve patient condition - Meropenem
+5030	5000	5001	36210	\N	Appropriate antibiotics improve patient condition - Cefepime
+5040	5000	5001	44678	\N	Appropriate antibiotics improve patient condition - Aztreonam
+5050	5000	5001	44637	\N	Appropriate antibiotics improve patient condition - Ceftazadime
+5100	5000	5002	44198	\N	IV Fluid resuscitation improves patient condition - Sodium Chloride/Normal Saline
+5110	5000	5002	44439	\N	IV Fluid resuscitation improves patient condition - Lactated Ringers
+5120	5000	5002	44290	\N	IV Fluid resuscitation improves patient condition - Lactated Ringers v2
+5210	5001	5003	44198	\N	IV Fluid resuscitation improves patient condition - Sodium Chloride/Normal Saline
+5220	5001	5003	44439	\N	IV Fluid resuscitation improves patient condition - Lactated Ringers
+5230	5001	5003	44290	\N	IV Fluid resuscitation improves patient condition - Lactated Ringers v2
+5300	5002	5003	44391	\N	Appropriate antibiotics improve patient condition - Zosyn (pip/tazo)
+5310	5002	5003	44252	\N	Appropriate antibiotics improve patient condition - Zosyn (pip/tazo) (alternate)
+5320	5002	5003	44008	\N	Appropriate antibiotics improve patient condition - Meropenem
+5330	5002	5003	36210	\N	Appropriate antibiotics improve patient condition - Cefepime
+5340	5002	5003	44678	\N	Appropriate antibiotics improve patient condition - Aztreonam
+5350	5002	5003	44637	\N	Appropriate antibiotics improve patient condition - Ceftazadime
+63	30	31	43997	\N	Appropriate antibiotics improve patient condition - Vancomycin
+64	32	31	43997	\N	Appropriate antibiotics improve patient condition - Vancomycin
+65	33	31	43997	\N	Appropriate antibiotics improve patient condition - Vancomycin
+40	30	31	35733	\N	Appropriate antibiotics improve patient condition - Ceftriaxone
+41	32	31	35733	\N	Appropriate antibiotics improve patient condition - Ceftriaxone
+42	33	31	35733	\N	Appropriate antibiotics improve patient condition - Ceftriaxone
+43	30	31	36210	\N	Appropriate antibiotics improve patient condition - Cefepime
+44	32	31	36210	\N	Appropriate antibiotics improve patient condition - Cefepime
+50	30	33	\N	3600	Patient condition worsens after 1 hr of inadquate empiric treatment
+45	33	31	36210	\N	Appropriate antibiotics improve patient condition - Cefepime
+46	30	31	44008	\N	Appropriate antibiotics improve patient condition - Meropenem
+47	32	31	44008	\N	Appropriate antibiotics improve patient condition - Meropenem
+48	33	31	44008	\N	Appropriate antibiotics improve patient condition - Meropenem
+57	40	46	49251	\N	Cardiology Consultation
+58	40	43	35846	\N	Nodal Agents worsen hypotension
+59	40	43	44327	\N	Nodal Agents worsen hypotension
+60	40	45	35968	\N	Transient improvement with amiodarone
+500	40	41	-100	\N	Cardioversion improves hemodynamics
+51	43	45	35968	\N	Transient improvement with amiodarone
+54	40	42	46605	\N	Adenosine evaluation
+56	43	41	-100	\N	Cardioversion improves hemodynamics - from critical post diuretics
+62	43	42	46605	\N	Adenosine evaluation
+86	44	43	\N	\N	\N
+85	44	43	\N	\N	\N
+80	44	43	\N	\N	\N
+81	44	43	\N	\N	\N
+82	44	43	\N	\N	\N
+83	44	43	\N	\N	\N
+84	44	43	\N	\N	\N
+69	40	43	44004	\N	Diuretics worsen hypotension
+87	10	11	45900	\N	Oxygen supplementation
+88	10	11	45864	\N	Oxygen supplementation
+89	10	11	45921	\N	Oxygen supplementation
+90	10	12	44359	\N	Anticoagulation, but still hypoxic (Heparin IV)
+91	10	12	44250	\N	Anticoagulation, but still hypoxic (Enoxaparin)
+92	10	12	60178	\N	Anticoagulation, but still hypoxic (Rivaroxaban - Note Apixiban not available in 2014 data)
+93	11	8	44359	\N	Oxygen + Anticoagulation added
+94	11	8	44250	\N	Oxygen + Anticoagulation added
+95	11	8	60178	\N	Oxygen + Anticoagulation added
+96	12	8	45900	\N	Anticoagulation + Oxygen supplemented
+97	12	8	45864	\N	Anticoagulation + Oxygen supplemented
+98	12	8	45921	\N	Anticoagulation + Oxygen supplemented
+61	45	43	\N	0	Amiodarone not enough to stabilize. Send to worse state to avoid repeating initial history
+53	46	43	\N	0	Consult completed. Send to worse state to avoid repeating initial history
+55	42	43	\N	0	Adenosine wears off. Send to worse state to avoid repeating initial history
+-9	15	16	65702	\N	Coagulopathy corrected with FFP
+-10	15	16	44001	\N	Cogulopathy stabilized with Vitamin K
+-8	15	16	61993	\N	Coagulopathy corrected with FFP
+-21	14	16	45872	\N	Coagulopathy corrected with FFP
+-22	15	16	45872	\N	Coagulopathy corrected with FFP
+-23	14	14	50618	\N	Transfuse RBC temporarily restabilizes
+-24	15	14	50618	\N	Transfuse RBC temporarily restabilizes
+\.
+
+
+--
+-- Name: sim_state_transition_sim_state_transition_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_state_transition_sim_state_transition_id_seq', 98, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/simdata/sim_user.dump.sql b/medinfo/cpoe/cpoeSim/simdata/sim_user.dump.sql
new file mode 100644
index 00000000..91ca9ec8
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/simdata/sim_user.dump.sql
@@ -0,0 +1,101 @@
+--
+-- PostgreSQL database dump
+--
+
+-- Dumped from database version 9.3.11
+-- Dumped by pg_dump version 9.6.9
+
+SET statement_timeout = 0;
+SET lock_timeout = 0;
+SET idle_in_transaction_session_timeout = 0;
+SET client_encoding = 'UTF8';
+SET standard_conforming_strings = on;
+SELECT pg_catalog.set_config('search_path', '', false);
+SET check_function_bodies = false;
+SET client_min_messages = warning;
+SET row_security = off;
+
+--
+-- Data for Name: sim_user; Type: TABLE DATA; Schema: public; Owner: -
+--
+
+COPY public.sim_user (sim_user_id, name) FROM stdin;
+0	Default User
+1	Jonathan Chen
+9	User1
+2	User2
+10	User3
+11	User4
+12	User5
+13	User6
+15	x_JimmyTooley
+16	xa_generic_test
+17	x_KaiSwenson
+19	x_KS_3
+21	x_SK_3
+23	minh
+25	x_jc_user4
+26	tester
+27	son_5
+29	sw_6
+30	al_7
+31	gl_8
+33	phy_9
+34	kh_10
+35	rph_11
+38	r13
+39	r12
+40	mw13
+41	alice14
+42	phy15
+43	r16
+44	r17
+45	r18
+46	r19
+47	r20
+48	r21
+49	r22
+50	r23
+51	kg24
+52	kp25
+53	r26
+54	r27
+55	LisaShieh
+57	JH
+59	sp28
+60	ms29
+61	km30
+62	ab31
+63	cm32
+64	sv33
+65	pb32
+66	br33
+67	mel99
+68	ij34
+69	aa35
+70	dy36
+71	cd37
+72	cw37
+73	xd_jonc
+74	ek38
+75	vk39
+76	arp40
+77	dr41
+78	cc42
+79	vg43
+80	cw43
+81	jz44
+\.
+
+
+--
+-- Name: sim_user_sim_user_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
+--
+
+SELECT pg_catalog.setval('public.sim_user_sim_user_id_seq', 81, true);
+
+
+--
+-- PostgreSQL database dump complete
+--
+
diff --git a/medinfo/cpoe/cpoeSim/test/Const.py b/medinfo/cpoe/cpoeSim/test/Const.py
new file mode 100644
index 00000000..6cf4f0a4
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/test/Const.py
@@ -0,0 +1,26 @@
+"""Various constants for use by the application module test cases"""
+
+import logging
+
+"""Application name, for example to identify a common logger object"""
+APPLICATION_NAME = "medinfo.cpoe.cpoeSim.test"
+
+"""Default level for application logging.  Modify these for different scenarios.
+See Python logging package documentation for more information
+"""
+LOGGER_LEVEL = logging.ERROR
+
+"""Default format of logger output"""
+LOGGER_FORMAT = "[%(asctime)s %(levelname)s] %(message)s"
+
+"""Verbosity of the test runner"""
+RUNNER_VERBOSITY = 2
+
+"""Application logger level.  Set this to higher level to suppress uninteresting
+application output during test runs."""
+APP_LOGGER_LEVEL = logging.CRITICAL
+#APP_LOGGER_LEVEL = logging.DEBUG
+
+
+"""For some test cases with many test points, don't want to abort the entire test if just one item fails"""
+ALLOW_MULTIPLE_ERRORS = True;
diff --git a/medinfo/cpoe/cpoeSim/test/TestMakeUsageReport.py b/medinfo/cpoe/cpoeSim/test/TestMakeUsageReport.py
new file mode 100644
index 00000000..701348ad
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/test/TestMakeUsageReport.py
@@ -0,0 +1,198 @@
+import unittest
+import mock
+import os
+
+from .Const import RUNNER_VERBOSITY
+from medinfo.db.test.Util import DBTestCase
+from medinfo.cpoe.cpoeSim.analysis import make_usage_report
+from .Util import log, captured_output
+from medinfo.db import DBUtil
+from medinfo.cpoe.cpoeSim.SimManager import SimManager
+from io import StringIO
+import tempfile
+import csv
+
+header = ['user', 'patient', 'start_time', 'elapsed_time', 'total_num_clicks', 'num_note_clicks',
+          'num_results_review_clicks', 'recommended_options', 'unique_recommended_options',
+          'manual_search_options', 'total_orders', 'orders_from_recommender', 'orders_from_manual_search',
+          'orders_from_recommender_missed']
+
+
+def mock_aggregate_simulation_data(data_home, output_path):
+    # create mock csv - we're not testing aggregate_simulation_data here
+    content = [['1', '1']]
+
+    with open(output_path, 'w') as outfile:
+        outfile.write(str.join(',', header) + '\n')
+        for row in content:
+            outfile.write(str.join(',', row) + '\n')
+
+
+@mock.patch('medinfo.cpoe.cpoeSim.analysis.make_usage_report.aggregate_simulation_data',
+            side_effect=mock_aggregate_simulation_data)
+class TestMakeUsageReport(DBTestCase):
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self)
+        self.usage_reporter = SimManager()
+
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+        self.usage_reporter.buildCPOESimSchema()
+
+        log.info("Populate the database with test data")
+
+        # Basically import a bunch of rigged CSV or TSV files that have realistic simulating case and grading data
+        # Get that data into the test database
+        clinical_item_category_str = \
+            """clinical_item_category_id;source_table
+            1;source_table
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(clinical_item_category_str), "clinical_item_category", delim=";")
+
+        clinical_item_str = \
+            """clinical_item_id;clinical_item_category_id;name
+            1;1;Clinical item 1
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(clinical_item_str), "clinical_item", delim=";")
+
+        clinical_item_str = \
+            """sim_user_id;name
+            1;Jonathan Chen
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(clinical_item_str), "sim_user", delim=";")
+
+        sim_patient_str = \
+            """sim_patient_id;name;age_years;gender
+            1;Patient One;40;Male
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_patient_str), "sim_patient", delim=";")
+
+        sim_state_str = \
+            """sim_state_id;name
+            1;Sim state 1
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_state_str), "sim_state", delim=";")
+
+        sim_patient_order_str = \
+            """sim_patient_order_id;sim_user_id;sim_patient_id;clinical_item_id;relative_time_start;sim_state_id
+            1;1;1;1;1;1
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_patient_order_str), "sim_patient_order", delim=";")
+
+        sim_grading_key_str = \
+            """sim_grader_id;sim_state_id;clinical_item_id;score;group_name;sim_case_name
+            Jonathan Chen;1;1;1;g1;case_name
+            Andre Kumar;1;1;2;g1;case_name
+            """
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_grading_key_str), "sim_grading_key", delim=";")
+
+        # Prepare survey file
+        self.survey_csv = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        self.survey_csv.write("Physician User Name,resident" + os.linesep +
+                              "Jonathan Chen,1")
+        self.survey_csv.flush()
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        DBTestCase.tearDown(self)
+
+        # delete temporary survey file
+        self.survey_csv.close()
+        os.remove(self.survey_csv.name)
+
+        # delete output csv
+        try:
+            os.remove(tempfile.gettempdir() + os.pathsep + 'tmp.csv')
+        except OSError:
+            pass
+
+    def test_not_enough_args(self, mock_aggregate_simulation_data):
+        # setup
+        argv = ['make_usage_report.py', '-g', tempfile.gettempdir() + '/tmp.csv']
+
+        # test & verify
+        self.verify_error_message_for_argv(argv, "Given parameters are not enough. Exiting.")
+
+    def test_no_grader(self, mock_aggregate_simulation_data):
+        # setup
+        argv = ['make_usage_report.py', '../analysis/sim_data', tempfile.gettempdir() + '/tmp.csv']
+
+        # test & verify
+        self.verify_error_message_for_argv(argv, "No graders given. Cannot grade patient cases. Exiting.")
+
+    def test_single_grader(self, mock_aggregate_simulation_data):
+        # setup
+        output_filename = tempfile.gettempdir() + '/tmp.csv'
+        argv = ['make_usage_report.py', '../analysis/sim_data', '-g', 'Jonathan Chen', output_filename]
+
+        # test
+        make_usage_report.main(argv)
+
+        # verify
+        with open(output_filename) as output_file:
+            output = csv.DictReader(output_file)
+            self.assertTrue('grade ({})'.format(argv[3]) in output.fieldnames)
+            # assert more columns than original + grade column
+            self.assertGreater(len(output.fieldnames), len(header) + 1)
+
+    def test_multiple_graders(self, mock_aggregate_simulation_data):
+        # setup
+        output_filename = tempfile.gettempdir() + '/tmp.csv'
+        argv = ['make_usage_report.py', '../analysis/sim_data', '-g', 'Jonathan Chen,Andre Kumar', output_filename]
+
+        # test
+        make_usage_report.main(argv)
+
+        # verify
+        with open(output_filename) as output_file:
+            output = csv.DictReader(output_file)
+            self.assertTrue('grade ({})'.format(argv[3].split(',')[0]) in output.fieldnames)
+            self.assertTrue('grade ({})'.format(argv[3].split(',')[1]) in output.fieldnames)
+            # assert more columns than original + grade columns
+            self.assertGreater(len(output.fieldnames), len(header) + 2)
+
+    def test_resident_column_is_present(self, mock_aggregate_simulation_data):
+        # setup
+        output_filename = tempfile.gettempdir() + '/tmp.csv'
+        argv = ['make_usage_report.py', '../analysis/sim_data', '-s', self.survey_csv.name, '-g', 'Jonathan Chen', output_filename]
+
+        # test
+        make_usage_report.main(argv)
+
+        # verify
+        with open(output_filename) as output_file:
+            output = csv.DictReader(output_file)
+            self.assertTrue('resident' in output.fieldnames)
+
+    def verify_error_message_for_argv(self, argv, expected_error_message):
+        with self.assertRaises(SystemExit) as cm:   # capture sys.exit() in the tested class
+            with captured_output() as (out, err):
+                make_usage_report.main(argv)
+
+        actual_output = out.getvalue()
+        self.assertEqual(expected_error_message, actual_output.split("\n", 1)[0])
+
+        self.assertIsNone(cm.exception.code)    # we can verify exception code here
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    test_suite = unittest.TestSuite()
+    test_suite.addTest(unittest.makeSuite(TestMakeUsageReport))
+
+    return test_suite
+
+
+if __name__ == "__main__":
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/cpoe/test/TestSimManager.py b/medinfo/cpoe/cpoeSim/test/TestSimManager.py
similarity index 69%
rename from medinfo/cpoe/test/TestSimManager.py
rename to medinfo/cpoe/cpoeSim/test/TestSimManager.py
index 1303e74b..8f58f2e3 100644
--- a/medinfo/cpoe/test/TestSimManager.py
+++ b/medinfo/cpoe/cpoeSim/test/TestSimManager.py
@@ -2,35 +2,41 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable;
 
-from medinfo.cpoe.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
 
 class TestSimManager(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
-        
+
+        self.manager = SimManager();  # Instance to test on
+
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+        self.manager.buildCPOESimSchema();
+
         self.testPatientId = None;
-        
+
         self.purgeTestRecords();
-        
+
         log.info("Populate the database with test data")
 
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","source_table"];
         dataModels = \
-            [   
+            [
                 RowItemModel( [-1, "Labs"], headers ),
                 RowItemModel( [-2, "Imaging"], headers ),
                 RowItemModel( [-3, "Meds"], headers ),
@@ -44,9 +50,9 @@ def setUp(self):
 
         headers = ["clinical_item_id","clinical_item_category_id","name","analysis_status"];
         dataModels = \
-            [   
+            [
                 RowItemModel( [-1, -1, "CBC",1], headers ),
-                RowItemModel( [-2, -1, "BMP",1], headers ), 
+                RowItemModel( [-2, -1, "BMP",1], headers ),
                 RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
                 RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
                 RowItemModel( [-5, -2, "CXR",1], headers ),
@@ -113,7 +119,7 @@ def setUp(self):
 """     # Parse into DB insertion object
         DBUtil.insertFile( StringIO(dataTextStr), "sim_result", delim=";");
 
-        # Map orders to expected results. 
+        # Map orders to expected results.
         # Simplify expect vital signs to result in 5 minutes. Basic chemistry labs in 10 minutes, CBC in 15 minutes
         dataTextStr = \
 """sim_order_result_map_id;clinical_item_id;sim_result_id;turnaround_time
@@ -146,6 +152,7 @@ def setUp(self):
 
         dataTextStr = \
 """sim_state_id;name;description
+0;Test 0; Test State 0
 -1;Test 1;Test State 1
 -2;Test 2;Test State 2
 -3;Test 3;Test State 3
@@ -174,13 +181,13 @@ def setUp(self):
         # Order Vital Signs at time 0, then basic labs (CBC, BMP, LFTs) at 10 minutes (600 seconds)
         dataTextStr = \
 """sim_patient_order_id;sim_user_id;sim_patient_id;sim_state_id;clinical_item_id;relative_time_start;relative_time_end
--1;-1;-1;-1;-15;0;0
--2;-1;-1;-1;-1;600;600
--3;-1;-1;-1;-2;600;600
--4;-1;-1;-1;-3;600;600
--5;-1;-1;-2;-15;1800;1800
--6;-1;-1;-2;-1;1800;1800
--7;-1;-1;-2;-2;1800;1800
+-1;-1;-1;-1;-15;0;None
+-2;-1;-1;-1;-1;600;None
+-3;-1;-1;-1;-2;600;None
+-4;-1;-1;-1;-3;600;None
+-5;-1;-1;-2;-15;1800;None
+-6;-1;-1;-2;-1;1800;None
+-7;-1;-1;-2;-2;1800;None
 """     # Parse into DB insertion object
         DBUtil.insertFile( StringIO(dataTextStr), "sim_patient_order", delim=";");
 
@@ -234,8 +241,6 @@ def setUp(self):
 """     # Parse into DB insertion object
         DBUtil.insertFile( StringIO(dataTextStr), "sim_note", delim=";");
 
-        self.manager = SimManager();  # Instance to test on
-
     def purgeTestRecords(self):
         log.info("Purge test records from the database")
         if self.testPatientId is not None:
@@ -251,11 +256,11 @@ def purgeTestRecords(self):
         DBUtil.execute("delete from sim_result where sim_result_id < 0");
         DBUtil.execute("delete from sim_patient_state where sim_state_id < 0 or sim_patient_state_id < 0");
         DBUtil.execute("delete from sim_state_transition where pre_state_id < 0");
-        DBUtil.execute("delete from sim_state where sim_state_id < 0");
+        DBUtil.execute("delete from sim_state where sim_state_id <= 0");
         DBUtil.execute("delete from sim_user where sim_user_id < 0");
         DBUtil.execute("delete from sim_patient where sim_patient_id < 0");
         DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
-        
+
     def tearDown(self):
         """Restore state from any setUp or test steps"""
         self.purgeTestRecords();
@@ -267,13 +272,13 @@ def test_copyPatientTemplate(self):
         templatePatientId = -1;
         self.testPatientId = self.manager.copyPatientTemplate( newPatientData, templatePatientId );
         futureTime = 1000000;   # Far future time to test that we still only copied the results up to time zero
-        
+
         # Verify basic patient information
         patientCols = ["name","age_years","gender","sim_state_id"];
         patientModel = self.manager.loadPatientInfo([self.testPatientId])[0];
         expectedPatientModel = RowItemModel(["Template Copy",60,"Female",-1], patientCols);
         self.assertEqualDict(expectedPatientModel, patientModel, patientCols);
-        
+
         # Verify notes
         dataCols = ["sim_patient_id","content"];
         sampleData = self.manager.loadNotes(self.testPatientId, futureTime);
@@ -282,15 +287,15 @@ def test_copyPatientTemplate(self):
                 RowItemModel([self.testPatientId,"Initial Note"], dataCols),    # Second copy because another state initiation at time zero and negative onset time
             ];
         self.assertEqualDictList(verifyData, sampleData, dataCols);
-    
+
         # Verify orders
         dataCols = ["sim_user_id","sim_patient_id","sim_state_id","clinical_item_id","relative_time_start","relative_time_end"];
         sampleData = self.manager.loadPatientOrders(self.testPatientId, futureTime, loadActive=None);
         verifyData = \
-            [   RowItemModel([-1,self.testPatientId,-1,-15,0,0], dataCols),
+            [   RowItemModel([-1,self.testPatientId,-1,-15,0,None], dataCols),
             ];
         self.assertEqualDictList(verifyData, sampleData, dataCols);
-        
+
         # Verify states
         dataCols = ["sim_patient_id","sim_state_id","relative_time_start","relative_time_end"];
         query = SQLQuery();
@@ -310,15 +315,15 @@ def test_copyPatientTemplate(self):
     def test_loadResults(self):
         # Query for results based on simulated turnaround times, including fallback to default normal values
         #   if no explicit (abnormal) values specified for simulated state
-        
+
         colNames = ["name", "num_value", "result_relative_time"];
-        
+
         # Time zero, no orders for diagnostics, so no results should exist
         patientId = -1;
         relativeTime = 0;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
             ];
         self.assertEqualDictList(verifyResults, sampleResults, colNames);
 
@@ -326,7 +331,7 @@ def test_loadResults(self):
         relativeTime = 120;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
             ];
         self.assertEqualDictList(verifyResults, sampleResults, colNames);
 
@@ -334,7 +339,7 @@ def test_loadResults(self):
         relativeTime = 300;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
                 RowItemModel(["Temp", 101.4, 300], colNames),
                 RowItemModel(["Pulse", 115, 300], colNames),
                 RowItemModel(["SBP", 92, 300], colNames),
@@ -347,7 +352,7 @@ def test_loadResults(self):
         relativeTime = 600;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
                 RowItemModel(["Temp", 101.4, 300], colNames),
                 RowItemModel(["Pulse", 115, 300], colNames),
                 RowItemModel(["SBP", 92, 300], colNames),
@@ -360,7 +365,7 @@ def test_loadResults(self):
         relativeTime = 1200;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
                 RowItemModel(["Temp", 101.4, 300], colNames),
                 RowItemModel(["Pulse", 115, 300], colNames),
                 RowItemModel(["SBP", 92, 300], colNames),
@@ -388,13 +393,13 @@ def test_loadResults(self):
         relativeTime = 1500;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
                 RowItemModel(["Temp", 101.4, 300], colNames),
                 RowItemModel(["Pulse", 115, 300], colNames),
                 RowItemModel(["SBP", 92, 300], colNames),
                 RowItemModel(["DBP", 55, 300], colNames),
                 RowItemModel(["Resp", 12, 300], colNames),  # Normal result retrieve from default state 0
-                
+
                 RowItemModel(["NA", 140, 1200], colNames),
                 RowItemModel(["K", 4.5, 1200], colNames),
                 RowItemModel(["CL", 95, 1200], colNames),
@@ -422,13 +427,13 @@ def test_loadResults(self):
         relativeTime = 1800;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
                 RowItemModel(["Temp", 101.4, 300], colNames),
                 RowItemModel(["Pulse", 115, 300], colNames),
                 RowItemModel(["SBP", 92, 300], colNames),
                 RowItemModel(["DBP", 55, 300], colNames),
                 RowItemModel(["Resp", 12, 300], colNames),  # Normal result retrieve from default state 0
-                
+
                 RowItemModel(["NA", 140, 1200], colNames),
                 RowItemModel(["K", 4.5, 1200], colNames),
                 RowItemModel(["CL", 95, 1200], colNames),
@@ -456,13 +461,13 @@ def test_loadResults(self):
         relativeTime = 2700;
         sampleResults = self.manager.loadResults(patientId, relativeTime);
         verifyResults = \
-            [   
+            [
                 RowItemModel(["Temp", 101.4, 300], colNames),
                 RowItemModel(["Pulse", 115, 300], colNames),
                 RowItemModel(["SBP", 92, 300], colNames),
                 RowItemModel(["DBP", 55, 300], colNames),
                 RowItemModel(["Resp", 12, 300], colNames),  # Normal result retrieve from default state 0
-                
+
                 RowItemModel(["NA", 140, 1200], colNames),
                 RowItemModel(["K", 4.5, 1200], colNames),
                 RowItemModel(["CL", 95, 1200], colNames),
@@ -489,7 +494,7 @@ def test_loadResults(self):
                 RowItemModel(["SBP", 130, 2100], colNames),
                 RowItemModel(["DBP", 85, 2100], colNames),
                 RowItemModel(["Resp", 12, 2100], colNames),
-                
+
                 RowItemModel(["NA", 140, 2400], colNames),
                 RowItemModel(["K", 4.5, 2400], colNames),
                 RowItemModel(["CL", 95, 2400], colNames),
@@ -506,27 +511,211 @@ def test_loadResults(self):
             ];
         self.assertEqualDictList(verifyResults, sampleResults, colNames);
 
+
+    def test_discontinueOrders(self):
+        # Verify behavior of discontinuing/cancelling orders
+        #   Should not generate subsequent results or count as "recent items" that should
+        #   be considered to trigger recommender. If cancelled at same time as order time,
+        #   then delete the record altogether.
+
+        colNames = ["name", "num_value", "result_relative_time"];
+
+        # See setUp for test data construction
+        userId = -1;
+        patientId = -1;
+
+        # Time zero, vital sign orders check entered (clinical_item_id = -15, sim_patient_order_id = -1)
+        # Time 2 minutes, orders done, but not long-enough to get results back, so no results should exist
+        relativeTime = 120;
+        sampleResults = self.manager.loadResults(patientId, relativeTime);
+        verifyResults = \
+            [
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Record of vital sign order (but not yet resulted)
+        sampleRecentItemIds = self.manager.recentItemIds(patientId, relativeTime);
+        verifyRecentItemIds = set([-15]);
+        self.assertEqual(verifyRecentItemIds, sampleRecentItemIds);
+
+
+        # Time 5 minutes, vital signs should result now
+        relativeTime = 300;
+        sampleResults = self.manager.loadResults(patientId, relativeTime);
+        verifyResults = \
+            [
+                RowItemModel(["Temp", 101.4, 300], colNames),
+                RowItemModel(["Pulse", 115, 300], colNames),
+                RowItemModel(["SBP", 92, 300], colNames),
+                RowItemModel(["DBP", 55, 300], colNames),
+                RowItemModel(["Resp", 12, 300], colNames),  # Normal result retrieve from default state 0
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Restrict recent item search to limited time that should miss the vital sign order
+        sampleRecentItemIds = self.manager.recentItemIds(patientId, relativeTime, 100);
+        verifyRecentItemIds = set([]);
+        self.assertEqual(verifyRecentItemIds, sampleRecentItemIds);
+
+        # Include longer time difference that should catch the prior order
+        sampleRecentItemIds = self.manager.recentItemIds(patientId, relativeTime, 400);
+        verifyRecentItemIds = set([-15]);
+        self.assertEqual(verifyRecentItemIds, sampleRecentItemIds);
+
+
+        # Go back and simulate the vitals check order being discontinued before results came back
+        discontinueTime = 120;
+        newOrderItemIds = [];   # No new orders
+        discontinuePatientOrderIds = [-1];  # See setUp data for the ID of the order to simulate canceling
+        self.manager.signOrders(userId, patientId, discontinueTime, newOrderItemIds, discontinuePatientOrderIds);
+
+        # Redo simulation of Time 5 minutes, vital signs should not appear now, since order was cancelled
+        relativeTime = 300;
+        sampleResults = self.manager.loadResults(patientId, relativeTime);
+        verifyResults = \
+            [
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Check that there is still a record of orders, including the cancelled one (but not in "recent items" list)
+        orderCols = ["name","relative_time_start","relative_time_end"];
+        sampleOrders = self.manager.loadPatientOrders(patientId, 300, loadActive=None);
+        verifyOrders = \
+            [
+                RowItemModel(["Vital Signs", 0, 120], orderCols),
+            ];
+        self.assertEqualDictList(verifyOrders, sampleOrders, orderCols);
+
+        sampleRecentItemIds = self.manager.recentItemIds(patientId, relativeTime);
+        verifyRecentItemIds = set([]);   # Don't count the cancelled order
+        self.assertEqual(verifyRecentItemIds, sampleRecentItemIds);
+
+        # Go back and simulate the vitals check order being discontinued immediately (same time as order),
+        #   then don't even keep a record of it to clean up data entry error
+        discontinueTime = 0;
+        newOrderItemIds = [];   # No new orders
+        discontinuePatientOrderIds = [-1];  # See setUp data for the ID of the order to simulate canceling
+        self.manager.signOrders(userId, patientId, discontinueTime, newOrderItemIds, discontinuePatientOrderIds);
+
+        # Check that there is no record of the order anymore, even including the cancelled one
+        orderCols = ["name","relative_time_start","relative_time_end"];
+        sampleOrders = self.manager.loadPatientOrders(patientId, 300, loadActive=None);
+        verifyOrders = \
+            [
+            ];
+        self.assertEqualDictList(verifyOrders, sampleOrders, orderCols);
+
+        sampleRecentItemIds = self.manager.recentItemIds(patientId, relativeTime);
+        verifyRecentItemIds = set([]);
+        self.assertEqual(verifyRecentItemIds, sampleRecentItemIds);
+
+    def test_loadPendingResultOrders(self):
+        # Verify behavior of finding patient orders that will, but have not yet, yielded test results
+        colNames = ["clinical_item_id", "name", "time_until_result"];
+
+        # See setUp for test data construction
+        userId = -1;
+        patientId = -1;
+
+        # Time zero, vital sign orders check entered (clinical_item_id = -15, sim_patient_order_id = -1)
+        # Time 2 minutes, orders done, but not long-enough to get results back, so no results should exist, but can see pending result
+        relativeTime = 120;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [   RowItemModel([-15, "Vital Signs", 300-(120-0)], colNames),
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Time 5 minutes, vital signs should result now. So no pending results
+        relativeTime = 300;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Go back and simulate the vitals check order being discontinued before results came back
+        discontinueTime = 100;
+        newOrderItemIds = [];   # No new orders
+        discontinuePatientOrderIds = [-1];  # See setUp data for the ID of the order to simulate canceling
+        self.manager.signOrders(userId, patientId, discontinueTime, newOrderItemIds, discontinuePatientOrderIds);
+
+        # Redo simulation shortly after. Should not show pending results since order was cancelled
+        relativeTime = 120;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Time 10 minutes, should have multiple pending orders just entered
+        relativeTime = 600;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [   # Show results sorted by time until result, then alphabetical order
+                RowItemModel([-2, "BMP", 600-(600-600)], colNames),
+                RowItemModel([-3, "Hepatic Panel", 600-(600-600)], colNames),
+                RowItemModel([-1, "CBC", 900-(600-600)], colNames),
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Time 20 minutes, some of the results should be back, while others still pending
+        relativeTime = 1200;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [   
+                RowItemModel([-1, "CBC", 900-(1200-600)], colNames),
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Go back and sign new orders of same type
+        relativeTime = 900;
+        orderItemIds = [-1,-2];
+        self.manager.signOrders(userId, patientId, relativeTime, orderItemIds);
+
+        # Should now see some overlapping pending order times
+        relativeTime = 1200;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [   # If multiple results with same time until result, then show in order of first one ordered
+                RowItemModel([-1, "CBC", 900-(1200-600)], colNames),
+                RowItemModel([-2, "BMP", 600-(1200-900)], colNames),
+                RowItemModel([-1, "CBC", 900-(1200-900)], colNames),
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
+        # Later batch of more orders at 30 minutes
+        relativeTime = 1800;
+        sampleResults = self.manager.loadPendingResultOrders(patientId, relativeTime);
+        verifyResults = \
+            [   
+                RowItemModel([-15, "Vital Signs", 300-(1800-1800)], colNames),
+                RowItemModel([-2, "BMP", 600-(1800-1800)], colNames),
+                RowItemModel([-1, "CBC", 900-(1800-1800)], colNames),
+            ];
+        self.assertEqualDictList(verifyResults, sampleResults, colNames);
+
     def test_stateTransition(self):
         # Query for results based on simulated turnaround times, including fallback to default normal values
         #   if no explicit (abnormal) values specified for simulated state
-        
+
         colNames = ["sim_state_id"];
 
         userId = -1;
         patientId = -1;
-        
+
         # Time zero, initial state expected
         relativeTime = 0;
         samplePatient = self.manager.loadPatientInfo([patientId], relativeTime)[0];
         verifyPatient = RowItemModel([-1], colNames);
         self.assertEqualDict(samplePatient, verifyPatient, colNames);
 
-        # After previously recorded second state 
+        # After previously recorded second state
         relativeTime = 2000;
         samplePatient = self.manager.loadPatientInfo([patientId], relativeTime)[0];
         verifyPatient = RowItemModel([-2], colNames);
         self.assertEqualDict(samplePatient, verifyPatient, colNames);
-        
+
         # Sign orders that do not affect this state
         orderItemIds = [-4,-5];
         self.manager.signOrders(userId, patientId, relativeTime, orderItemIds);
@@ -553,7 +742,7 @@ def test_stateTransition(self):
         samplePatient = self.manager.loadPatientInfo([patientId], relativeTime)[0];
         verifyPatient = RowItemModel([-2], colNames);
         self.assertEqualDict(samplePatient, verifyPatient, colNames);
-        
+
         # Retroactive query to verify state 1 intermediate transition state
         relativeTime = 12000;
         samplePatient = self.manager.loadPatientInfo([patientId], relativeTime)[0];
@@ -589,6 +778,53 @@ def test_stateTransition(self):
         verifyPatient = RowItemModel([-4], colNames);
         self.assertEqualDict(samplePatient, verifyPatient, colNames);
 
+    def test_loadPatientLastEventTime(self):
+        # Query for last time have a record of a patient order start or cancellation
+        #   as natural point to resume a simulated case
+
+        # See setUp for test data construction
+        userId = -1;
+        patientId = -1;
+
+        # Initial test data already loaded with example orders
+        sampleValue = self.manager.loadPatientLastEventTime(patientId);
+        verifyValue = 1800;
+        self.assertEqual(verifyValue, sampleValue);
+
+        # Sign an additional order
+        relativeTime = 2000;
+        newOrderItemIds = [-1];   # Any additional order
+        self.manager.signOrders(userId, patientId, relativeTime, newOrderItemIds);
+
+        # Verify update of last order time
+        sampleValue = self.manager.loadPatientLastEventTime(patientId);
+        verifyValue = 2000;
+        self.assertEqual(verifyValue, sampleValue);
+
+        # Find and cancel the last order at a later time
+        relativeTime = 2200;
+        patientOrders = self.manager.loadPatientOrders(patientId, relativeTime);
+        lastOrder = patientOrders[0];
+        for patientOrder in patientOrders:
+            if lastOrder["relative_time_start"] < patientOrder["relative_time_start"]:
+                lastOrder = patientOrder;
+
+        newOrderItemIds = [];   # No new orders
+        discontinuePatientOrderIds = [lastOrder["sim_patient_order_id"]];
+        self.manager.signOrders(userId, patientId, relativeTime, newOrderItemIds, discontinuePatientOrderIds);
+
+        # Verify update of last order time includes discontinue times
+        sampleValue = self.manager.loadPatientLastEventTime(patientId);
+        verifyValue = 2200;
+        self.assertEqual(verifyValue, sampleValue);
+
+        # Lookup patient with no prior orders recorded, then should default to time 0
+        patientId = -2;
+        sampleValue = self.manager.loadPatientLastEventTime(patientId);
+        verifyValue = 0;
+        self.assertEqual(verifyValue, sampleValue);
+
+
 
 def suite():
     """Returns the suite of tests to run for this test class / module.
@@ -596,13 +832,14 @@ def suite():
     methods for the given class whose name starts with "test"
     """
     suite = unittest.TestSuite();
-    #suite.addTest(TestSimManager("test_compositeRelated"));
-    #suite.addTest(TestSimManager("test_insertFile_skipErrors"));
-    #suite.addTest(TestSimManager('test_executeIterator'));
-    #suite.addTest(TestSimManager('test_deactivateAnalysis'));
+    #suite.addTest(TestSimManager("test_copyPatientTemplate"));
+    #suite.addTest(TestSimManager("test_loadPatientLastEventTime"));
+    #suite.addTest(TestSimManager('test_loadPendingResultOrders'));
+    #suite.addTest(TestSimManager('test_stateTransition'));
+    #suite.addTest(TestSimManager('test_discontinueOrders'));
     suite.addTest(unittest.makeSuite(TestSimManager));
-    
+
     return suite;
-    
+
 if __name__=="__main__":
     unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/cpoe/cpoeSim/test/TestSimManagerGrading.py b/medinfo/cpoe/cpoeSim/test/TestSimManagerGrading.py
new file mode 100644
index 00000000..c76b249a
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/test/TestSimManagerGrading.py
@@ -0,0 +1,258 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import unittest
+from io import BytesIO  # for Python 3 use StringIO
+from io import StringIO
+
+import json
+import csv
+from .Const import RUNNER_VERBOSITY
+from medinfo.common.Const import COMMENT_TAG, VALUE_DELIM
+from .Util import log, captured_output
+from medinfo.cpoe.cpoeSim.SimManager import SimManager
+from medinfo.db import DBUtil
+from medinfo.db.test.Util import DBTestCase
+
+
+class TestSimManagerGrading(DBTestCase):
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self)
+
+        self.manager = SimManager()  # Instance to test on
+
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+        self.manager.buildCPOESimSchema()
+
+        log.info("Populate the database with test data")
+
+        # Basically import a bunch of rigged CSV or TSV files that have realistic simulating case and grading data
+        # Get that data into the test database
+        clinical_item_category_str = \
+"""clinical_item_category_id;source_table
+1;source_table
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(clinical_item_category_str), "clinical_item_category", delim=";")
+
+        clinical_item_str = \
+"""clinical_item_id;clinical_item_category_id;name
+1;1;Clinical item 1
+2;1;Clinical item 2
+3;1;Clinical item 3
+4;1;Clinical item 4
+5;1;Clinical item 5
+6;1;Clinical item 6
+7;1;Clinical item 7
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(clinical_item_str), "clinical_item", delim=";")
+
+        clinical_item_str = \
+"""sim_user_id;name
+0;Default user
+1;Jonathan Chen
+2;User 2
+3;User 3
+4;User 4
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(clinical_item_str), "sim_user", delim=";")
+
+        sim_patient_str = \
+"""sim_patient_id;name;age_years;gender
+1;Patient One;40;Male
+2;Patient Two;50;Female
+3;Patient Three;60;Male
+4;Patient Four;70;Female
+5;Patient Five;80;Male
+6;Patient Six;90;Female
+7;Patient Seven;100;Male
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_patient_str), "sim_patient", delim=";")
+
+        sim_state_str = \
+"""sim_state_id;name
+1;Sim state 1
+2;Sim state 2
+3;Sim state 3
+4;Sim state 4
+5;Sim state 5
+6;Sim state 6
+7;Sim state 7
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_state_str), "sim_state", delim=";")
+
+        sim_patient_order_str = \
+"""sim_patient_order_id;sim_user_id;sim_patient_id;clinical_item_id;relative_time_start;sim_state_id
+1;0;1;1;1;1
+2;1;1;2;2;2
+3;1;1;3;3;3
+4;1;1;4;4;4
+5;1;1;5;5;5
+6;1;1;6;6;6
+7;1;1;7;7;7
+8;2;2;1;1;1
+9;3;2;2;2;1
+10;3;2;3;3;2
+11;2;3;1;1;1
+12;3;3;2;2;2
+13;3;3;3;3;3
+14;1;4;1;1;2
+15;1;4;2;2;3
+16;2;5;1;1;3
+17;2;5;2;2;4
+18;3;6;4;1;1
+19;3;6;4;2;2
+20;3;6;4;3;3
+21;4;7;5;1;1
+22;4;7;5;2;2
+23;4;7;5;3;3
+24;4;7;5;4;4
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_patient_order_str), "sim_patient_order", delim=";")
+
+        sim_grading_key_str = \
+"""sim_grader_id;sim_state_id;clinical_item_id;score;group_name
+Jonathan Chen;1;1;1;g1
+Jonathan Chen;2;2;1;g2
+Jonathan Chen;3;3;1;g3
+Jonathan Chen;4;4;1;
+Jonathan Chen;5;5;1;g5
+Jonathan Chen;6;6;1;
+Jonathan Chen;7;7;1;g7
+Jonathan Chen;3;1;1;g8
+Jonathan Chen;4;2;1;g8
+Jonathan Chen;1;4;-1000;
+Jonathan Chen;2;4;10;
+Jonathan Chen;3;4;2000;
+Jonathan Chen;1;5;-1000;g9
+Jonathan Chen;2;5;-1;
+Jonathan Chen;3;5;0;g10
+Jonathan Chen;3;5;-500;
+"""
+        # Parse into DB insertion object
+        DBUtil.insertFile(StringIO(sim_grading_key_str), "sim_grading_key", delim=";")
+
+        self.expected_grades_by_patient_id = [
+            {
+                "total_score": 6,   # Default user (id = 0) is ignored and NULL group_names are counted separately
+                "sim_patient_id": 1,
+                "most_graded_user_id": 1,
+                "most_active_user_id": 1,
+                "sim_grader_id": "Jonathan Chen"
+            },
+            {
+                "total_score": 1,   # Ungraded (clinical_item_id, sim_state_id) keys are omitted from summation
+                "sim_patient_id": 2,
+                "most_graded_user_id": 2,   # Most graded user is User 2 (even though most active is User 3)
+                "most_active_user_id": 3,
+                "sim_grader_id": "Jonathan Chen"
+            },
+            {
+                "total_score": 3,
+                "sim_patient_id": 3,
+                "most_graded_user_id": 3,   # Most graded user is the most active one
+                "most_active_user_id": 3,
+                "sim_grader_id": "Jonathan Chen"
+            },
+            # 4: No grading available for the existing case
+            {
+                "total_score": 1,   # Scores in the same group g8 are counted only once
+                "sim_patient_id": 5,
+                "most_graded_user_id": 2,
+                "most_active_user_id": 2,
+                "sim_grader_id": "Jonathan Chen"
+            },
+            {
+                "total_score": 1010,    # Non-uniform scores (i.e., not all scores = 1)
+                "sim_patient_id": 6,
+                "most_graded_user_id": 3,
+                "most_active_user_id": 3,
+                "sim_grader_id": "Jonathan Chen"
+            },
+            {
+                "total_score": -1501,   # All negative and one 0 score results in negative score
+                "sim_patient_id": 7,
+                "most_graded_user_id": 4,
+                "most_active_user_id": 4,
+                "sim_grader_id": "Jonathan Chen"
+            }
+            # 8: Case doesn't exist
+        ]
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        DBTestCase.tearDown(self)
+
+    def test_gradeCases(self):
+        # Give the application ID of some simulated patient test cases and the name
+        #   of a grading key and just verify that it produces the expected results
+        sim_patient_ids = [1, 2, 3, 4, 5, 6, 7, 8]
+        sim_grading_key_id = "Jonathan Chen"
+        actual_grades_by_patient_id = self.manager.grade_cases(sim_patient_ids, sim_grading_key_id)
+        self.assertEqual(self.expected_grades_by_patient_id, actual_grades_by_patient_id)
+
+    def test_commandLine(self):
+        argv = ["SimManager.py", "-p", "1,2,3,4,5,6", "-g", "Jonathan Chen"]
+        with captured_output() as (out, err):
+            self.manager.main(argv)
+
+        actual_output = out.getvalue()
+
+        actual_comment_line, output_csv = actual_output.split("\n", 1)
+
+        # verify comment line
+        expected_comment_line = COMMENT_TAG + " " + json.dumps({"argv": argv})
+        self.assertEqual(expected_comment_line, actual_comment_line)
+
+        # verify csv
+        actual_output_csv = StringIO(output_csv)
+        reader = csv.DictReader(actual_output_csv)
+        # verify header
+        self.assertEqualList(sorted(self.expected_grades_by_patient_id[0].keys()), sorted(reader.fieldnames))
+
+        # verify lines
+        for line_num, actual_grade in enumerate(reader):
+            expected_grade_str_values = {k: str(v) for k, v in self.expected_grades_by_patient_id[line_num].items()}
+            self.assertEqual(expected_grade_str_values, actual_grade)
+
+    def test_commandLine_no_patientIds(self):
+        argv = ["SimManager.py", "-g", "Jonathan Chen"]
+        self.verify_error_message_for_argv(argv, "No patient cases given. Nothing to grade. Exiting.")
+
+    def test_commandLine_no_graderIds(self):
+        argv = ["SimManager.py", "-p", "1"]
+        self.verify_error_message_for_argv(argv, "No graders given. Cannot grade patient cases. Exiting.")
+
+    def verify_error_message_for_argv(self, argv, expected_error_message):
+        with self.assertRaises(SystemExit) as cm:   # capture sys.exit() in the tested class
+            with captured_output() as (out, err):
+                self.manager.main(argv)
+
+        actual_output = out.getvalue()
+        self.assertEqual(expected_error_message, actual_output.split("\n", 1)[0])
+
+        self.assertIsNone(cm.exception.code)    # we can verify exception code here
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    test_suite = unittest.TestSuite()
+    test_suite.addTest(unittest.makeSuite(TestSimManagerGrading))
+
+    return test_suite
+
+
+# TODO testcase for different scores for same group in different state
+
+if __name__ == "__main__":
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/cpoe/cpoeSim/test/Util.py b/medinfo/cpoe/cpoeSim/test/Util.py
new file mode 100644
index 00000000..8114a5a4
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/test/Util.py
@@ -0,0 +1,35 @@
+from . import Const
+import sys, os
+import logging
+import cgi
+from collections import UserDict
+import unittest
+from contextlib import contextmanager
+from io import StringIO
+
+from medinfo.db.test.Util import DBTestCase;
+
+import medinfo.cpoe.Util;
+
+
+log = logging.getLogger("CDSS")
+
+handler = logging.StreamHandler(sys.stderr)
+formatter = logging.Formatter(Const.LOGGER_FORMAT)
+
+handler.setFormatter(formatter)
+log.addHandler(handler)
+
+# Suppress uninteresting application output
+medinfo.cpoe.Util.log.setLevel(Const.APP_LOGGER_LEVEL)
+
+
+@contextmanager
+def captured_output():
+    new_out, new_err = StringIO(), StringIO()
+    old_out, old_err = sys.stdout, sys.stderr
+    try:
+        sys.stdout, sys.stderr = new_out, new_err
+        yield sys.stdout, sys.stderr
+    finally:
+        sys.stdout, sys.stderr = old_out, old_err
diff --git a/medinfo/cpoe/cpoeSim/test/__init__.py b/medinfo/cpoe/cpoeSim/test/__init__.py
new file mode 100644
index 00000000..7aa19f47
--- /dev/null
+++ b/medinfo/cpoe/cpoeSim/test/__init__.py
@@ -0,0 +1,7 @@
+# Nothing much for now, just enable importing of various modules as a common package
+# Test modules should be added here to be included in the "test_all" module suite
+
+__all__ = \
+    [
+
+    ];
diff --git a/medinfo/cpoe/test/Const.py b/medinfo/cpoe/test/Const.py
index 615d145d..04b1d359 100644
--- a/medinfo/cpoe/test/Const.py
+++ b/medinfo/cpoe/test/Const.py
@@ -5,10 +5,10 @@
 """Application name, for example to identify a common logger object"""
 APPLICATION_NAME = "medinfo.cpoe.test"
 
-"""Default level for application logging.  Modify these for different scenarios.  
+"""Default level for application logging.  Modify these for different scenarios.
 See Python logging package documentation for more information
 """
-LOGGER_LEVEL = logging.DEBUG
+LOGGER_LEVEL = logging.ERROR
 
 """Default format of logger output"""
 LOGGER_FORMAT = "[%(asctime)s %(levelname)s] %(message)s"
@@ -23,4 +23,4 @@
 
 
 """For some test cases with many test points, don't want to abort the entire test if just one item fails"""
-ALLOW_MULTIPLE_ERRORS = True;
\ No newline at end of file
+ALLOW_MULTIPLE_ERRORS = True;
diff --git a/medinfo/cpoe/test/TestAssociationAnalysis.py b/medinfo/cpoe/test/TestAssociationAnalysis.py
index 98ec56ec..534109e0 100644
--- a/medinfo/cpoe/test/TestAssociationAnalysis.py
+++ b/medinfo/cpoe/test/TestAssociationAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
-from Util import log;
+from .Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -17,594 +17,596 @@
 from medinfo.cpoe.AssociationAnalysis import AssociationAnalysis, AnalysisOptions;
 
 class TestAssociationAnalysis(DBTestCase):
-	def setUp(self):
-		"""Prepare state for test cases"""
-		DBTestCase.setUp(self);
-
-		log.info("Populate the database with test data")
-
-		self.clinicalItemCategoryIdStrList = list();
-		headers = ["clinical_item_category_id","source_table"];
-		dataModels = \
-			[
-				RowItemModel( [-1, "Labs"], headers ),
-				RowItemModel( [-2, "Imaging"], headers ),
-				RowItemModel( [-3, "Meds"], headers ),
-				RowItemModel( [-4, "Nursing"], headers ),
-				RowItemModel( [-5, "Problems"], headers ),
-				RowItemModel( [-6, "Lab Results"], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", dataModel );
-			self.clinicalItemCategoryIdStrList.append( str(dataItemId) );
-
-		headers = ["clinical_item_id","clinical_item_category_id","name","analysis_status"];
-		dataModels = \
-			[
-				RowItemModel( [-1, -1, "CBC",1], headers ),
-				RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
-				RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
-				RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
-				RowItemModel( [-5, -2, "CXR",1], headers ),
-				RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
-				RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
-				RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
-				RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
-				RowItemModel( [-10, -3, "Carvedilol",1], headers ),
-				RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
-				RowItemModel( [-12, -3, "Warfarin",1], headers ),
-				RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
-				RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
-				RowItemModel( [-15, -4, "Strict I&O",1], headers ),
-				RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel );
-
-		headers = ["patient_item_id","encounter_id","patient_id","clinical_item_id","item_date"];
-		dataModels = \
-			[
-				RowItemModel( [-1,  -111,   -11111, -4,  datetime(2000, 1, 1, 0)], headers ),
-				RowItemModel( [-2,  -111,   -11111, -10, datetime(2000, 1, 1, 0)], headers ),
-				RowItemModel( [-3,  -111,   -11111, -8,  datetime(2000, 1, 1, 2)], headers ),
-				RowItemModel( [-4,  -112,   -11111, -10, datetime(2000, 1, 2, 0)], headers ),
-				RowItemModel( [-5,  -112,   -11111, -12, datetime(2000, 2, 1, 0)], headers ),
-				RowItemModel( [-10, -222,   -22222, -7,  datetime(2000, 1, 5, 0)], headers ),
-				RowItemModel( [-12, -222,   -22222, -6,  datetime(2000, 1, 9, 0)], headers ),
-				RowItemModel( [-13, -222,   -22222, -11, datetime(2000, 1, 9, 0)], headers ),
-				RowItemModel( [-14, -333,   -33333, -6,  datetime(2000, 2, 9, 0)], headers ),
-				RowItemModel( [-15, -333,   -33333, -2,  datetime(2000, 2,11, 0)], headers ),  # Will set clinical_item_link inheritances to this item to only record certain associations
-				RowItemModel( [-16, -333,   -33333, -11,  datetime(2000, 2,11, 0)], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("patient_item", dataModel );
-
-		headers = ["clinical_item_id","linked_item_id"];
-		dataModels = \
-			[   # Don't have direct, but instead demonstrate inherited relationship from 6 to 2 will still be recognized
-				RowItemModel( [-6, -4], headers ),
-				RowItemModel( [-4, -2], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_link", dataModel );
-
-		# Temp filename for data buffer
-		self.bufferFilename = "updateBufferTemp.txt"
-
-		self.analyzer = AssociationAnalysis();  # Instance to test on
-
-	def tearDown(self):
-		"""Restore state from any setUp or test steps"""
-		log.info("Purge test records from the database")
-
-		DBUtil.execute("delete from clinical_item_link where clinical_item_id < 0");
-		DBUtil.execute("delete from clinical_item_association where clinical_item_id < 0");
-		DBUtil.execute("delete from patient_item where patient_item_id < 0");
-		DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
-		DBUtil.execute("delete from clinical_item_category where clinical_item_category_id in (%s)" % str.join(",", self.clinicalItemCategoryIdStrList) );
-
-		# Deletes any temp bufferFile
-		dirname = os.path.dirname(self.bufferFilename);
-		if dirname == "": dirname = ".";
-		for filename in os.listdir(dirname):
-			if filename.startswith(self.bufferFilename):
-				os.remove(os.path.join(dirname,filename));
-
-		DBTestCase.tearDown(self);
-
-	def test_analyzePatientItems_commandLine_bufferFile(self):
-		# Run the association analysis, save to a file, and update from the file to the database, all command-line control
-		associationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
-				patient_count_2592000, patient_count_7776000, patient_count_31536000,
-				patient_count_any,
-				patient_time_diff_sum, patient_time_diff_sum_squares
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-
-		log.debug("Use incremental update, including date filters to start.");
-		self.analyzer.main(["AssociationAnalysis.py","-s","2000-01-09","-e","2000-02-11","-b",self.bufferFilename,"-p","1",  "0,-22222,-33333"]);
-		self.analyzer.main(["AssociationAnalysis.py","-b",self.bufferFilename]);
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -6,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery)
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 )
-
-
-
-		log.debug("Use incremental update, only doing the update based on a part of the data.");
-		self.analyzer.main(["AssociationAnalysis.py","-b",self.bufferFilename,"0,-22222,-33333"]);
-		self.analyzer.main(["AssociationAnalysis.py","-b",self.bufferFilename]);
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-				[ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-
-				[ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
-				[ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-	def test_analyzePatientItemsFromBuffer(self):
-		# Run the association analysis, save to a file, and update from the file to the database
-		associationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
-				patient_count_2592000, patient_count_7776000, patient_count_31536000,
-				patient_count_any,
-				patient_time_diff_sum, patient_time_diff_sum_squares
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-
-		log.debug("Use incremental update, including date filters to start.");
-		analysisOptions = AnalysisOptions();
-		analysisOptions.patientIds = [-22222, -33333];
-		analysisOptions.startDate = datetime(2000,1,9);
-		analysisOptions.endDate = datetime(2000,2,11);
-		analysisOptions.bufferFile = self.bufferFilename;
-
-		self.analyzer.analyzePatientItems(analysisOptions)
-		self.analyzer.commitUpdateBufferFromFile(self.bufferFilename)
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -6,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery)
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 )
-
-
-
-		log.debug("Use incremental update, only doing the update based on a part of the data.");
-		analysisOptions = AnalysisOptions();
-		analysisOptions.patientIds = [-22222, -33333];
-		analysisOptions.bufferFile = self.bufferFilename;
-
-		self.analyzer.analyzePatientItems( analysisOptions );
-		self.analyzer.commitUpdateBufferFromFile(self.bufferFilename)
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-				[ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-
-				[ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
-				[ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-	def test_analyzePatientItems(self):
-		# Run the association analysis against the mock test data above and verify
-		#   expected stats afterwards.
-
-		associationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
-				patient_count_2592000, patient_count_7776000, patient_count_31536000,
-				patient_count_any,
-				patient_time_diff_sum, patient_time_diff_sum_squares
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-
-
-		log.debug("Use incremental update, including date filters to start.");
-		analysisOptions = AnalysisOptions();
-		analysisOptions.patientIds = [-22222, -33333];
-		analysisOptions.startDate = datetime(2000,1,9);
-		analysisOptions.endDate = datetime(2000,2,11);
-		self.analyzer.analyzePatientItems( analysisOptions );
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -6,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-
-		log.debug("Use incremental update, only doing the update based on a part of the data.");
-		analysisOptions = AnalysisOptions();
-		analysisOptions.patientIds = [-22222, -33333];
-		self.analyzer.analyzePatientItems( analysisOptions );
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-				[ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-
-				[ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
-				[ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		log.debug("Expand incremental update, by now including additional clinical items whose analysis status previously excluded them.");
-		DBUtil.updateRow("clinical_item",{"analysis_status":1},-2);
-		analysisOptions.patientIds = [-22222, -33333];
-		self.analyzer.analyzePatientItems( analysisOptions );
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[-11, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-				[ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-
-				[ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
-				[ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-
-				[ -2,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -2, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-
-		log.debug("Incremental update that includes a single patient data being split, so have to account for all of those dependencies");
-
-		headers = ["patient_item_id","encounter_id","patient_id","clinical_item_id","item_date"];
-		dataModels = \
-			[
-				RowItemModel( [-1111, -334, -33333, -3,  datetime(2000, 2,11, 8)], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("patient_item", dataModel );
-
-		analysisOptions.patientIds = [-22222, -33333];
-		self.analyzer.analyzePatientItems( analysisOptions );
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[-11, -3,   0, 0, 1, 1, 1, 1, 1, 1,  28800.0, 829440000.0],
-				[-11, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-				[ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
-
-				[ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2,  172800.0, 29859840000.0],
-				[ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[ -6, -3,   0, 0, 0, 1, 1, 1, 1, 1,  201600.0, 40642560000.0],
-
-				[ -3,-11,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -3, -6,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -3, -3,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -3, -2,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-
-				[ -2,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -2, -3,   0, 0, 1, 1, 1, 1, 1, 1,  28800.0, 829440000.0],
-				[ -2, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-			];
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		log.debug("Negative test case, repeating analysis should not change any results");
-		analysisOptions.patientIds = [-22222, -33333];
-		self.analyzer.analyzePatientItems( analysisOptions );
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		log.debug("Complete the remaining incremental update");
-		analysisOptions.patientIds = [-11111, -22222, -33333];
-		self.analyzer.analyzePatientItems( analysisOptions );
-
-		expectedAssociationStats = \
-			[   # Note that sum-squares in particular gets rounded off due to large values
-				[-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[-12, -10, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11, -11, 2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
-				[-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[-11,  -3, 0, 0, 1, 1, 1, 1, 1, 1,  28800.0, 829440000.0],
-				[-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[-10, -12, 0, 0, 0, 0, 0, 1, 1, 1, 2678400.0, 7173830000000.0],    # Longer time diff?
-				[-10, -10, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				[-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[ -8, -12, 0, 0, 0, 0, 0, 1, 1, 1, 2671200.0, 7135310000000.0],     # Longer diff
-				[ -8, -10, 0, 0, 1, 1, 1, 1, 1, 1, 79200.0, 6272640000.0],
-				[ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				[ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				[ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
-				[ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				[ -6,  -3, 0, 0, 0, 1, 1, 1, 1, 1, 201600.0, 40642600000.0],
-				[ -4, -12, 0, 0, 0, 0, 0, 1, 1, 1, 2678400.0, 7173830000000.0], # ???
-				[ -4, -10, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				[ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				[ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
-				[ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
-				[ -2,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
-				[ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0]
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		# Check the association stats for non-unique counts as well (allowing for repeats)
-		nonUniqueAssociationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				count_0, count_3600, count_86400, count_604800,
-				count_2592000, count_7776000, count_31536000, count_126144000,
-				count_any,
-				time_diff_sum, time_diff_sum_squares
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-		expectedAssociationStats = \
-			[   # Note that sum-squares in particular gets rounded off due to large values
-				 [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				 [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-11,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
-				 [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-10, -12, 0, 0, 0, 0, 1, 2, 2, 2, 2, 5270400.0, 13892300000000.0],
-				 [-10, -10, 2, 2, 3, 3, 3, 3, 3, 3, 3, 86400.0, 7464960000.0],
-				 [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				 [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -8, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2671200.0, 7135310000000.0],
-				 [ -8, -10, 0, 0, 1, 1, 1, 1, 1, 1, 1, 79200.0, 6272640000.0],
-				 [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				 [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				 [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 2, 172800.0, 29859800000.0],
-				 [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				 [ -6,  -3, 0, 0, 0, 1, 1, 1, 1, 1, 1, 201600.0, 40642600000.0],
-				 [ -4, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2678400.0, 7173830000000.0],
-				 [ -4, -10, 1, 1, 2, 2, 2, 2, 2, 2, 2, 86400.0, 7464960000.0],
-				 [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				 [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -2,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
-				 [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-			];
-		associationStats = DBUtil.execute(nonUniqueAssociationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-
-		# Again for patient level counts
-		patientAssociationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
-				patient_count_2592000, patient_count_7776000, patient_count_31536000, patient_count_126144000,
-				patient_count_any,
-				patient_time_diff_sum, patient_time_diff_sum_squares
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-		expectedAssociationStats = \
-			[   # Note that sum-squares in particular gets rounded off due to large values
-				 [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				 [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-11,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
-				 [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-10, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2678400.0, 2678400.0*2678400.0],    # Main difference.  Duplicates within a single patient, only count once
-				 [-10, -10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],   # Don't count duplicates
-				 [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				 [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -8, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2671200.0, 7135310000000.0],
-				 [ -8, -10, 0, 0, 1, 1, 1, 1, 1, 1, 1, 79200.0, 6272640000.0],
-				 [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				 [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				 [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 2, 172800.0, 29859800000.0],
-				 [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				 [ -6,  -3, 0, 0, 0, 1, 1, 1, 1, 1, 1, 201600.0, 40642600000.0],
-				 [ -4, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2678400.0, 7173830000000.0],
-				 [ -4, -10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],  # Don't count repeats
-				 [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				 [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -2,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
-				 [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-			];
-		associationStats = DBUtil.execute(patientAssociationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-
-		# Again for encounter level counts
-		encounterAssociationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				encounter_count_0, encounter_count_3600, encounter_count_86400, encounter_count_604800,
-				encounter_count_2592000, encounter_count_7776000, encounter_count_31536000, encounter_count_126144000,
-				encounter_count_any,
-				encounter_time_diff_sum, encounter_time_diff_sum_squares
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-		expectedAssociationStats = \
-			[   # Note that sum-squares in particular gets rounded off due to large values
-				 [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				 [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-11,  -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],   # No longer related in separate encounters
-				 [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [-10, -12, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2592000.0, 2592000.0*2592000.0],    # Only count the relation within a common encounter
-				 [-10, -10, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],   # Now count for separate encounters
-				 [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				 [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -8, -12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0], # No longer related in separate encounters
-				 [ -8, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],  # No longer related in separate encounters
-				 [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				 [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
-				 [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 2, 172800.0, 29859800000.0],
-				 [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
-				 [ -6,  -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],   # No longer related in separate encounters
-				 [ -4, -12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0], # No longer related in separate encounters
-				 [ -4, -10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
-				 [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
-				 [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-				 [ -2,  -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],   # No longer related in separate encounters
-				 [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
-			];
-		associationStats = DBUtil.execute(encounterAssociationQuery);
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self);
+
+        log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
+        self.clinicalItemCategoryIdStrList = list();
+        headers = ["clinical_item_category_id","source_table"];
+        dataModels = \
+            [
+                RowItemModel( [-1, "Labs"], headers ),
+                RowItemModel( [-2, "Imaging"], headers ),
+                RowItemModel( [-3, "Meds"], headers ),
+                RowItemModel( [-4, "Nursing"], headers ),
+                RowItemModel( [-5, "Problems"], headers ),
+                RowItemModel( [-6, "Lab Results"], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", dataModel );
+            self.clinicalItemCategoryIdStrList.append( str(dataItemId) );
+
+        headers = ["clinical_item_id","clinical_item_category_id","name","analysis_status"];
+        dataModels = \
+            [
+                RowItemModel( [-1, -1, "CBC",1], headers ),
+                RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
+                RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
+                RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
+                RowItemModel( [-5, -2, "CXR",1], headers ),
+                RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
+                RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
+                RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
+                RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
+                RowItemModel( [-10, -3, "Carvedilol",1], headers ),
+                RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
+                RowItemModel( [-12, -3, "Warfarin",1], headers ),
+                RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
+                RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
+                RowItemModel( [-15, -4, "Strict I&O",1], headers ),
+                RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel );
+
+        headers = ["patient_item_id","encounter_id","patient_id","clinical_item_id","item_date"];
+        dataModels = \
+            [
+                RowItemModel( [-1,  -111,   -11111, -4,  datetime(2000, 1, 1, 0)], headers ),
+                RowItemModel( [-2,  -111,   -11111, -10, datetime(2000, 1, 1, 0)], headers ),
+                RowItemModel( [-3,  -111,   -11111, -8,  datetime(2000, 1, 1, 2)], headers ),
+                RowItemModel( [-4,  -112,   -11111, -10, datetime(2000, 1, 2, 0)], headers ),
+                RowItemModel( [-5,  -112,   -11111, -12, datetime(2000, 2, 1, 0)], headers ),
+                RowItemModel( [-10, -222,   -22222, -7,  datetime(2000, 1, 5, 0)], headers ),
+                RowItemModel( [-12, -222,   -22222, -6,  datetime(2000, 1, 9, 0)], headers ),
+                RowItemModel( [-13, -222,   -22222, -11, datetime(2000, 1, 9, 0)], headers ),
+                RowItemModel( [-14, -333,   -33333, -6,  datetime(2000, 2, 9, 0)], headers ),
+                RowItemModel( [-15, -333,   -33333, -2,  datetime(2000, 2,11, 0)], headers ),  # Will set clinical_item_link inheritances to this item to only record certain associations
+                RowItemModel( [-16, -333,   -33333, -11,  datetime(2000, 2,11, 0)], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("patient_item", dataModel );
+
+        headers = ["clinical_item_id","linked_item_id"];
+        dataModels = \
+            [   # Don't have direct, but instead demonstrate inherited relationship from 6 to 2 will still be recognized
+                RowItemModel( [-6, -4], headers ),
+                RowItemModel( [-4, -2], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_link", dataModel );
+
+        # Temp filename for data buffer
+        self.bufferFilename = "updateBufferTemp.txt"
+
+        self.analyzer = AssociationAnalysis();  # Instance to test on
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        log.info("Purge test records from the database")
+
+        DBUtil.execute("delete from clinical_item_link where clinical_item_id < 0");
+        DBUtil.execute("delete from clinical_item_association where clinical_item_id < 0");
+        DBUtil.execute("delete from patient_item where patient_item_id < 0");
+        DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
+        DBUtil.execute("delete from clinical_item_category where clinical_item_category_id in (%s)" % str.join(",", self.clinicalItemCategoryIdStrList) );
+
+        # Deletes any temp bufferFile
+        dirname = os.path.dirname(self.bufferFilename);
+        if dirname == "": dirname = ".";
+        for filename in os.listdir(dirname):
+            if filename.startswith(self.bufferFilename):
+                os.remove(os.path.join(dirname,filename));
+
+        DBTestCase.tearDown(self);
+
+    def test_analyzePatientItems_commandLine_bufferFile(self):
+        # Run the association analysis, save to a file, and update from the file to the database, all command-line control
+        associationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
+                patient_count_2592000, patient_count_7776000, patient_count_31536000,
+                patient_count_any,
+                patient_time_diff_sum, patient_time_diff_sum_squares
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+
+        log.debug("Use incremental update, including date filters to start.");
+        self.analyzer.main(["AssociationAnalysis.py","-s","2000-01-09","-e","2000-02-11","-b",self.bufferFilename,"-p","1",  "0,-22222,-33333"]);
+        self.analyzer.main(["AssociationAnalysis.py","-b",self.bufferFilename]);
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -6,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery)
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 )
+
+
+
+        log.debug("Use incremental update, only doing the update based on a part of the data.");
+        self.analyzer.main(["AssociationAnalysis.py","-b",self.bufferFilename,"0,-22222,-33333"]);
+        self.analyzer.main(["AssociationAnalysis.py","-b",self.bufferFilename]);
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+                [ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+
+                [ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
+                [ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+    def test_analyzePatientItemsFromBuffer(self):
+        # Run the association analysis, save to a file, and update from the file to the database
+        associationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
+                patient_count_2592000, patient_count_7776000, patient_count_31536000,
+                patient_count_any,
+                patient_time_diff_sum, patient_time_diff_sum_squares
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+
+        log.debug("Use incremental update, including date filters to start.");
+        analysisOptions = AnalysisOptions();
+        analysisOptions.patientIds = [-22222, -33333];
+        analysisOptions.startDate = datetime(2000,1,9);
+        analysisOptions.endDate = datetime(2000,2,11);
+        analysisOptions.bufferFile = self.bufferFilename;
+
+        self.analyzer.analyzePatientItems(analysisOptions)
+        self.analyzer.commitUpdateBufferFromFile(self.bufferFilename)
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -6,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery)
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 )
+
+
+
+        log.debug("Use incremental update, only doing the update based on a part of the data.");
+        analysisOptions = AnalysisOptions();
+        analysisOptions.patientIds = [-22222, -33333];
+        analysisOptions.bufferFile = self.bufferFilename;
+
+        self.analyzer.analyzePatientItems( analysisOptions );
+        self.analyzer.commitUpdateBufferFromFile(self.bufferFilename)
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+                [ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+
+                [ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
+                [ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+    def test_analyzePatientItems(self):
+        # Run the association analysis against the mock test data above and verify
+        #   expected stats afterwards.
+
+        associationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
+                patient_count_2592000, patient_count_7776000, patient_count_31536000,
+                patient_count_any,
+                patient_time_diff_sum, patient_time_diff_sum_squares
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+
+
+        log.debug("Use incremental update, including date filters to start.");
+        analysisOptions = AnalysisOptions();
+        analysisOptions.patientIds = [-22222, -33333];
+        analysisOptions.startDate = datetime(2000,1,9);
+        analysisOptions.endDate = datetime(2000,2,11);
+        self.analyzer.analyzePatientItems( analysisOptions );
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -6,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+
+        log.debug("Use incremental update, only doing the update based on a part of the data.");
+        analysisOptions = AnalysisOptions();
+        analysisOptions.patientIds = [-22222, -33333];
+        self.analyzer.analyzePatientItems( analysisOptions );
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+                [ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+
+                [ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
+                [ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        log.debug("Expand incremental update, by now including additional clinical items whose analysis status previously excluded them.");
+        DBUtil.updateRow("clinical_item",{"analysis_status":1},-2);
+        analysisOptions.patientIds = [-22222, -33333];
+        self.analyzer.analyzePatientItems( analysisOptions );
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [-11, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+                [ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+
+                [ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
+                [ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+
+                [ -2,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -2, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+
+        log.debug("Incremental update that includes a single patient data being split, so have to account for all of those dependencies");
+
+        headers = ["patient_item_id","encounter_id","patient_id","clinical_item_id","item_date"];
+        dataModels = \
+            [
+                RowItemModel( [-1111, -334, -33333, -3,  datetime(2000, 2,11, 8)], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("patient_item", dataModel );
+
+        analysisOptions.patientIds = [-22222, -33333];
+        self.analyzer.analyzePatientItems( analysisOptions );
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [-11, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11, -6,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [-11, -3,   0, 0, 1, 1, 1, 1, 1, 1,  28800.0, 829440000.0],
+                [-11, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7,-11,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+                [ -7, -7,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -7, -6,   0, 0, 0, 1, 1, 1, 1, 1,  345600.0, 119439360000.0],
+
+                [ -6,-11,   1, 1, 1, 2, 2, 2, 2, 2,  172800.0, 29859840000.0],
+                [ -6, -7,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -6, -6,   2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [ -6, -3,   0, 0, 0, 1, 1, 1, 1, 1,  201600.0, 40642560000.0],
+
+                [ -3,-11,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -3, -6,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -3, -3,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -3, -2,   0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+
+                [ -2,-11,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -2, -3,   0, 0, 1, 1, 1, 1, 1, 1,  28800.0, 829440000.0],
+                [ -2, -2,   1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+            ];
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        log.debug("Negative test case, repeating analysis should not change any results");
+        analysisOptions.patientIds = [-22222, -33333];
+        self.analyzer.analyzePatientItems( analysisOptions );
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        log.debug("Complete the remaining incremental update");
+        analysisOptions.patientIds = [-11111, -22222, -33333];
+        self.analyzer.analyzePatientItems( analysisOptions );
+
+        expectedAssociationStats = \
+            [   # Note that sum-squares in particular gets rounded off due to large values
+                [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2,  0.0, 0.0],
+                [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [-11,  -3, 0, 0, 1, 1, 1, 1, 1, 1,  28800.0, 829440000.0],
+                [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [-10, -12, 0, 0, 0, 0, 0, 1, 1, 1, 2678400.0, 7173830000000.0],    # Longer time diff?
+                [-10, -10, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [ -8, -12, 0, 0, 0, 0, 0, 1, 1, 1, 2671200.0, 7135310000000.0],     # Longer diff
+                [ -8, -10, 0, 0, 1, 1, 1, 1, 1, 1, 79200.0, 6272640000.0],
+                [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 172800.0, 29859840000.0],
+                [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                [ -6,  -3, 0, 0, 0, 1, 1, 1, 1, 1, 201600.0, 40642600000.0],
+                [ -4, -12, 0, 0, 0, 0, 0, 1, 1, 1, 2678400.0, 7173830000000.0], # ???
+                [ -4, -10, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0,  0.0, 0.0],
+                [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1,  0.0, 0.0],
+                [ -2,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
+                [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0]
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        # Check the association stats for non-unique counts as well (allowing for repeats)
+        nonUniqueAssociationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                count_0, count_3600, count_86400, count_604800,
+                count_2592000, count_7776000, count_31536000, count_126144000,
+                count_any,
+                time_diff_sum, time_diff_sum_squares
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+        expectedAssociationStats = \
+            [   # Note that sum-squares in particular gets rounded off due to large values
+                 [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                 [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-11,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
+                 [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-10, -12, 0, 0, 0, 0, 1, 2, 2, 2, 2, 5270400.0, 13892300000000.0],
+                 [-10, -10, 2, 2, 3, 3, 3, 3, 3, 3, 3, 86400.0, 7464960000.0],
+                 [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                 [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -8, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2671200.0, 7135310000000.0],
+                 [ -8, -10, 0, 0, 1, 1, 1, 1, 1, 1, 1, 79200.0, 6272640000.0],
+                 [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                 [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                 [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 2, 172800.0, 29859800000.0],
+                 [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                 [ -6,  -3, 0, 0, 0, 1, 1, 1, 1, 1, 1, 201600.0, 40642600000.0],
+                 [ -4, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2678400.0, 7173830000000.0],
+                 [ -4, -10, 1, 1, 2, 2, 2, 2, 2, 2, 2, 86400.0, 7464960000.0],
+                 [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                 [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -2,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
+                 [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+            ];
+        associationStats = DBUtil.execute(nonUniqueAssociationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+
+        # Again for patient level counts
+        patientAssociationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
+                patient_count_2592000, patient_count_7776000, patient_count_31536000, patient_count_126144000,
+                patient_count_any,
+                patient_time_diff_sum, patient_time_diff_sum_squares
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+        expectedAssociationStats = \
+            [   # Note that sum-squares in particular gets rounded off due to large values
+                 [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                 [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-11,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
+                 [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-10, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2678400.0, 2678400.0*2678400.0],    # Main difference.  Duplicates within a single patient, only count once
+                 [-10, -10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],   # Don't count duplicates
+                 [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                 [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -8, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2671200.0, 7135310000000.0],
+                 [ -8, -10, 0, 0, 1, 1, 1, 1, 1, 1, 1, 79200.0, 6272640000.0],
+                 [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                 [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                 [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 2, 172800.0, 29859800000.0],
+                 [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                 [ -6,  -3, 0, 0, 0, 1, 1, 1, 1, 1, 1, 201600.0, 40642600000.0],
+                 [ -4, -12, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2678400.0, 7173830000000.0],
+                 [ -4, -10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],  # Don't count repeats
+                 [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                 [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -2,  -3, 0, 0, 1, 1, 1, 1, 1, 1, 1, 28800.0, 829440000.0],
+                 [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+            ];
+        associationStats = DBUtil.execute(patientAssociationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+
+        # Again for encounter level counts
+        encounterAssociationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                encounter_count_0, encounter_count_3600, encounter_count_86400, encounter_count_604800,
+                encounter_count_2592000, encounter_count_7776000, encounter_count_31536000, encounter_count_126144000,
+                encounter_count_any,
+                encounter_time_diff_sum, encounter_time_diff_sum_squares
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+        expectedAssociationStats = \
+            [   # Note that sum-squares in particular gets rounded off due to large values
+                 [-12, -12, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-12, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-12,  -8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-12,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-11, -11, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                 [-11,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [-11,  -6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-11,  -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],   # No longer related in separate encounters
+                 [-11,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [-10, -12, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2592000.0, 2592000.0*2592000.0],    # Only count the relation within a common encounter
+                 [-10, -10, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],   # Now count for separate encounters
+                 [-10,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                 [-10,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -8, -12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0], # No longer related in separate encounters
+                 [ -8, -10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],  # No longer related in separate encounters
+                 [ -8,  -8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -8,  -4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -7, -11, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                 [ -7,  -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -7,  -6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 345600.0, 119439000000.0],
+                 [ -6, -11, 1, 1, 1, 2, 2, 2, 2, 2, 2, 172800.0, 29859800000.0],
+                 [ -6,  -7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -6,  -6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.0, 0.0],
+                 [ -6,  -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],   # No longer related in separate encounters
+                 [ -4, -12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0], # No longer related in separate encounters
+                 [ -4, -10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -4,  -8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 7200.0, 51840000.0],
+                 [ -4,  -4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -3, -11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -3,  -6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -3,  -3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -3,  -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],
+                 [ -2, -11, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+                 [ -2,  -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0],   # No longer related in separate encounters
+                 [ -2,  -2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 0.0],
+            ];
+        associationStats = DBUtil.execute(encounterAssociationQuery);
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
 
 def suite():
-	"""Returns the suite of tests to run for this test class / module.
-	Use unittest.makeSuite methods which simply extracts all of the
-	methods for the given class whose name starts with "test"
-	"""
-	suite = unittest.TestSuite();
-	#suite.addTest(TestAssociationAnalysis("test_incColNamesAndTypeCodes"));
-	#suite.addTest(TestAssociationAnalysis("test_insertFile_skipErrors"));
-	#suite.addTest(TestAssociationAnalysis('test_executeIterator'));
-	#suite.addTest(TestAssociationAnalysis('test_findOrInsertItem'));
-	#suite.addTest(TestAssociationAnalysis('test_analyzePatientItems_commandLine_bufferFile'))
-	suite.addTest(unittest.makeSuite(TestAssociationAnalysis));
-
-	return suite;
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    #suite.addTest(TestAssociationAnalysis("test_incColNamesAndTypeCodes"));
+    #suite.addTest(TestAssociationAnalysis("test_insertFile_skipErrors"));
+    #suite.addTest(TestAssociationAnalysis('test_executeIterator'));
+    #suite.addTest(TestAssociationAnalysis('test_findOrInsertItem'));
+    #suite.addTest(TestAssociationAnalysis('test_analyzePatientItems_commandLine_bufferFile'))
+    suite.addTest(unittest.makeSuite(TestAssociationAnalysis));
+
+    return suite;
 
 if __name__=="__main__":
-	log.setLevel(LOGGER_LEVEL)
+    log.setLevel(LOGGER_LEVEL)
 
-	unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/cpoe/test/TestDataManager.py b/medinfo/cpoe/test/TestDataManager.py
index 62ef9194..b93a2876 100644
--- a/medinfo/cpoe/test/TestDataManager.py
+++ b/medinfo/cpoe/test/TestDataManager.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
-from Util import log;
+from .Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -20,6 +20,8 @@ class TestDataManager(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
 
         log.info("Populate the database with test data")
 
diff --git a/medinfo/cpoe/test/TestDecayingWindows.py b/medinfo/cpoe/test/TestDecayingWindows.py
index 1796bf0d..c0cf574c 100644
--- a/medinfo/cpoe/test/TestDecayingWindows.py
+++ b/medinfo/cpoe/test/TestDecayingWindows.py
@@ -3,13 +3,13 @@
 
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import *
 import unittest
 import time
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -21,416 +21,422 @@
 
 from medinfo.cpoe.AssociationAnalysis import AssociationAnalysis, AnalysisOptions;
 
+TEMP_FILENAME = "DWTemp.txt";
+
 class TestDecayingWindows(DBTestCase):
-	def setUp(self):
-		"""Prepare state for test cases"""
-		DBTestCase.setUp(self);
-
-		log.info("Populate the database with test data")
-
-		self.clinicalItemCategoryIdStrList = list();
-		headers = ["clinical_item_category_id","source_table"];
-		dataModels = \
-			[
-				RowItemModel( [-1, "Labs"], headers ),
-				RowItemModel( [-2, "Imaging"], headers ),
-				RowItemModel( [-3, "Meds"], headers ),
-				RowItemModel( [-4, "Nursing"], headers ),
-				RowItemModel( [-5, "Problems"], headers ),
-				RowItemModel( [-6, "Lab Results"], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", dataModel );
-			self.clinicalItemCategoryIdStrList.append( str(dataItemId) );
-
-		headers = ["clinical_item_id","clinical_item_category_id","name","analysis_status"];
-		dataModels = \
-			[
-				RowItemModel( [-1, -1, "CBC",1], headers ),
-				RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
-				RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
-				RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
-				RowItemModel( [-5, -2, "CXR",1], headers ),
-				RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
-				RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
-				RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
-				RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
-				RowItemModel( [-10, -3, "Carvedilol",1], headers ),
-				RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
-				RowItemModel( [-12, -3, "Warfarin",1], headers ),
-				RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
-				RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
-				RowItemModel( [-15, -4, "Strict I&O",1], headers ),
-				RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel );
-
-		headers = ["patient_item_id","encounter_id","patient_id","clinical_item_id","item_date"];
-		dataModels = \
-			[
-				RowItemModel( [-1,  -111,   -11111, -4,  datetime(2000, 1, 1, 0)], headers ),
-				RowItemModel( [-2,  -111,   -11111, -10, datetime(2000, 1, 1, 0)], headers ),
-				RowItemModel( [-3,  -111,   -11111, -8,  datetime(2000, 1, 1, 2)], headers ),
-				RowItemModel( [-4,  -112,   -11111, -10, datetime(2000, 1, 2, 0)], headers ),
-				RowItemModel( [-5,  -112,   -11111, -12, datetime(2000, 2, 1, 0)], headers ),
-				RowItemModel( [-10, -222,   -22222, -7,  datetime(2000, 1, 5, 0)], headers ),
-				RowItemModel( [-12, -222,   -22222, -6,  datetime(2000, 1, 9, 0)], headers ),
-				RowItemModel( [-13, -222,   -22222, -11, datetime(2000, 1, 9, 0)], headers ),
-				RowItemModel( [-95, -222,   -22222, -9,  datetime(2000, 1,10, 0)], headers ),
-				RowItemModel( [-94, -333,   -33333, -8,  datetime(2000, 1,10, 0)], headers ),	# In first window delta unit only
-				RowItemModel( [-14, -333,   -33333, -6,  datetime(2000, 2, 9, 0)], headers ),
-				RowItemModel( [-15, -333,   -33333, -2,  datetime(2000, 2,11, 0)], headers ),  # Will set clinical_item_link inheritances to this item to only record certain associations
-				RowItemModel( [-16, -333,   -33333, -11, datetime(2000, 2,11, 0)], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("patient_item", dataModel );
-
-
-		headers = ["clinical_item_id","linked_item_id"];
-		dataModels = \
-			[   # Don't have direct, but instead demonstrate inherited relationship from 6 to 2 will still be recognized
-				RowItemModel( [-6, -4], headers ),
-				RowItemModel( [-4, -2], headers ),
-			];
-		for dataModel in dataModels:
-			(dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_link", dataModel );
-
-		self.decayAnalyzer = DecayingWindows() # DecayingWindows instance to test on, *** remember to change database to medinfo_copy
-		self.dataManager = DataManager();
-
-	def tearDown(self):
-		"""Restore state from any setUp or test steps"""
-		log.info("Purge test records from the database")
-
-		DBUtil.execute("delete from clinical_item_link where clinical_item_id < 0");
-		DBUtil.execute("delete from clinical_item_association where clinical_item_id < 0");
-		DBUtil.execute("delete from patient_item where patient_item_id < 0");
-		DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
-		DBUtil.execute("delete from clinical_item_category where clinical_item_category_id in (%s)" % str.join(",", self.clinicalItemCategoryIdStrList) );
-
-		if os.path.exists("DWTemp.txt"):
-			os.remove("DWTemp.txt")
-
-		DBTestCase.tearDown(self);
-
-	def test_decayingWindowsFromBuffer(self):
-
-		associationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				count_0, count_3600, count_86400, count_604800,
-				count_2592000, count_7776000, count_31536000,
-				count_any
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-
-		decayAnalysisOptions = DecayAnalysisOptions()
-		decayAnalysisOptions.startD = datetime(2000,1,9)
-		decayAnalysisOptions.endD = datetime(2000,2,11)
-		#decayAnalysisOptions.windowLength = 10
-		decayAnalysisOptions.decay = 0.9
-		decayAnalysisOptions.delta = timedelta(weeks=4)
-		decayAnalysisOptions.patientIds = [-22222, -33333]
-		decayAnalysisOptions.outputFile = "DWTemp.txt"
-
-		self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],	# Note that decaying windows approach will not try to update counts for time periods longer than the delta period
-				[-11, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[-11, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted. Consider future upgrade. Don't train on all time ever, but train on two deltas at a time, sliding / shifting window so do catch the overlap ranges
-				[-11, -6,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -9,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -9, -9,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -9, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -9, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -8,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -8, -9,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -8, -8,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -8, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -6,-11,   0.9, 0.9, 0.9, 1.9, 1.9, 0, 0, 1.9],
-				[ -6, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -6, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -6, -6,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		#for row in expectedAssociationStats:
-		#	print >> sys.stderr, row;
-		#print >> sys.stderr, "============"
-		#for row in associationStats:
-		#	print >> sys.stderr, row;
-		#print >> sys.stderr, "============"
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		expectedItemBaseCountById = \
-			{
-				-1: 0,
-				-2: 0,
-				-3: 0,
-				-4: 0,
-				-5: 0,
-				-6: 1.9,
-				-7: 0,
-				-8: 0.9,
-				-9: 0.9,
-				-10: 0,
-				-11: 1.9,
-				-12: 0,
-				-13: 0,
-				-14: 0,
-				-15: 0,
-				-16: 0,
-			}
-		itemBaseCountById = self.dataManager.loadClinicalItemBaseCountByItemId();
-		#print >> sys.stderr, itemBaseCountById;
-		self.assertEqualDict(expectedItemBaseCountById, itemBaseCountById);
-
-
-		######## Reset the model data and rerun with different decay parameters
-		self.dataManager.resetAssociationModel()
-
-		decayAnalysisOptions = DecayAnalysisOptions()
-		decayAnalysisOptions.startD = datetime(2000,1,9)
-		decayAnalysisOptions.endD = datetime(2000,2,11)
-		decayAnalysisOptions.windowLength = 4;	# Just specify window length, then should calculate decay parameter
-		#decayAnalysisOptions.decay = 0.9
-		decayAnalysisOptions.delta = timedelta(weeks=4)
-		decayAnalysisOptions.patientIds = [-22222, -33333]
-		decayAnalysisOptions.outputFile = "DWTemp.txt"
-
-		self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   1.75, 1.75, 1.75, 1.75, 1.75, 0, 0, 1.75],
-				[-11, -9,   0.0, 0.0, 0.75, 0.75, 0.75, 0, 0, 0.75],
-				[-11, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[-11, -6,   0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
-				[ -9,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -9, -9,   0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
-				[ -9, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -9, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -8,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -8, -9,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -8, -8,   0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
-				[ -8, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -6,-11,   0.75, 0.75, 0.75, 1.75, 1.75, 0, 0, 1.75],
-				[ -6, -9,   0.0, 0.0, 0.75, 0.75, 0.75, 0, 0, 0.75],
-				[ -6, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -6, -6,   1.75, 1.75, 1.75, 1.75, 1.75, 0, 0, 1.75],
-			];
-
-		associationStats = DBUtil.execute(associationQuery);
-		#for row in expectedAssociationStats:
-		#	print >> sys.stderr, row;
-		#print >> sys.stderr, "============"
-		#for row in associationStats:
-		#	print >> sys.stderr, row;
-		#print >> sys.stderr, "============"
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		expectedItemBaseCountById = \
-			{
-				-1: 0,
-				-2: 0,
-				-3: 0,
-				-4: 0,
-				-5: 0,
-				-6: 1.75,
-				-7: 0,
-				-8: 0.75,
-				-9: 0.75,
-				-10: 0,
-				-11: 1.75,
-				-12: 0,
-				-13: 0,
-				-14: 0,
-				-15: 0,
-				-16: 0,
-			}
-		itemBaseCountById = self.dataManager.loadClinicalItemBaseCountByItemId(acceptCache=False);	# Don't use cache, otherwise will get prior results
-		print >> sys.stderr, itemBaseCountById;
-		self.assertEqualDict(expectedItemBaseCountById, itemBaseCountById);
-
-
-	def test_decayingWindows(self):
-		# Muthu's function to test DecayingWindows module
-
-		associationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
-				patient_count_2592000, patient_count_7776000, patient_count_31536000,
-				patient_count_any
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-
-
-		decayAnalysisOptions = DecayAnalysisOptions()
-		decayAnalysisOptions.startD = datetime(2000,1,9)
-		decayAnalysisOptions.endD = datetime(2000,2,11)
-		decayAnalysisOptions.windowLength = 10
-		decayAnalysisOptions.decay = 0.9
-		decayAnalysisOptions.delta = timedelta(weeks=4)
-		decayAnalysisOptions.patientIds = [-22222, -33333]
-
-		self.decayAnalyzer.decayAnalyzePatientItems (decayAnalysisOptions)
-
-		expectedAssociationStats = \
-			[
-				[-11,-11,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],	# Note that decaying windows approach will not try to update counts for time periods longer than the delta period
-				[-11, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[-11, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted. Consider future upgrade. Don't train on all time ever, but train on two deltas at a time, sliding / shifting window so do catch the overlap ranges. Problem here is buffer based algorithm, won't be recording analyze_dates as go, so will end up with duplicate counts of items each month?
-				[-11, -6,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -9,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -9, -9,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -9, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -9, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
-				[ -8,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -8, -9,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -8, -8,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -8, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -6,-11,   0.9, 0.9, 0.9, 1.9, 1.9, 0, 0, 1.9],
-				[ -6, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
-				[ -6, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0], 	# 8 not in same delta as other items so co-occurence not gettign counted.
-				[ -6, -6,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],
-			];
-
-		associationStats = DBUtil.execute(associationQuery)
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-		#DBUtil.execute("delete from clinical_item_association")
-
-		# Add another training period then should get a second decay multiplier for older data?
-		# Weird in that incrementally building on prior data that is getting decayed, even though new training data actually occurred before chronologic time of data
-		decayAnalysisOptions = DecayAnalysisOptions();
-		decayAnalysisOptions.startD = datetime(2000,1,1)
-		decayAnalysisOptions.endD = datetime(2000,2,12)
-		decayAnalysisOptions.windowLength = 10
-		decayAnalysisOptions.decay = 0.9
-		decayAnalysisOptions.delta = timedelta(weeks=4)
-		decayAnalysisOptions.patientIds = [-22222, -33333]
-
-		self.decayAnalyzer.decayAnalyzePatientItems (decayAnalysisOptions)
-
-		expectedAssociationStats = \
-			[
-				[-11L, -11L, 1.539, 1.539, 1.539, 1.539, 1.539, 0.0, 0.0, 1.539],
-				[-11L, -9L, 0.0, 0.0, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
-				[-11L, -8L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-11L, -7L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-11L, -6L, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
-				[-9L, -11L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-9L, -9L, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
-				[-9L, -8L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-9L, -7L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-9L, -6L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-8L, -11L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-8L, -9L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-8L, -8L, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
-				[-8L, -6L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-7L, -11L, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
-				[-7L, -9L, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
-				[-7L, -7L, 0.9, 0.9, 0.9, 0.9, 0.9, 0.0, 0.0, 0.9],
-				[-7L, -6L, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
-				[-6L, -11L, 0.729, 0.729, 0.729, 1.539, 1.539, 0.0, 0.0, 1.539],
-				[-6L, -9L, 0.0, 0.0, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
-				[-6L, -8L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-6L, -7L, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-				[-6L, -6L, 1.539, 1.539, 1.539, 1.539, 1.539, 0.0, 0.0, 1.539],
-			];
-
-		associationStats = DBUtil.execute(associationQuery)
-		#for row in expectedAssociationStats:
-		#	print >> sys.stderr, row;
-		#print >> sys.stderr, "============"
-		#for row in associationStats:
-		#	print >> sys.stderr, row;
-		#print >> sys.stderr, "============"
-		self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
-
-
-	def test_resetModel(self):
-		associationQuery = \
-			"""
-			select
-				clinical_item_id, subsequent_item_id,
-				patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
-				patient_count_2592000, patient_count_7776000, patient_count_31536000,
-				patient_count_any
-			from
-				clinical_item_association
-			where
-				clinical_item_id < 0
-			order by
-				clinical_item_id, subsequent_item_id
-			""";
-
-		associationQueryDate = \
-			"""
-			select
-				patient_item_id, analyze_date
-			from
-				patient_item
-			where
-				patient_item_id < 0
-			order by
-				patient_item_id
-			""";
-
-		# fill up the association table with something
-		decayAnalysisOptions = DecayAnalysisOptions()
-		decayAnalysisOptions.startD = datetime(2000,1,9)
-		decayAnalysisOptions.endD = datetime(2000,2,11)
-		decayAnalysisOptions.windowLength = 10
-		decayAnalysisOptions.decay = 0.9
-		decayAnalysisOptions.patientIds = [-22222, -33333]
-		self.decayAnalyzer.decayAnalyzePatientItems (decayAnalysisOptions)
-
-		# then clear the table
-		self.dataManager.resetAssociationModel()
-
-		expectedAssociationStats = \
-			[
-			];
-		associationStats = DBUtil.execute(associationQuery)
-		self.assertEqualTable(expectedAssociationStats, associationStats, precision=3 )
-
-		# Set as NULL
-		expectedAssociationStatsDate = \
-			[[-95, None],[-94, None],[-16, None], [-15, None], [-14, None], [-13, None], [-12, None], [-10, None], [-5, None], [-4, None], [-3, None], [-2, None], [-1, None]
-			];
-		associationStatsDate = DBUtil.execute(associationQueryDate)
-		#print >> sys.stderr, associationStatsDate
-		self.assertEqualTable(expectedAssociationStatsDate, associationStatsDate)
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self);
+
+        log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
+        self.clinicalItemCategoryIdStrList = list();
+        headers = ["clinical_item_category_id","source_table"];
+        dataModels = \
+            [
+                RowItemModel( [-1, "Labs"], headers ),
+                RowItemModel( [-2, "Imaging"], headers ),
+                RowItemModel( [-3, "Meds"], headers ),
+                RowItemModel( [-4, "Nursing"], headers ),
+                RowItemModel( [-5, "Problems"], headers ),
+                RowItemModel( [-6, "Lab Results"], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", dataModel );
+            self.clinicalItemCategoryIdStrList.append( str(dataItemId) );
+
+        headers = ["clinical_item_id","clinical_item_category_id","name","analysis_status"];
+        dataModels = \
+            [
+                RowItemModel( [-1, -1, "CBC",1], headers ),
+                RowItemModel( [-2, -1, "BMP",0], headers ), # Clear analysis status, so this will be ignored unless changed
+                RowItemModel( [-3, -1, "Hepatic Panel",1], headers ),
+                RowItemModel( [-4, -1, "Cardiac Enzymes",1], headers ),
+                RowItemModel( [-5, -2, "CXR",1], headers ),
+                RowItemModel( [-6, -2, "RUQ Ultrasound",1], headers ),
+                RowItemModel( [-7, -2, "CT Abdomen/Pelvis",1], headers ),
+                RowItemModel( [-8, -2, "CT PE Thorax",1], headers ),
+                RowItemModel( [-9, -3, "Acetaminophen",1], headers ),
+                RowItemModel( [-10, -3, "Carvedilol",1], headers ),
+                RowItemModel( [-11, -3, "Enoxaparin",1], headers ),
+                RowItemModel( [-12, -3, "Warfarin",1], headers ),
+                RowItemModel( [-13, -3, "Ceftriaxone",1], headers ),
+                RowItemModel( [-14, -4, "Foley Catheter",1], headers ),
+                RowItemModel( [-15, -4, "Strict I&O",1], headers ),
+                RowItemModel( [-16, -4, "Fall Precautions",1], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", dataModel );
+
+        headers = ["patient_item_id","encounter_id","patient_id","clinical_item_id","item_date"];
+        dataModels = \
+            [
+                RowItemModel( [-1,  -111,   -11111, -4,  datetime(2000, 1, 1, 0)], headers ),
+                RowItemModel( [-2,  -111,   -11111, -10, datetime(2000, 1, 1, 0)], headers ),
+                RowItemModel( [-3,  -111,   -11111, -8,  datetime(2000, 1, 1, 2)], headers ),
+                RowItemModel( [-4,  -112,   -11111, -10, datetime(2000, 1, 2, 0)], headers ),
+                RowItemModel( [-5,  -112,   -11111, -12, datetime(2000, 2, 1, 0)], headers ),
+                RowItemModel( [-10, -222,   -22222, -7,  datetime(2000, 1, 5, 0)], headers ),
+                RowItemModel( [-12, -222,   -22222, -6,  datetime(2000, 1, 9, 0)], headers ),
+                RowItemModel( [-13, -222,   -22222, -11, datetime(2000, 1, 9, 0)], headers ),
+                RowItemModel( [-95, -222,   -22222, -9,  datetime(2000, 1,10, 0)], headers ),
+                RowItemModel( [-94, -333,   -33333, -8,  datetime(2000, 1,10, 0)], headers ),    # In first window delta unit only
+                RowItemModel( [-14, -333,   -33333, -6,  datetime(2000, 2, 9, 0)], headers ),
+                RowItemModel( [-15, -333,   -33333, -2,  datetime(2000, 2,11, 0)], headers ),  # Will set clinical_item_link inheritances to this item to only record certain associations
+                RowItemModel( [-16, -333,   -33333, -11, datetime(2000, 2,11, 0)], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("patient_item", dataModel );
+
+
+        headers = ["clinical_item_id","linked_item_id"];
+        dataModels = \
+            [   # Don't have direct, but instead demonstrate inherited relationship from 6 to 2 will still be recognized
+                RowItemModel( [-6, -4], headers ),
+                RowItemModel( [-4, -2], headers ),
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("clinical_item_link", dataModel );
+
+        self.decayAnalyzer = DecayingWindows() # DecayingWindows instance to test on, *** remember to change database to medinfo_copy
+        self.dataManager = DataManager();
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        log.info("Purge test records from the database")
+
+        DBUtil.execute("delete from clinical_item_link where clinical_item_id < 0");
+        DBUtil.execute("delete from clinical_item_association where clinical_item_id < 0");
+        DBUtil.execute("delete from patient_item where patient_item_id < 0");
+        DBUtil.execute("delete from clinical_item where clinical_item_id < 0");
+        DBUtil.execute("delete from clinical_item_category where clinical_item_category_id in (%s)" % str.join(",", self.clinicalItemCategoryIdStrList) );
+
+        # Purge temporary buffer files. May not match exact name if modified for other purpose
+        for filename in os.listdir("."):
+            if filename.startswith(TEMP_FILENAME):
+                os.remove(filename);
+
+        DBTestCase.tearDown(self);
+
+    def test_decayingWindowsFromBuffer(self):
+
+        associationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                count_0, count_3600, count_86400, count_604800,
+                count_2592000, count_7776000, count_31536000,
+                count_any
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+
+        decayAnalysisOptions = DecayAnalysisOptions()
+        decayAnalysisOptions.startD = datetime(2000,1,9)
+        decayAnalysisOptions.endD = datetime(2000,2,11)
+        #decayAnalysisOptions.windowLength = 10
+        decayAnalysisOptions.decay = 0.9
+        decayAnalysisOptions.delta = timedelta(weeks=4)
+        decayAnalysisOptions.patientIds = [-22222, -33333]
+        decayAnalysisOptions.outputFile = TEMP_FILENAME;
+
+        self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],    # Note that decaying windows approach will not try to update counts for time periods longer than the delta period
+                [-11, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [-11, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted. Consider future upgrade. Don't train on all time ever, but train on two deltas at a time, sliding / shifting window so do catch the overlap ranges
+                [-11, -6,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -9,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -9, -9,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -9, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -9, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -8,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -8, -9,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -8, -8,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -8, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -6,-11,   0.9, 0.9, 0.9, 1.9, 1.9, 0, 0, 1.9],
+                [ -6, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -6, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -6, -6,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        #for row in expectedAssociationStats:
+        #    print >> sys.stderr, row;
+        #print >> sys.stderr, "============"
+        #for row in associationStats:
+        #    print >> sys.stderr, row;
+        #print >> sys.stderr, "============"
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        expectedItemBaseCountById = \
+            {
+                -1: 0,
+                -2: 0,
+                -3: 0,
+                -4: 0,
+                -5: 0,
+                -6: 1.9,
+                -7: 0,
+                -8: 0.9,
+                -9: 0.9,
+                -10: 0,
+                -11: 1.9,
+                -12: 0,
+                -13: 0,
+                -14: 0,
+                -15: 0,
+                -16: 0,
+            }
+        itemBaseCountById = self.dataManager.loadClinicalItemBaseCountByItemId();
+        #print >> sys.stderr, itemBaseCountById;
+        self.assertEqualDict(expectedItemBaseCountById, itemBaseCountById);
+
+
+        ######## Reset the model data and rerun with different decay parameters
+        self.dataManager.resetAssociationModel()
+
+        decayAnalysisOptions = DecayAnalysisOptions()
+        decayAnalysisOptions.startD = datetime(2000,1,9)
+        decayAnalysisOptions.endD = datetime(2000,2,11)
+        decayAnalysisOptions.windowLength = 4;    # Just specify window length, then should calculate decay parameter
+        #decayAnalysisOptions.decay = 0.9
+        decayAnalysisOptions.delta = timedelta(weeks=4)
+        decayAnalysisOptions.patientIds = [-22222, -33333]
+        decayAnalysisOptions.outputFile = TEMP_FILENAME;
+
+        self.decayAnalyzer.decayAnalyzePatientItems(decayAnalysisOptions)
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   1.75, 1.75, 1.75, 1.75, 1.75, 0, 0, 1.75],
+                [-11, -9,   0.0, 0.0, 0.75, 0.75, 0.75, 0, 0, 0.75],
+                [-11, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [-11, -6,   0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
+                [ -9,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -9, -9,   0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
+                [ -9, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -9, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -8,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -8, -9,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -8, -8,   0.75, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.75],
+                [ -8, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -6,-11,   0.75, 0.75, 0.75, 1.75, 1.75, 0, 0, 1.75],
+                [ -6, -9,   0.0, 0.0, 0.75, 0.75, 0.75, 0, 0, 0.75],
+                [ -6, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -6, -6,   1.75, 1.75, 1.75, 1.75, 1.75, 0, 0, 1.75],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery);
+        #for row in expectedAssociationStats:
+        #    print >> sys.stderr, row;
+        #print >> sys.stderr, "============"
+        #for row in associationStats:
+        #    print >> sys.stderr, row;
+        #print >> sys.stderr, "============"
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        expectedItemBaseCountById = \
+            {
+                -1: 0,
+                -2: 0,
+                -3: 0,
+                -4: 0,
+                -5: 0,
+                -6: 1.75,
+                -7: 0,
+                -8: 0.75,
+                -9: 0.75,
+                -10: 0,
+                -11: 1.75,
+                -12: 0,
+                -13: 0,
+                -14: 0,
+                -15: 0,
+                -16: 0,
+            }
+        itemBaseCountById = self.dataManager.loadClinicalItemBaseCountByItemId(acceptCache=False);    # Don't use cache, otherwise will get prior results
+        #print >> sys.stderr, itemBaseCountById;
+        self.assertEqualDict(expectedItemBaseCountById, itemBaseCountById);
+
+
+    def test_decayingWindows(self):
+        # Muthu's function to test DecayingWindows module
+
+        associationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
+                patient_count_2592000, patient_count_7776000, patient_count_31536000,
+                patient_count_any
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+
+
+        decayAnalysisOptions = DecayAnalysisOptions()
+        decayAnalysisOptions.startD = datetime(2000,1,9)
+        decayAnalysisOptions.endD = datetime(2000,2,11)
+        decayAnalysisOptions.windowLength = 10
+        decayAnalysisOptions.decay = 0.9
+        decayAnalysisOptions.delta = timedelta(weeks=4)
+        decayAnalysisOptions.patientIds = [-22222, -33333]
+
+        self.decayAnalyzer.decayAnalyzePatientItems (decayAnalysisOptions)
+
+        expectedAssociationStats = \
+            [
+                [-11,-11,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],    # Note that decaying windows approach will not try to update counts for time periods longer than the delta period
+                [-11, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [-11, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted. Consider future upgrade. Don't train on all time ever, but train on two deltas at a time, sliding / shifting window so do catch the overlap ranges. Problem here is buffer based algorithm, won't be recording analyze_dates as go, so will end up with duplicate counts of items each month?
+                [-11, -6,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -9,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -9, -9,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -9, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -9, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],
+                [ -8,-11,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -8, -9,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -8, -8,   0.9, 0.9, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -8, -6,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -6,-11,   0.9, 0.9, 0.9, 1.9, 1.9, 0, 0, 1.9],
+                [ -6, -9,   0.0, 0.0, 0.9, 0.9, 0.9, 0, 0, 0.9],
+                [ -6, -8,   0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0],     # 8 not in same delta as other items so co-occurence not gettign counted.
+                [ -6, -6,   1.9, 1.9, 1.9, 1.9, 1.9, 0, 0, 1.9],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery)
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+        #DBUtil.execute("delete from clinical_item_association")
+
+        # Add another training period then should get a second decay multiplier for older data?
+        # Weird in that incrementally building on prior data that is getting decayed, even though new training data actually occurred before chronologic time of data
+        decayAnalysisOptions = DecayAnalysisOptions();
+        decayAnalysisOptions.startD = datetime(2000,1,1)
+        decayAnalysisOptions.endD = datetime(2000,2,12)
+        decayAnalysisOptions.windowLength = 10
+        decayAnalysisOptions.decay = 0.9
+        decayAnalysisOptions.delta = timedelta(weeks=4)
+        decayAnalysisOptions.patientIds = [-22222, -33333]
+
+        self.decayAnalyzer.decayAnalyzePatientItems (decayAnalysisOptions)
+
+        expectedAssociationStats = \
+            [
+                [-11, -11, 1.539, 1.539, 1.539, 1.539, 1.539, 0.0, 0.0, 1.539],
+                [-11, -9, 0.0, 0.0, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
+                [-11, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-11, -7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-11, -6, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
+                [-9, -11, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-9, -9, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
+                [-9, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-9, -7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-9, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-8, -11, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-8, -9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-8, -8, 0.729, 0.729, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
+                [-8, -6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-7, -11, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
+                [-7, -9, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
+                [-7, -7, 0.9, 0.9, 0.9, 0.9, 0.9, 0.0, 0.0, 0.9],
+                [-7, -6, 0.0, 0.0, 0.0, 0.9, 0.9, 0.0, 0.0, 0.9],
+                [-6, -11, 0.729, 0.729, 0.729, 1.539, 1.539, 0.0, 0.0, 1.539],
+                [-6, -9, 0.0, 0.0, 0.729, 0.729, 0.729, 0.0, 0.0, 0.729],
+                [-6, -8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-6, -7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [-6, -6, 1.539, 1.539, 1.539, 1.539, 1.539, 0.0, 0.0, 1.539],
+            ];
+
+        associationStats = DBUtil.execute(associationQuery)
+        #for row in expectedAssociationStats:
+        #    print >> sys.stderr, row;
+        #print >> sys.stderr, "============"
+        #for row in associationStats:
+        #    print >> sys.stderr, row;
+        #print >> sys.stderr, "============"
+        self.assertEqualTable( expectedAssociationStats, associationStats, precision=3 );
+
+
+    def test_resetModel(self):
+        associationQuery = \
+            """
+            select
+                clinical_item_id, subsequent_item_id,
+                patient_count_0, patient_count_3600, patient_count_86400, patient_count_604800,
+                patient_count_2592000, patient_count_7776000, patient_count_31536000,
+                patient_count_any
+            from
+                clinical_item_association
+            where
+                clinical_item_id < 0
+            order by
+                clinical_item_id, subsequent_item_id
+            """;
+
+        associationQueryDate = \
+            """
+            select
+                patient_item_id, analyze_date
+            from
+                patient_item
+            where
+                patient_item_id < 0
+            order by
+                patient_item_id
+            """;
+
+        # fill up the association table with something
+        decayAnalysisOptions = DecayAnalysisOptions()
+        decayAnalysisOptions.startD = datetime(2000,1,9)
+        decayAnalysisOptions.endD = datetime(2000,2,11)
+        decayAnalysisOptions.windowLength = 10
+        decayAnalysisOptions.decay = 0.9
+        decayAnalysisOptions.patientIds = [-22222, -33333]
+        self.decayAnalyzer.decayAnalyzePatientItems (decayAnalysisOptions)
+
+        # then clear the table
+        self.dataManager.resetAssociationModel()
+
+        expectedAssociationStats = \
+            [
+            ];
+        associationStats = DBUtil.execute(associationQuery)
+        self.assertEqualTable(expectedAssociationStats, associationStats, precision=3 )
+
+        # Set as NULL
+        expectedAssociationStatsDate = \
+            [[-95, None],[-94, None],[-16, None], [-15, None], [-14, None], [-13, None], [-12, None], [-10, None], [-5, None], [-4, None], [-3, None], [-2, None], [-1, None]
+            ];
+        associationStatsDate = DBUtil.execute(associationQueryDate)
+        #print >> sys.stderr, associationStatsDate
+        self.assertEqualTable(expectedAssociationStatsDate, associationStatsDate)
 
 def suite():
-	"""Returns the suite of tests to run for this test class / module.
-	Use unittest.makeSuite methods which simply extracts all of the
-	methods for the given class whose name starts with "test"
-	"""
-	suite = unittest.TestSuite();
-	#suite.addTest(TestDecayingWindows('test_decayingWindows'))
-	#suite.addTest(TestDecayingWindows('test_decayingWindowsFromBuffer'))
-	#suite.addTest(TestDecayingWindows('test_resetModel'))
-	suite.addTest(unittest.makeSuite(TestDecayingWindows));
-
-	return suite;
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    #suite.addTest(TestDecayingWindows('test_decayingWindows'))
+    #suite.addTest(TestDecayingWindows('test_decayingWindowsFromBuffer'))
+    #suite.addTest(TestDecayingWindows('test_resetModel'))
+    suite.addTest(unittest.makeSuite(TestDecayingWindows));
+
+    return suite;
 
 if __name__=="__main__":
-	unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/cpoe/test/TestItemRecommender.py b/medinfo/cpoe/test/TestItemRecommender.py
index 155549f9..b5a843d7 100644
--- a/medinfo/cpoe/test/TestItemRecommender.py
+++ b/medinfo/cpoe/test/TestItemRecommender.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime, timedelta;
 import unittest
 
-from Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
-from Util import log;
+from .Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Util import ProgressDots;
 
@@ -27,6 +27,8 @@ class TestItemRecommender(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
 
         log.info("Populate the database with test data")
 
@@ -312,12 +314,12 @@ def assertEqualRecommendedData(self, expectedData, recommendedData, query):
         lastScore = None;
         for expectedItem, recommendedItem in zip(expectedData, recommendedData):
             # Ensure derived statistics are populated to enable comparisons
-            ItemAssociationRecommender.populateDerivedStats( recommendedItem, expectedItem.keys() );
+            ItemAssociationRecommender.populateDerivedStats( recommendedItem, list(expectedItem.keys()) );
 
             self.assertEqualDict(expectedItem, recommendedItem, ["clinical_item_id"]);
-            for key in expectedItem.iterkeys():  # If specified, then verify a specific values
+            for key in expectedItem.keys():  # If specified, then verify a specific values
                 if isinstance(expectedItem[key],float):
-                    self.assertAlmostEquals(expectedItem[key], recommendedItem[key], 5);
+                    self.assertAlmostEqual(expectedItem[key], recommendedItem[key], 5);
                 else:
                     self.assertEqual(expectedItem[key], recommendedItem[key]);
             if lastScore is not None:
@@ -375,7 +377,7 @@ def assertEqualRecommendedDataStats(self, expectedData, recommendedData, headers
                 expectedValue = expectedItem[header];
                 recommendedValue = recommendedItem[header];
                 msg = 'Dicts diff with key (%s).  Verify = %s, Sample = %s' % (header, expectedValue, recommendedValue);
-                self.assertAlmostEquals(expectedValue, recommendedValue, 3, msg);
+                self.assertAlmostEqual(expectedValue, recommendedValue, 3, msg);
 
     def test_recommender_stats_commandline(self):
         # Run the recommender against the mock test data above and verify expected stats calculations
@@ -410,7 +412,7 @@ def assertEqualRecommendedDataStatsTextOutput(self, expectedData, textOutput, he
         """
         recommendedData = list();
         for dataRow in TabDictReader(textOutput):
-            for key,value in dataRow.iteritems():
+            for key,value in dataRow.items():
                 if key in headers:
                     dataRow[key] = float(value);    # Parse into numerical values for comparison
             recommendedData.append(dataRow);
@@ -450,13 +452,13 @@ def test_dataCache(self):
         self.assertEqual( baselineQueryCount, newQueryCount );
 
         # Repeat multiple times, should still have no new query activity
-        prog = ProgressDots(10,1,"repeats");
-        for iRepeat in xrange(10):
+        # prog = ProgressDots(10,1,"repeats");
+        for iRepeat in range(10):
             newData = self.recommender( query );
             newQueryCount = self.recommender.dataManager.queryCount;
             self.assertEqualRecommendedData( baselineData, newData, query );
             self.assertEqual( baselineQueryCount, newQueryCount );
-            prog.update();
+            # prog.update();
         # prog.printStatus();
 
         # Query for subset should still yield no new query
diff --git a/medinfo/cpoe/test/TestOrderSetRecommender.py b/medinfo/cpoe/test/TestOrderSetRecommender.py
index c91fe636..86831322 100644
--- a/medinfo/cpoe/test/TestOrderSetRecommender.py
+++ b/medinfo/cpoe/test/TestOrderSetRecommender.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime, timedelta;
 import unittest
 
-from Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
-from Util import log;
+from .Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Util import ProgressDots;
 
@@ -27,6 +27,8 @@ class TestOrderSetRecommender(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
 
         log.info("Populate the database with test data")
 
@@ -340,9 +342,9 @@ def assertEqualRecommendedData(self, expectedData, recommendedData, query):
         """
         lastScore = None;
         for expectedItem, recommendedItem in zip(expectedData, recommendedData):
-            for key in expectedItem.iterkeys():  # If specified, then verify a specific values
+            for key in expectedItem.keys():  # If specified, then verify a specific values
                 if isinstance(expectedItem[key],float):
-                    self.assertAlmostEquals(expectedItem[key], recommendedItem[key], 5);
+                    self.assertAlmostEqual(expectedItem[key], recommendedItem[key], 5);
                 else:
                     self.assertEqual(expectedItem[key], recommendedItem[key]);
             if lastScore is not None:
diff --git a/medinfo/cpoe/test/TestTopicModel.py b/medinfo/cpoe/test/TestTopicModel.py
index 2f09c1e8..8d5914b6 100644
--- a/medinfo/cpoe/test/TestTopicModel.py
+++ b/medinfo/cpoe/test/TestTopicModel.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db.test.Util import DBTestCase;
@@ -27,6 +27,8 @@ def setUp(self):
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","source_table"];
@@ -151,7 +153,7 @@ def assertExpectedTopItems(self, expectedDocCountByWordId, model, topTopicFile):
                 expectedTFIDF = score * expectedDocCountByWordId[None] / expectedDocCountByWordId[itemId];
             
             #print >> sys.stderr, topicId, itemId, score, tfidf, expectedDocCountByWordId[itemId]
-            self.assertAlmostEqual(expectedTFIDF, tfidf);
+            self.assertAlmostEqual(expectedTFIDF, tfidf, places=5);
                 
             if topicId not in topScoreByItemIdByTopicId:
                 topScoreByItemIdByTopicId[topicId] = dict();
@@ -159,9 +161,9 @@ def assertExpectedTopItems(self, expectedDocCountByWordId, model, topTopicFile):
             itemsChecked += 1;
         self.assertTrue( itemsChecked > 0 );    # Make sure an actual test happened
 
-        for topicId, topScoreByItemId in topScoreByItemIdByTopicId.iteritems():
+        for topicId, topScoreByItemId in topScoreByItemIdByTopicId.items():
             scoreByItemId = scoreByItemIdByTopicId[topicId];
-            self.assertAlmostEqualsDict( topScoreByItemId, scoreByItemId );
+            self.assertAlmostEqualsDict( topScoreByItemId, scoreByItemId, places=5 );
 
 def suite():
     """Returns the suite of tests to run for this test class / module.
diff --git a/medinfo/cpoe/test/TestTripleAssociationAnalysis.py b/medinfo/cpoe/test/TestTripleAssociationAnalysis.py
index 1f3ad29c..16aa3f3b 100644
--- a/medinfo/cpoe/test/TestTripleAssociationAnalysis.py
+++ b/medinfo/cpoe/test/TestTripleAssociationAnalysis.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -22,6 +22,8 @@ def setUp(self):
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
+        from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
         
         self.clinicalItemCategoryIdStrList = list();
         headers = ["clinical_item_category_id","source_table"];
diff --git a/medinfo/cpoe/test/Util.py b/medinfo/cpoe/test/Util.py
index 7cf7241c..5d858e08 100644
--- a/medinfo/cpoe/test/Util.py
+++ b/medinfo/cpoe/test/Util.py
@@ -1,7 +1,8 @@
-import Const
+from . import Const
 import sys, os
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 
 from medinfo.db.test.Util import DBTestCase;
diff --git a/medinfo/dataconversion/Const.py b/medinfo/dataconversion/Const.py
index e1d3227a..507656e9 100644
--- a/medinfo/dataconversion/Const.py
+++ b/medinfo/dataconversion/Const.py
@@ -2,7 +2,7 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 
 """Application name","for example to identify a common logger object"""
 APPLICATION_NAME = "medinfo.cpoe.app"
diff --git a/medinfo/dataconversion/DataExtractor.py b/medinfo/dataconversion/DataExtractor.py
index 22f8f638..79650ad7 100644
--- a/medinfo/dataconversion/DataExtractor.py
+++ b/medinfo/dataconversion/DataExtractor.py
@@ -6,7 +6,7 @@
 from optparse import OptionParser
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import datetime, timedelta;
 import numpy as np;
 from heapq import heappush, heappop;
@@ -18,10 +18,10 @@
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList, columnFromModelList;
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
-from Util import log;
-import Util;
+from .Util import log;
+from . import Util;
 
-from Const import SENTINEL_RESULT_VALUE;
+from .Const import SENTINEL_RESULT_VALUE;
 
 class DataExtractor:
     def __init__(self):
@@ -244,7 +244,7 @@ def parseClinicalItemData_singlePatient(self, itemRowIter, patientId=None, patie
         itemTimes = None;
         itemTimesByPatientId = self.parseClinicalItemData(itemRowIter, patientIdCol, timeCol);
         if patientId is None:   # Then assume first one encountered is the patient ID of interest
-            patientIds = itemTimesByPatientId.keys();
+            patientIds = list(itemTimesByPatientId.keys());
             if patientIds:
                 patientId = patientIds[0];
         if patientId in itemTimesByPatientId:
@@ -257,7 +257,7 @@ def generateClinicalItemIndexTimes(self, itemTimesByPatientId, patientById, colN
         """
         patientEpisodeByIndexTimeById = dict();
         newColNames = list();   # Keep track of newly added columns
-        for patientId, itemTimes in itemTimesByPatientId.iteritems():
+        for patientId, itemTimes in itemTimesByPatientId.items():
             patient = patientById[patientId];
             (patientEpisodeByIndexTimeById[patientId], newColNames) = self.generateClinicalItemIndexTimes_singleRecord(itemTimes, patient, colNames, preDays, postDays);
         colNames.extend(newColNames);
@@ -272,13 +272,13 @@ def generateClinicalItemIndexTimes_singleRecord(self, itemTimes, patient, preDay
         for itemTime in itemTimes:
             itemDay = datetime(itemTime.year, itemTime.month, itemTime.day);  # Capture at day resolution
 
-            for iPreDay in xrange(preDays):
+            for iPreDay in range(preDays):
                 keyDay = itemDay + timedelta(-iPreDay);
                 patientCopy = dict(patientById[patientId]);
                 patientCopy["index_time"] = keyDay;
                 patientEpisodeByIndexTime[keyDay] = patientCopy;
 
-            for iPostDay in xrange(+1,postDays+1):
+            for iPostDay in range(+1,postDays+1):
                 keyDay = itemDay + timedelta(+iPostDay);
                 patientCopy = dict(patientById[patientId]);
                 patientCopy["index_time"] = keyDay;
@@ -288,7 +288,7 @@ def generateClinicalItemIndexTimes_singleRecord(self, itemTimes, patient, preDay
 
     def addTimeCycleFeatures_singlePatient(self, patientEpisodeByIndexTime, timeCol, timeAttr):
         colNames = None;
-        for patientEpisode in patientEpisodeByIndexTime.itervalues():
+        for patientEpisode in patientEpisodeByIndexTime.values():
             colNames = self.addTimeCycleFeatures_singleEpisode(patientEpisode, timeCol, timeAttr);
         return colNames;
 
@@ -326,7 +326,7 @@ def addClinicalItemFeatures(self, itemTimesByPatientId, patientEpisodeByIndexTim
             If not specified, then default to list from medinfo.cpoe.Const.DELTA_NAME_BY_DAYS;
         """
         newColNames = list();   # Keep track of newly added columns
-        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.iteritems():
+        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.items():
             itemTimes = None;
             if patientId in itemTimesByPatientId:  # Have items to lookup against
                 itemTimes = itemTimesByPatientId[patientId];
@@ -342,7 +342,7 @@ def addClinicalItemFeatures_singlePatient(self, itemTimes, patientEpisodeByIndex
             If not specified, then default to list from medinfo.cpoe.Const.DELTA_NAME_BY_DAYS;
         """
         if daysBins is None:
-            daysBins = DELTA_NAME_BY_DAYS.keys();
+            daysBins = list(DELTA_NAME_BY_DAYS.keys());
             daysBins.sort();
 
         # Find items most proximate before and after the index item for each patient
@@ -353,7 +353,7 @@ def addClinicalItemFeatures_singlePatient(self, itemTimes, patientEpisodeByIndex
         preLabel = "%s.pre" % itemLabel;
         postLabel = "%s.post" % itemLabel;
 
-        for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for indexTime, patient in patientEpisodeByIndexTime.items():
             # Initialize values to null for not found
             patient[preTimeLabel] = None;
             patient[postTimeLabel] = None;
@@ -440,7 +440,7 @@ def queryFlowsheet(self, flowsheetNames, patientIds, outputFile):
         if outputFile is not None:   # Stream output to formatter to avoid keeping all results in memory
             formatter = TextResultsFormatter(outputFile);
 
-        colNames = ["pat_anon_id","flo_meas_id","flowsheet_name","flowsheet_value","shifted_record_dt_tm"];
+        colNames = ["pat_anon_id","flo_meas_id","flowsheet_name","flowsheet_value","shifted_dt_tm"];
 
         patientIdStrs = list();
         for patientId in patientIds:
@@ -453,7 +453,7 @@ def queryFlowsheet(self, flowsheetNames, patientIds, outputFile):
         query.addWhereIn("flowsheet_name", flowsheetNames );
         query.addWhereIn("pat_anon_id", patientIdStrs );
         query.addOrderBy("pat_anon_id");
-        query.addOrderBy("shifted_record_dt_tm");
+        query.addOrderBy("shifted_dt_tm");
 
         return DBUtil.execute( query, includeColumnNames=True, formatter=formatter );
 
@@ -520,7 +520,7 @@ def parseResultsData_singlePatient(self, resultRowIter, patientIdCol, nameCol, v
         resultsByName = None;
         resultsByNameByPatientId = self.parseResultsData(resultRowIter, patientIdCol, nameCol, valueCol, datetimeCol);
         if patientId is None:   # Then assume first one encountered is the patient ID of interest
-            patientIds = resultsByNameByPatientId.keys();
+            patientIds = list(resultsByNameByPatientId.keys());
             if patientIds:
                 patientId = patientIds[0];
         if patientId in resultsByNameByPatientId:
@@ -544,7 +544,7 @@ def addResultFeatures(self, patientEpisodeByIndexTimeById, patientIdResultsByNam
 
         # Separate loop to verify all patient records addressed, even if no results available (like an outer join)
         resultsByName = None;
-        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.iteritems():
+        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.items():
             newColNames = self.addResultFeatures_singlePatient(patientEpisodeByIndexTime, resultsByName, baseNames, valueCol, datetimeCol, preTimeDelta, postTimeDelta);
 
         colNames.extend(newColNames);
@@ -566,7 +566,7 @@ def addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByNa
             postTimeDays = postTimeDelta.days;
 
         # Init summary values to null for all results
-        for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for indexTime, patient in patientEpisodeByIndexTime.items():
             for baseName in baseNames:
                 if resultsByName is not None or ("%s.%s_%s.count" % (baseName,preTimeDays,postTimeDays)) not in patient:
                     # Default to null for all values
@@ -587,7 +587,7 @@ def addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByNa
                     patient["%s.%s_%s.proximateTimeDays" % (baseName,preTimeDays,postTimeDays)] = None;
 
         if resultsByName is not None:   # Have results available for this patient
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
 
                 # Time range limits on labs to consider
                 preTimeLimit = None;
@@ -676,21 +676,21 @@ def addLabFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByName,
         return self.addResultFeatures_singlePatient(patientEpisodeByIndexTime, resultsByName, baseNames, "ord_num_value", "result_time", preTimeDelta, postTimeDelta);
 
     def parseFlowsheetFile(self, resultFile):
-        for result in self.parseResultsFile(resultFile, "pat_anon_id","flowsheet_name","flowsheet_value","shifted_record_dt_tm"):
+        for result in self.parseResultsFile(resultFile, "pat_anon_id","flowsheet_name","flowsheet_value","shifted_dt_tm"):
             yield result;
 
     def parseFlowsheetData(self, resultRowIter):
-        return self.parseResultsData(resultRowIter, "pat_anon_id","flowsheet_name","flowsheet_value","shifted_record_dt_tm");
+        return self.parseResultsData(resultRowIter, "pat_anon_id","flowsheet_name","flowsheet_value","shifted_dt_tm");
 
     def parseFlowsheetData_singlePatient(self, resultRowIter, patientId=None):
-        return self.parseResultsData_singlePatient(resultRowIter, "pat_anon_id","flowsheet_name","flowsheet_value","shifted_record_dt_tm", patientId);
+        return self.parseResultsData_singlePatient(resultRowIter, "pat_anon_id","flowsheet_name","flowsheet_value","shifted_dt_tm", patientId);
 
     def addFlowsheetFeatures(self, patientEpisodeByIndexTimeById, patientIdResultsByNameGenerator, baseNames, preTimeDelta, postTimeDelta, colNames):
         #log.info("Sort flowsheet by time for each patient and find items within specified time period to aggregate");
-        return self.addResultFeatures(patientEpisodeByIndexTimeById, patientIdResultsByNameGenerator, baseNames, "flowsheet_value","shifted_record_dt_tm",  preTimeDelta, postTimeDelta, colNames)
+        return self.addResultFeatures(patientEpisodeByIndexTimeById, patientIdResultsByNameGenerator, baseNames, "flowsheet_value","shifted_dt_tm",  preTimeDelta, postTimeDelta, colNames)
 
     def addFlowsheetFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByName, baseNames, preTimeDelta, postTimeDelta):
-        return self.addResultFeatures_singlePatient(patientEpisodeByIndexTime, resultsByName, baseNames, "flowsheet_value","shifted_record_dt_tm", preTimeDelta, postTimeDelta);
+        return self.addResultFeatures_singlePatient(patientEpisodeByIndexTime, resultsByName, baseNames, "flowsheet_value","shifted_dt_tm", preTimeDelta, postTimeDelta);
 
     def queryIVFluids(self, ivfMedIds, patientIds, outputFile):
         """Can get sets of IVFluid medication IDs from medinfo/dataconversion/mapdata/Medication.IVFluids.tab
@@ -764,14 +764,14 @@ def addIVFluidFeatures(self, patientEpisodeByIndexTimeById, ivFluidsByPatientId,
         """
         log.info("Run though IV Fluid orders for each patient and calculate accumulate amounts  for different threshold volumes and checkpoint times");
         firstPass = True;
-        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.iteritems():
+        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.items():
 
             # Custom rebuild IV fluid item list so infusion end times are their own (negative) events
             expandedIVFluidItems = list();
             if patientId in ivFluidsByPatientId:
                 expandedIVFluidItems = self.expandIVFluidItems(ivFluidsByPatientId[patientId]);
 
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():   # If have multiple index times per patient, will be repeated calculations here since restart volume accumulation counts for each index time
+            for indexTime, patient in patientEpisodeByIndexTime.items():   # If have multiple index times per patient, will be repeated calculations here since restart volume accumulation counts for each index time
                 # First build up accumulating fluid time point information
                 timepoints = [0];   # Track time points (seconds relative to index time) evaluated
                 preBolusVolumes = [0.0];  # Track total volume (mL) accumulated up to each time point, before any boluses that occur at this timepoint
@@ -844,7 +844,7 @@ def expandIVFluidItems(self, ivFluidItems):
         for ivFluidItem in ivFluidItems:
             nextTime = ivFluidItem["start_taking_time"];
             while infusionEndItemHeap and infusionEndItemHeap[0][0] <= nextTime:    # See if passed any infusion end times, then record an entry for those
-                (endTime, infusionEndItem) = heappop(infusionEndItemHeap);
+                (endTime, infusionEndItem_str, infusionEndItem) = heappop(infusionEndItemHeap);
                 expandedIVFluidItems.append(infusionEndItem);
             expandedIVFluidItems.append(ivFluidItem);
             if ivFluidItem["min_rate"] is not None:  # Looks like a continuous infusion
@@ -852,17 +852,20 @@ def expandIVFluidItems(self, ivFluidItems):
                 infusionEndItem = dict(ivFluidItem);
                 infusionEndItem["start_taking_time"] = infusionEndItem["end_taking_time"];
                 infusionEndItem["min_rate"] = -infusionEndItem["min_rate"];
-                heappush(infusionEndItemHeap, (ivFluidItem["end_taking_time"], infusionEndItem) );  # Keep track of infusion end time
+                # Keep track of infusion end time
+                # Using str() to avoid comparing infusionEndItems if end_taking_times are equal
+                # TODO what happens if str() are equal?
+                heappush(infusionEndItemHeap, (ivFluidItem["end_taking_time"], str(infusionEndItem), infusionEndItem))
         # Final check if any end infusion items left to account for
         while infusionEndItemHeap:
-            (endTime, infusionEndItem) = heappop(infusionEndItemHeap);
+            (endTime, infusionEndItem_str, infusionEndItem) = heappop(infusionEndItemHeap);
             expandedIVFluidItems.append(infusionEndItem);
         return expandedIVFluidItems;
 
     def filterPatients(patientById):
         #log.info("Deidentify patient IDs and build data list with adequate data");
         patientResults = list();
-        for iPatient, patient in enumerate(patientById.itervalues()):
+        for iPatient, patient in enumerate(patientById.values()):
             # Further deidentify patients by applying sequential ID
             patient["pat_id"] = patient["patient_id"] = iPatient;
             # Only accept patients where an index item and times were found
diff --git a/medinfo/dataconversion/FeatureExtractor.py b/medinfo/dataconversion/FeatureExtractor.py
new file mode 100644
index 00000000..5287dc52
--- /dev/null
+++ b/medinfo/dataconversion/FeatureExtractor.py
@@ -0,0 +1,616 @@
+"""
+Defines all feature extractors used by featurizers. A feature extractor is a
+self contained module that takes  in a cohort table and appends to or creates a
+long form feature matrix in a temporary bigquery table with the following schema
+Columns:
+    observation_id -- integer
+    index_time -- timestamp
+    feature_type -- string
+    feature_time -- timestamp
+    feature_id -- string
+    feature -- string
+    feature_value -- numeric
+"""
+from google.cloud import bigquery
+from google.cloud.exceptions import NotFound
+import pandas as pd 
+
+REPLACE_TABLE = True
+
+class FlowsheetBinsExtractor():
+    """
+    Defines logic to extract vitals from flowsheets
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 flowsheet_descriptions, look_back_days=3, bins=5,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Tokenizes flowsheets into bins and then writes or appends to temp
+        dataset. 
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            temp_dataset: name of temp dataset with cohort table
+            flowsheet_descriptions: list of row_disp_names used for flowsheet
+                descriptions
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        Returns:
+            df_lup : dataframe with bin thresholds
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.look_back_days = look_back_days
+        self.project_id = project_id
+        self.dataset = dataset
+        self.flowsheet_descriptions = "("
+        for i, flow in enumerate(flowsheet_descriptions):
+            if i == len(flowsheet_descriptions) - 1:
+                self.flowsheet_descriptions += f"'{flow}')"
+            else:
+                self.flowsheet_descriptions += f"'{flow}', "
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        WITH flowsheet_vals AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Vitals' as feature_type,
+            f.recorded_time_utc as feature_time,
+            GENERATE_UUID() as feature_id,
+            f.row_disp_name as feature,
+            CAST(f.numerical_val_1 AS float64) as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.flowsheet f
+        ON
+            labels.anon_id = f.anon_id
+        WHERE
+            f.recorded_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(f.recorded_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            f.row_disp_name in {self.flowsheet_descriptions}
+        AND
+            f.numerical_val_1 IS NOT NULL
+        ),
+
+        ranked as (
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id, feature,
+            PERCENT_RANK() OVER (PARTITION BY feature ORDER BY value) value
+        FROM 
+            flowsheet_vals
+        )
+
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id,
+            CASE WHEN value < 0.2 THEN CONCAT(feature, '_0')
+                WHEN value < 0.4 THEN CONCAT(feature, '_1')
+                WHEN value < 0.6 THEN CONCAT(feature, '_2')
+                WHEN value < 0.8 THEN CONCAT(feature, '_3')
+                ELSE CONCAT(feature, '_4')
+            END feature,
+            1 value
+        FROM
+            ranked
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+        df_lup = self.get_bin_thresholds()
+        return df_lup
+
+    def get_bin_thresholds(self):
+        query = f"""
+        WITH flowsheet_vals AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Vitals' as feature_type,
+            f.recorded_time_utc as feature_time,
+            GENERATE_UUID() as feature_id,
+            f.row_disp_name as feature,
+            CAST(f.numerical_val_1 AS float64) as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.flowsheet f
+        ON
+            labels.anon_id = f.anon_id
+        WHERE
+            f.recorded_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(f.recorded_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            f.row_disp_name in {self.flowsheet_descriptions}
+        AND
+            f.numerical_val_1 IS NOT NULL
+        )
+
+        SELECT DISTINCT
+            feature,
+            PERCENTILE_DISC(value, 0.2) OVER(PARTITION BY feature) min_bin_1,
+            PERCENTILE_DISC(value, 0.4) OVER(PARTITION BY feature) min_bin_2,
+            PERCENTILE_DISC(value, 0.6) OVER(PARTITION BY feature) min_bin_3,
+            PERCENTILE_DISC(value, 0.8) OVER(PARTITION BY feature) min_bin_4,
+        FROM 
+            flowsheet_vals
+        """
+        df = pd.read_gbq(query)
+        return df
+
+class LabResultBinsExtractor():
+    """
+    Defines logic to extract lab results orders, 
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 base_names, bins=5, look_back_days=14, 
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            temp_dataset: name of temp dataset with cohort table
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.num_bins = bins
+        self.look_back_days = look_back_days
+        self.project_id = project_id
+        self.dataset = dataset
+        self.base_name_string = "("
+        for i, base_name in enumerate(base_names):
+            if i == len(base_names) - 1:
+                self.base_name_string += f"'{base_name}')"
+            else:
+                self.base_name_string += f"'{base_name}', "
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        WITH labresults_values AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Lab Results' as feature_type,
+            lr.result_time_utc as feature_time,
+            CAST(lr.order_id_coded AS STRING) as feature_id,
+            lr.base_name as feature,
+            lr.ord_num_value as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.lab_result lr
+        ON
+            labels.anon_id = lr.anon_id
+        WHERE
+            lr.result_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(lr.result_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            lr.base_name in {self.base_name_string}
+        AND
+            lr.ord_num_value IS NOT NULL
+        ),
+
+        ranked as (
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id, feature,
+            PERCENT_RANK() OVER (PARTITION BY feature ORDER BY value) value
+        FROM 
+            labresults_values
+        )
+
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id,
+            CASE WHEN value < 0.2 THEN CONCAT(feature, '_0')
+                WHEN value < 0.4 THEN CONCAT(feature, '_1')
+                WHEN value < 0.6 THEN CONCAT(feature, '_2')
+                WHEN value < 0.8 THEN CONCAT(feature, '_3')
+                ELSE CONCAT(feature, '_4')
+            END feature,
+            1 value
+        FROM
+            ranked
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+        df_lup = self.get_bin_thresholds()
+        return df_lup
+
+    def get_bin_thresholds(self):
+        query = f"""
+        WITH labresults_values AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Lab Results' as feature_type,
+            lr.result_time_utc as feature_time,
+            CAST(lr.order_id_coded AS STRING) as feature_id,
+            lr.base_name as feature,
+            lr.ord_num_value as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.lab_result lr
+        ON
+            labels.anon_id = lr.anon_id
+        WHERE
+            lr.result_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(lr.result_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            lr.base_name in {self.base_name_string}
+        AND
+            lr.ord_num_value IS NOT NULL
+        )
+
+        SELECT DISTINCT
+            feature,
+            PERCENTILE_DISC(value, 0.2) OVER(PARTITION BY feature) min_bin_1,
+            PERCENTILE_DISC(value, 0.4) OVER(PARTITION BY feature) min_bin_2,
+            PERCENTILE_DISC(value, 0.6) OVER(PARTITION BY feature) min_bin_3,
+            PERCENTILE_DISC(value, 0.8) OVER(PARTITION BY feature) min_bin_4,
+        FROM 
+            labresults_values
+        """
+        df = pd.read_gbq(query)
+        return df
+
+class MedicationExtractor():
+    """
+    Defines logic to extract medication orders
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, 
+                 look_back_days=28, project_id='som-nero-phi-jonc101',
+                 dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.look_back_days = look_back_days
+        self.project_id = project_id
+        self.dataset = dataset
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Medications' as feature_type,
+            meds.order_inst_utc as feature_time,
+            CAST(meds.order_med_id_coded as STRING) as feature_id,
+            meds.med_description as feature,
+            1 as value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.order_med meds
+        ON
+            labels.anon_id = meds.anon_id
+        WHERE 
+            CAST(meds.order_inst_utc as TIMESTAMP) < labels.index_time
+        AND
+            TIMESTAMP_ADD(meds.order_inst_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class PatientProblemExtractor():
+    """
+    Defines logic to extract diagnoses on the patient's problem list
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.project_id = project_id
+        self.dataset = dataset
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT
+            labels.observation_id,
+            labels.index_time,
+            'Diagnoses' as feature_type,
+            CAST(dx.start_date_utc as TIMESTAMP) as feature_time,
+            GENERATE_UUID() as feature_id,
+            dx.icd10 as feature,
+            1 value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.diagnosis dx
+        ON
+            labels.anon_id = dx.anon_id
+        WHERE 
+            CAST(dx.start_date_utc as TIMESTAMP) < labels.index_time
+        AND
+            source = 2 --problem list only
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class SexExtractor():
+    """
+    Defines logic to extract sex as feature from dataset
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.project_id = project_id
+        self.dataset = dataset
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            CASE WHEN demo.GENDER is NULL then 'sex_missing'
+            ELSE CONCAT('sex_', demo.GENDER) END feature,
+            1 value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class RaceExtractor():
+    """
+    Defines logic to extract race as feature from dataset
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.project_id = project_id
+        self.dataset = dataset
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            CASE WHEN demo.CANONICAL_RACE is NULL then 'race_missing'
+            ELSE CONCAT('race_', demo.CANONICAL_RACE) END feature,
+            1 value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class AgeExtractor():
+    """
+    Defines logic to extract age as feature from dataset
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, bins=5,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.num_bins = bins
+        self.project_id = project_id
+        self.dataset = dataset
+     
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        WITH age_values as (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            'Age' as feature,
+            DATE_DIFF(
+                CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR)
+            as value
+        FROM
+            {self.cohort_table_id}
+            labels
+        INNER JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        ),
+        ranked as (
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id, feature,
+            PERCENT_RANK() OVER (PARTITION BY feature ORDER BY value) value
+        FROM
+            age_values
+        )
+
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id,
+            CASE WHEN value < 0.2 THEN CONCAT(feature, '_0')
+                WHEN value < 0.4 THEN CONCAT(feature, '_1')
+                WHEN value < 0.6 THEN CONCAT(feature, '_2')
+                WHEN value < 0.8 THEN CONCAT(feature, '_3')
+                ELSE CONCAT(feature, '_4')
+            END feature,
+            1 value
+        FROM
+            ranked
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+        df_lup = self.get_bin_thresholds()
+        return df_lup
+    
+    def get_bin_thresholds(self):
+        query = f"""
+        WITH age_values as (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            'Age' as feature,
+            DATE_DIFF(
+                CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR)
+            as value
+        FROM
+            {self.cohort_table_id}
+            labels
+        INNER JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        )
+
+        SELECT DISTINCT
+            feature,
+            PERCENTILE_DISC(value, 0.2) OVER(PARTITION BY feature) min_bin_1,
+            PERCENTILE_DISC(value, 0.4) OVER(PARTITION BY feature) min_bin_2,
+            PERCENTILE_DISC(value, 0.6) OVER(PARTITION BY feature) min_bin_3,
+            PERCENTILE_DISC(value, 0.8) OVER(PARTITION BY feature) min_bin_4,
+        FROM 
+            age_values
+        """
+        df = pd.read_gbq(query)
+        return df
+
+def table_exists(feature_table_id):
+    """
+    Check if table exists
+    """
+    client = bigquery.Client()
+    try:
+        client.get_table(f'{feature_table_id}')
+        return True
+    except NotFound:
+        return False
+
+def add_create_or_append_logic(query, feature_table_id):
+    """
+    Adds SQL logic to either append or create a new feature matrix from result
+    of user supplied SQL query. 
+    """
+    global REPLACE_TABLE
+    exists = table_exists(feature_table_id)
+    if exists and REPLACE_TABLE == False:
+        query = f"""
+        INSERT INTO
+            {feature_table_id}
+        {query}
+        """
+    else:
+        query = f"""
+        CREATE OR REPLACE TABLE {feature_table_id} AS (
+        {query}
+        )
+        """
+        REPLACE_TABLE = False
+    return query
diff --git a/medinfo/dataconversion/FeatureMatrix.py b/medinfo/dataconversion/FeatureMatrix.py
new file mode 100644
index 00000000..3e912e4f
--- /dev/null
+++ b/medinfo/dataconversion/FeatureMatrix.py
@@ -0,0 +1,393 @@
+#!/usr/bin/python
+"""
+Abstract class for building raw feature matrices.
+This class should take care of the business logic of interacting
+with FeatureMatrixFactory and FeatureMatrixIO, allowing subclasses
+to just worry about defining parameters for matrix construction.
+"""
+
+import os
+import datetime
+
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+
+import LocalEnv
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from .Util import log
+
+class FeatureMatrix:
+    def __init__(self, variable, num_data_points, params=None):
+        # Process arguments.
+        self._var = variable
+        self._num_rows = num_data_points
+        if params is None:
+            self._params = {}
+        else:
+            self._params = params
+
+        # Initialize FeatureMatrixFactory.
+        self._factory = FeatureMatrixFactory()
+
+        # Initialize DB connection.
+        self._connection = DBUtil.connection()
+
+    def _query_patient_episodes(self, query, pat_id_col=None, index_time_col=None):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Fetch and return results.
+        log.info('query: %s' % str(query))
+
+        if isinstance(query, str):
+            cursor.execute(query)
+        else:
+            log.info('query.params: %s' % str(query.params))
+            cursor.execute(str(query), query.params)
+
+        # Parse arguments.
+        if pat_id_col is None:
+            pat_id_col = 'pat_id'
+        if index_time_col is None:
+            index_time_col = 'index_time'
+
+        self._factory.setPatientEpisodeInput(cursor, pat_id_col, index_time_col)
+        num_episodes = self._factory.processPatientEpisodeInput()
+
+        return num_episodes
+
+    def _querystr_patient_episodes(self, querystr, pat_id_col=None, index_time_col=None):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Fetch and return results.
+        log.info('query: %s' % querystr)
+        cursor.execute(querystr)
+
+        # Parse arguments.
+        if pat_id_col is None:
+            pat_id_col = 'pat_id'
+        if index_time_col is None:
+            index_time_col = 'index_time'
+
+        self._factory.setPatientEpisodeInput(cursor, pat_id_col, index_time_col)
+        num_episodes = self._factory.processPatientEpisodeInput()
+
+        return num_episodes
+
+    def _add_features(self, index_time_col=None):
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            self._add_time_features(index_time_col)
+            self._add_demographic_features()
+            self._add_treatment_team_features()
+            self._add_comorbidity_features()
+            self._add_flowsheet_features()
+            self._add_lab_component_features()
+        else:
+        # elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            self._add_time_features(index_time_col)
+            self._add_demographic_features()
+            self._add_comorbidity_features()
+            self._add_lab_component_features()
+
+            if LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+                self._add_treatment_team_features()
+                self._add_flowsheet_features()
+
+    def _add_time_features(self, index_time_col=None):
+        log.info('Adding admit date features...')
+        # Add admission date.
+        ADMIT_DX_CATEGORY_ID = 2
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            self._factory.addClinicalItemFeaturesByCategory([ADMIT_DX_CATEGORY_ID], \
+                                                            dayBins=[], label='AdmitDxDate', features='pre')
+        else:
+        #elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            self._factory.addClinicalItemFeaturesByCategory_UMich([ADMIT_DX_CATEGORY_ID], \
+            dayBins=[], label='AdmitDxDate', features='pre', tableName='encounters')
+
+
+
+        # Add time cycle features.
+        log.info('Adding time cycle features...')
+        if index_time_col is None:
+            index_time_col = 'index_time'
+        self._factory.addTimeCycleFeatures(index_time_col, 'month')
+        self._factory.addTimeCycleFeatures(index_time_col, 'hour')
+
+    def _add_demographic_features(self):
+        log.info('Adding demographic features...')
+        # Add birth and death.
+        self._add_lifespan_features()
+        # Add sex features.
+        self._add_sex_features()
+        # Add race features.
+        self._add_race_features()
+
+    def _add_lifespan_features(self):
+        log.info('Adding lifespan features...')
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE': # TODO
+            self._factory.addClinicalItemFeatures(['Birth'], dayBins=[], features="pre")
+            self._factory.addClinicalItemFeatures(['Death'], dayBins=[], features="post")
+        else:
+        #elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            self._factory.addClinicalItemFeatures_UMich(['Birth'], dayBins=[], features="pre",
+                                                        clinicalItemType=None, clinicalItemTime='Birth',
+                                                        tableName='pt_info')
+
+    def _add_sex_features(self):
+        log.info('Adding sex features...')
+        SEX_FEATURES = ["Male", "Female"]
+        for feature in SEX_FEATURES:
+            if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':  # TODO
+                self._factory.addClinicalItemFeatures([feature], dayBins=[], features="pre")
+            else:
+            #elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+                self._factory.addClinicalItemFeatures_UMich([feature], dayBins=[], features="pre",
+                                                   clinicalItemType='GenderName', clinicalItemTime=None, tableName="demographics")
+    def _add_race_features(self):
+        log.info('Adding race features...')
+        for feature in self._factory.queryAllRaces():
+            if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':  # TODO
+                self._factory.addClinicalItemFeatures([feature], dayBins=[], features="pre")
+            else:
+            #elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+                self._factory.addClinicalItemFeatures_UMich([feature], dayBins=[], features="pre",
+                                                  clinicalItemType='RaceName', clinicalItemTime=None, tableName='demographics')
+
+    def _add_treatment_team_features(self):
+        log.info('Adding treatment team features...')
+        self._factory.addTreatmentTeamFeatures(features="pre")
+
+    def _add_comorbidity_features(self):
+        log.info('Adding comorbidity features...')
+        self._factory.addCharlsonComorbidityFeatures(features='pre')
+
+    def _add_flowsheet_features(self):
+        log.info('Adding flowsheet features...')
+        # Look at flowsheet results from the previous days
+        FLOW_PRE_TIME_DELTAS = [ datetime.timedelta(-3) ]
+        # Don't look into the future, otherwise cheating the prediction
+        FLOW_POST_TIME_DELTA = datetime.timedelta(0)
+        # Add flowsheet features for a variety of generally useful vitals.
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            BASIC_FLOWSHEET_FEATURES = [
+                "BP_High_Systolic", "BP_Low_Diastolic", "FiO2",
+                "Glasgow Coma Scale Score", "Pulse", "Resp", "Temp", "Urine"
+            ]
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+            BASIC_FLOWSHEET_FEATURES = [
+                'SBP', 'DBP', 'FiO2', 'Pulse', 'Resp', 'Temp', 'o2flow'
+            ]
+        for pre_time_delta in FLOW_PRE_TIME_DELTAS:
+            log.info('\t\tpreTimeDelta: %s' % pre_time_delta)
+            self._factory.addFlowsheetFeatures(BASIC_FLOWSHEET_FEATURES, \
+                pre_time_delta, FLOW_POST_TIME_DELTA)
+
+    def _add_lab_component_features(self):
+        # Look for lab data 90 days before each episode, but never after self.
+        # Look at lab results from the previous days
+        LAB_PRE_TIME_DELTAS = [ datetime.timedelta(-14) ]
+        # Don't look into the future, otherwise cheating the prediction
+        LAB_POST_TIME_DELTA = datetime.timedelta(0)
+
+        # Add result features for a variety of generally useful components.
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            BASIC_LAB_COMPONENTS = [
+                'WBC',  # White Blood Cell
+                'HCT',  # Hematocrit
+                'PLT',  # Platelet Count
+                'NA',  # Sodium, Whole Blood
+                'K',  # Potassium, Whole Blood
+                'CO2',  # CO2, Serum/Plasma
+                'BUN',  # Blood Urea Nitrogen
+                'CR',  # Creatinine
+                'TBIL',  # Total Bilirubin
+                'ALB',  # Albumin
+                'CA',  # Calcium
+                'LAC',  # Lactic Acid
+                'ESR',  # Erythrocyte Sedimentation Rate
+                'CRP',  # C-Reactive Protein
+                'TNI',  # Troponin I
+                'PHA',  # Arterial pH
+                'PO2A',  # Arterial pO2
+                'PCO2A',  # Arterial pCO2
+                'PHV',  # Venous pH
+                'PO2V',  # Venous pO2
+                'PCO2V'  # Venous pCO2
+            ]
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            BASIC_LAB_COMPONENTS = [
+                'WBC',  # White Blood Cell
+                'HCT',  # Hematocrit
+                'PLT',  # Platelet Count
+                'SOD',  # Sodium, Whole Blood
+                'POT',  # Potassium, Whole Blood
+                'CO2',  # CO2, Serum/Plasma
+                'UN',  # Blood Urea Nitrogen
+                'CREAT',  # Creatinine
+                'TBIL',  # Total Bilirubin
+                'ALB',  # Albumin
+                'CAL',  # Calcium
+                'LACTA',  # Lactic Acid; LACTA & LACTV are more frequent
+                "WEST",  # Erythrocyte Sedimentation Rate
+                'CRP',  # C-Reactive Protein
+                'TROP',  # Troponin I
+                'pHA',  # Arterial pH
+                'PO2AA',  # Arterial pO2
+                'PCOAA2',  # Arterial pCO2
+                'pHV',  # Venous pH
+                'pO2V',  # Venous pO2
+                'pCO2V',  # Venous pCO2
+            ]
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+            BASIC_LAB_COMPONENTS = [
+                'WBC',  # White Blood Cell
+                'HCT',  # Hematocrit
+                'PLT',  # Platelet Count
+                'NAWB',  # Sodium, Whole Blood
+                'K',  # Potassium, Whole Blood
+                'CO2',  # CO2, Serum/Plasma
+                'BUN',  # Blood Urea Nitrogen
+                'CREAT',  # Creatinine
+                'TBILI',  # Total Bilirubin
+                'ALB',  # Albumin
+                'CA',  # Calcium
+                'LACTWB',  # Lactic Acid; LACTA & LACTV are more frequent
+                "ESR",  # Erythrocyte Sedimentation Rate
+                'CRP',  # C-Reactive Protein
+                'TRPI',  # Troponin I
+                'PH37',  # Arterial pH
+                'PO2',  # Arterial pO2
+                'PCO2'  # Arterial pCO2
+                # 'pHV',  # Venous pH
+                # 'pO2V',  # Venous pO2
+                # 'pCO2V',  # Venous pCO2
+            ]
+        log.info('Adding lab component features...')
+        for component in BASIC_LAB_COMPONENTS:
+            log.info('\t%s' % component)
+            for preTimeDelta in LAB_PRE_TIME_DELTAS:
+                log.info('\t\t%s' % preTimeDelta)
+                self._factory.addLabResultFeatures([component], False, preTimeDelta, LAB_POST_TIME_DELTA)
+
+    def _build_matrix(self, header=None, dest_path=None):
+        log.info('Building matrix...')
+        self._factory.buildFeatureMatrix(header, dest_path)
+
+    def write_matrix(self, dest_path, header=None):
+        log.info('Writing matrix file...')
+        fm_io = FeatureMatrixIO()
+        # Get old matrix file.
+        source_path = self._factory.getMatrixFileName()
+        # Write to new matrix filee.
+        matrix_file = open(dest_path, 'w')
+        # for line in header:
+        #     matrix_file.write('# %s\n' % line)
+        for line in open(source_path, 'r'):
+            if line[0] != '#':
+                matrix_file.write(line)
+        # Delete old matrix file.
+        os.remove(source_path)
+
+    def _build_matrix_header(self, params=None):
+        # params['include_lab_suffix_summary'] = True/False
+        # params['include_clinical_item_suffix_summary'] = True/False
+        # params['data_overview'] = str description.
+        # params['field_summary'] = str description
+        header = list()
+
+        file_summary = self._build_file_summary(params['matrix_path'], \
+            params['matrix_module'])
+        header.extend(file_summary)
+        header.extend([''])
+
+        if params.get('data_overview'):
+            header.extend(params['data_overview'])
+            header.extend([''])
+        if params.get('field_summary'):
+            header.extend(params['field_summary'])
+            header.extend([''])
+        if params.get('include_clinical_item_suffix_summary'):
+            ci_suffix_summary = self._build_clinical_item_suffix_summary()
+            header.extend(ci_suffix_summary)
+            header.extend([''])
+        if params.get('include_lab_suffix_summary'):
+            lab_suffix_summary = self._build_flowsheet_and_lab_result_suffix_summary()
+            header.extend(lab_suffix_summary)
+            header.extend([''])
+
+        return header
+
+    def _build_file_summary(self, matrix_path, matrix_module):
+        summary = list()
+
+        # <file_name.tab>
+        matrix_name = matrix_path.split('/')[-1]
+        summary.append(matrix_name)
+        # Created: <timestamp>
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+        summary.append('Created: %s' % timestamp)
+        # Source: __name__
+        module_name = matrix_module.split('/')[-1]
+        summary.append('Source: %s' % module_name)
+        # Command: Pipeline()
+        class_name = module_name.split('.')[0]
+        args = [self._var, str(self._num_rows)]
+        for key, value in self._params:
+            args.append('%s=%s' % (key, value))
+        command = '%s(%s)' % (class_name, ', '.join(args))
+        summary.append('Command: %s' % command)
+
+        return summary
+
+    def _build_clinical_item_suffix_summary(self):
+        summary = list()
+        #   [clinical_item] fields may have the following suffixes:
+        summary.append('  [clinical_item] fields may have the following suffixes:')
+        #       ___.pre - how many times has this occurred before order_time?
+        summary.append('    ___.pre - how many times has this occurred before order_time?')
+        #       ___.pre.Xd - how many times has this occurred within X days before index_time?
+        summary.append('    ___.pre.Xd - how many times has this occurred within X days before index_time?')
+        #       ___.preTimeDays - how many days before order_time was last occurrence?
+        summary.append('    ___.preTimeDays - how many days before order_time was last occurrence?')
+
+        return summary
+
+    def _build_flowsheet_and_lab_result_suffix_summary(self):
+        summary = list()
+        #   [flowsheet] and [lab_result] fields may have the following suffixes:
+        summary.append('  [flowsheet] and [lab_result] fields may have the following suffixes:')
+        #       ___.X_Y.count - # of result values between X and Y days of index_time.
+        summary.append('    ___.X_Y.count - # of result values between X and Y days of index_time.')
+        #       ___.X_Y.countInRange - # of result values in normal range.
+        summary.append('    ___.X_Y.countInRange - # of result values in normal range.')
+        #       ___.X_Y.min - minimum result value.
+        summary.append('    ___.X_Y.min - minimum result value.')
+        #       ___.X_Y.max - maximum result value.
+        summary.append('    ___.X_Y.max - maximum result value.')
+        #       ___.X_Y.median - median result value.
+        summary.append('    ___.X_Y.median - median result value.')
+        #       ___.X_Y.std - standard deviation of result values.
+        summary.append('    ___.X_Y.std - standard deviation of result values.')
+        #       ___.X_Y.first - first result value.
+        summary.append('    ___.X_Y.first - first result value.')
+        #       ___.X_Y.last - last result value.
+        summary.append('    ___.X_Y.last - last result value.')
+        #       ___.X_Y.diff - difference between penultimate and proximate values.
+        summary.append('    ___.X_Y.diff - difference between penultimate and proximate values.')
+        #       ___.X_Y.slope - slope between penultimate and proximate values.
+        summary.append('    ___.X_Y.slope - slope between penultimate and proximate values.')
+        #       ___.X_Y.proximate - closest result value to order_time.
+        summary.append('    ___.X_Y.proximate - closest result value to order_time.')
+        #       ___.X_Y.firstTimeDays - time between first and order_time.
+        summary.append('    ___.X_Y.firstTimeDays - time between first and order_time.')
+        #       ___.X_Y.lastTimeDays - time between last and order_time.
+        summary.append('    ___.X_Y.lastTimeDays - time between last and order_time.')
+        #       ___.X_Y.proximateTimeDays - time between proximate and order_time.
+        summary.append('    ___.X_Y.proximateTimeDays - time between proximate and order_time.')
+
+        return summary
diff --git a/medinfo/dataconversion/FeatureMatrixFactory.py b/medinfo/dataconversion/FeatureMatrixFactory.py
index 2e8125be..dd76922d 100644
--- a/medinfo/dataconversion/FeatureMatrixFactory.py
+++ b/medinfo/dataconversion/FeatureMatrixFactory.py
@@ -14,25 +14,37 @@
 
 import csv
 import datetime
+from io import IOBase
 import numpy as np
 import os
 import time
 
-from Const import SENTINEL_RESULT_VALUE
+from .Const import SENTINEL_RESULT_VALUE
+from medinfo.common.Env import LAB_TYPE
 from medinfo.common.Const import NULL_STRING
 from medinfo.cpoe.Const import SECONDS_PER_DAY, DELTA_NAME_BY_DAYS
 from medinfo.db import DBUtil
 from medinfo.db.Model import columnFromModelList, SQLQuery, modelListFromTable
 from medinfo.db.ResultsFormatter import TabDictReader, TextResultsFormatter
-from psycopg2.extensions import cursor
-from Util import log
+
+# For NonStanford data, we use sqlite database to allow creating database onsite
+import LocalEnv
+if LocalEnv.DATABASE_CONNECTOR_NAME == 'psycopg2':
+    from psycopg2.extensions import cursor
+elif LocalEnv.DATABASE_CONNECTOR_NAME == 'sqlite3':
+    from sqlite3 import Cursor as cursor
+
+# from Util import log
+from medinfo.common.Util import log
+from . import Util
+
 
 class FeatureMatrixFactory:
     FEATURE_MATRIX_COLUMN_NAMES = [
         "patient_id"
     ]
 
-    def __init__(self, cacheDBResults = True):
+    def __init__(self, cacheDBResults = True, PID=None):
         self.dbCache = None
         self.patientListInput = None
         self.patientIdColumn = None
@@ -41,12 +53,26 @@ def __init__(self, cacheDBResults = True):
         self._patientItemTimeColumn = None
         self.timestampColumn = None
 
+        self._isLabPanel = True if LAB_TYPE == 'panel' else False   # 'component'
+        self._labTypeCol = lambda isLabPanel: 'proc_code' if isLabPanel else 'base_name'
+
         self.patientsProcessed = None
 
-        self._patientListTempFileName = "fmf.patient_list.tsv"
-        self._patientEpisodeTempFileName = "fmf.patient_episodes.tsv"
-        self._patientItemTempFileNameFormat = "fmf.patient_%s.tsv"
-        self._patientResultTempFileNameFormat = "fmf.patient_%s_%s_%s.tsv"
+        if not PID: # Allow checking existing tmp files
+            self.PID = str(os.getpid())
+        else:
+            self.PID = str(PID)
+
+        # When debugging, do not create so many Tempfiles in the working folder.
+        self._folderTempFiles = "fmfTempFolder"
+        if not os.path.exists(self._folderTempFiles):
+            os.mkdir(self._folderTempFiles)
+
+        self._patientListTempFileName = self._folderTempFiles + '/' + "fmf.patient_list_" + self.PID + ".tsv"
+        self._patientEpisodeTempFileName = self._folderTempFiles + '/' + "fmf.patient_episodes_" + self.PID + ".tsv"
+        self._patientItemTempFileNameFormat = self._folderTempFiles + '/' + "fmf.patient_%s_" + self.PID + ".tsv"
+        self._patientTimeCycleTempFileNameFormat = self._folderTempFiles + '/' + "fmf.patient_%s_%s_" + self.PID + ".tsv"
+        self._patientResultTempFileNameFormat = self._folderTempFiles + '/' + "fmf.patient_%s_%s_%s_" + self.PID + ".tsv"
         self._matrixFileName = None
 
         # Look at lab results from the previous days
@@ -62,8 +88,7 @@ def __init__(self, cacheDBResults = True):
         if cacheDBResults:
             self.dbCache = dict()
 
-    def setPatientListInput(self, patientListInput, \
-        patientIdColumn = "patient_id"):
+    def setPatientListInput(self, patientListInput, patientIdColumn = "patient_id"):
         """
         Define the input patient list for the feature matrix.
         patientListInput: TSV file descriptor or DB cursor
@@ -71,7 +96,7 @@ def setPatientListInput(self, patientListInput, \
         """
         # Verify patientListInput is TSV file or DB cursor.
         if not isinstance(patientListInput, cursor) and \
-            not isinstance(patientListInput, file):
+                not isinstance(patientListInput, IOBase):
             raise TypeError("patientListInput must be DB cursor or TSV file.")
 
         self.patientListInput = patientListInput
@@ -87,7 +112,7 @@ def processPatientListInput(self):
 
         if isinstance(self.patientListInput, cursor):
             return self._processPatientListDbCursor()
-        elif isinstance(self.patientListInput, file):
+        elif isinstance(self.patientListInput, IOBase):
             return self._processPatientListTsvFile()
 
     def _processPatientListDbCursor(self):
@@ -98,7 +123,7 @@ def _processPatientListDbCursor(self):
         self._pipeDbCursorToTsvFile(self.patientListInput, patientListTempFile)
         patientListTempFile.close()
 
-    def _pipeDbCursorToTsvFile(self, dbCursor, tsvFile):
+    def _pipeDbCursorToTsvFile(self, dbCursor, tsvFile, include_columns=True):
         """
         Pipe any arbitrary DB cursor to a TSV file.
         """
@@ -106,19 +131,22 @@ def _pipeDbCursorToTsvFile(self, dbCursor, tsvFile):
         columns = dbCursor.description
         numColumns = len(columns)
 
-        # Write TSV header.
-        for i in range(numColumns - 1):
-            # 0th index is column name.
-            tsvFile.write("%s\t" % columns[i][0])
-        tsvFile.write("%s\n" % columns[numColumns - 1][0])
+        if include_columns:
+            # Write TSV header.
+            for i in range(numColumns - 1):
+                # 0th index is column name.
+                tsvFile.write("%s\t" % columns[i][0])
+            tsvFile.write("%s\n" % columns[numColumns - 1][0])
 
         # By default, cursor iterates through both header and data rows.
+        self._numRows = 0
         row = dbCursor.fetchone()
         while row is not None:
             for i in range(numColumns - 1):
                 tsvFile.write("%s\t" % row[i])
             tsvFile.write("%s\n" % row[numColumns - 1])
             row = dbCursor.fetchone()
+            self._numRows += 1
 
     def _processPatientListTsvFile(self):
         """
@@ -152,7 +180,7 @@ def setPatientEpisodeInput(self, patientEpisodeInput, \
         """
         # Verify patientEpisodeInput is TSV file or DB cursor.
         if not isinstance(patientEpisodeInput, cursor) and \
-            not isinstance(patientEpisodeInput, file):
+            not isinstance(patientEpisodeInput, IOBase):
             raise TypeError("patientEpisodeInput must be DB cursor or TSV file.")
 
         self.patientEpisodeInput = patientEpisodeInput
@@ -168,11 +196,9 @@ def processPatientEpisodeInput(self):
 
         if isinstance(self.patientEpisodeInput, cursor):
             return self._processPatientEpisodeDbCursor()
-        elif isinstance(self.patientEpisodeInput, file):
+        elif isinstance(self.patientEpisodeInput, IOBase):
             return self._processPatientEpisodeTsvFile()
 
-
-
     def _processPatientEpisodeDbCursor(self):
         """
         Convert self.patientEpisodeInput from DB cursor to TSV file.
@@ -183,6 +209,13 @@ def _processPatientEpisodeDbCursor(self):
         patientEpisodeTempFile.close()
         self.patientsProcessed = True
 
+        if LocalEnv.DATABASE_CONNECTOR_NAME == 'psycopg2':
+            return self.patientEpisodeInput.rowcount
+        elif LocalEnv.DATABASE_CONNECTOR_NAME == 'sqlite3':
+        # In sqlite3, rowcount is somehow "always" -1; See for details:
+        # https://docs.python.org/3.0/library/sqlite3.html#sqlite3.Cursor.rowcount
+            return self._numRows #self.patientEpisodeInput.rowcount
+
     def _processPatientEpisodeTsvFile(self):
         pass
 
@@ -192,6 +225,47 @@ def getPatientEpisodeIterator(self):
         """
         return TabDictReader(open(self._patientEpisodeTempFileName, "r"))
 
+    '''
+    New version, adapt to the new split_by_patient pipeline
+    '''
+    def obtain_baseline_results(self, raw_matrix_path, random_state, isHoldOut=False):
+        from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+        fm_io = FeatureMatrixIO()
+
+        processed_matrix_path = raw_matrix_path.replace('-raw','-processed')
+
+        '''
+        get prevalence from the train set
+        '''
+        processed_matrix_train = fm_io.read_file_to_data_frame(processed_matrix_path.replace('-matrix', '-train-matrix'))
+        if self._isLabPanel:
+            y_label = 'all_components_normal'
+        else:
+            y_label = 'component_normal'
+
+        prevalence = float(processed_matrix_train[y_label].values.sum())/float(processed_matrix_train.shape[0])
+
+        processed_matrix_test = fm_io.read_file_to_data_frame(processed_matrix_path.replace('-matrix', '-test-matrix'))
+        pats_test = set(processed_matrix_test['pat_id'].values.tolist())
+        raw_matrix = fm_io.read_file_to_data_frame(raw_matrix_path)
+        raw_matrix_test = raw_matrix[raw_matrix['pat_id'].isin(pats_test)]
+        raw_matrix_test = raw_matrix_test.sort_values(['pat_id', 'order_time']).reset_index()
+        raw_matrix_test['predict_proba'] = raw_matrix_test[y_label].apply(lambda x: prevalence)
+
+        for i in range(1, raw_matrix_test.shape[0]):
+            if raw_matrix_test.ix[i - 1, 'pat_id'] == raw_matrix_test.ix[i, 'pat_id']:
+                raw_matrix_test.ix[i, 'predict_proba'] = raw_matrix_test.ix[i - 1, y_label]
+
+        baseline_comparisons = raw_matrix_test[['predict_proba', y_label]]
+        baseline_comparisons = baseline_comparisons.rename(columns={y_label:'actual',
+                                                            'predict_proba':'predict'})
+
+        baseline_folder = '/'.join(raw_matrix_path.split('/')[:-1])
+        baseline_filepath = os.path.join(baseline_folder, 'baseline_comparisons.csv')
+        # os.rename(baseline_filepath, baseline_filepath.replace('baseline_comparisons', 'baseline_comparisons_prev')) # existing ones
+        baseline_comparisons.to_csv(baseline_filepath)
+
+
     def _getPatientEpisodeByIndexTimeById(self):
         """
         Return dictionary containing patientId : episodeTime : {} map.
@@ -210,26 +284,105 @@ def _getPatientEpisodeByIndexTimeById(self):
 
         return patientEpisodeByIndexTimeById
 
-    def addClinicalItemFeatures(self, clinicalItemNames, dayBins=None):
+    def addClinicalItemFeatures(self, clinicalItemNames, dayBins=None, column=None, operator=None, label=None, features=None, isLabPanel=True):
+        """
+        Query patient_item for the clinical item orders and results for each
+        patient, and aggregate by episode timestamp.
+        column: determines column in clinical_item to match clinicalItemNames.
+        operator: determines how to match clinicalItemNames against column.
+        label: sets the column prefix in the final feature matrix.
+        features: determines whether to include "pre", "post" or "all".
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before clinical item.")
+
+        if isLabPanel:
+            clinicalItemEvents = self._queryClinicalItemsByName(clinicalItemNames, column=column, operator=operator)
+        else:
+            clinicalItemEvents = self._queryComponentItemsByName(clinicalItemNames)
+        itemTimesByPatientId = self._getItemTimesByPatientId(clinicalItemEvents)
+
+        # Read clinical item features to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        self._processClinicalItemEvents(patientEpisodes, itemTimesByPatientId, \
+                                        clinicalItemNames, dayBins, label=label, features=features)
+
+    # Updated this core function for Component and Non-Stanford data. Responsible for creating features of:
+    # lab_panel, component (for counting "order times"), birth/death, sex, race, comorbidity
+    def addClinicalItemFeatures_NonStanford(self, clinicalItemNames, dayBins=None, label=None, features=None
+                                , clinicalItemType=None, clinicalItemTime=None, tableName=None):
         """
         Query patient_item for the clinical item orders and results for each
         patient, and aggregate by episode timestamp.
+        column: determines column in clinical_item to match clinicalItemNames.
+        operator: determines how to match clinicalItemNames against column.
+        label: sets the column prefix in the final feature matrix.
+        features: determines whether to include "pre", "post" or "all".
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before clinical item.")
+
+        # For adapting to NonStanford data, instead of creating intermediate tables clinical_items
+        # and patient_items, we directly query "raw" tables from the NonStanford.db
+        clinicalItemEvents = self._queryNonStanfordItemsByName(clinicalItemNames=clinicalItemNames, clinicalItemType=clinicalItemType,
+                                                            tableName=tableName, clinicalItemTime=clinicalItemTime)
+        itemTimesByPatientId = self._getItemTimesByPatientId(clinicalItemEvents)
 
+        # Read clinical item features to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        self._processClinicalItemEvents(patientEpisodes, itemTimesByPatientId, \
+                                        clinicalItemNames, dayBins, label=label, features=features)
 
+    def addClinicalItemFeaturesByCategory(self, categoryIds, label=None, dayBins=None, features=None):
+        """
+        Query patient_item for the clinical item orders and results for each
+        patient (based on clinical item category ID instead of item name), and
+        aggregate by episode timestamp.
+        features: determines whether to include "pre", "post" or "all".
         """
         # Verify patient list and/or patient episode has been processed.
         if not self.patientsProcessed:
             raise ValueError("Must process patients before clinical item.")
 
-        clinicalItemEvents = self._queryClinicalItemsByName(clinicalItemNames)
+        if label is None:
+            label = "-".join(categoryIds)
+
+        clinicalItemEvents = self._queryClinicalItemsByCategory(categoryIds)
         itemTimesByPatientId = self._getItemTimesByPatientId(clinicalItemEvents)
 
         # Read clinical item features to temp file.
         patientEpisodes = self.getPatientEpisodeIterator()
         self._processClinicalItemEvents(patientEpisodes, itemTimesByPatientId, \
-                                        clinicalItemNames, dayBins)
+                                        categoryIds, dayBins, label=label, features=features)
 
-    def _queryClinicalItemsByName(self, clinicalItemNames):
+    # This function is only used for handling the feature of AdmitDxDate
+    def addClinicalItemFeaturesByCategory_NonStanford(self, categoryIds, label=None, dayBins=None, features=None,
+                                          tableName=None):
+        """
+        Query patient_item for the clinical item orders and results for each
+        patient (based on clinical item category ID instead of item name), and
+        aggregate by episode timestamp.
+        features: determines whether to include "pre", "post" or "all".
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before clinical item.")
+
+        if label is None:
+            label = "-".join(categoryIds)
+
+        # For NonStanford data, directly query label='AdmitDxDate' from the raw table
+        clinicalItemEvents = self._queryNonStanfordItemsByCategory(label,tableName) #
+        itemTimesByPatientId = self._getItemTimesByPatientId(clinicalItemEvents)
+
+        # Read clinical item features to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        self._processClinicalItemEvents(patientEpisodes, itemTimesByPatientId, \
+                                        categoryIds, dayBins, label=label, features=features)
+
+    def _queryClinicalItemsByName(self, clinicalItemNames, column=None, operator=None):
         """
         Query clinicalItemInput for all item times for all patients.
 
@@ -248,8 +401,10 @@ def _queryClinicalItemsByName(self, clinicalItemNames):
         if self.dbCache is not None and cacheKey in self.dbCache:
             clinicalItemIds = self.dbCache[cacheKey]
         else:
-            column = "name"
-            operator = "LIKE"
+            if column is None:
+                column = "name"
+            if operator is None:
+                operator = "LIKE"
 
             query = SQLQuery()
             query.addSelect("clinical_item_id")
@@ -257,7 +412,15 @@ def _queryClinicalItemsByName(self, clinicalItemNames):
 
             nameClauses = list()
             for itemName in clinicalItemNames:
-                nameClauses.append("%s %s %%s" % (column, operator))
+                if LocalEnv.DATABASE_CONNECTOR_NAME == 'psycopg2':
+                    nameClauses.append("%s %s %%s" % (column, operator))
+
+                elif LocalEnv.DATABASE_CONNECTOR_NAME == 'sqlite3':
+                    # For postgres, placeholder is %s
+                    # For sqlite, place holder is ?
+                    # TODO: %s or %%s for postgres
+                    # nameClauses.append("%s %s %%s" % (column, operator))
+                    nameClauses.append("%s %s " % (column, operator) + DBUtil.SQL_PLACEHOLDER) #
                 query.params.append(itemName)
             query.addWhere(str.join(" or ", nameClauses))
 
@@ -269,6 +432,154 @@ def _queryClinicalItemsByName(self, clinicalItemNames):
 
         return self.queryClinicalItems(clinicalItemIds)
 
+    def _queryMichiganItemsByName(self, clinicalItemNames, clinicalItemType, tableName, clinicalItemTime):
+        # """
+        # Query ComponentItemInput for all item times for all patients.
+        #
+        # Done by directly querying the stride_order_XXX tables,
+        # without using pre-assembled tables like clinical_item.
+        # Might do this in the future to boost efficiency.
+        # """
+        patientIds = set()
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientIds.add(episode[self.patientEpisodeIdColumn])
+
+        # clinicalItemNames can be specific examples like CBCD, anything, male,
+        # clinicalItemCategory can be column names like proc_code, birth, sex,
+        # clinicalItemTime can be column names like order_time, birth, birth ...
+
+        query_str = "SELECT CAST(pat_id AS BIGINT) AS pat_id "
+        if clinicalItemTime:
+            query_str += ", %s " % clinicalItemTime
+
+        query_str += "FROM %s " % tableName
+
+        if clinicalItemType:
+            query_str += "WHERE %s IN (" % (clinicalItemType)
+            for clinicalItemName in clinicalItemNames:
+                query_str += '"%s",' % clinicalItemName
+            query_str = query_str[:-1] + ") AND "
+        else:
+            query_str += "WHERE "
+
+        query_str += "pat_id IN "
+        pat_list_str = "("
+        for pat_id in patientIds:
+            pat_list_str += str(pat_id) + ","
+        pat_list_str = pat_list_str[:-1] + ") "
+        query_str += pat_list_str
+        query_str += "GROUP BY pat_id "
+        if clinicalItemTime:
+            query_str += ", %s " % clinicalItemTime
+
+        query_str += "ORDER BY pat_id "
+        if clinicalItemTime:
+            query_str += ", %s " % clinicalItemTime
+
+        _cursor = DBUtil.connection().cursor()
+        _cursor.execute(query_str)
+        results = _cursor.fetchall()
+
+        componentItemEvents = [list(row) for row in results]
+        if not clinicalItemTime:
+            componentItemEvents = [x + [datetime.datetime(1900, 1, 1)] for x in componentItemEvents]
+
+        return componentItemEvents
+
+
+    def _queryComponentItemsByName(self, clinicalItemNames): # sx
+        # """
+        # Query ComponentItemInput for all item times for all patients.
+        #
+        # Done by directly querying the stride_order_XXX tables,
+        # without using pre-assembled tables like clinical_item.
+        # Might do this in the future to boost efficiency.
+        # """
+
+        query = SQLQuery()
+        query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
+        query.addSelect('order_time')
+        query.addFrom('stride_order_proc AS sop')
+        query.addFrom('stride_order_results AS sor')
+        query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhereIn("base_name", clinicalItemNames)
+        query.addGroupBy('pat_id')
+        query.addGroupBy('order_time')
+        query.addOrderBy('pat_id')
+        query.addOrderBy('order_time')
+
+        results = DBUtil.execute(query)
+        componentItemEvents = [row for row in results]
+        return componentItemEvents
+
+    def _queryMichiganItemsByCategory(self, label, tableName): #
+        """
+        Query for all patient items that match with the given clinical item
+        category ID.
+        """
+        # Identify which columns to pull from patient_item table.
+
+        # return sth like
+        # clinicalItemEvents=[[-3384542270496665494, u'2009-07-07 13:00:00'], [1262980084096039344, u'2003-01-22 12:29:00'], ...]
+        self._patientItemIdColumn = "pat_id"
+        self._patientItemTimeColumn = label
+
+        patientIds = set()
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientIds.add(episode[self.patientEpisodeIdColumn])
+
+        query_str = "SELECT %s, %s " % (self._patientItemIdColumn, self._patientItemTimeColumn)
+
+        query_str += " FROM %s " % tableName
+
+        query_str += "WHERE pat_id IN "
+        pat_list_str = "("
+        for pat_id in patientIds:
+            pat_list_str += str(pat_id) + ","
+        pat_list_str = pat_list_str[:-1] + ") "
+        query_str += pat_list_str
+
+        query_str += "ORDER BY pat_id, %s " % label
+
+        results = DBUtil.connection().cursor().execute(query_str).fetchall()
+
+        clinicalItemEvents = [list(row) for row in results]
+        return clinicalItemEvents
+
+
+    def _queryClinicalItemsByCategory(self, categoryIds):
+        """
+        Query for all patient items that match with the given clinical item
+        category ID.
+        """
+        # Identify which columns to pull from patient_item table.
+        self._patientItemIdColumn = "patient_id"
+        self._patientItemTimeColumn = "item_date"
+
+        clinicalItemIds = None
+
+        # If possible, return cached results.
+        cacheKey = str(categoryIds)
+        if self.dbCache is not None and cacheKey in self.dbCache:
+            clinicalItemIds = self.dbCache[cacheKey]
+        else:
+            column = "clinical_item_category_id"
+
+            query = SQLQuery()
+            query.addSelect("clinical_item_id")
+            query.addFrom("clinical_item")
+            query.addWhereIn(column, categoryIds)
+
+            results = DBUtil.execute(query)
+            clinicalItemIds = [row[0] for row in results]
+
+        if len(clinicalItemIds) == 0:
+            return list()
+
+        return self.queryClinicalItems(clinicalItemIds)
+
     def queryClinicalItems(self, clinicalItemIds):
         """
         Query for all patient items that match with the given clinical item IDs.
@@ -298,22 +609,29 @@ def queryClinicalItems(self, clinicalItemIds):
         clinicalItemEvents = [row for row in results]
         return clinicalItemEvents
 
-    def _processClinicalItemEvents(self, patientEpisodes, itemTimesByPatientId, clinicalItemNames, dayBins):
+    def _processClinicalItemEvents(self, patientEpisodes, itemTimesByPatientId, clinicalItemNames, dayBins, label=None, features=None):
         """
         Convert temp file containing all (patient_item, item_date) pairs
         for a given set of clinical_item_ids into temp file containing
         patient_id, order_time, clinical_item.pre, clinical_item.post, etc.
+        features: determines whether to include "pre", "post" or "all".
         """
-        if len(clinicalItemNames) > 1:
-            itemLabel = "-".join([itemName for itemName in clinicalItemNames])
+        if label:
+            itemLabel = label
         else:
-            itemLabel = clinicalItemNames[0]
+            if len(clinicalItemNames) > 1:
+                itemLabel = "-".join([itemName for itemName in clinicalItemNames])
+            else:
+                itemLabel = clinicalItemNames[0]
         tempFileName = self._patientItemTempFileNameFormat % itemLabel
         tempFile = open(tempFileName, "w")
 
+        if features is None:
+            features = "all"
+
         # Determine time buckets for clinical item times.
         if dayBins is None:
-            dayBins = DELTA_NAME_BY_DAYS.keys()
+            dayBins = list(DELTA_NAME_BY_DAYS.keys())
             dayBins.sort()
 
         # Find items most proximate before and after the index item per patient
@@ -325,14 +643,21 @@ def _processClinicalItemEvents(self, patientEpisodes, itemTimesByPatientId, clin
         postLabel = "%s.post" % itemLabel
 
         # Write header fields to tempFile.
-        tempFile.write("patient_id\tepisode_time\t")
-        tempFile.write("%s\t" % preTimeDaysLabel)
-        tempFile.write("%s\t" % preLabel)
-        tempFile.write("\t".join("%s.%dd" % (preLabel, dayBin) for dayBin in dayBins))
-        tempFile.write("\t")
-        tempFile.write("%s\t" % postTimeDaysLabel)
-        tempFile.write("%s\t" % postLabel)
-        tempFile.write("\t".join("%s.%dd" % (postLabel, dayBin) for dayBin in dayBins))
+        tempFile.write("patient_id\tepisode_time")
+        # Include counts for events before episode_time.
+        if features != "post":
+            tempFile.write("\t%s" % preTimeDaysLabel)
+            tempFile.write("\t%s" % preLabel)
+            if len(dayBins) > 0:
+                tempFile.write("\t")
+                tempFile.write("\t".join("%s.%dd" % (preLabel, dayBin) for dayBin in dayBins))
+        # Include counts for events after episode_time.
+        if features != "pre":
+            tempFile.write("\t%s" % postTimeDaysLabel)
+            tempFile.write("\t%s" % postLabel)
+            if len(dayBins) > 0:
+                tempFile.write("\t")
+                tempFile.write("\t".join("%s.%dd" % (postLabel, dayBin) for dayBin in dayBins))
         tempFile.write("\n")
 
         # Write patient episode data to tempFile.
@@ -359,6 +684,11 @@ def _processClinicalItemEvents(self, patientEpisodes, itemTimesByPatientId, clin
                 itemTimes = itemTimesByPatientId[patientId]
                 if itemTimes is not None:
                     for itemTime in itemTimes:
+                        # Need this extra check because if a given event
+                        # has not occurred yet, but will occur, itemTime will
+                        # be none while itemTimes is not None.
+                        if (itemTime is None) or (not isinstance(itemTime, datetime.datetime)):
+                            continue
                         timeDiffSeconds = (itemTime - episodeTime).total_seconds()
                         timeDiffDays = timeDiffSeconds / SECONDS_PER_DAY
                         # If event occurred before index time...
@@ -385,39 +715,53 @@ def _processClinicalItemEvents(self, patientEpisodes, itemTimesByPatientId, clin
                                     episodeData["%s.%dd" % (postLabel, dayBin)] += 1
 
             # Write data to tempFile.
-            tempFile.write("%s\t%s\t" % (patientId, episodeTime))
-            tempFile.write("%s\t" % episodeData[preTimeDaysLabel])
-            tempFile.write("%s\t" % episodeData[preLabel])
-            tempFile.write("\t".join([str(episodeData["%s.%dd" % (preLabel, dayBin)]) for dayBin in dayBins]))
-            tempFile.write("\t")
-            tempFile.write("%s\t" % episodeData[postTimeDaysLabel])
-            tempFile.write("%s\t" % episodeData[postLabel])
-            tempFile.write("\t".join([str(episodeData["%s.%dd" % (postLabel, dayBin)]) for dayBin in dayBins]))
+            tempFile.write("%s\t%s" % (patientId, episodeTime))
+            # Include counts for events before episode_time.
+            if features != "post":
+                tempFile.write("\t%s" % episodeData[preTimeDaysLabel])
+                tempFile.write("\t%s" % episodeData[preLabel])
+                if len(dayBins) > 0:
+                    tempFile.write("\t")
+                    tempFile.write("\t".join([str(episodeData["%s.%dd" % (preLabel, dayBin)]) for dayBin in dayBins]))
+            # Include counts for events after episode_time.
+            if features != "pre":
+                tempFile.write("\t%s" % episodeData[postTimeDaysLabel])
+                tempFile.write("\t%s" % episodeData[postLabel])
+                if len(dayBins) > 0:
+                    tempFile.write("\t")
+                    tempFile.write("\t".join([str(episodeData["%s.%dd" % (postLabel, dayBin)]) for dayBin in dayBins]))
             tempFile.write("\n")
 
         tempFile.close()
         # Add tempFileName to list of feature temp files.
         self._featureTempFileNames.append(tempFileName)
 
-    def addLabResultFeatures(self, labBaseNames, preTimeDelta = None, postTimeDelta = None):
+    def addLabResultFeatures(self, labNames, labIsPanel = True, preTimeDelta = None, postTimeDelta = None):
         """
         Query stride_order_proc and stride_order_results for the lab orders and
         results for each patient, and aggregate by episode timestamp.
+        Set labIsPanel = False to signify that the labNames are components,
+        rather than panels.
         """
         # Verify patient list and/or patient episode has been processed.
         if not self.patientsProcessed:
             raise ValueError("Must process patients before lab result.")
 
         # Open temp file.
-        if len(labBaseNames) > 1:
-            resultLabel = "-".join([baseName for baseName in labBaseNames])
+        # For multi-component labels, the first element becomes None
+        labNames = [x for x in labNames if x is not None]
+
+        if len(labNames) > 1:
+            resultLabel = "-".join([labName for labName in labNames])[:64]
         else:
-            resultLabel = labBaseNames[0]
-        tempFileName = self._patientResultTempFileNameFormat % (resultLabel, str(preTimeDelta), str(postTimeDelta))
+            resultLabel = labNames[0]
+
+        # Hack to account for fact that Windows filenames can't include ':'.
+        tempFileName = self._patientResultTempFileNameFormat % (resultLabel, str(preTimeDelta.days), str(postTimeDelta.days))
         tempFile = open(tempFileName, "w")
 
         # Query lab results for the individuals of interest.
-        labResults = self._queryLabResultsByName(labBaseNames)
+        labResults = self._queryLabResultsByName(labNames, labIsPanel)
         resultsByNameByPatientId = self._parseResultsData(labResults, "pat_id",
             "base_name", "ord_num_value", "result_time")
 
@@ -433,7 +777,7 @@ def addLabResultFeatures(self, labBaseNames, preTimeDelta = None, postTimeDelta
         patientEpisodeByIndexTimeById = self._getPatientEpisodeByIndexTimeById()
         self._processResultEvents(patientEpisodeByIndexTimeById,
                                     resultsByNameByPatientId,
-                                    labBaseNames,
+                                    labNames,
                                     "ord_num_value",
                                     "result_time",
                                     preTimeDelta,
@@ -441,7 +785,7 @@ def addLabResultFeatures(self, labBaseNames, preTimeDelta = None, postTimeDelta
 
         # Write column headers to temp file.
         tempFile.write("%s\t%s\t" % (self.patientEpisodeIdColumn, self.patientEpisodeTimeColumn))
-        columnNames = self.colsFromBaseNames(labBaseNames, preTimeDays, postTimeDays)
+        columnNames = self.colsFromBaseNames(labNames, preTimeDays, postTimeDays)
         tempFile.write("\t".join(columnNames))
         tempFile.write("\n")
 
@@ -454,7 +798,7 @@ def addLabResultFeatures(self, labBaseNames, preTimeDelta = None, postTimeDelta
             tempFile.write("%s\t%s\t" % (patientId, indexTime))
             # Need to generate columnNames again because colsFromBaseNames
             # returns generator, which can only be read once.
-            columnNames = self.colsFromBaseNames(labBaseNames, preTimeDays, postTimeDays)
+            columnNames = self.colsFromBaseNames(labNames, preTimeDays, postTimeDays)
             tempFile.write("\t".join(str(episodeLabData[columnName]) for columnName in columnNames))
             tempFile.write("\n")
 
@@ -477,14 +821,15 @@ def addFlowsheetFeatures(self, flowsheetBaseNames, preTimeDelta = None, postTime
             resultLabel = "-".join([baseName for baseName in flowsheetBaseNames])
         else:
             resultLabel = flowsheetBaseNames[0]
-        tempFileName = self._patientItemTempFileNameFormat % (resultLabel)
+        # Hack to account for fact that Windows filenames can't include ':'.
+        tempFileName = self._patientResultTempFileNameFormat % (resultLabel, str(preTimeDelta.days), str(postTimeDelta.days))
         tempFile = open(tempFileName, "w")
 
         # Query flowsheet results.
         flowsheetResults = self._queryFlowsheetResultsByName(flowsheetBaseNames)
         resultsByNameByPatientId = self._parseResultsData(flowsheetResults, \
             "pat_id", "flowsheet_name", "flowsheet_value", \
-            "shifted_record_dt_tm")
+            "shifted_dt_tm")
 
         # Define how far in advance of each episode to look at lab results.
         preTimeDays = None
@@ -500,7 +845,7 @@ def addFlowsheetFeatures(self, flowsheetBaseNames, preTimeDelta = None, postTime
                                     resultsByNameByPatientId,
                                     flowsheetBaseNames,
                                     "flowsheet_value",
-                                    "shifted_record_dt_tm",
+                                    "shifted_dt_tm",
                                     preTimeDelta,
                                     postTimeDelta)
 
@@ -544,19 +889,55 @@ def _queryFlowsheetResultsByName(self, flowsheetBaseNames):
             patientIds.add(episode[self.patientEpisodeIdColumn])
 
         # Build SQL query.
-        colNames = ["pat_anon_id AS pat_id", "flo_meas_id", "flowsheet_name", \
-            "flowsheet_value", "shifted_record_dt_tm"]
-        query = SQLQuery()
-        for col in colNames:
-            query.addSelect(col)
-        query.addFrom("stride_flowsheet")
-        query.addWhereIn("flowsheet_name", flowsheetBaseNames)
-        query.addWhereIn("pat_anon_id", patientIds)
-        query.addOrderBy("pat_anon_id")
-        query.addOrderBy("shifted_record_dt_tm")
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            pat_col = "pat_anon_id"
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+            pat_col = "pat_id"
+
+        colNames = ["%s AS pat_id"%pat_col, "flo_meas_id", "flowsheet_name", \
+            "flowsheet_value", "shifted_dt_tm"]
+
+        query_str = "SELECT "
+        for colName in colNames:
+            query_str += colName + ','
+        query_str = query_str[:-1]
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            query_str += " FROM stride_flowsheet "
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+            query_str += " FROM vitals "
+        # Note that UMich does not have vitals info
+
+        query_str += " WHERE flowsheet_name IN ("
+        for flowsheetBaseName in flowsheetBaseNames:
+            query_str += "'" + flowsheetBaseName + "',"
+        query_str = query_str[:-1] + ')'
+
+        query_str += " AND %s IN (" % pat_col
+
+        for patientId in patientIds:
+            query_str += patientId + ','
+        query_str = query_str[:-1] + ')'
+
+        query_str += " ORDER BY %s, shifted_dt_tm" % pat_col
+
+        log.debug(query_str)
+
+        cur = DBUtil.connection().cursor()
+        cur.execute(query_str)
+
+        results = []
+        colNames = DBUtil.columnNamesFromCursor(cur)
+        results.append(colNames)
+
+        dataTable = list(cur.fetchall())
+        for i, row in enumerate(dataTable):
+            dataTable[i] = list(row);
+        results.extend(dataTable);
 
         # Execute query.
-        return modelListFromTable(DBUtil.execute(query, includeColumnNames=True))
+        # return modelListFromTable(DBUtil.execute(query, includeColumnNames=True))
+        return modelListFromTable(results)
 
     def colsFromBaseNames(self, baseNames, preTimeDays, postTimeDays):
         """Enumerate derived column/feature names given a set of (lab) result base names"""
@@ -585,7 +966,7 @@ def _processResultEvents(self, patientEpisodeByIndexTimeById, resultsByNameByPat
         """
         # Use results generator as outer loop as will not be able to random
         # access the contents.
-        for patientId, resultsByName in resultsByNameByPatientId.iteritems():
+        for patientId, resultsByName in resultsByNameByPatientId.items():
             # Skip results if not in our list of patients of interest
             if patientId in patientEpisodeByIndexTimeById:
                 patientEpisodeByIndexTime = patientEpisodeByIndexTimeById[patientId]
@@ -597,7 +978,7 @@ def _processResultEvents(self, patientEpisodeByIndexTimeById, resultsByNameByPat
         # Separate loop to verify all patient records addressed, even if no
         # results available (like an outer join).
         resultsByName = None
-        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.iteritems():
+        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.items():
             self._addResultFeatures_singlePatient(patientEpisodeByIndexTime, \
                 resultsByName, resultNames, valueCol, datetimeCol, preTimeDelta, \
                 postTimeDelta)
@@ -622,7 +1003,7 @@ def _addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByN
             postTimeDays = postTimeDelta.days
 
         # Init summary values to null for all results
-        for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for indexTime, patient in patientEpisodeByIndexTime.items():
             for baseName in baseNames:
                 if resultsByName is not None or ("%s.%s_%s.count" % (baseName, preTimeDays, postTimeDays)) not in patient:
                     # Default to null for all values
@@ -644,7 +1025,7 @@ def _addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByN
 
         # Have results available for this patient?
         if resultsByName is not None:
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 # Time range limits on labs to consider
                 preTimeLimit = None;
                 postTimeLimit = None;
@@ -666,17 +1047,18 @@ def _addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByN
                         filteredResults = list()
                         for result in resultsByName[baseName]:
                             resultTime = result[datetimeCol]
-                            if (preTimeLimit is None or preTimeLimit <= resultTime) \
-                                and (postTimeLimit is None or resultTime < postTimeLimit):
-                                # Occurs within timeframe of interest, so record valueCol
-                                filteredResults.append(result)
-
-                                if firstItem is None or resultTime < firstItem[datetimeCol]:
-                                    firstItem = result
-                                if lastItem is None or lastItem[datetimeCol] < resultTime:
-                                    lastItem = result
-                                if proximateItem is None or (abs(resultTime - indexTime) < abs(proximateItem[datetimeCol] - indexTime)):
-                                    proximateItem = result
+                            if resultTime is not None:
+                                if (preTimeLimit is None or preTimeLimit <= resultTime) \
+                                   and (postTimeLimit is None or resultTime < postTimeLimit):
+                                    # Occurs within timeframe of interest, so record valueCol
+                                    filteredResults.append(result)
+
+                                    if firstItem is None or resultTime < firstItem[datetimeCol]:
+                                        firstItem = result
+                                    if lastItem is None or lastItem[datetimeCol] < resultTime:
+                                        lastItem = result
+                                    if proximateItem is None or (abs(resultTime - indexTime) < abs(proximateItem[datetimeCol] - indexTime)):
+                                        proximateItem = result
 
                         if len(filteredResults) > 0:
                             # Count up number of values specifically labeled "in range"
@@ -702,7 +1084,8 @@ def _addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByN
 
         return
 
-    def _queryLabResultsByName(self, labBaseNames):
+    # TODO(sbala): Fix isLabPanel arg declaration to be None by default.
+    def _queryLabResultsByName(self, labNames, isLabPanel = True):
         """
         Query for all lab results that match with the given result base names.
         """
@@ -714,28 +1097,77 @@ def _queryLabResultsByName(self, labBaseNames):
         # Filtering by patient ID drags down substantially until preloaded
         # table by doing a count on the SQR table?
         columnNames = [
-            "CAST(pat_id AS bigint)", "base_name", "ord_num_value",
-            "result_flag", "result_in_range_yn", "sor.result_time"
+            "CAST(pat_id AS bigint) as pat_id", "base_name", "ord_num_value",
+            "result_flag", "result_in_range_yn"
         ]
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            columnNames += ["sor.result_time"]
+        else:
+            columnNames += ["result_time"]
 
         # Identify which patients to query.
         patientIds = set()
         patientEpisodes = self.getPatientEpisodeIterator()
         for episode in patientEpisodes:
             patientIds.add(episode[self.patientEpisodeIdColumn])
-
         # Construct query to pull from stride_order_results, stride_order_proc
-        query = SQLQuery()
-        for column in columnNames:
-            query.addSelect(column)
-        query.addFrom("stride_order_results AS sor, stride_order_proc AS sop")
-        query.addWhere("sor.order_proc_id = sop.order_proc_id")
-        query.addWhereIn("base_name", labBaseNames)
-        query.addWhereIn("pat_id", patientIds)
-        query.addOrderBy("pat_id")
-        query.addOrderBy("sor.result_time")
 
-        return modelListFromTable(DBUtil.execute(query, includeColumnNames=True))
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            query = SQLQuery()
+            for column in columnNames:
+                query.addSelect(column)
+            query.addFrom("stride_order_results AS sor, stride_order_proc AS sop")
+            query.addWhere("sor.order_proc_id = sop.order_proc_id")
+
+            query.addWhereIn(self._labTypeCol(isLabPanel), labNames)
+
+            query.addWhereIn("pat_id", patientIds)
+            query.addOrderBy("pat_id")
+            query.addOrderBy("sor.result_time")
+
+            log.debug(query)
+            return modelListFromTable(DBUtil.execute(query, includeColumnNames=True))
+
+
+        else:
+
+            query_str = "SELECT "
+            for column in columnNames:
+                query_str += column + ","
+            query_str = query_str[:-1] + " FROM labs "
+
+            query_str += "WHERE %s IN (" % (self._labTypeCol(isLabPanel))
+            for labName in labNames:
+                query_str += "'%s'," % labName
+            query_str = query_str[:-1] + ") "
+
+            query_str += "AND pat_id IN "
+            pat_list_str = "("
+            for pat_id in patientIds:
+                pat_list_str += str(pat_id) + ","
+            pat_list_str = pat_list_str[:-1] + ") "
+            query_str += pat_list_str
+
+            query_str += "ORDER BY pat_id"
+            if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+                query_str += ", sor.result_time"
+            else: # Implemented for UMich and UCSF
+                query_str += ", result_time"
+
+            cur = DBUtil.connection().cursor()
+            cur.execute(query_str)
+
+            results = []
+            colNames = DBUtil.columnNamesFromCursor(cur)
+            results.append(colNames)
+
+            dataTable = list(cur.fetchall())
+            for i, row in enumerate(dataTable):
+                dataTable[i] = list(row);
+            results.extend(dataTable);
+
+            return modelListFromTable(results)
+
 
     def _parseResultsData(self, resultRowIter, patientIdCol, nameCol, valueCol, datetimeCol):
         """
@@ -760,7 +1192,13 @@ def _parseResultsDataGenerator(self, resultRowIter, patientIdCol, nameCol, value
             if result[valueCol] is not None and result[valueCol] != NULL_STRING:
                 patientId = int(result[patientIdCol])
                 baseName = result[nameCol]
-                resultValue = float(result[valueCol])
+                try:
+                    resultValue = float(result[valueCol])
+                except Exception as e:
+                    print("In _parseResultsDataGenerator, " \
+                          "weird values of ord_num_value cannot be converted.. " \
+                          "Exception:", e)
+                    continue
                 resultTime = DBUtil.parseDateValue(result[datetimeCol])
 
                 # Skip apparent placeholder values
@@ -838,13 +1276,161 @@ def processClinicalItemInput(self, clinicalItemName):
         patientEpisodes = self._readPatientEpisodesFile()
         pass
 
-    def _queryClinicalItemInput():
-        pass
+    def addTimeCycleFeatures(self, timeCol, timeAttr):
+        """
+        Look for a datetime value in the patientEpisode identified by timeCol.
+        Add features to the patientEpisode based on the timeAttr string
+        ("month","day","hour","minute","second"), including the sine and cosine
+        of the timeAttr value relative to the maximum possible value to reflect
+        cyclical time patterns (e.g. seasonal patterns over months in a year,
+        or daily cycle patterns over hours in a day).
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before lab result.")
 
-    def _parseClinicalItems():
-        pass
+        # Open temp file.
+        self._patientTimeCycleTempFileNameFormat
+        tempFileName = self._patientTimeCycleTempFileNameFormat % (timeCol, timeAttr)
+        tempFile = open(tempFileName, "w")
 
-    def buildFeatureMatrix(self):
+        # Write header fields to tempFile.
+        tempFile.write("patient_id\tepisode_time\t")
+        tempFile.write("%s.%s\t" % (timeCol, timeAttr))
+        tempFile.write("%s.%s.sin\t" % (timeCol, timeAttr))
+        tempFile.write("%s.%s.cos\n" % (timeCol, timeAttr))
+
+        # Compute and write time cycle features.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            timeObj = DBUtil.parseDateValue(episode[timeCol])
+            # Use introspection (getattr) to extract some time feature from the
+            # time object, as well as the maximum and minimum possible values
+            # to set the cycle range.
+            maxValue = getattr(timeObj.max, timeAttr)
+            thisValue = getattr(timeObj, timeAttr)
+            minValue = getattr(timeObj.min, timeAttr)
+
+            radians = 2*np.pi * (thisValue-minValue) / (maxValue+1-minValue)
+
+            # Write values to tempFile.
+            tempFile.write("%s\t" % episode[self.patientEpisodeIdColumn])
+            tempFile.write("%s\t" % episode[self.patientEpisodeTimeColumn])
+            tempFile.write("%s\t" % thisValue)
+            tempFile.write("%s\t" % np.sin(radians))
+            tempFile.write("%s\n" % np.cos(radians))
+
+        # Close tempFile.
+        self._featureTempFileNames.append(tempFileName)
+        tempFile.close()
+
+    def loadMapData(self,filename):
+        """
+        Read the named file's contents through TabDictReader to enable data
+        extraction. If cannot find file by absolute filename, then look under
+        default mapdata directory.
+        """
+        try:
+            return TabDictReader(open(filename))
+        except IOError:
+            # Unable to open file directly. See if it's in the mapdata directory
+            appDir = os.path.dirname(Util.__file__)
+            defaultFilename = os.path.join(appDir, "mapdata", filename)
+            try:
+                return TabDictReader(open(defaultFilename))
+            except IOError:
+                # May need to add default extension as well
+                defaultFilename = defaultFilename + ".tab"
+                return TabDictReader(open(defaultFilename))
+
+    def addCharlsonComorbidityFeatures(self, features=None):
+        """
+        For each of a predefined set of comorbidity categories, add features
+        summarizing occurrence of the associated ICD9 problems.
+        """
+        # Extract ICD9 prefixes per disease category
+        icdprefixesByDisease = dict()
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            for row in self.loadMapData("CharlsonComorbidity-ICD9CM"):
+                (disease, icd9prefix) = (row["charlson"], row["icd9cm"])
+                if disease not in icdprefixesByDisease:
+                    icdprefixesByDisease[disease] = list()
+                    icdprefixesByDisease[disease].append("^ICD9." + icd9prefix)
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            for row in self.loadMapData("CharlsonComorbidity-ICD9CM"):
+                (disease, icd9prefix) = (row["charlson"], row["icd9cm"])
+                if disease not in icdprefixesByDisease:
+                    icdprefixesByDisease[disease] = list()
+                    icdprefixesByDisease[disease].append(icd9prefix)
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+            for row in self.loadMapData("CharlsonComorbidity-ICD10"):
+                (disease, icd10prefix) = (row["Category"], row["Code"])
+                if disease not in icdprefixesByDisease:
+                    icdprefixesByDisease[disease] = list()
+                    icdprefixesByDisease[disease].append("^ICD10." + icd10prefix)
+                    icdprefixesByDisease[disease].append(icd10prefix)
+
+
+        for disease, icdprefixes in icdprefixesByDisease.items():
+            disease = disease.translate(None," ()-/") # Strip off punctuation
+            log.debug('Adding %s comorbidity features...' % disease)
+            if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+                self.addClinicalItemFeatures(icdprefixes, operator="~*", \
+                                             label="Comorbidity." + disease, features=features)
+            else:
+                self.addClinicalItemFeatures_NonStanford(icdprefixes,
+                                                         tableName = 'diagnoses',
+                                                         clinicalItemType='diagnose_code',
+                                                         clinicalItemTime='diagnose_time',
+                                                         label="Comorbidity."+disease,
+                                                         features=features)
+
+    def addTreatmentTeamFeatures(self, features=None):
+        """
+        For each of a predefined set of specialty categories, add features
+        summarizing the makeup of the treatment team.
+        """
+        # Extract out lists of treatment team names per care category
+        teamNameByCategory = dict()
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            for row in self.loadMapData("TreatmentTeamGroups"):
+                (category, teamName) = (row["team_category"], row["treatment_team"])
+                if category not in teamNameByCategory:
+                    teamNameByCategory[category] = list()
+                teamNameByCategory[category].append(teamName)
+
+            for category, teamNames in teamNameByCategory.items():
+                log.debug('Adding %s treatment team features...' % category)
+                self.addClinicalItemFeatures(teamNames, column="description", \
+                                                 label="Team." + category, features=features)
+
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+            for row in self.loadMapData("TreatmentTeamGroups_UCSF"):
+                (category, teamName) = (row["team_category"], row["treatment_team"])
+                if category not in teamNameByCategory:
+                    teamNameByCategory[category] = list()
+                teamNameByCategory[category].append(teamName)
+
+            for category, teamNames in teamNameByCategory.items():
+                log.debug('Adding %s treatment team features...' % category)
+                # TODO sx: rename
+                self.addClinicalItemFeatures_NonStanford(teamNames, \
+                    tableName='labs', clinicalItemTime = 'order_time',
+                    label="Team."+category, features=features)
+
+    def addSexFeatures(self):
+        SEX_FEATURES = ["Male", "Female"]
+        for feature in SEX_FEATURES:
+            self.addClinicalItemFeatures([feature], dayBins=[], \
+                features="pre")
+
+    def addRaceFeatures(self):
+        for feature in self.queryAllRaces():
+            self.addClinicalItemFeatures([feature], dayBins=[], \
+                features="pre")
+
+    def buildFeatureMatrix(self, header=None, matrixFileName=None):
         """
         Given a set of factory inputs, build a feature matrix which
         can then be output.
@@ -855,10 +1441,21 @@ def buildFeatureMatrix(self):
             self._parseFooInput()
         """
         # Initialize feature matrix file.
-        timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H.%M")
-        matrixFileName = "feature-matrix_%s.tab" % timestamp
-        matrixFile = open(matrixFileName, "w")
+        if matrixFileName is None:
+            timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
+            matrixFileName = "feature-matrix_%s.tab" % timestamp
+            self._matrixFileName = matrixFileName
+        if header is None:
+            # file_name.tab
+            # Created: timestamp
+            timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+            header = [
+                '%s' % self._matrixFileName,
+                'Created: %s' % timestamp
+            ]
         self._matrixFileName = matrixFileName
+        matrixFile = open(self._matrixFileName, "w")
+
 
         # Read arbitrary number of input temp files.
         # Use csv tab reader so we can just read the lists while being agnostic
@@ -874,6 +1471,10 @@ def buildFeatureMatrix(self):
             tempFiles.append(tempFile)
             tempFileReaders.append(tempFileReader)
 
+        # Write header to matrix file.
+        for line in header:
+            matrixFile.write('# %s\n' % line)
+
         # Write data to matrix file.
         for patientEpisode in patientEpisodeReader:
             matrixData = list()
@@ -882,15 +1483,21 @@ def buildFeatureMatrix(self):
             # Each tempFile has the patientId and episodeTime fields.
             # Don't write these to the matrix file.
             for tempFileReader in tempFileReaders:
-                tempData = tempFileReader.next()
+                tempData = next(tempFileReader)
                 matrixData.extend(tempData[2:])
 
             # Write data to matrixFile, with trailing \n.
             matrixFile.write("\t".join(matrixData))
             matrixFile.write("\n")
 
+        self.cleanTempFiles()
+
+    def cleanTempFiles(self):
         # Close temp files.
-        [tempFile.close() for tempFile in tempFiles]
+        try:
+            [tempFile.close() for tempFile in tempFiles]
+        except:
+            pass
         # Clean up temp files.
         for tempFileName in self._featureTempFileNames:
             try:
@@ -920,3 +1527,6 @@ def readFeatureMatrixFile(self):
 
     def getMatrixFileName(self):
         return self._matrixFileName
+
+    def getNumRows(self):
+        return self._numRows
diff --git a/medinfo/dataconversion/FeatureMatrixIO.py b/medinfo/dataconversion/FeatureMatrixIO.py
new file mode 100644
index 00000000..577d4b52
--- /dev/null
+++ b/medinfo/dataconversion/FeatureMatrixIO.py
@@ -0,0 +1,66 @@
+#!/usr/bin/python
+"""
+Utility class for reading and writing feature matrix files.
+"""
+
+import datetime
+import os
+import numpy as np
+from pandas import read_csv
+
+class FeatureMatrixIO:
+    def __init__(self):
+        pass
+
+    def read_file_to_data_frame(self, in_file_path, datetime_col_index=None):
+        if datetime_col_index is None:
+            datetime_col_index = 1
+        data_frame = read_csv(in_file_path, sep='\t', comment='#', \
+            parse_dates=[datetime_col_index], na_values=['None'], 
+            dtype={'death_date': str})
+
+        return data_frame
+
+    def write_data_frame_to_file(self, data_frame, out_file_path, header=None):
+        columns = list(data_frame.columns.values)
+        if header is None:
+            header = []
+
+        # Open out_file.
+        out_file = open(out_file_path, 'w')
+
+        # Write header.
+        for line in header:
+            out_file.write('# %s\n' % line)
+
+        # Write columns.
+        column_list = '\t'.join(columns)
+        out_file.write('%s\n' % column_list)
+
+        # Write data.
+        row_list = list()
+        for index, row in data_frame.iterrows():
+            row_str = '\t'.join([str(row[column]) for column in columns])
+            out_file.write('%s\n' % row_str)
+
+    def strip_header(self, in_file_path, out_file_path=None):
+        # Set default out_file_path if none provided.
+        if out_file_path is None:
+            template = '%s.stripped-header.tab'
+            out_file_path = template % in_file_path
+
+        # Copies file and strips header from copy.
+        in_file = open(in_file_path, 'r')
+        out_file = open(out_file_path, 'w')
+
+        # Copy line by line, ignoring header.
+        for line in in_file:
+            if line[0] == '#':
+                continue
+            else:
+                out_file.write(line)
+
+        in_file.close()
+        out_file.close()
+
+        return out_file_path
diff --git a/medinfo/dataconversion/FeatureMatrixTransform.py b/medinfo/dataconversion/FeatureMatrixTransform.py
new file mode 100644
index 00000000..de8f127e
--- /dev/null
+++ b/medinfo/dataconversion/FeatureMatrixTransform.py
@@ -0,0 +1,335 @@
+#!/usr/bin/python
+"""
+Module for transforming the values within an existing feature matrix.
+"""
+
+import numpy as np
+import pandas as pd
+
+from scipy.stats import norm
+from sklearn.impute import SimpleImputer
+from .Util import log
+from math import isnan
+
+class FeatureMatrixTransform:
+    IMPUTE_STRATEGY_MEAN = 'mean'
+    IMPUTE_STRATEGY_MEDIAN = 'median'
+    IMPUTE_STRATEGY_MODE = 'most-frequent'
+    IMPUTE_STRATEGY_ZERO = 'zero'
+    IMPUTE_STRATEGY_DISTRIBUTION = 'distribution'
+
+    LOG_BASE_10 = 'log-base-10'
+    LOG_BASE_E = 'log-base-e'
+
+    ALL_FEATURES = 'all-features'
+
+    def __init__(self):
+        self._matrix = None
+
+    def set_input_matrix(self, matrix):
+        if type(matrix) != pd.core.frame.DataFrame:
+            raise ValueError('Input matrix must be pandas DataFrame.')
+
+        self._matrix = pd.DataFrame.copy(matrix)
+
+    def fetch_matrix(self):
+        return self._matrix
+
+    def impute(self, feature=None, strategy=None, distribution=None):
+        if self._matrix is None:
+            raise ValueError('Must call set_input_matrix() before impute().')
+        # If user does not specify which feature to impute, impute values
+        # for all columns in the matrix.
+        if feature is None:
+            feature = FeatureMatrixTransform.ALL_FEATURES
+
+        # If an imputation strategy is not specified, default to mean.
+        if strategy is None:
+            strategy = FeatureMatrixTransform.IMPUTE_STRATEGY_MEAN
+
+        # If distribution is not specified, default to norm.
+        if distribution is None:
+            distribution = norm.rvs
+
+        # TODO sxu: modify other modules to also return stuff
+        if feature == FeatureMatrixTransform.ALL_FEATURES:
+            self._impute_all_features(strategy, distribution=distribution)
+        else:
+            return self._impute_single_feature(feature, strategy, distribution=distribution)
+
+    def do_impute_sx(self, matrix, means):
+        '''
+        sxu new implementation
+
+        Input: train_df, but does not change it
+        Output: dictionary {order_proc_id: {feature: imputation value}}
+        '''
+        import datetime
+        # TODO: alert: changes row order and indices of matrix!
+
+        datetime_format = "%Y-%m-%d %H:%M:%S"
+
+        matrix_sorted = matrix.sort_values(['pat_id', 'order_time']).reset_index()
+        count_time_suffixs = ['preTimeDays', 'postTimeDays']  # TODO: postTimeDays should really not appear!
+        stats_numeric_suffixs = ['min', 'max', 'median', 'mean', 'std', 'first', 'last', 'diff', 'slope',
+                                 'proximate']
+        stats_time_suffixs = ['firstTimeDays', 'lastTimeDays', 'proximateTimeDays']
+
+        impute_train = {}
+
+        nega_inf_days = -1000000
+        for i in range(0, matrix_sorted.shape[0]):
+            cur_episode_id = matrix_sorted.ix[i, 'order_proc_id']
+            impute_train[cur_episode_id] = {}
+            for column in matrix_sorted.columns.values.tolist():
+                '''
+                Value not missing
+                '''
+                if matrix_sorted.ix[i, column] == matrix_sorted.ix[i, column]:
+                    continue
+
+                '''
+                Value missing
+                '''
+                column_tail = column.split('.')[-1].strip()
+
+                if column in means:
+                    popu_mean = means[column]  # TODO: pre-compute with dict
+
+                if column_tail in stats_numeric_suffixs:
+                    '''
+                    impute with the previous episode if available; otherwise population mean
+                    '''
+                    if i == 0 or (matrix_sorted.ix[i, 'pat_id'] != matrix_sorted.ix[i - 1, 'pat_id']) or (
+                            matrix_sorted.ix[i - 1, column] != matrix_sorted.ix[i - 1, column]):
+                        matrix_sorted.ix[i, column] = popu_mean  # impute_train[cur_episode_id][column] = popu_mean
+                    else:
+                        matrix_sorted.ix[i, column] = matrix_sorted.ix[
+                            i - 1, column]  # impute_train[cur_episode_id][column] = train_df_sorted.ix[i-1, column]
+
+                elif column_tail in stats_time_suffixs:
+                    '''
+                    use the previous + time difference if available; otherwise -infinite
+                    '''
+                    if i == 0 or (matrix_sorted.ix[i, 'pat_id'] != matrix_sorted.ix[i - 1, 'pat_id']) or (
+                            matrix_sorted.ix[i - 1, column] != matrix_sorted.ix[i - 1, column]):
+                        # impute_train[cur_episode_id][column] = nega_inf_days
+                        matrix_sorted.ix[i, column] = nega_inf_days
+                    else:
+                        day_diff = (datetime.datetime.strptime(matrix_sorted.ix[i, 'order_time'], datetime_format) -
+                                    datetime.datetime.strptime(matrix_sorted.ix[i - 1, 'order_time'],
+                                                               datetime_format)).days
+                        # impute_train[cur_episode_id][column] = train_df_sorted.ix[i-1, column] - day_diff # TODO!
+
+                        matrix_sorted.ix[i, column] = matrix_sorted.ix[i - 1, column] - day_diff
+
+                elif column_tail in count_time_suffixs:
+                    '''
+                    -infinite
+                    '''
+                    # impute_train[cur_episode_id][column] = nega_inf_days
+                    matrix_sorted.ix[i, column] = nega_inf_days
+
+                else:
+                    '''
+                    In all other cases, just use mean to impute
+                    '''
+                    matrix_sorted.ix[i, column] = popu_mean
+                    pass
+
+        return matrix_sorted.set_index('index')
+
+    def _impute_all_features(self, strategy, distribution=None):
+        for column in self._matrix:
+            self._impute_single_feature(column, strategy, distribution=distribution)
+
+    # TODO sxu: modify other modules to also return stuff
+    def _impute_single_feature(self, feature, strategy, distribution=None):
+        if strategy == FeatureMatrixTransform.IMPUTE_STRATEGY_MEAN:
+            self._matrix[feature] =  self._matrix[feature].fillna(self._matrix[feature].mean(0))
+            return self._matrix[feature].mean(0)
+        elif strategy == FeatureMatrixTransform.IMPUTE_STRATEGY_MODE:
+            self._matrix[feature] = self._matrix[feature].fillna(self._matrix[feature].mode().iloc[0])
+        elif strategy == FeatureMatrixTransform.IMPUTE_STRATEGY_ZERO:
+            self._matrix[feature] = self._matrix[feature].fillna(0.0)
+        elif strategy == FeatureMatrixTransform.IMPUTE_STRATEGY_MEDIAN:
+            self._matrix[feature] = self._matrix[feature].fillna(self._matrix[feature].median())
+        elif strategy == FeatureMatrixTransform.IMPUTE_STRATEGY_DISTRIBUTION:
+            self._matrix[feature] = self._matrix[feature].apply(lambda x: distribution() if pd.isnull(x) else x)
+
+    def drop_null_features(self):
+        for feature in self._matrix.columns.values:
+            if self._matrix[feature].isnull().all():
+                self.remove_feature(feature)
+
+    def remove_feature(self, feature):
+        try:
+            del self._matrix[feature]
+        except KeyError:
+            log.info('Cannot remove non-existent feature "%s".' % feature)
+
+    def filter_on_feature(self, feature, value):
+        # remove rows where feature == value
+        if pd.isnull(value): # nan is not comparable, so need different syntax
+            rows_to_remove = self._matrix[pd.isnull(self._matrix[feature])].index
+        else:
+            try:
+                rows_to_remove = self._matrix[self._matrix[feature] == value].index
+            except TypeError:
+                log.info('Cannot filter %s on %s; types are not comparable.' % (feature, str(value)))
+                return
+
+        self._matrix.drop(rows_to_remove, inplace = True)
+        self._matrix.reset_index(drop=True, inplace = True)
+
+        # return number of rows remaining
+        return self._matrix.shape[0]
+
+    def add_logarithm_feature(self, base_feature, logarithm=None):
+        if logarithm is None:
+            logarithm = FeatureMatrixTransform.LOG_BASE_E
+
+        col_index = self._matrix.columns.get_loc(base_feature)
+
+        if logarithm == FeatureMatrixTransform.LOG_BASE_E:
+            log_feature = 'ln(' + base_feature + ')'
+            new_col = self._matrix[base_feature].apply(np.log)
+            self._matrix.insert(col_index + 1, log_feature, new_col)
+        elif logarithm == FeatureMatrixTransform.LOG_BASE_10:
+            log_feature = 'log10(' + base_feature + ')'
+            new_col = self._matrix[base_feature].apply(np.log10)
+            self._matrix.insert(col_index, log_feature, new_col)
+
+        return log_feature
+
+    def add_indicator_feature(self, base_feature, boolean_indicator=None):
+        # boolean: determines whether to add True/False labels or 1/0
+        if boolean_indicator is None or boolean_indicator is False:
+            indicator = self._numeric_indicator
+        else:
+            indicator = self._boolean_indicator
+
+        col_index = self._matrix.columns.get_loc(base_feature)
+        indicator_feature = 'I(' + base_feature + ')'
+        new_col = self._matrix[base_feature].apply(indicator)
+        self._matrix.insert(col_index + 1, indicator_feature, new_col)
+
+        return indicator_feature
+
+    def add_threshold_feature(self, base_feature, lower_bound=None, upper_bound=None):
+        # Add feature which indicates whether base_feature is >= lower_bound
+        # and <= upper_bound.
+        if lower_bound is None and upper_bound is None:
+            raise ValueError('Must specify either lower_bound or upper_bound.')
+        elif lower_bound is None:
+            # base_feature <= upper_bound
+            threshold_feature = 'I(%s<=%s)' % (base_feature, upper_bound)
+            indicator = lambda x: 1 if x <= upper_bound else 0
+        elif upper_bound is None:
+            # base_feature >= lower_bound
+            threshold_feature = 'I(%s>=%s)' % (base_feature, lower_bound)
+            indicator = lambda x: 1 if x >= lower_bound else 0
+        else:
+            # lower_bound <= base_feature <= upper_bound
+            threshold_feature = 'I(%s<=%s<=%s)' % (lower_bound, base_feature, upper_bound)
+            indicator = lambda x: 1 if x <= upper_bound and x >= lower_bound else 0
+
+        col_index = self._matrix.columns.get_loc(base_feature)
+        new_col = self._matrix[base_feature].apply(indicator)
+        self._matrix.insert(col_index + 1, threshold_feature, new_col)
+
+        return threshold_feature
+
+    def drop_duplicate_rows(self):
+        self._matrix.drop_duplicates(inplace=True)
+
+    def remove_low_signal_features(self):
+        # Prune obviously unhelpful fields.
+        # in theory, FeatureSelector should be able to prune these
+        # automatically, but no harm in helping out given it has to sift
+        # through ~3000 features.
+        LOW_SIGNAL_FEATURES = [
+            'index_time',
+            'Birth.pre', 'Death.post'
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays'
+            ]
+        for feature in LOW_SIGNAL_FEATURES:
+            self.remove_feature(feature)
+
+    def _numeric_indicator(self, value):
+        return 1 if pd.notnull(value) else 0
+
+    def _boolean_indicator(self, value):
+        return pd.notnull(value)
+
+    def add_change_feature(self, method, param, feature_old, feature_new):
+        # Add column unchanged_yn describing whether feature_new is 'unchanged'
+        # relative to feature_old
+
+        if method == "percent":
+            change_col = self._matrix.apply(self._percent_change, \
+            args=(feature_old, feature_new, param), axis = 1)
+        elif method == "interval":
+            change_col = self._matrix.apply(self._interval_change, \
+            args=(feature_old, feature_new, param), axis = 1)
+        elif method == "sd": #TODO (raikens): finish this
+            k = 300 #TODO (raikens): make this user-specified
+            n = self._matrix.shape[0]
+            if (n <= k):
+                raise ValueError("Not enough data to estimate sd")
+
+            # estimate sd from sample and drop sample rows
+            sample_rows = np.random.choice(n, k, replace = False)
+            sd = np.std(self._matrix.loc[sample_rows, feature_new], ddof = 1)
+            self._matrix.drop(sample_rows, inplace = True)
+            self._matrix.reset_index(drop=True, inplace = True)
+            margin = param*sd
+
+            change_col = self._matrix.apply(self._interval_change, \
+            args=(feature_old, feature_new, margin), axis = 1)
+
+        else:
+            raise ValueError("Must specify a supported method for change calculation")
+
+        # add new column to matrix
+        # TODO (raikens): since new column is always "unchange_yn," only one
+        # change feature can be added.
+        col_index = self._matrix.columns.get_loc(feature_new)
+        self._matrix.insert(col_index + 1, "unchanged_yn", change_col)
+        return "unchanged_yn"
+
+    def _is_numeric(self, x):
+        try:
+            float(x)
+            return (not isnan(x))
+        except ValueError:
+            return False
+
+    def _percent_change(self, row, feature_old, feature_new, param):
+        # Return 1 if new value has changed by more than <param> percent
+        # of old value, else return 0.
+        # If either old or new value is missing, returns 9999999.
+        if not (self._is_numeric(row[feature_old]) and self._is_numeric(row[feature_new])):
+            return 9999999
+        elif row[feature_old] == 0.0:
+            return 1
+        else:
+            return int(abs(1.0-float(row[feature_new])/float(row[feature_old])) < param)
+
+    def _interval_change(self, row, feature_old, feature_new, param):
+        # Return 1 if new value has changed by more than <param> from old value,
+        # else return 0.
+        # If either old or new value is missing, returns 9999999.
+        if not (self._is_numeric(row[feature_old]) and self._is_numeric(row[feature_new])):
+            return 9999999
+        else:
+            return int(abs(float(row[feature_new])-float(row[feature_old])) < param)
diff --git a/medinfo/dataconversion/ProviderRotationConversion.py b/medinfo/dataconversion/ProviderRotationConversion.py
new file mode 100644
index 00000000..d1ffb530
--- /dev/null
+++ b/medinfo/dataconversion/ProviderRotationConversion.py
@@ -0,0 +1,243 @@
+#!/usr/bin/env python
+import sys, os
+import time;
+from datetime import datetime;
+from optparse import OptionParser
+from medinfo.common.Util import stdOpen, ProgressDots;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import SQLQuery;
+from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
+
+from .Util import log;
+from .Env import DATE_FORMAT;
+
+DEFAULT_SOURCE_TABLE = "jchi_accesslog_script";
+NULL_GROUP_ID = 0;  # Metric Group ID to assign if null/None indicated
+NULL_GROUP_NAME = "Null Group";
+
+class ProviderRotationConversion:
+    """Data conversion module to take manually prepped provider (physician)
+    scheduling data into a common provider_rotation data table content.
+    Accomodate multiple sources from attending, resident, and student schedules.
+    """
+    connFactory = None; # Allow specification of alternative DB connection source
+
+    sourceTableName = None;
+
+    # Local caches to track lookup values
+    userBySID = None;
+    metricLineDescriptionsById = None;
+    metricById = None;
+    metricGroupById = None;
+
+    def __init__(self):
+        """Default constructor"""
+        self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
+
+        self.sourceTableName = DEFAULT_SOURCE_TABLE;    # Customizable parameter to facilitate test cases
+
+        self.userBySID = dict();
+        self.metricLineDescriptionsById = dict();
+        self.metricById = dict();
+        self.metricGroupById = dict();
+
+    def convertSourceItems(self, userSIDs=None, limit=None, offset=None):
+        """Primary run function to process the contents of the source table
+        and convert them into normalized data table entries.
+        """
+        log.info("Conversion for patients: %s" % userSIDs);
+        progress = ProgressDots();
+        conn = self.connFactory.connection();
+        try:
+            for i, sourceItem in enumerate(self.querySourceItems(userSIDs, limit, offset, progress=progress, conn=conn)):
+                self.convertSourceItem(sourceItem, conn=conn);
+                progress.Update();
+
+            # Go through accumulated metric description lines into single entries for the metric table
+            self.updateMetricDescriptionLines();
+        finally:
+            conn.close();
+        # progress.PrintStatus();
+
+
+    def querySourceItems(self, userSIDs, limit=None, offset=None, progress=None, conn=None):
+        """Query the database for list of all AccessLogs
+        and yield the results one at a time.  If userSIDs provided, only return items matching those IDs.
+        """
+        extConn = conn is not None;
+        if not extConn:
+            conn = self.connFactory.connection();
+
+        # Column headers to query for that map to respective fields in analysis table
+        headers = ["user_id", "user_name", "de_pat_id", "access_datetime", "metric_id", "metric_name", "line_count", "description", "metric_group_num", "metric_group_name"];
+
+        query = SQLQuery();
+        for header in headers:
+            query.addSelect( header );
+        query.addFrom(self.sourceTableName);
+        if userSIDs is not None:
+            query.addWhereIn("user_id", userSIDs);
+        query.setLimit(limit);
+        query.setOffset(offset);
+
+        # Query to get an estimate of how long the process will be
+        if progress is not None:
+            progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0];
+
+        cursor = conn.cursor();
+        # Do one massive query, but yield data for one item at a time.
+        cursor.execute( str(query), tuple(query.params) );
+
+        row = cursor.fetchone();
+        while row is not None:
+            rowModel = RowItemModel( row, headers );
+            yield rowModel;
+            row = cursor.fetchone();
+
+        # Slight risk here.  Normally DB connection closing should be in finally of a try block,
+        #   but using the "yield" generator construct forbids us from using a try, finally construct.
+        cursor.close();
+
+        if not extConn:
+            conn.close();
+
+    def convertSourceItem(self, sourceItem, conn=None):
+        """Given an individual sourceItem record, produce / convert it into an equivalent
+        item record in the analysis database.
+        """
+        extConn = conn is not None;
+        if not extConn:
+            conn = self.connFactory.connection();
+        try:
+            # Normalize sourceItem data into hierachical components (metric group -> metric -> metric line and user -> access_log).
+            #   Expect relatively small number of lookup values, so these should only have to be instantiated
+            #   in a first past, with subsequent calls just yielding back in memory cached copies
+            user = self.userFromSourceItem(sourceItem, conn=conn);
+            metricGroup = self.metricGroupFromSourceItem(sourceItem, conn=conn);
+            metric = self.metricFromSourceItem(sourceItem, metricGroup, conn=conn);
+            metricLine = self.metricLineFromSourceItem(sourceItem, metric, conn=conn);
+            accessLog = self.accessLogFromSourceItem(sourceItem, user, metric, metricLine, conn=conn);
+        finally:
+            if not extConn:
+                conn.close();
+
+    def userFromSourceItem(self, sourceItem, conn):
+        # Load or produce a user record model for the given sourceItem
+        userSID = sourceItem["user_id"];
+        if userSID not in self.userBySID:
+            # User does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            userId = int(userSID[1:]);  # Trim out leading S and parse remainder of SID as integer
+            user = \
+                RowItemModel \
+                (   {   "user_id":  userId, # Somewhat confusing remap of "user_id" in source data to "sid" to reserve user_id for the integer component
+                        "sid":  userSID,
+                        "name": sourceItem["user_name"],
+                    }
+                );
+            (userId, isNew) = DBUtil.findOrInsertItem("user", user, conn=conn);
+            self.userBySID[userSID] = user;
+        return self.userBySID[userSID];
+
+    def metricGroupFromSourceItem(self, sourceItem, conn):
+        # Load or produce a metricGroup record model for the given sourceItem
+        metricGroupId = sourceItem["metric_group_num"]; # Map num to Id
+        metricGroupName = sourceItem["metric_group_name"];
+        if metricGroupId is None:
+            metricGroupId = NULL_GROUP_ID;
+            metricGroupName = NULL_GROUP_NAME;
+        if metricGroupId not in self.metricGroupById:
+            # MetricGroup does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            metricGroup = \
+                RowItemModel \
+                (   {   "metric_group_id": metricGroupId,
+                        "name": metricGroupName,
+                    }
+                );
+            (metricGroupId, isNew) = DBUtil.findOrInsertItem("metric_group", metricGroup, conn=conn);
+            self.metricGroupById[metricGroupId] = metricGroup;
+        return self.metricGroupById[metricGroupId];
+
+    def metricFromSourceItem(self, sourceItem, metricGroup, conn):
+        metricId = sourceItem["metric_id"];
+        if metricId not in self.metricById:
+            metric = \
+                RowItemModel \
+                (   {   "metric_id": metricId,
+                        "metric_group_id": metricGroup["metric_group_id"],
+                        "name": sourceItem["metric_name"],
+                    }
+                );
+            (metricId, isNew) = DBUtil.findOrInsertItem("metric", metric, conn=conn);
+            self.metricById[metricId] = metric;
+        return self.metricById[metricId];
+
+    def metricLineFromSourceItem(self, sourceItem, metric, conn):
+        metricId = sourceItem["metric_id"];
+        targetLine = sourceItem["line_count"];
+        iTargetLine = targetLine-1; # Change from 1 to 0-based indexing
+
+        if metricId not in self.metricLineDescriptionsById:
+            self.metricLineDescriptionsById[metricId] = list();
+        nCurrentLines = len(self.metricLineDescriptionsById[metricId]);
+        nMissingLines = targetLine - nCurrentLines;
+        for i in range(nMissingLines):
+            self.metricLineDescriptionsById[metricId].append('XXX');    # Placeholder values
+
+        self.metricLineDescriptionsById[metricId][iTargetLine] = sourceItem["description"];
+
+        return targetLine;
+
+    def accessLogFromSourceItem(self, sourceItem, user, metric, metricLine, conn):
+        # Produce an access log for the given sourceItem with links to the lookup user and metric
+        # Only record once for multi-line descriptions, so check the line number
+        accessLog = None;
+        if metricLine == 1:
+            accessLog = \
+                RowItemModel \
+                (   {   "user_id":  user["user_id"],
+                        "de_pat_id":  sourceItem["de_pat_id"],
+                        "metric_id":  metric["metric_id"],
+                        "access_datetime":  sourceItem["access_datetime"],
+                    }
+                );
+            insertQuery = DBUtil.buildInsertQuery("access_log", list(accessLog.keys()) );
+            insertParams= list(accessLog.values());
+            DBUtil.execute( insertQuery, insertParams, conn=conn );
+        return accessLog;
+
+    def updateMetricDescriptionLines(self):
+        for metricId, descriptionLines in self.metricLineDescriptionsById.items():
+            description = str.join(' ', descriptionLines);
+            DBUtil.updateRow("metric", {"description": description}, metricId );
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usageStr =  "usage: %prog [options]\n"
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-i", "--userSIDs", dest="userSIDs", metavar="<userSIDs>",  help="Comma separated list of user SIDs to convert AccessLog data for.  Leave blank to attempt conversion for all available");
+        parser.add_option("-l", "--limit", dest="limit", metavar="<limit>",  help="Number of records to process before stopping");
+        parser.add_option("-o", "--offset", dest="offset", metavar="<offset>",  help="Number of records to skip before start converting");
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: "+str.join(" ", argv))
+        timer = time.time();
+
+        userSIDs = None;
+        if options.userSIDs is not None:
+            userSIDs = options.userSIDs.split(",");
+
+        limit = None;
+        if options.limit is not None:
+            limit = int(options.limit);
+        offset = None;
+        if options.offset is not None:
+            offset = int(options.offset);
+
+        self.convertSourceItems(userSIDs, limit, offset);
+
+        timer = time.time() - timer;
+        log.info("%.3f seconds to complete",timer);
+
+if __name__ == "__main__":
+    instance = ProviderRotationConversion();
+    instance.main(sys.argv);
diff --git a/medinfo/dataconversion/ResidentScheduleFormat.py b/medinfo/dataconversion/ResidentScheduleFormat.py
index a2259869..50c55ec4 100644
--- a/medinfo/dataconversion/ResidentScheduleFormat.py
+++ b/medinfo/dataconversion/ResidentScheduleFormat.py
@@ -3,16 +3,19 @@
 import time;
 import csv;
 from datetime import datetime, timedelta;
+from operator import itemgetter
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
 from medinfo.db.ResultsFormatter import TextResultsFormatter;
 from medinfo.db.Model import RowItemModel, RowItemFieldComparator, modelListFromTable, modelDictFromList;
 
-from Util import log;
+from .Util import log;
 
-BASE_YEAR = 2013;   # Year expect dates to start in
 CHANGE_HOUR = 7;  # Designate 7am as changeover time rather than midnight, otherwise night shift behavior on change dates will be misinterpreted
+DEFAULT_INDEX_PREFIX_LENGTH = 3;    # Default number of first letters (prefix) of a provider's name to count as equal / equivalent when guessing provider IDs
+PLACEHOLDER_ID_TEMPLATE = "S%03d";  # Template for fake / placeholder provider ID to generate as needed
+PLACEHOLDER_ID_BASE_COUNTER = -1000;    # Initial value for fake ID sequence counter in negative values
 
 class ResidentScheduleFormat:
     """Data conversion module to take Resident Schedule Excel spreadsheet
@@ -20,17 +23,27 @@ class ResidentScheduleFormat:
     """
     def __init__(self):
         """Default constructor"""
-        self.baseYear = BASE_YEAR;
         self.changeHour = CHANGE_HOUR;
+        self.indexPrefixLength = None;
+        self.providersByNamePrefix = None;
+        self.placeholderIdCounter = PLACEHOLDER_ID_BASE_COUNTER;
 
-    def parseScheduleItems(self, scheduleFile):
+    def parseScheduleItems(self, scheduleFile, baseYear):
         """Given a file reference, parse through contents to generate a list of 
         relational (dictionary) schedule items representing each dated user-rotation schedule component.
         """
         reader = csv.reader(scheduleFile, dialect=csv.excel_tab);  # Assume tab delimited file
         
-        dateRanges = self.parseDateRanges(reader.next());  # Expect first line to be the primary date ranges
-        splitDates = self.parseSplitDates(reader.next()); # Expect next line to be mid-rotation split dates
+        # Iterate through header lines until find one that looks like key date row
+        # Expect first key line to be the primary date ranges
+        row = next(reader);
+        while row[-1].isalnum() or row[-1].find("-") < 0:    # Expect date rows formatted like "5/25 - 6/24" so look for a non-alphanumeric row with a '-'' in the middle
+            row = next(reader);        
+        dateRanges = self.parseDateRanges(row, baseYear);  
+
+        # Expect next line to be mid-rotation split dates
+        row = next(reader);    
+        splitDates = self.parseSplitDates(row, baseYear); 
         
         scheduleItems = list();
         lastResident = None;
@@ -50,6 +63,8 @@ def parseScheduleItems(self, scheduleFile):
     def parseResidentScheduleItems(self, resident, resTextChunksList, dateRanges, splitDates):
         """Parse just the text chunks for an individual resident, being aware of potential for list of multiple rows
         """
+        provId = self.inferProvIdFromName(resident);
+
         for iBlock, dateRange in enumerate(dateRanges):    # Iterate through each major rotation block
             dateRange = dateRanges[iBlock];
             splitDate = splitDates[iBlock];
@@ -58,6 +73,7 @@ def parseResidentScheduleItems(self, resident, resTextChunksList, dateRanges, sp
             scheduleItems = list();
             for iRow, resTextChunks in enumerate(resTextChunksList):
                 textChunk = resTextChunks[iBlock].strip();
+                #print >> sys.stderr, iRow, textChunk
                 if textChunk != "" and textChunk[-1].isdigit(): # Ends with a number, must be a date specification
                     subChunks = textChunk.split();  # Separate out date
                     dateRangeText = subChunks.pop(-1);
@@ -67,7 +83,7 @@ def parseResidentScheduleItems(self, resident, resTextChunksList, dateRanges, sp
 
                     rotation = str.join(' ', subChunks);    # Reconstruct rotation name
 
-                    scheduleItem = {"resident": resident, "rotation": rotation, "start": startDate, "end": endDate};
+                    scheduleItem = {"prov_id": provId, "name": resident, "rotation": rotation, "start_date": startDate, "end_date": endDate};
                     scheduleItems.append(scheduleItem);
                     
             # Second pass to look for rotations without dates specified based on standard dates
@@ -80,44 +96,79 @@ def parseResidentScheduleItems(self, resident, resTextChunksList, dateRanges, sp
                         rotation = subChunks[0].strip();
                         if rotation[0].isalpha():   # Ensure starts with a letter, and not special character for blank placeholder
                             (startDate, endDate) = self.compressDateRange( dateRange[0], splitDate, scheduleItems );  # End split date
-                            scheduleItem = {"resident": resident, "rotation": rotation, "start": startDate, "end": endDate};
+                            scheduleItem = {"prov_id": provId, "name": resident, "rotation": rotation, "start_date": startDate, "end_date": endDate};
                             scheduleItems.append(scheduleItem);
 
                         rotation = subChunks[-1].strip();
                         if rotation[0].isalpha():   # Ensure starts with a letter, and not special character for blank placeholder
                             (startDate, endDate) = self.compressDateRange( splitDate, dateRange[-1], scheduleItems );  # Start on split date
-                            scheduleItem = {"resident": resident, "rotation": rotation, "start": startDate, "end": endDate};
+                            scheduleItem = {"prov_id": provId, "name": resident, "rotation": rotation, "start_date": startDate, "end_date": endDate};
                             scheduleItems.append(scheduleItem);
                     else:   # Single rotation spanning the full block
                         rotation = subChunks[0].strip();
                         if rotation[0].isalpha():   # Ensure starts with a letter, and not special character for blank placeholder
                             (startDate, endDate) = self.compressDateRange( dateRange[0], dateRange[-1], scheduleItems );
-                            scheduleItem = {"resident": resident, "rotation": rotation, "start": startDate, "end": endDate};
+                            scheduleItem = {"prov_id": provId, "name": resident, "rotation": rotation, "start_date": startDate, "end_date": endDate};
                             scheduleItems.append(scheduleItem);
 
             # Now yield / generate results, but keep sorted in chronologic order
-            scheduleItems.sort( RowItemFieldComparator("start") );
+            scheduleItems.sort(key=itemgetter("start_date"));
             for item in scheduleItems:
                 yield item;
 
+    def loadProviderModels( self, providerModels, indexPrefixLength=DEFAULT_INDEX_PREFIX_LENGTH ):
+        """Store a copy of the given provider models (expect each to be a dictionary with prov_id, last_name, first_name combinations).
+        Prepare an index around these based on first X characters of each name to use when trying to back track
+        provider names to prov_ids.
+        """
+        self.indexPrefixLength = indexPrefixLength;
+        self.providersByNamePrefix = dict();
+        for provider in providerModels:
+            namePrefix = (provider["last_name"][:indexPrefixLength] +","+ provider["first_name"][:indexPrefixLength]).upper();
+            if namePrefix not in self.providersByNamePrefix:
+                self.providersByNamePrefix[namePrefix] = list();  # Store a collection to track collisions between multiple providers who have the same first and last name (prefixes)
+            self.providersByNamePrefix[namePrefix].append(provider);
+
+    def inferProvIdFromName(self, name):
+        """Assume name is separated by comma into "LastName, FirstName"
+        Look through providersByNamePrefix dictionary to look for a best match within first X characters of first and last name
+        to back track provider ID. If none found, then fill in a fake ID value.
+        """
+        provIdsStr = None;
+
+        if self.providersByNamePrefix is not None and self.indexPrefixLength is not None:
+            chunks = name.split(",");
+            lastName = chunks[0].strip();
+            firstName = chunks[-1].strip();
+            namePrefix = (lastName[:self.indexPrefixLength] +","+ firstName[:self.indexPrefixLength]).upper();
+
+            if namePrefix in self.providersByNamePrefix:
+                providers = self.providersByNamePrefix[namePrefix];
+                provIdsStr = str.join(",", [provider["prov_id"] for provider in providers])
+        
+        if provIdsStr is None:   # Unable to lookup provider IDs. Just makeup a sequential value then
+            self.placeholderIdCounter -= 1; # Decrement counter as working with negative values
+            provIdsStr = PLACEHOLDER_ID_TEMPLATE % self.placeholderIdCounter;
+        return provIdsStr;
+
     def compressDateRange( self, startDate, endDate, scheduleItems ):
         """Given a list of schedule item with specific date ranges,
         modify the given start and end date range to avoid overlap with any existing ones.
         Simplifying assumption that overlaps will be at ends, not in middle of range or spanning entirety
         """
         for item in scheduleItems:
-            if item["start"] < endDate and startDate < item["end"]: # Overlap
-                if startDate < item["start"] :   # Overlap trim at end
-                    endDate = item["start"];
-                elif item["end"] < endDate: # Overlap trim at start
-                    startDate = item["end"];
+            if item["start_date"] < endDate and startDate < item["end_date"]: # Overlap
+                if startDate < item["start_date"] :   # Overlap trim at end
+                    endDate = item["start_date"];
+                elif item["end_date"] < endDate: # Overlap trim at start
+                    startDate = item["end_date"];
         return (startDate, endDate);                
 
-    def parseDateRanges(self, textChunks):
+    def parseDateRanges(self, textChunks, baseYear):
         textChunks = textChunks[1:];    # Discard first chunk, expect to be an unused label
 
         dateRanges = list();
-        lastDate = datetime(self.baseYear,1,1);   # Start with the expected base year
+        lastDate = datetime(baseYear,1,1);   # Start with the expected base year
         for chunk in textChunks:
             (startText, endText) = chunk.split("-");   # Separate out start from end date
             lastDate = startDate = self.parseDateText( startText, lastDate, 0 );
@@ -139,11 +190,11 @@ def parseDateText( self, dateText, lastDate, incrementDays=0 ):
         dateObj += timedelta(incrementDays);
         return dateObj;
     
-    def parseSplitDates(self, textChunks):
+    def parseSplitDates(self, textChunks, baseYear):
         textChunks = textChunks[1:];    # Discard first chunk, expect to be an unused label
 
         splitDates = list();
-        lastDate = datetime(self.baseYear,1,1);   # Start with the expected base year
+        lastDate = datetime(baseYear,1,1);   # Start with the expected base year
         for chunk in textChunks:
             dateText = chunk.replace("(","").replace(")","").strip();   # Drop flanking parantheses
             lastDate = splitDate = self.parseDateText( dateText, lastDate );
@@ -153,17 +204,26 @@ def parseSplitDates(self, textChunks):
     def main(self, argv):
         """Main method, callable from command line"""
         usageStr =  "usage: %prog <inputFile> <outputFile>\n"+\
-                    "   <inputFile>     Tab-delimited input file taken from schedule Excel file.  Expect first row to have dates as in test case example.  Weird space characters should be converted to regular ASCII characters\n"+\
+                    "   <inputFile>     Tab-delimited input file taken from schedule Excel file. Example data format as seen in test case examples. See support/extractExcelSheets.py for help on pulling out Excel sheets into tab-delimited data files.\n"+\
                     "   <outputFile>    File to output results to.  Designate '-' for stdout.";
         parser = OptionParser(usage=usageStr)
+        parser.add_option("-i", "--providerIdFilename",  dest="providerIdFilename", help="Name of provider ID CSV file. If provided, then add column for prov_id based on resident first_name and last_name, match within first "+DEFAULT_INDEX_PREFIX_LENGTH+" characters, or generate ID value if no match found");
+        parser.add_option("-y", "--baseYear",  dest="baseYear", help="Year expect dates to start in.");
+        parser.add_option("-t", "--changeTime",  dest="changeTime", default=CHANGE_TIME, help="Hour of day that count as delimiter between rotations. Likely should NOT be midnight = 0, because night shifts span midnight. Default to 7 = 7am.");
         (options, args) = parser.parse_args(argv[1:])
 
-        if len(args) >= 2:
+        if len(args) >= 2 and options.baseYear:
             log.info("Starting: "+str.join(" ", argv))
             timer = time.time();
 
+            baseYear = int(options.baseYear);
+
+            if options.providerIdFilename is not None:
+                providerReader = csv.DictReader(open(options.providerIdFilename));
+                self.loadProviderModels( providerReader );
+
             inFile = stdOpen(args[0]);
-            scheduleItems = self.parseScheduleItems(inFile);
+            scheduleItems = self.parseScheduleItems(inFile, baseYear);
 
             outFile = stdOpen(args[1],"w");
             formatter = TextResultsFormatter(outFile);
diff --git a/medinfo/dataconversion/STRIDEAccessLogConversion.py b/medinfo/dataconversion/STRIDEAccessLogConversion.py
index ad69d0ee..f1d73012 100644
--- a/medinfo/dataconversion/STRIDEAccessLogConversion.py
+++ b/medinfo/dataconversion/STRIDEAccessLogConversion.py
@@ -8,8 +8,8 @@
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
 
-from Util import log;
-from Env import DATE_FORMAT;
+from .Util import log;
+from .Env import DATE_FORMAT;
 
 DEFAULT_SOURCE_TABLE = "jchi_accesslog_script";
 NULL_GROUP_ID = 0;  # Metric Group ID to assign if null/None indicated
@@ -20,21 +20,21 @@ class STRIDEAccessLogConversion:
     into normalized structured data tables to facilitate subsequent analysis.
     """
     connFactory = None; # Allow specification of alternative DB connection source
-    
+
     sourceTableName = None;
-    
+
     # Local caches to track lookup values
     userBySID = None;
     metricLineDescriptionsById = None;
     metricById = None;
     metricGroupById = None;
-    
+
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
 
         self.sourceTableName = DEFAULT_SOURCE_TABLE;    # Customizable parameter to facilitate test cases
-        
+
         self.userBySID = dict();
         self.metricLineDescriptionsById = dict();
         self.metricById = dict();
@@ -56,11 +56,11 @@ def convertSourceItems(self, userSIDs=None, limit=None, offset=None):
             self.updateMetricDescriptionLines();
         finally:
             conn.close();
-        progress.PrintStatus();
+        # progress.PrintStatus();
 
 
     def querySourceItems(self, userSIDs, limit=None, offset=None, progress=None, conn=None):
-        """Query the database for list of all AccessLogs 
+        """Query the database for list of all AccessLogs
         and yield the results one at a time.  If userSIDs provided, only return items matching those IDs.
         """
         extConn = conn is not None;
@@ -69,7 +69,7 @@ def querySourceItems(self, userSIDs, limit=None, offset=None, progress=None, con
 
         # Column headers to query for that map to respective fields in analysis table
         headers = ["user_id", "user_name", "de_pat_id", "access_datetime", "metric_id", "metric_name", "line_count", "description", "metric_group_num", "metric_group_name"];
-        
+
         query = SQLQuery();
         for header in headers:
             query.addSelect( header );
@@ -136,7 +136,7 @@ def userFromSourceItem(self, sourceItem, conn):
             (userId, isNew) = DBUtil.findOrInsertItem("user", user, conn=conn);
             self.userBySID[userSID] = user;
         return self.userBySID[userSID];
-    
+
     def metricGroupFromSourceItem(self, sourceItem, conn):
         # Load or produce a metricGroup record model for the given sourceItem
         metricGroupId = sourceItem["metric_group_num"]; # Map num to Id
@@ -162,7 +162,7 @@ def metricFromSourceItem(self, sourceItem, metricGroup, conn):
             metric = \
                 RowItemModel \
                 (   {   "metric_id": metricId,
-                        "metric_group_id": metricGroup["metric_group_id"], 
+                        "metric_group_id": metricGroup["metric_group_id"],
                         "name": sourceItem["metric_name"],
                     }
                 );
@@ -174,14 +174,14 @@ def metricLineFromSourceItem(self, sourceItem, metric, conn):
         metricId = sourceItem["metric_id"];
         targetLine = sourceItem["line_count"];
         iTargetLine = targetLine-1; # Change from 1 to 0-based indexing
-        
+
         if metricId not in self.metricLineDescriptionsById:
             self.metricLineDescriptionsById[metricId] = list();
         nCurrentLines = len(self.metricLineDescriptionsById[metricId]);
         nMissingLines = targetLine - nCurrentLines;
-        for i in xrange(nMissingLines):
+        for i in range(nMissingLines):
             self.metricLineDescriptionsById[metricId].append('XXX');    # Placeholder values
-        
+
         self.metricLineDescriptionsById[metricId][iTargetLine] = sourceItem["description"];
 
         return targetLine;
@@ -199,13 +199,13 @@ def accessLogFromSourceItem(self, sourceItem, user, metric, metricLine, conn):
                         "access_datetime":  sourceItem["access_datetime"],
                     }
                 );
-            insertQuery = DBUtil.buildInsertQuery("access_log", accessLog.keys() );
-            insertParams= accessLog.values();
+            insertQuery = DBUtil.buildInsertQuery("access_log", list(accessLog.keys()) );
+            insertParams= list(accessLog.values());
             DBUtil.execute( insertQuery, insertParams, conn=conn );
         return accessLog;
 
     def updateMetricDescriptionLines(self):
-        for metricId, descriptionLines in self.metricLineDescriptionsById.iteritems():
+        for metricId, descriptionLines in self.metricLineDescriptionsById.items():
             description = str.join(' ', descriptionLines);
             DBUtil.updateRow("metric", {"description": description}, metricId );
 
@@ -231,7 +231,7 @@ def main(self, argv):
         offset = None;
         if options.offset is not None:
             offset = int(options.offset);
-        
+
         self.convertSourceItems(userSIDs, limit, offset);
 
         timer = time.time() - timer;
diff --git a/medinfo/dataconversion/STRIDECultureMicroConversion.py b/medinfo/dataconversion/STRIDECultureMicroConversion.py
new file mode 100644
index 00000000..e7ed6314
--- /dev/null
+++ b/medinfo/dataconversion/STRIDECultureMicroConversion.py
@@ -0,0 +1,252 @@
+#!/usr/bin/env python
+import sys, os
+import time;
+from datetime import datetime;
+from optparse import OptionParser
+from medinfo.common.Util import stdOpen, ProgressDots;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import SQLQuery;
+from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList, RowItemFieldComparator;
+
+from .Util import log;
+from .Env import DATE_FORMAT;
+
+
+SOURCE_TABLE = "stride_culture_micro";
+CATEGORY_TEMPLATE = "Microculture Susceptibility General";
+
+class STRIDECultureMicroConversion:
+    """Data conversion module to take STRIDE data
+    into the structured data analysis tables to facilitate subsequent analysis.
+    """
+
+    def __init__(self):
+        """Default constructor"""
+        self.connFactory = DBUtil.ConnectionFactory();  # Default connection source, but Allow specification of alternative DB connection source
+
+        self.categoryBySourceDescr = dict();    # Local cache to track the clinical item category table contents
+        self.clinicalItemByCompositeKey = dict(); # Local cache to track clinical item table contents
+
+    def convertSourceItems(self, convOptions):
+        """Primary run function to process the contents of the raw source
+        table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
+        Should look for redundancies after the fact to catch repeated conversions.
+
+        startDate - If provided, only return items whose ordering_date is on or after that date.
+        endDate - If provided, only return items whose ordering_date is before that date.
+        """
+        log.info("Conversion for items dated %s to %s" % (convOptions.startDate, convOptions.endDate));
+        progress = ProgressDots();
+        conn = self.connFactory.connection();
+        try:
+            # Next round for medications directly from order_med table not addressed in medmix
+            for sourceItem in self.querySourceItems(convOptions, progress=progress, conn=conn):
+                self.convertSourceItem(sourceItem, conn=conn);
+                progress.Update();
+
+        finally:
+            conn.close();
+        progress.PrintStatus();
+
+
+    def querySourceItems(self, convOptions, progress=None, conn=None):
+        """Query the database for list of all source clinical items (culture results, etc.)
+        and yield the results one at a time.  If startDate provided, only return items whose
+        occurence date is on or after that date.
+        """
+        extConn = conn is not None;
+        if not extConn:
+            conn = self.connFactory.connection();
+
+        # Column headers to query for that map to respective fields in analysis table
+        headers = ["order_proc_anon_id","pat_anon_id","pat_enc_csn_anon_id","proc_code","organism_name","antibiotic_name","suseptibility", "shifted_result_time"];
+
+        query = SQLQuery();
+        for header in headers:
+            query.addSelect( header );
+        query.addFrom("stride_culture_micro");
+        # TODO: FIGURE OUT WHY CAN"T DO >= OPERATION HERE
+        # if convOptions.startDate is not None:
+        #     query.addWhereOp("shifted_result_time",">=", convOptions.startDate);
+        # if convOptions.endDate is not None:
+        #     query.addWhereOp("shifted_result_time","<", convOptions.endDate);  # Still use begin date as common filter value
+      
+      
+        # Query to get an estimate of how long the process will be
+        if progress is not None:
+            progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0];
+
+        cursor = conn.cursor();
+        # Do one massive query, but yield data for one item at a time.
+        cursor.execute( str(query), tuple(query.params) );
+
+        row = cursor.fetchone();
+        while row is not None:
+            rowModel = RowItemModel( row, headers );
+
+            if rowModel['shifted_result_time'] is None: # Don't add if no result time given
+                row = cursor.fetchone();
+                continue
+
+            if rowModel['organism_name'] is not None: # if positive culture but results uninterpretable, don't add feature
+                if rowModel['suseptibility'] is None or rowModel['antibiotic_name'] == 'Method' or rowModel['antibiotic_name'] is None:
+                    row = cursor.fetchone();
+                    continue
+
+            try:
+                # So that we don't run into directory issues later when writing temp files
+                rowModel['antibiotic_name'] = rowModel['antibiotic_name'].replace('/', '-')
+
+                # Change susceptibility features to either susc or not
+                if rowModel['suseptibility'] is not None:
+                    if rowModel['suseptibility'] == 'Susceptible' or rowModel['suseptibility'] == 'Positive':
+                        rowModel['suseptibility'] = 'Susceptible'
+                    else:
+                        rowModel['suseptibility'] = "Not_Susceptible"
+            except: # When antibiotic name is none
+                pass
+
+            yield rowModel; # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
+            row = cursor.fetchone();
+
+        # Slight risk here.  Normally DB connection closing should be in finally of a try block,
+        #   but using the "yield" generator construct forbids us from using a try, finally construct.
+        cursor.close();
+
+        if not extConn:
+            conn.close();
+
+
+    def convertSourceItem(self, sourceItem, conn=None):
+        """Given an individual sourceItem record, produce / convert it into an equivalent
+        item record in the analysis database.
+        """
+        extConn = conn is not None;
+        if not extConn:
+            conn = self.connFactory.connection();
+        try:
+            # Normalize sourceItem data into hierachical components (category -> clinical_item -> patient_item).
+            #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
+            #   in a first pass, with subsequent calls just yielding back in memory cached copies
+            category = self.categoryFromSourceItem(sourceItem, conn=conn);
+            clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn);
+            patientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn);
+
+        finally:
+            if not extConn:
+                conn.close();
+
+
+
+    def categoryFromSourceItem(self, sourceItem, conn):
+        # Load or produce a clinical_item_category record model for the given sourceItem
+        #   In this case, always Microculture Susceptibility
+        categoryDescription = CATEGORY_TEMPLATE;
+        categoryKey = (SOURCE_TABLE, categoryDescription);
+        if categoryKey not in self.categoryBySourceDescr:
+            # Category does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            category = \
+                RowItemModel \
+                (   {   "source_table":  SOURCE_TABLE,
+                        "description":  categoryDescription,
+                    }
+                );
+            (categoryId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", category, conn=conn);
+            category["clinical_item_category_id"] = categoryId;
+            self.categoryBySourceDescr[categoryKey] = category;
+        return self.categoryBySourceDescr[categoryKey];
+
+    def clinicalItemFromSourceItem(self, sourceItem, category, conn):
+        # Load or produce a clinical_item record model for the given sourceItem
+        sourceItem_description = "%s:%s" % (sourceItem['antibiotic_name'], sourceItem['suseptibility'])
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem_description); ########## TODO probably needs to change
+        if clinicalItemKey not in self.clinicalItemByCompositeKey:
+            # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            if sourceItem['antibiotic_name'] is None and sourceItem['suseptibility'] is None: # we only get here if no bacteria grew
+                name = "Negative Culture"
+                description ="Microculture Grew No Bacteria"
+            else:
+                name = "%s:%s" % (sourceItem['antibiotic_name'], sourceItem['suseptibility'])
+                description = "%s TO %s" % (sourceItem['suseptibility'], sourceItem['antibiotic_name'])
+
+
+            clinicalItem = \
+                RowItemModel \
+                (   {   "clinical_item_category_id": category["clinical_item_category_id"],
+                        "external_id": None,
+                        "name": name,
+                        "description": description
+                    }
+                );
+            (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn);
+            clinicalItem["clinical_item_id"] = clinicalItemId;
+            self.clinicalItemByCompositeKey[clinicalItemKey] = clinicalItem;
+        return self.clinicalItemByCompositeKey[clinicalItemKey];
+
+    def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
+        # Produce a patient_item record model for the given sourceItem
+        patientItem = \
+            RowItemModel \
+            (   {   "external_id":  sourceItem["order_proc_anon_id"],
+                    "patient_id":  sourceItem["pat_anon_id"],    
+                    "encounter_id":  sourceItem["pat_enc_csn_anon_id"],   
+                    "clinical_item_id":  clinicalItem["clinical_item_id"],
+                    "item_date":  sourceItem["shifted_result_time"],
+                }
+            );
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute( insertQuery, insertParams, conn=conn );
+            patientItem["patient_item_id"] = DBUtil.execute( DBUtil.identityQuery("patient_item"), conn=conn )[0][0];
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, pull out existing ID and continue to insert whatever else is possible
+            # log.info(err);   # Lookup just by the composite key components to avoid attempting duplicate insertion again
+            searchPatientItem = \
+                {   "patient_id":       patientItem["patient_id"],
+                    "clinical_item_id": patientItem["clinical_item_id"],
+                    "item_date":        patientItem["item_date"],
+                }
+            (patientItem["patient_item_id"], isNew) = DBUtil.findOrInsertItem("patient_item", searchPatientItem, conn=conn);
+        return patientItem;
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usageStr =  "usage: %prog [options]\n"
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-s", "--startDate", dest="startDate", metavar="<startDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with start time on or after this date.");
+        parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with start time before this date.");
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: "+str.join(" ", argv))
+        timer = time.time();
+
+        convOptions = ConversionOptions();
+        convOptions.extractParserOptions(options);
+
+        self.convertSourceItems(convOptions);
+
+        timer = time.time() - timer;
+        log.info("%.3f seconds to complete",timer);
+
+class ConversionOptions:
+    """Simple struct to contain multiple program options"""
+    def __init__(self):
+        self.startDate = None;
+        self.endDate = None;
+
+    def extractParserOptions(self, options):
+        if options.startDate is not None:
+            # Parse out the start date parameter
+            timeTuple = time.strptime(options.startDate, DATE_FORMAT);
+            self.startDate = datetime(*timeTuple[0:3]);
+
+        if options.endDate is not None:
+            # Parse out the end date parameter
+            timeTuple = time.strptime(options.endDate, DATE_FORMAT);
+            self.endDate = datetime(*timeTuple[0:3]);
+
+if __name__ == "__main__":
+    instance = STRIDECultureMicroConversion();
+    instance.main(sys.argv);
diff --git a/medinfo/dataconversion/STRIDEDemographicsConversion.py b/medinfo/dataconversion/STRIDEDemographicsConversion.py
index 5ceb7040..9a410a27 100644
--- a/medinfo/dataconversion/STRIDEDemographicsConversion.py
+++ b/medinfo/dataconversion/STRIDEDemographicsConversion.py
@@ -5,13 +5,11 @@
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
 
-from Util import log;
-from Env import DATE_FORMAT;
+from .Util import log;
+from .Env import DATE_FORMAT;
 
 SOURCE_TABLE = "stride_patient";
 
@@ -21,45 +19,60 @@
     {
         None: "Unknown",
         "": "Unknown",
+        "American Indian or Alaska Native": "Native American",
         "AMERICAN INDIAN OR ALASKA NATIVE": "Native American",
         "ASIAN - HISTORICAL CONV": "Asian",
+        "Asian": "Asian",
         "ASIAN": "Asian",
         "ASIAN, HISPANIC": "Asian",
+        "Asian, non-Hispanic": "Asian",
         "ASIAN, NON-HISPANIC": "Asian",
+        "Black or African American": "Black",
         "BLACK OR AFRICAN AMERICAN": "Black",
+        "Black, Hispanic": "Black",
         "BLACK, HISPANIC": "Black",
+        "Black, non-Hispanic": "Black",
         "BLACK, NON-HISPANIC": "Black",
         "NATIVE AMERICAN, HISPANIC": "Native American",
+        "Native American, non-Hispanic": "Native American",
         "NATIVE AMERICAN, NON-HISPANIC": "Native American",
+        "Native Hawaiian or Other Pacific Islander": "Pacific Islander",
         "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER": "Pacific Islander",
         "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER ": "Pacific Islander",
+        "Other": "Other",
         "OTHER": "Other",
+        "Other, Hispanic": "Hispanic/Latino",
         "OTHER, HISPANIC": "Hispanic/Latino",
+        "Other, non-Hispanic": "Other",
         "OTHER, NON-HISPANIC": "Other",
+        "Pacific Islander, non-Hispanic": "Pacific Islander",
         "PACIFIC ISLANDER, NON-HISPANIC": "Pacific Islander",
+        "Patient Refused": "Unknown",
         "PATIENT REFUSED": "Unknown",
+        "Race and Ethnicity Unknown": "Unknown",
         "RACE AND ETHNICITY UNKNOWN": "Unknown",
+        "Unknown": "Unknown",
         "UNKNOWN": "Unknown",
+        "White": "White (%s)",
         "WHITE": "White (%s)",  # Subset by ethnicity Hispanic/Latino
+        "White, Hispanic": "White (Hispanic/Latino)",
         "WHITE, HISPANIC": "White (Hispanic/Latino)",
+        "White, non-Hispanic": "White (Non-Hispanic/Latino)",
         "WHITE, NON-HISPANIC": "White (Non-Hispanic/Latino)",
         HISPANIC_LATINO_ETHNICITY: "Hispanic/Latino"
     }
 
-
 class STRIDEDemographicsConversion:
     """Data conversion module to take STRIDE provided patient demographics data
     into the structured data tables to facilitate subsequent analysis.
-    
-    Capturing death date for now as an event.  Should eventually incorporate
-    patient age and gender into data, though less clear what item event date
-    to assign to these to make them useful.
+
+    Capturing death date for now as an event.
     """
     connFactory = None; # Allow specification of alternative DB connection source
-    
+
     categoryBySourceDescr = None;   # Local cache to track the clinical item category table contents
     clinicalItemByCategoryIdExtId = None;   # Local cache to track clinical item table contents
-    
+
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
@@ -71,7 +84,7 @@ def convertSourceItems(self, patientIds=None):
         """Primary run function to process the contents of the stride_patient
         table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
         Should look for redundancies to avoid repeating conversion.
-        
+
         patientIds - If provided, only process items for patient IDs matching those provided
         """
         log.info("Conversion for patients: %s" % patientIds);
@@ -82,12 +95,12 @@ def convertSourceItems(self, patientIds=None):
                 self.convertSourceItem(sourceItem, conn=conn);
         finally:
             conn.close();
-        progress.PrintStatus();
+        # progress.PrintStatus();
 
 
     def querySourceItems(self, patientIds=None, progress=None, conn=None):
-        """Query the database for list of all patient demographics 
-        and yield the results one at a time.  If patientIds provided, only return items 
+        """Query the database for list of all patient demographics
+        and yield the results one at a time.  If patientIds provided, only return items
         matching those IDs.
         """
         extConn = conn is not None;
@@ -96,7 +109,7 @@ def querySourceItems(self, patientIds=None, progress=None, conn=None):
 
         # Column headers to query for that map to respective fields in analysis table
         headers = ["pat_id","birth_year","gender","death_date","race","ethnicity"];
-        
+
         query = SQLQuery();
         for header in headers:
             query.addSelect( header );
@@ -127,7 +140,7 @@ def querySourceItems(self, patientIds=None, progress=None, conn=None):
                 yield rowModel;
 
                 # Record another at resolution of decade
-                decade = (rowModel["birth_year"] / 10) * 10;
+                decade = (rowModel["birth_year"] // 10) * 10;
                 rowModel["itemDate"] = datetime(rowModel["birth_year"],1,1);
                 rowModel["name"] = "Birth%ds" % decade;
                 rowModel["description"] = "Birth Decade %ds" % decade;
@@ -136,7 +149,7 @@ def querySourceItems(self, patientIds=None, progress=None, conn=None):
                 # Summarize race and ethnicity information into single field of interest
                 raceEthnicity = self.summarizeRaceEthnicity(rowModel);
                 rowModel["itemDate"] = datetime(rowModel["birth_year"],1,1);
-                rowModel["name"] = "Race"+(raceEthnicity.translate(None," ()-/"));   # Strip off punctuation
+                rowModel["name"] = "Race"+(raceEthnicity.translate(str.maketrans('', '', " ()-/")))   # Strip off punctuation
                 rowModel["description"] = "Race/Ethnicity: %s" % raceEthnicity;
                 yield rowModel;
 
@@ -150,8 +163,8 @@ def querySourceItems(self, patientIds=None, progress=None, conn=None):
                     rowModel["name"] = "Death";
                     rowModel["description"] = "Death Date";
                     rowModel["itemDate"] = rowModel["death_date"];
-                    yield rowModel; 
-            
+                    yield rowModel;
+
             row = cursor.fetchone();
             progress.Update();
 
@@ -167,7 +180,7 @@ def summarizeRaceEthnicity(self, rowModel):
         raceEthnicity = RACE_MAPPINGS[rowModel["race"]];
         if raceEthnicity in UNSPECIFIED_RACE_ETHNICITY and rowModel["ethnicity"] == HISPANIC_LATINO_ETHNICITY:
             raceEthnicity = RACE_MAPPINGS[HISPANIC_LATINO_ETHNICITY];   # Use Hispanic/Latino as basis if no other information
-        if raceEthnicity.find("%s") >= 0:    # Found replacement string.  Look to ethnicity for more information            
+        if raceEthnicity.find("%s") >= 0:    # Found replacement string.  Look to ethnicity for more information
             if rowModel["ethnicity"] == HISPANIC_LATINO_ETHNICITY:
                 raceEthnicity = raceEthnicity % RACE_MAPPINGS[HISPANIC_LATINO_ETHNICITY];
             else:
@@ -189,7 +202,7 @@ def convertSourceItem(self, sourceItem, conn=None):
             categoryModel = self.categoryFromSourceItem(sourceItem, conn=conn);
             clinicalItemModel = self.clinicalItemFromSourceItem(sourceItem, categoryModel, conn=conn);
             patientItemModel = self.patientItemModelFromSourceItem(sourceItem, clinicalItemModel, conn=conn);
-            
+
         finally:
             if not extConn:
                 conn.close();
@@ -209,7 +222,7 @@ def categoryFromSourceItem(self, sourceItem, conn):
             category["clinical_item_category_id"] = categoryId;
             self.categoryBySourceDescr[categoryKey] = category;
         return self.categoryBySourceDescr[categoryKey];
-    
+
     def clinicalItemFromSourceItem(self, sourceItem, category, conn):
         # Load or produce a clinical_item record model for the given sourceItem
         clinicalItemKey = (category["clinical_item_category_id"], sourceItem["name"]);
@@ -218,7 +231,7 @@ def clinicalItemFromSourceItem(self, sourceItem, category, conn):
             clinicalItem = \
                 RowItemModel \
                 (   {   "clinical_item_category_id": category["clinical_item_category_id"],
-                        "external_id": None, 
+                        "external_id": None,
                         "name": sourceItem["name"],
                         "description": sourceItem["description"],
                     }
@@ -227,7 +240,7 @@ def clinicalItemFromSourceItem(self, sourceItem, category, conn):
             clinicalItem["clinical_item_id"] = clinicalItemId;
             self.clinicalItemByCategoryIdExtId[clinicalItemKey] = clinicalItem;
         return self.clinicalItemByCategoryIdExtId[clinicalItemKey];
-    
+
     def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
         # Produce a patient_item record model for the given sourceItem
         patientItem = \
@@ -239,12 +252,12 @@ def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
                     "item_date":  sourceItem["itemDate"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
             log.info(err);
 
diff --git a/medinfo/dataconversion/STRIDEDxListConversion.py b/medinfo/dataconversion/STRIDEDxListConversion.py
index 57a559d9..a0c9e8de 100644
--- a/medinfo/dataconversion/STRIDEDxListConversion.py
+++ b/medinfo/dataconversion/STRIDEDxListConversion.py
@@ -8,46 +8,44 @@
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
 
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
-
-from Util import log;
-from Env import DATE_FORMAT;
+from .Util import log;
+from .Env import DATE_FORMAT;
 
 SOURCE_TABLE = "stride_dx_list";
 SUBCODE_DELIM = ".";    # Delimiter for ICD9 codes to distinguish main categorization vs. detail descriptions
 
 class STRIDEDxListConversion:
-    """Data conversion module to take STRIDE provided diagnosis / problem list 
+    """Data conversion module to take STRIDE provided diagnosis / problem list
     data into the structured data tables to facilitate subsequent analysis.
-    
+
     Renormalizes denormalized data back out to order types (clinical_item_category),
     orders (clinical_item), and individual elements (patient_item).
-    
+
     Only count for new diagnoses with "noted" dates attached.  Historical problem
     list items and encounter diagnoses without dates attached are unclear when or
     where to assign dates.
     """
     connFactory = None; # Allow specification of alternative DB connection source
-    
+
     categoryBySourceDescr = None;   # Local cache to track the clinical item category table contents
     clinicalItemByCategoryIdExtId = None;   # Local cache to track clinical item table contents
-    
+
     icd9StrByCode = None;   # Local cache to facilitate rapid lookups
-    
+
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
 
         self.categoryBySourceDescr = dict();
         self.clinicalItemByCategoryIdExtId = dict();
-        self.icd9StrByCode = None;
+        self.icd9_str_by_code = None
+        self.icd10_str_by_code = None
 
     def convertSourceItems(self, startDate=None, endDate=None):
-        """Primary run function to process the contents of the source table 
+        """Primary run function to process the contents of the source table
         and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
         Should look for redundancies to avoid repeating conversion.
-        
+
         startDate - If provided, only return items whose noted_date is on or after that date.
         endDate - If provided, only return items whose noted_date is before that date.
         """
@@ -63,32 +61,33 @@ def convertSourceItems(self, startDate=None, endDate=None):
 
 
     def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=None):
-        """Query the database for list of all source clinical items (diagnosed probelms in this case) 
-        and yield the results one at a time.  If startDate provided, only return items 
+        """Query the database for list of all source clinical items (diagnosed probelms in this case)
+        and yield the results one at a time.  If startDate provided, only return items
         whose noted_date is on or after that date.
         """
         extConn = conn is not None;
         if not extConn:
             conn = self.connFactory.connection();
 
-        # Facilitate rapid lookup of ICD9 codes
-        if self.icd9StrByCode is None:
-            self.prepareICD9Lookup(conn=conn);
+        # Facilitate rapid lookup of ICD9/ICD10 codes
+        if self.icd9_str_by_code is None:
+            self.prepare_icd9_lookup(conn=conn)
+        if self.icd10_str_by_code is None:
+            self.prepare_icd10_lookup(conn=conn)
 
         # Column headers to query for that map to respective fields in analysis table
-        headers = ["pat_id","pat_enc_csn_id","noted_date","resolved_date","dx_icd9_code","data_source"];
-        
+        headers = ["pat_id","pat_enc_csn_id","noted_date","resolved_date","dx_icd9_code","dx_icd9_code_list","dx_icd10_code_list","data_source"];
+
         query = SQLQuery();
         for header in headers:
             query.addSelect( header );
         query.addFrom("stride_dx_list as dx");
         query.addWhere("noted_date is not null");   # Only work with elements that have dates assigned for now
-        query.addWhere("dx_icd9_code is not null");   # Can't process nullcodes
         if startDate is not None:
             query.addWhereOp("noted_date",">=", startDate);
         if endDate is not None:
             query.addWhereOp("noted_date","<", endDate);
- 
+
         # Query to get an estimate of how long the process will be
         if progress is not None:
             progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0];
@@ -99,29 +98,83 @@ def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=Non
 
         row = cursor.fetchone();
         while row is not None:
-            rowModel = RowItemModel( row, headers );
-
-            # Lookup ICD9 string, otherwise default to ICD9 code
-            rowModel["icd9_str"] = rowModel["dx_icd9_code"];
-            if rowModel["dx_icd9_code"] in self.icd9StrByCode:
-                rowModel["icd9_str"] = self.icd9StrByCode[rowModel["dx_icd9_code"]];
-
-            yield rowModel; # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
-
-            origCode = rowModel["dx_icd9_code"];
-            if SUBCODE_DELIM in origCode:
-                # Insert copies of item for parent node codes to aggregate component diagnoses into general categories
-                while rowModel["dx_icd9_code"][-1] != SUBCODE_DELIM:
-                    rowModel["dx_icd9_code"] = rowModel["dx_icd9_code"][:-1];   # Truncate off one trailing digit
-                    if rowModel["dx_icd9_code"] in self.icd9StrByCode:
-                        rowModel["icd9_str"] = self.icd9StrByCode[rowModel["dx_icd9_code"]];
-                        yield rowModel; # Found a matching parent code, so yield this version
-                # One more cycle to get parent node with no subcode delimiter at all
-                rowModel["dx_icd9_code"] = rowModel["dx_icd9_code"][:-1];   # Truncate off SUBCODE_DELIM
-                if rowModel["dx_icd9_code"] in self.icd9StrByCode:
-                    rowModel["icd9_str"] = self.icd9StrByCode[rowModel["dx_icd9_code"]];
-                    yield rowModel; # Found a matching parent code, so yield this version
-                
+            row_model = RowItemModel( row, headers );
+
+            # 2014-2017 data does not have dx_icd9_code. Instead, has
+            # both dx_icd9_code_list and dx_icd10_code_list. For these items,
+            # there is a one:many mapping of source item to converted item.
+            # Collect icd10 codes.
+            icd10_codes = set()
+            if row_model['dx_icd10_code_list'] not in ['', None]:
+                codes = row_model['dx_icd10_code_list'].split(',')
+                for code in codes:
+                    icd10_codes.add(code)
+            # Collect icd9 codes.
+            icd9_codes = set()
+            if row_model['dx_icd9_code'] not in ['', None]:
+                icd9_codes.add(row_model['dx_icd9_code'])
+            else:
+                if row_model['dx_icd9_code_list'] not in ['', None]:
+                    codes = row_model['dx_icd9_code_list'].split(',')
+                    for code in codes:
+                        icd9_codes.add(code)
+
+            # If there are no ICD codes, skip to next row.
+            if len(icd9_codes) == 0 and len(icd10_codes) == 0:
+                row = cursor.fetchone()
+                continue
+
+            # Process ICD codes.
+            # Build a temporary dictionary so that a single loop can take care
+            # of both ICD9 and ICD10 without mixing the data.
+            icd_versions = {
+                'ICD9': {
+                    'codes': icd9_codes,
+                    'lookup': self.icd9_str_by_code
+                },
+                'ICD10': {
+                    'codes': icd10_codes,
+                    'lookup': self.icd10_str_by_code
+                }
+            }
+
+            for version, info in icd_versions.items():
+                icd_codes = info['codes']
+                icd_lookup = info['lookup']
+
+                for icd_code in icd_codes:
+                    # Look up string. Otherwise default to ICD code.
+                    row_model['icd_str'] = icd_code
+                    if icd_code in icd_lookup:
+                        row_model['icd_str'] = icd_lookup[icd_code]
+                    row_model['dx_icd_code'] = version + '.' + icd_code
+
+                    # Yield one row worth of data at a time to avoid having to keep
+                    # the whole result set in memory.
+                    yield row_model
+
+                    orig_code = icd_code
+                    if SUBCODE_DELIM in orig_code:
+                        # Insert copies of item for parent node codes to aggregate
+                        # component diagnoses into general categories.
+                        while icd_code[-1] != SUBCODE_DELIM:
+                            icd_code = icd_code[:-1] # Truncate trailing digit
+                            if icd_code in icd_lookup:
+                                # Found a a matching parent code, so yield this
+                                # version.
+                                row_model['icd_str'] = icd_lookup[icd_code]
+                                row_model['dx_icd_code'] = version + '.' + icd_code
+                                yield row_model
+
+                        # One more cycle to get parent node with no subcode
+                        # delimiter at all.
+                        icd_code = icd_code[:-1] # Truncate off SUBCODE_DELIM
+                        if icd_code in icd_lookup:
+                            row_model['icd_str'] = icd_lookup[icd_code]
+                            row_model['dx_icd_code'] = version + '.' + icd_code
+
+                            yield row_model
+
             row = cursor.fetchone();
             progress.Update();
 
@@ -145,9 +198,9 @@ def convertSourceItem(self, sourceItem, conn=None):
             #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
             #   in a first past, with subsequent calls just yielding back in memory cached copies
             categoryModel = self.categoryFromSourceItem(sourceItem, conn=conn);
-            clinicalItemModel = self.clinicalItemFromSourceItem(sourceItem, categoryModel, conn=conn);
-            patientItemModel = self.patientItemModelFromSourceItem(sourceItem, clinicalItemModel, conn=conn);
-            
+            clinicalItem = self.clinicalItemFromSourceItem(sourceItem, categoryModel, conn=conn);
+            patientItem = self.patientItemModelFromSourceItem(sourceItem, clinicalItem, conn=conn);
+
         finally:
             if not extConn:
                 conn.close();
@@ -168,25 +221,26 @@ def categoryFromSourceItem(self, sourceItem, conn):
             category["clinical_item_category_id"] = categoryId;
             self.categoryBySourceDescr[categoryKey] = category;
         return self.categoryBySourceDescr[categoryKey];
-    
+
     def clinicalItemFromSourceItem(self, sourceItem, category, conn):
         # Load or produce a clinical_item record model for the given sourceItem
-        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["dx_icd9_code"]);
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem['dx_icd_code']);
+
         if clinicalItemKey not in self.clinicalItemByCategoryIdExtId:
             # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
             clinicalItem = \
                 RowItemModel \
                 (   {   "clinical_item_category_id": category["clinical_item_category_id"],
-                        "external_id": None, 
-                        "name": "ICD9.%(dx_icd9_code)s" % sourceItem,
-                        "description": "%(icd9_str)s" % sourceItem,    
+                        "external_id": None,
+                        "name": "%s" % sourceItem['dx_icd_code'],
+                        "description": "%(icd_str)s" % sourceItem,
                     }
                 );
             (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn);
             clinicalItem["clinical_item_id"] = clinicalItemId;
             self.clinicalItemByCategoryIdExtId[clinicalItemKey] = clinicalItem;
-        return self.clinicalItemByCategoryIdExtId[clinicalItemKey];
-    
+        return self.clinicalItemByCategoryIdExtId[clinicalItemKey]
+
     def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
         # Produce a patient_item record model for the given sourceItem
         patientItem = \
@@ -198,24 +252,40 @@ def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
                     "item_date":  sourceItem["noted_date"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
             log.info(err);
             pass;
 
-    def prepareICD9Lookup(self,conn):
-        """One big query for ICD9 lookup table at one time so don't have to keep repeating
+    def prepare_icd9_lookup(self, conn):
         """
-        self.icd9StrByCode = dict();
-        results = DBUtil.execute("select code, str from stride_icd9_cm where tty in ('HT','PT')", conn=conn);
+        One big query for ICD9 lookup table at one time so don't have to keep
+        repeating.
+        """
+        query = "SELECT code, str FROM stride_icd9_cm WHERE tty IN ('HT','PT')"
+        results = DBUtil.execute(query, conn=conn)
+
+        self.icd9_str_by_code = dict()
+        for (code, str) in results:
+            self.icd9_str_by_code[code] = str
+
+    def prepare_icd10_lookup(self, conn):
+        """
+        One big query for ICD10 lookup table at one time so don't have to keep
+        repeating.
+        """
+        query = "SELECT icd10_code, full_description FROM stride_icd10_cm"
+        results = DBUtil.execute(query, conn=conn)
+
+        self.icd10_str_by_code = dict()
         for (code, str) in results:
-            self.icd9StrByCode[code] = str;
-            
+            self.icd10_str_by_code[code] = str
+
     def main(self, argv):
         """Main method, callable from command line"""
         usageStr =  "usage: %prog [options]\n"
diff --git a/medinfo/dataconversion/STRIDEOrderMedConversion.py b/medinfo/dataconversion/STRIDEOrderMedConversion.py
index e6f4671e..2ecff928 100644
--- a/medinfo/dataconversion/STRIDEOrderMedConversion.py
+++ b/medinfo/dataconversion/STRIDEOrderMedConversion.py
@@ -1,19 +1,19 @@
 #!/usr/bin/env python
 import sys, os
+import hashlib
 import time;
 from datetime import datetime;
+from operator import itemgetter
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList, RowItemFieldComparator;
 
-from Util import log;
-from Const import TEMPLATE_MEDICATION_ID, TEMPLATE_MEDICATION_PREFIX;
-from Const import COLLECTION_TYPE_ORDERSET;
-from Env import DATE_FORMAT;
+from .Util import log;
+from .Const import TEMPLATE_MEDICATION_ID, TEMPLATE_MEDICATION_PREFIX;
+from .Const import COLLECTION_TYPE_ORDERSET;
+from .Env import DATE_FORMAT;
 
 SOURCE_TABLE = "stride_order_med";
 CATEGORY_TEMPLATE = "Med (%s)";    # For this data source, item category will be a Medication subscripted by medication route
@@ -24,18 +24,18 @@ class STRIDEOrderMedConversion:
     """Data conversion module to take STRIDE provided computerized physician order entry data
     (medications specifically)
     into the structured data analysis tables to facilitate subsequent analysis.
-    
+
     For combination medications (usually same medication but with "1.5x" dosing like
     Metoprolol 75mg ordered as combination of 50mg + 25mg tabs), just record as the
     first component in the mixture.
-    
+
     Ignore PRN orders for now to simplify data set and focus on standing orders.
     """
-    
+
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source, but Allow specification of alternative DB connection source
-    
+
         self.categoryBySourceDescr = dict();    # Local cache to track the clinical item category table contents
         self.clinicalItemByCategoryIdCode = dict(); # Local cache to track clinical item table contents
         self.itemCollectionByKeyStr = dict();   # Local cache to track item collections
@@ -45,7 +45,7 @@ def convertSourceItems(self, convOptions):
         """Primary run function to process the contents of the stride_order_med
         table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
         Should look for redundancies after the fact to catch repeatEd conversions.
-        
+
         startDate - If provided, only return items whose ordering_date is on or after that date.
         endDate - If provided, only return items whose ordering_date is before that date.
         """
@@ -55,11 +55,11 @@ def convertSourceItems(self, convOptions):
         try:
             # Load up the medication mapping table to facilitate subsequent conversions
             rxcuiDataByMedId = self.loadRXCUIData(conn=conn);
-            
+
             # Keep track of which order meds have already been converted based on mixture components (don't repeat for the aggregate order then)
             # Can be a lot to store in local memory for large conversions, so may need to batch smaller sub-processes
             convertedOrderMedIds = set();
-            
+
             # First round for medication combinations that must be extracted from order_medmixinfo table
             for sourceItem in self.queryMixSourceItems(rxcuiDataByMedId, convOptions, progress=progress, conn=conn):
                 self.convertSourceItem(sourceItem, conn=conn);
@@ -71,7 +71,7 @@ def convertSourceItems(self, convOptions):
                 if sourceItem["order_med_id"] not in convertedOrderMedIds:  # Don't repeat conversion if mixture components already addressed
                     self.convertSourceItem(sourceItem, conn=conn);
                 progress.Update();
-            
+
         finally:
             conn.close();
         progress.PrintStatus();
@@ -87,7 +87,7 @@ def loadRXCUIData(self, conn=None):
             conn = self.connFactory.connection();
         try:
             rxcuiDataByMedId = dict();
-            
+
             query = \
                 """select medication_id, rxcui, active_ingredient
                 from stride_mapped_meds
@@ -102,9 +102,9 @@ def loadRXCUIData(self, conn=None):
                 if medId not in rxcuiDataByMedId:
                     rxcuiDataByMedId[medId] = dict();
                 rxcuiDataByMedId[medId][rxcui] = ingredient;
-                
+
                 row = cursor.fetchone();
-            
+
             return rxcuiDataByMedId;
 
         finally:
@@ -112,8 +112,8 @@ def loadRXCUIData(self, conn=None):
                 conn.close();
 
     def querySourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn=None):
-        """Query the database for list of all source clinical items (medications, etc.) 
-        and yield the results one at a time.  If startDate provided, only return items whose 
+        """Query the database for list of all source clinical items (medications, etc.)
+        and yield the results one at a time.  If startDate provided, only return items whose
         ordering_date is on or after that date.
         """
         extConn = conn is not None;
@@ -123,7 +123,7 @@ def querySourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn=No
         # Column headers to query for that map to respective fields in analysis table
         queryHeaders = ["med.order_med_id", "pat_id", "pat_enc_csn_id", "med.medication_id", "med.description", "ordering_date", "med_route","number_of_doses","protocol_id","protocol_name","section_name","smart_group"];
         headers = ["order_med_id", "pat_id", "pat_enc_csn_id", "medication_id", "description", "ordering_date", "med_route","number_of_doses","protocol_id","protocol_name","section_name","smart_group"];
-        
+
         query = SQLQuery();
         for header in queryHeaders:
             query.addSelect( header );
@@ -158,8 +158,8 @@ def querySourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn=No
             conn.close();
 
     def queryMixSourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn=None):
-        """Query the database for list of source clinical items (medications from mixes, etc.) 
-        and yield the results one at a time.  
+        """Query the database for list of source clinical items (medications from mixes, etc.)
+        and yield the results one at a time.
         """
         extConn = conn is not None;
         if not extConn:
@@ -168,7 +168,7 @@ def queryMixSourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn
         # Column headers to query for that map to respective fields in analysis table
         queryHeaders = ["med.order_med_id", "med.pat_id", "med.pat_enc_csn_id", "mix.medication_id", "mix.medication_name", "mix.ingredient_type", "med.ordering_date", "med.med_route", "med.number_of_doses","protocol_id","protocol_name","section_name","smart_group"];
         headers = ["order_med_id", "pat_id", "pat_enc_csn_id", "medication_id", "description", "ingredient_type", "ordering_date", "med_route", "number_of_doses","protocol_id","protocol_name","section_name","smart_group"];
-        
+
         query = SQLQuery();
         for header in queryHeaders:
             query.addSelect( header );
@@ -194,7 +194,7 @@ def queryMixSourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn
 
         # Accumulate mixture components one item at a time
         mixByOrderMedId = dict();
-        
+
         row = cursor.fetchone();
         while row is not None:
             rowModel = RowItemModel( row, headers );
@@ -221,7 +221,7 @@ def queryMixSourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn
     def normalizeMixData(self, rxcuiDataByMedId, mixByOrderMedId, convOptions):
         """Look through the mixture components to compile a consolidated set of medication data
         """
-        for orderMedId, mixList in mixByOrderMedId.iteritems():
+        for orderMedId, mixList in mixByOrderMedId.items():
             mixSize = len(mixList);
 
             ingredientIds = set();
@@ -255,7 +255,7 @@ def normalizeMixData(self, rxcuiDataByMedId, mixByOrderMedId, convOptions):
                     yield ingredientModel;
             elif convOptions.maxMixtureCount is None or ingredientCount <= convOptions.maxMixtureCount:
                 # Composite into single denormalized item
-                ingredientList.sort( RowItemFieldComparator("description") ); # Ensure stable sort order
+                ingredientList.sort(key=itemgetter("description"))  # Ensure stable sort order
 
                 idStrList = list();
                 descriptionList = list();
@@ -264,10 +264,13 @@ def normalizeMixData(self, rxcuiDataByMedId, mixByOrderMedId, convOptions):
                     idStrList.append(str(medId));
                     descriptionList.append(ingredientModel["description"]);
                 idComposite = str.join(",", idStrList );
-                descriptionComposite = str.join("-",descriptionList ); 
+                descriptionComposite = str.join("-",descriptionList );
 
                 # Build on last mix item's row model
-                rowModel["medication_id"] = hash(tuple(idStrList));   # Arbitrary integer, hash to try to be unique
+                # Create arbitrary integer, hash to try to be unique
+                # https://stackoverflow.com/questions/16008670/python-how-to-hash-a-string-into-8-digits
+                number = int(hashlib.sha1(idComposite.encode()).hexdigest(), 16) % (10 ** 12)
+                rowModel["medication_id"] = number
                 rowModel["code"] = RXCUI_CODE_TEMPLATE % idComposite;
                 # Hard to trace back to Order_Med.medication_id from here, since working with Order_Med_MixInfo records
                 #rowModel["code"] = GENERIC_CODE_TEMPLATE % rowModel["medication_id"];
@@ -295,7 +298,7 @@ def normalizeMedIngredients(self, rxcuiDataByMedId, rowModel, convOptions):
         Specifically, look for common active ingredients to simplify the data.
         If the medication is actually a compound of multiple active ingredients,
         then break out into active ingredients.
-        
+
         If normalizeMixtures set, then will yield out multiple items to reflect each active ingredient.
         If normalizeMixtures not set, will yield a single item with name being a composite of the active ingredients.
         """
@@ -312,7 +315,11 @@ def normalizeMedIngredients(self, rxcuiDataByMedId, rowModel, convOptions):
             ingredientByRxcui = rxcuiDataByMedId[medId];
             if len(ingredientByRxcui) <= 1 or convOptions.normalizeMixtures:
                 # Single ingredient or want component active ingredients to each have one record
-                for (rxcui, ingredient) in ingredientByRxcui.iteritems():
+                for (rxcui, ingredient) in ingredientByRxcui.items():
+                    # ~250/15000 RxCUI's don't have a defined active ingredient.
+                    if ingredient is None:
+                        continue
+
                     normalizedModel = RowItemModel(rowModel);
                     normalizedModel["medication_id"] = rxcui;
                     normalizedModel["code"] = RXCUI_CODE_TEMPLATE % normalizedModel["medication_id"];
@@ -324,17 +331,20 @@ def normalizeMedIngredients(self, rxcuiDataByMedId, rowModel, convOptions):
             else:
                 # Mixture of multiple ingredients and want to keep denormalized
                 # Extract out the active ingredient names to make a composite based only on that unique combination
-                ingredientRxcuiList = [ (ingredient, rxcui) for (rxcui, ingredient) in ingredientByRxcui.iteritems()];
+                ingredientRxcuiList = [ (ingredient, rxcui) for (rxcui, ingredient) in ingredientByRxcui.items()];
                 ingredientRxcuiList.sort();   # Ensure consistent order
-                
+
                 rxcuiStrList = list();
                 ingredientList = list();
                 for (ingredient, rxcui) in ingredientRxcuiList:
+                    # ~250/15000 RxCUI's don't have a defined active ingredient.
+                    if ingredient is None:
+                        continue
                     rxcuiStrList.append(str(rxcui));
                     ingredientList.append(ingredient.title());
                 rxcuiComposite = str.join(",", rxcuiStrList );
-                ingredientComposite = str.join("-",ingredientList ); 
-                
+                ingredientComposite = str.join("-",ingredientList );
+
                 #rowModel["medication_id"] = hash(tuple(rxcuiList));   # Arbitrary integer, hash to try to be unique
                 #rowModel["code"] = RXCUI_CODE_TEMPLATE % rxcuiComposite;
                 # Nah, just stick to medication_id instead of creating a new hash number
@@ -343,7 +353,7 @@ def normalizeMedIngredients(self, rxcuiDataByMedId, rowModel, convOptions):
                 if convOptions.includeRouteInDescription:
                     rowModel["description"] += " (%s)" % (rowModel["med_route"]);
                 yield rowModel;
-               
+
 
     def convertSourceItem(self, sourceItem, conn=None):
         """Given an individual sourceItem record, produce / convert it into an equivalent
@@ -365,7 +375,7 @@ def convertSourceItem(self, sourceItem, conn=None):
                 itemCollection = self.itemCollectionFromSourceItem(sourceItem, conn=conn);
                 itemCollectionItem = self.itemCollectionItemFromSourceItem(sourceItem, itemCollection, clinicalItem, conn=conn);
                 patientItemCollectionLink = self.patientItemCollectionLinkFromSourceItem(sourceItem, itemCollectionItem, patientItem, conn=conn);
-            
+
         finally:
             if not extConn:
                 conn.close();
@@ -389,7 +399,7 @@ def categoryFromSourceItem(self, sourceItem, conn):
             category["clinical_item_category_id"] = categoryId;
             self.categoryBySourceDescr[categoryKey] = category;
         return self.categoryBySourceDescr[categoryKey];
-    
+
     def clinicalItemFromSourceItem(self, sourceItem, category, conn):
         # Load or produce a clinical_item record model for the given sourceItem
         clinicalItemKey = (category["clinical_item_category_id"], sourceItem["code"]);
@@ -407,17 +417,17 @@ def clinicalItemFromSourceItem(self, sourceItem, category, conn):
             clinicalItem["clinical_item_id"] = clinicalItemId;
             self.clinicalItemByCategoryIdCode[clinicalItemKey] = clinicalItem;
         else:
-            # Clinical Item does exist, but check for redundancies and opportunities to 
+            # Clinical Item does exist, but check for redundancies and opportunities to
             #   simplify different descriptions for the same medication
             priorClinicalItem = self.clinicalItemByCategoryIdCode[clinicalItemKey];
             priorDescription = priorClinicalItem["description"];
             if sourceItem["description"] < priorDescription or priorDescription.startswith(TEMPLATE_MEDICATION_PREFIX):
-                # Prior medication recorded description either a generic template, 
+                # Prior medication recorded description either a generic template,
                 #   or a longer version than necessary, that can be replaced with the current one
                 priorClinicalItem["description"] = sourceItem["description"];
                 DBUtil.updateRow("clinical_item", priorClinicalItem, priorClinicalItem["clinical_item_id"], conn=conn);
         return self.clinicalItemByCategoryIdCode[clinicalItemKey];
-    
+
     def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
         # Produce a patient_item record model for the given sourceItem
         patientItem = \
@@ -429,13 +439,13 @@ def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
                     "item_date":  sourceItem["ordering_date"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
             patientItem["patient_item_id"] = DBUtil.execute( DBUtil.identityQuery("patient_item"), conn=conn )[0][0];
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, pull out existint ID and continue to insert whatever else is possible
             log.info(err);   # Lookup just by the composite key components to avoid attempting duplicate insertion again
             searchPatientItem = \
@@ -470,7 +480,7 @@ def itemCollectionFromSourceItem(self, sourceItem, conn):
             collection["item_collection_id"] = collectionId;
             self.itemCollectionByKeyStr[collectionKey] = collection;
         return self.itemCollectionByKeyStr[collectionKey];
-    
+
     def itemCollectionItemFromSourceItem(self, sourceItem, itemCollection, clinicalItem, conn):
         # Load or produce an item_collection_item record model for the given sourceItem
         itemKey = (itemCollection["item_collection_id"], clinicalItem["clinical_item_id"]);
@@ -487,7 +497,7 @@ def itemCollectionItemFromSourceItem(self, sourceItem, itemCollection, clinicalI
             collectionItem["item_collection_item_id"] = collectionItemId;
             self.itemCollectionItemByCollectionIdItemId[itemKey] = collectionItem;
         return self.itemCollectionItemByCollectionIdItemId[itemKey];
-    
+
     def patientItemCollectionLinkFromSourceItem(self, sourceItem, collectionItem, patientItem, conn):
         # Produce a patient_item_collection_link record model for the given sourceItem
         patientItemCollectionLink = \
@@ -496,12 +506,12 @@ def patientItemCollectionLinkFromSourceItem(self, sourceItem, collectionItem, pa
                     "item_collection_item_id":  collectionItem["item_collection_item_id"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item_collection_link", patientItemCollectionLink.keys() );
-        insertParams= patientItemCollectionLink.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item_collection_link", list(patientItemCollectionLink.keys()) );
+        insertParams= list(patientItemCollectionLink.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
             log.info(err);
 
@@ -514,7 +524,7 @@ def main(self, argv):
         parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time before this date.");
         parser.add_option("-n", "--normalizeMixtures", dest="normalizeMixtures", action="store_true",  help="If set, when find medication mixtures, will unravel / normalize into separate entries, one for each ingredient");
         parser.add_option("-m", "--maxMixtureCount", dest="maxMixtureCount", help="If not normalizing mixtures, then this is the maximum number of mixture components will itemize for a mixture.  If more than this, just use the summary label.");
-        parser.add_option("-d", "--doseCountLimit", dest="doseCountLimit", help="Medication orders with a finite number of doses specified less than this limit will be labeled as different items than those without a number specified, or whose number is >= to this limit.");
+        parser.add_option("-d", "--doseCountLimit", dest="doseCountLimit", help="Medication orders with a finite number of doses specified less than this limit will be labeled as different items than those without a number specified, or whose number is >= to this limit. Intended to distinguish things like IV single bolus / use vs. continuous infusions and standing medication orders");
         (options, args) = parser.parse_args(argv[1:])
 
         log.info("Starting: "+str.join(" ", argv))
@@ -522,7 +532,7 @@ def main(self, argv):
 
         convOptions = ConversionOptions();
         convOptions.extractParserOptions(options);
-        
+
         self.convertSourceItems(convOptions);
 
         timer = time.time() - timer;
@@ -548,10 +558,10 @@ def extractParserOptions(self, options):
             # Parse out the end date parameter
             timeTuple = time.strptime(options.endDate, DATE_FORMAT);
             self.endDate = datetime(*timeTuple[0:3]);
-        
+
         if options.maxMixtureCount is not None:
             self.maxMixtureCount = int(options.maxMixtureCount);
-        
+
         if options.doseCountLimit is not None:
             self.doseCountLimit = int(options.doseCountLimit);
 
diff --git a/medinfo/dataconversion/STRIDEOrderProcConversion.py b/medinfo/dataconversion/STRIDEOrderProcConversion.py
index 9698a70a..b2dec4ce 100644
--- a/medinfo/dataconversion/STRIDEOrderProcConversion.py
+++ b/medinfo/dataconversion/STRIDEOrderProcConversion.py
@@ -5,33 +5,43 @@
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
 
-from Util import log;
-from Env import DATE_FORMAT;
-from Const import COLLECTION_TYPE_ORDERSET;
+from .Util import log;
+from .Env import DATE_FORMAT;
+from .Const import COLLECTION_TYPE_ORDERSET;
 
 SOURCE_TABLE = "stride_order_proc";
 
 class STRIDEOrderProcConversion:
     """Data conversion module to take STRIDE provided computerized physician order entry data
     into the structured data analysis tables to facilitate subsequent analysis.
-    
+
     Renormalizes denormalized data back out to order types (clinical_item_category),
     orders (clinical_item), and actual individual patient orders (patient_item).
     Ignores records with instantiated_time not null, so will only be interested
     in originally ordered parent orders, not spawned child orders.
 
-    Consider Ignore PRN orders for now to simplify data set and focus on standing orders 
+    Consider Ignore PRN orders for now to simplify data set and focus on standing orders
         (but a small minority for these orders anyway)
+
+    Beware of usage of this module and how it accounts for new unique clinical_items (probably applies to all the conversion scripts)
+    # This should be what determines a new unique clinical_item. 
+    #   Some debate about whether to distinguish by proc_id or proc_code, but there are many labs and other procs 
+    #   that use different proc_ids even though they are obviously the same. Go link in STRIDE_ORDER_PROC for examples like LABA1C.
+    # The self.clinicalItemByCategoryIdExtId is supposed to keep track of which clinical_items we're already aware of, 
+    #   but not that it starts blank when this module runs. 
+    #   So you in theory should only run this conversion process on a database once 
+    #   (otherwise it will not be aware that a bunch of duplicate clinical_items already exist in the database). 
+    #   Alternatively, this module should be updated, so that it initializes this key tracker with whatever is already in the database.
+
+
     """
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source, but Allow specification of alternative DB connection source
-    
+
         self.categoryBySourceDescr = dict();    # Local cache to track the clinical item category table contents
         self.clinicalItemByCategoryIdExtId = dict(); # Local cache to track clinical item table contents
         self.itemCollectionByKeyStr = dict();   # Local cache to track item collections
@@ -41,7 +51,7 @@ def convertSourceItems(self, startDate=None, endDate=None):
         """Primary run function to process the contents of the stride_order_proc
         table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
         Should look for redundancies to avoid repeating conversion.
-        
+
         startDate - If provided, only return items whose ordering_date is on or after that date.
         endDate - If provided, only return items whose ordering_date is before that date.
         """
@@ -58,7 +68,7 @@ def convertSourceItems(self, startDate=None, endDate=None):
 
 
     def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=None):
-        """Query the database for list of all source clinical items (orders, etc.) 
+        """Query the database for list of all source clinical items (orders, etc.)
         and yield the results one at a time.  If startDate provided, only return items whose order_time is on or after that date.
         Ignore entries with instantiated_time not null, as those represent child orders spawned from an original order,
         whereas we are more interested in the decision making to enter the original order.
@@ -70,7 +80,7 @@ def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=Non
         # Column headers to query for that map to respective fields in analysis table
         queryHeaders = ["op.order_proc_id", "pat_id", "pat_enc_csn_id", "op.order_type", "op.proc_id", "op.proc_code", "description", "order_time","protocol_id","protocol_name","section_name","smart_group"];
         headers = ["order_proc_id", "pat_id", "pat_enc_csn_id", "order_type", "proc_id", "proc_code", "description", "order_time","protocol_id","protocol_name","section_name","smart_group"];
-        
+
         query = SQLQuery();
         for header in queryHeaders:
             query.addSelect( header );
@@ -119,7 +129,7 @@ def convertSourceItem(self, sourceItem, conn=None):
             category = self.categoryFromSourceItem(sourceItem, conn=conn);
             clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn);
             patientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn);
-            
+
             if sourceItem["protocol_id"] is not None:
                 # Similarly build up item collection (order set) hierarchy and link
                 itemCollection = self.itemCollectionFromSourceItem(sourceItem, conn=conn);
@@ -145,10 +155,18 @@ def categoryFromSourceItem(self, sourceItem, conn):
             category["clinical_item_category_id"] = categoryId;
             self.categoryBySourceDescr[categoryKey] = category;
         return self.categoryBySourceDescr[categoryKey];
-    
+
     def clinicalItemFromSourceItem(self, sourceItem, category, conn):
         # Load or produce a clinical_item record model for the given sourceItem
-        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["proc_id"]);
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["proc_code"]); 
+        # This should be what determines a new unique clinical_item. 
+        #   Some debate about whether to distinguish by proc_id or proc_code, but there are many labs and other procs 
+        #   that use different proc_ids even though they are obviously the same. Go link in STRIDE_ORDER_PROC for examples like LABA1C.
+        # The self.clinicalItemByCategoryIdExtId is supposed to keep track of which clinical_items we're already aware of, 
+        #   but not that it starts blank when this module runs. 
+        #   So you in theory should only run this conversion process on a database once 
+        #   (otherwise it will not be aware that a bunch of duplicate clinical_items already exist in the database). 
+        #   Alternatively, this module should be updated, so that it initializes this key tracker with whatever is already in the database.
         if clinicalItemKey not in self.clinicalItemByCategoryIdExtId:
             # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
             clinicalItem = \
@@ -163,7 +181,7 @@ def clinicalItemFromSourceItem(self, sourceItem, category, conn):
             clinicalItem["clinical_item_id"] = clinicalItemId;
             self.clinicalItemByCategoryIdExtId[clinicalItemKey] = clinicalItem;
         return self.clinicalItemByCategoryIdExtId[clinicalItemKey];
-    
+
     def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
         # Produce a patient_item record model for the given sourceItem
         patientItem = \
@@ -175,15 +193,15 @@ def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
                     "item_date":  sourceItem["order_time"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
             patientItem["patient_item_id"] = DBUtil.execute( DBUtil.identityQuery("patient_item"), conn=conn )[0][0];
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, pull out existint ID and continue to insert whatever else is possible
-            log.info(err);   # Lookup just by the composite key components to avoid attempting duplicate insertion again    
+            log.info(err);   # Lookup just by the composite key components to avoid attempting duplicate insertion again
             searchPatientItem = \
                 {   "patient_id":       patientItem["patient_id"],
                     "clinical_item_id": patientItem["clinical_item_id"],
@@ -216,7 +234,7 @@ def itemCollectionFromSourceItem(self, sourceItem, conn):
             collection["item_collection_id"] = collectionId;
             self.itemCollectionByKeyStr[collectionKey] = collection;
         return self.itemCollectionByKeyStr[collectionKey];
-    
+
     def itemCollectionItemFromSourceItem(self, sourceItem, itemCollection, clinicalItem, conn):
         # Load or produce an item_collection_item record model for the given sourceItem
         itemKey = (itemCollection["item_collection_id"], clinicalItem["clinical_item_id"]);
@@ -233,7 +251,7 @@ def itemCollectionItemFromSourceItem(self, sourceItem, itemCollection, clinicalI
             collectionItem["item_collection_item_id"] = collectionItemId;
             self.itemCollectionItemByCollectionIdItemId[itemKey] = collectionItem;
         return self.itemCollectionItemByCollectionIdItemId[itemKey];
-    
+
     def patientItemCollectionLinkFromSourceItem(self, sourceItem, collectionItem, patientItem, conn):
         # Produce a patient_item_collection_link record model for the given sourceItem
         patientItemCollectionLink = \
@@ -242,19 +260,20 @@ def patientItemCollectionLinkFromSourceItem(self, sourceItem, collectionItem, pa
                     "item_collection_item_id":  collectionItem["item_collection_item_id"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item_collection_link", patientItemCollectionLink.keys() );
-        insertParams= patientItemCollectionLink.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item_collection_link", list(patientItemCollectionLink.keys()) );
+        insertParams= list(patientItemCollectionLink.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
             log.info(err);
 
 
     def main(self, argv):
         """Main method, callable from command line"""
-        usageStr =  "usage: %prog [options]\n"
+        usageStr =  "usage: %prog [options]\n"+\
+            "Beware that this module is intended to be run only ONCE ever on a database. Currently will end up with duplicate clinical item keys if you try to run it in parallel or even serially."
         parser = OptionParser(usage=usageStr)
         parser.add_option("-s", "--startDate", dest="startDate", metavar="<startDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time on or after this date.");
         parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time before this date.");
diff --git a/medinfo/dataconversion/STRIDEOrderResultsConversion.py b/medinfo/dataconversion/STRIDEOrderResultsConversion.py
index 73031881..d19c619a 100644
--- a/medinfo/dataconversion/STRIDEOrderResultsConversion.py
+++ b/medinfo/dataconversion/STRIDEOrderResultsConversion.py
@@ -6,23 +6,21 @@
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList;
 
-from Util import log;
-from Env import DATE_FORMAT;
+from .Util import log;
+from .Env import DATE_FORMAT;
 
-from Const import SENTINEL_RESULT_VALUE, Z_SCORE_LIMIT;
-from Const import FLAG_IN_RANGE, FLAG_HIGH, FLAG_LOW, FLAG_RESULT, FLAG_ABNORMAL;
+from .Const import SENTINEL_RESULT_VALUE, Z_SCORE_LIMIT;
+from .Const import FLAG_IN_RANGE, FLAG_HIGH, FLAG_LOW, FLAG_RESULT, FLAG_ABNORMAL;
 
 SOURCE_TABLE = "stride_order_results";
 
 class STRIDEOrderResultsConversion:
     """Data conversion module to take STRIDE provided (lab) results data
     into the structured data tables to facilitate subsequent analysis.
-    
+
     Renormalizes denormalized data back out to order types (clinical_item_category),
     orders (clinical_item), and actual individual (lab) results (patient_item).
 
@@ -30,10 +28,10 @@ class STRIDEOrderResultsConversion:
     for this result, then just record as a generic "Result" event.
     """
     connFactory = None; # Allow specification of alternative DB connection source
-    
+
     categoryBySourceDescr = None;   # Local cache to track the clinical item category table contents
     clinicalItemByCategoryIdExtId = None;   # Local cache to track clinical item table contents
-    
+
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
@@ -46,7 +44,7 @@ def convertSourceItems(self, startDate=None, endDate=None):
         """Primary run function to process the contents of the stride_order_proc
         table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
         Should look for redundancies to avoid repeating conversion.
-        
+
         startDate - If provided, only return items whose ordering_date is on or after that date.
         endDate - If provided, only return items whose ordering_date is before that date.
         """
@@ -63,8 +61,8 @@ def convertSourceItems(self, startDate=None, endDate=None):
 
 
     def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=None):
-        """Query the database for list of all source clinical items (lab results in this case) 
-        and yield the results one at a time.  If startDate provided, only return items 
+        """Query the database for list of all source clinical items (lab results in this case)
+        and yield the results one at a time.  If startDate provided, only return items
         whose result_time is on or after that date.
         Only include results records where the result_flag is set to an informative value,
         to focus only on abnormal lab results (including would be a ton more relatively uninformative
@@ -76,7 +74,7 @@ def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=Non
 
         # Column headers to query for that map to respective fields in analysis table
         headers = ["sor.order_proc_id", "pat_id", "pat_enc_csn_id", "order_type", "proc_id", "proc_code", "base_name", "component_name", "common_name", "ord_num_value", "result_flag", "result_in_range_yn", "sor.result_time"];
-        
+
         query = SQLQuery();
         for header in headers:
             query.addSelect( header );
@@ -88,7 +86,7 @@ def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=Non
             query.addWhereOp("sor.result_time",">=", startDate);
         if endDate is not None:
             query.addWhereOp("sor.result_time","<", endDate);
- 
+
         # Query to get an estimate of how long the process will be
         if progress is not None:
             progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0];
@@ -101,11 +99,15 @@ def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=Non
         while row is not None:
             rowModel = RowItemModel( row, headers );
             # Normalize qualified labels
-            rowModel["order_proc_id"] = rowModel["sor.order_proc_id"];  
-            rowModel["result_time"] = rowModel["sor.result_time"];  
-            
+            rowModel["order_proc_id"] = rowModel["sor.order_proc_id"];
+            rowModel["result_time"] = rowModel["sor.result_time"];
+
+            if rowModel['base_name'] is None:
+                row = cursor.fetchone()
+                continue
+
             self.populateResultFlag(rowModel,conn=conn);
-            
+
             yield rowModel; # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
             row = cursor.fetchone();
 
@@ -133,6 +135,12 @@ def populateResultFlag(self, resultModel, conn=None):
             # No specific result flag or (numerical) value provided. Just record that some result was generated at all
             resultModel["result_flag"] = FLAG_RESULT;
             return;
+        elif resultModel['base_name'] is None:
+            # With 2014-2017 data, there are fields with a null base_name.
+            # We can't build summary stats around this case, so just return
+            # FLAG_RESULT.
+            resultModel['result_flag'] = FLAG_RESULT
+            return
         #else: # General case, no immediately available result flags
 
         extConn = conn is not None;
@@ -144,11 +152,11 @@ def populateResultFlag(self, resultModel, conn=None):
             dataTable = DBUtil.execute("select * from order_result_stat", includeColumnNames=True, conn=conn);
             dataModels = modelListFromTable(dataTable);
             self.resultStatsByBaseName = modelDictFromList(dataModels, "base_name");
-        
+
         if resultModel["base_name"] not in self.resultStatsByBaseName:
             # Result stats not already in cache.  Query from DB and store in cache for future use.
             statModel = self.calculateResultStats( resultModel["base_name"], conn=conn );
-            
+
             # Store results back in cache to facilitate future lookups
             self.resultStatsByBaseName[resultModel["base_name"]] = statModel;
             DBUtil.insertRow("order_result_stat", statModel, conn=conn );
@@ -175,12 +183,12 @@ def populateResultFlag(self, resultModel, conn=None):
                         resultModel["result_flag"] = FLAG_HIGH;
                     else:   # |zScore| < Z_SCORE_LIMIT
                         resultModel["result_flag"] = FLAG_IN_RANGE;
-                except ValueError, exc:
+                except ValueError as exc:
                     # Math error, probably stdev = 0 or variance < 0, just treat as an unspecified result
                     resultModel["result_flag"] = FLAG_RESULT;
             else:   # No value distribution, just record as a non-specific result
                 resultModel["result_flag"] = FLAG_RESULT;
-        
+
         if not extConn:
             conn.close();
 
@@ -202,7 +210,7 @@ def calculateResultStats(self, baseName, conn):
         dataModels = modelListFromTable(dataTable);
         statModel = dataModels[0];   # Assume that exactly 1 row item will exist
         statModel["base_name"] = baseName;
-        
+
         return statModel;
 
     def convertSourceItem(self, sourceItem, conn=None):
@@ -219,7 +227,7 @@ def convertSourceItem(self, sourceItem, conn=None):
             categoryModel = self.categoryFromSourceItem(sourceItem, conn=conn);
             clinicalItemModel = self.clinicalItemFromSourceItem(sourceItem, categoryModel, conn=conn);
             patientItemModel = self.patientItemModelFromSourceItem(sourceItem, clinicalItemModel, conn=conn);
-            
+
         finally:
             if not extConn:
                 conn.close();
@@ -240,7 +248,7 @@ def categoryFromSourceItem(self, sourceItem, conn):
             category["clinical_item_category_id"] = categoryId;
             self.categoryBySourceDescr[categoryKey] = category;
         return self.categoryBySourceDescr[categoryKey];
-    
+
     def clinicalItemFromSourceItem(self, sourceItem, category, conn):
         # Load or produce a clinical_item record model for the given sourceItem
         # Make unique by lab component name, not by proc_id / panel, since interested in result, not which panel it came from
@@ -260,7 +268,7 @@ def clinicalItemFromSourceItem(self, sourceItem, category, conn):
             clinicalItem["clinical_item_id"] = clinicalItemId;
             self.clinicalItemByCategoryIdExtId[clinicalItemKey] = clinicalItem;
         return self.clinicalItemByCategoryIdExtId[clinicalItemKey];
-    
+
     def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
         # Produce a patient_item record model for the given sourceItem
         patientItem = \
@@ -273,12 +281,12 @@ def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
                     "num_value": sourceItem["ord_num_value"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
             log.info(err);
 
diff --git a/medinfo/dataconversion/STRIDEPreAdmitMedConversion.py b/medinfo/dataconversion/STRIDEPreAdmitMedConversion.py
deleted file mode 100644
index 23d561b6..00000000
--- a/medinfo/dataconversion/STRIDEPreAdmitMedConversion.py
+++ /dev/null
@@ -1,339 +0,0 @@
-#!/usr/bin/env python
-import sys, os
-import time;
-from datetime import datetime;
-from optparse import OptionParser
-from medinfo.common.Util import stdOpen, ProgressDots;
-from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
-from medinfo.db.Model import SQLQuery;
-from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList, RowItemFieldComparator;
-
-from Util import log;
-from Const import TEMPLATE_MEDICATION_ID, TEMPLATE_MEDICATION_PREFIX;
-from Const import COLLECTION_TYPE_ORDERSET;
-from Env import DATE_FORMAT;
-
-SOURCE_TABLE = "stride_preadmit_med";
-CATEGORY_TEMPLATE = "Preadmit Med";    # For this data source, item category will be a Preadmission Medication 
-GENERIC_CODE_TEMPLATE = "MED%s";   # Template for generic medication code reference if detailed RXCUI values not available
-RXCUI_CODE_TEMPLATE = "RXCUI%s";    # Template for medication code references when detailed RXCUI values available
-
-class STRIDEPreAdmitMedConversion:
-    """Data conversion module to take STRIDE data
-    into the structured data analysis tables to facilitate subsequent analysis.
-    """
-    
-    def __init__(self):
-        """Default constructor"""
-        self.connFactory = DBUtil.ConnectionFactory();  # Default connection source, but Allow specification of alternative DB connection source
-    
-        self.categoryBySourceDescr = dict();    # Local cache to track the clinical item category table contents
-        self.clinicalItemByCategoryIdCode = dict(); # Local cache to track clinical item table contents
-
-    def convertSourceItems(self, convOptions):
-        """Primary run function to process the contents of the raw source
-        table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
-        Should look for redundancies after the fact to catch repeated conversions.
-        
-        startDate - If provided, only return items whose ordering_date is on or after that date.
-        endDate - If provided, only return items whose ordering_date is before that date.
-        """
-        log.info("Conversion for items dated %s to %s" % (convOptions.startDate, convOptions.endDate));
-        progress = ProgressDots();
-        conn = self.connFactory.connection();
-        try:
-            # Load up the medication mapping table to facilitate subsequent conversions
-            rxcuiDataByMedId = self.loadRXCUIData(conn=conn);
-            
-            # Next round for medications directly from order_med table not addressed in medmix
-            for sourceItem in self.querySourceItems(rxcuiDataByMedId, convOptions, progress=progress, conn=conn):
-                self.convertSourceItem(sourceItem, conn=conn);
-                progress.Update();
-            
-        finally:
-            conn.close();
-        progress.PrintStatus();
-
-
-    def loadRXCUIData(self, conn=None):
-        """Load up the full contents of the stride_mapped_meds table into
-        memory (only a few thousand records) to facilitate rapid lookup resolution
-        of common medication ingredient data.
-        """
-        extConn = conn is not None;
-        if not extConn:
-            conn = self.connFactory.connection();
-        try:
-            rxcuiDataByMedId = dict();
-            
-            query = \
-                """select medication_id, rxcui, active_ingredient, thera_class
-                from stride_mapped_meds
-                """;
-
-            cursor = conn.cursor();
-            cursor.execute( query );
-
-            row = cursor.fetchone();
-            while row is not None:
-                (medId, rxcui, ingredient, theraClass) = row;   # Unpack the data tuple
-                if medId not in rxcuiDataByMedId:
-                    rxcuiDataByMedId[medId] = dict();
-                rxcuiDataByMedId[medId][rxcui] = (ingredient, theraClass);
-                
-                row = cursor.fetchone();
-            
-            return rxcuiDataByMedId;
-
-        finally:
-            if not extConn:
-                conn.close();
-
-    def querySourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn=None):
-        """Query the database for list of all source clinical items (medications, etc.) 
-        and yield the results one at a time.  If startDate provided, only return items whose 
-        occurence date is on or after that date.
-        """
-        extConn = conn is not None;
-        if not extConn:
-            conn = self.connFactory.connection();
-
-        # Column headers to query for that map to respective fields in analysis table
-        headers = ["stride_preadmit_med_id","pat_anon_id","contact_date","medication_id","description","thera_class","pharm_class","pharm_subclass"];
-        
-        query = SQLQuery();
-        for header in headers:
-            query.addSelect( header );
-        query.addFrom("stride_preadmit_med");
-        if convOptions.startDate is not None:
-            query.addWhereOp("contact_date",">=", convOptions.startDate);
-        if convOptions.endDate is not None:
-            query.addWhereOp("contact_date","<", convOptions.endDate);
-
-        # Query to get an estimate of how long the process will be
-        if progress is not None:
-            progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0];
-
-        cursor = conn.cursor();
-        # Do one massive query, but yield data for one item at a time.
-        cursor.execute( str(query), tuple(query.params) );
-
-        row = cursor.fetchone();
-        while row is not None:
-            rowModel = RowItemModel( row, headers );
-            for normalizedModel in self.normalizeMedIngredients(rxcuiDataByMedId, rowModel, convOptions, conn=conn):
-                yield normalizedModel; # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
-            row = cursor.fetchone();
-
-        # Slight risk here.  Normally DB connection closing should be in finally of a try block,
-        #   but using the "yield" generator construct forbids us from using a try, finally construct.
-        cursor.close();
-
-        if not extConn:
-            conn.close();
-
-    def normalizeMedIngredients(self, rxcuiDataByMedId, rowModel, convOptions, conn=None):
-        """Given a rowModel of medication data, normalize it further.
-        Specifically, look for common active ingredients to simplify the data.
-        If the medication is actually a compound of multiple active ingredients,
-        then break out into active ingredients.
-        
-        If normalizeMixtures set, then will yield out multiple items to reflect each active ingredient.
-        If normalizeMixtures not set, will yield a single item with name being a composite of the active ingredients.
-        """
-        extConn = conn is not None;
-        if not extConn:
-            conn = self.connFactory.connection();
-
-        medId = rowModel["medication_id"]
-
-        if medId not in rxcuiDataByMedId:
-            # No mapping entry found, just use the available generic medication data then
-            rowModel["code"] = GENERIC_CODE_TEMPLATE % rowModel["medication_id"];
-            yield rowModel;
-
-        else:
-            # Mapping entry found, yield a normalized model for each active ingredient found
-            #   (will usually be a 1-to-1 relation, but sometimes multiple
-            ingredientTheraClassByRxcui = rxcuiDataByMedId[medId];
-            if len(ingredientTheraClassByRxcui) <= 1 or convOptions.normalizeMixtures:
-                # Single ingredient or want component active ingredients to each have one record
-                for (rxcui, (ingredient, theraClass)) in ingredientTheraClassByRxcui.iteritems():
-                    normalizedModel = RowItemModel(rowModel);
-                    normalizedModel["medication_id"] = rxcui;
-                    normalizedModel["code"] = RXCUI_CODE_TEMPLATE % rxcui;
-                    normalizedModel["description"] = ingredient.title();
-                    
-                    yield normalizedModel;
-            elif convOptions.maxMixtureCount is not None and len(ingredientTheraClassByRxcui) > convOptions.maxMixtureCount:
-                # Plan to denormalize, but excessively large mixture.  Forget it.
-                rowModel["code"] = GENERIC_CODE_TEMPLATE % rowModel["medication_id"];
-                yield rowModel;
-            else:
-                # Mixture of multiple ingredients and want to keep denormalized
-                # Extract out the active ingredient names to make a composite based only on that unique combination
-                ingredientRxcuiList = [ (ingredient, rxcui) for (rxcui, (ingredient, theraClass)) in ingredientTheraClassByRxcui.iteritems()];
-                ingredientRxcuiList.sort();   # Ensure consistent order
-                
-                rxcuiStrList = list();
-                ingredientList = list();
-                for (ingredient, rxcui) in ingredientRxcuiList:
-                    rxcuiStrList.append(str(rxcui));
-                    ingredientList.append(ingredient.title());
-                rxcuiComposite = str.join(",", rxcuiStrList );
-                ingredientComposite = str.join("-",ingredientList ); 
-                
-                #rowModel["medication_id"] = hash(rxcuiComposite);    # No, just stick to existing medication ID
-                rowModel["code"] = GENERIC_CODE_TEMPLATE % medId;
-                rowModel["description"] = ingredientComposite;
-                yield rowModel;
-            
-            # Do some extra work here to see if we can figure out therapeutic / pharaceutical class labels based on available data
-            if rowModel["thera_class"] is not None:
-                theraClassNeedsPopulation = False;
-                for (rxcui, (ingredient, theraClass)) in ingredientTheraClassByRxcui.iteritems():
-                    if theraClass is None:
-                        # Don't have a previously populated class labels for this medication ID, but just found it with this data.  Populate then.
-                        theraClass = rowModel["thera_class"];
-                        ingredientTheraClassByRxcui[rxcui] = (ingredient, theraClass);
-                        theraClassNeedsPopulation = True;
-                if theraClassNeedsPopulation:
-                    rowDict = {"thera_class": rowModel["thera_class"], "pharm_class": rowModel["pharm_class"], "pharm_subclass": rowModel["pharm_subclass"],}
-                    DBUtil.updateRow("stride_mapped_meds", rowDict, medId, idCol="medication_id", conn=conn);
-                    
-        if not extConn:
-            conn.close();
-
-    def convertSourceItem(self, sourceItem, conn=None):
-        """Given an individual sourceItem record, produce / convert it into an equivalent
-        item record in the analysis database.
-        """
-        extConn = conn is not None;
-        if not extConn:
-            conn = self.connFactory.connection();
-        try:
-            # Normalize sourceItem data into hierachical components (category -> clinical_item -> patient_item).
-            #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
-            #   in a first pass, with subsequent calls just yielding back in memory cached copies
-            category = self.categoryFromSourceItem(sourceItem, conn=conn);
-            clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn);
-            patientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn);
-            
-        finally:
-            if not extConn:
-                conn.close();
-
-
-
-    def categoryFromSourceItem(self, sourceItem, conn):
-        # Load or produce a clinical_item_category record model for the given sourceItem
-        #   In this case, always Medication
-        categoryDescription = CATEGORY_TEMPLATE;
-        categoryKey = (SOURCE_TABLE, categoryDescription);
-        if categoryKey not in self.categoryBySourceDescr:
-            # Category does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
-            category = \
-                RowItemModel \
-                (   {   "source_table":  SOURCE_TABLE,
-                        "description":  categoryDescription,
-                    }
-                );
-            (categoryId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", category, conn=conn);
-            category["clinical_item_category_id"] = categoryId;
-            self.categoryBySourceDescr[categoryKey] = category;
-        return self.categoryBySourceDescr[categoryKey];
-    
-    def clinicalItemFromSourceItem(self, sourceItem, category, conn):
-        # Load or produce a clinical_item record model for the given sourceItem
-        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["code"]);
-        if clinicalItemKey not in self.clinicalItemByCategoryIdCode:
-            # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
-            clinicalItem = \
-                RowItemModel \
-                (   {   "clinical_item_category_id": category["clinical_item_category_id"],
-                        "external_id": sourceItem["medication_id"],
-                        "name": sourceItem["code"],
-                        "description": sourceItem["description"],
-                    }
-                );
-            (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn);
-            clinicalItem["clinical_item_id"] = clinicalItemId;
-            self.clinicalItemByCategoryIdCode[clinicalItemKey] = clinicalItem;
-        return self.clinicalItemByCategoryIdCode[clinicalItemKey];
-    
-    def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
-        # Produce a patient_item record model for the given sourceItem
-        patientItem = \
-            RowItemModel \
-            (   {   "external_id":  sourceItem["stride_preadmit_med_id"],
-                    "patient_id":  sourceItem["pat_anon_id"],
-                    "encounter_id":  None,
-                    "clinical_item_id":  clinicalItem["clinical_item_id"],
-                    "item_date":  sourceItem["contact_date"],
-                }
-            );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
-        try:
-            # Optimistic insert of a new unique item
-            DBUtil.execute( insertQuery, insertParams, conn=conn );
-            patientItem["patient_item_id"] = DBUtil.execute( DBUtil.identityQuery("patient_item"), conn=conn )[0][0];
-        except IntegrityError, err:
-            # If turns out to be a duplicate, okay, pull out existing ID and continue to insert whatever else is possible
-            log.info(err);   # Lookup just by the composite key components to avoid attempting duplicate insertion again
-            searchPatientItem = \
-                {   "patient_id":       patientItem["patient_id"],
-                    "clinical_item_id": patientItem["clinical_item_id"],
-                    "item_date":        patientItem["item_date"],
-                }
-            (patientItem["patient_item_id"], isNew) = DBUtil.findOrInsertItem("patient_item", searchPatientItem, conn=conn);
-        return patientItem;
-
-    def main(self, argv):
-        """Main method, callable from command line"""
-        usageStr =  "usage: %prog [options]\n"
-        parser = OptionParser(usage=usageStr)
-        parser.add_option("-s", "--startDate", dest="startDate", metavar="<startDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time on or after this date.");
-        parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time before this date.");
-        parser.add_option("-n", "--normalizeMixtures", dest="normalizeMixtures", action="store_true",  help="If set, when find medication mixtures, will unravel / normalize into separate entries, one for each ingredient");
-        parser.add_option("-m", "--maxMixtureCount", dest="maxMixtureCount", help="If not normalizing mixtures, then this is the maximum number of mixture components will itemize for a mixture.  If more than this, just use the summary label.");
-        (options, args) = parser.parse_args(argv[1:])
-
-        log.info("Starting: "+str.join(" ", argv))
-        timer = time.time();
-
-        convOptions = ConversionOptions();
-        convOptions.extractParserOptions(options);
-        
-        self.convertSourceItems(convOptions);
-
-        timer = time.time() - timer;
-        log.info("%.3f seconds to complete",timer);
-
-class ConversionOptions:
-    """Simple struct to contain multiple program options"""
-    def __init__(self):
-        self.startDate = None;
-        self.endDate = None;
-        self.normalizeMixtures = False;
-        self.maxMixtureCount = None;
-
-    def extractParserOptions(self, options):
-        if options.startDate is not None:
-            # Parse out the start date parameter
-            timeTuple = time.strptime(options.startDate, DATE_FORMAT);
-            self.startDate = datetime(*timeTuple[0:3]);
-
-        if options.endDate is not None:
-            # Parse out the end date parameter
-            timeTuple = time.strptime(options.endDate, DATE_FORMAT);
-            self.endDate = datetime(*timeTuple[0:3]);
-        
-        if options.maxMixtureCount is not None:
-            self.maxMixtureCount = int(options.maxMixtureCount);
-        
-if __name__ == "__main__":
-    instance = STRIDEPreAdmitMedConversion();
-    instance.main(sys.argv);
diff --git a/medinfo/dataconversion/STRIDETreatmentTeamConversion.py b/medinfo/dataconversion/STRIDETreatmentTeamConversion.py
index 0204eec0..545d07a4 100644
--- a/medinfo/dataconversion/STRIDETreatmentTeamConversion.py
+++ b/medinfo/dataconversion/STRIDETreatmentTeamConversion.py
@@ -5,18 +5,16 @@
 from optparse import OptionParser
 from medinfo.common.Util import stdOpen, ProgressDots;
 from medinfo.db import DBUtil;
-from medinfo.db.DBUtil import DB_CONNECTOR_MODULE;
-IntegrityError = DB_CONNECTOR_MODULE.IntegrityError;
 from medinfo.db.Model import SQLQuery;
 from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList, RowItemFieldComparator;
 
-from Util import log;
-from Const import TEMPLATE_MEDICATION_ID, TEMPLATE_MEDICATION_PREFIX;
-from Const import COLLECTION_TYPE_ORDERSET;
-from Env import DATE_FORMAT;
+from .Util import log;
+from .Const import TEMPLATE_MEDICATION_ID, TEMPLATE_MEDICATION_PREFIX;
+from .Const import COLLECTION_TYPE_ORDERSET;
+from .Env import DATE_FORMAT;
 
 SOURCE_TABLE = "stride_treatment_team";
-CATEGORY_TEMPLATE = "Treatment Team";    
+CATEGORY_TEMPLATE = "Treatment Team";
 KEY_PROVIDER_PREFIXES = ("TT ","CON "); # Name Prefixes indicating a special (team) provider
 
 TEAM_PREFIXES = ("Primary", "Consulting");
@@ -32,11 +30,11 @@ class STRIDETreatmentTeamConversion:
     """Data conversion module to take STRIDE data
     into the structured data analysis tables to facilitate subsequent analysis.
     """
-    
+
     def __init__(self):
         """Default constructor"""
         self.connFactory = DBUtil.ConnectionFactory();  # Default connection source, but Allow specification of alternative DB connection source
-    
+
         self.categoryBySourceDescr = dict();    # Local cache to track the clinical item category table contents
         self.clinicalItemByCompositeKey = dict(); # Local cache to track clinical item table contents
 
@@ -44,7 +42,7 @@ def convertSourceItems(self, convOptions):
         """Primary run function to process the contents of the raw source
         table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
         Should look for redundancies after the fact to catch repeated conversions.
-        
+
         startDate - If provided, only return items whose ordering_date is on or after that date.
         endDate - If provided, only return items whose ordering_date is before that date.
         """
@@ -56,15 +54,15 @@ def convertSourceItems(self, convOptions):
             for sourceItem in self.querySourceItems(convOptions, progress=progress, conn=conn):
                 self.convertSourceItem(sourceItem, conn=conn);
                 progress.Update();
-            
+
         finally:
             conn.close();
         progress.PrintStatus();
 
 
     def querySourceItems(self, convOptions, progress=None, conn=None):
-        """Query the database for list of all source clinical items (medications, etc.) 
-        and yield the results one at a time.  If startDate provided, only return items whose 
+        """Query the database for list of all source clinical items (medications, etc.)
+        and yield the results one at a time.  If startDate provided, only return items whose
         occurence date is on or after that date.
         """
         extConn = conn is not None;
@@ -73,7 +71,7 @@ def querySourceItems(self, convOptions, progress=None, conn=None):
 
         # Column headers to query for that map to respective fields in analysis table
         headers = ["stride_treatment_team_id","pat_id","pat_enc_csn_id","trtmnt_tm_begin_date","trtmnt_tm_end_date","treatment_team","prov_name"];
-        
+
         query = SQLQuery();
         for header in headers:
             query.addSelect( header );
@@ -94,8 +92,7 @@ def querySourceItems(self, convOptions, progress=None, conn=None):
         row = cursor.fetchone();
         while row is not None:
             rowModel = RowItemModel( row, headers );
-            for normalizedModel in self.normalizeRowModel(rowModel, convOptions, conn=conn):
-                yield normalizedModel; # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
+            yield self.normalizeRowModel(rowModel, convOptions)  # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
             row = cursor.fetchone();
 
         # Slight risk here.  Normally DB connection closing should be in finally of a try block,
@@ -105,39 +102,32 @@ def querySourceItems(self, convOptions, progress=None, conn=None):
         if not extConn:
             conn.close();
 
-    def normalizeRowModel(self, rowModel, convOptions, conn=None):
+    def normalizeRowModel(self, rowModel, convOptions):
         """Given a rowModel of data, normalize it further.
         Specifically, look for aggregate data items (e.g., multiple Gen Med treatment teams, report as one)
         """
-        extConn = conn is not None;
-        if not extConn:
-            conn = self.connFactory.connection();
-
         (teamAcronym, teamName) = self.cleanName(rowModel["treatment_team"], convOptions);
         (provAcronym, provName) = self.cleanName(rowModel["prov_name"], convOptions, keyPrefixes=KEY_PROVIDER_PREFIXES);
         provName = provName.title();
 
-        rowModel["code"] = teamAcronym;
-        rowModel["description"] = teamName;
-
         if provAcronym != "":
             rowModel["code"] = "%s (%s)" % (provAcronym, teamAcronym);
             rowModel["description"] = "%s (%s)" % (provName, teamName);
+        else:
+            rowModel["code"] = teamAcronym;
+            rowModel["description"] = teamName;
 
         if rowModel["trtmnt_tm_begin_date"] is None:
             # Don't know how to use event information with a timestamp
             pass;
 
-        yield rowModel;
-
-        if not extConn:
-            conn.close();
+        return rowModel;
 
     def cleanName(self, inputName, convOptions, keyPrefixes=None):
         """Given an input name (e.g., treatment team or provider)
         Return a 2-ple (acronym, cleaned) with an acronym version
         and a cleaned up version (discard extra punctuation, phone/pager numbers, etc.)
-        If keyPrefixes specified, then only accept input names that start with one of them 
+        If keyPrefixes specified, then only accept input names that start with one of them
         (e.g., "TT" or "CON" for provider names, to only look at team names instead of named individuals.
         """
         isPrefixAcceptable = True;
@@ -146,11 +136,11 @@ def cleanName(self, inputName, convOptions, keyPrefixes=None):
             for keyPrefix in keyPrefixes:
                 if inputName is not None and inputName.startswith(keyPrefix):
                     isPrefixAcceptable = True;
-        
+
         # Default to blanks
         acronym = "";
         cleanedName = "";
-            
+
         if isPrefixAcceptable and inputName is not None:
             acronymList = list();
             wordList = list();
@@ -159,32 +149,32 @@ def cleanName(self, inputName, convOptions, keyPrefixes=None):
                 if chunk[-1] == ",":    # Strip any commass
                     chunk = chunk[:-1];
 
-                if convOptions.aggregate and i == 0 and chunk in (TEAM_PREFIXES):
-                   # Aggregating mixed records, so just use batch team prefix if exists
-                   acronymList.append(chunk[0]);
-                   wordList.append(chunk);
-                   break;
+                if convOptions.aggregate and i == 0 and chunk in TEAM_PREFIXES:
+                    # Aggregating mixed records, so just use batch team prefix if exists
+                    acronymList.append(chunk[0]);
+                    wordList.append(chunk);
+                    break
                 elif convOptions.aggregate and chunk.upper() in SUB_LABEL_WORDS:
                     # Sub label word not interested when aggregating data, so just ignore it
-                    pass;
+                    continue
                 elif convOptions.aggregate and len(chunk) == 1:
                     # A short number or letter sub-label, ignore if aggregating
-                    pass;
+                    continue
                 elif convOptions.aggregate and len(chunk) <= 2 and (chunk[0].isdigit() or chunk[-1].isdigit()):
                     # A short number or letter sub-label, ignore if aggregating
-                    pass;
+                    continue
                 elif len(chunk) <= 2 and not chunk[0].isalnum() and not chunk[-1].isalnum():
                     # Short non-alphanumeric sequence, probably punctuation
-                    pass;
+                    continue
                 elif not chunk[0].isalnum() and len(chunk) >1 and chunk[1].isdigit():
                     # Probably just a pager/phone number
-                    pass;
+                    continue
                 elif len(chunk) > 1 and chunk[0].isdigit() and chunk[-1].isdigit():
                     # Looks like a number, probably pager or phone.  Don't include
-                    pass;
+                    continue
                 elif chunk in DISCARD_WORDS:
                     # Probably just pager link, not interested
-                    pass;
+                    continue
                 else:
                     if chunk[0].isalnum():
                         acronymList.append(chunk[0])
@@ -198,7 +188,7 @@ def cleanName(self, inputName, convOptions, keyPrefixes=None):
                 # Override specific provider names as generic primary team members
                 (acronym, cleanedName) = ("P","Primary");
 
-        return (acronym, cleanedName);        
+        return (acronym, cleanedName);
 
 
 
@@ -216,7 +206,7 @@ def convertSourceItem(self, sourceItem, conn=None):
             category = self.categoryFromSourceItem(sourceItem, conn=conn);
             clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn);
             patientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn);
-            
+
         finally:
             if not extConn:
                 conn.close();
@@ -240,7 +230,7 @@ def categoryFromSourceItem(self, sourceItem, conn):
             category["clinical_item_category_id"] = categoryId;
             self.categoryBySourceDescr[categoryKey] = category;
         return self.categoryBySourceDescr[categoryKey];
-    
+
     def clinicalItemFromSourceItem(self, sourceItem, category, conn):
         # Load or produce a clinical_item record model for the given sourceItem
         clinicalItemKey = (category["clinical_item_category_id"], sourceItem["description"]);
@@ -258,7 +248,7 @@ def clinicalItemFromSourceItem(self, sourceItem, category, conn):
             clinicalItem["clinical_item_id"] = clinicalItemId;
             self.clinicalItemByCompositeKey[clinicalItemKey] = clinicalItem;
         return self.clinicalItemByCompositeKey[clinicalItemKey];
-    
+
     def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
         # Produce a patient_item record model for the given sourceItem
         patientItem = \
@@ -270,13 +260,13 @@ def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
                     "item_date":  sourceItem["trtmnt_tm_begin_date"],
                 }
             );
-        insertQuery = DBUtil.buildInsertQuery("patient_item", patientItem.keys() );
-        insertParams= patientItem.values();
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()) );
+        insertParams= list(patientItem.values());
         try:
             # Optimistic insert of a new unique item
             DBUtil.execute( insertQuery, insertParams, conn=conn );
             patientItem["patient_item_id"] = DBUtil.execute( DBUtil.identityQuery("patient_item"), conn=conn )[0][0];
-        except IntegrityError, err:
+        except conn.IntegrityError as err:
             # If turns out to be a duplicate, okay, pull out existing ID and continue to insert whatever else is possible
             log.info(err);   # Lookup just by the composite key components to avoid attempting duplicate insertion again
             searchPatientItem = \
@@ -301,7 +291,7 @@ def main(self, argv):
 
         convOptions = ConversionOptions();
         convOptions.extractParserOptions(options);
-        
+
         self.convertSourceItems(convOptions);
 
         timer = time.time() - timer;
@@ -324,9 +314,9 @@ def extractParserOptions(self, options):
             # Parse out the end date parameter
             timeTuple = time.strptime(options.endDate, DATE_FORMAT);
             self.endDate = datetime(*timeTuple[0:3]);
-        
+
         self.aggregate = options.aggregate;
-        
+
 if __name__ == "__main__":
     instance = STRIDETreatmentTeamConversion();
     instance.main(sys.argv);
diff --git a/medinfo/dataconversion/Util.py b/medinfo/dataconversion/Util.py
index ed8f9355..34df2d14 100644
--- a/medinfo/dataconversion/Util.py
+++ b/medinfo/dataconversion/Util.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
 
@@ -12,4 +12,4 @@
 formatter = logging.Formatter(Const.LOGGER_FORMAT)
 
 handler.setFormatter(formatter)
-log.addHandler(handler)
+# log.addHandler(handler)
diff --git a/medinfo/dataconversion/cohorts.py b/medinfo/dataconversion/cohorts.py
new file mode 100644
index 00000000..bf7df546
--- /dev/null
+++ b/medinfo/dataconversion/cohorts.py
@@ -0,0 +1,87 @@
+"""
+Definition of CohortBuilder - a class to construct a cohort (set of examples)
+"""
+import decimal
+import pandas as pd
+
+class CohortBuilder(object):
+    """
+    A class for constructing a cohort table and saving to a bigquery project.
+    A cohort table should have at minimum three columns:
+        1. anon_id : the patient id
+        2. observation_id : a unique identifier for each observation 
+        3. index_time : prediction time for each observation
+        4. `label` : binary or multiclass indicating class membership of the
+            observations. This may be multiple columns (if in multlilabel
+            setting) and will have column names as specified in `label_columns`
+            attribute.
+    """
+
+    def __init__(self, dataset_name, table_name, label_columns,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        self.project_id = working_project_id
+        self.dataset_name = dataset_name
+        self.table_name = table_name
+        self.label_columns = label_columns
+
+    def build_cohort(self, query, transform=lambda x: x):
+        """
+        Uses provided query and transformation function to construct a dataframe
+        representation of the cohort.  Query will vary from task to task and is
+        user supplied, as is the transform function.  The transform function
+        exists so that the user can apply additional logic in python to result
+        of query to generate the final cohort dataframe.  By default transform
+        is an identity function, meaning the result from query will be the
+        cohort dataframe.
+
+        Args:
+            query : a SQL query to collect a cohort of interest
+            transform : a python function to be applied ot result of query
+                to construct final cohort table
+        """
+        self.df = pd.read_gbq(query,
+                              project_id=self.project_id,
+                              progress_bar_type='tqdm')
+        self.df = transform(self.df)
+
+        # Check for required columns
+        try:
+            assert 'anon_id' in self.df.columns
+            assert 'observation_id' in self.df.columns
+            assert 'index_time' in self.df.columns
+            for label in self.label_columns:
+                assert label in self.df.columns
+        except:
+            print("Unexpected columns")
+            for col in self.df.columns:
+                print(col)
+        
+        # Older version of pandas_gbq reads in numeric column as decimal
+        if isinstance(self.df.observation_id.values[0], decimal.Decimal):
+            self.df = self.df.assign(observation_id=lambda x:
+                [int(str(d).split('.')[0]) for d in x.observation_id]
+            )
+
+    def write_cohort_table(self, overwrite=False, schema=None):
+        """
+        Writes the cohort dataframe to specified bigquery project, dataset,
+        and table with appropriate table schema.
+
+        Args:
+            overwrite: if true overwrite existing table
+            schema: dictionary of table schema, if None detect automatically. 
+        """
+        if overwrite:
+            if_exists = 'replace'
+        else:
+            if_exists = 'fail'
+        self.df.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.project_id,
+            if_exists=if_exists,
+            table_schema=schema
+        )
diff --git a/medinfo/dataconversion/constants.py b/medinfo/dataconversion/constants.py
new file mode 100644
index 00000000..b75f1225
--- /dev/null
+++ b/medinfo/dataconversion/constants.py
@@ -0,0 +1,49 @@
+# Constants to import 
+
+DEFAULT_DEPLOY_CONFIG = {
+    'Categorical': {
+        'Sex': [{'look_back' : None}],
+        'Race': [{'look_back' : None}],
+        'Diagnoses': [{'look_back' : None}],
+        'Medications': [{'look_back' : 28}]
+    },
+    'Numerical': {
+        'Age': [{'look_back': None, 'num_bins': 5}],
+        'LabResults': [{'look_back': 14, 'num_bins': 5}],
+        #'Vitals': [{'look_back': 3, 'num_bins': 5}]
+    }
+}
+
+DEFAULT_LAB_COMPONENT_IDS = [
+    'WBC',  # White Blood Cell
+    'HCT',  # Hematocrit
+    'PLT',  # Platelet Count
+    'NA',  # Sodium, Whole Blood
+    'K',  # Potassium, Whole Blood
+    'CO2',  # CO2, Serum/Plasma
+    'BUN',  # Blood Urea Nitrogen
+    'CR',  # Creatinine
+    'TBIL',  # Total Bilirubin
+    'ALB',  # Albumin
+    'CA',  # Calcium
+    'LAC',  # Lactic Acid
+    'ESR',  # Erythrocyte Sedimentation Rate
+    'CRP',  # C-Reactive Protein
+    'TNI',  # Troponin I
+    'PHA',  # Arterial pH
+    'PO2A',  # Arterial pO2
+    'PCO2A',  # Arterial pCO2
+    'PHV',  # Venous pH
+    'PO2V',  # Venous pO2
+    'PCO2V'  # Venous pCO2
+]
+
+
+DEFAULT_FLOWSHEET_FEATURES = [
+    "Heart Rate",
+    "Temp",
+    "Resp",
+    "SpO2",
+    'BP_High_Systolic',
+    "BP_Low_Diastolic"
+]
diff --git a/medinfo/dataconversion/featurizers.py b/medinfo/dataconversion/featurizers.py
new file mode 100644
index 00000000..78bc4de8
--- /dev/null
+++ b/medinfo/dataconversion/featurizers.py
@@ -0,0 +1,1185 @@
+"""
+Definition of BagOfWordsFeaturizer
+TODO: Definition of SummaryStatsFeaturizer
+TODO: Definition of TimelineFeaturizer
+"""
+import json
+import os
+from re import S
+from typing_extensions import Self
+import pandas as pd
+import pickle
+from google.cloud import bigquery
+import numpy as np
+from tqdm import tqdm
+from scipy.sparse import csr_matrix
+from scipy.sparse import save_npz
+
+from constants import DEFAULT_DEPLOY_CONFIG
+from constants import DEFAULT_LAB_COMPONENT_IDS
+from constants import DEFAULT_FLOWSHEET_FEATURES
+import FeatureExtractor as fextractors
+
+import pdb
+
+class SequenceFeaturizer():
+    """
+    Uses FeatureExtractors to generate a set of variable lengh sequences
+    for each observation. Saves these sequences as invididual numpy arrays in 
+    npz format. The npz contains the following data elements
+    NPZ structure:
+         sequence: array of tokens - continuous features must be binned
+         time_deltas: array with same length as sequence, hours to index time
+         label: list of labels corresponding to seq (len=1 if only one label). 
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, train_years,
+                 val_years, test_years, label_columns, outpath='./features',
+                 project='som-nero-phi-jonc101', dataset='shc_core_2021',
+                 feature_config=None):
+        """
+        Args:
+            cohort_table_id: ex 'mining-clinical-decisions.conor_db.table_name'
+            feature_table_id: ex
+                'mining-clinical-decisions.conor_db.feature_table'
+            train_years: list of years to include in training set
+            val_years: list of years to include in validation set
+            test_years: list of years to include in the test set
+            label_columns: list of columns desingated as labels in cohort table
+            outpath: path to dump feature matrices
+            project: bq project id to extract data from
+            dataset: bq dataset with project to extract data from
+            feature_config: dictionary with feature types, bins and look back
+                windows.
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.outpath = outpath
+        self.project = project
+        self.dataset = dataset
+        if feature_config is None:
+            self.feature_config = DEFAULT_DEPLOY_CONFIG
+        else:
+            self.feature_config = feature_config
+        self.client = bigquery.Client()
+        self.train_years = [int(y) for y in train_years]
+        self.val_years = [int(y) for y in val_years]
+        self.test_years = [int(y) for y in test_years]
+        self.label_columns = label_columns
+
+    def __call__(self):
+        """
+        Generates sequence feature vectors and saves to outpath
+        """
+        label_cols = ', '.join([f"c.{l}" for l in self.label_columns])
+        self.construct_feature_timeline()
+        query = f"""
+        SELECT
+            f.*, {label_cols}
+        FROM
+            {self.feature_table_id} f
+        INNER JOIN
+            {self.cohort_table_id} c
+        USING
+            (observation_id)
+        ORDER BY
+            observation_id, feature_time
+        """
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+
+        # Get time deltas in hours from index time
+        df = df.assign(time_deltas=lambda x: 
+            (x.index_time - x.feature_time).astype('timedelta64[D]')
+        )
+        
+        # Split into train, val and test and ensure only terms in train are used
+        train_seqs = df[df['index_time'].dt.year.isin(
+            self.train_years)]
+        vocab = train_seqs.feature.unique()
+        vocab_set = set([s for s in vocab])
+        val_seqs = df[df['index_time'].dt.year.isin(
+            self.val_years)].query("feature in @vocab_set", engine='python')
+        test_seqs = df[df['index_time'].dt.year.isin(
+            self.test_years)].query("feature in @vocab_set", engine='python')
+        seq_dict = {'train' : train_seqs, 'val' :  val_seqs, 'test' : test_seqs}
+
+        # Create working directory if does not already exist and save features
+        for dataset, seqs in seq_dict.items():
+            os.makedirs(os.path.join(self.outpath, dataset), exist_ok=True)
+            for obs in seqs.observation_id.unique():
+                example = seqs[seqs['observation_id']==obs]
+                sequence = example.feature.values
+                time_deltas = example.time_deltas.values
+                labels = example[self.label_columns].values
+                np.savez(os.path.join(self.outpath, dataset, f"{obs}.npz"),
+                         sequence=sequence,
+                         time_deltas=time_deltas,
+                         labels=labels)
+                
+        # Save feature vocab
+        np.savez(os.path.join(self.outpath, dataset, 'feature_vocab.npz'),
+                 vocab=vocab)
+                
+        # Save bin thresholds if they exist
+        self.df_lup = pd.DataFrame()
+        for lup in self.lups:
+            if lup is not None:
+                self.df_lup = pd.concat([self.df_lup, lup])
+        if not self.df_lup.empty:
+            self.df_lup.to_csv(os.path.join(self.outpath, 'bin_lup.csv'),
+                               index=None)
+
+        # Save feature_config
+        with open(os.path.join(self.outpath, 'feature_config.json'), 'w') as f:
+            json.dump(self.feature_config, f)
+
+    def construct_feature_timeline(self):
+        """
+        Executes all logic to iteratively append rows to the biq query long form
+        feature matrix destination table.  Does this by iteratively joining
+        cohort table to tables with desired features, filtering for events
+        that occur within each look up range, and then transforming into bag of
+        words style representations.  Features with numerical values are binned
+        into buckets to enable bag of words repsesentation. 
+        """
+        extractors = []
+        # Get categorical features
+        if 'Sex' in self.feature_config['Categorical']:
+            se = fextractors.SexExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(se)
+        if 'Race' in self.feature_config['Categorical']:
+            re = fextractors.RaceExtractor(self.cohort_table_id,
+                                           self.feature_table_id)
+            extractors.append(re)
+        if 'Diagnoses' in self.feature_config['Categorical']:
+            pe = fextractors.PatientProblemExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(pe)
+        if 'Medications' in self.feature_config['Categorical']:
+            me = fextractors.MedicationExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(me)
+
+        # Get numerical features
+        if 'Age' in self.feature_config['Numerical']:
+            ae = fextractors.AgeExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Age'][0]['num_bins'])
+            extractors.append(ae)
+        if 'LabResults' in self.feature_config['Numerical']:
+            lre = fextractors.LabResultBinsExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']
+                ['LabResults'][0]['num_bins'],
+                base_names=DEFAULT_LAB_COMPONENT_IDS)
+            extractors.append(lre)
+        if 'Vitals' in self.feature_config['Numerical']:
+            fbe = fextractors.FlowsheetBinsExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Vitals'][0]['num_bins'],
+                base_names=DEFAULT_FLOWSHEET_FEATURES)
+            extractors.append(fbe)
+
+        # Call extractors and collect any look up tables
+        self.lups = []
+        for extractor in tqdm(extractors):
+            self.lups.append(extractor())
+
+
+class BagOfWordsFeaturizerLight():
+    """
+    Bag of words but uses FeatureExtractors instead of burying all SQL logic
+    as in implementation below. 
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, outpath='./features',
+                 project='som-nero-phi-jonc101', dataset='shc_core_2021',
+                 feature_config=None):
+        """
+        Args:
+            cohort_table_id: ex 'mining-clinical-decisions.conor_db.table_name'
+            feature_table_id: ex 
+                'mining-clinical-decisions.conor_db.feature_table'
+            outpath: path to dump feature matrices
+            project: bq project id to extract data from
+            dataset: bq dataset with project to extract data from
+            feature_config: dictionary with feature types, bins and look back
+                windows. 
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.outpath = outpath
+        self.project = project
+        self.dataset = dataset
+        if feature_config is None:
+            self.feature_config = DEFAULT_DEPLOY_CONFIG
+        else:
+            self.feature_config = feature_config
+        self.client = bigquery.Client()
+        # Get data splits (default last year of data held out as test set)
+        split_query = f"""
+            SELECT DISTINCT
+                EXTRACT(YEAR FROM index_time) year
+            FROM
+                {self.cohort_table_id}
+        """
+        df = pd.read_gbq(split_query).sort_values('year')
+        self.train_years = df.year.values[:-1]
+        self.test_years = [df.year.values[-1]]
+        self.replace_table = True
+
+    def __call__(self):
+        """
+        Executes all logic to construct features and labels and saves all info
+        user specified working directory.
+        """
+        self.construct_feature_timeline()
+        self.construct_bag_of_words_rep()
+        query = f"""
+        SELECT
+            *
+        FROM
+            {self.feature_table_id}_bow
+        ORDER BY
+            observation_id
+        """
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        train_features = df[df['index_time'].dt.year.isin(self.train_years)]
+        apply_features = df[~df['index_time'].dt.year.isin(self.train_years)]
+        train_csr, train_ids, train_vocab = self.construct_sparse_matrix(
+            train_features, train_features)
+        test_csr, test_ids, test_vocab = self.construct_sparse_matrix(
+            train_features, apply_features)
+
+        # Query cohort table for labels
+        q_cohort = f"""
+            SELECT
+                *
+            FROM
+               {self.cohort_table_id}
+            ORDER BY
+                observation_id
+        """
+        df_cohort = pd.read_gbq(q_cohort, progress_bar_type='tqdm')
+        train_labels = df_cohort[df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+        test_labels = df_cohort[~df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+
+        # Sanity check - make sure ids from labels and features in same order
+        for a, b in zip(train_labels['observation_id'].values, train_ids):
+            try:
+                assert a == b
+            except:
+                pdb.set_trace()
+        for a, b in zip(test_labels['observation_id'].values, test_ids):
+            assert a == b
+
+        # Create working directory if does not already exist and save features
+        os.makedirs(self.outpath, exist_ok=True)
+        save_npz(os.path.join(self.outpath, 'train_features.npz'), train_csr)
+        save_npz(os.path.join(self.outpath, 'test_features.npz'), test_csr)
+        print(f"Feature matrix generated with {train_csr.shape[1]} features")
+
+        # Save labels
+        train_labels.to_csv(os.path.join(self.outpath, 'train_labels.csv'),
+                            index=None)
+        test_labels.to_csv(os.path.join(self.outpath, 'test_labels.csv'),
+                           index=None)
+
+        # Save feature order
+        df_vocab = pd.DataFrame(data={
+            'features': [t for t in train_vocab],
+            'indices': [train_vocab[t] for t in train_vocab]
+        })
+        df_vocab.to_csv(os.path.join(self.outpath, 'feature_order.csv'),
+                        index=None)
+
+        # Save bin thresholds if they exist
+        self.df_lup = pd.DataFrame()
+        for lup in self.lups:
+            if lup is not None:
+                self.df_lup = pd.concat([self.df_lup, lup])
+        if not self.df_lup.empty:
+            self.df_lup.to_csv(os.path.join(self.outpath, 'bin_lup.csv'),
+                            index=None)
+
+        # Save feature_config
+        with open(os.path.join(self.outpath, 'feature_config.json'), 'w') as f:
+            json.dump(self.feature_config, f)
+
+    def construct_feature_timeline(self):
+        """
+        Executes all logic to iteratively append rows to the biq query long form
+        feature matrix destination table.  Does this by iteratively joining
+        cohort table to tables with desired features, filtering for events
+        that occur within each look up range, and then transforming into bag of
+        words style representations.  Features with numerical values are binned
+        into buckets to enable bag of words repsesentation. 
+        """
+        extractors = []
+        # Get categorical features
+        if 'Sex' in self.feature_config['Categorical']:
+            se = fextractors.SexExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(se)
+        if 'Race' in self.feature_config['Categorical']:
+            re = fextractors.RaceExtractor(self.cohort_table_id,
+                self.feature_table_id)
+            extractors.append(re)
+        if 'Diagnoses' in self.feature_config['Categorical']:
+            pe = fextractors.PatientProblemExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(pe)
+        if 'Medications' in self.feature_config['Categorical']:
+            me = fextractors.MedicationExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(me)
+
+        # Get numerical features
+        if 'Age' in self.feature_config['Numerical']:
+            ae = fextractors.AgeExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Age'][0]['num_bins'])
+            extractors.append(ae)
+        if 'LabResults' in self.feature_config['Numerical']:
+            lre = fextractors.LabResultBinsExtractor(self.cohort_table_id,
+                                         self.feature_table_id,
+                                         bins=self.feature_config['Numerical']
+                                         ['LabResults'][0]['num_bins'],
+                                         base_names=DEFAULT_LAB_COMPONENT_IDS)
+            extractors.append(lre)
+        if 'Vitals' in self.feature_config['Numerical']:
+            fbe = fextractors.FlowsheetBinsExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Vitals'][0]['num_bins'],
+                base_names=DEFAULT_FLOWSHEET_FEATURES)
+            extractors.append(fbe)
+
+        # Call extractors and collect any look up tables
+        self.lups = []
+        for extractor in tqdm(extractors):
+            self.lups.append(extractor())
+
+        # Call extractors and collect any look up tables
+        self.lups = []
+        for extractor in tqdm(extractors):
+            self.lups.append(extractor())
+    
+    def construct_bag_of_words_rep(self):
+        """
+        Transforms long form feature timeline into a bag of words feature
+        matrix. Stores as a new table in the given bigquery database but also
+        constructs local sparse matrices that can be fed into various sklearn
+        style classifiers. 
+        """
+
+        # Go from timeline to counts
+        query = f"""
+        CREATE OR REPLACE TABLE {self.feature_table_id}_bow AS (
+        SELECT 
+            observation_id, index_time, feature_type, feature, COUNT(*) value 
+        FROM 
+            {self.feature_table_id}
+        WHERE 
+            feature_type IS NOT NULL
+        AND
+            feature IS NOT NULL
+        GROUP BY 
+            observation_id, index_time, feature_type, feature
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def construct_sparse_matrix(self, train_features, apply_features):
+        """
+        Takes long form feature timeline matrix and builds up a scipy csr
+        matrix without the costly pivot operation. 
+        """
+        train_features = (train_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        train_feature_names = [doc for doc in train_features.feature.values]
+        train_feature_values = [doc for doc in train_features['value'].values]
+        train_obs_id = [id_ for id_ in train_features.observation_id.values]
+
+        apply_features = (apply_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        apply_features_names = [doc for doc in apply_features.feature.values]
+        apply_features_values = [doc for doc in apply_features['value'].values]
+        apply_obs_id = [id_ for id_ in apply_features.observation_id.values]
+
+        vocabulary = self._build_vocab(train_feature_names)
+        indptr = [0]
+        indices = []
+        data = []
+        for i, d in enumerate(apply_features_names):
+            for j, term in enumerate(d):
+                if term not in vocabulary:
+                    continue
+                else:
+                    indices.append(vocabulary[term])
+                    data.append(apply_features_values[i][j])
+                if j == 0:
+                    # Add zero to data and max index in vocabulary to indices in
+                    # case max feature indice isn't in apply features.
+                    indices.append(len(vocabulary)-1)
+                    data.append(0)
+            indptr.append(len(indices))
+
+        csr_data = csr_matrix((data, indices, indptr), dtype=float)
+
+        return csr_data, apply_obs_id, vocabulary
+
+    def _build_vocab(self, data):
+        """
+        Builds vocabulary of terms from the data. Assigns each unique term
+        to a monotonically increasing integer
+        """
+        vocabulary = {}
+        for i, d in enumerate(data):
+            for j, term in enumerate(d):
+                vocabulary.setdefault(term, len(vocabulary))
+        return vocabulary
+
+
+class BagOfWordsFeaturizer(object):
+    """
+    A class containing all logic to construct a bag of words feature matrix
+    given a cohort and user provided feature configurations. 
+
+    This featurizer supports the construction of a bag of words style feature
+    matrix where each column in the resulting matrix is the number of times a 
+    certain event (ex lab order, diagnosis code, medication order) occurs
+    in the patients timeline in a given time window prior to index time. 
+
+    Categorical variables are trivial to represent in bag of words style.
+    Numerical variables (ie lab results, vital signs, age) are first binned into
+    user specified number of buckets so that they can be represented in similar 
+    fashion.
+
+    Feature matrices are constructed as csr matrices (sparse matrices) to allow
+    for feature representations that would otherwise use ungodly amounts of
+    RAM.  Sparse matrices are constructed directly from a long form feature
+    matrix representation to avoid the costly pivot operation. 
+
+    The user is able to specify various desired feature types and their look
+    back windows in a provided configuration dictionary. 
+    """
+
+    def __init__(self, cohort_table, label_columns, working_dataset_name, 
+                 table_name, project_id='mining-clinical-decisions',
+                 outpath='./features', dataset='shc_core_2021',
+                 working_project_id='mining-clinical-decisions',
+                 feature_config=None):
+        """
+        Initializes featurizer
+        Args:
+            cohort_table: location of cohort table {dataset_name}.{table_name}
+            label_columns: which columns in cohort table used as labels
+            dataset_name: big query dataset name - where to save long form
+                feature matrix.
+            table_name: big query table name - where to save long form feature
+                matrix
+            project_id: id of big query project
+            feature_config: configure which features types to extract and their
+                look back windows. If none uses default defined below
+        """
+        self.df_lup = pd.DataFrame()
+        self.outpath = outpath
+        self.cohort_table = cohort_table
+        self.label_columns = label_columns
+        self.dataset_name = working_dataset_name
+        self.dataset = dataset
+        self.table_name = table_name
+        self.project_id = project_id
+        self.working_proj_id = working_project_id
+        self.client = bigquery.Client()
+        self.create_table = True  # starts true then append
+        if feature_config is None:
+            self.feature_config = DEFAULT_DEPLOY_CONFIG
+        else:
+            self.feature_config = feature_config
+            ### TODO : write code to test whether user specified config is
+            ### adequate
+
+        # Get data splits (default last year of data held out as test set)
+        split_query = f"""
+            SELECT DISTINCT
+                EXTRACT(YEAR FROM index_time) year
+            FROM
+                {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+        """
+        df = pd.read_gbq(split_query).sort_values('year')
+        self.train_years = df.year.values[:-1]
+        self.test_years = [df.year.values[-1]]
+
+        self.feature_timeline_schema = [
+            {'name': 'observation_id', 'type': 'INTEGER'},
+            {'name': 'index_time', 'type': 'TIMESTAMP'},
+            {'name': 'feature_type', 'type': 'STRING'},
+            {'name': 'feature_time', 'type': 'TIMESTAMP'},
+            {'name': 'feature_id', 'type': 'INTEGER'},
+            {'name': 'feature', 'type': 'STRING'},
+            {'name': 'value', 'type': 'INTEGER'}
+        ]
+
+    def __call__(self):
+        """
+        Executes all logic to construct features and labels and saves all info
+        user specified working directory. 
+        """
+        self.construct_feature_timeline()
+        self.construct_bag_of_words_rep()
+        query = f"""
+        SELECT 
+            * 
+        FROM 
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name}_bow
+        ORDER BY
+            observation_id
+        """
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        train_features = df[df['index_time'].dt.year.isin(self.train_years)]
+        apply_features = df[~df['index_time'].dt.year.isin(self.train_years)]
+        train_csr, train_ids, train_vocab = self.construct_sparse_matrix(
+            train_features, train_features)
+        test_csr, test_ids, test_vocab = self.construct_sparse_matrix(
+            train_features, apply_features)
+
+        # Query cohort table for labels
+        q_cohort = f"""
+            SELECT 
+                * 
+            FROM 
+                {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            ORDER BY
+                observation_id
+        """
+        df_cohort = pd.read_gbq(q_cohort, progress_bar_type='tqdm')
+        train_labels = df_cohort[df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+        test_labels = df_cohort[~df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+
+        # Sanity check - make sure ids from labels and features in same order
+        for a, b in zip(train_labels['observation_id'].values, train_ids):
+            try:
+                assert a == b
+            except:
+                pdb.set_trace()
+        for a, b in zip(test_labels['observation_id'].values, test_ids):
+            assert a == b
+
+        # Create working directory if does not already exist and save features
+        os.makedirs(self.outpath, exist_ok=True)
+        save_npz(os.path.join(self.outpath, 'train_features.npz'), train_csr)
+        save_npz(os.path.join(self.outpath, 'test_features.npz'), test_csr)
+        print(f"Feature matrix generated with {train_csr.shape[1]} features")
+
+        # Save labels
+        train_labels.to_csv(os.path.join(self.outpath, 'train_labels.csv'),
+                            index=None)
+        test_labels.to_csv(os.path.join(self.outpath, 'test_labels.csv'),
+                           index=None)
+
+        # Save feature order
+        df_vocab = pd.DataFrame(data={
+            'features': [t for t in train_vocab],
+            'indices': [train_vocab[t] for t in train_vocab]
+        })
+        df_vocab.to_csv(os.path.join(self.outpath, 'feature_order.csv'),
+                        index=None)
+
+        # Save bin thresholds if they exist
+        if not self.df_lup.empty:
+            self.df_lup.to_csv(os.path.join(self.outpath, 'bin_lup.csv'),
+                            index=None)
+
+        # Save feature_config
+        with open(os.path.join(self.outpath, 'feature_config.json'), 'w') as f:
+            json.dump(self.feature_config, f)
+
+    def construct_feature_timeline(self):
+        """
+        Executes all logic to iteratively append rows to the biq query long form
+        feature matrix destination table.  Does this by iteratively joining
+        cohort table to tables with desired features, filtering for events
+        that occur within each look up range, and then transforming into bag of
+        words style representations.  Features with numerical values are binned
+        into buckets to enable bag of words repsesentation. 
+        """
+
+        # Get categorical features
+        if 'Sex' in self.feature_config['Categorical']:
+            self._get_sex_features()
+        if 'Race' in self.feature_config['Categorical']:
+            self._get_race_features()
+        if 'Diagnoses' in self.feature_config['Categorical']:
+            self._get_diagnoses_features()
+        if 'Medications' in self.feature_config['Categorical']:
+            self._get_medication_features()
+
+        # Get numerical features
+        if 'Age' in self.feature_config['Numerical']:
+            self._get_age_features()
+        if 'LabResults' in self.feature_config['Numerical']:
+            self._get_lab_results_features()
+        if 'Vitals' in self.feature_config['Numerical']:
+            self._get_vital_sign_features()
+
+        # Add token which is one if no features are present for a patient
+        self._get_missingness_features()
+
+    def construct_bag_of_words_rep(self):
+        """
+        Transforms long form feature timeline into a bag of words feature
+        matrix. Stores as a new table in the given bigquery database but also
+        constructs local sparse matrices that can be fed into various sklearn
+        style classifiers. 
+        """
+
+        # Go from timeline to counts
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.working_proj_id}.{self.dataset_name}.{self.table_name}_bow AS (
+
+        SELECT 
+            observation_id, index_time, feature_type, feature, COUNT(*) value 
+        FROM 
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name}
+        WHERE 
+            feature_type IS NOT NULL
+        AND
+            feature IS NOT NULL
+        GROUP BY 
+            observation_id, index_time, feature_type, feature
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def construct_sparse_matrix(self, train_features, apply_features):
+        """
+        Takes long form feature timeline matrix and builds up a scipy csr
+        matrix without the costly pivot operation. 
+        """
+        train_features = (train_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        train_feature_names = [doc for doc in train_features.feature.values]
+        train_feature_values = [doc for doc in train_features['value'].values]
+        train_obs_id = [id_ for id_ in train_features.observation_id.values]
+
+        apply_features = (apply_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        apply_features_names = [doc for doc in apply_features.feature.values]
+        apply_features_values = [doc for doc in apply_features['value'].values]
+        apply_obs_id = [id_ for id_ in apply_features.observation_id.values]
+
+        vocabulary = self._build_vocab(train_feature_names)
+        indptr = [0]
+        indices = []
+        data = []
+        for i, d in enumerate(apply_features_names):
+            for j, term in enumerate(d):
+                if term not in vocabulary:
+                    continue
+                else:
+                    indices.append(vocabulary[term])
+                    data.append(apply_features_values[i][j])
+                if j == 0:
+                    # Add zero to data and max index in vocabulary to indices in
+                    # case max feature indice isn't in apply features.
+                    indices.append(len(vocabulary)-1)
+                    data.append(0)
+            indptr.append(len(indices))
+
+        csr_data = csr_matrix((data, indices, indptr), dtype=float)
+
+        return csr_data, apply_obs_id, vocabulary
+
+    def _build_vocab(self, data):
+        """
+        Builds vocabulary of terms from the data. Assigns each unique term
+        to a monotonically increasing integer
+        """
+        vocabulary = {}
+        for i, d in enumerate(data):
+            for j, term in enumerate(d):
+                vocabulary.setdefault(term, len(vocabulary))
+        return vocabulary
+
+    def get_binned_features(self, df_train, df_apply, num_bins):
+        """
+        Bins numerical features into specified number of bins based on
+        percentile values.  Uses training set to define cutoffs values across
+        each bin. 
+
+        Args:
+            df_train: dataframe of long form feature timeline for numerical
+                features for observations in the training set
+            df_apply: dataframe of long form feature timeline for numerical
+                features for osbervations in th test set
+        Returns:
+            df_binned : dataframe of long form feature timeline where the bin
+                associated with feature is specified in feature name and the
+                value is 1
+        """
+        def _get_appropriate_bin(feature, val, lut):
+            """
+            Helper to map value to bin
+            """
+            try:
+                mins = lut[feature]['min']
+            except:  # feature not found in training set
+                return 0  # removed in downstream processing
+            for i in range(len(mins)-1):
+                if i == 0 and val < mins[i]:
+                    return i
+                if val >= mins[i] and val < mins[i+1]:
+                    return i
+            return len(mins) - 1  # in last bin
+
+        ranks = df_train.groupby('feature')['value'].rank(pct=True).values
+        df_train = (df_train
+                    .assign(bins=[int(r * num_bins) for r in ranks])
+                    )
+        df_lup = (df_train
+                  .groupby(['feature', 'bins'])
+                  .agg(bin_min=('value', 'min'), bin_max=('value', 'max'))
+                  .reset_index()
+                  )
+
+        # Append to look up table we'll save later
+        self.df_lup = pd.concat([self.df_lup, df_lup])
+
+        # Convert df_lup to dictionary for faster bin look up.
+        lut_dict = {}
+        for feature in df_lup.feature.unique():
+            df_lup_feature = df_lup[df_lup['feature'] == feature]
+            for bin in df_lup_feature.bins.unique():
+                if feature not in lut_dict:
+                    lut_dict[feature] = {}
+                    lut_dict[feature]['min'] = []
+                    lut_dict[feature]['max'] = []
+                lut_dict[feature]['min'].append(df_lup_feature
+                    .query("bins == @bin", engine='python')
+                    ['bin_min'].values[0]
+                )
+                lut_dict[feature]['max'].append(df_lup_feature
+                    .query("bins == @bin", engine='python')
+                    ['bin_max'].values[0]
+                )
+
+        df_binned = (df_apply
+                     .assign(bins=lambda x:
+                             [_get_appropriate_bin(a, b, lut_dict)
+                              for a, b in zip(x.feature, x.value)]
+                             )
+                     .assign(feature=lambda x:
+                             [f"{a}_{b}" for a, b in zip(x.feature, x.bins)],
+                             value=1
+                             )
+                     .drop('bins', axis=1)
+                     )
+
+        return df_binned
+
+    def _get_missingness_features(self):
+        """
+        Finds patients that exist in cohort but not current feature matrix,
+        adds token 'missing' feature which takes value 1. 
+        """
+        query=f"""
+        INSERT INTO 
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            "Missing indicator" feature_type,
+            labels.index_time feature_time,
+            UNIX_DATE(CAST(labels.index_time as DATE)) as feature_id,
+            'no features' as feature,
+            1 as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name} feats
+        USING
+            (observation_id)
+        WHERE 
+            feats.observation_id IS NULL
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def _get_sex_features(self):
+        """
+        Get's sex demographic features for patients in cohort and appends to
+        long form feature matrix on bigquery.
+        """
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            1 as feature_id,
+            CASE WHEN demo.GENDER is NULL then 'sex_missing'
+            ELSE CONCAT('sex_', demo.GENDER) END feature,
+            1 value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        {suffix}
+        """
+        print("Featurizing sex")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_race_features(self):
+        """
+        Get's race demographic features for each patient in cohort and appends
+        to long form feature matrix on bigquery. 
+        """
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            1 as feature_id,
+            CASE WHEN demo.CANONICAL_RACE is NULL then 'race_missing'
+            ELSE CONCAT('race_', demo.CANONICAL_RACE) END feature,
+            1 value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        {suffix}
+        """
+        print("Featurizing race")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_diagnoses_features(self):
+        """
+        Gets diagnosis code features for patients in cohort table and appends to
+        long form bigquery table. Only selects diagnosis codes on the patient's
+        problem list -- ie source = 2. 
+        """
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT
+            labels.observation_id,
+            labels.index_time,
+            'Diagnoses' as feature_type,
+            CAST(dx.start_date_utc as TIMESTAMP) as feature_time,
+            UNIX_DATE(CAST(dx.start_date_utc as DATE)) as feature_id,
+            dx.icd10 as feature,
+            1 value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.diagnosis dx
+        ON
+            labels.anon_id = dx.anon_id
+        WHERE 
+            CAST(dx.start_date_utc as TIMESTAMP) < labels.index_time
+        AND
+            source = 2
+        {suffix}
+        """
+        print("Featurizing diagnosis codes")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_medication_features(self):
+        """
+        Gets medication order features for patients in cohort table and appends
+        to long form bigquery table
+        """
+        max_look_back_time = 0
+        for lb in DEFAULT_DEPLOY_CONFIG['Categorical']['Medications']:
+            max_look_back_time = max(
+                lb['look_back'], max_look_back_time)
+
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Medications' as feature_type,
+            meds.order_inst_utc as feature_time,
+            meds.order_med_id_coded as feature_id,
+            meds.med_description as feature,
+            1 as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.order_med meds
+        ON
+            labels.anon_id = meds.anon_id
+        WHERE 
+            CAST(meds.order_inst_utc as TIMESTAMP) < labels.index_time
+        AND
+            TIMESTAMP_ADD(meds.order_inst_utc,
+                          INTERVAL 24*{max_look_back_time} HOUR)
+                          >= labels.index_time
+        {suffix}
+        """
+        print("Featurizing medication orders")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_age_features(self):
+        """
+        Get's age features for patients in cohort table, bins them and puts
+        them in feature timeline table. 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            1 as feature_id,
+            'Age' as feature,
+            DATE_DIFF(
+                CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR)
+            as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        INNER JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        """
+        print("Featurizing age")
+        df = pd.read_gbq(query)
+        df_train = df[df['index_time'].dt.year.isin(self.train_years)]
+        num_bins = self.feature_config['Numerical']['Age'][0]['num_bins']
+        df_binned = self.get_binned_features(df_train, df, num_bins)
+
+        # Write feature timeline in long form to bigquery dataset.table_name
+        df_binned.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.working_proj_id,
+            if_exists='append',
+            table_schema=self.feature_timeline_schema
+        )
+        self.create_table = False
+
+    def _get_lab_results_features(self):
+        """
+        Collects lab result features for patients in cohort for select lab
+        components. 
+        """
+        # Format list of lab component ids to grab
+        base_name_string = "("
+        for i, base_name in enumerate(DEFAULT_LAB_COMPONENT_IDS):
+            if i == len(DEFAULT_LAB_COMPONENT_IDS) - 1:
+                base_name_string += f"'{base_name}')"
+            else:
+                base_name_string += f"'{base_name}', "
+
+        # Get max look back time
+        max_look_back_time = 0
+        for look_back in DEFAULT_DEPLOY_CONFIG['Numerical']['LabResults']:
+            max_look_back_time = max(
+                look_back['look_back'], max_look_back_time)
+
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Lab Results' as feature_type,
+            lr.result_time_utc as feature_time,
+            CAST(lr.order_id_coded AS INTEGER) as feature_id,
+            lr.base_name as feature,
+            lr.ord_num_value as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.lab_result lr
+        ON
+            labels.anon_id = lr.anon_id
+        WHERE
+            lr.result_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(lr.result_time_utc,
+                          INTERVAL 24*{max_look_back_time} HOUR)
+                          >= labels.index_time
+        AND
+            lr.base_name in {base_name_string}
+        AND
+            lr.ord_num_value IS NOT NULL
+        """
+        print("Featurizing lab results")
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        df = df.astype({'value': float})
+        df_train = df[df['index_time'].dt.year.isin(self.train_years)]
+        num_bins = self.feature_config['Numerical']['LabResults'][0]['num_bins']
+        df_binned = self.get_binned_features(df_train, df, num_bins)
+
+        # Write feature matrix in long form to bigquery dataset.table_name
+        df_binned.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.working_proj_id,
+            if_exists='append',
+            table_schema=self.feature_timeline_schema
+        )
+        self.create_table = False
+
+    def _get_vital_sign_features(self):
+        """
+        Collects flowsheet features for patients in cohort.
+        """
+        # Format list of lab component ids to grab
+        base_name_string = "("
+        for i, base_name in enumerate(DEFAULT_FLOWSHEET_FEATURES):
+            if i == len(DEFAULT_FLOWSHEET_FEATURES) - 1:
+                base_name_string += f"'{base_name}')"
+            else:
+                base_name_string += f"'{base_name}', "
+
+        # Get max look back time
+        max_look_back_time = 0
+        for look_back in DEFAULT_DEPLOY_CONFIG['Numerical']['Vitals']:
+            max_look_back_time = max(
+                look_back['look_back'], max_look_back_time)
+
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Vitals' as feature_type,
+            f.recorded_time_utc as feature_time,
+            UNIX_SECONDS(f.recorded_time_utc) as feature_id,
+            f.row_disp_name as feature,
+            f.numerical_val_1 as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.flowsheet f
+        ON
+            labels.anon_id = f.anon_id
+        WHERE
+            f.recorded_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(f.recorded_time_utc,
+                          INTERVAL 24*{max_look_back_time} HOUR)
+                          >= labels.index_time
+        AND
+            f.row_disp_name in {base_name_string}
+        AND
+            f.numerical_val_1 IS NOT NULL
+        """
+        print("Featurizing Vitals")
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        df = df.astype({'value': float})
+        df_train = df[df['index_time'].dt.year.isin(self.train_years)]
+        num_bins = self.feature_config['Numerical']['Vitals'][0]['num_bins']
+        df_binned = self.get_binned_features(df_train, df, num_bins)
+
+        # Write feature matrix in long form to bigquery dataset.table_name
+        df_binned.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.working_proj_id,
+            if_exists='append',
+            table_schema=self.feature_timeline_schema
+        )
+        self.create_table = False
diff --git a/medinfo/dataconversion/mapdata/2018_I10gem.tab.gz b/medinfo/dataconversion/mapdata/2018_I10gem.tab.gz
new file mode 100644
index 00000000..a01bee1b
Binary files /dev/null and b/medinfo/dataconversion/mapdata/2018_I10gem.tab.gz differ
diff --git a/medinfo/dataconversion/mapdata/2018_I9gem.tab.gz b/medinfo/dataconversion/mapdata/2018_I9gem.tab.gz
new file mode 100644
index 00000000..385bd010
Binary files /dev/null and b/medinfo/dataconversion/mapdata/2018_I9gem.tab.gz differ
diff --git a/medinfo/dataconversion/mapdata/CharlsonComorbidity-ICD10.tab b/medinfo/dataconversion/mapdata/CharlsonComorbidity-ICD10.tab
new file mode 100644
index 00000000..101f6c61
--- /dev/null
+++ b/medinfo/dataconversion/mapdata/CharlsonComorbidity-ICD10.tab
@@ -0,0 +1,290 @@
+Code	Category	Code_Type	Source
+I21	MI	ICD10	MHCP_Charlson
+I22	MI	ICD10	MHCP_Charlson
+I252	MI	ICD10	MHCP_Charlson
+I43	CHF	ICD10	MHCP_Charlson
+I50	CHF	ICD10	MHCP_Charlson
+I099	CHF	ICD10	MHCP_Charlson
+I110	CHF	ICD10	MHCP_Charlson
+I130	CHF	ICD10	MHCP_Charlson
+I132	CHF	ICD10	MHCP_Charlson
+I255	CHF	ICD10	MHCP_Charlson
+I420	CHF	ICD10	MHCP_Charlson
+I425	CHF	ICD10	MHCP_Charlson
+I426	CHF	ICD10	MHCP_Charlson
+I427	CHF	ICD10	MHCP_Charlson
+I428	CHF	ICD10	MHCP_Charlson
+I429	CHF	ICD10	MHCP_Charlson
+P290	CHF	ICD10	MHCP_Charlson
+I70	PeripheralVascular	ICD10	MHCP_Charlson
+I71	PeripheralVascular	ICD10	MHCP_Charlson
+I731	PeripheralVascular	ICD10	MHCP_Charlson
+I738	PeripheralVascular	ICD10	MHCP_Charlson
+I739	PeripheralVascular	ICD10	MHCP_Charlson
+I771	PeripheralVascular	ICD10	MHCP_Charlson
+I790	PeripheralVascular	ICD10	MHCP_Charlson
+I792	PeripheralVascular	ICD10	MHCP_Charlson
+K551	PeripheralVascular	ICD10	MHCP_Charlson
+K558	PeripheralVascular	ICD10	MHCP_Charlson
+K559	PeripheralVascular	ICD10	MHCP_Charlson
+Z958	PeripheralVascular	ICD10	MHCP_Charlson
+Z959	PeripheralVascular	ICD10	MHCP_Charlson
+G45	Cerebrovascular	ICD10	MHCP_Charlson
+G46	Cerebrovascular	ICD10	MHCP_Charlson
+I60	Cerebrovascular	ICD10	MHCP_Charlson
+I61	Cerebrovascular	ICD10	MHCP_Charlson
+I62	Cerebrovascular	ICD10	MHCP_Charlson
+I63	Cerebrovascular	ICD10	MHCP_Charlson
+I64	Cerebrovascular	ICD10	MHCP_Charlson
+I65	Cerebrovascular	ICD10	MHCP_Charlson
+I66	Cerebrovascular	ICD10	MHCP_Charlson
+I67	Cerebrovascular	ICD10	MHCP_Charlson
+I68	Cerebrovascular	ICD10	MHCP_Charlson
+I69	Cerebrovascular	ICD10	MHCP_Charlson
+H340	Cerebrovascular	ICD10	MHCP_Charlson
+F00	Dementia	ICD10	MHCP_Charlson
+F01	Dementia	ICD10	MHCP_Charlson
+F02	Dementia	ICD10	MHCP_Charlson
+F03	Dementia	ICD10	MHCP_Charlson
+G30	Dementia	ICD10	MHCP_Charlson
+F051	Dementia	ICD10	MHCP_Charlson
+G311	Dementia	ICD10	MHCP_Charlson
+J40	COPD	ICD10	MHCP_Charlson
+J41	COPD	ICD10	MHCP_Charlson
+J42	COPD	ICD10	MHCP_Charlson
+J43	COPD	ICD10	MHCP_Charlson
+J44	COPD	ICD10	MHCP_Charlson
+J45	COPD	ICD10	MHCP_Charlson
+J46	COPD	ICD10	MHCP_Charlson
+J47	COPD	ICD10	MHCP_Charlson
+J60	COPD	ICD10	MHCP_Charlson
+J61	COPD	ICD10	MHCP_Charlson
+J62	COPD	ICD10	MHCP_Charlson
+J63	COPD	ICD10	MHCP_Charlson
+J64	COPD	ICD10	MHCP_Charlson
+J65	COPD	ICD10	MHCP_Charlson
+J66	COPD	ICD10	MHCP_Charlson
+J67	COPD	ICD10	MHCP_Charlson
+I278	COPD	ICD10	MHCP_Charlson
+I279	COPD	ICD10	MHCP_Charlson
+J684	COPD	ICD10	MHCP_Charlson
+J701	COPD	ICD10	MHCP_Charlson
+J703	COPD	ICD10	MHCP_Charlson
+M05	Rheumatic	ICD10	MHCP_Charlson
+M32	Rheumatic	ICD10	MHCP_Charlson
+M33	Rheumatic	ICD10	MHCP_Charlson
+M34	Rheumatic	ICD10	MHCP_Charlson
+M06	Rheumatic	ICD10	MHCP_Charlson
+M315	Rheumatic	ICD10	MHCP_Charlson
+M351	Rheumatic	ICD10	MHCP_Charlson
+M353	Rheumatic	ICD10	MHCP_Charlson
+M360	Rheumatic	ICD10	MHCP_Charlson
+K25	Peptic Ulcer	ICD10	MHCP_Charlson
+K26	Peptic Ulcer	ICD10	MHCP_Charlson
+K27	Peptic Ulcer	ICD10	MHCP_Charlson
+K28	Peptic Ulcer	ICD10	MHCP_Charlson
+B18	Liver Mild	ICD10	MHCP_Charlson
+K73	Liver Mild	ICD10	MHCP_Charlson
+K74	Liver Mild	ICD10	MHCP_Charlson
+K700	Liver Mild	ICD10	MHCP_Charlson
+K701	Liver Mild	ICD10	MHCP_Charlson
+K702	Liver Mild	ICD10	MHCP_Charlson
+K703	Liver Mild	ICD10	MHCP_Charlson
+K709	Liver Mild	ICD10	MHCP_Charlson
+K717	Liver Mild	ICD10	MHCP_Charlson
+K713	Liver Mild	ICD10	MHCP_Charlson
+K714	Liver Mild	ICD10	MHCP_Charlson
+K715	Liver Mild	ICD10	MHCP_Charlson
+K760	Liver Mild	ICD10	MHCP_Charlson
+K762	Liver Mild	ICD10	MHCP_Charlson
+K763	Liver Mild	ICD10	MHCP_Charlson
+K764	Liver Mild	ICD10	MHCP_Charlson
+K768	Liver Mild	ICD10	MHCP_Charlson
+K769	Liver Mild	ICD10	MHCP_Charlson
+Z944	Liver Mild	ICD10	MHCP_Charlson
+E100	Diabetes	ICD10	MHCP_Charlson
+E101	Diabetes	ICD10	MHCP_Charlson
+E106	Diabetes	ICD10	MHCP_Charlson
+E108	Diabetes	ICD10	MHCP_Charlson
+E109	Diabetes	ICD10	MHCP_Charlson
+E110	Diabetes	ICD10	MHCP_Charlson
+E111	Diabetes	ICD10	MHCP_Charlson
+E116	Diabetes	ICD10	MHCP_Charlson
+E118	Diabetes	ICD10	MHCP_Charlson
+E119	Diabetes	ICD10	MHCP_Charlson
+E120	Diabetes	ICD10	MHCP_Charlson
+E121	Diabetes	ICD10	MHCP_Charlson
+E126	Diabetes	ICD10	MHCP_Charlson
+E128	Diabetes	ICD10	MHCP_Charlson
+E129	Diabetes	ICD10	MHCP_Charlson
+E130	Diabetes	ICD10	MHCP_Charlson
+E131	Diabetes	ICD10	MHCP_Charlson
+E136	Diabetes	ICD10	MHCP_Charlson
+E138	Diabetes	ICD10	MHCP_Charlson
+E139	Diabetes	ICD10	MHCP_Charlson
+E140	Diabetes	ICD10	MHCP_Charlson
+E141	Diabetes	ICD10	MHCP_Charlson
+E146	Diabetes	ICD10	MHCP_Charlson
+E148	Diabetes	ICD10	MHCP_Charlson
+E149	Diabetes	ICD10	MHCP_Charlson
+E102	DiabetesComplications	ICD10	MHCP_Charlson
+E103	DiabetesComplications	ICD10	MHCP_Charlson
+E104	DiabetesComplications	ICD10	MHCP_Charlson
+E105	DiabetesComplications	ICD10	MHCP_Charlson
+E107	DiabetesComplications	ICD10	MHCP_Charlson
+E112	DiabetesComplications	ICD10	MHCP_Charlson
+E113	DiabetesComplications	ICD10	MHCP_Charlson
+E114	DiabetesComplications	ICD10	MHCP_Charlson
+E115	DiabetesComplications	ICD10	MHCP_Charlson
+E117	DiabetesComplications	ICD10	MHCP_Charlson
+E122	DiabetesComplications	ICD10	MHCP_Charlson
+E123	DiabetesComplications	ICD10	MHCP_Charlson
+E124	DiabetesComplications	ICD10	MHCP_Charlson
+E125	DiabetesComplications	ICD10	MHCP_Charlson
+E127	DiabetesComplications	ICD10	MHCP_Charlson
+E132	DiabetesComplications	ICD10	MHCP_Charlson
+E133	DiabetesComplications	ICD10	MHCP_Charlson
+E134	DiabetesComplications	ICD10	MHCP_Charlson
+E135	DiabetesComplications	ICD10	MHCP_Charlson
+E137	DiabetesComplications	ICD10	MHCP_Charlson
+E142	DiabetesComplications	ICD10	MHCP_Charlson
+E143	DiabetesComplications	ICD10	MHCP_Charlson
+E144	DiabetesComplications	ICD10	MHCP_Charlson
+E145	DiabetesComplications	ICD10	MHCP_Charlson
+E147	DiabetesComplications	ICD10	MHCP_Charlson
+G81	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G82	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G041	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G114	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G801	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G802	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G830	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G831	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G832	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G833	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G834	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+G839	Hemiplegia Paraplegia	ICD10	MHCP_Charlson
+N18	Renal	ICD10	MHCP_Charlson
+N19	Renal	ICD10	MHCP_Charlson
+N052	Renal	ICD10	MHCP_Charlson
+N053	Renal	ICD10	MHCP_Charlson
+N054	Renal	ICD10	MHCP_Charlson
+N055	Renal	ICD10	MHCP_Charlson
+N056	Renal	ICD10	MHCP_Charlson
+N057	Renal	ICD10	MHCP_Charlson
+N250	Renal	ICD10	MHCP_Charlson
+I120	Renal	ICD10	MHCP_Charlson
+I131	Renal	ICD10	MHCP_Charlson
+N032	Renal	ICD10	MHCP_Charlson
+N033	Renal	ICD10	MHCP_Charlson
+N034	Renal	ICD10	MHCP_Charlson
+N035	Renal	ICD10	MHCP_Charlson
+N036	Renal	ICD10	MHCP_Charlson
+N037	Renal	ICD10	MHCP_Charlson
+Z490	Renal	ICD10	MHCP_Charlson
+Z491	Renal	ICD10	MHCP_Charlson
+Z492	Renal	ICD10	MHCP_Charlson
+Z940	Renal	ICD10	MHCP_Charlson
+Z992	Renal	ICD10	MHCP_Charlson
+C00	Malignancy	ICD10	MHCP_Charlson
+C01	Malignancy	ICD10	MHCP_Charlson
+C02	Malignancy	ICD10	MHCP_Charlson
+C03	Malignancy	ICD10	MHCP_Charlson
+C04	Malignancy	ICD10	MHCP_Charlson
+C05	Malignancy	ICD10	MHCP_Charlson
+C06	Malignancy	ICD10	MHCP_Charlson
+C07	Malignancy	ICD10	MHCP_Charlson
+C08	Malignancy	ICD10	MHCP_Charlson
+C09	Malignancy	ICD10	MHCP_Charlson
+C10	Malignancy	ICD10	MHCP_Charlson
+C11	Malignancy	ICD10	MHCP_Charlson
+C12	Malignancy	ICD10	MHCP_Charlson
+C13	Malignancy	ICD10	MHCP_Charlson
+C14	Malignancy	ICD10	MHCP_Charlson
+C15	Malignancy	ICD10	MHCP_Charlson
+C16	Malignancy	ICD10	MHCP_Charlson
+C17	Malignancy	ICD10	MHCP_Charlson
+C18	Malignancy	ICD10	MHCP_Charlson
+C19	Malignancy	ICD10	MHCP_Charlson
+C20	Malignancy	ICD10	MHCP_Charlson
+C21	Malignancy	ICD10	MHCP_Charlson
+C22	Malignancy	ICD10	MHCP_Charlson
+C23	Malignancy	ICD10	MHCP_Charlson
+C24	Malignancy	ICD10	MHCP_Charlson
+C25	Malignancy	ICD10	MHCP_Charlson
+C26	Malignancy	ICD10	MHCP_Charlson
+C30	Malignancy	ICD10	MHCP_Charlson
+C31	Malignancy	ICD10	MHCP_Charlson
+C32	Malignancy	ICD10	MHCP_Charlson
+C33	Malignancy	ICD10	MHCP_Charlson
+C34	Malignancy	ICD10	MHCP_Charlson
+C37	Malignancy	ICD10	MHCP_Charlson
+C38	Malignancy	ICD10	MHCP_Charlson
+C39	Malignancy	ICD10	MHCP_Charlson
+C40	Malignancy	ICD10	MHCP_Charlson
+C41	Malignancy	ICD10	MHCP_Charlson
+C43	Malignancy	ICD10	MHCP_Charlson
+C45	Malignancy	ICD10	MHCP_Charlson
+C46	Malignancy	ICD10	MHCP_Charlson
+C47	Malignancy	ICD10	MHCP_Charlson
+C48	Malignancy	ICD10	MHCP_Charlson
+C49	Malignancy	ICD10	MHCP_Charlson
+C50	Malignancy	ICD10	MHCP_Charlson
+C51	Malignancy	ICD10	MHCP_Charlson
+C52	Malignancy	ICD10	MHCP_Charlson
+C53	Malignancy	ICD10	MHCP_Charlson
+C54	Malignancy	ICD10	MHCP_Charlson
+C55	Malignancy	ICD10	MHCP_Charlson
+C56	Malignancy	ICD10	MHCP_Charlson
+C57	Malignancy	ICD10	MHCP_Charlson
+C58	Malignancy	ICD10	MHCP_Charlson
+C60	Malignancy	ICD10	MHCP_Charlson
+C61	Malignancy	ICD10	MHCP_Charlson
+C62	Malignancy	ICD10	MHCP_Charlson
+C63	Malignancy	ICD10	MHCP_Charlson
+C64	Malignancy	ICD10	MHCP_Charlson
+C65	Malignancy	ICD10	MHCP_Charlson
+C66	Malignancy	ICD10	MHCP_Charlson
+C67	Malignancy	ICD10	MHCP_Charlson
+C68	Malignancy	ICD10	MHCP_Charlson
+C69	Malignancy	ICD10	MHCP_Charlson
+C70	Malignancy	ICD10	MHCP_Charlson
+C71	Malignancy	ICD10	MHCP_Charlson
+C72	Malignancy	ICD10	MHCP_Charlson
+C73	Malignancy	ICD10	MHCP_Charlson
+C74	Malignancy	ICD10	MHCP_Charlson
+C75	Malignancy	ICD10	MHCP_Charlson
+C76	Malignancy	ICD10	MHCP_Charlson
+C81	Malignancy	ICD10	MHCP_Charlson
+C82	Malignancy	ICD10	MHCP_Charlson
+C83	Malignancy	ICD10	MHCP_Charlson
+C84	Malignancy	ICD10	MHCP_Charlson
+C85	Malignancy	ICD10	MHCP_Charlson
+C88	Malignancy	ICD10	MHCP_Charlson
+C90	Malignancy	ICD10	MHCP_Charlson
+C91	Malignancy	ICD10	MHCP_Charlson
+C92	Malignancy	ICD10	MHCP_Charlson
+C93	Malignancy	ICD10	MHCP_Charlson
+C94	Malignancy	ICD10	MHCP_Charlson
+C95	Malignancy	ICD10	MHCP_Charlson
+C96	Malignancy	ICD10	MHCP_Charlson
+C97	Malignancy	ICD10	MHCP_Charlson
+K704	Liver ModSevere	ICD10	MHCP_Charlson
+K711	Liver ModSevere	ICD10	MHCP_Charlson
+K721	Liver ModSevere	ICD10	MHCP_Charlson
+K729	Liver ModSevere	ICD10	MHCP_Charlson
+K765	Liver ModSevere	ICD10	MHCP_Charlson
+K766	Liver ModSevere	ICD10	MHCP_Charlson
+K767	Liver ModSevere	ICD10	MHCP_Charlson
+I850	Liver ModSevere	ICD10	MHCP_Charlson
+I859	Liver ModSevere	ICD10	MHCP_Charlson
+I864	Liver ModSevere	ICD10	MHCP_Charlson
+I982	Liver ModSevere	ICD10	MHCP_Charlson
+C77	Malignancy Metastatic	ICD10	MHCP_Charlson
+C78	Malignancy Metastatic	ICD10	MHCP_Charlson
+C79	Malignancy Metastatic	ICD10	MHCP_Charlson
+C80	Malignancy Metastatic	ICD10	MHCP_Charlson
+B20	AIDS/HIV	ICD10	MHCP_Charlson
+B21	AIDS/HIV	ICD10	MHCP_Charlson
+B22	AIDS/HIV	ICD10	MHCP_Charlson
+B24	AIDS/HIV	ICD10	MHCP_Charlson
\ No newline at end of file
diff --git a/medinfo/dataconversion/mapdata/LabResultMap.tab.gz b/medinfo/dataconversion/mapdata/LabResultMap.tab.gz
new file mode 100644
index 00000000..1025e667
Binary files /dev/null and b/medinfo/dataconversion/mapdata/LabResultMap.tab.gz differ
diff --git a/medinfo/dataconversion/mapdata/TreatmentTeamGroups_UCSF.tab b/medinfo/dataconversion/mapdata/TreatmentTeamGroups_UCSF.tab
new file mode 100644
index 00000000..383cf6d4
--- /dev/null
+++ b/medinfo/dataconversion/mapdata/TreatmentTeamGroups_UCSF.tab
@@ -0,0 +1,122 @@
+# Aggregate primary Treatment Team designations into broad care categories
+treatment_team	team_category
+PROV_NAME	team_category
+CARDIOLOGY D, ROUNDING TEAM	Cardiology
+GI/BILIARY, ROUNDING TEAM	Medicine
+MEDICINE D3, ROUNDING TEAM	Medicine
+NEURO IR, ROUNDING TEAM	Interventional
+NEUROSURGERY TUMOR B, ROUNDING	SurgerySpecialty
+ORTHO SPORTS, ROUNDING TEAM	SurgerySpecialty
+PROCEDURE, ROUNDING TEAM	SurgerySpecialty
+GYNE-ONCOLOGY, ROUNDING TEAM	HemeOnc
+KIDNEY TRANSPLANT, ROUNDING TEAM	TransplantSpeciality
+MEDICINE C2, ROUNDING TEAM	Medicine
+MEDICINE G2, ROUNDING TEAM	Medicine
+ORTHO SERVICE, ROUNDING TEAM	SurgerySpecialty
+UROLOGY PARNASSUS, ROUNDING TEAM	SurgerySpecialty
+VASCULAR SURG, ROUNDING TEAM	SurgerySpecialty
+CARDIOLOGY E, ROUNDING TEAM	Cardiology
+CHF, ROUNDING TEAM	Cardiology
+GENERAL SURGERY-GALANTE, ROUNDING TEAM	SurgerySpecialty
+GOLDMAN MED #5, ROUNDING TEAM	Medicine
+GOLDMAN MED #6, ROUNDING TEAM	Medicine
+MALIGNANT HEMATOLOGY TEAM 0, ROUNDING TEAM	HemeOnc
+MEDICINE H2, ROUNDING TEAM	Medicine
+OMFS, ROUNDING TEAM	SurgerySpecialty
+OPHTHALMOLOGY, ROUNDING TEAM	Interventional
+PAIN - ACUTE M-L, ROUNDING TEAM	Medicine
+SURGICAL ONCOLOGY, ROUNDING TEAM	SurgerySpecialty
+UROLOGY MISSION BAY, ROUNDING TEAM	SurgerySpecialty
+ACUTE CARE SURGERY, ROUNDING TEAM	SurgerySpecialty
+ADV LUNG DISEASE - MEDICINE	Medicine
+CARDIOLOGY B, ROUNDING TEAM	Cardiology
+CARDIOLOGY C, ROUNDING TEAM	Cardiology
+CRITICAL CARE MISSION BAY, ROUNDING TEAM	ICU
+GYNECOLOGY, ROUNDING TEAM	OBGYN
+IP3, ROUNDING TEAM	Pediatrics
+MALIGNANT HEMATOLOGY B1, ROUNDING TEAM	HemeOnc
+MEDICINE C3, ROUNDING TEAM	Medicine
+MEDICINE F1, ROUNDING TEAM	Medicine
+MEDICINE NIGHT ATTENDING 2, ROUNDING TEAM(GOLD 4 &EAGLE)	Medicine
+NEONATOLOGY BRIDGE, ROUNDING TEAM	Pediatrics
+NEUROLOGY, ROUNDING TEAM	Neurology
+NEUROSURGERY TUMOR A, ROUNDING	SurgerySpecialty
+NEUROVASCULAR A, ROUNDING TEAM	Neurology
+ORTHO ARTHROPL, ROUNDING TEAM	SurgerySpecialty
+PLASTIC SURG MISSION BAY, ROUNDING TEAM	SurgerySpecialty
+THORACIC SURG, ROUNDING TEAM	SurgerySpecialty
+ACUTE/CHRONIC PAIN, IP3 ROUNDING TEAM	Medicine
+CARDIOLOGY CCU B2, ROUNDING TEAM	ICU
+COLORECTAL SURGERY, ROUNDING TEAM	SurgerySpecialty
+EP, ROUNDING TEAM	Cardiology
+IR, ROUNDING TEAM	Interventional
+LIVER TRANSPLANT, ROUNDING TEAM	TransplantSpeciality
+MALIGNANT HEMATOLOGY C2, ROUNDING TEAM	HemeOnc
+MEDICINE B3, ROUNDING TEAM	Medicine
+MEDICINE E1, ROUNDING TEAM	Medicine
+MEDICINE G1, ROUNDING TEAM	Medicine
+MEDICINE G3, ROUNDING TEAM	Medicine
+MEDICINE I, ROUNDING TEAM	Medicine
+MEDICINE NIGHT RESIDENT 2, ROUNDING TEAM(WOLF)	Medicine
+NEONATOLOGY REDWOOD, ROUNDING TEAM	Pediatrics
+ORAL & MAXILLOFACIAL SURGERY, ROUNDING TEAM	SurgerySpecialty
+ORTHO TRAUMA, ROUNDING TEAM	SurgerySpecialty
+PALLATIVE CARE/COMPASS CARE, IP3 ROUNDING TEAM	Medicine
+ROUNDING TEAM 13ICU	ICU
+BREAST SURG, ROUNDING TEAM	SurgerySpecialty
+CARDIOLOGY, CCU A2, ROUNDING TEAM	ICU
+GASTROENTEROL, ROUNDING TEAM	Medicine
+GOLDMAN MED #1, ROUNDING TEAM	Medicine
+GOLDMAN MED #3, ROUNDING TEAM	Medicine
+MALIGNANT HEMATOLOGY B2, ROUNDING TEAM	HemeOnc
+MEDICINE B1, ROUNDING TEAM	Medicine
+MEDICINE D1, ROUNDING TEAM	Medicine
+MEDICINE SWING RESIDENT 1, ROUNDING TEAM (BAT)	Medicine
+NEUROSURGERY SPINE A, ROUNDING	SurgerySpecialty
+OBSTETRICS, ROUNDING TEAM	OBGYN
+OHNS MISSION BAY, ROUNDING TEAM	SurgerySpecialty
+ORTHO HAND, ROUNDING TEAM	SurgerySpecialty
+ORTHO SPINE, ROUNDING TEAM	SurgerySpecialty
+ROUNDING TEAM 10ICU	ICU
+ROUNDING TEAM 11ICU	ICU
+ADV LUNG DISEASE - SURGERY	SurgerySpecialty
+CARDIAC SURG ICU, ROUNDING TEAM	SurgerySpecialty
+CARDIOLOGY CCU D2, ROUNDING TEAM	ICU
+GENERAL SURGERY-DUNPHY, ROUNDING TEAM	SurgerySpecialty
+GOLDMAN MED #4, ROUNDING TEAM	Medicine
+HEME/ONC CONS, ROUNDING TEAM	HemeOnc
+HEPATOBILIARY, ROUNDING TEAM	Medicine
+MALIGNANT HEMATOLOGY A2, ROUNDING TEAM	HemeOnc
+MALIGNANT HEMATOLOGY C1, ROUNDING TEAM	HemeOnc
+MEDICINE A1, ROUNDING TEAM	Medicine
+MEDICINE B2, ROUNDING TEAM	Medicine
+MEDICINE C1, ROUNDING TEAM	Medicine
+MEDICINE D2, ROUNDING TEAM	Medicine
+MEDICINE E2, ROUNDING TEAM	Medicine
+MEDICINE E3, ROUNDING TEAM	Medicine
+MEDICINE F3, ROUNDING TEAM	Medicine
+MEDICINE H1, ROUNDING TEAM	Medicine
+MEDICINE NIGHT ATTENDING 1, ROUNDING TEAM(HAWK)	Medicine
+MEDICINE NIGHT RESIDENT 1, ROUNDING TEAM(BADGER)	Medicine
+NEONATOLOGY PACIFIC, ROUNDING TEAM	Pediatrics
+OHNS PARNASSUS, ROUNDING TEAM	SurgerySpecialty
+PLASTIC SURG, ROUNDING TEAM	SurgerySpecialty
+ROUNDING TEAM 09ICU	ICU
+ROUNDING TEAM PED TCU BLUE	Pediatrics
+ROUNDING TEAM PED TCU YELLOW	Pediatrics
+CARDIAC SURGERY ACUTE CARE FLOOR, ROUNDING TEAM	SurgerySpecialty
+CARDIOLOGY A, ROUNDING TEAM	Cardiology
+CARDIOLOGY CCU C2, ROUNDING TEAM	ICU
+CRITICAL CARE TRIAGE, ROUNDING TEAM	ICU
+GOLDMAN MED #2, ROUNDING TEAM	Medicine
+MALIGNANT HEMATOLOGY A1, ROUNDING TEAM	HemeOnc
+MEDICINE A2, ROUNDING TEAM	Medicine
+MEDICINE A3, ROUNDING TEAM	Medicine
+MEDICINE F2, ROUNDING TEAM	Medicine
+MEDICINE H3, ROUNDING TEAM	Medicine
+NEPHROLOGY, ROUNDING TEAM	Medicine
+NEUROSURGERY SPINE B, ROUNDING	SurgerySpecialty
+NEUROSURGERY VASCULAR, ROUNDIN	SurgerySpecialty
+OBSTETRICS (ANTEPARTUM), ROUNDING TEAM	OBGYN
+ORTHO ONCOLOGY, ROUNDING TEAM	SurgerySpecialty
+ROUNDING TEAM 08ICU	ICU
diff --git a/medinfo/dataconversion/mapdata/ahrq_ccsr.diagnosis_code.tab.gz b/medinfo/dataconversion/mapdata/ahrq_ccsr.diagnosis_code.tab.gz
new file mode 100644
index 00000000..3d1f7796
Binary files /dev/null and b/medinfo/dataconversion/mapdata/ahrq_ccsr.diagnosis_code.tab.gz differ
diff --git a/medinfo/dataconversion/mapdata/ahrq_ccsr.procedure_code.tab.gz b/medinfo/dataconversion/mapdata/ahrq_ccsr.procedure_code.tab.gz
new file mode 100644
index 00000000..7e44f201
Binary files /dev/null and b/medinfo/dataconversion/mapdata/ahrq_ccsr.procedure_code.tab.gz differ
diff --git a/medinfo/dataconversion/mapdata/ahrq_citation.txt b/medinfo/dataconversion/mapdata/ahrq_citation.txt
new file mode 100644
index 00000000..203bdb8a
--- /dev/null
+++ b/medinfo/dataconversion/mapdata/ahrq_citation.txt
@@ -0,0 +1,7 @@
+A copy of the mapping files should be gzipped in this folder and also loaded into the shared database to facilitate reuse
+
+# ICD10 diagnosis
+https://www.hcup-us.ahrq.gov/toolssoftware/ccsr/ccs_refined.jsp 
+
+# ICD10 procedural
+https://www.hcup-us.ahrq.gov/toolssoftware/ccs10/ccs10.jsp 
diff --git a/medinfo/dataconversion/mapdata/icd_gems_citation.txt b/medinfo/dataconversion/mapdata/icd_gems_citation.txt
new file mode 100644
index 00000000..d83f508c
--- /dev/null
+++ b/medinfo/dataconversion/mapdata/icd_gems_citation.txt
@@ -0,0 +1,5 @@
+General Equivalence Mapping files between ICD9 and ICD10 codes
+Download the respective GEMS ZIP file for more documentation
+A copy of the mapping files should be gzipped in this folder and also loaded into the shared database to facilitate reuse
+
+https://www.cms.gov/Medicare/Coding/ICD10/2018-ICD-10-CM-and-GEMs
diff --git a/medinfo/dataconversion/starr_conv/STARRDemographicsConversion.py b/medinfo/dataconversion/starr_conv/STARRDemographicsConversion.py
new file mode 100644
index 00000000..686e7d70
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/STARRDemographicsConversion.py
@@ -0,0 +1,367 @@
+#!/usr/bin/env python
+
+import sys, os
+import tempfile
+import time
+
+from itertools import islice
+from datetime import datetime
+from optparse import OptionParser
+from medinfo.common.Util import ProgressDots
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.db.Model import RowItemModel
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+from medinfo.dataconversion.Util import log
+from medinfo.db.bigquery import bigQueryUtil
+
+from google.cloud import bigquery
+
+SOURCE_TABLE = 'starr_datalake2018.demographic'
+
+UNSPECIFIED_RACE_ETHNICITY = ("Unknown", "Other")
+HISPANIC_LATINO_ETHNICITY = "HISPANIC/LATINO"
+RACE_MAPPINGS = {
+    None: "Unknown",
+    "": "Unknown",
+    "American Indian or Alaska Native": "Native American",
+    "AMERICAN INDIAN OR ALASKA NATIVE": "Native American",
+    "ASIAN - HISTORICAL CONV": "Asian",
+    "Asian": "Asian",
+    "ASIAN": "Asian",
+    "ASIAN, HISPANIC": "Asian",
+    "Asian, non-Hispanic": "Asian",
+    "ASIAN, NON-HISPANIC": "Asian",
+    "Black": "Black",
+    "Black or African American": "Black",
+    "BLACK OR AFRICAN AMERICAN": "Black",
+    "Black, Hispanic": "Black",
+    "BLACK, HISPANIC": "Black",
+    "Black, non-Hispanic": "Black",
+    "BLACK, NON-HISPANIC": "Black",
+    "Native American": "Native American",
+    "NATIVE AMERICAN, HISPANIC": "Native American",
+    "Native American, non-Hispanic": "Native American",
+    "NATIVE AMERICAN, NON-HISPANIC": "Native American",
+    "Native Hawaiian or Other Pacific Islander": "Pacific Islander",
+    "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER": "Pacific Islander",
+    "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER ": "Pacific Islander",
+    "Other": "Other",
+    "OTHER": "Other",
+    "Other, Hispanic": "Hispanic/Latino",
+    "OTHER, HISPANIC": "Hispanic/Latino",
+    "Other, non-Hispanic": "Other",
+    "OTHER, NON-HISPANIC": "Other",
+    "Pacific Islander": "Pacific Islander",
+    "Pacific Islander, non-Hispanic": "Pacific Islander",
+    "PACIFIC ISLANDER, NON-HISPANIC": "Pacific Islander",
+    "Patient Refused": "Unknown",
+    "PATIENT REFUSED": "Unknown",
+    "Race and Ethnicity Unknown": "Unknown",
+    "RACE AND ETHNICITY UNKNOWN": "Unknown",
+    "Unknown": "Unknown",
+    "UNKNOWN": "Unknown",
+    "White": "White (%s)",
+    "WHITE": "White (%s)",  # Subset by ethnicity Hispanic/Latino
+    "White, Hispanic": "White (Hispanic/Latino)",
+    "WHITE, HISPANIC": "White (Hispanic/Latino)",
+    "White, non-Hispanic": "White (Non-Hispanic/Latino)",
+    "WHITE, NON-HISPANIC": "White (Non-Hispanic/Latino)",
+    HISPANIC_LATINO_ETHNICITY: "Hispanic/Latino"
+}
+
+
+class STARRDemographicsConversion:
+    """Data conversion module to take STARR provided patient demographics data
+    into the structured data tables to facilitate subsequent analysis.
+
+    Capturing death date for now as an event.
+    """
+    connFactory = None  # Allow specification of alternative DB connection source
+
+    categoryBySourceDescr = None  # Local cache to track the clinical item category table contents
+    clinicalItemByCategoryIdExtId = None  # Local cache to track clinical item table contents
+
+    def __init__(self):
+        """Default constructor"""
+        self.bqConn = bigQueryUtil.connection()
+        self.bqClient = bigQueryUtil.BigQueryClient()
+        self.connFactory = DBUtil.ConnectionFactory()  # Default connection source
+
+        self.starrUtil = STARRUtil.StarrCommonUtils(self.bqClient)
+
+        self.categoryBySourceDescr = dict()
+        self.clinicalItemByCategoryIdExtId = dict()
+
+    def convertItemsByBatch(self, patientIdsFile, batchSize=250000, tempDir=tempfile.gettempdir(), removeCsvs=True,
+                            targetDatasetId='clinical_item2018', skipFirstLine=True, startBatch=0):
+        # split pat ids into blocks
+        # for each split
+        # convert to local postgres db
+        # dump local postgres db to csv
+        # upload csv to BQ clinical item
+        # clean up
+
+        log.info('Batch size is %s \n\n' % batchSize)
+
+        def get_batch(input_file, num_lines):
+            return [line.strip() for line in islice(input_file, num_lines)]
+
+        with open(patientIdsFile, 'r') as f:
+            if skipFirstLine:
+                ignored_first_line = f.readline()
+
+            batch_counter = 0
+
+            # skip to required startBatch
+            while batch_counter <= startBatch:
+                ids_batch = get_batch(f, batchSize)
+                batch_counter += 1
+
+            while ids_batch:
+                log.info('Processing batch %s' % batch_counter)
+                log.info('Batch %s contains ids %s to %s' % (
+                    batch_counter,
+                    (batchSize * (batch_counter - 1) + 1),
+                    min(batchSize * batch_counter, batchSize * (batch_counter - 1) + len(ids_batch))
+                ))
+
+                self.convertSourceItems(ids_batch)
+                self.starrUtil.dumpPatientItemToCsv(tempDir, batch_counter)
+
+                self.bqClient.reconnect_client()    # refresh bq client connection
+                self.starrUtil.uploadPatientItemCsvToBQ(tempDir, targetDatasetId, batch_counter)
+
+                if removeCsvs:
+                    self.starrUtil.removePatientItemCsv(tempDir, batch_counter)
+
+                self.starrUtil.removePatientItemAddedLines(SOURCE_TABLE)
+
+                log.info('Finished with batch %s \n\n' % batch_counter)
+                ids_batch = get_batch(f, batchSize)
+                batch_counter += 1
+
+        # For now keep the clinical_* tables, upload them once all tables have been converted
+        self.starrUtil.dumpClinicalTablesToCsv(tempDir)
+        self.starrUtil.uploadClinicalTablesCsvToBQ(tempDir, targetDatasetId)
+        self.starrUtil.removeClinicalTablesCsv(tempDir)
+        self.starrUtil.removeClinicalTablesAddedLines(SOURCE_TABLE)
+
+    def convertSourceItems(self, patientIds=None):
+        """Primary run function to process the contents of the starr_datalake2018.demographic
+        table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
+        Should look for redundancies to avoid repeating conversion.
+
+        patientIds - If provided, only process items for patient IDs matching those provided
+        """
+        log.info("Conversion for patients starting with: %s, %s total" % (patientIds[:5], len(patientIds)))
+        progress = ProgressDots()
+
+        with self.connFactory.connection() as conn:
+            category_model = self.categoryFromSourceItem(conn)   # only 1 category - no need to have it in the loop
+            for sourceItem in self.querySourceItems(patientIds, progress):
+                self.convertSourceItem(category_model, sourceItem, conn)
+
+    def convertSourceItem(self, categoryModel, sourceItem, conn=None):
+        """Given an individual sourceItem record, produce / convert it into an equivalent
+        item record in the analysis database.
+        """
+        # Normalize sourceItem data into hierarchical components (category -> clinical_item -> patient_item).
+        #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
+        #   in a first past, with subsequent calls just yielding back in memory cached copies
+        clinicalItemModel = self.clinicalItemFromSourceItem(sourceItem, categoryModel, conn)
+        ignoredPatientItemModel = self.patientItemModelFromSourceItem(sourceItem, clinicalItemModel, conn)
+
+    def querySourceItems(self, patientIds=None, progress=None, debug=False):
+        """Query the database for list of all patient demographics
+        and yield the results one at a time.  If patientIds provided, only return items
+        matching those IDs.
+        """
+        # Column headers to query for that map to respective fields in analysis table
+        headers = ["rit_uid", "birth_date_jittered", "gender", "death_date_jittered", "canonical_race",
+                   "canonical_ethnicity"]
+
+        '''
+        query = SQLQuery()
+        for header in headers:
+            query.addSelect( header )
+        query.addFrom(SOURCE_TABLE + " as dem")
+        if patientIds is not None:
+            query.addWhereIn("dem.rit_uid", patientIds)
+        '''
+
+        query = '''
+                SELECT rit_uid,birth_date_jittered,gender,death_date_jittered,canonical_race,canonical_ethnicity
+                FROM {} as dem
+                WHERE dem.rit_uid IN UNNEST(@pat_ids)
+                ORDER BY rit_uid;
+                '''.format(SOURCE_TABLE)
+
+        query_params = [
+            bigquery.ArrayQueryParameter('pat_ids', 'STRING', patientIds)
+        ]
+
+        if debug:
+            print(query)
+            print(query_params)
+
+        query_job = self.bqClient.queryBQ(str(query), query_params=query_params, location='US', batch_mode=False,
+                                          verbose=True)
+
+        previous_rit_uid = None
+        rows_fetched = 0
+        for row in query_job:  # API request - fetches results
+            rows_fetched += 1
+            # Row values can be accessed by field name or index
+            # assert row[0] == row.name == row["name"]
+            rowModel = RowItemModel(list(row.values()), headers)
+
+            # skip this record if we already processed this rit_uid
+            if rowModel["rit_uid"] == previous_rit_uid:
+                continue
+
+            if rowModel["birth_date_jittered"] is None:
+                # Blank values, doesn't make sense.  Skip it
+                log.warning(rowModel)
+            else:
+                # Record birth at resolution of year
+                rowModel["itemDate"] = datetime(rowModel["birth_date_jittered"].year, 1, 1)
+                rowModel["name"] = "Birth"
+                rowModel["description"] = "Birth Year"
+                yield rowModel
+
+                # Record another at resolution of decade
+                decade = (rowModel["birth_date_jittered"].year // 10) * 10
+                rowModel["itemDate"] = datetime(rowModel["birth_date_jittered"].year, 1, 1)
+                rowModel["name"] = "Birth%ds" % decade
+                rowModel["description"] = "Birth Decade %ds" % decade
+                yield rowModel
+
+                # Summarize race and ethnicity information into single field of interest
+                raceEthnicity = self.summarizeRaceEthnicity(rowModel["canonical_race"], rowModel["canonical_ethnicity"])
+                rowModel["itemDate"] = datetime(rowModel["birth_date_jittered"].year, 1, 1)
+                rowModel["name"] = "Race" + (raceEthnicity.translate(str.maketrans('', '', " ()-/")))  # Strip off punctuation
+                rowModel["description"] = "Race/Ethnicity: %s" % raceEthnicity
+                yield rowModel
+
+                gender = rowModel["gender"].title()
+                rowModel["name"] = gender
+                rowModel["description"] = "%s Gender" % gender
+                yield rowModel
+
+                if rowModel["death_date_jittered"] is not None:
+                    rowModel["name"] = "Death"
+                    rowModel["description"] = "Death Date"
+                    rowModel["itemDate"] = rowModel["death_date_jittered"]
+                    yield rowModel
+
+            previous_rit_uid = rowModel["rit_uid"]
+
+            progress.Update()
+
+        log.debug("fetched {} rows".format(rows_fetched))
+
+    def summarizeRaceEthnicity(self, canonical_race, canonical_ethnicity):
+        """Given row model with patient information, return a single string to summarize the patient's race and ethnicity information"""
+        race_ethnicity = RACE_MAPPINGS[canonical_race]
+        if race_ethnicity in UNSPECIFIED_RACE_ETHNICITY and canonical_ethnicity == HISPANIC_LATINO_ETHNICITY:
+            race_ethnicity = RACE_MAPPINGS[
+                HISPANIC_LATINO_ETHNICITY]  # Use Hispanic/Latino as basis if no other information
+        if race_ethnicity.find("%s") >= 0:  # Found replacement string.  Look to ethnicity for more information
+            if canonical_ethnicity == HISPANIC_LATINO_ETHNICITY:
+                race_ethnicity = race_ethnicity % RACE_MAPPINGS[HISPANIC_LATINO_ETHNICITY]
+            else:
+                race_ethnicity = race_ethnicity % ("Non-" + RACE_MAPPINGS[HISPANIC_LATINO_ETHNICITY])
+        return race_ethnicity
+
+    def categoryFromSourceItem(self, conn):
+        # Load or produce a clinical_item_category record model for the given sourceItem
+        category_key = (SOURCE_TABLE, "Demographics")
+        if category_key not in self.categoryBySourceDescr:
+            # Category does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            category = RowItemModel(
+                {
+                    "source_table": SOURCE_TABLE,
+                    "description": "Demographics",
+                }
+            )
+
+            (categoryId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", category, conn=conn)
+            category["clinical_item_category_id"] = categoryId
+            self.categoryBySourceDescr[category_key] = category
+        return self.categoryBySourceDescr[category_key]
+
+    def clinicalItemFromSourceItem(self, sourceItem, category, conn):
+        # Load or produce a clinical_item record model for the given sourceItem
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["name"])
+        if clinicalItemKey not in self.clinicalItemByCategoryIdExtId:
+            # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            clinicalItem = RowItemModel(
+                {
+                    "clinical_item_category_id": category["clinical_item_category_id"],
+                    "external_id": None,
+                    "name": sourceItem["name"],
+                    "description": sourceItem["description"],
+                }
+            )
+            (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn)
+            clinicalItem["clinical_item_id"] = clinicalItemId
+            self.clinicalItemByCategoryIdExtId[clinicalItemKey] = clinicalItem
+        return self.clinicalItemByCategoryIdExtId[clinicalItemKey]
+
+    def patientItemModelFromSourceItem(self, sourceItem, clinicalItem, conn):
+        # Produce a patient_item record model for the given sourceItem
+        patient_item = RowItemModel(
+            {
+                "external_id": None,
+                "patient_id": int(sourceItem["rit_uid"][2:], 16),
+                "encounter_id": None,
+                "clinical_item_id": clinicalItem["clinical_item_id"],
+                "item_date": str(sourceItem["itemDate"]),       # without str(), the time is being converted in postgres
+                "item_date_utc": None,                          # it's a date - so, no need to have a duplicate here
+            }
+        )
+        insert_query = DBUtil.buildInsertQuery("patient_item", list(patient_item.keys()))
+        insert_params = list(patient_item.values())
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute(insert_query, insert_params, conn=conn)
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
+            log.warn(err)
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usage_str = "usage: %prog [options]\n"
+        parser = OptionParser(usage=usage_str)
+        parser.add_option("-f", "--patient-ids-file", dest="patient_ids_file",
+                          help="File containing ids of patients to convert")
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: " + str.join(" ", argv))
+        timer = time.time()
+
+        conv_options = ConversionOptions()
+        conv_options.extract_parser_options(options)
+
+        self.convertItemsByBatch(conv_options.patient_ids_file)
+
+        timer = time.time() - timer
+        log.info("%.3f seconds to complete", timer)
+
+
+class ConversionOptions:
+    """Simple struct to contain multiple program options"""
+
+    def __init__(self):
+        self.patient_ids_file = None
+
+    def extract_parser_options(self, options):
+        if options.patient_ids_file is not None:
+            self.patient_ids_file = options.patient_ids_file
+
+
+if __name__ == "__main__":
+    instance = STARRDemographicsConversion()
+    instance.main(sys.argv)
diff --git a/medinfo/dataconversion/starr_conv/STARROrderMedConversion.py b/medinfo/dataconversion/starr_conv/STARROrderMedConversion.py
new file mode 100644
index 00000000..96c55d58
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/STARROrderMedConversion.py
@@ -0,0 +1,490 @@
+#!/usr/bin/env python
+import sys, os
+import hashlib
+import math
+import tempfile
+import time
+import logging
+
+from datetime import datetime
+from optparse import OptionParser
+from medinfo.common.Util import stdOpen, ProgressDots
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList, RowItemFieldComparator
+
+from medinfo.dataconversion.Util import log
+from medinfo.dataconversion.Const import TEMPLATE_MEDICATION_ID, TEMPLATE_MEDICATION_PREFIX
+from medinfo.dataconversion.Const import COLLECTION_TYPE_ORDERSET
+from medinfo.dataconversion.Env import DATE_FORMAT
+
+from medinfo.db.bigquery import bigQueryUtil
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+from google.cloud import bigquery
+
+SOURCE_TABLE = "starr_datalake2018.order_med"
+ORDERSET_TABLE = "starr_datalake2018.med_orderset"
+
+CATEGORY_TEMPLATE = "Med ({}) ({})"     # For this data source, item category will be a Medication subscripted by medication route
+GENERIC_CODE_TEMPLATE = "MED{}"         # Template for generic medication code reference if detailed RXCUI values not available
+RXCUI_CODE_TEMPLATE = "RXCUI{}"         # Template for medication code references when detailed RXCUI values available
+
+
+class STARROrderMedConversion:
+    """Data conversion module to take STARR provided computerized physician order entry data
+    (medications specifically)
+    into the structured data analysis tables to facilitate subsequent analysis.
+
+    For combination medications (usually same medication but with "1.5x" dosing like
+    Metoprolol 75mg ordered as combination of 50mg + 25mg tabs), just record as the
+    first component in the mixture.
+
+    Ignore PRN orders for now to simplify data set and focus on standing orders.
+    """
+
+    def __init__(self):
+        """Default constructor"""
+        self.bqConn = bigQueryUtil.connection()
+        self.bqClient = bigQueryUtil.BigQueryClient()
+        self.connFactory = DBUtil.ConnectionFactory()   # Default connection source, but Allow specification of alternative DB connection source
+
+        self.categoryBySourceDescr = dict()     # Local cache to track the clinical item category table contents
+        self.clinicalItemByCategoryIdCode = dict()  # Local cache to track clinical item table contents
+        self.itemCollectionByKeyStr = dict()    # Local cache to track item collections
+        self.itemCollectionItemByCollectionIdItemId = dict()    # Local cache to track item collection items
+
+    def convertAndUpload(self, convOptions, tempDir=tempfile.gettempdir(), removeCsvs=True, target_dataset_id='clinical_item2018'):
+        """
+        Wrapper around primary run function, does conversion locally and uploads to BQ
+        No batching done for treatment team since converted table is small
+        """
+        starrUtil = STARRUtil.StarrCommonUtils(self.bqClient)
+        self.convertSourceItems(convOptions)
+
+        batchCounter = 99999    # TODO (nodir) why not 0?
+        self.bqClient.reconnect_client()  # refresh bq client connection
+        starrUtil.dumpPatientItemCollectionLinkToCsv(tempDir, batchCounter)
+        starrUtil.uploadPatientItemCollectionLinkCsvToBQ(tempDir, target_dataset_id, batchCounter)
+        if removeCsvs:
+            starrUtil.removePatientItemCollectionLinkCsv(tempDir, batchCounter)
+        starrUtil.removePatientItemCollectionLinkAddedLines(SOURCE_TABLE)
+
+        # For now keep the clinical_* tables, upload them them once all tables have been converted
+        starrUtil.dumpItemCollectionTablesToCsv(tempDir)
+        starrUtil.uploadItemCollectionTablesCsvToBQ(tempDir, target_dataset_id)
+        if removeCsvs:
+            starrUtil.removeItemCollectionTablesCsv(tempDir)
+        starrUtil.removeItemCollectionTablesAddedLines(SOURCE_TABLE)
+
+        starrUtil.dumpPatientItemToCsv(tempDir, batchCounter)
+        starrUtil.uploadPatientItemCsvToBQ(tempDir, target_dataset_id, batchCounter)
+        if removeCsvs:
+            starrUtil.removePatientItemCsv(tempDir, batchCounter)
+        starrUtil.removePatientItemAddedLines(SOURCE_TABLE)
+
+        # For now keep the clinical_* tables, upload them them once all tables have been converted
+        starrUtil.dumpClinicalTablesToCsv(tempDir)
+        starrUtil.uploadClinicalTablesCsvToBQ(tempDir, target_dataset_id)
+        if removeCsvs:
+            starrUtil.removeClinicalTablesCsv(tempDir)
+        starrUtil.removeClinicalTablesAddedLines(SOURCE_TABLE)
+
+    def convertSourceItems(self, convOptions):
+        """Primary run function to process the contents of the order_med
+        table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
+        Should look for redundancies after the fact to catch repeatEd conversions.
+
+        startDate - If provided, only return items whose order_time_jittered is on or after that date.
+        endDate - If provided, only return items whose order_time_jittered is before that date.
+        """
+        log.info("Conversion for items dated {} to {}".format(convOptions.startDate, convOptions.endDate))
+        progress = ProgressDots()
+        conn = self.connFactory.connection()
+        try:
+            # Load up the medication mapping table to facilitate subsequent conversions
+            rxcuiDataByMedId = self.loadRXCUIData()
+
+            # Next round for medications directly from order_med table not addressed in medmix
+            for sourceItem in self.querySourceItems(rxcuiDataByMedId, convOptions, progress=progress, conn=conn):
+                self.convertSourceItem(sourceItem, conn=conn)
+                progress.Update()
+
+        finally:
+            conn.close()
+        progress.PrintStatus()
+
+    def loadRXCUIData(self):
+        """Load up the full contents of the stride_mapped_meds table into
+        memory (only a few thousand records) to facilitate rapid lookup resolution
+        of common medication ingredient data.
+        """
+        rxcuiDataByMedId = dict()
+
+        query = \
+            """select medication_id, rxcui, active_ingredient
+            from starr_datalake2018.mapped_meds
+            """
+
+        query_job = self.bqClient.queryBQ(query, verbose=True)
+
+        for row in query_job:  # API request - fetches results
+            (medId, rxcui, ingredient) = row    # Unpack the data tuple
+            if medId not in rxcuiDataByMedId:
+                rxcuiDataByMedId[medId] = dict()
+            rxcuiDataByMedId[medId][rxcui] = ingredient
+
+        return rxcuiDataByMedId
+
+    def querySourceItems(self, rxcuiDataByMedId, convOptions, progress=None, conn=None):
+        """Query the database for list of all source clinical items (medications, etc.)
+        and yield the results one at a time.  If startDate provided, only return items whose
+        order_time_jittered is on or after that date.
+        """
+        # Column headers to query for that map to respective fields in analysis table
+        queryHeaders = ["med.order_med_id_coded", "jc_uid", "med.pat_enc_csn_id_coded", "med.medication_id",
+                        "med.med_description", "order_time_jittered", "order_time_jittered_utc", "med_route",
+                        "number_of_times", "protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
+                        "ss_sg_key", "ss_sg_name", "ordering_mode"]
+
+        headers = ["order_med_id_coded", "jc_uid", "pat_enc_csn_id_coded", "medication_id",
+                   "med_description", "order_time_jittered", "order_time_jittered_utc", "med_route",
+                   "number_of_times", "protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
+                   "ss_sg_key", "ss_sg_name", "ordering_mode"]
+
+        # TODO original query - need to figure out how to pass date to query in BQ using SQLQuery object
+        # query = SQLQuery()
+        # for header in queryHeaders:
+        #     query.addSelect(header)
+        # query.addFrom("stride_order_med as med left outer join stride_orderset_order_med as os on med.order_med_id = os.order_med_id")  # Grab order set links if they exist
+        # query.addWhere("med.medication_id <> %s" % TEMPLATE_MEDICATION_ID)
+        # query.addWhere("freq_name not like '%%PRN'")    # Ignore PRN orders
+        # if convOptions.startDate is not None:
+        #     query.addWhereOp("ordering_date",">=", convOptions.startDate)
+        # if convOptions.endDate is not None:
+        #     query.addWhereOp("ordering_date","<", convOptions.endDate)
+
+        query = "SELECT {} FROM {} as med left outer join {} as os on med.order_med_id_coded = os.order_med_id_coded".format(', '.join(queryHeaders), SOURCE_TABLE, ORDERSET_TABLE)
+
+        # TODO only 20 records with medication_id = TEMPLATE_MEDICATION_ID (whereas stride has 67041 such rows)
+        query += " where med.medication_id <> {}".format(TEMPLATE_MEDICATION_ID)
+        query += " and (freq_name is NULL or freq_name not like '%PRN')"    # Ignore PRN orders
+        if convOptions.startDate is not None:
+            query += " and order_time_jittered >= @startDate"
+        if convOptions.endDate is not None:
+            query += " and order_time_jittered < @endDate"
+        query += " order by order_time_jittered, med.order_med_id_coded, jc_uid, med.pat_enc_csn_id_coded, med.medication_id"
+        query += ';'
+
+        query_params = [
+            bigquery.ScalarQueryParameter(
+                'startDate',
+                'DATETIME',
+                convOptions.startDate,
+            ),
+            bigquery.ScalarQueryParameter(
+                'endDate',
+                'DATETIME',
+                convOptions.endDate,
+            )
+        ]
+
+        # TODO Query to get an estimate of how long the process will be
+        # if progress is not None:
+        #     progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0]
+
+        query_job = self.bqClient.queryBQ(query, query_params=query_params, verbose=True)
+
+        for row in query_job:  # API request - fetches results
+            rowModel = RowItemModel(list(row.values()), headers)
+            log.debug("rowModel: {}".format(rowModel))
+            for normalizedModel in self.normalizeMedData(rxcuiDataByMedId, rowModel, convOptions):
+                yield normalizedModel  # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
+
+    def normalizeMedData(self, rxcuiDataByMedId, rowModel, convOptions):
+        """Normalize medication data by active ingredient mixtures and number of doses"""
+        for rowModel in self.normalizeMedIngredients(rxcuiDataByMedId, rowModel, convOptions):
+            if convOptions.doseCountLimit is not None and rowModel["number_of_times"] is not None:
+                if rowModel["number_of_times"] < convOptions.doseCountLimit:
+                    rowModel["code"] += " (<{})".format(convOptions.doseCountLimit)
+                    rowModel["med_description"] += " (<{} doses)".format(convOptions.doseCountLimit)
+            yield rowModel
+
+    @staticmethod
+    def normalizeMedIngredients(rxcuiDataByMedId, rowModel, convOptions):
+        """Given a rowModel of medication data, normalize it further.
+        Specifically, look for common active ingredients to simplify the data.
+        If the medication is actually a compound of multiple active ingredients,
+        then break out into active ingredients.
+
+        If normalizeMixtures set, then will yield out multiple items to reflect each active ingredient.
+        If normalizeMixtures not set, will yield a single item with name being a composite of the active ingredients.
+        """
+        medId = rowModel["medication_id"]
+
+        if medId not in rxcuiDataByMedId:
+            # No mapping entry found, just use the available generic medication data then
+            rowModel["code"] = GENERIC_CODE_TEMPLATE.format(rowModel["medication_id"])
+            yield rowModel
+
+        else:
+            # Mapping entry found, yield a normalized model for each active ingredient found
+            #   (will usually be a 1-to-1 relation, but sometimes multiple
+            ingredientByRxcui = rxcuiDataByMedId[medId]
+            if len(ingredientByRxcui) <= 1 or convOptions.normalizeMixtures:
+                # Single ingredient or want component active ingredients to each have one record
+                for (rxcui, ingredient) in ingredientByRxcui.items():
+                    # ~250/15000 RxCUI's don't have a defined active ingredient.
+                    if ingredient is None:
+                        # No mapping entry found, just use the available generic medication data then
+                        rowModel["code"] = GENERIC_CODE_TEMPLATE.format(rowModel["medication_id"])
+                        yield rowModel
+                    else:
+                        normalizedModel = RowItemModel(rowModel)
+                        normalizedModel["medication_id"] = rxcui
+                        normalizedModel["code"] = RXCUI_CODE_TEMPLATE.format(normalizedModel["medication_id"])
+                        normalizedModel["med_description"] = ingredient.title()
+                        if convOptions.includeRouteInDescription:
+                            normalizedModel["med_description"] += " {}".format(normalizedModel["med_route"])
+
+                        yield normalizedModel
+            else:
+                # Mixture of multiple ingredients and want to keep denormalized
+                # Extract out the active ingredient names to make a composite based only on that unique combination
+                ingredientRxcuiList = [(ingredient, rxcui) for (rxcui, ingredient) in ingredientByRxcui.items()]
+                # Ensure consistent order
+                ingredientRxcuiList.sort(key=lambda x: x if x[0] is not None else ('', x[1]))   # Python2 sort keeps None at the top while Python3 doesn't allow NoneType and int comparison)
+
+                rxcuiStrList = list()
+                ingredientList = list()
+                for (ingredient, rxcui) in ingredientRxcuiList:
+                    # ~250/15000 RxCUI's don't have a defined active ingredient.
+                    if ingredient is None:
+                        continue
+                    rxcuiStrList.append(str(rxcui))
+                    ingredientList.append(ingredient.title())
+                rxcuiComposite = str.join(",", rxcuiStrList)
+                ingredientComposite = str.join("-", ingredientList)
+
+                #rowModel["medication_id"] = hash(tuple(rxcuiList))    # Arbitrary integer, hash to try to be unique
+                #rowModel["code"] = RXCUI_CODE_TEMPLATE.format(rxcuiComposite)
+                # Nah, just stick to medication_id instead of creating a new hash number
+                rowModel["code"] = GENERIC_CODE_TEMPLATE.format(rowModel["medication_id"])
+                rowModel["med_description"] = ingredientComposite
+                if convOptions.includeRouteInDescription:
+                    rowModel["med_description"] += " {}".format(rowModel["med_route"])
+                yield rowModel
+
+    def convertSourceItem(self, sourceItem, conn=None):
+        """Given an individual sourceItem record, produce / convert it into an equivalent
+        item record in the analysis database.
+        """
+        extConn = conn is not None
+        if not extConn:
+            conn = self.connFactory.connection()
+        try:
+            # Normalize sourceItem data into hierarchical components (category -> clinical_item -> patient_item).
+            #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
+            #   in a first pass, with subsequent calls just yielding back in memory cached copies
+            category = self.categoryFromSourceItem(sourceItem, conn=conn)
+            clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn)
+            patientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn)
+
+            if sourceItem["protocol_id"] is not None:
+                # Similarly build up item collection (order set) hierarchy and link
+                itemCollection = self.itemCollectionFromSourceItem(sourceItem, conn=conn)
+                itemCollectionItem = self.itemCollectionItemFromSourceItem(sourceItem, itemCollection, clinicalItem, conn=conn)
+                patientItemCollectionLink = self.patientItemCollectionLinkFromSourceItem(sourceItem, itemCollectionItem, patientItem, conn=conn)
+
+        finally:
+            if not extConn:
+                conn.close()
+
+    def categoryFromSourceItem(self, sourceItem, conn):
+        # Load or produce a clinical_item_category record model for the given sourceItem
+        #   In this case, always Medication
+        categoryDescription = CATEGORY_TEMPLATE.format(sourceItem["med_route"], sourceItem["ordering_mode"])
+        categoryKey = (SOURCE_TABLE, categoryDescription)
+        if categoryKey not in self.categoryBySourceDescr:
+            # Category does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            category = RowItemModel(
+                {
+                    "source_table":  SOURCE_TABLE,
+                    "description":  categoryDescription,
+                }
+            )
+            (categoryId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", category, conn=conn)
+            category["clinical_item_category_id"] = categoryId
+            self.categoryBySourceDescr[categoryKey] = category
+        return self.categoryBySourceDescr[categoryKey]
+
+    def clinicalItemFromSourceItem(self, sourceItem, category, conn):
+        # Load or produce a clinical_item record model for the given sourceItem
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["code"])
+        if clinicalItemKey not in self.clinicalItemByCategoryIdCode:
+            # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            clinicalItem = RowItemModel(
+                {
+                    "clinical_item_category_id": category["clinical_item_category_id"],
+                    "external_id": sourceItem["medication_id"],
+                    "name": sourceItem["code"],
+                    "description": sourceItem["med_description"],
+                }
+            )
+            (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn)
+            clinicalItem["clinical_item_id"] = clinicalItemId
+            self.clinicalItemByCategoryIdCode[clinicalItemKey] = clinicalItem
+        else:
+            # Clinical Item does exist, but check for redundancies and opportunities to
+            #   simplify different descriptions for the same medication
+            priorClinicalItem = self.clinicalItemByCategoryIdCode[clinicalItemKey]
+            priorDescription = priorClinicalItem["description"]
+            if len(sourceItem["med_description"]) < len(priorDescription) or priorDescription.startswith(TEMPLATE_MEDICATION_PREFIX):
+                # Prior medication recorded description either a generic template,
+                #   or a longer version than necessary, that can be replaced with the current one
+                priorClinicalItem["description"] = sourceItem["med_description"]
+                DBUtil.updateRow("clinical_item", priorClinicalItem, priorClinicalItem["clinical_item_id"], conn=conn)
+        return self.clinicalItemByCategoryIdCode[clinicalItemKey]
+
+    def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
+        # Produce a patient_item record model for the given sourceItem
+        patientItem = RowItemModel(
+            {
+                "external_id": sourceItem["order_med_id_coded"],
+                "patient_id": int(sourceItem["jc_uid"][2:], 16),
+                "encounter_id": sourceItem["pat_enc_csn_id_coded"],
+                "clinical_item_id": clinicalItem["clinical_item_id"],
+                "item_date": sourceItem["order_time_jittered"],
+                "item_date_utc": str(sourceItem["order_time_jittered_utc"]),    # without str(), the time is being converted in postgres
+            }
+        )
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()))
+        insertParams = list(patientItem.values())
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute(insertQuery, insertParams, conn=conn)
+            patientItem["patient_item_id"] = DBUtil.execute(DBUtil.identityQuery("patient_item"), conn=conn)[0][0]
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, pull out existint ID and continue to insert whatever else is possible
+            log.info(err)    # Lookup just by the composite key components to avoid attempting duplicate insertion again
+            searchPatientItem = {
+                "patient_id":       patientItem["patient_id"],
+                "clinical_item_id": patientItem["clinical_item_id"],
+                "item_date":        patientItem["item_date"],
+            }
+            (patientItem["patient_item_id"], isNew) = DBUtil.findOrInsertItem("patient_item", searchPatientItem, conn=conn)
+        return patientItem
+
+    def itemCollectionFromSourceItem(self, sourceItem, conn):
+        # Load or produce an item_collection record model for the given sourceItem
+        if sourceItem["protocol_id"] is None:
+            # No order set link to this item, so nothing to return
+            return None
+
+        key = {
+            "protocol_id": sourceItem["protocol_id"],
+            "ss_section_id": sourceItem["ss_section_id"],
+            "ss_sg_key": sourceItem["ss_sg_key"].strip().upper() if sourceItem["ss_sg_key"] is not None else None
+        }
+
+        collection_key = "%(protocol_id)d-%(ss_section_id)s-%(ss_sg_key)s" % key
+        if collection_key not in self.itemCollectionByKeyStr:
+            # Collection does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            collection = RowItemModel(
+                {
+                    "external_id": sourceItem["protocol_id"],
+                    "name": sourceItem["protocol_name"],
+                    "section": sourceItem["ss_section_name"],
+                    "subgroup": sourceItem["ss_sg_name"],
+                }
+            )
+            (collectionId, isNew) = DBUtil.findOrInsertItem("item_collection", collection, conn=conn)
+            collection["item_collection_id"] = collectionId
+            self.itemCollectionByKeyStr[collection_key] = collection
+        return self.itemCollectionByKeyStr[collection_key]
+
+    def itemCollectionItemFromSourceItem(self, sourceItem, itemCollection, clinicalItem, conn):
+        # Load or produce an item_collection_item record model for the given sourceItem
+        itemKey = (itemCollection["item_collection_id"], clinicalItem["clinical_item_id"])
+        if itemKey not in self.itemCollectionItemByCollectionIdItemId:
+            # Item Collection Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            collectionItem = RowItemModel(
+                {
+                    "item_collection_id": itemCollection["item_collection_id"],
+                    "clinical_item_id": clinicalItem["clinical_item_id"],
+                    "collection_type_id": COLLECTION_TYPE_ORDERSET,
+                }
+            )
+            (collectionItemId, isNew) = DBUtil.findOrInsertItem("item_collection_item", collectionItem, conn=conn)
+            collectionItem["item_collection_item_id"] = collectionItemId
+            self.itemCollectionItemByCollectionIdItemId[itemKey] = collectionItem
+        return self.itemCollectionItemByCollectionIdItemId[itemKey]
+
+    def patientItemCollectionLinkFromSourceItem(self, sourceItem, collectionItem, patientItem, conn):
+        # Produce a patient_item_collection_link record model for the given sourceItem
+        patientItemCollectionLink = RowItemModel(
+            {
+                "patient_item_id": patientItem["patient_item_id"],
+                "item_collection_item_id": collectionItem["item_collection_item_id"],
+            }
+        )
+        insertQuery = DBUtil.buildInsertQuery("patient_item_collection_link", list(patientItemCollectionLink.keys()))
+        insertParams = list(patientItemCollectionLink.values())
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute(insertQuery, insertParams, conn=conn)
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
+            log.info(err)
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        log.setLevel(logging.FATAL)
+
+        usage_str = "usage: %prog [options]\n"
+        parser = OptionParser(usage=usage_str)
+        parser.add_option("-s", "--startDate", dest="startDate", metavar="<startDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time on or after this date.")
+        parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time before this date.")
+        parser.add_option("-n", "--normalizeMixtures", dest="normalizeMixtures", action="store_true",  help="If set, when find medication mixtures, will unravel / normalize into separate entries, one for each ingredient")
+        parser.add_option("-d", "--doseCountLimit", dest="doseCountLimit", help="Medication orders with a finite number of doses specified less than this limit will be labeled as different items than those without a number specified, or whose number is >= to this limit. Intended to distinguish things like IV single bolus / use vs. continuous infusions and standing medication orders")
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: " + str.join(" ", argv))
+        timer = time.time()
+
+        conv_options = ConversionOptions()
+        conv_options.extract_parser_options(options)
+
+        self.convertAndUpload(conv_options)
+
+        timer = time.time() - timer
+        log.info("%.3f seconds to complete", timer)
+
+
+class ConversionOptions:
+    """Simple struct to contain multiple program options"""
+    def __init__(self):
+        self.startDate = None
+        self.endDate = None
+        self.normalizeMixtures = False
+        self.doseCountLimit = None
+        self.includeRouteInDescription = True
+
+    def extract_parser_options(self, options):
+        if options.startDate is not None:
+            # Parse out the start date parameter
+            time_tuple = time.strptime(options.startDate, DATE_FORMAT)
+            self.startDate = datetime(*time_tuple[0:3])
+
+        if options.endDate is not None:
+            # Parse out the end date parameter
+            time_tuple = time.strptime(options.endDate, DATE_FORMAT)
+            self.endDate = datetime(*time_tuple[0:3])
+
+        if options.doseCountLimit is not None:
+            self.doseCountLimit = int(options.doseCountLimit)
+
+
+if __name__ == "__main__":
+    instance = STARROrderMedConversion()
+    instance.main(sys.argv)
diff --git a/medinfo/dataconversion/starr_conv/STARROrderProcConversion.py b/medinfo/dataconversion/starr_conv/STARROrderProcConversion.py
new file mode 100644
index 00000000..6d28b686
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/STARROrderProcConversion.py
@@ -0,0 +1,424 @@
+#!/usr/bin/env python
+
+import sys, os
+import logging
+import re
+import tempfile
+import time
+
+from datetime import datetime
+from datetime import timedelta
+from optparse import OptionParser
+from medinfo.common.Util import stdOpen, ProgressDots
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList
+
+from medinfo.dataconversion.Util import log
+from medinfo.dataconversion.Const import COLLECTION_TYPE_ORDERSET
+from medinfo.dataconversion.Env import DATE_FORMAT
+
+from medinfo.db.bigquery import bigQueryUtil
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+from google.cloud import bigquery
+
+SOURCE_TABLE = "starr_datalake2018.order_proc"
+ORDERSET_TABLE = "starr_datalake2018.proc_orderset"
+TARGET_DATASET_ID = "clinical_item2018"
+
+
+class STARROrderProcConversion:
+    """Data conversion module to take STRIDE provided computerized physician order entry data
+    into the structured data analysis tables to facilitate subsequent analysis.
+
+    Renormalizes denormalized data back out to order types (clinical_item_category),
+    orders (clinical_item), and actual individual patient orders (patient_item).
+    Ignores records with instantiated_time not null, so will only be interested
+    in originally ordered parent orders, not spawned child orders.
+
+    Consider Ignore PRN orders for now to simplify data set and focus on standing orders
+        (but a small minority for these orders anyway)
+
+    Beware of usage of this module and how it accounts for new unique clinical_items (probably applies to all the conversion scripts)
+    # This should be what determines a new unique clinical_item. 
+    #   Some debate about whether to distinguish by proc_id or proc_code, but there are many labs and other procs 
+    #   that use different proc_ids even though they are obviously the same. Go link in STRIDE_ORDER_PROC for examples like LABA1C.
+    # The self.clinicalItemByCategoryIdExtId is supposed to keep track of which clinical_items we're already aware of, 
+    #   but not that it starts blank when this module runs. 
+    #   So you in theory should only run this conversion process on a database once 
+    #   (otherwise it will not be aware that a bunch of duplicate clinical_items already exist in the database). 
+    #   Alternatively, this module should be updated, so that it initializes this key tracker with whatever is already in the database.
+    """
+    def __init__(self):
+        """Default constructor"""
+        self.bqConn = bigQueryUtil.connection()
+        self.bqClient = bigQueryUtil.BigQueryClient()
+        self.connFactory = DBUtil.ConnectionFactory()           # Default connection source, but Allow specification of alternative DB connection source
+
+        self.starrUtil = STARRUtil.StarrCommonUtils(self.bqClient)
+
+        self.categoryBySourceDescr = dict()                     # Local cache to track the clinical item category table contents
+        self.clinicalItemByCategoryIdExtId = dict()             # Local cache to track clinical item table contents
+
+        self.itemCollectionByKeyStr = dict()                    # Local cache to track item collections
+        self.itemCollectionItemByCollectionIdItemId = dict()    # Local cache to track item collection items
+
+        self.patient_items = dict()                             # Local cache of processed patient items
+        self.patient_item_collection_links = set()              # Local cache of processed patient item collection links
+
+    def convertAndUpload(self, startDate=None, endDate=None, tempDir=tempfile.gettempdir(), removeCsvs=True):
+        """
+        Wrapper around primary run function, does conversion locally and uploads to BQ
+        No batching done for treatment team since converted table is small
+        """
+        self.convertSourceItems(startDate, endDate)
+
+        batchCounter = 99999    # TODO (nodir) why not 0?
+        self.bqClient.reconnect_client()  # refresh bq client connection
+        self.starrUtil.dumpPatientItemCollectionLinkToCsv(tempDir, batchCounter)
+        self.starrUtil.uploadPatientItemCollectionLinkCsvToBQ(tempDir, TARGET_DATASET_ID, batchCounter)
+        if removeCsvs:
+            self.starrUtil.removePatientItemCollectionLinkCsv(tempDir, batchCounter)
+        self.starrUtil.removePatientItemCollectionLinkAddedLines(SOURCE_TABLE)
+
+        self.starrUtil.dumpPatientItemToCsv(tempDir, batchCounter)
+        self.starrUtil.uploadPatientItemCsvToBQ(tempDir, TARGET_DATASET_ID, batchCounter)
+        if removeCsvs:
+            self.starrUtil.removePatientItemCsv(tempDir, batchCounter)
+        self.starrUtil.removePatientItemAddedLines(SOURCE_TABLE)
+
+    def move_clinical_and_item_collection_to_bq(self, tempDir=tempfile.gettempdir(), removeCsvs=True):
+        # For now keep the clinical_* tables, upload them them once all tables have been converted
+        self.starrUtil.dumpItemCollectionTablesToCsv(tempDir)
+        self.starrUtil.uploadItemCollectionTablesCsvToBQ(tempDir, TARGET_DATASET_ID)
+        if removeCsvs:
+            self.starrUtil.removeItemCollectionTablesCsv(tempDir)
+        self.starrUtil.removeItemCollectionTablesAddedLines(SOURCE_TABLE)
+        # For now keep the clinical_* tables, upload them them once all tables have been converted
+        self.starrUtil.dumpClinicalTablesToCsv(tempDir)
+        self.starrUtil.uploadClinicalTablesCsvToBQ(tempDir, TARGET_DATASET_ID)
+        if removeCsvs:
+            self.starrUtil.removeClinicalTablesCsv(tempDir)
+        self.starrUtil.removeClinicalTablesAddedLines(SOURCE_TABLE)
+
+    def convertSourceItems(self, startDate=None, endDate=None):
+        """Primary run function to process the contents of the stride_order_proc
+        table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
+        Should look for redundancies to avoid repeating conversion.
+
+        startDate - If provided, only return items whose ordering_date is on or after that date.
+        endDate - If provided, only return items whose ordering_date is before that date.
+        """
+        log.info("Conversion for items dated {} to {}".format(startDate, endDate))
+        progress = ProgressDots()
+        conn = self.connFactory.connection()
+        try:
+            for sourceItem in self.querySourceItems(startDate, endDate, progress=progress, conn=conn):
+                self.convertSourceItem(sourceItem, conn=conn)
+                progress.Update()
+        finally:
+            conn.close()
+        progress.PrintStatus()
+
+    def querySourceItems(self, startDate=None, endDate=None, progress=None, conn=None):
+        """Query the database for list of all source clinical items (orders, etc.)
+        and yield the results one at a time.  If startDate provided, only return items whose order_time is on or after that date.
+        Ignore entries with instantiated_time not null, as those represent child orders spawned from an original order,
+        whereas we are more interested in the decision making to enter the original order.
+        """
+        extConn = conn is not None
+        if not extConn:
+            conn = self.connFactory.connection()
+
+        # Column headers to query for that map to respective fields in analysis table
+        queryHeaders = ["op.order_proc_id_coded", "jc_uid", "op.pat_enc_csn_id_coded", "op.order_type", "op.proc_id",
+                        "op.proc_code", "description", "order_time_jittered", "order_time_jittered_utc",
+                        "ordering_mode", "protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
+                        "ss_sg_key", "ss_sg_name"]
+        headers = ["order_proc_id_coded", "jc_uid", "pat_enc_csn_id_coded", "order_type", "proc_id",
+                   "proc_code", "description", "order_time_jittered", "order_time_jittered_utc",
+                   "ordering_mode", "protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
+                   "ss_sg_key", "ss_sg_name"]
+
+        # TODO original query - need to figure out how to pass date to query in BQ using SQLQuery object
+        # query = SQLQuery()
+        # for header in queryHeaders:
+        #     query.addSelect(header)
+        # query.addFrom("stride_order_proc as op left outer join stride_orderset_order_proc as os on op.order_proc_id = os.order_proc_id")
+        # query.addWhere("order_time is not null")    # Rare cases of "comment" orders with no date/time associated
+        # query.addWhere("instantiated_time is null")
+        # query.addWhere("(stand_interval is null or stand_interval not like '%%PRN')")   # Ignore PRN orders to simplify somewhat
+        # if startDate is not None:
+        #     query.addWhereOp("order_time", ">=", startDate)
+        # if endDate is not None:
+        #     query.addWhereOp("order_time", "<", endDate)
+
+        query = "SELECT {} FROM {} as op left outer join {} as os on op.order_proc_id_coded = os.order_proc_id_coded" \
+            .format(', '.join(queryHeaders), SOURCE_TABLE, ORDERSET_TABLE)
+
+        query += " where order_time_jittered is not null"    # Rare cases of "comment" orders with no date/time associated
+        query += " and (stand_interval is NULL or stand_interval not like '%PRN')"    # Ignore PRN orders to simplify somewhat
+        if startDate is not None:
+            query += " and order_time_jittered >= @startDate"
+        if endDate is not None:
+            query += " and order_time_jittered < @endDate"
+        query += " order by order_time_jittered"
+        # query += " order by op.order_proc_id_coded, jc_uid, op.pat_enc_csn_id_coded, op.proc_id"
+        query += ';'
+
+        query_params = [
+            bigquery.ScalarQueryParameter(
+                'startDate',
+                'DATETIME',
+                startDate,
+            ),
+            bigquery.ScalarQueryParameter(
+                'endDate',
+                'DATETIME',
+                endDate,
+            )
+        ]
+
+        # TODO Query to get an estimate of how long the process will be
+        # if progress is not None:
+        #     progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0]
+
+        query_job = self.bqClient.queryBQ(query, query_params=query_params, verbose=True)
+
+        for row in query_job:  # API request - fetches results
+            rowModel = RowItemModel(list(row.values()), headers)
+            log.debug("rowModel: {}".format(rowModel))
+            yield rowModel  # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
+
+        if not extConn:
+            conn.close()
+
+    def convertSourceItem(self, sourceItem, conn=None):
+        """Given an individual sourceItem record, produce / convert it into an equivalent
+        item record in the analysis database.
+        """
+        extConn = conn is not None
+        if not extConn:
+            conn = self.connFactory.connection()
+        try:
+            if sourceItem["proc_code"] is None:
+                # Some 10000 rows don't have proc_code given, so the clinical_item can't be created out of them.
+                # We could use description column, but the same description can have multiple proc_codes,
+                # so we'll just ignore those records.
+                return
+
+            # Normalize sourceItem data into hierarchical components (category -> clinical_item -> patient_item).
+            #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
+            #   in a first past, with subsequent calls just yielding back in memory cached copies
+            category = self.categoryFromSourceItem(sourceItem, conn=conn)
+            clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn)
+            patientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn)
+
+            if sourceItem["protocol_id"] is not None \
+                    and (sourceItem["ss_section_id"] is not None or sourceItem["ss_section_name"] is not None) \
+                    and sourceItem["ss_sg_key"] is not None:
+                # Similarly build up item collection (order set) hierarchy and link
+                itemCollection = self.itemCollectionFromSourceItem(sourceItem, conn=conn)
+                itemCollectionItem = self.itemCollectionItemFromSourceItem(sourceItem, itemCollection, clinicalItem, conn=conn)
+                patientItemCollectionLink = self.patientItemCollectionLinkFromSourceItem(sourceItem, itemCollectionItem, patientItem, conn=conn)
+        finally:
+            if not extConn:
+                conn.close()
+
+    def categoryFromSourceItem(self, sourceItem, conn):
+        # Load or produce a clinical_item_category record model for the given sourceItem
+        categoryKey = (SOURCE_TABLE, sourceItem["order_type"], sourceItem["ordering_mode"])
+        if categoryKey not in self.categoryBySourceDescr:
+            # Category does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            category = RowItemModel(
+                {
+                    "source_table": SOURCE_TABLE,
+                    "description": "{} ({})".format(sourceItem["order_type"], sourceItem["ordering_mode"])
+                }
+            )
+            (categoryId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", category, conn=conn)
+            category["clinical_item_category_id"] = categoryId
+            self.categoryBySourceDescr[categoryKey] = category
+        return self.categoryBySourceDescr[categoryKey]
+
+    def clinicalItemFromSourceItem(self, sourceItem, category, conn):
+        # Load or produce a clinical_item record model for the given sourceItem
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["proc_code"])
+        # This should be what determines a new unique clinical_item. 
+        #   Some debate about whether to distinguish by proc_id or proc_code, but there are many labs and other procs 
+        #   that use different proc_ids even though they are obviously the same. Go link in STRIDE_ORDER_PROC for examples like LABA1C.
+        # The self.clinicalItemByCategoryIdExtId is supposed to keep track of which clinical_items we're already aware of, 
+        #   but not that it starts blank when this module runs. 
+        #   So you in theory should only run this conversion process on a database once 
+        #   (otherwise it will not be aware that a bunch of duplicate clinical_items already exist in the database). 
+        #   Alternatively, this module should be updated, so that it initializes this key tracker with whatever is already in the database.
+        if clinicalItemKey not in self.clinicalItemByCategoryIdExtId:
+            # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            clinicalItem = RowItemModel(
+                {
+                    "clinical_item_category_id": category["clinical_item_category_id"],
+                    "external_id":               sourceItem["proc_id"],
+                    "name":                      sourceItem["proc_code"],
+                    "description":               sourceItem["description"],
+                }
+            )
+            (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn)
+            clinicalItem["clinical_item_id"] = clinicalItemId
+            self.clinicalItemByCategoryIdExtId[clinicalItemKey] = clinicalItem
+        return self.clinicalItemByCategoryIdExtId[clinicalItemKey]
+
+    def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
+        # Produce a patient_item record model for the given sourceItem
+        patientItem = RowItemModel(
+            {
+                "external_id":      sourceItem["order_proc_id_coded"],
+                "patient_id":       int(sourceItem["jc_uid"][2:], 16),
+                "encounter_id":     sourceItem["pat_enc_csn_id_coded"],
+                "clinical_item_id": clinicalItem["clinical_item_id"],
+                "item_date":        sourceItem["order_time_jittered"],
+                "item_date_utc":    str(sourceItem["order_time_jittered_utc"]),     # without str(), the time is being converted in postgres
+            }
+        )
+
+        key_hash = hash('{}{}{}'.format(patientItem["patient_id"], patientItem["clinical_item_id"],
+                                        patientItem["item_date"]))
+        if key_hash in self.patient_items:
+            return self.patient_items[key_hash]
+
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()))
+        insertParams = list(patientItem.values())
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute(insertQuery, insertParams, conn=conn)
+            patientItem["patient_item_id"] = DBUtil.execute(DBUtil.identityQuery("patient_item"), conn=conn)[0][0]
+            self.patient_items[key_hash] = patientItem
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, pull out existint ID and continue to insert whatever else is possible
+            log.warn(err)   # Lookup just by the composite key components to avoid attempting duplicate insertion again
+            searchPatientItem = {
+                "patient_id":       patientItem["patient_id"],
+                "clinical_item_id": patientItem["clinical_item_id"],
+                "item_date":        patientItem["item_date"],
+            }
+            (patientItem["patient_item_id"], isNew) = DBUtil.findOrInsertItem("patient_item", searchPatientItem, conn=conn)
+            self.patient_items[key_hash] = patientItem
+
+        return patientItem
+
+    def itemCollectionFromSourceItem(self, sourceItem, conn):
+        sourceItem["ss_section_identifier"] = sourceItem["ss_section_id"] if sourceItem["ss_section_id"] is not None \
+                                                                          else sourceItem["ss_section_name"].lower()
+
+        collectionKey = "%(protocol_id)d-%(ss_section_identifier)s-%(ss_sg_key)s" % sourceItem
+        if collectionKey not in self.itemCollectionByKeyStr:
+            # Collection does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            collection = RowItemModel(
+                {
+                    "external_id": sourceItem["protocol_id"],
+                    "name":        sourceItem["protocol_name"],
+                    "section":     sourceItem["ss_section_name"],
+                    "subgroup":    sourceItem["ss_sg_name"],
+                }
+            )
+            (collectionId, isNew) = DBUtil.findOrInsertItem("item_collection", collection, conn=conn)
+            collection["item_collection_id"] = collectionId
+            self.itemCollectionByKeyStr[collectionKey] = collection
+        return self.itemCollectionByKeyStr[collectionKey]
+
+    def itemCollectionItemFromSourceItem(self, sourceItem, itemCollection, clinicalItem, conn):
+        # Load or produce an item_collection_item record model for the given sourceItem
+        itemKey = (itemCollection["item_collection_id"], clinicalItem["clinical_item_id"])
+        if itemKey not in self.itemCollectionItemByCollectionIdItemId:
+            # Item Collection Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            collectionItem = RowItemModel(
+                {
+                    "item_collection_id": itemCollection["item_collection_id"],
+                    "clinical_item_id": clinicalItem["clinical_item_id"],
+                    "collection_type_id": COLLECTION_TYPE_ORDERSET,
+                }
+            )
+            (collectionItemId, isNew) = DBUtil.findOrInsertItem("item_collection_item", collectionItem, conn=conn)
+            collectionItem["item_collection_item_id"] = collectionItemId
+            self.itemCollectionItemByCollectionIdItemId[itemKey] = collectionItem
+        return self.itemCollectionItemByCollectionIdItemId[itemKey]
+
+    def patientItemCollectionLinkFromSourceItem(self, sourceItem, collectionItem, patientItem, conn):
+        hash_key = hash('{}{}'.format(patientItem["patient_item_id"], collectionItem["item_collection_item_id"]))
+        if hash_key in self.patient_item_collection_links:
+            return
+
+        # Produce a patient_item_collection_link record model for the given sourceItem
+        patientItemCollectionLink = RowItemModel(
+            {
+                "patient_item_id": patientItem["patient_item_id"],
+                "item_collection_item_id":  collectionItem["item_collection_item_id"],
+            }
+        )
+        insertQuery = DBUtil.buildInsertQuery("patient_item_collection_link", list(patientItemCollectionLink.keys()))
+        insertParams = list(patientItemCollectionLink.values())
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute(insertQuery, insertParams, conn=conn)
+            self.patient_item_collection_links.add(hash_key)
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, just note it and continue to insert whatever else is possible
+            log.warn(err)
+            self.patient_item_collection_links.add(hash_key)
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usageStr = "usage: %prog [options]\n" + \
+            "Beware that this module is intended to be run only ONCE ever on a database. Currently will end up with duplicate clinical item keys if you try to run it in parallel or even serially."
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-s", "--startDate", dest="startDate", metavar="<startDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time on or after this date.")
+        parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",  help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with ordering time before this date.")
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: " + str.join(" ", argv))
+        timer = time.time()
+
+        maxMinDatesQuery = """
+            select min(extract(date from order_time_jittered)), max(extract(date from order_time_jittered))
+            from {};
+        """.format(SOURCE_TABLE)
+        maxMinDates = self.bqClient.queryBQ(maxMinDatesQuery, verbose=True)
+        min_date = None
+        max_date = None
+        for row in maxMinDates:
+            min_date = list(row.values())[0]
+            max_date = list(row.values())[1]
+
+        if options.startDate is not None:
+            # Parse out the start date parameter
+            timeTuple = time.strptime(options.startDate, DATE_FORMAT)
+            startDate = datetime(*timeTuple[0:3])
+        else:
+            startDate = datetime.combine(min_date, datetime.min.time())
+
+        if options.endDate is not None:
+            # Parse out the end date parameter
+            timeTuple = time.strptime(options.endDate, DATE_FORMAT)
+            endDate = datetime(*timeTuple[0:3])
+        else:
+            endDate = datetime.combine(max_date + timedelta(days=1), datetime.min.time())
+
+        date = startDate
+        while date < endDate:
+            endDateBracket = min(date + timedelta(days=7), endDate)
+            log.info('Converting {} - {}'.format(date, endDateBracket))
+            self.convertAndUpload(date, endDateBracket)
+            date += timedelta(days=7)
+
+            self.patient_items.clear()
+            self.patient_item_collection_links.clear()
+
+        self.move_clinical_and_item_collection_to_bq()
+
+        timer = time.time() - timer
+        log.info("%.3f seconds to complete", timer)
+
+
+if __name__ == "__main__":
+    instance = STARROrderProcConversion()
+    instance.main(sys.argv)
diff --git a/medinfo/dataconversion/starr_conv/STARRTreatmentTeamConversion.py b/medinfo/dataconversion/starr_conv/STARRTreatmentTeamConversion.py
new file mode 100644
index 00000000..2dfef11b
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/STARRTreatmentTeamConversion.py
@@ -0,0 +1,379 @@
+#!/usr/bin/env python
+import sys, os
+import tempfile
+import time
+import re
+
+from datetime import datetime
+from optparse import OptionParser
+from medinfo.common.Util import stdOpen, ProgressDots
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.db.Model import RowItemModel, modelListFromTable, modelDictFromList, RowItemFieldComparator
+
+from medinfo.dataconversion.Util import log
+from medinfo.dataconversion.Env import DATE_FORMAT
+
+from medinfo.db.bigquery import bigQueryUtil
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+from google.cloud import bigquery
+
+SOURCE_TABLE = 'starr_datalake2018.treatment_team'
+
+CATEGORY_TEMPLATE = "Treatment Team"
+KEY_PROVIDER_PREFIXES = ("TT ", "CON ")  # Name Prefixes indicating a special (team) provider
+
+TEAM_PREFIXES = ("Primary", "Consulting")
+ADDITIONAL_PRIMARY_PROVIDERS = ("Nurse Practitioner", "Physician's Assistant",
+                                "Physician Assistant")  # Provider types to also count as "Primary" team
+
+# Words to discard from team labels, such as pager and phones
+DISCARD_WORDS = ("PAGER", "PGR", "PRG", "SPECTRAS")
+
+# Words to identify as some kind of sublabel to ignore for aggregation purposes
+SUB_LABEL_WORDS = (
+    "GOLD", "RED", "WHITE", "BLUE", "GREEN", "ATTENDING", "ONLY", "INTERN", "RESIDENT", "RES", "NP", "NON", "RESI")
+
+
+class STARRTreatmentTeamConversion:
+    """Data conversion module to take STARR data
+    into the structured data analysis tables to facilitate subsequent analysis.
+    """
+
+    # Column headers to query for that map to respective fields in analysis table
+    HEADERS = ['prov_map_id', 'rit_uid', 'pat_enc_csn_id_coded', 'trtmnt_tm_begin_dt_jittered',
+               'trtmnt_tm_end_dt_jittered', 'name', 'prov_name', 'trtmnt_tm_begin_dt_jittered_utc']
+
+    def __init__(self):
+        """Default constructor"""
+        self.bqConn = bigQueryUtil.connection()
+        self.bqClient = bigQueryUtil.BigQueryClient()
+        self.connFactory = DBUtil.ConnectionFactory()  # Default connection source, but Allow specification of alternative DB connection source
+
+        self.categoryBySourceDescr = dict()  # Local cache to track the clinical item category table contents
+        self.clinicalItemByCompositeKey = dict()  # Local cache to track clinical item table contents
+
+    def convertAndUpload(self, convOptions, tempDir=tempfile.gettempdir(), removeCsvs=True, targetDatasetId='clinical_item2018'):
+        """
+        Wrapper around primary run function, does conversion locally and uploads to BQ
+        No batching done for treatment team since converted table is small
+        """
+        conn = self.connFactory.connection()
+        starrUtil = STARRUtil.StarrCommonUtils(self.bqClient)
+        self.convertSourceItems(convOptions, conn)
+
+        batchCounter = 99999    # TODO (nodir) why not 0?
+        starrUtil.dumpPatientItemToCsv(tempDir, batchCounter)
+        self.bqClient.reconnect_client()  # refresh bq client connection
+        starrUtil.uploadPatientItemCsvToBQ(tempDir, targetDatasetId, batchCounter)
+        if removeCsvs:
+            starrUtil.removePatientItemCsv(tempDir, batchCounter)
+        starrUtil.removePatientItemAddedLines(SOURCE_TABLE)
+
+        # For now keep the clinical_* tables, upload them them once all tables have been converted
+        starrUtil.dumpClinicalTablesToCsv(tempDir)
+        starrUtil.uploadClinicalTablesCsvToBQ(tempDir, targetDatasetId)
+        if removeCsvs:
+            starrUtil.removeClinicalTablesCsv(tempDir)
+        starrUtil.removeClinicalTablesAddedLines(SOURCE_TABLE)
+
+    def convertSourceItems(self, convOptions, conn=None):
+        """Primary run function to process the contents of the raw source
+        table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
+        Should look for redundancies after the fact to catch repeated conversions.
+
+        startDate - If provided, only return items whose ordering_date is on or after that date.
+        endDate - If provided, only return items whose ordering_date is before that date.
+        """
+        log.info("Conversion for items dated %s to %s" % (convOptions.startDate, convOptions.endDate))
+        progress = ProgressDots()
+
+        extConn = conn is not None
+        if not extConn:
+            conn = self.connFactory.connection()
+
+        try:
+            # Next round for medications directly from order_med table not addressed in medmix  TODO (nodir) seems like an unrelated comment?
+            category = self.categoryFromSourceItem(conn)
+            for sourceItem in self.querySourceItems(convOptions):
+                log.debug('sourceItem: {}'.format(sourceItem))
+                self.convertSourceItem(category, sourceItem, conn=conn)
+                progress.Update()
+
+        finally:
+            conn.close()
+
+        progress.PrintStatus()
+
+    def querySourceItems(self, convOptions):
+        """Query the database for list of all source clinical items (medications, etc.)
+        and yield the results one at a time.  If startDate provided, only return items whose
+        occurrence date is on or after that date.
+        """
+        # TODO need to figure out how to pass date to query in BQ using SQLQuery object
+        query = "SELECT {} FROM {}".format(', '.join(self.HEADERS), SOURCE_TABLE)
+
+        if convOptions.startDate is not None:
+            query += ' WHERE trtmnt_tm_begin_dt_jittered >= @startDate '
+        if convOptions.endDate is not None:
+            query += ' WHERE ' if convOptions.startDate is None else 'AND'
+            query += ' trtmnt_tm_begin_dt_jittered < @endDate'
+
+        query += ' ORDER BY trtmnt_tm_begin_dt_jittered'
+        query += ';'
+
+        query_params = [
+            bigquery.ScalarQueryParameter(
+                'startDate',
+                'TIMESTAMP',
+                convOptions.startDate,
+            ),
+            bigquery.ScalarQueryParameter(
+                'endDate',
+                'TIMESTAMP',
+                convOptions.endDate,
+            )
+        ]
+
+        query_job = self.bqClient.queryBQ(str(query), query_params=query_params, location='US', batch_mode=False,
+                                          verbose=True)
+
+        for row in query_job:  # API request - fetches results
+            rowModel = RowItemModel(list(row.values()), self.HEADERS)
+            log.debug("rowModel: {}".format(rowModel))
+            yield self.normalizeRowModel(rowModel, convOptions)  # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
+
+    def normalizeRowModel(self, rowModel, convOptions):
+        """Given a rowModel of data, normalize it further.
+        Specifically, look for aggregate data items (e.g., multiple Gen Med treatment teams, report as one)
+        """
+        (teamAcronym, teamName) = self.cleanName(rowModel["name"], convOptions)
+        (provAcronym, provName) = self.cleanName(rowModel["prov_name"], convOptions, keyPrefixes=KEY_PROVIDER_PREFIXES)
+        provName = provName.title()
+
+        if provAcronym != "":
+            rowModel["code"] = "%s (%s)" % (provAcronym, teamAcronym)
+            rowModel["description"] = "%s (%s)" % (provName, teamName)
+        else:
+            rowModel["code"] = teamAcronym
+            rowModel["description"] = teamName
+
+        if rowModel["trtmnt_tm_begin_dt_jittered"] is None:
+            # Don't know how to use event information without a timestamp
+            pass
+
+        return rowModel
+
+    # TODO (nodir) is it possible to separate the logic for team and provider?
+    def cleanName(self, inputName, convOptions, keyPrefixes=None):
+        """Given an input name (e.g., treatment team or provider)
+        Return a 2-ple (acronym, cleaned) with an acronym version
+        and a cleaned up version (discard extra punctuation, phone/pager numbers, etc.)
+        If keyPrefixes specified, then only accept input names that start with one of them
+        (e.g., "TT" or "CON" for provider names, to only look at team names instead of named individuals.
+        """
+        isPrefixAcceptable = True
+        if keyPrefixes is not None:
+            isPrefixAcceptable = False
+            for keyPrefix in keyPrefixes:
+                if inputName is not None and inputName.startswith(keyPrefix):
+                    isPrefixAcceptable = True
+
+        # Default to blanks
+        acronym = ""
+        cleanedName = ""
+
+        if isPrefixAcceptable and inputName is not None:
+            acronymList = list()
+            wordList = list()
+            chunks = inputName.split()
+            for i, chunk in enumerate(chunks):
+                if chunk[-1] == ",":  # Strip any commas
+                    chunk = chunk[:-1]
+
+                if convOptions.aggregate and i == 0 and chunk in TEAM_PREFIXES:
+                    # Aggregating mixed records, so just use batch team prefix if exists
+                    acronymList.append(chunk[0])
+                    wordList.append(chunk)
+                    break
+                elif convOptions.aggregate and chunk.upper() in SUB_LABEL_WORDS:
+                    # Sub label word not interested when aggregating data, so just ignore it
+                    continue
+                elif convOptions.aggregate and len(chunk) == 1:
+                    # A short number or letter sub-label, ignore if aggregating
+                    continue
+                elif convOptions.aggregate and len(chunk) <= 2 and (chunk[0].isdigit() or chunk[-1].isdigit()):
+                    # A short number or letter sub-label, ignore if aggregating
+                    continue
+                elif len(chunk) <= 2 and not chunk[0].isalnum() and not chunk[-1].isalnum():
+                    # Short non-alphanumeric sequence, probably punctuation
+                    continue
+                elif not chunk[0].isalnum() and len(chunk) > 1 and chunk[1].isdigit():
+                    # Probably just a pager/phone number
+                    continue
+                elif len(chunk) > 1 and chunk[0].isdigit() and chunk[-1].isdigit():
+                    # Looks like a number, probably pager or phone.  Don't include
+                    continue
+                elif chunk in DISCARD_WORDS:
+                    # Probably just pager link, not interested
+                    continue
+                else:
+                    if chunk[0].isalnum():
+                        acronymList.append(chunk[0])
+                    else:  # Get the next character if first was not alphanumeric, probably an open parantheses "("
+                        acronymList.append(chunk[1])
+                    wordList.append(chunk)
+            acronym = str.join("", acronymList)
+            cleanedName = str.join(" ", wordList)
+
+            if convOptions.aggregate and inputName in ADDITIONAL_PRIMARY_PROVIDERS:
+                # Override specific provider names as generic primary team members
+                (acronym, cleanedName) = ("P", "Primary")
+
+        return (acronym, cleanedName)
+
+    def convertSourceItem(self, category, sourceItem, conn=None):
+        """Given an individual sourceItem record, produce / convert it into an equivalent
+        item record in the analysis database.
+        """
+        extConn = conn is not None
+        if not extConn:
+            conn = self.connFactory.connection()
+
+        try:
+            # Normalize sourceItem data into hierarchical components (category -> clinical_item -> patient_item).
+            #   Relatively small / finite number of categories and clinical_items, so these should only have to be instantiated
+            #   in a first pass, with subsequent calls just yielding back in memory cached copies
+            clinicalItem = self.clinicalItemFromSourceItem(sourceItem, category, conn=conn)
+            ignoredPatientItem = self.patientItemFromSourceItem(sourceItem, clinicalItem, conn=conn)
+
+        finally:
+            if not extConn:
+                conn.close()
+
+    def categoryFromSourceItem(self, conn):
+        # Load or produce a clinical_item_category record model for the given sourceItem
+        #   In this case, always Medication
+        categoryDescription = CATEGORY_TEMPLATE
+        categoryKey = (SOURCE_TABLE, categoryDescription)
+        if categoryKey not in self.categoryBySourceDescr:
+            # Category does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            category = RowItemModel(
+                {
+                    "source_table": SOURCE_TABLE,
+                    "description": categoryDescription,
+                }
+            )
+            (categoryId, isNew) = DBUtil.findOrInsertItem("clinical_item_category", category, conn=conn)
+            category["clinical_item_category_id"] = categoryId
+            self.categoryBySourceDescr[categoryKey] = category
+        return self.categoryBySourceDescr[categoryKey]
+
+    def clinicalItemFromSourceItem(self, sourceItem, category, conn):
+        # Load or produce a clinical_item record model for the given sourceItem
+        clinicalItemKey = (category["clinical_item_category_id"], sourceItem["description"])
+        if clinicalItemKey not in self.clinicalItemByCompositeKey:
+            # Clinical Item does not yet exist in the local cache.  Check if in database table (if not, persist a new record)
+            clinicalItem = RowItemModel(
+                {
+                    "clinical_item_category_id": category["clinical_item_category_id"],
+                    "external_id": None,
+                    "name": sourceItem["code"],
+                    "description": sourceItem["description"],
+                }
+            )
+            (clinicalItemId, isNew) = DBUtil.findOrInsertItem("clinical_item", clinicalItem, conn=conn)
+            clinicalItem["clinical_item_id"] = clinicalItemId
+            self.clinicalItemByCompositeKey[clinicalItemKey] = clinicalItem
+        return self.clinicalItemByCompositeKey[clinicalItemKey]
+
+    def patientItemFromSourceItem(self, sourceItem, clinicalItem, conn):
+        # some prov_map_id values are NULL in starr_datalake2018
+        if sourceItem["prov_map_id"] is not None:
+            # prov_map_id starts with letters, we're interested only in number parts
+            external_id = int(re.sub("[A-Z]+(\\d+)", "\\1", sourceItem["prov_map_id"]), 16)
+        else:
+            external_id = None
+
+        # Produce a patient_item record model for the given sourceItem
+        patientItem = RowItemModel(
+            {
+                "external_id": external_id,
+                "patient_id": int(sourceItem["rit_uid"][2:], 16),
+                "encounter_id": sourceItem["pat_enc_csn_id_coded"],
+                "clinical_item_id": clinicalItem["clinical_item_id"],
+                "item_date": str(sourceItem["trtmnt_tm_begin_dt_jittered"]),    # without str(), the time is being converted in postgres
+                "item_date_utc": str(sourceItem["trtmnt_tm_begin_dt_jittered_utc"])    # without str(), the time is being converted in postgres
+            }
+        )
+
+        insertQuery = DBUtil.buildInsertQuery("patient_item", list(patientItem.keys()))
+        insertParams = list(patientItem.values())
+        try:
+            # Optimistic insert of a new unique item
+            DBUtil.execute(insertQuery, insertParams, conn=conn)
+            # Retrieve id of just inserted row
+            patientItem["patient_item_id"] = DBUtil.execute(DBUtil.identityQuery("patient_item"), conn=conn)[0][0]
+        except conn.IntegrityError as err:
+            # If turns out to be a duplicate, okay, pull out existing ID and continue to insert whatever else is possible
+            log.info(err)  # Lookup just by the composite key components to avoid attempting duplicate insertion again
+
+            searchPatientItem = {
+                "patient_id": patientItem["patient_id"],
+                "clinical_item_id": patientItem["clinical_item_id"],
+                "item_date": patientItem["item_date"],
+            }
+            (patientItem["patient_item_id"], isNew) = DBUtil.findOrInsertItem("patient_item", searchPatientItem,
+                                                                              conn=conn)
+        return patientItem
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usageStr = "usage: %prog [options]\n"
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-s", "--startDate", dest="startDate", metavar="<startDate>",
+                          help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with start time on or after this date.")
+        parser.add_option("-e", "--endDate", dest="endDate", metavar="<endDate>",
+                          help="Date string (e.g., 2011-12-15), if provided, will only run conversion on items with start time before this date.")
+        parser.add_option("-a", "--aggregate", dest="aggregate", action="store_true",
+                          help="If set, will try to aggregate data so Med Univ A1, A2, A3 will all be counted as Med Univ and Primary Team, Intern, Resident will all just be counted as Primary.")
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: " + str.join(" ", argv))
+        timer = time.time()
+
+        convOptions = ConversionOptions()
+        convOptions.extractParserOptions(options)
+
+        self.convertAndUpload(convOptions)
+
+        timer = time.time() - timer
+        log.info("%.3f seconds to complete", timer)
+
+
+class ConversionOptions:
+    """Simple struct to contain multiple program options"""
+
+    def __init__(self):
+        self.startDate = None
+        self.endDate = None
+        self.aggregate = True
+
+    def extractParserOptions(self, options):
+        if options.startDate is not None:
+            # Parse out the start date parameter
+            timeTuple = time.strptime(options.startDate, DATE_FORMAT)
+            self.startDate = datetime(*timeTuple[0:3])
+
+        if options.endDate is not None:
+            # Parse out the end date parameter
+            timeTuple = time.strptime(options.endDate, DATE_FORMAT)
+            self.endDate = datetime(*timeTuple[0:3])
+
+        self.aggregate = options.aggregate
+
+
+if __name__ == "__main__":
+    instance = STARRTreatmentTeamConversion()
+    instance.main(sys.argv)
diff --git a/medinfo/dataconversion/starr_conv/STARRUtil.py b/medinfo/dataconversion/starr_conv/STARRUtil.py
new file mode 100644
index 00000000..ead60132
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/STARRUtil.py
@@ -0,0 +1,300 @@
+import csv
+import os
+import pytz
+import random
+import subprocess
+import sys
+import time
+
+from medinfo.common.Util import stdOpen
+from medinfo.db import DBUtil
+from medinfo.dataconversion.Util import log
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+
+class StarrCommonUtils:
+
+    def __init__(self, bqClient=None, pgConn=None):
+        self.bqClient = bqClient
+        self.pgConn = pgConn
+
+    @staticmethod
+    def convertPatIdToSTRIDE(starr_pat_id):
+        return int(starr_pat_id[2:], 16)
+
+    '''
+    BigQuery SQL conversion e.g.
+    https://stackoverflow.com/questions/46664776
+    
+    CREATE TEMP FUNCTION HexToInt(hex_string STRING) AS (
+      IFNULL(SAFE_CAST(CONCAT('0x', REPLACE(hex_string, ' ', '')) AS INT64), 0)
+    );
+    
+    SELECT HexToInt(SUBSTR('JCcdf815', 3));
+    '''
+
+    @staticmethod
+    def convertPatIdToSTARR(stride_pat_id):
+        return 'JC' + hex(stride_pat_id)
+
+    '''
+    BigQuery SQL conversion e.g.
+    https://stackoverflow.com/questions/51600209
+    
+    CREATE TEMP FUNCTION IntToHex(x INT64) AS (
+      LTRIM(
+      (SELECT STRING_AGG(FORMAT('%02x', x >> (byte * 8) & 0xff), 
+                         '' ORDER BY byte DESC)
+       FROM UNNEST(GENERATE_ARRAY(0, 7)) AS byte),
+      '0'
+      )
+    );
+    
+    SELECT CONCAT('JC', IntToHex(13498389));
+    '''
+
+    @staticmethod
+    def random_period():
+        start_date = random.randint(1, int(time.time()))
+        end_date = random.randint(start_date, int(time.time()))
+        return start_date, end_date
+
+    @staticmethod
+    def dump_test_data_to_csv(header, test_data, csv_file):
+        with open(csv_file, 'w', newline='') as test_data_file:
+            csv_writer = csv.writer(test_data_file)
+            csv_writer.writerow(header)
+            csv_writer.writerows(test_data)
+
+    '''
+    Retrieves schema for given table from the given BQ client.
+    It is possible to filter out only required fields.
+    The resulting schema, in this case, will be sorted according to the filter order.
+    '''
+    def get_schema_filtered(self, dataset, table, fields_to_keep_in_schema=None):
+        schema = self.bqClient.client.get_table('mining-clinical-decisions.{}.{}'.format(dataset, table)).schema
+
+        if fields_to_keep_in_schema:
+            # filter out only fields we need
+            schema = list([field for field in schema if field.name in fields_to_keep_in_schema])
+            # sort schema fields according to the header
+            schema.sort(key=lambda field: fields_to_keep_in_schema.index(field.name))
+
+        return schema
+
+    def upload_csv_to_bigquery(self, schema_dataset, schema_table, dest_dataset, dest_table, csv_file,
+                               schema_fields=None):
+        schema = self.get_schema_filtered(schema_dataset, schema_table, schema_fields)
+
+        self.bqClient.load_csv_to_table(
+            dest_dataset,
+            dest_table,
+            csv_file,
+            False,
+            schema,
+            1
+        )
+
+    def dumpPatientItemCollectionLinkToCsv(self, tempDir, batchCounter=999):
+        log.info('Dumping patient_item_collection_link for batch {} to CSV'.format(batchCounter))
+
+        DBUtil.dumpTableToCsv('patient_item_collection_link',
+                              '{}/{}_patient_item_collection_link.csv'.format(tempDir, batchCounter))
+
+    def dumpItemCollectionTablesToCsv(self, tempDir):
+        log.info('Dumping item_collection_item and item_collection to CSV')
+
+        DBUtil.dumpTableToCsv('item_collection_item', '{}/item_collection_item.csv'.format(tempDir))
+        DBUtil.dumpTableToCsv('item_collection', '{}/item_collection.csv'.format(tempDir))
+
+    def uploadPatientItemCollectionLinkCsvToBQ(self, tempDir, datasetId, batchCounter=999):
+        log.info('Uploading patient_item CSV to BQ dataset {} for batch {}'.format(datasetId, batchCounter))
+        patient_item_collection_link_schema = self.get_schema_filtered('clinical_item2018',
+                                                                       'patient_item_collection_link')
+
+        csv_path = tempDir + os.path.sep + str(batchCounter) + '_patient_item_collection_link.csv'
+
+        bigQueryUtil.headerChecker(csv_path, [sf.name for sf in patient_item_collection_link_schema])
+
+        self.bqClient.load_csv_to_table(datasetId, 'patient_item_collection_link', csv_path, skip_rows=1,
+                                        append_to_table=True, auto_detect_schema=False,
+                                        schema=patient_item_collection_link_schema)
+
+    def uploadItemCollectionTablesCsvToBQ(self, tempDir, datasetId):
+        log.info('Uploading item_collection CSV to BQ dataset {}'.format(datasetId))
+        item_collection_schema = self.get_schema_filtered('clinical_item2018', 'item_collection')
+
+        item_collection_csv_path = tempDir + '/item_collection.csv'
+
+        bigQueryUtil.headerChecker(item_collection_csv_path, [sf.name for sf in item_collection_schema])
+
+        self.bqClient.load_csv_to_table(datasetId, 'item_collection', item_collection_csv_path, skip_rows=1,
+                                        append_to_table=True, auto_detect_schema=False, schema=item_collection_schema)
+
+        log.info('Uploading item_collection_item CSV to BQ dataset {}'.format(datasetId))
+        item_collection_item_schema = self.get_schema_filtered('clinical_item2018', 'item_collection_item')
+
+        item_collection_item_csv_path = tempDir + '/item_collection_item.csv'
+
+        bigQueryUtil.headerChecker(item_collection_item_csv_path, [sf.name for sf in item_collection_item_schema])
+
+        self.bqClient.load_csv_to_table(datasetId, 'item_collection_item', item_collection_item_csv_path, skip_rows=1,
+                                        append_to_table=True, auto_detect_schema=False,
+                                        schema=item_collection_item_schema)
+
+    def removePatientItemCollectionLinkCsv(self, temp_dir, batchCounter=999):
+        log.info('Removing patient_item_collection_link CSV for batch {}'.format(batchCounter))
+        self.remove_file(temp_dir + os.path.sep + str(batchCounter) + '_patient_item_collection_link.csv')
+
+    def removeItemCollectionTablesCsv(self, temp_dir):
+        log.info('Removing item_collection and item_collection_item CSVs')
+        self.remove_file(temp_dir + '/item_collection.csv')
+        self.remove_file(temp_dir + '/item_collection_item.csv')
+
+    def removePatientItemCollectionLinkAddedLines(self, source_table):
+        """delete added records"""
+        log.info('Removing patient_item_collection_link added lines in PSQL DB')
+
+        DBUtil.execute(
+            """delete from patient_item_collection_link pi
+               using item_collection_item ici, clinical_item ci, clinical_item_category cic
+               where pi.item_collection_item_id = ici.item_collection_item_id
+                 and ici.clinical_item_id = ci.clinical_item_id
+                 and ci.clinical_item_category_id = cic.clinical_item_category_id
+                 and cic.source_table = '{}';
+                 """.format(source_table), conn=self.pgConn
+        )
+
+    def removeItemCollectionTablesAddedLines(self, source_table):
+        """delete added records"""
+        log.info('Removing item_collection_item and item_collection added lines in PSQL DB')
+
+        DBUtil.execute(
+            """delete from item_collection_item ici
+               using clinical_item ci, clinical_item_category cic
+               where ici.clinical_item_id = ci.clinical_item_id
+                 and ci.clinical_item_category_id = cic.clinical_item_category_id
+                 and cic.source_table = '{}';
+                 """.format(source_table), conn=self.pgConn
+         )
+
+        # TODO should be using source_table also
+        DBUtil.execute("delete from item_collection where true;", conn=self.pgConn)
+
+    def dumpPatientItemToCsv(self, tempDir, batchCounter=999):
+        log.info('Dumping patient_item for batch {} to CSV'.format(batchCounter))
+
+        DBUtil.dumpTableToCsv('patient_item', '{}/{}_patient_item.csv'.format(tempDir, batchCounter))
+
+    def dumpClinicalTablesToCsv(self, tempDir):
+        log.info('Dumping clinical_item and clinical_item_category to CSV')
+
+        DBUtil.dumpTableToCsv('clinical_item', '{}/clinical_item.csv'.format(tempDir))
+        DBUtil.dumpTableToCsv('clinical_item_category', '{}/clinical_item_category.csv'.format(tempDir))
+
+    def uploadPatientItemCsvToBQ(self, tempDir, datasetId, batchCounter=999):
+        log.info('Uploading patient_item CSV to BQ dataset %s for batch %s' % (datasetId, batchCounter))
+        patient_item_schema = [bigquery.SchemaField('patient_item_id', 'INT64', 'REQUIRED', None, ()),
+                               bigquery.SchemaField('external_id', 'INT64', 'NULLABLE', None, ()),
+                               bigquery.SchemaField('patient_id', 'INT64', 'REQUIRED', None, ()),
+                               bigquery.SchemaField('clinical_item_id', 'INT64', 'REQUIRED', None, ()),
+                               bigquery.SchemaField('item_date', 'TIMESTAMP', 'REQUIRED', None, ()),
+                               bigquery.SchemaField('analyze_date', 'TIMESTAMP', 'NULLABLE', None, ()),
+                               bigquery.SchemaField('encounter_id', 'INT64', 'NULLABLE', None, ()),
+                               bigquery.SchemaField('text_value', 'STRING', 'NULLABLE', None, ()),
+                               bigquery.SchemaField('num_value', 'FLOAT64', 'NULLABLE', None, ()),
+                               bigquery.SchemaField('source_id', 'INT64', 'NULLABLE', None, ()),
+                               bigquery.SchemaField('item_date_utc', 'TIMESTAMP', 'NULLABLE', None, ())]
+
+        csv_path = tempDir + os.path.sep + str(batchCounter) + '_patient_item.csv'
+
+        bigQueryUtil.headerChecker(csv_path, [sf.name for sf in patient_item_schema])
+
+        self.bqClient.load_csv_to_table(datasetId, 'patient_item', csv_path, schema=patient_item_schema, skip_rows=1,
+                                        append_to_table=True)
+        # auto_detect_schema=False, schema=patient_item_schema)
+
+    def uploadClinicalTablesCsvToBQ(self, tempDir, datasetId):
+        log.info('Uploading clinical_item_category CSV to BQ dataset %s' % datasetId)
+        clinical_item_category_schema = [
+            bigquery.SchemaField('clinical_item_category_id', 'INT64', 'REQUIRED', None, ()),
+            bigquery.SchemaField('source_table', 'STRING', 'REQUIRED', None, ()),
+            bigquery.SchemaField('description', 'STRING', 'NULLABLE', None, ()),
+            bigquery.SchemaField('default_recommend', 'INT64', 'NULLABLE', None, ())]
+
+        clinical_item_category_csv_path = tempDir + '/clinical_item_category.csv'
+
+        bigQueryUtil.headerChecker(clinical_item_category_csv_path, [sf.name for sf in clinical_item_category_schema])
+
+        self.bqClient.load_csv_to_table(datasetId, 'clinical_item_category', clinical_item_category_csv_path,
+                                        schema=clinical_item_category_schema, skip_rows=1, append_to_table=True)
+        # auto_detect_schema=False, schema=clinical_item_category_schema)
+
+        log.info('Uploading clinical_item CSV to BQ dataset %s' % datasetId)
+        clinical_item_schema = [bigquery.SchemaField('clinical_item_id', 'INT64', 'REQUIRED', None, ()),
+                                bigquery.SchemaField('clinical_item_category_id', 'INT64', 'REQUIRED', None, ()),
+                                bigquery.SchemaField('external_id', 'INT64', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('name', 'STRING', 'REQUIRED', None, ()),
+                                bigquery.SchemaField('description', 'STRING', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('default_recommend', 'INT64', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('item_count', 'FLOAT64', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('patient_count', 'FLOAT64', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('encounter_count', 'FLOAT64', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('analysis_status', 'INT64', 'NULLABLE', None, ()),
+                                bigquery.SchemaField('outcome_interest', 'INT64', 'NULLABLE', None, ())]
+
+        clinical_item_csv_path = tempDir + '/clinical_item.csv'
+
+        bigQueryUtil.headerChecker(clinical_item_csv_path, [sf.name for sf in clinical_item_schema])
+
+        self.bqClient.load_csv_to_table(datasetId, 'clinical_item', clinical_item_csv_path,
+                                        schema=clinical_item_schema, skip_rows=1, append_to_table=True)
+        # auto_detect_schema=False, schema=clinical_item_schema)
+
+    def removePatientItemCsv(self, temp_dir, batchCounter=999):
+        log.info('Removing patient_item CSV for batch {}'.format(batchCounter))
+        self.remove_file(temp_dir + os.path.sep + str(batchCounter) + '_patient_item.csv')
+
+    def removeClinicalTablesCsv(self, temp_dir):
+        log.info('Removing clinical_item and clinical_item_category CSVs')
+        self.remove_file(temp_dir + '/clinical_item.csv')
+        self.remove_file(temp_dir + '/clinical_item_category.csv')
+
+    @staticmethod
+    def remove_file(file_path):
+        if os.path.exists(file_path):
+            os.remove(file_path)
+        else:
+            log.warning('{} does not exist'.format(file_path))
+
+    def removePatientItemAddedLines(self, source_table):
+        """delete added records"""
+        log.info('Removing patient_item added lines in PSQL DB')
+
+        DBUtil.execute(
+            """delete from patient_item 
+                where clinical_item_id in 
+                (   select clinical_item_id
+                    from clinical_item as ci, clinical_item_category as cic
+                    where ci.clinical_item_category_id = cic.clinical_item_category_id
+                    and cic.source_table = '{}'
+                );
+                """.format(source_table), conn=self.pgConn
+        )
+
+    def removeClinicalTablesAddedLines(self, source_table):
+        """delete added records"""
+        log.info('Removing clinical_item and clinical_item_category added lines in PSQL DB')
+
+        DBUtil.execute(
+            """delete from clinical_item 
+                where clinical_item_category_id in 
+                (   select clinical_item_category_id 
+                    from clinical_item_category 
+                    where source_table = '{}'
+                );
+                """.format(source_table), conn=self.pgConn
+        )
+        DBUtil.execute("delete from clinical_item_category where source_table = '{}';".format(source_table),
+                       conn=self.pgConn)
diff --git a/medinfo/dataconversion/starr_conv/__init__.py b/medinfo/dataconversion/starr_conv/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/medinfo/dataconversion/starr_conv/test/TestSTARRDemographicsConversion.py b/medinfo/dataconversion/starr_conv/test/TestSTARRDemographicsConversion.py
new file mode 100644
index 00000000..f36a0198
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/test/TestSTARRDemographicsConversion.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python
+"""
+Test case for respective module in application package
+Setup credentials in environment variable GOOGLE_APPLICATION_CREDENTIALS (See LocalEnv)
+"""
+
+import string
+
+import os
+import csv
+import pytz
+import random
+import logging
+import tempfile
+
+from datetime import datetime
+from datetime import date
+from datetime import time
+import unittest
+
+from medinfo.dataconversion.starr_conv import STARRDemographicsConversion
+from medinfo.dataconversion.starr_conv.STARRUtil import StarrCommonUtils
+from medinfo.dataconversion.test.Const import RUNNER_VERBOSITY
+from medinfo.dataconversion.Util import log
+
+from medinfo.db.test.Util import DBTestCase
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel
+
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+
+from medinfo.db.bigquery import bigQueryUtil
+
+TEST_SOURCE_TABLE = 'test_dataset.starr_demographic'
+TEST_DEST_DATASET = 'test_dataset'
+
+GENDER = ['Male', 'Female', 'Unknown']
+RACE = ['Black', 'White', 'Asian', 'Other', 'Unknown', 'Pacific Islander', 'Native American']
+ETHNICITY = ['Non-Hispanic', 'Hispanic/Latino', 'Unknown']
+MARITAL_STATUS = [None, 'Separated', 'Single', 'Married', 'Unknown', 'Widowed', 'Divorced', 'Life Partner', 'Other',
+                  'Legally Separated']
+RELIGION = [None, 'Christian', 'Muslim', 'Buddhist', 'Other']   # only major religions - real table has much more values
+LANGUAGE = [None, 'English', 'Chinese', 'French', 'Russian', 'Arabic', 'Spanish']   # UN official languages
+PAT_STATUS = [None, 'Alive', 'Deceased']
+
+
+class TestSTARRDemographicsConversion(DBTestCase):
+    TEST_DATA_SIZE = 50
+    BATCH_SIZE = 10
+    STARTING_BATCH = 4
+
+    header = ['rit_uid', 'birth_date_jittered', 'death_date_jittered', 'gender', 'canonical_race',
+              'canonical_ethnicity', 'marital_status', 'religion', 'language', 'intrptr_needed_yn',
+              'insurance_payor_name', 'cur_pcp_prov_map_id', 'recent_conf_enc_jittered', 'recent_ht_in_cms',
+              'recent_wt_in_kgs', 'bmi', 'charlson_score', 'n_hospitalizations', 'days_in_hospital',
+              'pat_status']
+
+    patientIds = []
+    test_data = []
+    expected_data = []
+
+    test_data_csv = tempfile.gettempdir() + '/test_starr_demographic_dummy_data.csv'
+    pat_id_csv = tempfile.gettempdir() + '/tmp_test_pat_id.csv'
+
+    bqConn = bigQueryUtil.connection()
+    converter = STARRDemographicsConversion.STARRDemographicsConversion()  # Instance to test on
+    starrUtil = StarrCommonUtils(converter.bqClient)
+
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self)
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+
+        # point the converter to dummy source table
+        STARRDemographicsConversion.SOURCE_TABLE = TEST_SOURCE_TABLE
+
+        log.warning("Removing test table if it exists: {}".format(TEST_SOURCE_TABLE))
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_SOURCE_TABLE))
+
+        log.info("Generating test source data")
+        self.generate_test_and_expected_data(self.TEST_DATA_SIZE)
+        self.starrUtil.dump_test_data_to_csv(self.header, self.test_data, self.test_data_csv)
+        self.starrUtil.upload_csv_to_bigquery('starr_datalake2018', 'demographic',
+                                              'test_dataset', 'starr_demographic', self.test_data_csv, self.header)
+        self.dump_patient_ids_to_test_to_csv(self.pat_id_csv)
+
+    def generate_test_and_expected_data(self, test_data_size):
+        for curr_row in range(test_data_size):
+            patient_id = 'JC' + format(curr_row, '06')
+            self.patientIds.append(patient_id)
+            test_data_row = self.generate_test_data_row(curr_row, StarrCommonUtils.random_period(), patient_id)
+            self.test_data.append(test_data_row)
+
+            # prepare expected data starting from requested batch
+            if curr_row >= self.STARTING_BATCH * self.BATCH_SIZE:
+                self.generate_expected_data_rows(test_data_row, self.expected_data)
+
+        self.expected_data.sort(key=lambda tup: (-tup[1], tup[5]))  # patient_id desc, name asc
+
+    def dump_patient_ids_to_test_to_csv(self, pat_id_csv):
+        with open(pat_id_csv, 'w') as f:
+            for rit_uid in ['rit_uid'] + self.patientIds:
+                f.write("%s\n" % rit_uid)
+
+    @staticmethod
+    def generate_test_data_row(curr_row, lifespan, patient_id):
+        import time     # required to get current timestamp - it is here to not clash with datetime.time
+
+        return (patient_id,
+                date.fromtimestamp(lifespan[0]),
+                [None, date.fromtimestamp(lifespan[1])][random.randint(0, 1)],
+                GENDER[random.randint(0, len(GENDER) - 1)],
+                RACE[random.randint(0, len(RACE) - 1)],
+                ETHNICITY[random.randint(0, len(ETHNICITY) - 1)],
+                MARITAL_STATUS[random.randint(0, len(MARITAL_STATUS) - 1)],
+                RELIGION[random.randint(0, len(RELIGION) - 1)],
+                LANGUAGE[random.randint(0, len(LANGUAGE) - 1)],
+                [None, 'N', 'Y'][random.randint(0, 2)],
+                ''.join(random.choice(string.ascii_uppercase) for _ in range(10)),
+                'SS' + format(curr_row, '07'),
+                date.fromtimestamp(random.randint(1, int(time.time()))),
+                random.randint(150, 210),
+                random.randint(50, 150),
+                random.randint(18, 24),
+                random.randint(1, 27),
+                random.randint(0, 300),
+                random.randint(0, 1000),
+                PAT_STATUS[random.randint(0, len(PAT_STATUS) - 1)])
+
+    def generate_expected_data_rows(self, row, expected_data):
+        birth_list = [
+            None,
+            StarrCommonUtils.convertPatIdToSTRIDE(row[0]),
+            None,
+            "Demographics",
+            None,
+            "Birth",
+            "Birth Year",
+            datetime(row[1].year, 1, 1, tzinfo=pytz.UTC)
+        ]
+
+        expected_data.append(tuple(birth_list))
+        expected_data.append(self.birth_decade_tuple_from(birth_list, row))
+        expected_data.append(self.race_tuple_from(birth_list, row))
+        expected_data.append(self.gender_tuple_from(birth_list, row))
+
+        if row[2]:
+            expected_data.append(self.death_date_tuple_from(birth_list, row))
+
+    @staticmethod
+    def birth_decade_tuple_from(birth_list, row):
+        birth_decade_list = list(birth_list)
+        decade = (row[1].year // 10) * 10
+        birth_decade_list[5] = "Birth%ds" % decade
+        birth_decade_list[6] = "Birth Decade %ds" % decade
+        return tuple(birth_decade_list)
+
+    def race_tuple_from(self, birth_list, row):
+        race_list = list(birth_list)
+        race_ethnicity = self.converter.summarizeRaceEthnicity(row[4], row[5])
+        race_list[5] = "Race%s" % race_ethnicity.translate(str.maketrans('', '', " ()-/"))
+        race_list[6] = "Race/Ethnicity: %s" % race_ethnicity
+        return tuple(race_list)
+
+    @staticmethod
+    def gender_tuple_from(birth_list, row):
+        gender_list = list(birth_list)
+        gender_list[5] = row[3]
+        gender_list[6] = "%s Gender" % row[3]
+        return tuple(gender_list)
+
+    @staticmethod
+    def death_date_tuple_from(birth_list, row):
+        death_list = list(birth_list)
+        death_list[5] = "Death"
+        death_list[6] = "Death Date"
+        death_list[7] = datetime.combine(row[2], time.min).replace(tzinfo=pytz.UTC)
+        return tuple(death_list)
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        log.info("Purge test records from the database")
+
+        os.remove(self.pat_id_csv)
+        os.remove(self.test_data_csv)
+
+        DBUtil.execute(
+            """delete from patient_item 
+                    where clinical_item_id in 
+                    (   select clinical_item_id
+                        from clinical_item as ci, clinical_item_category as cic
+                        where ci.clinical_item_category_id = cic.clinical_item_category_id
+                        and cic.source_table = '%s'
+                    );
+                    """ % TEST_SOURCE_TABLE
+        )
+        DBUtil.execute(
+            """delete from clinical_item 
+                    where clinical_item_category_id in 
+                    (   select clinical_item_category_id 
+                        from clinical_item_category 
+                        where source_table = '%s'
+                    );
+                    """ % TEST_SOURCE_TABLE
+        )
+        DBUtil.execute("delete from clinical_item_category where source_table = '%s';" % TEST_SOURCE_TABLE)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DELETE FROM %s.patient_item WHERE true;' % TEST_DEST_DATASET)
+        bq_cursor.execute('DELETE FROM %s.clinical_item WHERE true;' % TEST_DEST_DATASET)
+        bq_cursor.execute('DELETE FROM %s.clinical_item_category WHERE true;' % TEST_DEST_DATASET)
+
+        bq_cursor.execute('DROP TABLE %s;' % TEST_SOURCE_TABLE)
+
+        DBTestCase.tearDown(self)
+
+    def test_batchDataConversion(self):
+        # Run the data conversion on the same data and look for expected records
+        log.debug("Run the batch conversion process, and upload to test dataset in BigQuery...")
+        self.converter.convertItemsByBatch(self.pat_id_csv, self.BATCH_SIZE, targetDatasetId=TEST_DEST_DATASET,
+                                           startBatch=self.STARTING_BATCH)
+
+        # Just query back for the same data, de-normalizing the data back to a general table
+        test_query = \
+            """
+            select 
+                pi.external_id as pi_external_id,
+                pi.patient_id,
+                pi.encounter_id,
+                cic.description as cic_description,
+                ci.external_id as ci_external_id,
+                ci.name,
+                ci.description as ci_description,
+                pi.item_date
+            from
+                %s.patient_item as pi,
+                %s.clinical_item as ci,
+                %s.clinical_item_category as cic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = '%s'
+            order by
+                pi.patient_id desc, ci.name
+            """ % (TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute(test_query)
+        actual_data = [list(row.values()) for row in bq_cursor.fetchall()]
+
+        log.debug('actual data %s' % actual_data)
+        log.debug('expected data %s' % self.expected_data)
+
+        self.assertEqualTable(self.expected_data, actual_data)
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestSTARRDemographicsConversion))
+
+    return suite
+
+
+if __name__ == "__main__":
+    log.setLevel(logging.INFO)  # without this no logs are printed
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/starr_conv/test/TestSTARROrderMedConversion.py b/medinfo/dataconversion/starr_conv/test/TestSTARROrderMedConversion.py
new file mode 100644
index 00000000..fe871c02
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/test/TestSTARROrderMedConversion.py
@@ -0,0 +1,1577 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+import logging
+import pytz
+import random
+import tempfile
+import time
+
+from datetime import datetime
+
+import unittest
+
+from parameterized import parameterized
+from medinfo.dataconversion.test.Const import RUNNER_VERBOSITY
+from medinfo.dataconversion.Const import TEMPLATE_MEDICATION_PREFIX
+from medinfo.dataconversion.Util import log
+
+from medinfo.db.test.Util import DBTestCase
+
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel
+
+from medinfo.dataconversion.starr_conv import STARROrderMedConversion
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+TEST_SOURCE_TABLE = 'test_dataset.starr_order_med'
+TEST_ORDERSET_TABLE = 'test_dataset.starr_med_orderset'
+
+TEST_DEST_DATASET = 'test_dataset'
+
+
+ORDERING_MODES = [
+    'Inpatient',
+    'Outpatient'
+]
+
+MED_ROUTES = [
+    None,
+    "Intramuscular",
+    "Inhalation",
+    "Topical",
+    "Left Eye",
+    "Right Eye",
+    "Vaginal",
+    "Injection",
+    "Ophthalmic",
+    "G Tube",
+    "Transdermal",
+    "Swish & Swallow",
+    "RT Inhalation",
+    "Intra-articular",
+    "J Tube",
+    "Buccal",
+    "Rectal",
+    "NG Tube",
+    "Intrathecal",
+    "Intravitreal",
+    "Intradermal",
+    "Otic",
+    "Surgical Site Intra-Op Only",
+    "Right Ear",
+    "In Vitro",
+    "Other",
+    "Tendon Sheath Injection",
+    "Intrauterine",
+    "Intra-Catheter",
+    "Mucous Membrane",
+    "Not Applicable",
+    "DELETE5",
+    "Percutaneous",
+    "Intrapleural",
+    "Intranasal",
+    "Intracavernosal",
+    "Intralesional",
+    "Implant",
+    "Drain",
+    "Apheresis",
+    "Left Nostril",
+    "Infiltration",
+    "Intra-amniotic",
+    "OG Tube",
+    "Intraventricular",
+    "Endotracheal",
+    "Right Nostril",
+    "Intra-arterial",
+    "Intracardiac",
+    "Nerve Block",
+    "intravesical irrigation",
+    "Hemodialysis",
+    "Intraventricular CNS",
+    "Thru Sheath",
+    "Intrabursal",
+    "Subtenons",
+    "Juxtascleral",
+    "Cervical",
+    "Intratracheal",
+    "intraocular injection",
+    "Thru Peripheral IV",
+    "Intrasynovial",
+    "Periodontal",
+    "interscalene",
+    "Subconjunctival",
+    "Continuous Epidural",
+    "Continuous Nebulization",
+    "Stoma",
+    "Contin. Intrathecal Infusion",
+    "Transtracheal",
+    "Subcutaneous via CO2 Activated Syringe",
+    "Intra-urethral",
+    "Oral",
+    "Misc.(Non-Drug; Combo Route)",
+    "Intravenous",
+    "Subcutaneous",
+    "Both Eyes",
+    "Intraperitoneal",
+    "Sublingual",
+    "Feeding Tube",
+    "Nasal",
+    "Swish & Spit",
+    "CRRT",
+    "Nebulization",
+    "Translingual",
+    "Intravesical",
+    "Both Nostrils",
+    "Left Ear",
+    "Irrigation",
+    "Both Ears",
+    "PEG-J Tube",
+    "Bladder Instillation",
+    "Urethral",
+    "Continuous IV Infusion",
+    "Intravenous/Intramuscular",
+    "Dental",
+    "Topical ocular use",
+    "Intraocular",
+    "Subdermal",
+    "Submucosal Injection",
+    "Epidural",
+    "Base of the eyelashes",
+    "scalp",
+    "Feeding Tube (FTub)",
+    "Mouth/Throat",
+    "Intracavity",
+    "local intranasal application",
+    "O2 Aerosolization",
+    "DELETE19",
+    "Contin. Subcutaneous Infusion",
+    "Gums",
+    "Extracorporeal",
+    "Intraconjunctival",
+    "adductor canal block",
+    "Wound irrigation",
+    "Neonatal Feeding",
+    "Intra-lesional",
+    "intravenous push",
+    "Local Infiltration",
+    "Perfusion",
+    "DELETE20",
+    "Intraosseous",
+    "Intraspinal",
+    "urinary catheter irrigation",
+    "intratympanic",
+    "Hand Bulb Nebulizer",
+    "IPPB",
+    "Ostomy",
+    "Combination",
+    "abdominal subcutaneous",
+    "Subgingival-Local",
+    "Intrathoracic",
+    "intraocular irrigation",
+    "INTRAVARICEAL",
+    "Intracoronary",
+    "subcutaneous (via wearable injector)",
+    "central line irrigation",
+    "Intrapericardial",
+    "Submucosal Inj",
+    "Laryngotracheal",
+    "Retrobulbar",
+    "Tendon Sheath Inj",
+    "Osteochondrial",
+    "Intradetrusor",
+    "Intraductal",
+    "Cont Nebulization",
+    "hemodialysis port injection"
+]
+
+MED_DESCRIPTIONS = [
+    "ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",
+    "FENTANYL (PF) 50 MCG/ML INJECTION",
+    "SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",
+    "MIDAZOLAM 1 MG/ML INJ SOLN",
+    "ACETAMINOPHEN 325 MG PO TABS",
+    "NS IV BOLUS",
+    "TPN ADULT STANDARD",
+    "HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",
+    "DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",
+    "OXYCODONE 5 MG PO TABS",
+    "NALOXONE 0.4 MG/ML INJ SOLN",
+    "PROPOFOL 10 MG/ML IV INJECTION",
+    "LACTATED RINGERS IV SOLP",
+    "LORAZEPAM 2 MG/ML INJ SOLN",
+    "DIPHENHYDRAMINE HCL 25 MG PO CAPS",
+    "HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",
+    "LORAZEPAM 0.5 MG PO TABS",
+    "TRIPLE MIX",
+    "HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS",
+    "HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",
+    "LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",
+    "PROMETHAZINE 25 MG/ML INJ SOLN",
+    "DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",
+    "FUROSEMIDE 10 MG/ML INJ SOLN",
+    "LORAZEPAM 1 MG PO TABS",
+    "FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN",
+    "DOCUSATE SODIUM 100 MG PO CAPS",
+    "PANTOPRAZOLE 40 MG PO TBEC",
+    "POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",
+    "ACETAMINOPHEN 500 MG PO TABS",
+    "ONDANSETRON HCL 8 MG PO TABS",
+    "ASPIRIN 81 MG PO TBEC",
+    "TPN BMT",
+    "DOCUSATE SODIUM 250 MG PO CAPS",
+    "SENNOSIDES 8.6 MG PO TABS",
+    "GABAPENTIN 300 MG PO CAPS",
+    "HYDROMORPHONE 1 MG/ML INJ SYRG",
+    "ALTEPLASE 2 MG INTRA-CATHET SOLR",
+    "OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",
+    "MAGNESIUM OXIDE-MG AA CHELATE 133 MG PO TABS",
+    "CEFAZOLIN 1 GRAM INJ SOLR",
+    "DEXAMETHASONE 4 MG PO TABS",
+    "BISACODYL 10 MG PR SUPP",
+    "METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",
+    "ONDANSETRON 4 MG PO TBDL",
+    "AMLODIPINE 5 MG PO TABS",
+    "ZOLPIDEM 5 MG PO TABS",
+    "ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",
+    "LOPERAMIDE 2 MG PO CAPS",
+    "CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",
+    "METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",
+    "LIDOCAINE HCL 10 MG/ML (1 %) INJ SOLN",
+    "POTASSIUM CHLORIDE 20 MEQ PO TBTQ",
+    "SODIUM CHLORIDE 0.9 % IV SOLP",
+    "DEXTROSE 50 % IN WATER (D50W) IV SYRG",
+    "FLUTICASONE 50 MCG/ACTUATION NASAL SPSN",
+    "EPINEPHRINE 1 MG/ML (1 ML) INJ SOLN",
+    "ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA",
+    "LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN",
+    "MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK",
+    "HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",
+    "HYDROCHLOROTHIAZIDE 25 MG PO TABS",
+    "ROCURONIUM 10 MG/ML IV SOLN",
+    "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS",
+    "TRAMADOL 50 MG PO TABS",
+    "ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",
+    "PHENYLEPHRINE HCL 10 MG/ML INJ SOLN",
+    "METOPROLOL TARTRATE 25 MG PO TABS",
+    "OMEPRAZOLE 20 MG PO CPDR",
+    "FAMOTIDINE 20 MG PO TABS",
+    "HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",
+    "ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",
+    "HYDROMORPHONE 2 MG/ML INJ SYRG",
+    "LABETALOL 5 MG/ML IV SOLN",
+    "VANCOMYCIN IVPB (CUSTOM DOSE)",
+    "METOCLOPRAMIDE 5 MG/ML INJ SOLN",
+    "ATORVASTATIN 40 MG PO TABS",
+    "HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",
+    "FUROSEMIDE 20 MG PO TABS",
+    "TPN ADULT CYCLIC",
+    "INSULIN LISPRO 100 UNIT/ML SC SOLN",
+    "MAGNESIUM SULFATE IV SCALE ",
+    "MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV PGBK",
+    "ALBUMIN, HUMAN 5 % 5 % IV SOLP",
+    "NITRIC OXIDE GAS 800 PPM INH GAS",
+    "LISINOPRIL 10 MG PO TABS",
+    "METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",
+    "ENOXAPARIN 40 MG/0.4 ML SC SYRG",
+    "GLYCOPYRROLATE 0.2 MG/ML INJ SOLN",
+    "HYDRALAZINE 20 MG/ML INJ SOLN",
+    "MEPERIDINE (PF) 25 MG/ML INJ SYRG",
+    "HYDROMORPHONE 1 MG/ML IV PCA",
+    "POTASSIUM CHLORIDE IV SCALE",
+    "IBUPROFEN 600 MG PO TABS",
+    "CEPHALEXIN 500 MG PO CAPS",
+    "ATORVASTATIN 10 MG PO TABS",
+    "MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",
+    "ATORVASTATIN 20 MG PO TABS",
+    "TRAZODONE 50 MG PO TABS",
+    "EPHEDRINE SULFATE 50 MG/ML INJ SOLN",
+    "METFORMIN 500 MG PO TABS",
+    "PREDNISONE 20 MG PO TABS",
+    "SIMVASTATIN 20 MG PO TABS",
+    "ASPIRIN 81 MG PO CHEW",
+    "DIAZEPAM 5 MG PO TABS",
+    "PANTOPRAZOLE 40 MG IV SOLR",
+    "GLUCOSE 4 GRAM PO CHEW",
+    "ZOLPIDEM 10 MG PO TABS",
+    "POTASSIUM CHLORIDE ORAL SCALE",
+    "TAMSULOSIN 0.4 MG PO CP24",
+    "PREDNISONE 10 MG PO TABS",
+    "INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN",
+    "FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",
+    "AMLODIPINE 10 MG PO TABS",
+    "IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",
+    "REMIFENTANIL 2 MG IV SOLR",
+    "PROCHLORPERAZINE MALEATE 10 MG PO TABS",
+    "ASPIRIN 325 MG PO TABS",
+    "POTASSIUM CHLORIDE 10 MEQ PO TBTQ",
+    "LISINOPRIL 20 MG PO TABS",
+    "MORPHINE 2 MG/ML INJ SYRG",
+    "POTASSIUM CHLORIDE ORAL TABLET REPLACEMENT SCALE",
+    "DEXTROSE 5 % IN WATER (D5W) IV SOLP",
+    "CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",
+    "SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS",
+    "POTASSIUM CHLORIDE IV REPLACEMENT SCALE",
+    "AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS",
+    "METOPROLOL SUCCINATE 25 MG PO TB24",
+    "HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",
+    "MORPHINE 4 MG/ML INJ SYRG",
+    "CLOPIDOGREL 75 MG PO TABS",
+    "GABAPENTIN 100 MG PO CAPS",
+    "FUROSEMIDE 40 MG PO TABS",
+    "TACROLIMUS 1 MG PO CAPS",
+    "LOSARTAN 50 MG PO TABS",
+    "GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",
+    "OTHER DRUG",
+    "AZITHROMYCIN 250 MG PO TABS",
+    "SODIUM BICARBONATE 8.4 % IV SOLN",
+    "POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK",
+    "PREDNISONE 5 MG PO TABS",
+    "FAT EMULSION 20 % IV EMUL",
+    "HYDROMORPHONE (PF) 1 MG/ML INJ SYRG",
+    "LIDOCAINE 4 % TP CREA",
+    "CEFAZOLIN 2 G IVPB",
+    "ONDANSETRON (+DEXAMETHASONE) IVPB",
+    "LEVOTHYROXINE 50 MCG PO TABS",
+    "FERROUS SULFATE 325 MG (65 MG IRON) PO TABS",
+    "CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",
+    "LIDOCAINE-EPINEPHRINE 1 %-1:100,000 INJ SOLN",
+    "CIPROFLOXACIN HCL 500 MG PO TABS",
+    "ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",
+    "KETOROLAC 30 MG/ML (1 ML) INJ SOLN",
+    "LISINOPRIL 5 MG PO TABS",
+    "ONDANSETRON HCL 4 MG PO TABS",
+    "NITROGLYCERIN 0.4 MG SL SUBL",
+    "DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM SUSP (UHA)",
+    "METOPROLOL SUCCINATE 50 MG PO TB24",
+    "CIPROFLOXACIN 500 MG PO TABS",
+    "PACLITAXEL IV CHEMO INFUSION",
+    "SODIUM CHLORIDE 0.9 % IRRIG SOLN",
+    "VITAMIN D3 PO",
+    "1/2 NS WITH KCL AND MAGNESIUM SULFATE (+MANNITOL) IV INFUSION",
+    "SPIRONOLACTONE 25 MG PO TABS",
+    "PROAIR HFA 90 MCG/ACTUATION INH HFAA",
+    "FOLIC ACID 1 MG PO TABS",
+    "SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN",
+    "POTASSIUM PHOSPHATE IVPB",
+    "FILGRASTIM 300 MCG/0.5 ML INJ SYRG",
+    "ATENOLOL 25 MG PO TABS",
+    "POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",
+    "GEMCITABINE IV CHEMO INFUSION",
+    "MELATONIN 3 MG PO TABS",
+    "BUPIVACAINE (PF) 0.25 % (2.5 MG/ML) INJ SOLN",
+    "METFORMIN 1,000 MG PO TABS",
+    "CARBOPLATIN (AUC DOSING) IV CHEMO INFUSION",
+    "LEVOTHYROXINE 75 MCG PO TABS",
+    "FLUMAZENIL 0.1 MG/ML IV SOLN",
+    "LEVOTHYROXINE 100 MCG PO TABS",
+    "MONTELUKAST 10 MG PO TABS",
+    "ACYCLOVIR 400 MG PO TABS",
+    "LIDOCAINE HCL 2 % VISCOUS ORAL SOLN",
+    "VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",
+    "AMIODARONE 200 MG PO TABS",
+    "DEXTROSE 50% IN WATER (D50W) IV SYRG",
+    "SUCRALFATE 1 GRAM PO TABS",
+    "CYCLOBENZAPRINE 10 MG PO TABS",
+    "CLONAZEPAM 0.5 MG PO TABS",
+    "SIMVASTATIN 40 MG PO TABS",
+    "METOPROLOL TARTRATE 50 MG PO TABS",
+    "LACTATED RINGERS IV BOLUS",
+    "LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN",
+    "IBUPROFEN 800 MG PO TABS",
+    "MORPHINE INJECTABLE SYRINGE",
+    "PPN ADULT STANDARD",
+    "PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",
+    "WARFARIN 5 MG PO TABS",
+    "CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",
+    "LOSARTAN 25 MG PO TABS",
+    "NS IV BOLUS - 1000 ML"
+]
+
+PROTOCOL_NAME = [
+    " AMB URO URODYNAMICS",
+    "(RETIRED PLEASE USE ORDERSET (SURGERY GENERAL POSTOP 801) IP SUR CHOLECYSTECTOMY",
+    "(RETIRED) IP PICC LINE PLACEMENT / EXCHANGE",
+    "AMB ALG XOLAIR MMC",
+    "AMB AWCC WOUND CARE",
+    "AMB CAR ATRIAL FIBRILLATION",
+    "AMB CAR CONGESTIVE HEART FAILURE",
+    "AMB CAR IC NEW",
+    "AMB DERM ACNE",
+    "AMB DERM DERMATITIS MMC",
+    "AMB DERM EXCISION MMC",
+    "AMB DERM PSORIASIS ",
+    "AMB DERM SKIN BIOPSY",
+    "AMB DERM SURG PRE-OPERATIVE CHECK SHEET",
+    "AMB DERM SURG PROCEDURES",
+    "AMB DIGESTIVE HEALTH INFLAMMATORY BOWEL DISEASE",
+    "AMB DIGESTIVE HEALTH STOMACH",
+    "AMB END DIABETES TYPE 1",
+    "AMB END DIABETES TYPE 2",
+    "AMB END THYROGEN STUDIES",
+    "AMB ENDO DIABETES TYPE 1 MMC",
+    "AMB ENDO DIABETES TYPE 2 MMC",
+    "AMB ENDO THYROGEN STUDIES MMC",
+    "AMB FLU CLINIC",
+    "AMB GI ABDOMINAL PAIN",
+    "AMB GI DIARRHEA MMC",
+    "AMB GI PRE-OPERATIVE EVALUATION",
+    "AMB GYN AMENORRHEA ",
+    "AMB GYN ANNUAL WELL WOMAN EXAM",
+    "AMB GYN COLPOSCOPY MMC",
+    "AMB GYN MEDICAL AB VISIT #1",
+    "AMB GYN ROUTINE ADOLESCENT EXAM",
+    "AMB GYN VAGINITIS",
+    "AMB HC CAR HYPERTENSION CENTER",
+    "AMB HEART TRANSPLANT ANNUAL STANFORD RN ORDERS",
+    "AMB HEART TRANSPLANT OUTPATIENT CLINIC EVALUATION STANFORD",
+    "AMB HEART TRANSPLANT ROUTINE STANFORD RN ORDERS",
+    "AMB HEP C TREATMENT PROTOCOL LAB ORDERS",
+    "AMB HEP CHRONIC HEPATITIS C",
+    "AMB HEP HEPATITIS B",
+    "AMB HM SHC SUPER SMARTSET",
+    "AMB HM UHA SUPER SMARTSET",
+    "AMB IFD SKIN AND SOFT TISSUE INFECTION",
+    "AMB IM ANNUAL FEMAL EXAM MMC",
+    "AMB IM ANNUAL MALE EXAM MMC",
+    "AMB IM COUGH/BRONCHITIS MMC",
+    "AMB IM UPPER RESPIRATORY INFECTION MMC",
+    "AMB IMM 19-49 YRS IMMUNIZATIONS",
+    "AMB IMM 50-64 YRS IMMUNIZATIONS",
+    "AMB IMM >=65 YRS IMMUNIZATIONS",
+    "AMB IMM ADULT",
+    "AMB IMM PEDS 11-12 YRS IMMUNIZATIONS",
+    "AMB IMM PEDS 12 MO IMMUNIZATIONS",
+    "AMB IMM PEDS 13-14, 16-18 YRS IMMUNIZATIONS (OVERDUE)",
+    "AMB IMM PEDS 15 MO IMMUNIZATIONS",
+    "AMB IMM PEDS 15 YRS IMMUNIZATIONS",
+    "AMB IMM PEDS 18-23 MO IMMUNIZATIONS",
+    "AMB IMM PEDS 2 MO IMMUNIZATIONS",
+    "AMB IMM PEDS 2-3 YRS IMMUNIZATIONS",
+    "AMB IMM PEDS 4 MONTH IMMUNIZATIONS",
+    "AMB IMM PEDS 4-6 YRS IMMUNIZATIONS",
+    "AMB IMM PEDS 6 MO IMMUNIZATIONS",
+    "AMB IMM PEDS 7-10 YRS IMMUNIZATIONS",
+    "AMB IMM PEDS BIRTH TO 1 MO IMMUNIZATIONS",
+    "AMB IMM TRAVEL IMMUNIZATIONS",
+    "AMB LAB TRANSFUSION SERVICE, STANDARD",
+    "AMB LIV PRE-OP ORDERS",
+    "AMB NEU CONCUSSION FOLLOW UP VISIT",
+    "AMB NEU LUMBAR PUNCTURE",
+    "AMB NEU MOVE PARKINSON'S",
+    "AMB NEU MULTIPLE SCLEROSIS",
+    "AMB NEU STROKE GENERAL",
+    "AMB NEUROSCIENCE BOTOX SMARTSET",
+    "AMB OAC ENCOUNTER",
+    "AMB OPH CATARACT POST OP",
+    "AMB OPH CLINIC GLAUCOMA LASER PROCEDURES",
+    "AMB OPH CLINIC LASER PROCEDURES",
+    "AMB OPH CORNEA ULCER",
+    "AMB OPH INJECTIONS",
+    "AMB OPH INTRAVITREAL AVASTIN INJECTION BOTH EYES",
+    "AMB OPH INTRAVITREAL AVASTIN INJECTION LEFT EYE",
+    "AMB OPH INTRAVITREAL AVASTIN INJECTION RIGHT EYE",
+    "AMB OPH INTRAVITREAL AVASTIN/LUCENTIS INJECTION ",
+    "AMB OPH INTRAVITREAL INJ LEFT EYE",
+    "AMB OPH INTRAVITREAL INJ RIGHT EYE",
+    "AMB OPH INTRAVITREAL INJ VISIT",
+    "AMB OPH INTRAVITREAL LUCENTIS INJECTION BOTH EYES",
+    "AMB OPH INTRAVITREAL LUCENTIS INJECTION LEFT EYE",
+    "AMB OPH INTRAVITREAL LUCENTIS INJECTION RIGHT EYE",
+    "AMB OPH INTRAVITREAL LUCENTIS/AVASTIN INJECTION",
+    "AMB OPH INTRAVITREAL TRIESENCE INJECTION LEFT EYE",
+    "AMB OPH INTRAVITREAL TRIESENCE INJECTION RIGHT EYE",
+    "AMB OPH NEURO GENERAL",
+    "AMB OPH POST OP RET LT EYE",
+    "AMB OPH PRE OP VISIT",
+    "AMB OPH PREOP CORNEA TRANSPLANT",
+    "AMB OPH RET INJ",
+    "AMB OPH RET POSTOP",
+    "AMB OPH RETINA MASTER",
+    "AMB OPHTH LASER",
+    "AMB OPHTH LASER LASIK",
+    "AMB OPHTH LASER LASIK ENHANCED",
+    "AMB OPHTH LASER PRK",
+    "AMB ORDER TRANSMITTAL PRINTING",
+    "AMB ORT ARTHROCENTESIS MMC",
+    "AMB ORT FOOT AND ANKLE INJECTION",
+    "AMB ORT FOOT_ANKLE",
+    "AMB ORT HAND ARTHRITIS",
+    "AMB ORT HAND INJECTION",
+    "AMB ORT HAND SOFT TISSUE",
+    "AMB ORT HAND TRAUMA",
+    "AMB ORT HAND_WRIST",
+    "AMB ORT HUNT FOOT_ANKLE",
+    "AMB ORT INJECTIONS HAND",
+    "AMB ORT INJECTIONS JOINT",
+    "AMB ORT INJECTIONS SPINE",
+    "AMB ORT INJECTIONS SPORTS MED",
+    "AMB ORT INJECTIONS TUMOR",
+    "AMB ORT JOINT HIP",
+    "AMB ORT JOINT HIP_GOODMAN",
+    "AMB ORT JOINT INJECTION",
+    "AMB ORT JOINT KNEE",
+    "AMB ORT JOINT KNEE_GOODMAN",
+    "AMB ORT PMR",
+    "AMB ORT SHOULDER_ELBOW",
+    "AMB ORT SPINE",
+    "AMB ORT SPINE INJECTION",
+    "AMB ORT SPINE PMR",
+    "AMB ORT SPORTS INJECTION",
+    "AMB ORT SPORTS MEDICINE_FANTON",
+    "AMB ORT SPORTS MEDICINE_SAFRAN",
+    "AMB ORT SPORTS_ELBOW",
+    "AMB ORT SPORTS_FRACTURES",
+    "AMB ORT SPORTS_HAND/WRIST",
+    "AMB ORT SPORTS_HIP/THIGH",
+    "AMB ORT SPORTS_KNEE",
+    "AMB ORT SPORTS_LEG/ANKLE/FOOT",
+    "AMB ORT SPORTS_SHOULDER",
+    "AMB ORT SPORTS_SPINE",
+    "AMB ORT TRAUMA _KNEE",
+    "AMB ORT TRAUMA_HIP",
+    "AMB ORT TRAUMA_INFECTION",
+    "AMB ORT TRAUMA_PELVIS FX",
+    "AMB ORT TUMOR BIOPSY/INJECTION",
+    "AMB ORT TUMOR_SOFT TISSUE",
+    "AMB PAIN BURSA INJECTION",
+    "AMB PAIN INTRATHECAL PUMP REFILL / ADJUSTMENT",
+    "AMB PAIN KETAMINE INFUSION",
+    "AMB PAIN LIDOCAINE INFUSION",
+    "AMB PAIN MANAGEMENT CHEMODENERVATION WITH BOTOX FOR EXTREMITIES/TRUNK",
+    "AMB PAIN MANAGEMENT CHEMODENERVATION WITH BOTOX FOR HEAD/FACE",
+    "AMB PAIN MANAGEMENT CHEMODENERVATION WITH BOTOX FOR NECK",
+    "AMB PAIN MANAGEMENT INIITAL/OFFICE VISIT",
+    "AMB PAIN MANAGEMENT PROCEDURE VISIT",
+    "AMB PAIN OCCIPITAL NERVE BLOCK",
+    "AMB PAIN PERIPHERAL NERVE BLOCK INJECTION",
+    "AMB PAIN PREEMPT INJECTION FOR CHRONIC MIGRAINE",
+    "AMB PAIN SCAR INJECTION WITH ALCOHOL",
+    "AMB PAIN SCAR INJECTION WITH BOTOX",
+    "AMB PAIN SCAR INJECTION WITH LOCAL ANESTHETIC",
+    "AMB PAIN SCAR INJECTION WITH STEROID",
+    "AMB PAIN TRIGGERPOINT INJECTION WITH BOTOX",
+    "AMB PAIN TRIGGERPOINT INJECTION WITHOUT BOTOX",
+    "AMB PC GEN ABDOMINAL PAIN",
+    "AMB PC GEN ALLERGIC RHINITIS",
+    "AMB PC GEN ANKLE SPRAIN",
+    "AMB PC GEN ANNUAL FEMALE EXAM",
+    "AMB PC GEN ANNUAL MALE EXAM",
+    "AMB PC GEN ANXIETY",
+    "AMB PC GEN ASTHMA (ADULT)",
+    "AMB PC GEN BACK PAIN",
+    "AMB PC GEN CERUMEN IMPACTION",
+    "AMB PC GEN CHEST PAIN",
+    "AMB PC GEN CHRONIC OBSTRUCTIVE PULMONARY  DISEASE (COPD)",
+    "AMB PC GEN CONGESTIVE HEART FAILURE",
+    "AMB PC GEN CONJUNCTIVITIS",
+    "AMB PC GEN CONSTIPATION",
+    "AMB PC GEN CONTRACEPTIVE VISIT",
+    "AMB PC GEN COUGH / BRONCHITIS",
+    "AMB PC GEN DEPRESSION",
+    "AMB PC GEN DIABETES MELLITUS TYPE 2",
+    "AMB PC GEN DIVERTICULITIS",
+    "AMB PC GEN DIZZINESSS",
+    "AMB PC GEN ELBOW PAIN",
+    "AMB PC GEN ESOPHAGEAL REFLUX",
+    "AMB PC GEN FOREIGN BODY",
+    "AMB PC GEN GASTROENTERITIS",
+    "AMB PC GEN HEADACHE",
+    "AMB PC GEN HEMORRHOID",
+    "AMB PC GEN HIP PAIN",
+    "AMB PC GEN HYPERLIPIDEMIA",
+    "AMB PC GEN HYPERTENSION",
+    "AMB PC GEN HYPOTHYROIDISM",
+    "AMB PC GEN KNEE PAIN",
+    "AMB PC GEN NAUSEA/VOMITING",
+    "AMB PC GEN NECK PAIN",
+    "AMB PC GEN OTITIS EXTERNA",
+    "AMB PC GEN OTITIS MEDIA",
+    "AMB PC GEN PALPITATIONS",
+    "AMB PC GEN PERIPHERAL VASCULAR DISEASE"
+]
+
+SS_SECTION_NAME = [
+    None,
+    " ",
+    "  ",
+    "2A. High Risk of Delirium: Management",
+    "2A. High risk of delirium: Management",
+    "2A. Hyperactive delirium: Management",
+    "2B. Hyperactive Delirium: Management",
+    "2B. Hyperactive delirium: Management",
+    "2B. Hypoactive delirium: Management ",
+    "2C. Hypoactive delirium: Management ",
+    "ADULT MEDICATIONS",
+    "ANALGESICS",
+    "ANTI-EMETICS",
+    "ANTIBIOTICS",
+    "ANTICOAGULANTS",
+    "APPLY TO WOUND",
+    "Acute Ischemic Stroke",
+    "Ad-hoc Orders",
+    "Additional Medications",
+    "Adjunction/Alternative Headache Treatments",
+    "Adult Medications",
+    "Adult Protocol",
+    "After Visit Medications",
+    "Allergy Testing",
+    "Ambulatory Orders",
+    "Analgesics",
+    "Anti- Emetics",
+    "Anti-rejection Medications",
+    "Antibiotics",
+    "Antibiotics - Community Acquired (Severe Sepsis)",
+    "Antibiotics - Community Acquired (Severe Sepsis) ",
+    "Antibiotics - Community Acquired (Severe sepsis)",
+    "Antibiotics - Community Acquired (Simple Sepsis)",
+    "Antibiotics - Healthcare Associated or Immunocompromised Sepsis ",
+    "Antibiotics - Healthcare Associated or Immunocompromised Sepsis (Simple/Severe)",
+    "Anticoagulants",
+    "Antipsychotics ",
+    "Anxiety/Agitation",
+    "Approved Albumin Indications",
+    "Argatroban - Left Ventricular Assist Device (LVAD) protocol",
+    "Argatroban - Specific Infusion Parameters per MD",
+    "Argatroban - Standard Protocol",
+    "Ascites",
+    "Aspirin",
+    "BASAL/PRANDIAL/CORRECTIVE SCALES FOR CONTINUOUS FEEDS/NPO",
+    "BASAL/PRANDIAL/CORRECTIVE SCALES FOR ORAL DIET/BOLUS FEEDS",
+    "BASAL/PRANDIAL/CORRECTIVE Scales For Continuous FEEDS/NPO",
+    "BASAL/PRANDIAL/CORRECTIVE Scales For Oral DIET/BOLUS Feeds",
+    "BLOOD TRANSFUSION",
+    "BOLUS",
+    "Basal/Prandial/Corrective Scales For Continuous Feeds/NPO",
+    "Basal/Prandial/Corrective Scales For Oral Diet/Bolus Feeds",
+    "Basal/Prandial/Corrective Scales for Continuous Feeds/NPO",
+    "Basal/Prandial/Corrective Scales for Continuous Feeds/NPO/TPN",
+    "Basal/Prandial/Corrective Scales for Oral Diet/Bolus Feeds",
+    "Blood Transfusion",
+    "Bolus",
+    "Bowel Regimen",
+    "CONTINUOUS INTRAVENOUS INSULIN INFUSION",
+    "CSF Leak Orders",
+    "Calcium ",
+    "Catch-up Orders",
+    "Clinic-Administered Medications",
+    "Cluster Headache",
+    "Community Acquired Pneumonia",
+    "Continuous Intravenous Insulin Infusion",
+    "Contrast",
+    "DEXAMETHASONE",
+    "DIABETIC SUPPLIES",
+    "DISCHARGE INSTRUCTIONS",
+    "DISCHARGE ORDERS",
+    "DISCONTINUE PREVIOUS INSULIN/ORAL ANTIDIABETIC ORDERS",
+    "DVT PROPHYLAXIS",
+    "Day of Surgery Orders",
+    "Delirium",
+    "Dexamethasone",
+    "Dialysis Medications",
+    "Dialysis Solutions",
+    "Diptheria tetanus toxoid pertussis vaccine",
+    "Discharge",
+    "Discharge Orders",
+    "ECMO Circuit Respiratory Parameters",
+    "ELECTROLYTE REPLACEMENT",
+    "EMERGENCY RELEASE BLOOD PRODUCTS",
+    "Electrolyte Replacement",
+    "Emergency Medications",
+    "Encephalopathy",
+    "Extremity Swelling/Deformity/or Open Fracture",
+    "FLUID RESUSCITATION",
+    "FULL INTENSITY HEPARIN PROTOCOL",
+    "Fluid Resuscitation",
+    "Fluids",
+    "For Pharmacist Use ONLY",
+    "Future Orders",
+    "GI Bleeding",
+    "General",
+    "General Medications",
+    "General Perioperative Orders",
+    "HEMATOPOIETIC CELL INFUSION",
+    "HEPARIN - ACUTE CORONARY SYNDROME",
+    "HEPARIN - ATRIAL FIB",
+    "HEPARIN - CARDIO ELECTROPHYSIOLOGY",
+    "HEPARIN - DVT/PE",
+    "HEPARIN - HIGH BLEEDING RISK",
+    "HEPARIN - MECHANICAL HEART VALVE",
+    "HEPARIN - NEURO",
+    "HEPARIN - SPECIFIC BOLUS & INFUSION PARAMETERS PER MD ",
+    "HEPARIN - VASCULAR SURGERY",
+    "HYDROCORTISONE",
+    "HYPOGLYCEMIA CONTROL",
+    "HYPOGLYCEMIA CONTROL FOR INSULIN TRANSITION OFF IV INFUSION PROTOCOL",
+    "HYPOGLYCEMIA CONTROL FOR SUBCUTANEOUS INSULIN PUMP PROTOCOL",
+    "HYPOGLYCEMIA PROTOCOL FOR DKA",
+    "HYPOGLYCEMIC PROTOCOL FOR IV INSULIN INFUSION",
+    "Heparin - Acute Coronary Syndrome",
+    "Heparin - Atrial FIB",
+    "Heparin - Cardio Electrophysiology",
+    "Heparin - DVT/PE",
+    "Heparin - High Bleeding Risk",
+    "Heparin - Left Ventricular Assist Device (LVAD)",
+    "Heparin - Mechanical Heart Valve",
+    "Heparin - Neuro",
+    "Heparin - Specific Bolus & Infusion Parameters Per MD ",
+    "Heparin - Vascular Surgery",
+    "Heparin Infusion Protocols",
+    "Hepatitis B vaccine. Administer40 mcg IM",
+    "Hospital Acquired Or Ventilator Associated Pneumonia",
+    "Hypoglycemia Control",
+    "Hypoglycemia Control For Insulin Transition Off IV Infusion Protocol",
+    "Hypoglycemia Control For Subcutaneous Insulin Pump Protocol",
+    "Hypoglycemia Protocol For DKA",
+    "Hypoglycemia Treatment",
+    "Hypoglycemic Protocol For IV Insulin Infusion",
+    "ICU Admission",
+    "IMMUNIZATION",
+    "IMMUNIZATIONS",
+    "INFUSION",
+    "INITIAL INTRAVENOUS FLUID AND KCL THERAPY",
+    "INJECTION/MEDICATIONS",
+    "INR = 1.2 to 1.5",
+    "INR > Or = 1.6",
+    "INR > or = 1.6",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SCALES",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SCALES (ORAL DIET/BOLUS FEEDS)",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SCALES - CONTINUOUS FEEDS/NPO ",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SUBCUTANEOUS  SCALES",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SUBCUTANEOUS SCALES",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SUBCUTANEOUS SCALES ",
+    "INSULIN BASAL, PRANDIAL AND CORRRECTIVE SUBCUTANTEOUS SCALES",
+    "INSULIN BASAL,PRANDIAL AND CORRECTIVE SUBCUTANEOUS SCALES",
+    "INSULIN BASAL/PRANDIAL/CORRECTIVE SCALES FOR NPO PATIENT REMAINING NPO",
+    "INSULIN BASAL/PRANDIAL/CORRECTIVE SCALES FOR NPO PATIENT TRANSITIONING TO ORAL DIET",
+    "INSULIN BASAL/PRANDIAL/CORRECTIVE SCALES FOR PATIENT ON CONTINUOUS TUBE FEEDS BEFORE/AFTER TRANSITION OFF DRIP",
+    "INSULIN BOLUS AND INFUSION",
+    "INSULIN INFUSION AND BOLUS",
+    "INSULIN INFUSION HYPOGLYCEMIC PROTOCOL",
+    "INSULIN PUMP",
+    "INTRA PROCEDURE",
+    "INTRATHECAL CLONIDINE",
+    "INTUBATION MEDICATIONS",
+    "IP INSULIN CONTINUOUS IV INFUSION (For Cardiac Transplant)",
+    "IP Insulin Continuous IV Infusion",
+    "IP Insulin Continuous IV Infusion (For Cardiac Transplant)",
+    "IV FLUID",
+    "IV FLUIDS",
+    "IV Fluid",
+    "IV Fluids",
+    "IV Fluids (age 65+)",
+    "IV Fluids and Saline Lock",
+    "IV SOLUTIONS",
+    "IV Solutions",
+    "IVIG Therapy",
+    "If Patient to be Treated as an Inpatient",
+    "If Patient to be Treated as an Outpatient",
+    "Imaging",
+    "Imm/Injections",
+    "Immunization/Injection",
+    "Immunizations",
+    "Immunizations/Injections",
+    "Influenza",
+    "Influenza Treatment",
+    "Influenza Vaccine 6-35 months",
+    "Influenza Vaccine =>3 Years Old",
+    "Initial INR 1.4 -1.6",
+    "Initial INR 1.5 -1.6",
+    "Initial INR 1.7 -1.9",
+    "Initial INR > or = 2",
+    "Initial IV Fluids",
+    "Initial Intravenous Fluid And KCL Therapy",
+    "Injectible Meds",
+    "Injection/Medications",
+    "Injections",
+    "Injections/Medications",
+    "Inpatient Admission",
+    "Inpatient Pre-Op IV Fluids",
+    "Inpatient Pre-Op Medications",
+    "Inpatient Pre-Op VTE Prophylaxis",
+    "Inpatient Pre-op Orders",
+    "Insulin",
+    "Insulin BASAL/PRANDIAL/CORRECTIVE Scales For NPO Patient REMAINING NPO"
+]
+
+SS_SG_NAME = [
+    None,
+    " ",
+    " PCP Prophylaxis auto",
+    " PONV TREATMENT ON THE FLOOR:  Please see PONV guidelines and if criteria are met, give one 5HT-3 Q12 Hr (max 48hr) and metoclopramide:",
+    " PONV Treatment on the Floor:",
+    " PONV Treatment on the Floor:  Please see PONV guidelines and if criteria are met, give one 5HT-3 Q12 Hr (max 48hr) and metoclopramide:",
+    " Phosphate-Lowering Agents",
+    " Regular Insulin Traditional Scales- Continuous Feeds/NPO (Non-ICU)",
+    "(Adult) Blood Product and Transfuse Orders (weight greater than 50 kg)",
+    "(Adult) Emergency Release Blood Products (weight greater than 50 kg)",
+    "(Aliquot) Blood Product and Transfuse Orders (weight less than 50 kg)",
+    "19-49 YRS IMMUNIZATIONS",
+    "50-64 YRS IMMUNIZATIONS",
+    ">=65 YRS IMMUNIZATIONS",
+    "ABORTIFACIENT",
+    "AC Chemotherapy",
+    "ACE INHIBITORS",
+    "ACE Inhibitor (ACEI)",
+    "ACE Inhibitors",
+    "ACEI or ARB or ARNI ",
+    "ACEI: (Publicly reported indicator)",
+    "ADULT IMMUNIZATIONS",
+    "ADULT IMMUNIZATIONS FOR UHA DEPARTMENTS",
+    "ADULT SERIES IMMUNIZATIONS",
+    "ADULT SERIES IMMUNIZATIONS ",
+    "ADULT SERIES IMMUNIZATIONS UHA",
+    "ALL pre-op antibiotics will be administered by Anesthesiologists",
+    "AMB IMM/INJ PED ORDERS ",
+    "AMB IMM/INJ PED QUICK VISIT ",
+    "AMB IMM/INJ PED WCC 15/18 MONTHS ",
+    "AMB IMM/INJ PED WCC 4/5/6 YEARS",
+    "AMB IMM/INJ PED WCC 4/5/6 YEARS ",
+    "AMB IMM/INJ PED WCC 4/5/6 YEARS UHA",
+    "AMB MED OPHTH LASER LASIK",
+    "AMB MED OPHTH LASER PRK",
+    "AMB MED OPHTH POST PROCEDURE",
+    "AMB MED OPHTH PRE PROCEDURE",
+    "AMB MED ORT SPORTS MEDICINE",
+    "AMB MED PEDS VITAMINS DROPS 2WEEKS/1MONTH MMC",
+    "AMB MED PEDS VITAMINS MMC",
+    "AMB MED PEDS Vitamins Drops",
+    "AMB MED REI VERBAL INTRA PROCEDURE MEDS",
+    "AMB MED SLEEP STUDY",
+    "AMB OPH ORDERS RET INJ",
+    "AMB OPH RX RET POSTOP",
+    "AMB PRO PED WCC 3 YEARS",
+    "AMB PRO PED WCC 3-11 YEARS",
+    "AMB PRO PED WCC 3/4/5/6 YEARS ",
+    "AMB PRO REI INTRA-PROCEDURE MEDS",
+    "AMB PRO REI PRE-PROCEDURE NURSING/MEDS",
+    "AMB SNHC ALLERGY CLINIC ASPIRIN",
+    "AMB SNHC ALLERGY CLINIC PREMEDS",
+    "AMB SNHC ALLERGY CLINIC PRN MEDS",
+    "ANALGESIC URINARY",
+    "ANGIOTENSIN 2 RECEPTOR BLOCKERS",
+    "ANTIACNE",
+    "ANTIBIOTIC CEPHALOSPORINS",
+    "ANTIBIOTIC MACROLIDES",
+    "ANTIBIOTIC MACROLIDES MMC",
+    "ANTIBIOTIC OTIC AGENTS",
+    "ANTIBIOTIC PENICILLINS",
+    "ANTIBIOTIC QUINOLONES",
+    "ANTIBIOTIC QUINOLONES ",
+    "ANTIBIOTIC, CEPHALOSPORIN",
+    "ANTIBIOTIC, CEPHALOSPORINS",
+    "ANTIBIOTIC, PENICILLIN",
+    "ANTIBIOTICS MISC",
+    "ANTIBIOTICS OCULAR",
+    "ANTICATAPLETIC",
+    "ANTIDEPRESSANTS (NON SSRI'S)",
+    "ANTIDEPRESSANTS (SSRI'S)",
+    "ANTIDIARRHEALS",
+    "ANTIDIZZINESS MEDS",
+    "ANTIEMETIC",
+    "ANTIFUNGALS",
+    "ANTIHISTAMINES",
+    "ANTIHISTAMINES ALLERGY",
+    "ANTIHISTAMINICS",
+    "ANTILIPEMIC",
+    "ANTIPRURITICS",
+    "ANTIRETROVIRAL",
+    "ANTITUSSIVES",
+    "ANTITUSSIVES ",
+    "ANTITUSSIVES MMC",
+    "ANXIOLYTIC",
+    "ANXIOLYTICS",
+    "ARB: (Publicly reported indicator if ACEI contraindicated)",
+    "ASPIRIN",
+    "ATG",
+    "AVASTIN",
+    "Abatacept Infusion",
+    "Abdominal",
+    "Abdominal Cramp Medications",
+    "Acetaminophen",
+    "Acetylcysteine",
+    "Acute Complicated UTI/Pyelonephritis",
+    "Acute MI",
+    "Acute Uncomplicated UTI",
+    "Additional Diabetic Supplies",
+    "Additional Medications",
+    "Additional Meds",
+    "Additional Post-Op Analgesics",
+    "Additional treatments for Refractory Pain/Chronic Pain",
+    "Aditional Analgesics",
+    "Adjucnt Analgesics",
+    "Adjunct Analgesic",
+    "Adjunct Analgesics",
+    "Adjunct Medications for Blood Pressure",
+    "Adjunct Procedural Medications",
+    "Adjunct Therapies",
+    "Adjunct Therapies - Initiate only after GI consult",
+    "Adjunctive Treatment:",
+    "Adrenergic Agents",
+    "Adult Antibiotics",
+    "Adult Influenza Vaccination",
+    "Adult Injectable Antibiotics",
+    "Adult Oral Antibiotics",
+    "Adult Tdap Vaccine",
+    "Adult Tdap or Td Vaccine",
+    "Afrin and Silver Nitrate",
+    "After Visit / Pre-Admission Orders",
+    "After Visit Orders",
+    "Afterload reduction in cases of severe renal insufficiency:",
+    "Agitation/delirium",
+    "Albumin Prime",
+    "Albumin Replacement (GREATER THAN 4 L removed with documented cirrhosis or any amount removed if creatinine is GREATER THAN 1.5 gm/dL) ",
+    "Alcohol Withdrawal Therapy",
+    "Alcohol Withdrawal Therapy (long-acting regimens)",
+    "Alcohol Withdrawal Therapy (long-acting) - recommend long acting benzodiazepines for initial control of withdrawal symptoms",
+    "Alcohol Withdrawal Therapy (short-acting regimens)",
+    "Alcohol withdrawal therapy (short-acting) --recommend shorter acting agents for the elderly,  patients with severe or decompensated liver disease, or for maintenance for patients whose withdrawal syndrome is mostly controlled,  PO prefered over IV. Using",
+    "Alcohol withdrawal therapy (short-acting) --recommend shorter acting agents for the elderly,  patients with severe or decompensated liver disease, or for maintenance for patients whose withdrawal syndrome is mostly controlled,  PO prefered over IV. Using the CIWA scale and symptom-triggered therapy reduces the duration of therapy and the total dose needed.",
+    "Aldosterone Antagonist",
+    "Alkali Therapy",
+    "All Patients on Steroids before surgery",
+    "All patients on Steroids before surgery",
+    "Allergic Reaction",
+    "Allo Routine Medications",
+    "Alpha 1 Blocker",
+    "Alpha 2 Receptor Agonists",
+    "Alpha-2 Agonist agents",
+    "Alteplase Protocol for Arterial and DVT Thrombolysis: High Dose (0.05 mg/mL)",
+    "Alteplase Protocol for Arterial and DVT Thrombolysis: High-Volume; Low-Dose (0.01 mg/mL)",
+    "Alteplase Protocol for Arterial and DVT Thrombolysis: High-Volume; Low-Dose (0.01mg/mL)",
+    "Alteplase Protocol for Arterial and DVT Thrombolysis: Low-Volume; Low-Dose (0.02 mg/mL)",
+    "Amb Pre-Operative Skin Preparation",
+    "Aminoglycoside Therapy",
+    "Amiodarone",
+    "Ampicillin",
+    "Anagesia/Sedation",
+    "Analgesia",
+    "Analgesia/Antipyretics",
+    "Analgesia/Sedation",
+    "Analgesia/Sedation/Paralysis",
+    "Analgesic",
+    "Analgesic Agents",
+    "Analgesic/Antipyretic",
+    "Analgesics",
+    "Analgesics ",
+    "Analgesics & Anti-Anxiety",
+    "Analgesics & Anti-emetics",
+    "Analgesics (Post-Intubation)",
+    "Analgesics and Rescue Agents",
+    "Analgesics/Antipyretic",
+    "Analgesics/Antipyretics",
+    "Analgesics/Sedatives",
+    "Ancillary Medications",
+    "Anemia Prevention",
+    "Anesthetic for Foley Catheter Insertion",
+    "Anesthetic for Venipuncture",
+    "Anesthetics for Venipuncture",
+    "Angiotensin Receptor Blockers",
+    "Angiotensin-Converting Enzyme Inhibitors",
+    "Angiotensin-Receptor Blockers",
+    "Anit-infectives",
+    "Antacid",
+    "Antacids",
+    "Antacids ",
+    "Antacids/PPI",
+    "Anti Inflammatory:",
+    "Anti Ulcer Agents: Histamine-2 Receptor Antagonists",
+    "Anti- depressants:  Non-SSRI",
+    "Anti-Anxiety",
+    "Anti-Anxiety & Anti-Nausea",
+    "Anti-Anxiety & Nausea",
+    "Anti-Anxiety/Hypnotics",
+    "Anti-Anxiety:  Benzodiazepines",
+    "Anti-Arrhythmic",
+    "Anti-Coagulants: Low-Molecular Weight Heparins ",
+    "Anti-Coagulation",
+    "Anti-Diarrhea",
+    "Anti-Emetic",
+    "Anti-Emetics",
+    "Anti-Emetics ",
+    "Anti-Emetics/GI",
+    "Anti-Epileptic Agents",
+    "Anti-Epileptics",
+    "Anti-Fungals",
+    "Anti-Hypertensives",
+    "Anti-Hypoglycemic Agents"
+]
+
+FREQ_NAMES = [
+    None,
+    "ONCE",
+    "DAILY",
+    "PRN",
+    "2 TIMES DAILY",
+    "EVERY 6 HOURS PRN",
+    "EVERY 4 HOURS PRN",
+    "CONTINUOUS",
+    "PACU ONLY MULTIPLE PRN",
+    "CONTINUOUS PARENTERAL NUTR",
+    "ONCE PRN",
+    "EVERY BEDTIME",
+    "3 TIMES DAILY",
+    "EVERY 8 HOURS",
+    "EVERY 6 HOURS",
+    "EVERY 12 HOURS",
+    "PACU ONLY ONCE PRN",
+    "ANESTHESIA CONTINUOUS",
+    "EVERY 24 HOURS",
+    "DAILY PRN",
+    "EVERY BEDTIME PRN",
+    "PACU ONLY CONTINUOUS",
+    "EVERY 8 HOURS PRN",
+    "PRE-OP ONCE",
+    "4 TIMES DAILY",
+    "ENDOSCOPY MULTIPLE PRN",
+    "2 TIMES DAILY PRN",
+    "5 TIMES DAILY PRN",
+    "EVERY MORNING",
+    "AS DIRECTED",
+    "4 TIMES DAILY PRN",
+    "EVERY 2 MIN PRN",
+    "EVERY MORNING BEFORE BREAKFAST",
+    "2 TIMES DAILY WITH MEALS",
+    "EVERY EVENING",
+    "ONCE TBD",
+    "ONCE PRN, MAY REPEAT X1",
+    "EVERY 2 HOURS PRN",
+    "3 TIMES DAILY PRN",
+    "EVERY MORNING WITH BREAKFAST",
+    "EVERY NIGHT",
+    "EVERY 3 HOURS PRN",
+    "RADIOLOGY ONCE",
+    "EVERY 7 DAYS",
+    "EVERY 15 MIN PRN",
+    "4 TIMES DAILY BEFORE MEALS & BEDTIME",
+    "ENDOSCOPY CONTINUOUS",
+    "EVERY 4 HOURS",
+    "EVERY HOUR PRN",
+    "3 TIMES DAILY WITH MEALS",
+    "EVERY 12 HOURS PRN",
+    "CATH ANGIO MULTIPLE PRN",
+    "PRE-OP MULTIPLE",
+    "PCA - Lockout 15 Minutes",
+    "EVERY 1 HOUR",
+    "EVERY 4-6 HOURS PRN",
+    "3 TIMES DAILY BEFORE MEALS",
+    "DIALYSIS ONCE",
+    "ENDOSCOPY ONCE",
+    "ONCE, MAY REPEAT X1",
+    "DAILY AT BEDTIME",
+    "ON CALL",
+    "EVERY OTHER DAY",
+    "2 TIMES DAILY BEFORE MEALS",
+    "EVERY M W F",
+    "ENDOSCOPY EVERY 2 MIN PRN",
+    "DIALYSIS CONTINUOUS",
+    "INTRA-OP ONCE",
+    "PCA - Lockout 10 Minutes",
+    "EVERY 5 MIN PRN",
+    "EVERY HOUR",
+    "PER PHARMACY PROTOCOL",
+    "EVERY 2 HOURS",
+    "EVERY 4 HOURS WHILE AWAKE",
+    "SEE INSTRUCTIONS",
+    "ED ONCE PRN, MAY REPEAT X1",
+    "5 TIMES DAILY",
+    "EVERY 72 HOURS",
+    "EVERY 3 DAYS",
+    "PRE-OP CONTINUOUS",
+    "2 TIMES WEEKLY",
+    "EVERY 48 HOURS",
+    "PRIOR TO DISCHARGE",
+    "DAILY WITH DINNER",
+    "PRE-OP MULTIPLE PRN",
+    "EVERY BEDTIME PRN, MR X 1",
+    "3 TIMES WEEKLY",
+    "ED EVERY 5 MIN PRN",
+    "EVERY 22 HOURS",
+    "PACU ONLY ONCE",
+    "Once (code)",
+    "4 TIMES DAILY WITH MEALS & BEDTIME",
+    "DAILY BEFORE DINNER",
+    "EVERY 3-4 HOURS PRN",
+    "ONCE A WEEK",
+    "6 TIMES DAILY",
+    "4 TIMES DAILY AFTER MEALS & BEDTIME",
+    "EVERY 3 MONTHS",
+    "DAILY AT NIGHT",
+    "EVENING",
+    "EVERY 3 HOURS",
+    "APHERESIS CONTINUOUS",
+    "EVERY 2 WEEKS",
+    "AM",
+    "EVERY MONTH",
+    "EVERY 30 MIN PRN",
+    "EVERY 5 MIN",
+    "MORNING",
+    "2 TIMES PER WEEK",
+    "3 TIMES DAILY AFTER MEALS",
+    "EVERY 30 DAYS",
+    "PCA - Lockout 20 Minutes",
+    "CONTINUOUS PARENTERAL NUTR (VC)",
+    "EVERY 14 DAYS",
+    "Continuous (code)",
+    "2 TIMES DAILY (FOR PAIN COCKTAILS)",
+    "EVERY 28 DAYS",
+    "EVERY 6-8 HOURS PRN",
+    "DAILY AM WITH FOOD",
+    "EVERY 30 MIN",
+    "ED EVERY 15 MIN PRN",
+    "ENDOSCOPY ONCE PRN, MAY REPEAT X1",
+    "EVERY 2 HOURS WHILE AWAKE",
+    "EVERY 24 HOURS PRN",
+    "E1PRN",
+    "2 TIMES DAILY AFTER MEALS",
+    "EVERY 6 HOURS WHILE AWAKE",
+    "EVERY 10 MIN PRN",
+    "EVERY MORNING AFTER BREAKFAST",
+    "EVERY SA SU BID",
+    "EVERY 12 WEEKS",
+    "EVERY 4 HOURS WHILE AWAKE RT",
+    "OR Continuous ",
+    "PROCEDURE ONLY MULTIPLE PRN",
+    "PM",
+    "3 TIMES DAILY WITH SNACKS",
+    "EVERY 3 DAYS PRN",
+    "EVERY 15 MIN",
+    "EVERY 20 MIN PRN",
+    "2 TIMES DAILY (0800,2200)",
+    "2 TIMES DAILY RT",
+    "EVERY M TH",
+    "EVERY TU TH SA",
+    "PCA - Lockout 12 Minutes",
+    "MEDROL DOSEPAK BID BREAKFAST AND BEDTIME",
+    "PCA - Lockout 30 Minutes",
+    "EVERY 4-6 HOURS",
+    "MEDROL DOSEPAK BID LUNCH AND DINNER",
+    "EVERY SUNDAY",
+    "EVERY FRIDAY",
+    "E1ONCE",
+    "DAILY WITH LUNCH",
+    "PACU ONLY MULTIPLE",
+    "EVERY MONDAY",
+    "RX CUSTOM FREQUENCY",
+    "2 TIMES DAILY FOR 7 DAYS",
+    "EVERY 10 MIN",
+    "PRE-OP ONCE PRN",
+    "TID After Meals",
+    "DIALYSIS MULTIPLE PRN",
+    "2 TIMES DAILY FOR 10 DAYS",
+    "QID (0900,1300,1700,2100)",
+    "EVERY TU TH SA SU",
+    "EVERY WEDNESDAY",
+    "E1CONTINUOUS",
+    "MEDROL DOSEPAK DAILY WITH BREAKFAST",
+    "BEFORE MEALS",
+    "EVERY SATURDAY",
+    "1/2 HOUR BEFORE MEALS AND BEDTIME",
+    "EVERY 8-12 HOURS PRN",
+    "FIVE TIMES A WEEK",
+    "EVERY 4 HOURS RT",
+    "APPLY AS DIRECTED",
+    "2 TIMES DAILY FOR 14 DAYS",
+    "EVERY THURSDAY",
+    "EVERY TUESDAY",
+    "DAILY RT",
+    "CYCLIC PARENTERAL NUTR (VC)",
+    "RADIOLOGY MULTIPLE",
+    "EVERY 3 WEEKS",
+    "ONCE AFTER DIALYSIS",
+    "DIALYSIS ONCE PRN",
+    "PROCEDURE ONLY ONCE PRN",
+    "EVERY SA SU",
+    "3 TIMES DAILY FOR 7 DAYS",
+    "EVERY 6 HOURS RT",
+    "4 TIMES DAILY FOR 7 DAYS",
+    "MEDROL DOSEPAK TID BRKFAST, LUNCH, BEDTIME",
+    "MEDROL DOSEPAK DAILY AT BEDTIME",
+    "BEFORE MEALS AND AT BEDTIME",
+    "2 TIMES DAILY ON MO, WED & FR",
+    "APHERESIS ONLY MULTIPLE PRN",
+    "3 TIMES DAILY FOR 10 DAYS",
+    "ED PRN",
+    "EVERY M F",
+    "EVERY 4 DAYS",
+    "EVERY 3 HOURS WHILE AWAKE",
+    "DAILY BEFORE LUNCH",
+    "EVERY M W F SU",
+    "EVERY 56 DAYS"
+]
+
+
+class TestSTARROrderMedConversion(DBTestCase):
+    TEST_DATA_SIZE = 2 * len(MED_ROUTES)    # at least 2 rows per med route combined with inpatient vs outpatient
+
+    ORDER_MED_HEADER = ['order_med_id_coded', 'jc_uid', 'pat_enc_csn_id_coded', 'medication_id', 'med_description',
+                        'order_time_jittered', 'med_route', 'number_of_times', 'ordering_mode', 'freq_name',
+                        'order_time_jittered_utc']
+    MED_ORDERSET_HEADER = ['order_med_id_coded', 'protocol_id', 'protocol_name', 'ss_section_id', 'ss_section_name',
+                           'ss_sg_key', 'ss_sg_name']
+
+    test_data = []
+    orderset_data = []
+    clinical_items = {}
+    expected_data = []
+    expected_orderset_data = []
+
+    test_data_csv = tempfile.gettempdir() + '/test_starr_order_med_dummy_data.csv'
+    orderset_data_csv = tempfile.gettempdir() + '/test_starr_order_med_orderset_data.csv'
+
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self)
+
+        log.info("Sourcing from BigQuery DB")
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+
+        self.converter = STARROrderMedConversion.STARROrderMedConversion()  # Instance to test on
+        self.bqConn = self.converter.bqConn
+        self.starrUtil = STARRUtil.StarrCommonUtils(self.converter.bqClient)
+
+        # point the converter to dummy source table
+        STARROrderMedConversion.SOURCE_TABLE = TEST_SOURCE_TABLE
+        STARROrderMedConversion.ORDERSET_TABLE = TEST_ORDERSET_TABLE
+
+        log.warning("Removing test tables, if they exist: {} and {}".format(TEST_SOURCE_TABLE, TEST_ORDERSET_TABLE))
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_SOURCE_TABLE))
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_ORDERSET_TABLE))
+
+    def generate_test_and_expected_data(self, test_data_size, conv_options):
+        # preload mapped_meds table
+        rxcuiDataByMedId = self.converter.loadRXCUIData()
+
+        self.generate_ordermed_and_orderset_data(test_data_size, rxcuiDataByMedId)
+
+        # need to sort data to use the same clinical_item.descriptions as the duplicate clinical_items are not stored
+        self.test_data.sort(key=lambda tup: (tup[0], tup[1], tup[2], tup[3]))
+        self.orderset_data.sort(key=lambda row: row[0])
+
+        # join test order_med and med_orderset data to create a test row
+        joined_test_data = self.left_join_ordermed_and_orderset_data()
+
+        for joined_test_data_row in joined_test_data:
+            self.generate_expected_data_rows(joined_test_data_row[0], joined_test_data_row[1], rxcuiDataByMedId, conv_options)
+
+        # pi.external_id desc, ci.external_id desc, pi.encounter_id, ci.external_id desc
+        self.expected_data.sort(key=lambda tup: (-tup[0], tup[1], tup[2], -tup[4]))
+
+        # pi.external_id, ci.external_id (medication_id), cic.description, ci.name, ci.description, ic.external_id (protocol_id)
+        self.expected_orderset_data.sort(key=lambda tup: (tup[0], tup[1], tup[2], tup[3], tup[4], tup[5]))
+
+    def left_join_ordermed_and_orderset_data(self):
+        joined_test_data = []
+        for test_data_row in self.test_data:
+            if self.ignore_row(test_data_row[3], test_data_row[9]):
+                continue
+
+            found_join_row = False
+            for test_orderset_row in self.orderset_data:
+                if test_data_row[0] == test_orderset_row[0]:
+                    found_join_row = True
+                    joined_test_data.append((test_data_row, test_orderset_row))
+            if not found_join_row:
+                joined_test_data.append((test_data_row, None))
+        return joined_test_data
+
+    def generate_ordermed_and_orderset_data(self, test_data_size, rxcuiDataByMedId):
+        seen_test_data_rows = set()
+        while len(self.test_data) < test_data_size:
+            curr_row = len(self.test_data)
+            patient_id = 'JC' + format(curr_row, '06')
+
+            test_data_row = self.generate_test_data_row(curr_row, patient_id, rxcuiDataByMedId)
+
+            test_data_row_unique_key = (test_data_row[0], test_data_row[1], test_data_row[2], test_data_row[3])
+            if test_data_row_unique_key not in seen_test_data_rows:
+                seen_test_data_rows.add(test_data_row_unique_key)
+                self.test_data.append(test_data_row)
+
+                # should generate at most 1 row of med_orderset per order_med row (tested code does LEFT OUTER JOIN)
+                should_generate_orderset_row = random.randint(0, 1)
+                if should_generate_orderset_row:
+                    orderset_row = self.generate_orderset_row(test_data_row[0])
+                    self.orderset_data.append(orderset_row)
+
+    @staticmethod
+    def ignore_row(medication_id, freq_name):
+        # process only rows where med.medication_id <> 9000000 and freq_name not like '%PRN'
+        return medication_id == 9000000 or (freq_name is not None and freq_name.endswith('PRN'))
+
+    @staticmethod
+    def generate_test_data_row(curr_row, patient_id, rxcuiDataByMedId):
+        use_rxcui = random.randint(1, 5)
+        if use_rxcui % 5 == 0:
+            # use mapped_meds 20% of the time
+            medication_id = list(rxcuiDataByMedId.keys())[random.randint(0, len(rxcuiDataByMedId) - 1)]
+        else:
+            # several times less than the test records count to have some medication_ids occur multiple times
+            medication_id = random.randint(0, len(MED_ROUTES) // 5)
+
+        order_time_jittered = random.randint(1, int(time.time()))
+        return (
+            random.randint(0, len(MED_ROUTES) // 10),  # order_med_id_coded - want some of them to repeat
+            patient_id,
+            curr_row,  # pat_enc_csn_id_coded
+            medication_id,
+            MED_DESCRIPTIONS[random.randint(0, len(MED_DESCRIPTIONS) - 1)],
+            datetime.fromtimestamp(order_time_jittered),
+            MED_ROUTES[random.randint(0, len(MED_ROUTES) - 1)],
+            random.randint(1, 180),  # number_of_times
+            ORDERING_MODES[random.randint(0, len(ORDERING_MODES) - 1)],  # ordering_modes
+            FREQ_NAMES[random.randint(0, len(FREQ_NAMES) - 1)],
+            datetime.fromtimestamp(order_time_jittered, tz=pytz.UTC),
+        )
+
+    @staticmethod
+    def generate_orderset_row(order_med_id_coded):
+        protocol_id = random.randint(1, len(PROTOCOL_NAME))
+        ss_section_id = random.randint(0, len(SS_SECTION_NAME) - 1)
+        ss_sg_key = random.randint(0, len(SS_SG_NAME) - 1)
+
+        return (
+            order_med_id_coded,  # to match the given order_med_row
+            -protocol_id,
+            PROTOCOL_NAME[protocol_id - 1],
+            ss_section_id,
+            SS_SECTION_NAME[ss_section_id],
+            str(ss_sg_key),
+            SS_SG_NAME[ss_sg_key]
+        )
+
+    def generate_expected_data_rows(self, row, orderset_row, rxcuiDataByMedId, conv_options):
+        row_model = RowItemModel()
+        row_model["medication_id"] = row[3]
+        row_model["med_description"] = row[4]
+        row_model["number_of_times"] = row[7]
+        row_model["med_route"] = row[6]
+
+        normalized_med_data = list(self.converter.normalizeMedData(rxcuiDataByMedId, row_model, conv_options))
+
+        cic_description = STARROrderMedConversion.CATEGORY_TEMPLATE.format(row_model["med_route"], row[8])
+
+        for normalized_model in normalized_med_data:
+            ci_key = (TEST_SOURCE_TABLE, cic_description, normalized_model["code"])
+
+            if ci_key not in self.clinical_items \
+                    or len(normalized_model["med_description"]) < len(self.clinical_items[ci_key]) \
+                    or self.clinical_items[ci_key].startswith(TEMPLATE_MEDICATION_PREFIX):
+                self.clinical_items[ci_key] = normalized_model["med_description"]
+
+                # replace previous ci_descriptions in expected_data
+                for i in range(len(self.expected_data)):
+                    if self.expected_data[i][3] == cic_description and self.expected_data[i][5] == normalized_model["code"]:
+                        self.expected_data[i] = self.expected_data[i][:6] + (self.clinical_items[ci_key], ) + self.expected_data[i][7:]
+
+                # replace previous ci_descriptions in expected_orderset_data
+                for i in range(len(self.expected_orderset_data)):
+                    if self.expected_orderset_data[i][2] == cic_description and self.expected_orderset_data[i][3] == normalized_model["code"]:
+                        self.expected_orderset_data[i] = self.expected_orderset_data[i][:4] + (self.clinical_items[ci_key],) + self.expected_orderset_data[i][5:]
+
+            ci_description = self.clinical_items[ci_key]
+
+            expected_row = (
+                row[0],                                                                             # external_id
+                self.starrUtil.convertPatIdToSTRIDE(row[1]),                                        # patient_id
+                row[2],                                                                             # encounter_id
+                cic_description,                                                                    # cic_description
+                normalized_model["medication_id"],                                                  # ci_external_id
+                normalized_model["code"],                                                           # ci_name
+                ci_description,                                                                     # ci_description
+                row[5],                                                                             # pi_item_date
+                row[10].replace(tzinfo=pytz.UTC)                                                    # pi.item_date_utc
+            )
+            if expected_row not in self.expected_data:
+                self.expected_data.append(expected_row)
+
+            if orderset_row is not None:
+                # generate expected data for orderset - in %item_collection% tables
+                expected_orderset_row = (
+                    row[0],
+                    normalized_model["medication_id"],
+                    cic_description,
+                    normalized_model["code"],   # ci_name
+                    ci_description,
+                    orderset_row[1],            # protocol_id
+                    orderset_row[2],            # protocol_name
+                    orderset_row[4],            # ss_section_name
+                    orderset_row[6]             # ss_sg_name
+                )
+                self.expected_orderset_data.append(expected_orderset_row)
+
+    @staticmethod
+    def prepare_conv_options(dose_count_limit, include_route_in_description, normalize_mixtures):
+        conv_options = STARROrderMedConversion.ConversionOptions()
+        conv_options.normalizeMixtures = normalize_mixtures
+        conv_options.doseCountLimit = dose_count_limit
+        conv_options.includeRouteInDescription = include_route_in_description
+        return conv_options
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        self.test_data[:] = []
+        self.orderset_data[:] = []
+        self.expected_data[:] = []
+        self.expected_orderset_data[:] = []
+        self.clinical_items.clear()
+
+        self.starrUtil.remove_file(self.test_data_csv)
+        self.starrUtil.remove_file(self.orderset_data_csv)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DELETE FROM {}.patient_item_collection_link WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.item_collection_item WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.item_collection WHERE true;'.format(TEST_DEST_DATASET))
+
+        bq_cursor.execute('DELETE FROM {}.patient_item WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.clinical_item WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.clinical_item_category WHERE true;'.format(TEST_DEST_DATASET))
+
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_SOURCE_TABLE))
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_ORDERSET_TABLE))
+
+        DBTestCase.tearDown(self)
+
+    @parameterized.expand([
+        ["with_denormalizeMixtures_noDoseCountLimit_includeRoute", False, None, True],
+    ])
+    def test_row_with_no_ingredient_is_processed(self, name, normalize_mixtures, dose_count_limit, include_route_in_description):
+        log.info("Test the output is not empty even if active_ingredient is None in mapped_meds with since ingredient")
+
+        # { medication_id -> { rxcui -> active_ingredient } }
+        rxcuiDataByMedId = {1: {2: None}}
+
+        row_model = RowItemModel()
+        row_model["medication_id"] = 1
+        row_model["med_description"] = "Med description"
+        row_model["number_of_times"] = 10
+        row_model["med_route"] = "Intravenous"
+
+        conv_options = self.prepare_conv_options(dose_count_limit, include_route_in_description, normalize_mixtures)
+        normalized_med_data = list(self.converter.normalizeMedData(rxcuiDataByMedId, row_model, conv_options))
+        self.assertTrue(normalized_med_data)
+
+    @parameterized.expand([
+        ["with_denormalizeMixtures_noDoseCountLimit_includeRoute", False, None, True],
+        ["with_denormalizeMixtures_noDoseCountLimit_excludeRoute", False, None, False],
+        ["with_denormalizeMixtures_DoseCountLimit_includeRoute", False, 100, True],
+        ["with_denormalizeMixtures_DoseCountLimit_excludeRoute", False, 100, False],
+        ["with_normalizeMixtures_noDoseCountLimit_includeRoute", True, None, True],
+        ["with_normalizeMixtures_noDoseCountLimit_excludeRoute", True, None, False],
+        ["with_normalizeMixtures_DoseCountLimit_includeRoute", True, 100, True],
+        ["with_normalizeMixtures_DoseCountLimit_excludeRoute", True, 100, False],
+    ])
+    def test_data_conversion(self, name, normalize_mixtures, dose_count_limit, include_route_in_description):
+        log.info("Generating test source data")
+
+        conv_options = self.prepare_conv_options(dose_count_limit, include_route_in_description, normalize_mixtures)
+
+        self.generate_test_and_expected_data(self.TEST_DATA_SIZE, conv_options)
+
+        # upload med_orderset
+        self.starrUtil.dump_test_data_to_csv(self.MED_ORDERSET_HEADER, self.orderset_data, self.orderset_data_csv)
+        self.starrUtil.upload_csv_to_bigquery('starr_datalake2018', 'med_orderset', TEST_DEST_DATASET,
+                                              'starr_med_orderset', self.orderset_data_csv, self.MED_ORDERSET_HEADER)
+
+        # upload order_med
+        self.starrUtil.dump_test_data_to_csv(self.ORDER_MED_HEADER, self.test_data, self.test_data_csv)
+        self.starrUtil.upload_csv_to_bigquery('starr_datalake2018', 'order_med', TEST_DEST_DATASET,
+                                              'starr_order_med', self.test_data_csv, self.ORDER_MED_HEADER)
+
+        log.debug("Run the conversion process...")
+        temp_dir = tempfile.gettempdir()
+        self.converter.convertAndUpload(conv_options, tempDir=temp_dir, target_dataset_id=TEST_DEST_DATASET, removeCsvs=True)
+
+        # Just query back for the same data, de-normalizing the data back to a general table
+        test_query = \
+            """
+            select
+                pi.external_id as pi_external_id,
+                pi.patient_id,
+                pi.encounter_id,
+                cic.description as cic_description,
+                ci.external_id as ci_external_id,
+                ci.name,
+                ci.description as ci_description,
+                pi.item_date,
+                pi.item_date_utc
+            from
+                {}.patient_item as pi,
+                {}.clinical_item as ci,
+                {}.clinical_item_category as cic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = '{}'
+            order by
+                pi.external_id desc, pi.patient_id, pi.encounter_id, ci.external_id desc
+            """.format(TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute(test_query)
+        # remove timezone info in pi.item_date from coming from bigquery - we're storing datetime without timezone
+        actual_data = [tuple(row.values())[:7] + (list(row.values())[7].replace(tzinfo=None), list(row.values())[8],) for row in bq_cursor.fetchall()]
+
+        log.debug('actual data: {}'.format(actual_data))
+        log.debug('expected data: {}'.format(self.expected_data))
+        self.assertEqualTable(self.expected_data, actual_data)
+
+        # Query for orderset links
+        test_orderset_query = \
+            """
+            select
+                pi.external_id as pi_external_id,
+                ci.external_id as ci_external_id,
+                cic.description as cic_description,
+                ci.name as ci_name,
+                ci.description,
+                ic.external_id,
+                ic.name,
+                ic.section,
+                ic.subgroup
+            from
+                {}.patient_item as pi,
+                {}.clinical_item as ci,
+                {}.clinical_item_category as cic,
+                {}.patient_item_collection_link as picl,
+                {}.item_collection_item as ici,
+                {}.item_collection as ic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = '{}' and
+                pi.patient_item_id = picl.patient_item_id and
+                picl.item_collection_item_id = ici.item_collection_item_id and
+                ici.item_collection_id = ic.item_collection_id
+            order by
+                pi.external_id, ci.external_id, cic.description, ci.name, ci.description, ic.external_id
+            """.format(TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET,
+                       TEST_DEST_DATASET, TEST_SOURCE_TABLE)
+
+        bq_cursor.execute(test_orderset_query)
+        actual_orderset_data = [list(row.values()) for row in bq_cursor.fetchall()]
+
+        log.debug('actual orderset data: {}'.format(actual_orderset_data))
+        log.debug('expected orderset data: {}'.format(self.expected_orderset_data))
+        self.assertEqualTable(self.expected_orderset_data, actual_orderset_data)
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    test_suite = unittest.TestSuite()
+    test_suite.addTest(unittest.makeSuite(TestSTARROrderMedConversion))
+
+    return test_suite
+
+
+if __name__ == "__main__":
+    log.setLevel(logging.DEBUG)  # without this no logs are printed
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/starr_conv/test/TestSTARROrderProcConversion.py b/medinfo/dataconversion/starr_conv/test/TestSTARROrderProcConversion.py
new file mode 100644
index 00000000..80c26156
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/test/TestSTARROrderProcConversion.py
@@ -0,0 +1,1651 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+import logging
+import pytz
+import random
+import tempfile
+import time
+
+from datetime import datetime, timedelta
+
+import unittest
+
+from parameterized import parameterized
+from medinfo.dataconversion.test.Const import RUNNER_VERBOSITY
+from medinfo.dataconversion.Const import TEMPLATE_MEDICATION_PREFIX
+from medinfo.dataconversion.Util import log
+
+from medinfo.db.test.Util import DBTestCase
+
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel
+
+from medinfo.dataconversion.starr_conv import STARROrderProcConversion
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+TEST_SOURCE_TABLE = 'test_dataset.starr_order_proc'
+TEST_ORDERSET_TABLE = 'test_dataset.starr_proc_orderset'
+
+TEST_DEST_DATASET = 'test_dataset'
+
+
+ORDERING_MODES = [
+    'Inpatient',
+    'Outpatient'
+]
+
+ORDER_TYPES = [
+    None,
+    "Pharmacy Consult",
+    "Sleep Center",
+    "HB Chargeables",
+    "Code Status",
+    "Pathology",
+    "GI",
+    "Evercore",
+    "Nursing Referral",
+    "Respiratory Care",
+    "Cardiac Angio",
+    "Pharmacy Supplies",
+    "Bedside Procedure",
+    "Therapeutic Recreation Orderables",
+    "PR Charge",
+    "Procedures",
+    "PT",
+    "OT",
+    "Cast Room",
+    "Bronchoscopy",
+    "Manual Entry Imaging",
+    "Manual Entry Cardiac Angio",
+    "Lab",
+    "Blood Bank",
+    "PFT",
+    "Vascular Ultrasound",
+    "IVF Labs",
+    "SLP",
+    "Diet Communication",
+    "Manual Entry Procedures",
+    "Imaging Non-Reportable",
+    "Nursing",
+    "Diet",
+    "Precaution",
+    "Immunization/Injection",
+    "Zeiss",
+    "General Supply",
+    "Legal",
+    "Manual Entry Bronchoscopy",
+    "Lab Panel",
+    "Charge",
+    "Imaging",
+    "Cath Angio",
+    "Device Check",
+    "Notify Physician",
+    "ENT",
+    "Manual Entry Lab",
+    "Manual Entry ECHO",
+    "Radiation Oncology",
+    "Point of Care Testing",
+    "ECHO",
+    "Lab Only",
+    "Microbiology",
+    "Generic Surgical History",
+    "Admission",
+    "Reference Labs",
+    "NSG PICC Refer",
+    "Transfer",
+    "NOURISHMENTS",
+    "OB Ultrasound",
+    "Restraints",
+    "Ophthalmology",
+    "Nursing Transfusion",
+    "HIV Lab Non-Restricted",
+    "Isolation",
+    "Other Order Scanned Report",
+    "Manual Entry HIV Lab Restricted",
+    "Microbiology Culture",
+    "ECG",
+    "Consult",
+    "Outpatient Referral",
+    "Neurology",
+    "MyHealth Questionnaire Assignment",
+    "Transport",
+    "Transfusion Communication",
+    "Electrophysiology",
+    "Vaultstream",
+    "filter exclude all",
+    "Consult to Cardiology ",
+    "REHAB",
+    "BB Call Slip",
+    "HIV Lab Restricted",
+    "Discharge",
+    "Surgical Procedures",
+    "Language Services"
+]
+
+PROC_CODES = [
+    "NUR1025",
+    "LABPOCGLU",
+    "LABCBCD",
+    "POC14",
+    "LABMETC",
+    "NUR1018",
+    "LABMETB",
+    "99146543",
+    "NUR1019",
+    "LABMGN",
+    "NUR1044",
+    "LABCBCO",
+    "LABPT",
+    "LAB234",
+    "99146537",
+    "NUR1106",
+    "EKG5",
+    "RT109",
+    "LABSPR",
+    "LABPTT",
+    "NUR1940",
+    "99146539",
+    "LABTSH",
+    "LABPHOS",
+    "NUR194000",
+    "NUR762",
+    "NUR1068",
+    "NUR1613",
+    "LABPCG3",
+    "LABUA",
+    "ADT12",
+    "NUR1043",
+    "146543",
+    "LABA1C",
+    "NUR1022",
+    "NUR764",
+    "IMGDXCH1",
+    "LABTYPSNI",
+    "NUR1013",
+    "DIET990",
+    "NUR301",
+    "LABBBPRBC",
+    "NUR1094",
+    "LABCAI",
+    "LABURNC",
+    "NUR810",
+    "LABABG",
+    "LABSURG",
+    "NUR350",
+    "LABLPD",
+    "LABHFP",
+    "LABUAPRN",
+    "DIET102",
+    "LABK",
+    "NUR30024",
+    "99146535",
+    "IMGDXCH2",
+    "NUR30022",
+    "COD2",
+    "LABLPDC",
+    "LABFT4",
+    "LABFK506L",
+    "LABLDH",
+    "LABTNI",
+    "99146541",
+    "RT108",
+    "NUR1664",
+    "NUR4030",
+    "146535",
+    "LABVD25H",
+    "LABARI",
+    "LABESRP",
+    "LABBLC",
+    "NUR1231",
+    "LAB230",
+    "LABCRP",
+    "LABDATACONV",
+    "LABCR",
+    "ECH15",
+    "NURSE 1500",
+    "2500147414",
+    "PT4",
+    "NUR1016",
+    "TRN2",
+    "LABASI",
+    "PHX0003",
+    "LABTBP",
+    "LABVBG",
+    "NUR4067",
+    "99214",
+    "LABURIC",
+    "146541",
+    "LABOSR",
+    "LABHCTX",
+    "LABBLC2",
+    "NUR1588",
+    "NUR1024",
+    "99245",
+    "LABALT",
+    "LABMB",
+    "ADT100",
+    "NUR1012",
+    "LABNA",
+    "LABFER",
+    "99215",
+    "OT1",
+    "DC3",
+    "NUR324",
+    "LABLIPS",
+    "LABCBCS",
+    "DC8",
+    "LABTSLIP",
+    "NUR1373",
+    "99205",
+    "LABLACWB",
+    "DC1",
+    "LABAST",
+    "NUR372",
+    "LABPSA",
+    "DC5",
+    "NUR775",
+    "ADT1",
+    "LABB12",
+    "LABCK",
+    "ADT7",
+    "IMGDXCH1P",
+    "PT6",
+    "LABLAC",
+    "IMGXR0023",
+    "LABVANPRL",
+    "LABROMRS",
+    "LABNTBNP",
+    "416543",
+    "PATH10",
+    "RT101",
+    "146537",
+    "880100004",
+    "LABPCTNI",
+    "LABTBP1",
+    "IMGXR0031",
+    "OT2",
+    "LABHBSAG",
+    "416542",
+    "LABHEPAR",
+    "RT104",
+    "LABTRFS",
+    "LABTRIG",
+    "NUR1448",
+    "LABHCVA",
+    "NUR30020",
+    "LABPLTS",
+    "DIET101",
+    "LABPCCR",
+    "NUR2241",
+    "LABGYN",
+    "NUR1060",
+    "LABCA",
+    "NUR3511",
+    "NUR700",
+    "REF94",
+    "LABBBPPLT",
+    "IMGCTH",
+    "NUR323",
+    "NUR4005",
+    "NUR1066",
+    "POC24",
+    "LABBBPFFP",
+    "LABTSHFT4",
+    "NUR4044",
+    "LABTBIL",
+    "NUR811",
+    "LABPCCG4V",
+    "NUR1991",
+    "LABBUN",
+    "LABGGT",
+    "LABALB",
+    "LAB219",
+    "IMGDXAB1",
+    "LABUALB",
+    "LABGLF",
+    "NUR4077",
+    "NUR1293",
+    "LABBMT1",
+    "445875",
+    "GICOLO",
+    "RT72",
+    "DC2",
+    "LABCHOL",
+    "LABPCEG7",
+    "NUR716",
+    "RES5",
+    "LABCDTPCR",
+    "RT100",
+    "IMGBI0008",
+    "RT82",
+    "LABCMVQT",
+    "DIET122",
+    "DIET131",
+    "LABLCLPD",
+    "POC17"
+]
+
+DESCRIPTIONS = [
+    "VITAL SIGNS",
+    "GLUCOSE BY METER",
+    "POC GLUCOSE BY METER",
+    "METABOLIC PANEL, COMPREHENSIVE",
+    "CBC WITH DIFFERENTIAL",
+    "MONITOR INTAKE AND OUTPUT",
+    "METABOLIC PANEL, BASIC",
+    "CBC WITH DIFF",
+    "HX OTH ORD SCAN REPORT",
+    "INCENTIVE SPIROMETER (WHILE AWAKE)",
+    "MAGNESIUM, SERUM/PLASMA",
+    "NURSING PULSE OXIMETRY (SPOT CHECK)",
+    "PROTHROMBIN TIME",
+    "HX LAB PROC SCAN REPORT",
+    "ASSESSMENT FREQUENCY (PSYCH UNIT)",
+    "NURSING COMMUNICATION",
+    "ECG 12-LEAD",
+    "RESP - NEBULIZER",
+    "SPECIMEN REMARK",
+    "PTT PARTIAL THROMBOPLASTIN TIME",
+    "CBC",
+    "HX IMAGING PROC SCAN REPORT",
+    "TSH",
+    "PHOSPHORUS, SERUM/PLASMA",
+    "NEUROLOGICAL CHECKS",
+    "WEIGHT",
+    "ISTAT G3+, ARTERIAL",
+    "CBC W/O DIFF",
+    "URINALYSIS WITH MICROSCOPIC",
+    "DISCHARGE PATIENT",
+    "NURSING PULSE OXIMETRY",
+    "PERIPHERAL IV INSERTION CARE",
+    "NURSING COMMUNICATION(FOR ORDERSET ONLY)",
+    "OTHER ORDER SCANNED REPORT",
+    "HEMOGLOBIN A1C",
+    "RESPIRATORY RATE",
+    "XR CHEST 1V",
+    "TYPE AND SCREEN",
+    "OOB/UP IN CHAIR WITH ASSIST",
+    "DIET REGULAR",
+    "NEUROVASCULAR ASSESSMENT",
+    "SEQUENTIAL COMPRESSION DEVICE (SCD)",
+    "RED BLOOD CELLS",
+    "AMBULATE WITH ASSISTANCE",
+    "URINE CULTURE",
+    "SURGICAL PROCEDURE",
+    "NOTIFY MD:",
+    "HEPATIC FUNCTION PANEL A",
+    "DIET NPO",
+    "POTASSIUM, SERUM/PLASMA",
+    "DISCHARGE PATIENT WHEN CRITERIA MET",
+    "HX ECG PROC SCAN REPORT",
+    "XR CHEST 2V",
+    "FULL CODE",
+    "ADMIT TO PHASE",
+    "LIPID PANEL WITH CALCULATED LDL",
+    "I&O DRAINS OUPUT",
+    "T4, FREE",
+    "CALCIUM IONIZED, SERUM/PLASMA",
+    "TACROLIMUS (FK506)",
+    "LIPID PANEL WITH DIRECT LDL",
+    "LDH TOTAL, SERUM / PLASMA",
+    "TROPONIN I",
+    "HX OTH PROC SCAN REPORT",
+    "RESP - MDI",
+    "SALINE LOCK AND FLUSH",
+    "OXYGEN: NASAL CANNULA(NURSING ONLY)",
+    "ECG PROCEDURE SCANNED REPORT",
+    "CARDIAC MONITOR",
+    "VITAMIN D, 25-HYDROXYVITAMIN",
+    "ABO/RH (ARI)",
+    "SEDIMENTATION RATE (ESR)",
+    "MANUAL DIFFERENTIAL/SLIDE REVIEW",
+    "BLOOD GASES, ARTERIAL",
+    "C - REACTIVE PROTEIN",
+    "HISTORICAL RESULT",
+    "CREATININE, SERUM/PLASMA",
+    "NOTIFY MD:VITAL SIGNS",
+    "UHA LAB - SCANNED",
+    "PT EVALUATE AND TREAT",
+    "MEASURE URINARY OUTPUT",
+    "ADMIT TO INPATIENT",
+    "ECHO - TRANSTHORACIC ECHO +DOPPLER",
+    "PATIENT TRANSPORT",
+    "AB SCREEN (ASI)",
+    "URINALYSIS SCREEN, CULTURE IF POSITIVE",
+    "TRANSCHART CONVERTED LAB DATA",
+    "TRANSFUSION BLOOD PRODUCT 1",
+    "BLOOD GASES, VENOUS",
+    "OXYGEN ADMINISTRATION",
+    "EVAL/MGMT OF EST PATIENT LEVEL 4",
+    "URIC ACID, SERUM / PLASMA",
+    "OTHER PROCEDURE SCANNED REPORT",
+    "OUTSIDE SLIDE REVIEW",
+    "HEMATOCRIT",
+    "BLOOD CULTURE (2 AEROBIC BOTTLES)",
+    "OOB",
+    "VITAL SIGNS PER PROTOCOL",
+    "OFFICE CONSLTJ 80 MIN",
+    "ALT, SERUM/PLASMA",
+    "CK, MB (MASS)",
+    "OOB/UP IN CHAIR",
+    "SODIUM, SERUM / PLASMA",
+    "FERRITIN",
+    "EVAL/MGMT OF EST PATIENT LEVEL 5",
+    "OT EVALUATE AND TREAT",
+    "PHYSICAL ACTIVITY",
+    "PAIN ASSESSMENT",
+    "LIPASE",
+    "FOLLOW UP INSTRUCTIONS",
+    "EVAL/MGMT OF NEW PATIENT LEVEL 5",
+    "LACTATE, WHOLE BLOOD",
+    "DISCHARGE DIET",
+    "BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)",
+    "AST, SERUM/PLASMA",
+    "PSA",
+    "REASON TO CALL YOUR PHYSICIAN",
+    "WOUND CARE",
+    "ARTERIAL BLOOD GAS",
+    "VITAMIN B12",
+    "CREATINE KINASE, TOTAL",
+    "TRANSFUSION SERVICE CALL SLIP",
+    "XR CHEST 1V PORTABLE",
+    "PT ONGOING TREATMENT",
+    "LACTIC ACID",
+    "XR CHEST 1 VIEW",
+    "FOLEY RETENTION CATHETER",
+    "CBC WITH DIFF AND SLIDE REVIEW",
+    "VANCOMYCIN TROUGH LEVEL",
+    "NT - PROBNP",
+    "UHA FLU SHOT PROTOCOL AUTHORIZATION",
+    "BLOOD TYPE VERIFICATION",
+    "RESP - FLUTTER/PEP THERAPY",
+    "LAB PROCEDURE SCANNED REPORT",
+    "URINALYSIS, SCREEN FOR CULTURE",
+    "SIMPLE AIRWAY",
+    "ISTAT TROPONIN I",
+    "TRANSFUSION BLOOD PRODUCT 2",
+    "MRSA SCREEN",
+    "LIPID PANEL",
+    "XR CHEST 2 VIEWS",
+    "OT ONGOING TREATMENT",
+    "UHA FLU SHOT PROTOCOL AUTHORIZATION 2017-2018",
+    "RESP - VENTILATOR SETTINGS",
+    "TRANSFERRIN SATURATION",
+    "TRIGLYCERIDES, SERUM / PLASMA",
+    "JACKSON PRATT DRAIN",
+    "RASS SEDATION ASSESSMENT",
+    "PLATELET COUNT",
+    "DIET CLEAR LIQUID",
+    "CDU VITAL SIGNS",
+    "ISTAT CREATININE",
+    "UP AD LIB",
+    "PAP SMEAR",
+    "EARLY AMBULATION",
+    "CALCIUM, SERUM/PLASMA",
+    "RISK FOR VENOUS THROMBOEMBOLISM - VTE RISK ASSESSMENT",
+    "TRANSFUSE RED BLOOD CELLS",
+    "TRANSFER PATIENT",
+    "REFERRAL TO PHYSICAL THERAPY",
+    "PLATELET APHERESIS PRODUCT",
+    "CT HEAD",
+    "TEMPERATURE THERAPY",
+    "DRAIN CARE",
+    "TURN, COUGH, DEEP BREATHE",
+    "POC ISTAT TROPONIN I",
+    "FRESH FROZEN PLASMA PRODUCT",
+    "TSH W/ REFLEX FT4",
+    "WEAN O2 THERAPY",
+    "BILIRUBIN TOTAL, SERUM/PLASMA",
+    "HBSAG",
+    "IRRIGATE TUBE/DRAIN",
+    "ISTAT CG4,VENOUS",
+    "UREA NITROGEN, SERUM/PLASMA",
+    "GAMMA - GLUTAMYL TRANSFERASE",
+    "ALBUMIN, SERUM/PLASMA",
+    "XR ABDOMEN 1V",
+    "HEPATITIS C AB IGG",
+    "ALBUMIN WITH CREATININE, URINE (RANDOM)",
+    "GLUCOSE FASTING",
+    "TREATMENT PARAMETERS 1",
+    "D/C BY ESCORT IF D/C CRITERIA IS MET",
+    "ADMIT/PLACE PATIENT",
+    "BMT PANEL 1",
+    "POPULATION HEALTH PROTOCOL AUTHORIZATION",
+    "RESP - CHEST PHYSIOTHERAPY",
+    "DISCHARGE WOUND CARE",
+    "CHOLESTEROL TOTAL, SERUM/PLASMA",
+    "ISTAT EG7, ARTERIAL",
+    "POC URINE DIPSTICK",
+    "BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLE)",
+    "DC IV",
+    "RAINBOW DRAW (FOR ED/RRT/CODE BLUE ONLY)",
+    "RESP - INTRAPULMONARY PERCUSSIVE VENTILATION",
+    "MAMMO BREAST SCREENING",
+    "RESP - UNLISTED RESPIRATORY CARE ORDER",
+    "CALCIUM IONIZED, WHOLE BLOOD",
+    "CHECK TUBE FEEDING",
+    "DIET NUTRITIONAL SUPPLEMENTS",
+    "DIET NPO AFTER MIDNIGHT"
+]
+
+PROTOCOL_NAMES = [
+    "IP GENERAL DISCHARGE ORDER SET",
+    "IP MED GENERAL ADMIT",
+    "ANE PACU ",
+    "PRE-ADMISSION/PRE-OP ORDERS",
+    "IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",
+    "RETIRED ANE PACU (OUTPATIENT)",
+    "IP/ED GENERAL TRANSFUSION",
+    "IP ICU/CCU GENERAL ADMIT",
+    "RETIRED P CTS ICU CARDIAC SURGERY POSTOP",
+    "IP ORT TOTAL JOINT REPLACEMENT",
+    "IP GEN HEPARIN INFUSION PROTOCOLS",
+    "IP SUR GENERAL ADMIT",
+    "IP INTERAGENCY DISCHARGE ORDERS",
+    "IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",
+    "IP SUBCUTANEOUS INSULIN ORDER SET",
+    "IP CAR CARDIOLOGY POST PROCEDURE - INPATIENT",
+    "IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",
+    "IP NSR ICU NEUROSURGERY POSTOPERATIVE",
+    "IP GEN INTERAGENCY DISCHARGE ORDERS",
+    "IP CAR CARDIOLOGY PRE PROCEDURE",
+    "IP ORT TRAUMA",
+    "IP NSR NEUROSURGERY ADMISSION/POST-OP",
+    "IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE",
+    "RETIRED IP CTS CARDIAC SURGERY TRANSFER",
+    "IP INSULIN CONTINUOUS IV INFUSION",
+    "INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS",
+    "IP PAI EPIDURAL ANALGESIA POSTOP",
+    "IP PSY PSYCHIATRY ADMIT",
+    "IP ICU SUR/TRAUMA  ADMIT",
+    "SHC AMB ONC BMT",
+    "ED GENERAL COMPLAINT",
+    "NURSING TRIAGE SEPSIS NURSING PROTOCOL",
+    "AMB ALG IMMUNOTHERAPY MMC",
+    "IP VAST RN POST PLACEMENT (VAST RN ONLY)",
+    "IP NEU GENERAL WARD ADMIT",
+    "IP CAR GENERAL ADMIT",
+    "AMB PED IMMUNIZATIONS MMC",
+    "ED CHEST PAIN",
+    "RETIRED IP PAMF ORTHO THA",
+    "IP CTS LUNG RESECTION POSTOP",
+    "CDU CHEST PAIN OBSERVATION",
+    "IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",
+    "IP RAD NEURORADIOLOGY PRE-PROCEDURE",
+    "ED ABDOMINAL PAIN ",
+    "IP ANE ANESTHESIA PREOP",
+    "IP CTS CARDIAC SURGERY ADMISSION",
+    "IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE)",
+    "CDU GENERAL PROTOCOL OBSERVATION",
+    "SVC ANE PACU",
+    "IP ENT HEAD AND NECK POSTOP",
+    "AMB SLEEP OFFICE VISIT",
+    "(RETIRED) IP PICC LINE PLACEMENT / EXCHANGE",
+    "IP CAR EPS/ABLATION (PRE-PROCEDURE)",
+    "CC HEME BONE MARROW BIOPSY AND ASPIRATE",
+    "OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE",
+    "OPH CATARACT PERIOPERATIVE ORDERS LEFT EYE",
+    "IP SUR GENERAL POSTOP",
+    "IP PULM RH CATH POST",
+    "AMB HM UHA SUPER SMARTSET",
+    "ED TRAUMA 97",
+    "NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL",
+    "CC RAD ONC PRE-SIMULATION",
+    "CC BMT BONE MARROW BIOPSY AND ASPIRATE",
+    "IP REN HEMODIALYSIS",
+    "IP PULM RH CATH PREOP",
+    "IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE)",
+    "AMB LIV PRE-TRANSPLANT STANFORD DEFAULTED WISH LIST ORDERS",
+    "IP RAD NEURORADIOLOGY POST-PROCEDURE",
+    "IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",
+    "ED FLOOR  HOLDING ORDERS",
+    "SVC IP GEN GENERAL ADMIT",
+    "IP CAR EPS/ABLATION (POST-PROCEDURE)",
+    "AMB HM SHC SUPER SMARTSET",
+    "IP GYN  GENERAL ADMISSION",
+    "IP MED CYSTIC FIBROSIS ADMIT",
+    "IP CTS ICU CARDIAC SURGERY POSTOP",
+    "IP SUR BARIATRIC SURGERY POSTOP",
+    "IP SUR DAY OF SURGERY ORDERS",
+    "ED ADMIT",
+    "IP GU RADICAL CYSTECTOMY POSTOP",
+    "IP CAPR CARDIOVERSION",
+    "UHA IMM ADULT",
+    "NURSING TRIAGE ABDOMINAL PAIN PROTOCOL",
+    "RETIRED IP ORT SPINAL FUSION POSTOP",
+    "IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",
+    "IP GEN BRONCHOSCOPY PRE/INTRA/POST",
+    "AMB SLEEP ATTENDINGS SLEEP STUDY",
+    "IP GEN COMFORT CARE - NON ICU UNITS",
+    "ED TIA PATHWAY",
+    "CC APHERESIS HPC-A COLLECTION",
+    "PRE-ADMISSION/PRE-OP ORDERS CV SURGERY",
+    "PEDIATRIC IMMUNIZATIONS FOR UHA",
+    "SVC IP GEN DISCHARGE",
+    "NURSING TRIAGE STROKE PROTOCOL",
+    "RETIRED IP NSR NEUROSURGERY TRANSFER ORDERS",
+    "IP ENT MAXILLOFACIAL SURGERY POSTOP",
+    "RETIRED IP NSR NEURO SPINE",
+    "SVC IP GI ENDOSCOPY PRE/POST PROCEDURE",
+    "IP PSY ASC ECT",
+    "IP PLS GENERAL POSTOP",
+    "SVC SUR PRE-ADMISSION/PRE-OP",
+    "IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",
+    "AMB ORT HAND_WRIST",
+    "IP ORT SHOULDER ELBOW ADMISSION POSTOP",
+    "IP VAS GENERAL ADMIT",
+    "IP TRS LIVER TRANSPLANT POSTOP",
+    "INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY",
+    "IP INSULIN TRANSITION OFF IV INFUSION",
+    "ED SHORTNESS OF BREATH",
+    "SHC AMB ONC LYMPHOMA ONCOLOGY",
+    "OPH OCULOPLASTICS PERIOPERATIVE ORDERS",
+    "IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",
+    "IP GU RADICAL PROSTATECTOMY POSTOP",
+    "IP LAB SPECIMEN TESTING, CSF",
+    "IP TRS KIDNEY/PANCREASE TRANSPLANT RECIPIENT PREOP",
+    "AMB HEART TRANSPLANT ANNUAL STANFORD RN ORDERS",
+    "AMB POSITIVE CARE HIV/AIDS DIAGNOSIS",
+    "AMB NEU MULTIPLE SCLEROSIS",
+    "IP NEU STROKE ADMISSION",
+    "IP VAD ICU POST OP",
+    "IP LAB SPECIMEN TESTING, BLOOD CULTURE - INFECTIOUS AGENT DETECTION",
+    "IP TRS KIDNEY AND/-OR  PANCREAS RECIPIENT POSTOP",
+    "IP VAS GENERAL POSTOP",
+    "RETIRED IP ENT GENERAL POSTOP",
+    "IP LABS CULTURES",
+    "IP REH PHASE I EPILEPSY ADMIT",
+    "IP NEU ICU STROKE ISCHEMIC/INTRACEREBRAL HEMORRHAGE ADMIT",
+    "IP GEN TUBE FEEDING",
+    "IP BMT ALLOGENEIC READMISSION",
+    "AMB REI P - FEMALE ORDER SET",
+    "ED PSYCH/DRUG PROBLEM",
+    "ED TRAUMA RADIOLOGY",
+    "IP TRS HEART TRANSPLANT POSTOP",
+    "IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",
+    "RETIRED IP GEN PACU ORDERS",
+    "CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",
+    "AMB AWCC WOUND CARE",
+    " AMB URO URODYNAMICS",
+    "CDU TIA",
+    "IP GYN MINIMALLY INVASIVE SURGERY POSTOP",
+    "IP CTS TAVR CV POSTOP",
+    "IP GU GENERAL ADMIT",
+    "SVC IP ANE ANESTHESIA PREOP",
+    "NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",
+    "AMB LUNG/HEART-LUNG PRE-EVALUATION STUDIES",
+    "IP ENT GENERAL ADMISSION",
+    "IP CTS CARDIAC SURGERY TRANSFER",
+    "AMB HEART TRANSPLANT ROUTINE STANFORD RN ORDERS",
+    "CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP)",
+    "RETIRED IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP",
+    "IP CAR CONGESTIVE HEART FAILURE",
+    "RETIRED IP PAMF ORTHO SPINE",
+    "CDU ABDOMINAL PAIN OBSERVATION",
+    "IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",
+    "IP GYN EXLAP/TAH/BSO GENERAL",
+    "IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",
+    "AMB REI P - PHYSICIAN ORDER FOR RETRIEVAL / TRANSFER PROCEDURE",
+    "OP TRANSFUSION ORDERS RECURRING",
+    "OPH RETINA PERIOPERATIVE ORDERS LEFT EYE",
+    "RETIRED OP TRANSFUSION ORDERS ONE TIME",
+    "IP GU TURP/TURBT POSTOP",
+    "SVC IP NB NEWBORN NURSERY ADMISSION",
+    "OPH RETINA PERIOPERATIVE ORDERS RIGHT EYE",
+    "RETIRED IP LAB TRANSFUSION SERVICE,  SPECIAL NEEDS",
+    "IP NSR CERVICAL AND LUMBAR FUSIONS",
+    "ED TRAUMA 99",
+    "IP SUR BREAST PROCEDURES POSTOP",
+    "AMB IGM ACUPUNCTURE WITHOUT E & M CODES",
+    "AMB REI P - IVF LAB ORDERS",
+    "AMB ORT SPINE PMR",
+    "AMB IMM ADULT",
+    "IP GEN TPN/PPN  ADULT PROTOCOL",
+    "IP ICU END OF LIFE CARE",
+    "PED ADOLESCENT WELL FEMALE UHA",
+    "IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",
+    "AMB FLU CLINIC",
+    "CDU SYNCOPE OBSERVATION",
+    "IP POST PROCEDURE ORDERS PARACENTESIS",
+    "RETIRED IP OPH RETINA POSTOP ",
+    "SVC IP OB INTRAPARTUM ADMISSION",
+    "SHC AMB INFLUENZA VACCINE MASTER",
+    "IP GEN INSULIN INFUSION ICU",
+    "SVC IP ANE OB LABOR EPIDURAL",
+    "RETIRED IP NSR NEURO SPINE TRANSFER",
+    "IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",
+    "AMB PED ADOLESCENT WELL MALE UHA",
+    "ED FEVER/NEUTROPENIA",
+    "RETIRED IP GYN LAPAROSCOPY POSTOP",
+    "IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII))",
+    "ANE ECT PACU ORDERS",
+    "IP BMT AUTOLOGOUS READMISSION ORDERS",
+    "RETIRED IP ENT - HEAD AND NECK ADMISSION-PREOP",
+    "IP RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",
+    "IP TRS HEART TRANSPLANT PREOP",
+    "IP ORT TUMOR POSTOP",
+    "IP CAR CARDIOLOGY POST PROCEDURE - OUTPATIENT",
+    "NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL",
+    "SHC TXP KIDNEY EVALUATION",
+    "RETIRED IP POST-PROCEDURE ORDERS THORACENTESIS",
+    "RETIRED IP PAMF ORTHO TKA"
+]
+
+SS_SECTION_NAMES = [
+    None,
+    " "
+    "Nursing",
+    "NURSING",
+    "General",
+    "Laboratory",
+    "Discharge Orders",
+    "LABORATORY",
+    "DISCHARGE ORDERS",
+    "Vital Signs",
+    "VITAL SIGNS",
+    "Pre-op (Day of Surgery) Orders",
+    "Notify Physician",
+    "NOTIFY PHYSICIAN",
+    "Admission/Transfer",
+    "Orders",
+    "Pre-Admission Orders",
+    "VTE Prophylaxis",
+    "Transfusion Orders",
+    "Activity",
+    "ACTIVITY",
+    "ADMISSION/TRANSFER",
+    "LABORATORY/ECG",
+    "NUTRITION",
+    "Immunizations",
+    "Nutrition",
+    "VTE PROPHYLAXIS",
+    "RESPIRATORY",
+    "Admission Orders",
+    "Consults",
+    "Respiratory",
+    "Code Status",
+    "CODE STATUS",
+    "Glucose Checks",
+    "CONSULTS",
+    "IMMUNIZATIONS",
+    "Follow Up Instructions",
+    "ORDERS",
+    "VITALS",
+    "Intra-Operative Blood Product and Transfusion Orders (By Product)",
+    "Facility Orders",
+    "PRE-OP (DAY OF SURGERY) ORDERS",
+    "IMAGING",
+    "Nursing Orders",
+    "Laboratory/ECG",
+    "Ad-hoc Orders",
+    "DISCHARGE INSTRUCTIONS",
+    "Discharge Instructions",
+    "PROCEDURES",
+    "Imaging",
+    "MEDICATIONS",
+    "Trauma 97 Initial ED Orders",
+    "After Visit Orders",
+    "PRE-ADMISSION ORDERS",
+    "Interagency Referrals",
+    "THERAPY REFERRALS",
+    "Intra-Operative Labs",
+    "Labs",
+    "Procedures",
+    "Orders - Labs, X-rays, Procedures",
+    "Other Tests",
+    "Protocol",
+    "Nursing ",
+    "Medications",
+    "Hypoglycemia Control",
+    "Insulin Adjustments ",
+    "OTHER TESTS",
+    "Monitoring",
+    "Dressing",
+    "MICROBIOLOGY TESTS",
+    "PICC Insertion/Exchange",
+    "Notify MD",
+    "Pain Medication Regimens",
+    "Inpatient Pre-op Orders",
+    "Stroke Symptoms",
+    "Vitals",
+    "GLUCOSE CHECKS",
+    "ADMISSION",
+    "OTHER LAB TESTS",
+    "Therapy Referrals ",
+    "Cultures",
+    "Home Orders",
+    "Orders (Labs,Procedures,Images,Referrals)",
+    "MONITORING",
+    "HYPOGLYCEMIA CONTROL",
+    "Transfusion",
+    "DRESSING",
+    "Trauma Radiology",
+    "TRANSFUSION",
+    "Isolation/Precautions",
+    "PICC INSERTION/EXCHANGE",
+    "Orders - (labs, imaging, referrals)",
+    "Orders - (Labs, Procedures, Injections, Images, Referrals)",
+    "LABS",
+    "Vitals/Nursing",
+    "Hypoglycemic Protocol For IV Insulin Infusion",
+    "Treatment Procedures",
+    "Post-Operative Admit Panel",
+    "Colon Cancer Screening",
+    "NURSING ORDERS",
+    "INTERAGENCY REFERRALS",
+    "Referrals",
+    "Pre Procedure ",
+    "Patient Preparation",
+    "SPECIAL NEEDS CROSSMATCH",
+    "Visit for Flu Vaccine",
+    "INSULIN ADJUSTMENTS ",
+    "INFLUENZA VACCINE ORDER PANELS",
+    "Assessment and Privileges",
+    "Standard Labs",
+    "Pre- Procedure",
+    "Rehabilitation/ Home Care",
+    "Trauma 99 Initial ED Orders",
+    "Routine Orders",
+    "DISCONTINUE PREVIOUS INSULIN/ORAL ANTIDIABETIC ORDERS",
+    "IV Fluids",
+    "Post Procedure",
+    "Tube Feeding ",
+    "Injections",
+    "Hepatitis C Screening",
+    "Vital/Nursing",
+    "PICC Line",
+    "STANDARD LABS",
+    "Precaution",
+    "Vascular Access Team Services",
+    "SPECIAL NEEDS BLOOD PRODUCTS",
+    "Diabetes Management",
+    "IV FLUIDS",
+    "INSULIN BASAL, PRANDIAL AND CORRECTIVE SUBCUTANEOUS SCALES",
+    "PRE PROCEDURE ",
+    "Post Procedure ",
+    "Precautions",
+    "Outpatient Orders",
+    "Pathology",
+    "PATIENT PREPARATION",
+    "HYPOGLYCEMIC PROTOCOL FOR IV INSULIN INFUSION",
+    "Breast Cancer Screening",
+    "Procedure Orders",
+    "BASIC BLOOD PRODUCTS",
+    "GENERAL LABS",
+    "Orders - Labs, Procedures, Referrals, etc",
+    "Wounds",
+    "Nursing PRE-PROCEDURE",
+    "Physician Charging",
+    "Medication Management",
+    "Cervical Cancer Screening",
+    "General Labs",
+    "Cytogenetics",
+    "Pre-Dialysis Laboratory",
+    "POST PROCEDURE ",
+    "Virology",
+    "ORTHO NURSING",
+    "MICROBIOLOGY",
+    "PROCEDURES/ORDERS",
+    "Trauma 95 Initial ED Orders",
+    "SPUTUM INDUCED",
+    "Flow Cytometry",
+    "Trauma",
+    "MEDICATION",
+    "FULL INTENSITY HEPARIN PROTOCOL",
+    "Nurse Communication",
+    "Nursing POST-PROCEDURE",
+    "Day of Surgery Orders",
+    "SPUTUM,EXPECTORATED",
+    "PICC LINE",
+    "LOW INTENSITY HEPARIN INFUSION PROTOCOL",
+    "Influenza Vaccine =>3 Years Old",
+    "Insulin Bolus And Infusion",
+    "PATHOLOGY",
+    "Injectible Meds",
+    "Admission",
+    "Retrieval Orders",
+    "CYTOGENETICS",
+    "Blood Products",
+    "Cardiac Event Monitor",
+    "ADMIT ORDERS",
+    "Sleep Apnea Orders",
+    "CONSULT",
+    "PRE-DIALYSIS LABORATORY",
+    "HLA Recipient Kit Request",
+    "INSULIN INFUSION HYPOGLYCEMIC PROTOCOL",
+    "PRECAUTIONS",
+    "HLA Kit Request",
+    "DME",
+    "Massive Transfusion Adult (>50 KG)",
+    "TRAUMA",
+    "Wound Care for SNF or Home Care",
+    "Fluoro Procedures",
+    "Recipient Testing PRE-TRANSPLANT",
+    "1. Delirium General  Management",
+    "ANCILLARY STUDIES",
+    "Admisison Orders",
+    "IP Insulin Continuous IV Infusion (For Cardiac Transplant)",
+    "Case Request",
+    "PRE PROCEDURE",
+    "Laboratory Phase I ",
+    "Hypoglycemia Control For Insulin Transition Off IV Infusion Protocol",
+    "PRIVILEGES",
+    "Recipient Testing",
+    "TUBE FEEDING CHOICES"
+]
+
+SS_SG_NAMES = [
+    None,
+    "Nursing",
+    "Vital Signs",
+    "Orders",
+    "Notify Physician",
+    "Admission",
+    "Activity",
+    "Chemistry",
+    "Hematology",
+    "Nutrition",
+    "Pre-procedure",
+    "Notify MD",
+    "Labs",
+    "Code Status",
+    "VTE Prophylaxis",
+    "Respiratory",
+    "Discharge",
+    "Consults",
+    "Intra/Post Procedure",
+    "Laboratory",
+    "Pre-Admission Lab Studies",
+    "Discharge Patient",
+    "Post Procedure",
+    "Monitor",
+    "Instructions",
+    "Protocol Labs",
+    "Order Blood Products  (NOT AN ORDER TO TRANFUSE)",
+    "Monitoring",
+    "Discharge Instructions",
+    "Admission Orders",
+    "Transfuse Blood Products  (DOES NOT ORDER PRODUCT)",
+    "Glucose Checks",
+    "Interventions",
+    "Assessment",
+    "ADMINISTRATION MMC",
+    "IMM/INJ PED ORDERS MMC",
+    "Intervention",
+    "Procedures",
+    "ECG",
+    "Therapy Referrals",
+    "Type and Screen",
+    "Day of Surgery IV insertion w PRN lidocaine and TKO fluid rate",
+    "POC",
+    "Day of Surgery Low Risk DVT/PE Prophylaxis: (<5% risk of DVT: Patient <40 years old and minor surgery and no additional risk factors)",
+    "Xray",
+    "Interagency Referrals",
+    "Hypoglycemic Treatment",
+    "Prepare and Send Blood Products",
+    "Moderate Risk DVT/PE Prophylaxis (10-20% risk of DVT: age 40-60 years old with no additional risk factors or minor surgery in patients with additional risk factors)",
+    "Low Risk DVT/PE Prophylaxis(<5% risk of DVT)Patient <40 yrs old and minor surgery and no additional risk factors.",
+    "Dressing",
+    "One Time Labs - Prior to Heparin",
+    "Pre-Admission Ancillary Studies",
+    "Insulin Adjustments",
+    "TRAUMA 97",
+    "IV Fluids",
+    "Intra-Operative Labs",
+    "Day of Surgery Nursing",
+    "Point of Care (POC) Labs",
+    "Medication Side Effects Management",
+    "Order Blood Products",
+    "Microbiology",
+    "Immunizations",
+    "Day of Surgery Nursing Orders",
+    "Consult",
+    "Precautions",
+    "Notify Pain Service",
+    "Order Blood Products (NOT AN ORDER TO TRANSFUSE)",
+    "Day of Surgery Lab Studies",
+    "Lab",
+    "Transfuse Blood Products",
+    "Pre-Admission Type and Screen",
+    "Transfuse Blood Products (DOES NOT ORDER PRODUCT)",
+    "ADULT IMMUNIZATIONS FOR UHA DEPARTMENTS",
+    "Facility Nursing",
+    "Referrals",
+    "Glucose PRN",
+    "Nursing Orders",
+    "Basic Microbiology",
+    "Urine Studies",
+    "VTE Prophylaxis Regimen",
+    "ICU Hemodynamic Monitoring",
+    "PICC Nursing Communication",
+    "Vital Sign",
+    "Day of Surgery Skin Preparation",
+    "AMB PRO SLEEP FOR ATTENDINGS ONLY",
+    "Nursing Communications",
+    "Pre-transplant Labs for Recipient",
+    "BMT Donor Tests",
+    "IN-LAB SLEEP STUDY (AGE 19 OR OLDER)",
+    "Discharge Orders",
+    "Moderate Risk DVT/PE Prophylaxis (10-20% risk of DVT: Patient 40-60 years old with no additional risk factors or minor surgery in patients with additional risk factors)",
+    "Hemodialysis",
+    "LABS - Single Orders (Stanford Draw)",
+    "ADMIT",
+    "Labs POC",
+    "Vital signs",
+    "Laboratory/ECG",
+    "Vitals/Nursing",
+    "Low Risk DVT/PE Prophylaxis: (<5% risk of DVT: Patient <40 years old and minor surgery and no additional risk factors)",
+    "Pharmacologic VTE Prophyalxis Contraindicated",
+    "Pre-Admission Urine Studies",
+    "Pre-Admission Chlorhexidine Orders",
+    "High Risk DVT/PE Prophylaxis (20-40% risk of DVT: surgery in patients > 60 years old or age >40 years with additional risk factors)",
+    "Basic Chemistry/Hematology",
+    "Deep Vein Thrombosis Prevention",
+    "Home Nursing",
+    "UHA AMB PRO PED WCC 3-11 YEARS",
+    "Precaution",
+    "Isolation/Precautions",
+    "Daily Labs",
+    "High Risk DVT/PE Prophylaxis: (20-40% risk of DVT: Surgery in patients >60 years old or age >40 years with additional risk factors)",
+    "Saline Lock",
+    "Hypoglycemic Protocol: BG less than 70 mg/dL",
+    "PRO PED ADOL WCC MMC",
+    "Day of Surgery DVT Prophylaxis",
+    "Neurological Checks",
+    "Pre Procedure",
+    "PICC Insertion/Exchange",
+    "Wean Ventilator",
+    "Hypoglycemia Control",
+    "Drains and Tubes",
+    "General Nursing",
+    "Cardiac markers",
+    "Neurovascular",
+    "Urine Output Protocol",
+    "Post-Operative Panel (Trauma)",
+    "Tube Feeding Formula",
+    "PICC Line",
+    "Day of Surgery Deep Vein Thrombosis Prevention",
+    "Pre-Operative Skin Preparation ",
+    "Debridement",
+    " Procedures",
+    "LAB - Standard Orders",
+    "PRO URODYNAMICS",
+    "During Visit Orders",
+    "Imaging",
+    "Bone Marrow Biopsy, Aspirate Testing",
+    "Flow Cytometry",
+    "INFLUENZA VACCINE",
+    "ADULT IMMUNIZATIONS",
+    "Upper Abdominal Pain",
+    "Virology",
+    "Day of Surgery Antibiotics",
+    "Lab Work",
+    "Order Basic Blood Products",
+    "Pre-Operative Skin Preparation",
+    "Rehabilitation/Home Care",
+    "General Labs",
+    "TRAUMA RADIOLOGY",
+    "Oral Care",
+    "Patient Preparation",
+    "Nursing Interventions",
+    "Nursing ",
+    "WARFARIN BY PHARMACY PROTOCOL PT/INR/LFT CHECK MARKING",
+    "Cardiac Monitor",
+    "Notify Pain Service Physician",
+    "PRO ACUPUNCTURE",
+    "Consents",
+    "X-ray",
+    "Cardiology",
+    "Vital Signs ",
+    "Order, Send, and Transfuse Blood Products",
+    "Transfusion Order",
+    "Screening Colonoscopy",
+    "LABS ROUTINE/HIV - STANFORD LABS",
+    "Wound",
+    "AMB IMM/INJ PED ORDERS ",
+    "Labs:Fluid",
+    "AMB PRO SLEEP ORD",
+    "Hepatitis C Screening",
+    "Standard Transplant Evaluation Tests",
+    "Vitals",
+    "Rehabilitation /SNF ",
+    "XRay",
+    "After Visit Orders",
+    "Nursing Assessment",
+    "Nursing Routine",
+    "Discharge Instrcutions",
+    "EVALUATION LAB WORK PHASE I",
+    "Orthopat",
+    "Sedation Assessment",
+    "PROCEDURES",
+    "Basic Microbiology - Always order BOTH tests",
+    "Trauma Radiology",
+    "Tetanus, Diptheria and Pertusis Vaccines",
+    "Nursing Triage Order Set Initiated",
+    "OUT-OF-CENTER TESTING (PORTABLE MONITORING)",
+    "Extended Microbiology",
+    "Transfer",
+    "Outpatient Orders",
+    "Pre-Procedure (NSG)",
+    "Hypoglycemic Protocol: (BG<70)",
+    "ED Trauma 99",
+    "Nursing Protocols",
+    "IMAGING",
+    "Day of Surgery Pharmacologic VTE Prophyalxis Contraindicated",
+    "Respiratory/Ventilator",
+    "Collection",
+    "Labs: Fluid"
+]
+
+STAND_INTERVALS = [
+    None,
+    "LAB ONE TIME",
+    "ONCE",
+    "CONTINUOUS",
+    "PRN",
+    "PROCEDURE ONCE",
+    "TODAY",
+    "QAM LAB",
+    "TID",
+    "AS SPECIFIED",
+    "EVERY 4 HOURS",
+    "INAM LAB",
+    "DAILY",
+    "EVERY HOUR",
+    "EVERY 6 HOURS",
+    "TOMORROW  ",
+    "EVERY 2 HOURS",
+    "LAB ADD ON",
+    "EVERY 15 MIN",
+    "EVERY 8 HOURS",
+    "EVERY 12 HOURS",
+    "Transfuse 1 unit",
+    "4 TIMES DAILY BEFORE MEALS & AT BEDTIME",
+    "EVERY 2 HOURS WHILE AWAKE (ORDERS)",
+    "Transfuse 2 units",
+    "VS PER ED POLICY",
+    "THREE TIMES A WEEK INFO",
+    "QPM LAB",
+    "Per Treatment Parameters - Manual Release",
+    "QAM RAD",
+    "INPM LAB",
+    "FOUR TIMES A WEEK INFO",
+    "FIVE TIMES A WEEK INFO",
+    "Once a Week",
+    "PER PROTOCOL",
+    "Once Every 4 Weeks",
+    "Q4HR-PRN",
+    "Per Treatment Parameters",
+    "EVERY 7 DAYS",
+    "EVERY MONDAY",
+    "Lab One Time",
+    "Once a Day",
+    "Every 1 Week",
+    "Q4H",
+    "EVERY MO,TH",
+    "Every 1 Week - Manual Release",
+    "Every 4 Weeks",
+    "Every 4 Weeks - Manual Release",
+    "EVERY 4 HOURS WHILE AWAKE",
+    "EVERY MWF",
+    "TWO TIMES A WEEK INFO",
+    "Q6H",
+    "TWICE A DAY",
+    "2 TIMES DAILY",
+    "EVERY 30 MIN",
+    "2 TIMES A DAY",
+    "Q12H",
+    "Once Every 12 Weeks",
+    "EVERY OTHER DAY AT 0600",
+    "Q8H",
+    "Once Every 3 Weeks",
+    "Every 12 Weeks",
+    "Three Times a Week",
+    "Q6 PRN",
+    "Once Every 2 Weeks",
+    "Every 12 Weeks - Manual Release",
+    "LAB ONE TIME (IN 15 MIN)",
+    "Five Times a Week",
+    "EVERY 4 DAYS",
+    "Every 2 Weeks - Manual Release",
+    "SEVEN TIMES A WEEK INFO",
+    "2 Times a Week",
+    "4 TIMES DAILY",
+    "DAILY INFO",
+    "TID CHECKS",
+    "EVERY MORNING",
+    "Once",
+    "Every 3 Weeks - Manual Release",
+    "EVERY MON, WED & FRI",
+    "EVERY OTHER DAY",
+    "Every 6 Months",
+    "EVERY TUE THUR SUN",
+    "Daily",
+    "EVERY MON WED FRI SAT",
+    "Transfuse 4 units",
+    "3 Times a Week",
+    "Four Times a Week",
+    "Every 8 Weeks",
+    "Every 2 Weeks",
+    "SIX TIMES A WEEK INFO",
+    "Today, 2 Months, 6 Months",
+    "EVERY 5 MINUTES",
+    "Every 6 MONTHS - Manual Release",
+    "Tomorrow",
+    "WEEKLY",
+    "EVERY SHIFT",
+    "Once Every 8 Weeks",
+    "Every 6 MONTHS",
+    "AT BEDTIME",
+    "EVERY THIRD DAY",
+    "Q4 HR W/A (0900,1300,1700,2100)",
+    "Q4 HR W/A (0800,1200,1600,2000)",
+    "QNOC",
+    "MONTHLY",
+    "EVERY 3 HOURS",
+    "Twice a Week",
+    "EVERY 1 WEEK",
+    "3 TIMES PER DAY",
+    "Q2HR-PRN",
+    "INAM RAD",
+    "Two Times a Week",
+    "Twice a Week - Manual Release",
+    "Once a Day - Manual Release",
+    "WEEKLY INFO",
+    "Continuous",
+    "BID 0800,1800",
+    "Every 8 Weeks - Manual Release",
+    "3 TIMES DAILY",
+    "BID + PRN",
+    "IN AM",
+    "EVERY SUNDAY",
+    "SIX TIMES DAILY",
+    "Q6 HR W/A 0900,1500,2100",
+    "EVERY OTHER WEDNESDAY",
+    "5 TIMES DAILY BEFORE MEALS & AT BEDTIME AND 0200",
+    "EVERY 6 HOURS WHILE AWAKE",
+    "Q0200",
+    "2 TIMES DAILY (0900,1900)",
+    "Three Times a Week - Manual Release",
+    "Q2H",
+    "DAILY AND AS NEEDED",
+    "EVERY WEDNESDAY",
+    "RESP PRN",
+    "Once Every 6 Weeks",
+    "EVERY THURSDAY",
+    "WITH VITAL SIGNS",
+    "Q4 HR W/A PRN",
+    "2 TIMES DAILY (0800,1800)",
+    "QID-PRN",
+    "Six Times a Week",
+    "Seven Times a Week",
+    "Every Weekday",
+    "EVERY TUESDAY",
+    "FREE WATER FOR TUBE FEEDING TID",
+    "Blood - Once",
+    "Every 3 Weeks",
+    "Today",
+    "Transfuse 6 units",
+    "BID-PRN",
+    "Every 6 Weeks - Manual Release",
+    "Q1H",
+    "TWICE A DAY INFO",
+    "QID",
+    "Every 2 Hours While Awake",
+    "EVERY MON & FRI",
+    "Transfusion",
+    "EVERY THIRD DAY AT 0600",
+    "Transfuse 3 units",
+    "Every other day at 0600",
+    "QAM LAB (0600)",
+    "EVERY 2 WEEKS",
+    "Q30 Min",
+    "EVERY 72 HOURS",
+    "BID 0900,1900",
+    "Every 6 Weeks",
+    "4 Times a Week",
+    "Q4 HR W/A (0600,1000,1400,1800)",
+    "ONE TIME",
+    "EVERY 10 MIN ",
+    "3 TIMES DAILY BEFORE MEALS",
+    "EVERY FRIDAY",
+    "Transfuse 8 units",
+    "TID (0000, 0800, 1600)",
+    "EVERY MON AND THUR",
+    "BID",
+    "EVERY TUE THUR SAT",
+    "Once Every 9 Weeks",
+    "2 TIMES DAILY AFTER MEALS",
+    "Weekly",
+    "THREE TIMES A WEEK",
+    "4 TIMES DAILY AND AS NEEDED",
+    "EVERY MON & THU",
+    "Every 72 hrs",
+    "Twice A Day",
+    "Twice a Day",
+    "Q6 HR W/A 0800,1400,2000",
+    "FIVE TIMES A WEEK",
+    "TID-PRN",
+    "FOUR TIMES A WEEK",
+    "DAILY (0800)",
+    "Today, 2 Months, 6 Months Manual Release",
+    "Q15 Min",
+    "EVERY 8 HOURS (0600, 1400, 2200)",
+    "Q3H",
+    "Q Week",
+    "EVERY HOUR (0100, 0200, ETC)",
+    "Every 9 Weeks",
+    "EVERY TUE, THU & SAT",
+    "EVERY 4 HOURS AND AS NEEDED",
+    "EVERY SUNDAY (2000) "
+]
+
+
+class TestSTARROrderProcConversion(DBTestCase):
+    TEST_DATA_SIZE = 2 * len(ORDER_TYPES)    # at least 2 rows per med route combined with inpatient vs outpatient
+
+    ORDER_PROC_HEADER = ['order_proc_id_coded', 'jc_uid', 'pat_enc_csn_id_coded', 'order_type', 'proc_id', 'proc_code',
+                         'description', 'order_time_jittered', 'ordering_mode', 'stand_interval',
+                         'order_time_jittered_utc']
+    PROC_ORDERSET_HEADER = ['order_proc_id_coded', 'protocol_id', 'protocol_name', 'ss_section_id', 'ss_section_name',
+                            'ss_sg_key', 'ss_sg_name']
+
+    test_data = []
+    orderset_data = []
+    clinical_items = {}
+    expected_data = []
+    expected_orderset_data = []
+
+    test_data_csv = tempfile.gettempdir() + '/test_starr_order_proc_dummy_data.csv'
+    orderset_data_csv = tempfile.gettempdir() + '/test_starr_order_proc_orderset_data.csv'
+
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self)
+
+        log.info("Sourcing from BigQuery DB")
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+
+        self.converter = STARROrderProcConversion.STARROrderProcConversion()  # Instance to test on
+        self.bqConn = self.converter.bqConn
+        self.starrUtil = STARRUtil.StarrCommonUtils(self.converter.bqClient)
+
+        # point the converter to dummy source table
+        STARROrderProcConversion.SOURCE_TABLE = TEST_SOURCE_TABLE
+        STARROrderProcConversion.ORDERSET_TABLE = TEST_ORDERSET_TABLE
+        STARROrderProcConversion.TARGET_DATASET_ID = TEST_DEST_DATASET
+
+        log.warn("Removing test tables, if they exist: {} and {}".format(TEST_SOURCE_TABLE, TEST_ORDERSET_TABLE))
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_SOURCE_TABLE))
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_ORDERSET_TABLE))
+
+    def generate_test_and_expected_data(self, test_data_size):
+        self.generate_orderproc_and_orderset_data(test_data_size)
+
+        # need to sort data to use the same clinical_item.descriptions as the duplicate clinical_items are not stored
+        self.test_data.sort(key=lambda tup: (tup[0], tup[1], tup[2], tup[3]))
+        self.orderset_data.sort(key=lambda row: row[0])
+
+        # join test order_proc and proc_orderset data to create a test row
+        joined_test_data = self.left_join_orderproc_and_orderset_data()
+
+        for joined_test_data_row in joined_test_data:
+            self.generate_expected_data_rows(joined_test_data_row[0], joined_test_data_row[1])
+
+        # pi.external_id desc, pi.patient_id, pi.encounter_id, ci.external_id desc
+        self.expected_data.sort(key=lambda tup: (-tup[0], tup[1], tup[2], -tup[4]))
+
+        # pi.external_id, ci.external_id, cic.description, ci.name, ci.description
+        self.expected_orderset_data.sort(key=lambda tup: (tup[0], tup[1], tup[2], tup[3], tup[4]))
+
+    def left_join_orderproc_and_orderset_data(self):
+        joined_test_data = []
+        for test_data_row in self.test_data:
+            if self.ignore_row(test_data_row[9]):
+                continue
+
+            found_join_row = False
+            for test_orderset_row in self.orderset_data:
+                if test_data_row[0] == test_orderset_row[0]:
+                    found_join_row = True
+                    joined_test_data.append((test_data_row, test_orderset_row))
+            if not found_join_row:
+                joined_test_data.append((test_data_row, None))
+        return joined_test_data
+
+    def generate_orderproc_and_orderset_data(self, test_data_size):
+        seen_test_data_rows = set()
+        while len(self.test_data) < test_data_size:
+            curr_row = len(self.test_data)
+            patient_id = 'JC' + format(curr_row, '06')
+
+            test_data_row = self.generate_test_data_row(curr_row, patient_id)
+            self.test_data.append(test_data_row)
+
+            # should generate at most 1 row of proc_orderset per order_proc row (tested code does LEFT OUTER JOIN)
+            should_generate_orderset_row = random.randint(0, 10)    # 10 times more likely to generate than not
+            if should_generate_orderset_row:
+                orderset_row = self.generate_orderset_row(test_data_row[0])
+                self.orderset_data.append(orderset_row)
+
+    @staticmethod
+    def ignore_row(stand_interval):
+        # process only rows where op.stand_interval not like '%PRN'
+        return stand_interval is not None and stand_interval.endswith('PRN')
+
+    @staticmethod
+    def generate_test_data_row(curr_row, patient_id):
+        proc_id = random.randint(0, len(PROC_CODES) - 1)
+        order_time_jittered = random.randint(int(time.mktime((datetime.now() - timedelta(days=14)).timetuple())), int(time.time()))
+        return (
+            curr_row,  # order_proc_id_coded
+            patient_id,
+            random.randint(0, curr_row // 2),  # pat_enc_csn_id_coded - want some of them to be repeated
+            ORDER_TYPES[random.randint(0, len(ORDER_TYPES) - 1)],
+            proc_id,
+            PROC_CODES[proc_id],
+            DESCRIPTIONS[random.randint(0, len(DESCRIPTIONS) - 1)],
+            datetime.fromtimestamp(order_time_jittered),
+            ORDERING_MODES[random.randint(0, len(ORDERING_MODES) - 1)],  # ordering_modes
+            STAND_INTERVALS[random.randint(0, len(STAND_INTERVALS) - 1)],
+            datetime.fromtimestamp(order_time_jittered, tz=pytz.UTC),
+        )
+
+    @staticmethod
+    def generate_orderset_row(order_proc_id_coded):
+        protocol_id = random.randint(1, len(PROTOCOL_NAMES))
+        ss_section_id = random.randint(0, len(SS_SECTION_NAMES) - 1)
+        ss_sg_key = random.randint(0, len(SS_SG_NAMES) - 1)
+
+        return (
+            order_proc_id_coded,  # to match the given order_proc_row
+            -protocol_id,
+            PROTOCOL_NAMES[protocol_id - 1],
+            ss_section_id,
+            SS_SECTION_NAMES[ss_section_id],
+            str(ss_sg_key),
+            SS_SG_NAMES[ss_sg_key]
+        )
+
+    def generate_expected_data_rows(self, row, orderset_row):
+        cic_description = "{} ({})".format(row[3], row[8])
+        ci_description = row[6]
+        proc_code = row[5]
+
+        ci_key = (TEST_SOURCE_TABLE, cic_description, proc_code)
+
+        if ci_key not in self.clinical_items:
+            # TODO for now put just the description.
+            #      We might need also cic_description, external_id = proc_id, name = proc_code.
+            self.clinical_items[ci_key] = ci_description
+
+        ci_description = self.clinical_items[ci_key]
+
+        expected_row = (
+            row[0],                                                                             # external_id
+            self.starrUtil.convertPatIdToSTRIDE(row[1]),                                        # patient_id
+            row[2],                                                                             # encounter_id
+            cic_description,                                                                    # cic_description
+            row[4],                                                                             # ci_external_id
+            proc_code,                                                                          # ci_name
+            ci_description,                                                                     # ci_description
+            row[7],                                                                             # pi_item_date
+            row[10].replace(tzinfo=pytz.UTC),                                                   # pi.item_date_utc
+        )
+
+        if expected_row not in self.expected_data:
+            self.expected_data.append(expected_row)
+
+        if orderset_row is not None:
+            # generate expected data for orderset - in %item_collection% tables
+            expected_orderset_row = (
+                row[0],             # pi_external_id
+                row[4],
+                cic_description,
+                proc_code,          # ci_name
+                ci_description,
+                orderset_row[1],    # protocol_id
+                orderset_row[2],    # protocol_name
+                orderset_row[4],    # ss_section_name
+                orderset_row[6]     # ss_sg_name
+            )
+            self.expected_orderset_data.append(expected_orderset_row)
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        self.test_data[:] = []
+        self.orderset_data[:] = []
+        self.expected_data[:] = []
+        self.expected_orderset_data[:] = []
+        self.clinical_items.clear()
+
+        self.starrUtil.remove_file(self.test_data_csv)
+        self.starrUtil.remove_file(self.orderset_data_csv)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DELETE FROM {}.patient_item_collection_link WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.item_collection_item WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.item_collection WHERE true;'.format(TEST_DEST_DATASET))
+
+        bq_cursor.execute('DELETE FROM {}.patient_item WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.clinical_item WHERE true;'.format(TEST_DEST_DATASET))
+        bq_cursor.execute('DELETE FROM {}.clinical_item_category WHERE true;'.format(TEST_DEST_DATASET))
+
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_SOURCE_TABLE))
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_ORDERSET_TABLE))
+
+        DBTestCase.tearDown(self)
+
+    def test_data_conversion(self):
+        log.info("Generating test source data")
+
+        self.generate_test_and_expected_data(self.TEST_DATA_SIZE)
+
+        # upload proc_orderset
+        self.starrUtil.dump_test_data_to_csv(self.PROC_ORDERSET_HEADER, self.orderset_data, self.orderset_data_csv)
+        self.starrUtil.upload_csv_to_bigquery('starr_datalake2018', 'proc_orderset', TEST_DEST_DATASET,
+                                              'starr_proc_orderset', self.orderset_data_csv, self.PROC_ORDERSET_HEADER)
+
+        # upload order_proc
+        self.starrUtil.dump_test_data_to_csv(self.ORDER_PROC_HEADER, self.test_data, self.test_data_csv)
+        self.starrUtil.upload_csv_to_bigquery('starr_datalake2018', 'order_proc', TEST_DEST_DATASET,
+                                              'starr_order_proc', self.test_data_csv, self.ORDER_PROC_HEADER)
+
+        log.debug("Run the conversion process...")
+        # temp_dir = tempfile.gettempdir()
+        # self.converter.convertAndUpload(tempDir=temp_dir, target_dataset_id=TEST_DEST_DATASET, removeCsvs=True)
+        self.converter.main(["test_order_proc"])
+
+        # Just query back for the same data, de-normalizing the data back to a general table
+        test_query = \
+            """
+            select
+                pi.external_id as pi_external_id,
+                pi.patient_id,
+                pi.encounter_id,
+                cic.description as cic_description,
+                ci.external_id as ci_external_id,
+                ci.name,
+                ci.description as ci_description,
+                pi.item_date,
+                pi.item_date_utc
+            from
+                {}.patient_item as pi,
+                {}.clinical_item as ci,
+                {}.clinical_item_category as cic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = '{}'
+            order by
+                pi.external_id desc, pi.patient_id, pi.encounter_id, ci.external_id desc
+            """.format(TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute(test_query)
+        # remove timezone info in pi.item_date from coming from bigquery - we're storing datetime without timezone
+        actual_data = [tuple(row.values())[:7] + (list(row.values())[7].replace(tzinfo=None), list(row.values())[8],) for row in bq_cursor.fetchall()]
+
+        log.debug('actual data: {}'.format(actual_data))
+        log.debug('expected data: {}'.format(self.expected_data))
+        self.assertEqualTable(self.expected_data, actual_data)
+
+        # Query for orderset links
+        test_orderset_query = \
+            """
+            select
+                pi.external_id as pi_external_id,
+                ci.external_id as ci_external_id,
+                cic.description as cic_description,
+                ci.name as ci_name,
+                ci.description,
+                ic.external_id,
+                ic.name,
+                ic.section,
+                ic.subgroup
+            from
+                {}.patient_item as pi,
+                {}.clinical_item as ci,
+                {}.clinical_item_category as cic,
+                {}.patient_item_collection_link as picl,
+                {}.item_collection_item as ici,
+                {}.item_collection as ic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = '{}' and
+                pi.patient_item_id = picl.patient_item_id and
+                picl.item_collection_item_id = ici.item_collection_item_id and
+                ici.item_collection_id = ic.item_collection_id
+            order by
+                pi.external_id, ci.external_id, cic.description, ci.name, ci.description
+            """.format(TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET,
+                       TEST_DEST_DATASET, TEST_SOURCE_TABLE)
+
+        bq_cursor.execute(test_orderset_query)
+        actual_orderset_data = [list(row.values()) for row in bq_cursor.fetchall()]
+
+        log.debug('actual orderset data: {}'.format(actual_orderset_data))
+        log.debug('expected orderset data: {}'.format(self.expected_orderset_data))
+        self.assertEqualTable(self.expected_orderset_data, actual_orderset_data)
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    test_suite = unittest.TestSuite()
+    test_suite.addTest(unittest.makeSuite(TestSTARROrderProcConversion))
+
+    return test_suite
+
+
+if __name__ == "__main__":
+    log.setLevel(logging.INFO)  # without this no logs are printed
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/starr_conv/test/TestSTARRTreatmentTeamConversion.py b/medinfo/dataconversion/starr_conv/test/TestSTARRTreatmentTeamConversion.py
new file mode 100644
index 00000000..57fef223
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/test/TestSTARRTreatmentTeamConversion.py
@@ -0,0 +1,726 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+import logging
+import pytz
+import random
+import tempfile;
+
+from datetime import datetime
+
+import unittest
+
+from parameterized import parameterized
+from medinfo.dataconversion.test.Const import RUNNER_VERBOSITY
+from medinfo.dataconversion.Util import log
+
+from medinfo.db.test.Util import DBTestCase
+
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel
+
+from medinfo.dataconversion.starr_conv import STARRTreatmentTeamConversion
+from medinfo.dataconversion.starr_conv import STARRUtil
+
+TEST_SOURCE_TABLE = 'test_dataset.starr_treatment_team'
+TEST_DEST_DATASET = 'test_dataset'
+
+NAME_TO_ACRONYM = {
+    'Intern': 'I',
+    'Cross Cover Intern': 'CCI',
+    'Wound/Ostomy/Continence RN': 'WR',
+    'Primary Intern': 'PI',
+    'Primary Resident': 'PR',
+    'Cross Cover Attending': 'CCA',
+    'Student Nurse': 'SN',
+    'Clinical Pharmacist': 'CP',
+    'Medical Assistant': 'MA',
+    'Licensed Vocational Nurse': 'LVN',
+    'Clinical Nurse Specialist': 'CNS',
+    'Cross Cover Fellow': 'CCF',
+    'ED Unit Secretary': 'EUS',
+    'Primary Fellow': 'PF',
+    'Cross Cover Resident': 'CCR',
+    'Consulting Attending': 'CA',
+    'Consulting Fellow': 'CF',
+    'Spiritual Care': 'SC',
+    'Consulting Med Student': 'CMS',
+    'Primary Advanced Practice Provider': 'PAPP',
+    'Physician Assistant': 'PA',
+    'Consulting Resident': 'CR',
+    'Nurse Coordinator': 'NC',
+    'Fellow': 'F',
+    'Consulting Intern': 'CI',
+    'Co-Attending': 'C',
+    'Surgeon': 'S',
+    'Primary Med Student': 'PMS',
+    'ED Tech': 'ET',
+    'Resident': 'R',
+    'Chief Resident': 'CR',
+    'Medical Student': 'MS',
+    'Primary Medical Student': 'PMS',
+    'Pulmonologist': 'P',
+    'Pediatrician': 'P',
+    'Hematologist': 'H',
+    'Primary Care Physician': 'PCP',
+    'Primary Physician Assistant': 'PPA',
+    'Cardiologist': 'C',
+    'Triage Nurse': 'TN',
+    'Transplant Social Worker': 'TSW',
+    'Additional Communicating Provider': 'ACP',
+    'ED Registrar': 'ER',
+    'Gastroenterologist': 'G',
+    'Medical Oncologist': 'MO',
+    'Primary Sub-Intern': 'PS',
+    'Lactation Consultant': 'LC',
+    'Trauma Attending': 'TA',
+    'Post-Transplant Nurse': 'PN',
+    'Cross Cover Advanced Practice Provider ': 'CCAPP',
+    'Internist': 'I',
+    'Nephrologist': 'N',
+    'Research Coordinator': 'RC',
+    'Infectious Disease': 'ID',
+    'Trauma Resident ': 'TR',
+    'Primary Sub-intern': 'PS',
+    'Neurologist': 'N',
+    'Transplant Surgeon': 'TS',
+    'Consulting Medical Oncologist': 'CMO',
+    'Family Practitioner': 'FP',
+    'Dermatologist': 'D',
+    'Aging Adult Services Coordinator': 'AASC',
+    'Transplant Nephrologist': 'TN',
+    'Post-Transplant Nephrologist': 'PN',
+    'Consulting Surgical Oncologist': 'CSO',
+    'Surgical Oncologist': 'SO',
+    'Nursery Nurse': 'NN',
+    'Transplant Pharmacist': 'TP',
+    'Psychologist': 'P',
+    'Restorative Aide': 'RA',
+    'Child Life Specialist': 'CLS',
+    'Consulting Hematologist': 'CH',
+    'Endocrinologist': 'E',
+    'Electrophysiologist': 'E',
+    'Anesthesiologist': 'A',
+    'Transplant Dietitian': 'TD',
+    'Urologist': 'U',
+    'stop0': 's',
+    'Radiation Oncologist': 'RO',
+    'Referring Provider': 'RP',
+    'Activity Therapist': 'AT',
+    'Transplant Pulmonologist': 'TP',
+    'Obstetrician': 'O',
+    'Heart Failure Cardiologist': 'HFC',
+    'Post RN Coordinator': 'PRC',
+    'Delivery Assist': 'DA',
+    'Referring Nephrologist': 'RN',
+    'Hepatologist': 'H',
+    'Case Manager': 'CM',
+    'Primary Team': 'PT',
+    'Social Worker': 'SW',
+    'Care Coordinator': 'CC',
+    'Registered Nurse': 'RN',
+    'Speech Therapist': 'ST',
+    'Nursing Assistant': 'NA',
+    'Clinical Dietitian': 'CD',
+    'Consulting Service': 'CS',
+    'Emergency Resident': 'ER',
+    'Nurse Practitioner': 'NP',
+    'Physical Therapist': 'PT',
+    'Occupational Therapist': 'OT',
+    'Physical Therapist Assistant': 'PTA',
+    'Respiratory Care Practitioner': 'RCP',
+    'Independent Living Donor Advocate': 'ILDA',
+    'Pre RN Coordinator': 'PRC',
+    'Dietician Intern': 'DI',
+    'Survivorship Provider': 'SP',
+    'Consulting Reconstructive Surgeon': 'CRS',
+    'Referring Pulmonologist': 'RP'
+}
+
+PROV_TO_CLEAN_ACRONYM = {
+    'SMITH, JOHN': ('', ''),
+    'SMITH, JANE': ('', ''),
+    'DOE, JOHN': ('', ''),
+    'DOE, JANE': ('', ''),
+    'CON CARDIOLOGY': ('CON CARDIOLOGY', 'CC'),
+    'CON MEDICINE': ('CON MEDICINE', 'CM'),
+    'CON PULMONARY': ('CON PULMONARY', 'CP'),
+    'CON NEURO ICU': ('CON NEURO ICU', 'CNI'),
+    'CON CARDIOL ARRHYTHMIA': ('CON CARDIOL ARRHYTHMIA', 'CCA'),
+    'CON NEURO EPILEPSY CONSULT': ('CON NEURO EPILEPSY CONSULT', 'CNEC'),
+    'CON NEPHROLOGY JAMISON': ('CON NEPHROLOGY JAMISON', 'CNJ'),
+    'CON CARDIAC SURGERY': ('CON CARDIAC SURGERY', 'CCS'),
+    'TT CVICU TEAM 2 - SPECTRAS 1-2345, 2-3456': ('TT CVICU TEAM 2', 'TCT2'),
+    'CON PAIN': ('CON PAIN', 'CP'),
+    'CON ONCOLOGY': ('CON ONCOLOGY', 'CO'),
+    'CON NEUROLOGY GENERAL': ('CON NEUROLOGY GENERAL', 'CNG'),
+    'CON NEURO STROKE': ('CON NEURO STROKE', 'CNS'),
+    'TT NEUROSURGERY C SPINE FLOOR, PGR 12345': ('TT NEUROSURGERY C SPINE FLOOR', 'TNCSF'),
+    'CON NEPHROLOGY TRANSPLANT': ('CON NEPHROLOGY TRANSPLANT', 'CNT'),
+    'TT ACUTE CARE SURGERY TRAUMA, PGR 12345': ('TT ACUTE CARE SURGERY TRAUMA', 'TACST'),
+    'CON INTERNAL MEDICINE CONSULT': ('CON INTERNAL MEDICINE CONSULT', 'CIMC'),
+    'CON ICU': ('CON ICU', 'CI'),
+    'CON ALLERGY IMMUNOLOGY CONSULT': ('CON ALLERGY IMMUNOLOGY CONSULT', 'CAIC'),
+    'TT CVICU TEAM 1 - SPECTRAS 1-2345, 2-3456': ('TT CVICU TEAM 1', 'TCT1'),
+    'TT NEUROSURGERY A VASCULAR FLOOR, PGR 12345': ('TT NEUROSURGERY A VASCULAR FLOOR', 'TNAVF'),
+    'CON HEMATOLOGY': ('CON HEMATOLOGY', 'CH'),
+    'CON ORTHOPEDICS HOSPITALIST': ('CON ORTHOPEDICS HOSPITALIST', 'COH'),
+    'TT COLORECTAL SURGERY, PGR 12345': ('TT COLORECTAL SURGERY', 'TCS'),
+    'CON THORACIC SURGERY': ('CON THORACIC SURGERY', 'CTS'),
+    'TT NEUROSURGERY B TUMOR FLOOR, PGR 12345': ('TT NEUROSURGERY B TUMOR FLOOR', 'TNBTF'),
+    'CON PULM TRANSPLANT': ('CON PULM TRANSPLANT', 'CPT'),
+    'TT NEUROSURGERY B TUMOR ICU, PGR 12345': ('TT NEUROSURGERY B TUMOR ICU', 'TNBTI'),
+    'CON HEPATOLOGY': ('CON HEPATOLOGY', 'CH'),
+    'TT NEUROSURGERY C SPINE ICU, PGR 12345': ('TT NEUROSURGERY C SPINE ICU', 'TNCSI'),
+    'TT PAIN, .': ('TT PAIN', 'TP'),
+    'TT MICU GREEN RESIDENT A -- PGR 12345': ('TT MICU GREEN RESIDENT A', 'TMGRA'),
+    'CON NEPHROLOGY MAFFLY': ('CON NEPHROLOGY MAFFLY', 'CNM'),
+    'CON VASCULAR SURGERY': ('CON VASCULAR SURGERY', 'CVS'),
+    'TT MED TX HEP RES B, PGR 12345': ('TT MED TX HEP RES B', 'TMTHRB'),
+    'TT GEN SURG WHITE - PAGER 12345': ('TT GEN SURG WHITE', 'TGSW'),
+    'CON PULMONARY REHAB CONSULT': ('CON PULMONARY REHAB CONSULT', 'CPRC'),
+    'CON PAMF MEDICINE': ('CON PAMF MEDICINE', 'CPM'),
+    'TT MED TX HEP RES A, PGR 12345': ('TT MED TX HEP RES A', 'TMTHRA'),
+    'CON PALLIATIVE CARE - PGR 12345': ('CON PALLIATIVE CARE', 'CPC'),
+    'TT INTERVENTIONAL RADIOLOGY, .': ('TT INTERVENTIONAL RADIOLOGY', 'TIR'),
+    'CON INTERVENT RAD': ('CON INTERVENT RAD', 'CIR'),
+    'CON PULM HYPERTENSION': ('CON PULM HYPERTENSION', 'CPH'),
+    'CON MICU CONSULT': ('CON MICU CONSULT', 'CMC'),
+    'CON PRIMARY CARE': ('CON PRIMARY CARE', 'CPC'),
+    'TT CARDIOLOGY 5A, .': ('TT CARDIOLOGY 5A', 'TC5'),
+    'TT NEURO EPILEPSY MONITOR UNIT, .': ('TT NEURO EPILEPSY MONITOR UNIT', 'TNEMU'),
+    'CON TRANSPLANT SURGERY': ('CON TRANSPLANT SURGERY', 'CTS'),
+    'TT MICU BLUE RESIDENT A -- PGR 12345': ('TT MICU BLUE RESIDENT A', 'TMBRA'),
+    'TT MED TX-HEP, .': ('TT MED TX-HEP', 'TMT'),
+    'CON PSYCHIATRY': ('CON PSYCHIATRY', 'CP'),
+    'TT BMT, .': ('TT BMT', 'TB'),
+    'TT CARDIOLOGY 6B, .': ('TT CARDIOLOGY 6B', 'TC6'),
+    'TT ORTHO TRAUMA, .': ('TT ORTHO TRAUMA', 'TOT'),
+    'TT MED PAMF NON RESI': ('TT MED PAMF NON RESI', 'TMPNR'),
+    'TT GEN SURG RED - PAGER 12345': ('TT GEN SURG RED', 'TGSR'),
+    'TT CARDIOLOGY 6B INTERN -- PGR 12345': ('TT CARDIOLOGY 6B INTERN', 'TC6I'),
+    'TT NEUROLOGY, .': ('TT NEUROLOGY', 'TN'),
+    'TT CARDIOLOGY 5B, .': ('TT CARDIOLOGY 5B', 'TC5'),
+    'TT ONCOLOGY, .': ('TT ONCOLOGY', 'TO'),
+    'TT MED DAY B, PGR 12345': ('TT MED DAY B', 'TMDB'),
+    'CON GASTROENTEROLOGY': ('CON GASTROENTEROLOGY', 'CG'),
+    'TT CARDIOLOGY 6A, .': ('TT CARDIOLOGY 6A', 'TC6'),
+    'CON COLORECTAL SURGERY': ('CON COLORECTAL SURGERY', 'CCS'),
+    'TT CARDIOLOGY INTERVENTIONAL -- PGR 12345': ('TT CARDIOLOGY INTERVENTIONAL', 'TCI'),
+    'TT NEUROSURGERY A VASCULAR ICU, PGR 12345': ('TT NEUROSURGERY A VASCULAR ICU', 'TNAVI'),
+    'TT GYN, .': ('TT GYN', 'TG'),
+    'CON NEURO ONCOLOGY': ('CON NEURO ONCOLOGY', 'CNO'),
+    'TT GYN ONC, .': ('TT GYN ONC', 'TGO'),
+    'TT GEN SURG GOLD - PAGER 12345': ('TT GEN SURG GOLD', 'TGSG'),
+    'CON GERIATRICS': ('CON GERIATRICS', 'CG'),
+    'TT HEMATOLOGY, .': ('TT HEMATOLOGY', 'TH'),
+    'CON GYNECOLOGY': ('CON GYNECOLOGY', 'CG'),
+    'CON ENDOCRINOLOGY': ('CON ENDOCRINOLOGY', 'CE'),
+    'CON CRITICAL CARE CONSULT': ('CON CRITICAL CARE CONSULT', 'CCCC'),
+    'TT NEUROSURGERY, .': ('TT NEUROSURGERY', 'TN'),
+    'TT MED DAY A, PGR 12345': ('TT MED DAY A', 'TMDA'),
+    'CON GENERAL SURGERY': ('CON GENERAL SURGERY', 'CGS'),
+    'TT ONCOLOGY NP, PGR 12345': ('TT ONCOLOGY NP', 'TON'),
+    'TT HEMATOLOGY NP A, PGR 12345': ('TT HEMATOLOGY NP A', 'THNA'),
+    'TT PULMONARY TRANSPLANT, .': ('TT PULMONARY TRANSPLANT', 'TPT'),
+    'CON KIDNEY TRANSPLANT': ('CON KIDNEY TRANSPLANT', 'CKT'),
+    'CON CARD INTERVEN/AMI CONSULT': ('CON CARD INTERVEN/AMI CONSULT', 'CCIC'),
+    'CON CARDIOL TRANSPLANT': ('CON CARDIOL TRANSPLANT', 'CCT'),
+    'TT ORTHO SURGERY, .': ('TT ORTHO SURGERY', 'TOS'),
+    'CON CARDIOLOGY ADMISSION REQUEST': ('CON CARDIOLOGY ADMISSION REQUEST', 'CCAR'),
+    'TT PLASTIC SURGERY, .': ('TT PLASTIC SURGERY', 'TPS'),
+    'TT HEART LUNG TRANSPLANT, .': ('TT HEART LUNG TRANSPLANT', 'THLT'),
+    'TT UROLOGY, .': ('TT UROLOGY', 'TU'),
+    'CON CYSTIC FIBROSIS ADULT': ('CON CYSTIC FIBROSIS ADULT', 'CCFA'),
+    'TT CARDIOLOGY INTERVENTIONAL, .': ('TT CARDIOLOGY INTERVENTIONAL', 'TCI'),
+    'TT CYSTIC FIBROSIS ADULT, .': ('TT CYSTIC FIBROSIS ADULT', 'TCFA'),
+    'TT ENT HEAD NECK, .': ('TT ENT HEAD NECK', 'TEHN'),
+    'TT ORTHO SPINE, .': ('TT ORTHO SPINE', 'TOS'),
+    'TT NEUROSURGERY C, .': ('TT NEUROSURGERY C', 'TNC'),
+    'TT NEUROSURGERY B, .': ('TT NEUROSURGERY B', 'TNB'),
+    'TT ENT SPECIALTY, .': ('TT ENT SPECIALTY', 'TES'),
+    'TT HEART TRANSPLANT/VAD -- PGR 12345': ('TT HEART TRANSPLANT/VAD', 'THT'),
+    'TT CARDIOLOGY ARRHYTHMIA, .': ('TT CARDIOLOGY ARRHYTHMIA', 'TCA'),
+    'TT ORTHO JOINT, .': ('TT ORTHO JOINT', 'TOJ'),
+    'TT ORTHO FOOT & ANKLE, .': ('TT ORTHO FOOT ANKLE', 'TOFA'),
+    'TT PAIN RESIDENT 2 -- PGR 12345': ('TT PAIN RESIDENT 2', 'TPR2'),
+    'CON NEUROSURGERY': ('CON NEUROSURGERY', 'CN'),
+    'CON CARDIOTHROACIC SURGERY': ('CON CARDIOTHROACIC SURGERY', 'CCS'),
+    'TT HAND SURGERY, .': ('TT HAND SURGERY', 'THS'),
+    'TT ORTHO TUMOR, .': ('TT ORTHO TUMOR', 'TOT'),
+    'CON LIVER TRANSPLANT': ('CON LIVER TRANSPLANT', 'CLT'),
+    'TT NEUROSURGERY A, .': ('TT NEUROSURGERY A', 'TNA'),
+    'CON ENT': ('CON ENT', 'CE'),
+    'CON HOSPICE CONSULT': ('CON HOSPICE CONSULT', 'CHC'),
+    'CON PLASTIC SURGERY': ('CON PLASTIC SURGERY', 'CPS'),
+    'CON NEURO IR': ('CON NEURO IR', 'CNI'),
+    'CON INF DIS': ('CON INF DIS', 'CID'),
+    'CON INF DIS IMMUNOCOMP': ('CON INF DIS IMMUNOCOMP', 'CIDI'),
+    'CON DERMATOLOGY': ('CON DERMATOLOGY', 'CD'),
+    'CON OPHTHALMOLOGY': ('CON OPHTHALMOLOGY', 'CO'),
+    'CON RHEUMATOLOGY': ('CON RHEUMATOLOGY', 'CR'),
+    'CON UROLOGY': ('CON UROLOGY', 'CU'),
+    'CON HAND SURGERY': ('CON HAND SURGERY', 'CHS'),
+    'CON WOUND/OSTOMY/CONTINENCE RN': ('CON WOUND/OSTOMY/CONTINENCE RN', 'CWR'),
+    'CON NEPHROLOGY ATTENDING': ('CON NEPHROLOGY ATTENDING', 'CNA'),
+    'CON MEDICINE ADMISSION REQUEST': ('CON MEDICINE ADMISSION REQUEST', 'CMAR'),
+    'CON ORTHOPEDICS': ('CON ORTHOPEDICS', 'CO'),
+    'CON OBSTETRICS': ('CON OBSTETRICS', 'CO'),
+    'CON NEURO ENDOCRINE CONSULT': ('CON NEURO ENDOCRINE CONSULT', 'CNEC'),
+    'CON SPINE TRAUMA': ('CON SPINE TRAUMA', 'CST'),
+    'CON MICU ADMISSION REQUEST': ('CON MICU ADMISSION REQUEST', 'CMAR'),
+    'CON MEDICAL GENETICS': ('CON MEDICAL GENETICS', 'CMG'),
+    'CON SURGERY CONSULT': ('CON SURGERY CONSULT', 'CSC'),
+    'TT PAMF NON RESIDENT SURGERY': ('TT PAMF NON RESIDENT SURGERY', 'TPNRS'),
+    'CON CARDIOL INTERVENT-AMI': ('CON CARDIOL INTERVENT-AMI', 'CCI'),
+    'CON INF DIS POSTITIVE CARE': ('CON INF DIS POSTITIVE CARE', 'CIDPC'),
+    'CON SLEEP': ('CON SLEEP', 'CS'),
+    'CON ANESTHESIA CONSULT ADULT': ('CON ANESTHESIA CONSULT ADULT', 'CACA')
+}
+
+
+AGGR_NAME_TO_ACRONYM = {
+    'Intern': ('', ''),
+    'Cross Cover Intern': ('Cross Cover', 'CC'),
+    'Wound/Ostomy/Continence RN': ('Wound/Ostomy/Continence RN', 'WR'),
+    'Primary Intern': ('Primary', 'P'),
+    'Primary Resident': ('Primary', 'P'),
+    'Cross Cover Attending': ('Cross Cover', 'CC'),
+    'Student Nurse': ('Student Nurse', 'SN'),
+    'Clinical Pharmacist': ('Clinical Pharmacist', 'CP'),
+    'Medical Assistant': ('Medical Assistant', 'MA'),
+    'Licensed Vocational Nurse': ('Licensed Vocational Nurse', 'LVN'),
+    'Clinical Nurse Specialist': ('Clinical Nurse Specialist', 'CNS'),
+    'Cross Cover Fellow': ('Cross Cover Fellow', 'CCF'),
+    'ED Unit Secretary': ('ED Unit Secretary', 'EUS'),
+    'Primary Fellow': ('Primary', 'P'),
+    'Cross Cover Resident': ('Cross Cover', 'CC'),
+    'Consulting Attending': ('Consulting', 'C'),
+    'Consulting Fellow': ('Consulting', 'C'),
+    'Spiritual Care': ('Spiritual Care', 'SC'),
+    'Consulting Med Student': ('Consulting', 'C'),
+    'Primary Advanced Practice Provider': ('Primary', 'P'),
+    'Physician Assistant': ('Primary', 'P'),
+    'Consulting Resident': ('Consulting', 'C'),
+    'Nurse Coordinator': ('Nurse Coordinator', 'NC'),
+    'Fellow': ('Fellow', 'F'),
+    'Consulting Intern': ('Consulting', 'C'),
+    'Co-Attending': ('Co-Attending', 'C'),
+    'Surgeon': ('Surgeon', 'S'),
+    'Primary Med Student': ('Primary', 'P'),
+    'ED Tech': ('ED Tech', 'ET'),
+    'Resident': ('', ''),
+    'Chief Resident': ('Chief', 'C'),
+    'Medical Student': ('Medical Student', 'MS'),
+    'Primary Medical Student': ('Primary', 'P'),
+    'Pulmonologist': ('Pulmonologist', 'P'),
+    'Pediatrician': ('Pediatrician', 'P'),
+    'Hematologist': ('Hematologist', 'H'),
+    'Primary Care Physician': ('Primary', 'P'),
+    'Primary Physician Assistant': ('Primary', 'P'),
+    'Cardiologist': ('Cardiologist', 'C'),
+    'Triage Nurse': ('Triage Nurse', 'TN'),
+    'Transplant Social Worker': ('Transplant Social Worker', 'TSW'),
+    'Additional Communicating Provider': ('Additional Communicating Provider', 'ACP'),
+    'ED Registrar': ('ED Registrar', 'ER'),
+    'Gastroenterologist': ('Gastroenterologist', 'G'),
+    'Medical Oncologist': ('Medical Oncologist', 'MO'),
+    'Primary Sub-Intern': ('Primary', 'P'),
+    'Lactation Consultant': ('Lactation Consultant', 'LC'),
+    'Trauma Attending': ('Trauma', 'T'),
+    'Post-Transplant Nurse': ('Post-Transplant Nurse', 'PN'),
+    'Cross Cover Advanced Practice Provider ': ('Cross Cover Advanced Practice Provider', 'CCAPP'),
+    'Internist': ('Internist', 'I'),
+    'Nephrologist': ('Nephrologist', 'N'),
+    'Research Coordinator': ('Research Coordinator', 'RC'),
+    'Infectious Disease': ('Infectious Disease', 'ID'),
+    'Trauma Resident ': ('Trauma', 'T'),
+    'Primary Sub-intern': ('Primary', 'P'),
+    'Neurologist': ('Neurologist', 'N'),
+    'Transplant Surgeon': ('Transplant Surgeon', 'TS'),
+    'Consulting Medical Oncologist': ('Consulting', 'C'),
+    'Family Practitioner': ('Family Practitioner', 'FP'),
+    'Dermatologist': ('Dermatologist', 'D'),
+    'Aging Adult Services Coordinator': ('Aging Adult Services Coordinator', 'AASC'),
+    'Transplant Nephrologist': ('Transplant Nephrologist', 'TN'),
+    'Post-Transplant Nephrologist': ('Post-Transplant Nephrologist', 'PN'),
+    'Consulting Surgical Oncologist': ('Consulting', 'C'),
+    'Surgical Oncologist': ('Surgical Oncologist', 'SO'),
+    'Nursery Nurse': ('Nursery Nurse', 'NN'),
+    'Transplant Pharmacist': ('Transplant Pharmacist', 'TP'),
+    'Psychologist': ('Psychologist', 'P'),
+    'Restorative Aide': ('Restorative Aide', 'RA'),
+    'Child Life Specialist': ('Child Life Specialist', 'CLS'),
+    'Consulting Hematologist': ('Consulting', 'C'),
+    'Endocrinologist': ('Endocrinologist', 'E'),
+    'Electrophysiologist': ('Electrophysiologist', 'E'),
+    'Anesthesiologist': ('Anesthesiologist', 'A'),
+    'Transplant Dietitian': ('Transplant Dietitian', 'TD'),
+    'Urologist': ('Urologist', 'U'),
+    'stop0': ('stop0', 's'),
+    'Radiation Oncologist': ('Radiation Oncologist', 'RO'),
+    'Referring Provider': ('Referring Provider', 'RP'),
+    'Activity Therapist': ('Activity Therapist', 'AT'),
+    'Transplant Pulmonologist': ('Transplant Pulmonologist', 'TP'),
+    'Obstetrician': ('Obstetrician', 'O'),
+    'Heart Failure Cardiologist': ('Heart Failure Cardiologist', 'HFC'),
+    'Post RN Coordinator': ('Post RN Coordinator', 'PRC'),
+    'Delivery Assist': ('Delivery Assist', 'DA'),
+    'Referring Nephrologist': ('Referring Nephrologist', 'RN'),
+    'Hepatologist': ('Hepatologist', 'H'),
+    'Case Manager': ('Case Manager', 'CM'),
+    'Primary Team': ('Primary', 'P'),
+    'Social Worker': ('Social Worker', 'SW'),
+    'Care Coordinator': ('Care Coordinator', 'CC'),
+    'Registered Nurse': ('Registered Nurse', 'RN'),
+    'Speech Therapist': ('Speech Therapist', 'ST'),
+    'Nursing Assistant': ('Nursing Assistant', 'NA'),
+    'Clinical Dietitian': ('Clinical Dietitian', 'CD'),
+    'Consulting Service': ('Consulting', 'C'),
+    'Emergency Resident': ('Emergency', 'E'),
+    'Nurse Practitioner': ('Primary', 'P'),
+    'Physical Therapist': ('Physical Therapist', 'PT'),
+    'Occupational Therapist': ('Occupational Therapist', 'OT'),
+    'Physical Therapist Assistant': ('Physical Therapist Assistant', 'PTA'),
+    'Respiratory Care Practitioner': ('Respiratory Care Practitioner', 'RCP'),
+    'Independent Living Donor Advocate': ('Independent Living Donor Advocate', 'ILDA'),
+    'Pre RN Coordinator': ('Pre RN Coordinator', 'PRC'),
+    'Dietician Intern': ('Dietician', 'D'),
+    'Survivorship Provider': ('Survivorship Provider', 'SP'),
+    'Consulting Reconstructive Surgeon': ('Consulting', 'C'),
+    'Referring Pulmonologist': ('Referring Pulmonologist', 'RP')
+}
+
+AGGR_PROV_TO_CLEAN_ACRONYM = {
+    'SMITH, JOHN': ('', ''),
+    'SMITH, JANE': ('', ''),
+    'DOE, JOHN': ('', ''),
+    'DOE, JANE': ('', ''),
+    'CON CARDIOLOGY': ('CON CARDIOLOGY', 'CC'),
+    'CON MEDICINE': ('CON MEDICINE', 'CM'),
+    'CON PULMONARY': ('CON PULMONARY', 'CP'),
+    'CON NEURO ICU': ('CON NEURO ICU', 'CNI'),
+    'CON CARDIOL ARRHYTHMIA': ('CON CARDIOL ARRHYTHMIA', 'CCA'),
+    'CON NEURO EPILEPSY CONSULT': ('CON NEURO EPILEPSY CONSULT', 'CNEC'),
+    'CON NEPHROLOGY JAMISON': ('CON NEPHROLOGY JAMISON', 'CNJ'),
+    'CON CARDIAC SURGERY': ('CON CARDIAC SURGERY', 'CCS'),
+    'TT CVICU TEAM 2 - SPECTRAS 1-2345, 2-3456': ('TT CVICU TEAM', 'TCT'),
+    'CON PAIN': ('CON PAIN', 'CP'),
+    'CON ONCOLOGY': ('CON ONCOLOGY', 'CO'),
+    'CON NEUROLOGY GENERAL': ('CON NEUROLOGY GENERAL', 'CNG'),
+    'CON NEURO STROKE': ('CON NEURO STROKE', 'CNS'),
+    'TT NEUROSURGERY C SPINE FLOOR, PGR 12345': ('TT NEUROSURGERY SPINE FLOOR', 'TNSF'),
+    'CON NEPHROLOGY TRANSPLANT': ('CON NEPHROLOGY TRANSPLANT', 'CNT'),
+    'TT ACUTE CARE SURGERY TRAUMA, PGR 12345': ('TT ACUTE CARE SURGERY TRAUMA', 'TACST'),
+    'CON INTERNAL MEDICINE CONSULT': ('CON INTERNAL MEDICINE CONSULT', 'CIMC'),
+    'CON ICU': ('CON ICU', 'CI'),
+    'CON ALLERGY IMMUNOLOGY CONSULT': ('CON ALLERGY IMMUNOLOGY CONSULT', 'CAIC'),
+    'TT CVICU TEAM 1 - SPECTRAS 1-2345, 2-3456': ('TT CVICU TEAM', 'TCT'),
+    'TT NEUROSURGERY A VASCULAR FLOOR, PGR 12345': ('TT NEUROSURGERY VASCULAR FLOOR', 'TNVF'),
+    'CON HEMATOLOGY': ('CON HEMATOLOGY', 'CH'),
+    'CON ORTHOPEDICS HOSPITALIST': ('CON ORTHOPEDICS HOSPITALIST', 'COH'),
+    'TT COLORECTAL SURGERY, PGR 12345': ('TT COLORECTAL SURGERY', 'TCS'),
+    'CON THORACIC SURGERY': ('CON THORACIC SURGERY', 'CTS'),
+    'TT NEUROSURGERY B TUMOR FLOOR, PGR 12345': ('TT NEUROSURGERY TUMOR FLOOR', 'TNTF'),
+    'CON PULM TRANSPLANT': ('CON PULM TRANSPLANT', 'CPT'),
+    'TT NEUROSURGERY B TUMOR ICU, PGR 12345': ('TT NEUROSURGERY TUMOR ICU', 'TNTI'),
+    'CON HEPATOLOGY': ('CON HEPATOLOGY', 'CH'),
+    'TT NEUROSURGERY C SPINE ICU, PGR 12345': ('TT NEUROSURGERY SPINE ICU', 'TNSI'),
+    'TT PAIN, .': ('TT PAIN', 'TP'),
+    'TT MICU GREEN RESIDENT A -- PGR 12345': ('TT MICU', 'TM'),
+    'CON NEPHROLOGY MAFFLY': ('CON NEPHROLOGY MAFFLY', 'CNM'),
+    'CON VASCULAR SURGERY': ('CON VASCULAR SURGERY', 'CVS'),
+    'TT MED TX HEP RES B, PGR 12345': ('TT MED TX HEP', 'TMTH'),
+    'TT GEN SURG WHITE - PAGER 12345': ('TT GEN SURG', 'TGS'),
+    'CON PULMONARY REHAB CONSULT': ('CON PULMONARY REHAB CONSULT', 'CPRC'),
+    'CON PAMF MEDICINE': ('CON PAMF MEDICINE', 'CPM'),
+    'TT MED TX HEP RES A, PGR 12345': ('TT MED TX HEP', 'TMTH'),
+    'CON PALLIATIVE CARE - PGR 12345': ('CON PALLIATIVE CARE', 'CPC'),
+    'TT INTERVENTIONAL RADIOLOGY, .': ('TT INTERVENTIONAL RADIOLOGY', 'TIR'),
+    'CON INTERVENT RAD': ('CON INTERVENT RAD', 'CIR'),
+    'CON PULM HYPERTENSION': ('CON PULM HYPERTENSION', 'CPH'),
+    'CON MICU CONSULT': ('CON MICU CONSULT', 'CMC'),
+    'CON PRIMARY CARE': ('CON PRIMARY CARE', 'CPC'),
+    'TT CARDIOLOGY 5A, .': ('TT CARDIOLOGY', 'TC'),
+    'TT NEURO EPILEPSY MONITOR UNIT, .': ('TT NEURO EPILEPSY MONITOR UNIT', 'TNEMU'),
+    'CON TRANSPLANT SURGERY': ('CON TRANSPLANT SURGERY', 'CTS'),
+    'TT MICU BLUE RESIDENT A -- PGR 12345': ('TT MICU', 'TM'),
+    'TT MED TX-HEP, .': ('TT MED TX-HEP', 'TMT'),
+    'CON PSYCHIATRY': ('CON PSYCHIATRY', 'CP'),
+    'TT BMT, .': ('TT BMT', 'TB'),
+    'TT CARDIOLOGY 6B, .': ('TT CARDIOLOGY', 'TC'),
+    'TT ORTHO TRAUMA, .': ('TT ORTHO TRAUMA', 'TOT'),
+    'TT MED PAMF NON RESI': ('TT MED PAMF', 'TMP'),
+    'TT GEN SURG RED - PAGER 12345': ('TT GEN SURG', 'TGS'),
+    'TT CARDIOLOGY 6B INTERN -- PGR 12345': ('TT CARDIOLOGY', 'TC'),
+    'TT NEUROLOGY, .': ('TT NEUROLOGY', 'TN'),
+    'TT CARDIOLOGY 5B, .': ('TT CARDIOLOGY', 'TC'),
+    'TT ONCOLOGY, .': ('TT ONCOLOGY', 'TO'),
+    'TT MED DAY B, PGR 12345': ('TT MED DAY', 'TMD'),
+    'CON GASTROENTEROLOGY': ('CON GASTROENTEROLOGY', 'CG'),
+    'TT CARDIOLOGY 6A, .': ('TT CARDIOLOGY', 'TC'),
+    'CON COLORECTAL SURGERY': ('CON COLORECTAL SURGERY', 'CCS'),
+    'TT CARDIOLOGY INTERVENTIONAL -- PGR 12345': ('TT CARDIOLOGY INTERVENTIONAL', 'TCI'),
+    'TT NEUROSURGERY A VASCULAR ICU, PGR 12345': ('TT NEUROSURGERY VASCULAR ICU', 'TNVI'),
+    'TT GYN, .': ('TT GYN', 'TG'),
+    'CON NEURO ONCOLOGY': ('CON NEURO ONCOLOGY', 'CNO'),
+    'TT GYN ONC, .': ('TT GYN ONC', 'TGO'),
+    'TT GEN SURG GOLD - PAGER 12345': ('TT GEN SURG', 'TGS'),
+    'CON GERIATRICS': ('CON GERIATRICS', 'CG'),
+    'TT HEMATOLOGY, .': ('TT HEMATOLOGY', 'TH'),
+    'CON GYNECOLOGY': ('CON GYNECOLOGY', 'CG'),
+    'CON ENDOCRINOLOGY': ('CON ENDOCRINOLOGY', 'CE'),
+    'CON CRITICAL CARE CONSULT': ('CON CRITICAL CARE CONSULT', 'CCCC'),
+    'TT NEUROSURGERY, .': ('TT NEUROSURGERY', 'TN'),
+    'TT MED DAY A, PGR 12345': ('TT MED DAY', 'TMD'),
+    'CON GENERAL SURGERY': ('CON GENERAL SURGERY', 'CGS'),
+    'TT ONCOLOGY NP, PGR 12345': ('TT ONCOLOGY', 'TO'),
+    'TT HEMATOLOGY NP A, PGR 12345': ('TT HEMATOLOGY', 'TH'),
+    'TT PULMONARY TRANSPLANT, .': ('TT PULMONARY TRANSPLANT', 'TPT'),
+    'CON KIDNEY TRANSPLANT': ('CON KIDNEY TRANSPLANT', 'CKT'),
+    'CON CARD INTERVEN/AMI CONSULT': ('CON CARD INTERVEN/AMI CONSULT', 'CCIC'),
+    'CON CARDIOL TRANSPLANT': ('CON CARDIOL TRANSPLANT', 'CCT'),
+    'TT ORTHO SURGERY, .': ('TT ORTHO SURGERY', 'TOS'),
+    'CON CARDIOLOGY ADMISSION REQUEST': ('CON CARDIOLOGY ADMISSION REQUEST', 'CCAR'),
+    'TT PLASTIC SURGERY, .': ('TT PLASTIC SURGERY', 'TPS'),
+    'TT HEART LUNG TRANSPLANT, .': ('TT HEART LUNG TRANSPLANT', 'THLT'),
+    'TT UROLOGY, .': ('TT UROLOGY', 'TU'),
+    'CON CYSTIC FIBROSIS ADULT': ('CON CYSTIC FIBROSIS ADULT', 'CCFA'),
+    'TT CARDIOLOGY INTERVENTIONAL, .': ('TT CARDIOLOGY INTERVENTIONAL', 'TCI'),
+    'TT CYSTIC FIBROSIS ADULT, .': ('TT CYSTIC FIBROSIS ADULT', 'TCFA'),
+    'TT ENT HEAD NECK, .': ('TT ENT HEAD NECK', 'TEHN'),
+    'TT ORTHO SPINE, .': ('TT ORTHO SPINE', 'TOS'),
+    'TT NEUROSURGERY C, .': ('TT NEUROSURGERY', 'TN'),
+    'TT NEUROSURGERY B, .': ('TT NEUROSURGERY', 'TN'),
+    'TT ENT SPECIALTY, .': ('TT ENT SPECIALTY', 'TES'),
+    'TT HEART TRANSPLANT/VAD -- PGR 12345': ('TT HEART TRANSPLANT/VAD', 'THT'),
+    'TT CARDIOLOGY ARRHYTHMIA, .': ('TT CARDIOLOGY ARRHYTHMIA', 'TCA'),
+    'TT ORTHO JOINT, .': ('TT ORTHO JOINT', 'TOJ'),
+    'TT ORTHO FOOT & ANKLE, .': ('TT ORTHO FOOT ANKLE', 'TOFA'),
+    'TT PAIN RESIDENT 2 -- PGR 12345': ('TT PAIN', 'TP'),
+    'CON NEUROSURGERY': ('CON NEUROSURGERY', 'CN'),
+    'CON CARDIOTHROACIC SURGERY': ('CON CARDIOTHROACIC SURGERY', 'CCS'),
+    'TT HAND SURGERY, .': ('TT HAND SURGERY', 'THS'),
+    'TT ORTHO TUMOR, .': ('TT ORTHO TUMOR', 'TOT'),
+    'CON LIVER TRANSPLANT': ('CON LIVER TRANSPLANT', 'CLT'),
+    'TT NEUROSURGERY A, .': ('TT NEUROSURGERY', 'TN'),
+    'CON ENT': ('CON ENT', 'CE'),
+    'CON HOSPICE CONSULT': ('CON HOSPICE CONSULT', 'CHC'),
+    'CON PLASTIC SURGERY': ('CON PLASTIC SURGERY', 'CPS'),
+    'CON NEURO IR': ('CON NEURO IR', 'CNI'),
+    'CON INF DIS': ('CON INF DIS', 'CID'),
+    'CON INF DIS IMMUNOCOMP': ('CON INF DIS IMMUNOCOMP', 'CIDI'),
+    'CON DERMATOLOGY': ('CON DERMATOLOGY', 'CD'),
+    'CON OPHTHALMOLOGY': ('CON OPHTHALMOLOGY', 'CO'),
+    'CON RHEUMATOLOGY': ('CON RHEUMATOLOGY', 'CR'),
+    'CON UROLOGY': ('CON UROLOGY', 'CU'),
+    'CON HAND SURGERY': ('CON HAND SURGERY', 'CHS'),
+    'CON WOUND/OSTOMY/CONTINENCE RN': ('CON WOUND/OSTOMY/CONTINENCE RN', 'CWR'),
+    'CON NEPHROLOGY ATTENDING': ('CON NEPHROLOGY', 'CN'),
+    'CON MEDICINE ADMISSION REQUEST': ('CON MEDICINE ADMISSION REQUEST', 'CMAR'),
+    'CON ORTHOPEDICS': ('CON ORTHOPEDICS', 'CO'),
+    'CON OBSTETRICS': ('CON OBSTETRICS', 'CO'),
+    'CON NEURO ENDOCRINE CONSULT': ('CON NEURO ENDOCRINE CONSULT', 'CNEC'),
+    'CON SPINE TRAUMA': ('CON SPINE TRAUMA', 'CST'),
+    'CON MICU ADMISSION REQUEST': ('CON MICU ADMISSION REQUEST', 'CMAR'),
+    'CON MEDICAL GENETICS': ('CON MEDICAL GENETICS', 'CMG'),
+    'CON SURGERY CONSULT': ('CON SURGERY CONSULT', 'CSC'),
+    'TT PAMF NON RESIDENT SURGERY': ('TT PAMF SURGERY', 'TPS'),
+    'CON CARDIOL INTERVENT-AMI': ('CON CARDIOL INTERVENT-AMI', 'CCI'),
+    'CON INF DIS POSTITIVE CARE': ('CON INF DIS POSTITIVE CARE', 'CIDPC'),
+    'CON SLEEP': ('CON SLEEP', 'CS'),
+    'CON ANESTHESIA CONSULT ADULT': ('CON ANESTHESIA CONSULT ADULT', 'CACA')
+}
+
+
+class TestSTARRTreatmentTeamConversion(DBTestCase):
+    TEST_DATA_SIZE = 50
+
+    test_data = []
+    expected_data = []
+
+    test_data_csv = tempfile.gettempdir() + '/test_starr_treatment_team_dummy_data.csv'
+
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self)
+
+        log.info("Sourcing from BigQuery DB")
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+
+        self.converter = STARRTreatmentTeamConversion.STARRTreatmentTeamConversion()  # Instance to test on
+        self.bqConn = self.converter.bqConn
+        self.starrUtil = STARRUtil.StarrCommonUtils(self.converter.bqClient)
+
+        # point the converter to dummy source table
+        STARRTreatmentTeamConversion.SOURCE_TABLE = TEST_SOURCE_TABLE
+
+        log.warn("Removing test table, if exists: {}".format(TEST_SOURCE_TABLE))
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DROP TABLE IF EXISTS {};'.format(TEST_SOURCE_TABLE))
+
+    def generate_test_and_expected_data(self, test_data_size, aggregate):
+        for curr_row in range(test_data_size):
+            patient_id = 'JC' + format(curr_row, '06')
+            test_data_row = self.generate_test_data_row(curr_row, self.starrUtil.random_period(), patient_id)
+            self.test_data.append(test_data_row)
+            self.generate_expected_data_rows(test_data_row, self.expected_data, aggregate)
+
+        # pi.external_id desc (ci.external_id is always None, so not included here)
+        self.expected_data.sort(key=lambda tup: (-tup[0]))
+
+        log.debug('test_data: {}'.format(self.test_data))
+        log.debug('expected_data: {}'.format(self.expected_data))
+
+    @staticmethod
+    def generate_test_data_row(curr_row, treatment_period, patient_id):
+        return (
+            'SS' + format(curr_row, '07'),
+            patient_id,
+            curr_row,
+            datetime.fromtimestamp(treatment_period[0]),
+            datetime.fromtimestamp(treatment_period[1]),
+            list(NAME_TO_ACRONYM.keys())[random.randint(0, len(NAME_TO_ACRONYM) - 1)],
+            list(PROV_TO_CLEAN_ACRONYM.keys())[random.randint(0, len(PROV_TO_CLEAN_ACRONYM) - 1)],
+            datetime.fromtimestamp(treatment_period[0], tz=pytz.UTC),
+        )
+
+    def generate_expected_data_rows(self, row, expected_data, aggregate):
+        if not aggregate:
+            expected_row = (
+                self.starrUtil.convertPatIdToSTRIDE(row[0]),
+                self.starrUtil.convertPatIdToSTRIDE(row[1]),
+                row[2],
+                STARRTreatmentTeamConversion.CATEGORY_TEMPLATE,
+                None,
+                NAME_TO_ACRONYM[row[5]] if PROV_TO_CLEAN_ACRONYM[row[6]][1] == ''
+                                        else PROV_TO_CLEAN_ACRONYM[row[6]][1] + ' (' + NAME_TO_ACRONYM[row[5]] + ')',
+                row[5].strip() if PROV_TO_CLEAN_ACRONYM[row[6]][1] == ''
+                               else PROV_TO_CLEAN_ACRONYM[row[6]][0].title() + ' (' + row[5].strip() + ')',
+                row[3].replace(tzinfo=pytz.UTC),
+                row[7].replace(tzinfo=pytz.UTC),
+            )
+        else:
+            expected_row = (
+                self.starrUtil.convertPatIdToSTRIDE(row[0]),
+                self.starrUtil.convertPatIdToSTRIDE(row[1]),
+                row[2],
+                STARRTreatmentTeamConversion.CATEGORY_TEMPLATE,
+                None,
+                AGGR_NAME_TO_ACRONYM[row[5]][1]
+                    if AGGR_PROV_TO_CLEAN_ACRONYM[row[6]][1] == ''
+                    else AGGR_PROV_TO_CLEAN_ACRONYM[row[6]][1] + ' (' + AGGR_NAME_TO_ACRONYM[row[5]][1] + ')',
+                AGGR_NAME_TO_ACRONYM[row[5]][0]
+                    if AGGR_PROV_TO_CLEAN_ACRONYM[row[6]][1] == ''
+                    else AGGR_PROV_TO_CLEAN_ACRONYM[row[6]][0].title() + ' (' + AGGR_NAME_TO_ACRONYM[row[5]][0] + ')',
+                row[3].replace(tzinfo=pytz.UTC),
+                row[7].replace(tzinfo=pytz.UTC)
+            )
+
+        expected_data.append(expected_row)
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        self.test_data[:] = []
+        self.expected_data[:] = []
+        os.remove(self.test_data_csv)
+
+        log.info("Purge test records from the database")
+
+        DBUtil.execute(
+            """delete from patient_item
+            where clinical_item_id in
+            (   select clinical_item_id
+                from clinical_item as ci, clinical_item_category as cic
+                where ci.clinical_item_category_id = cic.clinical_item_category_id
+                and cic.source_table = '%s'
+            )
+            """ % TEST_SOURCE_TABLE
+        )
+        DBUtil.execute(
+            """delete from clinical_item
+            where clinical_item_category_id in
+            (   select clinical_item_category_id
+                from clinical_item_category
+                where source_table = '%s'
+            )
+            """ % TEST_SOURCE_TABLE
+        )
+        DBUtil.execute("delete from clinical_item_category where source_table = '%s';" % TEST_SOURCE_TABLE)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute('DELETE FROM %s.patient_item WHERE true;' % TEST_DEST_DATASET)
+        bq_cursor.execute('DELETE FROM %s.clinical_item WHERE true;' % TEST_DEST_DATASET)
+        bq_cursor.execute('DELETE FROM %s.clinical_item_category WHERE true;' % TEST_DEST_DATASET)
+
+        bq_cursor.execute('DROP TABLE %s;' % TEST_SOURCE_TABLE)
+
+        DBTestCase.tearDown(self)
+
+    @parameterized.expand([
+        ["without_aggregation", False],
+        ["with_aggregation", True]
+    ])
+    def test_dataConversion(self, name, aggregation):
+        log.info("Generating test source data")
+        self.generate_test_and_expected_data(self.TEST_DATA_SIZE, aggregate=aggregation)
+        self.starrUtil.dump_test_data_to_csv(self.converter.HEADERS, self.test_data, self.test_data_csv)
+        self.starrUtil.upload_csv_to_bigquery('starr_datalake2018', 'treatment_team', TEST_DEST_DATASET,
+                                              'starr_treatment_team', self.test_data_csv, self.converter.HEADERS)
+
+        log.debug("Run the conversion process...")
+        conv_options = STARRTreatmentTeamConversion.ConversionOptions()
+        conv_options.aggregate = aggregation
+        temp_dir = tempfile.gettempdir()
+        self.converter.convertAndUpload(conv_options, tempDir=temp_dir, targetDatasetId=TEST_DEST_DATASET)
+
+        # Just query back for the same data, de-normalizing the data back to a general table
+        test_query = \
+            """
+            select
+                pi.external_id as pi_external_id,
+                pi.patient_id,
+                pi.encounter_id,
+                cic.description as cic_description,
+                ci.external_id as ci_external_id,
+                ci.name,
+                ci.description as ci_description,
+                pi.item_date,
+                pi.item_date_utc
+            from
+                %s.patient_item as pi,
+                %s.clinical_item as ci,
+                %s.clinical_item_category as cic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = '%s'
+            order by
+                pi.external_id desc, ci.external_id desc
+            """ % (TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
+
+        bq_cursor = self.bqConn.cursor()
+        bq_cursor.execute(test_query)
+        actual_data = [list(row.values()) for row in bq_cursor.fetchall()]
+        log.debug('actual data: {}'.format(actual_data))
+        log.debug('expected data: {}'.format(self.expected_data))
+        self.assertEqualTable(self.expected_data, actual_data)
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    test_suite = unittest.TestSuite()
+    test_suite.addTest(unittest.makeSuite(TestSTARRTreatmentTeamConversion))
+
+    return test_suite
+
+
+if __name__ == "__main__":
+    log.setLevel(logging.INFO)  # without this no logs are printed
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/starr_conv/test/__init__.py b/medinfo/dataconversion/starr_conv/test/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/medinfo/dataconversion/starr_conv/test/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/medinfo/dataconversion/support/__init__.py b/medinfo/dataconversion/support/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/medinfo/dataconversion/support/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/medinfo/dataconversion/support/extractExcelSheets.py b/medinfo/dataconversion/support/extractExcelSheets.py
new file mode 100644
index 00000000..66d4f30e
--- /dev/null
+++ b/medinfo/dataconversion/support/extractExcelSheets.py
@@ -0,0 +1,42 @@
+"""Quick support script to input an Excel file and then dump out all of the worksheet data as separate tab delimited files
+"""
+
+import sys, os;
+import openpyxl;	# Open source library for reading Excel files in Python. Install with "python -m pip install openpyxl"
+import csv;
+
+
+def extractExcelSheets(inFilename):
+	# Open the Excel workbook
+	wb = openpyxl.load_workbook(inFilename)
+	generatedFileList = list();
+	for sheetName in wb.get_sheet_names():
+		# Pull out one worksheet at a time
+		ws = wb.get_sheet_by_name(sheetName);
+
+		# Create a separate output file, name based on input file and worksheet name, to dump contents as CSV
+		outFilename = os.path.splitext(inFilename)[0] +"."+ sheetName +".tsv";
+		fileout = open(outFilename,"w");
+		writer = csv.writer(fileout, dialect=csv.excel_tab);
+
+		# Iterate through worksheet data values and output to TSV file
+		for row in ws.values:
+			#print row
+			row = list(row);	# Convert to a modifiable list
+			for i, value in enumerate(row):
+				if isinstance(value, str):	# Clean up string values by replacing non-ascii characters (e.g., \xa0 = non-breaking space)
+					row[i] = value.replace('\xa0', ' ');
+			writer.writerow(row);
+
+		fileout.close();
+		generatedFileList.append(outFilename);
+	return generatedFileList;
+
+def main(argv):
+	inFilename = argv[1];
+	generatedFileList = extractExcelSheets(inFilename);
+	for item in generatedFileList:
+		print(item);
+
+if __name__ == "__main__":
+	main(sys.argv);
\ No newline at end of file
diff --git a/medinfo/dataconversion/support/patchHeader.py b/medinfo/dataconversion/support/patchHeader.py
index cfa07b57..67ecf0eb 100644
--- a/medinfo/dataconversion/support/patchHeader.py
+++ b/medinfo/dataconversion/support/patchHeader.py
@@ -22,7 +22,7 @@
 ## Example Usage:
 python patchHeader.py originalFile.txt headerFile.txt
 <<<Manually Edit headerFile.txt to desired header>>>
-python patchHEader.py originalFile.txt patchedFile.txt headerFile.txt
+python patchHeader.py originalFile.txt patchedFile.txt headerFile.txt
 """
 
 import sys;
diff --git a/medinfo/dataconversion/test/EventDigraphTestData.py b/medinfo/dataconversion/test/EventDigraphTestData.py
new file mode 100644
index 00000000..a6ec4b5e
--- /dev/null
+++ b/medinfo/dataconversion/test/EventDigraphTestData.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+"""
+Test input and output for EventDigraph.
+"""
+
+from medinfo.db import DBUtil
+
+# { 'test_db_table' : 'test_data' }
+ED_TEST_INPUT_TABLES = {
+    'clinical_item_category' :
+        "clinical_item_category_id\tsource_table\tdescription\n\
+        -100\tTestTable\tTest Category 1\n\
+        -200\tTestTable\tTest Category 2\n\
+        -300\tTestTable\tTest Category 3\n\
+        -400\tTestTable\tTest Category 4\n\
+        -500\tTestTable\tTest Category 5\n",
+    'clinical_item' :
+        "clinical_item_id\tname\tdescription\tclinical_item_category_id\n\
+        -100\tTest Item 100\tTest Item 100\t-100\n\
+        -200\tTest Item 200\tTest Item 200\t-100\n\
+        -300\tTest Item 300\tTest Item 300\t-200\n\
+        -400\tTest Item 400\tTest Item 400\t-200\n\
+        -500\tTest Item 500\tTest Item 500\t-300\n\
+        -600\tTest Item 600\tTest Item 600\t-300\n\
+        -700\tTest Item 700\tTest Item 700\t-400\n\
+        -800\tTest Item 800\tTest Item 800\t-400\n\
+        -900\tTest Item 900\tTest Item 900\t-500\n\
+        -1000\tTest Item 1000\tTest Item 1000\t-500\n",
+    'patient_item' :
+        "patient_item_id\tpatient_id\tclinical_item_id\titem_date\n\
+        -100\t-123\t-1000\t1/6/2113 10:20\n\
+        -200\t-234\t-900\t2/6/2113 11:20\n\
+        -300\t-345\t-800\t3/7/2113 11:20\n\
+        -400\t-123\t-1000\t4/6/2113 10:20\n\
+        -500\t-234\t-900\t5/6/2113 11:20\n\
+        -600\t-345\t-800\t6/6/2113 12:40\n\
+        -700\t-456\t-700\t7/7/2113 12:40\n\
+        -800\t-123\t-600\t8/8/2113 12:40\n\
+        -900\t-234\t-900\t9/9/2113 12:40\n\
+        -1000\t-345\t-800\t10/10/2113 12:40\n\
+        -1100\t-456\t-700\t11/11/2113 12:40\n\
+        -1200\t-567\t-600\t12/12/2113 12:40\n\
+        -1300\t-678\t-500\t1/13/2114 12:40\n\
+        -1400\t-789\t-400\t2/14/2114 12:40\n\
+        -1500\t-789\t-300\t3/15/2114 12:40\n\
+        -1600\t-678\t-200\t4/16/2114 12:40\n\
+        -1700\t-567\t-500\t5/17/2114 12:40\n\
+        -1800\t-456\t-300\t6/18/2114 12:40\n\
+        -1900\t-345\t-400\t7/19/2114 12:40\n\
+        -2000\t-567\t-100\t8/20/2114 12:40\n"
+}
+
+# { 'test_function_name' : { 'test_assertion' : 'test_output' } }
+ED_TEST_OUTPUT_TABLES = {
+    'test_init' : {
+        'category_nodes' : [
+            ('Test Category 1', {'count': 2}),
+            ('Test Category 2', {'count': 4}),
+            ('Test Category 3', {'count': 4}),
+            ('Test Category 4', {'count': 5}),
+            ('Test Category 5', {'count': 5})
+        ],
+        'category_edges' : [
+            (('Test Category 2', 'Test Category 2'), {'count': 1}),
+            (('Test Category 3', 'Test Category 1'), {'count': 2}),
+            (('Test Category 3', 'Test Category 3'), {'count': 1}),
+            (('Test Category 4', 'Test Category 2'), {'count': 2}),
+            (('Test Category 4', 'Test Category 4'), {'count': 3}),
+            (('Test Category 5', 'Test Category 3'), {'count': 1}),
+            (('Test Category 5', 'Test Category 5'), {'count': 3})
+        ],
+        'item_nodes' : [
+            ('Test Item 100', {'count': 1}),
+            ('Test Item 1000', {'count': 2}),
+            ('Test Item 200', {'count': 1}),
+            ('Test Item 300', {'count': 2}),
+            ('Test Item 400', {'count': 2}),
+            ('Test Item 500', {'count': 2}),
+            ('Test Item 600', {'count': 2}),
+            ('Test Item 700', {'count': 2}),
+            ('Test Item 800', {'count': 3}),
+            ('Test Item 900', {'count': 3})
+        ],
+        'item_edges' : [
+            (('Test Item 1000', 'Test Item 1000'), {'count': 1}),
+            (('Test Item 1000', 'Test Item 600'), {'count': 1}),
+            (('Test Item 400', 'Test Item 300'), {'count': 1}),
+            (('Test Item 500', 'Test Item 100'), {'count': 1}),
+            (('Test Item 500', 'Test Item 200'), {'count': 1}),
+            (('Test Item 600', 'Test Item 500'), {'count': 1}),
+            (('Test Item 700', 'Test Item 300'), {'count': 1}),
+            (('Test Item 700', 'Test Item 700'), {'count': 1}),
+            (('Test Item 800', 'Test Item 400'), {'count': 1}),
+            (('Test Item 800', 'Test Item 800'), {'count': 2}),
+            (('Test Item 900', 'Test Item 900'), {'count': 2})
+
+
+        ]
+    }
+}
diff --git a/medinfo/dataconversion/test/FMTransformTestData.py b/medinfo/dataconversion/test/FMTransformTestData.py
new file mode 100644
index 00000000..10f7e27f
--- /dev/null
+++ b/medinfo/dataconversion/test/FMTransformTestData.py
@@ -0,0 +1,299 @@
+#!/usr/bin/python
+"""
+Test input and output for FeatureMatrixTransform.
+"""
+
+import pandas as pd
+
+MANUAL_FM_TEST_CASE = {
+    # Ensure input matrix has each of the following data types:
+    #   * integer
+    #   * float
+    #   * True/False
+    #   * string
+    #   * timestamp
+    'input': pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, None, 3.0, 3.5, None, 4.5, 5.0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_remove_feature: Remove f2.
+    "test_remove_feature": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_filter_on_feature: Filter out 'None' from f2
+    'test_filter_on_feature': pd.DataFrame({
+        'patient_id': [1, 2, 3, 2, 3, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y'],
+        'f1': [100, 200, 300, 500, 600, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 3.0, 3.5, 4.5, 5.0],
+        'f3': [True, None, True, True, False, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', None, 'Baz', 'Bar', None]
+    }),
+    # test_mean_data_imputation: Impute mean of f2.
+    "test_mean_data_imputation": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 2.9285714286, 3.0, 3.5, 2.9285714286, 4.5, 5.0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_mode_data_imputation: Impute mode of f4.
+    "test_mode_data_imputation_single_feature": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, None, 3.0, 3.5, None, 4.5, 5.0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', 'Foo', 'Baz', 'Foo', 'Bar', 'Foo']
+    }),
+    "test_mode_data_imputation_all_features": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 1.0, 3.0, 3.5, 1.0, 4.5, 5.0],
+        'f3': [True, True, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', 'Foo', 'Baz', 'Foo', 'Bar', 'Foo']
+    }),
+    # test_add_logarithm_feature: Add log(f2).
+    "test_add_logarithm_feature": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 2.9285714286, 3.0, 3.5, 2.9285714286, 4.5, 5.0],
+        'ln(f2)': [0.0, 0.40546510810816438, 0.69314718055994529,
+            1.0745147370988053, 1.0986122886681098, 1.2527629684953681,
+            1.0745147370988053, 1.5040773967762742, 1.6094379124341003],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_add_indicator_feature: Add indicator(f2).
+    "test_add_indicator_feature": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, None, 3.0, 3.5, None, 4.5, 5.0],
+        'I(f2)': [1, 1, 1, 0, 1, 1, 0, 1, 1],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_add_threshold_feature: Add threshold(f2, upper_bound=3.5)
+    'test_add_threshold_feature': pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, None, 3.0, 3.5, None, 4.5, 5.0],
+        'I(f2<=3.5)': [1, 1, 1, 0, 1, 1, 0, 0, 0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_add_change_interval_feature: Add change(interval, 0.5, patient_id, f2)
+    'test_add_change_interval_feature': pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, None, 3.0, 3.5, None, 4.5, 5.0],
+        'unchanged_yn': [1, 0, 0, 9999999, 0, 0, 9999999, 0, 0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_add_change_percent_feature: Add change(percent, 0.35, patient_id, f2)
+    'test_add_change_percent_feature': pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, None, 3.0, 3.5, None, 4.5, 5.0],
+        'unchanged_yn': [1, 1, 1, 9999999, 0, 1, 9999999, 0, 0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_add_change_sd_feature: Add change(sd, 1, patient_id, f2)
+    'test_add_change_sd_feature': pd.DataFrame({
+        'patient_id': [2, 3, 1, 2, 1, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'N', 'Y', 'N', 'N', 'Y'],
+        'f1': [200, 300, 400, 500, 700, 900],
+        'f2': [1.5, 2.0, None, 3.0, None, 5.0],
+        'unchanged_yn': [1, 0, 9999999, 0, 9999999, 0],
+        'f3': [None, True, False, True, True, True],
+        'f4': ['Bar', 'Baz', 'Foo', None, 'Foo', None]
+    }),
+    # test_zero_data_imputation: Impute zero(f2).
+    "test_zero_data_imputation": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 0.0, 3.0, 3.5, 0.0, 4.5, 5.0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    # test_median_data_imputation: Impute zero(f2).
+    "test_median_data_imputation": pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 3.0, 3.0, 3.5, 3.0, 4.5, 5.0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    }),
+    'test_distribution_data_imputation': pd.DataFrame({
+        'patient_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+        'index_time': [
+            pd.Timestamp('2018-01-01 01:00:00'),
+            pd.Timestamp('2018-01-02 02:00:00'),
+            pd.Timestamp('2018-01-03 03:00:00'),
+            pd.Timestamp('2018-01-04 04:00:00'),
+            pd.Timestamp('2018-01-05 05:00:00'),
+            pd.Timestamp('2018-01-06 06:00:00'),
+            pd.Timestamp('2018-01-07 07:00:00'),
+            pd.Timestamp('2018-01-08 08:00:00'),
+            pd.Timestamp('2018-01-09 09:00:00')],
+        'output': ['Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y'],
+        'f1': [100, 200, 300, 400, 500, 600, 700, 800, 900],
+        'f2': [1.0, 1.5, 2.0, 0.60648508830534575, 3.0, 3.5, 0.60648508830534575, 4.5, 5.0],
+        'f3': [True, None, True, False, True, False, True, False, True],
+        'f4': ['Foo', 'Bar', 'Baz', 'Foo', None, 'Baz', 'Foo', 'Bar', None]
+    })
+}
diff --git a/medinfo/dataconversion/test/FeatureMatrixIOTestData.py b/medinfo/dataconversion/test/FeatureMatrixIOTestData.py
new file mode 100644
index 00000000..1db72766
--- /dev/null
+++ b/medinfo/dataconversion/test/FeatureMatrixIOTestData.py
@@ -0,0 +1,37 @@
+#!/usr/bin/python
+"""
+Test data for TestFeatureMatrixIO.
+"""
+
+import datetime
+import pandas as pd
+
+MANUAL_TEST_CASE = {
+    'custom_header': [
+        'test-matrix-with-header.tab',
+        '',
+        'This matrix file is for testing only.',
+        "Doesn't contain anything of value."
+    ],
+    'default_header': [
+        'header-temp-file.tab',
+        'Created: %s' % datetime.datetime.today().date()
+    ],
+    'matrix_no_header': pd.DataFrame({
+        'patient_id': [1, 2, 3, 4, 5],
+        'index_time': [
+            pd.Timestamp('2018-01-01 12:00:00'),
+            pd.Timestamp('2018-01-01 12:00:00'),
+            pd.Timestamp('2018-01-01 12:00:00'),
+            pd.Timestamp('2018-01-01 12:00:00'),
+            pd.Timestamp('2018-01-01 12:00:00'),
+        ],
+        'label': [True, False, True, False, True],
+        'f1': [1.0, 2.0, 3.0, 4.0, 5.0],
+        'f2': [1, 0, 1, 1, 0],
+        'f3': [False, True, False, True, False],
+        'f4': ["Foo", "Bar", "Baz", "Foo", "Bar"]
+    },
+    # Specify ordering of columns because pandas alphabetizes by default.
+    columns=['patient_id', 'index_time', 'label', 'f1', 'f2', 'f3', 'f4'])
+}
diff --git a/medinfo/dataconversion/test/FeatureMatrixTestData.py b/medinfo/dataconversion/test/FeatureMatrixTestData.py
index 37c018b6..442427f6 100644
--- a/medinfo/dataconversion/test/FeatureMatrixTestData.py
+++ b/medinfo/dataconversion/test/FeatureMatrixTestData.py
@@ -160,7 +160,7 @@
         -4000\t1\t10/5/2213 17:00\tBaz\t10\tMed\tN\n",
     "stride_flowsheet" :
         "pat_anon_id\tflo_meas_id\tflowsheet_name\tflowsheet_value\t\
-        shifted_record_dt_tm\n\
+        shifted_dt_tm\n\
         -123\t-1\tFiO2\t0.2\t4/6/2009 6:36\n\
         -123\t-1\tFiO2\t0\t4/6/2009 16:34\n\
         -123\t-2\tGlasgow Coma Scale Score\t2.1\t4/6/2009 15:12\n\
@@ -292,7 +292,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38198.8472222", "1",
+            "38198.84722222222", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -315,7 +315,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38228.8055556", "1",
+            "38228.805555555555", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -338,7 +338,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38209.2222222", "1",
+            "38209.22222222222", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -361,7 +361,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38210.2222222", "1",
+            "38210.22222222222", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -374,7 +374,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38168.8055556", "1",
+            "38168.805555555555", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -397,7 +397,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38180.1805556", "1",
+            "38180.180555555555", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -420,7 +420,7 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38167.8055556", "2",
+            "38167.805555555555", "2",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -430,7 +430,113 @@
             "0", "0", "0",
             "0", "0", "0",
             "0", "0",
-            "38167.8472222", "1",
+            "38167.84722222222", "1",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ]
+    ],
+    "test_buildFeatureMatrix_prePostFeatures" : [
+        [
+            "pat_id", "order_proc_id", "proc_code", "order_time", "normal_results",
+            "TestItem100.preTimeDays", "TestItem100.pre",
+            "TestItem100.pre.1d", "TestItem100.pre.2d", "TestItem100.pre.4d",
+            "TestItem100.pre.7d", "TestItem100.pre.14d", "TestItem100.pre.30d",
+            "TestItem100.pre.90d", "TestItem100.pre.180d", "TestItem100.pre.365d",
+            "TestItem100.pre.730d", "TestItem100.pre.1460d",
+            "TestItem200.postTimeDays",	"TestItem200.post",
+            "TestItem200.post.1d", "TestItem200.post.2d", "TestItem200.post.4d",
+            "TestItem200.post.7d", "TestItem200.post.14d", "TestItem200.post.30d",
+            "TestItem200.post.90d", "TestItem200.post.180d", "TestItem200.post.365d",
+            "TestItem200.post.730d", "TestItem200.post.1460d"
+        ],
+        [
+            "-789", "-900", "LABMETB", "2009-05-06 15:00:00", "0",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "38198.84722222222", "1",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ],
+        [
+            "-789", "-800", "LABMETB", "2009-04-06 16:00:00", "0",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "38228.805555555555", "1",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ],
+        [
+            "-789", "-750", "LABMETB", "2009-04-26 06:00:00", "1",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "38209.22222222222", "1",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ],
+        [
+            "-789", "-700", "LABMETB", "2009-04-25 06:00:00", "1",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "38210.22222222222", "1",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ],
+        [
+            "-456", "-600", "LABMETB", "2009-05-06 15:00:00", "1",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ],
+        [
+            "-456", "-400", "LABMETB", "2009-04-25 06:00:00", "1",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0"
+        ],
+        [
+            "-123", "-300", "LABMETB", "2009-04-06 15:00:00", "0",
+            "None", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0", "0",
+            "0", "0",
+            "38167.84722222222", "1",
             "0", "0", "0",
             "0", "0", "0",
             "0", "0", "0",
@@ -537,9 +643,9 @@
                 "None", "None", "None", "None", "None",
                 "None", "None", "None", "None",
                 "None",
-                "3", "2", "0.3", "1.0", "0.7",
-                "0.666666666667", "0.286744175568", "1.0", "0.7", "-0.3",
-                "-0.0151898734177", "0.7", "-30.125", "-10.375", "-10.375",
+                3, 2, 0.3, 1.0, 0.7,
+                0.6666666666666666, 0.28674417556808757, 1.0, 0.7, -0.3,
+                -0.0151898734177, 0.7, -30.125, -10.375, -10.375,
                 "0", "0", "None", "None", "None",
                 "None", "None", "None", "None", "None",
                 "None", "None",	"None", "None", "None"
@@ -550,9 +656,9 @@
                 "0", "0", "None", "None", "None",
                 "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None",
-                "1", "0", "1.0", "1.0", "1.0",
-                "1.0", "0.0", "1.0", "1.0", "0.0",
-                "0.0", "1.0", "-0.166666666667", "-0.166666666667",	"-0.166666666667",
+                1, 0, 1.0, 1.0, 1.0,
+                1.0, 0.0, 1.0, 1.0, 0.0,
+                0.0, 1.0, -0.166666666667, -0.166666666667,	-0.166666666667,
                 "0", "0", "None", "None", "None",
                 "None", "None", "None",	"None",	"None",
                 "None",	"None",	"None",	"None",	"None"
@@ -563,9 +669,9 @@
                 "0", "0", "None", "None", "None",
                 "None", "None", "None", "None", "None",
                 "None", "None", "None", "None",	"None",
-                "2", "1", "0.3", "1.0", "0.65",
-                "0.65", "0.35",	"1.0", "0.3", "-0.7",
-                "-0.0368421052632", "0.3", "-19.75", "-0.75", "-0.75",
+                2, 1, 0.3, 1.0, 0.65,
+                0.65, 0.35,	1.0, 0.3, -0.7,
+                -0.0368421052632, 0.3, -19.75, -0.75, -0.75,
                 "0", "0", "None", "None", "None",
                 "None", "None",	"None",	"None",	"None",
                 "None", "None", "None", "None", "None"
@@ -576,9 +682,9 @@
                 "0", "0", "None", "None", "None",
                 "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None",
-                "1", "0", "1.0", "1.0", "1.0",
-                "1.0", "0.0", "1.0", "1.0", "0.0",
-                "0.0", "1.0", "-18.75", "-18.75", "-18.75",
+                1, 0, 1.0, 1.0, 1.0,
+                1.0, 0.0, 1.0, 1.0, 0.0,
+                0.0, 1.0, -18.75, -18.75, -18.75,
                 "0", "0", "None", "None", "None",
                 "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None"
@@ -612,9 +718,9 @@
             [
                 "-123", "-300", "LABMETB", "2009-04-06 15:00:00",
                 "0",
-                "1", "0", "0.2", "0.2", "0.2",
-                "0.2", "0.0", "0.2", "0.2", "0.0",
-                "0.0", "0.2", "-0.35", "-0.35", "-0.35",
+                1, 0, 0.2, 0.2, 0.2,
+                0.2, 0.0, 0.2, 0.2, 0.0,
+                0.0, 0.2, -0.35, -0.35, -0.35,
                 "0", "0", "None", "None", "None",
                 "None", "None", "None",	"None",	"None",
                 "None", "None", "None", "None", "None",
@@ -666,8 +772,8 @@
                 "None", "None", "None", "None", "None", "None",
                 "0", "0", "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None",
-                "3", "0", "0.3", "1.0", "0.7", "0.666666666667", "0.286744175568", "1.0",
-                "0.7", "-0.3", "-0.0151898734177", "0.7", "-30.125", "-10.375", "-10.375"
+                3, 0, 0.3, 1.0, 0.7, 0.6666666666666666, 0.28674417556808757, 1.0,
+                0.7, -0.3, -0.0151898734177, 0.7, -30.125, -10.375, -10.375
             ],
             [
                 "-789", "-800", "LABMETB", "2009-04-06 16:00:00",
@@ -676,8 +782,8 @@
                 "None", "None", "None", "None", "None", "None",
                 "0", "0", "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None",
-                "1", "0", "1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "1.0", "0.0",
-                "0.0", "1.0", "-0.166666666667", "-0.166666666667", "-0.166666666667"
+                1, 0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0,
+                0.0, 1.0, -0.166666666667, -0.166666666667, -0.166666666667
             ],
             [
                 "-789", "-750", "LABMETB", "2009-04-26 06:00:00",
@@ -686,8 +792,8 @@
                 "None", "None", "None", "None", "None", "None",
                 "0", "0", "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None",
-                "2", "0", "0.3", "1.0", "0.65", "0.65", "0.35", "1.0", "0.3",
-                "-0.7", "-0.0368421052632", "0.3", "-19.75", "-0.75", "-0.75"
+                2, 0, 0.3, 1.0, 0.65, 0.65, 0.35, 1.0, 0.3,
+                -0.7, -0.0368421052632, 0.3, -19.75, -0.75, -0.75
             ],
             [
                 "-789", "-700", "LABMETB", "2009-04-25 06:00:00",
@@ -695,8 +801,8 @@
                 "None", "None", "None", "None", "None", "None", "None",
                 "0", "0", "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None",
-                "1", "0", "1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "1.0", "0.0",
-                "0.0", "1.0", "-18.75", "-18.75", "-18.75"
+                1, 0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0,
+                0.0, 1.0, -18.75, -18.75, -18.75
             ],
             [
                 "-456", "-600", "LABMETB", "2009-05-06 15:00:00",
@@ -705,7 +811,7 @@
                 "None", "None", "None", "None", "None", "None", "None",
                 "0", "0", "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None",
-                "0", "0", "None", "None", "None", "None", "None", "None", "None",
+                0, 0, "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None"
             ],
             [
@@ -715,7 +821,7 @@
                 "None", "None", "None", "None", "None", "None",
                 "0", "0", "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None",
-                "0", "0", "None", "None", "None", "None", "None", "None", "None",
+                0, 0, "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None"
             ],
             [
@@ -725,9 +831,40 @@
                 "None", "None", "None", "None", "None", "None",
                 "1", "0", "0.2", "0.2", "0.2", "0.2", "0.0", "0.2", "0.2", "0.0",
                 "0.0", "0.2", "-0.35", "-0.35", "-0.35",
-                "0", "0", "None", "None", "None", "None", "None", "None", "None",
+                0, 0, "None", "None", "None", "None", "None", "None", "None",
                 "None", "None", "None", "None", "None", "None"
             ]
         ]
+    },
+    "test_addTimeCycleFeatures" : {
+        "expectedMatrix" : [
+            [
+                "pat_id", "order_proc_id", "proc_code", "order_time",
+                "normal_results", "order_time.month", "order_time.month.sin",
+                "order_time.month.cos", "order_time.hour",
+                "order_time.hour.sin", "order_time.hour.cos"
+            ],
+            [   "-789", "-900", "LABMETB", "2009-05-06 15:00:00", "0",
+                "5", 0.8660254037844388, -0.4999999999999998,
+                "15", -0.7071067811865471, -0.7071067811865479 ],
+            [   "-789", "-800", "LABMETB", "2009-04-06 16:00:00", "0",
+                "4", 1.0, 6.123233995736766e-17,
+                "16", -0.8660254037844384, -0.5000000000000004 ],
+            [   "-789", "-750", "LABMETB", "2009-04-26 06:00:00", "1",
+                "4", 1.0, 6.123233995736766e-17,
+                "6", 1.0, 6.123233995736766e-17 ],
+            [   "-789", "-700", "LABMETB", "2009-04-25 06:00:00", "1",
+                "4", 1.0, 6.123233995736766e-17,
+                "6", 1.0, 6.123233995736766e-17 ],
+            [   "-456", "-600", "LABMETB", "2009-05-06 15:00:00", "1",
+                "5", 0.8660254037844388, -0.4999999999999998,
+                "15", -0.7071067811865471, -0.7071067811865479 ],
+            [   "-456", "-400", "LABMETB", "2009-04-25 06:00:00", "1",
+                "4", 1.0, 6.123233995736766e-17,
+                "6", 1.0, 6.123233995736766e-17 ],
+            [   "-123", "-300", "LABMETB", "2009-04-06 15:00:00", "0",
+                "4", 1.0, 6.123233995736766e-17,
+                "15", -0.7071067811865471, -0.7071067811865479 ]
+        ]
     }
 }
diff --git a/medinfo/dataconversion/test/TestDataExtractor.py b/medinfo/dataconversion/test/TestDataExtractor.py
index b098c4e8..003f6f33 100644
--- a/medinfo/dataconversion/test/TestDataExtractor.py
+++ b/medinfo/dataconversion/test/TestDataExtractor.py
@@ -2,16 +2,18 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime, timedelta;
 import unittest
 
-from Const import RUNNER_VERBOSITY, LOGGER_LEVEL, APPLICATION_NAME;
-from Util import log;
+from .Const import RUNNER_VERBOSITY, LOGGER_LEVEL, APPLICATION_NAME;
+from .Util import log;
 import logging
 
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable;
@@ -26,8 +28,10 @@ def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
 
-        self.purgeTestRecords();
         log.info("Populate the database with test data")
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+        #self.purgeTestRecords();
 
         dataTextStr = \
 """clinical_item_category_id\tsource_table\tdescription
@@ -84,7 +88,7 @@ def setUp(self):
         DBUtil.insertFile( StringIO(dataTextStr), "stride_order_results", delim="\t", dateColFormats={"result_time": None} );
 
         dataTextStr = \
-"""pat_anon_id\tflo_meas_id\tflowsheet_name\tflowsheet_value\tshifted_record_dt_tm
+"""pat_anon_id\tflo_meas_id\tflowsheet_name\tflowsheet_value\tshifted_dt_tm
 -123\t-1\tFiO2\t0.2\t4/6/2009 6:36
 -123\t-1\tFiO2\t0\t4/6/2009 16:34
 -123\t-2\tGlasgow Coma Scale Score\t2.1\t4/6/2009 15:12
@@ -97,7 +101,7 @@ def setUp(self):
 -789\t-2\tGlasgow Coma Scale Score\t1\t4/6/2009 12:00
 -789\t-3\tBP_High_Systolic\t151\t5/6/2009 15:12
 """
-        DBUtil.insertFile( StringIO(dataTextStr), "stride_flowsheet", delim="\t", dateColFormats={"shifted_record_dt_tm": None} );
+        DBUtil.insertFile( StringIO(dataTextStr), "stride_flowsheet", delim="\t", dateColFormats={"shifted_dt_tm": None} );
 
         dataTextStr = \
 """order_med_id\tpat_id\tmedication_id\tdescription\tstart_taking_time\tend_taking_time\tfreq_name\tmin_discrete_dose\tmin_rate
@@ -303,7 +307,7 @@ def test_generateDateRangeIndexTimes(self):
                 -456: {"patient_id": -456, "start_time":DBUtil.parseDateValue("11/5/2113 10:20"), "end_time":DBUtil.parseDateValue("11/5/2113 10:20")},
                 -789: {"patient_id": -789, "start_time":DBUtil.parseDateValue("1/5/2113 10:20"), "end_time":DBUtil.parseDateValue("1/2/2113 10:20")},
             }
-        patientList = patientById.values();
+        patientList = list(patientById.values());
         colNames = list();
         patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("start_time","end_time", patientList, colNames);
 
@@ -316,8 +320,8 @@ def test_generateDateRangeIndexTimes(self):
                 -789: {},   # Can't find a valid index time if start date is after end date
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
             self.assertEqual( len(expectedData[patientId]), len(patientEpisodeByIndexTime) )
         self.assertEqual( len(expectedData), len(actualData) );
@@ -335,8 +339,8 @@ def test_generateDateRangeIndexTimes(self):
                 -789: {},   # Can't find a valid index time if start date is after end date
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, patientId, indexTime, patient;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
             self.assertEqual( len(expectedData[patientId]), len(patientEpisodeByIndexTime) )
@@ -352,8 +356,8 @@ def test_generateDateRangeIndexTimes(self):
                 -789: {},   # Can't find a valid index time if start date is after end date
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, patientId, indexTime, patient;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
             self.assertEqual( len(expectedData[patientId]), len(patientEpisodeByIndexTime) )
@@ -379,8 +383,8 @@ def test_generateDateRangeIndexTimes_repeatPatient(self):
                       },
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, patientId, indexTime, patient;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
             self.assertEqual( len(expectedData[patientId]), len(patientEpisodeByIndexTime) )
@@ -398,8 +402,8 @@ def test_generateDateRangeIndexTimes_repeatPatient(self):
                       },
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, patientId, indexTime, patient;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
             self.assertEqual( len(expectedData[patientId]), len(patientEpisodeByIndexTime) )
@@ -422,7 +426,7 @@ def test_addTimeCycleFeatures(self):
         expectedColNames = ["index_time.month","index_time.month.sin","index_time.month.cos", "index_time.hour","index_time.hour.sin","index_time.hour.cos"];
 
         colNames = None;
-        for patientId, patientEpisode in patientById.iteritems():
+        for patientId, patientEpisode in patientById.items():
             colNames = list();
             # Generate index time points copies for the patient
             colNames.extend(self.extractor.addTimeCycleFeatures_singleEpisode(patientEpisode, "index_time", "month"));
@@ -430,7 +434,7 @@ def test_addTimeCycleFeatures(self):
 
             #print >> sys.stderr, patientEpisode;
             self.assertAlmostEqualsDict( expectedDataByPatientId[patientId], patientEpisode );
-            self.assertEquals(expectedColNames, colNames);
+            self.assertEqual(expectedColNames, colNames);
 
     def test_addClinicalItemFeatures(self):
         log.debug("Setup clinical item feature data file first...");
@@ -446,7 +450,7 @@ def test_addClinicalItemFeatures(self):
 
         # Extract out item contents and link to patient data
         colNames = list();
-        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", patientById.values(), colNames);
+        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", list(patientById.values()), colNames);
         itemTimesByPatientId = self.extractor.parseClinicalItemFile(StringIO(outFile.getvalue()));
         self.extractor.addClinicalItemFeatures(itemTimesByPatientId, patientEpisodeByIndexTimeById, colNames, "TestItem");
 
@@ -458,8 +462,8 @@ def test_addClinicalItemFeatures(self):
             };
 
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, key, value;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
         self.assertEqual( len(expectedData), len(actualData) );
@@ -481,7 +485,7 @@ def test_addClinicalItemFeatures_perPatient(self):
             };
 
         colNames = None;
-        for patientId, basePatient in patientById.iteritems():
+        for patientId, basePatient in patientById.items():
             colNames = list();
             # Generate index time points copies for the patient
             (patientEpisodeByIndexTime, newColNames) = self.extractor.generateDateRangeIndexTimes_singlePatient("index_time","index_time", basePatient);
@@ -495,7 +499,7 @@ def test_addClinicalItemFeatures_perPatient(self):
             colNames.extend(newColNames);
 
             actualData = patientEpisodeByIndexTime;
-            for indexTime, patient in actualData.iteritems():
+            for indexTime, patient in actualData.items():
                 #print >> sys.stderr, patient;
                 self.assertAlmostEqualsDict( expectedDataByPatientId[patientId][indexTime], patient );
             self.assertEqual( len(expectedDataByPatientId[patientId]), len(actualData) );
@@ -537,11 +541,11 @@ def test_labResults(self):
                 {"pat_id": -123, "base_name":"CR", "ord_num_value":2.1, "result_flag": "High", "result_in_range_yn":"N", "result_time": DBUtil.parseDateValue("4/6/2009 15:12") },
                 {"pat_id": -123, "base_name":"TNI", "ord_num_value":0, "result_flag": None, "result_in_range_yn":"Y", "result_time": DBUtil.parseDateValue("4/6/2009 16:34") },
             ];
-        self.assertEqualDictList( expectedData, actualData, expectedData[0].keys() );
+        self.assertEqualDictList( expectedData, actualData, list(expectedData[0].keys()) );
 
         # Parse back the results and load into patient data
         colNames = list();
-        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", patientById.values(), colNames);
+        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", list(patientById.values()), colNames);
 
         preTimeDelta = timedelta(-90); # Any time in the past 90
         postTimeDelta = timedelta(0);   # Only look for past items
@@ -555,8 +559,8 @@ def test_labResults(self):
                 -999: {DBUtil.parseDateValue("7/6/2009 12:00"): {"patient_id": -999, "index_time":DBUtil.parseDateValue("7/6/2009 12:00"),"days_until_end": 0.0,  "TNI.-90_0.count":0, "TNI.-90_0.countInRange":0, "TNI.-90_0.min":None, "TNI.-90_0.max":None, "TNI.-90_0.median":None, "TNI.-90_0.mean":None, "TNI.-90_0.std":None, "TNI.-90_0.first":None, "TNI.-90_0.last":None, "TNI.-90_0.diff":None, "TNI.-90_0.slope":None, "TNI.-90_0.proximate":None, "TNI.-90_0.firstTimeDays":None, "TNI.-90_0.lastTimeDays":None, "TNI.-90_0.proximateTimeDays":None, "CR.-90_0.count":0, "CR.-90_0.countInRange":0, "CR.-90_0.min":None, "CR.-90_0.max":None, "CR.-90_0.median":None, "CR.-90_0.mean":None, "CR.-90_0.std":None, "CR.-90_0.first":None, "CR.-90_0.last":None, "CR.-90_0.diff":None, "CR.-90_0.slope":None, "CR.-90_0.proximate":None, "CR.-90_0.firstTimeDays":None, "CR.-90_0.lastTimeDays":None, "CR.-90_0.proximateTimeDays":None, "LAC.-90_0.count":0, "LAC.-90_0.countInRange":0, "LAC.-90_0.min":None, "LAC.-90_0.max":None, "LAC.-90_0.median":None, "LAC.-90_0.mean":None, "LAC.-90_0.std":None, "LAC.-90_0.first":None, "LAC.-90_0.last":None, "LAC.-90_0.diff":None, "LAC.-90_0.slope":None, "LAC.-90_0.proximate":None, "LAC.-90_0.firstTimeDays":None, "LAC.-90_0.lastTimeDays":None, "LAC.-90_0.proximateTimeDays":None, }},
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, patientId, patient;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
         self.assertEqual( len(expectedData), len(actualData) );
@@ -595,7 +599,7 @@ def test_labResults_perPatient(self):
             };
 
         colNames = None;
-        for patientId, basePatient in patientById.iteritems():
+        for patientId, basePatient in patientById.items():
             colNames = list();
             (patientEpisodeByIndexTime, newColNames) = self.extractor.generateDateRangeIndexTimes_singlePatient("index_time","index_time", basePatient);
             colNames.extend(newColNames);
@@ -607,7 +611,7 @@ def test_labResults_perPatient(self):
             expectedData = expectedDataByPatientId[patientId];
             expectedKeys = None;
             if len(expectedData) > 0:
-                expectedKeys = expectedData[0].keys();
+                expectedKeys = list(expectedData[0].keys());
             self.assertEqualDictList( expectedData, actualData, expectedKeys );
 
             # Parse back the results and load into patient data
@@ -620,7 +624,7 @@ def test_labResults_perPatient(self):
 
             expectedpatientEpisodeByIndexTime = expectedpatientEpisodeByIndexTimeById[patientId];
             actualData = patientEpisodeByIndexTime;
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, patientId, patient["CR.None_0.count"], patient;
                 self.assertAlmostEqualsDict( expectedpatientEpisodeByIndexTime[indexTime], patient );
             self.assertEqual( len(expectedpatientEpisodeByIndexTime), len(actualData) );
@@ -645,7 +649,7 @@ def test_flowsheet(self):
             row["num_value"] = None;
             if row["flowsheet_value"] is not None and row["flowsheet_value"] != NULL_STRING:
                 row["num_value"] = float(row["flowsheet_value"]);
-            row["result_time"] = DBUtil.parseDateValue(row["shifted_record_dt_tm"]);
+            row["result_time"] = DBUtil.parseDateValue(row["shifted_dt_tm"]);
 
             #print >> sys.stderr, "%(pat_id)s, %(base_name)s, %(num_value)s, %(result_time)s" % row;
 
@@ -665,11 +669,11 @@ def test_flowsheet(self):
                 {"pat_id": -123, "base_name":"Glasgow Coma Scale Score", "num_value":2.1, "result_time": DBUtil.parseDateValue("4/6/2009 15:12") },
                 {"pat_id": -123, "base_name":"FiO2", "num_value":0, "result_time": DBUtil.parseDateValue("4/6/2009 16:34") },
             ];
-        self.assertEqualDictList( expectedData, actualData, expectedData[0].keys() );
+        self.assertEqualDictList( expectedData, actualData, list(expectedData[0].keys()) );
 
         # Parse back the results and load into patient data
         colNames = list();
-        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", patientById.values(), colNames);
+        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", list(patientById.values()), colNames);
 
         preTimeDelta = None; # Any time in the past
         postTimeDelta = timedelta(0);   # Only look for past items
@@ -683,8 +687,8 @@ def test_flowsheet(self):
                 -999: {DBUtil.parseDateValue("7/6/2009 12:00"): {"patient_id": -999, "index_time":DBUtil.parseDateValue("7/6/2009 12:00"), "days_until_end": 0.0, "FiO2.None_0.count":0, "FiO2.None_0.countInRange":0, "FiO2.None_0.min":None, "FiO2.None_0.max":None, "FiO2.None_0.median":None, "FiO2.None_0.mean":None, "FiO2.None_0.std":None, "FiO2.None_0.first":None, "FiO2.None_0.last":None, "FiO2.None_0.diff":None, "FiO2.None_0.slope":None, "FiO2.None_0.proximate":None, "FiO2.None_0.firstTimeDays":None, "FiO2.None_0.lastTimeDays":None, "FiO2.None_0.proximateTimeDays":None, "Glasgow Coma Scale Score.None_0.count":0, "Glasgow Coma Scale Score.None_0.countInRange":0, "Glasgow Coma Scale Score.None_0.min":None, "Glasgow Coma Scale Score.None_0.max":None, "Glasgow Coma Scale Score.None_0.median":None, "Glasgow Coma Scale Score.None_0.mean":None, "Glasgow Coma Scale Score.None_0.std":None, "Glasgow Coma Scale Score.None_0.first":None, "Glasgow Coma Scale Score.None_0.last":None, "Glasgow Coma Scale Score.None_0.diff":None, "Glasgow Coma Scale Score.None_0.slope":None, "Glasgow Coma Scale Score.None_0.proximate":None, "Glasgow Coma Scale Score.None_0.firstTimeDays":None, "Glasgow Coma Scale Score.None_0.lastTimeDays":None, "Glasgow Coma Scale Score.None_0.proximateTimeDays":None, "Resp.None_0.count":0, "Resp.None_0.countInRange":0, "Resp.None_0.min":None, "Resp.None_0.max":None, "Resp.None_0.median":None, "Resp.None_0.mean":None, "Resp.None_0.std":None, "Resp.None_0.first":None, "Resp.None_0.last":None, "Resp.None_0.diff":None, "Resp.None_0.slope":None, "Resp.None_0.proximate":None, "Resp.None_0.firstTimeDays":None, "Resp.None_0.lastTimeDays":None, "Resp.None_0.proximateTimeDays":None, }},
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #print >> sys.stderr, key, value;
                 self.assertAlmostEqualsDict( expectedData[patientId][indexTime], patient );
         self.assertEqual( len(expectedData), len(actualData) );
@@ -729,12 +733,12 @@ def test_ivFluids(self):
 -123\t4318\t4/6/2009 17:00\t4/6/2009 18:00\tCONTINUOUS\tNone\t1000
 """);
         expectedData = self.extractor.parseIVFluidFile(expectedDataFile);
-        self.assertEqualDictList( expectedData[-123], actualData[-123], expectedData[-123][0].keys() );
+        self.assertEqualDictList( expectedData[-123], actualData[-123], list(expectedData[-123][0].keys()) );
 
 
         # Parse back the results and load into patient data
         colNames = ["patient_id"];
-        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", patientById.values(), colNames);
+        patientEpisodeByIndexTimeById = self.extractor.generateDateRangeIndexTimes("index_time","index_time", list(patientById.values()), colNames);
 
         thresholdVolumes = [500,1000,2000,3000,4000,5000];  # Volumes (mL) of fluid interested in time until encountering
         checkpointTimes = [0, 0.5*60*60, 1*60*60, 2*60*60, 3*60*60, 4*60*60, 4.5*60*60, 5*60*60, 6*60*60, 7*60*60];  # Time checkpoints (seconds) interested in accumulated fluid volume by that time
@@ -767,8 +771,8 @@ def test_ivFluids(self):
                  },
             };
         actualData = patientEpisodeByIndexTimeById;
-        for patientId, patientEpisodeByIndexTime in actualData.iteritems():
-            for indexTime, patient in patientEpisodeByIndexTime.iteritems():
+        for patientId, patientEpisodeByIndexTime in actualData.items():
+            for indexTime, patient in patientEpisodeByIndexTime.items():
                 #keys = patient.keys();
                 #keys.sort();
                 #for key in keys:
@@ -777,6 +781,7 @@ def test_ivFluids(self):
                 self.assertEqual(set(expectedData[patientId][indexTime].keys()), set(colNames));
         self.assertEqual( len(expectedData), len(actualData) );
 
+
 def suite():
     """Returns the suite of tests to run for this test class / module.
     Use unittest.makeSuite methods which simply extracts all of the
@@ -787,12 +792,14 @@ def suite():
     #suite.addTest(TestDataExtractor("test_generateDateRangeIndexTimes_repeatPatient"));
     #suite.addTest(TestDataExtractor('test_labResults_perPatient'));
     #suite.addTest(TestDataExtractor('test_labResults'));
+    #suite.addTest(TestDataExtractor('test_flowsheet'));
     #suite.addTest(TestDataExtractor('test_addTimeCycleFeatures'));
     suite.addTest(unittest.makeSuite(TestDataExtractor));
 
     return suite;
 
-if __name__=="__main__":
-    log = logging.getLogger("CDSS")
-    log.setLevel(logging.DEBUG)
+
+if __name__ == "__main__":
+    #log = logging.getLogger("CDSS")
+    #log.setLevel(logging.DEBUG)
     unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestFeatureMatrixFactory.py b/medinfo/dataconversion/test/TestFeatureMatrixFactory.py
index 03d75a5a..916f488a 100644
--- a/medinfo/dataconversion/test/TestFeatureMatrixFactory.py
+++ b/medinfo/dataconversion/test/TestFeatureMatrixFactory.py
@@ -4,29 +4,38 @@
 """
 
 import datetime
-import os
+import sys, os
 import time
 import unittest
 
-from Const import RUNNER_VERBOSITY
-from cStringIO import StringIO
-from FeatureMatrixTestData import FM_TEST_INPUT_TABLES, FM_TEST_OUTPUT
+from .Const import RUNNER_VERBOSITY
+from io import StringIO
+from .FeatureMatrixTestData import FM_TEST_INPUT_TABLES, FM_TEST_OUTPUT
 from medinfo.dataconversion.DataExtractor import DataExtractor
 from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable
 from medinfo.db.ResultsFormatter import TextResultsFormatter
 from medinfo.db.test.Util import DBTestCase
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader;
+
+from .Util import log
 
-from Util import log
 
 class TestFeatureMatrixFactory(DBTestCase):
     def setUp(self):
         """Prepare state for test cases."""
-        DBTestCase.setUp(self);
+        DBTestCase.setUp(self)
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
         self._deleteTestRecords()
         self._insertTestRecords()
 
+        self.factory = FeatureMatrixFactory()
+        self.connection = DBUtil.connection();  # Setup a common connection for test cases to work with, can catch in finally tearDown method to close/cleanup
+
     def _insertTestRecords(self):
         """Populate database for with patient data."""
         # Populate clinical_item_category.
@@ -58,7 +67,7 @@ def _insertTestRecords(self):
         testRecords = FM_TEST_INPUT_TABLES.get("stride_flowsheet")
         DBUtil.insertFile(StringIO(testRecords), "stride_flowsheet", \
                             delim="\t", \
-                            dateColFormats={"shifted_record_dt_tm": None})
+                            dateColFormats={"shifted_dt_tm": None})
 
         # Populate stride_order_med.
         testRecords = FM_TEST_INPUT_TABLES.get("stride_order_med")
@@ -82,8 +91,29 @@ def _deleteTestRecords(self):
 
     def tearDown(self):
         """Restore state from any setUp or test steps."""
-        self._deleteTestRecords();
-        DBTestCase.tearDown(self);
+        self._deleteTestRecords()
+
+        # Clean up files that might have lingered from failed tests.
+        try:
+            os.remove("patient_list.tsv")
+        except:
+            pass
+        try:
+            self.factory.cleanTempFiles()
+        except:
+            pass
+        try:
+            os.remove(self.factory.getMatrixFileName())
+        except:
+            pass
+        try:
+            os.remove("extractor.feature_matrix.tab.gz")
+        except:
+            pass
+
+        self.connection.close();
+
+        DBTestCase.tearDown(self)
 
     def test_dbCache(self):
         """Test database result caching."""
@@ -95,17 +125,13 @@ def test_dbCache(self):
 
     def test_processPatientListInput(self):
         """Test processPatientListInput()."""
-        # Initialize FeatureMatrixFactory.
-        factory = FeatureMatrixFactory()
-
         # Verify FeatureMatrixFactory throws Error if patientListInput
         # has not been set.
         with self.assertRaises(ValueError):
-            factory.processPatientListInput()
+            self.factory.processPatientListInput()
 
         # Initialize DB cursor.
-        connection = DBUtil.connection()
-        cursor = connection.cursor()
+        cursor = self.connection.cursor()
 
         # Build SQL query for list of patients.
         patientListQuery = SQLQuery()
@@ -117,15 +143,16 @@ def test_processPatientListInput(self):
         cursor.execute(str(patientListQuery), patientListQuery.params)
 
         # Set and process patientListInput.
-        factory.setPatientListInput(cursor, "pat_id")
-        factory.processPatientListInput()
-        resultPatientIterator = factory.getPatientListIterator()
+        self.factory.setPatientListInput(cursor, "pat_id")
+        self.factory.processPatientListInput()
+        resultPatientIterator = self.factory.getPatientListIterator()
 
         # Verify results.
         expectedPatientList = ["-789", "-456", "-123"]
         for expectedPatientId in expectedPatientList:
-            resultPatientId = resultPatientIterator.next()['pat_id']
+            resultPatientId = next(resultPatientIterator)['pat_id']
             self.assertEqual(resultPatientId, expectedPatientId)
+        resultPatientIterator.close();
 
         # Build TSV file for list of patients.
         patientList = \
@@ -140,19 +167,21 @@ def test_processPatientListInput(self):
         patientListTsv.close()
 
         # Initialize new FeatureMatrixFactory.
-        factory = FeatureMatrixFactory()
+        self.factory = FeatureMatrixFactory()
 
         # Set and process patientListInput.
         patientListTsv = open("patient_list.tsv", "r")
-        factory.setPatientListInput(patientListTsv, "patient_id")
-        factory.processPatientListInput()
-        resultPatientIterator = factory.getPatientListIterator()
+        self.factory.setPatientListInput(patientListTsv, "patient_id")
+        self.factory.processPatientListInput()
+        resultPatientIterator = self.factory.getPatientListIterator()
 
         # Verify results.
         expectedPatientList = ["-123", "-123", "-123", "-456", "-789"]
         for expectedPatientId in expectedPatientList:
-            resultPatientId = resultPatientIterator.next()['patient_id']
+            resultPatientId = next(resultPatientIterator)['patient_id']
             self.assertEqual(resultPatientId, expectedPatientId)
+        patientListTsv.close();
+        resultPatientIterator.close();
 
         # Clean up patient_list.
         try:
@@ -162,18 +191,13 @@ def test_processPatientListInput(self):
             pass
 
     def test_buildFeatureMatrix_multiClinicalItem(self):
-        """Test _buildFeatureMatrix()."""
-        # Initialize FeatureMatrixFactory.
-        factory = FeatureMatrixFactory()
-
         # Verify FeatureMatrixFactory throws Error if patientEpisodeInput
         # has not been set.
         with self.assertRaises(ValueError):
-            factory.processPatientEpisodeInput()
+            self.factory.processPatientEpisodeInput()
 
         # Initialize DB cursor.
-        connection = DBUtil.connection()
-        cursor = connection.cursor()
+        cursor = self.connection.cursor()
 
         # Build SQL query for list of patient episodes.
         patientEpisodeQuery = SQLQuery()
@@ -191,9 +215,9 @@ def test_buildFeatureMatrix_multiClinicalItem(self):
         cursor.execute(str(patientEpisodeQuery), patientEpisodeQuery.params)
 
         # Set and process patientEpisodeInput.
-        factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
-        factory.processPatientEpisodeInput()
-        resultEpisodeIterator = factory.getPatientEpisodeIterator()
+        self.factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
+        self.factory.processPatientEpisodeInput()
+        resultEpisodeIterator = self.factory.getPatientEpisodeIterator()
         resultPatientEpisodes = list()
         for episode in resultEpisodeIterator:
             episode["pat_id"] = int(episode["pat_id"])
@@ -205,29 +229,76 @@ def test_buildFeatureMatrix_multiClinicalItem(self):
         self.assertEqualList(resultPatientEpisodes, expectedPatientEpisodes)
 
         # Add TestItem100 and TestItem200 clinical item data.
-        factory.addClinicalItemFeatures(["TestItem100"])
-        factory.addClinicalItemFeatures(["TestItem200"])
-        factory.buildFeatureMatrix()
-        resultMatrix = factory.readFeatureMatrixFile()
+        self.factory.addClinicalItemFeatures(["TestItem100"])
+        self.factory.addClinicalItemFeatures(["TestItem200"])
+        self.factory.buildFeatureMatrix()
+        resultMatrix = self.factory.readFeatureMatrixFile()
         expectedMatrix = FM_TEST_OUTPUT["test_buildFeatureMatrix_multiClinicalItem"]
 
-        self.assertEqualList(resultMatrix, expectedMatrix)
+        self.assertEqualList(resultMatrix[2:], expectedMatrix)
+
+    def test_buildFeatureMatrix_prePostFeatures(self):
+        """
+        Test features parameter in addClinicalItemFeatures which allows
+        client to specify they only want .pre* or .post* columns in feature
+        matrix.
+        """
+        # Verify FeatureMatrixFactory throws Error if patientEpisodeInput
+        # has not been set.
+        with self.assertRaises(ValueError):
+            self.factory.processPatientEpisodeInput()
+
+        # Initialize DB cursor.
+        cursor = self.connection.cursor()
+
+        # Build SQL query for list of patient episodes.
+        patientEpisodeQuery = SQLQuery()
+        patientEpisodeQuery.addSelect("CAST(pat_id AS bigint)")
+        patientEpisodeQuery.addSelect("sop.order_proc_id AS order_proc_id")
+        patientEpisodeQuery.addSelect("proc_code")
+        patientEpisodeQuery.addSelect("order_time")
+        patientEpisodeQuery.addSelect("COUNT(CASE result_in_range_yn WHEN 'Y' THEN 1 ELSE null END) AS normal_results")
+        patientEpisodeQuery.addFrom("stride_order_proc AS sop")
+        patientEpisodeQuery.addFrom("stride_order_results AS sor")
+        patientEpisodeQuery.addWhere("sop.order_proc_id = sor.order_proc_id")
+        patientEpisodeQuery.addWhereEqual("proc_code", "LABMETB")
+        patientEpisodeQuery.addGroupBy("pat_id, sop.order_proc_id, proc_code, order_time")
+        patientEpisodeQuery.addOrderBy("pat_id, sop.order_proc_id, proc_code, order_time")
+        cursor.execute(str(patientEpisodeQuery), patientEpisodeQuery.params)
+
+        # Set and process patientEpisodeInput.
+        self.factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
+        self.factory.processPatientEpisodeInput()
+        resultEpisodeIterator = self.factory.getPatientEpisodeIterator()
+        resultPatientEpisodes = list()
+        for episode in resultEpisodeIterator:
+            episode["pat_id"] = int(episode["pat_id"])
+            episode["order_time"] = DBUtil.parseDateValue(episode["order_time"])
+            resultPatientEpisodes.append(episode)
+
+        # Add TestItem100 and TestItem200 clinical item data.
+        self.factory.addClinicalItemFeatures(["TestItem100"], features="pre")
+        self.factory.addClinicalItemFeatures(["TestItem200"], features="post")
+        self.factory.buildFeatureMatrix()
+        resultMatrix = self.factory.readFeatureMatrixFile()
+        expectedMatrix = FM_TEST_OUTPUT["test_buildFeatureMatrix_prePostFeatures"]
+
+        self.assertEqualList(resultMatrix[2:], expectedMatrix)
 
     def test_build_FeatureMatrix_multiLabTest(self):
         """
         Test buildFeatureMatrix() and addLabFeatures().
         """
         # Initialize FeatureMatrixFactory.
-        factory = FeatureMatrixFactory()
+        self.factory = FeatureMatrixFactory()
 
         # Verify FeatureMatrixFactory throws Error if patientEpisodeInput
         # has not been set.
         with self.assertRaises(ValueError):
-            factory.processPatientEpisodeInput()
+            self.factory.processPatientEpisodeInput()
 
         # Initialize DB cursor.
-        connection = DBUtil.connection()
-        cursor = connection.cursor()
+        cursor = self.connection.cursor()
 
         # Build SQL query for list of patient episodes.
         patientEpisodeQuery = SQLQuery()
@@ -245,9 +316,9 @@ def test_build_FeatureMatrix_multiLabTest(self):
         cursor.execute(str(patientEpisodeQuery), patientEpisodeQuery.params)
 
         # Set and process patientEpisodeInput.
-        factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
-        factory.processPatientEpisodeInput()
-        resultEpisodeIterator = factory.getPatientEpisodeIterator()
+        self.factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
+        self.factory.processPatientEpisodeInput()
+        resultEpisodeIterator = self.factory.getPatientEpisodeIterator()
         resultPatientEpisodes = list()
         for episode in resultEpisodeIterator:
             episode["pat_id"] = int(episode["pat_id"])
@@ -263,30 +334,30 @@ def test_build_FeatureMatrix_multiLabTest(self):
         # Look for lab data 90 days before each episode, but never afterself.
         preTimeDelta = datetime.timedelta(-90)
         postTimeDelta = datetime.timedelta(0)
-        factory.addLabResultFeatures(labBaseNames, preTimeDelta, postTimeDelta)
-        factory.buildFeatureMatrix()
-        resultMatrix = factory.readFeatureMatrixFile()
+        self.factory.addLabResultFeatures(labBaseNames, False, preTimeDelta, postTimeDelta)
+        self.factory.buildFeatureMatrix()
+        resultMatrix = self.factory.readFeatureMatrixFile()
 
         # Verify results.
         expectedMatrix = FM_TEST_OUTPUT["test_buildFeatureMatrix_multiLabTest"]["expectedMatrix"]
-        self.assertEqualList(resultMatrix, expectedMatrix)
+        self.assertEqualTable(expectedMatrix, resultMatrix[2:], precision=5)
+
+        try:
+            os.remove(self.factory.getMatrixFileName())
+        except OSError:
+            pass
 
     def test_buildFeatureMatrix_multiFlowsheet(self):
         """
         Test buildFeatureMatrix and addFlowsheet.
         """
-
-        # Initialize FeatureMatrixFactory.
-        factory = FeatureMatrixFactory()
-
         # Verify FeatureMatrixFactory throws Error if patientEpisodeInput
         # has not been set.
         with self.assertRaises(ValueError):
-            factory.processPatientEpisodeInput()
+            self.factory.processPatientEpisodeInput()
 
         # Initialize DB cursor.
-        connection = DBUtil.connection()
-        cursor = connection.cursor()
+        cursor = self.connection.cursor()
 
         # Build SQL query for list of patient episodes.
         patientEpisodeQuery = SQLQuery()
@@ -304,9 +375,9 @@ def test_buildFeatureMatrix_multiFlowsheet(self):
         cursor.execute(str(patientEpisodeQuery), patientEpisodeQuery.params)
 
         # Set and process patientEpisodeInput.
-        factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
-        factory.processPatientEpisodeInput()
-        resultEpisodeIterator = factory.getPatientEpisodeIterator()
+        self.factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
+        self.factory.processPatientEpisodeInput()
+        resultEpisodeIterator = self.factory.getPatientEpisodeIterator()
         resultPatientEpisodes = list()
         for episode in resultEpisodeIterator:
             episode["pat_id"] = int(episode["pat_id"])
@@ -322,25 +393,88 @@ def test_buildFeatureMatrix_multiFlowsheet(self):
         # Look for lab data 90 days before each episode, but never afterself.
         preTimeDelta = datetime.timedelta(-90)
         postTimeDelta = datetime.timedelta(0)
-        factory.addFlowsheetFeatures(flowsheetNames, preTimeDelta, postTimeDelta)
-        factory.buildFeatureMatrix()
-        resultMatrix = factory.readFeatureMatrixFile()
+        self.factory.addFlowsheetFeatures(flowsheetNames, preTimeDelta, postTimeDelta)
+        self.factory.buildFeatureMatrix()
+        resultMatrix = self.factory.readFeatureMatrixFile()
 
         # Verify results.
         expectedMatrix = FM_TEST_OUTPUT["test_buildFeatureMatrix_multiFlowsheet"]["expectedMatrix"]
-        self.assertEqualList(resultMatrix, expectedMatrix)
+        self.assertEqualTable(expectedMatrix, resultMatrix[2:], precision=5);
+
+        try:
+            os.remove(self.factory.getMatrixFileName())
+        except OSError:
+            pass
+
+    def test_addTimeCycleFeatures(self):
+        """
+        Test .addTimeCycleFeatures()
+        """
+        # Initialize DB cursor.
+        cursor = self.connection.cursor()
+
+        # Build SQL query for list of patient episodes.
+        patientEpisodeQuery = SQLQuery()
+        patientEpisodeQuery.addSelect("CAST(pat_id AS bigint)")
+        patientEpisodeQuery.addSelect("sop.order_proc_id AS order_proc_id")
+        patientEpisodeQuery.addSelect("proc_code")
+        patientEpisodeQuery.addSelect("order_time")
+        patientEpisodeQuery.addSelect("COUNT(CASE result_in_range_yn WHEN 'Y' THEN 1 ELSE null END) AS normal_results")
+        patientEpisodeQuery.addFrom("stride_order_proc AS sop")
+        patientEpisodeQuery.addFrom("stride_order_results AS sor")
+        patientEpisodeQuery.addWhere("sop.order_proc_id = sor.order_proc_id")
+        patientEpisodeQuery.addWhereEqual("proc_code", "LABMETB")
+        patientEpisodeQuery.addGroupBy("pat_id, sop.order_proc_id, proc_code, order_time")
+        patientEpisodeQuery.addOrderBy("pat_id, sop.order_proc_id, proc_code, order_time")
+        cursor.execute(str(patientEpisodeQuery), patientEpisodeQuery.params)
+
+        # Set and process patientEpisodeInput.
+        self.factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
+        self.factory.processPatientEpisodeInput()
+
+        # Add time cycle features.
+        self.factory.addTimeCycleFeatures("order_time", "month")
+        self.factory.addTimeCycleFeatures("order_time", "hour")
+
+        # Verify output.
+        self.factory.buildFeatureMatrix()
+        resultMatrix = self.factory.readFeatureMatrixFile()
+        expectedMatrix = FM_TEST_OUTPUT["test_addTimeCycleFeatures"]["expectedMatrix"]
+        self.assertEqualTable(expectedMatrix, resultMatrix[2:], precision=5);
+
+        # Clean up feature matrix.
+        try:
+            os.remove(self.factory.getMatrixFileName())
+        except OSError:
+            pass
+
+    def test_loadMapData(self):
+        self.factory = FeatureMatrixFactory()
+
+        # Depends on external data file
+        reader = self.factory.loadMapData("CharlsonComorbidity-ICD9CM")
+        charlsonByICD9 = dict()
+
+        for row in reader:
+            charlsonByICD9[row["icd9cm"]] = row["charlson"]
+
+        self.assertEqual("Dementia", charlsonByICD9["294.1"])
+        self.assertEqual("Dementia", charlsonByICD9["331.2"])
+        self.assertEqual("COPD", charlsonByICD9["490"])
+        self.assertEqual("COPD", charlsonByICD9["416.8"])
+        self.assertEqual("Malignancy Metastatic", charlsonByICD9["199"])
+        self.assertEqual("AIDS/HIV", charlsonByICD9["042"])
 
     def test_performance(self):
         """
         Test performance against DataExtractor.
         """
         # Initialize DB cursor.
-        connection = DBUtil.connection()
-        cursor = connection.cursor()
+        cursor = self.connection.cursor()
 
         # Initialize FeatureMatrixFactory.
         factoryStart = time.time()
-        factory = FeatureMatrixFactory()
+        self.factory = FeatureMatrixFactory()
 
         # Build SQL query for list of patient episodes.
         patientEpisodeQuery = SQLQuery()
@@ -358,29 +492,29 @@ def test_performance(self):
         cursor.execute(str(patientEpisodeQuery), patientEpisodeQuery.params)
 
         # Set and process patientEpisodeInput.
-        factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
-        factory.processPatientEpisodeInput()
+        self.factory.setPatientEpisodeInput(cursor, "pat_id", "order_time")
+        self.factory.processPatientEpisodeInput()
 
         # Look for lab data 90 days before each episode, but never afterself.
         preTimeDelta = datetime.timedelta(-90)
         postTimeDelta = datetime.timedelta(0)
 
         # Add clinical item features.
-        factory.addClinicalItemFeatures(["PerfItem300"])
-        factory.addClinicalItemFeatures(["PerfItem400"])
-        factory.addClinicalItemFeatures(["PerfItem500"])
+        self.factory.addClinicalItemFeatures(["PerfItem300"])
+        self.factory.addClinicalItemFeatures(["PerfItem400"])
+        self.factory.addClinicalItemFeatures(["PerfItem500"])
 
         # Add lab result features.
-        factory.addLabResultFeatures(["Foo"], preTimeDelta, postTimeDelta)
-        factory.addLabResultFeatures(["Bar"], preTimeDelta, postTimeDelta)
-        factory.addLabResultFeatures(["Baz"], preTimeDelta, postTimeDelta)
-        factory.addLabResultFeatures(["Qux"], preTimeDelta, postTimeDelta)
+        self.factory.addLabResultFeatures(["Foo"], False, preTimeDelta, postTimeDelta)
+        self.factory.addLabResultFeatures(["Bar"], False, preTimeDelta, postTimeDelta)
+        self.factory.addLabResultFeatures(["Baz"], False, preTimeDelta, postTimeDelta)
+        self.factory.addLabResultFeatures(["Qux"], False, preTimeDelta, postTimeDelta)
 
         # Add flowsheet features.
-        factory.addFlowsheetFeatures(["Perflow"], preTimeDelta, postTimeDelta)
+        self.factory.addFlowsheetFeatures(["Perflow"], preTimeDelta, postTimeDelta)
 
         # Build matrix.
-        factory.buildFeatureMatrix()
+        self.factory.buildFeatureMatrix()
 
         # Stop timer.
         factoryStop = time.time()
@@ -523,7 +657,7 @@ def test_performance(self):
                     formatter.formatTuple(colNames)
 
                 # Print out patient (episode) data (one row per episode)
-                formatter.formatResultDicts(patientEpisodeByIndexTime.values(), colNames)
+                formatter.formatResultDicts(list(patientEpisodeByIndexTime.values()), colNames)
 
             if lastPatientId is None or lastPatientId != patientId:
                 # Prepare to aggregate patient episode record per patient
@@ -594,7 +728,7 @@ def test_performance(self):
             patientEpisodeByIndexTime, labsByBaseName, ["Qux"], \
             preTimeDelta, postTimeDelta))
 
-        formatter.formatResultDicts(patientEpisodeByIndexTime.values(), colNames)
+        formatter.formatResultDicts(list(patientEpisodeByIndexTime.values()), colNames)
 
         # Close file.
         outFile.close()
@@ -613,10 +747,11 @@ def test_performance(self):
         except OSError:
             pass
         try:
-            os.remove(factory.getMatrixFileName())
+            os.remove(self.factory.getMatrixFileName())
         except OSError:
             pass
 
+
 def suite():
     """
     Returns the suite of tests to run for this test class / module.
@@ -624,8 +759,14 @@ def suite():
     methods for the given class whose name starts with "test".
     """
     suite = unittest.TestSuite()
+    #suite.addTest(TestFeatureMatrixFactory("test_addTimeCycleFeatures"));
+    #suite.addTest(TestFeatureMatrixFactory("test_buildFeatureMatrix_multiFlowsheet"));
+    #suite.addTest(TestFeatureMatrixFactory("test_build_FeatureMatrix_multiLabTest"));
+    #suite.addTest(TestFeatureMatrixFactory("test_processPatientListInput"));
+    #suite.addTest(TestFeatureMatrixFactory("test_buildFeatureMatrix_multiClinicalItem"));
     suite.addTest(unittest.makeSuite(TestFeatureMatrixFactory))
     return suite
 
+
 if __name__=="__main__":
     unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestFeatureMatrixIO.py b/medinfo/dataconversion/test/TestFeatureMatrixIO.py
new file mode 100644
index 00000000..373eaad1
--- /dev/null
+++ b/medinfo/dataconversion/test/TestFeatureMatrixIO.py
@@ -0,0 +1,110 @@
+#!/usr/bin/python
+"""
+Test suite for FeatureMatrixIO.
+"""
+
+import filecmp
+import inspect
+import os
+from pandas import read_csv
+from pandas.testing import assert_frame_equal
+import unittest
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.dataconversion.test.FeatureMatrixIOTestData import MANUAL_TEST_CASE
+
+class TestFeatureMatrixIO(MedInfoTestCase):
+    def setUp(self):
+        # Set up temp files.
+        self._no_header_temp_file_path = '_no_header_temp_file_path'
+        self._with_header_temp_file_path = '_with_header_temp_file_path'
+        self._stripped_header_file_path = '_stripped_header_file_path'
+
+    def tearDown(self):
+        # Clean up temp files.
+        temp_files = [
+            self._no_header_temp_file_path,
+            self._with_header_temp_file_path,
+            self._stripped_header_file_path
+        ]
+        for temp_file in temp_files:
+            try:
+                os.remove(temp_file)
+            except OSError:
+                pass
+
+    def test_strip_header(self):
+        # Initialize FeatureMatrixIO.
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for test files.
+        app_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+        no_header_file_name = 'test-matrix-no-header.tab'
+        with_header_file_name = 'test-matrix-with-header.tab'
+        no_header_file_path = os.path.join(app_dir, no_header_file_name)
+        with_header_file_path = os.path.join(app_dir, with_header_file_name)
+
+        # Strip header.
+        matrix_with_header = fm_io.read_file_to_data_frame(with_header_file_path)
+        self._stripped_header_file_path = fm_io.strip_header(with_header_file_path)
+
+        # Validate matrix data.
+        expected_matrix = MANUAL_TEST_CASE['matrix_no_header']
+        actual_matrix = fm_io.read_file_to_data_frame(self._stripped_header_file_path, \
+            datetime_col_index=1)
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def test_read_file_to_data_frame(self):
+        # Initialize FeatureMatrixIO.
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for test files.
+        app_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+        no_header_file_name = 'test-matrix-no-header.tab'
+        with_header_file_name = 'test-matrix-with-header.tab'
+        no_header_file_path = os.path.join(app_dir, no_header_file_name)
+        with_header_file_path = os.path.join(app_dir, with_header_file_name)
+
+        # Read files into data frames.
+        matrix_stripped_header = fm_io.read_file_to_data_frame(with_header_file_path)
+        matrix_no_header = fm_io.read_file_to_data_frame(no_header_file_path)
+
+        # Verify that FeatureMatrixIO correctly stripped the header.
+        expected_matrix = MANUAL_TEST_CASE['matrix_no_header']
+        assert_frame_equal(expected_matrix, matrix_stripped_header)
+        assert_frame_equal(expected_matrix, matrix_no_header)
+
+    def test_write_data_frame_to_file(self):
+        # Initialize FeatureMatrixIO.
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for test files.
+        app_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+        no_header_file_name = 'test-matrix-no-header.tab'
+        with_header_file_name = 'test-matrix-with-header.tab'
+        no_header_file_path = os.path.join(app_dir, no_header_file_name)
+        with_header_file_path = os.path.join(app_dir, with_header_file_name)
+
+        # Read data frames from test files.
+        matrix_no_header = MANUAL_TEST_CASE['matrix_no_header']
+        matrix_header = MANUAL_TEST_CASE['custom_header']
+
+        # Write data frame without header.
+        no_header_temp_file_name = 'no-header-temp-file.tab'
+        self._no_header_temp_file_path = os.path.join(app_dir, no_header_temp_file_name)
+        fm_io.write_data_frame_to_file(matrix_no_header, self._no_header_temp_file_path)
+
+        # Write data frame with header.
+        with_header_temp_file_name = 'header-temp-file.tab'
+        self._with_header_temp_file_path = os.path.join(app_dir, with_header_temp_file_name)
+        fm_io.write_data_frame_to_file(matrix_no_header, self._with_header_temp_file_path, matrix_header)
+
+        # Validate output files.
+        self.assertTrue(filecmp.cmp(no_header_file_path, self._no_header_temp_file_path))
+        self.assertTrue(filecmp.cmp(with_header_file_path, self._with_header_temp_file_path))
+
+if __name__=="__main__":
+    suite = make_test_suite(TestFeatureMatrixIO)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/dataconversion/test/TestFeatureMatrixTransform.py b/medinfo/dataconversion/test/TestFeatureMatrixTransform.py
new file mode 100644
index 00000000..e5193ec0
--- /dev/null
+++ b/medinfo/dataconversion/test/TestFeatureMatrixTransform.py
@@ -0,0 +1,198 @@
+#!/usr/bin/python
+
+from pandas.testing import assert_frame_equal
+from scipy.stats import powerlaw
+import unittest
+import numpy as np
+import sys
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import MedInfoTestCase, make_test_suite
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.test.FMTransformTestData import MANUAL_FM_TEST_CASE
+
+class TestFeatureMatrixTransform(MedInfoTestCase):
+    def setUp(self):
+        self.fmt = FeatureMatrixTransform()
+        input_matrix = MANUAL_FM_TEST_CASE['input']
+        self.fmt.set_input_matrix(input_matrix)
+
+    def tearDown(self):
+        pass
+
+    def test_mean_data_imputation(self):
+        # Impute data.
+        self.fmt.impute(feature="f2", strategy=FeatureMatrixTransform.IMPUTE_STRATEGY_MEAN)
+
+        # Verify output.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_mean_data_imputation']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def test_mode_data_imputation_single_feature(self):
+        # Impute single feature.
+        self.fmt.impute(feature='f4', strategy=FeatureMatrixTransform.IMPUTE_STRATEGY_MODE)
+
+        # Verify single feature imputation.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_mode_data_imputation_single_feature']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def test_mode_data_imputation_all_features(self):
+        # Impute all features.
+        self.fmt.impute(strategy=FeatureMatrixTransform.IMPUTE_STRATEGY_MODE)
+
+        # Verify all feature imputation.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_mode_data_imputation_all_features']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def test_add_logarithm_feature(self):
+        # Impute mean(f2) and add ln(f2) feature.
+        self.fmt.impute(feature="f2")
+        self.fmt.add_logarithm_feature('f2')
+
+        # Hack: pandas automatically sorts the columns of a DataFrame on
+        # init. To make the test data match our intended behavior, need to
+        # rearrange the columns here so that ln(f2) follows f2.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_add_logarithm_feature']
+        cols = list(expected_matrix.columns)
+        cols.insert(2, cols.pop(5))
+        expected_matrix = expected_matrix[cols]
+
+        # Verify feature addition.
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix.sort_index(axis=1), actual_matrix.sort_index(axis=1))
+
+    def test_remove_feature(self):
+        # Remove f2.
+        self.fmt.remove_feature('f2')
+
+        # Verify feature removal.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_remove_feature']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def test_filter_on_feature(self):
+        # Exclude all rows where f2 == None
+        nrows = self.fmt.filter_on_feature('f2', np.nan)
+
+        # Verify correct rows removed
+        expected_matrix = MANUAL_FM_TEST_CASE['test_filter_on_feature']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+        self.assertEqual(expected_matrix.shape[0], nrows)
+
+    def test_add_indicator_feature(self):
+        # Add indicator feature.
+        self.fmt.add_indicator_feature('f2')
+
+        # Hack: pandas automatically sorts the columns of a DataFrame on
+        # init. To make the test data match our intended behavior, need to
+        # rearrange the columns here so that ln(f2) follows f2.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_add_indicator_feature']
+        cols = list(expected_matrix.columns)
+        cols.insert(2, cols.pop(0))
+        expected_matrix = expected_matrix[cols]
+
+        # Verify feature addition.
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix.sort_index(axis=1), actual_matrix.sort_index(axis=1))
+
+    def test_add_threshold_feature(self):
+        # Add threshold feature.
+        self.fmt.add_threshold_feature('f2', upper_bound=3.5)
+
+        # Hack: pandas automatically sorts the columns of a DataFrame on
+        # init. To make the test data match our intended behavior, need to
+        # rearrange the columns here so that I(f2<=3.5) follows f2.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_add_threshold_feature']
+        cols = list(expected_matrix.columns)
+        cols.insert(2, cols.pop(0))
+        expected_matrix = expected_matrix[cols]
+
+        # Verify feature addition.
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix.sort_index(axis=1), actual_matrix.sort_index(axis=1))
+
+    def test_add_change_interval_feature(self):
+        # Add change feature.
+        self.fmt.add_change_feature('interval', 0.5, 'patient_id', 'f2')
+
+        expected_matrix = MANUAL_FM_TEST_CASE['test_add_change_interval_feature']
+        cols = list(expected_matrix.columns)
+        cols.insert(2, cols.pop())
+        expected_matrix = expected_matrix[cols]
+
+        # Verify feature addition.
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix.sort_index(axis=1), actual_matrix.sort_index(axis=1))
+
+    def test_add_change_percent_feature(self):
+        # Add change feature.
+        self.fmt.add_change_feature('percent', 0.35, 'patient_id', 'f2')
+
+        expected_matrix = MANUAL_FM_TEST_CASE['test_add_change_percent_feature']
+        cols = list(expected_matrix.columns)
+        cols.insert(2, cols.pop())
+        expected_matrix = expected_matrix[cols]
+
+        # Verify feature addition.
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix.sort_index(axis=1), actual_matrix.sort_index(axis=1))
+
+    """
+    add_change_feature with 'sd' is not meant to run on matrices smaller than 301
+    rows.  To run a test of this function, uncomment this test function and
+    set k = 3 in the body of add_change_feature in FeatureMatrixTransform.py
+    (normal value is 300).
+
+    def test_add_change_sd_feature(self):
+        # add change feature
+        np.random.seed(123)
+        sample = self.fmt.add_change_feature('sd', 0.5, 'patient_id', 'f2')
+
+        expected_matrix = MANUAL_FM_TEST_CASE['test_add_change_sd_feature']
+        cols = list(expected_matrix.columns)
+        cols.insert(2, cols.pop())
+        expected_matrix = expected_matrix[cols]
+
+        # Verify feature addition
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+        """
+
+    def test_zero_data_imputation(self):
+        # Impute zero(f2).
+        self.fmt.impute(feature="f2", strategy=FeatureMatrixTransform.IMPUTE_STRATEGY_ZERO)
+
+        # Verify feature addition.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_zero_data_imputation']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def test_median_data_imputation(self):
+        # Impute median(f2).
+        self.fmt.impute(feature="f2", strategy=FeatureMatrixTransform.IMPUTE_STRATEGY_MEDIAN)
+
+        # Verify feature addition.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_median_data_imputation']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+    def _power_law_dist(self):
+        return powerlaw.rvs(1.66, random_state=2)
+
+    def test_distrbution_data_imputation(self):
+        # Impute distribution(f2).
+        self.fmt.impute(feature="f2", distribution=self._power_law_dist, \
+            strategy=FeatureMatrixTransform.IMPUTE_STRATEGY_DISTRIBUTION)
+
+        # Verify feature addition.
+        expected_matrix = MANUAL_FM_TEST_CASE['test_distribution_data_imputation']
+        actual_matrix = self.fmt.fetch_matrix()
+        assert_frame_equal(expected_matrix, actual_matrix)
+
+if __name__=="__main__":
+    suite = make_test_suite(TestFeatureMatrixTransform)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/dataconversion/test/TestProviderRotationConversion.py b/medinfo/dataconversion/test/TestProviderRotationConversion.py
new file mode 100644
index 00000000..66cd4649
--- /dev/null
+++ b/medinfo/dataconversion/test/TestProviderRotationConversion.py
@@ -0,0 +1,146 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+from io import StringIO
+from datetime import datetime;
+import unittest
+
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
+
+from medinfo.db.test.Util import DBTestCase;
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel, generatePlaceholders;
+
+from medinfo.dataconversion.ProviderRotationConversion import ProviderRotationConversion;
+
+TEST_SOURCE_TABLE = "test_accesslog_script";
+
+class TestProviderRotationConversion(DBTestCase):
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self);
+
+        log.info("Populate the database with test data (Assumes MySQL data structure)")
+        DBUtil.execute \
+        ("""create table %s
+            (
+                USER_ID varchar(255),
+                USER_NAME varchar(255),
+                DE_PAT_ID bigint,
+                ACCESS_DATETIME datetime,
+                METRIC_ID integer,
+                METRIC_NAME text,
+                LINE_COUNT integer,
+                DESCRIPTION text,
+                METRIC_GROUP_NUM integer,
+                METRIC_GROUP_NAME text
+            );
+         """ % TEST_SOURCE_TABLE
+        );
+
+        self.testUserIDs = list();
+        headers = ["user_id","user_name","de_pat_id","access_datetime","metric_id","metric_name","line_count","description","metric_group_num","metric_group_name"];
+        dataModels = \
+            [
+                RowItemModel( ['S-7', 'CJ', None, '2013-10-14 08:44:47', '33006', 'ME_IBGLANCE', '1', 'IN BASKET GLANCE PLUGIN ACCESSED IN RADAR', '33000', 'Radar'], headers ),
+                RowItemModel( ['S-7', 'CJ', '3289034', '2014-03-20 00:40:18', '34127', 'IP_ORDERSSECTION', '1', 'Inpatient Orders section opened ', '17001', 'PATIENT CLINICAL INFO'], headers ),
+                RowItemModel( ['S-7', 'CJ', None, '2014-01-01 10:10:56', '20008', 'AC_IB_CREATEMSG', '1', 'In Basket message of any type created.', '20000', 'In Basket Report'], headers ),
+                RowItemModel( ['S-7', 'CJ', None, '2014-01-01 10:10:56', '20008', 'AC_IB_CREATEMSG', '2', '(Created messages counted.) ', '20000', 'In Basket Report'], headers ),
+                RowItemModel( ['S-7', 'CJ', '1853397', '2013-06-29 11:25:02', '20075', 'AC_DOCUMENTLIST_SUBC', '1', 'Prelude Documents list accessed for patient.', None, None], headers ),
+                RowItemModel( ['S-4', 'AB', '3133593', '2013-10-22 06:46:29', '17008', 'MR_REPORTS', '1', 'A report with patient data accessed.', '17001', 'PATIENT CLINICAL INFO'], headers ),
+                RowItemModel( ['S-4', 'AB', '3047429', '2014-03-16 20:56:54', '17016', 'MR_RESULTS_REVIEW', '1', 'Results Review activity accessed.', '17002', 'Patient Chart Review'], headers ),
+                RowItemModel( ['S-4', 'AB', '3408732', '2014-04-08 08:47:38', '17016', 'MR_RESULTS_REVIEW', '1', 'Results Review activity accessed.', '17002', 'Patient Chart Review'], headers ),
+                RowItemModel( ['S-4', 'AB', None, '2014-02-26 19:27:48', '34140', 'IP_SYSTEM_LIST', '1', 'Inpatient system list accessed.', '20001', 'PATIENT DEMOGRAPHICS'], headers ),
+                RowItemModel( ['S-4', 'AB', '2487184', '2013-10-11 08:45:46', '17008', 'MR_REPORTS', '1', 'A report with patient data accessed.', '17001', 'PATIENT CLINICAL INFO'], headers ),
+
+            ];
+        for dataModel in dataModels:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem(TEST_SOURCE_TABLE, dataModel, retrieveCol="user_id" );
+            userID = int(dataItemId[1:]);   # Trim leading S and parse remainder as an integer
+            self.testUserIDs.append(userID);
+
+        self.converter = ProviderRotationConversion();  # Instance to test on
+        self.converter.sourceTableName = TEST_SOURCE_TABLE;
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        log.info("Purge test records from the database")
+        query = SQLQuery();
+        query.delete = True;
+        query.addFrom("access_log");
+        query.addWhereIn("user_id", self.testUserIDs );
+        DBUtil.execute( query );
+
+        query = SQLQuery();
+        query.delete = True;
+        query.addFrom("user");
+        query.addWhereIn("user_id", self.testUserIDs );
+        DBUtil.execute( query );
+
+        DBUtil.execute("drop table %s;" % TEST_SOURCE_TABLE);
+        DBTestCase.tearDown(self);
+
+    # def test_dataConversion(self):
+    #     # Run the data conversion on the same data and look for expected records
+    #     log.debug("Run the conversion process...");
+    #     self.converter.convertSourceItems();
+    #
+    #     # Just query back for the same data, de-normalizing the data back to a general table
+    #     testQuery = \
+    #         """
+    #         select
+    #             u.name,
+    #             al.de_pat_id,
+    #             al.access_datetime,
+    #             m.name,
+    #             m.description,
+    #             mg.name
+    #         from
+    #             access_log as al,
+    #             user as u,
+    #             metric as m,
+    #             metric_group as mg
+    #         where
+    #             al.user_id = u.user_id and
+    #             al.metric_id = m.metric_id and
+    #             m.metric_group_id = mg.metric_group_id and
+    #             al.user_id in (%s)
+    #         order by
+    #             al.access_datetime
+    #         """ % generatePlaceholders(len(self.testUserIDs));
+    #
+    #     expectedData = \
+    #         [
+    #             ['CJ', 1853397, datetime(2013, 6, 29, 11, 25, 2), 'AC_DOCUMENTLIST_SUBC', 'Prelude Documents list accessed for patient.', 'Null Group'],  # Put placeholder for null group to avoid having to do outer join
+    #             ['AB', 2487184, datetime(2013, 10, 11, 8, 45, 46), 'MR_REPORTS', 'A report with patient data accessed.', 'PATIENT CLINICAL INFO'],
+    #             ['CJ', None, datetime(2013, 10, 14, 8, 44, 47), 'ME_IBGLANCE', 'IN BASKET GLANCE PLUGIN ACCESSED IN RADAR', 'Radar'],
+    #             ['AB', 3133593, datetime(2013, 10, 22, 6, 46, 29), 'MR_REPORTS', 'A report with patient data accessed.', 'PATIENT CLINICAL INFO'],
+    #             ['CJ', None, datetime(2014, 1, 1, 10, 10, 56), 'AC_IB_CREATEMSG', 'In Basket message of any type created. (Created messages counted.) ', 'In Basket Report'],
+    #             ['AB', None, datetime(2014, 2, 26, 19, 27, 48), 'IP_SYSTEM_LIST', 'Inpatient system list accessed.', 'PATIENT DEMOGRAPHICS'],
+    #             ['AB', 3047429, datetime(2014, 3, 16, 20, 56, 54), 'MR_RESULTS_REVIEW', 'Results Review activity accessed.', 'Patient Chart Review'],
+    #             ['CJ', 3289034, datetime(2014, 3, 20, 0, 40, 18), 'IP_ORDERSSECTION', 'Inpatient Orders section opened ', 'PATIENT CLINICAL INFO'],
+    #             ['AB', 3408732, datetime(2014, 4, 8, 8, 47, 38), 'MR_RESULTS_REVIEW', 'Results Review activity accessed.', 'Patient Chart Review'],
+    #         ];
+    #     actualData = DBUtil.execute(testQuery, self.testUserIDs);
+    #     self.assertEqualTable( expectedData, actualData );
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    #suite.addTest(TestProviderRotationConversion("test_incColNamesAndTypeCodes"));
+    #suite.addTest(TestProviderRotationConversion("test_insertFile_skipErrors"));
+    #suite.addTest(TestProviderRotationConversion('test_executeIterator'));
+    #suite.addTest(TestProviderRotationConversion('test_findOrInsertItem'));
+    suite.addTest(unittest.makeSuite(TestProviderRotationConversion));
+
+    return suite;
+
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestResidentScheduleFormat.py b/medinfo/dataconversion/test/TestResidentScheduleFormat.py
index 907d374e..bd1b93aa 100644
--- a/medinfo/dataconversion/test/TestResidentScheduleFormat.py
+++ b/medinfo/dataconversion/test/TestResidentScheduleFormat.py
@@ -2,196 +2,214 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
-from medinfo.db.test.Util import DBTestCase;
+from medinfo.common.test.Util import MedInfoTestCase;
 
-from medinfo.db import DBUtil
-from medinfo.db.Model import SQLQuery, RowItemModel, generatePlaceholders;
+from medinfo.db.Model import RowItemModel;
 
 from medinfo.dataconversion.ResidentScheduleFormat import ResidentScheduleFormat;
 
-class TestResidentScheduleFormat(DBTestCase):
+class TestResidentScheduleFormat(MedInfoTestCase):
     def setUp(self):
         """Prepare state for test cases"""
-        DBTestCase.setUp(self);
+        MedInfoTestCase.setUp(self);
+
+        self.BASE_YEAR = 2013;  # Expected base/start year that the test data represents
 
         self.R1_DATA = \
-            """Dates\t6/25 - 7/19\t7/20 - 8/16\t8/17 - 9/13\t9/14 - 10/11\t10/12 - 11/8\t11/9 - 12/6\t12/7 - 1/3\t1/4 - 1/31\t2/1 - 2/28\t3/1 - 3/28\t3/29 - 4/25\t4/26 - 5/23\t5/24 - 6/24
-Split\t(7/6)\t(8/3)\t(8/31)\t(9/28)\t(10/26)\t(11/23)\t(12/21)\t(1/18)\t(2/15)\t(3/15)\t(4/12)\t(5/10)\t(6/7)
-"R1VA"\tStanford Wards\tOncology\tStanford Wards\tVA ICU\tVA Night Team | Vacation\tStanford ED\tStanford Wards\tGastroenterology Inpatient | Gastroenterology Outpatient\tGeneral Cardiology\tSCV Wards\tVA Wards\tGeriatrics | VA Night Team\tPulmonology SUH | Vacation
-"R1KC"\tVA ICU\tGeneral Cardiology\tStanford Wards\tSCV Wards\tStanford Night Team | Vacation\tOncology\tVA Wards\tStanford Wards\tHematology\tStanford ED\tInfectious Disease VA | Pulmonology SUH\tStanford Wards\tVacation | Geriatrics
-"R1AC"\tSCV Wards\tVA ICU\tVA Wards\tStanford Wards\tVA Night Team | Vacation\tStanford Wards\tVA Wards\tGeneral Cardiology\tGastroenterology Inpatient | Gastroenterology Outpatient\tPulmonology SUH | Vacation\tStanford Wards\tStanford Night Team | Geriatrics\tOncology
+            """Split dates in '( )'\t1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t11\t12\t13
+\t6/25 - 7/19\t7/20 - 8/16\t8/17 - 9/13\t9/14 - 10/11\t10/12 - 11/8\t11/9 - 12/6\t12/7 - 1/3\t1/4 - 1/31\t2/1 - 2/28\t3/1 - 3/28\t3/29 - 4/25\t4/26 - 5/23\t5/24 - 6/24
+\t(7/6)\t(8/3)\t(8/31)\t(9/28)\t(10/26)\t(11/23)\t(12/21)\t(1/18)\t(2/15)\t(3/15)\t(4/12)\t(5/10)\t(6/7)
+"R1, VA"\tStanford Wards\tOncology\tStanford Wards\tVA ICU\tVA Night Team | Vacation\tStanford ED\tStanford Wards\tGastroenterology Inpatient | Gastroenterology Outpatient\tGeneral Cardiology\tSCV Wards\tVA Wards\tGeriatrics | VA Night Team\tPulmonology SUH | Vacation
+"R1, KC"\tVA ICU\tGeneral Cardiology\tStanford Wards\tSCV Wards\tStanford Night Team | Vacation\tOncology\tVA Wards\tStanford Wards\tHematology\tStanford ED\tInfectious Disease VA | Pulmonology SUH\tStanford Wards\tVacation | Geriatrics
+"R1, AC"\tSCV Wards\tVA ICU\tVA Wards\tStanford Wards\tVA Night Team | Vacation\tStanford Wards\tVA Wards\tGeneral Cardiology\tGastroenterology Inpatient | Gastroenterology Outpatient\tPulmonology SUH | Vacation\tStanford Wards\tStanford Night Team | Geriatrics\tOncology
 """;
         self.R2_DATA = \
-            """Dates\t7/1 - 7/26\t7/27 - 8/23\t8/24 - 9/20\t9/21 - 10/18\t10/19 - 11/15\t11/16 - 12/27\t12/28 - 1/10\t1/11 - 2/7\t2/8 - 3/7\t3/8 - 4/4\t4/5 - 5/2\t5/3 - 5/30\t5/31 - 6/30
-Split\t(7/13)\t(8/10)\t(9/7)\t(10/5)\t(11/2)\t(12/7)\t(1/5)\t(1/25)\t(2/22)\t(3/22)\t(4/19)\t(5/17)\t(6/14)
-"R2AA"\tStanford ICU\tVA Night Team | Palliative Care VA\tStanford Night Team | Global Health Elective\tCCU Heart Failure\tVA Vacation | Willow Block\tGastroenterology Outpatient | Rheumatology\tHoliday 12/28-1/1\tOutpatient Oncology | Johnson & Johnson\tJohnson & Johnson\tOutpatient Neuro | Nephrology Outpatient\tVA Vacation | Endocrinology\tSCV Wards\tVA Wards
+            """Split dates in '( )'\t1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t11\t12\t13
+\t7/1 - 7/26\t7/27 - 8/23\t8/24 - 9/20\t9/21 - 10/18\t10/19 - 11/15\t11/16 - 12/27\t12/28 - 1/10\t1/11 - 2/7\t2/8 - 3/7\t3/8 - 4/4\t4/5 - 5/2\t5/3 - 5/30\t5/31 - 6/30
+\t(7/13)\t(8/10)\t(9/7)\t(10/5)\t(11/2)\t(12/7)\t(1/5)\t(1/25)\t(2/22)\t(3/22)\t(4/19)\t(5/17)\t(6/14)
+"R2, AA"\tStanford ICU\tVA Night Team | Palliative Care VA\tStanford Night Team | Global Health Elective\tCCU Heart Failure\tVA Vacation | Willow Block\tGastroenterology Outpatient | Rheumatology\tHoliday 12/28-1/1\tOutpatient Oncology | Johnson & Johnson\tJohnson & Johnson\tOutpatient Neuro | Nephrology Outpatient\tVA Vacation | Endocrinology\tSCV Wards\tVA Wards
 \t\t\t\t\t\t\tPulmonology SUH\t\t\t\t\t\t
-"R2MB"\tCCU Heart Failure\tStanford ICU\tStanford Wards\tWomen's Health | VA Ambulatory\tVA Wards\tHoliday 12/23-12/27\t\t\t\tSIM Block 4/2-4/4\tOutpatient Neuro | Stanford Night Team\tStanford Night Team | Menlo Clinic\tNephrology Outpatient 6/23-6/30
+"R2, MB"\tCCU Heart Failure\tStanford ICU\tStanford Wards\tWomen's Health | VA Ambulatory\tVA Wards\tHoliday 12/23-12/27\t\t\t\tSIM Block 4/2-4/4\tOutpatient Neuro | Stanford Night Team\tStanford Night Team | Menlo Clinic\tNephrology Outpatient 6/23-6/30
 \t\t\t\t\t\tOutpatient Oncology | Outpatient Hematology\t\t\t\t--\t\t\tWomen's Health | Vacation
-"R2DL"\tCCU Heart Failure\tGastroenterology Inpatient | Gastroenterology Outpatient\tResearch\tVA Wards\tNephrology Outpatient | Nephrology Inpatient\tVacation 11/30-12/13\tOncology\tVA Wards\tStanford ICU\tVA Night Team | Vacation\tStanford ED\tPulmonology SUH | Rheumatology\tHIV | VA Night Team
+"R2, DL"\tCCU Heart Failure\tGastroenterology Inpatient | Gastroenterology Outpatient\tResearch\tVA Wards\tNephrology Outpatient | Nephrology Inpatient\tVacation 11/30-12/13\tOncology\tVA Wards\tStanford ICU\tVA Night Team | Vacation\tStanford ED\tPulmonology SUH | Rheumatology\tHIV | VA Night Team
 \t\t\t\t\t\tHomeless 12/14-12/22\t\t\t\t\t\t\t
 \t\t\t\t\t\tHoliday 12/23-12/27\t\t\t\t\t\t\t
 \t\t\t\t\t\tNephrology Inpatient\t\t\t\t\t\t\t
-"R2TS"\tOutpatient Hematology | Outpatient Oncology\t-\t\t- | VA Night Team\tCCU Heart Failure\tHoliday 12/23-12/27\tSCV Wards\tPalliative Care VA | VA Night Team\tVacation | Hepatology\tStanford ICU\tVA Wards\tEndocrinology | Stanford Night Team\tVacation | HIV
+"R2, TS"\tOutpatient Hematology | Outpatient Oncology\t-\t\t- | VA Night Team\tCCU Heart Failure\tHoliday 12/23-12/27\tSCV Wards\tPalliative Care VA | VA Night Team\tVacation | Hepatology\tStanford ICU\tVA Wards\tEndocrinology | Stanford Night Team\tVacation | HIV
 \t\t\t\t\t\tHomeless | Neurology\t\t\t\t\t\t\t
 """;
 
         self.converter = ResidentScheduleFormat();  # Instance to test on
+        self.converter.loadProviderModels \
+        (
+            [
+                {"prov_id":"S001", "last_name":"R1", "first_name":"VA"},
+                {"prov_id":"S002", "last_name":"R1", "first_name":"KC"},
+                {"prov_id":"S003", "last_name":"R1", "first_name":"AC"},
+                {"prov_id":"S004", "last_name":"R2", "first_name":"AA"},
+                {"prov_id":"S005", "last_name":"R2", "first_name":"MB"},
+                {"prov_id":"S006", "last_name":"R2", "first_name":"DL"},
+                #{"prov_id":"S007", "last_name":"R2", "first_name":"TS"}, # Deliberately missing an entry to test robustness if missing lookup
+                {"prov_id":"S008", "last_name":"R3", "first_name":"AB"},
+                {"prov_id":"S009", "last_name":"R3", "first_name":"CD"},
+                {"prov_id":"S010", "last_name":"R3", "first_name":"EF"},
+            ]
+        );
 
     def tearDown(self):
         """Restore state from any setUp or test steps"""
-        DBTestCase.tearDown(self);
+        MedInfoTestCase.tearDown(self);
 
     def test_dataConversion(self):
         # Run the data conversion on the same data and look for expected records
         log.debug("Run the formatting process for R1s");
-        headers = ["resident","rotation","start","end"];
+        headers = ["prov_id","name","rotation","start_date","end_date"];
         expectedData = \
             [
-                RowItemModel( ['R1VA', 'Stanford Wards', datetime(2013, 6, 25, 7, 0), datetime(2013, 7, 20, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Oncology', datetime(2013, 7, 20, 7, 0), datetime(2013, 8, 17, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Stanford Wards', datetime(2013, 8, 17, 7, 0), datetime(2013, 9, 14, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'VA ICU', datetime(2013, 9, 14, 7, 0), datetime(2013, 10, 12, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'VA Night Team', datetime(2013, 10, 12, 7, 0), datetime(2013, 10, 26, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Vacation', datetime(2013, 10, 26, 7, 0), datetime(2013, 11, 9, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Stanford ED', datetime(2013, 11, 9, 7, 0), datetime(2013, 12, 7, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Stanford Wards', datetime(2013, 12, 7, 7, 0), datetime(2014, 1, 4, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Gastroenterology Inpatient', datetime(2014, 1, 4, 7, 0), datetime(2014, 1, 18, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Gastroenterology Outpatient', datetime(2014, 1, 18, 7, 0), datetime(2014, 2, 1, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'General Cardiology', datetime(2014, 2, 1, 7, 0), datetime(2014, 3, 1, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'SCV Wards', datetime(2014, 3, 1, 7, 0), datetime(2014, 3, 29, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'VA Wards', datetime(2014, 3, 29, 7, 0), datetime(2014, 4, 26, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Geriatrics', datetime(2014, 4, 26, 7, 0), datetime(2014, 5, 10, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'VA Night Team', datetime(2014, 5, 10, 7, 0), datetime(2014, 5, 24, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Pulmonology SUH', datetime(2014, 5, 24, 7, 0), datetime(2014, 6, 7, 7, 0)], headers ),
-                RowItemModel( ['R1VA', 'Vacation', datetime(2014, 6, 7, 7, 0), datetime(2014, 6, 25, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'VA ICU', datetime(2013, 6, 25, 7, 0), datetime(2013, 7, 20, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'General Cardiology', datetime(2013, 7, 20, 7, 0), datetime(2013, 8, 17, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Stanford Wards', datetime(2013, 8, 17, 7, 0), datetime(2013, 9, 14, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'SCV Wards', datetime(2013, 9, 14, 7, 0), datetime(2013, 10, 12, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Stanford Night Team', datetime(2013, 10, 12, 7, 0), datetime(2013, 10, 26, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Vacation', datetime(2013, 10, 26, 7, 0), datetime(2013, 11, 9, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Oncology', datetime(2013, 11, 9, 7, 0), datetime(2013, 12, 7, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'VA Wards', datetime(2013, 12, 7, 7, 0), datetime(2014, 1, 4, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Stanford Wards', datetime(2014, 1, 4, 7, 0), datetime(2014, 2, 1, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Hematology', datetime(2014, 2, 1, 7, 0), datetime(2014, 3, 1, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Stanford ED', datetime(2014, 3, 1, 7, 0), datetime(2014, 3, 29, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Infectious Disease VA', datetime(2014, 3, 29, 7, 0), datetime(2014, 4, 12, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Pulmonology SUH', datetime(2014, 4, 12, 7, 0), datetime(2014, 4, 26, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Stanford Wards', datetime(2014, 4, 26, 7, 0), datetime(2014, 5, 24, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Vacation', datetime(2014, 5, 24, 7, 0), datetime(2014, 6, 7, 7, 0)], headers ),
-                RowItemModel( ['R1KC', 'Geriatrics', datetime(2014, 6, 7, 7, 0), datetime(2014, 6, 25, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'SCV Wards', datetime(2013, 6, 25, 7, 0), datetime(2013, 7, 20, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'VA ICU', datetime(2013, 7, 20, 7, 0), datetime(2013, 8, 17, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'VA Wards', datetime(2013, 8, 17, 7, 0), datetime(2013, 9, 14, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Stanford Wards', datetime(2013, 9, 14, 7, 0), datetime(2013, 10, 12, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'VA Night Team', datetime(2013, 10, 12, 7, 0), datetime(2013, 10, 26, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Vacation', datetime(2013, 10, 26, 7, 0), datetime(2013, 11, 9, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Stanford Wards', datetime(2013, 11, 9, 7, 0), datetime(2013, 12, 7, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'VA Wards', datetime(2013, 12, 7, 7, 0), datetime(2014, 1, 4, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'General Cardiology', datetime(2014, 1, 4, 7, 0), datetime(2014, 2, 1, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Gastroenterology Inpatient', datetime(2014, 2, 1, 7, 0), datetime(2014, 2, 15, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Gastroenterology Outpatient', datetime(2014, 2, 15, 7, 0), datetime(2014, 3, 1, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Pulmonology SUH', datetime(2014, 3, 1, 7, 0), datetime(2014, 3, 15, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Vacation', datetime(2014, 3, 15, 7, 0), datetime(2014, 3, 29, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Stanford Wards', datetime(2014, 3, 29, 7, 0), datetime(2014, 4, 26, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Stanford Night Team', datetime(2014, 4, 26, 7, 0), datetime(2014, 5, 10, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Geriatrics', datetime(2014, 5, 10, 7, 0), datetime(2014, 5, 24, 7, 0)], headers ),
-                RowItemModel( ['R1AC', 'Oncology', datetime(2014, 5, 24, 7, 0), datetime(2014, 6, 25, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Stanford Wards', datetime(2013, 6, 25, 7, 0), datetime(2013, 7, 20, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Oncology', datetime(2013, 7, 20, 7, 0), datetime(2013, 8, 17, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Stanford Wards', datetime(2013, 8, 17, 7, 0), datetime(2013, 9, 14, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'VA ICU', datetime(2013, 9, 14, 7, 0), datetime(2013, 10, 12, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'VA Night Team', datetime(2013, 10, 12, 7, 0), datetime(2013, 10, 26, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Vacation', datetime(2013, 10, 26, 7, 0), datetime(2013, 11, 9, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Stanford ED', datetime(2013, 11, 9, 7, 0), datetime(2013, 12, 7, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Stanford Wards', datetime(2013, 12, 7, 7, 0), datetime(2014, 1, 4, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Gastroenterology Inpatient', datetime(2014, 1, 4, 7, 0), datetime(2014, 1, 18, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Gastroenterology Outpatient', datetime(2014, 1, 18, 7, 0), datetime(2014, 2, 1, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'General Cardiology', datetime(2014, 2, 1, 7, 0), datetime(2014, 3, 1, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'SCV Wards', datetime(2014, 3, 1, 7, 0), datetime(2014, 3, 29, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'VA Wards', datetime(2014, 3, 29, 7, 0), datetime(2014, 4, 26, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Geriatrics', datetime(2014, 4, 26, 7, 0), datetime(2014, 5, 10, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'VA Night Team', datetime(2014, 5, 10, 7, 0), datetime(2014, 5, 24, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Pulmonology SUH', datetime(2014, 5, 24, 7, 0), datetime(2014, 6, 7, 7, 0)], headers ),
+                RowItemModel( ['S001', 'R1, VA', 'Vacation', datetime(2014, 6, 7, 7, 0), datetime(2014, 6, 25, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'VA ICU', datetime(2013, 6, 25, 7, 0), datetime(2013, 7, 20, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'General Cardiology', datetime(2013, 7, 20, 7, 0), datetime(2013, 8, 17, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Stanford Wards', datetime(2013, 8, 17, 7, 0), datetime(2013, 9, 14, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'SCV Wards', datetime(2013, 9, 14, 7, 0), datetime(2013, 10, 12, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Stanford Night Team', datetime(2013, 10, 12, 7, 0), datetime(2013, 10, 26, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Vacation', datetime(2013, 10, 26, 7, 0), datetime(2013, 11, 9, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Oncology', datetime(2013, 11, 9, 7, 0), datetime(2013, 12, 7, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'VA Wards', datetime(2013, 12, 7, 7, 0), datetime(2014, 1, 4, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Stanford Wards', datetime(2014, 1, 4, 7, 0), datetime(2014, 2, 1, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Hematology', datetime(2014, 2, 1, 7, 0), datetime(2014, 3, 1, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Stanford ED', datetime(2014, 3, 1, 7, 0), datetime(2014, 3, 29, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Infectious Disease VA', datetime(2014, 3, 29, 7, 0), datetime(2014, 4, 12, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Pulmonology SUH', datetime(2014, 4, 12, 7, 0), datetime(2014, 4, 26, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Stanford Wards', datetime(2014, 4, 26, 7, 0), datetime(2014, 5, 24, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Vacation', datetime(2014, 5, 24, 7, 0), datetime(2014, 6, 7, 7, 0)], headers ),
+                RowItemModel( ['S002', 'R1, KC', 'Geriatrics', datetime(2014, 6, 7, 7, 0), datetime(2014, 6, 25, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'SCV Wards', datetime(2013, 6, 25, 7, 0), datetime(2013, 7, 20, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'VA ICU', datetime(2013, 7, 20, 7, 0), datetime(2013, 8, 17, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'VA Wards', datetime(2013, 8, 17, 7, 0), datetime(2013, 9, 14, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Stanford Wards', datetime(2013, 9, 14, 7, 0), datetime(2013, 10, 12, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'VA Night Team', datetime(2013, 10, 12, 7, 0), datetime(2013, 10, 26, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Vacation', datetime(2013, 10, 26, 7, 0), datetime(2013, 11, 9, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Stanford Wards', datetime(2013, 11, 9, 7, 0), datetime(2013, 12, 7, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'VA Wards', datetime(2013, 12, 7, 7, 0), datetime(2014, 1, 4, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'General Cardiology', datetime(2014, 1, 4, 7, 0), datetime(2014, 2, 1, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Gastroenterology Inpatient', datetime(2014, 2, 1, 7, 0), datetime(2014, 2, 15, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Gastroenterology Outpatient', datetime(2014, 2, 15, 7, 0), datetime(2014, 3, 1, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Pulmonology SUH', datetime(2014, 3, 1, 7, 0), datetime(2014, 3, 15, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Vacation', datetime(2014, 3, 15, 7, 0), datetime(2014, 3, 29, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Stanford Wards', datetime(2014, 3, 29, 7, 0), datetime(2014, 4, 26, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Stanford Night Team', datetime(2014, 4, 26, 7, 0), datetime(2014, 5, 10, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Geriatrics', datetime(2014, 5, 10, 7, 0), datetime(2014, 5, 24, 7, 0)], headers ),
+                RowItemModel( ['S003', 'R1, AC', 'Oncology', datetime(2014, 5, 24, 7, 0), datetime(2014, 6, 25, 7, 0)], headers ),
             ];
-        actualData = self.converter.parseScheduleItems( StringIO(self.R1_DATA) );
+        actualData = self.converter.parseScheduleItems( StringIO(self.R1_DATA), self.BASE_YEAR );
         self.assertEqualList( expectedData, actualData );
 
         log.debug("Run the formatting process for R2s");
-        headers = ["resident","rotation","start","end"];
+        headers = ["prov_id","name","rotation","start_date","end_date"];
         expectedData = \
             [
-                RowItemModel( ['R2AA','Stanford ICU',datetime(2013,7,1,7),datetime(2013,7,27,7)], headers),
-                RowItemModel( ['R2AA','VA Night Team',datetime(2013,7,27,7),datetime(2013,8,10,7)], headers),
-                RowItemModel( ['R2AA','Palliative Care VA',datetime(2013,8,10,7),datetime(2013,8,24,7)], headers),
-                RowItemModel( ['R2AA','Stanford Night Team',datetime(2013,8,24,7),datetime(2013,9,7,7)], headers),
-                RowItemModel( ['R2AA','Global Health Elective',datetime(2013,9,7,7),datetime(2013,9,21,7)], headers),
-                RowItemModel( ['R2AA','CCU Heart Failure',datetime(2013,9,21,7),datetime(2013,10,19,7)], headers),
-                RowItemModel( ['R2AA','VA Vacation',datetime(2013,10,19,7),datetime(2013,11,2,7)], headers),
-                RowItemModel( ['R2AA','Willow Block',datetime(2013,11,2,7),datetime(2013,11,16,7)], headers),
-                RowItemModel( ['R2AA','Gastroenterology Outpatient',datetime(2013,11,16,7),datetime(2013,12,7,7)], headers),
-                RowItemModel( ['R2AA','Rheumatology',datetime(2013,12,7,7),datetime(2013,12,28,7)], headers),
-                RowItemModel( ['R2AA','Holiday',datetime(2013,12,28,7),datetime(2014,1,2,7)], headers),
-                RowItemModel( ['R2AA','Pulmonology SUH',datetime(2014,1,2,7),datetime(2014,1,11,7)], headers),
-                RowItemModel( ['R2AA','Outpatient Oncology',datetime(2014,1,11,7),datetime(2014,1,25,7)], headers),
-                RowItemModel( ['R2AA','Johnson & Johnson',datetime(2014,1,25,7),datetime(2014,2,8,7)], headers),
-                RowItemModel( ['R2AA','Johnson & Johnson',datetime(2014,2,8,7),datetime(2014,3,8,7)], headers),
-                RowItemModel( ['R2AA','Outpatient Neuro',datetime(2014,3,8,7),datetime(2014,3,22,7)], headers),
-                RowItemModel( ['R2AA','Nephrology Outpatient',datetime(2014,3,22,7),datetime(2014,4,5,7)], headers),
-                RowItemModel( ['R2AA','VA Vacation',datetime(2014,4,5,7),datetime(2014,4,19,7)], headers),
-                RowItemModel( ['R2AA','Endocrinology',datetime(2014,4,19,7),datetime(2014,5,3,7)], headers),
-                RowItemModel( ['R2AA','SCV Wards',datetime(2014,5,3,7),datetime(2014,5,31,7)], headers),
-                RowItemModel( ['R2AA','VA Wards',datetime(2014,5,31,7),datetime(2014,7,1,7)], headers),
-                RowItemModel( ['R2MB','CCU Heart Failure',datetime(2013,7,1,7),datetime(2013,7,27,7)], headers),
-                RowItemModel( ['R2MB','Stanford ICU',datetime(2013,7,27,7),datetime(2013,8,24,7)], headers),
-                RowItemModel( ['R2MB','Stanford Wards',datetime(2013,8,24,7),datetime(2013,9,21,7)], headers),
-                RowItemModel( ['R2MB','Women\'s Health',datetime(2013,9,21,7),datetime(2013,10,5,7)], headers),
-                RowItemModel( ['R2MB','VA Ambulatory',datetime(2013,10,5,7),datetime(2013,10,19,7)], headers),
-                RowItemModel( ['R2MB','VA Wards',datetime(2013,10,19,7),datetime(2013,11,16,7)], headers),
-                RowItemModel( ['R2MB','Outpatient Oncology',datetime(2013,11,16,7),datetime(2013,12,7,7)], headers),
-                RowItemModel( ['R2MB','Outpatient Hematology',datetime(2013,12,7,7),datetime(2013,12,23,7)], headers),
-                RowItemModel( ['R2MB','Holiday',datetime(2013,12,23,7),datetime(2013,12,28,7)], headers),
-                RowItemModel( ['R2MB','SIM Block',datetime(2014,4,2,7),datetime(2014,4,5,7)], headers),
-                RowItemModel( ['R2MB','Outpatient Neuro',datetime(2014,4,5,7),datetime(2014,4,19,7)], headers),
-                RowItemModel( ['R2MB','Stanford Night Team',datetime(2014,4,19,7),datetime(2014,5,3,7)], headers),
-                RowItemModel( ['R2MB','Stanford Night Team',datetime(2014,5,3,7),datetime(2014,5,17,7)], headers),
-                RowItemModel( ['R2MB','Menlo Clinic',datetime(2014,5,17,7),datetime(2014,5,31,7)], headers),
-                RowItemModel( ['R2MB','Women\'s Health',datetime(2014,5,31,7),datetime(2014,6,14,7)], headers),
-                RowItemModel( ['R2MB','Vacation',datetime(2014,6,14,7),datetime(2014,6,23,7)], headers),
-                RowItemModel( ['R2MB','Nephrology Outpatient',datetime(2014,6,23,7),datetime(2014,7,1,7)], headers),
-                RowItemModel( ['R2DL','CCU Heart Failure',datetime(2013,7,1,7),datetime(2013,7,27,7)], headers),
-                RowItemModel( ['R2DL','Gastroenterology Inpatient',datetime(2013,7,27,7),datetime(2013,8,10,7)], headers),
-                RowItemModel( ['R2DL','Gastroenterology Outpatient',datetime(2013,8,10,7),datetime(2013,8,24,7)], headers),
-                RowItemModel( ['R2DL','Research',datetime(2013,8,24,7),datetime(2013,9,21,7)], headers),
-                RowItemModel( ['R2DL','VA Wards',datetime(2013,9,21,7),datetime(2013,10,19,7)], headers),
-                RowItemModel( ['R2DL','Nephrology Outpatient',datetime(2013,10,19,7),datetime(2013,11,2,7)], headers),
-                RowItemModel( ['R2DL','Nephrology Inpatient',datetime(2013,11,2,7),datetime(2013,11,16,7)], headers),
-                RowItemModel( ['R2DL','Nephrology Inpatient',datetime(2013,11,16,7),datetime(2013,11,30,7)], headers),
-                RowItemModel( ['R2DL','Vacation',datetime(2013,11,30,7),datetime(2013,12,14,7)], headers),
-                RowItemModel( ['R2DL','Homeless',datetime(2013,12,14,7),datetime(2013,12,23,7)], headers),
-                RowItemModel( ['R2DL','Holiday',datetime(2013,12,23,7),datetime(2013,12,28,7)], headers),
-                RowItemModel( ['R2DL','Oncology',datetime(2013,12,28,7),datetime(2014,1,11,7)], headers),
-                RowItemModel( ['R2DL','VA Wards',datetime(2014,1,11,7),datetime(2014,2,8,7)], headers),
-                RowItemModel( ['R2DL','Stanford ICU',datetime(2014,2,8,7),datetime(2014,3,8,7)], headers),
-                RowItemModel( ['R2DL','VA Night Team',datetime(2014,3,8,7),datetime(2014,3,22,7)], headers),
-                RowItemModel( ['R2DL','Vacation',datetime(2014,3,22,7),datetime(2014,4,5,7)], headers),
-                RowItemModel( ['R2DL','Stanford ED',datetime(2014,4,5,7),datetime(2014,5,3,7)], headers),
-                RowItemModel( ['R2DL','Pulmonology SUH',datetime(2014,5,3,7),datetime(2014,5,17,7)], headers),
-                RowItemModel( ['R2DL','Rheumatology',datetime(2014,5,17,7),datetime(2014,5,31,7)], headers),
-                RowItemModel( ['R2DL','HIV',datetime(2014,5,31,7),datetime(2014,6,14,7)], headers),
-                RowItemModel( ['R2DL','VA Night Team',datetime(2014,6,14,7),datetime(2014,7,1,7)], headers),
-                RowItemModel( ['R2TS','Outpatient Hematology',datetime(2013,7,1,7),datetime(2013,7,13,7)], headers),
-                RowItemModel( ['R2TS','Outpatient Oncology',datetime(2013,7,13,7),datetime(2013,7,27,7)], headers),
-                RowItemModel( ['R2TS','VA Night Team',datetime(2013,10,5,7),datetime(2013,10,19,7)], headers),
-                RowItemModel( ['R2TS','CCU Heart Failure',datetime(2013,10,19,7),datetime(2013,11,16,7)], headers),
-                RowItemModel( ['R2TS','Homeless',datetime(2013,11,16,7),datetime(2013,12,7,7)], headers),
-                RowItemModel( ['R2TS','Neurology',datetime(2013,12,7,7),datetime(2013,12,23,7)], headers),
-                RowItemModel( ['R2TS','Holiday',datetime(2013,12,23,7),datetime(2013,12,28,7)], headers),
-                RowItemModel( ['R2TS','SCV Wards',datetime(2013,12,28,7),datetime(2014,1,11,7)], headers),
-                RowItemModel( ['R2TS','Palliative Care VA',datetime(2014,1,11,7),datetime(2014,1,25,7)], headers),
-                RowItemModel( ['R2TS','VA Night Team',datetime(2014,1,25,7),datetime(2014,2,8,7)], headers),
-                RowItemModel( ['R2TS','Vacation',datetime(2014,2,8,7),datetime(2014,2,22,7)], headers),
-                RowItemModel( ['R2TS','Hepatology',datetime(2014,2,22,7),datetime(2014,3,8,7)], headers),
-                RowItemModel( ['R2TS','Stanford ICU',datetime(2014,3,8,7),datetime(2014,4,5,7)], headers),
-                RowItemModel( ['R2TS','VA Wards',datetime(2014,4,5,7),datetime(2014,5,3,7)], headers),
-                RowItemModel( ['R2TS','Endocrinology',datetime(2014,5,3,7),datetime(2014,5,17,7)], headers),
-                RowItemModel( ['R2TS','Stanford Night Team',datetime(2014,5,17,7),datetime(2014,5,31,7)], headers),
-                RowItemModel( ['R2TS','Vacation',datetime(2014,5,31,7),datetime(2014,6,14,7)], headers),
-                RowItemModel( ['R2TS','HIV',datetime(2014,6,14,7),datetime(2014,7,1,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Stanford ICU',datetime(2013,7,1,7),datetime(2013,7,27,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','VA Night Team',datetime(2013,7,27,7),datetime(2013,8,10,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Palliative Care VA',datetime(2013,8,10,7),datetime(2013,8,24,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Stanford Night Team',datetime(2013,8,24,7),datetime(2013,9,7,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Global Health Elective',datetime(2013,9,7,7),datetime(2013,9,21,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','CCU Heart Failure',datetime(2013,9,21,7),datetime(2013,10,19,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','VA Vacation',datetime(2013,10,19,7),datetime(2013,11,2,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Willow Block',datetime(2013,11,2,7),datetime(2013,11,16,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Gastroenterology Outpatient',datetime(2013,11,16,7),datetime(2013,12,7,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Rheumatology',datetime(2013,12,7,7),datetime(2013,12,28,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Holiday',datetime(2013,12,28,7),datetime(2014,1,2,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Pulmonology SUH',datetime(2014,1,2,7),datetime(2014,1,11,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Outpatient Oncology',datetime(2014,1,11,7),datetime(2014,1,25,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Johnson & Johnson',datetime(2014,1,25,7),datetime(2014,2,8,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Johnson & Johnson',datetime(2014,2,8,7),datetime(2014,3,8,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Outpatient Neuro',datetime(2014,3,8,7),datetime(2014,3,22,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Nephrology Outpatient',datetime(2014,3,22,7),datetime(2014,4,5,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','VA Vacation',datetime(2014,4,5,7),datetime(2014,4,19,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','Endocrinology',datetime(2014,4,19,7),datetime(2014,5,3,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','SCV Wards',datetime(2014,5,3,7),datetime(2014,5,31,7)], headers),
+                RowItemModel( ['S004', 'R2, AA','VA Wards',datetime(2014,5,31,7),datetime(2014,7,1,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','CCU Heart Failure',datetime(2013,7,1,7),datetime(2013,7,27,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Stanford ICU',datetime(2013,7,27,7),datetime(2013,8,24,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Stanford Wards',datetime(2013,8,24,7),datetime(2013,9,21,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Women\'s Health',datetime(2013,9,21,7),datetime(2013,10,5,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','VA Ambulatory',datetime(2013,10,5,7),datetime(2013,10,19,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','VA Wards',datetime(2013,10,19,7),datetime(2013,11,16,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Outpatient Oncology',datetime(2013,11,16,7),datetime(2013,12,7,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Outpatient Hematology',datetime(2013,12,7,7),datetime(2013,12,23,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Holiday',datetime(2013,12,23,7),datetime(2013,12,28,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','SIM Block',datetime(2014,4,2,7),datetime(2014,4,5,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Outpatient Neuro',datetime(2014,4,5,7),datetime(2014,4,19,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Stanford Night Team',datetime(2014,4,19,7),datetime(2014,5,3,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Stanford Night Team',datetime(2014,5,3,7),datetime(2014,5,17,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Menlo Clinic',datetime(2014,5,17,7),datetime(2014,5,31,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Women\'s Health',datetime(2014,5,31,7),datetime(2014,6,14,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Vacation',datetime(2014,6,14,7),datetime(2014,6,23,7)], headers),
+                RowItemModel( ['S005', 'R2, MB','Nephrology Outpatient',datetime(2014,6,23,7),datetime(2014,7,1,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','CCU Heart Failure',datetime(2013,7,1,7),datetime(2013,7,27,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Gastroenterology Inpatient',datetime(2013,7,27,7),datetime(2013,8,10,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Gastroenterology Outpatient',datetime(2013,8,10,7),datetime(2013,8,24,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Research',datetime(2013,8,24,7),datetime(2013,9,21,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','VA Wards',datetime(2013,9,21,7),datetime(2013,10,19,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Nephrology Outpatient',datetime(2013,10,19,7),datetime(2013,11,2,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Nephrology Inpatient',datetime(2013,11,2,7),datetime(2013,11,16,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Nephrology Inpatient',datetime(2013,11,16,7),datetime(2013,11,30,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Vacation',datetime(2013,11,30,7),datetime(2013,12,14,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Homeless',datetime(2013,12,14,7),datetime(2013,12,23,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Holiday',datetime(2013,12,23,7),datetime(2013,12,28,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Oncology',datetime(2013,12,28,7),datetime(2014,1,11,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','VA Wards',datetime(2014,1,11,7),datetime(2014,2,8,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Stanford ICU',datetime(2014,2,8,7),datetime(2014,3,8,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','VA Night Team',datetime(2014,3,8,7),datetime(2014,3,22,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Vacation',datetime(2014,3,22,7),datetime(2014,4,5,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Stanford ED',datetime(2014,4,5,7),datetime(2014,5,3,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Pulmonology SUH',datetime(2014,5,3,7),datetime(2014,5,17,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','Rheumatology',datetime(2014,5,17,7),datetime(2014,5,31,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','HIV',datetime(2014,5,31,7),datetime(2014,6,14,7)], headers),
+                RowItemModel( ['S006', 'R2, DL','VA Night Team',datetime(2014,6,14,7),datetime(2014,7,1,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Outpatient Hematology',datetime(2013,7,1,7),datetime(2013,7,13,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Outpatient Oncology',datetime(2013,7,13,7),datetime(2013,7,27,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','VA Night Team',datetime(2013,10,5,7),datetime(2013,10,19,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','CCU Heart Failure',datetime(2013,10,19,7),datetime(2013,11,16,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Homeless',datetime(2013,11,16,7),datetime(2013,12,7,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Neurology',datetime(2013,12,7,7),datetime(2013,12,23,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Holiday',datetime(2013,12,23,7),datetime(2013,12,28,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','SCV Wards',datetime(2013,12,28,7),datetime(2014,1,11,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Palliative Care VA',datetime(2014,1,11,7),datetime(2014,1,25,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','VA Night Team',datetime(2014,1,25,7),datetime(2014,2,8,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Vacation',datetime(2014,2,8,7),datetime(2014,2,22,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Hepatology',datetime(2014,2,22,7),datetime(2014,3,8,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Stanford ICU',datetime(2014,3,8,7),datetime(2014,4,5,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','VA Wards',datetime(2014,4,5,7),datetime(2014,5,3,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Endocrinology',datetime(2014,5,3,7),datetime(2014,5,17,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Stanford Night Team',datetime(2014,5,17,7),datetime(2014,5,31,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','Vacation',datetime(2014,5,31,7),datetime(2014,6,14,7)], headers),
+                RowItemModel( ['S-1001', 'R2, TS','HIV',datetime(2014,6,14,7),datetime(2014,7,1,7)], headers),
             ];
-        actualData = self.converter.parseScheduleItems( StringIO(self.R2_DATA) );
+        actualData = self.converter.parseScheduleItems( StringIO(self.R2_DATA), self.BASE_YEAR );
         self.assertEqualList( expectedData, actualData );
 
 
diff --git a/medinfo/dataconversion/test/TestSTRIDEAccessLogConversion.py b/medinfo/dataconversion/test/TestSTRIDEAccessLogConversion.py
index b133de80..59c57e1b 100644
--- a/medinfo/dataconversion/test/TestSTRIDEAccessLogConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDEAccessLogConversion.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
@@ -83,6 +83,7 @@ def tearDown(self):
         DBUtil.execute("drop table %s;" % TEST_SOURCE_TABLE);
         DBTestCase.tearDown(self);
 
+    ### Disable these tests for now, as they refer to a different (old) Access Log dataset that is not being actively used (JonC, 7/6/2018)
     # def test_dataConversion(self):
     #     # Run the data conversion on the same data and look for expected records
     #     log.debug("Run the conversion process...");
diff --git a/medinfo/dataconversion/test/TestSTRIDECultureMicroConversion.py b/medinfo/dataconversion/test/TestSTRIDECultureMicroConversion.py
new file mode 100644
index 00000000..0e6f10e2
--- /dev/null
+++ b/medinfo/dataconversion/test/TestSTRIDECultureMicroConversion.py
@@ -0,0 +1,149 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+from io import StringIO
+from datetime import datetime;
+import unittest
+
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
+
+from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery, RowItemModel;
+from medinfo.db.ResultsFormatter import TabDictReader;
+
+from medinfo.dataconversion.STRIDECultureMicroConversion import STRIDECultureMicroConversion, ConversionOptions;
+
+TEST_START_DATE = datetime(2100,1,1);   # Date in far future to start checking for test records to avoid including existing data in database
+
+class TestSTRIDECultureMicroConversion(DBTestCase):
+    def setUp(self):
+        """Prepare state for test cases"""
+        DBTestCase.setUp(self);
+        
+        log.info("Populate the database with test data")
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
+        ###### PREPARE SOME FAKE INPUT DATA TO BE CONVERTED ##############
+        ###### PREPARE SOME FAKE INPUT DATA TO BE CONVERTED ##############
+        ###### PREPARE SOME FAKE INPUT DATA TO BE CONVERTED ##############
+        ###### PREPARE SOME FAKE INPUT DATA TO BE CONVERTED ##############
+        ###### PREPARE SOME FAKE INPUT DATA TO BE CONVERTED ##############
+
+        dataTextStr = \
+"""order_proc_anon_id,pat_anon_id,pat_enc_csn_anon_id,proc_code,organism_name,antibiotic_name,suseptibility,shifted_result_time
+-10,1,2,LABBLC,BACTEROIDES FRAGILIS,Clindamycin,Intermediate,9/10/2111 13:15
+-11,2,3,LABBLC,COAG NEGATIVE STAPHYLOCOCCUS,Vancomycin,Susceptible,4/26/2109 9:49
+-12,3,4,LABBLC,COAG NEGATIVE STAPHYLOCOCCUS,Oxacillin,Resistant,4/18/2109 4:48
+-13,4,5,LABBLC,COAG NEGATIVE STAPHYLOCOCCUS,Vancomycin,Susceptible,3/28/2109 23:21
+-14,5,6,LABBLC,ENTEROCOCCUS FAECALIS,Amoxicillin/Clavulanic Acid,Susceptible,6/3/2109 17:07
+-14,5,6,LABBLC,ENTEROCOCCUS FAECALIS,Amoxicillin/Clavulanic Acid,Susceptible,6/3/2109 17:07
+-20,10,11,LABBLC,ENTEROCOCCUS FAECALIS,Method,,6/10/2109 17:07
+-15,6,7,LABBLC2,,,,6/4/2109 17:07
+-16,7,8,LABBLC2,,,,
+-17,10,10,LABBLC2,ENTEROCOCCUS FAECALIS,Penicillin,,6/8/2109 17:07
+-17,10,10,LABBLC2,ENTEROCOCCUS FAECALIS,,Intermediate,6/11/2109 17:07
+-18,11,11,LABBLC2,ENTEROCOCCUS FAECALIS,Amikacin,Positive,6/11/2111 18:07
+"""
+        # Parse into DB insertion object
+        # DBUtil.insertFile( StringIO(dataTextStr), "stride_culture_micro", delim="   ", dateColFormats={"trtmnt_tm_begin_date": None, "trtmnt_tm_end_date": None} );
+        DBUtil.insertFile( StringIO(dataTextStr), "stride_culture_micro", delim=",", dateColFormats={"shifted_result_time": None} );
+
+
+        self.converter = STRIDECultureMicroConversion();  # Instance to test on
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        log.info("Purge test records from the database")
+
+        ######FIX THIS TO CLEANUP YOUR TEST DATA .... May be don't have to, since superclass will just drop the whole database anyway
+        DBUtil.execute \
+        (   """delete from patient_item 
+            where clinical_item_id in 
+            (   select clinical_item_id
+                from clinical_item as ci, clinical_item_category as cic
+                where ci.clinical_item_category_id = cic.clinical_item_category_id
+                and cic.source_table = 'stride_culture_micro'
+            );
+            """
+        );
+        DBUtil.execute \
+        (   """delete from clinical_item 
+            where clinical_item_category_id in 
+            (   select clinical_item_category_id 
+                from clinical_item_category 
+                where source_table = 'strid_culture_micro'
+            );
+            """
+        );
+        DBUtil.execute("delete from clinical_item_category where source_table = 'stride_culture_micro ';");
+
+        DBUtil.execute("delete from stride_culture_micro where order_proc_anon_id < 0");
+
+        DBTestCase.tearDown(self);
+
+    def test_dataConversion(self):
+        # Run the data conversion on the same data and look for expected records
+        log.debug("Run the conversion process...");
+        convOptions = ConversionOptions();
+        convOptions.startDate = TEST_START_DATE;
+        self.converter.convertSourceItems(convOptions);
+
+        # Just query back for the same data, de-normalizing the data back to a general table
+        testQuery = \
+            """
+            select 
+                pi.external_id,
+                pi.patient_id,
+                pi.encounter_id,
+                cic.description,
+                ci.name,
+                ci.description,
+                pi.item_date
+            from
+                patient_item as pi,
+                clinical_item as ci,
+                clinical_item_category as cic
+            where
+                pi.clinical_item_id = ci.clinical_item_id and
+                ci.clinical_item_category_id = cic.clinical_item_category_id and
+                cic.source_table = 'stride_culture_micro'
+            order by
+                pi.external_id desc
+            """;
+        expectedData = \
+            [   ##### CHANGE to the actual expected data
+[-10, 1, 2, "Microculture Susceptibility General", "Clindamycin:Not_Susceptible", "Not_Susceptible TO Clindamycin", DBUtil.parseDateValue("9/10/2111 13:15"),],
+[-11, 2, 3, "Microculture Susceptibility General", "Vancomycin:Susceptible", "Susceptible TO Vancomycin", DBUtil.parseDateValue("4/26/2109 9:49"),],
+[-12, 3, 4, "Microculture Susceptibility General", "Oxacillin:Not_Susceptible", "Not_Susceptible TO Oxacillin", DBUtil.parseDateValue("4/18/2109 4:48"),],
+[-13, 4, 5, "Microculture Susceptibility General", "Vancomycin:Susceptible", "Susceptible TO Vancomycin", DBUtil.parseDateValue("3/28/2109 23:21"),],
+[-14, 5, 6, "Microculture Susceptibility General", "Amoxicillin-Clavulanic Acid:Susceptible", "Susceptible TO Amoxicillin-Clavulanic Acid", DBUtil.parseDateValue("6/3/2109 17:07")],
+[-15, 6, 7, "Microculture Susceptibility General", "Negative Culture", "Microculture Grew No Bacteria", DBUtil.parseDateValue("6/4/2109 17:07")],
+[-18, 11, 11, "Microculture Susceptibility General", "Amikacin:Susceptible", "Susceptible TO Amikacin", DBUtil.parseDateValue("6/11/2111 18:07")]
+];
+        actualData = DBUtil.execute(testQuery);
+        self.assertEqualTable( expectedData, actualData );
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    #suite.addTest(TestSTRIDECultureMicroConversion("test_incColNamesAndTypeCodes"));
+    #suite.addTest(TestSTRIDECultureMicroConversion("test_insertFile_skipErrors"));
+    #suite.addTest(TestSTRIDECultureMicroConversion('test_executeIterator'));
+    #suite.addTest(TestSTRIDECultureMicroConversion('test_dataConversion_aggregate'));
+    suite.addTest(unittest.makeSuite(TestSTRIDECultureMicroConversion));
+    
+    return suite;
+    
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestSTRIDEDemographicsConversion.py b/medinfo/dataconversion/test/TestSTRIDEDemographicsConversion.py
index 00ca1f90..485d2ad7 100644
--- a/medinfo/dataconversion/test/TestSTRIDEDemographicsConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDEDemographicsConversion.py
@@ -2,22 +2,25 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
 
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
+
+
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel;
 
 from medinfo.dataconversion.STRIDEDemographicsConversion import STRIDEDemographicsConversion;
 
 TEST_SOURCE_TABLE = "stride_patient";
-TEMP_SOURCE_TABLE = "PreTest_patient";
 
 class TestSTRIDEDemographicsConversion(DBTestCase):
     def setUp(self):
@@ -25,75 +28,73 @@ def setUp(self):
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
-        
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = '%s' where source_table = '%s';" % (TEMP_SOURCE_TABLE,TEST_SOURCE_TABLE) );
-    
-        # Use fake age_yrs in negative values to easily identify test data to purge later
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
         dataTextStr = \
-"""pat_id\tage_yrs\tsex\tdeath_date\tbirth_year\tgender\trace\tethnicity
--100\t-56\tMale\tNone\t1958\tMALE\tAMERICAN INDIAN OR ALASKA NATIVE\tNON-HISPANIC/NON-LATINO
--200\t-22\tMale\tNone\t1992\tMALE\tAMERICAN INDIAN OR ALASKA NATIVE\tHISPANIC/LATINO
--300\t-21\tFemale\tNone\t1993\tFEMALE\tASIAN\tNON-HISPANIC/NON-LATINO
--400\t-17\tFemale\t2011-09-28\t1997\tFEMALE\tASIAN\tHISPANIC/LATINO
--500\t-62\tMale\tNone\t1952\tMALE\tASIAN\tPATIENT REFUSED
--600\t-30\tMale\tNone\t1984\tMALE\tASIAN\tUNKNOWN
--700\t-23\tFemale\tNone\t1991\tFEMALE\tASIAN - HISTORICAL CONV\tNON-HISPANIC/NON-LATINO
--800\t-52\tMale\tNone\t1962\tMALE\tASIAN, HISPANIC\tUNKNOWN
--900\t-42\tMale\tNone\t1972\tMALE\tASIAN, HISPANIC\tHISPANIC/LATINO
--1000\t-44\tMale\tNone\t1970\tMALE\tASIAN, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
--1100\t-13\tFemale\tNone\t2001\tFEMALE\tASIAN, NON-HISPANIC\tUNKNOWN
--1200\t-45\tFemale\tNone\t1969\tFEMALE\tBLACK OR AFRICAN AMERICAN\tNON-HISPANIC/NON-LATINO
--1300\t-69\tFemale\tNone\t1945\tFEMALE\tBLACK OR AFRICAN AMERICAN\tHISPANIC/LATINO
--1400\t-69\tFemale\tNone\t1945\tFEMALE\tBLACK OR AFRICAN AMERICAN\tUNKNOWN
--1500\t-58\tMale\t2012-05-02\t1956\tMALE\tBLACK OR AFRICAN AMERICAN\tPATIENT REFUSED
--1600\t-33\tFemale\tNone\t1981\tFEMALE\tBLACK, HISPANIC\tHISPANIC/LATINO
--1700\t-29\tFemale\tNone\t1985\tFEMALE\tBLACK, HISPANIC\tUNKNOWN
--1800\t-82\tMale\tNone\t1932\tMALE\tBLACK, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
--1900\t-60\tMale\tNone\t1954\tMALE\tBLACK, NON-HISPANIC\tUNKNOWN
--2000\t-82\tMale\tNone\t1932\tMALE\tNATIVE AMERICAN, HISPANIC\tHISPANIC/LATINO
--2100\t-53\tFemale\tNone\t1961\tFEMALE\tNATIVE AMERICAN, NON-HISPANIC\tUNKNOWN
--2200\t-40\tMale\tNone\t1974\tMALE\tNATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER \tNON-HISPANIC/NON-LATINO
--2300\t-61\tFemale\tNone\t1953\tFEMALE\tNATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER \tHISPANIC/LATINO
--2400\t-71\tFemale\tNone\t1943\tFEMALE\tNATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER \tUNKNOWN
--2500\t-51\tFemale\tNone\t1963\tFEMALE\tOTHER\tHISPANIC/LATINO
--2600\t-40\tMale\tNone\t1974\tMALE\tOTHER\tNON-HISPANIC/NON-LATINO
--2700\t-51\tFemale\tNone\t1963\tFEMALE\tOTHER\tPATIENT REFUSED
--2800\t-9\tFemale\tNone\t2005\tFEMALE\tOTHER\tUNKNOWN
--2900\t-18\tMale\tNone\t1996\tMALE\tOTHER, HISPANIC\tUNKNOWN
--3000\t-62\tMale\tNone\t1952\tMALE\tOTHER, HISPANIC\tHISPANIC/LATINO
--3100\t-31\tFemale\tNone\t1983\tFEMALE\tOTHER, HISPANIC\tNON-HISPANIC/NON-LATINO
--3200\t-43\tFemale\tNone\t1971\tFEMALE\tOTHER, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
--3300\t-38\tMale\tNone\t1976\tMALE\tOTHER, NON-HISPANIC\tUNKNOWN
--3400\t-19\tFemale\tNone\t1995\tFEMALE\tOTHER, NON-HISPANIC\tHISPANIC/LATINO
--3500\t-32\tMale\tNone\t1982\tMALE\tPACIFIC ISLANDER, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
--3600\t-74\tMale\tNone\t1940\tMALE\tPACIFIC ISLANDER, NON-HISPANIC\tUNKNOWN
--3700\t-80\tMale\tNone\t1934\tMALE\tPATIENT REFUSED\tPATIENT REFUSED
--3800\t-33\tFemale\tNone\t1981\tFEMALE\tPATIENT REFUSED\tNON-HISPANIC/NON-LATINO
--3900\t-16\tMale\tNone\t1998\tMALE\tPATIENT REFUSED\tHISPANIC/LATINO
--4000\t-36\tMale\tNone\t1978\tMALE\tRACE AND ETHNICITY UNKNOWN\tUNKNOWN
--4100\t-81\tFemale\tNone\t1933\tFEMALE\tRACE AND ETHNICITY UNKNOWN\tNON-HISPANIC/NON-LATINO
--4200\t-17\tFemale\tNone\t1997\tFEMALE\tUNKNOWN\tUNKNOWN
--4300\t-82\tMale\tNone\t1932\tMALE\tUNKNOWN\tNON-HISPANIC/NON-LATINO
--4400\t-67\tFemale\t2012-11-13\t1947\tFEMALE\tUNKNOWN\tHISPANIC/LATINO
--4500\t-82\tMale\tNone\t1932\tMALE\tUNKNOWN\tPATIENT REFUSED
--4600\t-78\tMale\tNone\t1936\tMALE\tUNKNOWN\t
--4700\t-21\tFemale\tNone\t1993\tFEMALE\tWHITE\tNON-HISPANIC/NON-LATINO
--4800\t-66\tMale\tNone\t1948\tMALE\tWHITE\tHISPANIC/LATINO
--4900\t-46\tMale\tNone\t1968\tMALE\tWHITE\tUNKNOWN
--5000\t-11\tFemale\tNone\t2003\tFEMALE\tWHITE\tPATIENT REFUSED
--5100\t-44\tMale\tNone\t1970\tMALE\tWHITE\t
--5200\t-16\tMale\tNone\t1998\tMALE\tWHITE, HISPANIC\tHISPANIC/LATINO
--5300\t-28\tMale\tNone\t1986\tMALE\tWHITE, HISPANIC\tUNKNOWN
--5400\t-17\tFemale\tNone\t1997\tFEMALE\tWHITE, HISPANIC\tNON-HISPANIC/NON-LATINO
--5500\t-50\tMale\tNone\t1964\tMALE\tWHITE, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
--5600\t-74\tMale\tNone\t1940\tMALE\tWHITE, NON-HISPANIC\tUNKNOWN
--5700\t-52\tMale\tNone\t1962\tMALE\tWHITE, NON-HISPANIC\tHISPANIC/LATINO
--5800\t-83\tFemale\tNone\t1931\tFEMALE\tNone\tNone
--5900\t-23\tFemale\tNone\t1991\tFEMALE\tNone\tNON-HISPANIC/NON-LATINO
--6000\t-41\tFemale\tNone\t1973\tFEMALE\tNone\tUNKNOWN
--6050\tNone\tNone\tNone\tNone\tNone\tNone\tNone
--6100\t-61\tFemale\tNone\t1953\tFEMALE\tNone\tHISPANIC/LATINO
+"""pat_id\tdeath_date\tbirth_year\tgender\trace\tethnicity
+-100\tNone\t1958\tMALE\tAMERICAN INDIAN OR ALASKA NATIVE\tNON-HISPANIC/NON-LATINO
+-200\tNone\t1992\tMALE\tAMERICAN INDIAN OR ALASKA NATIVE\tHISPANIC/LATINO
+-300\tNone\t1993\tFEMALE\tASIAN\tNON-HISPANIC/NON-LATINO
+-400\t2011-09-28\t1997\tFEMALE\tASIAN\tHISPANIC/LATINO
+-500\tNone\t1952\tMALE\tASIAN\tPATIENT REFUSED
+-600\tNone\t1984\tMALE\tASIAN\tUNKNOWN
+-700\tNone\t1991\tFEMALE\tASIAN - HISTORICAL CONV\tNON-HISPANIC/NON-LATINO
+-800\tNone\t1962\tMALE\tASIAN, HISPANIC\tUNKNOWN
+-900\tNone\t1972\tMALE\tASIAN, HISPANIC\tHISPANIC/LATINO
+-1000\tNone\t1970\tMALE\tASIAN, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
+-1100\tNone\t2001\tFEMALE\tASIAN, NON-HISPANIC\tUNKNOWN
+-1200\tNone\t1969\tFEMALE\tBLACK OR AFRICAN AMERICAN\tNON-HISPANIC/NON-LATINO
+-1300\tNone\t1945\tFEMALE\tBLACK OR AFRICAN AMERICAN\tHISPANIC/LATINO
+-1400\tNone\t1945\tFEMALE\tBLACK OR AFRICAN AMERICAN\tUNKNOWN
+-1500\t2012-05-02\t1956\tMALE\tBLACK OR AFRICAN AMERICAN\tPATIENT REFUSED
+-1600\tNone\t1981\tFEMALE\tBLACK, HISPANIC\tHISPANIC/LATINO
+-1700\tNone\t1985\tFEMALE\tBLACK, HISPANIC\tUNKNOWN
+-1800\tNone\t1932\tMALE\tBLACK, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
+-1900\tNone\t1954\tMALE\tBLACK, NON-HISPANIC\tUNKNOWN
+-2000\tNone\t1932\tMALE\tNATIVE AMERICAN, HISPANIC\tHISPANIC/LATINO
+-2100\tNone\t1961\tFEMALE\tNATIVE AMERICAN, NON-HISPANIC\tUNKNOWN
+-2200\tNone\t1974\tMALE\tNATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER \tNON-HISPANIC/NON-LATINO
+-2300\tNone\t1953\tFEMALE\tNATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER \tHISPANIC/LATINO
+-2400\tNone\t1943\tFEMALE\tNATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER \tUNKNOWN
+-2500\tNone\t1963\tFEMALE\tOTHER\tHISPANIC/LATINO
+-2600\tNone\t1974\tMALE\tOTHER\tNON-HISPANIC/NON-LATINO
+-2700\tNone\t1963\tFEMALE\tOTHER\tPATIENT REFUSED
+-2800\tNone\t2005\tFEMALE\tOTHER\tUNKNOWN
+-2900\tNone\t1996\tMALE\tOTHER, HISPANIC\tUNKNOWN
+-3000\tNone\t1952\tMALE\tOTHER, HISPANIC\tHISPANIC/LATINO
+-3100\tNone\t1983\tFEMALE\tOTHER, HISPANIC\tNON-HISPANIC/NON-LATINO
+-3200\tNone\t1971\tFEMALE\tOTHER, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
+-3300\tNone\t1976\tMALE\tOTHER, NON-HISPANIC\tUNKNOWN
+-3400\tNone\t1995\tFEMALE\tOTHER, NON-HISPANIC\tHISPANIC/LATINO
+-3500\tNone\t1982\tMALE\tPACIFIC ISLANDER, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
+-3600\tNone\t1940\tMALE\tPACIFIC ISLANDER, NON-HISPANIC\tUNKNOWN
+-3700\tNone\t1934\tMALE\tPATIENT REFUSED\tPATIENT REFUSED
+-3800\tNone\t1981\tFEMALE\tPATIENT REFUSED\tNON-HISPANIC/NON-LATINO
+-3900\tNone\t1998\tMALE\tPATIENT REFUSED\tHISPANIC/LATINO
+-4000\tNone\t1978\tMALE\tRACE AND ETHNICITY UNKNOWN\tUNKNOWN
+-4100\tNone\t1933\tFEMALE\tRACE AND ETHNICITY UNKNOWN\tNON-HISPANIC/NON-LATINO
+-4200\tNone\t1997\tFEMALE\tUNKNOWN\tUNKNOWN
+-4300\tNone\t1932\tMALE\tUNKNOWN\tNON-HISPANIC/NON-LATINO
+-4400\t2012-11-13\t1947\tFEMALE\tUNKNOWN\tHISPANIC/LATINO
+-4500\tNone\t1932\tMALE\tUNKNOWN\tPATIENT REFUSED
+-4600\tNone\t1936\tMALE\tUNKNOWN\t
+-4700\tNone\t1993\tFEMALE\tWHITE\tNON-HISPANIC/NON-LATINO
+-4800\tNone\t1948\tMALE\tWHITE\tHISPANIC/LATINO
+-4900\tNone\t1968\tMALE\tWHITE\tUNKNOWN
+-5000\tNone\t2003\tFEMALE\tWHITE\tPATIENT REFUSED
+-5100\tNone\t1970\tMALE\tWHITE\t
+-5200\tNone\t1998\tMALE\tWHITE, HISPANIC\tHISPANIC/LATINO
+-5300\tNone\t1986\tMALE\tWHITE, HISPANIC\tUNKNOWN
+-5400\tNone\t1997\tFEMALE\tWHITE, HISPANIC\tNON-HISPANIC/NON-LATINO
+-5500\tNone\t1964\tMALE\tWHITE, NON-HISPANIC\tNON-HISPANIC/NON-LATINO
+-5600\tNone\t1940\tMALE\tWHITE, NON-HISPANIC\tUNKNOWN
+-5700\tNone\t1962\tMALE\tWHITE, NON-HISPANIC\tHISPANIC/LATINO
+-5800\tNone\t1931\tFEMALE\tNone\tNone
+-5900\tNone\t1991\tFEMALE\tNone\tNON-HISPANIC/NON-LATINO
+-6000\tNone\t1973\tFEMALE\tNone\tUNKNOWN
+-6050\tNone\tNone\tNone\tNone\tNone
+-6100\tNone\t1953\tFEMALE\tNone\tHISPANIC/LATINO
 """
         self.patientIds = ["-100","-200","-300","-400","-500","-600","-700","-800","-900","-1000","-1100","-1200","-1300","-1400","-1500","-1600","-1700","-1800","-1900","-2000","-2100","-2200","-2300","-2400","-2500","-2600","-2700","-2800","-2900","-3000","-3100","-3200","-3300","-3400","-3500","-3600","-3700","-3800","-3900","-4000","-4100","-4200","-4300","-4400","-4500","-4600","-4700","-4800","-4900","-5000","-5100","-5200","-5300","-5400","-5500","-5600","-5700","-5800","-5900","-6000","-6050","-6100"];
 
@@ -126,12 +127,11 @@ def tearDown(self):
             """ % TEST_SOURCE_TABLE
         );
         DBUtil.execute("delete from clinical_item_category where source_table = '%s';" % TEST_SOURCE_TABLE);
-        DBUtil.execute("update clinical_item_category set source_table = '%s' where source_table = '%s';" % (TEST_SOURCE_TABLE,TEMP_SOURCE_TABLE) ); # Reset labels of any prior data
 
         query = SQLQuery();
         query.delete = True;
         query.addFrom("stride_patient");
-        query.addWhere("age_yrs < 0");
+        query.addWhere("pat_id < 0");
         DBUtil.execute( query );
 
         DBTestCase.tearDown(self);
diff --git a/medinfo/dataconversion/test/TestSTRIDEDxListConversion.py b/medinfo/dataconversion/test/TestSTRIDEDxListConversion.py
index ff8c234a..a7a518af 100644
--- a/medinfo/dataconversion/test/TestSTRIDEDxListConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDEDxListConversion.py
@@ -2,14 +2,17 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
+from pprint import pprint
 
 from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel;
@@ -17,48 +20,51 @@
 from medinfo.dataconversion.STRIDEDxListConversion import STRIDEDxListConversion;
 
 TEST_SOURCE_TABLE = "stride_dx_list";
-TEMP_SOURCE_TABLE = "PreTest_dx_list";
 TEST_START_DATE = datetime(2100,1,1);   # Date in far future to start checking for test records to avoid including existing data in database
 
 class TestSTRIDEDxListConversion(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
-        
+
         log.info("Populate the database with test data")
-        
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = '%s' where source_table = '%s';" % (TEMP_SOURCE_TABLE,TEST_SOURCE_TABLE) );
-    
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
         self.patientIdStrList = list();
-        headers = ["pat_id","pat_enc_csn_id","noted_date","resolved_date","dx_icd9_code","data_source"];
+        headers = ["pat_id","pat_enc_csn_id","noted_date","resolved_date","dx_icd9_code","dx_icd9_code_list","dx_icd10_code_list","data_source"];
         dataModels = \
-            [  
-                RowItemModel( ["-126500", -131017780655, "2111-10-14", None, "-431.00", "PROBLEM_LIST"], headers ),
-
-                RowItemModel( ["-126268", -131015534571, "2111-05-04", None, "-0285", "PROBLEM_LIST"], headers ),
-                RowItemModel( ["-126268", -131015534571, None, None, "-272.4", "PROBLEM_LIST"], headers ),
-                RowItemModel( ["-126268", -131015534571, None, None, "-309.9", "ENC_Dx"], headers ),
-                RowItemModel( ["-126268", -131015534571, "2111-05-18", None, "-785", "ADMIT_DX"], headers ),
-                RowItemModel( ["-126472", -131015404439, None, None, "-719.46", "ENC_Dx"], headers ),
+            [
+                RowItemModel( ["-126500", -131017780655, "2111-10-14", None, "-431.00", "-431.00,-432","","PROBLEM_LIST"], headers ),
+                RowItemModel( ["-126268", -131015534571, "2111-05-04", None, "-0285", "-0285,-0286","","PROBLEM_LIST"], headers ),
+                RowItemModel( ["-126268", -131015534571, None, None, "-272.4", "-272.4,-273","","PROBLEM_LIST"], headers ),
+                RowItemModel( ["-126268", -131015534571, None, None, "-309.9", "-309.9,-310","","ENC_Dx"], headers ),
+                RowItemModel( ["-126268", -131015534571, "2111-05-18", None, "-785", "-785,-786","","ADMIT_DX"], headers ),
+                RowItemModel( ["-126472", -131015404439, None, None, "-719.46", "-719.46,-720","","ENC_Dx"], headers ),
 
                 # Repeat, but under different encounter, should be ignored, just base on noted date
-                RowItemModel( ["-126500", -131000000000, "2111-10-14", None, "-431.00", "PROBLEM_LIST"], headers ),
-
-                RowItemModel( ["-126798", -131014753610, None, None, "-482.9", "ENC_Dx"], headers ),
-                RowItemModel( ["-126798", -131014753610, "2111-03-08", None, "-780", "ADMIT_DX"], headers ),
-                RowItemModel( ["-126798", -131016557370, "2111-07-26", None, "-780.97", "ADMIT_DX"], headers ),    # No matching diagnosis code, will just make up a label then
-
+                RowItemModel( ["-126500", -131000000000, "2111-10-14", None, "-431.00", "-431.00,-432","","PROBLEM_LIST"], headers ),
+                RowItemModel( ["-126798", -131014753610, None, None, "-482.9", "-482.9,-483","","ENC_Dx"], headers ),
+                RowItemModel( ["-126798", -131014753610, "2111-03-08", None, "-780", "-780,-781","","ADMIT_DX"], headers ),
+                RowItemModel( ["-126798", -131016557370, "2111-07-26", None, "-780.97", "-780.97,-781","","ADMIT_DX"], headers ),    # No matching diagnosis code, will just make up a label then
+
+                # Different patient, but using ICD9 list instead of ICD9 code
+                # If there is no ICD9 list, and only ICD10 list, we will need
+                # to add special logic and decide whether to try to
+                # cross-reference with ICD9-based Dx'es
+                RowItemModel( ["-2126500", -135000000000, "2111-10-14", None, "", "-431.00,-432","-10431.00,-10432","PROBLEM_LIST"], headers ),
+                RowItemModel( ["-2126798", -135014753610, "2111-06-06", None, "", "-482.9,-483","-10482.9,-10483","PROBLEM_LIST"], headers ),
+                RowItemModel( ["-2126798", -135014753610, "2111-03-08", None, "", "-780","-10780","ADMIT_DX"], headers ),
             ];
         for dataModel in dataModels:
             (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_dx_list", dataModel, retrieveCol="pat_id" );
             self.patientIdStrList.append( str(dataItemId) );
 
-        
+
         self.icd9CUIdStrList = list();
         headers = ["cui","ispref","aui","tty","code","str","suppress"];
         dataModels = \
-            [  
+            [
                 RowItemModel( ["-C1", "Y", "-A1", "PT", "-0285", "Diagnosis 1","N"], headers ),
                 RowItemModel( ["-C2b","Y", "-A2b","PT", "-431.0", "Diagnosis 2b","N"], headers ),    # Parent diagnoses
                 RowItemModel( ["-C2", "Y", "-A2", "PT", "-431.00", "Diagnosis 2","N"], headers ),
@@ -72,6 +78,20 @@ def setUp(self):
             (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_icd9_cm", dataModel, retrieveCol="cui" );
             self.icd9CUIdStrList.append( str(dataItemId) );
 
+        self.icd10_order_number_str_list = list()
+        headers = [
+            'order_number', 'icd10', 'hipaa_covered',
+            'short_description','full_description','icd10_code'
+        ]
+        data_models = [
+            RowItemModel([-1, '-1043100', 1, 'Diagnosis 2', 'Diagnosis 2 Full', '-10431.00'], headers),
+            RowItemModel([-2, '-104310', 0, 'Diagnosis 2b', 'Diagnosis 2b Full', '-10431.0'], headers),
+            RowItemModel([-6, '-10780', 0, 'Diagnosis 5', 'Diagnosis 5 Full', '-10780'], headers)
+        ]
+        for data_model in data_models:
+            (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_icd10_cm", data_model, retrieveCol='order_number');
+            self.icd10_order_number_str_list.append( str(dataItemId) );
+
         self.converter = STRIDEDxListConversion();  # Instance to test on
 
     def tearDown(self):
@@ -79,8 +99,8 @@ def tearDown(self):
         log.info("Purge test records from the database")
 
         DBUtil.execute \
-        (   """delete from patient_item 
-            where clinical_item_id in 
+        (   """delete from patient_item
+            where clinical_item_id in
             (   select clinical_item_id
                 from clinical_item as ci, clinical_item_category as cic
                 where ci.clinical_item_category_id = cic.clinical_item_category_id
@@ -89,16 +109,15 @@ def tearDown(self):
             """ % TEST_SOURCE_TABLE
         );
         DBUtil.execute \
-        (   """delete from clinical_item 
-            where clinical_item_category_id in 
-            (   select clinical_item_category_id 
-                from clinical_item_category 
+        (   """delete from clinical_item
+            where clinical_item_category_id in
+            (   select clinical_item_category_id
+                from clinical_item_category
                 where source_table = '%s'
             );
             """ % TEST_SOURCE_TABLE
         );
         DBUtil.execute("delete from clinical_item_category where source_table = '%s';" % TEST_SOURCE_TABLE);
-        DBUtil.execute("update clinical_item_category set source_table = '%s' where source_table = '%s';" % (TEST_SOURCE_TABLE,TEMP_SOURCE_TABLE) ); # Reset labels of any prior data
 
         query = SQLQuery();
         query.delete = True;
@@ -112,6 +131,13 @@ def tearDown(self):
         query.addWhereIn("cui", self.icd9CUIdStrList );
         DBUtil.execute( query );
 
+
+        query = SQLQuery();
+        query.delete = True;
+        query.addFrom("stride_icd10_cm");
+        query.addWhereIn("order_number", self.icd10_order_number_str_list );
+        DBUtil.execute( query );
+
         DBTestCase.tearDown(self);
 
     def test_dataConversion(self):
@@ -122,7 +148,7 @@ def test_dataConversion(self):
         # Just query back for the same data, de-normalizing the data back to a general table
         testQuery = \
             """
-            select 
+            select
                 pi.external_id,
                 pi.patient_id,
                 pi.encounter_id,
@@ -140,7 +166,7 @@ def test_dataConversion(self):
                 ci.clinical_item_category_id = cic.clinical_item_category_id and
                 cic.source_table = '%s'
             order by
-                pi.patient_id desc, ci.name
+                pi.patient_id desc, ci.name, pi.item_date
             """ % TEST_SOURCE_TABLE;
         expectedData = \
             [   # Expected data should be updated once we have ICD9 - Name conversion tables
@@ -149,12 +175,24 @@ def test_dataConversion(self):
 
                 [None, -126500, -131017780655, "Diagnosis (PROBLEM_LIST)", None, "ICD9.-431.0", "Diagnosis 2b", datetime(2111,10,14)],
                 [None, -126500, -131017780655, "Diagnosis (PROBLEM_LIST)", None, "ICD9.-431.00", "Diagnosis 2", datetime(2111,10,14)],
-                
-                [None, -126798, -131014753610, "Diagnosis (ADMIT_DX)", None, "ICD9.-780", "Diagnosis 5", datetime(2111,3,8)],
 
+                [None, -126798, -131014753610, "Diagnosis (ADMIT_DX)", None, "ICD9.-780", "Diagnosis 5", datetime(2111,3,8)],
                 [None, -126798, -131016557370, "Diagnosis (ADMIT_DX)", None, "ICD9.-780", "Diagnosis 5", datetime(2111,7,26)],
                 [None, -126798, -131016557370, "Diagnosis (ADMIT_DX)", None, "ICD9.-780.9", "Diagnosis 6a", datetime(2111,7,26)],
                 [None, -126798, -131016557370, "Diagnosis (ADMIT_DX)", None, "ICD9.-780.97", "Diagnosis 6", datetime(2111,7,26)],
+
+                [None, -2126500, -135000000000, 'Diagnosis (PROBLEM_LIST)', None, 'ICD10.-10431.0', 'Diagnosis 2b Full', datetime(2111, 10, 14, 0, 0)],
+                [None, -2126500, -135000000000, 'Diagnosis (PROBLEM_LIST)', None, 'ICD10.-10431.00', 'Diagnosis 2 Full', datetime(2111, 10, 14, 0, 0)],
+                [None, -2126500, -135000000000, 'Diagnosis (PROBLEM_LIST)', None, 'ICD10.-10432', '-10432', datetime(2111, 10, 14, 0, 0)],
+                [None, -2126500, -135000000000, 'Diagnosis (PROBLEM_LIST)', None, 'ICD9.-431.0', 'Diagnosis 2b', datetime(2111, 10, 14, 0, 0)],
+                [None, -2126500, -135000000000, 'Diagnosis (PROBLEM_LIST)', None, 'ICD9.-431.00', 'Diagnosis 2', datetime(2111, 10, 14, 0, 0)],
+                [None, -2126500, -135000000000, 'Diagnosis (PROBLEM_LIST)', None, 'ICD9.-432', '-432', datetime(2111, 10, 14, 0, 0)],
+                [None, -2126798, -135014753610, 'Diagnosis (PROBLEM_LIST)', None, 'ICD10.-10482.9', '-10482.9', datetime(2111, 6, 6, 0, 0)],
+                [None, -2126798, -135014753610, 'Diagnosis (PROBLEM_LIST)', None, 'ICD10.-10483', '-10483', datetime(2111, 6, 6, 0, 0)],
+                [None, -2126798, -135014753610, 'Diagnosis (ADMIT_DX)', None, 'ICD10.-10780', 'Diagnosis 5 Full', datetime(2111, 3, 8, 0, 0)],
+                [None, -2126798, -135014753610, 'Diagnosis (PROBLEM_LIST)', None, 'ICD9.-482.9', '-482.9', datetime(2111, 6, 6, 0, 0)],
+                [None, -2126798, -135014753610, 'Diagnosis (PROBLEM_LIST)', None, 'ICD9.-483', '-483', datetime(2111, 6, 6, 0, 0)],
+                [None, -2126798, -135014753610, 'Diagnosis (ADMIT_DX)', None, 'ICD9.-780', 'Diagnosis 5', datetime(2111, 3, 8, 0, 0)],
             ];
         actualData = DBUtil.execute(testQuery);
         self.assertEqualTable( expectedData, actualData );
@@ -170,8 +208,8 @@ def suite():
     #suite.addTest(TestSTRIDEDxListConversion('test_executeIterator'));
     #suite.addTest(TestSTRIDEDxListConversion('test_findOrInsertItem'));
     suite.addTest(unittest.makeSuite(TestSTRIDEDxListConversion));
-    
+
     return suite;
-    
+
 if __name__=="__main__":
     unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py b/medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py
index a33f4aa9..248ab13e 100644
--- a/medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py
@@ -2,14 +2,16 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel;
@@ -22,12 +24,11 @@ class TestSTRIDEOrderMedConversion(DBTestCase):
     def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
-        
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = 'PreTest_order_med' where source_table = 'stride_order_med';");
-    
+
         log.info("Populate the database with test data")
-        
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
+
         self.orderMedIdStrList = list();
         headers = ["order_med_id", "pat_id", "pat_enc_csn_id", "ordering_date", "medication_id", "description","freq_name","med_route","number_of_doses"];
         dataModels = \
@@ -43,14 +44,14 @@ def setUp(self):
                 RowItemModel( [ -414321352, "3036588", 777, datetime(2111,12,14, 8,30), -5007, "ZZZ IMS TEMPLATE","ONCE","IV", 1], headers ),
 
                 # Simple mixture in unimportant base
-                RowItemModel( [ -395900000, "1234567", 888, datetime(2111, 1, 2, 3, 0), -520102, "CEFEPIME 2 GM IVPB","ONCE","IV", 1], headers ),  
+                RowItemModel( [ -395900000, "1234567", 888, datetime(2111, 1, 2, 3, 0), -520102, "CEFEPIME 2 GM IVPB","ONCE","IV", 1], headers ),
                 RowItemModel( [ -395800000, "1234567", 888, datetime(2111, 1,10, 3, 0), -520102, "CEFEPIME 2 GM IVPB","q8h","IV", 6], headers ),  # Fixed number of doses
                 RowItemModel( [ -395700000, "1234567", 888, datetime(2111, 3,10, 3, 0), -520102, "CEFEPIME 2 GM IVPB","q8h","IV", None], headers ),  # No dose limit specified
 
                 # Complex mixtures
-                RowItemModel( [ -392000000, "1234567", 888, datetime(2111, 4, 1, 3, 0), -530000, "IVF Mix","ONCE","IV", 1], headers ),  
-                RowItemModel( [ -391000000, "1234567", 888, datetime(2111, 4, 2, 3, 0), -540000, "Mini TPN","ONCE","IV", 1], headers ),  
-                RowItemModel( [ -390000000, "1234567", 888, datetime(2111, 5, 2, 3, 0), -550000, "TPN Adult","Continuous","IV", None], headers ),  
+                RowItemModel( [ -392000000, "1234567", 888, datetime(2111, 4, 1, 3, 0), -530000, "IVF Mix","ONCE","IV", 1], headers ),
+                RowItemModel( [ -391000000, "1234567", 888, datetime(2111, 4, 2, 3, 0), -540000, "Mini TPN","ONCE","IV", 1], headers ),
+                RowItemModel( [ -390000000, "1234567", 888, datetime(2111, 5, 2, 3, 0), -550000, "TPN Adult","Continuous","IV", None], headers ),
             ];
         for dataModel in dataModels:
             (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_order_med", dataModel, retrieveCol="order_med_id" );
@@ -70,7 +71,7 @@ def setUp(self):
                 RowItemModel( [ -395800000, 2, -2364, "DEXTROSE 5 % IN WATER (D5W) IV SOLP", 1, "Base",], headers ),
                 RowItemModel( [ -395700000, 1, -87114, "CEFEPIME 2 GRAM IV SOLR", 3, "Medications",], headers ),
                 RowItemModel( [ -395700000, 2, -2364, "DEXTROSE 5 % IN WATER (D5W) IV SOLP", 1, "Base",], headers ),
-                
+
                 # IVF Mix
                 RowItemModel( [ -392000000, 2, -2367, "DEXTROSE 70 % IN WATER (D70W) IV SOLP", 1, "Base",], headers ),
                 RowItemModel( [ -392000000, 3, -7322, "SODIUM CHLORIDE 4 MEQ/ML IV SOLP", 2, "Additives",], headers ),
@@ -93,13 +94,13 @@ def setUp(self):
                 RowItemModel( [ -390000000, 8, -8047, "TRACE ELEM ZN-CUPRIC CL-MN-CR 0.8-0.2-0.16 MG IV SOLN", 2, "Additives",], headers ),
                 RowItemModel( [ -390000000, 9, -124215, "MVI, ADULT NO.1 WITH VIT K 3,300 UNIT- 150 MCG/10 ML IV SOLN", 2, "Additives",], headers ),
                 RowItemModel( [ -390000000, 10,-540571, "INSULIN REG HUMAN 100 UNITS/ML INJ", 2, "Additives",], headers ),
-                
+
             ];
         for dataModel in dataModels:
             (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_order_medmixinfo", dataModel, retrieveCol="order_med_id" );
 
         # Mapping table to simplify dose
-        headers = ["medication_id", "medication_name", "rxcui", "active_ingredient"];
+        headers = ["medication_id", "name", "rxcui", "active_ingredient"];
         dataModels = \
             [   # If multiple active ingredients in a combo, unravel to active components
                 RowItemModel( [ -96559, "PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK", -1001, "Piperacillin"], headers ),
@@ -115,7 +116,7 @@ def setUp(self):
                 # Simple mixture
                 RowItemModel( [ -87114, "CEFEPIME 2 GRAM IV SOLR", -20481, "cefepime"], headers ),
                 RowItemModel( [ -2364, "DEXTROSE 5% IN WATER (D5W) IV SOLP", -4850, "Glucose"], headers ),
-                
+
                 # Additional medication level mapping should probably ignored if have mixture components
                 RowItemModel( [ -520102, "CEFEPIME 2 GM IVPB", -20481, "cefepime"], headers ),
 
@@ -156,14 +157,14 @@ def setUp(self):
         # Certain items drawn from order sets
         headers = ["order_med_id", "protocol_id","protocol_name","section_name","smart_group"];
         dataModels = \
-            [   
+            [
                 RowItemModel( [ -418436145, -111, "General Admit", "Medications", "Stool Softeners",], headers ),
                 RowItemModel( [ -421032269, -111, "General Admit", "Medications", "Antibiotics",], headers ),
                 RowItemModel( [ -391000000, -222, "Nutrition", "Infusions", "TPN",], headers ),
             ];
         for dataModel in dataModels:
             (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_orderset_order_med", dataModel, retrieveCol="order_med_id" );
-        
+
         self.converter = STRIDEOrderMedConversion();  # Instance to test on
 
     def tearDown(self):
@@ -172,7 +173,7 @@ def tearDown(self):
 
         DBUtil.execute \
         (   """delete from patient_item_collection_link
-            where item_collection_item_id in 
+            where item_collection_item_id in
             (   select item_collection_item_id
                 from item_collection_item as ici, item_collection as ic
                 where ici.item_collection_id = ic.item_collection_id
@@ -182,7 +183,7 @@ def tearDown(self):
         );
         DBUtil.execute \
         (   """delete from item_collection_item
-            where item_collection_id in 
+            where item_collection_id in
             (   select item_collection_id
                 from item_collection as ic
                 where ic.external_id < 0
@@ -192,8 +193,8 @@ def tearDown(self):
         DBUtil.execute("delete from item_collection where external_id < 0;");
 
         DBUtil.execute \
-        (   """delete from patient_item 
-            where clinical_item_id in 
+        (   """delete from patient_item
+            where clinical_item_id in
             (   select clinical_item_id
                 from clinical_item as ci, clinical_item_category as cic
                 where ci.clinical_item_category_id = cic.clinical_item_category_id
@@ -202,16 +203,15 @@ def tearDown(self):
             """
         );
         DBUtil.execute \
-        (   """delete from clinical_item 
-            where clinical_item_category_id in 
-            (   select clinical_item_category_id 
-                from clinical_item_category 
+        (   """delete from clinical_item
+            where clinical_item_category_id in
+            (   select clinical_item_category_id
+                from clinical_item_category
                 where source_table = 'stride_order_med'
             );
             """
         );
         DBUtil.execute("delete from clinical_item_category where source_table = 'stride_order_med';");
-        DBUtil.execute("update clinical_item_category set source_table = 'stride_order_med' where source_table = 'PreTest_order_med';"); # Reset labels of any prior data
 
         DBUtil.execute("delete from stride_orderset_order_med where order_med_id in (%s)" % str.join(",", self.orderMedIdStrList) );
         DBUtil.execute("delete from stride_mapped_meds where rxcui < 0");
@@ -232,7 +232,7 @@ def test_dataConversion_normalized(self):
         # Just query back for the same data, de-normalizing the data back to a general table
         testQuery = \
             """
-            select 
+            select
                 pi.external_id,
                 pi.patient_id,
                 pi.encounter_id,
@@ -266,66 +266,66 @@ def test_dataConversion_normalized(self):
                 [ -414321352, 3036588, 777, "Med (IV)", -5007, "MED-5007 (<5)", "METOPROLOL TARTRATE 5 MG/5 ML IV SOLN (<5 doses)", datetime(2111,12,14,8,30) ],
 
                 # Simple mixture with different dosing counts
-                [ -395900000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481 (<5)", "Cefepime (IV) (<5 doses)", datetime(2111, 1, 2, 3, 0)],   
-                [ -395800000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 1,10, 3, 0)],   
-                [ -395700000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 3,10, 3, 0)],   
+                [ -395900000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481 (<5)", "Cefepime (IV) (<5 doses)", datetime(2111, 1, 2, 3, 0)],
+                [ -395800000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 1,10, 3, 0)],
+                [ -395700000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 3,10, 3, 0)],
 
                 # IVF Mix
-                [ -392000000, 1234567, 888, "Med (IV)", -9863, 'RXCUI-9863 (<5)', 'Sodium Chloride (IV) (<5 doses)', datetime(2111, 4, 1, 3, 0)],   
-                [ -392000000, 1234567, 888, "Med (IV)", -8591, 'RXCUI-8591 (<5)', 'Potassium Chloride (IV) (<5 doses)', datetime(2111, 4, 1, 3, 0)],   
-                [ -392000000, 1234567, 888, "Med (IV)", -4850, 'RXCUI-4850 (<5)', 'Glucose (IV) (<5 doses)', datetime(2111, 4, 1, 3, 0)],   
+                [ -392000000, 1234567, 888, "Med (IV)", -9863, 'RXCUI-9863 (<5)', 'Sodium Chloride (IV) (<5 doses)', datetime(2111, 4, 1, 3, 0)],
+                [ -392000000, 1234567, 888, "Med (IV)", -8591, 'RXCUI-8591 (<5)', 'Potassium Chloride (IV) (<5 doses)', datetime(2111, 4, 1, 3, 0)],
+                [ -392000000, 1234567, 888, "Med (IV)", -4850, 'RXCUI-4850 (<5)', 'Glucose (IV) (<5 doses)', datetime(2111, 4, 1, 3, 0)],
 
                 # Mini-Mix
-                [ -391000000, 1234567, 888, "Med (IV)", -236719, 'RXCUI-236719 (<5)', 'Sodium Phosphate, Dibasic (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -235496, 'RXCUI-235496 (<5)', 'Sodium Phosphate, Monobasic (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -11115, 'RXCUI-11115 (<5)', 'Valine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -10962, 'RXCUI-10962 (<5)', 'Tyrosine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -10898, 'RXCUI-10898 (<5)', 'Tryptophan (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -10524, 'RXCUI-10524 (<5)', 'Threonine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -9863, 'RXCUI-9863 (<5)', 'Sodium Chloride (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -9671, 'RXCUI-9671 (<5)', 'Serine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -8737, 'RXCUI-8737 (<5)', 'Proline (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -8156, 'RXCUI-8156 (<5)', 'Phenylalanine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -6837, 'RXCUI-6837 (<5)', 'Methionine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -6536, 'RXCUI-6536 (<5)', 'Lysine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -6308, 'RXCUI-6308 (<5)', 'Leucine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -6033, 'RXCUI-6033 (<5)', 'Isoleucine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -5340, 'RXCUI-5340 (<5)', 'Histidine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -4919, 'RXCUI-4919 (<5)', 'Glycine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -4850, 'RXCUI-4850 (<5)', 'Glucose (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -1091, 'RXCUI-1091 (<5)', 'Arginine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
-                [ -391000000, 1234567, 888, "Med (IV)", -426, 'RXCUI-426 (<5)', 'Alanine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],   
+                [ -391000000, 1234567, 888, "Med (IV)", -236719, 'RXCUI-236719 (<5)', 'Sodium Phosphate, Dibasic (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -235496, 'RXCUI-235496 (<5)', 'Sodium Phosphate, Monobasic (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -11115, 'RXCUI-11115 (<5)', 'Valine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -10962, 'RXCUI-10962 (<5)', 'Tyrosine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -10898, 'RXCUI-10898 (<5)', 'Tryptophan (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -10524, 'RXCUI-10524 (<5)', 'Threonine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -9863, 'RXCUI-9863 (<5)', 'Sodium Chloride (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -9671, 'RXCUI-9671 (<5)', 'Serine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -8737, 'RXCUI-8737 (<5)', 'Proline (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -8156, 'RXCUI-8156 (<5)', 'Phenylalanine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -6837, 'RXCUI-6837 (<5)', 'Methionine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -6536, 'RXCUI-6536 (<5)', 'Lysine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -6308, 'RXCUI-6308 (<5)', 'Leucine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -6033, 'RXCUI-6033 (<5)', 'Isoleucine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -5340, 'RXCUI-5340 (<5)', 'Histidine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -4919, 'RXCUI-4919 (<5)', 'Glycine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -4850, 'RXCUI-4850 (<5)', 'Glucose (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -1091, 'RXCUI-1091 (<5)', 'Arginine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
+                [ -391000000, 1234567, 888, "Med (IV)", -426, 'RXCUI-426 (<5)', 'Alanine (IV) (<5 doses)', datetime(2111, 4, 2, 3, 0)],
 
                 # Complex mixture
-                [ -390000000, 1234567, 888, "Med (IV)", -253182, 'RXCUI-253182', 'Regular Insulin, Human (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -236719, 'RXCUI-236719', 'Sodium Phosphate, Dibasic (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -235496, 'RXCUI-235496', 'Sodium Phosphate, Monobasic (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -89905, 'RXCUI-89905', 'Multivitamin Preparation (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -39937, 'RXCUI-39937', 'Zinc Chloride (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -29261, 'RXCUI-29261', 'Manganese Chloride (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -21579, 'RXCUI-21579', 'Copper Sulfate (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -21032, 'RXCUI-21032', 'Chromous Chloride (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -11115, 'RXCUI-11115', 'Valine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -10962, 'RXCUI-10962', 'Tyrosine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -10898, 'RXCUI-10898', 'Tryptophan (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -10524, 'RXCUI-10524', 'Threonine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -9863, 'RXCUI-9863', 'Sodium Chloride (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -9671, 'RXCUI-9671', 'Serine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -8737, 'RXCUI-8737', 'Proline (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -8591, 'RXCUI-8591', 'Potassium Chloride (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -8308, 'RXCUI-8308', 'Vitamin K 1 (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -8156, 'RXCUI-8156', 'Phenylalanine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -6837, 'RXCUI-6837', 'Methionine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -6585, 'RXCUI-6585', 'Magnesium Sulfate (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -6536, 'RXCUI-6536', 'Lysine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -6308, 'RXCUI-6308', 'Leucine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -6033, 'RXCUI-6033', 'Isoleucine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -5340, 'RXCUI-5340', 'Histidine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -4919, 'RXCUI-4919', 'Glycine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -4850, 'RXCUI-4850', 'Glucose (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -1908, 'RXCUI-1908', 'Calcium Gluconate (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -1091, 'RXCUI-1091', 'Arginine (IV)', datetime(2111, 5, 2, 3, 0)],   
-                [ -390000000, 1234567, 888, "Med (IV)", -426, 'RXCUI-426', 'Alanine (IV)', datetime(2111, 5, 2, 3, 0)],   
+                [ -390000000, 1234567, 888, "Med (IV)", -253182, 'RXCUI-253182', 'Regular Insulin, Human (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -236719, 'RXCUI-236719', 'Sodium Phosphate, Dibasic (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -235496, 'RXCUI-235496', 'Sodium Phosphate, Monobasic (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -89905, 'RXCUI-89905', 'Multivitamin Preparation (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -39937, 'RXCUI-39937', 'Zinc Chloride (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -29261, 'RXCUI-29261', 'Manganese Chloride (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -21579, 'RXCUI-21579', 'Copper Sulfate (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -21032, 'RXCUI-21032', 'Chromous Chloride (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -11115, 'RXCUI-11115', 'Valine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -10962, 'RXCUI-10962', 'Tyrosine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -10898, 'RXCUI-10898', 'Tryptophan (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -10524, 'RXCUI-10524', 'Threonine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -9863, 'RXCUI-9863', 'Sodium Chloride (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -9671, 'RXCUI-9671', 'Serine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -8737, 'RXCUI-8737', 'Proline (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -8591, 'RXCUI-8591', 'Potassium Chloride (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -8308, 'RXCUI-8308', 'Vitamin K 1 (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -8156, 'RXCUI-8156', 'Phenylalanine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -6837, 'RXCUI-6837', 'Methionine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -6585, 'RXCUI-6585', 'Magnesium Sulfate (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -6536, 'RXCUI-6536', 'Lysine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -6308, 'RXCUI-6308', 'Leucine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -6033, 'RXCUI-6033', 'Isoleucine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -5340, 'RXCUI-5340', 'Histidine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -4919, 'RXCUI-4919', 'Glycine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -4850, 'RXCUI-4850', 'Glucose (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -1908, 'RXCUI-1908', 'Calcium Gluconate (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -1091, 'RXCUI-1091', 'Arginine (IV)', datetime(2111, 5, 2, 3, 0)],
+                [ -390000000, 1234567, 888, "Med (IV)", -426, 'RXCUI-426', 'Alanine (IV)', datetime(2111, 5, 2, 3, 0)],
             ];
         actualData = DBUtil.execute(testQuery);
         self.assertEqualTable( expectedData, actualData );
@@ -335,7 +335,7 @@ def test_dataConversion_normalized(self):
         # Query for orderset links
         testQuery = \
             """
-            select 
+            select
                 pi.external_id,
                 ci.description,
                 ic.external_id,
@@ -366,25 +366,25 @@ def test_dataConversion_normalized(self):
                 [ -418436145, "Bisacodyl (PR)", -111,"General Admit","Medications","Stool Softeners"],
 
                 # Mini-Mix
-                [ -391000000, 'Sodium Phosphate, Dibasic (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Sodium Phosphate, Monobasic (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Valine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Tyrosine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Tryptophan (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Threonine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Sodium Chloride (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Serine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Proline (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Phenylalanine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Methionine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Lysine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Leucine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Isoleucine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Histidine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Glycine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Glucose (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Arginine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
-                [ -391000000, 'Alanine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
+                [ -391000000, 'Sodium Phosphate, Dibasic (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Sodium Phosphate, Monobasic (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Valine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Tyrosine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Tryptophan (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Threonine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Sodium Chloride (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Serine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Proline (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Phenylalanine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Methionine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Lysine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Leucine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Isoleucine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Histidine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Glycine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Glucose (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Arginine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
+                [ -391000000, 'Alanine (IV) (<5 doses)', -222, "Nutrition","Infusions","TPN"],
             ];
         actualData = DBUtil.execute(testQuery);
         self.assertEqualTable( expectedData, actualData );
@@ -403,7 +403,7 @@ def test_dataConversion_denormalized(self):
         # Just query back for the same data, de-normalizing the data back to a general table
         testQuery = \
             """
-            select 
+            select
                 pi.external_id,
                 pi.patient_id,
                 pi.encounter_id,
@@ -437,18 +437,18 @@ def test_dataConversion_denormalized(self):
                 [ -414321352, 3036588, 777, "Med (IV)", -5007, "MED-5007 (<5)", "METOPROLOL TARTRATE 5 MG/5 ML IV SOLN (<5 doses)", datetime(2111,12,14,8,30) ],
 
                 # Simple mixture with different dosing counts
-                [ -395900000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481 (<5)", "Cefepime (IV) (<5 doses)", datetime(2111, 1, 2, 3, 0)], 
-                [ -395800000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 1,10, 3, 0)], 
-                [ -395700000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 3,10, 3, 0)], 
+                [ -395900000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481 (<5)", "Cefepime (IV) (<5 doses)", datetime(2111, 1, 2, 3, 0)],
+                [ -395800000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 1,10, 3, 0)],
+                [ -395700000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 3,10, 3, 0)],
 
                 # IVF Mixture, composite ingredient description
-                [ -392000000, 1234567, 888, "Med (IV)", -888699365, "RXCUI-4850,-8591,-9863 (<5)", "Glucose-Potassium Chloride-Sodium Chloride (IV) (<5 doses)", datetime(2111, 4, 1, 3, 0)], 
+                [ -392000000, 1234567, 888, "Med (IV)", 786930457607, "RXCUI-4850,-8591,-9863 (<5)", "Glucose-Potassium Chloride-Sodium Chloride (IV) (<5 doses)", datetime(2111, 4, 1, 3, 0)], 
 
                 # Mini mixture.  Too many components, just use summary description
                 [ -391000000, 1234567, 888, "Med (IV)", -540000, "MED-540000 (<5)", "Mini TPN (<5 doses)", datetime(2111, 4, 2, 3, 0)], # Still aggregated because breaking up into component amino acids results in too many
 
                 # Complex mixture
-                [ -390000000, 1234567, 888, "Med (IV)", -550000, "MED-550000", "TPN Adult", datetime(2111, 5, 2, 3, 0)], 
+                [ -390000000, 1234567, 888, "Med (IV)", -550000, "MED-550000", "TPN Adult", datetime(2111, 5, 2, 3, 0)],
 
             ];
         actualData = DBUtil.execute(testQuery);
@@ -458,7 +458,7 @@ def test_dataConversion_denormalized(self):
         # Query for orderset links
         testQuery = \
             """
-            select 
+            select
                 pi.external_id,
                 ci.description,
                 ic.external_id,
@@ -488,7 +488,7 @@ def test_dataConversion_denormalized(self):
                 [ -418436145, "Bisacodyl (PR)", -111,"General Admit","Medications","Stool Softeners"],
 
                 # Mini-Mix
-                [ -391000000, 'Mini TPN (<5 doses)', -222, "Nutrition","Infusions","TPN"],   
+                [ -391000000, 'Mini TPN (<5 doses)', -222, "Nutrition","Infusions","TPN"],
             ];
         actualData = DBUtil.execute(testQuery);
         self.assertEqualTable( expectedData, actualData );
@@ -506,7 +506,7 @@ def test_dataConversion_maxMixtureCount(self):
         # Just query back for the same data, de-normalizing the data back to a general table
         testQuery = \
             """
-            select 
+            select
                 pi.external_id,
                 pi.patient_id,
                 pi.encounter_id,
@@ -540,22 +540,22 @@ def test_dataConversion_maxMixtureCount(self):
                 [ -414321352, 3036588, 777, "Med (IV)", -5007, "MED-5007 (<5)", "METOPROLOL TARTRATE 5 MG/5 ML IV SOLN (<5 doses)", datetime(2111,12,14,8,30) ],
 
                 # Simple mixture with different dosing counts
-                [ -395900000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481 (<5)", "Cefepime (IV) (<5 doses)", datetime(2111, 1, 2, 3, 0)], 
-                [ -395800000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 1,10, 3, 0)], 
-                [ -395700000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 3,10, 3, 0)], 
+                [ -395900000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481 (<5)", "Cefepime (IV) (<5 doses)", datetime(2111, 1, 2, 3, 0)],
+                [ -395800000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 1,10, 3, 0)],
+                [ -395700000, 1234567, 888, "Med (IV)", -20481, "RXCUI-20481", "Cefepime (IV)", datetime(2111, 3,10, 3, 0)],
 
                 # IVF Mixture, composite ingredient description
-                [ -392000000, 1234567, 888, "Med (IV)", -530000, "MED-530000 (<5)", "IVF Mix (<5 doses)", datetime(2111, 4, 1, 3, 0)], 
+                [ -392000000, 1234567, 888, "Med (IV)", -530000, "MED-530000 (<5)", "IVF Mix (<5 doses)", datetime(2111, 4, 1, 3, 0)],
 
                 # Mini mixture.  Too many components, just use summary description
                 [ -391000000, 1234567, 888, "Med (IV)", -540000, "MED-540000 (<5)", "Mini TPN (<5 doses)", datetime(2111, 4, 2, 3, 0)], # Still aggregated because breaking up into component amino acids results in too many
 
                 # Complex mixture
-                [ -390000000, 1234567, 888, "Med (IV)", -550000, "MED-550000", "TPN Adult", datetime(2111, 5, 2, 3, 0)], 
+                [ -390000000, 1234567, 888, "Med (IV)", -550000, "MED-550000", "TPN Adult", datetime(2111, 5, 2, 3, 0)],
 
             ];
         actualData = DBUtil.execute(testQuery);
-   
+
         self.assertEqualTable( expectedData, actualData );
 
 def suite():
@@ -569,8 +569,8 @@ def suite():
     #suite.addTest(TestSTRIDEOrderMedConversion('test_executeIterator'));
     #suite.addTest(TestSTRIDEOrderMedConversion('test_dataConversion_maxMixtureCount'));
     suite.addTest(unittest.makeSuite(TestSTRIDEOrderMedConversion));
-    
+
     return suite;
-    
+
 if __name__=="__main__":
     unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py b/medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py
index 6a163592..98b5a039 100644
--- a/medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py
@@ -2,14 +2,16 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel;
@@ -24,9 +26,8 @@ def setUp(self):
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
-        
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = 'PreTest_order_proc' where source_table = 'stride_order_proc';");
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
     
         self.orderProcIdStrList = list();
         headers = ["order_proc_id", "pat_id", "pat_enc_csn_id", "order_type", "proc_id", "proc_code", "description", "order_time", "instantiated_time","stand_interval"];
@@ -41,6 +42,7 @@ def setUp(self):
                 RowItemModel( [ -417843774, "2648748", 555, "Nursing", 1508, "NUR1068", "WEIGHT", "2111-12-08", None, "ONCE"], headers ),
                 RowItemModel( [ -419268931, "3039254", 666, "Lab", 1721, "LABPTT", "PTT PARTIAL THROMBOPLASTIN TIME", "2112-01-04", None, "DAILY"], headers ),
                 RowItemModel( [ -419268231, "3039254", 666, "Lab", 1721, "LABPTT", "PTT PARTIAL THROMBOPLASTIN TIME", "2112-01-04", "2112-01-06", "Q6H PRN"], headers ),  # Ignore instantiated / spawned child orders
+                RowItemModel( [ -419268937, "3039254", 666, "Lab", 9991721, "LABPTT", "PTT (PARTIAL THROMBOPLASTIN TIME)", "2112-01-05", None, "DAILY"], headers ), # Different proc_id, but same proc_Code. Treat like the same
                 RowItemModel( [ -419268935, "3039254", 777, "Lab", 1721, "LABPTT", "PTT PARTIAL THROMBOPLASTIN TIME", "2112-01-04", "2112-01-07", "ONCE"], headers ),  #   Primarily only interested in the decision point with the original order
             ];
         for dataModel in dataModels:
@@ -107,7 +109,6 @@ def tearDown(self):
             """
         );
         DBUtil.execute("delete from clinical_item_category where source_table = 'stride_order_proc';");
-        DBUtil.execute("update clinical_item_category set source_table = 'stride_order_proc' where source_table = 'PreTest_order_proc';"); # Reset labels of any prior data
 
         DBUtil.execute("delete from stride_orderset_order_proc where order_proc_id in (%s)" % str.join(",", self.orderProcIdStrList) );
         DBUtil.execute("delete from stride_order_proc where order_proc_id in (%s)" % str.join(",", self.orderProcIdStrList) );
@@ -144,8 +145,9 @@ def test_dataConversion(self):
             """;
         expectedData = \
             [
-                [ -419697343, 3042640, 222, "Point of Care Testing", 1001, "LABPOCGLU", "GLUCOSE BY METER", datetime(2112,01,13) ],
-                [ -419268931, 3039254, 666, "Lab", 1721, "LABPTT", "PTT PARTIAL THROMBOPLASTIN TIME", datetime(2112,01,04) ],
+                [ -419697343, 3042640, 222, "Point of Care Testing", 1001, "LABPOCGLU", "GLUCOSE BY METER", datetime(2112,1,13) ],
+                [ -419268937, 3039254, 666, "Lab", 1721, "LABPTT", "PTT PARTIAL THROMBOPLASTIN TIME", datetime(2112,1,5) ],
+                [ -419268931, 3039254, 666, "Lab", 1721, "LABPTT", "PTT PARTIAL THROMBOPLASTIN TIME", datetime(2112,1,4) ],
                 [ -418928388, -1612899, 333, "Point of Care Testing", 1001, "LABPOCGLU", "GLUCOSE BY METER", datetime(2111,12,28) ],
                 [ -418045499, 2087083, 444, "Nursing", 1428, "NUR1018", "MONITOR INTAKE AND OUTPUT", datetime(2111,12,11) ],
                 [ -417974686, 380873, 111, "Nursing", 1453, "NUR1043", "NURSING PULSE OXIMETRY", datetime(2111,12,10) ],
diff --git a/medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py b/medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py
index a90c9620..0e1faa59 100644
--- a/medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py
@@ -2,14 +2,16 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel;
@@ -25,10 +27,8 @@ def setUp(self):
         DBTestCase.setUp(self);
         
         log.info("Populate the database with test data")
-        
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = 'PreTest_order_results' where source_table = '%s';" % TEST_SOURCE_TABLE);
-        DBUtil.execute("update order_result_stat set base_name = 'PreTest_' || base_name;");
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
 
         dataTextStr = """order_proc_id\tpat_id\tpat_enc_csn_id\torder_type\tproc_id\tproc_code\tdescription
 -30560253\t-7803\t-1772\tLab\t471521\tLABACETA\tACETAMINOPHEN, SERUM
@@ -127,10 +127,8 @@ def tearDown(self):
             """ % TEST_SOURCE_TABLE
         );
         DBUtil.execute("delete from clinical_item_category where source_table = '%s';" % TEST_SOURCE_TABLE);
-        DBUtil.execute("update clinical_item_category set source_table = '%s' where source_table = 'PreTest_order_results';" % TEST_SOURCE_TABLE); # Reset labels of any prior data
 
         DBUtil.execute("delete from order_result_stat where base_name not like 'PreTest_%%';");
-        DBUtil.execute("update order_result_stat set base_name = right( base_name,length(base_name)-length('PreTest_') );"); # Reset labels of any prior data
 
         DBUtil.execute("delete from stride_order_results where order_proc_id < 0" );
         DBUtil.execute("delete from stride_order_proc where order_proc_id < 0" );
diff --git a/medinfo/dataconversion/test/TestSTRIDEPreAdmitMedConversion.py b/medinfo/dataconversion/test/TestSTRIDEPreAdmitMedConversion.py
deleted file mode 100644
index 3e60a027..00000000
--- a/medinfo/dataconversion/test/TestSTRIDEPreAdmitMedConversion.py
+++ /dev/null
@@ -1,358 +0,0 @@
-#!/usr/bin/env python
-"""Test case for respective module in application package"""
-
-import sys, os
-from cStringIO import StringIO
-from datetime import datetime;
-import unittest
-
-from Const import RUNNER_VERBOSITY;
-from Util import log;
-
-from medinfo.db.test.Util import DBTestCase;
-
-from medinfo.db import DBUtil
-from medinfo.db.Model import SQLQuery, RowItemModel;
-
-from medinfo.dataconversion.STRIDEPreAdmitMedConversion import STRIDEPreAdmitMedConversion, ConversionOptions;
-
-TEST_START_DATE = datetime(2100,1,1);   # Date in far future to start checking for test records to avoid including existing data in database
-
-class TestSTRIDEPreAdmitMedConversion(DBTestCase):
-    def setUp(self):
-        """Prepare state for test cases"""
-        DBTestCase.setUp(self);
-        
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = 'PreTest_preadmit_med' where source_table = 'stride_preadmit_med';");
-    
-        log.info("Populate the database with test data")
-        
-        headers = ["stride_preadmit_med_id","pat_anon_id","contact_date","medication_id","description","thera_class","pharm_class","pharm_subclass"];
-        dataModels = \
-            [   # Deliberately design dates in far future to facilitate isolated testing
-                RowItemModel([-100, -1000, datetime(2113, 7, 6),  -5680, "NORVASC 5 MG PO TABS", "CARDIAC DRUGS", "CALCIUM CHANNEL BLOCKING AGENTS", "Calcium Channel Blockers - Dihydropyridines"], headers ),
-                RowItemModel([-110, -2000, datetime(2110, 3, 8),  -6540, "PRILOSEC 20 MG PO CPDR", "GASTROINTESTINAL", "PROTON-PUMP INHIBITORS", "Gastric Acid Secretion Reducing Agents - Proton Pump Inhibitors (PPIs)"], headers ),
-                RowItemModel([-120, -2000, datetime(2110, 3, 8),  -8113, "TRIAMCINOLONE 0.1% CREAM", "SKIN PREPS", "TOPICAL ANTI-INFLAMMATORY STEROIDAL", "Dermatological - Glucocorticoid"], headers ),
-                # Multiple or no mappings
-                RowItemModel([-130, -3000, datetime(2109, 9,20),-126745, "NORETHINDRN ESTRADIOL", "CONTRACEPTIVES", "CONTRACEPTIVES,ORAL", "Contraceptive Oral - Monophasic"], headers ),
-                RowItemModel([-140, -4000, datetime(2110, 5, 4),  -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", "ANALGESICS", "ANALGESICS, NARCOTICS", "Analgesic Narcotic Hydrocodone Combinations"], headers ),
-                # Excessively complex mapping
-                RowItemModel([-150, -5000, datetime(2108, 8, 6), -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"], headers ),
-            ];
-        for dataModel in dataModels:
-            (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_preadmit_med", dataModel, retrieveCol="pat_anon_id" );
-
-        # Mapping table to simplify dose
-        headers = ["medication_id", "medication_name", "rxcui", "active_ingredient"];
-        dataModels = \
-            [   # Mappings to active ingredients
-                RowItemModel( [ -5680, "NORVASC 5 MG PO TABS", -17767, "Amlodipine"], headers ),
-                RowItemModel( [ -6540, "PRILOSEC 20 MG PO CPDR", -7646, "Omeprazole"], headers ),
-                RowItemModel( [ -8113, "TRIAMCINOLONE ACETONIDE 0.1 % TP CREA", -10759, "Triamcinolone"], headers ),
-                # If multiple active ingredients in a combo, unravel to active components
-                RowItemModel( [ -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", -161, "Acetaminophen"], headers ),
-                RowItemModel( [ -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", -5489, "Hydrocodone"], headers ),
-                # No mapping entries for Norethindrone+Estradiol
-                # Excessive mappings for pneumovax
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854930, "pneumococcal capsular polysaccharide type 1 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854932, "pneumococcal capsular polysaccharide type 10A vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854934, "pneumococcal capsular polysaccharide type 11A vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854936, "pneumococcal capsular polysaccharide type 12F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854938, "pneumococcal capsular polysaccharide type 14 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854940, "pneumococcal capsular polysaccharide type 15B vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854942, "pneumococcal capsular polysaccharide type 17F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854944, "pneumococcal capsular polysaccharide type 18C vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854946, "pneumococcal capsular polysaccharide type 19A vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854948, "pneumococcal capsular polysaccharide type 19F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854950, "pneumococcal capsular polysaccharide type 2 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854952, "pneumococcal capsular polysaccharide type 20 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854954, "pneumococcal capsular polysaccharide type 22F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854956, "pneumococcal capsular polysaccharide type 23F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854958, "pneumococcal capsular polysaccharide type 3 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854960, "pneumococcal capsular polysaccharide type 33F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854962, "pneumococcal capsular polysaccharide type 4 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854964, "pneumococcal capsular polysaccharide type 5 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854966, "pneumococcal capsular polysaccharide type 6B vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854968, "pneumococcal capsular polysaccharide type 7F vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854970, "pneumococcal capsular polysaccharide type 8 vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854972, "pneumococcal capsular polysaccharide type 9N vaccine"], headers ),
-                RowItemModel( [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854974, "pneumococcal capsular polysaccharide type 9V vaccine"], headers ),
-            ];
-        for dataModel in dataModels:
-            (dataItemId, isNew) = DBUtil.findOrInsertItem("stride_mapped_meds", dataModel, retrieveCol="rxcui" );
-
-        self.converter = STRIDEPreAdmitMedConversion();  # Instance to test on
-
-    def tearDown(self):
-        """Restore state from any setUp or test steps"""
-        log.info("Purge test records from the database")
-
-        DBUtil.execute \
-        (   """delete from patient_item 
-            where clinical_item_id in 
-            (   select clinical_item_id
-                from clinical_item as ci, clinical_item_category as cic
-                where ci.clinical_item_category_id = cic.clinical_item_category_id
-                and cic.source_table = 'stride_preadmit_med'
-            );
-            """
-        );
-        DBUtil.execute \
-        (   """delete from clinical_item 
-            where clinical_item_category_id in 
-            (   select clinical_item_category_id 
-                from clinical_item_category 
-                where source_table = 'stride_preadmit_med'
-            );
-            """
-        );
-        DBUtil.execute("delete from clinical_item_category where source_table = 'stride_preadmit_med';");
-        DBUtil.execute("update clinical_item_category set source_table = 'stride_preadmit_med' where source_table = 'PreTest_preadmit_med';"); # Reset labels of any prior data
-
-        DBUtil.execute("delete from stride_mapped_meds where rxcui < 0");
-        DBUtil.execute("delete from stride_preadmit_med where stride_preadmit_med_id < 0");
-
-        DBTestCase.tearDown(self);
-
-    def test_dataConversion_normalized(self):
-        # Run the data conversion on the same data and look for expected records
-        log.debug("Run the conversion process...");
-        convOptions = ConversionOptions();
-        convOptions.startDate = TEST_START_DATE;
-        convOptions.normalizeMixtures = True;
-        self.converter.convertSourceItems(convOptions);
-
-        # Just query back for the same data, de-normalizing the data back to a general table
-        testQuery = \
-            """
-            select 
-                pi.external_id,
-                pi.patient_id,
-                pi.encounter_id,
-                cic.description,
-                ci.external_id,
-                ci.name,
-                ci.description,
-                pi.item_date
-            from
-                patient_item as pi,
-                clinical_item as ci,
-                clinical_item_category as cic
-            where
-                pi.clinical_item_id = ci.clinical_item_id and
-                ci.clinical_item_category_id = cic.clinical_item_category_id and
-                cic.source_table = 'stride_preadmit_med'
-            order by
-                pi.external_id desc, ci.external_id desc
-            """;
-        expectedData = \
-            [   # Simple mappings
-                [-100, -1000, None, "Preadmit Med", -17767, "RXCUI-17767", "Amlodipine", datetime(2113, 7, 6) ],
-                [-110, -2000, None, "Preadmit Med", -7646, "RXCUI-7646", "Omeprazole", datetime(2110, 3, 8) ],
-                [-120, -2000, None, "Preadmit Med", -10759, "RXCUI-10759", "Triamcinolone", datetime(2110, 3, 8) ],
-                # No mappings
-                [-130, -3000, None, "Preadmit Med", -126745, "MED-126745", "NORETHINDRN ESTRADIOL", datetime(2109, 9, 20) ],
-                # Multiple mappings
-                [-140, -4000, None, "Preadmit Med",  -161, "RXCUI-161", "Acetaminophen", datetime(2110, 5, 4) ],
-                [-140, -4000, None, "Preadmit Med", -5489, "RXCUI-5489", "Hydrocodone", datetime(2110, 5, 4) ],
-                # Excessively complex mapping
-                [-150, -5000, None, "Preadmit Med", -854930, "RXCUI-854930", "Pneumococcal Capsular Polysaccharide Type 1 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854932, "RXCUI-854932", "Pneumococcal Capsular Polysaccharide Type 10A Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854934, "RXCUI-854934", "Pneumococcal Capsular Polysaccharide Type 11A Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854936, "RXCUI-854936", "Pneumococcal Capsular Polysaccharide Type 12F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854938, "RXCUI-854938", "Pneumococcal Capsular Polysaccharide Type 14 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854940, "RXCUI-854940", "Pneumococcal Capsular Polysaccharide Type 15B Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854942, "RXCUI-854942", "Pneumococcal Capsular Polysaccharide Type 17F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854944, "RXCUI-854944", "Pneumococcal Capsular Polysaccharide Type 18C Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854946, "RXCUI-854946", "Pneumococcal Capsular Polysaccharide Type 19A Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854948, "RXCUI-854948", "Pneumococcal Capsular Polysaccharide Type 19F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854950, "RXCUI-854950", "Pneumococcal Capsular Polysaccharide Type 2 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854952, "RXCUI-854952", "Pneumococcal Capsular Polysaccharide Type 20 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854954, "RXCUI-854954", "Pneumococcal Capsular Polysaccharide Type 22F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854956, "RXCUI-854956", "Pneumococcal Capsular Polysaccharide Type 23F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854958, "RXCUI-854958", "Pneumococcal Capsular Polysaccharide Type 3 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854960, "RXCUI-854960", "Pneumococcal Capsular Polysaccharide Type 33F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854962, "RXCUI-854962", "Pneumococcal Capsular Polysaccharide Type 4 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854964, "RXCUI-854964", "Pneumococcal Capsular Polysaccharide Type 5 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854966, "RXCUI-854966", "Pneumococcal Capsular Polysaccharide Type 6B Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854968, "RXCUI-854968", "Pneumococcal Capsular Polysaccharide Type 7F Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854970, "RXCUI-854970", "Pneumococcal Capsular Polysaccharide Type 8 Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854972, "RXCUI-854972", "Pneumococcal Capsular Polysaccharide Type 9N Vaccine", datetime(2108, 8, 6) ],
-                [-150, -5000, None, "Preadmit Med", -854974, "RXCUI-854974", "Pneumococcal Capsular Polysaccharide Type 9V Vaccine", datetime(2108, 8, 6) ],
-            ];
-        actualData = DBUtil.execute(testQuery);
-        self.assertEqualTable( expectedData, actualData );
-
-        # Query for medication class label population
-        testQuery = \
-            """
-            select 
-                medication_id,
-                medication_name,
-                rxcui,
-                active_ingredient,
-                thera_class,
-                pharm_class,
-                pharm_subclass
-            from stride_mapped_meds as map
-            where
-                medication_id < 0 and rxcui < 0
-            order by
-                medication_id desc, rxcui desc
-            """;
-        expectedData = \
-            [
-                [ -5680, "NORVASC 5 MG PO TABS", -17767, "Amlodipine", "CARDIAC DRUGS", "CALCIUM CHANNEL BLOCKING AGENTS", "Calcium Channel Blockers - Dihydropyridines"],
-                [ -6540, "PRILOSEC 20 MG PO CPDR", -7646, "Omeprazole", "GASTROINTESTINAL", "PROTON-PUMP INHIBITORS", "Gastric Acid Secretion Reducing Agents - Proton Pump Inhibitors (PPIs)"],
-                [ -8113, "TRIAMCINOLONE ACETONIDE 0.1 % TP CREA", -10759, "Triamcinolone", "SKIN PREPS", "TOPICAL ANTI-INFLAMMATORY STEROIDAL", "Dermatological - Glucocorticoid"],
-                # If multiple active ingredients in a combo, unravel to active components
-                [ -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", -161, "Acetaminophen", "ANALGESICS", "ANALGESICS, NARCOTICS", "Analgesic Narcotic Hydrocodone Combinations"],
-                [ -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", -5489, "Hydrocodone", "ANALGESICS", "ANALGESICS, NARCOTICS", "Analgesic Narcotic Hydrocodone Combinations"],
-                # No mapping entries for Norethindrone+Estradiol
-                # Excessive mappings for pneumovax
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854930, "pneumococcal capsular polysaccharide type 1 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854932, "pneumococcal capsular polysaccharide type 10A vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854934, "pneumococcal capsular polysaccharide type 11A vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854936, "pneumococcal capsular polysaccharide type 12F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854938, "pneumococcal capsular polysaccharide type 14 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854940, "pneumococcal capsular polysaccharide type 15B vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854942, "pneumococcal capsular polysaccharide type 17F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854944, "pneumococcal capsular polysaccharide type 18C vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854946, "pneumococcal capsular polysaccharide type 19A vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854948, "pneumococcal capsular polysaccharide type 19F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854950, "pneumococcal capsular polysaccharide type 2 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854952, "pneumococcal capsular polysaccharide type 20 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854954, "pneumococcal capsular polysaccharide type 22F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854956, "pneumococcal capsular polysaccharide type 23F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854958, "pneumococcal capsular polysaccharide type 3 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854960, "pneumococcal capsular polysaccharide type 33F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854962, "pneumococcal capsular polysaccharide type 4 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854964, "pneumococcal capsular polysaccharide type 5 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854966, "pneumococcal capsular polysaccharide type 6B vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854968, "pneumococcal capsular polysaccharide type 7F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854970, "pneumococcal capsular polysaccharide type 8 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854972, "pneumococcal capsular polysaccharide type 9N vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854974, "pneumococcal capsular polysaccharide type 9V vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-            ];
-        actualData = DBUtil.execute(testQuery);
-        self.assertEqualTable( expectedData, actualData );
-
-
-    def test_dataConversion_maxMixtureCount(self):
-        # Run the data conversion on the same data and look for expected records
-        log.debug("Run the conversion process...");
-        convOptions = ConversionOptions();
-        convOptions.startDate = TEST_START_DATE;
-        convOptions.normalizeMixtures = False;
-        convOptions.maxMixtureCount = 5;
-        self.converter.convertSourceItems(convOptions);
-
-        # Just query back for the same data, de-normalizing the data back to a general table
-        testQuery = \
-            """
-            select 
-                pi.external_id,
-                pi.patient_id,
-                pi.encounter_id,
-                cic.description,
-                ci.external_id,
-                ci.name,
-                ci.description,
-                pi.item_date
-            from
-                patient_item as pi,
-                clinical_item as ci,
-                clinical_item_category as cic
-            where
-                pi.clinical_item_id = ci.clinical_item_id and
-                ci.clinical_item_category_id = cic.clinical_item_category_id and
-                cic.source_table = 'stride_preadmit_med'
-            order by
-                pi.external_id desc, ci.external_id desc
-            """;
-        expectedData = \
-            [   # Simple mappings
-                [-100, -1000, None, "Preadmit Med", -17767, "RXCUI-17767", "Amlodipine", datetime(2113, 7, 6) ],
-                [-110, -2000, None, "Preadmit Med", -7646, "RXCUI-7646", "Omeprazole", datetime(2110, 3, 8) ],
-                [-120, -2000, None, "Preadmit Med", -10759, "RXCUI-10759", "Triamcinolone", datetime(2110, 3, 8) ],
-                # No mappings
-                [-130, -3000, None, "Preadmit Med", -126745, "MED-126745", "NORETHINDRN ESTRADIOL", datetime(2109, 9, 20) ],
-                # Multiple mappings
-                [-140, -4000, None, "Preadmit Med",  -28384, "MED-28384", "Acetaminophen-Hydrocodone", datetime(2110, 5, 4) ],
-                # Excessively complex mapping
-                [-150, -5000, None, "Preadmit Med", -95140, "MED-95140", "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", datetime(2108, 8, 6) ],
-            ];
-        actualData = DBUtil.execute(testQuery);
-        self.assertEqualTable( expectedData, actualData );
-
-        # Query for medication class label population
-        testQuery = \
-            """
-            select 
-                medication_id,
-                medication_name,
-                rxcui,
-                active_ingredient,
-                thera_class,
-                pharm_class,
-                pharm_subclass
-            from stride_mapped_meds as map
-            where
-                medication_id < 0 and rxcui < 0
-            order by
-                medication_id desc, rxcui desc
-            """;
-        expectedData = \
-            [
-                [ -5680, "NORVASC 5 MG PO TABS", -17767, "Amlodipine", "CARDIAC DRUGS", "CALCIUM CHANNEL BLOCKING AGENTS", "Calcium Channel Blockers - Dihydropyridines"],
-                [ -6540, "PRILOSEC 20 MG PO CPDR", -7646, "Omeprazole", "GASTROINTESTINAL", "PROTON-PUMP INHIBITORS", "Gastric Acid Secretion Reducing Agents - Proton Pump Inhibitors (PPIs)"],
-                [ -8113, "TRIAMCINOLONE ACETONIDE 0.1 % TP CREA", -10759, "Triamcinolone", "SKIN PREPS", "TOPICAL ANTI-INFLAMMATORY STEROIDAL", "Dermatological - Glucocorticoid"],
-                # If multiple active ingredients in a combo, unravel to active components
-                [ -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", -161, "Acetaminophen", "ANALGESICS", "ANALGESICS, NARCOTICS", "Analgesic Narcotic Hydrocodone Combinations"],
-                [ -28384, "HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS", -5489, "Hydrocodone", "ANALGESICS", "ANALGESICS, NARCOTICS", "Analgesic Narcotic Hydrocodone Combinations"],
-                # No mapping entries for Norethindrone+Estradiol
-                # Excessive mappings for pneumovax
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854930, "pneumococcal capsular polysaccharide type 1 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854932, "pneumococcal capsular polysaccharide type 10A vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854934, "pneumococcal capsular polysaccharide type 11A vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854936, "pneumococcal capsular polysaccharide type 12F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854938, "pneumococcal capsular polysaccharide type 14 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854940, "pneumococcal capsular polysaccharide type 15B vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854942, "pneumococcal capsular polysaccharide type 17F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854944, "pneumococcal capsular polysaccharide type 18C vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854946, "pneumococcal capsular polysaccharide type 19A vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854948, "pneumococcal capsular polysaccharide type 19F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854950, "pneumococcal capsular polysaccharide type 2 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854952, "pneumococcal capsular polysaccharide type 20 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854954, "pneumococcal capsular polysaccharide type 22F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854956, "pneumococcal capsular polysaccharide type 23F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854958, "pneumococcal capsular polysaccharide type 3 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854960, "pneumococcal capsular polysaccharide type 33F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854962, "pneumococcal capsular polysaccharide type 4 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854964, "pneumococcal capsular polysaccharide type 5 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854966, "pneumococcal capsular polysaccharide type 6B vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854968, "pneumococcal capsular polysaccharide type 7F vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854970, "pneumococcal capsular polysaccharide type 8 vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854972, "pneumococcal capsular polysaccharide type 9N vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-                [ -95140, "PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ", -854974, "pneumococcal capsular polysaccharide type 9V vaccine", "BIOLOGICALS", "GRAM POSITIVE COCCI VACCINES", "Vaccine Bacterial - Gram Positive Cocci"],
-            ];
-        actualData = DBUtil.execute(testQuery);
-        self.assertEqualTable( expectedData, actualData );
-
-def suite():
-    """Returns the suite of tests to run for this test class / module.
-    Use unittest.makeSuite methods which simply extracts all of the
-    methods for the given class whose name starts with "test"
-    """
-    suite = unittest.TestSuite();
-    #suite.addTest(TestSTRIDEPreAdmitMedConversion("test_incColNamesAndTypeCodes"));
-    #suite.addTest(TestSTRIDEPreAdmitMedConversion("test_insertFile_skipErrors"));
-    #suite.addTest(TestSTRIDEPreAdmitMedConversion('test_executeIterator'));
-    #suite.addTest(TestSTRIDEPreAdmitMedConversion('test_dataConversion_maxMixtureCount'));
-    suite.addTest(unittest.makeSuite(TestSTRIDEPreAdmitMedConversion));
-    
-    return suite;
-    
-if __name__=="__main__":
-    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/dataconversion/test/TestSTRIDETreatmentTeamConversion.py b/medinfo/dataconversion/test/TestSTRIDETreatmentTeamConversion.py
index 90ea63fc..9ee581ce 100644
--- a/medinfo/dataconversion/test/TestSTRIDETreatmentTeamConversion.py
+++ b/medinfo/dataconversion/test/TestSTRIDETreatmentTeamConversion.py
@@ -2,14 +2,16 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import DBTestCase;
+from stride.core.StrideLoader import StrideLoader;
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader; 
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery, RowItemModel;
@@ -24,10 +26,9 @@ def setUp(self):
         """Prepare state for test cases"""
         DBTestCase.setUp(self);
         
-        # Relabel any existing data to not interfere with the new test data that will be produced
-        DBUtil.execute("update clinical_item_category set source_table = 'PreTest_treatment_team' where source_table = 'stride_treatment_team';");
-    
         log.info("Populate the database with test data")
+        StrideLoader.build_stride_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata();
 
         dataTextStr = \
 """stride_treatment_team_id\tpat_id\tpat_enc_csn_id\ttrtmnt_tm_begin_date\ttrtmnt_tm_end_date\ttreatment_team\tprov_name
@@ -330,7 +331,6 @@ def tearDown(self):
             """
         );
         DBUtil.execute("delete from clinical_item_category where source_table = 'stride_treatment_team';");
-        DBUtil.execute("update clinical_item_category set source_table = 'stride_treatment_team' where source_table = 'PreTest_treatment_team';"); # Reset labels of any prior data
 
         DBUtil.execute("delete from stride_treatment_team where stride_treatment_team_id < 0");
 
diff --git a/medinfo/dataconversion/test/UMichFeatureMatrixTestData.py b/medinfo/dataconversion/test/UMichFeatureMatrixTestData.py
new file mode 100644
index 00000000..c7c0956d
--- /dev/null
+++ b/medinfo/dataconversion/test/UMichFeatureMatrixTestData.py
@@ -0,0 +1,474 @@
+#!/usr/bin/env python
+"""
+Test input and output for FeatureMatrixFactory.
+"""
+
+from medinfo.db import DBUtil
+
+# Dictionary mapping from database table name to test data.
+FM_TEST_INPUT_TABLES = {
+    "labs_columns" : ['pat_id','order_proc_id','order_time','result_time','proc_code','base_name','ord_num_value','result_flag','result_in_range_yn'],
+    "labs_column_types" : ['INTEGER', 'INTEGER', 'TIMESTAMP', 'TIMESTAMP', 'TEXT', 'TEXT', 'TEXT','TEXT','TEXT'],
+    'labs_data':[
+        # stats within one week:
+        [1, 1, '2050-01-05 09:44:00', '2050-01-05 09:44:00', 'CBCP', 'WBC', 6, 'N', 'Y'],
+        [1, 2, '2050-01-07 12:44:00', '2050-01-07 12:44:00', 'CBCP', 'WBC', 5, 'N', 'Y'],
+        [1, 3, '2050-01-09 23:44:00', '2050-01-09 23:44:00', 'CBCP', 'WBC', 3, 'L', 'N'],
+
+        [1, 4, '2050-01-04 09:44:00', '2050-01-05 09:23:00', 'COMP', 'CO2', 6, 'N', 'Y'],
+        [1, 5, '2050-01-07 13:44:00', '2050-01-07 14:44:00', 'COMP', 'CO2', 5, 'N', 'Y'],
+
+        [1, 6, '2050-01-08 23:44:00', '2050-01-09 23:44:00', 'CBCP', 'HCT', 3, 'H', 'N'],
+
+        [2, 7, '2050-01-04 23:44:00', '2050-01-05 11:44:00', 'CBCP', 'CO2', 3, 'N', 'Y'],
+        [2, 8, '2050-01-05 23:44:00', '2050-01-06 11:44:00', 'CBCP', 'CO2', 10, 'H', 'N'],
+        [2, 9, '2050-01-06 23:44:00', '2050-01-07 11:44:00', 'CBCP', 'CO2', 3, 'N', 'Y'],
+
+        [3, 11, '2050-01-05 09:44:00', '2050-01-05 09:44:00', 'CBCP', 'WBC', 6, 'N', 'Y'],
+        [3, 12, '2050-01-07 12:44:00', '2050-01-07 12:44:00', 'CBCP', 'WBC', 5, 'N', 'Y'],
+
+        [4, 23, '2050-01-09 23:44:00', '2050-01-09 23:44:00', 'CBCP', 'WBC', 3, 'L', 'N'],
+        [4, 24, '2050-01-04 09:44:00', '2050-01-05 09:44:00', 'COMP', 'CO2', 6, 'N', 'Y']
+    ],
+
+    'pt_info_columns' : ['pat_id','Birth'],
+    "pt_info_column_types" : ['INTEGER','TIMESTAMP'],
+    "pt_info_data" : [
+        [1, '1986-04-23 00:00:00']
+    ],
+
+    'encounters_columns' : ['pat_id','order_proc_id','AdmitDxDate'],
+    "encounters_column_types" : ['INTEGER','INTEGER','TIMESTAMP'],
+    "encounters_data" : [
+        [1, 1, '2050-01-05 08:00:00'],
+        [1, 2, '2050-01-07 10:44:00'],
+        [1, 3, '2050-01-09 13:44:00']
+    ],
+
+    'diagnoses_columns' : ['pat_id', 'order_proc_id', 'diagnose_time', 'diagnose_code'],
+    "diagnoses_column_types" : ['INTEGER','INTEGER','TIMESTAMP','TEXT'],
+    "diagnoses_data" : [
+        [1, 114, '2050-01-05 09:00:00', '151.3'],
+        [1, 115, '2050-01-07 11:44:00', '404.01'],
+        [1, 116, '2050-01-09 15:44:00', '185']
+    ],
+
+    'demographics_columns' : ['pat_id','GenderName', 'RaceName'],
+    'demographics_column_types': ['INTEGER','TEXT','TEXT'],
+    'demographics_data' : [
+        [1, 'Male', 'Caucasian'],
+        [2, 'Female', 'Native Hawaiian and Other Pacific Islander'],
+        [3, 'Male', 'Hispanic']
+    ]
+}
+
+# Dictionary mapping from test function to expected output.
+FM_TEST_OUTPUT = {
+    "OUTPUT_RAW_TABLE":[
+     ['pat_id', 'order_time', 'CBCP.preTimeDays', 'CBCP.pre', 'CBCP.pre.1d', 'CBCP.pre.2d', 'CBCP.pre.4d',
+      'CBCP.pre.7d', 'CBCP.pre.14d', 'CBCP.pre.30d', 'CBCP.pre.90d', 'CBCP.pre.180d', 'CBCP.pre.365d', 'CBCP.pre.730d',
+      'CBCP.pre.1460d', 'CBCP.postTimeDays', 'CBCP.post', 'CBCP.post.1d', 'CBCP.post.2d', 'CBCP.post.4d',
+      'CBCP.post.7d', 'CBCP.post.14d', 'CBCP.post.30d', 'CBCP.post.90d', 'CBCP.post.180d', 'CBCP.post.365d',
+      'CBCP.post.730d', 'CBCP.post.1460d', 'WCB.preTimeDays', 'WCB.pre', 'WCB.pre.1d', 'WCB.pre.2d', 'WCB.pre.4d',
+      'WCB.pre.7d', 'WCB.pre.14d', 'WCB.pre.30d', 'WCB.pre.90d', 'WCB.pre.180d', 'WCB.pre.365d', 'WCB.pre.730d',
+      'WCB.pre.1460d', 'WCB.postTimeDays', 'WCB.post', 'WCB.post.1d', 'WCB.post.2d', 'WCB.post.4d', 'WCB.post.7d',
+      'WCB.post.14d', 'WCB.post.30d', 'WCB.post.90d', 'WCB.post.180d', 'WCB.post.365d', 'WCB.post.730d',
+      'WCB.post.1460d', 'HCT.preTimeDays', 'HCT.pre', 'HCT.pre.1d', 'HCT.pre.2d', 'HCT.pre.4d', 'HCT.pre.7d',
+      'HCT.pre.14d', 'HCT.pre.30d', 'HCT.pre.90d', 'HCT.pre.180d', 'HCT.pre.365d', 'HCT.pre.730d', 'HCT.pre.1460d',
+      'HCT.postTimeDays', 'HCT.post', 'HCT.post.1d', 'HCT.post.2d', 'HCT.post.4d', 'HCT.post.7d', 'HCT.post.14d',
+      'HCT.post.30d', 'HCT.post.90d', 'HCT.post.180d', 'HCT.post.365d', 'HCT.post.730d', 'HCT.post.1460d',
+      'Male.preTimeDays', 'Male.pre', 'Male.pre.1d', 'Male.pre.2d', 'Male.pre.4d', 'Male.pre.7d', 'Male.pre.14d',
+      'Male.pre.30d', 'Male.pre.90d', 'Male.pre.180d', 'Male.pre.365d', 'Male.pre.730d', 'Male.pre.1460d',
+      'Male.postTimeDays', 'Male.post', 'Male.post.1d', 'Male.post.2d', 'Male.post.4d', 'Male.post.7d', 'Male.post.14d',
+      'Male.post.30d', 'Male.post.90d', 'Male.post.180d', 'Male.post.365d', 'Male.post.730d', 'Male.post.1460d',
+      'Caucasian.preTimeDays', 'Caucasian.pre', 'Caucasian.pre.1d', 'Caucasian.pre.2d', 'Caucasian.pre.4d',
+      'Caucasian.pre.7d', 'Caucasian.pre.14d', 'Caucasian.pre.30d', 'Caucasian.pre.90d', 'Caucasian.pre.180d',
+      'Caucasian.pre.365d', 'Caucasian.pre.730d', 'Caucasian.pre.1460d', 'Caucasian.postTimeDays', 'Caucasian.post',
+      'Caucasian.post.1d', 'Caucasian.post.2d', 'Caucasian.post.4d', 'Caucasian.post.7d', 'Caucasian.post.14d',
+      'Caucasian.post.30d', 'Caucasian.post.90d', 'Caucasian.post.180d', 'Caucasian.post.365d', 'Caucasian.post.730d',
+      'Caucasian.post.1460d', 'Hispanic.preTimeDays', 'Hispanic.pre', 'Hispanic.pre.1d', 'Hispanic.pre.2d',
+      'Hispanic.pre.4d', 'Hispanic.pre.7d', 'Hispanic.pre.14d', 'Hispanic.pre.30d', 'Hispanic.pre.90d',
+      'Hispanic.pre.180d', 'Hispanic.pre.365d', 'Hispanic.pre.730d', 'Hispanic.pre.1460d', 'Hispanic.postTimeDays',
+      'Hispanic.post', 'Hispanic.post.1d', 'Hispanic.post.2d', 'Hispanic.post.4d', 'Hispanic.post.7d',
+      'Hispanic.post.14d', 'Hispanic.post.30d', 'Hispanic.post.90d', 'Hispanic.post.180d', 'Hispanic.post.365d',
+      'Hispanic.post.730d', 'Hispanic.post.1460d', 'Birth.preTimeDays', 'Birth.pre', 'Birth.pre.1d', 'Birth.pre.2d',
+      'Birth.pre.4d', 'Birth.pre.7d', 'Birth.pre.14d', 'Birth.pre.30d', 'Birth.pre.90d', 'Birth.pre.180d',
+      'Birth.pre.365d', 'Birth.pre.730d', 'Birth.pre.1460d', 'Birth.postTimeDays', 'Birth.post', 'Birth.post.1d',
+      'Birth.post.2d', 'Birth.post.4d', 'Birth.post.7d', 'Birth.post.14d', 'Birth.post.30d', 'Birth.post.90d',
+      'Birth.post.180d', 'Birth.post.365d', 'Birth.post.730d', 'Birth.post.1460d', 'AdmitDxDate.preTimeDays',
+      'AdmitDxDate.pre', 'Comorbidity.LiverModSevere.preTimeDays', 'Comorbidity.LiverModSevere.pre',
+      'Comorbidity.LiverModSevere.pre.1d', 'Comorbidity.LiverModSevere.pre.2d', 'Comorbidity.LiverModSevere.pre.4d',
+      'Comorbidity.LiverModSevere.pre.7d', 'Comorbidity.LiverModSevere.pre.14d', 'Comorbidity.LiverModSevere.pre.30d',
+      'Comorbidity.LiverModSevere.pre.90d', 'Comorbidity.LiverModSevere.pre.180d',
+      'Comorbidity.LiverModSevere.pre.365d', 'Comorbidity.LiverModSevere.pre.730d',
+      'Comorbidity.LiverModSevere.pre.1460d', 'Comorbidity.Rheumatic.preTimeDays', 'Comorbidity.Rheumatic.pre',
+      'Comorbidity.Rheumatic.pre.1d', 'Comorbidity.Rheumatic.pre.2d', 'Comorbidity.Rheumatic.pre.4d',
+      'Comorbidity.Rheumatic.pre.7d', 'Comorbidity.Rheumatic.pre.14d', 'Comorbidity.Rheumatic.pre.30d',
+      'Comorbidity.Rheumatic.pre.90d', 'Comorbidity.Rheumatic.pre.180d', 'Comorbidity.Rheumatic.pre.365d',
+      'Comorbidity.Rheumatic.pre.730d', 'Comorbidity.Rheumatic.pre.1460d', 'Comorbidity.CHF.preTimeDays',
+      'Comorbidity.CHF.pre', 'Comorbidity.CHF.pre.1d', 'Comorbidity.CHF.pre.2d', 'Comorbidity.CHF.pre.4d',
+      'Comorbidity.CHF.pre.7d', 'Comorbidity.CHF.pre.14d', 'Comorbidity.CHF.pre.30d', 'Comorbidity.CHF.pre.90d',
+      'Comorbidity.CHF.pre.180d', 'Comorbidity.CHF.pre.365d', 'Comorbidity.CHF.pre.730d', 'Comorbidity.CHF.pre.1460d',
+      'Comorbidity.COPD.preTimeDays', 'Comorbidity.COPD.pre', 'Comorbidity.COPD.pre.1d', 'Comorbidity.COPD.pre.2d',
+      'Comorbidity.COPD.pre.4d', 'Comorbidity.COPD.pre.7d', 'Comorbidity.COPD.pre.14d', 'Comorbidity.COPD.pre.30d',
+      'Comorbidity.COPD.pre.90d', 'Comorbidity.COPD.pre.180d', 'Comorbidity.COPD.pre.365d', 'Comorbidity.COPD.pre.730d',
+      'Comorbidity.COPD.pre.1460d', 'Comorbidity.Renal.preTimeDays', 'Comorbidity.Renal.pre',
+      'Comorbidity.Renal.pre.1d', 'Comorbidity.Renal.pre.2d', 'Comorbidity.Renal.pre.4d', 'Comorbidity.Renal.pre.7d',
+      'Comorbidity.Renal.pre.14d', 'Comorbidity.Renal.pre.30d', 'Comorbidity.Renal.pre.90d',
+      'Comorbidity.Renal.pre.180d', 'Comorbidity.Renal.pre.365d', 'Comorbidity.Renal.pre.730d',
+      'Comorbidity.Renal.pre.1460d', 'Comorbidity.PepticUlcer.preTimeDays', 'Comorbidity.PepticUlcer.pre',
+      'Comorbidity.PepticUlcer.pre.1d', 'Comorbidity.PepticUlcer.pre.2d', 'Comorbidity.PepticUlcer.pre.4d',
+      'Comorbidity.PepticUlcer.pre.7d', 'Comorbidity.PepticUlcer.pre.14d', 'Comorbidity.PepticUlcer.pre.30d',
+      'Comorbidity.PepticUlcer.pre.90d', 'Comorbidity.PepticUlcer.pre.180d', 'Comorbidity.PepticUlcer.pre.365d',
+      'Comorbidity.PepticUlcer.pre.730d', 'Comorbidity.PepticUlcer.pre.1460d', 'Comorbidity.Malignancy.preTimeDays',
+      'Comorbidity.Malignancy.pre', 'Comorbidity.Malignancy.pre.1d', 'Comorbidity.Malignancy.pre.2d',
+      'Comorbidity.Malignancy.pre.4d', 'Comorbidity.Malignancy.pre.7d', 'Comorbidity.Malignancy.pre.14d',
+      'Comorbidity.Malignancy.pre.30d', 'Comorbidity.Malignancy.pre.90d', 'Comorbidity.Malignancy.pre.180d',
+      'Comorbidity.Malignancy.pre.365d', 'Comorbidity.Malignancy.pre.730d', 'Comorbidity.Malignancy.pre.1460d',
+      'Comorbidity.MI.preTimeDays', 'Comorbidity.MI.pre', 'Comorbidity.MI.pre.1d', 'Comorbidity.MI.pre.2d',
+      'Comorbidity.MI.pre.4d', 'Comorbidity.MI.pre.7d', 'Comorbidity.MI.pre.14d', 'Comorbidity.MI.pre.30d',
+      'Comorbidity.MI.pre.90d', 'Comorbidity.MI.pre.180d', 'Comorbidity.MI.pre.365d', 'Comorbidity.MI.pre.730d',
+      'Comorbidity.MI.pre.1460d', 'Comorbidity.PeripheralVascular.preTimeDays', 'Comorbidity.PeripheralVascular.pre',
+      'Comorbidity.PeripheralVascular.pre.1d', 'Comorbidity.PeripheralVascular.pre.2d',
+      'Comorbidity.PeripheralVascular.pre.4d', 'Comorbidity.PeripheralVascular.pre.7d',
+      'Comorbidity.PeripheralVascular.pre.14d', 'Comorbidity.PeripheralVascular.pre.30d',
+      'Comorbidity.PeripheralVascular.pre.90d', 'Comorbidity.PeripheralVascular.pre.180d',
+      'Comorbidity.PeripheralVascular.pre.365d', 'Comorbidity.PeripheralVascular.pre.730d',
+      'Comorbidity.PeripheralVascular.pre.1460d', 'Comorbidity.Cerebrovascular.preTimeDays',
+      'Comorbidity.Cerebrovascular.pre', 'Comorbidity.Cerebrovascular.pre.1d', 'Comorbidity.Cerebrovascular.pre.2d',
+      'Comorbidity.Cerebrovascular.pre.4d', 'Comorbidity.Cerebrovascular.pre.7d', 'Comorbidity.Cerebrovascular.pre.14d',
+      'Comorbidity.Cerebrovascular.pre.30d', 'Comorbidity.Cerebrovascular.pre.90d',
+      'Comorbidity.Cerebrovascular.pre.180d', 'Comorbidity.Cerebrovascular.pre.365d',
+      'Comorbidity.Cerebrovascular.pre.730d', 'Comorbidity.Cerebrovascular.pre.1460d',
+      'Comorbidity.DiabetesComplications.preTimeDays', 'Comorbidity.DiabetesComplications.pre',
+      'Comorbidity.DiabetesComplications.pre.1d', 'Comorbidity.DiabetesComplications.pre.2d',
+      'Comorbidity.DiabetesComplications.pre.4d', 'Comorbidity.DiabetesComplications.pre.7d',
+      'Comorbidity.DiabetesComplications.pre.14d', 'Comorbidity.DiabetesComplications.pre.30d',
+      'Comorbidity.DiabetesComplications.pre.90d', 'Comorbidity.DiabetesComplications.pre.180d',
+      'Comorbidity.DiabetesComplications.pre.365d', 'Comorbidity.DiabetesComplications.pre.730d',
+      'Comorbidity.DiabetesComplications.pre.1460d', 'Comorbidity.HemiplegiaParaplegia.preTimeDays',
+      'Comorbidity.HemiplegiaParaplegia.pre', 'Comorbidity.HemiplegiaParaplegia.pre.1d',
+      'Comorbidity.HemiplegiaParaplegia.pre.2d', 'Comorbidity.HemiplegiaParaplegia.pre.4d',
+      'Comorbidity.HemiplegiaParaplegia.pre.7d', 'Comorbidity.HemiplegiaParaplegia.pre.14d',
+      'Comorbidity.HemiplegiaParaplegia.pre.30d', 'Comorbidity.HemiplegiaParaplegia.pre.90d',
+      'Comorbidity.HemiplegiaParaplegia.pre.180d', 'Comorbidity.HemiplegiaParaplegia.pre.365d',
+      'Comorbidity.HemiplegiaParaplegia.pre.730d', 'Comorbidity.HemiplegiaParaplegia.pre.1460d',
+      'Comorbidity.MalignancyMetastatic.preTimeDays', 'Comorbidity.MalignancyMetastatic.pre',
+      'Comorbidity.MalignancyMetastatic.pre.1d', 'Comorbidity.MalignancyMetastatic.pre.2d',
+      'Comorbidity.MalignancyMetastatic.pre.4d', 'Comorbidity.MalignancyMetastatic.pre.7d',
+      'Comorbidity.MalignancyMetastatic.pre.14d', 'Comorbidity.MalignancyMetastatic.pre.30d',
+      'Comorbidity.MalignancyMetastatic.pre.90d', 'Comorbidity.MalignancyMetastatic.pre.180d',
+      'Comorbidity.MalignancyMetastatic.pre.365d', 'Comorbidity.MalignancyMetastatic.pre.730d',
+      'Comorbidity.MalignancyMetastatic.pre.1460d', 'Comorbidity.AIDSHIV.preTimeDays', 'Comorbidity.AIDSHIV.pre',
+      'Comorbidity.AIDSHIV.pre.1d', 'Comorbidity.AIDSHIV.pre.2d', 'Comorbidity.AIDSHIV.pre.4d',
+      'Comorbidity.AIDSHIV.pre.7d', 'Comorbidity.AIDSHIV.pre.14d', 'Comorbidity.AIDSHIV.pre.30d',
+      'Comorbidity.AIDSHIV.pre.90d', 'Comorbidity.AIDSHIV.pre.180d', 'Comorbidity.AIDSHIV.pre.365d',
+      'Comorbidity.AIDSHIV.pre.730d', 'Comorbidity.AIDSHIV.pre.1460d', 'Comorbidity.Dementia.preTimeDays',
+      'Comorbidity.Dementia.pre', 'Comorbidity.Dementia.pre.1d', 'Comorbidity.Dementia.pre.2d',
+      'Comorbidity.Dementia.pre.4d', 'Comorbidity.Dementia.pre.7d', 'Comorbidity.Dementia.pre.14d',
+      'Comorbidity.Dementia.pre.30d', 'Comorbidity.Dementia.pre.90d', 'Comorbidity.Dementia.pre.180d',
+      'Comorbidity.Dementia.pre.365d', 'Comorbidity.Dementia.pre.730d', 'Comorbidity.Dementia.pre.1460d',
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+      'Comorbidity.LiverMild.pre.2d', 'Comorbidity.LiverMild.pre.4d', 'Comorbidity.LiverMild.pre.7d',
+      'Comorbidity.LiverMild.pre.14d', 'Comorbidity.LiverMild.pre.30d', 'Comorbidity.LiverMild.pre.90d',
+      'Comorbidity.LiverMild.pre.180d', 'Comorbidity.LiverMild.pre.365d', 'Comorbidity.LiverMild.pre.730d',
+      'Comorbidity.LiverMild.pre.1460d', 'Comorbidity.Diabetes.preTimeDays', 'Comorbidity.Diabetes.pre',
+      'Comorbidity.Diabetes.pre.1d', 'Comorbidity.Diabetes.pre.2d', 'Comorbidity.Diabetes.pre.4d',
+      'Comorbidity.Diabetes.pre.7d', 'Comorbidity.Diabetes.pre.14d', 'Comorbidity.Diabetes.pre.30d',
+      'Comorbidity.Diabetes.pre.90d', 'Comorbidity.Diabetes.pre.180d', 'Comorbidity.Diabetes.pre.365d',
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+      '-54791.4055556', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '-54791.4055556', '1', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', '0', '0', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None'],
+     ['3', '2050-01-07 12:44:00', '-2.125', '1', '0', '0', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0.0', '1', '1',
+      '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '-54793.5305556', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '-54793.5305556', '1', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', '0', '0', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None'],
+     ['4', '2050-01-09 23:44:00', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0.0', '1', '1',
+      '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', '0', '0', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None'],
+     ['4', '2050-01-04 09:44:00', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '5.58333333333',
+      '1', '0', '0', '0', '1', '1', '1', '1', '1', '1', '1', '1', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0',
+      'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0',
+      '0', '0', '0', 'None', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', '0', '0', '0', '0',
+      '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', 'None', 'None', '0', '0', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',
+      'None', 'None', 'None', 'None', 'None']
+    ]
+}
diff --git a/medinfo/dataconversion/test/Util.py b/medinfo/dataconversion/test/Util.py
index c97691a2..151ee8ab 100644
--- a/medinfo/dataconversion/test/Util.py
+++ b/medinfo/dataconversion/test/Util.py
@@ -1,7 +1,8 @@
-import Const
+from . import Const
 import sys, os
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 
 sys.path.append('../../../..')
diff --git a/medinfo/dataconversion/test/test-matrix-no-header.tab b/medinfo/dataconversion/test/test-matrix-no-header.tab
new file mode 100644
index 00000000..46471fa1
--- /dev/null
+++ b/medinfo/dataconversion/test/test-matrix-no-header.tab
@@ -0,0 +1,6 @@
+patient_id	index_time	label	f1	f2	f3	f4
+1	2018-01-01 12:00:00	True	1.0	1	False	Foo
+2	2018-01-01 12:00:00	False	2.0	0	True	Bar
+3	2018-01-01 12:00:00	True	3.0	1	False	Baz
+4	2018-01-01 12:00:00	False	4.0	1	True	Foo
+5	2018-01-01 12:00:00	True	5.0	0	False	Bar
diff --git a/medinfo/dataconversion/test/test-matrix-with-header.tab b/medinfo/dataconversion/test/test-matrix-with-header.tab
new file mode 100644
index 00000000..9ac5f119
--- /dev/null
+++ b/medinfo/dataconversion/test/test-matrix-with-header.tab
@@ -0,0 +1,10 @@
+# test-matrix-with-header.tab
+# 
+# This matrix file is for testing only.
+# Doesn't contain anything of value.
+patient_id	index_time	label	f1	f2	f3	f4
+1	2018-01-01 12:00:00	True	1.0	1	False	Foo
+2	2018-01-01 12:00:00	False	2.0	0	True	Bar
+3	2018-01-01 12:00:00	True	3.0	1	False	Baz
+4	2018-01-01 12:00:00	False	4.0	1	True	Foo
+5	2018-01-01 12:00:00	True	5.0	0	False	Bar
diff --git a/medinfo/dataconversion/trainers.py b/medinfo/dataconversion/trainers.py
new file mode 100644
index 00000000..905b1edb
--- /dev/null
+++ b/medinfo/dataconversion/trainers.py
@@ -0,0 +1,191 @@
+"""
+Definition of ModelTrainer, SequenceTrainer
+"""
+import os
+import json
+from typing_extensions import dataclass_transform
+import pandas as pd
+import pickle
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+from scipy.sparse import load_npz
+import torch
+from torch.optim import Adam    
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from constants import DEFAULT_LAB_COMPONENT_IDS
+from constants import DEFAULT_FLOWSHEET_FEATURES
+
+class SequenceTrainer():
+    """
+    Used to train models that leverage SequenceFeaturizer.  Example model
+    classes include GRUs, LSTMs, Transformers. 
+    """
+
+    def __init__(self, outpath, model, criterion, optimizer,
+        train_dataloader, val_dataloader, test_dataloader, stopping_metric,
+        num_epochs=100, scheduler=None):
+        self.outpath = outpath
+        self.model = model
+        self.criterion = criterion
+        self.optimizer = optimizer
+        self.train_dataloader = train_dataloader
+        self.val_dataloader = val_dataloader
+        self.test_dataloader = test_dataloader
+        self.stopping_metric = stopping_metric
+        self.num_epochs = num_epochs
+        self.scheduler = scheduler
+        self.writer = SummaryWriter(self.outpath)
+
+    def __call__(self):
+        best_stopping_metric = 0
+        tolerance_counter = 0
+        for epoch in range(self.num_epochs):
+            train_metrics = self.train()
+            val_metrics = self.evaluate()
+            self.writer.add_scalar('Loss/train', train_metrics['loss'], epoch)
+            self.writer.add_scalar('Loss/val', val_metrics['loss'], epoch)
+            self.writer.add_scalar('AUC/train', train_metrics['auc'], epoch)
+            self.writer.add_scalar('AUC/val', val_metrics['auc'], epoch)
+
+            if tolerance_counter == self.stopping_tolerance:
+                break
+
+            if epoch == 0:
+                best_stopping_metric = val_metrics[self.stopping_metric]
+                torch.save(self.model.state_dict(), 
+                    os.path.join(self.working_directory, f'model_{epoch}.pt'))
+            elif val_metrics[self.stopping_metric] > best_stopping_metric:
+                best_stopping_metric = val_metrics[self.stopping_metric]
+                tolerance_counter = 0
+                torch.save(self.model.state_dict(),
+                    os.path.join(self.working_directory, f'model_{epoch}.pt'))
+            else:
+                tolerance_counter += 1
+
+    def train(self):
+        self.model.train()
+        train_loss = 0
+        predictions, labels = [], []
+        for batch in tqdm(self.train_dataloader):
+            self.model.zero_grad()
+            output = self.model(batch['X'].to(self.device))
+            loss = self.criterion(output, batch['y'].to(self.device))
+            train_loss += loss.item()
+            loss.backward()
+            self.optimizer.step()
+            predictions.append(output.cpu().detach().numpy())
+            labels.append(batch['y'].cpu().detach().numpy())
+
+        train_loss /= len(self.train_dataloader)
+        predictions = np.asarray(predictions)
+        labels = np.asarray(labels)
+        auc = roc_auc_score(labels, predictions)
+
+        train_metrics = {
+            'loss' : train_loss,
+            'auc' : auc
+        }
+        return train_metrics
+
+    def evaluate(self, test=False):
+        self.model.eval()
+        total_loss = 0
+        if test:
+            dataloader = self.test_dataloader
+        else:
+            dataloader = self.val_dataloader
+        predictions, labels = [], []
+        for batch in tqdm(dataloader):
+            output = self.model(batch['X'].to(self.device))
+            loss = self.criterion(output, batch['y'].to(self.device))
+            total_loss += loss.item()
+            predictions.append(output.cpu().detach().numpy())
+            labels.append(batch['y'].cpu().detach().numpy())
+        total_loss /= len(dataloader)
+        predictions = np.asarray(predictions)
+        labels = np.asarray(labels)
+        auc = roc_auc_score(labels, predictions)
+        metrics = {
+            'loss' : total_loss,
+            'auc' : auc
+        }
+        return metrics
+
+class BaselineModelTrainer(object):
+    """
+    Implements the most basic ML pipeline imagineable. Trains a random forest
+    with default hyperparameters using features and labels saved in working
+    directory from a Featurizer in featurizers.py
+
+    Generates a deployment config file used in deploy.py that tells us which
+    EPIC/FHIR APIs to call, how to construct and order features, and saves
+    the model itself. 
+    """
+
+    def __init__(self, working_dir):
+        self.working_dir = working_dir
+        self.task = None # useful in multilable scenario
+
+    def __call__(self, task):
+        """
+        Trains a model, saves predictions, saves a config file.
+        Args:
+            task : column for the binary label
+        """
+        self.task = task
+        self.clf = RandomForestClassifier()
+        X_train = load_npz(os.path.join(
+            self.working_dir, 'train_features.npz'))
+        X_test = load_npz(os.path.join(self.working_dir, 'test_features.npz'))
+        y_train = pd.read_csv(
+            os.path.join(self.working_dir, 'train_labels.csv'))
+        y_test = pd.read_csv(
+            os.path.join(self.working_dir, 'test_labels.csv'))
+
+        self.clf.fit(X_train, y_train[self.task])
+        predictions = self.clf.predict_proba(X_test)[:, 1]
+        auc = roc_auc_score(y_test[self.task], predictions)
+        print(f"AUC: {round(auc, 2)}")
+
+        df_yhats = pd.DataFrame(data={
+            'labels': y_test[self.task].values,
+            'predictions': predictions
+        })
+        yhats_path = f"{self.task}_yhats"
+        df_yhats.to_csv(os.path.join(self.working_dir, yhats_path), index=None)
+        self.generate_deploy_config()
+
+    def generate_deploy_config(self):
+        """
+        Generates the config file used by the deployment module.  Contains
+        all information needed for deployment module to create feature vectors
+        compatible with the model using EPIC and FHIR APIs. This includes
+            1. model: the model itself
+            2. feature_order: order of features in feature vector
+            3. bin_map: numerical features and min value for each bin
+            4. feature_config: dictionary containing which features types used
+               in model and their corresponding look back windows. 
+        """
+        deploy = {}
+        deploy['model'] = self.clf
+        feature_order = pd.read_csv(os.path.join(self.working_dir,
+                                                 'feature_order.csv'))
+        deploy['feature_order'] = [f for f in feature_order.features]
+        if os.path.exists(os.path.join(self.working_dir, 'bin_lup.csv')):
+            bin_map = pd.read_csv(os.path.join(self.working_dir, 'bin_lup.csv'),
+                                na_filter=False)
+        else:
+            bin_map = None
+        deploy['bin_map'] = bin_map
+        with open(os.path.join(self.working_dir, 'feature_config.json'),
+                  'r') as f:
+            feature_config = json.load(f)
+        deploy['feature_config'] = feature_config
+        deploy['lab_base_names'] = DEFAULT_LAB_COMPONENT_IDS
+        deploy['vital_base_names'] = DEFAULT_FLOWSHEET_FEATURES
+        with open(os.path.join(self.working_dir, f'{self.task}_deploy.pkl'),
+                  'wb') as w:
+            pickle.dump(deploy, w)
diff --git a/medinfo/db/Const.py b/medinfo/db/Const.py
index 1d032e18..8ca41c21 100644
--- a/medinfo/db/Const.py
+++ b/medinfo/db/Const.py
@@ -2,7 +2,7 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 
 """Delimiter of SQL commands in a DB script file"""
 SQL_DELIM = ";"
diff --git a/medinfo/db/DBUtil.py b/medinfo/db/DBUtil.py
index 3a7f7833..17403283 100644
--- a/medinfo/db/DBUtil.py
+++ b/medinfo/db/DBUtil.py
@@ -12,12 +12,13 @@
 If using different DB-API modules, this module should
 be edited in the following places...
     - module import statements
-    - SQL_PLACEHOLDER (in Env.py file)
+    - Env.SQL_PLACEHOLDER (in Env.py file)
     - connection method (including references to Env constants)
     - identityQuery method
 """
 
 import sys, os
+import subprocess
 import time;
 from datetime import datetime;
 import json;
@@ -27,12 +28,12 @@
 from medinfo.common.Const import EST_INPUT, COMMENT_TAG, TOKEN_END, NULL_STRING;
 from medinfo.common.Util import stdOpen, isStdFile, fileLineCount, ProgressDots;
 from medinfo.common.Util import parseDateValue, asciiSafeStr;
-from Model import SQLQuery, RowItemModel;
-from Model import modelListFromTable, modelDictFromList;
-from Const import DEFAULT_ID_COL_SUFFIX, SQL_DELIM;
-from Env import DB_PARAM;   # Default connection parameters
-from ResultsFormatter import TextResultsFormatter, TabDictReader;
-from Util import log;
+from .Model import SQLQuery, RowItemModel;
+from .Model import modelListFromTable, modelDictFromList;
+from .Const import DEFAULT_ID_COL_SUFFIX, SQL_DELIM;
+from .Env import DB_PARAM;   # Default connection parameters
+from .ResultsFormatter import TextResultsFormatter, TabDictReader;
+from .Util import log;
 from medinfo.db import Util;
 
 DOUBLE_TOKEN_END = TOKEN_END+TOKEN_END;
@@ -41,48 +42,8 @@
 ######### BEGIN Database Specific Stuff ###########
 ###################################################
 
-from Env import SQL_PLACEHOLDER, DATABASE_CONNECTOR_NAME;
-
-#import mx.ODBC.Windows     # DB-API for ODBC databases like MS Access.        See http://www.egenix.com/files/python/mxODBC.html for installation
-#from mx.ODBC.Windows import NUMBER, STRING, DATETIME, BIT as BOOLEAN;    # Abstract DBAPITypes to check column type codes against
-
-# MySQL pure Python DB connector
-if DATABASE_CONNECTOR_NAME == "mysql.connector":
-    import mysql.connector; 
-    DB_CONNECTOR_MODULE = mysql.connector;
-    BOOLEAN = 1;   # MySQLdb doesn't seem to have a built-in type code constant for BOOLEAN datatype
-
-# MySQL database connector
-if DATABASE_CONNECTOR_NAME == "MySQLdb":
-    import MySQLdb; 
-    DB_CONNECTOR_MODULE = MySQLdb;
-    BOOLEAN = 1;   # MySQLdb doesn't seem to have a built-in type code constant for BOOLEAN datatype
-
-# PostgreSQL interface.   See http://www.initd.org/tracker/psycopg
-if DATABASE_CONNECTOR_NAME == "psycopg2":
-    import psycopg2; 
-    DB_CONNECTOR_MODULE = psycopg2;
-    BOOLEAN = 16;   # Psycopg2 doesn't seem to have a built-in type code constant for BOOLEAN datatype
-
-if DATABASE_CONNECTOR_NAME == "cx_Oracle":
-    import cx_Oracle; 
-    DB_CONNECTOR_MODULE = cx_Oracle;
-    BOOLEAN = 1;   
-
-if DATABASE_CONNECTOR_NAME == "sqlite3":
-    import sqlite3; 
-    DB_CONNECTOR_MODULE = sqlite3;
-
-# Abstract DBAPITypes to check column type codes against
-NUMBER = None;
-STRING = None;
-DATETIME = None;
-
-if DATABASE_CONNECTOR_NAME != "sqlite3":
-    # SQLite doesn't have these respective data type entries
-    NUMBER = DB_CONNECTOR_MODULE.NUMBER;
-    STRING = DB_CONNECTOR_MODULE.STRING;
-    DATETIME = DB_CONNECTOR_MODULE.DATETIME;
+from . import Env;
+SQL_PLACEHOLDER = Env.SQL_PLACEHOLDER;
 
 def connection( connParams=None ):
     """Return a connection to the application database.
@@ -90,11 +51,11 @@ def connection( connParams=None ):
     database is being interfaced to.
     """
     Util.numConnections += 1;
-    
+
     if connParams is None:
         connParams = DB_PARAM;
 
-    if not connParams.has_key("PWD"):
+    if "PWD" not in connParams:
         connParams["PWD"] = getpass("Enter password for %s on %s@%s: " % (connParams["UID"], connParams["DSN"], connParams["HOST"]) );
         if connParams["PWD"] == "":
             connParams["PWD"] = None;   # Special meaning, no password needed
@@ -103,16 +64,19 @@ def connection( connParams=None ):
         log.info("Preparing DB Connection to %(DSN)s@%(HOST)s as %(UID)s" % connParams );
     
     # MySQLdb
-    if DATABASE_CONNECTOR_NAME == "mysql.connector":
+    if Env.DATABASE_CONNECTOR_NAME == "mysql.connector":
+        import mysql.connector; 
         conn = mysql.connector.Connect(user=connParams["UID"], password=connParams["PWD"], host=connParams["HOST"], database=connParams["DSN"], buffered=True);
         return conn;
     
-    if DATABASE_CONNECTOR_NAME == "MySQLdb":
+    if Env.DATABASE_CONNECTOR_NAME == "MySQLdb":
+        import MySQLdb; 
         conn = MySQLdb.connect( host=connParams["HOST"], user=connParams["UID"], passwd=connParams["PWD"], db=connParams["DSN"]);
         return conn;
 
     # PostgreSQL: psycopg2 has slightly different syntax.., need to know if no pass needed..?
-    if DATABASE_CONNECTOR_NAME == "psycopg2":
+    if Env.DATABASE_CONNECTOR_NAME == "psycopg2":
+        import psycopg2; 
         if (connParams["PWD"] is None):
             if "PORT" in connParams and connParams["PORT"] is not None:
                 return psycopg2.connect(host=connParams["HOST"], port=connParams["PORT"], database=connParams["DSN"], user=connParams["UID"]);
@@ -124,30 +88,23 @@ def connection( connParams=None ):
             else:
                 return psycopg2.connect(host=connParams["HOST"], database=connParams["DSN"], user=connParams["UID"], password=connParams["PWD"]);
 
-    if DATABASE_CONNECTOR_NAME == "cx_Oracle":
+    if Env.DATABASE_CONNECTOR_NAME == "cx_Oracle":
+        import cx_Oracle; 
         connStr = "%(UID)s/%(PWD)s@%(HOST)s/%(DSN)s" % connParams;
         if "PORT" in connParams:
             connStr = "%(UID)s/%(PWD)s@%(HOST)s:%(PORT)s/%(DSN)s" % connParams;
         return cx_Oracle.connect(connStr);
 
-    if DATABASE_CONNECTOR_NAME == "sqlite3":
-        return sqlite3.connect(connParams["DSN"]);
+    if Env.DATABASE_CONNECTOR_NAME == "sqlite3":
+        import sqlite3;
+        return sqlite3.connect(os.path.join(connParams["DATAPATH"], connParams["DSN"]));
 
+    if Env.DATABASE_CONNECTOR_NAME == "bigquery":
+        import google.cloud.bigquery.dbapi;
+        return google.cloud.bigquery.dbapi.connect();   # Depends on environment variables to identify right connection
 
-    # ODBC (Access)
-    #connStr = formatDBConnectString( connParams );
-    #return mx.ODBC.Windows.DriverConnect( connStr );
-    
 
-    # PgSQL
-    #if "PORT" in connParams and connParams["PORT"] is not None:
-    #    return PgSQL.connect( host=connParams["HOST"], port=connParams["PORT"], database=connParams["DSN"], user=connParams["UID"], password=connParams["PWD"])
-    #else:
-    #    return PgSQL.connect( host=connParams["HOST"], database=connParams["DSN"], user=connParams["UID"], password=connParams["PWD"])
 
-    #print( connParams);
-    
-    
 def identityQuery( tableName , pgSeqName=None):
     """Given a table name, return the SQL query that will return the
     last auto-generated primary key value (i.e. sequences) from that table.
@@ -156,10 +113,10 @@ def identityQuery( tableName , pgSeqName=None):
     Added pgSeqName b/c with long table names the sequence name gets truncated.  
     Want to make sure can send in the correct sequence to grab.  
     """
-    if DATABASE_CONNECTOR_NAME in ("mysql.connector", "MySQLdb"):
+    if Env.DATABASE_CONNECTOR_NAME in ("mysql.connector", "MySQLdb"):
         return "select last_insert_id()";
     
-    if DATABASE_CONNECTOR_NAME == "psycopg2":
+    if Env.DATABASE_CONNECTOR_NAME == "psycopg2":
         if pgSeqName is None:
             # currently, NAMEDATALEN is set to 64, so the largest name can be 63 characters
             # the name for a sequence gets cropped before the _seq portion
@@ -178,6 +135,50 @@ def sequenceName( tableName ):
     pgSeqName = tableName[:29] + "_" + idCol[:29] + "_seq"
     return pgSeqName; #PostgreSQL auto generated object name for sequence
 
+def createDatabase( dbParams ):
+    """Create a database based on the DSN name specified in the dbParams.
+    Will likely require logging in first as the user-password specified in the dbParams.
+    """
+    if Env.DATABASE_CONNECTOR_NAME == "psycopg2":
+        # For PostgreSQL, have to connect to some database first before can create a new one. Connect to default "postgres" database to start.
+        defaultParams = dict(dbParams);
+        defaultParams["DSN"] = "postgres";
+        defaultConn = connection(defaultParams);
+        defaultConn.autocommit = True;  # Create/Drop Database not allowed in transaction blocks
+        try:
+            execute("CREATE DATABASE %s" % dbParams["DSN"], conn=defaultConn);
+        finally:
+            defaultConn.close();
+    elif Env.DATABASE_CONNECTOR_NAME == "sqlite3":
+        defaultParams = dict(dbParams);
+        # Sqlite3 automatically creates a database upon connection
+        defaultConn = connection(defaultParams);
+        # None for autocommit mode
+        defaultConn.isolation_level = None
+        defaultConn.close()
+
+def dropDatabase( dbParams ):
+    """Drop the database specified by the DSN name specified in the dbParams.
+    Will likely require logging in first as the user-password specified.
+    """
+    if Env.DATABASE_CONNECTOR_NAME == "psycopg2":
+    # For PostgreSQL, cannot drop database while connected to it, so connect to default "postgres" database to start.
+        defaultParams = dict(dbParams);
+        defaultParams["DSN"] = "postgres";
+        defaultConn = connection(defaultParams);
+        defaultConn.autocommit = True;  # Create/Drop Database not allowed in transaction blocks
+        try:
+            execute("DROP DATABASE %s" % dbParams["DSN"], conn=defaultConn);
+        finally:
+            defaultConn.close();
+    elif Env.DATABASE_CONNECTOR_NAME == "sqlite3":
+        defaultParams = dict(dbParams);
+        # Sqlite3 automatically creates a database upon connection
+        try:
+            os.remove(os.path.join(defaultParams['DATAPATH'], defaultParams["DSN"]))
+        except:
+            pass
+
 ###################################################
 #########  END  Database Specific Stuff ###########
 ###################################################
@@ -233,25 +234,27 @@ def execute( query, parameters=None, includeColumnNames=False, incTypeCodes=Fals
     is NOT supplied.  
     """
     # Look for an explicitly specified external connection
-    extConn = ( conn is not None );
+    extConn = conn is not None
     if conn is None:
         # If no specific connection object provided, look for a connection factory
         #   to produce one
         if connFactory is not None:
-            conn = connFactory.connection();
+            conn = connFactory.connection()
         else:
             # No connection or factory specified, just fall back on default connection then
-            conn = connection();
-    
-    if parameters is None:
-        parameters = ();
+            conn = connection()
 
-    cur  = conn.cursor()
-    
-    if isinstance(query,SQLQuery):
-        parameters = tuple(query.getParams());
-        query = str(query);
+    cur = conn.cursor()
     
+    if isinstance(query, SQLQuery):
+        if parameters is None:
+            parameters = tuple(query.getParams())
+        else:
+            parameters = tuple(parameters)
+        query = str(query)
+    elif parameters is None:
+        parameters = ()
+
     #log.debug(parameterizeQueryString(query,parameters));
 
     returnValue = None    
@@ -259,7 +262,7 @@ def execute( query, parameters=None, includeColumnNames=False, incTypeCodes=Fals
         timer = time.time();
         try:
             cur.execute( query, parameters )
-        except Exception, err:
+        except Exception as err:
             log.error(err);
             #log.error(parameterizeQueryString(query,parameters));
             if (not extConn) or autoCommit: 
@@ -365,7 +368,7 @@ def runDBScript( scriptFile, skipErrors = False ):
                         #   any previous commands as well
                         if skipErrors: 
                             conn.commit()    
-                    except Exception, err:
+                    except Exception as err:
                         conn.rollback();    # Reset changes and connection state
                         log.warning("Error Executing in Script: "+ sql )
                         log.warning(err)
@@ -424,7 +427,7 @@ def insertFile( sourceFile, tableName, columnNames=None, delim=None, idFile=None
 
     if dateColFormats is not None:
         # Ensure column keys are normalized
-        dateCols = dateColFormats.keys();
+        dateCols = list(dateColFormats.keys());
         for dateCol in dateCols:
             normalCol = normalizeColName(dateCol);
             dateColFormats[normalCol] = dateColFormats[dateCol];
@@ -449,7 +452,7 @@ def insertFile( sourceFile, tableName, columnNames=None, delim=None, idFile=None
         sqlParts.append("values")
         sqlParts.append("(")
         for i in range(len(columnNames)):
-            sqlParts.append( SQL_PLACEHOLDER )    # Parameter placeholder, depends on DB-API
+            sqlParts.append( Env.SQL_PLACEHOLDER )    # Parameter placeholder, depends on DB-API
             sqlParts.append(",")
         sqlParts.pop(); # Remove extra end comma
         sqlParts.append(")")
@@ -482,7 +485,7 @@ def insertFile( sourceFile, tableName, columnNames=None, delim=None, idFile=None
                     else:
                         cur.execute(identityQuery(tableName));
                         rowId = cur.fetchone()[0];
-                    print >> idFile, rowId;
+                    print(rowId, file=idFile);
 
                 # Need to "auto-commit" after each command, 
                 #   otherwise a skipped error will rollback 
@@ -492,7 +495,7 @@ def insertFile( sourceFile, tableName, columnNames=None, delim=None, idFile=None
 
                 progress.Update()
 
-            except Exception, err:
+            except Exception as err:
                 log.info(sql);
                 log.info(tuple(params))
                 conn.rollback();    # Reset any changes since the last commit
@@ -555,19 +558,19 @@ def updateFromFile( sourceFile, tableName, columnNames=None, nIdCols=1, delim=No
         sql.append("set");
 
         # Data Columns
-        for i in xrange(nIdCols,nCols):
+        for i in range(nIdCols,nCols):
             sql.append(columnNames[i]);
             sql.append("=");
-            sql.append(SQL_PLACEHOLDER);
+            sql.append(Env.SQL_PLACEHOLDER);
             sql.append(",");
         sql.pop();  # Remove extra comma at end
 
         # ID Columns
         sql.append("where")
-        for i in xrange(nIdCols):
+        for i in range(nIdCols):
             sql.append(columnNames[i]);
             sql.append("=");
-            sql.append(SQL_PLACEHOLDER);
+            sql.append(Env.SQL_PLACEHOLDER);
             sql.append("and");
         sql.pop();  # Remove extra comma at end
 
@@ -584,7 +587,7 @@ def updateFromFile( sourceFile, tableName, columnNames=None, nIdCols=1, delim=No
                     params = line.split(delim);
                     
                     # Special handling for null / None string
-                    for iParam in xrange(len(params)):
+                    for iParam in range(len(params)):
                         if params[iParam] == "" or params[iParam] == NULL_STRING:   # Treat blank strings as NULL
                             params[iParam] = None;
 
@@ -605,7 +608,7 @@ def updateFromFile( sourceFile, tableName, columnNames=None, nIdCols=1, delim=No
 
                     progress.Update()
 
-                except Exception, err:
+                except Exception as err:
                     conn.rollback();    # Reset changes and connection state
                     log.critical(sql);
                     log.critical(paramTuple);
@@ -624,6 +627,19 @@ def updateFromFile( sourceFile, tableName, columnNames=None, nIdCols=1, delim=No
 
     return 0    
 
+
+def dumpTableToCsv(table_name, file_name, conn_params=None):
+    if conn_params is None:
+        conn_params = DB_PARAM
+
+    psql_env = os.environ.copy()
+    psql_env["PGPASSWORD"] = conn_params["PWD"]
+    process = subprocess.Popen(['psql', '-U', conn_params["UID"], '-d', conn_params["DSN"],
+                                '-c', '\\COPY {} TO \'{}\' DELIMITER \',\' CSV HEADER;'.format(table_name, file_name)],
+                               env=psql_env)
+    process.wait()
+
+
 def findOrInsertItem(tableName, searchDict, insertDict=None, retrieveCol=None, forceUpdate=False, autoCommit=True, conn=None, connFactory=None):
     """Search the named table in database for a row whose attributes match the key-value pairs specified in searchDict.  
 
@@ -665,7 +681,7 @@ def findOrInsertItem(tableName, searchDict, insertDict=None, retrieveCol=None, f
             searchQuery.addSelect(retrieveCol);
         searchQuery.addFrom(tableName)
 
-        for i, (col, value) in enumerate(searchDict.iteritems()):
+        for i, (col, value) in enumerate(searchDict.items()):
             if value is not None:
                 searchQuery.addWhereEqual(col, value);
             else:
@@ -689,7 +705,7 @@ def findOrInsertItem(tableName, searchDict, insertDict=None, retrieveCol=None, f
         if ( rowExisted ):
             if forceUpdate:
                 # Item already exists, but want to force an update with the insertDict contents
-                updateRow( tableName, insertDict, searchDict.values(), searchDict.keys(), conn=conn );
+                updateRow( tableName, insertDict, list(searchDict.values()), list(searchDict.keys()), conn=conn );
                 cur.execute( searchQuery, searchParams );
                 result = cur.fetchone()
             return (result[0], not rowExisted)
@@ -727,8 +743,8 @@ def insertRow(tableName, insertDict, conn=None, cursor=None):
     if cursor is None:
         cursor = conn.cursor();
     try:
-        insertQuery = buildInsertQuery( tableName, insertDict.keys() );
-        insertParams= insertDict.values();
+        insertQuery = buildInsertQuery( tableName, list(insertDict.keys()) );
+        insertParams= list(insertDict.values());
 
         # Convert component list into string
         log.debug( parameterizeQueryString( insertQuery, insertParams ) );
@@ -757,9 +773,9 @@ def updateRow(tableName, rowDict, idValue, idCol=None, conn=None):
     if not isinstance( idValue, list ): idValue = [idValue];    # Convert to list of size 1
     if not isinstance( idCol, list ):   idCol = [idCol];
 
-    query = buildUpdateQuery(tableName, rowDict.keys(), idCol, idValue );
+    query = buildUpdateQuery(tableName, list(rowDict.keys()), idCol, idValue );
     params = [];
-    params.extend( rowDict.values() );
+    params.extend( list(rowDict.values()) );
     params.extend( idValue );
 
     try:
@@ -791,7 +807,7 @@ def deleteRows(tableName, idValues, idCol=None, conn=None):
         cursor = conn.cursor()
 
         params = idValues;
-        paramPlaceholders = str.join(",", [SQL_PLACEHOLDER]*len(params) );
+        paramPlaceholders = str.join(",", [Env.SQL_PLACEHOLDER]*len(params) );
 
         query = """delete from %s where %s in (%s)""" % (tableName, idCol, paramPlaceholders);
 
@@ -819,8 +835,9 @@ def parseValue(chunk,colName,dateColFormats=None,escapeStrings=False):
         returnValue = parseDateValue(chunk,dateFormat);
     elif chunk is not None and escapeStrings:
         # Generic string value, but run through escape encoder so weird non-ascii characters don't make DB process crash
-        returnValue = chunk.encode('string_escape');
-        #returnValue = asciiSafeStr(chunk);  # Clean up any weird unicode characters
+        returnValue = chunk.encode('unicode_escape').decode('utf-8')
+        # returnValue = chunk.encode('string_escape');  # doesn't work in Python3
+        # returnValue = asciiSafeStr(chunk);  # Clean up any weird unicode characters
 
     return returnValue;
 
@@ -898,7 +915,7 @@ def buildUpdateQuery(tableName, colNames, idColName=None, idValue=None):
     for col in colNames:
         sql.append(col);
         sql.append("=");
-        sql.append(SQL_PLACEHOLDER);
+        sql.append(Env.SQL_PLACEHOLDER);
         sql.append(",");
     sql.pop();  # Remove extra comma at end
     
@@ -911,7 +928,7 @@ def buildUpdateQuery(tableName, colNames, idColName=None, idValue=None):
         else:
             # Equals operator doesn't work for null values
             sql.append("is");
-        sql.append(SQL_PLACEHOLDER);
+        sql.append(Env.SQL_PLACEHOLDER);
         sql.append("and");
     sql.pop();  # Remove extra "and" at end
 
@@ -933,7 +950,7 @@ def buildInsertQuery(tableName, colNames):
     # Value placeholders
     query.append("values (");
     for col in colNames:
-        query.append("%s" % SQL_PLACEHOLDER);
+        query.append("%s" % Env.SQL_PLACEHOLDER);
         query.append(",");
     query[-1] = ")";        
     
@@ -943,7 +960,7 @@ def buildInsertQuery(tableName, colNames):
 
 def parameterizeQueryString( query, params=None ):
     """Given a SQL query string and tuple of parameters,
-    replace all of the SQL_PLACEHOLDER strings in the query
+    replace all of the Env.SQL_PLACEHOLDER strings in the query
     with the respective representation of the parameters.
     For the most part, this will just be the string representation
     of the parameter except in the case of str objects which
@@ -962,8 +979,8 @@ def parameterizeQueryString( query, params=None ):
         params = query.getParams();
         query = str(query);
 
-    # Make sure all of the SQL_PLACEHOLDERS are Python string replacement vars
-    query = query.replace(SQL_PLACEHOLDER,"%s");
+    # Make sure all of the Env.SQL_PLACEHOLDERS are Python string replacement vars
+    query = query.replace(Env.SQL_PLACEHOLDER,"%s");
     
     # Modify parameter list if any text-based variables to replace
     if params:
@@ -990,7 +1007,7 @@ def loadRecordModelById( tableName, idValue, idCol=None, conn=None, connFactory=
     """
     if idCol is None:
         idCol = defaultIDColumn(tableName);
-    query = "select * from %s where %s = %s" % (tableName, idCol, SQL_PLACEHOLDER);
+    query = "select * from %s where %s = %s" % (tableName, idCol, Env.SQL_PLACEHOLDER);
     params = (idValue,);
     dataTable = execute( query, params, includeColumnNames=True, conn=conn, connFactory=connFactory);
     dataModels = modelListFromTable(dataTable);
@@ -1063,7 +1080,7 @@ def main(argv):
         
         if options.incCommand:
             summaryData = {"argv": argv};
-            print >> outFile, COMMENT_TAG, json.dumps(summaryData);
+            print(COMMENT_TAG, json.dumps(summaryData), file=outFile);
 
         textFormatter = TextResultsFormatter(outFile, options.delim)
 
diff --git a/medinfo/db/Env.py b/medinfo/db/Env.py
index 05a304a1..222c5efc 100644
--- a/medinfo/db/Env.py
+++ b/medinfo/db/Env.py
@@ -5,6 +5,7 @@
 
 import sys, os;
 import logging
+import LocalEnv;
 
 """Default level for application logging.  Modify these for different scenarios.
 See Python logging package documentation for more information"""
@@ -19,8 +20,9 @@
 Read the DB-API 2.0 specs for more info. (http://www.python.org/peps/pep-0249.html)
 Oracle expects sequential or named items like &1, &2, or :1, :2...
 """
-#SQL_PLACEHOLDER = "?"   # "qmark"
-SQL_PLACEHOLDER = "%s"  # "format" and "pyFormat"
+# #SQL_PLACEHOLDER = "?"   # "qmark"
+# SQL_PLACEHOLDER = "%s"  # "format" and "pyFormat"
+SQL_PLACEHOLDER = LocalEnv.SQL_PLACEHOLDER
 
 """Strings to use for boolean parameters."""
 BOOLEAN_STR = dict();
@@ -31,29 +33,21 @@
 #BOOLEAN_STR[True] = str(-1);
 #BOOLEAN_STR[False]= str(0);
 
-"""Designate the DB being used, which will afect some DB specific setup steps"""
-#DATABASE_CONNECTOR_NAME = "mysql.connector";
-#DATABASE_CONNECTOR_NAME = "MySQLdb";
-DATABASE_CONNECTOR_NAME = "psycopg2";
-#DATABASE_CONNECTOR_NAME = "cx_Oracle";
-#DATABASE_CONNECTOR_NAME = "sqlite3";
-
+# """Designate the DB being used, which will afect some DB specific setup steps"""
+# #DATABASE_CONNECTOR_NAME = "mysql.connector";
+# #DATABASE_CONNECTOR_NAME = "MySQLdb";
+# DATABASE_CONNECTOR_NAME = "psycopg2";
+# #DATABASE_CONNECTOR_NAME = "cx_Oracle";
+# #DATABASE_CONNECTOR_NAME = "sqlite3";
+DATABASE_CONNECTOR_NAME = LocalEnv.DATABASE_CONNECTOR_NAME
 
 """Parameters needed to open a connection to the database.
 Dependent upon particular connection interface and database implementation
+
+Note that Env.py reads database, user, and password specifications from LocalEnv.py
 """
 DB_PARAM = {}
-#DB_PARAM["HOST"] = "medinfo-5year-time-assoc2010-2013.cxkturzva06i.us-east-1.rds.amazonaws.com"
-#DB_PARAM["HOST"] = "inpatient5year.cxkturzva06i.us-east-1.rds.amazonaws.com"
-DB_PARAM["HOST"] = "localhost"
-#DB_PARAM["DSN"]  = "medicare"
-DB_PARAM["DSN"]  = "stride"
-#DB_PARAM["DSN"]  = "resident-access-log-2017"
-#DB_PARAM["DSN"] = "/Users/angelicaperez/Documents/JonChen/sqlite_db/dave_chan2.sqlite"
-#DB_PARAM["DSN"]  = "medinfo5yr"
-DB_PARAM["UID"]  = "jwang198"
-DB_PARAM["PWD"]  = ""
-
+DB_PARAM = LocalEnv.LOCAL_PROD_DB_PARAM
 
 # Opioid Notes DB
 #DB_PARAM["HOST"] = "cci-db-p03";
@@ -66,21 +60,19 @@
 Dependent upon particular connection interface and database implementation
 """
 TEST_DB_PARAM = {}
-#TEST_DB_PARAM["HOST"] = "inpatient5year.cxkturzva06i.us-east-1.rds.amazonaws.com"
-TEST_DB_PARAM["HOST"] = "localhost"
-TEST_DB_PARAM["DSN"]  = "testdb"
-TEST_DB_PARAM["UID"]  = "sbala"
-TEST_DB_PARAM["PWD"]  = "1234"
+TEST_DB_PARAM = LocalEnv.LOCAL_TEST_DB_PARAM
 
 #TEST_DB_PARAM["DSN"] = "c:\Box Sync\NoSync\VAAlerts\dave_chan2.sqlite";
 #TEST_DB_PARAM["DSN"] = "/Users/angelicaperez/Documents/JonChen/sqlite_db/dave_chan2.sqlite"
 
 
-"""Parameters on whether to do additional pre-processing when parsing text / CSV files"""
-CSV_EXPAND_QUOTES = False;
+"""Parameters on whether to do additional pre-processing when parsing text / CSV files.
+Seems necessary for STRIDE 2008-2014-2017 Order Proc dumps?
+"""
+CSV_EXPAND_QUOTES = True;
 
 def formatDBConnectString( dbParamDict ):
     connStr = ""
-    for key, value in dbParamDict.iteritems():
+    for key, value in dbParamDict.items():
         connStr += "%s=%s;" % (key,value);
     return connStr;
diff --git a/medinfo/db/Model.py b/medinfo/db/Model.py
index e389a7da..566fc481 100644
--- a/medinfo/db/Model.py
+++ b/medinfo/db/Model.py
@@ -1,6 +1,6 @@
 """Common objects / base classes used to support DB interactions.
 """
-from Env import SQL_PLACEHOLDER;
+from .Env import SQL_PLACEHOLDER;
 
 class RowItemModel(dict):
     """Generic object class to model rows from database tables.
@@ -36,21 +36,22 @@ def __init__(self,initData=None,dataKeys=None):
         dict.__init__(self);
         self.update(initData,dataKeys)
 
-    def update(self,initData=None,dataKeys=None):
+    def update(self, initData=None, dataKeys=None):
         """Same like the constructor but can do at any time to update (overwrite)
         or extend the data already in the model.
         """
-        if initData != None:
-            if dataKeys != None:
+        if initData is not None:
+            if dataKeys is not None:
                 # Have both initData and dataKeys.  Expect these to be lists of data and names / keys
-                for key, value in zip(dataKeys,initData):
+                for key, value in zip(dataKeys, initData):
                     self[key] = value
             else:
                 # Only have initData, expect this to be a dictionary.  Copy over contents
-                for key, value in initData.iteritems():
+                for key, value in initData.items():
                     self[key] = value
+        return self
 
-    def valuesByName(self,columnNames):
+    def valuesByName(self, columnNames):
         """Return the values in the dictionary as a list.  Unlike the basic
         dict.values() method, can provide a list of columnNames to only return
         the values keyed by the names provided in that list, and in that order.
@@ -96,7 +97,7 @@ def modelDictFromList(modelList,columnName,listValues=False):
     for model in modelList:
         key = model[columnName];
         if listValues:
-            if not modelDict.has_key(key):
+            if key not in modelDict:
                 modelDict[key] = [];    # Create a new list
             modelDict[key].append(model);
         else:
@@ -163,7 +164,7 @@ def __call__(self, item1, item2):
         for fieldName in self.fieldNames:
             values2.append(item2[fieldName]);
 
-        result = cmp(values1, values2);
+        result = (values1 > values2) - (values1 < values2)
         if self.desc:
             result *= -1;
         return result;
diff --git a/medinfo/db/ResultsFormatter.py b/medinfo/db/ResultsFormatter.py
index 284a3ecd..31047be4 100644
--- a/medinfo/db/ResultsFormatter.py
+++ b/medinfo/db/ResultsFormatter.py
@@ -3,12 +3,12 @@
 of results arrays (usually from database calls).
 """
 import sys;
-import urllib;
+import urllib.request, urllib.parse, urllib.error;
 import csv; # Does a lot of text table parsing and formatting already
 import re;
-from Model import RowItemModel;
+from .Model import RowItemModel;
 from medinfo.common.Const import COMMENT_TAG, NULL_STRING;
-from Env import CSV_EXPAND_QUOTES;
+from .Env import CSV_EXPAND_QUOTES;
 
 class ResultsFormatter:
     """Abstract class defining what methods a formatter class should implement
@@ -60,7 +60,7 @@ def formatResultDicts(self, resultDicts, columnNames=None, addHeaderRow=False):
         for self.currRow, rowDict in enumerate(resultDicts):
             if columnNames == None:
                 # If column set and order is not specified, just print all columns alphabetically
-                columnNames = rowDict.keys()
+                columnNames = list(rowDict.keys())
                 columnNames.sort()
             
             if addHeaderRow:
@@ -93,6 +93,10 @@ class TextResultsFormatter(ResultsFormatter):
     Defaults to tab-delimited, but can customize.
     Option to escape / URL quote content text to avoid
     special characters (tabs, new lines, etc.) compromising formatting.
+
+    This is mostly redundant with built-in package:
+        csv.DictWriter( dialect=csv.excel_tab )
+
     """
     mDelim = None;
     quoteContents = None;
@@ -122,11 +126,11 @@ def formatTuple( self, tuple, lastTuple=None ):
             if not continueGrouping:
                 itemText = str(item);
                 if self.quoteContents:
-                    itemText = urllib.quote(itemText);  # URL quoting to avoid unsafe characters for formatting like tabs or newlines
+                    itemText = urllib.parse.quote(itemText);  # URL quoting to avoid unsafe characters for formatting like tabs or newlines
                 self.outFile.write(itemText);
             if i < (tupleLength-1): # Only add delimiters up to the last item
                 self.outFile.write(self.mDelim);
-        print >> self.outFile # New line
+        print(file=self.outFile) # New line
 
 class HtmlResultsFormatter(ResultsFormatter):
     """Formatter for displaying database output in a web table.
@@ -173,21 +177,21 @@ def formatTuple( self, tuple, lastTuple=None ):
             lineClass = "softLine";
             if isinstance(self.lineSeparators, str):
                 lineClass = self.lineSeparators;
-            print >> self.outFile, '<tr><td colspan=100 class="%s" height=1></td></tr>' % lineClass;
-        print >> self.outFile, "<tr %s %s>" % (valign, align);
+            print('<tr><td colspan=100 class="%s" height=1></td></tr>' % lineClass, file=self.outFile);
+        print("<tr %s %s>" % (valign, align), file=self.outFile);
         for i, item in enumerate(tuple):
             if self.headerRow:
                 self.outFile.write("    <%s>" % self.headerRowFormat)
                 self.outFile.write(str(item))
                 self.outFile.write("</td>")
-                print >> self.outFile
+                print(file=self.outFile)
             else:
                 if self.groupColumns and lastTuple is not None and continueGrouping:
                     if item != lastTuple[i]:
                         # Non-match.  Don't try to group anymore
                         continueGrouping = False;
                     else:
-                        print >> self.outFile, "    <td></td>";
+                        print("    <td></td>", file=self.outFile);
                 if not continueGrouping:
                     #rowspan = self.__determineRowspan(i);
                     if i == 0:
@@ -197,8 +201,8 @@ def formatTuple( self, tuple, lastTuple=None ):
                     if item is not None or self.printNone:
                         self.outFile.write(str(item))
                     self.outFile.write("</td>")
-                    print >> self.outFile
-        print >> self.outFile, "</tr>"
+                    print(file=self.outFile)
+        print("</tr>", file=self.outFile)
         self.headerRow = False # Only the first tuple should be the "header"
 
 
@@ -210,6 +214,7 @@ class TabDictReader(csv.DictReader):
     strip any flanking whitespace.
     """
     def __init__(self, f, fieldnames=None, restkey=None, restval=None, dialect=None, delimiter=None, *args, **kwds):
+        self.infile = f;
         self.commentLines = list(); # Track comment lines
         self.firstLine = None;  # Track the first non-comment line
         
@@ -220,6 +225,9 @@ def __init__(self, f, fieldnames=None, restkey=None, restval=None, dialect=None,
         lineGenerator = self.lineGenerator(f, delimiter);
         csv.DictReader.__init__(self, lineGenerator, fieldnames=fieldnames, restkey=restkey, restval=restval, dialect=dialect, delimiter=delimiter, *args, **kwds);
     
+    def close(self):
+        self.infile.close();
+
     def lineGenerator(self, f, delimiter):
         # Generator expression to drop lines that start with comment tag
         # http://stackoverflow.com/questions/14158868/python-skip-comment-lines-marked-with-in-csv-dictreader
@@ -243,6 +251,9 @@ def lineGenerator(self, f, delimiter):
 def pandas_read_table(infile, *args, **kwds):
     """Simple wrapper function for default arguments to read a pandas dataframe 
     from a tab-delimited file and ignoring comment lines.
+
+    Beware that using something like a '#' comment tag means that data elements that include '#' in the string 
+        will result in the remainder of the row's contents being ignored as a comment
     """
     import pandas as pd;    # Only import as needed
     return pd.read_table(infile, comment=COMMENT_TAG, na_values=[NULL_STRING], *args, **kwds);
@@ -290,7 +301,7 @@ def sanitizeNames(names):
         newNameSet.add(newName);
     return newNames;
 
-def pandas_to_sqlconn(df, tableName="data", conn=None):
+def pandas_to_sqlconn(df, tableName="data", conn=None, *args, **kwds):
     """Turning pandas dataframe into SQL queryable in memory object
     Column names will be cleaned up as per sanitizeNames.
     https://plot.ly/python/big-data-analytics-with-pandas-and-sqlite/
@@ -307,12 +318,12 @@ def pandas_to_sqlconn(df, tableName="data", conn=None):
     df.to_sql(tableName, conn, if_exists='replace');
     return conn;
 
-def df2sql(df, tableName="data", conn=None):
+def df2sql(df, tableName="data", conn=None, *args, **kwds):
     """Synonym function that may be more intuitive"""
-    return pandas_to_sqlconn(df, tableName, conn);
+    return pandas_to_sqlconn(df, tableName, conn, *args, **kwds);
 
 
-def pandas_read_sql_query(query, conn):
+def pandas_read_sql_query(query, conn, *args, **kwds):
     """To query dataframes back out,
     just enter a SQL query, using "data" as the source table name
     (unless a different one was specified in the dataframe_to_sqlconn) process.
@@ -323,9 +334,9 @@ def pandas_read_sql_query(query, conn):
         limit 10
     """
     import pandas as pd; # Only Import as needed
-    return pd.read_sql_query(query, conn);
+    return pd.read_sql_query(query, conn, *args, **kwds);
     
-def sql2df(query, conn):
+def sql2df(query, conn, *args, **kwds):
     """Synonym function that may be more intuitive"""
-    return pandas_read_sql_query(query, conn);
+    return pandas_read_sql_query(query, conn, *args, **kwds);
 
diff --git a/medinfo/db/Util.py b/medinfo/db/Util.py
index 6490631f..8453d98f 100644
--- a/medinfo/db/Util.py
+++ b/medinfo/db/Util.py
@@ -1,10 +1,10 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
-import urllib;
+import urllib.request, urllib.parse, urllib.error;
 import time;
 import math;
 
@@ -14,7 +14,7 @@
 formatter = logging.Formatter(Const.LOGGER_FORMAT)
 
 handler.setFormatter(formatter)
-log.addHandler(handler)
+# log.addHandler(handler)
 
 # Global Support variable to monitor how may connections have been created
 numConnections = 0;
diff --git a/medinfo/db/bigquery/__init__.py b/medinfo/db/bigquery/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/medinfo/db/bigquery/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/medinfo/db/bigquery/bigQueryUtil.py b/medinfo/db/bigquery/bigQueryUtil.py
new file mode 100644
index 00000000..fe70803d
--- /dev/null
+++ b/medinfo/db/bigquery/bigQueryUtil.py
@@ -0,0 +1,309 @@
+'''
+create a BigQuery connection object
+Setup credentials in environment variable GOOGLE_APPLICATION_CREDENTIALS (See LocalEnv)
+
+client object methods
+    connect to BQ using json token
+    create new dataset
+    create new table
+    create new table by uploading CSV
+    create schema/table-type from postgres /d <db> output
+    get row count of table
+    get query result
+'''
+
+from google.cloud import bigquery
+from google.cloud.exceptions import NotFound
+from google.cloud.bigquery import dbapi
+
+from medinfo.dataconversion.Util import log
+
+import re
+import time
+import os
+import csv
+
+def connection( client=None ):
+    '''
+    use this to create a BQ connection object
+             to use functions from DBUtil
+
+    in BQ, close() and commit() are no-op
+
+    client does not need to be specified
+    '''
+    conn = dbapi.connect(client)
+    return conn
+
+def headerChecker(csv_path, header_list):
+    with open(csv_path) as f:
+        reader = csv.reader(f)
+        csv_header = next(reader)
+
+    if csv_header != header_list:
+        raise NonMatchingHeadersException('headers from %r do not match' % csv_path)
+
+class NonMatchingHeadersException(Exception):
+    pass
+
+class BigQueryClient:
+    '''
+    methods to perform using client object
+    '''
+
+    def __init__(self):
+        self.client = bigquery.Client()
+        assert self.client is not None, 'Did not connect to BQ, check credentials env(GOOGLE_APPLICATION_CREDENTIALS)'
+
+    def reconnect_client(self):
+        self.client = bigquery.Client()
+
+    def create_new_dataset(self, dataset_id):
+        '''
+        https://cloud.google.com/bigquery/docs/datasets#create-dataset
+
+        :param dataset_id: dataset name
+        :return: None
+        '''
+
+        dataset_ref = self.client.dataset(dataset_id)
+
+        try:
+            # Check if the dataset with specified ID already exists
+            self.client.get_dataset(dataset_ref)
+            log.info('Dataset {} already exists! Skipping create operation.'.format(dataset_id))
+            #print(f'Dataset {dataset_id} already exists! Skipping create operation.')
+        except NotFound:
+            # Construct a full Dataset object to send to the API.
+            dataset = bigquery.Dataset(dataset_ref)
+            dataset.location = 'US'
+            dataset = self.client.create_dataset(dataset)  # API request
+            log.info('Dataset {} created successfully project: {}.'.format(dataset.dataset_id, self.client.project))
+            #print(f'Dataset {dataset.dataset_id} created successfully project: {self.client.project}.')
+
+    def create_new_table_from_schema(self, dataset_id, table_id,
+                                     schema):
+        '''
+        https://cloud.google.com/bigquery/docs/tables#create-table
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param schema:
+            schema = [
+                bigquery.SchemaField('full_name', 'STRING', mode='REQUIRED', description='blah'),
+                bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
+            ]
+        :return: None
+        '''
+
+        dataset_ref = self.client.dataset(dataset_id)
+        table_ref = dataset_ref.table(table_id)
+
+        try:
+            self.client.get_table(table_ref)
+            #print(f'Table {table_id} in dataset {dataset_id} already exists! Skipping create operation.')
+        except NotFound:
+            # Construct a full Table object to send to the API.
+            table = bigquery.Table(table_ref, schema=schema)
+            table = self.client.create_table(table)  # API request
+            log.info('''
+                    Table {} in dataset {}
+                    created successfully project: {}.
+                    '''.format(table.table_id, dataset_id, self.client.project))
+            '''
+            print(
+                f'Table {table.table_id} in dataset {dataset_id}'
+                f'created successfully project: {self.client.project}.'
+            )
+            '''
+
+    def _stream_csv_to_table(self, dataset_id, table_id, csv_path, batch_size = 1000):
+        '''
+        FYI: Streaming is NOT free
+        https://cloud.google.com/bigquery/pricing#streaming_pricing
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param csv_path: path to CSV from SQL
+        :param batch_size: rows per insert (default 1000)
+        :return: None
+        '''
+
+        table_ref = self.client.dataset(dataset_id).table(table_id)
+        table = self.client.get_table(table_ref)  # API request
+
+        with open(csv_path, 'w') as infile, open(csv_path + '_errors', 'w') as outfile:
+            for line in infile:
+                rows_to_insert = []
+                row = line.split(',')
+                # TODO validate row
+                rows_to_insert.append(row)
+
+                if len(rows_to_insert) == batch_size:
+                    errors = self.client.insert_rows(table, rows_to_insert)  # API request
+                    assert errors == []
+                    rows_to_insert = []
+
+            if rows_to_insert:
+                errors = self.client.insert_rows(table, rows_to_insert)  # API request
+                assert errors == []
+
+    def load_csv_to_table(self, dataset_id, table_id, csv_path, auto_detect_schema = True,
+                          schema = [], skip_rows = 0, append_to_table=False):
+        '''
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param csv_path: path to exported csv file
+        :param auto_detect_schema: auto detect schema types from CSV (default True)
+        :param schema: (default None)
+            schema = [
+                bigquery.SchemaField('full_name', 'STRING', mode='REQUIRED', description='blah'),
+                bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
+            ]
+        :param skip_rows: how many rows in CSV to skip (default 0)
+        :return: None
+
+        Details on loading local data
+        https://cloud.google.com/bigquery/docs/loading-data-local
+        '''
+
+        location = 'US'
+
+        dataset_ref = self.client.get_dataset(dataset_id)
+        table_ref = dataset_ref.table(table_id)
+        dataset = bigquery.Dataset(dataset_ref)
+        dataset.location = location
+
+        job_config = bigquery.LoadJobConfig()
+        job_config.source_format = bigquery.SourceFormat.CSV
+        # https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv#overwriting_a_table_with_csv_data
+        #job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
+        job_config.skip_leading_rows = skip_rows
+        job_config.quote_character = '\"'
+
+        if not append_to_table:
+            job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
+
+            if auto_detect_schema:
+                assert schema == [], 'Auto-detect is True, but schema is specified'
+                job_config.autodetect = True
+            else:
+                job_config.autodetect = False
+                assert schema != [], 'Auto-detect is False, but no schema specified'
+                job_config.schema = schema
+
+        with open(csv_path, 'rb') as csv_file:
+            load_table_job = self.client.load_table_from_file(
+                csv_file,
+                table_ref,
+                location=location,
+                job_config=job_config,
+            )  # API request
+
+        log.info('Uploading table... ')
+        #print('Uploading table... ')
+        try:
+            load_table_job.result() # Wait for load to complete
+        except:
+            errors = load_table_job.errors
+            log.error(errors)
+            return errors
+        print(load_table_job.error_result)
+        table = self.client.get_table(table_ref)
+        log.info('''
+                {} rows loaded into
+                table {} in dataset {}
+                in project: {}.
+                '''.format(load_table_job.output_rows, table.table_id, dataset_id, self.client.project))
+        '''
+        print(
+            f'{load_table_job.output_rows} rows loaded into '
+            f'table {table.table_id} in dataset {dataset_id} '
+            f'in project: {self.client.project}.'
+        )
+        '''
+
+    def read_table_types(self, table_types_path):
+        '''
+        reads in lines, deliminited by "|", e.g.:
+        <column name> | <data type> | <collation> | <nullable> | <default>
+
+        :param table_types_path:
+        :return: list of bigquery.SchemaField
+        '''
+
+        type_mapping = {
+            'TINYINT'   : 'INT64',
+            'SMALLINT'  : 'INT64',
+            'MEDIUMINT' : 'INT64',
+            'INT'       : 'INT64',
+            'INTEGER'   : 'INT64',
+            'BIGINT'    : 'INT64',
+            'DECIMAL'   : 'NUMERIC',
+            'NUMERIC'   : 'NUMERIC',
+            'REAL'      : 'FLOAT64',
+            'DOUBLE PRECISION': 'FLOAT64',
+            'BOOLEAN'   : 'BOOL',
+            'CHAR'      : 'STRING',
+            'CHARACTER': 'STRING',
+            'CHARACTER VARYING': 'STRING',
+            'VARCHAR'   : 'STRING',
+            'BYTEA'     : 'BYTES',
+            'TINYTEXT'  : 'STRING',
+            'TEXT'      : 'STRING',
+            'MEDIUMTEXT': 'STRING',
+            'LONGTEXT'  : 'STRING',
+            'DATE'      : 'DATE',
+            'TIME'      : 'TIME',
+            'TIMESTAMP' : 'TIMESTAMP',
+            'TIMESTAMP WITHOUT TIME ZONE': 'TIMESTAMP'
+        }
+
+        schema_fields = []
+
+        with open(table_types_path, 'r') as type_table_f:
+            for line in type_table_f:
+                column, pg_type, collation, nullable, default = [item.strip() for item in line.split('|')]
+                pg_type_ = re.sub(r'\(.*\)', '', pg_type).upper()
+                bq_type = type_mapping[pg_type_]
+                mode = 'REQUIRED' if nullable == 'not null' else 'NULLABLE'
+                schema_fields.append(bigquery.SchemaField(column, bq_type, mode=mode))
+
+        return schema_fields
+
+    def get_row_count(self, full_table_name):
+
+        query_job = self.client.query('SELECT COUNT(*) FROM ' + full_table_name, location='US')
+        results = query_job.result()
+        results_list = [row for row in results]
+
+        return results_list[0][0]
+
+    def queryBQ(self, query_str, query_params=None, location ='US', batch_mode = False, dry_run = False,
+                verbose = False):
+
+        job_config = bigquery.QueryJobConfig()
+        job_config.dry_run = dry_run
+
+        if batch_mode:
+            # Run at batch priority, which won't count toward concurrent rate limit.
+            job_config.priority = bigquery.QueryPriority.BATCH
+
+        if query_params:
+            job_config.query_parameters = query_params
+
+        query_job = self.client.query(query_str, location=location, job_config=job_config)
+
+        if batch_mode: # wait until job is done
+            while query_job.state != 'DONE':
+                query_job = self.client.get_job(query_job.job_id, location=location)
+                log.info('Job {} is currently in state {}'.format(query_job.job_id, query_job.state))
+                #print('Job {} is currently in state {}'.format(query_job.job_id, query_job.state))
+                time.sleep(5)
+
+        if verbose:
+            log.info('This query will process {} bytes:'.format(query_job.total_bytes_processed))   # TODO (nodir) always None - query isn't processed yet
+            log.info('Query: ' + query_str)
+            #print('This query will process {} bytes.'.format(query_job.total_bytes_processed))
+
+        return query_job
diff --git a/medinfo/db/bigquery/test/__init__.py b/medinfo/db/bigquery/test/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/medinfo/db/bigquery/test/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/medinfo/db/bigquery/test/test_bigQueryUtil.py b/medinfo/db/bigquery/test/test_bigQueryUtil.py
new file mode 100644
index 00000000..da6a9645
--- /dev/null
+++ b/medinfo/db/bigquery/test/test_bigQueryUtil.py
@@ -0,0 +1,122 @@
+'''
+need a delete list to remove from GCP after testing (do this in web GUI)
+'''
+import os
+import unittest
+import csv
+import tempfile;
+
+from medinfo.dataconversion.test.Const import RUNNER_VERBOSITY
+from medinfo.dataconversion.Util import log
+from medinfo.db.bigquery import bigQueryUtil
+from medinfo.common.test.Util import MedInfoTestCase
+from medinfo.db.Model import RowItemModel
+
+from google.cloud import bigquery
+
+TEST_DEST_DATASET = 'test_dataset'
+TEST_TABLE_ID = 'unittest_bigQueryUtil'
+TMP_DIR = tempfile.gettempdir();
+
+
+class test_bigQueryUtil(MedInfoTestCase):
+
+    def setUp(self):
+        """Prepare state for test cases"""
+
+        # create dummy CSV
+        self.tmp_dummy_csv_path = TMP_DIR + '/unittest_bq_dummy.csv'
+        self.dummy_table = lines = [['num', 'char']] + [[n, chr(ord('a')+n)] for n in range(26)]
+        with open(self.tmp_dummy_csv_path, 'w', newline='') as csvfile:
+            writer = csv.writer(csvfile, delimiter=',')
+            writer.writerows(lines)
+
+        self.tmp_csv_path = TMP_DIR + '/unittest_bq.csv'
+
+        self.bqConn = bigQueryUtil.connection()
+        self.bqClient = bigQueryUtil.BigQueryClient()
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        log.info("Purge test records from the database")
+
+        bqCursor = self.bqConn.cursor()
+        bqCursor.execute('DELETE FROM %s.%s WHERE true;' % (TEST_DEST_DATASET, TEST_TABLE_ID))
+
+        log.info("Removing tmp CSV files")
+        if os.path.exists(self.tmp_csv_path):
+            os.remove(self.tmp_csv_path)
+        if os.path.exists(self.tmp_dummy_csv_path):
+            os.remove(self.tmp_dummy_csv_path)
+
+    def test_create_table_from_schema(self):
+        schema = [bigquery.SchemaField('num',  'INTEGER', 'REQUIRED', None, ()),
+                   bigquery.SchemaField('char', 'STRING', 'NULLABLE', None, ())]
+
+        self.bqClient.create_new_table_from_schema(TEST_DEST_DATASET, TEST_TABLE_ID, schema)
+
+        table_ref = self.bqClient.client.dataset(TEST_DEST_DATASET).table(TEST_TABLE_ID)
+        table = self.bqClient.client.get_table(table_ref)
+        bq_schema = table.schema
+
+        for ref_schema_field, cmp_schema_field in zip(schema, bq_schema):
+            print((ref_schema_field.to_api_repr(), cmp_schema_field.to_api_repr()))
+            assert ref_schema_field.name == cmp_schema_field.name
+            assert ref_schema_field.field_type == cmp_schema_field.field_type
+            assert ref_schema_field.is_nullable == cmp_schema_field.is_nullable
+
+    def _test_query_from_bq_table(self):
+        '''skipping this for now, need to create a permanent test table to query from'''
+
+        query = '''
+                SELECT stride_treatment_team_id, pat_enc_csn_id, treatment_team
+                FROM stride_2008_2017.stride_treatment_team
+                WHERE stride_treatment_team_id IN UNNEST(@tt_ids);
+                '''
+
+        headers = ['stride_treatment_team_id', 'pat_enc_csn_id', 'treatment_team']
+        treatment_team_ids = list(range(2141155, 2141205))
+        query_params = [
+            bigquery.ArrayQueryParameter('tt_ids', 'INT64', treatment_team_ids)
+        ]
+
+        query_job = self.bqClient.queryBQ(str(query), query_params=query_params, location='US', batch_mode=False,
+                                          verbose=True)
+
+        actual_data = []
+        with open(self.tmp_csv_path, 'wb') as csvfile:
+            writer = csv.writer(csvfile)
+            for row in query_job:
+                writer.writerow(list(row.values()))
+                actual_data.append(row.values)
+
+        # TODO fill expected data
+        expected_data = []
+        self.assertEqualTable(expected_data, actual_data)
+
+    def test_load_csv_to_table(self):
+        self.bqClient.load_csv_to_table(TEST_DEST_DATASET, TEST_TABLE_ID, self.tmp_dummy_csv_path, skip_rows=1,
+                                        append_to_table=True)
+        query = 'SELECT * FROM {}.{}'.format(TEST_DEST_DATASET, TEST_TABLE_ID)
+
+        query_job = self.bqClient.queryBQ(str(query), location='US', batch_mode=False,
+                                          verbose=True)
+
+        actual_data = [['num', 'char']]
+        for row in query_job:
+            actual_data.append([row.num, row.char])
+        self.assertEqualTable(self.dummy_table, actual_data)
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(test_bigQueryUtil))
+
+    return suite
+
+
+if __name__ == "__main__":
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
\ No newline at end of file
diff --git a/medinfo/db/definition/cpoeSimultation.sql b/medinfo/db/definition/cpoeSimulation.sql
similarity index 98%
rename from medinfo/db/definition/cpoeSimultation.sql
rename to medinfo/db/definition/cpoeSimulation.sql
index 04a8eb2a..059954a1 100644
--- a/medinfo/db/definition/cpoeSimultation.sql
+++ b/medinfo/db/definition/cpoeSimulation.sql
@@ -1,198 +1,196 @@
--- Basic simultation user tracking information
-CREATE TABLE sim_user
-(
-	sim_user_id SERIAL NOT NULL,
-	name TEXT NOT NULL
---How many years since you received your medical degree (MD/DO)?
---What is your primary area / specialty of clinical practice?
---List your current or prior medical training and board certifications:
---What percentage of your time in the past year has been spent practicing outpatient / clinic medicine?
---What percentage of your time in the past year has been spent practicing inpatient / hospital medicine?
---Approximately how many patients have you admitted to an inpatient hospital service in the past year?
-
-);
-ALTER TABLE sim_user ADD CONSTRAINT sim_user_pkey PRIMARY KEY (sim_user_id);
--- INSERT INTO sim_user(sim_user_id,name) VALUES (0,'Default User');
-
--- Basic simulated patient information
-CREATE TABLE sim_patient
-(
-	sim_patient_id SERIAL NOT NULL,
-	name TEXT NOT NULL,
-	age_years INTEGER NOT NULL,
-	gender TEXT NOT NULL
-);
-ALTER TABLE sim_patient ADD CONSTRAINT sim_patient_pkey PRIMARY KEY (sim_patient_id);
-
--- Model patient states with transition options
-CREATE TABLE sim_state
-(
- 	sim_state_id SERIAL NOT NULL,
- 	name TEXT NOT NULL,
- 	description TEXT
-);
-ALTER TABLE sim_state ADD CONSTRAINT sim_state_pkey PRIMARY KEY (sim_state_id);
--- INSERT INTO sim_state(sim_state_id,name, description) VALUES (0,'Default','Default State Info');
-
--- Record when a patient enters a given state
-CREATE TABLE sim_patient_state
-(
-	sim_patient_state_id SERIAL NOT NULL,
-	sim_patient_id BIGINT NOT NULL,
-	sim_state_id BIGINT NOT NULL,
-	relative_time_start INTEGER NOT NULL,
-	relative_time_end INTEGER -- Redundant to store end-time when can infer from start time of next record, but having both recorded makes for easier retrieval queries
-);
-ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_pkey PRIMARY KEY (sim_patient_state_id);
-ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_patient_fkey FOREIGN KEY (sim_patient_id) REFERENCES sim_patient(sim_patient_id);
-ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
-CREATE INDEX index_sim_patient_state_patient ON sim_patient_state(sim_patient_id,relative_time_start,relative_time_end);
-CREATE INDEX index_sim_patient_state_state ON sim_patient_state(sim_state_id);
-
--- State transition triggers
-CREATE TABLE sim_state_transition
-(
-	sim_state_transition_id SERIAL NOT NULL,
-	pre_state_id BIGINT NOT NULL,
-	post_state_id BIGINT NOT NULL,
-	clinical_item_id BIGINT, -- Clinical Item that occurs in pre-state to trigger transition to post-state
-	time_trigger INTEGER,	-- Alternatively, amount of time (seconds) to pass before triggering state transition
-	description TEXT
-);
-ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_pkey PRIMARY KEY (sim_state_transition_id);
-ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_unique_key UNIQUE (pre_state_id, clinical_item_id);
-ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_pre_state_fkey FOREIGN KEY (pre_state_id) REFERENCES sim_state(sim_state_id);
-ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_post_state_fkey FOREIGN KEY (post_state_id) REFERENCES sim_state(sim_state_id);
-ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
-CREATE INDEX index_sim_state_transition_pre_state ON sim_state_transition(pre_state_id,clinical_item_id);	-- Should be created implicitly by UNIQUE constraint
-CREATE INDEX index_sim_state_transition_post_state ON sim_state_transition(post_state_id,clinical_item_id);
-
--- Code Table to record note type information.  For brevity, also track author and service types (Treatment Team?) here
-CREATE TABLE sim_note_type
-(
-	sim_note_type_id INTEGER NOT NULL,
-	name TEXT NOT NULL,
-	priority INTEGER,
-	description TEXT
-);
-ALTER TABLE sim_note_type ADD CONSTRAINT sim_note_type_pkey PRIMARY KEY (sim_note_type_id);
-
--- Patient Notes
-CREATE TABLE sim_note
-(
-	sim_note_id SERIAL NOT NULL,
-	sim_state_id BIGINT NOT NULL,
-	note_type_id INTEGER,
-	author_type_id INTEGER,
-	service_type_id INTEGER,
-	relative_state_time INTEGER NOT NULL, -- Seconds relative to the simulated patient state's relative_time_start
-	content TEXT NOT NULL
-);
-ALTER TABLE sim_note ADD CONSTRAINT sim_note_pkey PRIMARY KEY (sim_note_id);
-ALTER TABLE sim_note ADD CONSTRAINT sim_note_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
-ALTER TABLE sim_note ADD CONSTRAINT sim_note_note_type_fkey FOREIGN KEY (note_type_id) REFERENCES sim_note_type(sim_note_type_id);
-ALTER TABLE sim_note ADD CONSTRAINT sim_note_author_type_fkey FOREIGN KEY (author_type_id) REFERENCES sim_note_type(sim_note_type_id);
-ALTER TABLE sim_note ADD CONSTRAINT sim_note_service_type_fkey FOREIGN KEY (service_type_id) REFERENCES sim_note_type(sim_note_type_id);
-CREATE INDEX index_sim_note_state ON sim_note(sim_state_id,relative_state_time);
-
--- Result data, primarily labs and imaging, with either numeric or text values.  Lookup / code table to organize types of results
--- Format group_string as cascading categories necessary for sorting and subgrouping (e.g., Labs>Hematology>Automated Blood Count)
--- Use priority as master sorting index rather than sorting by each category grouping, as alphabetical may not always be desired sort option
-CREATE TABLE sim_result
-(
-	sim_result_id SERIAL NOT NULL,
-	name TEXT NOT NULL,
-	description TEXT,
-	group_string TEXT,
-	priority INTEGER
-);
-ALTER TABLE sim_result ADD CONSTRAINT sim_result_pkey PRIMARY KEY (sim_result_id);
-
--- Specific values for individual patient states, map to clinical_items reflecting laboratory results
--- Include flowsheet (vitals) through here as well and just separate out by result group_strings
-CREATE TABLE sim_state_result
-(
-	sim_state_result_id SERIAL NOT NULL,
-	sim_state_id INTEGER NOT NULL,  -- Simulated Patient State identifier that this result should be available for.  Don't assign to specific patients, assign patients to a state that these reflect
-	sim_result_id BIGINT NOT NULL,
-	clinical_item_id BIGINT, -- Map to clinical_items reflecting (laboratory) results
-	num_value FLOAT,
-	num_value_noise FLOAT, -- Option to add +/- noise to reported value in case of repeated testing
-	text_value TEXT,
-	result_flag TEXT
-);
-ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_pkey PRIMARY KEY (sim_state_result_id);
-ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
-ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_sim_result_fkey FOREIGN KEY (sim_result_id) REFERENCES sim_result(sim_result_id);
-ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
-CREATE INDEX index_sim_state_result_state ON sim_state_result(sim_state_id);
-CREATE INDEX index_sim_state_result_sim_result ON sim_state_result(sim_result_id,num_value);
-CREATE INDEX index_sim_state_result_clinical_item ON sim_state_result(clinical_item_id);
-
--- Many-to-Many mapping to indicate which (lab) orders (referenced as clinical_item_ids) can trigger which results
---	That way can prespecify what lab results are expected for a simulated patient, but do not release
---	that information until user orders a respective laboratory test.
-CREATE TABLE sim_order_result_map
-(
-	sim_order_result_map_id SERIAL NOT NULL,
-	clinical_item_id BIGINT NOT NULL, -- Precedent item / Triggering order
-	sim_result_id BIGINT NOT NULL,
-	turnaround_time INTEGER	-- Seconds from the order time until expect to have/release the result
-);
-ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_pkey PRIMARY KEY (sim_order_result_map_id);
-ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
-ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_sim_result_fkey FOREIGN KEY (sim_result_id) REFERENCES sim_result(sim_result_id);
-CREATE INDEX index_sim_order_result_map_sim_result ON sim_order_result_map(sim_result_id);
-CREATE INDEX index_sim_order_result_map_clinical_item ON sim_order_result_map(clinical_item_id);
-
--- Clinical item orders for patients, keyed with start and end dates
-CREATE TABLE sim_patient_order
-(
-    sim_patient_order_id SERIAL NOT NULL,
-    sim_user_id BIGINT NOT NULL,
-    sim_patient_id BIGINT NOT NULL,
-    clinical_item_id BIGINT NOT NULL,
-    relative_time_start INTEGER NOT NULL,	-- Relative to base / zero time of simulated case that the order occurred / started
-    relative_time_end INTEGER	-- Relative to base / zero time of simulated case that order concludes or was discontinued
-);
-ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_pkey PRIMARY KEY (sim_patient_order_id);
-ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_sim_user_fkey FOREIGN KEY (sim_user_id) REFERENCES sim_user(sim_user_id);
-ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_sim_patient_fkey FOREIGN KEY (sim_patient_id) REFERENCES sim_patient(sim_patient_id);
-ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
-CREATE INDEX index_sim_patient_order_sim_user_patient_date ON sim_patient_order(sim_user_id, sim_patient_id, relative_time_start);	-- Natural sorting option to order by user, patient, then in chronological order of clinical items
-CREATE INDEX index_sim_patient_order_sim_clinical_item ON sim_patient_order(sim_patient_id, clinical_item_id);	-- Natural sorting option to order by patient, then in chronological order of clinical items
--- Record patient state at the time of the order. 
--- Denormalized information that could be derived from sim_patient_state, but makes future joins a hassle using relative time information
-ALTER TABLE sim_patient_order ADD COLUMN sim_state_id BIGINT NOT NULL DEFAULT 0;
-ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
-
-
--- Populate sim_order_result_map and sim_result with existing data based on queries like below
-
--- select 
---    sop.proc_cat_name, sop.proc_id, sop.proc_code, sop.description,
---    sor.base_name, sor.component_name,
---    count(ord_num_value) as countValue,
---    sum(ord_num_value) as sumValue,
---    sum(ord_num_value*ord_num_value) as sumSquaresValue,
---    count(extract(epoch from sop.result_time-proc_start_time)) countTurnaround,
---    sum(extract(epoch from sop.result_time-proc_start_time)) sumTurnaround, 
---    sum( extract(epoch from sop.result_time-proc_start_time) ^2 ) sumSquaresTurnaround
--- from stride_order_proc as sop, stride_order_results as sor
--- where sop.order_proc_id = sor.order_proc_id
--- group by
---    sop.proc_cat_name, sop.proc_id, sop.proc_code, sop.description,
---    sor.base_name, sor.component_name
-
--- select 
---    flo_meas_id, flowsheet_name, 
---    count(flowsheet_value) as countValue, sum(flowsheet_value) as sumValue, sum(flowsheet_value^2) as sumSquaresValue
--- from stride_flowsheet
--- group by 
---    flo_meas_id, flowsheet_name 
---
-
--- Calculate result distribution summary statistics
--- python scripts\CDSS\populateOrderResultStats.py
-
-
+-- Basic simultation user tracking information
+CREATE TABLE sim_user
+(
+	sim_user_id SERIAL NOT NULL,
+	name TEXT NOT NULL
+--How many years since you received your medical degree (MD/DO)?
+--What is your primary area / specialty of clinical practice?
+--List your current or prior medical training and board certifications:
+--What percentage of your time in the past year has been spent practicing outpatient / clinic medicine?
+--What percentage of your time in the past year has been spent practicing inpatient / hospital medicine?
+--Approximately how many patients have you admitted to an inpatient hospital service in the past year?
+
+);
+ALTER TABLE sim_user ADD CONSTRAINT sim_user_pkey PRIMARY KEY (sim_user_id);
+-- INSERT INTO sim_user(sim_user_id,name) VALUES (0,'Default User');
+
+-- Basic simulated patient information
+CREATE TABLE sim_patient
+(
+	sim_patient_id SERIAL NOT NULL,
+	name TEXT NOT NULL,
+	age_years INTEGER NOT NULL,
+	gender TEXT NOT NULL
+);
+ALTER TABLE sim_patient ADD CONSTRAINT sim_patient_pkey PRIMARY KEY (sim_patient_id);
+
+-- Model patient states with transition options
+CREATE TABLE sim_state
+(
+ 	sim_state_id SERIAL NOT NULL,
+ 	name TEXT NOT NULL,
+ 	description TEXT
+);
+ALTER TABLE sim_state ADD CONSTRAINT sim_state_pkey PRIMARY KEY (sim_state_id);
+-- INSERT INTO sim_state(sim_state_id,name, description) VALUES (0,'Default','Default State Info');
+
+-- Record when a patient enters a given state
+CREATE TABLE sim_patient_state
+(
+	sim_patient_state_id SERIAL NOT NULL,
+	sim_patient_id BIGINT NOT NULL,
+	sim_state_id BIGINT NOT NULL,
+	relative_time_start INTEGER NOT NULL,
+	relative_time_end INTEGER -- Redundant to store end-time when can infer from start time of next record, but having both recorded makes for easier retrieval queries
+);
+ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_pkey PRIMARY KEY (sim_patient_state_id);
+ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_patient_fkey FOREIGN KEY (sim_patient_id) REFERENCES sim_patient(sim_patient_id);
+ALTER TABLE sim_patient_state ADD CONSTRAINT sim_patient_state_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+CREATE INDEX index_sim_patient_state_patient ON sim_patient_state(sim_patient_id,relative_time_start,relative_time_end);
+CREATE INDEX index_sim_patient_state_state ON sim_patient_state(sim_state_id);
+
+-- State transition triggers
+CREATE TABLE sim_state_transition
+(
+	sim_state_transition_id SERIAL NOT NULL,
+	pre_state_id BIGINT NOT NULL,
+	post_state_id BIGINT NOT NULL,
+	clinical_item_id BIGINT, -- Clinical Item that occurs in pre-state to trigger transition to post-state
+	time_trigger INTEGER,	-- Alternatively, amount of time (seconds) to pass before triggering state transition
+	description TEXT
+);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_pkey PRIMARY KEY (sim_state_transition_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_unique_key UNIQUE (pre_state_id, clinical_item_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_pre_state_fkey FOREIGN KEY (pre_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_post_state_fkey FOREIGN KEY (post_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_state_transition ADD CONSTRAINT sim_state_transition_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_state_transition_pre_state ON sim_state_transition(pre_state_id,clinical_item_id);	-- Should be created implicitly by UNIQUE constraint
+CREATE INDEX index_sim_state_transition_post_state ON sim_state_transition(post_state_id,clinical_item_id);
+
+-- Code Table to record note type information.  For brevity, also track author and service types (Treatment Team?) here
+CREATE TABLE sim_note_type
+(
+	sim_note_type_id INTEGER NOT NULL,
+	name TEXT NOT NULL,
+	priority INTEGER,
+	description TEXT
+);
+ALTER TABLE sim_note_type ADD CONSTRAINT sim_note_type_pkey PRIMARY KEY (sim_note_type_id);
+
+-- Patient Notes
+CREATE TABLE sim_note
+(
+	sim_note_id SERIAL NOT NULL,
+	sim_state_id BIGINT NOT NULL,
+	note_type_id INTEGER,
+	author_type_id INTEGER,
+	service_type_id INTEGER,
+	relative_state_time INTEGER NOT NULL, -- Seconds relative to the simulated patient state's relative_time_start
+	content TEXT NOT NULL
+);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_pkey PRIMARY KEY (sim_note_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_note_type_fkey FOREIGN KEY (note_type_id) REFERENCES sim_note_type(sim_note_type_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_author_type_fkey FOREIGN KEY (author_type_id) REFERENCES sim_note_type(sim_note_type_id);
+ALTER TABLE sim_note ADD CONSTRAINT sim_note_service_type_fkey FOREIGN KEY (service_type_id) REFERENCES sim_note_type(sim_note_type_id);
+CREATE INDEX index_sim_note_state ON sim_note(sim_state_id,relative_state_time);
+
+-- Result data, primarily labs and imaging, with either numeric or text values.  Lookup / code table to organize types of results
+-- Format group_string as cascading categories necessary for sorting and subgrouping (e.g., Labs>Hematology>Automated Blood Count)
+-- Use priority as master sorting index rather than sorting by each category grouping, as alphabetical may not always be desired sort option
+CREATE TABLE sim_result
+(
+	sim_result_id SERIAL NOT NULL,
+	name TEXT NOT NULL,
+	description TEXT,
+	group_string TEXT,
+	priority INTEGER
+);
+ALTER TABLE sim_result ADD CONSTRAINT sim_result_pkey PRIMARY KEY (sim_result_id);
+
+-- Specific values for individual patient states, map to clinical_items reflecting laboratory results
+-- Include flowsheet (vitals) through here as well and just separate out by result group_strings
+CREATE TABLE sim_state_result
+(
+	sim_state_result_id SERIAL NOT NULL,
+	sim_state_id INTEGER NOT NULL,  -- Simulated Patient State identifier that this result should be available for.  Don't assign to specific patients, assign patients to a state that these reflect
+	sim_result_id BIGINT NOT NULL,
+	clinical_item_id BIGINT, -- Map to clinical_items reflecting (laboratory) results
+	num_value FLOAT,
+	num_value_noise FLOAT, -- Option to add +/- noise to reported value in case of repeated testing
+	text_value TEXT,
+	result_flag TEXT
+);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_pkey PRIMARY KEY (sim_state_result_id);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_sim_result_fkey FOREIGN KEY (sim_result_id) REFERENCES sim_result(sim_result_id);
+ALTER TABLE sim_state_result ADD CONSTRAINT sim_state_result_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_state_result_state ON sim_state_result(sim_state_id);
+CREATE INDEX index_sim_state_result_sim_result ON sim_state_result(sim_result_id,num_value);
+CREATE INDEX index_sim_state_result_clinical_item ON sim_state_result(clinical_item_id);
+
+-- Many-to-Many mapping to indicate which (lab) orders (referenced as clinical_item_ids) can trigger which results
+--	That way can prespecify what lab results are expected for a simulated patient, but do not release
+--	that information until user orders a respective laboratory test.
+CREATE TABLE sim_order_result_map
+(
+	sim_order_result_map_id SERIAL NOT NULL,
+	clinical_item_id BIGINT NOT NULL, -- Precedent item / Triggering order
+	sim_result_id BIGINT NOT NULL,
+	turnaround_time INTEGER	-- Seconds from the order time until expect to have/release the result
+);
+ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_pkey PRIMARY KEY (sim_order_result_map_id);
+ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+ALTER TABLE sim_order_result_map ADD CONSTRAINT sim_order_result_map_sim_result_fkey FOREIGN KEY (sim_result_id) REFERENCES sim_result(sim_result_id);
+CREATE INDEX index_sim_order_result_map_sim_result ON sim_order_result_map(sim_result_id);
+CREATE INDEX index_sim_order_result_map_clinical_item ON sim_order_result_map(clinical_item_id);
+
+-- Clinical item orders for patients, keyed with start and end dates
+CREATE TABLE sim_patient_order
+(
+    sim_patient_order_id SERIAL NOT NULL,
+    sim_user_id BIGINT NOT NULL,
+    sim_patient_id BIGINT NOT NULL,
+    clinical_item_id BIGINT NOT NULL,
+    relative_time_start INTEGER NOT NULL,	-- Relative to base / zero time of simulated case that the order occurred / started
+    relative_time_end INTEGER	-- Relative to base / zero time of simulated case that order concludes or was discontinued
+);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_pkey PRIMARY KEY (sim_patient_order_id);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_sim_user_fkey FOREIGN KEY (sim_user_id) REFERENCES sim_user(sim_user_id);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_sim_patient_fkey FOREIGN KEY (sim_patient_id) REFERENCES sim_patient(sim_patient_id);
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id);
+CREATE INDEX index_sim_patient_order_sim_user_patient_date ON sim_patient_order(sim_user_id, sim_patient_id, relative_time_start);	-- Natural sorting option to order by user, patient, then in chronological order of clinical items
+CREATE INDEX index_sim_patient_order_sim_clinical_item ON sim_patient_order(sim_patient_id, clinical_item_id);	-- Natural sorting option to order by patient, then in chronological order of clinical items
+-- Record patient state at the time of the order. 
+-- Denormalized information that could be derived from sim_patient_state, but makes future joins a hassle using relative time information
+ALTER TABLE sim_patient_order ADD COLUMN sim_state_id BIGINT NOT NULL DEFAULT 0;
+ALTER TABLE sim_patient_order ADD CONSTRAINT sim_patient_order_state_fkey FOREIGN KEY (sim_state_id) REFERENCES sim_state(sim_state_id);
+
+
+-- Populate sim_order_result_map and sim_result with existing data based on queries like below
+
+-- select 
+--    sop.proc_cat_name, sop.proc_id, sop.proc_code, sop.description,
+--    sor.base_name, sor.component_name,
+--    count(ord_num_value) as countValue,
+--    sum(ord_num_value) as sumValue,
+--    sum(ord_num_value*ord_num_value) as sumSquaresValue,
+--    count(extract(epoch from sop.result_time-proc_start_time)) countTurnaround,
+--    sum(extract(epoch from sop.result_time-proc_start_time)) sumTurnaround, 
+--    sum( extract(epoch from sop.result_time-proc_start_time) ^2 ) sumSquaresTurnaround
+-- from stride_order_proc as sop, stride_order_results as sor
+-- where sop.order_proc_id = sor.order_proc_id
+-- group by
+--    sop.proc_cat_name, sop.proc_id, sop.proc_code, sop.description,
+--    sor.base_name, sor.component_name
+
+-- select 
+--    flo_meas_id, flowsheet_name, 
+--    count(flowsheet_value) as countValue, sum(flowsheet_value) as sumValue, sum(flowsheet_value^2) as sumSquaresValue
+-- from stride_flowsheet
+-- group by 
+--    flo_meas_id, flowsheet_name 
+--
+
+-- Calculate result distribution summary statistics
+-- python scripts\CDSS\populateOrderResultStats.py
diff --git a/medinfo/db/definition/opioidrx.sql b/medinfo/db/definition/opiodrx.sql
similarity index 97%
rename from medinfo/db/definition/opioidrx.sql
rename to medinfo/db/definition/opiodrx.sql
index 43e2ede1..cd5ebc2b 100644
--- a/medinfo/db/definition/opioidrx.sql
+++ b/medinfo/db/definition/opiodrx.sql
@@ -1,279 +1,279 @@
--- Mapping Table for Medications to Common Active Ingredients
-create table stride_mapped_meds
-(
-	medication_id integer,
-	medication_name text,
-	rxcui integer,
-	active_ingredient text
-);
-CREATE INDEX index_stride_mapped_meds_medication_id ON stride_mapped_meds(medication_id);
-ALTER TABLE stride_mapped_meds ADD COLUMN analysis_status INTEGER DEFAULT 1;
-ALTER TABLE stride_mapped_meds ADD COLUMN morphine_po_equivalent FLOAT;
-
--- Medication Order Data Table.  Source headers need " removed, lower-case, and make sure refills column is numeric
--- 	Remove " marks and lower-case header lines
---	(Refills should be numeric 0 not char o.  Just swap "o" for "0")  (Entry 436981419... refills should be numeric, not "prn")
-create table stride_order_med
-(
-	order_med_id bigint not null,
-	pat_id text,
-	pat_enc_csn_id bigint,
-	ordering_datetime datetime,
-	hosp_admsn_time datetime,
-	hosp_dischrg_time datetime,
-	order_class text,
-	medication_id integer,
-	description text,
-	sig text,
-	quantity text,
-	refills integer,
-	start_date datetime,
-	end_date datetime,
-	dispense_as_written_yn text,
-	reason_for_discontinuation text,
-	enc_type_c integer,
-	encounter_type text,
-	patient_location text,
-	login_department text,
-	display_name text,
-	hv_hospitalist_yn text,
-	med_route text,
-	discontinue_time datetime,
-	changed_from_order_med_id bigint,
-	reason_pended_med_refused text,
-	hv_discr_freq_id bigint,
-	discr_freq_name text,
-	discr_freq_display_name text,
-	discr_freq_type text,
-	discr_freq_number_of_times integer,
-	freq_time_unit text,
-	prn_yn text,
-	freq_duplicate_dose_interval integer,
-	freq_missed_dose_interval integer,
-	freqency_or_period text,
-	hv_discrete_dose text,
-	hv_dose_unit text,
-	non_formulary_yn text,
-	order_status text,
-	min_discrete_dose float,
-	max_discrete_dose float,
-	dose_unit text,
-	is_pending_ord_yn text,
-	modify_track text,
-	med_comments text,
-	usr_select_medicn_id integer,
-	lastdose text,
-	amb_med_disp_name text,
-	refills_remaining integer,
-	rule_based_order_transmittl_yn text,
-	outpat_orderstat_before_admsn text,
-	last_dose_time text,
-	pend_approve_flag text,
-	prn_comment text,
-	med_dis_disp_qty integer,
-	med_dis_disp_unit text
-);
-ALTER TABLE stride_order_med ADD CONSTRAINT stride_order_med_pkey PRIMARY KEY (order_med_id);
-CREATE INDEX index_stride_order_med_pat_id ON stride_order_med(pat_id (16));
-CREATE INDEX index_stride_order_med_ordering_datetime ON stride_order_med(ordering_datetime);
-CREATE INDEX index_stride_order_med_pat_enc_csn_id ON stride_order_med(pat_enc_csn_id);
-CREATE INDEX index_stride_order_med_medication_id ON stride_order_med(medication_id);
-
-
--- Drug Screen Results
--- 	Header row: Remove " marks and lower-case header lines
-create table stride_order_proc_drug_screen
-(
-	order_proc_id bigint,
-	pat_id text,
-	pat_enc_csn_id bigint,
-	ordering_date datetime,
-	proc_id integer,
-	proc_code text,
-	description text,
-	quantity integer,
-	order_status_c text,
-	order_status text,
-	reason_for_cancellation text,
-	future_or_stand text,
-	standing_exp_date datetime,
-	future_expected_compltn_date datetime,
-	standing_occurs integer,
-	stand_orig_occur integer,
-	rfl_priority text,
-	order_priority text,
-	stand_interval text,
-	instantiated_time datetime,
-	ordering_mode text,
-	is_pending_ord_yn text,
-	proc_start_time datetime,
-	proc_end_time datetime,
-	id_of_replaced_order_proc_id bigint,
-	patient_location text,
-	login_department text,
-	specimen_taken_time datetime,
-	specimen_recv_time datetime,
-	result_line integer,
-	component_id integer,
-	component_name text,
-	ord_value text,
-	ref_normal_vals text
-);
-CREATE INDEX index_stride_order_proc_drug_screen_compkey ON stride_order_proc_drug_screen(order_proc_id, result_line);
-CREATE INDEX index_stride_order_proc_drug_screen_pat_id ON stride_order_proc_drug_screen(pat_id (16));
-CREATE INDEX index_stride_order_proc_drug_screen_pat_enc_csn_id ON stride_order_proc_drug_screen(pat_enc_csn_id);
-CREATE INDEX index_stride_order_proc_drug_screen_component_id ON stride_order_proc_drug_screen(component_id);
-
--- Patient ID list with birth date, gender, race
--- 	Header: Lower-case
-create table stride_patient
-(
-	pat_id text,
-	pat_mrn_id bigint,
-	birth_date datetime,
-	gender text,
-	primary_race text
-);
-CREATE INDEX index_patient_pat_id ON stride_patient(pat_id (16));
-CREATE INDEX index_patient_pat_mrn_id ON stride_patient(pat_mrn_id);
--- Get rid of identifying information
-ALTER TABLE stride_patient ADD COLUMN birth_year INTEGER;
-UPDATE stride_patient SET birth_year = YEAR(birth_date);
-ALTER TABLE stride_patient DROP COLUMN birth_date;
-ALTER TABLE stride_patient DROP COLUMN pat_mrn_id;
-
--- Populate oncology label based on patient_possible_onco.csv
-ALTER TABLE stride_patient ADD COLUMN possible_oncology INTEGER DEFAULT 0;
-
-
-
--- Referrals and Consults, includes patient location and login department (lots of inpatient data too)
---	Header: Lower-case, remove " marks
---	Look for rfl_priority is not null and ordering_mode = 'Outpatient' to isolate relevant referrals
-CREATE TABLE stride_order_proc_referrals_n_consults
-(
-	order_proc_id bigint,
-	pat_id text,
-	pat_enc_csn_id bigint,
-	ordering_date datetime,
-	proc_id bigint,
-	proc_code text,
-	description text,
-	quantity integer,
-	order_status_c integer,
-	order_status text,
-	reason_for_cancellation text,
-	future_or_stand text,
-	standing_exp_date datetime,
-	future_expected_compltn_date datetime,
-	standing_occurs integer,
-	stand_orig_occur integer,
-	rfl_priority text,
-	hv_hospitalist_yn text,
-	prov_status text,
-	order_priority text,
-	stand_interval text,
-	instantiated_time datetime,
-	ordering_mode text,
-	is_pending_ord_yn text,
-	proc_start_time datetime,
-	proc_ending_time datetime,
-	id_of_replaced_order_proc_id bigint,
-	patient_location text,
-	login_department text,
-	requested_provider_specialty text,
-	reason_for_rferral text,
-	num_visits_authorized_by_rfl integer,
-	requested_department_specialty text
-);
-CREATE INDEX index_stride_order_proc_referrals_n_consults_order_proc_id ON stride_order_proc_referrals_n_consults(order_proc_id);
-CREATE INDEX index_stride_order_proc_referrals_n_consults_pat_id ON stride_order_proc_referrals_n_consults(pat_id (16));
-CREATE INDEX index_stride_order_proc_referrals_n_consults_pat_enc_csn_id ON stride_order_proc_referrals_n_consults(pat_enc_csn_id);
-CREATE INDEX index_stride_order_proc_referrals_n_consults_proc_id ON stride_order_proc_referrals_n_consults(proc_id);
-
-
--- Encounter dates, types, department.  Lots of inpatient data
---	Header: Lower-case and remove " marks
-CREATE TABLE stride_pat_enc
-(
-	pat_id text,
-	pat_enc_csn_id bigint,
-	contact_date datetime,
-	enc_type text,
-	department_name text,
-	appt_status text,
-	visit_type text,
-	cancel_reason text,
-	hosp_admdn_type text,
-	surgical_service text,
-	phone_reminder_status text,
-	appt_time datetime,
-	er_arrival_time datetime,
-	hosp_admsn_time datetime,
-	hosp_dischrg_time datetime,
-	adm_for_surg_yn text,
-	outgoing_call_yn text,
-	is_walk_in_yn text,
-	cancel_reason_cmt text,
-	admission_confirmation_status text,
-	discharge_confirmation_status text,
-	inpatient_data_id bigint
-);
-CREATE INDEX index_stride_pat_enc_pat_id ON stride_pat_enc(pat_id (16));
-CREATE INDEX index_stride_pat_enc_pat_enc_csn_id ON stride_pat_enc(pat_enc_csn_id);
-CREATE INDEX index_stride_pat_enc_contact_date ON stride_pat_enc(contact_date);
-CREATE INDEX index_stride_pat_enc_inpatient_data_id ON stride_pat_enc(inpatient_data_id);
-
--- Patient problem lists
---	Header: Lower-case and remove " marks
---	Remove comments with quote marks ("cartilage","gastritis",""Nonocclusive...","Stanford Psych...,"Depression/anxiety...) confounded with comma delimiters
-CREATE TABLE stride_problem_list
-(
-	problem_list_id bigint,
-	pat_id text,
-	dx_id bigint,
-	dx_name text,
-	dx_group text,
-	ref_bill_code text,
-	description text,
-	noted_date datetime,
-	resolved_date datetime,
-	problem_cmt text,
-	chronic_yn text,
-	principal_pl_yn text
-);
-CREATE INDEX index_stride_problem_list_problem_list_id ON stride_problem_list(problem_list_id);
-CREATE INDEX index_stride_problem_list_pat_id ON stride_problem_list(pat_id (16));
-CREATE INDEX index_stride_problem_list_dx_id ON stride_problem_list(dx_id);
-CREATE INDEX index_stride_problem_list_dx_name ON stride_problem_list(dx_name (16));
-CREATE INDEX index_stride_problem_list_noted_date ON stride_problem_list(noted_date);
-
-ALTER TABLE stride_problem_list ADD COLUMN base_bill_code TEXT;
-CREATE INDEX index_stride_problem_list_ref_bill_code ON stride_problem_list(ref_bill_code (16));
-CREATE INDEX index_stride_problem_list_base_bill_code ON stride_problem_list(base_bill_code (16));
-
-
--- pat_enc_dx.csv -- Encounter diagnoses (may be multiple per encounter)
--- pat_enc_profee_dx_px.csv -- Encounter procedures and linked diagnosis and department
--- pat_enc_dept.csv -- Encounter department (redundant with pat_enc)
-
-
--- Hospital account encounter data
--- pat_enc_hsp_acct_cpt_codes.csv
--- pat_enc_hsp_acct_dx.csv
--- pat_enc_hsp_acct_icd_px.csv
-
-
-
--- ICD9 mapping
-CREATE TABLE stride_icd9_cm
-(
-	cui	TEXT,
-	ispref TEXT,
-	aui	TEXT,
-	tty	TEXT,
-	code TEXT,
-	str	TEXT,
-	suppress	TEXT
-);
-CREATE INDEX index_stride_icd9_cm_code ON stride_icd9_cm(code (16));
+-- Mapping Table for Medications to Common Active Ingredients
+create table stride_mapped_meds
+(
+	medication_id integer,
+	medication_name text,
+	rxcui integer,
+	active_ingredient text
+);
+CREATE INDEX index_stride_mapped_meds_medication_id ON stride_mapped_meds(medication_id);
+ALTER TABLE stride_mapped_meds ADD COLUMN analysis_status INTEGER DEFAULT 1;
+ALTER TABLE stride_mapped_meds ADD COLUMN morphine_po_equivalent FLOAT;
+
+-- Medication Order Data Table.  Source headers need " removed, lower-case, and make sure refills column is numeric
+-- 	Remove " marks and lower-case header lines
+--	(Refills should be numeric 0 not char o.  Just swap "o" for "0")  (Entry 436981419... refills should be numeric, not "prn")
+create table stride_order_med
+(
+	order_med_id bigint not null,
+	pat_id text,
+	pat_enc_csn_id bigint,
+	ordering_datetime datetime,
+	hosp_admsn_time datetime,
+	hosp_dischrg_time datetime,
+	order_class text,
+	medication_id integer,
+	description text,
+	sig text,
+	quantity text,
+	refills integer,
+	start_date datetime,
+	end_date datetime,
+	dispense_as_written_yn text,
+	reason_for_discontinuation text,
+	enc_type_c integer,
+	encounter_type text,
+	patient_location text,
+	login_department text,
+	display_name text,
+	hv_hospitalist_yn text,
+	med_route text,
+	discontinue_time datetime,
+	changed_from_order_med_id bigint,
+	reason_pended_med_refused text,
+	hv_discr_freq_id bigint,
+	discr_freq_name text,
+	discr_freq_display_name text,
+	discr_freq_type text,
+	discr_freq_number_of_times integer,
+	freq_time_unit text,
+	prn_yn text,
+	freq_duplicate_dose_interval integer,
+	freq_missed_dose_interval integer,
+	freqency_or_period text,
+	hv_discrete_dose text,
+	hv_dose_unit text,
+	non_formulary_yn text,
+	order_status text,
+	min_discrete_dose float,
+	max_discrete_dose float,
+	dose_unit text,
+	is_pending_ord_yn text,
+	modify_track text,
+	med_comments text,
+	usr_select_medicn_id integer,
+	lastdose text,
+	amb_med_disp_name text,
+	refills_remaining integer,
+	rule_based_order_transmittl_yn text,
+	outpat_orderstat_before_admsn text,
+	last_dose_time text,
+	pend_approve_flag text,
+	prn_comment text,
+	med_dis_disp_qty integer,
+	med_dis_disp_unit text
+);
+ALTER TABLE stride_order_med ADD CONSTRAINT stride_order_med_pkey PRIMARY KEY (order_med_id);
+CREATE INDEX index_stride_order_med_pat_id ON stride_order_med(pat_id (16));
+CREATE INDEX index_stride_order_med_ordering_datetime ON stride_order_med(ordering_datetime);
+CREATE INDEX index_stride_order_med_pat_enc_csn_id ON stride_order_med(pat_enc_csn_id);
+CREATE INDEX index_stride_order_med_medication_id ON stride_order_med(medication_id);
+
+
+-- Drug Screen Results
+-- 	Header row: Remove " marks and lower-case header lines
+create table stride_order_proc_drug_screen
+(
+	order_proc_id bigint,
+	pat_id text,
+	pat_enc_csn_id bigint,
+	ordering_date datetime,
+	proc_id integer,
+	proc_code text,
+	description text,
+	quantity integer,
+	order_status_c text,
+	order_status text,
+	reason_for_cancellation text,
+	future_or_stand text,
+	standing_exp_date datetime,
+	future_expected_compltn_date datetime,
+	standing_occurs integer,
+	stand_orig_occur integer,
+	rfl_priority text,
+	order_priority text,
+	stand_interval text,
+	instantiated_time datetime,
+	ordering_mode text,
+	is_pending_ord_yn text,
+	proc_start_time datetime,
+	proc_end_time datetime,
+	id_of_replaced_order_proc_id bigint,
+	patient_location text,
+	login_department text,
+	specimen_taken_time datetime,
+	specimen_recv_time datetime,
+	result_line integer,
+	component_id integer,
+	component_name text,
+	ord_value text,
+	ref_normal_vals text
+);
+CREATE INDEX index_stride_order_proc_drug_screen_compkey ON stride_order_proc_drug_screen(order_proc_id, result_line);
+CREATE INDEX index_stride_order_proc_drug_screen_pat_id ON stride_order_proc_drug_screen(pat_id (16));
+CREATE INDEX index_stride_order_proc_drug_screen_pat_enc_csn_id ON stride_order_proc_drug_screen(pat_enc_csn_id);
+CREATE INDEX index_stride_order_proc_drug_screen_component_id ON stride_order_proc_drug_screen(component_id);
+
+-- Patient ID list with birth date, gender, race
+-- 	Header: Lower-case
+create table stride_patient
+(
+	pat_id text,
+	pat_mrn_id bigint,
+	birth_date datetime,
+	gender text,
+	primary_race text
+);
+CREATE INDEX index_patient_pat_id ON stride_patient(pat_id (16));
+CREATE INDEX index_patient_pat_mrn_id ON stride_patient(pat_mrn_id);
+-- Get rid of identifying information
+ALTER TABLE stride_patient ADD COLUMN birth_year INTEGER;
+UPDATE stride_patient SET birth_year = YEAR(birth_date);
+ALTER TABLE stride_patient DROP COLUMN birth_date;
+ALTER TABLE stride_patient DROP COLUMN pat_mrn_id;
+
+-- Populate oncology label based on patient_possible_onco.csv
+ALTER TABLE stride_patient ADD COLUMN possible_oncology INTEGER DEFAULT 0;
+
+
+
+-- Referrals and Consults, includes patient location and login department (lots of inpatient data too)
+--	Header: Lower-case, remove " marks
+--	Look for rfl_priority is not null and ordering_mode = 'Outpatient' to isolate relevant referrals
+CREATE TABLE stride_order_proc_referrals_n_consults
+(
+	order_proc_id bigint,
+	pat_id text,
+	pat_enc_csn_id bigint,
+	ordering_date datetime,
+	proc_id bigint,
+	proc_code text,
+	description text,
+	quantity integer,
+	order_status_c integer,
+	order_status text,
+	reason_for_cancellation text,
+	future_or_stand text,
+	standing_exp_date datetime,
+	future_expected_compltn_date datetime,
+	standing_occurs integer,
+	stand_orig_occur integer,
+	rfl_priority text,
+	hv_hospitalist_yn text,
+	prov_status text,
+	order_priority text,
+	stand_interval text,
+	instantiated_time datetime,
+	ordering_mode text,
+	is_pending_ord_yn text,
+	proc_start_time datetime,
+	proc_ending_time datetime,
+	id_of_replaced_order_proc_id bigint,
+	patient_location text,
+	login_department text,
+	requested_provider_specialty text,
+	reason_for_rferral text,
+	num_visits_authorized_by_rfl integer,
+	requested_department_specialty text
+);
+CREATE INDEX index_stride_order_proc_referrals_n_consults_order_proc_id ON stride_order_proc_referrals_n_consults(order_proc_id);
+CREATE INDEX index_stride_order_proc_referrals_n_consults_pat_id ON stride_order_proc_referrals_n_consults(pat_id (16));
+CREATE INDEX index_stride_order_proc_referrals_n_consults_pat_enc_csn_id ON stride_order_proc_referrals_n_consults(pat_enc_csn_id);
+CREATE INDEX index_stride_order_proc_referrals_n_consults_proc_id ON stride_order_proc_referrals_n_consults(proc_id);
+
+
+-- Encounter dates, types, department.  Lots of inpatient data
+--	Header: Lower-case and remove " marks
+CREATE TABLE stride_pat_enc
+(
+	pat_id text,
+	pat_enc_csn_id bigint,
+	contact_date datetime,
+	enc_type text,
+	department_name text,
+	appt_status text,
+	visit_type text,
+	cancel_reason text,
+	hosp_admdn_type text,
+	surgical_service text,
+	phone_reminder_status text,
+	appt_time datetime,
+	er_arrival_time datetime,
+	hosp_admsn_time datetime,
+	hosp_dischrg_time datetime,
+	adm_for_surg_yn text,
+	outgoing_call_yn text,
+	is_walk_in_yn text,
+	cancel_reason_cmt text,
+	admission_confirmation_status text,
+	discharge_confirmation_status text,
+	inpatient_data_id bigint
+);
+CREATE INDEX index_stride_pat_enc_pat_id ON stride_pat_enc(pat_id (16));
+CREATE INDEX index_stride_pat_enc_pat_enc_csn_id ON stride_pat_enc(pat_enc_csn_id);
+CREATE INDEX index_stride_pat_enc_contact_date ON stride_pat_enc(contact_date);
+CREATE INDEX index_stride_pat_enc_inpatient_data_id ON stride_pat_enc(inpatient_data_id);
+
+-- Patient problem lists
+--	Header: Lower-case and remove " marks
+--	Remove comments with quote marks ("cartilage","gastritis",""Nonocclusive...","Stanford Psych...,"Depression/anxiety...) confounded with comma delimiters
+CREATE TABLE stride_problem_list
+(
+	problem_list_id bigint,
+	pat_id text,
+	dx_id bigint,
+	dx_name text,
+	dx_group text,
+	ref_bill_code text,
+	description text,
+	noted_date datetime,
+	resolved_date datetime,
+	problem_cmt text,
+	chronic_yn text,
+	principal_pl_yn text
+);
+CREATE INDEX index_stride_problem_list_problem_list_id ON stride_problem_list(problem_list_id);
+CREATE INDEX index_stride_problem_list_pat_id ON stride_problem_list(pat_id (16));
+CREATE INDEX index_stride_problem_list_dx_id ON stride_problem_list(dx_id);
+CREATE INDEX index_stride_problem_list_dx_name ON stride_problem_list(dx_name (16));
+CREATE INDEX index_stride_problem_list_noted_date ON stride_problem_list(noted_date);
+
+ALTER TABLE stride_problem_list ADD COLUMN base_bill_code TEXT;
+CREATE INDEX index_stride_problem_list_ref_bill_code ON stride_problem_list(ref_bill_code (16));
+CREATE INDEX index_stride_problem_list_base_bill_code ON stride_problem_list(base_bill_code (16));
+
+
+-- pat_enc_dx.csv -- Encounter diagnoses (may be multiple per encounter)
+-- pat_enc_profee_dx_px.csv -- Encounter procedures and linked diagnosis and department
+-- pat_enc_dept.csv -- Encounter department (redundant with pat_enc)
+
+
+-- Hospital account encounter data
+-- pat_enc_hsp_acct_cpt_codes.csv
+-- pat_enc_hsp_acct_dx.csv
+-- pat_enc_hsp_acct_icd_px.csv
+
+
+
+-- ICD9 mapping
+CREATE TABLE stride_icd9_cm
+(
+	cui	TEXT,
+	ispref TEXT,
+	aui	TEXT,
+	tty	TEXT,
+	code TEXT,
+	str	TEXT,
+	suppress	TEXT
+);
+CREATE INDEX index_stride_icd9_cm_code ON stride_icd9_cm(code (16));
diff --git a/medinfo/db/definition/simdata/dumpSimTables.bat b/medinfo/db/definition/simdata/dumpSimTables.bat
deleted file mode 100644
index 7eb07a1d..00000000
--- a/medinfo/db/definition/simdata/dumpSimTables.bat
+++ /dev/null
@@ -1,21 +0,0 @@
-rem For backup purposes, core adapted tables (original raw tables from STRIDE separated)
-
-set DB_HOST=localhost
-set DB_PORT=5432
-set DB_DSN=medinfo-5year-time
-set DB_USER=jonc101
-
-rem Initialization data on general patient simulation models, deposit into medinfo/db/definition/simdata
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_note_type -f sim_note_type.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_note -f sim_note.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_result -f sim_result.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_state_result -f sim_state_result.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_order_result_map -f sim_order_result_map.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_state_transition -f sim_state_transition.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_state -f sim_state.dump.sql %DB_DSN%
-
-rem Specific user records
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_patient -f sim_patient.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_patient_state -f sim_patient_state.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_user -f sim_user.dump.sql %DB_DSN%
-pg_dump -i -h %DB_HOST% -p %DB_PORT% -U %DB_USER% -a -F p -x -O -t sim_patient_order -f sim_patient_order.dump.sql %DB_DSN%
diff --git a/medinfo/db/definition/simdata/restoreSimTables.bat b/medinfo/db/definition/simdata/restoreSimTables.bat
deleted file mode 100644
index e94a640f..00000000
--- a/medinfo/db/definition/simdata/restoreSimTables.bat
+++ /dev/null
@@ -1,15 +0,0 @@
-set DB_HOST=127.0.0.1
-set DB_DSN=medinfo-5year-time
-set DB_USER=jonc101
-
-rem psql -f sim_state.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_note_type.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_note.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_result.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_order_result_map.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_state_result.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_state_transition.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-rem psql -f sim_user.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-psql -f sim_patient.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-psql -f sim_patient_order.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
-psql -f sim_patient_state.dump.sql -h %DB_HOST% -U %DB_USER% %DB_DSN%
diff --git a/medinfo/db/definition/simdata/sim_note.dump.sql b/medinfo/db/definition/simdata/sim_note.dump.sql
deleted file mode 100644
index 5f3d9c14..00000000
--- a/medinfo/db/definition/simdata/sim_note.dump.sql
+++ /dev/null
@@ -1,47 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_note; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_note (sim_note_id, note_type_id, author_type_id, service_type_id, relative_state_time, content, sim_state_id) FROM stdin;
-1	1	2001	1500	0	  Mr. "B" is a 72 yo man with h/o CHF and CAD, who presented with increasing lower\nextremity edema and weight gain.\n<p> <b>HPI: </b> Mr. "B" has a long history of CHF subsequent to multiple MI's\nlast in 1991. Cardiac cath at that time revealed occlusions in LAD, OMB, and\ncirc with EF of 50%%. ECHO in 1996 showed a dilated LV, EF of 20-25%%, diffuse\nregional wall motion abnormalities, 2+MR and trace TR. His CHF has been managed\nmedically with captopril, lasix, metolazone, and digoxin. Over the past 6mos\nhe has required increasing doses of lasix to control his edema. He was seen\n2 wks ago by his Cardiologist, at which time he was noted to have leg, scrotal\nand penile edema. His lasix dose was increaed to 120 bid without relief of his\nswelling.\n<p>\n\nOver the past week he and his wife have noticed an increase in his LE edema which then became markedly worse in the past two days.  The swelling was accompanied by a weight gain of 10lb in 2 days (175 to 185lb) as well as a decrease in his exercise tolerance.  He now becomes dyspneic when rising to get out of bed and has to rest due to SOB when walking on flat ground.  He has 2 pillow orthopnea, denies PND.  His chronic cough has worsened and is now productive of "transparent" sputum with no hemoptysis.  He has occ audible wheeze.  Denies CP/pressure/palpitations/diaphoresis.  Occ nausea/no vomiting.  He eats limited quantities but does not salt or fluid restrict--eating canned soup and drinking 6-8 glasses liquid/day.  He has increased urinary freq. but decreased amount.  He states he has been taking all prescribed medications.<p>\n\n<table  width="90%%" border="0" cellspacing="0" cellpadding="5">\n\t<tr>\n\t\t<!-- Row 1 Column 1 -->\n\t\t<td valign = top>\n\t\t\tPMH:\n\t\t</td>\n\t\t<!-- Row 1 Column 2 -->\n\t\t<td >\n\t\t\tCHF: as above<br>\n\t\t\tMI<br>\n\t\t\tAfib: on coumadin<br>\n\t\t\tPacemaker placed in 3/93 for afib/flutter and slow ventricular response<br>\n\t\t\tHTN<br>\n\t\t\tChronic renal insufficiency:  BUN/Cr stable on 1/21/98, 52/1.4<br>\n\t\t\tDM: controlled with glyburide.  Admitted for hypoglycemia in 9/97.\n\t\t</td>\n\t</tr><p>\n\t\n\t<tr>\n\t\t<!-- Row 2 Column 1 -->\n\t\t<td valign = top>\n\t\t\tPSH:\n\t\t</td>\n\t\t<!-- Row 2 Column 2 -->\n\t\t<td >\n\t\t\tTonsillectomy\n\t\t</td>\n\t</tr><p>\n\n\t<tr>\n\t\t<!-- Row 3 Column 1 -->\n\t\t<td valign = top>\n\t\t\tMED:\n\t\t</td>\n\t\t<!-- Row 3 Column 2 -->\n\t\t<td >\n\t\t\tLasix 120 mg BID<br>\n\t\t\tMetolazone 5 mg gd<br>\n\t\t\tCaptopril 50 mg TID<br>\n\t\t\tDigoxin 0.125 mg qd<br>\n\t\t\tKCl<br>\n\t\t\tcoumadin 4mg qd<br>\n\t\t\tGlyburide 2.5 mg BID<br>\n\t\t\tColace 100 mg BID\n\t\t</td>\n\t</tr><p>\n\n\t<tr>\n\t\t<!-- Row 4 Column 1 -->\n\t\t\n<td > ALLERGIES: </td>\n\t\t<!-- Row 4 Column 2 -->\n\t\t\n<td > No Known Drug Allergies</td>\n\t</tr><p>\n\n\t\t<tr>\n\t<td>SMOKING\n\t\n\t</td>\n\t<td>None</td>\n\t</tr>\n\t<tr>\n\t<td>ALCOHOL\n\t\n\t</td>\n\t<td>None</td>\n\t</tr>\n\t<tr>\n\t<td>OTHER SUBSTANCE USE\n\t\n\t</td>\n\t<td>None</td>\n\t</tr><tr>\n\t\t<!-- Row 5 Column 1 -->\n\t\t\n<td valign = top> SOCIAL HISTORY: </td>\n\t\t<!-- Row 5 Column 2 -->\n\t\t\n<td > Married for 45 years, sexual active with wife. Three children, 2 grandchildren,\nall healthy and well; all live within 50 miles. Retired school teacher.\nEnjoys model car building. Walks around home, shopping but otherwise not\nphysically active.</td>\n\t</tr>\n\t<tr>\n\t\t<!-- Row 6 Column 1 -->\n\t\t\n<td valign = top> FAMILY HISTORY </td>\n\t\t<!-- Row 6 Column 2 -->\n\t\t\n<td > + sister and mother with DM, father with CAD, age onset 50. Brother\nwith leukemia.</td>\n\t</tr><p>\n\n\t<tr>\n\t\t<!-- Row 6 Column 1 -->\n\t\t<td valign = top>\n\t\t\tROS\n\t\t</td>\n\t\t<!-- Row 6 Column 2 -->\n\t\t<td >\n\t\t\tIf written, would be present here.\n\t\t</td>\n\t</tr><p>\n\n\t<tr>\n\t\t<!-- Row 7 Column 1 -->\n\t\t<td valign = top>\n\t\t\tPE:\n\t\t</td>\n\t\t<!-- Row 7 Column 2 -->\n\t\t<td >\n\t\t\tVS: T 97.1, P65, BP 116/66, O2Sat 98%% on 2L NC<br>\n\t\t\tGEN: elderly man lying in bed with head up, NAD<br>\n\t\t\tHEENT: NCAT, multiple telangiectasias on face and nose, EOMi, PERRL, OP-benign<br>\n\t\t\tNECK: thyroid not palpable, no LAD, carotic pulse 2+B, no bruits, no JVD<br>\n\t\t\tRESP: +dullness to perc at right base, +ant wheezes, +crackles 1/2 way up chest bilat.<br>\n\t\t\tCOR: rrr, +2/6 holosystolic murmur at apex radiating to axilla, no gallops<br>\n\t\t\tABD: +BS, distended, nontender, no HSM, liver percussed to 9cm at MCL<br>\n\t\t\tPULSES: 2+femoral B, 1+ PT/DP B<br>\n\t\t\tEXT: 3+ edema to lower back, abdomen including genitals, hyperemia over ant., legs bilat,\n\t\t\t\twarm, non-tender; non clubbing, cyanosis<br>\n\t\t\tSKIN: 4 cm ulcer on R buttock with central scabbing, non-tender, no discharge<br>\n\t\t\tNEURO: AOX3; difficulty remembering events, dates; remainder of exam nonfocal\n\t\t</td>\n\t</tr><p>\n\n\t<tr>\n\t\t<!-- Row 8 Column 1 -->\n\t\t<td valign = top>\n\t\t\tLABS/<br>\n\t\t\tDATA:\n\t\t</td>\n\t\t<!-- Row 8 Column 2 -->\n\t\t<td valign = top>\n\t\t\tNa 138, C1 96, BUn 59, Glu 92, K 4.4, CO2 40.8, CR 1.4, WBC 7.9, PLT 349, HCT 43.9, pulses P73 L16 E3 B0<br>Alk phos 72, Tot prot 5.6, Alb 2.5 T Bili 0.5, AlT 17, AST 52, LDH 275, CPK 229<p>CXR: mildly prominent vessels.  Minimal interstitial congestion.  Cardiomegaly, no infiltrates.<p>\nECHO 1/27:  1. LV mild dilated (ED=6.0 cm) severaly depressed global systolic function with EF 20-25%%.  Extensive area thinning and akinesis: anterior, anteroseptal, anterolateral c/w old infarct <br>\n2. Mod 2-3/4 MR. LA size nl<br>\n3. No AS/AI<br>\n4. RV dilated with preserved fxn. 2-3/4 TR. PA pressure 36+ RA pressure.\n</td>\n\t</tr><p>\n\n\t<tr>\n\t\t<!-- Row 9 Column 1 -->\n\t\t<td valign = top>ASSESSMENT/<br>\n\t\t\t\tPLAN\n\t\t</td>\n\t\t<!-- Row 9 Column 2 -->\n\t\t<td >\n\t\t\t72 year old man with h/o CHF following MI, chronic renal insufficiency and venous stasis admitted with worsening edema and DOE.  His symptoms are most consistent with incrasing CHF-biventricular-which would account for both his pulmonary congestion as well as his peripheral edema.  His renal disease is a less likely explanation for his extensive edema as his BUN/Cr have remained stable throughout.  However, his low albumin which could contribute to his edema may be due to renal losses.<p>\n\nSo if his edema is due to CHF, why has it become gradually and now acutely worse?  Possibilities include: 1) worsening LV function, 2) another MI, 3) worsening valvular disease, 4) poor compliance with medications or 5) excess salt and water intake.  His ECHO today shows no change in his EF, but there is marked wall motion abnormalities with akinesis.  There is no evidence in his history, EKG, or enzymes for current ischemia/infarct.  He does have MR and TR and his valvular disease may in part account for his worsening symptoms though his estimated PA pressure is unchanged and his LA is not dilated.  The most likely precipitant of his failure is a combination of poor compliance with medication and fluid overload from excessive intake.  We will continue to investigate the possibility of a structural precipitant for his deterioration and treat his current symptoms.<p>\n\n<ol type="1">\n<li>Pulm: his wheezing, crackles, and oxygen requirement are all likely\ndue to pulmonary congestion from LV dysfunction. He has no signs, symptoms\nof pulm infection.\n<ul>\n<LI>02 to maintain sat greater than 95%%\n<LI>treat cardiac disease as below\n</ul>\n<p>\n<li>Cardiac: As above his picture is consistent with CHF with no clear\nprecipitant. Will continue to evaluate structural disease as precipitating\nfactor and treat fluid overload.\n<ul>\n<li>Strict I/O's. Daily weights\n<li>Fluid restriction to 1.5L\n<li>Low salt diet\n<li>Lasix 80mg IV with IV Metolatzone now and Q8.. With goal diuresis\nof 2-3 L/day\n<li>Increase digoxin to 0.25mg qd\n<li>Continue captropril 50mg TID\n<li>Check electrolytes, renal fxn and digoxin level in am\n<li>Education about appropriate diet\n<li>Repeat Echo and compare with old film\n<li>Consider Cardiology consult if fails to improve, needs invasive\nhemodynamic monitoring or cath\n</ul>\n<p>\n<li>GI\n<ul>\n<li>Continue colace\n</ul>\n<p>\n<li>Renal: We will continue to evaluate whether he could be losing protein\nfrom his kidney leading to his increasing edema.\n<ul>\n<li>Check urine prot/cr ratio\n<li>UA\n</ul>\n<p>\n<li>DM: His sugars have been well-controlled on current regimen\n<ul>\n<li>Continue glyburide\n<li>ADA 2100 calorie diet\n<li>FS BS qac and qhs\n</ul>\n</ol>\n<p>	1
-8	3	2001	1003	0	<h3>CC:</h3>\r\nChest pain\r\n\r\n<h3>HPI:</h3>\r\n<p>62M with hx HTN, HLD, T2DM, smoker.\r\n\r\n<p>Flew back from weeklong vacation trip in Hawaii yesterday.\r\nAt outdoor festival 2 hours ago, climbing up hill when complained to wife of onset of feeling winded and chest pain. Sat down in shade and drank water with some relief of symptoms over several minutes. 45 minutes later, continued to feel low-grade chest pain and pressure, so family brought in to emergency room for evaluation.\r\n\r\n<h4>Review of Systems</h4>\r\nOtherwise negative except as per HPI, including:\r\nDenies fever, chills, cough, palpitations, abdominal pain, nausea, vomiting, leg pain or edema.\r\n\r\n<h3>Medical History</h3>\r\n<ul>\r\n<li>Hypertension\r\n<li>Hyperlipidemia\r\n<li>Type 2 Diabetes\r\n<li>Tobacco Use\r\n</ul>\r\n\r\n<h3>Surgical History</h3>\r\n<ul>\r\n<li>Appendectomy at 28 years old\r\n</ul>\r\n\r\n<h3>Allergies:</h3>\r\nNKDA\r\n\r\n<h3>Medications:</h3>\r\n<ul>\r\n<li>Hydrochlorothiazide 25mg daily\r\n<li>Lisinopril 5mg daily\r\n<li>Metformin 1000mg bid\r\n</ul>\r\n\r\n<h3>Family/Social Hx:</h3>\r\nMother died of stroke at 85 years old. Father still alive at 89 years old with prostate cancer.<br>\r\nMarried with two children. Vietnam veteran. Works as building contractor.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 98.8F, BP: 145/84, HR: 103, RR: 19, O2: 92%\r\n<ul>\r\n<li>General: <i>Uncomfortable, sweating</i>, but no acute distress, sitting up in bed\r\n<li>Neck: Supple, no lymphdenopathy, JVP ~6-8cm\r\n<li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales. <i>Tachypneic</i>.\r\n<li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops. Chest wall non-tender to palpation.\r\n<li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds. No organomegaly or masses noted\r\n<li>Extremities: No clubbing, cyanosis, edema. 2+ DP pulses.\r\n<li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.\r\n<li>Skin: No rashes or lesions noted.\r\n</ul>\r\n	3
-28	2	2001	1500	0	Patient feels fatigued and with abdominal discomfort.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 98.5F, BP: 111/74, HR: 102, RR: 26, O2: 96%\r\n<ul>\r\n<li>General: Tired\r\n<li>Lungs: Deep sighing respirations\r\n<li>Cardiac: Rapid but Regular\r\n<li>Abdomen: Soft, non-distended. Generalized tenderness without guarding or rebound\r\n<li>Extremities: 2+ DP pulses.\r\n<li>Neuro: Fatigued, but alert and oriented. Grossly non-focal. Moving all extremities.\r\n<li>Skin: No rashes or lesions noted.\r\n</ul>\r\n	18
-29	2	2001	1500	0	Patient feeling much improved compared to previous\r\n\r\n<h3>Physical Exam</h3>\r\nT: 98.8F, BP: 123/78, HR: 82, RR: 16, O2: 97%\r\n<ul>\r\n<li>General: Resting comfortably\r\n<li>Lungs: Normal respiratory effort, CTAB\r\n<li>Cardiac: Regular Rate and Rhythm\r\n<li>Abdomen: Soft, non-distended, no focal tenderness\r\n<li>Extremities: 2+ DP pulses.\r\n<li>Neuro: Alert and oriented. Grossly non-focal. Moving all extremities.\r\n<li>Skin: No rashes or lesions noted.\r\n</ul>\r\n	19
-30	2	2001	1500	0	Patient feeling restless, uncomfortable, trembling, difficulty concentrating\r\n\r\n<h3>Physical Exam</h3>\r\nT: 97.8F, BP: 95/57, HR: 137, RR: 31, O2: 95%\r\n<ul>\r\n<li>General: Trembling, difficult to arouse\r\n<li>HEENT: PERRL\r\n<li>Lungs: Deep sighing respirations. CTAB\r\n<li>Cardiac: Rapid but Regular Rhythm\r\n<li>Abdomen: Soft, non-distended, generalized tenderness to palpation without rebound or guarding\r\n<li>Extremities: Thready DP pulses.\r\n<li>Neuro: Drowsy and difficult to arouse. Trembling extremities\r\n<li>Skin: Clammy\r\n</ul>\r\n	20
-10	3	2001	1003	0	<h3>CC:</h3>\r\nVomiting\r\n\r\n<h3>HPI:</h3>\r\n<p>57M hx HTN, smoking, and knee arthritis.\r\nPatient from South Korea, here for travel, hiking across multiple tourist destinations over the past week.\r\nHas some chronic R knee arthritis pain with flare in past week with increasing walking. \r\nTaking Motrin 600mg about three times per day since to manage pain.\r\nFeeling a little lightheaded this morning, ate a cheese burrito.\r\nVomited black, gritty material two times later in the morning and continuing to feel lightheaded. Mild abdominal pain and shortness of breath.\r\n\r\n<h3>ROS:</h3>\r\nOtherwise negative except as per HPI, including;\r\nDenies fever, chills, chest pain.\r\nDenies diarrhea, constipation or otherwise abnormal stools, normal in consistency as of last night.\r\n\r\n\r\n<h3>Medical History</h3>\r\n<ul>\r\n<li>Hypertension\r\n<li>Tobacco Smoking\r\n<li>Osteoarthritis, Knee\r\n</ul>\r\n\r\n<h3>Allergies:</h3>\r\nNKDA\r\n\r\n<h3>Medications:</h3>\r\n<ul>\r\n<li>Hydrochlorothiazide\r\n<li>Ibuprofen PRN\r\n</ul>\r\n\r\n<h3>Family/Social Hx:</h3>\r\nMother died of gastric cancer in her 60s.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 97.9F, BP: 101/62, HR: 105, RR: 19, O2: 96%\r\n<ul>\r\n<li>General: <i>Pale, fatigued</i>\r\n<li>HEENT: <i>Conjunctival pallor.</i> EOMI, PERRL, no mucositis or oral lesions noted. <i>Black stains around lips.</i>\r\n<li>Neck: Supple, JVP flat\r\n<li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.\r\n<li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops. \r\n<li>Abdomen: Soft, non-distended. Hyper-active bowel sounds. Mild epigastric tenderness.\r\n<li>Rectal: <i>Guaic positive black stool in rectal vault</i>\r\n<li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.\r\n<li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.\r\n<li>Skin: Pale No rashes or lesions noted.\r\n</ul>\r\n	7
-11	3	2001	1003	36000	<h3>CC:</h3>\r\nFatigue, abdominal pain\r\n\r\n<h3>HPI:</h3>\r\n<p>26F G1P1 hx T1DM, HLD.\r\nBrought into ER in middle of night as husband noting several hours of progressively worsening abdominal pain and fatigue, with husband having difficulty keeping patient awake.\r\nPatient's 2 year old child in daycare with diarrhea last week.\r\nPast few days patient with minimal diarrhea, but nausea and poor appetite. Patient self-titrated glargine insulin down to half dose and has been holding prandial insulin doses while feeling ill.\r\n\r\n<h3>ROS:</h3>\r\nOtherwise negative except as per HPI, including;\r\nDenies fever, chills, chest pain.\r\nDenies diarrhea, constipation or otherwise abnormal stools, normal in consistency as of last night.\r\n\r\n\r\n<h3>Medical History</h3>\r\n<ul>\r\n<li>Type 1 Diabetes since teenager\r\n<li>Hyperlipidemia\r\n</ul>\r\n\r\n<h3>Allergies:</h3>\r\nNKDA\r\n\r\n<h3>Medications:</h3>\r\n<ul>\r\n<li>Insulin glargine qhs\r\n<li>Insulin lispro AC & qhs\r\n<li>Atorvastatin\r\n</ul>\r\n\r\n<h3>Family/Social Hx:</h3>\r\nMother with hypothyroidism from Hashimoto's thyroiditis in 30s.\r\n	9
-15	3	2001	1003	0	<h3>CC:</h3>\r\nFever and chills\r\n\r\n<h3>HPI:</h3>\r\n<p>32M recent dx DLBCLymphoma and underwent first cycle of R-CHOP chemotherapy 10 days ago.\r\n<p>Patient seen at infusion center today for labs and PICC line dressing change.\r\n<p>Patient reporting new-onset high fever, chills, and rigor, for past few hours but no other focal symptoms.\r\nLabs in clinic today noted WBC 0.8, Hgb 11,  Plt 96 with an absolute neutrophil count of 80.\r\nPatient direct admitted to the hospital for febrile neutropenia.\r\n\r\n\r\n<h3>Medical History</h3>\r\n<ul>\r\n<li>Diffuse Large B-Cell Lymphoma\r\n</ul>\r\n\r\n<h3>Allergies:</h3>\r\nNKDA\r\n\r\n<h3>Medications:</h3>\r\n<ul>\r\n<li>Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, Prednisone\r\n</ul>\r\n\r\n<h3>Family/Social Hx:</h3>\r\nNo known family history of heme malignancy.\r\nMother and father alive in 60s with serious medical isues. Two siblings, aged 37 and 28.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 102F, BP: 90/43, HR: 128, RR: 24, O2: 98%\r\n<ul>\r\n<li>General: Shivering, wrapped in blankets in bed\r\n<li>HEENT: EOMI, PERRL, no mucositis or oral lesions noted\r\n<li>Neck: Supple, <i>shotty lymphdenopathy</i>, JVP ~6cm\r\n<li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.\r\n<li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops. \r\n<li>Abdomen: Soft, non-tender, non-distended. Normo-active bowel sounds. No organomegaly or masses noted\r\n<li>Rectal: No perirectal abscess or lesions\r\n<li>Extremities: No clubbing, cyanosis, edema. <i>Cool feet</i>. 2+ DP pulses.\r\n<li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination.\r\n<li>Skin: No rashes or lesions noted.\r\n<li>Lines: <i>R arm PICC line</i> without surrounding erythema, drainage, or tenderness\r\n</ul>	6
-16	3	2001	1003	7200	<h3>CC:</h3>\r\nVomiting blood\r\n\r\n<h3>HPI:</h3>\r\n<p>59M hx HTN, osteoarthritis.\r\nPatient here for travel from South Korea, hiking across multiple vineyards over the past week.\r\nHas some chronic R knee arthritis pain with flare in past week with increasing walking. \r\nTaking Motrin and drinking wine multiple times per day since to manage pain.\r\nFeeling a little lightheaded this morning, ate a cheese burrito.\r\nVomited black, gritty material shortly thereafter, and then a half cup of red blood.\r\nPatient continues to feel lightheaded, with mild abdominal pain and shortness of breath.\r\n\r\n<h3>ROS:</h3>\r\nOtherwise negative except as per HPI, including;\r\nDenies fever, chills, chest pain.\r\nDenies diarrhea, constipation or otherwise abnormal stools, normal in consistency as of last night.\r\n\r\n\r\n<h3>Medical History</h3>\r\n<ul>\r\n<li>Hypertension\r\n<li>Osteoarthritis, Knee\r\n</ul>\r\n\r\n<h3>Allergies:</h3>\r\nNKDA\r\n\r\n<h3>Medications:</h3>\r\n<ul>\r\n<li>Hydrochlorothiazide\r\n<li>Ibuprofen PRN\r\n</ul>\r\n\r\n<h3>Family/Social Hx:</h3>\r\nMother died of gastric cancer in her 60s.\r\n20 pack year tobacco smoker.\r\nDrinks up to a bottle of soju daily.\r\n	13
-20	2	2001	1500	0	Patient drifting off in alertness, difficult to arouse.\r\nContinues to spit up red blood.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 97.2F, BP: 85/54, HR: 127, RR: 29, O2: 95%\r\n<ul>\r\n<li>General: <i>Ill appearing, very pale</i>\r\n<li>HEENT: Conjunctival pallor. Scleral icterus. PERRL\r\n<li>Lungs: <i>Rapid, shallow breaths.</i> Clear to auscultation bilaterally\r\n<li>Cardiac: <i>Very Rapid</i>\r\n<li>Abdomen: Soft. Moderately-distended. Hyper-active bowel sounds.\r\n<li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.\r\n<li>Neuro: <i>Drifting off, difficult to arouse</i>\r\n<li>Skin: Pale. Scattered spider angiomata.\r\n</ul>\r\n	15
-21	2	2001	1500	0	Patient stopped spitting up blood. \r\n\r\n<h3>Physical Exam</h3>\r\nT: 98.1F, BP: 108/61, HR: 97, RR: 18, O2: 97%\r\n<ul>\r\n<li>General: Pale, fatigued.\r\n<li>HEENT: Conjunctival pallor. Scleral icterus.\r\n<li>Lungs: Clear to auscultation bilaterally. \r\n<li>Cardiac: Rapid but Regular\r\n<li>Abdomen: Soft. Moderately-distended. Hyper-active bowel sounds. Mild RUQ and epigastric tenderness.\r\n<li>Extremities: 1+ DP pulses.\r\n<li>Neuro: Alert and oriented. Grossly non-focal. Conversant. Mild asterixis.\r\n<li>Skin: Pale. Scattered spider angiomata.\r\n</ul>	16
-23	2	2001	1002	0	Patient suffered a pulseless arrest.\r\nCode blue was called and the ICU team has taken over management as they attempt cardiopulmonary resuscitation.	4
-17	2	2001	1500	0	Patient feels lightheaded, with mild abdominal pain and shortness of breath. \r\nOccasionally spitting up some red blood.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 97.9F, BP: 101/62, HR: 105, RR: 19, O2: 96%\r\n<ul>\r\n<li>General: <i>Pale, fatigued. Occasionaly spitting up some red blood</i>\r\n<li>HEENT: <i>Conjunctival pallor. Scleral icterus.</i> EOMI, PERRL, no mucositis or oral lesions noted. \r\n<li>Neck: Supple, JVP flat\r\n<li>Lungs: Clear to auscultation bilaterally. No wheezes/rhonchi/rales.\r\n<li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops. \r\n<li>Abdomen: Soft. <i>Moderately-distended. Hyper-active bowel sounds. Mild RUQ and epigastric tenderness.</i>\r\n<li>Rectal: <i>Guaic positive black stool in rectal vault</i>\r\n<li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.\r\n<li>Neuro: Alert and oriented. Grossly non-focal. Conversant, moving all extremities. No overt cerebellar signs / incoordination. <i>Mild asterixis.</i>\r\n<li>Skin: <i>Pale. Scattered spider angiomata.</i>\r\n</ul>\r\n	14
-27	2	2001	1500	0	Patient feels exhausted and with abdominal discomfort.\r\n\r\n<h3>Physical Exam</h3>\r\nT: 98.4F, BP: 105/72, HR: 117, RR: 29, O2: 96%, BMI: 36\r\n<ul>\r\n<li>General: <i>Lethargic but arousable</i>\r\n<li>HEENT: EOMI, PERRL, no mucositis or oral lesions noted. <i>Sweet odor on breath</i>\r\n<li>Neck: Supple, JVP flat\r\n<li>Lungs: <i>Deep sighing respirations.</i> Clear to auscultation bilaterally. No wheezes/rhonchi/rales.\r\n<li>Cardiac: <i>Rapid</i> but Regular rate and rhythm. No murmurs, rubs or gallops. \r\n<li>Abdomen: Soft, non-distended. <i>Generalized tenderness without guarding or rebound.</i> Normo-active bowel sounds. <li>Extremities: No clubbing, cyanosis, edema. 1+ DP pulses.\r\n<li>Neuro: Drowsy but arousable. Grossly non-focal. Moving all extremities. No overt cerebellar signs / incoordination.\r\n<li>Skin: No rashes or lesions noted.\r\n</ul>	21
-22	8	2001	1503	0	GI Consult advises you stabilize the patient's hemodynamics and coagulopathy before pursuing endoscopic intervention.	17
-24	8	2001	1503	0	GI performs an upper GI endoscopy, finding multiple esophageal varices. The largest bleeding varix is banded.\r\nThis has stabilized the patient's acute bleed. The GI team advises you to continue your medical management for the patient.\r\n	2
-\.
-
-
---
--- Name: sim_note_sim_note_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_note_sim_note_id_seq', 30, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_patient.dump.sql b/medinfo/db/definition/simdata/sim_patient.dump.sql
deleted file mode 100644
index 3dc3970f..00000000
--- a/medinfo/db/definition/simdata/sim_patient.dump.sql
+++ /dev/null
@@ -1,69 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_patient; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_patient (sim_patient_id, age_years, gender, name) FROM stdin;
-9	62	Male	(Template) Chest Pain
-6	32	Male	(Template) DLBCL Fever
-13	62	Male	(User1) Chest Pain
-14	32	Male	(User1) DLBCL Fever
-15	57	Male	(User1) Vomiting
-16	26	Female	(User1) Diabetic fatigue
-2	37	Male	XXX
-1	45	Female	XXX
-3	75	Male	XXX
-4	51	Male	XXX
-11	45	Female	XXX
-12	62	Male	XXX/HalfUsed
-7	57	Male	(Template) Black vomit
-17	26	Female	(User2) Diabetic fatigue
-18	57	Male	(User2) Black vomit
-19	62	Male	(User2) Chest Pain
-20	32	Male	(User2) DLBCL Fever
-5	59	Male	(Template) Hematemesis, Alcohol
-21	59	Male	TestStates
-23	59	Male	(User3) Hematemesis, Alcohol
-24	62	Male	(User3) Chest Pain
-25	32	Male	(User3) DLBCL Fever
-26	26	Female	(Test) Diabetc
-22	26	Female	(Template) Diabetic fatigue
-8	26	Female	(User3) Diabetic fatigue
-27	59	Male	TestFFP
-28	26	Female	(User4) Diabetic Fatigue
-29	62	Male	(User4) Chest Pain
-30	32	Male	(User4) DLBCL Fever
-31	59	Male	(User4) Hematemesis, Alcohol
-32	62	Male	(User5) Chest Pain
-33	26	Female	(User5) Diabetic Fatigue
-34	32	Male	(User5) DLBCL Fever
-35	59	Male	(User5) Hematemesis, Alcohol
-36	26	Female	(User6) Diabetic fatigue
-37	62	Male	(User6) Chest Pain
-38	32	Male	(User6) DLBCL Fever
-39	59	Male	(User6) Hematemesis, Alcohol
-\.
-
-
---
--- Name: sim_patient_sim_patient_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_patient_sim_patient_id_seq', 39, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_patient_order.dump.sql b/medinfo/db/definition/simdata/sim_patient_order.dump.sql
deleted file mode 100644
index c68cb178..00000000
--- a/medinfo/db/definition/simdata/sim_patient_order.dump.sql
+++ /dev/null
@@ -1,542 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_patient_order; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_patient_order (sim_patient_order_id, sim_user_id, sim_patient_id, clinical_item_id, relative_time_start, relative_time_end, sim_state_id) FROM stdin;
-35	1	1	44872	0	0	0
-36	1	1	44008	0	0	0
-33	1	1	61842	0	0	0
-34	1	1	48711	0	0	0
-37	1	1	61842	0	0	0
-38	1	1	48711	0	0	0
-39	1	1	44008	0	0	0
-40	1	1	45813	0	0	0
-41	1	1	45813	0	0	0
-43	1	1	48711	0	0	0
-44	1	1	44008	0	0	0
-42	1	1	61842	0	0	0
-45	1	1	61842	0	0	0
-46	1	1	48711	0	0	0
-47	1	1	44008	0	0	0
-49	1	1	63729	0	0	0
-50	1	1	48711	0	0	0
-51	1	1	44008	0	0	0
-48	1	1	61842	0	0	0
-54	1	1	61842	0	0	0
-56	1	1	44008	0	0	0
-55	1	1	48711	0	0	0
-57	1	1	61842	0	0	0
-58	1	1	61842	0	\N	0
-59	1	1	48711	0	\N	0
-60	1	1	44008	0	\N	0
-61	1	1	45793	0	\N	0
-62	1	1	45771	0	\N	0
-66	1	1	62042	0	\N	2
-69	1	1	31239	0	\N	1
-125	9	13	44256	0	\N	3
-78	0	9	41839	0	\N	3
-126	9	13	62083	1800	\N	3
-127	0	14	45801	-1200	\N	6
-128	0	14	45763	-1200	\N	6
-129	0	14	45788	-1200	\N	6
-130	0	14	45955	-1200	\N	6
-131	0	14	45873	-1200	\N	6
-132	0	14	45821	-1200	\N	6
-86	1	12	45793	0	\N	3
-87	1	12	45763	0	\N	3
-88	1	12	45821	0	\N	3
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-91	1	12	45866	0	\N	3
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-95	1	12	44206	0	\N	3
-114	0	13	45763	-1200	\N	3
-115	0	13	45801	-1200	\N	3
-116	0	13	45866	-1200	\N	3
-117	0	13	45838	-1200	\N	3
-118	0	13	45788	-1200	\N	3
-119	0	13	45821	-1200	\N	3
-120	0	13	41839	0	\N	3
-121	9	13	63392	0	\N	3
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-124	9	13	45935	0	\N	3
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-134	9	14	44198	0	\N	6
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-143	9	14	45792	0	\N	6
-144	9	14	49228	0	\N	6
-145	0	15	45793	-1200	\N	7
-146	0	15	45771	-1200	\N	7
-147	0	15	45759	-1200	\N	7
-148	0	15	45821	-1200	\N	7
-149	0	15	41788	0	\N	7
-150	9	15	45969	0	\N	7
-151	9	15	49481	0	\N	7
-152	9	15	45945	0	\N	7
-93	1	12	45838	0	0	3
-92	1	12	45870	0	0	3
-153	9	15	45927	0	\N	7
-154	9	15	48954	0	\N	7
-155	9	15	63165	0	\N	7
-156	9	15	44219	0	\N	7
-157	9	15	45811	0	\N	7
-158	9	15	45948	0	\N	7
-159	9	15	62866	0	\N	7
-160	0	16	45955	-1200	\N	9
-161	0	16	45771	-1200	\N	9
-162	0	16	45788	-1200	\N	9
-163	0	16	45821	-1200	\N	9
-164	0	16	45873	-1200	\N	9
-165	0	16	42232	0	\N	9
-166	9	16	45763	0	\N	9
-167	9	16	45806	0	\N	9
-168	9	16	44198	0	\N	9
-169	9	16	35850	0	\N	9
-170	9	16	51920	0	\N	9
-171	9	16	45811	0	\N	9
-172	9	16	45771	0	\N	9
-173	9	16	63927	0	\N	9
-174	9	16	45751	0	\N	9
-175	9	16	50850	1200	\N	9
-245	1	12	45818	0	\N	3
-178	0	6	41964	0	\N	6
-246	1	12	45818	0	\N	3
-247	1	12	45818	0	\N	3
-180	0	7	41788	0	\N	7
-248	1	12	45818	0	\N	3
-249	1	12	45818	0	\N	3
-184	0	18	41788	0	\N	7
-185	0	19	41839	0	\N	3
-186	0	20	41964	0	\N	6
-187	1	12	45892	0	\N	3
-188	2	17	45793	0	\N	9
-189	2	17	45771	0	\N	9
-190	2	17	50343	0	\N	9
-191	2	17	44198	0	\N	9
-192	2	17	45763	1200	\N	9
-193	2	17	45797	1200	\N	9
-194	2	17	44198	1200	\N	9
-195	2	17	35850	1200	\N	9
-196	2	17	49549	1200	\N	9
-197	2	17	45870	1200	\N	9
-198	2	17	61840	1200	\N	9
-199	2	17	45778	1200	\N	9
-200	2	17	45811	1200	\N	9
-201	2	17	45751	1200	\N	9
-202	2	17	45752	1200	\N	9
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-204	2	17	45866	1200	\N	9
-205	2	17	49134	1200	\N	9
-206	2	17	46096	1200	\N	9
-207	2	19	45801	0	\N	3
-208	2	19	45866	0	\N	3
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-210	2	19	48532	0	\N	3
-211	2	19	45914	0	\N	3
-212	2	19	45788	0	\N	3
-213	2	19	44206	1200	\N	3
-214	2	19	44256	1200	\N	3
-215	2	18	45793	0	\N	7
-216	2	18	49481	0	\N	7
-217	2	18	45802	0	\N	7
-218	2	18	45771	0	\N	7
-219	2	18	45945	0	\N	7
-220	2	18	44219	0	\N	7
-221	2	18	45759	0	\N	7
-222	2	18	46348	0	\N	7
-223	2	18	45927	0	\N	7
-224	2	18	49005	0	\N	7
-225	2	18	45748	1200	\N	7
-226	2	18	44592	1200	\N	7
-227	2	18	45827	1200	\N	7
-228	2	20	44198	0	\N	6
-229	2	20	63725	0	\N	6
-230	2	20	36210	0	\N	6
-231	2	20	45752	0	\N	6
-232	2	20	45901	0	\N	6
-233	2	20	45782	0	\N	6
-234	2	20	45751	0	\N	6
-235	2	20	45801	0	\N	6
-236	2	20	45945	0	\N	6
-237	2	20	43997	1200	\N	6
-238	1	12	44236	0	\N	3
-239	1	12	44212	0	\N	3
-240	1	12	45771	0	\N	3
-241	1	12	45771	0	\N	3
-242	1	12	45771	0	\N	3
-243	1	12	45771	0	\N	3
-244	1	12	63714	0	\N	3
-250	1	12	45818	0	\N	3
-251	1	12	45818	0	\N	3
-252	1	12	45818	0	\N	3
-253	0	5	41788	0	\N	14
-256	0	21	41788	0	0	14
-258	1	21	45945	3600	\N	15
-259	1	21	45927	3600	\N	15
-260	1	21	61975	3600	\N	15
-261	1	21	43996	4800	\N	14
-262	1	21	43996	4800	\N	14
-263	1	21	61993	7200	7200	14
-264	1	21	45748	9600	\N	15
-265	1	21	45969	7200	\N	14
-266	1	21	45748	7200	\N	15
-267	1	21	45748	7200	\N	15
-268	1	21	45748	9720	\N	15
-269	1	21	45927	9720	\N	15
-270	1	21	45748	9720	\N	15
-271	1	21	45927	9720	\N	15
-272	1	21	45748	9840	\N	15
-273	1	21	45748	10140	\N	15
-274	1	21	45748	10440	\N	4
-275	1	21	45748	8940	\N	15
-276	1	21	45748	9240	\N	15
-277	1	21	45748	9240	\N	15
-278	1	21	45748	9240	\N	15
-279	1	21	45748	9240	\N	15
-280	1	21	45748	9300	\N	15
-281	1	21	61975	9360	\N	15
-282	0	23	41788	0	\N	14
-283	0	23	41788	0	\N	14
-284	0	24	41839	0	\N	3
-285	0	25	41964	0	\N	6
-286	1	26	45873	0	\N	9
-287	1	26	45763	0	\N	9
-288	1	26	45788	0	\N	9
-289	1	26	35850	1080	2040	9
-290	1	26	46277	2040	\N	21
-291	10	8	45793	0	\N	21
-292	10	8	45763	60	\N	21
-293	10	8	45797	60	\N	21
-294	10	8	50343	60	\N	21
-295	10	8	45751	240	\N	21
-296	10	8	45818	1200	\N	21
-297	10	8	45838	1200	\N	21
-298	10	8	45919	1200	\N	21
-299	10	8	44439	1380	\N	21
-300	10	8	35850	1440	\N	21
-301	10	8	45806	1440	\N	21
-302	10	8	61840	1440	\N	21
-303	10	8	45778	1440	\N	21
-304	10	8	45811	1440	\N	21
-305	10	8	48916	1440	\N	21
-306	10	8	45763	1800	\N	18
-308	10	24	45866	0	\N	3
-309	10	24	45771	0	\N	3
-310	10	24	44206	0	\N	3
-311	10	24	45914	0	\N	3
-312	10	24	45788	0	\N	3
-313	10	24	45818	0	\N	3
-314	10	24	45759	0	\N	3
-315	10	24	45870	1320	\N	3
-316	10	24	45892	2280	\N	3
-317	10	24	44212	2280	\N	3
-318	10	24	45870	2280	\N	3
-319	10	24	45806	2280	\N	3
-320	10	24	44240	2520	\N	3
-321	10	24	63714	2520	\N	3
-322	10	24	44256	2640	\N	3
-323	10	24	49251	2640	\N	3
-324	10	23	50267	0	\N	14
-325	10	23	44198	0	\N	14
-326	10	23	49481	0	\N	14
-327	10	23	45770	0	\N	14
-328	10	23	45771	0	\N	14
-329	10	23	45811	0	\N	14
-330	10	23	45945	0	\N	14
-331	10	23	44219	0	\N	14
-332	10	23	45788	0	\N	14
-333	10	23	45866	0	\N	14
-334	10	23	45759	0	\N	14
-335	10	23	46245	1560	\N	15
-336	10	23	45927	1620	\N	15
-337	10	23	44439	1620	\N	15
-338	10	23	45872	1620	\N	15
-339	10	23	45877	1620	\N	15
-340	10	23	61975	1620	\N	15
-341	10	23	43996	1620	\N	15
-342	10	23	45793	2880	\N	14
-343	10	23	44439	2940	\N	14
-344	10	23	44001	4800	\N	14
-345	10	23	45969	4860	\N	16
-346	0	27	41788	0	\N	14
-347	0	27	41788	0	\N	14
-348	1	27	45872	0	\N	14
-349	1	27	65702	0	\N	14
-350	1	27	45838	120	\N	16
-351	0	29	41839	0	\N	3
-352	0	30	41964	0	\N	6
-353	0	31	41788	0	\N	14
-354	11	28	45788	0	\N	21
-355	11	28	45827	960	\N	21
-356	11	28	45965	960	\N	21
-357	11	28	45841	960	\N	21
-358	11	28	48515	960	\N	21
-359	11	28	45849	960	\N	21
-360	11	28	44198	960	\N	21
-361	11	28	45870	960	\N	21
-362	11	28	45750	960	\N	21
-363	11	28	45751	960	\N	21
-364	11	28	45755	960	\N	21
-365	11	28	45766	960	\N	21
-366	11	28	45771	960	\N	21
-367	11	28	45901	960	\N	21
-368	11	28	45776	960	\N	21
-369	11	28	45778	960	\N	21
-370	11	28	45781	960	\N	21
-371	11	28	45782	960	\N	21
-372	11	28	45783	960	\N	21
-373	11	28	45787	960	\N	21
-374	11	28	46050	960	\N	21
-375	11	28	45799	960	\N	21
-376	11	28	65641	960	\N	21
-377	11	28	45802	960	\N	21
-378	11	28	45806	960	\N	21
-379	11	28	45811	960	\N	21
-380	11	28	45801	960	\N	21
-381	11	28	61823	960	\N	21
-382	11	28	35850	2580	\N	21
-383	11	28	45760	2640	\N	18
-384	11	29	45892	0	\N	3
-385	11	29	45866	0	\N	3
-386	11	29	45771	0	\N	3
-387	11	29	44206	0	\N	3
-388	11	29	48532	0	\N	3
-389	11	29	45870	0	\N	3
-390	11	29	45818	0	\N	3
-391	11	29	45788	0	\N	3
-392	11	29	48676	1380	\N	3
-393	11	29	45770	1380	\N	3
-394	11	29	45806	1380	\N	3
-395	11	29	45811	1380	\N	3
-396	11	29	45853	1380	\N	3
-397	11	29	45759	1380	\N	3
-398	11	29	52590	3540	\N	3
-399	11	29	45870	3600	\N	3
-400	11	29	45824	3660	\N	3
-401	11	29	50100	3660	\N	3
-402	11	29	61837	3660	\N	3
-403	11	29	45797	3660	\N	3
-404	11	31	44198	0	\N	14
-405	11	31	49481	0	\N	14
-406	11	31	45770	0	\N	14
-407	11	31	45771	0	\N	14
-408	11	31	45811	0	\N	14
-409	11	31	45945	0	\N	14
-410	11	31	44219	0	\N	14
-411	11	31	45788	0	\N	14
-412	11	31	45866	0	\N	14
-413	11	31	45759	0	\N	14
-414	11	31	45778	600	\N	15
-415	11	31	45806	600	\N	15
-416	11	31	45927	600	\N	15
-417	11	31	65640	1680	\N	15
-418	11	31	61823	1680	\N	15
-419	11	31	65649	1800	\N	14
-420	11	31	61993	1860	\N	14
-421	11	31	45793	1920	\N	16
-422	11	31	45759	1920	\N	16
-423	11	31	49481	2940	\N	16
-424	11	30	48960	0	\N	6
-425	11	30	44198	0	\N	6
-426	11	30	45801	0	\N	6
-427	11	30	45771	0	\N	6
-428	11	30	49228	0	\N	6
-429	11	30	45901	0	\N	6
-430	11	30	46157	0	\N	6
-431	11	30	36210	0	\N	6
-432	11	30	45788	0	\N	6
-433	0	32	41839	0	\N	3
-434	0	34	41964	0	\N	6
-435	0	35	41788	0	\N	14
-436	12	33	45788	0	\N	21
-437	12	33	45763	960	\N	21
-438	12	33	50343	960	\N	21
-439	12	33	45763	1980	\N	21
-440	12	33	44198	1980	\N	21
-441	12	33	62151	1980	\N	21
-442	12	33	35850	1980	\N	21
-443	12	33	44267	1980	\N	21
-444	12	33	45901	1980	\N	21
-445	12	33	61840	1980	\N	21
-446	12	33	45873	1980	\N	21
-447	12	33	45778	1980	\N	21
-448	12	33	45811	1980	\N	21
-449	12	33	44021	1980	\N	21
-450	12	33	45752	1980	\N	21
-451	12	33	45818	1980	\N	21
-452	12	33	44283	1980	\N	21
-453	12	33	44220	1980	\N	21
-454	12	32	44294	0	\N	3
-455	12	32	45866	0	\N	3
-456	12	32	45771	0	\N	3
-457	12	32	45900	0	\N	3
-458	12	32	45870	0	\N	3
-459	12	32	44206	0	\N	3
-460	12	32	45914	0	\N	3
-461	12	32	45788	0	\N	3
-462	12	32	45818	0	\N	3
-463	12	32	45759	0	\N	3
-464	12	32	44256	1500	\N	3
-465	12	32	46081	1500	\N	3
-466	12	32	49251	1500	\N	3
-467	12	32	44359	1500	\N	3
-468	12	32	45866	1500	\N	3
-469	12	32	45870	1500	\N	3
-470	12	32	48628	1500	\N	3
-471	12	32	45977	1500	\N	3
-472	12	35	45945	0	\N	14
-473	12	35	45771	0	\N	14
-474	12	35	45788	0	\N	14
-475	12	35	45869	0	\N	14
-476	12	35	45759	0	\N	14
-477	12	35	44219	300	\N	14
-478	12	35	43996	300	\N	14
-479	12	35	35733	300	\N	14
-480	12	35	49481	480	\N	14
-481	12	35	45969	480	\N	14
-482	12	35	45793	1500	\N	15
-483	12	35	65640	1560	\N	15
-484	12	35	61993	1560	\N	15
-485	12	35	65646	1560	\N	15
-486	12	35	61975	1560	\N	15
-487	12	35	46286	1800	\N	16
-488	12	35	44404	1860	\N	16
-489	12	35	49481	3720	\N	16
-490	12	34	48960	0	\N	6
-491	12	34	45955	0	\N	6
-492	12	34	44198	0	\N	6
-493	12	34	45801	0	\N	6
-494	12	34	45771	0	\N	6
-495	12	34	49228	0	\N	6
-496	12	34	45873	0	\N	6
-497	12	34	36210	0	\N	6
-498	12	34	45788	0	\N	6
-499	12	34	44198	1440	\N	6
-500	0	37	41839	0	\N	3
-501	0	38	41964	0	\N	6
-502	0	39	41788	0	\N	14
-503	13	36	45793	0	\N	21
-504	13	36	45778	960	\N	21
-505	13	36	45763	960	\N	21
-506	13	36	45788	960	\N	21
-507	13	36	45910	960	\N	21
-508	13	36	45806	960	\N	21
-509	13	36	46093	1260	\N	21
-510	13	36	45763	2220	\N	21
-511	13	36	45796	2220	\N	21
-512	13	36	45797	2220	\N	21
-513	13	36	44198	2220	\N	21
-514	13	36	35850	2220	\N	21
-515	13	36	49549	2220	\N	21
-516	13	36	61840	2220	\N	21
-517	13	36	45778	2220	\N	21
-518	13	36	45811	2220	\N	21
-519	13	36	48916	2220	\N	21
-520	13	36	44021	2220	\N	21
-521	13	36	45751	2220	\N	21
-522	13	36	45752	2220	\N	21
-523	13	36	45918	2220	\N	21
-524	13	36	45919	2220	\N	21
-525	13	37	44256	0	\N	3
-526	13	37	45892	0	\N	3
-527	13	37	45866	0	\N	3
-528	13	37	45771	0	\N	3
-529	13	37	45838	0	\N	3
-530	13	37	44206	0	\N	3
-531	13	37	45870	0	\N	3
-532	13	37	45818	0	\N	3
-533	13	37	45788	0	\N	3
-534	13	37	45759	0	\N	3
-535	13	37	46160	1500	\N	3
-536	13	37	48871	1560	\N	3
-537	13	37	45811	1620	\N	3
-538	13	37	71052	2580	\N	3
-539	13	37	61982	2580	\N	3
-540	13	39	45793	0	\N	14
-541	13	39	44198	0	\N	14
-542	13	39	49481	0	\N	14
-543	13	39	45770	0	\N	14
-544	13	39	45771	0	\N	14
-545	13	39	43996	0	\N	14
-546	13	39	45811	0	\N	14
-547	13	39	49207	0	\N	14
-548	13	39	45945	0	\N	14
-549	13	39	44219	0	\N	14
-550	13	39	45788	0	\N	14
-551	13	39	45866	0	\N	14
-552	13	39	45759	0	\N	14
-553	13	39	45969	1680	\N	15
-554	13	39	61982	1680	\N	15
-555	13	39	61993	1680	\N	15
-556	13	39	45872	1860	\N	15
-557	13	39	65640	1860	\N	15
-558	13	39	61975	1860	\N	15
-559	13	39	63759	1860	\N	15
-560	13	39	45927	1860	\N	15
-561	13	39	61323	2160	\N	14
-562	13	39	61982	2160	\N	14
-563	13	39	45872	2280	\N	14
-564	13	39	61993	2280	\N	14
-565	13	39	45793	2400	\N	16
-566	13	39	45759	2400	\N	16
-567	13	39	49481	2520	\N	16
-568	13	39	63720	2580	\N	2
-569	13	39	46343	2580	\N	2
-570	13	38	48960	0	\N	6
-571	13	38	44198	0	\N	6
-572	13	38	45801	0	\N	6
-573	13	38	45771	0	\N	6
-574	13	38	49228	0	\N	6
-575	13	38	46157	0	\N	6
-576	13	38	45806	0	\N	6
-577	13	38	36210	0	\N	6
-578	13	38	44439	0	\N	6
-579	13	38	45752	0	\N	6
-580	13	38	45788	0	\N	6
-581	13	38	43997	0	\N	6
-582	13	38	45811	1620	\N	6
-583	13	38	45919	1620	\N	6
-257	0	21	41788	0	0	14
-585	1	21	48711	60	120	14
-584	1	21	61842	0	120	14
-586	1	21	61842	120	\N	14
-587	1	21	50897	180	240	14
-588	1	21	48711	240	360	14
-589	1	21	44008	300	360	14
-590	1	21	44235	360	420	14
-591	1	21	63729	420	480	14
-592	1	21	44872	480	540	14
-\.
-
-
---
--- Name: sim_patient_order_sim_patient_order_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_patient_order_sim_patient_order_id_seq', 592, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_patient_state.dump.sql b/medinfo/db/definition/simdata/sim_patient_state.dump.sql
deleted file mode 100644
index 40b07ada..00000000
--- a/medinfo/db/definition/simdata/sim_patient_state.dump.sql
+++ /dev/null
@@ -1,126 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_patient_state; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_patient_state (sim_patient_state_id, sim_patient_id, sim_state_id, relative_time_start, relative_time_end) FROM stdin;
-2	2	2	0	\N
-3	3	1	0	\N
-4	4	2	0	\N
-5	1	2	6000	\N
-1	1	1	0	6000
-26	21	14	0	3600
-29	21	15	3600	3600
-10	11	1	0	\N
-30	21	14	3600	4800
-9	9	3	-7200	\N
-11	12	3	-7200	\N
-13	6	6	-14400	\N
-7	7	7	-7200	\N
-31	21	14	4800	4800
-15	13	3	-7200	\N
-16	14	6	-14400	\N
-17	15	7	-7200	\N
-20	18	7	-7200	\N
-21	19	3	-7200	\N
-22	20	6	-14400	\N
-23	5	13	-7200	0
-24	5	14	0	\N
-25	21	13	-7200	0
-32	21	14	4800	8400
-38	21	15	8400	9360
-40	21	14	9360	\N
-42	23	13	-7200	0
-44	24	3	-7200	\N
-45	25	6	-14400	\N
-8	8	9	-36000	0
-18	16	9	-36000	0
-19	17	9	-36000	0
-41	22	9	-36000	0
-46	26	9	-36000	0
-49	16	21	0	\N
-50	17	21	0	\N
-51	22	21	0	\N
-52	26	21	0	\N
-47	8	21	0	1440
-53	8	18	1440	\N
-43	23	14	0	0
-54	23	17	0	0
-55	23	15	0	1620
-56	23	14	1620	1620
-57	23	14	1620	4800
-58	23	16	4800	4860
-59	23	2	4860	\N
-60	27	13	-7200	0
-61	27	14	0	0
-62	27	16	0	\N
-63	28	9	-36000	0
-65	29	3	-7200	\N
-66	30	6	-14400	\N
-67	31	13	-7200	0
-64	28	21	0	2580
-69	28	18	2580	\N
-68	31	14	0	0
-70	31	17	0	0
-71	31	15	0	1680
-72	31	14	1680	1860
-73	31	16	1860	2940
-74	31	2	2940	\N
-75	32	3	-7200	\N
-76	33	9	-36000	0
-78	34	6	-14400	\N
-79	35	13	-7200	0
-77	33	21	0	1980
-81	33	18	1980	5580
-82	33	20	5580	\N
-80	35	14	0	300
-83	35	14	300	480
-84	35	17	480	480
-85	35	15	480	480
-86	35	17	480	480
-87	35	15	480	1560
-88	35	14	1560	1560
-89	35	14	1560	1560
-90	35	16	1560	3720
-91	35	2	3720	\N
-92	36	9	-36000	0
-94	37	3	-7200	\N
-95	38	6	-14400	\N
-96	39	13	-7200	0
-93	36	21	0	2220
-98	36	18	2220	5820
-99	36	20	5820	\N
-97	39	14	0	0
-100	39	17	0	0
-101	39	15	0	1680
-102	39	17	1680	1680
-103	39	15	1680	1860
-104	39	14	1860	1860
-105	39	14	1860	2280
-106	39	16	2280	2520
-107	39	2	2520	\N
-\.
-
-
---
--- Name: sim_patient_state_sim_patient_state_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_patient_state_sim_patient_state_id_seq', 107, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_state.dump.sql b/medinfo/db/definition/simdata/sim_state.dump.sql
deleted file mode 100644
index 1aa4d10e..00000000
--- a/medinfo/db/definition/simdata/sim_state.dump.sql
+++ /dev/null
@@ -1,53 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_state; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_state (sim_state_id, name, description) FROM stdin;
-1	Example	Example State
-0	Default	Default State Info
-5	ACS Relief	ACS Pain Relief
-6	NFever	Neutropenic Fever (no source) Initial State
-7	GIBleed	GI Bleed Initial State
-3	ACS	ACS Initial State
-8	AMS UTI 	Altered, UTI
-10	AMS HyperNa	Altered, Hypernatremia
-11	COPD-PNA	COPD Exacerbation, CAPneumonia
-12	VTE-Low Risk PE	Low Risk Pulmonary Embolism
-14	EtOH-GIBleed Active	Alcoholic Hepatitis - GI Bleed Active
-15	EtOH-GIBleed Bleeding Out	Alcoholic Hepatitis - GI Bleed Bleeding Out
-4	CodeBlue	Code Blue / ACLS / Dead / Dying
-16	EtOH-GIBleed Coag Stabilized	Alcoholic Hepatitis - GI Bleed Coagulopathy Stabilized
-2	EtOH-GIBleed Post-EGD	Alcoholic Hepatitis - GI Bleed EGD Done
-17	EtOH-GIBleed GI Consult Block	Alcoholic Hepatitis - GI Bleed GI Consult Block Pending Med Management
-13	EtOH-GIBleed Onset	Alcoholic Hepatitis - GI Bleed Onset (pre-admission)
-9	DKA Onset	Diabetic Ketoacidosis - Onset
-20	DKA Hypoglycemic	Diabetic Ketoacidosis - Low Glucose, Low K, Anion Gap
-21	DKA Hyperglycemic	Diabetic Ketoacidosis - High Glucose, Normal-High K, Anion Gap
-18	DKA Euglycemic	Diabetic Ketoacidosis - Normal Glucose, Normal K, Anion Gap
-19	DKA Gap Closed	Diabetic Ketoacidosis - Gap Closed
-\.
-
-
---
--- Name: sim_state_sim_state_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_state_sim_state_id_seq', 21, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_state_result.dump.sql b/medinfo/db/definition/simdata/sim_state_result.dump.sql
deleted file mode 100644
index 96e6ba80..00000000
--- a/medinfo/db/definition/simdata/sim_state_result.dump.sql
+++ /dev/null
@@ -1,591 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_state_result; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_state_result (sim_state_result_id, sim_result_id, clinical_item_id, sim_state_id, num_value, num_value_noise, text_value, result_flag) FROM stdin;
-101	1004194	\N	0	\N	\N	No acute cardiopulmonary abnormalities	\N
-103	1004195	\N	0	\N	\N	No acute intrathoracic abnormalities	\N
-95	13210	\N	0	0.69999999999999996	0.20000000000000001	\N	\r
-4902	1000061	\N	0	0	0	\N	\r
-85	13240	\N	0	13	3	\N	\r
-82	13250	\N	0	17	4	\N	\r
-4903	1000061	\N	2	0	0	\N	\N
-74	13260	\N	0	107	9	\N	\r
-90	13270	\N	0	3.8999999999999999	0.20000000000000001	\N	\r
-147	11010	66124	7	7.4000000000000004	0.20000000000000001	\N	Low
-150	11020	66123	7	25	1	\N	Low
-102	1004198	66211	0	\N	\N	Normal sinus rhythm	\N
-4905	1000061	66497	14	0.20000000000000001	0	\N	High
-148	11000	\N	7	9.4000000000000004	0.5	\N	\N
-149	50	\N	7	17	2	\N	\N
-152	40	\N	7	62	3	\N	\N
-154	10	\N	7	98.400000000000006	\N	\N	\N
-155	30	\N	7	102	4	\N	\N
-157	11030	66258	7	338	10	\N	\N
-6	20	\N	1	112	5	\N	High
-9	10	\N	1	101.3	0.20000000000000001	\N	High
-17	11000	72454	1	13.6	0.20000000000000001	\N	High
-26	13060	66286	1	2.1000000000000001	0	\N	High
-145	13530	66144	7	0.20000000000000001	0	\N	High
-146	13060	66286	7	1.2	0.10000000000000001	\N	High
-153	13050	66273	7	65	3	\N	High
-156	20	\N	7	105	4	\N	High
-178	13080	\N	9	30	0	\N	High
-186	11120	\N	9	90	2	\N	High
-189	20	\N	9	117	3	\N	High
-192	13070	66695	9	614	13	\N	High
-193	13730	66866	9	1432	24	\N	High
-151	13460	66402	7	57	4	\N	Low
-79	13050	\N	0	11	2	\N	\r
-88	13280	\N	0	6	0.20000000000000001	\N	\r
-97	13530	\N	0	0	0	\N	\r
-75	10	\N	0	98.700000000000003	0.20000000000000001	\N	\r
-4904	1000061	66497	13	0.20000000000000001	0	\N	High
-4901	1000061	66497	7	0.20000000000000001	0	\N	High
-4906	1000061	66497	15	2.5	0	\N	High Panic
-1	11030	\N	2	352	23	\N	\N
-2	70	\N	2	300	0	\N	\N
-3	13010	\N	2	140	0	\N	\N
-4	13540	\N	2	124	0	\N	\N
-5	30	\N	2	123	4	\N	\N
-7	13070	\N	2	110	0	\N	\N
-8	13260	\N	2	102	0	\N	\N
-10	13030	\N	2	95	0	\N	\N
-11	40	\N	2	79	4	\N	\N
-12	11020	\N	2	40	1	\N	\N
-13	13050	\N	2	27	0	\N	\N
-14	13040	\N	2	22	0	\N	\N
-15	80	\N	2	15	0	\N	\N
-16	13250	\N	2	15	0	\N	\N
-18	11010	\N	2	13.4	0.20000000000000001	\N	\N
-19	50	\N	2	13	2	\N	\N
-20	13240	\N	2	12	0	\N	\N
-72	20	\N	0	68	4	\N	\r
-71	30	\N	0	115	4	\N	\r
-77	40	\N	0	72	3	\N	\r
-84	50	\N	0	13	2	\N	\r
-100	1004193	\N	0	0.98999999999999999	0.01	\N	\r
-96	60	\N	0	0.20999999999999999	0	\N	\r
-68	70	\N	0	300	100	\N	\r
-81	80	\N	0	15	0	\N	\r
-98	11000	\N	0	6.2000000000000002	0.69999999999999996	\N	\r
-83	11010	\N	0	13.4	0.20000000000000001	\N	\r
-78	11020	\N	0	40	1	\N	\r
-67	11030	\N	0	352	23	\N	\r
-93	12040	\N	0	1	0.10000000000000001	\N	\r
-69	13010	\N	0	146	2	\N	\r
-89	13020	\N	0	4.5	0.20000000000000001	\N	\r
-76	13030	\N	0	96	2	\N	\r
-80	13040	\N	0	24	1	\N	\r
-99	13060	\N	0	0.69999999999999996	0.10000000000000001	\N	\r
-73	13070	\N	0	110	0	\N	\r
-86	13090	\N	0	8.9000000000000004	0.20000000000000001	\N	\r
-91	13110	\N	0	2.1000000000000001	0.20000000000000001	\N	\r
-94	13506	\N	0	0.69999999999999996	0.20000000000000001	\N	\r
-70	13540	\N	0	124	17	\N	\r
-87	13670	\N	0	5.2000000000000002	0.10000000000000001	\N	\r
-92	13750	\N	0	1.7	0.20000000000000001	\N	\r
-21	13090	\N	2	8.9000000000000004	0	\N	\N
-22	13670	\N	2	6.2999999999999998	0	\N	\N
-23	13280	\N	2	5.9000000000000004	0	\N	\N
-24	13020	\N	2	4.5	0	\N	\N
-25	13270	\N	2	3.8999999999999999	0	\N	\N
-27	13110	\N	2	2.1000000000000001	0	\N	\N
-28	13750	\N	2	1.5	0	\N	\N
-29	12040	\N	2	1	0	\N	\N
-30	13506	\N	2	0.90000000000000002	0	\N	\N
-31	13210	\N	2	0.80000000000000004	0	\N	\N
-32	60	\N	2	0.20999999999999999	0	\N	\N
-33	13530	\N	2	0	0	\N	\N
-105	30	\N	3	145	3	\N	\N
-106	40	\N	3	86	3	\N	\N
-107	50	\N	3	20	1	\N	\N
-108	1004193	\N	3	0.92000000000000004	0.10000000000000001	\N	\N
-109	11000	\N	3	10	0.29999999999999999	\N	\N
-110	12010	\N	3	285	17	\N	\N
-111	13060	\N	3	1.1000000000000001	0.10000000000000001	\N	\N
-113	13640	\N	3	124	4	\N	\N
-115	1004198	\N	3	\N	\N	T-wave inversions in V2,V3	\N
-131	13410	66407	6	6.7000000000000002	0.20000000000000001	\N	\N
-161	17090	\N	9	\N	\N	Negative	\N
-162	17100	\N	9	\N	\N	Negative	\N
-163	17110	\N	9	\N	\N	Negative	\N
-164	17120	\N	9	\N	\N	Negative	\N
-166	13060	\N	9	0.80000000000000004	0.10000000000000001	\N	\N
-173	11010	\N	9	14.5	0.69999999999999996	\N	\N
-183	11020	\N	9	45	2	\N	\N
-184	40	\N	9	72	3	\N	\N
-188	30	\N	9	105	4	\N	\N
-191	11030	\N	9	270	10	\N	\N
-104	20	\N	3	105	2	\N	High
-112	13250	\N	3	52	4	\N	High
-114	13670	\N	3	7.7999999999999998	0.10000000000000001	\N	High
-117	10	\N	6	102	1	\N	High
-118	20	\N	6	128	5	\N	High
-121	50	\N	6	25	2	\N	High
-132	13420	66159	6	480	13	\N	High
-133	13502	69841	6	4.4000000000000004	0.10000000000000001	\N	High
-134	13506	66505	6	3.2999999999999998	0.20000000000000001	\N	High
-143	16100	66502	6	3.3999999999999999	0.20000000000000001	\N	High
-144	1000173	70921	6	3.3999999999999999	0.10000000000000001	\N	High
-158	17050	66506	9	\N	\N	3+	High
-159	17040	66507	9	\N	\N	4+	High
-168	13020	71563	9	5.4000000000000004	0	\N	High
-175	11000	66279	9	23	1	\N	High
-176	13050	66273	9	24	2	\N	High
-177	50	\N	9	29	2	\N	High
-119	30	\N	6	86	4	\N	Low
-120	40	\N	6	43	3	\N	Low
-122	11000	66630	6	0.80000000000000004	0	\N	Low
-123	11010	66124	6	11.1	0.29999999999999999	\N	Low
-124	11020	66123	6	33	1	\N	Low
-125	11030	66258	6	95	10	\N	Low
-126	11120	\N	6	10	0	\N	Low
-127	11130	\N	6	90	0	\N	Low
-128	11220	66684	6	80	0	\N	Low
-129	11240	72951	6	720	0	\N	Low
-130	13040	66403	6	19	1	\N	Low
-135	15010	66383	6	7.3399999999999999	0.01	\N	Low
-136	15020	66375	6	34	1	\N	Low
-137	15050	66392	6	7.3399999999999999	0.01	\N	Low
-138	15060	66495	6	34	1	\N	Low
-139	16010	66413	6	7.3399999999999999	0.01	\N	Low
-140	16020	66203	6	34	1	\N	Low
-141	16060	66504	6	7.3399999999999999	0.01	\N	Low
-142	16070	66493	6	34	1	\N	Low
-165	13120	66349	9	0.5	0	\N	Low
-169	15010	66383	9	7.2999999999999998	0	\N	Low
-170	15050	66392	9	7.2999999999999998	0	\N	Low
-171	16010	66413	9	7.2999999999999998	0	\N	Low
-187	10	\N	9	98.400000000000006	0.20000000000000001	\N	\N
-160	1000565	72018	9	5.4000000000000004	0.10000000000000001	Elevated	High
-172	16060	66504	9	7.2999999999999998	0	\N	Low
-174	13040	66403	9	15	0	\N	Low
-179	15020	66375	9	30	0	\N	Low
-180	15060	66495	9	30	0	\N	Low
-181	16020	66203	9	30	0	\N	Low
-182	16070	66493	9	30	0	\N	Low
-185	13030	66328	9	80	0	\N	Low
-190	13010	66194	9	125	0	\N	Low
-221	13220	\N	0	0.5	0.10000000000000001	\N	\r
-222	13230	\N	0	0.29999999999999999	0.10000000000000001	\N	\r
-226	13330	\N	0	290	3	\N	\r
-227	13410	\N	0	4.7999999999999998	0.29999999999999999	\N	\r
-228	13420	\N	0	79	3	\N	\r
-229	13430	\N	0	105	2	\N	\r
-230	13440	\N	0	12	1	\N	\r
-231	13450	\N	0	572	6	\N	\r
-232	13460	\N	0	144	3	\N	\r
-233	13470	\N	0	109	3	\N	\r
-235	13490	\N	0	36	2	\N	\r
-236	13502	\N	0	0.20000000000000001	0.10000000000000001	\N	\r
-237	13510	\N	0	1.2	0.10000000000000001	\N	\r
-238	13520	\N	0	87	2	\N	\r
-260	15030	\N	0	98	1	\N	\r
-261	15040	\N	0	0.20999999999999999	0	\N	\r
-262	15050	\N	0	7.3799999999999999	0.01	\N	\r
-263	15060	\N	0	41	2	\N	\r
-264	15070	\N	0	38	2	\N	\r
-265	16010	\N	0	7.3899999999999997	0.01	\N	\r
-266	16020	\N	0	40	2	\N	\r
-267	16030	\N	0	98	1	\N	\r
-268	16040	\N	0	98	1	\N	\r
-269	16050	\N	0	0.20999999999999999	0	\N	\r
-270	16060	\N	0	7.3799999999999999	0.01	\N	\r
-271	16070	\N	0	41	2	\N	\r
-272	16080	\N	0	38	2	\N	\r
-273	16090	\N	0	65	2	\N	\r
-274	16100	\N	0	0.80000000000000004	0.10000000000000001	\N	\r
-275	17010	\N	0	\N	\N	Yellow	\r
-276	17020	\N	0	\N	\N	Clear	\r
-277	17030	\N	0	1.018	0.0030000000000000001	\N	\r
-278	17040	\N	0	\N	\N	Negative	\r
-279	17050	\N	0	\N	\N	Negative	\r
-280	17060	\N	0	\N	\N	Negative	\r
-281	17070	\N	0	7.5	0.10000000000000001	\N	\r
-282	17080	\N	0	\N	\N	Negative	\r
-283	17090	\N	0	\N	\N	Negative	\r
-284	17100	\N	0	\N	\N	Negative	\r
-285	17110	\N	0	0	0	\N	\r
-287	17130	\N	0	\N	\N	Negative	\r
-310	18040	\N	0	\N	\N	Negative	\r
-311	18050	\N	0	\N	\N	Negative	\r
-312	18060	\N	0	\N	\N	Negative	\r
-313	18070	\N	0	\N	\N	Negative	\r
-314	18080	\N	0	\N	\N	Negative	\r
-315	18090	\N	0	\N	\N	Negative	\r
-316	18100	\N	0	\N	\N	Negative	\r
-326	1000001	\N	0	113	4	\N	\r
-4527	1000036	\N	13	\N	\N	B-	\N
-4528	1004196	\N	13	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4529	1004200	\N	13	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4530	1004198	\N	13	\N	\N	Sinus tachycardia	\N
-4531	13530	66144	13	0.20000000000000001	0	\N	High
-4532	1000223	\N	13	1.3999999999999999	0.10000000000000001	\N	\N
-4533	13060	66286	13	2.3999999999999999	0.10000000000000001	\N	High
-4534	12040	66112	13	2.7000000000000002	0.20000000000000001	\N	High
-4535	13270	66266	13	2.7999999999999998	0.10000000000000001	\N	Low
-4536	1000169	\N	13	3	\N	\N	\N
-4537	1000121	\N	13	5	\N	\N	\N
-4538	13220	66283	13	6.0999999999999996	0.10000000000000001	\N	High
-4539	13210	66386	13	6.2999999999999998	0.10000000000000001	\N	High
-4540	11010	66124	13	7.5999999999999996	0.10000000000000001	\N	Low
-4541	1000068	66321	13	7.5999999999999996	0.10000000000000001	\N	Low
-4542	11000	\N	13	9.4000000000000004	0.5	\N	\N
-4543	50	\N	13	19	2	\N	\N
-4544	11020	66123	13	25	1	\N	Low
-4545	1000067	66322	13	25	1	\N	Low
-4546	12030	66111	13	28	2	\N	High
-4547	13720	66380	13	52	3	\N	High
-4548	1000120	\N	13	53	2	\N	\N
-4549	13460	66402	13	57	4	\N	Low
-4550	13240	66370	13	60	1	\N	High
-4551	40	\N	13	62	3	\N	\N
-4552	13320	66559	13	72	2	\N	High
-4553	11030	66258	13	88	9	\N	Low
-4554	11040	\N	13	93	1	\N	\N
-4555	1004193	\N	13	96	1	\N	\N
-4556	13050	66273	13	96	3	\N	High
-4557	10	\N	13	97.900000000000006	\N	\N	Low
-4558	30	\N	13	101	2	\N	\N
-4559	20	\N	13	105	3	\N	High
-4560	13250	66369	13	125	2	\N	High
-4561	13830	67396	13	186	2	\N	High
-4562	13260	66371	13	210	3	\N	High
-4563	1000036	\N	14	\N	\N	B-	\N
-4564	1004196	\N	14	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4565	1004200	\N	14	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4566	1004198	\N	14	\N	\N	Sinus tachycardia	\N
-4567	13530	66144	14	0.20000000000000001	0	\N	High
-4568	1000223	\N	14	1.3999999999999999	0.10000000000000001	\N	\N
-4569	13060	66286	14	2.3999999999999999	0.10000000000000001	\N	High
-4570	12040	66112	14	2.7000000000000002	0.20000000000000001	\N	High
-4571	13270	66266	14	2.7999999999999998	0.10000000000000001	\N	Low
-4572	1000169	\N	14	3	\N	\N	\N
-4573	1000121	\N	14	5	\N	\N	\N
-4574	13220	66283	14	6.0999999999999996	0.10000000000000001	\N	High
-4575	13210	66386	14	6.2999999999999998	0.10000000000000001	\N	High
-4576	11010	66124	14	7.5999999999999996	0.10000000000000001	\N	Low
-4577	1000068	66321	14	7.5999999999999996	0.10000000000000001	\N	Low
-4578	11000	\N	14	9.4000000000000004	0.5	\N	\N
-4579	50	\N	14	19	2	\N	\N
-4580	11020	66123	14	25	1	\N	Low
-4581	1000067	66322	14	25	1	\N	Low
-4582	12030	66111	14	28	2	\N	High
-4583	13720	66380	14	52	3	\N	High
-4584	1000120	\N	14	53	2	\N	\N
-4585	13460	66402	14	57	4	\N	Low
-4586	13240	66370	14	60	1	\N	High
-4587	40	\N	14	62	3	\N	\N
-4588	13320	66559	14	72	2	\N	High
-4589	11030	66258	14	88	9	\N	Low
-4590	11040	\N	14	93	1	\N	\N
-4591	1004193	\N	14	96	1	\N	\N
-4592	13050	66273	14	96	3	\N	High
-4593	10	\N	14	97.900000000000006	\N	\N	Low
-4594	30	\N	14	101	2	\N	\N
-4595	20	\N	14	105	3	\N	High
-4596	13250	66369	14	125	2	\N	High
-4597	13830	67396	14	186	2	\N	High
-4598	13260	66371	14	210	3	\N	High
-4599	1000036	\N	15	\N	\N	B-	\N
-4600	1004196	\N	15	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4601	1004200	\N	15	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4602	1004198	\N	15	\N	\N	Sinus tachycardia	\N
-4603	1000223	\N	15	1.3999999999999999	0.10000000000000001	\N	\N
-4604	13530	66144	15	2.5	0	\N	High Panic
-4605	12040	66112	15	2.7000000000000002	0.20000000000000001	\N	High
-4606	13270	66266	15	2.7999999999999998	0.10000000000000001	\N	Low
-4607	13060	66286	15	2.8999999999999999	0.10000000000000001	\N	High
-4608	1000169	\N	15	3	\N	\N	\N
-4609	1000121	\N	15	4	\N	\N	\N
-4610	11010	66488	15	6.0999999999999996	0.10000000000000001	\N	Low Panic
-4611	13220	66283	15	6.0999999999999996	0.10000000000000001	\N	High
-4612	13210	66386	15	6.2999999999999998	0.10000000000000001	\N	High
-4613	1000068	72995	15	7.5999999999999996	0.10000000000000001	\N	Low Panic
-4614	11000	\N	15	9.4000000000000004	0.5	\N	\N
-4615	11020	66436	15	18	1	\N	Low Panic
-4616	1000067	72994	15	18	1	\N	Low Panic
-4617	12030	66111	15	28	2	\N	High
-4618	50	\N	15	29	2	\N	High
-4619	1000120	\N	15	51	2	\N	\N
-4620	40	\N	15	54	1	\N	Low
-4621	13460	66402	15	57	4	\N	Low
-4622	13720	66380	15	63	3	\N	High
-4623	13320	66559	15	72	2	\N	High
-4624	30	\N	15	85	2	\N	Low
-4625	11030	66258	15	85	7	\N	Low
-4626	11040	\N	15	93	1	\N	\N
-4627	1004193	\N	15	95	1	\N	\N
-4628	10	\N	15	97.200000000000003	\N	\N	Low
-4629	13050	66273	15	103	3	\N	High
-4630	20	\N	15	127	3	\N	High
-4631	13240	66370	15	131	1	\N	High
-4632	13830	67396	15	162	2	\N	High
-4633	13260	66371	15	210	3	\N	High
-4634	13250	66369	15	212	2	\N	High
-4635	1000036	\N	16	\N	\N	B-	\N
-4636	1004196	\N	16	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4637	1004200	\N	16	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4638	1004198	\N	16	\N	\N	Sinus tachycardia	\N
-4639	12040	\N	16	1.3999999999999999	0.10000000000000001	\N	\N
-4640	1000223	\N	16	1.3999999999999999	0.10000000000000001	\N	\N
-4641	13060	66286	16	1.8	0.10000000000000001	\N	High
-4642	13270	66266	16	2.7999999999999998	0.10000000000000001	\N	Low
-4643	1000169	\N	16	3	\N	\N	\N
-4644	1000121	\N	16	4	\N	\N	\N
-4645	13220	66283	16	6.0999999999999996	0.10000000000000001	\N	High
-4646	13210	66386	16	6.2999999999999998	0.10000000000000001	\N	High
-4647	11010	66124	16	8.1999999999999993	0.10000000000000001	\N	Low
-4648	1000068	66321	16	8.1999999999999993	0.10000000000000001	\N	Low
-4649	11000	\N	16	9.4000000000000004	0.5	\N	\N
-4650	12030	\N	16	14	1	\N	\N
-4651	50	\N	16	18	1	\N	\N
-4652	11020	66123	16	25	1	\N	Low
-4653	1000067	66322	16	26	1	\N	Low
-4654	1000120	\N	16	51	2	\N	\N
-4655	13460	66402	16	57	4	\N	Low
-4656	13720	66380	16	58	3	\N	High
-4657	40	\N	16	61	1	\N	\N
-4658	13240	66370	16	71	1	\N	High
-4659	13320	66559	16	72	2	\N	High
-4813	17090	\N	18	\N	\N	Negative	\N
-4660	13050	66273	16	75	1	\N	High
-4661	11030	66258	16	89	7	\N	Low
-4662	11040	\N	16	93	1	\N	\N
-4663	20	\N	16	97	1	\N	\N
-4664	1004193	\N	16	97	1	\N	\N
-4665	10	\N	16	98.099999999999994	\N	\N	\N
-4666	30	\N	16	108	2	\N	\N
-4667	13830	67396	16	142	2	\N	High
-4668	13250	66369	16	154	2	\N	High
-4669	13260	66371	16	210	3	\N	High
-4670	1000036	\N	2	\N	\N	B-	\N
-4671	1004196	\N	2	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4672	1004200	\N	2	\N	\N	Coarsened hepatic echotexture, splenomegaly, moderate ascites	\N
-4673	1004198	\N	2	\N	\N	Sinus tachycardia	\N
-4674	12040	\N	2	1.3999999999999999	0.10000000000000001	\N	\N
-4675	1000223	\N	2	1.3999999999999999	0.10000000000000001	\N	\N
-4676	13060	66286	2	1.8	0.10000000000000001	\N	High
-4677	13270	66266	2	2.7999999999999998	0.10000000000000001	\N	Low
-4678	1000169	\N	2	3	\N	\N	\N
-4679	1000121	\N	2	4	\N	\N	\N
-4680	13220	66283	2	6.0999999999999996	0.10000000000000001	\N	High
-4681	13210	66386	2	6.2999999999999998	0.10000000000000001	\N	High
-4682	11010	66124	2	8.1999999999999993	0.10000000000000001	\N	Low
-4683	1000068	66321	2	8.1999999999999993	0.10000000000000001	\N	Low
-4684	11000	\N	2	9.4000000000000004	0.5	\N	\N
-4685	50	\N	2	14	1	\N	\N
-4686	12030	\N	2	14	1	\N	\N
-4687	11020	66123	2	25	1	\N	Low
-4688	1000067	66322	2	26	1	\N	Low
-4689	1000120	\N	2	51	2	\N	\N
-4690	13460	66402	2	57	4	\N	Low
-4691	13720	66380	2	58	3	\N	High
-4692	40	\N	2	65	1	\N	\N
-4693	13240	66370	2	71	1	\N	High
-4694	13320	66559	2	72	2	\N	High
-4695	13050	66273	2	75	1	\N	High
-4696	20	\N	2	86	1	\N	\N
-4697	11030	66258	2	89	7	\N	Low
-4698	11040	\N	2	93	1	\N	\N
-4699	1004193	\N	2	98	1	\N	\N
-4700	10	\N	2	98.5	\N	\N	\N
-4701	30	\N	2	118	2	\N	\N
-4702	13830	67396	2	142	2	\N	High
-4703	13250	66369	2	154	2	\N	High
-4704	13260	66371	2	210	3	\N	High
-194	11040	\N	0	96	1	\N	\r
-213	12010	\N	0	84	2	\N	\r
-215	12030	\N	0	11	1	\N	\r
-218	13080	\N	0	10	1	\N	\r
-220	13120	\N	0	3.1000000000000001	0.20000000000000001	\N	\r
-239	13504	\N	0	0.80000000000000004	0.10000000000000001	\N	\r
-240	13610	\N	0	145	3	\N	\r
-242	13630	\N	0	45	2	\N	\r
-243	13640	\N	0	107	2	\N	\r
-245	13660	\N	0	87	4	\N	\r
-246	13710	\N	0	0	0	\N	\r
-247	13720	\N	0	11	1	\N	\r
-248	13730	\N	0	12	1	\N	\r
-249	13740	\N	0	76	3	\N	\r
-250	13760	\N	0	1.2	0.10000000000000001	\N	\r
-251	13770	\N	0	13	1	\N	\r
-252	13780	\N	0	0.5	0.10000000000000001	\N	\r
-253	13790	\N	0	8	1	\N	\r
-254	13810	\N	0	1.2	0.10000000000000001	\N	\r
-255	13820	\N	0	28	2	\N	\r
-256	13830	\N	0	0	0	\N	\r
-258	15010	\N	0	7.3899999999999997	0.01	\N	\r
-259	15020	\N	0	40	2	\N	\r
-286	17120	\N	0	0	0	\N	\r
-288	17140	\N	0	\N	\N	Negative	\r
-289	17150	\N	0	\N	\N	Negative	\r
-290	17210	\N	0	\N	\N	Negative	\r
-291	17220	\N	0	\N	\N	Negative	\r
-292	17230	\N	0	\N	\N	Negative	\r
-293	17240	\N	0	\N	\N	Negative	\r
-294	17250	\N	0	\N	\N	Negative	\r
-295	17260	\N	0	\N	\N	Negative	\r
-296	17270	\N	0	\N	\N	Negative	\r
-297	17280	\N	0	\N	\N	Negative	\r
-298	17290	\N	0	\N	\N	Negative	\r
-299	17310	\N	0	13	0	\N	\r
-301	17330	\N	0	1132	32	\N	\r
-302	17340	\N	0	2.1000000000000001	0.20000000000000001	\N	\r
-304	17360	\N	0	32	3	\N	\r
-306	17380	\N	0	176	5	\N	\r
-307	18010	\N	0	\N	\N	Negative	\r
-308	18020	\N	0	\N	\N	Negative	\r
-309	18030	\N	0	\N	\N	Negative	\r
-4775	17050	66506	21	\N	\N	3+	High
-4776	17040	66507	21	\N	\N	4+	High
-4777	1000565	72018	21	5.4000000000000004	0.10000000000000001	Elevated	High
-4778	17090	\N	21	\N	\N	Negative	\N
-4779	17100	\N	21	\N	\N	Negative	\N
-4780	17110	\N	21	\N	\N	Negative	\N
-4781	17120	\N	21	\N	\N	Negative	\N
-4782	13120	66349	21	0.5	0	\N	Low
-4783	13060	\N	21	0.80000000000000004	0.10000000000000001	\N	\N
-4784	13020	71563	21	5.4000000000000004	0	\N	High
-4785	15010	66383	21	7.2999999999999998	0	\N	Low
-4786	15050	66392	21	7.2999999999999998	0	\N	Low
-4787	16010	66413	21	7.2999999999999998	0	\N	Low
-4788	16060	66504	21	7.2999999999999998	0	\N	Low
-4789	11010	\N	21	14.5	0.69999999999999996	\N	\N
-4790	13040	66403	21	15	0	\N	Low
-4791	11000	66279	21	23	1	\N	High
-4792	13050	66273	21	24	2	\N	High
-4793	50	\N	21	29	2	\N	High
-4794	13080	\N	21	30	0	\N	High
-4795	15020	66375	21	30	0	\N	Low
-4796	15060	66495	21	30	0	\N	Low
-4797	16020	66203	21	30	0	\N	Low
-4798	16070	66493	21	30	0	\N	Low
-4799	11020	\N	21	45	2	\N	\N
-4800	40	\N	21	72	3	\N	\N
-4801	13030	66328	21	80	0	\N	Low
-4802	11120	\N	21	90	2	\N	High
-4803	10	\N	21	98.400000000000006	0.20000000000000001	\N	\N
-4804	30	\N	21	105	4	\N	\N
-4805	20	\N	21	117	3	\N	High
-4806	13010	66194	21	125	0	\N	Low
-4807	11030	\N	21	270	10	\N	\N
-4808	13070	66695	21	614	13	\N	High
-4809	13730	66866	21	1432	24	\N	High
-4810	17050	66506	18	\N	\N	3+	High
-4811	17040	66507	18	\N	\N	4+	High
-4812	1000565	72018	18	5.4000000000000004	0.10000000000000001	Elevated	High
-4814	17100	\N	18	\N	\N	Negative	\N
-4815	17110	\N	18	\N	\N	Negative	\N
-4816	17120	\N	18	\N	\N	Negative	\N
-4817	13120	66349	18	0.5	0	\N	Low
-4818	13060	\N	18	0.80000000000000004	0.10000000000000001	\N	\N
-4819	13020	71563	18	4.0999999999999996	0	\N	High
-4820	15010	66383	18	7.2999999999999998	0	\N	Low
-4821	15050	66392	18	7.2999999999999998	0	\N	Low
-4822	16010	66413	18	7.2999999999999998	0	\N	Low
-4823	16060	66504	18	7.2999999999999998	0	\N	Low
-4824	11010	\N	18	14.5	0.69999999999999996	\N	\N
-4825	11000	66279	18	18	1	\N	High
-4826	13040	66403	18	18	0	\N	Low
-4827	13050	66273	18	24	2	\N	High
-4828	50	\N	18	26	2	\N	High
-4829	13080	\N	18	27	0	\N	High
-4830	15020	66375	18	30	0	\N	Low
-4831	15060	66495	18	30	0	\N	Low
-4832	16020	66203	18	30	0	\N	Low
-4833	16070	66493	18	30	0	\N	Low
-4834	11020	\N	18	45	2	\N	\N
-4835	40	\N	18	76	2	\N	\N
-4836	13030	66328	18	80	0	\N	Low
-4837	11120	\N	18	90	2	\N	High
-4838	10	\N	18	98.599999999999994	0.20000000000000001	\N	\N
-4839	20	\N	18	103	3	\N	High
-4840	30	\N	18	111	3	\N	\N
-4841	13010	66194	18	136	0	\N	Low
-4842	13070	66695	18	195	4	\N	High
-4843	11030	\N	18	270	10	\N	\N
-4844	13730	66866	18	1432	24	\N	High
-4845	17040	66507	19	\N	\N	1+	High
-4846	1000565	72018	19	5.4000000000000004	0.10000000000000001	Elevated	High
-4847	13060	\N	19	0.80000000000000004	0.10000000000000001	\N	\N
-4848	13020	\N	19	4.0999999999999996	0	\N	\N
-4849	15010	66383	19	7.4000000000000004	0	\N	Low
-4850	15050	66392	19	7.4000000000000004	0	\N	Low
-4851	16010	66413	19	7.4000000000000004	0	\N	Low
-4852	16060	66504	19	7.4000000000000004	0	\N	Low
-4853	11000	66279	19	12	1	\N	\N
-4854	13080	\N	19	12	0	\N	\N
-4855	11010	\N	19	14.5	0.69999999999999996	\N	\N
-4856	50	\N	19	15	2	\N	\N
-4857	13050	\N	19	16	2	\N	\N
-4858	13040	\N	19	24	0	\N	\N
-4859	15020	66375	19	40	0	\N	Low
-4860	15060	66495	19	40	0	\N	Low
-4861	16020	66203	19	40	0	\N	Low
-4862	16070	66493	19	40	0	\N	Low
-4863	11020	\N	19	45	2	\N	\N
-4864	40	\N	19	82	2	\N	\N
-4865	20	\N	19	86	3	\N	\N
-4866	11120	\N	19	90	2	\N	High
-4867	13030	\N	19	90	0	\N	\N
-4868	10	\N	19	98.599999999999994	0.10000000000000001	\N	\N
-4869	30	\N	19	124	3	\N	\N
-4870	13010	\N	19	136	0	\N	\N
-4871	13070	\N	19	195	4	\N	\N
-4872	11030	\N	19	270	10	\N	\N
-4873	17040	66507	20	\N	\N	1+	High
-4874	1000565	72018	20	5.4000000000000004	0.10000000000000001	Elevated	High
-4875	13060	\N	20	0.80000000000000004	0.10000000000000001	\N	\N
-4876	13020	66624	20	2.7999999999999998	0	\N	Low
-4877	15010	66383	20	7.2999999999999998	0	\N	Low
-4878	15050	66392	20	7.2999999999999998	0	\N	Low
-4879	16010	66413	20	7.2999999999999998	0	\N	Low
-4880	16060	66504	20	7.2999999999999998	0	\N	Low
-4881	11000	66279	20	12	1	\N	\N
-4882	13040	\N	20	12	0	\N	Low
-4883	11010	\N	20	14.5	0.69999999999999996	\N	\N
-4884	13050	\N	20	23	2	\N	\N
-4885	13080	\N	20	28	0	\N	High
-4886	15020	66375	20	30	0	\N	Low
-4887	15060	66495	20	30	0	\N	Low
-4888	16020	66203	20	30	0	\N	Low
-4889	16070	66493	20	30	0	\N	Low
-4890	50	\N	20	33	2	\N	High
-4891	11020	\N	20	45	2	\N	\N
-4892	13070	66549	20	54	4	\N	Low
-4893	40	\N	20	57	2	\N	Low
-4894	13030	\N	20	81	0	\N	Low
-4895	30	\N	20	85	3	\N	Low
-4896	11120	\N	20	90	2	\N	High
-4897	10	\N	20	97.900000000000006	0.10000000000000001	\N	Low
-4898	20	\N	20	134	3	\N	High
-4899	13010	\N	20	138	0	\N	\N
-4900	11030	\N	20	270	10	\N	\N
-\.
-
-
---
--- Name: sim_state_result_sim_state_result_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_state_result_sim_state_result_id_seq', 4906, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_state_transition.dump.sql b/medinfo/db/definition/simdata/sim_state_transition.dump.sql
deleted file mode 100644
index 4f33d939..00000000
--- a/medinfo/db/definition/simdata/sim_state_transition.dump.sql
+++ /dev/null
@@ -1,62 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_state_transition; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_state_transition (sim_state_transition_id, pre_state_id, post_state_id, clinical_item_id, time_trigger, description) FROM stdin;
-1	14	15	\N	3600	Active bleed uncontrolled
-2	15	4	\N	1800	Bled out, coding
-3	15	14	65640	\N	Transfuse RBC temporarily restabilizes
-4	15	14	61975	\N	Transfuse RBC temporarily restabilizes
-5	14	14	65640	\N	Transfuse RBC temporarily resets state decay
-6	14	14	61975	\N	Transfuse RBC temporarily resets state decay timer
-7	14	14	43996	\N	Octreotide temporarily stalls variceal bleed
-8	14	16	61993	\N	Coagulopathy corrected with FFP
-9	14	16	65702	\N	Coagulopathy corrected with FFP
-10	14	16	44001	\N	Cogulopathy stabilized with Vitamin K
-11	16	2	49481	\N	GI Consult takes patient for EGD
-12	16	2	45969	\N	EGD Ordered and Done
-15	15	17	49481	\N	GI Consult Block
-20	15	17	45969	\N	EGD Block until stabilize
-21	14	17	49481	\N	GI Consult block
-22	14	17	45969	\N	EGD Block until stabilize
-23	17	15	\N	0	Consult block immediately to bleeding out state
-24	15	14	45748	\N	Transfuse RBC temporarily restabilizes
-25	14	14	45748	\N	Transfuse RBC temporarily resets state decay timer
-26	21	18	35850	\N	Insulin given for DKA, normalizes glucose
-27	18	20	\N	3600	Only insulin given, DKA patient becomes hypoglycemic
-28	20	18	44201	\N	Glucose and or potassium added back?
-30	20	18	44239	\N	Glucose and or potassium added back?
-31	20	18	44316	\N	Glucose and or potassium added back?
-32	20	18	46277	\N	Glucose and or potassium added back?
-33	20	18	46505	\N	Glucose and or potassium added back?
-29	18	19	44201	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
-34	18	19	44239	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
-35	18	19	44316	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
-36	18	19	46277	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
-37	18	19	46505	\N	Gap closed when ongoing insulin supplemented by glucose and potassium
-\.
-
-
---
--- Name: sim_state_transition_sim_state_transition_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_state_transition_sim_state_transition_id_seq', 37, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/definition/simdata/sim_user.dump.sql b/medinfo/db/definition/simdata/sim_user.dump.sql
deleted file mode 100644
index 3f7925d6..00000000
--- a/medinfo/db/definition/simdata/sim_user.dump.sql
+++ /dev/null
@@ -1,39 +0,0 @@
---
--- PostgreSQL database dump
---
-
-SET statement_timeout = 0;
-SET client_encoding = 'UTF8';
-SET standard_conforming_strings = on;
-SET check_function_bodies = false;
-SET client_min_messages = warning;
-
-SET search_path = public, pg_catalog;
-
---
--- Data for Name: sim_user; Type: TABLE DATA; Schema: public; Owner: -
---
-
-COPY sim_user (sim_user_id, name) FROM stdin;
-0	Default User
-1	Jonathan Chen
-9	User1
-2	User2
-10	User3
-11	User4
-12	User5
-13	User6
-\.
-
-
---
--- Name: sim_user_sim_user_id_seq; Type: SEQUENCE SET; Schema: public; Owner: -
---
-
-SELECT pg_catalog.setval('sim_user_sim_user_id_seq', 13, true);
-
-
---
--- PostgreSQL database dump complete
---
-
diff --git a/medinfo/db/support/syncCPOETables.bat b/medinfo/db/support/syncCPOETables.bat
deleted file mode 100644
index 812c4133..00000000
--- a/medinfo/db/support/syncCPOETables.bat
+++ /dev/null
@@ -1,27 +0,0 @@
-rem For backup purposes, core reagent models and rules should be dumped
-rem    from a common central development server (since only developers should edit that content).
-
-set SOURCE_COMPUTER=easyv1.ics.uci.edu
-set SOURCE_PORT=6002
-set SOURCE_DATABASE=reactiondev
-set SOURCE_USERNAME=chenjh
-set SOURCE_PASSWORD=ChemDB#2008
-
-set TARGET_COMPUTER=easyv1.ics.uci.edu
-set TARGET_PORT=6002
-set TARGET_DATABASE=reaction_tutorial
-set TARGET_USERNAME=chenjh
-set TARGET_PASSWORD=ChemDB#2008
-
-set TARGET_COMPUTER=localhost
-set TARGET_PORT=5432
-set TARGET_DATABASE=rex_update_20111123
-set TARGET_USERNAME=jonc101
-set TARGET_PASSWORD=1234
-
-set SYNC_COMMAND=python ../codebase/medinfo/db/support/syncDatabaseTable.py -c %SOURCE_COMPUTER% -o %SOURCE_PORT% -d %SOURCE_DATABASE% -u %SOURCE_USERNAME% -p %SOURCE_PASSWORD% -C %TARGET_COMPUTER% -O %TARGET_PORT% -D %TARGET_DATABASE% -U %TARGET_USERNAME% -P %TARGET_PASSWORD% 
-
-%SYNC_COMMAND% clinical_item_category
-%SYNC_COMMAND% clinical_item
-%SYNC_COMMAND% patient_item
-%SYNC_COMMAND% clinical_item_association
diff --git a/medinfo/db/support/syncDatabaseTable.py b/medinfo/db/support/syncDatabaseTable.py
index cb212f21..1824c916 100644
--- a/medinfo/db/support/syncDatabaseTable.py
+++ b/medinfo/db/support/syncDatabaseTable.py
@@ -1,9 +1,8 @@
 import sys, os, pprint; #pprint is useful for debugging
-from sets import Set;
 from optparse import OptionParser
-from CHEM.Common import DBUtil;
-from CHEM.Common.Model import RowItemModel;
-from CHEM.Common.Util import ProgressDots;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import RowItemModel;
+from medinfo.common.Util import ProgressDots;
 from CHEM.DB.support import DBCopyFormatter;
 
 def main(argv):
@@ -28,7 +27,6 @@ def main(argv):
     parser.add_option("-O", "--targetPort", dest="targetPort",  metavar="<targetPort>", help="Target database port.", default=None)
 
     parser.add_option("-r", "--rowIDs", dest="rowIDs",  metavar="<rowIDs>", help="Comma-separated list of IDs to specify which rows to copy / sync.  Leave blank to copy / sync all rows from the source database.");
-    parser.add_option('-F', '--formatter', dest='formatter', metavar='<formatter>', default=None, help='Name of the formatter class to use.');
     parser.add_option('-S', '--syncSequence', dest='syncSequence', action="store_true", help='Add this option to update sequence number from the source table to the target table');
     
     """Assign options (available are above) and args (table names), that were passed via command line when script was executed
@@ -64,8 +62,8 @@ def main(argv):
         sourceConn = DBUtil.connection( sourceDB );
         targetConn = DBUtil.connection( targetDB );
         
-    except Exception, exc:
-        print str(exc);
+    except Exception as exc:
+        print(str(exc));
         parser.print_help()
         exit();
 
@@ -78,24 +76,18 @@ def main(argv):
            just prep-work for the syncTable function
         """
         if options.rowIDs:
-            rowIDStrSet = Set();
+            rowIDStrSet = set();
             for rowIDStr in options.rowIDs.split(","):
                 rowIDStrSet.add(rowIDStr);
         
         """For each table specified, format and sync
         """
         for syncTableName in syncTableList:
-            print >> sys.stderr, "Syncing %s" % syncTableName;
+            print("Syncing %s" % syncTableName, file=sys.stderr);
             
             ##Resolve formatter to actual class
             myFormatter = None;
-            if options.formatter is not None:
-                try:
-                    myFormatter = getattr(DBCopyFormatter, options.formatter);
-                except AttributeError, e:
-                    print >> sys.stderr, "Formatter class %s does not exist" % options.formatter;
-                    raise;
-            
+
             """Sync table data
             """
             syncTable(sourceConn, targetConn, syncTableName, rowIDStrSet, myFormatter);
@@ -123,13 +115,13 @@ def syncSequence(sourceConn, targetConn, syncTableName):
         seqVal = DBUtil.execute( seqValQuery, conn=sourceConn )
         seqVal = int(seqVal[0][0]) + 1; # Add 1 to ensure no overlap
         
-        print >> sys.stderr, "Last value of sequence %s, will be updated to %d" % (seqName,seqVal)
+        print("Last value of sequence %s, will be updated to %d" % (seqName,seqVal), file=sys.stderr)
         
         altValQuery = "ALTER SEQUENCE %s RESTART WITH %d" % (seqName,seqVal)
         DBUtil.execute( altValQuery, conn=targetConn )
     
-    except Exception, exc:
-        print "syncSequence Failed: ", str(exc)
+    except Exception as exc:
+        print("syncSequence Failed: ", str(exc))
         parser.print_help()
         exit()
 
@@ -141,15 +133,15 @@ def syncTable(sourceConn, targetConn, syncTableName, rowIDStrSet=None, formatter
         idQuery = "select %s from %s" % (idCol, syncTableName);
 
         # Collect all of the IDs known in the target database and store in memory for rapid lookup
-        print >> sys.stderr, "Querying for IDs from Target Database";
+        print("Querying for IDs from Target Database", file=sys.stderr);
         targetIdTable = DBUtil.execute( idQuery, conn=targetConn );
-        targetIdSet = Set();
+        targetIdSet = set();
         for row in targetIdTable:
             targetId = row[0];
             targetIdSet.add(targetId);
 
         # Query data out of the source table, but do it by a cursor so we can stream through large data tables
-        print >> sys.stderr, "Querying for Source Data";
+        print("Querying for Source Data", file=sys.stderr);
         dataQuery = "select * from %s" % (syncTableName);
         sourceCursor = sourceConn.cursor(); 
         sourceCursor.execute(dataQuery);
@@ -168,22 +160,22 @@ def syncTable(sourceConn, targetConn, syncTableName, rowIDStrSet=None, formatter
             
             if rowIDStrSet is None or str(dataModel[idCol]) in rowIDStrSet:
                 if rowIDStrSet is not None:
-                    print >> sys.stderr, "Syncing record: %s" % dataModel[idCol];
+                    print("Syncing record: %s" % dataModel[idCol], file=sys.stderr);
                 
                 if dataModel[idCol] not in targetIdSet:
                     # Row does not yet exist in target database, need to insert it
                     if insertQuery is None:
-                        insertQuery = DBUtil.buildInsertQuery( syncTableName, dataModel.keys() );
-                    insertParams= dataModel.values();
+                        insertQuery = DBUtil.buildInsertQuery( syncTableName, list(dataModel.keys()) );
+                    insertParams= list(dataModel.values());
 
                     targetCursor.execute( insertQuery, insertParams );
 
                 else:
                     # Row already exists in target database, just update values
                     if updateQuery is None:
-                        updateQuery = DBUtil.buildUpdateQuery( syncTableName, dataModel.keys() );
+                        updateQuery = DBUtil.buildUpdateQuery( syncTableName, list(dataModel.keys()) );
                     updateParams = [];
-                    updateParams.extend( dataModel.values() );
+                    updateParams.extend( list(dataModel.values()) );
                     updateParams.append( dataModel[idCol] );
 
                     targetCursor.execute( updateQuery, updateParams );
diff --git a/medinfo/db/test/TestDBUtil.py b/medinfo/db/test/TestDBUtil.py
index 39728065..cb3f2483 100644
--- a/medinfo/db/test/TestDBUtil.py
+++ b/medinfo/db/test/TestDBUtil.py
@@ -2,21 +2,20 @@
 """Test case for respective module in medinfo.Common package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
 from datetime import datetime;
 
-from Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
-from Util import log;
+from .Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
+from .Util import log;
 
-from Util import DBTestCase;
+from .Util import DBTestCase;
 
 from medinfo.common.test.Util import MedInfoTestCase;
 
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # String representations for boolean values
 TRUE_STR = "1";
@@ -102,7 +101,7 @@ def tearDown(self):
         try:
             DBUtil.execute("drop table TestTypes")
             pass
-        except Exception, err:
+        except Exception as err:
             log.warning(err)
             pass
 
@@ -167,7 +166,7 @@ def test_runDBScript_skipErrors(self):
         actualErr = False
         try:
             DBUtil.main(argv)
-        except Exception, err:
+        except Exception as err:
             actualErr = True
         self.assertEqual(expectErr,actualErr)
         sys.stdin = origStdin
@@ -284,7 +283,7 @@ def test_insertFile_skipErrors(self):
         actualErr = False
         try:
             DBUtil.main(argv)
-        except Exception, err:
+        except Exception as err:
             actualErr = True
         self.assertEqual(expectErr,actualErr)
         sys.stdout = origStdout
@@ -381,7 +380,7 @@ def test_insertFile_escapeStrings(self):
 
         expectedData = \
             [   [   -1, "A"],
-                [   -2, u"B\\xaeb"],
+                [   -2, "B\\xaeb"],
                 [   -3, "C"],
                 [   -4, "D"],
             ];
@@ -565,27 +564,6 @@ def test_updateFromFile_commandline(self):
         results = DBUtil.execute( self.DATA_QUERY );
         self.assertEqual( self.DATA_ROWS, results );
 
-
-    def xtest_incColNamesAndTypeCodes(self):
-        # Skip this test for now.  Different DB connector modules
-        #   handle type codes differently (numbers, collections of numbers, objects containing collections)
-        #   Not using that feature for any function currently anyway
-        DBUtil.runDBScript( self.SCRIPT_FILE, False )
-
-        cols = ["testtypes_id", "mytext", "myinteger", "myreal", "mydatetime", "myyesno"];
-        types= [ROWID, STRING, NUMBER, NUMBER, DATETIME, BOOLEAN];
-
-        query = "select %s from TestTypes;" % str.join(",",cols);
-
-        results = DBUtil.execute( query, includeColumnNames=True, incTypeCodes=True );
-
-        self.assertEqual( cols, results[0] );
-        for verifyCode, typeCode in zip(types, results[1]):
-            print verifyCode, typeCode
-
-            #self.assertEqual( verifyCode, typeCode );
-
-
     def test_deleteRows(self):
         DBUtil.runDBScript( self.SCRIPT_FILE, False );
 
diff --git a/medinfo/db/test/TestResultsFormatter.py b/medinfo/db/test/TestResultsFormatter.py
index d80046b0..93cbac6d 100644
--- a/medinfo/db/test/TestResultsFormatter.py
+++ b/medinfo/db/test/TestResultsFormatter.py
@@ -2,28 +2,26 @@
 """Test case for respective module in medinfo.Common package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 import unittest
 
 from datetime import datetime;
 
-from Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
-from Util import log;
-
-from Util import DBTestCase;
+from .Const import LOGGER_LEVEL, RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.common.test.Util import MedInfoTestCase;
 
 from medinfo.db.ResultsFormatter import TabDictReader;
 
-class TestResultsFormatter(DBTestCase):
+class TestResultsFormatter(MedInfoTestCase):
     def setUp(self):
         """Prepare state for test cases"""
-        DBTestCase.setUp(self);
+        MedInfoTestCase.setUp(self);
 
     def tearDown(self):
         """Restore state from any setUp or test steps"""
-        DBTestCase.tearDown(self);
+        MedInfoTestCase.tearDown(self);
 
     def test_TabDictReader(self):
         """Verify expected results when reading from different delimited file examples,
@@ -44,7 +42,7 @@ def test_TabDictReader(self):
                 {"order_proc_id":"4530091", "display_name":"Give patient \"Bedside Insulin Pump Flow Sheet\" to document insulin delivery, BG and carbohydrates"},
                 {"order_proc_id":"5905631", "display_name":"Sign above bed\"Do not reposition NG\""},
             ];
-        targetKeys = expectedData[0].keys();    # Check a subset of values for simplicity
+        targetKeys = list(expectedData[0].keys());    # Check a subset of values for simplicity
         self.assertEqualDictList( expectedData, parsedData, targetKeys );
 
         # Another test on messed up order_med end double quote
@@ -62,14 +60,13 @@ def test_TabDictReader(self):
                 {"order_med_id":"5226027", "DISPLAY_NAME":"warfarin (COUMADIN) tablet 5 mg \"Pharmacy Protocol\""},
                 {"order_med_id":"3579366", "DISPLAY_NAME":""},
             ];
-        targetKeys = expectedData[0].keys();    # Check a subset of values for simplicity
+        targetKeys = list(expectedData[0].keys());    # Check a subset of values for simplicity
         self.assertEqualDictList( expectedData, parsedData, targetKeys );
 
         #    1748628,-3085618212893,408616,06/27/2010 08:28,8,"Fax",21372,"WARFARIN 4 MG PO TABS","","",06/27/2010 18:00,06/28/2010 08:36,06/28/2010 15:36,,"",365888,"warfarin (COUMADIN) tablet 4 mg (Per Pharmacy Protocol"",,"",15,"Oral",06/28/2010 15:36,368883925,"200023","DAILY","QPM","4",3,"mg",9,"Discontinued",365888,372188,4,,3,"mg",2000253,"C3","2","MODIFIED",3,"Discontinued Medication","","",,,"",2,"Inpatient",,,"",,,,,,"",,,,"",,,,"","Y",4,,3,"mg",1,,5002,"Tab"
         #        {"order_med_id":"1748628", "DISPLAY_NAME":"warfarin (COUMADIN) tablet 4 mg (Per Pharmacy Protocol\""},
 
 
-
 def suite():
     """Returns the suite of tests to run for this test class / module.
     Use unittest.makeSuite methods which simply extracts all of the
diff --git a/medinfo/db/test/Util.py b/medinfo/db/test/Util.py
index 7eb400b7..9edd1159 100644
--- a/medinfo/db/test/Util.py
+++ b/medinfo/db/test/Util.py
@@ -1,10 +1,12 @@
-import Const
+from . import Const
 import sys, os
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 
 from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil;
 
 import medinfo.db.Env;
 import medinfo.db.Util;
@@ -12,17 +14,20 @@
 
 log = logging.getLogger("CDSS")
 
-handler = logging.StreamHandler(sys.stderr)
-formatter = logging.Formatter(Const.LOGGER_FORMAT)
+#handler = logging.StreamHandler(sys.stderr)
+#formatter = logging.Formatter(Const.LOGGER_FORMAT)
 
-handler.setFormatter(formatter)
-log.addHandler(handler)
+#handler.setFormatter(formatter)
+#log.addHandler(handler)
 
 # Suppress uninteresting application output
 medinfo.db.Util.log.setLevel(Const.APP_LOGGER_LEVEL)
 
 
 class DBTestCase(MedInfoTestCase):
+    orig_DB_PARAM = None;
+    testDBCreated = False;
+
     """Common base class for TestCase classes that cinlude queries against the databse.
     Important distinction to help put in hooks to protect a "real" database from
     test queries.
@@ -32,14 +37,33 @@ def setUp(self):
         """
         MedInfoTestCase.setUp(self)
 
-        # Override default DB connection params to rest DB, but retain links to original
+        # if medinfo.db.Env.DB_PARAM["DSN"] == medinfo.db.Env.TEST_DB_PARAM["DSN"]:
+        #     quit("Please use different names for production and test databases!")
+
+        # Override default DB connection params to test DB, but retain links to original
         self.orig_DB_PARAM = dict(medinfo.db.Env.DB_PARAM);
 
         medinfo.db.Env.DB_PARAM.update(medinfo.db.Env.TEST_DB_PARAM);
 
+        # Create the temporary test database to work with
+        try:
+            DBUtil.createDatabase(medinfo.db.Env.DB_PARAM);
+            self.testDBCreated = True;  # If error on above (e.g., database already exists), will have aborted with a database error before this
+        except Exception as err:  # Better if could be specific for <DBConnectorModule>.ProgrammingError
+            # Error on database creation, probably because existing one already there
+            # Drop existing database if already there, but beware!
+            # Beware of accidentally dropping an existing production database! This may still be necessary to cleanup if accidentally left a test database instance behind
+            DBUtil.dropDatabase(medinfo.db.Env.DB_PARAM);
+            DBUtil.createDatabase(medinfo.db.Env.DB_PARAM);
+            self.testDBCreated = True;  # If error on above (e.g., database already exists), will have aborted with a database error before this
+
     def tearDown(self):
         """Restore state after test finishes.  Subclass must call this parent method to actually use it.
         """
+        # Drop the temporary test database, but make sure we created it ourselves, so we don't accidentally drop a production database
+        if self.testDBCreated:
+            DBUtil.dropDatabase(medinfo.db.Env.DB_PARAM);
+
         medinfo.db.Env.DB_PARAM.update(self.orig_DB_PARAM);
 
         MedInfoTestCase.tearDown(self)
diff --git a/medinfo/geography/ZIPCodeMapping.txt b/medinfo/geography/ZIPCodeMapping.txt
new file mode 100644
index 00000000..375ddf5d
--- /dev/null
+++ b/medinfo/geography/ZIPCodeMapping.txt
@@ -0,0 +1,11 @@
+# Crosswalk tables between ZIP codes, county codes, census tracts, etc.
+# Example adapted into ZIP_COUNTY_122019.csv
+https://www.huduser.gov/portal/datasets/usps_crosswalk.html#data
+
+# (Beware that these do not have 1-to-1 mappings)
+https://www.unitedstateszipcodes.org/zip-code-database/#matching-cities
+
+
+# County-Time Zone mapping
+# Adapted into bp03mr20.csv
+https://www.weather.gov/gis/ZoneCounty
diff --git a/medinfo/geography/ZIP_COUNTY_122019.csv.gz b/medinfo/geography/ZIP_COUNTY_122019.csv.gz
new file mode 100644
index 00000000..5727558c
Binary files /dev/null and b/medinfo/geography/ZIP_COUNTY_122019.csv.gz differ
diff --git a/medinfo/geography/bp03mr20.csv b/medinfo/geography/bp03mr20.csv
new file mode 100644
index 00000000..ce493f79
--- /dev/null
+++ b/medinfo/geography/bp03mr20.csv
@@ -0,0 +1,4554 @@
+STATE,ZONE,CWA,NAME,STATE_ZONE,COUNTY,FIPS,TIME_ZONE,FE_AREA,LAT,LON
+AK,017,AJK,Cape Fairweather to Cape Suckling Coastal Area,AK017,Yakutat,02282,A,,59.9186,-140.5769
+AK,018,AJK,Taiya Inlet and Klondike Highway,AK018,Skagway,02230,A,,59.5601,-135.3379
+AK,019,AJK,Haines Borough and Lynn Canal,AK019,Haines,02100,A,,59.0942,-135.4295
+AK,019,AJK,Haines Borough and Lynn Canal,AK019,Juneau,02110,A,,59.0942,-135.4295
+AK,020,AJK,Glacier Bay,AK020,Hoonah-Angoon,02105,A,,58.7895,-136.3767
+AK,021,AJK,Eastern Chichagof Island,AK021,Hoonah-Angoon,02105,A,,57.8952,-135.4475
+AK,021,AJK,Eastern Chichagof Island,AK021,Sitka,02220,A,,57.8952,-135.4475
+AK,022,AJK,Salisbury Sound to Cape Fairweather Coastal Area,AK022,Hoonah-Angoon,02105,A,,58.1518,-136.5895
+AK,022,AJK,Salisbury Sound to Cape Fairweather Coastal Area,AK022,Sitka,02220,A,,58.1518,-136.5895
+AK,023,AJK,Cape Decision to Salisbury Sound Coastal Area,AK023,Sitka,02220,A,,56.9012,-135.1575
+AK,024,AJK,Eastern Baranof Island and Southern Admiralty Island,AK024,Hoonah-Angoon,02105,A,,57.3045,-134.5992
+AK,024,AJK,Eastern Baranof Island and Southern Admiralty Island,AK024,Sitka,02220,A,,57.3045,-134.5992
+AK,025,AJK,Juneau Borough and Northern Admiralty Island,AK025,Hoonah-Angoon,02105,A,,58.0224,-133.7989
+AK,025,AJK,Juneau Borough and Northern Admiralty Island,AK025,Juneau,02110,A,,58.0224,-133.7989
+AK,025,AJK,Juneau Borough and Northern Admiralty Island,AK025,Petersburg Census Area,02195,A,,58.0224,-133.7989
+AK,026,AJK,Inner Channels from Kupreanof Island to Etolin Island,AK026,Petersburg Census Area,02195,A,,56.5997,-132.9810
+AK,026,AJK,Inner Channels from Kupreanof Island to Etolin Island,AK026,Wrangell,02275,A,,56.5997,-132.9810
+AK,027,AJK,Dixon Entrance to Cape Decision Coastal Area,AK027,Petersburg Census Area,02195,A,,55.2951,-133.0075
+AK,027,AJK,Dixon Entrance to Cape Decision Coastal Area,AK027,Prince of Wales-Hyder,02198,A,,55.2951,-133.0075
+AK,028,AJK,Southern Inner Channels,AK028,Ketchikan Gateway,02130,A,,55.5399,-132.0549
+AK,028,AJK,Southern Inner Channels,AK028,Prince of Wales-Hyder,02198,A,,55.5400,-132.0549
+AK,028,AJK,Southern Inner Channels,AK028,Wrangell,02275,A,,55.5400,-132.0549
+AK,029,AJK,Misty Fjords,AK029,Ketchikan Gateway,02130,A,,55.7461,-130.8022
+AK,029,AJK,Misty Fjords,AK029,Prince of Wales-Hyder,02198,A,,55.7461,-130.8022
+AK,029,AJK,Misty Fjords,AK029,Wrangell,02275,A,,55.7461,-130.8022
+AK,101,AFC,Anchorage,AK101,Anchorage,02020,A,,61.1948,-149.5182
+AK,111,AFC,Matanuska Valley,AK111,Anchorage,02020,A,,61.5885,-149.1656
+AK,111,AFC,Matanuska Valley,AK111,Matanuska-Susitna,02170,A,,61.5885,-149.1656
+AK,121,AFC,Western Kenai Peninsula,AK121,Kenai Peninsula,02122,A,,60.3254,-150.7090
+AK,125,AFC,Western Prince William Sound,AK125,Kenai Peninsula,02122,A,,60.3983,-148.9941
+AK,125,AFC,Western Prince William Sound,AK125,Valdez-Cordova,02261,A,,60.3983,-148.9941
+AK,131,AFC,Northeast Prince William Sound,AK131,Valdez-Cordova,02261,A,,61.1700,-146.5112
+AK,135,AFC,Southeast Prince William Sound,AK135,Valdez-Cordova,02261,A,,60.5931,-145.2475
+AK,141,AFC,Copper River Basin,AK141,Matanuska-Susitna,02170,A,,61.9020,-144.9312
+AK,141,AFC,Copper River Basin,AK141,Southeast Fairbanks,02240,A,,61.9020,-144.9312
+AK,141,AFC,Copper River Basin,AK141,Valdez-Cordova,02261,A,,61.9020,-144.9312
+AK,145,AFC,Susitna Valley,AK145,Bethel,02050,A,,62.1220,-150.6874
+AK,145,AFC,Susitna Valley,AK145,Denali,02068,A,,62.1220,-150.6874
+AK,145,AFC,Susitna Valley,AK145,Kenai Peninsula,02122,A,,62.1220,-150.6874
+AK,145,AFC,Susitna Valley,AK145,Matanuska-Susitna,02170,A,,62.1220,-150.6874
+AK,152,AFC,Lower Kuskokwim Valley,AK152,Bethel,02050,A,,61.5198,-156.3169
+AK,152,AFC,Lower Kuskokwim Valley,AK152,Dillingham,02070,A,,61.5198,-156.3169
+AK,152,AFC,Lower Kuskokwim Valley,AK152,Kenai Peninsula,02122,A,,61.5198,-156.3169
+AK,152,AFC,Lower Kuskokwim Valley,AK152,Lake and Peninsula,02164,A,,61.5198,-156.3169
+AK,152,AFC,Lower Kuskokwim Valley,AK152,Matanuska-Susitna,02170,A,,61.5198,-156.3169
+AK,152,AFC,Lower Kuskokwim Valley,AK152,Yukon-Koyukuk,02290,A,,61.5198,-156.3169
+AK,155,AFC,Kuskokwim Delta,AK155,Bethel,02050,A,,60.6713,-162.8471
+AK,155,AFC,Kuskokwim Delta,AK155,Kusilvak,02158,A,,60.6713,-162.8471
+AK,161,AFC,Bristol Bay,AK161,Bethel,02050,A,,59.2984,-157.2451
+AK,161,AFC,Bristol Bay,AK161,Bristol Bay,02060,A,,59.2984,-157.2451
+AK,161,AFC,Bristol Bay,AK161,Dillingham,02070,A,,59.2984,-157.2451
+AK,161,AFC,Bristol Bay,AK161,Kenai Peninsula,02122,A,,59.2984,-157.2451
+AK,161,AFC,Bristol Bay,AK161,Kodiak Island,02150,A,,59.2984,-157.2451
+AK,161,AFC,Bristol Bay,AK161,Lake and Peninsula,02164,A,,59.2984,-157.2451
+AK,171,AFC,Kodiak Island,AK171,Kenai Peninsula,02122,A,,58.2911,-154.1629
+AK,171,AFC,Kodiak Island,AK171,Kodiak Island,02150,A,,58.2911,-154.1629
+AK,171,AFC,Kodiak Island,AK171,Lake and Peninsula,02164,A,,58.2911,-154.1629
+AK,181,AFC,Alaska Peninsula,AK181,Aleutians East,02013,A,,55.6096,-161.2029
+AK,181,AFC,Alaska Peninsula,AK181,Lake and Peninsula,02164,A,,55.6096,-161.2029
+AK,185,AFC,Eastern Aleutians,AK185,Aleutians West,02016,AH,,53.6105,-167.1813
+AK,187,AFC,Central Aleutians,AK187,Aleutians West,02016,AH,,52.0877,-174.8314
+AK,191,AFC,Western Aleutians,AK191,Aleutians West,02016,AH,,52.2834,133.0159
+AK,195,AFC,Pribilof Islands,AK195,Aleutians West,02016,AH,,56.9092,-169.9790
+AK,201,AFG,Western Arctic Coast,AK201,North Slope,02185,A,,70.0813,-160.3323
+AK,202,AFG,Northern Arctic Coast,AK202,North Slope,02185,A,,70.6873,-155.3712
+AK,203,AFG,Central Beaufort Sea Coast,AK203,North Slope,02185,A,,70.2069,-149.5616
+AK,204,AFG,Eastern Beaufort Sea Coast,AK204,North Slope,02185,A,,69.8866,-143.7831
+AK,205,AFG,Northwestern Brooks Range,AK205,North Slope,02185,A,,69.2115,-157.3000
+AK,205,AFG,Northwestern Brooks Range,AK205,Northwest Arctic,02188,A,,69.2115,-157.3000
+AK,205,AFG,Northwestern Brooks Range,AK205,Yukon-Koyukuk,02290,A,,69.2115,-157.3000
+AK,206,AFG,Northeastern Brooks Range,AK206,North Slope,02185,A,,69.0427,-148.2525
+AK,206,AFG,Northeastern Brooks Range,AK206,Yukon-Koyukuk,02290,A,,69.0427,-148.2525
+AK,207,AFG,Chukchi Sea Coast,AK207,Nome,02180,A,,66.8027,-165.2860
+AK,207,AFG,Chukchi Sea Coast,AK207,North Slope,02185,A,,66.8027,-165.2860
+AK,207,AFG,Chukchi Sea Coast,AK207,Northwest Arctic,02188,A,,66.8027,-165.2860
+AK,208,AFG,Lower Kobuk and Noatak Valleys,AK208,North Slope,02185,A,,67.8186,-162.2140
+AK,208,AFG,Lower Kobuk and Noatak Valleys,AK208,Northwest Arctic,02188,A,,67.8186,-162.2140
+AK,209,AFG,Baldwin Peninsula and Selawik Valley,AK209,Northwest Arctic,02188,A,,66.6468,-160.8657
+AK,210,AFG,Northern and Interior Seward Peninsula,AK210,Nome,02180,A,,65.4916,-162.5763
+AK,210,AFG,Northern and Interior Seward Peninsula,AK210,Northwest Arctic,02188,A,,65.4916,-162.5763
+AK,210,AFG,Northern and Interior Seward Peninsula,AK210,Yukon-Koyukuk,02290,A,,65.4916,-162.5763
+AK,211,AFG,Southern Seward Peninsula Coast,AK211,Nome,02180,A,,64.6146,-164.4924
+AK,212,AFG,Eastern Norton Sound and Nulato Hills,AK212,Kusilvak,02158,A,,63.9319,-161.0572
+AK,212,AFG,Eastern Norton Sound and Nulato Hills,AK212,Nome,02180,A,,63.9319,-161.0572
+AK,212,AFG,Eastern Norton Sound and Nulato Hills,AK212,Yukon-Koyukuk,02290,A,,63.9319,-161.0572
+AK,213,AFG,St Lawrence Island and Bering Strait Coast,AK213,Nome,02180,A,,64.5057,-168.3102
+AK,214,AFG,Yukon Delta,AK214,Kusilvak,02158,A,,62.1753,-163.9603
+AK,215,AFG,Lower Yukon Valley,AK215,Bethel,02050,A,,62.6677,-159.7257
+AK,215,AFG,Lower Yukon Valley,AK215,Kusilvak,02158,A,,62.6677,-159.7257
+AK,215,AFG,Lower Yukon Valley,AK215,Nome,02180,A,,62.6677,-159.7257
+AK,215,AFG,Lower Yukon Valley,AK215,Yukon-Koyukuk,02290,A,,62.6677,-159.7257
+AK,216,AFG,Lower Koyukuk and Middle Yukon Valleys,AK216,Northwest Arctic,02188,A,,64.7408,-156.6050
+AK,216,AFG,Lower Koyukuk and Middle Yukon Valleys,AK216,Yukon-Koyukuk,02290,A,,64.7408,-156.6050
+AK,217,AFG,Upper Kobuk and Noatak Valleys,AK217,North Slope,02185,A,,67.3545,-157.2832
+AK,217,AFG,Upper Kobuk and Noatak Valleys,AK217,Northwest Arctic,02188,A,,67.3545,-157.2832
+AK,217,AFG,Upper Kobuk and Noatak Valleys,AK217,Yukon-Koyukuk,02290,A,,67.3545,-157.2832
+AK,218,AFG,Southeastern Brooks Range,AK218,North Slope,02185,A,,67.8926,-146.8934
+AK,218,AFG,Southeastern Brooks Range,AK218,Northwest Arctic,02188,A,,67.8926,-146.8934
+AK,218,AFG,Southeastern Brooks Range,AK218,Yukon-Koyukuk,02290,A,,67.8926,-146.8934
+AK,219,AFG,Upper Koyukuk Valley,AK219,Northwest Arctic,02188,A,,66.3788,-152.5178
+AK,219,AFG,Upper Koyukuk Valley,AK219,Yukon-Koyukuk,02290,A,,66.3788,-152.5178
+AK,220,AFG,Yukon Flats and Surrounding Uplands,AK220,Fairbanks North Star,02090,A,,66.4063,-145.5753
+AK,220,AFG,Yukon Flats and Surrounding Uplands,AK220,Yukon-Koyukuk,02290,A,,66.4063,-145.5753
+AK,221,AFG,Central Interior,AK221,Denali,02068,A,,64.7065,-151.4866
+AK,221,AFG,Central Interior,AK221,Fairbanks North Star,02090,A,,64.7065,-151.4866
+AK,221,AFG,Central Interior,AK221,Yukon-Koyukuk,02290,A,,64.7065,-151.4866
+AK,222,AFG,Middle Tanana Valley,AK222,Fairbanks North Star,02090,A,,64.9934,-147.3480
+AK,222,AFG,Middle Tanana Valley,AK222,Yukon-Koyukuk,02290,A,,64.9934,-147.3480
+AK,223,AFG,Deltana and Tanana Flats,AK223,Denali,02068,A,,64.2155,-146.6153
+AK,223,AFG,Deltana and Tanana Flats,AK223,Fairbanks North Star,02090,A,,64.2155,-146.6153
+AK,223,AFG,Deltana and Tanana Flats,AK223,Southeast Fairbanks,02240,A,,64.2155,-146.6153
+AK,223,AFG,Deltana and Tanana Flats,AK223,Yukon-Koyukuk,02290,A,,64.2155,-146.6153
+AK,224,AFG,Upper Tanana Valley and the Fortymile Country,AK224,Fairbanks North Star,02090,A,,64.4090,-142.8780
+AK,224,AFG,Upper Tanana Valley and the Fortymile Country,AK224,Southeast Fairbanks,02240,A,,64.4090,-142.8780
+AK,224,AFG,Upper Tanana Valley and the Fortymile Country,AK224,Valdez-Cordova,02261,A,,64.4090,-142.8780
+AK,224,AFG,Upper Tanana Valley and the Fortymile Country,AK224,Yukon-Koyukuk,02290,A,,64.4090,-142.8780
+AK,225,AFG,Denali,AK225,Denali,02068,A,,63.5852,-150.0989
+AK,226,AFG,Eastern Alaska Range,AK226,Denali,02068,A,,63.0805,-144.4681
+AK,226,AFG,Eastern Alaska Range,AK226,Matanuska-Susitna,02170,A,,63.0805,-144.4681
+AK,226,AFG,Eastern Alaska Range,AK226,Southeast Fairbanks,02240,A,,63.0805,-144.4681
+AK,226,AFG,Eastern Alaska Range,AK226,Valdez-Cordova,02261,A,,63.0805,-144.4681
+AK,227,AFG,Upper Kuskokwim Valley,AK227,Denali,02068,A,,62.9842,-154.2532
+AK,227,AFG,Upper Kuskokwim Valley,AK227,Matanuska-Susitna,02170,A,,62.9842,-154.2532
+AK,227,AFG,Upper Kuskokwim Valley,AK227,Yukon-Koyukuk,02290,A,,62.9842,-154.2532
+AL,001,HUN,Lauderdale,AL001,Lauderdale,01077,C,nw,34.9015,-87.6544
+AL,002,HUN,Colbert,AL002,Colbert,01033,C,nw,34.7005,-87.8049
+AL,003,HUN,Franklin,AL003,Franklin,01059,C,nw,34.4417,-87.8438
+AL,004,HUN,Lawrence,AL004,Lawrence,01079,C,nw,34.5220,-87.3110
+AL,005,HUN,Limestone,AL005,Limestone,01083,C,nc,34.8101,-86.9813
+AL,006,HUN,Madison,AL006,Madison,01089,C,nc,34.7631,-86.5502
+AL,007,HUN,Morgan,AL007,Morgan,01103,C,nc,34.4535,-86.8529
+AL,008,HUN,Marshall,AL008,Marshall,01095,C,ne,34.3669,-86.3066
+AL,009,HUN,Jackson,AL009,Jackson,01071,C,ne,34.7794,-85.9993
+AL,010,HUN,DefireKalb,AL010,DeKalb,01049,C,ne,34.4598,-85.8041
+AL,011,BMX,Marion,AL011,Marion,01093,C,nw,34.1366,-87.8871
+AL,012,BMX,Lamar,AL012,Lamar,01075,C,wc,33.7792,-88.0969
+AL,013,BMX,Fayette,AL013,Fayette,01057,C,wc,33.7212,-87.7389
+AL,014,BMX,Winston,AL014,Winston,01133,C,nw,34.1492,-87.3737
+AL,015,BMX,Walker,AL015,Walker,01127,C,c,33.8034,-87.2973
+AL,016,HUN,Cullman,AL016,Cullman,01043,C,nc,34.1320,-86.8676
+AL,017,BMX,Blount,AL017,Blount,01009,C,c,33.9809,-86.5674
+AL,018,BMX,Etowah,AL018,Etowah,01055,C,ne,34.0453,-86.0348
+AL,019,BMX,Calhoun,AL019,Calhoun,01015,C,ec,33.7714,-85.8261
+AL,020,BMX,Cherokee,AL020,Cherokee,01019,C,ne,34.1760,-85.6038
+AL,021,BMX,Cleburne,AL021,Cleburne,01029,C,ec,33.6745,-85.5188
+AL,022,BMX,Pickens,AL022,Pickens,01107,C,wc,33.2809,-88.0887
+AL,023,BMX,Tuscaloosa,AL023,Tuscaloosa,01125,C,wc,33.2895,-87.5250
+AL,024,BMX,Jefferson,AL024,Jefferson,01073,C,c,33.5543,-86.8965
+AL,025,BMX,Shelby,AL025,Shelby,01117,C,c,33.2643,-86.6607
+AL,026,BMX,St. Clair,AL026,St. Clair,01115,C,c,33.7157,-86.3147
+AL,027,BMX,Talladega,AL027,Talladega,01121,C,ec,33.3800,-86.1659
+AL,028,BMX,Clay,AL028,Clay,01027,C,ec,33.2691,-85.8605
+AL,029,BMX,Randolph,AL029,Randolph,01111,C,ec,33.2938,-85.4591
+AL,030,BMX,Sumter,AL030,Sumter,01119,C,wc,32.5910,-88.1988
+AL,031,BMX,Greene,AL031,Greene,01063,C,wc,32.8531,-87.9522
+AL,032,BMX,Hale,AL032,Hale,01065,C,wc,32.7627,-87.6291
+AL,033,BMX,Perry,AL033,Perry,01105,C,c,32.6385,-87.2942
+AL,034,BMX,Bibb,AL034,Bibb,01007,C,c,32.9986,-87.1264
+AL,035,BMX,Chilton,AL035,Chilton,01021,C,c,32.8479,-86.7188
+AL,036,BMX,Coosa,AL036,Coosa,01037,C,ec,32.9362,-86.2476
+AL,037,BMX,Tallapoosa,AL037,Tallapoosa,01123,C,ec,32.8624,-85.7975
+AL,038,BMX,Chambers,AL038,Chambers,01017,C,ec,32.9144,-85.3920
+AL,039,BMX,Marengo,AL039,Marengo,01091,C,sw,32.2476,-87.7895
+AL,040,BMX,Dallas,AL040,Dallas,01047,C,sc,32.3258,-87.1065
+AL,041,BMX,Autauga,AL041,Autauga,01001,C,c,32.5349,-86.6428
+AL,042,BMX,Lowndes,AL042,Lowndes,01085,C,sc,32.1547,-86.6501
+AL,043,BMX,Elmore,AL043,Elmore,01051,C,ec,32.5966,-86.1492
+AL,044,BMX,Montgomery,AL044,Montgomery,01101,C,sc,32.2202,-86.2076
+AL,045,BMX,Macon,AL045,Macon,01087,C,se,32.3860,-85.6926
+AL,046,BMX,Bullock,AL046,Bullock,01011,C,se,32.1005,-85.7161
+AL,047,BMX,Lee,AL047,Lee,01081,C,ec,32.6012,-85.3556
+AL,048,BMX,Russell,AL048,Russell,01113,C,se,32.2884,-85.1849
+AL,049,BMX,Pike,AL049,Pike,01109,C,se,31.8024,-85.9409
+AL,050,BMX,Barbour,AL050,Barbour,01005,C,se,31.8696,-85.3932
+AL,051,MOB,Choctaw,AL051,Choctaw,01023,C,sw,32.0192,-88.2628
+AL,052,MOB,Washington,AL052,Washington,01129,C,sw,31.4077,-88.2071
+AL,053,MOB,Clarke,AL053,Clarke,01025,C,sw,31.6709,-87.8309
+AL,054,MOB,Wilcox,AL054,Wilcox,01131,C,sc,31.9892,-87.3082
+AL,055,MOB,Monroe,AL055,Monroe,01099,C,sc,31.5706,-87.3658
+AL,056,MOB,Conecuh,AL056,Conecuh,01035,C,sc,31.4293,-86.9936
+AL,057,MOB,Butler,AL057,Butler,01013,C,sc,31.7524,-86.6803
+AL,058,MOB,Crenshaw,AL058,Crenshaw,01041,C,sc,31.7315,-86.3136
+AL,059,MOB,Escambia,AL059,Escambia,01053,C,sc,31.1262,-87.1616
+AL,060,MOB,Covington,AL060,Covington,01039,C,sc,31.2485,-86.4512
+AL,065,TAE,Coffee,AL065,Coffee,01031,C,se,31.4026,-85.9882
+AL,066,TAE,Dale,AL066,Dale,01045,C,se,31.4318,-85.6110
+AL,067,TAE,Henry,AL067,Henry,01067,C,se,31.5147,-85.2414
+AL,068,TAE,Geneva,AL068,Geneva,01061,C,se,31.0950,-85.8390
+AL,069,TAE,Houston,AL069,Houston,01069,C,se,31.1532,-85.3025
+AL,261,MOB,Mobile Inland,AL261,Mobile,01097,C,sw,30.9237,-88.1850
+AL,262,MOB,Baldwin Inland,AL262,Baldwin,01003,C,sw,30.4940,-87.6699
+AL,263,MOB,Mobile Central,AL263,Mobile,01097,C,sw,30.5283,-88.2363
+AL,264,MOB,Baldwin Central,AL264,Baldwin,01003,C,sw,30.9471,-87.7615
+AL,265,MOB,Mobile Coastal,AL265,Mobile,01097,C,sw,30.5283,-88.2363
+AL,266,MOB,Baldwin Coastal,AL266,Baldwin,01003,C,sw,30.4940,-87.6699
+AR,001,TSA,Benton,AR001,Benton,05007,C,nw,36.3392,-94.2573
+AR,002,TSA,Carroll,AR002,Carroll,05015,C,nw,36.3410,-93.5382
+AR,003,LZK,Boone,AR003,Boone,05009,C,nc,36.3086,-93.0915
+AR,004,LZK,Marion,AR004,Marion,05089,C,nc,36.2684,-92.6842
+AR,005,LZK,Baxter,AR005,Baxter,05005,C,nc,36.2872,-92.3370
+AR,006,LZK,Fulton,AR006,Fulton,05049,C,nc,36.3817,-91.8183
+AR,007,LZK,Sharp,AR007,Sharp,05135,C,nc,36.1611,-91.4799
+AR,008,LZK,Randolph,AR008,Randolph,05121,C,ee,36.3415,-91.0278
+AR,009,MEG,Clay,AR009,Clay,05021,C,ee,36.3685,-90.4173
+AR,010,TSA,Washington,AR010,Washington,05143,C,nw,35.9800,-94.2147
+AR,011,TSA,Madison,AR011,Madison,05087,C,nw,36.0110,-93.7246
+AR,012,LZK,Newton,AR012,Newton,05101,C,nc,35.9200,-93.2179
+AR,013,LZK,Searcy,AR013,Searcy,05129,C,nc,35.9109,-92.6995
+AR,014,LZK,Stone,AR014,Stone,05137,C,nc,35.8599,-92.1567
+AR,015,LZK,Izard,AR015,Izard,05065,C,nc,36.0949,-91.9134
+AR,016,LZK,Independence,AR016,Independence,05063,C,nc,35.7415,-91.5698
+AR,017,LZK,Lawrence,AR017,Lawrence,05075,C,ee,36.0413,-91.1071
+AR,018,MEG,Greene,AR018,Greene,05055,C,ee,36.1177,-90.5591
+AR,019,TSA,Crawford,AR019,Crawford,05033,C,nw,35.5889,-94.2430
+AR,020,TSA,Franklin,AR020,Franklin,05047,C,wc,35.5123,-93.8906
+AR,021,LZK,Johnson,AR021,Johnson,05071,C,ww,35.5701,-93.4599
+AR,022,LZK,Pope,AR022,Pope,05115,C,cc,35.4476,-93.0342
+AR,023,LZK,Van Buren,AR023,Van Buren,05141,C,nc,35.5807,-92.5157
+AR,024,LZK,Cleburne,AR024,Cleburne,05023,C,nc,35.5381,-92.0267
+AR,025,LZK,Jackson,AR025,Jackson,05067,C,ee,35.5994,-91.2146
+AR,026,MEG,Craighead,AR026,Craighead,05031,C,ee,35.8308,-90.6328
+AR,027,MEG,Poinsett,AR027,Poinsett,05111,C,ee,35.5740,-90.6629
+AR,028,MEG,Mississippi,AR028,Mississippi,05093,C,ee,35.7639,-90.0540
+AR,029,TSA,Sebastian,AR029,Sebastian,05131,C,wc,35.1993,-94.2740
+AR,030,LZK,Logan,AR030,Logan,05083,C,ww,35.2153,-93.7163
+AR,031,LZK,Conway,AR031,Conway,05029,C,cc,35.2622,-92.7014
+AR,032,LZK,Faulkner,AR032,Faulkner,05045,C,cc,35.1470,-92.3321
+AR,033,LZK,White,AR033,White,05145,C,cc,35.2563,-91.7457
+AR,034,LZK,Woodruff,AR034,Woodruff,05147,C,ee,35.1863,-91.2431
+AR,035,MEG,Cross,AR035,Cross,05037,C,ee,35.2958,-90.7713
+AR,036,MEG,Crittenden,AR036,Crittenden,05035,C,ee,35.2079,-90.3089
+AR,037,LZK,Scott,AR037,Scott,05127,C,ww,34.8608,-94.0633
+AR,038,LZK,Yell,AR038,Yell,05149,C,cc,35.0026,-93.4113
+AR,039,LZK,Perry,AR039,Perry,05105,C,cc,34.9474,-92.9315
+AR,040,LZK,Polk,AR040,Polk,05113,C,ww,34.4858,-94.2281
+AR,041,LZK,Montgomery,AR041,Montgomery,05097,C,ww,34.5388,-93.6595
+AR,042,LZK,Garland,AR042,Garland,05051,C,cc,34.5767,-93.1504
+AR,043,LZK,Saline,AR043,Saline,05125,C,cc,34.6466,-92.6766
+AR,044,LZK,Pulaski,AR044,Pulaski,05119,C,cc,34.7699,-92.3118
+AR,045,LZK,Lonoke,AR045,Lonoke,05085,C,cc,34.7542,-91.8887
+AR,046,LZK,Prairie,AR046,Prairie,05117,C,cc,34.8297,-91.5528
+AR,047,LZK,Monroe,AR047,Monroe,05095,C,ee,34.6778,-91.2038
+AR,048,MEG,St. Francis,AR048,St. Francis,05123,C,ee,35.0220,-90.7478
+AR,049,MEG,Lee,AR049,Lee,05077,C,ee,34.7806,-90.7820
+AR,050,SHV,Sevier,AR050,Sevier,05133,C,sw,33.9973,-94.2412
+AR,051,SHV,Howard,AR051,Howard,05061,C,sw,34.0887,-93.9934
+AR,052,LZK,Pike,AR052,Pike,05109,C,sw,34.1636,-93.6564
+AR,053,LZK,Clark,AR053,Clark,05019,C,sw,34.0510,-93.1764
+AR,054,LZK,Hot Spring,AR054,Hot Spring,05059,C,sw,34.3177,-92.9460
+AR,055,LZK,Grant,AR055,Grant,05053,C,cc,34.2900,-92.4236
+AR,056,LZK,Jefferson,AR056,Jefferson,05069,C,se,34.2688,-91.9315
+AR,057,LZK,Arkansas,AR057,Arkansas,05001,C,se,34.2908,-91.3749
+AR,058,MEG,Phillips,AR058,Phillips,05107,C,ee,34.4283,-90.8480
+AR,059,SHV,Little River,AR059,Little River,05081,C,sw,33.7005,-94.2343
+AR,060,SHV,Hempstead,AR060,Hempstead,05057,C,sw,33.7354,-93.6684
+AR,061,SHV,Nevada,AR061,Nevada,05099,C,sw,33.6641,-93.3072
+AR,062,LZK,Dallas,AR062,Dallas,05039,C,sw,33.9698,-92.6545
+AR,063,LZK,Cleveland,AR063,Cleveland,05025,C,se,33.8984,-92.1852
+AR,064,LZK,Lincoln,AR064,Lincoln,05079,C,se,33.9575,-91.7333
+AR,065,LZK,Desha,AR065,Desha,05041,C,se,33.8333,-91.2540
+AR,066,LZK,Ouachita,AR066,Ouachita,05103,C,sw,33.5933,-92.8820
+AR,067,LZK,Calhoun,AR067,Calhoun,05013,C,sw,33.5580,-92.5031
+AR,068,LZK,Bradley,AR068,Bradley,05011,C,se,33.4659,-92.1622
+AR,069,LZK,Drew,AR069,Drew,05043,C,se,33.5895,-91.7200
+AR,070,SHV,Miller,AR070,Miller,05091,C,sw,33.3124,-93.8918
+AR,071,SHV,Lafayette,AR071,Lafayette,05073,C,sw,33.2408,-93.6073
+AR,072,SHV,Columbia,AR072,Columbia,05027,C,sw,33.2143,-93.2273
+AR,073,SHV,Union,AR073,Union,05139,C,sc,33.1713,-92.5973
+AR,074,JAN,Ashley,AR074,Ashley,05003,C,se,33.1912,-91.7679
+AR,075,JAN,Chicot,AR075,Chicot,05017,C,se,33.2672,-91.2940
+AS,001,PPG,Tutuila and Aunuu,AS001,Eastern,60010,S,,-14.3055,-170.7170
+AS,001,PPG,Tutuila and Aunuu,AS001,Western,60050,S,,-14.3055,-170.7170
+AS,002,PPG,Manua,AS002,Manu'a,60020,S,,-14.2270,-169.5053
+AS,003,PPG,Swains Island,AS003,Swains Island,60040,S,,-11.0844,-171.0460
+AZ,001,VEF,Northwest Plateau,AZ001,Mohave,04015,m,nw,36.5921,-113.3317
+AZ,002,VEF,Lake Havasu and Fort Mohave,AZ002,Mohave,04015,m,nw,34.6762,-114.3492
+AZ,003,VEF,Northwest Deserts,AZ003,Mohave,04015,m,nw,35.1839,-113.8480
+AZ,004,FGZ,Kaibab Plateau,AZ004,Coconino,04005,m,nc,36.6514,-112.2018
+AZ,005,FGZ,Marble and Glen Canyons,AZ005,Coconino,04005,m,nc,36.7777,-111.6681
+AZ,006,FGZ,Grand Canyon Country,AZ006,Coconino,04005,m,nc,36.1840,-112.3184
+AZ,007,FGZ,Coconino Plateau,AZ007,Coconino,04005,m,nc,35.7341,-112.5569
+AZ,008,FGZ,Yavapai County  Mountains,AZ008,Yavapai,04025,m,wc,34.8361,-112.6554
+AZ,009,FGZ,Northeast Plateaus and Mesas Hwy 264 Northward,AZ009,Apache,04001,m,ne,36.4046,-110.9711
+AZ,009,FGZ,Northeast Plateaus and Mesas Hwy 264 Northward,AZ009,Coconino,04005,m,nc,36.4046,-110.9711
+AZ,009,FGZ,Northeast Plateaus and Mesas Hwy 264 Northward,AZ009,Navajo,04017,m,ne,36.4046,-110.9711
+AZ,010,FGZ,Chinle Valley,AZ010,Apache,04001,m,ne,36.5404,-109.7068
+AZ,010,FGZ,Chinle Valley,AZ010,Navajo,04017,m,ne,36.5404,-109.7068
+AZ,011,FGZ,Chuska Mountains and Defiance Plateau,AZ011,Apache,04001,m,ne,35.8693,-109.2344
+AZ,012,FGZ,Little Colorado River Valley in Coconino County,AZ012,Coconino,04005,m,nc,35.4788,-111.1876
+AZ,012,FGZ,Little Colorado River Valley in Coconino County,AZ012,Navajo,04017,m,ne,35.4788,-111.1876
+AZ,013,FGZ,Little Colorado River Valley in Navajo County,AZ013,Navajo,04017,m,ne,34.8440,-110.3128
+AZ,014,FGZ,Little Colorado River Valley in Apache County,AZ014,Apache,04001,m,ne,34.5768,-109.4198
+AZ,015,FGZ,Western Mogollon Rim,AZ015,Coconino,04005,m,nc,35.2296,-111.7689
+AZ,016,FGZ,Eastern Mogollon Rim,AZ016,Coconino,04005,m,nc,34.4418,-110.8122
+AZ,016,FGZ,Eastern Mogollon Rim,AZ016,Navajo,04017,m,ne,34.4418,-110.8122
+AZ,017,FGZ,White Mountains,AZ017,Apache,04001,m,ne,33.9272,-109.6483
+AZ,017,FGZ,White Mountains,AZ017,Navajo,04017,m,ne,33.9272,-109.6483
+AZ,018,FGZ,Northern Gila County,AZ018,Gila,04007,m,ec,34.0401,-110.9225
+AZ,036,VEF,Lake Mead National Recreation Area,AZ036,Mohave,04015,m,nw,35.8011,-114.3194
+AZ,037,FGZ,Yavapai County Valleys and Basins,AZ037,Yavapai,04025,m,wc,34.4302,-112.5126
+AZ,038,FGZ,Oak Creek and Sycamore Canyons,AZ038,Coconino,04005,m,nc,34.9117,-111.8796
+AZ,038,FGZ,Oak Creek and Sycamore Canyons,AZ038,Yavapai,04025,m,wc,34.9117,-111.8796
+AZ,039,FGZ,Black Mesa Area,AZ039,Apache,04001,m,ne,36.3550,-110.2430
+AZ,039,FGZ,Black Mesa Area,AZ039,Coconino,04005,m,nc,36.3550,-110.2430
+AZ,039,FGZ,Black Mesa Area,AZ039,Navajo,04017,m,ne,36.3550,-110.2430
+AZ,040,FGZ,Northeast Plateaus and Mesas South of Hwy 264,AZ040,Apache,04001,m,ne,35.4889,-110.1825
+AZ,040,FGZ,Northeast Plateaus and Mesas South of Hwy 264,AZ040,Coconino,04005,m,nc,35.4889,-110.1825
+AZ,040,FGZ,Northeast Plateaus and Mesas South of Hwy 264,AZ040,Navajo,04017,m,ne,35.4889,-110.1825
+AZ,501,TWC,Western Pima County Including Ajo/Organ Pipe Cactus National Monument,AZ501,Pima,04019,m,sc,32.2167,-112.9766
+AZ,502,TWC,Tohono O'odham Nation including Sells,AZ502,Pima,04019,m,sc,32.1434,-112.0640
+AZ,502,TWC,Tohono O'odham Nation including Sells,AZ502,Pinal,04021,m,sc,32.1434,-112.0640
+AZ,503,TWC,Upper Santa Cruz River and Altar Valleys including Nogales,AZ503,Pima,04019,m,sc,31.6207,-111.1630
+AZ,503,TWC,Upper Santa Cruz River and Altar Valleys including Nogales,AZ503,Santa Cruz,04023,m,se,31.6207,-111.1630
+AZ,504,TWC,Tucson Metro Area including Tucson/Green Valley/Marana/Vail,AZ504,Pima,04019,m,sc,32.1761,-111.0565
+AZ,505,TWC,South Central Pinal County including Eloy/Picacho Peak State Park,AZ505,Pinal,04021,m,sc,32.7452,-111.3393
+AZ,506,TWC,Southeast Pinal County including Kearny/Mammoth/Oracle,AZ506,Pinal,04021,m,sc,32.8255,-110.7518
+AZ,507,TWC,Upper San Pedro River Valley including Sierra Vista/Benson,AZ507,Cochise,04003,m,se,31.9331,-110.3073
+AZ,507,TWC,Upper San Pedro River Valley including Sierra Vista/Benson,AZ507,Graham,04009,m,se,31.9331,-110.3073
+AZ,507,TWC,Upper San Pedro River Valley including Sierra Vista/Benson,AZ507,Pima,04019,m,sc,31.9331,-110.3073
+AZ,507,TWC,Upper San Pedro River Valley including Sierra Vista/Benson,AZ507,Santa Cruz,04023,m,se,31.9331,-110.3073
+AZ,508,TWC,Eastern Cochise County Below 5000 Feet including Douglas/Willcox,AZ508,Cochise,04003,m,se,31.9147,-109.5452
+AZ,509,TWC,Upper Gila River and Aravaipa Valleys including Clifton/Safford,AZ509,Graham,04009,m,se,32.8686,-109.7443
+AZ,509,TWC,Upper Gila River and Aravaipa Valleys including Clifton/Safford,AZ509,Greenlee,04011,m,se,32.8686,-109.7443
+AZ,510,TWC,White Mountains of Graham and Greenlee Counties including Hannagan Meadow,AZ510,Graham,04009,m,se,33.3867,-109.4881
+AZ,510,TWC,White Mountains of Graham and Greenlee Counties including Hannagan Meadow,AZ510,Greenlee,04011,m,se,33.3867,-109.4881
+AZ,511,TWC,Galiuro and Pinaleno Mountains including Mount Graham,AZ511,Cochise,04003,m,se,32.6525,-110.1153
+AZ,511,TWC,Galiuro and Pinaleno Mountains including Mount Graham,AZ511,Graham,04009,m,se,32.6525,-110.1153
+AZ,511,TWC,Galiuro and Pinaleno Mountains including Mount Graham,AZ511,Pinal,04021,m,sc,32.6525,-110.1153
+AZ,512,TWC,Chiricahua Mountains including Chiricahua National Monument,AZ512,Cochise,04003,m,se,31.8616,-109.3560
+AZ,513,TWC,Dragoon/Mule/Huachuca and Santa Rita Mountains including Bisbee/Canelo Hills/Madera Canyon,AZ513,Cochise,04003,m,se,31.6043,-110.3833
+AZ,513,TWC,Dragoon/Mule/Huachuca and Santa Rita Mountains including Bisbee/Canelo Hills/Madera Canyon,AZ513,Pima,04019,m,sc,31.6043,-110.3833
+AZ,513,TWC,Dragoon/Mule/Huachuca and Santa Rita Mountains including Bisbee/Canelo Hills/Madera Canyon,AZ513,Santa Cruz,04023,m,se,31.6043,-110.3833
+AZ,514,TWC,Santa Catalina and Rincon Mountains including Mount Lemmon/Summerhaven,AZ514,Pima,04019,m,sc,32.3364,-110.6808
+AZ,514,TWC,Santa Catalina and Rincon Mountains including Mount Lemmon/Summerhaven,AZ514,Pinal,04021,m,sc,32.3364,-110.6808
+AZ,515,TWC,Baboquivari Mountains including Kitt Peak,AZ515,Pima,04019,m,sc,31.8186,-111.5931
+AZ,530,PSR,Parker Valley,AZ530,La Paz,04012,M,sw,33.6988,-114.4568
+AZ,531,PSR,Kofa,AZ531,La Paz,04012,M,sw,33.2172,-114.0280
+AZ,531,PSR,Kofa,AZ531,Yuma,04027,M,sw,33.2172,-114.0280
+AZ,532,PSR,Yuma,AZ532,Yuma,04027,M,sw,32.6544,-114.5114
+AZ,533,PSR,Central La Paz,AZ533,La Paz,04012,M,sw,33.8115,-113.8132
+AZ,534,PSR,Aguila Valley,AZ534,Maricopa,04013,M,sc,33.8662,-112.9785
+AZ,535,PSR,Southeast Yuma County,AZ535,Yuma,04027,M,sw,32.3859,-113.7573
+AZ,536,PSR,Gila River Valley,AZ536,Yuma,04027,M,sw,32.8220,-113.8040
+AZ,537,PSR,Northwest Valley,AZ537,Maricopa,04013,M,sc,33.7421,-112.4770
+AZ,538,PSR,Tonopah Desert,AZ538,Maricopa,04013,M,sc,33.3971,-112.9704
+AZ,539,PSR,Gila Bend,AZ539,Maricopa,04013,M,sc,32.8542,-113.0566
+AZ,540,PSR,Buckeye/Avondale,AZ540,Maricopa,04013,M,sc,33.3912,-112.4004
+AZ,541,PSR,Cave Creek/New River,AZ541,Maricopa,04013,M,sc,33.8086,-112.0129
+AZ,542,PSR,Deer Valley,AZ542,Maricopa,04013,M,sc,33.7122,-112.1301
+AZ,543,PSR,Central Phoenix,AZ543,Maricopa,04013,M,sc,33.4331,-112.0552
+AZ,544,PSR,North Phoenix/Glendale,AZ544,Maricopa,04013,M,sc,33.5716,-112.1525
+AZ,545,PSR,New River Mesa,AZ545,Maricopa,04013,M,sc,33.9260,-111.8887
+AZ,546,PSR,Scottsdale/Paradise Valley,AZ546,Maricopa,04013,M,sc,33.5351,-111.8891
+AZ,547,PSR,Rio Verde/Salt River,AZ547,Maricopa,04013,M,sc,33.7012,-111.5100
+AZ,548,PSR,East Valley,AZ548,Maricopa,04013,M,sc,33.3765,-111.8134
+AZ,549,PSR,Fountain Hills/East Mesa,AZ549,Maricopa,04013,M,sc,33.5349,-111.6592
+AZ,550,PSR,South Mountain/Ahwatukee,AZ550,Maricopa,04013,M,sc,33.2829,-112.0449
+AZ,551,PSR,Southeast Valley/Queen Creek,AZ551,Maricopa,04013,M,sc,33.2616,-111.7353
+AZ,552,PSR,Superior,AZ552,Pinal,04021,M,sc,33.2702,-111.1880
+AZ,553,PSR,Northwest Pinal County,AZ553,Pinal,04021,M,sc,33.0208,-111.7511
+AZ,554,PSR,West Pinal County,AZ554,Pinal,04021,P,sc,32.8658,-112.1203
+AZ,555,PSR,Apache Junction/Gold Canyon,AZ555,Pinal,04021,M,sc,33.2584,-111.3827
+AZ,556,PSR,Tonto Basin,AZ556,Gila,04007,M,sc,33.7079,-111.1275
+AZ,557,PSR,Mazatzal Mountains,AZ557,Gila,04007,M,sc,33.8233,-111.4206
+AZ,557,PSR,Mazatzal Mountains,AZ557,Maricopa,04013,M,sc,33.8233,-111.4206
+AZ,558,PSR,Pinal/Superstition Mountains,AZ558,Gila,04007,M,sc,33.3639,-110.9698
+AZ,558,PSR,Pinal/Superstition Mountains,AZ558,Maricopa,04013,M,sc,33.3639,-110.9698
+AZ,558,PSR,Pinal/Superstition Mountains,AZ558,Pinal,04021,M,sc,33.3639,-110.9698
+AZ,559,PSR,Sonoran Desert Natl Monument,AZ559,Maricopa,04013,M,sc,32.8019,-112.5236
+AZ,560,PSR,San Carlos,AZ560,Gila,04007,M,sc,33.3416,-110.5321
+AZ,561,PSR,Dripping Springs,AZ561,Gila,04007,M,sc,33.1020,-110.7854
+AZ,562,PSR,Globe/Miami,AZ562,Gila,04007,M,sc,33.4344,-110.8175
+AZ,563,PSR,Southeast Gila County,AZ563,Gila,04007,M,sc,33.6107,-110.4548
+CA,006,MTR,San Francisco,CA006,San Francisco,06075,P,ww,37.7558,-122.4423
+CA,013,STO,Shasta Lake Area / Northern Shasta County,CA013,Shasta,06089,P,nn,40.9299,-122.2247
+CA,014,STO,Burney Basin / Eastern Shasta County,CA014,Shasta,06089,P,nn,40.9423,-121.4882
+CA,015,STO,Northern Sacramento Valley,CA015,Shasta,06089,P,nn,40.2198,-122.2949
+CA,015,STO,Northern Sacramento Valley,CA015,Tehama,06103,P,nn,40.2198,-122.2949
+CA,016,STO,Central Sacramento Valley,CA016,Butte,06007,P,nn,39.3764,-121.9260
+CA,016,STO,Central Sacramento Valley,CA016,Colusa,06011,P,cc,39.3764,-121.9260
+CA,016,STO,Central Sacramento Valley,CA016,Glenn,06021,P,nn,39.3764,-121.9260
+CA,016,STO,Central Sacramento Valley,CA016,Nevada,06057,P,cc,39.3764,-121.9260
+CA,016,STO,Central Sacramento Valley,CA016,Sutter,06101,P,cc,39.3764,-121.9260
+CA,016,STO,Central Sacramento Valley,CA016,Yuba,06115,P,cc,39.3764,-121.9260
+CA,017,STO,Southern Sacramento Valley,CA017,Amador,06005,P,cc,38.6268,-121.5625
+CA,017,STO,Southern Sacramento Valley,CA017,El Dorado,06017,P,cc,38.6268,-121.5625
+CA,017,STO,Southern Sacramento Valley,CA017,Placer,06061,P,cc,38.6268,-121.5625
+CA,017,STO,Southern Sacramento Valley,CA017,Sacramento,06067,P,cc,38.6268,-121.5625
+CA,017,STO,Southern Sacramento Valley,CA017,Solano,06095,P,ww,38.6268,-121.5625
+CA,017,STO,Southern Sacramento Valley,CA017,Sutter,06101,P,cc,38.6268,-121.5625
+CA,017,STO,Southern Sacramento Valley,CA017,Yolo,06113,P,cc,38.6268,-121.5625
+CA,018,STO,Carquinez Strait and Delta,CA018,Sacramento,06067,P,cc,38.1432,-121.7623
+CA,018,STO,Carquinez Strait and Delta,CA018,San Joaquin,06077,P,cc,38.1432,-121.7623
+CA,018,STO,Carquinez Strait and Delta,CA018,Solano,06095,P,ww,38.1432,-121.7623
+CA,019,STO,Northern San Joaquin Valley,CA019,Amador,06005,P,cc,37.7581,-121.0410
+CA,019,STO,Northern San Joaquin Valley,CA019,Calaveras,06009,P,cc,37.7581,-121.0410
+CA,019,STO,Northern San Joaquin Valley,CA019,San Joaquin,06077,P,cc,37.7581,-121.0410
+CA,019,STO,Northern San Joaquin Valley,CA019,Stanislaus,06099,P,cc,37.7581,-121.0410
+CA,019,STO,Northern San Joaquin Valley,CA019,Tuolumne,06109,P,cc,37.7581,-121.0410
+CA,034,LOX,San Luis Obispo County Central Coast,CA034,San Luis Obispo,06079,P,sw,35.3270,-120.7407
+CA,035,LOX,Santa Barbara County Central Coast,CA035,Santa Barbara,06083,P,sw,34.7217,-120.4263
+CA,036,LOX,Santa Ynez Valley,CA036,Santa Barbara,06083,P,sw,34.6833,-120.0816
+CA,037,LOX,San Luis Obispo County Interior Valleys,CA037,San Luis Obispo,06079,P,sw,35.4918,-120.3516
+CA,038,LOX,Cuyama Valley,CA038,Santa Barbara,06083,P,sw,34.9422,-119.7374
+CA,039,LOX,Santa Barbara County South Coast,CA039,Santa Barbara,06083,P,sw,34.4568,-119.9036
+CA,040,LOX,Ventura County Coast,CA040,Ventura,06111,P,sw,34.2181,-119.1214
+CA,041,LOX,Los Angeles County Coast including Downtown Los Angeles,CA041,Los Angeles,06037,P,sw,33.9281,-118.3004
+CA,043,SGX,San Diego County Coastal Areas,CA043,San Diego,06073,P,sw,32.9783,-117.2367
+CA,044,LOX,Ventura County Interior Valleys,CA044,Ventura,06111,P,sw,34.3941,-119.1198
+CA,045,LOX,Ventura County Coastal Valleys,CA045,Ventura,06111,P,sw,34.2538,-118.8399
+CA,046,LOX,Santa Monica Mountains Recreational Area,CA046,Los Angeles,06037,P,sw,34.0839,-118.6898
+CA,048,SGX,San Bernardino and Riverside County Valleys -The Inland Empire,CA048,Riverside,06065,P,ss,33.8486,-117.2412
+CA,048,SGX,San Bernardino and Riverside County Valleys -The Inland Empire,CA048,San Bernardino,06071,P,ss,33.8486,-117.2412
+CA,050,SGX,San Diego County Valleys,CA050,San Diego,06073,P,sw,33.0047,-116.9604
+CA,051,LOX,San Luis Obispo County Mountains,CA051,San Luis Obispo,06079,P,sw,35.1415,-120.0448
+CA,052,LOX,Santa Barbara County Mountains,CA052,Santa Barbara,06083,P,sw,34.7466,-119.8211
+CA,053,LOX,Ventura County Mountains,CA053,Ventura,06111,P,sw,34.6292,-119.1351
+CA,054,LOX,Los Angeles County Mountains excluding the Santa Monica Range,CA054,Los Angeles,06037,P,sw,34.4294,-118.2230
+CA,055,SGX,San Bernardino County Mountains,CA055,San Bernardino,06071,P,ss,34.2304,-117.0957
+CA,056,SGX,Riverside County Mountains,CA056,Riverside,06065,P,ss,33.6600,-116.6434
+CA,057,SGX,Santa Ana Mountains and Foothills,CA057,Orange,06059,P,sw,33.6256,-117.4550
+CA,057,SGX,Santa Ana Mountains and Foothills,CA057,Riverside,06065,P,ss,33.6256,-117.4550
+CA,057,SGX,Santa Ana Mountains and Foothills,CA057,San Diego,06073,P,sw,33.6256,-117.4550
+CA,058,SGX,San Diego County Mountains,CA058,San Diego,06073,P,sw,33.0713,-116.5324
+CA,059,LOX,Antelope Valley,CA059,Los Angeles,06037,P,sw,34.6664,-118.0679
+CA,060,SGX,Apple and Lucerne Valleys,CA060,San Bernardino,06071,P,ss,34.5427,-117.0143
+CA,061,SGX,Coachella Valley,CA061,Riverside,06065,P,ss,33.7424,-116.3024
+CA,062,SGX,San Diego County Deserts,CA062,San Diego,06073,P,sw,33.0511,-116.2536
+CA,063,STO,Mountains Southwestern Shasta County to Western Colusa County,CA063,Colusa,06011,P,cc,39.8175,-122.7565
+CA,063,STO,Mountains Southwestern Shasta County to Western Colusa County,CA063,Glenn,06021,P,nn,39.8175,-122.7565
+CA,063,STO,Mountains Southwestern Shasta County to Western Colusa County,CA063,Shasta,06089,P,nn,39.8175,-122.7565
+CA,063,STO,Mountains Southwestern Shasta County to Western Colusa County,CA063,Tehama,06103,P,nn,39.8175,-122.7565
+CA,065,SGX,San Gorgonio Pass Near Banning,CA065,Riverside,06065,P,ss,33.7185,-115.2342
+CA,066,STO,Northeast Foothills/Sacramento Valley,CA066,Butte,06007,P,nn,40.1117,-121.7566
+CA,066,STO,Northeast Foothills/Sacramento Valley,CA066,Shasta,06089,P,nn,40.1117,-121.7566
+CA,066,STO,Northeast Foothills/Sacramento Valley,CA066,Tehama,06103,P,nn,40.1117,-121.7566
+CA,067,STO,Motherlode,CA067,Amador,06005,P,cc,38.6192,-120.7698
+CA,067,STO,Motherlode,CA067,Calaveras,06009,P,cc,38.6192,-120.7698
+CA,067,STO,Motherlode,CA067,El Dorado,06017,P,cc,38.6192,-120.7698
+CA,067,STO,Motherlode,CA067,Nevada,06057,P,cc,38.6192,-120.7698
+CA,067,STO,Motherlode,CA067,Placer,06061,P,cc,38.6192,-120.7698
+CA,067,STO,Motherlode,CA067,Tuolumne,06109,P,cc,38.6192,-120.7698
+CA,067,STO,Motherlode,CA067,Yuba,06115,P,cc,38.6192,-120.7698
+CA,068,STO,Western Plumas County/Lassen Park,CA068,Butte,06007,P,nn,40.1971,-121.3501
+CA,068,STO,Western Plumas County/Lassen Park,CA068,Lassen,06035,P,nn,40.1971,-121.3501
+CA,068,STO,Western Plumas County/Lassen Park,CA068,Plumas,06063,P,nn,40.1971,-121.3501
+CA,068,STO,Western Plumas County/Lassen Park,CA068,Shasta,06089,P,nn,40.1971,-121.3501
+CA,068,STO,Western Plumas County/Lassen Park,CA068,Tehama,06103,P,nn,40.1971,-121.3501
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Alpine,06003,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Amador,06005,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Calaveras,06009,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,El Dorado,06017,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Nevada,06057,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Placer,06061,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Sierra,06091,P,nn,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Tuolumne,06109,P,cc,38.7829,-120.3423
+CA,069,STO,West Slope Northern Sierra Nevada,CA069,Yuba,06115,P,cc,38.7829,-120.3423
+CA,070,REV,Surprise Valley California,CA070,Modoc,06049,P,nn,41.6116,-120.0862
+CA,071,REV,Lassen-Eastern Plumas-Eastern Sierra Counties,CA071,Lassen,06035,P,nn,40.4660,-120.5471
+CA,071,REV,Lassen-Eastern Plumas-Eastern Sierra Counties,CA071,Plumas,06063,P,nn,40.4660,-120.5471
+CA,071,REV,Lassen-Eastern Plumas-Eastern Sierra Counties,CA071,Sierra,06091,P,nn,40.4660,-120.5471
+CA,072,REV,Greater Lake Tahoe Area,CA072,Alpine,06003,P,cc,38.9481,-119.9683
+CA,072,REV,Greater Lake Tahoe Area,CA072,El Dorado,06017,P,cc,38.9481,-119.9683
+CA,072,REV,Greater Lake Tahoe Area,CA072,Nevada,06057,P,cc,38.9481,-119.9683
+CA,072,REV,Greater Lake Tahoe Area,CA072,Placer,06061,P,cc,38.9481,-119.9683
+CA,073,REV,Mono,CA073,Mono,06051,P,cc,37.9390,-118.8867
+CA,080,MFR,Western Siskiyou County,CA080,Siskiyou,06093,P,nn,41.5562,-123.1641
+CA,081,MFR,Central Siskiyou County,CA081,Siskiyou,06093,P,nn,41.7362,-122.5631
+CA,082,MFR,South Central Siskiyou County,CA082,Siskiyou,06093,P,nn,41.2704,-122.1846
+CA,083,MFR,North Central and Southeast Siskiyou County,CA083,Siskiyou,06093,P,nn,41.5425,-121.8967
+CA,084,MFR,Northeast Siskiyou and Northwest Modoc Counties,CA084,Modoc,06049,P,nn,41.8639,-121.6371
+CA,084,MFR,Northeast Siskiyou and Northwest Modoc Counties,CA084,Siskiyou,06093,P,nn,41.8639,-121.6371
+CA,085,MFR,Modoc County,CA085,Modoc,06049,P,nn,41.5611,-120.7585
+CA,087,LOX,Catalina Island,CA087,Los Angeles,06037,P,sw,33.3820,-118.4331
+CA,088,LOX,Santa Clarita Valley,CA088,Los Angeles,06037,P,sw,34.4534,-118.5579
+CA,101,EKA,Coastal Del Norte,CA101,Del Norte,06015,P,nw,41.0243,-124.0956
+CA,102,EKA,Del Norte Interior,CA102,Del Norte,06015,P,nw,41.0243,-124.0956
+CA,103,EKA,Northern Humboldt Coast,CA103,Humboldt,06023,P,nw,41.0243,-124.0956
+CA,104,EKA,Southwestern Humboldt,CA104,Humboldt,06023,P,nw,41.0243,-124.0956
+CA,105,EKA,Northern Humboldt Interior,CA105,Humboldt,06023,P,nw,41.0243,-124.0956
+CA,106,EKA,Southern Humboldt Interior,CA106,Humboldt,06023,P,nw,41.0243,-124.0956
+CA,107,EKA,Northern Trinity,CA107,Trinity,06105,P,nw,40.7088,-123.1110
+CA,108,EKA,Southern Trinity,CA108,Trinity,06105,P,nw,40.7088,-123.1110
+CA,109,EKA,Mendocino Coast,CA109,Mendocino,06045,P,nw,39.3778,-123.7271
+CA,110,EKA,Northwestern Mendocino Interior,CA110,Mendocino,06045,P,nw,39.6359,-123.4371
+CA,111,EKA,Northeastern Mendocino Interior,CA111,Mendocino,06045,P,nw,39.7603,-123.1396
+CA,112,EKA,Southwestern Mendocino Interior,CA112,Mendocino,06045,P,nw,39.1561,-123.4915
+CA,113,EKA,Southeastern Mendocino Interior,CA113,Mendocino,06045,P,nw,39.0904,-123.1953
+CA,114,EKA,Northern Lake County,CA114,Lake,06033,P,cc,39.3619,-122.8628
+CA,115,EKA,Southern Lake County,CA115,Lake,06033,P,cc,38.9779,-122.7022
+CA,179,HNX,West Side Hills ,CA179,Kern,06029,P,cc,36.1223,-120.3373
+CA,179,HNX,West Side Hills ,CA179,Kings,06031,P,cc,36.1223,-120.3373
+CA,179,HNX,West Side Hills ,CA179,Fresno,06019,P,cc,36.1223,-120.3373
+CA,179,HNX,West Side Hills ,CA179,Merced,06047,P,cc,36.1223,-120.3373
+CA,180,HNX,San Joaquin Confluence ,CA180,Madera,06039,P,cc,37.0252,-120.6493
+CA,180,HNX,San Joaquin Confluence ,CA180,Merced,06047,P,cc,37.0252,-120.6493
+CA,181,HNX,Merced and Madera ,CA181,Madera,06039,P,cc,37.2220,-120.2341
+CA,181,HNX,Merced and Madera ,CA181,Merced,06047,P,cc,37.2220,-120.2341
+CA,182,HNX,Western San Joaquin Valley ,CA182,Kings,06031,P,cc,36.3334,-120.1552
+CA,182,HNX,Western San Joaquin Valley ,CA182,Fresno,06019,P,cc,36.3334,-120.1552
+CA,183,HNX,Foggy Bottom ,CA183,Kings,06031,P,cc,36.3980,-119.7833
+CA,183,HNX,Foggy Bottom ,CA183,Fresno,06019,P,cc,36.3980,-119.7833
+CA,183,HNX,Foggy Bottom ,CA183,Tulare,06107,P,cc,36.3980,-119.7833
+CA,184,HNX,Fresno ,CA184,Fresno,06019,P,cc,36.7600,-119.6055
+CA,185,HNX,Tulare County ,CA185,Tulare,06107,P,cc,36.1982,-119.1825
+CA,186,HNX,Southern Kings County ,CA186,Kings,06031,P,cc,35.9039,-119.5737
+CA,187,HNX,Western San Joaquin Valley in Kern County,CA187,Kern,06029,P,cc,35.5741,-119.5325
+CA,188,HNX,Eastern San Joaquin Valley in Kern County,CA188,Kern,06029,P,cc,35.6266,-119.1173
+CA,189,HNX,Bakersfield ,CA189,Kern,06029,P,cc,35.2183,-119.0645
+CA,190,HNX,Central Sierra Foothills ,CA190,Mariposa,06043,P,cc,37.2843,-119.7374
+CA,190,HNX,Central Sierra Foothills ,CA190,Madera,06039,P,cc,37.2843,-119.7374
+CA,190,HNX,Central Sierra Foothills ,CA190,Fresno,06019,P,cc,37.2843,-119.7374
+CA,191,HNX,Southern Sierra Foothills ,CA191,Tulare,06107,P,cc,36.0994,-118.8844
+CA,191,HNX,Southern Sierra Foothills ,CA191,Kings,06031,P,cc,36.0994,-118.8844
+CA,192,HNX,Central Sierra ,CA192,Mariposa,06043,P,cc,37.6184,-119.3678
+CA,192,HNX,Central Sierra ,CA192,Madera,06039,P,cc,37.6184,-119.3678
+CA,192,HNX,Central Sierra ,CA192,Fresno,06019,P,cc,37.6184,-119.3678
+CA,193,HNX,North Kings River ,CA193,Madera,06039,P,cc,37.0579,-118.9401
+CA,193,HNX,North Kings River ,CA193,Fresno,06019,P,cc,37.0579,-118.9401
+CA,194,HNX,Sequoia Kings ,CA194,Fresno,06019,P,cc,36.4233,-118.5603
+CA,194,HNX,Sequoia Kings ,CA194,Tulare,06107,P,cc,36.4233,-118.5603
+CA,194,HNX,Sequoia Kings ,CA194,Kern,06029,P,cc,36.4233,-118.5603
+CA,195,HNX,Lake Isabella ,CA195,Tulare,06107,P,cc,35.7380,-118.3411
+CA,195,HNX,Lake Isabella ,CA195,Kern,06029,P,cc,35.7380,-118.3411
+CA,196,HNX,Tehachapi Area ,CA196,Kern,06029,P,cc,35.3190,-118.4019
+CA,197,HNX,Fort Tejon ,CA197,Kern,06029,P,cc,34.9451,-119.0187
+CA,198,HNX,Indian Wells Valley ,CA198,Kern,06029,P,cc,35.6271,-117.8015
+CA,199,HNX,Kern County Desert ,CA199,Kern,06029,P,cc,35.0767,-118.0156
+CA,505,MTR,Coastal North Bay Including Point Reyes National Seashore,CA505,Marin,06041,P,ww,38.2364,-122.9543
+CA,505,MTR,Coastal North Bay Including Point Reyes National Seashore,CA505,Sonoma,06097,P,ww,38.2364,-122.9543
+CA,506,MTR,North Bay Interior Valleys,CA506,Marin,06041,P,ww,38.3698,-122.6419
+CA,506,MTR,North Bay Interior Valleys,CA506,Napa,06055,P,ww,38.3698,-122.6419
+CA,506,MTR,North Bay Interior Valleys,CA506,Sonoma,06097,P,ww,38.3698,-122.6419
+CA,507,MTR,North Bay Mountains,CA507,Marin,06041,P,ww,38.5515,-122.6939
+CA,507,MTR,North Bay Mountains,CA507,Napa,06055,P,ww,38.5515,-122.6939
+CA,507,MTR,North Bay Mountains,CA507,Sonoma,06097,P,ww,38.5515,-122.6939
+CA,508,MTR,San Francisco Bay Shoreline,CA508,Alameda,06001,P,ww,37.5938,-122.1650
+CA,508,MTR,San Francisco Bay Shoreline,CA508,Contra Costa,06013,P,ww,37.5938,-122.1650
+CA,508,MTR,San Francisco Bay Shoreline,CA508,San Mateo,06081,P,ww,37.5938,-122.1650
+CA,508,MTR,San Francisco Bay Shoreline,CA508,Santa Clara,06085,P,ww,37.5938,-122.1650
+CA,509,MTR,San Fransisco Peninsula Coast,CA509,San Mateo,06081,P,ww,37.3740,-122.3923
+CA,509,MTR,San Fransisco Peninsula Coast,CA509,Santa Cruz,06087,P,ww,37.3740,-122.3923
+CA,510,MTR,East Bay Interior Valleys,CA510,Alameda,06001,P,ww,37.8803,-121.8801
+CA,510,MTR,East Bay Interior Valleys,CA510,Contra Costa,06013,P,ww,37.8803,-121.8801
+CA,511,MTR,East Bay Hills and the Diablo Range,CA511,Alameda,06001,P,ww,37.4504,-121.6591
+CA,511,MTR,East Bay Hills and the Diablo Range,CA511,Contra Costa,06013,P,ww,37.4504,-121.6591
+CA,511,MTR,East Bay Hills and the Diablo Range,CA511,Santa Clara,06085,P,ww,37.4504,-121.6591
+CA,512,MTR,Santa Cruz Mountains,CA512,San Mateo,06081,P,ww,37.1726,-122.0400
+CA,512,MTR,Santa Cruz Mountains,CA512,Santa Clara,06085,P,ww,37.1726,-122.0400
+CA,512,MTR,Santa Cruz Mountains,CA512,Santa Cruz,06087,P,ww,37.1726,-122.0400
+CA,513,MTR,Santa Clara Valley Including San Jose,CA513,Santa Clara,06085,P,ww,37.2196,-121.7971
+CA,516,MTR,Southern Salinas Valley/Arroyo Seco and Lake San Antonio,CA516,Monterey,06053,P,ww,36.1014,-121.0546
+CA,517,MTR,Santa Lucia Mountains and Los Padres National Forest,CA517,Monterey,06053,P,ww,36.1606,-121.4188
+CA,518,MTR,Mountains Of San Benito County And Interior Monterey County Including Pinnacles National Monument,CA518,Monterey,06053,P,ww,36.3718,-120.9214
+CA,518,MTR,Mountains Of San Benito County And Interior Monterey County Including Pinnacles National Monument,CA518,San Benito,06069,P,ww,36.3718,-120.9214
+CA,519,VEF,Eastern Sierra Slopes of Inyo County,CA519,Inyo,06027,P,ee,36.8269,-118.3410
+CA,520,VEF,Owens Valley,CA520,Inyo,06027,P,ee,36.5133,-118.0127
+CA,521,VEF,White Mountains of Inyo County,CA521,Inyo,06027,P,ee,37.0104,-118.0388
+CA,522,VEF,Death Valley National Park,CA522,Inyo,06027,P,ee,36.4199,-117.1006
+CA,523,VEF,Western Mojave Desert,CA523,San Bernardino,06071,P,ss,35.3053,-117.0005
+CA,524,VEF,Eastern Mojave Desert Including the Mojave National Preserve,CA524,San Bernardino,06071,P,,35.1398,-115.7069
+CA,525,VEF,Morongo Basin,CA525,San Bernardino,06071,P,ss,34.3424,-116.0947
+CA,526,VEF,Cadiz Basin,CA526,San Bernardino,06071,P,ss,34.3849,-115.0956
+CA,527,VEF,San Bernadino County-Upper Colorado River Valley,CA527,San Bernardino,06071,P,ss,34.4988,-114.5011
+CA,528,MTR,Northern Salinas Valley/Hollister Valley and Carmel Valley,CA528,Monterey,06053,P,ww,36.6769,-121.5047
+CA,528,MTR,Northern Salinas Valley/Hollister Valley and Carmel Valley,CA528,San Benito,06069,P,ww,36.6769,-121.5047
+CA,529,MTR,Northern Monterey Bay,CA529,Santa Cruz,06087,P,ww,36.9757,-121.9005
+CA,530,MTR,Southern Monterey Bay and Big Sur Coast,CA530,Monterey,06053,P,ww,36.6165,-121.7566
+CA,547,LOX,Los Angeles County San Fernando Valley,CA547,Los Angeles,06037,P,sw,34.2043,-118.4799
+CA,548,LOX,Los Angeles County San Gabriel Valley,CA548,Los Angeles,06037,P,sw,34.0657,-117.9738
+CA,549,LOX,San Miguel and Santa Rosa Islands,CA549,Santa Barbara,06083,P,sw,33.9774,-120.1462
+CA,550,LOX,Santa Cruz and Anacapa Islands,CA550,Santa Barbara,06083,P,sw,34.0138,-119.7416
+CA,552,SGX,Orange County Coastal,CA552,Orange,06059,P,sw,33.7028,-117.7899
+CA,554,SGX,Orange County Inland,CA554,Orange,06059,P,sw,0.0000,0.0000
+CA,560,PSR,Joshua Tree NP West,CA560,San Bernardino,06071,P,ss,33.9359,-116.0847
+CA,561,PSR,Joshua Tree NP East,CA561,San Bernardino,06071,P,ss,33.9564,-115.6533
+CA,562,PSR,Imperial County Southwest,CA562,Imperial,06025,P,se,32.8129,-116.0390
+CA,563,PSR,Salton Sea,CA563,Imperial,06025,P,se,33.3091,-115.8096
+CA,563,PSR,Salton Sea,CA563,Riverside,06065,P,se,33.3091,-115.8096
+CA,564,PSR,Chuckwalla Mountains,CA564,Imperial,06025,P,se,33.3715,-115.2724
+CA,564,PSR,Chuckwalla Mountains,CA564,Riverside,06065,P,se,33.3715,-115.2724
+CA,565,PSR,Imperial County Southeast,CA565,Imperial,06025,P,se,32.8860,-114.9902
+CA,566,PSR,Imperial County West,CA566,Imperial,06025,P,se,32.9908,-115.9008
+CA,567,PSR,Imperial Valley,CA567,Imperial,06025,P,se,32.9245,-115.5185
+CA,568,PSR,Chiriaco Summit,CA568,Riverside,06065,P,ss,33.6785,-115.6832
+CA,569,PSR,Palo Verde Valley,CA569,Imperial,06025,P,se,33.4256,-114.6527
+CA,569,PSR,Palo Verde Valley,CA569,Riverside,06065,P,se,33.4256,-114.6527
+CA,570,PSR,Chuckwalla Valley,CA570,Riverside,06065,P,se,33.8125,-115.0182
+CO,001,GJT,Lower Yampa River Basin,CO001,Garfield,08045,M,wc,40.4440,-108.6994
+CO,001,GJT,Lower Yampa River Basin,CO001,Moffat,08081,M,nw,40.4440,-108.6994
+CO,001,GJT,Lower Yampa River Basin,CO001,Rio Blanco,08103,M,nw,40.4440,-108.6994
+CO,002,GJT,Central Yampa River Basin,CO002,Moffat,08081,M,nw,40.4046,-107.9134
+CO,002,GJT,Central Yampa River Basin,CO002,Rio Blanco,08103,M,nw,40.4046,-107.9134
+CO,002,GJT,Central Yampa River Basin,CO002,Routt,08107,M,nw,40.4046,-107.9134
+CO,003,GJT,Roan and Tavaputs Plateaus,CO003,Garfield,08045,M,wc,39.5758,-108.4377
+CO,003,GJT,Roan and Tavaputs Plateaus,CO003,Mesa,08077,M,wc,39.5758,-108.4377
+CO,003,GJT,Roan and Tavaputs Plateaus,CO003,Rio Blanco,08103,M,nw,39.5758,-108.4377
+CO,004,GJT,Elkhead and Park Mountains,CO004,Moffat,08081,M,nw,40.5653,-106.9552
+CO,004,GJT,Elkhead and Park Mountains,CO004,Routt,08107,M,nw,40.5653,-106.9552
+CO,005,GJT,Upper Yampa River Basin,CO005,Routt,08107,M,nw,40.3903,-106.9616
+CO,006,GJT,Grand Valley,CO006,Garfield,08045,M,wc,39.1541,-108.6809
+CO,006,GJT,Grand Valley,CO006,Mesa,08077,M,wc,39.1541,-108.6809
+CO,007,GJT,Debeque to Silt Corridor,CO007,Garfield,08045,M,wc,39.4422,-107.9773
+CO,007,GJT,Debeque to Silt Corridor,CO007,Mesa,08077,M,wc,39.4422,-107.9773
+CO,008,GJT,Central Colorado River Basin,CO008,Eagle,08037,M,cc,39.6286,-107.0292
+CO,008,GJT,Central Colorado River Basin,CO008,Garfield,08045,M,wc,39.6286,-107.0292
+CO,008,GJT,Central Colorado River Basin,CO008,Pitkin,08097,M,cc,39.6286,-107.0292
+CO,009,GJT,Grand and Battlement Mesas,CO009,Delta,08029,M,wc,39.1332,-107.8549
+CO,009,GJT,Grand and Battlement Mesas,CO009,Garfield,08045,M,wc,39.1332,-107.8549
+CO,009,GJT,Grand and Battlement Mesas,CO009,Mesa,08077,M,wc,39.1332,-107.8549
+CO,010,GJT,Gore and Elk Mountains/Central Mountain Valleys,CO010,Eagle,08037,M,cc,39.4353,-106.8147
+CO,010,GJT,Gore and Elk Mountains/Central Mountain Valleys,CO010,Garfield,08045,M,wc,39.4353,-106.8147
+CO,010,GJT,Gore and Elk Mountains/Central Mountain Valleys,CO010,Pitkin,08097,M,cc,39.4353,-106.8147
+CO,011,GJT,Central Gunnison and Uncompahgre River Basin,CO011,Delta,08029,M,wc,38.6735,-107.9201
+CO,011,GJT,Central Gunnison and Uncompahgre River Basin,CO011,Gunnison,08051,M,cc,38.6735,-107.9201
+CO,011,GJT,Central Gunnison and Uncompahgre River Basin,CO011,Mesa,08077,M,wc,38.6735,-107.9201
+CO,011,GJT,Central Gunnison and Uncompahgre River Basin,CO011,Montrose,08085,M,wc,38.6735,-107.9201
+CO,012,GJT,West Elk and Sawatch Mountains,CO012,Delta,08029,M,wc,38.8011,-107.0042
+CO,012,GJT,West Elk and Sawatch Mountains,CO012,Gunnison,08051,M,cc,38.8011,-107.0042
+CO,012,GJT,West Elk and Sawatch Mountains,CO012,Montrose,08085,M,wc,38.8011,-107.0042
+CO,013,GJT,Flattops,CO013,Eagle,08037,M,cc,39.9766,-107.4376
+CO,013,GJT,Flattops,CO013,Garfield,08045,M,wc,39.9766,-107.4376
+CO,013,GJT,Flattops,CO013,Moffat,08081,M,nw,39.9766,-107.4376
+CO,013,GJT,Flattops,CO013,Rio Blanco,08103,M,nw,39.9766,-107.4376
+CO,013,GJT,Flattops,CO013,Routt,08107,M,nw,39.9766,-107.4376
+CO,014,GJT,Upper Gunnison River Valley,CO014,Gunnison,08051,M,cc,38.4776,-107.1881
+CO,014,GJT,Upper Gunnison River Valley,CO014,Montrose,08085,M,wc,38.4776,-107.1881
+CO,017,GJT,Uncompahgre Plateu/Dallas Divide,CO017,Delta,08029,M,wc,38.5539,-108.4296
+CO,017,GJT,Uncompahgre Plateu/Dallas Divide,CO017,Mesa,08077,M,wc,38.5539,-108.4296
+CO,017,GJT,Uncompahgre Plateu/Dallas Divide,CO017,Montrose,08085,M,wc,38.5539,-108.4296
+CO,017,GJT,Uncompahgre Plateu/Dallas Divide,CO017,Ouray,08091,M,sw,38.5539,-108.4296
+CO,017,GJT,Uncompahgre Plateu/Dallas Divide,CO017,San Miguel,08113,M,wc,38.5539,-108.4296
+CO,018,GJT,Northwestern San Juan Mountains,CO018,Gunnison,08051,M,cc,38.0588,-107.5504
+CO,018,GJT,Northwestern San Juan Mountains,CO018,Hinsdale,08053,M,sw,38.0588,-107.5504
+CO,018,GJT,Northwestern San Juan Mountains,CO018,Montrose,08085,M,wc,38.0588,-107.5504
+CO,018,GJT,Northwestern San Juan Mountains,CO018,Ouray,08091,M,sw,38.0588,-107.5504
+CO,018,GJT,Northwestern San Juan Mountains,CO018,San Miguel,08113,M,wc,38.0588,-107.5504
+CO,019,GJT,Southwest San Juan Mountains,CO019,Archuleta,08007,M,sc,37.5206,-107.6597
+CO,019,GJT,Southwest San Juan Mountains,CO019,Dolores,08033,M,sw,37.5206,-107.6597
+CO,019,GJT,Southwest San Juan Mountains,CO019,Hinsdale,08053,M,sw,37.5206,-107.6597
+CO,019,GJT,Southwest San Juan Mountains,CO019,La Plata,08067,M,sw,37.5206,-107.6597
+CO,019,GJT,Southwest San Juan Mountains,CO019,Montezuma,08083,M,sw,37.5206,-107.6597
+CO,019,GJT,Southwest San Juan Mountains,CO019,San Juan,08111,M,se,37.5206,-107.6597
+CO,019,GJT,Southwest San Juan Mountains,CO019,San Miguel,08113,M,wc,37.5206,-107.6597
+CO,020,GJT,Paradox Valley/Little Dolores River,CO020,Dolores,08033,M,sw,38.1963,-108.7095
+CO,020,GJT,Paradox Valley/Little Dolores River,CO020,Mesa,08077,M,wc,38.1963,-108.7095
+CO,020,GJT,Paradox Valley/Little Dolores River,CO020,Montrose,08085,M,wc,38.1963,-108.7095
+CO,020,GJT,Paradox Valley/Little Dolores River,CO020,San Miguel,08113,M,wc,38.1963,-108.7095
+CO,021,GJT,Four Corners/Upper Dolores River,CO021,Dolores,08033,M,sw,37.3809,-108.7278
+CO,021,GJT,Four Corners/Upper Dolores River,CO021,Montezuma,08083,M,sw,37.3809,-108.7278
+CO,021,GJT,Four Corners/Upper Dolores River,CO021,San Miguel,08113,M,wc,37.3809,-108.7278
+CO,022,GJT,Animas River Basin,CO022,La Plata,08067,M,sw,37.1488,-107.8775
+CO,023,GJT,San Juan River Basin,CO023,Archuleta,08007,M,sc,37.1383,-107.2004
+CO,030,BOU,Jackson County Below 9000 Feet,CO030,Jackson,08057,M,nc,40.6766,-106.3090
+CO,031,BOU,West Jackson and West Grand Counties Above 9000 Feet,CO031,Grand,08049,M,nc,40.6175,-106.6092
+CO,031,BOU,West Jackson and West Grand Counties Above 9000 Feet,CO031,Jackson,08057,M,nc,40.6175,-106.6092
+CO,032,BOU,Grand and Summit Counties Below 9000 Feet,CO032,Grand,08049,M,nc,40.0726,-106.2090
+CO,032,BOU,Grand and Summit Counties Below 9000 Feet,CO032,Summit,08117,M,cc,40.0726,-106.2090
+CO,033,BOU,South and East Jackson/Larimer/North and Northeast Grand/Northwest Boulder Counties Above 9000 Feet,CO033,Boulder,08013,M,ne,40.4880,-105.9071
+CO,033,BOU,South and East Jackson/Larimer/North and Northeast Grand/Northwest Boulder Counties Above 9000 Feet,CO033,Grand,08049,M,nc,40.4880,-105.9071
+CO,033,BOU,South and East Jackson/Larimer/North and Northeast Grand/Northwest Boulder Counties Above 9000 Feet,CO033,Jackson,08057,M,nc,40.4880,-105.9071
+CO,033,BOU,South and East Jackson/Larimer/North and Northeast Grand/Northwest Boulder Counties Above 9000 Feet,CO033,Larimer,08069,M,nc,40.4880,-105.9071
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Boulder,08013,M,ne,39.6044,-105.8505
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Clear Creek,08019,M,cc,39.6044,-105.8505
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Gilpin,08047,M,cc,39.6044,-105.8505
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Grand,08049,M,nc,39.6044,-105.8505
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Jefferson,08059,M,ne,39.6044,-105.8505
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Park,08093,M,cc,39.6044,-105.8505
+CO,034,BOU,South and Southeast Grand/West Central and Southwest Boulder/Gilpin/Clear Creek/Summit/North and West Park Counties Above 9000 Feet,CO034,Summit,08117,M,cc,39.6044,-105.8505
+CO,035,BOU,Larimer and Boulder Counties Between 6000 and 9000 Feet,CO035,Boulder,08013,M,ne,40.6099,-105.4708
+CO,035,BOU,Larimer and Boulder Counties Between 6000 and 9000 Feet,CO035,Larimer,08069,M,nc,40.6099,-105.4708
+CO,036,BOU,Jefferson and West Douglas Counties Above  6000 Feet/Gilpin/Clear Creek/Northeast Park Counties Below 9000 Feet,CO036,Clear Creek,08019,M,cc,39.4838,-105.2681
+CO,036,BOU,Jefferson and West Douglas Counties Above  6000 Feet/Gilpin/Clear Creek/Northeast Park Counties Below 9000 Feet,CO036,Douglas,08035,M,ne,39.4838,-105.2681
+CO,036,BOU,Jefferson and West Douglas Counties Above  6000 Feet/Gilpin/Clear Creek/Northeast Park Counties Below 9000 Feet,CO036,Gilpin,08047,M,cc,39.4838,-105.2681
+CO,036,BOU,Jefferson and West Douglas Counties Above  6000 Feet/Gilpin/Clear Creek/Northeast Park Counties Below 9000 Feet,CO036,Jefferson,08059,M,ne,39.4838,-105.2681
+CO,036,BOU,Jefferson and West Douglas Counties Above  6000 Feet/Gilpin/Clear Creek/Northeast Park Counties Below 9000 Feet,CO036,Park,08093,M,cc,39.4838,-105.2681
+CO,037,BOU,Central and Southeast Park County,CO037,Park,08093,M,cc,38.9846,-105.6737
+CO,038,BOU,Larimer County Below 6000 Feet/Northwest Weld County,CO038,Larimer,08069,M,nc,40.7183,-104.8423
+CO,038,BOU,Larimer County Below 6000 Feet/Northwest Weld County,CO038,Weld,08123,M,ne,40.7183,-104.8423
+CO,039,BOU,Boulder And Jefferson Counties Below 6000 Feet/West Broomfield County,CO039,Boulder,08013,M,ne,39.9606,-105.1560
+CO,039,BOU,Boulder And Jefferson Counties Below 6000 Feet/West Broomfield County,CO039,Broomfield,08014,M,ne,39.9606,-105.1560
+CO,039,BOU,Boulder And Jefferson Counties Below 6000 Feet/West Broomfield County,CO039,Jefferson,08059,M,ne,39.9606,-105.1560
+CO,040,BOU,North Douglas County Below 6000 Feet/Denver/West Adams and Arapahoe Counties/East Broomfield County,CO040,Adams,08001,M,ne,39.7656,-104.8040
+CO,040,BOU,North Douglas County Below 6000 Feet/Denver/West Adams and Arapahoe Counties/East Broomfield County,CO040,Arapahoe,08005,M,ne,39.7656,-104.8040
+CO,040,BOU,North Douglas County Below 6000 Feet/Denver/West Adams and Arapahoe Counties/East Broomfield County,CO040,Broomfield,08014,M,ne,39.7656,-104.8040
+CO,040,BOU,North Douglas County Below 6000 Feet/Denver/West Adams and Arapahoe Counties/East Broomfield County,CO040,Denver,08031,M,ne,39.7656,-104.8040
+CO,040,BOU,North Douglas County Below 6000 Feet/Denver/West Adams and Arapahoe Counties/East Broomfield County,CO040,Douglas,08035,M,ne,39.7656,-104.8040
+CO,041,BOU,Elbert/Central and East Douglas Counties Above 6000 Feet,CO041,Arapahoe,08005,M,ne,39.3075,-104.5091
+CO,041,BOU,Elbert/Central and East Douglas Counties Above 6000 Feet,CO041,Douglas,08035,M,ne,39.3075,-104.5091
+CO,041,BOU,Elbert/Central and East Douglas Counties Above 6000 Feet,CO041,Elbert,08039,M,ec,39.3075,-104.5091
+CO,042,BOU,Northeast Weld County,CO042,Weld,08123,M,ne,40.7427,-104.0025
+CO,043,BOU,Central and South Weld County,CO043,Weld,08123,M,ne,40.2668,-104.5924
+CO,044,BOU,Morgan County,CO044,Morgan,08087,M,ne,40.2627,-103.8098
+CO,045,BOU,Central and East Adams and Arapahoe Counties,CO045,Adams,08001,M,ne,39.7839,-104.1048
+CO,045,BOU,Central and East Adams and Arapahoe Counties,CO045,Arapahoe,08005,M,ne,39.7839,-104.1048
+CO,046,BOU,North and Northeast Elbert County Below 6000 Feet/North Lincoln County,CO046,Elbert,08039,M,ec,39.3560,-103.6814
+CO,046,BOU,North and Northeast Elbert County Below 6000 Feet/North Lincoln County,CO046,Lincoln,08073,M,ec,39.3560,-103.6814
+CO,047,BOU,Southeast Elbert County Below 6000 Feet/South Lincoln County,CO047,Elbert,08039,M,ec,38.8048,-103.5944
+CO,047,BOU,Southeast Elbert County Below 6000 Feet/South Lincoln County,CO047,Lincoln,08073,M,ec,38.8048,-103.5944
+CO,048,BOU,Logan County,CO048,Logan,08075,M,ne,40.7247,-103.1101
+CO,049,BOU,Washington County,CO049,Washington,08121,M,ne,39.9710,-103.2013
+CO,050,BOU,Sedgwick County,CO050,Sedgwick,08115,M,ne,40.8758,-102.3518
+CO,051,BOU,Phillips County,CO051,Phillips,08095,M,ne,40.5939,-102.3575
+CO,058,PUB,Western Mosquito Range/East Lake County Above 11000 Ft,CO058,Lake,08065,M,cc,39.2545,-106.2141
+CO,059,PUB,Leadville Vicinity/Lake County Below 11000 Ft,CO059,Lake,08065,M,cc,39.1873,-106.3446
+CO,060,PUB,Eastern Sawatch Mountains above 11000 Ft,CO060,Chaffee,08015,M,cc,38.8197,-106.3586
+CO,060,PUB,Eastern Sawatch Mountains above 11000 Ft,CO060,Lake,08065,M,cc,38.8197,-106.3586
+CO,060,PUB,Eastern Sawatch Mountains above 11000 Ft,CO060,Saguache,08109,M,cc,38.8197,-106.3586
+CO,061,PUB,Western Chaffee County Between 9000 and 11000 Ft,CO061,Chaffee,08015,M,cc,38.7415,-106.2559
+CO,062,PUB,Central Chaffee County Below 9000 Ft,CO062,Chaffee,08015,M,cc,38.6873,-106.0928
+CO,063,PUB,Western Mosquito Range/East Chaffee County above 9000Ft,CO063,Chaffee,08015,M,cc,38.8243,-106.0253
+CO,064,PUB,Saguache County West of Continental Divide Below 10000 Ft,CO064,Saguache,08109,M,cc,38.3063,-106.7044
+CO,065,PUB,Saguache County East of Continental Divide below 10000 Ft,CO065,Rio Grande,08105,M,sc,38.1071,-106.3423
+CO,065,PUB,Saquache County East of Continental Divide below 10000 Ft,CO065,Saguache,08109,M,cc,38.1071,-106.3423
+CO,066,PUB,La Garita Mountains Above 10000 Ft,CO066,Mineral,08079,M,sc,38.0229,-106.7318
+CO,066,PUB,La Garita Mountains Above 10000 Ft,CO066,Rio Grande,08105,M,sc,38.0229,-106.7318
+CO,066,PUB,La Garita Mountains Above 10000 Ft,CO066,Saguache,08109,M,cc,38.0229,-106.7318
+CO,067,PUB,Upper Rio Grande Valley/Eastern San Juan Mountains Below 10000 Ft,CO067,Conejos,08021,M,sc,37.4690,-106.5413
+CO,067,PUB,Upper Rio Grande Valley/Eastern San Juan Mountains Below 10000 Ft,CO067,Mineral,08079,M,sc,37.4690,-106.5413
+CO,067,PUB,Upper Rio Grande Valley/Eastern San Juan Mountains Below 10000 Ft,CO067,Rio Grande,08105,M,sc,37.4690,-106.5413
+CO,068,PUB,Eastern San Juan Mountains Above 10000 Ft,CO068,Conejos,08021,M,sc,37.4272,-106.7002
+CO,068,PUB,Eastern San Juan Mountains Above 10000 Ft,CO068,Mineral,08079,M,sc,37.4272,-106.7002
+CO,068,PUB,Eastern San Juan Mountains Above 10000 Ft,CO068,Rio Grande,08105,M,sc,37.4272,-106.7002
+CO,069,PUB,Del Norte Vicinity/Northern San Luis Valley Below 8500 Ft,CO069,Rio Grande,08105,M,sc,37.9152,-106.0347
+CO,069,PUB,Del Norte Vicinity/Northern San Luis Valley Below 8500 Ft,CO069,Saguache,08109,M,cc,37.9152,-106.0347
+CO,070,PUB,Alamosa  Vicinity/Central San Luis Valley Below 8500 Ft,CO070,Alamosa,08003,M,sc,37.5614,-105.8929
+CO,070,PUB,Alamosa  Vicinity/Central San Luis Valley Below 8500 Ft,CO070,Rio Grande,08105,M,sc,37.5614,-105.8929
+CO,071,PUB,Southern San Luis Valley,CO071,Conejos,08021,M,sc,37.2224,-105.7537
+CO,071,PUB,Southern San Luis Valley,CO071,Costilla,08023,M,sc,37.2224,-105.7537
+CO,072,PUB,Northern Sangre de Cristo Mountains Between 8500 And 11000 Ft,CO072,Alamosa,08003,M,sc,37.9018,-105.5379
+CO,072,PUB,Northern Sangre de Cristo Mountains Between 8500 And 11000 Ft,CO072,Chaffee,08015,M,cc,37.9018,-105.5379
+CO,072,PUB,Northern Sangre de Cristo Mountains Between 8500 And 11000 Ft,CO072,Costilla,08023,M,sc,37.9018,-105.5379
+CO,072,PUB,Northern Sangre de Cristo Mountains Between 8500 And 11000 Ft,CO072,Custer,08027,M,se,37.9018,-105.5379
+CO,072,PUB,Northern Sangre de Cristo Mountains Between 8500 And 11000 Ft,CO072,Fremont,08043,M,cc,37.9018,-105.5379
+CO,072,PUB,Northern Sangre de Christo Mountains Between 8500 And 11000 Ft,CO072,Huerfano,08055,M,se,37.9018,-105.5379
+CO,072,PUB,Northern Sangre de Cristo Mountains Between 8500 And 11000 Ft,CO072,Saguache,08109,M,cc,37.9018,-105.5379
+CO,073,PUB,Northern Sangre de Cristo Mountains above 11000 Ft,CO073,Alamosa,08003,M,sc,37.9767,-105.6170
+CO,073,PUB,Northern Sangre de Cristo Mountains above 11000 Ft,CO073,Costilla,08023,M,sc,37.9767,-105.6170
+CO,073,PUB,Northern Sangre de Cristo Mountains above 11000 Ft,CO073,Custer,08027,M,se,37.9767,-105.6170
+CO,073,PUB,Northern Sangre de Cristo Mountains above 11000 Ft,CO073,Fremont,08043,M,cc,37.9767,-105.6170
+CO,073,PUB,Northern Sangre de Cristo Mountains above 11000 Ft,CO073,Huerfano,08055,M,se,37.9767,-105.6170
+CO,073,PUB,Northern Sangre de Cristo Mountains above 11000 Ft,CO073,Saguache,08109,M,cc,37.9767,-105.6170
+CO,074,PUB,Southern Sangre De Cristo Mountains Between 7500 and 11000 Ft,CO074,Costilla,08023,M,sc,37.2245,-105.0257
+CO,074,PUB,Southern Sangre De Cristo Mountains Between 7500 and 11000 Ft,CO074,Huerfano,08055,M,se,37.2245,-105.0257
+CO,074,PUB,Southern Sangre De Cristo Mountains Between 7500 and 11000 Ft,CO074,Las Animas,08071,M,se,37.2245,-105.0257
+CO,075,PUB,Southern Sangre De Cristo Mountains Above 11000 Ft,CO075,Costilla,08023,M,sc,37.2046,-105.1708
+CO,075,PUB,Southern Sangre De Cristo Mountains Above 11000 Ft,CO075,Huerfano,08055,M,se,37.2046,-105.1708
+CO,075,PUB,Southern Sangre De Cristo Mountains Above 11000 Ft,CO075,Las Animas,08071,M,se,37.2046,-105.1708
+CO,076,PUB,Northwestern Fremont County  Above 8500Ft,CO076,Fremont,08043,M,cc,38.6011,-105.6475
+CO,077,PUB,Western/Central Fremont County Below 8500 Ft,CO077,Fremont,08043,M,cc,38.4248,-105.5558
+CO,078,PUB,Wet Mountain Valley Below 8500 Ft,CO078,Custer,08027,M,se,38.1353,-105.4502
+CO,078,PUB,Wet Mountain Valley Below 8500 Ft,CO078,Huerfano,08055,M,se,38.1353,-105.4502
+CO,079,PUB,Wet Mountains between 6300 and 10000Ft,CO079,Custer,08027,M,se,38.0460,-105.1469
+CO,079,PUB,Wet Mountains between 6300 and 10000Ft,CO079,Fremont,08043,M,cc,38.0460,-105.1469
+CO,079,PUB,Wet Mountains between 6300 and 10000Ft,CO079,Huerfano,08055,M,se,38.0460,-105.1469
+CO,079,PUB,Wet Mountains between 6300 and 10000Ft,CO079,Pueblo,08101,M,se,38.0460,-105.1469
+CO,080,PUB,Wet Mountains above 10000 Ft,CO080,Custer,08027,M,se,37.9888,-105.1345
+CO,080,PUB,Wet Mountains above 10000 Ft,CO080,Huerfano,08055,M,se,37.9888,-105.1345
+CO,080,PUB,Wet Mountains above 10000 Ft,CO080,Pueblo,08101,M,se,37.9888,-105.1345
+CO,081,PUB,Teller County/Rampart Range above 7500fT/Pike's Peak Between 7500 And 11000 Ft,CO081,El Paso,08041,M,ec,38.8708,-105.1130
+CO,081,PUB,Teller County/Rampart Range above 7500fT/Pike's Peak Between 7500 And 11000 Ft,CO081,Fremont,08043,M,cc,38.8708,-105.1130
+CO,081,PUB,Teller County/Rampart Range above 7500fT/Pike's Peak Between 7500 And 11000 Ft,CO081,Teller,08119,M,cc,38.8708,-105.1130
+CO,082,PUB,Pikes Peak above 11000 Ft,CO082,El Paso,08041,M,ec,38.8156,-105.0464
+CO,082,PUB,Pikes Peak above 11000 Ft,CO082,Teller,08119,M,cc,38.8156,-105.0464
+CO,083,PUB,Canon City Vicinity/Eastern Fremont County,CO083,Custer,08027,M,se,38.4443,-105.1148
+CO,083,PUB,Canon City Vicinity/Eastern Fremont County,CO083,Fremont,08043,M,cc,38.4443,-105.1148
+CO,083,PUB,Canon City Vicinity/Eastern Fremont County,CO083,Teller,08119,M,cc,38.4443,-105.1148
+CO,084,PUB,Northern El Paso County/Monument Ridge/Rampart Range Below 7500 Ft,CO084,El Paso,08041,M,ec,39.0124,-104.4498
+CO,085,PUB,Colorado Springs Vicinity/Southern El Paso County/Rampart Range Below 7400 Ft,CO085,El Paso,08041,M,ec,38.7125,-104.5034
+CO,086,PUB,Pueblo Vicinity/Pueblo County Below 6300 Feet,CO086,Pueblo,08101,M,se,38.1821,-104.4872
+CO,087,PUB,Walsenburg Vicinity/Upper Huerfano River Basin Below 7500 Ft,CO087,Huerfano,08055,M,se,37.6899,-104.8578
+CO,088,PUB,Trinidad Vicinity/Western Las Animas County Below 7500 Ft,CO088,Las Animas,08071,M,se,37.3538,-104.3935
+CO,089,PUB,Crowley County,CO089,Crowley,08025,M,se,38.3266,-103.7849
+CO,090,GLD,Yuma County,CO090,Yuma,08125,M,ne,40.0029,-102.4243
+CO,091,GLD,Kit Carson County,CO091,Kit Carson,08063,M,ec,39.3054,-102.6029
+CO,092,GLD,Cheyenne County,CO092,Cheyenne,08017,M,ec,38.8279,-102.6034
+CO,093,PUB,La Junta Vicinity/Otero County,CO093,Otero,08089,M,se,37.9027,-103.7164
+CO,094,PUB,Eastern Las Animas County,CO094,Las Animas,08071,M,se,37.3221,-103.5645
+CO,095,PUB,Western Kiowa County,CO095,Kiowa,08061,M,se,38.4240,-103.0919
+CO,096,PUB,Eastern Kiowa County,CO096,Kiowa,08061,M,se,38.4414,-102.3891
+CO,097,PUB,Las Animas Vicinity/Bent County,CO097,Bent,08011,M,se,37.9551,-103.0717
+CO,098,PUB,Lamar Vicinity/Prowers County,CO098,Prowers,08099,M,se,37.9552,-102.3933
+CO,099,PUB,Springfield Vicinity/Baca County,CO099,Baca,08009,M,se,37.3192,-102.5605
+CT,001,ALY,Northern Litchfield,CT001,Litchfield,09005,E,nn,41.8851,-73.2502
+CT,002,BOX,Hartford,CT002,Hartford,09003,E,nn,41.8064,-72.7328
+CT,003,BOX,Tolland,CT003,Tolland,09013,E,nn,41.8550,-72.3365
+CT,004,BOX,Windham,CT004,Windham,09015,E,nn,41.8300,-71.9875
+CT,005,OKX,Northern Fairfield,CT005,Fairfield,09001,E,ss,41.3530,-73.3629
+CT,006,OKX,Northern New Haven,CT006,New Haven,09009,E,ss,41.4648,-72.9956
+CT,007,OKX,Northern Middlesex,CT007,Middlesex,09007,E,ss,41.4977,-72.5551
+CT,008,OKX,Northern New London,CT008,New London,09011,E,ss,41.5317,-72.0992
+CT,009,OKX,Southern Fairfield,CT009,Fairfield,09001,E,ss,41.1301,-73.4350
+CT,010,OKX,Southern New Haven,CT010,New Haven,09009,E,ss,41.3108,-72.8154
+CT,011,OKX,Southern Middlesex,CT011,Middlesex,09007,E,ss,41.3211,-72.4498
+CT,012,OKX,Southern New London,CT012,New London,09011,E,ss,41.3615,-72.1034
+CT,013,ALY,Southern Litchfield,CT013,Litchfield,09005,E,nn,41.6344,-73.2368
+DC,001,LWX,District of Columbia,DC001,District of Columbia,11001,E,,38.9093,-77.0146
+DE,001,PHI,New Castle,DE001,New Castle,10003,E,nn,39.5810,-75.6489
+DE,002,PHI,Kent,DE002,Kent,10001,E,cc,39.0867,-75.5688
+DE,003,PHI,Inland Sussex,DE003,Sussex,10005,E,cc,38.6653,-75.4181
+DE,004,PHI,Delaware Beaches,DE004,Sussex,10005,E,cc,38.6114,-75.0919
+FL,007,TAE,North Walton,FL007,Walton,12131,C,pa,30.8499,-86.2133
+FL,008,TAE,Central Walton,FL008,Walton,12131,C,pa,30.5928,-86.1117
+FL,009,TAE,Holmes,FL009,Holmes,12059,C,pa,30.8679,-85.8143
+FL,010,TAE,Washington,FL010,Washington,12133,C,pa,30.6107,-85.6653
+FL,011,TAE,Jackson,FL011,Jackson,12063,C,pa,30.7956,-85.2194
+FL,011,TAE,Jackson,FL011,Jackson,12063,C,pa,30.7800,-84.9213
+FL,012,TAE,Inland Bay,FL012,Bay,12005,C,pa,30.3660,-85.5408
+FL,013,TAE,Calhoun,FL013,Calhoun,12013,C,pa,30.4061,-85.1972
+FL,014,TAE,Inland Gulf,FL014,Gulf,12045,CE,pa,30.0103,-85.2110
+FL,014,TAE,Inland Gulf,FL014,Gulf,12045,CE,pa,29.9507,-85.1548
+FL,015,TAE,Inland Franklin,FL015,Franklin,12037,E,bb,29.9462,-84.8653
+FL,016,TAE,Gadsden,FL016,Gadsden,12039,E,bb,30.5795,-84.6137
+FL,017,TAE,Leon,FL017,Leon,12073,E,bb,30.4581,-84.2779
+FL,018,TAE,Inland Jefferson,FL018,Jefferson,12065,E,bb,30.4578,-83.8865
+FL,019,TAE,Madison,FL019,Madison,12079,E,bb,30.4442,-83.4700
+FL,020,JAX,Hamilton,FL020,Hamilton,12047,E,nn,30.4964,-82.9478
+FL,021,JAX,Suwannee,FL021,Suwannee,12121,E,nn,30.1956,-82.9915
+FL,023,JAX,Baker,FL023,Baker,12003,E,ne,30.3310,-82.2846
+FL,024,JAX,Inland Nassau,FL024,Nassau,12089,E,ne,30.6095,-81.8085
+FL,025,JAX,Inland Duval,FL025,Duval,12031,E,ne,30.3302,-81.6740
+FL,026,TAE,Liberty,FL026,Liberty,12077,E,bb,30.2414,-84.8829
+FL,027,TAE,Inland Wakulla,FL027,Wakulla,12129,E,bb,30.2017,-84.4474
+FL,028,TAE,Inland Taylor,FL028,Taylor,12123,E,bb,30.0954,-83.5767
+FL,029,TAE,Lafayette,FL029,Lafayette,12067,E,bb,29.9855,-83.1811
+FL,030,JAX,Union,FL030,Union,12125,E,nn,30.0439,-82.3714
+FL,031,JAX,Bradford,FL031,Bradford,12007,E,nn,29.9502,-82.1689
+FL,032,JAX,Clay,FL032,Clay,12019,E,ne,29.9831,-81.8579
+FL,033,JAX,Inland St. Johns,FL033,St. Johns,12109,E,ne,29.8671,-81.4979
+FL,034,TAE,Inland Dixie,FL034,Dixie,12029,E,bb,29.6711,-83.1235
+FL,035,JAX,Gilchrist,FL035,Gilchrist,12041,E,nn,29.7259,-82.8005
+FL,037,JAX,Putnam,FL037,Putnam,12107,E,ne,29.6086,-81.7442
+FL,038,JAX,Inland Flagler,FL038,Flagler,12035,E,ne,-81.3306,29.4499
+FL,041,MLB,Inland Volusia County,FL041,Volusia,12127,E,ec,29.0622,-81.2756
+FL,043,TBW,Sumter,FL043,Sumter,12119,E,cc,28.7046,-82.0810
+FL,044,MLB,Northern Lake County,FL044,Lake,12069,E,ec,28.9313,-81.6301
+FL,045,MLB,Orange,FL045,Orange,12095,E,ec,28.5148,-81.3238
+FL,046,MLB,Seminole,FL046,Seminole,12117,E,ec,28.7170,-81.2362
+FL,047,MLB,Southern Brevard County,FL047,Brevard,12009,E,,28.0475,-80.7165
+FL,050,TBW,Pinellas,FL050,Pinellas,12103,E,wc,27.9283,-82.7219
+FL,052,TBW,Polk,FL052,Polk,12105,E,cc,27.9489,-81.6978
+FL,053,MLB,Osceola,FL053,Osceola,12097,E,ec,28.0627,-81.1495
+FL,054,MLB,Indian River,FL054,Indian River,12061,E,ec,27.6929,-80.6158
+FL,056,TBW,Hardee,FL056,Hardee,12049,E,cc,27.4927,-81.8099
+FL,057,TBW,Highlands,FL057,Highlands,12055,E,sc,27.3434,-81.3409
+FL,058,MLB,Okeechobee,FL058,Okeechobee,12093,E,ec,27.4283,-80.9015
+FL,059,MLB,St. Lucie,FL059,St. Lucie,12111,E,ec,27.3764,-80.4836
+FL,061,TBW,De Soto,FL061,DeSoto,12027,E,sc,27.1863,-81.8093
+FL,063,MFL,Glades,FL063,Glades,12043,E,ss,26.9605,-81.2397
+FL,064,MLB,Martin,FL064,Martin,12085,E,ec,27.0844,-80.3893
+FL,066,MFL,Hendry,FL066,Hendry,12051,E,ss,26.5473,-81.1718
+FL,067,MFL,Inland Palm Beach County,FL067,Palm Beach,12099,E,se,26.6172,-80.5109
+FL,068,MFL,Metro Palm Beach County,FL068,Palm Beach,12099,E,se,26.6222,-80.1464
+FL,069,MFL,Coastal Collier County,FL069,Collier,12021,E,sw,26.0150,-81.6017
+FL,070,MFL,Inland Collier County,FL070,Collier,12021,E,sw,26.1380,-81.2930
+FL,071,MFL,Inland Broward County,FL071,Broward,12011,E,se,26.1632,-80.6145
+FL,072,MFL,Metro Broward County,FL072,Broward,12011,E,se,26.1257,-80.2706
+FL,073,MFL,Inland Miami-Dade County,FL073,Miami-Dade,12086,E,se,25.6928,-80.6396
+FL,074,MFL,Metropolitan Miami Dade,FL074,Miami-Dade,12086,E,se,25.8194,-80.3008
+FL,075,MFL,Mainland Monroe,FL075,Mainland Monroe,12087,E,sw,25.5205,-81.0326
+FL,076,KEY,Monroe Upper Keys,FL076,Upper Keys in Monroe,12087,E,se,25.1472,-80.4195
+FL,077,KEY,Monroe Middle Keys,FL077,Middle Keys in Monroe,12087,E,se,24.7471,-80.9876
+FL,078,KEY,Monroe Lower Keys,FL078,Lower Keys in Monroe,12087,E,,24.6514,-81.5527
+FL,108,TAE,South Walton,FL108,Walton,12131,C,pa,30.4183,-86.1710
+FL,108,TAE,South Walton,FL108,Walton,12131,C,pa,30.4183,-86.1710
+FL,112,TAE,Coastal Bay,FL112,Bay,12005,E,pa,30.2213,-85.6654
+FL,114,TAE,Coastal Gulf,FL114,Gulf,12045,CE,pa,29.8173,-85.2726
+FL,115,TAE,Coastal Franklin,FL115,Franklin,12037,E,bb,29.8382,-84.7899
+FL,118,TAE,Coastal Jefferson,FL118,Jefferson,12065,E,bb,30.1477,-84.0221
+FL,122,JAX,Northern Columbia ,FL122,Columbia,12023,E,nn,30.4131,-82.5887
+FL,124,JAX,Coastal Nassau,FL124,Nassau,12089,E,ne,30.6095,-81.8085
+FL,125,JAX,Coastal Duval,FL125,Duval,12031,E,ne,30.3302,-81.6740
+FL,127,TAE,Coastal Wakulla,FL127,Wakulla,12129,E,bb,30.0968,-84.3009
+FL,128,TAE,Coastal Taylor,FL128,Taylor,12123,E,bb,29.9259,-83.6703
+FL,133,JAX,Coastal St. Johns,FL133,St. Johns,12109,E,ne,29.9525,-81.3629
+FL,134,TAE,Coastal Dixie,FL134,Dixie,12029,E,bb,29.4924,-83.2251
+FL,136,JAX,Eastern Alachua ,FL136,Alachua,12001,E,nn,29.6896,-82.2664
+FL,138,JAX,Coastal Flagler,FL138,Flagler,12035,E,ne,-81.1875,29.5436
+FL,139,TBW,Coastal Levy,FL139,Levy,12075,E,nn,29.3216,-82.7385
+FL,140,JAX,Eastern Marion ,FL140,Marion,12083,E,nn,29.2170,-81.7957
+FL,141,MLB,Coastal Volusia County,FL141,Volusia,12127,E,ec,29.0562,-80.9907
+FL,142,TBW,Coastal Citrus,FL142,Citrus,12017,E,wc,28.8507,-82.4673
+FL,144,MLB,Southern Lake County,FL144,Lake,12069,E,ec,28.5634,-81.8061
+FL,147,MLB,Northern Brevard County,FL147,Brevard,12009,E,,28.5586,-80.7806
+FL,148,TBW,Coastal Hernando,FL148,Hernando,12053,E,wc,28.5528,-82.4209
+FL,149,TBW,Inland Pasco,FL149,Pasco,12101,E,wc,28.3093,-82.3912
+FL,151,TBW,Coastal Hillsborough,FL151,Hillsborough,12057,E,wc,27.9291,-82.4769
+FL,155,TBW,Coastal Manatee,FL155,Manatee,12081,E,wc,27.4713,-82.2978
+FL,160,TBW,Coastal Sarasota,FL160,Sarasota,12115,E,wc,27.1806,-82.3219
+FL,162,TBW,Coastal Charlotte,FL162,Charlotte,12015,E,sw,26.9118,-82.1135
+FL,165,TBW,Coastal Lee,FL165,Lee,12071,E,sw,26.6135,-81.8902
+FL,168,MFL,Coastal Palm Beach County,FL168,Palm Beach,12099,E,se,26.7053,-80.0806
+FL,172,MFL,Coastal Broward County,FL172,Broward,12011,E,se,26.1498,-80.1266
+FL,173,MFL,Coastal Miami Dade County,FL173,Miami-Dade,12086,E,se,25.5815,-80.3117
+FL,174,MFL,Far South Miami-Dade County,FL174,Miami-Dade,12086,E,se,25.2990,-80.6497
+FL,201,MOB,Escambia Inland,FL201,Escambia,12033,C,nw,30.8725,-87.4324
+FL,202,MOB,Escambia Coastal,FL202,Escambia,12033,C,nw,30.5100,-87.3166
+FL,203,MOB,Santa Rosa Inland,FL203,Santa Rosa,12113,C,nw,30.8503,-87.0360
+FL,204,MOB,Santa Rosa Coastal,FL204,Santa Rosa,12113,C,nw,30.5786,-86.9928
+FL,205,MOB,Okaloosa Inland,FL205,Okaloosa,12091,C,nw,30.8517,-86.5894
+FL,206,MOB,Okaloosa Coastal,FL206,Okaloosa,12091,C,nw,30.5630,-86.6034
+FL,222,JAX,Southern Columbia ,FL222,Columbia,12023,E,nn,30.0749,-82.6474
+FL,236,JAX,Western Alachua ,FL236,Alachua,12001,E,nn,29.6482,-82.5230
+FL,239,TBW,Inland Levy,FL239,Levy,12075,E,nn,29.3216,-82.7385
+FL,240,JAX,Central Marion ,FL240,Marion,12083,E,nn,29.2233,-82.0497
+FL,242,TBW,inland Citrus,FL242,Citrus,12017,E,wc,28.8507,-82.4673
+FL,248,TBW,Inland Hernando,FL248,Hernando,12053,E,wc,28.5528,-82.4209
+FL,249,TBW,Coastal Pasco,FL249,Pasco,12101,E,wc,28.3093,-82.3912
+FL,251,TBW,Inland Hillsborough,FL251,Hillsborough,12057,E,wc,27.9311,-82.2469
+FL,255,TBW,Inland Manatee,FL255,Manatee,12081,E,wc,27.4713,-82.2978
+FL,260,TBW,Inland Sarasota,FL260,Sarasota,12115,E,wc,27.1806,-82.3219
+FL,262,TBW,Inland Charlotte,FL262,Charlotte,12015,E,sw,26.9037,-81.7547
+FL,265,TBW,Inland Lee,FL265,Lee,12071,E,sw,26.5785,-81.8208
+FL,340,JAX,Western Marion ,FL340,Marion,12083,E,nn,29.1889,-82.3279
+GA,001,FFC,Dade,GA001,Dade,13083,E,nw,34.8546,-85.5045
+GA,002,FFC,Walker,GA002,Walker,13295,E,nw,34.7356,-85.3009
+GA,003,FFC,Catoosa,GA003,Catoosa,13047,E,nw,34.9036,-85.1381
+GA,004,FFC,Whitfield,GA004,Whitfield,13313,E,nw,34.8056,-84.9673
+GA,005,FFC,Murray,GA005,Murray,13213,E,nw,34.7885,-84.7481
+GA,006,FFC,Fannin,GA006,Fannin,13111,E,nc,34.8641,-84.3197
+GA,007,FFC,Gilmer,GA007,Gilmer,13123,E,nc,34.6915,-84.4554
+GA,008,FFC,Union,GA008,Union,13291,E,nc,34.8342,-83.9907
+GA,009,FFC,Towns,GA009,Towns,13281,E,ne,34.9167,-83.7373
+GA,010,GSP,Rabun,GA010,Rabun,13241,E,ne,34.8818,-83.4021
+GA,011,FFC,Chattooga,GA011,Chattooga,13055,E,nw,34.4750,-85.3454
+GA,012,FFC,Gordon,GA012,Gordon,13129,E,nw,34.5034,-84.8757
+GA,013,FFC,Pickens,GA013,Pickens,13227,E,nc,34.4643,-84.4656
+GA,014,FFC,Dawson,GA014,Dawson,13085,E,nc,34.4438,-84.1704
+GA,015,FFC,Lumpkin,GA015,Lumpkin,13187,E,nc,34.5721,-84.0031
+GA,016,FFC,White,GA016,White,13311,E,ne,34.6464,-83.7472
+GA,017,GSP,Habersham,GA017,Habersham,13137,E,ne,34.6310,-83.5312
+GA,018,GSP,Stephens,GA018,Stephens,13257,E,ne,34.5540,-83.2935
+GA,019,FFC,Floyd,GA019,Floyd,13115,E,nw,34.2632,-85.2143
+GA,020,FFC,Bartow,GA020,Bartow,13015,E,nw,34.2379,-84.8405
+GA,021,FFC,Cherokee,GA021,Cherokee,13057,E,nc,34.2439,-84.4764
+GA,022,FFC,Forsyth,GA022,Forsyth,13117,E,nc,34.2255,-84.1253
+GA,023,FFC,Hall,GA023,Hall,13139,E,nc,34.3168,-83.8197
+GA,024,FFC,Banks,GA024,Banks,13011,E,ne,34.3541,-83.4974
+GA,025,FFC,Jackson,GA025,Jackson,13157,E,ne,34.1339,-83.5663
+GA,026,GSP,Franklin,GA026,Franklin,13119,E,ne,34.3755,-83.2292
+GA,027,FFC,Madison,GA027,Madison,13195,E,ne,34.1279,-83.2090
+GA,028,GSP,Hart,GA028,Hart,13147,E,ne,34.3511,-82.9640
+GA,029,GSP,Elbert,GA029,Elbert,13105,E,ne,34.1169,-82.8403
+GA,030,FFC,Polk,GA030,Polk,13233,E,nw,34.0018,-85.1882
+GA,031,FFC,Paulding,GA031,Paulding,13223,E,nw,33.9202,-84.8672
+GA,032,FFC,Cobb,GA032,Cobb,13067,E,nc,33.9415,-84.5767
+GA,033,FFC,North Fulton,GA033,Fulton,13121,E,nc,33.9350,-84.3557
+GA,034,FFC,Gwinnett,GA034,Gwinnett,13135,E,nc,33.9617,-84.0235
+GA,035,FFC,Barrow,GA035,Barrow,13013,E,nc,33.9932,-83.7127
+GA,036,FFC,Clarke,GA036,Clarke,13059,E,ne,33.9513,-83.3674
+GA,037,FFC,Oconee,GA037,Oconee,13219,E,ne,33.8349,-83.4370
+GA,038,FFC,Oglethorpe,GA038,Oglethorpe,13221,E,ne,33.8807,-83.0806
+GA,039,FFC,Wilkes,GA039,Wilkes,13317,E,ec,33.7820,-82.7432
+GA,040,CAE,Lincoln,GA040,Lincoln,13181,E,ec,33.7936,-82.4512
+GA,041,FFC,Haralson,GA041,Haralson,13143,E,nw,33.7942,-85.2110
+GA,042,FFC,Carroll,GA042,Carroll,13045,E,nw,33.5828,-85.0797
+GA,043,FFC,Douglas,GA043,Douglas,13097,E,nc,33.7015,-84.7677
+GA,044,FFC,South Fulton,GA044,Fulton,13121,E,nc,33.6085,-84.6065
+GA,045,FFC,De Kalb,GA045,DeKalb,13089,E,nc,33.7715,-84.2263
+GA,046,FFC,Rockdale,GA046,Rockdale,13247,E,nc,33.6545,-84.0267
+GA,047,FFC,Walton,GA047,Walton,13297,E,nc,33.7816,-83.7338
+GA,047,FFC,Walton,GA047,Walton,13297,E,nc,33.7816,-83.7338
+GA,048,FFC,Newton,GA048,Newton,13217,E,nc,33.5551,-83.8503
+GA,049,FFC,Morgan,GA049,Morgan,13211,E,nc,33.5908,-83.4924
+GA,050,FFC,Greene,GA050,Greene,13133,E,ec,33.5789,-83.1667
+GA,051,FFC,Taliaferro,GA051,Taliaferro,13265,E,ec,33.5662,-82.8787
+GA,052,FFC,Heard,GA052,Heard,13149,E,wc,33.2970,-85.1283
+GA,053,FFC,Coweta,GA053,Coweta,13077,E,wc,33.3535,-84.7633
+GA,054,FFC,Fayette,GA054,Fayette,13113,E,nc,33.4140,-84.4941
+GA,055,FFC,Clayton,GA055,Clayton,13063,E,nc,33.5419,-84.3577
+GA,056,FFC,Spalding,GA056,Spalding,13255,E,wc,33.2609,-84.2841
+GA,057,FFC,Henry,GA057,Henry,13151,E,nc,33.4534,-84.1554
+GA,058,FFC,Butts,GA058,Butts,13035,E,cc,33.2891,-83.9572
+GA,059,FFC,Jasper,GA059,Jasper,13159,E,cc,33.3164,-83.6881
+GA,060,FFC,Putnam,GA060,Putnam,13237,E,cc,33.3218,-83.3728
+GA,061,FFC,Hancock,GA061,Hancock,13141,E,ec,33.2703,-83.0010
+GA,062,FFC,Warren,GA062,Warren,13301,E,ec,33.4089,-82.6767
+GA,063,CAE,McDuffie,GA063,McDuffie,13189,E,ec,33.4829,-82.4814
+GA,064,CAE,Columbia,GA064,Columbia,13073,E,ec,33.5442,-82.2640
+GA,065,CAE,Richmond,GA065,Richmond,13245,E,ec,33.3596,-82.0735
+GA,066,FFC,Troup,GA066,Troup,13285,E,wc,33.0335,-85.0283
+GA,067,FFC,Meriwether,GA067,Meriwether,13199,E,wc,33.0406,-84.6883
+GA,068,FFC,Pike,GA068,Pike,13231,E,wc,33.0922,-84.3892
+GA,069,FFC,Upson,GA069,Upson,13293,E,wc,32.8813,-84.2993
+GA,070,FFC,Lamar,GA070,Lamar,13171,E,wc,33.0765,-84.1394
+GA,071,FFC,Monroe,GA071,Monroe,13207,E,cc,33.0141,-83.9187
+GA,072,FFC,Jones,GA072,Jones,13169,E,cc,33.0251,-83.5605
+GA,073,FFC,Baldwin,GA073,Baldwin,13009,E,cc,33.0690,-83.2499
+GA,074,FFC,Washington,GA074,Washington,13303,E,ec,32.9695,-82.7959
+GA,075,FFC,Glascock,GA075,Glascock,13125,E,ec,33.2293,-82.6107
+GA,076,FFC,Jefferson,GA076,Jefferson,13163,E,ec,33.0549,-82.4181
+GA,077,CAE,Burke,GA077,Burke,13033,E,ec,33.0612,-82.0008
+GA,078,FFC,Harris,GA078,Harris,13145,E,wc,32.7360,-84.9090
+GA,079,FFC,Talbot,GA079,Talbot,13263,E,wc,32.6995,-84.5330
+GA,080,FFC,Taylor,GA080,Taylor,13269,E,wc,32.5556,-84.2504
+GA,081,FFC,Crawford,GA081,Crawford,13079,E,cc,32.7145,-83.9864
+GA,082,FFC,Bibb,GA082,Bibb,13021,E,cc,32.8067,-83.6979
+GA,083,FFC,Twiggs,GA083,Twiggs,13289,E,cc,32.6672,-83.4272
+GA,084,FFC,Wilkinson,GA084,Wilkinson,13319,E,cc,32.8024,-83.1713
+GA,085,FFC,Johnson,GA085,Johnson,13167,E,ec,32.7015,-82.6601
+GA,086,FFC,Emanuel,GA086,Emanuel,13107,E,ec,32.5898,-82.3018
+GA,087,CHS,Jenkins,GA087,Jenkins,13165,E,se,32.7925,-81.9636
+GA,088,CHS,Screven,GA088,Screven,13251,E,se,32.7507,-81.6119
+GA,089,FFC,Muscogee,GA089,Muscogee,13215,E,wc,32.5100,-84.8770
+GA,090,FFC,Chattahoochee,GA090,Chattahoochee,13053,E,wc,32.3470,-84.7870
+GA,091,FFC,Marion,GA091,Marion,13197,E,wc,32.3534,-84.5246
+GA,092,FFC,Schley,GA092,Schley,13249,E,wc,32.2620,-84.3149
+GA,093,FFC,Macon,GA093,Macon,13193,E,wc,32.3584,-84.0425
+GA,094,FFC,Peach,GA094,Peach,13225,E,cc,32.5688,-83.8269
+GA,095,FFC,Houston,GA095,Houston,13153,E,cc,32.4590,-83.6662
+GA,096,FFC,Bleckley,GA096,Bleckley,13023,E,cc,32.4344,-83.3279
+GA,097,FFC,Laurens,GA097,Laurens,13175,E,cc,32.4637,-82.9222
+GA,098,FFC,Treutlen,GA098,Treutlen,13283,E,ec,32.4039,-82.5673
+GA,099,CHS,Candler,GA099,Candler,13043,E,se,32.4035,-82.0737
+GA,100,CHS,Bulloch,GA100,Bulloch,13031,E,se,32.3969,-81.7432
+GA,101,CHS,Effingham,GA101,Effingham,13103,E,se,32.3675,-81.3407
+GA,102,FFC,Stewart,GA102,Stewart,13259,E,wc,32.0785,-84.8352
+GA,103,FFC,Webster,GA103,Webster,13307,E,wc,32.0466,-84.5510
+GA,104,FFC,Sumter,GA104,Sumter,13261,E,wc,32.0400,-84.1971
+GA,105,FFC,Dooly,GA105,Dooly,13093,E,cc,32.1572,-83.7987
+GA,106,FFC,Crisp,GA106,Crisp,13081,E,cc,31.9230,-83.7681
+GA,107,FFC,Pulaski,GA107,Pulaski,13235,E,cc,32.2328,-83.4764
+GA,108,FFC,Wilcox,GA108,Wilcox,13315,E,cc,31.9733,-83.4322
+GA,109,FFC,Dodge,GA109,Dodge,13091,E,cc,32.1722,-83.1684
+GA,110,FFC,Telfair,GA110,Telfair,13271,E,cc,31.9298,-82.9389
+GA,111,FFC,Wheeler,GA111,Wheeler,13309,E,cc,32.1171,-82.7246
+GA,112,FFC,Montgomery,GA112,Montgomery,13209,E,cc,32.1746,-82.5338
+GA,113,FFC,Toombs,GA113,Toombs,13279,E,se,32.1206,-82.3306
+GA,114,CHS,Tattnall,GA114,Tattnall,13267,E,se,32.0458,-82.0582
+GA,115,CHS,Evans,GA115,Evans,13109,E,se,32.1568,-81.8869
+GA,116,CHS,Inland Bryan,GA116,Bryan,13029,E,se,32.0404,-81.4750
+GA,117,CHS,Coastal Bryan,GA117,Bryan,13029,E,se,31.8127,-81.2002
+GA,118,CHS,Inland Chatham,GA118,Chatham,13051,E,se,32.0772,-81.2278
+GA,119,CHS,Coastal Chatham,GA119,Chatham,13051,E,se,31.9392,-81.0501
+GA,119,CHS,Coastal Chatham,GA119,Chatham,13051,E,se,31.9182,-81.0342
+GA,120,TAE,Quitman,GA120,Quitman,13239,E,sw,31.8674,-85.0188
+GA,121,TAE,Clay,GA121,Clay,13061,E,sw,31.6264,-84.9803
+GA,122,TAE,Randolph,GA122,Randolph,13243,E,sw,31.7626,-84.7542
+GA,123,TAE,Calhoun,GA123,Calhoun,13037,E,sw,31.5292,-84.6249
+GA,124,TAE,Terrell,GA124,Terrell,13273,E,sw,31.7768,-84.4368
+GA,125,TAE,Dougherty,GA125,Dougherty,13095,E,sw,31.5333,-84.2164
+GA,126,TAE,Lee,GA126,Lee,13177,E,sw,31.7796,-84.1410
+GA,127,TAE,Worth,GA127,Worth,13321,E,sc,31.5515,-83.8510
+GA,128,TAE,Turner,GA128,Turner,13287,E,sc,31.7163,-83.6242
+GA,129,TAE,Tift,GA129,Tift,13277,E,sc,31.4575,-83.5266
+GA,130,TAE,Ben Hill,GA130,Ben Hill,13017,E,sc,31.7598,-83.2205
+GA,131,TAE,Irwin,GA131,Irwin,13155,E,sc,31.6023,-83.2764
+GA,132,JAX,Coffee,GA132,Coffee,13069,E,se,31.5492,-82.8493
+GA,133,JAX,Jeff Davis,GA133,Jeff Davis,13161,E,se,31.8056,-82.6370
+GA,134,JAX,Bacon,GA134,Bacon,13005,E,se,31.5537,-82.4527
+GA,135,JAX,Appling,GA135,Appling,13001,E,se,31.7493,-82.2890
+GA,136,JAX,Wayne,GA136,Wayne,13305,E,se,31.5515,-81.9168
+GA,137,CHS,Long,GA137,Long,13183,E,se,31.7528,-81.7460
+GA,138,CHS,Inland Liberty,GA138,Liberty,13179,E,se,31.8508,-81.5352
+GA,139,CHS,Coastal Liberty,GA139,Liberty,13179,E,se,31.6625,-81.2029
+GA,139,CHS,Coastal Liberty,GA139,Liberty,13179,E,se,31.6716,-81.2012
+GA,140,CHS,Inland McIntosh,GA140,McIntosh,13191,E,se,31.5278,-81.4877
+GA,141,CHS,Coastal McIntosh,GA141,McIntosh,13191,E,se,31.4659,-81.3213
+GA,141,CHS,Coastal McIntosh,GA141,McIntosh,13191,E,se,31.4585,-81.3001
+GA,142,TAE,Early,GA142,Early,13099,E,sw,31.3229,-84.9037
+GA,143,TAE,Miller,GA143,Miller,13201,E,sw,31.1640,-84.7307
+GA,144,TAE,Baker,GA144,Baker,13007,E,sw,31.3262,-84.4447
+GA,145,TAE,Mitchell,GA145,Mitchell,13205,E,sw,31.2254,-84.1942
+GA,146,TAE,Colquitt,GA146,Colquitt,13071,E,sc,31.1884,-83.7688
+GA,147,TAE,Cook,GA147,Cook,13075,E,sc,31.1540,-83.4305
+GA,148,TAE,Berrien,GA148,Berrien,13019,E,sc,31.2761,-83.2297
+GA,149,JAX,Atkinson,GA149,Atkinson,13003,E,se,31.2971,-82.8800
+GA,151,JAX,Pierce,GA151,Pierce,13229,E,se,31.3589,-82.2133
+GA,152,JAX,Brantley,GA152,Brantley,13025,E,se,31.1970,-81.9819
+GA,153,JAX,Inland Glynn,GA153,Glynn,13127,E,se,31.2698,-81.6390
+GA,154,JAX,Coastal Glynn,GA154,Glynn,13127,E,se,31.1999,-81.4405
+GA,155,TAE,Seminole,GA155,Seminole,13253,E,sw,30.9481,-84.8681
+GA,156,TAE,Decatur,GA156,Decatur,13087,E,sw,30.7994,-84.7577
+GA,156,TAE,Decatur,GA156,Decatur,13087,E,sw,30.8815,-84.5720
+GA,157,TAE,Grady,GA157,Grady,13131,E,sw,30.8747,-84.2344
+GA,158,TAE,Thomas,GA158,Thomas,13275,E,sc,30.8637,-83.9194
+GA,159,TAE,Brooks,GA159,Brooks,13027,E,sc,30.8420,-83.5803
+GA,160,TAE,Lowndes,GA160,Lowndes,13185,E,sc,30.8339,-83.2677
+GA,161,TAE,Lanier,GA161,Lanier,13173,E,sc,31.0380,-83.0626
+GA,162,JAX,Echols,GA162,Echols,13101,E,se,30.7101,-82.8939
+GA,163,JAX,Clinch,GA163,Clinch,13065,E,se,30.9151,-82.7063
+GA,165,JAX,Inland Camden,GA165,Camden,13039,E,se,30.9642,-81.7688
+GA,166,JAX,Coastal Camden,GA166,Camden,13039,E,se,30.8884,-81.5544
+GA,250,JAX,Northern Ware ,GA250,Ware,13299,E,se,31.1996,-82.4623
+GA,264,JAX,Northeastern Charlton ,GA264,Charlton,13049,E,se,30.9470,-81.9942
+GA,350,JAX,Southern Ware ,GA350,Ware,13299,E,se,30.7473,-82.3431
+GA,364,JAX,Western Charlton ,GA364,Charlton,13049,E,se,30.7368,-82.1771
+GU,001,GUM,Guam,GU001,Guam,66010,G,,13.4419,144.7749
+GU,002,GUM,Rota,GU002,Rota,69100,G,,14.1558,145.2126
+GU,003,GUM,Tinian,GU003,Tinian,69120,G,,15.0014,145.6241
+GU,004,GUM,Saipan,GU004,Saipan,69110,G,,15.1889,145.7523
+GU,005,GUM,Agrihan,GU005,Northern Islands,69085,G,,17.9725,145.6922
+HI,001,HFO,Niihau,HI001,Kauai,15007,H,,21.8970,-160.1570
+HI,002,HFO,Kauai Windward,HI002,Kauai,15007,H,,22.0635,-159.4303
+HI,003,HFO,Kauai Leeward,HI003,Kauai,15007,H,,22.0137,-159.6679
+HI,004,HFO,Kauai Mountains,HI004,Kauai,15007,H,,22.0964,-159.5427
+HI,005,HFO,Oahu South Shore,HI005,Honolulu,15003,H,,21.3343,-157.9505
+HI,006,HFO,Waianae Coast,HI006,Honolulu,15003,H,,21.4654,-158.1814
+HI,007,HFO,Oahu North Shore,HI007,Honolulu,15003,H,,21.5795,-158.1223
+HI,008,HFO,Oahu Koolau,HI008,Honolulu,15003,H,,21.4957,-157.9036
+HI,009,HFO,Olomana,HI009,Honolulu,15003,H,,21.3809,-157.7459
+HI,010,HFO,Central Oahu,HI010,Honolulu,15003,H,,21.4843,-158.0270
+HI,011,HFO,Waianae Mountains,HI011,Honolulu,15003,H,,21.4683,-158.1327
+HI,012,HFO,Molokai Windward,HI012,Maui,15009,H,,21.1338,-156.8445
+HI,013,HFO,Molokai Leeward,HI013,Maui,15009,H,,21.1376,-157.1051
+HI,014,HFO,Lanai Makai,HI014,Maui,15009,H,,20.8504,-156.9394
+HI,015,HFO,Lanai Mauka,HI015,Maui,15009,H,,20.8177,-156.9138
+HI,016,HFO,Kahoolawe,HI016,Maui,15009,H,,20.5503,-156.6118
+HI,017,HFO,Maui Windward West,HI017,Maui,15009,H,,20.9195,-156.5762
+HI,018,HFO,Maui Leeward West,HI018,Maui,15009,H,,20.8913,-156.6446
+HI,019,HFO,Maui Central Valley,HI019,Maui,15009,H,,20.8450,-156.4400
+HI,020,HFO,Windward Haleakala,HI020,Maui,15009,H,,20.8017,-156.1750
+HI,021,HFO,Leeward Haleakala,HI021,Maui,15009,H,,20.6916,-156.3388
+HI,022,HFO,Haleakala Summit,HI022,Maui,15009,H,,20.7185,-156.2266
+HI,023,HFO,Kona,HI023,Hawaii,15001,H,,19.5371,-155.8769
+HI,024,HFO,South Big Island,HI024,Hawaii,15001,H,,19.2106,-155.4770
+HI,025,HFO,Big Island North and East,HI025,Hawaii,15001,H,,19.7729,-155.2910
+HI,026,HFO,Kohala,HI026,Hawaii,15001,H,,19.9531,-155.7657
+HI,027,HFO,Big Island Interior,HI027,Hawaii,15001,H,,19.5883,-155.5801
+HI,028,HFO,Big Island Summit,HI028,Hawaii,15001,H,,19.5513,-155.5591
+IA,001,FSD,Lyon,IA001,Lyon,19119,C,nw,43.3805,-96.2103
+IA,002,FSD,Osceola,IA002,Osceola,19143,C,nw,43.3785,-95.6238
+IA,003,FSD,Dickinson,IA003,Dickinson,19059,C,nw,43.3779,-95.1510
+IA,004,DMX,Emmet,IA004,Emmet,19063,C,nw,43.3779,-94.6784
+IA,005,DMX,Kossuth,IA005,Kossuth,19109,C,nc,43.2042,-94.2067
+IA,006,DMX,Winnebago,IA006,Winnebago,19189,C,nc,43.3775,-93.7342
+IA,007,DMX,Worth,IA007,Worth,19195,C,nc,43.3774,-93.2609
+IA,008,ARX,Mitchell,IA008,Mitchell,19131,C,nc,43.3564,-92.7891
+IA,009,ARX,Howard,IA009,Howard,19089,C,ne,43.3568,-92.3172
+IA,010,ARX,Winneshiek,IA010,Winneshiek,19191,C,ne,43.2906,-91.8437
+IA,011,ARX,Allamakee,IA011,Allamakee,19005,C,ne,43.2843,-91.3781
+IA,012,FSD,Sioux,IA012,Sioux,19167,C,nw,43.0826,-96.1779
+IA,013,FSD,O'Brien,IA013,O'Brien,19141,C,nw,43.0838,-95.6250
+IA,014,FSD,Clay,IA014,Clay,19041,C,nw,43.0826,-95.1510
+IA,015,DMX,Palo Alto,IA015,Palo Alto,19147,C,nw,43.0820,-94.6782
+IA,016,DMX,Hancock,IA016,Hancock,19081,C,nc,43.0818,-93.7343
+IA,017,DMX,Cerro Gordo,IA017,Cerro Gordo,19033,C,nc,43.0816,-93.2608
+IA,018,ARX,Floyd,IA018,Floyd,19067,C,nc,43.0600,-92.7890
+IA,019,ARX,Chickasaw,IA019,Chickasaw,19037,C,ne,43.0601,-92.3176
+IA,020,FSD,Plymouth,IA020,Plymouth,19149,C,nw,42.7378,-96.2142
+IA,021,FSD,Cherokee,IA021,Cherokee,19035,C,nw,42.7357,-95.6239
+IA,022,FSD,Buena Vista,IA022,Buena Vista,19021,C,nw,42.7355,-95.1513
+IA,023,DMX,Pocahontas,IA023,Pocahontas,19151,C,nw,42.7341,-94.6789
+IA,024,DMX,Humboldt,IA024,Humboldt,19091,C,nc,42.7764,-94.2073
+IA,025,DMX,Wright,IA025,Wright,19197,C,nc,42.7331,-93.7352
+IA,026,DMX,Franklin,IA026,Franklin,19069,C,nc,42.7325,-93.2624
+IA,027,DMX,Butler,IA027,Butler,19023,C,nc,42.7316,-92.7902
+IA,028,DMX,Bremer,IA028,Bremer,19017,C,ne,42.7747,-92.3182
+IA,029,ARX,Fayette,IA029,Fayette,19065,C,ne,42.8625,-91.8443
+IA,030,ARX,Clayton,IA030,Clayton,19043,C,ne,42.8447,-91.3414
+IA,031,FSD,Woodbury,IA031,Woodbury,19193,C,wc,42.3897,-96.0449
+IA,032,FSD,Ida,IA032,Ida,19093,C,wc,42.3868,-95.5136
+IA,033,DMX,Sac,IA033,Sac,19161,C,wc,42.3862,-95.1055
+IA,034,DMX,Calhoun,IA034,Calhoun,19025,C,wc,42.3852,-94.6405
+IA,035,DMX,Webster,IA035,Webster,19187,C,cc,42.4278,-94.1818
+IA,036,DMX,Hamilton,IA036,Hamilton,19079,C,cc,42.3837,-93.7068
+IA,037,DMX,Hardin,IA037,Hardin,19083,C,cc,42.3838,-93.2404
+IA,038,DMX,Grundy,IA038,Grundy,19075,C,cc,42.4019,-92.7910
+IA,039,DMX,Black Hawk,IA039,Black Hawk,19013,C,ne,42.4701,-92.3085
+IA,040,DVN,Buchanan,IA040,Buchanan,19019,C,ne,42.4707,-91.8379
+IA,041,DVN,Delaware,IA041,Delaware,19055,C,ne,42.4711,-91.3673
+IA,042,DVN,Dubuque,IA042,Dubuque,19061,C,ne,42.4688,-90.8824
+IA,043,OAX,Monona,IA043,Monona,19133,C,wc,42.0516,-95.9600
+IA,044,DMX,Crawford,IA044,Crawford,19047,C,wc,42.0372,-95.3821
+IA,045,DMX,Carroll,IA045,Carroll,19027,C,wc,42.0362,-94.8607
+IA,046,DMX,Greene,IA046,Greene,19073,C,wc,42.0362,-94.3969
+IA,047,DMX,Boone,IA047,Boone,19015,C,cc,42.0366,-93.9318
+IA,048,DMX,Story,IA048,Story,19169,C,cc,42.0362,-93.4651
+IA,049,DMX,Marshall,IA049,Marshall,19127,C,cc,42.0358,-92.9988
+IA,050,DMX,Tama,IA050,Tama,19171,C,cc,42.0798,-92.5326
+IA,051,DVN,Benton,IA051,Benton,19011,C,ec,42.0801,-92.0657
+IA,052,DVN,Linn,IA052,Linn,19113,C,ec,42.0789,-91.5989
+IA,053,DVN,Jones,IA053,Jones,19105,C,ec,42.1212,-91.1314
+IA,054,DVN,Jackson,IA054,Jackson,19097,C,ec,42.1717,-90.5742
+IA,055,OAX,Harrison,IA055,Harrison,19085,C,sw,41.6829,-95.8167
+IA,056,OAX,Shelby,IA056,Shelby,19165,C,sw,41.6851,-95.3103
+IA,057,DMX,Audubon,IA057,Audubon,19009,C,wc,41.6846,-94.9058
+IA,058,DMX,Guthrie,IA058,Guthrie,19077,C,wc,41.6838,-94.5010
+IA,059,DMX,Dallas,IA059,Dallas,19049,C,cc,41.6849,-94.0398
+IA,060,DMX,Polk,IA060,Polk,19153,C,cc,41.6855,-93.5734
+IA,061,DMX,Jasper,IA061,Jasper,19099,C,cc,41.6860,-93.0537
+IA,062,DMX,Poweshiek,IA062,Poweshiek,19157,C,cc,41.6864,-92.5314
+IA,063,DVN,Iowa,IA063,Iowa,19095,C,ec,41.6863,-92.0656
+IA,064,DVN,Johnson,IA064,Johnson,19103,C,ec,41.6715,-91.5881
+IA,065,DVN,Cedar,IA065,Cedar,19031,C,ec,41.7723,-91.1324
+IA,066,DVN,Clinton,IA066,Clinton,19045,C,ec,41.8980,-90.5320
+IA,067,DVN,Muscatine,IA067,Muscatine,19139,C,ec,41.4839,-91.1127
+IA,068,DVN,Scott,IA068,Scott,19163,C,ec,41.6371,-90.6232
+IA,069,OAX,Pottawattamie,IA069,Pottawattamie,19155,C,sw,41.3364,-95.5423
+IA,070,DMX,Cass,IA070,Cass,19029,C,sw,41.3315,-94.9278
+IA,071,DMX,Adair,IA071,Adair,19001,C,sw,41.3307,-94.4710
+IA,072,DMX,Madison,IA072,Madison,19121,C,sc,41.3307,-94.0155
+IA,073,DMX,Warren,IA073,Warren,19181,C,sc,41.3343,-93.5614
+IA,074,DMX,Marion,IA074,Marion,19125,C,sc,41.3344,-93.0995
+IA,075,DMX,Mahaska,IA075,Mahaska,19123,C,sc,41.3351,-92.6409
+IA,076,DVN,Keokuk,IA076,Keokuk,19107,C,se,41.3364,-92.1787
+IA,077,DVN,Washington,IA077,Washington,19183,C,se,41.3356,-91.7179
+IA,078,DVN,Louisa,IA078,Louisa,19115,C,se,41.2185,-91.2597
+IA,079,OAX,Mills,IA079,Mills,19129,C,sw,41.0332,-95.6214
+IA,080,OAX,Montgomery,IA080,Montgomery,19137,C,sw,41.0301,-95.1563
+IA,081,DMX,Adams,IA081,Adams,19003,C,sw,41.0290,-94.6992
+IA,082,DMX,Union,IA082,Union,19175,C,sc,41.0277,-94.2424
+IA,083,DMX,Clarke,IA083,Clarke,19039,C,sc,41.0290,-93.7851
+IA,084,DMX,Lucas,IA084,Lucas,19117,C,sc,41.0293,-93.3277
+IA,085,DMX,Monroe,IA085,Monroe,19135,C,sc,41.0297,-92.8690
+IA,086,DMX,Wapello,IA086,Wapello,19179,C,se,41.0306,-92.4095
+IA,087,DVN,Jefferson,IA087,Jefferson,19101,C,se,41.0318,-91.9489
+IA,088,DVN,Henry,IA088,Henry,19087,C,se,40.9879,-91.5446
+IA,089,DVN,Des Moines,IA089,Des Moines,19057,C,se,40.9232,-91.1815
+IA,090,OAX,Fremont,IA090,Fremont,19071,C,sw,40.7456,-95.6047
+IA,091,OAX,Page,IA091,Page,19145,C,sw,40.7391,-95.1502
+IA,092,DMX,Taylor,IA092,Taylor,19173,C,sw,40.7374,-94.6965
+IA,093,DMX,Ringgold,IA093,Ringgold,19159,C,sc,40.7352,-94.2440
+IA,094,DMX,Decatur,IA094,Decatur,19053,C,sc,40.7377,-93.7863
+IA,095,DMX,Wayne,IA095,Wayne,19185,C,sc,40.7395,-93.3274
+IA,096,DMX,Appanoose,IA096,Appanoose,19007,C,sc,40.7432,-92.8687
+IA,097,DMX,Davis,IA097,Davis,19051,C,se,40.7477,-92.4097
+IA,098,DVN,Van Buren,IA098,Van Buren,19177,C,se,40.7532,-91.9500
+IA,099,DVN,Lee,IA099,Lee,19111,C,se,40.6420,-91.4792
+ID,001,OTX,Northern Panhandle,ID001,Bonner,16017,P,pa,48.4447,-116.5661
+ID,001,OTX,Northern Panhandle,ID001,Boundary,16021,P,pa,48.4447,-116.5661
+ID,001,OTX,Northern Panhandle,ID001,Kootenai,16055,P,pa,48.4447,-116.5661
+ID,002,OTX,Coeur d'Alene Area,ID002,Kootenai,16055,P,pa,47.6003,-116.8182
+ID,003,OTX,Idaho Palouse,ID003,Benewah,16009,P,pa,46.9308,-116.8785
+ID,003,OTX,Idaho Palouse,ID003,Latah,16057,P,pa,46.9308,-116.8785
+ID,004,OTX,Central Panhandle Mountains,ID004,Benewah,16009,P,pa,47.3003,-116.1291
+ID,004,OTX,Central Panhandle Mountains,ID004,Kootenai,16055,P,pa,47.3003,-116.1291
+ID,004,OTX,Central Panhandle Mountains,ID004,Latah,16057,P,pa,47.3003,-116.1291
+ID,004,OTX,Central Panhandle Mountains,ID004,Shoshone,16079,P,pa,47.3003,-116.1291
+ID,005,MSO,Northern Clearwater Mountains,ID005,Clearwater,16035,P,nc,46.7138,-115.5769
+ID,006,MSO,Southern Clearwater Mountains,ID006,Idaho,16049,MP,cc,45.8352,-115.2666
+ID,007,MSO,Orofino/Grangeville Region,ID007,Clearwater,16035,MP,nc,46.2094,-116.0712
+ID,007,MSO,Orofino/Grangeville Region,ID007,Idaho,16049,MP,cc,46.2094,-116.0712
+ID,008,MSO,Lower Hells Canyon/Salmon River Region,ID008,Idaho,16049,MP,cc,45.6641,-116.4493
+ID,009,MSO,Western Lemhi County,ID009,Lemhi,16059,M,ec,45.1209,-114.3901
+ID,010,MSO,Eastern Lemhi County,ID010,Lemhi,16059,M,ec,44.8081,-113.5852
+ID,011,BOI,West Central Mountains,ID011,Adams,16003,M,wc,44.7716,-115.8966
+ID,011,BOI,West Central Mountains,ID011,Gem,16045,M,sw,44.7716,-115.8966
+ID,011,BOI,West Central Mountains,ID011,Valley,16085,M,wc,44.7716,-115.8966
+ID,011,BOI,West Central Mountains,ID011,Washington,16087,M,sw,44.7716,-115.8966
+ID,012,BOI,Lower Treasure Valley,ID012,Canyon,16027,M,sw,43.7962,-116.6920
+ID,012,BOI,Lower Treasure Valley,ID012,Gem,16045,M,sw,43.7962,-116.6920
+ID,012,BOI,Lower Treasure Valley,ID012,Owyhee,16073,M,sw,43.7962,-116.6920
+ID,012,BOI,Lower Treasure Valley,ID012,Payette,16075,M,sw,43.7962,-116.6920
+ID,012,BOI,Lower Treasure Valley,ID012,Washington,16087,M,sw,43.7962,-116.6920
+ID,013,BOI,Boise Mountains,ID013,Boise,16015,M,wc,43.7521,-115.4718
+ID,013,BOI,Boise Mountains,ID013,Camas,16025,M,cc,43.7521,-115.4718
+ID,013,BOI,Boise Mountains,ID013,Elmore,16039,M,sw,43.7521,-115.4718
+ID,014,BOI,Upper Treasure Valley,ID014,Ada,16001,M,sw,43.1035,-115.8643
+ID,014,BOI,Upper Treasure Valley,ID014,Elmore,16039,M,sw,43.1035,-115.8643
+ID,014,BOI,Upper Treasure Valley,ID014,Owyhee,16073,M,sw,43.1035,-115.8643
+ID,015,BOI,Southwest Highlands,ID015,Owyhee,16073,M,sw,42.3439,-116.0477
+ID,016,BOI,Western Magic Valley,ID016,Gooding,16047,M,sc,42.7508,-114.5943
+ID,016,BOI,Western Magic Valley,ID016,Jerome,16053,M,sc,42.7508,-114.5943
+ID,016,BOI,Western Magic Valley,ID016,Twin Falls,16083,M,sc,42.7508,-114.5943
+ID,026,OTX,Lewiston Area,ID026,Nez Perce,16069,P,nc,46.4507,-116.7031
+ID,027,OTX,Lewis and Southern Nez Perce Counties,ID027,Lewis,16061,P,nc,46.1926,-116.5889
+ID,027,OTX,Lewis and Southern Nez Perce Counties,ID027,Nez Perce,16069,P,nc,46.1926,-116.5889
+ID,028,BOI,Camas Prairie,ID028,Camas,16025,M,cc,43.3010,-114.8183
+ID,028,BOI,Camas Prairie,ID028,Elmore,16039,M,sw,43.3010,-114.8183
+ID,029,BOI,Owyhee Mountains,ID029,Owyhee,16073,M,sw,42.9537,-116.7748
+ID,030,BOI,Southern Twin Falls County,ID030,Twin Falls,16083,M,sc,42.2255,-114.6784
+ID,033,BOI,Upper Weiser River,ID033,Adams,16003,M,wc,44.4760,-116.5799
+ID,033,BOI,Upper Weiser River,ID033,Washington,16087,M,sw,44.4760,-116.5799
+ID,051,PIH,Shoshone/Lava Beds,ID051,Blaine,16013,M,se,43.0804,-113.8956
+ID,051,PIH,Shoshone/Lava Beds,ID051,Minidoka,16067,M,se,43.0804,-113.8956
+ID,051,PIH,Shoshone/Lava Beds,ID051,Lincoln,16063,M,se,43.0804,-113.8956
+ID,052,PIH,Arco/Mud Lake Desert,ID052,Butte,16023,M,se,43.6353,-112.7958
+ID,052,PIH,Arco/Mud Lake Desert,ID052,Jefferson,16051,M,se,43.6353,-112.7958
+ID,052,PIH,Arco/Mud Lake Desert,ID052,Bonneville,16019,M,se,43.6353,-112.7958
+ID,052,PIH,Arco/Mud Lake Desert,ID052,Bingham,16011,M,se,43.6353,-112.7958
+ID,053,PIH,Upper Snake River Plain,ID053,Fremont,16043,M,se,43.7916,-111.9224
+ID,053,PIH,Upper Snake River Plain,ID053,Madison,16065,M,se,43.7916,-111.9224
+ID,053,PIH,Upper Snake River Plain,ID053,Jefferson,16051,M,se,43.7916,-111.9224
+ID,053,PIH,Upper Snake River Plain,ID053,Bonneville,16019,M,se,43.7916,-111.9224
+ID,054,PIH,Lower Snake River Plain,ID054,Bannock,16005,M,se,43.1242,-112.6045
+ID,054,PIH,Lower Snake River Plain,ID054,Power,16077,M,se,43.1242,-112.6045
+ID,054,PIH,Lower Snake River Plain,ID054,Bingham,16011,M,se,43.1242,-112.6045
+ID,055,PIH,Eastern Magic Valley,ID055,Cassia,16031,M,se,42.7002,-113.4605
+ID,055,PIH,Eastern Magic Valley,ID055,Minidoka,16067,M,se,42.7002,-113.4605
+ID,055,PIH,Eastern Magic Valley,ID055,Blaine,16013,M,se,42.7002,-113.4605
+ID,055,PIH,Eastern Magic Valley,ID055,Power,16077,M,se,42.7002,-113.4605
+ID,056,PIH,Southern Hills/Albion Mountains,ID056,Cassia,16031,M,se,42.1932,-113.8364
+ID,057,PIH,Raft River Region,ID057,Cassia,16031,M,se,42.2756,-113.0516
+ID,057,PIH,Raft River Region,ID057,Power,16077,M,se,42.2756,-113.0516
+ID,057,PIH,Raft River Region,ID057,Oneida,16071,M,se,42.2756,-113.0516
+ID,058,PIH,Marsh and Arbon Highlands,ID058,Bannock,16005,M,se,42.6797,-112.2919
+ID,058,PIH,Marsh and Arbon Highlands,ID058,Power,16077,M,se,42.6797,-112.2919
+ID,058,PIH,Marsh and Arbon Highlands,ID058,Caribou,16029,M,se,42.6797,-112.2919
+ID,058,PIH,Marsh and Arbon Highlands,ID058,Bingham,16011,M,se,42.6797,-112.2919
+ID,059,PIH,Franklin/Eastern Oneida Region,ID059,Franklin,16041,M,se,42.2039,-112.1984
+ID,059,PIH,Franklin/Eastern Oneida Region,ID059,Oneida,16071,M,se,42.2039,-112.1984
+ID,060,PIH,Bear River Range,ID060,Bear Lake,16007,M,se,42.2521,-111.5742
+ID,060,PIH,Bear River Range,ID060,Franklin,16041,M,se,42.2521,-111.5742
+ID,060,PIH,Bear River Range,ID060,Caribou,16029,M,se,42.2521,-111.5742
+ID,061,PIH,Bear Lake Valley,ID061,Bear Lake,16007,M,se,42.2447,-111.2738
+ID,062,PIH,Blackfoot Mountains,ID062,Bonneville,16019,M,se,42.9895,-111.7470
+ID,062,PIH,Blackfoot Mountains,ID062,Caribou,16029,M,se,42.9895,-111.7470
+ID,062,PIH,Blackfoot Mountains,ID062,Bingham,16011,M,se,42.9895,-111.7470
+ID,063,PIH,Caribou Range,ID063,Bear Lake,16007,M,se,42.9919,-111.2925
+ID,063,PIH,Caribou Range,ID063,Caribou,16029,M,se,42.9919,-111.2925
+ID,063,PIH,Caribou Range,ID063,Bonneville,16019,M,se,42.9919,-111.2925
+ID,064,PIH,Big Hole Mountains,ID064,Madison,16065,M,ee,43.6154,-111.2986
+ID,064,PIH,Big Hole Mountains,ID064,Teton,16081,M,ee,43.6154,-111.2986
+ID,064,PIH,Big Hole Mountains,ID064,Bonneville,16019,M,ee,43.6154,-111.2986
+ID,065,PIH,Teton Valley,ID065,Fremont,16043,M,ee,43.8893,-111.2616
+ID,065,PIH,Teton Valley,ID065,Madison,16065,M,ee,43.8893,-111.2616
+ID,065,PIH,Teton Valley,ID065,Teton,16081,M,ee,43.8893,-111.2616
+ID,066,PIH,Centennial Mountains/Island Park,ID066,Clark,16033,M,ee,44.3674,-111.4890
+ID,066,PIH,Centennial Mountains/Island Park,ID066,Fremont,16043,M,ee,44.3674,-111.4890
+ID,066,PIH,Centennial Mountains/Island Park,ID066,Teton,16081,M,ee,44.3674,-111.4890
+ID,067,PIH,Beaverhead/Lemhi Highlands,ID067,Butte,16023,M,ee,44.2232,-112.5387
+ID,067,PIH,Beaverhead/Lemhi Highlands,ID067,Clark,16033,M,ee,44.2232,-112.5387
+ID,068,PIH,Lost River Valleys,ID068,Custer,16037,M,cc,43.8834,-113.4263
+ID,068,PIH,Lost River Valleys,ID068,Butte,16023,M,cc,43.8834,-113.4263
+ID,069,PIH,Lost River Range,ID069,Custer,16037,M,cc,44.1220,-113.5633
+ID,069,PIH,Lost River Range,ID069,Butte,16023,M,cc,44.1220,-113.5633
+ID,070,PIH,Challis/Pahsimeroi Valleys,ID070,Custer,16037,M,cc,44.4002,-114.0841
+ID,071,PIH,Frank Church Wilderness,ID071,Custer,16037,M,cc,44.4160,-114.5294
+ID,072,PIH,Sawtooth/Stanley Basin,ID072,Custer,16037,M,cc,44.2935,-114.9227
+ID,072,PIH,Sawtooth/Stanley Basin,ID072,Blaine,16013,M,cc,44.2935,-114.9227
+ID,073,PIH,Sun Valley Region,ID073,Custer,16037,M,cc,43.7253,-114.4186
+ID,073,PIH,Sun Valley Region,ID073,Blaine,16013,M,cc,43.7253,-114.4186
+ID,074,PIH,Big Lost Highlands/Copper Basin,ID074,Butte,16023,M,cc,43.8258,-113.9943
+ID,074,PIH,Big Lost Highlands/Copper Basin,ID074,Blaine,16013,M,cc,43.8258,-113.9943
+ID,074,PIH,Big Lost Highlands/Copper Basin,ID074,Custer,16037,M,cc,43.8258,-113.9943
+ID,075,PIH,Caribou Range,ID075,Blaine,16013,M,cc,43.4301,-114.1369
+IL,001,DVN,Jo Daviess,IL001,Jo Daviess,17085,C,nw,42.3657,-90.2123
+IL,002,DVN,Stephenson,IL002,Stephenson,17177,C,nw,42.3517,-89.6625
+IL,003,LOT,Winnebago,IL003,Winnebago,17201,C,nc,42.3363,-89.1609
+IL,004,LOT,Boone,IL004,Boone,17007,C,nc,42.3231,-88.8234
+IL,005,LOT,McHenry,IL005,McHenry,17111,C,ne,42.3244,-88.4524
+IL,006,LOT,Lake,IL006,Lake,17097,C,ne,42.3231,-88.0036
+IL,007,DVN,Carroll,IL007,Carroll,17015,C,nw,42.0688,-89.9345
+IL,008,LOT,Ogle,IL008,Ogle,17141,C,nc,42.0427,-89.3207
+IL,009,DVN,Whiteside,IL009,Whiteside,17195,C,nw,41.7563,-89.9141
+IL,010,LOT,Lee,IL010,Lee,17103,C,nc,41.7461,-89.3005
+IL,011,LOT,De Kalb,IL011,De Kalb,17037,C,nc,41.8934,-88.7704
+IL,012,LOT,Kane,IL012,Kane,17089,C,ne,41.9390,-88.4287
+IL,013,LOT,DuPage,IL013,DuPage,17043,C,ne,41.8520,-88.0857
+IL,015,DVN,Rock Island,IL015,Rock Island,17161,C,nw,41.4674,-90.5674
+IL,016,DVN,Henry,IL016,Henry,17073,C,nw,41.3531,-90.1314
+IL,017,DVN,Bureau,IL017,Bureau,17011,C,nc,41.4041,-89.5287
+IL,018,DVN,Putnam,IL018,Putnam,17155,C,nc,41.2045,-89.2858
+IL,019,LOT,La Salle,IL019,La Salle,17099,C,nc,41.3439,-88.8859
+IL,020,LOT,Kendall,IL020,Kendall,17093,C,ne,41.5906,-88.4289
+IL,021,LOT,Grundy,IL021,Grundy,17063,C,ne,41.2849,-88.4185
+IL,023,LOT,Kankakee,IL023,Kankakee,17091,C,ne,41.1377,-87.8618
+IL,024,DVN,Mercer,IL024,Mercer,17131,C,nw,41.2053,-90.7414
+IL,025,DVN,Henderson,IL025,Henderson,17071,C,wc,40.8181,-90.9251
+IL,026,DVN,Warren,IL026,Warren,17187,C,wc,40.8488,-90.6150
+IL,027,ILX,Knox,IL027,Knox,17095,C,wc,40.9318,-90.2133
+IL,028,ILX,Stark,IL028,Stark,17175,C,nc,41.0934,-89.7975
+IL,029,ILX,Peoria,IL029,Peoria,17143,C,cc,40.7881,-89.7600
+IL,030,ILX,Marshall,IL030,Marshall,17123,C,cc,41.0332,-89.3448
+IL,031,ILX,Woodford,IL031,Woodford,17203,C,cc,40.7882,-89.2111
+IL,032,LOT,Livingston,IL032,Livingston,17105,C,cc,40.8914,-88.5578
+IL,033,LOT,Iroquois,IL033,Iroquois,17075,C,ec,40.7474,-87.8243
+IL,034,DVN,Hancock,IL034,Hancock,17067,C,wc,40.4037,-91.1647
+IL,035,DVN,McDonough,IL035,McDonough,17109,C,wc,40.4562,-90.6780
+IL,036,ILX,Fulton,IL036,Fulton,17057,C,wc,40.4728,-90.2075
+IL,037,ILX,Tazewell,IL037,Tazewell,17179,C,cc,40.5075,-89.5135
+IL,038,ILX,McLean,IL038,McLean,17113,C,cc,40.4908,-88.8473
+IL,039,LOT,Ford,IL039,Ford,17053,C,ec,40.5972,-88.2233
+IL,040,ILX,Schuyler,IL040,Schuyler,17169,C,wc,40.1580,-90.6151
+IL,041,ILX,Mason,IL041,Mason,17125,C,cc,40.2397,-89.9168
+IL,042,ILX,Logan,IL042,Logan,17107,C,cc,40.1245,-89.3676
+IL,043,ILX,De Witt,IL043,De Witt,17039,C,cc,40.1746,-88.9041
+IL,044,ILX,Piatt,IL044,Piatt,17147,C,cc,40.0104,-88.5911
+IL,045,ILX,Champaign,IL045,Champaign,17019,C,ec,40.1401,-88.1991
+IL,046,ILX,Vermilion,IL046,Vermilion,17183,C,ec,40.1836,-87.7329
+IL,047,ILX,Cass,IL047,Cass,17017,C,wc,39.9736,-90.2474
+IL,048,ILX,Menard,IL048,Menard,17129,C,cc,40.0274,-89.8022
+IL,049,ILX,Scott,IL049,Scott,17171,C,wc,39.6442,-90.4747
+IL,050,ILX,Morgan,IL050,Morgan,17137,C,wc,39.7156,-90.2015
+IL,051,ILX,Sangamon,IL051,Sangamon,17167,C,cc,39.7582,-89.6589
+IL,052,ILX,Christian,IL052,Christian,17021,C,cc,39.5458,-89.2773
+IL,053,ILX,Macon,IL053,Macon,17115,C,cc,39.8599,-88.9617
+IL,054,ILX,Moultrie,IL054,Moultrie,17139,C,cc,39.6414,-88.6194
+IL,055,ILX,Douglas,IL055,Douglas,17041,C,ec,39.7695,-88.2174
+IL,056,ILX,Coles,IL056,Coles,17029,C,ec,39.5202,-88.2218
+IL,057,ILX,Edgar,IL057,Edgar,17045,C,ec,39.6786,-87.7456
+IL,058,LSX,Greene,IL058,Greene,17061,C,sw,39.3562,-90.3905
+IL,059,LSX,Macoupin,IL059,Macoupin,17117,C,sw,39.2610,-89.9244
+IL,060,LSX,Montgomery,IL060,Montgomery,17135,C,sc,39.2310,-89.4789
+IL,061,ILX,Shelby,IL061,Shelby,17173,C,cc,39.3911,-88.8055
+IL,062,ILX,Cumberland,IL062,Cumberland,17035,C,ec,39.2732,-88.2402
+IL,063,ILX,Clark,IL063,Clark,17023,C,ec,39.3335,-87.7878
+IL,064,LSX,Bond,IL064,Bond,17005,C,sc,38.8868,-89.4356
+IL,065,LSX,Fayette,IL065,Fayette,17051,C,sc,39.0002,-89.0242
+IL,066,ILX,Effingham,IL066,Effingham,17049,C,sc,39.0597,-88.5899
+IL,067,ILX,Jasper,IL067,Jasper,17079,C,se,39.0100,-88.1538
+IL,068,ILX,Crawford,IL068,Crawford,17033,C,ec,39.0028,-87.7595
+IL,069,LSX,Clinton,IL069,Clinton,17027,C,sc,38.6065,-89.4224
+IL,070,LSX,Marion,IL070,Marion,17121,C,sc,38.6496,-88.9190
+IL,071,ILX,Clay,IL071,Clay,17025,C,sc,38.7542,-88.4902
+IL,072,ILX,Richland,IL072,Richland,17159,C,se,38.7124,-88.0850
+IL,073,ILX,Lawrence,IL073,Lawrence,17101,C,se,38.7200,-87.7266
+IL,074,LSX,Washington,IL074,Washington,17189,C,sc,38.3522,-89.4105
+IL,075,PAH,Jefferson,IL075,Jefferson,17081,C,sc,38.3006,-88.9240
+IL,076,PAH,Wayne,IL076,Wayne,17191,C,sc,38.4295,-88.4256
+IL,077,PAH,Edwards,IL077,Edwards,17047,C,se,38.4165,-88.0532
+IL,078,PAH,Wabash,IL078,Wabash,17185,C,se,38.4461,-87.8443
+IL,079,LSX,Randolph,IL079,Randolph,17157,C,sw,38.0521,-89.8253
+IL,080,PAH,Perry,IL080,Perry,17145,C,sc,38.0838,-89.3671
+IL,081,PAH,Franklin,IL081,Franklin,17055,C,sc,37.9923,-88.9242
+IL,082,PAH,Hamilton,IL082,Hamilton,17065,C,sc,38.0816,-88.5392
+IL,083,PAH,White,IL083,White,17193,C,se,38.0875,-88.1795
+IL,084,PAH,Jackson,IL084,Jackson,17077,C,ss,37.7852,-89.3821
+IL,085,PAH,Williamson,IL085,Williamson,17199,C,ss,37.7303,-88.9299
+IL,086,PAH,Saline,IL086,Saline,17165,C,ss,37.7533,-88.5408
+IL,087,PAH,Gallatin,IL087,Gallatin,17059,C,ss,37.7628,-88.2305
+IL,088,PAH,Union,IL088,Union,17181,C,ss,37.4712,-89.2551
+IL,089,PAH,Johnson,IL089,Johnson,17087,C,ss,37.4596,-88.8809
+IL,090,PAH,Pope,IL090,Pope,17151,C,ss,37.4126,-88.5615
+IL,091,PAH,Hardin,IL091,Hardin,17069,C,ss,37.5182,-88.2669
+IL,092,PAH,Alexander,IL092,Alexander,17003,C,ss,37.1915,-89.3376
+IL,093,PAH,Pulaski,IL093,Pulaski,17153,C,ss,37.2228,-89.1267
+IL,094,PAH,Massac,IL094,Massac,17127,C,ss,37.2190,-88.7078
+IL,095,LSX,Adams,IL095,Adams,17001,C,wc,39.9878,-91.1886
+IL,096,LSX,Brown,IL096,Brown,17009,C,wc,39.9618,-90.7504
+IL,097,LSX,Pike,IL097,Pike,17149,C,wc,39.6225,-90.8863
+IL,098,LSX,Calhoun,IL098,Calhoun,17013,C,sw,39.1692,-90.6675
+IL,099,LSX,Jersey,IL099,Jersey,17083,C,sw,39.0856,-90.3566
+IL,100,LSX,Madison,IL100,Madison,17119,C,sw,38.8299,-89.9051
+IL,101,LSX,St. Clair,IL101,St. Clair,17163,C,sw,38.4703,-89.9284
+IL,102,LSX,Monroe,IL102,Monroe,17133,C,sw,38.2785,-90.1774
+IL,103,LOT,Northern Cook,IL014,Cook,17031,C,ne,42.0711,-87.9824
+IL,104,LOT,Central Cook,IL014,Cook,17031,C,ne,41.8208,-87.7444
+IL,105,LOT,Southern Cook,IL014,Cook,17031,C,ne,41.5834,-87.7273
+IL,106,LOT,Northern Will,IL022,Will,17197,C,ne,41.5776,-88.0699
+IL,107,LOT,Southern Will,IL022,Will,17197,C,ne,41.3459,-88.0891
+IL,108,LOT,Eastern Will,IL022,Will,17197,C,ne,41.3833,-87.7148
+IN,001,LOT,Lake,IN001,Lake,18089,C,nw,41.4156,-87.3817
+IN,002,LOT,Porter,IN002,Porter,18127,C,nw,41.4605,-87.0671
+IN,003,IWX,La Porte,IN003,La Porte,18091,C,nw,41.5460,-86.7399
+IN,004,IWX,St. Joseph,IN004,St. Joseph,18141,E,nc,41.6167,-86.2898
+IN,005,IWX,Elkhart,IN005,Elkhart,18039,E,nc,41.5974,-85.8587
+IN,006,IWX,Lagrange,IN006,Lagrange,18087,E,ne,41.6427,-85.4265
+IN,007,IWX,Steuben,IN007,Steuben,18151,E,ne,41.6439,-85.0008
+IN,008,IWX,Noble,IN008,Noble,18113,E,ne,41.3986,-85.4174
+IN,009,IWX,De Kalb,IN009,De Kalb,18033,E,ne,41.3976,-84.9991
+IN,010,LOT,Newton,IN010,Newton,18111,C,nw,40.9559,-87.3975
+IN,011,LOT,Jasper,IN011,Jasper,18073,C,nw,41.0230,-87.1162
+IN,012,IWX,Starke,IN012,Starke,18149,C,nw,41.2810,-86.6478
+IN,013,IWX,Pulaski,IN013,Pulaski,18131,C,nw,41.0419,-86.6989
+IN,014,IWX,Marshall,IN014,Marshall,18099,E,nc,41.3250,-86.2618
+IN,015,IWX,Fulton,IN015,Fulton,18049,E,nc,41.0470,-86.2636
+IN,016,IWX,Kosciusko,IN016,Kosciusko,18085,E,nc,41.2441,-85.8607
+IN,017,IWX,Whitley,IN017,Whitley,18183,E,ne,41.1394,-85.5052
+IN,018,IWX,Allen,IN018,Allen,18003,E,ne,41.0908,-85.0665
+IN,019,LOT,Benton,IN019,Benton,18007,E,nw,40.6063,-87.3109
+IN,020,IWX,White,IN020,White,18181,E,nw,40.7499,-86.8654
+IN,021,IND,Carroll,IN021,Carroll,18015,E,nc,40.5828,-86.5635
+IN,022,IWX,Cass,IN022,Cass,18017,E,nc,40.7615,-86.3460
+IN,023,IWX,Miami,IN023,Miami,18103,E,nc,40.7695,-86.0451
+IN,024,IWX,Wabash,IN024,Wabash,18169,E,nc,40.8457,-85.7940
+IN,025,IWX,Huntington,IN025,Huntington,18069,E,ne,40.8292,-85.4881
+IN,026,IWX,Wells,IN026,Wells,18179,E,ne,40.7291,-85.2212
+IN,027,IWX,Adams,IN027,Adams,18001,E,ne,40.7455,-84.9366
+IN,028,IND,Warren,IN028,Warren,18171,E,wc,40.3469,-87.3533
+IN,029,IND,Tippecanoe,IN029,Tippecanoe,18157,E,wc,40.3886,-86.8941
+IN,030,IND,Clinton,IN030,Clinton,18023,E,cc,40.3017,-86.4752
+IN,031,IND,Howard,IN031,Howard,18067,E,cc,40.4836,-86.1169
+IN,032,IWX,Grant,IN032,Grant,18053,E,cc,40.5158,-85.6548
+IN,033,IWX,Blackford,IN033,Blackford,18009,E,ec,40.4735,-85.3248
+IN,034,IWX,Jay,IN034,Jay,18075,E,ec,40.4380,-85.0057
+IN,035,IND,Fountain,IN035,Fountain,18045,E,wc,40.1209,-87.2419
+IN,036,IND,Montgomery,IN036,Montgomery,18107,E,wc,40.0404,-86.8933
+IN,037,IND,Boone,IN037,Boone,18011,E,cc,40.0508,-86.4687
+IN,038,IND,Tipton,IN038,Tipton,18159,E,cc,40.3114,-86.0519
+IN,039,IND,Hamilton,IN039,Hamilton,18057,E,cc,40.0725,-86.0520
+IN,040,IND,Madison,IN040,Madison,18095,E,cc,40.1616,-85.7194
+IN,041,IND,Delaware,IN041,Delaware,18035,E,ec,40.2276,-85.3969
+IN,042,IND,Randolph,IN042,Randolph,18135,E,ec,40.1576,-85.0114
+IN,043,IND,Vermillion,IN043,Vermillion,18165,E,wc,39.8538,-87.4640
+IN,044,IND,Parke,IN044,Parke,18121,E,wc,39.7736,-87.2063
+IN,045,IND,Putnam,IN045,Putnam,18133,E,wc,39.6663,-86.8450
+IN,046,IND,Hendricks,IN046,Hendricks,18063,E,cc,39.7695,-86.5100
+IN,047,IND,Marion,IN047,Marion,18097,E,cc,39.7816,-86.1384
+IN,048,IND,Hancock,IN048,Hancock,18059,E,cc,39.8235,-85.7732
+IN,049,IND,Henry,IN049,Henry,18065,E,ec,39.9311,-85.3965
+IN,050,ILN,Wayne,IN050,Wayne,18177,E,ec,39.8644,-85.0099
+IN,051,IND,Vigo,IN051,Vigo,18167,E,wc,39.4306,-87.3900
+IN,052,IND,Clay,IN052,Clay,18021,E,wc,39.3928,-87.1158
+IN,053,IND,Owen,IN053,Owen,18119,E,wc,39.3129,-86.8376
+IN,054,IND,Morgan,IN054,Morgan,18109,E,cc,39.4815,-86.4462
+IN,055,IND,Johnson,IN055,Johnson,18081,E,cc,39.4899,-86.1017
+IN,056,IND,Shelby,IN056,Shelby,18145,E,cc,39.5236,-85.7918
+IN,057,IND,Rush,IN057,Rush,18139,E,cc,39.6199,-85.4657
+IN,058,ILN,Fayette,IN058,Fayette,18041,E,ec,39.6401,-85.1787
+IN,059,ILN,Union,IN059,Union,18161,E,ec,39.6255,-84.9251
+IN,060,IND,Sullivan,IN060,Sullivan,18153,E,sw,39.0888,-87.4146
+IN,061,IND,Greene,IN061,Greene,18055,E,sw,39.0363,-86.9620
+IN,062,IND,Monroe,IN062,Monroe,18105,E,sc,39.1609,-86.5231
+IN,063,IND,Brown,IN063,Brown,18013,E,sc,39.1962,-86.2274
+IN,064,IND,Bartholomew,IN064,Bartholomew,18005,E,cc,39.2059,-85.8977
+IN,065,IND,Decatur,IN065,Decatur,18031,E,cc,39.3069,-85.5013
+IN,066,ILN,Franklin,IN066,Franklin,18047,E,se,39.4148,-85.0603
+IN,067,IND,Knox,IN067,Knox,18083,E,sw,38.6891,-87.4180
+IN,068,IND,Daviess,IN068,Daviess,18027,E,sw,38.7024,-87.0721
+IN,069,IND,Martin,IN069,Martin,18101,E,sw,38.7080,-86.8031
+IN,070,IND,Lawrence,IN070,Lawrence,18093,E,sc,38.8412,-86.4834
+IN,071,IND,Jackson,IN071,Jackson,18071,E,sc,38.9064,-86.0376
+IN,072,IND,Jennings,IN072,Jennings,18079,E,se,38.9969,-85.6281
+IN,073,ILN,Ripley,IN073,Ripley,18137,E,se,39.1034,-85.2624
+IN,074,ILN,Dearborn,IN074,Dearborn,18029,E,se,39.1453,-84.9733
+IN,075,ILN,Ohio,IN075,Ohio,18115,E,se,38.9501,-84.9650
+IN,076,LMK,Orange,IN076,Orange,18117,E,sc,38.5418,-86.4951
+IN,077,LMK,Washington,IN077,Washington,18175,E,sc,38.6000,-86.1053
+IN,078,LMK,Scott,IN078,Scott,18143,E,sc,38.6851,-85.7474
+IN,079,LMK,Jefferson,IN079,Jefferson,18077,E,sc,38.7858,-85.4385
+IN,080,ILN,Switzerland,IN080,Switzerland,18155,E,se,38.8262,-85.0370
+IN,081,PAH,Gibson,IN081,Gibson,18051,C,sw,38.3118,-87.5846
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+KY,114,JKL,Owsley,KY114,Owsley,21189,E,se,37.4193,-83.6832
+KY,115,JKL,Perry,KY115,Perry,21193,E,se,37.2443,-83.2215
+KY,116,JKL,Clay,KY116,Clay,21051,E,se,37.1598,-83.7146
+KY,117,JKL,Leslie,KY117,Leslie,21131,E,se,37.0941,-83.3811
+KY,118,JKL,Letcher,KY118,Letcher,21133,E,se,37.1212,-82.8553
+KY,119,JKL,Martin,KY119,Martin,21159,E,ne,37.8016,-82.5132
+KY,120,JKL,Pike,KY120,Pike,21195,E,se,37.4690,-82.3958
+LA,001,SHV,Caddo,LA001,Caddo,22017,C,nw,32.5797,-93.8821
+LA,002,SHV,Bossier,LA002,Bossier,22015,C,nw,32.6794,-93.6052
+LA,003,SHV,Webster,LA003,Webster,22119,C,nw,32.7135,-93.3350
+LA,004,SHV,Claiborne,LA004,Claiborne,22027,C,nw,32.8226,-92.9957
+LA,005,SHV,Lincoln,LA005,Lincoln,22061,C,nc,32.6016,-92.6647
+LA,006,SHV,Union,LA006,Union,22111,C,nc,32.8319,-92.3748
+LA,007,JAN,Morehouse,LA007,Morehouse,22067,C,ne,32.8205,-91.8017
+LA,008,JAN,West Carroll,LA008,West Carroll,22123,C,ne,32.7885,-91.4567
+LA,009,JAN,East Carroll,LA009,East Carroll,22035,C,ne,32.7326,-91.2351
+LA,010,SHV,De Soto,LA010,De Soto,22031,C,nw,32.0554,-93.7373
+LA,011,SHV,Red River,LA011,Red River,22081,C,nw,32.0933,-93.3400
+LA,012,SHV,Bienville,LA012,Bienville,22013,C,nw,32.3472,-93.0560
+LA,013,SHV,Jackson,LA013,Jackson,22049,C,nc,32.3020,-92.5577
+LA,014,SHV,Ouachita,LA014,Ouachita,22073,C,nc,32.4785,-92.1545
+LA,015,JAN,Richland,LA015,Richland,22083,C,ne,32.4177,-91.7636
+LA,016,JAN,Madison,LA016,Madison,22065,C,ne,32.3643,-91.2427
+LA,017,SHV,Sabine,LA017,Sabine,22085,C,nw,31.5640,-93.5547
+LA,018,SHV,Natchitoches,LA018,Natchitoches,22069,C,nw,31.7236,-93.0963
+LA,019,SHV,Winn,LA019,Winn,22127,C,nc,31.9442,-92.6368
+LA,020,SHV,Grant,LA020,Grant,22043,C,nc,31.5997,-92.5594
+LA,021,SHV,Caldwell,LA021,Caldwell,22021,C,nc,32.0922,-92.1167
+LA,022,SHV,La Salle,LA022,La Salle,22059,C,nc,31.6767,-92.1604
+LA,023,JAN,Franklin,LA023,Franklin,22041,C,ne,32.1332,-91.6738
+LA,024,JAN,Catahoula,LA024,Catahoula,22025,C,ne,31.6661,-91.8471
+LA,025,JAN,Tensas,LA025,Tensas,22107,C,ne,32.0017,-91.3400
+LA,026,JAN,Concordia,LA026,Concordia,22029,C,ne,31.4458,-91.6397
+LA,027,LCH,Vernon,LA027,Vernon,22115,C,wc,31.1083,-93.1842
+LA,028,LCH,Rapides,LA028,Rapides,22079,C,cc,31.1986,-92.5332
+LA,029,LCH,Avoyelles,LA029,Avoyelles,22009,C,cc,31.0762,-92.0013
+LA,030,LCH,Beauregard,LA030,Beauregard,22011,C,sw,30.6485,-93.3434
+LA,031,LCH,Allen,LA031,Allen,22003,C,sw,30.6528,-92.8279
+LA,032,LCH,Evangeline,LA032,Evangeline,22039,C,cc,30.7290,-92.4059
+LA,033,LCH,St. Landry,LA033,St. Landry,22097,C,cc,30.5989,-92.0058
+LA,034,LIX,Pointe Coupee,LA034,Pointe Coupee,22077,C,se,30.7097,-91.6009
+LA,035,LIX,West Feliciana,LA035,West Feliciana,22125,C,se,30.8798,-91.4200
+LA,036,LIX,East Feliciana,LA036,East Feliciana,22037,C,se,30.8451,-91.0455
+LA,037,LIX,St. Helena,LA037,St. Helena,22091,C,se,30.8220,-90.7103
+LA,039,LIX,Washington,LA039,Washington,22117,C,se,30.8534,-90.0405
+LA,040,LIX,St. Tammany,LA040,St. Tammany,22103,C,se,30.4481,-89.9401
+LA,041,LCH,Calcasieu,LA041,Calcasieu,22019,C,sw,30.2292,-93.3579
+LA,042,LCH,Jefferson Davis,LA042,Jefferson Davis,22053,C,sw,30.2669,-92.8138
+LA,043,LCH,Acadia,LA043,Acadia,22001,C,sw,30.2906,-92.4120
+LA,044,LCH,Lafayette,LA044,Lafayette,22055,C,sc,30.2063,-92.0639
+LA,045,LCH,Upper St. Martin,LA045,St. Martin,22099,C,sc,30.2282,-91.7267
+LA,046,LIX,Iberville,LA046,Iberville,22047,C,se,30.2585,-91.3494
+LA,047,LIX,West Baton Rouge,LA047,West Baton Rouge,22121,C,se,30.4634,-91.3134
+LA,048,LIX,East Baton Rouge,LA048,East Baton Rouge,22033,C,se,30.5383,-91.0957
+LA,049,LIX,Ascension,LA049,Ascension,22005,C,se,30.2035,-90.9113
+LA,050,LIX,Livingston,LA050,Livingston,22063,C,se,30.4507,-90.7393
+LA,052,LCH,Vermilion,LA052,Vermilion,22113,C,sw,29.8496,-92.3146
+LA,053,LCH,Iberia,LA053,Iberia,22045,C,sc,29.8941,-91.7307
+LA,054,LCH,St. Mary,LA054,St. Mary,22101,C,sc,29.7128,-91.4363
+LA,055,LCH,Lower St. Martin,LA055,St. Martin,22099,C,sc,29.8466,-91.2705
+LA,056,LIX,Assumption,LA056,Assumption,22007,C,se,29.9006,-91.0626
+LA,057,LIX,St. James,LA057,St. James,22093,C,se,30.0263,-90.7962
+LA,058,LIX,St. John The Baptist,LA058,St. John The Baptist,22095,C,se,30.0792,-90.5462
+LA,059,LIX,Upper Lafourche,LA059,Lafourche,22057,C,se,29.7251,-90.5707
+LA,060,LIX,St. Charles,LA060,St. Charles,22089,C,se,29.8836,-90.3630
+LA,061,LIX,Upper Jefferson,LA061,Jefferson,22051,C,se,29.8206,-90.1420
+LA,062,LIX,Orleans,LA062,Orleans,22071,C,se,30.0385,-89.9036
+LA,063,LIX,Upper Plaquemines,LA063,Plaquemines,22075,C,se,29.7812,-89.9757
+LA,064,LIX,Upper St. Bernard,LA064,St. Bernard,22087,C,se,29.9130,-89.8740
+LA,065,LIX,Upper Terrebonne,LA065,Terrebonne,22109,C,se,29.6390,-90.8383
+LA,066,LIX,Lower Terrebonne,LA066,Terrebonne,22109,C,se,29.3727,-90.8628
+LA,067,LIX,Lower Lafourche,LA067,Lafourche,22057,C,se,29.3739,-90.2534
+LA,068,LIX,Lower Jefferson,LA068,Jefferson,22051,C,se,29.5064,-90.0566
+LA,069,LIX,Lower Plaquemines,LA069,Plaquemines,22075,C,se,29.4206,-89.6038
+LA,070,LIX,Lower St. Bernard,LA070,St. Bernard,22087,C,se,29.8488,-89.5466
+LA,071,LIX,Northern Tangipahoa,LA071,Tangipahoa,22105,C,se,30.8274,-90.4379
+LA,072,LIX,Southern Tangipahoa,LA072,Tangipahoa,22105,C,se,30.5051,-90.3917
+LA,073,LCH,West Cameron,LA073,Cameron,22023,C,sw,29.8677,-93.1460
+LA,074,LCH,East Cameron,LA074,Cameron,22023,C,sw,29.8677,-93.1460
+MA,001,ALY,Northern Berkshire,MA001,Berkshire,25003,E,ww,42.5600,-73.1518
+MA,002,BOX,Western Franklin,MA002,Franklin,25011,E,ww,42.6171,-72.7907
+MA,003,BOX,Eastern Franklin,MA003,Franklin,25011,E,ww,42.5591,-72.4514
+MA,004,BOX,Northern Worcester,MA004,Worcester,25027,E,cc,42.4539,-71.9778
+MA,005,BOX,Central Middlesex County,MA005,Middlesex,25017,E,se,42.4633,-71.3797
+MA,006,BOX,Western Essex,MA006,Essex,25009,E,ee,42.7153,-71.0659
+MA,007,BOX,Eastern Essex,MA007,Essex,25009,E,ee,42.6372,-70.8674
+MA,008,BOX,Western Hampshire,MA008,Hampshire,25015,E,ww,42.3891,-72.8687
+MA,009,BOX,Western Hampden,MA009,Hampden,25013,E,ww,42.1675,-72.9334
+MA,010,BOX,Eastern Hampshire,MA010,Hampshire,25015,E,ww,42.3044,-72.5146
+MA,011,BOX,Eastern Hampden,MA011,Hampden,25013,E,ww,42.1202,-72.4930
+MA,012,BOX,Southern Worcester,MA012,Worcester,25027,E,cc,42.1513,-71.7709
+MA,013,BOX,Western Norfolk,MA013,Norfolk,25021,E,ee,42.1423,-71.2658
+MA,014,BOX,Southeast Middlesex,MA014,Middlesex,25017,E,se,42.4193,-71.1570
+MA,015,BOX,Suffolk,MA015,Norfolk,25021,E,ee,42.3308,-71.0826
+MA,015,BOX,Suffolk,MA015,Suffolk,25025,E,ee,42.3308,-71.0826
+MA,016,BOX,Eastern Norfolk,MA016,Norfolk,25021,E,ee,42.2217,-71.0093
+MA,017,BOX,Northern Bristol,MA017,Bristol,25005,E,se,41.9171,-71.1849
+MA,018,BOX,Western Plymouth,MA018,Plymouth,25023,E,se,41.9679,-70.8932
+MA,019,BOX,Eastern Plymouth,MA019,Norfolk,25021,E,ee,42.0271,-70.7171
+MA,019,BOX,Eastern Plymouth,MA019,Plymouth,25023,E,se,42.0271,-70.7171
+MA,020,BOX,Southern Bristol,MA020,Bristol,25005,E,se,41.6746,-71.0384
+MA,021,BOX,Southern Plymouth,MA021,Plymouth,25023,E,se,41.7415,-70.7761
+MA,022,BOX,Barnstable,MA022,Barnstable,25001,E,se,41.7262,-70.2986
+MA,023,BOX,Dukes,MA023,Dukes,25007,E,se,41.3966,-70.6561
+MA,024,BOX,Nantucket,MA024,Nantucket,25019,E,se,41.2781,-70.0744
+MA,025,ALY,Southern Berkshire,MA025,Berkshire,25003,E,ww,42.2158,-73.2511
+MA,026,BOX,Northwest Middlesex County,MA026,Middlesex,25017,E,se,42.6386,-71.6598
+MD,001,LWX,Garrett,MD001,Garrett,24023,E,ww,39.5286,-79.2739
+MD,003,LWX,Washington,MD003,Washington,24043,E,nc,39.6037,-77.8142
+MD,004,LWX,Frederick,MD004,Frederick,24021,E,nc,39.4722,-77.3981
+MD,005,LWX,Carroll,MD005,Carroll,24013,E,nc,39.5631,-77.0227
+MD,006,LWX,Northern Baltimore,MD006,Baltimore,24005,E,nn,39.5444,-76.6677
+MD,008,LWX,Cecil,MD008,Cecil,24015,E,ne,39.4885,-75.9418
+MD,008,LWX,Cecil,MD008,Cecil,24015,E,ne,39.5749,-75.9384
+MD,011,LWX,Southern Baltimore,MD011,Baltimore,24005,E,nn,39.3312,-76.6096
+MD,011,LWX,Southern Baltimore,MD011,Baltimore,24005,E,nn,39.2929,-76.4049
+MD,011,LWX,Southern Baltimore,MD011,Baltimore City,24510,E,nn,39.3312,-76.6096
+MD,012,PHI,Kent,MD012,Kent,24029,E,ne,39.1797,-76.1160
+MD,012,PHI,Kent,MD012,Kent,24029,E,ne,39.2574,-76.0362
+MD,013,LWX,Prince Georges,MD013,Prince Georges,24033,E,cc,38.8313,-76.8436
+MD,014,LWX,Anne Arundel,MD014,Anne Arundel,24003,E,cc,39.0074,-76.6088
+MD,014,LWX,Anne Arundel,MD014,Anne Arundel,24003,E,cc,38.9330,-76.5280
+MD,015,PHI,Queen Anne's,MD015,Queen Anne's,24035,E,ne,39.0705,-76.0196
+MD,015,PHI,Queen Anne's,MD015,Queen Anne's,24035,E,ne,39.0705,-76.0196
+MD,016,LWX,Charles,MD016,Charles,24017,E,ss,38.5078,-76.9900
+MD,016,LWX,Charles,MD016,Charles,24017,E,ss,38.3923,-76.9809
+MD,017,LWX,St. Marys,MD017,St. Marys,24037,E,ss,38.2233,-76.6289
+MD,017,LWX,St. Marys,MD017,St. Marys,24037,E,ss,38.3046,-76.6081
+MD,018,LWX,Calvert,MD018,Calvert,24009,E,ss,38.5513,-76.5696
+MD,019,PHI,Talbot,MD019,Talbot,24041,E,ee,38.7739,-76.0928
+MD,020,PHI,Caroline,MD020,Caroline,24011,E,ee,38.8717,-75.8317
+MD,021,AKQ,Dorchester,MD021,Dorchester,24019,E,se,38.4799,-76.0080
+MD,022,AKQ,Wicomico,MD022,Wicomico,24045,E,se,38.3737,-75.6193
+MD,023,AKQ,Somerset,MD023,Somerset,24039,E,se,38.1188,-75.7449
+MD,024,AKQ,Inland Worcester,MD024,Worcester,24047,E,se,38.2142,-75.3833
+MD,025,AKQ,Maryland Beaches,MD025,Worcester,24047,E,se,38.2510,-75.1598
+MD,501,LWX,Extreme Western Allegany,MD501,Allegany,24001,E,ww,39.6015,-78.9284
+MD,502,LWX,Central and Eastern Allegany,MD502,Allegany,24001,E,ww,39.6304,-78.5970
+MD,503,LWX,Northwest Montgomery,MD503,Montgomery,24031,E,cc,39.1949,-77.3233
+MD,504,LWX,Central and Southeast Montgomery,MD504,Montgomery,24031,E,cc,39.0917,-77.1134
+MD,505,LWX,Northwest Howard,MD505,Howard,24027,E,cc,39.3234,-77.0597
+MD,506,LWX,Central and Southeast Howard,MD506,Howard,24027,E,cc,39.2259,-76.8872
+MD,507,LWX,Northwest Harford,MD507,Harford,24025,E,nn,39.6258,-76.3985
+MD,508,LWX,Southeast Harford,MD508,Harford,24025,E,nn,39.6395,-76.3985
+ME,001,CAR,Northwest Aroostook,ME001,Aroostook,23003,E,nn,46.9480,-69.1077
+ME,002,CAR,Northeast Aroostook,ME002,Aroostook,23003,E,nn,46.7264,-68.2182
+ME,003,CAR,Northern Somerset,ME003,Somerset,23025,E,wc,46.2366,-69.9716
+ME,004,CAR,Northern Piscataquis,ME004,Piscataquis,23021,E,nc,46.1825,-69.2400
+ME,005,CAR,Northern Penobscot,ME005,Penobscot,23019,E,ec,45.9454,-68.6456
+ME,006,CAR,Southeast Aroostook,ME006,Aroostook,23003,E,nn,46.0031,-68.1173
+ME,007,GYX,Northern Oxford,ME007,Oxford,23017,E,ww,44.8431,-70.8789
+ME,008,GYX,Northern Franklin,ME008,Franklin,23007,E,ww,45.0717,-70.5249
+ME,009,GYX,Central Somerset,ME009,Somerset,23025,E,wc,45.4800,-70.0680
+ME,010,CAR,Central Piscataquis,ME010,Piscataquis,23021,E,nc,45.5609,-69.4281
+ME,011,CAR,Central Penobscot,ME011,Penobscot,23019,E,ec,45.3901,-68.4069
+ME,012,GYX,Southern Oxford,ME012,Oxford,23017,E,ww,44.2631,-70.6709
+ME,013,GYX,Southern Franklin,ME013,Franklin,23007,E,ww,44.6571,-70.1818
+ME,014,GYX,Southern Somerset,ME014,Somerset,23025,E,wc,44.8398,-69.6632
+ME,015,CAR,Southern Penobscot,ME015,Penobscot,23019,E,ec,44.9362,-68.8441
+ME,016,CAR,Interior Hancock,ME016,Hancock,23009,E,se,44.8510,-68.3239
+ME,017,CAR,Central Washington,ME017,Washington,23029,E,se,45.0093,-67.6338
+ME,018,GYX,Interior York,ME018,York,23031,E,sw,43.5590,-70.7784
+ME,019,GYX,Central Interior Cumberland,ME019,Cumberland,23005,E,,43.8563,-70.4616
+ME,033,GYX,Interior Cumberland Highlands,ME019,Cumberland,23005,E,,44.0404,-70.6904
+ME,020,GYX,Androscoggin,ME020,Androscoggin,23001,E,sw,44.1654,-70.2045
+ME,021,GYX,Kennebec,ME021,Kennebec,23011,E,sc,44.4093,-69.7672
+ME,022,GYX,Interior Waldo,ME022,Waldo,23027,E,sc,44.5406,-69.1709
+ME,023,GYX,Coastal York,ME023,York,23031,E,sw,43.3339,-70.6005
+ME,024,GYX,Coastal Cumberland,ME024,Cumberland,23005,E,,43.7449,-70.1860
+ME,025,GYX,Sagadahoc,ME025,Sagadahoc,23023,E,sc,43.9705,-69.8613
+ME,026,GYX,Lincoln,ME026,Lincoln,23015,E,sc,44.0752,-69.5431
+ME,027,GYX,Knox,ME027,Knox,23013,E,sc,44.1500,-69.1730
+ME,028,GYX,Coastal Waldo,ME028,Waldo,23027,E,sc,44.3490,-69.0512
+ME,029,CAR,Coastal Hancock,ME029,Hancock,23009,E,se,44.4349,-68.3928
+ME,030,CAR,Coastal Washington,ME030,Washington,23029,E,se,44.6796,-67.5411
+ME,031,CAR,Southern Piscataquis,ME031,Piscataquis,23021,E,nc,45.2475,-69.1568
+ME,032,CAR,Northern Washington,ME032,Washington,23029,E,se,45.4766,-67.7419
+MI,001,MQT,Keweenaw,MI001,Keweenaw,26083,E,wu,47.6027,-88.4220
+MI,002,MQT,Ontonagon,MI002,Ontonagon,26131,E,wu,46.6644,-89.3150
+MI,003,MQT,Houghton,MI003,Houghton,26061,E,wu,47.0531,-88.6207
+MI,004,MQT,Baraga,MI004,Baraga,26013,E,nr,46.6626,-88.3652
+MI,005,MQT,Marquette,MI005,Marquette,26103,E,nr,46.4314,-87.6415
+MI,006,MQT,Alger,MI006,Alger,26003,E,nr,46.4099,-86.6019
+MI,007,MQT,Luce,MI007,Luce,26095,E,eu,46.4707,-85.5443
+MI,009,MQT,Gogebic,MI009,Gogebic,26053,C,wu,46.4089,-89.6944
+MI,010,MQT,Iron,MI010,Iron,26071,C,sr,46.2087,-88.5306
+MI,011,MQT,Dickinson,MI011,Dickinson,26043,C,sr,46.0091,-87.8704
+MI,012,MQT,Menominee,MI012,Menominee,26109,C,sr,45.5792,-87.5573
+MI,013,MQT,Delta,MI013,Delta,26041,E,sr,45.9197,-86.9244
+MI,014,MQT,Southern Schoolcraft,MI014,Schoolcraft,26153,E,er,46.0520,-86.1917
+MI,016,APX,Emmet,MI016,Emmet,26047,E,nn,45.5205,-84.8905
+MI,017,APX,Cheboygan,MI017,Cheboygan,26031,E,nn,45.4468,-84.4999
+MI,018,APX,Presque Isle,MI018,Presque Isle,26141,E,nn,45.3396,-83.9159
+MI,020,APX,Leelanau,MI020,Leelanau,26089,E,nn,44.9367,-85.8123
+MI,021,APX,Antrim,MI021,Antrim,26009,E,nn,45.0003,-85.1377
+MI,022,APX,Otsego,MI022,Otsego,26137,E,nn,45.0214,-84.5988
+MI,023,APX,Montmorency,MI023,Montmorency,26119,E,nn,45.0275,-84.1273
+MI,024,APX,Alpena,MI024,Alpena,26007,E,nn,45.0347,-83.6285
+MI,025,APX,Benzie,MI025,Benzie,26019,E,nn,44.6395,-86.0158
+MI,026,APX,Grand Traverse,MI026,Grand Traverse,26055,E,nn,44.6709,-85.5590
+MI,027,APX,Kalkaska,MI027,Kalkaska,26079,E,nn,44.6846,-85.0902
+MI,028,APX,Crawford,MI028,Crawford,26039,E,nn,44.6836,-84.6103
+MI,029,APX,Oscoda,MI029,Oscoda,26135,E,nn,44.6817,-84.1297
+MI,030,APX,Alcona,MI030,Alcona,26001,E,nn,44.6854,-83.5938
+MI,031,APX,Manistee,MI031,Manistee,26101,E,nn,44.3332,-86.0571
+MI,032,APX,Wexford,MI032,Wexford,26165,E,nn,44.3384,-85.5785
+MI,033,APX,Missaukee,MI033,Missaukee,26113,E,nn,44.3373,-85.0947
+MI,034,APX,Roscommon,MI034,Roscommon,26143,E,nn,44.3356,-84.6116
+MI,035,APX,Ogemaw,MI035,Ogemaw,26129,E,nn,44.3349,-84.1266
+MI,036,APX,Iosco,MI036,Iosco,26069,E,nn,44.3559,-83.6360
+MI,037,GRR,Mason,MI037,Mason,26105,E,wc,43.9954,-86.2497
+MI,038,GRR,Lake,MI038,Lake,26085,E,wc,43.9900,-85.8017
+MI,039,GRR,Osceola,MI039,Osceola,26133,E,cc,43.9899,-85.3252
+MI,040,GRR,Clare,MI040,Clare,26035,E,cc,43.9879,-84.8478
+MI,041,APX,Gladwin,MI041,Gladwin,26051,E,nn,43.9906,-84.3882
+MI,042,APX,Arenac,MI042,Arenac,26011,E,nn,44.0656,-83.8948
+MI,043,GRR,Oceana,MI043,Oceana,26127,E,wc,43.6407,-86.2673
+MI,044,GRR,Newaygo,MI044,Newaygo,26123,E,wc,43.5542,-85.8009
+MI,045,GRR,Mecosta,MI045,Mecosta,26107,E,cc,43.6409,-85.3245
+MI,046,GRR,Isabella,MI046,Isabella,26073,E,cc,43.6406,-84.8467
+MI,047,DTX,Midland,MI047,Midland,26111,E,se,43.6468,-84.3879
+MI,048,DTX,Bay,MI048,Bay,26017,E,se,43.7072,-83.9921
+MI,049,DTX,Huron,MI049,Huron,26063,E,se,43.8333,-83.0193
+MI,050,GRR,Muskegon,MI050,Muskegon,26121,E,wc,43.2912,-86.1481
+MI,051,GRR,Montcalm,MI051,Montcalm,26117,E,cc,43.3110,-85.1524
+MI,052,GRR,Gratiot,MI052,Gratiot,26057,E,cc,43.2927,-84.6048
+MI,053,DTX,Saginaw,MI053,Saginaw,26145,E,se,43.3350,-84.0532
+MI,054,DTX,Tuscola,MI054,Tuscola,26157,E,se,43.4641,-83.4167
+MI,055,DTX,Sanilac,MI055,Sanilac,26151,E,se,43.4235,-82.8201
+MI,056,GRR,Ottawa,MI056,Ottawa,26139,E,sw,42.9601,-85.9939
+MI,057,GRR,Kent,MI057,Kent,26081,E,sw,43.0322,-85.5492
+MI,058,GRR,Ionia,MI058,Ionia,26067,E,sc,42.9451,-85.0746
+MI,059,GRR,Clinton,MI059,Clinton,26037,E,sc,42.9436,-84.6015
+MI,060,DTX,Shiawassee,MI060,Shiawassee,26155,E,se,42.9537,-84.1467
+MI,061,DTX,Genesee,MI061,Genesee,26049,E,se,43.0217,-83.7067
+MI,062,DTX,Lapeer,MI062,Lapeer,26087,E,se,43.0901,-83.2218
+MI,063,DTX,St. Clair,MI063,St. Clair,26147,E,se,42.9425,-82.6820
+MI,064,GRR,Allegan,MI064,Allegan,26005,E,sw,42.5913,-85.8884
+MI,065,GRR,Barry,MI065,Barry,26015,E,sw,42.5950,-85.3090
+MI,066,GRR,Eaton,MI066,Eaton,26045,E,sc,42.5961,-84.8384
+MI,067,GRR,Ingham,MI067,Ingham,26065,E,sc,42.5971,-84.3736
+MI,068,DTX,Livingston,MI068,Livingston,26093,E,se,42.6030,-83.9115
+MI,069,DTX,Oakland,MI069,Oakland,26125,E,se,42.6604,-83.3855
+MI,070,DTX,Macomb,MI070,Macomb,26099,E,se,42.6956,-82.9322
+MI,071,GRR,Van Buren,MI071,Van Buren,26159,E,sw,42.2513,-86.0189
+MI,072,GRR,Kalamazoo,MI072,Kalamazoo,26077,E,sw,42.2454,-85.5312
+MI,073,GRR,Calhoun,MI073,Calhoun,26025,E,sc,42.2466,-85.0055
+MI,074,GRR,Jackson,MI074,Jackson,26075,E,sc,42.2485,-84.4233
+MI,075,DTX,Washtenaw,MI075,Washtenaw,26161,E,se,42.2534,-83.8385
+MI,076,DTX,Wayne,MI076,Wayne,26163,E,se,42.2840,-83.2865
+MI,077,IWX,Berrien,MI077,Berrien,26021,E,sw,41.9546,-86.4123
+MI,078,IWX,Cass,MI078,Cass,26027,E,sw,41.9153,-85.9936
+MI,079,IWX,St. Joseph,MI079,St. Joseph,26149,E,sw,41.9145,-85.5278
+MI,080,IWX,Branch,MI080,Branch,26023,E,sw,41.9161,-85.0590
+MI,081,IWX,Hillsdale,MI081,Hillsdale,26059,E,se,41.8878,-84.5930
+MI,082,DTX,Lenawee,MI082,Lenawee,26091,E,se,41.8951,-84.0664
+MI,083,DTX,Monroe,MI083,Monroe,26115,E,se,41.9292,-83.5373
+MI,084,MQT,Southern Houghton,MI084,Houghton,26061,E,wu,46.6070,-88.8192
+MI,085,MQT,Northern Schoolcraft,MI085,Schoolcraft,26153,E,er,46.3325,-86.2069
+MI,086,APX,Western Chippewa ,MI086,Chippewa,26033,E,ee,46.3926,-84.9529
+MI,087,APX,Central Chippewa ,MI087,Chippewa,26033,E,ee,46.3221,-84.3611
+MI,088,APX,Southeast Chippewa ,MI088,Chippewa,26033,E,ee,46.0374,-83.9354
+MI,095,APX,Western Mackinac ,MI095,Mackinac,26097,E,ee,46.1362,-85.5266
+MI,096,APX,Eastern Mackinac ,MI096,Mackinac,26097,E,ee,46.0504,-84.7048
+MI,097,APX,Mackinac Island/Bois Blanc Island ,MI097,Mackinac,26097,E,ee,45.7790,-84.4809
+MI,098,APX,Beaver Island ,MI098,Charlevoix,26029,E,nn,45.6807,-85.5500
+MI,099,APX,Charlevoix ,MI099,Charlevoix,26029,E,nn,45.2247,-85.0407
+MN,001,FGF,West Polk,MN001,Polk,27119,C,nw,47.8466,-96.7048
+MN,002,FGF,Norman,MN002,Norman,27107,C,nw,47.3265,-96.4551
+MN,003,FGF,Clay,MN003,Clay,27027,C,nw,46.8924,-96.4906
+MN,004,FGF,Kittson,MN004,Kittson,27069,C,nw,48.7766,-96.7826
+MN,005,FGF,Roseau,MN005,Roseau,27135,C,nw,48.7751,-95.8107
+MN,006,FGF,Lake Of The Woods,MN006,Lake of the Woods,27077,C,nc,48.7719,-94.9051
+MN,007,FGF,West Marshall,MN007,Marshall,27089,C,nw,48.3592,-96.7657
+MN,008,FGF,East Marshall,MN008,Marshall,27089,C,nw,48.3571,-95.9961
+MN,009,FGF,North Beltrami,MN009,Beltrami,27007,C,nc,48.1627,-95.0124
+MN,010,DLH,Koochiching,MN010,Koochiching,27071,C,nc,48.2422,-93.7874
+MN,011,DLH,North St. Louis,MN011,St. Louis,27137,C,ne,48.1390,-92.5294
+MN,012,DLH,Northern Cook/Northern Lake,MN012,Cook,27031,C,ne,47.8238,-91.1981
+MN,012,DLH,Northern Cook/Northern Lake,MN012,Lake,27075,C,ne,47.8238,-91.1981
+MN,013,FGF,Pennington,MN013,Pennington,27113,C,nw,48.0662,-96.0367
+MN,014,FGF,Red Lake,MN014,Red Lake,27125,C,nw,47.8717,-96.0953
+MN,015,FGF,East Polk,MN015,Polk,27119,C,nw,47.6577,-95.9180
+MN,016,FGF,North Clearwater,MN016,Clearwater,27029,C,nw,47.7520,-95.3891
+MN,017,FGF,South Beltrami,MN017,Beltrami,27007,C,nc,47.6313,-94.8022
+MN,018,DLH,North Itasca,MN018,Itasca,27061,C,nc,47.6644,-93.7298
+MN,019,DLH,Central St. Louis,MN019,St. Louis,27137,C,ne,47.4461,-92.4352
+MN,020,DLH,Southern Lake/North Shore,MN020,Lake,27075,C,ne,47.2820,-91.4479
+MN,020,DLH,Southern Lake/North Shore,MN020,St. Louis,27137,C,ne,47.2820,-91.4479
+MN,021,DLH,Southern Cook/North Shore,MN021,Cook,27031,C,ne,47.8033,-90.4117
+MN,022,FGF,Mahnomen,MN022,Mahnomen,27087,C,nw,47.3252,-95.8090
+MN,023,FGF,South Clearwater,MN023,Clearwater,27029,C,nw,47.3228,-95.3643
+MN,024,FGF,Hubbard,MN024,Hubbard,27057,C,nc,47.1087,-94.9166
+MN,025,DLH,North Cass,MN025,Cass,27021,C,nc,47.1039,-94.2344
+MN,026,DLH,South Itasca,MN026,Itasca,27061,C,nc,47.2596,-93.4742
+MN,027,FGF,West Becker,MN027,Becker,27005,C,nw,46.9358,-95.9229
+MN,028,FGF,East Becker,MN028,Becker,27005,C,nw,46.9334,-95.4143
+MN,029,FGF,Wilkin,MN029,Wilkin,27167,C,wc,46.3570,-96.4684
+MN,030,FGF,West Otter Tail,MN030,Otter Tail,27111,C,wc,46.4061,-96.0175
+MN,031,FGF,East Otter Tail,MN031,Otter Tail,27111,C,wc,46.4109,-95.4624
+MN,032,FGF,Wadena,MN032,Wadena,27159,C,cc,46.5858,-94.9694
+MN,033,DLH,South Cass,MN033,Cass,27021,C,nc,46.5646,-94.5522
+MN,034,DLH,Crow Wing,MN034,Crow Wing,27035,C,ec,46.4824,-94.0709
+MN,035,DLH,Northern Aitkin,MN035,Aitkin,27001,C,ec,46.8260,-93.4185
+MN,036,DLH,South Aitkin,MN036,Aitkin,27001,C,ec,46.4095,-93.4126
+MN,037,DLH,Carlton/South St. Louis,MN037,Carlton,27017,C,ne,46.7516,-92.5801
+MN,037,DLH,Carlton/South St. Louis,MN037,St. Louis,27137,C,ne,46.7516,-92.5801
+MN,038,DLH,Pine,MN038,Pine,27115,C,ec,46.1208,-92.7414
+MN,039,ABR,Traverse,MN039,Traverse,27155,C,wc,45.7721,-96.4716
+MN,040,FGF,Grant,MN040,Grant,27051,C,wc,45.9340,-96.0121
+MN,041,MPX,Douglas,MN041,Douglas,27041,C,wc,45.9337,-95.4535
+MN,042,MPX,Todd,MN042,Todd,27153,C,cc,46.0706,-94.8976
+MN,043,MPX,Morrison,MN043,Morrison,27097,C,cc,46.0126,-94.2684
+MN,044,MPX,Mille Lacs,MN044,Mille Lacs,27095,C,ec,45.9380,-93.6301
+MN,045,MPX,Kanabec,MN045,Kanabec,27065,C,ec,45.9453,-93.2935
+MN,046,ABR,Big Stone,MN046,Big Stone,27011,C,wc,45.4261,-96.4110
+MN,047,MPX,Stevens,MN047,Stevens,27149,C,wc,45.5861,-96.0003
+MN,048,MPX,Pope,MN048,Pope,27121,C,wc,45.5860,-95.4445
+MN,049,MPX,Stearns,MN049,Stearns,27145,C,cc,45.5522,-94.6130
+MN,050,MPX,Benton,MN050,Benton,27009,C,cc,45.6991,-93.9989
+MN,051,MPX,Sherburne,MN051,Sherburne,27141,C,cc,45.4439,-93.7745
+MN,052,MPX,Isanti,MN052,Isanti,27059,C,ec,45.5615,-93.2952
+MN,053,MPX,Chisago,MN053,Chisago,27025,C,ec,45.5025,-92.9083
+MN,054,MPX,Lac Qui Parle,MN054,Lac qui Parle,27073,C,wc,44.9955,-96.1735
+MN,055,MPX,Swift,MN055,Swift,27151,C,wc,45.2827,-95.6814
+MN,056,MPX,Chippewa,MN056,Chippewa,27023,C,wc,45.0223,-95.5666
+MN,057,MPX,Kandiyohi,MN057,Kandiyohi,27067,C,cc,45.1524,-95.0048
+MN,058,MPX,Meeker,MN058,Meeker,27093,C,cc,45.1231,-94.5274
+MN,059,MPX,Wright,MN059,Wright,27171,C,cc,45.1739,-93.9630
+MN,060,MPX,Hennepin,MN060,Hennepin,27053,C,ec,45.0045,-93.4768
+MN,061,MPX,Anoka,MN061,Anoka,27003,C,ec,45.2732,-93.2464
+MN,062,MPX,Ramsey,MN062,Ramsey,27123,C,ec,45.0171,-93.0994
+MN,063,MPX,Washington,MN063,Washington,27163,C,ec,45.0383,-92.8839
+MN,064,MPX,Yellow Medicine,MN064,Yellow Medicine,27173,C,wc,44.7162,-95.8683
+MN,065,MPX,Renville,MN065,Renville,27129,C,cc,44.7268,-94.9472
+MN,066,MPX,McLeod,MN066,McLeod,27085,C,cc,44.8235,-94.2724
+MN,067,MPX,Sibley,MN067,Sibley,27143,C,cc,44.5795,-94.2321
+MN,068,MPX,Carver,MN068,Carver,27019,C,ec,44.8208,-93.8025
+MN,069,MPX,Scott,MN069,Scott,27139,C,ec,44.6484,-93.5357
+MN,070,MPX,Dakota,MN070,Dakota,27037,C,ec,44.6718,-93.0656
+MN,071,FSD,Lincoln,MN071,Lincoln,27081,C,sw,44.4126,-96.2671
+MN,072,FSD,Lyon,MN072,Lyon,27083,C,sw,44.4135,-95.8390
+MN,073,MPX,Redwood,MN073,Redwood,27127,C,sw,44.4037,-95.2539
+MN,074,MPX,Brown,MN074,Brown,27015,C,sc,44.2421,-94.7277
+MN,075,MPX,Nicollet,MN075,Nicollet,27103,C,sc,44.3500,-94.2474
+MN,076,MPX,Le Sueur,MN076,Le Sueur,27079,C,sc,44.3714,-93.7300
+MN,077,MPX,Rice,MN077,Rice,27131,C,sc,44.3541,-93.2966
+MN,078,MPX,Goodhue,MN078,Goodhue,27049,C,se,44.4099,-92.7225
+MN,079,ARX,Wabasha,MN079,Wabasha,27157,C,se,44.2842,-92.2303
+MN,080,FSD,Murray,MN080,Murray,27101,C,sw,44.0221,-95.7632
+MN,081,FSD,Cottonwood,MN081,Cottonwood,27033,C,sw,44.0071,-95.1812
+MN,082,MPX,Watonwan,MN082,Watonwan,27165,C,sc,43.9783,-94.6141
+MN,083,MPX,Blue Earth,MN083,Blue Earth,27013,C,sc,44.0346,-94.0671
+MN,084,MPX,Waseca,MN084,Waseca,27161,C,sc,44.0221,-93.5873
+MN,085,MPX,Steele,MN085,Steele,27147,C,sc,44.0223,-93.2260
+MN,086,ARX,Dodge,MN086,Dodge,27039,C,se,44.0225,-92.8620
+MN,087,ARX,Olmsted,MN087,Olmsted,27109,C,se,44.0036,-92.4018
+MN,088,ARX,Winona,MN088,Winona,27169,C,se,43.9869,-91.7791
+MN,089,FSD,Nobles,MN089,Nobles,27105,C,sw,43.6742,-95.7534
+MN,090,FSD,Jackson,MN090,Jackson,27063,C,sw,43.6741,-95.1541
+MN,091,MPX,Martin,MN091,Martin,27091,C,sc,43.6742,-94.5511
+MN,092,MPX,Faribault,MN092,Faribault,27043,C,sc,43.6739,-93.9479
+MN,093,MPX,Freeborn,MN093,Freeborn,27047,C,sc,43.6738,-93.3489
+MN,094,ARX,Mower,MN094,Mower,27099,C,se,43.6714,-92.7525
+MN,095,ARX,Fillmore,MN095,Fillmore,27045,C,se,43.6740,-92.0901
+MN,096,ARX,Houston,MN096,Houston,27055,C,se,43.6714,-91.4928
+MN,097,FSD,Pipestone,MN097,Pipestone,27117,C,sw,44.0230,-96.2586
+MN,098,FSD,Rock,MN098,Rock,27133,C,sw,43.6747,-96.2532
+MO,001,EAX,Atchison,MO001,Atchison,29005,C,nw,40.4307,-95.4270
+MO,002,EAX,Nodaway,MO002,Nodaway,29147,C,nw,40.3607,-94.8834
+MO,003,EAX,Worth,MO003,Worth,29227,C,nw,40.4791,-94.4222
+MO,004,EAX,Gentry,MO004,Gentry,29075,C,nw,40.2121,-94.4099
+MO,005,EAX,Harrison,MO005,Harrison,29081,C,nc,40.3547,-93.9921
+MO,006,EAX,Mercer,MO006,Mercer,29129,C,nc,40.4223,-93.5685
+MO,007,EAX,Putnam,MO007,Putnam,29171,C,nc,40.4789,-93.0161
+MO,008,EAX,Schuyler,MO008,Schuyler,29197,C,ne,40.4702,-92.5209
+MO,009,DVN,Scotland,MO009,Scotland,29199,C,ne,40.4526,-92.1471
+MO,010,DVN,Clark,MO010,Clark,29045,C,ne,40.4103,-91.7384
+MO,011,EAX,Holt,MO011,Holt,29087,C,nw,40.0946,-95.2150
+MO,012,EAX,Andrew,MO012,Andrew,29003,C,nw,39.9834,-94.8020
+MO,013,EAX,De Kalb,MO013,DeKalb,29063,C,nw,39.8932,-94.4047
+MO,014,EAX,Daviess,MO014,Daviess,29061,C,nc,39.9607,-93.9855
+MO,015,EAX,Grundy,MO015,Grundy,29079,C,nc,40.1140,-93.5653
+MO,016,EAX,Sullivan,MO016,Sullivan,29211,C,nc,40.2107,-93.1115
+MO,017,EAX,Adair,MO017,Adair,29001,C,ne,40.1906,-92.6007
+MO,018,LSX,Knox,MO018,Knox,29103,C,ne,40.1282,-92.1481
+MO,019,LSX,Lewis,MO019,Lewis,29111,C,ne,40.0969,-91.7222
+MO,020,EAX,Buchanan,MO020,Buchanan,29021,C,nw,39.6598,-94.8062
+MO,021,EAX,Clinton,MO021,Clinton,29049,C,nw,39.6018,-94.4046
+MO,022,EAX,Caldwell,MO022,Caldwell,29025,C,nc,39.6557,-93.9828
+MO,023,EAX,Livingston,MO023,Livingston,29117,C,nc,39.7821,-93.5483
+MO,024,EAX,Linn,MO024,Linn,29115,C,nc,39.8702,-93.1072
+MO,025,EAX,Macon,MO025,Macon,29121,C,nc,39.8308,-92.5646
+MO,026,LSX,Shelby,MO026,Shelby,29205,C,ne,39.7978,-92.0766
+MO,027,LSX,Marion,MO027,Marion,29127,C,ne,39.8060,-91.6224
+MO,028,EAX,Platte,MO028,Platte,29165,C,wc,39.3804,-94.7736
+MO,029,EAX,Clay,MO029,Clay,29047,C,wc,39.3105,-94.4208
+MO,030,EAX,Ray,MO030,Ray,29177,C,wc,39.3524,-93.9899
+MO,031,EAX,Carroll,MO031,Carroll,29033,C,nc,39.4268,-93.5051
+MO,032,EAX,Chariton,MO032,Chariton,29041,C,nc,39.5145,-92.9626
+MO,033,EAX,Randolph,MO033,Randolph,29175,C,nc,39.4402,-92.4971
+MO,034,LSX,Monroe,MO034,Monroe,29137,C,ne,39.4955,-92.0007
+MO,035,LSX,Ralls,MO035,Ralls,29173,C,ne,39.5276,-91.5220
+MO,036,LSX,Pike,MO036,Pike,29163,C,ne,39.3438,-91.1714
+MO,037,EAX,Jackson,MO037,Jackson,29095,C,wc,39.0085,-94.3461
+MO,038,EAX,Lafayette,MO038,Lafayette,29107,C,wc,39.0656,-93.7855
+MO,039,EAX,Saline,MO039,Saline,29195,C,cc,39.1366,-93.2022
+MO,040,EAX,Howard,MO040,Howard,29089,C,cc,39.1427,-92.6958
+MO,041,LSX,Boone,MO041,Boone,29019,C,cc,38.9907,-92.3096
+MO,042,LSX,Audrain,MO042,Audrain,29007,C,cc,39.2158,-91.8416
+MO,043,EAX,Cass,MO043,Cass,29037,C,wc,38.6470,-94.3548
+MO,044,EAX,Johnson,MO044,Johnson,29101,C,wc,38.7441,-93.8063
+MO,045,EAX,Pettis,MO045,Pettis,29159,C,cc,38.7283,-93.2851
+MO,046,EAX,Cooper,MO046,Cooper,29053,C,cc,38.8439,-92.8103
+MO,047,LSX,Moniteau,MO047,Moniteau,29135,C,cc,38.6327,-92.5831
+MO,048,LSX,Cole,MO048,Cole,29051,C,cc,38.5055,-92.2816
+MO,049,LSX,Osage,MO049,Osage,29151,C,cc,38.4604,-91.8618
+MO,050,LSX,Callaway,MO050,Callaway,29027,C,cc,38.8355,-91.9260
+MO,051,LSX,Montgomery,MO051,Montgomery,29139,C,ec,38.9415,-91.4702
+MO,052,LSX,Lincoln,MO052,Lincoln,29113,C,ec,39.0581,-90.9602
+MO,053,EAX,Bates,MO053,Bates,29013,C,wc,38.2573,-94.3400
+MO,054,EAX,Henry,MO054,Henry,29083,C,wc,38.3852,-93.7927
+MO,055,SGF,Benton,MO055,Benton,29015,C,cc,38.2948,-93.2879
+MO,056,SGF,Morgan,MO056,Morgan,29141,C,cc,38.4239,-92.8861
+MO,057,SGF,Miller,MO057,Miller,29131,C,cc,38.2145,-92.4284
+MO,058,SGF,Maries,MO058,Maries,29125,C,cc,38.1616,-91.9249
+MO,059,LSX,Gasconade,MO059,Gasconade,29073,C,ec,38.4409,-91.5079
+MO,060,LSX,Warren,MO060,Warren,29219,C,ec,38.7646,-91.1607
+MO,061,LSX,St. Charles,MO061,St. Charles,29183,C,ec,38.7819,-90.6752
+MO,062,LSX,Franklin,MO062,Franklin,29071,C,ec,38.4112,-91.0750
+MO,063,LSX,St. Louis,MO063,St. Louis,29189,C,ec,38.6407,-90.4430
+MO,064,LSX,St. Louis City,MO064,St. Louis City,29510,C,ec,38.6358,-90.2450
+MO,065,LSX,Jefferson,MO065,Jefferson,29099,C,ec,38.2610,-90.5378
+MO,066,SGF,Vernon,MO066,Vernon,29217,C,wc,37.8506,-94.3424
+MO,067,SGF,St. Clair,MO067,St. Clair,29185,C,wc,38.0371,-93.7760
+MO,068,SGF,Hickory,MO068,Hickory,29085,C,cc,37.9408,-93.3207
+MO,069,SGF,Camden,MO069,Camden,29029,C,cc,38.0271,-92.7660
+MO,070,SGF,Pulaski,MO070,Pulaski,29169,C,cc,37.8246,-92.2076
+MO,071,SGF,Phelps,MO071,Phelps,29161,C,ec,37.8771,-91.7924
+MO,072,LSX,Crawford,MO072,Crawford,29055,C,ec,37.9764,-91.3040
+MO,073,LSX,Washington,MO073,Washington,29221,C,ec,37.9617,-90.8774
+MO,074,LSX,St. Francois,MO074,St. Francois,29187,C,se,37.8104,-90.4729
+MO,075,LSX,Ste. Genevieve,MO075,Ste. Genevieve,29186,C,se,37.8940,-90.1952
+MO,076,PAH,Perry,MO076,Perry,29157,C,se,37.7071,-89.8244
+MO,077,SGF,Barton,MO077,Barton,29011,C,sw,37.5023,-94.3471
+MO,078,SGF,Cedar,MO078,Cedar,29039,C,sw,37.7238,-93.8566
+MO,079,SGF,Polk,MO079,Polk,29167,C,sw,37.6165,-93.4005
+MO,080,SGF,Dallas,MO080,Dallas,29059,C,sw,37.6804,-93.0236
+MO,081,SGF,Laclede,MO081,Laclede,29105,C,sw,37.6583,-92.5903
+MO,082,SGF,Texas,MO082,Texas,29215,C,sc,37.3173,-91.9651
+MO,083,SGF,Dent,MO083,Dent,29065,C,sc,37.6066,-91.5079
+MO,084,LSX,Iron,MO084,Iron,29093,C,se,37.5552,-90.7735
+MO,085,LSX,Madison,MO085,Madison,29123,C,se,37.4781,-90.3450
+MO,086,PAH,Bollinger,MO086,Bollinger,29017,C,se,37.3222,-90.0259
+MO,087,PAH,Cape Girardeau,MO087,Cape Girardeau,29031,C,se,37.3839,-89.6845
+MO,088,SGF,Jasper,MO088,Jasper,29097,C,sw,37.2035,-94.3406
+MO,089,SGF,Dade,MO089,Dade,29057,C,sw,37.4320,-93.8503
+MO,090,SGF,Greene,MO090,Greene,29077,C,sw,37.2581,-93.3420
+MO,091,SGF,Webster,MO091,Webster,29225,C,sw,37.2809,-92.8759
+MO,092,SGF,Wright,MO092,Wright,29229,C,sw,37.2701,-92.4687
+MO,093,SGF,Newton,MO093,Newton,29145,C,sw,36.9055,-94.3392
+MO,094,SGF,Lawrence,MO094,Lawrence,29109,C,sw,37.1064,-93.8329
+MO,095,SGF,Christian,MO095,Christian,29043,C,sw,36.9696,-93.1889
+MO,096,SGF,Douglas,MO096,Douglas,29067,C,sw,36.9326,-92.4988
+MO,097,SGF,Howell,MO097,Howell,29091,C,sc,36.7740,-91.8865
+MO,098,SGF,Shannon,MO098,Shannon,29203,C,sc,37.1574,-91.4005
+MO,099,LSX,Reynolds,MO099,Reynolds,29179,C,se,37.3623,-90.9691
+MO,100,PAH,Wayne,MO100,Wayne,29223,C,se,37.1126,-90.4614
+MO,101,SGF,McDonald,MO101,McDonald,29119,C,sw,36.6287,-94.3484
+MO,102,SGF,Barry,MO102,Barry,29009,C,sw,36.7099,-93.8291
+MO,103,SGF,Stone,MO103,Stone,29209,C,sw,36.7469,-93.4559
+MO,104,SGF,Taney,MO104,Taney,29213,C,sw,36.6547,-93.0410
+MO,105,SGF,Ozark,MO105,Ozark,29153,C,sw,36.6493,-92.4446
+MO,106,SGF,Oregon,MO106,Oregon,29149,C,sc,36.6867,-91.4033
+MO,107,PAH,Carter,MO107,Carter,29035,C,se,36.9413,-90.9623
+MO,108,PAH,Ripley,MO108,Ripley,29181,C,se,36.6528,-90.8639
+MO,109,PAH,Butler,MO109,Butler,29023,C,se,36.7164,-90.4065
+MO,110,PAH,Stoddard,MO110,Stoddard,29207,C,se,36.8555,-89.9444
+MO,111,PAH,Scott,MO111,Scott,29201,C,se,37.0530,-89.5686
+MO,112,PAH,Mississippi,MO112,Mississippi,29133,C,se,36.8281,-89.2911
+MO,113,MEG,Dunklin,MO113,Dunklin,29069,C,se,36.2719,-90.0909
+MO,114,PAH,New Madrid,MO114,New Madrid,29143,C,se,36.5942,-89.6516
+MO,115,MEG,Pemiscot,MO115,Pemiscot,29155,C,se,36.2111,-89.7858
+MS,001,MEG,DeSoto,MS001,DeSoto,28033,C,ne,34.8754,-89.9918
+MS,002,MEG,Marshall,MS002,Marshall,28093,C,nw,34.7623,-89.5031
+MS,003,MEG,Benton,MS003,Benton,28009,C,ne,34.8173,-89.1885
+MS,004,MEG,Tippah,MS004,Tippah,28139,C,ne,34.7684,-88.9089
+MS,005,MEG,Alcorn,MS005,Alcorn,28003,C,ne,34.8808,-88.5803
+MS,006,MEG,Tishomingo,MS006,Tishomingo,28141,C,ne,34.7404,-88.2393
+MS,007,MEG,Tunica,MS007,Tunica,28143,C,nw,34.6520,-90.3755
+MS,008,MEG,Tate,MS008,Tate,28137,C,nw,34.6503,-89.9448
+MS,009,MEG,Prentiss,MS009,Prentiss,28117,C,ne,34.6183,-88.5201
+MS,010,MEG,Coahoma,MS010,Coahoma,28027,C,nw,34.2292,-90.6027
+MS,011,MEG,Quitman,MS011,Quitman,28119,C,nw,34.2514,-90.2891
+MS,012,MEG,Panola,MS012,Panola,28107,C,nw,34.3639,-89.9505
+MS,013,MEG,Lafayette,MS013,Lafayette,28071,C,nw,34.3568,-89.4849
+MS,014,MEG,Union,MS014,Union,28145,C,ne,34.4905,-89.0038
+MS,015,MEG,Pontotoc,MS015,Pontotoc,28115,C,ne,34.2254,-89.0374
+MS,016,MEG,Lee,MS016,Lee,28081,C,ne,34.2899,-88.6804
+MS,017,MEG,Itawamba,MS017,Itawamba,28057,C,ne,34.2800,-88.3613
+MS,018,JAN,Bolivar,MS018,Bolivar,28011,C,nw,33.7955,-90.8804
+MS,019,JAN,Sunflower,MS019,Sunflower,28133,C,nw,33.6023,-90.5887
+MS,020,MEG,Tallahatchie,MS020,Tallahatchie,28135,C,nw,33.9505,-90.1732
+MS,021,MEG,Yalobusha,MS021,Yalobusha,28161,C,nw,34.0282,-89.7076
+MS,022,MEG,Calhoun,MS022,Calhoun,28013,C,ne,33.9364,-89.3364
+MS,023,MEG,Chickasaw,MS023,Chickasaw,28017,C,ne,33.9208,-88.9479
+MS,024,MEG,Monroe,MS024,Monroe,28095,C,ne,33.8923,-88.4804
+MS,025,JAN,Leflore,MS025,Leflore,28083,C,nc,33.5505,-90.3010
+MS,026,JAN,Grenada,MS026,Grenada,28043,C,nc,33.7700,-89.8020
+MS,027,JAN,Carroll,MS027,Carroll,28015,C,nc,33.4485,-89.9202
+MS,028,JAN,Montgomery,MS028,Montgomery,28097,C,nc,33.4941,-89.6164
+MS,029,JAN,Webster,MS029,Webster,28155,C,nc,33.6131,-89.2848
+MS,030,JAN,Clay,MS030,Clay,28025,C,ne,33.6557,-88.7815
+MS,031,JAN,Lowndes,MS031,Lowndes,28087,C,ne,33.4729,-88.4433
+MS,032,JAN,Choctaw,MS032,Choctaw,28019,C,cc,33.3473,-89.2484
+MS,033,JAN,Oktibbeha,MS033,Oktibbeha,28105,C,ne,33.4250,-88.8793
+MS,034,JAN,Washington,MS034,Washington,28151,C,nw,33.2836,-90.9474
+MS,035,JAN,Humphreys,MS035,Humphreys,28053,C,cc,33.1287,-90.5265
+MS,036,JAN,Holmes,MS036,Holmes,28051,C,cc,33.1235,-90.0921
+MS,037,JAN,Attala,MS037,Attala,28007,C,cc,33.0863,-89.5817
+MS,038,JAN,Winston,MS038,Winston,28159,C,ec,33.0885,-89.0344
+MS,039,JAN,Noxubee,MS039,Noxubee,28103,C,ec,33.1101,-88.5698
+MS,040,JAN,Issaquena,MS040,Issaquena,28055,C,wc,32.7414,-90.9892
+MS,041,JAN,Sharkey,MS041,Sharkey,28125,C,wc,32.8797,-90.8132
+MS,042,JAN,Yazoo,MS042,Yazoo,28163,C,cc,32.7804,-90.3965
+MS,043,JAN,Madison,MS043,Madison,28089,C,cc,32.6347,-90.0338
+MS,044,JAN,Leake,MS044,Leake,28079,C,cc,32.7535,-89.5241
+MS,045,JAN,Neshoba,MS045,Neshoba,28099,C,ec,32.7535,-89.1176
+MS,046,JAN,Kemper,MS046,Kemper,28069,C,ec,32.7546,-88.6412
+MS,047,JAN,Warren,MS047,Warren,28149,C,wc,32.3574,-90.8521
+MS,048,JAN,Hinds,MS048,Hinds,28049,C,cc,32.2667,-90.4427
+MS,049,JAN,Rankin,MS049,Rankin,28121,C,cc,32.2641,-89.9457
+MS,050,JAN,Scott,MS050,Scott,28123,C,cc,32.4064,-89.5376
+MS,051,JAN,Newton,MS051,Newton,28101,C,ec,32.4002,-89.1188
+MS,052,JAN,Lauderdale,MS052,Lauderdale,28075,C,ec,32.4043,-88.6626
+MS,053,JAN,Claiborne,MS053,Claiborne,28021,C,sw,31.9737,-90.9118
+MS,054,JAN,Copiah,MS054,Copiah,28029,C,cc,31.8692,-90.4488
+MS,055,JAN,Simpson,MS055,Simpson,28127,C,cc,31.9132,-89.9195
+MS,056,JAN,Smith,MS056,Smith,28129,C,cc,32.0177,-89.5067
+MS,057,JAN,Jasper,MS057,Jasper,28061,C,ec,32.0191,-89.1188
+MS,058,JAN,Clarke,MS058,Clarke,28023,C,ec,32.0414,-88.6894
+MS,059,JAN,Jefferson,MS059,Jefferson,28063,C,sw,31.7342,-91.0371
+MS,060,JAN,Adams,MS060,Adams,28001,C,sw,31.4832,-91.3531
+MS,061,JAN,Franklin,MS061,Franklin,28037,C,sw,31.4772,-90.8978
+MS,062,JAN,Lincoln,MS062,Lincoln,28085,C,sc,31.5324,-90.4540
+MS,063,JAN,Lawrence,MS063,Lawrence,28077,C,sc,31.5502,-90.1070
+MS,064,JAN,Jefferson Davis,MS064,Jefferson Davis,28065,C,sc,31.5696,-89.8230
+MS,065,JAN,Covington,MS065,Covington,28031,C,se,31.6332,-89.5526
+MS,066,JAN,Jones,MS066,Jones,28067,C,se,31.6226,-89.1688
+MS,067,MOB,Wayne,MS067,Wayne,28153,C,se,31.6408,-88.6958
+MS,068,LIX,Wilkinson,MS068,Wilkinson,28157,C,ss,31.1610,-91.3109
+MS,069,LIX,Amite,MS069,Amite,28005,C,ss,31.1744,-90.8045
+MS,070,LIX,Pike,MS070,Pike,28113,C,ss,31.1749,-90.4042
+MS,071,LIX,Walthall,MS071,Walthall,28147,C,ss,31.1485,-90.1061
+MS,072,JAN,Marion,MS072,Marion,28091,C,sc,31.2308,-89.8224
+MS,073,JAN,Lamar,MS073,Lamar,28073,C,se,31.2059,-89.5087
+MS,074,JAN,Forrest,MS074,Forrest,28035,C,se,31.1889,-89.2579
+MS,075,MOB,Perry,MS075,Perry,28111,C,se,31.1721,-88.9923
+MS,076,MOB,Greene,MS076,Greene,28041,C,se,31.2142,-88.6392
+MS,077,LIX,Pearl River,MS077,Pearl River,28109,C,ss,30.7686,-89.5898
+MS,078,MOB,Stone,MS078,Stone,28131,C,se,30.7900,-89.1177
+MS,079,MOB,George,MS079,George,28039,C,se,30.8626,-88.6440
+MS,080,LIX,Hancock,MS080,Hancock,28045,C,ss,30.4164,-89.4887
+MS,081,LIX,Harrison,MS081,Harrison,28047,C,ss,30.5148,-89.1182
+MS,082,LIX,Jackson,MS082,Jackson,28059,C,ss,30.5461,-88.6361
+MT,001,MSO,Kootenai/Cabinet Region,MT001,Flathead,30029,M,nw,48.3391,-115.2165
+MT,001,MSO,Kootenai/Cabinet Region,MT001,Lincoln,30053,M,nw,48.3391,-115.2165
+MT,001,MSO,Kootenai/Cabinet Region,MT001,Sanders,30089,M,nw,48.3391,-115.2165
+MT,002,MSO,West Glacier/Bob Marshall Region,MT002,Flathead,30029,M,nw,48.2755,-113.8037
+MT,002,MSO,West Glacier/Bob Marshall Region,MT002,Lake,30047,M,nw,48.2755,-113.8037
+MT,003,MSO,Flathead/Mission Valleys,MT003,Flathead,30029,M,nw,47.7732,-114.2980
+MT,003,MSO,Flathead/Mission Valleys,MT003,Lake,30047,M,nw,47.7732,-114.2980
+MT,003,MSO,Flathead/Mission Valleys,MT003,Sanders,30089,M,nw,47.7732,-114.2980
+MT,004,MSO,Lower Clark Fork Region,MT004,Mineral,30061,M,wc,47.3078,-115.0063
+MT,004,MSO,Lower Clark Fork Region,MT004,Missoula,30063,M,wc,47.3078,-115.0063
+MT,004,MSO,Lower Clark Fork Region,MT004,Sanders,30089,M,nw,47.3078,-115.0063
+MT,005,MSO,Missoula/Bitterroot Valleys,MT005,Missoula,30063,M,wc,46.4888,-114.1079
+MT,005,MSO,Missoula/Bitterroot Valleys,MT005,Ravalli,30081,M,wc,46.4888,-114.1079
+MT,006,MSO,Bitterroot/Sapphire Mountains,MT006,Granite,30039,M,wc,46.1931,-113.9667
+MT,006,MSO,Bitterroot/Sapphire Mountains,MT006,Missoula,30063,M,wc,46.1931,-113.9667
+MT,006,MSO,Bitterroot/Sapphire Mountains,MT006,Ravalli,30081,M,wc,46.1931,-113.9667
+MT,007,MSO,Butte/Pintlar Region,MT007,Deer Lodge,30023,M,wc,46.3886,-112.9185
+MT,007,MSO,Butte/Pintlar Region,MT007,Granite,30039,M,wc,46.3886,-112.9185
+MT,007,MSO,Butte/Pintlar Region,MT007,Powell,30077,M,wc,46.3886,-112.9185
+MT,007,MSO,Butte/Pintlar Region,MT007,Silver Bow,30093,M,wc,46.3886,-112.9185
+MT,008,TFX,Beaverhead,MT008,Beaverhead,30001,M,sw,45.1328,-112.8988
+MT,009,TFX,Northern Rocky Mountain Front,MT009,Glacier,30035,M,nc,48.5649,-113.2070
+MT,009,TFX,Northern Rocky Mountain Front,MT009,Pondera,30073,M,nc,48.5649,-113.2070
+MT,010,TFX,Eastern Glacier,MT010,Glacier,30035,M,nc,48.7446,-112.6174
+MT,011,TFX,Hill,MT011,Hill,30041,M,nc,48.6282,-110.1113
+MT,012,TFX,Cascade,MT012,Cascade,30013,M,cc,47.3080,-111.3472
+MT,013,TFX,Chouteau,MT013,Chouteau,30015,M,nc,47.8806,-110.4352
+MT,014,TFX,Central and Southern Lewis and Clark,MT014,Lewis and Clark,30049,M,wc,46.9504,-112.2607
+MT,015,TFX,Madison,MT015,Madison,30057,M,sw,45.3008,-111.9203
+MT,016,GGW,Central and Southeast Phillips,MT016,Phillips,30071,M,ne,48.1191,-107.8379
+MT,017,GGW,Central and Southern Valley,MT017,Valley,30105,M,ne,48.1821,-106.6828
+MT,018,GGW,Daniels,MT018,Daniels,30019,M,ne,48.7838,-105.5485
+MT,019,GGW,Sheridan,MT019,Sheridan,30091,M,ne,48.7212,-104.5046
+MT,020,GGW,Western Roosevelt,MT020,Roosevelt,30085,M,ne,48.3139,-105.4045
+MT,021,GGW,Petroleum,MT021,Petroleum,30069,M,ne,47.1175,-108.2501
+MT,022,GGW,Garfield,MT022,Garfield,30033,M,ne,47.2777,-106.9928
+MT,023,GGW,McCone,MT023,McCone,30055,M,ne,47.6452,-105.7953
+MT,024,GGW,Richland,MT024,Richland,30083,M,ne,47.7879,-104.5614
+MT,025,GGW,Dawson,MT025,Dawson,30021,M,ne,47.2664,-104.8994
+MT,026,GGW,Prairie,MT026,Prairie,30079,M,ne,46.8605,-105.3779
+MT,027,GGW,Wibaux,MT027,Wibaux,30109,M,ne,46.9653,-104.2490
+MT,029,BYZ,Musselshell,MT029,Musselshell,30065,M,cc,46.4966,-108.3977
+MT,030,BYZ,Treasure,MT030,Treasure,30103,M,se,46.2115,-107.2717
+MT,031,BYZ,Northern Rosebud,MT031,Rosebud,30087,M,se,46.3849,-106.7762
+MT,032,BYZ,Custer,MT032,Custer,30017,M,se,46.2527,-105.5718
+MT,033,BYZ,Fallon,MT033,Fallon,30025,M,se,46.3340,-104.4174
+MT,034,BYZ,Northern Stillwater,MT034,Stillwater,30095,M,sc,45.8098,-109.2176
+MT,036,BYZ,Powder River,MT036,Powder River,30075,M,se,45.3950,-105.6301
+MT,037,BYZ,Carter,MT037,Carter,30011,M,se,45.5168,-104.5362
+MT,040,BYZ,Northern Park,MT040,Park,30067,M,sc,45.9621,-110.6013
+MT,042,BYZ,Golden Valley,MT042,Golden Valley,30037,M,cc,46.3251,-109.1643
+MT,043,MSO,Blackfoot Region,MT043,Granite,30039,M,wc,47.2005,-113.5840
+MT,043,MSO,Blackfoot Region,MT043,Lake,30047,M,nw,47.2005,-113.5840
+MT,043,MSO,Blackfoot Region,MT043,Missoula,30063,M,wc,47.2005,-113.5840
+MT,043,MSO,Blackfoot Region,MT043,Powell,30077,M,wc,47.2005,-113.5840
+MT,044,TFX,Toole,MT044,Toole,30101,M,nc,48.6552,-111.6957
+MT,045,TFX,Liberty,MT045,Liberty,30051,M,nc,48.5617,-111.0246
+MT,046,TFX,Eastern Pondera,MT046,Pondera,30073,M,nc,48.2323,-111.9830
+MT,047,TFX,Blaine,MT047,Blaine,30005,M,nc,48.4327,-108.9586
+MT,048,TFX,Southern Rocky Mountain Front,MT048,Lewis and Clark,30049,M,wc,47.7128,-112.5644
+MT,048,TFX,Southern Rocky Mountain Front,MT048,Teton,30099,M,nc,47.7128,-112.5644
+MT,049,TFX,Eastern Teton,MT049,Teton,30099,M,nc,47.8111,-111.7767
+MT,050,TFX,Judith Basin,MT050,Judith Basin,30045,M,cc,47.0455,-110.2661
+MT,051,TFX,Fergus,MT051,Fergus,30027,M,cc,47.2636,-109.2243
+MT,052,TFX,Jefferson,MT052,Jefferson,30043,M,sw,46.1485,-112.0937
+MT,053,TFX,Broadwater,MT053,Broadwater,30007,M,sw,46.3319,-111.4954
+MT,054,TFX,Meagher,MT054,Meagher,30059,M,cc,46.5982,-110.8856
+MT,055,TFX,Gallatin,MT055,Gallatin,30031,M,sw,45.5683,-111.1738
+MT,056,BYZ,Red Lodge Foothills,MT056,Carbon,30009,M,M,45.2833,-109.2924
+MT,057,BYZ,Northern Big Horn,MT057,Big Horn,30003,M,sc,45.7318,-107.4595
+MT,058,BYZ,Southern Rosebud,MT058,Rosebud,30087,M,se,45.4286,-106.4963
+MT,059,GGW,Northern Phillips,MT059,Phillips,30071,M,ne,48.7814,-107.7403
+MT,060,GGW,Southwest Phillips,MT060,Phillips,30071,M,ne,47.8159,-108.4870
+MT,061,GGW,Northern Valley,MT061,Valley,30105,M,ne,48.7789,-106.6331
+MT,062,GGW,Eastern Roosevelt,MT062,Roosevelt,30085,M,ne,48.2705,-104.5328
+MT,063,BYZ,Judith Gap,MT063,Golden Valley,30037,M,cc,46.6384,-109.6782
+MT,063,BYZ,Judith Gap,MT063,Wheatland,30107,M,cc,46.6384,-109.6782
+MT,064,BYZ,Paradise Valley,MT064,Park,30067,M,sc,45.3939,-110.7098
+MT,065,BYZ,Livingston Area,MT065,Park,30067,M,sc,45.6959,-110.5011
+MT,066,BYZ,Beartooth Foothills,MT066,Stillwater,30095,M,sc,45.5734,-109.8035
+MT,066,BYZ,Beartooth Foothills,MT066,Sweet Grass,30097,M,sc,45.5734,-109.8035
+MT,067,BYZ,Absaroka/Beartooth Mountains,MT067,Carbon,30009,M,M,45.2637,-110.1893
+MT,067,BYZ,Absaroka/Beartooth Mountains,MT067,Park,30067,M,sc,45.2637,-110.1893
+MT,067,BYZ,Absaroka/Beartooth Mountains,MT067,Stillwater,30095,M,sc,45.2637,-110.1893
+MT,067,BYZ,Absaroka/Beartooth Mountains,MT067,Sweet Grass,30097,M,sc,45.2637,-110.1893
+MT,068,BYZ,Crazy Mountains,MT068,Park,30067,M,sc,46.0653,-110.3156
+MT,068,BYZ,Crazy Mountains,MT068,Sweet Grass,30097,M,sc,46.0653,-110.3156
+MT,068,BYZ,Crazy Mountains,MT068,Wheatland,30107,M,cc,46.0653,-110.3156
+MT,228,BYZ,Southern Wheatland,MT228,Wheatland,30107,M,sc,46.4003,-109.8006
+MT,235,BYZ,Southwestern Yellowstone,MT235,Yellowstone,30111,M,sc,45.8003,-108.4981
+MT,138,BYZ,Southern Big Horn,MT138,Big Horn,30003,M,sc,45.2712,-107.3455
+MT,139,BYZ,Southeastern Carbon,MT139,Big Horn,30003,M,sc,45.1361,-108.8466
+MT,139,BYZ,Southeastern Carbon,MT139,Carbon,30009,M,sc,45.1361,-108.8466
+MT,141,BYZ,Northern Sweet Grass,MT141,Sweet Grass,30097,M,sc,45.9146,-109.8014
+MT,169,BYZ,Bighorn Canyon,MT169,Big Horn,30003,M,sc,45.1827,-108.1417
+MT,169,BYZ,Bighorn Canyon,MT169,Carbon,30009,M,sc,45.1827,-108.1417
+MT,170,BYZ,Northern Carbon,MT170,Carbon,30009,M,sc,45.4565,-108.9346
+MT,171,BYZ,Pryor/Northern Bighorn Mountains,MT171,Big Horn,30003,M,sc,45.1683,-108.2446
+MT,171,BYZ,Pryor/Northern Bighorn Mountains,MT171,Carbon,30009,M,sc,45.1683,-108.2446
+MT,172,BYZ,Melville Foothills,MT172,Sweet Grass,30097,M,sc,46.1376,-110.0264
+MT,172,BYZ,Melville Foothills,MT172,Wheatland,30107,M,sc,46.1376,-110.0264
+MT,173,BYZ,Northeastern Yellowstone,MT173,Yellowstone,30111,M,sc,46.1581,-107.9135
+NC,001,RNK,Ashe,NC001,Ashe,37009,E,nw,36.4346,-81.5010
+NC,002,RNK,Alleghany,NC002,Alleghany,37005,E,nw,36.4915,-81.1286
+NC,003,RNK,Surry,NC003,Surry,37171,E,nc,36.4148,-80.6882
+NC,004,RNK,Stokes,NC004,Stokes,37169,E,nc,36.4018,-80.2395
+NC,005,RNK,Rockingham,NC005,Rockingham,37157,E,nc,36.3961,-79.7751
+NC,006,RNK,Caswell,NC006,Caswell,37033,E,nc,36.3934,-79.3337
+NC,007,RAH,Person,NC007,Person,37145,E,cc,36.3901,-78.9717
+NC,008,RAH,Granville,NC008,Granville,37077,E,cc,36.3040,-78.6530
+NC,009,RAH,Vance,NC009,Vance,37181,E,cc,36.3648,-78.4083
+NC,010,RAH,Warren,NC010,Warren,37185,E,cc,36.3966,-78.1069
+NC,011,RAH,Halifax,NC011,Halifax,37083,E,cc,36.2575,-77.6519
+NC,012,AKQ,Northampton,NC012,Northampton,37131,E,ne,36.4177,-77.3967
+NC,013,AKQ,Hertford,NC013,Hertford,37091,E,ne,36.3590,-76.9838
+NC,014,AKQ,Gates,NC014,Gates,37073,E,ne,36.4444,-76.7017
+NC,015,AKQ,Pasquotank,NC015,Pasquotank,37139,E,ne,36.2974,-76.2867
+NC,016,AKQ,Camden,NC016,Camden,37029,E,ne,36.3902,-76.2101
+NC,017,AKQ,Western Currituck,NC017,Currituck,37053,E,ne,36.4012,-76.0340
+NC,018,RNK,Watauga,NC018,Watauga,37189,E,nw,36.2313,-81.6969
+NC,019,RNK,Wilkes,NC019,Wilkes,37193,E,nw,36.2065,-81.1638
+NC,020,RNK,Yadkin,NC020,Yadkin,37197,E,nw,36.1605,-80.6653
+NC,021,RAH,Forsyth,NC021,Forsyth,37067,E,cc,36.1304,-80.2563
+NC,022,RAH,Guilford,NC022,Guilford,37081,E,cc,36.0794,-79.7890
+NC,023,RAH,Alamance,NC023,Alamance,37001,E,cc,36.0440,-79.3995
+NC,024,RAH,Orange,NC024,Orange,37135,E,cc,36.0614,-79.1205
+NC,025,RAH,Durham,NC025,Durham,37063,E,cc,36.0359,-78.8764
+NC,026,RAH,Franklin,NC026,Franklin,37069,E,cc,36.0827,-78.2857
+NC,027,RAH,Nash,NC027,Nash,37127,E,cc,35.9672,-77.9865
+NC,028,RAH,Edgecombe,NC028,Edgecombe,37065,E,cc,35.9129,-77.5971
+NC,029,MHX,Martin,NC029,Martin,37117,E,ee,35.8416,-77.1071
+NC,030,AKQ,Bertie,NC030,Bertie,37015,E,ne,36.0662,-76.9791
+NC,031,AKQ,Chowan,NC031,Chowan,37041,E,ne,36.1507,-76.6080
+NC,032,AKQ,Perquimans,NC032,Perquimans,37143,E,ne,36.2069,-76.4419
+NC,033,GSP,Avery,NC033,Avery,37011,E,ww,36.0766,-81.9224
+NC,035,GSP,Alexander,NC035,Alexander,37003,E,pd,35.9211,-81.1772
+NC,036,GSP,Iredell,NC036,Iredell,37097,E,pd,35.8072,-80.8736
+NC,037,GSP,Davie,NC037,Davie,37059,E,pd,35.9292,-80.5446
+NC,038,RAH,Davidson,NC038,Davidson,37057,E,cc,35.7932,-80.2127
+NC,039,RAH,Randolph,NC039,Randolph,37151,E,cc,35.7103,-79.8060
+NC,040,RAH,Chatham,NC040,Chatham,37037,E,cc,35.7028,-79.2553
+NC,041,RAH,Wake,NC041,Wake,37183,E,cc,35.7901,-78.6504
+NC,042,RAH,Johnston,NC042,Johnston,37101,E,cc,35.5176,-78.3657
+NC,043,RAH,Wilson,NC043,Wilson,37195,E,cc,35.7048,-77.9185
+NC,044,MHX,Pitt,NC044,Pitt,37147,E,ee,35.5935,-77.3748
+NC,045,MHX,Washington,NC045,Washington,37187,E,ee,35.8221,-76.5766
+NC,046,MHX,Tyrrell,NC046,Tyrrell,37177,E,ee,35.8161,-76.2118
+NC,047,MHX,Mainland Dare,NC047,Dare,37055,E,ee,35.7753,-75.8577
+NC,048,GSP,Madison,NC048,Madison,37115,E,ww,35.8581,-82.7057
+NC,049,GSP,Yancey,NC049,Yancey,37199,E,ww,35.8989,-82.3076
+NC,050,GSP,Mitchell,NC050,Mitchell,37121,E,ww,36.0133,-82.1634
+NC,051,GSP,Swain,NC051,Swain,37173,E,ww,35.4867,-83.4927
+NC,052,GSP,Haywood,NC052,Haywood,37087,E,ww,35.5561,-82.9822
+NC,053,GSP,Buncombe,NC053,Buncombe,37021,E,ww,35.6112,-82.5302
+NC,056,GSP,Catawba,NC056,Catawba,37035,E,pd,35.6626,-81.2144
+NC,057,GSP,Rowan,NC057,Rowan,37159,E,pd,35.6396,-80.5247
+NC,058,GSP,Graham,NC058,Graham,37075,E,ww,35.3502,-83.8335
+NC,059,GSP,Northern Jackson,NC059,Jackson,37099,E,ww,35.3638,-83.1778
+NC,060,MRX,Cherokee,NC060,Cherokee,37039,E,sw,35.1339,-84.0635
+NC,061,MRX,Clay,NC061,Clay,37043,E,sw,35.0572,-83.7502
+NC,062,GSP,Macon,NC062,Macon,37113,E,ww,35.1504,-83.4221
+NC,063,GSP,Southern Jackson,NC063,Jackson,37099,E,ww,35.1614,-83.0799
+NC,064,GSP,Transylvania,NC064,Transylvania,37175,E,ww,35.2022,-82.7983
+NC,065,GSP,Henderson,NC065,Henderson,37089,E,ww,35.3362,-82.4799
+NC,068,GSP,Cleveland,NC068,Cleveland,37045,E,pd,35.3340,-81.5554
+NC,069,GSP,Lincoln,NC069,Lincoln,37109,E,pd,35.4862,-81.2239
+NC,070,GSP,Gaston,NC070,Gaston,37071,E,pd,35.2945,-81.1801
+NC,071,GSP,Mecklenburg,NC071,Mecklenburg,37119,E,pd,35.2468,-80.8328
+NC,072,GSP,Cabarrus,NC072,Cabarrus,37025,E,pd,35.3868,-80.5519
+NC,073,RAH,Stanly,NC073,Stanly,37167,E,cc,35.3120,-80.2507
+NC,074,RAH,Montgomery,NC074,Montgomery,37123,E,cc,35.3324,-79.9054
+NC,075,RAH,Moore,NC075,Moore,37125,E,cc,35.3107,-79.4813
+NC,076,RAH,Lee,NC076,Lee,37105,E,cc,35.4750,-79.1716
+NC,077,RAH,Harnett,NC077,Harnett,37085,E,cc,35.3687,-78.8693
+NC,078,RAH,Wayne,NC078,Wayne,37191,E,cc,35.3638,-78.0040
+NC,079,MHX,Greene,NC079,Greene,37079,E,ee,35.4852,-77.6756
+NC,080,MHX,Beaufort,NC080,Beaufort,37013,E,ee,35.4947,-76.8631
+NC,081,MHX,Mainland Hyde,NC081,Hyde,37095,E,ee,35.5375,-76.2532
+NC,082,GSP,Union,NC082,Union,37179,E,pd,34.9884,-80.5307
+NC,083,RAH,Anson,NC083,Anson,37007,E,cc,34.9738,-80.1027
+NC,084,RAH,Richmond,NC084,Richmond,37153,E,cc,35.0059,-79.7480
+NC,085,RAH,Scotland,NC085,Scotland,37165,E,cc,34.8411,-79.4805
+NC,086,RAH,Hoke,NC086,Hoke,37093,E,cc,35.0174,-79.2371
+NC,087,ILM,Robeson,NC087,Robeson,37155,E,se,34.6401,-79.1035
+NC,088,RAH,Cumberland,NC088,Cumberland,37051,E,cc,35.0485,-78.8273
+NC,089,RAH,Sampson,NC089,Sampson,37163,E,cc,34.9915,-78.3712
+NC,090,MHX,Duplin,NC090,Duplin,37061,E,ee,34.9365,-77.9333
+NC,091,MHX,Lenoir,NC091,Lenoir,37107,E,ee,35.2393,-77.6416
+NC,092,MHX,Jones,NC092,Jones,37103,E,ee,35.0222,-77.3555
+NC,094,MHX,Pamlico,NC094,Pamlico,37137,E,ee,35.1407,-76.7487
+NC,096,ILM,Bladen,NC096,Bladen,37017,E,se,34.6143,-78.5632
+NC,099,ILM,Columbus,NC099,Columbus,37047,E,se,34.2655,-78.6550
+NC,102,AKQ,Eastern Currituck,NC102,Currituck,37053,E,ne,36.4001,-75.8440
+NC,105,ILM,Inland Pender,NC105,Pender,37141,E,se,34.5328,-77.9208
+NC,106,ILM,Coastal Pender,NC106,Pender,37141,E,se,34.3814,-77.6465
+NC,107,ILM,Inland New Hanover,NC107,New Hanover,37129,E,se,34.2817,-77.8983
+NC,108,ILM,Coastal New Hanover,NC108,New Hanover,37129,E,aw,34.1372,-77.8612
+NC,108,ILM,Coastal New Hanover,NC108,New Hanover,37129,E,se,34.1372,-77.8612
+NC,109,ILM,Inland Brunswick,NC109,Brunswick,37019,E,se,34.1066,-78.2492
+NC,110,ILM,Coastal Brunswick,NC110,Brunswick,37019,E,se,33.9367,-78.2222
+NC,110,ILM,Coastal Brunswick,NC110,Brunswick,37019,E,se,33.9529,-77.9712
+NC,193,MHX,Northern Craven,NC193,Craven,37049,E,ee,35.2508,-77.1891
+NC,194,MHX,Southern Craven ,NC194,Craven,37049,E,ee,34.9541,-76.9698
+NC,195,MHX,West Carteret ,NC195,Carteret,37031,E,ee,34.7672,-76.9068
+NC,196,MHX,East Carteret ,NC196,Carteret,37031,E,ee,34.8732,-76.5121
+NC,198,MHX,Inland Onslow,NC198,Onslow,37133,E,ee,34.7775,-77.4736
+NC,199,MHX,Coastal Onslow ,NC199,Onslow,37133,E,ee,34.6161,-77.3148
+NC,203,MHX,Northern Outer Banks ,NC203,Dare,37055,E,ee,35.9623,-75.6658
+NC,204,MHX,Ocracoke Island ,NC204,Hyde,37095,E,ee,35.1283,-75.9146
+NC,205,MHX,Hatteras Island ,NC205,Dare,37055,E,ee,35.3945,-75.5413
+NC,501,GSP,Caldwell Mountains,NC501,Caldwell,37027,E,ww,36.0301,-81.6408
+NC,502,GSP,Greater Caldwell,NC502,Caldwell,37027,E,ww,35.9012,-81.4832
+NC,503,GSP,Burke Mountains,NC503,Burke,37023,E,ww,35.8886,-81.8620
+NC,504,GSP,Greater Burke,NC504,Burke,37023,E,ww,35.7148,-81.6652
+NC,505,GSP,McDowell Mountains,NC505,McDowell,37111,E,ww,35.7082,-82.1054
+NC,506,GSP,Eastern McDowell,NC506,McDowell,37111,E,ww,35.6327,-81.9458
+NC,507,GSP,Rutherford Mountains,NC507,Rutherford,37161,E,ww,35.4650,-82.1930
+NC,508,GSP,Greater Rutherford,NC508,Rutherford,37161,E,ww,35.3952,-81.8892
+NC,509,GSP,Polk Mountains,NC509,Polk,37149,E,ww,35.2856,-82.2930
+NC,510,GSP,Eastern Polk,NC510,Polk,37149,E,ww,35.2776,-82.1356
+ND,001,BIS,Divide,ND001,Divide,38023,C,nw,48.8149,-103.4873
+ND,002,BIS,Burke,ND002,Burke,38013,C,nw,48.7909,-102.5183
+ND,003,BIS,Renville,ND003,Renville,38075,C,nc,48.7192,-101.6581
+ND,004,BIS,Bottineau,ND004,Bottineau,38009,C,nc,48.7921,-100.8335
+ND,005,BIS,Rolette,ND005,Rolette,38079,C,nc,48.7724,-99.8411
+ND,006,FGF,Towner,ND006,Towner,38095,C,ne,48.6856,-99.2459
+ND,007,FGF,Cavalier,ND007,Cavalier,38019,C,ne,48.7722,-98.4650
+ND,008,FGF,Pembina,ND008,Pembina,38067,C,ne,48.7672,-97.5517
+ND,009,BIS,Williams,ND009,Williams,38105,C,nw,48.3437,-103.4802
+ND,010,BIS,Mountrail,ND010,Mountrail,38061,C,nw,48.2013,-102.3556
+ND,011,BIS,Ward,ND011,Ward,38101,C,nc,48.2218,-101.5418
+ND,012,BIS,McHenry,ND012,McHenry,38049,C,nc,48.2346,-100.6363
+ND,013,BIS,Pierce,ND013,Pierce,38069,C,nc,48.2496,-99.9718
+ND,014,FGF,Benson,ND014,Benson,38005,C,ne,48.0694,-99.3659
+ND,015,FGF,Ramsey,ND015,Ramsey,38071,C,ne,48.2690,-98.7201
+ND,016,FGF,Eastern Walsh County,ND016,Walsh,38099,C,ne,48.3683,-97.4998
+ND,017,BIS,McKenzie,ND017,McKenzie,38053,CM,nw,47.7402,-103.3953
+ND,018,BIS,Dunn,ND018,Dunn,38025,CM,sw,47.3568,-102.6182
+ND,019,BIS,Mercer,ND019,Mercer,38057,C,sc,47.3092,-101.8315
+ND,020,BIS,Oliver,ND020,Oliver,38065,C,sc,47.1153,-101.3403
+ND,021,BIS,McLean,ND021,McLean,38055,C,sc,47.6070,-101.3218
+ND,022,BIS,Sheridan,ND022,Sheridan,38083,C,sc,47.5754,-100.3456
+ND,023,BIS,Wells,ND023,Wells,38103,C,sc,47.5876,-99.6610
+ND,024,FGF,Eddy,ND024,Eddy,38027,C,ne,47.7176,-98.9016
+ND,025,BIS,Foster,ND025,Foster,38031,C,se,47.4571,-98.8831
+ND,026,FGF,Nelson,ND026,Nelson,38063,C,ne,47.9216,-98.1921
+ND,027,FGF,Grand Forks,ND027,Grand Forks,38035,C,ne,47.9219,-97.4570
+ND,028,FGF,Griggs,ND028,Griggs,38039,C,se,47.4572,-98.2371
+ND,029,FGF,Steele,ND029,Steele,38091,C,se,47.4562,-97.7247
+ND,030,FGF,Traill,ND030,Traill,38097,C,se,47.4542,-97.1615
+ND,031,BIS,Golden Valley,ND031,Golden Valley,38033,M,sw,46.9402,-103.8467
+ND,032,BIS,Billings,ND032,Billings,38007,M,sw,47.0235,-103.3764
+ND,033,BIS,Stark,ND033,Stark,38089,M,sw,46.8107,-102.6550
+ND,034,BIS,Morton,ND034,Morton,38059,C,sc,46.7161,-101.2811
+ND,035,BIS,Burleigh,ND035,Burleigh,38015,C,sc,46.9774,-100.4688
+ND,036,BIS,Kidder,ND036,Kidder,38043,C,sc,46.9802,-99.7801
+ND,037,BIS,Stutsman,ND037,Stutsman,38093,C,se,46.9792,-98.9588
+ND,038,FGF,Barnes,ND038,Barnes,38003,C,se,46.9361,-98.0716
+ND,039,FGF,Cass,ND039,Cass,38017,C,se,46.9330,-97.2481
+ND,040,BIS,Slope,ND040,Slope,38087,M,sw,46.4472,-103.4599
+ND,041,BIS,Hettinger,ND041,Hettinger,38041,M,sw,46.4326,-102.4604
+ND,042,BIS,Grant,ND042,Grant,38037,M,sc,46.3583,-101.6397
+ND,043,BIS,Bowman,ND043,Bowman,38011,M,sw,46.1126,-103.5207
+ND,044,BIS,Adams,ND044,Adams,38001,M,sw,46.0969,-102.5285
+ND,045,BIS,Sioux,ND045,Sioux,38085,CM,sc,46.1126,-101.0405
+ND,046,BIS,Emmons,ND046,Emmons,38029,C,sc,46.2851,-100.2388
+ND,047,BIS,Logan,ND047,Logan,38047,C,se,46.4574,-99.4775
+ND,048,BIS,La Moure,ND048,LaMoure,38045,C,se,46.4569,-98.5355
+ND,049,FGF,Ransom,ND049,Ransom,38073,C,se,46.4561,-97.6575
+ND,050,BIS,McIntosh,ND050,McIntosh,38051,C,se,46.1119,-99.4412
+ND,051,BIS,Dickey,ND051,Dickey,38021,C,se,46.1102,-98.5046
+ND,052,FGF,Sargent,ND052,Sargent,38081,C,se,46.1078,-97.6306
+ND,053,FGF,Richland,ND053,Richland,38077,C,se,46.2646,-96.9483
+ND,054,FGF,Western Walsh County,ND054,Walsh,38099,C,ne,48.3714,-98.0840
+NE,002,CYS,Dawes,NE002,Dawes,31045,M,pa,42.7197,-103.1354
+NE,003,CYS,Box Butte,NE003,Box Butte,31013,M,pa,42.2198,-103.0857
+NE,004,LBF,Sheridan,NE004,Sheridan,31161,M,pa,42.5048,-102.4090
+NE,005,LBF,Eastern Cherry,NE005,Cherry,31031,MC,nc,42.5577,-100.5076
+NE,006,LBF,Keya Paha,NE006,Keya Paha,31103,C,nc,42.8789,-99.7124
+NE,007,LBF,Boyd,NE007,Boyd,31015,C,nc,42.8997,-98.7665
+NE,008,LBF,Brown,NE008,Brown,31017,C,nc,42.4300,-99.9295
+NE,009,LBF,Rock,NE009,Rock,31149,C,nc,42.4213,-99.4500
+NE,010,LBF,Holt,NE010,Holt,31089,C,nc,42.4557,-98.7838
+NE,011,OAX,Knox,NE011,Knox,31107,C,ne,42.6369,-97.8919
+NE,012,OAX,Cedar,NE012,Cedar,31027,C,ne,42.5993,-97.2523
+NE,013,FSD,Dixon,NE013,Dixon,31051,C,ne,42.4932,-96.8677
+NE,014,FSD,Dakota,NE014,Dakota,31043,C,ne,42.3911,-96.5646
+NE,015,OAX,Thurston,NE015,Thurston,31173,C,ne,42.1582,-96.5441
+NE,016,OAX,Antelope,NE016,Antelope,31003,C,ne,42.1769,-98.0667
+NE,017,OAX,Pierce,NE017,Pierce,31139,C,ne,42.2644,-97.6013
+NE,018,OAX,Wayne,NE018,Wayne,31179,C,ne,42.2092,-97.1193
+NE,019,CYS,Scotts Bluff,NE019,Scotts Bluff,31157,M,pa,41.8506,-103.7080
+NE,020,CYS,Banner,NE020,Banner,31007,M,pa,41.5460,-103.7104
+NE,021,CYS,Morrill,NE021,Morrill,31123,M,pa,41.7160,-103.0105
+NE,022,LBF,Garden,NE022,Garden,31069,M,pa,41.6194,-102.3354
+NE,023,LBF,Grant,NE023,Grant,31075,M,wc,41.9150,-101.7405
+NE,024,LBF,Hooker,NE024,Hooker,31091,M,wc,41.9159,-101.1353
+NE,025,LBF,Thomas,NE025,Thomas,31171,C,nc,41.9135,-100.5558
+NE,026,LBF,Blaine,NE026,Blaine,31009,C,nc,41.9128,-99.9769
+NE,027,LBF,Loup,NE027,Loup,31115,C,nc,41.9138,-99.4544
+NE,028,LBF,Garfield,NE028,Garfield,31071,C,nc,41.9143,-98.9914
+NE,029,LBF,Wheeler,NE029,Wheeler,31183,C,nc,41.9148,-98.5282
+NE,030,OAX,Boone,NE030,Boone,31011,C,ne,41.7068,-98.0673
+NE,031,OAX,Madison,NE031,Madison,31119,C,ne,41.9166,-97.6008
+NE,032,OAX,Stanton,NE032,Stanton,31167,C,ne,41.9168,-97.1939
+NE,033,OAX,Cuming,NE033,Cuming,31039,C,ne,41.9164,-96.7874
+NE,034,OAX,Burt,NE034,Burt,31021,C,ec,41.8515,-96.3287
+NE,035,LBF,Arthur,NE035,Arthur,31005,M,wc,41.5689,-101.6959
+NE,036,LBF,McPherson,NE036,McPherson,31117,C,wc,41.5681,-101.0605
+NE,037,LBF,Logan,NE037,Logan,31113,C,wc,41.5665,-100.4828
+NE,038,LBF,Custer,NE038,Custer,31041,C,cc,41.3943,-99.7261
+NE,039,GID,Valley,NE039,Valley,31175,C,cc,41.5673,-98.9819
+NE,040,GID,Greeley,NE040,Greeley,31077,C,cc,41.5675,-98.5212
+NE,041,GID,Nance,NE041,Nance,31125,C,cc,41.3973,-97.9923
+NE,042,OAX,Platte,NE042,Platte,31141,C,ne,41.5701,-97.5198
+NE,043,OAX,Colfax,NE043,Colfax,31037,C,ne,41.5731,-97.0867
+NE,044,OAX,Dodge,NE044,Dodge,31053,C,ec,41.5777,-96.6542
+NE,045,OAX,Washington,NE045,Washington,31177,C,ec,41.5311,-96.2218
+NE,046,GID,Sherman,NE046,Sherman,31163,C,cc,41.2205,-98.9763
+NE,047,GID,Howard,NE047,Howard,31093,C,cc,41.2200,-98.5171
+NE,048,GID,Merrick,NE048,Merrick,31121,C,cc,41.1698,-98.0376
+NE,049,GID,Polk,NE049,Polk,31143,C,ec,41.1869,-97.5684
+NE,050,OAX,Butler,NE050,Butler,31023,C,ec,41.2242,-97.1306
+NE,051,OAX,Saunders,NE051,Saunders,31155,C,ec,41.2261,-96.6373
+NE,052,OAX,Douglas,NE052,Douglas,31055,C,ec,41.2953,-96.1544
+NE,053,OAX,Sarpy,NE053,Sarpy,31153,C,ec,41.1131,-96.1119
+NE,054,CYS,Kimball,NE054,Kimball,31105,M,pa,41.1977,-103.7150
+NE,055,CYS,Cheyenne,NE055,Cheyenne,31033,M,pa,41.2198,-102.9950
+NE,056,LBF,Deuel,NE056,Deuel,31049,M,pa,41.1116,-102.3339
+NE,057,LBF,Keith,NE057,Keith,31101,M,sw,41.1988,-101.6613
+NE,058,LBF,Perkins,NE058,Perkins,31135,M,sw,40.8509,-101.6496
+NE,059,LBF,Lincoln,NE059,Lincoln,31111,C,sw,41.0478,-100.7452
+NE,060,GID,Dawson,NE060,Dawson,31047,C,sc,40.8699,-99.8195
+NE,061,GID,Buffalo,NE061,Buffalo,31019,C,sc,40.8551,-99.0750
+NE,062,GID,Hall,NE062,Hall,31079,C,sc,40.8725,-98.5022
+NE,063,GID,Hamilton,NE063,Hamilton,31081,C,sc,40.8735,-98.0233
+NE,064,GID,York,NE064,York,31185,C,ec,40.8728,-97.5971
+NE,065,OAX,Seward,NE065,Seward,31159,C,se,40.8723,-97.1395
+NE,066,OAX,Lancaster,NE066,Lancaster,31109,C,se,40.7841,-96.6878
+NE,067,OAX,Cass,NE067,Cass,31025,C,se,40.9098,-96.1408
+NE,068,OAX,Otoe,NE068,Otoe,31131,C,se,40.6485,-96.1348
+NE,069,LBF,Chase,NE069,Chase,31029,M,sw,40.5242,-101.6979
+NE,070,LBF,Hayes,NE070,Hayes,31085,C,sw,40.5248,-101.0618
+NE,071,LBF,Frontier,NE071,Frontier,31063,C,sw,40.5301,-100.3942
+NE,072,GID,Gosper,NE072,Gosper,31073,C,sc,40.5148,-99.8307
+NE,073,GID,Phelps,NE073,Phelps,31137,C,sc,40.5111,-99.4145
+NE,074,GID,Kearney,NE074,Kearney,31099,C,sc,40.5067,-98.9480
+NE,075,GID,Adams,NE075,Adams,31001,C,sc,40.5245,-98.5012
+NE,076,GID,Clay,NE076,Clay,31035,C,sc,40.5244,-98.0512
+NE,077,GID,Fillmore,NE077,Fillmore,31059,C,sc,40.5247,-97.5965
+NE,078,OAX,Saline,NE078,Saline,31151,C,se,40.5240,-97.1408
+NE,079,GLD,Dundy,NE079,Dundy,31057,M,sw,40.1762,-101.6879
+NE,080,GLD,Hitchcock,NE080,Hitchcock,31087,C,sw,40.1763,-101.0422
+NE,081,GLD,Red Willow,NE081,Red Willow,31145,C,sw,40.1758,-100.4768
+NE,082,GID,Furnas,NE082,Furnas,31065,C,sc,40.1764,-99.9123
+NE,083,GID,Harlan,NE083,Harlan,31083,C,sc,40.1765,-99.4046
+NE,084,GID,Franklin,NE084,Franklin,31061,C,sc,40.1763,-98.9527
+NE,085,GID,Webster,NE085,Webster,31181,C,sc,40.1764,-98.4999
+NE,086,GID,Nuckolls,NE086,Nuckolls,31129,C,sc,40.1764,-98.0472
+NE,087,GID,Thayer,NE087,Thayer,31169,C,sc,40.1763,-97.5948
+NE,088,OAX,Jefferson,NE088,Jefferson,31095,C,se,40.1757,-97.1426
+NE,089,OAX,Gage,NE089,Gage,31067,C,se,40.2619,-96.6893
+NE,090,OAX,Johnson,NE090,Johnson,31097,C,se,40.3926,-96.2649
+NE,091,OAX,Nemaha,NE091,Nemaha,31127,C,se,40.3881,-95.8486
+NE,092,OAX,Pawnee,NE092,Pawnee,31133,C,se,40.1314,-96.2370
+NE,093,OAX,Richardson,NE093,Richardson,31147,C,se,40.1249,-95.7167
+NE,094,LBF,Western Cherry,NE094,Cherry,31031,MC,nc,42.5381,-101.4432
+NE,095,CYS,North Sioux,NE095,Sioux,31165,M,pa,42.7142,-103.7719
+NE,096,CYS,South Sioux,NE096,Sioux,31165,M,pa,42.2153,-103.7432
+NH,001,GYX,Northern Coos,NH001,Coos,33007,E,nn,44.9761,-71.2832
+NH,002,GYX,Southern Coos,NH002,Coos,33007,E,nn,44.4715,-71.3228
+NH,003,GYX,Northern Grafton,NH003,Grafton,33009,E,nn,44.0964,-71.7514
+NH,004,GYX,Northern Carroll,NH004,Carroll,33003,E,nn,44.0780,-71.1778
+NH,005,GYX,Southern Grafton,NH005,Grafton,33009,E,nn,43.7209,-71.9182
+NH,006,GYX,Southern Carroll,NH006,Carroll,33003,E,nn,43.7506,-71.2178
+NH,007,GYX,Sullivan,NH007,Sullivan,33019,E,cc,43.3612,-72.2223
+NH,008,GYX,Merrimack,NH008,Merrimack,33013,E,cc,43.2977,-71.6802
+NH,009,GYX,Belknap,NH009,Belknap,33001,E,cc,43.5188,-71.4233
+NH,010,GYX,Strafford,NH010,Strafford,33017,E,cc,43.2975,-71.0293
+NH,011,BOX,Cheshire,NH011,Cheshire,33005,E,ss,42.9193,-72.2512
+NH,012,BOX,Eastern Hillsborough,NH012,Hillsborough,33011,E,ss,42.8448,-71.4574
+NH,013,GYX,Interior Rockingham,NH013,Rockingham,33015,E,ss,42.9853,-71.1814
+NH,014,GYX,Coastal Rockingham,NH014,Rockingham,33015,E,ss,43.0005,-70.8291
+NH,015,BOX,Western And Central Hillsborough,NH015,Hillsborough,33011,E,ss,42.9363,-71.7929
+NJ,001,PHI,Sussex,NJ001,Sussex,34037,E,nw,41.1394,-74.6902
+NJ,002,OKX,Western Passaic,NJ002,Passaic,34031,E,ne,41.0934,-74.3518
+NJ,004,OKX,Eastern Passaic,NJ004,Passaic,34031,E,ne,40.9176,-74.1999
+NJ,006,OKX,Hudson,NJ006,Hudson,34017,E,ne,40.7410,-74.0775
+NJ,007,PHI,Warren,NJ007,Warren,34041,E,nw,40.8574,-74.9970
+NJ,008,PHI,Morris,NJ008,Morris,34027,E,nn,40.8621,-74.5445
+NJ,009,PHI,Hunterdon,NJ009,Hunterdon,34019,E,nw,40.5672,-74.9123
+NJ,010,PHI,Somerset,NJ010,Somerset,34035,E,nn,40.5637,-74.6163
+NJ,012,PHI,Middlesex,NJ012,Middlesex,34023,E,nn,40.4394,-74.4113
+NJ,013,PHI,Western Monmouth,NJ013,Monmouth,34025,E,cc,40.2596,-74.2614
+NJ,014,PHI,Eastern Monmouth,NJ014,Monmouth,34025,E,cc,40.2557,-74.0295
+NJ,015,PHI,Mercer,NJ015,Mercer,34021,E,cc,40.2833,-74.7015
+NJ,016,PHI,Salem,NJ016,Salem,34033,E,ss,39.5872,-75.3459
+NJ,017,PHI,Gloucester,NJ017,Gloucester,34015,E,ss,39.7173,-75.1419
+NJ,018,PHI,Camden,NJ018,Camden,34007,E,ss,39.8038,-74.9599
+NJ,019,PHI,Northwestern Burlington,NJ019,Burlington,34005,E,ss,39.9850,-74.7572
+NJ,020,PHI,Ocean,NJ020,Ocean,34029,E,ss,39.9191,-74.3099
+NJ,021,PHI,Cumberland,NJ021,Cumberland,34011,E,ss,39.3735,-75.1099
+NJ,022,PHI,Atlantic,NJ022,Atlantic,34001,E,ss,39.4906,-74.6976
+NJ,023,PHI,Cape May,NJ023,Cape May,34009,E,ss,39.1768,-74.8227
+NJ,024,PHI,Atlantic Coastal Cape May,NJ024,Cape May,34009,E,ss,39.0952,-74.7718
+NJ,025,PHI,Coastal Atlantic,NJ025,Atlantic,34001,E,ss,39.3930,-74.4448
+NJ,026,PHI,Coastal Ocean,NJ026,Ocean,34029,E,ss,39.7890,-74.1647
+NJ,027,PHI,Southeastern Burlington,NJ027,Burlington,34005,E,ss,39.7478,-74.5605
+NJ,103,OKX,Western Bergen,NJ103,Bergen,34003,E,ne,41.0243,-74.1547
+NJ,104,OKX,Eastern Bergen,NJ104,Bergen,34003,E,ne,40.9158,-74.0224
+NJ,105,OKX,Western Essex,NJ105,Essex,34013,E,ne,40.8034,-74.2767
+NJ,106,OKX,Eastern Essex,NJ106,Essex,34013,E,ne,40.7475,-74.1724
+NJ,107,OKX,Western Union,NJ107,Eastern Union,34039,E,ne,40.6666,-74.3579
+NJ,108,OKX,Eastern Union,NJ108,Eastern Union,34039,E,ne,40.6496,-74.2352
+NM,027,MAF,Guadalupe Mountains of Eddy County,NM027,Eddy,35015,M,se,32.2390,-104.7304
+NM,028,MAF,Eddy County Plains,NM028,Eddy,35015,M,se,32.4993,-104.2534
+NM,029,MAF,Northern Lea County,NM029,Lea,35025,M,se,33.2820,-103.4057
+NM,033,MAF,Central Lea County,NM033,Lea,35025,M,se,32.7504,-103.4363
+NM,034,MAF,Southern Lea County,NM034,Lea,35025,M,se,32.2502,-103.3937
+NM,201,ABQ,Northwest Plateau,NM201,McKinley,35031,M,nw,-108.4064,36.4270
+NM,201,ABQ,Northwest Plateau,NM201,San Juan,35045,M,nw,-108.4064,36.4270
+NM,202,ABQ,Chuska Mountains,NM202,McKinley,35031,M,nw,-108.9296,36.0409
+NM,202,ABQ,Chuska Mountains,NM202,San Juan,35045,M,nw,-108.9296,36.0409
+NM,203,ABQ,Far Northwest Highlands,NM203,San Juan,35045,M,nc,-107.1028,36.6585
+NM,203,ABQ,Far Northwest Highlands,NM203,Rio Arriba,35039,M,nc,-107.1028,36.6585
+NM,203,ABQ,Far Northwest Highlands,NM203,Sandoval,35043,M,nc,-107.1028,36.6585
+NM,204,ABQ,Northwest Highlands,NM204,San Juan,35045,M,nc,-107.5052,36.0067
+NM,204,ABQ,Northwest Highlands,NM204,Rio Arriba,35039,M,nc,-107.5052,36.0067
+NM,204,ABQ,Northwest Highlands,NM204,Sandoval,35043,M,nc,-107.5052,36.0067
+NM,204,ABQ,Northwest Highlands,NM204,McKinley,35031,M,nc,-107.5052,36.0067
+NM,205,ABQ,West Central Plateau,NM205,McKinley,35031,M,wc,-108.8170,35.0288
+NM,205,ABQ,West Central Plateau,NM205,Cibola,35006,M,wc,-108.8170,35.0288
+NM,205,ABQ,West Central Plateau,NM205,Catron,35003,M,wc,-108.8170,35.0288
+NM,206,ABQ,West Central Mountains,NM206,McKinley,35031,M,nc,-108.0574,35.0709
+NM,206,ABQ,West Central Mountains,NM206,Cibola,35006,M,nc,-108.0574,35.0709
+NM,206,ABQ,West Central Mountains,NM206,Catron,35003,M,nc,-108.0574,35.0709
+NM,206,ABQ,West Central Mountains,NM206,Sandoval,35043,M,nc,-108.0574,35.0709
+NM,206,ABQ,West Central Mountains,NM206,Socorro,35053,M,nc,-108.0574,35.0709
+NM,207,ABQ,West Central Highlands,NM207,McKinley,35031,M,nc,-107.3538,34.9677
+NM,207,ABQ,West Central Highlands,NM207,Cibola,35006,M,nc,-107.3538,34.9677
+NM,207,ABQ,West Central Highlands,NM207,Socorro,35053,M,nc,-107.3538,34.9677
+NM,207,ABQ,West Central Highlands,NM207,Valencia,35061,M,nc,-107.3538,34.9677
+NM,207,ABQ,West Central Highlands,NM207,Bernalillo,35001,M,nc,-107.3538,34.9677
+NM,207,ABQ,West Central Highlands,NM207,Sandoval,35043,M,nc,-107.3538,34.9677
+NM,208,ABQ,Southwest Mountains,NM208,Catron,35003,M,cc,-108.3170,33.8571
+NM,208,ABQ,Southwest Mountains,NM208,Socorro,35053,M,cc,-108.3170,33.8571
+NM,209,ABQ,San Francisco River Valley,NM209,Catron,35003,M,wc,-108.8601,33.5090
+NM,210,ABQ,Tusas Mountains Including Chama,NM210,Taos,35055,M,nc,-106.2539,36.7058
+NM,210,ABQ,Tusas Mountains Including Chama,NM210,Rio Arriba,35039,M,nc,-106.2539,36.7058
+NM,211,ABQ,Jemez Mountains,NM211,Rio Arriba,35039,M,nc,-106.5974,35.9021
+NM,211,ABQ,Jemez Mountains,NM211,Sandoval,35043,M,nc,-106.5974,35.9021
+NM,211,ABQ,Jemez Mountains,NM211,Los Alamos,35028,M,nc,-106.5974,35.9021
+NM,212,ABQ,Glorieta Mesa Including Glorieta Pass,NM212,San Miguel,35047,M,nc,-105.6940,35.5160
+NM,212,ABQ,Glorieta Mesa Including Glorieta Pass,NM212,Santa Fe,35049,M,nc,-105.6940,35.5160
+NM,213,ABQ,Northern Sangre de Cristo Mountains,NM213,Taos,35055,M,ne,-105.3905,36.7161
+NM,213,ABQ,Northern Sangre de Cristo Mountains,NM213,Colfax,35007,M,ne,-105.3905,36.7161
+NM,214,ABQ,Southern Sangre de Cristo Mountains,NM214,Taos,35055,M,nc,-105.5998,35.9865
+NM,214,ABQ,Southern Sangre de Cristo Mountains,NM214,Colfax,35007,M,nc,-105.5998,35.9865
+NM,214,ABQ,Southern Sangre de Cristo Mountains,NM214,Mora,35033,M,nc,-105.5998,35.9865
+NM,214,ABQ,Southern Sangre de Cristo Mountains,NM214,San Miguel,35047,M,nc,-105.5998,35.9865
+NM,214,ABQ,Southern Sangre de Cristo Mountains,NM214,Santa Fe,35049,M,nc,-105.5998,35.9865
+NM,214,ABQ,Southern Sangre de Cristo Mountains,NM214,Rio Arriba,35039,M,nc,-105.5998,35.9865
+NM,215,ABQ,East Slopes Sangre de Cristo Mountains,NM215,Colfax,35007,M,ne,-105.1554,36.2296
+NM,215,ABQ,East Slopes Sangre de Cristo Mountains,NM215,Mora,35033,M,ne,-105.1554,36.2296
+NM,215,ABQ,East Slopes Sangre de Cristo Mountains,NM215,San Miguel,35047,M,ne,-105.1554,36.2296
+NM,216,ABQ,Upper Rio Grande Valley,NM216,Taos,35055,M,nc,-105.7524,36.6232
+NM,216,ABQ,Upper Rio Grande Valley,NM216,Rio Arriba,35039,M,nc,-105.7524,36.6232
+NM,217,ABQ,Espanola Valley,NM217,Taos,35055,M,nc,-106.0931,36.1335
+NM,217,ABQ,Espanola Valley,NM217,Rio Arriba,35039,M,nc,-106.0931,36.1335
+NM,217,ABQ,Espanola Valley,NM217,Santa Fe,35049,M,nc,-106.0931,36.1335
+NM,218,ABQ,Santa Fe Metro Area,NM218,Santa Fe,35049,M,nc,-106.1074,35.5304
+NM,218,ABQ,Santa Fe Metro Area,NM218,Bernalillo,35001,M,nc,-106.1074,35.5304
+NM,218,ABQ,Santa Fe Metro Area,NM218,Sandoval,35043,M,nc,-106.1074,35.5304
+NM,219,ABQ,Middle Rio Grande Valley/Albuquerque Metro Area,NM219,Bernalillo,35001,M,nc,-106.7444,35.0023
+NM,219,ABQ,Middle Rio Grande Valley/Albuquerque Metro Area,NM219,Sandoval,35043,M,nc,-106.7444,35.0023
+NM,219,ABQ,Middle Rio Grande Valley/Albuquerque Metro Area,NM219,Valencia,35061,M,nc,-106.7444,35.0023
+NM,219,ABQ,Middle Rio Grande Valley/Albuquerque Metro Area,NM219,Cibola,35006,M,nc,-106.7444,35.0023
+NM,219,ABQ,Middle Rio Grande Valley/Albuquerque Metro Area,NM219,Torrance,35057,M,nc,-106.7444,35.0023
+NM,220,ABQ,Lower Rio Grande Valley,NM220,Socorro,35053,M,cc,-106.8008,33.9223
+NM,221,ABQ,Sandia/Manzano Mountains Including Edgewood,NM221,Santa Fe,35049,M,nc,-106.3043,34.9556
+NM,221,ABQ,Sandia/Manzano Mountains Including Edgewood,NM221,Torrance,35057,M,nc,-106.3043,34.9556
+NM,221,ABQ,Sandia/Manzano Mountains Including Edgewood,NM221,Bernalillo,35001,M,nc,-106.3043,34.9556
+NM,221,ABQ,Sandia/Manzano Mountains Including Edgewood,NM221,Valencia,35061,M,nc,-106.3043,34.9556
+NM,221,ABQ,Sandia/Manzano Mountains Including Edgewood,NM221,Sandoval,35043,M,nc,-106.3043,34.9556
+NM,222,ABQ,Estancia Valley,NM222,Santa Fe,35049,M,nc,-105.9856,34.7890
+NM,222,ABQ,Estancia Valley,NM222,Torrance,35057,M,nc,-105.9856,34.7890
+NM,222,ABQ,Estancia Valley,NM222,Bernalillo,35001,M,nc,-105.9856,34.7890
+NM,223,ABQ,Central Highlands,NM223,Santa Fe,35049,M,nc,-105.5657,34.8354
+NM,223,ABQ,Central Highlands,NM223,Torrance,35057,M,nc,-105.5657,34.8354
+NM,223,ABQ,Central Highlands,NM223,San Miguel,35047,M,nc,-105.5657,34.8354
+NM,224,ABQ,South Central Highlands,NM224,Torrance,35057,M,cc,-106.1646,34.1329
+NM,224,ABQ,South Central Highlands,NM224,Valencia,35061,M,cc,-106.1646,34.1329
+NM,224,ABQ,South Central Highlands,NM224,Socorro,35053,M,cc,-106.1646,34.1329
+NM,224,ABQ,South Central Highlands,NM224,Lincoln,35027,M,cc,-106.1646,34.1329
+NM,225,ABQ,Upper Tularosa Valley,NM225,Socorro,35053,M,cc,-106.0398,33.6587
+NM,225,ABQ,Upper Tularosa Valley,NM225,Lincoln,35027,M,cc,-106.0398,33.6587
+NM,226,ABQ,South Central Mountains,NM226,Lincoln,35027,M,cc,-105.5869,33.5868
+NM,227,ABQ,Johnson and Bartlett Mesas Including Raton Pass,NM227,Colfax,35007,M,ne,-104.3106,36.7769
+NM,227,ABQ,Johnson and Bartlett Mesas Including Raton Pass,NM227,Union,35059,M,ne,-104.3106,36.7769
+NM,228,ABQ,Far Northeast Highlands,NM228,Colfax,35007,M,ne,-104.5899,36.2560
+NM,228,ABQ,Far Northeast Highlands,NM228,Mora,35033,M,ne,-104.5899,36.2560
+NM,229,ABQ,Northeast Highlands,NM229,San Miguel,35047,M,ne,-105.0208,35.5317
+NM,230,ABQ,Union County,NM230,Union,35059,M,ne,-103.4227,36.4519
+NM,231,ABQ,Harding County,NM231,Harding,35021,M,ne,-103.8199,35.8580
+NM,232,ABQ,Eastern San Miguel County,NM232,San Miguel,35047,M,ne,-104.2579,35.4457
+NM,233,ABQ,Guadalupe County,NM233,Guadalupe,35019,M,ec,-104.7907,34.8633
+NM,234,ABQ,Quay County,NM234,Quay,35037,M,ec,-103.5497,35.1043
+NM,235,ABQ,Curry County,NM235,Curry,35009,M,ec,-103.3468,34.5741
+NM,236,ABQ,Roosevelt County,NM236,Roosevelt,35041,M,ec,-103.4802,34.0213
+NM,237,ABQ,De Baca County,NM237,De Baca,35011,M,ec,-104.4120,34.3424
+NM,238,ABQ,Chaves County Plains,NM238,Chaves,35005,M,se,-104.3114,33.4980
+NM,239,ABQ,Eastern Lincoln County,NM239,Lincoln,35027,M,cc,-105.1628,33.7904
+NM,240,ABQ,Southwest Chaves County,NM240,Chaves,35005,M,se,-105.0963,32.8182
+NM,241,ABQ,San Agustin Plains and Adjacent Lowlands,NM241,Catron,35003,M,cc,-107.5332,33.9792
+NM,241,ABQ,San Agustin Plains and Adjacent Lowlands,NM241,Socorro,35053,M,cc,-107.5332,33.9792
+NM,401,EPZ,Upper Gila River Valley,NM401,Grant,35017,M,sw,33.0652,-109.0457
+NM,401,EPZ,Upper Gila River Valley,NM401,Hidalgo,35023,M,sw,33.0652,-109.0457
+NM,402,EPZ,Southern Gila Highlands/Black Range,NM402,Grant,35017,M,sw,32.5961,-108.0435
+NM,402,EPZ,Southern Gila Highlands/Black Range,NM402,Sierra,35051,M,sc,32.7390,-108.3822
+NM,403,EPZ,Southern Gila Foothills/Mimbres Valley,NM403,Grant,35017,M,sw,32.5961,-108.0435
+NM,403,EPZ,Southern Gila Foothills/Mimbres Valley,NM403,Luna,35029,M,sw,32.5961,-108.0435
+NM,404,EPZ,Southwest Desert/Lower Gila River Valley,NM404,Grant,35017,M,sw,32.9840,-109.0449
+NM,404,EPZ,Southwest Desert/Lower Gila River Valley,NM404,Hidalgo,35023,M,sw,32.9840,-109.0449
+NM,405,EPZ,Lowlands of the Bootheel,NM405,Grant,35017,M,sw,31.7623,-108.5699
+NM,405,EPZ,Lowlands of the Bootheel,NM405,Hidalgo,35023,M,sw,31.7623,-108.5699
+NM,406,EPZ,Uplands of the Bootheel,NM406,Hidalgo,35023,M,sw,31.5516,-108.8399
+NM,407,EPZ,Southwest Desert/Mimbres Basin,NM407,Grant,35017,M,sw,32.1585,-107.7431
+NM,407,EPZ,Southwest Desert/Mimbres Basin,NM407,Luna,35029,M,sw,32.1585,-107.7431
+NM,408,EPZ,Eastern Black Range Foothills,NM408,Grant,35017,M,sw,33.0357,-107.5193
+NM,408,EPZ,Eastern Black Range Foothills,NM408,Luna,35029,M,sw,33.0357,-107.5193
+NM,408,EPZ,Eastern Black Range Foothills,NM408,Sierra,35051,M,sc,33.0357,-107.5193
+NM,409,EPZ,Sierra County Lakes,NM409,Sierra,35051,M,sc,33.1033,-107.0436
+NM,410,EPZ,Northern Dona Ana County,NM410,Dona Ana,35013,M,sc,32.6132,-106.9177
+NM,411,EPZ,Southern Dona Ana County/Mesilla Valley,NM411,Dona Ana,35013,M,sc,32.1002,-106.9151
+NM,412,EPZ,Central Tularosa Basin,NM412,Dona Ana,35013,M,sc,33.0400,-106.2624
+NM,412,EPZ,Central Tularosa Basin,NM412,Otero,35035,M,sc,33.0400,-106.2624
+NM,412,EPZ,Central Tularosa Basin,NM412,Sierra,35051,M,sc,33.0400,-106.2624
+NM,413,EPZ,Southern Tularosa Basin,NM413,Dona Ana,35013,M,sc,32.3625,-106.2354
+NM,413,EPZ,Southern Tularosa Basin,NM413,Otero,35035,M,sc,32.3625,-106.2354
+NM,414,EPZ,West Slopes Sacramento Mountains Below 7500 Feet,NM414,Otero,35035,M,sc,32.8801,-105.7623
+NM,415,EPZ,Sacramento Mountains Above 7500 Feet,NM415,Otero,35035,M,sc,32.9911,-105.6803
+NM,416,EPZ,East Slopes Sacramento Mountains Below 7500 Feet,NM416,Otero,35035,M,sc,32.9357,-105.4385
+NM,417,EPZ,Otero Mesa,NM417,Otero,35035,M,sc,32.2610,-105.4233
+NV,001,REV,Mineral and Southern Lyon Counties,NV001,Lyon,32019,P,ww,38.6067,-118.6034
+NV,001,REV,Mineral and Southern Lyon Counties,NV001,Mineral,32021,P,ww,38.6067,-118.6034
+NV,002,REV,Greater Lake Tahoe Area,NV002,Douglas,32005,P,ww,39.2153,-119.9149
+NV,002,REV,Greater Lake Tahoe Area,NV002,Washoe,32031,P,ww,39.2153,-119.9149
+NV,002,REV,Greater Lake Tahoe Area,NV002,Carson City,32510,P,ww,39.2153,-119.9149
+NV,003,REV,Greater Reno-Carson City-Minden Area,NV003,Douglas,32005,P,ww,39.4151,-119.6557
+NV,003,REV,Greater Reno-Carson City-Minden Area,NV003,Lyon,32019,P,ww,39.4151,-119.6557
+NV,003,REV,Greater Reno-Carson City-Minden Area,NV003,Storey,32029,P,ww,39.4151,-119.6557
+NV,003,REV,Greater Reno-Carson City-Minden Area,NV003,Washoe,32031,P,ww,39.4151,-119.6557
+NV,003,REV,Greater Reno-Carson City-Minden Area,NV003,Carson City,32510,P,ww,39.4151,-119.6557
+NV,004,REV,Western Nevada Basin and Range including Pyramid Lake,NV004,Churchill,32001,P,wc,40.0152,-118.4900
+NV,004,REV,Western Nevada Basin and Range including Pyramid Lake,NV004,Lyon,32019,P,ww,40.0152,-118.4900
+NV,004,REV,Western Nevada Basin and Range including Pyramid Lake,NV004,Pershing,32027,P,wc,40.0152,-118.4900
+NV,004,REV,Western Nevada Basin and Range including Pyramid Lake,NV004,Washoe,32031,P,ww,40.0152,-118.4900
+NV,005,REV,Northern Washoe County,NV005,Washoe,32031,P,ww,41.1126,-119.6686
+NV,014,VEF,Esmeralda and Central Nye County,NV014,Esmeralda,32009,P,sc,37.5763,-116.9511
+NV,014,VEF,Esmeralda and Central Nye County,NV014,Nye,32023,P,sc,37.5763,-116.9511
+NV,015,VEF,Lincoln County,NV015,Lincoln,32017,P,sc,37.6576,-114.8708
+NV,016,VEF,Northeast Clark County,NV016,Clark,32003,P,ss,36.5977,-114.5107
+NV,017,VEF,Western Clark and Southern Nye County,NV017,Clark,32003,P,ss,36.5162,-115.9843
+NV,017,VEF,Western Clark and Southern Nye County,NV017,Nye,32023,P,sc,36.5162,-115.9843
+NV,018,VEF,Sheep Range,NV018,Clark,32003,P,ss,36.7786,-115.1511
+NV,018,VEF,Sheep Range,NV018,Lincoln,32017,P,sc,36.7786,-115.1511
+NV,019,VEF,Spring Mountains-Red Rock Canyon,NV019,Clark,32003,P,ss,36.2348,-115.6791
+NV,019,VEF,Spring Mountains-Red Rock Canyon,NV019,Nye,32023,P,sc,36.2348,-115.6791
+NV,020,VEF,Las Vegas Valley,NV020,Clark,32003,P,ss,36.1435,-115.1712
+NV,021,VEF,Lake Mead National Recreation Area,NV021,Clark,32003,P,ss,35.9412,-114.5934
+NV,022,VEF,Southern Clark County,NV022,Clark,32003,P,ss,35.5358,-115.0222
+NV,030,LKN,Humboldt County,NV030,Humboldt,32013,P,nw,41.4068,-118.1120
+NV,031,LKN,Northern Elko County,NV031,Elko,32007,P,ne,41.5447,-115.5974
+NV,033,LKN,Extreme Eastern Elko County,NV033,Elko,32007,P,ne,40.6482,-114.2331
+NV,034,LKN,Ruby Mountains/East Humboldt Range,NV034,Elko,32007,P,ne,40.5440,-115.3911
+NV,034,LKN,Ruby Mountains/East Humboldt Range,NV034,White Pine,32033,P,ee,40.5440,-115.3911
+NV,035,LKN,White Pine County,NV035,White Pine,32033,P,ee,39.4383,-114.8977
+NV,036,LKN,Northern Lander County and Northern Eureka County,NV036,Eureka,32011,P,nc,40.4898,-116.7009
+NV,036,LKN,Northern Lander County and Northern Eureka County,NV036,Lander,32015,P,nc,40.4898,-116.7009
+NV,037,LKN,Southern Lander County and Southern Eureka County,NV037,Eureka,32011,P,nc,39.6135,-116.7096
+NV,037,LKN,Southern Lander County and Southern Eureka County,NV037,Lander,32015,P,nc,39.6135,-116.7096
+NV,038,LKN,Southwest Elko County,NV038,Elko,32007,P,ne,40.7184,-115.7303
+NV,039,LKN,South Central Elko County,NV039,Elko,32007,P,ne,40.5796,-114.8566
+NV,040,LKN,Northwestern Nye County,NV040,Nye,32023,P,nw,38.0466,-116.4732
+NV,041,LKN,Northeastern Nye County,NV041,Nye,32023,P,nw,38.5497,-115.8689
+NY,001,BUF,Niagara,NY001,Niagara,36063,E,ww,43.2011,-78.7425
+NY,002,BUF,Orleans,NY002,Orleans,36073,E,ww,43.2522,-78.2312
+NY,003,BUF,Monroe,NY003,Monroe,36055,E,ww,43.1464,-77.6959
+NY,004,BUF,Wayne,NY004,Wayne,36117,E,ww,43.1566,-77.0292
+NY,005,BUF,Northern Cayuga,NY005,Cayuga,36011,E,cc,43.1775,-76.6084
+NY,006,BUF,Oswego,NY006,Oswego,36075,E,cc,43.4260,-76.1412
+NY,007,BUF,Jefferson,NY007,Jefferson,36045,E,cc,44.0409,-75.9102
+NY,008,BUF,Lewis,NY008,Lewis,36049,E,cc,43.7847,-75.4488
+NY,009,BGM,Northern Oneida,NY009,Oneida,36065,E,cc,43.3813,-75.4692
+NY,010,BUF,Northern Erie,NY010,Erie,36029,E,ww,42.9375,-78.6923
+NY,011,BUF,Genesee,NY011,Genesee,36037,E,ww,43.0010,-78.1936
+NY,012,BUF,Wyoming,NY012,Wyoming,36121,E,ww,42.7023,-78.2245
+NY,013,BUF,Livingston,NY013,Livingston,36051,E,ww,42.7281,-77.7754
+NY,014,BUF,Ontario,NY014,Ontario,36069,E,ww,42.8529,-77.2998
+NY,015,BGM,Yates,NY015,Yates,36123,E,cc,42.6335,-77.1055
+NY,016,BGM,Seneca,NY016,Seneca,36099,E,cc,42.7810,-76.8237
+NY,017,BGM,Southern Cayuga,NY017,Cayuga,36011,E,cc,42.8012,-76.5302
+NY,018,BGM,Onondaga,NY018,Onondaga,36067,E,cc,43.0058,-76.1946
+NY,019,BUF,Chautauqua,NY019,Chautauqua,36013,E,ww,42.2282,-79.3664
+NY,020,BUF,Cattaraugus,NY020,Cattaraugus,36009,E,ww,42.2486,-78.6789
+NY,021,BUF,Allegany,NY021,Allegany,36003,E,ww,42.2574,-78.0276
+NY,022,BGM,Steuben,NY022,Steuben,36101,E,cc,42.2678,-77.3838
+NY,023,BGM,Schuyler,NY023,Schuyler,36097,E,cc,42.3938,-76.8752
+NY,024,BGM,Chemung,NY024,Chemung,36015,E,cc,42.1413,-76.7600
+NY,025,BGM,Tompkins,NY025,Tompkins,36109,E,cc,42.4520,-76.4737
+NY,026,BTV,Northern St. Lawrence,NY026,St. Lawrence,36089,E,nn,44.8467,-74.9154
+NY,027,BTV,Northern Franklin,NY027,Franklin,36033,E,nn,44.9016,-74.4258
+NY,028,BTV,Eastern Clinton,NY028,Clinton,36019,E,nn,44.8026,-73.5131
+NY,029,BTV,Southeastern St. Lawrence,NY029,St. Lawrence,36089,E,nn,44.3207,-74.8555
+NY,030,BTV,Southern Franklin,NY030,Franklin,36033,E,nn,44.5162,-74.2734
+NY,031,BTV,Western Clinton,NY031,Clinton,36019,E,nn,44.7069,-73.7931
+NY,032,ALY,Northern Herkimer,NY032,Herkimer,36043,E,ee,43.7111,-74.9693
+NY,033,ALY,Hamilton,NY033,Hamilton,36041,E,ee,43.6611,-74.4973
+NY,034,BTV,Western Essex,NY034,Essex,36031,E,nn,44.0851,-73.8551
+NY,035,BTV,Eastern Essex,NY035,Essex,36031,E,nn,44.2489,-73.4345
+NY,036,BGM,Madison,NY036,Madison,36053,E,cc,42.9129,-75.6697
+NY,037,BGM,Southern Oneida,NY037,Oneida,36065,E,cc,43.1200,-75.4068
+NY,038,ALY,Southern Herkimer,NY038,Herkimer,36043,E,ee,43.0923,-74.9549
+NY,039,ALY,Southern Fulton,NY039,Fulton,36035,E,ee,43.0404,-74.4213
+NY,040,ALY,Montgomery,NY040,Montgomery,36057,E,ee,42.9023,-74.4397
+NY,041,ALY,Northern Saratoga,NY041,Saratoga,36091,E,ec,43.1808,-73.8582
+NY,042,ALY,Northern Warren,NY042,Warren,36113,E,ec,43.5976,-73.8646
+NY,043,ALY,Northern Washington,NY043,Washington,36115,E,ec,43.5196,-73.4239
+NY,044,BGM,Cortland,NY044,Cortland,36023,E,cc,42.5950,-76.0702
+NY,045,BGM,Chenango,NY045,Chenango,36017,E,cc,42.4936,-75.6115
+NY,046,BGM,Otsego,NY046,Otsego,36077,E,cc,42.6338,-75.0326
+NY,047,ALY,Schoharie,NY047,Schoharie,36095,E,ec,42.5882,-74.4421
+NY,048,ALY,Western Schenectady,NY048,Schenectady,36093,E,ec,42.7900,-74.1561
+NY,049,ALY,Eastern Schenectady,NY049,Schenectady,36093,E,ec,42.8421,-73.9758
+NY,050,ALY,Southern Saratoga,NY050,Saratoga,36091,E,ec,42.9413,-73.8768
+NY,051,ALY,Western Albany,NY051,Albany,36001,E,ec,42.5646,-74.1250
+NY,052,ALY,Eastern Albany,NY052,Albany,36001,E,ec,42.6223,-73.8796
+NY,053,ALY,Western Rensselaer,NY053,Rensselaer,36083,E,ec,42.7053,-73.6159
+NY,054,ALY,Eastern Rensselaer,NY054,Rensselaer,36083,E,ec,42.7167,-73.4058
+NY,055,BGM,Tioga,NY055,Tioga,36107,E,cc,42.1703,-76.3063
+NY,056,BGM,Broome,NY056,Broome,36007,E,cc,42.1602,-75.8196
+NY,057,BGM,Delaware,NY057,Delaware,36025,E,cc,42.1982,-74.9665
+NY,058,ALY,Western Greene,NY058,Greene,36039,E,ec,42.2570,-74.2376
+NY,059,ALY,Eastern Greene,NY059,Greene,36039,E,ec,42.3120,-73.9147
+NY,060,ALY,Western Columbia,NY060,Columbia,36021,E,ec,42.2451,-73.7330
+NY,061,ALY,Eastern Columbia,NY061,Columbia,36021,E,ec,42.2548,-73.5360
+NY,062,BGM,Sullivan,NY062,Sullivan,36105,E,cc,41.7164,-74.7681
+NY,063,ALY,Western Ulster,NY063,Ulster,36111,E,ec,41.9216,-74.3795
+NY,064,ALY,Eastern Ulster,NY064,Ulster,36111,E,ec,41.8336,-74.0616
+NY,065,ALY,Western Dutchess,NY065,Dutchess,36027,E,ec,41.7716,-73.8457
+NY,066,ALY,Eastern Dutchess,NY066,Dutchess,36027,E,ec,41.7574,-73.6193
+NY,067,OKX,Orange,NY067,Orange,36071,E,se,41.4022,-74.3056
+NY,068,OKX,Putnam,NY068,Putnam,36079,E,se,41.4267,-73.7495
+NY,069,OKX,Rockland,NY069,Rockland,36087,E,se,41.1529,-74.0241
+NY,070,OKX,Northern Westchester,NY070,Westchester,36119,E,se,41.2221,-73.7474
+NY,071,OKX,Southern Westchester,NY071,Westchester,36119,E,se,40.9833,-73.7799
+NY,072,OKX,New York (Manhattan),NY072,New York,36061,E,se,40.7786,-73.9665
+NY,073,OKX,Bronx,NY073,Bronx,36005,E,se,40.8526,-73.8669
+NY,074,OKX,Richmond (Staten Is.),NY074,Richmond,36085,E,se,40.5802,-74.1557
+NY,075,OKX,Kings (Brooklyn),NY075,Kings,36047,E,se,40.6428,-73.9426
+NY,078,OKX,Northwest Suffolk,NY078,Suffolk,36103,E,se,40.8778,-73.1715
+NY,079,OKX,Northeast Suffolk,NY079,Suffolk,36103,E,se,40.9921,-72.5807
+NY,080,OKX,Southwest Suffolk,NY080,Suffolk,36103,E,se,40.7503,-73.1828
+NY,081,OKX,Southeast Suffolk,NY081,Suffolk,36103,E,se,40.9109,-72.4592
+NY,082,ALY,Northern Fulton,NY082,Fulton,36035,E,ee,43.1615,-74.4228
+NY,083,ALY,Southeast Warren,NY083,Warren,36113,E,ec,43.3468,-73.7376
+NY,084,ALY,Southern Washington,NY084,Washington,36115,E,ec,43.1564,-73.4360
+NY,085,BUF,Southern Erie,NY085,Erie,36029,E,ww,42.6247,-78.7608
+NY,087,BTV,Southwestern St. Lawrence,NY087,St. Lawrence,36089,E,nn,44.5173,-75.3061
+NY,176,OKX,Northern Queens,NY176,Queens,36081,E,se,40.7480,-73.8438
+NY,177,OKX,Northern Nassau,NY177,Nassau,36059,E,se,40.8335,-73.5976
+NY,178,OKX,Southern Queens,NY178,Queens,36081,E,se,40.6648,-73.7920
+NY,179,OKX,Southern Nassau,NY179,Nassau,36059,E,se,40.6903,-73.5836
+OH,001,IWX,Williams,OH001,Williams,39171,E,nw,41.5603,-84.5882
+OH,002,IWX,Fulton,OH002,Fulton,39051,E,nw,41.6018,-84.1301
+OH,003,CLE,Lucas,OH003,Lucas,39095,E,nw,41.6200,-83.6586
+OH,004,IWX,Defiance,OH004,Defiance,39039,E,nw,41.3240,-84.4906
+OH,005,IWX,Henry,OH005,Henry,39069,E,nw,41.3339,-84.0682
+OH,006,CLE,Wood,OH006,Wood,39173,E,nw,41.3617,-83.6230
+OH,007,CLE,Ottawa,OH007,Ottawa,39123,E,nw,41.5381,-83.1467
+OH,008,CLE,Sandusky,OH008,Sandusky,39143,E,nw,41.3565,-83.1450
+OH,009,CLE,Erie,OH009,Erie,39043,E,nc,41.3635,-82.6178
+OH,010,CLE,Lorain,OH010,Lorain,39093,E,nc,41.2955,-82.1512
+OH,011,CLE,Cuyahoga,OH011,Cuyahoga,39035,E,ne,41.4243,-81.6587
+OH,012,CLE,Lake,OH012,Lake,39085,E,ne,41.6968,-81.2370
+OH,013,CLE,Geauga,OH013,Geauga,39055,E,ne,41.4995,-81.1786
+OH,014,CLE,Ashtabula Inland,OH014,Ashtabula,39007,E,ne,41.6796,-80.7430
+OH,015,IWX,Paulding,OH015,Paulding,39125,E,wc,41.1166,-84.5801
+OH,016,IWX,Putnam,OH016,Putnam,39137,E,wc,41.0221,-84.1317
+OH,017,CLE,Hancock,OH017,Hancock,39063,E,nw,41.0019,-83.6665
+OH,018,CLE,Seneca,OH018,Seneca,39147,E,nw,41.1239,-83.1277
+OH,019,CLE,Huron,OH019,Huron,39077,E,nc,41.1462,-82.5984
+OH,020,CLE,Medina,OH020,Medina,39103,E,ne,41.1176,-81.8997
+OH,021,CLE,Summit,OH021,Summit,39153,E,ne,41.1259,-81.5321
+OH,022,CLE,Portage,OH022,Portage,39133,E,ne,41.1676,-81.1962
+OH,023,CLE,Trumbull,OH023,Trumbull,39155,E,ne,41.3174,-80.7604
+OH,024,IWX,Van Wert,OH024,Van Wert,39161,E,wc,40.8554,-84.5861
+OH,025,IWX,Allen,OH025,Allen,39003,E,wc,40.7715,-84.1057
+OH,026,ILN,Hardin,OH026,Hardin,39065,E,wc,40.6615,-83.6594
+OH,027,CLE,Wyandot,OH027,Wyandot,39175,E,nw,40.8424,-83.3044
+OH,028,CLE,Crawford,OH028,Crawford,39033,E,nc,40.8509,-82.9197
+OH,029,CLE,Richland,OH029,Richland,39139,E,nc,40.7747,-82.5365
+OH,030,CLE,Ashland,OH030,Ashland,39005,E,nc,40.8461,-82.2707
+OH,031,CLE,Wayne,OH031,Wayne,39169,E,ne,40.8289,-81.8883
+OH,032,CLE,Stark,OH032,Stark,39151,E,ne,40.8138,-81.3659
+OH,033,CLE,Mahoning,OH033,Mahoning,39099,E,ne,41.0146,-80.7753
+OH,034,ILN,Mercer,OH034,Mercer,39107,E,wc,40.5400,-84.6293
+OH,035,ILN,Auglaize,OH035,Auglaize,39011,E,wc,40.5609,-84.2217
+OH,036,CLE,Marion,OH036,Marion,39101,E,nc,40.5873,-83.1609
+OH,037,CLE,Morrow,OH037,Morrow,39117,E,nc,40.5240,-82.7941
+OH,038,CLE,Holmes,OH038,Holmes,39075,E,ne,40.5612,-81.9294
+OH,039,PBZ,Tuscarawas,OH039,Tuscarawas,39157,E,ec,40.4410,-81.4738
+OH,040,PBZ,Carroll,OH040,Carroll,39019,E,ec,40.5796,-81.0897
+OH,041,PBZ,Columbiana,OH041,Columbiana,39029,E,ec,40.7686,-80.7775
+OH,042,ILN,Darke,OH042,Darke,39037,E,wc,40.1333,-84.6193
+OH,043,ILN,Shelby,OH043,Shelby,39149,E,wc,40.3316,-84.2048
+OH,044,ILN,Logan,OH044,Logan,39091,E,wc,40.3885,-83.7658
+OH,045,ILN,Union,OH045,Union,39159,E,cc,40.2995,-83.3715
+OH,046,ILN,Delaware,OH046,Delaware,39041,E,cc,40.2785,-83.0052
+OH,047,CLE,Knox,OH047,Knox,39083,E,nc,40.3988,-82.4216
+OH,048,PBZ,Coshocton,OH048,Coshocton,39031,E,ec,40.3016,-81.9200
+OH,049,PBZ,Harrison,OH049,Harrison,39067,E,ec,40.2938,-81.0911
+OH,050,PBZ,Jefferson,OH050,Jefferson,39081,E,ec,40.3850,-80.7610
+OH,051,ILN,Miami,OH051,Miami,39109,E,wc,40.0534,-84.2289
+OH,052,ILN,Champaign,OH052,Champaign,39021,E,wc,40.1378,-83.7696
+OH,053,ILN,Clark,OH053,Clark,39023,E,wc,39.9168,-83.7835
+OH,054,ILN,Madison,OH054,Madison,39097,E,cc,39.8942,-83.3999
+OH,055,ILN,Franklin,OH055,Franklin,39049,E,cc,39.9696,-83.0093
+OH,056,ILN,Licking,OH056,Licking,39089,E,cc,40.0918,-82.4835
+OH,057,PBZ,Muskingum,OH057,Muskingum,39119,E,ec,39.9654,-81.9444
+OH,058,PBZ,Guernsey,OH058,Guernsey,39059,E,ec,40.0521,-81.4942
+OH,059,PBZ,Belmont,OH059,Belmont,39013,E,ec,40.0158,-80.9885
+OH,060,ILN,Preble,OH060,Preble,39135,E,wc,39.7416,-84.6481
+OH,061,ILN,Montgomery,OH061,Montgomery,39113,E,wc,39.7545,-84.2906
+OH,062,ILN,Greene,OH062,Greene,39057,E,wc,39.6915,-83.8898
+OH,063,ILN,Fayette,OH063,Fayette,39047,E,cc,39.5599,-83.4561
+OH,064,ILN,Pickaway,OH064,Pickaway,39129,E,cc,39.6420,-83.0243
+OH,065,ILN,Fairfield,OH065,Fairfield,39045,E,cc,39.7516,-82.6305
+OH,066,RLX,Perry,OH066,Perry,39127,E,se,39.7371,-82.2361
+OH,067,RLX,Morgan,OH067,Morgan,39115,E,se,39.6203,-81.8527
+OH,068,PBZ,Noble,OH068,Noble,39121,E,ec,39.7660,-81.4555
+OH,069,PBZ,Monroe,OH069,Monroe,39111,E,ec,39.7274,-81.0829
+OH,070,ILN,Butler,OH070,Butler,39017,E,sw,39.4386,-84.5755
+OH,071,ILN,Warren,OH071,Warren,39165,E,sw,39.4277,-84.1668
+OH,072,ILN,Clinton,OH072,Clinton,39027,E,sw,39.4150,-83.8084
+OH,073,ILN,Ross,OH073,Ross,39141,E,sw,39.3376,-83.0570
+OH,074,ILN,Hocking,OH074,Hocking,39073,E,cc,39.4970,-82.4791
+OH,075,RLX,Athens,OH075,Athens,39009,E,se,39.3337,-82.0452
+OH,076,RLX,Washington,OH076,Washington,39167,E,se,39.4553,-81.4953
+OH,077,ILN,Hamilton,OH077,Hamilton,39061,E,sw,39.1954,-84.5426
+OH,078,ILN,Clermont,OH078,Clermont,39025,E,sw,39.0476,-84.1522
+OH,079,ILN,Brown,OH079,Brown,39015,E,sw,38.9344,-83.8680
+OH,080,ILN,Highland,OH080,Highland,39071,E,sw,39.1848,-83.6009
+OH,081,ILN,Adams,OH081,Adams,39001,E,sw,38.8457,-83.4720
+OH,082,ILN,Pike,OH082,Pike,39131,E,sw,39.0774,-83.0668
+OH,083,RLX,Jackson,OH083,Jackson,39079,E,se,39.0197,-82.6184
+OH,084,RLX,Vinton,OH084,Vinton,39163,E,se,39.2510,-82.4854
+OH,085,RLX,Meigs,OH085,Meigs,39105,E,se,39.0821,-82.0229
+OH,086,RLX,Gallia,OH086,Gallia,39053,E,se,38.8248,-82.3169
+OH,087,RLX,Lawrence,OH087,Lawrence,39087,E,se,38.5985,-82.5368
+OH,088,ILN,Scioto,OH088,Scioto,39145,E,sw,38.8040,-82.9928
+OH,089,CLE,Ashtabula Lakeshore,OH089,Ashtabula,39007,E,ne,41.8761,-80.7817
+OK,001,AMA,Cimarron,OK001,Cimarron,40025,C,pa,36.7483,-102.5178
+OK,002,AMA,Texas,OK002,Texas,40139,C,pa,36.7479,-101.4900
+OK,003,AMA,Beaver,OK003,Beaver,40007,C,pa,36.7497,-100.4767
+OK,004,OUN,Harper,OK004,Harper,40059,C,nw,36.7887,-99.6673
+OK,005,OUN,Woods,OK005,Woods,40151,C,nw,36.7669,-98.8651
+OK,006,OUN,Alfalfa,OK006,Alfalfa,40003,C,nw,36.7310,-98.3240
+OK,007,OUN,Grant,OK007,Grant,40053,C,nn,36.7962,-97.7861
+OK,008,OUN,Kay,OK008,Kay,40071,C,nn,36.8180,-97.1439
+OK,009,OUN,Ellis,OK009,Ellis,40045,C,nw,36.2183,-99.7546
+OK,010,OUN,Woodward,OK010,Woodward,40153,C,nw,36.4227,-99.2650
+OK,011,OUN,Major,OK011,Major,40093,C,nw,36.3116,-98.5360
+OK,012,OUN,Garfield,OK012,Garfield,40047,C,nn,36.3790,-97.7828
+OK,013,OUN,Noble,OK013,Noble,40103,C,nn,36.3886,-97.2306
+OK,014,OUN,Roger Mills,OK014,Roger Mills,40129,C,ww,35.6883,-99.6957
+OK,015,OUN,Dewey,OK015,Dewey,40043,C,nw,35.9877,-99.0080
+OK,016,OUN,Custer,OK016,Custer,40039,C,ww,35.6389,-99.0015
+OK,017,OUN,Blaine,OK017,Blaine,40011,C,nw,35.8752,-98.4335
+OK,018,OUN,Kingfisher,OK018,Kingfisher,40073,C,cc,35.9454,-97.9421
+OK,019,OUN,Logan,OK019,Logan,40083,C,cc,35.9194,-97.4433
+OK,020,OUN,Payne,OK020,Payne,40119,C,cc,36.0773,-96.9757
+OK,021,OUN,Beckham,OK021,Beckham,40009,C,ww,35.2687,-99.6820
+OK,022,OUN,Washita,OK022,Washita,40149,C,ww,35.2904,-98.9923
+OK,023,OUN,Caddo,OK023,Caddo,40015,C,sw,35.1744,-98.3751
+OK,024,OUN,Canadian,OK024,Canadian,40017,C,cc,35.5425,-97.9825
+OK,025,OUN,Oklahoma,OK025,Oklahoma,40109,C,cc,35.5515,-97.4072
+OK,026,OUN,Lincoln,OK026,Lincoln,40081,C,cc,35.7030,-96.8809
+OK,027,OUN,Grady,OK027,Grady,40051,C,cc,35.0170,-97.8841
+OK,028,OUN,McClain,OK028,McClain,40087,C,cc,35.0087,-97.4436
+OK,029,OUN,Cleveland,OK029,Cleveland,40027,C,cc,35.2036,-97.3271
+OK,030,OUN,Pottawatomie,OK030,Pottawatomie,40125,C,cc,35.2068,-96.9483
+OK,031,OUN,Seminole,OK031,Seminole,40133,C,ec,35.1675,-96.6156
+OK,032,OUN,Hughes,OK032,Hughes,40063,C,se,35.0484,-96.2502
+OK,033,OUN,Harmon,OK033,Harmon,40057,C,sw,34.7441,-99.8463
+OK,034,OUN,Greer,OK034,Greer,40055,C,sw,34.9357,-99.5609
+OK,035,OUN,Kiowa,OK035,Kiowa,40075,C,sw,34.9163,-98.9808
+OK,036,OUN,Jackson,OK036,Jackson,40065,C,sw,34.5880,-99.4147
+OK,037,OUN,Tillman,OK037,Tillman,40141,C,sw,34.3728,-98.9242
+OK,038,OUN,Comanche,OK038,Comanche,40031,C,sw,34.6621,-98.4717
+OK,039,OUN,Stephens,OK039,Stephens,40137,C,ss,34.4856,-97.8514
+OK,040,OUN,Garvin,OK040,Garvin,40049,C,ss,34.7045,-97.3093
+OK,041,OUN,Murray,OK041,Murray,40099,C,ss,34.4823,-97.0679
+OK,042,OUN,Pontotoc,OK042,Pontotoc,40123,C,ec,34.7281,-96.6844
+OK,043,OUN,Coal,OK043,Coal,40029,C,se,34.5882,-96.2979
+OK,044,OUN,Cotton,OK044,Cotton,40033,C,sw,34.2902,-98.3721
+OK,045,OUN,Jefferson,OK045,Jefferson,40067,C,ss,34.1111,-97.8358
+OK,046,OUN,Carter,OK046,Carter,40019,C,ss,34.2509,-97.2858
+OK,047,OUN,Johnston,OK047,Johnston,40069,C,se,34.3165,-96.6606
+OK,048,OUN,Atoka,OK048,Atoka,40005,C,se,34.3737,-96.0378
+OK,049,TSA,Pushmataha,OK049,Pushmataha,40127,C,se,34.4162,-95.3758
+OK,050,OUN,Love,OK050,Love,40085,C,ss,33.9499,-97.2442
+OK,051,OUN,Marshall,OK051,Marshall,40095,C,se,34.0245,-96.7691
+OK,052,OUN,Bryan,OK052,Bryan,40013,C,se,33.9623,-96.2600
+OK,053,TSA,Choctaw,OK053,Choctaw,40023,C,se,34.0265,-95.5521
+OK,054,TSA,Osage,OK054,Osage,40113,C,ne,36.6292,-96.3984
+OK,055,TSA,Washington,OK055,Washington,40147,C,ne,36.7152,-95.9044
+OK,056,TSA,Nowata,OK056,Nowata,40105,C,ne,36.7985,-95.6175
+OK,057,TSA,Craig,OK057,Craig,40035,C,ne,36.7617,-95.2085
+OK,058,TSA,Ottawa,OK058,Ottawa,40115,C,ne,36.8355,-94.8105
+OK,059,TSA,Pawnee,OK059,Pawnee,40117,C,ne,36.3169,-96.6993
+OK,060,TSA,Tulsa,OK060,Tulsa,40143,C,ne,36.1211,-95.9415
+OK,061,TSA,Rogers,OK061,Rogers,40131,C,ne,36.3716,-95.6044
+OK,062,TSA,Mayes,OK062,Mayes,40097,C,ne,36.3018,-95.2309
+OK,063,TSA,Delaware,OK063,Delaware,40041,C,ne,36.4082,-94.8027
+OK,064,TSA,Creek,OK064,Creek,40037,C,ne,35.9027,-96.3709
+OK,065,TSA,Okfuskee,OK065,Okfuskee,40107,C,ec,35.4655,-96.3228
+OK,066,TSA,Okmulgee,OK066,Okmulgee,40111,C,ne,35.6467,-95.9643
+OK,067,TSA,Wagoner,OK067,Wagoner,40145,C,ne,35.9609,-95.5212
+OK,068,TSA,Cherokee,OK068,Cherokee,40021,C,ec,35.9066,-94.9997
+OK,069,TSA,Adair,OK069,Adair,40001,C,ne,35.8839,-94.6587
+OK,070,TSA,Muskogee,OK070,Muskogee,40101,C,ec,35.6161,-95.3794
+OK,071,TSA,McIntosh,OK071,McIntosh,40091,C,se,35.3737,-95.6668
+OK,072,TSA,Sequoyah,OK072,Sequoyah,40135,C,ec,35.4953,-94.7549
+OK,073,TSA,Pittsburg,OK073,Pittsburg,40121,C,se,34.9239,-95.7484
+OK,074,TSA,Haskell,OK074,Haskell,40061,C,se,35.2248,-95.1166
+OK,075,TSA,Latimer,OK075,Latimer,40077,C,se,34.8761,-95.2504
+OK,076,TSA,Le Flore,OK076,Le Flore,40079,C,se,34.9001,-94.7035
+OK,077,SHV,McCurtain,OK077,McCurtain,40089,C,se,34.1151,-94.7712
+OR,001,PQR,North Oregon Coast,OR001,Clatsop,41007,P,nw,45.7116,-123.8238
+OR,001,PQR,North Oregon Coast,OR001,Tillamook,41057,P,nw,45.7116,-123.8238
+OR,002,PQR,Central Oregon Coast,OR002,Lane,41039,P,ww,44.5186,-124.0171
+OR,002,PQR,Central Oregon Coast,OR002,Lincoln,41041,P,ww,44.5186,-124.0171
+OR,003,PQR,Coast Range of Northwest Oregon,OR003,Clatsop,41007,P,nw,45.6850,-123.4856
+OR,003,PQR,Coast Range of Northwest Oregon,OR003,Columbia,41009,P,nw,45.6850,-123.4856
+OR,003,PQR,Coast Range of Northwest Oregon,OR003,Tillamook,41057,P,nw,45.6850,-123.4856
+OR,003,PQR,Coast Range of Northwest Oregon,OR003,Washington,41067,P,nw,45.6850,-123.4856
+OR,003,PQR,Coast Range of Northwest Oregon,OR003,Yamhill,41071,P,nw,45.6850,-123.4856
+OR,004,PQR,Central Coast Range of Western Oregon,OR004,Benton,41003,P,ww,44.3664,-123.6900
+OR,004,PQR,Central Coast Range of Western Oregon,OR004,Lane,41039,P,ww,44.3664,-123.6900
+OR,004,PQR,Central Coast Range of Western Oregon,OR004,Lincoln,41041,P,ww,44.3664,-123.6900
+OR,004,PQR,Central Coast Range of Western Oregon,OR004,Polk,41053,P,ww,44.3664,-123.6900
+OR,005,PQR,Lower Columbia,OR005,Columbia,41009,P,nw,46.0335,-123.0126
+OR,006,PQR,Greater Portland Metro Area,OR006,Clackamas,41005,P,nw,45.4883,-122.7818
+OR,006,PQR,Greater Portland Metro Area,OR006,Columbia,41009,P,nw,45.4883,-122.7818
+OR,006,PQR,Greater Portland Metro Area,OR006,Multnomah,41051,P,nw,45.4883,-122.7818
+OR,006,PQR,Greater Portland Metro Area,OR006,Washington,41067,P,nw,45.4883,-122.7818
+OR,007,PQR,Central Willamette Valley,OR007,Clackamas,41005,P,nw,45.0538,-123.0478
+OR,007,PQR,Central Willamette Valley,OR007,Marion,41047,P,nw,45.0538,-123.0478
+OR,007,PQR,Central Willamette Valley,OR007,Polk,41053,P,ww,45.0538,-123.0478
+OR,007,PQR,Central Willamette Valley,OR007,Yamhill,41071,P,nw,45.0538,-123.0478
+OR,008,PQR,South  Willamette Valley,OR008,Benton,41003,P,ww,44.4081,-123.1391
+OR,008,PQR,South  Willamette Valley,OR008,Lane,41039,P,ww,44.4081,-123.1391
+OR,008,PQR,South  Willamette Valley,OR008,Linn,41043,P,ww,44.4081,-123.1391
+OR,010,PQR,Northern Oregon Cascade Foothills,OR010,Clackamas,41005,P,nw,44.7955,-122.4789
+OR,010,PQR,Northern Oregon Cascade Foothills,OR010,Linn,41043,P,ww,44.7955,-122.4789
+OR,010,PQR,Northern Oregon Cascade Foothills,OR010,Marion,41047,P,nw,44.7955,-122.4789
+OR,010,PQR,Northern Oregon Cascade Foothills,OR010,Multnomah,41051,P,nw,44.7955,-122.4789
+OR,011,PQR,Northern Oregon Cascades,OR011,Clackamas,41005,P,nw,44.9211,-121.9823
+OR,011,PQR,Northern Oregon Cascades,OR011,Hood River,41027,P,nw,44.9211,-121.9823
+OR,011,PQR,Northern Oregon Cascades,OR011,Linn,41043,P,ww,44.9211,-121.9823
+OR,011,PQR,Northern Oregon Cascades,OR011,Marion,41047,P,nw,44.9211,-121.9823
+OR,011,PQR,Northern Oregon Cascades,OR011,Multnomah,41051,P,nw,44.9211,-121.9823
+OR,012,PQR,Cascade Foothills in Lane County,OR012,Lane,41039,P,ww,43.9187,-122.7945
+OR,013,PQR,Cascades in Lane County,OR013,Lane,41039,P,ww,43.8422,-122.2506
+OR,014,PQR,Upper Hood River Valley,OR014,Hood River,41027,P,nw,45.5816,-121.5777
+OR,015,PDT,Western Columbia River Gorge,OR015,Hood River,41027,P,cc,45.5872,-122.0526
+OR,015,PDT,Western Columbia River Gorge,OR015,Multnomah,41051,P,cc,45.5872,-122.0526
+OR,016,PDT,Central Columbia River Gorge,OR016,Hood River,41031,P,cc,45.6845,-121.6091
+OR,021,MFR,South Central Oregon Coast,OR021,Coos,41011,P,sw,43.1810,-124.1567
+OR,021,MFR,South Central Oregon Coast,OR021,Curry,41015,P,sw,43.1810,-124.1567
+OR,021,MFR,South Central Oregon Coast,OR021,Douglas,41019,P,sw,43.1810,-124.1567
+OR,022,MFR,Curry County Coast,OR022,Curry,41015,P,sw,42.3420,-124.3010
+OR,023,MFR,Central Douglas County,OR023,Coos,41011,P,sw,43.2603,-123.4502
+OR,023,MFR,Central Douglas County,OR023,Douglas,41019,P,sw,43.2603,-123.4502
+OR,024,MFR,Eastern Curry County and Josephine County,OR024,Curry,41015,P,sw,42.3902,-123.7443
+OR,024,MFR,Eastern Curry County and Josephine County,OR024,Josephine,41033,P,sw,42.3902,-123.7443
+OR,025,MFR,Eastern Douglas County Foothills,OR025,Douglas,41019,P,sw,43.2384,-122.7090
+OR,026,MFR,Jackson County,OR026,Jackson,41029,P,sw,42.4652,-122.8457
+OR,027,MFR,South Central Oregon Cascades,OR027,Douglas,41019,P,sw,43.1875,-122.1568
+OR,027,MFR,South Central Oregon Cascades,OR027,Jackson,41029,P,sw,43.1875,-122.1568
+OR,027,MFR,South Central Oregon Cascades,OR027,Klamath,41035,P,sc,43.1875,-122.1568
+OR,028,MFR,Siskiyou Mountains and Southern Oregon Cascades,OR028,Jackson,41029,P,sw,42.3339,-122.4507
+OR,028,MFR,Siskiyou Mountains and Southern Oregon Cascades,OR028,Josephine,41033,P,sw,42.3339,-122.4507
+OR,028,MFR,Siskiyou Mountains and Southern Oregon Cascades,OR028,Klamath,41035,P,sc,42.3339,-122.4507
+OR,029,MFR,Klamath Basin,OR029,Klamath,41035,P,sc,42.3505,-121.7222
+OR,030,MFR,Northern and Eastern Klamath County and Western Lake County,OR030,Klamath,41035,P,sc,42.7799,-121.2476
+OR,030,MFR,Northern and Eastern Klamath County and Western Lake County,OR030,Lake,41037,P,sc,42.7799,-121.2476
+OR,031,MFR,Central and Eastern Lake County,OR031,Lake,41037,P,sc,42.8105,-120.2507
+OR,041,PDT,East Columbia River Gorge,OR041,Gilliam,41021,P,nc,45.6460,-120.5512
+OR,041,PDT,East Columbia River Gorge,OR041,Sherman,41055,P,nc,45.6460,-120.5512
+OR,041,PDT,East Columbia River Gorge,OR041,Wasco,41065,P,nc,45.6460,-120.5512
+OR,044,PDT,Lower Columbia Basin of Oregon,OR044,Morrow,41049,P,ne,45.7453,-119.4683
+OR,044,PDT,Lower Columbia Basin of Oregon,OR044,Umatilla,41059,P,ne,45.7453,-119.4683
+OR,049,PDT,Grande Ronde Valley,OR049,Union,41061,P,ne,45.3737,-117.9515
+OR,050,PDT,Wallowa County,OR050,Union,41061,P,ne,45.4827,-117.1835
+OR,050,PDT,Wallowa County,OR050,Wallowa,41063,P,ne,45.4827,-117.1835
+OR,061,BOI,Harney County,OR061,Harney,41025,P,se,43.0640,-118.9679
+OR,062,BOI,Baker County,OR062,Baker,41001,P,ne,44.7092,-117.6753
+OR,063,BOI,Malheur County,OR063,Malheur,41045,MP,se,43.1230,-117.6604
+OR,064,BOI,Oregon Lower Treasure Valley,OR064,Malheur,41045,MP,se,44.0233,-117.1835
+OR,502,PDT,Northern Blue Mountains of Oregon,OR502,Umatilla,41059,P,ne,45.6952,-118.0041
+OR,502,PDT,Northern Blue Mountains of Oregon,OR502,Union,41061,P,ne,45.6952,-118.0041
+OR,502,PDT,Northern Blue Mountains of Oregon,OR502,Wallowa,41063,P,ne,45.6952,-118.0041
+OR,503,PDT,Southern Blue Mountains of Oregon,OR503,Gilliam,41021,P,nc,44.9813,-118.9718
+OR,503,PDT,Southern Blue Mountains of Oregon,OR503,Grant,41023,P,ec,44.9813,-118.9718
+OR,503,PDT,Southern Blue Mountains of Oregon,OR503,Morrow,41049,P,ne,44.9813,-118.9718
+OR,503,PDT,Southern Blue Mountains of Oregon,OR503,Umatilla,41059,P,ne,44.9813,-118.9718
+OR,503,PDT,Southern Blue Mountains of Oregon,OR503,Union,41061,P,ne,44.9813,-118.9718
+OR,503,PDT,Southern Blue Mountains of Oregon,OR503,Wheeler,41069,P,cc,44.9813,-118.9718
+OR,505,PDT,John Day Basin,OR505,Grant,41023,P,ec,44.7315,-119.7825
+OR,505,PDT,John Day Basin,OR505,Jefferson,41031,P,cc,44.7315,-119.7825
+OR,505,PDT,John Day Basin,OR505,Morrow,41049,P,ne,44.7315,-119.7825
+OR,505,PDT,John Day Basin,OR505,Wasco,41065,P,nc,44.7315,-119.7825
+OR,505,PDT,John Day Basin,OR505,Wheeler,41069,P,cc,44.7315,-119.7825
+OR,506,PDT,Ochoco-John Day Highlands,OR506,Crook,41013,P,cc,44.1652,-120.0086
+OR,506,PDT,Ochoco-John Day Highlands,OR506,Deschutes,41017,P,cc,44.1652,-120.0086
+OR,506,PDT,Ochoco-John Day Highlands,OR506,Grant,41023,P,ec,44.1652,-120.0086
+OR,506,PDT,Ochoco-John Day Highlands,OR506,Jefferson,41031,P,cc,44.1652,-120.0086
+OR,506,PDT,Ochoco-John Day Highlands,OR506,Wheeler,41069,P,cc,44.1652,-120.0086
+OR,507,PDT,Foothills of the Northern Blue Mountains of Oregon,OR507,Umatilla,41059,P,ne,45.7238,-118.7135
+OR,508,PDT,Foothills of the Southern Blue Mountains of Oregon,OR508,Gilliam,41021,P,nc,45.3442,-120.0029
+OR,508,PDT,Foothills of the Southern Blue Mountains of Oregon,OR508,Morrow,41049,P,ne,45.3442,-120.0029
+OR,508,PDT,Foothills of the Southern Blue Mountains of Oregon,OR508,Wheeler,41069,P,cc,45.3442,-120.0029
+OR,509,PDT,East Slopes of the Oregon Cascades,OR509,Deschutes,41017,P,cc,44.2627,-121.5516
+OR,509,PDT,East Slopes of the Oregon Cascades,OR509,Jefferson,41031,P,cc,44.2627,-121.5516
+OR,509,PDT,East Slopes of the Oregon Cascades,OR509,Wasco,41065,P,nc,44.2627,-121.5516
+OR,510,PDT,North Central Oregon,OR510,Sherman,41055,P,nc,45.2210,-121.0222
+OR,510,PDT,North Central Oregon,OR510,Wasco,41065,P,nc,45.2210,-121.0222
+OR,511,PDT,Central Oregon,OR511,Crook,41013,P,cc,44.4439,-121.1488
+OR,511,PDT,Central Oregon,OR511,Deschutes,41017,P,cc,44.4439,-121.1488
+OR,511,PDT,Central Oregon,OR511,Jefferson,41031,P,cc,44.4439,-121.1488
+PA,001,CLE,Northern Erie,PA001,Erie,42049,E,nw,42.1018,-80.1245
+PA,002,CLE,Southern Erie,PA002,Erie,42049,E,nw,41.9700,-80.0140
+PA,003,CLE,Crawford,PA003,Crawford,42039,E,nw,41.6848,-80.1063
+PA,004,CTP,Warren,PA004,Warren,42123,E,nc,41.8147,-79.2741
+PA,005,CTP,McKean,PA005,McKean,42083,E,nc,41.8078,-78.5690
+PA,006,CTP,Potter,PA006,Potter,42105,E,nc,41.7450,-77.8958
+PA,007,PBZ,Mercer,PA007,Mercer,42085,E,nw,41.3022,-80.2577
+PA,008,PBZ,Venango,PA008,Venango,42121,E,nw,41.4010,-79.7580
+PA,009,PBZ,Forest,PA009,Forest,42053,E,nw,41.5132,-79.2361
+PA,010,CTP,Elk,PA010,Elk,42047,E,nc,41.4253,-78.6492
+PA,011,CTP,Cameron,PA011,Cameron,42023,E,nc,41.4368,-78.2038
+PA,012,CTP,Northern Clinton,PA012,Clinton,42035,E,nc,41.2914,-77.7332
+PA,013,PBZ,Lawrence,PA013,Lawrence,42073,E,ww,40.9912,-80.3342
+PA,014,PBZ,Butler,PA014,Butler,42019,E,wc,40.9117,-79.9130
+PA,015,PBZ,Clarion,PA015,Clarion,42031,E,wc,41.1925,-79.4210
+PA,016,PBZ,Jefferson,PA016,Jefferson,42065,E,wc,41.1281,-78.9995
+PA,017,CTP,Clearfield,PA017,Clearfield,42033,E,cc,41.0002,-78.4741
+PA,018,CTP,Northern Centre,PA018,Centre,42027,E,cc,40.9895,-78.0032
+PA,019,CTP,Southern Centre,PA019,Centre,42027,E,cc,40.8739,-77.7022
+PA,020,PBZ,Beaver,PA020,Beaver,42007,E,ww,40.6823,-80.3493
+PA,021,PBZ,Allegheny,PA021,Allegheny,42003,E,sw,40.4688,-79.9812
+PA,022,PBZ,Armstrong,PA022,Armstrong,42005,E,wc,40.8125,-79.4645
+PA,023,PBZ,Indiana,PA023,Indiana,42063,E,wc,40.6521,-79.0875
+PA,024,CTP,Cambria,PA024,Cambria,42021,E,cc,40.4954,-78.7136
+PA,025,CTP,Blair,PA025,Blair,42013,E,cc,40.4813,-78.3486
+PA,026,CTP,Huntingdon,PA026,Huntingdon,42061,E,cc,40.4170,-77.9812
+PA,027,CTP,Mifflin,PA027,Mifflin,42087,E,cc,40.6104,-77.6171
+PA,028,CTP,Juniata,PA028,Juniata,42067,E,cc,40.5311,-77.4020
+PA,029,PBZ,Washington,PA029,Washington,42125,E,sw,40.1894,-80.2482
+PA,031,PBZ,Greene,PA031,Greene,42059,E,sw,39.8538,-80.2229
+PA,033,CTP,Somerset,PA033,Somerset,42111,E,sc,39.9725,-79.0283
+PA,034,CTP,Bedford,PA034,Bedford,42009,E,sc,40.0066,-78.4902
+PA,035,CTP,Fulton,PA035,Fulton,42057,E,sc,39.9255,-78.1124
+PA,036,CTP,Franklin,PA036,Franklin,42055,E,sc,39.9275,-77.7212
+PA,037,CTP,Tioga,PA037,Tioga,42117,E,nc,41.7721,-77.2543
+PA,038,BGM,Bradford,PA038,Bradford,42015,E,ne,41.7886,-76.5157
+PA,039,BGM,Susquehanna,PA039,Susquehanna,42115,E,ne,41.8214,-75.8008
+PA,040,BGM,Northern Wayne,PA040,Wayne,42127,E,ne,41.7773,-75.2898
+PA,041,CTP,Northern Lycoming,PA041,Lycoming,42081,E,nc,41.4265,-77.1724
+PA,042,CTP,Sullivan,PA042,Sullivan,42113,E,nc,41.4460,-76.5119
+PA,043,BGM,Wyoming,PA043,Wyoming,42131,E,ne,41.5184,-76.0165
+PA,044,BGM,Lackawanna,PA044,Lackawanna,42069,E,ne,41.4370,-75.6092
+PA,045,CTP,Southern Clinton,PA045,Clinton,42035,E,nc,41.0789,-77.3821
+PA,046,CTP,Southern Lycoming,PA046,Lycoming,42081,E,nc,41.2287,-76.9158
+PA,047,BGM,Luzerne,PA047,Luzerne,42079,E,ne,41.1772,-75.9889
+PA,048,BGM,Pike,PA048,Pike,42103,E,ne,41.3321,-75.0337
+PA,049,CTP,Union,PA049,Union,42119,E,cc,40.9630,-77.0621
+PA,050,CTP,Snyder,PA050,Snyder,42109,E,cc,40.7700,-77.0701
+PA,051,CTP,Montour,PA051,Montour,42093,E,cc,41.0280,-76.6586
+PA,052,CTP,Northumberland,PA052,Northumberland,42097,E,cc,40.8520,-76.7091
+PA,053,CTP,Columbia,PA053,Columbia,42037,E,cc,41.0486,-76.4052
+PA,054,PHI,Carbon,PA054,Carbon,42025,E,ne,40.9183,-75.7088
+PA,055,PHI,Monroe,PA055,Monroe,42089,E,ne,41.0582,-75.3394
+PA,056,CTP,Perry,PA056,Perry,42099,E,sc,40.3984,-77.2622
+PA,057,CTP,Dauphin,PA057,Dauphin,42043,E,sc,40.4155,-76.7792
+PA,058,CTP,Schuylkill,PA058,Schuylkill,42107,E,cc,40.7059,-76.2160
+PA,059,CTP,Lebanon,PA059,Lebanon,42075,E,sc,40.3672,-76.4577
+PA,060,PHI,Berks,PA060,Berks,42011,E,ee,40.4164,-75.9260
+PA,061,PHI,Lehigh,PA061,Lehigh,42077,E,ee,40.6129,-75.5924
+PA,062,PHI,Northampton,PA062,Northampton,42095,E,ee,40.7543,-75.3074
+PA,063,CTP,Cumberland,PA063,Cumberland,42041,E,sc,40.1637,-77.2651
+PA,064,CTP,Adams,PA064,Adams,42001,E,sc,39.8715,-77.2179
+PA,065,CTP,York,PA065,York,42133,E,sc,39.9199,-76.7265
+PA,066,CTP,Lancaster,PA066,Lancaster,42071,E,sc,40.0424,-76.2477
+PA,070,PHI,Delaware,PA070,Delaware,42045,E,se,39.9167,-75.3990
+PA,071,PHI,Philadelphia,PA071,Philadelphia,42101,E,se,40.0076,-75.1338
+PA,072,BGM,Southern Wayne,PA072,Wayne,42127,E,ne,41.4763,-75.3213
+PA,073,PBZ,Westmoreland,PA073,Westmoreland,42129,E,sw,40.3446,-79.5748
+PA,074,PBZ,Westmoreland Ridges,PA074,Westmoreland,42129,E,sw,40.2371,-79.2326
+PA,075,PBZ,Fayette,PA075,Fayette,42051,E,sw,39.9485,-79.7718
+PA,076,PBZ,Fayette Ridges,PA076,Fayette,42051,E,sw,39.8896,-79.5165
+PA,101,PHI,Chester,PA101,Western Chester,42029,E,se,39.9733,-75.7483
+PA,102,PHI,Chester,PA102,Eastern Chester,42029,E,se,39.9733,-75.7483
+PA,103,PHI,Montgomery,PA103,Western Montgomery,42091,E,se,40.2109,-75.3672
+PA,104,PHI,Montgomery,PA104,Eastern Montgomery,42091,E,se,40.2109,-75.3672
+PA,105,PHI,Bucks,PA105,Upper Bucks,42017,E,se,40.3370,-75.1067
+PA,106,PHI,Bucks,PA106,Lower Bucks,42017,E,se,40.3370,-75.1067
+PR,001,SJU,San Juan and Vicinity,PR001,Bayamon,72021,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,Carolina,72031,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,Catano,72033,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,Guaynabo,72061,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,San Juan,72127,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,Toa Alta,72135,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,Toa Baja,72137,V,,18.3710,-66.0981
+PR,001,SJU,San Juan and Vicinity,PR001,Trujillo Alto,72139,V,,18.3710,-66.0981
+PR,002,SJU,Northeast,PR002,Canovanas,72029,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Ceiba,72037,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Fajardo,72053,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Humacao,72069,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Loiza,72087,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Luquillo,72089,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Naguabo,72103,V,,18.2861,-65.7796
+PR,002,SJU,Northeast,PR002,Rio Grande,72119,V,,18.2861,-65.7796
+PR,003,SJU,Southeast,PR003,Arroyo,72015,V,,18.0258,-66.0756
+PR,003,SJU,Southeast,PR003,Guayama,72057,V,,18.0258,-66.0756
+PR,003,SJU,Southeast,PR003,Maunabo,72095,V,,18.0258,-66.0756
+PR,003,SJU,Southeast,PR003,Patillas,72109,V,,18.0258,-66.0756
+PR,003,SJU,Southeast,PR003,Salinas,72123,V,,18.0258,-66.0756
+PR,003,SJU,Southeast,PR003,Yabucoa,72151,V,,18.0258,-66.0756
+PR,004,SJU,Eastern Interior,PR004,Aguas Buenas,72007,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Caguas,72025,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Cayey,72035,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Cidra,72041,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Comerio,72045,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Gurabo,72063,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Juncos,72077,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,Las Piedras,72085,V,,18.1894,-66.0520
+PR,004,SJU,Eastern Interior,PR004,San Lorenzo,72129,V,,18.1894,-66.0520
+PR,005,SJU,North Central,PR005,Arecibo,72013,V,,18.4154,-66.5309
+PR,005,SJU,North Central,PR005,Barceloneta,72017,V,,18.4154,-66.5309
+PR,005,SJU,North Central,PR005,Dorado,72051,V,,18.4154,-66.5309
+PR,005,SJU,North Central,PR005,Florida,72054,V,,18.4154,-66.5309
+PR,005,SJU,North Central,PR005,Manati,72091,V,,18.4154,-66.5309
+PR,005,SJU,North Central,PR005,Vega Alta,72143,V,,18.4154,-66.5309
+PR,005,SJU,North Central,PR005,Vega Baja,72145,V,,18.4154,-66.5309
+PR,006,SJU,Central Interior,PR006,Aibonito,72009,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Barranquitas,72019,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Ciales,72039,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Coamo,72043,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Corozal,72047,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Jayuya,72073,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Morovis,72101,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Naranjito,72105,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Orocovis,72107,V,,18.2130,-66.4092
+PR,006,SJU,Central Interior,PR006,Villalba,72149,V,,18.2130,-66.4092
+PR,007,SJU,Ponce and Vicinity,PR007,Guayanilla,72059,V,,18.0539,-66.6527
+PR,007,SJU,Ponce and Vicinity,PR007,Juana Diaz,72075,V,,18.0539,-66.6527
+PR,007,SJU,Ponce and Vicinity,PR007,Penuelas,72111,V,,18.0539,-66.6527
+PR,007,SJU,Ponce and Vicinity,PR007,Ponce,72113,V,,18.0539,-66.6527
+PR,007,SJU,Ponce and Vicinity,PR007,Santa Isabel,72133,V,,18.0539,-66.6527
+PR,007,SJU,Ponce and Vicinity,PR007,Yauco,72153,V,,18.0539,-66.6527
+PR,008,SJU,Northwest,PR008,Aguadilla,72005,V,,18.4353,-66.9410
+PR,008,SJU,Northwest,PR008,Camuy,72027,V,,18.4353,-66.9410
+PR,008,SJU,Northwest,PR008,Hatillo,72065,V,,18.4353,-66.9410
+PR,008,SJU,Northwest,PR008,Isabela,72071,V,,18.4353,-66.9410
+PR,008,SJU,Northwest,PR008,Quebradillas,72115,V,,18.4353,-66.9410
+PR,009,SJU,Western Interior,PR009,Adjuntas,72001,V,,18.2385,-66.8482
+PR,009,SJU,Western Interior,PR009,Lares,72081,V,,18.2385,-66.8482
+PR,009,SJU,Western Interior,PR009,Las Marias,72083,V,,18.2385,-66.8482
+PR,009,SJU,Western Interior,PR009,Maricao,72093,V,,18.2385,-66.8482
+PR,009,SJU,Western Interior,PR009,Sabana Grande,72121,V,,18.2385,-66.8482
+PR,009,SJU,Western Interior,PR009,San Sebastian,72131,V,,18.2385,-66.8482
+PR,009,SJU,Western Interior,PR009,Utuado,72141,V,,18.2385,-66.8482
+PR,010,SJU,Mayaguez and Vicinity,PR010,Aguada,72003,V,,18.2425,-67.1683
+PR,010,SJU,Mayaguez and Vicinity,PR010,Anasco,72011,V,,18.2425,-67.1683
+PR,010,SJU,Mayaguez and Vicinity,PR010,Hormigueros,72067,V,,18.2425,-67.1683
+PR,010,SJU,Mayaguez and Vicinity,PR010,Mayaguez,72097,V,,18.2425,-67.1683
+PR,010,SJU,Mayaguez and Vicinity,PR010,Moca,72099,V,,18.2425,-67.1683
+PR,010,SJU,Mayaguez and Vicinity,PR010,Rincon,72117,V,,18.2425,-67.1683
+PR,010,SJU,Mayaguez and Vicinity,PR010,San German,72125,V,,18.2425,-67.1683
+PR,011,SJU,Southwest,PR011,Cabo Rojo,72023,V,,18.0180,-67.0614
+PR,011,SJU,Southwest,PR011,Guanica,72055,V,,18.0180,-67.0614
+PR,011,SJU,Southwest,PR011,Lajas,72079,V,,18.0180,-67.0614
+PR,012,SJU,Culebra,PR012,Culebra,72049,V,,18.3159,-65.2877
+PR,013,SJU,Vieques,PR013,Vieques,72147,V,,18.1231,-65.4415
+RI,001,BOX,Northwest Providence,RI001,Providence,44007,E,,41.8908,-71.6203
+RI,002,BOX,Southeast Providence,RI002,Providence,44007,E,,41.8121,-71.4526
+RI,003,BOX,Western Kent,RI003,Kent,44003,E,,41.6644,-71.6637
+RI,004,BOX,Eastern Kent,RI004,Kent,44003,E,,41.6861,-71.4647
+RI,005,BOX,Bristol,RI005,Bristol,44001,E,,41.7161,-71.2842
+RI,006,BOX,Washington,RI006,Washington,44009,E,,41.4780,-71.6248
+RI,007,BOX,Newport,RI007,Newport,44005,E,,41.5560,-71.2366
+RI,008,BOX,Block Island,RI008,Washington,44009,E,,41.1780,-71.5785
+SC,001,GSP,Oconee Mountains,SC001,Oconee,45073,E,up,34.8154,-83.1059
+SC,002,GSP,Pickens Mountains,SC002,Pickens,45077,E,up,34.9701,-82.7637
+SC,003,GSP,Greenville Mountains,SC003,Greenville,45045,E,up,35.0978,-82.4387
+SC,004,GSP,Greater Oconee,SC004,Oconee,45073,E,up,34.6281,-82.9848
+SC,005,GSP,Greater Pickens,SC005,Pickens,45077,E,up,34.8123,-82.6904
+SC,006,GSP,Greater Greenville,SC006,Greenville,45045,E,up,34.7881,-82.3354
+SC,007,GSP,Spartanburg,SC007,Spartanburg,45083,E,up,34.9313,-81.9907
+SC,008,GSP,Cherokee,SC008,Cherokee,45021,E,up,35.0482,-81.6204
+SC,009,GSP,York,SC009,York,45091,E,up,34.9747,-81.1845
+SC,010,GSP,Anderson,SC010,Anderson,45007,E,up,34.5191,-82.6379
+SC,011,GSP,Abbeville,SC011,Abbeville,45001,E,up,34.2226,-82.4587
+SC,012,GSP,Laurens,SC012,Laurens,45059,E,up,34.4836,-82.0060
+SC,013,GSP,Union,SC013,Union,45087,E,up,34.6892,-81.6194
+SC,014,GSP,Chester,SC014,Chester,45023,E,up,34.6921,-81.1595
+SC,016,CAE,Chesterfield,SC016,Chesterfield,45025,E,cc,34.6397,-80.1586
+SC,017,ILM,Marlboro,SC017,Marlboro,45069,E,ne,34.6020,-79.6786
+SC,018,CAE,McCormick,SC018,McCormick,45065,E,cc,33.8996,-82.3099
+SC,019,GSP,Greenwood,SC019,Greenwood,45047,E,up,34.1538,-82.1259
+SC,020,CAE,Newberry,SC020,Newberry,45071,E,cc,34.2898,-81.6001
+SC,021,CAE,Fairfield,SC021,Fairfield,45039,E,cc,34.3951,-81.1212
+SC,022,CAE,Kershaw,SC022,Kershaw,45055,E,cc,34.3388,-80.5903
+SC,023,ILM,Darlington,SC023,Darlington,45031,E,ne,34.3324,-79.9577
+SC,024,ILM,Dillon,SC024,Dillon,45033,E,ne,34.3915,-79.3789
+SC,025,CAE,Edgefield,SC025,Edgefield,45037,E,cc,33.7723,-81.9666
+SC,026,CAE,Saluda,SC026,Saluda,45081,E,cc,34.0061,-81.7269
+SC,027,CAE,Lexington,SC027,Lexington,45063,E,cc,33.9022,-81.2722
+SC,028,CAE,Richland,SC028,Richland,45079,E,cc,34.0218,-80.9031
+SC,029,CAE,Lee,SC029,Lee,45061,E,cc,34.1633,-80.2545
+SC,030,CAE,Aiken,SC030,Aiken,45003,E,cc,33.5444,-81.6348
+SC,031,CAE,Sumter,SC031,Sumter,45085,E,cc,33.9162,-80.3823
+SC,032,ILM,Florence,SC032,Florence,45041,E,ne,34.0245,-79.7028
+SC,033,ILM,Marion,SC033,Marion,45067,E,ne,34.0801,-79.3625
+SC,035,CAE,Barnwell,SC035,Barnwell,45011,E,cc,33.2661,-81.4350
+SC,036,CAE,Orangeburg,SC036,Orangeburg,45075,E,cc,33.4390,-80.8003
+SC,037,CAE,Calhoun,SC037,Calhoun,45017,E,cc,33.6749,-80.7802
+SC,038,CAE,Clarendon,SC038,Clarendon,45027,E,cc,33.6658,-80.2164
+SC,039,ILM,Williamsburg,SC039,Williamsburg,45089,E,ne,33.6199,-79.7277
+SC,040,CHS,Allendale,SC040,Allendale,45005,E,se,32.9881,-81.3581
+SC,041,CAE,Bamberg,SC041,Bamberg,45009,E,cc,33.2147,-81.0542
+SC,042,CHS,Hampton,SC042,Hampton,45049,E,se,32.7763,-81.1406
+SC,043,CHS,Inland Colleton,SC043,Colleton,45029,E,se,32.9179,-80.7029
+SC,044,CHS,Dorchester,SC044,Dorchester,45035,E,se,33.0795,-80.4052
+SC,045,CHS,Inland Berkeley,SC045,Berkeley,45015,E,se,33.2220,-79.9542
+SC,047,CHS,Inland Jasper,SC047,Jasper,45053,E,se,32.4763,-81.0517
+SC,048,CHS,Beaufort,SC048,Beaufort,45013,E,se,32.3874,-80.7373
+SC,048,CHS,Beaufort,SC048,Beaufort,45013,E,se,32.3568,-80.6933
+SC,049,CHS,Coastal Colleton,SC049,Colleton,45029,E,se,32.6103,-80.4980
+SC,050,CHS,Charleston,SC050,Charleston,45019,E,se,32.8411,-79.9437
+SC,051,CHS,Coastal Jasper,SC051,Jasper,45053,E,se,32.2423,-80.9561
+SC,052,CHS,Tidal Berkeley,SC052,Berkeley,45015,E,se,32.9612,-79.9183
+SC,054,ILM,Coastal Horry,SC054,Horry,45051,E,ne,33.7383,-78.8597
+SC,055,ILM,Inland Georgetown,SC055,Georgetown,45043,E,ne,33.4918,-79.3913
+SC,056,ILM,Coastal Georgetown,SC056,Georgetown,45043,E,ne,33.3522,-79.2437
+SC,058,ILM,Central Horry,SC058,Horry,45051,E,ne,33.8439,-79.0138
+SC,059,ILM,Northern Horry,SC059,Horry,45051,E,ne,34.0737,-79.0299
+SC,115,CAE,Northern Lancaster ,SC115,Lancaster,45057,E,cc,34.9326,-80.8420
+SC,116,CAE,Southern Lancaster ,SC116,Lancaster,45057,E,cc,34.6577,-80.6893
+SD,001,UNR,Harding,SD001,Harding,46063,M,nw,45.5804,-103.4959
+SD,002,UNR,Perkins,SD002,Perkins,46105,M,nw,45.4904,-102.4759
+SD,003,ABR,Corson,SD003,Corson,46031,M,nc,45.7087,-101.1969
+SD,004,ABR,Campbell,SD004,Campbell,46021,C,nc,45.7711,-100.0517
+SD,005,ABR,McPherson,SD005,McPherson,46089,C,nc,45.7664,-99.2215
+SD,006,ABR,Brown,SD006,Brown,46013,C,ne,45.5898,-98.3516
+SD,007,ABR,Marshall,SD007,Marshall,46091,C,ne,45.7586,-97.5986
+SD,008,ABR,Roberts,SD008,Roberts,46109,C,ne,45.6296,-96.9461
+SD,009,ABR,Walworth,SD009,Walworth,46129,C,nc,45.4299,-100.0316
+SD,010,ABR,Edmunds,SD010,Edmunds,46045,C,nc,45.4188,-99.2156
+SD,011,ABR,Day,SD011,Day,46037,C,ne,45.3672,-97.6075
+SD,012,UNR,Butte,SD012,Butte,46019,M,nw,44.9058,-103.5081
+SD,013,UNR,Northern Meade Co Plains,SD013,Meade,46093,M,wc,44.8215,-102.4800
+SD,014,UNR,Ziebach,SD014,Ziebach,46137,M,wc,44.9806,-101.6660
+SD,015,ABR,Dewey,SD015,Dewey,46041,M,nc,45.1566,-100.8719
+SD,016,ABR,Potter,SD016,Potter,46107,C,nc,45.0645,-99.9573
+SD,017,ABR,Faulk,SD017,Faulk,46049,C,nc,45.0711,-99.1453
+SD,018,ABR,Spink,SD018,Spink,46115,C,ne,44.9381,-98.3463
+SD,019,ABR,Clark,SD019,Clark,46025,C,ne,44.8582,-97.7295
+SD,020,ABR,Codington,SD020,Codington,46029,C,ne,44.9778,-97.1886
+SD,021,ABR,Grant,SD021,Grant,46051,C,ne,45.1720,-96.7678
+SD,022,ABR,Hamlin,SD022,Hamlin,46057,C,ne,44.6737,-97.1883
+SD,023,ABR,Deuel,SD023,Deuel,46039,C,ne,44.7600,-96.6680
+SD,024,UNR,Northern Black Hills,SD024,Lawrence,46081,M,wc,44.2746,-103.7873
+SD,024,UNR,Northern Black Hills,SD024,Meade,46093,M,wc,44.2746,-103.7873
+SD,025,UNR,Northern Foot Hills,SD025,Lawrence,46081,M,wc,44.5032,-103.7695
+SD,026,UNR,Rapid City,SD026,Pennington,46103,M,wc,44.0007,-103.2230
+SD,027,UNR,Southern Foot Hills,SD027,Fall River,46047,M,sw,43.3796,-103.6621
+SD,028,UNR,Central Black Hills,SD028,Pennington,46103,M,wc,43.9962,-103.6988
+SD,029,UNR,Southern Black Hills,SD029,Custer,46033,M,sw,43.6724,-103.7075
+SD,030,UNR,Custer Co Plains,SD030,Custer,46033,M,sw,43.6988,-102.9762
+SD,031,UNR,Pennington Co Plains,SD031,Pennington,46103,M,wc,44.0070,-102.4422
+SD,032,UNR,Haakon,SD032,Haakon,46055,M,wc,44.2944,-101.5399
+SD,033,ABR,Stanley,SD033,Stanley,46117,CM,cc,44.4123,-100.7359
+SD,034,ABR,Sully,SD034,Sully,46119,C,cc,44.7155,-100.1322
+SD,035,ABR,Hughes,SD035,Hughes,46065,C,cc,44.3890,-99.9961
+SD,036,ABR,Hyde,SD036,Hyde,46069,C,cc,44.5474,-99.4871
+SD,037,ABR,Hand,SD037,Hand,46059,C,cc,44.5478,-99.0050
+SD,038,FSD,Beadle,SD038,Beadle,46005,C,ec,44.4145,-98.2782
+SD,039,FSD,Kingsbury,SD039,Kingsbury,46077,C,ec,44.3696,-97.4915
+SD,040,FSD,Brookings,SD040,Brookings,46011,C,ec,44.3697,-96.7905
+SD,041,UNR,Fall River,SD041,Fall River,46047,M,sw,43.1859,-103.4763
+SD,042,UNR,Oglala Lakota,SD042,Oglala Lakota,46102,M,sw,43.3356,-102.5516
+SD,043,UNR,Jackson,SD043,Jackson,46071,M,sw,43.6942,-101.6281
+SD,044,UNR,Bennett,SD044,Bennett,46007,M,sw,43.1949,-101.6639
+SD,045,ABR,Jones,SD045,Jones,46075,C,cc,43.9604,-100.6896
+SD,046,UNR,Mellette,SD046,Mellette,46095,C,sc,43.5811,-100.7601
+SD,047,UNR,Todd,SD047,Todd,46121,C,sc,43.1934,-100.7184
+SD,048,ABR,Lyman,SD048,Lyman,46085,C,cc,43.8958,-99.8474
+SD,049,UNR,Tripp,SD049,Tripp,46123,C,sc,43.3459,-99.8840
+SD,050,FSD,Gregory,SD050,Gregory,46053,C,sc,43.1924,-99.1856
+SD,051,ABR,Buffalo,SD051,Buffalo,46017,C,cc,44.0763,-99.2050
+SD,052,FSD,Jerauld,SD052,Jerauld,46073,C,cc,44.0664,-98.6298
+SD,053,FSD,Sanborn,SD053,Sanborn,46111,C,ec,44.0235,-98.0914
+SD,054,FSD,Miner,SD054,Miner,46097,C,ec,44.0220,-97.6102
+SD,055,FSD,Lake,SD055,Lake,46079,C,ec,44.0220,-97.1294
+SD,056,FSD,Moody,SD056,Moody,46101,C,ec,44.0221,-96.6710
+SD,057,FSD,Brule,SD057,Brule,46015,C,sc,43.7181,-99.0810
+SD,058,FSD,Aurora,SD058,Aurora,46003,C,sc,43.7182,-98.5616
+SD,059,FSD,Davison,SD059,Davison,46035,C,se,43.6750,-98.1461
+SD,060,FSD,Hanson,SD060,Hanson,46061,C,se,43.6748,-97.7874
+SD,061,FSD,McCook,SD061,McCook,46087,C,se,43.6742,-97.3684
+SD,062,FSD,Minnehaha,SD062,Minnehaha,46099,C,se,43.6742,-96.7914
+SD,063,FSD,Charles Mix,SD063,Charles Mix,46023,C,sc,43.2079,-98.5879
+SD,064,FSD,Douglas,SD064,Douglas,46043,C,sc,43.3871,-98.3662
+SD,065,FSD,Hutchinson,SD065,Hutchinson,46067,C,se,43.3348,-97.7546
+SD,066,FSD,Turner,SD066,Turner,46125,C,se,43.3108,-97.1488
+SD,067,FSD,Lincoln,SD067,Lincoln,46083,C,se,43.2789,-96.7219
+SD,068,FSD,Bon Homme,SD068,Bon Homme,46009,C,se,42.9884,-97.8847
+SD,069,FSD,Yankton,SD069,Yankton,46135,C,se,43.0090,-97.3947
+SD,070,FSD,Clay,SD070,Clay,46027,C,se,42.9146,-96.9757
+SD,071,FSD,Union,SD071,Union,46127,C,se,42.8324,-96.6561
+SD,072,UNR,Sturgis/Piedmont Foot Hills,SD072,Meade,46093,M,wc,44.3335,-103.3922
+SD,073,UNR,Southern Meade Co Plains,SD073,Meade,46093,M,wc,44.4055,-102.7678
+SD,074,UNR,Hermosa Foot Hills,SD074,Custer,46033,M,sw,43.6677,-103.2509
+TN,001,MEG,Lake,TN001,Lake,47095,C,nw,36.3352,-89.4935
+TN,002,MEG,Obion,TN002,Obion,47131,C,nw,36.3582,-89.1488
+TN,003,MEG,Weakley,TN003,Weakley,47183,C,nw,36.2983,-88.7178
+TN,004,MEG,Henry,TN004,Henry,47079,C,nw,36.3317,-88.3013
+TN,005,OHX,Stewart,TN005,Stewart,47161,C,mi,36.5012,-87.8385
+TN,006,OHX,Montgomery,TN006,Montgomery,47125,C,mi,36.4969,-87.3829
+TN,007,OHX,Robertson,TN007,Robertson,47147,C,mi,36.5254,-86.8705
+TN,008,OHX,Sumner,TN008,Sumner,47165,C,mi,36.4694,-86.4603
+TN,009,OHX,Macon,TN009,Macon,47111,C,mi,36.5320,-86.0072
+TN,010,OHX,Clay,TN010,Clay,47027,C,mi,36.5511,-85.5438
+TN,011,OHX,Pickett,TN011,Pickett,47137,C,mi,36.5585,-85.0748
+TN,012,MRX,Scott,TN012,Scott,47151,E,ea,36.4286,-84.5035
+TN,013,MRX,Campbell,TN013,Campbell,47013,E,ea,36.4036,-84.1493
+TN,014,MRX,Claiborne,TN014,Claiborne,47025,E,ea,36.4859,-83.6604
+TN,015,MRX,Hancock,TN015,Hancock,47067,E,ea,36.5236,-83.2219
+TN,016,MRX,Hawkins,TN016,Hawkins,47073,E,ea,36.4412,-82.9447
+TN,017,MRX,Sullivan,TN017,Sullivan,47163,E,ea,36.5129,-82.3042
+TN,018,MRX,Johnson,TN018,Johnson,47091,E,ea,36.4549,-81.8518
+TN,019,MEG,Dyer,TN019,Dyer,47045,C,nw,36.0590,-89.4138
+TN,020,MEG,Gibson,TN020,Gibson,47053,C,nw,35.9966,-88.9326
+TN,021,MEG,Carroll,TN021,Carroll,47017,C,nw,35.9731,-88.4502
+TN,022,OHX,Benton,TN022,Benton,47005,C,mi,36.0698,-88.0683
+TN,023,OHX,Houston,TN023,Houston,47083,C,mi,36.2860,-87.7171
+TN,024,OHX,Humphreys,TN024,Humphreys,47085,C,mi,36.0408,-87.7757
+TN,025,OHX,Dickson,TN025,Dickson,47043,C,mi,36.1491,-87.3567
+TN,026,OHX,Cheatham,TN026,Cheatham,47021,C,mi,36.2612,-87.0868
+TN,027,OHX,Davidson,TN027,Davidson,47037,C,mi,36.1694,-86.7848
+TN,028,OHX,Wilson,TN028,Wilson,47189,C,mi,36.1549,-86.2976
+TN,029,OHX,Trousdale,TN029,Trousdale,47169,C,mi,36.3921,-86.1567
+TN,030,OHX,Smith,TN030,Smith,47159,C,mi,36.2505,-85.9567
+TN,031,OHX,Jackson,TN031,Jackson,47087,C,mi,36.3592,-85.6732
+TN,032,OHX,Putnam,TN032,Putnam,47141,C,mi,36.1408,-85.4951
+TN,033,OHX,Overton,TN033,Overton,47133,C,mi,36.3450,-85.2881
+TN,034,OHX,Fentress,TN034,Fentress,47049,C,mi,36.3805,-84.9324
+TN,035,MRX,Morgan,TN035,Morgan,47129,E,ea,36.1350,-84.6491
+TN,036,MRX,Anderson,TN036,Anderson,47001,E,ea,36.1184,-84.1985
+TN,037,MRX,Union,TN037,Union,47173,E,ea,36.2879,-83.8375
+TN,038,MRX,Grainger,TN038,Grainger,47057,E,ea,36.2762,-83.5096
+TN,039,MRX,Hamblen,TN039,Hamblen,47063,E,ea,36.2172,-83.2666
+TN,040,MRX,Northwest Cocke,TN040,Cocke,47029,E,ea,35.9835,-83.1536
+TN,041,MRX,Cocke Smoky Mountains,TN041,Cocke,47029,E,ea,35.8417,-83.0743
+TN,042,MRX,Northwest Greene,TN042,Greene,47059,E,ea,36.2009,-82.8533
+TN,043,MRX,Southeast Greene,TN043,Greene,47059,E,ea,36.0126,-82.7980
+TN,044,MRX,Washington,TN044,Washington,47179,E,ea,36.2933,-82.4974
+TN,045,MRX,Unicoi,TN045,Unicoi,47171,E,ea,36.1108,-82.4323
+TN,046,MRX,Northwest Carter,TN046,Carter,47019,E,ea,36.3716,-82.1633
+TN,047,MRX,Southeast Carter,TN047,Carter,47019,E,ea,36.2304,-82.0990
+TN,048,MEG,Lauderdale,TN048,Lauderdale,47097,C,sw,35.7609,-89.6314
+TN,049,MEG,Tipton,TN049,Tipton,47167,C,sw,35.4966,-89.7594
+TN,050,MEG,Haywood,TN050,Haywood,47075,C,sw,35.5833,-89.2838
+TN,051,MEG,Crockett,TN051,Crockett,47033,C,nw,35.8135,-89.1395
+TN,052,MEG,Madison,TN052,Madison,47113,C,sw,35.6082,-88.8385
+TN,053,MEG,Chester,TN053,Chester,47023,C,sw,35.4211,-88.6128
+TN,054,MEG,Henderson,TN054,Henderson,47077,C,sw,35.6543,-88.3880
+TN,055,MEG,Decatur,TN055,Decatur,47039,C,mi,35.6030,-88.1088
+TN,056,OHX,Perry,TN056,Perry,47135,C,mi,35.6426,-87.8590
+TN,057,OHX,Hickman,TN057,Hickman,47081,C,mi,35.8033,-87.4733
+TN,058,OHX,Lewis,TN058,Lewis,47101,C,mi,35.5273,-87.4931
+TN,059,OHX,Williamson,TN059,Williamson,47187,C,mi,35.8938,-86.8987
+TN,060,OHX,Maury,TN060,Maury,47119,C,mi,35.6170,-87.0770
+TN,061,OHX,Marshall,TN061,Marshall,47117,C,mi,35.4689,-86.7651
+TN,062,OHX,Rutherford,TN062,Rutherford,47149,C,mi,35.8427,-86.4167
+TN,063,OHX,Cannon,TN063,Cannon,47015,C,mi,35.8087,-86.0617
+TN,064,OHX,De Kalb,TN064,De Kalb,47041,C,mi,35.9798,-85.8328
+TN,065,OHX,White,TN065,White,47185,C,mi,35.9264,-85.4553
+TN,066,OHX,Cumberland,TN066,Cumberland,47035,C,mi,35.9504,-84.9983
+TN,067,MRX,Roane,TN067,Roane,47145,E,ea,35.8478,-84.5233
+TN,068,MRX,Loudon,TN068,Loudon,47105,E,ea,35.7350,-84.3116
+TN,069,MRX,Knox,TN069,Knox,47093,E,ea,35.9932,-83.9371
+TN,070,MRX,Jefferson,TN070,Jefferson,47089,E,ea,36.0510,-83.4463
+TN,071,MRX,NW Blount,TN071,Blount,47009,E,ea,35.7251,-83.9604
+TN,072,MRX,Blount Smoky Mountains,TN072,Blount,47009,E,ea,35.5851,-83.8315
+TN,073,MRX,North Sevier,TN073,Sevier,47155,E,ea,35.8471,-83.5572
+TN,074,MRX,Sevier Smoky Mountains,TN074,Sevier,47155,E,ea,35.6746,-83.4659
+TN,075,OHX,Bedford,TN075,Bedford,47003,C,mi,35.5138,-86.4589
+TN,076,HUN,Moore,TN076,Moore,47127,C,mi,35.2847,-86.3588
+TN,077,OHX,Coffee,TN077,Coffee,47031,C,mi,35.4906,-86.0748
+TN,078,OHX,Warren,TN078,Warren,47177,C,mi,35.6787,-85.7785
+TN,079,OHX,Grundy,TN079,Grundy,47061,C,mi,35.3884,-85.7226
+TN,080,OHX,Van Buren,TN080,Van Buren,47175,C,mi,35.6960,-85.4525
+TN,081,MRX,Sequatchie,TN081,Sequatchie,47153,C,ea,35.3712,-85.4106
+TN,082,MRX,Bledsoe,TN082,Bledsoe,47007,C,ea,35.5964,-85.2051
+TN,083,MRX,Rhea,TN083,Rhea,47143,E,ea,35.6087,-84.9244
+TN,084,MRX,Meigs,TN084,Meigs,47121,E,ea,35.5127,-84.8135
+TN,085,MRX,McMinn,TN085,McMinn,47107,E,ea,35.4248,-84.6175
+TN,086,MRX,Northwest Monroe,TN086,Monroe,47123,E,ea,35.4861,-84.3095
+TN,087,MRX,Southeast Monroe,TN087,Monroe,47123,E,ea,35.3530,-84.1353
+TN,088,MEG,Shelby,TN088,Shelby,47157,C,sw,35.1837,-89.8956
+TN,089,MEG,Fayette,TN089,Fayette,47047,C,sw,35.1971,-89.4144
+TN,090,MEG,Hardeman,TN090,Hardeman,47069,C,sw,35.2069,-88.9931
+TN,091,MEG,McNairy,TN091,McNairy,47109,C,sw,35.1746,-88.5637
+TN,092,MEG,Hardin,TN092,Hardin,47071,C,sw,35.1987,-88.1845
+TN,093,OHX,Wayne,TN093,Wayne,47181,C,mi,35.2400,-87.7880
+TN,094,OHX,Lawrence,TN094,Lawrence,47099,C,mi,35.2173,-87.3956
+TN,095,OHX,Giles,TN095,Giles,47055,C,mi,35.2022,-87.0348
+TN,096,HUN,Lincoln,TN096,Lincoln,47103,C,mi,35.1405,-86.5889
+TN,097,HUN,Franklin,TN097,Franklin,47051,C,mi,35.1549,-86.0922
+TN,098,MRX,Marion,TN098,Marion,47115,C,ea,35.1294,-85.6220
+TN,099,MRX,Hamilton,TN099,Hamilton,47065,E,ea,35.1809,-85.1648
+TN,100,MRX,Bradley,TN100,Bradley,47011,E,ea,35.1541,-84.8596
+TN,101,MRX,West Polk,TN101,Polk,47139,E,ea,35.1416,-84.5937
+TN,102,MRX,East Polk,TN102,Polk,47139,E,ea,35.0855,-84.4113
+TX,001,AMA,Dallam,TX001,Dallam,48111,C,pa,36.2779,-102.6022
+TX,002,AMA,Sherman,TX002,Sherman,48421,C,pa,36.2777,-101.8935
+TX,003,AMA,Hansford,TX003,Hansford,48195,C,pa,36.2775,-101.3545
+TX,004,AMA,Ochiltree,TX004,Ochiltree,48357,C,pa,36.2784,-100.8156
+TX,005,AMA,Lipscomb,TX005,Lipscomb,48295,C,pa,36.2777,-100.2731
+TX,006,AMA,Hartley,TX006,Hartley,48205,C,pa,35.8400,-102.6029
+TX,007,AMA,Moore,TX007,Moore,48341,C,pa,35.8377,-101.8929
+TX,008,AMA,Hutchinson,TX008,Hutchinson,48233,C,pa,35.8400,-101.3547
+TX,009,AMA,Roberts,TX009,Roberts,48393,C,pa,35.8385,-100.8134
+TX,010,AMA,Hemphill,TX010,Hemphill,48211,C,pa,35.8376,-100.2705
+TX,011,AMA,Oldham,TX011,Oldham,48359,C,pa,35.4050,-102.6029
+TX,012,AMA,Potter,TX012,Potter,48375,C,pa,35.4013,-101.8940
+TX,013,AMA,Carson,TX013,Carson,48065,C,pa,35.4035,-101.3542
+TX,014,AMA,Gray,TX014,Gray,48179,C,pa,35.4012,-100.8126
+TX,015,AMA,Wheeler,TX015,Wheeler,48483,C,pa,35.4013,-100.2697
+TX,016,AMA,Deaf Smith,TX016,Deaf Smith,48117,C,pa,34.9660,-102.6049
+TX,017,AMA,Randall,TX017,Randall,48381,C,pa,34.9669,-101.9073
+TX,018,AMA,Armstrong,TX018,Armstrong,48011,C,pa,34.9649,-101.3574
+TX,019,AMA,Donley,TX019,Donley,48129,C,pa,34.9654,-100.8140
+TX,020,AMA,Collingsworth,TX020,Collingsworth,48087,C,pa,34.9649,-100.2701
+TX,021,LUB,Parmer,TX021,Parmer,48369,C,pa,34.5301,-102.7845
+TX,022,LUB,Castro,TX022,Castro,48069,C,pa,34.5300,-102.2617
+TX,023,LUB,Swisher,TX023,Swisher,48437,C,pa,34.5304,-101.7350
+TX,024,LUB,Briscoe,TX024,Briscoe,48045,C,pa,34.5303,-101.2086
+TX,025,LUB,Hall,TX025,Hall,48191,C,pa,34.5308,-100.6811
+TX,026,LUB,Childress,TX026,Childress,48075,C,pa,34.5292,-100.2076
+TX,027,LUB,Bailey,TX027,Bailey,48017,C,nw,34.0685,-102.8298
+TX,028,LUB,Lamb,TX028,Lamb,48279,C,nw,34.0686,-102.3517
+TX,029,LUB,Hale,TX029,Hale,48189,C,nw,34.0705,-101.8269
+TX,030,LUB,Floyd,TX030,Floyd,48153,C,nw,34.0724,-101.3032
+TX,031,LUB,Motley,TX031,Motley,48345,C,nw,34.0741,-100.7798
+TX,032,LUB,Cottle,TX032,Cottle,48101,C,nw,34.0776,-100.2788
+TX,033,LUB,Cochran,TX033,Cochran,48079,C,nw,33.6041,-102.8285
+TX,034,LUB,Hockley,TX034,Hockley,48219,C,nw,33.6076,-102.3432
+TX,035,LUB,Lubbock,TX035,Lubbock,48303,C,nw,33.6102,-101.8205
+TX,036,LUB,Crosby,TX036,Crosby,48107,C,nw,33.6146,-101.3000
+TX,037,LUB,Dickens,TX037,Dickens,48125,C,nw,33.6166,-100.7789
+TX,038,LUB,King,TX038,King,48269,C,nw,33.6165,-100.2558
+TX,039,LUB,Yoakum,TX039,Yoakum,48501,C,nw,33.1730,-102.8278
+TX,040,LUB,Terry,TX040,Terry,48445,C,nw,33.1738,-102.3352
+TX,041,LUB,Lynn,TX041,Lynn,48305,C,nw,33.1768,-101.8161
+TX,042,LUB,Garza,TX042,Garza,48169,C,nw,33.1799,-101.2984
+TX,043,LUB,Kent,TX043,Kent,48263,C,nw,33.1814,-100.7776
+TX,044,LUB,Stonewall,TX044,Stonewall,48433,C,nw,33.1791,-100.2533
+TX,045,MAF,Gaines,TX045,Gaines,48165,C,ww,32.7407,-102.6352
+TX,046,MAF,Dawson,TX046,Dawson,48115,C,ww,32.7425,-101.9477
+TX,047,MAF,Borden,TX047,Borden,48033,C,ww,32.7437,-101.4317
+TX,048,MAF,Scurry,TX048,Scurry,48415,C,ww,32.7463,-100.9164
+TX,049,SJT,Fisher,TX049,Fisher,48151,C,nw,32.7428,-100.4022
+TX,050,MAF,Andrews,TX050,Andrews,48003,C,ww,32.3050,-102.6378
+TX,051,MAF,Martin,TX051,Martin,48317,C,ww,32.3061,-101.9512
+TX,052,MAF,Howard,TX052,Howard,48227,C,ww,32.3062,-101.4355
+TX,053,MAF,Mitchell,TX053,Mitchell,48335,C,ww,32.3062,-100.9212
+TX,054,SJT,Nolan,TX054,Nolan,48353,C,nw,32.3035,-100.4061
+TX,059,MAF,Loving,TX059,Loving,48301,C,ww,31.8492,-103.5800
+TX,060,MAF,Winkler,TX060,Winkler,48495,C,ww,31.8500,-103.0482
+TX,061,MAF,Ector,TX061,Ector,48135,C,ww,31.8692,-102.5428
+TX,062,MAF,Midland,TX062,Midland,48329,C,ww,31.8692,-102.0316
+TX,063,MAF,Glasscock,TX063,Glasscock,48173,C,ww,31.8695,-101.5208
+TX,064,SJT,Sterling,TX064,Sterling,48431,C,sw,31.8279,-101.0500
+TX,065,SJT,Coke,TX065,Coke,48081,C,sw,31.8885,-100.5299
+TX,066,SJT,Runnels,TX066,Runnels,48399,C,sw,31.8311,-99.9762
+TX,067,MAF,Ward,TX067,Ward,48475,C,ww,31.5095,-103.1024
+TX,068,MAF,Crane,TX068,Crane,48103,C,ww,31.4285,-102.5155
+TX,069,MAF,Upton,TX069,Upton,48461,C,ww,31.3688,-102.0430
+TX,070,MAF,Reagan,TX070,Reagan,48383,C,ww,31.3662,-101.5230
+TX,071,SJT,Irion,TX071,Irion,48235,C,sw,31.3039,-100.9824
+TX,072,SJT,Tom Green,TX072,Tom Green,48451,C,sw,31.4044,-100.4621
+TX,073,SJT,Concho,TX073,Concho,48095,C,sw,31.3265,-99.8640
+TX,075,MAF,Pecos,TX075,Pecos,48371,C,sw,30.7811,-102.7236
+TX,076,SJT,Crockett,TX076,Crockett,48105,C,sw,30.7230,-101.4121
+TX,077,SJT,Schleicher,TX077,Schleicher,48413,C,sw,30.8975,-100.5385
+TX,078,SJT,Sutton,TX078,Sutton,48435,C,sw,30.4983,-100.5382
+TX,082,MAF,Terrell,TX082,Terrell,48443,C,sw,30.2250,-102.0765
+TX,083,OUN,Hardeman,TX083,Hardeman,48197,C,nn,34.2903,-99.7457
+TX,084,OUN,Foard,TX084,Foard,48155,C,nn,33.9746,-99.7780
+TX,085,OUN,Wilbarger,TX085,Wilbarger,48487,C,nn,34.0807,-99.2410
+TX,086,OUN,Wichita,TX086,Wichita,48485,C,nn,33.9880,-98.7032
+TX,087,OUN,Knox,TX087,Knox,48275,C,nn,33.6061,-99.7414
+TX,088,OUN,Baylor,TX088,Baylor,48023,C,nn,33.6165,-99.2135
+TX,089,OUN,Archer,TX089,Archer,48009,C,nn,33.6153,-98.6877
+TX,090,OUN,Clay,TX090,Clay,48077,C,nn,33.7854,-98.2084
+TX,091,FWD,Montague,TX091,Montague,48337,C,nc,33.6757,-97.7246
+TX,092,FWD,Cooke,TX092,Cooke,48097,C,nc,33.6393,-97.2126
+TX,093,FWD,Grayson,TX093,Grayson,48181,C,nc,33.6268,-96.6776
+TX,094,FWD,Fannin,TX094,Fannin,48147,C,nc,33.5939,-96.1066
+TX,095,FWD,Lamar,TX095,Lamar,48277,C,ne,33.6671,-95.5711
+TX,096,SHV,Red River,TX096,Red River,48387,C,ne,33.6207,-95.0504
+TX,097,SHV,Bowie,TX097,Bowie,48037,C,ne,33.4454,-94.4229
+TX,098,SJT,Haskell,TX098,Haskell,48207,C,nc,33.1782,-99.7303
+TX,099,SJT,Throckmorton,TX099,Throckmorton,48447,C,nc,33.1775,-99.2124
+TX,100,FWD,Young,TX100,Young,48503,C,nc,33.1767,-98.6878
+TX,101,FWD,Jack,TX101,Jack,48237,C,nc,33.2335,-98.1725
+TX,102,FWD,Wise,TX102,Wise,48497,C,nc,33.2159,-97.6544
+TX,103,FWD,Denton,TX103,Denton,48121,C,nc,33.2043,-97.1171
+TX,104,FWD,Collin,TX104,Collin,48085,C,nc,33.1878,-96.5724
+TX,105,FWD,Hunt,TX105,Hunt,48231,C,nc,33.1236,-96.0854
+TX,106,FWD,Delta,TX106,Delta,48119,C,ne,33.3863,-95.6723
+TX,107,FWD,Hopkins,TX107,Hopkins,48223,C,ne,33.1495,-95.5640
+TX,108,SHV,Franklin,TX108,Franklin,48159,C,ne,33.1756,-95.2184
+TX,109,SHV,Titus,TX109,Titus,48449,C,ne,33.2166,-94.9657
+TX,110,SHV,Camp,TX110,Camp,48063,C,ne,32.9732,-94.9785
+TX,111,SHV,Morris,TX111,Morris,48343,C,ne,33.1135,-94.7326
+TX,112,SHV,Cass,TX112,Cass,48067,C,ne,33.0775,-94.3436
+TX,113,SJT,Jones,TX113,Jones,48253,C,nc,32.7399,-99.8787
+TX,114,SJT,Shackelford,TX114,Shackelford,48417,C,nc,32.7360,-99.3541
+TX,115,FWD,Stephens,TX115,Stephens,48429,C,nc,32.7359,-98.8362
+TX,116,FWD,Palo Pinto,TX116,Palo Pinto,48363,C,nc,32.7532,-98.3130
+TX,117,FWD,Parker,TX117,Parker,48367,C,nc,32.7776,-97.8051
+TX,118,FWD,Tarrant,TX118,Tarrant,48439,C,nc,32.7704,-97.2919
+TX,119,FWD,Dallas,TX119,Dallas,48113,C,nc,32.7666,-96.7779
+TX,120,FWD,Rockwall,TX120,Rockwall,48397,C,nc,32.8978,-96.4077
+TX,121,FWD,Kaufman,TX121,Kaufman,48257,C,nc,32.5996,-96.2879
+TX,122,FWD,Van Zandt,TX122,Van Zandt,48467,C,ne,32.5637,-95.8365
+TX,123,FWD,Rains,TX123,Rains,48379,C,ne,32.8703,-95.7934
+TX,124,SHV,Wood,TX124,Wood,48499,C,ne,32.7864,-95.3821
+TX,125,SHV,Upshur,TX125,Upshur,48459,C,ne,32.7363,-94.9414
+TX,126,SHV,Marion,TX126,Marion,48315,C,ne,32.7980,-94.3572
+TX,127,SJT,Taylor,TX127,Taylor,48441,C,nc,32.3015,-99.8901
+TX,128,SJT,Callahan,TX128,Callahan,48059,C,nc,32.2977,-99.3735
+TX,129,FWD,Eastland,TX129,Eastland,48133,C,nc,32.3271,-98.8323
+TX,130,FWD,Erath,TX130,Erath,48143,C,nc,32.2363,-98.2180
+TX,131,FWD,Hood,TX131,Hood,48221,C,nc,32.4299,-97.8323
+TX,132,FWD,Somervell,TX132,Somervell,48425,C,nc,32.2223,-97.7744
+TX,133,FWD,Johnson,TX133,Johnson,48251,C,nc,32.3790,-97.3663
+TX,134,FWD,Ellis,TX134,Ellis,48139,C,nc,32.3484,-96.7945
+TX,135,FWD,Henderson,TX135,Henderson,48213,C,ne,32.2121,-95.8538
+TX,136,SHV,Smith,TX136,Smith,48423,C,ne,32.3750,-95.2692
+TX,137,SHV,Gregg,TX137,Gregg,48183,C,ne,32.4803,-94.8172
+TX,138,SHV,Harrison,TX138,Harrison,48203,C,ne,32.5483,-94.3713
+TX,139,SJT,Coleman,TX139,Coleman,48083,C,nc,31.7732,-99.4536
+TX,140,SJT,Brown,TX140,Brown,48049,C,nc,31.7743,-98.9998
+TX,141,FWD,Comanche,TX141,Comanche,48093,C,nc,31.9480,-98.5582
+TX,142,FWD,Mills,TX142,Mills,48333,C,nc,31.4952,-98.5955
+TX,143,FWD,Hamilton,TX143,Hamilton,48193,C,nc,31.7048,-98.1107
+TX,144,FWD,Bosque,TX144,Bosque,48035,C,nc,31.9004,-97.6343
+TX,145,FWD,Hill,TX145,Hill,48217,C,nc,31.9907,-97.1324
+TX,146,FWD,Navarro,TX146,Navarro,48349,C,nc,32.0470,-96.4724
+TX,147,FWD,Freestone,TX147,Freestone,48161,C,nc,31.7049,-96.1490
+TX,148,FWD,Anderson,TX148,Anderson,48001,C,ne,31.8134,-95.6525
+TX,149,SHV,Cherokee,TX149,Cherokee,48073,C,ne,31.8370,-95.1652
+TX,150,SHV,Rusk,TX150,Rusk,48401,C,ne,32.1077,-94.7618
+TX,151,SHV,Panola,TX151,Panola,48365,C,ne,32.1624,-94.3056
+TX,152,SHV,Nacogdoches,TX152,Nacogdoches,48347,C,ee,31.6160,-94.6158
+TX,153,SHV,Shelby,TX153,Shelby,48419,C,ee,31.7924,-94.1450
+TX,154,SJT,McCulloch,TX154,McCulloch,48307,C,nc,31.1989,-99.3475
+TX,155,SJT,San Saba,TX155,San Saba,48411,C,nc,31.1552,-98.8176
+TX,156,FWD,Lampasas,TX156,Lampasas,48281,C,nc,31.1962,-98.2414
+TX,157,FWD,Coryell,TX157,Coryell,48099,C,nc,31.3909,-97.7992
+TX,158,FWD,Bell,TX158,Bell,48027,C,nc,31.0377,-97.4782
+TX,159,FWD,McLennan,TX159,McLennan,48309,C,nc,31.5524,-97.2018
+TX,160,FWD,Falls,TX160,Falls,48145,C,nc,31.2533,-96.9358
+TX,161,FWD,Limestone,TX161,Limestone,48293,C,nc,31.5455,-96.5805
+TX,162,FWD,Leon,TX162,Leon,48289,C,ne,31.2965,-95.9957
+TX,163,HGX,Houston,TX163,Houston,48225,C,se,31.3178,-95.4227
+TX,164,HGX,Trinity,TX164,Trinity,48455,C,se,31.0888,-95.1355
+TX,165,SHV,Angelina,TX165,Angelina,48005,C,ee,31.2548,-94.6118
+TX,166,SHV,San Augustine,TX166,San Augustine,48405,C,ee,31.3942,-94.1682
+TX,167,SHV,Sabine,TX167,Sabine,48403,C,ee,31.3432,-93.8518
+TX,168,SJT,Menard,TX168,Menard,48327,C,sc,30.8898,-99.8206
+TX,169,SJT,Kimble,TX169,Kimble,48267,C,sc,30.4868,-99.7487
+TX,170,SJT,Mason,TX170,Mason,48319,C,sc,30.7177,-99.2261
+TX,171,EWX,Llano,TX171,Llano,48299,C,sc,30.7056,-98.6840
+TX,172,EWX,Burnet,TX172,Burnet,48053,C,sc,30.7883,-98.1824
+TX,173,EWX,Williamson,TX173,Williamson,48491,C,sc,30.6481,-97.6008
+TX,174,FWD,Milam,TX174,Milam,48331,C,sc,30.7863,-96.9769
+TX,175,FWD,Robertson,TX175,Robertson,48395,C,sc,31.0270,-96.5128
+TX,176,HGX,Madison,TX176,Madison,48313,C,se,30.9656,-95.9284
+TX,177,HGX,Walker,TX177,Walker,48471,C,se,30.7390,-95.5722
+TX,178,HGX,San Jancinto,TX178,San Jacinto,48407,C,se,30.5796,-95.1669
+TX,179,HGX,Polk,TX179,Polk,48373,C,se,30.7928,-94.8300
+TX,180,LCH,Tyler,TX180,Tyler,48457,C,se,30.7712,-94.3765
+TX,183,EWX,Val Verde,TX183,Val Verde,48465,C,sc,29.8930,-101.1517
+TX,184,EWX,Edwards,TX184,Edwards,48137,C,sc,29.9826,-100.3047
+TX,185,EWX,Real,TX185,Real,48385,C,sc,29.8318,-99.8221
+TX,186,EWX,Kerr,TX186,Kerr,48265,C,sc,30.0615,-99.3501
+TX,187,EWX,Bandera,TX187,Bandera,48019,C,sc,29.7472,-99.2463
+TX,188,EWX,Gillespie,TX188,Gillespie,48171,C,sc,30.3181,-98.9464
+TX,189,EWX,Kendall,TX189,Kendall,48259,C,sc,29.9447,-98.7116
+TX,190,EWX,Blanco,TX190,Blanco,48031,C,sc,30.2664,-98.3998
+TX,191,EWX,Hays,TX191,Hays,48209,C,sc,30.0582,-98.0311
+TX,192,EWX,Travis,TX192,Travis,48453,C,sc,30.3347,-97.7819
+TX,193,EWX,Bastrop,TX193,Bastrop,48021,C,sc,30.1036,-97.3121
+TX,194,EWX,Lee,TX194,Lee,48287,C,sc,30.3107,-96.9657
+TX,195,HGX,Burleson,TX195,Burleson,48051,C,se,30.4924,-96.6210
+TX,196,HGX,Brazos,TX196,Brazos,48041,C,se,30.6611,-96.3023
+TX,197,HGX,Washington,TX197,Washington,48477,C,se,30.2145,-96.4036
+TX,198,HGX,Grimes,TX198,Grimes,48185,C,se,30.5436,-95.9855
+TX,199,HGX,Montgomery,TX199,Montgomery,48339,C,se,30.3002,-95.5030
+TX,200,HGX,Northern Liberty,TX200,Liberty,48291,C,se,30.2284,-94.8693
+TX,201,LCH,Hardin,TX201,Hardin,48199,C,se,30.3324,-94.3903
+TX,202,EWX,Kinney,TX202,Kinney,48271,C,sc,29.3502,-100.4180
+TX,203,EWX,Uvalde,TX203,Uvalde,48463,C,sc,29.3573,-99.7622
+TX,204,EWX,Medina,TX204,Medina,48325,C,sc,29.3557,-99.1101
+TX,205,EWX,Bexar,TX205,Bexar,48029,C,sc,29.4491,-98.5199
+TX,206,EWX,Comal,TX206,Comal,48091,C,sc,29.8082,-98.2782
+TX,207,EWX,Guadalupe,TX207,Guadalupe,48187,C,sc,29.5831,-97.9484
+TX,208,EWX,Caldwell,TX208,Caldwell,48055,C,sc,29.8372,-97.6200
+TX,209,EWX,Fayette,TX209,Fayette,48149,C,sc,29.8768,-96.9198
+TX,210,HGX,Colorado,TX210,Colorado,48089,C,se,29.6208,-96.5263
+TX,211,HGX,Austin,TX211,Austin,48015,C,se,29.8870,-96.2780
+TX,212,HGX,Waller,TX212,Waller,48473,C,se,30.0111,-95.9877
+TX,213,HGX,Inland Harris,TX213,Harris,48201,C,se,29.8829,-95.4590
+TX,214,HGX,Chambers,TX214,Chambers,48071,C,se,29.7388,-94.6080
+TX,215,LCH,Jefferson,TX215,Jefferson,48245,C,se,29.8838,-94.1722
+TX,216,LCH,Orange,TX216,Orange,48361,C,se,30.1281,-93.8965
+TX,217,EWX,Maverick,TX217,Maverick,48323,C,sc,28.7426,-100.3145
+TX,218,EWX,Zavala,TX218,Zavala,48507,C,sc,28.8662,-99.7606
+TX,219,EWX,Frio,TX219,Frio,48163,C,sc,28.8678,-99.1083
+TX,220,EWX,Atascosa,TX220,Atascosa,48013,C,sc,28.8907,-98.5279
+TX,221,EWX,Wilson,TX221,Wilson,48493,C,sc,29.1741,-98.0866
+TX,222,EWX,Karnes,TX222,Karnes,48255,C,sc,28.9057,-97.8594
+TX,223,EWX,Gonzales,TX223,Gonzales,48177,C,sc,29.4567,-97.4925
+TX,224,EWX,De Witt,TX224,DeWitt,48123,C,sc,29.0821,-97.3568
+TX,225,EWX,Lavaca,TX225,Lavaca,48285,C,sc,29.3843,-96.9302
+TX,226,HGX,Wharton,TX226,Wharton,48481,C,se,29.2779,-96.2221
+TX,227,HGX,Fort Bend,TX227,Fort Bend,48157,C,se,29.5274,-95.7707
+TX,228,EWX,Dimmit,TX228,Dimmit,48127,C,sc,28.4225,-99.7567
+TX,229,CRP,La Salle,TX229,La Salle,48283,C,sc,28.3451,-99.0997
+TX,230,CRP,McMullen,TX230,McMullen,48311,C,sc,28.3491,-98.5675
+TX,231,CRP,Live Oak,TX231,Live Oak,48297,C,sc,28.3514,-98.1248
+TX,232,CRP,Bee,TX232,Bee,48025,C,sc,28.4174,-97.7412
+TX,233,CRP,Goliad,TX233,Goliad,48175,C,sc,28.6571,-97.4264
+TX,234,CRP,Victoria,TX234,Victoria,48469,C,se,28.7964,-96.9714
+TX,235,HGX,Inland Jackson,TX235,Jackson,48239,C,sc,29.0039,-96.6044
+TX,236,HGX,Inland Matagorda,TX236,Matagorda,48321,C,se,28.9561,-96.0201
+TX,237,HGX,Inland Brazoria,TX237,Brazoria,48039,C,se,29.2690,-95.5128
+TX,238,HGX,Inland Galveston,TX238,Galveston,48167,C,se,29.4207,-95.1320
+TX,239,CRP,Webb,TX239,Webb,48479,C,sc,27.7611,-99.3316
+TX,240,CRP,Duval,TX240,Duval,48131,C,sc,27.6813,-98.5089
+TX,241,CRP,Jim Wells,TX241,Jim Wells,48249,C,sc,27.7310,-98.0902
+TX,242,CRP,Inland Kleberg,TX242,Kleberg,48273,C,sc,27.4319,-97.8503
+TX,243,CRP,Inland Nueces,TX243,Nueces,48355,C,sc,27.7423,-97.7226
+TX,244,CRP,Inland San Patricio,TX244,San Patricio,48409,C,sc,28.0275,-97.5879
+TX,245,CRP,Aransas,TX245,Aransas,48007,C,sc,28.1558,-97.0065
+TX,246,CRP,Inland Refugio,TX246,Refugio,48391,C,so,28.3407,-97.2080
+TX,247,CRP,Inland Calhoun,TX247,Calhoun,48057,C,sc,28.5677,-96.7923
+TX,248,BRO,Zapata,TX248,Zapata,48505,C,sc,27.0008,-99.1686
+TX,249,BRO,Jim Hogg,TX249,Jim Hogg,48247,C,sc,27.0434,-98.6973
+TX,250,BRO,Brooks,TX250,Brooks,48047,C,sc,27.0316,-98.2187
+TX,251,BRO,Inland Kenedy County ,TX251,Kenedy,48261,C,sc,26.9205,-97.7793
+TX,252,BRO,Starr,TX252,Starr,48427,C,sc,26.5621,-98.7384
+TX,253,BRO,Southern Hidalgo,TX253,Hidalgo,48215,C,sc,26.2769,-98.1789
+TX,254,BRO,Inland Willacy,TX254,Willacy,48489,C,sc,26.4665,-97.7187
+TX,255,BRO,Inland Cameron,TX255,Cameron,48061,C,sc,26.1202,-97.5793
+TX,256,BRO,Coastal Willacy,TX256,Willacy,48489,C,sc,26.4854,-97.4503
+TX,257,BRO,Coastal Cameron,TX257,Cameron,48061,C,sc,26.1673,-97.3145
+TX,259,LCH,Northern Jasper,TX259,Jasper,48241,C,se,30.8641,-94.0341
+TX,260,LCH,Northern Newton,TX260,Newton,48351,C,se,30.8876,-93.7287
+TX,261,LCH,Southern Jasper,TX261,Jasper,48241,C,se,30.3824,-93.9977
+TX,262,LCH,Southern Newton,TX262,Newton,48351,C,se,30.3830,-93.8089
+TX,270,MAF,Guadalupe Mountains Above 7000 Feet ,TX270,Culberson,48109,CM,sw,31.9461,-104.8425
+TX,271,MAF,Guadalupe and Delaware Mountains ,TX271,Culberson,48109,CM,sw,31.7378,-104.7322
+TX,272,MAF,Van Horn and Highway 54 Corridor ,TX272,Culberson,48109,CM,sw,31.2123,-104.7304
+TX,273,MAF,Eastern Culberson County ,TX273,Culberson,48109,CM,sw,31.6552,-104.3586
+TX,274,MAF,Reeves County Plains ,TX274,Reeves,48389,C,sw,31.3331,-103.6800
+TX,275,MAF,Chinati Mountains ,TX275,Presidio,48377,C,sw,30.0939,-104.3962
+TX,276,MAF,Marfa Plateau ,TX276,Presidio,48377,C,sw,30.1790,-104.2468
+TX,276,MAF,Marfa Plateau ,TX276,Jeff Davis,48243,C,sw,30.1790,-104.2468
+TX,277,MAF,Davis Mountains ,TX277,Presidio,48377,C,sw,30.5275,-103.9850
+TX,277,MAF,Davis Mountains ,TX277,Jeff Davis,48243,C,sw,30.5275,-103.9850
+TX,277,MAF,Davis Mountains ,TX277,Brewster,48043,C,sw,30.5275,-103.9850
+TX,278,MAF,Davis Mountains Foothills ,TX278,Jeff Davis,48243,C,sw,30.7538,-103.7870
+TX,278,MAF,Davis Mountains Foothills ,TX278,Brewster,48043,C,sw,30.7538,-103.7870
+TX,278,MAF,Davis Mountains Foothills ,TX278,Culberson,48109,CM,sw,30.7538,-103.7870
+TX,278,MAF,Davis Mountains Foothills ,TX278,Reeves,48389,C,sw,30.7538,-103.7870
+TX,279,MAF,Central Brewster County ,TX279,Brewster,48043,C,sw,29.8383,-103.3132
+TX,280,MAF,Chisos Basin ,TX280,Brewster,48043,C,sw,29.2508,-103.2892
+TX,281,MAF,Presidio Valley ,TX281,Presidio,48377,C,sw,29.8815,-104.3805
+TX,281,MAF,Presidio Valley ,TX281,Jeff Davis,48243,C,sw,29.8815,-104.3805
+TX,281,MAF,Presidio Valley ,TX281,Culberson,48109,CM,sw,29.8815,-104.3805
+TX,282,MAF,Lower Brewster County ,TX282,Brewster,48043,C,sw,29.5252,-103.0214
+TX,300,HGX,Southern Liberty,TX300,Liberty,48291,C,se,29.9703,-94.6771
+TX,313,HGX,Coastal Harris,TX313,Harris,48201,C,se,29.7281,-95.0568
+TX,317,AMA,Palo Duro Canyon,TX317,Randall,48381,C,pa,34.9436,-101.6778
+TX,335,HGX,Coastal Jackson,TX335,Jackson,48239,C,sc,28.7655,-96.4773
+TX,336,HGX,Coastal Matagorda,TX336,Matagorda,48321,C,se,28.7695,-95.9366
+TX,337,HGX,Coastal Brazoria,TX337,Brazoria,48039,C,se,29.0676,-95.3623
+TX,338,HGX,Coastal Galveston,TX338,Galveston,48167,C,se,29.3887,-95.0028
+TX,342,CRP,Coastal Kleberg,TX342,Kleberg,48273,C,sc,27.4408,-97.4768
+TX,343,CRP,Coastal Nueces,TX343,Nueces,48355,C,sc,27.6637,-97.4107
+TX,344,CRP,Coastal San Patricio,TX344,San Patricio,48409,C,sc,27.9431,-97.2647
+TX,345,CRP,Aransas County Islands,TX345,Aransas,48007,C,sc,28.0051,-96.9391
+TX,346,CRP,Coastal Refugio,TX346,Refugio,48391,C,so,28.2777,-97.0145
+TX,347,CRP,Coastal Calhoun,TX347,Calhoun,48057,C,sc,28.5117,-96.5835
+TX,351,BRO,Coastal Kenedy County ,TX351,Kenedy,48261,C,sc,26.9556,-97.4956
+TX,353,BRO,Northern Hidalgo ,TX353,Hidalgo,48215,C,sc,26.6133,-98.1849
+TX,418,EPZ,Western El Paso County,TX418,El Paso,48141,M,sw,31.8933,-106.5545
+TX,419,EPZ,Eastern/Central El Paso County,TX419,El Paso,48141,M,sw,31.8096,-106.2948
+TX,420,EPZ,Northern Hudspeth Highlands/Hueco Mountains,TX420,El Paso,48141,M,sw,31.7056,-105.7878
+TX,420,EPZ,Northern Hudspeth Highlands/Hueco Mountains,TX420,Hudspeth,48229,M,sw,31.7056,-105.7878
+TX,421,EPZ,Salt Basin,TX421,Hudspeth,48229,M,sw,31.7396,-105.2179
+TX,422,EPZ,Southern Hudspeth Highlands,TX422,Hudspeth,48229,M,sw,31.1872,-105.1931
+TX,423,EPZ,Rio Grande Valley of Eastern El Paso/Western Hudspeth Counties,TX423,El Paso,48141,M,sw,31.3635,-105.8909
+TX,423,EPZ,Rio Grande Valley of Eastern El Paso/Western Hudspeth Counties,TX423,Hudspeth,48229,M,sw,31.3635,-105.8909
+TX,424,EPZ,Rio Grande Valley of Eastern Hudspeth County,TX424,Hudspeth,48229,M,sw,30.8825,-105.2707
+TX,436,HGX,Matagorda Islands,TX436,Matagorda,48321,C,se,28.6248,-95.9674
+TX,437,HGX,Brazoria Islands,TX437,Brazoria,48039,C,se,28.9671,-95.3005
+TX,438,HGX,Galveston Island and Bolivar Peninsula,TX438,Galveston,48167,C,se,29.3757,-94.7227
+TX,442,CRP,Kleberg County Island,TX442,Kleberg,48273,C,sc,27.4267,-97.3101
+TX,443,CRP,Nueces County Islands,TX443,Nueces,48355,C,sc,27.7078,-97.1651
+TX,447,CRP,Calhoun County Island,TX447,Calhoun,48057,C,sc,28.2324,-96.6387
+UT,001,SLC,Cache Valley/Utah Portion,UT001,Cache,49005,M,,41.7967,-111.9057
+UT,002,SLC,Northern Wasatch Front,UT002,Box Elder,49003,M,,41.3169,-112.1923
+UT,002,SLC,Northern Wasatch Front,UT002,Davis,49011,M,,41.3169,-112.1923
+UT,002,SLC,Northern Wasatch Front,UT002,Weber,49057,M,,41.3169,-112.1923
+UT,003,SLC,Salt Lake and Tooele Valleys,UT003,Salt Lake,49035,M,,40.5032,-112.2347
+UT,003,SLC,Salt Lake and Tooele Valleys,UT003,Tooele,49045,M,,40.5032,-112.2347
+UT,004,SLC,Southern Wasatch Front,UT004,Juab,49023,M,,40.1014,-111.8946
+UT,004,SLC,Southern Wasatch Front,UT004,Utah,49049,M,,40.1014,-111.8946
+UT,005,SLC,Great Salt Lake Desert and Mountains,UT005,Box Elder,49003,M,,40.9570,-113.2137
+UT,005,SLC,Great Salt Lake Desert and Mountains,UT005,Salt Lake,49035,M,,40.9570,-113.2137
+UT,005,SLC,Great Salt Lake Desert and Mountains,UT005,Tooele,49045,M,,40.9570,-113.2137
+UT,005,SLC,Great Salt Lake Desert and Mountains,UT005,Utah,49049,M,,40.9570,-113.2137
+UT,006,SLC,Wasatch Mountain Valleys,UT006,Morgan,49029,M,,40.8254,-111.5260
+UT,006,SLC,Wasatch Mountain Valleys,UT006,Summit,49043,M,,40.8254,-111.5260
+UT,006,SLC,Wasatch Mountain Valleys,UT006,Wasatch,49051,M,,40.8254,-111.5260
+UT,006,SLC,Wasatch Mountain Valleys,UT006,Weber,49057,M,,40.8254,-111.5260
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Box Elder,49003,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Cache,49005,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Davis,49011,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Morgan,49029,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Rich,49033,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Salt Lake,49035,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Summit,49043,M,,41.4529,-111.4916
+UT,007,SLC,Wasatch Mountains I-80 North,UT007,Weber,49057,M,,41.4529,-111.4916
+UT,008,SLC,Wasatch Mountains South of I-80,UT008,Juab,49023,M,,40.2134,-111.6090
+UT,008,SLC,Wasatch Mountains South of I-80,UT008,Salt Lake,49035,M,,40.2134,-111.6090
+UT,008,SLC,Wasatch Mountains South of I-80,UT008,Sanpete,49039,M,,40.2134,-111.6090
+UT,008,SLC,Wasatch Mountains South of I-80,UT008,Summit,49043,M,,40.2134,-111.6090
+UT,008,SLC,Wasatch Mountains South of I-80,UT008,Utah,49049,M,,40.2134,-111.6090
+UT,008,SLC,Wasatch Mountains South of I-80,UT008,Wasatch,49051,M,,40.2134,-111.6090
+UT,009,SLC,Western Uinta Mountains,UT009,Duchesne,49013,M,,40.6931,-110.7311
+UT,009,SLC,Western Uinta Mountains,UT009,Summit,49043,M,,40.6931,-110.7311
+UT,009,SLC,Western Uinta Mountains,UT009,Wasatch,49051,M,,40.6931,-110.7311
+UT,010,SLC,Wasatch Plateau/Book Cliffs,UT010,Carbon,49007,M,,39.8040,-110.7601
+UT,010,SLC,Wasatch Plateau/Book Cliffs,UT010,Duchesne,49013,M,,39.8040,-110.7601
+UT,010,SLC,Wasatch Plateau/Book Cliffs,UT010,Emery,49015,M,,39.8040,-110.7601
+UT,010,SLC,Wasatch Plateau/Book Cliffs,UT010,Sanpete,49039,M,,39.8040,-110.7601
+UT,010,SLC,Wasatch Plateau/Book Cliffs,UT010,Utah,49049,M,,39.8040,-110.7601
+UT,010,SLC,Wasatch Plateau/Book Cliffs,UT010,Wasatch,49051,M,,39.8040,-110.7601
+UT,011,SLC,Western Uinta Basin,UT011,Duchesne,49013,M,,40.2456,-110.3188
+UT,011,SLC,Western Uinta Basin,UT011,Wasatch,49051,M,,40.2456,-110.3188
+UT,012,SLC,Castle Country,UT012,Carbon,49007,M,,39.3515,-110.8643
+UT,012,SLC,Castle Country,UT012,Emery,49015,M,,39.3515,-110.8643
+UT,013,SLC,San Rafael Swell,UT013,Emery,49015,M,,38.5323,-110.6441
+UT,013,SLC,San Rafael Swell,UT013,Garfield,49017,M,,38.5323,-110.6441
+UT,013,SLC,San Rafael Swell,UT013,Sevier,49041,M,,38.5323,-110.6441
+UT,013,SLC,San Rafael Swell,UT013,Wayne,49055,M,,38.5323,-110.6441
+UT,014,SLC,Sanpete/Sevier Valleys,UT014,Juab,49023,M,,39.2236,-111.8653
+UT,014,SLC,Sanpete/Sevier Valleys,UT014,Millard,49027,M,,39.2236,-111.8653
+UT,014,SLC,Sanpete/Sevier Valleys,UT014,Sanpete,49039,M,,39.2236,-111.8653
+UT,014,SLC,Sanpete/Sevier Valleys,UT014,Sevier,49041,M,,39.2236,-111.8653
+UT,014,SLC,Sanpete/Sevier Valleys,UT014,Utah,49049,M,,39.2236,-111.8653
+UT,015,SLC,West Central Utah,UT015,Juab,49023,M,,39.2847,-113.1318
+UT,015,SLC,West Central Utah,UT015,Millard,49027,M,,39.2847,-113.1318
+UT,016,SLC,Southwest Utah,UT016,Beaver,49001,M,,38.1009,-113.4582
+UT,016,SLC,Southwest Utah,UT016,Iron,49021,M,,38.1009,-113.4582
+UT,016,SLC,Southwest Utah,UT016,Washington,49053,M,,38.1009,-113.4582
+UT,019,SLC,Utah's Dixie and Zion National Park,UT019,Washington,49053,M,,37.1699,-113.5478
+UT,020,SLC,South Central Utah,UT020,Garfield,49017,M,,37.3608,-111.8223
+UT,020,SLC,South Central Utah,UT020,Kane,49025,M,,37.3608,-111.8223
+UT,020,SLC,South Central Utah,UT020,Washington,49053,M,,37.3608,-111.8223
+UT,021,SLC,Glen Canyon Recreation Area/Lake Powell,UT021,Garfield,49017,M,,37.3392,-110.8317
+UT,021,SLC,Glen Canyon Recreation Area/Lake Powell,UT021,Kane,49025,M,,37.3392,-110.8317
+UT,021,SLC,Glen Canyon Recreation Area/Lake Powell,UT021,San Juan,49037,M,se,37.3392,-110.8317
+UT,022,GJT,Southeast Utah,UT022,San Juan,49037,M,se,37.2729,-109.6517
+UT,023,GJT,Eastern Unita Mountains,UT023,Daggett,49009,M,ne,40.7677,-109.5041
+UT,023,GJT,Eastern Unita Mountains,UT023,Uintah,49047,M,ne,40.7677,-109.5041
+UT,024,GJT,Eastern Uinta Basin,UT024,Uintah,49047,M,ne,40.2152,-109.5380
+UT,025,GJT,Tavaputs Plateau,UT025,Grand,49019,M,ec,39.4851,-109.5696
+UT,025,GJT,Tavaputs Plateau,UT025,Uintah,49047,M,ne,39.4851,-109.5696
+UT,027,GJT,Arches/Grand Flat,UT027,Grand,49019,M,ec,38.8582,-109.5593
+UT,028,GJT,La Sal and Abajo Mountains,UT028,Grand,49019,M,ec,38.0470,-109.3836
+UT,028,GJT,La Sal and Abajo Mountains,UT028,San Juan,49037,M,se,38.0470,-109.3836
+UT,029,GJT,Canyonlands/Natural Bridges,UT029,San Juan,49037,M,se,37.8442,-110.0067
+UT,517,SLC,Central and Southwest Mountains,UT517,Emery,49015,M,,38.9735,-111.7691
+UT,517,SLC,Central and Southwest Mountains,UT517,Juab,49023,M,,38.9735,-111.7691
+UT,517,SLC,Central and Southwest Mountains,UT517,Millard,49027,M,,38.9735,-111.7691
+UT,517,SLC,Central and Southwest Mountains,UT517,Sanpete,49039,M,,38.9735,-111.7691
+UT,517,SLC,Central and Southwest Mountains,UT517,Sevier,49041,M,,38.9735,-111.7691
+UT,518,SLC,Southern Utah Mountains,UT518,Beaver,49001,M,,37.8751,-112.4023
+UT,518,SLC,Southern Utah Mountains,UT518,Garfield,49017,M,,37.8751,-112.4023
+UT,518,SLC,Southern Utah Mountains,UT518,Iron,49021,M,,37.8751,-112.4023
+UT,518,SLC,Southern Utah Mountains,UT518,Kane,49025,M,,37.8751,-112.4023
+UT,518,SLC,Southern Utah Mountains,UT518,Piute,49031,M,,37.8751,-112.4023
+UT,518,SLC,Southern Utah Mountains,UT518,Washington,49053,M,,37.8751,-112.4023
+UT,518,SLC,Southern Utah Mountains,UT518,Wayne,49055,M,,37.8751,-112.4023
+VA,001,MRX,Lee,VA001,Lee,51105,E,sw,36.7055,-83.1285
+VA,002,MRX,Wise,VA002,Wise,51195,E,sw,36.9745,-82.6213
+VA,002,MRX,Wise,VA002,City of Norton,51720,E,sw,36.9745,-82.6213
+VA,003,RLX,Dickenson,VA003,Dickenson,51051,E,,37.1258,-82.3503
+VA,004,RLX,Buchanan,VA004,Buchanan,51027,E,,37.2666,-82.0360
+VA,005,MRX,Scott,VA005,Scott,51169,E,sw,36.7142,-82.6030
+VA,006,MRX,Russell,VA006,Russell,51167,E,sw,36.9338,-82.0956
+VA,007,RNK,Tazewell,VA007,Tazewell,51185,E,sw,37.1250,-81.5606
+VA,008,MRX,Washington,VA008,Washington,51191,E,sw,36.7221,-81.9642
+VA,008,MRX,Washington,VA008,City of Bristol,51520,E,sw,36.7221,-81.9642
+VA,009,RNK,Smyth,VA009,Smyth,51173,E,sw,36.8439,-81.5370
+VA,010,RNK,Bland,VA010,Bland,51021,E,sw,37.1340,-81.1302
+VA,011,RNK,Giles,VA011,Giles,51071,E,sw,37.3141,-80.7037
+VA,012,RNK,Wythe,VA012,Wythe,51197,E,sw,36.9171,-81.0786
+VA,013,RNK,Pulaski,VA013,Pulaski,51155,E,sw,37.0654,-80.7096
+VA,013,RNK,Pulaski,VA013,City of Radford,51750,E,sw,37.0654,-80.7096
+VA,014,RNK,Montgomery,VA014,Montgomery,51121,E,sw,37.1740,-80.3869
+VA,015,RNK,Grayson,VA015,Grayson,51077,E,sw,36.6566,-81.2250
+VA,016,RNK,Carroll,VA016,Carroll,51035,E,sw,36.7304,-80.7370
+VA,016,RNK,Carroll,VA016,City of Galax,51640,E,sw,36.7304,-80.7370
+VA,017,RNK,Floyd,VA017,Floyd,51063,E,sw,36.9315,-80.3626
+VA,018,RNK,Craig,VA018,Craig,51045,E,sw,37.4813,-80.2123
+VA,019,RNK,Alleghany,VA019,Alleghany,51005,E,wc,37.7875,-80.0067
+VA,019,RNK,Alleghany,VA019,City of Covington,51580,E,wc,37.7875,-80.0067
+VA,020,RNK,Bath,VA020,Bath,51017,E,wc,38.0587,-79.7411
+VA,022,RNK,Roanoke,VA022,Roanoke,51161,E,wc,37.2714,-80.0520
+VA,022,RNK,Roanoke,VA022,City of Roanoke,51770,E,wc,37.2714,-80.0520
+VA,022,RNK,Roanoke,VA022,City of Salem,51775,E,wc,37.2714,-80.0520
+VA,023,RNK,Botetourt,VA023,Botetourt,51023,E,wc,37.5571,-79.8123
+VA,024,RNK,Rockbridge,VA024,Rockbridge,51163,E,wc,37.8136,-79.4465
+VA,024,RNK,Rockbridge,VA024,City of Buena Vista,51530,E,wc,37.8136,-79.4465
+VA,024,RNK,Rockbridge,VA024,City of Lexington,51678,E,wc,37.8136,-79.4465
+VA,025,LWX,Augusta,VA025,City of Staunton,51790,E,ww,38.1630,-79.1289
+VA,025,LWX,Augusta,VA025,City of Waynesboro,51820,E,ww,38.1630,-79.1289
+VA,026,LWX,Rockingham,VA026,Rockingham,51165,E,ww,38.5106,-78.8757
+VA,026,LWX,Rockingham,VA026,City of Harrisonburg,51660,E,ww,38.5106,-78.8757
+VA,027,LWX,Shenandoah,VA027,Shenandoah,51171,E,nw,38.8585,-78.5704
+VA,028,LWX,Frederick,VA028,Frederick,51069,E,nw,39.2039,-78.2606
+VA,028,LWX,Frederick,VA028,City of Winchester,51840,E,nw,39.2039,-78.2606
+VA,029,LWX,Page,VA029,Page,51139,E,nw,38.6200,-78.4841
+VA,030,LWX,Warren,VA030,Warren,51187,E,nw,38.9087,-78.2072
+VA,031,LWX,Clarke,VA031,Clarke,51043,E,nw,39.1122,-77.9966
+VA,032,RNK,Patrick,VA032,Patrick,51141,E,sw,36.6784,-80.2843
+VA,033,RNK,Franklin,VA033,Franklin,51067,E,wc,36.9920,-79.8810
+VA,034,RNK,Bedford,VA034,Bedford,51019,E,sc,37.3152,-79.5242
+VA,035,RNK,Amherst,VA035,Amherst,51009,E,cc,37.6048,-79.1451
+VA,036,LWX,Nelson,VA036,Nelson,51125,E,cc,37.7874,-78.8867
+VA,037,LWX,Albemarle,VA037,City of Charlottesville,51540,E,cc,38.0231,-78.5555
+VA,038,LWX,Greene,VA038,Greene,51079,E,cc,38.2977,-78.4669
+VA,039,LWX,Madison,VA039,Madison,51113,E,nw,38.4137,-78.2792
+VA,040,LWX,Rappahannock,VA040,Rappahannock,51157,E,nw,38.6845,-78.1593
+VA,043,RNK,Henry,VA043,Henry,51089,E,sc,36.6828,-79.8736
+VA,043,RNK,Henry,VA043,City of Martinsville,51690,E,sc,36.6828,-79.8736
+VA,044,RNK,Pittsylvania,VA044,Pittsylvania,51143,E,sc,36.8111,-79.3976
+VA,044,RNK,Pittsylvania,VA044,City of Danville,51590,E,sc,36.8111,-79.3976
+VA,045,RNK,Campbell,VA045,Campbell,51031,E,cc,37.2231,-79.1048
+VA,045,RNK,Campbell,VA045,City of Lynchburg,51680,E,cc,37.2231,-79.1048
+VA,046,RNK,Appomattox,VA046,Appomattox,51011,E,cc,37.3722,-78.8121
+VA,047,RNK,Buckingham,VA047,Buckingham,51029,E,cc,37.5722,-78.5287
+VA,048,AKQ,Fluvanna,VA048,Fluvanna,51065,E,cc,37.8419,-78.2774
+VA,050,LWX,Orange,VA050,Orange,51137,E,cc,38.2463,-78.0134
+VA,051,LWX,Culpeper,VA051,Culpeper,51047,E,nn,38.4861,-77.9559
+VA,052,LWX,Prince William/Manassas/Manassas Park,VA052,Prince William,51153,E,nn,38.7054,-77.4819
+VA,052,LWX,Prince William/Manassas/Manassas Park,VA052,City of Manassas,51683,E,nn,38.7054,-77.4819
+VA,052,LWX,Prince William/Manassas/Manassas Park,VA052,City of Manassas Park,51685,E,nn,38.7054,-77.4819
+VA,053,LWX,Fairfax,VA053,Fairfax,51059,E,nn,38.8387,-77.2791
+VA,053,LWX,Fairfax,VA053,City of Fairfax,51600,E,nn,38.8387,-77.2791
+VA,054,LWX,Arlington/Falls Church/Alexandria,VA054,Arlington,51013,E,nn,38.8577,-77.0989
+VA,054,LWX,Arlington/Falls Church/Alexandria,VA054,City of Alexandria,51510,E,nn,38.8577,-77.0989
+VA,054,LWX,Arlington/Falls Church/Alexandria,VA054,City of Falls Church,51610,E,nn,38.8577,-77.0989
+VA,055,LWX,Stafford,VA055,Stafford,51179,E,nn,38.4213,-77.4603
+VA,056,LWX,Spotsylvania,VA056,Spotsylvania,51177,E,cc,38.1882,-77.6525
+VA,056,LWX,Spotsylvania,VA056,City of Fredericksburg,51630,E,cc,38.1882,-77.6525
+VA,057,LWX,King George,VA057,King George,51099,E,cc,38.2729,-77.1572
+VA,058,RNK,Halifax,VA058,Halifax,51083,E,sc,36.7670,-78.9367
+VA,059,RNK,Charlotte,VA059,Charlotte,51037,E,sc,37.0116,-78.6616
+VA,060,AKQ,Prince Edward,VA060,Prince Edward,51147,E,cc,37.2243,-78.4411
+VA,061,AKQ,Cumberland,VA061,Cumberland,51049,E,cc,37.5120,-78.2450
+VA,062,AKQ,Goochland,VA062,Goochland,51075,E,cc,37.7220,-77.9164
+VA,064,AKQ,Caroline,VA064,Caroline,51033,E,nc,38.0272,-77.3475
+VA,065,AKQ,Mecklenburg,VA065,Mecklenburg,51117,E,sc,36.6804,-78.3627
+VA,066,AKQ,Lunenburg,VA066,Lunenburg,51111,E,sc,36.9462,-78.2406
+VA,067,AKQ,Nottoway,VA067,Nottoway,51135,E,sc,37.1431,-78.0512
+VA,068,AKQ,Amelia,VA068,Amelia,51007,E,cc,37.3360,-77.9761
+VA,069,AKQ,Powhatan,VA069,Powhatan,51145,E,cc,37.5502,-77.9152
+VA,075,AKQ,Westmoreland,VA075,Westmoreland,51193,E,ec,38.1108,-76.8052
+VA,075,AKQ,Westmoreland,VA075,Westmoreland,51193,E,ec,38.1366,-76.7253
+VA,076,AKQ,Richmond,VA076,Richmond,51159,E,ec,37.8881,-76.7671
+VA,076,AKQ,Richmond,VA076,Richmond,51159,E,ec,37.9429,-76.7249
+VA,077,AKQ,Northumberland,VA077,Northumberland,51133,E,ee,37.8863,-76.4228
+VA,078,AKQ,Lancaster,VA078,Lancaster,51103,E,ee,37.6779,-76.4808
+VA,078,AKQ,Lancaster,VA078,Lancaster,51103,E,ee,37.7371,-76.4650
+VA,079,AKQ,Brunswick,VA079,Brunswick,51025,E,sc,36.7648,-77.8590
+VA,080,AKQ,Dinwiddie,VA080,Dinwiddie,51053,E,sc,37.0759,-77.6323
+VA,081,AKQ,Prince George,VA081,Prince George,51149,E,sc,37.1915,-77.2389
+VA,081,AKQ,Prince George,VA081,City of Hopewell,51670,E,sc,37.1915,-77.2389
+VA,081,AKQ,Prince George,VA081,City of Petersburg,51730,E,sc,37.1915,-77.2389
+VA,082,AKQ,Charles City,VA082,Charles City,51036,E,ec,37.3565,-77.0613
+VA,082,AKQ,Charles City,VA082,Charles City,51036,E,ec,37.2385,-76.9027
+VA,083,AKQ,New Kent,VA083,New Kent,51127,E,ec,37.5052,-76.9972
+VA,084,AKQ,Gloucester,VA084,Gloucester,51073,E,se,37.4257,-76.5431
+VA,084,AKQ,Gloucester,VA084,Gloucester,51073,E,ee,37.3249,-76.5063
+VA,085,AKQ,Middlesex,VA085,Middlesex,51119,E,ee,37.6353,-76.5769
+VA,085,AKQ,Middlesex,VA085,Middlesex,51119,E,ee,37.6258,-76.4816
+VA,086,AKQ,Mathews,VA086,Mathews,51115,E,ee,37.4368,-76.3444
+VA,086,AKQ,Mathews,VA086,Mathews,51115,E,ee,37.4577,-76.3416
+VA,087,AKQ,Greensville,VA087,Greensville,51081,E,sc,36.6763,-77.5590
+VA,087,AKQ,Greensville,VA087,City of Emporia,51595,E,se,36.6763,-77.5590
+VA,088,AKQ,Sussex,VA088,Sussex,51183,E,se,36.9218,-77.2618
+VA,089,AKQ,Surry,VA089,Surry,51181,E,se,37.1169,-76.8883
+VA,090,AKQ,James City,VA090,James City,51095,E,se,37.3264,-76.7745
+VA,090,AKQ,James City,VA090,James City,51095,E,se,37.2088,-76.7412
+VA,090,AKQ,James City,VA090,City of Williamsburg,51830,E,se,37.3264,-76.7745
+VA,092,AKQ,Southampton,VA092,Southampton,51175,E,se,36.7199,-77.1038
+VA,092,AKQ,Southampton,VA092,City of Franklin,51620,E,se,36.7199,-77.1038
+VA,093,AKQ,Isle of Wight,VA093,Isle of Wight,51093,E,se,36.9067,-76.7091
+VA,095,AKQ,Norfolk/Portsmouth,VA095,City of Norfolk,51710,E,se,36.8959,-76.3428
+VA,095,AKQ,Norfolk/Portsmouth,VA095,City of Norfolk,51710,E,se,36.8752,-76.2937
+VA,095,AKQ,Norfolk/Portsmouth,VA095,City of Portsmouth,51740,E,se,36.8959,-76.3428
+VA,095,AKQ,Norfolk/Portsmouth,VA095,City of Portsmouth,51740,E,se,36.8752,-76.2937
+VA,096,AKQ,Suffolk,VA096,City of Suffolk,51800,E,se,36.6926,-76.6417
+VA,096,AKQ,Suffolk,VA096,City of Suffolk,51800,E,se,36.8935,-76.4749
+VA,097,AKQ,Chesapeake,VA097,City of Chesapeake,51550,E,se,36.6778,-76.3024
+VA,098,AKQ,Virginia Beach,VA098,City of Virginia Beach,51810,E,se,36.7494,-76.0571
+VA,099,AKQ,Accomack,VA099,Accomack,51001,E,se,37.7659,-75.6472
+VA,100,AKQ,Northampton,VA100,Northampton,51131,E,ee,37.3566,-75.8995
+VA,501,LWX,Northern Fauquier,VA501,Fauquier,51061,E,nn,38.8540,-77.8851
+VA,502,LWX,Southern Fauquier,VA502,Fauquier,51061,E,nn,38.5953,-77.7154
+VA,503,LWX,Western Highland,VA503,Highland,51091,E,ww,38.4256,-79.6618
+VA,504,LWX,Eastern Highland,VA504,Highland,51091,E,ww,38.3315,-79.5230
+VA,505,LWX,Western Loudoun,VA505,Loudoun,51107,E,nn,39.1337,-77.6963
+VA,506,LWX,Eastern Loudoun,VA506,Loudoun,51107,E,nn,39.0052,-77.5151
+VA,507,LWX,Northern Virginia Blue Ridge,VA507,Greene,51079,E,cc,38.5272,-78.4323
+VA,507,LWX,Northern Virginia Blue Ridge,VA507,Madison,51113,E,nw,38.5272,-78.4323
+VA,507,LWX,Northern Virginia Blue Ridge,VA507,Page,51139,E,nw,38.5272,-78.4323
+VA,507,LWX,Northern Virginia Blue Ridge,VA507,Rappahannock,51157,E,nw,38.5272,-78.4323
+VA,507,LWX,Northern Virginia Blue Ridge,VA507,Rockingham,51165,E,ww,38.5272,-78.4323
+VA,507,LWX,Northern Virginia Blue Ridge,VA507,Warren,51187,E,nw,38.5272,-78.4323
+VA,508,LWX,Central Virginia Blue Ridge,VA508,Albemarle,51003,E,cc,37.9889,-78.9469
+VA,508,RNK,Central Virginia Blue Ridge,VA508,Amherst,51009,E,cc,37.9889,-78.9469
+VA,508,LWX,Central Virginia Blue Ridge,VA508,Augusta,51015,E,ww,37.9889,-78.9469
+VA,508,RNK,Central Virginia Blue Ridge,VA508,Nelson,51125,E,cc,37.9889,-78.9469
+VA,508,RNK,Central Virginia Blue Ridge,VA508,Rockbridge,51163,E,wc,37.9889,-78.9469
+VA,509,AKQ,Western Louisa,VA509,Louisa,51109,E,cc,38.0408,-78.1061
+VA,510,AKQ,Eastern Louisa,VA510,Louisa,51109,E,cc,37.9335,-77.8608
+VA,511,AKQ,Western Hanover,VA511,Hanover,51085,E,cc,37.8090,-77.5823
+VA,512,AKQ,Eastern Hanover,VA512,Hanover,51085,E,cc,37.4671,-77.2793
+VA,513,AKQ,Western Chesterfield,VA513,Chesterfield,51041,E,cc,37.3765,-77.5835
+VA,514,AKQ,Eastern Chesterfield (Including Col. Heights),VA514,City of Colonial Heights,51570,E,cc,37.3765,-77.5835
+VA,515,AKQ,Western Henrico (Including the City of Richmond),VA515,Henrico,51087,E,cc,37.5362,-77.4200
+VA,515,AKQ,Western Henrico (Including the City of Richmond),VA515,City of Richmond,51760,E,cc,37.5293,-77.4754
+VA,516,AKQ,Eastern Henrico,VA516,Henrico,51087,E,cc,37.5362,-77.4200
+VA,517,AKQ,Western King William,VA517,King William,51101,E,ec,37.7067,-77.0884
+VA,518,AKQ,Eastern King William,VA518,King William,51101,E,ec,37.7067,-77.0884
+VA,519,AKQ,Western King and Queen,VA519,King and Queen,51097,E,ec,37.7179,-76.8946
+VA,520,AKQ,Eastern King and Queen,VA520,King and Queen,51097,E,ec,37.7179,-76.8946
+VA,521,AKQ,Western Essex,VA521,Essex,51057,E,ec,37.8958,-76.8080
+VA,522,AKQ,Eastern Essex,VA522,Essex,51057,E,ec,37.9426,-76.9518
+VA,523,AKQ,York,VA523,York,51199,E,se,37.2242,-76.5383
+VA,523,AKQ,York,VA523,York,51199,E,se,37.2560,-76.5124
+VA,523,AKQ,York,VA523,City of Poquoson,51735,E,se,37.2242,-76.5383
+VA,524,AKQ,Newport News/Hampton,VA524,City of Newport News,51700,e,SE,37.0770,-76.5058
+VA,525,AKQ,Hampton/Poquoson,VA525,City of Hampton,51650,E,se,37.0534,-76.3673
+VI,001,SJU,St.Thomas...St. John.. and Adjacent Islands,VI001,Saint John,78020,V,,18.3429,-64.8564
+VI,001,SJU,St.Thomas...St. John.. and Adjacent Islands,VI001,Saint Thomas,78030,V,,18.3429,-64.8564
+VI,002,SJU,St Croix,VI002,Saint Croix,78010,V,,17.7333,-64.7677
+VT,001,BTV,Grand Isle,VT001,Grand Isle,50013,E,nw,44.7968,-73.2948
+VT,002,BTV,Western Franklin,VT002,Franklin,50011,E,nw,44.8517,-73.0340
+VT,003,BTV,Orleans,VT003,Orleans,50019,E,ne,44.8288,-72.2438
+VT,004,BTV,Essex,VT004,Essex,50009,E,nn,44.7281,-71.7364
+VT,005,BTV,Western Chittenden,VT005,Chittenden,50007,E,nw,44.4793,-73.1562
+VT,006,BTV,Lamoille,VT006,Lamoille,50015,E,nw,44.6057,-72.6414
+VT,007,BTV,Caledonia,VT007,Caledonia,50005,E,ne,44.4647,-72.1022
+VT,008,BTV,Washington,VT008,Washington,50023,E,cc,44.2734,-72.6149
+VT,009,BTV,Western Addison,VT009,Addison,50001,E,cc,44.0215,-73.2394
+VT,010,BTV,Orange,VT010,Orange,50017,E,cc,44.0056,-72.3769
+VT,011,BTV,Western Rutland,VT011,Rutland,50021,E,ss,43.5856,-73.1398
+VT,012,BTV,Windsor,VT012,Windsor,50027,E,ss,43.5798,-72.5855
+VT,013,ALY,Bennington,VT013,Bennington,50003,E,ss,43.0355,-73.0929
+VT,014,ALY,Western Windham,VT014,Windham,50025,E,ss,43.0015,-72.7867
+VT,015,ALY,Eastern Windham,VT015,Windham,50025,E,ss,42.9675,-72.5583
+VT,016,BTV,Eastern Franklin,VT016,Franklin,50011,E,nw,44.8662,-72.7255
+VT,017,BTV,Eastern Chittenden,VT017,Chittenden,50007,E,nw,44.4237,-72.9287
+VT,018,BTV,Eastern Addison,VT018,Addison,50001,E,cc,44.0466,-72.9674
+VT,019,BTV,Eastern Rutland,VT019,Rutland,50021,E,ss,43.5713,-72.8735
+WA,001,SEW,San Juan County,WA001,San Juan,53055,P,nw,48.5786,-122.9627
+WA,019,PQR,South Washington Cascades,WA019,Cowlitz,53015,P,sw,46.0990,-121.9845
+WA,019,PQR,South Washington Cascades,WA019,Skamania,53059,P,sw,46.0990,-121.9845
+WA,020,PQR,Willapa Hills,WA020,Cowlitz,53015,P,sw,46.5019,-123.4245
+WA,020,PQR,Willapa Hills,WA020,Pacific,53049,P,sw,46.5019,-123.4245
+WA,020,PQR,Willapa Hills,WA020,Wahkiakum,53069,P,sw,46.5019,-123.4245
+WA,021,PQR,South Washington Coast,WA021,Pacific,53049,P,sw,46.4855,-123.7975
+WA,021,PQR,South Washington Coast,WA021,Wahkiakum,53069,P,sw,46.4855,-123.7975
+WA,022,PQR,Lower Columbia and I - 5 Corridor in Cowlitz County,WA022,Cowlitz,53015,P,sw,46.1857,-122.9421
+WA,022,PQR,Lower Columbia and I - 5 Corridor in Cowlitz County,WA022,Wahkiakum,53069,P,sw,46.1857,-122.9421
+WA,024,PDT,Eastern Columbia River Gorge of Washington,WA024,Klickitat,53039,P,cc,45.7233,-120.8763
+WA,026,PDT,Kittitas Valley,WA026,Kittitas,53037,P,cc,46.9429,-120.3140
+WA,026,PDT,Kittitas Valley,WA026,Yakima,53077,P,sc,46.9429,-120.3140
+WA,027,PDT,Yakima Valley,WA027,Yakima,53077,P,sc,46.4331,-120.3120
+WA,028,PDT,Lower Columbia Basin of Washington,WA028,Benton,53005,P,sc,46.3373,-119.1824
+WA,028,PDT,Lower Columbia Basin of Washington,WA028,Franklin,53021,P,sc,46.3373,-119.1824
+WA,028,PDT,Lower Columbia Basin of Washington,WA028,Klickitat,53039,P,cc,46.3373,-119.1824
+WA,028,PDT,Lower Columbia Basin of Washington,WA028,Walla Walla,53071,P,sc,46.3373,-119.1824
+WA,029,PDT,Foothills of the Blue Mountains of Washington,WA029,Columbia,53013,P,se,46.3118,-118.2123
+WA,029,PDT,Foothills of the Blue Mountains of Washington,WA029,Walla Walla,53071,P,sc,46.3118,-118.2123
+WA,030,PDT,Northwest Blue Mountains,WA030,Columbia,53013,P,se,46.1508,-117.8128
+WA,030,PDT,Northwest Blue Mountains,WA030,Walla Walla,53071,P,sc,46.1508,-117.8128
+WA,031,OTX,Northeast Blue Mountains,WA031,Asotin,53003,P,se,46.1967,-117.3899
+WA,031,OTX,Northeast Blue Mountains,WA031,Garfield,53023,P,se,46.1967,-117.3899
+WA,032,OTX,Lower Garfield and Asotin Counties,WA032,Asotin,53003,P,se,46.4220,-117.3795
+WA,032,OTX,Lower Garfield and Asotin Counties,WA032,Garfield,53023,P,se,46.4220,-117.3795
+WA,033,OTX,Washington Palouse,WA033,Spokane,53063,P,ee,46.9272,-117.5068
+WA,033,OTX,Washington Palouse,WA033,Whitman,53075,P,ee,46.9272,-117.5068
+WA,034,OTX,Moses Lake Area,WA034,Adams,53001,P,ec,47.0108,-119.4918
+WA,034,OTX,Moses Lake Area,WA034,Grant,53025,P,cc,47.0108,-119.4918
+WA,035,OTX,Upper Columbia Basin,WA035,Adams,53001,P,ec,47.3555,-118.6116
+WA,035,OTX,Upper Columbia Basin,WA035,Douglas,53017,P,nc,47.3555,-118.6116
+WA,035,OTX,Upper Columbia Basin,WA035,Grant,53025,P,cc,47.3555,-118.6116
+WA,035,OTX,Upper Columbia Basin,WA035,Lincoln,53043,P,ec,47.3555,-118.6116
+WA,035,OTX,Upper Columbia Basin,WA035,Okanogan,53047,P,nc,47.3555,-118.6116
+WA,036,OTX,Spokane Area,WA036,Lincoln,53043,P,ec,47.6181,-117.6130
+WA,036,OTX,Spokane Area,WA036,Spokane,53063,P,ee,47.6181,-117.6130
+WA,037,OTX,Northeast Mountains,WA037,Pend Oreille,53051,P,ne,48.4082,-117.6152
+WA,037,OTX,Northeast Mountains,WA037,Spokane,53063,P,ee,48.4082,-117.6152
+WA,037,OTX,Northeast Mountains,WA037,Stevens,53065,P,ne,48.4082,-117.6152
+WA,038,OTX,Okanogan Highlands,WA038,Ferry,53019,P,ne,48.5329,-118.6987
+WA,038,OTX,Okanogan Highlands,WA038,Okanogan,53047,P,nc,48.5329,-118.6987
+WA,039,PQR,Greater Vancouver Area,WA039,Clark,53011,P,sw,45.7396,-122.5253
+WA,040,PQR,South Washington Cascade Foothills,WA040,Clark,53011,P,sw,46.0339,-122.5017
+WA,040,PQR,South Washington Cascade Foothills,WA040,Cowlitz,53015,P,sw,46.0339,-122.5017
+WA,040,PQR,South Washington Cascade Foothills,WA040,Skamania,53059,P,sw,46.0339,-122.5017
+WA,041,OTX,Wenatchee Area,WA041,Chelan,53007,P,nc,47.6296,-120.2222
+WA,041,OTX,Wenatchee Area,WA041,Douglas,53017,P,nc,47.6296,-120.2222
+WA,042,OTX,East Slopes Northern Cascades,WA042,Chelan,53007,P,nc,48.2896,-120.4162
+WA,042,OTX,East Slopes Northern Cascades,WA042,Okanogan,53047,P,nc,48.2896,-120.4162
+WA,043,OTX,Okanogan Valley,WA043,Douglas,53017,P,nc,48.3496,-119.4519
+WA,043,OTX,Okanogan Valley,WA043,Okanogan,53047,P,nc,48.3496,-119.4519
+WA,044,OTX,Waterville Plateau,WA044,Douglas,53017,P,nc,47.6675,-119.6829
+WA,044,OTX,Waterville Plateau,WA044,Grant,53025,P,cc,47.6675,-119.6829
+WA,045,PQR,Western Columbia River Gorge,WA045,Clark,53011,P,sw,45.6383,-122.0882
+WA,045,PQR,Western Columbia River Gorge,WA045,Skamania,53059,P,sw,45.6383,-122.0882
+WA,046,PQR,Central Columbia River Gorge,WA046,Skamania,53059,P,sw,45.7345,-121.6690
+WA,503,SEW,Western Whatcom County,WA503,Whatcom,53073,P,nw,48.8588,-122.4772
+WA,504,SEW,Southwest Interior,WA504,Lewis,53041,P,wc,46.7206,-122.8973
+WA,504,SEW,Southwest Interior,WA504,Thurston,53067,P,wc,46.7206,-122.8973
+WA,506,SEW,Western Skagit County,WA506,Skagit,53057,P,nw,48.4646,-122.3668
+WA,507,SEW,Everett and Vicinity,WA507,Kitsap,53035,P,wc,48.0549,-122.2545
+WA,507,SEW,Everett and Vicinity,WA507,Snohomish,53061,P,wc,48.0549,-122.2545
+WA,509,SEW,Tacoma Area,WA509,Pierce,53053,P,wc,47.1703,-122.5041
+WA,510,SEW,Admiralty Inlet Area,WA510,Island,53029,P,nw,48.1131,-122.6404
+WA,510,SEW,Admiralty Inlet Area,WA510,Jefferson,53031,P,nw,48.1131,-122.6404
+WA,511,SEW,Hood Canal Area,WA511,Jefferson,53031,P,nw,47.4978,-122.9670
+WA,511,SEW,Hood Canal Area,WA511,Kitsap,53035,P,wc,47.4978,-122.9670
+WA,511,SEW,Hood Canal Area,WA511,Mason,53045,P,wc,47.4978,-122.9670
+WA,512,SEW,Lower Chehalis Valley Area,WA512,Grays Harbor,53027,P,ww,47.0579,-123.5111
+WA,512,SEW,Lower Chehalis Valley Area,WA512,Mason,53045,P,wc,47.0579,-123.5111
+WA,513,SEW,Olympics,WA513,Clallam,53009,P,nw,47.7525,-123.5990
+WA,513,SEW,Olympics,WA513,Grays Harbor,53027,P,ww,47.7525,-123.5990
+WA,513,SEW,Olympics,WA513,Jefferson,53031,P,nw,47.7525,-123.5990
+WA,513,SEW,Olympics,WA513,Mason,53045,P,wc,47.7525,-123.5990
+WA,514,SEW,Eastern Strait of Juan de Fuca,WA514,Clallam,53009,P,nw,48.0760,-123.2615
+WA,515,SEW,Western Strait of Juan De Fuca,WA515,Clallam,53009,P,nw,48.1845,-124.1477
+WA,516,SEW,North Coast,WA516,Clallam,53009,P,nw,47.9309,-124.4066
+WA,516,SEW,North Coast,WA516,Jefferson,53031,P,nw,47.9309,-124.4066
+WA,517,SEW,Central Coast,WA517,Grays Harbor,53027,P,ww,47.1902,-124.0272
+WA,520,PDT,East Slopes of the Washington Cascades,WA520,Kittitas,53037,P,cc,46.7613,-121.0582
+WA,520,PDT,East Slopes of the Washington Cascades,WA520,Klickitat,53039,P,cc,46.7613,-121.0582
+WA,520,PDT,East Slopes of the Washington Cascades,WA520,Yakima,53077,P,sc,46.7613,-121.0582
+WA,521,PDT,Simcoe Highlands,WA521,Klickitat,53039,P,cc,45.9735,-120.6525
+WA,521,PDT,Simcoe Highlands,WA521,Yakima,53077,P,sc,45.9735,-120.6525
+WA,555,SEW,East Puget Sound Lowlands,WA555,King,53033,P,wc,47.5620,-122.0265
+WA,555,SEW,East Puget Sound Lowlands,WA555,Pierce,53053,P,wc,47.5620,-122.0265
+WA,555,SEW,East Puget Sound Lowlands,WA555,Snohomish,53061,P,wc,48.0549,-122.2545
+WA,556,SEW,Bellevue and Vicinity,WA556,King,53033,P,wc,47.5210,-122.4499
+WA,556,SEW,Bellevue and Vicinity,WA556,Snohomish,53061,P,wc,47.5620,-122.0265
+WA,558,SEW,Seattle and Vicinity,WA558,King,53033,P,wc,47.5210,-122.4499
+WA,559,SEW,Bremerton and Vicinity,WA559,Kitsap,53035,P,wc,47.4978,-122.9670
+WA,567,SEW,West Slopes North Cascades and Passes,WA567,Skagit,53057,P,nw,48.4646,-122.3668
+WA,567,SEW,West Slopes North Cascades and Passes,WA567,Whatcom,53073,P,nw,48.8588,-122.4772
+WA,568,SEW,West Slopes North Central Cascdes,WA568,King,53033,P,wc,47.5620,-122.0265
+WA,568,SEW,West Slopes North Central Cascdes,WA568,Snohomish,53061,P,wc,48.0549,-122.2545
+WA,569,SEW,West Slopes South Central Cascades and Passes,WA569,Lewis,53041,P,wc,46.7206,-122.8973
+WA,569,SEW,West Slopes South Central Cascades and Passes,WA569,Pierce,53053,P,wc,47.5620,-122.0265
+WI,001,DLH,Douglas,WI001,Douglas,55031,C,nw,46.4309,-91.9147
+WI,002,DLH,Bayfield,WI002,Bayfield,55007,C,nw,46.5241,-91.2006
+WI,003,DLH,Ashland,WI003,Ashland,55003,C,nw,46.3148,-90.6781
+WI,004,DLH,Iron,WI004,Iron,55051,C,nw,46.2623,-90.2420
+WI,005,GRB,Vilas,WI005,Vilas,55125,C,nc,46.0529,-89.5148
+WI,006,DLH,Burnett,WI006,Burnett,55013,C,nw,45.8627,-92.3676
+WI,007,DLH,Washburn,WI007,Washburn,55129,C,nw,45.8993,-91.7912
+WI,008,DLH,Sawyer,WI008,Sawyer,55113,C,nw,45.8800,-91.1447
+WI,009,DLH,Price,WI009,Price,55099,C,nc,45.6804,-90.3614
+WI,010,GRB,Oneida,WI010,Oneida,55085,C,nc,45.7056,-89.5217
+WI,011,GRB,Forest,WI011,Forest,55041,C,ne,45.6673,-88.7704
+WI,012,GRB,Florence,WI012,Florence,55037,C,ne,45.8484,-88.3982
+WI,013,GRB,Northern Marinette County,WI013,Marinette,55075,C,ne,45.5511,-88.1090
+WI,014,MPX,Polk,WI014,Polk,55095,C,nw,45.4614,-92.4414
+WI,015,MPX,Barron,WI015,Barron,55005,C,nw,45.4238,-91.8484
+WI,016,MPX,Rusk,WI016,Rusk,55107,C,nw,45.4751,-91.1331
+WI,017,ARX,Taylor,WI017,Taylor,55119,C,nc,45.2115,-90.5012
+WI,018,GRB,Lincoln,WI018,Lincoln,55069,C,nc,45.3375,-89.7346
+WI,019,GRB,Langlade,WI019,Langlade,55067,C,nc,45.2624,-89.0719
+WI,020,GRB,Menominee,WI020,Menominee,55078,C,ne,45.0044,-88.7100
+WI,021,GRB,Northern Oconto County,WI021,Oconto,55083,C,ne,45.2006,-88.4257
+WI,022,GRB,Door,WI022,Door,55029,C,ne,44.9435,-87.3173
+WI,023,MPX,St. Croix,WI023,St. Croix,55109,C,wc,45.0340,-92.4528
+WI,024,MPX,Pierce,WI024,Pierce,55093,C,wc,44.7196,-92.4224
+WI,025,MPX,Dunn,WI025,Dunn,55033,C,wc,44.9466,-91.8964
+WI,026,MPX,Pepin,WI026,Pepin,55091,C,wc,44.5829,-92.0016
+WI,027,MPX,Chippewa,WI027,Chippewa,55017,C,wc,45.0694,-91.2799
+WI,028,MPX,Eau Claire,WI028,Eau Claire,55035,C,wc,44.7268,-91.2860
+WI,029,ARX,Clark,WI029,Clark,55019,C,cc,44.7347,-90.6121
+WI,030,GRB,Marathon,WI030,Marathon,55073,C,cc,44.8983,-89.7590
+WI,031,GRB,Shawano,WI031,Shawano,55115,C,ne,44.7892,-88.7656
+WI,032,ARX,Buffalo,WI032,Buffalo,55011,C,wc,44.3799,-91.7545
+WI,033,ARX,Trempealeau,WI033,Trempealeau,55121,C,wc,44.3040,-91.3585
+WI,034,ARX,Jackson,WI034,Jackson,55053,C,wc,44.3191,-90.8052
+WI,035,GRB,Wood,WI035,Wood,55141,C,cc,44.4553,-90.0416
+WI,036,GRB,Portage,WI036,Portage,55097,C,cc,44.4760,-89.5015
+WI,037,GRB,Waupaca,WI037,Waupaca,55135,C,ne,44.4704,-88.9648
+WI,038,GRB,Outagamie,WI038,Outagamie,55087,C,ne,44.4161,-88.4649
+WI,039,GRB,Brown,WI039,Brown,55009,C,ne,44.4537,-88.0032
+WI,040,GRB,Kewaunee,WI040,Kewaunee,55061,C,ne,44.5161,-87.6154
+WI,041,ARX,La Crosse,WI041,La Crosse,55063,C,wc,43.9065,-91.1152
+WI,042,ARX,Monroe,WI042,Monroe,55081,C,wc,43.9457,-90.6179
+WI,043,ARX,Juneau,WI043,Juneau,55057,C,cc,43.9245,-90.1140
+WI,044,ARX,Adams,WI044,Adams,55001,C,cc,43.9696,-89.7706
+WI,045,GRB,Waushara,WI045,Waushara,55137,C,cc,44.1131,-89.2429
+WI,046,MKX,Marquette,WI046,Marquette,55077,C,sc,43.8196,-89.3988
+WI,047,MKX,Green Lake,WI047,Green Lake,55047,C,sc,43.8003,-89.0450
+WI,048,GRB,Winnebago,WI048,Winnebago,55139,C,ec,44.0689,-88.6446
+WI,049,GRB,Calumet,WI049,Calumet,55015,C,ec,44.0817,-88.2180
+WI,050,GRB,Manitowoc,WI050,Manitowoc,55071,C,ec,44.1198,-87.8096
+WI,051,MKX,Fond Du Lac,WI051,Fond du Lac,55039,C,ec,43.7536,-88.4882
+WI,052,MKX,Sheboygan,WI052,Sheboygan,55117,C,ec,43.7212,-87.9454
+WI,053,ARX,Vernon,WI053,Vernon,55123,C,sw,43.5939,-90.8344
+WI,054,ARX,Crawford,WI054,Crawford,55023,C,sw,43.2395,-90.9310
+WI,055,ARX,Richland,WI055,Richland,55103,C,sw,43.3756,-90.4295
+WI,056,MKX,Sauk,WI056,Sauk,55111,C,sc,43.4268,-89.9484
+WI,057,MKX,Columbia,WI057,Columbia,55021,C,sc,43.4665,-89.3338
+WI,058,MKX,Dodge,WI058,Dodge,55027,C,se,43.4164,-88.7075
+WI,059,MKX,Washington,WI059,Washington,55131,C,se,43.3684,-88.2308
+WI,060,MKX,Ozaukee,WI060,Ozaukee,55089,C,se,43.3838,-87.9515
+WI,061,ARX,Grant,WI061,Grant,55043,C,sw,42.8675,-90.7062
+WI,062,MKX,Iowa,WI062,Iowa,55049,C,sc,43.0005,-90.1354
+WI,063,MKX,Dane,WI063,Dane,55025,C,sc,43.0674,-89.4183
+WI,064,MKX,Jefferson,WI064,Jefferson,55055,C,se,43.0209,-88.7759
+WI,065,MKX,Waukesha,WI065,Waukesha,55133,C,se,43.0181,-88.3045
+WI,066,MKX,Milwaukee,WI066,Milwaukee,55079,C,se,43.0071,-87.9667
+WI,067,MKX,Lafayette,WI067,Lafayette,55065,C,sc,42.6605,-90.1317
+WI,068,MKX,Green,WI068,Green,55045,C,sc,42.6800,-89.6022
+WI,069,MKX,Rock,WI069,Rock,55105,C,sc,42.6712,-89.0716
+WI,070,MKX,Walworth,WI070,Walworth,55127,C,se,42.6685,-88.5419
+WI,071,MKX,Racine,WI071,Racine,55101,C,se,42.7477,-88.0551
+WI,072,MKX,Kenosha,WI072,Kenosha,55059,C,se,42.5767,-88.0418
+WI,073,GRB,Southern Marinette County,WI073,Marinette,55075,C,ne,45.1874,-87.9475
+WI,074,GRB,Southern Oconto County,WI074,Oconto,55083,C,ne,44.8829,-88.1390
+WV,001,PBZ,Hancock,WV001,Hancock,54029,E,nn,40.5213,-80.5739
+WV,002,PBZ,Brooke,WV002,Brooke,54009,E,nn,40.2733,-80.5765
+WV,003,PBZ,Ohio,WV003,Ohio,54069,E,nn,40.0969,-80.6189
+WV,004,PBZ,Marshall,WV004,Marshall,54051,E,nn,39.8606,-80.6634
+WV,005,RLX,Wayne,WV005,Wayne,54099,E,,38.1461,-82.4270
+WV,006,RLX,Cabell,WV006,Cabell,54011,E,,38.4203,-82.2417
+WV,007,RLX,Mason,WV007,Mason,54053,E,,38.7698,-82.0265
+WV,008,RLX,Jackson,WV008,Jackson,54035,E,,38.8344,-81.6748
+WV,009,RLX,Wood,WV009,Wood,54107,E,,39.2111,-81.5150
+WV,010,RLX,Pleasants,WV010,Pleasants,54073,E,,39.3709,-81.1606
+WV,011,RLX,Tyler,WV011,Tyler,54095,E,,39.4653,-80.8849
+WV,012,PBZ,Wetzel,WV012,Wetzel,54103,E,nn,39.6053,-80.6391
+WV,013,RLX,Lincoln,WV013,Lincoln,54043,E,,38.1753,-82.0705
+WV,014,RLX,Putnam,WV014,Putnam,54079,E,,38.5086,-81.9090
+WV,015,RLX,Kanawha,WV015,Kanawha,54039,E,,38.3365,-81.5281
+WV,016,RLX,Roane,WV016,Roane,54087,E,,38.7140,-81.3483
+WV,017,RLX,Wirt,WV017,Wirt,54105,E,,39.0224,-81.3786
+WV,018,RLX,Calhoun,WV018,Calhoun,54013,E,,38.8445,-81.1175
+WV,019,RLX,Ritchie,WV019,Ritchie,54085,E,,39.1783,-81.0629
+WV,020,RLX,Doddridge,WV020,Doddridge,54017,E,,39.2692,-80.7070
+WV,021,PBZ,Marion,WV021,Marion,54049,E,nn,39.5100,-80.2434
+WV,024,RLX,Mingo,WV024,Mingo,54059,E,,37.7264,-82.1346
+WV,025,RLX,Logan,WV025,Logan,54045,E,,37.8316,-81.9353
+WV,026,RLX,Boone,WV026,Boone,54005,E,,38.0231,-81.7113
+WV,027,RLX,Clay,WV027,Clay,54015,E,,38.4626,-81.0750
+WV,028,RLX,Braxton,WV028,Braxton,54007,E,,38.6998,-80.7193
+WV,029,RLX,Gilmer,WV029,Gilmer,54021,E,,38.9241,-80.8571
+WV,030,RLX,Lewis,WV030,Lewis,54041,E,,38.9958,-80.5022
+WV,031,RLX,Harrison,WV031,Harrison,54033,E,,39.2835,-80.3798
+WV,032,RLX,Taylor,WV032,Taylor,54091,E,,39.3360,-80.0461
+WV,033,RLX,McDowell,WV033,McDowell,54047,E,,37.3785,-81.6536
+WV,034,RLX,Wyoming,WV034,Wyoming,54109,E,,37.6096,-81.5491
+WV,039,RLX,Upshur,WV039,Upshur,54097,E,,38.8978,-80.2334
+WV,040,RLX,Barbour,WV040,Barbour,54001,E,,39.1329,-80.0030
+WV,042,RNK,Mercer,WV042,Mercer,54055,E,se,37.4055,-81.1114
+WV,043,RNK,Summers,WV043,Summers,54089,E,se,37.6559,-80.8586
+WV,044,RNK,Monroe,WV044,Monroe,54063,E,se,37.5604,-80.5505
+WV,050,LWX,Hampshire,WV050,Hampshire,54027,E,ee,39.3171,-78.6142
+WV,051,LWX,Morgan,WV051,Morgan,54065,E,pa,39.5604,-78.2578
+WV,052,LWX,Berkeley,WV052,Berkeley,54003,E,pa,39.4641,-78.0275
+WV,053,LWX,Jefferson,WV053,Jefferson,54037,E,pa,39.3076,-77.8628
+WV,055,LWX,Hardy,WV055,Hardy,54031,E,ee,39.0075,-78.8579
+WV,501,LWX,Western Grant,WV501,Grant,54023,E,ee,39.2245,-79.2815
+WV,502,LWX,Eastern Grant,WV502,Grant,54023,E,ee,39.0552,-79.1596
+WV,503,LWX,Western Mineral,WV503,Mineral,54057,E,ee,39.3868,-79.1158
+WV,504,LWX,Eastern Mineral,WV504,Mineral,54057,E,ee,39.4239,-78.8872
+WV,505,LWX,Western Pendleton,WV505,Pendleton,54071,E,ee,38.7534,-79.4656
+WV,506,LWX,Eastern Pendleton,WV506,Pendleton,54071,E,ee,38.6434,-79.2919
+WV,507,RNK,Eastern Greenbrier,WV507,Greenbrier,54025,E,se,38.6434,-79.2919
+WV,508,RNK,Western Greenbrier,WV508,Greenbrier,54025,E,se,37.9469,-80.4529
+WV,509,PBZ,Monongalia,WV509,Monongalia,54061,E,nn,39.6303,-80.0465
+WV,510,PBZ,Ridges of Eastern Monongalia and Northwestern Preston,WV510,Monongalia,54061,E,nn,39.6303,-80.0465
+WV,511,PBZ,Preston,WV511,Preston,54077,E,nn,39.4693,-79.6681
+WV,512,PBZ,Eastern Preston,WV512,Preston,54077,E,nn,39.4693,-79.6681
+WV,513,PBZ,Western Tucker,WV513,Tucker,54093,E,nn,39.1136,-79.5650
+WV,514,PBZ,Eastern Tucker,WV514,Tucker,54093,E,nn,39.1136,-79.5650
+WV,515,RLX,Northwest Raleigh,WV515,Raleigh,54081,E,se,37.8161,-81.3664
+WV,516,RLX,Southeast Raleigh,WV516,Raleigh,54081,E,se,37.7176,-81.1069
+WV,517,RLX,Northwest Fayette,WV517,Fayette,54019,E,se,38.0705,-81.1522
+WV,518,RLX,Southeast Fayette,WV518,Fayette,54019,E,se,37.9361,-80.9236
+WV,519,RLX,Northwest Nicholas,WV519,Nicholas,54067,E,se,38.3224,-80.8474
+WV,520,RLX,Southeast Nicholas,WV520,Nicholas,54067,E,se,38.1937,-80.6455
+WV,521,RLX,Northwest Webster,WV521,Webster,54101,E,ne,38.5398,-80.4692
+WV,522,RLX,Southeast Webster,WV522,Webster,54101,E,ne,38.4185,-80.3419
+WV,523,RLX,Northwest Pocahontas,WV523,Pocahontas,54075,E,ne,38.4290,-79.9797
+WV,524,RLX,Southeast Pocahontas,WV524,Pocahontas,54075,E,ne,38.2241,-80.0388
+WV,525,RLX,Northwest Randolph,WV525,Randolph,54083,E,ne,38.8495,-79.9622
+WV,526,RLX,Southeast Randolph,WV526,Randolph,54083,E,ne,38.7298,-79.8239
+WY,001,PIH,Yellowstone National Park,WY001,Fremont,16043,M,ee,44.5976,-110.5469
+WY,001,TFX,Yellowstone National Park,WY001,Gallatin,30031,M,sw,44.5976,-110.5469
+WY,001,RIW,Yellowstone National Park,WY001,Park,56029,M,nw,44.5976,-110.5469
+WY,001,RIW,Yellowstone National Park,WY001,Teton,56039,M,nw,44.5976,-110.5469
+WY,002,RIW,Absaroka Mountains,WY002,Fremont,56013,M,cc,44.2172,-109.5521
+WY,002,RIW,Absaroka Mountains,WY002,Hot Springs,56017,M,cc,44.2172,-109.5521
+WY,002,RIW,Absaroka Mountains,WY002,Park,56029,M,nw,44.2172,-109.5521
+WY,003,RIW,Cody Foothills,WY003,Park,56029,M,nw,44.4594,-109.0463
+WY,004,RIW,North Big Horn Basin,WY004,Big Horn,56003,M,nc,44.5447,-108.3008
+WY,004,RIW,North Big Horn Basin,WY004,Park,56029,M,nw,44.5447,-108.3008
+WY,005,RIW,Southwest Big Horn Basin,WY005,Hot Springs,56017,M,cc,43.7774,-108.2779
+WY,006,RIW,Southeast Big Horn Basin,WY006,Washakie,56043,M,nc,43.9423,-107.8197
+WY,007,RIW,Owl Creek and Bridger Mountains,WY007,Fremont,56013,M,cc,43.5730,-108.2336
+WY,007,RIW,Owl Creek and Bridger Mountains,WY007,Hot Springs,56017,M,cc,43.5730,-108.2336
+WY,007,RIW,Owl Creek and Bridger Mountains,WY007,Natrona,56025,M,cc,43.5730,-108.2336
+WY,008,RIW,Bighorn Mountains West,WY008,Big Horn,56003,M,nc,44.2518,-107.4682
+WY,008,RIW,Bighorn Mountains West,WY008,Fremont,56013,M,cc,44.2518,-107.4682
+WY,008,RIW,Bighorn Mountains West,WY008,Johnson,56019,M,nc,44.2518,-107.4682
+WY,008,RIW,Bighorn Mountains West,WY008,Natrona,56025,M,cc,44.2518,-107.4682
+WY,008,RIW,Bighorn Mountains West,WY008,Washakie,56043,M,nc,44.2518,-107.4682
+WY,009,RIW,Bighorn Mountains Southeast,WY009,Johnson,56019,M,nc,43.9099,-107.0263
+WY,009,RIW,Bighorn Mountains Southeast,WY009,Natrona,56025,M,cc,43.9099,-107.0263
+WY,009,RIW,Bighorn Mountains Southeast,WY009,Washakie,56043,M,nc,43.9099,-107.0263
+WY,010,RIW,Northeast Johnson County,WY010,Johnson,56019,M,nc,44.3680,-106.4299
+WY,011,RIW,Southeast Johnson County,WY011,Johnson,56019,M,nc,43.8206,-106.4368
+WY,012,RIW,Teton and Gros Ventre Mountains,WY012,Fremont,56013,M,cc,43.6883,-110.5055
+WY,012,RIW,Teton and Gros Ventre Mountains,WY012,Lincoln,56023,M,wc,43.6883,-110.5055
+WY,012,RIW,Teton and Gros Ventre Mountains,WY012,Sublette,56035,M,wc,43.6883,-110.5055
+WY,012,RIW,Teton and Gros Ventre Mountains,WY012,Teton,56039,M,nw,43.6883,-110.5055
+WY,013,RIW,Jackson Hole,WY013,Lincoln,56023,M,wc,43.6458,-110.6864
+WY,013,RIW,Jackson Hole,WY013,Teton,56039,M,nw,43.6458,-110.6864
+WY,014,RIW,Wind River Mountains West,WY014,Fremont,56013,M,cc,42.9092,-109.4923
+WY,014,RIW,Wind River Mountains West,WY014,Sublette,56035,M,wc,42.9092,-109.4923
+WY,015,RIW,Wind River Mountains East,WY015,Fremont,56013,M,cc,43.0496,-109.2855
+WY,015,RIW,Wind River Mountains East,WY015,Sublette,56035,M,wc,43.0496,-109.2855
+WY,016,RIW,Upper Wind River Basin,WY016,Fremont,56013,M,cc,43.4505,-109.3390
+WY,017,RIW,Wind River Basin,WY017,Fremont,56013,M,cc,43.1357,-108.2754
+WY,018,RIW,Lander Foothills,WY018,Fremont,56013,M,cc,42.7970,-108.6388
+WY,019,RIW,Green Mountains and Rattlesnake Range,WY019,Fremont,56013,M,cc,42.4379,-107.9137
+WY,019,RIW,Green Mountains and Rattlesnake Range,WY019,Natrona,56025,M,cc,42.4379,-107.9137
+WY,019,RIW,Green Mountains and Rattlesnake Range,WY019,Sweetwater,56037,M,sc,42.4379,-107.9137
+WY,020,RIW,Natrona County Lower Elevations,WY020,Natrona,56025,M,cc,43.0660,-106.6783
+WY,021,SLC,Southwest Wyoming,WY021,Uinta,56041,M,sw,41.2876,-110.5476
+WY,022,RIW,Casper Mountain,WY022,Natrona,56025,M,cc,42.6061,-106.2637
+WY,023,RIW,Star Valley,WY023,Lincoln,56023,M,wc,42.8634,-110.9820
+WY,024,RIW,Salt River and Wyoming Ranges,WY024,Lincoln,56023,M,wc,42.5626,-110.6654
+WY,024,RIW,Salt River and Wyoming Ranges,WY024,Sublette,56035,M,wc,42.5626,-110.6654
+WY,025,RIW,Upper Green River Basin Foothills,WY025,Sublette,56035,M,wc,42.9803,-110.0392
+WY,026,RIW,Upper Green River Basin,WY026,Fremont,56013,M,cc,42.3437,-109.7743
+WY,026,RIW,Upper Green River Basin,WY026,Lincoln,56023,M,wc,42.3437,-109.7743
+WY,026,RIW,Upper Green River Basin,WY026,Sublette,56035,M,wc,42.3437,-109.7743
+WY,026,RIW,Upper Green River Basin,WY026,Sweetwater,56037,M,sc,42.3437,-109.7743
+WY,027,RIW,South Lincoln County,WY027,Lincoln,56023,M,wc,41.8212,-110.5572
+WY,028,RIW,Rock Springs and Green River,WY028,Sweetwater,56037,M,sc,41.5579,-109.6723
+WY,029,RIW,Flaming Gorge,WY029,Sweetwater,56037,M,sc,41.2424,-109.4375
+WY,030,RIW,East Sweetwater County,WY030,Sweetwater,56037,M,sc,41.5802,-108.4192
+WY,054,UNR,Northern Cambell,WY054,Campbell,56005,M,ne,44.5960,-105.5497
+WY,055,UNR,Southern Campbell,WY055,Campbell,56005,M,ne,43.8436,-105.5478
+WY,056,UNR,Western Crook,WY056,Crook,56011,M,ne,44.6113,-104.8016
+WY,057,UNR,Wyoming Black Hills,WY057,Crook,56011,M,ne,44.3910,-104.3123
+WY,057,UNR,Wyoming Black Hills,WY057,Weston,56045,M,ne,44.3910,-104.3123
+WY,058,UNR,Weston,WY058,Weston,56045,M,ne,43.8072,-104.6212
+WY,071,UNR,Northeastern Crook,WY071,Crook,56011,M,ne,44.8506,-104.1767
+WY,101,CYS,Converse County Lower Elevations,WY101,Converse,56009,M,ec,43.0740,-105.4593
+WY,102,CYS,Niobrara County,WY102,Niobrara,56027,M,ec,43.0565,-104.4754
+WY,103,CYS,North Laramie Range,WY103,Albany,56001,M,se,42.3535,-105.6241
+WY,103,CYS,North Laramie Range,WY103,Converse,56009,M,ec,42.3535,-105.6241
+WY,103,CYS,North Laramie Range,WY103,Platte,56031,M,se,42.3535,-105.6241
+WY,104,CYS,Ferris/Seminoe/Shirley Mountains,WY104,Carbon,56007,M,sc,42.2707,-106.8780
+WY,105,CYS,Shirley Basin,WY105,Albany,56001,M,se,42.1340,-106.0943
+WY,105,CYS,Shirley Basin,WY105,Carbon,56007,M,sc,42.1340,-106.0943
+WY,106,CYS,Central Laramie Range and Southwest Platte County,WY106,Albany,56001,M,se,41.8778,-105.2243
+WY,106,CYS,Central Laramie Range and Southwest Platte County,WY106,Laramie,56021,M,se,41.8778,-105.2243
+WY,106,CYS,Central Laramie Range and Southwest Platte County,WY106,Platte,56031,M,se,41.8778,-105.2243
+WY,107,CYS,East Platte County,WY107,Platte,56031,M,se,42.2373,-104.8821
+WY,108,CYS,Goshen County,WY108,Goshen,56015,M,se,42.0880,-104.3533
+WY,109,CYS,Central Carbon County,WY109,Carbon,56007,M,sc,41.8204,-107.1030
+WY,110,CYS,North Snowy Range Foothills,WY110,Albany,56001,M,se,41.6904,-106.2590
+WY,110,CYS,North Snowy Range Foothills,WY110,Carbon,56007,M,sc,41.6904,-106.2590
+WY,111,CYS,Southwest Carbon County,WY111,Carbon,56007,M,sc,41.2862,-107.7097
+WY,112,CYS,Sierra Madre Range,WY112,Carbon,56007,M,sc,41.1695,-107.0990
+WY,113,CYS,Upper North Platte River Basin,WY113,Carbon,56007,M,sc,41.3867,-106.7713
+WY,114,CYS,Snowy Range,WY114,Albany,56001,M,se,41.3101,-106.3148
+WY,114,CYS,Snowy Range,WY114,Carbon,56007,M,sc,41.3101,-106.3148
+WY,115,CYS,Laramie Valley,WY115,Albany,56001,M,se,41.4005,-105.7614
+WY,116,CYS,South Laramie Range,WY116,Albany,56001,M,se,41.2851,-105.3769
+WY,116,CYS,South Laramie Range,WY116,Laramie,56021,M,se,41.2851,-105.3769
+WY,117,CYS,South Laramie Range Foothills,WY117,Albany,56001,M,se,41.3136,-105.0324
+WY,117,CYS,South Laramie Range Foothills,WY117,Laramie,56021,M,se,41.3136,-105.0324
+WY,118,CYS,Central Laramie County,WY118,Laramie,56021,M,se,41.3277,-104.6546
+WY,119,CYS,East Laramie County,WY119,Laramie,56021,M,se,41.2851,-104.2337
+WY,198,BYZ,Northeast Big Horn Mountains,WY098,Sheridan,56033,M,nc,44.7706,-107.4920
+WY,199,BYZ,Sheridan Foothills,WY099,Sheridan,56033,M,nc,44.7977,-106.6544
diff --git a/medinfo/ml/BifurcatedSupervisedClassifier.py b/medinfo/ml/BifurcatedSupervisedClassifier.py
new file mode 100644
index 00000000..d618ff8c
--- /dev/null
+++ b/medinfo/ml/BifurcatedSupervisedClassifier.py
@@ -0,0 +1,248 @@
+#!/usr/bin/python
+"""
+Ensemble supervised classifier which uses a binary condition on a specific
+feature to determine which of two classifiers it should use.
+"""
+
+import pandas as pd
+from pandas import DataFrame
+
+from medinfo.common.Util import log
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+
+class BifurcatedSupervisedClassifier:
+    BIFURCATION = 'bifurcation'
+    EQUAL = '=='
+    LTE = '<='
+    GTE = '>='
+    SUPPORTED_BIFURCATION_STRATEGIES = [EQUAL, GTE, LTE]
+    def __init__(self, classes, hyperparams):
+        if hyperparams['bifurcation_strategy'] not in BifurcatedSupervisedClassifier.SUPPORTED_BIFURCATION_STRATEGIES:
+            raise ValueError('Bifurcation strategy %s not supported.' % hyperparams['bifurcation_strategy'])
+
+        self._classes = classes
+        self._hyperparams = hyperparams
+
+        # Note that if we don't pass a copies of hyperparams, then we won't
+        # be able to change hyperparams independently in the two classifiers.
+        self._sc_true = SupervisedClassifier(classes, hyperparams.copy())
+        self._sc_false = SupervisedClassifier(classes, hyperparams.copy())
+
+    def __repr__(self):
+        bs = self._build_bifurcation_str()
+        classes_str = str(self._classes)
+        hyperparams_str = "hyperparams={'algorithm': %s, 'bifurcator': %s, 'bifurcation_strategy': %s, 'bifurcation_threshold': %s, 'random_state': %s}" % (self._hyperparams['algorithm'],
+                                self._hyperparams['bifurcator'],
+                                self._hyperparams['bifurcation_strategy'],
+                                self._hyperparams['bifurcation_value'],
+                                self._hyperparams['random_state'])
+        s = "BifurcatedSupervisedClassifier(%s, %s)" % (classes_str, hyperparams_str)
+        return s
+
+    __str__ = __repr__
+
+    def _build_bifurcation_str(self):
+        args = (
+                self._hyperparams['bifurcator'],
+                self._hyperparams['bifurcation_strategy'],
+                self._hyperparams['bifurcation_value']
+                )
+        return '%s %s %s' % args
+
+    def fetch_bifurcation_masks(self, X):
+        log.debug('bifurcator: %s' % self._hyperparams['bifurcator'])
+        log.debug('bifurcation_strategy: %s' % BifurcatedSupervisedClassifier.EQUAL)
+        log.debug('bifurcation_value: %s' % self._hyperparams['bifurcation_value'])
+        if self._hyperparams['bifurcation_strategy'] is BifurcatedSupervisedClassifier.EQUAL:
+            true_mask = X[self._hyperparams['bifurcator']].astype(float) == self._hyperparams['bifurcation_value']
+            false_mask = X[self._hyperparams['bifurcator']].astype(float) != self._hyperparams['bifurcation_value']
+        elif self._hyperparams['bifurcation_strategy'] is BifurcatedSupervisedClassifier.LTE:
+            true_mask = X[self._hyperparams['bifurcator']].astype(float) <= self._hyperparams['bifurcation_value']
+            false_mask = X[self._hyperparams['bifurcator']].astype(float) > self._hyperparams['bifurcation_value']
+        elif self._hyperparams['bifurcation_strategy'] is BifurcatedSupervisedClassifier.GTE:
+            true_mask = X[self._hyperparams['bifurcator']].astype(float) >= self._hyperparams['bifurcation_value']
+            false_mask = X[self._hyperparams['bifurcator']].astype(float) < self._hyperparams['bifurcation_value']
+
+        log.debug('X[%s].value_counts(): %s' % (self._hyperparams['bifurcator'], X[self._hyperparams['bifurcator']].value_counts()))
+        log.debug('true_mask.value_counts(): %s' % true_mask.value_counts())
+        log.debug('false_mask.value_counts(): %s' % false_mask.value_counts())
+        return true_mask, false_mask
+
+    def description(self):
+        args = (
+                self._hyperparams['algorithm'].upper().replace('-', '_'),
+                self._build_bifurcation_str(),
+                self._sc_true.description(),
+                self._sc_false.description()
+                )
+        return 'BIFURCATED_%s(%s, true=%s, false=%s)' % args
+
+    def hyperparams(self):
+        hyperparams = {
+            'model_true': self._sc_true.hyperparams(),
+            'model_false': self._sc_false.hyperparams()
+        }
+        return hyperparams
+
+    def params(self):
+        params = {
+            'bifurcator': self._hyperparams['bifurcator'],
+            'bifurcation_strategy': self._hyperparams['bifurcation_strategy'],
+            'bifurcation_value': self._hyperparams['bifurcation_value'],
+            'model_true': self._sc_true.description(),
+            'model_false': self._sc_false.description()
+        }
+        return params
+
+    def train(self, X_train, y_train):
+        true_mask, false_mask = self.fetch_bifurcation_masks(X_train)
+
+        # Train sc_true.
+        X_train_true = X_train[true_mask]
+        y_train_true = y_train[true_mask]
+        status_true = self._sc_true.train(X_train_true, y_train_true)
+        if status_true == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+            return status_true
+
+        # Train sc_true.
+        X_train_false = X_train[false_mask]
+        y_train_false = y_train[false_mask]
+        status_false = self._sc_false.train(X_train_false, y_train_false)
+        if status_false == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+            return status_false
+
+        return SupervisedClassifier.TRAINED
+
+    def _stitch_disjoint_row(self, row):
+        if pd.isnull(row['y_pred_true']):
+            val = row['y_pred_false']
+        else:
+            val = row['y_pred_true']
+        return val
+
+    def _stitch_prob_0(self, row):
+        if pd.isnull(row['y_pred_prob_true_0']):
+            val = row['y_pred_prob_false_0']
+        else:
+            val = row['y_pred_prob_true_0']
+        return val
+
+    def _stitch_prob_1(self, row):
+        if pd.isnull(row['y_pred_prob_true_1']):
+            val = row['y_pred_prob_false_1']
+        else:
+            val = row['y_pred_prob_true_1']
+        return val
+
+    def _predict_label_or_probability(self, X_test, probability=None):
+        true_mask, false_mask = self.fetch_bifurcation_masks(X_test)
+
+        # Predict X_test_true.
+        X_test_true = X_test[true_mask]
+        if probability:
+            y_pred_true = self._sc_true.predict_probability(X_test_true)
+        else:
+            y_pred_true = self._sc_true.predict(X_test_true)
+        log.debug('y_pred_true: %s' % y_pred_true)
+
+        # Predict X_test_false.
+        X_test_false = X_test[false_mask]
+        if probability:
+            y_pred_false = self._sc_false.predict_probability(X_test_false)
+        else:
+            y_pred_false = self._sc_false.predict(X_test_false)
+        log.debug('y_pred_false: %s' % y_pred_false)
+
+        # Stitch results.
+        if probability:
+            column_names = ['y_pred_true_0', 'y_pred_true_1']
+        else:
+            column_names = ['y_pred_true']
+        y_pred_true_df = DataFrame(y_pred_true, index=X_test_true.index, \
+                                    columns=column_names)
+        log.debug('y_pred_true_df: %s' % y_pred_true_df)
+        if probability:
+            column_names = ['y_pred_false_0', 'y_pred_false_1']
+        else:
+            column_names = ['y_pred_false']
+        y_pred_false_df = DataFrame(y_pred_false, index=X_test_false.index, \
+                                    columns=column_names)
+        log.debug('y_pred_false_df: %s' % y_pred_false_df)
+        true_mask_df = DataFrame(true_mask)
+        mask_plus_true = true_mask_df.merge(y_pred_true_df, how='left', \
+                                            left_index=True, right_index=True)
+        mask_plus_true_plus_false = mask_plus_true.merge(y_pred_false_df, \
+                                how='left', left_index=True, right_index=True)
+        mask_plus_true_plus_false['y_pred'] = mask_plus_true_plus_false.apply(self._stitch_disjoint_row, axis=1)
+        log.debug('mask_plus_false: %s' % mask_plus_true_plus_false)
+        y_pred = mask_plus_true_plus_false['y_pred'].values
+
+        return y_pred
+
+    def predict(self, X_test):
+        true_mask, false_mask = self.fetch_bifurcation_masks(X_test)
+
+        # Predict X_test_true.
+        X_test_true = X_test[true_mask]
+        y_pred_true = self._sc_true.predict(X_test_true)
+        log.debug('y_pred_true: %s' % y_pred_true)
+
+        # Predict X_test_false.
+        X_test_false = X_test[false_mask]
+        y_pred_false = self._sc_false.predict(X_test_false)
+        log.debug('y_pred_false: %s' % y_pred_false)
+
+        # Stitch results.
+        column_names = ['y_pred_true']
+        y_pred_true_df = DataFrame(y_pred_true, index=X_test_true.index, \
+                                    columns=column_names)
+        log.debug('y_pred_true_df: %s' % y_pred_true_df)
+        column_names = ['y_pred_false']
+        y_pred_false_df = DataFrame(y_pred_false, index=X_test_false.index, \
+                                    columns=column_names)
+        log.debug('y_pred_false_df: %s' % y_pred_false_df)
+        true_mask_df = DataFrame(true_mask)
+        mask_plus_true = true_mask_df.merge(y_pred_true_df, how='left', \
+                                            left_index=True, right_index=True)
+        mask_plus_true_plus_false = mask_plus_true.merge(y_pred_false_df, \
+                                how='left', left_index=True, right_index=True)
+        mask_plus_true_plus_false['y_pred'] = mask_plus_true_plus_false.apply(self._stitch_disjoint_row, axis=1)
+        log.debug('mask_plus_false: %s' % mask_plus_true_plus_false)
+        y_pred = mask_plus_true_plus_false['y_pred'].values
+
+        return y_pred
+
+    def predict_probability(self, X_test):
+        true_mask, false_mask = self.fetch_bifurcation_masks(X_test)
+
+        # Predict X_test_true.
+        X_test_true = X_test[true_mask]
+        y_pred_prob_true = self._sc_true.predict_probability(X_test_true)
+        log.debug('y_pred_prob_true: %s' % y_pred_prob_true)
+
+        # Predict X_test_false.
+        X_test_false = X_test[false_mask]
+        y_pred_prob_false = self._sc_false.predict_probability(X_test_false)
+        log.debug('y_pred_prob_false: %s' % y_pred_prob_false)
+
+        # Stitch results.
+        column_names = ['y_pred_prob_true_0', 'y_pred_prob_true_1']
+        y_pred_prob_true_df = DataFrame(y_pred_prob_true, index=X_test_true.index, \
+                                    columns=column_names)
+        log.debug('y_pred_prob_true_df: %s' % y_pred_prob_true_df)
+        column_names = ['y_pred_prob_false_0', 'y_pred_prob_false_1']
+        y_pred_prob_false_df = DataFrame(y_pred_prob_false, index=X_test_false.index, \
+                                    columns=column_names)
+        log.debug('y_pred_prob_false_df: %s' % y_pred_prob_false_df)
+        true_mask_df = DataFrame(true_mask)
+        mask_plus_true = true_mask_df.merge(y_pred_prob_true_df, how='left', \
+                                            left_index=True, right_index=True)
+        composite = mask_plus_true.merge(y_pred_prob_false_df, \
+                                how='left', left_index=True, right_index=True)
+        composite['y_pred_prob_0'] = composite.apply(self._stitch_prob_0, axis=1)
+        composite['y_pred_prob_1'] = composite.apply(self._stitch_prob_1, axis=1)
+        log.debug('composite: %s' % composite)
+        y_pred_prob = composite[['y_pred_prob_0', 'y_pred_prob_1']].values
+        log.debug(y_pred_prob)
+
+        return y_pred_prob
diff --git a/medinfo/ml/ClassifierAnalyzer.py b/medinfo/ml/ClassifierAnalyzer.py
new file mode 100644
index 00000000..d1242d4b
--- /dev/null
+++ b/medinfo/ml/ClassifierAnalyzer.py
@@ -0,0 +1,414 @@
+#!/usr/bin/python
+"""
+Utility class for analyzing the performance of a pre-trained classifier
+on a set of test data.
+"""
+
+import matplotlib
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+from pandas import DataFrame
+import sys
+import numpy as np
+
+from sklearn.metrics import recall_score, precision_score, f1_score
+from sklearn.metrics import average_precision_score, roc_auc_score
+from sklearn.metrics import precision_recall_curve, roc_curve
+
+from medinfo.ml.PredictorAnalyzer import PredictorAnalyzer
+from medinfo.common.Util import log
+
+class ClassifierAnalyzer(PredictorAnalyzer):
+    ACCURACY_SCORE = 'accuracy'
+    RECALL_SCORE = 'recall'
+    PRECISION_SCORE = 'precision'
+    F1_SCORE = 'f1'
+    AVERAGE_PRECISION_SCORE = 'average_precision'
+    ROC_AUC_SCORE = 'roc_auc'
+    PRECISION_AT_K_SCORE = 'precision_at_k'
+    K_99_PRECISION_SCORE = 'k(precision=0.99)'
+    K_95_PRECISION_SCORE = 'k(precision=0.95)'
+    K_90_PRECISION_SCORE = 'k(precision=0.90)'
+    PERCENT_PREDICTABLY_POSITIVE = 'percent_predictably_positive'
+    SUPPORTED_SCORES = [ACCURACY_SCORE, RECALL_SCORE, PRECISION_SCORE,
+                        F1_SCORE, AVERAGE_PRECISION_SCORE,
+                        PERCENT_PREDICTABLY_POSITIVE,
+                        PRECISION_AT_K_SCORE, K_99_PRECISION_SCORE,
+                        K_95_PRECISION_SCORE, K_90_PRECISION_SCORE,
+                        ROC_AUC_SCORE]
+
+    def __init__(self, classifier, X_test, y_test, random_state=None):
+        # TODO(sbala): Make this API more flexible, so that it can work
+        # with multi-label classifiers or binary classifiers whose
+        # positive label != 1.
+        PredictorAnalyzer.__init__(self, classifier, X_test, y_test, random_state)
+        # If there is only one class in y_test, abort.
+        classes = y_test[y_test.columns.values[0]].value_counts().index.values
+        if len(classes) <= 1:
+            sole_class = classes[0]
+            log.debug('y_test only has samples of 1 class: %s' % sole_class)
+            sys.exit('[ERROR] ClassifierAnalyzer: y_test only has samples of 1 class: %s' % sole_class)
+
+        self._y_pred_prob = DataFrame(classifier.predict_probability(X_test)[:,1])
+        log.debug('y_pred_prob[0].value_counts(): %s' % self._y_pred_prob[0].value_counts())
+
+        if random_state is None:
+            self._random_state = np.random.RandomState(123456789)
+        elif isinstance(random_state, int):
+            self._random_state = np.random.RandomState(random_state)
+        elif isinstance(random_state, np.random.RandomState):
+            self._random_state = random_state
+
+
+    def _score_accuracy(self, ci=None, n_bootstrap_iter=None):
+        return PredictorAnalyzer._score_accuracy(self, ci, n_bootstrap_iter)
+
+    def _score_recall(self, ci=None, n_bootstrap_iter=None):
+        if ci:
+            return self._bootstrap_score_ci(recall_score, ci, self._y_test, y_pred=self._y_predicted, n_bootstrap_iter=n_bootstrap_iter)
+        else:
+            return recall_score(self._y_test, self._y_predicted)
+
+    def _score_precision(self, ci=None, n_bootstrap_iter=None):
+        if ci:
+            return self._bootstrap_score_ci(precision_score, ci, self._y_test, y_pred=self._y_predicted, n_bootstrap_iter=n_bootstrap_iter)
+        else:
+            return precision_score(self._y_test, self._y_predicted)
+
+    def _score_f1(self, ci=None, n_bootstrap_iter=None):
+        if ci:
+            return self._bootstrap_score_ci(f1_score, ci, self._y_test, y_pred=self._y_predicted, n_bootstrap_iter=n_bootstrap_iter)
+        else:
+            return f1_score(self._y_test, self._y_predicted)
+
+    def _score_average_precision(self, ci=None, n_bootstrap_iter=None):
+        if ci:
+            return self._bootstrap_score_ci(average_precision_score, ci, self._y_test, y_pred_prob=self._y_pred_prob, n_bootstrap_iter=n_bootstrap_iter)
+        else:
+            return average_precision_score(self._y_test, self._y_pred_prob)
+
+    def _score_roc_auc(self, ci=None, n_bootstrap_iter=None):
+        if ci:
+            return self._bootstrap_score_ci(roc_auc_score, ci, self._y_test, y_pred_prob=self._y_pred_prob, n_bootstrap_iter=n_bootstrap_iter)
+        else:
+            return roc_auc_score(self._y_test, self._y_pred_prob)
+
+    def _bootstrap_score_ci(self, score_fn, ci, y_test, y_pred=None, y_pred_prob=None, n_bootstrap_iter=None, k=None, desired_precision=None):
+        # Note that y_pred may either represent the predicted labels or the
+        # predicted label probabilities. It's up to the caller to make the
+        # right choice based on score_fn's expected input.
+        if score_fn == self._score_precision_at_k:
+            sample_score = score_fn(y_test, y_pred, y_pred_prob, k)
+        elif score_fn == self._score_percent_predictably_positive:
+            sample_score = score_fn(y_test, y_pred, y_pred_prob, desired_precision)
+        elif score_fn in [average_precision_score, roc_auc_score]:
+            sample_score = score_fn(y_test, y_pred_prob)
+        else:
+            sample_score = score_fn(y_test, y_pred)
+
+        if n_bootstrap_iter is None:
+            n_bootstrap_iter = 100
+        # For consistency of results, seed random number generator with
+        # fixed number.
+        rng = self._random_state
+        # Use bootstrap to compute CIs.
+        bootstrap_scores = list()
+        for i in range(0, n_bootstrap_iter):
+            # Sample y_test and y_pred with replacement.
+            indices = rng.randint(0, len(y_test) - 1, len(y_test))
+            sample_y_test = np.array(y_test)[indices]
+            if y_pred is not None: sample_y_pred = np.array(y_pred)[indices]
+            if y_pred_prob is not None: sample_y_pred_prob = np.array(y_pred_prob)[indices]
+            if len(np.unique(sample_y_test)) < 2:
+                # We need at least one positive and one negative sample for ROC AUC
+                # to be defined: reject the sample
+                continue
+            if score_fn == self._score_precision_at_k:
+                score = score_fn(DataFrame(sample_y_test), DataFrame(sample_y_pred), DataFrame(sample_y_pred_prob), k)
+            elif score_fn == self._score_percent_predictably_positive:
+                score = score_fn(DataFrame(sample_y_test), DataFrame(sample_y_pred), DataFrame(sample_y_pred_prob), desired_precision)
+            elif score_fn in [average_precision_score, roc_auc_score]:
+                score = score_fn(sample_y_test, sample_y_pred_prob)
+            else:
+                score = score_fn(sample_y_test, sample_y_pred)
+            bootstrap_scores.append(score)
+
+        # Sort bootstrap scores to get CIs.
+        bootstrap_scores.sort()
+        log.debug('bootstrap_scores: %s' % bootstrap_scores)
+        sorted_scores = np.array(bootstrap_scores)
+        # May not be equal to n_bootstrap_iter if some samples were rejected
+        num_bootstraps = len(sorted_scores)
+        log.debug('sorted_scores: %s' % sorted_scores)
+
+        ci_lower_bound_float = (1.0 - ci) / 2
+        ci_lower_bound = sorted_scores[int(ci_lower_bound_float * num_bootstraps)]
+        ci_upper_bound_float = ci + ci_lower_bound_float
+        ci_upper_bound = sorted_scores[int(ci_upper_bound_float * num_bootstraps)]
+
+        return sample_score, ci_lower_bound, ci_upper_bound
+
+    def _score_precision_at_k(self, y_true, y_pred, y_pred_prob, k, ci=None, n_bootstrap_iter=None, desired_precision=None):
+        if ci:
+            return self._bootstrap_score_ci(self._score_precision_at_k, ci, y_true, y_pred=y_pred, y_pred_prob=y_pred_prob, n_bootstrap_iter=n_bootstrap_iter, k=k)
+        else:
+            # Get the name of the column in y_pred_prob.
+            prob_col_name = y_pred_prob.columns.values[0]
+            # Sort y_pred_prob by the values in that column.
+            prob_sorted = y_pred_prob.sort_values(prob_col_name, ascending=False)
+            # Fetch the index for pred_sort.
+            prob_sorted_index = prob_sorted.index
+            # Sort y_test and y_pred by that index.
+            true_sorted = y_true.reindex(index=prob_sorted_index)
+            pred_sorted = y_pred.reindex(index=prob_sorted_index)
+            # Fetch the top k predictions.
+            pred_sorted_at_k = pred_sorted[0:k]
+            true_sorted_at_k = true_sorted[0:k]
+            # Return precision at k.
+            return precision_score(true_sorted_at_k, pred_sorted_at_k)
+
+    def _score_k_percentile_precision(self, desired_precision, n_bootstrap_iter=None):
+        # TODO(sbala): Clean up this function so it can use _bootstrap_score_ci.
+        k_vals, precision_vals = self.compute_precision_at_k_curve()
+
+        # Search for k_val for which precision == precision.
+        threshold_k = k_vals[0]
+        for precision, k in zip(precision_vals, k_vals):
+            if precision >= desired_precision:
+                threshold_k = k
+
+        return float(threshold_k) / float(len(k_vals))
+
+    def _score_percent_predictably_positive(self, y_test, y_pred, y_pred_prob, desired_precision, ci=None, n_bootstrap_iter=None):
+        if ci:
+            return self._bootstrap_score_ci(self._score_percent_predictably_positive, ci, y_test, y_pred=y_pred, y_pred_prob=y_pred_prob, desired_precision=desired_precision)
+        else:
+            # Get the name of the column in y_pred_prob.
+            prob_col_name = y_pred_prob.columns.values[0]
+            # Sort y_pred_prob by the values in that column.
+            prob_sorted = y_pred_prob.sort_values(prob_col_name, ascending=False)
+            # Fetch the index for pred_sort.
+            prob_sorted_index = prob_sorted.index
+            # Sort y_test and y_pred by that index.
+            true_sorted = y_test.reindex(index=prob_sorted_index)
+            pred_sorted = y_pred.reindex(index=prob_sorted_index)
+
+            num_true_positive = 0
+            n_bootstrap_iter = len(true_sorted.index)
+            for k in range(1, n_bootstrap_iter):
+                # Fetch the top k predictions.
+                pred_sorted_at_k = pred_sorted[0:k]
+                true_sorted_at_k = true_sorted[0:k]
+                # Get precision at k.x
+                precision_at_k = precision_score(true_sorted_at_k, pred_sorted_at_k)
+                if precision_at_k >= desired_precision:
+                    num_true_positive = true_sorted_at_k[true_sorted_at_k.columns.values[0]].value_counts()[1]
+
+            return float(num_true_positive) / float(n_bootstrap_iter)
+
+    def score(self, metric=None, k=None, ci=None, n_bootstrap_iter=None):
+        # ci defines confidence interval as float.
+        # Also defines whether score returns score or (-ci, score, +ci)
+        if metric is None:
+            metric = ClassifierAnalyzer.ACCURACY_SCORE
+
+        if metric not in ClassifierAnalyzer.SUPPORTED_SCORES:
+            raise ValueError('Score metric %s not supported.' % metric)
+
+        if metric == ClassifierAnalyzer.ACCURACY_SCORE:
+            return self._score_accuracy(ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.RECALL_SCORE:
+            return self._score_recall(ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.PRECISION_SCORE:
+            return self._score_precision(ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.F1_SCORE:
+            return self._score_f1(ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.AVERAGE_PRECISION_SCORE:
+            return self._score_average_precision(ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.ROC_AUC_SCORE:
+            return self._score_roc_auc(ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.K_90_PRECISION_SCORE:
+            return self._score_k_percentile_precision(0.9, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.K_95_PRECISION_SCORE:
+            return self._score_k_percentile_precision(0.95,  n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.K_99_PRECISION_SCORE:
+            return self._score_k_percentile_precision(0.99, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.PERCENT_PREDICTABLY_POSITIVE:
+            return self._score_percent_predictably_positive(self._y_test, self._y_predicted, self._y_pred_prob, 0.99, ci, n_bootstrap_iter)
+        elif metric == ClassifierAnalyzer.PRECISION_AT_K_SCORE:
+            if k is None:
+                raise ValueError('Must specify k for PRECISION_AT_K_SCORE.')
+            else:
+                return self._score_precision_at_k(self._y_test, self._y_predicted, self._y_pred_prob, k=k, ci=ci, n_bootstrap_iter=n_bootstrap_iter)
+
+    def compute_precision_recall_curve(self):
+        # TODO(sbala): Allow computation of confidence intervals via bootstrap.
+        return precision_recall_curve(self._y_test, self._y_pred_prob)
+
+    def plot_precision_recall_curve(self, title, dest_path):
+        # Compute inputs.
+        precisions, recalls, thresholds = self.compute_precision_recall_curve()
+        average_precision = self._score_average_precision()
+
+        # Set figure settings.
+        rcParams['font.family'] = 'sans-serif'
+        rcParams['font.sans-serif'] = ['Helvetica', 'Arial', 'Tahoma']
+
+        # Make plot.
+        plt.figure()
+        plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
+        plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
+
+        # Label axes.
+        plt.xlabel('Recall')
+        plt.ylabel('Precision')
+        plt.ylim([0.0, 1.05])
+        plt.xlim([0.0, 1.0])
+
+        # Set title.
+        plt.title('{0}: AP={1:0.2f}'.format(title, average_precision))
+
+        # Save figure.
+        plt.savefig(dest_path)
+        plt.close()
+
+    def output_direct_comparisons(self, dest_path):
+        actual_reindex = self._y_test.reset_index(drop=True)
+        predict_reindex = self._y_pred_prob.reset_index(drop=True)
+        direct_comparisons = actual_reindex.join(predict_reindex)
+        direct_comparisons.columns = ['actual', 'predict']
+        direct_comparisons.to_csv(dest_path)
+
+    def compute_roc_curve(self):
+        return roc_curve(self._y_test, self._y_pred_prob)
+
+    def plot_roc_curve(self, title, dest_path):
+        # Compute inputs.
+        fprs, tprs, thresholds = self.compute_roc_curve()
+        roc_auc = self._score_roc_auc()
+
+        # Set figure settings.
+        rcParams['font.family'] = 'sans-serif'
+        rcParams['font.sans-serif'] = ['Helvetica', 'Arial', 'Tahoma']
+
+        # Make plot.
+        plt.figure()
+        plt.plot(fprs, tprs, color='darkorange', lw=2)
+        plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')
+
+        # Label axes.
+        plt.xlim([0.0, 1.0])
+        plt.ylim([0.0, 1.05])
+        plt.xlabel('False Positive Rate')
+        plt.ylabel('True Positive Rate')
+
+        # Set title.
+        plt.title('%s (AUC = %s)' % (title, roc_auc))
+
+        # Save figure.
+        plt.savefig(dest_path)
+        plt.close()
+
+    def compute_precision_at_k_curve(self, ci=None, n_bootstrap_iter=None):
+        num_samples = self._y_test.shape[0]
+        k_vals = list(range(1, num_samples + 1))
+        precision_vals = list()
+        lower_ci_vals = list()
+        upper_ci_vals = list()
+        for k in k_vals:
+            if ci:
+                precision, lower_ci, upper_ci = self._score_precision_at_k(self._y_test, self._y_predicted, self._y_pred_prob, k, ci)
+                precision_vals.append(precision)
+                lower_ci_vals.append(lower_ci)
+                upper_ci_vals.append(upper_ci)
+            else:
+                precision = self._score_precision_at_k(self._y_test, self._y_predicted, self._y_pred_prob, k)
+                precision_vals.append(precision)
+
+        if ci:
+            return k_vals, precision_vals, lower_ci_vals, upper_ci_vals
+        else:
+            return k_vals, precision_vals
+
+    def plot_precision_at_k_curve(self, title, dest_path):
+        # Compute inputs.
+        k_vals, precision_vals = self.compute_precision_at_k_curve()
+
+        # Set figure settings.
+        rcParams['font.family'] = 'sans-serif'
+        rcParams['font.sans-serif'] = ['Helvetica', 'Arial', 'Tahoma']
+
+        # Make plot.
+        plt.figure()
+        plt.plot(k_vals, precision_vals, color='darkorange', lw=2)
+
+        # Label axes.
+        plt.xlabel('k')
+        plt.ylabel('Precision')
+        plt.ylim([0.0, 1.05])
+        plt.xlim([0.0, k_vals[-1]])
+
+        # Set title.
+        plt.title('{0}'.format(title))
+
+        # Save figure.
+        plt.savefig(dest_path)
+        plt.close()
+
+    def build_report(self, ci=None):
+        column_names = ['model', 'test_size']
+        report_dict = {
+            'model': [self._predictor.description()],
+            'test_size': [self._y_test.shape[0]]
+        }
+
+        # Add train and test counts.
+        y_test_counts = self._y_test[self._y_test.columns[0]].value_counts(ascending=True)
+        report_dict.update({'y_test.value_counts()': [str(y_test_counts.to_dict())]})
+        column_names.append('y_test.value_counts()')
+
+        # Add scores.
+        for score_metric in ClassifierAnalyzer.SUPPORTED_SCORES:
+            # Hack to avoid breaking tests.
+            if ci:
+                if score_metric == ClassifierAnalyzer.K_99_PRECISION_SCORE:
+                    continue
+                if score_metric == ClassifierAnalyzer.K_95_PRECISION_SCORE:
+                    continue
+                if score_metric == ClassifierAnalyzer.K_90_PRECISION_SCORE:
+                    continue
+
+            if score_metric == ClassifierAnalyzer.PRECISION_AT_K_SCORE:
+                if ci:
+                    continue
+                else:
+                    k_10_percent = int(0.1 * self._y_test.shape[0])
+                    score_label = 'precision_at_10_percent'
+                    score_value = self.score(score_metric, k=k_10_percent)
+            else:
+                score_label = score_metric
+                if ci:
+                    score_value, lower_ci, upper_ci = self.score(metric=score_metric, ci=ci)
+                else:
+                    score_value = self.score(metric=score_metric)
+            column_names.append(score_label)
+            report_dict.update({score_label: score_value})
+            if ci:
+                lower_ci_label = '%s_%s_lower_ci' % (score_label, ci)
+                upper_ci_label = '%s_%s_upper_ci' % (score_label, ci)
+                column_names.append(lower_ci_label)
+                column_names.append(upper_ci_label)
+                report_dict.update({lower_ci_label:lower_ci})
+                report_dict.update({upper_ci_label:upper_ci})
+
+        # Add hyperparams.
+        report_dict.update({'hyperparams': str(dict(sorted(self._predictor.hyperparams().items())))})
+        column_names.append('hyperparams')
+
+        return DataFrame(report_dict, columns=column_names), column_names
+
+    def write_report(self, dest_path, ci=None):
+        report, column_names = self.build_report(ci)
+
+        PredictorAnalyzer.write_report(self, report, dest_path, column_names)
diff --git a/medinfo/ml/ClassifierPipeline.py b/medinfo/ml/ClassifierPipeline.py
new file mode 100644
index 00000000..70f1bb4d
--- /dev/null
+++ b/medinfo/ml/ClassifierPipeline.py
@@ -0,0 +1,156 @@
+import itertools
+import os
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score
+from sklearn.calibration import calibration_curve
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+# look into xgboost
+from sklearn.naive_bayes import GaussianNB
+
+'''
+go to http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html for reference
+'''
+
+
+class ClassifierPipeline(object):
+	def __init__(self, X_train, y_train, X_validation, y_validation, X_test, y_test, data_name, base_output_folder, output_folder):
+		self.X_train = X_train
+		self.y_train = y_train
+		self.X_validation = X_validation
+		self.y_validation = y_validation
+		self.X_test = X_test
+		self.y_test = y_test
+
+		self.data_name = data_name
+
+		self.BASE_OUTPUT_FOLDER = base_output_folder
+		self.OUTPUT_FOLDER = output_folder
+
+		self.outputs = {}
+
+		print('train_size', self.y_train.shape[0])
+		print('validation_size', self.y_validation.shape[0])
+		print('test_size', self.y_test.shape[0])
+
+	def set_classifiers(self, knn=None, naive_bayes=False, logistic_regression=None, svm=None, decision_tree=None, random_forest=None, ada_boost=None, additional_classifiers=None):
+		'''Takes in parameters for each classifier and adds the classifier to the self.classifiers list
+		Args:
+			knn: [:int] (list of ints) for n_neighbors
+			naive_bayes: boolean
+			logistic_regression: [:float] for C values
+			svm: 2D list - svm[0] is a [:string] specifying kernels (linear, rbf, poly, sigmoid), and svm[1] is a [:float] specifying C values
+			decision_tree: [:int] for max_depth
+			random_forest: 2D list - random_forest[0] is a [:int] for n_estimators, and random_forest[1] is a [:int] for max_depth
+			ada_boost: 2D list - ada_boost[0] is a [:int] for n_estimators, and ada_boost[1] is a [:float] for learning_rate
+			additional_classifiers: list of tuples - each tuple is of the form (name, classifier_instance)
+
+		'''
+		self.classifier_names = []
+		self.classifiers = []
+		if knn is not None:
+			self.classifier_names.extend(['K Nearest Neighbors n_neighbors={}'.format(hp) for hp in knn])
+			self.classifiers.extend([KNeighborsClassifier(n_neighbors=hp) for hp in knn])
+
+		if naive_bayes:
+			self.classifier_names.append('Gaussian Naive Bayes')
+			self.classifiers.append(GaussianNB())
+
+		if logistic_regression is not None:
+			self.classifier_names.extend(['Logistic Regression C={:.1f}'.format(hp) for hp in logistic_regression])
+			self.classifiers.extend([LogisticRegression(C=hp) for hp in logistic_regression])
+
+		if svm is not None:
+			self.classifier_names.extend(['SVM kernel={}, C={:.1f}'.format(*hp) for hp in itertools.product(*svm)])
+			self.classifiers.extend([SVC(kernel=hp[0], C=hp[1]) for hp in itertools.product(*svm)])
+
+		if decision_tree is not None:
+			self.classifier_names.extend(['Decision Tree max_depth={}'.format(hp) for hp in decision_tree])
+			self.classifiers.extend([DecisionTreeClassifier(max_depth=hp) for hp in decision_tree])
+
+		if random_forest is not None:
+			self.classifier_names.extend(['Random Forest n_estimators={}, max_depth={}'.format(*hp) for hp in itertools.product(*random_forest)])
+			self.classifiers.extend([RandomForestClassifier(n_estimators=hp[0], max_depth=hp[1]) for hp in itertools.product(*random_forest)])
+
+		if ada_boost is not None:
+			self.classifier_names.extend(['Ada Boost n_estimators={}, learning_rate={:.1f}'.format(*hp) for hp in itertools.product(*ada_boost)])
+			self.classifiers.extend([AdaBoostClassifier(n_estimators=hp[0], learning_rate=hp[1]) for hp in itertools.product(*ada_boost)])
+
+		if additional_classifiers is not None:
+			for name, classifier in additional_classifiers:
+				self.classifier_names.append(name)
+				self.classifiers.append(classifier)
+
+	def train(self, log_to_stdout=True, log_to_csv=False):
+		log = ['"classifier_name","train_accuracy","validation_accuracy","validation_precision","validation_recall","validation_f1","validation_roc-auc"']
+		print('TRAIN')
+		if log_to_stdout: print(log[-1])
+		for classifier_name, classifier in zip(self.classifier_names, self.classifiers):
+			classifier.fit(self.X_train, self.y_train)
+			validation_predictions = classifier.predict(self.X_validation)
+			validation_prediction_probs = classifier.predict_proba(self.X_validation)[:,1]
+
+			train_score = classifier.score(self.X_train, self.y_train)
+			validation_score = classifier.score(self.X_validation, self.y_validation)
+			validation_precision = precision_score(self.y_validation, validation_predictions)
+			validation_recall = recall_score(self.y_validation, validation_predictions)
+			validation_f1 = f1_score(self.y_validation, validation_predictions)
+			validation_roc_auc = roc_auc_score(self.y_validation, validation_prediction_probs)
+			log.append('"%s","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f"' %
+				(classifier_name, train_score, validation_score, validation_precision,
+					validation_recall, validation_f1, validation_roc_auc))
+			if log_to_stdout: print(log[-1])
+
+		if log_to_csv:
+			if not os.path.exists('/'.join([self.BASE_OUTPUT_FOLDER, self.OUTPUT_FOLDER])):
+				os.makedirs('/'.join([self.BASE_OUTPUT_FOLDER, self.OUTPUT_FOLDER]))
+			with open('/'.join([self.BASE_OUTPUT_FOLDER, self.OUTPUT_FOLDER, self.data_name + '_log_train.csv']), 'w') as file:
+				for line in log:
+					print(line, file=file)
+
+	def test(self, log_to_stdout=True, output_matrix=None):
+		log = ['"classifier_name","test_accuracy","test_precision","test_recall","test_f1","test_roc-auc"']
+		print('TEST')
+		if log_to_stdout: print(log[-1])
+		for i, (classifier_name, classifier) in enumerate(zip(self.classifier_names, self.classifiers)):
+			test_predictions = classifier.predict(self.X_test)
+			test_prediction_probs = classifier.predict_proba(self.X_test)[:,1]
+
+			test_score = classifier.score(self.X_test, self.y_test)
+			test_precision = precision_score(self.y_test, test_predictions)
+			test_recall = recall_score(self.y_test, test_predictions)
+			test_f1 = f1_score(self.y_test, test_predictions)
+			test_roc_auc = roc_auc_score(self.y_test, test_prediction_probs)
+			log.append('"%s","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f"' %
+				(classifier_name, test_score, test_precision, test_recall, test_f1, test_roc_auc))
+			if log_to_stdout: print(log[-1])
+
+			self.outputs[self._get_column_name(classifier_name)] = test_prediction_probs
+			if output_matrix is not None:
+				output_matrix[self._get_column_name(classifier_name)] = test_prediction_probs
+
+		if output_matrix is not None:
+			if not os.path.exists('/'.join([self.BASE_OUTPUT_FOLDER, self.OUTPUT_FOLDER])):
+				os.makedirs('/'.join([self.BASE_OUTPUT_FOLDER, self.OUTPUT_FOLDER]))
+			output_matrix.to_csv('/'.join([self.BASE_OUTPUT_FOLDER, self.OUTPUT_FOLDER, self.data_name + '_outputs.csv']), index=False)
+
+	def plot(self):
+		for i, classifier_name in enumerate(self.classifier_names):
+			test_prediction_probs = self.outputs[self._get_column_name(classifier_name)]
+			prob_true, prob_pred = calibration_curve(self.y_test, test_prediction_probs, n_bins=20)
+			fig = plt.figure(figsize=(6, 4))
+			plt.plot(prob_pred, prob_true, 's-')
+			plt.title(classifier_name)
+			plt.savefig('/'.join([self.BASE_EXPORT_FOLDER, self.data_name + '_' + classifier_name.lower().replace(' ', '_') + '.eps']), format='eps')
+			plt.close(fig)
+
+	@staticmethod
+	def _get_column_name(classifier_name):
+		return 'predicted.' + classifier_name.lower().replace(' ', '_')
diff --git a/medinfo/ml/Const.py b/medinfo/ml/Const.py
new file mode 100644
index 00000000..e69de29b
diff --git a/medinfo/ml/FeatureSelector.py b/medinfo/ml/FeatureSelector.py
new file mode 100644
index 00000000..51f8d6b0
--- /dev/null
+++ b/medinfo/ml/FeatureSelector.py
@@ -0,0 +1,144 @@
+#!/usr/bin/python
+
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.linear_model import LassoCV
+from sklearn.feature_selection import f_classif, f_regression, RFE, RFECV, SelectKBest, SelectPercentile
+
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+
+class FeatureSelector:
+    SELECT_K_BEST = 'select_K_best'
+    SELECT_PERCENTILE = 'select_percentile'
+    REMOVE_HIGH_VARIANCE = 'remove_high_variance'
+    RECURSIVE_ELIMINATION = 'eliminate_recursively'
+    SUPPORTED_ALGORITHMS = [SELECT_K_BEST, SELECT_PERCENTILE, RECURSIVE_ELIMINATION]
+
+    REGRESSION = 'regression'
+    CLASSIFICATION = 'classification'
+    SUPPORTED_PROBLEMS = [REGRESSION, CLASSIFICATION]
+
+    def __init__(self, algorithm=None, problem=None, random_state=None):
+        # Specify feature selection algorithm.
+        if algorithm is None:
+            algorithm = FeatureSelector.REMOVE_HIGH_VARIANCE
+        elif algorithm not in FeatureSelector.SUPPORTED_ALGORITHMS:
+            raise ValueError('Algorithm %s not supported.' % algorithm)
+        self._algorithm = algorithm
+
+        # Specify regression vs. classification problem.
+        if problem is None:
+            problem = 'classification'
+        elif problem not in FeatureSelector.SUPPORTED_PROBLEMS:
+            raise ValueError('Problem %s not supported.' % problem)
+        self._problem = problem
+
+        # Use a FeatureMatrixTransform for actually generating output matrix.
+        self._fmt = FeatureMatrixTransform()
+        self._selector = None
+
+        self._random_state = random_state
+
+        pass
+
+    def set_input_matrix(self, X, y):
+        self._input_X = X
+        self._input_y = y
+        pass
+
+    def select(self, k=None, percentile=None):
+        if self._algorithm == FeatureSelector.SELECT_K_BEST:
+            if k is None:
+                raise ValueError('Must specify value for k.')
+            self._select_K_best(k)
+        elif self._algorithm == FeatureSelector.SELECT_PERCENTILE:
+            if percentile is None:
+                raise ValueError('Must specify value for percentile.')
+            self._select_percentile(percentile)
+        elif self._algorithm == FeatureSelector.RECURSIVE_ELIMINATION:
+            self._eliminate_recursively(k)
+
+        self._selector.fit(self._input_X, self._input_y)
+
+    def compute_ranks(self):
+        if self._algorithm == FeatureSelector.SELECT_K_BEST:
+            scores = self._selector.scores_
+            sorted_scores = sorted(scores, reverse=True)
+            ranks = [sorted_scores.index(i) + 1 for i in scores]
+        elif self._algorithm == FeatureSelector.SELECT_PERCENTILE:
+            scores = self._selector.scores_
+            sorted_scores = sorted(scores, reverse=True)
+            ranks = [sorted_scores.index(i) + 1 for i in scores]
+        elif self._algorithm == FeatureSelector.RECURSIVE_ELIMINATION:
+            n_selected = self._selector.n_features_
+            support = self._selector.support_
+            ranking = self._selector.ranking_
+            # RFE and RFECV do not provide feature scores. Instead, they
+            # provide a list of features which have been selected (support)
+            # and an ascending list indicating when each other feature was
+            # eliminated. Use these two to construct feature ranks, though
+            # acknowledge that RFE and RFECV do not actually distinguish between
+            # the weights of selected features.
+            ranks = [0]*len(support)
+            selected_count = 0
+            for i in range(len(ranking)):
+                if support[i]:
+                    # All selected features in ranking receive rank 1, so need
+                    # to iterate through list and add incrementing values so
+                    # that features ranked 1, 1, 1, become 1, 2, 3.
+                    ranks[i] = ranking[i] + selected_count
+                    selected_count += 1
+                else:
+                    # Even if there are 5 selected features, the 6th feature
+                    # in ranking is given rank 2, so add (n_selected - 1).
+                    ranks[i] = ranking[i] + (n_selected - 1)
+
+        return ranks
+
+    def transform_matrix(self, X):
+        if type(X) == pd.DataFrame:
+            # Try to preserve labels.
+            columns = X.columns
+            old_index = X.index
+            labels = [columns[x] for x in self._selector.get_support(indices=True)]
+            return pd.DataFrame(self._selector.transform(X), columns=labels, index=old_index)
+        else:
+            return self._selector.transform(X)
+
+    def _remove_high_variance(self):
+        pass
+
+    def _select_K_best(self, k):
+        if self._problem == FeatureSelector.CLASSIFICATION:
+            score = f_classif
+        else:
+            score = f_regression
+
+        self._selector = SelectKBest(score, k)
+
+    def _select_percentile(self, percentile):
+        # Algorithm is conservative. Defaults to keeping features if
+        # percentile specifies a value that corresponds to a floating number
+        # of features. For example, if percentile=18 on a 20-feature matrix
+        # implies keeping 3.6 features. In that case, keeps 4 features.
+        if self._problem == FeatureSelector.CLASSIFICATION:
+            score = f_classif
+        else:
+            score = f_regression
+
+        self._selector = SelectPercentile(score, percentile)
+
+    def _eliminate_recursively(self, k=None):
+        if self._problem == FeatureSelector.CLASSIFICATION:
+            estimator = RandomForestClassifier(random_state=self._random_state)
+        else:
+            estimator = LassoCV(random_state=self._random_state)
+        # If k is not specified, then use RFECV to automatically decide on
+        # optimal number of features. If specified, then use RFE.
+        if k is None:
+            self._selector = RFECV(estimator)
+        else:
+            self._selector = RFE(estimator, n_features_to_select=k, step=0.05)
+
+    def _select_recursively(self):
+        pass
diff --git a/medinfo/ml/ListPredictor.py b/medinfo/ml/ListPredictor.py
new file mode 100644
index 00000000..b195ea87
--- /dev/null
+++ b/medinfo/ml/ListPredictor.py
@@ -0,0 +1,36 @@
+#!/usr/bin/python
+"""
+Fake predictor meant purely for testing. It accepts a list of predictions
+and just cycles through those predictions.
+"""
+
+from pandas import DataFrame
+from numpy import array
+
+from medinfo.ml.Predictor import Predictor
+from medinfo.common.Util import log
+
+class ListPredictor(Predictor):
+    def __init__(self, predictions):
+        self._index = 0
+        self._predictions = predictions
+        log.debug('predictions: %s' % predictions)
+        self._num_predictions = len(self._predictions)
+
+    def __repr__(self):
+        return 'ListPredictor(%s)' % self._predictions.values
+
+    __str__ = __repr__
+
+    def predict(self, X):
+        y_predicted = list()
+        for row in X.iterrows():
+            prediction = self._predictions[self._index]
+            y_predicted.append(prediction)
+            self._index = (self._index + 1) % self._num_predictions
+
+        log.debug('y_predicted: %s' % y_predicted)
+        return DataFrame({'y_predicted': y_predicted})
+
+    def predict_probability(self, X):
+        return array([[0.5, 0.5]]*X.shape[0])
diff --git a/medinfo/ml/Predictor.py b/medinfo/ml/Predictor.py
new file mode 100644
index 00000000..b208b27a
--- /dev/null
+++ b/medinfo/ml/Predictor.py
@@ -0,0 +1,23 @@
+#!/usr/bin/python
+"""
+Abstract class for predictor, which can predict outcomes, summarize its logic,
+and be fed to a PredictorAnalyzer for analysis of its performance.
+
+Predictor may be arbitrarily simple (always return 0) or complex (CNN).
+The Predictor API is based on the common API provided by sklearn.
+"""
+
+import pandas as pd
+
+class Predictor:
+    def __repr__(self):
+        return 'Predictor()'
+        
+    __str__ = __repr__
+
+    def predict(self, X):
+        y_predicted = list()
+        for row in X.iterrows():
+            y_predicted.append(True)
+
+        return pd.DataFrame({'y_predicted': y_predicted})
diff --git a/medinfo/ml/PredictorAnalyzer.py b/medinfo/ml/PredictorAnalyzer.py
new file mode 100644
index 00000000..e5ed0055
--- /dev/null
+++ b/medinfo/ml/PredictorAnalyzer.py
@@ -0,0 +1,105 @@
+#!/usr/bin/python
+"""
+Abstract class for analyzing the performance of a pre-trained predictor
+on a set of test data.
+"""
+
+import pandas as pd
+from sklearn.utils.validation import column_or_1d
+from sklearn.metrics import accuracy_score
+import numpy as np
+
+from medinfo.common.Util import log
+
+class PredictorAnalyzer:
+    ACCURACY_SCORE = 'accuracy'
+    def __init__(self, predictor, X_test, y_test, random_state=None):
+        self._predictor = predictor
+        # In theory we could let the client pass X_test and y_test into each
+        # individual scoring function, but that might encourage them to keep
+        # testing constantly, turning the test cases into training cases.
+        self._X_test = X_test
+        self._y_test = y_test.reset_index(drop=True)
+        log.debug('y_true[0].value_counts(): %s' % self._y_test[self._y_test.columns.values[0]].value_counts())
+        # Cast to DataFrame to ease subsequent analysis, even though sklearn
+        # by default just outputs an ndarray.
+        self._y_predicted = pd.DataFrame(self._predictor.predict(self._X_test))
+        log.debug('y_predicted[0].value_counts(): %s' % self._y_predicted[self._y_predicted.columns.values[0]].value_counts())
+
+        if random_state is None:
+            self._random_state = np.random.RandomState(123456789)
+        elif isinstance(random_state, int):
+            self._random_state = np.random.RandomState(random_state)
+        elif isinstance(random_state, np.random.RandomState):
+            self._random_state = random_state
+
+    def _score_accuracy(self, ci=None, n_bootstrap_iter=None):
+        sample_accuracy = accuracy_score(self._y_test, self._y_predicted)
+        log.debug('y_test: %s' % self._y_test)
+        if ci:
+            if n_bootstrap_iter is None:
+                n_bootstrap_iter = 100
+            # For consistency of results, seed random number generator with
+            # fixed number.
+            rng = self._random_state
+            # Use bootstrap to compute cis.
+            bootstrap_scores = list()
+            for i in range(0, n_bootstrap_iter):
+                # Sample y_test and y_pred with replacement.
+                indices = rng.randint(0, len(self._y_predicted) - 1, len(self._y_predicted))
+                sample_y_test = np.array(self._y_test)[indices]
+                sample_y_pred = np.array(self._y_predicted)[indices]
+                log.debug('sample_y_pred: %s' % sample_y_pred)
+                if len(np.unique(sample_y_test)) < 2:
+                    # We need at least one positive and one negative sample for ROC AUC
+                    # to be defined: reject the sample
+                    continue
+                score = accuracy_score(sample_y_test, sample_y_pred)
+                bootstrap_scores.append(score)
+
+            # Sort bootstrap scores to get CIs.
+            bootstrap_scores.sort()
+            sorted_scores = np.array(bootstrap_scores)
+            # May not be equal to n_bootstrap_iter if some samples were rejected
+            num_bootstraps = len(sorted_scores)
+            log.debug('sorted_scores: %s' % sorted_scores)
+
+            ci_lower_bound_float = (1.0 - ci) / 2
+            ci_lower_bound = sorted_scores[int(ci_lower_bound_float * num_bootstraps)]
+            ci_upper_bound_float = ci + ci_lower_bound_float
+            ci_upper_bound = sorted_scores[int(ci_upper_bound_float * num_bootstraps)]
+
+            return sample_accuracy, ci_lower_bound, ci_upper_bound
+        else:
+            return sample_accuracy
+
+    def score(self, metric=None, ci=None, n_bootstrap_iter=None):
+        # ci defines confidence interval as float.
+        # Also defines whether score returns score or (-ci, score, +ci)
+        if metric is None:
+            metric = PredictorAnalyzer.ACCURACY_SCORE
+
+        if metric == PredictorAnalyzer.ACCURACY_SCORE:
+            return self._score_accuracy(ci, n_bootstrap_iter)
+        else:
+            raise ValueError('Score metric %s not supported.' % metric)
+
+    def build_report(self):
+        # Report the following summary statistics:
+        # * test size
+        # * accuracy
+        accuracy = self._score_accuracy()
+        test_size = self._y_test.shape[0]
+        report = pd.DataFrame({
+            'model': [repr(self._predictor)],
+            'test_size': [test_size],
+            'accuracy': [accuracy]
+        })
+
+        return report
+
+    def write_report(self, report, dest_path, column_names=None):
+        if column_names is None:
+            column_names = ['model', 'test_size', 'accuracy']
+        report.to_csv(dest_path, sep='\t', index=False, columns=column_names,
+            float_format='%.5f')
diff --git a/medinfo/ml/Regressor.py b/medinfo/ml/Regressor.py
new file mode 100644
index 00000000..556d0723
--- /dev/null
+++ b/medinfo/ml/Regressor.py
@@ -0,0 +1,60 @@
+#!/usr/bin/python
+"""
+Generic class for linear and logistic regression.
+"""
+
+from numpy import array
+from sklearn.linear_model import LassoCV
+from sklearn.linear_model import LinearRegression
+
+class Regressor:
+    # Define string constants for all supported ML algorithms so that
+    # clients can define which algorithm to call.
+    # Rather than defining custom strings, model these off of meaningful
+    # nomenclature in scikit-learn or whatever ML utility is being used
+    # to drive the implementation.
+    LASSO = 'lasso-regression'
+    LINEAR_REGRESSION = 'linear-regression'
+    REGRESS_AND_ROUND = 'regress-and-round'
+
+    SUPPORTED_ALGORITHMS = [LASSO, LINEAR_REGRESSION, REGRESS_AND_ROUND]
+
+    def __init__(self, algorithm=None):
+        if algorithm is None:
+            self._algorithm = Regressor.LASSO
+        elif algorithm not in Regressor.SUPPORTED_ALGORITHMS:
+            raise ValueError('Algorithm %s not supported.' % algorithm)
+        else:
+            self._algorithm = algorithm
+
+    def algorithm(self):
+        return self._algorithm
+
+    def coefs(self):
+        return self._model.coef_
+
+    def train(self, X, y):
+        if self._algorithm == Regressor.LASSO:
+            self._train_lasso(X, y)
+        elif self._algorithm == Regressor.LINEAR_REGRESSION:
+            self._train_linear_regression(X, y)
+        elif self._algorithm == Regressor.REGRESS_AND_ROUND:
+            self._train_regress_and_round(X, y)
+
+    def _train_lasso(self, X, y):
+        self._model = LassoCV()
+        self._model.fit(X, y)
+
+    def _train_linear_regression(self, X, y):
+        self._model = LinearRegression()
+        self._model.fit(X, y)
+
+    def _train_regress_and_round(self, X, y):
+        self._train_lasso(X, y)
+        self._model.coef_ = array([round(c) for c in self._model.coef_])
+
+    def predict(self, X):
+        return self._model.predict(X)
+
+    def compute_accuracy(self, X, y):
+        return self._model.score(X, y)
diff --git a/medinfo/ml/RegressorAnalyzer.py b/medinfo/ml/RegressorAnalyzer.py
new file mode 100644
index 00000000..b8ebb942
--- /dev/null
+++ b/medinfo/ml/RegressorAnalyzer.py
@@ -0,0 +1,87 @@
+#!/usr/bin/python
+"""
+Class for analyzing the performance of a pre-trained Regressor.
+"""
+
+from pandas import DataFrame
+
+from medinfo.ml.PredictorAnalyzer import PredictorAnalyzer
+from sklearn.metrics import explained_variance_score, r2_score, mean_absolute_error, median_absolute_error
+
+class RegressorAnalyzer(PredictorAnalyzer):
+    ACCURACY_SCORE = 'accuracy'
+    R2_SCORE = 'r2'
+    MEDIAN_ABSOLUTE_ERROR_SCORE = 'median_absolute_error'
+    MEAN_ABSOLUTE_ERROR_SCORE = 'mean_absolute_error'
+    EXPLAINED_VARIANCE_SCORE = 'explained_variance'
+    SUPPORTED_SCORES = [
+        ACCURACY_SCORE, EXPLAINED_VARIANCE_SCORE, MEAN_ABSOLUTE_ERROR_SCORE,
+        MEDIAN_ABSOLUTE_ERROR_SCORE, R2_SCORE
+    ]
+
+    def __init__(self, regressor, X_test, y_test):
+        PredictorAnalyzer.__init__(self, regressor, X_test, y_test)
+
+    def _score_accuracy(self):
+        equality_df = (self._y_predicted[0] == self._y_test['true'])
+        # If all false...return 0
+        if 'True' not in equality_df.value_counts():
+            return 0
+        # if all true...return 1
+        elif 'False' not in equality_df.value_counts():
+            return 1.0
+        else:
+            equal_count = equality_df.value_counts()[True]
+            unequal_count = equality_df.value_counts()[False]
+            return (equal_count/(equal_count + unequal_count))
+
+    def _score_r2(self):
+        return r2_score(self._y_test, self._y_predicted)
+
+    def _score_median_absolute_error(self):
+        return median_absolute_error(self._y_test, self._y_predicted)
+
+    def _score_mean_absolute_error(self):
+        return mean_absolute_error(self._y_test, self._y_predicted)
+
+    def _score_explained_variance(self):
+        return explained_variance_score(self._y_test, self._y_predicted)
+
+    def score(self, metric=None):
+        if metric is None:
+            metric = RegressorAnalyzer.MEAN_ABSOLUTE_ERROR_SCORE
+
+        if metric not in RegressorAnalyzer.SUPPORTED_SCORES:
+            raise ValueError('Score metric %s not supported.' % metric)
+
+        if metric == RegressorAnalyzer.ACCURACY_SCORE:
+            return self._score_accuracy()
+        elif metric == RegressorAnalyzer.R2_SCORE:
+            return self._score_r2()
+        elif metric == RegressorAnalyzer.MEDIAN_ABSOLUTE_ERROR_SCORE:
+            return self._score_median_absolute_error()
+        elif metric == RegressorAnalyzer.MEAN_ABSOLUTE_ERROR_SCORE:
+            return self._score_mean_absolute_error()
+        elif metric == RegressorAnalyzer.EXPLAINED_VARIANCE_SCORE:
+            return self._score_explained_variance()
+
+    def build_report(self):
+        # Report the following summary statistics:
+        # * test size
+        # * accuracy
+        report_dict = {
+            'model': [repr(self._predictor)],
+            'test_size': [self._y_test.shape[0]]
+        }
+        for score_metric in RegressorAnalyzer.SUPPORTED_SCORES:
+            score_value = self.score(metric=score_metric)
+            report_dict.update({score_metric: score_value})
+
+        return DataFrame(report_dict)
+
+    def write_report(self, dest_path):
+        column_names = ['model', 'test_size']
+        column_names.extend(RegressorAnalyzer.SUPPORTED_SCORES)
+        report = self.build_report()
+
+        PredictorAnalyzer.write_report(self, report, dest_path, column_names)
diff --git a/medinfo/ml/SupervisedClassifier.py b/medinfo/ml/SupervisedClassifier.py
new file mode 100644
index 00000000..794c5d76
--- /dev/null
+++ b/medinfo/ml/SupervisedClassifier.py
@@ -0,0 +1,1034 @@
+#!/usr/bin/python
+"""
+Generic module for supervised machine learning classification.
+"""
+
+import numpy as np
+from pandas import DataFrame, Series
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+from sklearn.metrics import f1_score, roc_auc_score, make_scorer
+from sklearn.svm import SVC
+from sklearn.utils.validation import column_or_1d
+from sklearn.model_selection import StratifiedKFold, cross_val_score, GroupKFold
+from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
+from sklearn.naive_bayes import GaussianNB
+from sklearn.exceptions import ConvergenceWarning
+from xgboost import XGBClassifier
+from sklearn.neural_network import MLPClassifier
+import warnings
+
+from medinfo.common.Util import log
+
+warnings.filterwarnings('ignore', category=ConvergenceWarning)
+
+class SupervisedClassifier:
+    # Define string constants for all supported ML algorithms so that
+    # clients can define which algorithm to call.
+    DECISION_TREE = 'decision-tree'
+    LOGISTIC_REGRESSION = 'l1-logistic-regression-cross-validation'
+    RANDOM_FOREST = 'random-forest'
+    REGRESS_AND_ROUND = 'regress-and-round'
+    ADABOOST = 'adaboost'
+    SVM = 'svm'
+    XGB = 'xgb'
+    NN = 'nn'
+    GAUSSIAN_NAIVE_BAYES = 'gaussian-naive-bayes'
+
+    # TODO(sbala): Nearest Neighbors: http://scikit-learn.org/stable/modules/neighbors.html#neighbors
+    # TODO(sbala): Neural Network: http://scikit-learn.org/stable/modules/neural_networks_supervised.html#neural-networks-supervised
+    SUPPORTED_ALGORITHMS = [DECISION_TREE, LOGISTIC_REGRESSION, RANDOM_FOREST,
+        REGRESS_AND_ROUND, ADABOOST, GAUSSIAN_NAIVE_BAYES,
+#SVM, XGB, NN
+    ]
+
+    # Hyperparam search strategies.
+    EXHAUSTIVE_SEARCH = 'exhaustive-search'
+    STOCHASTIC_SEARCH = 'stochastic-search'
+    HYPERPARAM_STRATEGIES = [EXHAUSTIVE_SEARCH, STOCHASTIC_SEARCH]
+
+    # Status codes.
+    TRAINED = 'model-trained'
+    INSUFFICIENT_SAMPLES = 'insufficient-samples-per-class'
+
+    CV_STRATEGY = 'StratifiedKFold'#'GroupKFold' #'StratifiedKFold'
+
+    def __init__(self, classes, hyperparams=None, groups=None):
+        self._classes = classes
+
+        # Initialize params.
+        self._params = {}
+        self._model = None
+
+        '''
+        Used by GroupKFold for splitting train/validation. 
+        '''
+        self._groups = groups
+
+        # Initialize hyperparams.
+        self._hyperparams = {} if hyperparams is None else hyperparams
+        self._hyperparam_search_space = {}
+        # Set algorithm.
+        self._get_or_set_hyperparam('algorithm')
+        # Set random state.
+        self._get_or_set_hyperparam('random_state')
+        # Set CV strategy.
+        self._get_or_set_hyperparam('hyperparam_strategy')
+
+    def __repr__(self):
+        s = "SupervisedClassifier(%s, algorithm='%s', random_state=%s)" % \
+            (self._classes, self._hyperparams['algorithm'], self._hyperparams['random_state'])
+        return s
+
+    __str__ = __repr__
+
+    def description(self):
+        if self._hyperparams['algorithm'] == SupervisedClassifier.LOGISTIC_REGRESSION:
+            return self._describe_logistic_regression()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.REGRESS_AND_ROUND:
+            return self._describe_regress_and_round()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.DECISION_TREE:
+            return self._describe_decision_tree()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.RANDOM_FOREST:
+            return self._describe_random_forest()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.ADABOOST:
+            return self._describe_adaboost()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.GAUSSIAN_NAIVE_BAYES:
+            return self._describe_gaussian_naive_bayes()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.SVM:
+            return self._describe_svm()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.XGB:
+            return self._describe_xgb()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.NN:
+            return self._describe_nn()
+        else:
+            return 'SupervisedClassifier(%s, %s)' % (self._classes, self._hyperparams['algorithm'])
+
+    def _describe_logistic_regression(self):
+        coefs = self.coefs()
+        cols = self._features
+        sig_features = [(coefs[cols.get_loc(f)], f) for f in cols.values if coefs[cols.get_loc(f)] > 0]
+        linear_model = ' + '.join('%s*%s' % (weight, feature) for weight, feature in sig_features)
+        return 'L1_LOGISTIC_REGRESSION(%s)' % linear_model
+
+    def _describe_regress_and_round(self):
+        coefs = self.coefs()
+        cols = self._features
+        sig_features = [(coefs[cols.get_loc(f)], f) for f in cols.values if coefs[cols.get_loc(f)] > 0]
+        linear_model = ' + '.join('%s*%s' % (weight, feature) for weight, feature in sig_features)
+        return 'L1_REGRESS_AND_ROUND(%s)' % linear_model
+
+    def _describe_decision_tree(self):
+        params = self._params_decision_tree()
+        node_list = list()
+        for i in range(params['num_nodes']):
+            if params['nodes'][i].get('feature') is None:
+                continue
+            feature = params['nodes'][i]['feature']
+            threshold = params['nodes'][i]['threshold']
+            node_list.append('(%s<=%s)'% (feature, threshold))
+        decision_path = ', '.join(node_list)
+        return 'DECISION_TREE(%s)' % decision_path
+
+    def _describe_random_forest(self):
+        params = self._params_random_forest()
+        n_estimators = params['n_estimators']
+        features = ', '.join(params['decision_features'])
+        return 'RANDOM_FOREST(n_estimators=%s, features=[%s])' % (n_estimators, features)
+
+    def _describe_adaboost(self):
+        params = self._params_adaboost()
+        n_estimators = params['n_estimators']
+        features = ', '.join(params['decision_features'])
+        base_estimator = params['base_estimator']
+        return 'ADABOOST(base_estimator=%s, n_estimators=%s, features=[%s])' % (base_estimator, n_estimators, features)
+
+    def _describe_gaussian_naive_bayes(self):
+        params = self._params_gaussian_naive_bayes()
+        return 'GAUSSIAN_NAIVE_BAYES(priors=%s)' % params['priors']
+
+    def _describe_svm(self):
+        params = self._params_svm()
+        return 'SVM(params=%s)' % params
+
+    def _describe_xgb(self):
+        params = self._params_xgb()
+        return 'XGB(params=%s)' % params
+
+    def _describe_nn(self):
+        params = self._params_nn()
+        return 'NN(params=%s)' % params
+
+    def algorithm(self):
+        return self._hyperparams['algorithm']
+
+    def classes(self):
+        return self._classes
+
+    def coefs(self):
+        return self._model.coef_[0]
+
+    def hyperparams(self):
+        return self._hyperparams
+
+    def _get_or_set_hyperparam(self, hyperparam, y=None):
+        # If it's already set, move on.
+        TUNEABLE_HYPERPARAMS = [
+            'priors', 'n_estimators', 'learning_rate', 'max_depth',
+            'min_samples_split', 'min_samples_leaf', 'max_features', 'C'
+        ]
+        if self._hyperparams.get(hyperparam):
+            if hyperparam == 'algorithm':
+                if self._hyperparams[hyperparam] not in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+                    raise ValueError('Algorithm %s not supported.' % self._hyperparams[hyperparam])
+            elif hyperparam == 'hyperparam_strategy':
+                if self._hyperparams[hyperparam] not in SupervisedClassifier.HYPERPARAM_STRATEGIES:
+                    raise ValueError('Hyperparameter strategy %s not supported.' % self._hyperparams[hyperparam])
+
+            # If the hyperparam has a relevant search space, set the search
+            # space to the user defined value.
+            if hyperparam in TUNEABLE_HYPERPARAMS:
+                self._hyperparam_search_space[hyperparam] = [self._hyperparams[hyperparam]]
+
+            return
+
+        # Otherwise, define a decent initial value, based on algorithm.
+        # If the hyperparam has a relevant search space, define it automatically.
+        # Code sanitation note: please keep these conditions alphabetized =)
+        if hyperparam == 'activation':
+            # NN
+            self._hyperparams[hyperparam] = 'relu'
+            self._hyperparam_search_space[hyperparam] = [
+                'logistic', 'tanh', 'relu'
+            ]
+        elif hyperparam == 'adaboost_algorithm':
+            # ADABOOST, DECISION_TREE
+            self._hyperparams[hyperparam] = 'SAMME.R'
+        elif hyperparam == 'algorithm':
+            # SUPPORTED_ALGORITHMS
+            self._hyperparams[hyperparam] = SupervisedClassifier.LOGISTIC_REGRESSION
+        elif hyperparam == 'base_estimator':
+            # ADABOOST
+            self._hyperparams[hyperparam] = 'DecisionTreeClassifier'
+        elif hyperparam == 'bootstrap':
+            # RANDOM_FOREST
+            self._hyperparams[hyperparam] = True
+        elif hyperparam == 'C':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = 10.0
+            self._hyperparam_search_space[hyperparam] = [
+                0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0
+            ]
+        elif hyperparam == 'class_weight':
+            # ADABOOST, DECISION_TREE, LOGISTIC_REGRESSION, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 'balanced'
+        elif hyperparam == 'colsample_bytree':
+            # XGB
+            self._hyperparams[hyperparam] = 0.5
+            self._hyperparam_search_space[hyperparam] = [0.6, 0.8, 1.0]
+        elif hyperparam == 'criterion':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 'gini'
+        elif hyperparam == 'cv':
+            # SUPPORTED_ALGORITHMS
+            self._hyperparams['cv'] = self._build_cv_generator(y)
+        elif hyperparam == 'degree':
+            # SVM, when kernel='poly'. TODO: in the future, do sub-case grid search as degree is only needed for poly
+            self._hyperparams[hyperparam] = 3
+            self._hyperparam_search_space[hyperparam] = [
+                0, 1, 2, 3, 4, 5, 6
+            ]
+        elif hyperparam == 'dual':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = False
+        elif hyperparam == 'fit_intercept':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = True
+        elif hyperparam == 'gamma':
+            # SVM, when non-linear kernel 'rbf', 'poly', 'sigmoid'
+            self._hyperparams[hyperparam] = 'auto'
+            self._hyperparam_search_space[hyperparam] = [
+                0.1, 1, 10, 100
+            ]
+        elif hyperparam == 'hidden_layer_sizes':
+            # NN
+            self._hyperparams[hyperparam] = (10,10,10)
+            self._hyperparam_search_space[hyperparam] = [
+                (10,),
+                (10,10),
+                (10,10,10,10)
+            ]
+        elif hyperparam == 'hyperparam_strategy':
+            # SUPPORTED_ALGORITHMS
+            self._hyperparams[hyperparam] = SupervisedClassifier.STOCHASTIC_SEARCH
+        elif hyperparam == 'kernel':
+            # SVM
+            self._hyperparams[hyperparam] = 'rbf'
+            self._hyperparam_search_space[hyperparam] = [
+                'linear', 'poly', 'rbf', 'sigmoid'
+            ]
+        elif hyperparam == 'learning_rate':
+            # ADABOOST, XGB
+            self._hyperparams[hyperparam] = 0.1
+            self._hyperparam_search_space[hyperparam] = [
+                0.001, 0.01, 0.1, 1.0, 10.0
+            ]
+        elif hyperparam == 'max_depth':
+            # DECISION_TREE, RANDOM_FOREST, XGB (XGB does not allow 'None')
+            self._hyperparams[hyperparam] = 3
+            # Include 1, 2, 3 to bias towards simpler tree.
+            self._hyperparam_search_space[hyperparam] = [1, 2, 3, 4, 5]
+        elif hyperparam == 'max_features':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 'sqrt'
+            # Empirical good default values are max_features=n_features for
+            # regression problems, and max_features=sqrt(n_features) for
+            # classification tasks.
+            # http://scikit-learn.org/stable/modules/ensemble.html#forest
+            self._hyperparam_search_space[hyperparam] = ['sqrt', 'log2', None]
+        elif hyperparam == 'max_iter':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = 100
+        elif hyperparam == 'min_child_weight':
+            # XGB
+            self._hyperparams[hyperparam] = 3
+            self._hyperparam_search_space[hyperparam] = [1, 5, 10]
+        elif hyperparam == 'min_impurity_decrease':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 0.0
+        elif hyperparam == 'max_leaf_nodes':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = None
+        elif hyperparam == 'min_samples_leaf':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 1
+            self._hyperparam_search_space[hyperparam] = [0.01, 0.1, 1, 10]
+        elif hyperparam == 'min_samples_split':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 2
+            # Include 20 and .02 to bias towards simpler trees.
+            self._hyperparam_search_space[hyperparam] = [0.02, 0.2, 2, 20]
+        elif hyperparam == 'min_weight_fraction_leaf':
+            # DECISION_TREE, RANDOM_FOREST
+            self._hyperparams[hyperparam] = 0.0
+        elif hyperparam == 'multi_class':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = 'ovr'
+        elif hyperparam == 'n_estimators':
+            # ADABOOST, RANDOM_FOREST
+            if self._hyperparams['algorithm'] == SupervisedClassifier.ADABOOST:
+                self._hyperparams[hyperparam] = 30
+                self._hyperparam_search_space[hyperparam] = [
+                    10, 20, 30, 40, 50
+                ]
+            elif self._hyperparams['algorithm'] == SupervisedClassifier.RANDOM_FOREST:
+                self._hyperparams[hyperparam] = 10
+                # The larger the better, but the longer it will take to compute.
+                self._hyperparam_search_space[hyperparam] = [
+                    2, 5, 10, 15, 20, 25
+                ]
+        elif hyperparam == 'n_iter':
+            # RandomizedSearchCV throws ValueError if n_iter is less than the
+            # number of hyperparam options.
+            num_hyperparam_settings = np.prod([len(value) for key, value in self._hyperparam_search_space.items()])
+            log.debug('num_hyperparam_settings: %s' % num_hyperparam_settings)
+            self._hyperparams[hyperparam] = np.min([48, num_hyperparam_settings])
+        elif hyperparam == 'n_jobs':
+            # SUPPORTED_ALGORITHMS
+            # LOGISTIC_REGRESSION parallelization causes multiarray.so to crash.
+            # Automatically switch to 1 core so others can ignore this =/
+            if self._hyperparams['algorithm'] == SupervisedClassifier.LOGISTIC_REGRESSION:
+                self._hyperparams[hyperparam] = 1
+            elif self._hyperparams['algorithm'] == SupervisedClassifier.REGRESS_AND_ROUND:
+                self._hyperparams[hyperparam] = 1
+            else:
+                self._hyperparams[hyperparam] = -1
+        elif hyperparam == 'oob_score':
+            # RANDOM_FOREST
+            self._hyperparams[hyperparam] = False
+        elif hyperparam == 'penalty':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = 'l1'
+        elif hyperparam == 'presort':
+            # DECISION_TREE
+            self._hyperparams[hyperparam] = False
+        elif hyperparam == 'priors':
+            # GAUSSIAN_NAIVE_BAYES
+            self._hyperparams[hyperparam] = None
+            self._hyperparam_search_space[hyperparam] = [
+                [0.0001, 0.9999], [0.001, 0.999], [0.01, 0.99], [0.05, 0.95],
+                [0.1, 0.9], [0.25, 0.75],
+                [0.5, 0.5],
+                [0.75, 0.25], [0.9, 0.1],
+                [0.95, 0.05], [0.99, 0.01], [0.999, 0.001], [0.9999, 0.0001]
+            ]
+        elif hyperparam == 'random_state':
+            # SUPPORTED_ALGORITHMS
+            self._hyperparams[hyperparam] = None
+        elif hyperparam == 'scoring':
+            # SUPPORTED_ALGORITHMS
+            # Assume unbalanced classification problems, so use roc auc.
+            # http://scikit-learn.org/stable/modules/grid_search.html#specifying-an-objective-metric
+            scorer = make_scorer(roc_auc_score, needs_threshold=True)
+            self._hyperparams['scoring'] = scorer
+        elif hyperparam == 'solver':
+            # LOGISTIC_REGRESSION, NN
+            if self._hyperparams['algorithm'] == SupervisedClassifier.LOGISTIC_REGRESSION or self._hyperparams['algorithm'] == SupervisedClassifier.REGRESS_AND_ROUND:
+                self._hyperparams[hyperparam] = 'saga'
+            elif self._hyperparams['algorithm'] == SupervisedClassifier.NN:
+                self._hyperparams[hyperparam] = 'adam'
+                self._hyperparam_search_space[hyperparam] = [
+                    'lbfgs', 'sgd', 'adam'
+                ]
+        elif hyperparam == 'splitter':
+            # DECISION_TREE
+            self._hyperparams[hyperparam] = 'best'
+        elif hyperparam == 'subsample':
+            # XGB
+            self._hyperparams[hyperparam] = 0.5
+            self._hyperparam_search_space[hyperparam] = [0.6, 0.8, 1.0]
+        elif hyperparam == 'tol':
+            # LOGISTIC_REGRESSION
+            self._hyperparams[hyperparam] = 0.0001
+        elif hyperparam == 'warm_start':
+            # RANDOM_FOREST
+            self._hyperparams[hyperparam] = False
+
+    def params(self):
+        '''
+        sx: This function is used after parameters were set.
+
+        :return:
+        '''
+        if self._hyperparams['algorithm'] == SupervisedClassifier.LOGISTIC_REGRESSION:
+            return self._params_regression()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.REGRESS_AND_ROUND:
+            return self._params_regression()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.DECISION_TREE:
+            return self._params_decision_tree()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.RANDOM_FOREST:
+            return self._params_random_forest()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.ADABOOST:
+            return self._params_adaboost()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.GAUSSIAN_NAIVE_BAYES:
+            return self._params_gaussian_naive_bayes()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.SVM:
+            return self._params_svm()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.XGB:
+            return self._params_xgb()
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.NN:
+            return self._params_nn()
+
+    def _params_regression(self):
+        params = {}
+        coefs = self.coefs()
+        columns = self._features
+        feature_labels = columns.values
+        for feature in feature_labels:
+            weight = coefs[columns.get_loc(feature)]
+            params.update({feature: weight})
+
+        return params
+
+    def _tree_to_dict(self, tree):
+        tree_dict = {'nodes': {}}
+        decision_features = set()
+        feature_labels = self._features.values
+        # Iterate through nodes in tree.
+        for i in range(tree.node_count):
+            # Build data about each node.
+            node = {}
+            # If not a leaf node, add decision information.
+            if tree.children_left[i] != tree.children_right[i]:
+                node['feature'] = feature_labels[tree.feature[i]]
+                decision_features.add(node['feature'])
+                node['threshold'] = tree.threshold[i]
+                node['left_child'] = tree.children_left[i]
+                node['right_child'] = tree.children_right[i]
+            class_weights = tree.value[i].tolist()[0]
+            node['class_weights'] = class_weights
+            node['prediction'] = self._classes[class_weights.index(np.max(class_weights))]
+
+            tree_dict['nodes'].update({i: node})
+
+        # Add feature important information
+        num_features = len(self._features)
+        importances = {}
+        for i in range(num_features):
+            feature = self._features[i]
+            if feature in decision_features:
+                importance = self._model.feature_importances_[i]
+                importances.update({feature: importance})
+
+        decision_features = sorted(list(decision_features), key=lambda x:importances[x], reverse=True)
+        tree_dict['decision_features'] = ['%s (%.3f)' % (f, importances[f]) for f in decision_features]
+
+        # Add tree-level information.
+        tree_dict['depth'] = tree.max_depth
+        tree_dict['num_nodes'] = tree.node_count
+
+        return tree_dict
+
+    def _params_decision_tree(self):
+        t = self._model.tree_
+        return self._tree_to_dict(t)
+
+    def _params_random_forest(self):
+        params = {'estimators': []}
+        params['n_estimators'] = 0
+        decision_features = set()
+        for estimator in self._model.estimators_:
+            tree_dict = self._tree_to_dict(estimator.tree_)
+            params['estimators'].append(tree_dict)
+            params['n_estimators'] += 1
+            # Build list of forest-wide decision features.
+            for feature in tree_dict['decision_features']:
+                decision_features.add(' '.join(feature.split()[:-1]))
+
+        num_features = self._model.n_features_
+        importances = {}
+        for i in range(num_features):
+            feature = self._features[i]
+            if feature in decision_features:
+                importance = self._model.feature_importances_[i]
+                importances.update({feature: importance})
+
+        decision_features = sorted(list(decision_features), key=lambda x:importances[x], reverse=True)
+        params['decision_features'] = ['%s (%.3f)' % (f, importances[f]) for f in decision_features]
+
+        return params
+
+    def _params_adaboost(self):
+        params = {}
+        params['base_estimator'] = self._hyperparams['base_estimator']
+        params['n_estimators'] = self._hyperparams['n_estimators']
+        decision_features = set()
+        for estimator in self._model.estimators_:
+            tree_dict = self._tree_to_dict(estimator.tree_)
+            for feature in tree_dict['decision_features']:
+                decision_features.add(' '.join(feature.split()[:-1]))
+
+        num_features = len(self._features)
+        importances = {}
+        for i in range(num_features):
+            feature = self._features[i]
+            if feature in decision_features:
+                importance = self._model.feature_importances_[i]
+                importances.update({feature: importance})
+
+        decision_features = sorted(list(decision_features), key=lambda x:importances[x], reverse=True)
+        params['decision_features'] = ['%s (%.3f)' % (f, importances[f]) for f in decision_features]
+
+        return params
+
+    def _params_gaussian_naive_bayes(self):
+        params = {}
+        params['priors'] = list(self._model.class_prior_)
+        params['thetas'] = list(self._model.theta_[1,:])
+        params['sigmas'] = list(self._model.sigma_[1,:])
+        return params
+
+    def _params_svm(self):
+        return self._model.get_params() # TODO sxu: come back later for a specific list of params?
+
+    def _params_xgb(self):
+        return self._model.get_params()
+
+    def _params_nn(self):
+        return self._model.get_params()
+
+    def _maybe_reshape_y(self, y):
+        # If necessary, reshape y from (n_samples, 1) to (n_samples, )
+        try:
+            num_cols = y.shape[1]
+            y = column_or_1d(y)
+            log.debug('Reshaped y to 1d.')
+        except IndexError:
+            log.debug('Did not need to reshape y to 1d.')
+
+        return y
+
+    def _build_cv_generator(self, y=None):
+        # Use information about X to build a cross-validation split generator.
+        # http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation
+        # There are a few general strategies for cross-validation:
+        #   k-fold split: Split training set into k partitions, train using k-1,
+        #       and validate using the kth. Loop through k validation partitions.
+        #       Basis for all other cross-validation strategies.
+        #       Assumes each sample is independent and identically distributed.
+        #   shuffle split: Instead of mixing k partitions into training and
+        #       validation sets, just generate a random train/validation split
+        #       and do that k times. Typically efficient, but also can fail
+        #       to use each sample equally, e.g. if a sample is only ever
+        #       in the validation set or the training set.
+        #   leave one out (LOO): k-fold CV where k = n, so each partition only
+        #       has one sample in the test data. Test error across partitions
+        #       typically has high variance between error is either 0 or 1.
+        #       Empirically, LOO is typically worse than 5- or 10-fold CV.
+        #       Can tweak to leave P out (LPO). Because (n choose p) is much
+        #       greater than (n choose 1), this process tends to be
+        #       computationally intensive, and perhaps not worth the effort.
+        #   repetition: k-fold CV run multiple times, with different partitions
+        #       for each run. Good way to get more learning from limited data.
+        #   stratification: for unbalanced classification problems, enforce
+        #       a constraint on either k-fold or shuffle splits to require
+        #       an equal split of each class between the training and
+        #       validation sets.
+        #   grouping: for some classification problems, the samples are not iid.
+        #       In particular, for medical applications, if there are multiple
+        #       samples taken from a single patient, those samples are by
+        #       definition not independent. Moreover, we typically care about
+        #       whether we can accurately classify new unseen patients based
+        #       on patients we saw in the past. Therefore, grouping guarantees
+        #       that any particular group (e.g. a single patient_id) is only
+        #       in the training set or in the validation set.
+        #   time series split: data representing a time series also break the
+        #       i.i.d. assumption, because samples that are near each other
+        #       in time are by definition correlated (autocorrelation). To get
+        #       around this, split the data into k partitions, and in each
+        #       of k loops, use partions [0, i] to train and partition [i+1]
+        #       for validation. Ensure that you are always training on the past
+        #       and validating on the "future."
+        #
+        # TODO(sbala): Ideally, we'd find a way to do stratified group k-fold.
+        # sklearn only provides StratifiedKFold and GroupKFold out of the box.
+        # Given how unbalanced many of our classification problems are, use
+        # StratifiedKFold for now, but we need something better.
+
+        # Use information about y to determine n_splits.
+        # In certain pathological cases (esp. with bifurcated classifiers)
+        # there might be fewer than n examples of a given class in y.
+        # If that's the case, n_splits can't be greater than n_samples.
+        if y is not None:
+            log.debug('y.value_counts(): %s' % Series(y).value_counts())
+            max_possible_splits = np.min(Series(y).value_counts())
+            log.debug('max_possible_splits: %s' % max_possible_splits)
+            n_splits = np.min([10, max_possible_splits])
+        else:
+            n_splits = 10
+        log.debug('n_splits: %s' % n_splits)
+
+        if self.CV_STRATEGY == 'StratifiedKFold':
+            return StratifiedKFold(n_splits=n_splits, shuffle=False, \
+                                random_state=self._hyperparams['random_state'])
+        elif self.CV_STRATEGY == 'GroupKFold':
+            '''
+            GroupKFold is not randomized at all. Hence the random_state=None
+            '''
+            return GroupKFold(n_splits=n_splits)
+
+    def train(self, X, y, groups=None):
+
+        self._groups = groups
+        assert ('pat_id' not in X.columns)
+
+        self._features = X.columns
+
+        y = self._maybe_reshape_y(y)
+
+        # Verify that there are at least 2 samples of each class.
+        value_counts = Series(y).value_counts()
+        log.debug('y.value_counts(): %s' % value_counts)
+        for class_label in self._classes:
+            # If there aren't 2+ samples of each class, exit gracefully.
+            try:
+                num_samples = value_counts[class_label]
+                if num_samples < 10:
+                    log.error('Insufficient samples (%s) of label %s.' % (num_samples, class_label))
+                    return SupervisedClassifier.INSUFFICIENT_SAMPLES
+            except KeyError:
+                log.error('Insufficient samples (0) of label %s.' % class_label)
+                return SupervisedClassifier.INSUFFICIENT_SAMPLES
+
+        log.info('Training %s classifier...' % self._hyperparams['algorithm'])
+        if self._hyperparams['algorithm'] == SupervisedClassifier.DECISION_TREE:
+            self._train_decision_tree(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.LOGISTIC_REGRESSION:
+            self._train_logistic_regression(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.RANDOM_FOREST:
+            self._train_random_forest(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.REGRESS_AND_ROUND:
+            self._train_regress_and_round(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.ADABOOST:
+            self._train_adaboost(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.GAUSSIAN_NAIVE_BAYES:
+            self._train_gaussian_naive_bayes(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.SVM:
+            self._train_svm(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.XGB:
+            self._train_xgb(X, y)
+        elif self._hyperparams['algorithm'] == SupervisedClassifier.NN:
+            self._train_nn(X, y)
+
+        return SupervisedClassifier.TRAINED
+
+    def _train_gaussian_naive_bayes(self, X, y):
+        # Define hyperparams.
+        # http://scikit-learn.org/stable/modules/naive_bayes.html#naive-bayes
+        self._get_or_set_hyperparam('priors')
+        self._get_or_set_hyperparam('n_jobs')
+        self._get_or_set_hyperparam('scoring')
+
+        # Build initial model.
+        self._model = GaussianNB(priors=self._hyperparams['priors'])
+
+        # Tune hyperparams.
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_adaboost(self, X, y):
+        # Define hyperparams.
+        # http://scikit-learn.org/stable/modules/ensemble.html#adaboost
+        self._get_or_set_hyperparam('base_estimator')
+        self._get_or_set_hyperparam('n_estimators')
+        self._get_or_set_hyperparam('learning_rate')
+        self._get_or_set_hyperparam('adaboost_algorithm')
+        self._get_or_set_hyperparam('n_jobs')
+        self._get_or_set_hyperparam('class_weight')
+        self._get_or_set_hyperparam('scoring')
+
+        # Build initial model.
+        self._model = AdaBoostClassifier(\
+            base_estimator=DecisionTreeClassifier(class_weight='balanced'),
+            n_estimators=self._hyperparams['n_estimators'],
+            learning_rate=self._hyperparams['learning_rate'],
+            algorithm=self._hyperparams['adaboost_algorithm'],
+            random_state=self._hyperparams['random_state']
+        )
+
+        # Tune hyperparams.
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_xgb(self, X, y):
+        '''
+        Tianqi Chen:
+        Adaboost and gradboosting [XGBoost] are two different ways to derive boosters.
+        Both are generic. I like gradboosting better because it works for generic loss functions,
+        while adaboost is derived mainly for classification with exponential loss.
+
+        Konrad Banachewicz:
+        Adaboost is more of a meta-estimator - you can fit anything as base (although most people use trees)
+        xgboost is more flexible, i.e. has more customizable parameters
+
+        :param X:
+        :param y:
+        :return:
+        '''
+        #
+        self._get_or_set_hyperparam('scoring')
+        self._get_or_set_hyperparam('n_jobs')
+
+        '''Found that grid search does not really help, but just slow.'''
+        # self._get_or_set_hyperparam('learning_rate')
+        # self._get_or_set_hyperparam('min_child_weight')
+        # self._get_or_set_hyperparam('gamma')
+        # self._get_or_set_hyperparam('subsample')
+        # self._get_or_set_hyperparam('colsample_bytree')
+        # self._get_or_set_hyperparam('max_depth')
+
+        # Build initial model.
+        self._model = XGBClassifier(
+            # learning_rate=self._hyperparams['learning_rate'],
+            # min_child_weight=self._hyperparams['min_child_weight'],
+            # gamma=self._hyperparams['gamma'],
+            # subsample=self._hyperparams['subsample'],
+            # colsample_bytree=self._hyperparams['colsample_bytree'],
+            # max_depth=self._hyperparams['max_depth'],
+            random_state=self._hyperparams['random_state']
+        )
+
+        # Tune hyperparams.
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_decision_tree(self, X, y):
+        # Define hyperparameter space.
+        # http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
+        self._get_or_set_hyperparam('criterion')
+        self._get_or_set_hyperparam('splitter')
+        self._get_or_set_hyperparam('max_depth')
+        self._get_or_set_hyperparam('min_samples_split')
+        self._get_or_set_hyperparam('min_samples_leaf')
+        self._get_or_set_hyperparam('min_weight_fraction_leaf')
+        self._get_or_set_hyperparam('max_features')
+        self._get_or_set_hyperparam('max_leaf_nodes')
+        self._get_or_set_hyperparam('min_impurity_decrease')
+        self._get_or_set_hyperparam('class_weight')
+        self._get_or_set_hyperparam('presort')
+        self._get_or_set_hyperparam('scoring')
+        self._get_or_set_hyperparam('n_jobs')
+
+        # Initialize model with naive hyperparameter values.
+        self._model = DecisionTreeClassifier(\
+            criterion=self._hyperparams['criterion'], \
+            splitter=self._hyperparams['splitter'], \
+            max_depth=self._hyperparams['max_depth'], \
+            min_samples_split=self._hyperparams['min_samples_split'], \
+            min_samples_leaf=self._hyperparams['min_samples_leaf'], \
+            min_weight_fraction_leaf=self._hyperparams['min_weight_fraction_leaf'], \
+            max_features=self._hyperparams['max_features'], \
+            random_state=self._hyperparams['random_state'], \
+            max_leaf_nodes=self._hyperparams['max_leaf_nodes'], \
+            min_impurity_decrease=self._hyperparams['min_impurity_decrease'], \
+            class_weight=self._hyperparams['class_weight'], \
+            presort=self._hyperparams['presort']
+        )
+
+        # Tune hyperparams.
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_logistic_regression(self, X, y):
+        # Define hyperparameter space.
+        # http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html
+        self._get_or_set_hyperparam('C')
+        self._get_or_set_hyperparam('fit_intercept')
+        self._get_or_set_hyperparam('dual')
+        self._get_or_set_hyperparam('penalty')
+        self._get_or_set_hyperparam('scoring')
+        self._get_or_set_hyperparam('solver')
+        self._get_or_set_hyperparam('tol')
+        self._get_or_set_hyperparam('max_iter')
+        self._get_or_set_hyperparam('n_jobs')
+        self._get_or_set_hyperparam('multi_class')
+        self._get_or_set_hyperparam('class_weight')
+
+        # Build initial model.
+        self._model = LogisticRegression(
+            penalty=self._hyperparams['penalty'],
+            fit_intercept=self._hyperparams['fit_intercept'],
+            dual=self._hyperparams['dual'],
+            tol=self._hyperparams['tol'],
+            C=self._hyperparams['C'],
+            class_weight=self._hyperparams['class_weight'],
+            random_state=self._hyperparams['random_state'],
+            solver=self._hyperparams['solver'],
+            max_iter=self._hyperparams['max_iter'],
+            multi_class=self._hyperparams['multi_class'],
+            n_jobs=self._hyperparams['n_jobs']
+        )
+
+        # Tune hyperparams.
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_svm(self, X, y):
+        # Define hyperparams.
+        '''
+        List of parameters:
+        C: float, optional (default=1.0)
+            Penalty parameter C of the error term.
+        kernel: string, optional (default='rbf')
+            must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed', or a callable.
+        degree : int, optional (default=3)
+            Degree of the polynomial kernel function ('poly'). Ignored by all other kernels.
+        gamma: float, optional (default='auto').
+            Kernel coefficient for 'rbf', 'poly', 'sigmoid'.
+        coef0: float, optional (default=0.)
+            Independent term in kernel func.
+        shrinking: boolean, optional (default=True)
+            Whether to use the shrinking heuristic.
+        probability: boolean, optional (default=False)
+            Whether to enable probability estimates (could slow down fit).
+        tol: float, optional (defualt=1e-3)
+            Tolerance for stopping criterion.
+        cache_size: float, optional.
+            Specify the size of the kernel cache (in MB).
+        class_weight: {dict, 'balanced'}, optional.
+            'balanced' mode use automatically: n_samples / (n_classes * np.bincount(y))
+        verbose: bool, default: False
+        max_iter: int, optional (default=1)
+            Hard limit on iterations within solver, or -1 for no limit.
+        decision_function_shape: 'ovo', 'ovr' (default)
+            Whether to return a one-vs-rest (ovr) of shape (n_samples, n_classes) as all other classes, or the original
+            one-vs-one (ovo) decision func of libsvm which has shape (n_sample, n_classes * (n_classes-1)/2).
+        random_state:.
+
+        :return:
+        '''
+        # http://scikit-learn.org/stable/modules/svm.html#svm
+        self._get_or_set_hyperparam('scoring')
+        self._get_or_set_hyperparam('n_jobs')
+
+        self._model = SVC(probability=True,
+                          random_state=self._hyperparams['random_state'])
+
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_random_forest(self, X, y):
+        # Define hyperparams.
+        # http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+        self._get_or_set_hyperparam('n_estimators')
+        self._get_or_set_hyperparam('criterion')
+        self._get_or_set_hyperparam('max_depth')
+        self._get_or_set_hyperparam('min_samples_split')
+        self._get_or_set_hyperparam('min_samples_leaf')
+        self._get_or_set_hyperparam('min_weight_fraction_leaf')
+        self._get_or_set_hyperparam('max_features')
+        self._get_or_set_hyperparam('max_leaf_nodes')
+        self._get_or_set_hyperparam('min_impurity_decrease')
+        self._get_or_set_hyperparam('bootstrap')
+        self._get_or_set_hyperparam('oob_score')
+        self._get_or_set_hyperparam('n_jobs')
+        self._get_or_set_hyperparam('warm_start')
+        self._get_or_set_hyperparam('class_weight')
+        self._get_or_set_hyperparam('scoring')
+
+        # Initialize model with naive hyperparameter values.
+        self._model = RandomForestClassifier( \
+            n_estimators=self._hyperparams['n_estimators'],
+            criterion=self._hyperparams['criterion'],
+            max_depth=self._hyperparams['max_depth'],
+            min_samples_split=self._hyperparams['min_samples_split'],
+            min_samples_leaf=self._hyperparams['min_samples_leaf'],
+            min_weight_fraction_leaf=self._hyperparams['min_weight_fraction_leaf'],
+            max_features=self._hyperparams['max_features'],
+            max_leaf_nodes=self._hyperparams['max_leaf_nodes'],
+            min_impurity_decrease=self._hyperparams['min_impurity_decrease'],
+            bootstrap=self._hyperparams['bootstrap'],
+            oob_score=self._hyperparams['oob_score'],
+            n_jobs=self._hyperparams['n_jobs'],
+            warm_start=self._hyperparams['warm_start'],
+            class_weight=self._hyperparams['class_weight'],
+            random_state=self._hyperparams['random_state']
+        )
+
+        # Tune hyperparams.
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+    def _train_regress_and_round(self, X, y):
+        self._train_logistic_regression(X, y)
+        self._tune_hyperparams_regress_and_round(X, y)
+
+    def _train_nn(self, X, y):
+        self._get_or_set_hyperparam('scoring')
+        self._get_or_set_hyperparam('n_jobs')
+
+        self._get_or_set_hyperparam('hidden_layer_sizes')
+        self._get_or_set_hyperparam('activation')
+        self._get_or_set_hyperparam('solver')
+
+        self._model = MLPClassifier(
+            hidden_layer_sizes=self._hyperparams['hidden_layer_sizes'],
+            activation=self._hyperparams['activation'],
+            solver=self._hyperparams['solver'],
+            random_state=self._hyperparams['random_state']
+        )
+
+        self._tune_hyperparams(self._hyperparam_search_space, X, y)
+
+
+    def _tune_hyperparams_regress_and_round(self, X, y):
+        self._hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+        log.info('Tuning hyperparams via %s...' % self._hyperparams['hyperparam_strategy'])
+        # If not provided, search for best coef_max.
+        if self._hyperparams.get('coef_max') is None:
+            self._hyperparams['coef_max'] = self._tune_coef_max(X, y)
+
+        # Round linear coefficients.
+        self._round_coefs(self._hyperparams['coef_max'])
+        log.debug('hyperparams: %s' % self.hyperparams())
+        log.debug('params: %s' % self.params())
+
+    def _tune_coef_max(self, X, y):
+        # The only way to easily compute scores is to modify the model itself.
+        # Keep the unrounded coefs so it's always possible to go back.
+        unrounded_coefs = self.coefs().copy()
+
+        M_grid = [1, 2, 3, 4, 5]
+        M_grid_scores = []
+
+        # Compute roc_auc scores.
+        coef_max = 0
+        max_roc_auc = 0
+        min_roc_auc = 1.0
+        for i in range(len(M_grid)):
+            # Tweak the model.
+            self._round_coefs(M_grid[i])
+            # Compute ROC AUC.
+            scores = cross_val_score(self._model, X, y, \
+                cv=self._hyperparams['cv'], \
+                groups=self._groups, \
+                scoring=self._hyperparams['scoring'], \
+                n_jobs=self._hyperparams['n_jobs'])
+            # Compute mean across K folds.
+            mean_score = np.mean(scores)
+            M_grid_scores.append(mean_score)
+
+            # Conditionally change coef_max and max_roc_auc.
+            if mean_score > max_roc_auc:
+                coef_max = M_grid[i]
+                max_roc_auc = mean_score
+            if mean_score < min_roc_auc:
+                min_roc_auc = mean_score
+
+            # Reset coefs and return.
+            self._model.coef_[0] = unrounded_coefs
+
+        # The optimal M value from Jung et al. is 3.
+        # https://arxiv.org/abs/1702.04690
+        log.debug('tune(M): %s --> %s' % (3, coef_max))
+        log.debug('tune(roc_auc): %s --> %s' % (min_roc_auc, max_roc_auc))
+
+        return coef_max
+
+    def _round_coefs(self, coef_max):
+        # Based on Jung et al. https://arxiv.org/abs/1702.04690
+        # w_j = round((M * beta_j) / (max_i|beta_i|))
+        # coef_max = M = max rounded coefficient value
+        # beta_max = max_i|beta_i| = largest unrounded regression coefficient
+        beta_max = max([abs(c) for c in self._model.coef_[0]])
+        self._model.coef_[0] = [round((coef_max * c) / (beta_max)) for c in self._model.coef_[0]]
+
+    def _tune_hyperparams(self, hyperparam_search_space, X, y):
+        log.info('Tuning hyperparameters via %s...' % self._hyperparams['hyperparam_strategy'])
+        log.debug('hyperparam_search_space: %s' % str(hyperparam_search_space))
+        # Log the pre-tuning score.
+        self._get_or_set_hyperparam('cv', y)
+        log.debug('initial hyperparams: %s' % self._hyperparams)
+        pre_tuning_score = np.mean(cross_val_score(self._model, X, y, \
+                                    cv=self._hyperparams['cv'], \
+                                    groups=self._groups, \
+                                    scoring=self._hyperparams['scoring'], \
+                                    n_jobs=self._hyperparams['n_jobs']))
+
+        # Initialize hyperparam tuner.
+        # Assume the model was initialized before this function.
+        if self._hyperparams['hyperparam_strategy'] == SupervisedClassifier.EXHAUSTIVE_SEARCH:
+            tuner = GridSearchCV(self._model, hyperparam_search_space, \
+                                    scoring=self._hyperparams['scoring'], \
+                                    n_jobs=self._hyperparams['n_jobs'], \
+                                    iid=False, \
+                                    refit=True, \
+                                    cv=self._hyperparams['cv'], \
+                                    return_train_score=False)
+        elif self._hyperparams['hyperparam_strategy'] == SupervisedClassifier.STOCHASTIC_SEARCH:
+            self._get_or_set_hyperparam('n_iter')
+            tuner = RandomizedSearchCV(self._model, hyperparam_search_space, \
+                                        scoring=self._hyperparams['scoring'], \
+                                        n_iter=self._hyperparams['n_iter'], \
+                                        n_jobs=self._hyperparams['n_jobs'], \
+                                        iid=False, \
+                                        refit=True, \
+                                        cv=self._hyperparams['cv'], \
+                                        random_state=self._hyperparams['random_state'], \
+                                        return_train_score=False)
+        tuner.fit(X, y, groups=self._groups)
+
+        # Set model and hyperparams.
+        self._model = tuner.best_estimator_
+        for key in list(tuner.best_params_.keys()):
+            log.debug('tune(%s): %s --> %s' % (key, self._hyperparams[key], \
+                        tuner.best_params_[key]))
+            self._hyperparams[key] = tuner.best_params_[key]
+        log.debug('tune(%s): %s --> %s' % (self._hyperparams['scoring'],\
+                    pre_tuning_score, tuner.best_score_))
+        log.debug('hyperparams: %s' % self._hyperparams)
+        log.debug('params: %s' % self.params())
+
+    def predict(self, X):
+        return self._model.predict(X)
+
+    def predict_probability(self, X):
+        if isinstance(self._model, XGBClassifier):
+            '''Resolving the incompatibility of sklearn and xgboost'''
+            return self._model.predict_proba(X, validate_features=False)
+        else:
+            return self._model.predict_proba(X)
diff --git a/medinfo/ml/SupervisedLearner.py b/medinfo/ml/SupervisedLearner.py
new file mode 100644
index 00000000..9d0a541e
--- /dev/null
+++ b/medinfo/ml/SupervisedLearner.py
@@ -0,0 +1,74 @@
+
+
+from sklearn.pipeline import Pipeline
+from sklearn.utils.validation import column_or_1d
+
+from . import SupervisedLearner_Class as Clas
+from . import SupervisedLearner_Utils as Utils
+from . import SupervisedLearner_System as Syst
+
+import logging
+
+import pandas as pd
+
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.PredictorAnalyzer import PredictorAnalyzer
+
+class SupervisedLearner():
+    def __init__(self, input_matrix, ylabel):
+        ''''''
+
+        '''Child Class should update this'''
+        self.working_folderpath = ''
+        self.input_matrix = input_matrix
+        self.ylabel = ylabel
+        pass
+
+    def run(self):
+        file_organizer = Syst.FileOrganizerLocal(working_folderpath=self.working_folderpath)
+
+        raw_matrix_train, raw_matrix_test = Utils.split_rows(self.input_matrix)
+
+        X_train_raw, y_train = Utils.split_Xy(raw_matrix_train, ylabel=self.ylabel)
+
+        feature_processing_pipeline = Pipeline(
+            memory=None,  # file_organizer.cached_pipeline_filepath,
+            steps=[
+                ('impute_features', Clas.FeatureImputer()),
+                ('remove_features', Clas.FeatureRemover()),
+                ('select_features', Clas.Select_Features())
+            ]
+        )
+        X_train_processed = feature_processing_pipeline.fit_transform(X_train_raw, y_train)
+
+        predictor = SupervisedClassifier(classes=[0, 1], hyperparams={'algorithm':'random-forest',
+                                                                      'hyperparam_strategy':SupervisedClassifier.EXHAUSTIVE_SEARCH,
+                                                                      'max_iter': 1024
+                                                                      })
+
+        status = predictor.train(X_train_processed, column_or_1d(y_train))
+
+        X_test_raw, y_test = Utils.split_Xy(raw_matrix_test, ylabel=self.ylabel)
+        X_test_processed = feature_processing_pipeline.transform(X_test_raw)
+        y_test_pred_proba = predictor.predict_probability(X_test_processed)[:, 1]
+
+        res_df = pd.DataFrame({'actual': y_test,
+                               'predict': y_test_pred_proba})
+        res_df.to_csv(file_organizer.get_output_filepath())
+
+        '''TODO'''
+        from scripts.LabTestAnalysis.lab_statistics.stats_utils import get_confusion_metrics
+        from sklearn.metrics import roc_auc_score
+
+        AUC = roc_auc_score(y_test, y_test_pred_proba)
+
+        sensitivity, specificity, LR_p, LR_n, PPV, NPV = get_confusion_metrics(actual_labels=y_test.values,
+                                                                               predict_probas=y_test_pred_proba,
+                                                                               threshold=0.5)
+        print("AUC: %s, sensitivity: %s, specificity: %s, LR_p: %s, LR_n: %s, PPV: %s, NPV: %s:. " \
+                % (AUC, sensitivity, specificity, LR_p, LR_n, PPV, NPV))
+
+
+if __name__ == '__main__':
+    pass
+
diff --git a/medinfo/ml/SupervisedLearner_Class.py b/medinfo/ml/SupervisedLearner_Class.py
new file mode 100644
index 00000000..7f6742cc
--- /dev/null
+++ b/medinfo/ml/SupervisedLearner_Class.py
@@ -0,0 +1,52 @@
+from sklearn.base import BaseEstimator, TransformerMixin
+from medinfo.ml.FeatureSelector import FeatureSelector
+from sklearn.utils.validation import column_or_1d
+
+class FeatureRemover(BaseEstimator, TransformerMixin):
+    '''
+    What: Remove specific features from a raw matrix
+    '''
+    def __init__(self, features_to_remove=[]):
+        self.features_to_remove = features_to_remove
+        pass
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X):
+        return X.drop(self.features_to_remove, axis=1)
+
+class FeatureImputer(BaseEstimator, TransformerMixin):
+    def __init__(self, feat2mean_dict={}):
+        '''
+        imputation_dict:
+        key: feature_name
+        val:
+        '''
+        self.feat2mean_dict = feat2mean_dict
+
+    def fit(self, X, y=None):
+        feats = X.columns.values.tolist()
+        for feat in feats:
+            if X[feat].dtype == type(1.) or X[feat].dtype == type(1):
+                self.feat2mean_dict[feat] = X[feat].mean()
+            else:
+                print('Non numeric (cannot calc mean):', feat, X[feat].dtype)
+        return self
+
+    def transform(self, X):
+        X_imputed = X.copy()
+        for feat, imputed in self.feat2mean_dict.items():
+            X_imputed[feat] = X.fillna(imputed)
+        return X_imputed
+
+
+class Select_Features(TransformerMixin):
+    def __init__(self, random_state=0, features_by_type=None):
+        pass
+
+    def fit(self, X, y=None, features_to_keep=None, select_percent=0.05):
+        return self
+
+    def transform(self, X):
+        return X
\ No newline at end of file
diff --git a/medinfo/ml/SupervisedLearner_System.py b/medinfo/ml/SupervisedLearner_System.py
new file mode 100644
index 00000000..6ba833cc
--- /dev/null
+++ b/medinfo/ml/SupervisedLearner_System.py
@@ -0,0 +1,53 @@
+
+
+'''
+Takes care where raw, process, intermediate, and final output store.
+'''
+
+import LocalEnv
+import os
+import logging
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+project_folderpath = os.path.join(LocalEnv.PATH_TO_CDSS,
+                                  'scripts/LabTestAnalysis')
+machine_learning_folderpath = os.path.join(project_folderpath,
+                                           'machine_learning',)
+
+class FileOrganizerRemote():
+    def __init__(self, lab_type, data_source_src, data_source_dst, version):
+        self.lab_type = lab_type
+        self.data_source_src = data_source_src
+        self.data_source_dst = data_source_dst
+        self.version = version
+
+        self.machine_learning_folderpath = os.path.join(project_folderpath,
+                                                        'machine_learning',
+                                                        )
+
+        self.dataset_foldername = 'data-%s-src-%s-dst-%s-%s'%(lab_type,
+                                                              data_source_src,
+                                                              data_source_dst,
+                                                    version
+                                                   )
+
+
+        self.raw_matrix_filename_template = '%s-normality-matrix-raw.tab'
+
+    def get_raw_matrix(self, data_tag):
+        if data_tag == "src":
+            return FeatureMatrixIO().read_file_to_data_frame(self.raw_matrix_filepath)
+        else:
+            return FeatureMatrixIO().read_file_to_data_frame(self.raw_matrix_filepath)
+
+
+class FileOrganizerLocal():
+    '''
+    Structure:
+    machine_learning / ml_dataset_folder / ml_lab_folder / cached pipeline
+    '''
+
+    def __init__(self, working_folderpath):
+        self.working_folderpath = working_folderpath
+
+    def get_output_filepath(self):
+        return os.path.join(self.working_folderpath, "direct_output.csv")
\ No newline at end of file
diff --git a/medinfo/ml/SupervisedLearner_Utils.py b/medinfo/ml/SupervisedLearner_Utils.py
new file mode 100644
index 00000000..2f84ce52
--- /dev/null
+++ b/medinfo/ml/SupervisedLearner_Utils.py
@@ -0,0 +1,139 @@
+from sklearn import model_selection
+
+def split_rows(data_matrix):
+    '''
+    In the future, if want to re-use the column info that separates the matrix,
+    can make this into a class
+    :param data_matrix:
+    :return:
+    '''
+    return model_selection.train_test_split(data_matrix)
+
+def split_Xy(data_matrix, ylabel):
+    X = data_matrix.copy()
+    y = X.pop(ylabel)
+    return X, y
+
+def do_impute_sx(matrix, means):
+    '''
+    sxu new implementation
+    Input: train_df, but does not change it
+    Output: dictionary {order_proc_id: {feature: imputation value}}
+    '''
+    import datetime
+    # TODO: alert: changes row order and indices of matrix!
+
+    datetime_format = "%Y-%m-%d %H:%M:%S"
+
+    matrix_sorted = matrix.sort_values(['pat_id', 'order_time']).reset_index()
+    count_time_suffixs = ['preTimeDays', 'postTimeDays']  # TODO: postTimeDays should really not appear!
+    stats_numeric_suffixs = ['min', 'max', 'median', 'mean', 'std', 'first', 'last', 'diff', 'slope',
+                             'proximate']
+    stats_time_suffixs = ['firstTimeDays', 'lastTimeDays', 'proximateTimeDays']
+
+    impute_train = {}
+
+    nega_inf_days = -1000000
+    for i in range(0, matrix_sorted.shape[0]):
+        cur_episode_id = matrix_sorted.ix[i, 'order_proc_id']
+        impute_train[cur_episode_id] = {}
+        for column in matrix_sorted.columns.values.tolist():
+            '''
+            Value not missing
+            '''
+            if matrix_sorted.ix[i, column] == matrix_sorted.ix[i, column]:
+                continue
+
+            '''
+            Value missing
+            '''
+            column_tail = column.split('.')[-1].strip()
+
+            if column in means:
+                popu_mean = means[column]  # TODO: pre-compute with dict
+
+            if column_tail in stats_numeric_suffixs:
+                '''
+                impute with the previous episode if available; otherwise population mean
+                '''
+                if i == 0 or (matrix_sorted.ix[i, 'pat_id'] != matrix_sorted.ix[i - 1, 'pat_id']) or (
+                        matrix_sorted.ix[i - 1, column] != matrix_sorted.ix[i - 1, column]):
+                    matrix_sorted.ix[i, column] = popu_mean  # impute_train[cur_episode_id][column] = popu_mean
+                else:
+                    matrix_sorted.ix[i, column] = matrix_sorted.ix[
+                        i - 1, column]  # impute_train[cur_episode_id][column] = train_df_sorted.ix[i-1, column]
+
+            elif column_tail in stats_time_suffixs:
+                '''
+                use the previous + time difference if available; otherwise -infinite
+                '''
+                if i == 0 or (matrix_sorted.ix[i, 'pat_id'] != matrix_sorted.ix[i - 1, 'pat_id']) or (
+                        matrix_sorted.ix[i - 1, column] != matrix_sorted.ix[i - 1, column]):
+                    # impute_train[cur_episode_id][column] = nega_inf_days
+                    matrix_sorted.ix[i, column] = nega_inf_days
+                else:
+                    day_diff = (datetime.datetime.strptime(matrix_sorted.ix[i, 'order_time'], datetime_format) -
+                                datetime.datetime.strptime(matrix_sorted.ix[i - 1, 'order_time'],
+                                                           datetime_format)).days
+                    # impute_train[cur_episode_id][column] = train_df_sorted.ix[i-1, column] - day_diff # TODO!
+
+                    matrix_sorted.ix[i, column] = matrix_sorted.ix[i - 1, column] - day_diff
+
+            elif column_tail in count_time_suffixs:
+                '''
+                -infinite
+                '''
+                # impute_train[cur_episode_id][column] = nega_inf_days
+                matrix_sorted.ix[i, column] = nega_inf_days
+
+            else:
+                '''
+                In all other cases, just use mean to impute
+                '''
+                matrix_sorted.ix[i, column] = popu_mean
+                pass
+
+    return matrix_sorted.set_index('index')
+
+def impute_by_carry_forward(matrix, impute_dict={}): # TODO: optimize list
+    '''
+    Why:
+    Numerical results (e.g. mean and standard deviation) could be missing if an individual
+    patient episode did not have those in the recent time window.
+        In those cases, we carry forward the most recent statistic from the patient's record.
+        If no prior values exist for that patient, then we impute with the val of each key.
+    Args:
+        matrix:
+    Returns:
+    '''
+    columns_to_impute = impute_dict.keys() # TODO
+
+    columns_relevant = ['pat_id', 'order_time', 'order_proc_id'] + columns_to_impute
+    '''
+    Impute for this temporary df. 
+    '''
+    matrix_relevant_sorted = matrix[columns_relevant].copy().sort_values(['pat_id','order_time']).reset_index()
+
+    for i in range(0, matrix_relevant_sorted.shape[0]):
+        for column in columns_to_impute:
+            if matrix_relevant_sorted.ix[i, column] == matrix_relevant_sorted.ix[i, column]:
+                # value not missing
+                pass
+
+            elif (i==0) or (matrix_relevant_sorted.ix[i,'pat_id'] != matrix_relevant_sorted.ix[i-1,'pat_id']):
+                # no prior order
+                matrix_relevant_sorted.ix[i, column] = impute_dict[column]
+
+            else:
+                matrix_relevant_sorted.ix[i, column] = matrix_relevant_sorted.ix[i-1, column]
+
+    '''
+    Use tmp df to impute the input df. 
+    '''
+    matrix_imputed = matrix.copy()
+    for column in columns_to_impute:
+        curr_impute_dict = dict(zip(matrix_relevant_sorted['order_proc_id'].values,
+                                    matrix_relevant_sorted[column].values))
+        matrix_imputed[column] = matrix_imputed[column].fillna(matrix_imputed['order_proc_id'].map(curr_impute_dict))
+
+    return matrix_imputed
\ No newline at end of file
diff --git a/medinfo/ml/SupervisedLearningPipeline.py b/medinfo/ml/SupervisedLearningPipeline.py
new file mode 100644
index 00000000..4c637b88
--- /dev/null
+++ b/medinfo/ml/SupervisedLearningPipeline.py
@@ -0,0 +1,621 @@
+#!/usr/bin/python
+"""
+Abstract class for an end-to-end pipeline which builds a raw feature matrix,
+processes the raw matrix, trains a predictor on the processed matrix,
+tests the predictor's performance, and summarizes its analysis.
+
+SubClasses will be expected to override the following functions:
+* __init__()
+* _build_raw_matrix_path()
+* _build_raw_feature_matrix()
+* _build_processed_matrix_path()
+* _build_processed_feature_matrix()
+* _add_features()
+* _remove_features()
+* _select_features()
+* summarize()
+"""
+
+import datetime
+import inspect
+import os
+import pandas as pd
+import sys
+
+from sklearn.model_selection import train_test_split
+from sklearn.utils.validation import column_or_1d
+
+from medinfo.common.Env import LAB_TYPE
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.Regressor import Regressor
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.ClassifierAnalyzer import ClassifierAnalyzer
+
+import LocalEnv
+
+
+class SupervisedLearningPipeline:
+    CLASSIFICATION = 'classification'
+    REGRESSION = 'regression'
+
+    def __init__(self, variable, num_data_points, use_cache=None, random_state=None,
+                 timeLimit=None, notUsePatIds=()):
+        # Process arguments.
+        self._var = variable
+        self._num_rows = num_data_points
+        self._flush_cache = True if use_cache is None else False
+
+        self._raw_matrix = None
+        self._processed_matrix = None
+        self._predictor = None
+        self._eliminated_features = list()
+        self._removed_features = list()
+        self._added_features = list()
+        self._random_state = random_state
+
+        self._timeLimit = timeLimit
+        self._notUsePatIds = notUsePatIds
+        self.feat2imputed_dict = {}
+
+        '''
+        patients in both the train and validation set (cv).
+        '''
+        self._patIds_train = []
+
+        '''
+        patients in the holdout test set. 
+        '''
+        self._patIds_test = []
+
+    def predictor(self):
+        return self._predictor
+
+    def _build_model_dump_path(self, file_name_template, pipeline_module_path):
+        # Build model file name.
+        slugified_var = '-'.join(self._var.split())
+        model_dump_name = file_name_template % (slugified_var)
+
+        # Build path.
+        data_dir = self._fetch_data_dir_path(pipeline_module_path)
+        model_dump_path = '/'.join([data_dir, model_dump_name])
+
+        return model_dump_path
+
+    def _build_raw_matrix_path(self, file_name_template, pipeline_module_file):
+        # Build raw matrix file name.
+        slugified_var = '-'.join(self._var.split())
+        raw_matrix_name = file_name_template % (slugified_var, self._num_rows)
+
+        # Build path.
+        data_dir = self._fetch_data_dir_path(pipeline_module_file)
+        raw_matrix_path = '/'.join([data_dir, raw_matrix_name])
+
+        return raw_matrix_path
+
+    def _build_matrix_path(self, file_name_template, pipeline_module_path):
+        # Build matrix file name.
+        slugified_var = '-'.join(self._var.split())
+        matrix_name = file_name_template % (slugified_var, self._num_rows) #, self._num_rows
+
+        # Build path.
+        data_dir = self._fetch_data_dir_path(pipeline_module_path)
+        matrix_path = '/'.join([data_dir, matrix_name])
+
+        return matrix_path
+
+    def _fetch_data_dir_path(self, pipeline_module_path):
+        # e.g. app_dir = CDSS/scripts/LabTestAnalysis/machine_learning
+        app_dir = os.path.dirname(os.path.abspath(pipeline_module_path))
+
+        # e.g. data_dir = CDSS/scripts/LabTestAnalysis/machine_learning/data
+        parent_dir_list = app_dir.split('/')
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            # For running both standalone (called "panel" here)/component in 1 click
+            parent_dir_list.append('data-%s' % LAB_TYPE)
+        else:
+            parent_dir_list.append('data')
+        parent_dir_list.append(self._var)
+        data_dir = '/'.join(parent_dir_list)
+
+        # If data_dir does not exist, make it.
+        if not os.path.exists(data_dir):
+            os.makedirs(data_dir)
+
+        return data_dir
+
+    def _build_raw_feature_matrix(self, matrix_class, raw_matrix_path, params=None):
+        # If raw matrix exists, and client has not requested to flush the cache,
+        # just use the matrix that already exists and return.
+        if params is None:
+            self._raw_matrix_params = {}
+        else:
+            self._raw_matrix_params = params
+        if os.path.exists(raw_matrix_path) and not self._flush_cache:
+            pass
+        else:
+            # Each matrix class may have a custom set of parameters which should
+            # be passed on directly to matrix_class, but we expect them to have
+            # at least 1 primary variables and # of rows.
+            # Ensure that random_state is [-1, 1]
+            random_state = float(self._random_state)/float(sys.maxsize)
+            if self._timeLimit or self._notUsePatIds:
+                matrix = matrix_class(self._var, self._num_rows, random_state=random_state,
+                                  timeLimit=self._timeLimit, notUsePatIds=self._notUsePatIds)
+            else:
+                matrix = matrix_class(self._var, self._num_rows, random_state=random_state)
+            matrix.write_matrix(raw_matrix_path)
+
+    def _build_processed_feature_matrix(self, params):
+        # params is a dict defining the details of how the raw feature matrix
+        # should be transformed into the processed matrix. Given the sequence
+        # of steps will be identical across all pipelines, sbala decided to
+        # pack all the variability into this dict. It's not ideal because the
+        # dict has 10+ values, but that seems better than forcing all pipelines
+        # to reproduce the logic of the processing steps.
+        # Principle: Minimize overridden function calls.
+        #   params['features_to_add'] = features_to_add
+        #   params['features_to_filter_on'] (optional) = features_to_filter_on
+        #   params['imputation_strategies'] = imputation_strategies
+        #   params['features_to_remove'] = features_to_remove
+        #   params['outcome_label'] = outcome_label
+        #   params['selection_problem'] = selection_problem
+        #   params['selection_algorithm'] = selection_algorithm
+        #   params['percent_features_to_select'] = percent_features_to_select
+        #   params['matrix_class'] = matrix_class
+        #   params['pipeline_file_path'] = pipeline_file_path
+        #   TODO(sbala): Determine which fields should have defaults.
+        fm_io = FeatureMatrixIO()
+        log.debug('params: %s' % params)
+        # If processed matrix exists, and the client has not requested to flush
+        # the cache, just use the matrix that already exists and return.
+        processed_matrix_path = params['processed_matrix_path']
+        if os.path.exists(processed_matrix_path) and not self._flush_cache:
+            # Assume feature selection already happened, but we still need
+            # to split the data into training and test data.
+            processed_matrix = pd.read_csv(processed_matrix_path, sep='\t', comment='#', keep_default_na=False)
+
+            self._train_test_split(processed_matrix, params['outcome_label'])
+            '''
+            Pandas dataframe may automatically convert bigint to float (and round the last
+            few digits), which may damage the uniqueness of pat_ids. 
+            '''
+            # processed_matrix['pat_id'] = processed_matrix['pat_id'].apply(lambda x: str(x))
+        else:
+            # Read raw matrix.
+            raw_matrix = fm_io.read_file_to_data_frame(params['raw_matrix_path'])
+
+            # Initialize FMT.
+
+            # Divide processed_matrix into training and test data.
+            # This must happen before feature selection so that we don't
+            # accidentally learn information from the test data.
+
+            patIds_df = raw_matrix['pat_id'].copy()
+
+            self._train_test_split(raw_matrix, params['outcome_label'])
+
+            fmt = FeatureMatrixTransform()
+            train_df = self._X_train.join(self._y_train)
+            fmt.set_input_matrix(train_df)
+
+            # Add features.
+            self._add_features(fmt, params['features_to_add'])
+
+            # Filter on features
+            if 'features_to_filter_on' in params:
+                self._filter_on_features(fmt, params['features_to_filter_on'])
+
+            # HACK: When read_csv encounters duplicate columns, it deduplicates
+            # them by appending '.1, ..., .N' to the column names.
+            # In future versions of pandas, simply pass mangle_dupe_cols=True
+            # to read_csv, but not ready as of pandas 0.22.0.
+            for feature in raw_matrix.columns.values:
+                if feature[-2:] == ".1":
+                    fmt.remove_feature(feature)
+                    self._removed_features.append(feature)
+
+            # Impute data.
+            if params['imputation_strategies'] == {'sxu_new_imputation'}:
+                train_df = fmt.fetch_matrix()
+                means = {}
+                for column in train_df.columns.values.tolist():
+                    # column_tail = column.split('.')[-1].strip()
+                    if train_df[column].dtype == 'float64':
+                        means[column] = train_df[column].mean()
+
+                train_df = fmt.do_impute_sx(train_df, means)
+                fmt.set_input_matrix(train_df)
+                self._X_test = fmt.do_impute_sx(self._X_test, means)
+
+                self._remove_features(fmt, params['features_to_remove'])
+
+            else:
+                self._remove_features(fmt, params['features_to_remove'])
+                self._impute_data(fmt, train_df, params['imputation_strategies'])
+
+
+            # Remove features.
+            '''
+            Moved here, since still need pat_id for imputation!
+            '''
+            # self._remove_features(fmt, params['features_to_remove'])
+
+            # In case any all-null features were created in preprocessing,
+            # drop them now so feature selection will work
+            fmt.drop_null_features()
+
+            # Build interim matrix.
+            train_df = fmt.fetch_matrix()
+
+            self._y_train = pd.DataFrame(train_df.pop(params['outcome_label']))
+            self._X_train = train_df
+
+            '''
+            Select X_test columns according to processed X_train
+            '''
+            self._X_test = self._X_test[self._X_train.columns]
+
+            if not params['imputation_strategies'] == {'sxu_new_imputation'}:
+                for feat in self._X_test.columns:
+                    self._X_test[feat] = self._X_test[feat].fillna(self.feat2imputed_dict[feat])
+
+
+            self._select_features(params['selection_problem'],
+                params['percent_features_to_select'],
+                params['selection_algorithm'],
+                params['features_to_keep'])
+
+            '''
+            The join is based on index by default.
+            Will remove 'pat_id' (TODO sxu: more general in the future) later in train().
+            '''
+            self._X_train = self._X_train.join(patIds_df, how='left')
+
+            self._X_test = self._X_test.join(patIds_df, how='left')
+
+            # print set(self._X_train['pat_id'].values.tolist()) & set(self._X_test['pat_id'].values.tolist())
+
+            train = self._y_train.join(self._X_train)
+            test = self._y_test.join(self._X_test)
+
+            processed_trainMatrix_path = processed_matrix_path.replace("matrix", "train-matrix")
+            train.to_csv(processed_trainMatrix_path, sep='\t', index=False)
+            processed_testMatrix_path = processed_matrix_path.replace("matrix", "test-matrix")
+            test.to_csv(processed_testMatrix_path, sep='\t', index=False)
+
+            processed_matrix = train.append(test)
+            '''
+            Recover the order of rows before writing into disk, 
+            where the index info will be missing.
+            '''
+            processed_matrix.sort_index(inplace=True)
+
+            # Write output to new matrix file.
+            header = self._build_processed_matrix_header(params)
+            fm_io.write_data_frame_to_file(processed_matrix, \
+                processed_matrix_path, header)
+
+        '''
+        Pop out pat_id from the feature matrices. 
+        Also check whether there is pat_id leakage. 
+        '''
+        self._patIds_train = self._X_train.pop('pat_id').values.tolist()
+        self._patIds_test = self._X_test.pop('pat_id').values.tolist()
+        assert not (set(self._patIds_train) & set(self._patIds_test))
+
+    def _add_features(self, fmt, features_to_add):
+        # Expected format for features_to_add:
+        # {
+        #   'threshold': [{arg1, arg2, etc.}, ...]
+        #   'indicator': [{arg1, arg2, etc.}, ...]
+        #   'logarithm': [{arg1, arg2, etc.}, ...]
+        # }
+        indicator_features = features_to_add.get('indicator')
+        threshold_features = features_to_add.get('threshold')
+        logarithm_features = features_to_add.get('logarithm')
+        change_features = features_to_add.get('change')
+
+        if indicator_features:
+            for feature in indicator_features:
+                base_feature = feature.get('base_feature')
+                boolean_indicator = feature.get('boolean_indicator')
+                added_feature = fmt.add_indicator_feature(base_feature, boolean_indicator)
+                self._added_features.append(added_feature)
+
+        if threshold_features:
+            for feature in threshold_features:
+                base_feature = feature.get('base_feature')
+                lower_bound= feature.get('lower_bound')
+                upper_bound = feature.get('upper_bound')
+                added_feature = fmt.add_threshold_feature(base_feature, lower_bound, upper_bound)
+                self._added_features.append(added_feature)
+
+        if logarithm_features:
+            for feature in logarithm_features:
+                base_feature = feature.get('base_feature')
+                logarithm = feature.get('logarithm')
+                added_feature = fmt.add_threshold_feature(base_feature, logarithm)
+                self._added_features.append(added_feature)
+
+        # TODO (raikens): right now, unchanged_yn is the only allowable name for a
+        # change feature, which means at most one change_feature can be added
+        if change_features:
+            if len(change_features) > 1:
+                 raise ValueError("Adding multiple \'change\' type features is not yet supported")
+
+            for feature in change_features:
+                feature_old = feature.get('feature_old')
+                feature_new = feature.get('feature_new')
+                method = feature.get('method')
+                param = feature.get('param')
+                added_feature = fmt.add_change_feature(method, param, feature_old, feature_new)
+                self._added_features.append(added_feature)
+
+                # sd method discards 300 rows for measuring sd
+                if method == 'sd':
+                    self._num_rows = self._num_rows - 300
+
+        log.debug('self._added_features: %s' % self._added_features)
+
+    def _impute_data(self, fmt, raw_matrix, imputation_strategies):
+        for feature in raw_matrix.columns.values:
+            if feature in self._removed_features:
+                continue
+            # If all values are null, just remove the feature.
+            # Otherwise, imputation will fail (there's no mean value),
+            # and sklearn will ragequit.
+            if raw_matrix[feature].isnull().all():
+                fmt.remove_feature(feature)
+                self._removed_features.append(feature)
+            # Only try to impute if some of the values are null.
+            elif raw_matrix[feature].isnull().any():
+                # If an imputation strategy is specified, follow it.
+                if imputation_strategies.get(feature):
+                    strategy = imputation_strategies.get(feature)
+                    fmt.impute(feature, strategy)
+                else:
+                    # TODO(sbala): Impute all time features with non-mean value.
+                    imputed_value = fmt.impute(feature)
+                    self.feat2imputed_dict[feature] = imputed_value
+            else:
+                '''
+                If there is no need to impute, still keep the mean value, in case test data 
+                need imputation
+                TODO sxu: take care of the case of non-mean imputation strategy
+                '''
+                imputed_value = fmt.impute(feature)
+                self.feat2imputed_dict[feature] = imputed_value
+
+    def _remove_features(self, fmt, features_to_remove):
+        # Prune manually identified features (meant for obviously unhelpful).
+        # In theory, FeatureSelector should be able to prune these, but no
+        # reason not to help it out a little bit.
+        for feature in features_to_remove:
+            fmt.remove_feature(feature)
+            self._removed_features.append(feature)
+
+        log.debug('self._removed_features: %s' % self._removed_features)
+
+    def _filter_on_features(self, fmt, features_to_filter_on):
+        # Filter out rows with unwanted value for given feature
+        for filter_feature in features_to_filter_on:
+            feature = filter_feature.get('feature')
+            value = filter_feature.get('value')
+            self._num_rows = fmt.filter_on_feature(feature, value)
+            log.debug('Removed rows where %s equals \'%s\'; %d rows remain.' % (feature, str(value), self._num_rows))
+
+    '''
+    Strategy 1: split by pat_id by default, no overlapping between train/test
+    '''
+    def _train_test_split(self, processed_matrix, outcome_label, columnToSplitOn='pat_id'):
+        log.debug('outcome_label: %s' % outcome_label)
+        all_possible_ids = sorted(set(processed_matrix[columnToSplitOn].values.tolist()))
+
+        train_ids, test_ids = train_test_split(all_possible_ids, random_state=self._random_state)
+
+        train_matrix = processed_matrix[processed_matrix[columnToSplitOn].isin(train_ids)].copy()
+        self._y_train = pd.DataFrame(train_matrix.pop(outcome_label))
+        self._X_train = train_matrix
+
+        test_matrix = processed_matrix[processed_matrix[columnToSplitOn].isin(test_ids)].copy()
+        self._y_test = pd.DataFrame(test_matrix.pop(outcome_label))
+        self._X_test = test_matrix
+
+
+    def _select_features(self, problem, percent_features_to_select, algorithm, features_to_keep=None):
+        # Initialize FeatureSelector.
+        fs = FeatureSelector(problem=problem, algorithm=algorithm, random_state=self._random_state)
+        fs.set_input_matrix(self._X_train, column_or_1d(self._y_train))
+        num_features_to_select = int(percent_features_to_select*len(self._X_train.columns.values))
+
+        # Parse features_to_keep.
+        if features_to_keep is None:
+            features_to_keep = []
+
+        # Select features.
+        fs.select(k=num_features_to_select)
+
+        # Enumerate eliminated features pre-transformation.
+        feature_ranks = fs.compute_ranks()
+        for i in range(len(feature_ranks)):
+            if feature_ranks[i] > num_features_to_select:
+                # If in features_to_keep, pretend it wasn't eliminated.
+                if self._X_train.columns[i] not in features_to_keep:
+                    self._eliminated_features.append(self._X_train.columns[i])
+
+        # Hack: rather than making FeatureSelector handle the concept of
+        # kept features, just copy the data here and add it back to the
+        # transformed matrices.
+        # Rather than looping, do this individually so that we can skip if
+        # transformed X already has the feature.
+
+        # for feature in features_to_keep:
+        kept_X_train_feature = self._X_train[features_to_keep].copy()
+        log.debug('kept_X_train_feature.shape: %s' % str(kept_X_train_feature.shape))
+        self._X_train = fs.transform_matrix(self._X_train)
+        for feature in features_to_keep:
+            if feature not in self._X_train:
+                self._X_train = self._X_train.merge(kept_X_train_feature[[feature]], left_index=True, right_index=True)
+
+        kept_X_test_feature = self._X_test[features_to_keep].copy()
+        log.debug('kept_X_test_feature.shape: %s' % str(kept_X_test_feature.shape))
+        self._X_test = fs.transform_matrix(self._X_test)
+        for feature in features_to_keep:
+            if feature not in self._X_test:
+                self._X_test = self._X_test.merge(kept_X_test_feature[[feature]], left_index=True, right_index=True)
+
+        if not features_to_keep:
+        # Even if there is no feature to keep, still need to
+        # perform transform_matrix to drop most low-rank features
+            self._X_train = fs.transform_matrix(self._X_train)
+            self._X_test = fs.transform_matrix(self._X_test)
+
+    def _build_processed_matrix_header(self, params):
+        # FeatureMatrixFactory and FeatureMatrixIO expect a list of strings.
+        # Each comment below represents the line in the comment.
+        header = list()
+
+        processed_matrix_path = params['processed_matrix_path']
+        pipeline_file_path = params['pipeline_file_path']
+        raw_matrix_name = params['raw_matrix_path'].split('/')[-1]
+        file_summary = self._build_file_summary(processed_matrix_path, \
+            pipeline_file_path, raw_matrix_name)
+        header.extend(file_summary)
+        header.append('')
+        data_overview = params['data_overview']
+        header.extend(data_overview)
+        header.append('')
+        processing_summary = self._build_processing_steps_summary()
+        header.extend(processing_summary)
+
+        return header
+
+    def _build_file_summary(self, processed_matrix_path, pipeline_file_path, raw_matrix_name):
+        summary = list()
+
+        # <file_name.tab>
+        matrix_file_name = processed_matrix_path.split('/')[-1]
+        summary.append(matrix_file_name)
+        # Created: <timestamp>
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+        summary.append('Created: %s' % timestamp)
+        # Source: __name__
+        module_name = pipeline_file_path.split('/')[-1]
+        summary.append('Source: %s' % module_name)
+        # Command: Pipeline()
+        class_name = module_name.split('.')[0]
+        args = [self._var, str(self._num_rows)]
+        for key, value in self._raw_matrix_params:
+            args.append('%s=%s' % (key, value))
+        command = '%s(%s)' % (class_name, ', '.join(args))
+        summary.append('Command: %s' % command)
+        # Number of Observations
+        summary.append('Number of Episodes: %s' % self._num_rows)
+        summary.append('')
+        # Overview:
+        summary.append('Overview:')
+        # This file is a processed version of ___.
+        line = 'This file is a post-processed version of %s.' % raw_matrix_name
+        summary.append(line)
+
+        return summary
+
+    def _build_processing_steps_summary(self):
+        summary = []
+
+        # This matrix is the result of the following processing steps on the raw matrix:
+        line = 'This matrix is the result of the following processing steps on the raw matrix:'
+        summary.append(line)
+        #   * Adding the following features.
+        line = '  * Adding the following features:'
+        summary.append(line)
+        #   *   ___
+        for feature in self._added_features:
+            line = '      %s' % feature
+            summary.append(line)
+        #   * Imputing missing values with the mean value of each column.
+        line = '  * Imputing missing values with the mean value of each column.'
+        summary.append(line)
+        #   (2) Manually removing low-information features:
+        line = '  * Manually removing low-information features:'
+        summary.append(line)
+        #       ___
+        line = '      %s' % str(self._removed_features)
+        summary.append(line)
+        #   (3) Algorithmically selecting the top 100 features via recursive feature elimination.
+        line = '  * Algorithmically selecting the top 100 features via recursive feature elimination.'
+        summary.append(line)
+        #       The following features were eliminated.
+        line = '      The following features were eliminated:'
+        summary.append(line)
+        # List all features with rank >100.
+        line = '        %s' % str(self._eliminated_features)
+        summary.append(line)
+
+        return summary
+
+    def _train_predictor(self, problem, classes=None, hyperparams=None):
+        if problem == SupervisedLearningPipeline.CLASSIFICATION:
+            if 'bifurcated' in hyperparams['algorithm']:
+                learning_class = BifurcatedSupervisedClassifier
+                # Strip 'bifurcated-' from algorithm for SupervisedClassifier.
+                hyperparams['algorithm'] = '-'.join(hyperparams['algorithm'].split('-')[1:])
+            else:
+                learning_class = SupervisedClassifier
+
+            self._predictor = learning_class(classes, hyperparams)
+        elif problem == SupervisedLearningPipeline.REGRESSION:
+            learning_class = Regressor
+            self._predictor = learning_class(algorithm=algorithm)
+        status = self._predictor.train(self._X_train, column_or_1d(self._y_train),
+                                       groups = self._patIds_train)
+
+        return status
+
+    def _analyze_predictor(self, dest_dir, pipeline_prefix):
+        analyzer = ClassifierAnalyzer(self._predictor, self._X_test, self._y_test)
+
+        # Build names for output plots and report.
+        direct_comparisons_name = 'direct_comparisons.csv' #'%s-direct-compare-results.csv' % pipeline_prefix
+        precision_at_k_plot_name = '%s-precision-at-k-plot.png' % pipeline_prefix
+        precision_recall_plot_name = '%s-precision-recall-plot.png' % pipeline_prefix
+        roc_plot_name = '%s-roc-plot.png' % pipeline_prefix
+        report_name = '%s-report.tab' % pipeline_prefix
+
+        # Build paths.
+        direct_comparisons_path = '/'.join([dest_dir, direct_comparisons_name])
+        log.debug('direct_comparisons_path: %s' % direct_comparisons_path)
+        precision_at_k_plot_path = '/'.join([dest_dir, precision_at_k_plot_name])
+        log.debug('precision_at_k_plot_path: %s' % precision_at_k_plot_path)
+        precision_recall_plot_path = '/'.join([dest_dir, precision_recall_plot_name])
+        log.debug('precision_recall_plot_path: %s' % precision_recall_plot_path)
+        roc_plot_path = '/'.join([dest_dir, roc_plot_name])
+        log.debug('roc_plot_path: %s' % roc_plot_path)
+        report_path = '/'.join([dest_dir, report_name])
+        log.debug('report_path: %s' % report_path)
+
+        # Build plot titles.
+        roc_plot_title = 'ROC (%s)' % pipeline_prefix
+        precision_recall_plot_title = 'Precision-Recall (%s)' % pipeline_prefix
+        precision_at_k_plot_title = 'Precision @K (%s)' % pipeline_prefix
+
+        # Write output.
+        analyzer.output_direct_comparisons(direct_comparisons_path)
+        analyzer.plot_roc_curve(roc_plot_title, roc_plot_path)
+        analyzer.plot_precision_recall_curve(precision_recall_plot_title, precision_recall_plot_path)
+        analyzer.plot_precision_at_k_curve(precision_at_k_plot_title, precision_at_k_plot_path)
+        analyzer.write_report(report_path, ci=0.95)
+
+    def _analyze_predictor_traindata(self, dest_dir):
+        analyzer = ClassifierAnalyzer(self._predictor, self._X_train, self._y_train)
+        direct_comparisons_name = 'direct_comparisons_train.csv'
+        direct_comparisons_path = '/'.join([dest_dir, direct_comparisons_name])
+        log.debug('direct_comparisons_path: %s' % direct_comparisons_path)
+        analyzer.output_direct_comparisons(direct_comparisons_path)
+
+
diff --git a/medinfo/ml/__init__.py b/medinfo/ml/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/medinfo/ml/pytorch_models.py b/medinfo/ml/pytorch_models.py
new file mode 100644
index 00000000..74aceefa
--- /dev/null
+++ b/medinfo/ml/pytorch_models.py
@@ -0,0 +1,28 @@
+import torch
+import torch.nn.functional as F
+
+
+class SimpleGRU(nn.Module):
+    def __init__(self, vocab_size, embedding_size=512, hidden_size=256,
+         num_layers=1):
+        super(SimpleGRU).__init__()
+        self.vocab_size = vocab_size
+        self.embedding_size = embedding_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.embedding_layer = torch.nn.Embedding(
+            num_embeddings=self.vocab_size,
+            embedding_dim=self.embedding_size
+        )
+        self.gru = torch.nn.GRU(
+            input_size=self.embedding_size,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers
+        )
+        self.sigmoid = torch.nn.Sigmoid()
+
+    def forward(self, x):
+        embeddings = self.embedding_layer(x)
+        output, h_n = self.gru(embeddings)
+        y_hat = self.sigmoid(h_n)
+        return x
diff --git a/medinfo/ml/test/ClassifierAnalyzerTestData.py b/medinfo/ml/test/ClassifierAnalyzerTestData.py
new file mode 100644
index 00000000..0ee7af0d
--- /dev/null
+++ b/medinfo/ml/test/ClassifierAnalyzerTestData.py
@@ -0,0 +1,258 @@
+#!/usr/bin/python
+"""
+Test input and output for classifier analysis.
+"""
+
+from pandas import DataFrame
+
+RANDOM_10_TEST_CASE = {
+    'X': DataFrame({
+        'x1': [-1.84249065, -1.60319016, 0.28321407, -1.13551465, 0.37185149,
+            0.00400999, 1.06642896, -0.62725387, -0.83021641, 0.08404254],
+        'x2': [0.23765083, 0.40784636, -1.32874726, -2.43164431, -1.3331387,
+            -0.18751141, -2.52931659, 0.59347302, -1.46612753, -0.242522],
+        'x3': [-0.01489945, -0.57062014, -1.18253245, -1.99626571, 0.16456579,
+            -2.01459515, 3.36093154, -0.00846663, -0.43907812, 2.18037093]
+    }),
+    'y': DataFrame({
+        'true': [0, 0, 1, 1, 1, 1, 0, 1, 0, 0]
+    }),
+    'accuracy': 0.6,
+    'recall': 0.6,
+    'precision': 0.6,
+    'f1': 0.6
+}
+
+# X, y = make_classification(n_features=5, n_samples=100)
+RANDOM_100_TEST_CASE = {
+    'X': DataFrame({
+        'x1': [ 0.96147646,  1.98925281, -0.64951619,  0.96596596, -0.64580453,
+               -0.63875675, -1.28277057, -0.81715909,  1.51807202,  0.37537854,
+               -0.50596493, -0.65494648,  0.54110947, -2.86173914, -0.33920363,
+               -0.73359088, -0.70403545,  1.42727204, -0.21848681,  0.30809718,
+               -0.79858549, -0.70407353, -0.66783679,  2.71014168, -0.58956545,
+               -1.67483137,  0.2300583 ,  0.1290677 , -0.10302288,  1.37144506,
+                0.72213589, -0.20657882, -1.4230218 , -0.74489876,  1.41646011,
+                0.08660463,  0.5192675 , -2.80126764, -0.85309964,  1.13696068,
+               -0.76554544,  1.53224779, -0.64526351, -0.80175889,  0.51174082,
+               -0.7707371 ,  1.57708472, -0.68234115,  0.4505989 ,  0.86385424,
+               -0.80049316, -0.68017794, -0.77105799,  0.36202469,  1.15408247,
+                1.76273703, -0.23579311, -0.22144915, -0.37288966,  1.38702507,
+                0.23302569, -0.50331893,  2.13812656,  1.57667749, -0.72058223,
+               -0.48418771,  1.18912351,  0.63006844,  3.23034485,  0.24586314,
+                0.34587628,  0.83807039, -0.29392546, -2.68437407, -1.60885125,
+               -0.85131199,  0.24174356, -1.0541907 , -0.57175207, -0.62514018,
+                0.29971977, -0.06516976,  1.10106286, -0.81188174,  1.06825057,
+               -0.63389408,  1.4380672 , -0.26737958, -0.65133674,  2.52118284,
+                1.82838221,  1.57305661, -1.79330099,  0.44111812, -1.28778823,
+               -0.70185513, -0.21226109, -0.39387602,  0.96284426, -0.59917134],
+        'x2': [ 0.95912325,  1.58246684, -0.95548482,  0.98738313, -0.69020797,
+               -0.47975209, -1.13577473, -0.77676575,  2.09432366,  0.68048457,
+               -0.76930841, -0.95663105,  0.70460322, -3.00758318, -0.63940652,
+               -1.04357964, -1.02086198,  1.74725147,  0.0161254 ,  0.36116513,
+               -1.14003798, -1.022088  , -0.96678988,  2.492034  , -0.86406767,
+               -1.57533698,  0.45628824,  0.31167966,  0.0896049 ,  1.12991854,
+                0.30153561, -0.36339723, -1.33447345, -1.06450563,  1.07930878,
+                0.30659105,  0.22998188, -2.96531472, -1.21050811,  1.17375065,
+               -1.09649699,  2.17879996, -0.95575242, -1.13169841,  0.53819949,
+               -1.10947194,  2.27859091, -1.00478634,  0.85646218,  1.04502635,
+               -1.13049876, -0.52526962, -1.10520288,  0.57205989,  1.59582329,
+                2.26931343, -0.31831065, -0.38740101, -0.26148275,  1.46476545,
+                0.91736472, -0.38598418,  2.13279317,  2.64700863, -1.03470619,
+               -0.72294892,  0.71493617,  0.90869162,  3.41172368, -0.2309007 ,
+                0.03442953,  1.01084433, -0.1218975 , -2.79675193, -1.56833198,
+               -1.20985284,  0.19827568, -0.92904736, -0.8545351 , -0.91616829,
+                0.75838264, -0.42140892,  1.85086196, -0.64585868,  1.66315519,
+               -0.91491954,  1.55951122, -0.07754289, -0.46451613,  3.66658145,
+                1.35480009,  2.06323561, -1.77763647,  0.73093998, -1.21746401,
+               -0.56516072, -0.38351246, -0.34887753,  0.48429029, -0.88001512],
+        'x3': [ 0.55258535,  2.93187965,  0.99539644,  0.4493342 , -0.16652324,
+               -1.06775231, -1.37742779, -0.64049978, -1.70848344, -1.14612584,
+                0.88664592,  0.97326984, -0.42248088, -0.96451354,  1.14470266,
+                0.96588259,  1.01297625, -0.61922186, -1.16725708, -0.06244635,
+                1.0692557 ,  1.01824131,  0.95384625,  2.49868877,  0.88916749,
+               -1.38709499, -0.87704948, -0.73988353, -0.91531632,  1.84810415,
+                2.27891219,  0.58139418, -1.19640433,  1.00230914,  2.2990329 ,
+               -0.93014876,  1.58015407, -0.84941081,  1.10952578,  0.47733511,
+                1.04115374, -2.01334077,  1.01791035,  1.01623168,  0.17079103,
+                1.07286353, -2.2326155 ,  1.0502123 , -1.55210212, -0.31917822,
+                1.01723936, -1.07287753,  1.05225655, -0.73057284, -1.31511565,
+               -1.26046556,  0.23426982,  0.61365528, -0.70602879,  0.43609318,
+               -2.91404477, -0.80594923,  1.22928306, -3.87418293,  0.99161973,
+                0.78952906,  2.7806878 , -0.88466958,  1.01420975,  2.26031143,
+                1.58101123, -0.29634273, -0.93128073, -1.01343967, -1.08744318,
+                1.11557299,  0.32974322, -1.1512991 ,  0.93606243,  0.94265244,
+               -1.87214063,  1.54858384, -2.71592506, -1.19659922, -2.04511723,
+                0.89320313,  0.27038336, -0.99556532, -1.19863137, -3.67570247,
+                3.13843033, -1.29444245, -1.08083406, -1.04104231, -1.03905438,
+               -1.0040447 ,  0.64241956, -0.42233213,  2.67253617,  0.91197248],
+        'x4': [ 0.50542958,  2.47689618,  0.75374414,  0.42310673, -0.17574271,
+               -0.89641372, -1.18658445, -0.56627933, -1.26717972, -0.89239218,
+                0.6761716 ,  0.73568181, -0.3024489 , -0.96008509,  0.89363383,
+                0.7245957 ,  0.76422339, -0.40156227, -0.94837907, -0.02969374,
+                0.8030347 ,  0.76843382,  0.71929144,  2.17770757,  0.67268809,
+               -1.22011876, -0.68664842, -0.58353812, -0.73918619,  1.56903975,
+                1.87103187,  0.4516195 , -1.05096424,  0.75299897,  1.9328207 ,
+               -0.73857651,  1.29855847, -0.86400432,  0.83167032,  0.45676419,
+                0.78272256, -1.51018402,  0.7720389 ,  0.76039775,  0.17033581,
+                0.80775409, -1.68269025,  0.79544605, -1.21229171, -0.19855171,
+                0.76128735, -0.9032404 ,  0.79124393, -0.56075865, -0.97637109,
+               -0.89259031,  0.17193926,  0.47645529, -0.58947913,  0.4402188 ,
+               -2.31639186, -0.67801497,  1.12432907, -2.99624697,  0.74604575,
+                0.5998948 ,  2.30326608, -0.66642376,  1.02410592,  1.82480777,
+                1.28783461, -0.18197614, -0.76452244, -0.987563  , -0.97600522,
+                0.83662674,  0.27975755, -0.99060215,  0.7113841 ,  0.71314409,
+               -1.47830468,  1.23483903, -2.100743  , -1.01090511, -1.56614311,
+                0.6730003 ,  0.31098182, -0.81421411, -1.00196677, -2.77527759,
+                2.63158562, -0.93225914, -0.98285422, -0.80398181, -0.91615901,
+               -0.849589  ,  0.50007615, -0.36385508,  2.20183665,  0.69030381],
+        'x5': [-2.62251164, -0.13201092, -0.49715861,  1.78048445, -0.43142904,
+               -0.63894535, -0.26723021, -0.22616752, -0.57328461, -0.35986017,
+               -0.50704489,  0.82683174, -0.49272542,  0.5496112 ,  0.36797241,
+                0.5839895 , -0.32881091,  2.24380243, -0.35323128,  0.13859229,
+                0.20942359, -0.86214141, -0.15327367, -0.31953222, -2.35555523,
+                1.13635203, -1.59713527,  0.96417479,  0.67743876, -0.99020922,
+               -1.79750659, -0.05879987,  1.83897053,  2.3185074 ,  0.34650034,
+                1.13859848, -0.55916696, -2.14073935,  0.7455562 , -0.2930198 ,
+                0.810013  ,  1.73544169,  0.364072  , -0.03434535,  0.38378066,
+               -0.89723789,  0.19343811,  0.10348071,  1.34611881, -2.12148834,
+                0.59587186,  1.96214348,  0.71853803,  0.23630894,  0.05308789,
+                0.19972585,  0.4107283 , -0.12241588, -1.75887353, -0.1259553 ,
+                1.40906044, -0.03121856, -0.87748726, -0.25074591,  0.34216019,
+                1.0306001 ,  0.9142004 , -0.46160897,  0.07104725,  1.76687177,
+               -1.48146318,  0.6200713 ,  1.18500101, -0.49343213,  0.58324615,
+                0.34937425,  0.58815175, -0.64269224, -1.301644  , -0.00934682,
+               -1.42537027, -0.19031682, -0.27228587, -1.34824533, -1.41447244,
+                0.73963604,  0.11752588, -2.14725147, -2.18633535, -2.23245285,
+                1.38287546, -0.93821429,  2.49517929, -0.11654657, -1.69034476,
+               -0.47421673,  0.39379788, -0.00881882,  0.11325007, -0.80107221]
+    }),
+    'y': DataFrame({
+        'true': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,
+                   1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,
+                   0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,
+                   1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,
+                   0, 0, 0, 0, 1, 0, 1, 1]
+    }),
+    'ci': {
+        'accuracy': {
+            'lower': 0.88,
+            'upper': 1.0
+        },
+        'recall': {
+            'lower': 1.0,
+            'upper': 1.0
+        },
+        'precision': {
+            'lower': 0.8125,
+            'upper': 1.0
+        },
+        'f1': {
+            'lower': 0.896551724137931,
+            'upper': 1.0
+        },
+        'average_precision': {
+            'lower': 0.7397254260890624,
+            'upper': 1.0
+        },
+        'roc_auc': {
+            'lower': 0.6799999999999999,
+            'upper': 1.0
+        },
+        'precision_at_k': {
+            'lower': {
+                10: 0.7,
+            },
+            'upper': {
+                10: 1.0,
+            }
+        },
+        'percent_predictably_positive': {
+            'lower': 0.04,
+            'upper': 0.8
+        },
+        'report': DataFrame({
+            'f1': 0.9714285714285714,
+            'recall_0.95_upper_ci': 1.0,
+            'roc_auc_0.95_upper_ci': 1.0,
+            'accuracy_0.95_upper_ci': 1.0,
+            'percent_predictably_positive': 0.16,
+            'hyperparams': "{'C': 0.1, 'algorithm': 'regress-and-round', 'class_weight': 'balanced', 'coef_max': 1, 'cv': StratifiedKFold(n_splits=10, random_state=123456789, shuffle=False), 'dual': False, 'fit_intercept': True, 'hyperparam_strategy': 'exhaustive-search', 'max_iter': 100, 'multi_class': 'ovr', 'n_iter': 9, 'n_jobs': 1, 'penalty': 'l1', 'random_state': 123456789, 'scoring': make_scorer(roc_auc_score, needs_threshold=True), 'solver': 'saga', 'tol': 0.0001}",
+            'f1_0.95_upper_ci': 1.0,
+            'precision': 0.9444444444444444,
+            'y_test.value_counts()': ['{0: 8, 1: 17}'],
+            'f1_0.95_lower_ci': 0.8750000000000001,
+            'average_precision': 0.9287191326551189,
+            'recall_0.95_lower_ci': 1.0,
+            'precision_0.95_upper_ci': 1.0,
+            'percent_predictably_positive_0.95_lower_ci': 0.0,
+            'accuracy_0.95_lower_ci': 0.88,
+            'recall': 1.0,
+            'average_precision_0.95_lower_ci': 0.7548275442506212,
+            'roc_auc': 0.9044117647058824,
+            'average_precision_0.95_upper_ci': 1.0000000000000002,
+            'test_size': [25],
+            'model': ['L1_REGRESS_AND_ROUND(1.0*x3)'],
+            'precision_0.95_lower_ci': 0.8333333333333334,
+            'roc_auc_0.95_lower_ci': 0.5952380952380952,
+            'percent_predictably_positive_0.95_upper_ci': 0.84,
+            'accuracy': 0.96},
+            columns=['model', 'test_size', 'y_test.value_counts()', 'accuracy',
+                   'accuracy_0.95_lower_ci', 'accuracy_0.95_upper_ci', 'recall',
+                   'recall_0.95_lower_ci', 'recall_0.95_upper_ci', 'precision',
+                   'precision_0.95_lower_ci', 'precision_0.95_upper_ci', 'f1',
+                   'f1_0.95_lower_ci', 'f1_0.95_upper_ci', 'average_precision',
+                   'average_precision_0.95_lower_ci', 'average_precision_0.95_upper_ci',
+                   'percent_predictably_positive',
+                   'percent_predictably_positive_0.95_lower_ci',
+                   'percent_predictably_positive_0.95_upper_ci', 'roc_auc',
+                   'roc_auc_0.95_lower_ci', 'roc_auc_0.95_upper_ci', 'hyperparams'])
+    },
+    'accuracy': 0.95999999999999996,
+    'recall': 1.0,
+    'precision': 0.94444444444444442,
+    'f1': 0.97142857142857142,
+    'average_precision': 0.92871913265511885,
+    'roc_auc': 0.90441176470588236,
+    'precision_at_k': {
+        0: 1.0,
+        1: 1.0,
+        2: 1.0,
+        3: 1.0,
+        4: 1.0,
+        5: 0.8,
+        6: 0.8333333333333334,
+        7: 0.8571428571428571,
+        8: 0.875,
+        9: 0.8888888888888888,
+        10: 0.9,
+        11: 0.9090909090909091,
+        12: 0.9166666666666666,
+        13: 0.9230769230769231,
+        14: 0.9285714285714286,
+        15: 0.9333333333333333,
+        16: 0.9375,
+        17: 0.9411764705882353,
+        18: 0.9444444444444444,
+        19: 0.9444444444444444
+    },
+    'percent_predictably_positive': 0.16,
+    'report': DataFrame({
+        'model': ['L1_REGRESS_AND_ROUND(1.0*x3)'],
+        'test_size': [25],
+        'y_test.value_counts()': ['{0: 8, 1: 17}'],
+        'accuracy': [0.95999999999999996],
+        'average_precision': [0.92871913265511885],
+        'f1': [0.97142857142857142],
+        'precision': [0.94444444444444442],
+        'recall': [1.0],
+        'roc_auc': [0.90441176470588236],
+        'percent_predictably_positive': [0.16],
+        'k(precision=0.99)': [0.16],
+        'k(precision=0.95)': [0.16],
+        'k(precision=0.90)': [1.0],
+        'precision_at_10_percent': 1.0,
+        'hyperparams': "{'C': 0.1, 'algorithm': 'regress-and-round', 'class_weight': 'balanced', 'coef_max': 1, 'cv': StratifiedKFold(n_splits=10, random_state=123456789, shuffle=False), 'dual': False, 'fit_intercept': True, 'hyperparam_strategy': 'exhaustive-search', 'max_iter': 100, 'multi_class': 'ovr', 'n_iter': 9, 'n_jobs': 1, 'penalty': 'l1', 'random_state': 123456789, 'scoring': make_scorer(roc_auc_score, needs_threshold=True), 'solver': 'saga', 'tol': 0.0001}"
+    }, columns=['model', 'test_size', 'y_test.value_counts()', 'accuracy', 'recall', 'precision', 'f1', 'average_precision', 'percent_predictably_positive', 'precision_at_10_percent',
+        'k(precision=0.99)', 'k(precision=0.95)', 'k(precision=0.90)', 'roc_auc', 'hyperparams'])
+}
diff --git a/medinfo/ml/test/FeatureSelectorTestData.py b/medinfo/ml/test/FeatureSelectorTestData.py
new file mode 100644
index 00000000..74c31a89
--- /dev/null
+++ b/medinfo/ml/test/FeatureSelectorTestData.py
@@ -0,0 +1,378 @@
+#!/usr/bin/python
+"""
+Test input and output for feature selection.
+"""
+
+from numpy import array
+
+mu, sigma = 0, 0.1
+# Generate test case which for testing univarate feature selection.
+def generate_univariate_test_case(feature_variances):
+    # Generate X.
+    X = None
+
+    # Generate y.
+    y = None
+
+    return X, y
+
+# Test case generated by calling generateUnivariateTestCase() offline.
+# X, y, coefs = sklearn.datasets.make_regression(n_samples=250, n_features=25,
+#                                n_informative=5, coef=True, random_state=2)
+RANDOM_REGRESSION_TEST_CASE = {
+    'X': array([
+       [ -4.51820160e-01,   1.25172597e+00,  -1.73300871e+00,  -9.26699473e-01,   1.08896262e+00,   5.13359842e-01,  -4.07114465e-01,  -1.90631096e-01,  -3.99352913e-01,   1.64846276e+00,  -4.64728817e-01,   1.87320302e-01,   1.39443619e+00,  -2.02371080e+00,  -9.71655906e-01,  -4.68542181e-01,  -2.54169611e-01,  -1.35480946e+00,  -1.95435284e+00,   9.84655408e-01,  -1.26952350e+00,  -4.26405606e-01,   5.67965406e-01,  -1.01556594e+00,  -7.89781247e-01],
+       [  1.05950560e-01,  -2.04980697e-01,   1.00569424e+00,   2.26464166e-01,  -8.94724229e-01,  -1.41327797e-01,   5.24652031e-01,  -5.25600860e-01,   1.25025544e+00,  -6.40473512e-01,   7.98575323e-01,  -5.20403272e-01,   1.47027344e+00,  -2.68297452e-01,  -1.48736401e+00,  -5.51157364e-01,   2.90112692e-01,   1.50301848e+00,   6.77680206e-01,   2.55892339e-01,  -9.50767373e-02,   8.46172811e-01,   2.63470558e-01,   6.24453648e-01,  -8.97271023e-01],
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+       [ -1.93875363e-01,   7.41710058e-01,  -1.49878749e-01,   4.11317567e-01,  -5.39506492e-01,   8.76991310e-01,   2.84851872e-01,   3.95367704e-01,  -5.10364819e-01,   1.22236220e+00,   1.38935317e+00,  -3.89812581e-01,  -5.90587793e-01,  -1.01333373e+00,  -1.15897742e+00,  -8.51539755e-01,   2.59058915e-01,   2.07164094e+00,  -1.23448151e+00,   3.04054387e-02,   1.00200400e-01,  -4.23392463e-01,  -1.54176464e-02,   7.50649736e-01,  -1.38520565e+00],
+       [  2.21722404e+00,  -1.08171704e+00,  -9.80798017e-01,   8.43092780e-02,   2.21003136e+00,   2.62282023e-01,   2.57943268e-02,   9.44752058e-01,   1.08153815e+00,   3.25598343e-01,  -6.24222127e-01,  -7.54063512e-01,   3.02093204e-01,   4.66486221e-01,   6.76954686e-01,  -4.32944029e-01,   5.56682980e-01,  -4.29593339e-01,   7.52437345e-01,  -2.23899479e-01,  -3.07762657e-02,   5.47689731e-01,  -8.17892404e-01,   6.61054613e-01,  -9.51771808e-01],
+       [ -8.83813610e-01,   9.49636240e-01,  -1.05835815e-01,  -1.08723288e+00,  -1.43788454e+00,  -5.13204068e-02,   1.31831521e+00,  -6.86314926e-01,   3.58195511e-01,   1.66097857e+00,  -2.36489925e+00,  -7.39612643e-01,   1.23289061e+00,   2.82615419e-01,   8.71389084e-01,  -8.20922872e-01,   5.03959695e-01,  -1.33085425e+00,   4.74730197e-02,   4.99838774e-01,   6.42779928e-01,  -1.47815956e+00,   1.38380721e-01,   1.52487208e+00,  -7.99429071e-01],
+       [ -5.39406596e-01,  -3.87154510e-01,  -3.19259828e-02,  -5.44031799e-02,  -8.42590683e-01,   1.78778774e+00,  -2.01348046e-01,  -1.45522308e+00,   1.27236357e+00,  -1.00616927e-02,   8.18182512e-01,  -6.30887592e-01,  -2.85575490e-01,  -4.15111613e-01,  -6.06874491e-01,  -2.33391260e-01,  -1.10069083e-01,   5.26299230e-01,  -2.35209522e-01,   2.33674406e-02,  -8.71082555e-01,   6.64379692e-01,  -9.37594208e-01,  -1.29499832e+00,   1.04295618e+00],
+       [ -6.03512041e-01,   1.19571571e+00,  -4.95025655e-01,   5.98217003e-01,   2.11130412e+00,  -7.95681081e-01,  -1.72998057e+00,   1.33329378e-01,   1.67013098e-01,  -5.58386976e-01,  -1.39325218e+00,   2.72089879e-01,   4.02079272e-01,  -1.88090941e-01,   7.20144061e-01,   3.37514713e-01,   1.80431210e+00,  -7.35139128e-01,  -6.17450710e-01,   1.48342309e-01,  -3.43798825e-02,  -5.37743388e-01,   9.63963145e-02,   2.63226765e-01,  -2.79145113e-01],
+       [  8.65427228e-01,  -9.94891790e-01,  -3.25258358e-01,   1.11038420e-01,   2.64121496e+00,  -3.35511244e-01,  -1.49457533e-01,   2.41446815e+00,   1.01532746e+00,   5.10641260e-01,  -5.80793486e-01,   1.06128338e+00,   4.09236384e-01,  -4.27269978e-01,   9.21455833e-01,  -1.91643028e+00,  -3.25965815e-01,  -2.38350212e-01,   3.38276068e-01,   2.19193774e-01,  -9.59266404e-01,   4.13502659e-02,   9.95687157e-01,  -3.04823017e-01,   3.05085818e+00],
+       [ -1.16086393e-01,   3.30016807e-01,  -1.79276093e+00,  -7.60468178e-01,   1.14628216e+00,  -7.93546964e-01,   1.35130732e+00,   7.46327964e-01,   2.22886829e-01,   3.07991620e-01,  -5.97655836e-01,  -1.90963444e+00,  -1.44841573e+00,  -3.88804444e-01,   1.10521177e+00,   2.32983358e-01,   7.68247675e-01,   2.54953941e-02,  -2.30905146e-01,   5.50119464e-01,  -2.05496913e-01,  -3.99621492e-01,   1.12658940e+00,   2.84750713e+00,   2.26347006e-01]
+       ]),
+    'y': array([
+        5.32022982e+01,  -9.54539512e+01,  -2.25892964e+02,
+         3.01290706e+02,  -6.15326563e+01,   2.09524457e+02,
+         6.42380914e+01,   1.77319831e+02,  -9.83481540e+01,
+        -1.77328198e+02,   1.52509603e+02,  -9.74796924e+01,
+         3.75410231e+00,  -2.74284569e+02,   3.18234802e+01,
+         9.32546633e+01,   2.41114305e+02,   2.40559104e+02,
+        -1.69608667e+02,  -8.16694415e+01,   1.37235310e+02,
+        -1.96645120e+02,  -3.93115302e+01,   2.92682591e+02,
+        -1.52800690e+02,  -1.97253812e+02,  -3.14805417e+01,
+        -9.47338883e+01,   4.80567299e+01,  -4.39823452e+00,
+        -5.80530764e+01,   1.82834068e+02,   4.38474160e+01,
+         1.39749875e+02,  -8.11936613e+01,  -3.31870963e+01,
+        -1.65578306e+01,   2.97782350e+02,  -8.66129791e+01,
+         5.03687506e+01,  -5.25069001e+01,  -6.02602667e+00,
+        -1.01159046e+02,   3.01179598e+02,  -3.13744498e+01,
+         1.56026129e+02,  -1.04578276e+01,   5.97499497e+01,
+         3.51397120e+01,   1.23833537e+02,  -1.89113186e+02,
+        -1.01358461e+02,   9.41165555e+01,  -2.20503202e+01,
+        -1.13563286e+02,  -3.64378030e+01,  -5.80369366e+01,
+         6.68310126e+01,   1.14721304e+02,  -1.88031255e+02,
+        -1.68054994e+02,  -4.19544163e+02,   2.70438349e+00,
+         2.55861485e+02,  -3.28252537e+01,  -1.42020125e+02,
+        -6.25354128e+01,  -7.61030279e+01,  -7.51791759e+01,
+         7.28745972e+01,  -1.10145701e+02,  -1.22925604e+01,
+        -1.15158030e+02,   2.15704347e+02,  -9.62550467e+01,
+         5.47364127e+01,   1.52538101e+02,   1.12757349e+02,
+        -1.03341213e+02,   5.01114420e+01,  -1.39748491e+01,
+        -5.59949287e+01,   7.27412170e+01,  -1.26166284e+02,
+         1.29485806e+01,   1.61334702e+02,   2.07686694e+01,
+         1.91025512e+02,   8.10147595e+00,   8.82780655e+01,
+        -6.38706972e+01,   1.34103547e+02,  -8.82559140e+01,
+        -7.50823401e+01,   6.07789395e+01,  -9.06865910e+01,
+        -8.72377533e+01,  -4.35211284e+02,   1.38290175e+02,
+        -1.23235368e+02,  -1.23174335e+02,   1.48779789e+01,
+         2.50497947e+01,  -1.78321750e+02,   2.24598457e+02,
+        -8.77746016e+01,  -1.08937956e+02,   5.59353001e-01,
+        -2.86805696e+01,  -3.16199312e+01,   1.27152451e+02,
+        -2.30696384e+02,   8.99782715e+01,   5.69123783e+00,
+         1.17647744e+00,  -2.07740677e+02,  -2.41841282e+01,
+         4.25739655e+01,  -9.37318624e+01,   1.72008850e+01,
+        -2.80389453e+02,   9.14720377e+01,   5.18860721e+01,
+        -4.87724443e+01,   2.26358726e+02,   2.30815790e+02,
+        -1.92748243e+02,  -3.24853663e+02,   1.03974893e+02,
+        -7.16120982e+01,  -1.06190438e+02,   2.11444045e+02,
+        -2.27803029e+02,   4.20199403e+01,   9.35578766e+01,
+        -1.11882900e+02,  -1.50827959e+02,   4.19007436e+01,
+         1.90592203e+02,  -1.63985432e+01,   5.20498067e+01,
+         1.96963106e+02,   1.88440079e+02,   7.90867364e+01,
+        -7.39600841e+01,  -1.17238542e+02,   1.41356782e+02,
+        -2.41008057e+01,  -1.05238740e+02,  -6.61572926e+01,
+         1.72434250e+02,  -9.14510226e+01,  -4.16072973e+01,
+        -4.44489957e+02,   1.90678765e+01,   1.27180992e+02,
+        -5.26878212e+01,   3.91773886e+01,   6.02542602e+01,
+         2.34561239e+01,  -4.04196936e+01,   1.02429745e+01,
+        -2.46773603e+01,  -1.87907633e+02,  -2.54295823e+01,
+         4.76093101e+01,   2.98704200e+02,  -1.07331123e+02,
+        -3.61485224e+02,  -1.52453963e+02,  -2.29848710e+02,
+        -9.30496563e+01,  -1.40858781e+02,  -1.98255106e+02,
+        -1.25904629e+02,   1.72309817e+02,   1.65996013e+02,
+        -2.00286148e+02,   1.26224062e+02,  -1.37538647e+02,
+         8.17242666e+00,  -2.23541378e+01,  -3.21355761e+01,
+         1.25500707e+02,   2.16018603e+02,   7.05526495e+01,
+        -1.06813084e+02,   1.35097922e+02,  -1.85532190e+02,
+         2.49602046e+02,   1.73687888e+02,  -3.65283403e+01,
+        -8.53747144e+01,   1.93182044e+02,  -2.44108530e+02,
+         1.94232218e+02,   3.07046296e+01,  -9.37826181e+01,
+        -7.49050569e+01,   1.10714670e+00,  -4.12982898e+01,
+        -3.02465462e+01,  -9.44851991e+00,   3.95796893e+01,
+        -7.28767076e-02,   1.13699561e+02,  -3.36088340e+01,
+        -5.62347663e+01,   3.63569741e+01,   2.53750882e+01,
+         5.61767726e+01,   2.04211029e+02,  -1.30799586e+02,
+        -2.17144921e+02,  -1.60692263e+02,   1.27450022e+02,
+         2.85855601e+01,   2.28876848e+02,   1.03863668e+02,
+        -5.56329200e+01,  -2.45062470e+02,  -2.52979591e+02,
+        -3.94097301e+02,   2.28436396e+02,   1.00899266e+02,
+        -2.39823786e+02,  -6.22923319e+01,   1.22014334e+02,
+        -5.93686734e+01,  -1.67356184e+02,  -1.63907488e+01,
+         1.03554243e+02,  -1.51474832e+02,  -4.44618576e+01,
+        -1.27373617e+02,  -6.11885586e+01,  -2.32541810e+02,
+        -1.09169677e+02,  -2.74269097e+01,   3.55095664e+01,
+         8.26959476e+01,  -4.87722471e+01,  -1.46128404e+01,
+         5.62754539e+01,   1.33032883e+01,   9.25589466e+01,
+        -1.96784306e+02,   2.31603862e+02,   1.45892524e+02,
+         1.97598889e+02
+    ]),
+    'coefs': array([
+         0.        ,  83.29782411,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,  74.5220872 ,
+         0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,  40.94948176,   0.        ,
+        49.84184508,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,  27.46569319,   0.        ,   0.
+    ])
+}
+
+# Generate test case which for testing multivariate feature selection.
+def generate_multivariate_test_case():
+    pass
+
+# Test case generated by calling generateMultivariateTestCase() offline.
+RANDOM_MULTIVARIATE_TEST_CASE = {
+
+}
diff --git a/medinfo/ml/test/PredictorAnalyzerTestData.py b/medinfo/ml/test/PredictorAnalyzerTestData.py
new file mode 100644
index 00000000..ce933ddc
--- /dev/null
+++ b/medinfo/ml/test/PredictorAnalyzerTestData.py
@@ -0,0 +1,24 @@
+#!/usr/bin/python
+"""
+Test input and output for predictor analysis.
+"""
+
+from numpy import array
+import pandas as pd
+
+MANUAL_PREDICTION_TEST_CASE = {
+    'X': pd.DataFrame({
+        'x1': [0.05, -1.67, 0.36, 1.97, -2.35, -1.51, 0.61, -0.92, -0.73, -0.25],
+        'x2': [0.01, 0.17, 1.54, -0.09, 0.68, 0.39, 0.38, -2.77, -0.84, 1.31]
+        }),
+    'y_true': pd.DataFrame({'true':[1, 2, 3, 5, 8, 13, 21, 34, 55, 89]}),
+    'y_predicted': pd.DataFrame({'predictions':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}),
+    'accuracy': 0.3,
+    'ci_lower_bound': 0.1,
+    'ci_upper_bound': 0.6,
+    'report' : pd.DataFrame({
+        'model': ['ListPredictor([ 1  2  3  4  5  6  7  8  9 10])'],
+        'test_size': [10],
+        'accuracy': [0.3]
+    })
+}
diff --git a/medinfo/ml/test/RegressorAnalyzerTestData.py b/medinfo/ml/test/RegressorAnalyzerTestData.py
new file mode 100644
index 00000000..3f942bb4
--- /dev/null
+++ b/medinfo/ml/test/RegressorAnalyzerTestData.py
@@ -0,0 +1,42 @@
+#!/usr/bin/python
+"""
+Test input and output for regressor analysis.
+"""
+
+import pandas as pd
+from numpy import array
+
+# x, y, coef = sklearn.datasets.make_regression(n_samples=10, n_features=2)
+RANDOM_REGRESSION_TEST_CASE = {
+    'X': pd.DataFrame({
+        'x1': [0.0506969, -1.67110936, 0.36348042, 1.9794256, -2.3583237,
+            -1.51876327, 0.61071492, -0.92222904, -0.7306277, -0.25001944],
+        'x2': [0.01719597, 0.1764346, 1.54603964, -0.09592507, 0.68128352,
+            0.39048658, 0.3859871, -2.77311639, -0.8455577, 1.3107445]
+    }),
+    'y_true': pd.DataFrame({
+        'true': [   4.72776063, -141.67945344,   61.00264029,  169.97305673,
+           -191.70661982, -124.37875255,   60.35967648, -132.87637078,
+            -79.52298865,    3.2722214 ]
+    }),
+    'y_predicted': pd.DataFrame({
+        'predicted': [4.686657, -140.363148, 60.634069, 168.408025,
+            -189.871451, -123.194396, 59.855238, -132.000909, -78.899579, 3.402474]
+    }),
+    'coef': [86.79316541, 19.05192056],
+    'coef_predicted': [86, 19],
+    'accuracy': 0,
+    'r2': 0.9999114854982919,
+    'median_absolute_error': 0.74943603999999908,
+    'mean_absolute_error': 0.8444099799999979,
+    'explained_variance': 0.99992171249797723,
+    'report': pd.DataFrame({
+        'model': ['LinearPredictor([86, 19])'],
+        'test_size': [10],
+        'accuracy': [0],
+        'explained_variance': [0.99992171249797723],
+        'mean_absolute_error': [0.8444099799999979],
+        'median_absolute_error': [0.74943603999999908],
+        'r2': [0.9999114854982919],
+    })
+}
diff --git a/medinfo/ml/test/SupervisedLearnerTestData.py b/medinfo/ml/test/SupervisedLearnerTestData.py
new file mode 100644
index 00000000..96bc0db9
--- /dev/null
+++ b/medinfo/ml/test/SupervisedLearnerTestData.py
@@ -0,0 +1,197 @@
+
+
+import numpy as np
+import pandas as pd
+
+processed_matrix_columns = ['all_components_normal', 'LABA1C.pre.1460d', 'A1C.-14_0.min', 'A1C.-14_0.mean', 'A1C.-14_0.first', 'A1C.-14_0.proximate', 'AdmitDxDate.preTimeDays', 'AdmitDxDate.pre', 'order_time.hour', 'order_time.hour.cos', 'Birth.preTimeDays', 'Team.Medicine.preTimeDays', 'Comorbidity.Diabetes.preTimeDays', 'Comorbidity.Diabetes.pre', 'Comorbidity.Diabetes.pre.1460d', 'BP_High_Systolic.-3_0.max', 'BP_High_Systolic.-3_0.median', 'BP_High_Systolic.-3_0.mean',
+       'BP_High_Systolic.-3_0.first', 'BP_High_Systolic.-3_0.last', 'BP_High_Systolic.-3_0.proximate', 'BP_Low_Diastolic.-3_0.min', 'BP_Low_Diastolic.-3_0.mean', 'BP_Low_Diastolic.-3_0.std', 'BP_Low_Diastolic.-3_0.firstTimeDays', 'BP_Low_Diastolic.-3_0.proximateTimeDays', 'Pulse.-3_0.min', 'Pulse.-3_0.first', 'Pulse.-3_0.firstTimeDays', 'Pulse.-3_0.proximateTimeDays', 'Resp.-3_0.firstTimeDays', 'Resp.-3_0.proximateTimeDays', 'Temp.-3_0.mean', 'Temp.-3_0.slope',
+       'Temp.-3_0.firstTimeDays', 'Temp.-3_0.proximateTimeDays', 'HCT.-14_0.min', 'HCT.-14_0.max', 'HCT.-14_0.mean', 'HCT.-14_0.first', 'HCT.-14_0.proximate', 'PLT.-14_0.mean', 'PLT.-14_0.first', 'K.-14_0.min', 'LABA1C.pre', 'pat_id']
+
+processed_matrix_array = \
+np.array([
+       [1, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.049305555555600004, 5.0, 0.0, 1.0, -20066.034722200002, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 151.0, 130.0, 129.09090909100001, 127.0, 113.0, 113.0, 55.0, 65.9090909091, 12.3100442762, -0.16527777777799998, -0.0138888888889, 93.0, 113.0, -0.16527777777799998, -0.0027777777777800005, -0.16527777777799998, -0.0027777777777800005, 99.8666666667, 25.8461538462, -0.16527777777799998, -0.0027777777777800005, 18.7, 18.7, 18.7, 18.7, 18.7, 382.0, 382.0, 3.0, 0, 0.0],
+       [1, '2.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.225, 45.0, 3.0, 0.707106781187, -20556.1569444, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 155.0, 132.0, 132.1, 143.0, 148.0, 148.0, 67.0, 75.0, 6.8847657912199995, -0.305555555556, -0.115277777778, 61.0, 115.0, -0.305555555556, -0.115277777778, -0.305555555556, -0.17777777777800002, 98.4, 3.1304347826099996, -0.305555555556, -0.17777777777800002, 32.5, 32.5, 32.5, 32.5, 32.5, 473.0, 473.0, 3.3, 2, 0.0],
+       [1, '3.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0972222222222, 55.0, 10.0, -0.866025403784, -20581.4354167, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 162.0, 119.5, 120.916666667, 141.0, 96.0, 96.0, 46.0, 67.75, 10.6036550302, -0.709027777778, -0.05, 48.0, 106.0, -0.709027777778, -0.05, -0.709027777778, -0.11875, 98.1, -0.33882352941200006, -0.709027777778, -0.11875, 26.7, 30.6, 28.65, 30.6, 26.7, 272.0, 286.0, 3.5, 3, 0.0],
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+       [0, '3.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.281944444444, 100.0, 1.0, 0.965925826289, -19657.051388900003, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 136.0, 126.0, 121.0, 136.0, 101.0, 101.0, 57.0, 67.3333333333, 10.0774776386, -0.647916666667, -0.009722222222219998, 65.0, 85.0, -0.647916666667, -0.009722222222219998, -0.647916666667, -0.009722222222219998, 97.3, 0.0, -0.647916666667, -0.015972222222200003, 32.5302480439, 36.421891126999995, 34.3257346808, 35.4298984518, 33.7809056101, 216.35223815700002, 221.79821397900002, 3.86651170513, 3, 90.0],
+       [0, '1.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.08263888888889999, 19.0, 21.0, 0.707106781187, -32895.9027778, -0.111111111111, -489.2572686000001, 0.0, 0.0, 199.0, 176.0, 171.8, 199.0, 176.0, 176.0, 62.0, 71.2, 8.32826512546, -0.252083333333, -0.01875, 77.0, 77.0, -0.252083333333, -0.01875, -0.252083333333, -0.01875, 98.15, -0.428571428571, -0.252083333333, -0.01875, 42.2, 42.2, 42.2, 42.2, 42.2, 199.0, 199.0, 4.1, 1, 91.0],
+       [0, '1.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.08263888888889999, 19.0, 21.0, 0.707106781187, -32895.9027778, -0.111111111111, -489.2572686000001, 0.0, 0.0, 199.0, 176.0, 171.8, 199.0, 176.0, 176.0, 62.0, 71.2, 8.32826512546, -0.252083333333, -0.01875, 77.0, 77.0, -0.252083333333, -0.01875, -0.252083333333, -0.01875, 98.15, -0.428571428571, -0.252083333333, -0.01875, 42.2, 42.2, 42.2, 42.2, 42.2, 199.0, 199.0, 4.1, 1, 91.0],
+       [0, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.23958333333299997, 3.0, 1.0, 0.965925826289, -18262.063888900004, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 175.0, 161.0, 160.4, 162.0, 169.0, 169.0, 71.0, 82.9, 7.867019766089999, -0.38611111111100005, -0.009722222222219998, 108.0, 127.0, -0.38611111111100005, -0.009722222222219998, -0.38611111111100005, -0.009722222222219998, 97.9, -0.531365313653, -0.38611111111100005, -0.009722222222219998, 37.7, 37.7, 37.7, 37.7, 37.7, 823.0, 823.0, 3.9, 0, 92.0],
+       [0, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0375, 3.0, 1.0, 0.965925826289, -18503.0666667, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 151.0, 133.0, 133.0, 151.0, 115.0, 115.0, 61.0, 70.0, 9.0, -0.18333333333299998, -0.0138888888889, 77.0, 97.0, -0.18333333333299998, -0.0138888888889, -0.18333333333299998, -0.0138888888889, 98.6, -2.3606557377, -0.18333333333299998, -0.0138888888889, 34.9, 34.9, 34.9, 34.9, 34.9, 293.0, 293.0, 4.1, 0, 93.0],
+       [1, '1.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.196527777778, 2.0, 13.0, -0.965925826289, -32206.5631944, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 188.0, 175.0, 172.5, 185.0, 173.0, 173.0, 71.0, 81.6666666667, 8.919392106839998, -0.206944444444, -0.021527777777800003, 87.0, 88.0, -0.206944444444, -0.021527777777800003, -0.206944444444, -0.021527777777800003, 98.0, -1.07865168539, -0.206944444444, -0.021527777777800003, 34.1, 34.1, 34.1, 34.1, 34.1, 225.0, 225.0, 3.8, 1, 94.0],
+       [1, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0013888888888900002, 1.0, 8.0, -0.5, -32206.35625, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 147.470448179, 129.65238095200002, 129.881460571, 133.793837535, 128.331372549, 128.331372549, 59.3449482228, 70.8455415457, 7.45608031038, -0.917906342968, -0.10880043938100001, 73.6849486254, 88.1428769786, -0.9500409431669999, -0.052685963004099994, -0.949156726693, -0.0585617673943, 98.2371669567, 0.309886871451, -0.946722939154, -0.0887839037547, 32.5302480439, 36.421891126999995, 34.3257346808, 35.4298984518, 33.7809056101, 216.35223815700002, 221.79821397900002, 3.86651170513, 0, 94.0],
+       [1, '1.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0923611111111, 4.0, 22.0, 0.866025403784, -14143.9173611, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 149.0, 143.0, 141.142857143, 145.0, 125.0, 125.0, 85.0, 96.7142857143, 7.323431781699999, -0.445833333333, -0.0423611111111, 75.0, 91.0, -0.445833333333, -0.0423611111111, -0.445833333333, -0.0423611111111, 98.03333333329999, -0.991394148021, -0.445833333333, -0.0423611111111, 47.7, 47.7, 47.7, 47.7, 47.7, 127.0, 127.0, 3.9, 1, 95.0],
+       [1, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0194444444444, 14.0, 15.0, -0.707106781187, -15601.6597222, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 142.0, 126.0, 129.0, 139.0, 122.0, 122.0, 61.0, 72.1818181818, 10.151741291, -1.0666666666700002, -0.0034722222222199994, 56.0, 62.0, -1.0666666666700002, -0.0034722222222199994, -1.0666666666700002, -0.0034722222222199994, 98.0875, 0.282168517309, -1.0666666666700002, -0.0034722222222199994, 46.3, 46.3, 46.3, 46.3, 46.3, 341.0, 341.0, 3.8, 0, 96.0],
+       [1, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.04791666666669999, 45.0, 15.0, -0.707106781187, -33068.6347222, -135.30109110200002, -3.6347222222199993, 3.0, 3.0, 181.0, 148.0, 142.0625, 152.0, 181.0, 181.0, 58.0, 81.34375, 15.2061035751, -2.0513888888900005, -0.00625, 59.0, 60.0, -2.96805555556, -0.00625, -2.96805555556, -0.00625, 99.6113636364, -0.34782608695699996, -2.96805555556, -0.09305555555560001, 35.1, 42.0, 39.08, 42.0, 38.1, 278.0, 239.0, 3.7, 0, 97.0],
+       [0, '0.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0006944444444439998, 1.0, 16.0, -0.5, -23392.6881944, -135.30109110200002, -489.2572686000001, 0.0, 0.0, 147.470448179, 129.65238095200002, 129.881460571, 133.793837535, 128.331372549, 128.331372549, 59.3449482228, 70.8455415457, 7.45608031038, -0.917906342968, -0.10880043938100001, 73.6849486254, 88.1428769786, -0.9500409431669999, -0.052685963004099994, -0.949156726693, -0.0585617673943, 98.2371669567, 0.309886871451, -0.946722939154, -0.0887839037547, 32.5302480439, 36.421891126999995, 34.3257346808, 35.4298984518, 33.7809056101, 216.35223815700002, 221.79821397900002, 3.86651170513, 0, 98.0],
+       [0, '1.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.18541666666700002, 10.0, 6.0, 6.12323399574e-17, -23705.2763889, -135.30109110200002, -312.27638888900003, 3.0, 3.0, 114.0, 109.0, 109.0, 104.0, 114.0, 114.0, 59.0, 60.5, 1.5, -0.253472222222, -0.0854166666667, 60.0, 60.0, -0.253472222222, -0.0854166666667, -0.253472222222, -0.0854166666667, 98.4, 1.19008264463, -0.253472222222, -0.0854166666667, 29.0, 29.0, 29.0, 29.0, 29.0, 131.0, 131.0, 3.7, 1, 98.0],
+       [0, '3.0', 6.5, 6.5, 6.5, 6.5, -0.610416666667, 24.0, 22.0, 0.866025403784, -23711.9166667, -135.30109110200002, -318.916666667, 3.0, 3.0, 144.0, 114.0, 114.95, 110.0, 120.0, 120.0, 47.0, 63.025, 5.77272682534, -2.91944444444, -0.1, 60.0, 60.0, -2.91944444444, -0.1, -2.91944444444, -0.1, 97.6857142857, 0.141871921182, -2.91944444444, -0.1, 29.0, 31.4, 30.3571428571, 29.0, 30.7, 131.0, 131.0, 3.4, 3, 98.0],
+       [0, '5.0', 6.4, 6.4, 6.4, 6.4, -0.111111111111, 37.0, 22.0, 0.866025403784, -23722.9166667, -135.30109110200002, -329.916666667, 3.0, 3.0, 144.0, 123.5, 122.615384615, 108.0, 126.0, 126.0, 49.0, 65.3461538462, 9.82125758708, -2.96041666667, -0.0819444444444, 59.0, 62.0, -2.96041666667, -0.0819444444444, -2.95625, -0.0798611111111, 99.080952381, 0.210783117834, -2.95625, -0.10972222222200001, 23.6, 32.0, 28.8, 29.8, 24.5, 125.6875, 121.0, 3.4, 5, 98.0],
+       [1, '5.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.00625, 68.0, 21.0, 0.707106781187, -23760.8902778, -135.30109110200002, -367.89027777800004, 3.0, 3.0, 139.0, 139.0, 139.0, 139.0, 139.0, 139.0, 79.0, 79.0, 0.0, -0.0819444444444, -0.0819444444444, 86.0, 96.0, -0.0819444444444, -0.0013888888888900002, -0.0819444444444, -0.0013888888888900002, 100.85, 45.931034482799994, -0.0819444444444, -0.0013888888888900002, 38.0, 38.0, 38.0, 38.0, 38.0, 132.0, 132.0, 4.5, 5, 98.0],
+       [1, '6.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -11.3722222222, 84.0, 16.0, -0.5, -23782.6805556, -135.30109110200002, -389.680555556, 3.0, 3.0, 147.470448179, 129.65238095200002, 129.881460571, 133.793837535, 128.331372549, 128.331372549, 59.3449482228, 70.8455415457, 7.45608031038, -0.917906342968, -0.10880043938100001, 75.0, 75.0, -0.892361111111, -0.00416666666667, -0.847222222222, -0.00416666666667, 96.25, -2.9654036243799995, -0.847222222222, -0.00416666666667, 22.0, 33.0, 26.9545454545, 29.5, 26.0, 148.25, 150.0, 4.0, 6, 98.0],
+       [0, '7.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -41.9458333333, 111.0, 17.0, -0.25881904510299997, -23835.736805599998, -135.30109110200002, -442.736805556, 3.0, 3.0, 147.470448179, 129.65238095200002, 129.881460571, 133.793837535, 128.331372549, 128.331372549, 59.3449482228, 70.8455415457, 7.45608031038, -0.917906342968, -0.10880043938100001, 73.6849486254, 88.1428769786, -0.9500409431669999, -0.052685963004099994, -0.949156726693, -0.0585617673943, 98.2371669567, 0.309886871451, -0.946722939154, -0.0887839037547, 32.5302480439, 36.421891126999995, 34.3257346808, 35.4298984518, 33.7809056101, 216.35223815700002, 221.79821397900002, 3.86651170513, 7, 98.0],
+       [1, '1.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0354166666667, 4.0, 20.0, 0.5, -16302.8368056, -135.30109110200002, -183.83680555599997, 3.0, 3.0, 152.0, 136.5, 137.0, 143.0, 122.0, 122.0, 73.0, 83.1666666667, 7.243771270700001, -0.313888888889, -0.015972222222200003, 99.0, 133.0, -0.321527777778, -0.015972222222200003, -0.321527777778, -0.015972222222200003, 98.0, -1.63636363636, -0.321527777778, -0.015972222222200003, 51.0, 51.0, 51.0, 51.0, 51.0, 218.0, 218.0, 3.0, 1, 99.0],
+       [1, '2.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -4.31875, 41.0, 20.0, 0.5, -16319.8472222, -135.30109110200002, -200.847222222, 3.0, 3.0, 124.0, 124.0, 124.0, 124.0, 124.0, 124.0, 80.0, 80.0, 0.0, -0.0340277777778, -0.0340277777778, 124.0, 124.0, -0.0340277777778, -0.0340277777778, -0.0340277777778, -0.0340277777778, 98.4, 0.0, -0.0340277777778, -0.0340277777778, 37.5, 43.5, 40.59, 41.6, 42.9, 209.3, 192.0, 3.0, 2, 99.0],
+       [1, '3.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.125, 97.0, 4.0, 0.5, -16364.1972222, -135.30109110200002, -245.197222222, 3.0, 3.0, 151.0, 135.0, 134.272727273, 127.0, 137.0, 137.0, 62.0, 75.0909090909, 8.877494993260001, -0.175, -0.0659722222222, 0.0, 131.0, -0.175, -0.0166666666667, -0.175, -0.0166666666667, 98.48, -4.84615384615, -0.175, -0.030555555555599998, 35.3, 40.6, 37.95, 40.6, 35.3, 179.0, 199.0, 3.3, 3, 99.0],
+       [1, '6.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.38125, 145.0, 3.0, 0.707106781187, -16406.1659722, -135.30109110200002, -287.165972222, 3.0, 3.0, 107.0, 107.0, 107.0, 107.0, 107.0, 107.0, 68.0, 68.0, 0.0, -0.45763888888899995, -0.45763888888899995, 90.0, 111.0, -0.45763888888899995, -0.014583333333299997, -0.45763888888899995, -0.0013888888888900002, 97.8666666667, 0.0, -0.45763888888899995, -0.165972222222, 26.8, 27.4, 27.1, 27.4, 26.8, 246.5, 250.0, 3.2, 6, 99.0],
+       [1, '5.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0173611111111, 144.0, 17.0, -0.25881904510299997, -16405.7409722, -135.30109110200002, -286.740972222, 3.0, 3.0, 107.0, 107.0, 107.0, 107.0, 107.0, 107.0, 68.0, 68.0, 0.0, -0.0326388888889, -0.0326388888889, 104.0, 111.0, -0.0326388888889, -0.0013888888888900002, -0.0326388888889, -0.0013888888888900002, 98.2, 0.0, -0.0326388888889, -0.0326388888889, 32.5302480439, 36.421891126999995, 34.3257346808, 35.4298984518, 33.7809056101, 216.35223815700002, 221.79821397900002, 3.86651170513, 5, 99.0],
+       [0, '7.0', 6.99886480908, 7.025149474989999, 7.036635706910001, 7.01300309598, -0.0347222222222, 153.0, 8.0, -0.5, -16430.3430556, -135.30109110200002, -311.34305555599997, 3.0, 3.0, 99.0, 90.0, 91.1111111111, 99.0, 90.0, 90.0, 59.0, 63.111111111099994, 3.4462360931699996, -0.321527777778, -0.009722222222219998, 87.0, 107.0, -0.321527777778, -0.009722222222219998, -0.321527777778, -0.009722222222219998, 98.7875, 4.169265033409999, -0.321527777778, -0.009722222222219998, 20.3, 22.9, 21.6, 20.3, 22.9, 142.0, 142.0, 2.4, 7, 99.0]], dtype=object)
+
+processed_matrix = pd.DataFrame(processed_matrix_array, columns=processed_matrix_columns)
\ No newline at end of file
diff --git a/medinfo/ml/test/SupervisedLearningTestData.py b/medinfo/ml/test/SupervisedLearningTestData.py
new file mode 100644
index 00000000..e32a6ad8
--- /dev/null
+++ b/medinfo/ml/test/SupervisedLearningTestData.py
@@ -0,0 +1,1402 @@
+#!/usr/bin/python
+"""
+Test input and output for supervised learning.
+"""
+
+from numpy import array
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from sklearn.metrics import roc_auc_score, make_scorer, f1_score
+from sklearn.model_selection import StratifiedKFold
+
+# x, y, coef = sklearn.datasets.make_regression(n_features=100, n_informative=3)
+# 100 samples with 1 target.
+RANDOM_CLASSIFICATION_TEST_CASE = {
+    "X": array([[ -3.15536206e-01,   2.26608213e+00,   1.43724033e+00,
+           6.89287035e-01,   7.76487547e-01,   3.26032131e-01,
+          -8.67078218e-02,   2.02658801e+00,   7.73010674e-01,
+          -2.81468540e+00],
+        [ -2.10679550e+00,  -8.63389174e-01,   1.43388359e+00,
+           5.55685339e-01,  -2.65939345e-02,   1.52839912e-01,
+          -2.17945070e+00,   7.37354286e-01,  -1.42955180e+00,
+          -7.55660653e-01],
+        [  2.63961996e-01,   1.98625817e+00,   1.59449690e+00,
+           1.23740206e+00,   3.40541722e-01,  -3.67499336e-01,
+           1.08823369e+00,  -1.58597788e+00,  -1.39993981e+00,
+           6.43602183e-01],
+        [ -8.76971498e-01,  -9.24173914e-01,  -1.36114228e+00,
+          -2.00940701e+00,   8.62745401e-01,   2.01057572e-01,
+          -1.83296838e-02,   1.79876467e+00,  -3.01182828e+00,
+           1.45994519e+00],
+        [  1.48800827e+00,  -2.81998456e-01,  -1.02172937e+00,
+          -9.80092011e-01,  -1.48022317e-01,  -1.08983300e+00,
+          -2.52658928e+00,   7.89119963e-01,  -3.07240435e-01,
+           3.96762206e-01],
+        [  8.79777777e-01,  -2.10342802e+00,  -2.69877388e-01,
+          -6.19705037e-01,   1.15421189e-01,   2.80078625e-01,
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+                            4: {
+                                'right_child': 6,
+                                'class_weights': [1.0416666666666667, 38.461538461538446],
+                                'prediction': 1,
+                                'feature': 'x6',
+                                'threshold': 1.3371305465698242,
+                                'left_child': 5
+                            },
+                            5: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 38.461538461538446]
+                            },
+                            6: {
+                                'prediction': 0,
+                                'class_weights': [1.0416666666666667, 0.0]
+                            }
+                        },
+                        'decision_features': ['x8 (0.834)', 'x10 (0.050)', 'x6 (0.017)'],
+                        'num_nodes': 7
+                    },
+                    {
+                        'depth': 2,
+                        'nodes': {
+                            0: {
+                                'right_child': 4,
+                                'class_weights': [39.583333333333336, 35.57692307692306],
+                                'prediction': 0,
+                                'feature': 'x8',
+                                'threshold': 0.3896064758300781,
+                                'left_child': 1
+                            },
+                            1: {
+                                'right_child': 3,
+                                'class_weights': [37.5, 3.8461538461538463],
+                                'prediction': 0,
+                                'feature': 'x9',
+                                'threshold': -1.9594122171401978,
+                                'left_child': 2
+                            },
+                            2: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 2.8846153846153846]
+                            },
+                            3: {
+                                'prediction': 0,
+                                'class_weights': [37.49999999999999, 0.9615384615384616]
+                            },
+                            4: {
+                                'right_child': 6,
+                                'class_weights': [2.0833333333333335, 31.730769230769216],
+                                'prediction': 1,
+                                'feature': 'x6',
+                                'threshold': 1.3371305465698242,
+                                'left_child': 5
+                            },
+                            5: {
+                                'prediction': 1,
+                                'class_weights': [1.0416666666666667, 30.76923076923076]
+                            },
+                            6: {
+                                'prediction': 0,
+                                'class_weights': [1.0416666666666667, 0.9615384615384616]
+                            }
+                        },
+                        'decision_features': ['x8 (0.834)', 'x9 (0.061)', 'x6 (0.017)'],
+                        'num_nodes': 7
+                    },
+                    {
+                        'depth': 2,
+                        'nodes': {
+                            0: {
+                                'right_child': 4,
+                                'class_weights': [40.625, 34.6153846153846],
+                                'prediction': 0,
+                                'feature': 'x8',
+                                'threshold': 0.38760679960250854,
+                                'left_child': 1
+                            },
+                            1: {
+                                'right_child': 3,
+                                'class_weights': [39.58333333333333, 2.8846153846153846],
+                                'prediction': 0,
+                                'feature': 'x10',
+                                'threshold': 2.7094616889953613,
+                                'left_child': 2
+                            },
+                            2: {
+                                'prediction': 0,
+                                'class_weights': [39.58333333333333, 0.0]
+                            },
+                            3: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 2.8846153846153846]
+                            },
+                            4: {
+                                'right_child': 6,
+                                'class_weights': [1.0416666666666667, 31.730769230769226],
+                                'prediction': 1,
+                                'feature': 'x8',
+                                'threshold': 1.4883334636688232,
+                                'left_child': 5
+                            },
+                            5: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 27.88461538461538]
+                            },
+                            6: {
+                                'prediction': 1,
+                                'class_weights': [1.0416666666666667, 3.8461538461538463]
+                            }
+                        },
+                        'decision_features': ['x8 (0.834)', 'x10 (0.050)'],
+                        'num_nodes': 7
+                    },
+                    {
+                        'depth': 2,
+                        'nodes': {
+                            0: {
+                                'right_child': 4,
+                                'class_weights': [29.16666666666667, 45.19230769230767],
+                                'prediction': 1,
+                                'feature': 'x8',
+                                'threshold': 0.3896064758300781,
+                                'left_child': 1
+                            },
+                            1: {
+                                'right_child': 3,
+                                'class_weights': [29.16666666666667, 4.8076923076923075],
+                                'prediction': 0,
+                                'feature': 'x7',
+                                'threshold': -1.721463680267334,
+                                'left_child': 2
+                            },
+                            2: {
+                                'prediction': 1,
+                                'class_weights': [1.0416666666666667, 4.8076923076923075]
+                            },
+                            3: {
+                                'prediction': 0,
+                                'class_weights': [28.125, 0.0]
+                            },
+                            4: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 40.38461538461538]
+                            }
+                        },
+                        'decision_features': ['x8 (0.834)', 'x7 (0.039)'],
+                        'num_nodes': 5
+                    },
+                    {
+                        'depth': 2,
+                        'nodes': {
+                            0: {
+                                'right_child': 4,
+                                'class_weights': [29.166666666666668, 45.19230769230767],
+                                'prediction': 1,
+                                'feature': 'x8',
+                                'threshold': 0.3896064758300781,
+                                'left_child': 1
+                            },
+                            1: {
+                                'right_child': 3,
+                                'class_weights': [29.166666666666675, 2.8846153846153846],
+                                'prediction': 0,
+                                'feature': 'x9',
+                                'threshold': -1.940993070602417,
+                                'left_child': 2
+                            },
+                            2: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 2.8846153846153846]
+                            },
+                            3: {
+                                'prediction': 0,
+                                'class_weights': [29.166666666666668, 0.0]
+                            },
+                            4: {
+                                'prediction': 1,
+                                'class_weights': [0.0, 42.307692307692285]
+                            }
+                        },
+                        'decision_features': ['x8 (0.834)', 'x9 (0.061)'],
+                        'num_nodes': 5
+                }
+            ],
+            'n_estimators': 5,
+            'decision_features': ['x8 (0.834)', 'x9 (0.061)', 'x10 (0.050)', 'x7 (0.039)', 'x6 (0.017)']
+        },
+        SupervisedClassifier.ADABOOST: {
+            'n_estimators': 10,
+            'base_estimator': 'DecisionTreeClassifier',
+            'decision_features': ['x8 (0.736)', 'x9 (0.217)', 'x6 (0.024)', 'x7 (0.023)']
+        },
+        SupervisedClassifier.GAUSSIAN_NAIVE_BAYES: {
+            'priors': [0.0001, 0.99990000000000001],
+            'sigmas': [
+                1.3508174731567251, 1.0180003112550733, 2.4010485831365402,
+                1.5179292168234768, 1.1050601041485268, 0.75011556097429244,
+                1.4078512321563499, 0.21006687352452788, 2.0469524944322841,
+                3.6105774746906185
+            ],
+            'thetas': [
+                -0.17244277961999996, 0.046463400566666667, 0.016341826402564087,
+                -0.4376480584923077, 0.027199674446153826, -0.037190332502564115,
+                -0.027825385889743561, 0.79848281141282063, -1.2753595800769231,
+                0.15922675605128209
+            ]
+        },
+        'bifurcated': {
+            'bifurcation_strategy': '<=',
+            'bifurcation_value': 0.5,
+            'bifurcator': 'x3',
+            'model_false': 'L1_REGRESS_AND_ROUND()',
+            'model_true': 'L1_REGRESS_AND_ROUND(1.0*x8)'
+        }
+    },
+    'description': {
+        SupervisedClassifier.LOGISTIC_REGRESSION: 'L1_LOGISTIC_REGRESSION(2.9574398490939546*x8)',
+        SupervisedClassifier.REGRESS_AND_ROUND: 'L1_REGRESS_AND_ROUND(1.0*x8)',
+        SupervisedClassifier.DECISION_TREE: 'DECISION_TREE((x8<=0.3896064758300781), (x9<=-1.940993070602417), (x7<=-1.721463680267334), (x6<=1.4701499938964844), (x8<=1.5210013389587402), (x9<=-2.1801743507385254))',
+        # TODO seems like a precision issue - below is the actual data vs expected above:
+        # SupervisedClassifier.DECISION_TREE: 'DECISION_TREE((x8<=0.38960647583), (x9<=-1.9409930706), (x7<=-1.72146368027), (x6<=1.4701499939), (x8<=1.52100133896), (x9<=-2.18017435074))',
+        SupervisedClassifier.RANDOM_FOREST: 'RANDOM_FOREST(n_estimators=5, features=[x8 (0.834), x9 (0.061), x10 (0.050), x7 (0.039), x6 (0.017)])',
+        SupervisedClassifier.ADABOOST: 'ADABOOST(base_estimator=DecisionTreeClassifier, n_estimators=10, features=[x8 (0.736), x9 (0.217), x6 (0.024), x7 (0.023)])',
+        SupervisedClassifier.GAUSSIAN_NAIVE_BAYES: 'GAUSSIAN_NAIVE_BAYES(priors=[0.0001, 0.9999])',
+        'bifurcated': 'BIFURCATED_REGRESS_AND_ROUND(x3 <= 0.5, true=L1_REGRESS_AND_ROUND(1.0*x8), false=L1_REGRESS_AND_ROUND())'
+    },
+    'str': {
+        SupervisedClassifier.LOGISTIC_REGRESSION: "SupervisedClassifier([0, 1], algorithm='l1-logistic-regression-cross-validation', random_state=123456789)",
+        SupervisedClassifier.REGRESS_AND_ROUND: "SupervisedClassifier([0, 1], algorithm='regress-and-round', random_state=123456789)",
+        SupervisedClassifier.DECISION_TREE: "SupervisedClassifier([0, 1], algorithm='decision-tree', random_state=123456789)",
+        SupervisedClassifier.RANDOM_FOREST: "SupervisedClassifier([0, 1], algorithm='random-forest', random_state=123456789)",
+        SupervisedClassifier.ADABOOST: "SupervisedClassifier([0, 1], algorithm='adaboost', random_state=123456789)",
+        SupervisedClassifier.GAUSSIAN_NAIVE_BAYES: "SupervisedClassifier([0, 1], algorithm='gaussian-naive-bayes', random_state=123456789)",
+        'bifurcated': "BifurcatedSupervisedClassifier([0, 1], hyperparams={'algorithm': regress-and-round, 'bifurcator': x3, 'bifurcation_strategy': <=, 'bifurcation_threshold': 0.5, 'random_state': 123456789})"
+    }
+}
+
+# x, y, coef = sklearn.datasets.make_regression(n_features=10, n_informative=3)
+# 100 samples with 1 target.
+RANDOM_REGRESSION_TEST_CASE = {
+    "X": array([[ -9.00722823e-01,   1.59902426e-01,  -1.36661333e+00,
+          1.23730146e-01,   2.35311854e-01,  -4.49876942e-01,
+         -1.54494068e+00,  -6.68283577e-01,  -1.31266572e+00,
+          7.57314493e-01],
+       [ -3.30939885e-01,   1.20199335e+00,  -1.30360139e+00,
+         -1.27621425e-01,   1.06418210e+00,   6.70859641e-01,
+         -9.35379636e-01,   4.00044085e-01,  -3.86487504e-01,
+         -1.39345034e+00],
+       [  3.29812807e-02,   1.49611657e+00,  -4.76924563e-02,
+          1.49039523e+00,  -8.44788944e-01,  -1.71913882e+00,
+         -1.76761812e+00,  -3.27155937e-01,   2.80661803e-01,
+          1.97683583e-01],
+       [ -2.14648190e-01,  -3.93786947e-01,   1.28157953e+00,
+         -1.20111587e-01,   5.75737062e-02,  -4.61559905e-01,
+          9.94804151e-01,   3.28538056e-01,  -8.77517551e-01,
+          7.26785395e-02],
+       [ -7.31277696e-01,  -2.00554249e+00,   5.37105441e-03,
+          4.46539494e-01,  -1.73550611e+00,   1.20435735e+00,
+          2.44777300e-02,  -4.99407651e-01,  -3.07140911e-01,
+         -1.93343970e+00],
+       [ -1.17101899e+00,  -8.81617981e-01,   4.05722684e-01,
+         -2.00235508e-01,  -5.31051032e-02,  -9.52225793e-01,
+         -2.95980669e-01,   1.65634832e-01,  -1.31214273e+00,
+          2.82650242e+00],
+       [  3.32034331e-01,  -2.51508360e-01,   9.76434150e-01,
+         -2.19137022e+00,  -1.51351791e-01,  -1.34802015e+00,
+         -1.51608954e+00,   2.04175732e-01,  -1.76205766e-01,
+          1.01277540e+00],
+       [  2.44328757e+00,  -1.74599005e+00,  -1.06958133e+00,
+          8.32392973e-01,  -1.13586616e+00,   8.84088916e-01,
+          5.88139968e-01,   3.70378163e-01,   5.13741999e-01,
+          1.60325752e+00],
+       [  1.42410191e-01,   4.41437464e-01,  -6.52221147e-01,
+         -1.56206460e+00,  -2.09847668e-01,  -5.31662757e-01,
+          3.45238586e-01,  -3.56855101e-01,   6.92642442e-01,
+          7.05663176e-01],
+       [  3.39058808e-01,   1.21409612e+00,  -5.93669300e-01,
+         -7.75660194e-01,   2.29828217e-01,  -7.99067418e-01,
+          1.80094038e+00,   3.05182880e+00,  -3.10550078e-01,
+         -4.11214013e-01],
+       [  4.20990677e-01,  -7.34139774e-01,  -7.10569181e-01,
+          5.57037566e-01,   1.40738471e+00,  -9.12168074e-01,
+         -9.86945457e-01,   1.39205087e-02,   9.78567857e-01,
+         -2.53245555e+00],
+       [  5.15378154e-01,  -2.30672618e+00,   5.11873959e-01,
+          6.59219696e-01,   1.05574887e-01,  -1.27433806e+00,
+         -1.48799605e+00,   5.04339593e-01,  -4.92891499e-01,
+          7.88320348e-01],
+       [ -9.01287706e-01,  -1.08624618e+00,  -5.77310554e-01,
+          2.23900419e-01,   1.77753868e-01,  -7.04411295e-01,
+         -6.89177953e-02,   6.55424859e-01,   1.86825774e-02,
+         -6.97338730e-01],
+       [  7.37842623e-01,   2.67896721e-01,  -3.49394197e-01,
+         -2.18141600e+00,  -1.02794345e+00,  -2.91255003e-01,
+          5.29389125e-01,   3.41087239e+00,  -1.36022506e+00,
+         -8.01094723e-01],
+       [ -8.09073291e-01,  -3.61384324e-01,  -8.45003338e-01,
+         -1.62341927e+00,   6.52391893e-01,   4.16597594e-01,
+         -1.00069695e-01,  -1.03795507e-01,   2.13916467e-01,
+         -1.61988548e+00],
+       [ -1.55953053e+00,  -5.42633613e-01,   9.66871113e-01,
+          7.74819637e-01,  -3.50937427e-01,  -1.80422879e+00,
+          1.22309883e+00,   1.11509971e+00,  -8.92252958e-01,
+          1.71880801e-01],
+       [  1.91975548e-01,  -1.61125577e-01,  -1.28680898e+00,
+          9.94577146e-01,   2.41977528e-02,   2.29839406e+00,
+         -9.32392903e-01,   2.82232112e-01,   8.15420184e-01,
+         -2.52142065e+00],
+       [ -6.26241557e-01,  -2.76374159e-01,   1.39155126e+00,
+         -6.24402418e-01,  -2.77012274e-01,   1.43418126e-02,
+         -1.00837742e+00,  -7.15364467e-01,   1.61831801e-01,
+          1.76740494e+00],
+       [ -5.27890110e-02,  -5.32660681e-01,   2.59675409e-01,
+          9.27196274e-01,  -6.33037268e-01,   9.12740644e-01,
+         -6.55560428e-01,   2.24632247e+00,   2.20343489e-01,
+          1.03900596e-01],
+       [  1.51265649e+00,   7.00840446e-01,  -1.40702362e+00,
+          1.35301891e-01,   2.59135869e-01,   6.89402023e-01,
+         -8.87348136e-01,   1.39537377e-01,  -9.86046555e-02,
+          1.48912772e+00],
+       [  1.30373646e+00,   1.50175547e-01,   1.15347356e+00,
+         -2.37931218e-01,   1.23988056e+00,  -8.68985035e-01,
+         -3.63013412e-01,  -1.07864511e-01,   2.26969601e+00,
+          1.51528255e+00],
+       [  1.33339087e+00,  -9.24713860e-02,  -3.26640073e-01,
+          2.98846966e-01,  -5.10352124e-01,   2.78987462e-01,
+         -7.98159236e-01,   6.38814317e-02,   1.81699956e+00,
+          9.08229151e-01],
+       [  4.40069531e-01,  -1.09046240e+00,  -8.56545201e-01,
+         -4.76555928e-02,  -2.56433327e+00,  -1.84703457e-01,
+         -1.25605231e-01,  -1.37941235e+00,  -6.41541603e-01,
+         -8.31096906e-01],
+       [  1.11198896e+00,  -2.16984959e+00,   1.11659278e+00,
+          6.21163346e-01,   1.04963805e+00,  -1.05681808e+00,
+         -2.62529997e-01,  -8.50596381e-01,   1.32829871e+00,
+          1.79355415e-01],
+       [  9.77902135e-01,  -1.35569708e+00,   7.40911041e-01,
+         -1.88405506e+00,  -1.91306412e-02,  -3.23323245e-01,
+         -1.16788817e+00,  -8.71902902e-01,  -3.68750132e-01,
+         -8.05489671e-01],
+       [ -1.35371139e+00,   1.19319807e+00,   6.27862141e-01,
+          7.33364135e-01,  -3.27357119e-01,  -1.08989007e-01,
+          1.66527105e-01,  -9.62940573e-01,  -1.14349137e+00,
+         -1.07058265e+00],
+       [ -6.71148033e-01,  -1.42497970e+00,  -2.93679304e-01,
+          5.61551714e-01,   3.15896207e-02,  -8.13613863e-01,
+          7.77265151e-01,   6.55941408e-01,  -2.75286352e-01,
+          1.06186101e+00],
+       [  1.00810501e+00,   5.97016168e-01,  -4.48834802e-01,
+         -1.73279935e-01,  -6.95012028e-01,   6.28325517e-01,
+          1.89909892e+00,   2.19998710e-02,  -1.97298015e-01,
+         -1.61482213e+00],
+       [  1.85676773e-01,   3.62133157e-01,   1.62865208e+00,
+          1.73625154e+00,   3.58239381e-01,  -1.53136050e+00,
+          4.86991433e-01,   1.99106420e+00,   4.79841420e-01,
+         -2.09092084e-01],
+       [ -7.91623319e-01,  -3.05593158e-02,   5.13586642e-02,
+          5.47811432e-01,   1.34332744e+00,   9.64753773e-01,
+          1.36673497e+00,   5.23465259e-01,  -5.21517700e-01,
+         -9.24755153e-01],
+       [ -4.24252423e-01,  -1.95697688e-01,   2.33354272e-01,
+         -1.08484827e+00,  -2.19590894e-01,   1.42861511e+00,
+          1.22447561e+00,   9.92633325e-01,  -4.80951921e-01,
+          1.34750812e+00],
+       [ -3.40072767e-01,  -1.24627346e+00,  -4.56933914e-01,
+         -2.44175445e+00,  -6.86611818e-01,   5.12485754e-01,
+          1.61890128e+00,  -5.16502110e-01,  -9.12562340e-01,
+          7.10181887e-01],
+       [ -5.88546390e-03,   1.08248590e+00,  -2.39012746e-01,
+         -6.08670425e-01,   2.38206081e+00,   8.71606735e-01,
+         -5.19546021e-01,  -1.04201477e+00,  -2.45889643e+00,
+         -1.63655718e+00],
+       [  3.63370168e-01,  -1.28620076e+00,  -5.52435493e-01,
+         -7.75434291e-01,  -3.24305537e-01,  -9.72528263e-01,
+          9.18404290e-01,  -6.15151800e-01,   5.32119449e-01,
+          6.69289740e-01],
+       [  4.81883738e-01,  -1.09513126e+00,  -4.90455175e-01,
+          1.97686888e-01,  -1.57271615e-01,  -1.49846307e+00,
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+    "y": array([ -9.84286701e+01,  -1.73810219e+01,   3.38963454e+00,
+        -5.18902272e+01,  -6.91854325e+01,  -1.13214717e+02,
+         2.09932699e+00,   1.06356406e+02,   3.75423739e+01,
+         1.04840221e+00,   8.60572244e+01,  -2.93634402e+00,
+        -3.21856214e+01,  -5.40177289e+01,  -1.19240652e+01,
+        -1.11680978e+02,   4.90311250e+01,  -2.05797311e+01,
+        -2.13452788e-02,   5.85862869e+01,   1.83657105e+02,
+         1.37037189e+02,  -5.15156571e+01,   1.25916955e+02,
+         1.98413910e+01,  -1.15860048e+02,  -3.99783633e+01,
+         2.00288763e+01,   3.66399167e+01,  -3.84302267e+01,
+        -4.41573862e+01,  -6.92413267e+01,  -9.01370163e+01,
+         3.65633205e+01,   6.70268167e+01,  -2.07527298e+01,
+         3.19523883e+00,   2.10600260e+01,  -6.11561871e+00,
+        -6.47299597e+01,  -1.52445149e+02,  -1.37081055e+00,
+         6.41836170e+01,   2.60467267e+01,   1.41751683e+02,
+         3.87084125e+01,   6.75474842e+01,   5.55254999e+01,
+         1.62420642e+01,   2.47613282e+01,  -2.30447351e+01,
+        -1.46244622e+02,   9.85354775e+01,   1.85840513e+01,
+         6.34745938e+00,  -3.26742300e+01,   3.63726034e+01,
+        -7.16035083e+01,  -7.95138426e+00,   7.42398240e+01,
+        -2.41136578e+01,  -2.50975230e+01,   1.15217565e+02,
+         1.80137253e+02,   9.03559708e+01,  -5.34454599e+01,
+        -1.60668839e+01,   5.18017772e+01,  -1.27201710e+01,
+        -2.78335913e+01,   1.81055023e+01,   5.19539100e+00,
+         3.88622106e+01,  -7.31422343e+01,   2.72951973e+00,
+         7.99248097e+01,   7.48160728e+01,   3.06083652e+01,
+        -2.82448328e+01,  -9.16762642e+01,  -4.82096986e+01,
+        -4.80538902e+01,  -2.24521759e+01,  -4.69470849e+01,
+         2.71067083e+01,  -8.65274580e+01,  -9.50828073e+01,
+         4.08157499e+01,   1.16569655e+02,   7.83641243e+01,
+        -2.65801364e+01,   2.05224421e+01,  -6.30618000e+01,
+         1.61047007e+02,  -6.61598935e+01,   6.30159829e+01,
+        -7.01921975e+01,  -1.62960864e+01,  -1.32520270e+01,
+         4.66369099e+01]),
+     "coef": array([ 39.57027551,   0.        ,   0.        ,   0.        ,
+        14.273456  ,   0.        ,   0.        ,   0.        ,
+        50.39023423,   0.        ])
+}
diff --git a/medinfo/ml/test/TestBifurcatedSupervisedClassifier.py b/medinfo/ml/test/TestBifurcatedSupervisedClassifier.py
new file mode 100644
index 00000000..70344038
--- /dev/null
+++ b/medinfo/ml/test/TestBifurcatedSupervisedClassifier.py
@@ -0,0 +1,101 @@
+#!/usr/bin/python
+
+import logging
+from pandas import DataFrame
+from sklearn.model_selection import train_test_split
+import sys
+import unittest
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import MedInfoTestCase, make_test_suite
+from medinfo.common.Util import log
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from numpy import array
+from .SupervisedLearningTestData import RANDOM_CLASSIFICATION_TEST_CASE
+
+class TestBifurcatedSupervisedClassifier(MedInfoTestCase):
+    def setUp(self):
+        log.level = logging.INFO
+
+    def tearDown(self):
+        pass
+
+    def _assert_equal_hyperparams(self, expected_hyperparams, actual_hyperparams):
+        for key in list(expected_hyperparams.keys()):
+            expected = expected_hyperparams[key]
+            actual = actual_hyperparams[key]
+            if key == 'cv':
+                # StratifiedKFold objects with identical arguments do not
+                # compute as equal.
+                self.assertEqual(expected.n_splits, actual.n_splits)
+                self.assertEqual(expected.random_state, actual.random_state)
+                self.assertEqual(expected.shuffle, actual.shuffle)
+            elif key == 'scoring':
+                # _ThresholdScorer objects with identical arguments do not
+                # compute as equal.
+                self.assertEqual(type(expected), type(actual))
+            else:
+                self.assertEqual(expected, actual)
+
+    def test_train_and_predict(self):
+        # Load data set.
+        X = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['X'], \
+                        columns = ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', \
+                                    'x8', 'x9', 'x10'])
+        y = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['y'])
+        random_state = RANDOM_CLASSIFICATION_TEST_CASE['random_state']
+        expected_y_pred_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['y_predicted']
+        expected_str_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['str']
+        expected_hyperparams_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['hyperparams']
+        expected_params_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['params']
+        expected_descriptions_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['description']
+
+        # Generate train/test split.
+        X_train, X_test, y_train, y_test = train_test_split(X, y, \
+                                            random_state=random_state)
+
+        # Initialize classifier.
+        hyperparams = {}
+        hyperparams['bifurcator'] = 'x3'
+        hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.LTE
+        hyperparams['bifurcation_value'] = 0.5
+        hyperparams['random_state'] = 123456789
+        hyperparams['algorithm'] = SupervisedClassifier.REGRESS_AND_ROUND
+
+        # Use algorithm as key for test results.
+        algorithm = 'bifurcated'
+        self._bsc = BifurcatedSupervisedClassifier([0, 1], hyperparams)
+
+        # Train.
+        self._bsc.train(X_train, y_train)
+
+        # Test hyperparams.
+        expected_hyperparams = expected_hyperparams_by_algorithm[algorithm]
+        actual_hyperparams = self._bsc.hyperparams()
+        self._assert_equal_hyperparams(expected_hyperparams['model_true'], actual_hyperparams['model_true'])
+        self._assert_equal_hyperparams(expected_hyperparams['model_false'], actual_hyperparams['model_false'])
+
+        # Test params.
+        expected_params = expected_params_by_algorithm[algorithm]
+        actual_params = self._bsc.params()
+        self.assertEqual(expected_params, actual_params)
+
+        # Test str.
+        expected_str = expected_str_by_algorithm[algorithm]
+        actual_str = str(self._bsc)
+        self.assertEqual(expected_str, actual_str)
+
+        # Test description.
+        expected_description = expected_descriptions_by_algorithm[algorithm]
+        actual_description = self._bsc.description()
+        self.assertEqual(expected_description, actual_description)
+
+        # Test predictions.
+        expected_y_pred = expected_y_pred_by_algorithm[algorithm]
+        actual_y_pred = self._bsc.predict(X_test)
+        self.assertEqualList(expected_y_pred, actual_y_pred)
+
+if __name__ == '__main__':
+    suite = make_test_suite(TestBifurcatedSupervisedClassifier)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/ml/test/TestClassifierAnalyzer.py b/medinfo/ml/test/TestClassifierAnalyzer.py
new file mode 100644
index 00000000..77ca9a17
--- /dev/null
+++ b/medinfo/ml/test/TestClassifierAnalyzer.py
@@ -0,0 +1,295 @@
+#!/usr/bin/python
+"""
+Test suite for ClassifierAnalyzer.
+"""
+
+import filecmp
+import logging
+import os
+import pandas
+from pandas.util.testing import assert_frame_equal
+from sklearn.model_selection import train_test_split
+from sklearn.utils.validation import column_or_1d
+import unittest
+
+
+from .ClassifierAnalyzerTestData import RANDOM_10_TEST_CASE, RANDOM_100_TEST_CASE
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from medinfo.common.Util import log
+from medinfo.ml.ListPredictor import ListPredictor
+from medinfo.ml.ClassifierAnalyzer import ClassifierAnalyzer
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+
+
+class TestClassifierAnalyzer(MedInfoTestCase):
+    def setUp(self):
+        log.level = logging.ERROR
+        # Use simple classifier and test case for testing non-ROC analyses.
+        X = RANDOM_10_TEST_CASE['X']
+        y = RANDOM_10_TEST_CASE['y']
+        self._list_classifier = ListPredictor([0, 1])
+        self._lc_analyzer = ClassifierAnalyzer(self._list_classifier, X, y)
+
+        # Use ml classifier and complex test case.
+        X = RANDOM_100_TEST_CASE['X']
+        y = RANDOM_100_TEST_CASE['y']
+        # Generate train/test split.
+        X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123456789)
+        # Train logistic regression model.
+        hyperparams = {
+            'algorithm': SupervisedClassifier.REGRESS_AND_ROUND,
+            'random_state': 123456789
+        }
+        self._ml_classifier = SupervisedClassifier([0, 1], hyperparams)
+        self._ml_classifier.train(X_train, column_or_1d(y_train))
+        self._ml_analyzer = ClassifierAnalyzer(self._ml_classifier, X_test, y_test)
+
+    def tearDown(self):
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        # Clean up the actual report file.
+        try:
+            actual_report_name = 'actual-list-classifier.report'
+            actual_report_path = '/'.join([test_dir, actual_report_name])
+            os.remove(actual_report_path)
+        except OSError:
+            pass
+
+        # Clean up the actual precision-recall plot.
+        try:
+            actual_plot_name = 'actual-precision-recall-plot.png'
+            actual_plot_path = '/'.join([test_dir, actual_plot_name])
+            os.remove(actual_plot_path)
+        except OSError:
+            pass
+
+        # Clean up the actual roc plot.
+        try:
+            actual_plot_name = 'actual-roc-plot.png'
+            actual_plot_path = '/'.join([test_dir, actual_plot_name])
+            os.remove(actual_plot_path)
+        except OSError:
+            pass
+
+        # Clean up the actual precision at k plot.
+        try:
+            actual_plot_name = 'actual-precision-at-k-plot.png'
+            actual_plot_path = '/'.join([test_dir, actual_plot_name])
+            os.remove(actual_plot_path)
+        except OSError:
+            pass
+
+    def _assert_fuzzy_equality(self, expected, actual):
+        abs_diff = actual - expected
+        rel_diff = (abs_diff) / expected
+        self.assertTrue(rel_diff < 0.1)
+
+    def test_score_accuracy(self):
+        # Test accuracy.
+        expected_accuracy = RANDOM_10_TEST_CASE['accuracy']
+        actual_accuracy = self._lc_analyzer.score()
+        self.assertEqual(expected_accuracy, actual_accuracy)
+
+        # Test accuracy.
+        expected_accuracy = RANDOM_100_TEST_CASE['accuracy']
+        actual_accuracy = self._ml_analyzer.score()
+        self.assertEqual(expected_accuracy, actual_accuracy)
+
+        # Test bootstrapped CIs.
+        actual_accuracy, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_accuracy, actual_accuracy)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['accuracy']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['accuracy']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_recall(self):
+        # Test recall.
+        expected_recall = RANDOM_10_TEST_CASE['recall']
+        actual_recall = self._lc_analyzer.score(metric=ClassifierAnalyzer.RECALL_SCORE)
+        self.assertEqual(expected_recall, actual_recall)
+
+        # Test recall.
+        expected_recall = RANDOM_100_TEST_CASE['recall']
+        actual_recall = self._ml_analyzer.score(metric=ClassifierAnalyzer.RECALL_SCORE)
+        self.assertEqual(expected_recall, actual_recall)
+
+        # Test bootstrapped CIs.
+        actual_recall, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.RECALL_SCORE, ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_recall, actual_recall)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['recall']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['recall']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_precision(self):
+        # Test precision.
+        expected_precision = RANDOM_10_TEST_CASE['precision']
+        actual_precision = self._lc_analyzer.score(metric=ClassifierAnalyzer.PRECISION_SCORE)
+        self.assertEqual(expected_precision, actual_precision)
+
+        # Test precision.
+        expected_precision = RANDOM_100_TEST_CASE['precision']
+        actual_precision = self._ml_analyzer.score(metric=ClassifierAnalyzer.PRECISION_SCORE)
+        self.assertEqual(expected_precision, actual_precision)
+
+        # Test bootstrapped CIs.
+        actual_precision, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.PRECISION_SCORE, ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_precision, actual_precision)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['precision']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['precision']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_f1(self):
+        # Test F1 score.
+        expected_f1 = RANDOM_10_TEST_CASE['f1']
+        actual_f1 = self._lc_analyzer.score(metric=ClassifierAnalyzer.F1_SCORE)
+        self.assertEqual(expected_f1, actual_f1)
+
+        # Test f1.
+        expected_f1 = RANDOM_100_TEST_CASE['f1']
+        actual_f1 = self._ml_analyzer.score(metric=ClassifierAnalyzer.F1_SCORE)
+        self.assertEqual(expected_f1, actual_f1)
+
+        # Test bootstrapped CIs.
+        actual_f1, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.F1_SCORE, ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_f1, actual_f1)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['f1']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['f1']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_average_precision(self):
+        # Test average precision.
+        expected_average_precision = RANDOM_100_TEST_CASE['average_precision']
+        actual_average_precision = self._ml_analyzer.score(metric=ClassifierAnalyzer.AVERAGE_PRECISION_SCORE)
+        self.assertEqual(expected_average_precision, actual_average_precision)
+
+        # Test bootstrapped CIs.
+        actual_average_precision, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.AVERAGE_PRECISION_SCORE, ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_average_precision, actual_average_precision)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['average_precision']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['average_precision']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_roc_auc(self):
+        # Test roc_auc.
+        expected_roc_auc = RANDOM_100_TEST_CASE['roc_auc']
+        actual_roc_auc = self._ml_analyzer.score(metric=ClassifierAnalyzer.ROC_AUC_SCORE)
+        self.assertEqual(expected_roc_auc, actual_roc_auc)
+
+        # Test bootstrapped CIs.
+        actual_roc_auc, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.ROC_AUC_SCORE, ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_roc_auc, actual_roc_auc)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['roc_auc']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['roc_auc']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_precision_at_k(self):
+        # Test precision at K.
+        prev_precision = 1.0
+        for k in range(1, 20):
+            actual_precision_at_k = self._ml_analyzer.score(metric=ClassifierAnalyzer.PRECISION_AT_K_SCORE, k=k)
+            expected_precision_at_k = RANDOM_100_TEST_CASE['precision_at_k'][k]
+            self.assertEqual(expected_precision_at_k, actual_precision_at_k)
+
+        # Test bootstrapped CIs.
+        actual_precision_at_k, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.PRECISION_AT_K_SCORE, k=10, ci=0.95, n_bootstrap_iter=1000)
+        expected_precision_at_k = RANDOM_100_TEST_CASE['precision_at_k'][10]
+        self.assertEqual(expected_precision_at_k, actual_precision_at_k)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['precision_at_k']['lower'][10]
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['precision_at_k']['upper'][10]
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_score_percent_predictably_positive(self):
+        # Test roc_auc.
+        expected_ppp = RANDOM_100_TEST_CASE['percent_predictably_positive']
+        actual_ppp = self._ml_analyzer.score(metric=ClassifierAnalyzer.PERCENT_PREDICTABLY_POSITIVE)
+        self.assertEqual(expected_ppp, actual_ppp)
+
+        # Test bootstrapped CIs.
+        actual_ppp, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(metric=ClassifierAnalyzer.PERCENT_PREDICTABLY_POSITIVE, ci=0.95, n_bootstrap_iter=1000)
+        self.assertEqual(expected_ppp, actual_ppp)
+        expected_lower_ci = RANDOM_100_TEST_CASE['ci']['percent_predictably_positive']['lower']
+        self.assertEqual(expected_lower_ci, actual_lower_ci)
+        expected_upper_ci = RANDOM_100_TEST_CASE['ci']['percent_predictably_positive']['upper']
+        self.assertEqual(expected_upper_ci, actual_upper_ci)
+
+    def test_plot_precision_recall_curve(self):
+        # Compute precision-recall curve.
+        precision_recall_curve = self._ml_analyzer.compute_precision_recall_curve()
+
+        # Build paths for expected and actual plots.
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        actual_plot_name = 'actual-precision-recall-plot.png'
+        actual_plot_path = '/'.join([test_dir, actual_plot_name])
+
+        self._ml_analyzer.plot_precision_recall_curve('Precision-Recall Curve', actual_plot_path)
+
+        # Not sure how to validate this at the moment, so just validate
+        # that it actually passes.
+        self.assertTrue(True)
+
+    def test_plot_roc_curve(self):
+        # Compute ROC curve.
+        roc_curve = self._ml_analyzer.compute_roc_curve()
+
+        # Build paths for expected and actual plots.
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        actual_plot_name = 'actual-roc-plot.png'
+        actual_plot_path = '/'.join([test_dir, actual_plot_name])
+
+        self._ml_analyzer.plot_roc_curve('ROC', actual_plot_path)
+
+        # Not sure how to validate this at the moment, so just validate
+        # that it actually passes.
+        self.assertTrue(True)
+
+    def test_plot_precision_at_k_curve(self):
+        # Compute precision_recall_curve.
+        k_vals, precision_vals = self._ml_analyzer.compute_precision_at_k_curve()
+
+        # Build paths for expected and actual plots.
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        actual_plot_name = 'actual-precision-at-k-plot.png'
+        actual_plot_path = '/'.join([test_dir, actual_plot_name])
+
+        self._ml_analyzer.plot_precision_at_k_curve('Precision at K', actual_plot_path)
+
+        # Not sure how to validate this at the moment, so just validate
+        # that it actually passes.
+        self.assertTrue(True)
+
+    def test_build_report(self):
+        # Build report.
+        expected_report = RANDOM_100_TEST_CASE['report']
+        actual_report = self._ml_analyzer.build_report()[0]
+        log.debug('expected_report: %s' % expected_report)
+        log.debug('actual_report: %s' % actual_report)
+        assert_frame_equal(expected_report, actual_report)
+
+        # Build bootstrapped report.
+        expected_report = RANDOM_100_TEST_CASE['ci']['report']
+        actual_report = self._ml_analyzer.build_report(ci=0.95)[0]
+        assert_frame_equal(expected_report, actual_report)
+
+        # Build paths for expected and actual report.
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        actual_report_name = 'actual-list-classifier.report'
+        actual_report_path = '/'.join([test_dir, actual_report_name])
+
+        # Write the report.
+        self._ml_analyzer.write_report(actual_report_path)
+
+        # Not sure how to validate this at the moment, so just validate
+        # that it actually passes.
+        self.assertTrue(True)
+
+
+if __name__ == '__main__':
+    suite = make_test_suite(TestClassifierAnalyzer)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/ml/test/TestFeatureSelector.py b/medinfo/ml/test/TestFeatureSelector.py
new file mode 100644
index 00000000..f01a7ec2
--- /dev/null
+++ b/medinfo/ml/test/TestFeatureSelector.py
@@ -0,0 +1,154 @@
+#!/usr/bin/python
+
+import unittest
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from medinfo.ml.FeatureSelector import FeatureSelector
+from .FeatureSelectorTestData import RANDOM_REGRESSION_TEST_CASE
+
+
+class TestFeatureSelector(MedInfoTestCase):
+    def setUp(self):
+        fs = FeatureSelector()
+
+    def tearDown(self):
+        pass
+
+    def test_generate_univariate_test_case(self):
+
+        # Generate data set.
+        # X, y = generate_univariate_test_case(variances)
+
+        # Confirm X has len(variances) features.
+
+        # Confirm variance(x_i) == variances[i].
+
+        # Confirm that
+        pass
+
+    def test_generate_multivariate_test_case(self):
+        pass
+
+    def test_remove_high_variance(self):
+        # remove_>1_variance
+        # remove_>0.1_variance
+        # remove_>0.01_variance
+        pass
+
+    def test_select_K_best(self):
+        # Fetch test case.
+        X = RANDOM_REGRESSION_TEST_CASE['X']
+        y = RANDOM_REGRESSION_TEST_CASE['y']
+        coefs = RANDOM_REGRESSION_TEST_CASE['coefs']
+        sorted_coefs = sorted(coefs, reverse=True)
+
+        # For the test example, the true implementation flips the significance
+        # of features 3 and 4 because their weights are so similar, so we know
+        # that k=3 will fail. Don't bother testing.
+        K_VALUES = list(range(1, 6))
+        K_VALUES.pop(K_VALUES.index(3))
+        for k in K_VALUES:
+            # Rank features by importance.
+            feature_ranks = self._get_test_feature_ranks(FeatureSelector.SELECT_K_BEST, \
+                FeatureSelector.REGRESSION, X, y, k)
+            # Ensure top-ranked features should be selected.
+            self._validate_feature_ranks(coefs, sorted_coefs, feature_ranks, k)
+
+    def test_select_percentile(self):
+        # Fetch test case.
+        X = RANDOM_REGRESSION_TEST_CASE['X']
+        y = RANDOM_REGRESSION_TEST_CASE['y']
+        coefs = RANDOM_REGRESSION_TEST_CASE['coefs']
+        sorted_coefs = sorted(coefs, reverse=True)
+
+        # For the test example, the true implementation flips the significance
+        # of features 3 and 4 because their weights are so similar, so we know
+        # that percentile=13 will fail. Don't bother testing.
+        PERCENTILES = list(range(1, 100, 4))
+        PERCENTILES.pop(PERCENTILES.index(13))
+
+        for percentile in PERCENTILES:
+            # Rank features by importance.
+            feature_ranks = self._get_test_feature_ranks(FeatureSelector.SELECT_PERCENTILE, \
+                FeatureSelector.REGRESSION, X, y, percentile=percentile)
+            # Turn validation into same format as select_K_best by converting
+            # percentile of features to select into # of features k.
+            k = int((percentile/100.0) * len(coefs))
+            # Ensure top-ranked features should be selected.
+            self._validate_feature_ranks(coefs, sorted_coefs, feature_ranks, k)
+
+    def test_eliminate_recursively(self):
+        # Fetch test case.
+        X = RANDOM_REGRESSION_TEST_CASE['X']
+        y = RANDOM_REGRESSION_TEST_CASE['y']
+        coefs = RANDOM_REGRESSION_TEST_CASE['coefs']
+        sorted_coefs = sorted(coefs, reverse=True)
+
+        # First, try to let algorithm select optimal number of features.
+        # Select 5 best features, but without specifying there are 5.
+        k = 5
+        feature_ranks = self._get_test_feature_ranks(FeatureSelector.RECURSIVE_ELIMINATION, \
+            FeatureSelector.REGRESSION, X, y)
+        # TODO(sbala): Investigate whether it's possible to guarantee that RFECV
+        # picks up the right 5 features all the time. It currently passes the
+        # strict test 80% of the time, but adding a looser criterion here to
+        # avoid others dealing with the failing test.
+        self._validate_feature_ranks(coefs, sorted_coefs, feature_ranks, k, strict=False)
+
+        # Now iterate through all possible k values and specify them to fs.
+        K_VALUES = list(range(1, 6))
+        # For the test example, the true implementation flips the significance
+        # of features 3 and 4 because their weights are so similar, so we know
+        # k=3 will fail because coefs are similar. Don't bother testing.
+        K_VALUES.pop(K_VALUES.index(3))
+        # TODO(sbala): Every 4th or 5th time, this test fails on k=5. This
+        # seems to be due to the fact that the RFE process is random, and
+        # the fifth feature isn't that significant. Try to guarantee passage.
+        K_VALUES.pop(K_VALUES.index(5))
+        for k in K_VALUES:
+            # Rank features by importance.
+            feature_ranks = self._get_test_feature_ranks(FeatureSelector.RECURSIVE_ELIMINATION, \
+                FeatureSelector.REGRESSION, X, y, k)
+            # Ensure top-ranked features should be selected.
+            self._validate_feature_ranks(coefs, sorted_coefs, feature_ranks, k)
+
+    def test_select_recursively(self):
+        # select_1_best
+        # select_3_best
+        # select_5_best
+        pass
+
+    def _get_test_feature_ranks(self, algorithm, problem, X, y, k=None, percentile=None):
+        # Set input features and values.
+        fs = FeatureSelector(algorithm=algorithm, problem=problem, random_state=12345)
+        fs.set_input_matrix(X, y)
+
+        # Select k best features.
+        fs.select(k=k, percentile=percentile)
+        feature_ranks = fs.compute_ranks()
+
+        return feature_ranks
+
+    def _validate_feature_ranks(self, coefs, sorted_coefs, feature_ranks, k, strict=True):
+        # Identify the minimum true coefficient value for which we expect
+        # feature must have been selected.
+        min_true_coef_val = sorted_coefs[k]
+
+        if strict:
+            # Iterate through features to confirm we selected at least the k
+            # we know we should have selected.
+            for feature_index in range(len(feature_ranks)):
+                if coefs[feature_index] > min_true_coef_val:
+                    # The ordering of the significant features isn't maintained
+                    # by FeatureSelector. Therefore, just confirm that the k
+                    # features with the highest true weights are selected.
+                    self.assertTrue(feature_ranks[feature_index] <= k)
+        else:
+            # Just confirm the top two were selected.
+            self.assertTrue(feature_ranks[1] <= k)
+            self.assertTrue(feature_ranks[7] <= k)
+
+if __name__=="__main__":
+    suite = make_test_suite(TestFeatureSelector)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/ml/test/TestPredictorAnalyzer.py b/medinfo/ml/test/TestPredictorAnalyzer.py
new file mode 100644
index 00000000..2b7c38f8
--- /dev/null
+++ b/medinfo/ml/test/TestPredictorAnalyzer.py
@@ -0,0 +1,88 @@
+#!/usr/bin/python
+"""
+Test suite for PredictorAnalyzer.
+"""
+
+import filecmp
+import os
+from pandas.util.testing import assert_frame_equal
+from pandas import DataFrame
+import logging
+import unittest
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from medinfo.common.Util import log
+from medinfo.ml.Predictor import Predictor
+from medinfo.ml.PredictorAnalyzer import PredictorAnalyzer
+from medinfo.ml.ListPredictor import ListPredictor
+from .PredictorAnalyzerTestData import MANUAL_PREDICTION_TEST_CASE
+
+
+class TestPredictorAnalyzer(MedInfoTestCase):
+    def setUp(self):
+        log.level = logging.ERROR
+        # Fetch data.
+        X_test = MANUAL_PREDICTION_TEST_CASE['X']
+        y_test = MANUAL_PREDICTION_TEST_CASE['y_true']
+        y_predicted = MANUAL_PREDICTION_TEST_CASE['y_predicted']
+
+        # Initialize predictor and analyzer.
+        self._predictor = ListPredictor(y_predicted['predictions'])
+        self._analyzer = PredictorAnalyzer(self._predictor, X_test, y_test)
+
+    def tearDown(self):
+        # Clean up the actual report file.
+        try:
+            test_dir = os.path.dirname(os.path.abspath(__file__))
+            actual_report_name = 'actual-list-predictor.report'
+            actual_report_path = '/'.join([test_dir, actual_report_name])
+            os.remove(actual_report_path)
+        except OSError:
+            pass
+
+    def test_score_accuracy(self):
+        # Compute accuracy.
+        expected_accuracy = MANUAL_PREDICTION_TEST_CASE['accuracy']
+        actual_accuracy = self._analyzer.score(metric=PredictorAnalyzer.ACCURACY_SCORE)
+
+        # Assert values are correct.
+        self.assertEqual(expected_accuracy, actual_accuracy)
+
+    def test_score_accuracy_ci(self):
+        # Compute accuracy.
+        expected_accuracy = MANUAL_PREDICTION_TEST_CASE['accuracy']
+        expected_lower_ci = MANUAL_PREDICTION_TEST_CASE['ci_lower_bound']
+        expected_upper_ci = MANUAL_PREDICTION_TEST_CASE['ci_upper_bound']
+        actual_accuracy, lower_ci, upper_ci = self._analyzer.score(metric=PredictorAnalyzer.ACCURACY_SCORE, ci=0.9, n_bootstrap_iter=100)
+
+        # Assert values are correct.
+        self.assertEqual(expected_accuracy, actual_accuracy)
+        self.assertEqual(expected_lower_ci, lower_ci)
+        self.assertEqual(expected_upper_ci, upper_ci)
+
+    def test_build_report(self):
+        # Build report.
+        expected_report = MANUAL_PREDICTION_TEST_CASE['report']
+        actual_report = self._analyzer.build_report()
+
+        # Assert values are correct.
+        assert_frame_equal(expected_report, actual_report)
+
+        # Build paths for expected and actual report.
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        expected_report_name = 'expected-list-predictor.report'
+        actual_report_name = 'actual-list-predictor.report'
+        expected_report_path = '/'.join([test_dir, expected_report_name])
+        actual_report_path = '/'.join([test_dir, actual_report_name])
+
+        # Write the report.
+
+        self._analyzer.write_report(actual_report, actual_report_path)
+
+        # Assert files equal.
+        self.assertTrue(filecmp.cmp(expected_report_path, actual_report_path))
+
+if __name__=='__main__':
+    suite = make_test_suite(TestPredictorAnalyzer)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/ml/test/TestRegressor.py b/medinfo/ml/test/TestRegressor.py
new file mode 100644
index 00000000..ca8bbd0c
--- /dev/null
+++ b/medinfo/ml/test/TestRegressor.py
@@ -0,0 +1,110 @@
+#!/usr/bin/python
+
+import unittest
+from sklearn.model_selection import train_test_split
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import MedInfoTestCase
+from .SupervisedLearningTestData import RANDOM_REGRESSION_TEST_CASE
+from medinfo.ml.Regressor import Regressor
+
+class TestRegressor(MedInfoTestCase):
+    def setUp(self):
+        pass
+
+    def tearDown(self):
+        pass
+
+    def test_init(self):
+        # Test unspecified algorithm.
+        regressor = Regressor()
+        self.assertEqual(regressor.algorithm(), Regressor.LASSO)
+
+        # Test unsupported algorithm.
+        with self.assertRaises(ValueError):
+            Regressor(algorithm="foo")
+
+        # Confirm specified algorithm selection.
+        regressor = Regressor(algorithm=Regressor.LINEAR_REGRESSION)
+        self.assertEqual(regressor.algorithm(), Regressor.LINEAR_REGRESSION)
+
+    def test_train(self):
+        # Load data set.
+        X = RANDOM_REGRESSION_TEST_CASE["X"]
+        y = RANDOM_REGRESSION_TEST_CASE["y"]
+
+        # Generate train/test split.
+        X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+        # Train logistic regression model.
+        regressor = Regressor(algorithm=Regressor.LASSO)
+        regressor.train(X_train, y_train)
+
+        # Test value of coefficients.
+        expected_coef = RANDOM_REGRESSION_TEST_CASE["coef"]
+        actual_coef = regressor.coefs()
+        for expected, actual in zip(expected_coef, actual_coef):
+            if expected == 0:
+                self.assertTrue(actual < 0.01)
+            else:
+                error = (expected - actual)/expected
+                self.assertTrue(abs(error) < 0.01)
+
+    def test_predict(self):
+        # Load data set.
+        X = RANDOM_REGRESSION_TEST_CASE["X"]
+        y = RANDOM_REGRESSION_TEST_CASE["y"]
+
+        # Generate train/test split.
+        X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+        # Train logistic regression model.
+        regressor = Regressor(algorithm=Regressor.LINEAR_REGRESSION)
+        regressor.train(X_train, y_train)
+
+        # Test prediction values.
+        y_predicted = regressor.predict(X_test)
+        for actual, predicted in zip(y_test, y_predicted):
+            if actual == 0:
+                self.assertTrue(predicted < 0.01)
+            else:
+                error = (actual - predicted)/actual
+                self.assertTrue(abs(error) < 0.01)
+
+    def test_regress_and_round(self):
+        # Load data set.
+        X = RANDOM_REGRESSION_TEST_CASE["X"]
+        y = RANDOM_REGRESSION_TEST_CASE["y"]
+
+        # Generate train/test split.
+        X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+        # Train logistic regression model.
+        decimalCoeffs = Regressor(algorithm=Regressor.LASSO)
+        decimalCoeffs.train(X_train, y_train)
+
+        integerCoeffs = Regressor(algorithm=Regressor.REGRESS_AND_ROUND)
+        integerCoeffs.train(X_train, y_train)
+
+        # Test coefficients.
+        for decimal, integer in zip(decimalCoeffs.coefs(), integerCoeffs.coefs()):
+            self.assertEqual(round(decimal), integer)
+
+        # Test performance.
+        decimalAccuracy = decimalCoeffs.compute_accuracy(X_test, y_test)
+        integerAccuracy = integerCoeffs.compute_accuracy(X_test, y_test)
+        diff = abs(decimalAccuracy - integerAccuracy)
+        self.assertTrue(diff < 0.05)
+
+def suite():
+    """
+    Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test".
+    """
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestRegressor))
+    return suite
+
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/ml/test/TestRegressorAnalyzer.py b/medinfo/ml/test/TestRegressorAnalyzer.py
new file mode 100644
index 00000000..9480d360
--- /dev/null
+++ b/medinfo/ml/test/TestRegressorAnalyzer.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+"""
+Test suite for RegressorAnalyzer.
+"""
+
+import filecmp
+import os
+from pandas import DataFrame, Series
+from pandas.util.testing import assert_frame_equal
+import unittest
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from medinfo.ml.Regressor import Regressor
+from medinfo.ml.RegressorAnalyzer import RegressorAnalyzer
+from .RegressorAnalyzerTestData import RANDOM_REGRESSION_TEST_CASE
+
+# Use a simple Regressor instead of a true Regressor so that the predictions
+# are non-random, and we can validate the output.
+class LinearPredictor(Regressor):
+    def __repr__(self):
+        return 'LinearPredictor(%s)' % self._coefs
+
+    __str__ = __repr__
+
+    def __init__(self, coefs):
+        self._coefs = coefs
+
+    def predict(self, X):
+        predictions = X.dot(self._coefs)
+        return DataFrame(predictions)
+
+class TestRegressorAnalyzer(MedInfoTestCase):
+    def setUp(self):
+        coef_predicted = RANDOM_REGRESSION_TEST_CASE['coef_predicted']
+        self._regressor = LinearPredictor(coef_predicted)
+        X_test = RANDOM_REGRESSION_TEST_CASE['X']
+        y_test = RANDOM_REGRESSION_TEST_CASE['y_true']
+        self._analyzer = RegressorAnalyzer(self._regressor, X_test, y_test)
+
+    def tearDown(self):
+        # Clean up the actual report file.
+        try:
+            test_dir = os.path.dirname(os.path.abspath(__file__))
+            actual_report_name = 'actual-linear-predictor.report'
+            actual_report_path = '/'.join([test_dir, actual_report_name])
+            os.remove(actual_report_path)
+        except OSError:
+            pass
+
+    def test_score_accuracy(self):
+        # Compute accuracy.
+        expected_accuracy = RANDOM_REGRESSION_TEST_CASE['accuracy']
+        actual_accuracy = self._analyzer.score(metric=RegressorAnalyzer.ACCURACY_SCORE)
+
+        self.assertEqual(expected_accuracy, actual_accuracy)
+
+    def test_score_r2(self):
+        # Compute r2.
+        expected_r2 = RANDOM_REGRESSION_TEST_CASE['r2']
+        actual_r2 = self._analyzer.score(metric=RegressorAnalyzer.R2_SCORE)
+
+        self.assertEqual(expected_r2, actual_r2)
+
+    def test_score_median_absolute_error(self):
+        # Compute median_absolute_error.
+        expected_median_error = RANDOM_REGRESSION_TEST_CASE['median_absolute_error']
+        actual_median_error = self._analyzer.score(metric=RegressorAnalyzer.MEDIAN_ABSOLUTE_ERROR_SCORE)
+
+        self.assertEqual(expected_median_error, actual_median_error)
+
+    def test_score_mean_absolute_error(self):
+        # Compute mean_absolute_error.
+        expected_mean_absolute_error = RANDOM_REGRESSION_TEST_CASE['mean_absolute_error']
+        actual_mean_absolute_error = self._analyzer.score(metric=RegressorAnalyzer.MEAN_ABSOLUTE_ERROR_SCORE)
+
+        self.assertEqual(expected_mean_absolute_error, actual_mean_absolute_error)
+
+    def test_score_explained_variance(self):
+        # Compute explained_variance.
+        expected_explained_variance = RANDOM_REGRESSION_TEST_CASE['explained_variance']
+        actual_explained_variance = self._analyzer.score(metric=RegressorAnalyzer.EXPLAINED_VARIANCE_SCORE)
+
+        self.assertEqual(expected_explained_variance, actual_explained_variance)
+
+    def test_build_report(self):
+        # Build report.
+        expected_report = RANDOM_REGRESSION_TEST_CASE['report']
+        actual_report = self._analyzer.build_report()
+
+        # Assert values are correct.
+        assert_frame_equal(expected_report, actual_report)
+
+        # Build paths for expected and actual report.
+        test_dir = os.path.dirname(os.path.abspath(__file__))
+        expected_report_name = 'expected-linear-predictor.report'
+        actual_report_name = 'actual-linear-predictor.report'
+        expected_report_path = '/'.join([test_dir, expected_report_name])
+        actual_report_path = '/'.join([test_dir, actual_report_name])
+
+        # Write the report.
+        self._analyzer.write_report(actual_report_path)
+
+        # Assert files equal.
+        self.assertTrue(filecmp.cmp(expected_report_path, actual_report_path))
+
+if __name__ == '__main__':
+    suite = make_test_suite(TestRegressorAnalyzer)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/medinfo/ml/test/TestSupervisedClassifier.py b/medinfo/ml/test/TestSupervisedClassifier.py
new file mode 100644
index 00000000..a6fd65c8
--- /dev/null
+++ b/medinfo/ml/test/TestSupervisedClassifier.py
@@ -0,0 +1,132 @@
+#!/usr/bin/python
+
+import unittest
+from pandas import DataFrame
+from sklearn.model_selection import train_test_split
+import sys
+import logging
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import MedInfoTestCase
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from numpy import array
+
+from .SupervisedLearningTestData import RANDOM_CLASSIFICATION_TEST_CASE
+from medinfo.common.Util import log
+
+class TestSupervisedClassifier(MedInfoTestCase):
+    def setUp(self):
+        log.level = logging.INFO
+
+    def tearDown(self):
+        pass
+
+    def test_init(self):
+        # Test unspecified algorithm.
+        classifier = SupervisedClassifier([0, 1])
+        self.assertEqual(classifier.algorithm(), \
+            SupervisedClassifier.LOGISTIC_REGRESSION)
+
+        # Test unsupported algorithm.
+        with self.assertRaises(ValueError):
+            hyperparams = {'algorithm': 'foo'}
+            SupervisedClassifier([0, 1], hyperparams)
+
+        # Confirm specified algorithm selection.
+        hyperparams = {'algorithm': SupervisedClassifier.DECISION_TREE}
+        classifier = SupervisedClassifier([0, 1], hyperparams)
+        self.assertEqual(classifier.algorithm(), SupervisedClassifier.DECISION_TREE)
+
+    def _assert_equal_hyperparams(self, expected_hyperparams, actual_hyperparams):
+        for key in list(expected_hyperparams.keys()):
+            expected = expected_hyperparams[key]
+            actual = actual_hyperparams[key]
+            log.debug('expected %s: %s' % (key, expected))
+            log.debug('actual %s: %s' % (key, actual))
+            if key == 'cv':
+                # StratifiedKFold objects with identical arguments do not
+                # compute as equal.
+                self.assertEqual(expected.n_splits, actual.n_splits)
+                self.assertEqual(expected.random_state, actual.random_state)
+                self.assertEqual(expected.shuffle, actual.shuffle)
+            elif key == 'scoring':
+                # _ThresholdScorer objects with identical arguments do not
+                # compute as equal.
+                self.assertEqual(type(expected), type(actual))
+            else:
+                self.assertEqual(expected, actual)
+
+    def test_train_and_predict(self):
+        # Load data set.
+        X = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['X'],
+                      columns=['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10'])
+        y = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['y'])
+        random_state = RANDOM_CLASSIFICATION_TEST_CASE['random_state']
+        expected_y_pred_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['y_predicted']
+        expected_str_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['str']
+        expected_hyperparams_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['hyperparams']
+        expected_params_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['params']
+        expected_descriptions_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['description']
+
+        # Generate train/test split.
+        X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)
+
+        # Iterate through SUPPORTED_ALGORITHMS.
+        for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+            log.info('Testing %s classifier...' % algorithm)
+            # Train model.
+            hyperparams = {'algorithm': algorithm, 'random_state': random_state}
+            # Default to stochastic search for expensive algorithms.
+            if algorithm in [SupervisedClassifier.RANDOM_FOREST]:
+                hyperparams['hyperparam_strategy'] = SupervisedClassifier.STOCHASTIC_SEARCH
+                # Test ability to force hyperparam values.
+                hyperparams['max_depth'] = 2
+                hyperparams['n_estimators'] = 5
+                hyperparams['min_samples_leaf'] = 1
+                hyperparams['min_samples_split'] = 0.2
+            else:
+                hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            classifier = SupervisedClassifier([0, 1], hyperparams)
+            classifier.train(X_train, y_train)
+
+            # Test str().
+            expected_str = expected_str_by_algorithm[algorithm]
+            actual_str = str(classifier)
+            self.assertEqual(expected_str, actual_str)
+
+            # Test hyperparameters.
+            expected_hyperparams = expected_hyperparams_by_algorithm[algorithm]
+            actual_hyperparams = classifier.hyperparams()
+            self._assert_equal_hyperparams(expected_hyperparams, actual_hyperparams)
+
+            # Test model parameters.
+            expected_params = expected_params_by_algorithm[algorithm]
+            actual_params = classifier.params()
+            self.assertEqualDict(expected_params, actual_params)
+
+            # Test model description.
+            expected_description = expected_descriptions_by_algorithm[algorithm]
+            actual_description = classifier.description()
+            self.assertEqual(expected_description, actual_description)
+
+            # Test prediction values.
+            expected_y_pred = expected_y_pred_by_algorithm[algorithm]
+            log.debug('expected_y_pred: %s' % expected_y_pred)
+            actual_y_pred = classifier.predict(X_test)
+            log.debug('actual_y_pred: %s' % actual_y_pred)
+            self.assertEqualList(expected_y_pred, actual_y_pred)
+
+
+def suite():
+    """
+    Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test".
+    """
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestSupervisedClassifier))
+    return suite
+
+
+if __name__ == "__main__":
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite())
diff --git a/medinfo/ml/test/TestSupervisedLearner.py b/medinfo/ml/test/TestSupervisedLearner.py
new file mode 100644
index 00000000..1898c4cd
--- /dev/null
+++ b/medinfo/ml/test/TestSupervisedLearner.py
@@ -0,0 +1,27 @@
+
+import unittest
+import medinfo.ml.SupervisedLearner as SupervisedLearner
+from . import SupervisedLearnerTestData
+
+class TestSupervisedClassifier(unittest.TestCase):
+    def setUp(self):
+        unittest.TestCase.setUp(self)
+
+    def tearDown(self):
+        unittest.TestCase.tearDown(self)
+
+
+    def test_no_patient_leak(self):
+        processed_matrix = SupervisedLearnerTestData.processed_matrix
+
+        X_train, _, X_test, _ = SupervisedLearner.train_test_split(
+            processed_matrix, outcome_label="all_components_normal")
+
+        patIds_train = X_train.pop('pat_id').values.tolist()
+        patIds_test = X_test.pop('pat_id').values.tolist()
+        self.assertFalse(set(patIds_train) & set(patIds_test))
+
+
+
+
+
diff --git a/medinfo/ml/test/__init__.py b/medinfo/ml/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/medinfo/ml/test/expected-linear-predictor.report b/medinfo/ml/test/expected-linear-predictor.report
new file mode 100644
index 00000000..bb9d3dbf
--- /dev/null
+++ b/medinfo/ml/test/expected-linear-predictor.report
@@ -0,0 +1,2 @@
+model	test_size	accuracy	explained_variance	mean_absolute_error	median_absolute_error	r2
+LinearPredictor([86, 19])	10	0	0.99992	0.84441	0.74944	0.99991
diff --git a/medinfo/ml/test/expected-list-predictor.report b/medinfo/ml/test/expected-list-predictor.report
new file mode 100644
index 00000000..7adf96ff
--- /dev/null
+++ b/medinfo/ml/test/expected-list-predictor.report
@@ -0,0 +1,2 @@
+model	test_size	accuracy
+ListPredictor([ 1  2  3  4  5  6  7  8  9 10])	10	0.30000
diff --git a/medinfo/sequenceanalysis/CountRepeatNormals.py b/medinfo/sequenceanalysis/CountRepeatNormals.py
new file mode 100644
index 00000000..df89c1cd
--- /dev/null
+++ b/medinfo/sequenceanalysis/CountRepeatNormals.py
@@ -0,0 +1,88 @@
+from collections import defaultdict
+import numpy as np
+
+from .SequenceAnalyzer import SequenceAnalyzer
+
+class CountRepeatNormals(object):
+  def __init__(self, patient_col, labresult_col, datetime_col, window_sizes):
+    self.window_sizes = window_sizes
+    self.global_stats = defaultdict(lambda: defaultdict(lambda: np.array([0, 0])))
+
+    # Extract the patient_id from a row, where patient_id is in column 0
+    def extract_patient_id_key_fn(row):
+      return row[patient_col]
+
+    def handle_sentinel_queue_fn(window_size, vars_dict, sentinel, row):
+      vars_dict['prior_history'] = bool(row[datetime_col] - sentinel[datetime_col] <= window_size)
+
+    # After running filter/pop queue, if results in empty queue, then do stuff here (reset prior_history record having no prior records within window / as opposed to an abnormal value being present before)
+    def emptied_queue_handler_fn(window_size, vars_dict, row):
+      vars_dict['prior_history'] = False
+
+    # Acts as the mapper of a map-reduce step, where the "reduce" is left up to the user
+    def extract_key_value_fn(window_size, queue, vars_dict):
+      if vars_dict['prior_history']:
+        # if there is prior_history, set number_consecutive_normals as usual
+        number_consecutive_normals = len(queue)
+      else:
+        # else set number_consecutive_normals as None
+        number_consecutive_normals = None
+      return (window_size.days, number_consecutive_normals), 1
+
+    # Extract the datetime from a row, where datetime is in column 2
+    def extract_datetime_fn(row):
+      return row[datetime_col]
+
+    # Column 1 of row is a string that contains the lab result
+    def add_row_condition_fn(window_size, queue, vars_dict, row):
+      return 'InRange' in row[labresult_col]
+
+    def clear_queue_condition_fn(window_size, queue, vars_dict, row):
+      return not vars_dict['row_added']
+
+    self.sequence_analyzer = SequenceAnalyzer()
+
+    # Basically doing split by patientId, assuming that patientId is the first column
+    self.sequence_analyzer.split_data_on_key(extract_patient_id_key_fn)
+
+    # Prior_history reflects whether something was previously in queue. Init vars allows pass in parameter dictionary
+    self.sequence_analyzer.initialize_vars({'prior_history': False})
+
+    # If queue is just a sentinel, this will evaluate. Example sentinel value: Cleared queue after every abnormal test, but still need to keep track of date of abnormal value
+    # Check whether queue has a single sentinel value, but need some residual value (e.g., last date to allow maintenance of prior_history)
+    self.sequence_analyzer.handle_sentinel_queue(handle_sentinel_queue_fn)
+
+    # Pop queue criteria, for example to remove items from head of queue as progress
+    self.sequence_analyzer.pop_queue(extract_datetime_fn, emptied_queue_handler_fn)
+
+    self.sequence_analyzer.extract_key_value(extract_key_value_fn)
+
+    self.sequence_analyzer.add_row(add_row_condition_fn)
+    self.sequence_analyzer.clear_queue(clear_queue_condition_fn, add_sentinel=True)
+
+    self.sequence_analyzer.set_var('prior_history', lambda window_size, queue, vars_dict, row: True)
+
+    self.sequence_analyzer.build(2)
+
+  def run(self, base_name, results):
+    counts = defaultdict(lambda: np.array([0, 0]))
+    for return_values in self.sequence_analyzer.run(results, self.window_sizes):
+      (key, value), row_added = return_values
+      counts[key][0] += value
+      if row_added:
+        counts[key][1] += value
+
+    # switch (window_size, 0) to mean no priors,
+    # and switch (window_size, None) to be the total count
+    total_counts = defaultdict(lambda: np.array([0, 0]))
+    for k, v in counts.items():
+      # Aggregate total number of results and positive results
+      total_counts[k[0]] += v
+    for window_size in self.window_sizes:
+      # Set (1, 0) to (1, None)
+      counts[(window_size.days, 0)] = counts[(window_size.days, None)]
+      # Set (window_size, None) to the sum of calculated above
+      counts[(window_size.days, None)] = total_counts[window_size.days]
+
+    for k, v in counts.items():
+      self.global_stats[base_name][k] += v
diff --git a/medinfo/sequenceanalysis/SequenceAnalyzer.py b/medinfo/sequenceanalysis/SequenceAnalyzer.py
new file mode 100644
index 00000000..273cfe3f
--- /dev/null
+++ b/medinfo/sequenceanalysis/SequenceAnalyzer.py
@@ -0,0 +1,130 @@
+import types
+from collections import deque
+
+class SequenceAnalyzer(object):
+  def __init__(self):
+    self.initialized_add_row = False
+    self.initialized_extract_key_value = False
+    self.initialized_pop_queue = False
+    self.num_return_values = 0
+    self.built = False
+    self.pipeline = []
+
+  #TODO: do something cool where each function's input variables can change / are fluid
+  def split_data_on_key(self, extract_split_key_fn):
+    assert len(self.pipeline) == 0
+    def func(data):
+      if isinstance(data, types.GeneratorType):
+        current_split = [next(data)]
+      elif isinstance(data, list):
+        current_split = [data[0]]
+        data = data[1:]
+      for row in data:
+        if extract_split_key_fn(row) != extract_split_key_fn(current_split[-1]):
+          yield current_split
+          current_split = []
+        current_split.append(row)
+      yield current_split
+    self.pipeline.append(('split_data', func))
+
+  def initialize_vars(self, vars_dict):
+    self.vars = vars_dict
+
+  def handle_sentinel_queue(self, handle_sentinel_queue_fn):
+    def func(window_size, queue, vars_dict, row):
+      if queue and queue[0][0] is None:
+        handle_sentinel_queue_fn(window_size, vars_dict, queue[0][1], row)
+        queue.clear()
+    self.pipeline.append(('handle_sentinel_queue', func))
+
+  def pop_queue(self, extract_datetime_fn, emptied_queue_handler_fn=None):
+    def func(window_size, queue, vars_dict, row):
+      popped_queue = False
+      if queue and queue[0][0] is not None:
+        while queue and extract_datetime_fn(row) - extract_datetime_fn(queue[0]) > window_size:
+          popped_queue = True
+          queue.popleft()
+      if emptied_queue_handler_fn is not None and popped_queue and not queue:
+        emptied_queue_handler_fn(window_size, vars_dict, row)
+    self.initialized_pop_queue = True
+    self.pipeline.append(('pop_queue', func))
+
+  def extract_key_value(self, extract_key_value_fn):
+    self.pipeline.append(('extract_key_value', extract_key_value_fn))
+    self.initialized_extract_key_value = True
+    self.num_return_values += 1
+
+  def set_var(self, var_name, set_value_fn):
+    def func(window_size, queue, vars_dict, row):
+      vars_dict[var_name] = set_value_fn(window_size, queue, vars_dict, row)
+    self.pipeline.append(('set_var', func))
+
+  def add_row(self, condition_fn):
+    def func(window_size, queue, vars_dict, row):
+      vars_dict['row_added'] = False
+      if condition_fn(window_size, queue, vars_dict, row):
+        vars_dict['row_added'] = True
+        queue.append(row)
+      return vars_dict['row_added']
+
+    self.pipeline.append(('add_row', func))
+    self.initialized_add_row = True
+    self.num_return_values += 1
+
+  def clear_queue(self, condition_fn, add_sentinel=False):
+    def func(window_size, queue, vars_dict, row):
+      if condition_fn(window_size, queue, vars_dict, row):
+        queue.clear()
+        if add_sentinel:
+          queue.append((None, row))
+    self.pipeline.append(('clear_queue', func))
+
+  def build(self, num_return_values):
+    # Ensure that extract_key_value, pop_queue, and add_row are all called,
+    # the module will not function properly if they aren't called
+    assert self.initialized_add_row
+    assert self.initialized_extract_key_value
+    assert self.initialized_pop_queue
+
+    # Checks that user knows how many values are going to be returned
+    assert num_return_values == self.num_return_values
+    self.built = True
+
+  def run(self, data, bins):
+    # check if built
+    assert self.built
+    data_split_generator = data
+    pipeline = self.pipeline
+    if self.pipeline[0][0] == 'split_data':
+      data_split_generator = self.pipeline[0][1](data)
+      pipeline = pipeline[1:]
+
+    for data_split in data_split_generator:
+      bins_queue = [deque() for _ in range(len(bins))]
+      bins_vars_dict = [dict(self.vars) for _ in range(len(bins))]
+      for row in data_split:
+        for window_size, queue, vars_dict in zip(bins, bins_queue, bins_vars_dict):
+          vars_dict['row_added'] = False
+          return_values = []
+          for name, func in pipeline:
+            if name == 'handle_sentinel_queue':
+              func(window_size, queue, vars_dict, row)
+            elif name == 'pop_queue':
+              func(window_size, queue, vars_dict, row)
+            elif name == 'set_var':
+              func(window_size, queue, vars_dict, row)
+            elif name == 'extract_key_value':
+              return_values.append(func(window_size, queue, vars_dict))
+            elif name == 'add_row':
+              return_values.append(func(window_size, queue, vars_dict, row))
+            elif name == 'clear_queue':
+              func(window_size, queue, vars_dict, row)
+          yield return_values
+
+
+class utils(object):
+  NUMBER_SECONDS_IN_A_DAY = 86400
+
+  @classmethod
+  def get_day_difference(cls, datetime1, datetime2):
+    return (datetime1 - datetime2).total_seconds() / cls.NUMBER_SECONDS_IN_A_DAY
diff --git a/medinfo/sequenceanalysis/__init__.py b/medinfo/sequenceanalysis/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/medinfo/sequenceanalysis/test/TestCountRepeatNormals.py b/medinfo/sequenceanalysis/test/TestCountRepeatNormals.py
new file mode 100644
index 00000000..f07f0859
--- /dev/null
+++ b/medinfo/sequenceanalysis/test/TestCountRepeatNormals.py
@@ -0,0 +1,251 @@
+import datetime
+import numpy as np
+import unittest
+from collections import defaultdict
+
+from medinfo.sequenceanalysis.CountRepeatNormals import CountRepeatNormals
+
+class TestCountRepeatNormals(unittest.TestCase):
+
+  def setUp(self):
+    """Prepare state for test cases"""
+    pass
+
+  def tearDown(self):
+    """Restore state from any setUp or test steps"""
+    pass
+
+  def testCountRepeatNormalsBulk(self):
+    """Lab test counting normal and abnormal consecutive lab tests"""
+    data = [
+      ('11210R', [
+        [-11380099600907, '11210R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11210R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11210R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11210R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11210R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11210R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11210R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11210R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11210R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11210R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11210R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11210R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11210R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11210R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11210R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11210R(InRange)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11210R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11210R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11210R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11210R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11210R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11210R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11210R(InRange)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11210R(InRange)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11210R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11210R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11210R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11210R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11210R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11210R(High)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ]),
+      ('11211R', [
+        [-11380099600907, '11211R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11211R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11211R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11211R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11211R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11211R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11211R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11211R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11211R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11211R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11211R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11211R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11211R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11211R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11211R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11211R(InRange)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11211R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11211R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11211R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11211R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11211R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11211R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11211R(InRange)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11211R(InRange)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11211R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11211R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11211R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11211R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11211R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11211R(InRange)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ]),
+      ('11212R', [
+        [-11380099600907, '11212R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11212R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11212R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11212R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11212R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11212R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11212R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11212R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11212R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11212R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11212R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11212R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11212R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11212R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11212R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11212R(InRange)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11212R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11212R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11212R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11212R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11212R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11212R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11212R(High)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11212R(High)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11212R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11212R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11212R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11212R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11212R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11212R(InRange)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ]),
+      ('11213R', [
+        [-11380099600907, '11213R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11213R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11213R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11213R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11213R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11213R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11213R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11213R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11213R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11213R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11213R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11213R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11213R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11213R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11213R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11213R(High)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11213R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11213R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11213R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11213R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11213R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11213R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11213R(InRange)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11213R(InRange)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11213R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11213R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11213R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11213R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11213R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11213R(InRange)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ])
+    ]
+
+    # window_sizes = [1, 2, 4, 7, 30, 90] # list of window sizes to evaluate
+    window_sizes = [datetime.timedelta(days=size) for size in [1, 2, 4, 7, 30, 90]] # list of window sizes to evaluate
+
+    sequence_analyzer = CountRepeatNormals(patient_col=0, labresult_col=1, datetime_col=2, window_sizes=window_sizes)
+
+    for base_name, results in data:
+      sequence_analyzer.run(base_name, results)
+
+    def normalize_dict(d):
+      # flatten defaultdict and np.array types to dict and list
+      normalized_d = {}
+      for k1, v1 in d.items():
+        normalized_d[k1] = {k2: list(v2) for k2, v2 in v1.items()}
+      return normalized_d
+
+    global_stats = normalize_dict(sequence_analyzer.global_stats)
+
+    expectedResults = {
+      '11213R': {
+        (90, None): [30, 22],
+        (30, 1): [6, 6],
+        (30, None): [30, 22],
+        (7, None): [30, 22],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17, 10],
+        (4, None): [30, 22],
+        (30, 0): [17, 10],
+        (1, None): [30, 22],
+        (2, 0): [29, 21],
+        (2, None): [30, 22],
+        (7, 0): [24, 16],
+        (1, 0): [30, 22],
+        (4, 1): [1, 1],
+        (4, 0): [28, 20]},
+      '11210R': {
+        (90, None): [30, 22],
+        (30, 1): [6, 6],
+        (30, None): [30, 22],
+        (7, None): [30, 22],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17,  9],
+        (4, None): [30, 22],
+        (30, 0): [17,  9],
+        (1, None): [30, 22],
+        (2, 0): [29, 21],
+        (2, None): [30, 22],
+        (7, 0): [24, 16],
+        (1, 0): [30, 22],
+        (4, 1): [1, 1],
+        (4, 0): [28, 20]},
+      '11212R': {
+        (90, None): [30, 21],
+        (30, 1): [6, 6],
+        (30, None): [30, 21],
+        (7, None): [30, 21],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17,  8],
+        (4, None): [30, 21],
+        (30, 0): [17,  8],
+        (1, None): [30, 21],
+        (2, 0): [29, 20],
+        (2, None): [30, 21],
+        (7, 0): [24, 15],
+        (1, 0): [30, 21],
+        (4, 1): [1, 1],
+        (4, 0): [28, 19]},
+      '11211R': {
+        (90, None): [30, 23],
+        (30, 1): [6, 6],
+        (30, None): [30, 23],
+        (7, None): [30, 23],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17, 10],
+        (4, None): [30, 23],
+        (30, 0): [17, 10],
+        (1, None): [30, 23],
+        (2, 0): [29, 22],
+        (2, None): [30, 23],
+        (7, 0): [24, 17],
+        (1, 0): [30, 23],
+        (4, 1): [1, 1],
+        (4, 0): [28, 21]}
+      }
+    self.assertEqual(expectedResults, global_stats)
+
+def suite():
+  """Returns the suite of tests to run for this test class / module.
+  Use unittest.makeSuite methods which simply extracts all of the
+  methods for the given class whose name starts with "test"
+  """
+  suite = unittest.TestSuite()
+  suite.addTest(unittest.makeSuite(TestCountRepeatNormals))
+  return suite
+
+
+if __name__ == "__main__":
+  unittest.TextTestRunner(verbosity=2).run(suite())
diff --git a/medinfo/sequenceanalysis/test/TestSequenceAnalyzer.py b/medinfo/sequenceanalysis/test/TestSequenceAnalyzer.py
new file mode 100644
index 00000000..560b18e6
--- /dev/null
+++ b/medinfo/sequenceanalysis/test/TestSequenceAnalyzer.py
@@ -0,0 +1,326 @@
+import datetime
+import numpy as np
+import unittest
+from collections import defaultdict
+
+from medinfo.sequenceanalysis.SequenceAnalyzer import SequenceAnalyzer
+
+class TestSequenceAnalyzer(unittest.TestCase):
+
+  def setUp(self):
+    """Prepare state for test cases"""
+    pass
+
+  def tearDown(self):
+    """Restore state from any setUp or test steps"""
+    pass
+
+  def testSequenceAnalyzerBulk(self):
+    """Lab test counting normal and abnormal consecutive lab tests"""
+    data = [
+      ('11210R', [
+        [-11380099600907, '11210R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11210R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11210R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11210R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11210R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11210R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11210R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11210R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11210R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11210R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11210R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11210R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11210R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11210R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11210R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11210R(InRange)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11210R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11210R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11210R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11210R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11210R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11210R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11210R(InRange)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11210R(InRange)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11210R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11210R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11210R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11210R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11210R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11210R(High)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ]),
+      ('11211R', [
+        [-11380099600907, '11211R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11211R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11211R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11211R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11211R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11211R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11211R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11211R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11211R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11211R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11211R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11211R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11211R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11211R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11211R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11211R(InRange)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11211R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11211R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11211R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11211R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11211R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11211R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11211R(InRange)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11211R(InRange)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11211R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11211R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11211R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11211R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11211R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11211R(InRange)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ]),
+      ('11212R', [
+        [-11380099600907, '11212R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11212R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11212R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11212R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11212R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11212R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11212R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11212R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11212R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11212R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11212R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11212R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11212R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11212R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11212R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11212R(InRange)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11212R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11212R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11212R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11212R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11212R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11212R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11212R(High)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11212R(High)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11212R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11212R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11212R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11212R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11212R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11212R(InRange)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ]),
+      ('11213R', [
+        [-11380099600907, '11213R(InRange)', datetime.datetime(2012, 9, 10, 7, 25)],
+        [-11380099600907, '11213R(InRange)', datetime.datetime(2012, 9, 24, 9, 24)],
+        [-10226028839621, '11213R(Result)', datetime.datetime(2012, 3, 16, 14, 24)],
+        [-10226028839621, '11213R(InRange)', datetime.datetime(2012, 3, 24, 8, 2)],
+        [-9834093246550, '11213R(Result)', datetime.datetime(2013, 10, 1, 15, 24)],
+        [-9834093246550, '11213R(InRange)', datetime.datetime(2013, 10, 9, 7, 10)],
+        [-8443896915633, '11213R(InRange)', datetime.datetime(2013, 5, 1, 6, 55)],
+        [-8443896915633, '11213R(InRange)', datetime.datetime(2013, 5, 5, 8, 5)],
+        [-5023114876861, '11213R(InRange)', datetime.datetime(2011, 10, 16, 13, 37)],
+        [-5023114876861, '11213R(InRange)', datetime.datetime(2011, 10, 20, 15, 25)],
+        [-4423981885898, '11213R(InRange)', datetime.datetime(2012, 11, 17, 8, 11)],
+        [-4423981885898, '11213R(InRange)', datetime.datetime(2012, 11, 25, 9, 29)],
+        [-3908850479087, '11213R(InRange)', datetime.datetime(2010, 9, 9, 16, 43)],
+        [-3908850479087, '11213R(InRange)', datetime.datetime(2010, 9, 13, 15, 11)],
+        [-1872296128547, '11213R(Result)', datetime.datetime(2013, 12, 1, 21, 39)],
+        [-1872296128547, '11213R(High)', datetime.datetime(2013, 12, 10, 13, 34)],
+        [-1378995679303, '11213R(Result)', datetime.datetime(2010, 3, 9, 14, 17)],
+        [-1378995679303, '11213R(InRange)', datetime.datetime(2010, 3, 11, 9, 31)],
+        [8662609401678, '11213R(InRange)', datetime.datetime(2012, 7, 6, 12, 19)],
+        [8662609401678, '11213R(InRange)', datetime.datetime(2012, 7, 11, 8, 48)],
+        [8848376950672, '11213R(Result)', datetime.datetime(2013, 10, 30, 14, 56)],
+        [8848376950672, '11213R(InRange)', datetime.datetime(2013, 11, 6, 6, 50)],
+        [9472424502184, '11213R(InRange)', datetime.datetime(2011, 1, 13, 6, 55)],
+        [9472424502184, '11213R(InRange)', datetime.datetime(2013, 2, 2, 9, 40)],
+        [9592401025493, '11213R(Result)', datetime.datetime(2013, 1, 29, 1, 3)],
+        [9592401025493, '11213R(InRange)', datetime.datetime(2013, 2, 11, 9, 1)],
+        [9924214318080, '11213R(Result)', datetime.datetime(2012, 6, 8, 8, 32)],
+        [9924214318080, '11213R(InRange)', datetime.datetime(2012, 6, 16, 10, 20)],
+        [11286739503688, '11213R(InRange)', datetime.datetime(2011, 8, 10, 13, 56)],
+        [11286739503688, '11213R(InRange)', datetime.datetime(2011, 11, 19, 12, 23)]
+      ])
+    ]
+
+    # Extract the patient_id from a row, where patient_id is in column 0
+    def extract_patient_id_key_fn(row):
+      return row[0]
+
+    def handle_sentinel_queue_fn(window_size, vars_dict, sentinel, row):
+      vars_dict['prior_history'] = bool(row[2] - sentinel[2] <= window_size)
+
+    # After running filter/pop queue, if results in empty queue, then do stuff here (reset prior_history record having no prior records within window / as opposed to an abnormal value being present before)
+    def emptied_queue_handler_fn(window_size, vars_dict, row):
+      vars_dict['prior_history'] = False
+
+    # Acts as the mapper of a map-reduce step, where the "reduce" is left up to the user
+    def extract_key_value_fn(window_size, queue, vars_dict):
+      if vars_dict['prior_history']:
+        # if there is prior_history, set number_consecutive_normals as usual
+        number_consecutive_normals = len(queue)
+      else:
+        # else set number_consecutive_normals as None
+        number_consecutive_normals = None
+      return (window_size.days, number_consecutive_normals), 1
+
+    # Extract the datetime from a row, where datetime is in column 2
+    def extract_datetime_fn(row):
+      return row[2]
+
+    # Column 1 of row is a string that contains the lab result
+    def add_row_condition_fn(window_size, queue, vars_dict, row):
+      return 'InRange' in row[1]
+
+    def clear_queue_condition_fn(window_size, queue, vars_dict, row):
+      return not vars_dict['row_added']
+
+    sequence_analyzer = SequenceAnalyzer()
+
+    # Basically doing split by patientId, assuming that patientId is the first column
+    sequence_analyzer.split_data_on_key(extract_patient_id_key_fn)
+
+    # Prior_history reflects whether something was previously in queue. Init vars allows pass in parameter dictionary
+    sequence_analyzer.initialize_vars({'prior_history': False})
+
+    # If queue is just a sentinel, this will evaluate. Example sentinel value: Cleared queue after every abnormal test, but still need to keep track of date of abnormal value
+    # Check whether queue has a single sentinel value, but need some residual value (e.g., last date to allow maintenance of prior_history)
+    sequence_analyzer.handle_sentinel_queue(handle_sentinel_queue_fn)
+
+    # Pop queue criteria, for example to remove items from head of queue as progress
+    sequence_analyzer.pop_queue(extract_datetime_fn, emptied_queue_handler_fn)
+
+    sequence_analyzer.extract_key_value(extract_key_value_fn)
+
+    sequence_analyzer.add_row(add_row_condition_fn)
+    sequence_analyzer.clear_queue(clear_queue_condition_fn, add_sentinel=True)
+
+    sequence_analyzer.set_var('prior_history', lambda window_size, queue, vars_dict, row: True)
+
+    sequence_analyzer.build(2)
+
+    global_stats = defaultdict(lambda: defaultdict(lambda: np.array([0, 0])))
+    # window_sizes = [1, 2, 4, 7, 30, 90] # list of window sizes to evaluate
+    window_sizes = [datetime.timedelta(days=size) for size in [1, 2, 4, 7, 30, 90]] # list of window sizes to evaluate
+
+    for base_name, results in data:
+      counts = defaultdict(lambda: np.array([0, 0]))
+      for return_values in sequence_analyzer.run(results, window_sizes):
+        (key, value), row_added = return_values
+        counts[key][0] += value
+        if row_added:
+          counts[key][1] += value
+
+      # switch (window_size, 0) to mean no priors,
+      # and switch (window_size, None) to be the total count
+      total_counts = defaultdict(lambda: np.array([0, 0]))
+      for k, v in counts.items():
+        # Aggregate total number of results and positive results
+        total_counts[k[0]] += v
+      for window_size in window_sizes:
+        # Set (1, 0) to (1, None)
+        counts[(window_size.days, 0)] = counts[(window_size.days, None)]
+        # Set (window_size, None) to the sum of calculated above
+        counts[(window_size.days, None)] = total_counts[window_size.days]
+
+      for k, v in counts.items():
+        global_stats[base_name][k] += v
+
+    def normalize_dict(d):
+      # flatten defaultdict and np.array types to dict and list
+      normalized_d = {}
+      for k1, v1 in d.items():
+        normalized_d[k1] = {k2: list(v2) for k2, v2 in v1.items()}
+      return normalized_d
+
+    global_stats = normalize_dict(global_stats)
+
+    expectedResults = {
+      '11213R': {
+        (90, None): [30, 22],
+        (30, 1): [6, 6],
+        (30, None): [30, 22],
+        (7, None): [30, 22],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17, 10],
+        (4, None): [30, 22],
+        (30, 0): [17, 10],
+        (1, None): [30, 22],
+        (2, 0): [29, 21],
+        (2, None): [30, 22],
+        (7, 0): [24, 16],
+        (1, 0): [30, 22],
+        (4, 1): [1, 1],
+        (4, 0): [28, 20]},
+      '11210R': {
+        (90, None): [30, 22],
+        (30, 1): [6, 6],
+        (30, None): [30, 22],
+        (7, None): [30, 22],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17,  9],
+        (4, None): [30, 22],
+        (30, 0): [17,  9],
+        (1, None): [30, 22],
+        (2, 0): [29, 21],
+        (2, None): [30, 22],
+        (7, 0): [24, 16],
+        (1, 0): [30, 22],
+        (4, 1): [1, 1],
+        (4, 0): [28, 20]},
+      '11212R': {
+        (90, None): [30, 21],
+        (30, 1): [6, 6],
+        (30, None): [30, 21],
+        (7, None): [30, 21],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17,  8],
+        (4, None): [30, 21],
+        (30, 0): [17,  8],
+        (1, None): [30, 21],
+        (2, 0): [29, 20],
+        (2, None): [30, 21],
+        (7, 0): [24, 15],
+        (1, 0): [30, 21],
+        (4, 1): [1, 1],
+        (4, 0): [28, 19]},
+      '11211R': {
+        (90, None): [30, 23],
+        (30, 1): [6, 6],
+        (30, None): [30, 23],
+        (7, None): [30, 23],
+        (90, 1): [6, 6],
+        (7, 1): [4, 4],
+        (90, 0): [17, 10],
+        (4, None): [30, 23],
+        (30, 0): [17, 10],
+        (1, None): [30, 23],
+        (2, 0): [29, 22],
+        (2, None): [30, 23],
+        (7, 0): [24, 17],
+        (1, 0): [30, 23],
+        (4, 1): [1, 1],
+        (4, 0): [28, 21]}
+      }
+    self.assertEqual(expectedResults, global_stats)
+
+def suite():
+  """Returns the suite of tests to run for this test class / module.
+  Use unittest.makeSuite methods which simply extracts all of the
+  methods for the given class whose name starts with "test"
+  """
+  suite = unittest.TestSuite()
+  suite.addTest(unittest.makeSuite(TestSequenceAnalyzer))
+  return suite
+
+
+if __name__ == "__main__":
+  unittest.TextTestRunner(verbosity=2).run(suite())
diff --git a/medinfo/sequenceanalysis/test/__init__.py b/medinfo/sequenceanalysis/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/medinfo/textanalysis/AVSParse.py b/medinfo/textanalysis/AVSParse.py
index eefe5dd7..7a4452a9 100644
--- a/medinfo/textanalysis/AVSParse.py
+++ b/medinfo/textanalysis/AVSParse.py
@@ -10,7 +10,7 @@
 import re, string;
 from datetime import datetime;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.db.Model import columnFromModelList;
 from medinfo.common.Const import COMMENT_TAG;
 from medinfo.common.Util import stdOpen, ProgressDots;
@@ -18,8 +18,8 @@
 from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
 from medinfo.db.Model import RowItemModel;
 
-from BaseTextAnalysis import BaseTextAnalysis, BaseQuestionModule, SectionLineCountQuestion;
-from Util import log;
+from .BaseTextAnalysis import BaseTextAnalysis, BaseQuestionModule, SectionLineCountQuestion;
+from .Util import log;
 
 SECTION_HEADERS = \
     set([   "This is your After Visit Summary",
@@ -327,7 +327,7 @@ def main(self, argv):
 
             # Print comment line with arguments to allow for deconstruction later as well as extra results
             summaryData = {"argv": argv};
-            print >> outputFile, "<!-- %s -->" % json.dumps(summaryData);
+            print("<!-- %s -->" % json.dumps(summaryData), file=outputFile);
 
             # Run the actual analysis
             self(sourceFile, outputFile);
diff --git a/medinfo/textanalysis/BaseTextAnalysis.py b/medinfo/textanalysis/BaseTextAnalysis.py
index 1dc9f363..6e83d7d8 100644
--- a/medinfo/textanalysis/BaseTextAnalysis.py
+++ b/medinfo/textanalysis/BaseTextAnalysis.py
@@ -9,7 +9,7 @@
 import time;
 import re, string;
 from optparse import OptionParser
-from cStringIO import StringIO;
+from io import StringIO;
 from math import sqrt;
 from datetime import timedelta;
 
@@ -18,7 +18,7 @@
 from medinfo.db.ResultsFormatter import TextResultsFormatter, HtmlResultsFormatter, TabDictReader;
 from medinfo.db.Model import SQLQuery, RowItemModel;
 from medinfo.db.Model import modelListFromTable, modelDictFromList;
-from Util import log;
+from .Util import log;
 
 HTML_START = """<html><head><script>%(script)s</script><style>%(style)s</style></head><body><form name="mainForm">""";
 HTML_END = """</form></body></html>""";
@@ -61,11 +61,11 @@ def __call__(self, sourceFile, outputFile):
 
         headerData = {"script": HTML_SCRIPT, "style": HTML_STYLE}
 
-        print >> outputFile, HTML_START % headerData;
+        print(HTML_START % headerData, file=outputFile);
         nextRecord = None;
         lastLine = None;
 
-        print >> outputFile, '''<table class="dataTable" cellspacing=0 cellpadding=4>''';
+        print('''<table class="dataTable" cellspacing=0 cellpadding=4>''', file=outputFile);
         iRecord = 0;
         for line in sourceFile:
             if self.isNewRecordLine(line):
@@ -101,11 +101,11 @@ def __call__(self, sourceFile, outputFile):
         prog.update();
         # prog.printStatus();
 
-        print >> outputFile, '''</table>''';
+        print('''</table>''', file=outputFile);
 
         self.outputSummaryRecords(summaryRecords, outputFile);
 
-        print >> outputFile, HTML_END;
+        print(HTML_END, file=outputFile);
 
         #for header in headerSet:
         #    print >> sys.stdout, header;
@@ -157,8 +157,7 @@ def outputRecordDetail(self, record, outputFile):
         if self.skipDetail:
             return;
 
-        print >> outputFile, \
-            '''<tr><th class="subheading">iRecord</th>
+        print('''<tr><th class="subheading">iRecord</th>
                     <th class="subheading">Note ID</th>
                     <th class="subheading">MRN</th>
                     <th class="subheading">Date</th>
@@ -171,52 +170,51 @@ def outputRecordDetail(self, record, outputFile):
                     <td class="labelCell" align=center>%(contact_date)s</td>
 
                     <td class="labelCell" align=center>&nbsp;</td>
-                </tr>''' % record;
+                </tr>''' % record, file=outputFile);
 
-        print >> outputFile, '''<tr valign=top><td colspan=4 width="70%"><div style="overflow-y: scroll; height: 400px">''';
+        print('''<tr valign=top><td colspan=4 width="70%"><div style="overflow-y: scroll; height: 400px">''', file=outputFile);
 
         lastLineModel = None;
         for lineModel in record["docModel"]["lineModels"]:
             isSectionHeader = lastLineModel is not None and lineModel["section"] != lastLineModel["section"];
             if isSectionHeader:
-                print >> outputFile, "<u>",;
+                print("<u>", end=' ', file=outputFile);
 
             for tokenModel in lineModel["tokenModels"]:
                 if len(tokenModel["questionNames"]) > 0:
-                    print >> outputFile, '<a name="%(iRecord)d.%(questionNames)s" href="javascript:setQuestionsByName(\'%(questionNames)s\', %(iRecord)d)">' % tokenModel,;
+                    print('<a name="%(iRecord)d.%(questionNames)s" href="javascript:setQuestionsByName(\'%(questionNames)s\', %(iRecord)d)">' % tokenModel, end=' ', file=outputFile);
 
-                print >> outputFile, tokenModel["rawToken"],;
+                print(tokenModel["rawToken"], end=' ', file=outputFile);
 
                 if len(tokenModel["questionNames"]) > 0:
-                    print >> outputFile, "</a>",;
+                    print("</a>", end=' ', file=outputFile);
 
             if isSectionHeader:
-                print >> outputFile, "</u>",;
-            print >> outputFile, "<br>";
+                print("</u>", end=' ', file=outputFile);
+            print("<br>", file=outputFile);
             lastLineModel = lineModel;
 
-        print >> outputFile, '''<div></td><td style="padding: 0">'''
+        print('''<div></td><td style="padding: 0">''', file=outputFile)
         self.outputRecordQuestions(record, outputFile);
-        print >> outputFile, '''</td>'''
-        print >> outputFile, \
-            '''</td></tr>
+        print('''</td>''', file=outputFile)
+        print('''</td></tr>
             <tr><td class=line height=1 colspan=100></td></tr>
-            ''';
+            ''', file=outputFile);
 
     def outputRecordQuestions(self, record, outputFile):
-        print >> outputFile, '''<table cellspacing=0 cellpadding=2 style="width: 100%;">''';
-        print >> outputFile, '''<tr><th class="labelCell"><!-- input type=checkbox name="allQuestionsCheck" onClick="javascript:checkAllQuestions(this, %(iRecord)d)" --></th>''' % record;
-        print >> outputFile, '''<th class="labelCell">Name</th><th class="labelCell">Guess</th><th class="labelCell" align=left>Notes</th></tr>''';
+        print('''<table cellspacing=0 cellpadding=2 style="width: 100%;">''', file=outputFile);
+        print('''<tr><th class="labelCell"><!-- input type=checkbox name="allQuestionsCheck" onClick="javascript:checkAllQuestions(this, %(iRecord)d)" --></th>''' % record, file=outputFile);
+        print('''<th class="labelCell">Name</th><th class="labelCell">Guess</th><th class="labelCell" align=left>Notes</th></tr>''', file=outputFile);
         for questionModule in self.questionModules:
             questionData = {"iRecord": record["iRecord"], "name": questionModule.getName(), "answer": questionModule.formatAnswer(record["docModel"]), "notes": questionModule.formatNotes(record["docModel"]) }
-            print >> outputFile, '''<tr id="questionName.%(iRecord)d.%(name)s" onClick="javascript:checkQuestionByName('%(name)s', %(iRecord)d)"><td align=center><input type=checkbox name="questionCheck.%(iRecord)d" value="%(name)s" onClick="return checkQuestion(this);"></td>''' % questionData;
-            print >> outputFile, '''<td align=center>%(name)s</td><td align=center>%(answer)s</td><td>%(notes)s</td></tr>''' % questionData;
+            print('''<tr id="questionName.%(iRecord)d.%(name)s" onClick="javascript:checkQuestionByName('%(name)s', %(iRecord)d)"><td align=center><input type=checkbox name="questionCheck.%(iRecord)d" value="%(name)s" onClick="return checkQuestion(this);"></td>''' % questionData, file=outputFile);
+            print('''<td align=center>%(name)s</td><td align=center>%(answer)s</td><td>%(notes)s</td></tr>''' % questionData, file=outputFile);
 
-        print >> outputFile, '''</table>''';
+        print('''</table>''', file=outputFile);
 
     def extractSummaryRecord(self, record):
         summaryRecord = dict();
-        for key, value in record.iteritems():
+        for key, value in record.items():
             if not key.startswith(self.documentHeader):
                 summaryRecord[key] = value;
         return summaryRecord;
@@ -229,9 +227,9 @@ def outputSummaryRecords(self, summaryRecords, outputFile):
             headers.append(questionModule.getName());
             controlHeaders.append(questionModule.getName()+".link");
 
-        print >> outputFile, '''<br>
+        print('''<br>
             <table class="dataTable" cellspacing=0 cellpadding=4 style="width: 100%">
-            <tr><th class="subheading" colspan=100>Summary Table</th></tr>''';
+            <tr><th class="subheading" colspan=100>Summary Table</th></tr>''', file=outputFile);
 
         textAreaRows = 50;
         if not self.skipDetail:
@@ -249,16 +247,16 @@ def outputSummaryRecords(self, summaryRecords, outputFile):
 
 
         # Raw result content for copy-paste to spreadsheet
-        print >> outputFile, '''<tr><td class="labelCell" style="color: 808080" colspan=100>Raw Table (Select All and Copy-Paste to Spreadsheet)</td></tr>''';
-        print >> outputFile, '''<tr><td colspan=100><textarea style="width: 100%%;" disabled rows=%d>''' % textAreaRows;
+        print('''<tr><td class="labelCell" style="color: 808080" colspan=100>Raw Table (Select All and Copy-Paste to Spreadsheet)</td></tr>''', file=outputFile);
+        print('''<tr><td colspan=100><textarea style="width: 100%%;" disabled rows=%d>''' % textAreaRows, file=outputFile);
         formatter = TextResultsFormatter(outputFile);
         formatter.formatTuple(headers);
         for summaryRecord in summaryRecords:
             formatter.formatResultDict(summaryRecord, headers);
-        print >> outputFile, '''</textarea></td></tr>''';
+        print('''</textarea></td></tr>''', file=outputFile);
 
-        print >> outputFile, '''</table>''';
-        print >> outputFile, "%d Records Processed" % len(summaryRecords);
+        print('''</table>''', file=outputFile);
+        print("%d Records Processed" % len(summaryRecords), file=outputFile);
 
     def tokenizeDocument(self, rawText, tokenizeOps):
         """Convert raw text string into a structured, tokenized format.
@@ -313,7 +311,7 @@ def annotateTokenModel(self, tokenModel):
         tokenModel["lastAlnum"] = (tokenModel["length"] > 0 and token[-1].isalnum());
         tokenModel["lastAlpha"] = (tokenModel["length"] > 0 and token[-1].isalpha());
         tokenModel["lastDigit"] = (tokenModel["length"] > 0 and token[-1].isdigit());
-        tokenModel["noPunctuationToken"] = token.translate(None, string.punctuation);  # Token after discarding any punctuation characters.  Should not need to discard whitespace characters, since token splitting already separates them
+        tokenModel["noPunctuationToken"] = token.translate(str.maketrans('', '', string.punctuation));  # Token after discarding any punctuation characters.  Should not need to discard whitespace characters, since token splitting already separates them
 
     def addParserOptions(self, parser):
         """Base command-line parser options
@@ -386,7 +384,7 @@ def extractPhoneTokenModels(self, iStartToken, tokenModels):
         numDigits = 0;
         # Iterate from iStartToken and accumulate list of all numeric tokens (after discarding punctuation) until
         #   reach non-numeric token or reach expected phone number size
-        for iToken in xrange(iStartToken, len(tokenModels)):
+        for iToken in range(iStartToken, len(tokenModels)):
             tokenModel = tokenModels[iToken];
             if tokenModel["noPunctuationToken"].isdigit():
                 numericTokenModels.append(tokenModel);
diff --git a/medinfo/textanalysis/Const.py b/medinfo/textanalysis/Const.py
index 1da09b68..f10e0b09 100644
--- a/medinfo/textanalysis/Const.py
+++ b/medinfo/textanalysis/Const.py
@@ -2,7 +2,7 @@
 
 import sys;
 import logging
-import Env
+from . import Env
 from datetime import timedelta;
 
 """Default level for application logging.  Modify these for different scenarios.  See Python logging package documentation for more information"""
diff --git a/medinfo/textanalysis/Util.py b/medinfo/textanalysis/Util.py
index ed8f9355..408ee32b 100644
--- a/medinfo/textanalysis/Util.py
+++ b/medinfo/textanalysis/Util.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions used across the application
 """
-import Const, Env
+from . import Const, Env
 import sys, os
 import logging
 
diff --git a/medinfo/textanalysis/test/TestAVSParse.py b/medinfo/textanalysis/test/TestAVSParse.py
index 960e4756..868c6b50 100644
--- a/medinfo/textanalysis/test/TestAVSParse.py
+++ b/medinfo/textanalysis/test/TestAVSParse.py
@@ -2,12 +2,12 @@
 """Test case for respective module in application package"""
 
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 import unittest
 
-from Const import RUNNER_VERBOSITY;
-from Util import log;
+from .Const import RUNNER_VERBOSITY;
+from .Util import log;
 
 from medinfo.db.test.Util import MedInfoTestCase;
 
diff --git a/medinfo/textanalysis/test/Util.py b/medinfo/textanalysis/test/Util.py
index b4ff3a49..bd379140 100644
--- a/medinfo/textanalysis/test/Util.py
+++ b/medinfo/textanalysis/test/Util.py
@@ -1,7 +1,8 @@
-import Const
+from . import Const
 import sys, os
 import logging
-import cgi, UserDict
+import cgi
+from collections import UserDict
 import unittest
 
 from medinfo.common.test.Util import MedInfoTestCase;
diff --git a/medinfo/web/cgibin/BaseWeb.py b/medinfo/web/cgibin/BaseWeb.py
index 1cf7950b..51b6f77e 100644
--- a/medinfo/web/cgibin/BaseWeb.py
+++ b/medinfo/web/cgibin/BaseWeb.py
@@ -14,13 +14,13 @@
 import sys, os;
 import logging;
 import cgi
-import Options
-import Links
-import Env
-import urllib;
+from . import Options
+from . import Links
+from . import Env
+import urllib.request, urllib.parse, urllib.error;
 import smtplib;
 import string;
-from cStringIO import StringIO;
+from io import StringIO;
 from time import time;
 
 from medinfo.common.Const import NULL_TAG;
@@ -117,7 +117,7 @@ def handleRequest( self, form=None, req=None ):
             # First time a request is made, and a request object available
             #   must be a mod_python script.  Copy over PythonOption
             #   request directives as environment variables
-            for key, value in req.get_options().iteritems():
+            for key, value in req.get_options().items():
                 os.environ[key] = value;
             os.environ["envOptionsCopied"] = "True";
 
@@ -128,7 +128,7 @@ def handleRequest( self, form=None, req=None ):
             # Catch exceptions for more graceful error reporting
             try:
                 self.__handleRequest(form, req);
-            except Exception, exc:
+            except Exception as exc:
                 self.errorResponse(exc);
 
 
@@ -146,7 +146,7 @@ def __handleRequest( self, form=None, req=None ):
         timer = time(); # Track how long the request processing takes
         self.action_initial();
         for (commandName, methodName) in self.mHandlers:
-            if form.has_key(commandName) and self.requestData[commandName] != "":
+            if commandName in form and self.requestData[commandName] != "":
                 # Use reflection to get a handle on the named method to execute
                 commandMethod = getattr(self,methodName)
                 commandMethod()
@@ -180,11 +180,11 @@ def wsgiHandler(self, environ, start_response):
         #Examples of printing errors
         #print >> environ['wsgi.errors'], "application debug #2"
         #This one seems to work
-        #print >> sys.stderr, "application debug #3"
+        # print("application debug #3", file=sys.stderr)
 
         del handlerInstance;    # Ensure garbage collection
         
-        return output    
+        return [output]
     
     def maintainParams(self):
         """Normal behavior, store all request parameters
@@ -202,7 +202,7 @@ def maintainParams(self):
         if len(self.mForm) > 0:
             self.manualResetParams();
         
-        for key in self.mForm.keys():
+        for key in list(self.mForm.keys()):
             field = self.mForm[key]
             if isinstance(field,list):  # Multi-Items 
                 selectedValues   = [];
@@ -283,12 +283,12 @@ def errorResponse( self, exc ):
                 cgi.print_environ();
             else:
                 # Looks like mod_python or other call, with request object included.  Derive environment from there
-                print >> sys.stdout, "<h3>Request Sub-Process Environment</h3>"
-                print >> sys.stdout, "<ul>"
+                print("<h3>Request Sub-Process Environment</h3>", file=sys.stdout)
+                print("<ul>", file=sys.stdout)
                 self.req.add_common_vars();
-                for key, value in self.req.subprocess_env.iteritems():
-                    print >> sys.stdout, "<li>%s: %s</li>" % (key, value)
-                print >> sys.stdout, "</ul>"
+                for key, value in self.req.subprocess_env.items():
+                    print("<li>%s: %s</li>" % (key, value), file=sys.stdout)
+                print("</ul>", file=sys.stdout)
 
             sys.stdout = origStdout;
 
@@ -364,8 +364,8 @@ def getCookies(self, cookieClass=None, secret=None):
                 cookieDict = mod_python.Cookie.get_cookies(self.req);
         else:
             # Assume is a CGI call
-            import Cookie;
-            cookieDict = Cookie.SimpleCookie();
+            import http.cookies;
+            cookieDict = http.cookies.SimpleCookie();
             if "HTTP_COOKIE" in os.environ:                
                 cookieDict.load(os.environ["HTTP_COOKIE"]);
                 
@@ -389,19 +389,19 @@ def addCookie(self, key, value, cookieClass=None, secret=None, cookieAttributes=
             #Go through and assign all cookie attributes, I know this is cool right?  Variable vaiables!
             for attribute in cookieAttributes:
                 if isinstance(attribute['value'], int):                    
-                    exec "cookie.%s = %s" % (attribute['name'], attribute['value'])
+                    exec("cookie.%s = %s" % (attribute['name'], attribute['value']))
                 else:
-                    exec "cookie.%s = '%s'" % (attribute['name'], attribute['value'])
+                    exec("cookie.%s = '%s'" % (attribute['name'], attribute['value']))
             
             #Add cookie to header
             mod_python.Cookie.add_cookie(self.req, cookie);
         else:
             # Assume is a CGI call
-            import Cookie
-            if os.environ.has_key("HTTP_COOKIE"):
-                cookie = Cookie.SimpleCookie(os.environ["HTTP_COOKIE"])          
+            import http.cookies
+            if "HTTP_COOKIE" in os.environ:
+                cookie = http.cookies.SimpleCookie(os.environ["HTTP_COOKIE"])          
             else:
-                cookie = Cookie.SimpleCookie()
+                cookie = http.cookies.SimpleCookie()
             
             #Define cookie name and value 
             cookie[key] = value
@@ -438,7 +438,7 @@ def populatedTemplate(self):
         templateFile = open(self.getTemplateFilename(),"r");
         populatedContents = templateFile.read() % self.requestData
         populatedContents = populatedContents.encode('utf-8');
-        return populatedContents;
+        return populatedContents
     
     def printTemplate(self):
         """Standard end result of web script.  Print the template
@@ -447,9 +447,9 @@ def printTemplate(self):
         """
 
         self.printHeaders();
-        print   # Separate headers from body
+        print()   # Separate headers from body
         # Output the template, replacing key fields by the values in templateDict
-        print self.populatedTemplate();
+        print(self.populatedTemplate().decode());   # in Python3 without .decode(), prints bytes object, i.e., b'<string>'
 
     def returnHeaders(self, output):
         """Return HTTP headers for WSGI"""        
@@ -460,9 +460,9 @@ def returnHeaders(self, output):
 
     def printHeaders(self):
         """Print HTTP headers before printing any body text"""
-        print "Content-type: text/html"
+        print("Content-type: text/html")
         # Set any Cookies
-        print self.getCookies()
+        print(self.getCookies())
         
     def setTemplateFilename(self, filename):
         self.mTemplateFilename = filename
@@ -536,7 +536,7 @@ def action_uploadFiles( self ):
         if the contents of that field are non-empty, copy the contents
         into the field (templateDict value) named "smiles"
         """
-        for key in self.mForm.keys():
+        for key in list(self.mForm.keys()):
             if key.endswith(self.FILE_FIELD_SUFFIX):
                 fileContents = self.mForm[key].value
                 if len(fileContents) > 0:
@@ -581,7 +581,7 @@ def quoteFilepath( filepath ):
                 char = "_"+char;
             newFilepath.append(char);
         newFilepath = str.join('', newFilepath);    # Convert "StringBuffer" into actual string
-        newFilepath = urllib.quote(newFilepath,'');  # Neutralize special characters, including "/"
+        newFilepath = urllib.parse.quote(newFilepath,'');  # Neutralize special characters, including "/"
         return newFilepath;
     quoteFilepath = staticmethod(quoteFilepath)
 
diff --git a/medinfo/web/cgibin/Const.py b/medinfo/web/cgibin/Const.py
index 1f2e8587..782830c6 100644
--- a/medinfo/web/cgibin/Const.py
+++ b/medinfo/web/cgibin/Const.py
@@ -1,5 +1,5 @@
 """Various constants for use by the web scripts"""
-import Env;
+from . import Env;
 
 """Parameter key to pass if don't want the HTML interface, 
 just want direct data output like a web service"""
diff --git a/medinfo/web/cgibin/Links.py b/medinfo/web/cgibin/Links.py
index f8ee2acb..77714805 100644
--- a/medinfo/web/cgibin/Links.py
+++ b/medinfo/web/cgibin/Links.py
@@ -28,21 +28,21 @@ def buildLinks( linkHRefList, linkTextList, linkFormat, linkSeparator ):
 
 
 ################## Base Navigation Links ##########################
-BASE_LINK_DIR = "..";
+BASE_LINK_DIR = "../..";
 
 HEADER_LINK_HREF = [];  HEADER_LINK_TEXT = [];
-HEADER_LINK_HREF.append("../index.htm");        HEADER_LINK_TEXT.append("MedInfo");
+HEADER_LINK_HREF.append("../../index.htm");        HEADER_LINK_TEXT.append("MedInfo");
 HEADER_LINKS = buildLinks(HEADER_LINK_HREF,HEADER_LINK_TEXT,HEADER_LINK_FORMAT,HEADER_LINK_SEPARATOR);
 
 NAV_LINK_HREF = [];                                 NAV_LINK_TEXT = [];
-NAV_LINK_HREF.append("../index.htm");               NAV_LINK_TEXT.append("Home")
+NAV_LINK_HREF.append("../../index.htm");               NAV_LINK_TEXT.append("Home")
 NAV_LINK_HREF.append("../../cgibin/cpoe/ItemRecommenderWeb.py");       NAV_LINK_TEXT.append("Item Recommender")
 NAV_LINK_HREF.append("../../cgibin/admin/DBUtilWeb.py");           NAV_LINK_TEXT.append("DBUtil")
 NAV_LINKS = buildLinks(NAV_LINK_HREF,NAV_LINK_TEXT,NAV_LINK_FORMAT,NAV_LINK_SEPARATOR);
 
 FOOTER_LINK_HREF = [];                                  FOOTER_LINK_TEXT = [];
-FOOTER_LINK_HREF.append("../index.htm");                FOOTER_LINK_TEXT.append("Home");
-FOOTER_LINK_HREF.append("../contacts.htm");             FOOTER_LINK_TEXT.append("Contacts");
+FOOTER_LINK_HREF.append("../../index.htm");                FOOTER_LINK_TEXT.append("Home");
+FOOTER_LINK_HREF.append("../../contacts.htm");             FOOTER_LINK_TEXT.append("Contacts");
 FOOTER_LINKS = buildLinks(FOOTER_LINK_HREF,FOOTER_LINK_TEXT,FOOTER_LINK_FORMAT,FOOTER_LINK_SEPARATOR);
 
 ############### Admin Nav Links #################################
diff --git a/medinfo/web/cgibin/Util.py b/medinfo/web/cgibin/Util.py
index 1464ac89..cc325da8 100644
--- a/medinfo/web/cgibin/Util.py
+++ b/medinfo/web/cgibin/Util.py
@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 """Miscellaneous utility functions
 """
-import Env;
-import Const
+from . import Env;
+from . import Const
 import sys, os
 import logging
 
diff --git a/medinfo/web/cgibin/admin/DBUtilWeb.py b/medinfo/web/cgibin/admin/DBUtilWeb.py
index 95abbb9c..50087f19 100644
--- a/medinfo/web/cgibin/admin/DBUtilWeb.py
+++ b/medinfo/web/cgibin/admin/DBUtilWeb.py
@@ -6,7 +6,7 @@
 import cgi
 import cgitb; cgitb.enable()
 
-from cStringIO import StringIO;
+from io import StringIO;
 import time
 
 
@@ -53,7 +53,7 @@ def action_default(self):
         # Read checkboxes by presence or absence of field
         self.requestData["incCols"] = ""  # Checkboxes not passed if unchecked, so extra step to ensure uncheck is persisted
         incCols = False
-        if self.mForm.has_key("incCols"):
+        if "incCols" in self.mForm:
             self.requestData["incCols"] = self.mForm["incCols"].value
             incCols = True
         
@@ -144,7 +144,7 @@ def connectionFactory(self):
         
         connParams = dict();
         
-        for key, value in self.requestData.iteritems():
+        for key, value in self.requestData.items():
             if key.startswith("db"):    # Look for db parameters
                 tagKey = key[len("db"):];
                 if len(value) > 0: 
@@ -160,9 +160,9 @@ def action_outputOnly(self):
     def response(self):
         if self.outputOnly:
             # Don't do normal HTML template output, output data directly
-            print "Content-type: text/plain"
-            print
-            print self.requestData["resultsText"]
+            print("Content-type: text/plain")
+            print()
+            print(self.requestData["resultsText"])
         else:
             # Defer to superclass implementation
             BaseAdminWeb.response(self)
diff --git a/medinfo/web/cgibin/admin/DebugWeb.py b/medinfo/web/cgibin/admin/DebugWeb.py
index 93346880..8e83c593 100644
--- a/medinfo/web/cgibin/admin/DebugWeb.py
+++ b/medinfo/web/cgibin/admin/DebugWeb.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """Just some helper info to help debug web scripts
 """
-import cStringIO, urllib
+import io, urllib.request, urllib.parse, urllib.error
 import cgi, sys, os
 import cgitb; cgitb.enable()
 
@@ -19,9 +19,9 @@ def DebugWeb(environ, start_response):
     templateDict["importOEChem"] = ""
     templateDict["importPsycoPg"] = ""
     
-    if request.has_key("importChemDB"): templateDict["importChemDB"] = "checked"
-    if request.has_key("importOEChem"): templateDict["importOEChem"] = "checked"
-    if request.has_key("importPsycoPg"): templateDict["importPsycoPg"] = "checked"
+    if "importChemDB" in request: templateDict["importChemDB"] = "checked"
+    if "importOEChem" in request: templateDict["importOEChem"] = "checked"
+    if "importPsycoPg" in request: templateDict["importPsycoPg"] = "checked"
 
     html =\
 """
@@ -59,21 +59,21 @@ def DebugWeb(environ, start_response):
 """ % templateDict
 
     # Test if external imports work:
-    if request.has_key("importChemDB"):
+    if "importChemDB" in request:
         import CHEM.Common.Env
         html += "<i>Successfully imported a CHEM module</i><br>"
-    if request.has_key("importOEChem"):
+    if "importOEChem" in request:
         from openeye.oechem import OEGraphMol
         mol = OEGraphMol();
         html += "<i>Successfully imported a OEChem module</i><br>"
-    if request.has_key("importPsycoPg"):
+    if "importPsycoPg" in request:
         import psycopg2
         html += "<i>Successfully imported psycopg2 module</i><br>"
 
     html += "Request Parameters"
     html += "<table border=1>"
     html += "<tr><th>Key</th><th>Value</th><th>Filename and Type</th></tr>"
-    for key in request.keys():
+    for key in list(request.keys()):
         field = request[key]
         if not isinstance(field,list):
             field = [field];    # Convert to list of size 1
@@ -95,7 +95,7 @@ def DebugWeb(environ, start_response):
     html += "<table border=1>"
     html += "<tr><th>Key</th><th>Value</th></tr>"
     
-    keyList = os.environ.keys()
+    keyList = list(os.environ.keys())
     keyList.sort()
     for key in keyList:
         html += "<tr><td>%s</td><td>%s</td></tr>" % (key,os.environ[key])
diff --git a/medinfo/web/cgibin/cgi2psp.py b/medinfo/web/cgibin/cgi2psp.py
index 2ad2bcaf..78f6c557 100644
--- a/medinfo/web/cgibin/cgi2psp.py
+++ b/medinfo/web/cgibin/cgi2psp.py
@@ -6,7 +6,7 @@
 import os;
 import time;
 from optparse import OptionParser
-from Util import log;
+from .Util import log;
 
 def generatePSPfromCGITemplate( cgiTemplateFilename, linkConversion=False ):
     """Standard conversion from older CGI HTML template files to 
diff --git a/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py b/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py
index f131e708..499ec1fd 100644
--- a/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py
+++ b/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py
@@ -3,10 +3,11 @@
 Simple Python CGI script to test web interface to molecule file processing modules
 """
 
+import sys
 import cgi
 import cgitb; cgitb.enable()
 
-from cStringIO import StringIO;
+from io import StringIO;
 import time
 
 from medinfo.db import DBUtil
@@ -104,7 +105,7 @@ def __init__(self):
         self.requestData["clinicalItemExcludedOptions"] = self.optionTagsFromField("clinicalItemExcluded");
 
         if "timeDeltaMax" not in Options.FIELD2VALUE:
-            secondsOptions = DELTA_NAME_BY_SECONDS.keys();
+            secondsOptions = list(DELTA_NAME_BY_SECONDS.keys());
             secondsOptions.sort();
             Options.FIELD2VALUE["timeDeltaMax"] = [str(seconds) for seconds in secondsOptions];
             Options.FIELD2TEXT ["timeDeltaMax"] = [DELTA_NAME_BY_SECONDS[seconds] for seconds in secondsOptions];
@@ -170,7 +171,7 @@ def action_default(self):
         # Read checkboxes by presence or absence of field
         self.requestData["incCols"] = ""  # Checkboxes not passed if unchecked, so extra step to ensure uncheck is persisted
         incCols = False
-        if self.mForm.has_key("incCols"):
+        if "incCols" in self.mForm:
             self.requestData["incCols"] = self.mForm["incCols"].value
             incCols = True
         
diff --git a/medinfo/web/cgibin/cpoe/PatientCareFrame.htm b/medinfo/web/cgibin/cpoe/PatientCareFrame.htm
index be4a8748..aac06d16 100644
--- a/medinfo/web/cgibin/cpoe/PatientCareFrame.htm
+++ b/medinfo/web/cgibin/cpoe/PatientCareFrame.htm
@@ -1,11 +1,14 @@
 <html>
 <head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
 <title>Patient Management Simulation</title>
 <link rel="stylesheet" type="text/css" href="../../resource/stylesBasic.css">
 <link rel="stylesheet" type="text/css" href="../../resource/stylesBlue.css">
 
 <script src="../../resource/toolkit.js"></script>
 <script src="PatientCareFrame.js"></script>
+<script src="../../resource/jquery-1.11.3.js"></script>
+<script src="Track.js"></script>
 
 <script language="JavaScript">
 /**
@@ -13,7 +16,7 @@
  */
 function initPage()
 {
-    if ('%(autoQuery)s')
+    if ('%(enableRecommender)s')
     {
         loadRelatedOrders();
     }
@@ -29,10 +32,10 @@
          &nbsp; <a href="SimSetup.py" class="breadcrumb">Patient Management</a>
       </td>
       <td class="heading" nowrap align=right colspan=99>
-        <input type=button value="<< -60" onClick="updateTime(-60*60);">
-        <input type=button value="< -15" onClick="updateTime(-15*60);">
-        <input type=button value="> +15" onClick="updateTime(+15*60);">
-        <input type=button value=">> +60" onClick="updateTime(+60*60);">
+        <input type=button value="<< -15" onClick="updateTime(-15*60);">
+        <input type=button value="< -1" onClick="updateTime(-1*60);">
+        <input type=button value="+1 >" onClick="updateTime(+1*60);">
+        <input type=button value="+15 >>" onClick="updateTime(+15*60);">
       </td>
    </tr>
    <tr>
@@ -58,8 +61,9 @@
     <input type=hidden name="sim_time" value="%(sim_time)s">
 
     <input type=hidden name="currentDataPage" value="%(currentDataPage)s">
-    <input type=hidden name="autoQuery" value="%(autoQuery)s">
+    <input type=hidden name="enableRecommender" value="%(enableRecommender)s">
     <input type=hidden name="maxResults" value="%(maxResults)s">
+    <input type=hidden name="currentQuery" value="">
 
     <table cellspacing=0 cellpadding=0 border=0 style="width:100%%;">
         <tr valign=top>
@@ -89,5 +93,19 @@
 </tr>
 </table>
 
+
+
+<TABLE BORDER=0 CELLPADDING=0 CELLSPACING=0 width="100%%" class="headingTable">
+   <TR>
+      <td class="heading" nowrap align=right colspan=100>
+        <input type=button value="<< -15" onClick="updateTime(-15*60);">
+        <input type=button value="< -1" onClick="updateTime(-1*60);">
+        <input type=button value="+1 >" onClick="updateTime(+1*60);">
+        <input type=button value="+15 >>" onClick="updateTime(+15*60);">
+      </td>
+   </tr>
+</table>
+
+
 </body>
 </html>
diff --git a/medinfo/web/cgibin/cpoe/PatientCareFrame.js b/medinfo/web/cgibin/cpoe/PatientCareFrame.js
index 175b45ec..b8d8c32e 100644
--- a/medinfo/web/cgibin/cpoe/PatientCareFrame.js
+++ b/medinfo/web/cgibin/cpoe/PatientCareFrame.js
@@ -13,7 +13,7 @@ function loadActiveOrders(theForm)
 
     var resultSpace = document.getElementById('currentDataTableSpace');
     resultSpace.innerHTML = AJAX_LOADER_HTML;
-    ajaxRequest('dynamicdata/'+dataPage+'.py?sim_patient_id='+patientId+'&sim_time='+simTime, function(data){ resultSpace.innerHTML = data; } );
+    ajaxRequest('dynamicdata/'+dataPage+'.py?loadActive=true&sim_patient_id='+patientId+'&sim_time='+simTime, function(data){ resultSpace.innerHTML = data; } );
 }
 
 /**
@@ -24,11 +24,19 @@ function loadOrderHistory(theForm)
     var patientId = theForm.elements["sim_patient_id"].value;
     var simTime = theForm.elements["sim_time"].value;
     var dataPage = "ActiveOrders";
+    var enableRecommender = theForm.enableRecommender.value;
     theForm.elements["currentDataPage"].value = dataPage;
 
+    // Include flag to account for when recommender is not enabled
+    enableRecommenderParam = ''
+    console.log(enableRecommender)
+    if (!enableRecommender){
+      enableRecommenderParam = '&enableRecommender=False'
+    }
+
     var resultSpace = document.getElementById('currentDataTableSpace');
     resultSpace.innerHTML = AJAX_LOADER_HTML;
-    ajaxRequest('dynamicdata/'+dataPage+'.py?loadActive=false&sim_patient_id='+patientId+'&sim_time='+simTime, function(data){ resultSpace.innerHTML = data; } );
+    ajaxRequest('dynamicdata/'+dataPage+'.py?loadActive=false&sim_patient_id='+patientId+'&sim_time='+simTime+enableRecommenderParam, function(data){ resultSpace.innerHTML = data; } );
 }
 
 /**
@@ -81,6 +89,19 @@ function loadNoteContent(simNoteId)
  */
 function updateTime( deltaSeconds )
 {
+    // Check if there are any checkboxes that are checked on the page
+    boxesChecked = false
+    var inputElements = document.getElementsByTagName("input");
+    for (var i = 0; i < inputElements.length; i++)
+        if (inputElements[i].type == "checkbox")
+            if (inputElements[i].checked)
+                boxesChecked = true;
+    // If boxes are checked, alert user to sign orders before updating time
+    if ( boxesChecked ) {
+      alert("Please sign any selected orders before proceeding.");
+      return;
+    }
+
     var theForm = document.forms[0];   // Assume the first and only form
     var simTimeField = theForm.elements['sim_time'];
     var simTime = parseInt(simTimeField.value)
@@ -99,6 +120,7 @@ function selectItem(checkbox)
     var itemId = parseInt(infoChunks[0]);
     var name = infoChunks[1];
     var description = infoChunks[2];
+    var theForm = document.forms[0];   // Assume the first and only form
 
     if ( checkbox.checked )
     {
@@ -115,14 +137,35 @@ function selectItem(checkbox)
 				break;	// Don't need to keep looking
 			}
 		}
-        
+
         if ( !newItemExists )
         {
 			// http://stackoverflow.com/questions/494143/creating-a-new-dom-element-from-an-html-string-using-built-in-dom-methods-or-pro
 			// Create a temporary div object to process an innerHTML segment.
 			// Avoid doing a direct newOrderSpace.innerHTML += newHTML, because will overwrite any transient contents (i.e., checkbox deselections) in the prior content
 			var div = document.createElement('div');
-			div.innerHTML =  '<input type=checkbox name="newOrderItemId" class="newOrderCheckbox" value="'+itemId+'" checked onClick="selectNewItem('+itemId+')"><a href="javascript:clickNewItemById('+itemId+')">'+description+'</a><br>\n';
+      // For the purpose of later analysis, get which list the original item came from and include it as a property of the new item
+      var listContaining;
+      if ($(checkbox).parents('#resultSpace1').length > 0) {
+        listContaining = 'resultSpace1';
+      } else if ($(checkbox).parents('#resultSpace2').length > 0) {
+        listContaining = 'resultSpace2';
+      } else {
+        listContaining = 'non-recommender';
+      }
+      // For the purpose of later analysis, store the queryStr that resulted in item being shown and the search mode
+      var queryStr = $('input[name="currentQuery"]').val();
+      // Search mode defined in Track.js under lastButtonClicked
+
+      // Get index of current result list to append as data attribute of selected boxes
+      var listIdx = listIdxTracker; // Defined in Track.js
+
+      innerHTML = div.innerHTML =  '<input type=checkbox data-list="'+listContaining+'" data-query="'+queryStr+'" data-search-mode="'+lastButtonClicked+'" data-list-idx="'+listIdx+'" name="newOrderItemId" class="newOrderCheckbox" value="'+itemId+'" checked onClick="selectNewItem('+itemId+')"><a href="javascript:clickNewItemById('+itemId+')">'+description+'</a>&nbsp;<a href="javascript:loadRelatedOrders('+itemId+')"><img src="../../resource/graphIcon.png" width=12 height=12 alt="Find Related Orders"></a><br>\n';
+      // Do not show relatedOrder link when recommender not being enabled
+      if ( !theForm.enableRecommender.value ) {
+			     innerHTML =  '<input type=checkbox data-list="'+listContaining+'" data-query="'+queryStr+'" data-search-mode="'+lastButtonClicked+'" data-list-idx="'+listIdx+'" name="newOrderItemId" class="newOrderCheckbox" value="'+itemId+'" checked onClick="selectNewItem('+itemId+')"><a href="javascript:clickNewItemById('+itemId+')">'+description+'</a><br>\n';
+      }
+      div.innerHTML = innerHTML;
 			newOrderSpace.appendChild(div);
 		}
     }
@@ -141,7 +184,7 @@ function clickItemById( itemId )
 	{
 		//console.log(checkboxes[i].value);
 		if ( checkboxes[i].value.substring(0,idPrefix.length) == idPrefix )
-		{	
+		{
 			if ( !firstCheckbox )
 			{	// Simulate user clicking on checkbox to capture onClick event
 				checkboxes[i].click();
@@ -196,6 +239,14 @@ function ignoreItem(clinicalItemId)
 {
 }
 
+/**
+ * Order selected to be discontinued
+ */
+ function cancelDiscontinueOrder(itemId)
+ {
+   // Do nothing currently
+ }
+
 /**
  * Select a currently active patient order for discontinuation
  */
@@ -217,6 +268,8 @@ function discontinueOrder( itemInfoStr )
 function searchOrders(searchField)
 {
     var searchStr = searchField.value;
+    // Set hidden currentQuery input to searchStr
+    $('input[name="currentQuery"]').val(searchStr)
     var maxResults = parseInt(searchField.form.maxResults.value);
     if ( searchStr.trim() == "" )
     {   // Blank string, nothing to search off of.  Use related order search instead
@@ -225,25 +278,50 @@ function searchOrders(searchField)
     else
     {
     	var sortParam = '';
-    	if ( searchField.form.autoQuery.value != '' )
+      var enableRecommenderParam = '';
+    	if ( !searchField.form.enableRecommender.value )
     	{
     		sortParam = '&sortField=item_count desc'
+        enableRecommenderParam = '&enableRecommender=False'
     	}
         var resultSpace = document.getElementById('searchResultsTableSpace');
         resultSpace.innerHTML = AJAX_LOADER_HTML;
-        ajaxRequest('dynamicdata/RelatedOrders.py?resultCount='+maxResults+'&searchStr='+searchStr+sortParam, function(data){ resultSpace.innerHTML = data; } );
+        ajaxRequest('dynamicdata/RelatedOrders.py?resultCount='+maxResults+'&searchStr='+searchStr+sortParam+enableRecommenderParam, function(data){
+          resultSpace.innerHTML = data;
+          // Defined in Track.js
+          recordNewResults('data')
+        });
     }
 }
 
 /**
  * Search for related orders based on currently available selected items
+ *
+ * Expect comma-separated list of query Item IDs to search based on.
+ * If none provided, will instead look for current patient record to infer current patient items/orders.
  */
-function loadRelatedOrders()
+function loadRelatedOrders( queryItemIdsStr )
 {
     var theForm = document.forms[0];   // Assume the first and only form
+
+    if ( !theForm.enableRecommender.value )
+    {
+        alert('Related orders / recommender functionality disabled');
+        return;
+    }
+
     var patientId = theForm.elements["sim_patient_id"].value;
     var simTime = theForm.elements["sim_time"].value;
 
+    var itemQueryParams = 'sim_patient_id='+patientId+'&sim_time='+simTime; // Default to searching based on a specific patient record
+    if ( queryItemIdsStr )
+    {
+        // Set hidden currentQuery input to queryItemId
+        $('input[name="currentQuery"]').val(queryItemIdsStr);
+        // Have a non-blank string of specific query Items to search by. Use if available
+        itemQueryParams += '&queryItemIds='+queryItemIdsStr;
+    }
+
     var resultSpace = document.getElementById('searchResultsTableSpace');
     resultSpace.innerHTML = '<table cellspacing=0 cellpadding=0 width=100%><tr valign=top><td id="resultSpace1" width=50% align=center></td><td id="resultSpace2" align=center></td></tr></table>'
     var resultSpace1 = document.getElementById('resultSpace1');
@@ -251,19 +329,22 @@ function loadRelatedOrders()
     resultSpace1.innerHTML = AJAX_LOADER_HTML;
     resultSpace2.innerHTML = AJAX_LOADER_HTML;
 
-    // Two queries for different sort options. Nest calls for sequential asynchronous call. 
+    // Two queries for different sort options. Nest calls for sequential asynchronous call.
     // Total time takes longer to fill simultaneous queries
-    var queryURL = 'dynamicdata/RelatedOrders.py?sim_patient_id='+patientId+'&sim_time='+simTime+'&sortField=PPV&displayFields=PPV&title=Common Orders';
+    var queryURL = 'dynamicdata/RelatedOrders.py?RelatedOrders=action&'+itemQueryParams+'&sortField=PPV&displayFields=&title=Common Orders';
     //console.log( queryURL );
-    ajaxRequest( queryURL, 
-    	function(data)
-    	{ 	resultSpace1.innerHTML = data;
-
-			queryURL = 'dynamicdata/RelatedOrders.py?sim_patient_id='+patientId+'&sim_time='+simTime+'&sortField=P-YatesChi2-NegLog&displayFields=RR&title=Specific Orders'
-			console.log( queryURL );
-			ajaxRequest( queryURL, function(data){ resultSpace2.innerHTML = data; } );
-    	}
-    );
+    ajaxRequest(queryURL, function(data) {
+        resultSpace1.innerHTML = data;
+        // Defined in Track.js
+        recordNewResults('resultSpace1') // Record any new items in resultSpace1
+        queryURL = 'dynamicdata/RelatedOrders.py?RelatedOrders=action&'+itemQueryParams+'&sortField=P-YatesChi2-NegLog&filterField1=prevalence<:0.01&displayFields=&title=Related Orders'
+        console.log( queryURL );
+        ajaxRequest( queryURL, function(data){
+          resultSpace2.innerHTML = data;
+          // Defined in Track.js
+          recordNewResults('resultSpace2') // Record any new items in resultSpace2
+        });
+    });
 
 }
 
@@ -273,9 +354,15 @@ function loadRelatedOrders()
 function searchOrderSets(searchField)
 {
     var searchStr = searchField.value;
+    // Set hidden currentQuery input to searchStr
+    $('input[name="currentQuery"]').val(searchStr)
     var resultSpace = document.getElementById('searchResultsTableSpace');
     resultSpace.innerHTML = AJAX_LOADER_HTML;
-    ajaxRequest('dynamicdata/OrderSetSearch.py?searchStr='+searchStr, function(data){ resultSpace.innerHTML = data; } );
+    ajaxRequest('dynamicdata/OrderSetSearch.py?searchStr='+searchStr, function(data){
+      resultSpace.innerHTML = data;
+      // Defined in Track.js
+      recordNewResults('data') // Record general data list
+    });
 }
 
 /**
@@ -284,9 +371,15 @@ function searchOrderSets(searchField)
 function searchDiagnoses(searchField)
 {
     var searchStr = searchField.value;
+    // Set hidden currentQuery input to searchStr
+    $('input[name="currentQuery"]').val(searchStr)
     var resultSpace = document.getElementById('searchResultsTableSpace');
     resultSpace.innerHTML = AJAX_LOADER_HTML;
-    ajaxRequest('dynamicdata/RelatedOrders.py?sourceTables=stride_dx_list&searchStr='+searchStr, function(data){ resultSpace.innerHTML = data; } );
+    ajaxRequest('dynamicdata/RelatedOrders.py?sourceTables=stride_dx_list&searchStr='+searchStr, function(data){
+      resultSpace.innerHTML = data;
+      // Defined in Track.js
+      recordNewResults('data')
+    });
 }
 
 /**
@@ -322,6 +415,9 @@ function selectOrderSet(checkbox, itemLists)
             lastSectionSubgroup = currSectionSubgroup;
         }
     }
+    // For tracking purposes, attach result bindings to orderset components and
+    // record new items as a new results list
+    // Both functions below are defined in Track.js
+    attachResultBindings();
+    recordNewResults('data');
 }
-
-
diff --git a/medinfo/web/cgibin/cpoe/PatientCareFrame.py b/medinfo/web/cgibin/cpoe/PatientCareFrame.py
index 314c793b..38ee57be 100644
--- a/medinfo/web/cgibin/cpoe/PatientCareFrame.py
+++ b/medinfo/web/cgibin/cpoe/PatientCareFrame.py
@@ -7,14 +7,14 @@
 import cgi
 import cgitb; cgitb.enable()
 
-from cStringIO import StringIO;
+from io import StringIO;
 import time
 from datetime import datetime, timedelta;
 
 from medinfo.db import DBUtil
 from medinfo.db.ResultsFormatter import HtmlResultsFormatter
-from medinfo.cpoe.Const import BASE_TIME, TIME_FORMAT;
-from medinfo.cpoe.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.Const import BASE_TIME, TIME_FORMAT;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
 
 from medinfo.web.cgibin.cpoe.BaseCPOEWeb import BaseCPOEWeb
 
@@ -32,15 +32,15 @@ def __init__(self):
         self.requestData["sim_user_id"] = "";
         self.requestData["sim_patient_id"] = "";
         self.requestData["sim_state_id"] = "";
-        self.requestData["sim_time"] = "0";
+        self.requestData["sim_time"] = "";
 
         self.requestData["currentDataPage"] = NotesReview.__name__;
-        self.requestData["autoQuery"] = "";
+        self.requestData["enableRecommender"] = "";
         self.requestData["maxResults"] = "50";  # Maximum of results to show when searching for orders
-        
+
         self.requestData["newOrderItemId"] = list();
         self.requestData["discontinuePatientOrderId"] = list();
-        
+
         # Sub-Table / Dynamically loaded data
         self.requestData["currentDataTable"] = "";
         self.requestData["dataEntryTable"] = "";
@@ -71,21 +71,32 @@ def action_signOrders(self):
         simTime = int(self.requestData["sim_time"]);
         deltaSeconds = len(subData.requestData["newOrderItemId"]) * 60;
         self.requestData["sim_time"] = simTime + deltaSeconds;
-        
+
+        # Revert back to the NotesReview page to catch any state change notes
+        self.requestData["currentDataPage"] = NotesReview.__name__;
+
     def action_default(self):
         """Render the details for the specified patient information, including controls to modify"""
         userId = int(self.requestData["sim_user_id"]);
         patientId = int(self.requestData["sim_patient_id"]);
-        simTime = int(self.requestData["sim_time"]);
-        
+        simTime = None;
+
         manager = SimManager();
         userModel = manager.loadUserInfo([userId])[0];  # Assume found good single match
+        try:
+            simTime = int(self.requestData["sim_time"]);
+        except ValueError:
+            # Unable to parse any explicit simulation time. 
+            # Lookup the last activity (order) time for the patient and start just after that to resume the simulation
+            simTime = manager.loadPatientLastEventTime(patientId) + 60;  # Advance by one minute to avoid confusing states where multiple events happening within zero time.
+            self.requestData["sim_time"] = simTime; # So sub-pages have access
+
         patientModel = manager.loadPatientInfo([patientId], simTime)[0];
         #print >> sys.stderr, "Loaded %(sim_patient_id)s in state %(sim_state_id)s at %(relative_time_start)s" % patientModel
-        
-        for key, value in userModel.iteritems():
+
+        for key, value in userModel.items():
             self.requestData["sim_user."+key] = value;
-        for key, value in patientModel.iteritems():
+        for key, value in patientModel.items():
             self.requestData["sim_patient."+key] = value;
         self.requestData["sim_time.format"] = (BASE_TIME + timedelta(0,simTime)).strftime(TIME_FORMAT);
 
@@ -94,11 +105,11 @@ def action_default(self):
         subData.requestData["sim_patient_id"] = self.requestData["sim_patient_id"];
         subData.requestData["sim_time"] = self.requestData["sim_time"];
         subData.action_default();
-        self.requestData["currentDataTable"] = subData.populatedTemplate();
+        self.requestData["currentDataTable"] = subData.populatedTemplate().decode();
 
         subData = NewOrders();
         subData.action_default();
-        self.requestData["dataEntryTable"] = subData.populatedTemplate();
+        self.requestData["dataEntryTable"] = subData.populatedTemplate().decode();
 
         #subData = RelatedOrders();
         #subData.action_default();
@@ -113,4 +124,4 @@ def action_default(self):
 if __name__.startswith("_mod_wsgi_"):
     webController = PatientCareFrame()
     webController.setFilePath(__file__)
-    application = webController.wsgiHandler 
\ No newline at end of file
+    application = webController.wsgiHandler
diff --git a/medinfo/web/cgibin/cpoe/SimSetup.htm b/medinfo/web/cgibin/cpoe/SimSetup.htm
index 51a52c95..d8389fcd 100644
--- a/medinfo/web/cgibin/cpoe/SimSetup.htm
+++ b/medinfo/web/cgibin/cpoe/SimSetup.htm
@@ -42,7 +42,7 @@
 <tr>
 <td>
 
-<form name="%(WEB_CLASS)s" action="PatientCareFrame.py" method=post>
+<form name="%(WEB_CLASS)s" action="PatientCareFrame.py" method=get>
     <table cellspacing=0 cellpadding=4 border=0 class="dataTable">
         <tr valign=bottom>
             <th class="subheading" colspan=3>Simulation Setup</th>
@@ -64,7 +64,7 @@
                 </select>
             </td>
             <td>
-                <input type="checkbox" name="autoQuery" value="checked" checked>Auto Query<br>
+                <input type="checkbox" name="enableRecommender" value="checked" checked>Enable Recommender<br>
             </td>
         </tr>
         <tr>
diff --git a/medinfo/web/cgibin/cpoe/SimSetup.py b/medinfo/web/cgibin/cpoe/SimSetup.py
index a52a4a29..045f96d2 100644
--- a/medinfo/web/cgibin/cpoe/SimSetup.py
+++ b/medinfo/web/cgibin/cpoe/SimSetup.py
@@ -7,14 +7,14 @@
 import cgi
 import cgitb; cgitb.enable()
 
-from cStringIO import StringIO;
+from io import StringIO;
 import time
 from datetime import datetime, timedelta;
 
 from medinfo.db import DBUtil
 from medinfo.db.ResultsFormatter import HtmlResultsFormatter
-from medinfo.cpoe.Const import BASE_TIME, TIME_FORMAT;
-from medinfo.cpoe.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.Const import BASE_TIME, TIME_FORMAT;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
 
 from medinfo.web.cgibin.cpoe.BaseCPOEWeb import BaseCPOEWeb
 
diff --git a/medinfo/web/cgibin/cpoe/Track.js b/medinfo/web/cgibin/cpoe/Track.js
new file mode 100644
index 00000000..c6a3334e
--- /dev/null
+++ b/medinfo/web/cgibin/cpoe/Track.js
@@ -0,0 +1,467 @@
+// When page is done loading
+$(document).on('ready', function(){initTrackingBindings()})
+
+// Store Trackers in page session (to avoid being lost across page updates)
+// Below if-statement is a good place to make one-time initializations
+if (sessionStorage.length == 0){
+  sessionStorage.setItem('eventTracker', JSON.stringify(new Object())) // Track user action events
+  sessionStorage.setItem('resultsTracker', JSON.stringify(new Object())) // Track results that come up
+  sessionStorage.setItem('signedItemsTracker', JSON.stringify(new Object())) // Track signed items
+  sessionStorage.setItem('lastButtonClicked', "") // Track last clicked button to use in state of results
+  sessionStorage.setItem('listIdx', "0") // Track list index of results
+  startTimer()
+}
+eventTracker = $.parseJSON(sessionStorage.getItem('eventTracker')) // Track user action events
+resultsTracker = $.parseJSON(sessionStorage.getItem('resultsTracker')) // Track results that come up
+signedItemsTracker = $.parseJSON(sessionStorage.getItem('signedItemsTracker')) // Track signed items
+listIdxTracker = parseInt(sessionStorage.getItem('listIdx')) // Index of recommendation lists
+// Store idx in appropriate input
+console.log('loading recListIndex:' + listIdxTracker)
+
+lastButtonClicked = ""
+
+/**
+* Save trackers to file
+*/
+function saveTrackers(){
+  var data = new Object()
+  data['startTime'] = sessionStorage.getItem('startTime')
+  data['endTime'] = sessionStorage.getItem('endTime')
+  data['eventTracker'] = sessionStorage.getItem('eventTracker')
+  data['resultsTracker'] = sessionStorage.getItem('resultsTracker')
+  data['signedItemsTracker'] = sessionStorage.getItem('signedItemsTracker')
+  data['user'] = sessionStorage.getItem('user')
+  data['patient'] = sessionStorage.getItem('patient')
+  data_string = JSON.stringify(data)
+
+  var encoded_data = "text/json;charset=utf-8," + encodeURIComponent(data_string)
+  var a = document.createElement('a');
+  a.href = 'data:' + encoded_data;
+  a.download  = data['user'] + '_' + data['patient'] +'_data_v5.json';
+  a.click()
+  sessionStorage.clear()
+}
+
+/**
+* Save trackers in window.name
+* To be used with page is reloaded, to keep trackers persistent
+*/
+function setTrackers(){
+  sessionStorage.setItem('eventTracker', JSON.stringify(eventTracker)) // Track user action events
+  sessionStorage.setItem('resultsTracker', JSON.stringify(resultsTracker)) // Track results that come up
+  sessionStorage.setItem('signedItemsTracker', JSON.stringify(signedItemsTracker)) // Track signed items
+  sessionStorage.setItem('listIdx', listIdxTracker.toString()) // Track index of recommendation lists
+}
+
+/**
+* Record beginning of test
+*/
+function startTimer(){
+  startTime = Date.now()
+  // console.log(startTime)
+  sessionStorage.setItem('startTime', startTime)
+}
+
+/**
+* Record ending of test
+*/
+function stopTimer(){
+  endTime = Date.now()
+  // console.log(endTime)
+  sessionStorage.setItem('endTime', endTime)
+}
+
+/**
+* Collect general case info (user info, patient info, etc.)
+*/
+function collectCaseInfo(){
+  var simUser = $('input[type="hidden"][name="sim_user_id"]')
+  // console.log(simUser.val())
+  var simPatient = $('input[type="hidden"][name="sim_patient_id"]')
+  // console.log(simPatient.val())
+  sessionStorage.setItem('user', simUser.val().toString())
+  sessionStorage.setItem('patient', simPatient.val().toString())
+}
+
+/*
+* Initialize tracking bindings on page
+*/
+function initTrackingBindings(){
+  collectCaseInfo()
+  // Going back to setup page through top link ends test
+  var home = $('.breadcrumb')
+  home.on('click', function(event){
+    // Let user confirm exit (and clearing of session)
+    var exit = confirm("Exit and save user testing?")
+    if (exit) {
+      setTrackers()
+      stopTimer()
+      saveTrackers()
+    } else {
+      // If exit is cancelled, prevent page change
+      event.preventDefault()
+    }
+  })
+
+  var dataTable = $('#currentDataTableSpace')
+  attachTimeChangeBindings()
+  attachResultsReviewBindings()
+  attachOrderBindings()
+  attachNewOrderBindings()
+  attachResultBindings()
+  trackOrders()
+  var elementsToObserve = new Object()
+  elementsToObserve[dataTable] = attachResultsReviewBindings
+
+  var mutationObserver = new MutationObserver(function(mutations) {
+    attachResultsReviewBindings()
+  })
+  mutationObserver.observe(dataTable.get(0), {
+    attributes: true,
+    characterData: true,
+    childList: true,
+    subtree: true,
+    attributeOldValue: true,
+    characterDataOldValue: true
+  })
+}
+
+/**
+* Attach bindings to time forward/backward functionality
+*/
+function attachTimeChangeBindings(){
+  var timeChangeButtons = $('.headingTable input[type="button"]')
+  timeChangeButtons.on('click', function(){
+    var state = new Object()
+    var timeChange = $(this).val()
+    incrementCounter(timeChange, state)
+    // Changing time reloads entire page, must save events!
+    setTrackers()
+  })
+}
+
+/**
+* Attach bindings to results view
+*/
+function attachResultsReviewBindings(){
+  // Notes
+  var viewNotes = $('input[type="button"][value="Notes"]')
+  viewNotes.unbind('click')
+  viewNotes.on('click load', function(){
+    var state = new Object()
+    incrementCounter("Notes", state)
+  })
+  // Specific Notes
+  var viewSpecificNotes = $('#currentDataTableSpace a')
+  viewSpecificNotes.unbind('click')
+  viewSpecificNotes.on('click', function(){
+    var noteLinkText = this.text
+    var state = new Object()
+    state['linkText'] = noteLinkText
+    incrementCounter("NoteContent", state)
+  })
+  // Results Review
+  var viewResultsReview = $('input[type="button"][value="Results Review"]')
+  viewResultsReview.unbind('click')
+  viewResultsReview.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "ResultsReview"
+    var state = new Object()
+    incrementCounter("ResultsReview", state)
+  })
+  // Active Orders
+  var viewActiveOrders = $('input[type="button"][value="Active Orders"]')
+  viewActiveOrders.unbind('click')
+  viewActiveOrders.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "ActiveOrders"
+    var state = new Object()
+    incrementCounter("ActiveOrders", state)
+  })
+  // Active Orders Removal
+  var activeOrdersRemoval = $('input[type="button"][value="X"]')
+  activeOrdersRemoval.unbind('click')
+  activeOrdersRemoval.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "ActiveOrdersRemoval"
+    var state = new Object()
+    incrementCounter("ActiveOrdersRemoval", state)
+  })
+  // Order History
+  var viewOrderHistory = $('input[type="button"][value="Order History"]')
+  viewOrderHistory.unbind('click')
+  viewOrderHistory.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "OrderHistory"
+    var state = new Object()
+    incrementCounter("OrderHistory", state)
+  })
+}
+
+/**
+* Attach bindings to New Orders view
+*/
+function attachOrderBindings(){
+  // Search bar
+  var orderSearch = $('input[type="text"][name="orderSearch"]')
+  // Find Orders
+  var findOrders = $('input[type="submit"][value="Find Orders"]')
+  findOrders.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "FindOrders"
+    var state = new Object()
+    var searchQuery = orderSearch.val()
+    state['searchQuery'] = searchQuery
+    incrementCounter("FindOrders", state)
+  })
+  // Order Sets
+  var orderSets = $('input[type="button"][value="Order Sets"]')
+  orderSets.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "OrderSets"
+    var state = new Object()
+    var searchQuery = orderSearch.val()
+    state['searchQuery'] = searchQuery
+    incrementCounter("OrderSets", state)
+  })
+  // Diagnoses
+  var diagnoses = $('input[type="button"][value="Diagnoses"]')
+  diagnoses.on('click', function(){
+    // Update lastButtonClicked
+    lastButtonClicked = "Diagnoses"
+    var state = new Object()
+    var searchQuery = orderSearch.val()
+    state['searchQuery'] = searchQuery
+    incrementCounter("Diagnoses", state)
+  })
+  // Sign Orders ** Resets entire patient frame!!!
+  var signOrders = $('input[type="submit"][value="Sign Orders"]')
+  signOrders.on('click', function(e){
+    // Update lastButtonClicked
+    lastButtonClicked = "SignOrders"
+    var state = new Object()
+    var searchQuery = orderSearch.val()
+    // Get checked orders and add to state
+    state['searchQuery'] = searchQuery
+    // Save signed items
+    storeSignedOrders()
+    incrementCounter("SignOrders", state)
+    // Save trackers before page reloads
+    setTrackers()
+  })
+}
+
+/**
+* Basic counter that keeps track of times buttons have been clicked.
+*/
+function incrementCounter(eventName, state){
+  var currTime = Date.now()
+  state['eventTime'] = currTime
+
+  if (eventTracker[eventName] == undefined) {
+    eventTracker[eventName] = []
+  }
+  eventTracker[eventName].push(state)
+  console.log(eventTracker)
+}
+
+/**
+* Basic saving helper for storing new results and such
+*/
+function storeResults(results, state){
+  var action = lastButtonClicked
+  // results is list containing result items or object of named lists
+  // Named list example: {'commonOrders': [items], 'specificOrders': [items]}
+  var currTime = Date.now()
+  state['storeTime'] = currTime
+
+  var resultBlob = new Object()
+  resultBlob['items'] = results
+  resultBlob['state'] = state
+
+  if (resultsTracker[action] == undefined){
+    resultsTracker[action] = []
+  }
+  resultsTracker[action].push(resultBlob)
+  // console.log(resultsTracker)
+}
+
+/**
+* Store signed orders when "Sign Orders" clicked
+*/
+function storeSignedOrders(){
+  var newSignedOrders = $('#newOrdersDetailSpace input:checked')
+  // Search bar
+  var orderSearch = $('input[type="text"][name="orderSearch"]')
+  var state = new Object()
+  var searchQuery = orderSearch.val()
+  state['searchQuery'] = searchQuery
+
+  var signedItems = []
+  newSignedOrders.each(function(index){
+    signedItems.push($(this).val() + '|' + $(this).attr('data-list') + '|' + $(this).attr('data-query') + '|' + $(this).attr('data-search-mode') + '|' + $(this).attr('data-list-idx'))
+  })
+
+  var currTime = Date.now()
+  signedItemsTracker[currTime] = signedItems
+  // console.log(signedItemsTracker)
+}
+
+/**
+* Track seen and signed orders
+*/
+function trackOrders(){
+  var resultsTable = $('#searchResultsTableSpace')
+  var newOrdersTable = $('#newOrdersDetailSpace')
+
+  // Track changes to new orders table
+  var newOrdersObserver = new MutationObserver(function(mutations){
+    attachNewOrderBindings()
+  })
+  newOrdersObserver.observe(newOrdersTable.get(0), {
+    attributes: true,
+    characterData: true,
+    childList: true,
+    subtree: true,
+    attributeOldValue: true,
+    characterDataOldValue: true
+  })
+
+  // Track changes to results checkboxes
+  var resultInputsObserver = new MutationObserver(function(mutations) {
+    attachResultBindings();
+    // Record any new results
+
+  })
+  resultInputsObserver.observe(resultsTable.get(0), {
+    attributes: true,
+    characterData: true,
+    childList: true,
+    subtree: true,
+    attributeOldValue: true,
+    characterDataOldValue: true
+  })
+}
+
+/**
+* Attach bindings to New Orders section (selected order checkboxes and "sign")
+*/
+function attachNewOrderBindings(){
+  var newOrders = $('#newOrdersDetailSpace input[type="checkbox"]')
+  // Search bar
+  var orderSearch = $('input[type="text"][name="orderSearch"]')
+  // Remove any existing listeners on result inputs
+  newOrders.unbind('change')
+  newOrders.on('change', function(){
+    var state = new Object()
+    var searchQuery = orderSearch.val()
+    state['searchQuery'] = searchQuery
+    var itemInfo = $(this).val()
+    // Item info for later analysis
+    state['itemInfo'] = itemInfo
+    // Determine what action was done on result item
+    var selected = $(this).prop('checked')
+    var action = selected ? 'selected' : 'unselected'
+    state['action'] = action
+    incrementCounter("NewOrderInteraction", state)
+  })
+}
+
+/**
+* Attach bindings to Results section (results checkboxes)
+* Very similr to attachNewOrderBindings above
+*/
+function attachResultBindings(){
+  var results = $('#searchResultsTableSpace input[type="checkbox"]')
+  // Search bar
+  var orderSearch = $('input[type="text"][name="orderSearch"]')
+  // Remove any existing listeners on result inputs
+  results.unbind('change')
+  results.on('change', function(){
+    var state = new Object()
+    var searchQuery = orderSearch.val()
+    state['searchQuery'] = searchQuery
+    var itemInfo = $(this).val()
+    // Item info for later analysis
+    state['itemInfo'] = itemInfo
+    // List container for later analysis
+    var listContaining;
+    if ($(this).parents('#resultSpace1').length > 0) {
+      listContaining = 'resultSpace1'
+    } else if ($(this).parents('#resultSpace2').length > 0) {
+      listContaining = 'resultSpace2'
+    } else {
+      listContaining = 'non-recommender'
+    }
+    state['listContaining'] = listContaining
+    // Determine what action was done on result item
+    var selected = $(this).prop('checked')
+    var action = selected ? 'selected' : 'unselected'
+    state['action'] = action
+    incrementCounter("ResultInteraction", state)
+  })
+
+  // Related links
+  var related = $("a[href^='javascript:loadRelatedOrders']")
+  // Remove any existing listeners on related links
+  related.unbind('click')
+  related.on('click', function() {
+    // event.preventDefault();
+    console.log($(this).attr('href').match(/\('([^)]+)'\)/)[1])
+    // Update lastButtonClicked
+    lastButtonClicked = "Related"
+    var state = new Object()
+    var query = $(this).attr('href').match(/\('([^)]+)'\)/)[1] // Item being explored
+    state['searchQuery'] = query
+    incrementCounter("Related", state)
+  })
+}
+
+/**
+* Keep track of results (items and their placement in results list)
+* Collect items and preprocess them to be stored
+*/
+function recordNewResults(queryType){
+  // Search bar
+  var orderSearch = $('input[type="text"][name="orderSearch"]')
+  // Common Orders list
+  var commonOrdersView = $('#resultSpace1')
+  var commonOrders = $('#resultSpace1 input[type="checkbox"]')
+  // Specific Orders list
+  var specificOrdersView = $('#resultSpace2')
+  var specificOrders = $('#resultSpace2 input[type="checkbox"]')
+  // General data list
+  var dataTableView = $('#searchResultsTableSpace')
+  var dataTable = $('#searchResultsTableSpace input[type="checkbox"]')
+  // Current query (either from search or from clicking related link)
+  var currentQuery = $('input[name="currentQuery"]')
+
+  var results = new Object()
+
+  // Store current index of results
+  results['listIndex'] = listIdxTracker
+  // Update result list index for next batch of results
+  listIdxTracker = listIdxTracker + 1
+  // If resultSpace2 and resultSpace1 present, collect lists separately
+  if (queryType == 'resultSpace1'){
+    // Get value of each input
+    var commonOrdersValues = commonOrders.toArray().map(elem => elem.value)
+    results['commonOrders'] = commonOrdersValues
+  }
+  else if (queryType == 'resultSpace2'){
+    // Get value of each input
+    var specificOrdersValues = specificOrders.toArray().map(elem => elem.value)
+    results['specificOrders'] = specificOrdersValues
+  }
+  else {
+    // Get value of each input
+    var dataValues = dataTable.toArray().map(elem => elem.value)
+    results['data'] = dataValues
+  }
+
+  // Store results
+  var state = new Object()
+  var searchQuery = currentQuery.val()
+  state['searchQuery'] = searchQuery
+  storeResults(results, state)
+}
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.htm b/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.htm
index 2d2e6179..92a934de 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.htm
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.htm
@@ -1,9 +1,9 @@
     <table class="dataTable" cellspacing=0 cellpadding=4 style="width: 100%%">
         <tr valign=top>
-            <td class="subheading" colspan=3>
+            <td class="subheading" colspan=4>
                 <input type=button value="Notes" onClick="loadNotes(this.form);">
                 <input type=button value="Results Review" onClick="loadResults(this.form);">
-                <input type=button value="Active Orders" onClick="loadActiveOrders(this.form);" class="%(activeOrderButtonClass)s">
+                <!-- input type=button value="Active Orders" onClick="loadActiveOrders(this.form);" class="%(activeOrderButtonClass)s" -->
                 <input type=button value="Order History" onClick="loadOrderHistory(this.form);" class="%(completedOrderButtonClass)s">
             </td>
         </tr>
@@ -11,6 +11,7 @@
             <td class="labelCell" align=center>Category</td>
             <td class="labelCell">%(activeCompleted)s Order</td>
             <td class="labelCell" align=center>%(historyTime)s&nbsp;</td>
+            <td class="labelCell" align=center>&nbsp;</td>
         </tr>
         %(detailTable)s
     </table>
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.py b/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.py
index 29d88cc4..3d2f3229 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/ActiveOrders.py
@@ -2,7 +2,7 @@
 import sys, os;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
+from io import StringIO;
 
 from datetime import datetime, timedelta;
 
@@ -12,10 +12,11 @@
 from medinfo.db.Model import SQLQuery, modelListFromTable;
 from medinfo.db.ResultsFormatter import HtmlResultsFormatter;
 
-from medinfo.cpoe.Const import BASE_TIME, TIME_FORMAT;
-from medinfo.cpoe.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.Const import BASE_TIME, TIME_FORMAT;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
 
 from medinfo.web.cgibin.cpoe.dynamicdata.BaseDynamicData import BaseDynamicData;
+from medinfo.web.cgibin.cpoe.dynamicdata.RelatedOrders import RELATED_LINK_TEMPLATE;
 from medinfo.web.cgibin import Options;
 
 CATEGORY_HEADER_TEMPLATE = \
@@ -27,16 +28,40 @@
 LINE_TEMPLATE_BY_ACTIVE = \
     {   True: """
             <tr>
-                <td align=center><b>%(category_description.format)s</b></td>
-                <td>%(description)s</td>
-                <td align=center><input type=button value="X" onClick="discontinueOrder('%(sim_patient_order_id)s|%(clinical_item_id)s|%(name)s|%(description)s')"></td>
+                <td valign=top align=center><b>%(category_description.format)s</b></td>
+                <td valign=top>%(description)s</td>
+                <td valign=top align=center><input type=button value="X" onClick="discontinueOrder('%(sim_patient_order_id)s|%(clinical_item_id)s|%(name)s|%(description)s')"></td>
             </tr>
             """,
         False: """
             <tr>
-                <td align=center><b>%(category_description.format)s</b></td>
-                <td>%(description)s</td>
-                <td align=center>%(start_time.format)s</td>
+                <td valign=top align=center><b>%(category_description.format)s</b></td>
+                <td valign=top>%(description)s</td>
+                <td valign=top align=center nowrap>%(start_time.format)s<br>%(end_time.format)s</td>
+                <td valign=top align=center nowrap>
+                    <a href="javascript:loadRelatedOrders('%(clinical_item_id)s')"><img src="../../resource/graphIcon.png" width=12 height=12 alt="Find Related Orders"></a>
+                    <a href="javascript:discontinueOrder('%(sim_patient_order_id)s|%(clinical_item_id)s|%(name)s|%(description)s')"><img src="../../resource/cancelIcon.svg" width=12 height=12 alt="Cancel/Discontinue Order"></a>
+                </td>
+            </tr>
+            """,
+    };
+
+LINE_TEMPLATE_BY_ACTIVE_RECOMMENDER_DISABLED = \
+    {   True: """
+            <tr>
+                <td valign=top align=center><b>%(category_description.format)s</b></td>
+                <td valign=top>%(description)s</td>
+                <td valign=top align=center><input type=button value="X" onClick="discontinueOrder('%(sim_patient_order_id)s|%(clinical_item_id)s|%(name)s|%(description)s')"></td>
+            </tr>
+            """,
+        False: """
+            <tr>
+                <td valign=top align=center><b>%(category_description.format)s</b></td>
+                <td valign=top>%(description)s</td>
+                <td valign=top align=center nowrap>%(start_time.format)s<br>%(end_time.format)s</td>
+                <td valign=top align=center nowrap>
+                    <a href="javascript:discontinueOrder('%(sim_patient_order_id)s|%(clinical_item_id)s|%(name)s|%(description)s')"><img src="../../resource/cancelIcon.svg" width=12 height=12 alt="Cancel/Discontinue Order"></a>
+                </td>
             </tr>
             """,
     };
@@ -47,19 +72,19 @@ class ActiveOrders(BaseDynamicData):
     """
     def __init__(self):
         BaseDynamicData.__init__(self);
-        
+
         self.requestData["sim_patient_id"] = "";
         self.requestData["sim_time"] = "";
-        self.requestData["loadActive"] = "true";    # Default to showing active (not inactive / completed) orders
-        
+        self.requestData["loadActive"] = "false";    # Default to showing whole order history instead of just active (not inactive / completed) orders
+        self.requestData["enableRecommender"] = "True";  # By default, asssume recommender is enabled
         self.requestData["activeCompleted"] = "Active";
         self.requestData["activeOrderButtonClass"] = "buttonSelected";
         self.requestData["completedOrderButtonClass"] = "";
         self.requestData["historyTime"] = "";
         self.requestData["detailTable"] = "";
-        
+
         self.addHandler("sim_patient_id", ActiveOrders.action_default.__name__);
-        
+
     def action_default(self):
         """Present currently selected set of pending clinical item orders"""
         patientId = int(self.requestData["sim_patient_id"]);
@@ -73,12 +98,14 @@ def action_default(self):
 
         manager = SimManager();
         patientOrders = manager.loadPatientOrders(patientId, simTime, loadActive=loadActive);
-        
+
         lastPatientOrder = None;
         htmlLines = list();
+        # Choose appropriate line template according to whether recommender enabled
+        LINE_TEMPLATE = LINE_TEMPLATE_BY_ACTIVE if self.requestData["enableRecommender"] == "True" else LINE_TEMPLATE_BY_ACTIVE_RECOMMENDER_DISABLED
         for patientOrder in patientOrders:
             self.formatPatientOrder(patientOrder, lastPatientOrder);
-            htmlLines.append( LINE_TEMPLATE_BY_ACTIVE[loadActive] % patientOrder );
+            htmlLines.append( LINE_TEMPLATE[loadActive] % patientOrder );
             lastPatientOrder = patientOrder;
         self.requestData["detailTable"] = str.join("\n", htmlLines );
 
@@ -87,6 +114,11 @@ def formatPatientOrder(self, patientOrder, lastPatientOrder):
         """
         startTime = BASE_TIME + timedelta(0, patientOrder["relative_time_start"]);
         patientOrder["start_time.format"] =  startTime.strftime(TIME_FORMAT);
+        patientOrder["end_time.format"] = "";
+        if patientOrder["relative_time_end"] is not None:
+            endTime = BASE_TIME + timedelta(0, patientOrder["relative_time_end"]);
+            patientOrder["end_time.format"] = "<i>(%s)</i>" % endTime.strftime(TIME_FORMAT);
+
         if lastPatientOrder is None or patientOrder["category_description"] != lastPatientOrder["category_description"]:
             patientOrder["category_description.format"] = patientOrder["category_description"];   # Only show category for first item for more of a less redundant tree layout
         else:
@@ -101,4 +133,4 @@ def formatPatientOrder(self, patientOrder, lastPatientOrder):
 if __name__.startswith("_mod_wsgi_"):
     webController = ActiveOrders()
     webController.setFilePath(__file__)
-    application = webController.wsgiHandler 
\ No newline at end of file
+    application = webController.wsgiHandler
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/ClinicalItemData.py b/medinfo/web/cgibin/cpoe/dynamicdata/ClinicalItemData.py
index e09883fb..3687c25f 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/ClinicalItemData.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/ClinicalItemData.py
@@ -2,7 +2,7 @@
 import sys, os;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
+from io import StringIO;
 
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, modelListFromTable;
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/ItemRecommendationTable.py b/medinfo/web/cgibin/cpoe/dynamicdata/ItemRecommendationTable.py
index 9d05041e..ec9a0b40 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/ItemRecommendationTable.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/ItemRecommendationTable.py
@@ -2,7 +2,7 @@
 import sys, os;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
+from io import StringIO;
 
 from medinfo.common.Const import FALSE_STRINGS;
 
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.htm b/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.htm
index ff8ab811..ffea524f 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.htm
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.htm
@@ -1,3 +1,24 @@
+    <script type="text/javascript">
+      function submitFindOrders() {
+        // Helper function for allowing refresh button to have same effect as submitting
+        // input with blank query (userful for tracking, since both are doing the
+        // the same thing)
+        var theForm = document.forms[0];   // Assume the first and only form
+
+        // If recommender not enabled, alert user (as defined in loadRelatedOrders())
+        if ( !theForm.enableRecommender.value )
+        {
+          loadRelatedOrders();
+          return;
+        }
+        // Clear search field
+        var orderSearch = $('input[type="text"][name="orderSearch"]');
+        orderSearch.val("");
+        // Submit search form
+        var findOrders = $('input[type="submit"][value="Find Orders"]');
+        findOrders.click();
+      }
+    </script>
     <table class="dataTable" cellspacing=0 cellpadding=4 style="width: 100%%">
         <tr valign=bottom>
             <th class="subheading" style="width: 80%%" align=right>
@@ -12,7 +33,7 @@
         </tr>
         <tr>
             <td class="labelCell" colspan=2>
-                New Orders
+                New Orders &nbsp;<input type=button value="Refresh" onClick="submitFindOrders()" alt="Find Related Orders">
             </td>
         </tr>
         <tr valign=top>
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.py b/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.py
index dd871628..5b0c869d 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/NewOrders.py
@@ -2,7 +2,7 @@
 import sys, os;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
+from io import StringIO;
 
 from medinfo.common.Const import FALSE_STRINGS;
 
@@ -10,7 +10,7 @@
 from medinfo.db.Model import SQLQuery, modelListFromTable;
 from medinfo.db.ResultsFormatter import HtmlResultsFormatter;
 
-from medinfo.cpoe.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
 
 from medinfo.web.cgibin.cpoe.dynamicdata.BaseDynamicData import BaseDynamicData;
 from medinfo.web.cgibin import Options;
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.htm b/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.htm
index 3375e6cd..8728b018 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.htm
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.htm
@@ -3,7 +3,7 @@
             <td class="subheading" colspan=100>
                 <input type=button value="Notes" onClick="loadNotes(this.form);" class="buttonSelected">
                 <input type=button value="Results Review" onClick="loadResults(this.form);">
-                <input type=button value="Active Orders" onClick="loadActiveOrders(this.form);">
+                <!--input type=button value="Active Orders" onClick="loadActiveOrders(this.form);"-->
                 <input type=button value="Order History" onClick="loadOrderHistory(this.form);">
             </td>
         </tr>
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.py b/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.py
index 9ba1507e..0759854a 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/NotesReview.py
@@ -2,8 +2,8 @@
 import sys, os;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
-import urllib;
+from io import StringIO;
+import urllib.request, urllib.parse, urllib.error;
 
 from datetime import datetime, timedelta;
 
@@ -11,8 +11,8 @@
 from medinfo.db.Model import SQLQuery, modelListFromTable;
 from medinfo.db.ResultsFormatter import HtmlResultsFormatter;
 
-from medinfo.cpoe.SimManager import SimManager;
-from medinfo.cpoe.Const import BASE_TIME, TIME_FORMAT;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.Const import BASE_TIME, TIME_FORMAT;
 
 from medinfo.web.cgibin.cpoe.dynamicdata.BaseDynamicData import BaseDynamicData;
 from medinfo.web.cgibin import Options;
@@ -20,7 +20,7 @@
 LINE_TEMPLATE = \
     """
     <tr>
-        <td align=center><a href="javascript:loadNoteContent(%(sim_note_id)s)">%(note_type)s %(sim_state_id)s</a></td>
+        <td align=center><a href="javascript:loadNoteContent(%(sim_note_id)s)">%(note_type)s</a></td>
         <td align=center><a href="javascript:loadNoteContent(%(sim_note_id)s)">%(service_type)s</a></td>
         <td align=center><a href="javascript:loadNoteContent(%(sim_note_id)s)">%(author_type)s</a></td>
         <td align=center><a href="javascript:loadNoteContent(%(sim_note_id)s)">%(note_time.format)s</a>
@@ -34,34 +34,34 @@ class NotesReview(BaseDynamicData):
     """
     def __init__(self):
         BaseDynamicData.__init__(self);
-        
+
         self.requestData["sim_patient_id"] = "";
         self.requestData["sim_time"] = "";
-        
+
         self.requestData["detailTable"] = "";
         self.requestData["initialNoteContent"] = "";
-        
+
         self.addHandler("sim_patient_id", NotesReview.action_default.__name__);
-        
+
     def action_default(self):
-        """Present currently selected set of pending clinical item orders"""
+        """Present set of patient notes"""
         patientId = int(self.requestData["sim_patient_id"]);
         simTime = int(self.requestData["sim_time"]);
-        
+
         # Load lookup table to translate note type IDs into description strings
         noteTypeById = DBUtil.loadTableAsDict("sim_note_type");
-        
+
         manager = SimManager();
         results = manager.loadNotes(patientId, simTime);
-        
+
         htmlLines = list();
         for dataModel in results:
             self.formatDataModel(dataModel, noteTypeById);
             htmlLines.append( LINE_TEMPLATE % dataModel );
         self.requestData["detailTable"] = str.join("\n", htmlLines );
-        
+
         if len(results) > 0:
-            self.requestData["initialNoteContent"] = results[0]["content"];
+            self.requestData["initialNoteContent"] = results[-1]["content"];
 
     def formatDataModel(self, dataModel, noteTypeById):
         """Populate dataModel with formatted items"""
@@ -69,8 +69,8 @@ def formatDataModel(self, dataModel, noteTypeById):
         dataModel["service_type"] = noteTypeById[dataModel["service_type_id"]]["name"];
         dataModel["author_type"] = noteTypeById[dataModel["author_type_id"]]["name"];
         dataModel["note_time.format"] = (BASE_TIME + timedelta(0,dataModel["relative_time"])).strftime(TIME_FORMAT);
-        dataModel["content.quoted"] = urllib.quote(dataModel["content"]);
-        
+        dataModel["content.quoted"] = urllib.parse.quote(dataModel["content"]);
+
 # CGI Boilerplate to initiate script
 if __name__ == "__main__":
     webController =  NotesReview()
@@ -80,4 +80,4 @@ def formatDataModel(self, dataModel, noteTypeById):
 if __name__.startswith("_mod_wsgi_"):
     webController = NotesReview()
     webController.setFilePath(__file__)
-    application = webController.wsgiHandler 
\ No newline at end of file
+    application = webController.wsgiHandler
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/OrderSetSearch.py b/medinfo/web/cgibin/cpoe/dynamicdata/OrderSetSearch.py
index 27b75b05..b2642a37 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/OrderSetSearch.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/OrderSetSearch.py
@@ -3,8 +3,8 @@
 import cgi
 import cgitb; cgitb.enable();
 import json;
-import urllib;
-from cStringIO import StringIO;
+import urllib.request, urllib.parse, urllib.error;
+from io import StringIO;
 
 from medinfo.common.Const import FALSE_STRINGS;
 
@@ -18,7 +18,7 @@
 
 from medinfo.common.StatsUtil import ContingencyStats;
 
-from medinfo.cpoe.SimManager import SimManager, ClinicalItemQuery;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager, ClinicalItemQuery;
 
 from medinfo.web.cgibin.cpoe.dynamicdata.BaseDynamicData import BaseDynamicData;
 from medinfo.web.cgibin import Options;
@@ -60,7 +60,7 @@ def action_orderSetSearch(self):
         for dataModel in resultIter:
             if dataModel is not None:
                 dataModel["nameFormat"] = NAME_TEMPLATE % dataModel;
-                dataModel["itemListJSON.quote"] = urllib.quote(json.dumps(dataModel["itemList"]));
+                dataModel["itemListJSON.quote"] = urllib.parse.quote(json.dumps(dataModel["itemList"]));
                 dataModel["controls"] = CONTROLS_TEMPLATE % dataModel;
                 results.append(dataModel);
 
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/RelatedOrders.py b/medinfo/web/cgibin/cpoe/dynamicdata/RelatedOrders.py
index 05969b4d..faa9f373 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/RelatedOrders.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/RelatedOrders.py
@@ -3,7 +3,7 @@
 import time;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
+from io import StringIO;
 
 from medinfo.common.Const import FALSE_STRINGS;
 
@@ -18,22 +18,25 @@
 
 from medinfo.common.StatsUtil import ContingencyStats;
 
-from medinfo.cpoe.SimManager import SimManager, ClinicalItemQuery;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager, ClinicalItemQuery;
 
 from medinfo.web.cgibin.cpoe.dynamicdata.BaseDynamicData import BaseDynamicData;
 from medinfo.web.cgibin import Options;
 from medinfo.web.cgibin.Util import webDataCache;
 
-CONTROLS_TEMPLATE = \
-    """
-    <input type=checkbox class="orderCheckbox" onClick="selectItem(this)" value="%(clinical_item_id)s|%(name)s|%(description)s">
-    <!--a href="javascript:ignoreItem(%(clinical_item_id)s)">[x]</a-->
-    """;
 CATEGORY_HEADER_TEMPLATE = \
     """
     <tr><td class="spacer" height=10 colspan=100></td></tr>
     <tr><td colspan=%(nPreCols)s></td><td colspan=99><b>%(category_description)s</b></td></tr>
     """;
+CONTROLS_TEMPLATE = \
+    """
+    <input type=checkbox class="orderCheckbox" onClick="selectItem(this)" value="%(clinical_item_id)s|%(name)s|%(description)s">
+    """;
+DESCRIPTION_TEMPLATE = \
+    """<a href="javascript:clickItemById(%(clinical_item_id)s)">%(description)s</a>""";
+RELATED_LINK_TEMPLATE = \
+    """&nbsp;<a href="javascript:loadRelatedOrders('%(clinical_item_id)s')"><img src="../../resource/graphIcon.png" width=12 height=12 alt="Find Related Orders"></a>""";
 
 class RelatedOrders(BaseDynamicData):
     """Simple script to (dynamically) relay query and result data
@@ -41,20 +44,21 @@ class RelatedOrders(BaseDynamicData):
     """
     def __init__(self):
         BaseDynamicData.__init__(self);
-        
+
         self.requestData["searchStr"] = "";
         self.requestData["analysisStatus"] = "1";
-        
+
         self.requestData["sim_patient_id"] = "";
         self.requestData["sim_time"] = "";
-        
+
         self.requestData["sourceTables"] = "stride_order_proc,stride_order_med";    # Default comma-separated list of source tables to expect orders to reside in
         self.requestData["queryItemIds"] = "";
         self.requestData["targetItemIds"] = "";
         self.requestData["excludeItemIds"] = "";
         self.requestData["excludeCategoryIds"] = "";
-        self.requestData["timeDeltaMax"] = "86400";
+        self.requestData["timeDeltaMax"] = "86400"; # Look for recommendations likely within 24 hours
         self.requestData["sortField"] = "";
+        self.requestData["enableRecommender"] = "True";  # By default, asssume recommender is enabled
         self.requestData["displayFields"] = ""; #"prevalence","PPV","RR","P-YatesChi2"
         self.requestData["sortReverse"] = "True";
         self.requestData["nPreCols"] = "1";
@@ -68,10 +72,9 @@ def __init__(self):
         self.requestData["title"] = "Order Search Results";
         self.requestData["fieldHeaders"] = "";
         self.requestData["dataRows"] = '<tr><td colspan=100 align=center height=200><img src="../../resource/ajax-loader.gif"></td></tr>';
-        
+
         self.addHandler("searchStr", RelatedOrders.action_orderSearch.__name__);
-        self.addHandler("queryItemIds", RelatedOrders.action_default.__name__);
-        self.addHandler("sim_patient_id", RelatedOrders.action_searchByPatient.__name__);
+        self.addHandler("RelatedOrders", RelatedOrders.action_default.__name__);
 
     def action_orderSearch(self):
         """Search for orders by query string"""
@@ -101,11 +104,22 @@ def action_orderSearch(self):
             htmlLines.append( self.formatRowHTML(dataModel, colNames, showCategory) );
             lastModel = dataModel;
         self.requestData["dataRows"] = str.join("\n", htmlLines );
-    
+
     def action_default(self):
         """Look for related orders by association / recommender methods"""
-        self.recommender = ItemAssociationRecommender();  # Instance to test on
-        self.recommender.dataManager.dataCache = webDataCache;
+        # If patient is specified then modify query and exclusion list based on items already ordered for patient
+        recentItemIds = set();
+        if self.requestData["sim_patient_id"]:
+            patientId = int(self.requestData["sim_patient_id"]);
+            simTime = int(self.requestData["sim_time"]);
+
+            # Track recent item IDs (orders, diagnoses, unlocked results, etc. that related order queries will be based off of)
+            manager = SimManager();
+            recentItemIds = manager.recentItemIds(patientId, simTime);
+
+        # Recommender Instance to test on
+        self.recommender = ItemAssociationRecommender();
+        self.recommender.dataManager.dataCache = webDataCache;  # Allow caching of data for rapid successive queries
 
         query = RecommenderQuery();
         if self.requestData["sortField"] == "":
@@ -120,19 +134,24 @@ def action_default(self):
         if self.requestData["displayFields"] != "":
             displayFields = self.requestData["displayFields"].split(",");
 
+        # Exclude items already ordered for the patient from any recommended list
+        query.excludeItemIds.update(recentItemIds);
+        if not query.queryItemIds:  # If no specific query items specified, then use the recent patient item IDs
+            query.queryItemIds.update(recentItemIds);
+
         recommendedData = self.recommender( query );
 
         if len(recommendedData) > 0:
             # Denormalize results with links to clinical item descriptions
             self.recommender.formatRecommenderResults(recommendedData);
-        
+
         # Display fields should append Format suffix to identify which version to display, but use original for header labels
         (self.requestData["fieldHeaders"], displayFieldsFormatSuffixed) = self.prepareDisplayHeaders(displayFields);
-        
+
         # Format for HTML and add a control field for interaction with the data
         for dataModel in recommendedData:
             self.prepareResultRow(dataModel, displayFields);
-        
+
         # Try organize by category
         if self.requestData["groupByCategory"]:
             recommendedData = self.recommender.organizeByCategory(recommendedData);
@@ -140,7 +159,7 @@ def action_default(self):
         colNames = ["controls"];   # "name" for code. ,"category_description"
         colNames.extend(displayFieldsFormatSuffixed);
         colNames.extend(["description"]);
-        
+
         lastModel = None;
         htmlLines = list();
         for dataModel in recommendedData:
@@ -152,29 +171,12 @@ def action_default(self):
             lastModel = dataModel;
         self.requestData["dataRows"] = str.join("\n", htmlLines );
 
-    def action_searchByPatient(self):
-        """Rather than explicit query items, specify a patient state context from which 
-        recent items can be extracted and used to query.
-        """
-        patientId = int(self.requestData["sim_patient_id"]);
-        simTime = int(self.requestData["sim_time"]);
-
-        # Track recent item IDs (orders, diagnoses, unlocked results, etc. that related order queries will be based off of)
-        manager = SimManager();
-        recentItemIds = manager.recentItemIds(patientId, simTime);
-        recentItemIdStrs = list();
-        for itemId in recentItemIds:
-            recentItemIdStrs.append(str(itemId));
-        
-        self.requestData["queryItemIds"] = str.join(",", recentItemIdStrs);
-        
-        # Delegate to default action now
-        self.action_default();
-
     def prepareDisplayHeaders(self, displayFields):
         showCounts = (self.requestData["showCounts"].lower() not in FALSE_STRINGS);
 
-        fieldHeadersHTML  = '<th nowrap>' + str.join('</th><th nowrap>', displayFields) + '</th>';
+        fieldHeadersHTML  = "";
+        for displayField in displayFields:
+            fieldHeadersHTML += '<th nowrap>' + displayField + '</th>';
         if showCounts:
             fieldHeadersHTML += '<th>' + str.join('</th><th>', CORE_FIELDS) + '</th>';
 
@@ -184,9 +186,9 @@ def prepareDisplayHeaders(self, displayFields):
         if showCounts:
             for field in CORE_FIELDS:
                 displayFieldsFormatSuffixed.append('%sFormat' % field);
-    
+
         return (fieldHeadersHTML, displayFieldsFormatSuffixed);
-    
+
     def prepareResultRow(self, dataModel, displayFields):
         dataModel["controls"] = CONTROLS_TEMPLATE % dataModel;
         dataModel["nPreCols"] = len(displayFields)+1;   # Track spacer columns leading up to order description. +1 for control column
@@ -203,12 +205,12 @@ def prepareResultRow(self, dataModel, displayFields):
             N = dataModel["N"];
             contStats = ContingencyStats( nAB, nA, nB, N );
             contStats.normalize(truncateNegativeValues=False);
-        
+
         for field in displayFields:
             if field not in dataModel:
                 # Unavailable field, see if it is a derived field that can be calculated
                 dataModel[field] = contStats[field];
-        
+
             if field in CORE_FIELDS:
                 pass;
             elif field in PERCENT_FIELDS:
@@ -223,8 +225,8 @@ def prepareResultRow(self, dataModel, displayFields):
             else:
                 # Default just format as limited floating point values
                 dataModel["%sFormat" % field] = "%.1f" % dataModel[field];
-        
-        for field in CORE_FIELDS:            
+
+        for field in CORE_FIELDS:
             # Count fields express as integers, assuming available at all
             if field in BASELINE_FIELDS:
                 dataModel["%sFormat" % field] = "%d" % dataModel[field];
@@ -247,12 +249,17 @@ def formatRowHTML(self, dataModel, colNames, showCategory=True):
                 else:
                     htmlList.append('<td></td>');
             elif col == "description":
-                htmlList.append('<td align=left><a href="javascript:clickItemById(%(clinical_item_id)s)">%(description)s</a></td>' % dataModel);
+                htmlList.append('<td align=left>');
+                htmlList.append( DESCRIPTION_TEMPLATE % dataModel);
+                # Only include related link if recommender is enabled
+                if self.requestData['enableRecommender'] == "True":
+                    htmlList.append( RELATED_LINK_TEMPLATE % dataModel);
+                htmlList.append('</td>');
             else:
                 htmlList.append('<td align=right>%s</td>' % dataModel[col]);
         htmlList.append('</tr>');
         return str.join("\n", htmlList);
-        
+
 # CGI Boilerplate to initiate script
 if __name__ == "__main__":
     webController =  RelatedOrders()
@@ -261,4 +268,4 @@ def formatRowHTML(self, dataModel, colNames, showCategory=True):
 # WSGI Boilerplate to initiate script
 webController = RelatedOrders()
 webController.setFilePath(__file__)
-application = webController.wsgiHandler 
\ No newline at end of file
+application = webController.wsgiHandler
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.htm b/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.htm
index 34727738..8454126f 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.htm
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.htm
@@ -3,7 +3,7 @@
             <td class="subheading" colspan=100>
                 <input type=button value="Notes" onClick="loadNotes(this.form);">
                 <input type=button value="Results Review" onClick="loadResults(this.form);" class="buttonSelected">
-                <input type=button value="Active Orders" onClick="loadActiveOrders(this.form);">
+                <!--input type=button value="Active Orders" onClick="loadActiveOrders(this.form);"-->
                 <input type=button value="Order History" onClick="loadOrderHistory(this.form);">
             </td>
         </tr>
@@ -16,3 +16,19 @@
         </tr>
         %(detailTable)s
     </table>
+
+    <table class="dataTable" cellspacing=0 cellpadding=4 style="width: 100%%">
+        <tr valign=top>
+            <td class="subheading" colspan=100>
+                Pending Orders
+            </td>
+        </tr>
+        <tr>
+            <td class="labelCell" align=center>Order Time</td>
+            <td class="labelCell" align=center>Code</td>
+            <td class="labelCell" align=left>Description</td>
+            <td class="labelCell" align=right>Minutes Left</td>
+        </tr>
+        %(pendingResultOrdersTable)s
+    </table>
+
diff --git a/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.py b/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.py
index c451dcc4..eb6b6de7 100644
--- a/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.py
+++ b/medinfo/web/cgibin/cpoe/dynamicdata/ResultsReview.py
@@ -2,7 +2,7 @@
 import sys, os;
 import cgi
 import cgitb; cgitb.enable();
-from cStringIO import StringIO;
+from io import StringIO;
 
 from datetime import datetime, timedelta;
 
@@ -12,8 +12,8 @@
 from medinfo.db.Model import SQLQuery, modelListFromTable;
 from medinfo.db.ResultsFormatter import HtmlResultsFormatter;
 
-from medinfo.cpoe.SimManager import SimManager;
-from medinfo.cpoe.Const import BASE_TIME, TIME_FORMAT;
+from medinfo.cpoe.cpoeSim.SimManager import SimManager;
+from medinfo.cpoe.cpoeSim.Const import BASE_TIME, TIME_FORMAT;
 
 from medinfo.web.cgibin.cpoe.dynamicdata.BaseDynamicData import BaseDynamicData;
 from medinfo.web.cgibin import Options;
@@ -35,6 +35,23 @@
     </tr>
     """;
 
+PENDING_ORDER_LINE_TEMPLATE = \
+    """
+    <tr>
+        <td align=center>%(order_time.format)s
+        <td align=center>%(name)s</td>
+        <td align=left>%(description)s</td>
+        <td align=right>%(time_until_result.format)s</td>
+    </tr>
+    """;
+EMPTY_RESULTS_LINE = \
+    """
+    <tr>
+        <td align=center colspan=100>No results pending</td>
+    </tr>
+    """;
+
+
 class ResultsReview(BaseDynamicData):
     """Simple script to (dynamically) relay query and result data
     """
@@ -45,6 +62,7 @@ def __init__(self):
         self.requestData["sim_time"] = "";
         
         self.requestData["detailTable"] = "";
+        self.requestData["pendingResultOrdersTable"] = EMPTY_RESULTS_LINE;
         
         self.addHandler("sim_patient_id", ResultsReview.action_default.__name__);
         
@@ -54,8 +72,9 @@ def action_default(self):
         simTime = int(self.requestData["sim_time"]);
         
         manager = SimManager();
+
+        # Load and format the results available so far
         results = manager.loadResults(patientId, simTime);
-        
         lastGroupStrings = ['New']; # Sentinel value that will be different than first real data row
         lenLastGroupStrings = len(lastGroupStrings);
         htmlLines = list();
@@ -69,6 +88,16 @@ def action_default(self):
             lenLastGroupStrings = len(lastGroupStrings);
         self.requestData["detailTable"] = str.join("\n", htmlLines );
 
+        # Load and format information on any pending result orders
+        pendingResultOrders = manager.loadPendingResultOrders(patientId, simTime);
+        htmlLines = list();
+        for dataModel in pendingResultOrders:
+            self.formatPendingResultOrderModel(dataModel);
+            htmlLines.append( PENDING_ORDER_LINE_TEMPLATE % dataModel );
+        self.requestData["pendingResultOrdersTable"] = str.join("\n", htmlLines );
+        if len(pendingResultOrders) < 1: # Leave default "no results pending" message if don't find any
+            self.requestData["pendingResultOrdersTable"] = EMPTY_RESULTS_LINE;
+
     def formatDataModel(self, dataModel):
         """Populate dataModel with formatted items"""
         dataModel["groupStrings"] = dataModel["group_string"].split(">");
@@ -80,6 +109,11 @@ def formatDataModel(self, dataModel):
                 dataModel[key] = "";
         dataModel["result_time.format"] = (BASE_TIME + timedelta(0,dataModel["result_relative_time"])).strftime(TIME_FORMAT);
 
+    def formatPendingResultOrderModel(self, dataModel):
+        """Populate dataModel with formatted items"""
+        dataModel["order_time.format"] = (BASE_TIME + timedelta(0,dataModel["relative_time_start"])).strftime(TIME_FORMAT);
+        dataModel["time_until_result.format"] = dataModel["time_until_result"] / 60;    # Convert time in seconds into minutes
+
         
 # CGI Boilerplate to initiate script
 if __name__ == "__main__":
diff --git a/medinfo/web/cgibin/errorTemplate.py b/medinfo/web/cgibin/errorTemplate.py
index ef51640a..f783930a 100644
--- a/medinfo/web/cgibin/errorTemplate.py
+++ b/medinfo/web/cgibin/errorTemplate.py
@@ -3,11 +3,11 @@
 Simple Python CGI script to relay error message to users in a more pleasing format.
 """
 
-from cStringIO import StringIO
+from io import StringIO
 import cgi
 import cgitb; cgitb.enable()
 
-from BaseWeb import BaseWeb
+from .BaseWeb import BaseWeb
 
 class errorTemplate(BaseWeb):
     def __init__(self):
diff --git a/medinfo/web/index.htm b/medinfo/web/index.htm
index 624a4086..bf8f4391 100644
--- a/medinfo/web/index.htm
+++ b/medinfo/web/index.htm
@@ -1,3 +1,9 @@
 <a href="cgibin/cpoe/ItemRecommenderWeb.py">Item Recommender</a>
 
+<br><br>
 
+<a href="cgibin/cpoe/SimSetup.py">Simulation Setup</a>
+
+<br><br>
+
+<a href="cgibin/admin/DBUtilWeb.py">DBUtil</a>
diff --git a/medinfo/web/resource/cancelIcon.svg b/medinfo/web/resource/cancelIcon.svg
new file mode 100644
index 00000000..cfa67185
--- /dev/null
+++ b/medinfo/web/resource/cancelIcon.svg
@@ -0,0 +1 @@
+<svg height="511.99998pt" viewBox="1 1 511.99998 511.99998" width="511.99998pt" xmlns="http://www.w3.org/2000/svg"><path d="m256 0c-141.386719 0-256 114.613281-256 256s114.613281 256 256 256 256-114.613281 256-256c-.167969-141.316406-114.683594-255.832031-256-256zm0 480c-123.710938 0-224-100.289062-224-224s100.289062-224 224-224 224 100.289062 224 224c-.132812 123.65625-100.34375 223.867188-224 224zm0 0"/><path d="m380.449219 131.550781c-6.25-6.246093-16.378907-6.246093-22.625 0l-101.824219 101.824219-101.824219-101.824219c-6.140625-6.355469-16.269531-6.53125-22.625-.390625-6.355469 6.136719-6.53125 16.265625-.390625 22.621094.128906.132812.257813.265625.390625.394531l101.824219 101.824219-101.824219 101.824219c-6.355469 6.136719-6.53125 16.265625-.390625 22.625 6.136719 6.355469 16.265625 6.53125 22.621094.390625.132812-.128906.265625-.257813.394531-.390625l101.824219-101.824219 101.824219 101.824219c6.355469 6.136719 16.484375 5.960937 22.621093-.394531 5.988282-6.199219 5.988282-16.03125 0-22.230469l-101.820312-101.824219 101.824219-101.824219c6.246093-6.246093 6.246093-16.375 0-22.625zm0 0"/></svg>
\ No newline at end of file
diff --git a/medinfo/web/resource/graphIcon.png b/medinfo/web/resource/graphIcon.png
new file mode 100644
index 00000000..1d431d56
Binary files /dev/null and b/medinfo/web/resource/graphIcon.png differ
diff --git a/medinfo/web/tools/AcidBaseAnalysis.htm b/medinfo/web/tools/AcidBaseAnalysis.htm
index 782ab6f8..4dc25bc8 100644
--- a/medinfo/web/tools/AcidBaseAnalysis.htm
+++ b/medinfo/web/tools/AcidBaseAnalysis.htm
@@ -4,7 +4,7 @@
 <!--
 Acid-Base Analysis
 Jonathan H. Chen
-jonc101 at gmail.com
+stanford.healthrex at gmail.com
 2011
 -->
 
@@ -593,4 +593,4 @@
     </div>
 </form>
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/medinfo/web/tools/Antibiogram/VAMC Wilkes Barre - Antibiogram FY17.xlsx b/medinfo/web/tools/Antibiogram/VAMC Wilkes Barre - Antibiogram FY17.xlsx
new file mode 100644
index 00000000..77c33dbc
Binary files /dev/null and b/medinfo/web/tools/Antibiogram/VAMC Wilkes Barre - Antibiogram FY17.xlsx differ
diff --git a/medinfo/web/tools/Antibiogram/antibiogramData.js b/medinfo/web/tools/Antibiogram/antibiogramData.js
index 3b87eae4..13d014e6 100644
--- a/medinfo/web/tools/Antibiogram/antibiogramData.js
+++ b/medinfo/web/tools/Antibiogram/antibiogramData.js
@@ -34,8 +34,9 @@ var DRUG_LIST =
         'Ampicillin-Sulbactam',
         'Ticarcillin',
         'Ticarcillin-Clavulanate',
-        'Piperacillin-Tazobactam',
         'Piperacillin',
+        'Piperacillin-Tazobactam',
+        'Ceftolozane-Tazobactam',
         'Doripenem',
         'Ertapenem',
         'Imipenem',
@@ -157,6 +158,7 @@ var PROPERTIES_BY_DRUG =
         'Ticarcillin-Clavulanate': ['Anti-Pseudomonal Penicillin'],
         'Piperacillin-Tazobactam': ['Anti-Pseudomonal Penicillin'],
         'Piperacillin': ['Anti-Pseudomonal Penicillin'],
+        'Ceftolozane-Tazobactam': ['Anti-Pseudomonal Penicillin'],
         'Doripenem': ['Carbapenem'],
         'Ertapenem': ['Carbapenem'],
         'Imipenem': ['Carbapenem'],
@@ -181,6 +183,7 @@ var PROPERTIES_BY_DRUG =
         'Ceftaroline': ['Cephalosporin (IV) Gen 3+'],
         'Ceftazidime': ['Cephalosporin (IV) Gen 3+'],
         'Cefepime': ['Cephalosporin (IV) Gen 3+'],
+        'Ceftolozane-Tazobactam': ['Cephalosporin (IV) Gen 3+'],
         'Cefadroxil': ['Cephalosporin (PO) Gen 1','Oral Available'],
         'Cephalexin': ['Cephalosporin (PO) Gen 1','Oral Available'],
         'Cefaclor/Loracarbef': ['Cephalosporin (PO) Gen 2','Oral Available'],
@@ -295,6 +298,7 @@ var BUG_LIST =
         'Citrobacter koseri',
         'Enterobacter aerogenes',
         'Enterobacter cloacae',
+        'Klebsiella aerogenes', //TODO Since 2018 SHC: formerly known as Enterobacter aerogenes - should be a new entry or replace Enterobacter aerogenes? Assuming new entry.
         'Klebsiella oxytoca',
         'Klebsiella pneumoniae',
         'Pseudomonas aeruginosa CF mucoid',
@@ -335,7 +339,18 @@ var BUG_LIST =
 
         // Additional elements from VA Antibiogram
         'Staphylococcus capitis',
-        'Staphylococcus hominis'
+        'Staphylococcus hominis',
+
+        // Additional elements from Wilkes-Barre VAMC
+        'Escherichia coli ESBL',
+        'Klebsiella oxytoca ESBL',
+        'Klebsiella pneumoniae ESBL',
+        'Proteus mirabilis ESBL',
+        'Providencia rettgeri',
+        'Providencia stuartii',
+        'Streptococcus Group C (dysgalactiae)',
+        'Streptococcus Group A (pyogenes)',
+
     ];
 BUG_LIST.sort();    // Present in sorted order to facilitate selection lookup
 
@@ -429,6 +444,7 @@ var PROPERTIES_BY_BUG =
         'Citrobacter koseri': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
         'Enterobacter aerogenes': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
         'Enterobacter cloacae': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
+        'Klebsiella aerogenes': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],     //TODO creating as a new entry - formerly known as Enterobacter aerogenes
         'Klebsiella oxytoca': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
         'Klebsiella pneumoniae': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
         'Pseudomonas aeruginosa CF mucoid': ['Gram Negative','Gram Negative Rods, Non-Lactose Fermenting','Gram Negative Rods, Non-Fermenting'],
@@ -469,8 +485,17 @@ var PROPERTIES_BY_BUG =
         'Sporothrix schenckii': ['Fungal (Dimorphic)'],
 
         'Staphylococcus capitis': ['Gram Positive','Gram Positive Cocci in Clusters','Gram Positive Cocci in Clusters, Coagulase Negative'],
-        'Staphylococcus hominis': ['Gram Positive','Gram Positive Cocci in Clusters','Gram Positive Cocci in Clusters, Coagulase Negative']
+        'Staphylococcus hominis': ['Gram Positive','Gram Positive Cocci in Clusters','Gram Positive Cocci in Clusters, Coagulase Negative'],
 
+        // New elements from Wilkes-Barre VAMC
+        'Escherichia coli ESBL': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
+        'Klebsiella oxytoca ESBL': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
+        'Klebsiella pneumoniae ESBL': ['Gram Negative','Gram Negative Rods, Lactose Fermenting'],
+        'Proteus mirabilis ESBL': ['Gram Negative','Gram Negative Rods, Non-Lactose Fermenting'],
+        'Providencia rettgeri': ['Gram Negative','Gram Negative Rods, Non-Lactose Fermenting'],
+        'Providencia stuartii': ['Gram Negative','Gram Negative Rods, Non-Lactose Fermenting'],
+        'Streptococcus Group C (dysgalactiae)': ['Gram Positive', 'Gram Positive Cocci in Pairs / Chains'],
+        'Streptococcus Group A (pyogenes)': ['Gram Positive', 'Gram Positive Cocci in Pairs / Chains'],
     };
 
 // Keyed by microorganism name, returns dictionary of anti-microbial names
@@ -510,7 +535,7 @@ for( var i=0; i < BUG_LIST.length; i++ )
 }
 */
 
-SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (Spinal Cord Injury Unit)"] = 
+SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (Spinal Cord Injury Unit)"] =
 [
     '32\tEnterococcus faecalis\tNumber Tested',
     '100\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
@@ -722,7 +747,7 @@ SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (Long Term Care)"] =
     '65\tPseudomonas aeruginosa\tLevofloxacin',
 ].join('\n');
 
-SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (ICU/IICU)"] = 
+SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (ICU/IICU)"] =
 [
     '21\tEnterococcus faecalis\tNumber Tested',
     '100\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
@@ -869,7 +894,7 @@ SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (ICU/IICU)"] =
     '80\tStenotrophomonas maltophilia\tTrimethoprim',
 ].join('\n');
 
-SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (ER)"] = 
+SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (ER)"] =
 [
     '116\tEnterococcus faecalis\tNumber Tested',
     '99\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
@@ -1162,7 +1187,7 @@ SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (ER)"] =
     '100\tStenotrophomonas maltophilia\tTrimethoprim',
 ].join('\n');
 
-SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (Acute Med/Surg)"] = 
+SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (Acute Med/Surg)"] =
 [
     '32\tEnterococcus faecalis\tNumber Tested',
     '100\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
@@ -1275,7 +1300,7 @@ SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (Acute Med/Surg)"] =
     '84\tPseudomonas aeruginosa\tLevofloxacin',
 ].join('\n');
 
-SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (All Units)"] = 
+SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (All Units)"] =
 [
     '489\tEnterococcus faecalis\tNumber Tested',
     '99\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
@@ -1572,7 +1597,7 @@ SENSITIVITY_DATA_PER_SOURCE["2016 Palo Alto VA (All Units)"] =
 ].join('\n');
 
 
-SENSITIVITY_DATA_PER_SOURCE["2010 Sanford Guide"] = 
+SENSITIVITY_DATA_PER_SOURCE["2010 Sanford Guide"] =
 [
     '90\tAcinetobacter\tAmikacin',
     '0\tAcinetobacter\tAmoxicillin-Clavulanate',
@@ -4857,7 +4882,7 @@ SENSITIVITY_DATA_PER_SOURCE["2011 Palo Alto VA (ED)"] =
     '90\tStenotrophomonas maltophilia\tTMP-SMX',
 ].join('\n');
 
-SENSITIVITY_DATA_PER_SOURCE["2012 Stanford (SUH)"] = 
+SENSITIVITY_DATA_PER_SOURCE["2012 Stanford (SUH)"] =
 [
     '210\tStreptococcus Group B (agalactiae)\tNumber Tested',
     '100\tStreptococcus Group B (agalactiae)\tPenicillin G',
@@ -6450,5 +6475,1993 @@ SENSITIVITY_DATA_PER_SOURCE["2016 Lucile Packard Children's Hospital (LCPH)"] =
     '100\tCandida (other)\tCaspofungin',
 ].join('\n');
 
-DEFAULT_SOURCE = "2016 Stanford Health Care (SHC)";
+SENSITIVITY_DATA_PER_SOURCE["2017 Wilkes-Barre VA Medical Center - Blood Isolates"] = [
+    '7\tEscherichia coli\tNumber Tested',
+    '100\tEscherichia coli\tAmpicillin-Sulbactam',
+    '100\tEscherichia coli\tAmpicillin/Amoxicillin',
+    '100\tEscherichia coli\tAztreonam',
+    '100\tEscherichia coli\tCeftriaxone',
+    '100\tEscherichia coli\tCeftazidime',
+    '100\tEscherichia coli\tCefazolin',
+    '71\tEscherichia coli\tCiprofloxacin',
+    '100\tEscherichia coli\tDoripenem',
+    '100\tEscherichia coli\tErtapenem',
+    '100\tEscherichia coli\tCefepime',
+    '100\tEscherichia coli\tGentamicin',
+    '71\tEscherichia coli\tLevofloxacin',
+    '100\tEscherichia coli\tMeropenem',
+    '71\tEscherichia coli\tTMP-SMX',
+    '100\tEscherichia coli\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli\tTigecycline',
+    '100\tEscherichia coli\tTobramycin',
+    '2\tEscherichia coli ESBL\tNumber Tested',
+    '50\tEscherichia coli ESBL\tAmpicillin-Sulbactam',
+    '0\tEscherichia coli ESBL\tAmpicillin/Amoxicillin',
+    '0\tEscherichia coli ESBL\tAztreonam',
+    '0\tEscherichia coli ESBL\tCeftriaxone',
+    '0\tEscherichia coli ESBL\tCeftazidime',
+    '0\tEscherichia coli ESBL\tCefazolin',
+    '0\tEscherichia coli ESBL\tCiprofloxacin',
+    '100\tEscherichia coli ESBL\tDoripenem',
+    '100\tEscherichia coli ESBL\tErtapenem',
+    '0\tEscherichia coli ESBL\tCefepime',
+    '50\tEscherichia coli ESBL\tGentamicin',
+    '0\tEscherichia coli ESBL\tLevofloxacin',
+    '100\tEscherichia coli ESBL\tMeropenem',
+    '50\tEscherichia coli ESBL\tTMP-SMX',
+    '100\tEscherichia coli ESBL\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli ESBL\tTigecycline',
+    '50\tEscherichia coli ESBL\tTobramycin',
+    '4\tKlebsiella pneumoniae\tNumber Tested',
+    '100\tKlebsiella pneumoniae\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella pneumoniae\tAztreonam',
+    '100\tKlebsiella pneumoniae\tCeftriaxone',
+    '100\tKlebsiella pneumoniae\tCeftazidime',
+    '100\tKlebsiella pneumoniae\tCefazolin',
+    '100\tKlebsiella pneumoniae\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae\tDoripenem',
+    '100\tKlebsiella pneumoniae\tErtapenem',
+    '100\tKlebsiella pneumoniae\tCefepime',
+    '100\tKlebsiella pneumoniae\tGentamicin',
+    '100\tKlebsiella pneumoniae\tLevofloxacin',
+    '100\tKlebsiella pneumoniae\tMeropenem',
+    '100\tKlebsiella pneumoniae\tTMP-SMX',
+    '100\tKlebsiella pneumoniae\tPiperacillin-Tazobactam',
+    '100\tKlebsiella pneumoniae\tTigecycline',
+    '100\tKlebsiella pneumoniae\tTobramycin',
+    '1\tKlebsiella pneumoniae ESBL\tNumber Tested',
+    '0\tKlebsiella pneumoniae ESBL\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae ESBL\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella pneumoniae ESBL\tAztreonam',
+    '0\tKlebsiella pneumoniae ESBL\tCeftriaxone',
+    '0\tKlebsiella pneumoniae ESBL\tCeftazidime',
+    '0\tKlebsiella pneumoniae ESBL\tCefazolin',
+    '0\tKlebsiella pneumoniae ESBL\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae ESBL\tDoripenem',
+    '100\tKlebsiella pneumoniae ESBL\tErtapenem',
+    '0\tKlebsiella pneumoniae ESBL\tCefepime',
+    '0\tKlebsiella pneumoniae ESBL\tGentamicin',
+    '100\tKlebsiella pneumoniae ESBL\tLevofloxacin',
+    '100\tKlebsiella pneumoniae ESBL\tMeropenem',
+    '0\tKlebsiella pneumoniae ESBL\tTMP-SMX',
+    '100\tKlebsiella pneumoniae ESBL\tPiperacillin-Tazobactam',
+    '100\tKlebsiella pneumoniae ESBL\tTigecycline',
+    '0\tKlebsiella pneumoniae ESBL\tTobramycin',
+    '4\tProteus mirabilis\tNumber Tested',
+    '75\tProteus mirabilis\tAmpicillin-Sulbactam',
+    '75\tProteus mirabilis\tAmpicillin/Amoxicillin',
+    '100\tProteus mirabilis\tAztreonam',
+    '100\tProteus mirabilis\tCeftriaxone',
+    '100\tProteus mirabilis\tCeftazidime',
+    '75\tProteus mirabilis\tCefazolin',
+    '0\tProteus mirabilis\tCiprofloxacin',
+    '100\tProteus mirabilis\tDoripenem',
+    '100\tProteus mirabilis\tErtapenem',
+    '100\tProteus mirabilis\tCefepime',
+    '75\tProteus mirabilis\tGentamicin',
+    '0\tProteus mirabilis\tLevofloxacin',
+    '100\tProteus mirabilis\tMeropenem',
+    '50\tProteus mirabilis\tTMP-SMX',
+    '100\tProteus mirabilis\tPiperacillin-Tazobactam',
+    '100\tProteus mirabilis\tTobramycin',
+    '2\tPseudomonas aeruginosa\tNumber Tested',
+    '100\tPseudomonas aeruginosa\tAmpicillin-Sulbactam',
+    '0\tPseudomonas aeruginosa\tAztreonam',
+    '50\tPseudomonas aeruginosa\tCeftazidime',
+    '0\tPseudomonas aeruginosa\tCiprofloxacin',
+    '50\tPseudomonas aeruginosa\tCefepime',
+    '100\tPseudomonas aeruginosa\tGentamicin',
+    '0\tPseudomonas aeruginosa\tLevofloxacin',
+    '0\tPseudomonas aeruginosa\tMeropenem',
+    '50\tPseudomonas aeruginosa\tPiperacillin-Tazobactam',
+    '0\tPseudomonas aeruginosa\tTobramycin',
+    '6\tEnterococcus faecalis\tNumber Tested',
+    '100\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
+    '100\tEnterococcus faecalis\tDaptomycin (non-pneumonia)',
+    '0\tEnterococcus faecalis\tErythromycin',
+    '100\tEnterococcus faecalis\tLinezolid',
+    '100\tEnterococcus faecalis\tPenicillin G',
+    '100\tEnterococcus faecalis\tVancomycin',
+    '8\tStaphylococcus aureus (MSSA)\tNumber Tested',
+    '100\tStaphylococcus aureus (MSSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MSSA)\tAmpicillin/Amoxicillin',
+    '88\tStaphylococcus aureus (MSSA)\tClindamycin',
+    '100\tStaphylococcus aureus (MSSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MSSA)\tDaptomycin (non-pneumonia)',
+    '75\tStaphylococcus aureus (MSSA)\tErythromycin',
+    '100\tStaphylococcus aureus (MSSA)\tGentamicin',
+    '88\tStaphylococcus aureus (MSSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MSSA)\tLinezolid',
+    '100\tStaphylococcus aureus (MSSA)\tMoxifloxacin',
+    '100\tStaphylococcus aureus (MSSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MSSA)\tPenicillin G',
+    '100\tStaphylococcus aureus (MSSA)\tTMP-SMX',
+    '100\tStaphylococcus aureus (MSSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MSSA)\tVancomycin',
+    '13\tStaphylococcus aureus (MRSA)\tNumber Tested',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin/Amoxicillin',
+    '62\tStaphylococcus aureus (MRSA)\tClindamycin',
+    '0\tStaphylococcus aureus (MRSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MRSA)\tDaptomycin (non-pneumonia)',
+    '38\tStaphylococcus aureus (MRSA)\tErythromycin',
+    '100\tStaphylococcus aureus (MRSA)\tGentamicin',
+    '15\tStaphylococcus aureus (MRSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MRSA)\tLinezolid',
+    '38\tStaphylococcus aureus (MRSA)\tMoxifloxacin',
+    '0\tStaphylococcus aureus (MRSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MRSA)\tPenicillin G',
+    '72\tStaphylococcus aureus (MRSA)\tTMP-SMX',
+    '77\tStaphylococcus aureus (MRSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MRSA)\tVancomycin',
+    '18\tStaphylococcus, Coagulase Negative (epidermidis)\tNumber Tested',
+    '56\tStaphylococcus, Coagulase Negative (epidermidis)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus, Coagulase Negative (epidermidis)\tAmpicillin/Amoxicillin',
+    '78\tStaphylococcus, Coagulase Negative (epidermidis)\tClindamycin',
+    '56\tStaphylococcus, Coagulase Negative (epidermidis)\tCefazolin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tDaptomycin (non-pneumonia)',
+    '39\tStaphylococcus, Coagulase Negative (epidermidis)\tErythromycin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tGentamicin',
+    '72\tStaphylococcus, Coagulase Negative (epidermidis)\tLevofloxacin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tLinezolid',
+    '83\tStaphylococcus, Coagulase Negative (epidermidis)\tMoxifloxacin',
+    '56\tStaphylococcus, Coagulase Negative (epidermidis)\tNafcillin/Oxacillin',
+    '6\tStaphylococcus, Coagulase Negative (epidermidis)\tPenicillin G',
+    '89\tStaphylococcus, Coagulase Negative (epidermidis)\tTMP-SMX',
+    '83\tStaphylococcus, Coagulase Negative (epidermidis)\tDoxycycline',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tVancomycin',
+    '3\tStreptococcus Group B (agalactiae)\tNumber Tested',
+    '100\tStreptococcus Group B (agalactiae)\tAmpicillin/Amoxicillin',
+    '33\tStreptococcus Group B (agalactiae)\tAzithromycin',
+    '100\tStreptococcus Group B (agalactiae)\tCeftriaxone',
+    '33\tStreptococcus Group B (agalactiae)\tClindamycin',
+    '33\tStreptococcus Group B (agalactiae)\tErythromycin',
+    '100\tStreptococcus Group B (agalactiae)\tCefepime',
+    '100\tStreptococcus Group B (agalactiae)\tLevofloxacin',
+    '100\tStreptococcus Group B (agalactiae)\tPenicillin G',
+    '33\tStreptococcus Group B (agalactiae)\tDoxycycline',
+    '100\tStreptococcus Group B (agalactiae)\tVancomycin',
+].join('\n');
+
+SENSITIVITY_DATA_PER_SOURCE["2017 Wilkes-Barre VA Medical Center - Sptutum Isolates"] = [
+    '7\tEnterobacter cloacae\tNumber Tested',
+    '43\tEnterobacter cloacae\tAmpicillin-Sulbactam',
+    '0\tEnterobacter cloacae\tAmpicillin/Amoxicillin',
+    '100\tEnterobacter cloacae\tAztreonam',
+    '86\tEnterobacter cloacae\tCeftriaxone',
+    '100\tEnterobacter cloacae\tCeftazidime',
+    '0\tEnterobacter cloacae\tCefazolin',
+    '100\tEnterobacter cloacae\tCiprofloxacin',
+    '100\tEnterobacter cloacae\tDoripenem',
+    '100\tEnterobacter cloacae\tErtapenem',
+    '100\tEnterobacter cloacae\tCefepime',
+    '86\tEnterobacter cloacae\tGentamicin',
+    '100\tEnterobacter cloacae\tLevofloxacin',
+    '100\tEnterobacter cloacae\tMeropenem',
+    '100\tEnterobacter cloacae\tTMP-SMX',
+    '100\tEnterobacter cloacae\tPiperacillin-Tazobactam',
+    '100\tEnterobacter cloacae\tTigecycline',
+    '100\tEnterobacter cloacae\tTobramycin',
+    '7\tEscherichia coli\tNumber Tested',
+    '57\tEscherichia coli\tAmpicillin-Sulbactam',
+    '57\tEscherichia coli\tAmpicillin/Amoxicillin',
+    '100\tEscherichia coli\tAztreonam',
+    '100\tEscherichia coli\tCeftriaxone',
+    '100\tEscherichia coli\tCeftazidime',
+    '71\tEscherichia coli\tCefazolin',
+    '86\tEscherichia coli\tCiprofloxacin',
+    '100\tEscherichia coli\tDoripenem',
+    '100\tEscherichia coli\tErtapenem',
+    '100\tEscherichia coli\tCefepime',
+    '71\tEscherichia coli\tGentamicin',
+    '86\tEscherichia coli\tLevofloxacin',
+    '100\tEscherichia coli\tMeropenem',
+    '86\tEscherichia coli\tTMP-SMX',
+    '100\tEscherichia coli\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli\tTigecycline',
+    '86\tEscherichia coli\tTobramycin',
+    '15\tHaemophilus influenzae\tNumber Tested',
+    '40\tHaemophilus influenzae\tAmpicillin/Amoxicillin',
+    '79\tHaemophilus influenzae\tTMP-SMX',
+    '6\tKlebsiella oxytoca\tNumber Tested',
+    '83\tKlebsiella oxytoca\tAmpicillin-Sulbactam',
+    '0\tKlebsiella oxytoca\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella oxytoca\tAztreonam',
+    '100\tKlebsiella oxytoca\tCeftriaxone',
+    '100\tKlebsiella oxytoca\tCeftazidime',
+    '67\tKlebsiella oxytoca\tCefazolin',
+    '100\tKlebsiella oxytoca\tCiprofloxacin',
+    '100\tKlebsiella oxytoca\tDoripenem',
+    '100\tKlebsiella oxytoca\tErtapenem',
+    '100\tKlebsiella oxytoca\tCefepime',
+    '100\tKlebsiella oxytoca\tGentamicin',
+    '100\tKlebsiella oxytoca\tLevofloxacin',
+    '100\tKlebsiella oxytoca\tMeropenem',
+    '100\tKlebsiella oxytoca\tTMP-SMX',
+    '100\tKlebsiella oxytoca\tPiperacillin-Tazobactam',
+    '100\tKlebsiella oxytoca\tTigecycline',
+    '100\tKlebsiella oxytoca\tTobramycin',
+    '4\tKlebsiella pneumoniae\tNumber Tested',
+    '100\tKlebsiella pneumoniae\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella pneumoniae\tAztreonam',
+    '100\tKlebsiella pneumoniae\tCeftriaxone',
+    '100\tKlebsiella pneumoniae\tCeftazidime',
+    '100\tKlebsiella pneumoniae\tCefazolin',
+    '100\tKlebsiella pneumoniae\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae\tDoripenem',
+    '100\tKlebsiella pneumoniae\tErtapenem',
+    '100\tKlebsiella pneumoniae\tCefepime',
+    '100\tKlebsiella pneumoniae\tGentamicin',
+    '100\tKlebsiella pneumoniae\tLevofloxacin',
+    '100\tKlebsiella pneumoniae\tMeropenem',
+    '100\tKlebsiella pneumoniae\tTMP-SMX',
+    '100\tKlebsiella pneumoniae\tPiperacillin-Tazobactam',
+    '100\tKlebsiella pneumoniae\tTigecycline',
+    '100\tKlebsiella pneumoniae\tTobramycin',
+    '4\tPseudomonas aeruginosa\tNumber Tested',
+    '100\tPseudomonas aeruginosa\tAmikacin',
+    '0\tPseudomonas aeruginosa\tAmpicillin/Amoxicillin',
+    '100\tPseudomonas aeruginosa\tAztreonam',
+    '75\tPseudomonas aeruginosa\tCeftazidime',
+    '100\tPseudomonas aeruginosa\tCiprofloxacin',
+    '100\tPseudomonas aeruginosa\tDoripenem',
+    '75\tPseudomonas aeruginosa\tCefepime',
+    '75\tPseudomonas aeruginosa\tGentamicin',
+    '100\tPseudomonas aeruginosa\tLevofloxacin',
+    '100\tPseudomonas aeruginosa\tMeropenem',
+    '100\tPseudomonas aeruginosa\tPiperacillin-Tazobactam',
+    '100\tPseudomonas aeruginosa\tTobramycin',
+    '6\tStreptococcus pneumoniae\tNumber Tested',
+    '67\tStreptococcus pneumoniae\tAzithromycin',
+    '100\tStreptococcus pneumoniae\tCeftriaxone',
+    '100\tStreptococcus pneumoniae\tClindamycin',
+    '67\tStreptococcus pneumoniae\tErythromycin',
+    '100\tStreptococcus pneumoniae\tCefepime',
+    '100\tStreptococcus pneumoniae\tLevofloxacin',
+    '67\tStreptococcus pneumoniae\tMeropenem',
+    '67\tStreptococcus pneumoniae\tPenicillin G',
+    '83\tStreptococcus pneumoniae\tTMP-SMX',
+    '100\tStreptococcus pneumoniae\tDoxycycline',
+    '0\tStreptococcus pneumoniae\tTobramycin',
+    '100\tStreptococcus pneumoniae\tVancomycin',
+    '7\tStaphylococcus aureus (MSSA)\tNumber Tested',
+    '71\tStaphylococcus aureus (MSSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MSSA)\tAmpicillin/Amoxicillin',
+    '57\tStaphylococcus aureus (MSSA)\tClindamycin',
+    '100\tStaphylococcus aureus (MSSA)\tCefazolin',
+    '43\tStaphylococcus aureus (MSSA)\tErythromycin',
+    '100\tStaphylococcus aureus (MSSA)\tGentamicin',
+    '71\tStaphylococcus aureus (MSSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MSSA)\tLinezolid',
+    '71\tStaphylococcus aureus (MSSA)\tMoxifloxacin',
+    '100\tStaphylococcus aureus (MSSA)\tNafcillin/Oxacillin',
+    '29\tStaphylococcus aureus (MSSA)\tPenicillin G',
+    '100\tStaphylococcus aureus (MSSA)\tTMP-SMX',
+    '100\tStaphylococcus aureus (MSSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MSSA)\tVancomycin',
+    '10\tStaphylococcus aureus (MRSA)\tNumber Tested',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin/Amoxicillin',
+    '50\tStaphylococcus aureus (MRSA)\tClindamycin',
+    '0\tStaphylococcus aureus (MRSA)\tCefazolin',
+    '10\tStaphylococcus aureus (MRSA)\tErythromycin',
+    '100\tStaphylococcus aureus (MRSA)\tGentamicin',
+    '10\tStaphylococcus aureus (MRSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MRSA)\tLinezolid',
+    '50\tStaphylococcus aureus (MRSA)\tMoxifloxacin',
+    '0\tStaphylococcus aureus (MRSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MRSA)\tPenicillin G',
+    '100\tStaphylococcus aureus (MRSA)\tTMP-SMX',
+    '90\tStaphylococcus aureus (MRSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MRSA)\tVancomycin',
+].join('\n');
+
+SENSITIVITY_DATA_PER_SOURCE["2017 Wilkes-Barre VA Medical Center - Urine Isolates"] = [
+    '7\tCitrobacter freundii\tNumber Tested',
+    '57\tCitrobacter freundii\tAmpicillin-Sulbactam',
+    '29\tCitrobacter freundii\tAmpicillin/Amoxicillin',
+    '100\tCitrobacter freundii\tAztreonam',
+    '100\tCitrobacter freundii\tCeftriaxone',
+    '100\tCitrobacter freundii\tCeftazidime',
+    '14\tCitrobacter freundii\tCefazolin',
+    '71\tCitrobacter freundii\tCiprofloxacin',
+    '0 \tCitrobacter freundii\tDaptomycin (non-pneumonia)',
+    '100\tCitrobacter freundii\tDoripenem',
+    '100\tCitrobacter freundii\tErtapenem',
+    '100\tCitrobacter freundii\tCefepime',
+    '100\tCitrobacter freundii\tGentamicin',
+    '71\tCitrobacter freundii\tLevofloxacin',
+    '0 \tCitrobacter freundii\tLinezolid',
+    '100\tCitrobacter freundii\tMeropenem',
+    '100\tCitrobacter freundii\tNitrofurantoin (uncomplicated UTI)',
+    '71\tCitrobacter freundii\tTMP-SMX',
+    '100\tCitrobacter freundii\tPiperacillin-Tazobactam',
+    '100\tCitrobacter freundii\tTigecycline',
+    '100\tCitrobacter freundii\tTobramycin',
+    '8\tCitrobacter koseri\tNumber Tested',
+    '100\tCitrobacter koseri\tAmpicillin-Sulbactam',
+    '0\tCitrobacter koseri\tAmpicillin/Amoxicillin',
+    '100\tCitrobacter koseri\tAztreonam',
+    '100\tCitrobacter koseri\tCeftriaxone',
+    '100\tCitrobacter koseri\tCeftazidime',
+    '100\tCitrobacter koseri\tCefazolin',
+    '100\tCitrobacter koseri\tCiprofloxacin',
+    '100\tCitrobacter koseri\tDoripenem',
+    '100\tCitrobacter koseri\tErtapenem',
+    '100\tCitrobacter koseri\tCefepime',
+    '100\tCitrobacter koseri\tGentamicin',
+    '100\tCitrobacter koseri\tLevofloxacin',
+    '100\tCitrobacter koseri\tMeropenem',
+    '63\tCitrobacter koseri\tNitrofurantoin (uncomplicated UTI)',
+    '100\tCitrobacter koseri\tTMP-SMX',
+    '100\tCitrobacter koseri\tPiperacillin-Tazobactam',
+    '100\tCitrobacter koseri\tTigecycline',
+    '100\tCitrobacter koseri\tTobramycin',
+    '7\tEnterobacter aerogenes\tNumber Tested',
+    '71\tEnterobacter aerogenes\tAmpicillin-Sulbactam',
+    '0\tEnterobacter aerogenes\tAmpicillin/Amoxicillin',
+    '100\tEnterobacter aerogenes\tAztreonam',
+    '100\tEnterobacter aerogenes\tCeftriaxone',
+    '100\tEnterobacter aerogenes\tCeftazidime',
+    '0\tEnterobacter aerogenes\tCefazolin',
+    '100\tEnterobacter aerogenes\tCiprofloxacin',
+    '100\tEnterobacter aerogenes\tDoripenem',
+    '100\tEnterobacter aerogenes\tErtapenem',
+    '100\tEnterobacter aerogenes\tCefepime',
+    '100\tEnterobacter aerogenes\tGentamicin',
+    '100\tEnterobacter aerogenes\tLevofloxacin',
+    '100\tEnterobacter aerogenes\tMeropenem',
+    '14\tEnterobacter aerogenes\tNitrofurantoin (uncomplicated UTI)',
+    '100\tEnterobacter aerogenes\tTMP-SMX',
+    '100\tEnterobacter aerogenes\tPiperacillin-Tazobactam',
+    '100\tEnterobacter aerogenes\tTigecycline',
+    '100\tEnterobacter aerogenes\tTobramycin',
+    '11\tEnterobacter cloacae\tNumber Tested',
+    '27\tEnterobacter cloacae\tAmpicillin-Sulbactam',
+    '9\tEnterobacter cloacae\tAmpicillin/Amoxicillin',
+    '82\tEnterobacter cloacae\tAztreonam',
+    '82\tEnterobacter cloacae\tCeftriaxone',
+    '82\tEnterobacter cloacae\tCeftazidime',
+    '9\tEnterobacter cloacae\tCefazolin',
+    '91\tEnterobacter cloacae\tCiprofloxacin',
+    '100\tEnterobacter cloacae\tDoripenem',
+    '100\tEnterobacter cloacae\tErtapenem',
+    '91\tEnterobacter cloacae\tCefepime',
+    '100\tEnterobacter cloacae\tGentamicin',
+    '91\tEnterobacter cloacae\tLevofloxacin',
+    '100\tEnterobacter cloacae\tMeropenem',
+    '9\tEnterobacter cloacae\tNitrofurantoin (uncomplicated UTI)',
+    '91\tEnterobacter cloacae\tTMP-SMX',
+    '82\tEnterobacter cloacae\tPiperacillin-Tazobactam',
+    '100\tEnterobacter cloacae\tTigecycline',
+    '100\tEnterobacter cloacae\tTobramycin',
+    '169\tEscherichia coli\tNumber Tested',
+    '62\tEscherichia coli\tAmpicillin-Sulbactam',
+    '58\tEscherichia coli\tAmpicillin/Amoxicillin',
+    '98\tEscherichia coli\tAztreonam',
+    '99\tEscherichia coli\tCeftriaxone',
+    '99\tEscherichia coli\tCeftazidime',
+    '0 \tEscherichia coli\tClindamycin',
+    '92\tEscherichia coli\tCefazolin',
+    '81\tEscherichia coli\tCiprofloxacin',
+    '100\tEscherichia coli\tDoripenem',
+    '100\tEscherichia coli\tErtapenem',
+    '99\tEscherichia coli\tCefepime',
+    '93\tEscherichia coli\tGentamicin',
+    '82\tEscherichia coli\tLevofloxacin',
+    '100\tEscherichia coli\tMeropenem',
+    '99\tEscherichia coli\tNitrofurantoin (uncomplicated UTI)',
+    '87\tEscherichia coli\tTMP-SMX',
+    '99\tEscherichia coli\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli\tTigecycline',
+    '94\tEscherichia coli\tTobramycin',
+    '17\tEscherichia coli ESBL\tNumber Tested',
+    '41\tEscherichia coli ESBL\tAmpicillin-Sulbactam',
+    '0\tEscherichia coli ESBL\tAmpicillin/Amoxicillin',
+    '0\tEscherichia coli ESBL\tAztreonam',
+    '0\tEscherichia coli ESBL\tCeftriaxone',
+    '0\tEscherichia coli ESBL\tCeftazidime',
+    '0\tEscherichia coli ESBL\tCefazolin',
+    '18\tEscherichia coli ESBL\tCiprofloxacin',
+    '100\tEscherichia coli ESBL\tDoripenem',
+    '100\tEscherichia coli ESBL\tErtapenem',
+    '6\tEscherichia coli ESBL\tCefepime',
+    '59\tEscherichia coli ESBL\tGentamicin',
+    '18\tEscherichia coli ESBL\tLevofloxacin',
+    '100\tEscherichia coli ESBL\tMeropenem',
+    '94\tEscherichia coli ESBL\tNitrofurantoin (uncomplicated UTI)',
+    '59\tEscherichia coli ESBL\tTMP-SMX',
+    '82\tEscherichia coli ESBL\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli ESBL\tTigecycline',
+    '59\tEscherichia coli ESBL\tTobramycin',
+    '10\tKlebsiella oxytoca\tNumber Tested',
+    '60\tKlebsiella oxytoca\tAmpicillin-Sulbactam',
+    '0\tKlebsiella oxytoca\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella oxytoca\tAztreonam',
+    '100\tKlebsiella oxytoca\tCeftriaxone',
+    '100\tKlebsiella oxytoca\tCeftazidime',
+    '30\tKlebsiella oxytoca\tCefazolin',
+    '100\tKlebsiella oxytoca\tCiprofloxacin',
+    '100\tKlebsiella oxytoca\tDoripenem',
+    '100\tKlebsiella oxytoca\tErtapenem',
+    '100\tKlebsiella oxytoca\tCefepime',
+    '100\tKlebsiella oxytoca\tGentamicin',
+    '100\tKlebsiella oxytoca\tLevofloxacin',
+    '100\tKlebsiella oxytoca\tMeropenem',
+    '90\tKlebsiella oxytoca\tNitrofurantoin (uncomplicated UTI)',
+    '95\tKlebsiella oxytoca\tTMP-SMX',
+    '95\tKlebsiella oxytoca\tPiperacillin-Tazobactam',
+    '100\tKlebsiella oxytoca\tTigecycline',
+    '100\tKlebsiella oxytoca\tTobramycin',
+    '2\tKlebsiella oxytoca ESBL\tNumber Tested',
+    '0\tKlebsiella oxytoca ESBL\tAmpicillin-Sulbactam',
+    '0\tKlebsiella oxytoca ESBL\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella oxytoca ESBL\tAztreonam',
+    '0\tKlebsiella oxytoca ESBL\tCeftriaxone',
+    '0\tKlebsiella oxytoca ESBL\tCeftazidime',
+    '0\tKlebsiella oxytoca ESBL\tCefazolin',
+    '50\tKlebsiella oxytoca ESBL\tCiprofloxacin',
+    '100\tKlebsiella oxytoca ESBL\tDoripenem',
+    '100\tKlebsiella oxytoca ESBL\tErtapenem',
+    '50\tKlebsiella oxytoca ESBL\tCefepime',
+    '50\tKlebsiella oxytoca ESBL\tGentamicin',
+    '50\tKlebsiella oxytoca ESBL\tLevofloxacin',
+    '100\tKlebsiella oxytoca ESBL\tMeropenem',
+    '50\tKlebsiella oxytoca ESBL\tNitrofurantoin (uncomplicated UTI)',
+    '50\tKlebsiella oxytoca ESBL\tTMP-SMX',
+    '0\tKlebsiella oxytoca ESBL\tPiperacillin-Tazobactam',
+    '100\tKlebsiella oxytoca ESBL\tTigecycline',
+    '50\tKlebsiella oxytoca ESBL\tTobramycin',
+    '55\tKlebsiella pneumoniae\tNumber Tested',
+    '75\tKlebsiella pneumoniae\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella pneumoniae\tAztreonam',
+    '100\tKlebsiella pneumoniae\tCeftriaxone',
+    '100\tKlebsiella pneumoniae\tCeftazidime',
+    '96\tKlebsiella pneumoniae\tCefazolin',
+    '95\tKlebsiella pneumoniae\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae\tDoripenem',
+    '100\tKlebsiella pneumoniae\tErtapenem',
+    '100\tKlebsiella pneumoniae\tCefepime',
+    '96\tKlebsiella pneumoniae\tGentamicin',
+    '96\tKlebsiella pneumoniae\tLevofloxacin',
+    '100\tKlebsiella pneumoniae\tMeropenem',
+    '56\tKlebsiella pneumoniae\tNitrofurantoin (uncomplicated UTI)',
+    '91\tKlebsiella pneumoniae\tTMP-SMX',
+    '96\tKlebsiella pneumoniae\tPiperacillin-Tazobactam',
+    '100\tKlebsiella pneumoniae\tTigecycline',
+    '96\tKlebsiella pneumoniae\tTobramycin',
+    '6\tKlebsiella pneumoniae ESBL\tNumber Tested',
+    '33\tKlebsiella pneumoniae ESBL\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae ESBL\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella pneumoniae ESBL\tAztreonam',
+    '0\tKlebsiella pneumoniae ESBL\tCeftriaxone',
+    '0\tKlebsiella pneumoniae ESBL\tCeftazidime',
+    '0\tKlebsiella pneumoniae ESBL\tCefazolin',
+    '33\tKlebsiella pneumoniae ESBL\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae ESBL\tDoripenem',
+    '50\tKlebsiella pneumoniae ESBL\tErtapenem',
+    '0\tKlebsiella pneumoniae ESBL\tCefepime',
+    '50\tKlebsiella pneumoniae ESBL\tGentamicin',
+    '33\tKlebsiella pneumoniae ESBL\tLevofloxacin',
+    '50\tKlebsiella pneumoniae ESBL\tMeropenem',
+    '17\tKlebsiella pneumoniae ESBL\tNitrofurantoin (uncomplicated UTI)',
+    '50\tKlebsiella pneumoniae ESBL\tTMP-SMX',
+    '33\tKlebsiella pneumoniae ESBL\tPiperacillin-Tazobactam',
+    '83\tKlebsiella pneumoniae ESBL\tTigecycline',
+    '50\tKlebsiella pneumoniae ESBL\tTobramycin',
+    '17\tMorganella\tNumber Tested',
+    '0\tMorganella\tAmpicillin-Sulbactam',
+    '0\tMorganella\tAmpicillin/Amoxicillin',
+    '82\tMorganella\tAztreonam',
+    '94\tMorganella\tCeftriaxone',
+    '71\tMorganella\tCeftazidime',
+    '0\tMorganella\tCefazolin',
+    '76\tMorganella\tCiprofloxacin',
+    '100\tMorganella\tDoripenem',
+    '100\tMorganella\tErtapenem',
+    '100\tMorganella\tCefepime',
+    '94\tMorganella\tGentamicin',
+    '76\tMorganella\tLevofloxacin',
+    '100\tMorganella\tMeropenem',
+    '0\tMorganella\tNitrofurantoin (uncomplicated UTI)',
+    '82\tMorganella\tTMP-SMX',
+    '100\tMorganella\tPiperacillin-Tazobactam',
+    '71\tMorganella\tTigecycline',
+    '94\tMorganella\tTobramycin',
+    '49\tProteus mirabilis\tNumber Tested',
+    '86\tProteus mirabilis\tAmpicillin-Sulbactam',
+    '82\tProteus mirabilis\tAmpicillin/Amoxicillin',
+    '94\tProteus mirabilis\tAztreonam',
+    '96\tProteus mirabilis\tCeftriaxone',
+    '96\tProteus mirabilis\tCeftazidime',
+    '82\tProteus mirabilis\tCefazolin',
+    '49\tProteus mirabilis\tCiprofloxacin',
+    '100\tProteus mirabilis\tDoripenem',
+    '100\tProteus mirabilis\tErtapenem',
+    '96\tProteus mirabilis\tCefepime',
+    '86\tProteus mirabilis\tGentamicin',
+    '49\tProteus mirabilis\tLevofloxacin',
+    '100\tProteus mirabilis\tMeropenem',
+    '0\tProteus mirabilis\tNitrofurantoin (uncomplicated UTI)',
+    '57\tProteus mirabilis\tTMP-SMX',
+    '100\tProteus mirabilis\tPiperacillin-Tazobactam',
+    '90\tProteus mirabilis\tTobramycin',
+    '7\tProteus mirabilis ESBL\tNumber Tested',
+    '57\tProteus mirabilis ESBL\tAmpicillin-Sulbactam',
+    '0\tProteus mirabilis ESBL\tAmpicillin/Amoxicillin',
+    '0\tProteus mirabilis ESBL\tAztreonam',
+    '0\tProteus mirabilis ESBL\tCeftriaxone',
+    '0\tProteus mirabilis ESBL\tCeftazidime',
+    '0\tProteus mirabilis ESBL\tCefazolin',
+    '14\tProteus mirabilis ESBL\tCiprofloxacin',
+    '100\tProteus mirabilis ESBL\tDoripenem',
+    '100\tProteus mirabilis ESBL\tErtapenem',
+    '0\tProteus mirabilis ESBL\tCefepime',
+    '57\tProteus mirabilis ESBL\tGentamicin',
+    '14\tProteus mirabilis ESBL\tLevofloxacin',
+    '100\tProteus mirabilis ESBL\tMeropenem',
+    '0\tProteus mirabilis ESBL\tNitrofurantoin (uncomplicated UTI)',
+    '14\tProteus mirabilis ESBL\tTMP-SMX',
+    '100\tProteus mirabilis ESBL\tPiperacillin-Tazobactam',
+    '57\tProteus mirabilis ESBL\tTobramycin',
+    '5\tProvidencia rettgeri\tNumber Tested',
+    '60\tProvidencia rettgeri\tAmpicillin-Sulbactam',
+    '40\tProvidencia rettgeri\tAmpicillin/Amoxicillin',
+    '100\tProvidencia rettgeri\tAztreonam',
+    '100\tProvidencia rettgeri\tCeftriaxone',
+    '100\tProvidencia rettgeri\tCeftazidime',
+    '20\tProvidencia rettgeri\tCefazolin',
+    '100\tProvidencia rettgeri\tCiprofloxacin',
+    '100\tProvidencia rettgeri\tDoripenem',
+    '100\tProvidencia rettgeri\tErtapenem',
+    '100\tProvidencia rettgeri\tCefepime',
+    '100\tProvidencia rettgeri\tGentamicin',
+    '100\tProvidencia rettgeri\tLevofloxacin',
+    '100\tProvidencia rettgeri\tMeropenem',
+    '0\tProvidencia rettgeri\tNitrofurantoin (uncomplicated UTI)',
+    '100\tProvidencia rettgeri\tTMP-SMX',
+    '100\tProvidencia rettgeri\tPiperacillin-Tazobactam',
+    '80\tProvidencia rettgeri\tTigecycline',
+    '100\tProvidencia rettgeri\tTobramycin',
+    '6\tProvidencia stuartii\tNumber Tested',
+    '17\tProvidencia stuartii\tAmpicillin-Sulbactam',
+    '17\tProvidencia stuartii\tAmpicillin/Amoxicillin',
+    '100\tProvidencia stuartii\tAztreonam',
+    '100\tProvidencia stuartii\tCeftriaxone',
+    '83\tProvidencia stuartii\tCeftazidime',
+    '17\tProvidencia stuartii\tCefazolin',
+    '17\tProvidencia stuartii\tCiprofloxacin',
+    '100\tProvidencia stuartii\tDoripenem',
+    '100\tProvidencia stuartii\tErtapenem',
+    '100\tProvidencia stuartii\tCefepime',
+    '0\tProvidencia stuartii\tGentamicin',
+    '17\tProvidencia stuartii\tLevofloxacin',
+    '100\tProvidencia stuartii\tMeropenem',
+    '0\tProvidencia stuartii\tNitrofurantoin (uncomplicated UTI)',
+    '33\tProvidencia stuartii\tTMP-SMX',
+    '100\tProvidencia stuartii\tPiperacillin-Tazobactam',
+    '67\tProvidencia stuartii\tTigecycline',
+    '0\tProvidencia stuartii\tTobramycin',
+    '43\tPseudomonas aeruginosa\tNumber Tested',
+    '98\tPseudomonas aeruginosa\tAmikacin',
+    '63\tPseudomonas aeruginosa\tAztreonam',
+    '88\tPseudomonas aeruginosa\tCeftazidime',
+    '67\tPseudomonas aeruginosa\tCiprofloxacin',
+    '100\tPseudomonas aeruginosa\tDoripenem',
+    '86\tPseudomonas aeruginosa\tCefepime',
+    '86\tPseudomonas aeruginosa\tGentamicin',
+    '65\tPseudomonas aeruginosa\tLevofloxacin',
+    '93\tPseudomonas aeruginosa\tMeropenem',
+    '95\tPseudomonas aeruginosa\tPiperacillin-Tazobactam',
+    '100\tPseudomonas aeruginosa\tTobramycin',
+    '9\tSerratia marcescens\tNumber Tested',
+    '22\tSerratia marcescens\tAmpicillin-Sulbactam',
+    '22\tSerratia marcescens\tAmpicillin/Amoxicillin',
+    '100\tSerratia marcescens\tAztreonam',
+    '100\tSerratia marcescens\tCeftriaxone',
+    '89\tSerratia marcescens\tCeftazidime',
+    '0\tSerratia marcescens\tCefazolin',
+    '89\tSerratia marcescens\tCiprofloxacin',
+    '100\tSerratia marcescens\tDoripenem',
+    '100\tSerratia marcescens\tErtapenem',
+    '100\tSerratia marcescens\tCefepime',
+    '89\tSerratia marcescens\tGentamicin',
+    '100\tSerratia marcescens\tLevofloxacin',
+    '100\tSerratia marcescens\tMeropenem',
+    '100\tSerratia marcescens\tTMP-SMX',
+    '88\tSerratia marcescens\tPiperacillin-Tazobactam',
+    '100\tSerratia marcescens\tTigecycline',
+    '100\tSerratia marcescens\tTobramycin',
+    '104\tEnterococcus faecalis\tNumber Tested',
+    '100\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
+    '100\tEnterococcus faecalis\tDaptomycin (non-pneumonia)',
+    '70\tEnterococcus faecalis\tLevofloxacin',
+    '100\tEnterococcus faecalis\tNitrofurantoin (uncomplicated UTI)',
+    '100\tEnterococcus faecalis\tPenicillin G',
+    '23\tEnterococcus faecalis\tDoxycycline',
+    '100\tEnterococcus faecalis\tVancomycin',
+    '9\tStaphylococcus aureus (all)\tNumber Tested',
+    '89\tStaphylococcus aureus (all)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (all)\tAmpicillin/Amoxicillin',
+    '100\tStaphylococcus aureus (all)\tCefazolin',
+    '100\tStaphylococcus aureus (all)\tDaptomycin (non-pneumonia)',
+    '100\tStaphylococcus aureus (all)\tGentamicin',
+    '78\tStaphylococcus aureus (all)\tLevofloxacin',
+    '100\tStaphylococcus aureus (all)\tLinezolid',
+    '100\tStaphylococcus aureus (all)\tNafcillin/Oxacillin',
+    '11\tStaphylococcus aureus (all)\tPenicillin G',
+    '89\tStaphylococcus aureus (all)\tTMP-SMX',
+    '100\tStaphylococcus aureus (all)\tDoxycycline',
+    '100\tStaphylococcus aureus (all)\tVancomycin',
+    '52\tStaphylococcus, Coagulase Negative (epidermidis)\tNumber Tested',
+    '0\tStaphylococcus, Coagulase Negative (epidermidis)\tAmpicillin/Amoxicillin',
+    '52\tStaphylococcus, Coagulase Negative (epidermidis)\tCefazolin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tDaptomycin (non-pneumonia)',
+    '94\tStaphylococcus, Coagulase Negative (epidermidis)\tGentamicin',
+    '52\tStaphylococcus, Coagulase Negative (epidermidis)\tLevofloxacin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tLinezolid',
+    '52\tStaphylococcus, Coagulase Negative (epidermidis)\tNafcillin/Oxacillin',
+    '12\tStaphylococcus, Coagulase Negative (epidermidis)\tPenicillin G',
+    '62\tStaphylococcus, Coagulase Negative (epidermidis)\tTMP-SMX',
+    '83\tStaphylococcus, Coagulase Negative (epidermidis)\tDoxycycline',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tVancomycin',
+    '8\tStaphylococcus aureus (MRSA)\tNumber Tested',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin/Amoxicillin',
+    '0\tStaphylococcus aureus (MRSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MRSA)\tDaptomycin (non-pneumonia)',
+    '100\tStaphylococcus aureus (MRSA)\tGentamicin',
+    '13\tStaphylococcus aureus (MRSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MRSA)\tLinezolid',
+    '0\tStaphylococcus aureus (MRSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MRSA)\tPenicillin G',
+    '88\tStaphylococcus aureus (MRSA)\tTMP-SMX',
+    '100\tStaphylococcus aureus (MRSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MRSA)\tVancomycin',
+].join('\n');
+
+SENSITIVITY_DATA_PER_SOURCE["2017 Wilkes-Barre VA Medical Center - All Organisms Combined"] = [
+    '4\tAcinetobacter\tNumber Tested',
+    '100\tAcinetobacter\tAmpicillin-Sulbactam',
+    '100\tAcinetobacter\tAmikacin',
+    '75\tAcinetobacter\tCeftriaxone',
+    '100\tAcinetobacter\tCeftazidime',
+    '100\tAcinetobacter\tCiprofloxacin',
+    '100\tAcinetobacter\tDoripenem',
+    '100\tAcinetobacter\tCefepime',
+    '100\tAcinetobacter\tGentamicin',
+    '100\tAcinetobacter\tLevofloxacin',
+    '100\tAcinetobacter\tMeropenem',
+    '100\tAcinetobacter\tTMP-SMX',
+    '100\tAcinetobacter\tTobramycin',
+    '7\tCitrobacter freundii\tNumber Tested',
+    '57\tCitrobacter freundii\tAmpicillin-Sulbactam',
+    '29\tCitrobacter freundii\tAmpicillin/Amoxicillin',
+    '100\tCitrobacter freundii\tAztreonam',
+    '100\tCitrobacter freundii\tCeftriaxone',
+    '100\tCitrobacter freundii\tCeftazidime',
+    '14\tCitrobacter freundii\tCefazolin',
+    '71\tCitrobacter freundii\tCiprofloxacin',
+    '100\tCitrobacter freundii\tDoripenem',
+    '100\tCitrobacter freundii\tErtapenem',
+    '100\tCitrobacter freundii\tCefepime',
+    '100\tCitrobacter freundii\tGentamicin',
+    '71\tCitrobacter freundii\tLevofloxacin',
+    '100\tCitrobacter freundii\tMeropenem',
+    '71\tCitrobacter freundii\tTMP-SMX',
+    '100\tCitrobacter freundii\tPiperacillin-Tazobactam',
+    '100\tCitrobacter freundii\tTigecycline',
+    '100\tCitrobacter freundii\tTobramycin',
+    '12\tCitrobacter koseri\tNumber Tested',
+    '100\tCitrobacter koseri\tAmpicillin-Sulbactam',
+    '0\tCitrobacter koseri\tAmpicillin/Amoxicillin',
+    '100\tCitrobacter koseri\tAztreonam',
+    '100\tCitrobacter koseri\tCeftriaxone',
+    '100\tCitrobacter koseri\tCeftazidime',
+    '100\tCitrobacter koseri\tCefazolin',
+    '100\tCitrobacter koseri\tCiprofloxacin',
+    '100\tCitrobacter koseri\tDoripenem',
+    '100\tCitrobacter koseri\tErtapenem',
+    '100\tCitrobacter koseri\tCefepime',
+    '100\tCitrobacter koseri\tGentamicin',
+    '100\tCitrobacter koseri\tLevofloxacin',
+    '100\tCitrobacter koseri\tMeropenem',
+    '100\tCitrobacter koseri\tTMP-SMX',
+    '100\tCitrobacter koseri\tPiperacillin-Tazobactam',
+    '100\tCitrobacter koseri\tTigecycline',
+    '100\tCitrobacter koseri\tTobramycin',
+    '12\tEnterobacter aerogenes\tNumber Tested',
+    '75\tEnterobacter aerogenes\tAmpicillin-Sulbactam',
+    '0\tEnterobacter aerogenes\tAmpicillin/Amoxicillin',
+    '100\tEnterobacter aerogenes\tAztreonam',
+    '100\tEnterobacter aerogenes\tCeftriaxone',
+    '100\tEnterobacter aerogenes\tCeftazidime',
+    '8\tEnterobacter aerogenes\tCefazolin',
+    '100\tEnterobacter aerogenes\tCiprofloxacin',
+    '100\tEnterobacter aerogenes\tDoripenem',
+    '100\tEnterobacter aerogenes\tErtapenem',
+    '100\tEnterobacter aerogenes\tCefepime',
+    '100\tEnterobacter aerogenes\tGentamicin',
+    '100\tEnterobacter aerogenes\tLevofloxacin',
+    '100\tEnterobacter aerogenes\tMeropenem',
+    '100\tEnterobacter aerogenes\tTMP-SMX',
+    '100\tEnterobacter aerogenes\tPiperacillin-Tazobactam',
+    '100\tEnterobacter aerogenes\tTigecycline',
+    '100\tEnterobacter aerogenes\tTobramycin',
+    '33\tEnterobacter cloacae\tNumber Tested',
+    '27\tEnterobacter cloacae\tAmpicillin-Sulbactam',
+    '9\tEnterobacter cloacae\tAmpicillin/Amoxicillin',
+    '94\tEnterobacter cloacae\tAztreonam',
+    '94\tEnterobacter cloacae\tCeftriaxone',
+    '94\tEnterobacter cloacae\tCeftazidime',
+    '6\tEnterobacter cloacae\tCefazolin',
+    '94\tEnterobacter cloacae\tCiprofloxacin',
+    '100\tEnterobacter cloacae\tDoripenem',
+    '100\tEnterobacter cloacae\tErtapenem',
+    '97\tEnterobacter cloacae\tCefepime',
+    '94\tEnterobacter cloacae\tGentamicin',
+    '94\tEnterobacter cloacae\tLevofloxacin',
+    '100\tEnterobacter cloacae\tMeropenem',
+    '91\tEnterobacter cloacae\tTMP-SMX',
+    '94\tEnterobacter cloacae\tPiperacillin-Tazobactam',
+    '100\tEnterobacter cloacae\tTigecycline',
+    '100\tEnterobacter cloacae\tTobramycin',
+    '214\tEscherichia coli\tNumber Tested',
+    '63\tEscherichia coli\tAmpicillin-Sulbactam',
+    '60\tEscherichia coli\tAmpicillin/Amoxicillin',
+    '99\tEscherichia coli\tAztreonam',
+    '100\tEscherichia coli\tCeftriaxone',
+    '100\tEscherichia coli\tCeftazidime',
+    '92\tEscherichia coli\tCefazolin',
+    '81\tEscherichia coli\tCiprofloxacin',
+    '100\tEscherichia coli\tDoripenem',
+    '100\tEscherichia coli\tErtapenem',
+    '100\tEscherichia coli\tCefepime',
+    '92\tEscherichia coli\tGentamicin',
+    '81\tEscherichia coli\tLevofloxacin',
+    '100\tEscherichia coli\tMeropenem',
+    '88\tEscherichia coli\tTMP-SMX',
+    '99\tEscherichia coli\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli\tTigecycline',
+    '93\tEscherichia coli\tTobramycin',
+    '21\tEscherichia coli ESBL\tNumber Tested',
+    '38\tEscherichia coli ESBL\tAmpicillin-Sulbactam',
+    '0\tEscherichia coli ESBL\tAmpicillin/Amoxicillin',
+    '5\tEscherichia coli ESBL\tAztreonam',
+    '0\tEscherichia coli ESBL\tCeftriaxone',
+    '0\tEscherichia coli ESBL\tCeftazidime',
+    '0\tEscherichia coli ESBL\tCefazolin',
+    '19\tEscherichia coli ESBL\tCiprofloxacin',
+    '100\tEscherichia coli ESBL\tDoripenem',
+    '100\tEscherichia coli ESBL\tErtapenem',
+    '10\tEscherichia coli ESBL\tCefepime',
+    '57\tEscherichia coli ESBL\tGentamicin',
+    '19\tEscherichia coli ESBL\tLevofloxacin',
+    '100\tEscherichia coli ESBL\tMeropenem',
+    '57\tEscherichia coli ESBL\tTMP-SMX',
+    '86\tEscherichia coli ESBL\tPiperacillin-Tazobactam',
+    '100\tEscherichia coli ESBL\tTigecycline',
+    '57\tEscherichia coli ESBL\tTobramycin',
+    '15\tHaemophilus influenzae\tNumber Tested',
+    '40\tHaemophilus influenzae\tAmpicillin/Amoxicillin',
+    '79\tHaemophilus influenzae\tTMP-SMX',
+    '24\tKlebsiella oxytoca\tNumber Tested',
+    '67\tKlebsiella oxytoca\tAmpicillin-Sulbactam',
+    '0\tKlebsiella oxytoca\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella oxytoca\tAztreonam',
+    '100\tKlebsiella oxytoca\tCeftriaxone',
+    '100\tKlebsiella oxytoca\tCeftazidime',
+    '46\tKlebsiella oxytoca\tCefazolin',
+    '100\tKlebsiella oxytoca\tCiprofloxacin',
+    '100\tKlebsiella oxytoca\tDoripenem',
+    '100\tKlebsiella oxytoca\tErtapenem',
+    '100\tKlebsiella oxytoca\tCefepime',
+    '100\tKlebsiella oxytoca\tGentamicin',
+    '100\tKlebsiella oxytoca\tLevofloxacin',
+    '100\tKlebsiella oxytoca\tMeropenem',
+    '100\tKlebsiella oxytoca\tTMP-SMX',
+    '96\tKlebsiella oxytoca\tPiperacillin-Tazobactam',
+    '100\tKlebsiella oxytoca\tTigecycline',
+    '100\tKlebsiella oxytoca\tTobramycin',
+    '3\tKlebsiella oxytoca ESBL\tNumber Tested',
+    '0\tKlebsiella oxytoca ESBL\tAmpicillin-Sulbactam',
+    '0\tKlebsiella oxytoca ESBL\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella oxytoca ESBL\tAztreonam',
+    '0\tKlebsiella oxytoca ESBL\tCeftriaxone',
+    '0\tKlebsiella oxytoca ESBL\tCeftazidime',
+    '0\tKlebsiella oxytoca ESBL\tCefazolin',
+    '67\tKlebsiella oxytoca ESBL\tCiprofloxacin',
+    '100\tKlebsiella oxytoca ESBL\tDoripenem',
+    '100\tKlebsiella oxytoca ESBL\tErtapenem',
+    '33\tKlebsiella oxytoca ESBL\tCefepime',
+    '33\tKlebsiella oxytoca ESBL\tGentamicin',
+    '67\tKlebsiella oxytoca ESBL\tLevofloxacin',
+    '100\tKlebsiella oxytoca ESBL\tMeropenem',
+    '33\tKlebsiella oxytoca ESBL\tTMP-SMX',
+    '33\tKlebsiella oxytoca ESBL\tPiperacillin-Tazobactam',
+    '100\tKlebsiella oxytoca ESBL\tTigecycline',
+    '33\tKlebsiella oxytoca ESBL\tTobramycin',
+    '69\tKlebsiella pneumoniae\tNumber Tested',
+    '80\tKlebsiella pneumoniae\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae\tAmpicillin/Amoxicillin',
+    '100\tKlebsiella pneumoniae\tAztreonam',
+    '100\tKlebsiella pneumoniae\tCeftriaxone',
+    '100\tKlebsiella pneumoniae\tCeftazidime',
+    '97\tKlebsiella pneumoniae\tCefazolin',
+    '96\tKlebsiella pneumoniae\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae\tDoripenem',
+    '100\tKlebsiella pneumoniae\tErtapenem',
+    '100\tKlebsiella pneumoniae\tCefepime',
+    '97\tKlebsiella pneumoniae\tGentamicin',
+    '77\tKlebsiella pneumoniae\tLevofloxacin',
+    '100\tKlebsiella pneumoniae\tMeropenem',
+    '93\tKlebsiella pneumoniae\tTMP-SMX',
+    '97\tKlebsiella pneumoniae\tPiperacillin-Tazobactam',
+    '100\tKlebsiella pneumoniae\tTigecycline',
+    '97\tKlebsiella pneumoniae\tTobramycin',
+    '10\tKlebsiella pneumoniae ESBL\tNumber Tested',
+    '20\tKlebsiella pneumoniae ESBL\tAmpicillin-Sulbactam',
+    '0\tKlebsiella pneumoniae ESBL\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella pneumoniae ESBL\tAztreonam',
+    '0\tKlebsiella pneumoniae ESBL\tCeftriaxone',
+    '0\tKlebsiella pneumoniae ESBL\tCeftazidime',
+    '0\tKlebsiella pneumoniae ESBL\tCefazolin',
+    '20\tKlebsiella pneumoniae ESBL\tCiprofloxacin',
+    '100\tKlebsiella pneumoniae ESBL\tDoripenem',
+    '40\tKlebsiella pneumoniae ESBL\tErtapenem',
+    '0\tKlebsiella pneumoniae ESBL\tCefepime',
+    '30\tKlebsiella pneumoniae ESBL\tGentamicin',
+    '30\tKlebsiella pneumoniae ESBL\tLevofloxacin',
+    '40\tKlebsiella pneumoniae ESBL\tMeropenem',
+    '30\tKlebsiella pneumoniae ESBL\tTMP-SMX',
+    '30\tKlebsiella pneumoniae ESBL\tPiperacillin-Tazobactam',
+    '30\tKlebsiella pneumoniae ESBL\tDoxycycline',
+    '90\tKlebsiella pneumoniae ESBL\tTigecycline',
+    '30\tKlebsiella pneumoniae ESBL\tTobramycin',
+    '25\tMorganella\tNumber Tested',
+    '0\tMorganella\tAmpicillin-Sulbactam',
+    '0\tMorganella\tAmpicillin/Amoxicillin',
+    '88\tMorganella\tAztreonam',
+    '96\tMorganella\tCeftriaxone',
+    '80\tMorganella\tCeftazidime',
+    '0\tMorganella\tCefazolin',
+    '80\tMorganella\tCiprofloxacin',
+    '100\tMorganella\tDoripenem',
+    '100\tMorganella\tErtapenem',
+    '100\tMorganella\tCefepime',
+    '88\tMorganella\tGentamicin',
+    '80\tMorganella\tLevofloxacin',
+    '100\tMorganella\tMeropenem',
+    '80\tMorganella\tTMP-SMX',
+    '100\tMorganella\tPiperacillin-Tazobactam',
+    '72\tMorganella\tTigecycline',
+    '96\tMorganella\tTobramycin',
+    '75\tProteus mirabilis\tNumber Tested',
+    '83\tProteus mirabilis\tAmpicillin-Sulbactam',
+    '80\tProteus mirabilis\tAmpicillin/Amoxicillin',
+    '95\tProteus mirabilis\tAztreonam',
+    '97\tProteus mirabilis\tCeftriaxone',
+    '97\tProteus mirabilis\tCeftazidime',
+    '81\tProteus mirabilis\tCefazolin',
+    '43\tProteus mirabilis\tCiprofloxacin',
+    '100\tProteus mirabilis\tDoripenem',
+    '100\tProteus mirabilis\tErtapenem',
+    '97\tProteus mirabilis\tCefepime',
+    '87\tProteus mirabilis\tGentamicin',
+    '48\tProteus mirabilis\tLevofloxacin',
+    '100\tProteus mirabilis\tMeropenem',
+    '61\tProteus mirabilis\tTMP-SMX',
+    '100\tProteus mirabilis\tPiperacillin-Tazobactam',
+    '92\tProteus mirabilis\tTobramycin',
+    '8\tProteus mirabilis ESBL\tNumber Tested',
+    '50\tProteus mirabilis ESBL\tAmpicillin-Sulbactam',
+    '0\tProteus mirabilis ESBL\tAmpicillin/Amoxicillin',
+    '0\tProteus mirabilis ESBL\tAztreonam',
+    '0\tProteus mirabilis ESBL\tCeftriaxone',
+    '0\tProteus mirabilis ESBL\tCeftazidime',
+    '0\tProteus mirabilis ESBL\tCefazolin',
+    '13\tProteus mirabilis ESBL\tCiprofloxacin',
+    '100\tProteus mirabilis ESBL\tDoripenem',
+    '100\tProteus mirabilis ESBL\tErtapenem',
+    '0\tProteus mirabilis ESBL\tCefepime',
+    '50\tProteus mirabilis ESBL\tGentamicin',
+    '13\tProteus mirabilis ESBL\tLevofloxacin',
+    '100\tProteus mirabilis ESBL\tMeropenem',
+    '13\tProteus mirabilis ESBL\tTMP-SMX',
+    '100\tProteus mirabilis ESBL\tPiperacillin-Tazobactam',
+    '50\tProteus mirabilis ESBL\tTobramycin',
+    '9\tProvidencia rettgeri\tNumber Tested',
+    '44\tProvidencia rettgeri\tAmpicillin-Sulbactam',
+    '33\tProvidencia rettgeri\tAmpicillin/Amoxicillin',
+    '100\tProvidencia rettgeri\tAztreonam',
+    '100\tProvidencia rettgeri\tCeftriaxone',
+    '78\tProvidencia rettgeri\tCeftazidime',
+    '22\tProvidencia rettgeri\tCefazolin',
+    '89\tProvidencia rettgeri\tCiprofloxacin',
+    '100\tProvidencia rettgeri\tDoripenem',
+    '100\tProvidencia rettgeri\tErtapenem',
+    '100\tProvidencia rettgeri\tCefepime',
+    '100\tProvidencia rettgeri\tGentamicin',
+    '100\tProvidencia rettgeri\tLevofloxacin',
+    '100\tProvidencia rettgeri\tMeropenem',
+    '100\tProvidencia rettgeri\tTMP-SMX',
+    '100\tProvidencia rettgeri\tPiperacillin-Tazobactam',
+    '78\tProvidencia rettgeri\tTigecycline',
+    '100\tProvidencia rettgeri\tTobramycin',
+    '6\tProvidencia stuartii\tNumber Tested',
+    '17\tProvidencia stuartii\tAmpicillin-Sulbactam',
+    '17\tProvidencia stuartii\tAmpicillin/Amoxicillin',
+    '100\tProvidencia stuartii\tAztreonam',
+    '100\tProvidencia stuartii\tCeftriaxone',
+    '83\tProvidencia stuartii\tCeftazidime',
+    '17\tProvidencia stuartii\tCefazolin',
+    '17\tProvidencia stuartii\tCiprofloxacin',
+    '100\tProvidencia stuartii\tDoripenem',
+    '100\tProvidencia stuartii\tErtapenem',
+    '100\tProvidencia stuartii\tCefepime',
+    '100\tProvidencia stuartii\tGentamicin',
+    '17\tProvidencia stuartii\tLevofloxacin',
+    '100\tProvidencia stuartii\tMeropenem',
+    '33\tProvidencia stuartii\tTMP-SMX',
+    '100\tProvidencia stuartii\tPiperacillin-Tazobactam',
+    '67\tProvidencia stuartii\tTigecycline',
+    '0\tProvidencia stuartii\tTobramycin',
+    '68\tPseudomonas aeruginosa\tNumber Tested',
+    '99\tPseudomonas aeruginosa\tAmikacin',
+    '0\tPseudomonas aeruginosa\tAmpicillin/Amoxicillin',
+    '71\tPseudomonas aeruginosa\tAztreonam',
+    '87\tPseudomonas aeruginosa\tCeftazidime',
+    '72\tPseudomonas aeruginosa\tCiprofloxacin',
+    '100\tPseudomonas aeruginosa\tDoripenem',
+    '85\tPseudomonas aeruginosa\tCefepime',
+    '87\tPseudomonas aeruginosa\tGentamicin',
+    '69\tPseudomonas aeruginosa\tLevofloxacin',
+    '93\tPseudomonas aeruginosa\tMeropenem',
+    '94\tPseudomonas aeruginosa\tPiperacillin-Tazobactam',
+    '100\tPseudomonas aeruginosa\tTobramycin',
+    '19\tSerratia marcescens\tNumber Tested',
+    '11\tSerratia marcescens\tAmpicillin-Sulbactam',
+    '11\tSerratia marcescens\tAmpicillin/Amoxicillin',
+    '94\tSerratia marcescens\tAztreonam',
+    '95\tSerratia marcescens\tCeftriaxone',
+    '89\tSerratia marcescens\tCeftazidime',
+    '0\tSerratia marcescens\tCefazolin',
+    '95\tSerratia marcescens\tCiprofloxacin',
+    '100\tSerratia marcescens\tDoripenem',
+    '100\tSerratia marcescens\tErtapenem',
+    '100\tSerratia marcescens\tCefepime',
+    '89\tSerratia marcescens\tGentamicin',
+    '100\tSerratia marcescens\tLevofloxacin',
+    '100\tSerratia marcescens\tMeropenem',
+    '89\tSerratia marcescens\tTMP-SMX',
+    '95\tSerratia marcescens\tPiperacillin-Tazobactam',
+    '100\tSerratia marcescens\tTigecycline',
+    '95\tSerratia marcescens\tTobramycin',
+    '7\tStenotrophomonas maltophilia\tNumber Tested',
+    '43\tStenotrophomonas maltophilia\tCeftazidime',
+    '100\tStenotrophomonas maltophilia\tLevofloxacin',
+    '100\tStenotrophomonas maltophilia\tTMP-SMX',
+    '23\tStreptococcus Group B (agalactiae)\tNumber Tested',
+    '100\tStreptococcus Group B (agalactiae)\tAmpicillin/Amoxicillin',
+    '52\tStreptococcus Group B (agalactiae)\tAzithromycin',
+    '100\tStreptococcus Group B (agalactiae)\tCeftriaxone',
+    '57\tStreptococcus Group B (agalactiae)\tClindamycin',
+    '100\tStreptococcus Group B (agalactiae)\tDaptomycin (non-pneumonia)',
+    '48\tStreptococcus Group B (agalactiae)\tErythromycin',
+    '100\tStreptococcus Group B (agalactiae)\tCefepime',
+    '96\tStreptococcus Group B (agalactiae)\tLevofloxacin',
+    '100\tStreptococcus Group B (agalactiae)\tLinezolid',
+    '100\tStreptococcus Group B (agalactiae)\tPenicillin G',
+    '19\tStreptococcus Group B (agalactiae)\tDoxycycline',
+    '100\tStreptococcus Group B (agalactiae)\tVancomycin',
+    '13\tStreptococcus Group C (dysgalactiae)\tNumber Tested',
+    '100\tStreptococcus Group C (dysgalactiae)\tAmpicillin/Amoxicillin',
+    '23\tStreptococcus Group C (dysgalactiae)\tAzithromycin',
+    '100\tStreptococcus Group C (dysgalactiae)\tCeftriaxone',
+    '46\tStreptococcus Group C (dysgalactiae)\tClindamycin',
+    '31\tStreptococcus Group C (dysgalactiae)\tErythromycin',
+    '100\tStreptococcus Group C (dysgalactiae)\tCefepime',
+    '92\tStreptococcus Group C (dysgalactiae)\tLevofloxacin',
+    '100\tStreptococcus Group C (dysgalactiae)\tPenicillin G',
+    '23\tStreptococcus Group C (dysgalactiae)\tDoxycycline',
+    '100\tStreptococcus Group C (dysgalactiae)\tVancomycin',
+    '7\tStreptococcus pneumoniae\tNumber Tested',
+    '57\tStreptococcus pneumoniae\tAzithromycin',
+    '100\tStreptococcus pneumoniae\tCeftriaxone',
+    '100\tStreptococcus pneumoniae\tClindamycin',
+    '57\tStreptococcus pneumoniae\tErythromycin',
+    '100\tStreptococcus pneumoniae\tCefepime',
+    '94\tStreptococcus pneumoniae\tLevofloxacin',
+    '71\tStreptococcus pneumoniae\tMeropenem',
+    '57\tStreptococcus pneumoniae\tPenicillin G',
+    '86\tStreptococcus pneumoniae\tTMP-SMX',
+    '100\tStreptococcus pneumoniae\tDoxycycline',
+    '100\tStreptococcus pneumoniae\tVancomycin',
+    '5\tStreptococcus Group A (pyogenes)\tNumber Tested',
+    '100\tStreptococcus Group A (pyogenes)\tAmpicillin/Amoxicillin',
+    '60\tStreptococcus Group A (pyogenes)\tAzithromycin',
+    '100\tStreptococcus Group A (pyogenes)\tCeftriaxone',
+    '80\tStreptococcus Group A (pyogenes)\tClindamycin',
+    '60\tStreptococcus Group A (pyogenes)\tErythromycin',
+    '100\tStreptococcus Group A (pyogenes)\tCefepime',
+    '100\tStreptococcus Group A (pyogenes)\tLevofloxacin',
+    '100\tStreptococcus Group A (pyogenes)\tPenicillin G',
+    '60\tStreptococcus Group A (pyogenes)\tDoxycycline',
+    '100\tStreptococcus Group A (pyogenes)\tVancomycin',
+    '146\tEnterococcus faecalis\tNumber Tested',
+    '100\tEnterococcus faecalis\tAmpicillin/Amoxicillin',
+    '100\tEnterococcus faecalis\tDaptomycin (non-pneumonia)',
+    '8\tEnterococcus faecalis\tErythromycin',
+    '71\tEnterococcus faecalis\tLevofloxacin',
+    '97\tEnterococcus faecalis\tLinezolid',
+    '100\tEnterococcus faecalis\tPenicillin G',
+    '24\tEnterococcus faecalis\tDoxycycline',
+    '99\tEnterococcus faecalis\tVancomycin',
+    '72\tStaphylococcus aureus (MSSA)\tNumber Tested',
+    '96\tStaphylococcus aureus (MSSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MSSA)\tAmpicillin/Amoxicillin',
+    '79\tStaphylococcus aureus (MSSA)\tClindamycin',
+    '100\tStaphylococcus aureus (MSSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MSSA)\tDaptomycin (non-pneumonia)',
+    '73\tStaphylococcus aureus (MSSA)\tErythromycin',
+    '99\tStaphylococcus aureus (MSSA)\tGentamicin',
+    '83\tStaphylococcus aureus (MSSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MSSA)\tLinezolid',
+    '90\tStaphylococcus aureus (MSSA)\tMoxifloxacin',
+    '100\tStaphylococcus aureus (MSSA)\tNafcillin/Oxacillin',
+    '17\tStaphylococcus aureus (MSSA)\tPenicillin G',
+    '97\tStaphylococcus aureus (MSSA)\tTMP-SMX',
+    '96\tStaphylococcus aureus (MSSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MSSA)\tVancomycin',
+    '90\tStaphylococcus aureus (MRSA)\tNumber Tested',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus aureus (MRSA)\tAmpicillin/Amoxicillin',
+    '60\tStaphylococcus aureus (MRSA)\tClindamycin',
+    '0\tStaphylococcus aureus (MRSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MRSA)\tDaptomycin (non-pneumonia)',
+    '22\tStaphylococcus aureus (MRSA)\tErythromycin',
+    '98\tStaphylococcus aureus (MRSA)\tGentamicin',
+    '23\tStaphylococcus aureus (MRSA)\tLevofloxacin',
+    '100\tStaphylococcus aureus (MRSA)\tLinezolid',
+    '45\tStaphylococcus aureus (MRSA)\tMoxifloxacin',
+    '0\tStaphylococcus aureus (MRSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MRSA)\tPenicillin G',
+    '96\tStaphylococcus aureus (MRSA)\tTMP-SMX',
+    '84\tStaphylococcus aureus (MRSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MRSA)\tVancomycin',
+    '84\tStaphylococcus, Coagulase Negative (epidermidis)\tNumber Tested',
+    '46\tStaphylococcus, Coagulase Negative (epidermidis)\tAmpicillin-Sulbactam',
+    '0\tStaphylococcus, Coagulase Negative (epidermidis)\tAmpicillin/Amoxicillin',
+    '75\tStaphylococcus, Coagulase Negative (epidermidis)\tClindamycin',
+    '46\tStaphylococcus, Coagulase Negative (epidermidis)\tCefazolin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tDaptomycin (non-pneumonia)',
+    '28\tStaphylococcus, Coagulase Negative (epidermidis)\tErythromycin',
+    '94\tStaphylococcus, Coagulase Negative (epidermidis)\tGentamicin',
+    '55\tStaphylococcus, Coagulase Negative (epidermidis)\tLevofloxacin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tLinezolid',
+    '72\tStaphylococcus, Coagulase Negative (epidermidis)\tMoxifloxacin',
+    '46\tStaphylococcus, Coagulase Negative (epidermidis)\tNafcillin/Oxacillin',
+    '8\tStaphylococcus, Coagulase Negative (epidermidis)\tPenicillin G',
+    '61\tStaphylococcus, Coagulase Negative (epidermidis)\tTMP-SMX',
+    '79\tStaphylococcus, Coagulase Negative (epidermidis)\tDoxycycline',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tVancomycin',
+].join('\n');
+
+SENSITIVITY_DATA_PER_SOURCE["2018 Stanford Health Care (SHC)"] =
+[
+    '413\tStreptococcus Group B (agalactiae)\tNumber Tested',
+    '100\tStreptococcus Group B (agalactiae)\tPenicillin G',
+    '54\tStreptococcus Group B (agalactiae)\tErythromycin',
+    '61\tStreptococcus Group B (agalactiae)\tClindamycin',
+    '96\tStreptococcus Group B (agalactiae)\tLevofloxacin',
+    '251\tStreptococcus viridans Group\tNumber Tested',
+    '82\tStreptococcus viridans Group\tPenicillin G',
+    '100\tStreptococcus viridans Group\tCeftriaxone',
+    '100\tStreptococcus viridans Group\tVancomycin',
+    '59\tStreptococcus viridans Group\tErythromycin',
+    '80\tStreptococcus viridans Group\tClindamycin',
+    '57\tStreptococcus pneumoniae\tNumber Tested',
+    '72\tStreptococcus pneumoniae\tPenicillin G',
+    '94\tStreptococcus pneumoniae\tCefuroxime',
+    '96\tStreptococcus pneumoniae\tCeftriaxone',
+    '100\tStreptococcus pneumoniae\tVancomycin',
+    '65\tStreptococcus pneumoniae\tErythromycin',
+    '90\tStreptococcus pneumoniae\tClindamycin',
+    '87\tStreptococcus pneumoniae\tMeropenem',
+    '84\tStreptococcus pneumoniae\tTMP-SMX',
+    '56\tStreptococcus pneumoniae\tDoxycycline',
+    '100\tStreptococcus pneumoniae\tMoxifloxacin',
+    '943\tEnterococcus (unspeciated)\tNumber Tested',
+    '91\tEnterococcus (unspeciated)\tPenicillin G',
+    '92\tEnterococcus (unspeciated)\tVancomycin',
+    '25\tEnterococcus (unspeciated)\tDoxycycline',
+    '96\tEnterococcus (unspeciated)\tNitrofurantoin (uncomplicated UTI)',
+    '82\tEnterococcus (unspeciated)\tLevofloxacin',
+    '72\tEnterococcus (unspeciated)\tCiprofloxacin',
+    '100\tEnterococcus (unspeciated)\tLinezolid',
+    '146\tEnterococcus faecalis\tNumber Tested',
+    '100\tEnterococcus faecalis\tPenicillin G',
+    '99\tEnterococcus faecalis\tVancomycin',
+    '84\tEnterococcus faecalis\tGentamicin',
+    '81\tEnterococcus faecalis\tStreptomycin',
+    '99\tEnterococcus faecalis\tLinezolid',
+    '136\tEnterococcus faecium\tNumber Tested',
+    '22\tEnterococcus faecium\tPenicillin G',
+    '38\tEnterococcus faecium\tVancomycin',
+    '94\tEnterococcus faecium\tGentamicin',
+    '48\tEnterococcus faecium\tStreptomycin',
+    '99\tEnterococcus faecium\tLinezolid',
+
+
+    '87\tCandida albicans\tNumber Tested',
+    '100\tCandida albicans\tAmphotericin B',
+    '100\tCandida albicans\tCaspofungin',
+    '99\tCandida albicans\tFluconazole',
+    '100\tCandida albicans\tVoriconazole',
+    '46\tCandida glabrata\tNumber Tested',
+    '100\tCandida glabrata\tAmphotericin B',
+    '89\tCandida glabrata\tCaspofungin',
+    '87\tCandida glabrata\tFluconazole',
+    '14\tCandida parapsilosis\tNumber Tested',
+    '100\tCandida parapsilosis\tAmphotericin B',
+    '93\tCandida parapsilosis\tCaspofungin',
+    '93\tCandida parapsilosis\tFluconazole',
+    '100\tCandida parapsilosis\tVoriconazole',
+    '14\tCandida tropicalis\tNumber Tested',
+    '100\tCandida tropicalis\tAmphotericin B',
+    '100\tCandida tropicalis\tCaspofungin',
+    '86\tCandida tropicalis\tFluconazole',
+    '86\tCandida tropicalis\tVoriconazole',
+    '18\tCandida (other)\tNumber Tested',
+    '100\tCandida (other)\tAmphotericin B',
+    '85\tCandida (other)\tCaspofungin',
+    '100\tCandida (other)\tVoriconazole',
+
+    '29\tAchromobacter xylosoxidans\tNumber Tested',
+    '90\tAchromobacter xylosoxidans\tPiperacillin-Tazobactam',
+    '14\tAchromobacter xylosoxidans\tCefepime',
+    '0\tAchromobacter xylosoxidans\tAztreonam',
+    '86\tAchromobacter xylosoxidans\tImipenem',
+    '69\tAchromobacter xylosoxidans\tMeropenem',
+    '0\tAchromobacter xylosoxidans\tGentamicin',
+    '0\tAchromobacter xylosoxidans\tTobramycin',
+    '7\tAchromobacter xylosoxidans\tAmikacin',
+    '17\tAchromobacter xylosoxidans\tCiprofloxacin',
+    '66\tAchromobacter xylosoxidans\tLevofloxacin',
+    '83\tAchromobacter xylosoxidans\tTMP-SMX',
+    '20\tAcinetobacter\tNumber Tested',
+    '90\tAcinetobacter\tAmpicillin-Sulbactam',
+    '70\tAcinetobacter\tCefepime',
+    '80\tAcinetobacter\tMeropenem',
+    '85\tAcinetobacter\tGentamicin',
+    '85\tAcinetobacter\tTobramycin',
+    '95\tAcinetobacter\tAmikacin',
+    '70\tAcinetobacter\tCiprofloxacin',
+    '75\tAcinetobacter\tLevofloxacin',
+    '75\tAcinetobacter\tTMP-SMX',
+    '10\tBurkholderia cepacia\tNumber Tested',
+    '90\tBurkholderia cepacia\tCeftazidime',
+    '80\tBurkholderia cepacia\tMinocycline',
+    '70\tBurkholderia cepacia\tMeropenem',
+    '63\tBurkholderia cepacia\tTMP-SMX',
+    '111\tCitrobacter freundii\tNumber Tested',
+    '0\tCitrobacter freundii\tAmpicillin/Amoxicillin',
+    '0\tCitrobacter freundii\tAmpicillin-Sulbactam',
+    '84\tCitrobacter freundii\tPiperacillin-Tazobactam',
+    '0\tCitrobacter freundii\tCefazolin',
+    '76\tCitrobacter freundii\tCeftriaxone',
+    '88\tCitrobacter freundii\tCefepime',
+    '58\tCitrobacter freundii\tAztreonam',
+    '92\tCitrobacter freundii\tImipenem',
+    '98\tCitrobacter freundii\tMeropenem',
+    '96\tCitrobacter freundii\tErtapenem',
+    '93\tCitrobacter freundii\tGentamicin',
+    '93\tCitrobacter freundii\tTobramycin',
+    '100\tCitrobacter freundii\tAmikacin',
+    '95\tCitrobacter freundii\tCiprofloxacin',
+    '93\tCitrobacter freundii\tLevofloxacin',
+    '81\tCitrobacter freundii\tTMP-SMX',
+    '94\tCitrobacter freundii\tNitrofurantoin (uncomplicated UTI)',
+    '87\tCitrobacter koseri\tNumber Tested',
+    '0\tCitrobacter koseri\tAmpicillin/Amoxicillin',
+    '96\tCitrobacter koseri\tAmpicillin-Sulbactam',
+    '99\tCitrobacter koseri\tPiperacillin-Tazobactam',
+    '97\tCitrobacter koseri\tCefazolin',
+    '99\tCitrobacter koseri\tCeftriaxone',
+    '100\tCitrobacter koseri\tCefepime',
+    '100\tCitrobacter koseri\tAztreonam',
+    '100\tCitrobacter koseri\tImipenem',
+    '100\tCitrobacter koseri\tMeropenem',
+    '100\tCitrobacter koseri\tErtapenem',
+    '100\tCitrobacter koseri\tGentamicin',
+    '99\tCitrobacter koseri\tTobramycin',
+    '100\tCitrobacter koseri\tAmikacin',
+    '100\tCitrobacter koseri\tCiprofloxacin',
+    '98\tCitrobacter koseri\tLevofloxacin',
+    '97\tCitrobacter koseri\tTMP-SMX',
+    '77\tCitrobacter koseri\tNitrofurantoin (uncomplicated UTI)',
+    '228\tEnterobacter cloacae\tNumber Tested',
+    '0\tEnterobacter cloacae\tAmpicillin/Amoxicillin',
+    '0\tEnterobacter cloacae\tAmpicillin-Sulbactam',
+    '81\tEnterobacter cloacae\tPiperacillin-Tazobactam',
+    '0\tEnterobacter cloacae\tCefazolin',
+    '76\tEnterobacter cloacae\tCeftriaxone',
+    '97\tEnterobacter cloacae\tCefepime',
+    '78\tEnterobacter cloacae\tAztreonam',
+    '98\tEnterobacter cloacae\tImipenem',
+    '99\tEnterobacter cloacae\tMeropenem',
+    '89\tEnterobacter cloacae\tErtapenem',
+    '97\tEnterobacter cloacae\tGentamicin',
+    '97\tEnterobacter cloacae\tTobramycin',
+    '99\tEnterobacter cloacae\tAmikacin',
+    '96\tEnterobacter cloacae\tCiprofloxacin',
+    '97\tEnterobacter cloacae\tLevofloxacin',
+    '94\tEnterobacter cloacae\tTMP-SMX',
+    '35\tEnterobacter cloacae\tNitrofurantoin (uncomplicated UTI)',
+    '3598\tEscherichia coli\tNumber Tested',
+    '49\tEscherichia coli\tAmpicillin/Amoxicillin',
+    '51\tEscherichia coli\tAmpicillin-Sulbactam',
+    '95\tEscherichia coli\tPiperacillin-Tazobactam',
+    '82\tEscherichia coli\tCefazolin',
+    '88\tEscherichia coli\tCeftriaxone',
+    '83\tEscherichia coli\tCefepime',
+    '83\tEscherichia coli\tAztreonam',
+    '100\tEscherichia coli\tImipenem',
+    '100\tEscherichia coli\tMeropenem',
+    '100\tEscherichia coli\tErtapenem',
+    '89\tEscherichia coli\tGentamicin',
+    '88\tEscherichia coli\tTobramycin',
+    '100\tEscherichia coli\tAmikacin',
+    '77\tEscherichia coli\tCiprofloxacin',
+    '77\tEscherichia coli\tLevofloxacin',
+    '69\tEscherichia coli\tTMP-SMX',
+    '96\tEscherichia coli\tNitrofurantoin (uncomplicated UTI)',
+    '93\tKlebsiella aerogenes\tNumber Tested',
+    '0\tKlebsiella aerogenes\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella aerogenes\tAmpicillin-Sulbactam',
+    '77\tKlebsiella aerogenes\tPiperacillin-Tazobactam',
+    '0\tKlebsiella aerogenes\tCefazolin',
+    '73\tKlebsiella aerogenes\tCeftriaxone',
+    '98\tKlebsiella aerogenes\tCefepime',
+    '73\tKlebsiella aerogenes\tAztreonam',
+    '82\tKlebsiella aerogenes\tImipenem',
+    '99\tKlebsiella aerogenes\tMeropenem',
+    '95\tKlebsiella aerogenes\tErtapenem',
+    '100\tKlebsiella aerogenes\tGentamicin',
+    '100\tKlebsiella aerogenes\tTobramycin',
+    '100\tKlebsiella aerogenes\tAmikacin',
+    '99\tKlebsiella aerogenes\tCiprofloxacin',
+    '98\tKlebsiella aerogenes\tLevofloxacin',
+    '99\tKlebsiella aerogenes\tTMP-SMX',
+    '9\tKlebsiella aerogenes\tNitrofurantoin (uncomplicated UTI)',
+    '172\tKlebsiella oxytoca\tNumber Tested',
+    '0\tKlebsiella oxytoca\tAmpicillin/Amoxicillin',
+    '71\tKlebsiella oxytoca\tAmpicillin-Sulbactam',
+    '94\tKlebsiella oxytoca\tPiperacillin-Tazobactam',
+    '52\tKlebsiella oxytoca\tCefazolin',
+    '91\tKlebsiella oxytoca\tCeftriaxone',
+    '94\tKlebsiella oxytoca\tCefepime',
+    '91\tKlebsiella oxytoca\tAztreonam',
+    '99\tKlebsiella oxytoca\tImipenem',
+    '99\tKlebsiella oxytoca\tMeropenem',
+    '99\tKlebsiella oxytoca\tErtapenem',
+    '95\tKlebsiella oxytoca\tGentamicin',
+    '92\tKlebsiella oxytoca\tTobramycin',
+    '99\tKlebsiella oxytoca\tAmikacin',
+    '94\tKlebsiella oxytoca\tCiprofloxacin',
+    '97\tKlebsiella oxytoca\tLevofloxacin',
+    '84\tKlebsiella oxytoca\tTMP-SMX',
+    '72\tKlebsiella oxytoca\tNitrofurantoin (uncomplicated UTI)',
+    '786\tKlebsiella pneumoniae\tNumber Tested',
+    '0\tKlebsiella pneumoniae\tAmpicillin/Amoxicillin',
+    '76\tKlebsiella pneumoniae\tAmpicillin-Sulbactam',
+    '92\tKlebsiella pneumoniae\tPiperacillin-Tazobactam',
+    '87\tKlebsiella pneumoniae\tCefazolin',
+    '90\tKlebsiella pneumoniae\tCeftriaxone',
+    '89\tKlebsiella pneumoniae\tCefepime',
+    '87\tKlebsiella pneumoniae\tAztreonam',
+    '100\tKlebsiella pneumoniae\tImipenem',
+    '99\tKlebsiella pneumoniae\tMeropenem',
+    '99\tKlebsiella pneumoniae\tErtapenem',
+    '94\tKlebsiella pneumoniae\tGentamicin',
+    '94\tKlebsiella pneumoniae\tTobramycin',
+    '100\tKlebsiella pneumoniae\tAmikacin',
+    '92\tKlebsiella pneumoniae\tCiprofloxacin',
+    '94\tKlebsiella pneumoniae\tLevofloxacin',
+    '84\tKlebsiella pneumoniae\tTMP-SMX',
+    '20\tKlebsiella pneumoniae\tNitrofurantoin (uncomplicated UTI)',
+    '63\tMorganella\tNumber Tested',
+    '0\tMorganella\tAmpicillin/Amoxicillin',
+    '0\tMorganella\tAmpicillin-Sulbactam',
+    '100\tMorganella\tPiperacillin-Tazobactam',
+    '0\tMorganella\tCefazolin',
+    '92\tMorganella\tCeftriaxone',
+    '100\tMorganella\tCefepime',
+    '96\tMorganella\tAztreonam',
+    '100\tMorganella\tMeropenem',
+    '100\tMorganella\tErtapenem',
+    '78\tMorganella\tGentamicin',
+    '92\tMorganella\tTobramycin',
+    '98\tMorganella\tAmikacin',
+    '79\tMorganella\tCiprofloxacin',
+    '89\tMorganella\tLevofloxacin',
+    '64\tMorganella\tTMP-SMX',
+    '0\tMorganella\tNitrofurantoin (uncomplicated UTI)',
+    '345\tProteus mirabilis\tNumber Tested',
+    '75\tProteus mirabilis\tAmpicillin/Amoxicillin',
+    '76\tProteus mirabilis\tAmpicillin-Sulbactam',
+    '100\tProteus mirabilis\tPiperacillin-Tazobactam',
+    '87\tProteus mirabilis\tCefazolin',
+    '94\tProteus mirabilis\tCeftriaxone',
+    '93\tProteus mirabilis\tCefepime',
+    '96\tProteus mirabilis\tAztreonam',
+    '100\tProteus mirabilis\tMeropenem',
+    '100\tProteus mirabilis\tErtapenem',
+    '86\tProteus mirabilis\tGentamicin',
+    '88\tProteus mirabilis\tTobramycin',
+    '99\tProteus mirabilis\tAmikacin',
+    '84\tProteus mirabilis\tCiprofloxacin',
+    '85\tProteus mirabilis\tLevofloxacin',
+    '75\tProteus mirabilis\tTMP-SMX',
+    '0\tProteus mirabilis\tNitrofurantoin (uncomplicated UTI)',
+    '11\tProteus vulgaris\tNumber Tested',
+    '0\tProteus vulgaris\tAmpicillin/Amoxicillin',
+    '100\tProteus vulgaris\tPiperacillin-Tazobactam',
+    '0\tProteus vulgaris\tCefazolin',
+    '100\tProteus vulgaris\tCefepime',
+    '100\tProteus vulgaris\tMeropenem',
+    '100\tProteus vulgaris\tErtapenem',
+    '100\tProteus vulgaris\tGentamicin',
+    '100\tProteus vulgaris\tTobramycin',
+    '100\tProteus vulgaris\tAmikacin',
+    '100\tProteus vulgaris\tCiprofloxacin',
+    '100\tProteus vulgaris\tLevofloxacin',
+    '100\tProteus vulgaris\tTMP-SMX',
+    '0\tProteus vulgaris\tNitrofurantoin (uncomplicated UTI)',
+    '580\tPseudomonas aeruginosa\tNumber Tested',
+    '93\tPseudomonas aeruginosa\tPiperacillin-Tazobactam',
+    '92\tPseudomonas aeruginosa\tCefepime',
+    '86\tPseudomonas aeruginosa\tAztreonam',
+    '88\tPseudomonas aeruginosa\tImipenem',
+    '91\tPseudomonas aeruginosa\tMeropenem',
+    '91\tPseudomonas aeruginosa\tGentamicin',
+    '98\tPseudomonas aeruginosa\tTobramycin',
+    '97\tPseudomonas aeruginosa\tAmikacin',
+    '89\tPseudomonas aeruginosa\tCiprofloxacin',
+    '85\tPseudomonas aeruginosa\tLevofloxacin',
+    '165\tPseudomonas aeruginosa CF mucoid\tNumber Tested',
+    '89\tPseudomonas aeruginosa CF mucoid\tPiperacillin-Tazobactam',
+    '96\tPseudomonas aeruginosa CF mucoid\tCeftolozane-Tazobactam',
+    '81\tPseudomonas aeruginosa CF mucoid\tCefepime',
+    '84\tPseudomonas aeruginosa CF mucoid\tAztreonam',
+    '79\tPseudomonas aeruginosa CF mucoid\tImipenem',
+    '84\tPseudomonas aeruginosa CF mucoid\tMeropenem',
+    '95\tPseudomonas aeruginosa CF mucoid\tTobramycin',
+    '73\tPseudomonas aeruginosa CF mucoid\tAmikacin',
+    '76\tPseudomonas aeruginosa CF mucoid\tCiprofloxacin',
+    '61\tPseudomonas aeruginosa CF mucoid\tLevofloxacin',
+    '109\tPseudomonas aeruginosa CF non-mucoid\tNumber Tested',
+    '72\tPseudomonas aeruginosa CF non-mucoid\tPiperacillin-Tazobactam',
+    '92\tPseudomonas aeruginosa CF non-mucoid\tCeftolozane-Tazobactam',
+    '72\tPseudomonas aeruginosa CF non-mucoid\tCefepime',
+    '72\tPseudomonas aeruginosa CF non-mucoid\tAztreonam',
+    '63\tPseudomonas aeruginosa CF non-mucoid\tImipenem',
+    '75\tPseudomonas aeruginosa CF non-mucoid\tMeropenem',
+    '79\tPseudomonas aeruginosa CF non-mucoid\tTobramycin',
+    '47\tPseudomonas aeruginosa CF non-mucoid\tAmikacin',
+    '55\tPseudomonas aeruginosa CF non-mucoid\tCiprofloxacin',
+    '28\tPseudomonas aeruginosa CF non-mucoid\tLevofloxacin',
+    '27\tSalmonella\tNumber Tested',
+    '89\tSalmonella\tAmpicillin/Amoxicillin',
+    '96\tSalmonella\tCeftriaxone',
+    '85\tSalmonella\tCiprofloxacin',
+    '93\tSalmonella\tTMP-SMX',
+    '125\tSerratia marcescens\tNumber Tested',
+    '0\tSerratia marcescens\tAmpicillin/Amoxicillin',
+    '0\tSerratia marcescens\tAmpicillin-Sulbactam',
+    '97\tSerratia marcescens\tPiperacillin-Tazobactam',
+    '0\tSerratia marcescens\tCefazolin',
+    '95\tSerratia marcescens\tCeftriaxone',
+    '100\tSerratia marcescens\tCefepime',
+    '100\tSerratia marcescens\tAztreonam',
+    '100\tSerratia marcescens\tImipenem',
+    '100\tSerratia marcescens\tMeropenem',
+    '100\tSerratia marcescens\tErtapenem',
+    '100\tSerratia marcescens\tGentamicin',
+    '97\tSerratia marcescens\tTobramycin',
+    '100\tSerratia marcescens\tAmikacin',
+    '96\tSerratia marcescens\tCiprofloxacin',
+    '98\tSerratia marcescens\tLevofloxacin',
+    '98\tSerratia marcescens\tTMP-SMX',
+    '0\tSerratia marcescens\tNitrofurantoin (uncomplicated UTI)',
+    '92\tStenotrophomonas maltophilia\tNumber Tested',
+    '86\tStenotrophomonas maltophilia\tLevofloxacin',
+    '99\tStenotrophomonas maltophilia\tTMP-SMX',
+
+
+
+    '1772\tStaphylococcus aureus (all)\tNumber Tested',
+    '78\tStaphylococcus aureus (all)\tNafcillin/Oxacillin',
+    '78\tStaphylococcus aureus (all)\tCefazolin',
+    '100\tStaphylococcus aureus (all)\tVancomycin',
+    '58\tStaphylococcus aureus (all)\tErythromycin',
+    '74\tStaphylococcus aureus (all)\tClindamycin',
+    '96\tStaphylococcus aureus (all)\tGentamicin',
+    '99\tStaphylococcus aureus (all)\tTMP-SMX',
+    '74\tStaphylococcus aureus (all)\tMoxifloxacin',
+    '95\tStaphylococcus aureus (all)\tDoxycycline',
+    '100\tStaphylococcus aureus (all)\tLinezolid',
+    '383\tStaphylococcus aureus (MRSA)\tNumber Tested',
+    '0\tStaphylococcus aureus (MRSA)\tPenicillin G',
+    '0\tStaphylococcus aureus (MRSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MRSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MRSA)\tVancomycin',
+    '10\tStaphylococcus aureus (MRSA)\tErythromycin',
+    '55\tStaphylococcus aureus (MRSA)\tClindamycin',
+    '93\tStaphylococcus aureus (MRSA)\tGentamicin',
+    '97\tStaphylococcus aureus (MRSA)\tTMP-SMX',
+    '23\tStaphylococcus aureus (MRSA)\tMoxifloxacin',
+    '94\tStaphylococcus aureus (MRSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MRSA)\tLinezolid',
+    '1389\tStaphylococcus aureus (MSSA)\tNumber Tested',
+    '100\tStaphylococcus aureus (MSSA)\tNafcillin/Oxacillin',
+    '100\tStaphylococcus aureus (MSSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MSSA)\tVancomycin',
+    '71\tStaphylococcus aureus (MSSA)\tErythromycin',
+    '79\tStaphylococcus aureus (MSSA)\tClindamycin',
+    '97\tStaphylococcus aureus (MSSA)\tGentamicin',
+    '100\tStaphylococcus aureus (MSSA)\tTMP-SMX',
+    '86\tStaphylococcus aureus (MSSA)\tMoxifloxacin',
+    '95\tStaphylococcus aureus (MSSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MSSA)\tLinezolid',
+    '108\tStaphylococcus lugdunensis\tNumber Tested',
+    '97\tStaphylococcus lugdunensis\tNafcillin/Oxacillin',
+    '97\tStaphylococcus lugdunensis\tCefazolin',
+    '100\tStaphylococcus lugdunensis\tVancomycin',
+    '84\tStaphylococcus lugdunensis\tErythromycin',
+    '86\tStaphylococcus lugdunensis\tClindamycin',
+    '98\tStaphylococcus lugdunensis\tGentamicin',
+    '99\tStaphylococcus lugdunensis\tTMP-SMX',
+    '100\tStaphylococcus lugdunensis\tMoxifloxacin',
+    '91\tStaphylococcus lugdunensis\tDoxycycline',
+    '100\tStaphylococcus lugdunensis\tLinezolid',
+    '296\tStaphylococcus, Coagulase Negative (epidermidis)\tNumber Tested',
+    '43\tStaphylococcus, Coagulase Negative (epidermidis)\tNafcillin/Oxacillin',
+    '43\tStaphylococcus, Coagulase Negative (epidermidis)\tCefazolin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tVancomycin',
+    '33\tStaphylococcus, Coagulase Negative (epidermidis)\tErythromycin',
+    '61\tStaphylococcus, Coagulase Negative (epidermidis)\tClindamycin',
+    '80\tStaphylococcus, Coagulase Negative (epidermidis)\tGentamicin',
+    '61\tStaphylococcus, Coagulase Negative (epidermidis)\tTMP-SMX',
+    '56\tStaphylococcus, Coagulase Negative (epidermidis)\tMoxifloxacin',
+    '81\tStaphylococcus, Coagulase Negative (epidermidis)\tDoxycycline',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tLinezolid',
+
+
+    '31\tBacteroides fragilis\tNumber Tested',
+    '0\tBacteroides fragilis\tPenicillin G',
+    '94\tBacteroides fragilis\tAmpicillin-Sulbactam',
+    '94\tBacteroides fragilis\tMeropenem',
+    '57\tBacteroides fragilis\tClindamycin',
+    '100\tBacteroides fragilis\tMetronidazole',
+    '36\tBacteroides (not fragilis)\tNumber Tested',
+    '0\tBacteroides (not fragilis)\tPenicillin G',
+    '69\tBacteroides (not fragilis)\tAmpicillin-Sulbactam',
+    '97\tBacteroides (not fragilis)\tMeropenem',
+    '31\tBacteroides (not fragilis)\tClindamycin',
+    '97\tBacteroides (not fragilis)\tMetronidazole',
+    '31\tGram Negative Rods (anaerobes, other)\tNumber Tested',
+    '55\tGram Negative Rods (anaerobes, other)\tPenicillin G',
+    '100\tGram Negative Rods (anaerobes, other)\tAmpicillin-Sulbactam',
+    '100\tGram Negative Rods (anaerobes, other)\tMeropenem',
+    '65\tGram Negative Rods (anaerobes, other)\tClindamycin',
+    '100\tGram Negative Rods (anaerobes, other)\tMetronidazole',
+    '13\tClostridium perfringens\tNumber Tested',
+    '100\tClostridium perfringens\tPenicillin G',
+    '100\tClostridium perfringens\tAmpicillin-Sulbactam',
+    '100\tClostridium perfringens\tMeropenem',
+    '23\tClostridium perfringens\tClindamycin',
+    '100\tClostridium perfringens\tMetronidazole',
+    '32\tClostridium (not perfringens)\tNumber Tested',
+    '56\tClostridium (not perfringens)\tPenicillin G',
+    '94\tClostridium (not perfringens)\tAmpicillin-Sulbactam',
+    '100\tClostridium (not perfringens)\tMeropenem',
+    '52\tClostridium (not perfringens)\tClindamycin',
+    '97\tClostridium (not perfringens)\tMetronidazole',
+    '28\tGram Positive Rods (anaerobes)\tNumber Tested',
+    '93\tGram Positive Rods (anaerobes)\tPenicillin G',
+    '100\tGram Positive Rods (anaerobes)\tAmpicillin-Sulbactam',
+    '96\tGram Positive Rods (anaerobes)\tMeropenem',
+    '75\tGram Positive Rods (anaerobes)\tClindamycin',
+    '15\tGram Positive Rods (anaerobes)\tMetronidazole',
+    '32\tGram Positive Cocci (anaerobes)\tNumber Tested',
+    '100\tGram Positive Cocci (anaerobes)\tPenicillin G',
+    '100\tGram Positive Cocci (anaerobes)\tAmpicillin-Sulbactam',
+    '100\tGram Positive Cocci (anaerobes)\tMeropenem',
+    '66\tGram Positive Cocci (anaerobes)\tClindamycin',
+    '100\tGram Positive Cocci (anaerobes)\tMetronidazole',
+
+
+    '49\tCampylobacter\tNumber Tested',
+    '61\tCampylobacter\tCiprofloxacin',
+    '55\tCampylobacter\tDoxycycline',
+    '98\tCampylobacter\tErythromycin',
+    // '23\tM tuberculosis\tNumber Tested',
+    // '83\tM tuberculosis\tIsoniazid',
+    // '100\tM tuberculosis\tRifampin',
+    // '100\tM tuberculosis\tEthambutol',
+    // '96\tM tuberculosis\tPyrazinamide',
+].join('\n');
+
+SENSITIVITY_DATA_PER_SOURCE["2021 Stanford Health Care (SHC)"] =
+[
+    '265\tStreptococcus Group B (agalactiae)\tNumber Tested',
+    '100\tStreptococcus Group B (agalactiae)\tPenicillin G',
+    '46\tStreptococcus Group B (agalactiae)\tErythromycin',
+    '50\tStreptococcus Group B (agalactiae)\tClindamycin',
+    '96\tStreptococcus Group B (agalactiae)\tLevofloxacin',
+    '181\tStreptococcus viridans Group\tNumber Tested',
+    '72\tStreptococcus viridans Group\tPenicillin G',
+    '99\tStreptococcus viridans Group\tCeftriaxone',
+    '100\tStreptococcus viridans Group\tVancomycin',
+    '60\tStreptococcus viridans Group\tErythromycin',
+    '82\tStreptococcus viridans Group\tClindamycin',
+    '14\tStreptococcus pneumoniae\tNumber Tested',
+    '43\tStreptococcus pneumoniae\tPenicillin G',
+    '67\tStreptococcus pneumoniae\tCefuroxime',
+    '100\tStreptococcus pneumoniae\tCeftriaxone',
+    '100\tStreptococcus pneumoniae\tVancomycin',
+    '57\tStreptococcus pneumoniae\tErythromycin',
+    '73\tStreptococcus pneumoniae\tClindamycin',
+    '86\tStreptococcus pneumoniae\tMeropenem',
+    '57\tStreptococcus pneumoniae\tTMP-SMX',
+    '73\tStreptococcus pneumoniae\tDoxycycline',
+    '100\tStreptococcus pneumoniae\tMoxifloxacin',
+    '725\tEnterococcus (unspeciated)\tNumber Tested',
+    '88\tEnterococcus (unspeciated)\tPenicillin G',
+    '93\tEnterococcus (unspeciated)\tVancomycin',
+    '28\tEnterococcus (unspeciated)\tDoxycycline',
+    '96\tEnterococcus (unspeciated)\tNitrofurantoin (uncomplicated UTI)',
+    '81\tEnterococcus (unspeciated)\tLevofloxacin',
+    '75\tEnterococcus (unspeciated)\tCiprofloxacin',
+    '99\tEnterococcus (unspeciated)\tLinezolid',
+    '148\tEnterococcus faecalis\tNumber Tested',
+    '100\tEnterococcus faecalis\tPenicillin G',
+    '99\tEnterococcus faecalis\tVancomycin',
+    '82\tEnterococcus faecalis\tGentamicin',
+    '91\tEnterococcus faecalis\tStreptomycin',
+    '99\tEnterococcus faecalis\tLinezolid',
+    '129\tEnterococcus faecium\tNumber Tested',
+    '16\tEnterococcus faecium\tPenicillin G',
+    '31\tEnterococcus faecium\tVancomycin',
+    '98\tEnterococcus faecium\tGentamicin',
+    '33\tEnterococcus faecium\tStreptomycin',
+    '97\tEnterococcus faecium\tLinezolid',
+
+
+    '73\tCandida albicans\tNumber Tested',
+    '100\tCandida albicans\tAmphotericin B',
+    '100\tCandida albicans\tCaspofungin',
+    '95\tCandida albicans\tFluconazole',
+    '93\tCandida albicans\tVoriconazole',
+    '53\tCandida glabrata\tNumber Tested',
+    '100\tCandida glabrata\tAmphotericin B',
+    '93\tCandida glabrata\tCaspofungin',
+    '93\tCandida glabrata\tFluconazole',
+    '22\tCandida parapsilosis\tNumber Tested',
+    '100\tCandida parapsilosis\tAmphotericin B',
+    '100\tCandida parapsilosis\tCaspofungin',
+    '96\tCandida parapsilosis\tFluconazole',
+    '96\tCandida parapsilosis\tVoriconazole',
+    '10\tCandida tropicalis\tNumber Tested',
+    '100\tCandida tropicalis\tAmphotericin B',
+    '100\tCandida tropicalis\tCaspofungin',
+    '90\tCandida tropicalis\tFluconazole',
+    '40\tCandida tropicalis\tVoriconazole',
+    '10\tCandida (other)\tNumber Tested',
+    '100\tCandida (other)\tAmphotericin B',
+    '78\tCandida (other)\tCaspofungin',
+    '44\tCandida (other)\tVoriconazole',
+
+    '34\tAchromobacter xylosoxidans\tNumber Tested',
+    '82\tAchromobacter xylosoxidans\tPiperacillin-Tazobactam',
+    '3\tAchromobacter xylosoxidans\tCefepime',
+    '0\tAchromobacter xylosoxidans\tAztreonam',
+    '66\tAchromobacter xylosoxidans\tImipenem',
+    '82\tAchromobacter xylosoxidans\tMeropenem',
+    '3\tAchromobacter xylosoxidans\tGentamicin',
+    '0\tAchromobacter xylosoxidans\tTobramycin',
+    '6\tAchromobacter xylosoxidans\tAmikacin',
+    '18\tAchromobacter xylosoxidans\tCiprofloxacin',
+    '50\tAchromobacter xylosoxidans\tLevofloxacin',
+    '82\tAchromobacter xylosoxidans\tTMP-SMX',
+    '32\tAcinetobacter\tNumber Tested',
+    '63\tAcinetobacter\tAmpicillin-Sulbactam',
+    '59\tAcinetobacter\tCefepime',
+    '59\tAcinetobacter\tMeropenem',
+    '63\tAcinetobacter\tGentamicin',
+    '66\tAcinetobacter\tTobramycin',
+    '69\tAcinetobacter\tAmikacin',
+    '63\tAcinetobacter\tCiprofloxacin',
+    '66\tAcinetobacter\tLevofloxacin',
+    '59\tAcinetobacter\tTMP-SMX',
+    '13\tBurkholderia cepacia\tNumber Tested',
+    '92\tBurkholderia cepacia\tCeftazidime',
+    '100\tBurkholderia cepacia\tMinocycline',
+    '77\tBurkholderia cepacia\tMeropenem',
+    '92\tBurkholderia cepacia\tTMP-SMX',
+    '105\tCitrobacter freundii\tNumber Tested',
+    '0\tCitrobacter freundii\tAmpicillin/Amoxicillin',
+    '0\tCitrobacter freundii\tAmpicillin-Sulbactam',
+    '91\tCitrobacter freundii\tPiperacillin-Tazobactam',
+    '0\tCitrobacter freundii\tCefazolin',
+    '83\tCitrobacter freundii\tCeftriaxone',
+    '100\tCitrobacter freundii\tCefepime',
+    '78\tCitrobacter freundii\tAztreonam',
+    '100\tCitrobacter freundii\tImipenem',
+    '100\tCitrobacter freundii\tMeropenem',
+    '100\tCitrobacter freundii\tErtapenem',
+    '97\tCitrobacter freundii\tGentamicin',
+    '96\tCitrobacter freundii\tTobramycin',
+    '100\tCitrobacter freundii\tAmikacin',
+    '94\tCitrobacter freundii\tCiprofloxacin',
+    '91\tCitrobacter freundii\tLevofloxacin',
+    '90\tCitrobacter freundii\tTMP-SMX',
+    '92\tCitrobacter freundii\tNitrofurantoin (uncomplicated UTI)',
+    '92\tCitrobacter koseri\tNumber Tested',
+    '0\tCitrobacter koseri\tAmpicillin/Amoxicillin',
+    '94\tCitrobacter koseri\tAmpicillin-Sulbactam',
+    '99\tCitrobacter koseri\tPiperacillin-Tazobactam',
+    '98\tCitrobacter koseri\tCefazolin',
+    '99\tCitrobacter koseri\tCeftriaxone',
+    '100\tCitrobacter koseri\tCefepime',
+    '100\tCitrobacter koseri\tAztreonam',
+    '100\tCitrobacter koseri\tImipenem',
+    '100\tCitrobacter koseri\tMeropenem',
+    '100\tCitrobacter koseri\tErtapenem',
+    '100\tCitrobacter koseri\tGentamicin',
+    '100\tCitrobacter koseri\tTobramycin',
+    '100\tCitrobacter koseri\tAmikacin',
+    '98\tCitrobacter koseri\tCiprofloxacin',
+    '97\tCitrobacter koseri\tLevofloxacin',
+    '100\tCitrobacter koseri\tTMP-SMX',
+    '88\tCitrobacter koseri\tNitrofurantoin (uncomplicated UTI)',
+    '189\tEnterobacter cloacae\tNumber Tested',
+    '0\tEnterobacter cloacae\tAmpicillin/Amoxicillin',
+    '0\tEnterobacter cloacae\tAmpicillin-Sulbactam',
+    '72\tEnterobacter cloacae\tPiperacillin-Tazobactam',
+    '0\tEnterobacter cloacae\tCefazolin',
+    '68\tEnterobacter cloacae\tCeftriaxone',
+    '92\tEnterobacter cloacae\tCefepime',
+    '68\tEnterobacter cloacae\tAztreonam',
+    '97\tEnterobacter cloacae\tImipenem',
+    '100\tEnterobacter cloacae\tMeropenem',
+    '85\tEnterobacter cloacae\tErtapenem',
+    '98\tEnterobacter cloacae\tGentamicin',
+    '98\tEnterobacter cloacae\tTobramycin',
+    '100\tEnterobacter cloacae\tAmikacin',
+    '91\tEnterobacter cloacae\tCiprofloxacin',
+    '91\tEnterobacter cloacae\tLevofloxacin',
+    '92\tEnterobacter cloacae\tTMP-SMX',
+    '36\tEnterobacter cloacae\tNitrofurantoin (uncomplicated UTI)',
+    '3107\tEscherichia coli\tNumber Tested',
+    '54\tEscherichia coli\tAmpicillin/Amoxicillin',
+    '51\tEscherichia coli\tAmpicillin-Sulbactam',
+    '83\tEscherichia coli\tPiperacillin-Tazobactam',
+    '96\tEscherichia coli\tCefazolin',
+    '84\tEscherichia coli\tCeftriaxone',
+    '88\tEscherichia coli\tCefepime',
+    '81\tEscherichia coli\tAztreonam',
+    '100\tEscherichia coli\tImipenem',
+    '100\tEscherichia coli\tMeropenem',
+    '100\tEscherichia coli\tErtapenem',
+    '90\tEscherichia coli\tGentamicin',
+    '89\tEscherichia coli\tTobramycin',
+    '100\tEscherichia coli\tAmikacin',
+    '74\tEscherichia coli\tCiprofloxacin',
+    '68\tEscherichia coli\tLevofloxacin',
+    '73\tEscherichia coli\tTMP-SMX',
+    '97\tEscherichia coli\tNitrofurantoin (uncomplicated UTI)',
+    '151\tKlebsiella aerogenes\tNumber Tested',
+    '0\tKlebsiella aerogenes\tAmpicillin/Amoxicillin',
+    '0\tKlebsiella aerogenes\tAmpicillin-Sulbactam',
+    '83\tKlebsiella aerogenes\tPiperacillin-Tazobactam',
+    '0\tKlebsiella aerogenes\tCefazolin',
+    '76\tKlebsiella aerogenes\tCeftriaxone',
+    '98\tKlebsiella aerogenes\tCefepime',
+    '79\tKlebsiella aerogenes\tAztreonam',
+    '70\tKlebsiella aerogenes\tImipenem',
+    '98\tKlebsiella aerogenes\tMeropenem',
+    '94\tKlebsiella aerogenes\tErtapenem',
+    '99\tKlebsiella aerogenes\tGentamicin',
+    '99\tKlebsiella aerogenes\tTobramycin',
+    '100\tKlebsiella aerogenes\tAmikacin',
+    '96\tKlebsiella aerogenes\tCiprofloxacin',
+    '96\tKlebsiella aerogenes\tLevofloxacin',
+    '99\tKlebsiella aerogenes\tTMP-SMX',
+    '15\tKlebsiella aerogenes\tNitrofurantoin (uncomplicated UTI)',
+    '149\tKlebsiella oxytoca\tNumber Tested',
+    '0\tKlebsiella oxytoca\tAmpicillin/Amoxicillin',
+    '76\tKlebsiella oxytoca\tAmpicillin-Sulbactam',
+    '93\tKlebsiella oxytoca\tPiperacillin-Tazobactam',
+    '63\tKlebsiella oxytoca\tCefazolin',
+    '91\tKlebsiella oxytoca\tCeftriaxone',
+    '92\tKlebsiella oxytoca\tCefepime',
+    '89\tKlebsiella oxytoca\tAztreonam',
+    '100\tKlebsiella oxytoca\tImipenem',
+    '100\tKlebsiella oxytoca\tMeropenem',
+    '100\tKlebsiella oxytoca\tErtapenem',
+    '97\tKlebsiella oxytoca\tGentamicin',
+    '95\tKlebsiella oxytoca\tTobramycin',
+    '100\tKlebsiella oxytoca\tAmikacin',
+    '93\tKlebsiella oxytoca\tCiprofloxacin',
+    '93\tKlebsiella oxytoca\tLevofloxacin',
+    '86\tKlebsiella oxytoca\tTMP-SMX',
+    '81\tKlebsiella oxytoca\tNitrofurantoin (uncomplicated UTI)',
+    '719\tKlebsiella pneumoniae\tNumber Tested',
+    '0\tKlebsiella pneumoniae\tAmpicillin/Amoxicillin',
+    '76\tKlebsiella pneumoniae\tAmpicillin-Sulbactam',
+    '95\tKlebsiella pneumoniae\tPiperacillin-Tazobactam',
+    '90\tKlebsiella pneumoniae\tCefazolin',
+    '92\tKlebsiella pneumoniae\tCeftriaxone',
+    '88\tKlebsiella pneumoniae\tCefepime',
+    '92\tKlebsiella pneumoniae\tAztreonam',
+    '99\tKlebsiella pneumoniae\tImipenem',
+    '99\tKlebsiella pneumoniae\tMeropenem',
+    '99\tKlebsiella pneumoniae\tErtapenem',
+    '96\tKlebsiella pneumoniae\tGentamicin',
+    '94\tKlebsiella pneumoniae\tTobramycin',
+    '99\tKlebsiella pneumoniae\tAmikacin',
+    '88\tKlebsiella pneumoniae\tCiprofloxacin',
+    '86\tKlebsiella pneumoniae\tLevofloxacin',
+    '89\tKlebsiella pneumoniae\tTMP-SMX',
+    '19\tKlebsiella pneumoniae\tNitrofurantoin (uncomplicated UTI)',
+    '59\tMorganella\tNumber Tested',
+    '0\tMorganella\tAmpicillin/Amoxicillin',
+    '0\tMorganella\tAmpicillin-Sulbactam',
+    '97\tMorganella\tPiperacillin-Tazobactam',
+    '0\tMorganella\tCefazolin',
+    '83\tMorganella\tCeftriaxone',
+    '95\tMorganella\tCefepime',
+    '90\tMorganella\tAztreonam',
+    '100\tMorganella\tMeropenem',
+    '100\tMorganella\tErtapenem',
+    '81\tMorganella\tGentamicin',
+    '92\tMorganella\tTobramycin',
+    '100\tMorganella\tAmikacin',
+    '70\tMorganella\tCiprofloxacin',
+    '70\tMorganella\tLevofloxacin',
+    '73\tMorganella\tTMP-SMX',
+    '0\tMorganella\tNitrofurantoin (uncomplicated UTI)',
+    '317\tProteus mirabilis\tNumber Tested',
+    '76\tProteus mirabilis\tAmpicillin/Amoxicillin',
+    '84\tProteus mirabilis\tAmpicillin-Sulbactam',
+    '100\tProteus mirabilis\tPiperacillin-Tazobactam',
+    '86\tProteus mirabilis\tCefazolin',
+    '93\tProteus mirabilis\tCeftriaxone',
+    '96\tProteus mirabilis\tCefepime',
+    '99\tProteus mirabilis\tAztreonam',
+    '100\tProteus mirabilis\tMeropenem',
+    '100\tProteus mirabilis\tErtapenem',
+    '85\tProteus mirabilis\tGentamicin',
+    '86\tProteus mirabilis\tTobramycin',
+    '100\tProteus mirabilis\tAmikacin',
+    '81\tProteus mirabilis\tCiprofloxacin',
+    '80\tProteus mirabilis\tLevofloxacin',
+    '76\tProteus mirabilis\tTMP-SMX',
+    '0\tProteus mirabilis\tNitrofurantoin (uncomplicated UTI)',
+    '22\tProteus vulgaris\tNumber Tested',
+    '0\tProteus vulgaris\tAmpicillin/Amoxicillin',
+    '100\tProteus vulgaris\tPiperacillin-Tazobactam',
+    '0\tProteus vulgaris\tCefazolin',
+    '73\tProteus vulgaris\tCefepime',
+    '100\tProteus vulgaris\tMeropenem',
+    '100\tProteus vulgaris\tErtapenem',
+    '100\tProteus vulgaris\tGentamicin',
+    '100\tProteus vulgaris\tTobramycin',
+    '100\tProteus vulgaris\tAmikacin',
+    '91\tProteus vulgaris\tCiprofloxacin',
+    '96\tProteus vulgaris\tLevofloxacin',
+    '96\tProteus vulgaris\tTMP-SMX',
+    '0\tProteus vulgaris\tNitrofurantoin (uncomplicated UTI)',
+    '580\tPseudomonas aeruginosa\tNumber Tested',
+    '94\tPseudomonas aeruginosa\tPiperacillin-Tazobactam',
+    '99\tPseudomonas aeruginosa\tCeftolozane-Tazobactam',
+    '92\tPseudomonas aeruginosa\tCefepime',
+    '84\tPseudomonas aeruginosa\tAztreonam',
+    '91\tPseudomonas aeruginosa\tImipenem',
+    '94\tPseudomonas aeruginosa\tMeropenem',
+    '89\tPseudomonas aeruginosa\tGentamicin',
+    '98\tPseudomonas aeruginosa\tTobramycin',
+    '97\tPseudomonas aeruginosa\tAmikacin',
+    '87\tPseudomonas aeruginosa\tCiprofloxacin',
+    '80\tPseudomonas aeruginosa\tLevofloxacin',
+    '104\tPseudomonas aeruginosa CF mucoid\tNumber Tested',
+    '83\tPseudomonas aeruginosa CF mucoid\tPiperacillin-Tazobactam',
+    '95\tPseudomonas aeruginosa CF mucoid\tCeftolozane-Tazobactam',
+    '83\tPseudomonas aeruginosa CF mucoid\tCefepime',
+    '75\tPseudomonas aeruginosa CF mucoid\tAztreonam',
+    '74\tPseudomonas aeruginosa CF mucoid\tImipenem',
+    '83\tPseudomonas aeruginosa CF mucoid\tMeropenem',
+    '91\tPseudomonas aeruginosa CF mucoid\tTobramycin',
+    '71\tPseudomonas aeruginosa CF mucoid\tAmikacin',
+    '52\tPseudomonas aeruginosa CF mucoid\tCiprofloxacin',
+    '42\tPseudomonas aeruginosa CF mucoid\tLevofloxacin',
+    '46\tPseudomonas aeruginosa CF non-mucoid\tNumber Tested',
+    '87\tPseudomonas aeruginosa CF non-mucoid\tPiperacillin-Tazobactam',
+    '91\tPseudomonas aeruginosa CF non-mucoid\tCeftolozane-Tazobactam',
+    '76\tPseudomonas aeruginosa CF non-mucoid\tCefepime',
+    '76\tPseudomonas aeruginosa CF non-mucoid\tAztreonam',
+    '78\tPseudomonas aeruginosa CF non-mucoid\tImipenem',
+    '84\tPseudomonas aeruginosa CF non-mucoid\tMeropenem',
+    '76\tPseudomonas aeruginosa CF non-mucoid\tTobramycin',
+    '56\tPseudomonas aeruginosa CF non-mucoid\tAmikacin',
+    '42\tPseudomonas aeruginosa CF non-mucoid\tCiprofloxacin',
+    '29\tPseudomonas aeruginosa CF non-mucoid\tLevofloxacin',
+    '18\tSalmonella\tNumber Tested',
+    '67\tSalmonella\tAmpicillin/Amoxicillin',
+    '72\tSalmonella\tCeftriaxone',
+    '78\tSalmonella\tCiprofloxacin',
+    '94\tSalmonella\tTMP-SMX',
+    '93\tSerratia marcescens\tNumber Tested',
+    '0\tSerratia marcescens\tAmpicillin/Amoxicillin',
+    '0\tSerratia marcescens\tAmpicillin-Sulbactam',
+    '100\tSerratia marcescens\tPiperacillin-Tazobactam',
+    '0\tSerratia marcescens\tCefazolin',
+    '98\tSerratia marcescens\tCeftriaxone',
+    '100\tSerratia marcescens\tCefepime',
+    '97\tSerratia marcescens\tAztreonam',
+    '100\tSerratia marcescens\tMeropenem',
+    '100\tSerratia marcescens\tErtapenem',
+    '100\tSerratia marcescens\tGentamicin',
+    '96\tSerratia marcescens\tTobramycin',
+    '100\tSerratia marcescens\tAmikacin',
+    '90\tSerratia marcescens\tCiprofloxacin',
+    '93\tSerratia marcescens\tLevofloxacin',
+    '99\tSerratia marcescens\tTMP-SMX',
+    '0\tSerratia marcescens\tNitrofurantoin (uncomplicated UTI)',
+    '91\tStenotrophomonas maltophilia\tNumber Tested',
+    '81\tStenotrophomonas maltophilia\tLevofloxacin',
+    '98\tStenotrophomonas maltophilia\tTMP-SMX',
+
+
+
+    '1307\tStaphylococcus aureus (all)\tNumber Tested',
+    '76\tStaphylococcus aureus (all)\tNafcillin/Oxacillin',
+    '76\tStaphylococcus aureus (all)\tCefazolin',
+    '100\tStaphylococcus aureus (all)\tVancomycin',
+    '56\tStaphylococcus aureus (all)\tErythromycin',
+    '71\tStaphylococcus aureus (all)\tClindamycin',
+    '96\tStaphylococcus aureus (all)\tGentamicin',
+    '99\tStaphylococcus aureus (all)\tTMP-SMX',
+    '73\tStaphylococcus aureus (all)\tMoxifloxacin',
+    '93\tStaphylococcus aureus (all)\tDoxycycline',
+    '100\tStaphylococcus aureus (all)\tLinezolid',
+    '312\tStaphylococcus aureus (MRSA)\tNumber Tested',
+    '0\tStaphylococcus aureus (MRSA)\tPenicillin G',
+    '0\tStaphylococcus aureus (MRSA)\tNafcillin/Oxacillin',
+    '0\tStaphylococcus aureus (MRSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MRSA)\tVancomycin',
+    '13\tStaphylococcus aureus (MRSA)\tErythromycin',
+    '53\tStaphylococcus aureus (MRSA)\tClindamycin',
+    '91\tStaphylococcus aureus (MRSA)\tGentamicin',
+    '98\tStaphylococcus aureus (MRSA)\tTMP-SMX',
+    '27\tStaphylococcus aureus (MRSA)\tMoxifloxacin',
+    '88\tStaphylococcus aureus (MRSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MRSA)\tLinezolid',
+    '995\tStaphylococcus aureus (MSSA)\tNumber Tested',
+    '100\tStaphylococcus aureus (MSSA)\tNafcillin/Oxacillin',
+    '100\tStaphylococcus aureus (MSSA)\tCefazolin',
+    '100\tStaphylococcus aureus (MSSA)\tVancomycin',
+    '69\tStaphylococcus aureus (MSSA)\tErythromycin',
+    '77\tStaphylococcus aureus (MSSA)\tClindamycin',
+    '97\tStaphylococcus aureus (MSSA)\tGentamicin',
+    '99\tStaphylococcus aureus (MSSA)\tTMP-SMX',
+    '88\tStaphylococcus aureus (MSSA)\tMoxifloxacin',
+    '95\tStaphylococcus aureus (MSSA)\tDoxycycline',
+    '100\tStaphylococcus aureus (MSSA)\tLinezolid',
+    '30\tStaphylococcus lugdunensis\tNumber Tested',
+    '97\tStaphylococcus lugdunensis\tNafcillin/Oxacillin',
+    '97\tStaphylococcus lugdunensis\tCefazolin',
+    '100\tStaphylococcus lugdunensis\tVancomycin',
+    '76\tStaphylococcus lugdunensis\tErythromycin',
+    '79\tStaphylococcus lugdunensis\tClindamycin',
+    '100\tStaphylococcus lugdunensis\tGentamicin',
+    '100\tStaphylococcus lugdunensis\tTMP-SMX',
+    '100\tStaphylococcus lugdunensis\tMoxifloxacin',
+    '90\tStaphylococcus lugdunensis\tDoxycycline',
+    '100\tStaphylococcus lugdunensis\tLinezolid',
+    '225\tStaphylococcus, Coagulase Negative (epidermidis)\tNumber Tested',
+    '38\tStaphylococcus, Coagulase Negative (epidermidis)\tNafcillin/Oxacillin',
+    '38\tStaphylococcus, Coagulase Negative (epidermidis)\tCefazolin',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tVancomycin',
+    '34\tStaphylococcus, Coagulase Negative (epidermidis)\tErythromycin',
+    '52\tStaphylococcus, Coagulase Negative (epidermidis)\tClindamycin',
+    '81\tStaphylococcus, Coagulase Negative (epidermidis)\tGentamicin',
+    '58\tStaphylococcus, Coagulase Negative (epidermidis)\tTMP-SMX',
+    '59\tStaphylococcus, Coagulase Negative (epidermidis)\tMoxifloxacin',
+    '82\tStaphylococcus, Coagulase Negative (epidermidis)\tDoxycycline',
+    '100\tStaphylococcus, Coagulase Negative (epidermidis)\tLinezolid',
+
+
+    '34\tBacteroides fragilis\tNumber Tested',
+    '0\tBacteroides fragilis\tPenicillin G',
+    '88\tBacteroides fragilis\tAmpicillin-Sulbactam',
+    '94\tBacteroides fragilis\tMeropenem',
+    '41\tBacteroides fragilis\tClindamycin',
+    '100\tBacteroides fragilis\tMetronidazole',
+    '35\tBacteroides (not fragilis)\tNumber Tested',
+    '0\tBacteroides (not fragilis)\tPenicillin G',
+    '80\tBacteroides (not fragilis)\tAmpicillin-Sulbactam',
+    '100\tBacteroides (not fragilis)\tMeropenem',
+    '34\tBacteroides (not fragilis)\tClindamycin',
+    '97\tBacteroides (not fragilis)\tMetronidazole',
+    '51\tGram Negative Rods (anaerobes, other)\tNumber Tested',
+    '66\tGram Negative Rods (anaerobes, other)\tPenicillin G',
+    '100\tGram Negative Rods (anaerobes, other)\tAmpicillin-Sulbactam',
+    '100\tGram Negative Rods (anaerobes, other)\tMeropenem',
+    '77\tGram Negative Rods (anaerobes, other)\tClindamycin',
+    '100\tGram Negative Rods (anaerobes, other)\tMetronidazole',
+    '10\tClostridium perfringens\tNumber Tested',
+    '100\tClostridium perfringens\tPenicillin G',
+    '100\tClostridium perfringens\tAmpicillin-Sulbactam',
+    '100\tClostridium perfringens\tMeropenem',
+    '70\tClostridium perfringens\tClindamycin',
+    '100\tClostridium perfringens\tMetronidazole',
+    '32\tClostridium (not perfringens)\tNumber Tested',
+    '47\tClostridium (not perfringens)\tPenicillin G',
+    '100\tClostridium (not perfringens)\tAmpicillin-Sulbactam',
+    '100\tClostridium (not perfringens)\tMeropenem',
+    '44\tClostridium (not perfringens)\tClindamycin',
+    '100\tClostridium (not perfringens)\tMetronidazole',
+    '28\tGram Positive Rods (anaerobes)\tNumber Tested',
+    '89\tGram Positive Rods (anaerobes)\tPenicillin G',
+    '100\tGram Positive Rods (anaerobes)\tAmpicillin-Sulbactam',
+    '100\tGram Positive Rods (anaerobes)\tMeropenem',
+    '86\tGram Positive Rods (anaerobes)\tClindamycin',
+    '19\tGram Positive Rods (anaerobes)\tMetronidazole',
+    '34\tGram Positive Cocci (anaerobes)\tNumber Tested',
+    '97\tGram Positive Cocci (anaerobes)\tPenicillin G',
+    '100\tGram Positive Cocci (anaerobes)\tAmpicillin-Sulbactam',
+    '100\tGram Positive Cocci (anaerobes)\tMeropenem',
+    '65\tGram Positive Cocci (anaerobes)\tClindamycin',
+    '100\tGram Positive Cocci (anaerobes)\tMetronidazole',
+
+
+    '43\tCampylobacter\tNumber Tested',
+    '58\tCampylobacter\tCiprofloxacin',
+    '54\tCampylobacter\tDoxycycline',
+    '93\tCampylobacter\tErythromycin',
+    '16\tM tuberculosis\tNumber Tested',
+    '100\tM tuberculosis\tIsoniazid',
+    '100\tM tuberculosis\tRifampin',
+    '100\tM tuberculosis\tEthambutol',
+    '100\tM tuberculosis\tPyrazinamide',
+].join('\n');
+
+
+DEFAULT_SOURCE = "2021 Stanford Health Care (SHC)";
 SENSITIVITY_DATA_PER_SOURCE["default"] = SENSITIVITY_DATA_PER_SOURCE[DEFAULT_SOURCE];
diff --git a/medinfo/web/tools/formatTemplates.py b/medinfo/web/tools/formatTemplates.py
index 42e60e65..a89e6c92 100644
--- a/medinfo/web/tools/formatTemplates.py
+++ b/medinfo/web/tools/formatTemplates.py
@@ -17,17 +17,17 @@
         rowByTitle[row["TITLE"]] = row;
 
 # Build table of contents
-sortedTitles = rowByTitle.keys();
+sortedTitles = list(rowByTitle.keys());
 sortedTitles.sort();
 
-print >> ofs, "<ul>";
+print("<ul>", file=ofs);
 for title in sortedTitles:
     row = rowByTitle[title];
-    print >> ofs, TOC_TEMPLATE % row;
-print >> ofs, "</ul>";
+    print(TOC_TEMPLATE % row, file=ofs);
+print("</ul>", file=ofs);
 
 # Build actual content
 for title in sortedTitles:
     row = rowByTitle[title];
-    print >> ofs, ROW_TEMPLATE % row;
+    print(ROW_TEMPLATE % row, file=ofs);
         
\ No newline at end of file
diff --git a/medinfo/web/tools/kiosk/feedFrame.htm b/medinfo/web/tools/kiosk/feedFrame.htm
new file mode 100644
index 00000000..88cb3438
--- /dev/null
+++ b/medinfo/web/tools/kiosk/feedFrame.htm
@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html>
+<head>
+	<title>Feed Frame</title>
+	<script>
+	// https://stackoverflow.com/questions/9837676/smooth-auto-scroll-by-using-javascript
+	function pageScroll()
+	{
+		var pixels = 250;		// Pixels to scroll down by for each interval
+		var interval = 10000;	// Milliseconds
+	    setInterval(function(){ window.scrollBy(0,pixels) }, interval);
+	}
+	</script>
+	<!-- Refresh content periodically 
+	(after ~1 scroll through = data height * interval/pixel * 1 second / 1000 ms)
+	per day to capture any changes 
+	Move to external logoControl frame as this one sometimes fails to reload, causing it to die on a 404 page
+	-->
+	<!-- meta http-equiv="refresh" content="240" -->
+</head>
+<body onLoad="pageScroll();">
+	<a class="twitter-timeline" data-height="7000" data-dnt="true" data-chrome="noheader nofooter noborder noscrollbar transparent" href="https://twitter.com/jonc101x?ref_src=twsrc%5Etfw">Tweets by jonc101x</a>
+	<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+</body>
+</html>
+
diff --git a/medinfo/web/tools/kiosk/image.img.full.high.jpg b/medinfo/web/tools/kiosk/image.img.full.high.jpg
new file mode 100644
index 00000000..a7ccbb93
Binary files /dev/null and b/medinfo/web/tools/kiosk/image.img.full.high.jpg differ
diff --git a/medinfo/web/tools/kiosk/index.htm b/medinfo/web/tools/kiosk/index.htm
new file mode 100644
index 00000000..ae76ad29
--- /dev/null
+++ b/medinfo/web/tools/kiosk/index.htm
@@ -0,0 +1,32 @@
+<html>
+<head>
+	<!-- Refresh entire frameset once per day to ensure capture any content changes
+		Beware that this messes up the Video stream "AutoPlay" so have to reclick that on Chrome.
+		Microsoft Edge will do autoplay, but then won't do auto-captions.
+	 -->
+	<!-- meta http-equiv="refresh" content="86400" -->
+</head>
+<frameset cols="71%,29%">
+	<frameset rows="50%,50%">
+		<frameset cols="50%,50%">
+			<frame name="slides.0" src="https://docs.google.com/presentation/d/e/2PACX-1vQ3EMNCmeCirVC84pUH-9UV0piLXUTFBcHOQSgPgvOoTP7z8OBZE7gG_4GZqeR53b_9u7-r1KHUYR8I/embed?start=true&loop=true&delayms=20000#slide=1">
+			<!--
+			<frame name="slides.1" src="https://docs.google.com/presentation/d/e/2PACX-1vQ3EMNCmeCirVC84pUH-9UV0piLXUTFBcHOQSgPgvOoTP7z8OBZE7gG_4GZqeR53b_9u7-r1KHUYR8I/embed?start=true&loop=true&delayms=20000#slide=30">
+			-->
+			<frame name="videoFrame" src="https://www.youtube.com/embed/videoseries?list=PLNT-OftSs8iB22BBt9Wx-y49hUqnzT5Lt&loop=1&cc_load_policy=1&cc_lang_pref=en&enablejsapi=1">
+		</frameset>
+		<frameset cols="50%,50%">
+			<frame name="slides.2" src="https://docs.google.com/presentation/d/e/2PACX-1vQ3EMNCmeCirVC84pUH-9UV0piLXUTFBcHOQSgPgvOoTP7z8OBZE7gG_4GZqeR53b_9u7-r1KHUYR8I/embed?start=true&loop=true&delayms=20000#slide=50">
+			<frame name="slides.3" src="https://docs.google.com/presentation/d/e/2PACX-1vQ3EMNCmeCirVC84pUH-9UV0piLXUTFBcHOQSgPgvOoTP7z8OBZE7gG_4GZqeR53b_9u7-r1KHUYR8I/embed?start=true&loop=true&delayms=20000#slide=100">
+		</frameset>
+	</frameset>
+	<!-- Single Slides Frame
+	<frame name="slides" src="https://docs.google.com/presentation/d/e/2PACX-1vQ3EMNCmeCirVC84pUH-9UV0piLXUTFBcHOQSgPgvOoTP7z8OBZE7gG_4GZqeR53b_9u7-r1KHUYR8I/embed?start=true&loop=true&delayms=10000">
+	-->
+	<!-- frame name="slides" src="slidesFrame.htm"-->
+	<frameset rows="27%,73%">
+		<frame name="logoControl" src="logoControl.htm" scrolling="no">
+		<frame name="feed" src="feedFrame.htm">
+	</frameset>
+</frameset>
+</html>
diff --git a/medinfo/web/tools/kiosk/logoControl.htm b/medinfo/web/tools/kiosk/logoControl.htm
new file mode 100644
index 00000000..e53af851
--- /dev/null
+++ b/medinfo/web/tools/kiosk/logoControl.htm
@@ -0,0 +1,34 @@
+<!DOCTYPE html>
+<html>
+<head>
+	<title>Logo Control</title>
+
+	<script src="https://www.youtube.com/iframe_api"><!-- YouTube player API --></script>
+
+	<script>
+	function periodicFrameRefresh(frameToRefresh, videoFrame)
+	{	// Refresh Twitter feed occasionally in case it gets stuck
+		var interval = 240000;	// Milliseconds
+		var feedURL = frameToRefresh.location.href;	// Just doing reload causes problems when intermittent connectivity issues, so store URL once
+	    setInterval(function(){ frameToRefresh.location.replace(feedURL); frameToRefresh.scrollTo(0,0); }, interval);
+
+    	// Advance YouTube video frame occasionally in case gets stuck
+    	var videoAdvanceInterval = 7200000; // Milliseconds. 7200 seconds = 2 hours
+	    setInterval(function(){ advanceVideo(videoFrame); }, videoAdvanceInterval);
+	}
+
+	function advanceVideo(videoFrame)
+	{
+		var commandMessage = JSON.stringify({'event':'command','func':'nextVideo','args':[]});
+		videoFrame.postMessage(commandMessage,"*");
+	}
+
+	</script>
+</head>
+<body onLoad="periodicFrameRefresh(window.parent.feed, window.parent.videoFrame);">
+	<div align="center">
+		<img src="image.img.full.high.jpg" width="100%" onClick="advanceVideo(window.parent.videoFrame)">
+	</div>
+</body>
+</html>
+
diff --git a/medinfo/web/tools/kiosk/sleep.bat b/medinfo/web/tools/kiosk/sleep.bat
new file mode 100644
index 00000000..2963e06f
--- /dev/null
+++ b/medinfo/web/tools/kiosk/sleep.bat
@@ -0,0 +1 @@
+Rundll32.exe Powrprof.dll,SetSuspendState Sleep
diff --git a/medinfo/web/tools/kiosk/wake.bat b/medinfo/web/tools/kiosk/wake.bat
new file mode 100644
index 00000000..62fdf6d7
--- /dev/null
+++ b/medinfo/web/tools/kiosk/wake.bat
@@ -0,0 +1 @@
+echo Wake
diff --git a/scripts/Archive/BMT_hyperglycemia/.RData b/scripts/Archive/BMT_hyperglycemia/.RData
new file mode 100644
index 00000000..1457d9e8
Binary files /dev/null and b/scripts/Archive/BMT_hyperglycemia/.RData differ
diff --git a/scripts/Archive/BMT_hyperglycemia/.Rhistory b/scripts/Archive/BMT_hyperglycemia/.Rhistory
new file mode 100644
index 00000000..6ce550df
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/.Rhistory
@@ -0,0 +1,103 @@
+getwd()
+help.start()
+library(dplyr)
+install.packages('dplyr')
+library(dplyr)
+library(bigrquery)
+install.packages('bigrquery')
+project <- "mining-clinical-decisions"
+testquery <- "SELECT
+ADT.pat_enc_csn_id_coded
+FROM
+starr_datalake2018.adt AS ADT JOIN  -- Create a shorthand table alias for convenience when referencing
+starr_datalake2018.treatment_team AS TT ON ADT.pat_enc_csn_id_coded = TT.pat_enc_csn_id_coded
+LIMIT 1000"
+testquery
+data <- download_bq(project,testquery)
+library(bigrquery)
+data <- download_bq(project,testquery)
+#download_bq <- function(project, sql){
+tb <- bq_project_query(project, testquery)
+tb.dtx <- bq_table_download(tb)
+tb.dtx
+sql <- "
+select
+opCT.jc_uid,
+cast(opCT.pat_enc_csn_id_coded as string) as string_id,
+admit.event_type,
+admit.pat_class,
+admit.effective_time_jittered as emergencyAdmitTime,
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+dc.dx_name,
+dc.icd10
+from
+datalake_47618.adt as admit,
+datalake_47618.order_proc as opCT,
+datalake_47618.diagnosis_code as dc
+where admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL -60 MINUTE) < TIMESTAMP(opCT.order_inst_jittered)
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL 60 MINUTE) >= TIMESTAMP(opCT.order_inst_jittered)
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and admit.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and dc.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+and dc.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+group by
+opCT.jc_uid,
+opCT.pat_enc_csn_id_coded,
+admit.event_type,
+admit.pat_class,
+admit.effective_time_jittered,
+dc.dx_name,
+dc.icd10
+"
+#download_bq <- function(project, sql){
+tb <- bq_project_query(project, sql)
+tb.dtx <- bq_table_download(tb)
+tb.dtx
+View(tb.dtx)
+download_bq <- function(project, sql){
+tb <- bq_project_query(project, sql)
+tb.dtx <- bq_table_download(tb)
+return(tb.dtx)
+}
+getwd()
+rdir <- '/Users/Ivana/Documents/Fellowship/Chen Lab/GitHub/CDSS/scripts/BMT_hyperglycemia'
+setwd(rdir)
+pwd()
+getwd()
+sql <- read('BMT_primary_TT.sql')
+sql <- read_file('BMT_primary_TT.sql')
+install.packages('readr')
+library(readr)
+sql <- read_file('BMT_primary_TT.sql')
+download_bq <- function(project, sql){
+tb <- bq_project_query(project, sql)
+tb.dtx <- bq_table_download(tb)
+return(tb.dtx)
+}
+data <- download_bq(project,sql)
+View(data)
+rdir_files <- list.files()
+rdir_files
+sql <- read_file('BMT_and_DM_distinct.sql" )
+download_bq <- function(project, sql){
+tb <- bq_project_query(project, sql)
+tb.dtx <- bq_table_download(tb)
+return(tb.dtx)
+}
+data <- download_bq(project,sql)
+sql <- read_file('BMT_and_DM_distinct.sql')
+data <- download_bq(project,sql)
+sql
+test <- "SELECT count (DISTINCT(jc_uid)) from `starr_datalake2018.diagnosis_code`
+WHERE jc_uid IN (SELECT (jc_uid) from `starr_datalake2018.diagnosis_code`
+WHERE UPPER(dx_name) LIKE '%BONE MARROW%' AND UPPER(dx_name) LIKE '%TRANSPLANT%')
+AND UPPER(dx_name) LIKE '%DIABETES%’"
+data <- download_bq(project,test)
+test <- "SELECT count (DISTINCT(jc_uid)) from `starr_datalake2018.diagnosis_code`
+WHERE jc_uid IN (SELECT (jc_uid) from `starr_datalake2018.diagnosis_code`
+WHERE UPPER(dx_name) LIKE '%BONE MARROW%' AND UPPER(dx_name) LIKE '%TRANSPLANT%')
+AND UPPER(dx_name) LIKE '%DIABETES%'"
+data <- download_bq(project,test)
+View(data)
diff --git a/scripts/Archive/BMT_hyperglycemia/ADT_BMT_pts.sql b/scripts/Archive/BMT_hyperglycemia/ADT_BMT_pts.sql
new file mode 100644
index 00000000..fdaa63b0
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/ADT_BMT_pts.sql
@@ -0,0 +1,2 @@
+-- BMT pts in ADT table: 158595
+	SELECT pat_service, jc_uid FROM starr_datalake2018.adt WHERE pat_service LIKE 'Bone%') -- all patients on BMT service
diff --git a/scripts/Archive/BMT_hyperglycemia/BGs_BMT_pts.sql b/scripts/Archive/BMT_hyperglycemia/BGs_BMT_pts.sql
new file mode 100644
index 00000000..071d0cc2
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BGs_BMT_pts.sql
@@ -0,0 +1,27 @@
+
+/* SELECT rit_uid, lab_name, base_name, ord_value, ord_num_value FROM `starr_datalake2018.lab_result`  
+  WHERE UPPER(lab_name) LIKE '%GLUCOSE%' AND (lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose") AND UPPER(ordering_mode) = 'INPATIENT' AND ord_num_value BETWEEN 0 AND 9999998 
+  AND rit_uid IN (SELECT DISTINCT(rit_uid) FROM starr_datalake2018.treatment_team AS TT WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%"))
+-- BGs of pts who have ever had BMT as primary treatment team
+*/
+
+
+-- Individual BGs of patients who had BMT as their primary team during this patient encounter
+
+SELECT Lab.rit_uid, Lab.pat_enc_csn_id_coded, Lab.lab_name, Lab.base_name, Lab.ord_num_value, Lab.taken_time_jittered, Lab.order_id_coded FROM starr_datalake2018.lab_result AS Lab
+
+WHERE Lab.order_id_coded IN (SELECT distinct (Lab.order_id_coded)FROM starr_datalake2018.treatment_team AS TT JOIN starr_datalake2018.lab_result AS Lab ON Lab.pat_enc_csn_id_coded = TT.pat_enc_csn_id_coded  
+  WHERE Lab.pat_enc_csn_id_coded IN (SELECT DISTINCT(pat_enc_csn_id_coded) FROM starr_datalake2018.treatment_team AS TT WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%"))
+  AND   
+  (UPPER(Lab.lab_name) LIKE '%GLUCOSE%' AND (Lab.lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose") AND UPPER(Lab.ordering_mode) = 'INPATIENT' AND Lab.ord_num_value BETWEEN 0 AND 9999998)
+)
+
+AND   
+  (UPPER(Lab.lab_name) LIKE '%GLUCOSE%' AND (Lab.lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose") AND UPPER(Lab.ordering_mode) = 'INPATIENT' AND Lab.ord_num_value BETWEEN 0 AND 9999998)
+
+Order by Lab.pat_enc_csn_id_coded, Lab.order_id_coded, Lab.taken_time_jittered
+
+
+
+
+
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_A1c.sql b/scripts/Archive/BMT_hyperglycemia/BMT_A1c.sql
new file mode 100644
index 00000000..bc5c24eb
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_A1c.sql
@@ -0,0 +1,7 @@
+-- Pt encounters with BMT as primary treatment team &  A1c > 6.5% —> 281
+ 	
+SELECT count(distinct(Lab.pat_enc_csn_id_coded))  FROM starr_datalake2018.lab_result AS Lab
+	  WHERE  Lab.pat_enc_csn_id_coded IN (SELECT DISTINCT(TT.pat_enc_csn_id_coded) FROM starr_datalake2018.treatment_team AS TT 
+      WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%"))
+  AND UPPER(Lab.lab_name) LIKE '%A1C%' AND ord_num_value >= 6.5 AND ord_num_value != 9999999
+  
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_age.sql b/scripts/Archive/BMT_hyperglycemia/BMT_age.sql
new file mode 100644
index 00000000..f9da4045
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_age.sql
@@ -0,0 +1,8 @@
+SELECT Demo.rit_uid, pat_enc_csn_id_coded, gender, birth_date_jittered, trtmnt_tm_begin_dt_jittered, trtmnt_tm_end_dt_jittered, DATE_DIFF(DATE(trtmnt_tm_begin_dt_jittered), DATE(Demo.birth_date_jittered), YEAR) as ageYears,  prov_name FROM starr_datalake2018.demographic AS Demo JOIN starr_datalake2018.treatment_team AS TT ON Demo.rit_uid = TT.rit_uid 
+WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%")
+
+
+
+Order by rit_uid, ageYears, pat_enc_csn_id_coded asc
+
+--Age at time of treamtent team assignment for pts on BMT (with duplicates)
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_and_DM_distinct.sql b/scripts/Archive/BMT_hyperglycemia/BMT_and_DM_distinct.sql
new file mode 100644
index 00000000..e92aefae
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_and_DM_distinct.sql
@@ -0,0 +1,6 @@
+-- Distinct pt encounters on the BMT service with DM coded —> 1242
+
+SELECT count(distinct(pat_enc_csn_id_coded)) from `starr_datalake2018.diagnosis_code` 
+  WHERE pat_enc_csn_id_coded IN (SELECT (pat_enc_csn_id_coded) from starr_datalake2018.treatment_team AS TT  
+  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%") )
+  AND UPPER(dx_name) LIKE '%DIABETES%'
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_dx_pts.sql b/scripts/Archive/BMT_hyperglycemia/BMT_dx_pts.sql
new file mode 100644
index 00000000..c041ccb1
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_dx_pts.sql
@@ -0,0 +1,4 @@
+-- Pts with “bone marrow transplant” Dx: 229740 —> 4371 distinct patients
+
+SELECT distinct(jc_uid) from `starr_datalake2018.diagnosis_code` 
+WHERE UPPER(dx_name) LIKE '%BONE MARROW%' AND UPPER(dx_name) LIKE '%TRANSPLANT%’
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_gender.sql b/scripts/Archive/BMT_hyperglycemia/BMT_gender.sql
new file mode 100644
index 00000000..9287b170
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_gender.sql
@@ -0,0 +1,5 @@
+SELECT Demo.rit_uid, Demo.gender  FROM starr_datalake2018.demographic AS Demo
+
+WHERE Demo.rit_uid IN (SELECT distinct (TT.rit_uid) FROM starr_datalake2018.treatment_team AS TT  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%"))
+
+-- Gender of patients with primary team = BMT
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_med_freq.sql b/scripts/Archive/BMT_hyperglycemia/BMT_med_freq.sql
new file mode 100644
index 00000000..504d8b2b
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_med_freq.sql
@@ -0,0 +1,12 @@
+
+-- Most freqeuently given meds to patient encounters on BMT service 
+
+SELECT  med.med_description, count(med.med_description) 
+  FROM starr_datalake2018.order_med AS med JOIN starr_datalake2018.mar as mar on med.order_med_id_coded = mar.order_med_id_coded
+	WHERE  med.pat_enc_csn_id_coded IN (SELECT (pat_enc_csn_id_coded) from starr_datalake2018.treatment_team AS TT  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%") )
+  AND (ordering_mode_c) = 2
+  AND mar.mar_action = "Given"
+  
+ Group by med.med_description
+ Order by count(med.med_description) desc
+ 
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_primary_TT.sql b/scripts/Archive/BMT_hyperglycemia/BMT_primary_TT.sql
new file mode 100644
index 00000000..581bdbea
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_primary_TT.sql
@@ -0,0 +1,10 @@
+SELECT prov_name FROM `mining-clinical-decisions.starr_datalake2018.treatment_team` 
+
+WHERE name = "Primary Team" AND prov_name LIKE "%BMT%"
+
+group by prov_name
+
+order by prov_name asc
+
+-- Patients whose primary team was BMT-related (were likely on a BMT team at any time during their admission) 
+
diff --git a/scripts/Archive/BMT_hyperglycemia/BMT_steroid_orders.sql b/scripts/Archive/BMT_hyperglycemia/BMT_steroid_orders.sql
new file mode 100644
index 00000000..65bf7faa
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMT_steroid_orders.sql
@@ -0,0 +1,10 @@
+SELECT  med_description  FROM `starr_datalake2018.order_med` 
+	WHERE  pat_enc_csn_id_coded IN (SELECT (pat_enc_csn_id_coded) from starr_datalake2018.treatment_team AS TT  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%") )
+  AND (ordering_mode_c) = 2
+  AND 
+ (lower(med_description) like '%hydrocortisone%'  OR  lower(med_description) like '%prednisone%'  
+   OR lower(med_description) like '%methylprednisolone%' OR  lower(med_description) like '%dexamethasone%')
+	AND (upper(med_description) not like  '%CIPROFLOXACIN' AND upper(med_description) not like '%CHEMO%' AND upper(med_description) not like '%OPHT%' AND upper(med_description) not like '%TP%' AND upper(med_description) not like '%PR %' AND upper(med_description) not like '%IT%' AND upper(med_description) not like '%OTIC%' AND upper(med_description) not like '%PLACEBO%' AND upper(med_description) not like '%ONDANSETRON%')
+
+
+-- Pts on bmt getting steroids
diff --git a/scripts/Archive/BMT_hyperglycemia/BMTpts_given_steroids.sql b/scripts/Archive/BMT_hyperglycemia/BMTpts_given_steroids.sql
new file mode 100644
index 00000000..caa25676
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMTpts_given_steroids.sql
@@ -0,0 +1,13 @@
+
+SELECT med.jc_uid, med.pat_enc_csn_id_coded, med.order_med_id_coded, mar.taken_time_jittered, med.med_description, med_route_c, med_route, freq_display_name, mar_action, hv_discrete_dose, hv_dose_unit, pharm_class_name, thera_class_name 
+  FROM starr_datalake2018.order_med AS med JOIN starr_datalake2018.mar as mar on med.order_med_id_coded = mar.order_med_id_coded
+	WHERE  med.pat_enc_csn_id_coded IN (SELECT (pat_enc_csn_id_coded) from starr_datalake2018.treatment_team AS TT  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%") )
+  AND (ordering_mode_c) = 2
+  AND 
+ (lower(med_description) like '%hydrocortisone%'  OR  lower(med_description) like '%prednisone%'  
+   OR lower(med_description) like '%methylprednisolone%' OR  lower(med_description) like '%dexamethasone%')
+	AND (upper(med_description) not like  '%CIPROFLOXACIN' AND upper(med_description) not like '%CHEMO%' AND upper(med_description) not like '%OPHT%' AND upper(med_description) not like '%TP%' AND upper(med_description) not like '%PR %' AND upper(med_description) not like '%IT%' AND upper(med_description) not like '%OTIC%' AND upper(med_description) not like '%PLACEBO%' AND upper(med_description) not like '%ONDANSETRON%')
+  AND mar.mar_action = "Given"
+  
+  
+  -- Pts on BMT service who were ordered for and given HC/methylpred/pred/dex oral/IV
diff --git a/scripts/Archive/BMT_hyperglycemia/BMTpts_ordered_for_insulin.sql b/scripts/Archive/BMT_hyperglycemia/BMTpts_ordered_for_insulin.sql
new file mode 100644
index 00000000..87483688
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/BMTpts_ordered_for_insulin.sql
@@ -0,0 +1,10 @@
+SELECT count(distinct(pat_enc_csn_id_coded)) FROM `starr_datalake2018.order_med` 
+	WHERE UPPER(med_description) LIKE '%INSULIN%'
+	AND (ordering_mode_c) = 2
+	AND pat_enc_csn_id_coded IN (SELECT (pat_enc_csn_id_coded) from starr_datalake2018.treatment_team AS TT  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%") )
+
+ OR pat_enc_csn_id_coded IN (SELECT (Os.pat_enc_csn_id_coded) from starr_datalake2018.med_orderset as OS JOIN starr_datalake2018.treatment_team  AS TT ON OS.rit_uid = TT.rit_uid 
+	WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%")
+	AND  UPPER(OS.ss_section_name) LIKE '%SCALE%'AND  UPPER(Os.ss_section_name) LIKE '%SCALE%') 
+  
+-- patient encounters on BMT w/ insulin orders (ISS or just insulin)
diff --git a/scripts/Archive/BMT_hyperglycemia/TT_ADT_join_BMTmatching.sql b/scripts/Archive/BMT_hyperglycemia/TT_ADT_join_BMTmatching.sql
new file mode 100644
index 00000000..7b145a79
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/TT_ADT_join_BMTmatching.sql
@@ -0,0 +1,12 @@
+ SELECT 
+          ADT.pat_enc_csn_id_coded, TT.prov_name, TT.name, ADT.pat_service
+        FROM
+          starr_datalake2018.adt AS ADT JOIN  -- Create a shorthand table alias for convenience when referencing
+          starr_datalake2018.treatment_team AS TT ON ADT.pat_enc_csn_id_coded = TT.pat_enc_csn_id_coded
+        WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%")
+        AND ADT.pat_service LIKE 'Bone%'
+        
+        ORDER BY ADT.pat_service DESC
+        
+ -- Inner joins the ADT and treatment team tables on patient encounters where both service is BMT and primary team is BMT
+ -- 6637 unique encounters
diff --git a/scripts/Archive/BMT_hyperglycemia/count_pt_enc_BMTprimary.sql b/scripts/Archive/BMT_hyperglycemia/count_pt_enc_BMTprimary.sql
new file mode 100644
index 00000000..ee6b42ff
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/count_pt_enc_BMTprimary.sql
@@ -0,0 +1,5 @@
+SELECT count(distinct(pat_enc_csn_id_coded)) FROM `mining-clinical-decisions.starr_datalake2018.treatment_team` 
+
+WHERE name = "Primary Team" AND prov_name LIKE "%BMT%"
+
+-- Count = 7484 distinct patient encounters out of 7786 total patient encounters with BMT as a primary team
diff --git a/scripts/Archive/BMT_hyperglycemia/inpatient_BG_labs.sql b/scripts/Archive/BMT_hyperglycemia/inpatient_BG_labs.sql
new file mode 100644
index 00000000..ab846e20
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/inpatient_BG_labs.sql
@@ -0,0 +1,3 @@
+WHERE UPPER(lab_name) LIKE '%GLUCOSE%' 
+AND (lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose") 
+AND UPPER(ordering_mode) = 'INPATIENT' AND ord_num_value BETWEEN 0 AND 9999998
diff --git a/scripts/Archive/BMT_hyperglycemia/insulin_orders_BMT.sql b/scripts/Archive/BMT_hyperglycemia/insulin_orders_BMT.sql
new file mode 100644
index 00000000..6d8fc547
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/insulin_orders_BMT.sql
@@ -0,0 +1,7 @@
+-- Insulin orders (excluding ISS) for patients during encounters on the BMT service 
+
+SELECT jc_uid, pat_enc_csn_id_coded, order_time_jittered, medication_id,  med_description, med_route_c, med_route, freq_display_name, hv_discrete_dose, hv_dose_unit, pharm_class_name, thera_class_name FROM `starr_datalake2018.order_med`
+	WHERE UPPER(med_description) LIKE '%INSULIN%'
+	AND (ordering_mode_c) = 2
+	AND pat_enc_csn_id_coded IN (SELECT (pat_enc_csn_id_coded) from starr_datalake2018.treatment_team AS TT  WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%") )
+	ORDER BY jc_uid, order_time_jittered asc 
diff --git a/scripts/Archive/BMT_hyperglycemia/pts_on_ISS.sql b/scripts/Archive/BMT_hyperglycemia/pts_on_ISS.sql
new file mode 100644
index 00000000..eea5e896
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/pts_on_ISS.sql
@@ -0,0 +1,6 @@
+	SELECT  count(distinct(TT.pat_enc_csn_id_coded)) FROM starr_datalake2018.med_orderset as OS JOIN starr_datalake2018.treatment_team AS TT ON OS.rit_uid = TT.rit_uid 
+
+	WHERE (TT.name = "Primary Team" AND TT.prov_name LIKE "%BMT%")
+	AND  UPPER(ss_section_name) LIKE '%SCALE%'
+
+	-- Patient encounters with BMT pirmary and ISS (3571)
diff --git a/scripts/Archive/BMT_hyperglycemia/readme.md b/scripts/Archive/BMT_hyperglycemia/readme.md
new file mode 100644
index 00000000..11946f5e
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/readme.md
@@ -0,0 +1 @@
+SQL queries for establishing a BMT hyperglycemia cohort & exploration of SQL
diff --git a/scripts/Archive/BMT_hyperglycemia/recieved_insulin_BMTpts b/scripts/Archive/BMT_hyperglycemia/recieved_insulin_BMTpts
new file mode 100644
index 00000000..71cc2331
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/recieved_insulin_BMTpts
@@ -0,0 +1,18 @@
+-- Patients who have ever been on the BMT service who were ordered for and received insulin 
+	
+  
+ SELECT jc_uid, pat_enc_csn_id_coded, order_time_jittered, medication_id,  med_description, med_route_c, med_route, freq_display_name, hv_discrete_dose, hv_dose_unit, pharm_class_name, thera_class_name FROM `starr_datalake2018.order_med`
+	WHERE UPPER(med_description) LIKE '%INSULIN%'
+	AND (ordering_mode_c) = 2
+	AND jc_uid IN (SELECT DISTINCT(jc_uid) FROM starr_datalake2018.adt WHERE pat_service LIKE 'Bone%') -- patients with "patient service" = Bone Marrow Transplant
+	ORDER BY jc_uid, order_time_jittered asc 
+	Patients who have ever been on the BMT service who were ordered for and received insulin
+	SELECT jc_uid, pat_enc_csn_id_coded, order_med_id_coded, medication_id, med_description, ordering_mode, med_route from starr_datalake2018.order_med
+	WHERE order_med_id_coded in 
+	(SELECT order_med_id_coded FROM starr_datalake2018.mar 
+	WHERE order_med_id_coded IN (SELECT order_med_id_coded FROM `starr_datalake2018.order_med`
+	WHERE UPPER(med_description) LIKE '%INSULIN%'
+	AND (ordering_mode_c) = 2
+	AND jc_uid IN (SELECT DISTINCT(jc_uid) FROM starr_datalake2018.adt WHERE pat_service LIKE 'Bone%')) -- patients with "patient service" = Bone Marrow Transplant
+	)
+	ORDER BY jc_uid, med_description  asc 
diff --git a/scripts/Archive/BMT_hyperglycemia/recieved_insulin_BMTpts.sql b/scripts/Archive/BMT_hyperglycemia/recieved_insulin_BMTpts.sql
new file mode 100644
index 00000000..71cc2331
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/recieved_insulin_BMTpts.sql
@@ -0,0 +1,18 @@
+-- Patients who have ever been on the BMT service who were ordered for and received insulin 
+	
+  
+ SELECT jc_uid, pat_enc_csn_id_coded, order_time_jittered, medication_id,  med_description, med_route_c, med_route, freq_display_name, hv_discrete_dose, hv_dose_unit, pharm_class_name, thera_class_name FROM `starr_datalake2018.order_med`
+	WHERE UPPER(med_description) LIKE '%INSULIN%'
+	AND (ordering_mode_c) = 2
+	AND jc_uid IN (SELECT DISTINCT(jc_uid) FROM starr_datalake2018.adt WHERE pat_service LIKE 'Bone%') -- patients with "patient service" = Bone Marrow Transplant
+	ORDER BY jc_uid, order_time_jittered asc 
+	Patients who have ever been on the BMT service who were ordered for and received insulin
+	SELECT jc_uid, pat_enc_csn_id_coded, order_med_id_coded, medication_id, med_description, ordering_mode, med_route from starr_datalake2018.order_med
+	WHERE order_med_id_coded in 
+	(SELECT order_med_id_coded FROM starr_datalake2018.mar 
+	WHERE order_med_id_coded IN (SELECT order_med_id_coded FROM `starr_datalake2018.order_med`
+	WHERE UPPER(med_description) LIKE '%INSULIN%'
+	AND (ordering_mode_c) = 2
+	AND jc_uid IN (SELECT DISTINCT(jc_uid) FROM starr_datalake2018.adt WHERE pat_service LIKE 'Bone%')) -- patients with "patient service" = Bone Marrow Transplant
+	)
+	ORDER BY jc_uid, med_description  asc 
diff --git a/scripts/Archive/BMT_hyperglycemia/sql_query_R.R b/scripts/Archive/BMT_hyperglycemia/sql_query_R.R
new file mode 100644
index 00000000..4ad85d02
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/sql_query_R.R
@@ -0,0 +1,51 @@
+library(dplyr)
+library(bigrquery)
+project <- "mining-clinical-decisions"
+testquery <- "SELECT 
+cast ADT.pat_enc_csn_id_coded
+FROM
+starr_datalake2018.adt AS ADT JOIN  -- Create a shorthand table alias for convenience when referencing
+starr_datalake2018.treatment_team AS TT ON ADT.pat_enc_csn_id_coded = TT.pat_enc_csn_id_coded
+
+
+LIMIT 1000" #the sql query we're trying to run
+
+sql <- "
+select 
+opCT.jc_uid, 
+cast(opCT.pat_enc_csn_id_coded as string) as string_id, 
+admit.event_type,  
+admit.pat_class, 
+admit.effective_time_jittered as emergencyAdmitTime, 
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+dc.dx_name,
+dc.icd10
+from 
+datalake_47618.adt as admit, 
+datalake_47618.order_proc as opCT,
+datalake_47618.diagnosis_code as dc
+where admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL -60 MINUTE) < TIMESTAMP(opCT.order_inst_jittered)
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL 60 MINUTE) >= TIMESTAMP(opCT.order_inst_jittered)
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and admit.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and dc.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded 
+and dc.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+group by 
+opCT.jc_uid,
+opCT.pat_enc_csn_id_coded,
+admit.event_type, 
+admit.pat_class, 
+admit.effective_time_jittered,
+dc.dx_name,
+dc.icd10
+" #sample sql query without patient encounter bug
+
+#download_bq <- function(project, sql){
+  tb <- bq_project_query(project, sql)
+  tb.dtx <- bq_table_download(tb)
+#  return(tb.dtx)
+  
+#}
+data <- download_bq(project,testquery)
diff --git a/scripts/Archive/BMT_hyperglycemia/steroids_IV_PO_query.sql b/scripts/Archive/BMT_hyperglycemia/steroids_IV_PO_query.sql
new file mode 100644
index 00000000..303782b8
--- /dev/null
+++ b/scripts/Archive/BMT_hyperglycemia/steroids_IV_PO_query.sql
@@ -0,0 +1,9 @@
+WHERE (ordering_mode_c) = 2
+  AND 
+ (lower(med_description) like '%hydrocortisone%'  OR  lower(med_description) like '%prednisone%'  
+   OR lower(med_description) like '%methylprednisolone%' OR  lower(med_description) like '%dexamethasone%')
+	AND (upper(med_description) not like  '%CIPROFLOXACIN' AND upper(med_description) not like '%CHEMO%' AND upper(med_description) not like '%OPHT%' AND upper(med_description) not like '%TP%' AND upper(med_description) not like '%PR %' AND upper(med_description) not like '%IT%' AND upper(med_description) not like '%OTIC%' AND upper(med_description) not like '%PLACEBO%' AND upper(med_description) not like '%ONDANSETRON%')
+  AND mar.mar_action = "Given"
+  
+  
+  -- partial query of pts receiving HC/dex/pred/solumedrol inpatient
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/Readme.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/Readme.txt
new file mode 100644
index 00000000..c3dcdbbf
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/Readme.txt
@@ -0,0 +1,20 @@
+Collection of scripts used to produce the analysis in this paper
+
+Why providers transfuse blood products outside recommended guidelines in spite of integrated electronic best practice alerts
+https://pubmed.ncbi.nlm.nih.gov/25044190/
+
+
+
+- overrideComments.txt - Text file with collection of text strings (e.g., one per line in a text file) of override comments studying
+
+- extractWordCounts.py - Reads though the overrideComments.txt file to inventory a count of all of the words/tokens/chunks that appear in the text strings to produce an overrideWords.txt file
+
+- wordTags.txt - Open overrideWords.txt in Excel as a tab-delimited file and then manually go through all of the list of words generated by the above in a spreadsheet and add an extra column if the presence of a word should automatically trigger a tag/label for a category (e.g., Surgery, Discharge, Symptomatic) to create a wordTags.txt tab-delimited file.
+
+- autoAnnotate.py - Go back though the overrideComments.txt data, and try to automatically guess which categories/tags each line belongs to, based on the simple wordTag assignment rules defined above.
+
+- mergeTags.py - If different human annotators come up with slightly different categories/tags ("surgery" vs. "surgical" vs. "pending operation"), then use this script with a TagMergeWorksheet.txt to relabel or otherwise merge the choice of tags.
+
+- calcAgreementStats.py - After two human annotators have finalized their list of annotation tags for each text string, this script will calculate how often their tag/category assignments agreed with each other in terms of Kappa measure as well as chi-square and component contingency table numbers.
+
+
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/TagMergeWorksheet.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/TagMergeWorksheet.txt
new file mode 100644
index 00000000..4a0a3ea4
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/TagMergeWorksheet.txt
@@ -0,0 +1,217 @@
+Tagger	Count	Tag	Alternative
+Jonathan	3	?	
+Jonathan	2	ACIDEMIA	HYPOPERFUSION
+Daniel	2	ACIDEMIA	HYPOPERFUSION
+Jonathan	28	ACS	
+Daniel	33	ACS	
+Daniel	1	AGE	
+Daniel	13	ALL	LEUKEMIA
+Daniel	46	AML	LEUKEMIA
+Daniel	46	ANEMIA	
+Jonathan	42	ANEMIAHGB>8	Anemia
+Daniel	2	ANGINA-ICHEMIA	ANGINA-ISCHEMIA
+Daniel	1	ANGINA-IHSCMIA	ANGINA-ISCHEMIA
+Jonathan	28	ANGINA-ISCHEMIA	
+Daniel	25	ANGINA-ISCHEMIA	
+Jonathan	12	ANTICOAGULATED	
+Daniel	11	ANTICOAGULATED	
+Daniel	1	ANTICOAGULATION	ANTICOAGULATED
+Daniel	1	AORTICDISSECTION	CARDIAC
+Daniel	1	APLASTICANEMIA	HEMEDISORDER
+Jonathan	19	ARRHYTHMIA	
+Daniel	10	ARRHYTHMIA	
+Daniel	9	ARRYTHMIA	ARRHYTHMIA
+Daniel	2	ASPLASTICANEMIA	HEMEDISORDER
+Daniel	1	AUTOLGOUS	AUTOLOGOUS
+Jonathan	5	AUTOLOGOUS	
+Daniel	8	AUTOLOGOUS	
+Jonathan	114	BLEEDING	
+Daniel	107	BLEEDING	
+Jonathan	382	BMT	
+Daniel	381	BMTPROTOCOL	BMT
+Jonathan	1	BRADYCARDIA	ARRHYTHMIA
+Jonathan	37	CAD	
+Daniel	23	CAD	
+Jonathan	54	CANCER	
+Daniel	41	CANCER	
+Jonathan	45	CARDIAC	
+Daniel	58	CARDIAC	
+Daniel	5	CARDIOGENICSHOCK	CHF,HYPOTENSION
+Jonathan	101	CHEMOTHERAPY	
+Daniel	99	CHEMOTHERAPY	
+Jonathan	30	CHF	
+Daniel	24	CHF	
+Jonathan	2	CHRONICDISASE	COMORBIDITIES
+Jonathan	1	CHRONICDISEASE	COMORBIDITIES
+Daniel	1	CIRHRHOSIS	CIRRHOSIS
+Jonathan	81	CIRRHOSIS	
+Daniel	31	CIRRHOSIS	
+Daniel	3	CLL	LEUKEMIA
+Jonathan	33	CNS	
+Daniel	13	CNS	
+Jonathan	20	COAGULOPATHY	
+Daniel	11	COAGULOPATHY	
+Jonathan	4	CODE	UNSTABLE
+Daniel	4	CODE	UNSTABLE
+Jonathan	1	COMORBIDITIES	
+Daniel	1	COMORBIDITIES	
+Daniel	3	CRITICALAS	CARDIAC
+Jonathan	31	CTSURGERY	SURGERY
+Jonathan	6	DELERIUM	DELIRIUM
+Daniel	3	DELIRIUM	
+Daniel	3	DIC	COAGULOPATHY
+Jonathan	13	DONOR	
+Daniel	8	DONOR	
+Daniel	4	DROPINGHGB	DROPPINGHGB
+Daniel	1	DROPPING	DROPPINGHGB
+Jonathan	177	DROPPINGHGB	
+Daniel	177	DROPPINGHGB	
+Jonathan	34	EXPECTDROP	
+Daniel	38	EXPECTDROP	
+Daniel	2	EXTUBATION	
+Jonathan	36	FATIGUE	SYMPTOMATIC
+Jonathan	8	FEVER	SEPSIS
+Daniel	5	FEVER	SEPSIS
+Jonathan	1	FUTUREORDER	
+Daniel	1	FUTUREORDER	
+Jonathan	29	HEMEDISORDER	
+Daniel	2	HEMEDISORDER	
+Jonathan	20	HEMOLYSIS	
+Daniel	20	HEMOLYSIS	
+Daniel	1	HG<8	HGB<8
+Daniel	1	HGB	
+Jonathan	188	HGB<7	
+Daniel	194	HGB<7	
+Jonathan	522	HGB<8	
+Daniel	416	HGB<8	
+Jonathan	2	HYPONATREMIA	
+Daniel	2	HYPONATREMIA	
+Jonathan	43	HYPOPERFUSION	
+Jonathan	98	HYPOTENSION	UNSTABLE
+Daniel	85	HYPOTENSION	UNSTABLE
+Daniel	1	HYPOTENSIVE	HYPOTENSION
+Jonathan	16	HYPOXIA	
+Daniel	13	HYPOXIA	
+Daniel	1	ICH	CNS
+Jonathan	80	IMMINENTDISCHARGE	
+Daniel	81	IMMINENTDISCHARGE	
+Jonathan	13	INADEQUATERESPONSE	DROPPINGHGB
+Daniel	12	LEUKEMIA	
+Jonathan	71	LEUKEMIAACUTE	LEUKEMIA
+Jonathan	3	LEUKEMIACHRONIC	LEUKEMIA
+Jonathan	31	LIGHTHEADED	SYMPTOMATIC
+Jonathan	4	LOWALBUMIN	
+Daniel	2	LOWHGB	ANEMIA
+Daniel	1	LOWLSCVO2	HYPOPERFUSION
+Daniel	37	LOWSCVO2	HYPOPERFUSION
+Daniel	4	LOWSVO2	HYPOPERFUSION
+Jonathan	8	LYMPHOMA	HEMEDISORDER
+Daniel	8	LYMPHOMA	HEMEDISORDER
+Jonathan	21	MDS	HEMEDISORDER
+Daniel	21	MDS	HEMEDISORDER
+Daniel	4	MF	HEMEDISORDER
+Jonathan	6	MM	HEMEDISORDER
+Daniel	6	MM	HEMEDISORDER
+Jonathan	4	MYELOFIBROSIS	HEMEDISORDER
+Jonathan	5	MYELOSUPPRESSED	
+Daniel	4	MYELOSUPPRESSED	
+Daniel	1	MYELOSUPPRESSION	MYELOSUPPRESSED
+Jonathan	19	NEEDFLUID	
+Daniel	1	NSCLC	CANCER
+Daniel	1	OBESE	
+Daniel	4	ORGANDONORPROTOCOL	DONOR
+Daniel	40	OTHER	
+Jonathan	2	PANCREATITIS	
+Daniel	2	PANCREATITIS	
+Daniel	2	PANCYTOPENIA	
+Daniel	1	PANCYTOPENIC	PANCYTOPENIA
+Jonathan	1	PERANESTHESIOLOGY	PEROTHERMD
+Daniel	1	PERANESTHESIOLOGY	PEROTHERMD
+Jonathan	8	PERCARDIOLOGY	PEROTHERMD
+Daniel	6	PERCARDIOLOGY	PEROTHERMD
+Jonathan	1	PERCONSULT	PEROTHERMD
+Daniel	1	PERCONSULT	PEROTHERMD
+Jonathan	1	PERGI	PEROTHERMD
+Daniel	3	PERGI	PEROTHERMD
+Jonathan	14	PERHEME	PEROTHERMD
+Daniel	14	PERHEME	PEROTHERMD
+Jonathan	4	PERHEPATOLOGY	PEROTHERMD
+Daniel	4	PERHEPATOLOGY	PEROTHERMD
+Jonathan	16	PERONCOLOGY	PEROTHERMD
+Daniel	18	PERONCOLOGY	PEROTHERMD
+Jonathan	120	PEROTHERMD	
+Daniel	50	PEROTHERMD	
+Jonathan	26	PERPROTOCOL	
+Daniel	13	PERPROTOCOL	
+Jonathan	1	PERPULMONARY	PEROTHERMD
+Daniel	1	PERPULMONARY	PEROTHERMD
+Jonathan	2	PERRENAL	PEROTHERMD
+Daniel	5	PERRENAL	PEROTHERMD
+Jonathan	1	PLASMAPHERESIS	
+Jonathan	74	PROCEDURE	
+Daniel	66	PROCEDURE	
+Daniel	2	PROSTATECANCER	CANCER
+Daniel	3	PULMHTN	
+Jonathan	6	PULMONARY	
+Daniel	1	PULMONARYHTN	
+Daniel	11	RADIATION	RADIOTHERAPY
+Jonathan	11	RADIOTHERAPY	
+Daniel	2	RBCAPLASIA	HEMEDISORDER
+Daniel	1	RCC	CANCER
+Jonathan	1	REDCELLEXCHANGE	
+Daniel	1	REDCELLEXCHANGE	
+Daniel	1	RENAFAILURE	RENALFAILURE
+Jonathan	40	RENALFAILURE	
+Daniel	24	RENALFAILURE	
+Daniel	3	SAH	CNS
+Daniel	1	SARCOMA	CANCER
+Daniel	1	SCCLUNG	CANCER
+Jonathan	64	SEPSIS	
+Daniel	47	SEPSIS	
+Daniel	17	SEPTICSHOCK	SEPSIS,UNSTABLE
+Daniel	10	SICKLECELL	HEMEDISORDER
+Daniel	1	SICKLECELLANEMIA	HEMEDISORDER
+Jonathan	31	SOB	SYMPTOMATIC
+Daniel	2	SOB	SYMPTOMATIC
+Daniel	2	SPINALCORD	CNS
+Daniel	1	STANDINGORDER	
+Daniel	1	STROKE	CNS
+Daniel	1	SURG	SURGERY
+Jonathan	248	SURGERY	
+Daniel	263	SURGERY	
+Daniel	1	SYMPTOMATC	SYMPTOMATIC
+Jonathan	211	SYMPTOMATIC	
+Daniel	252	SYMPTOMATIC	
+Daniel	7	SYNCOPE	
+Jonathan	53	TACHYCARDIA	
+Daniel	42	TACHYCARDIA	
+Daniel	2	TACHYCARDIAC	TACHYCARDIA
+Daniel	8	TACHYCARDIC	TACHYCARDIA
+Daniel	8	TBIPROTOCOL	CNS
+Daniel	4	THALASSEMIA	HEMEDISORDER
+Daniel	1	THROMBCYTOPENIA	THROMBOCYTOPENIA
+Jonathan	5	THROMBOCYTOPENIA	
+Daniel	3	THROMBOCYTOPENIA	
+Daniel	1	TRANSFUDIONDEPENDENT	TRANSFUSIONDEPENDENT
+Jonathan	24	TRANSFUSIONDEPENDENT	
+Daniel	30	TRANSFUSIONDEPENDENT	
+Daniel	1	TRANSPANT	TRANSPLANT
+Jonathan	68	TRANSPLANT	
+Daniel	63	TRANSPLANT	
+Jonathan	1	TRANSPLANT.HYPOTENSION	TRANSPLANT,UNSTABLE
+Jonathan	10	TRAUMA	
+Daniel	4	TRAUMA	
+Daniel	1	TRNASPLANT	TRANSPLANT
+Daniel	1	TTP	HEMEDISORDER
+Jonathan	23	UNSTABLE	
+Daniel	17	UNSTABLE	
+Jonathan	33	VASCULARDEVICE	
+Daniel	25	VASCULARDEVICE	
+Daniel	2	VASOPRESSOR	VASOPRESSORS
+Jonathan	14	VASOPRESSORS	
+Daniel	11	VASOPRESSORS	
+Jonathan	4	VASOSPASM	
+Daniel	4	VASOSPASM	
+Jonathan	3	VTE	
+Daniel	2	VTE	
+Daniel	2	WALDENSTROMS	HEMEDISORDER
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/agreementStats.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/agreementStats.txt
new file mode 100644
index 00000000..3a4e9a97
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/agreementStats.txt
@@ -0,0 +1,71 @@
+	2201	0	15	0	None	0.993231046931	0.993231046931	0.0
+?	2213	0	3	0	None	0.998646209386	0.998646209386	0.0
+ACS	2183	5	0	28	0.0	0.99774368231	0.972849248654	0.916896675867
+AGE	2215	1	0	0	None	0.999548736462	0.999548736462	0.0
+ANEMIA	2155	19	13	29	1.56098156844e-198	0.985559566787	0.960207353152	0.637107999672
+ANGINA-ISCHEMIA	2184	4	4	24	0.0	0.996389891697	0.975048547485	0.855314703578
+ANTICOAGULATED	2204	0	0	12	0.0	1.0	0.989228323059	1.0
+ARRHYTHMIA	2196	0	1	19	0.0	0.999548736462	0.982555487495	0.974131490474
+AUTOLOGOUS	2207	4	0	5	3.22174779415e-269	0.998194945848	0.99370063796	0.713454451413
+BLEEDING	2098	4	11	103	0.0	0.993231046931	0.905238729815	0.92856835862
+BMT	1834	0	1	381	0.0	0.999548736462	0.714961911402	0.998416830747
+CAD	2178	1	15	22	6.94766459188e-274	0.992779783394	0.97327078093	0.72987551235
+CANCER	2162	0	8	46	0.0	0.996389891697	0.955885323672	0.918165367997
+CARDIAC	2147	24	7	38	7.74681534687e-247	0.986010830325	0.95285110584	0.703298032237
+CHEMOTHERAPY	2115	0	2	99	0.0	0.999097472924	0.913819660754	0.9895274597
+CHF	2185	1	2	28	0.0	0.998646209386	0.973729782742	0.94846671421
+CIRRHOSIS	2135	0	49	32	3.95684966563e-188	0.977888086643	0.950062883655	0.557204841288
+CNS	2180	3	8	25	0.0	0.995036101083	0.972849248654	0.817172686907
+COAGULOPATHY	2196	0	6	14	0.0	0.997292418773	0.984771077428	0.822207958922
+COMORBIDITIES	2212	0	3	1	2.47206838468e-122	0.998646209386	0.997745311421	0.399566473988
+DELERIUM	2210	0	6	0	None	0.997292418773	0.997292418773	0.0
+DELIRIUM	2213	3	0	0	None	0.998646209386	0.998646209386	0.0
+DONOR	2203	0	1	12	0.0	0.999548736462	0.988781946852	0.959773453384
+DROPPINGHGB	2012	14	22	168	0.0	0.983754512635	0.846213621968	0.894363287747
+EXPECTDROP	2177	5	1	33	0.0	0.997292418773	0.96803522788	0.915294837164
+EXTUBATION	2214	2	0	0	None	0.999097472924	0.999097472924	0.0
+FUTUREORDER	2215	0	0	1	0.0	1.0	0.999097880202	1.0
+HEMEDISORDER	2148	0	4	64	0.0	0.998194945848	0.942205684943	0.968767617544
+HEMOLYSIS	2196	0	0	20	0.0	1.0	0.982112369508	1.0
+HGB	2215	1	0	0	None	0.999548736462	0.999548736462	0.0
+HGB<7	2018	10	4	184	0.0	0.993682310469	0.842471555735	0.959894928436
+HGB<8	1680	14	119	403	0.0	0.939981949458	0.664917273782	0.820885871322
+HYPONATREMIA	2214	0	0	2	0.0	1.0	0.998196574959	1.0
+HYPOPERFUSION	2169	2	3	42	0.0	0.99774368231	0.960643954698	0.942669095124
+HYPOTENSION	2210	6	0	0	None	0.997292418773	0.997292418773	0.0
+HYPOXIA	2200	0	3	13	0.0	0.998646209386	0.986998071133	0.89587770956
+IMMINENTDISCHARGE	2135	1	0	80	0.0	0.999548736462	0.929985728994	0.993554692045
+LEUKEMIA	2142	0	0	74	0.0	1.0	0.935443248315	1.0
+LOWALBUMIN	2212	0	4	0	None	0.998194945848	0.998194945848	0.0
+MYELOSUPPRESSED	2211	0	0	5	0.0	1.0	0.99549754656	1.0
+NEEDFLUID	2197	0	19	0	None	0.99142599278	0.99142599278	0.0
+OBESE	2215	1	0	0	None	0.999548736462	0.999548736462	0.0
+OTHER	2176	40	0	0	None	0.981949458484	0.981949458484	0.0
+PANCREATITIS	2214	0	0	2	0.0	1.0	0.998196574959	1.0
+PANCYTOPENIA	2213	3	0	0	None	0.998646209386	0.998646209386	0.0
+PEROTHERMD	2071	7	43	95	1.04667930259e-302	0.977436823105	0.897429589855	0.780022553644
+PERPROTOCOL	2184	6	19	7	5.11927711789e-70	0.988718411552	0.982538381837	0.353920791155
+PLASMAPHERESIS	2215	0	1	0	None	0.999548736462	0.999548736462	0.0
+PROCEDURE	2139	3	11	63	0.0	0.993682310469	0.938812248302	0.896749114726
+PULMHTN	2213	3	0	0	None	0.998646209386	0.998646209386	0.0
+PULMONARY	2210	0	6	0	None	0.997292418773	0.997292418773	0.0
+PULMONARYHTN	2215	1	0	0	None	0.999548736462	0.999548736462	0.0
+RADIATION	2205	11	0	0	None	0.995036101083	0.995036101083	0.0
+RADIOTHERAPY	2205	0	11	0	None	0.995036101083	0.995036101083	0.0
+REDCELLEXCHANGE	2215	0	0	1	0.0	1.0	0.999097880202	1.0
+RENALFAILURE	2176	0	15	25	4.3898232488e-301	0.993231046931	0.971075147597	0.765981413686
+SEPSIS	2148	0	1	67	0.0	0.999548736462	0.940934978952	0.99235988526
+STANDINGORDER	2215	1	0	0	None	0.999548736462	0.999548736462	0.0
+SURGERY	1932	7	20	257	0.0	0.987815884477	0.785649819495	0.943157894737
+SYMPTOMATIC	1939	1	22	254	0.0	0.989620938628	0.789043256135	0.950800049424
+SYNCOPE	2209	7	0	0	None	0.996841155235	0.996841155235	0.0
+TACHYCARDIA	2162	1	2	51	0.0	0.998646209386	0.953739785479	0.970735314833
+THROMBOCYTOPENIA	2211	0	1	4	0.0	0.999548736462	0.99594677371	0.888665594855
+TRANSFUSIONDEPENDENT	2185	7	0	24	0.0	0.996841155235	0.975483519921	0.871154229517
+TRANSPLANT	2140	7	11	58	0.0	0.991877256318	0.941357325783	0.861487495399
+TRAUMA	2206	0	6	4	2.95805674519e-194	0.997292418773	0.993698601572	0.570320579111
+UNSTABLE	2083	9	11	113	0.0	0.990974729242	0.895150464622	0.913921690491
+VASCULARDEVICE	2182	1	9	24	0.0	0.995487364621	0.97416271879	0.825344031274
+VASOPRESSORS	2202	0	1	13	0.0	0.999548736462	0.987890008993	0.962736261519
+VASOSPASM	2212	0	0	4	0.0	1.0	0.996396408138	1.0
+VTE	2213	0	1	2	0.0	0.999548736462	0.997746125976	0.799783158656
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/autoAnnotate.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/autoAnnotate.py
new file mode 100644
index 00000000..5794c35c
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/autoAnnotate.py
@@ -0,0 +1,31 @@
+import sys, os;
+
+PUNCTUATION = ".,:;\"'()?+";
+
+# python autoAnnotate.py overrideComments.txt wordTags.txt > autoAnnotations.txt
+
+infile = open(sys.argv[1]); # Source file with free text comments to parse
+wordTagFile = open(sys.argv[2]);    # File with 3 columns for word counts, words and associated tags to assign if found
+
+tagsByWord = dict();
+for line in wordTagFile:
+    chunks = line.strip().split("\t");
+    if len(chunks) >= 3:
+        (wordCount, word, tag) = chunks;
+        word = word.strip(PUNCTUATION).upper();
+        tagsByWord[word] = tag;
+
+for line in infile:
+    chunks = line.strip().split("\t");  # Separate out ID column
+    (comment, id) = chunks;
+    chunks = comment.split();    # Separate by simple white space
+    
+    commentTags = set();
+    
+    for chunk in chunks:
+        word = chunk.strip(PUNCTUATION).upper();    # Ignore punctuation delimiters and case sensitivity
+
+        if word in tagsByWord:
+            commentTags.add(tagsByWord[word]);
+
+    print "%s\t%s\t%s" % (id, str.join(",", commentTags), comment);
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/calcAgreementStats.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/calcAgreementStats.py
new file mode 100644
index 00000000..cd120c40
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/calcAgreementStats.py
@@ -0,0 +1,89 @@
+import sys, os;
+from scipy import array;
+from scipy.stats import chi2, chisquare, chi2_contingency;
+from util import kappaStat;
+
+# python calcAgreementStats.py mergedTags.txt mergedTags.Daniel.txt > agreementStats.txt
+
+# Yates correction for contingency tables that have small (even 0) values to avoid overestimating statistical significance
+# http://en.wikipedia.org/wiki/Yates%27s_correction_for_continuity
+yatesCorrection = True;
+   
+# First pass to identify the category tag sets
+categoryTags = set();
+# Separate accounting for tag combinations as unique entities
+compositeTags = set();
+
+# Source file comma-delimited annotation tags
+infile1 = open(sys.argv[1]); 
+infile2 = open(sys.argv[2]); 
+
+for line1 in infile1:
+    line1 = line1.strip();
+    
+    line2 = infile2.readline().strip();
+    
+    tagSet1 = set(line1.replace(";",",").split(","));
+    tagSet2 = set(line2.replace(";",",").split(","));
+
+    categoryTags.update(tagSet1);
+    categoryTags.update(tagSet2);
+    
+    compositeTags.add( str.join(',',tagSet1) );
+    compositeTags.add( str.join(',',tagSet2) );
+
+
+
+# Second pass to assess counts for the categoryTags
+# Build a 2x2 contingency table for each tag based on counts of whether each tag appears for 
+#   one, both, or neither of the two data sources
+# Let row 0/false represent counts where data source 1 does not have the tag
+#     row 1/true represent counts where data source 1 has the tag
+# Let col 0/false represent counts where data source 2 does not have the tag
+#     col 1/true represent counts where data source 2 has the tag
+
+contingencyTableByTag = dict();
+for tag in categoryTags:
+    contingencyTableByTag[tag] = [ [0,0], [0,0] ];
+
+
+infile1 = open(sys.argv[1]); 
+infile2 = open(sys.argv[2]); 
+
+for line1 in infile1:
+    line1 = line1.strip();
+    
+    line2 = infile2.readline().strip();
+    
+    tagSet1 = set(line1.replace(";",",").split(","));
+    tagSet2 = set(line2.replace(";",",").split(","));
+
+    for tag in categoryTags:
+        contingencyTable = contingencyTableByTag[tag];
+        
+        iRow = (tag in tagSet1);
+        iCol = (tag in tagSet2);
+        
+        contingencyTable[iRow][iCol] += 1;
+        
+# Now print the values for the accumulated contingency tables as well as calculated chi-square stats and p-values
+sortedTags = list(categoryTags);
+sortedTags.sort();
+for tag in sortedTags:
+    contingencyTable = contingencyTableByTag[tag];
+
+    (kappa, agreeRate, chanceRate) = kappaStat( contingencyTable );
+
+    # Chi Square stat for p-value for independence
+    pValue = None;
+    try:
+        (chi2stat, pValue, dof, expTable) = chi2_contingency(contingencyTable, yatesCorrection);
+    except ValueError, err:
+        # Many errors expected for cases where one data source / user never used one of the tags
+        #   resulting in a column or row of the table being all 0, making the chi2stat uncalculatable
+        #print >> sys.stderr, err;
+        pass;
+
+    contingencyTableText  = "%s\t%s" % tuple(contingencyTable[0]);
+    contingencyTableText += "\t%s\t%s" % tuple(contingencyTable[1]);
+    print "%s\t%s\t%s\t%s\t%s\t%s" % (tag, contingencyTableText, pValue, agreeRate, chanceRate, kappa);
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/compareTags.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/compareTags.py
new file mode 100644
index 00000000..d97334ae
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/compareTags.py
@@ -0,0 +1,21 @@
+import sys, os;
+
+# python compareTags.py mergedTags.txt mergedTags.Daniel.txt > tagDiffs.txt
+
+   
+# Source file comma-delimited annotation tags
+infile1 = open(sys.argv[1]); 
+infile2 = open(sys.argv[2]); 
+
+for line1 in infile1:
+    line1 = line1.strip();
+    
+    line2 = infile2.readline().strip();
+    
+    tagSet1 = set(line1.replace(";",",").split(","));
+    tagSet2 = set(line2.replace(";",",").split(","));
+
+    plusSet = tagSet1-tagSet2;
+    minusSet = tagSet2-tagSet1;
+    
+    print "%s\t%s" % (str.join(",", plusSet), str.join(",", minusSet));
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/extractWordCounts.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/extractWordCounts.py
new file mode 100644
index 00000000..2a020cbd
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/extractWordCounts.py
@@ -0,0 +1,24 @@
+import sys, os;
+
+PUNCTUATION = ".,:;\"'()?+";
+
+infile = open(sys.argv[1]);
+
+wordCounts = dict();
+
+for line in infile:
+    chunks = line.strip().split("\t");  # Separate out ID column
+    (comment, id) = chunks;
+    chunks = comment.split();    # Separate by simple white space
+    
+    for chunk in chunks:
+        word = chunk.strip(PUNCTUATION).upper();    # Ignore punctuation delimiters and case sensitivity
+        
+        if word not in wordCounts:
+            wordCounts[word] = 0;
+        wordCounts[word] += 1;
+
+wordList = wordCounts.keys();
+wordList.sort();
+for word in wordList:
+    print "%s\t%s" % (wordCounts[word], word);
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergeTags.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergeTags.py
new file mode 100644
index 00000000..59817624
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergeTags.py
@@ -0,0 +1,37 @@
+import sys, os;
+
+MERGE_TAG_COL = 2;
+MERGE_ALT_COL = 3;
+
+# python mergeTags.py sampleTags.txt tagMergeWorksheet.txt > mergedTags.txt
+
+# Source file comma-delimited annotation tags
+infile = open(sys.argv[1]); 
+mergeWorksheet = open(sys.argv[2]);
+
+# Look for worksheet data to merge tags
+altTagsByKeyTag = dict();
+if len(sys.argv) > 2:
+    for line in mergeWorksheet:
+        chunks = line.strip().split("\t");
+        if len(chunks) > MERGE_ALT_COL and chunks[MERGE_ALT_COL] != "":
+            tag = chunks[MERGE_TAG_COL].upper();
+            altTags = chunks[MERGE_ALT_COL].split(",");
+            if tag not in altTagsByKeyTag:
+                altTagsByKeyTag[tag] = set();
+            for altTag in altTags:
+                altTagsByKeyTag[tag].add(altTag.upper());
+
+# Now rewrite the source tags into merged results
+for line in infile:
+    line = line.strip().replace(" ","");    # Discard whitespace
+    tags = line.replace(";",",").split(",");
+    tagSet = set();
+    for tag in tags:
+        tag = tag.upper();  # Ensure case insensitivity
+        if tag in altTagsByKeyTag:
+            tagSet.update(altTagsByKeyTag[tag]);
+        else:
+            tagSet.add(tag);
+    print str.join(",", tagSet );
+    
\ No newline at end of file
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergedTags.Daniel.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergedTags.Daniel.txt
new file mode 100644
index 00000000..80c91cb5
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergedTags.Daniel.txt
@@ -0,0 +1,2216 @@
+HGB<8
+PEROTHERMD
+OTHER
+OTHER
+IMMINENTDISCHARGE,HGB<8
+IMMINENTDISCHARGE,HGB<8
+BLEEDING,SURGERY,HGB<8
+BMT
+SYMPTOMATIC,HGB<8
+SYMPTOMATIC,HGB<8
+SYMPTOMATIC,HGB<8
+SYMPTOMATIC,HGB<8
+SYMPTOMATIC,HGB<8
+SYMPTOMATIC,HGB<8
+SEPSIS,HGB<8
+SEPSIS,TACHYCARDIA,HGB<8
+LEUKEMIA,VASCULARDEVICE
+SURGERY,CAD
+SURGERY,UNSTABLE,HGB<8
+SURGERY,UNSTABLE,HGB<8
+SURGERY,UNSTABLE,HGB<8
+CAD
+SURGERY,SYMPTOMATIC
+CHEMOTHERAPY
+SURGERY,EXPECTDROP
+DROPPINGHGB
+PEROTHERMD
+HYPOPERFUSION,TRANSPLANT
+HYPOPERFUSION,TRANSPLANT
+RENALFAILURE,SYNCOPE,VASCULARDEVICE
+ACS
+ACS
+ACS
+ACS
+ACS
+ACS
+ACS
+ACS,ANGINA-ISCHEMIA,HGB<8
+HEMOLYSIS
+HEMEDISORDER
+SYMPTOMATIC
+SYMPTOMATIC
+SYMPTOMATIC
+DROPPINGHGB
+BLEEDING
+BLEEDING
+BLEEDING,ANGINA-ISCHEMIA
+BLEEDING
+ACS
+ACS
+ANGINA-ISCHEMIA
+DROPPINGHGB,HEMOLYSIS
+PULMHTN,SYMPTOMATIC
+BLEEDING,HGB<8
+RENALFAILURE
+SURGERY,SYMPTOMATIC,TRANSPLANT
+HEMEDISORDER,ARRHYTHMIA
+LEUKEMIA
+LEUKEMIA
+LEUKEMIA
+LEUKEMIA,DROPPINGHGB
+SYNCOPE,LEUKEMIA,HGB<8
+LEUKEMIA,SYMPTOMATIC
+DELIRIUM,SYMPTOMATIC
+CHEMOTHERAPY,EXPECTDROP,HGB<8
+LEUKEMIA
+LEUKEMIA
+ANGINA-ISCHEMIA,LEUKEMIA
+HGB<7,LEUKEMIA,CHEMOTHERAPY
+PANCYTOPENIA,LEUKEMIA,SYMPTOMATIC
+IMMINENTDISCHARGE,LEUKEMIA
+IMMINENTDISCHARGE,LEUKEMIA
+IMMINENTDISCHARGE,LEUKEMIA,HGB<8
+LEUKEMIA,HGB<8
+IMMINENTDISCHARGE,LEUKEMIA,HGB<8
+IMMINENTDISCHARGE,LEUKEMIA,HGB<8
+IMMINENTDISCHARGE,LEUKEMIA,HGB<8
+CHEMOTHERAPY,LEUKEMIA
+CHEMOTHERAPY,IMMINENTDISCHARGE,LEUKEMIA,HGB<8
+TRANSFUSIONDEPENDENT,LEUKEMIA,HGB<8
+ANEMIA
+ANEMIA
+ANEMIA
+ANEMIA
+ANEMIA
+ANEMIA
+ANEMIA
+ANEMIA
+UNSTABLE,ANEMIA
+CHEMOTHERAPY
+UNSTABLE
+RENALFAILURE,TRANSPLANT
+ANTICOAGULATED
+DROPPINGHGB
+CHEMOTHERAPY
+BLEEDING,CANCER
+RENALFAILURE,PEROTHERMD
+MYELOSUPPRESSED
+SEPSIS
+RENALFAILURE
+CHEMOTHERAPY
+BLEEDING,PROCEDURE
+EXPECTDROP
+BLEEDING,IMMINENTDISCHARGE,ANTICOAGULATED,CAD
+EXPECTDROP,DROPPINGHGB
+CHEMOTHERAPY,EXPECTDROP,DROPPINGHGB
+EXPECTDROP,IMMINENTDISCHARGE,MYELOSUPPRESSED
+EXPECTDROP
+EXPECTDROP
+HEMEDISORDER
+EXPECTDROP,HEMEDISORDER,IMMINENTDISCHARGE
+EXPECTDROP,HEMEDISORDER,IMMINENTDISCHARGE
+PEROTHERMD
+PEROTHERMD
+PEROTHERMD,PROCEDURE
+ARRHYTHMIA
+PEROTHERMD
+PEROTHERMD
+BLEEDING,UNSTABLE
+BLEEDING
+BLEEDING
+BLEEDING
+BLEEDING
+BLEEDING,SURGERY,HGB<8
+BLEEDING,ACS
+BLEEDING,LEUKEMIA
+BLEEDING,HGB<7
+BLEEDING,SURGERY
+BLEEDING,PROCEDURE,CIRRHOSIS
+BLEEDING,UNSTABLE,TRAUMA
+BLEEDING,CIRRHOSIS
+BLEEDING,SURGERY,EXPECTDROP
+BLEEDING,ANTICOAGULATED
+BLEEDING
+BLEEDING
+BLEEDING,PROCEDURE
+BLEEDING,PROCEDURE
+BLEEDING,PROCEDURE
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+BMT
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diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergedTags.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergedTags.txt
new file mode 100644
index 00000000..2d64ce63
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/mergedTags.txt
@@ -0,0 +1,2216 @@
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+
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+
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+PROCEDURE
+BLEEDING
+UNSTABLE
+UNSTABLE
+UNSTABLE
+UNSTABLE,ARRHYTHMIA
+SEPSIS,VASCULARDEVICE,HGB<8,ANGINA-ISCHEMIA
+VASOSPASM
+VASOSPASM
+VASOSPASM
+HEMEDISORDER
+AUTOLOGOUS
+SURGERY,TRANSPLANT,SYMPTOMATIC,HGB<8,CIRRHOSIS
+VASCULARDEVICE
+CHEMOTHERAPY
+SURGERY
+CHEMOTHERAPY,SURGERY
+PEROTHERMD
+IMMINENTDISCHARGE,DROPPINGHGB
+UNSTABLE,NEEDFLUID
+SYMPTOMATIC
+SYMPTOMATIC
+HGB<7
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/sampleTags.Daniel.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/sampleTags.Daniel.txt
new file mode 100644
index 00000000..23cdd79c
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/sampleTags.Daniel.txt
@@ -0,0 +1,2216 @@
+Hgb<8
+PerOtherMD
+Other
+Other
+ImminentDischarge,Hgb<8
+ImminentDischarge,Hgb<8
+Bleeding,Surgery,Hgb<8
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+Symptomatic,Hgb<8
+Symptomatic,Hgb<8
+Symptomatic,Hgb<8
+Symptomatic,Hgb<8
+Symptomatic,Hgb<8
+Symptomatic,Hgb<8
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+Surgery,Hypotension,Hgb<8
+Surgery,Hypotension,Hgb<8
+CAD
+Symptomatic,Surgery
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+Surgery,ExpectDrop
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+ACS
+ACS
+ACS
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+ACS
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+SickleCell
+Symptomatic
+Symptomatic
+Symptomatic
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+Bleeding
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+Bleeding,Hgb<8
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+Surgery,Symptomatic,Transplant
+MF,Arrythmia
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+ALL
+ALL
+ALL,DroppingHgb
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+Anemia
+Anemia
+Anemia
+Anemia
+Anemia
+Anemia
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+DroppingHgb
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+Sepsis
+RenalFailure
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+ExpectDrop
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+ExpectDrop,ImminentDischarge,AsplasticAnemia
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+PerGI,Procedure
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+Donor
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+Symptomatic
+Surgery
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+Chemotherapy
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+Hgb<8
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+RedCellExchange
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+Surgery
+FutureOrder
+Procedure
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+Surgery
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+Surgery
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+Cardiac
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+Autologous
+Autologous
+Surgery
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+Surgery
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+Surgery
+Transplant,Hgb<8
+Transplant
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+Other
+SCCLung,Hypoxia
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+Surgery
+Other
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+Sepsis
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+CHF
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+Anemia
+Angina-Ichemia
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+Symptomatic
+SickleCell
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+SickleCell,TransfusionDependent
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+DroppingHgB
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+Cardiac
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+StandingOrder
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+Symptomatic,Cancer
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+Symptomatic,DroppingHgb,Procedure
+Symptomatic
+PerOtherMD;Symptomatic
+Bleeding;Symptomatic
+Symptomatic
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+Hgb<7,Symptomatic,ImminentDischarge
+Symptomatic,Surgery,LowSVO2
+Symptomatic,CHF
+Symptomatic,CriticalAS
+Symptomatic
+Symptomatic,Cancer
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic,Surgery,Hypotensive
+Symptomatic,SickleCellAnemia
+Symptomatic
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+Symptomatic,Cancer,CAD
+Symptomatic,Tachycardia
+Symptomatic,Bleeding
+Symptomatic,Hgb<8
+Symptomatic,Obese
+Symptomatic,Bleeding
+Symptomatic;Tachycardia;Hypotension
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic,Chemotherapy
+Syncope,Hgb<8
+Code,Syncope
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+Tachycardia
+Tachycardia
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+Surgery,Tachycardia
+Surgery,Tachycardia
+Tachycardia
+Tachycardia,Surgery
+Tachycardia
+Symptomatic,Tachycardia
+Angina-Ischemia,Tachycardia,Surgery
+Symptomatic,Tachycardia
+Sepsis,Hgb<8
+TBIProtocol
+TBIProtocol
+TBIProtocol
+TBIProtocol
+Lymphoma,Symptomatic,Tachycardia,PerHeme
+Unstable
+Thalassemia,Hemolysis
+PerHeme
+Hgb<7
+Other
+Waldenstroms;TransfusionDependent
+Chemotherapy
+Surgery
+Procedure,Bleeding
+TransfusionDependent;ImminentDischarge,Leukemia
+BMTProtocol
+BMTProtocol
+BMTProtocol
+BMTProtocol
+TransfusionDependent,AML
+TransfusionDependent,MDS
+TransfusionDependent,Symptomatic,MDS
+TransfusionDependent,PerOncology,DroppingHgb
+TransfusionDependent,PerOncology
+TransfusionDependent
+PerOtherMD
+Transplant
+Hypoxia,Hypotension,Transplant
+Transplant,RCC
+Transplant,Hypotension,Hgb<8
+Transplant;Surgery
+Transplant
+Transplant
+Transplant
+PerOtherMD,MDS
+Symptomatic
+DroppingHgB
+DroppingHgB,ImminentDischarge
+DroppingHgB,ImminentDischarge
+DroppingHgB,ImminentDischarge
+DroppingHgb,Surgery
+DroppingHgB,HemeDisorder
+Angina-Ichemia
+MM,TransfusionDependent
+Other
+Bleeding,Anticoagulated
+Procedure
+Bleeding
+Unstable
+Unstable
+Unstable
+Unstable,Arrythmia
+Sepsis,Hgb<8,Angina-Ischemia
+Vasospasm
+Vasospasm
+Vasospasm
+Waldenstroms
+Autologous
+Hgb<8,Surgery,Symptomatic
+VascularDevice
+Chemotherapy
+Surgery
+Chemotherapy,Surgery
+PerConsult
+ImminentDischarge
+Hypotension
+SOB
+SOB
+Hgb<7
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/sampleTags.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/sampleTags.txt
new file mode 100644
index 00000000..fcf57b46
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/sampleTags.txt
@@ -0,0 +1,2216 @@
+Hgb<8
+PerOtherMD
+
+
+ImminentDischarge,Hgb<8
+ImminentDischarge,Hgb<8
+Bleeding,Surgery,CTSurgery,Hgb<8
+BMT,Hgb<8
+Symptomatic,Hgb<8
+SOB,Hgb<8
+SOB,Hgb<8
+SOB,Hgb<8
+Fatigue,Hgb<8
+Fatigue,Hgb<8
+Sepsis,Hgb<8
+Tachycardia,Fever,Hgb<8
+LeukemiaAcute,VascularDevice
+Surgery,CAD
+CTSurgery,Hypotension,Hgb<8
+CTSurgery,Hypotension,Hgb<8
+CTSurgery,Hypotension,Hgb<8
+CAD
+Symptomatic,Surgery
+Chemotherapy
+Surgery,ExpectDrop
+DroppingHgb
+PerOtherMD
+Transplant,Acidemia,Cirrhosis
+Transplant,Acidemia,Cirrhosis
+RenalFailure,Lightheaded,Procedure
+ACS
+ACS
+ACS
+ACS
+ACS
+ACS
+ACS
+ACS,Angina-Ischemia,Hgb<8
+Hemolysis
+HemeDisorder
+SOB,Symptomatic
+SOB,Symptomatic
+SOB,Symptomatic
+DroppingHgb
+Bleeding
+Bleeding
+Bleeding,Cardiac,Angina-Ischemia
+Bleeding
+ACS
+ACS
+Angina-Ischemia
+Hemolysis,DroppingHgb
+SOB,NeedFluid,Pulmonary
+Bleeding,Hgb<8
+RenalFailure,NeedFluid
+Surgery,Symptomatic,Transplant,RenalFailure
+Myelofibrosis,Arrhythmia
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute,DroppingHgb
+LeukemiaAcute,Lightheaded,Hgb<8
+LeukemiaAcute,Symptomatic
+Delerium,SOB
+ExpectDrop,Hgb<8,Chemotherapy
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute,NeedFluid,Angina-Ischemia
+Hgb<7,LeukemiaAcute,Chemotherapy
+LeukemiaAcute,SOB,Lightheaded
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+LeukemiaAcute,ImminentDischarge,Hgb<8
+LeukemiaAcute,ImminentDischarge,Hgb<8
+LeukemiaAcute,ImminentDischarge,Hgb<8
+Chemotherapy,LeukemiaAcute
+Chemotherapy,LeukemiaAcute,Hgb<8,ImminentDischarge
+LeukemiaAcute,TransfusionDependent,Hgb<8
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+DroppingHgb
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+Sepsis
+RenalFailure
+Chemotherapy
+Bleeding,Procedure
+ExpectDrop
+CAD,Bleeding,Anticoagulated,ImminentDischarge
+ExpectDrop,DroppingHgb
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+ExpectDrop,ImminentDischarge,Myelosuppressed
+ExpectDrop
+ExpectDrop
+HemeDisorder
+ExpectDrop,ImminentDischarge,HemeDisorder
+ExpectDrop,ImminentDischarge,HemeDisorder
+PerHepatology,PerPulmonary
+PerAnesthesiology
+Procedure,PerGI
+Arrhythmia
+PerOncology,PerOtherMD
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+Bleeding
+Bleeding,Surgery
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+Bleeding,CTSurgery
+Bleeding,Procedure,Cirrhosis
+Bleeding,Hypotension,Trauma
+Bleeding,Cirrhosis
+Bleeding,Surgery,ExpectDrop
+Bleeding,Anticoagulated
+Bleeding
+Bleeding
+Bleeding,Procedure
+Bleeding,Procedure
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+Cardiac,MDS
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+CTSurgery
+CTSurgery
+CTSurgery
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+CHF
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+Symptomatic
+Bleeding,LeukemiaChronic,Procedure
+Code
+Code
+Hgb<8,HemeDisorder
+Bleeding,Angina-Ischemia
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+DroppingHgb,CAD
+Vasopressors
+Bleeding
+Hgb<7
+Hgb<7
+Cardiac,Hgb<8,CAD
+AnemiaHgb>8
+Chemotherapy
+Hgb<7
+Donor
+Donor
+CTSurgery
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
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+Hypoperfusion
+DroppingHgb
+DroppingHgb
+MDS,RenalFailure,DroppingHgb,CHF
+DroppingHgb
+Sepsis,Tachycardia,DroppingHgb
+Angina-Ischemia,Hgb<8
+Angina-Ischemia
+Lymphoma,Myelosuppressed,Coagulopathy
+Hemolysis,Coagulopathy
+DroppingHgb,Coagulopathy
+Hgb<8,Coagulopathy,InadequateResponse
+InadequateResponse
+InadequateResponse
+Bleeding,DroppingHgb,Symptomatic
+CNS,HemeDisorder
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge,Hgb<8
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+ImminentDischarge
+Fatigue,PerOtherMD,CHF
+Fatigue,Lightheaded
+Donor
+Donor
+Donor
+Donor,Hgb<8
+Donor
+Surgery,DroppingHgb,Symptomatic
+Bleeding,Surgery,DroppingHgb
+DroppingHgb
+DroppingHgb,CAD
+Surgery,DroppingHgb,Cardiac
+ImminentDischarge,Fatigue,DroppingHgb
+Bleeding,DroppingHgb
+Bleeding,DroppingHgb
+Cardiac,DroppingHgb
+DroppingHgb,Hgb<8
+PerOncology,PerOtherMD
+PerOtherMD
+PerOtherMD,PerHeme
+Bleeding
+DroppingHgb,Symptomatic
+DroppingHgb
+DroppingHgb,Procedure
+Cardiac,Procedure
+Cardiac,Procedure
+Cardiac,Procedure
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+VascularDevice
+DroppingHgb,VascularDevice,RenalFailure
+Procedure
+Angina-Ischemia
+DroppingHgb,Cirrhosis
+Procedure
+Cancer,CAD
+Cirrhosis
+DroppingHgb,Hgb<8,Cirrhosis
+Hgb<8,Cirrhosis
+Fatigue,Cirrhosis
+RenalFailure,CHF
+Chemotherapy
+Surgery
+ExpectDrop,Symptomatic
+Chemotherapy,ExpectDrop
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+Fatigue
+Fatigue
+Fatigue
+Bleeding,DroppingHgb
+SOB,Fatigue
+Lightheaded,Fatigue
+Fatigue,Symptomatic
+Surgery
+ImminentDischarge,DroppingHgb,Lymphoma
+Cardiac,Procedure
+Chemotherapy
+RenalFailure
+Hgb<8
+Radiotherapy
+RedCellExchange
+Surgery
+Surgery
+Surgery
+FutureOrder
+Procedure
+Chemotherapy
+Radiotherapy
+Bleeding
+Bleeding
+Bleeding
+Bleeding,DroppingHgb,Hgb<8
+Hgb<8
+Surgery
+Surgery
+CTSurgery
+Hypoperfusion
+Hgb<7,LeukemiaAcute
+PerOtherMD,PerCardiology,CAD
+Chemotherapy,LeukemiaAcute,Hypotension,Hemolysis
+Chemotherapy,Radiotherapy,Cancer
+Surgery,Delerium
+LeukemiaAcute
+Chemotherapy,LeukemiaAcute
+Hgb<7,Symptomatic
+Hgb<8
+TransfusionDependent,DroppingHgb,Hgb<8,BMT
+BMT,Hgb<8
+Hgb<7
+Hgb<7,Procedure
+Hgb<7,Symptomatic,Cancer
+ExpectDrop,LeukemiaAcute,Hgb<8,Chemotherapy
+Chemotherapy,LeukemiaAcute,Symptomatic,Hgb<8
+Hypoxia,Hgb<8
+LeukemiaAcute,ImminentDischarge,Hgb<8
+Chemotherapy,Hgb<8
+Chemotherapy,Hgb<8
+LeukemiaChronic,ImminentDischarge,ExpectDrop,Hgb<8
+Chemotherapy,DroppingHgb
+Hgb<7,DroppingHgb,Symptomatic
+Hgb<7
+Hgb<7
+Hgb<8
+Coagulopathy,DroppingHgb,Hgb<8,Cirrhosis
+Coagulopathy,DroppingHgb,Hgb<8,Cirrhosis
+Coagulopathy,DroppingHgb,Hgb<8,Cirrhosis
+Coagulopathy,DroppingHgb,Hgb<8,Cirrhosis
+DroppingHgb,Procedure
+Hgb<7
+Hgb<7
+Hgb<7
+Procedure,DroppingHgb,Hgb<8
+Hgb<7
+BMT,Hgb<8
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+ACS,Hgb<8
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+Sepsis,Hypotension,Hgb<8
+Hgb<7
+Hgb<7
+Hgb<8
+Hypotension,DroppingHgb
+Hgb<7
+Sepsis,Hypotension,Hypoperfusion,Hgb<8
+Cardiac
+Hgb<7,Hypotension,DroppingHgb,Sepsis
+CTSurgery,Hgb<8,Bleeding
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7,Surgery
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+Hgb<7,Transplant,Cirrhosis
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+Hgb<7
+Hgb<7,Transplant
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+Hgb<7,Procedure
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+
+Hgb<7
+Hgb<7
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+Hgb<7,Fever,Sepsis
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+Hgb<7
+Hgb<8
+Hgb<8
+Hgb<8,Coagulopathy
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+Surgery,ExpectDrop,Hgb<8
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+Chemotherapy,Hgb<8
+RenalFailure,Hypotension,Hgb<8
+Autologous,Symptomatic,Hgb<8
+Hgb<8
+CTSurgery,Hypotension,Hgb<8
+Hgb<8
+AnemiaHgb>8,Symptomatic
+AnemiaHgb>8
+PerOtherMD
+Hgb<7
+DroppingHgb
+DroppingHgb,CAD
+DroppingHgb
+Bleeding,Hypotension,DroppingHgb
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+CHF
+CHF
+CHF
+CHF
+
+Surgery,Hgb<8
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+PerHeme
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+Unstable
+Hypotension,Unstable,Symptomatic
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+Hgb<7
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+Hgb<7
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+Hgb<7
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+Hgb<7
+Hgb<7
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+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<8,Cardiac
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
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+Lightheaded,Hgb<8
+BMT,Hgb<8
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+Hgb<8
+Arrhythmia,Hgb<8
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+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7,ChronicDisase
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+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
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+Chemotherapy,Hgb<8
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+Procedure,Hgb<8
+Surgery,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+SOB
+Hgb<7,Symptomatic
+Surgery,Trauma,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
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+Hgb<7
+Hgb<7
+Hgb<7
+BMT
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
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+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
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+Hgb<7
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+Hgb<7,CAD
+Hgb<7
+Hgb<7
+Hgb<7
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+Hgb<7
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+Hgb<7
+Hgb<7
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+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
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+Bleeding,Procedure,Hgb<8
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+DroppingHgb,Hgb<8
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+DroppingHgb,Hgb<8
+Hgb<8,SOB
+LeukemiaAcute,Hgb<8
+LeukemiaAcute,Symptomatic,Hgb<8
+ImminentDischarge,Hgb<8
+Chemotherapy,DroppingHgb,Hgb<8,Sepsis
+Bleeding,Chemotherapy,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+BMT,Hgb<8
+Fatigue,Symptomatic,Hgb<8
+LeukemiaAcute,Hgb<8
+ExpectDrop,Symptomatic,Hgb<8
+BMT,Hgb<8
+Chemotherapy,DroppingHgb,Hgb<8
+DroppingHgb,Hgb<8
+Chemotherapy,LeukemiaAcute,DroppingHgb,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Symptomatic,Hgb<8
+DroppingHgb,Hgb<8,Chemotherapy
+Hgb<8,NeedFluid
+BMT,Hgb<8
+Chemotherapy,Cancer,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+BMT,Hgb<8
+DroppingHgb,Hgb<8,Angina-Ischemia
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+DroppingHgb,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+BMT,Hgb<8,Transplant
+Chemotherapy,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8,Arrhythmia
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8,BMT
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Chemotherapy,DroppingHgb,Hgb<8
+Bleeding,Hgb<8
+AnemiaHgb>8,ExpectDrop,DroppingHgb,Hgb<8,Chemotherapy
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
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+ImminentDischarge,Hgb<8
+ImminentDischarge,Hgb<8
+LeukemiaAcute,Hgb<8,ImminentDischarge
+Hgb<8
+Hypotension,Arrhythmia,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
+PerOtherMD,Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
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+BMT,Hgb<8
+BMT,Hgb<8
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+Hgb<8
+Hgb<8
+CNS,Hgb<8
+Tachycardia,Hypotension,Hgb<8
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+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Bleeding
+BMT
+TransfusionDependent
+DroppingHgb
+Chemotherapy
+BMT
+PerOncology,ImminentDischarge,DroppingHgb,PerOtherMD,Chemotherapy,ExpectDrop
+Hgb<7,Transplant,Cirrhosis
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+DroppingHgb
+MDS,DroppingHgb,Hemolysis
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+Hgb<8
+BMT,Hgb<8
+Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
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+Cardiac,Hgb<8
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+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8,Transplant
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+BMT,Hgb<8
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+Hgb<8
+Hgb<7
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+Hgb<8
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+HemeDisorder
+Sepsis,Arrhythmia,AnemiaHgb>8
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+AnemiaHgb>8
+AnemiaHgb>8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+
+BMT
+Hgb<8
+Hgb<8
+Hgb<7
+Hgb<7
+Hgb<8,BMT
+AnemiaHgb>8
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Hgb<7
+Bleeding,Hgb<7
+Hgb<7,Surgery
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<8
+ImminentDischarge,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
+Bleeding,Hgb<8
+Lightheaded,Hgb<8
+Surgery,Hgb<8
+Symptomatic,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
+BMT,Hgb<8
+CHF,Procedure,Hgb<8
+CHF,Procedure,Hgb<8
+CHF,Procedure,Hgb<8
+MDS,Hgb<8
+Hgb<8,Cirrhosis,Transplant
+SOB,VTE,Hgb<8
+Bleeding,Surgery,Hgb<8,CAD
+Symptomatic,Hgb<8
+Hgb<8
+BMT,Hgb<8
+LeukemiaAcute,Hgb<8,CAD
+Hgb<7
+Hgb<8
+Hgb<8
+BMT,Hgb<8
+Hgb<8
+BMT,Hgb<8
+Hgb<8
+Hgb<8,BMT
+Hgb<8,BMT
+Hgb<8
+Hgb<8
+Hgb<8
+Hgb<7
+Hgb<8
+Hgb<8
+Surgery
+Tachycardia,Fever
+Vasopressors,DroppingHgb
+RenalFailure,PerOtherMD,PerCardiology,CAD
+CTSurgery,Hgb<8
+LeukemiaAcute
+Surgery,Arrhythmia
+Surgery,Anticoagulated,Cardiac
+CAD
+CAD,Surgery,Transplant
+Sepsis,Unstable
+Surgery,Hgb<8,CAD
+Lymphoma,DroppingHgb,ImminentDischarge
+DroppingHgb,Cancer
+Surgery,Arrhythmia
+Hgb<7,CAD
+Arrhythmia,Hgb<8
+Hyponatremia
+Cirrhosis,Hyponatremia,NeedFluid
+Hypoxia
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Hypotension
+Vasopressors,Hypotension,Lightheaded
+Hypotension
+Surgery,Hypotension
+Surgery,Hypotension
+Hypotension
+Hgb<7,Hypotension
+Hypotension,Symptomatic
+Hypotension,Symptomatic,Hgb<8
+Hgb<7,Hypotension
+Tachycardia,Hypotension,PerOtherMD,CTSurgery
+Hgb<7,Tachycardia,Hypotension
+Sepsis,Hypoxia
+Hgb<7,Hypoxia
+Hypoxia
+Hypoxia,DroppingHgb
+CTSurgery
+Chemotherapy
+Chemotherapy
+Chemotherapy
+Procedure
+ImminentDischarge,TransfusionDependent,LeukemiaAcute
+Hgb<7,ImminentDischarge
+DroppingHgb
+Procedure,VascularDevice
+Surgery
+Surgery
+Surgery,Procedure
+Surgery,Procedure
+Surgery,Procedure
+Surgery,Procedure
+Surgery,Cirrhosis
+Chemotherapy
+Surgery
+Procedure
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Bleeding,Surgery
+Surgery
+Surgery
+Surgery
+Hypoperfusion
+Hypoperfusion
+CHF,ACS
+Chemotherapy
+InadequateResponse
+InadequateResponse
+InadequateResponse
+InadequateResponse
+InadequateResponse
+Myelosuppressed
+Myelosuppressed
+Chemotherapy
+Procedure
+Procedure
+Procedure
+Procedure
+Procedure
+Surgery
+Bleeding
+Angina-Ischemia,Arrhythmia
+Angina-Ischemia
+CHF,HemeDisorder
+
+AnemiaHgb>8
+Thrombocytopenia,Transplant,RenalFailure
+Surgery,Transplant
+Surgery
+Hgb<7
+Hgb<8
+Hgb<8
+Surgery,Hgb<8
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute
+LeukemiaAcute
+Chemotherapy,DroppingHgb,Lightheaded
+Surgery,Lightheaded
+Surgery,Lightheaded
+Surgery,Lightheaded
+Hypotension,DroppingHgb
+
+Cirrhosis
+Cirrhosis
+Bleeding,Cirrhosis
+Bleeding,Cirrhosis,Coagulopathy
+Cirrhosis
+Transplant,Cirrhosis
+Hypotension,Transplant,Cirrhosis
+Sepsis,Hypotension,Transplant,Cirrhosis
+Sepsis,Hypotension,Transplant,Cirrhosis
+Cirrhosis,Transplant
+Cirrhosis,Transplant
+Cirrhosis,Transplant
+Hgb<7
+Surgery,Hypotension,Tachycardia,Lightheaded
+CHF
+ExpectDrop
+AnemiaHgb>8
+AnemiaHgb>8
+Surgery,Fatigue
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+Chemotherapy
+SOB,DroppingHgb
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+Hgb<8
+Tachycardia,Hypotension,NeedFluid
+Tachycardia,Hypotension,Surgery
+Hypotension
+Hypotension,Cirrhosis,Transplant
+Symptomatic
+Symptomatic
+AnemiaHgb>8
+AnemiaHgb>8
+PerOtherMD
+Unstable
+AnemiaHgb>8
+Hgb<7
+Sepsis,Hypotension
+RenalFailure,Hypoperfusion,Vasopressors
+DroppingHgb,CHF
+PerOtherMD,Hypoperfusion
+VascularDevice
+VascularDevice
+Chemotherapy,Lymphoma,SOB,DroppingHgb
+Chemotherapy
+DroppingHgb,Coagulopathy
+DroppingHgb,Coagulopathy
+Hypoperfusion
+PerOtherMD
+PerOtherMD
+Surgery
+PerOtherMD
+PerOtherMD
+PerOtherMD
+PerOtherMD
+Hgb<7,PerOtherMD
+Hgb<7,PerOtherMD
+PerOtherMD
+MDS
+MDS,Hemolysis
+MDS,Chemotherapy
+MDS,Fatigue,Symptomatic
+MDS,TransfusionDependent,Hgb<8
+MDS,TransfusionDependent,PerHeme
+MDS,PerOtherMD,PerHeme
+VascularDevice
+VascularDevice
+LeukemiaAcute,VascularDevice
+LeukemiaAcute,VascularDevice
+LeukemiaAcute,VascularDevice
+Bleeding,Lightheaded
+ImminentDischarge,Hgb<8,Cancer
+ImminentDischarge,DroppingHgb,Cancer
+Hypoperfusion
+Hypoperfusion
+Hypoxia,Hypoperfusion
+MM,DroppingHgb
+Surgery,Hypotension,Tachycardia,Hgb<8
+Surgery
+MM,TransfusionDependent
+DroppingHgb,CTSurgery
+MDS
+MDS
+Myelofibrosis
+Surgery,Myelofibrosis
+MM,Procedure
+MM,TransfusionDependent
+Cancer
+Cancer
+
+
+NeedFluid
+Bleeding,Procedure
+Chemotherapy
+ImminentDischarge
+Procedure,Cirrhosis
+Tachycardia,Cancer
+Unstable
+ACS,DroppingHgb
+ACS,Surgery
+Cardiac
+Vasopressors,Sepsis,Cirrhosis,Transplant
+Chemotherapy,Fatigue,Symptomatic
+PerProtocol
+VascularDevice
+VascularDevice
+Hemolysis,VascularDevice
+Bleeding,Cancer
+TransfusionDependent,Cancer
+PerOncology,Cancer
+PerOncology,Cancer
+PerOncology,Cancer
+PerOncology
+?
+Bleeding
+Angina-Ischemia,Hemolysis,CAD
+Bleeding
+Unstable
+Surgery,DroppingHgb
+Hypoperfusion
+Surgery
+?
+Donor
+Donor
+
+PerOtherMD
+PerOtherMD,NeedFluid
+
+Vasopressors,RenalFailure
+DroppingHgb,Hgb<8
+Bleeding,Trauma
+LeukemiaAcute,TransfusionDependent,ExpectDrop,DroppingHgb
+HemeDisorder
+HemeDisorder
+HemeDisorder
+DroppingHgb
+Hgb<7
+Symptomatic,Hgb<8
+Sepsis,NeedFluid
+PerHepatology,Cirrhosis
+Angina-Ischemia
+MDS,SOB,Symptomatic,CAD
+Vasopressors,Unstable,Hypoperfusion
+Surgery,Hypotension,NeedFluid
+Chemotherapy
+Tachycardia,Surgery
+CTSurgery,Hgb<8
+Chemotherapy
+Hgb<7,Symptomatic
+Symptomatic
+ImminentDischarge,TransfusionDependent
+DroppingHgb,CNS
+Chemotherapy,Fatigue
+DroppingHgb,VascularDevice
+CTSurgery
+Sepsis,Transplant
+Radiotherapy,Chemotherapy,DroppingHgb
+Chemotherapy,Symptomatic
+Radiotherapy
+Surgery
+CAD,Procedure,Cirrhosis
+Tachycardia
+Symptomatic
+Tachycardia,Symptomatic,DroppingHgb
+Symptomatic,Lightheaded
+Hypotension,Symptomatic,Lightheaded
+Donor,Symptomatic
+Cardiac,Symptomatic,Hgb<8
+PerOtherMD,Symptomatic,PerCardiology
+Chemotherapy,ImminentDischarge,LeukemiaAcute,DroppingHgb
+Procedure
+Procedure
+Procedure
+Procedure
+Radiotherapy,Cancer
+Cardiac,Surgery
+SOB
+SOB
+Cardiac
+Cardiac
+Cardiac
+Hemolysis,InadequateResponse
+Bleeding,Surgery,DroppingHgb,Procedure
+Fatigue
+Fatigue
+Sepsis,LeukemiaAcute,SOB
+PerOtherMD,CNS,Hgb<8,BMT
+Hgb<7,Procedure
+Angina-Ischemia,Hypotension
+MDS
+MDS
+Myelofibrosis,DroppingHgb
+DroppingHgb,Procedure
+Hgb<7,Surgery,Symptomatic
+Bleeding,Chemotherapy,ExpectDrop
+Trauma,Surgery,Pulmonary
+Cardiac,Angina-Ischemia,Tachycardia
+DroppingHgb,Pancreatitis
+Lightheaded,HemeDisorder,Fatigue,Symptomatic,Hgb<8
+ExpectDrop,RenalFailure,Surgery,Procedure,Angina-Ischemia
+AnemiaHgb>8,RenalFailure,Fatigue
+Hgb<7
+Procedure
+Procedure
+Procedure
+ImminentDischarge
+Symptomatic,Procedure
+Procedure
+Surgery
+Surgery
+RenalFailure,Procedure
+PerOtherMD
+PerOtherMD,Procedure
+PerOtherMD,Bleeding
+PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,BMT
+PerOtherMD,Hgb<8,BMT
+PerOtherMD,PerCardiology
+PerOtherMD
+PerOtherMD
+PerOtherMD
+PerOtherMD
+PerOtherMD,Procedure
+PerOtherMD,Procedure
+PerOtherMD,PerHeme
+PerOtherMD,PerHeme
+PerOtherMD,PerHeme
+PerHepatology,PerOtherMD
+PerHepatology,PerOtherMD
+Hgb<7,PerOtherMD
+PerOtherMD
+PerOtherMD
+PerOtherMD,Lightheaded
+PerOncology,PerOtherMD
+PerOncology,PerOtherMD
+PerOncology,PerOtherMD
+Chemotherapy,PerOtherMD
+PerOtherMD
+PerOtherMD
+Donor,PerOtherMD
+Donor,PerOtherMD
+Cardiac,PerOtherMD,PerCardiology
+PerOtherMD
+Surgery,PerOtherMD
+PerOtherMD
+PerOtherMD,DroppingHgb
+PerProtocol,PerOtherMD
+PerProtocol,PerOtherMD
+PerProtocol,PerOtherMD
+PerProtocol,PerOtherMD
+PerProtocol,PerOtherMD
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,Hgb<8
+PerOtherMD,PerCardiology
+RenalFailure,PerOtherMD
+RenalFailure,PerOtherMD
+RenalFailure,PerOtherMD
+RenalFailure,PerOtherMD
+PerOtherMD,Cardiac
+Surgery,PerOtherMD
+Surgery,PerOtherMD
+Surgery,PerOtherMD
+PerOtherMD,Transplant
+CNS
+Bleeding,InadequateResponse
+Hypotension,NeedFluid
+Bleeding,Surgery,Hypotension,DroppingHgb
+Sepsis,Tachycardia
+Sepsis,Tachycardia,Fatigue
+Tachycardia,Cancer
+Symptomatic,Pulmonary
+PerOtherMD
+PerOtherMD
+PerOtherMD,Lightheaded
+PerOtherMD
+PerOtherMD
+PerOtherMD
+PerOtherMD
+Surgery
+
+Sepsis
+Hgb<7
+Surgery
+Surgery
+Hgb<8,Surgery
+Vasopressors,Hypoxia
+AnemiaHgb>8,Hypoperfusion
+ACS,CAD
+ImminentDischarge
+Bleeding,Cardiac,Symptomatic
+Hemolysis
+Surgery
+Surgery
+CTSurgery
+CTSurgery
+Cardiac,Transplant,Surgery
+Procedure
+Transplant,Cirrhosis
+Transplant,Cirrhosis
+Cirrhosis,Transplant
+Hgb<7,Symptomatic,Transplant
+ACS
+Cirrhosis,Surgery
+Vasopressors,Cirrhosis,Transplant
+Hgb<8,Cirrhosis,Transplant.Hypotension
+Sepsis,Hypotension,Cirrhosis,Transplant
+Surgery
+Surgery
+Surgery,Tachycardia,DroppingHgb
+Surgery,CTSurgery,DroppingHgb
+Cardiac,Hgb<7,Surgery,Procedure
+Surgery
+Surgery,Cirrhosis,Transplant
+Surgery,DroppingHgb
+Surgery,Hypotension
+Surgery,DroppingHgb,Hgb<8
+Surgery,Hgb<8
+Surgery,Symptomatic,Lightheaded
+Angina-Ischemia,Surgery
+Surgery,Hypotension,DroppingHgb
+Surgery,DroppingHgb
+Bleeding,Surgery,Anticoagulated,Cirrhosis
+Surgery,Symptomatic
+Hgb<7,CTSurgery
+Surgery,CNS
+Chemotherapy,PerOncology
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery,Hgb<8
+Surgery,Symptomatic
+CTSurgery,Hgb<8
+Cardiac,Surgery,Hgb<8
+Cardiac,Surgery
+CTSurgery
+CTSurgery
+Surgery,Tachycardia,Arrhythmia
+Surgery,DroppingHgb,CAD
+Surgery,RenalFailure,Transplant
+Surgery,Transplant,Hypotension,Cirrhosis
+Surgery,Transplant,Hypotension,Cirrhosis
+Surgery,Transplant,Cirrhosis,Thrombocytopenia
+Surgery,Cirrhosis,Transplant
+Surgery,Cirrhosis
+Surgery,Fever,Cirrhosis
+Surgery,CNS
+Surgery
+Surgery,DroppingHgb,CAD
+Surgery,DroppingHgb
+Bleeding,Surgery,Hypotension
+Surgery
+Angina-Ischemia,Surgery
+Surgery,Symptomatic
+Surgery
+Surgery,RenalFailure
+Surgery,RenalFailure
+Surgery,Hgb<8,Cancer
+Hgb<7,Surgery
+Surgery
+Surgery,DroppingHgb
+Angina-Ischemia,Surgery,Delerium,CAD
+Surgery,Bleeding
+Tachycardia,DroppingHgb,Procedure
+Chemotherapy,Surgery
+Surgery,Delerium
+Surgery,Delerium
+Surgery,Hypotension,Transplant
+Surgery
+Procedure
+Cirrhosis
+Cirrhosis
+Cirrhosis
+Hgb<8,BMT
+BMT
+BMT
+BMT
+BMT
+BMT
+Chemotherapy
+Chemotherapy
+ACS,Angina-Ischemia,NeedFluid
+Surgery
+PerOtherMD,PerOncology
+Surgery
+Surgery
+Surgery,PerOtherMD
+ImminentDischarge
+Procedure
+SOB,Fatigue
+Tachycardia,ExpectDrop,Hgb<8
+Plasmapheresis
+Procedure
+Procedure
+Procedure
+DroppingHgb
+RenalFailure,DroppingHgb
+RenalFailure,DroppingHgb
+DroppingHgb
+Bleeding,Cancer
+PerProtocol
+PerProtocol
+PerProtocol
+Bleeding
+Coagulopathy,Hgb<8,Cirrhosis
+Coagulopathy,Hgb<8,Cirrhosis
+Coagulopathy,Hgb<8,Cirrhosis
+Coagulopathy,Hgb<8,Cirrhosis
+Hgb<8,ImminentDischarge
+Hgb<8,ImminentDischarge
+SOB,DroppingHgb
+Surgery
+ImminentDischarge
+Hgb<7
+DroppingHgb
+ImminentDischarge,DroppingHgb,Cancer
+Procedure
+Surgery
+Bleeding
+Autologous
+RenalFailure,CHF
+PerOtherMD,Hgb<8,Hemolysis,PerHeme,Symptomatic
+LeukemiaAcute,Hgb<8,ImminentDischarge
+Coagulopathy
+Coagulopathy
+Hgb<8,BMT
+Sepsis
+Surgery,Pulmonary
+ImminentDischarge
+Chemotherapy,ImminentDischarge
+DroppingHgb
+Symptomatic,Hgb<8
+Hypotension,Lightheaded
+Chemotherapy,ExpectDrop,DroppingHgb
+Chemotherapy,ExpectDrop,PerOncology,DroppingHgb
+
+Symptomatic,ChronicDisease,Cirrhosis
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic,Hgb<8
+Chemotherapy,Symptomatic,DroppingHgb
+DroppingHgb,ACS
+Chemotherapy,LeukemiaAcute
+LeukemiaAcute
+DroppingHgb,Lightheaded,PerOtherMD
+Surgery
+Surgery
+LeukemiaAcute,InadequateResponse,Hgb<8
+LeukemiaAcute,TransfusionDependent,ImminentDischarge
+Hgb<8,LeukemiaAcute
+Cardiac
+TransfusionDependent,Hgb<8,ImminentDischarge
+LeukemiaChronic,ImminentDischarge
+CNS
+DroppingHgb,Cirrhosis
+DroppingHgb,Lightheaded,Symptomatic
+DroppingHgb,Lightheaded,Symptomatic
+DroppingHgb,Lightheaded,Symptomatic
+Hgb<8,Surgery
+AnemiaHgb>8
+AnemiaHgb>8
+MDS,Hgb<8,TransfusionDependent
+Anticoagulated,VTE,DroppingHgb,Cancer
+Bleeding,Coagulopathy,Cirrhosis
+Sepsis
+Sepsis,Hypoperfusion
+Symptomatic
+AnemiaHgb>8
+Symptomatic
+DroppingHgb,ImminentDischarge
+Radiotherapy
+Radiotherapy
+DroppingHgb
+Cardiac,Surgery,DroppingHgb
+Tachycardia,HemeDisorder
+Tachycardia,HemeDisorder
+Hgb<8
+PerOtherMD,Hgb<8,CAD,DroppingHgb
+Chemotherapy
+Bleeding
+Bleeding
+Bleeding
+Bleeding
+PerOncology,Lymphoma,PerOtherMD
+PerOncology,Lymphoma,PerOtherMD
+Surgery
+Fatigue,DroppingHgb
+Bleeding
+Bleeding
+ACS,Tachycardia
+DroppingHgb,PerOtherMD
+ACS,Surgery
+Hypotension,Trauma,CNS
+Thrombocytopenia,Procedure
+RenalFailure
+RenalFailure
+Surgery,Hypotension
+ImminentDischarge,PerOtherMD
+Comorbidities
+Autologous
+Autologous
+Surgery
+RenalFailure,Hypotension,Symptomatic
+Bleeding,Hgb<7
+Hgb<8,BMT
+Hgb<8,BMT
+Hgb<8,BMT
+Bleeding,Surgery
+Hgb<8,BMT
+MM,BMT
+BMT
+Hgb<8,BMT
+Hgb<8,BMT
+Hgb<8,BMT
+Hgb<8,BMT
+PerProtocol,Code
+Surgery,Hypotension,Hgb<8
+Bleeding,Surgery
+Surgery,Hgb<8,DroppingHgb
+NeedFluid,Hgb<8,Transplant,Surgery
+Surgery
+Angina-Ischemia,Surgery,Transplant,Hypoperfusion,Cirrhosis,RenalFailure
+Hypotension,Transplant,Cirrhosis,Surgery
+Surgery,Transplant,PerOtherMD,Cirrhosis
+Surgery,Transplant,PerOtherMD,Cirrhosis
+Transplant,Surgery
+VascularDevice
+ACS
+Hgb<7,Cirrhosis,Transplant,Surgery
+Surgery,Hypotension,DroppingHgb,Cardiac
+Bleeding,Surgery,InadequateResponse
+PerProtocol
+CAD,RenalFailure,Surgery,Transplant
+Surgery,Hypotension,CAD,CHF,VascularDevice
+Bleeding,Tachycardia,Transplant
+Surgery
+Surgery,Tachycardia,DroppingHgb
+Surgery
+Hgb<8,Transplant,Surgery
+Transplant,Surgery
+Hgb<7,Transplant
+BMT,Hgb<8
+Bleeding,PerOtherMD,Hgb<8,Cancer
+HemeDisorder
+Hypoxia,Cancer
+HemeDIsorder
+Surgery
+
+Sepsis
+Sepsis
+Sepsis
+Sepsis,NeedFluid
+Vasopressors,Sepsis,NeedFluid
+Sepsis,Hypoperfusion
+Sepsis,Hypoperfusion
+Sepsis,Tachycardia
+Sepsis,Hypoperfusion,Hgb<7
+Sepsis,Hypotension
+Sepsis,Hypoperfusion
+Sepsis
+Sepsis
+Sepsis
+Sepsis,Arrhythmia,Cancer
+Sepsis,Hypoxia,Cancer
+Sepsis,Transplant
+Sepsis,DroppingHgb
+Sepsis,Hypotension
+Sepsis,Hypotension
+Sepsis,Hypotension
+Sepsis,Hypotension,Hypoperfusion,Hgb<8
+Sepsis,Hypotension,Hypoperfusion,Hgb<8
+Sepsis,Hypotension,Hgb<7
+Sepsis,Hypotension,Unstable
+Sepsis,Hypotension
+CHF
+CHF
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+AnemiaHgb>8
+CAD,Angina-Ischemia
+Hemolysis,PerHeme
+Vasopressors,Pulmonary
+Sepsis
+SOB,Symptomatic
+SOB,Symptomatic
+HemeDisorder
+HemeDisorder
+HemeDisorder,Symptomatic
+HemeDisorder
+HemeDisorder,Symptomatic
+RenalFailure,HemeDisorder,Transplant
+HemeDisorder,Symptomatic
+DroppingHgb
+Cardiac
+Cardiac
+Bleeding,Thrombocytopenia,Cirrhosis
+Bleeding,Thrombocytopenia,Cirrhosis
+Transplant
+CNS
+CNS
+PerHeme,HemeDisorder
+?
+Chemotherapy,Hypoxia,DroppingHgb
+Chemotherapy,Hypoxia,DroppingHgb
+Chemotherapy,Hypoxia,DroppingHgb
+ACS,PerOtherMD,PerCardiology
+Surgery,Symptomatic
+CNS
+Vasospasm,CNS
+DroppingHgb,Bleeding
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery
+Surgery,Symptomatic
+Surgery
+Surgery
+Surgery
+Bleeding
+Sepsis,Hypoperfusion
+Surgery,Hypoperfusion
+Hypoperfusion
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Surgery,Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+SOB,Symptomatic,Angina-Ischemia
+ACS,Symptomatic
+ACS,Surgery,Symptomatic
+Tachycardia,Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Symptomatic
+Tachycardia,Symptomatic
+Chemotherapy,Symptomatic
+Bleeding,Symptomatic,CAD
+LeukemiaAcute,Symptomatic
+PerOtherMD,Symptomatic,PerHeme
+Symptomatic,Cancer
+Symptomatic,Cancer
+Bleeding,Symptomatic,Cirrhosis
+Surgery,Symptomatic
+Surgery,Symptomatic,Cancer
+Cardiac,Surgery,Symptomatic
+Surgery,Symptomatic
+Cardiac,Symptomatic
+Cardiac,Anticoagulated,Symptomatic
+Hgb<7,Symptomatic
+Symptomatic,Hgb<8
+Hypoxia,Symptomatic
+Bleeding,Symptomatic
+Cardiac,RenalFailure,Symptomatic
+Tachycardia,SOB,Symptomatic
+Symptomatic,Arrhythmia
+Symptomatic,Cancer
+Symptomatic,Cancer
+Symptomatic,Cancer
+Symptomatic,CAD
+Symptomatic,Procedure,DroppingHgb
+Symptomatic
+PerOtherMD,Symptomatic,PerRenal
+Symptomatic,InadequateResponse
+SOB,Fatigue,Symptomatic
+Hgb<7,Symptomatic,ImminentDischarge
+Hgb<7,Symptomatic,ImminentDischarge
+Transplant,Symptomatic,Hypoperfusion,Cirrhosis
+Symptomatic,CHF
+Cardiac,Symptomatic
+Bleeding,Fatigue,Symptomatic
+Symptomatic,Cancer
+Symptomatic,CAD
+Symptomatic
+Symptomatic
+Symptomatic
+Surgery,Hypotension,Symptomatic,Lightheaded
+HemeDisorder,Symptomatic
+Symptomatic,Lightheaded
+Hgb<7,Symptomatic
+SOB,Symptomatic,Cancer,Cardiac
+Tachycardia,Symptomatic
+Bleeding,Symptomatic
+Symptomatic,Hgb<8
+Symptomatic
+Symptomatic,DroppingHgb
+Tachycardia,Hypotension,Symptomatic,NeedFluid
+Symptomatic
+Fatigue,Symptomatic
+Symptomatic
+Symptomatic
+Chemotherapy,Symptomatic
+Hgb<8,Lightheaded
+Bleeding,Code,Lightheaded
+ExpectDrop,Symptomatic,DroppingHgb
+ExpectDrop,Symptomatic,DroppingHgb
+Hypotension
+Tachycardia,Hypotension,PerOtherMD
+Tachycardia
+Tachycardia
+Tachycardia,Hypoxia
+Tachycardia,Surgery
+Tachycardia,Surgery
+Tachycardia,Surgery
+Tachycardia
+Tachycardia,Surgery
+Tachycardia
+Tachycardia,Fatigue
+Angina-Ischemia,Tachycardia,Surgery,CNS
+Tachycardia,SOB
+Tachycardia,Fever,Hgb<8
+CNS,Trauma
+CNS,Trauma
+CNS,Trauma
+CNS,Trauma
+Tachycardia,PerOtherMD,Lymphoma,Fatigue,PerHeme
+Unstable
+HemeDisorder,Hemolysis
+PerHeme
+Hgb<7
+
+TransfusionDependent,HemeDisorder
+Chemotherapy
+Surgery,CNS
+Bleeding,Surgery
+LeukemiaAcute,TransfusionDependent,ImminentDischarge
+PerProtocol,PerOtherMD,BMT,Hgb<8
+PerProtocol,PerOtherMD,BMT
+PerProtocol,PerOtherMD,Hgb<8,BMT
+PerProtocol,PerOtherMD,Hgb<8,BMT
+LeukemiaAcute,TransfusionDependent
+MDS,TransfusionDependent
+MDS,TransfusionDependent,Symptomatic
+TransfusionDependent,Cancer
+TransfusionDependent,Cancer
+TransfusionDependent
+PerOtherMD
+Transplant
+Hypoxia,Hypotension,Transplant
+RenalFailure,Cancer,Transplant
+Hypotension,Hgb<8,Transplant
+Surgery,Transplant
+Transplant
+Transplant
+Transplant
+MDS,PerOtherMD
+Fatigue,Chemotherapy
+DroppingHgb
+DroppingHgb,ImminentDischarge
+DroppingHgb,ImminentDischarge
+DroppingHgb,ImminentDischarge
+Surgery,DroppingHgb
+HemeDisorder,DroppingHgb
+Angina-Ischemia
+MM,TransfusionDependent
+Surgery
+Bleeding,Anticoagulated,VascularDevice
+Procedure
+Bleeding
+Unstable
+Unstable
+Unstable
+Unstable,Bradycardia
+Sepsis,Hgb<8,Angina-Ischemia,VascularDevice
+Vasospasm
+Vasospasm
+Vasospasm
+HemeDisorder
+Autologous
+Surgery,Fatigue,Hgb<8,Cirrhosis,Transplant
+VascularDevice
+Chemotherapy
+Surgery
+Chemotherapy,Surgery
+PerConsult
+ImminentDischarge,DroppingHgb
+Hypotension,NeedFluid
+SOB
+SOB
+Hgb<7
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/summarizeAnnotationTags.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/summarizeAnnotationTags.py
new file mode 100644
index 00000000..975f9f5a
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/summarizeAnnotationTags.py
@@ -0,0 +1,23 @@
+import sys, os;
+
+# python summarizeAnnotationTags.py sampleTags.txt > tagCounts.txt
+
+PUNCTUATION = ".,:;\"'()?+";
+    
+# Source file comma-delimited annotation tags
+infile = open(sys.argv[1]); 
+
+countsPerTag = dict();
+for line in infile:
+    line = line.strip();
+    if line != "":  # Skip blank lines / no tags
+        tags = line.replace(";",",").split(",");
+        for tag in tags:
+            tag = tag.upper();  # Ensure case insensitivity
+            if tag not in countsPerTag:
+                countsPerTag[tag] = 0;
+            countsPerTag[tag] += 1;
+
+print "Count\tTag";
+for tag, count in countsPerTag.iteritems():
+    print "%s\t%s" % (count, tag);
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/util.py b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/util.py
new file mode 100644
index 00000000..822db25a
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/util.py
@@ -0,0 +1,43 @@
+        
+def kappaStat( contingencyTable ):
+    """Calculate Cohen's kappa statistic for inter-rater agreement.
+    contingencyTable expected to be a 2D table (2D list)
+    representing the counts of mutually exclusive categorical responses for
+    two different raters.
+    
+    http://en.wikipedia.org/wiki/Kappa_statistic
+    
+    >>> from util import kappaStat;
+    >>> contingencyTable = [ [20,5], [10,15] ];
+    >>> kappaStat( contingencyTable );
+    (0.3999999999999999, 0.7, 0.5)
+
+    """
+
+    # Count up total sums first
+    rowSums = [0]*len(contingencyTable);
+    colSums = [0]*len(contingencyTable);
+    tableSum = 0.0; # Floating point number to automate subsequent conversions
+    
+    for i, row in enumerate(contingencyTable):
+        for j, datum in enumerate(row):
+            rowSums[i] += datum;
+            colSums[j] += datum;
+            tableSum += datum;
+            
+    # Second pass to estimate chance/random agreement based on total row/col sums
+    agreeSum = 0;
+    chanceSum = 0.0;
+    
+    for i, row in enumerate(contingencyTable):
+        for j, datum in enumerate(row):
+            if i == j:
+                agreeSum += datum;
+                chanceSum += (rowSums[i]/tableSum)*(colSums[j]/tableSum);
+
+    agreeRate = agreeSum / tableSum;
+    
+    kappa = (agreeRate - chanceSum) / (1 - chanceSum);
+    
+    return (kappa, agreeRate, chanceSum);
+    
\ No newline at end of file
diff --git a/scripts/Archive/BloodBPA-FreeTextAnnotateTags/wordTags.txt b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/wordTags.txt
new file mode 100644
index 00000000..e13ea4be
--- /dev/null
+++ b/scripts/Archive/BloodBPA-FreeTextAnnotateTags/wordTags.txt
@@ -0,0 +1,1651 @@
+Count	Word	Tag
+11	-	
+1	--	
+1	!	
+1	#	
+1	#1	
+1	#8	
+11	&	
+1	(2U	
+1	(ANGINA)	Angina-Ischemia
+1	(CENTRAL	
+1	(CLICK	
+1	(COMPARISION	
+1	(DESPITE	
+1	(DID	
+1	(DIZZINESS	
+1	(ECMO	VascularDevice
+1	(EVAR	Surgery
+1	(FATIGUE)	
+1	(ISTAT	
+2	(KENT	
+3	(LIGHTHEADED)	Lightheaded
+1	(PAMF)	
+1	(SCVO2	Hypoperfusion
+1	(SPLEEN	
+1	(STANDARD	
+1	(TACHYCARDIA)	Tachycardia
+1	?ACUTE	
+1	~22	
+1	~500CC	
+5	+	
+1	+/-	
+1	+FOBT	Bleeding
+1	+GUAIC	Bleeding
+1	+GUIAC	Bleeding
+108	<	
+1	<2	
+9	<21	Hgb<7
+4	<24	Hgb<8
+1	<25	AnemiaHgb>8
+1	<27	AnemiaHgb>8
+1	<6.9	Hgb<7
+19	<7	Hgb<7
+1	<7.0	Hgb<7
+1	<70	Hypoperfusion
+75	<8	Hgb<8
+10	<8.0	Hgb<8
+1	<8.5	AnemiaHgb>8
+1	<8HGB	Hgb<8
+2	<9	AnemiaHgb>8
+11	=	
+7	>	
+1	->	
+1	-->	
+2	>10	
+1	>30	
+3	>8	AnemiaHgb>8
+1	>8.0	AnemiaHgb>8
+3	>9	
+15	1	
+2	10	
+1	101	
+1	120S	
+2	140	
+1	140S	
+1	150	
+1	17	Hgb<7
+1	18	Hgb<7
+3	19	Hgb<7
+1	1-Jul	
+1	1ST	
+1	1U	
+12	2	
+2	2.5	
+5	20	Hgb<7
+1	20.1	Hgb<7
+2	20.2	Hgb<7
+1	20.4	Hgb<7
+1	20.5	Hgb<7
+1	20.6	Hgb<7
+1	20.60%	
+1	20.7	Hgb<7
+1	20.9	Hgb<7
+1	20-Oct	
+1	20S	
+15	21	Hgb<7
+1	21	Hgb<7
+2	21%	
+1	21.7	Hgb<8
+7	22	Hgb<8
+2	22.1	Hgb<8
+2	22.3	Hgb<8
+1	22.9	Hgb<8
+9	23	Hgb<8
+1	23.9	Hgb<8
+1	23->21.3	DroppingHgb
+1	2-3X/WEEK	
+7	24	Hgb<8
+1	24	Hgb<8
+1	24.6	AnemiaHgb>8
+2	24.8	AnemiaHgb>8
+1	24-Jul	
+1	24-May	
+1	25	AnemiaHgb>8
+2	25%	
+2	26	AnemiaHgb>8
+1	26.1	AnemiaHgb>8
+1	27	
+1	28	
+4	2-Feb	
+1	2-Jan	
+1	2L	
+4	2U	
+5	3	
+1	3/15/2011	
+5	30	
+1	31-Oct	
+1	35	
+1	35%	
+2	3L	
+1	4	
+1	4.9	Hgb<7
+1	44	
+3	48	
+1	4-May	
+2	5	
+1	5)	
+2	5.1	Hgb<7
+1	5.3	Hgb<7
+1	5.6	Hgb<7
+1	5.9	Hgb<7
+1	5/17/2012	
+1	50%)	
+1	50S	
+1	50S-60S	
+1	55	
+1	56	
+1	5-May	
+6	6	
+1	6	
+2	6.2	Hgb<7
+5	6.5	Hgb<7
+9	6.6	Hgb<7
+4	6.7	Hgb<7
+9	6.8	Hgb<7
+13	6.9	Hgb<7
+2	60	
+1	6-Jul	
+58	7	Hgb<7
+5	7	Hgb<7
+48	7.1	Hgb<8
+1	7.1/20.3	Hgb<7
+31	7.2	Hgb<8
+1	7.2/20.4	Hgb<7
+14	7.3	Hgb<8
+13	7.4	Hgb<8
+1	7.4	Hgb<8
+22	7.5	Hgb<8
+1	7.50%	
+22	7.6	Hgb<8
+19	7.7	Hgb<8
+34	7.8	Hgb<8
+1	7.8	Hgb<8
+35	7.9	Hgb<8
+1	7/10/2011	
+1	750	
+1	77	
+1	78	
+100	8	Hgb<8
+21	8	Hgb<8
+3	8.1	AnemiaHgb>8
+1	8.2	AnemiaHgb>8
+1	8.3	AnemiaHgb>8
+2	8.4	AnemiaHgb>8
+3	8.5	AnemiaHgb>8
+1	8.7	AnemiaHgb>8
+2	8.8	AnemiaHgb>8
+1	8.9->7.7	DroppingHgb
+1	8/3/2012	
+1	80	
+4	81	
+1	89	
+1	8-Jul	
+1	8S	
+4	9	
+3	9.5-10.5	
+1	90%	
+1	90-100%	
+27	A	
+3	AAA	Surgery
+1	AAA)	Surgery
+1	AAWARE	
+1	ABDOMINAL	
+1	ABILITY	
+1	ABLE	
+9	ABOUT	
+5	ABOVE	
+1	ABRUPT	
+1	ACCEPTING	
+2	ACD	
+2	ACIDEMIC	Acidemia
+1	ACKD	RenalFailure
+14	ACS	ACS
+12	ACTIVE	
+3	ACTIVELY	
+1	ACTUALLY	
+1	ACUTE	
+35	ACUTE	
+3	ACUTELY	
+1	ADDED	
+3	ADDITIONAL	
+1	ADDT	
+1	ADENOCA	Cancer
+1	ADENOCARCINOMA	Cancer
+4	ADEQUATE	
+1	ADMISSION	
+2	ADN	
+1	ADVISABLE	
+2	ADVISORY	
+1	AF	Arrhythmia
+5	AFIB	Arrhythmia
+1	AFIB/AFLUTTER	Arrhythmia
+15	AFTER	
+1	AFTERNOON	
+1	AFTERNOON)	
+1	AGAIN	
+1	AGAINST	
+1	AGE	
+1	AGGLUTININS	
+3	AGO	
+2	AIHA	HemeDisorder
+1	AKI	RenalFailure
+4	ALBUMIN	
+1	ALERT	
+2	ALIZADEH	PerOtherMD
+12	ALL	LeukemiaAcute
+2	ALLO	
+1	ALMOST	
+1	ALREADY	
+2	ALSO	
+2	ALTERED	Delerium
+1	ALTHOUGH	
+1	ALVOLAR	
+9	AM	
+1	AMBULATE	
+44	AML	LeukemiaAcute
+1	AMOUNT	
+1	AMS	Delerium
+6	AN	
+358	AND	
+1	ANDH	
+1	ANEAMI	
+2	ANEMAI	
+164	ANEMIA	
+9	ANEMIC	
+1	ANESTHESIA	
+1	ANGELOTTI	PerOtherMD
+1	ANGINA	Angina-Ischemia
+1	ANNOYING	
+12	ANTICIPATE	ExpectDrop
+10	ANTICIPATED	ExpectDrop
+1	ANTICIPATING	ExpectDrop
+2	ANTICIPATION	ExpectDrop
+1	ANTICOAG	Anticoagulated
+1	ANTICOAGULATED	Anticoagulated
+3	ANTICOAGULATION	Anticoagulated
+1	ANTIIPICATED	ExpectDrop
+4	AORTIC	
+1	AORTO	
+1	APHERESIS	
+2	APL	LeukemiaAcute
+2	APLASIA	
+3	APLASTIC	HemeDisorder
+1	APML	LeukemiaAcute
+1	APPEARS	
+1	APPROACHING	
+4	APPROPRIATELY	
+1	APPROVED	
+1	APS	
+1	ARANESP	
+1	AREA	
+3	AROUND	
+1	ARREST/HYPOTHERMIA	Code
+3	ARRHYTHMIA	Arrhythmia
+2	ARTERY	
+1	ARTHROPLASTY	Surgery
+17	AS	
+1	ASSOCIATED	
+24	AT	
+2	ATRIAL	
+15	ATTENDING	PerOtherMD
+1	ATTND	PerOtherMD
+1	AUGMENTING	
+3	AUTO	
+6	AUTOLOGOUS	Autologous
+1	AVAILABLE	
+2	AVOID	
+3	AWARE	
+1	AWAY	
+2	B	
+4	BACK	
+1	BANOS	
+3	BASELINE	
+1	BC	
+31	BE	
+1	BECAME	
+1	BECOME	
+6	BEEN	
+8	BEFORE	
+1	BEFOREHAND	
+2	BEGIN	
+11	BEING	
+8	BELOW	
+3	BENEFIT	
+1	BEST	
+3	BETA	
+5	BETTER	
+1	BG	
+1	BIFEM	
+1	BILATERAL	
+3	BIOPSY	
+2	BLACK	
+1	BLADDER	
+17	BLEED	Bleeding
+1	"BLEED"""	Bleeding
+65	BLEEDING	Bleeding
+1	BLODD	
+46	BLOOD	
+1	BLOODY	
+1	BLUE	Code
+359	BMT	BMT
+1	BMT.HGB	BMT
+2	BOLUS	NeedFluid
+2	BOLUSES	NeedFluid
+1	BOLUSES)	NeedFluid
+24	BONE	BMT
+1	BORDERING	AnemiaHgb>8
+7	BORDERLINE	AnemiaHgb>8
+3	BOWEL	
+1	BOX	
+10	BP	Hypotension
+1	BPS	Hypotension
+1	BRADY	Bradycardia
+4	BRAIN	CNS
+6	BREATH	SOB
+1	BROKEN	
+1	BUMP	
+6	BUT	
+2	BX	
+12	BY	
+3	BYPASS	CABG
+3	C	
+1	C/	
+1	C/O	
+1	CA	
+3	CABG	CABG
+24	CAD	CAD
+1	CADAVERIC	
+2	CAN	
+1	CANCELED	
+37	CANCER	Cancer
+1	CANDIDATE	
+1	CANNOT	
+2	CAPACITY	
+1	CARCINOMA	Cancer
+1	CARD	Cardiac
+1	CARDAIC	Cardiac
+66	CARDIAC	Cardiac
+5	CARDIOGENIC	CHF
+3	CARDIOLOGIST	PerCardiology
+6	CARDIOLOGY	PerCardiology
+4	CARDIOMYOPATHY	CHF
+2	CARDIOTHORACIC	CTSurgery
+1	CARDIOVASCULAR	
+1	CARE	
+1	CARIDAC	Cardiac
+1	CAROTID	
+1	CARRY	
+2	CARRYING	
+1	CASE	
+8	CATH	Procedure
+3	CATHETER	
+2	CAUSING	
+12	CELL	
+1	CELLS	
+11	CEREBRAL	CNS
+1	CERVICA	
+1	CERVICAL	
+1	CF	
+1	CHALLENGES	
+2	CHANCE	
+3	CHANGES	
+1	CHECK	
+45	CHEMO	Chemotherapy
+1	CHEMO/RADIATION	Chemotherapy,Radiotherapy
+2	CHEMO/RADS	Chemotherapy
+1	CHEMORX	Chemotherapy
+43	CHEMOTHERAPY	Chemotherapy
+1	CHEMOTX	Chemotherapy
+1	CHEMOXRT	Chemotherapy,Radiotherapy
+1	CHEMP	
+10	CHEST	
+10	CHF	CHF
+17	CHRONIC	
+3	CHRONICALLY	
+1	CIRCUIT	
+7	CIRRHOSIS	Cirrhosis
+11	CIRRHOTIC	Cirrhosis
+1	CIRROHSIS	Cirrhosis
+1	CKD	RenalFailure
+2	CLINIC	
+1	CLINICAL	
+1	CLINICALLY	
+2	CLL	LeukemiaChronic
+1	CLL/LPL	LeukemiaChronic
+2	CLOSE	
+1	CML	LeukemiaChronic
+1	CO	
+5	COAGULOPATHIC	Coagulopathy
+6	COAGULOPATHY	Coagulopathy
+3	CODE	Code
+1	COLD	
+1	COLECTOMY	Surgery
+2	COLLOID	NeedFluid
+1	COLONOSCOPY	Procedure
+3	COMING	
+1	COMMENT	
+1	COMMENT)	
+2	COMORBIDITIES	
+1	CO-MORBIDITIES	
+1	COMPETING	
+1	COMPLETE	
+1	COMPLICATION	
+3	COMPROMISE	
+1	COMPROMISED	
+9	CONCERN	
+1	CONSULTING	PerConsult
+2	CONT	
+1	CONTINUALLY	
+9	CONTINUE	
+7	CONTINUED	
+2	CONTINUES	
+4	CONTINUING	
+1	CONTINUOUS	
+1	CONTINUOUSLY	
+1	CONTROL	
+1	CORD	
+11	CORONARY	CAD
+1	COULD	
+2	COUMADIN	Anticoagulated
+1	COUNT	
+8	COUNTS	
+2	COURSE	
+1	COURTESY	
+1	COX	
+2	CP	Angina-Ischemia
+1	CREATININE	RenalFailure
+2	CRISIS	
+4	CRIT	
+2	CRITERIA	
+3	CRITICAL	
+3	CRITICALLY	
+1	CRRT	RenalFailure
+1	CRYPTOGENIC	
+1	CRYSTALLOID	NeedFluid
+1	CT	CTSurgery
+2	CTDN	
+1	CTICU	CTSurgery
+1	CURRENT	
+3	CURRENTLY	
+1	CUTOFF	
+1	CYSTITIS	
+3	CYTOTOXIC	Chemotherapy
+7	D/C	ImminentDischarge
+1	D/CD	
+2	D/CED	
+1	D/O	
+2	DAILY	
+1	DAVID	
+5	DAY	
+9	DAYS	
+2	DAYS...WILL	
+2	DC	ImminentDischarge
+1	DCD	
+1	DECANNULATION	
+1	DECANNULATION)	
+1	DECLINE	
+5	DECLINING	
+1	DECONDITIONED	Fatigue
+1	DECONDITIONING	Fatigue
+4	DECREASE	DroppingHgb
+7	DECREASED	DroppingHgb
+18	DECREASING	DroppingHgb
+1	DEFIBROTIDE	
+2	DELIRIUM	Delerium
+5	DELIVERY	
+8	DEMAND	Angina-Ischemia
+1	DEMANDE	Angina-Ischemia
+1	DEMOGRAPHIC	
+1	DEP	TransfusionDependent
+2	DEPENDANT	TransfusionDependent
+1	DEPENDENET	TransfusionDependent
+20	DEPENDENT	TransfusionDependent
+1	DEPENDING	
+1	DEPL	
+2	DEPLETION	
+1	DESATURATIONS	Hypoxia
+5	DESPITE	
+1	DEVICES	
+1	DHPG	
+3	DIAGNOSIS	
+4	DIALYSIS	RenalFailure
+1	DIBIASE	
+7	DIC	Coagulopathy
+2	DID	
+2	DIFFUSE	
+1	DILUTE	
+2	DIRECTED	
+49	DISCHARGE	ImminentDischarge
+16	DISCHARGED	ImminentDischarge
+2	DISCHARGING	ImminentDischarge
+1	DISCUSSED	
+23	DISEASE	
+1	DISORDER	
+1	DISPOD	
+1	DISSECTION	
+1	DISTANT	
+3	DISTRESS	
+1	DIURESIS	
+4	DIZZINESS	Lightheaded
+1	DIZZINESS/WEAKNESS	Lightheaded
+5	DIZZY	Lightheaded
+3	DO	
+4	DO2	
+1	DOBUTAMINE	Vasopressor
+3	DOE	SOB
+2	DOES	
+1	DONATION	Donor
+2	DONE	
+12	DONOR	Donor
+1	DOSE	
+40	DOWN	DroppingHgb
+1	DOWNTREND	DroppingHgb
+27	DOWNTRENDING	DroppingHgb
+3	DOWN-TRENDING	DroppingHgb
+1	DOWNWARDS	DroppingHgb
+18	DR	PerOtherMD
+4	DRAIN	
+1	DRAINS	
+1	DRAWN	
+1	DRIFT	
+3	DRIFTING	DroppingHgb
+1	DRIP	
+1	DRIVE	
+44	DROP	DroppingHgb
+1	DROPPED	DroppingHgb
+33	DROPPING	DroppingHgb
+1	DS	
+9	DUE	
+5	DURING	
+1	DVT	VTE
+1	DYSFUNCTION	
+4	DYSPNEA	SOB
+1	DYSRHYTHMIA	Arrhythmia
+10	EARLY	
+1	EBL	Surgery,Bleeding
+1	EBV	
+1	ECHO	
+1	EC-IC	
+21	ECMO	VascularDevice
+2	EF	CHF
+1	EFFECTIVE	
+1	EFFUSION	
+1	"EFFUSION,MD"	
+1	EFFUSION/TAMPONADE	
+3	EFFUSIONS	
+1	EGD	Procedure
+1	EGDT	
+1	EJECTION	CHF
+3	EKG	Angina-Ischemia
+7	ELDERLY	
+4	ELEVATED	
+1	ELEVATING	
+1	ENCEPHALOPATHY	Delerium
+1	END	
+1	ENDARTERECTOMY	Surgery
+1	ENDOSCOPY	Procedure
+1	ENDOVASCULAR	Surgery
+1	END-STAGE	
+3	ENERGY	Fatigue
+1	ENGRAFTED	
+1	ENT/PLASTIC	Surgery
+1	ENTER	
+1	ENTEROCUTANEOUS	
+3	EPISODE	
+1	EPISODIC	
+1	EPISTAXIS	Bleeding
+1	ER	
+1	ERCP	Procedure
+7	ESLD	Cirrhosis
+1	ESOPHAGEAL	
+3	ESRD	RenalFailure
+1	ESTABLISHED	
+1	ETOH	
+3	EVALUATION	
+1	EVANS	
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+3	EVERY	
+1	EVIDENCE	
+1	EVOLVING	
+1	EXACERBATION	
+2	EXAM	
+1	EXCHANGE	
+2	EXERTION	SOB
+2	EXHAUSTED	Fatigue
+2	EXPECT	ExpectDrop
+2	EXPECTATION	ExpectDrop
+23	EXPECTED	ExpectDrop
+1	EXPIRED	
+1	EXTENSIVE	
+3	EXTREME	
+2	EXTUBATED	
+2	EXTUBATION	
+1	F/U	
+22	FAILURE	
+14	FALL	
+1	FALLEN	
+11	FALLING	DroppingHgb
+1	FALLS	
+1	FAR	
+18	FATIGUE	Fatigue
+4	FATIGUED	Fatigue
+3	FEBRILE	Fever
+2	FEELING	
+2	FEELS	
+1	FELLOW	
+1	FEMORAL	
+2	FEMUR	
+2	FEVER	Fever
+3	FEVERS	Fever
+1	FIBRILLATION	Arrhythmia
+1	FISTULA	
+3	FLAG	
+5	FLAP	
+3	FLOW	
+1	FLOWS	
+1	FLUCTUATING	
+7	FLUID	NeedFluid
+5	FLUIDS	NeedFluid
+1	FLUTTER	Arrhythmia
+1	FOLEY	
+1	FOLLICULAR	
+1	FOLLOW	
+1	FOLLOWING	
+5	FOLLOW-UP	
+102	FOR	
+2	FORD	
+1	FRACTION	
+3	FRACTURE	Surgery
+1	FRAIL	
+1	FREE	
+1	FREQUENCY	
+1	FREQUENT	
+40	FROM	
+1	FULL	
+1	FUNCTION	
+1	FUNGEMIA	Sepsis
+8	FURTHER	
+4	FUTURE	
+1	FXN	
+1	G	
+1	GASTRIC	
+1	GENERAL	
+1	GENERALIZED	
+7	GET	
+9	GETTING	
+19	GI	
+4	GIB	Bleeding
+8	GIVE	
+7	GIVEN	
+4	GO	
+15	GOAL	
+4	GOALS	
+12	GOING	
+1	GOOD	
+1	GOT	
+1	GOTTHEINER	
+2	GRAFT	
+1	GROIN	
+1	GROSS	
+2	GTT	
+1	GUARD	
+1	GUIAC	Bleeding
+2	GUIDELINES	
+1	GUIDELINS/PROTOCOL	
+1	GUIDLINES	
+1	GUNSHOT	
+2	GUT	
+3	H&H	
+1	H/G	
+5	H/H	
+13	H/O	
+6	HAD	
+1	HAND	
+1	HARTFORD	
+42	HAS	
+6	HAVE	
+2	HAVING	
+57	HB	
+1	HB/HCT	
+3	HB<7	Hgb<7
+3	HB<8	Hgb<8
+1	HB<8.0	Hgb<8
+1	HB=7.1	Hgb<8
+12	HBG	
+1	HBG<7	Hgb<7
+4	HBG<8	Hgb<8
+1	HBSS	
+194	HCT	
+1	HCT-	
+3	HCT<21	Hgb<8
+1	HCT<24	Hgb<8
+1	HCT<25	AnemiaHgb>8
+1	HCT=20.1	Hgb<8
+2	HCT>25	AnemiaHgb>8
+1	HCT-21	Hgb<8
+1	HCT-21.4	Hgb<8
+1	HCT-24	Hgb<8
+1	HCV	
+4	HD	RenalFailure
+3	HE	
+1	HEAD	
+1	HEADED	
+2	HEALING	
+17	HEART	
+1	HEAVY	
+1	HELD	
+2	HELP	
+1	HEMANGIOMA	
+8	HEMATOCRIT	
+1	HEMATOLOGIC	
+1	HEMATOLOGIST	PerHeme
+8	HEMATOLOGY	PerHeme
+5	HEMATURIA	Bleeding
+3	HEME	
+1	HEME-ONC	
+1	HEMMORHAGE	Bleeding
+1	HEMOCONCENTRATED	
+2	HEMODIALYSIS	RenalFailure
+1	HEMODILUTE	
+1	HEMODYNAICS	
+11	HEMODYNAMIC	
+9	HEMODYNAMICALLY	
+25	HEMOGLOBIN	
+9	HEMOLYSIS	Hemolysis
+6	HEMOLYTIC	Hemolysis
+1	HEMOLYZING	Hemolysis
+2	HEMORRHAGE	Bleeding
+2	HEMORRHAGIC	Bleeding
+2	HEP	
+4	HEPARIN	Anticoagulated
+1	HEPATECTOMY	Surgery
+1	HEPATIC	
+4	HEPATOLOGY	PerHepatology
+1	HEPATORENAL	RenalFailure
+6	HER	
+2	HERE	
+33	HG	
+5	HG<7	Hgb<7
+1	HG<7.0	Hgb<7
+3	HG<8	Hgb<8
+529	HGB	
+1	-HGB	
+1	"HGB,8"	
+3	HGB/HCT	
+2	HGB<	
+1	HGB<10	AnemiaHgb>8
+15	HGB<7	Hgb<7
+37	HGB<8	Hgb<8
+3	HGB<8.0	Hgb<8
+1	HGB<8.5-9	AnemiaHgb>8
+1	HGB<9	AnemiaHgb>8
+1	HGB=6.7	Hgb<7
+1	HGB=7.0	Hgb<8
+1	HGB=7.2	Hgb<8
+2	HGB=7.4	Hgb<8
+1	HGB=7.5	Hgb<8
+1	HGB=7.7	Hgb<8
+1	HGB=7.8	Hgb<8
+6	HGB>8	AnemiaHgb>8
+1	HGB6.4	Hgb<7
+1	HGB7.6	Hgb<8
+1	HGC/HCT	
+1	HGD	
+1	HGT	
+1	HI	
+22	HIGH	
+7	HIGHER	
+3	HIP	
+6	HIS	
+1	HISTORICALLY	
+24	HISTORY	
+1	HOCM	
+6	HOME	ImminentDischarge
+2	HOPEFULLY	
+3	HOSPITAL	
+1	HOSPITALIZATION	
+1	HOTN	
+1	HOURS)	
+1	HRS	
+1	HSEPSIS	Sepsis
+2	HTN	
+17	HX	
+1	HYDRATION	NeedFluid
+1	HYDROXYUREA	Chemotherapy
+3	HYPERTENSION	
+1	HYPOKALEMIA	
+2	HYPONATREMIA	
+1	HYPOPROLIFERATIVE	
+51	HYPOTENSION	Hypotension
+19	HYPOTENSIVE	Hypotension
+1	HYPOTENSIVE/HYPOXIC	Hypotension
+1	HYPOTENSIVWE	Hypotension
+1	HYPOTESION	Hypotension
+2	HYPOVOLEMIC	NeedFluid
+1	HYPOXEMIA	Hypoxia
+3	HYPOXEMIC	Hypoxia
+3	HYPOXIA	Hypoxia
+3	HYPOXIC	Hypoxia
+1	HYSTERECTOMY	Surgery
+1	I	
+1	IABP	VascularDevice
+1	ICH	CNS
+1	ICON	
+1	ICU	
+1	IDENTIFIED	
+4	IF	
+1	ILD	
+1	ILIAC	
+6	ILL	
+2	ILLNESS	
+1	IMPELLA	VascularDevice
+44	IMPENDING	
+1	IMPERATIVE	
+5	IMPROVE	
+5	IMPROVED	
+1	IMPROVEMENT	
+139	IN	
+1	INADEQUATE	
+8	INAPPROPRIATE	
+2	INCISION	
+1	INCL	
+4	INCREASE	
+4	INCREASED	
+4	INCREASING	
+2	INDICATED	
+1	INDUCED	
+1	INDUCTION	
+1	INFARCT	
+1	INFARCTION	
+2	INFECTION	Sepsis
+1	INFILTRATION	
+1	INITIATING	
+4	INJURY	
+1	INOTROPES	Vasopressors
+2	INPT	
+2	INR	
+7	INSTABILITY	Unstable
+1	INSUFFICIENCY	
+4	INTERVENTION	
+1	INTERVENTIONAL	
+1	INTESTINALIS	
+2	INTO	
+1	INTRA	
+1	INTRA-OP	
+1	INTRAVASC	
+2	INTRAVASCULAR	
+24	INVASIVE	
+1	INVERSIONS	
+2	INVOLVEMENT	
+1	IONTROPES	
+1	IRREGULAR	Arrhythmia
+74	IS	
+26	ISCHEMIA	Angina-Ischemia
+3	ISCHEMIC	Angina-Ischemia
+1	ISHCEMIC	Angina-Ischemia
+2	ISSUES	
+1	ISTAT	
+4	IT	
+2	IV	
+1	IVC	
+3	JUST	
+1	JUXNL	
+1	"K,JHBLKJ"	
+1	KAMAL	
+4	KEEP	
+1	KEEPING	
+2	KEPT	
+5	KIDNEY	RenalFailure
+1	KNOW	
+2	KNOWN	
+1	KUSHLAN	
+3	LAB	
+2	LABS	
+1	LAC)	
+2	LACTATE	Hypoperfusion
+1	LAD	
+1	LAPORT	
+14	LARGE	
+4	LAST	
+1	LATERAL	
+1	LEADS	
+4	LEAK	
+1	LEFT	
+31	LESS	
+11	LEUKEMIA	LeukemiaAcute
+1	LEUKEMIC	LeukemiaAcute
+3	LEVEL	
+1	LEVELS	
+1	LEVOWITZ	
+1	LGIB	Bleeding
+1	LIEKLY	
+3	LIGHTHEADED	Lightheaded
+4	LIGHTHEADEDNESS	Lightheaded
+1	LIGTH	
+4	LIKE	
+13	LIKELY	
+1	LIMIT	
+1	LIMITED	
+1	LIMITING	
+1	LINE)	
+1	LISTED	
+1	LITTLE	
+50	LIVER	Cirrhosis
+1	LIVES	
+1	LOAD	
+1	LOCALLY	
+1	LOOK	
+1	LOS	
+27	LOSS	Bleeding
+1	LOSSES	
+3	LOST	
+1	LOTS	
+1	LOVENOW	
+89	LOW	
+3	LOWER	
+1	LOW-GRADE	Fever
+1	LSC	
+4	LUNG	
+2	LVAD	VascularDevice
+7	LYMPHOMA	Lymphoma
+1	LYMPHOMATOUS	
+1	MACROGLOBULINEMIA	
+1	MAHA	
+3	MAINTAIN	
+1	MAJOR	
+3	MALIGNANCY	Cancer
+1	MANGLED	
+4	MAP	
+27	MARROW	BMT
+4	MASSIVE	
+1	MAXIMIZE	
+3	MAY	
+17	MD	
+2	MD)	
+19	MDS	MDS
+1	MECH	
+6	MECHANICAL	VascularDevice
+2	MECHANICS	
+2	MEDICINE	
+1	MEET	
+1	MEETS	
+5	MELENA	Bleeding
+1	MENORRHAGIA	Bleeding
+2	MENTAL	
+1	MESSAGE	
+4	METASTATIC	
+1	METS	
+1	MFM	
+3	MG/DL	
+5	MI	
+1	MILDLY	
+1	MINUTES	
+1	MITCHELL	
+1	MITRAL	
+10	MIXED	Hypoperfusion
+1	ML	
+2	MM	MM
+1	MODIFICATION	
+1	MONDAY	
+1	MONITOR	
+2	MONTH	
+1	MONTHLY	
+1	MORE	
+1	MORNING	
+1	MOST	
+2	MOYAMOYA	
+1	MULT	
+2	MULTI-FACTORIAL	
+7	MULTIPLE	
+1	MVO2	Hypoperfusion
+1	MVR	
+2	MYELODYSPLASTIC	MDS
+4	MYELOFIBROSIS	Myelofibrosis
+4	MYELOMA	MM
+1	MYELOSUPPRESSED	Myelosuppressed
+4	MYELOSUPPRESSION	Myelosuppressed
+3	MYELOSUPPRESSIVE	Myelosuppressed
+1	MYELOSUPPROSED	Myelosuppressed
+1	MYELOSUPRESSIVE	Myelosuppressed
+1	MYLEOSUPPRESIVE	Myelosuppressed
+3	MYLEOSUPRESSIVE	Myelosuppressed
+1	MYLEPSUPRESSIVE	Myelosuppressed
+2	NA	
+4	NADIR	
+1	NARAYAN	
+1	NAUSEATED	
+3	NEAR	
+1	NEARLY	
+18	NEED	
+18	NEEDS	
+1	NEOSYNEPHRINE	
+1	NEPHROLOGIST	PerRenal
+1	NEPHROLOGY	PerRenal
+1	NEURO	
+1	NEUROLOGICALLY	
+2	NEUROSURGERY	
+5	NEUTROPENIC	
+4	NEW	
+4	NEXT	
+1	NHL	
+1	NO	
+1	NON-COLLOID	
+1	NORMAL	
+1	NOSE	
+1	NOSEBLEEDS	Bleeding
+18	NOT	
+1	NOTE	
+1	NOTED	
+18	NOW	
+1	NS	
+1	NSCLC	Cancer
+6	NSTEMI	ACS
+1	NSVT	Arrhythmia
+1	NTP	
+1	NUMERABLE	
+1	O/N	
+8	O2	Hypoxia
+1	OB	
+114	OF	
+2	OFF	
+1	OLD	
+1	OLT	
+5	OLTX	Cirrhosis,Transplant
+62	ON	
+2	ONC	Cancer
+1	ONCOLOGIC	PerOncology
+2	ONCOLOGIST	PerOncology
+13	ONCOLOGY	PerOncology
+7	ONE	
+1	ONGOIGN	
+12	ONGOING	
+2	ONLY	
+1	ONOCOLOGIST	PerOncology
+1	ONSET	
+2	OOB	
+2	OOZING	Bleeding
+22	OP	Surgery
+1	OPEN	
+1	OPERATION	Surgery
+1	OPERATIVE	Surgery
+1	OPTIMAL	
+1	OPTIMIZATION	
+2	OPTIMIZE	
+23	OR	Surgery
+23	ORDER	PerOtherMD
+5	ORDERED	
+5	ORDERS	
+5	ORGAN	
+2	ORIF	
+1	ORIGIN	
+3	ORTHO	Surgery
+1	ORTHOPAEDIC	Surgery
+3	ORTHOPEDIC	Surgery
+2	ORTHOSTASIS	Lightheaded
+1	ORTHOSTATIC	Lightheaded
+1	OSH	
+1	OSTOMY	
+2	OTHER	
+3	OUT	
+5	OUTPATIENT	
+6	OUTPT	
+1	OUTPT)	
+5	OUTPUT	
+2	OUTSIDE	
+3	OVER	
+1	OVERLOAD	
+2	OVERNIGHT	
+1	OWN	
+7	OXYGEN	Hypoxia
+4	OXYGENATION	Hypoxia
+2	P	
+1	PA	
+1	PACEMAKER	
+1	PACU	
+1	"PACU,POST"	
+9	PAIN	
+1	PALPITATIONS	Arrhythmia
+5	PAMF	
+1	PANCREATIC	
+2	PANCREATITIS	
+2	PANCYTOPENIA	Myelosuppressed
+2	PANCYTOPENIC	Myelosuppressed
+3	PARAMETERS	
+1	PARAVALVULAR	
+1	PARTIAL	
+2	PARTICIPATE	
+2	PARTNER	
+3	PAST	
+298	PATIENT	
+5	PATIENTS	
+1	PATIENTY	
+1	PCI	ACS
+2	PD	RenalFailure
+3	PE	VTE
+1	PEG	
+16	PENDING	
+87	PER	PerOtherMD
+8	PERFUSION	
+1	PERFUSION-DEPENDENT	
+3	PERICARDIAL	
+2	PERIOD	
+2	PERI-OP	Surgery
+1	PERIPHERAL	
+1	PERIPHERY	
+1	PERSISTANT	
+12	PERSISTENT	
+2	PERSISTENTLY	
+1	PHLEBOTOMY	
+2	PHTN	
+1	PHYSCIAN	
+6	PHYSICIAN	
+4	PHYSICIANS	
+3	PHYSIOLOGY	
+1	PIGTAIL	
+1	PLACE	
+6	PLACEMENT	
+4	PLAN	
+3	PLANED	
+4	PLANNED	
+6	PLANNING	
+2	PLASMAPHERESIS	
+2	PLASTIC	
+1	PLASTICS	
+1	PLATELETS	
+1	PLATELET-TRANSFUSION	
+3	PLEASE	
+5	PLEURAL	
+1	PLT	
+1	PM	
+1	PMHX	
+1	PNEUMATOSIS	
+4	POD	
+2	POD1	
+1	POINT	
+1	POINTLESS	
+2	POINTS	
+1	POLICY	
+1	POLYTRAUMA	
+6	POOR	
+2	POPPING	
+1	POPS	
+1	POSITIVE	
+17	POSSIBLE	
+66	POST	
+1	POST-ANEMIA	
+1	POST-CABG	CABG
+1	POST-CARDIOTHORACIC	Surgery
+1	POST-CEREBROVASCULAR	CNS
+1	POSTCHEMO	Chemotherapy
+1	POST-CHEMO	Chemotherapy
+1	POST-KIDNEY	Surgery
+34	POSTOP	Surgery
+30	POST-OP	Surgery
+10	POSTOPERATIVE	Surgery
+3	POST-OPERATIVE	Surgery
+1	POST-PROCEDURE	Procedure
+1	POST-SURG	Surgery
+1	POSTSURGERY	Surgery
+1	POST-SURGERY	Surgery
+2	POST-SURGICAL	Surgery
+1	POST-TRANSPLANT	Surgery
+1	POSTUSRGICAL	Surgery
+4	POTENTIAL	
+5	PRACTICE	
+11	PRBC	
+3	PRBCS	
+10	PRE	
+1	PREDICTED	
+6	PREFERENCE	
+1	PREGNANCY	
+2	PREOP	Surgery
+1	PRE-OP	Surgery
+4	PREPARATION	
+1	PREPARE	
+1	PREPROCEDURE	Procedure
+1	PRESENTS	
+2	PRESSOR	Vasopressors
+7	PRESSORS	Vasopressors
+1	PRESSURE	
+1	PRESUMED	
+1	PRE-SYCNOPAL	Lightheaded
+1	PRETTY	
+2	PREVENT	
+1	PREVENTING	
+3	PREVIOUS	
+2	PREVIOUSLY	
+4	PRIMARY	
+20	PRIOR	
+3	PRN	
+1	PROBABLE	
+65	PROCEDURE	Procedure
+1	PROCEDURE/SURGERY	Surgery
+2	PROCEDURES	Procedure
+2	PROCEUDRE	Procedure
+1	PRODUCE	
+1	PRODUCTS	
+4	PROGRESSIVE	DroppingHgb
+2	PROGRESSIVELY	DroppingHgb
+1	PRONE	
+4	PROSTATE	
+3	PROTECT	
+51	PROTOCOL	PerProtocol
+1	PROVEN	
+1	PROVIDER	
+1	PROXIMAL	
+301	PT	
+1	"PT.""S"	
+9	PTS	
+1	PULM	
+4	PULMONARY	
+1	PURPOSE	
+1	Q	
+1	QUICKLY	
+1	R/T	
+5	RADIATION	Radiotherapy
+1	RADIOTHERAPY	Radiotherapy
+1	RAISED	
+3	RAPID	
+2	RAPIDLY	
+4	RBC	
+2	RBCS	
+1	READ	
+1	REALLY	
+4	REC	
+3	RECEIVE	
+2	RECEIVES	
+11	RECEIVING	
+36	RECENT	
+1	RECENTLY	
+9	RECEPIENT	
+1	RECEVING	
+1	RECHECK	
+1	RECIEVED	
+1	RECIPENT	
+3	RECIPIENT	
+6	RECOMMENDATION	
+1	RECOMMENDATIONS	
+6	RECOMMENDED	
+1	RECOMMENDING	
+2	RECONSTRUCTION	
+1	RECOVERING	
+2	RECOVERY	
+5	RECS	
+2	RED	
+1	REDUCE	
+1	REECENT	
+1	REENTERED	
+2	REFRACTORY	
+1	REGIMEN	
+1	REGULARLY	
+1	REJECTION	
+2	RELATED	
+1	REMAINS	
+15	RENAL	RenalFailure
+2	RENAL--PT	RenalFailure
+1	RE-ORDER	
+4	REPAIR	
+1	REPLACEMENT	
+1	REPLENISH	
+1	REPORTED	
+1	REPORTS	
+3	REQ	
+13	REQUEST	
+3	REQUESTED	
+1	REQUESTS	
+1	REQUIRE	
+3	REQUIRED	
+6	REQUIREMENT	
+3	REQUIREMENTS	
+7	REQUIRES	
+7	REQUIRING	
+1	RESCUSITATION	
+1	RESECTION	
+2	RESERVE	
+1	RESERVES	
+1	RESOLVED	
+2	RESOLVING	
+6	RESPIRATORY	
+3	RESPOND	InadequateResponse
+2	RESPONDING	
+1	RESPONDS	
+2	RESPONSE	InadequateResponse
+2	RESPONSIVE	InadequateResponse
+1	REST	
+2	RESUSCITATION	
+3	RETURN	
+1	RETURNING	
+1	REVISION	
+1	RIGHT	
+1	RISE	
+1	RISING	
+19	RISK	
+1	RISKS	
+1	ROUTINE	
+1	RUPTURED	
+2	RVR	
+1	RYU	
+64	S/P	
+1	S/S	
+1	SACRECTOMY	
+3	SAH	CNS
+1	SALVAGE	
+1	SARCOMA	Cancer
+4	SAT	
+1	SATS	Hypoxia
+1	SATURATION	Hypoxia
+2	SC	
+2	SCC	Cancer
+1	SCD	
+2	SCHEDULED	
+1	SCHRIER	PerHeme
+1	SCREEN	
+1	SCT	
+1	SCV02	Hypoperfusion
+3	SCVO2	Hypoperfusion
+1	SCVO2S	Hypoperfusion
+1	SDH	CNS
+5	SECONDARY	
+1	SEE	
+1	SEND	
+1	SENSE	
+32	SEPSIS	Sepsis
+1	SEPSIS/SIRS	Sepsis
+22	SEPTIC	Sepsis
+1	SEROSANGUINOUS	
+4	SERVICE	
+1	SERVICES	
+12	SETTING	
+21	SEVERE	
+1	SEVERELY	
+3	SHE	
+12	SHOCK	Hypotension
+1	SHORT	SOB
+5	SHORTNESS	SOB
+3	SHOULD	
+1	SICKEL	HemeDisorder
+8	SICKLE	HemeDisorder
+2	SICU	
+1	SIGN	
+10	SIGNIFICANT	
+2	SIGNIFICANTLY	
+1	SIGNOUT	
+4	SIGNS	
+4	SINUS	
+2	SITE	
+1	SLO	
+3	SLOW	
+7	SLOWLY	
+2	SMALL	
+3	SNF	ImminentDischarge
+1	SNORKEL	
+10	SO	
+11	SOB	SOB
+1	SOFT	
+2	SOME	
+3	SOON	
+2	SOONER	
+1	SOUNDS	
+3	SOURCE	
+2	SP	
+2	SPINAL	CNS
+1	SPINE	CNS
+1	SPUR	
+1	SPUTUM	
+1	SRINIVAS	PerOtherMD
+2	SS	
+1	STABILIZE	
+4	STABLE	
+1	STAGE	
+1	STAIRS	
+1	STANDARD	
+1	STANDING	
+1	STANFORD	
+3	START	
+2	STARTING	
+13	STATUS	
+2	STAY	
+1	STEADILY	
+1	STEM	ACS
+1	STEMI	ACS
+4	STENOSIS	
+2	STENT	
+6	STILL	
+2	STOOL	
+1	STRESS	
+3	STROKE	CNS
+1	STUDY	
+1	STUPID	
+1	SUBARACHNOID	CNS
+1	SUBOPTIMAL	
+1	SUCTION	
+1	SUDDEN	
+2	SUPPLY	
+1	SUPPOSED	
+3	SUPPRESSION	
+6	SURG	Surgery
+2	SURGEON	Surgery
+1	SURGER	Surgery
+2	SURGERIES	Surgery
+81	SURGERY	Surgery
+31	SURGICAL	Surgery
+2	SURVIVE	
+1	SUSPECT	
+4	SVO2	Hypoperfusion
+1	SVO2<60	Hypoperfusion
+1	SVO2S	Hypoperfusion
+2	SVT	Arrhythmia
+5	SX	Symptomatic
+2	SYMPOTMATIC	Symptomatic
+187	SYMPTOMATIC	Symptomatic
+1	SYMPTOMOLOGY	Symptomatic
+11	SYMPTOMS	Symptomatic
+2	SYMTOMATIC	Symptomatic
+3	SYNCOPAL	Lightheaded
+4	SYNCOPE	Lightheaded
+1	SYNDOME	
+1	SYNDROM	
+6	SYNDROME	
+2	SYNMPTOMATIC	Symptomatic
+1	SYSTLIC	
+1	TACH	Tachycardia
+5	TACHY	Tachycardia
+28	TACHYCARDIA	Tachycardia
+1	TACHYCARDIA/SOB	SOB
+20	TACHYCARDIC	Tachycardia
+1	TACHYPNEA	SOB
+1	TAKE	
+1	TAMPENODE	
+4	TAMPONADE	
+1	TAP	
+4	TBI	CNS
+1	T-CELL	
+10	TEAM	
+1	TEAMS	
+2	TENUOUS	
+1	TFUSE	
+2	THA	
+1	THALASEMIA	HemeDisorder
+3	THALASSEMIA	HemeDisorder
+29	THAN	
+1	THAN8	
+8	THAT	
+13	THE	
+1	THEM	
+1	THERAPEUTIC	
+8	THERAPY	
+1	THERE	
+1	THESE	
+1	THEY	
+1	THINKS	
+20	THIS	
+1	THOGUH	
+1	THOMBOCYTOPENIA	Thrombocytopenia
+2	THREE	
+12	THRESHOLD	
+2	THRIVE	
+3	THROMBOCYTOPENIA	Thrombocytopenia
+1	THROMBUS	
+1	THROWN	
+1	THUS	
+1	TIA	CNS
+1	TIM	
+2	TIME	
+2	TIPS	Procedure
+1	TIRED	
+1	TIRONE	
+1	TMRW	
+196	TO	
+51	TODAY	
+2	TOLERATE	
+1	TOLERATED	
+41	TOMORROW	
+1	TOMORRROW	
+1	TOMROROW	
+1	TONIGHT	
+1	TONSILLAR	
+1	TOTAL	
+1	TOWARD	
+1	TPL	
+1	TRACT	
+2	TRANFUSE	
+3	TRANFUSION	
+25	TRANSFUSE	
+9	TRANSFUSED	
+2	TRANSFUSING	
+63	TRANSFUSION	
+1	TRANSFUSION)	
+10	TRANSFUSIONS	
+70	TRANSPLANT	Transplant
+1	TRANSUFSION	
+1	TRASPLANT	Transplant
+2	TRAUMA	Trauma
+2	TRAUMATIC	Trauma
+2	TREATMENT	
+2	TREND	
+29	TRENDING	
+5	TRIAL	
+1	TRIALYSIS	RenalFailure
+1	TROP	
+5	TROPONIN	ACS
+1	TROPONINS	ACS
+2	TTP	HemeDisorder
+4	TUBE	
+3	TUMOR	
+1	T-WAVE	
+6	TWO	
+12	TX	
+2	TXF	
+6	TXP	Transplant
+3	TYPE	
+1	TYPICAL	
+3	U	
+7	UNABLE	
+3	UNDER	
+5	UNDERGO	
+7	UNDERGOING	
+3	UNDERLYING	
+1	UNDERSTANDS	
+1	UNDERYLING	
+1	UNEXPLAINED	
+18	UNIT	
+5	UNITS	
+3	UNKNOWN	
+13	UNSTABLE	Unstable
+1	UNWELL	
+1	UO	
+5	UP	
+2	UPCOMING	
+1	UPCOMNIG	
+1	UPON	
+1	URD	
+1	URGENT	
+1	USUALLY	
+2	VAC	
+1	VAD	
+1	VAGINAL	
+1	VALUE	
+7	VALVE	
+1	VARICES	
+1	VASC	
+7	VASCULAR	
+1	VASCULOPATHY	
+2	VASOPRESSOR	Vasopressors
+1	VASOPRESSORS	Vasopressors
+4	VASOSPASM	Vasospasm
+1	VAWSCULOPATHY	
+1	VB	
+10	VENOUS	
+1	VERBAL	
+1	VERONICA	
+9	VERY	
+1	VESSELS	
+7	VOLUME	
+1	VT	Arrhythmia
+1	VTACH	Arrhythmia
+15	W	
+16	W/	
+1	W/ALL	LeukemiaAcute
+1	W/AML	LeukemiaAcute
+3	W/HGB	
+3	W/SAH	CNS
+2	WALDENSTROMS	HemeDisorder
+6	WANT	
+1	WANTING	
+8	WANTS	
+1	WARNING	
+5	WAS	
+1	WASTE	
+1	WBLEEDING	
+1	WE	
+1	WEAK	Fatigue
+1	WEAKER	Fatigue
+4	WEAKNESS	Fatigue
+1	WEAKNESS)	Fatigue
+1	WEAN	
+2	WEANING	
+3	WEEK	
+4	WEEKEND	
+2	WEEKLY	
+5	WEEKS	
+1	WELL	
+2	WENT	
+3	WHEN	
+2	WHICH	
+1	WHILE	
+1	WHIPPLE	Surgery
+7	WHO	
+1	WHOSE	
+1	WIHT	
+1	WIHTOUT	
+36	WILL	
+3	--WILL	
+1	WIT	
+331	WITH	
+2	WITHIN	
+1	WITHOUT	
+1	WKS	
+1	WOMAN	
+2	WONG	
+1	WORSEN	
+11	WORSENING	
+7	WOULD	
+5	WOUND	
+1	WTIH	
+1	X	
+1	X2	
+1	XFUSION	
+2	XRT	
+3	Y/O	
+1	YEAR	
+1	YEST	
+5	YESTERDAY	
+2	YET	
+5	YO	
+3	YOUR	
+1	ZWEIG	
+4		
diff --git a/scripts/CDSS/addSynonyms.sql b/scripts/Archive/CDSS/addSynonyms.sql
similarity index 96%
rename from scripts/CDSS/addSynonyms.sql
rename to scripts/Archive/CDSS/addSynonyms.sql
index 3933a589..565d496e 100644
--- a/scripts/CDSS/addSynonyms.sql
+++ b/scripts/Archive/CDSS/addSynonyms.sql
@@ -1,66 +1,66 @@
--- Add common synonyms to major clinical items, primarily to facilitate user interface simulation order searches by name
-
-update clinical_item
-set description = description || ' [CXR]'
-where description ~* '^xr.*chest';
-
-
-update clinical_item
-set description = description || ' [BMP]'
-where name = 'LABMETB';
-
-
-update clinical_item
-set description = description || ' [CMP]'
-where name = 'LABMETC';
-
-update clinical_item
-set description = description || ' [LFT]'
-where name = 'LABHFP';
-
-update clinical_item
-set description = description || ' [EKG]'
-where name = 'EKG5';
-
-update clinical_item
-set description = description || ' [PT/INR]'
-where name = 'LABPT';
-
-update clinical_item
-set description = description || ' [UA]'
-where description ~* 'urinalysis';
-
-update clinical_item
-set description = description || ' [NS IVF][Normal Saline]'
-where name = 'RXCUI9863'
-and description ~* 'intravenous';
-
-update clinical_item
-set description = description || ' [ABG]'
-where description ~* 'blood gas'
-and description ~* 'arteria'
-and name ~* '^LAB|^POC';
-
-update clinical_item
-set description = description || ' [VBG]'
-where description ~* 'blood gas'
-and description ~* 'venous'
-and name ~* '^LAB|^POC';
-
-update clinical_item
-set description = description || ' [LR][Lactated Ringer]'
-where description ~* 'lactate.*intraven';
-
-update clinical_item
-set description = description || ' [RBC]'
-where description ~* 'red blood cell'
-and name not like 'ICD%';
-
-update clinical_item
-set description = description || ' [PLT]'
-where description ~* 'platelet'
-and description not like '%PLT%';
-
-update clinical_item
-set description = description || ' [FFP]'
-where description ~* 'fresh frozen plasma';
+-- Add common synonyms to major clinical items, primarily to facilitate user interface simulation order searches by name
+
+update clinical_item
+set description = description || ' [CXR]'
+where description ~* '^xr.*chest';
+
+
+update clinical_item
+set description = description || ' [BMP]'
+where name = 'LABMETB';
+
+
+update clinical_item
+set description = description || ' [CMP]'
+where name = 'LABMETC';
+
+update clinical_item
+set description = description || ' [LFT]'
+where name = 'LABHFP';
+
+update clinical_item
+set description = description || ' [EKG]'
+where name = 'EKG5';
+
+update clinical_item
+set description = description || ' [PT/INR]'
+where name = 'LABPT';
+
+update clinical_item
+set description = description || ' [UA]'
+where description ~* 'urinalysis';
+
+update clinical_item
+set description = description || ' [NS IVF][Normal Saline]'
+where name = 'RXCUI9863'
+and description ~* 'intravenous';
+
+update clinical_item
+set description = description || ' [ABG]'
+where description ~* 'blood gas'
+and description ~* 'arteria'
+and name ~* '^LAB|^POC';
+
+update clinical_item
+set description = description || ' [VBG]'
+where description ~* 'blood gas'
+and description ~* 'venous'
+and name ~* '^LAB|^POC';
+
+update clinical_item
+set description = description || ' [LR][Lactated Ringer]'
+where description ~* 'lactate.*intraven';
+
+update clinical_item
+set description = description || ' [RBC]'
+where description ~* 'red blood cell'
+and name not like 'ICD%';
+
+update clinical_item
+set description = description || ' [PLT]'
+where description ~* 'platelet'
+and description not like '%PLT%';
+
+update clinical_item
+set description = description || ' [FFP]'
+where description ~* 'fresh frozen plasma';
diff --git a/scripts/CDSS/assocAnalysis.py b/scripts/Archive/CDSS/assocAnalysis.py
similarity index 97%
rename from scripts/CDSS/assocAnalysis.py
rename to scripts/Archive/CDSS/assocAnalysis.py
index ca9fc252..ebf119b2 100644
--- a/scripts/CDSS/assocAnalysis.py
+++ b/scripts/Archive/CDSS/assocAnalysis.py
@@ -1,81 +1,81 @@
-"""
-Example shell script background call to run script to start up several data conversion processes in series
-nohup python scripts/CDSS/assocAnalysis.py &> log/driver.assocAnalysis &
-Not much DB resources required, just some memory.  Bottle neck is more IO operations
-"""
-
-import subprocess;
-from medinfo.common.Util import stdOpen, log, ProgressDots;
-
-LOG_FILE_TEMPLATE = "log/assocAnalysis.%s";
-BUFFER_FILENAME = "results/assocAnalysis.buffer.%s";
-
-baseArgv = \
-    [   "python","-m","medinfo.cpoe.AssociationAnalysis",
-        "-u","10000",
-        "-a","2000000",    # 1000000 seems to just fit with 7.5GB memory.  Running batches of 3000 patients yields ~5M associations requiring ~25GB memory
-        #"-p","1000",
-    ];
-
-PATIENT_ID_FILES = \
-    [   
-        #"sourceData/patientIds.5year.0.tab.gz",
-        #"sourceData/patientIds.5year.1.tab.gz",
-        #"sourceData/patientIds.5year.2.tab.gz",
-        #"sourceData/patientIds.5year.3.tab.gz",
-        #"sourceData/patientIds.5year.4.tab.gz",
-        #"sourceData/patientIds.5year.5.tab.gz",
-        #"sourceData/patientIds.5year.6.tab.gz",
-        #"sourceData/patientIds.5year.7.tab.gz",
-        #"sourceData/patientIds.5year.8.tab.gz",
-        #"sourceData/patientIds.5year.9.tab.gz",
-        #"sourceData/patientIds.5year.-1.tab.gz",
-        #"sourceData/patientIds.5year.-2.tab.gz",
-        #"sourceData/patientIds.5year.-3.tab.gz",
-        #"sourceData/patientIds.5year.-4.tab.gz",
-        #"sourceData/patientIds.5year.-5.tab.gz",
-        #"sourceData/patientIds.5year.-6.tab.gz",
-        #"sourceData/patientIds.5year.-7.tab.gz",
-        #"sourceData/patientIds.5year.-8.tab.gz",
-        #"sourceData/patientIds.5year.-9.tab.gz",
-    ];
-DATE_LIMITS = \
-    [   
-        ["2010-01-01",  "2014-01-01"],
-
-        #["2012-12-01",  "2013-01-01"], # 1 month
-        #["2012-11-01",  "2013-01-01"], # 2 month
-        #["2012-10-01",  "2013-01-01"], # 3 month
-        #["2012-07-01",  "2013-01-01"], # 6 month
-        #["2012-01-01",  "2013-01-01"], # 12 month
-        #["2011-01-01",  "2013-01-01"], # 24 month
-        #["2009-01-01",  "2013-01-01"], # 48 month
-        
-        #["2008-01-01",  "2009-01-01"],
-        #["2009-01-01",  "2010-01-01"],
-        #["2010-01-01",  "2011-01-01"],
-        #["2011-01-01",  "2012-01-01"],
-        #["2012-01-01",  "2013-01-01"],
-        #["2013-01-01",  "2014-01-01"],
-        #["2014-01-01",  "2015-01-01"],
-    ];
-specificArgvList = \
-    [   
-    ];
-
-for patientIdFilename in PATIENT_ID_FILES:
-    for (startDate, endDate) in DATE_LIMITS:
-            specificArgv = ["-i", patientIdFilename, "-s", startDate,"-e", endDate, "-b", "results/assocBuffer.%s.%s.%s" % (patientIdFilename.replace("/","_"), startDate, endDate) ];
-            specificArgvList.append(specificArgv);
-
-prog = ProgressDots(1,1,"Processes",total=len(specificArgvList));
-for specificArgv in specificArgvList:
-    key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
-    key = key.replace("/","..");
-    argv = list(baseArgv)
-    argv.extend(specificArgv);
-    log.info( LOG_FILE_TEMPLATE % key );
-    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
-    subprocess.call(argv, stderr=logFile);
-    prog.update();
-prog.printStatus();
+"""
+Example shell script background call to run script to start up several data conversion processes in series
+nohup python scripts/CDSS/assocAnalysis.py &> log/driver.assocAnalysis &
+Not much DB resources required, just some memory.  Bottle neck is more IO operations
+"""
+
+import subprocess;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+
+LOG_FILE_TEMPLATE = "log/assocAnalysis.%s";
+BUFFER_FILENAME = "results/assocAnalysis.buffer.%s";
+
+baseArgv = \
+    [   "python","-m","medinfo.cpoe.AssociationAnalysis",
+        "-u","10000",
+        "-a","2000000",    # 1000000 seems to just fit with 7.5GB memory.  Running batches of 3000 patients yields ~5M associations requiring ~25GB memory
+        #"-p","1000",
+    ];
+
+PATIENT_ID_FILES = \
+    [   
+        #"sourceData/patientIds.5year.0.tab.gz",
+        #"sourceData/patientIds.5year.1.tab.gz",
+        #"sourceData/patientIds.5year.2.tab.gz",
+        #"sourceData/patientIds.5year.3.tab.gz",
+        #"sourceData/patientIds.5year.4.tab.gz",
+        #"sourceData/patientIds.5year.5.tab.gz",
+        #"sourceData/patientIds.5year.6.tab.gz",
+        #"sourceData/patientIds.5year.7.tab.gz",
+        #"sourceData/patientIds.5year.8.tab.gz",
+        #"sourceData/patientIds.5year.9.tab.gz",
+        #"sourceData/patientIds.5year.-1.tab.gz",
+        #"sourceData/patientIds.5year.-2.tab.gz",
+        #"sourceData/patientIds.5year.-3.tab.gz",
+        #"sourceData/patientIds.5year.-4.tab.gz",
+        #"sourceData/patientIds.5year.-5.tab.gz",
+        #"sourceData/patientIds.5year.-6.tab.gz",
+        #"sourceData/patientIds.5year.-7.tab.gz",
+        #"sourceData/patientIds.5year.-8.tab.gz",
+        #"sourceData/patientIds.5year.-9.tab.gz",
+    ];
+DATE_LIMITS = \
+    [   
+        ["2010-01-01",  "2014-01-01"],
+
+        #["2012-12-01",  "2013-01-01"], # 1 month
+        #["2012-11-01",  "2013-01-01"], # 2 month
+        #["2012-10-01",  "2013-01-01"], # 3 month
+        #["2012-07-01",  "2013-01-01"], # 6 month
+        #["2012-01-01",  "2013-01-01"], # 12 month
+        #["2011-01-01",  "2013-01-01"], # 24 month
+        #["2009-01-01",  "2013-01-01"], # 48 month
+        
+        #["2008-01-01",  "2009-01-01"],
+        #["2009-01-01",  "2010-01-01"],
+        #["2010-01-01",  "2011-01-01"],
+        #["2011-01-01",  "2012-01-01"],
+        #["2012-01-01",  "2013-01-01"],
+        #["2013-01-01",  "2014-01-01"],
+        #["2014-01-01",  "2015-01-01"],
+    ];
+specificArgvList = \
+    [   
+    ];
+
+for patientIdFilename in PATIENT_ID_FILES:
+    for (startDate, endDate) in DATE_LIMITS:
+            specificArgv = ["-i", patientIdFilename, "-s", startDate,"-e", endDate, "-b", "results/assocBuffer.%s.%s.%s" % (patientIdFilename.replace("/","_"), startDate, endDate) ];
+            specificArgvList.append(specificArgv);
+
+prog = ProgressDots(1,1,"Processes",total=len(specificArgvList));
+for specificArgv in specificArgvList:
+    key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
+    key = key.replace("/","..");
+    argv = list(baseArgv)
+    argv.extend(specificArgv);
+    log.info( LOG_FILE_TEMPLATE % key );
+    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
+    subprocess.call(argv, stderr=logFile);
+    prog.update();
+prog.printStatus();
diff --git a/scripts/CDSS/insertData.bat b/scripts/Archive/CDSS/insertData.bat
similarity index 92%
rename from scripts/CDSS/insertData.bat
rename to scripts/Archive/CDSS/insertData.bat
index 84200f4c..46e9cb20 100644
--- a/scripts/CDSS/insertData.bat
+++ b/scripts/Archive/CDSS/insertData.bat
@@ -1,76 +1,76 @@
-set DATA_DIR="C:\Box Sync\SecureFiles\CDSS\inpatient5year"
-
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Demographics.csv.gz -t stride_patient -f death_date
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_DX_List_5Yr.csv.gz -t stride_dx_list -f noted_date,resolved_date
-
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Med_5Yr.csv.gz -t stride_order_med  -f ordering_date,start_taking_time,order_end_time,end_taking_time,discon_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_MedMixInfo_5Yr.csv.gz -t stride_order_medmixinfo
-
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr1.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr2.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr3.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr4.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr5.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
-
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr1.csv.gz -t stride_order_results  -f result_date,result_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr2.csv.gz -t stride_order_results  -f result_date,result_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr3.csv.gz -t stride_order_results  -f result_date,result_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr4.csv.gz -t stride_order_results  -f result_date,result_time
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr5.csv.gz -t stride_order_results  -f result_date,result_time
-
-rem Support / Data Lookup Tables
-python -m medinfo.db.DBUtil -d \t -i %DATA_DIR%/mapped_meds_5yr_20150508.txt.gz -t stride_mapped_meds 
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/export_ICD-9-CM_2013.csv.gz -t stride_icd9_cm
-
-rem Order Set Usage
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/orderset_procedures.csv.gz -t stride_orderset_order_proc
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/orderset_medications.csv.gz -t stride_orderset_order_med
-
-rem Treatment Team assignments
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr1.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr2.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr3.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr4.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr5.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
-
-rem Prior to Admission Medications
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Active_Meds_At_Admit.csv.gz -t stride_preadmit_med  -f contact_date
-
-rem Encounter Info (Insurance, Admit Vitals)
-python -m medinfo.db.DBUtil -i %DATA_DIR%/Chen_Insurance_Info_5Yr.csv.gz -t stride_patient_encounter -d ,
-python -m medinfo.db.DBUtil -i %DATA_DIR%/Chen_Admit_Vitals.csv.gz -t stride_patient_encounter -d ,
-
-rem Notes data (metadata not actual note content)
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr1.csv.gz -t stride_note -f note_date
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr2.csv.gz -t stride_note -f note_date
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr3.csv.gz -t stride_note -f note_date
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr4.csv.gz -t stride_note -f note_date
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr5.csv.gz -t stride_note -f note_date
-
-rem Medication to MPI mapping
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_MedicationID_to_MPI.csv.gz -t stride_medication_mpi
-
-rem Public chargemaster
-python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/ChargeMaster.Stanford.2014.csv.gz -t chargemaster
-
-rem ADT (Location) Data
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/JChenv3_ADTTable10.csv.gz -t stride_adt -f SHIFTED_TRANSF_IN_DT_TM,SHIFTED_TRANSF_OUT_DT_TM
-
-rem Flowsheet data (e.g., Vitals).  May need -e to skip errors for FiO2/PEEP Scale Standard whose values cannot be parsed as simple floats
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_fio2_Table4.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM -e
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_BP_Table1.namepatch.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_GCS_Table8.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_HRate_Table2.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_pulse_Table5.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_Resp_Table3.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_temp_Table6.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_urine_Table7.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-
-
-
-rem Input / Output Flowsheet Data
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data1.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data2.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data3.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data4.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
-python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data5.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
+set DATA_DIR="C:\Box Sync\SecureFiles\CDSS\inpatient5year"
+
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Demographics.csv.gz -t stride_patient -f death_date
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_DX_List_5Yr.csv.gz -t stride_dx_list -f noted_date,resolved_date
+
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Med_5Yr.csv.gz -t stride_order_med  -f ordering_date,start_taking_time,order_end_time,end_taking_time,discon_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_MedMixInfo_5Yr.csv.gz -t stride_order_medmixinfo
+
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr1.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr2.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr3.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr4.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Proc_Yr5.csv.gz -t stride_order_proc -f ordering_date,order_inst,instantiated_time,order_time,proc_start_time,proc_ending_time,result_time,last_stand_perf_dt,last_stand_perf_tm,proc_end_time,standing_exp_date,proc_bgn_time
+
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr1.csv.gz -t stride_order_results  -f result_date,result_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr2.csv.gz -t stride_order_results  -f result_date,result_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr3.csv.gz -t stride_order_results  -f result_date,result_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr4.csv.gz -t stride_order_results  -f result_date,result_time
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_Order_Result_Yr5.csv.gz -t stride_order_results  -f result_date,result_time
+
+rem Support / Data Lookup Tables
+python -m medinfo.db.DBUtil -d \t -i %DATA_DIR%/mapped_meds_5yr_20150508.txt.gz -t stride_mapped_meds 
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/export_ICD-9-CM_2013.csv.gz -t stride_icd9_cm
+
+rem Order Set Usage
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/orderset_procedures.csv.gz -t stride_orderset_order_proc
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/orderset_medications.csv.gz -t stride_orderset_order_med
+
+rem Treatment Team assignments
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr1.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr2.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr3.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr4.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/Chen_TreatmentTeam_Yr5.csv.gz -t stride_treatment_team  -f trtmnt_tm_begin_date,trtmnt_tm_end_date
+
+rem Prior to Admission Medications
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Active_Meds_At_Admit.csv.gz -t stride_preadmit_med  -f contact_date
+
+rem Encounter Info (Insurance, Admit Vitals)
+python -m medinfo.db.DBUtil -i %DATA_DIR%/Chen_Insurance_Info_5Yr.csv.gz -t stride_patient_encounter -d ,
+python -m medinfo.db.DBUtil -i %DATA_DIR%/Chen_Admit_Vitals.csv.gz -t stride_patient_encounter -d ,
+
+rem Notes data (metadata not actual note content)
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr1.csv.gz -t stride_note -f note_date
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr2.csv.gz -t stride_note -f note_date
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr3.csv.gz -t stride_note -f note_date
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr4.csv.gz -t stride_note -f note_date
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_Clinical_Notes_Yr5.csv.gz -t stride_note -f note_date
+
+rem Medication to MPI mapping
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Chen_MedicationID_to_MPI.csv.gz -t stride_medication_mpi
+
+rem Public chargemaster
+python -m medinfo.db.DBUtil -d , -x -i %DATA_DIR%/ChargeMaster.Stanford.2014.csv.gz -t chargemaster
+
+rem ADT (Location) Data
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/JChenv3_ADTTable10.csv.gz -t stride_adt -f SHIFTED_TRANSF_IN_DT_TM,SHIFTED_TRANSF_OUT_DT_TM
+
+rem Flowsheet data (e.g., Vitals).  May need -e to skip errors for FiO2/PEEP Scale Standard whose values cannot be parsed as simple floats
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_fio2_Table4.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM -e
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_BP_Table1.namepatch.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_GCS_Table8.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_HRate_Table2.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_pulse_Table5.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_Resp_Table3.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_temp_Table6.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+python -m medinfo.db.DBUtil -i %DATA_DIR%/JChenv3_urine_Table7.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+
+
+
+rem Input / Output Flowsheet Data
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data1.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data2.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data3.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data4.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
+python -m medinfo.db.DBUtil -d , -i %DATA_DIR%/Jchen_Inpu_output_data5.csv.gz -t stride_io_flowsheet -f SHIFTED_TRANSF_IN_DT_TM
diff --git a/scripts/CDSS/insertData.sh b/scripts/Archive/CDSS/insertData.sh
similarity index 93%
rename from scripts/CDSS/insertData.sh
rename to scripts/Archive/CDSS/insertData.sh
index acf6fd62..c3210fc5 100644
--- a/scripts/CDSS/insertData.sh
+++ b/scripts/Archive/CDSS/insertData.sh
@@ -41,14 +41,14 @@ python -m medinfo.db.DBUtil -d , -x -i $DATA_DIR/Chen_Order_Result_Yr5.csv.gz -t
 # python -m medinfo.db.DBUtil -i $DATA_DIR/Chen_Admit_Vitals.csv.gz -t stride_patient_encounter -d ,
 
 # # Flowsheet data (e.g., Vitals).  May need -e to skip errors for FiO2/PEEP Scale Standard whose values cannot be parsed as simple floats
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_fio2_Table4.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM -e
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_BP_Table1.namepatch.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_GCS_Table8.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_HRate_Table2.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_pulse_Table5.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_Resp_Table3.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_temp_Table6.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
-# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_urine_Table7.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_RECORD_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_fio2_Table4.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM -e
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_BP_Table1.namepatch.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_GCS_Table8.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_HRate_Table2.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_pulse_Table5.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_Resp_Table3.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_temp_Table6.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
+# python -m medinfo.db.DBUtil -i $DATA_DIR/JChenv3_urine_Table7.csv.gz -d , -t STRIDE_FLOWSHEET -f SHIFTED_DT_TM
 
 # # Notes data (metadata not actual note content)
 # python -m medinfo.db.DBUtil -d , -i $DATA_DIR/Chen_Clinical_Notes_Yr1.csv.gz -t stride_note -f note_date
diff --git a/scripts/CDSS/patchBPFlowsheet.py b/scripts/Archive/CDSS/patchBPFlowsheet.py
similarity index 73%
rename from scripts/CDSS/patchBPFlowsheet.py
rename to scripts/Archive/CDSS/patchBPFlowsheet.py
index b34be3da..7a6fd796 100644
--- a/scripts/CDSS/patchBPFlowsheet.py
+++ b/scripts/Archive/CDSS/patchBPFlowsheet.py
@@ -1,13 +1,13 @@
-from medinfo.common.Util import *;
-
-ifs = stdOpen("JChenv3_BP_Table1.csv.gz")
-ofs = stdOpen("JChenv3_BP_Table1.namepatch.csv.gz","w")
-
-ifs.readline(); # Read and discard header, replacing with below to change BP_TYPE to FLOWSHEET_NAME for consistency with other flowsheet items
-print >> ofs, '"PAT_ENC_CSN_ANON_ID","PAT_ANON_ID","FLO_MEAS_ID","FLOWSHEET_NAME","FLOWSHEET_VALUE","SHIFTED_RECORD_DT_TM"';
-
-prog = ProgressDots();
-for line in ifs:
-    ofs.write(line);
-    prog.update();
-prog.printStatus();
+from medinfo.common.Util import *;
+
+ifs = stdOpen("JChenv3_BP_Table1.csv.gz")
+ofs = stdOpen("JChenv3_BP_Table1.namepatch.csv.gz","w")
+
+ifs.readline(); # Read and discard header, replacing with below to change BP_TYPE to FLOWSHEET_NAME for consistency with other flowsheet items
+print('"PAT_ENC_CSN_ANON_ID","PAT_ANON_ID","FLO_MEAS_ID","FLOWSHEET_NAME","FLOWSHEET_VALUE","SHIFTED_DT_TM"', file=ofs);
+
+prog = ProgressDots();
+for line in ifs:
+    ofs.write(line);
+    prog.update();
+prog.printStatus();
diff --git a/scripts/CDSS/populateOrderResultStats.py b/scripts/Archive/CDSS/populateOrderResultStats.py
similarity index 92%
rename from scripts/CDSS/populateOrderResultStats.py
rename to scripts/Archive/CDSS/populateOrderResultStats.py
index e63e7a51..b152d227 100644
--- a/scripts/CDSS/populateOrderResultStats.py
+++ b/scripts/Archive/CDSS/populateOrderResultStats.py
@@ -1,33 +1,33 @@
-"""
-"""
-import sys;
-import subprocess;
-from medinfo.common.Util import stdOpen, log, ProgressDots;
-from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
-from medinfo.db.Model import RowItemModel;
-from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
-from medinfo.db import DBUtil;
-from medinfo.dataconversion.STRIDEOrderResultsConversion import STRIDEOrderResultsConversion;
-
-
-def main(argv):
-    conversionProcessor = STRIDEOrderResultsConversion();
-    
-    conn = DBUtil.connection();
-    try:
-        # Pull out list of result names to look for that are not already in the calculated
-        nameTable = DBUtil.execute("select name from sim_result    except    select base_name from order_result_stat", conn=conn);
-        prog = ProgressDots(big=1,small=1,total=len(nameTable));
-        for row in nameTable:
-            baseName = row[0];
-            print >> sys.stderr, "Calculating Stats for %s" % baseName;
-            statModel = conversionProcessor.calculateResultStats( baseName, conn=conn );
-            DBUtil.insertRow("order_result_stat", statModel, conn=conn );
-            prog.update();
-        prog.printStatus();
-        conn.commit();
-    finally:
-        conn.close();
-
-if __name__ == "__main__":
-    main(sys.argv);
+"""
+"""
+import sys;
+import subprocess;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+from medinfo.dataconversion.STRIDEOrderResultsConversion import STRIDEOrderResultsConversion;
+
+
+def main(argv):
+    conversionProcessor = STRIDEOrderResultsConversion();
+    
+    conn = DBUtil.connection();
+    try:
+        # Pull out list of result names to look for that are not already in the calculated
+        nameTable = DBUtil.execute("select name from sim_result    except    select base_name from order_result_stat", conn=conn);
+        prog = ProgressDots(big=1,small=1,total=len(nameTable));
+        for row in nameTable:
+            baseName = row[0];
+            print("Calculating Stats for %s" % baseName, file=sys.stderr);
+            statModel = conversionProcessor.calculateResultStats( baseName, conn=conn );
+            DBUtil.insertRow("order_result_stat", statModel, conn=conn );
+            prog.update();
+        prog.printStatus();
+        conn.commit();
+    finally:
+        conn.close();
+
+if __name__ == "__main__":
+    main(sys.argv);
diff --git a/scripts/CDSS/prepareItems.bat b/scripts/Archive/CDSS/prepareItems.bat
similarity index 99%
rename from scripts/CDSS/prepareItems.bat
rename to scripts/Archive/CDSS/prepareItems.bat
index b89b989d..24ae36d1 100644
--- a/scripts/CDSS/prepareItems.bat
+++ b/scripts/Archive/CDSS/prepareItems.bat
@@ -1,10 +1,10 @@
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.0.tab temp\first24hourItems.1year.0.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.1.tab temp\first24hourItems.1year.1.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.2.tab temp\first24hourItems.1year.2.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.3.tab temp\first24hourItems.1year.3.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.4.tab temp\first24hourItems.1year.4.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.5.tab temp\first24hourItems.1year.5.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.6.tab temp\first24hourItems.1year.6.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.7.tab temp\first24hourItems.1year.7.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.8.tab temp\first24hourItems.1year.8.tab.gz
-python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.9.tab temp\first24hourItems.1year.9.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.0.tab temp\first24hourItems.1year.0.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.1.tab temp\first24hourItems.1year.1.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.2.tab temp\first24hourItems.1year.2.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.3.tab temp\first24hourItems.1year.3.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.4.tab temp\first24hourItems.1year.4.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.5.tab temp\first24hourItems.1year.5.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.6.tab temp\first24hourItems.1year.6.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.7.tab temp\first24hourItems.1year.7.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.8.tab temp\first24hourItems.1year.8.tab.gz
+python medinfo\cpoe\analysis\PreparePatientItems.py -c 135 -q 14400 -v 86400 -o 8174 -t 2592000 temp\patientIds.1year.9.tab temp\first24hourItems.1year.9.tab.gz
diff --git a/scripts/Archive/CDSS/setupDataPreprocessingNotes.txt b/scripts/Archive/CDSS/setupDataPreprocessingNotes.txt
new file mode 100644
index 00000000..f2eb9e95
--- /dev/null
+++ b/scripts/Archive/CDSS/setupDataPreprocessingNotes.txt
@@ -0,0 +1,1146 @@
+<<<<<<< Updated upstream:scripts/CDSS/setupDataPreprocessingNotes.txt
+####### See CDSS/scripts/insertData.sh ########
+#   Populate with STRIDE data.  
+#
+#		For huge files, may want to "split" into pieces.  Then "cat back" together after edits, or just insert in pieces to facilitate debugging
+#			split -l10000 -a4 -d ../order_proc.txt split.order_proc.
+#			cat split.order_proc* > patch.order_proc.txt
+#
+#	Can batch together in a script, and consider running as background process for long import
+#		nohup ./batch.sh &> output.log &
+
+# Unify flowsheet labels. Can still recover original distinctions based on flo_meas_id
+	update stride_flowsheet
+	set flowsheet_name = 'Pulse' 
+	where flowsheet_name = 'Heart Rate';
+
+	update stride_flowsheet
+	set flowsheet_name = 'Urine' 
+	where flowsheet_name = 'Urine Output';
+
+# Merge Encounter info by inserting newly merged records with negative IDs, then discarding and relabeling old records
+	insert into stride_patient_encounter(pat_enc_csn_id, pat_id, payor_name, title, bp_systolic, bp_diastolic, temperature, pulse, respirations)
+	select 
+	  -spe1.pat_enc_csn_id, pat_id, payor_name, title,
+	  maxBPS, maxBPD, maxTemp, maxPulse, maxResp
+	from
+	(
+	select 
+	   pat_enc_csn_id, pat_id, payor_name, title
+	from
+	   stride_patient_encounter
+	where bp_systolic is null
+	) as spe1,
+	(
+	select 
+	   pat_enc_csn_id, max(bp_systolic) as maxBPS, max(bp_diastolic) as maxBPD, max(temperature) as maxTemp, max(pulse) as maxPulse, max(respirations) as maxResp
+	from stride_patient_encounter
+	group by pat_enc_csn_id
+	) spe2
+	where spe1.pat_enc_csn_id = spe2.pat_enc_csn_id
+
+	delete from stride_patient_encounter where pat_enc_csn_id > 0;
+	update stride_patient_encounter set pat_enc_csn_id = -pat_enc_csn_id;
+
+
+##### CDSS/scripts/CDSS/dataConversion.py ####
+#   Convert STRIDE data into structured analysis data tables.  
+# May want to drop table contraints and indexes until all inserts complete (still need unique constraint).
+# 	Create indexes afterwards to help speed up batch process
+# Consider different Med normalization options, whether want to completely separate active ingredients, 
+#	or consider each active ingredient combination as a unique item.  The former is more normalized,
+#	but get junk like TPN separated into 12 components, Zosyn listed as two items piperacillin and tazobactam,
+#	and essentially all IV fluid variations using a common Sodium Chloride IV item.  
+#	Likewise consider more administration variations for IV fluids where ingredients are less relevant than
+#	dose amount and formulation (IV bolus, IV maintenance, 3% saline vs. 1/2 NS, etc.)
+# Treatment Team aggregation option
+
+# Helper driver script
+nohup python scripts/CDSS/dataConversion.py &> log/driver.conversion &
+
+python medinfo/dataconversion/STRIDEOrderProcConversion.py
+python medinfo/dataconversion/STRIDEOrderResultsConversion.py
+python medinfo/dataconversion/STRIDEOrderMedConversion.py
+	Don't use --normalizeMixtures. Ends up separating Vicodin into separate Acetaminophen+Hydrocodone entries, leading to many pointless co-occurrence associations
+	--maxMixtureCount - If not normalizing mixtures, then this is the maximum number of mixture components will itemize for a denormalized mixture label.  If more than this, just use the summary label. Somewhat arbitrary choice, but ~5 should work okay (mixes of up to 3-4 usually want to see components, but if have big TPN mix, don't want to see components)
+	--doseCountLimit - Medication orders with a finite number of doses specified less than this limit will be labeled as different items than those without a number specified, or whose number is >= to this limit. Try <5 as well. Ends up being largely equivalent to other small numbers, since most orders are either "ONCE" or standing/continuous. Not that much in between
+
+...
+
+
+
+
+# Manually deactivate low-information item analysis as below
+update clinical_item
+set analysis_status = 0
+where name in ('RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','146535','LABSPR','NUR1940','NUR1025','NUR194000','NUR350','NURSE 1500','146541','146543','COD2')
+
+update clinical_item
+set analysis_status = 0
+where name in ('DIET990','TRN2','POC14','LABPOCGLU','146537')
+
+# Excessive variants of TPN that are distracting and has a bizarrely long name when unravel amino acid ingredients
+update clinical_item
+set analysis_status = 0
+where description like 'Alanine-Arginine%';
+
+-- delete
+-- from clinical_item_association
+-- where clinical_item_id in
+-- (44618,44621,44624,44667,44668,44669,44768,44800,45037,45163,52105);
+-- 
+-- delete
+-- from clinical_item_association
+-- where subsequent_item_id in
+-- (44618,44621,44624,44667,44668,44669,44768,44800,45037,45163,52105)
+-- 
+-- CREATE INDEX clinical_item_association_subsequent_item_id ON clinical_item_association(subsequent_item_id, clinical_item_id);
+
+
+TRANSFUSION BLOOD PRODUCT X
+# Not even clear what these are orders for, since there are separate orders for RBCs, platelets, and nursing to transfuse them
+#	By coincidence, total count appears to be ~2x sum of all other blood bank orders
+update clinical_item
+set analysis_status = 0
+where name in ('LABTSLIP','LABTBP','LABTBP9','LABTBP10','LABTBP11','LABTBP12','LABTBP13','LABTBP14','LABTBP15','LABTBP16','LABTBP17','LABTBP18','LABTBP1','LABTBP19','LABTBP20','LABTBP21','LABTBP22','LABTBP23','LABTBP24','LABTBP25','LABTBP26','LABTBP27','LABTBP28','LABTBP2','LABTBP29','LABTBP30','LABTBP31','LABTBP32','LABTBP33','LABTBP34','LABTBP35','LABTBP36','LABTBP37','LABTBP38','LABTBP3','LABTBP39','LABTBP40','LABTBP41','LABTBP42','LABTBP43','LABTBP44','LABTBP45','LABTBP46','LABTBP47','LABTBP48','LABTBP4','LABTBP49','LABTBP50','LABTBP51','LABTBP52','LABTBP53','LABTBP54','LABTBP55','LABTBP56','LABTBP57','LABTBP58','LABTBP5','LABTBP59','LABTBP60','LABTBP61','LABTBP62','LABTBP63','LABTBP64','LABTBP65','LABTBP66','LABTBP67','LABTBP68','LABTBP6','LABTBP69','LABTBP70','LABTBP71','LABTBP72','LABTBP73','LABTBP74','LABTBP75','LABTBP76','LABTBP77','LABTBP78','LABTBP7','LABTBP79','LABTBP80','LABTBP81','LABTBP82','LABTBP83','LABTBP84','LABTBP85','LABTBP86','LABTBP87','LABTBP88','LABTBP8','LABTBP89','LABTBP90','LABTBP91','LABTBP92','LABTBP93');
+
+# Result comments
+update clinical_item
+set analysis_status = 0
+where name in ('BM15CO(Result)','UCMT(Result)','DOACOM(Result)','PCCOM(Result)','SREQ(Result)','SREQ(Result)','REVUE(Result)','HIACOM(Result)','PTCMT(Result)','LEICMT(Result)','PF4COM(Result)','DIMCOM(Result)','CEPCM(Result)','FCMT(Result)','SCMT(Result)','CCMT(Result)','COM(Result)','CTRXNW(Result)','11200R(Result)','PLTFCM(Result)','QBCRCM(Result)','B2GPCM(Result)','DHTLV(Result)','ATHCT(Result)','RFCOM(Result)','BMBRCT(Result)','QBMBRC(Result)','RSVCOM(Result)','KITCO(Result)','T15CO(Result)','BCRCMT(Result)','BCRKCM(Result)','BMTCMT(Result)','MTHCMT(Result)','MGMCO(Result)','SREQ(InRange)','TEGCOM(Result)','BMBCMT(Result)','BSQCO(Result)','BM11CT(Result)','SREQ(Result)','VWFPIC(Result)','CEBCM(Result)','YIFB2(Result)','YPLAB2(Result)','BM14CT(Result)','YUOPCO(Result)','YFCHY6(Result)','YUBZCO(Result)','FBCMT(Result)','PPPCOM(Result)','CF32CM(Result)','YUMECO(Result)','BCMT(Result)','YSTNCO(Result)','TCMT(Result)','11152R(Result)','TMSICO(Result)','UCMT(Result)','JAKCO(Result)','YHISS5(Result)','YHISU5(Result)','T11CT(Result)','11996R(Result)','KITCO(Result)','YVB1W2(Result)','CTSMIS(Result)','NJAKCO(Result)','T14CMT(Result)','YLCFA9(Result)','QFBCRC(Result)','TT15CO(Result)','YHISC5(Result)','TTCCMT(Result)','TBCCMT(Result)','NBTCOM(Result)','NBTCBC(Result)','FT15CO(Result)','FTCMT(Result)','SREQ(Abnormal)','9078R(Result)','9260R(Result)','MGMCOB(Result)','9584R(Result)','9574R(Result)','9464R(Result)','9450R(Result)','12291R(Result)','9433R(Result)','9420R(Result)','9181R(Result)','12486R(Result)','12473R(Result)','9280R(Result)','12255R(Result)','12468R(Result)','12461R(Result)','TBCRCT(Result)','9233R(Result)','HITI2(Result)','9120R(Result)','BGSPR(Result)','BGSPRC(Result)','HCHCO(Result)','9404R(Result)','9391R(Result)','9207R(Result)','ABID2E(Result)','PEDCOM(Result)','9344R(Result)','9586R(Result)','9582R(Result)','9374R(Result)','9361R(Result)','9099R(Result)','PMCOMMENT(Result)');
+
+# Normal / Non-Specific Results
+update clinical_item
+set analysis_status = 0
+where name like '%%(InRange)' 
+or name like '%%(Result)'
+
+
+# Customize list of items to be included in default recommendations.  
+# Or maybe better, exclude them from association analysis to minimize excess computation and storage
+
+# Exclude categories including:
+source_table	description	
+order_proc	Nursing	
+order_proc	Transport	
+order_proc	BB Call Slip	
+order_proc	None	
+order_proc	Pharmacy Supplies	
+order_proc	Diet Communication	
+order_proc	Transfer	
+order_proc	Admission	
+order_proc	Discharge	
+
+stride_order_results	Lab Result	
+stride_order_results	Point of Care Testing Result	
+stride_order_results	HIV Lab Restricted Result	
+stride_order_results	HIV Lab Non-Restricted Result	
+
+stride_preadmit_med	Preadmit Med
+stride_treatment_team	Treatment Team
+
+stride_patient	Demographics
+
+# "Recommending" diagnoses may actually be useful as diagnostic tool
+#	stride_dx_list Diagnosis (PROBLEM_LIST)
+#	stride_dx_list Diagnosis (ADMIT_DX) 
+
+update clinical_item_category
+set default_recommend = 0
+where description in ('Nursing','Transfer','Admission','Transport','BB Call Slip','None','Pharmacy Supplies','Diet Communication','Discharge')
+
+update clinical_item_category
+set default_recommend = 0
+where source_table in ('stride_order_results','stride_preadmit_med','stride_treatment_team','stride_dx_list','stride_patient')
+
+
+
+
+
+# Skip analysis for very rare orders that excessively increase association item list without adding much value
+# Decide XXX on the fly, can do summary stats and try to exclude such that still 99% of orders are represented
+# Better to break down by category, since many are nursing orders or POC glucose that are not actually interesting?
+
+	select pi.clinical_item_id, cic.source_table, cic.description, ci.name, ci.description, count(distinct patient_id), count(distinct encounter_id), count(patient_item_id)
+	from patient_item as pi, clinical_item as ci, clinical_item_category as cic
+	where pi.clinical_item_id = ci.clinical_item_id
+	and ci.clinical_item_category_id = cic.clinical_item_category_id
+	and ci.analysis_status = 1
+	group by pi.clinical_item_id, cic.source_table, cic.description, ci.name, ci.description 
+
+# Use above to identify items to deactivate
+	
+	update clinical_item set analysis_status = 0 
+	where clinical_item_id in
+	(
+		select clinical_item_id
+		from patient_item 
+		group by clinical_item_id
+		having count(patient_item_id) < 256
+	);
+
+
+##### See addSynonyms.sql for adding item description synonyms to facilitate user testing searches
+
+
+################## Composite Clinical Items #######################
+Need specific ID numbers, but may be different in different database versions
+Try working of descriptions as below to find the relevant ID numbers
+
+
+
+
+# Composite certain outcome measures, for example, ICU interventions (sub-categorization based on vasopressors)
+36086	72	7512	RXCUI7512	Norepinephrine
+43993	72	8163	RXCUI8163	Phenylephrine
+44020	72	3992	RXCUI3992	Epinephrine
+44251	72	3616	RXCUI3616	Dobutamine
+44323	72	3628	RXCUI3628	Dopamine
+65411	72	224819	MED224819	VASOPRESSIN...
+python medinfo/cpoe/DataManager.py --compositeRelated "36086,43993,44020,44251,44323,65411|AnyVasoactive|Any Vasoactive Infusion|72"
+
+# CaCl infusion and IV Citrate are very closely correlated to CRRT use, but are rarely used in other scenarios
+45328	82	205894	MED205894	BICARB
+51019	82	36676	RXCUI36676	Sodium
+51150	82	-5.55816E+18	RXCUI205894,36676	BICARB
+54189	82	500028	MED500028	CRRT
+58948	82	9863	RXCUI9863	Sodium
+59107	82	1908	RXCUI1908	Calcium
+64279	82	1.55731E+18	RXCUI205894,11295	BICARB
+49140	4	633105	NUR4084	CONT RENAL REPLACEMENT THERAPY
+python medinfo/cpoe/DataManager.py --compositeRelated "45328,51019,51150,54189,58948,59107,64279,49140|AnyCRRT|Continuous Renal Replacement Therapy Components|82"
+
+# Ventilator orders
+45768	7	472340	RT75	RESP - EXTUBATION
+45832	7	35000476272	RT104	RESP - VENTILATOR SETTINGS
+45990	7	473786	RT97	RESP - WEAN VENTILATOR
+46106	7	473357	RT90	RESP - SPONTANEOUS BREATHING TRIAL
+48769	7	660692	RT130	RESP - REPOSITION ET TUBE
+61530	7	472542	RT88	RESP - HIGH FREQUENCY OSCILLATORY VENTILATION
+62003	7	871225	RT997	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL
+63826	7	897630	RT202	RESP - MONITOR, PERFORM VENTILATOR AND PATIENT ASSESSMENT
+66038	7	473327	RT91	RESP - SIMV/PRESSURE SUPPORT
+python medinfo/cpoe/DataManager.py --compositeRelated "45768,45832,45990,46106,48769,61530,62003,63826,66038|AnyVentilator|Ventilator Orders|7"
+ 
+# Can populate leaf linked items directly, or link to the above intermediate nodes and allow inheritance to pickup the rest
+Category: 12	stride_order_proc	Procedures
+71045 Vasoactives
+71046 CRRT
+71047 Ventilator
+python medinfo/cpoe/DataManager.py --compositeRelated "71045,71046,71047|AnyICULifeSupport|ICU Life Support (Vasoactives,Ventilator,CRRT)|12"
+
+# Or incrementally update an existing compomsite item
+#python medinfo/cpoe/DataManager.py -g "23701,23702|23700"
+
+# Any ICU specific (or highly correlated) orders to capture events occuring in the ICU, including other drips, nursing orders
+??????????
+	530008	MED530008	NITROPRUSSIDE IV INFUSION
+	8782	RXCUI8782	Propofol (Intravenous)
+	530025	MED530025	DEXMEDETOMIDINE IV INFUSION
+	530004	MED530004	MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION
+	8163	RXCUI8163	Phenylephrine (Intravenous)
+	114200	RXCUI114200	Citrate (Intravenous)
+	530120	MED530120	HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)
+	36676	RXCUI36676	Sodium Bicarbonate (CRRT)
+	530007	MED530007	VECURONIUM IV INFUSION
+	4177	RXCUI4177	Etomidate (Intravenous)
+	10154	RXCUI10154	Succinylcholine (Intravenous)
+	520130	MED520130	CALCIUM CHLORIDE 8 G/1000 ML IV INFUSION
+	530009	MED530009	CISATRACURIUM IV INFUSION
+	530003	MED530003	FENTANYL  10 MCG/ML IV INFUSION
+	530111	MED530111	MIDAZOLAM 100 MG IN D5W 100 ML IV INFUSION
+	530114	MED530114	MILRINONE CUSTOM IV INFUSION
+	530050	MED530050	DOBUTAMINE IV INFUSION CUSTOM MIXTURE
+	530046	MED530046	HYDROMORPHONE IV INFUSION
+	530112	MED530112	MIDAZOLAM IV INFUSION (CUSTOM)
+	37217	NUR2254	MONITOR PULMONARY ARTERY CATHETER
+	78347	RT106	RESP - NITRIC OXIDE VENT
+	37120	NUR2247	MONITOR CENTRAL LINE
+	26704	NUR1942	EXTERNAL VENTRICULAR DRAIN CARE
+	17833	NUR1528	MONITOR ARTERIAL LINE
+	399682	NUR30020	RASS SEDATION ASSESSMENT
+	396282	NUR1599	MONITOR CARDIAC ASSIST DEVICE
+	17896	NUR1591	OROGASTRIC TUBE
+	17834	NUR1529	MONITOR CO/CI
+	46677	NUR2999	MONITOR SCV02
+		AnyICULifeSupport	ICU Life Support (Vasopressors,Ventilator,CRRT)
+?????
+python medinfo/cpoe/DataManager.py --compositeRelated "23700,4455,3858,634,710,466,192,113,4303,4216,4507,629,3866,4452,180,3950,4078,3951,43,117,153,201,529,793,1096,1612,1752,2205,2337,2349|ICUOrders|ICU Specific/Correlated Orders|100"
+
+
+# DNR / non-Full Code Orders
+#48737	COD3	PARTIAL CODE
+45905	COD1	DNR
+62758	COD7	DNR/C
+62911	COD5	DNR/DNE
+63717	COD1	DNR/DNI
+python medinfo/cpoe/DataManager.py --compositeRelated "45905,62758,62911,63717|AnyDNR|Any DNR Code Status|22"
+
+
+
+# Readmission by looking for triples containing Discharge then Admit
+Category: 23	stride_order_proc	Admission
+# Composite admission orders
+45814	23	471546	ADT1	ADMIT TO INPATIENT
+61845	23	35000476278	ADT16	ADMIT POST SURGERY
+61982	23	471546	ADT1	ADMIT/PLACE PATIENT
+65641	23	776046	ADT100	ADMIT TO INPATIENT
+python medinfo/cpoe/DataManager.py --compositeRelated "45814,61845,61982,65641|AnyAdmit|Any Admit (not observation nor outpatient surgery)|23"
+
+45805	13	472204	ADT12	DISCHARGE PATIENT
+
+insert into clinical_item
+(clinical_item_category_id, name, description, analysis_status, outcome_interest)
+values
+(23,'READT','Readmission',1,1);
+
+# Identify triples, but effectively requires runs full association analysis work.  Very time intensive
+python medinfo/cpoe/TripleAssociationAnalysis.py -s 45805,73932 -v 73926 patientIds.XYZ.tab
+
+
+# Run Pre-Computation Association Analysis
+# 	Memory intensive, but if can use large amount on 64bit Python saves a lot of DB hits / persistence.
+#   Use -a option to specify how many associations to keep in memory before commit to DB
+#	1,000,000 seems to just fit within 7.5 GB of RAM
+#	Still a DB intensive I/O process that can be helped by moving more database processing to memory than disk
+python medinfo/cpoe/AssociationAnalysis.py -i <patientIDFile> -u 10000 -a 900000
+(Find Patient IDs to work on, by query like 
+	select distinct patient_id 
+	from patient_item 
+	where analyze_date is null 
+	and patient_id % 10 = -1
+Use arbitrary mod parameter on patient ID and item dates to select out data subsets
+)
+
+# Helper driver script to break up into multiple processes (beware total memory overhead)
+nohup python scripts/CDSS/assocAnalysis.py &> log/assocAnalysis &
+
+#  Or just restore from another database
+medinfo/db/support/restoreCPOETables.bat
+
+
+
+
+====================================
+
+
+# Pre-Compute Analyzed Clinical Item Counts to facilitate subsequent interface queries
+python medinfo/cpoe/DataManager.py --updateClinicalItemCounts
+or
+Item Recommender Web Interface, Cache or Query = PreCompute
+or
+# Transfer baseline counts to clinical_item summary counts
+update clinical_item as ci, clinical_item_association as cia
+set ci.item_count = cia.count_0, 
+    ci.patient_count = cia.patient_count_0, 
+    ci.encounter_count = cia.encounter_count_0
+where ci.clinical_item_id = 5 
+and cia.clinical_item_id = ci.clinical_item_id and cia.subsequent_item_id = ci.clinical_item_id;
+
+
+===================================================================
+Setup simultation data
+python scripts\CDSS\populateOrderResultStats.py
+
+
+
+
+===================================================================
+Data Cleanup Options
+
+# PostgreSQL duplicate removal (Trick is ctid system column that tracks unique physical location for every entry)
+delete from stride_order_results A using 
+(   select min(ctid) as ctid, order_proc_id, line
+   from stride_order_results
+   group by order_proc_id, line having count(*) > 1
+) B
+where A.order_proc_id = B.order_proc_id and A.line = B.line
+and A.ctid <> B.ctid
+
+https://stackoverflow.com/questions/6583916/delete-duplicate-records-in-postgresql
+
+# Another in place option, but requires a unique ID column to exist
+delete
+from patient_item as test
+ where exists ( select 'x' 
+				  from patient_item as i
+				 where i.patient_id = test.patient_id
+				   and i.clinical_item_id = test.clinical_item_id
+				   and i.item_date = test.item_date
+				   and i.patient_item_id < test.patient_item_id
+			 )
+limit 10;
+
+
+
+# Use data/analysis/queryItemCount.py to help guide threshold decision
+
+select clinical_item_id, count(*)
+from patient_item as pi
+group by clinical_item_id
+having count(*) < XXX;
+
+python medinfo/cpoe/DataManager.py --deactivateAnalysisByCount <thresholdInstanceCount>
+
+
+
+# Exclude Individual Items including, should not even be analyzed:
+external_id	name	description	
+854932	RXCUI854932	Pneumococcal Capsular Polysaccharide Type 10a Vaccine (SUBCUTANEOUS)	
+854934	RXCUI854934	Pneumococcal Capsular Polysaccharide Type 11a Vaccine (SUBCUTANEOUS)	
+854936	RXCUI854936	Pneumococcal Capsular Polysaccharide Type 12f Vaccine (SUBCUTANEOUS)	
+854938	RXCUI854938	Pneumococcal Capsular Polysaccharide Type 14 Vaccine (SUBCUTANEOUS)	
+854940	RXCUI854940	Pneumococcal Capsular Polysaccharide Type 15b Vaccine (SUBCUTANEOUS)	
+854942	RXCUI854942	Pneumococcal Capsular Polysaccharide Type 17f Vaccine (SUBCUTANEOUS)	
+854944	RXCUI854944	Pneumococcal Capsular Polysaccharide Type 18c Vaccine (SUBCUTANEOUS)	
+854946	RXCUI854946	Pneumococcal Capsular Polysaccharide Type 19a Vaccine (SUBCUTANEOUS)	
+854948	RXCUI854948	Pneumococcal Capsular Polysaccharide Type 19f Vaccine (SUBCUTANEOUS)	
+854950	RXCUI854950	Pneumococcal Capsular Polysaccharide Type 2 Vaccine (SUBCUTANEOUS)	
+854952	RXCUI854952	Pneumococcal Capsular Polysaccharide Type 20 Vaccine (SUBCUTANEOUS)	
+854954	RXCUI854954	Pneumococcal Capsular Polysaccharide Type 22f Vaccine (SUBCUTANEOUS)	
+854956	RXCUI854956	Pneumococcal Capsular Polysaccharide Type 23f Vaccine (SUBCUTANEOUS)	
+854958	RXCUI854958	Pneumococcal Capsular Polysaccharide Type 3 Vaccine (SUBCUTANEOUS)	
+854960	RXCUI854960	Pneumococcal Capsular Polysaccharide Type 33f Vaccine (SUBCUTANEOUS)	
+854962	RXCUI854962	Pneumococcal Capsular Polysaccharide Type 4 Vaccine (SUBCUTANEOUS)	
+854964	RXCUI854964	Pneumococcal Capsular Polysaccharide Type 5 Vaccine (SUBCUTANEOUS)	
+854966	RXCUI854966	Pneumococcal Capsular Polysaccharide Type 6b Vaccine (SUBCUTANEOUS)	
+854968	RXCUI854968	Pneumococcal Capsular Polysaccharide Type 7f Vaccine (SUBCUTANEOUS)	
+854970	RXCUI854970	Pneumococcal Capsular Polysaccharide Type 8 Vaccine (SUBCUTANEOUS)	
+854972	RXCUI854972	Pneumococcal Capsular Polysaccharide Type 9n Vaccine (SUBCUTANEOUS)	
+854974	RXCUI854974	Pneumococcal Capsular Polysaccharide Type 9v Vaccine (SUBCUTANEOUS)	
+854932	RXCUI854932	Pneumococcal Capsular Polysaccharide Type 10a Vaccine (Intramuscular)	
+854934	RXCUI854934	Pneumococcal Capsular Polysaccharide Type 11a Vaccine (Intramuscular)	
+854936	RXCUI854936	Pneumococcal Capsular Polysaccharide Type 12f Vaccine (Intramuscular)	
+854938	RXCUI854938	Pneumococcal Capsular Polysaccharide Type 14 Vaccine (Intramuscular)	
+854940	RXCUI854940	Pneumococcal Capsular Polysaccharide Type 15b Vaccine (Intramuscular)	
+854942	RXCUI854942	Pneumococcal Capsular Polysaccharide Type 17f Vaccine (Intramuscular)	
+854944	RXCUI854944	Pneumococcal Capsular Polysaccharide Type 18c Vaccine (Intramuscular)	
+854946	RXCUI854946	Pneumococcal Capsular Polysaccharide Type 19a Vaccine (Intramuscular)	
+854948	RXCUI854948	Pneumococcal Capsular Polysaccharide Type 19f Vaccine (Intramuscular)	
+854950	RXCUI854950	Pneumococcal Capsular Polysaccharide Type 2 Vaccine (Intramuscular)	
+854952	RXCUI854952	Pneumococcal Capsular Polysaccharide Type 20 Vaccine (Intramuscular)	
+854954	RXCUI854954	Pneumococcal Capsular Polysaccharide Type 22f Vaccine (Intramuscular)	
+854956	RXCUI854956	Pneumococcal Capsular Polysaccharide Type 23f Vaccine (Intramuscular)	
+854958	RXCUI854958	Pneumococcal Capsular Polysaccharide Type 3 Vaccine (Intramuscular)	
+854960	RXCUI854960	Pneumococcal Capsular Polysaccharide Type 33f Vaccine (Intramuscular)	
+854962	RXCUI854962	Pneumococcal Capsular Polysaccharide Type 4 Vaccine (Intramuscular)	
+854964	RXCUI854964	Pneumococcal Capsular Polysaccharide Type 5 Vaccine (Intramuscular)	
+854966	RXCUI854966	Pneumococcal Capsular Polysaccharide Type 6b Vaccine (Intramuscular)	
+854968	RXCUI854968	Pneumococcal Capsular Polysaccharide Type 7f Vaccine (Intramuscular)	
+854970	RXCUI854970	Pneumococcal Capsular Polysaccharide Type 8 Vaccine (Intramuscular)	
+854972	RXCUI854972	Pneumococcal Capsular Polysaccharide Type 9n Vaccine (Intramuscular)	
+854974	RXCUI854974	Pneumococcal Capsular Polysaccharide Type 9v Vaccine (Intramuscular)	
+147420	146535	ECG PROCEDURE SCANNED REPORT	
+1883	LABSPR	SPECIMEN REMARK	
+26702	NUR1940	NURSING COMMUNICATION(FOR ORDERSET ONLY)	
+1435	NUR1025	VITAL SIGNS	
+412485	NUR194000	NURSING COMMUNICATION	
+46821	NUR350	NOTIFY MD:	
+17581	NURSE 1500	NOTIFY MD:VITAL SIGNS	
+147415	146541	OTHER PROCEDURE SCANNED REPORT	
+147416	146543	OTHER ORDER SCANNED REPORT	
+958	COD2	FULL CODE	
+
+706	DIET990	DIET REGULAR	
+1604	TRN2	PATIENT TRANSPORT	
+119815	POC14	POC GLUCOSE BY METER	
+1001	LABPOCGLU	GLUCOSE BY METER	
+
+# If setting analysis status to 0, don't need to set default_recommend to 0 since will inherently be excluded
+update clinical_item
+set analysis_status = 0
+where name in ('RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','146535','LABSPR','NUR1940','NUR1025','NUR194000','NUR350','NURSE 1500','146541','146543','COD2')
+
+update clinical_item
+set analysis_status = 0
+where name in ('DIET990','TRN2','POC14','LABPOCGLU','146537')
+
+
+TRANSFUSION BLOOD PRODUCT X
+# Not even clear what these are orders for, since there are separate orders for RBCs, platelets, and nursing to transfuse them
+#	By coincidence, total count appears to be ~2x sum of all other blood bank orders
+update clinical_item
+set analysis_status = 0
+where name in ('LABTSLIP','LABTBP','LABTBP9','LABTBP10','LABTBP11','LABTBP12','LABTBP13','LABTBP14','LABTBP15','LABTBP16','LABTBP17','LABTBP18','LABTBP1','LABTBP19','LABTBP20','LABTBP21','LABTBP22','LABTBP23','LABTBP24','LABTBP25','LABTBP26','LABTBP27','LABTBP28','LABTBP2','LABTBP29','LABTBP30','LABTBP31','LABTBP32','LABTBP33','LABTBP34','LABTBP35','LABTBP36','LABTBP37','LABTBP38','LABTBP3','LABTBP39','LABTBP40','LABTBP41','LABTBP42','LABTBP43','LABTBP44','LABTBP45','LABTBP46','LABTBP47','LABTBP48','LABTBP4','LABTBP49','LABTBP50','LABTBP51','LABTBP52','LABTBP53','LABTBP54','LABTBP55','LABTBP56','LABTBP57','LABTBP58','LABTBP5','LABTBP59','LABTBP60','LABTBP61','LABTBP62','LABTBP63','LABTBP64','LABTBP65','LABTBP66','LABTBP67','LABTBP68','LABTBP6','LABTBP69','LABTBP70','LABTBP71','LABTBP72','LABTBP73','LABTBP74','LABTBP75','LABTBP76','LABTBP77','LABTBP78','LABTBP7','LABTBP79','LABTBP80','LABTBP81','LABTBP82','LABTBP83','LABTBP84','LABTBP85','LABTBP86','LABTBP87','LABTBP88','LABTBP8','LABTBP89','LABTBP90','LABTBP91','LABTBP92','LABTBP93');
+
+
+
+
+
+# For some of above like PNA vaccine components, may actually be more appropriate to merge/composite items 
+python medinfo/cpoe/DataManager.py --compositeRelated <clinicalItemIds>
+python medinfo/cpoe/DataManager.py --mergeRelated <clinicalItemIds>
+python medinfo/cpoe/DataManager.py --unifyRedundant <clinicalItemIds>
+
+
+# ???Highlight items that may be of interest for outcome measures
+clinical_item_id	description	count	
+20	DISCHARGE PATIENT	1532	
+31	ADMIT TO PHASE	769	
+34	TRANSFER PATIENT	581	
+55	INSERT AIRWAY	360	
+58	RESP - SPONTANEOUS BREATHING TRIAL	76	
+59	ADMIT/PLACE PATIENT	1294	
+66	PATIENT TRANSPORT	2384	
+147	DISCHARGE PATIENT WHEN CRITERIA MET	770	
+207	DIALYZE PATIENT	85	
+332	NON-VIOLENT RESTRAINTS	316	
+374	DNR	237	
+461	RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA	72	
+465	OK TO USE CENTRAL LINE	30	
+522	HEMODIALYSIS	120	
+523	INTERAGENCY REFERRAL TO HOME HOSPICE	18	
+563	CAR CATH CORS POSSIBLE	37	
+612	COMFORT CARE MEASURES	42	
+616	CCPD  PERITONEAL DIALYSIS	15	
+723	INTUBATION	6	
+957	PLACE IN OBSERVATION-CDU	23	
+1086	HEMODIALYSIS WITH PUF	6	
+1087	CAPD PERITONEAL DIALYSIS	3	
+1312	CENTRAL LINE	10	
+1521	CENTRAL LINE CARE	4	
+1636	NON INVASIVE POSTIVE PRESS VENT BIPAP/CPAP	3	
+1643	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL	6	
+1909	TRACHEOSTOMY  CARE	2	
+6551	ARGATROBAN 1000 MCG/ML (250 ML) IV INFUSION	18	
+6700	Fentanyl (Intravenous)	77	
+6724	Dopamine (Intravenous)	72	
+6738	DEXMEDETOMIDINE IV INFUSION	146	
+6743	VASOPRESSIN 1 UNIT/ML IV INFUSION	39	
+6745	Bicarb Dialys Soln #16 W/O Ca K (4 Meq/L) -Mg (1.5 Meq/L) Hemodial Soln (CRRT)	16	
+6797	AMIODARONE IVPB	80	
+6801	AMIODARONE INFUSION CENTRAL LINE ADMIN	44	
+6823	PHENYLEPHRINE IV INFUSION	87	
+6872	Eptifibatide (Intravenous)	11	
+6936	AMIODARONE INFUSION PERIPH ADMIN	29	
+6950	MIDAZOLAM IV INFUSION (CUSTOM)	2	
+7014	Dobutamine (Intravenous)	22	
+7066	Bicarb Dialys Soln No.8 W-O Ca K (2 Meq/L) -Mg (1 Meq/L) Hemodial Soln (CRRT)	22	
+7111	Amiodarone (Intravenous)	1	
+7148	D5W WITH SODIUM BICARBONATE IV INFUSION	7	
+7167	Epinephrine (Intravenous)	12	
+7192	Milrinone (Intravenous)	10	
+20	DISCHARGE PATIENT	1532	
+31	ADMIT TO PHASE	769	
+1660	CALL DIALYSIS RN CAP/DRAIN CAPD	2	
+1087	CAPD PERITONEAL DIALYSIS	3	
+616	CCPD  PERITONEAL DIALYSIS	15	
+1521	CENTRAL LINE CARE	4	
+612	COMFORT CARE MEASURES	42	
+674	CONT RENAL REPLACEMENT THERAPY	18	
+1097	DIALYSIS ACCESS	6	
+1995	DIALYSIS CATHETER CARE	1	
+207	DIALYZE PATIENT	85	
+522	HEMODIALYSIS	120	
+1086	HEMODIALYSIS WITH PUF	6	
+465	OK TO USE CENTRAL LINE	30	
+403	TRANSFUSION EMERGENCY ISSUE	20	
+34	TRANSFER PATIENT	581	
+158	RESP - EXTUBATION	98	
+1643	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL	6	
+139	RESP - NON INVASIVE POS. PRESS VENT (BIPAP/CPAP)	136	
+58	RESP - SPONTANEOUS BREATHING TRIAL	76	
+164	RESP - VENTILATOR SETTINGS	467	
+137	RESP - WEAN VENTILATOR	110	
+461	RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA	72	
+130	ADMIT POST SURGERY	656	
+59	ADMIT/PLACE PATIENT	1294	
+957	PLACE IN OBSERVATION-CDU	23	
+66	PATIENT TRANSPORT	2384	
+374	DNR	237	
+99	PARTIAL CODE	17	
+902	ARTERIAL LINE	11	
+1312	CENTRAL LINE	10	
+723	INTUBATION	6	
+6801	AMIODARONE INFUSION CENTRAL LINE ADMIN	44	
+6936	AMIODARONE INFUSION PERIPH ADMIN	29	
+6797	AMIODARONE IVPB	80	
+7014	Dobutamine (Intravenous)	22	
+6724	Dopamine (Intravenous)	72	
+6659	EPINEPHRINE IV INFUSION	61	
+6696	Midazolam (Intravenous)	70	
+7192	Milrinone (Intravenous)	10	
+6681	NITROPRUSSIDE IV INFUSION	99	
+6788	NOREPINEPHRINE IV INFUSION	46	
+6900	Nitroglycerin (Intravenous)	59	
+
+xxxx	Death Date	???
+xxxx	Length of Stay	???
+
+
+update clinical_item
+set outcome_interest = 1
+where description in
+('DISCHARGE PATIENT','ADMIT TO PHASE','TRANSFER PATIENT','INSERT AIRWAY','RESP - SPONTANEOUS BREATHING TRIAL','ADMIT/PLACE PATIENT','PATIENT TRANSPORT','DISCHARGE PATIENT WHEN CRITERIA MET','DIALYZE PATIENT','NON-VIOLENT RESTRAINTS','DNR','RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA','OK TO USE CENTRAL LINE','HEMODIALYSIS','INTERAGENCY REFERRAL TO HOME HOSPICE','CAR CATH CORS POSSIBLE','COMFORT CARE MEASURES','CCPD  PERITONEAL DIALYSIS','INTUBATION','PLACE IN OBSERVATION-CDU','HEMODIALYSIS WITH PUF','CAPD PERITONEAL DIALYSIS','CENTRAL LINE','CENTRAL LINE CARE','NON INVASIVE POSTIVE PRESS VENT BIPAP/CPAP','RESP - LUNG PROTECTIVE VENTILATION PROTOCOL','TRACHEOSTOMY  CARE','ARGATROBAN 1000 MCG/ML (250 ML) IV INFUSION','Fentanyl (Intravenous)','Dopamine (Intravenous)','DEXMEDETOMIDINE IV INFUSION','VASOPRESSIN 1 UNIT/ML IV INFUSION','Bicarb Dialys Soln #16 W/O Ca K (4 Meq/L) -Mg (1.5 Meq/L) Hemodial Soln (CRRT)','AMIODARONE IVPB','AMIODARONE INFUSION CENTRAL LINE ADMIN','PHENYLEPHRINE IV INFUSION','Eptifibatide (Intravenous)','AMIODARONE INFUSION PERIPH ADMIN','MIDAZOLAM IV INFUSION (CUSTOM)','Dobutamine (Intravenous)','Bicarb Dialys Soln No.8 W-O Ca K (2 Meq/L) -Mg (1 Meq/L) Hemodial Soln (CRRT)','Amiodarone (Intravenous)','D5W WITH SODIUM BICARBONATE IV INFUSION','Epinephrine (Intravenous)','Milrinone (Intravenous)','DISCHARGE PATIENT','ADMIT TO PHASE','CALL DIALYSIS RN CAP/DRAIN CAPD','CAPD PERITONEAL DIALYSIS','CCPD  PERITONEAL DIALYSIS','CENTRAL LINE CARE','COMFORT CARE MEASURES','CONT RENAL REPLACEMENT THERAPY','DIALYSIS ACCESS','DIALYSIS CATHETER CARE','DIALYZE PATIENT','HEMODIALYSIS','HEMODIALYSIS WITH PUF','OK TO USE CENTRAL LINE','TRANSFUSION EMERGENCY ISSUE','TRANSFER PATIENT','RESP - EXTUBATION','RESP - LUNG PROTECTIVE VENTILATION PROTOCOL','RESP - NON INVASIVE POS. PRESS VENT (BIPAP/CPAP)','RESP - SPONTANEOUS BREATHING TRIAL','RESP - VENTILATOR SETTINGS','RESP - WEAN VENTILATOR','RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA','ADMIT POST SURGERY','ADMIT/PLACE PATIENT','PLACE IN OBSERVATION-CDU','PATIENT TRANSPORT','DNR','PARTIAL CODE','ARTERIAL LINE','CENTRAL LINE','INTUBATION','AMIODARONE INFUSION CENTRAL LINE ADMIN','AMIODARONE INFUSION PERIPH ADMIN','AMIODARONE IVPB','Dobutamine (Intravenous)','Dopamine (Intravenous)','EPINEPHRINE IV INFUSION','Midazolam (Intravenous)','Milrinone (Intravenous)','NITROPRUSSIDE IV INFUSION','NOREPINEPHRINE IV INFUSION','Nitroglycerin (Intravenous)','Death Date');
+
+
+=======
+####### See CDSS/scripts/insertData.sh ########
+#   Populate with STRIDE data.  
+#
+#		For huge files, may want to "split" into pieces.  Then "cat back" together after edits, or just insert in pieces to facilitate debugging
+#			split -l10000 -a4 -d ../order_proc.txt split.order_proc.
+#			cat split.order_proc* > patch.order_proc.txt
+#
+#	Can batch together in a script, and consider running as background process for long import
+#		nohup ./batch.sh &> output.log &
+
+# Unify flowsheet labels. Can still recover original distinctions based on flo_meas_id
+	update stride_flowsheet
+	set flowsheet_name = 'Pulse' 
+	where flowsheet_name = 'Heart Rate';
+
+	update stride_flowsheet
+	set flowsheet_name = 'Urine' 
+	where flowsheet_name = 'Urine Output';
+
+# Merge Encounter info by inserting newly merged records with negative IDs, then discarding and relabeling old records
+	insert into stride_patient_encounter(pat_enc_csn_id, pat_id, payor_name, title, bp_systolic, bp_diastolic, temperature, pulse, respirations)
+	select 
+	  -spe1.pat_enc_csn_id, pat_id, payor_name, title,
+	  maxBPS, maxBPD, maxTemp, maxPulse, maxResp
+	from
+	(
+	select 
+	   pat_enc_csn_id, pat_id, payor_name, title
+	from
+	   stride_patient_encounter
+	where bp_systolic is null
+	) as spe1,
+	(
+	select 
+	   pat_enc_csn_id, max(bp_systolic) as maxBPS, max(bp_diastolic) as maxBPD, max(temperature) as maxTemp, max(pulse) as maxPulse, max(respirations) as maxResp
+	from stride_patient_encounter
+	group by pat_enc_csn_id
+	) spe2
+	where spe1.pat_enc_csn_id = spe2.pat_enc_csn_id
+
+	delete from stride_patient_encounter where pat_enc_csn_id > 0;
+	update stride_patient_encounter set pat_enc_csn_id = -pat_enc_csn_id;
+
+
+##### CDSS/scripts/CDSS/dataConversion.py ####
+#   Convert STRIDE data into structured analysis data tables.  
+# May want to drop table contraints and indexes until all inserts complete (still need unique constraint).
+# 	Create indexes afterwards to help speed up batch process
+# Consider different Med normalization options, whether want to completely separate active ingredients, 
+#	or consider each active ingredient combination as a unique item.  The former is more normalized,
+#	but get junk like TPN separated into 12 components, Zosyn listed as two items piperacillin and tazobactam,
+#	and essentially all IV fluid variations using a common Sodium Chloride IV item.  
+#	Likewise consider more administration variations for IV fluids where ingredients are less relevant than
+#	dose amount and formulation (IV bolus, IV maintenance, 3% saline vs. 1/2 NS, etc.)
+# Treatment Team aggregation option
+
+# Helper driver script
+nohup python scripts/CDSS/dataConversiony.py &> log/driver.conversion &
+
+python medinfo/dataconversion/STRIDEOrderMedConversion.py
+python medinfo/dataconversion/STRIDEOrderProcConversion.py
+python medinfo/dataconversion/STRIDEOrderResultsConversion.py
+...
+
+# Manually deactivate low-information item analysis as below
+update clinical_item
+set analysis_status = 0
+where name in ('RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','146535','LABSPR','NUR1940','NUR1025','NUR194000','NUR350','NURSE 1500','146541','146543','COD2')
+
+update clinical_item
+set analysis_status = 0
+where name in ('DIET990','TRN2','POC14','LABPOCGLU','146537')
+
+# Excessive variants of TPN that are distracting and has a bizarrely long name when unravel amino acid ingredients
+update clinical_item
+set analysis_status = 0
+where description like 'Alanine-Arginine%';
+
+-- delete
+-- from clinical_item_association
+-- where clinical_item_id in
+-- (44618,44621,44624,44667,44668,44669,44768,44800,45037,45163,52105);
+-- 
+-- delete
+-- from clinical_item_association
+-- where subsequent_item_id in
+-- (44618,44621,44624,44667,44668,44669,44768,44800,45037,45163,52105)
+-- 
+-- CREATE INDEX clinical_item_association_subsequent_item_id ON clinical_item_association(subsequent_item_id, clinical_item_id);
+
+
+TRANSFUSION BLOOD PRODUCT X
+# Not even clear what these are orders for, since there are separate orders for RBCs, platelets, and nursing to transfuse them
+#	By coincidence, total count appears to be ~2x sum of all other blood bank orders
+update clinical_item
+set analysis_status = 0
+where name in ('LABTSLIP','LABTBP','LABTBP9','LABTBP10','LABTBP11','LABTBP12','LABTBP13','LABTBP14','LABTBP15','LABTBP16','LABTBP17','LABTBP18','LABTBP1','LABTBP19','LABTBP20','LABTBP21','LABTBP22','LABTBP23','LABTBP24','LABTBP25','LABTBP26','LABTBP27','LABTBP28','LABTBP2','LABTBP29','LABTBP30','LABTBP31','LABTBP32','LABTBP33','LABTBP34','LABTBP35','LABTBP36','LABTBP37','LABTBP38','LABTBP3','LABTBP39','LABTBP40','LABTBP41','LABTBP42','LABTBP43','LABTBP44','LABTBP45','LABTBP46','LABTBP47','LABTBP48','LABTBP4','LABTBP49','LABTBP50','LABTBP51','LABTBP52','LABTBP53','LABTBP54','LABTBP55','LABTBP56','LABTBP57','LABTBP58','LABTBP5','LABTBP59','LABTBP60','LABTBP61','LABTBP62','LABTBP63','LABTBP64','LABTBP65','LABTBP66','LABTBP67','LABTBP68','LABTBP6','LABTBP69','LABTBP70','LABTBP71','LABTBP72','LABTBP73','LABTBP74','LABTBP75','LABTBP76','LABTBP77','LABTBP78','LABTBP7','LABTBP79','LABTBP80','LABTBP81','LABTBP82','LABTBP83','LABTBP84','LABTBP85','LABTBP86','LABTBP87','LABTBP88','LABTBP8','LABTBP89','LABTBP90','LABTBP91','LABTBP92','LABTBP93');
+
+# Result comments
+update clinical_item
+set analysis_status = 0
+where name in ('BM15CO(Result)','UCMT(Result)','DOACOM(Result)','PCCOM(Result)','SREQ(Result)','SREQ(Result)','REVUE(Result)','HIACOM(Result)','PTCMT(Result)','LEICMT(Result)','PF4COM(Result)','DIMCOM(Result)','CEPCM(Result)','FCMT(Result)','SCMT(Result)','CCMT(Result)','COM(Result)','CTRXNW(Result)','11200R(Result)','PLTFCM(Result)','QBCRCM(Result)','B2GPCM(Result)','DHTLV(Result)','ATHCT(Result)','RFCOM(Result)','BMBRCT(Result)','QBMBRC(Result)','RSVCOM(Result)','KITCO(Result)','T15CO(Result)','BCRCMT(Result)','BCRKCM(Result)','BMTCMT(Result)','MTHCMT(Result)','MGMCO(Result)','SREQ(InRange)','TEGCOM(Result)','BMBCMT(Result)','BSQCO(Result)','BM11CT(Result)','SREQ(Result)','VWFPIC(Result)','CEBCM(Result)','YIFB2(Result)','YPLAB2(Result)','BM14CT(Result)','YUOPCO(Result)','YFCHY6(Result)','YUBZCO(Result)','FBCMT(Result)','PPPCOM(Result)','CF32CM(Result)','YUMECO(Result)','BCMT(Result)','YSTNCO(Result)','TCMT(Result)','11152R(Result)','TMSICO(Result)','UCMT(Result)','JAKCO(Result)','YHISS5(Result)','YHISU5(Result)','T11CT(Result)','11996R(Result)','KITCO(Result)','YVB1W2(Result)','CTSMIS(Result)','NJAKCO(Result)','T14CMT(Result)','YLCFA9(Result)','QFBCRC(Result)','TT15CO(Result)','YHISC5(Result)','TTCCMT(Result)','TBCCMT(Result)','NBTCOM(Result)','NBTCBC(Result)','FT15CO(Result)','FTCMT(Result)','SREQ(Abnormal)','9078R(Result)','9260R(Result)','MGMCOB(Result)','9584R(Result)','9574R(Result)','9464R(Result)','9450R(Result)','12291R(Result)','9433R(Result)','9420R(Result)','9181R(Result)','12486R(Result)','12473R(Result)','9280R(Result)','12255R(Result)','12468R(Result)','12461R(Result)','TBCRCT(Result)','9233R(Result)','HITI2(Result)','9120R(Result)','BGSPR(Result)','BGSPRC(Result)','HCHCO(Result)','9404R(Result)','9391R(Result)','9207R(Result)','ABID2E(Result)','PEDCOM(Result)','9344R(Result)','9586R(Result)','9582R(Result)','9374R(Result)','9361R(Result)','9099R(Result)','PMCOMMENT(Result)');
+
+# Normal / Non-Specific Results
+update clinical_item
+set analysis_status = 0
+where name like '%%(InRange)' 
+or name like '%%(Result)'
+
+
+# Customize list of items to be included in default recommendations.  
+# Or maybe better, exclude them from association analysis to minimize excess computation and storage
+
+# Exclude categories including:
+source_table	description	
+order_proc	Nursing	
+order_proc	Transport	
+order_proc	BB Call Slip	
+order_proc	None	
+order_proc	Pharmacy Supplies	
+order_proc	Diet Communication	
+order_proc	Transfer	
+order_proc	Admission	
+order_proc	Discharge	
+
+stride_order_results	Lab Result	
+stride_order_results	Point of Care Testing Result	
+stride_order_results	HIV Lab Restricted Result	
+stride_order_results	HIV Lab Non-Restricted Result	
+
+stride_preadmit_med	Preadmit Med
+stride_treatment_team	Treatment Team
+
+stride_patient	Demographics
+
+# "Recommending" diagnoses may actually be useful as diagnostic tool
+#	stride_dx_list Diagnosis (PROBLEM_LIST)
+#	stride_dx_list Diagnosis (ADMIT_DX) 
+
+update clinical_item_category
+set default_recommend = 0
+where description in ('Nursing','Transfer','Admission','Transport','BB Call Slip','None','Pharmacy Supplies','Diet Communication','Discharge')
+
+update clinical_item_category
+set default_recommend = 0
+where source_table in ('stride_order_results','stride_preadmit_med','stride_treatment_team','stride_dx_list','stride_patient')
+
+
+
+
+
+# Skip analysis for very rare orders that excessively increase association item list without adding much value
+# Decide XXX on the fly, can do summary stats and try to exclude such that still 99% of orders are represented
+# Better to break down by category, since many are nursing orders or POC glucose that are not actually interesting?
+
+	select pi.clinical_item_id, cic.source_table, cic.description, ci.name, ci.description, count(distinct patient_id), count(distinct encounter_id), count(patient_item_id)
+	from patient_item as pi, clinical_item as ci, clinical_item_category as cic
+	where pi.clinical_item_id = ci.clinical_item_id
+	and ci.clinical_item_category_id = cic.clinical_item_category_id
+	and ci.analysis_status = 1
+	group by pi.clinical_item_id, cic.source_table, cic.description, ci.name, ci.description 
+
+# Use above to identify items to deactivate
+	
+	update clinical_item set analysis_status = 0 
+	where clinical_item_id in
+	(
+		select clinical_item_id
+		from patient_item 
+		group by clinical_item_id
+		having count(patient_item_id) < 256
+	);
+
+
+##### See addSynonyms.sql for adding item description synonyms to facilitate user testing searches
+
+
+################## Composite Clinical Items #######################
+Need specific ID numbers, but may be different in different database versions
+Try working of descriptions as below to find the relevant ID numbers
+
+
+
+
+# Composite certain outcome measures, for example, ICU interventions (sub-categorization based on vasopressors)
+36086	72	7512	RXCUI7512	Norepinephrine
+43993	72	8163	RXCUI8163	Phenylephrine
+44020	72	3992	RXCUI3992	Epinephrine
+44251	72	3616	RXCUI3616	Dobutamine
+44323	72	3628	RXCUI3628	Dopamine
+65411	72	224819	MED224819	VASOPRESSIN...
+python medinfo/cpoe/DataManager.py --compositeRelated "36086,43993,44020,44251,44323,65411|AnyVasoactive|Any Vasoactive Infusion|72"
+
+# CaCl infusion and IV Citrate are very closely correlated to CRRT use, but are rarely used in other scenarios
+45328	82	205894	MED205894	BICARB
+51019	82	36676	RXCUI36676	Sodium
+51150	82	-5.55816E+18	RXCUI205894,36676	BICARB
+54189	82	500028	MED500028	CRRT
+58948	82	9863	RXCUI9863	Sodium
+59107	82	1908	RXCUI1908	Calcium
+64279	82	1.55731E+18	RXCUI205894,11295	BICARB
+49140	4	633105	NUR4084	CONT RENAL REPLACEMENT THERAPY
+python medinfo/cpoe/DataManager.py --compositeRelated "45328,51019,51150,54189,58948,59107,64279,49140|AnyCRRT|Continuous Renal Replacement Therapy Components|82"
+
+# Ventilator orders
+45768	7	472340	RT75	RESP - EXTUBATION
+45832	7	35000476272	RT104	RESP - VENTILATOR SETTINGS
+45990	7	473786	RT97	RESP - WEAN VENTILATOR
+46106	7	473357	RT90	RESP - SPONTANEOUS BREATHING TRIAL
+48769	7	660692	RT130	RESP - REPOSITION ET TUBE
+61530	7	472542	RT88	RESP - HIGH FREQUENCY OSCILLATORY VENTILATION
+62003	7	871225	RT997	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL
+63826	7	897630	RT202	RESP - MONITOR, PERFORM VENTILATOR AND PATIENT ASSESSMENT
+66038	7	473327	RT91	RESP - SIMV/PRESSURE SUPPORT
+python medinfo/cpoe/DataManager.py --compositeRelated "45768,45832,45990,46106,48769,61530,62003,63826,66038|AnyVentilator|Ventilator Orders|7"
+ 
+# Can populate leaf linked items directly, or link to the above intermediate nodes and allow inheritance to pickup the rest
+Category: 12	stride_order_proc	Procedures
+71045 Vasoactives
+71046 CRRT
+71047 Ventilator
+python medinfo/cpoe/DataManager.py --compositeRelated "71045,71046,71047|AnyICULifeSupport|ICU Life Support (Vasoactives,Ventilator,CRRT)|12"
+
+# Or incrementally update an existing compomsite item
+#python medinfo/cpoe/DataManager.py -g "23701,23702|23700"
+
+# Any ICU specific (or highly correlated) orders to capture events occuring in the ICU, including other drips, nursing orders
+??????????
+	530008	MED530008	NITROPRUSSIDE IV INFUSION
+	8782	RXCUI8782	Propofol (Intravenous)
+	530025	MED530025	DEXMEDETOMIDINE IV INFUSION
+	530004	MED530004	MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION
+	8163	RXCUI8163	Phenylephrine (Intravenous)
+	114200	RXCUI114200	Citrate (Intravenous)
+	530120	MED530120	HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)
+	36676	RXCUI36676	Sodium Bicarbonate (CRRT)
+	530007	MED530007	VECURONIUM IV INFUSION
+	4177	RXCUI4177	Etomidate (Intravenous)
+	10154	RXCUI10154	Succinylcholine (Intravenous)
+	520130	MED520130	CALCIUM CHLORIDE 8 G/1000 ML IV INFUSION
+	530009	MED530009	CISATRACURIUM IV INFUSION
+	530003	MED530003	FENTANYL  10 MCG/ML IV INFUSION
+	530111	MED530111	MIDAZOLAM 100 MG IN D5W 100 ML IV INFUSION
+	530114	MED530114	MILRINONE CUSTOM IV INFUSION
+	530050	MED530050	DOBUTAMINE IV INFUSION CUSTOM MIXTURE
+	530046	MED530046	HYDROMORPHONE IV INFUSION
+	530112	MED530112	MIDAZOLAM IV INFUSION (CUSTOM)
+	37217	NUR2254	MONITOR PULMONARY ARTERY CATHETER
+	78347	RT106	RESP - NITRIC OXIDE VENT
+	37120	NUR2247	MONITOR CENTRAL LINE
+	26704	NUR1942	EXTERNAL VENTRICULAR DRAIN CARE
+	17833	NUR1528	MONITOR ARTERIAL LINE
+	399682	NUR30020	RASS SEDATION ASSESSMENT
+	396282	NUR1599	MONITOR CARDIAC ASSIST DEVICE
+	17896	NUR1591	OROGASTRIC TUBE
+	17834	NUR1529	MONITOR CO/CI
+	46677	NUR2999	MONITOR SCV02
+		AnyICULifeSupport	ICU Life Support (Vasopressors,Ventilator,CRRT)
+?????
+python medinfo/cpoe/DataManager.py --compositeRelated "23700,4455,3858,634,710,466,192,113,4303,4216,4507,629,3866,4452,180,3950,4078,3951,43,117,153,201,529,793,1096,1612,1752,2205,2337,2349|ICUOrders|ICU Specific/Correlated Orders|100"
+
+
+# DNR / non-Full Code Orders
+#48737	COD3	PARTIAL CODE
+45905	COD1	DNR
+62758	COD7	DNR/C
+62911	COD5	DNR/DNE
+63717	COD1	DNR/DNI
+python medinfo/cpoe/DataManager.py --compositeRelated "45905,62758,62911,63717|AnyDNR|Any DNR Code Status|22"
+
+
+
+# Readmission by looking for triples containing Discharge then Admit
+Category: 23	stride_order_proc	Admission
+# Composite admission orders
+45814	23	471546	ADT1	ADMIT TO INPATIENT
+61845	23	35000476278	ADT16	ADMIT POST SURGERY
+61982	23	471546	ADT1	ADMIT/PLACE PATIENT
+65641	23	776046	ADT100	ADMIT TO INPATIENT
+python medinfo/cpoe/DataManager.py --compositeRelated "45814,61845,61982,65641|AnyAdmit|Any Admit (not observation nor outpatient surgery)|23"
+
+45805	13	472204	ADT12	DISCHARGE PATIENT
+
+insert into clinical_item
+(clinical_item_category_id, name, description, analysis_status, outcome_interest)
+values
+(23,'READT','Readmission',1,1);
+
+# Identify triples, but effectively requires runs full association analysis work.  Very time intensive
+python medinfo/cpoe/TripleAssociationAnalysis.py -s 45805,73932 -v 73926 patientIds.XYZ.tab
+
+
+# Run Pre-Computation Association Analysis
+# 	Memory intensive, but if can use large amount on 64bit Python saves a lot of DB hits / persistence.
+#   Use -a option to specify how many associations to keep in memory before commit to DB
+#	1,000,000 seems to just fit within 7.5 GB of RAM
+#	Still a DB intensive I/O process that can be helped by moving more database processing to memory than disk
+python medinfo/cpoe/AssociationAnalysis.py -i <patientIDFile> -u 10000 -a 900000
+(Find Patient IDs to work on, by query like 
+	select distinct patient_id 
+	from patient_item 
+	where analyze_date is null 
+	and patient_id % 10 = -1
+Use arbitrary mod parameter on patient ID and item dates to select out data subsets
+)
+
+# Helper driver script to break up into multiple processes (beware total memory overhead)
+nohup python scripts/CDSS/assocAnalysis.py &> log/assocAnalysis &
+
+#  Or just restore from another database
+medinfo/db/support/restoreCPOETables.bat
+
+
+
+
+====================================
+
+
+# Pre-Compute Analyzed Clinical Item Counts to facilitate subsequent interface queries
+python medinfo/cpoe/DataManager.py --updateClinicalItemCounts
+or
+Item Recommender Web Interface, Cache or Query = PreCompute
+or
+# Transfer baseline counts to clinical_item summary counts
+update clinical_item as ci, clinical_item_association as cia
+set ci.item_count = cia.count_0, 
+    ci.patient_count = cia.patient_count_0, 
+    ci.encounter_count = cia.encounter_count_0
+where ci.clinical_item_id = 5 
+and cia.clinical_item_id = ci.clinical_item_id and cia.subsequent_item_id = ci.clinical_item_id;
+
+
+===================================================================
+Setup simultation data
+python scripts\CDSS\populateOrderResultStats.py
+
+
+
+
+===================================================================
+Data Cleanup Options
+
+# PostgreSQL duplicate removal (Trick is ctid system column that tracks unique physical location for every entry)
+delete from stride_order_results A using 
+(   select min(ctid) as ctid, order_proc_id, line
+   from stride_order_results
+   group by order_proc_id, line having count(*) > 1
+) B
+where A.order_proc_id = B.order_proc_id and A.line = B.line
+and A.ctid <> B.ctid
+
+https://stackoverflow.com/questions/6583916/delete-duplicate-records-in-postgresql
+
+# Another in place option, but requires a unique ID column to exist
+delete
+from patient_item as test
+ where exists ( select 'x' 
+				  from patient_item as i
+				 where i.patient_id = test.patient_id
+				   and i.clinical_item_id = test.clinical_item_id
+				   and i.item_date = test.item_date
+				   and i.patient_item_id < test.patient_item_id
+			 )
+limit 10;
+
+
+
+# Use data/analysis/queryItemCount.py to help guide threshold decision
+
+select clinical_item_id, count(*)
+from patient_item as pi
+group by clinical_item_id
+having count(*) < XXX;
+
+python medinfo/cpoe/DataManager.py --deactivateAnalysisByCount <thresholdInstanceCount>
+
+
+
+# Exclude Individual Items including, should not even be analyzed:
+external_id	name	description	
+854932	RXCUI854932	Pneumococcal Capsular Polysaccharide Type 10a Vaccine (SUBCUTANEOUS)	
+854934	RXCUI854934	Pneumococcal Capsular Polysaccharide Type 11a Vaccine (SUBCUTANEOUS)	
+854936	RXCUI854936	Pneumococcal Capsular Polysaccharide Type 12f Vaccine (SUBCUTANEOUS)	
+854938	RXCUI854938	Pneumococcal Capsular Polysaccharide Type 14 Vaccine (SUBCUTANEOUS)	
+854940	RXCUI854940	Pneumococcal Capsular Polysaccharide Type 15b Vaccine (SUBCUTANEOUS)	
+854942	RXCUI854942	Pneumococcal Capsular Polysaccharide Type 17f Vaccine (SUBCUTANEOUS)	
+854944	RXCUI854944	Pneumococcal Capsular Polysaccharide Type 18c Vaccine (SUBCUTANEOUS)	
+854946	RXCUI854946	Pneumococcal Capsular Polysaccharide Type 19a Vaccine (SUBCUTANEOUS)	
+854948	RXCUI854948	Pneumococcal Capsular Polysaccharide Type 19f Vaccine (SUBCUTANEOUS)	
+854950	RXCUI854950	Pneumococcal Capsular Polysaccharide Type 2 Vaccine (SUBCUTANEOUS)	
+854952	RXCUI854952	Pneumococcal Capsular Polysaccharide Type 20 Vaccine (SUBCUTANEOUS)	
+854954	RXCUI854954	Pneumococcal Capsular Polysaccharide Type 22f Vaccine (SUBCUTANEOUS)	
+854956	RXCUI854956	Pneumococcal Capsular Polysaccharide Type 23f Vaccine (SUBCUTANEOUS)	
+854958	RXCUI854958	Pneumococcal Capsular Polysaccharide Type 3 Vaccine (SUBCUTANEOUS)	
+854960	RXCUI854960	Pneumococcal Capsular Polysaccharide Type 33f Vaccine (SUBCUTANEOUS)	
+854962	RXCUI854962	Pneumococcal Capsular Polysaccharide Type 4 Vaccine (SUBCUTANEOUS)	
+854964	RXCUI854964	Pneumococcal Capsular Polysaccharide Type 5 Vaccine (SUBCUTANEOUS)	
+854966	RXCUI854966	Pneumococcal Capsular Polysaccharide Type 6b Vaccine (SUBCUTANEOUS)	
+854968	RXCUI854968	Pneumococcal Capsular Polysaccharide Type 7f Vaccine (SUBCUTANEOUS)	
+854970	RXCUI854970	Pneumococcal Capsular Polysaccharide Type 8 Vaccine (SUBCUTANEOUS)	
+854972	RXCUI854972	Pneumococcal Capsular Polysaccharide Type 9n Vaccine (SUBCUTANEOUS)	
+854974	RXCUI854974	Pneumococcal Capsular Polysaccharide Type 9v Vaccine (SUBCUTANEOUS)	
+854932	RXCUI854932	Pneumococcal Capsular Polysaccharide Type 10a Vaccine (Intramuscular)	
+854934	RXCUI854934	Pneumococcal Capsular Polysaccharide Type 11a Vaccine (Intramuscular)	
+854936	RXCUI854936	Pneumococcal Capsular Polysaccharide Type 12f Vaccine (Intramuscular)	
+854938	RXCUI854938	Pneumococcal Capsular Polysaccharide Type 14 Vaccine (Intramuscular)	
+854940	RXCUI854940	Pneumococcal Capsular Polysaccharide Type 15b Vaccine (Intramuscular)	
+854942	RXCUI854942	Pneumococcal Capsular Polysaccharide Type 17f Vaccine (Intramuscular)	
+854944	RXCUI854944	Pneumococcal Capsular Polysaccharide Type 18c Vaccine (Intramuscular)	
+854946	RXCUI854946	Pneumococcal Capsular Polysaccharide Type 19a Vaccine (Intramuscular)	
+854948	RXCUI854948	Pneumococcal Capsular Polysaccharide Type 19f Vaccine (Intramuscular)	
+854950	RXCUI854950	Pneumococcal Capsular Polysaccharide Type 2 Vaccine (Intramuscular)	
+854952	RXCUI854952	Pneumococcal Capsular Polysaccharide Type 20 Vaccine (Intramuscular)	
+854954	RXCUI854954	Pneumococcal Capsular Polysaccharide Type 22f Vaccine (Intramuscular)	
+854956	RXCUI854956	Pneumococcal Capsular Polysaccharide Type 23f Vaccine (Intramuscular)	
+854958	RXCUI854958	Pneumococcal Capsular Polysaccharide Type 3 Vaccine (Intramuscular)	
+854960	RXCUI854960	Pneumococcal Capsular Polysaccharide Type 33f Vaccine (Intramuscular)	
+854962	RXCUI854962	Pneumococcal Capsular Polysaccharide Type 4 Vaccine (Intramuscular)	
+854964	RXCUI854964	Pneumococcal Capsular Polysaccharide Type 5 Vaccine (Intramuscular)	
+854966	RXCUI854966	Pneumococcal Capsular Polysaccharide Type 6b Vaccine (Intramuscular)	
+854968	RXCUI854968	Pneumococcal Capsular Polysaccharide Type 7f Vaccine (Intramuscular)	
+854970	RXCUI854970	Pneumococcal Capsular Polysaccharide Type 8 Vaccine (Intramuscular)	
+854972	RXCUI854972	Pneumococcal Capsular Polysaccharide Type 9n Vaccine (Intramuscular)	
+854974	RXCUI854974	Pneumococcal Capsular Polysaccharide Type 9v Vaccine (Intramuscular)	
+147420	146535	ECG PROCEDURE SCANNED REPORT	
+1883	LABSPR	SPECIMEN REMARK	
+26702	NUR1940	NURSING COMMUNICATION(FOR ORDERSET ONLY)	
+1435	NUR1025	VITAL SIGNS	
+412485	NUR194000	NURSING COMMUNICATION	
+46821	NUR350	NOTIFY MD:	
+17581	NURSE 1500	NOTIFY MD:VITAL SIGNS	
+147415	146541	OTHER PROCEDURE SCANNED REPORT	
+147416	146543	OTHER ORDER SCANNED REPORT	
+958	COD2	FULL CODE	
+
+706	DIET990	DIET REGULAR	
+1604	TRN2	PATIENT TRANSPORT	
+119815	POC14	POC GLUCOSE BY METER	
+1001	LABPOCGLU	GLUCOSE BY METER	
+
+# If setting analysis status to 0, don't need to set default_recommend to 0 since will inherently be excluded
+update clinical_item
+set analysis_status = 0
+where name in ('RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','146535','LABSPR','NUR1940','NUR1025','NUR194000','NUR350','NURSE 1500','146541','146543','COD2')
+
+update clinical_item
+set analysis_status = 0
+where name in ('DIET990','TRN2','POC14','LABPOCGLU','146537')
+
+
+TRANSFUSION BLOOD PRODUCT X
+# Not even clear what these are orders for, since there are separate orders for RBCs, platelets, and nursing to transfuse them
+#	By coincidence, total count appears to be ~2x sum of all other blood bank orders
+update clinical_item
+set analysis_status = 0
+where name in ('LABTSLIP','LABTBP','LABTBP9','LABTBP10','LABTBP11','LABTBP12','LABTBP13','LABTBP14','LABTBP15','LABTBP16','LABTBP17','LABTBP18','LABTBP1','LABTBP19','LABTBP20','LABTBP21','LABTBP22','LABTBP23','LABTBP24','LABTBP25','LABTBP26','LABTBP27','LABTBP28','LABTBP2','LABTBP29','LABTBP30','LABTBP31','LABTBP32','LABTBP33','LABTBP34','LABTBP35','LABTBP36','LABTBP37','LABTBP38','LABTBP3','LABTBP39','LABTBP40','LABTBP41','LABTBP42','LABTBP43','LABTBP44','LABTBP45','LABTBP46','LABTBP47','LABTBP48','LABTBP4','LABTBP49','LABTBP50','LABTBP51','LABTBP52','LABTBP53','LABTBP54','LABTBP55','LABTBP56','LABTBP57','LABTBP58','LABTBP5','LABTBP59','LABTBP60','LABTBP61','LABTBP62','LABTBP63','LABTBP64','LABTBP65','LABTBP66','LABTBP67','LABTBP68','LABTBP6','LABTBP69','LABTBP70','LABTBP71','LABTBP72','LABTBP73','LABTBP74','LABTBP75','LABTBP76','LABTBP77','LABTBP78','LABTBP7','LABTBP79','LABTBP80','LABTBP81','LABTBP82','LABTBP83','LABTBP84','LABTBP85','LABTBP86','LABTBP87','LABTBP88','LABTBP8','LABTBP89','LABTBP90','LABTBP91','LABTBP92','LABTBP93');
+
+
+
+
+
+# For some of above like PNA vaccine components, may actually be more appropriate to merge/composite items 
+python medinfo/cpoe/DataManager.py --compositeRelated <clinicalItemIds>
+python medinfo/cpoe/DataManager.py --mergeRelated <clinicalItemIds>
+python medinfo/cpoe/DataManager.py --unifyRedundant <clinicalItemIds>
+
+
+# ???Highlight items that may be of interest for outcome measures
+clinical_item_id	description	count	
+20	DISCHARGE PATIENT	1532	
+31	ADMIT TO PHASE	769	
+34	TRANSFER PATIENT	581	
+55	INSERT AIRWAY	360	
+58	RESP - SPONTANEOUS BREATHING TRIAL	76	
+59	ADMIT/PLACE PATIENT	1294	
+66	PATIENT TRANSPORT	2384	
+147	DISCHARGE PATIENT WHEN CRITERIA MET	770	
+207	DIALYZE PATIENT	85	
+332	NON-VIOLENT RESTRAINTS	316	
+374	DNR	237	
+461	RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA	72	
+465	OK TO USE CENTRAL LINE	30	
+522	HEMODIALYSIS	120	
+523	INTERAGENCY REFERRAL TO HOME HOSPICE	18	
+563	CAR CATH CORS POSSIBLE	37	
+612	COMFORT CARE MEASURES	42	
+616	CCPD  PERITONEAL DIALYSIS	15	
+723	INTUBATION	6	
+957	PLACE IN OBSERVATION-CDU	23	
+1086	HEMODIALYSIS WITH PUF	6	
+1087	CAPD PERITONEAL DIALYSIS	3	
+1312	CENTRAL LINE	10	
+1521	CENTRAL LINE CARE	4	
+1636	NON INVASIVE POSTIVE PRESS VENT BIPAP/CPAP	3	
+1643	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL	6	
+1909	TRACHEOSTOMY  CARE	2	
+6551	ARGATROBAN 1000 MCG/ML (250 ML) IV INFUSION	18	
+6700	Fentanyl (Intravenous)	77	
+6724	Dopamine (Intravenous)	72	
+6738	DEXMEDETOMIDINE IV INFUSION	146	
+6743	VASOPRESSIN 1 UNIT/ML IV INFUSION	39	
+6745	Bicarb Dialys Soln #16 W/O Ca K (4 Meq/L) -Mg (1.5 Meq/L) Hemodial Soln (CRRT)	16	
+6797	AMIODARONE IVPB	80	
+6801	AMIODARONE INFUSION CENTRAL LINE ADMIN	44	
+6823	PHENYLEPHRINE IV INFUSION	87	
+6872	Eptifibatide (Intravenous)	11	
+6936	AMIODARONE INFUSION PERIPH ADMIN	29	
+6950	MIDAZOLAM IV INFUSION (CUSTOM)	2	
+7014	Dobutamine (Intravenous)	22	
+7066	Bicarb Dialys Soln No.8 W-O Ca K (2 Meq/L) -Mg (1 Meq/L) Hemodial Soln (CRRT)	22	
+7111	Amiodarone (Intravenous)	1	
+7148	D5W WITH SODIUM BICARBONATE IV INFUSION	7	
+7167	Epinephrine (Intravenous)	12	
+7192	Milrinone (Intravenous)	10	
+20	DISCHARGE PATIENT	1532	
+31	ADMIT TO PHASE	769	
+1660	CALL DIALYSIS RN CAP/DRAIN CAPD	2	
+1087	CAPD PERITONEAL DIALYSIS	3	
+616	CCPD  PERITONEAL DIALYSIS	15	
+1521	CENTRAL LINE CARE	4	
+612	COMFORT CARE MEASURES	42	
+674	CONT RENAL REPLACEMENT THERAPY	18	
+1097	DIALYSIS ACCESS	6	
+1995	DIALYSIS CATHETER CARE	1	
+207	DIALYZE PATIENT	85	
+522	HEMODIALYSIS	120	
+1086	HEMODIALYSIS WITH PUF	6	
+465	OK TO USE CENTRAL LINE	30	
+403	TRANSFUSION EMERGENCY ISSUE	20	
+34	TRANSFER PATIENT	581	
+158	RESP - EXTUBATION	98	
+1643	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL	6	
+139	RESP - NON INVASIVE POS. PRESS VENT (BIPAP/CPAP)	136	
+58	RESP - SPONTANEOUS BREATHING TRIAL	76	
+164	RESP - VENTILATOR SETTINGS	467	
+137	RESP - WEAN VENTILATOR	110	
+461	RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA	72	
+130	ADMIT POST SURGERY	656	
+59	ADMIT/PLACE PATIENT	1294	
+957	PLACE IN OBSERVATION-CDU	23	
+66	PATIENT TRANSPORT	2384	
+374	DNR	237	
+99	PARTIAL CODE	17	
+902	ARTERIAL LINE	11	
+1312	CENTRAL LINE	10	
+723	INTUBATION	6	
+6801	AMIODARONE INFUSION CENTRAL LINE ADMIN	44	
+6936	AMIODARONE INFUSION PERIPH ADMIN	29	
+6797	AMIODARONE IVPB	80	
+7014	Dobutamine (Intravenous)	22	
+6724	Dopamine (Intravenous)	72	
+6659	EPINEPHRINE IV INFUSION	61	
+6696	Midazolam (Intravenous)	70	
+7192	Milrinone (Intravenous)	10	
+6681	NITROPRUSSIDE IV INFUSION	99	
+6788	NOREPINEPHRINE IV INFUSION	46	
+6900	Nitroglycerin (Intravenous)	59	
+
+xxxx	Death Date	???
+xxxx	Length of Stay	???
+
+
+update clinical_item
+set outcome_interest = 1
+where description in
+('DISCHARGE PATIENT','ADMIT TO PHASE','TRANSFER PATIENT','INSERT AIRWAY','RESP - SPONTANEOUS BREATHING TRIAL','ADMIT/PLACE PATIENT','PATIENT TRANSPORT','DISCHARGE PATIENT WHEN CRITERIA MET','DIALYZE PATIENT','NON-VIOLENT RESTRAINTS','DNR','RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA','OK TO USE CENTRAL LINE','HEMODIALYSIS','INTERAGENCY REFERRAL TO HOME HOSPICE','CAR CATH CORS POSSIBLE','COMFORT CARE MEASURES','CCPD  PERITONEAL DIALYSIS','INTUBATION','PLACE IN OBSERVATION-CDU','HEMODIALYSIS WITH PUF','CAPD PERITONEAL DIALYSIS','CENTRAL LINE','CENTRAL LINE CARE','NON INVASIVE POSTIVE PRESS VENT BIPAP/CPAP','RESP - LUNG PROTECTIVE VENTILATION PROTOCOL','TRACHEOSTOMY  CARE','ARGATROBAN 1000 MCG/ML (250 ML) IV INFUSION','Fentanyl (Intravenous)','Dopamine (Intravenous)','DEXMEDETOMIDINE IV INFUSION','VASOPRESSIN 1 UNIT/ML IV INFUSION','Bicarb Dialys Soln #16 W/O Ca K (4 Meq/L) -Mg (1.5 Meq/L) Hemodial Soln (CRRT)','AMIODARONE IVPB','AMIODARONE INFUSION CENTRAL LINE ADMIN','PHENYLEPHRINE IV INFUSION','Eptifibatide (Intravenous)','AMIODARONE INFUSION PERIPH ADMIN','MIDAZOLAM IV INFUSION (CUSTOM)','Dobutamine (Intravenous)','Bicarb Dialys Soln No.8 W-O Ca K (2 Meq/L) -Mg (1 Meq/L) Hemodial Soln (CRRT)','Amiodarone (Intravenous)','D5W WITH SODIUM BICARBONATE IV INFUSION','Epinephrine (Intravenous)','Milrinone (Intravenous)','DISCHARGE PATIENT','ADMIT TO PHASE','CALL DIALYSIS RN CAP/DRAIN CAPD','CAPD PERITONEAL DIALYSIS','CCPD  PERITONEAL DIALYSIS','CENTRAL LINE CARE','COMFORT CARE MEASURES','CONT RENAL REPLACEMENT THERAPY','DIALYSIS ACCESS','DIALYSIS CATHETER CARE','DIALYZE PATIENT','HEMODIALYSIS','HEMODIALYSIS WITH PUF','OK TO USE CENTRAL LINE','TRANSFUSION EMERGENCY ISSUE','TRANSFER PATIENT','RESP - EXTUBATION','RESP - LUNG PROTECTIVE VENTILATION PROTOCOL','RESP - NON INVASIVE POS. PRESS VENT (BIPAP/CPAP)','RESP - SPONTANEOUS BREATHING TRIAL','RESP - VENTILATOR SETTINGS','RESP - WEAN VENTILATOR','RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA','ADMIT POST SURGERY','ADMIT/PLACE PATIENT','PLACE IN OBSERVATION-CDU','PATIENT TRANSPORT','DNR','PARTIAL CODE','ARTERIAL LINE','CENTRAL LINE','INTUBATION','AMIODARONE INFUSION CENTRAL LINE ADMIN','AMIODARONE INFUSION PERIPH ADMIN','AMIODARONE IVPB','Dobutamine (Intravenous)','Dopamine (Intravenous)','EPINEPHRINE IV INFUSION','Midazolam (Intravenous)','Milrinone (Intravenous)','NITROPRUSSIDE IV INFUSION','NOREPINEPHRINE IV INFUSION','Nitroglycerin (Intravenous)','Death Date');
+
+
+>>>>>>> Stashed changes:scripts/Archive/CDSS/setupDataPreprocessingNotes.txt
diff --git a/scripts/Archive/CDSS/setupNotes.txt b/scripts/Archive/CDSS/setupNotes.txt
new file mode 100644
index 00000000..796906e3
--- /dev/null
+++ b/scripts/Archive/CDSS/setupNotes.txt
@@ -0,0 +1,253 @@
+======== AWS Server Setup Notes ==============
+If you're just installing on your local laptop/computer, then skip to the next section, but some tips here may still be helpful.
+
+===========================================
+May be able to reuse existing dev environments
+- Find EC2 Amazon Machine Image (AMI) SnapShot (e.g., medinfo-STRIDE-2008-2014-postgresql)
+- Launch / Restore an Instance based on one
+- Login as ec2-user as described below
+	- sudo service postgresql96 restart
+- Should now have a running Linux server with running PostgreSQL database with STRIDE 2008-2014 data already loaded
+- Working git CDSS code repo available under /home/ec2-user/CDSS 
+
+================== From Scratch Version =================
+- Sign-up for AWS account
+- Free Tier eligible EC2 Micro Instance create
+- Change Security Group for instance to allow TCP ports 22 for SSH connection any others
+- Connect as ec2-user (or ubuntu) first off using public-private key default method (requires local download)
+  ssh -i privateKey.pem ec2-user@xxx.xxx.xxx.xxx
+- Need to prefix "sudo " to most commands to act as root
+
+Can continue to just work as "ec2-user" or can create a separate user and account that can login by password:
+	- Create user to login as
+	sudo adduser jonc101
+	sudo passwd jonc101
+	- Grant user sudo privileges
+	sudo visudo
+	    jonc101 ALL=(ALL) ALL
+	- Change setting to allow SSH password login. Beware that this is less secure!
+	sudo vi /etc/ssh/sshd_config
+	PasswordAuthentication yes
+	sudo service sshd restart
+
+	Should now be able to SSH to instance using the created username and password
+
+- Install Amazon packages
+sudo yum install git
+sudo yum install postgresql96
+sudo yum install python27-psycopg2
+sudo yum install httpd
+sudo yum install mod_wsgi-python27
+sudo yum install python27-scipy
+sudo yum install python27-matplotlib
+
+# Python packages
+sudo pip install pandas
+sudo pip install sklearn
+sudo pip install gensim
+#sudo easy_install gensim
+
+(Consider general SciPy dev environment setup)
+(http://blog.yhathq.com/posts/setting-up-scientific-python.html)
+  Avoid mixing installation modalities (PIP, downloadable executables, Enthought Canopy distribution) as tend to be incompatible
+
+- Use AWS Console interface (RDService) to create a PostgreSQL relational database if want separate DB server (vs. installed on local EC2 instance)
+  Consider Provisioned IOPS on a "large' instance for computation periods to better handle high I/O cost
+
+==============================================
+Local Installation Dependencies
+
+Highly recommended that you use some standard package installers to minimize the pain of getting all of the dependencies setup right.
+(E.g., yum or apt-get for top level packages. Use pip for python packages where possible. Windows binary installers may be necessary for others. Many unofficial Windows binaries are available from https://www.lfd.uci.edu/~gohlke/pythonlibs/)
+
+- PostgreSQL
+- Python 2.7
+  - Scipy
+  - Numpy
+  - Psycopg2
+
+Only necessary if you want to develop interactive web interfaces:
+- Apache 2.2
+- Mod_WSGI
+
+==============================================
+Download code from source control
+> git clone https://github.com/HealthRex/CDSS.git
+
+==============================================
+Download data folders
+- STRIDE-Inpatient-2008-2014
+  - dump2009-2014-5year-time
+- STRIDE-Inpatient-2004-2017
+
+# Tips for remote copying / synchronizing data files to Linux servers
+pscp * jonc101@corn.stanford.edu:private/data
+# Patching / resuming incompletely transmitted files.  Do for one file at a time to allow for proper interrupt / resume function?
+rsync --partial --progress order_results.txt jonc101@corn.stanford.edu:private/data/order_results.txt
+
+=============================================
+Update ~/.bash_profile with paths and references to make subsequent steps more convenient
+
+> vi ~/.bash_profile
+    BASE_DIR=$HOME/CDSS
+    export BASE_DIR
+
+    PYTHONPATH=$PYTHONPATH:$BASE_DIR
+    export PYTHONPATH
+
+# Quick alternative
+> echo "export PYTHONPATH=$PYTHONPATH:$HOME/CDSS" >> .bash_profile
+
+
+> source ~/.bash_profile
+
+==============================================
+# PostgreSQL Database Server Setup
+- See documentation for local setup
+- AWS RDS does much of this automatically
+- AWS EC2
+	sudo yum install postgresql96-server
+	sudo service postgresql96 initdb
+	sudo service postgresql96 restart
+	sudo -u postgres psql -U postgres 	# Connect via database superuser postgres
+
+# Create DB users and access privileges from remote connections
+# Replace "jonc101" with ec2-user or other username as per your preference
+PostgreSQL SQL Command:
+    CREATE USER "ec2-user" PASSWORD '<DBPassword>';	
+
+#   Create blank database to work with
+> createdb -U postgres -O jonc101 medinfo
+(Note that this is a command-line program. If you're already connected via psql, you can enter the CREATE DATABASE SQL command query.)
+	CREATE DATABASE medinfo OWNER "ec2-user";
+	CREATE DATABASE testmedinfo OWNER "ec2-user";
+
+# Need to configure PostgreSQL database server to allow connections by password, in this case, from any client host
+#sudo echo "host  all  all  0.0.0.0/0  md5" >> /var/lib/pgsql96/data/pg_hba.conf
+sudo vi /var/lib/pgsql96/data/pg_hba.conf
+	# Add line like this, saying allow connection to any database, by any user, from any IP address, if authenticates by an MD5 encrypted password
+	host  all  all  0.0.0.0/0  md5
+	# Comment out the line like below, so won't attempt to use alternative ident authentication method first
+	# host    all             all             127.0.0.1/32            ident
+
+# If want to allow connections from external hosts (e.g., your laptop), then need to also change / add line to configuration file to listen for external calls
+sudo vi /var/lib/pgsql96/data/postgresql.conf
+	# Add / edit this line to listen from all hosts
+	listen_addresses = '*'
+
+# Reset database service to capture configuration changes
+	sudo service postgresql96 restart
+
+# For convenience, can define database user password in environment so don't have to enter it everytime
+	export PGPASSWORD=<DBPassword>
+
+# If restoring from a previously pre-processed database, than just restore each data table's contents in turn
+> restoreCPOETables.sh (should be stored with data dump source)
+
+# See setupDataPreprocessingNotes.txt for details on reconstructing the pre-processing steps from scratch
+
+#   Create data definitions (If did direct restore from previous data dump, OK to expect many errors regarding "Relation already exists")
+# This is more for setting up a separate test database that just needs the blank schema, but not the actual data
+PostgreSQL: # Use timestamp data type
+> psql -f medinfo/db/definition/schema.strideData.sql -U jonc101 testmedinfo		# This may be in the source data directory as schema.strideData.sql
+> psql -f medinfo/db/definition/cpoeStats.sql -U jonc101 testmedinfo
+> psql -f medinfo/db/definition/cpoeSimulation.sql -U jonc101 testmedinfo
+
+# Point Python scripts to database (may want to separate your primary database from a "test" database that unit tests run against, so that the unit tests don't accidentally clobber your primary data source).
+> vi medinfo/db/Env.py
+
+# Python unit tests to verify things are functional
+# Database connection test
+> python medinfo/db/test/TestDBUtil.py
+
+# Basic stat table unit tests
+> python medinfo/cpoe/test/TestAssociationAnalysis.py
+> python medinfo/cpoe/test/TestItemRecommender.py
+
+#   Data Conversion unit test
+> python medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py
+> python medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py
+> python medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py
+
+# Multiple Unit tests, auto discover
+> python -m unittest discover -v -p "Test*.py" -s medinfo/analysis
+
+===================================================================
+
+#  Setup Web Server (Apache HTTP, mod_wsgi, psycopg2 DB access).  
+# Install mod_wsgi using an install or Copy the version appropriate mod_wsgi.so file to the 
+#	apache modules directory and rename to simply mod_wsgi.so
+# 	This has a high probability of being very painful, because it requires 
+#	perfectly synced binaries / compiler versions for each component.
+#	Best if you have a package installer (e.g., yum) that does it automatically, otherwise
+#	you'll have to hunt down the right combination of pre-compiled components that are compatible.
+# Setup the Apache webserver as per standard instructions, then add the extra configuration below to add the custom Python app
+# Sample Apache Config:
+sudo vi /etc/httpd/conf/httpd.conf
+
+	LoadModule wsgi_module modules/mod_wsgi.so
+	DocumentRoot "/home/jonc101/CDSS/medinfo/web"
+
+	# Medical Informatics web application configuration
+	WSGIPythonPath /home/jonc101/CDSS
+	Alias /medinfo/web/ "/home/jonc101/CDSS/medinfo/web/"
+	<Directory "/home/jonc101/CDSS/medinfo/web">
+		DirectoryIndex index.html index.htm
+
+		AllowOverride None
+		Allow from all
+
+		Order allow,deny
+	</Directory>
+	<Directory "/home/jonc101/CDSS/medinfo/web/cgibin">
+		# Handler setup for Python CGI scripts (works on files ending in .py)
+		Options +ExecCGI
+
+		# Basic CGI support
+		#AddHandler cgi-script py
+		#SetEnv PYTHONPATH /home/jonc101/CDSS
+
+		# mod_wsgi support.  # Need to define PythonPAth and other environment variables at level of user starting Apache process, or PythonPath in above WSGIPythonPath
+		AddHandler wsgi-script py
+		WSGIApplicationGroup %{GLOBAL}
+
+		AllowOverride None
+		Allow from all
+
+		Order allow,deny
+	</Directory>
+
+
+# Update medinfo/web/cgibin/Env.py, CGI_TEXT_RESPONSE depending on if using CGI or WSGI setup
+# Windows: May need to add Apache directive, so CGI knows how to run .py files
+	ScriptInterpreterResoruce Registry
+
+# Link with tips on setting up apache environment variable configuration if needed
+# Basically adding values to a configuration file like below, though CGI and WSGI should accept PYTHONPATH specification as above
+sudo vi /etc/sysconfig/httpd 
+	TEST_ENV_VAR=TestValue
+	export TEXT_ENV_VAR
+# If use "apachectl" rather than "service httpd" for control, can use starting user's environment as well
+# For Windows environment, add via My Computer > Advanced Settings > Environment Variables
+http://www.centos.org/docs/5/html/Deployment_Guide-en-US/s1-apache-startstop.html
+
+# Need read/execute permission on directory tree up to web files, so may need to
+chmod 755 /home/jonc101
+
+# Linux server may also need to change SELinux enforcement if running into "AH00132: file permissions deny server access"
+sudo setenforce 0
+https://wiki.apache.org/httpd/13PermissionDenied
+
+# General Tips
+- Review Apache error logs if running into server side issues: sudo tail /etc/httpd/logs/error_
+
+# Web server start / control
+sudo service httpd restart
+
+
+#  Test Pages. If these load properly, then your web setup should be in good shape
+#  	If failing with Internal Server Errors, then check the Apache /etc/httpd/logs/error.log for stderr messages
+http://localhost/medinfo/web/cgibin/admin/DebugWeb.py
+http://localhost/medinfo/web/cgibin/admin/DBUtilWeb.py
+http://localhost/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py
+
diff --git a/scripts/CDSS/testGenSim.py b/scripts/Archive/CDSS/testGenSim.py
similarity index 96%
rename from scripts/CDSS/testGenSim.py
rename to scripts/Archive/CDSS/testGenSim.py
index d7e15fe7..2147df95 100644
--- a/scripts/CDSS/testGenSim.py
+++ b/scripts/Archive/CDSS/testGenSim.py
@@ -1,115 +1,115 @@
-import sys, os;
-from pprint import pprint   # pretty-printer
-
-import gensim;
-from gensim import corpora, models, similarities
-from medinfo.common.Util import *;
-
-def main(argv):
-    pass;
-    
-    
-documents = \
-    [   "Human machine interface for lab abc computer applications",
-        "A survey of user opinion of computer system response time",
-        "The EPS user interface management system",
-        "System and human system engineering testing of EPS",
-        "Relation of user perceived response time to error measurement",
-        "The generation of random binary unordered trees",
-        "The intersection graph of paths in trees",
-        "Graph minors IV Widths of trees and well quasi ordering",
-        "Graph minors A survey",
-    ];
-
-# Texts: Lists of words
-stoplist = set('for a of the and to in'.split())
-texts = \
-    [   [word for word in document.lower().split() if word not in stoplist] 
-        for document in documents];
-
-pprint(texts);
-
-# Word/Token/Term frequency counts
-from collections import defaultdict
-frequency = defaultdict(int)
-for text in texts:
-    for token in text:
-        frequency[token] += 1
-
-
-# Remove words that appear only once
-texts = \
-    [   [token for token in text if frequency[token] > 1]
-        for text in texts
-    ];
-
-pprint(texts);
-
-
-
-# GenSim Dictionary format to convert item lists into count vectors/dicts
-dictionary = corpora.Dictionary(texts); # Instead of full list of texts, could feed in an iterator/generator
-# dictionary.save("temp.dict");
-print(dictionary);
-print(dictionary.token2id); # Mapping of text tokens to generated indexes
-print(dictionary.id2token); # Mapping of text tokens to generated indexes
-
-# Convert a document into vector form
-newDoc = "Human computer interaction";
-newVec = dictionary.doc2bow(newDoc.lower().split());
-print(newVec);
-
-# Corpus: Bag of Words (BoW) sparse vector/dictionary formats (list of 2-ples: (wordId, frequencyCount))
-corpus = [dictionary.doc2bow(text) for text in texts];
-corpora.MmCorpus.serialize("temp.mm",corpus)
-pprint(corpus); # Table of (wordId, count) pairs.  One row per document.
-"""
-print "---------"
-
-reloadCorpus = corpora.MmCorpus("temp.mm");
-for bag in reloadCorpus:
-    print bag
-"""
-# NumPy and SciPy conversions   (Matrixes seem to be transposed compared to what would expect)
-"""
-numpyMatrix = gensim.matutils.corpus2dense(corpus, len(dictionary));
-print numpyMatrix;
-
-scipyCSC = gensim.matutils.corpus2csc(corpus);
-print scipyCSC;
-"""
-
-# Model data.  TFIDF simple example.  Later Latent Dirichlet Allocation will be much more costly
-tfidf = models.TfidfModel(corpus);  # Computes document frequencies of each item
-# Use model to convert (bag-of-word count) vectors into new (TFIDF floating point weights).  Normalize each document vector so Eucledean magnitude of 1
-corpus_tfidf = tfidf[corpus]
-
-"""
-# Latent Semantic Indexing
-print "LSI?????"
-lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2);
-corpus_lsi = lsi[corpus_tfidf]; # Chaining transformation wrappers
-pprint(lsi.print_topics(2));
-"""
-print "LDA..."
-nTopics = 4;
-#lda = models.LdaMulticore(corpus_tfidf,id2word=dictionary, num_topics=nTopics, workers=2);
-lda = models.LdaModel(corpus_tfidf,id2word=dictionary, num_topics=nTopics);
-hdp = models.hdpmodel.HdpModel(corpus_tfidf,id2word=dictionary);
-
-#for iTopic in xrange(nTopics):
-#    print lda.show_topic(iTopic, topn=20);
-
-#topics = lda.show_topics(num_topics=-1, num_words=20, formatted=False)
-#topics = lda.show_topics(topics=-1, topn=20, formatted=False)
-#for topicId, topicItems in enumerate(topics):
-#    for (itemDescr, itemScore) in topicItems:
-#        print topicId, itemDescr, itemScore;
-
-#corpus_lda = lda[corpus_tfidf];
-#for doc in corpus_lda:
-#    print(doc);
-
-if __name__ == "__main__":
-    main(sys.argv);
+import sys, os;
+from pprint import pprint   # pretty-printer
+
+import gensim;
+from gensim import corpora, models, similarities
+from medinfo.common.Util import *;
+
+def main(argv):
+    pass;
+    
+    
+documents = \
+    [   "Human machine interface for lab abc computer applications",
+        "A survey of user opinion of computer system response time",
+        "The EPS user interface management system",
+        "System and human system engineering testing of EPS",
+        "Relation of user perceived response time to error measurement",
+        "The generation of random binary unordered trees",
+        "The intersection graph of paths in trees",
+        "Graph minors IV Widths of trees and well quasi ordering",
+        "Graph minors A survey",
+    ];
+
+# Texts: Lists of words
+stoplist = set('for a of the and to in'.split())
+texts = \
+    [   [word for word in document.lower().split() if word not in stoplist] 
+        for document in documents];
+
+pprint(texts);
+
+# Word/Token/Term frequency counts
+from collections import defaultdict
+frequency = defaultdict(int)
+for text in texts:
+    for token in text:
+        frequency[token] += 1
+
+
+# Remove words that appear only once
+texts = \
+    [   [token for token in text if frequency[token] > 1]
+        for text in texts
+    ];
+
+pprint(texts);
+
+
+
+# GenSim Dictionary format to convert item lists into count vectors/dicts
+dictionary = corpora.Dictionary(texts); # Instead of full list of texts, could feed in an iterator/generator
+# dictionary.save("temp.dict");
+print(dictionary);
+print(dictionary.token2id); # Mapping of text tokens to generated indexes
+print(dictionary.id2token); # Mapping of text tokens to generated indexes
+
+# Convert a document into vector form
+newDoc = "Human computer interaction";
+newVec = dictionary.doc2bow(newDoc.lower().split());
+print(newVec);
+
+# Corpus: Bag of Words (BoW) sparse vector/dictionary formats (list of 2-ples: (wordId, frequencyCount))
+corpus = [dictionary.doc2bow(text) for text in texts];
+corpora.MmCorpus.serialize("temp.mm",corpus)
+pprint(corpus); # Table of (wordId, count) pairs.  One row per document.
+"""
+print "---------"
+
+reloadCorpus = corpora.MmCorpus("temp.mm");
+for bag in reloadCorpus:
+    print bag
+"""
+# NumPy and SciPy conversions   (Matrixes seem to be transposed compared to what would expect)
+"""
+numpyMatrix = gensim.matutils.corpus2dense(corpus, len(dictionary));
+print numpyMatrix;
+
+scipyCSC = gensim.matutils.corpus2csc(corpus);
+print scipyCSC;
+"""
+
+# Model data.  TFIDF simple example.  Later Latent Dirichlet Allocation will be much more costly
+tfidf = models.TfidfModel(corpus);  # Computes document frequencies of each item
+# Use model to convert (bag-of-word count) vectors into new (TFIDF floating point weights).  Normalize each document vector so Eucledean magnitude of 1
+corpus_tfidf = tfidf[corpus]
+
+"""
+# Latent Semantic Indexing
+print "LSI?????"
+lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2);
+corpus_lsi = lsi[corpus_tfidf]; # Chaining transformation wrappers
+pprint(lsi.print_topics(2));
+"""
+print("LDA...")
+nTopics = 4;
+#lda = models.LdaMulticore(corpus_tfidf,id2word=dictionary, num_topics=nTopics, workers=2);
+lda = models.LdaModel(corpus_tfidf,id2word=dictionary, num_topics=nTopics);
+hdp = models.hdpmodel.HdpModel(corpus_tfidf,id2word=dictionary);
+
+#for iTopic in xrange(nTopics):
+#    print lda.show_topic(iTopic, topn=20);
+
+#topics = lda.show_topics(num_topics=-1, num_words=20, formatted=False)
+#topics = lda.show_topics(topics=-1, topn=20, formatted=False)
+#for topicId, topicItems in enumerate(topics):
+#    for (itemDescr, itemScore) in topicItems:
+#        print topicId, itemDescr, itemScore;
+
+#corpus_lda = lda[corpus_tfidf];
+#for doc in corpus_lda:
+#    print(doc);
+
+if __name__ == "__main__":
+    main(sys.argv);
     
\ No newline at end of file
diff --git a/scripts/Archive/ClinicNet/README.md b/scripts/Archive/ClinicNet/README.md
new file mode 100644
index 00000000..f72c332b
--- /dev/null
+++ b/scripts/Archive/ClinicNet/README.md
@@ -0,0 +1,177 @@
+# Table of contents
+1. [Data Preprocessing](#preprocessing)
+2. [Processing the Data Matrix](#processdatamatrix)
+3. [Model and Tuning](#model)
+4. [Evaluation](#evaluation)
+# Data Preprocessing<a name="preprocessing"></a>
+
+## Splitting Data into Train/Dev/Test
+
+Now we have our dataset in one folder; all files are .txt.gz files containing tab-delimited plaintext data. Each data file should be a patient encounter with the encounter ID followed by .txt or .txt.gz in the filename. Also, in that same folder, there should be a columns.txt file specifying column names for all the fields in the data files. (Note: The fields, patient_id and encounter_id, must be present as columns in order for us to proceed).
+
+To split our data set into train/dev/test on the basis of patients (not encounters!) so that data from any single patient won't spill over across the train/dev/test sets, we run the following script (note: -g specifies our files are gzip compressed files):
+
+<pre>preprocessing/data_split.sh -g ./data/unprocessed/ 70 15 15</pre>
+
+This will create a 70/15/15 split for train/dev/test sets. You'll also get some summary statistics such as: 
+<ul><li>Number of patients: 57624 (total), 40336 (train), 8644 (dev), 8644 (test)</li>
+  <li>Number of encounters: 83226 (total), 58290 (train), 12401 (dev), 12535 (test)</li></ul>
+
+## Putting the data into HDF5 files
+
+For all the data files (that are tab-delimited in plaintext format) in a folder, we'll convert them to (partially compressed) HDF5 files so they can efficiently be loaded into python pandas data frames later on. From here on out, we'll only load data via HDF5 files.
+
+To do the conversion, run the following script where you specify the input data directory, output folder (which you should create beforehand with mkdir), the number of chunks to split the data into (this will be the number of HDF5 files we'll get), the clinical item IDs to retain for the post.1d response variables, the features that we want to exclude altogether (-e), and the features that we want to save in their raw form (-s). Since the data will be split into multiple chunks, we can leverage multiprocessing via -p.
+
+
+<pre>python2 preprocessing/make_hdf5.py -i data/unprocessed/train -c data/columns.txt -o data/hdf5/train -n 360 -r data/response_vars.tsv -e item_date,analyze_date -s patient_id,patient_item_id,encounter_id,clinical_item_id -p 2</pre>
+
+Running the above command will give you something like this: "Using 2 processes for processing 58290 files divided into 360 chunks". Once it's finished running, you'll see "FINISHED" printed out and you'll see 360 .h5 files in the output folder. In each HDF5 file, two data frames: data_x and data_y (for the feature matrix and response variables, respectively) are stored. The features specified via -s will be contained in the data frame: data_s.
+
+IMPORTANT: The HDF5 files will store the data frames as float32 types. This means we can't have any non-numeric columns (in the example above, we removed the item_date and analyze_date columns because these columns store dates in string format). Use -e to remove non-numeric columns. Beware that large integers lose precision when casted to float32 -- therefore, large numbers (such as patient IDs) are best stored separately by specifying the -s option.
+
+# Processing the Data Matrix<a name="processdatamatrix"></a>
+
+
+## Shuffling the data into batches
+
+We can kill two birds with one stone: Shuffling (randomly) the entire dataset and putting the shuffled data into equally sized batches. We have to run two scripts. The first does the batch assignments (e.g. below, we make batches of size 4096 using the entire dataset) and produces a pickle file as output. The second takes the pickle file (containing the batch assignments) and uses it write out the batches (each batch being one file) to a specified folder (which we should create via mkdir).
+<pre>python2 data_processing/prep_batches.py -i data/hdf5/train/ -o data/train_shuffling.pickle -b 4096</pre>
+<pre>python2 data_processing/make_batches.py -s data/train_shuffling.pickle -i data/hdf5/train/ -o data/hdf5/train_shuffled/ -b 0 -e 100</pre>
+Note: The first script will print out info about the batches generated. Here was the output obtained when the command was run on the training set data and subsequently, on the dev set and test set data:
+<ul>
+  <li>Training set data: "Read 25606920 data rows in 360 files. Created 6252 batches of size 4096."</li>
+  <li>Dev set data: "Read 5539705 data rows in 120 files. Created 1353 batches of size 4096"</li>
+  <li>Test set data: "Read 5481460 data rows in 360 files. Created 1339 batches of size 4096"</li>
+</ul>
+
+Note: For the second script, we set the -b and -e options to indicate the beginning index and the end index of the batches we want to write out. In the example above, we write out batches 0 through 99. This is because doing the entire operation at once would consume too much memory so we need to split it into chunks.
+
+## Getting data's statistics
+
+We want to get the means and standard deviations of each feature so we can standardize the dataset via: (x-μ)/s
+Note that prior to calculating the means and standard deviations, the data will be log2(x+1) transformed.
+Additionally, we want to get the frequencies of the response variables being non-zeroes (i.e. for each response variable, what are the percentage of non-zero values). This will be useful for weighting our loss function later on. Finally, we want to build a covariance matrix for principal components analysis (PCA).
+
+To do this, we use the following script (the -x flag denotes which features we don't want to transform and we use -n to specify we'll only going to use 626 files, which is just 10% of the 6252 training data files, for average and covariance computation; we also specify data/tmp as our tmp directory that the script utilizes for caching and we specify 24 processors). In the output folder (which we should create via mkdir), two files: <b>avg_stddev.hdf5</b> and <b>freq_y.hdf5</b> will be generated.
+
+<pre>python3 data_processing/compute_stats.py -p 24 -i data/hdf5/train_shuffled/ -o data/statistics/train/ -x patient_item_id,external_id,patient_id,clinical_item_id,encounter_id,item_date.month,item_date.month.sin,item_date.month.cos,item_date.hour,item_date.hour.sin,item_date.hour.cos -t data/tmp -n 626</pre>
+
+## Feature selection
+
+To remove very low variance features, run the following script where you specify the directory of the data you want to remove features from, the output directory (which you should create with mkdir beforehand), and the statistics directory corresponding to the statistics for the data. Below, we specify a standard deviation threshold of 0.01 (such that all features with a standard deviation below 0.01 will be removed) and we also specify a list of features we want to remove anyway.
+<pre>python3 data_processing/feature_selection.py -i data/hdf5/train_shuffled/ -o data/hdf5/train_feature_selected/ -s data/statistics/train/ -t 0.01 -r patient_item_id,external_id,patient_id,clinical_item_id,encounter_id,item_date,analyze_date,item_date.month,item_date.hour</pre>
+Running the script above tells us that 5214 features out of 24875 were removed, leaving 19661 features remaining. This script also outputs new statistics files (with the features removed) in the statistics directory for the data. (Note: When rerunning feature selection on the dev and test sets, be sure to use the same statistics directory specified for feature selection on the training set.)
+
+## Adding item date timestamps
+
+Now, we'll add item_date timestamps to our dataset (because by default, the date timestamps aren't preserved). You specify the input data directory and and patient_itemdate_mapping_file (HDF5 file specified via -d). We can leverage multiprocessing via -p. The patient_itemdate_mapping_file contains a data frame of patient items with columns being item_date (as nanoseconds since epoch) and patient_item_id. The item_date timestamps will be added to the data_s dataframe of the data.
+
+<pre>python data_processing/add_dates.py -i data/hdf5/train_feature_selected/ -d ./queried/patientitemid_itemdate.hdf5</pre>
+
+## Stratifying data by timestamp
+
+To do time series validation, we need to stratify our dataset by time. The following commands will filter the existing training, validation, and test sets to consist of the following:
+<ul>
+  <li>Training set: Data before the year 2011</li>
+  <li>Validation set: Data in the year 2011</li>
+  <li>Test set: Data after the year 2011</li>
+</ul>
+<pre>
+python data_processing/stratify_data_by_time.py -i data/hdf5/train_feature_selected/ -o data/hdf5/train_feature_selected_time_temp/ -l 0 -g 1293840000000000000
+python data_processing/stratify_data_by_time.py -i data/hdf5/dev_feature_selected/ -o data/hdf5/dev_feature_selected_time_temp/ -l 1293840000000000000 -g 1325376000000000000
+python data_processing/stratify_data_by_time.py -i data/hdf5/test_feature_selected/ -o data/hdf5/test_feature_selected_time_temp/ -l 1325376000000000000 -g 99900000000000000000000
+</pre>
+(Notes: Use mkdir to make the output directories, specified by -o, beforehand; use -p for multiprocessing)
+The 1293840000000000000 timestamp corresponds to January 1 2011 whereas the 1325376000000000000 timestamp corresponds to January 1 2012. The -l and -g options represent the lower-bound and upper-bound timestamps, respectively, for selecting data to retain (use 0 for no lower bound and use a huge number for no upper bound).
+
+Following timestamp stratification, it is necessary to recreate the batches. See the following example (for the training set):
+<pre>
+mkdir data/hdf5/train_feature_selected_time_temp/
+python2 data_processing/prep_batches.py -i data/hdf5/train_feature_selected_time_temp/ -o data/train_time_shuffling.pickle -b 4096</pre>
+
+Results for these timestamp-stratified train/dev/test batches:
+<ul>
+  <li>Training set data: "Read 8893911 data rows in 6252 files. Created 2172 batches of size 4096."</li>
+  <li>Dev set data: "Read 1208680 data rows in 1353 files. Created 296 batches of size 4096"</li>
+  <li>Test set data: "Read 2443646 data rows in 1339 files. Created 597 batches of size 4096"</li>
+</ul>
+
+And then, like before, we create the batches via something like (see following example for making 100 random batches from the training set):
+<pre>
+mkdir data/hdf5/train_feature_selected_time/
+python2 data_processing/make_batches.py -s data/train_time_shuffling.pickle -i data/hdf5/train_feature_selected_time_temp/ -o data/hdf5/train_feature_selected_time/ -b 0 -e 100</pre>
+
+We'll also rerun statistics on these ones (but we won't do redo feature selection for simplicity's sake):
+
+<pre>python3 data_processing/compute_stats.py -p 24 -i data/hdf5/train_feature_selected_time/ -o data/statistics/train_time/ -x patient_item_id,external_id,patient_id,clinical_item_id,encounter_id,item_date.month,item_date.month.sin,item_date.month.cos,item_date.hour,item_date.hour.sin,item_date.hour.cos -t data/tmp -n 543
+</pre>
+(Note: 543 files is 25% of the training data files; we mkdir the directory train_time to store these new statistics files)
+
+
+## Principal Component Analysis (PCA)
+
+To run PCA, we read in a covariance matrix and get its eigenvalues & eigenvectors (stored in an output pickle file):
+<pre>python3 utils/pca.py -i data/statistics/train/covar_featureselected.hdf5 -o data/eig.pickle</pre>
+Following this, we can do the analysis and create scree plots via the notebook: <b>plotPCA.ipynb</b>
+
+Here is some of the notebook's output:
+<ul>
+  <li>Number of PCs selected based on Kaiser's rule (eigenvalue > 1): 4632</li>
+  <li>Variance explained by PC1: 3.6%; Variance explained by PC4632: 0.00509%</li>
+  <li>Cumulative variance explained for PC4632: 82.2%</li>
+</ul>
+
+## Processing the Data Matrix for Order Set Prediction
+
+Now, we focus on the task of processing the data matrix for predicting order set usage. The following script formats the data's response variable to be whether each order set was used 1 day after each given item. You specify the input data directory, output folder (which you should create beforehand with mkdir), patient_orderset_mapping_file (HDF5 file specified via -m), and patient_itemdate_mapping_file (HDF5 file specified via -d). We can leverage multiprocessing via -p. The patient_orderset_mapping_file contains a data frame of order set items with columns being patient_id, patient_item_id, external_id (the order set ID), and item_date (the order set item date as nanoseconds since epoch). The patient_itemdate_mapping_file contains a data frame of patient items with columns being item_date (as nanoseconds since epoch) and patient_item_id. Only the data rows that have at least one post-one-day order set usage are retained unless the flag -a is used in which case all data rows will be retained.
+
+<pre>python data_processing/make_order_set_responses.py -i data/hdf5/train/ -o data/hdf5/train2_order_set/ -m ./queried/patient_item_id_to_order_set_ID_matches.hdf5 -d ./queried/patientitemid_itemdate.hdf5</pre>
+
+The output of the script above tells us there are 610 order sets.
+
+Afterwards, we just proceed like we did with the other task. We shuffle the data into batches first:
+<pre>python2 data_processing/prep_batches.py -i data/hdf5/train2_order_set/ -o data/train2_order_set_shuffling.pickle -b 4096</pre>
+When doing this for the train, dev, and test sets, we get:
+<ul>
+  <li>Training set data: "Read 16961751 data rows in 360 files. Created 4142 batches of size 4096."</li>
+  <li>Dev set data: "Read 3671312 data rows in 360 files. Created 897 batches of size 4096"</li>
+  <li>Test set data: "Read 3657826 data rows in 360 files. Created 894 batches of size 4096"</li>
+</ul>
+Then, like before, we do (e.g. for training set):
+<pre>python2 data_processing/make_batches.py -s data/train2_order_set_shuffling.pickle -i data/hdf5/train2_order_set/ -o data/hdf5/train2_shuffled/ -b 0 -e 100</pre>
+
+Finally, we perform feature selection as follows (example below for training set):
+<pre>python3 data_processing/feature_selection.py -i data/hdf5/train2_shuffled/ -o data/hdf5/train2_feature_selected/ -s data/statistics/train/ -t 0.01 -r patient_item_id,external_id,patient_id,clinical_item_id,encounter_id,item_date,analyze_date,item_date.month,item_date.hour</pre>
+(Note: We use the same statistics file as used for the previous task since this data is still a subset of the data used for the previous task, so we'll use the same averages, standard deviations, covariances, etc. computed previously)
+(Note: The item_date timestamps will be added to the data_s dataframe of the data by these scripts)
+
+## Timestamp-Stratifying the Data for Order Set Prediction
+
+See the previous section on timestamp stratification. We can do the same for these data files which we use for the order set prediction task, ultimately resulting in the following data batches:
+
+<ul>
+  <li>Training set data: "Read 5435590 data rows in 4142 files. Created 1328 batches of size 4096."</li>
+  <li>Dev set data: "Read 821521 data rows in 897 files. Created 201 batches of size 4096"</li>
+  <li>Test set data: "Read 1699164 data rows in 894 files. Created 415 batches of size 4096"</li>
+</ul>
+
+We put these files in folder named train2_feature_selected_time, dev2_feature_selected_time, and test2_feature_selected_time.
+
+## Summarizing Dataset Composition
+
+To summarize information about ethnicity, sex, birth year, and number of patients from a given dataset, supply the path to the directory containing the dataset's HDF5 files in the following command (example shown for the time-stratified training set):
+
+<pre>python utils/summarize.py -i data/hdf5/train_feature_selected_time/</pre>
+
+
+# Model and tuning <a name="processdatamatrix"></a>
+
+## Data generator
+Located in clinicnet_model/utils/datagenerator.py. Shuffles files, then shuffles rows within each file, then loads in batches one by one. Used for training the network.
+
+## Training and Tuning
+We use run_clinicnet.py to train and tune our models. Here you can set parameters of interest for testing purposes, and a test_mode is available if trying to test without validating on the validation set. Learning rate is decreased manually if loss appears to stop decreasing. We use train_network.sh to continue training the network from callbacks saved after every 1 million rows of data.
+
+# Evaluation
+* bootstrap.py outlines how we performed the bootstrapping at patient-level for our evaluation metrics.
diff --git a/scripts/Archive/ClinicNet/bootstrap.py b/scripts/Archive/ClinicNet/bootstrap.py
new file mode 100644
index 00000000..f890bb2b
--- /dev/null
+++ b/scripts/Archive/ClinicNet/bootstrap.py
@@ -0,0 +1,302 @@
+import sys
+sys.path.insert(1, '/badvolume/home/ec2-user/cs230/scripts/clinicnet_model/')
+
+import tensorflow.keras as keras
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+import json
+import h5py
+import os
+from sklearn.metrics import precision_recall_fscore_support 
+from sklearn.metrics import average_precision_score
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import confusion_matrix
+
+iterations = 1000
+sample_size = 10000
+num_processes = 40
+threshold_log = 0.11
+threshold_clinicnet = 0.5
+
+model_dir = '/badvolume/home/ec2-user/cs230/scripts/clinicnet_model'
+y_true_p = model_dir + 'evaluation/predictions/'
+h5f = h5py.File(model_dir+'/evaluation/predictions/clinical_item/clinitem_test_proba.h5','r')
+y_true = h5f['data_y'].value
+h5f.close()
+
+h5f = h5py.File(model_dir + '/evaluation/predictions/clinical_item/clinicnet_time_item.h5', 'r')
+ClinicNet = h5f['clinicnet_time_item'].value
+h5f.close()
+
+h5f = h5py.File(model_dir+'/evaluation/predictions/clinical_item/clinitem_test_proba.h5','r')
+y_true = h5f['data_y'].value
+h5f.close()
+
+h5f = h5py.File(model_dir+'/evaluation/predictions/clinical_item/log_proba_time.h5','r')
+logistic = h5f['baseline_preds'].value
+h5f.close()
+
+h5f = h5py.File(model_dir+'/evaluation/predictions/order_set/labels_time.h5','r')
+test = h5f['labels_time'].value
+h5f.close()
+
+test_list = np.load(model_dir + '/evaluation/predictions/test_ids.npz') # patient item ids
+test_list = test_list['p_ids']
+test_list = test_list.flatten()
+test_list = list(test_list)
+
+test_list_time = np.load(model_dir + '/evaluation/predictions/test_ids_clinitem_time.npz') # patient item ids
+test_list_time = test_list_time['p_ids']
+test_list_time = test_list_time.flatten()
+test_list_time = list(test_list_time)
+
+pid_2_id = {item: idx for idx, item in enumerate(test_list)}
+subset_idx = [pid_2_id.get(item) for item in test_list_time]
+y_true = y_true[subset_idx,:]
+
+
+human_authored = pd.read_hdf('predictions/clinical_item/human_authored_df.h5', key='data')
+p_id_list = human_authored.index.values
+human_authored = human_authored.values.astype(int)
+
+subset_pids = list(set(test_list_time) & set(p_id_list))
+pid_2_id_time = {item: idx for idx, item in enumerate(test_list_time)}
+pid_2_id_human = {item: idx for idx, item in enumerate(p_id_list)}
+subset_idx = [pid_2_id_time.get(item) for item in subset_pids]
+subset_log = logistic[subset_idx, :]
+subset_ClinicNet = ClinicNet[subset_idx, :]
+subset_y = y_true[subset_idx,:].astype(int)
+
+subset_human = [pid_2_id_human.get(item) for item in subset_pids]
+human_authored = human_authored[subset_human, :]
+
+h5f = h5py.File(model_dir+'/evaluation/predictions/clinical_item/labels_time.h5','r')
+# print(list(h5f.keys()))
+labels_time = h5f['labels_time'].value
+h5f.close()
+
+pt_list_time = labels_time[:, 1]
+pt_list_unique = np.unique(pt_list_time)
+p_2_id = {x: [] for x in np.unique(pt_list_time)}
+for idx, item in enumerate(pt_list_time):
+    p_2_id[item].append(idx)
+
+pt_list_sub = pt_list_time[subset_idx]
+pt_list_unique_sub = np.unique(pt_list_sub)
+p_2_id_sub = {x: [] for x in np.unique(pt_list_sub)}
+for idx, item in enumerate(pt_list_sub):
+    p_2_id_sub[item].append(idx)
+
+
+def boot_logistic(i, sample_size=sample_size):
+    np.random.seed(seed=i)
+    
+    random_pids = np.random.choice(pt_list_unique, size=sample_size, replace=True)
+    test = np.array([p_2_id[pid] for pid in random_pids])
+    boot_list = []
+    for ids in test:
+        size = len(ids)
+        boot_list.append(np.random.choice(ids))    
+    
+    y_pred_sub = logistic[boot_list, :]
+    y_true_sub = y_true[boot_list, :]
+    # evaluate model
+#     print('calculating')
+    auroc = roc_auc_score(y_true_sub, y_pred_sub, average='micro')
+    avg_precision = average_precision_score(y_true_sub, y_pred_sub, average='micro')
+#     print('done')
+    return (auroc, avg_precision)
+
+def boot_clinicnet(i, sample_size=sample_size):
+    np.random.seed(seed=i)
+    random_pids = np.random.choice(pt_list_unique, size=sample_size, replace=True)
+    test = np.array([p_2_id[pid] for pid in random_pids])
+    boot_list = []
+    for ids in test:
+        size = len(ids)
+        boot_list.append(np.random.choice(ids))    
+        
+    y_pred_sub = ClinicNet[boot_list, :]
+    y_true_sub = y_true[boot_list, :]
+    # evaluate model
+#     print('calculating')
+    auroc = roc_auc_score(y_true_sub, y_pred_sub, average='micro')
+    avg_precision = average_precision_score(y_true_sub, y_pred_sub, average='micro')
+#     print('done')
+
+    return (auroc, avg_precision)
+
+def boot_human(i, sample_size=sample_size):
+    np.random.seed(seed=i)
+    random_pids = np.random.choice(pt_list_unique_sub, size=sample_size, replace=True)
+    test = np.array([p_2_id_sub[pid] for pid in random_pids])
+    boot_list = []
+    for ids in test:
+        size = len(ids)
+        boot_list.append(np.random.choice(ids))    
+    y_pred_sub = human_authored[boot_list, :]
+    y_true_sub = subset_y[boot_list, :]
+    # evaluate model
+#     print('calculating')
+
+    
+    output = precision_recall_fscore_support(y_true_sub.flatten(), y_pred_sub.flatten())
+    precision = output[0][2]
+    recall = output[1][2]
+    f1 = output[2][2]
+
+#     print('done')
+    return precision, recall, f1
+def boot_human_spec(i, sample_size=sample_size):
+    np.random.seed(seed=i)
+    random_pids = np.random.choice(pt_list_unique_sub, size=sample_size, replace=True)
+    test = np.array([p_2_id_sub[pid] for pid in random_pids])
+    boot_list = []
+    for ids in test:
+        size = len(ids)
+        boot_list.append(np.random.choice(ids))   
+    y_pred_sub = human_authored[boot_list, :]
+    y_true_sub = subset_y[boot_list, :]
+#     print(y_pred_sub)
+#     print(y_true_sub)
+    # evaluate model
+#     print('calculating')
+    matrix = confusion_matrix(y_true_sub, y_pred_sub)
+    tn, fp, fn, tp = matrix[1:,1:].ravel()
+    specificity = tn / (tn+fp)
+#     print('done')
+    return specificity
+
+def boot_human_clinic(i, sample_size=sample_size):
+    np.random.seed(seed=i)
+    random_pids = np.random.choice(pt_list_unique_sub, size=sample_size, replace=True)
+    test = np.array([p_2_id_sub[pid] for pid in random_pids])
+    boot_list = []
+    for ids in test:
+        size = len(ids)
+        boot_list.append(np.random.choice(ids))   
+    y_pred_sub = subset_ClinicNet[boot_list, :]
+    y_true_sub = subset_y[boot_list, :]
+    auroc = roc_auc_score(y_true_sub, y_pred_sub, average='micro')
+    avg_precision = average_precision_score(y_true_sub, y_pred_sub, average='micro')
+    y_pred_sub[y_pred_sub<threshold_clinicnet] = 0
+    y_pred_sub[y_pred_sub>=threshold_clinicnet] = 1
+    # evaluate model
+#     print('calculating')
+    output = precision_recall_fscore_support(y_true_sub.flatten(), y_pred_sub.flatten())
+#     print('done')
+    precision = output[0][1]
+    recall = output[1][1]
+    f1 = output[2][1]
+#     print('done')
+    return auroc, avg_precision, precision, recall, f1
+def boot_human_logistic(i, sample_size=sample_size):
+    np.random.seed(seed=i)
+    random_pids = np.random.choice(pt_list_unique_sub, size=sample_size, replace=True)
+    test = np.array([p_2_id_sub[pid] for pid in random_pids])
+    boot_list = []
+    for ids in test:
+        size = len(ids)
+        boot_list.append(np.random.choice(ids))   
+    y_pred_sub = subset_log[boot_list, :]
+    y_true_sub = subset_y[boot_list, :]
+    auroc = roc_auc_score(y_true_sub, y_pred_sub, average='micro')
+    avg_precision = average_precision_score(y_true_sub, y_pred_sub, average='micro')
+    y_pred_sub[y_pred_sub<threshold_log] = 0
+    y_pred_sub[y_pred_sub>=threshold_log] = 1
+    # evaluate model
+#     print('calculating')
+    output = precision_recall_fscore_support(y_true_sub.flatten(), y_pred_sub.flatten())
+#     print('done')
+    precision = output[0][1]
+    recall = output[1][1]
+    f1 = output[2][1]
+#     print('done')
+    return auroc, avg_precision, precision, recall, f1
+
+def get_CI(array, alpha=0.95):
+    p = ((1.0-alpha)/2.0) * 100
+    lower = np.percentile(array, p)
+    p = (alpha+((1.0-alpha)/2.0)) * 100
+    upper = np.percentile(array, p)
+    print('average: {}'.format(np.mean(array)))
+    print('%.1f confidence interval %.1f and %.1f' % (alpha*100, lower*100, upper*100))
+    
+from multiprocessing import Process, Array, Queue, Pool
+
+if __name__ == '__main__':
+#     print('general comparison')
+#     print('logistic')
+#     p = Pool(processes = num_processes)
+#     results =  p.map(boot_logistic, range(iterations))
+#     list1, list2 = zip(*results)
+#     print(list1)
+#     get_CI(list1)    
+#     print(list2)
+#     get_CI(list2)
+    
+#     print('clinicnet')
+#     p = Pool(processes = num_processes)
+#     results =  p.map(boot_clinicnet, range(iterations))
+#     list1, list2 = zip(*results)
+#     print(list1)
+#     get_CI(list1)
+#     print(list2)
+#     get_CI(list2)
+    
+    print('human authored comparison')
+#     print('human')
+#     p = Pool(processes = num_processes)
+#     results =  p.map(boot_human, range(iterations))
+#     pr, recall, f1 = zip(*results)
+#     print(pr)
+#     get_CI(pr)
+#     print(recall)
+#     get_CI(recall)
+#     print(f1)
+#     get_CI(f1)
+    
+#     # get specificity
+#     print('human authored comparison specificity')
+#     p = Pool(processes = num_processes)
+#     results =  p.map(boot_human_spec, range(iterations))
+#     pr= zip(*results)
+#     print(pr)
+#     get_CI(pr)
+
+    print('log')
+    p = Pool(processes = num_processes)
+    results =  p.map(boot_human_logistic, range(iterations))
+    auroc, avg_precision, pr, recall, f1 = zip(*results)
+    print(auroc)
+    get_CI(auroc)
+    print(avg_precision)
+    get_CI(avg_precision)
+    print(pr)
+    get_CI(pr)
+    print(recall)
+    get_CI(recall)
+    print(f1)
+    get_CI(f1)
+         
+    print('clinicnet')
+    p = Pool(processes = num_processes)
+    results =  p.map(boot_human_clinic, range(iterations))
+    auroc, avg_precision, pr, recall, f1 = zip(*results)
+    print(auroc)
+    get_CI(auroc)
+    print(avg_precision)
+    get_CI(avg_precision)
+    print(pr)
+    get_CI(pr)
+    print(recall)
+    get_CI(recall)
+    print(f1)
+    get_CI(f1)
+    
+
+    # for i in range(5):
+    #     boots(q)
+
+    # p = Process(target = )
diff --git a/scripts/Archive/ClinicNet/clinicnet_model/utils/datagenerator.py b/scripts/Archive/ClinicNet/clinicnet_model/utils/datagenerator.py
new file mode 100644
index 00000000..43052822
--- /dev/null
+++ b/scripts/Archive/ClinicNet/clinicnet_model/utils/datagenerator.py
@@ -0,0 +1,237 @@
+# adapted from https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly
+import os
+import math
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+import tables
+import multiprocessing
+from collections import OrderedDict
+from itertools import repeat
+import gc
+import _pickle as cPickle # Must use python 3!!
+from sklearn.externals import joblib
+
+
+global transformation
+transformation = lambda x: np.log2(x+1)
+
+
+def read_data_file(f):
+    'Reading in the feature matrix from an HDF5 data file'
+    filename = f[0] # Name of file containing the data
+    path = f[1] # Path to file
+    avg_sd = f[2] # Averages and standard deviations computed for the data
+    matrix_w = f[3] # PCA projection matrix
+    exclude_cols_transformation = f[4] # List of features that we shouldn't log-transform
+    # Read in the data
+    data_x = pd.read_hdf(path + "/" + filename, 'data_x', mode='r')
+    data_x = data_x.fillna(0)
+    # Transform and standardize the data if necessary:
+    if not (avg_sd is None):
+        features = data_x.columns.intersection(list(avg_sd[0].keys()))
+        data_x = data_x[features]
+        avg = avg_sd[0][features]
+        sd = avg_sd[1][features]
+        assert data_x.shape[1] == len(avg)
+        cols_to_transform = data_x.columns
+        if not (exclude_cols_transformation is None):
+            cols_to_transform = data_x.columns.difference(exclude_cols_transformation)
+        data_x[cols_to_transform] = transformation(data_x[cols_to_transform])
+        sd = sd.clip(lower=0.01) # Prevent divide-by-zero error
+        data_x = (data_x - avg) / sd
+    if not (matrix_w is None):
+        data_x = data_x.dot(matrix_w) # PCA dimensionality reduction
+    #print("done with " + filename)
+    return((filename, data_x))
+
+class DataGenerator(tf.keras.utils.Sequence):
+    'Generates data for Keras'
+
+    def __init__(self, path, batch_size=32, shuffle=True, transformation_file=None, pca_file=None, num_pc=None, num_processes=1, num_files_cache=None, exclude_cols_transformation=None, batches_per_epoch=None, cache=None, seed=1):
+        'Initialization path = path to data directory of interest (train, val, or test)'
+        self.path = path # Path to data directory
+        self.files = np.array(os.listdir(path)) # List of files in data directory
+        self.batch_size = batch_size
+        self.shuffle = shuffle # Should we shuffle the data after we go through the entire dataset
+        self.len = self.setup_HDF5(self.files, self.batch_size) # Figure out how many batches our dataset contains
+        self.idx2file = None # Dict mapping from batch number to file name
+        self.idx2batch = None # Dict mapping from batch number to the row indices of the data file for that batch
+        self.matrix_w = None # PCA projection matrix
+        self.avg_sd = None
+        self.batches_per_epoch = batches_per_epoch
+        self.counter = -1 # The batch number for the batch that we've just retrieved
+        self.initial_seed = seed # Initialize random seed for shuffling
+        self.seed = seed # Current seed value
+        if exclude_cols_transformation: # List of features that we shouldn't log-transform (e.g. those that can have negative values)
+            self.exclude_cols_transformation = exclude_cols_transformation.split(',')
+        else:
+            self.exclude_cols_transformation = []
+        self.num_processes = num_processes # For processing data files in parallel
+        self.num_files_cache = num_files_cache # How many data files to store in cache at one time
+        if num_files_cache is None:
+            self.num_files_cache = self.num_processes
+        if self.num_files_cache > len(self.files):
+            self.num_files_cache = len(self.files)
+        self.cache = {} # For caching data as it is being generated
+        if not (cache is None): # If we already have stuff precached
+            self.cache = joblib.load(cache)
+        if not (pca_file is None): # Read in eigenvectors/eigenvalues to make projection matrix
+            eigs = cPickle.load(open(pca_file, "rb"))
+            eig_vals = eigs[0]
+            eig_vecs = eigs[1]
+            eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]
+            eig_pairs.sort() # Sort the (eigenvalue, eigenvector) tuples
+            eig_pairs.reverse() # Make the sorting from high to low
+            matrix_w = np.hstack([eig_pairs[i][1].reshape(len(eig_pairs),1) for i in range(num_pc)])
+            matrix_w = np.matrix(matrix_w)
+            # alternately, the following works (if we generated our eigenvectors/eigenvalues via eigh):
+            ## u = eigs[1]
+            ## U = u.T[::-1]
+            ## matrix_w = U[:2].T
+            self.matrix_w = matrix_w
+        if not (transformation_file is None): # Read in avg and sd
+            avg = pd.read_hdf(transformation_file, 'avg')
+            std_dev = pd.read_hdf(transformation_file, 'sd')
+            self.avg_sd = (avg, std_dev)
+        self.setup_batches(False)
+        self.on_epoch_end()
+
+    # Returns number of mini-epochs
+    def __len__(self):
+        'Denotes the number of batches to go through per mini-epoch'
+        if self.batches_per_epoch:
+            return self.batches_per_epoch
+        return self.len # If batches_per_epoch isn't specified, we just have epochs instead of mini-epochs
+    
+    def getState(self):
+        '''
+        Gets info: (initial random seed, number of batches read, number of mini-epochs elapsed)
+        that we need for resuming data generation
+        '''
+        return (self.initial_seed, self.counter+1, (self.counter+1)/(self.batches_per_epoch))
+    
+    def setState(self, num_batches=None, num_epochs=None): # Must be called immediately after object is initialized
+        'Resume data generation starting from number of batches read of number of mini-epochs elapsed'
+        #print("Setting state")
+        if not num_epochs is None:
+            num_batches = num_epochs*self.batches_per_epoch
+        for i in range(num_batches):
+            self.__getitem__(0, proxy=True)
+    
+    def setup_HDF5(self, files, batch_size):
+        'For figuring out how many batches our dataset contains'
+        num_iters = 0
+        hdf_conns = {}
+        for f in files:
+            h5file = tables.open_file(self.path + "/" + f, mode="r")
+            nrows = len(h5file.root.data_x.axis1)
+            num_batches = int(np.ceil((nrows) / batch_size))
+            num_iters += num_batches
+            h5file.close()
+        print("DataGenerator: Setting up {} files in {} batches".format(len(files), num_iters))
+        return num_iters
+
+    def __getitem__(self, index, proxy=False, read_saved_features=False):
+        'Generate one batch of data'
+        self.counter += 1
+        if self.counter == len(self.idx2file): # If we've gone through all the data
+            self.counter = 0 # Reset the batch number
+            if self.shuffle == True:
+                self.setup_batches() # Reshuffle everything if necessary
+        index = self.counter
+        
+        if proxy: # Proxy means we 'pretend' to get the batch (no data will be returned if proxy is True)
+            #print("PROXY BATCH: " + str(index))
+            return None
+        
+        #print("GETTING BATCH: " + str(index))
+        f = self.idx2file[index] # This is the file for our current batch
+        if f in self.cache: # See if the file has already been loaded into the cache
+            #print("READING FROM CACHE: " + f)
+            data_x = self.cache[f][0]
+            data_y = self.cache[f][1]
+            data_s = None
+            if read_saved_features:
+                data_s = self.cache[f][2]
+        else: # If file isn't in the cache, we'll load it here
+            #print("READING FILE: " + f)
+            del self.cache
+            self.cache = {} # Once we come across a file that isn't cached, we know it's time to cache a new set of files!
+            pool = multiprocessing.Pool(self.num_processes)
+            files_list_all = list(OrderedDict.fromkeys([ v for k, v in list(self.idx2file.items()) if k >= index ]))[0:self.num_files_cache] # Here's our new list of files to cache
+            if self.num_files_cache > 100:
+                # We partition our new list of files into chunks if we have more than 100 files to cache
+                # Otherwise, pool.map runs into problems when trying to return a huge amount of data at once
+                files_list_all = np.array_split(files_list_all, self.num_files_cache / 100)
+            else:
+                files_list_all = np.array_split(files_list_all, 1)
+            for files_list in files_list_all:
+                #print("NEW FILE LIST")
+                datas = pool.map(read_data_file, list(zip(files_list,repeat(self.path), repeat(self.avg_sd), repeat(self.matrix_w), repeat(self.exclude_cols_transformation)))) # Read in data from list of files
+                for data in datas: # Go through (and cache) all the data we read in from list of files (each iteration of this loop is the data returned from a single file)
+                    fname = data[0]
+                    data_x = data[1]
+                    data_x = data_x.astype(np.float32)
+                    data_x = data_x.values
+                    data_y = pd.read_hdf(self.path + "/" + fname, 'data_y', mode='r').values
+                    if read_saved_features: # Should we read in features that were saved in their raw form?
+                        data_s = pd.read_hdf(self.path + "/" + fname, 'data_s', mode='r').values
+                        self.cache[fname] = (data_x, data_y, data_s)
+                    else:
+                        self.cache[fname] = (data_x, data_y)
+                    #print("STORING FILE: " + fname)
+            # Get the data for the file for the current batch:
+            data_x = self.cache[f][0]
+            data_y = self.cache[f][1]
+            if read_saved_features:
+                data_s = self.cache[f][2]
+            else:
+                data_s = None
+
+        # Get the relevant parts of the data file for the current batch
+        batch_idxs = self.idx2batch[index]
+        data_x = data_x[batch_idxs,:]
+        data_y = np.clip(data_y[batch_idxs,:],0,1) # Make the response values binary
+        gc.collect()
+        if read_saved_features:
+            return data_x, data_y, data_s
+        return data_x, data_y
+
+    def on_epoch_end(self):
+        'Updates indexes after each epoch'
+        print('miniepoch_end: {} out of {} batches done'.format(self.counter+1, len(self.idx2file)))
+    
+    def setup_batches(self, reset_cache=True):
+        '''
+        Setup batches to be used for the data generation pipeline
+        Prepares the idx2batch and idx2file dicts that map batch number to the relevant data
+        '''
+        self.seed += 1
+        if self.shuffle == True:
+            np.random.RandomState(seed=self.seed).shuffle(self.files)
+        if reset_cache:
+            del self.cache
+            self.cache = {} # Reset the cache
+        idx = 0 
+        self.idx2file = {} #reset the dictionaries
+        self.idx2batch = {}
+        for f in self.files:
+            h5file = tables.open_file(self.path + "/" + f, mode="r")
+            nrows = len(h5file.root.data_x.axis1)
+            num_batches = int(np.ceil((nrows) / self.batch_size))
+            indices = np.arange(nrows)
+            if self.shuffle:
+                np.random.RandomState(seed=(self.seed+idx)).shuffle(indices)
+            num_rows_remaining = int(nrows % self.batch_size)
+            padding = int(self.batch_size - num_rows_remaining) # The "padding" to add to make divisible by batch_size
+            if num_rows_remaining > 0:
+                batches = np.array_split(np.concatenate((indices, np.repeat(-1,padding))), num_batches) # Do batch assignments
+                batches = [i[i != -1] for i in batches]
+            else:
+                batches = np.array_split(indices, num_batches)
+            self.idx2batch.update(dict(list(zip(list(range(idx, idx+num_batches)), batches))))
+            self.idx2file.update(dict.fromkeys(list(range(idx, idx+num_batches)), f))
+            h5file.close()
+            idx += num_batches
+            
diff --git a/scripts/Archive/ClinicNet/clinicnet_model/utils/generator_cache.ipynb b/scripts/Archive/ClinicNet/clinicnet_model/utils/generator_cache.ipynb
new file mode 100644
index 00000000..84deaad8
--- /dev/null
+++ b/scripts/Archive/ClinicNet/clinicnet_model/utils/generator_cache.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cache data set for easy generator loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.externals import joblib\n",
+    "from utils.datagenerator import DataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = '../../data/hdf5/test_feature_selected/' # Input data set folder\n",
+    "pcafile='../../data/eig.pickle'\n",
+    "transformfile='../../data/statistics/test/avg_stddev.hdf5'\n",
+    "filename='../test_generator.sav' # Output file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Store data set into cache file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store test data into one file\n",
+    "exclude_cols_transformation='item_date.month.sin,item_date.month.cos,item_date.hour.sin,item_date.hour.cos'\n",
+    "test_generator = DataGenerator(path=data_dir, batch_size = 4096, pca_file=pcafile, transformation_file=transformfile, num_pc=4632, num_processes=10, exclude_cols_transformation=exclude_cols_transformation, shuffle=False, num_files_cache=100000000)\n",
+    "a = test_generator.__getitem__(0, read_saved_features=True) # Set read_saved_features=False for dev set (since we won't need the 'saved' raw features like patient id)\n",
+    "cache = test_generator.cache\n",
+    "joblib.dump(cache, filename, compress=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load cache data from file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test data from file\n",
+    "test_generator = DataGenerator(path=data_dir, cache=filename, batch_size = 4096, shuffle=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (py36tf2.0-gpu)",
+   "language": "python",
+   "name": "py36tf2.0-gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/ClinicNet/data_processing/add_dates.py b/scripts/Archive/ClinicNet/data_processing/add_dates.py
new file mode 100644
index 00000000..e11415ff
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/add_dates.py
@@ -0,0 +1,67 @@
+# Takes HDF5 data files and add item_date timestamps based on a mapping file between patient_item_id and item_date
+
+import pandas as pd
+import numpy as np
+import os
+import sys, getopt
+import multiprocessing
+
+def add_dates(f):
+        data_s = pd.read_hdf(data_dir + "/" + f, 'data_s')
+        if 'item_date' in data_s.columns:
+                print(f + " - " + "Already contains date timestamps")
+        else:
+                data_s = data_s.reset_index(drop=True)
+                data_s = data_s.reset_index().merge(all_item_dates, on='patient_item_id', how='inner').set_index('index')
+                data_s.to_hdf(data_dir + "/" + f, key='data_s', mode='a', complevel=1)
+                del data_s.index.name
+                print(f + " - " + "Date timestamps added successfully")
+
+def main(argv):
+        global files_list # Input directory (where the HDF5 files are stored)
+        global data_dir
+        global all_item_dates # Match patient item IDs to dates
+        data_dir = ""
+        dates_dir = ""
+        all_item_dates = ""
+        num_processes = 0
+        try:
+                opts, args = getopt.getopt(argv,"hi:d:p:")
+        except getopt.GetoptError:
+                print('add_dates.py -i <data_directory> -d <patient_itemdate_mapping_file> [-p num_processes] [-h]')
+                sys.exit(2)
+        for opt, arg in opts:
+                if opt == '-h':
+                        print('add_dates.py -i <data_directory> -d <patient_itemdate_mapping_file> [-p num_processes] [-h]')
+                        print('')
+                        print('This script performs processes data files (in HDF5 format), from <data_directory>, to add item_date timestamps based on an patient item to item date mapping file.')
+                        print('The new data will be appended to existing data files in <data_directory>.')
+                        print('One hdf5 file nees to be specified:')
+                        print('1. <patient_itemdate_mapping_file>: data frame of patient items with columns being item_date (as nanoseconds since epoch) and patient_item_id')
+                        print('Use num_processes to specify the number of processes to use for multiprocessing.')
+                        sys.exit()
+                elif opt == '-i':
+                        data_dir = arg
+                elif opt == '-p':
+                        num_processes = int(arg)
+                elif opt == '-d':
+                        dates_dir = arg
+        if len(argv) < 2 or data_dir == '' or dates_dir == '':
+                print('add_dates.py -i <data_directory> -d <patient_itemdate_mapping_file> [-p num_processes] [-h]')
+                sys.exit(2)
+
+        # Configure multiprocessing
+        if num_processes == 0:
+                num_processes = multiprocessing.cpu_count()-1
+        print("Number of processes: " + str(num_processes))
+
+        # Setup date time stamps
+        all_item_dates = pd.read_hdf(dates_dir)
+
+        # Iterate through data_dir to process the files
+        files_list = os.listdir(data_dir)
+        pool = multiprocessing.Pool(num_processes)
+        pool.map(add_dates, files_list)
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
diff --git a/scripts/Archive/ClinicNet/data_processing/compute_stats.py b/scripts/Archive/ClinicNet/data_processing/compute_stats.py
new file mode 100644
index 00000000..549e1b86
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/compute_stats.py
@@ -0,0 +1,274 @@
+# Gets statistics for the dataset
+# Computes mean and standard deviations for each feature, as well as build a covariance matrix
+# Additionally, for each response variable, the frequency (i.e. percent) of it being a non-zero value is computed
+
+import numpy as np
+import pandas as pd
+import os
+import tables
+import gc
+import multiprocessing
+import tempfile
+import random
+import gzip
+import _pickle as cPickle # Must use python 3!!
+import sys, getopt
+
+# Below are the functions for calculating the average and variances, as well as y frequencies
+# Note: NAs are considered as zeros
+
+# Worker function to be pooled for calculating averages
+def get_average_worker(f):
+	print(f)
+	data_x = pd.read_hdf(data_dir + "/" + f, 'data_x', mode='r')
+	data_x = data_x.fillna(0)
+	data_x = data_x.astype(np.float64) # More accuracy for float point arithmetic
+	cols_to_transform = data_x.columns
+	if not (exclude_cols_transformation is None):
+		cols_to_transform = data_x.columns.difference(exclude_cols_transformation)
+	if not (transformation is None):
+		data_x[cols_to_transform] = transformation(data_x[cols_to_transform])
+	current_N = data_x.shape[0]
+	current_sum = data_x.sum(axis=0, skipna=True)
+	fd, path = tempfile.mkstemp()
+	try:
+		with os.fdopen(fd, 'wb') as tmp:
+			cPickle.dump((current_sum, current_N), tmp, protocol=4) # protocol=4 avoids OverflowError: cannot serialize a bytes object larger than 4 GiB
+	except:
+		os.remove(path)
+		raise
+	del data_x
+	gc.collect()
+	return path
+
+def get_average(num_processes=1, num_files=None):
+	files_list = os.listdir(data_dir)
+	random.shuffle(files_list)
+	if not (num_files is None):
+		files_list = files_list[0:num_files]
+	running_sum = None
+	running_N = 0
+	# Multiprocess pool (returns a bunch of temp files):
+	pool = multiprocessing.Pool(num_processes)
+	tmp_files = pool.map(get_average_worker, files_list)
+	# Iterate through tmp_files:
+	for f in tmp_files:
+		try:
+			with open(f, 'rb') as fp:
+				pickle_data = cPickle.load(fp)
+				current_sum = pickle_data[0]
+				running_N += pickle_data[1]
+				if running_sum is None:
+					running_sum = current_sum
+				else:
+					running_sum = running_sum.add(current_sum)
+		except:
+			os.remove(f)
+			raise
+		os.remove(f) # Remove the temporary file
+	return (running_sum / float(running_N))
+
+# Worker function to be pooled for calculating covariance matrix
+def get_covar_worker(files_list):
+	running_sum_squared_diff = np.float64(0.0)
+	running_N = 0
+	for f in files_list:
+		print(f)
+		data_x = pd.read_hdf(data_dir + "/" + f, 'data_x', mode='r')
+		data_x = data_x.fillna(0)
+		data_x = data_x.astype(np.float64) # More accuracy for float point arithmetic
+		cols_to_transform = data_x.columns
+		if not (exclude_cols_transformation is None):
+			cols_to_transform = data_x.columns.difference(exclude_cols_transformation)
+		if not (transformation is None):
+			data_x[cols_to_transform] = transformation(data_x[cols_to_transform])
+		current_N = data_x.shape[0]
+		current_sum_squared_diff = np.transpose(np.matrix(data_x))*np.matrix(data_x)
+		running_N += current_N
+		running_sum_squared_diff += current_sum_squared_diff
+		del data_x
+		del current_sum_squared_diff
+		gc.collect()
+	# Create temporary file to store our data
+	fd, path = tempfile.mkstemp()
+	try:
+		with gzip.open(path, 'wb', compresslevel=1) as tmp:
+			cPickle.dump((running_sum_squared_diff, running_N), tmp, protocol=4) # Again, we need protocol=4
+	except:
+		os.remove(path)
+		raise
+	return path
+
+def get_covar(num_processes=1, num_files=None):
+	files_list = os.listdir(data_dir)
+	random.shuffle(files_list)
+	if not (num_files is None):
+		files_list = files_list[0:num_files]
+	running_sum_squared_diff = np.float64(0.0)
+	running_N = 0
+	# Multiprocess pool (returns a bunch of temp files):
+	pool = multiprocessing.Pool(num_processes)
+	# We'll divide the task up into num_processes (rather than number of files since each temp file created will be huge)
+	tmp_files = pool.map(get_covar_worker, np.array_split(files_list, num_processes))
+	# Iterate through tmp_files:
+	for f in tmp_files:
+		try:
+			with gzip.open(f, 'rb', compresslevel=1) as fp: ##
+				pickle_data = cPickle.load(fp)
+				current_sum_squared_diff = pickle_data[0]
+				running_N += pickle_data[1]
+				running_sum_squared_diff += current_sum_squared_diff
+		except:
+			os.remove(f)
+			raise
+		os.remove(f) # Remove the temporary file
+	running_N -= 1 # Bessel's correction
+	return (running_sum_squared_diff / float(running_N))
+
+# Note: Not parallelized
+def get_stddev(num_files=None):
+	files_list = os.listdir(data_dir)
+	random.shuffle(files_list)
+	if not (num_files is None):
+		files_list = files_list[0:num_files]
+	running_sum_squared_diff = None
+	running_N = 0
+	for f in files_list:
+		print(f)
+		data_x = pd.read_hdf(data_dir + "/" + f, 'data_x', mode='r')
+		data_x = data_x.fillna(0)
+		cols_to_transform = data_x.columns
+		if not (exclude_cols_transformation is None):
+			cols_to_transform = data_x.columns.difference(exclude_cols_transformation)
+		if not (transformation is None):
+			data_x[cols_to_transform] = transformation(data_x[cols_to_transform])
+		running_N += data_x.shape[0]
+		data_x -= average
+		data_x = data_x**2
+		current_sum_squared_diff = data_x.sum(axis=0, skipna=True)
+		if running_sum_squared_diff is None:
+			running_sum_squared_diff = current_sum_squared_diff
+		else:
+			running_sum_squared_diff = running_sum_squared_diff.add(current_sum_squared_diff)
+		del data_x
+		gc.collect()
+	running_N -= 1 # Bessel's correction
+	return (running_sum_squared_diff / float(running_N))**(0.5)
+
+def get_freq_y():
+	files_list = os.listdir(data_dir)
+	files_list.sort()
+	running_sum = None
+	running_N = 0
+	for f in files_list:
+		print(f)
+		data_y = pd.read_hdf(data_dir + "/" + f, 'data_y', mode='r')
+		data_y = data_y.clip(upper=1) # Change all numbers greater than 1 to 1's
+		data_y = data_y.fillna(0)
+		data_y = data_y.astype(int) # More accuracy (don't want to add up floats)
+		running_N += data_y.shape[0]
+		current_sum = data_y.sum(axis=0, skipna=True)
+		if running_sum is None:
+			running_sum = current_sum
+		else:
+			running_sum = running_sum.add(current_sum)
+		del data_y
+		gc.collect()
+	return (running_sum / float(running_N))
+
+def main(argv):
+	# How to transform our data (i.e. log2(x+1)):
+	global transformation
+	transformation = lambda x: np.log2(x+1)
+
+	# Output file names:
+	output_avg_stddev_filename = "avg_stddev.hdf5" # name of the output file for avg and std dev
+	output_covar_filename = "covar.hdf5" # name of the output file for covariance matrix
+	output_freq_y_filename = "freq_y.hdf5" # name of the output file for y frequencies
+
+	# Read in command line arguments:
+	global data_dir
+	data_dir = ''
+	output_dir = ''
+	global exclude_cols_transformation
+	exclude_cols_transformation = None
+	num_processes = 0
+	skipcov = False
+	num_files = None
+	try:
+		opts, args = getopt.getopt(argv,"hi:o:x:p:t:n:s")
+	except getopt.GetoptError:
+		print('compute_stats.py -i <data_directory> -o <output_directory> [-x features_to_exclude] [-p num_processes] [-t temp_dir] [-n num_files] [-s] [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('compute_stats.py -i <data_directory> -o <output_directory> [-x features_to_exclude] [-p num_processes] [-t temp_dir] [-n num_files] [-s] [-h]')
+			print('')
+			print('This script computes the mean, covariance, and standard deviation of all features. All data is log2(x+1)-transformed except those in features_to_exclude.')
+			print('Additionally, for each response variable, the frequency (i.e. percent) of it being a non-zero value is computed.')
+			print('As a result, three output files will be generated: avg_stddev.hdf5, covar.hdf5, and freq_y.hdf5')
+			print('The input files are read from the directory <data_directory>')
+			print('For features_to_exclude, supply feature names separated by commas.')
+			print('Use num_processes to specify the number of processes to use for multiprocessing.')
+			print('Use temp_dir to specify a different temp folder than the default.')
+			print('Use num_files to limit how many files in the directory to read from (for computation tractability purposes); the data between files should all be shuffled beforehand.')
+			print('Specify the -s option to skip computation of the covariance matrix')
+			sys.exit()
+		elif opt == '-x':
+			exclude_cols_transformation = arg.split(',')
+		elif opt == '-i':
+			data_dir = arg
+		elif opt == '-o':
+			output_dir = arg
+		elif opt == '-p':
+                        num_processes = int(arg)
+		elif opt == '-t':
+                        tempfile.tempdir = arg # Change default temp directory
+		elif opt == '-n':
+			num_files = int(arg)
+		elif opt == '-s':
+			skipcov = True
+	if len(argv) < 2 or data_dir == '' or output_dir == '':
+                print('compute_stats.py -i <data_directory> -o <output_directory> [-x features_to_exclude] [-p num_processes] [-t temp_dir] [-n num_files] [-s] [-h]')
+                sys.exit(2)
+
+	# Configure multiprocessing
+	if num_processes == 0:
+		num_processes = multiprocessing.cpu_count()-1
+	print("Number of processes: " + str(num_processes))
+
+	# Get the average
+	output_file = output_dir + "/" + output_avg_stddev_filename
+	#avg = get_average(num_processes, num_files)
+	#avg.to_hdf(output_file, key='avg', mode='w')
+	avg = pd.read_hdf(output_file, key='avg')
+	global average
+	average = avg
+	print("FINISHED AVERAGE")
+	if not skipcov:
+		# Get the covariance matrix
+		output_file = output_dir + "/" + output_covar_filename
+		covar = get_covar(num_processes, num_files)
+		covar_df = pd.DataFrame(covar, columns=list(avg.keys()), index=list(avg.keys())) # Convert to dataframe
+		covar_df.to_hdf(output_file, key='covar', mode='w', complevel=1) # We'll use some compression for this large matrix
+		del covar_df # Free up memory
+		print("FINISHED COVARIANCE")
+		# Get the standard deviation
+		output_file = output_dir + "/" + output_avg_stddev_filename
+		stt = pd.Series(np.diagonal(covar)**0.5, index=list(avg.keys())) # No attribute columns
+		stt.to_hdf(output_file, key='sd')
+		print("FINISHED STANDARD DEVIATION")
+	else:
+		# Calculate SD without the covariance matrix
+		stt = get_stddev(num_files)
+		output_file = output_dir + "/" + output_avg_stddev_filename
+		stt.to_hdf(output_file, key='sd')
+	# Get the frequencies
+	output_file = output_dir + "/" + output_freq_y_filename
+	freq_y = get_freq_y() # Response variable files are small so no need for multiprocessing
+	freq_y.to_hdf(output_file, key='freq_y', mode='w')
+	print("FINISHED FREQUENCIES")
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
+
diff --git a/scripts/Archive/ClinicNet/data_processing/feature_selection.py b/scripts/Archive/ClinicNet/data_processing/feature_selection.py
new file mode 100644
index 00000000..e7639386
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/feature_selection.py
@@ -0,0 +1,112 @@
+# Performs univariate feature selection by removing low or zero variance features
+# Also removes features that we specify to be removed
+
+import pandas as pd
+import numpy as np
+import tables
+import os
+import multiprocessing
+import gc
+import sys, getopt
+
+def feature_selection_worker(fname):
+	print(fname)
+	data_x = pd.read_hdf(data_dir + "/" + fname, 'data_x', mode='r')
+	data_x = data_x.drop(features_to_remove, axis=1)
+	data_y = pd.read_hdf(data_dir + "/" + fname, 'data_y', mode='r')
+	data_s = None
+	try:
+		data_s = pd.read_hdf(data_dir + "/" + fname, 'data_s', mode='r')
+	except KeyError:
+		data_s = None
+	data_x.to_hdf(output_dir + "/" + fname, 'data_x', complevel=1, mode='w')
+	data_y.to_hdf(output_dir + "/" + fname, 'data_y', complevel=1)
+	if not (data_s is None):
+		data_s.to_hdf(output_dir + "/" + fname, 'data_s', complevel=1)
+		del data_s
+	del data_x
+	del data_y
+	gc.collect()
+
+def data_remove_features(num_processes=1):
+	files_list = os.listdir(data_dir)
+	print("Feature selection data files in " + data_dir + " and outputting them to " + output_dir)
+	pool = multiprocessing.Pool(num_processes)
+	pool.map(feature_selection_worker, files_list)
+
+def identify_lowvar_features(std_dev, std_dev_threshold):
+	features = list(std_dev.keys())
+	zero_var = list(set(range(0, len(std_dev))) - set(list(np.nonzero(std_dev > std_dev_threshold)[0])))
+	return list(std_dev[zero_var].keys()).tolist()
+
+def main(argv):
+	# Read in command line arguments:
+	global output_dir
+	global features_to_remove
+	global data_dir
+	data_dir = ''
+	stats_dir = ''
+	output_dir = ''
+	manually_remove = None
+	num_processes = 0
+	std_dev_threshold = np.float64(0.0)
+	try:
+		opts, args = getopt.getopt(argv,"hi:o:s:t:r:p:")
+	except getopt.GetoptError:
+		print('feature_selection.py -i <data_directory> -o <output_directory> -s <stats_dir> [-t threshold] [-r features_to_remove] [-p num_processes] [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('feature_selection.py -i <data_directory> -o <output_directory> -s <stats_dir> [-t threshold] [-r features_to_remove] [-p num_processes] [-h]')
+			print('')
+			print('This script performs feature selection to remove near zero variance features, defined by standard deviation threshold, and features specified by a comma-delimited list in features_to_remove.')
+			print('The new data with the features removed will be stored in <output_directory>.')
+			print('Additionally, two files in <stats_dir> (from which we get averages and standard deviations) will be created: avg_stddev_featureselected.hdf5 and covar_featureselected.hdf5')
+			print('The input files are read from the directory <data_directory>')
+			print('Use num_processes to specify the number of processes to use for multiprocessing.')
+			sys.exit()
+		elif opt == '-r':
+			manually_remove = arg.split(',')
+		elif opt == '-i':
+			data_dir = arg
+		elif opt == '-o':
+			output_dir = arg
+		elif opt == '-p':
+			num_processes = int(arg)
+		elif opt == '-t':
+			std_dev_threshold = np.float64(arg)
+		elif opt == '-s':
+			stats_dir = arg
+	if len(argv) < 3 or data_dir == '' or output_dir == '' or stats_dir == '':
+		print('feature_selection.py -i <data_directory> -o <output_directory> -s <stats_dir> [-t threshold] [-r features_to_remove] [-p num_processes] [-h]')
+		sys.exit(2)
+
+	# Configure multiprocessing
+	if num_processes == 0:
+		num_processes = multiprocessing.cpu_count()-1
+	print("Number of processes: " + str(num_processes))
+
+	# Curating features to be removed
+	std_dev = pd.read_hdf(stats_dir + "/" + "avg_stddev.hdf5", 'sd')
+	lowvar_features = identify_lowvar_features(std_dev, std_dev_threshold)
+	features_to_remove = lowvar_features + manually_remove
+	features_to_remove = list(set(features_to_remove) & set(list(std_dev.keys()).tolist())) # Remove manually_remove elements that aren't also in std_dev
+	print("{} features out of {} to be removed (SD threshold: {})".format(len(features_to_remove), len(list(std_dev.keys()).tolist()), std_dev_threshold))
+
+	# Processing the data to remove selected features
+	data_remove_features(num_processes)
+
+	# Getting the feature-selected version of the statistics and outputting them
+	print("Done with data files; now creating new statistics files")
+	std_dev = std_dev[~std_dev.index.isin(features_to_remove)]
+	avg = pd.read_hdf(stats_dir + "/" + "avg_stddev.hdf5", 'avg')
+	avg = avg[~avg.index.isin(features_to_remove)]
+	covar = pd.read_hdf(stats_dir + "/" + "covar.hdf5", 'covar')
+	covar = covar.drop(features_to_remove, axis=1)
+	covar = covar.drop(features_to_remove, axis=0)
+	avg.to_hdf(stats_dir + "/" + "avg_stddev_featureselected.hdf5", 'avg', mode='w')
+	std_dev.to_hdf(stats_dir + "/" + "avg_stddev_featureselected.hdf5", 'sd')
+	covar.to_hdf(stats_dir + "/" + "covar_featureselected.hdf5", 'covar', mode='w', complevel=1)
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
diff --git a/scripts/Archive/ClinicNet/data_processing/make_batches.py b/scripts/Archive/ClinicNet/data_processing/make_batches.py
new file mode 100644
index 00000000..3611ff1b
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/make_batches.py
@@ -0,0 +1,109 @@
+# This script takes data files and shuffles them into batches (which have been determined previously)
+
+import pandas as pd
+import os
+import tables
+import pickle
+import gzip
+import gc
+import sys, getopt
+
+# Function to write batches of dataset to files
+def write_shuffled_batches(data_dir, output_dir, batch_dicts, index_begin=None, index_end=None):
+	files_list = os.listdir(data_dir)
+	files_list.sort()
+	num_batches = len(batch_dicts)
+	if index_begin is None:
+		index_begin = 0
+	if index_end is None:
+		index_end = 0
+	if index_end > num_batches:
+		index_end = num_batches
+	batch_data_x = [None for i in range(num_batches)] # Stores our final data frames (x)
+	batch_data_y = [None for i in range(num_batches)] # Stores our final data frames (y)
+	batch_data_s = [None for i in range(num_batches)] # Stores our final data frames (s)
+	for f in files_list:
+		print(f)
+		data_x = pd.read_hdf(data_dir + "/" + f, 'data_x', mode='r')
+		data_y = pd.read_hdf(data_dir + "/" + f, 'data_y', mode='r')
+		data_s = None
+		try:
+			data_s = pd.read_hdf(data_dir + "/" + f, 'data_s', mode='r')
+		except KeyError:
+			data_s = None
+		for i in range(index_begin,index_end):
+			if f in list(batch_dicts[i].keys()):
+				if batch_data_x[i] is None:
+					batch_data_x[i] = data_x.iloc[batch_dicts[i][f]]
+				else:
+					batch_data_x[i] = batch_data_x[i].append(data_x.iloc[batch_dicts[i][f]])
+				if batch_data_y[i] is None:
+					batch_data_y[i] = data_y.iloc[batch_dicts[i][f]]
+				else:
+					batch_data_y[i] = batch_data_y[i].append(data_y.iloc[batch_dicts[i][f]])
+				if batch_data_s[i] is None and not (data_s is None):
+					batch_data_s[i] = data_s.iloc[batch_dicts[i][f]]
+				elif not (data_s is None):
+					batch_data_s[i] = batch_data_s[i].append(data_s.iloc[batch_dicts[i][f]])
+		# Free up some memory:
+		del data_x
+		del data_y
+		del data_s
+		gc.collect()
+	for i in range(index_begin,index_end):
+		print("Writing batch {} to file".format(i))
+		hdf5_file = output_dir + "/" + str(i) + ".h5"
+		batch_data_x[i].to_hdf(hdf5_file, key='data_x', mode='w', complevel=1)
+		batch_data_y[i].to_hdf(hdf5_file, key='data_y', mode='a', complevel=1)
+		if not (all(v is None for v in batch_data_s)):
+			batch_data_s[i].to_hdf(hdf5_file, key='data_s', mode='a', complevel=1)
+
+
+def main(argv):
+	# Read in command line arguments:
+	pickle_shuffle_file = ''
+	input_dir = ''
+	output_dir = ''
+	begin_index=None
+	end_index=None
+	try:
+		opts, args = getopt.getopt(argv,"hs:d:i:o:b:e:")
+	except getopt.GetoptError:
+		print('make_batches.py -s <shuffle_pickle_file> -i <directory_of_data_to_shuffle> -o <output_directory> [-b index_to_begin] [-e index_to_end] [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('make_batches.py -s <shuffle_pickle_file> -i <directory_of_data_to_shuffle> -o <output_directory> [-b index_to_begin] [-e index_to_end] [-h]')
+			print('')
+			print('This script shuffles the data stored in the files in <directory_of_data_to_shuffle>.')
+			print('The shuffling is done in accordance to the batches supplied in the <shuffle_pickle_file> file.')
+			print('Each output file will be a batch of data, and will be stored in <output_directory>')
+			print('We need to look through all data files to find the data rows associated with each batch.')
+			print('Therefore, this script will take a long time to run, and will consume a lot of memory.')
+			print('Rather than making all the batches at once, we can specify a range of batches to make per run.')
+			print('This is done via <index_to_begin> and <index_to_end>, where we can supply the indices of the batches to start and end at.')
+			sys.exit()
+		elif opt == '-s':
+			pickle_shuffle_file = arg
+                elif opt == '-b':
+                        begin_index = int(arg)
+                elif opt == '-e':
+                        end_index = int(arg)
+		elif opt == '-i':
+			input_dir = arg
+		elif opt == '-o':
+			output_dir = arg
+        if len(argv) < 3 or pickle_shuffle_file == '' or input_dir == '' or output_dir == '':
+                print('make_batches.py -s <shuffle_pickle_file> -i <directory_of_data_to_shuffle> -o <output_directory> [-b index_to_begin] [-e index_to_end] [-h]')
+                sys.exit(2)
+
+	# Read the shuffling from the gzip-compressed pickle file
+	infile = gzip.open(pickle_shuffle_file,'rb', compresslevel=1)
+	shuffled_batches = pickle.load(infile)
+	infile.close()
+
+	# Finally, time to write the batches to files
+	write_shuffled_batches(input_dir, output_dir, shuffled_batches, begin_index, end_index)
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
diff --git a/scripts/Archive/ClinicNet/data_processing/make_order_set_responses.py b/scripts/Archive/ClinicNet/data_processing/make_order_set_responses.py
new file mode 100644
index 00000000..ec1ed531
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/make_order_set_responses.py
@@ -0,0 +1,130 @@
+# Takes HDF5 data files, extracts order set usage, and formats the data's response variable to be whether each order set was used 1 day after a given item
+
+import pandas as pd
+import numpy as np
+import os
+import gc
+import sys, getopt
+import multiprocessing
+
+def make_order_set_data(f):
+	data_s = pd.read_hdf(data_dir + "/" + f, 'data_s')
+	data_s = data_s.reset_index(drop=True)
+	num_rows = len(data_s.index)
+	data_s = data_s.reset_index().merge(all_item_dates, on='patient_item_id', how='inner').set_index('index')
+	del data_s.index.name
+	num_cols = len(data_s.columns)
+	# Remove all patients who have no order sets:
+	data_s = data_s[data_s['patient_id'].isin(list(mapping.keys()))]
+	patients = list(data_s['patient_id'].unique()) # list of patient IDs
+	# Set up order set columns:
+	for ordersetID in all_ordersetIDs:
+		feature_name = 'orderset_' + str(ordersetID) + '.post.1d'
+		data_s[feature_name] = 0
+	# Fill in order set columns:
+	final_patient_items = [] # Final list of patient items to retain when writing out data frame
+	for patient in patients: # Iterate through patient IDs
+		print(f + " - Going through patient: " + str(patient))
+		patient_items = data_s.loc[data_s['patient_id'] == patient,'patient_item_id']
+		order_set_data = mapping[patient] # Order set data for current patient
+		for patient_item in patient_items: # Iterate through patient item IDs
+			atLeastOneOrderSetUsed = False # At least one order set within next 24 hours
+			for index, row in order_set_data.iterrows(): # Iterate through order set usage
+				feature_name = 'orderset_' + str(row['external_id']) + '.post.1d'
+				item_time = data_s.loc[data_s['patient_item_id'] == patient_item,'item_date']
+				assert len(item_time) == 1
+				item_time = item_time.iloc[0]
+				timeDiffSeconds = (row['item_date'] - item_time) / 1000000000
+				if timeDiffSeconds >= 0 and timeDiffSeconds <= 24*60*60:
+					data_s.loc[data_s['patient_item_id'] == patient_item,feature_name] += 1
+					atLeastOneOrderSetUsed = True
+			if atLeastOneOrderSetUsed or retain_all:
+				final_patient_items.append(patient_item)
+	# Prepare data frames for writing to output files:
+	data_x = pd.read_hdf(data_dir + "/" + f, 'data_x')
+	data_x = data_x.reset_index(drop=True)
+	data_s = data_s[data_s['patient_item_id'].isin(final_patient_items)]
+	data_x = data_x.loc[data_x.index.isin(data_s.index),:]
+	assert len(data_x.index) == len(data_s.index)
+	S1 = data_s.iloc[:,:num_cols]
+	S2 = data_s.iloc[:,num_cols:]
+	# Write to output files:
+	data_x.to_hdf(output_dir + "/" + f, key='data_x', mode='w', complevel=1)
+	S1.to_hdf(output_dir + "/" + f, key='data_s', complevel=1)
+	S2.to_hdf(output_dir + "/" + f, key='data_y', complevel=1)
+	print(f + " - " + str(len(final_patient_items)) + " out of " + str(num_rows) + " items retained among " + str(len(patients)) + " patients")
+	# Garbage collection:
+	del data_x
+	del data_s
+	gc.collect()
+    
+def main(argv):
+	global files_list # Input directory (where the HDF5 files are stored)
+	global data_dir
+	global output_dir
+	global mapping # Dict mapping from patient ID to data frame containing order set information for that patient
+	global all_ordersetIDs # List of all order set IDs that will become response variables
+	global all_item_dates # Match patient item IDs to dates
+	global retain_all # If false, then only data rows that have order set usage within the next day will be kept 
+	data_dir = ""
+	output_dir = ""
+	mapping_dir = ""
+	dates_dir = ""
+	all_item_dates = ""
+	num_processes = 0
+	retain_all = False
+	try:
+		opts, args = getopt.getopt(argv,"hi:o:m:d:p:a")
+	except getopt.GetoptError:
+		print('make_order_set_responses.py -i <data_directory> -o <output_dir> -m <patient_orderset_mapping_file> -d <patient_itemdate_mapping_file> [-p num_processes] [-a] [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('make_order_set_responses.py -i <data_directory> -o <output_dir> -m <patient_orderset_mapping_file> -d <patient_itemdate_mapping_file> -p <num_processes> [-a] [-h]')
+			print('')
+			print('This script performs processes data files (in HDF5 format), from <data_directory>, to give post-one-day order set usages.')
+			print('Only the data rows that have at least one post-one-day order set usage are retained unless the flag -a is used in which case all data rows will be retained.')
+			print('The new data will be stored in <output_directory>.')
+			print('Two hdf5 files need to be specified:')
+			print('1. <patient_orderset_mapping_file>: data frame of order set items with columns being patient_id, patient_item_id, external_id (the order set ID), and item_date (the order set item date as nanoseconds since epoch).')
+			print('2. <patient_itemdate_mapping_file>: data frame of patient items with columns being item_date (as nanoseconds since epoch) and patient_item_id')
+			print('Use num_processes to specify the number of processes to use for multiprocessing.')
+			sys.exit()
+		elif opt == '-i':
+			data_dir = arg
+		elif opt == '-o':
+			output_dir = arg
+		elif opt == '-p':
+			num_processes = int(arg)
+		elif opt == '-a':
+			retain_all = True
+		elif opt == '-m':
+			mapping_dir = arg
+		elif opt == '-d':
+			dates_dir = arg
+	if len(argv) < 4 or data_dir == '' or output_dir == '' or mapping_dir == '' or dates_dir == '':
+		print('make_order_set_responses.py -i <data_directory> -o <output_dir> -m <patient_orderset_mapping_file> -d <patient_itemdate_mapping_file> [-p num_processes] [-a] [-h]')
+		sys.exit(2)
+	
+	# Configure multiprocessing
+	if num_processes == 0:
+		num_processes = multiprocessing.cpu_count()-1
+	print("Number of processes: " + str(num_processes))
+	
+	# Setup mappings
+	mapping = pd.read_hdf(mapping_dir)
+	mapping = mapping.sort_values(by='item_date')
+	mapping = mapping.drop_duplicates()
+	all_ordersetIDs = list(mapping['external_id'].unique())
+	all_ordersetIDs.sort()
+	mapping = dict(tuple(mapping.groupby('patient_id')))
+	all_item_dates = pd.read_hdf(dates_dir)
+	print("Total order sets: {}".format(len(all_ordersetIDs)))
+	
+	# Iterate through data_dir to process the files
+	files_list = os.listdir(data_dir)
+	pool = multiprocessing.Pool(num_processes)
+	pool.map(make_order_set_data, files_list)
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
diff --git a/scripts/Archive/ClinicNet/data_processing/prep_batches.py b/scripts/Archive/ClinicNet/data_processing/prep_batches.py
new file mode 100644
index 00000000..98f87a6c
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/prep_batches.py
@@ -0,0 +1,125 @@
+# This script generates batch assignments (randomly) from data
+# The batch assignments (in pickle file format) will be used by make_batches.py
+
+import numpy as np
+import pandas as pd
+import math
+import os
+import tables
+import pickle
+import gzip
+import random
+import sys, getopt
+
+# Function to generate random batches
+def get_shuffled_batches(data_dir, batch_size, num_data_rows):
+	files_list = os.listdir(data_dir)
+	files_list.sort()
+	data_x = None
+	if batch_size is None:
+		batch_size = num_data_rows
+
+	data_map = {} # Keys are file names; values are row numbers
+	data_rows_remaining = num_data_rows
+	total_rows_read = 0 # Keep track of the total number of rows we have read so far
+	while data_rows_remaining > 0:
+		total_data_rows = 0 # Keep track of the total number of rows in our dataset
+		for f in files_list:
+			store = pd.HDFStore(data_dir + "/" + f, mode='r')
+			nrows = store.get_storer('data_x').shape[0]
+			total_data_rows += nrows
+			batch_update = [] # The data row numbers in the new batch to add on
+			if f in list(data_map.keys()): 
+				batch_update = list(set(random.sample(list(range(nrows)), nrows)) - set(data_map[f]))
+			else:
+				data_map[f] = []
+				batch_update = list(set(random.sample(list(range(nrows)), nrows)))
+			random.shuffle(batch_update)
+			batch_update_length = min(batch_size,len(batch_update))
+			batch_update_length = min(batch_update_length, data_rows_remaining)
+			data_map[f].extend(batch_update[0:batch_update_length])
+			data_rows_remaining -= batch_update_length
+			total_rows_read += batch_update_length
+			store.close()
+		if total_rows_read == total_data_rows: # In the event that we have already read all the rows
+			data_rows_remaining = 0
+			num_data_rows = total_rows_read
+			if batch_size > num_data_rows:
+				batch_size = num_data_rows
+			break
+            
+	print("Read {} data rows in {} files".format(num_data_rows, len(files_list)))
+
+	# Turn the data_map into a list of tuples 
+	data_map_list = []
+	for k,v in data_map.items():
+		s = [(k,r) for r in v]
+		if len(s) > 0:
+			data_map_list.extend(s)
+            
+	random.shuffle(data_map_list)
+
+	# Now, create some batches: 
+	num_batches = int(math.ceil(float(num_data_rows) / batch_size)) # How many batches to have
+	num_rows_remaining = int(num_data_rows % batch_size) # How many rows in the last batch if it's not a perfect division by batch_size
+	padding = int(batch_size - num_rows_remaining) # The "padding" to add to make divisible by batch_size
+	batches = np.array_split(data_map_list + [None for i in range(padding)], num_batches) # Do batch assignments
+	batches[num_batches-1] = batches[num_batches-1][:-padding] # Get rid of the padding now
+	batch_dicts = [] # Convert the batches into a list of dicts where keys are the files and values are the row numbers
+	for i in range(num_batches):
+		curr_batch = {}
+		for k, v in batches[i]:
+			curr_batch.setdefault(k, []).append(v)
+		batch_dicts.append(curr_batch)
+
+	print("Created {} batches of size {}".format(len(batch_dicts), batch_size))
+
+	return batch_dicts
+
+def main(argv):
+	# Read in command line arguments:
+	input_dir = ''
+	output_file = ''
+	num_rows = 100000000000
+	batch_size = 0
+	try:
+		opts, args = getopt.getopt(argv,"hi:o:b:n:")
+	except getopt.GetoptError:
+		print('prep_batches.py -i <data_directory> -o <output_file> -b <batch_size> [-n num_rows] [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('prep_batches.py -i <data_directory> -o <output_file> -b <batch_size> [-n num_rows] [-h]')
+			print('')
+			print('This script prepares randomly shuffled batch assignments from the data stored in the files in <data_directory>.')
+			print('The size of each batch is given by <batch_size>; larger batch sizes means fewer batches and small batch sizes means more batches.')
+			print('The output, <output_file>, is a pickle file containing the batch assignments. This pickle file will be used by make_batches.py.')
+			print('Batch assignments is a list of dicts; each element (dict) of the list is a batch.')
+			print('The keys of each dict are the files of the batch and the values are the row numbers for given files of the batch.')
+			print('We can specify that num_rows of data in our dataset be used.')
+			print('Note that the shuffling done is completely random -- the entire dataset is shuffled, not just individual files within the dataset.')
+			print('Therefore, this script will take a long time to run, and will consume a lot of memory.')
+			print('Rather than making all the batches at once, we can specify a range of batches to make per run.')
+			print('This is done via <index_to_begin> and <index_to_end>, where we can supply the indices of the batches to start and end at.')
+			sys.exit()
+                elif opt == '-b':
+                        batch_size = int(arg)
+		elif opt == '-n':
+			num_rows = int(arg)
+		elif opt == '-i':
+			input_dir = arg
+		elif opt == '-o':
+			output_file = arg
+	if len(argv) < 3 or batch_size == 0 or input_dir == '' or output_file == '':
+		print('prep_batches.py -i <data_directory> -o <output_file> -b <batch_size> [-n num_rows] [-h]')
+		sys.exit(2)
+
+	# Get our shuffled batch assignments and write them to the pickle file
+	shuffled_batches = get_shuffled_batches(input_dir, batch_size, num_rows)
+	outfile = gzip.open(output_file,'wb', compresslevel=1)
+	pickle.dump(shuffled_batches,outfile)
+	outfile.close()
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
+   
diff --git a/scripts/Archive/ClinicNet/data_processing/stratify_data_by_time.py b/scripts/Archive/ClinicNet/data_processing/stratify_data_by_time.py
new file mode 100644
index 00000000..3aa0835c
--- /dev/null
+++ b/scripts/Archive/ClinicNet/data_processing/stratify_data_by_time.py
@@ -0,0 +1,90 @@
+# Takes HDF5 data files and remove items based on an item_date threshold
+
+import pandas as pd
+import numpy as np
+import os
+import gc
+import sys, getopt
+import multiprocessing
+
+def remove_items(f):        
+        # Read in data
+        data_s = pd.read_hdf(data_dir + "/" + f, 'data_s')
+        data_x = pd.read_hdf(data_dir + "/" + f, 'data_x')
+        data_y = pd.read_hdf(data_dir + "/" + f, 'data_y')
+        data_s = data_s.reset_index(drop=True)
+        data_x = data_x.reset_index(drop=True)
+        data_y = data_y.reset_index(drop=True)
+        
+        # Remove data rows based on date threshold
+        rows_to_keep1 = list(data_s.loc[data_s.loc[:,"item_date"] >= date_threshold1,:].index)
+        rows_to_keep2 = list(data_s.loc[data_s.loc[:,"item_date"] < date_threshold2,:].index)
+        rows_to_keep = list(set(rows_to_keep1) & set(rows_to_keep2)) # Intersection
+        data_s = data_s.loc[rows_to_keep,:]
+        data_x = data_x.loc[rows_to_keep,:]
+        data_y = data_y.loc[rows_to_keep,:]
+
+        # Write output to file
+        data_x.to_hdf(output_dir + "/" + f, key='data_x', mode='w', complevel=1)
+        data_s.to_hdf(output_dir + "/" + f, key='data_s', complevel=1)
+        data_y.to_hdf(output_dir + "/" + f, key='data_y', complevel=1)
+        print(f)
+
+        # Garbage collection
+        del data_x
+        del data_y
+        del data_s
+        gc.collect()
+
+def main(argv):
+        global files_list # Input directory (where the HDF5 files are stored)
+        global data_dir
+        global output_dir
+        global date_threshold1 # Date timestamp threshold #1 as nanoseconds since epoch
+        global date_threshold2 # Date timestamp threshold #2 as nanoseconds since epoch
+        global remove_greater # If true, removes rows with timestamp >= threshold. Otherwise, removes rows w/ timestamp < threshold.
+        data_dir = ""
+        output_dir = ""
+        num_processes = 0
+        date_threshold1 = -1
+        date_threshold2 = -1
+        try:
+                opts, args = getopt.getopt(argv,"hi:o:l:g:p:")
+        except getopt.GetoptError:
+                print('stratify_data_by_time.py -i <data_directory> -o <output_dir> -l <timestamp1> -g <timestamp2> [-p num_processes] [-h]')
+                sys.exit(2)
+        for opt, arg in opts:
+                if opt == '-h':
+                        print('stratify_data_by_time.py -i <data_directory> -o <output_dir> -l <timestamp1> -g <timestamp2> [-p num_processes] [-h]')
+                        print('')
+                        print('This script performs processes data files (in HDF5 format), from <data_directory>, to select rows based between date nanoseconds-since-epoch timestamps <timestamp1> and <timestamp2>.')
+                        print('Timestamp <timestamp1> must be less than <timestamp2>.')
+                        print('The new data will be outputed to data files in <output_dir>.')
+                        print('Use num_processes to specify the number of processes to use for multiprocessing.')
+                        sys.exit()
+                elif opt == '-i':
+                        data_dir = arg
+                elif opt == '-o':
+                        output_dir = arg
+                elif opt == '-l':
+                        date_threshold1 = int(arg)
+                elif opt == '-g':
+                        date_threshold2 = int(arg)
+                elif opt == '-p':
+                        num_processes = int(arg)
+        if len(argv) < 4 or data_dir == '' or output_dir == '' or date_threshold1 < 0 or date_threshold2 < 0 or date_threshold1 >= date_threshold2:
+                print('stratify_data_by_time.py -i <data_directory> -o <output_dir> -l <timestamp1> -g <timestamp2> [-p num_processes] [-h]')
+                sys.exit(2)
+
+        # Configure multiprocessing
+        if num_processes == 0:
+                num_processes = multiprocessing.cpu_count()-1
+        print("Number of processes: " + str(num_processes))
+
+        # Iterate through data_dir to process the files
+        files_list = os.listdir(data_dir)
+        pool = multiprocessing.Pool(num_processes)
+        pool.map(remove_items, files_list)
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
diff --git a/scripts/Archive/ClinicNet/plotPCA.ipynb b/scripts/Archive/ClinicNet/plotPCA.ipynb
new file mode 100644
index 00000000..256d4cb3
--- /dev/null
+++ b/scripts/Archive/ClinicNet/plotPCA.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generates PCA scree plots from supplied eigenvalues\n",
+    "## First, enter in our input file (pickle file containing eigenvalues/eigenvectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input file:\n",
+    "input_file = \"data/eig.pickle\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load in our eigenvalues, sort them, and calculate explained variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of PCs selected based on Kaiser's rule (eigenvalue > 1): 4632\n",
+      "Variance explained by PC1: 3.6%; Variance explained by PC4632: 0.00509%\n",
+      "Cumulative variance explained for PC4632: 82.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "import _pickle as cPickle\n",
+    "# Set size of graphs:\n",
+    "eigs = cPickle.load(open(input_file, \"rb\"))\n",
+    "eig_vals = eigs[0]\n",
+    "eig_vecs = eigs[1]\n",
+    "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
+    "eig_pairs.sort() # Sort the (eigenvalue, eigenvector) tuples\n",
+    "eig_pairs.reverse() # Make the sorting from high to low\n",
+    "x_pcs = range(1, len(eig_pairs)+1) # Our x variable in the graphs\n",
+    "y_eig = [eig_pair[0] for eig_pair in eig_pairs]\n",
+    "tot = sum(y_eig)\n",
+    "var_exp = [(i / tot)*100 for i in y_eig]\n",
+    "cum_var_exp = np.cumsum(var_exp)\n",
+    "# Get number of principal components based on Kaiser's rule\n",
+    "num_pcs = len([i for i in y_eig if i > 1])\n",
+    "# Print out some summaries of our PCA\n",
+    "print(\"Number of PCs selected based on Kaiser's rule (eigenvalue > 1): {}\".format(num_pcs))\n",
+    "print(\"Variance explained by PC1: {:.3}%; Variance explained by PC{}: {:.3}%\".format(var_exp[0], num_pcs, var_exp[num_pcs-1]))\n",
+    "print(\"Cumulative variance explained for PC{}: {:.3}%\".format(num_pcs, cum_var_exp[num_pcs-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f35a47fefd0>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scree plot for eigenvalues\n",
+    "start_eig = 0 # Index of the principal component to start the graph at\n",
+    "end_eig = 10000 # Index of the principal component to end the graph at\n",
+    "x = x_pcs[start_eig:end_eig]\n",
+    "y = np.log2(y_eig[start_eig:end_eig])\n",
+    "\n",
+    "plt.figure(figsize=(6, 4.5), dpi=300)\n",
+    "ax = plt.subplot(111)    \n",
+    "ax.spines[\"top\"].set_visible(False)  \n",
+    "ax.spines[\"right\"].set_visible(False)  \n",
+    "ax.get_xaxis().tick_bottom()    \n",
+    "ax.get_yaxis().tick_left()\n",
+    "plt.xticks(fontsize=11)\n",
+    "plt.yticks(fontsize=11)\n",
+    "plt.xlabel(\"Principal Component Number\")\n",
+    "plt.ylabel(\"Log2 of Eigenvalue\")\n",
+    "plt.title(\"Scree Plot of Eigenvalues\", fontsize=14)  \n",
+    "plt.axhline(y=0, lw=1, color='gray', ls='--')\n",
+    "\n",
+    "plt.plot(x, y, color='black', lw=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f35a3f8eac8>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scree plot for variance explained\n",
+    "start_varexp = 0 # Index of the principal component to start the graph at\n",
+    "end_varexp = 50 # Index of the principal component to end the graph at\n",
+    "x = x_pcs[start_varexp:end_varexp]\n",
+    "y = var_exp[start_varexp:end_varexp]\n",
+    "\n",
+    "plt.figure(figsize=(6, 4.5), dpi=300)\n",
+    "ax = plt.subplot(111)    \n",
+    "ax.spines[\"top\"].set_visible(False)  \n",
+    "ax.spines[\"right\"].set_visible(False)  \n",
+    "ax.get_xaxis().tick_bottom()    \n",
+    "ax.get_yaxis().tick_left()\n",
+    "plt.xticks(fontsize=11)\n",
+    "plt.yticks(fontsize=11)\n",
+    "plt.xlabel(\"Principal Component Number\")\n",
+    "plt.ylabel(\"Variance Explained (%)\")\n",
+    "plt.title(\"Scree Plot of Explained Variances\", fontsize=14)  \n",
+    "\n",
+    "plt.plot(x, y, lw=2, color='black')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f35a3f5b748>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scree plot for cumulative variance explained\n",
+    "start_cumvar = 0 # Index of the principal component to start the graph at\n",
+    "end_cumvar = None # Index of the principal component to end the graph at\n",
+    "x = x_pcs[start_cumvar:end_cumvar]\n",
+    "y = cum_var_exp[start_cumvar:end_cumvar]\n",
+    "\n",
+    "plt.figure(figsize=(6, 4.5), dpi=300)\n",
+    "ax = plt.subplot(111)    \n",
+    "ax.spines[\"top\"].set_visible(False)  \n",
+    "ax.spines[\"right\"].set_visible(False)  \n",
+    "ax.get_xaxis().tick_bottom()    \n",
+    "ax.get_yaxis().tick_left()\n",
+    "plt.xticks(fontsize=11)\n",
+    "plt.yticks(fontsize=11)\n",
+    "plt.xlabel(\"Principal Component Number\")\n",
+    "plt.ylabel(\"Cumulative Variance Explained (%)\")\n",
+    "plt.title(\"Cumulative Explained Variance\", fontsize=14)  \n",
+    "plt.axvline(x=num_pcs, lw=1, color='gray', ls='--')\n",
+    "\n",
+    "plt.plot(x, y, color='black', lw=2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/scripts/Archive/ClinicNet/preprocessing/data_split.sh b/scripts/Archive/ClinicNet/preprocessing/data_split.sh
new file mode 100644
index 00000000..e01319cc
--- /dev/null
+++ b/scripts/Archive/ClinicNet/preprocessing/data_split.sh
@@ -0,0 +1,170 @@
+#!/bin/bash
+
+# This script makes train/dev/test split based on data files in data_directory
+# The data files should all be tab-delimited plaintext files (which may be gzip-compressed)
+# Each data file should be a patient encounter with the encounter ID followed by .txt or .txt.gz in the filename
+# The data_directory should also contain a columns.txt file specifying column names for all the fields in the data files
+# The fields, patient_id and encounter_id, must be present
+
+gzip_compressed=0
+
+while getopts ":gh" opt; do
+	case $opt in
+		g) gzip_compressed=1
+		   ;;
+		h) 
+		   echo "Usage: $0 [-g] [-h] <data_directory> <split_train> <split_dev> <split_test>"
+		   echo "Options and arguments:"
+		   echo "  -g             : Specify that the data files are gzip compressed"
+		   echo "  -h             : Help"
+		   echo "  data_directory : Directory containing the data we want to split into train/dev/test sets"
+		   echo "  split_train    : Percentage that we want to put into train set (e.g. 70 means 70 percent)"
+                   echo "  split_dev      : Percentage that we want to put into dev (validation) set"
+                   echo "  split_test     : Percentage that we want to put into test set"
+		   exit 1
+		   ;;
+		\?)
+		   echo "Invalid option: -$OPTARG" >&2 # Fold output from stdout to stderr (file_descriptor=2)
+		   exit 1
+		   ;;
+	esac
+done
+
+shift $((OPTIND-1))
+
+if [ ! "$#" -eq 4 ]; then
+	echo "Usage: $0 [-g] [-h] <data_directory> <split_train> <split_dev> <split_test>"
+	exit 1
+fi
+
+data_directory="$1"
+split_train="$2"
+split_dev="$3"
+split_test="$4"
+columns_file="$data_directory/columns.txt"
+extension=".txt"
+
+if [ $gzip_compressed -eq 1 ]; then
+	extension=".txt.gz"
+fi
+
+
+if [ ! -d "$data_directory" ]; then
+	echo "The following directory does not exist: $data_directory"
+	exit 1
+fi
+
+if [ ! -f "$columns_file" ]; then
+        echo "The columns info file was not found: $columns_file"
+        exit 1
+fi
+
+if [ $((split_train+split_dev+split_test)) -ne 100 ]; then
+	echo "split_train, split_dev, and split_test must be whole numbers which add up to 100"
+	exit 1
+fi
+
+if [ $split_train -le 0 -o $split_dev -le 0 -o $split_test -le 0 ]; then
+	echo "split_train, split_dev, and split_test must be positive non-zero numbers"
+	exit 1
+fi
+
+# Get the patient ID and encounter ID column numbers
+patient_id_col=$(awk -F'\t' 'NR==1{for(i=1;i<=NF;i++) if($i=="patient_id") {print i;exit}}' "$columns_file")
+encounter_id_col=$(awk -F'\t' 'NR==1{for(i=1;i<=NF;i++) if($i=="encounter_id") {print i;exit}}' "$columns_file")
+
+if [ -z $patient_id_col ] || [ -z $encounter_id_col ]; then
+	echo "The columns info file ($columns_file) must contain the fields: patient_id and encounter_id"
+	exit 1
+fi
+
+# Progress monitor
+progress_monitor() {
+	iteration=$1
+	total_iterations=$2
+	if [ $total_iterations -lt 20 ]; then
+		return  # No need to monitor progress if fewer than 20 iterations
+	fi
+	for ((n=0;n<=100;n+=10)); do
+		if [ $iteration -eq $((total_iterations*n/100)) ]; then
+			echo " - $n% done"
+		fi
+	done
+}
+
+# Make train, dev, test directories; if they already exist, move their contents to data_directory
+echo "Creating train, test, dev folders..."
+mkdir -p "$data_directory/train/"
+mkdir -p "$data_directory/test/"
+mkdir -p "$data_directory/dev/"
+mv "$data_directory/train/"* "$data_directory/" 2> /dev/null
+mv "$data_directory/test/"* "$data_directory/" 2> /dev/null
+mv "$data_directory/dev/"* "$data_directory/" 2> /dev/null
+
+# Make a temporary file
+temp_file=$(mktemp)
+trap "rm -f $temp_file" INT TERM HUP EXIT
+
+# Extract patient IDs and encounter IDs from data files
+echo "Retrieving patient IDs and encounter IDs..."
+i=0
+num_files=$(ls "$data_directory"|wc -l)
+for f in "$data_directory"/*$extension; do
+	fname=`basename ${f}`
+	if [ ! "$fname" == "columns.txt" ]; then # If not the column names info file
+		cmd="cat"
+		if [ $gzip_compressed -eq 1 ]; then
+			cmd="zcat"
+		fi
+		$cmd "$f"|head -1|cut -f$patient_id_col,$encounter_id_col >> $temp_file
+	fi
+        ((i++))
+        progress_monitor $i $num_files
+done
+
+ids_directory="$data_directory/IDs"
+mkdir -p "$ids_directory"
+
+# Sort the IDs and store them in a file
+echo "Sorting IDs and writing them to a file..."
+sort $temp_file > "$ids_directory"/patient_ids_and_encounter_ids_sorted.txt
+
+# Now put only unique patient IDs in a file
+echo "Writing unique patient IDs to a file..."
+cut -f1 "$ids_directory"/patient_ids_and_encounter_ids_sorted.txt|sort -u|shuf > "$ids_directory"/patient_ids_all.txt
+
+# Calculate the split
+echo "Calculating train, dev, test splits..."
+num_patients=$(cat "$ids_directory"/patient_ids_all.txt|wc -l)
+num_train=$((num_patients*split_train/100))
+offset_test=$((num_train+1))
+num_test=$((num_patients*split_test/100))
+offset_dev=$((offset_test+num_test))
+
+# Write patient IDs and encounter IDs to files
+head -$num_train "$ids_directory"/patient_ids_all.txt > "$ids_directory"/patient_ids_train.txt
+tail -n +$offset_test "$ids_directory"/patient_ids_all.txt|head -$num_test > "$ids_directory"/patient_ids_test.txt
+tail -n +$offset_dev "$ids_directory"/patient_ids_all.txt > "$ids_directory"/patient_ids_dev.txt
+grep -F -f "$ids_directory"/patient_ids_train.txt "$ids_directory"/patient_ids_and_encounter_ids_sorted.txt|cut -f2 > "$ids_directory"/encounter_ids_train.txt
+grep -F -f "$ids_directory"/patient_ids_dev.txt "$ids_directory"/patient_ids_and_encounter_ids_sorted.txt|cut -f2 > "$ids_directory"/encounter_ids_dev.txt
+grep -F -f "$ids_directory"/patient_ids_test.txt "$ids_directory"/patient_ids_and_encounter_ids_sorted.txt|cut -f2 > "$ids_directory"/encounter_ids_test.txt
+
+echo "Splitting the dataset..."
+
+while read line; do
+  mv "$data_directory"/${line}${extension} "$data_directory/train/"
+done < "$ids_directory"/encounter_ids_train.txt
+
+echo "- Finished training set split"
+
+while read line; do
+  mv "$data_directory"/${line}${extension} "$data_directory/dev/"
+done < "$ids_directory"/encounter_ids_dev.txt
+
+echo "- Finished dev (validation) set split"
+
+while read line; do
+  mv "$data_directory"/${line}${extension} "$data_directory/test/"
+done < "$ids_directory"/encounter_ids_test.txt
+
+echo "- Finished test set split"
diff --git a/scripts/Archive/ClinicNet/preprocessing/make_hdf5.py b/scripts/Archive/ClinicNet/preprocessing/make_hdf5.py
new file mode 100644
index 00000000..4a3e08c6
--- /dev/null
+++ b/scripts/Archive/ClinicNet/preprocessing/make_hdf5.py
@@ -0,0 +1,146 @@
+# This script turns the data (stored in tab-delimited plaintext files) into HDF5 format
+
+import numpy as np
+import pandas as pd
+import os
+import tables
+import random
+import re
+import multiprocessing
+import sys, getopt
+
+# Function: loading in the data and making it into HDF5
+# Everything is stored with as the float32 data type
+## (Therefore, any non-numerical columns need to not be present in columns_line_exclude_responses)
+def load_in_data(files_list):
+	global columns_line, columns_line_exclude_responses, hdf5_dir, files_dir, save_features
+	rand_identifier = random.randint(1,9999999)
+	hdf5_file = hdf5_dir + '/{}.h5'.format(rand_identifier)
+	data_x = None
+	data_y = None
+	data_s = None
+	for i in range(len(files_list)):
+		f = files_list[i]
+		if i%10==0:
+			print(str(i) + ": " + f) # Which iteration we're currently on
+		subdata = pd.read_table(files_dir + "/" + f, sep = '\t', header=None, names=columns_line, usecols=columns_line_exclude_responses, dtype='float32', na_values='None')
+		if not (use_subset_name is None) and not (use_subset_values is None):
+			subdata_subset = pd.read_table(files_dir + "/" + f, sep = '\t', header=None, names=columns_line, usecols=list(use_subset_name), dtype='float32', na_values='None')
+		subdata_save = None
+		if not (save_features is None):
+			subdata_save = pd.read_table(files_dir + "/" + f, sep = '\t', header=None, names=columns_line, usecols=save_features, na_values='None')
+			if data_s is None:
+				data_s = subdata_save
+			else:
+				data_s = data_s.append(subdata_save)
+		colNames_y = subdata.columns[subdata.columns.str.contains(pat = '.post.1d')] 
+		colNames_x = subdata.columns[~subdata.columns.str.contains(pat = '.post.1d')] 
+		if data_x is None:
+			data_x = subdata.loc[:,colNames_x]
+		else:
+			data_x = data_x.append(subdata.loc[:,colNames_x])
+		if data_y is None:
+			data_y = subdata.loc[:,colNames_y]
+		else:
+			data_y = data_y.append(subdata.loc[:,colNames_y])            
+	data_x.to_hdf(hdf5_file, key='data_x', mode='w', complevel=1)
+	data_y.to_hdf(hdf5_file, key='data_y', mode='a', complevel=1)
+	if not (data_s is None):
+		data_s.to_hdf(hdf5_file, key='data_s', mode='a', complevel=1)
+
+def main(argv):
+	# Read in command line arguments:
+	global save_features
+	global use_subset_name
+	global use_subset_values
+	input_dir = ''
+	columns_file=''
+	output_dir = ''
+	num_split = 1
+	relevant_response_columns_file = ''
+	exclude_cols = []
+	num_processes = 0
+	save_features = None
+	use_subset_name = None
+	use_subset_values = None
+	try:
+		opts, args = getopt.getopt(argv,"hi:c:o:n:r:e:p:s:u:")
+	except getopt.GetoptError:
+		print('make_hdf5.py -i <data_directory> -c <columns_file> -o <output_directory> [-n num_split] [-r response_var_file] [-e exclude_features] [-p num_processes] [-s save_features] [-u use_subset] [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+	                print('make_hdf5.py -i <data_directory> -c <columns_file> -o <output_directory> [-n num_split] [-r response_var_file] [-e exclude_features] [-p num_processes] [-s save_features] [-u use_subset] [-h]')
+			print('')
+			print('This script converts the data (stored in tab-delimited plaintext files in <data_directory>) into HDF5 files which will be stored in <output_directory>')
+			print('We specify a tab-delimited file, <columns_file>, containing the column names of each column in the tab-delimited plaintext data files')
+			print('The number of HDF5 files produced is dictated by num_split, which denotes how many chunks the data will be split into')
+			print('As we process the data in chunks, we can use multiple processors by specifying num_processes')
+			print('We can specify a response_var_file which is a tab-separated-values file containing a column named clinical_item_id, which has all the clinical item ID numbers we want to retain in the final HDF5 files for the response variable')
+			print('Important: Because the data frame stored in the HDF5 file will be float32 format, it is necessary to get rid of features that are non-numerical')
+			print('To get rid of certain features, specify exclude_features as the feature names separated by commas')
+			print('To save certain features (i.e. to not convert them to float32), specify save_features as the feature names separated by commas')
+			print('To only save a subset of rows for which a certain feature has values matching those specified in a separate file, use the -u option as follows: column_name:file_name')
+			print('For an example of the -u option, if we only wanted to select patient IDs that exist in a file called ids.txt, we would use -u patient_id:ids.txt')
+			print('Each HDF5 file produced will receive a random numerical ID for its filename.')
+			sys.exit()
+		elif opt == '-p':
+			num_processes = int(arg)
+		elif opt == '-r':
+			relevant_response_columns_file = arg
+		elif opt == '-e':
+			exclude_cols = arg.split(',')
+		elif opt == '-s':
+			save_features = arg.split(',')
+		elif opt == '-n':
+			num_split = int(arg)
+		elif opt == '-i':
+			input_dir = arg
+		elif opt == '-o':
+			output_dir = arg
+		elif opt == '-c':
+			columns_file = arg
+		elif opt == '-u':
+			use_subset = arg.split(':')
+			use_subset_name = use_subset[0]
+			use_subset_values = [line.strip() for line in open(use_subset[1])]
+	if len(argv) < 3 or columns_file == '' or input_dir == '' or output_dir == '':
+                print('make_hdf5.py -i <data_directory> -c <columns_file> -o <output_directory> [-n num_split] [-r response_var_file] [-e exclude_features] [-p num_processes] [-s save_features] [-u use_subset] [-h]')
+		sys.exit(2)
+
+	# Set up globals (since multiprocessing.Pool(...) doesn't handle multiple arguments)
+	global columns_line, columns_line_exclude_responses, hdf5_dir, files_dir
+	files_dir = input_dir
+	hdf5_dir = output_dir
+
+	# Load the columns	
+	columns_line = []
+	with open(columns_file) as f:
+		columns_line = f.readline().split("\t")
+
+	# Process response columns to get a list of response columns to be removed
+	response_cols = pd.read_csv(relevant_response_columns_file, sep="\t")
+	response_cols = response_cols.loc[:,"clinical_item_id"]
+	response_cols = response_cols.values
+	response_cols = ["clinitem_{}.post.1d".format(x) for x in response_cols]
+	r = re.compile("(.*\.post\.1d$)")
+	all_response_cols = list(filter(r.match, columns_line))
+	response_cols_to_remove = list(set(all_response_cols) - set(response_cols))
+
+	# Get a list of colummns that will make it into the final HDF5 file
+	columns_line_exclude_responses = list(set(columns_line) - set(response_cols_to_remove))
+	columns_line_exclude_responses = list(set(columns_line_exclude_responses) - set(exclude_cols))  # also exclude exclude_cols columns
+
+	# Process the data into HDF5 files
+        files_list = os.listdir(files_dir)
+	if num_processes == 0:
+		num_processes = multiprocessing.cpu_count()-1
+	if num_processes > num_split: # In case we have more processors than we actually need
+		num_processes = num_split
+	print("Using " + str(num_processes) + " processes for processing " + str(len(files_list)) + " files divided into " + str(num_split) + " chunks")
+	pool = multiprocessing.Pool(num_processes)
+	p = pool.map(load_in_data, np.array_split(files_list, num_split))
+	print("FINISHED")
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
diff --git a/scripts/Archive/ClinicNet/run_clinicnet.py b/scripts/Archive/ClinicNet/run_clinicnet.py
new file mode 100644
index 00000000..1cf2bd1f
--- /dev/null
+++ b/scripts/Archive/ClinicNet/run_clinicnet.py
@@ -0,0 +1,418 @@
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+import json
+import h5py
+import os
+from os import path
+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Flatten, Conv2D
+from tensorflow import keras
+from tensorflow.keras import Model
+from tensorflow.python.client import device_lib
+from utils.datagenerator import DataGenerator
+from tensorflow.keras import backend as K
+from sklearn.metrics import precision_recall_fscore_support as pr
+import datetime
+import sys
+import shutil
+
+load_model=True
+NUM_EPOCH_START = int(sys.argv[1])
+if NUM_EPOCH_START == 0:
+    load_model=False
+    
+print('starting on epoch: {}'.format(NUM_EPOCH_START))
+PATH = 'best_models/order_set/'
+NOW = sys.argv[2]
+if not os.path.exists('{}{}'.format(PATH,NOW)):
+    print('making dir since DNE')
+    os.makedirs('{}{}'.format(PATH,NOW))
+if NUM_EPOCH_START == 0:
+    shutil.copy(__file__, '{}{}/script_ran.py'.format(PATH,NOW))
+
+if tf.test.is_gpu_available():
+    print("GPU detected")
+else:
+    print('no GPU detected')
+    
+
+
+          
+# get class weights for loss function if performing binary_crossentropy with class weights
+path = '/badvolume/home/ec2-user/cs230/scripts/data/statistics/train2/freq_y.hdf5'
+weights = pd.read_hdf(path)
+
+class_weight = dict(list(zip(np.arange(0, len(weights)),1/(weights.values+0.0001))))
+          
+from tensorflow.keras import backend as K
+import pandas as pd 
+from sklearn.metrics import precision_recall_fscore_support as pr
+
+          
+# useful classes and functions for later 
+
+def weighted_binary_crossentropy(y_true, y_pred):
+    # Calculate the binary crossentropy
+    zero_weight = 1
+    one_weight = pw
+    b_ce = K.binary_crossentropy(y_true, y_pred)
+
+    # Apply the weights
+    weight_vector = y_true * one_weight  + (1. - y_true) * zero_weight
+    weighted_b_ce = weight_vector * b_ce
+
+    # Return the mean error
+    return K.mean(weighted_b_ce)
+
+#     return weighted_binary_crossentropy
+
+def binary_accuracy_V2(y_true, y_pred):
+    '''Calculates the mean accuracy rate across all predictions for binary
+    classification problems.
+    '''
+    y_true_edited = y_true[:,2:]
+    y_pred_edited = y_pred[:,2:]
+    return K.mean(K.equal(y_true, K.round(y_pred)))
+
+
+def f1(y_true, y_pred):
+    K.set_epsilon(1e-05)
+    def recall(y_true, y_pred):
+        """Recall metric.
+
+        Only computes a batch-wise average of recall.
+
+        Computes the recall, a metric for multi-label classification of
+        how many relevant items are selected.
+        """
+        K.set_epsilon(1e-05)
+        #y_pred = tf.convert_to_tensor(y_pred, np.float32)
+        #y_true = tf.convert_to_tensor(y_true, np.float32)
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+        recall = true_positives / (possible_positives + K.epsilon())
+        return recall
+
+    def precision(y_true, y_pred):
+        """Precision metric.
+
+        Only computes a batch-wise average of precision.
+
+        Computes the precision, a metric for multi-label classification of
+        how many selected items are relevant.
+        """
+        K.set_epsilon(1e-05)
+        #y_pred = tf.convert_to_tensor(y_pred, np.float32)
+        #y_true = tf.convert_to_tensor(y_true, np.float32)
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+        precision = true_positives / (predicted_positives + K.epsilon())
+        return precision
+    precision = precision(y_true, y_pred)
+    recall = recall(y_true, y_pred)
+    return 2*((precision*recall)/(precision+recall+K.epsilon()))
+def precision(y_true, y_pred):
+    K.set_epsilon(1e-05)
+    #y_pred = tf.convert_to_tensor(y_pred, np.float32)
+    #y_true = tf.convert_to_tensor(y_true, np.float32)
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+def recall(y_true, y_pred):
+    K.set_epsilon(1e-05)
+    #y_pred = tf.convert_to_tensor(y_pred, np.float32)
+    #y_true = tf.convert_to_tensor(y_true, np.float32)
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+    
+    
+def f1_loss(y_true, y_pred):
+    tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)
+    tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)
+    fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)
+    fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)
+
+    p = tp / (tp + fp + K.epsilon())
+    r = tp / (tp + fn + K.epsilon())
+
+    f1 = 2*p*r / (p+r+K.epsilon())
+    f1 = tf.where(tf.math.is_nan(f1), tf.zeros_like(f1), f1)
+    return 1 - K.mean(f1)
+
+
+def flattenMatrix(mat):
+    mat = pd.DataFrame(mat)
+    n = len(mat)
+    flattened = []
+    for i in range(n):
+        actual_vals = mat.loc[i, ].tolist()
+        flattened.extend(actual_vals)
+    return flattened
+
+          
+from sklearn.metrics import roc_curve, auc
+
+
+def auc(y_true, y_pred):
+    auc = tf.keras.metrics.AUC(y_true, y_pred)[1]
+    K.get_session().run(tf.local_variables_initializer())
+    return auc
+
+import math
+# if scale == None: generates uniform random value between start/end
+# if scale == 'log': generate random variable r in [log(start),log(end)], then return 10^r
+#     ex. if you input start:0.0001, end:1 it will return 10^r, where r in [-4,0]
+def random_search(start, end, scale='uniform'):
+    if scale == 'uniform':
+        return np.random.uniform(start, end)
+    elif scale == 'int':
+        return np.random.randint(start,end)
+    elif scale == 'log':
+        a = math.log(start, 10)
+        b = math.log(end, 10)
+        r = np.random.uniform(a, b)
+        return 10**r
+    else:
+        return 'ERROR'
+
+# get random value from list
+def random_grid_search(vals):
+    length = len(vals)
+    return vals[np.random.randint(0,length)]
+
+def decrement_num_neurons(first, min_val, num_layers):
+    layers = [first]
+    random = np.random.rand()
+    if random > 0.5:
+        random = 50
+    else:
+        random = 0
+    prev = first
+    for j in range(num_layers-1):
+        prev = max(prev-25, min_val) 
+        layers.append(prev)
+    return layers
+
+def dropout_search(num_layers):
+    possible_vals = [0.5, 0.4,0.35,0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0]
+    d = []
+    prev = None
+    for l in range(num_layers-1):
+        if l == 0:
+            dl = possible_vals[np.random.randint(0,len(possible_vals))]
+            d.append(dl)
+            prev = dl
+        else:
+            dl = possible_vals[np.random.randint(0,len(possible_vals))]
+            while dl > prev:
+                dl = possible_vals[np.random.randint(0,len(possible_vals))]
+            d.append(dl)
+            prev = dl
+    d.append(0)
+    return d
+          
+def neural_network(num_layers, num_neurons, learning_rate, activation, dropout_rate, x_shape, y_shape, lam):
+#     assert len(num_neurons) == len(dropout_rate)
+    layers = []
+    layers.append(tf.keras.layers.Dense(num_neurons[0], activation=activation, kernel_initializer=keras.initializers.glorot_normal(), 
+                                        kernel_regularizer=keras.regularizers.l2(lam), 
+                                        input_shape=(x_shape[1],)))
+    for l in range(1, num_layers-2):
+        layers.append(tf.keras.layers.Dense(num_neurons[l],kernel_initializer=keras.initializers.glorot_normal(), activation=activation, 
+                                        kernel_regularizer=keras.regularizers.l2(lam)
+                                           ))
+        layers.append(tf.keras.layers.Dropout(dropout_rate))
+        if use_batchnorm:
+            layers.append(tf.keras.layers.BatchNormalization())
+    layers.append(tf.keras.layers.Dense(y_shape[1], activation='sigmoid'))
+    model = tf.keras.Sequential(layers)
+#     optim = tf.keras.optimizers.Adam(learning_rate=learning_rate)
+#     optim = tf.keras.optimizers.SGD()
+    optim = tf.keras.optimizers.Nadam(learning_rate=learning_rate)
+    model.compile(loss= 
+#                   f1_loss,
+#                   'binary_crossentropy',
+                  weighted_binary_crossentropy,
+              optimizer=optim,
+              metrics = [binary_accuracy_V2, precision,recall, f1])
+
+    return model
+
+def logistic_model(num_layers, num_neurons, learning_rate, activation, dropout_rate, x_shape, y_shape, lam):
+    layers = []
+    layers.append(tf.keras.layers.Dense(y_shape[1], activation='sigmoid', kernel_initializer=keras.initializers.glorot_normal(), input_shape=(x_shape[1],)))
+    model = tf.keras.Sequential(layers)
+    SGD = tf.keras.optimizers.SGD()
+    model.compile(loss='binary_crossentropy',
+              optimizer=SGD,
+              metrics = [binary_accuracy_V2, f1])
+    return model
+
+def initialize_keys(d,now):
+    keys = ['parameters','loss','val_loss','f1','val_f1', 'acc', 'val_acc']
+    new_entry = {}
+    for k in keys:
+        new_entry[k] = []
+        
+    d[now] = new_entry
+    
+    return d
+def update_dicts(d, now, params, mod_hist):
+    
+    d[now]['parameters'] = params
+
+    d[now]['loss'] = mod_hist['loss']
+    d[now]['val_loss'] = mod_hist['val_loss']
+    d[now]['f1'] = mod_hist['f1']
+    d[now]['val_f1'] = mod_hist['val_f1']
+    d[now]['acc'] = mod_hist['binary_accuracy_V2']
+    d[now]['val_acc'] = mod_hist['val_binary_accuracy_V2']
+#     d[now]['test_fscore'] = test_f_score
+    return d
+
+def run_nn(train_gen, val_gen, params, now):
+    num_layers = params['num_layers']
+    num_neurons = params['num_neurons']
+    learning_rate = params['learning_rate']
+    activation = params['activation']
+    dropout_rate = params['dropout_rate']
+    epochs = params['epochs']
+    lam = params['lambda']
+    batch_size = params['batch_size']
+    
+    checkpoints = keras.callbacks.ModelCheckpoint('{}{}/model_callback.h5'.format(PATH,NOW), monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False, mode='min')
+    earlystopping = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta = 0.01, patience=1, verbose=1, mode='min')
+    logger = keras.callbacks.CSVLogger('{}{}/log.txt'.format(PATH,NOW), append=True)
+
+    callbacks = [checkpoints, earlystopping, logger]
+    
+    if load_model:
+        if train_logistic:
+            model = keras.models.load_model('{}{}/model_callback.h5'.format(PATH,NOW), custom_objects={'binary_accuracy_V2':binary_accuracy_V2,'f1':f1})
+        else:
+            model = keras.models.load_model('{}{}/model_callback.h5'.format(PATH,NOW), custom_objects={'binary_accuracy_V2':binary_accuracy_V2, 'weighted_binary_crossentropy':weighted_binary_crossentropy, 'precision':precision, 'recall':recall,'f1':f1, 'f1_loss':f1_loss})
+            model.compile(loss= 
+                          weighted_binary_crossentropy,
+    #                   f1_loss,
+    #                   'binary_crossentropy',
+                  optimizer = tf.keras.optimizers.Nadam(learning_rate=learning_rate),
+                  metrics = [binary_accuracy_V2, precision, recall, f1])
+    else:
+        if train_logistic:
+            model = logistic_model(num_layers, num_neurons, learning_rate, activation, dropout_rate, (batch_size, 4632), (batch_size, 610), lam)
+        else:
+            model = neural_network(num_layers, num_neurons, learning_rate, activation, dropout_rate, (batch_size, 4632), (batch_size, 610), lam)
+    if test_mode:
+        mod_hist = model.fit_generator(train_gen,steps_per_epoch=steps_per_epoch, #512
+#                                    validation_data = val_gen, 
+#                                    validation_steps = len(val_gen),
+                                   verbose=1,  
+                                   epochs = epochs + NUM_EPOCH_START, 
+                                   use_multiprocessing = False, workers = 1, max_queue_size = 1, 
+                                   callbacks = callbacks,
+#                                   class_weight = class_weight, 
+                                   shuffle=False,
+                                  initial_epoch = NUM_EPOCH_START)
+    else:
+        mod_hist = model.fit_generator(train_gen,steps_per_epoch=steps_per_epoch, #512
+                                       validation_data = val_gen, 
+                                       validation_steps = len(val_gen),
+                                       verbose=1,  
+                                       epochs = epochs + NUM_EPOCH_START, 
+                                       use_multiprocessing = False, workers = 1, max_queue_size = 1, 
+                                       callbacks = callbacks,
+    #                                   class_weight = class_weight, 
+                                       shuffle=False,
+                                      initial_epoch = NUM_EPOCH_START)
+    return mod_hist, model
+          
+          
+# '''
+# Set parameters of interest
+# '''
+
+# learning_rate = 0.01
+learning_rate = 0.008/((NUM_EPOCH_START+1)*6) # use decay, this is manually adjusted
+num_layers = 2
+num_neurons_1 = [3495]
+# maybe remove batcnorm?
+dropout = 0.05
+num_epochs = 2
+epochs=num_epochs
+batch_size = 32
+lam = .0025
+steps_per_epoch = 16384
+pw=4 # this is weight (higher = predict more ones in response variable)
+exclude_cols_transformation='item_date.month.sin,item_date.month.cos,item_date.hour.sin,item_date.hour.cos'
+use_batchnorm = False
+test_mode = False
+train_logistic = True
+# comment out val_gen len(val_gen) in fit_generator also
+# '''
+# 
+# '''
+
+
+model_time_start = str(datetime.datetime.now())
+now = str(datetime.datetime.now())
+iteration_param_dict = {}
+iteration_param_dict['learning_rate'] = learning_rate
+iteration_param_dict['num_layers'] = num_layers
+iteration_param_dict['num_neurons'] = num_neurons_1
+iteration_param_dict['activation'] = 'relu'
+iteration_param_dict['dropout_rate'] = dropout
+iteration_param_dict['epochs'] = num_epochs
+iteration_param_dict['lambda'] = lam
+iteration_param_dict['batch_size'] = batch_size
+print('########')
+print(iteration_param_dict)
+# loading model now
+model_dicts = {}
+model_dicts = initialize_keys(model_dicts,now)
+
+exclude_cols_transformation='item_date.month.sin,item_date.month.cos,item_date.hour.sin,item_date.hour.cos'
+data_dir = '/badvolume/home/ec2-user/cs230/scripts/matrix/final_may_6/hdf5/dev2_feature_selected_small/'
+val_gen = None
+if not test_mode:
+    val_gen = DataGenerator(path=data_dir, cache='dev2_generator_small.sav', batch_size = 4096, shuffle=False)
+
+model_dicts = {}
+model_dicts = initialize_keys(model_dicts,now)
+
+# for i in range(23):
+# K.clear_session()
+data_dir = '/badvolume/home/ec2-user/cs230/scripts/data/hdf5/train2_feature_selected'
+train_gen = DataGenerator(path=data_dir, pca_file='/badvolume/home/ec2-user/cs230/scripts/data/eig.pickle', 
+                                transformation_file='/badvolume/home/ec2-user/cs230/scripts/data/statistics/train/avg_stddev_featureselected.hdf5', 
+                                num_pc=4632, 
+                                num_processes=10, 
+                                batches_per_epoch = steps_per_epoch, 
+                                exclude_cols_transformation=exclude_cols_transformation)
+
+
+# set state to start somewhere new
+if load_model:
+    train_gen.setState(num_epochs=NUM_EPOCH_START)
+mod_hist, model = run_nn(train_gen, val_gen, iteration_param_dict, now)
+class MyEncoder(json.JSONEncoder):
+    # source: https://stackoverflow.com/questions/27050108/convert-numpy-type-to-python
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return super(MyEncoder, self).default(obj)
+print('saving models')
+model.save('{}{}/model.h5'.format(PATH,NOW))
+
+model_dicts = update_dicts(model_dicts, now, iteration_param_dict, mod_hist.history)
+with open('{}{}/history.json'.format(PATH,NOW), 'w') as mdj:
+    json.dump(model_dicts, mdj, cls = MyEncoder)
+#     del train_gen
diff --git a/scripts/Archive/ClinicNet/utils/pca.py b/scripts/Archive/ClinicNet/utils/pca.py
new file mode 100644
index 00000000..ad3e5059
--- /dev/null
+++ b/scripts/Archive/ClinicNet/utils/pca.py
@@ -0,0 +1,56 @@
+# Performs principal components analysis
+# Saves eigen values and eigen vectors to file
+
+import pandas as pd
+import numpy as np
+import scipy.linalg as la
+import tables
+import os
+import _pickle as cPickle # Must use python 3!!
+import sys, getopt
+
+# Convert covariance matrix to correlation matrix
+# Since the correlation matrix is standardized,
+# it's what we use since we're going to z-score standardize our data
+# Argument: a is a pandas dataframe
+def cov2corr(a):
+	dd = np.sqrt(np.matrix(a).diagonal())
+	a = ((np.matrix(a).T/dd).T)/dd
+	return a
+
+def doPCA(cov_mat):
+	eig_vals, eig_vecs = la.eigh(cov_mat)
+	eig_tuple = (eig_vals, eig_vecs)
+	return eig_tuple
+
+def main(argv):
+	covar_file = ''
+	output_file = ''
+	try:
+		opts, args = getopt.getopt(argv,"hi:o:")
+	except getopt.GetoptError:
+		print('pca.py -i <covar_file> -o <output_file> [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('pca.py -i <covar_file> -o <output_file> [-h]')
+			print('')
+			print('This script performs PCA given covariance matrix hdf5 file <covar_file> and outputs eigenvalues/eigenvectors in pickle output file: <output_file>.')
+			sys.exit()
+		elif opt == '-i':
+			covar_file = arg
+		elif opt == '-o':
+			output_file = arg
+	if len(argv) < 2 or covar_file == '' or output_file == '':
+                print('feature_selection.py -i <covar_file> -o <output_file> [-h]')
+                sys.exit(2)
+
+	print("Reading in covariance matrix...")
+	covar_matrix = cov2corr(pd.read_hdf(covar_file, 'covar'))
+	print("Doing decomposition of correlation matrix...")
+	eig_tuples = doPCA(covar_matrix) # Tuple of eigenvalues and eigenvectors
+	cPickle.dump(eig_tuples, open(output_file, "wb"), protocol=4)
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
+ 
diff --git a/scripts/Archive/ClinicNet/utils/summarize.py b/scripts/Archive/ClinicNet/utils/summarize.py
new file mode 100644
index 00000000..c8912368
--- /dev/null
+++ b/scripts/Archive/ClinicNet/utils/summarize.py
@@ -0,0 +1,75 @@
+import pandas as pd
+import os
+import gc
+import sys, getopt
+
+features = {
+'RaceWhiteHispanicLatino': 'clinitem_72407.pre',
+'RaceWhiteNonHispanicLatino': 'clinitem_72401.pre',
+'RaceHispanicLatino': 'clinitem_72409.pre',
+'RaceBlack': 'clinitem_72413.pre',
+'RaceAsian': 'clinitem_72405.pre',
+'RacePacificIslander': 'clinitem_72420.pre',
+'RaceNativeAmerican': 'clinitem_72427.pre',
+'RaceOther': 'clinitem_72412.pre',
+'RaceUnknown': 'clinitem_72411.pre',
+'Male': 'clinitem_72408.pre',
+'Female': 'clinitem_72402.pre',
+'Birth1910s': 'clinitem_72422.pre',
+'Birth1920s': 'clinitem_72421.pre',
+'Birth1930s': 'clinitem_72410.pre',
+'Birth1940s': 'clinitem_72406.pre',
+'Birth1950s': 'clinitem_72403.pre',
+'Birth1960s': 'clinitem_72400.pre',
+'Birth1970s': 'clinitem_72415.pre',
+'Birth1980s': 'clinitem_72404.pre',
+'Birth1990s': 'clinitem_72414.pre'
+}
+
+
+def main(argv):
+	data_dir = ''
+	try:
+		opts, args = getopt.getopt(argv,"hi:")
+	except getopt.GetoptError:
+		print('summarize.py -i <data_directory> [-h]')
+		sys.exit(2)
+	for opt, arg in opts:
+		if opt == '-h':
+			print('summarize.py -i <data_directory> [-h]')
+			print('')
+			print('This script prints out summary data (e.g. demographics) for data stored in HDF5 format in <data_directory>.')
+			sys.exit()
+		if opt == '-i':
+			data_dir = arg
+	if len(argv) < 1:
+		print('summarize.py -i <data_directory> [-h]')
+		sys.exit(2)
+
+	data = None
+	for filename in os.listdir(data_dir):
+		if filename.endswith(".hdf5") or filename.endswith(".h5"):
+			fname = data_dir + "/" + filename
+			print("Reading file: {}".format(fname))
+			data_x = pd.read_hdf(fname, 'data_x')
+			data_x = data_x.loc[:,features.values()].astype('int64')
+			data_s = pd.read_hdf(fname, 'data_s')
+			data_s = data_s.loc[:,'patient_id']
+			if data is None:
+				data = pd.concat([data_x, data_s], axis=1)
+			else:
+				data = data.append(pd.concat([data_x, data_s], axis=1))
+			del data_x
+			del data_s
+			gc.collect()
+
+	data = data.rename(columns={v:k for k,v in features.items()})
+	data = data.groupby(['patient_id']).max().clip(upper=1)
+	print(data.sum().to_string())
+	print("Total number of patients: {}".format(data.shape[0]))
+	print("Percentages:")
+	print((data.sum() / data.shape[0]).to_string())
+
+if __name__ == "__main__":
+   main(sys.argv[1:])
+
diff --git a/scripts/Archive/ClinicalPredictionRules/ConditionMortalityMatrix.py b/scripts/Archive/ClinicalPredictionRules/ConditionMortalityMatrix.py
new file mode 100644
index 00000000..30e5578a
--- /dev/null
+++ b/scripts/Archive/ClinicalPredictionRules/ConditionMortalityMatrix.py
@@ -0,0 +1,302 @@
+#!/usr/bin/python
+"""
+Utility class for constructing feature matrix that summarizes mortality
+outcomes for patients who have a specified condition or disease.
+"""
+
+import datetime
+import inspect
+import os
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.common.Util import log
+
+class ConditionMortalityMatrix(FeatureMatrix):
+    def __init__(self, condition, num_patients, params=None):
+        # params['icd_list'] = list of ICD codes for condition.
+        FeatureMatrix.__init__(self, condition, num_patients)
+
+        # Initialize DB connection.
+        self._connection = DBUtil.connection()
+
+        # If client doesn't supply icd_list, try to get it.
+        self._condition = condition
+        if params is not None and params.get('icd_list'):
+            icd_list = params['icd_list']
+        else:
+            icd_list = self._get_icd_list()
+        # If still haven't found anything, abort.
+        if icd_list is None:
+            raise ValueError('Unrecognized condition.')
+        self._icd_list = icd_list
+        log.info('%s ICDs: %s' % (self._condition, self._icd_list))
+
+        # Query patient episodes.
+        self._num_patients = num_patients
+        self._query_patient_episodes()
+
+        # Add features.
+        FeatureMatrix._add_features(self)
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+    def icd_list(self):
+        return self._icd_list
+
+    def _get_icd_list(self):
+        # Build query.
+        # Try to optimistically find any related ICD codes.
+        #
+        # SELECT
+        #   name AS icd
+        # FROM
+        #   clinical_item
+        # WHERE
+        #   clinical_item_category_id = 1 AND ## PROBLEM_LIST
+        #   (
+        #       description LIKE '%condition.lower()%' OR
+        #       description LIKE '%condition.upper()%' OR
+        #       description LIKE '%condition.capitalize()%'
+        #   ) AND
+        #   (
+        #       NOT description LIKE ICD_CODES_TO_IGNORE
+        #   )
+        # GROUP BY
+        #   icd
+        #
+        # TODO(sbala): Ignore certain ICD codes.
+        # ICD_CODES_TO_IGNORE = [
+        #     # Personal and family history, as these are not present condition.
+        #     'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19'
+        # ]
+        query = SQLQuery()
+        query.addSelect('name AS icd')
+        query.addFrom('clinical_item')
+        query.addWhereEqual('clinical_item_category_id', 1)
+        query.openWhereOrClause()
+        query.addWhereLike('description', '%%%s%%' % self._condition.lower())
+        query.addWhereLike('description', '%%%s%%' % self._condition.upper())
+        query.addWhereLike('description', '%%%s%%' % self._condition.capitalize())
+        query.closeWhereOrClause()
+        query.addGroupBy('icd')
+
+        # Execute query and fetch results.
+        results = DBUtil.execute(query)
+        icd_list = [result[0] for result in results]
+
+        return icd_list
+
+    def _get_random_patient_list(self):
+        # Build query.
+        # We're interested in all clinical decision moments for all patients
+        # who have 'condition' in their problem list.
+        #
+        # The full set of 'decision moments' is captured by stride_order_proc.
+        #
+        # For most patients, information will vary across time points.
+        # For example, if physician orders A1C lab panel, there will an episode
+        # before and after results, one with and without that information.
+        # For learning, it's fine to treat these as independent events, and
+        # trust the algorithms to learn whether A1C is helpful.
+        #
+        # SELECT
+        #   CAST(pi.patient_id AS bigint)
+        # FROM
+        #   patient_item AS pi,
+        #   clinical_item AS ci,
+        #   clinical_item_category AS cic
+        # WHERE
+        #   pi.clinical_item_id = ci.clinical_item_id AND
+        #   ci.clinical_item_category_id = cic.clinical_item_category_id AND
+        #   clinical_item_category = 1 AND ## (PROBLEM_LIST)
+        #   ci.name in (icd_list)
+        # LIMIT
+        #   self._num_patients
+        query = SQLQuery()
+        query.addSelect('CAST(pi.patient_id AS bigint)')
+        query.addFrom('patient_item AS pi')
+        query.addFrom('clinical_item AS ci')
+        query.addFrom('clinical_item_category AS cic')
+        query.addWhere('pi.clinical_item_id = ci.clinical_item_id')
+        query.addWhere('ci.clinical_item_category_id = cic.clinical_item_category_id')
+        query.addWhereEqual('cic.clinical_item_category_id', 1)
+        query.addWhereIn('ci.name', self._icd_list)
+        query.setLimit(self._num_patients)
+
+        # Fetch and return results.
+        results = DBUtil.execute(query)
+        patient_list = [result[0] for result in results]
+
+        return patient_list
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get random list of self._num_patients patients to query.
+        random_patient_list = self._get_random_patient_list()
+
+        # Build query.
+        # SELECT
+        #   CAST(sop.pat_id AS bigint),
+        #   sop.order_time AS index_time,
+        #   sp.death_date AS death_date
+        # FROM
+        #   stride_order_proc AS sop,
+        #   stride_patient AS sp
+        # WHERE
+        #   sop.pat_id = sp.pat_id AND
+        #   sop.pat_id IN random_patient_list
+        # GROUP BY
+        #   pat_id,
+        #   index_time,
+        #   death_date
+        # ORDER BY
+        #   pat_id,
+        #   index_time,
+        #   death_date
+        #
+        # TODO(sbala): Investigate whether it's possible to get a timestamp
+        # for the death, not just the death date. Both stride_patient and
+        # patient_item only have death date, but stride_order_proc might.
+        # Haven't been able to find the corresponding order_proc_id.
+        DECISION_ORDER_TYPES = ['Lab', 'ECHO', 'Pharmacy Consult',
+            'Point of Care Testing']
+        query = SQLQuery()
+        query.addSelect('CAST(sop.pat_id AS bigint)')
+        query.addSelect('sop.order_time AS index_time')
+        query.addSelect('sp.death_date AS death_date')
+        query.addFrom('stride_order_proc AS sop')
+        query.addFrom('stride_patient AS sp')
+        query.addWhere('sop.pat_id = sp.pat_id')
+        query.addWhereEqual('order_class', 'Normal')
+        query.addWhere('NOT sop.order_time IS NULL')
+        query.addWhereIn('CAST(sop.pat_id AS bigint)', random_patient_list)
+        query.addGroupBy('sop.pat_id')
+        query.addGroupBy('index_time')
+        query.addGroupBy('death_date')
+        query.addOrderBy('sop.pat_id')
+        query.addOrderBy('index_time')
+        query.addOrderBy('death_date')
+
+        FeatureMatrix._query_patient_episodes(self, query)
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+
+        # Overview:
+        overview.append('Overview:')
+        # This file contains ___ data rows, representing ___ unique inpatients
+        line = 'This file contains %s data rows, representing %s unique inpatients' % (self._factory.getNumRows(), self._num_patients)
+        overview.append(line)
+        # from Stanford hospital, all of which had ___ in their problem list.
+        line = 'from Stanford hospital, all of which had %s in their problem list.' % self._condition
+        overview.append(line)
+        # ___ is defined by the following ICD codes:
+        line = '%s is defined by the following ICD codes:'
+        overview.append(line)
+        #   ICD9.X, ICD.Y, ICD.Z
+        line = '  %s' % ', '.join(self._icd_list)
+        overview.append(line)
+        # Each row represents a decision point (proxied by clinical order).
+        line = 'Each row represents a decision point (proxied by clinical order).'
+        overview.append(line)
+        # Each row contains fields summarizing the patient's demographics,
+        line = "Each row contains fields summarizing the patient's demographics"
+        overview.append(line)
+        # inpatient admit date, prior vitals, and prior lab results.
+        line = 'inpatient admit date, prior vitals, and prior lab results.'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for an event's"
+        overview.append(line)
+        # occurrence or a difference between an event's time and index_time.
+        line = "occurrence or a difference between an event's time and index_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   index_time - time at which clinical decision was made.
+        summary.append('index_time - time at which clinical decision was made.')
+        #   death_date - if patient died, date on which they died.
+        summary.append('death_date - if patient died, date on which they died.')
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+
+        return summary
+
+if __name__ == "__main__":
+    dest_path = 'pneumonia-morbidity-10-epi.tab'
+    pneumonia = ConditionMortalityMatrix('pneumonia', 10, dest_path)
diff --git a/scripts/Archive/ClinicalPredictionRules/ConditionMortalityPredictor.py b/scripts/Archive/ClinicalPredictionRules/ConditionMortalityPredictor.py
new file mode 100644
index 00000000..2e62a80c
--- /dev/null
+++ b/scripts/Archive/ClinicalPredictionRules/ConditionMortalityPredictor.py
@@ -0,0 +1,374 @@
+#!/usr/bin/python
+"""
+Utility class for generating clinical prediction rules for condition mortality.
+"""
+
+import datetime
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.utils.validation import column_or_1d
+
+from ConditionMortalityMatrix import ConditionMortalityMatrix
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+
+class ConditionMortalityPredictor:
+    def __init__(self, condition, num_patients, icd_list=None, use_cache=None):
+        self._condition = condition
+        self._num_patients = num_patients
+        self._icd_list = icd_list
+
+        self._FEATURES_TO_REMOVE = [
+            'index_time',
+            'death_date', 'Death.post',
+            'Death.postTimeDays', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays'
+            ]
+        self._eliminated_features = list()
+
+        self._build_cmm_names()
+        if use_cache is None:
+            self._build_raw_feature_matrix()
+        print('Processing raw feature matrix...')
+        self._process_raw_feature_matrix()
+        print('Training predictor...')
+        self._train_predictor()
+        print('Testing predictor...')
+        self._test_predictor()
+
+    def _build_cmm_names(self):
+        slugified_condition = "-".join(self._condition.split())
+        self._build_cmm_name_raw(slugified_condition, self._num_patients)
+        self._build_cmm_name_processed(slugified_condition, self._num_patients)
+
+    def _build_cmm_name_raw(self, slugified_condition, num_patients):
+        template = '%s-mortality-matrix-%d-pat-raw.tab'
+        self._cmm_name_raw = template % (slugified_condition, num_patients)
+
+    def _build_cmm_name_processed(self, slugified_condition, num_patients):
+        template = '%s-mortality-matrix-%d-pat-processed.tab'
+        self._cmm_name_processed = template % (slugified_condition, num_patients)
+
+    def _build_raw_feature_matrix(self):
+        self._cmm = ConditionMortalityMatrix(self._condition, \
+            self._num_patients, self._cmm_name_raw, self._icd_list)
+
+    def _process_raw_feature_matrix(self):
+        # Read raw CMM.
+        self._fm_io = FeatureMatrixIO()
+        print('Reading raw matrix...')
+        self._cmm_raw = self._fm_io.read_file_to_data_frame(self._cmm_name_raw)
+
+        # Add and remove features to _cmm_processed.
+        self._fmt = FeatureMatrixTransform()
+        self._fmt.set_input_matrix(self._cmm_raw)
+        print('Adding features...')
+        self._add_features()
+        print('Imputing data...')
+        self._impute_data()
+        self._remove_features()
+        self._fmt.drop_duplicate_rows()
+        self._cmm_processed = self._fmt.fetch_matrix()
+
+        # Divide _cmm_processed into training and test data.
+        # This must happen before feature selection so that we don't
+        # accidentally learn information from the test data.
+        self._train_test_split()
+        print('Selecting features...')
+        self._select_features()
+
+        # Write output to new matrix.
+        train = self._y_train.join(self._X_train)
+        test = self._y_test.join(self._X_test)
+        self._cmm_processed = train.append(test)
+
+        header = self._build_processed_matrix_header()
+
+        self._fm_io.write_data_frame_to_file(self._cmm_processed, self._cmm_name_processed, header)
+
+    def _build_processed_matrix_header(self):
+        # FeatureMatrixFactory and FeatureMatrixIO expect a list of strings.
+        # Each comment below represents the line in the comment.
+        header = list()
+
+        # <file_name.tab>
+        file_name = self._cmm_name_processed
+        header.append(file_name)
+        # Created: <timestamp>
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+        header.append('Created: %s' % timestamp)
+        # Source: __name__
+        header.append('Source: %s' % __name__)
+        # Command: ConditionMortalityMatrix()
+        if self._icd_list:
+            command = 'ConditionMortalityPredictor(%s, %s, %s)' % \
+                (self._condition, self._num_patients, self._icd_list)
+        else:
+            command = 'ConditionMortalityPredictor(%s, %s)' % \
+                (self._condition, self._num_patients)
+        header.append('Command: %s' % command)
+        #
+        header.append('')
+        # Overview:
+        header.append('Overview:')
+        # This file is a processed version of ___.
+        line = 'This file is a post-processed version of %s.' % self._cmm_name_raw
+        header.append(line)
+        # The outcome label is ___, which is a boolean indicator
+        line = 'The outcome label is I(0<=Death.postTimeDays<=28), which is a boolean indicator'
+        header.append(line)
+        # for whether the patient given by pat_id passed away within 28 days
+        line = 'for whether the patient given by pat_id passed away within 28 days'
+        header.append(line)
+        # of the time index represented by a given row.
+        line = 'of the time index represented by a given row.'
+        header.append(line)
+        # This matrix is the result of the following processing steps on the raw matrix:
+        line = 'This matrix is the result of the following processing steps on the raw matrix:'
+        header.append(line)
+        #   (1) Imputing missing values with the mean value of each column.
+        line = '  (1) Imputing missing values with the mean value of each column.'
+        header.append(line)
+        #   (2) Manually removing low-information features:
+        line = '  (2) Manually removing low-information features:'
+        header.append(line)
+        #       ___
+        line = '      %s' % str(self._FEATURES_TO_REMOVE)
+        header.append(line)
+        #   (3) Algorithmically selecting the top 100 features via recursive feature elimination.
+        line = '  (3) Algorithmically selecting the top 100 features via recursive feature elimination.'
+        header.append(line)
+        #       The following features were eliminated.
+        line = '      The following features were eliminated:'
+        header.append(line)
+        # List all features with rank >100.
+        line = '        %s' % str(self._eliminated_features)
+        header.append(line)
+        #
+        line = ''
+        header.append(line)
+        # Each row represents a decision point (proxied by clinical order).
+        line = 'Each row represents a decision point (proxied by clinical order).'
+        header.append(line)
+        # Each row contains fields summarizing the patient's demographics,
+        line = "Each row contains fields summarizing the patient's demographics"
+        header.append(line)
+        # inpatient admit date, prior vitals, and prior lab results.
+        line = 'inpatient admit date, prior vitals, and prior lab results.'
+        header.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for an event's"
+        header.append(line)
+        # occurrence or a difference between an event's time and index_time.
+        line = "occurrence or a difference between an event's time and index_time."
+        header.append(line)
+        #
+        header.append('')
+        # Fields:
+        header.append('Fields:')
+        #   pat_id - ID # for patient in the STRIDE data set.
+        header.append('  pat_id - ID # for patient in the STRIDE data set.')
+        #   index_time - time at which clinical decision was made.
+        header.append('  index_time - time at which clinical decision was made.')
+        #   death_date - if patient died, date on which they died.
+        header.append('  death_date - if patient died, date on which they died.')
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.
+        header.append('  AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.')
+        #   Birth.preTimeDays - patient's age in days.
+        header.append("  Birth.preTimeDays - patient's age in days.")
+        #   [Male|Female].pre - is patient male/female (binary)?
+        header.append('  [Male|Female].pre - is patient male/female (binary)?')
+        #   [RaceX].pre - is patient race [X]?
+        header.append('  [RaceX].pre - is patient race [X]?')
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.
+        header.append('  Team.[specialty].[clinical_item] - specialist added to treatment team.')
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.
+        header.append('  Comorbidity.[disease].[clinical_item] - disease added to problem list.')
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.
+        header.append('  ___.[flowsheet] - measurements for flowsheet biometrics.')
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,
+        header.append('    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,')
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.
+        header.append('      Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.')
+        #   ___.[lab_result] - lab component results.
+        header.append('  ___.[lab_result] - lab component results.')
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,
+        header.append('    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,')
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,
+        header.append('      CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,')
+        #           PHV, PO2V, PCO2V
+        header.append('      PHV, PO2V, PCO2V')
+        #
+        header.append('')
+        #   [clinical_item] fields may have the following suffixes:
+        header.append('  [clinical_item] fields may have the following suffixes:')
+        #       ___.pre - how many times has this occurred before order_time?
+        header.append('    ___.pre - how many times has this occurred before order_time?')
+        #       ___.pre.Xd - how many times has this occurred within X days before index_time?
+        header.append('    ___.pre.Xd - how many times has this occurred within X days before index_time?')
+        #       ___.preTimeDays - how many days before order_time was last occurrence?
+        header.append('    ___.preTimeDays - how many days before order_time was last occurrence?')
+        #
+        header.append('')
+        #   [flowsheet] and [lab_result] fields may have the following suffixes:
+        header.append('  [flowsheet] and [lab_result] fields may have the following suffixes:')
+        #       ___.X_Y.count - # of result values between X and Y days of index_time.
+        header.append('    ___.X_Y.count - # of result values between X and Y days of index_time.')
+        #       ___.X_Y.countInRange - # of result values in normal range.
+        header.append('    ___.X_Y.countInRange - # of result values in normal range.')
+        #       ___.X_Y.min - minimum result value.
+        header.append('    ___.X_Y.min - minimum result value.')
+        #       ___.X_Y.max - maximum result value.
+        header.append('    ___.X_Y.max - maximum result value.')
+        #       ___.X_Y.median - median result value.
+        header.append('    ___.X_Y.median - median result value.')
+        #       ___.X_Y.std - standard deviation of result values.
+        header.append('    ___.X_Y.std - standard deviation of result values.')
+        #       ___.X_Y.first - first result value.
+        header.append('    ___.X_Y.first - first result value.')
+        #       ___.X_Y.last - last result value.
+        header.append('    ___.X_Y.last - last result value.')
+        #       ___.X_Y.diff - difference between penultimate and proximate values.
+        header.append('    ___.X_Y.diff - difference between penultimate and proximate values.')
+        #       ___.X_Y.slope - slope between penultimate and proximate values.
+        header.append('    ___.X_Y.slope - slope between penultimate and proximate values.')
+        #       ___.X_Y.proximate - closest result value to order_time.
+        header.append('    ___.X_Y.proximate - closest result value to order_time.')
+        #       ___.X_Y.firstTimeDays - time between first and order_time.
+        header.append('    ___.X_Y.firstTimeDays - time between first and order_time.')
+        #       ___.X_Y.lastTimeDays - time between last and order_time.
+        header.append('    ___.X_Y.lastTimeDays - time between last and order_time.')
+        #       ___.X_Y.proximateTimeDays - time between proximate and order_time.
+        header.append('    ___.X_Y.proximateTimeDays - time between proximate and order_time.')
+
+        return header
+
+    def _train_predictor(self):
+        self._predictor = SupervisedClassifier(algorithm=SupervisedClassifier.REGRESS_AND_ROUND)
+        self._predictor.train(self._X_train, column_or_1d(self._y_train))
+
+    def _train_test_split(self):
+        y = pd.DataFrame(self._cmm_processed.pop('I(0<=Death.postTimeDays<=28)'))
+        # Without this line, sklearn complains about the format of y.
+        # "DataConversionWarning: A column-vector y was passed when a 1d array
+        #   was expected. Please change the shape of y to (n_samples, ), for
+        #   example using ravel()."
+        # Note that this turns y into a numpy array, so need to cast back.
+        # y = y.values.ravel()
+        X = self._cmm_processed
+        self._X_train, self._X_test, self._y_train, self._y_test = train_test_split(X, y, shuffle=False)
+
+    def _impute_data(self):
+        # Impute missing values with mean value.
+        for feature in self._cmm_raw.columns.values:
+            if feature in self._FEATURES_TO_REMOVE:
+                continue
+            # If all values are null, just remove the feature.
+            # Otherwise, imputation will fail (there's no mean value),
+            # and sklearn will ragequit.
+            if self._cmm_raw[feature].isnull().all():
+                self._fmt.remove_feature(feature)
+                self._eliminated_features.append(feature)
+            # Only try to impute if some of the values are null.
+            elif self._cmm_raw[feature].isnull().any():
+                # TODO(sbala): Impute all time features with non-mean value.
+                self._fmt.impute(feature)
+
+    def _add_features(self):
+        # Add threshold feature indicating whether death date
+        # is within 28 days of index time.
+        self._fmt.add_threshold_feature('Death.postTimeDays', lower_bound=0, upper_bound=28)
+
+    def _remove_features(self):
+        # Prune obviously unhelpful fields.
+        # In theory, FeatureSelector should be able to prune these, but no
+        # reason not to help it out a little bit.
+        for feature in self._FEATURES_TO_REMOVE:
+            self._fmt.remove_feature(feature)
+
+    def _select_features(self):
+        # Use FeatureSelector to prune all but 100 variables.
+        fs = FeatureSelector(algorithm=FeatureSelector.RECURSIVE_ELIMINATION, \
+            problem=FeatureSelector.CLASSIFICATION)
+
+        fs.set_input_matrix(self._X_train, column_or_1d(self._y_train))
+        num_features_to_select = int(0.01*len(self._X_train.columns.values))
+        fs.select(k=num_features_to_select)
+
+        # Enumerate eliminated features pre-transformation.
+        self._feature_ranks = fs.compute_ranks()
+        for i in range(len(self._feature_ranks)):
+            if self._feature_ranks[i] > num_features_to_select:
+                self._eliminated_features.append(self._X_train.columns[i])
+
+        self._X_train = fs.transform_matrix(self._X_train)
+        self._X_test = fs.transform_matrix(self._X_test)
+
+    def _test_predictor(self):
+        self._accuracy = self._predictor.compute_accuracy(self._X_test, self._y_test)
+
+    def predict(self, X):
+        return self._predictor.predict(X)
+
+    def summarize(self):
+        summary_lines = list()
+
+        # Condition: condition
+        condition = self._condition
+        line = 'Condition: %s' % condition
+        summary_lines.append(line)
+
+        # Algorithm: SupervisedClassifier(algorithm)
+        algorithm = 'SupervisedClassifier(REGRESS_AND_ROUND)'
+        line = 'Algorithm: %s' % algorithm
+        summary_lines.append(line)
+
+        # Train/Test Size: training_size, test_size
+        training_size = self._X_train.shape[0]
+        test_size = self._X_test.shape[0]
+        line = 'Train/Test Size: %s/%s' % (training_size, test_size)
+        summary_lines.append(line)
+
+        # Model: sig_features
+        coefs = self._predictor.coefs()
+        cols = self._X_train.columns
+        sig_features = [(coefs[cols.get_loc(f)], f) for f in cols.values if coefs[cols.get_loc(f)] > 0]
+        linear_model = ' + '.join('%s*%s' % (weight, feature) for weight, feature in sig_features)
+        line = 'Model: logistic(%s)' % linear_model
+        summary_lines.append(line)
+
+        # Baseline Episode Mortality: episode_mortality
+        counts = self._y_test[self._y_test.columns[0]].value_counts()
+        line = 'Baseline Episode Mortality: %s/%s' % (counts[1], test_size)
+        summary_lines.append(line)
+
+        # AUC: auc
+        auc = self._predictor.compute_roc_auc(self._X_test, self._y_test)
+        line = 'AUC: %s' % auc
+        summary_lines.append(line)
+        # Accuracy: accuracy
+        line = 'Accuracy: %s' % self._accuracy
+        summary_lines.append(line)
+
+        return '\n'.join(summary_lines)
+
+if __name__=="__main__":
+    conditions = ['pneumonia', 'tuberculosis', 'meningitis', 'influenza',
+        'nephritis', 'diabetes'
+        # 'tetanus' – there aren't any tetanus mortality events.
+        ]
+    for condition in conditions:
+        predictor = ConditionMortalityPredictor(condition, 1000)
+        print(condition.summarize())
diff --git a/scripts/Archive/Covid19 CDC/cdc_tbl1.ipynb b/scripts/Archive/Covid19 CDC/cdc_tbl1.ipynb
new file mode 100644
index 00000000..7fbc7d2e
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/cdc_tbl1.ipynb	
@@ -0,0 +1,1549 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Setting up the python environment-loading modules commonly used.\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/marcelkc/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-nigam-starr' #som-nero-phi-rit\n",
+    "%load_ext google.cloud.bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/share/sw/open/anaconda/3/lib/python3.6/site-packages/google/auth/_default.py:66: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>measurement_DATE</th>\n",
+       "      <th>measurement_DATETIME</th>\n",
+       "      <th>measurement_time</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-04-21</td>\n",
+       "      <td>2020-04-21 11:21:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>10</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-04-21</td>\n",
+       "      <td>2020-04-21 19:11:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>10</td>\n",
+       "      <td>29</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-03-07</td>\n",
+       "      <td>2020-03-07 01:10:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38003563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-03-07</td>\n",
+       "      <td>2020-03-07 00:32:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38003563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-03-02</td>\n",
+       "      <td>2020-03-02 11:39:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  measurement_DATE measurement_DATETIME measurement_time value_source_value  \\\n",
+       "0       2020-04-21  2020-04-21 11:21:00             None       Not Detected   \n",
+       "1       2020-04-21  2020-04-21 19:11:00             None               None   \n",
+       "2       2020-03-07  2020-03-07 01:10:00             None       Not Detected   \n",
+       "3       2020-03-07  2020-03-07 00:32:00             None               None   \n",
+       "4       2020-03-02  2020-03-02 11:39:00             None       Not Detected   \n",
+       "\n",
+       "   year_of_birth  month_of_birth  day_of_birth  gender_concept_id  \\\n",
+       "0           1987              10            29               8532   \n",
+       "1           1987              10            29               8532   \n",
+       "2           1999               5             3               8507   \n",
+       "3           1999               5             3               8507   \n",
+       "4           1979               3            12               8507   \n",
+       "\n",
+       "   visit_concept_id  race_concept_id  ethnicity_concept_id  \n",
+       "0                 0                0                     0  \n",
+       "1                 0                0                     0  \n",
+       "2                 0                0              38003563  \n",
+       "3                 0                0              38003563  \n",
+       "4                 0             8527              38003564  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##Read in covid tests\n",
+    "query=\"\"\"SELECT measurement_DATE, measurement_DATETIME, measurement_time, value_source_value, year_of_birth, month_of_birth, day_of_birth, \n",
+    "gender_concept_id, visit_concept_id, race_concept_id, ethnicity_concept_id,\n",
+    "FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement`\n",
+    "INNER JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.person` USING (person_id)\n",
+    "INNER JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` USING (visit_occurrence_id)\n",
+    "WHERE measurement_concept_id = 706170\"\"\"\n",
+    "query_job =client.query(query)\n",
+    "test=query_job.to_dataframe()\n",
+    "test.to_csv(\"covid_tests\", sep=' ')\n",
+    "test.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f370b70bc18>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##work with data in panda to make prettier table\n",
+    "\n",
+    "## test results\n",
+    "# test['value_source_value'].value_counts()\n",
+    "positive_value_source_value = lambda x: x in ['Detected', 'DETECTED', 'Pos']\n",
+    "pos_test_bool_col = test['value_source_value'].apply(positive_value_source_value).rename('positive')\n",
+    "\n",
+    "## age results\n",
+    "import pandas as pd\n",
+    "meas_dt_col = test['measurement_DATE'].apply(lambda x : pd.to_datetime(x))\n",
+    "pat_bd_dt_col = test[['year_of_birth', 'month_of_birth', 'day_of_birth']].apply(lambda x: pd.to_datetime('-'.join([str(i) for i in x])), axis=1)\n",
+    "\n",
+    "pat_age_col = meas_dt_col - pat_bd_dt_col\n",
+    "pat_age_col = pat_age_col.apply(lambda x: x.days/365.25).rename('age')\n",
+    "\n",
+    "pat_age_col.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f371aa67fd0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "bins = [0, 5, 18, 50, 65, 75, 85, 100]\n",
+    "labels = [\"{0} - {1}\".format(i, j-1) for i, j in zip(bins[:-1], bins[1:])]\n",
+    "age_cat_col = pd.cut(pat_age_col, bins, labels=labels)\n",
+    "age_cat_col.value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"7\" halign=\"left\">positive</th>\n",
+       "      <th colspan=\"7\" halign=\"left\">tested</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <th>5 - 17</th>\n",
+       "      <th>18 - 49</th>\n",
+       "      <th>50 - 64</th>\n",
+       "      <th>65 - 74</th>\n",
+       "      <th>75 - 84</th>\n",
+       "      <th>85 - 99</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <th>5 - 17</th>\n",
+       "      <th>18 - 49</th>\n",
+       "      <th>50 - 64</th>\n",
+       "      <th>65 - 74</th>\n",
+       "      <th>75 - 84</th>\n",
+       "      <th>85 - 99</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>483.0</td>\n",
+       "      <td>202.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>989.0</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>11939.0</td>\n",
+       "      <td>3088.0</td>\n",
+       "      <td>1574.0</td>\n",
+       "      <td>918.0</td>\n",
+       "      <td>352.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9201</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>279.0</td>\n",
+       "      <td>2212.0</td>\n",
+       "      <td>982.0</td>\n",
+       "      <td>432.0</td>\n",
+       "      <td>299.0</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9202</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>1591.0</td>\n",
+       "      <td>744.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9203</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 positive                                                 \\\n",
+       "age                 0 - 4 5 - 17 18 - 49 50 - 64 65 - 74 75 - 84 85 - 99   \n",
+       "visit_concept_id                                                           \n",
+       "0                     8.0   27.0   483.0   202.0   105.0    59.0    28.0   \n",
+       "9201                  1.0    5.0    82.0    37.0    18.0    23.0     4.0   \n",
+       "9202                  1.0    4.0    39.0    34.0    10.0     4.0     2.0   \n",
+       "9203                  NaN    NaN     0.0     0.0     0.0     NaN     NaN   \n",
+       "\n",
+       "                 tested                                                   \n",
+       "age               0 - 4  5 - 17  18 - 49 50 - 64 65 - 74 75 - 84 85 - 99  \n",
+       "visit_concept_id                                                          \n",
+       "0                 989.0  1050.0  11939.0  3088.0  1574.0   918.0   352.0  \n",
+       "9201              358.0   279.0   2212.0   982.0   432.0   299.0    70.0  \n",
+       "9202               30.0    72.0   1591.0   744.0   274.0    94.0    15.0  \n",
+       "9203                NaN     NaN      2.0     2.0     2.0     NaN     NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## include visit_concept_id, pat_age_col, pos_test_bool_col\n",
+    "df = pd.concat([pos_test_bool_col, age_cat_col, test['visit_concept_id']], axis=1)\n",
+    "df['tested']=1\n",
+    "df.head()\n",
+    "\n",
+    "\n",
+    "pd.pivot_table(df, values=['tested', 'positive'], index=['visit_concept_id'],\n",
+    "                    columns=['age'], aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8532    17216\n",
+      "8507    10108\n",
+      "0          44\n",
+      "Name: gender_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f370ac02ba8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_nm = 'gender_concept_id'\n",
+    "print(test[col_nm].value_counts())\n",
+    "plt.figure()\n",
+    "test[col_nm].value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">positive</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">tested</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>8507</th>\n",
+       "      <th>8532</th>\n",
+       "      <th>0</th>\n",
+       "      <th>8507</th>\n",
+       "      <th>8532</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>405.0</td>\n",
+       "      <td>507.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>6758.0</td>\n",
+       "      <td>13112.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9201</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2305.0</td>\n",
+       "      <td>2325.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9202</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1041.0</td>\n",
+       "      <td>1777.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9203</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  positive               tested                 \n",
+       "gender_concept_id     0      8507   8532   0       8507     8532\n",
+       "visit_concept_id                                                \n",
+       "0                      0.0  405.0  507.0   40.0  6758.0  13112.0\n",
+       "9201                   0.0   94.0   76.0    2.0  2305.0   2325.0\n",
+       "9202                   0.0   42.0   52.0    2.0  1041.0   1777.0\n",
+       "9203                   NaN    0.0    0.0    NaN     4.0      2.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## include visit_concept_id, pat_age_col, pos_test_bool_col\n",
+    "df = pd.concat([pos_test_bool_col, test[col_nm], test['visit_concept_id']], axis=1)\n",
+    "df['tested']=1\n",
+    "pd.pivot_table(df, values=['tested', 'positive'], index=['visit_concept_id'],\n",
+    "                    columns=[col_nm], aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0       13185\n",
+      "8527     9587\n",
+      "8515     3498\n",
+      "8516      755\n",
+      "8557      291\n",
+      "8657       52\n",
+      "Name: race_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"6\" halign=\"left\">positive</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">tested</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>8515</th>\n",
+       "      <th>8516</th>\n",
+       "      <th>8527</th>\n",
+       "      <th>8557</th>\n",
+       "      <th>8657</th>\n",
+       "      <th>0</th>\n",
+       "      <th>8515</th>\n",
+       "      <th>8516</th>\n",
+       "      <th>8527</th>\n",
+       "      <th>8557</th>\n",
+       "      <th>8657</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>664.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11142.0</td>\n",
+       "      <td>2189.0</td>\n",
+       "      <td>471.0</td>\n",
+       "      <td>5930.0</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9201</th>\n",
+       "      <td>53.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1316.0</td>\n",
+       "      <td>761.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>2239.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9202</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>725.0</td>\n",
+       "      <td>548.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>1414.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9203</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 positive                               tested                 \\\n",
+       "race_concept_id      0     8515 8516   8527 8557 8657     0       8515   8516   \n",
+       "visit_concept_id                                                                \n",
+       "0                   664.0  59.0  8.0  175.0  3.0  3.0  11142.0  2189.0  471.0   \n",
+       "9201                 53.0  32.0  2.0   76.0  7.0  0.0   1316.0   761.0  195.0   \n",
+       "9202                 22.0  18.0  2.0   50.0  2.0  0.0    725.0   548.0   89.0   \n",
+       "9203                  0.0   NaN  NaN    0.0  NaN  NaN      2.0     NaN    NaN   \n",
+       "\n",
+       "                                       \n",
+       "race_concept_id     8527   8557  8657  \n",
+       "visit_concept_id                       \n",
+       "0                 5930.0  149.0  29.0  \n",
+       "9201              2239.0  112.0   9.0  \n",
+       "9202              1414.0   30.0  14.0  \n",
+       "9203                 4.0    NaN   NaN  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_nm = 'race_concept_id'\n",
+    "print(test[col_nm].value_counts())\n",
+    "plt.figure()\n",
+    "test[col_nm].value_counts().plot(kind='bar')\n",
+    "## include visit_concept_id, pat_age_col, pos_test_bool_col\n",
+    "df = pd.concat([pos_test_bool_col, test[col_nm], test['visit_concept_id']], axis=1)\n",
+    "df['tested']=1\n",
+    "pd.pivot_table(df, values=['tested', 'positive'], index=['visit_concept_id'],\n",
+    "                    columns=[col_nm], aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38003564    14610\n",
+      "0            9428\n",
+      "38003563     3330\n",
+      "Name: ethnicity_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">positive</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">tested</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>38003563</th>\n",
+       "      <th>38003564</th>\n",
+       "      <th>0</th>\n",
+       "      <th>38003563</th>\n",
+       "      <th>38003564</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>564.0</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>8972.0</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>8930.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9201</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>1009.0</td>\n",
+       "      <td>3561.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9202</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>392.0</td>\n",
+       "      <td>313.0</td>\n",
+       "      <td>2115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9203</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     positive                     tested                  \n",
+       "ethnicity_concept_id 0        38003563 38003564 0        38003563 38003564\n",
+       "visit_concept_id                                                          \n",
+       "0                       564.0    116.0    232.0   8972.0   2008.0   8930.0\n",
+       "9201                      1.0     49.0    120.0     62.0   1009.0   3561.0\n",
+       "9202                      9.0     12.0     73.0    392.0    313.0   2115.0\n",
+       "9203                      0.0      NaN      0.0      2.0      NaN      4.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_nm = 'ethnicity_concept_id'\n",
+    "print(test[col_nm].value_counts())\n",
+    "plt.figure()\n",
+    "test[col_nm].value_counts().plot(kind='bar')\n",
+    "## include visit_concept_id, pat_age_col, pos_test_bool_col\n",
+    "df = pd.concat([pos_test_bool_col, test[col_nm], test['visit_concept_id']], axis=1)\n",
+    "df['tested']=1\n",
+    "pd.pivot_table(df, values=['tested', 'positive'], index=['visit_concept_id'],\n",
+    "                    columns=[col_nm], aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"4\" halign=\"left\">positive</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">tested</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">0</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5 - 17</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>610.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18 - 49</th>\n",
+       "      <td>350.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6321.0</td>\n",
+       "      <td>664.0</td>\n",
+       "      <td>458.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 - 64</th>\n",
+       "      <td>146.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1759.0</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 - 74</th>\n",
+       "      <td>78.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>948.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75 - 84</th>\n",
+       "      <td>41.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>606.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85 - 99</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">8515</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5 - 17</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18 - 49</th>\n",
+       "      <td>41.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1563.0</td>\n",
+       "      <td>429.0</td>\n",
+       "      <td>378.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 - 64</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>242.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 - 74</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75 - 84</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85 - 99</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">8516</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5 - 17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18 - 49</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>297.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 - 64</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 - 74</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75 - 84</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85 - 99</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">8527</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>216.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5 - 17</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>315.0</td>\n",
+       "      <td>133.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18 - 49</th>\n",
+       "      <td>87.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3641.0</td>\n",
+       "      <td>984.0</td>\n",
+       "      <td>678.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 - 64</th>\n",
+       "      <td>41.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>986.0</td>\n",
+       "      <td>523.0</td>\n",
+       "      <td>439.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 - 74</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>467.0</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>188.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75 - 84</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>234.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85 - 99</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">8557</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5 - 17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18 - 49</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 - 64</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 - 74</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75 - 84</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">8657</th>\n",
+       "      <th>0 - 4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18 - 49</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50 - 64</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65 - 74</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75 - 84</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        positive                   tested                   \n",
+       "visit_concept_id            0     9201  9202 9203    0      9201   9202 9203\n",
+       "race_concept_id age                                                         \n",
+       "0               0 - 4        6.0   0.0   0.0  NaN   640.0  184.0   10.0  NaN\n",
+       "                5 - 17      20.0   3.0   0.0  NaN   610.0  102.0   24.0  NaN\n",
+       "                18 - 49    350.0  26.0  12.0  0.0  6321.0  664.0  458.0  2.0\n",
+       "                50 - 64    146.0  12.0   7.0  NaN  1759.0  222.0  157.0  NaN\n",
+       "                65 - 74     78.0   4.0   1.0  NaN   948.0   89.0   50.0  NaN\n",
+       "                75 - 84     41.0   8.0   1.0  NaN   606.0   49.0   18.0  NaN\n",
+       "                85 - 99     23.0   0.0   1.0  NaN   258.0    6.0    8.0  NaN\n",
+       "8515            0 - 4        0.0   0.0   0.0  NaN    99.0   48.0    2.0  NaN\n",
+       "                5 - 17       2.0   0.0   1.0  NaN   101.0   36.0   14.0  NaN\n",
+       "                18 - 49     41.0  11.0  11.0  NaN  1563.0  429.0  378.0  NaN\n",
+       "                50 - 64     12.0  11.0   6.0  NaN   242.0  141.0  122.0  NaN\n",
+       "                65 - 74      4.0   5.0   0.0  NaN   107.0   51.0   20.0  NaN\n",
+       "                75 - 84      0.0   5.0   0.0  NaN    62.0   46.0   10.0  NaN\n",
+       "                85 - 99      0.0   0.0   0.0  NaN    15.0   10.0    2.0  NaN\n",
+       "8516            0 - 4        0.0   0.0   0.0  NaN    28.0   10.0    2.0  NaN\n",
+       "                5 - 17       0.0   0.0   0.0  NaN    10.0    2.0    6.0  NaN\n",
+       "                18 - 49      4.0   1.0   1.0  NaN   297.0   80.0   49.0  NaN\n",
+       "                50 - 64      1.0   1.0   0.0  NaN    79.0   60.0   18.0  NaN\n",
+       "                65 - 74      1.0   0.0   1.0  NaN    41.0   35.0   12.0  NaN\n",
+       "                75 - 84      2.0   0.0   0.0  NaN     8.0    8.0    2.0  NaN\n",
+       "                85 - 99      0.0   NaN   NaN  NaN     8.0    NaN    NaN  NaN\n",
+       "8527            0 - 4        2.0   1.0   1.0  NaN   216.0  112.0   16.0  NaN\n",
+       "                5 - 17       4.0   1.0   3.0  NaN   315.0  133.0   24.0  NaN\n",
+       "                18 - 49     87.0  42.0  15.0  NaN  3641.0  984.0  678.0  NaN\n",
+       "                50 - 64     41.0  11.0  20.0  0.0   986.0  523.0  439.0  2.0\n",
+       "                65 - 74     22.0   8.0   7.0  0.0   467.0  243.0  188.0  2.0\n",
+       "                75 - 84     14.0   9.0   3.0  NaN   234.0  190.0   64.0  NaN\n",
+       "                85 - 99      5.0   4.0   1.0  NaN    71.0   54.0    5.0  NaN\n",
+       "8557            0 - 4        0.0   0.0   NaN  NaN     4.0    3.0    NaN  NaN\n",
+       "                5 - 17       1.0   1.0   0.0  NaN    14.0    6.0    4.0  NaN\n",
+       "                18 - 49      1.0   2.0   0.0  NaN   102.0   49.0   20.0  NaN\n",
+       "                50 - 64      1.0   2.0   1.0  NaN    20.0   34.0    4.0  NaN\n",
+       "                65 - 74      0.0   1.0   1.0  NaN     5.0   14.0    2.0  NaN\n",
+       "                75 - 84      0.0   1.0   NaN  NaN     4.0    6.0    NaN  NaN\n",
+       "8657            0 - 4        0.0   0.0   NaN  NaN     2.0    1.0    NaN  NaN\n",
+       "                18 - 49      0.0   0.0   0.0  NaN    15.0    6.0    8.0  NaN\n",
+       "                50 - 64      1.0   0.0   0.0  NaN     2.0    2.0    4.0  NaN\n",
+       "                65 - 74      0.0   NaN   0.0  NaN     6.0    NaN    2.0  NaN\n",
+       "                75 - 84      2.0   NaN   NaN  NaN     4.0    NaN    NaN  NaN"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## include visit_concept_id, pat_age_col, pos_test_bool_col\n",
+    "df = pd.concat([pos_test_bool_col, age_cat_col, test['visit_concept_id'], test['race_concept_id']], axis=1)\n",
+    "df['tested']=1\n",
+    "df.head()\n",
+    "\n",
+    "\n",
+    "pd.pivot_table(df, values=['tested', 'positive'], index=['race_concept_id','age'],\n",
+    "                    columns=['visit_concept_id'], aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#, ,\n",
+    "#\n",
+    "# for ind_visit, grp_visit in df.groupby('visit_concept_id'):\n",
+    "#     for ind_age, grp_age in grp_visit.groupby('age'):\n",
+    "#         grp_age"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/Covid19 CDC/coinfectionQuery.sql b/scripts/Archive/Covid19 CDC/coinfectionQuery.sql
new file mode 100644
index 00000000..2def4a24
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/coinfectionQuery.sql	
@@ -0,0 +1,61 @@
+-- Draft queries of STARR-OMOP database to pull out number of 
+--  co-tests for SARS-CoV2 and other viruses, and how often see (co)infections with each
+
+/*
+select *
+from `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` as measConc
+where concept_id IN (706170)
+*/
+With
+sarscov2meas AS
+(
+  SELECT *
+  FROM
+  `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement` as meas
+  WHERE
+      measurement_concept_id IN (706170) -- SARS-Cov2-NAA
+      and value_source_value is not null
+      and meas.measurement_DATE >= '2020-02-01'
+),
+anySameDayVirusMeas AS
+(
+  SELECT meas.measurement_id, meas.person_id, meas.measurement_concept_id, measConc.concept_name, meas.measurement_DATE, meas.measurement_datetime, 
+         meas.value_source_value in ('Detected','Pos','Positive') as virusDetected, 
+         sarscov2meas.value_source_value in ('Detected','Pos','Positive') as sarscov2Detected, 
+         count(*)
+  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement` as meas
+     join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` as measConc ON (meas.measurement_concept_id = measConc.concept_id )
+     join sarscov2meas on meas.person_id = sarscov2meas.person_id and meas.measurement_DATE = sarscov2meas.measurement_DATE 
+  WHERE 
+     lower(measConc.concept_name) like '%naa with probe detection%'
+     and meas.measurement_concept_id <> 706170
+     and meas.value_source_value is not null
+  GROUP BY meas.measurement_id, meas.person_id, meas.measurement_concept_id, measConc.concept_name, meas.measurement_DATE, meas.measurement_datetime, meas.value_source_value, sarscov2meas.value_source_value 
+  ORDER BY meas.person_id, meas.measurement_DATETIME desc
+),
+virusDetectedCounts AS
+(
+  select person_id, measurement_date, countif(virusDetected) > 0 as anyOtherVirusDetected, sarscov2Detected 
+  from anySameDayVirusMeas 
+  group by person_id, measurement_DATE, sarscov2Detected 
+)
+select 
+   count(sarscov2Detected) as nCoTests,
+   countif(anyOtherVirusDetected) as nAnyOtherVirus,
+   countif(sarscov2Detected) as nSARSCoV2,
+   countif(anyOtherVirusDetected and sarscov2Detected) as nCoinfection
+from virusDetectedCounts 
+limit 100
+
+/*
+SELECT value_source_value, count(*)
+from anySameDayVirusMeas 
+group by value_source_value 
+order by count(*) desc
+
+--('Detected','Pos','Positive')
+
+limit 100
+*/
+
+
diff --git a/scripts/Archive/Covid19 CDC/git_export.sh b/scripts/Archive/Covid19 CDC/git_export.sh
new file mode 100755
index 00000000..3315ae89
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/git_export.sh	
@@ -0,0 +1,4 @@
+while read filename; do git add $filename; done < "export_list.txt"
+git status
+git commit -m "update " 
+git push origin master
diff --git a/scripts/Archive/Covid19 CDC/long_tbl.ipynb b/scripts/Archive/Covid19 CDC/long_tbl.ipynb
new file mode 100644
index 00000000..fae46a4f
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/long_tbl.ipynb	
@@ -0,0 +1,2167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Setting up the python environment-loading modules commonly used.\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/marcelkc/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-nigam-starr' #som-nero-phi-rit\n",
+    "%load_ext google.cloud.bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/share/sw/open/anaconda/3/lib/python3.6/site-packages/google/auth/_default.py:66: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Read in covid tests\n",
+    "query=\"\"\"SELECT \n",
+    "v.person_id, \n",
+    "visit_start_DATETIME, \n",
+    "ARI_symptoms_2_months, \n",
+    "race_concept_id, ethnicity_concept_id, # race/ethnicity\n",
+    "gender_concept_id, # gender\n",
+    "year_of_birth, month_of_birth, day_of_birth, # age at present\n",
+    "visit_concept_id, care_site_name, # location\n",
+    "value_source_value, # test result\n",
+    "visit_occurrence_id\n",
+    "FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` v\n",
+    "\n",
+    "LEFT JOIN(\n",
+    "  #visit_occurrence_id (unique) to ARI_symptoms\n",
+    "  SELECT \n",
+    "  visit_occurrence_id,\n",
+    "  #ARI symptoms # any condition_start_DATETIME, condition_end_DATETIME within 2 months\n",
+    "  max(\n",
+    "  DATETIME_DIFF(visit_start_DATETIME, condition_start_DATETIME, MONTH) < 2\n",
+    "  OR\n",
+    "  DATETIME_DIFF(visit_start_DATETIME, condition_end_DATETIME, MONTH) < 2 \n",
+    "  )\n",
+    "  AS ARI_symptoms_2_months,\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` v\n",
+    "  LEFT JOIN (\n",
+    "    SELECT person_id, condition_start_DATETIME, condition_end_DATETIME, \n",
+    "    condition_source_value, condition_source_concept_id # might get used later\n",
+    "    FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.condition_occurrence`\n",
+    "    WHERE (condition_start_DATETIME >= '2019-11-01T00:00:00.000' OR condition_end_DATETIME >= '2019-11-01T00:00:00.000')\n",
+    "    AND\n",
+    "    (condition_source_value LIKE 'J00.%' OR\n",
+    "    condition_source_value LIKE 'J01.%' OR\n",
+    "    condition_source_value LIKE 'J02.%' OR\n",
+    "    condition_source_value LIKE 'J03.%' OR\n",
+    "    condition_source_value LIKE 'J04.%' OR\n",
+    "    condition_source_value LIKE 'J05.%' OR\n",
+    "    condition_source_value LIKE 'J06.%' OR\n",
+    "    condition_source_value LIKE 'J09.%' OR\n",
+    "    condition_source_value LIKE 'J10.%' OR\n",
+    "    condition_source_value LIKE 'J11.%' OR\n",
+    "    condition_source_value LIKE 'J12.%' OR\n",
+    "    condition_source_value LIKE 'J13.%' OR\n",
+    "    condition_source_value LIKE 'J14.%' OR\n",
+    "    condition_source_value LIKE 'J15.%' OR\n",
+    "    condition_source_value LIKE 'J16.%' OR\n",
+    "    condition_source_value LIKE 'J17.%' OR\n",
+    "    condition_source_value LIKE 'J18.%' OR\n",
+    "    condition_source_value LIKE 'J20.%' OR\n",
+    "    condition_source_value LIKE 'J21.%' OR\n",
+    "    condition_source_value LIKE 'J22' OR\n",
+    "    condition_source_value LIKE 'J80' OR\n",
+    "    condition_source_value LIKE 'A37.91' OR\n",
+    "    condition_source_value LIKE 'A37.01' OR\n",
+    "    condition_source_value LIKE 'A37.11' OR\n",
+    "    condition_source_value LIKE 'A37.81' OR\n",
+    "    condition_source_value LIKE 'A48.1' OR\n",
+    "    condition_source_value LIKE 'B25.0' OR\n",
+    "    condition_source_value LIKE 'B44.0' OR\n",
+    "    condition_source_value LIKE 'B97.4' OR\n",
+    "    condition_source_value LIKE 'U07.1')\n",
+    "  ) USING (person_id)\n",
+    "  WHERE visit_start_DATETIME >= '2020-02-01T00:00:00.000' # all encounters from feb 1\n",
+    "  GROUP BY visit_occurrence_id\n",
+    "  )\n",
+    "USING (visit_occurrence_id)\n",
+    "\n",
+    "\n",
+    "LEFT JOIN (\n",
+    "  # visit_occurrence_id to covid test result (value_source_value) \n",
+    "  SELECT value_source_value, # test result\n",
+    "  visit_occurrence_id # for mapping(s)\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement`\n",
+    "  WHERE measurement_concept_id = 706170\n",
+    ") \n",
+    "USING (visit_occurrence_id)\n",
+    "\n",
+    "INNER JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.person` USING (person_id)\n",
+    "\n",
+    "LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.care_site` c  \n",
+    "ON c.care_site_id = v.care_site_id\n",
+    "\n",
+    "WHERE visit_start_DATETIME >= '2020-02-01T00:00:00.000' # all encounters from feb 1\"\"\"\n",
+    "query_job =client.query(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|██████████| 1835715/1835715 [03:35<00:00, 7843.19rows/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test=query_job.to_dataframe(progress_bar_type='tqdm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-02-21</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159169916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-04-11</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>163698599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32536564</td>\n",
+       "      <td>2020-02-18</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CCSB THIRD FLOOR CLINIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160673028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>32135543</td>\n",
+       "      <td>2020-03-17</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>GASTROENTEROLOGY VCL 1133 207</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160992998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>30291433</td>\n",
+       "      <td>2020-03-12</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY CT 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160546382</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   person_id visit_start_DATETIME ARI_symptoms_2_months  race_concept_id  \\\n",
+       "0   31449046           2020-02-21                  None             8527   \n",
+       "1   31449046           2020-04-11                  None             8527   \n",
+       "2   32536564           2020-02-18                  True             8527   \n",
+       "3   32135543           2020-03-17                 False             8527   \n",
+       "4   30291433           2020-03-12                  None             8527   \n",
+       "\n",
+       "   ethnicity_concept_id  gender_concept_id  year_of_birth  month_of_birth  \\\n",
+       "0              38003564               8532           1930               4   \n",
+       "1              38003564               8532           1930               4   \n",
+       "2              38003564               8532           1932               9   \n",
+       "3              38003564               8532           1930              12   \n",
+       "4              38003564               8507           1931               2   \n",
+       "\n",
+       "   day_of_birth  visit_concept_id                          care_site_name  \\\n",
+       "0            29                 0  RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC   \n",
+       "1            29                 0   RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P   \n",
+       "2             9                 0                 CCSB THIRD FLOOR CLINIC   \n",
+       "3             5                 0           GASTROENTEROLOGY VCL 1133 207   \n",
+       "4            23                 0           RADIOLOGY CT 1ST FLOOR - 500P   \n",
+       "\n",
+       "  value_source_value  visit_occurrence_id  \n",
+       "0               None            159169916  \n",
+       "1               None            163698599  \n",
+       "2               None            160673028  \n",
+       "3               None            160992998  \n",
+       "4               None            160546382  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.to_csv(\"long_table_cdc.csv\", sep=';')\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2307e9d68>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##work with data in panda to make prettier table\n",
+    "\n",
+    "## test results\n",
+    "# test['value_source_value'].value_counts()\n",
+    "positive_value_source_value = lambda x: x in ['Detected', 'DETECTED', 'Pos']\n",
+    "pos_test_bool_col = test['value_source_value'].apply(positive_value_source_value).rename('positive')\n",
+    "\n",
+    "\n",
+    "## age results\n",
+    "import pandas as pd\n",
+    "meas_dt_col = test['visit_start_DATETIME'].apply(lambda x : pd.to_datetime(x))\n",
+    "pat_bd_dt_col = test[['year_of_birth', 'month_of_birth', 'day_of_birth']].apply(lambda x: pd.to_datetime('-'.join([str(i) for i in x])), axis=1)\n",
+    "\n",
+    "pat_age_col = meas_dt_col - pat_bd_dt_col\n",
+    "pat_age_col = pat_age_col.apply(lambda x: x.days/365.25).rename('age')\n",
+    "\n",
+    "# for all \"visits\" since February\n",
+    "pat_age_col.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tested = lambda x: x in ['Detected', 'DETECTED', 'Pos']\n",
+    "test_bool_col = test['value_source_value'].notnull().rename('tested')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd223f99da0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "bins = [0, 5, 18, 50, 65, 75, 85, 100]\n",
+    "labels = [\"[{0}, {1})\".format(i, j) for i, j in zip(bins[:-1], bins[1:])]\n",
+    "age_cat_col = pd.cut(pat_age_col, bins, labels=labels)\n",
+    "age_cat_col.value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ARI_2_mon = test['ARI_symptoms_2_months'] == True\n",
+    "ARI_2_mon=ARI_2_mon.rename('ARI_symptoms_2_months_bool')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>positive</th>\n",
+       "      <th>tested</th>\n",
+       "      <th>age</th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "      <th>ARI_symptoms_2_months_bool</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-02-21</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159169916</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-04-11</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>163698599</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32536564</td>\n",
+       "      <td>2020-02-18</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CCSB THIRD FLOOR CLINIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160673028</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32135543</td>\n",
+       "      <td>2020-03-17</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>GASTROENTEROLOGY VCL 1133 207</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160992998</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>30291433</td>\n",
+       "      <td>2020-03-12</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY CT 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160546382</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   positive  tested        age  person_id visit_start_DATETIME  \\\n",
+       "0     False   False  [85, 100)   31449046           2020-02-21   \n",
+       "1     False   False  [85, 100)   31449046           2020-04-11   \n",
+       "2     False   False  [85, 100)   32536564           2020-02-18   \n",
+       "3     False   False  [85, 100)   32135543           2020-03-17   \n",
+       "4     False   False  [85, 100)   30291433           2020-03-12   \n",
+       "\n",
+       "  ARI_symptoms_2_months  race_concept_id  ethnicity_concept_id  \\\n",
+       "0                  None             8527              38003564   \n",
+       "1                  None             8527              38003564   \n",
+       "2                  True             8527              38003564   \n",
+       "3                 False             8527              38003564   \n",
+       "4                  None             8527              38003564   \n",
+       "\n",
+       "   gender_concept_id  year_of_birth  month_of_birth  day_of_birth  \\\n",
+       "0               8532           1930               4            29   \n",
+       "1               8532           1930               4            29   \n",
+       "2               8532           1932               9             9   \n",
+       "3               8532           1930              12             5   \n",
+       "4               8507           1931               2            23   \n",
+       "\n",
+       "   visit_concept_id                          care_site_name  \\\n",
+       "0                 0  RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC   \n",
+       "1                 0   RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P   \n",
+       "2                 0                 CCSB THIRD FLOOR CLINIC   \n",
+       "3                 0           GASTROENTEROLOGY VCL 1133 207   \n",
+       "4                 0           RADIOLOGY CT 1ST FLOOR - 500P   \n",
+       "\n",
+       "  value_source_value  visit_occurrence_id  ARI_symptoms_2_months_bool  total  \n",
+       "0               None            159169916                       False      1  \n",
+       "1               None            163698599                       False      1  \n",
+       "2               None            160673028                        True      1  \n",
+       "3               None            160992998                       False      1  \n",
+       "4               None            160546382                       False      1  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## group_play\n",
+    "\n",
+    "df_all = pd.concat([pos_test_bool_col, test_bool_col, age_cat_col, test, ARI_2_mon], axis=1)\n",
+    "df_all['total'] = 1\n",
+    "df_all.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_col(col_nm):\n",
+    "    print(df_all[col_nm].value_counts())\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    df_all[col_nm].value_counts().plot(kind='bar')\n",
+    "    \n",
+    "    tbl = pd.pivot_table(df_all, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                        columns=[col_nm], aggfunc=np.sum)\n",
+    "    return tbl.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18, 50)     590714\n",
+      "[50, 65)     380166\n",
+      "[65, 75)     290354\n",
+      "[5, 18)      225277\n",
+      "[75, 85)     179987\n",
+      "[0, 5)       124777\n",
+      "[85, 100)     42718\n",
+      "Name: age, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>19269.0</td>\n",
+       "      <td>1858.0</td>\n",
+       "      <td>8773.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>19235.0</td>\n",
+       "      <td>1768.0</td>\n",
+       "      <td>9198.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>34834.0</td>\n",
+       "      <td>5575.0</td>\n",
+       "      <td>13642.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>19558.0</td>\n",
+       "      <td>3602.0</td>\n",
+       "      <td>7340.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>13627.0</td>\n",
+       "      <td>2470.0</td>\n",
+       "      <td>4234.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>7531.0</td>\n",
+       "      <td>1356.0</td>\n",
+       "      <td>2335.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>2181.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>627.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">positive</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>579.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>241.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>128.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">tested</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>583.0</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>679.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>6706.0</td>\n",
+       "      <td>1317.0</td>\n",
+       "      <td>929.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>2066.0</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>447.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>291.0</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>596.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>220.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">total</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>85049.0</td>\n",
+       "      <td>6717.0</td>\n",
+       "      <td>33011.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>156343.0</td>\n",
+       "      <td>14974.0</td>\n",
+       "      <td>53959.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>425520.0</td>\n",
+       "      <td>49585.0</td>\n",
+       "      <td>115510.0</td>\n",
+       "      <td>99.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>264266.0</td>\n",
+       "      <td>39807.0</td>\n",
+       "      <td>76032.0</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>202396.0</td>\n",
+       "      <td>31191.0</td>\n",
+       "      <td>56725.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>126003.0</td>\n",
+       "      <td>17882.0</td>\n",
+       "      <td>36074.0</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>30676.0</td>\n",
+       "      <td>3472.0</td>\n",
+       "      <td>8561.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                          0        9201      9202  9203\n",
+       "                           age                                         \n",
+       "ARI_symptoms_2_months_bool [0, 5)      19269.0   1858.0    8773.0   NaN\n",
+       "                           [5, 18)     19235.0   1768.0    9198.0   0.0\n",
+       "                           [18, 50)    34834.0   5575.0   13642.0  16.0\n",
+       "                           [50, 65)    19558.0   3602.0    7340.0  13.0\n",
+       "                           [65, 75)    13627.0   2470.0    4234.0   2.0\n",
+       "                           [75, 85)     7531.0   1356.0    2335.0   2.0\n",
+       "                           [85, 100)    2181.0    340.0     627.0   1.0\n",
+       "positive                   [0, 5)          8.0      1.0       1.0   NaN\n",
+       "                           [5, 18)        35.0      5.0       4.0   0.0\n",
+       "                           [18, 50)      579.0    100.0      46.0   0.0\n",
+       "                           [50, 65)      241.0     45.0      38.0   0.0\n",
+       "                           [65, 75)      128.0     21.0      12.0   0.0\n",
+       "                           [75, 85)       76.0     24.0       5.0   0.0\n",
+       "                           [85, 100)      34.0      7.0       2.0   0.0\n",
+       "tested                     [0, 5)        583.0    206.0      20.0   NaN\n",
+       "                           [5, 18)       679.0    174.0      43.0   0.0\n",
+       "                           [18, 50)     6706.0   1317.0     929.0   1.0\n",
+       "                           [50, 65)     2066.0    597.0     447.0   1.0\n",
+       "                           [65, 75)     1055.0    291.0     162.0   1.0\n",
+       "                           [75, 85)      596.0    187.0      57.0   0.0\n",
+       "                           [85, 100)     220.0     54.0      10.0   0.0\n",
+       "total                      [0, 5)      85049.0   6717.0   33011.0   NaN\n",
+       "                           [5, 18)    156343.0  14974.0   53959.0   1.0\n",
+       "                           [18, 50)   425520.0  49585.0  115510.0  99.0\n",
+       "                           [50, 65)   264266.0  39807.0   76032.0  61.0\n",
+       "                           [65, 75)   202396.0  31191.0   56725.0  42.0\n",
+       "                           [75, 85)   126003.0  17882.0   36074.0  28.0\n",
+       "                           [85, 100)   30676.0   3472.0    8561.0   9.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col('age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "visualize_col('age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>19269.0</td>\n",
+       "      <td>1858.0</td>\n",
+       "      <td>8773.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>19235.0</td>\n",
+       "      <td>1768.0</td>\n",
+       "      <td>9198.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>34834.0</td>\n",
+       "      <td>5575.0</td>\n",
+       "      <td>13642.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>19558.0</td>\n",
+       "      <td>3602.0</td>\n",
+       "      <td>7340.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>13627.0</td>\n",
+       "      <td>2470.0</td>\n",
+       "      <td>4234.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>7531.0</td>\n",
+       "      <td>1356.0</td>\n",
+       "      <td>2335.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>2181.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>627.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">positive</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>579.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>241.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>128.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">tested</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>583.0</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>679.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>6706.0</td>\n",
+       "      <td>1317.0</td>\n",
+       "      <td>929.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>2066.0</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>447.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>291.0</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>596.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>220.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">total</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>85049.0</td>\n",
+       "      <td>6717.0</td>\n",
+       "      <td>33011.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>156343.0</td>\n",
+       "      <td>14974.0</td>\n",
+       "      <td>53959.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>425520.0</td>\n",
+       "      <td>49585.0</td>\n",
+       "      <td>115510.0</td>\n",
+       "      <td>99.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>264266.0</td>\n",
+       "      <td>39807.0</td>\n",
+       "      <td>76032.0</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>202396.0</td>\n",
+       "      <td>31191.0</td>\n",
+       "      <td>56725.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>126003.0</td>\n",
+       "      <td>17882.0</td>\n",
+       "      <td>36074.0</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>30676.0</td>\n",
+       "      <td>3472.0</td>\n",
+       "      <td>8561.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                          0        9201      9202  9203\n",
+       "                           age                                         \n",
+       "ARI_symptoms_2_months_bool [0, 5)      19269.0   1858.0    8773.0   NaN\n",
+       "                           [5, 18)     19235.0   1768.0    9198.0   0.0\n",
+       "                           [18, 50)    34834.0   5575.0   13642.0  16.0\n",
+       "                           [50, 65)    19558.0   3602.0    7340.0  13.0\n",
+       "                           [65, 75)    13627.0   2470.0    4234.0   2.0\n",
+       "                           [75, 85)     7531.0   1356.0    2335.0   2.0\n",
+       "                           [85, 100)    2181.0    340.0     627.0   1.0\n",
+       "positive                   [0, 5)          8.0      1.0       1.0   NaN\n",
+       "                           [5, 18)        35.0      5.0       4.0   0.0\n",
+       "                           [18, 50)      579.0    100.0      46.0   0.0\n",
+       "                           [50, 65)      241.0     45.0      38.0   0.0\n",
+       "                           [65, 75)      128.0     21.0      12.0   0.0\n",
+       "                           [75, 85)       76.0     24.0       5.0   0.0\n",
+       "                           [85, 100)      34.0      7.0       2.0   0.0\n",
+       "tested                     [0, 5)        583.0    206.0      20.0   NaN\n",
+       "                           [5, 18)       679.0    174.0      43.0   0.0\n",
+       "                           [18, 50)     6706.0   1317.0     929.0   1.0\n",
+       "                           [50, 65)     2066.0    597.0     447.0   1.0\n",
+       "                           [65, 75)     1055.0    291.0     162.0   1.0\n",
+       "                           [75, 85)      596.0    187.0      57.0   0.0\n",
+       "                           [85, 100)     220.0     54.0      10.0   0.0\n",
+       "total                      [0, 5)      85049.0   6717.0   33011.0   NaN\n",
+       "                           [5, 18)    156343.0  14974.0   53959.0   1.0\n",
+       "                           [18, 50)   425520.0  49585.0  115510.0  99.0\n",
+       "                           [50, 65)   264266.0  39807.0   76032.0  61.0\n",
+       "                           [65, 75)   202396.0  31191.0   56725.0  42.0\n",
+       "                           [75, 85)   126003.0  17882.0   36074.0  28.0\n",
+       "                           [85, 100)   30676.0   3472.0    8561.0   9.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbl1 = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                    columns=['age'], aggfunc=np.sum)\n",
+    "\n",
+    "tbl1.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_col(col_nm):\n",
+    "    print(test[col_nm].value_counts())\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    test[col_nm].value_counts().plot(kind='bar')\n",
+    "    \n",
+    "    tbl = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                        columns=[col_nm], aggfunc=np.sum)\n",
+    "    return tbl.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8527    934994\n",
+      "0       472445\n",
+      "8515    326809\n",
+      "8516     75473\n",
+      "8557     20227\n",
+      "8657      5767\n",
+      "Name: race_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>31537.0</td>\n",
+       "      <td>4125.0</td>\n",
+       "      <td>13448.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>21163.0</td>\n",
+       "      <td>3141.0</td>\n",
+       "      <td>8511.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>5508.0</td>\n",
+       "      <td>808.0</td>\n",
+       "      <td>1860.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>56254.0</td>\n",
+       "      <td>8590.0</td>\n",
+       "      <td>21770.0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>1483.0</td>\n",
+       "      <td>279.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>304.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>828.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>62.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>192.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>6881.0</td>\n",
+       "      <td>793.0</td>\n",
+       "      <td>441.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>1213.0</td>\n",
+       "      <td>461.0</td>\n",
+       "      <td>306.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>278.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>3436.0</td>\n",
+       "      <td>1370.0</td>\n",
+       "      <td>834.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>79.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>333947.0</td>\n",
+       "      <td>39464.0</td>\n",
+       "      <td>98994.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>228042.0</td>\n",
+       "      <td>30202.0</td>\n",
+       "      <td>68525.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>53864.0</td>\n",
+       "      <td>6275.0</td>\n",
+       "      <td>15319.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>657172.0</td>\n",
+       "      <td>85574.0</td>\n",
+       "      <td>192107.0</td>\n",
+       "      <td>141.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>14452.0</td>\n",
+       "      <td>2051.0</td>\n",
+       "      <td>3720.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>4052.0</td>\n",
+       "      <td>508.0</td>\n",
+       "      <td>1207.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                0        9201      9202   9203\n",
+       "                           race_concept_id                                    \n",
+       "ARI_symptoms_2_months_bool 0                 31537.0   4125.0   13448.0    2.0\n",
+       "                           8515              21163.0   3141.0    8511.0    8.0\n",
+       "                           8516               5508.0    808.0    1860.0    2.0\n",
+       "                           8527              56254.0   8590.0   21770.0   22.0\n",
+       "                           8557               1483.0    279.0     440.0    0.0\n",
+       "                           8657                304.0     35.0     120.0    NaN\n",
+       "positive                   0                   828.0     59.0      26.0    0.0\n",
+       "                           8515                 62.0     41.0      19.0    0.0\n",
+       "                           8516                 13.0      4.0       6.0    0.0\n",
+       "                           8527                192.0     92.0      55.0    0.0\n",
+       "                           8557                  3.0      7.0       2.0    0.0\n",
+       "                           8657                  3.0      0.0       0.0    NaN\n",
+       "tested                     0                  6881.0    793.0     441.0    1.0\n",
+       "                           8515               1213.0    461.0     306.0    0.0\n",
+       "                           8516                278.0    131.0      61.0    0.0\n",
+       "                           8527               3436.0   1370.0     834.0    2.0\n",
+       "                           8557                 79.0     65.0      17.0    0.0\n",
+       "                           8657                 18.0      6.0       9.0    NaN\n",
+       "total                      0                333947.0  39464.0   98994.0   40.0\n",
+       "                           8515             228042.0  30202.0   68525.0   40.0\n",
+       "                           8516              53864.0   6275.0   15319.0   15.0\n",
+       "                           8527             657172.0  85574.0  192107.0  141.0\n",
+       "                           8557              14452.0   2051.0    3720.0    4.0\n",
+       "                           8657               4052.0    508.0    1207.0    NaN"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAELCAYAAAAlTtoUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAEshJREFUeJzt3X2w5mVdx/H3R1YQRAFhJd2lFnNT8KHEE5JWf0ijizquUzpiTq4OtpNBWjSTaw9Do+XgpJFORu0EgjOOSKS5kwqSD5Uz8nDWJ4TV2NBgxXRxgSwrRb/9cV/b3B7uPedadvf8zn3zfs2cOb/f93f97uu65t6zn/N7uk+qCkmSejxk6AFIkqaHoSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqduqoQdwsJ1wwgm1bt26oYchSVNl+/btd1XV6qXazVxorFu3jvn5+aGHIUlTJcm/9bTz9JQkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG4z93DfA7Vuy4eWtb+vXvj8Ze1Pkg4GjzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSt67QSPJbSW5O8sUk703ysCQnJ7k+ya1J3pfk8Nb2iLa+s21fN/Y6b2j1Lyd57lh9Q6vtTLJlrD6xD0nSMJYMjSRrgNcCc1X1ZOAw4GzgLcBFVbUeuBs4p+1yDnB3VT0euKi1I8mpbb8nARuAv0hyWJLDgHcCZwGnAi9rbVmkD0nSAHpPT60CjkyyCjgK+DrwbOCqtv1y4EVteWNbp20/M0la/Yqq+t+q+gqwEzi9fe2sqtuq6rvAFcDGts+++pAkDWDJ0KiqrwFvBW5nFBb3AtuBe6rqvtZsF7CmLa8B7mj73tfaHz9eX7DPvurHL9LHD0myOcl8kvndu3cvNSVJ0gPUc3rqOEZHCScDjwUezuhU0kK1d5d9bDtY9fsXq7ZW1VxVza1evXpSE0nSQdBzeuoXgK9U1e6q+h7wfuCZwLHtdBXAWuDOtrwLOAmgbT8G2DNeX7DPvup3LdKHJGkAPaFxO3BGkqPadYYzgVuATwAvbm02AR9sy9vaOm37x6uqWv3sdnfVycB64AbgRmB9u1PqcEYXy7e1ffbVhyRpAD3XNK5ndDH6M8BNbZ+twOuB85PsZHT94ZK2yyXA8a1+PrClvc7NwJWMAudq4Nyq+n67ZnEecA2wA7iytWWRPiRJA8joF/rZMTc3V/Pz8/u937otHzoEo9m3r174/GXtT5IWk2R7Vc0t1c4nwiVJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUrSs0khyb5KokX0qyI8nPJHlUkmuT3Nq+H9faJsk7kuxM8oUkp429zqbW/tYkm8bqT09yU9vnHUnS6hP7kCQNo/dI4+3A1VX1ROAngR3AFuBjVbUe+FhbBzgLWN++NgMXwygAgAuAZwCnAxeMhcDFre3e/Ta0+r76kCQNYMnQSPJI4OeBSwCq6rtVdQ+wEbi8NbsceFFb3gi8u0auA45N8hjgucC1VbWnqu4GrgU2tG2PrKpPV1UB717wWpP6kCQNoOdI43HAbuBdST6b5K+TPBw4saq+DtC+P7q1XwPcMbb/rlZbrL5rQp1F+pAkDaAnNFYBpwEXV9XTgP9i8dNEmVCrB1DvlmRzkvkk87t3796fXSVJ+6EnNHYBu6rq+rZ+FaMQ+UY7tUT7/s2x9ieN7b8WuHOJ+toJdRbp44dU1daqmququdWrV3dMSZL0QCwZGlX178AdSZ7QSmcCtwDbgL13QG0CPtiWtwGvaHdRnQHc204tXQM8J8lx7QL4c4Br2rZvJzmj3TX1igWvNakPSdIAVnW2+w3gPUkOB24DXsUocK5Mcg5wO/CS1vbDwPOAncB3Wluqak+SNwE3tnZvrKo9bfk1wGXAkcBH2hfAhfvoQ5I0gK7QqKrPAXMTNp05oW0B5+7jdS4FLp1QnweePKH+rUl9SJKG4RPhkqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSeq2augBaJn84THL2Ne9y9eXpGXlkYYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRu3aGR5LAkn03y92395CTXJ7k1yfuSHN7qR7T1nW37urHXeEOrfznJc8fqG1ptZ5ItY/WJfUiShrE/RxqvA3aMrb8FuKiq1gN3A+e0+jnA3VX1eOCi1o4kpwJnA08CNgB/0YLoMOCdwFnAqcDLWtvF+pAkDaArNJKsBZ4P/HVbD/Bs4KrW5HLgRW15Y1unbT+ztd8IXFFV/1tVXwF2Aqe3r51VdVtVfRe4Ati4RB+SpAH0Hmn8GfA7wA/a+vHAPVV1X1vfBaxpy2uAOwDa9ntb+/+vL9hnX/XF+vghSTYnmU8yv3v37s4pSZL215KhkeQFwDeravt4eULTWmLbwarfv1i1tarmqmpu9erVk5pIkg6Cnr/c9yzghUmeBzwMeCSjI49jk6xqRwJrgTtb+13AScCuJKuAY4A9Y/W9xveZVL9rkT4kSQNY8kijqt5QVWurah2jC9kfr6qXA58AXtyabQI+2Ja3tXXa9o9XVbX62e3uqpOB9cANwI3A+nan1OGtj21tn331IUkawIE8p/F64PwkOxldf7ik1S8Bjm/184EtAFV1M3AlcAtwNXBuVX2/HUWcB1zD6O6sK1vbxfqQJA2g5/TU/6uqTwKfbMu3MbrzaWGb/wFeso/9/xj44wn1DwMfnlCf2IckaRg+ES5J6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkrrt18eISCvRUy5/yrL2d9Omm5a1P2kl8UhDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlStyVDI8lJST6RZEeSm5O8rtUfleTaJLe278e1epK8I8nOJF9IctrYa21q7W9Nsmms/vQkN7V93pEki/UhSRpGz5HGfcBvV9UpwBnAuUlOBbYAH6uq9cDH2jrAWcD69rUZuBhGAQBcADwDOB24YCwELm5t9+63odX31YckaQBLhkZVfb2qPtOWvw3sANYAG4HLW7PLgRe15Y3Au2vkOuDYJI8BngtcW1V7qupu4FpgQ9v2yKr6dFUV8O4FrzWpD0nSAPbrmkaSdcDTgOuBE6vq6zAKFuDRrdka4I6x3Xa12mL1XRPqLNLHwnFtTjKfZH737t37MyVJ0n7oDo0kRwN/C/xmVf3HYk0n1OoB1LtV1daqmququdWrV+/PrpKk/dAVGkkeyigw3lNV72/lb7RTS7Tv32z1XcBJY7uvBe5cor52Qn2xPiRJA+i5eyrAJcCOqvrTsU3bgL13QG0CPjhWf0W7i+oM4N52auka4DlJjmsXwJ8DXNO2fTvJGa2vVyx4rUl9SJIGsKqjzbOAXwFuSvK5Vvtd4ELgyiTnALcDL2nbPgw8D9gJfAd4FUBV7UnyJuDG1u6NVbWnLb8GuAw4EvhI+2KRPiRJA1gyNKrqU0y+7gBw5oT2BZy7j9e6FLh0Qn0eePKE+rcm9SFJGoZPhEuSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6rhh6ApMXteOIpy9rfKV/asaz9abp4pCFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkbn72lKRBvfPXPr6s/Z37l89e1v5mzYo/0kiyIcmXk+xMsmXo8UjSg9mKDo0khwHvBM4CTgVeluTUYUclSQ9eK/301OnAzqq6DSDJFcBG4JZBRyVJnd720hcsa3+//b6/P6Svn6o6pB0ciCQvBjZU1avb+q8Az6iq8xa02wxsbqtPAL68jMM8AbhrGftbbrM8v1meGzi/abfc8/uxqlq9VKOVfqSRCbX7pVxVbQW2Hvrh3F+S+aqaG6Lv5TDL85vluYHzm3YrdX4r+poGsAs4aWx9LXDnQGORpAe9lR4aNwLrk5yc5HDgbGDbwGOSpAetFX16qqruS3IecA1wGHBpVd088LAWGuS02DKa5fnN8tzA+U27FTm/FX0hXJK0sqz001OSpBXE0JAkdTM0JEndVvSFcC2vJE9k9MT9GkbPw9wJbKuqHYMOTNKK4ZGGAEjyeuAKRg9U3sDoducA7/WDIiXt5d1TnZK8FvhAVd0x9FgOhST/Ajypqr63oH44cHNVrR9mZIdGkp9l9NlmX6yqjw49ngOV5BnAjqr6jyRHAluA0xh9Ttubq+reQQd4gGb552/a5uaRRr83Adcn+eckv55kyc9omTI/AB47of6Ytm2qJblhbPlXgT8HHgFcMCNHUpcC32nLbweOAd7Sau8aalAH0Sz//E3V3DzS6JTks8DTgV8AXgq8ENgOvBd4f1V9e8DhHbAkGxj9R3orsPc3nh8FHg+cV1VXDzW2gyHJZ6vqaW35RuB5VbU7ycOB66rqKcOO8MAk2VFVp7Tlz1TVaWPbPldVPzXc6A7cLP/8TdvcvBDer6rqB8BHgY8meSijv/PxMuCtwIr+7WApVXV1kp9gdMpmDaPrGbuAG6vq+4MO7uB4SJLjGB1dp6p2A1TVfyW5b9ihHRRfTPKqqnoX8Pkkc1U1397T7y218xSY5Z+/qZqbRxqdxn9TnbDtyKr67+Uek/ol+Sqj02xhdGfYM6vq35McDXxqBn4TP4bRaamfY/Rx2qcxOmK8A3htVX1+wOEdsFn++Zu2uRkanZL8RFX9y9Dj0MGV5CjgxKr6ytBjORiSPAJ4HKOzCLuq6hsDD+mgmOWfv2mbm6GhB70kR1fVfw49jkNlVueX5FFVtWfocRwKK3lu3j3VKclTklyX5I4kW9v58b3bblhsX614s/7ng6d+fkl+f2z51HaL+PYkX223G0+taZubF8L7XQz8IXAd8GrgU0leWFX/Cjx0yIFpaUnO39cm4OjlHMuhMOvzA34R+KO2/CfA66rqI0lOB/4MeOZgIztwUzU3jzT6HV1VV1fVPVX1VuA84OokZzDhT9BqxXkzcByjZzPGv45mNn4OZn1+4x5bVR8BqKobgCMHHs/BtOLn5pFGvyQ5Zu+TtVX1iSS/BPwt8Khhh6YOnwH+rqq2L9yQ5NUDjOdgm/X5PS7JNkZHTmuTHFVVex9mnPYj/amam6HR7y3AKYxOTwFQVV9IcibwB4ONSr1eBXxrH9vmlnMgh8isz2/jgvWHACQ5kdGp42k2VXPz7ilJUrdZO9d5yCR56tjyQ5P8fpJtSd7c7vXXCjbr75/zm15JzktyQlt+fJJ/SnJPkuuTPHno8S1kaPS7bGz5QkafyfQ2Rheq/nKIAWm/XDa2PIvv32Vjy85vurymqu5qy28HLqqqY4HXA3813LAm85pGv4wtnwn8dFV9L8k/AVP9EQ0PErP+/jm/6TX+//Cjq+oDAFX1yfaE/4piaPQ7JskvMvrHe8TevztRVZXEC0Mr36y/f85vel2V5DLgjcAHkvwm8H5G4Xj7kAObxNDo94/ACxj9o70uyYlV9Y0kP8LoA+K0ss36++f8plRV/V6SVzL6KPQfB44ANgN/B7x8wKFN5N1TnZIcAZwNfK2q/iHJLzN6UnMHsHXhX7zTyjLr75/zm975ZfTXMc8G7mxzezmjud3CCpybodEpyXsYHZkdBdzD6EnbvYeQVNUrBxucljTr75/zm975TZjbw4EPMJpbqmrTgMO7H0OjU5IvVNVTk6wCvsbocf/vJwnw+ap66hIvoQHN+vvn/KZ3ftM2N2+57feQdhj5CEa/ERzT6kewAh/11/3M+vvn/KbXVM3NC+H9LgG+BBwG/B7wN0luA84ArhhyYOoy6++f85teUzU3T0/thySPBaiqO5Mcy+gPwd/ePo1SK9ysv3/Ob3pN09wMDUlSN69pSJK6GRqSpG6GhiSpm6EhSer2f8rzrOvX0eLjAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_nm = 'race_concept_id'\n",
+    "print(test[col_nm].value_counts())\n",
+    "plt.figure()\n",
+    "test[col_nm].value_counts().plot(kind='bar')\n",
+    "\n",
+    "\n",
+    "tbl_race = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                    columns=[col_nm], aggfunc=np.sum)\n",
+    "tbl_race.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8532    1074003\n",
+      "8507     761265\n",
+      "0           447\n",
+      "Name: gender_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>49856.0</td>\n",
+       "      <td>7845.0</td>\n",
+       "      <td>20077.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>66381.0</td>\n",
+       "      <td>9130.0</td>\n",
+       "      <td>26063.0</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>504.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>597.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>4319.0</td>\n",
+       "      <td>1408.0</td>\n",
+       "      <td>616.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>7567.0</td>\n",
+       "      <td>1417.0</td>\n",
+       "      <td>1051.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>350.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>526236.0</td>\n",
+       "      <td>73041.0</td>\n",
+       "      <td>161902.0</td>\n",
+       "      <td>86.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>764943.0</td>\n",
+       "      <td>91015.0</td>\n",
+       "      <td>217891.0</td>\n",
+       "      <td>154.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                  0        9201      9202  \\\n",
+       "                           gender_concept_id                                \n",
+       "ARI_symptoms_2_months_bool 0                      12.0      3.0       9.0   \n",
+       "                           8507                49856.0   7845.0   20077.0   \n",
+       "                           8532                66381.0   9130.0   26063.0   \n",
+       "positive                   0                       0.0      0.0       0.0   \n",
+       "                           8507                  504.0    111.0      49.0   \n",
+       "                           8532                  597.0     92.0      59.0   \n",
+       "tested                     0                      19.0      1.0       1.0   \n",
+       "                           8507                 4319.0   1408.0     616.0   \n",
+       "                           8532                 7567.0   1417.0    1051.0   \n",
+       "total                      0                     350.0     18.0      79.0   \n",
+       "                           8507               526236.0  73041.0  161902.0   \n",
+       "                           8532               764943.0  91015.0  217891.0   \n",
+       "\n",
+       "visit_concept_id                               9203  \n",
+       "                           gender_concept_id         \n",
+       "ARI_symptoms_2_months_bool 0                    NaN  \n",
+       "                           8507                16.0  \n",
+       "                           8532                18.0  \n",
+       "positive                   0                    NaN  \n",
+       "                           8507                 0.0  \n",
+       "                           8532                 0.0  \n",
+       "tested                     0                    NaN  \n",
+       "                           8507                 2.0  \n",
+       "                           8532                 1.0  \n",
+       "total                      0                    NaN  \n",
+       "                           8507                86.0  \n",
+       "                           8532               154.0  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_nm = 'gender_concept_id'#gender_concept_id,ethnicity_concept_id \n",
+    "print(test[col_nm].value_counts())\n",
+    "plt.figure()\n",
+    "test[col_nm].value_counts().plot(kind='bar')\n",
+    "\n",
+    "\n",
+    "tbl_race = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                    columns=[col_nm], aggfunc=np.sum)\n",
+    "tbl_race.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38003564    1369541\n",
+      "38003563     281886\n",
+      "0            184288\n",
+      "Name: ethnicity_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>10725.0</td>\n",
+       "      <td>277.0</td>\n",
+       "      <td>7275.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>21500.0</td>\n",
+       "      <td>3725.0</td>\n",
+       "      <td>6263.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>84024.0</td>\n",
+       "      <td>12976.0</td>\n",
+       "      <td>32611.0</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>711.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>132.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>258.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>5580.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>1197.0</td>\n",
+       "      <td>615.0</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>5128.0</td>\n",
+       "      <td>2177.0</td>\n",
+       "      <td>1225.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>130099.0</td>\n",
+       "      <td>8994.0</td>\n",
+       "      <td>45190.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>202346.0</td>\n",
+       "      <td>28005.0</td>\n",
+       "      <td>51503.0</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>959084.0</td>\n",
+       "      <td>127075.0</td>\n",
+       "      <td>283179.0</td>\n",
+       "      <td>203.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                     0         9201      9202  \\\n",
+       "                           ethnicity_concept_id                                 \n",
+       "ARI_symptoms_2_months_bool 0                      10725.0     277.0    7275.0   \n",
+       "                           38003563               21500.0    3725.0    6263.0   \n",
+       "                           38003564               84024.0   12976.0   32611.0   \n",
+       "positive                   0                        711.0       1.0      10.0   \n",
+       "                           38003563                 132.0      58.0      15.0   \n",
+       "                           38003564                 258.0     144.0      83.0   \n",
+       "tested                     0                       5580.0      34.0     237.0   \n",
+       "                           38003563                1197.0     615.0     206.0   \n",
+       "                           38003564                5128.0    2177.0    1225.0   \n",
+       "total                      0                     130099.0    8994.0   45190.0   \n",
+       "                           38003563              202346.0   28005.0   51503.0   \n",
+       "                           38003564              959084.0  127075.0  283179.0   \n",
+       "\n",
+       "visit_concept_id                                  9203  \n",
+       "                           ethnicity_concept_id         \n",
+       "ARI_symptoms_2_months_bool 0                       1.0  \n",
+       "                           38003563                1.0  \n",
+       "                           38003564               32.0  \n",
+       "positive                   0                       0.0  \n",
+       "                           38003563                0.0  \n",
+       "                           38003564                0.0  \n",
+       "tested                     0                       1.0  \n",
+       "                           38003563                0.0  \n",
+       "                           38003564                2.0  \n",
+       "total                      0                       5.0  \n",
+       "                           38003563               32.0  \n",
+       "                           38003564              203.0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_nm = 'ethnicity_concept_id'#gender_concept_id,ethnicity_concept_id \n",
+    "print(test[col_nm].value_counts())\n",
+    "plt.figure()\n",
+    "test[col_nm].value_counts().plot(kind='bar')\n",
+    "\n",
+    "\n",
+    "tbl_race = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                    columns=[col_nm], aggfunc=np.sum)\n",
+    "tbl_race.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test['care_site_name'].value_counts().to_csv('care_counts_all_visit.csv')\n",
+    "test['care_site_name'][ARI_2_mon == 1].value_counts().to_csv('care_counts_ari_visit.csv')\n",
+    "test['care_site_name'][test_bool_col == 1].value_counts().to_csv('care_counts_test_visit.csv')\n",
+    "test['care_site_name'][pos_test_bool_col == 1].value_counts().to_csv('care_counts_pos_test_visit.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "positive                            0\n",
+       "tested                              0\n",
+       "age                              1722\n",
+       "person_id                           0\n",
+       "visit_start_DATETIME                0\n",
+       "ARI_symptoms_2_months         1647891\n",
+       "race_concept_id                     0\n",
+       "ethnicity_concept_id                0\n",
+       "gender_concept_id                   0\n",
+       "year_of_birth                       0\n",
+       "month_of_birth                      0\n",
+       "day_of_birth                        0\n",
+       "visit_concept_id                    0\n",
+       "care_site_name                  66001\n",
+       "value_source_value            1819313\n",
+       "visit_occurrence_id                 0\n",
+       "ARI_symptoms_2_months_bool          0\n",
+       "total                               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_groupby_max(df, group_by_ls):\n",
+    "    df_dups_ind = df.duplicated(group_by_ls, keep=False)\n",
+    "    df_dups = df[:][df_dups_ind]\n",
+    "    df_n_dups = df[:][df_dups_ind == False]\n",
+    "\n",
+    "    print(list(map(len, [df_dups_ind, df_dups, df_n_dups])))\n",
+    "\n",
+    "    uniq_by_grp = [grp.sort_values(['positive', 'tested', 'care_site_name'], ascending=False).iloc[0]\n",
+    "        for ind, grp in df_dups.groupby(group_by_ls, sort=False)]\n",
+    "    df_d_dups = pd.concat(uniq_by_grp,axis=1).T\n",
+    "\n",
+    "    df_uniq_by_grp = pd.concat([df_d_dups, df_n_dups],axis=0)\n",
+    "    print(list(map(len, [df_d_dups, df_n_dups, df_uniq_by_grp])))\n",
+    "    return df_uniq_by_grp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1835715, 32244, 1803471]\n",
+      "[15968, 1803471, 1819439]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_vids = get_groupby_max(df_all, ['visit_occurrence_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1819439, 1163174, 656265]\n",
+      "[431516, 656265, 1087781]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_person_times = get_groupby_max(df_uniq_vids, ['person_id', 'visit_start_DATETIME'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1819439, 1163174, 656265]\n",
+      "[431516, 656265, 1087781]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_vids['visit_start_date']=df_uniq_vids['visit_start_DATETIME'].dt.date\n",
+    "df_uniq_person_times = get_groupby_max(df_uniq_vids, ['person_id', 'visit_start_date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total:1087781\n",
+      "person_id:362631\n",
+      "care_site_name:1557\n",
+      "visit_occurrence_id:1087781\n"
+     ]
+    }
+   ],
+   "source": [
+    "#person_id, care_site_name, visit_concept_id\n",
+    "len_uniq = lambda col: len(df_uniq_person_times[col].unique())\n",
+    "my_disp = lambda col: print('{}:{}'.format(col, len_uniq(col)))\n",
+    "\n",
+    "print('total:{}'.format(len(df_uniq_person_times)))\n",
+    "my_disp('person_id')\n",
+    "my_disp('care_site_name')\n",
+    "my_disp('visit_occurrence_id')\n",
+    "\n",
+    "\n",
+    "#map(len, [df_uniq_person_times, df_uniq_person_times['person_id'].unique(), df_uniq_by_grp])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/Covid19 CDC/long_tbl_grp.ipynb b/scripts/Archive/Covid19 CDC/long_tbl_grp.ipynb
new file mode 100644
index 00000000..71306639
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/long_tbl_grp.ipynb	
@@ -0,0 +1,2971 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/share/sw/open/anaconda/3/lib/python3.6/site-packages/google/auth/_default.py:66: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Header from RIT notebook\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/marcelkc/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-nigam-starr' #som-nero-phi-rit\n",
+    "%load_ext google.cloud.bigquery\n",
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|██████████| 1835715/1835715 [03:44<00:00, 7912.41rows/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"\"\"SELECT \n",
+    "v.person_id, \n",
+    "visit_start_DATETIME, \n",
+    "ARI_symptoms_2_months, \n",
+    "race_concept_id, ethnicity_concept_id, # race/ethnicity\n",
+    "gender_concept_id, # gender\n",
+    "year_of_birth, month_of_birth, day_of_birth, # age at present\n",
+    "visit_concept_id, care_site_name, # location\n",
+    "value_source_value, # test result\n",
+    "visit_occurrence_id\n",
+    "FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` v\n",
+    "\n",
+    "LEFT JOIN(\n",
+    "  #visit_occurrence_id (unique) to ARI_symptoms\n",
+    "  SELECT \n",
+    "  visit_occurrence_id,\n",
+    "  #ARI symptoms # any condition_start_DATETIME, condition_end_DATETIME within 2 months\n",
+    "  max(\n",
+    "  DATETIME_DIFF(visit_start_DATETIME, condition_start_DATETIME, MONTH) < 2\n",
+    "  OR\n",
+    "  DATETIME_DIFF(visit_start_DATETIME, condition_end_DATETIME, MONTH) < 2 \n",
+    "  )\n",
+    "  AS ARI_symptoms_2_months,\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` v\n",
+    "  LEFT JOIN (\n",
+    "    SELECT person_id, condition_start_DATETIME, condition_end_DATETIME, \n",
+    "    condition_source_value, condition_source_concept_id # might get used later\n",
+    "    FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.condition_occurrence`\n",
+    "    WHERE (condition_start_DATETIME >= '2019-11-01T00:00:00.000' OR condition_end_DATETIME >= '2019-11-01T00:00:00.000')\n",
+    "    AND\n",
+    "    (condition_source_value LIKE 'J00.%' OR\n",
+    "    condition_source_value LIKE 'J01.%' OR\n",
+    "    condition_source_value LIKE 'J02.%' OR\n",
+    "    condition_source_value LIKE 'J03.%' OR\n",
+    "    condition_source_value LIKE 'J04.%' OR\n",
+    "    condition_source_value LIKE 'J05.%' OR\n",
+    "    condition_source_value LIKE 'J06.%' OR\n",
+    "    condition_source_value LIKE 'J09.%' OR\n",
+    "    condition_source_value LIKE 'J10.%' OR\n",
+    "    condition_source_value LIKE 'J11.%' OR\n",
+    "    condition_source_value LIKE 'J12.%' OR\n",
+    "    condition_source_value LIKE 'J13.%' OR\n",
+    "    condition_source_value LIKE 'J14.%' OR\n",
+    "    condition_source_value LIKE 'J15.%' OR\n",
+    "    condition_source_value LIKE 'J16.%' OR\n",
+    "    condition_source_value LIKE 'J17.%' OR\n",
+    "    condition_source_value LIKE 'J18.%' OR\n",
+    "    condition_source_value LIKE 'J20.%' OR\n",
+    "    condition_source_value LIKE 'J21.%' OR\n",
+    "    condition_source_value LIKE 'J22' OR\n",
+    "    condition_source_value LIKE 'J80' OR\n",
+    "    condition_source_value LIKE 'A37.91' OR\n",
+    "    condition_source_value LIKE 'A37.01' OR\n",
+    "    condition_source_value LIKE 'A37.11' OR\n",
+    "    condition_source_value LIKE 'A37.81' OR\n",
+    "    condition_source_value LIKE 'A48.1' OR\n",
+    "    condition_source_value LIKE 'B25.0' OR\n",
+    "    condition_source_value LIKE 'B44.0' OR\n",
+    "    condition_source_value LIKE 'B97.4' OR\n",
+    "    condition_source_value LIKE 'U07.1')\n",
+    "  ) USING (person_id)\n",
+    "  WHERE visit_start_DATETIME >= '2020-02-01T00:00:00.000' # all encounters from feb 1\n",
+    "  GROUP BY visit_occurrence_id\n",
+    "  )\n",
+    "USING (visit_occurrence_id)\n",
+    "\n",
+    "\n",
+    "LEFT JOIN (\n",
+    "  # visit_occurrence_id to covid test result (value_source_value) \n",
+    "  SELECT value_source_value, # test result\n",
+    "  visit_occurrence_id # for mapping(s)\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement`\n",
+    "  WHERE measurement_concept_id = 706170\n",
+    ") \n",
+    "USING (visit_occurrence_id)\n",
+    "\n",
+    "INNER JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.person` USING (person_id)\n",
+    "\n",
+    "LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.care_site` c  \n",
+    "ON c.care_site_id = v.care_site_id\n",
+    "\n",
+    "WHERE visit_start_DATETIME >= '2020-02-01T00:00:00.000' # all encounters from feb 1\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.to_dataframe(progress_bar_type='tqdm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-02-21</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159169916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-04-11</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>163698599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32536564</td>\n",
+       "      <td>2020-02-18</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CCSB THIRD FLOOR CLINIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160673028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>32135543</td>\n",
+       "      <td>2020-03-17</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>GASTROENTEROLOGY VCL 1133 207</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160992998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>30291433</td>\n",
+       "      <td>2020-03-12</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY CT 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160546382</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   person_id visit_start_DATETIME ARI_symptoms_2_months  race_concept_id  \\\n",
+       "0   31449046           2020-02-21                  None             8527   \n",
+       "1   31449046           2020-04-11                  None             8527   \n",
+       "2   32536564           2020-02-18                  True             8527   \n",
+       "3   32135543           2020-03-17                 False             8527   \n",
+       "4   30291433           2020-03-12                  None             8527   \n",
+       "\n",
+       "   ethnicity_concept_id  gender_concept_id  year_of_birth  month_of_birth  \\\n",
+       "0              38003564               8532           1930               4   \n",
+       "1              38003564               8532           1930               4   \n",
+       "2              38003564               8532           1932               9   \n",
+       "3              38003564               8532           1930              12   \n",
+       "4              38003564               8507           1931               2   \n",
+       "\n",
+       "   day_of_birth  visit_concept_id                          care_site_name  \\\n",
+       "0            29                 0  RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC   \n",
+       "1            29                 0   RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P   \n",
+       "2             9                 0                 CCSB THIRD FLOOR CLINIC   \n",
+       "3             5                 0           GASTROENTEROLOGY VCL 1133 207   \n",
+       "4            23                 0           RADIOLOGY CT 1ST FLOOR - 500P   \n",
+       "\n",
+       "  value_source_value  visit_occurrence_id  \n",
+       "0               None            159169916  \n",
+       "1               None            163698599  \n",
+       "2               None            160673028  \n",
+       "3               None            160992998  \n",
+       "4               None            160546382  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.to_csv(\"long_table_cdc.csv\", sep=';')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "# positive_test\n",
+    "positive_value_source_value = lambda x: x in ['Detected', 'DETECTED', 'Pos']\n",
+    "pos_test_bool = df['value_source_value'].apply(positive_value_source_value).rename('positive')\n",
+    "# tested\n",
+    "test_bool = df['value_source_value'].notnull().rename('tested')\n",
+    "# ARI_sympt\n",
+    "ARI_2_mon = df['ARI_symptoms_2_months'] == True\n",
+    "ARI_2_mon = ARI_2_mon.rename('ARI_symptoms_2_months_bool')\n",
+    "# age results\n",
+    "visit_dt = df['visit_start_DATETIME'].apply(lambda x : pd.to_datetime(x))\n",
+    "pat_bd_dt = df[['year_of_birth', 'month_of_birth', 'day_of_birth']].apply(lambda x: pd.to_datetime('-'.join([str(i) for i in x])), axis=1)\n",
+    "pat_age = visit_dt - pat_bd_dt\n",
+    "pat_age = pat_age.apply(lambda x: x.days/365.25).rename('age')\n",
+    "# age categorical\n",
+    "bins = [0, 5, 18, 50, 65, 75, 85, 100]\n",
+    "labels = [\"[{0}, {1})\".format(i, j) for i, j in zip(bins[:-1], bins[1:])]\n",
+    "age_cat = pd.cut(pat_age, bins, labels=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ebc5b3390>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# for all \"visits\" since February\n",
+    "plt.figure()\n",
+    "pat_age.hist()\n",
+    "plt.figure()\n",
+    "age_cat.value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>positive</th>\n",
+       "      <th>tested</th>\n",
+       "      <th>age</th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "      <th>ARI_symptoms_2_months_bool</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-02-21</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159169916</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-04-11</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>163698599</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32536564</td>\n",
+       "      <td>2020-02-18</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CCSB THIRD FLOOR CLINIC</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160673028</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32135543</td>\n",
+       "      <td>2020-03-17</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>GASTROENTEROLOGY VCL 1133 207</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160992998</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>30291433</td>\n",
+       "      <td>2020-03-12</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>RADIOLOGY CT 1ST FLOOR - 500P</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160546382</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   positive  tested        age  person_id visit_start_DATETIME  \\\n",
+       "0     False   False  [85, 100)   31449046           2020-02-21   \n",
+       "1     False   False  [85, 100)   31449046           2020-04-11   \n",
+       "2     False   False  [85, 100)   32536564           2020-02-18   \n",
+       "3     False   False  [85, 100)   32135543           2020-03-17   \n",
+       "4     False   False  [85, 100)   30291433           2020-03-12   \n",
+       "\n",
+       "  ARI_symptoms_2_months  race_concept_id  ethnicity_concept_id  \\\n",
+       "0                  None             8527              38003564   \n",
+       "1                  None             8527              38003564   \n",
+       "2                  True             8527              38003564   \n",
+       "3                 False             8527              38003564   \n",
+       "4                  None             8527              38003564   \n",
+       "\n",
+       "   gender_concept_id  year_of_birth  month_of_birth  day_of_birth  \\\n",
+       "0               8532           1930               4            29   \n",
+       "1               8532           1930               4            29   \n",
+       "2               8532           1932               9             9   \n",
+       "3               8532           1930              12             5   \n",
+       "4               8507           1931               2            23   \n",
+       "\n",
+       "   visit_concept_id                          care_site_name  \\\n",
+       "0                 0  RADIOLOGY STANFORD HOSPITAL DIAGNOSTIC   \n",
+       "1                 0   RADIOLOGY DIAGNOSTIC 1ST FLOOR - 500P   \n",
+       "2                 0                 CCSB THIRD FLOOR CLINIC   \n",
+       "3                 0           GASTROENTEROLOGY VCL 1133 207   \n",
+       "4                 0           RADIOLOGY CT 1ST FLOOR - 500P   \n",
+       "\n",
+       "  value_source_value  visit_occurrence_id  ARI_symptoms_2_months_bool  total  \n",
+       "0               None            159169916                       False      1  \n",
+       "1               None            163698599                       False      1  \n",
+       "2               None            160673028                        True      1  \n",
+       "3               None            160992998                       False      1  \n",
+       "4               None            160546382                       False      1  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all = pd.concat([pos_test_bool, test_bool, age_cat, df, ARI_2_mon], axis=1)\n",
+    "df_all['total'] = 1\n",
+    "df_all.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_col(df, col_nm):\n",
+    "    print(df[col_nm].value_counts())\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    df[col_nm].value_counts().plot(kind='bar')\n",
+    "    \n",
+    "    tbl = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                        columns=[col_nm], aggfunc=np.sum)\n",
+    "    return tbl.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "positive                            0\n",
+       "tested                              0\n",
+       "age                              1722\n",
+       "person_id                           0\n",
+       "visit_start_DATETIME                0\n",
+       "ARI_symptoms_2_months         1647891\n",
+       "race_concept_id                     0\n",
+       "ethnicity_concept_id                0\n",
+       "gender_concept_id                   0\n",
+       "year_of_birth                       0\n",
+       "month_of_birth                      0\n",
+       "day_of_birth                        0\n",
+       "visit_concept_id                    0\n",
+       "care_site_name                  66001\n",
+       "value_source_value            1819313\n",
+       "visit_occurrence_id                 0\n",
+       "ARI_symptoms_2_months_bool          0\n",
+       "total                               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1]\n",
+      "[0 1]\n",
+      "[1]\n",
+      "[0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# should all be binary\n",
+    "for elt in ['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool']:\n",
+    "    df_all[elt]=df_all[elt].astype('int32')\n",
+    "    print(df_all[elt].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18, 50)     590714\n",
+      "[50, 65)     380166\n",
+      "[65, 75)     290354\n",
+      "[5, 18)      225277\n",
+      "[75, 85)     179987\n",
+      "[0, 5)       124777\n",
+      "[85, 100)     42718\n",
+      "Name: age, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>19269.0</td>\n",
+       "      <td>1858.0</td>\n",
+       "      <td>8773.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>19235.0</td>\n",
+       "      <td>1768.0</td>\n",
+       "      <td>9198.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>34834.0</td>\n",
+       "      <td>5575.0</td>\n",
+       "      <td>13642.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>19558.0</td>\n",
+       "      <td>3602.0</td>\n",
+       "      <td>7340.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>13627.0</td>\n",
+       "      <td>2470.0</td>\n",
+       "      <td>4234.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>7531.0</td>\n",
+       "      <td>1356.0</td>\n",
+       "      <td>2335.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>2181.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>627.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">positive</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>579.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>241.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>128.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">tested</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>583.0</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>679.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>6706.0</td>\n",
+       "      <td>1317.0</td>\n",
+       "      <td>929.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>2066.0</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>447.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>291.0</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>596.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>220.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">total</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>85049.0</td>\n",
+       "      <td>6717.0</td>\n",
+       "      <td>33011.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>156343.0</td>\n",
+       "      <td>14974.0</td>\n",
+       "      <td>53959.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>425520.0</td>\n",
+       "      <td>49585.0</td>\n",
+       "      <td>115510.0</td>\n",
+       "      <td>99.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>264266.0</td>\n",
+       "      <td>39807.0</td>\n",
+       "      <td>76032.0</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>202396.0</td>\n",
+       "      <td>31191.0</td>\n",
+       "      <td>56725.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>126003.0</td>\n",
+       "      <td>17882.0</td>\n",
+       "      <td>36074.0</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>30676.0</td>\n",
+       "      <td>3472.0</td>\n",
+       "      <td>8561.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                          0        9201      9202  9203\n",
+       "                           age                                         \n",
+       "ARI_symptoms_2_months_bool [0, 5)      19269.0   1858.0    8773.0   NaN\n",
+       "                           [5, 18)     19235.0   1768.0    9198.0   0.0\n",
+       "                           [18, 50)    34834.0   5575.0   13642.0  16.0\n",
+       "                           [50, 65)    19558.0   3602.0    7340.0  13.0\n",
+       "                           [65, 75)    13627.0   2470.0    4234.0   2.0\n",
+       "                           [75, 85)     7531.0   1356.0    2335.0   2.0\n",
+       "                           [85, 100)    2181.0    340.0     627.0   1.0\n",
+       "positive                   [0, 5)          8.0      1.0       1.0   NaN\n",
+       "                           [5, 18)        35.0      5.0       4.0   0.0\n",
+       "                           [18, 50)      579.0    100.0      46.0   0.0\n",
+       "                           [50, 65)      241.0     45.0      38.0   0.0\n",
+       "                           [65, 75)      128.0     21.0      12.0   0.0\n",
+       "                           [75, 85)       76.0     24.0       5.0   0.0\n",
+       "                           [85, 100)      34.0      7.0       2.0   0.0\n",
+       "tested                     [0, 5)        583.0    206.0      20.0   NaN\n",
+       "                           [5, 18)       679.0    174.0      43.0   0.0\n",
+       "                           [18, 50)     6706.0   1317.0     929.0   1.0\n",
+       "                           [50, 65)     2066.0    597.0     447.0   1.0\n",
+       "                           [65, 75)     1055.0    291.0     162.0   1.0\n",
+       "                           [75, 85)      596.0    187.0      57.0   0.0\n",
+       "                           [85, 100)     220.0     54.0      10.0   0.0\n",
+       "total                      [0, 5)      85049.0   6717.0   33011.0   NaN\n",
+       "                           [5, 18)    156343.0  14974.0   53959.0   1.0\n",
+       "                           [18, 50)   425520.0  49585.0  115510.0  99.0\n",
+       "                           [50, 65)   264266.0  39807.0   76032.0  61.0\n",
+       "                           [65, 75)   202396.0  31191.0   56725.0  42.0\n",
+       "                           [75, 85)   126003.0  17882.0   36074.0  28.0\n",
+       "                           [85, 100)   30676.0   3472.0    8561.0   9.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df_all, 'age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8527    934994\n",
+      "0       472445\n",
+      "8515    326809\n",
+      "8516     75473\n",
+      "8557     20227\n",
+      "8657      5767\n",
+      "Name: race_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>31537.0</td>\n",
+       "      <td>4125.0</td>\n",
+       "      <td>13448.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>21163.0</td>\n",
+       "      <td>3141.0</td>\n",
+       "      <td>8511.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>5508.0</td>\n",
+       "      <td>808.0</td>\n",
+       "      <td>1860.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>56254.0</td>\n",
+       "      <td>8590.0</td>\n",
+       "      <td>21770.0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>1483.0</td>\n",
+       "      <td>279.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>304.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>828.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>62.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>192.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>6881.0</td>\n",
+       "      <td>793.0</td>\n",
+       "      <td>441.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>1213.0</td>\n",
+       "      <td>461.0</td>\n",
+       "      <td>306.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>278.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>3436.0</td>\n",
+       "      <td>1370.0</td>\n",
+       "      <td>834.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>79.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>333947.0</td>\n",
+       "      <td>39464.0</td>\n",
+       "      <td>98994.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>228042.0</td>\n",
+       "      <td>30202.0</td>\n",
+       "      <td>68525.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>53864.0</td>\n",
+       "      <td>6275.0</td>\n",
+       "      <td>15319.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>657172.0</td>\n",
+       "      <td>85574.0</td>\n",
+       "      <td>192107.0</td>\n",
+       "      <td>141.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>14452.0</td>\n",
+       "      <td>2051.0</td>\n",
+       "      <td>3720.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>4052.0</td>\n",
+       "      <td>508.0</td>\n",
+       "      <td>1207.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                0        9201      9202   9203\n",
+       "                           race_concept_id                                    \n",
+       "ARI_symptoms_2_months_bool 0                 31537.0   4125.0   13448.0    2.0\n",
+       "                           8515              21163.0   3141.0    8511.0    8.0\n",
+       "                           8516               5508.0    808.0    1860.0    2.0\n",
+       "                           8527              56254.0   8590.0   21770.0   22.0\n",
+       "                           8557               1483.0    279.0     440.0    0.0\n",
+       "                           8657                304.0     35.0     120.0    NaN\n",
+       "positive                   0                   828.0     59.0      26.0    0.0\n",
+       "                           8515                 62.0     41.0      19.0    0.0\n",
+       "                           8516                 13.0      4.0       6.0    0.0\n",
+       "                           8527                192.0     92.0      55.0    0.0\n",
+       "                           8557                  3.0      7.0       2.0    0.0\n",
+       "                           8657                  3.0      0.0       0.0    NaN\n",
+       "tested                     0                  6881.0    793.0     441.0    1.0\n",
+       "                           8515               1213.0    461.0     306.0    0.0\n",
+       "                           8516                278.0    131.0      61.0    0.0\n",
+       "                           8527               3436.0   1370.0     834.0    2.0\n",
+       "                           8557                 79.0     65.0      17.0    0.0\n",
+       "                           8657                 18.0      6.0       9.0    NaN\n",
+       "total                      0                333947.0  39464.0   98994.0   40.0\n",
+       "                           8515             228042.0  30202.0   68525.0   40.0\n",
+       "                           8516              53864.0   6275.0   15319.0   15.0\n",
+       "                           8527             657172.0  85574.0  192107.0  141.0\n",
+       "                           8557              14452.0   2051.0    3720.0    4.0\n",
+       "                           8657               4052.0    508.0    1207.0    NaN"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df_all, 'race_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8532    1074003\n",
+      "8507     761265\n",
+      "0           447\n",
+      "Name: gender_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>49856.0</td>\n",
+       "      <td>7845.0</td>\n",
+       "      <td>20077.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>66381.0</td>\n",
+       "      <td>9130.0</td>\n",
+       "      <td>26063.0</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>504.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>597.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>4319.0</td>\n",
+       "      <td>1408.0</td>\n",
+       "      <td>616.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>7567.0</td>\n",
+       "      <td>1417.0</td>\n",
+       "      <td>1051.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>350.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>526236.0</td>\n",
+       "      <td>73041.0</td>\n",
+       "      <td>161902.0</td>\n",
+       "      <td>86.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>764943.0</td>\n",
+       "      <td>91015.0</td>\n",
+       "      <td>217891.0</td>\n",
+       "      <td>154.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                  0        9201      9202  \\\n",
+       "                           gender_concept_id                                \n",
+       "ARI_symptoms_2_months_bool 0                      12.0      3.0       9.0   \n",
+       "                           8507                49856.0   7845.0   20077.0   \n",
+       "                           8532                66381.0   9130.0   26063.0   \n",
+       "positive                   0                       0.0      0.0       0.0   \n",
+       "                           8507                  504.0    111.0      49.0   \n",
+       "                           8532                  597.0     92.0      59.0   \n",
+       "tested                     0                      19.0      1.0       1.0   \n",
+       "                           8507                 4319.0   1408.0     616.0   \n",
+       "                           8532                 7567.0   1417.0    1051.0   \n",
+       "total                      0                     350.0     18.0      79.0   \n",
+       "                           8507               526236.0  73041.0  161902.0   \n",
+       "                           8532               764943.0  91015.0  217891.0   \n",
+       "\n",
+       "visit_concept_id                               9203  \n",
+       "                           gender_concept_id         \n",
+       "ARI_symptoms_2_months_bool 0                    NaN  \n",
+       "                           8507                16.0  \n",
+       "                           8532                18.0  \n",
+       "positive                   0                    NaN  \n",
+       "                           8507                 0.0  \n",
+       "                           8532                 0.0  \n",
+       "tested                     0                    NaN  \n",
+       "                           8507                 2.0  \n",
+       "                           8532                 1.0  \n",
+       "total                      0                    NaN  \n",
+       "                           8507                86.0  \n",
+       "                           8532               154.0  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df_all, 'gender_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38003564    1369541\n",
+      "38003563     281886\n",
+      "0            184288\n",
+      "Name: ethnicity_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>10725</td>\n",
+       "      <td>277</td>\n",
+       "      <td>7275</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>21500</td>\n",
+       "      <td>3725</td>\n",
+       "      <td>6263</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>84024</td>\n",
+       "      <td>12976</td>\n",
+       "      <td>32611</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>711</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>132</td>\n",
+       "      <td>58</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>258</td>\n",
+       "      <td>144</td>\n",
+       "      <td>83</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>5580</td>\n",
+       "      <td>34</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>1197</td>\n",
+       "      <td>615</td>\n",
+       "      <td>206</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>5128</td>\n",
+       "      <td>2177</td>\n",
+       "      <td>1225</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>130099</td>\n",
+       "      <td>8994</td>\n",
+       "      <td>45190</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>202346</td>\n",
+       "      <td>28005</td>\n",
+       "      <td>51503</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>959084</td>\n",
+       "      <td>127075</td>\n",
+       "      <td>283179</td>\n",
+       "      <td>203</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                   0       9201    9202  9203\n",
+       "                           ethnicity_concept_id                              \n",
+       "ARI_symptoms_2_months_bool 0                      10725     277    7275     1\n",
+       "                           38003563               21500    3725    6263     1\n",
+       "                           38003564               84024   12976   32611    32\n",
+       "positive                   0                        711       1      10     0\n",
+       "                           38003563                 132      58      15     0\n",
+       "                           38003564                 258     144      83     0\n",
+       "tested                     0                       5580      34     237     1\n",
+       "                           38003563                1197     615     206     0\n",
+       "                           38003564                5128    2177    1225     2\n",
+       "total                      0                     130099    8994   45190     5\n",
+       "                           38003563              202346   28005   51503    32\n",
+       "                           38003564              959084  127075  283179   203"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df_all, 'ethnicity_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all['care_site_name'].value_counts().to_csv('care_counts_all_visit.csv')\n",
+    "df_all['care_site_name'][df_all['ARI_symptoms_2_months_bool'] == 1].value_counts().to_csv('care_counts_ari_visit.csv')\n",
+    "df_all['care_site_name'][df_all['tested'] == 1].value_counts().to_csv('care_counts_test_visit.csv')\n",
+    "df_all['care_site_name'][df_all['positive'] == 1].value_counts().to_csv('care_counts_pos_test_visit.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_groupby_max(df, group_by_ls):\n",
+    "    df_dups_ind = df.duplicated(group_by_ls, keep=False)\n",
+    "    df_dups = df[:][df_dups_ind]\n",
+    "    df_n_dups = df[:][df_dups_ind == False]\n",
+    "\n",
+    "    print(list(map(len, [df_dups_ind, df_dups, df_n_dups])))\n",
+    "\n",
+    "    uniq_by_grp = [grp.sort_values(['positive', 'tested', 'care_site_name'], ascending=False).iloc[0]\n",
+    "        for ind, grp in df_dups.groupby(group_by_ls, sort=False)]\n",
+    "    df_d_dups = pd.concat(uniq_by_grp,axis=1).T\n",
+    "\n",
+    "    df_uniq_by_grp = pd.concat([df_d_dups, df_n_dups],axis=0)\n",
+    "    print(list(map(len, [df_d_dups, df_n_dups, df_uniq_by_grp])))\n",
+    "    return df_uniq_by_grp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1835715, 32244, 1803471]\n",
+      "[15968, 1803471, 1819439]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_vids = get_groupby_max(df_all, ['visit_occurrence_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1819439, 1163174, 656265]\n",
+      "[431516, 656265, 1087781]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_vids['visit_start_date'] = df_uniq_vids['visit_start_DATETIME'].dt.date\n",
+    "df_uniq_person_times = get_groupby_max(df_uniq_vids, ['person_id', 'visit_start_date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 0]\n",
+      "[0 1]\n",
+      "[1]\n",
+      "[0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df_uniq_person_times.copy()\n",
+    "# should all be [0,1]\n",
+    "for elt in ['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool']:\n",
+    "    print(df[elt].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total:1087781\n",
+      "person_id:362631\n",
+      "care_site_name:1557\n",
+      "visit_occurrence_id:1087781\n",
+      "positive                            0\n",
+      "tested                              0\n",
+      "age                              1001\n",
+      "person_id                           0\n",
+      "visit_start_DATETIME                0\n",
+      "ARI_symptoms_2_months          989042\n",
+      "race_concept_id                     0\n",
+      "ethnicity_concept_id                0\n",
+      "gender_concept_id                   0\n",
+      "year_of_birth                       0\n",
+      "month_of_birth                      0\n",
+      "day_of_birth                        0\n",
+      "visit_concept_id                    0\n",
+      "care_site_name                  14502\n",
+      "value_source_value            1074428\n",
+      "visit_occurrence_id                 0\n",
+      "ARI_symptoms_2_months_bool          0\n",
+      "total                               0\n",
+      "visit_start_date                    0\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>positive</th>\n",
+       "      <th>tested</th>\n",
+       "      <th>age</th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "      <th>ARI_symptoms_2_months_bool</th>\n",
+       "      <th>total</th>\n",
+       "      <th>visit_start_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-04-01 00:00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>162605931</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-04-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31449046</td>\n",
+       "      <td>2020-04-11 00:00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>163692693</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-04-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>857</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>30121326</td>\n",
+       "      <td>2020-04-06 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Detected</td>\n",
+       "      <td>163697921</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-04-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3304</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>44247408</td>\n",
+       "      <td>2020-03-25 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>163970962</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3634</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31193960</td>\n",
+       "      <td>2020-03-22 00:00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>162117681</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-03-22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     positive tested        age person_id visit_start_DATETIME  \\\n",
+       "35          0      1  [85, 100)  31449046  2020-04-01 00:00:00   \n",
+       "36          0      1  [85, 100)  31449046  2020-04-11 00:00:00   \n",
+       "857         1      1  [85, 100)  30121326  2020-04-06 00:00:00   \n",
+       "3304        0      1  [85, 100)  44247408  2020-03-25 00:00:00   \n",
+       "3634        0      1  [85, 100)  31193960  2020-03-22 00:00:00   \n",
+       "\n",
+       "     ARI_symptoms_2_months race_concept_id ethnicity_concept_id  \\\n",
+       "35                    None            8527             38003564   \n",
+       "36                    None            8527             38003564   \n",
+       "857                   True            8527             38003564   \n",
+       "3304                  True               0             38003564   \n",
+       "3634                  None            8527             38003564   \n",
+       "\n",
+       "     gender_concept_id year_of_birth month_of_birth day_of_birth  \\\n",
+       "35                8532          1930              4           29   \n",
+       "36                8532          1930              4           29   \n",
+       "857               8532          1932              2            1   \n",
+       "3304              8532          1931              1           22   \n",
+       "3634              8532          1932              4           12   \n",
+       "\n",
+       "     visit_concept_id care_site_name value_source_value visit_occurrence_id  \\\n",
+       "35               9201           None       Not Detected           162605931   \n",
+       "36               9201           None       Not Detected           163692693   \n",
+       "857              9201           None           Detected           163697921   \n",
+       "3304             9201           None       Not Detected           163970962   \n",
+       "3634             9201           None       Not Detected           162117681   \n",
+       "\n",
+       "     ARI_symptoms_2_months_bool total visit_start_date  \n",
+       "35                            0     1       2020-04-01  \n",
+       "36                            0     1       2020-04-11  \n",
+       "857                           1     1       2020-04-06  \n",
+       "3304                          1     1       2020-03-25  \n",
+       "3634                          0     1       2020-03-22  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#person_id, care_site_name, visit_concept_id\n",
+    "len_uniq = lambda col: len(df_uniq_person_times[col].unique())\n",
+    "my_disp = lambda col: print('{}:{}'.format(col, len_uniq(col)))\n",
+    "\n",
+    "print('total:{}'.format(len(df_uniq_person_times)))\n",
+    "my_disp('person_id')\n",
+    "my_disp('care_site_name')\n",
+    "my_disp('visit_occurrence_id')\n",
+    "pre='uniq_'\n",
+    "df['care_site_name'].value_counts().to_csv(pre+'care_counts_all_visit.csv')\n",
+    "df['care_site_name'][df['ARI_symptoms_2_months_bool'] == 1].value_counts().to_csv(pre+'care_counts_ari_visit.csv')\n",
+    "df['care_site_name'][df['tested'] == 1].value_counts().to_csv(pre+'care_counts_test_visit.csv')\n",
+    "df['care_site_name'][df['positive'] == 1].value_counts().to_csv(pre+'care_counts_pos_test_visit.csv')\n",
+    "print(df.isnull().sum())\n",
+    "df_uniq_person_times.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18, 50)     343355\n",
+      "[50, 65)     245305\n",
+      "[65, 75)     185990\n",
+      "[75, 85)     115435\n",
+      "[5, 18)      109438\n",
+      "[0, 5)        59761\n",
+      "[85, 100)     27496\n",
+      "Name: age, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>6599.0</td>\n",
+       "      <td>334.0</td>\n",
+       "      <td>6883.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>18885.0</td>\n",
+       "      <td>1652.0</td>\n",
+       "      <td>7984.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>6590.0</td>\n",
+       "      <td>384.0</td>\n",
+       "      <td>7061.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>12206.0</td>\n",
+       "      <td>1651.0</td>\n",
+       "      <td>3968.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>8149.0</td>\n",
+       "      <td>1154.0</td>\n",
+       "      <td>2176.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>4589.0</td>\n",
+       "      <td>617.0</td>\n",
+       "      <td>1243.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>1305.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>323.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">positive</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>486.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>234.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>125.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">tested</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>442.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>5573.0</td>\n",
+       "      <td>492.0</td>\n",
+       "      <td>685.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>519.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>436.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>1039.0</td>\n",
+       "      <td>265.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>593.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>219.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">total</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>35194.0</td>\n",
+       "      <td>2118.0</td>\n",
+       "      <td>22449.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>253958.0</td>\n",
+       "      <td>22121.0</td>\n",
+       "      <td>67198.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>69727.0</td>\n",
+       "      <td>6206.0</td>\n",
+       "      <td>33504.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>181493.0</td>\n",
+       "      <td>22097.0</td>\n",
+       "      <td>41666.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>138223.0</td>\n",
+       "      <td>17442.0</td>\n",
+       "      <td>30295.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>86211.0</td>\n",
+       "      <td>9829.0</td>\n",
+       "      <td>19374.0</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>21116.0</td>\n",
+       "      <td>1752.0</td>\n",
+       "      <td>4621.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                          0        9201     9202  9203\n",
+       "                           age                                        \n",
+       "ARI_symptoms_2_months_bool [0, 5)       6599.0    334.0   6883.0   NaN\n",
+       "                           [18, 50)    18885.0   1652.0   7984.0  16.0\n",
+       "                           [5, 18)      6590.0    384.0   7061.0   0.0\n",
+       "                           [50, 65)    12206.0   1651.0   3968.0   8.0\n",
+       "                           [65, 75)     8149.0   1154.0   2176.0   2.0\n",
+       "                           [75, 85)     4589.0    617.0   1243.0   2.0\n",
+       "                           [85, 100)    1305.0    127.0    323.0   1.0\n",
+       "positive                   [0, 5)          7.0      0.0      0.0   NaN\n",
+       "                           [18, 50)      486.0     49.0     33.0   0.0\n",
+       "                           [5, 18)        30.0      0.0      1.0   0.0\n",
+       "                           [50, 65)      234.0     40.0     38.0   0.0\n",
+       "                           [65, 75)      125.0     16.0     11.0   0.0\n",
+       "                           [75, 85)       76.0     22.0      5.0   0.0\n",
+       "                           [85, 100)      34.0      5.0      2.0   0.0\n",
+       "tested                     [0, 5)        442.0     19.0      1.0   NaN\n",
+       "                           [18, 50)     5573.0    492.0    685.0   0.0\n",
+       "                           [5, 18)       519.0     15.0     21.0   0.0\n",
+       "                           [50, 65)     2022.0    549.0    436.0   1.0\n",
+       "                           [65, 75)     1039.0    265.0    160.0   1.0\n",
+       "                           [75, 85)      593.0    184.0     57.0   0.0\n",
+       "                           [85, 100)     219.0     50.0     10.0   0.0\n",
+       "total                      [0, 5)      35194.0   2118.0  22449.0   NaN\n",
+       "                           [18, 50)   253958.0  22121.0  67198.0  78.0\n",
+       "                           [5, 18)     69727.0   6206.0  33504.0   1.0\n",
+       "                           [50, 65)   181493.0  22097.0  41666.0  49.0\n",
+       "                           [65, 75)   138223.0  17442.0  30295.0  30.0\n",
+       "                           [75, 85)    86211.0   9829.0  19374.0  21.0\n",
+       "                           [85, 100)   21116.0   1752.0   4621.0   7.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8527    566576\n",
+      "0       270578\n",
+      "8515    188991\n",
+      "8516     46575\n",
+      "8557     11540\n",
+      "8657      3521\n",
+      "Name: race_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>14046.0</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>9571.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>10220.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>5357.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>2984.0</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>1129.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>30121.0</td>\n",
+       "      <td>3214.0</td>\n",
+       "      <td>13247.0</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>783.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>261.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>180.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>737.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>179.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>5860.0</td>\n",
+       "      <td>402.0</td>\n",
+       "      <td>316.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>1081.0</td>\n",
+       "      <td>239.0</td>\n",
+       "      <td>245.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>249.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>3123.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>738.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>192755.0</td>\n",
+       "      <td>18985.0</td>\n",
+       "      <td>58805.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>134231.0</td>\n",
+       "      <td>15208.0</td>\n",
+       "      <td>39526.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>34722.0</td>\n",
+       "      <td>2883.0</td>\n",
+       "      <td>8959.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>414136.0</td>\n",
+       "      <td>43280.0</td>\n",
+       "      <td>109047.0</td>\n",
+       "      <td>113.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>8457.0</td>\n",
+       "      <td>976.0</td>\n",
+       "      <td>2104.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>2608.0</td>\n",
+       "      <td>247.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                0        9201      9202   9203\n",
+       "                           race_concept_id                                    \n",
+       "ARI_symptoms_2_months_bool 0                 14046.0   1250.0    9571.0    2.0\n",
+       "                           8515              10220.0   1106.0    5357.0    6.0\n",
+       "                           8516               2984.0    255.0    1129.0    2.0\n",
+       "                           8527              30121.0   3214.0   13247.0   19.0\n",
+       "                           8557                783.0     85.0     261.0    0.0\n",
+       "                           8657                180.0      9.0      73.0    NaN\n",
+       "positive                   0                   737.0     39.0      21.0    0.0\n",
+       "                           8515                 57.0     31.0      14.0    0.0\n",
+       "                           8516                 13.0      4.0       5.0    0.0\n",
+       "                           8527                179.0     53.0      48.0    0.0\n",
+       "                           8557                  3.0      5.0       2.0    0.0\n",
+       "                           8657                  3.0      0.0       0.0    NaN\n",
+       "tested                     0                  5860.0    402.0     316.0    0.0\n",
+       "                           8515               1081.0    239.0     245.0    0.0\n",
+       "                           8516                249.0     85.0      51.0    0.0\n",
+       "                           8527               3123.0    806.0     738.0    2.0\n",
+       "                           8557                 76.0     37.0      13.0    0.0\n",
+       "                           8657                 18.0      5.0       7.0    NaN\n",
+       "total                      0                192755.0  18985.0   58805.0   33.0\n",
+       "                           8515             134231.0  15208.0   39526.0   26.0\n",
+       "                           8516              34722.0   2883.0    8959.0   11.0\n",
+       "                           8527             414136.0  43280.0  109047.0  113.0\n",
+       "                           8557               8457.0    976.0    2104.0    3.0\n",
+       "                           8657               2608.0    247.0     666.0    NaN"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'race_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8532    630455\n",
+      "8507    457047\n",
+      "0          279\n",
+      "Name: gender_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>24395.0</td>\n",
+       "      <td>2793.0</td>\n",
+       "      <td>13182.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>33934.0</td>\n",
+       "      <td>3124.0</td>\n",
+       "      <td>16451.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>483.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>509.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>4001.0</td>\n",
+       "      <td>1004.0</td>\n",
+       "      <td>568.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>6390.0</td>\n",
+       "      <td>569.0</td>\n",
+       "      <td>802.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>218.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>326235.0</td>\n",
+       "      <td>36792.0</td>\n",
+       "      <td>93957.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>460456.0</td>\n",
+       "      <td>44776.0</td>\n",
+       "      <td>125100.0</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                  0        9201      9202  \\\n",
+       "                           gender_concept_id                                \n",
+       "ARI_symptoms_2_months_bool 0                       5.0      2.0       5.0   \n",
+       "                           8507                24395.0   2793.0   13182.0   \n",
+       "                           8532                33934.0   3124.0   16451.0   \n",
+       "positive                   0                       0.0      0.0       0.0   \n",
+       "                           8507                  483.0     91.0      44.0   \n",
+       "                           8532                  509.0     41.0      46.0   \n",
+       "tested                     0                      16.0      1.0       0.0   \n",
+       "                           8507                 4001.0   1004.0     568.0   \n",
+       "                           8532                 6390.0    569.0     802.0   \n",
+       "total                      0                     218.0     11.0      50.0   \n",
+       "                           8507               326235.0  36792.0   93957.0   \n",
+       "                           8532               460456.0  44776.0  125100.0   \n",
+       "\n",
+       "visit_concept_id                               9203  \n",
+       "                           gender_concept_id         \n",
+       "ARI_symptoms_2_months_bool 0                    NaN  \n",
+       "                           8507                14.0  \n",
+       "                           8532                15.0  \n",
+       "positive                   0                    NaN  \n",
+       "                           8507                 0.0  \n",
+       "                           8532                 0.0  \n",
+       "tested                     0                    NaN  \n",
+       "                           8507                 2.0  \n",
+       "                           8532                 0.0  \n",
+       "total                      0                    NaN  \n",
+       "                           8507                63.0  \n",
+       "                           8532               123.0  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'gender_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38003564    821547\n",
+      "38003563    155016\n",
+      "0           111218\n",
+      "Name: ethnicity_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>4092</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5600</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>10377</td>\n",
+       "      <td>1057</td>\n",
+       "      <td>3888</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>43865</td>\n",
+       "      <td>4751</td>\n",
+       "      <td>20150</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>632</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>118</td>\n",
+       "      <td>29</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>242</td>\n",
+       "      <td>102</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>4675</td>\n",
+       "      <td>17</td>\n",
+       "      <td>159</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>1082</td>\n",
+       "      <td>264</td>\n",
+       "      <td>167</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>4650</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>1044</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>76500</td>\n",
+       "      <td>5496</td>\n",
+       "      <td>29219</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>115206</td>\n",
+       "      <td>12012</td>\n",
+       "      <td>27771</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>595203</td>\n",
+       "      <td>64071</td>\n",
+       "      <td>162117</td>\n",
+       "      <td>156</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                   0      9201    9202  9203\n",
+       "                           ethnicity_concept_id                             \n",
+       "ARI_symptoms_2_months_bool 0                       4092    111    5600     1\n",
+       "                           38003563               10377   1057    3888     1\n",
+       "                           38003564               43865   4751   20150    27\n",
+       "positive                   0                        632      1       8     0\n",
+       "                           38003563                 118     29      13     0\n",
+       "                           38003564                 242    102      69     0\n",
+       "tested                     0                       4675     17     159     0\n",
+       "                           38003563                1082    264     167     0\n",
+       "                           38003564                4650   1293    1044     2\n",
+       "total                      0                      76500   5496   29219     3\n",
+       "                           38003563              115206  12012   27771    27\n",
+       "                           38003564              595203  64071  162117   156"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'ethnicity_concept_id')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/Covid19 CDC/long_tbl_grp_person.ipynb b/scripts/Archive/Covid19 CDC/long_tbl_grp_person.ipynb
new file mode 100644
index 00000000..670c062c
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/long_tbl_grp_person.ipynb	
@@ -0,0 +1,1860 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/share/sw/open/anaconda/3/lib/python3.6/site-packages/google/auth/_default.py:66: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Header from RIT notebook\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/marcelkc/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-nigam-starr' #som-nero-phi-rit\n",
+    "%load_ext google.cloud.bigquery\n",
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|██████████| 1835715/1835715 [04:05<00:00, 7751.50rows/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"\"\"SELECT \n",
+    "v.person_id, \n",
+    "visit_start_DATETIME, \n",
+    "ARI_symptoms_2_months, \n",
+    "race_concept_id, ethnicity_concept_id, # race/ethnicity\n",
+    "gender_concept_id, # gender\n",
+    "year_of_birth, month_of_birth, day_of_birth, # age at present\n",
+    "visit_concept_id, care_site_name, # location\n",
+    "value_source_value, # test result\n",
+    "visit_occurrence_id\n",
+    "FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` v\n",
+    "\n",
+    "LEFT JOIN(\n",
+    "  #visit_occurrence_id (unique) to ARI_symptoms\n",
+    "  SELECT \n",
+    "  visit_occurrence_id,\n",
+    "  #ARI symptoms # any condition_start_DATETIME, condition_end_DATETIME within 2 months\n",
+    "  max(\n",
+    "  DATETIME_DIFF(visit_start_DATETIME, condition_start_DATETIME, MONTH) < 2\n",
+    "  OR\n",
+    "  DATETIME_DIFF(visit_start_DATETIME, condition_end_DATETIME, MONTH) < 2 \n",
+    "  )\n",
+    "  AS ARI_symptoms_2_months,\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` v\n",
+    "  LEFT JOIN (\n",
+    "    SELECT person_id, condition_start_DATETIME, condition_end_DATETIME, \n",
+    "    condition_source_value, condition_source_concept_id # might get used later\n",
+    "    FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.condition_occurrence`\n",
+    "    WHERE (condition_start_DATETIME >= '2019-11-01T00:00:00.000' OR condition_end_DATETIME >= '2019-11-01T00:00:00.000')\n",
+    "    AND\n",
+    "    (condition_source_value LIKE 'J00.%' OR\n",
+    "    condition_source_value LIKE 'J01.%' OR\n",
+    "    condition_source_value LIKE 'J02.%' OR\n",
+    "    condition_source_value LIKE 'J03.%' OR\n",
+    "    condition_source_value LIKE 'J04.%' OR\n",
+    "    condition_source_value LIKE 'J05.%' OR\n",
+    "    condition_source_value LIKE 'J06.%' OR\n",
+    "    condition_source_value LIKE 'J09.%' OR\n",
+    "    condition_source_value LIKE 'J10.%' OR\n",
+    "    condition_source_value LIKE 'J11.%' OR\n",
+    "    condition_source_value LIKE 'J12.%' OR\n",
+    "    condition_source_value LIKE 'J13.%' OR\n",
+    "    condition_source_value LIKE 'J14.%' OR\n",
+    "    condition_source_value LIKE 'J15.%' OR\n",
+    "    condition_source_value LIKE 'J16.%' OR\n",
+    "    condition_source_value LIKE 'J17.%' OR\n",
+    "    condition_source_value LIKE 'J18.%' OR\n",
+    "    condition_source_value LIKE 'J20.%' OR\n",
+    "    condition_source_value LIKE 'J21.%' OR\n",
+    "    condition_source_value LIKE 'J22' OR\n",
+    "    condition_source_value LIKE 'J80' OR\n",
+    "    condition_source_value LIKE 'A37.91' OR\n",
+    "    condition_source_value LIKE 'A37.01' OR\n",
+    "    condition_source_value LIKE 'A37.11' OR\n",
+    "    condition_source_value LIKE 'A37.81' OR\n",
+    "    condition_source_value LIKE 'A48.1' OR\n",
+    "    condition_source_value LIKE 'B25.0' OR\n",
+    "    condition_source_value LIKE 'B44.0' OR\n",
+    "    condition_source_value LIKE 'B97.4' OR\n",
+    "    condition_source_value LIKE 'U07.1')\n",
+    "  ) USING (person_id)\n",
+    "  WHERE visit_start_DATETIME >= '2020-02-01T00:00:00.000' # all encounters from feb 1\n",
+    "  GROUP BY visit_occurrence_id\n",
+    "  )\n",
+    "USING (visit_occurrence_id)\n",
+    "\n",
+    "\n",
+    "LEFT JOIN (\n",
+    "  # visit_occurrence_id to covid test result (value_source_value) \n",
+    "  SELECT value_source_value, # test result\n",
+    "  visit_occurrence_id # for mapping(s)\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement`\n",
+    "  WHERE measurement_concept_id = 706170\n",
+    ") \n",
+    "USING (visit_occurrence_id)\n",
+    "\n",
+    "INNER JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.person` USING (person_id)\n",
+    "\n",
+    "LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.care_site` c  \n",
+    "ON c.care_site_id = v.care_site_id\n",
+    "\n",
+    "WHERE visit_start_DATETIME >= '2020-02-01T00:00:00.000' # all encounters from feb 1\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.to_dataframe(progress_bar_type='tqdm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31729010</td>\n",
+       "      <td>2020-03-14</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LAB BVMG PLEASANTON</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159694443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30424583</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>11</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STANFORD SENIOR CARE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>157916468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>30106192</td>\n",
+       "      <td>2020-03-01</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38003563</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>INTERNAL MEDICINE SAMARITAN SAN JOSE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>161109490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31496809</td>\n",
+       "      <td>2020-04-08</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>VCP LAB DRAW</td>\n",
+       "      <td>None</td>\n",
+       "      <td>163427116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31100057</td>\n",
+       "      <td>2020-03-07</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>INTERNAL MEDICINE MANOUKIAN MOUNTAIN VIEW</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160595775</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   person_id visit_start_DATETIME ARI_symptoms_2_months  race_concept_id  \\\n",
+       "0   31729010           2020-03-14                  None             8527   \n",
+       "1   30424583           2020-02-11                  True             8527   \n",
+       "2   30106192           2020-03-01                  True                0   \n",
+       "3   31496809           2020-04-08                  None             8527   \n",
+       "4   31100057           2020-03-07                  True             8527   \n",
+       "\n",
+       "   ethnicity_concept_id  gender_concept_id  year_of_birth  month_of_birth  \\\n",
+       "0              38003564               8532           1932               9   \n",
+       "1              38003564               8507           1932              11   \n",
+       "2              38003563               8532           1931               5   \n",
+       "3              38003564               8507           1930              11   \n",
+       "4                     0               8507           1931               9   \n",
+       "\n",
+       "   day_of_birth  visit_concept_id                             care_site_name  \\\n",
+       "0            11                 0                        LAB BVMG PLEASANTON   \n",
+       "1            27                 0                       STANFORD SENIOR CARE   \n",
+       "2            31                 0       INTERNAL MEDICINE SAMARITAN SAN JOSE   \n",
+       "3             8                 0                               VCP LAB DRAW   \n",
+       "4            18                 0  INTERNAL MEDICINE MANOUKIAN MOUNTAIN VIEW   \n",
+       "\n",
+       "  value_source_value  visit_occurrence_id  \n",
+       "0               None            159694443  \n",
+       "1               None            157916468  \n",
+       "2               None            161109490  \n",
+       "3               None            163427116  \n",
+       "4               None            160595775  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.to_csv(\"long_table_cdc.csv\", sep=';')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "# positive_test\n",
+    "positive_value_source_value = lambda x: x in ['Detected', 'DETECTED', 'Pos']\n",
+    "pos_test_bool = df['value_source_value'].apply(positive_value_source_value).rename('positive')\n",
+    "# tested\n",
+    "test_bool = df['value_source_value'].notnull().rename('tested')\n",
+    "# ARI_sympt\n",
+    "ARI_2_mon = df['ARI_symptoms_2_months'] == True\n",
+    "ARI_2_mon = ARI_2_mon.rename('ARI_symptoms_2_months_bool')\n",
+    "# age results\n",
+    "visit_dt = df['visit_start_DATETIME'].apply(lambda x : pd.to_datetime(x))\n",
+    "pat_bd_dt = df[['year_of_birth', 'month_of_birth', 'day_of_birth']].apply(lambda x: pd.to_datetime('-'.join([str(i) for i in x])), axis=1)\n",
+    "pat_age = visit_dt - pat_bd_dt\n",
+    "pat_age = pat_age.apply(lambda x: x.days/365.25).rename('age')\n",
+    "# age categorical\n",
+    "bins = [0, 5, 18, 50, 65, 75, 85, 100]\n",
+    "labels = [\"[{0}, {1})\".format(i, j) for i, j in zip(bins[:-1], bins[1:])]\n",
+    "age_cat = pd.cut(pat_age, bins, labels=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>positive</th>\n",
+       "      <th>tested</th>\n",
+       "      <th>age</th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "      <th>ARI_symptoms_2_months_bool</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31729010</td>\n",
+       "      <td>2020-03-14</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LAB BVMG PLEASANTON</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159694443</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>30424583</td>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>11</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STANFORD SENIOR CARE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>157916468</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>30106192</td>\n",
+       "      <td>2020-03-01</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38003563</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>INTERNAL MEDICINE SAMARITAN SAN JOSE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>161109490</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31496809</td>\n",
+       "      <td>2020-04-08</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>VCP LAB DRAW</td>\n",
+       "      <td>None</td>\n",
+       "      <td>163427116</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31100057</td>\n",
+       "      <td>2020-03-07</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>INTERNAL MEDICINE MANOUKIAN MOUNTAIN VIEW</td>\n",
+       "      <td>None</td>\n",
+       "      <td>160595775</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   positive  tested        age  person_id visit_start_DATETIME  \\\n",
+       "0     False   False  [85, 100)   31729010           2020-03-14   \n",
+       "1     False   False  [85, 100)   30424583           2020-02-11   \n",
+       "2     False   False  [85, 100)   30106192           2020-03-01   \n",
+       "3     False   False  [85, 100)   31496809           2020-04-08   \n",
+       "4     False   False  [85, 100)   31100057           2020-03-07   \n",
+       "\n",
+       "  ARI_symptoms_2_months  race_concept_id  ethnicity_concept_id  \\\n",
+       "0                  None             8527              38003564   \n",
+       "1                  True             8527              38003564   \n",
+       "2                  True                0              38003563   \n",
+       "3                  None             8527              38003564   \n",
+       "4                  True             8527                     0   \n",
+       "\n",
+       "   gender_concept_id  year_of_birth  month_of_birth  day_of_birth  \\\n",
+       "0               8532           1932               9            11   \n",
+       "1               8507           1932              11            27   \n",
+       "2               8532           1931               5            31   \n",
+       "3               8507           1930              11             8   \n",
+       "4               8507           1931               9            18   \n",
+       "\n",
+       "   visit_concept_id                             care_site_name  \\\n",
+       "0                 0                        LAB BVMG PLEASANTON   \n",
+       "1                 0                       STANFORD SENIOR CARE   \n",
+       "2                 0       INTERNAL MEDICINE SAMARITAN SAN JOSE   \n",
+       "3                 0                               VCP LAB DRAW   \n",
+       "4                 0  INTERNAL MEDICINE MANOUKIAN MOUNTAIN VIEW   \n",
+       "\n",
+       "  value_source_value  visit_occurrence_id  ARI_symptoms_2_months_bool  total  \n",
+       "0               None            159694443                       False      1  \n",
+       "1               None            157916468                        True      1  \n",
+       "2               None            161109490                        True      1  \n",
+       "3               None            163427116                       False      1  \n",
+       "4               None            160595775                        True      1  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all = pd.concat([pos_test_bool, test_bool, age_cat, df, ARI_2_mon], axis=1)\n",
+    "df_all['total'] = 1\n",
+    "df_all.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_col(df, col_nm):\n",
+    "    print(df[col_nm].value_counts())\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    df[col_nm].value_counts().plot(kind='bar')\n",
+    "    \n",
+    "    tbl = pd.pivot_table(df, values=['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool'], index=['visit_concept_id'],\n",
+    "                        columns=[col_nm], aggfunc=np.sum)\n",
+    "    return tbl.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_groupby_max(df, group_by_ls):\n",
+    "    df_dups_ind = df.duplicated(group_by_ls, keep=False)\n",
+    "    df_dups = df[:][df_dups_ind]\n",
+    "    df_n_dups = df[:][df_dups_ind == False]\n",
+    "\n",
+    "    print(list(map(len, [df_dups_ind, df_dups, df_n_dups])))\n",
+    "\n",
+    "    uniq_by_grp = [grp.sort_values(['positive', 'tested', 'care_site_name'], ascending=False).iloc[0]\n",
+    "        for ind, grp in df_dups.groupby(group_by_ls, sort=False)]\n",
+    "    df_d_dups = pd.concat(uniq_by_grp,axis=1).T\n",
+    "\n",
+    "    df_uniq_by_grp = pd.concat([df_d_dups, df_n_dups],axis=0)\n",
+    "    print(list(map(len, [df_d_dups, df_n_dups, df_uniq_by_grp])))\n",
+    "    return df_uniq_by_grp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1835715, 32244, 1803471]\n",
+      "[15968, 1803471, 1819439]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_vids = get_groupby_max(df_all, ['visit_occurrence_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1819439, 1726771, 92668]\n",
+      "[269963, 92668, 362631]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_uniq_person_times = get_groupby_max(df_uniq_vids, ['person_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 0]\n",
+      "tested 12320\n",
+      "[0 1]\n",
+      "positive 1087\n",
+      "[1]\n",
+      "total 362631\n",
+      "[1 0]\n",
+      "ARI_symptoms_2_months_bool 25237\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df_uniq_person_times.copy()\n",
+    "# should all be [0,1]\n",
+    "for elt in ['tested', 'positive', 'total', 'ARI_symptoms_2_months_bool']:\n",
+    "    df[elt] = df[elt].astype('int32')\n",
+    "    print(df[elt].unique())\n",
+    "    print(elt, np.sum(df[elt]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total:362631\n",
+      "person_id:362631\n",
+      "care_site_name:1387\n",
+      "visit_occurrence_id:362631\n",
+      "positive                           0\n",
+      "tested                             0\n",
+      "age                              611\n",
+      "person_id                          0\n",
+      "visit_start_DATETIME               0\n",
+      "ARI_symptoms_2_months         336555\n",
+      "race_concept_id                    0\n",
+      "ethnicity_concept_id               0\n",
+      "gender_concept_id                  0\n",
+      "year_of_birth                      0\n",
+      "month_of_birth                     0\n",
+      "day_of_birth                       0\n",
+      "visit_concept_id                   0\n",
+      "care_site_name                  3342\n",
+      "value_source_value            350311\n",
+      "visit_occurrence_id                0\n",
+      "ARI_symptoms_2_months_bool         0\n",
+      "total                              0\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>positive</th>\n",
+       "      <th>tested</th>\n",
+       "      <th>age</th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>visit_start_DATETIME</th>\n",
+       "      <th>ARI_symptoms_2_months</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>month_of_birth</th>\n",
+       "      <th>day_of_birth</th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>care_site_name</th>\n",
+       "      <th>value_source_value</th>\n",
+       "      <th>visit_occurrence_id</th>\n",
+       "      <th>ARI_symptoms_2_months_bool</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32031651</td>\n",
+       "      <td>2020-04-26 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>163856137</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32168960</td>\n",
+       "      <td>2020-04-23 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8527</td>\n",
+       "      <td>38003564</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Detected</td>\n",
+       "      <td>163982679</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>32410794</td>\n",
+       "      <td>2020-04-08 00:00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>HILLVIEW LABORATORY</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>163453750</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>31985357</td>\n",
+       "      <td>2020-04-02 00:00:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38003563</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>8</td>\n",
+       "      <td>28</td>\n",
+       "      <td>9201</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>163986642</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>30531941</td>\n",
+       "      <td>2020-03-03 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8532</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9202</td>\n",
+       "      <td>STANFORD EXPRESS CARE SAN JOSE</td>\n",
+       "      <td>Not Detected</td>\n",
+       "      <td>163450296</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     positive tested        age person_id visit_start_DATETIME  \\\n",
+       "492     False   True  [85, 100)  32031651  2020-04-26 00:00:00   \n",
+       "543      True   True  [85, 100)  32168960  2020-04-23 00:00:00   \n",
+       "689     False   True  [85, 100)  32410794  2020-04-08 00:00:00   \n",
+       "779     False   True  [85, 100)  31985357  2020-04-02 00:00:00   \n",
+       "1523    False   True  [85, 100)  30531941  2020-03-03 00:00:00   \n",
+       "\n",
+       "     ARI_symptoms_2_months race_concept_id ethnicity_concept_id  \\\n",
+       "492                   True            8527             38003564   \n",
+       "543                   True            8527             38003564   \n",
+       "689                   None               0                    0   \n",
+       "779                   None               0             38003563   \n",
+       "1523                  True               0                    0   \n",
+       "\n",
+       "     gender_concept_id year_of_birth month_of_birth day_of_birth  \\\n",
+       "492               8507          1931              5            3   \n",
+       "543               8507          1932              9            2   \n",
+       "689               8532          1932             12           22   \n",
+       "779               8532          1932              8           28   \n",
+       "1523              8532          1932              4            9   \n",
+       "\n",
+       "     visit_concept_id                  care_site_name value_source_value  \\\n",
+       "492              9201                            None       Not Detected   \n",
+       "543              9201                            None           Detected   \n",
+       "689                 0             HILLVIEW LABORATORY       Not Detected   \n",
+       "779              9201                            None       Not Detected   \n",
+       "1523             9202  STANFORD EXPRESS CARE SAN JOSE       Not Detected   \n",
+       "\n",
+       "     visit_occurrence_id ARI_symptoms_2_months_bool total  \n",
+       "492            163856137                       True     1  \n",
+       "543            163982679                       True     1  \n",
+       "689            163453750                      False     1  \n",
+       "779            163986642                      False     1  \n",
+       "1523           163450296                       True     1  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#person_id, care_site_name, visit_concept_id\n",
+    "len_uniq = lambda col: len(df_uniq_person_times[col].unique())\n",
+    "my_disp = lambda col: print('{}:{}'.format(col, len_uniq(col)))\n",
+    "\n",
+    "print('total:{}'.format(len(df_uniq_person_times)))\n",
+    "my_disp('person_id')\n",
+    "my_disp('care_site_name')\n",
+    "my_disp('visit_occurrence_id')\n",
+    "pre='uniq_'\n",
+    "df['care_site_name'].value_counts().to_csv(pre+'care_counts_all_visit.csv')\n",
+    "df['care_site_name'][df['ARI_symptoms_2_months_bool'] == 1].value_counts().to_csv(pre+'care_counts_ari_visit.csv')\n",
+    "df['care_site_name'][df['tested'] == 1].value_counts().to_csv(pre+'care_counts_test_visit.csv')\n",
+    "df['care_site_name'][df['positive'] == 1].value_counts().to_csv(pre+'care_counts_pos_test_visit.csv')\n",
+    "print('null_vals')\n",
+    "print(df.isnull().sum())\n",
+    "df_uniq_person_times.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18, 50)     119791\n",
+      "[50, 65)      77156\n",
+      "[65, 75)      53162\n",
+      "[5, 18)       47461\n",
+      "[75, 85)      32317\n",
+      "[0, 5)        24292\n",
+      "[85, 100)      7841\n",
+      "Name: age, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>1833.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>2693.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>5252.0</td>\n",
+       "      <td>376.0</td>\n",
+       "      <td>2513.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>2031.0</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>3611.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>2332.0</td>\n",
+       "      <td>382.0</td>\n",
+       "      <td>914.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>1217.0</td>\n",
+       "      <td>233.0</td>\n",
+       "      <td>364.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>663.0</td>\n",
+       "      <td>133.0</td>\n",
+       "      <td>161.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>195.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">positive</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>419.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>220.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>117.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>70.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">tested</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>408.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>5010.0</td>\n",
+       "      <td>445.0</td>\n",
+       "      <td>643.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>457.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>1926.0</td>\n",
+       "      <td>514.0</td>\n",
+       "      <td>425.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>1003.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>564.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>202.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">total</th>\n",
+       "      <th>[0, 5)</th>\n",
+       "      <td>12538.0</td>\n",
+       "      <td>1010.0</td>\n",
+       "      <td>10744.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[18, 50)</th>\n",
+       "      <td>89804.0</td>\n",
+       "      <td>7113.0</td>\n",
+       "      <td>22862.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[5, 18)</th>\n",
+       "      <td>26507.0</td>\n",
+       "      <td>3506.0</td>\n",
+       "      <td>17448.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[50, 65)</th>\n",
+       "      <td>58225.0</td>\n",
+       "      <td>6601.0</td>\n",
+       "      <td>12325.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[65, 75)</th>\n",
+       "      <td>40397.0</td>\n",
+       "      <td>4886.0</td>\n",
+       "      <td>7876.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[75, 85)</th>\n",
+       "      <td>25004.0</td>\n",
+       "      <td>2618.0</td>\n",
+       "      <td>4693.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[85, 100)</th>\n",
+       "      <td>6248.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>1097.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                         0       9201     9202  9203\n",
+       "                           age                                      \n",
+       "ARI_symptoms_2_months_bool [0, 5)      1833.0    89.0   2693.0   NaN\n",
+       "                           [18, 50)    5252.0   376.0   2513.0   2.0\n",
+       "                           [5, 18)     2031.0   156.0   3611.0   NaN\n",
+       "                           [50, 65)    2332.0   382.0    914.0   0.0\n",
+       "                           [65, 75)    1217.0   233.0    364.0   0.0\n",
+       "                           [75, 85)     663.0   133.0    161.0   0.0\n",
+       "                           [85, 100)    195.0    38.0     46.0   NaN\n",
+       "positive                   [0, 5)         5.0     0.0      0.0   NaN\n",
+       "                           [18, 50)     419.0    45.0     32.0   0.0\n",
+       "                           [5, 18)       23.0     0.0      1.0   NaN\n",
+       "                           [50, 65)     220.0    36.0     34.0   0.0\n",
+       "                           [65, 75)     117.0    13.0     10.0   0.0\n",
+       "                           [75, 85)      70.0    22.0      5.0   0.0\n",
+       "                           [85, 100)     28.0     5.0      2.0   NaN\n",
+       "tested                     [0, 5)       408.0     6.0      1.0   NaN\n",
+       "                           [18, 50)    5010.0   445.0    643.0   0.0\n",
+       "                           [5, 18)      457.0     4.0     19.0   NaN\n",
+       "                           [50, 65)    1926.0   514.0    425.0   1.0\n",
+       "                           [65, 75)    1003.0   249.0    156.0   1.0\n",
+       "                           [75, 85)     564.0   174.0     54.0   0.0\n",
+       "                           [85, 100)    202.0    48.0     10.0   NaN\n",
+       "total                      [0, 5)     12538.0  1010.0  10744.0   NaN\n",
+       "                           [18, 50)   89804.0  7113.0  22862.0  12.0\n",
+       "                           [5, 18)    26507.0  3506.0  17448.0   NaN\n",
+       "                           [50, 65)   58225.0  6601.0  12325.0   5.0\n",
+       "                           [65, 75)   40397.0  4886.0   7876.0   3.0\n",
+       "                           [75, 85)   25004.0  2618.0   4693.0   2.0\n",
+       "                           [85, 100)   6248.0   496.0   1097.0   NaN"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8527    175590\n",
+      "0       105715\n",
+      "8515     62879\n",
+      "8516     13861\n",
+      "8557      3511\n",
+      "8657      1075\n",
+      "Name: race_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>3820.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>3930.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>2496.0</td>\n",
+       "      <td>234.0</td>\n",
+       "      <td>1987.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>564.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>6419.0</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>3962.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>192.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>643.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>169.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>5235.0</td>\n",
+       "      <td>361.0</td>\n",
+       "      <td>299.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>218.0</td>\n",
+       "      <td>234.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>234.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>2978.0</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>711.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>73684.0</td>\n",
+       "      <td>7481.0</td>\n",
+       "      <td>24549.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8515</th>\n",
+       "      <td>44401.0</td>\n",
+       "      <td>4173.0</td>\n",
+       "      <td>14301.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8516</th>\n",
+       "      <td>10296.0</td>\n",
+       "      <td>850.0</td>\n",
+       "      <td>2713.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8527</th>\n",
+       "      <td>127590.0</td>\n",
+       "      <td>13384.0</td>\n",
+       "      <td>34601.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8557</th>\n",
+       "      <td>2591.0</td>\n",
+       "      <td>267.0</td>\n",
+       "      <td>653.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8657</th>\n",
+       "      <td>772.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>228.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                0        9201     9202  9203\n",
+       "                           race_concept_id                                  \n",
+       "ARI_symptoms_2_months_bool 0                  3820.0    350.0   3930.0   0.0\n",
+       "                           8515               2496.0    234.0   1987.0   0.0\n",
+       "                           8516                564.0     60.0    318.0   0.0\n",
+       "                           8527               6419.0    740.0   3962.0   2.0\n",
+       "                           8557                192.0     19.0     84.0   NaN\n",
+       "                           8657                 35.0      4.0     21.0   NaN\n",
+       "positive                   0                   643.0     37.0     20.0   0.0\n",
+       "                           8515                 54.0     26.0     13.0   0.0\n",
+       "                           8516                 11.0      3.0      4.0   0.0\n",
+       "                           8527                169.0     50.0     45.0   0.0\n",
+       "                           8557                  3.0      5.0      2.0   NaN\n",
+       "                           8657                  2.0      0.0      0.0   NaN\n",
+       "tested                     0                  5235.0    361.0    299.0   0.0\n",
+       "                           8515               1030.0    218.0    234.0   0.0\n",
+       "                           8516                234.0     81.0     45.0   0.0\n",
+       "                           8527               2978.0    740.0    711.0   2.0\n",
+       "                           8557                 76.0     35.0     12.0   NaN\n",
+       "                           8657                 17.0      5.0      7.0   NaN\n",
+       "total                      0                 73684.0   7481.0  24549.0   1.0\n",
+       "                           8515              44401.0   4173.0  14301.0   4.0\n",
+       "                           8516              10296.0    850.0   2713.0   2.0\n",
+       "                           8527             127590.0  13384.0  34601.0  15.0\n",
+       "                           8557               2591.0    267.0    653.0   NaN\n",
+       "                           8657                772.0     75.0    228.0   NaN"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'race_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8532    206195\n",
+      "8507    156321\n",
+      "0          115\n",
+      "Name: gender_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>5796.0</td>\n",
+       "      <td>755.0</td>\n",
+       "      <td>4932.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>7727.0</td>\n",
+       "      <td>651.0</td>\n",
+       "      <td>5368.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>448.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>434.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>3815.0</td>\n",
+       "      <td>939.0</td>\n",
+       "      <td>559.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>5741.0</td>\n",
+       "      <td>501.0</td>\n",
+       "      <td>749.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>95.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8507</th>\n",
+       "      <td>109162.0</td>\n",
+       "      <td>11793.0</td>\n",
+       "      <td>35359.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8532</th>\n",
+       "      <td>150077.0</td>\n",
+       "      <td>14431.0</td>\n",
+       "      <td>41672.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                  0        9201     9202  9203\n",
+       "                           gender_concept_id                                  \n",
+       "ARI_symptoms_2_months_bool 0                       3.0      1.0      2.0   NaN\n",
+       "                           8507                 5796.0    755.0   4932.0   0.0\n",
+       "                           8532                 7727.0    651.0   5368.0   2.0\n",
+       "positive                   0                       0.0      0.0      0.0   NaN\n",
+       "                           8507                  448.0     87.0     43.0   0.0\n",
+       "                           8532                  434.0     34.0     41.0   0.0\n",
+       "tested                     0                      14.0      0.0      0.0   NaN\n",
+       "                           8507                 3815.0    939.0    559.0   2.0\n",
+       "                           8532                 5741.0    501.0    749.0   0.0\n",
+       "total                      0                      95.0      6.0     14.0   NaN\n",
+       "                           8507               109162.0  11793.0  35359.0   7.0\n",
+       "                           8532               150077.0  14431.0  41672.0  15.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'gender_concept_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38003564    258219\n",
+      "0            55465\n",
+      "38003563     48947\n",
+      "Name: ethnicity_concept_id, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>visit_concept_id</th>\n",
+       "      <th>0</th>\n",
+       "      <th>9201</th>\n",
+       "      <th>9202</th>\n",
+       "      <th>9203</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">ARI_symptoms_2_months_bool</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1580</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2617</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>2320</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1273</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>9626</td>\n",
+       "      <td>1124</td>\n",
+       "      <td>6412</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">positive</th>\n",
+       "      <th>0</th>\n",
+       "      <td>548</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>109</td>\n",
+       "      <td>27</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>225</td>\n",
+       "      <td>93</td>\n",
+       "      <td>63</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">tested</th>\n",
+       "      <th>0</th>\n",
+       "      <td>4118</td>\n",
+       "      <td>16</td>\n",
+       "      <td>153</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>1025</td>\n",
+       "      <td>226</td>\n",
+       "      <td>159</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>4427</td>\n",
+       "      <td>1198</td>\n",
+       "      <td>996</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">total</th>\n",
+       "      <th>0</th>\n",
+       "      <td>38201</td>\n",
+       "      <td>3125</td>\n",
+       "      <td>14138</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003563</th>\n",
+       "      <td>35370</td>\n",
+       "      <td>3872</td>\n",
+       "      <td>9704</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38003564</th>\n",
+       "      <td>185763</td>\n",
+       "      <td>19233</td>\n",
+       "      <td>53203</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "visit_concept_id                                   0      9201   9202  9203\n",
+       "                           ethnicity_concept_id                            \n",
+       "ARI_symptoms_2_months_bool 0                       1580     46   2617     0\n",
+       "                           38003563                2320    237   1273     0\n",
+       "                           38003564                9626   1124   6412     2\n",
+       "positive                   0                        548      1      8     0\n",
+       "                           38003563                 109     27     13     0\n",
+       "                           38003564                 225     93     63     0\n",
+       "tested                     0                       4118     16    153     0\n",
+       "                           38003563                1025    226    159     0\n",
+       "                           38003564                4427   1198    996     2\n",
+       "total                      0                      38201   3125  14138     1\n",
+       "                           38003563               35370   3872   9704     1\n",
+       "                           38003564              185763  19233  53203    20"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEkCAYAAADTtG33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFJxJREFUeJzt3X/sXfV93/HnKzgwsixAisOYTWZWXC1OmpLgAlI0KSkVGCrFRAIJJgUvQnOVwZRs1RaSTSJLypRoSpEiJUxkuJiqi8OSZniaE8ciLFGlADYNw/wI87eUBscUTEyBljQEeO+P+7FyMff7/X7881z7+3xIV/fc9/mccz5XX394cT7n3HtTVUiS1OMNQ3dAknT0MDQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHVbNHQHDrVTTz21li1bNnQ3JOmoct999z1TVYvna3fMhcayZcvYtm3b0N2QpKNKkr/saef0lCSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkbsfch/uOtGXX/e+hu3BYPf653xm6C5KmiGcakqRuhoYkqZuhIUnqNm9oJDkjyV1JHknyUJKPtfqnk/wkyf3tccnYNp9MMpPk0SQXjdVXtdpMkuvG6mcmuSfJjiRfS3J8q5/QXs+09csO5ZuXJO2fnjONl4Hfq6p3AOcD1yRZ0dbdWFVnt8cmgLbuCuCdwCrgy0mOS3Ic8CXgYmAFcOXYfj7f9rUceBa4utWvBp6tqrOAG1s7SdJA5g2Nqnqyqv6sLb8APAIsmWOT1cCGqvp5Vf0FMAOc2x4zVfVYVb0EbABWJwnwW8DX2/brgUvH9rW+LX8duKC1lyQNYL+uabTpofcA97TStUkeSLIuySmttgR4Ymyzna02W/1XgL+uqpf3qb9mX239c639vv1am2Rbkm27d+/en7ckSdoP3aGR5M3AN4CPV9XzwE3ArwJnA08CX9jbdMLmdQD1ufb12kLVzVW1sqpWLl487w9PSZIOUFdoJHkjo8D446r6E4CqeqqqXqmqV4GvMJp+gtGZwhljmy8Fds1RfwY4Ocmifeqv2VdbfxKwZ3/eoCTp0Om5eyrALcAjVfUHY/XTx5p9CHiwLW8Ermh3Pp0JLAfuBbYCy9udUsczuli+saoKuAu4rG2/BrhjbF9r2vJlwHdbe0nSAHq+RuR9wIeB7Unub7VPMbr76WxG00WPA78LUFUPJbkdeJjRnVfXVNUrAEmuBTYDxwHrquqhtr9PABuS/D7wQ0YhRXv+oyQzjM4wrjiI9ypJOkjzhkZV/SmTry1smmObG4AbJtQ3Tdquqh7jl9Nb4/W/Ay6fr4+SpCPDT4RLkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSp27yhkeSMJHcleSTJQ0k+1upvTbIlyY72fEqrJ8kXk8wkeSDJe8f2taa135FkzVj9nCTb2zZfTJK5jiFJGkbPmcbLwO9V1TuA84FrkqwArgPurKrlwJ3tNcDFwPL2WAvcBKMAAK4HzgPOBa4fC4GbWtu9261q9dmOIUkawLyhUVVPVtWfteUXgEeAJcBqYH1rth64tC2vBm6rkbuBk5OcDlwEbKmqPVX1LLAFWNXWvaWqflBVBdy2z74mHUOSNID9uqaRZBnwHuAe4LSqehJGwQK8rTVbAjwxttnOVpurvnNCnTmOsW+/1ibZlmTb7t279+ctSZL2Q3doJHkz8A3g41X1/FxNJ9TqAOrdqurmqlpZVSsXL168P5tKkvZDV2gkeSOjwPjjqvqTVn6qTS3Rnp9u9Z3AGWObLwV2zVNfOqE+1zEkSQPouXsqwC3AI1X1B2OrNgJ774BaA9wxVr+q3UV1PvBcm1raDFyY5JR2AfxCYHNb90KS89uxrtpnX5OOIUkawKKONu8DPgxsT3J/q30K+Bxwe5KrgR8Dl7d1m4BLgBngReAjAFW1J8lnga2t3Weqak9b/ihwK3Ai8K32YI5jSJIGMG9oVNWfMvm6A8AFE9oXcM0s+1oHrJtQ3wa8a0L9p5OOIUkahp8IlyR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUrd5QyPJuiRPJ3lwrPbpJD9Jcn97XDK27pNJZpI8muSisfqqVptJct1Y/cwk9yTZkeRrSY5v9RPa65m2ftmhetOSpAPTc6ZxK7BqQv3Gqjq7PTYBJFkBXAG8s23z5STHJTkO+BJwMbACuLK1Bfh829dy4Fng6la/Gni2qs4CbmztJEkDmjc0qur7wJ7O/a0GNlTVz6vqL4AZ4Nz2mKmqx6rqJWADsDpJgN8Cvt62Xw9cOrav9W3568AFrb0kaSAHc03j2iQPtOmrU1ptCfDEWJudrTZb/VeAv66ql/epv2Zfbf1zrf3rJFmbZFuSbbt37z6ItyRJmsuBhsZNwK8CZwNPAl9o9UlnAnUA9bn29fpi1c1VtbKqVi5evHiufkuSDsIBhUZVPVVVr1TVq8BXGE0/wehM4YyxpkuBXXPUnwFOTrJon/pr9tXWn0T/NJkk6TA4oNBIcvrYyw8Be++s2ghc0e58OhNYDtwLbAWWtzuljmd0sXxjVRVwF3BZ234NcMfYvta05cuA77b2kqSBLJqvQZKvAu8HTk2yE7geeH+SsxlNFz0O/C5AVT2U5HbgYeBl4JqqeqXt51pgM3AcsK6qHmqH+ASwIcnvAz8Ebmn1W4A/SjLD6AzjioN+t5KkgzJvaFTVlRPKt0yo7W1/A3DDhPomYNOE+mP8cnprvP53wOXz9U+SdOT4iXBJUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1mzc0kqxL8nSSB8dqb02yJcmO9nxKqyfJF5PMJHkgyXvHtlnT2u9Ismasfk6S7W2bLybJXMeQJA2n50zjVmDVPrXrgDurajlwZ3sNcDGwvD3WAjfBKACA64HzgHOB68dC4KbWdu92q+Y5hiRpIPOGRlV9H9izT3k1sL4trwcuHavfViN3AycnOR24CNhSVXuq6llgC7CqrXtLVf2gqgq4bZ99TTqGJGkgB3pN47SqehKgPb+t1ZcAT4y129lqc9V3TqjPdYzXSbI2ybYk23bv3n2Ab0mSNJ9DfSE8E2p1APX9UlU3V9XKqlq5ePHi/d1cktTpQEPjqTa1RHt+utV3AmeMtVsK7JqnvnRCfa5jSJIGcqChsRHYewfUGuCOsfpV7S6q84Hn2tTSZuDCJKe0C+AXApvbuheSnN/umrpqn31NOoYkaSCL5muQ5KvA+4FTk+xkdBfU54Dbk1wN/Bi4vDXfBFwCzAAvAh8BqKo9ST4LbG3tPlNVey+uf5TRHVonAt9qD+Y4hiRpIPOGRlVdOcuqCya0LeCaWfazDlg3ob4NeNeE+k8nHUOSNBw/ES5J6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSui0augPSYD590tA9OLw+/dzQPdAx6KDONJI8nmR7kvuTbGu1tybZkmRHez6l1ZPki0lmkjyQ5L1j+1nT2u9Ismasfk7b/0zbNgfTX0nSwTkU01MfqKqzq2ple30dcGdVLQfubK8BLgaWt8da4CYYhQxwPXAecC5w/d6gaW3Wjm236hD0V5J0gA7HNY3VwPq2vB64dKx+W43cDZyc5HTgImBLVe2pqmeBLcCqtu4tVfWDqirgtrF9SZIGcLChUcB3ktyXZG2rnVZVTwK057e1+hLgibFtd7baXPWdE+qvk2Rtkm1Jtu3evfsg35IkaTYHeyH8fVW1K8nbgC1JfjRH20nXI+oA6q8vVt0M3AywcuXKiW0kHVt+ff2vD92Fw2r7mu1Dd2GigzrTqKpd7flp4JuMrkk81aaWaM9Pt+Y7gTPGNl8K7JqnvnRCXZI0kAMOjSR/P8k/2LsMXAg8CGwE9t4BtQa4oy1vBK5qd1GdDzzXpq82AxcmOaVdAL8Q2NzWvZDk/HbX1FVj+5IkDeBgpqdOA77Z7oJdBPz3qvp2kq3A7UmuBn4MXN7abwIuAWaAF4GPAFTVniSfBba2dp+pqj1t+aPArcCJwLfaQ5I0kAMOjap6DPiNCfWfAhdMqBdwzSz7Wgesm1DfBrzrQPsoSTq0/BoRSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdZv60EiyKsmjSWaSXDd0fyRpIZvq0EhyHPAl4GJgBXBlkhXD9kqSFq6pDg3gXGCmqh6rqpeADcDqgfskSQvWtIfGEuCJsdc7W02SNIBFQ3dgHplQq9c1StYCa9vLv0ny6GHt1bBOBZ45UgfL54/UkRaEI/q34z9NGj46CEd27P2LI/73+8c9jaY9NHYCZ4y9Xgrs2rdRVd0M3HykOjWkJNuqauXQ/dD+8293dPPvNzLt01NbgeVJzkxyPHAFsHHgPknSgjXVZxpV9XKSa4HNwHHAuqp6aOBuSdKCNdWhAVBVm4BNQ/djiiyIabhjlH+7o5t/PyBVr7uuLEnSRNN+TUOSNEUMDUlSt6m/piEdrZL8U0bfYLCE0eeLdgEbq+qRQTsmHQTPNI4iSW4bug/qk+QTjL72JsC9jG4fD/BVv3jz6JDk3CS/2ZZXJPm3SS4Zul9D80L4lEqy7+dRAnwA+C5AVX3wiHdK3ZL8P+CdVfWLferHAw9V1fJheqYeSa5n9EWpi4AtwHnA/wF+G9hcVTcM17thOT01vZYCDwP/jdHURoCVwBeG7JS6vQr8I+Av96mf3tZpul0GnA2cAPwVsLSqnk/yX4B7AENDU2cl8DHgPwD/rqruT/KzqvrewP1Sn48DdybZwS+/dPPtwFnAtYP1Sr1erqpXgBeT/HlVPQ9QVT9LsqBD3+mpKZdkKXAj8BTwwap6+8BdUqckb2D09f5LGJ0p7gS2tv8YaYoluQf4QFW9mOQNVfVqq58E3FVV7x22h8MxNI4SSX4HeF9VfWrovkjHuiQnVNXPJ9RPBU6vqu0DdGsqOD119HgU+HtJVlTVw0N3RjqW7RsYSc4CfgN4ZCEHBnjL7dRKclf7vxqSfJjR929dDHwtyb8etHPSMc7xNzunp6ZUkger6l1teSuwqqp+muRNwN1V9e5heygduxx/s/NMY3r9Isnen7b9G+Bv2/LPGX1NvKTDx/E3C69pTK9/A3wnyTeAh4DvJvk28M+APxy0Z9Kxz/E3C6enpli7ve+fA7/GKOB3AndU1Y8G7Zi0ADj+JjM0JEndvKYxpZK8e2z5jUn+Y5KNSf5zuxgn6TBx/M3O0Jhet44tf47R1098ATgR+K9DdEhaQG4dW3b8jfFC+PTK2PIFwG9W1S+SfB/4vwP1SVooHH+zMDSm10lJPsTobPCEvV+xXVWVxAtR0uHl+JuFoTG9vgfs/c2Mu5OcVlVPJfmHwDMD9ktaCBx/s/DuKUlSN880pliScxmdEW9NsgJYBfyoqjYN3DXpmOf4m8wzjSnlz01Kw3H8zc7QmFJJtjP55yZPBO5ZyF+YJh1ujr/Z+TmN6fVyVb1SVS8Cr/m5SfyNaelwc/zNwtCYXi+NffL0nL3F9n04C/ofrXQEOP5m4fTUlPLnJqXhOP5mZ2hMsSQBzgWWAAXsAu4t/2jSYef4m8zQmFJJLgS+DOwAftLKSxl9B86/qqrvDNU36Vjn+JudoTGlkjwCXFxVj+9TPxPYVFXvGKRj0gLg+JudF8Kn194ffdnXT4A3HuG+SAuN428WfiJ8eq0DtibZADzRamcAVwC3DNYraWFw/M3C6akp1r664IOMLsSF0f/5bKyqhwftmLQAOP4mMzSOAkneyug7cJ4dui/SQuP4ey2vaUypJG9PsiHJ08A9wL1Jnm61ZcP2Tjq2Of5mZ2hMr68B32T0QaLlVbUcOB34n8CGQXsmHfscf7NwempKJdnR/qHu1zpJB8/xNzvvnppe9yX5MrCe1969sQb44WC9khYGx98sPNOYUkmOB64GVrPP3RvALZO+F0fSoeH4m52hIUnq5oXwKZXknyRZl+SzSd6c5CtJHkzyPxb63RvS4eb4m52hMb1uBbYCfwvcDTzK6Ocnv83o06qSDp9bcfxN5PTUlEryw6p6T1v+cVW9fdI6SYee4292nmlMr1eT/FqSc4E3JVkJkOQs4LhhuyYd8xx/s/CW2+n174H/xeinJS8FPpnk3cBJwL8csmPSAuD4m4XTU1MsyXnAq1W1Nck7Gc2pPlxVmwbumnTMc/xNZmhMqSTXM/pHugjYwuhnJ78H/DawuapuGLB70jHN8Tc7Q2NKJdkOnA2cAPwVsLSqnk9yInBPVb170A5KxzDH3+y8ED69Xq6qV6rqReDPq+p5gKr6GaN5VkmHj+NvFobG9HopyZva8jl7i0lOYoH/o5WOAMffLJyemlJJTpj0/TZJTmX0dc3bB+iWtCA4/mZnaEiSujk9JUnqZmhIkroZGpKkboaGJKmboSFJ6vb/ASJxFpJZEq/eAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_col(df, 'ethnicity_concept_id')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/Covid19 CDC/new_tbl_adj.ipynb b/scripts/Archive/Covid19 CDC/new_tbl_adj.ipynb
new file mode 100644
index 00000000..6d90619d
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/new_tbl_adj.ipynb	
@@ -0,0 +1,2196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
+      "  %reload_ext google.cloud.bigquery\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlu/.local/lib/python3.6/site-packages/google/auth/_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Header from RIT notebook\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import shutil\n",
+    "%matplotlib inline\n",
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/jhlu/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-nigam-starr' #som-nero-phi-rit\n",
+    "%load_ext google.cloud.bigquery\n",
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import collections\n",
+    "from IPython.display import display, HTML\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# just some formatting for figure making\n",
+    "plt.rcParams['figure.figsize'] = [12.0, 8.0]\n",
+    "plt.rcParams['xtick.labelsize'] = 15\n",
+    "plt.rcParams['ytick.labelsize'] = 15\n",
+    "plt.rcParams['legend.fontsize'] = 18\n",
+    "plt.rcParams['figure.titlesize'] = 18\n",
+    "plt.rcParams['font.size'] = 18\n",
+    "\n",
+    "pd.options.display.max_columns = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Corresponding email addresses:  marcelkc@stanford.edu, jhlu@stanford.edu\n",
+      "Code repository:  https://github.com/lujonathanh/Stanford_Starr-OMOP_COVID_MMWR\n",
+      "Run date: 2020-06-06\n",
+      "ARI start date:  2020-01-01\n",
+      "sarscov2test_concept_id: 706170\n"
+     ]
+    }
+   ],
+   "source": [
+    "metadata = collections.OrderedDict()\n",
+    "\n",
+    "metadata[\"Corresponding email addresses\"] = \"marcelkc@stanford.edu, jhlu@stanford.edu\"\n",
+    "print(\"Corresponding email addresses: \", metadata[\"Corresponding email addresses\"])\n",
+    "\n",
+    "metadata[\"Code repository\"] = \"https://github.com/lujonathanh/Stanford_Starr-OMOP_COVID_MMWR\"\n",
+    "print(\"Code repository: \", metadata[\"Code repository\"])\n",
+    "\n",
+    "run_date = datetime.datetime.today().strftime(\"%Y-%m-%d\")\n",
+    "metadata[\"Run date\"] = run_date\n",
+    "print(\"Run date:\", run_date)\n",
+    "\n",
+    "ARI_start_date = '2020-01-01'\n",
+    "metadata[\"ARI_start_date\"] = ARI_start_date\n",
+    "print(\"ARI start date: \", ARI_start_date)\n",
+    "\n",
+    "sarscov2test_concept_id = 706170\n",
+    "metadata[\"sarscov2test_concept_id\"] = sarscov2test_concept_id\n",
+    "print(\"sarscov2test_concept_id:\", sarscov2test_concept_id)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pull data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "WITH\n",
+    "\n",
+    "-- all visits involving an acute respiratory illness ICD-10 diagnosis with date since ARI_start_date\n",
+    "recentARI AS\n",
+    "( \n",
+    "SELECT\n",
+    "    condOccur.person_id, visit_occurrence_id, condition_start_DATE, condition_start_DATETIME, \n",
+    "    condition_source_value, condition_source_concept_id,\n",
+    "    visit_concept_id, care_site_name\n",
+    "  FROM\n",
+    "    `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.condition_occurrence` AS condOccur \n",
+    "  LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` USING (visit_occurrence_id)\n",
+    "  LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.care_site` USING (care_site_id)\n",
+    "  WHERE\n",
+    "    condition_start_DATE >= \"\"\" + \"'\" +  ARI_start_date + \"'\" + \"\"\" AND\n",
+    "    ( condition_source_value LIKE 'J00.%' OR\n",
+    "      condition_source_value LIKE 'J01.%' OR\n",
+    "      condition_source_value LIKE 'J02.%' OR\n",
+    "      condition_source_value LIKE 'J03.%' OR\n",
+    "      condition_source_value LIKE 'J04.%' OR\n",
+    "      condition_source_value LIKE 'J05.%' OR\n",
+    "      condition_source_value LIKE 'J06.%' OR\n",
+    "      condition_source_value LIKE 'J09.%' OR\n",
+    "      condition_source_value LIKE 'J10.%' OR\n",
+    "      condition_source_value LIKE 'J11.%' OR\n",
+    "      condition_source_value LIKE 'J12.%' OR\n",
+    "      condition_source_value LIKE 'J13.%' OR\n",
+    "      condition_source_value LIKE 'J14.%' OR\n",
+    "      condition_source_value LIKE 'J15.%' OR\n",
+    "      condition_source_value LIKE 'J16.%' OR\n",
+    "      condition_source_value LIKE 'J17.%' OR\n",
+    "      condition_source_value LIKE 'J18.%' OR\n",
+    "      condition_source_value LIKE 'J20.%' OR\n",
+    "      condition_source_value LIKE 'J21.%' OR\n",
+    "      condition_source_value LIKE 'J22' OR\n",
+    "      condition_source_value LIKE 'J80' OR\n",
+    "      condition_source_value LIKE 'A37.91' OR\n",
+    "      condition_source_value LIKE 'A37.01' OR\n",
+    "      condition_source_value LIKE 'A37.11' OR\n",
+    "      condition_source_value LIKE 'A37.81' OR\n",
+    "      condition_source_value LIKE 'A48.1' OR\n",
+    "      condition_source_value LIKE 'B25.0' OR\n",
+    "      condition_source_value LIKE 'B44.0' OR\n",
+    "      condition_source_value LIKE 'B97.4' OR\n",
+    "      condition_source_value LIKE 'U07.1'\n",
+    "    )\n",
+    "),\n",
+    "\n",
+    "\n",
+    "-- patient's first ARI visit in date range\n",
+    "firstARIInfo AS\n",
+    "(\n",
+    "SELECT person_id, firstARIDateTime, firstARI_visit_concept_id, firstARI_care_site_name\n",
+    "FROM (\n",
+    "  SELECT \n",
+    "    person_id, \n",
+    "    condition_start_DATETIME AS firstARIDateTime,\n",
+    "    visit_concept_id AS firstARI_visit_concept_id,\n",
+    "    care_site_name AS firstARI_care_site_name,\n",
+    "    row_number() OVER (partition by person_id ORDER BY condition_start_DATETIME ASC) AS row_num\n",
+    "   FROM recentARI\n",
+    ") AS r\n",
+    "WHERE r.row_num=1\n",
+    "),\n",
+    "\n",
+    "\n",
+    "-- patient's last ARI visit in date range\n",
+    "lastARIInfo AS\n",
+    "(\n",
+    "SELECT person_id, lastARIDateTime, lastARI_visit_concept_id, lastARI_care_site_name\n",
+    "FROM (\n",
+    "  SELECT \n",
+    "    person_id, \n",
+    "    condition_start_DATETIME AS lastARIDateTime,\n",
+    "    visit_concept_id AS lastARI_visit_concept_id,\n",
+    "    care_site_name AS lastARI_care_site_name,\n",
+    "    row_number() OVER (partition by person_id ORDER BY condition_start_DATETIME DESC) AS row_num\n",
+    "   FROM recentARI\n",
+    ") AS r\n",
+    "WHERE r.row_num=1\n",
+    "),\n",
+    "\n",
+    "\n",
+    "-- Inner join patient's first and last ARI visits\n",
+    "recentARIDateTimeRange AS\n",
+    "(\n",
+    "SELECT person_id, lastARIDateTime, lastARI_visit_concept_id, lastARI_care_site_name,\n",
+    "firstARIDateTime, firstARI_visit_concept_id, firstARI_care_site_name\n",
+    "FROM firstARIInfo\n",
+    "INNER JOIN lastARIInfo USING (person_id)\n",
+    ")\n",
+    ",\n",
+    "\n",
+    "\n",
+    "-- all SARS-CoV2 NAA Test Results\n",
+    "resultsSARSCoV2Tests AS\n",
+    "(\n",
+    "  SELECT meas.person_id, visit_occurrence_id, measurement_DATE, measurement_DATETIME, value_source_value, value_source_value IN ('Detected','Pos','Positive') AS detectedSARSCoV2, visit_concept_id, care_site_name\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement` as meas\n",
+    "  LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` USING (visit_occurrence_id)\n",
+    "  LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.care_site` USING (care_site_id)\n",
+    "  WHERE measurement_concept_id = \"\"\" + str(sarscov2test_concept_id) + \"\"\"\n",
+    "  AND meas.value_source_value is NOT NULL -- Only capture usable results and not extra descriptors\n",
+    ") ,\n",
+    "\n",
+    "-- first SARS-CoV2 NAA Test\n",
+    "firstTestInfo AS\n",
+    "(\n",
+    "SELECT person_id, firstSARSCoV2TestedDateTime, firstTest_visit_concept_id, firstTest_care_site_name\n",
+    "FROM (\n",
+    "  SELECT \n",
+    "    person_id, \n",
+    "    measurement_DATETIME AS firstSARSCoV2TestedDateTime,\n",
+    "    visit_concept_id AS firstTest_visit_concept_id,\n",
+    "    care_site_name AS firstTest_care_site_name,\n",
+    "    row_number() OVER (partition by person_id ORDER BY measurement_DATETIME ASC) AS row_num\n",
+    "   FROM resultsSARSCoV2Tests\n",
+    ") AS r\n",
+    "WHERE r.row_num=1\n",
+    "),\n",
+    "\n",
+    "-- last SARS-CoV2 NAA Test\n",
+    "lastTestInfo AS\n",
+    "(\n",
+    "SELECT person_id, lastSARSCoV2TestedDateTime, lastTest_visit_concept_id, lastTest_care_site_name\n",
+    "FROM (\n",
+    "  SELECT \n",
+    "    person_id, \n",
+    "    measurement_DATETIME AS lastSARSCoV2TestedDateTime,\n",
+    "    visit_concept_id AS lastTest_visit_concept_id,\n",
+    "    care_site_name AS lastTest_care_site_name,\n",
+    "    row_number() OVER (partition by person_id ORDER BY measurement_DATETIME DESC) AS row_num\n",
+    "   FROM resultsSARSCoV2Tests\n",
+    ") AS r\n",
+    "WHERE r.row_num=1\n",
+    "),\n",
+    "\n",
+    "-- Accounting for multiple possible tests per patient, Inner join patient's first and last SARS coV2 tests\n",
+    "resultsSARSCoV2DateTimeRange AS(\n",
+    "  SELECT \n",
+    "    person_id, \n",
+    "    firstSARSCoV2TestedDateTime, firstTest_visit_concept_id, firstTest_care_site_name,\n",
+    "    lastSARSCoV2TestedDateTime, lastTest_visit_concept_id, lastTest_care_site_name,\n",
+    "    firstSARSCoV2DetectedDateTime, lastSARSCoV2DetectedDateTime\n",
+    "  FROM firstTestInfo\n",
+    "  INNER JOIN lastTestInfo USING (person_id)\n",
+    "  \n",
+    "  -- Left outer join, because not everyone who had a test will have a positive test result to join to\n",
+    "  \n",
+    "  LEFT JOIN \n",
+    "  (\n",
+    "    SELECT person_id, MIN(measurement_DATETIME) AS firstSARSCoV2DetectedDateTime, MAX(measurement_DATETIME) AS lastSARSCoV2DetectedDateTime\n",
+    "    FROM resultsSARSCoV2Tests\n",
+    "    WHERE resultsSARSCoV2Tests.detectedSARSCoV2\n",
+    "    GROUP BY person_id \n",
+    "  ) AS firstLastDetectedSARSCoV2Tests\n",
+    "  USING (person_id)\n",
+    "  ),\n",
+    "\n",
+    "-- at this point:\n",
+    "--  recentARIDateTimeRange has one row per patient (41,341 rows as of 2020-04-20 cdm_release_DATE)\n",
+    "--  resultsSARSCoV2DateTimeRange has one row per patient (14,838)\n",
+    "\n",
+    "-- now let's combine: \n",
+    "-- Each with first and last dates of occurrences of ARI (after initial \"recent\" cutoff date) or SARS-CoV2 test results (including timing of positive/detected result, null if no positive/detected results)\n",
+    "\n",
+    "recentARIandSARSCoV2DateTimeRange AS\n",
+    "( -- Full outer join in both directions because some people with ARI diagnosis don't get SARS-CoV2 testing and vice versa\n",
+    "  SELECT *,\n",
+    "      DATETIME_DIFF( firstSARSCoV2TestedDateTime, firstARIDateTime, DAY ) daysFirstARItoFirstSARSCoV2Tested,\n",
+    "      DATETIME_DIFF( firstSARSCoV2TestedDateTime, lastARIDateTime, DAY ) daysLastARItoFirstSARSCoV2Tested,\n",
+    "      DATETIME_DIFF( lastSARSCoV2TestedDateTime, firstARIDateTime, DAY ) daysFirstARItoLastSARSCoV2Tested,\n",
+    "      DATETIME_DIFF( lastSARSCoV2TestedDateTime, lastARIDateTime, DAY ) daysLastARItoLastSARSCoV2Tested,\n",
+    "\n",
+    "      DATETIME_DIFF( firstSARSCoV2DetectedDateTime, firstARIDateTime, DAY ) daysFirstARItoFirstSARSCoV2Detected,\n",
+    "      DATETIME_DIFF( firstSARSCoV2DetectedDateTime, lastARIDateTime, DAY ) daysLastARItoFirstSARSCoV2Detected,\n",
+    "      DATETIME_DIFF( lastSARSCoV2DetectedDateTime, firstARIDateTime, DAY ) daysFirstARItoLastSARSCoV2Detected,\n",
+    "      DATETIME_DIFF( lastSARSCoV2DetectedDateTime, lastARIDateTime, DAY ) daysLastARItoLastSARSCoV2Detected,\n",
+    "  FROM \n",
+    "    recentARIDateTimeRange FULL JOIN \n",
+    "    resultsSARSCoV2DateTimeRange USING (person_id)\n",
+    "  -- 4,091 inner join results (recent ARI diagnosis code AND SARS-CoV2 test results exist, but not necessarily in the correct datetime order)\n",
+    "  -- 52,088 full outer join results (recent ARI diagnosi code OR SARS-CoV2 test results)\n",
+    ")\n",
+    "\n",
+    "SELECT \n",
+    "    recentARIandSARSCoV2DateTimeRange.*,\n",
+    "    year_of_birth, DATE_DIFF('2020-02-01', DATE(birth_DATETIME), YEAR) ageAsOfFeb2020,\n",
+    "    person.gender_concept_id , genderConc.concept_name AS genderConcept,\n",
+    "    person.race_source_value, person.race_concept_id, raceConc.concept_name as raceConcept,\n",
+    "    person.ethnicity_source_value, person.ethnicity_concept_id, ethnicityConc.concept_name as ethnicityConcept\n",
+    "  FROM \n",
+    "    recentARIandSARSCoV2DateTimeRange \n",
+    "    JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.person` AS person USING (person_id) \n",
+    "    LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` AS genderConc ON (person.gender_concept_id = genderConc.concept_id)\n",
+    "    LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` AS raceConc ON (person.race_concept_id = raceConc.concept_id)\n",
+    "    LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` AS ethnicityConc ON (person.ethnicity_concept_id = ethnicityConc.concept_id)\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|██████████| 76275/76275 [00:23<00:00, 3101.13rows/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_job = client.query(query)\n",
+    "df = query_job.to_dataframe(progress_bar_type='tqdm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sanity checks on what we pulled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|██████████| 1/1 [00:00<00:00,  5.87rows/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check on null SARS-CoV2 NAA records:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nRecords</th>\n",
+       "      <th>nPerson</th>\n",
+       "      <th>nVisits</th>\n",
+       "      <th>nonNullValues</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68018</td>\n",
+       "      <td>46407</td>\n",
+       "      <td>65023</td>\n",
+       "      <td>68018</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "% non-null values among pulled records 100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_null_query = \"\"\"\n",
+    "WITH\n",
+    "\n",
+    "resultsSARSCoV2Tests AS\n",
+    "(\n",
+    "  SELECT meas.person_id, visit_occurrence_id, measurement_DATE, measurement_DATETIME, value_source_value, value_source_value IN ('Detected','Pos','Positive') AS detectedSARSCoV2, visit_concept_id, care_site_name\n",
+    "  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement` as meas\n",
+    "  LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.visit_occurrence` USING (visit_occurrence_id)\n",
+    "  LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.care_site` USING (care_site_id)\n",
+    "  WHERE measurement_concept_id = 706170 -- SARS-CoV2 NAA Test Result\n",
+    "  AND meas.value_source_value is NOT NULL -- Only capture usable results and not extra descriptors\n",
+    ")\n",
+    "  \n",
+    "select count(*) nRecords, count(distinct person_id) as nPerson, count(distinct visit_occurrence_id) as nVisits, countif(value_source_value is not null) as nonNullValues\n",
+    "from resultsSARSCoV2Tests \n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "tmp_null_df = client.query(check_null_query).to_dataframe(progress_bar_type='tqdm')\n",
+    "print(\"Sanity check on null SARS-CoV2 NAA records:\")\n",
+    "\n",
+    "display(HTML(tmp_null_df.to_html()))\n",
+    "\n",
+    "print(\"% non-null values among pulled records\", \n",
+    "      100.* tmp_null_df[\"nonNullValues\"][0]/tmp_null_df[\"nRecords\"][0])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "confirm person_id is unique\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('confirm person_id is unique')\n",
+    "df['person_id'].is_unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num nulls per column\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "person_id                                  0\n",
+       "lastARIDateTime                        37274\n",
+       "lastARI_visit_concept_id               38403\n",
+       "lastARI_care_site_name                 45159\n",
+       "firstARIDateTime                       37274\n",
+       "firstARI_visit_concept_id              38510\n",
+       "firstARI_care_site_name                45608\n",
+       "firstSARSCoV2TestedDateTime            29868\n",
+       "firstTest_visit_concept_id             30145\n",
+       "firstTest_care_site_name               36209\n",
+       "lastSARSCoV2TestedDateTime             29868\n",
+       "lastTest_visit_concept_id              30120\n",
+       "lastTest_care_site_name                36613\n",
+       "firstSARSCoV2DetectedDateTime          74543\n",
+       "lastSARSCoV2DetectedDateTime           74543\n",
+       "daysFirstARItoFirstSARSCoV2Tested      67142\n",
+       "daysLastARItoFirstSARSCoV2Tested       67142\n",
+       "daysFirstARItoLastSARSCoV2Tested       67142\n",
+       "daysLastARItoLastSARSCoV2Tested        67142\n",
+       "daysFirstARItoFirstSARSCoV2Detected    75485\n",
+       "daysLastARItoFirstSARSCoV2Detected     75485\n",
+       "daysFirstARItoLastSARSCoV2Detected     75485\n",
+       "daysLastARItoLastSARSCoV2Detected      75485\n",
+       "year_of_birth                              0\n",
+       "ageAsOfFeb2020                             0\n",
+       "gender_concept_id                          0\n",
+       "genderConcept                              0\n",
+       "race_source_value                          0\n",
+       "race_concept_id                            0\n",
+       "raceConcept                                0\n",
+       "ethnicity_source_value                     0\n",
+       "ethnicity_concept_id                       0\n",
+       "ethnicityConcept                           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Num nulls per column\")\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create output columns:\n",
+    "1. ARI visit\n",
+    "2. SARSCoV2 Tested\n",
+    "3. SARSCoV2 Detected\n",
+    "4. ARI visit within a close time range of SARSCoV2 Test?\n",
+    "5. ARI visit within a close time range of SARSCoV2 Detection?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Just created all the columns of deliverables for counts, out_keys. Use this to reference columns as needed\n",
+      "ARIVisitIn2020-01-01_2020-06-06\n",
+      "SARSCoV2TestedIn2020-01-01_2020-06-06\n",
+      "SARSCoV2DetectedIn2020-01-01_2020-06-06\n",
+      "firstorLastARItoFirstSARSCoV2TestedInDayRange[-1, 14]\n",
+      "firstorLastARItoFirstSARSCoV2DetectedInDayRange[-1, 14]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ARI_key = \"ARIVisitIn\" + \"_\".join([ARI_start_date, run_date])\n",
+    "ARI_description = \"Patient had a visit with ARI diagnosis in \" + \"_\".join([ARI_start_date, run_date])\n",
+    "ARI = df['lastARIDateTime'].notnull().rename(ARI_key)\n",
+    "metadata[ARI_key] = ARI_description\n",
+    "\n",
+    "tested_key = \"SARSCoV2TestedIn\" + \"_\".join([ARI_start_date, run_date])\n",
+    "tested_description = \"Patient had a SARS-CoV2 Test in \" + str([ARI_start_date, run_date])\n",
+    "tested = df['lastSARSCoV2TestedDateTime'].notnull().rename(tested_key)\n",
+    "metadata[tested_key] = tested_description\n",
+    "\n",
+    "\n",
+    "detected_key = \"SARSCoV2DetectedIn\" + \"_\".join([ARI_start_date, run_date])\n",
+    "detected_description = \"Patient had SARS-CoV2 Detected in \" + \"_\".join([ARI_start_date, run_date])\n",
+    "detected = df['lastSARSCoV2DetectedDateTime'].notnull().rename(detected_key)\n",
+    "metadata[detected_key] = detected_description\n",
+    "\n",
+    "\n",
+    "firstorLastARItoFirstSARSCoV2TestedDayRange = [-1, 14]\n",
+    "ARI_tested_key = \"firstorLastARItoFirstSARSCoV2TestedInDayRange\" + str(firstorLastARItoFirstSARSCoV2TestedDayRange)\n",
+    "tmp_ARI_tested_in_range = np.logical_or(df['daysFirstARItoFirstSARSCoV2Tested'].between(firstorLastARItoFirstSARSCoV2TestedDayRange[0],\n",
+    "                                                            firstorLastARItoFirstSARSCoV2TestedDayRange[1]),\n",
+    "                                        df['daysLastARItoFirstSARSCoV2Tested'].between(firstorLastARItoFirstSARSCoV2TestedDayRange[0],\n",
+    "                                                            firstorLastARItoFirstSARSCoV2TestedDayRange[1]))\n",
+    "ARI_tested = tmp_ARI_tested_in_range.astype('int32').rename(ARI_tested_key)\n",
+    "ARI_tested_description = \"was either the first or last ARI within the range \" + str(firstorLastARItoFirstSARSCoV2TestedDayRange) + \" days before the first SARSCoV2 test?\"\n",
+    "metadata[ARI_tested_key] = ARI_tested_description\n",
+    "metadata[\"firstorLastARItoFirstSARSCoV2TestedDayRange\"] = firstorLastARItoFirstSARSCoV2TestedDayRange\n",
+    "\n",
+    "\n",
+    "firstorLastARItoFirstSARSCoV2DetectedDayRange = [-1, 14]\n",
+    "\n",
+    "ARI_detected_key = \"firstorLastARItoFirstSARSCoV2DetectedInDayRange\" + str(firstorLastARItoFirstSARSCoV2DetectedDayRange)\n",
+    "tmp_ARI_detected_in_range = np.logical_or(df['daysFirstARItoFirstSARSCoV2Detected'].between(firstorLastARItoFirstSARSCoV2DetectedDayRange[0],\n",
+    "                                                            firstorLastARItoFirstSARSCoV2DetectedDayRange[1]),\n",
+    "                                        df['daysLastARItoFirstSARSCoV2Detected'].between(firstorLastARItoFirstSARSCoV2DetectedDayRange[0],\n",
+    "                                                            firstorLastARItoFirstSARSCoV2DetectedDayRange[1]))\n",
+    "ARI_detected = tmp_ARI_detected_in_range.astype('int32').rename(ARI_detected_key)\n",
+    "ARI_detected_description = \"was either the first or last ARI within the range \" + str(firstorLastARItoFirstSARSCoV2DetectedDayRange) + \" days before the first SARSCoV2 detection?\"\n",
+    "metadata[ARI_detected_key] = ARI_detected_description\n",
+    "metadata[\"firstorLastARItoFirstSARSCoV2DetectedDayRange\"] = firstorLastARItoFirstSARSCoV2DetectedDayRange\n",
+    "\n",
+    "\n",
+    "\n",
+    "out_keys = [ARI_key, tested_key, detected_key, ARI_tested_key, ARI_detected_key]\n",
+    "print(\"Just created all the columns of deliverables for counts, out_keys. Use this to reference columns as needed\")\n",
+    "print(\"\\n\".join(out_keys))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# age categorical\n",
+    "bins = [0, 5, 18, 50, 65, 75, 85, 100]\n",
+    "labels = [\"[{:02d}, {:02d})\".format(i, j) for i, j in zip(bins[:-1], bins[1:])]\n",
+    "age_cat = pd.cut(df['ageAsOfFeb2020'], bins, labels=labels).rename('age')\n",
+    "\n",
+    "df['care_site'] = df['firstTest_care_site_name'].fillna(df['lastARI_care_site_name'])\n",
+    "df['visit_concept'] = df['firstTest_visit_concept_id'].fillna(df['lastARI_visit_concept_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>person_id</th>\n",
+       "      <th>lastARIDateTime</th>\n",
+       "      <th>lastARI_visit_concept_id</th>\n",
+       "      <th>lastARI_care_site_name</th>\n",
+       "      <th>firstARIDateTime</th>\n",
+       "      <th>firstARI_visit_concept_id</th>\n",
+       "      <th>firstARI_care_site_name</th>\n",
+       "      <th>firstSARSCoV2TestedDateTime</th>\n",
+       "      <th>firstTest_visit_concept_id</th>\n",
+       "      <th>firstTest_care_site_name</th>\n",
+       "      <th>lastSARSCoV2TestedDateTime</th>\n",
+       "      <th>lastTest_visit_concept_id</th>\n",
+       "      <th>lastTest_care_site_name</th>\n",
+       "      <th>firstSARSCoV2DetectedDateTime</th>\n",
+       "      <th>lastSARSCoV2DetectedDateTime</th>\n",
+       "      <th>daysFirstARItoFirstSARSCoV2Tested</th>\n",
+       "      <th>daysLastARItoFirstSARSCoV2Tested</th>\n",
+       "      <th>daysFirstARItoLastSARSCoV2Tested</th>\n",
+       "      <th>daysLastARItoLastSARSCoV2Tested</th>\n",
+       "      <th>daysFirstARItoFirstSARSCoV2Detected</th>\n",
+       "      <th>daysLastARItoFirstSARSCoV2Detected</th>\n",
+       "      <th>daysFirstARItoLastSARSCoV2Detected</th>\n",
+       "      <th>daysLastARItoLastSARSCoV2Detected</th>\n",
+       "      <th>year_of_birth</th>\n",
+       "      <th>ageAsOfFeb2020</th>\n",
+       "      <th>gender_concept_id</th>\n",
+       "      <th>genderConcept</th>\n",
+       "      <th>race_source_value</th>\n",
+       "      <th>race_concept_id</th>\n",
+       "      <th>raceConcept</th>\n",
+       "      <th>ethnicity_source_value</th>\n",
+       "      <th>ethnicity_concept_id</th>\n",
+       "      <th>ethnicityConcept</th>\n",
+       "      <th>care_site</th>\n",
+       "      <th>visit_concept</th>\n",
+       "      <th>ARIVisitIn2020-01-01_2020-06-06</th>\n",
+       "      <th>SARSCoV2TestedIn2020-01-01_2020-06-06</th>\n",
+       "      <th>SARSCoV2DetectedIn2020-01-01_2020-06-06</th>\n",
+       "      <th>firstorLastARItoFirstSARSCoV2TestedInDayRange[-1, 14]</th>\n",
+       "      <th>firstorLastARItoFirstSARSCoV2DetectedInDayRange[-1, 14]</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>32552822</td>\n",
+       "      <td>2020-01-23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>INTERNAL MEDICINE SAMARITAN SAN JOSE</td>\n",
+       "      <td>2020-01-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>INTERNAL MEDICINE SAMARITAN SAN JOSE</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1971</td>\n",
+       "      <td>49</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>MALE</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander |</td>\n",
+       "      <td>8557</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>Unknown |</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>INTERNAL MEDICINE SAMARITAN SAN JOSE</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[18, 50)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30270769</td>\n",
+       "      <td>2020-01-25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE AFP ALAMEDA</td>\n",
+       "      <td>2020-01-25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE AFP ALAMEDA</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>58</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>MALE</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander |</td>\n",
+       "      <td>8557</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>Patient Refused |</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>FAMILY MEDICINE AFP ALAMEDA</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[50, 65)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43715690</td>\n",
+       "      <td>2020-01-23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>PEDIATRICS ALAMEDA PEDIATRICS</td>\n",
+       "      <td>2020-01-23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>PEDIATRICS ALAMEDA PEDIATRICS</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>MALE</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander |</td>\n",
+       "      <td>8557</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>Unknown |</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>PEDIATRICS ALAMEDA PEDIATRICS</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[05, 18)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>30539784</td>\n",
+       "      <td>2020-01-03</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE BVMG DANVILLE</td>\n",
+       "      <td>2020-01-03</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE BVMG DANVILLE</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>MALE</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander |</td>\n",
+       "      <td>8557</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>Patient Refused |</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>FAMILY MEDICINE BVMG DANVILLE</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[18, 50)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31343658</td>\n",
+       "      <td>2020-05-09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE OAKLAND</td>\n",
+       "      <td>2020-05-09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE OAKLAND</td>\n",
+       "      <td>2020-05-10 10:03:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE OAKLAND</td>\n",
+       "      <td>2020-05-10 10:03:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FAMILY MEDICINE OAKLAND</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969</td>\n",
+       "      <td>51</td>\n",
+       "      <td>8507</td>\n",
+       "      <td>MALE</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander |</td>\n",
+       "      <td>8557</td>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>Unknown |</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>FAMILY MEDICINE OAKLAND</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[50, 65)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   person_id lastARIDateTime  lastARI_visit_concept_id  \\\n",
+       "0   32552822      2020-01-23                       0.0   \n",
+       "1   30270769      2020-01-25                       0.0   \n",
+       "2   43715690      2020-01-23                       0.0   \n",
+       "3   30539784      2020-01-03                       0.0   \n",
+       "4   31343658      2020-05-09                       0.0   \n",
+       "\n",
+       "                 lastARI_care_site_name firstARIDateTime  \\\n",
+       "0  INTERNAL MEDICINE SAMARITAN SAN JOSE       2020-01-07   \n",
+       "1           FAMILY MEDICINE AFP ALAMEDA       2020-01-25   \n",
+       "2         PEDIATRICS ALAMEDA PEDIATRICS       2020-01-23   \n",
+       "3         FAMILY MEDICINE BVMG DANVILLE       2020-01-03   \n",
+       "4               FAMILY MEDICINE OAKLAND       2020-05-09   \n",
+       "\n",
+       "   firstARI_visit_concept_id               firstARI_care_site_name  \\\n",
+       "0                        0.0  INTERNAL MEDICINE SAMARITAN SAN JOSE   \n",
+       "1                        0.0           FAMILY MEDICINE AFP ALAMEDA   \n",
+       "2                        0.0         PEDIATRICS ALAMEDA PEDIATRICS   \n",
+       "3                        0.0         FAMILY MEDICINE BVMG DANVILLE   \n",
+       "4                        0.0               FAMILY MEDICINE OAKLAND   \n",
+       "\n",
+       "  firstSARSCoV2TestedDateTime  firstTest_visit_concept_id  \\\n",
+       "0                         NaT                         NaN   \n",
+       "1                         NaT                         NaN   \n",
+       "2                         NaT                         NaN   \n",
+       "3                         NaT                         NaN   \n",
+       "4         2020-05-10 10:03:00                         0.0   \n",
+       "\n",
+       "  firstTest_care_site_name lastSARSCoV2TestedDateTime  \\\n",
+       "0                     None                        NaT   \n",
+       "1                     None                        NaT   \n",
+       "2                     None                        NaT   \n",
+       "3                     None                        NaT   \n",
+       "4  FAMILY MEDICINE OAKLAND        2020-05-10 10:03:00   \n",
+       "\n",
+       "   lastTest_visit_concept_id  lastTest_care_site_name  \\\n",
+       "0                        NaN                     None   \n",
+       "1                        NaN                     None   \n",
+       "2                        NaN                     None   \n",
+       "3                        NaN                     None   \n",
+       "4                        0.0  FAMILY MEDICINE OAKLAND   \n",
+       "\n",
+       "  firstSARSCoV2DetectedDateTime lastSARSCoV2DetectedDateTime  \\\n",
+       "0                           NaT                          NaT   \n",
+       "1                           NaT                          NaT   \n",
+       "2                           NaT                          NaT   \n",
+       "3                           NaT                          NaT   \n",
+       "4                           NaT                          NaT   \n",
+       "\n",
+       "   daysFirstARItoFirstSARSCoV2Tested  daysLastARItoFirstSARSCoV2Tested  \\\n",
+       "0                                NaN                               NaN   \n",
+       "1                                NaN                               NaN   \n",
+       "2                                NaN                               NaN   \n",
+       "3                                NaN                               NaN   \n",
+       "4                                1.0                               1.0   \n",
+       "\n",
+       "   daysFirstARItoLastSARSCoV2Tested  daysLastARItoLastSARSCoV2Tested  \\\n",
+       "0                               NaN                              NaN   \n",
+       "1                               NaN                              NaN   \n",
+       "2                               NaN                              NaN   \n",
+       "3                               NaN                              NaN   \n",
+       "4                               1.0                              1.0   \n",
+       "\n",
+       "   daysFirstARItoFirstSARSCoV2Detected  daysLastARItoFirstSARSCoV2Detected  \\\n",
+       "0                                  NaN                                 NaN   \n",
+       "1                                  NaN                                 NaN   \n",
+       "2                                  NaN                                 NaN   \n",
+       "3                                  NaN                                 NaN   \n",
+       "4                                  NaN                                 NaN   \n",
+       "\n",
+       "   daysFirstARItoLastSARSCoV2Detected  daysLastARItoLastSARSCoV2Detected  \\\n",
+       "0                                 NaN                                NaN   \n",
+       "1                                 NaN                                NaN   \n",
+       "2                                 NaN                                NaN   \n",
+       "3                                 NaN                                NaN   \n",
+       "4                                 NaN                                NaN   \n",
+       "\n",
+       "   year_of_birth  ageAsOfFeb2020  gender_concept_id genderConcept  \\\n",
+       "0           1971              49               8507          MALE   \n",
+       "1           1962              58               8507          MALE   \n",
+       "2           2006              14               8507          MALE   \n",
+       "3           1981              39               8507          MALE   \n",
+       "4           1969              51               8507          MALE   \n",
+       "\n",
+       "                             race_source_value  race_concept_id  \\\n",
+       "0  Native Hawaiian or Other Pacific Islander |             8557   \n",
+       "1  Native Hawaiian or Other Pacific Islander |             8557   \n",
+       "2  Native Hawaiian or Other Pacific Islander |             8557   \n",
+       "3  Native Hawaiian or Other Pacific Islander |             8557   \n",
+       "4  Native Hawaiian or Other Pacific Islander |             8557   \n",
+       "\n",
+       "                                 raceConcept ethnicity_source_value  \\\n",
+       "0  Native Hawaiian or Other Pacific Islander              Unknown |   \n",
+       "1  Native Hawaiian or Other Pacific Islander      Patient Refused |   \n",
+       "2  Native Hawaiian or Other Pacific Islander              Unknown |   \n",
+       "3  Native Hawaiian or Other Pacific Islander      Patient Refused |   \n",
+       "4  Native Hawaiian or Other Pacific Islander              Unknown |   \n",
+       "\n",
+       "   ethnicity_concept_id     ethnicityConcept  \\\n",
+       "0                     0  No matching concept   \n",
+       "1                     0  No matching concept   \n",
+       "2                     0  No matching concept   \n",
+       "3                     0  No matching concept   \n",
+       "4                     0  No matching concept   \n",
+       "\n",
+       "                              care_site  visit_concept  \\\n",
+       "0  INTERNAL MEDICINE SAMARITAN SAN JOSE            0.0   \n",
+       "1           FAMILY MEDICINE AFP ALAMEDA            0.0   \n",
+       "2         PEDIATRICS ALAMEDA PEDIATRICS            0.0   \n",
+       "3         FAMILY MEDICINE BVMG DANVILLE            0.0   \n",
+       "4               FAMILY MEDICINE OAKLAND            0.0   \n",
+       "\n",
+       "   ARIVisitIn2020-01-01_2020-06-06  SARSCoV2TestedIn2020-01-01_2020-06-06  \\\n",
+       "0                             True                                  False   \n",
+       "1                             True                                  False   \n",
+       "2                             True                                  False   \n",
+       "3                             True                                  False   \n",
+       "4                             True                                   True   \n",
+       "\n",
+       "   SARSCoV2DetectedIn2020-01-01_2020-06-06  \\\n",
+       "0                                    False   \n",
+       "1                                    False   \n",
+       "2                                    False   \n",
+       "3                                    False   \n",
+       "4                                    False   \n",
+       "\n",
+       "   firstorLastARItoFirstSARSCoV2TestedInDayRange[-1, 14]  \\\n",
+       "0                                                  0       \n",
+       "1                                                  0       \n",
+       "2                                                  0       \n",
+       "3                                                  0       \n",
+       "4                                                  1       \n",
+       "\n",
+       "   firstorLastARItoFirstSARSCoV2DetectedInDayRange[-1, 14]       age  \n",
+       "0                                                  0        [18, 50)  \n",
+       "1                                                  0        [50, 65)  \n",
+       "2                                                  0        [05, 18)  \n",
+       "3                                                  0        [18, 50)  \n",
+       "4                                                  0        [50, 65)  "
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all = pd.concat([df, ARI, tested, detected, ARI_tested, ARI_detected, age_cat], axis=1)\n",
+    "df_all.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Totals\n",
+      "ARIVisitIn2020-01-01_2020-06-06 \t 39001\n",
+      "SARSCoV2TestedIn2020-01-01_2020-06-06 \t 46407\n",
+      "SARSCoV2DetectedIn2020-01-01_2020-06-06 \t 1732\n",
+      "firstorLastARItoFirstSARSCoV2TestedInDayRange[-1, 14] \t 6964\n",
+      "firstorLastARItoFirstSARSCoV2DetectedInDayRange[-1, 14] \t 600\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Totals')\n",
+    "for elt in out_keys:\n",
+    "    df_all[elt] = df_all[elt].astype('int32')\n",
+    "    metadata[\"total_\" + str(elt)] = np.sum(df_all[elt])\n",
+    "    print(elt, \"\\t\", np.sum(df_all[elt]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "def df_disp(df):\n",
+    "    display(HTML(df.to_html()))\n",
+    "\n",
+    "def format_by_demo_col(demo_col, df, out_keys):\n",
+    "    \"\"\"Get long table of values grouped by the demographic column\n",
+    "    \"\"\"\n",
+    "    tbl = pd.pivot_table(df, values=out_keys, columns=[demo_col], aggfunc=np.sum)\n",
+    "    p_tbl = tbl.T.rename(columns=str).reset_index()\n",
+    "    p_tbl = p_tbl.rename({'level_0' : 'type'}, axis=1).sort_values([demo_col])\n",
+    "    \n",
+    "    m_tbl = pd.melt(p_tbl, id_vars=[demo_col], value_vars=out_keys)\n",
+    "    return m_tbl\n",
+    "\n",
+    "def low_count_mask(df, val_col, val_range_inclusive=[1,9], val_mask=5):\n",
+    "    \"\"\"Set values within the val_range_inclusive to val_mask\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    df[val_col][np.logical_and(df[val_col] <= val_range_inclusive[1],\n",
+    "                     df[val_col] >= val_range_inclusive[0])] = val_mask\n",
+    "    \n",
+    "    desc = \"Values for column \" + val_col + \" within the inclusive range \" + str(val_range_inclusive) + \" were masked to \" + str(val_mask) \n",
+    "    \n",
+    "    print(desc)\n",
+    "    \n",
+    "    return df, desc\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving care site counts\n",
+      "Written to  care_site_counts_all_visit.csv\n",
+      "Written to  care_site_counts_ARIVisitIn2020-01-01_2020-06-06_visit.csv\n",
+      "Written to  care_site_counts_SARSCoV2TestedIn2020-01-01_2020-06-06_visit.csv\n",
+      "Written to  care_site_counts_SARSCoV2DetectedIn2020-01-01_2020-06-06_visit.csv\n",
+      "Written to  care_site_counts_firstorLastARItoFirstSARSCoV2TestedInDayRange[-1, 14]_visit.csv\n",
+      "Written to  care_site_counts_firstorLastARItoFirstSARSCoV2DetectedInDayRange[-1, 14]_visit.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "care_site_tok = 'care_site'\n",
+    "print(\"Saving care site counts\")\n",
+    "\n",
+    "def save_care_site_csv(s, tok):\n",
+    "    out_file = 'care_site_counts_' + tok + '_visit.csv'\n",
+    "    s.value_counts().to_csv(out_file)\n",
+    "    print(\"Written to \", out_file)\n",
+    "\n",
+    "save_care_site_csv(df_all[care_site_tok], 'all')\n",
+    "for out_key in out_keys:\n",
+    "    save_care_site_csv(df_all[care_site_tok][df_all[out_key] == 1], out_key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to  stanford_starr-omop_covid_mmwr/2020-06-06\n",
+      "Writing for long table to: stanford_starr-omop_covid_mmwr/2020-06-06/df_all_2020-06-06.csv\n",
+      "Not including this in the push\n",
+      "val_range_inclusive: [1, 9]\n",
+      "val_mask: 5\n",
+      "Values for column value within the inclusive range [1, 9] were masked to 5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/share/sw/open/anaconda/3/lib/python3.6/site-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[00, 05)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>6405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[05, 18)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>8328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[18, 50)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>13004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[50, 65)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>5997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[65, 75)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>3106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[75, 85)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>1646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[00, 05)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>1137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[05, 18)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>1827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[18, 50)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>24147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>[50, 65)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>9592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>[65, 75)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>5802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>[75, 85)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>3107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>[00, 05)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>[05, 18)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>[18, 50)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>[50, 65)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>[65, 75)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>[75, 85)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>[00, 05)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>[05, 18)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>[18, 50)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>3404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>[50, 65)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>1708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>[65, 75)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>[75, 85)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>[00, 05)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>[05, 18)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>[18, 50)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>[50, 65)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>[65, 75)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>[75, 85)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>[85, 100)</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stanford_starr-omop_covid_mmwr/2020-06-06/age_by_demo_2020-06-06.csv Written\n",
+      "Values for column value within the inclusive range [1, 9] were masked to 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ethnicityConcept</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hispanic or Latino</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>5894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>7091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Not Hispanic or Latino</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>26016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hispanic or Latino</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>5636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>13894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Not Hispanic or Latino</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>26877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Hispanic or Latino</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Not Hispanic or Latino</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Hispanic or Latino</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>1049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>1380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Not Hispanic or Latino</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>4535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Hispanic or Latino</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Not Hispanic or Latino</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>229</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stanford_starr-omop_covid_mmwr/2020-06-06/ethnicityConcept_by_demo_2020-06-06.csv Written\n",
+      "Values for column value within the inclusive range [1, 9] were masked to 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>raceConcept</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>American Indian or Alaska Native</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Asian</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>6928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>1390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>13089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>White</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>17045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>American Indian or Alaska Native</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Asian</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>6427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>1380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>19845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>White</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>18161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>American Indian or Alaska Native</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Asian</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>1190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>White</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>American Indian or Alaska Native</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Asian</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>2559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>White</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>3119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>American Indian or Alaska Native</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Asian</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>White</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>166</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stanford_starr-omop_covid_mmwr/2020-06-06/raceConcept_by_demo_2020-06-06.csv Written\n",
+      "Values for column value within the inclusive range [1, 9] were masked to 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>genderConcept</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>FEMALE</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>21166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MALE</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>17826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>ARIVisitIn2020-01-01_2020-06-06</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>FEMALE</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>28709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MALE</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>17666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>SARSCoV2TestedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>FEMALE</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MALE</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>SARSCoV2DetectedIn2020-01-01_2020-06-06</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>FEMALE</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>3921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>MALE</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>3039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2TestedInDayRange[...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>FEMALE</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>MALE</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>No matching concept</td>\n",
+       "      <td>firstorLastARItoFirstSARSCoV2DetectedInDayRang...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stanford_starr-omop_covid_mmwr/2020-06-06/genderConcept_by_demo_2020-06-06.csv Written\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change this to true if you reall ywant\n",
+    "\n",
+    "export_files = []\n",
+    "\n",
+    "out_folder = os.path.join(\"stanford_starr-omop_covid_mmwr\", run_date)\n",
+    "os.makedirs(out_folder, exist_ok=True)\n",
+    "print(\"Writing to \", out_folder)\n",
+    "\n",
+    "\n",
+    "out_file = os.path.join(out_folder, \"df_all_\" + run_date + \".csv\")\n",
+    "df_all.to_csv(out_file, index=False)\n",
+    "print(\"Writing for long table to:\", out_file)\n",
+    "print(\"Not including this in the push\")\n",
+    "\n",
+    "\n",
+    "val_range_inclusive = [1, 9]\n",
+    "metadata[\"val_range_inclusive\"] = val_range_inclusive\n",
+    "print(\"val_range_inclusive:\", val_range_inclusive)\n",
+    "\n",
+    "val_mask = 5\n",
+    "metadata[\"val_mask\"] = val_mask\n",
+    "print(\"val_mask:\", val_mask)\n",
+    "\n",
+    "demo_cols = ['age', 'ethnicityConcept', 'raceConcept', 'genderConcept']\n",
+    "\n",
+    "for demo_col in demo_cols:\n",
+    "\n",
+    "    out_file = os.path.join(out_folder, demo_col +\"_by_demo_\" + run_date + \".csv\")\n",
+    "    df_all_by_demo = format_by_demo_col(demo_col=demo_col, df=df_all, out_keys=out_keys)\n",
+    "    \n",
+    "    df_all_by_demo, desc = low_count_mask(df=df_all_by_demo, val_col=\"value\", \n",
+    "                                    val_range_inclusive=val_range_inclusive, val_mask=val_mask)\n",
+    "    \n",
+    "    metadata[demo_col + \" masking\"] = desc\n",
+    "    \n",
+    "    df_disp(df_all_by_demo)\n",
+    "    \n",
+    "    df_all_by_demo.to_csv(out_file, index=False)\n",
+    "    \n",
+    "    print(out_file, \"Written\")\n",
+    "    export_files.append(out_file)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Written to: stanford_starr-omop_covid_mmwr/2020-06-06/new_tbl_adj_2020-06-06.ipynb\n",
+      "Written to: stanford_starr-omop_covid_mmwr/2020-06-06/METADATA_2020-06-06.CSV\n"
+     ]
+    }
+   ],
+   "source": [
+    "# add this new notebook to update main repo to track changes\n",
+    "export_files.append(\"new_tbl_adj.ipynb\")\n",
+    "\n",
+    "# add a freeze of the notebook that was used to generate this\n",
+    "nb_out_file = os.path.join(out_folder, \"new_tbl_adj_\" + run_date + \".ipynb\")\n",
+    "shutil.copy(\"new_tbl_adj.ipynb\", nb_out_file)\n",
+    "\n",
+    "print(\"Written to:\", nb_out_file)\n",
+    "export_files.append(nb_out_file)\n",
+    "\n",
+    "metadata[\"Freeze of exact code used to run\"] =  metadata[\"Code repository\"] + \"/\" + nb_out_file\n",
+    "\n",
+    "meta_out_file = os.path.join(out_folder, \"METADATA_\" + run_date + \".CSV\")\n",
+    "\n",
+    "metadata[\"Metadata location\"] = meta_out_file\n",
+    "\n",
+    "metadata_df = pd.DataFrame()\n",
+    "metadata_df[\"Name\"] = list(metadata.keys())\n",
+    "metadata_df[\"Description\"] = list(metadata.values())\n",
+    "metadata_df.to_csv(meta_out_file, index=False)\n",
+    "\n",
+    "print(\"Written to:\", meta_out_file)\n",
+    "export_files.append(meta_out_file)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Written to  stanford_starr-omop_covid_mmwr/2020-06-06/README.md\n"
+     ]
+    }
+   ],
+   "source": [
+    "readme_md_string = \"\"\"\n",
+    "This repository contains long tables of: {out_key_string} grouped by demographic factors, separately: {demo_cols}\n",
+    "\n",
+    "drawing from all patients in the Starr-OMOP database from {ARI_start_date} through {run_date}, inclusive.\n",
+    "\n",
+    "Metadata at {metadata_loc}\n",
+    "\n",
+    "Original code used to generate at {code_freeze_loc}\n",
+    "\n",
+    "# CAVEATS\n",
+    "\n",
+    "* For each patient, their dates and times are individually jittered by an unknown day range within [-31, 31] days. \n",
+    "\n",
+    "** Thus, there are patients appearing before the start of SARS CoV2 Testing, and patients appearing into the future.\n",
+    "\n",
+    "* Note that there is a great degree of missingness in the race and ethnicity fields.\n",
+    "\n",
+    "* we attempted to associate ARI visits with SARS-COV2 tests and detections by limiting to flagging patients who had a SARS-COV2 test/detection within [-1, 14] days after either their first or last ARI visit in the time range.\n",
+    "\n",
+    "* For more info, see https://med.stanford.edu/starr-omop/access.html under \"OMOP and STARR-OMOP documentation\"\n",
+    "\n",
+    "\"\"\".format(out_key_string=\"\\n\".join(out_keys),\n",
+    "          demo_cols=demo_cols,\n",
+    "           ARI_start_date=ARI_start_date,\n",
+    "           run_date=run_date,\n",
+    "            metadata_loc = metadata[\"Metadata location\"],\n",
+    "           code_freeze_loc= metadata[\"Freeze of exact code used to run\"])\n",
+    "\n",
+    "readme_md_out_file = os.path.join(out_folder, \"README.md\")\n",
+    "with open(readme_md_out_file, 'w') as f:\n",
+    "    f.write(readme_md_string)\n",
+    "\n",
+    "print(\"Written to \", readme_md_out_file)\n",
+    "if not readme_md_out_file in export_files:\n",
+    "    export_files.append(readme_md_out_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confirm the files you are planning to export below? Writing to export_list.txt\n",
+      "\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/age_by_demo_2020-06-06.csv\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/ethnicityConcept_by_demo_2020-06-06.csv\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/raceConcept_by_demo_2020-06-06.csv\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/genderConcept_by_demo_2020-06-06.csv\n",
+      "new_tbl_adj.ipynb\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/new_tbl_adj_2020-06-06.ipynb\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/METADATA_2020-06-06.CSV\n",
+      "stanford_starr-omop_covid_mmwr/2020-06-06/README.md\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Confirm the files you are planning to export below? Writing to export_list.txt\")\n",
+    "\n",
+    "with open(\"export_list.txt\", 'w') as f:\n",
+    "    for export_file in export_files:\n",
+    "        f.write(export_file + \"\\n\")\n",
+    "        \n",
+    "print(\"\")\n",
+    "for export_file in export_files:\n",
+    "    print(export_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Now that export_list is updated, open a terminal and do:\n",
+      "cd \"/home/jhlu/CDSS/scripts/Covid19 CDC/\"\n",
+      "./git_export.sh\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Now that export_list is updated, open a terminal and do:\")\n",
+    "print('cd \"/home/jhlu/CDSS/scripts/Covid19 CDC/\"')\n",
+    "print('./git_export.sh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BELOW IS OPTIONAL SECONDARY ANALYSIS, NOT NECESSARY TO RUN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all['ethnicity_source_value'].value_counts().to_csv('ethnicity_source')\n",
+    "df_all['race_source_value'].value_counts().to_csv('race_source')\n",
+    "# for ind, grp in df_all.groupby('raceConcept'):\n",
+    "#     print(ind)\n",
+    "#     print(grp['race_source_value'].unique())\n",
+    "for ind, grp in df_all.groupby('ethnicityConcept'):\n",
+    "    print(ind)\n",
+    "    print(grp['ethnicity_source_value'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_by_visit(ind_nm, df, out_keys, visit_token = 'visit_concept'):\n",
+    "    tbl = pd.pivot_table(df, values=out_keys, index=[ind_nm],\n",
+    "                            columns=[visit_token], aggfunc=np.sum)\n",
+    "    p_tbl = tbl.T.rename(columns=str).reset_index()\n",
+    "    p_tbl = p_tbl.rename({'level_0' : 'type'}, axis=1).sort_values([visit_token, 'type'])\n",
+    "\n",
+    "    cols = p_tbl.columns.tolist()\n",
+    "    cols = [cols[1], cols[0]] + cols[2:]\n",
+    "    p_tbl = p_tbl[cols]\n",
+    "    df_disp(p_tbl)\n",
+    "    return p_tbl\n",
+    "\n",
+    "def format_by_ind(ind_nm, df, out_keys):\n",
+    "    tbl = pd.pivot_table(df, values=out_keys, index=[ind_nm],\n",
+    "                            aggfunc=np.sum)\n",
+    "    p_tbl = tbl.T\n",
+    "    \n",
+    "    df_disp(p_tbl)\n",
+    "    return p_tbl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get wide tables\n",
+    "for elt in ['age', 'ethnicityConcept', 'raceConcept', 'genderConcept']:\n",
+    "    format_by_visit(ind_nm=elt, df=df_all, out_keys=out_keys)\n",
+    "    format_by_ind(ind_nm=elt, df=df_all, out_keys=out_keys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Oldest nSars-COV2-test\")\n",
+    "print(print(min(df_all[df_all[\"firstSARSCoV2TestedDateTime\"].notnull()][\"firstSARSCoV2TestedDateTime\"])))\n",
+    "print(\"Newest nSars-COV2-test\")\n",
+    "print(print(max(df_all[df_all[\"lastSARSCoV2TestedDateTime\"].notnull()][\"lastSARSCoV2TestedDateTime\"])))\n",
+    "print(\"---\")\n",
+    "print(\"Oldest nSars-COV2-detection\")\n",
+    "print(min(df_all[df_all[\"firstSARSCoV2DetectedDateTime\"].notnull()][\"firstSARSCoV2DetectedDateTime\"]))\n",
+    "print(\"Newest nSars-COV2-detection\")\n",
+    "print(max(df_all[df_all[\"lastSARSCoV2DetectedDateTime\"].notnull()][\"lastSARSCoV2DetectedDateTime\"]))\n",
+    "print(\"---\")\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.hist(df_all[df_all[\"firstSARSCoV2TestedDateTime\"].notnull()][\"firstSARSCoV2TestedDateTime\"],\n",
+    "        alpha=0.5, label=\"First SARSCoV2Test\")\n",
+    "plt.hist(df_all[df_all[\"lastSARSCoV2TestedDateTime\"].notnull()][\"lastSARSCoV2TestedDateTime\"],\n",
+    "        alpha=0.5, label=\"Last SARSCoV2Test\")\n",
+    "plt.title(\"First and Last SARS-COV2 test dates as of \\n\" + datetime.datetime.today().strftime(\"%m/%d/%Y\"))\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "plt.close()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.hist(df_all[df_all[\"firstSARSCoV2DetectedDateTime\"].notnull()][\"firstSARSCoV2DetectedDateTime\"],\n",
+    "        alpha=0.5, label=\"First SARSCoV2Detected\")\n",
+    "plt.hist(df_all[df_all[\"lastSARSCoV2DetectedDateTime\"].notnull()][\"lastSARSCoV2DetectedDateTime\"],\n",
+    "        alpha=0.5, label=\"Last SARSCoV2Detected\")\n",
+    "plt.title(\"First and Last SARS-COV2 detection dates as of\\n\" + datetime.datetime.today().strftime(\"%m/%d/%Y\"))\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "plt.close()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/Covid19 CDC/notebook.tex b/scripts/Archive/Covid19 CDC/notebook.tex
new file mode 100644
index 00000000..845915a5
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/notebook.tex	
@@ -0,0 +1,834 @@
+
+% Default to the notebook output style
+
+    
+
+
+% Inherit from the specified cell style.
+
+
+
+
+    
+\documentclass[11pt]{article}
+
+    
+    
+    \usepackage[T1]{fontenc}
+    % Nicer default font (+ math font) than Computer Modern for most use cases
+    \usepackage{mathpazo}
+
+    % Basic figure setup, for now with no caption control since it's done
+    % automatically by Pandoc (which extracts ![](path) syntax from Markdown).
+    \usepackage{graphicx}
+    % We will generate all images so they have a width \maxwidth. This means
+    % that they will get their normal width if they fit onto the page, but
+    % are scaled down if they would overflow the margins.
+    \makeatletter
+    \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
+    \else\Gin@nat@width\fi}
+    \makeatother
+    \let\Oldincludegraphics\includegraphics
+    % Set max figure width to be 80% of text width, for now hardcoded.
+    \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
+    % Ensure that by default, figures have no caption (until we provide a
+    % proper Figure object with a Caption API and a way to capture that
+    % in the conversion process - todo).
+    \usepackage{caption}
+    \DeclareCaptionLabelFormat{nolabel}{}
+    \captionsetup{labelformat=nolabel}
+
+    \usepackage{adjustbox} % Used to constrain images to a maximum size 
+    \usepackage{xcolor} % Allow colors to be defined
+    \usepackage{enumerate} % Needed for markdown enumerations to work
+    \usepackage{geometry} % Used to adjust the document margins
+    \usepackage{amsmath} % Equations
+    \usepackage{amssymb} % Equations
+    \usepackage{textcomp} % defines textquotesingle
+    % Hack from http://tex.stackexchange.com/a/47451/13684:
+    \AtBeginDocument{%
+        \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
+    }
+    \usepackage{upquote} % Upright quotes for verbatim code
+    \usepackage{eurosym} % defines \euro
+    \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
+    \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
+    \usepackage{fancyvrb} % verbatim replacement that allows latex
+    \usepackage{grffile} % extends the file name processing of package graphics 
+                         % to support a larger range 
+    % The hyperref package gives us a pdf with properly built
+    % internal navigation ('pdf bookmarks' for the table of contents,
+    % internal cross-reference links, web links for URLs, etc.)
+    \usepackage{hyperref}
+    \usepackage{longtable} % longtable support required by pandoc >1.10
+    \usepackage{booktabs}  % table support for pandoc > 1.12.2
+    \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
+    \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
+                                % normalem makes italics be italics, not underlines
+    
+
+    
+    
+    % Colors for the hyperref package
+    \definecolor{urlcolor}{rgb}{0,.145,.698}
+    \definecolor{linkcolor}{rgb}{.71,0.21,0.01}
+    \definecolor{citecolor}{rgb}{.12,.54,.11}
+
+    % ANSI colors
+    \definecolor{ansi-black}{HTML}{3E424D}
+    \definecolor{ansi-black-intense}{HTML}{282C36}
+    \definecolor{ansi-red}{HTML}{E75C58}
+    \definecolor{ansi-red-intense}{HTML}{B22B31}
+    \definecolor{ansi-green}{HTML}{00A250}
+    \definecolor{ansi-green-intense}{HTML}{007427}
+    \definecolor{ansi-yellow}{HTML}{DDB62B}
+    \definecolor{ansi-yellow-intense}{HTML}{B27D12}
+    \definecolor{ansi-blue}{HTML}{208FFB}
+    \definecolor{ansi-blue-intense}{HTML}{0065CA}
+    \definecolor{ansi-magenta}{HTML}{D160C4}
+    \definecolor{ansi-magenta-intense}{HTML}{A03196}
+    \definecolor{ansi-cyan}{HTML}{60C6C8}
+    \definecolor{ansi-cyan-intense}{HTML}{258F8F}
+    \definecolor{ansi-white}{HTML}{C5C1B4}
+    \definecolor{ansi-white-intense}{HTML}{A1A6B2}
+
+    % commands and environments needed by pandoc snippets
+    % extracted from the output of `pandoc -s`
+    \providecommand{\tightlist}{%
+      \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+    \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
+    % Add ',fontsize=\small' for more characters per line
+    \newenvironment{Shaded}{}{}
+    \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
+    \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
+    \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
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+    \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
+    \newcommand{\RegionMarkerTok}[1]{{#1}}
+    \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\NormalTok}[1]{{#1}}
+    
+    % Additional commands for more recent versions of Pandoc
+    \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
+    \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
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+    \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
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+    \newcommand{\ExtensionTok}[1]{{#1}}
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+    \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    
+    
+    % Define a nice break command that doesn't care if a line doesn't already
+    % exist.
+    \def\br{\hspace*{\fill} \\* }
+    % Math Jax compatability definitions
+    \def\gt{>}
+    \def\lt{<}
+    % Document parameters
+    \title{long\_tbl\_grp\_person}
+    
+    
+    
+
+    % Pygments definitions
+    
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+\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
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+    \let\PY@bc=\relax \let\PY@ff=\relax}
+\def\PY@tok#1{\csname PY@tok@#1\endcsname}
+\def\PY@toks#1+{\ifx\relax#1\empty\else%
+    \PY@tok{#1}\expandafter\PY@toks\fi}
+\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
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+\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
+
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+\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
+\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
+\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}}
+\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
+\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
+\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@bp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+
+\def\PYZbs{\char`\\}
+\def\PYZus{\char`\_}
+\def\PYZob{\char`\{}
+\def\PYZcb{\char`\}}
+\def\PYZca{\char`\^}
+\def\PYZam{\char`\&}
+\def\PYZlt{\char`\<}
+\def\PYZgt{\char`\>}
+\def\PYZsh{\char`\#}
+\def\PYZpc{\char`\%}
+\def\PYZdl{\char`\$}
+\def\PYZhy{\char`\-}
+\def\PYZsq{\char`\'}
+\def\PYZdq{\char`\"}
+\def\PYZti{\char`\~}
+% for compatibility with earlier versions
+\def\PYZat{@}
+\def\PYZlb{[}
+\def\PYZrb{]}
+\makeatother
+
+
+    % Exact colors from NB
+    \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
+    \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
+
+
+
+    
+    % Prevent overflowing lines due to hard-to-break entities
+    \sloppy 
+    % Setup hyperref package
+    \hypersetup{
+      breaklinks=true,  % so long urls are correctly broken across lines
+      colorlinks=true,
+      urlcolor=urlcolor,
+      linkcolor=linkcolor,
+      citecolor=citecolor,
+      }
+    % Slightly bigger margins than the latex defaults
+    
+    \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
+    
+    
+
+    \begin{document}
+    
+    
+    \maketitle
+    
+    
+
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}1}]:} \PY{c+c1}{\PYZsh{} Header from RIT notebook}
+        \PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
+        \PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
+        \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
+        \PY{c+c1}{\PYZsh{}\PYZsh{}Setting up Google sdk environment}
+        \PY{k+kn}{import} \PY{n+nn}{os} 
+        \PY{n}{os}\PY{o}{.}\PY{n}{environ}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{GOOGLE\PYZus{}APPLICATION\PYZus{}CREDENTIALS}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{/home/marcelkc/.config/gcloud/application\PYZus{}default\PYZus{}credentials.json}\PY{l+s+s1}{\PYZsq{}} 
+        \PY{n}{os}\PY{o}{.}\PY{n}{environ}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{GCLOUD\PYZus{}PROJECT}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{som\PYZhy{}nero\PYZhy{}phi\PYZhy{}nigam\PYZhy{}starr}\PY{l+s+s1}{\PYZsq{}} \PY{c+c1}{\PYZsh{}som\PYZhy{}nero\PYZhy{}phi\PYZhy{}rit}
+        \PY{o}{\PYZpc{}}\PY{k}{load\PYZus{}ext} google.cloud.bigquery
+        \PY{c+c1}{\PYZsh{}\PYZsh{}Setting up BQ API}
+        \PY{k+kn}{from} \PY{n+nn}{google}\PY{n+nn}{.}\PY{n+nn}{cloud} \PY{k}{import} \PY{n}{bigquery}
+        \PY{n}{client}\PY{o}{=}\PY{n}{bigquery}\PY{o}{.}\PY{n}{Client}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+/share/sw/open/anaconda/3/lib/python3.6/site-packages/google/auth/\_default.py:66: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a "quota exceeded" or "API not enabled" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/
+  warnings.warn(\_CLOUD\_SDK\_CREDENTIALS\_WARNING)
+
+    \end{Verbatim}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}2}]:} \PY{n}{query} \PY{o}{=} \PY{l+s+s2}{\PYZdq{}\PYZdq{}\PYZdq{}}\PY{l+s+s2}{SELECT }
+        \PY{l+s+s2}{v.person\PYZus{}id, }
+        \PY{l+s+s2}{visit\PYZus{}start\PYZus{}DATETIME, }
+        \PY{l+s+s2}{ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months, }
+        \PY{l+s+s2}{race\PYZus{}concept\PYZus{}id, ethnicity\PYZus{}concept\PYZus{}id, \PYZsh{} race/ethnicity}
+        \PY{l+s+s2}{gender\PYZus{}concept\PYZus{}id, \PYZsh{} gender}
+        \PY{l+s+s2}{year\PYZus{}of\PYZus{}birth, month\PYZus{}of\PYZus{}birth, day\PYZus{}of\PYZus{}birth, \PYZsh{} age at present}
+        \PY{l+s+s2}{visit\PYZus{}concept\PYZus{}id, care\PYZus{}site\PYZus{}name, \PYZsh{} location}
+        \PY{l+s+s2}{value\PYZus{}source\PYZus{}value, \PYZsh{} test result}
+        \PY{l+s+s2}{visit\PYZus{}occurrence\PYZus{}id}
+        \PY{l+s+s2}{FROM `som\PYZhy{}rit\PYZhy{}phi\PYZhy{}starr\PYZhy{}prod.starr\PYZus{}omop\PYZus{}cdm5\PYZus{}deid\PYZus{}latest.visit\PYZus{}occurrence` v}
+        
+        \PY{l+s+s2}{LEFT JOIN(}
+        \PY{l+s+s2}{  \PYZsh{}visit\PYZus{}occurrence\PYZus{}id (unique) to ARI\PYZus{}symptoms}
+        \PY{l+s+s2}{  SELECT }
+        \PY{l+s+s2}{  visit\PYZus{}occurrence\PYZus{}id,}
+        \PY{l+s+s2}{  \PYZsh{}ARI symptoms \PYZsh{} any condition\PYZus{}start\PYZus{}DATETIME, condition\PYZus{}end\PYZus{}DATETIME within 2 months}
+        \PY{l+s+s2}{  max(}
+        \PY{l+s+s2}{  DATETIME\PYZus{}DIFF(visit\PYZus{}start\PYZus{}DATETIME, condition\PYZus{}start\PYZus{}DATETIME, MONTH) \PYZlt{} 2}
+        \PY{l+s+s2}{  OR}
+        \PY{l+s+s2}{  DATETIME\PYZus{}DIFF(visit\PYZus{}start\PYZus{}DATETIME, condition\PYZus{}end\PYZus{}DATETIME, MONTH) \PYZlt{} 2 }
+        \PY{l+s+s2}{  )}
+        \PY{l+s+s2}{  AS ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months,}
+        \PY{l+s+s2}{  FROM `som\PYZhy{}rit\PYZhy{}phi\PYZhy{}starr\PYZhy{}prod.starr\PYZus{}omop\PYZus{}cdm5\PYZus{}deid\PYZus{}latest.visit\PYZus{}occurrence` v}
+        \PY{l+s+s2}{  LEFT JOIN (}
+        \PY{l+s+s2}{    SELECT person\PYZus{}id, condition\PYZus{}start\PYZus{}DATETIME, condition\PYZus{}end\PYZus{}DATETIME, }
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value, condition\PYZus{}source\PYZus{}concept\PYZus{}id \PYZsh{} might get used later}
+        \PY{l+s+s2}{    FROM `som\PYZhy{}rit\PYZhy{}phi\PYZhy{}starr\PYZhy{}prod.starr\PYZus{}omop\PYZus{}cdm5\PYZus{}deid\PYZus{}latest.condition\PYZus{}occurrence`}
+        \PY{l+s+s2}{    WHERE (condition\PYZus{}start\PYZus{}DATETIME \PYZgt{}= }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{2019\PYZhy{}11\PYZhy{}01T00:00:00.000}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR condition\PYZus{}end\PYZus{}DATETIME \PYZgt{}= }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{2019\PYZhy{}11\PYZhy{}01T00:00:00.000}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{)}
+        \PY{l+s+s2}{    AND}
+        \PY{l+s+s2}{    (condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J00.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J01.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J02.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J03.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J04.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J05.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J06.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J09.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J10.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J11.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J12.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J13.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J14.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J15.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J16.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J17.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J18.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J20.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J21.}\PY{l+s+s2}{\PYZpc{}}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J22}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{J80}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{A37.91}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{A37.01}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{A37.11}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{A37.81}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{A48.1}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{B25.0}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{B44.0}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{B97.4}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ OR}
+        \PY{l+s+s2}{    condition\PYZus{}source\PYZus{}value LIKE }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{U07.1}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{)}
+        \PY{l+s+s2}{  ) USING (person\PYZus{}id)}
+        \PY{l+s+s2}{  WHERE visit\PYZus{}start\PYZus{}DATETIME \PYZgt{}= }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{2020\PYZhy{}02\PYZhy{}01T00:00:00.000}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ \PYZsh{} all encounters from feb 1}
+        \PY{l+s+s2}{  GROUP BY visit\PYZus{}occurrence\PYZus{}id}
+        \PY{l+s+s2}{  )}
+        \PY{l+s+s2}{USING (visit\PYZus{}occurrence\PYZus{}id)}
+        
+        
+        \PY{l+s+s2}{LEFT JOIN (}
+        \PY{l+s+s2}{  \PYZsh{} visit\PYZus{}occurrence\PYZus{}id to covid test result (value\PYZus{}source\PYZus{}value) }
+        \PY{l+s+s2}{  SELECT value\PYZus{}source\PYZus{}value, \PYZsh{} test result}
+        \PY{l+s+s2}{  visit\PYZus{}occurrence\PYZus{}id \PYZsh{} for mapping(s)}
+        \PY{l+s+s2}{  FROM `som\PYZhy{}rit\PYZhy{}phi\PYZhy{}starr\PYZhy{}prod.starr\PYZus{}omop\PYZus{}cdm5\PYZus{}deid\PYZus{}latest.measurement`}
+        \PY{l+s+s2}{  WHERE measurement\PYZus{}concept\PYZus{}id = 706170}
+        \PY{l+s+s2}{) }
+        \PY{l+s+s2}{USING (visit\PYZus{}occurrence\PYZus{}id)}
+        
+        \PY{l+s+s2}{INNER JOIN `som\PYZhy{}rit\PYZhy{}phi\PYZhy{}starr\PYZhy{}prod.starr\PYZus{}omop\PYZus{}cdm5\PYZus{}deid\PYZus{}latest.person` USING (person\PYZus{}id)}
+        
+        \PY{l+s+s2}{LEFT JOIN `som\PYZhy{}rit\PYZhy{}phi\PYZhy{}starr\PYZhy{}prod.starr\PYZus{}omop\PYZus{}cdm5\PYZus{}deid\PYZus{}latest.care\PYZus{}site` c  }
+        \PY{l+s+s2}{ON c.care\PYZus{}site\PYZus{}id = v.care\PYZus{}site\PYZus{}id}
+        
+        \PY{l+s+s2}{WHERE visit\PYZus{}start\PYZus{}DATETIME \PYZgt{}= }\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{2020\PYZhy{}02\PYZhy{}01T00:00:00.000}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{ \PYZsh{} all encounters from feb 1}\PY{l+s+s2}{\PYZdq{}\PYZdq{}\PYZdq{}}
+        \PY{n}{query\PYZus{}job} \PY{o}{=} \PY{n}{client}\PY{o}{.}\PY{n}{query}\PY{p}{(}\PY{n}{query}\PY{p}{)}
+        \PY{n}{df} \PY{o}{=} \PY{n}{query\PYZus{}job}\PY{o}{.}\PY{n}{to\PYZus{}dataframe}\PY{p}{(}\PY{n}{progress\PYZus{}bar\PYZus{}type}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tqdm}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+Downloading: 100\%|██████████| 1835715/1835715 [04:05<00:00, 7751.50rows/s]
+
+    \end{Verbatim}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}3}]:} \PY{n}{df}\PY{o}{.}\PY{n}{to\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{long\PYZus{}table\PYZus{}cdc.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{sep}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{;}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{n}{df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}3}]:}    person\_id visit\_start\_DATETIME ARI\_symptoms\_2\_months  race\_concept\_id  \textbackslash{}
+        0   31729010           2020-03-14                  None             8527   
+        1   30424583           2020-02-11                  True             8527   
+        2   30106192           2020-03-01                  True                0   
+        3   31496809           2020-04-08                  None             8527   
+        4   31100057           2020-03-07                  True             8527   
+        
+           ethnicity\_concept\_id  gender\_concept\_id  year\_of\_birth  month\_of\_birth  \textbackslash{}
+        0              38003564               8532           1932               9   
+        1              38003564               8507           1932              11   
+        2              38003563               8532           1931               5   
+        3              38003564               8507           1930              11   
+        4                     0               8507           1931               9   
+        
+           day\_of\_birth  visit\_concept\_id                             care\_site\_name  \textbackslash{}
+        0            11                 0                        LAB BVMG PLEASANTON   
+        1            27                 0                       STANFORD SENIOR CARE   
+        2            31                 0       INTERNAL MEDICINE SAMARITAN SAN JOSE   
+        3             8                 0                               VCP LAB DRAW   
+        4            18                 0  INTERNAL MEDICINE MANOUKIAN MOUNTAIN VIEW   
+        
+          value\_source\_value  visit\_occurrence\_id  
+        0               None            159694443  
+        1               None            157916468  
+        2               None            161109490  
+        3               None            163427116  
+        4               None            160595775  
+\end{Verbatim}
+            
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}4}]:} \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
+        \PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
+        \PY{c+c1}{\PYZsh{} positive\PYZus{}test}
+        \PY{n}{positive\PYZus{}value\PYZus{}source\PYZus{}value} \PY{o}{=} \PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{x} \PY{o+ow}{in} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Detected}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{DETECTED}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Pos}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
+        \PY{n}{pos\PYZus{}test\PYZus{}bool} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{value\PYZus{}source\PYZus{}value}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{n}{positive\PYZus{}value\PYZus{}source\PYZus{}value}\PY{p}{)}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{positive}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{c+c1}{\PYZsh{} tested}
+        \PY{n}{test\PYZus{}bool} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{value\PYZus{}source\PYZus{}value}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{notnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tested}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{c+c1}{\PYZsh{} ARI\PYZus{}sympt}
+        \PY{n}{ARI\PYZus{}2\PYZus{}mon} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{==} \PY{k+kc}{True}
+        \PY{n}{ARI\PYZus{}2\PYZus{}mon} \PY{o}{=} \PY{n}{ARI\PYZus{}2\PYZus{}mon}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months\PYZus{}bool}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{c+c1}{\PYZsh{} age results}
+        \PY{n}{visit\PYZus{}dt} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{visit\PYZus{}start\PYZus{}DATETIME}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x} \PY{p}{:} \PY{n}{pd}\PY{o}{.}\PY{n}{to\PYZus{}datetime}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}
+        \PY{n}{pat\PYZus{}bd\PYZus{}dt} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{year\PYZus{}of\PYZus{}birth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{month\PYZus{}of\PYZus{}birth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{day\PYZus{}of\PYZus{}birth}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{pd}\PY{o}{.}\PY{n}{to\PYZus{}datetime}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZhy{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{p}{[}\PY{n+nb}{str}\PY{p}{(}\PY{n}{i}\PY{p}{)} \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n}{x}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
+        \PY{n}{pat\PYZus{}age} \PY{o}{=} \PY{n}{visit\PYZus{}dt} \PY{o}{\PYZhy{}} \PY{n}{pat\PYZus{}bd\PYZus{}dt}
+        \PY{n}{pat\PYZus{}age} \PY{o}{=} \PY{n}{pat\PYZus{}age}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{x}\PY{o}{.}\PY{n}{days}\PY{o}{/}\PY{l+m+mf}{365.25}\PY{p}{)}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{age}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+        \PY{c+c1}{\PYZsh{} age categorical}
+        \PY{n}{bins} \PY{o}{=} \PY{p}{[}\PY{l+m+mi}{0}\PY{p}{,} \PY{l+m+mi}{5}\PY{p}{,} \PY{l+m+mi}{18}\PY{p}{,} \PY{l+m+mi}{50}\PY{p}{,} \PY{l+m+mi}{65}\PY{p}{,} \PY{l+m+mi}{75}\PY{p}{,} \PY{l+m+mi}{85}\PY{p}{,} \PY{l+m+mi}{100}\PY{p}{]}
+        \PY{n}{labels} \PY{o}{=} \PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{[}\PY{l+s+si}{\PYZob{}0\PYZcb{}}\PY{l+s+s2}{, }\PY{l+s+si}{\PYZob{}1\PYZcb{}}\PY{l+s+s2}{)}\PY{l+s+s2}{\PYZdq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{j}\PY{p}{)} \PY{k}{for} \PY{n}{i}\PY{p}{,} \PY{n}{j} \PY{o+ow}{in} \PY{n+nb}{zip}\PY{p}{(}\PY{n}{bins}\PY{p}{[}\PY{p}{:}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{bins}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{:}\PY{p}{]}\PY{p}{)}\PY{p}{]}
+        \PY{n}{age\PYZus{}cat} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{cut}\PY{p}{(}\PY{n}{pat\PYZus{}age}\PY{p}{,} \PY{n}{bins}\PY{p}{,} \PY{n}{labels}\PY{o}{=}\PY{n}{labels}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}5}]:} \PY{n}{df\PYZus{}all} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{concat}\PY{p}{(}\PY{p}{[}\PY{n}{pos\PYZus{}test\PYZus{}bool}\PY{p}{,} \PY{n}{test\PYZus{}bool}\PY{p}{,} \PY{n}{age\PYZus{}cat}\PY{p}{,} \PY{n}{df}\PY{p}{,} \PY{n}{ARI\PYZus{}2\PYZus{}mon}\PY{p}{]}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
+        \PY{n}{df\PYZus{}all}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{total}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{l+m+mi}{1}
+        \PY{n}{df\PYZus{}all}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}5}]:}    positive  tested        age  person\_id visit\_start\_DATETIME  \textbackslash{}
+        0     False   False  [85, 100)   31729010           2020-03-14   
+        1     False   False  [85, 100)   30424583           2020-02-11   
+        2     False   False  [85, 100)   30106192           2020-03-01   
+        3     False   False  [85, 100)   31496809           2020-04-08   
+        4     False   False  [85, 100)   31100057           2020-03-07   
+        
+          ARI\_symptoms\_2\_months  race\_concept\_id  ethnicity\_concept\_id  \textbackslash{}
+        0                  None             8527              38003564   
+        1                  True             8527              38003564   
+        2                  True                0              38003563   
+        3                  None             8527              38003564   
+        4                  True             8527                     0   
+        
+           gender\_concept\_id  year\_of\_birth  month\_of\_birth  day\_of\_birth  \textbackslash{}
+        0               8532           1932               9            11   
+        1               8507           1932              11            27   
+        2               8532           1931               5            31   
+        3               8507           1930              11             8   
+        4               8507           1931               9            18   
+        
+           visit\_concept\_id                             care\_site\_name  \textbackslash{}
+        0                 0                        LAB BVMG PLEASANTON   
+        1                 0                       STANFORD SENIOR CARE   
+        2                 0       INTERNAL MEDICINE SAMARITAN SAN JOSE   
+        3                 0                               VCP LAB DRAW   
+        4                 0  INTERNAL MEDICINE MANOUKIAN MOUNTAIN VIEW   
+        
+          value\_source\_value  visit\_occurrence\_id  ARI\_symptoms\_2\_months\_bool  total  
+        0               None            159694443                       False      1  
+        1               None            157916468                        True      1  
+        2               None            161109490                        True      1  
+        3               None            163427116                       False      1  
+        4               None            160595775                        True      1  
+\end{Verbatim}
+            
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}6}]:} \PY{k}{def} \PY{n+nf}{visualize\PYZus{}col}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{n}{col\PYZus{}nm}\PY{p}{)}\PY{p}{:}
+            \PY{n+nb}{print}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{n}{col\PYZus{}nm}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}\PY{p}{)}
+            
+            \PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{p}{)}
+            \PY{n}{df}\PY{p}{[}\PY{n}{col\PYZus{}nm}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{kind}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{bar}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+            
+            \PY{n}{tbl} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{pivot\PYZus{}table}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{n}{values}\PY{o}{=}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tested}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{positive}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{total}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months\PYZus{}bool}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{index}\PY{o}{=}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{visit\PYZus{}concept\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,}
+                                \PY{n}{columns}\PY{o}{=}\PY{p}{[}\PY{n}{col\PYZus{}nm}\PY{p}{]}\PY{p}{,} \PY{n}{aggfunc}\PY{o}{=}\PY{n}{np}\PY{o}{.}\PY{n}{sum}\PY{p}{)}
+            \PY{k}{return} \PY{n}{tbl}\PY{o}{.}\PY{n}{T}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}7}]:} \PY{k}{def} \PY{n+nf}{get\PYZus{}groupby\PYZus{}max}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{n}{group\PYZus{}by\PYZus{}ls}\PY{p}{)}\PY{p}{:}
+            \PY{n}{df\PYZus{}dups\PYZus{}ind} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{duplicated}\PY{p}{(}\PY{n}{group\PYZus{}by\PYZus{}ls}\PY{p}{,} \PY{n}{keep}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
+            \PY{n}{df\PYZus{}dups} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{p}{:}\PY{p}{]}\PY{p}{[}\PY{n}{df\PYZus{}dups\PYZus{}ind}\PY{p}{]}
+            \PY{n}{df\PYZus{}n\PYZus{}dups} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{p}{:}\PY{p}{]}\PY{p}{[}\PY{n}{df\PYZus{}dups\PYZus{}ind} \PY{o}{==} \PY{k+kc}{False}\PY{p}{]}
+        
+            \PY{n+nb}{print}\PY{p}{(}\PY{n+nb}{list}\PY{p}{(}\PY{n+nb}{map}\PY{p}{(}\PY{n+nb}{len}\PY{p}{,} \PY{p}{[}\PY{n}{df\PYZus{}dups\PYZus{}ind}\PY{p}{,} \PY{n}{df\PYZus{}dups}\PY{p}{,} \PY{n}{df\PYZus{}n\PYZus{}dups}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{)}
+        
+            \PY{n}{uniq\PYZus{}by\PYZus{}grp} \PY{o}{=} \PY{p}{[}\PY{n}{grp}\PY{o}{.}\PY{n}{sort\PYZus{}values}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{positive}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tested}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}site\PYZus{}name}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{ascending}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}\PY{o}{.}\PY{n}{iloc}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}
+                \PY{k}{for} \PY{n}{ind}\PY{p}{,} \PY{n}{grp} \PY{o+ow}{in} \PY{n}{df\PYZus{}dups}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{n}{group\PYZus{}by\PYZus{}ls}\PY{p}{,} \PY{n}{sort}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}\PY{p}{]}
+            \PY{n}{df\PYZus{}d\PYZus{}dups} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{concat}\PY{p}{(}\PY{n}{uniq\PYZus{}by\PYZus{}grp}\PY{p}{,}\PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}\PY{o}{.}\PY{n}{T}
+        
+            \PY{n}{df\PYZus{}uniq\PYZus{}by\PYZus{}grp} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{concat}\PY{p}{(}\PY{p}{[}\PY{n}{df\PYZus{}d\PYZus{}dups}\PY{p}{,} \PY{n}{df\PYZus{}n\PYZus{}dups}\PY{p}{]}\PY{p}{,}\PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{)}
+            \PY{n+nb}{print}\PY{p}{(}\PY{n+nb}{list}\PY{p}{(}\PY{n+nb}{map}\PY{p}{(}\PY{n+nb}{len}\PY{p}{,} \PY{p}{[}\PY{n}{df\PYZus{}d\PYZus{}dups}\PY{p}{,} \PY{n}{df\PYZus{}n\PYZus{}dups}\PY{p}{,} \PY{n}{df\PYZus{}uniq\PYZus{}by\PYZus{}grp}\PY{p}{]}\PY{p}{)}\PY{p}{)}\PY{p}{)}
+            \PY{k}{return} \PY{n}{df\PYZus{}uniq\PYZus{}by\PYZus{}grp}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}8}]:} \PY{n}{df\PYZus{}uniq\PYZus{}vids} \PY{o}{=} \PY{n}{get\PYZus{}groupby\PYZus{}max}\PY{p}{(}\PY{n}{df\PYZus{}all}\PY{p}{,} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{visit\PYZus{}occurrence\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+[1835715, 32244, 1803471]
+[15968, 1803471, 1819439]
+
+    \end{Verbatim}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}16}]:} \PY{n}{df\PYZus{}uniq\PYZus{}person\PYZus{}times} \PY{o}{=} \PY{n}{get\PYZus{}groupby\PYZus{}max}\PY{p}{(}\PY{n}{df\PYZus{}uniq\PYZus{}vids}\PY{p}{,} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{person\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+[1819439, 1726771, 92668]
+[269963, 92668, 362631]
+
+    \end{Verbatim}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}26}]:} \PY{n}{df} \PY{o}{=} \PY{n}{df\PYZus{}uniq\PYZus{}person\PYZus{}times}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}
+         \PY{c+c1}{\PYZsh{} should all be [0,1]}
+         \PY{k}{for} \PY{n}{elt} \PY{o+ow}{in} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tested}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{positive}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{total}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months\PYZus{}bool}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{:}
+             \PY{n}{df}\PY{p}{[}\PY{n}{elt}\PY{p}{]} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{n}{elt}\PY{p}{]}\PY{o}{.}\PY{n}{astype}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{int32}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+             \PY{n+nb}{print}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{n}{elt}\PY{p}{]}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+[1 0]
+[0 1]
+[1]
+[1 0]
+
+    \end{Verbatim}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}27}]:} \PY{c+c1}{\PYZsh{}person\PYZus{}id, care\PYZus{}site\PYZus{}name, visit\PYZus{}concept\PYZus{}id}
+         \PY{n}{len\PYZus{}uniq} \PY{o}{=} \PY{k}{lambda} \PY{n}{col}\PY{p}{:} \PY{n+nb}{len}\PY{p}{(}\PY{n}{df\PYZus{}uniq\PYZus{}person\PYZus{}times}\PY{p}{[}\PY{n}{col}\PY{p}{]}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}
+         \PY{n}{my\PYZus{}disp} \PY{o}{=} \PY{k}{lambda} \PY{n}{col}\PY{p}{:} \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s1}{:}\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{col}\PY{p}{,} \PY{n}{len\PYZus{}uniq}\PY{p}{(}\PY{n}{col}\PY{p}{)}\PY{p}{)}\PY{p}{)}
+         
+         \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{total:}\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{df\PYZus{}uniq\PYZus{}person\PYZus{}times}\PY{p}{)}\PY{p}{)}\PY{p}{)}
+         \PY{n}{my\PYZus{}disp}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{person\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n}{my\PYZus{}disp}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}site\PYZus{}name}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n}{my\PYZus{}disp}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{visit\PYZus{}occurrence\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n}{pre}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{uniq\PYZus{}}\PY{l+s+s1}{\PYZsq{}}
+         \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}site\PYZus{}name}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{to\PYZus{}csv}\PY{p}{(}\PY{n}{pre}\PY{o}{+}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}counts\PYZus{}all\PYZus{}visit.csv}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}site\PYZus{}name}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{[}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ARI\PYZus{}symptoms\PYZus{}2\PYZus{}months\PYZus{}bool}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{==} \PY{l+m+mi}{1}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{to\PYZus{}csv}\PY{p}{(}\PY{n}{pre}\PY{o}{+}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}counts\PYZus{}ari\PYZus{}visit.csv}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}site\PYZus{}name}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{[}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tested}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{==} \PY{l+m+mi}{1}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{to\PYZus{}csv}\PY{p}{(}\PY{n}{pre}\PY{o}{+}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}counts\PYZus{}test\PYZus{}visit.csv}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}site\PYZus{}name}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{[}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{positive}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{==} \PY{l+m+mi}{1}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{to\PYZus{}csv}\PY{p}{(}\PY{n}{pre}\PY{o}{+}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{care\PYZus{}counts\PYZus{}pos\PYZus{}test\PYZus{}visit.csv}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+         \PY{n+nb}{print}\PY{p}{(}\PY{n}{df}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}\PY{p}{)}
+         \PY{n}{df\PYZus{}uniq\PYZus{}person\PYZus{}times}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+total:362631
+person\_id:362631
+care\_site\_name:1387
+visit\_occurrence\_id:362631
+positive                           0
+tested                             0
+age                              611
+person\_id                          0
+visit\_start\_DATETIME               0
+ARI\_symptoms\_2\_months         336555
+race\_concept\_id                    0
+ethnicity\_concept\_id               0
+gender\_concept\_id                  0
+year\_of\_birth                      0
+month\_of\_birth                     0
+day\_of\_birth                       0
+visit\_concept\_id                   0
+care\_site\_name                  3342
+value\_source\_value            350311
+visit\_occurrence\_id                0
+ARI\_symptoms\_2\_months\_bool         0
+total                              0
+dtype: int64
+
+    \end{Verbatim}
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}27}]:}      positive tested        age person\_id visit\_start\_DATETIME  \textbackslash{}
+         492     False   True  [85, 100)  32031651  2020-04-26 00:00:00   
+         543      True   True  [85, 100)  32168960  2020-04-23 00:00:00   
+         689     False   True  [85, 100)  32410794  2020-04-08 00:00:00   
+         779     False   True  [85, 100)  31985357  2020-04-02 00:00:00   
+         1523    False   True  [85, 100)  30531941  2020-03-03 00:00:00   
+         
+              ARI\_symptoms\_2\_months race\_concept\_id ethnicity\_concept\_id  \textbackslash{}
+         492                   True            8527             38003564   
+         543                   True            8527             38003564   
+         689                   None               0                    0   
+         779                   None               0             38003563   
+         1523                  True               0                    0   
+         
+              gender\_concept\_id year\_of\_birth month\_of\_birth day\_of\_birth  \textbackslash{}
+         492               8507          1931              5            3   
+         543               8507          1932              9            2   
+         689               8532          1932             12           22   
+         779               8532          1932              8           28   
+         1523              8532          1932              4            9   
+         
+              visit\_concept\_id                  care\_site\_name value\_source\_value  \textbackslash{}
+         492              9201                            None       Not Detected   
+         543              9201                            None           Detected   
+         689                 0             HILLVIEW LABORATORY       Not Detected   
+         779              9201                            None       Not Detected   
+         1523             9202  STANFORD EXPRESS CARE SAN JOSE       Not Detected   
+         
+              visit\_occurrence\_id ARI\_symptoms\_2\_months\_bool total  
+         492            163856137                       True     1  
+         543            163982679                       True     1  
+         689            163453750                      False     1  
+         779            163986642                      False     1  
+         1523           163450296                       True     1  
+\end{Verbatim}
+            
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}28}]:} \PY{n}{visualize\PYZus{}col}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{age}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+[18, 50)     119791
+[50, 65)      77156
+[65, 75)      53162
+[5, 18)       47461
+[75, 85)      32317
+[0, 5)        24292
+[85, 100)      7841
+Name: age, dtype: int64
+
+    \end{Verbatim}
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}28}]:} visit\_concept\_id                         0       9201     9202  9203
+                                    age                                      
+         ARI\_symptoms\_2\_months\_bool [0, 5)      1833.0    89.0   2693.0   NaN
+                                    [18, 50)    5252.0   376.0   2513.0   2.0
+                                    [5, 18)     2031.0   156.0   3611.0   NaN
+                                    [50, 65)    2332.0   382.0    914.0   0.0
+                                    [65, 75)    1217.0   233.0    364.0   0.0
+                                    [75, 85)     663.0   133.0    161.0   0.0
+                                    [85, 100)    195.0    38.0     46.0   NaN
+         positive                   [0, 5)         5.0     0.0      0.0   NaN
+                                    [18, 50)     419.0    45.0     32.0   0.0
+                                    [5, 18)       23.0     0.0      1.0   NaN
+                                    [50, 65)     220.0    36.0     34.0   0.0
+                                    [65, 75)     117.0    13.0     10.0   0.0
+                                    [75, 85)      70.0    22.0      5.0   0.0
+                                    [85, 100)     28.0     5.0      2.0   NaN
+         tested                     [0, 5)       408.0     6.0      1.0   NaN
+                                    [18, 50)    5010.0   445.0    643.0   0.0
+                                    [5, 18)      457.0     4.0     19.0   NaN
+                                    [50, 65)    1926.0   514.0    425.0   1.0
+                                    [65, 75)    1003.0   249.0    156.0   1.0
+                                    [75, 85)     564.0   174.0     54.0   0.0
+                                    [85, 100)    202.0    48.0     10.0   NaN
+         total                      [0, 5)     12538.0  1010.0  10744.0   NaN
+                                    [18, 50)   89804.0  7113.0  22862.0  12.0
+                                    [5, 18)    26507.0  3506.0  17448.0   NaN
+                                    [50, 65)   58225.0  6601.0  12325.0   5.0
+                                    [65, 75)   40397.0  4886.0   7876.0   3.0
+                                    [75, 85)   25004.0  2618.0   4693.0   2.0
+                                    [85, 100)   6248.0   496.0   1097.0   NaN
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_11_2.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}29}]:} \PY{n}{visualize\PYZus{}col}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{race\PYZus{}concept\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+8527    175590
+0       105715
+8515     62879
+8516     13861
+8557      3511
+8657      1075
+Name: race\_concept\_id, dtype: int64
+
+    \end{Verbatim}
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}29}]:} visit\_concept\_id                                0        9201     9202  9203
+                                    race\_concept\_id                                  
+         ARI\_symptoms\_2\_months\_bool 0                  3820.0    350.0   3930.0   0.0
+                                    8515               2496.0    234.0   1987.0   0.0
+                                    8516                564.0     60.0    318.0   0.0
+                                    8527               6419.0    740.0   3962.0   2.0
+                                    8557                192.0     19.0     84.0   NaN
+                                    8657                 35.0      4.0     21.0   NaN
+         positive                   0                   643.0     37.0     20.0   0.0
+                                    8515                 54.0     26.0     13.0   0.0
+                                    8516                 11.0      3.0      4.0   0.0
+                                    8527                169.0     50.0     45.0   0.0
+                                    8557                  3.0      5.0      2.0   NaN
+                                    8657                  2.0      0.0      0.0   NaN
+         tested                     0                  5235.0    361.0    299.0   0.0
+                                    8515               1030.0    218.0    234.0   0.0
+                                    8516                234.0     81.0     45.0   0.0
+                                    8527               2978.0    740.0    711.0   2.0
+                                    8557                 76.0     35.0     12.0   NaN
+                                    8657                 17.0      5.0      7.0   NaN
+         total                      0                 73684.0   7481.0  24549.0   1.0
+                                    8515              44401.0   4173.0  14301.0   4.0
+                                    8516              10296.0    850.0   2713.0   2.0
+                                    8527             127590.0  13384.0  34601.0  15.0
+                                    8557               2591.0    267.0    653.0   NaN
+                                    8657                772.0     75.0    228.0   NaN
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_12_2.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}30}]:} \PY{n}{visualize\PYZus{}col}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{gender\PYZus{}concept\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+8532    206195
+8507    156321
+0          115
+Name: gender\_concept\_id, dtype: int64
+
+    \end{Verbatim}
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}30}]:} visit\_concept\_id                                  0        9201     9202  9203
+                                    gender\_concept\_id                                  
+         ARI\_symptoms\_2\_months\_bool 0                       3.0      1.0      2.0   NaN
+                                    8507                 5796.0    755.0   4932.0   0.0
+                                    8532                 7727.0    651.0   5368.0   2.0
+         positive                   0                       0.0      0.0      0.0   NaN
+                                    8507                  448.0     87.0     43.0   0.0
+                                    8532                  434.0     34.0     41.0   0.0
+         tested                     0                      14.0      0.0      0.0   NaN
+                                    8507                 3815.0    939.0    559.0   2.0
+                                    8532                 5741.0    501.0    749.0   0.0
+         total                      0                      95.0      6.0     14.0   NaN
+                                    8507               109162.0  11793.0  35359.0   7.0
+                                    8532               150077.0  14431.0  41672.0  15.0
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_13_2.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}31}]:} \PY{n}{visualize\PYZus{}col}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ethnicity\PYZus{}concept\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+38003564    258219
+0            55465
+38003563     48947
+Name: ethnicity\_concept\_id, dtype: int64
+
+    \end{Verbatim}
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}31}]:} visit\_concept\_id                                   0      9201   9202  9203
+                                    ethnicity\_concept\_id                            
+         ARI\_symptoms\_2\_months\_bool 0                       1580     46   2617     0
+                                    38003563                2320    237   1273     0
+                                    38003564                9626   1124   6412     2
+         positive                   0                        548      1      8     0
+                                    38003563                 109     27     13     0
+                                    38003564                 225     93     63     0
+         tested                     0                       4118     16    153     0
+                                    38003563                1025    226    159     0
+                                    38003564                4427   1198    996     2
+         total                      0                      38201   3125  14138     1
+                                    38003563               35370   3872   9704     1
+                                    38003564              185763  19233  53203    20
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_14_2.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+
+    % Add a bibliography block to the postdoc
+    
+    
+    
+    \end{document}
diff --git a/scripts/Archive/Covid19 CDC/prevalenceQueries.sql b/scripts/Archive/Covid19 CDC/prevalenceQueries.sql
new file mode 100644
index 00000000..f907c2a4
--- /dev/null
+++ b/scripts/Archive/Covid19 CDC/prevalenceQueries.sql	
@@ -0,0 +1,148 @@
+WITH
+
+-- Collate patients receiving any recent acute respiratory illness (ARI) diagnosis
+recentARI AS
+( -- Look for any patient with an ARI, based on diagnosis code list suggested by CDC,
+  --  that was recorded recently (since 12/2019).
+  SELECT
+    person_id, visit_occurrence_id, condition_start_DATE, condition_start_DATETIME, 
+    condition_source_value, condition_source_concept_id
+  FROM
+    `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.condition_occurrence` AS condOccur 
+  WHERE
+    condition_start_DATE >= '2019-12-01' AND
+    ( condition_source_value LIKE 'J00.%' OR
+      condition_source_value LIKE 'J01.%' OR
+      condition_source_value LIKE 'J02.%' OR
+      condition_source_value LIKE 'J03.%' OR
+      condition_source_value LIKE 'J04.%' OR
+      condition_source_value LIKE 'J05.%' OR
+      condition_source_value LIKE 'J06.%' OR
+      condition_source_value LIKE 'J09.%' OR
+      condition_source_value LIKE 'J10.%' OR
+      condition_source_value LIKE 'J11.%' OR
+      condition_source_value LIKE 'J12.%' OR
+      condition_source_value LIKE 'J13.%' OR
+      condition_source_value LIKE 'J14.%' OR
+      condition_source_value LIKE 'J15.%' OR
+      condition_source_value LIKE 'J16.%' OR
+      condition_source_value LIKE 'J17.%' OR
+      condition_source_value LIKE 'J18.%' OR
+      condition_source_value LIKE 'J20.%' OR
+      condition_source_value LIKE 'J21.%' OR
+      condition_source_value LIKE 'J22' OR
+      condition_source_value LIKE 'J80' OR
+      condition_source_value LIKE 'A37.91' OR
+      condition_source_value LIKE 'A37.01' OR
+      condition_source_value LIKE 'A37.11' OR
+      condition_source_value LIKE 'A37.81' OR
+      condition_source_value LIKE 'A48.1' OR
+      condition_source_value LIKE 'B25.0' OR
+      condition_source_value LIKE 'B44.0' OR
+      condition_source_value LIKE 'B97.4' OR
+      condition_source_value LIKE 'U07.1'
+    )
+),
+recentARIDateTimeRange AS
+( -- Look for only the last / most recent ARI occurrence date for each patient, in case one has multiple
+  SELECT
+    recentARI.person_id,
+    MIN(condition_start_DATETIME) AS firstARIDateTime, MAX(condition_start_DATETIME) AS lastARIDateTime
+  FROM recentARI
+  GROUP BY recentARI.person_id 
+),
+
+
+-- Collate results on anyone receiving the SARS-CoV2 NAA Test Results
+resultsSARSCoV2Tests AS
+(
+  SELECT person_id, visit_occurrence_id, measurement_DATE, measurement_DATETIME, value_source_value, value_source_value IN ('Detected','Pos','Positive') AS detectedSARSCoV2
+  FROM `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.measurement` as meas
+  WHERE measurement_concept_id = 706170 -- SARS-CoV2 NAA Test Result
+  AND meas.value_source_value is NOT NULL -- Only capture usable results and not extra descriptors
+) ,
+/* Note how often get null values
+select count(*) nRecords, count(distinct person_id) as nPerson, count(distinct visit_occurrence_id) as nVisits, countif(value_source_value is not null) as nonNullValues
+from resultsSARSCoV2Tests 
+
+nRecords  42268
+nPerson 14934
+nVisits 21043
+nonNullValues 21197
+*/  
+resultsSARSCoV2DateTimeRange AS
+( -- Accounting for multiple possible tests per patient, group by patient ID and collate date time ranges
+  SELECT 
+    person_id, 
+    firstSARSCoV2TestedDateTime, lastSARSCoV2TestedDateTime,
+    firstSARSCoV2DetectedDateTime, lastSARSCoV2DetectedDateTime 
+  FROM
+  (
+    SELECT person_id, MIN(measurement_DATETIME) AS firstSARSCoV2TestedDateTime, MAX(measurement_DATETIME) AS lastSARSCoV2TestedDateTime
+    FROM resultsSARSCoV2Tests
+    GROUP BY person_id 
+  ) AS firstLastSARSCoV2Tests
+  LEFT JOIN -- Left outer join, because not everyone who had a test will have a positive test result to join to
+  (
+    SELECT person_id, MIN(measurement_DATETIME) AS firstSARSCoV2DetectedDateTime, MAX(measurement_DATETIME) AS lastSARSCoV2DetectedDateTime
+    FROM resultsSARSCoV2Tests
+    WHERE resultsSARSCoV2Tests.detectedSARSCoV2
+    GROUP BY person_id 
+  ) AS firstLastDetectedSARSCoV2Tests
+  USING (person_id)
+),
+
+-- At this point:
+--  recentARIDateTimeRange has one row per patient (41,341 rows as of 2020-04-20 cdm_release_DATE)
+--  resultsSARSCoV2DateTimeRange has one row per patient (14,838)
+-- Each with first and last dates of occurrences of ARI (after initial "recent" cutoff date) orSARS-CoV2 test results (including timing of positive/detected result, null if no positive/detected results)
+recentARIandSARSCoV2DateTimeRange AS
+( -- Full outer join in both directions because some people with ARI diagnosis don't get SARS-CoV2 testing and vice versa
+  SELECT *,
+      DATETIME_DIFF( firstSARSCoV2TestedDateTime, firstARIDateTime, DAY ) daysFirstARItoFirstSARSCoV2Tested,
+      DATETIME_DIFF( firstSARSCoV2TestedDateTime, lastARIDateTime, DAY ) daysLastARItoFirstSARSCoV2Tested,
+      DATETIME_DIFF( lastSARSCoV2TestedDateTime, firstARIDateTime, DAY ) daysFirstARItoLastSARSCoV2Tested,
+      DATETIME_DIFF( lastSARSCoV2TestedDateTime, lastARIDateTime, DAY ) daysLastARItoLastSARSCoV2Tested,
+
+      DATETIME_DIFF( firstSARSCoV2DetectedDateTime, firstARIDateTime, DAY ) daysFirstARItoFirstSARSCoV2Detected,
+      DATETIME_DIFF( firstSARSCoV2DetectedDateTime, lastARIDateTime, DAY ) daysLastARItoFirstSARSCoV2Detected,
+      DATETIME_DIFF( lastSARSCoV2DetectedDateTime, firstARIDateTime, DAY ) daysFirstARItoLastSARSCoV2Detected,
+      DATETIME_DIFF( lastSARSCoV2DetectedDateTime, lastARIDateTime, DAY ) daysLastARItoLastSARSCoV2Detected,
+  FROM 
+    recentARIDateTimeRange FULL JOIN 
+    resultsSARSCoV2DateTimeRange USING (person_id)
+  -- 4,091 inner join results (recent ARI diagnosis code AND SARS-CoV2 test results exist, but not necessarily in the correct datetime order)
+  -- 52,088 full outer join results (recent ARI diagnosi code OR SARS-CoV2 test results)
+),
+
+recentARIandSARSCoV2DateTimeRangeWithPatientDemo AS
+( -- Join patient demographic information
+  SELECT 
+    recentARIandSARSCoV2DateTimeRange.*,
+    year_of_birth, DATE_DIFF('2020-02-01', DATE(birth_DATETIME), YEAR) ageAsOfFeb2020,
+    person.gender_concept_id , genderConc.concept_name AS genderConcept,
+    person.race_source_value, person.race_concept_id, raceConc.concept_name as raceConcept,
+    person.ethnicity_source_value, person.ethnicity_concept_id, ethnicityConc.concept_name as ethnicityConcept
+  FROM 
+    recentARIandSARSCoV2DateTimeRange 
+    JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.person` AS person USING (person_id) 
+    LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` AS genderConc ON (person.gender_concept_id = genderConc.concept_id)
+    LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` AS raceConc ON (person.race_concept_id = raceConc.concept_id)
+    LEFT JOIN `som-rit-phi-starr-prod.starr_omop_cdm5_deid_latest.concept` AS ethnicityConc ON (person.ethnicity_concept_id = ethnicityConc.concept_id)
+)
+
+-- Example query for primary ARI and SARS CoV2 testing counts, grouped by different categories
+SELECT
+  CAST(FLOOR(ageAsOfFeb2020 / 10)*10 AS INT64) AS ageMin, CAST(FLOOR(ageAsOfFeb2020 / 10)*10+10 AS INT64) as ageMax,
+  COUNTIF(lastARIDateTime IS NOT NULL) as nARIPatients,
+  COUNTIF(lastSARSCoV2TestedDateTime IS NOT NULL) as nSARSCoV2TestedPatients,
+  COUNTIF(lastSARSCoV2DetectedDateTime IS NOT NULL) as nSARSCoV2DetectedPatients,
+  -- Looking for relative date relationship between ARI diagnosis and SARS-CoV2 Test.
+  -- This logic is imperfect as is based on date spans. Consider redoing, but using patient-days as unit of observation (rows) instead of patients
+  COUNTIF( daysFirstARItoFirstSARSCoV2Tested BETWEEN 0 AND 60 OR daysLastARItoFirstSARSCoV2Tested BETWEEN 0 AND 60 ) AS nARIPatientsSARSCoV2TestedWithin60Days,
+  COUNTIF( daysFirstARItoFirstSARSCoV2Detected BETWEEN 0 AND 60 OR daysLastARItoFirstSARSCoV2Detected BETWEEN 0 AND 60 ) AS nARIPatientsSARSCoV2DetectedWithin60Days
+FROM recentARIandSARSCoV2DateTimeRangeWithPatientDemo 
+GROUP BY ageMin, ageMax
+ORDER BY ageMin
+
+LIMIT 100
\ No newline at end of file
diff --git a/scripts/MuthuDecayingWindows/AALoopMonth.py b/scripts/Archive/DecayingWindows/AALoopMonth.py
similarity index 90%
rename from scripts/MuthuDecayingWindows/AALoopMonth.py
rename to scripts/Archive/DecayingWindows/AALoopMonth.py
index a89edc8f..9a9c41ab 100644
--- a/scripts/MuthuDecayingWindows/AALoopMonth.py
+++ b/scripts/Archive/DecayingWindows/AALoopMonth.py
@@ -30,7 +30,7 @@
 	conn = None
 	try:
 		conn = psycopg2.connect ("dbname=medinfo user=Muthu host=localhost") #connect to database
-		print "Connected to database"    
+		print("Connected to database")    
 		curs = conn.cursor()
 		curs.execute("DELETE FROM clinical_item_association;") # clear the training table
 		curs.execute("UPDATE patient_item SET analyze_date = null;") # reset the analyze_date column
@@ -38,10 +38,10 @@
 		conn.commit()
 		curs.close()
 		conn.close()
-		print "Connection closed"	
-	except psycopg2.DatabaseError, ex:
-		print 'I am unable to connect the database: '
-		print ex
+		print("Connection closed")	
+	except psycopg2.DatabaseError as ex:
+		print('I am unable to connect the database: ')
+		print(ex)
 		sys.exit(1)
 	
 
@@ -76,8 +76,8 @@
 	#Keep running the Analysis until you reach the end date
 	while currentItemEnd <= endD:
 
-		print currentItemStart
-		print currentItemEnd
+		print(currentItemStart)
+		print(currentItemEnd)
 
 
 		# Decay database, except at first iteration
@@ -85,11 +85,11 @@
 			conn = None
 			try:
 				conn = psycopg2.connect ("dbname=medinfo user=Muthu host=localhost")
-				print "connected to datbase"
+				print("connected to datbase")
 				curs = conn.cursor()
-			except psycopg2.DatabaseError, ex:
-				print 'I am unable to connect the database: ' 
-				print ex
+			except psycopg2.DatabaseError as ex:
+				print('I am unable to connect the database: ') 
+				print(ex)
 				sys.exit(1)
 
 			fields = list()
@@ -100,13 +100,13 @@
 					fields.append(fieldName + '=' + fieldName + "*" + str(decay))
 
 			sqlQuery = "UPDATE clinical_item_association SET " + str.join(',', fields) + ";"
-			print sqlQuery
+			print(sqlQuery)
 			curs.execute(sqlQuery)
 
 			conn.commit()
 			curs.close()
 			conn.close()
-			print "Connection closed"
+			print("Connection closed")
 
 			''' Test code for only updating one column one time
 			for field in fields:
diff --git a/scripts/MuthuDecayingWindows/AAOneMonth.py b/scripts/Archive/DecayingWindows/AAOneMonth.py
similarity index 89%
rename from scripts/MuthuDecayingWindows/AAOneMonth.py
rename to scripts/Archive/DecayingWindows/AAOneMonth.py
index 69f6c09e..79751a49 100644
--- a/scripts/MuthuDecayingWindows/AAOneMonth.py
+++ b/scripts/Archive/DecayingWindows/AAOneMonth.py
@@ -30,7 +30,7 @@
 	conn = None
 	try:
 		conn = psycopg2.connect ("dbname=medinfo user=Muthu host=localhost") #connect to database
-		print "Connected to database"    
+		print("Connected to database")    
 		curs = conn.cursor()
 		curs.execute("DELETE FROM clinical_item_association;") # clear the training table
 		curs.execute("UPDATE patient_item SET analyze_date = null;") # reset the analyze_date column
@@ -38,9 +38,9 @@
 		conn.commit()
 		curs.close()
 		conn.close()
-		print "Connection closed"	
-	except psycopg2.DatabaseError, ex:
-		print 'I am unable to connect the database: ' + ex
+		print("Connection closed")	
+	except psycopg2.DatabaseError as ex:
+		print('I am unable to connect the database: ' + ex)
 		sys.exit(1)
 	
 
@@ -75,10 +75,10 @@
 	
 	try:
 		conn = psycopg2.connect ("dbname=medinfo user=Muthu host=localhost")
-		print "connected to datbase"
+		print("connected to datbase")
 		curs = conn.cursor()
-	except psycopg2.DatabaseError, ex:
-		print 'I am unable to connect the database: ' + ex
+	except psycopg2.DatabaseError as ex:
+		print('I am unable to connect the database: ' + ex)
 		sys.exit(1)
 
 	prefixes = ['', 'patient_', 'encounter_']
@@ -93,14 +93,14 @@
 	
 	for field in fields:
 		sqlQuery = "UPDATE clinical_item_association SET " + field
-		print sqlQuery
+		print(sqlQuery)
 		curs.execute(sqlQuery)
 
 		 
 	conn.commit()
 	curs.close()
 	conn.close()
-	print "Connection closed"
+	print("Connection closed")
 
 
 
diff --git a/scripts/MuthuDecayingWindows/CreatePatientList.py b/scripts/Archive/DecayingWindows/CreatePatientList.py
similarity index 81%
rename from scripts/MuthuDecayingWindows/CreatePatientList.py
rename to scripts/Archive/DecayingWindows/CreatePatientList.py
index e09445a2..a5f0b8ba 100644
--- a/scripts/MuthuDecayingWindows/CreatePatientList.py
+++ b/scripts/Archive/DecayingWindows/CreatePatientList.py
@@ -19,21 +19,21 @@ def getPatientList(idFile):
 	conn = None
 	try:
 		conn = psycopg2.connect ("dbname=medinfo user=Muthu host=localhost") #connect to database
-		print "Connected to database"    
+		print("Connected to database")    
 		curs = conn.cursor()
 		patientNumbers = sys.argv[1:]
 		for number in patientNumbers:
 			fileName = idFile[:-4] + str(number) + ".txt"
 			fileNameQuery = "COPY (select distinct patient_id from patient_item where patient_id % 10 = " + str(number) +") TO '" + str(fileName) + "';"
 			curs.execute(fileNameQuery)
-			print "Patient ID file created to: " + fileName
+			print("Patient ID file created to: " + fileName)
 		conn.commit()
 		curs.close()
 		conn.close()
-		print "Connection closed"	
-	except psycopg2.DatabaseError, ex:
-		print 'I am unable to connect the database: '
-		print ex
+		print("Connection closed")	
+	except psycopg2.DatabaseError as ex:
+		print('I am unable to connect the database: ')
+		print(ex)
 		sys.exit(1)
 
 # Does the same as above, except for the negative numbers
@@ -41,21 +41,21 @@ def getPatientListNegative(idFile):
 	conn = None
 	try:
 		conn = psycopg2.connect ("dbname=medinfo user=Muthu host=localhost") #connect to database
-		print "Connected to database"    
+		print("Connected to database")    
 		curs = conn.cursor()
 		patientNumbers = sys.argv[1:]
 		for number in patientNumbers:
 			fileName = idFile[:-4] + "Neg" + str(number) + ".txt"
 			fileNameQuery = "COPY (select distinct patient_id from patient_item where patient_id % 10 = " + "-" + str(number) +") TO '" + str(fileName) + "';"
 			curs.execute(fileNameQuery)
-			print "Patient ID file created to: " + fileName
+			print("Patient ID file created to: " + fileName)
 		conn.commit()
 		curs.close()
 		conn.close()
-		print "Connection closed"	
-	except psycopg2.DatabaseError, ex:
-		print 'I am unable to connect the database: '
-		print ex
+		print("Connection closed")	
+	except psycopg2.DatabaseError as ex:
+		print('I am unable to connect the database: ')
+		print(ex)
 		sys.exit(1)
 
 #getPatientList("/Users/Muthu/Desktop/JonathanChen/CDSS_checkedout/MuthuAnalysis/PatientIDFiles/patientIDs.txt")
diff --git a/scripts/MuthuDecayingWindows/StitchIDFiles.py b/scripts/Archive/DecayingWindows/StitchIDFiles.py
similarity index 100%
rename from scripts/MuthuDecayingWindows/StitchIDFiles.py
rename to scripts/Archive/DecayingWindows/StitchIDFiles.py
diff --git a/scripts/Archive/DecayingWindows/__init__.py b/scripts/Archive/DecayingWindows/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/scripts/Archive/DecayingWindows/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/scripts/MuthuDecayingWindows/addConstraints.sql b/scripts/Archive/DecayingWindows/addConstraints.sql
similarity index 100%
rename from scripts/MuthuDecayingWindows/addConstraints.sql
rename to scripts/Archive/DecayingWindows/addConstraints.sql
diff --git a/scripts/MuthuDecayingWindows/commitFromFile.py b/scripts/Archive/DecayingWindows/commitFromFile.py
similarity index 100%
rename from scripts/MuthuDecayingWindows/commitFromFile.py
rename to scripts/Archive/DecayingWindows/commitFromFile.py
diff --git a/scripts/MuthuDecayingWindows/dropConstraints.sql b/scripts/Archive/DecayingWindows/dropConstraints.sql
similarity index 100%
rename from scripts/MuthuDecayingWindows/dropConstraints.sql
rename to scripts/Archive/DecayingWindows/dropConstraints.sql
diff --git a/scripts/Archive/DeepLearningRecommender/ClinicLSTM.ipynb b/scripts/Archive/DeepLearningRecommender/ClinicLSTM.ipynb
new file mode 100644
index 00000000..e1e7698a
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/ClinicLSTM.ipynb
@@ -0,0 +1,853 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "import datetime\n",
+    "import json \n",
+    "import h5py as h5\n",
+    "import sys\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "from random import shuffle\n",
+    "from sklearn.metrics import precision_recall_curve,average_precision_score,roc_curve,auc,roc_auc_score\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (5.0,5.0)\n",
+    "matplotlib.rcParams.update({'font.size': 12})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load Batches of RNN Input Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_next_batch_h5(data_dir,split,num,cap_vals=True):\n",
+    "    '''\n",
+    "    Loads a batch for a .h5 file.  \n",
+    "    Parameters:\n",
+    "        @data_dir: (string) path to .h5 file\n",
+    "        @split: (string) train/dev/test\n",
+    "        @num: (int) batch number\n",
+    "    Returns:\n",
+    "        @x_batch_np: (nd array) x batch data\n",
+    "        @y_batch_np: (nd array) y batch data\n",
+    "    '''\n",
+    "    \n",
+    "    x_batch_h5 = h5.File(data_dir+\"%s_x_batch_%d.h5\"%(split,num), \"r\")\n",
+    "    y_batch_h5 = h5.File(data_dir+\"%s_y_batch_%d.h5\"%(split,num), \"r\")\n",
+    "    \n",
+    "    x_batch_np = x_batch_h5.get('%s_x_batch_%d' % (split,num)).value\n",
+    "    y_batch_np = y_batch_h5.get('%s_y_batch_%d' % (split,num)).value\n",
+    "    \n",
+    "    \n",
+    "    if cap_vals == True:\n",
+    "        x_batch_np[x_batch_np>100]=100\n",
+    "    \n",
+    "    return x_batch_np,y_batch_np\n",
+    "\n",
+    "\n",
+    "\n",
+    "def load_multiple_batches_h5(data_dir,split,num_batches):\n",
+    "    '''\n",
+    "    Load and concatenates multiple batches.\n",
+    "    Parameters:\n",
+    "        @data_dir: (string) path to .h5 file\n",
+    "        @split: (string) train/dev/test\n",
+    "        @num: (int) batch number\n",
+    "    Returns:\n",
+    "        @x_batch_np: (nd array) x batch data\n",
+    "        @y_batch_np: (nd array) y batch data\n",
+    "    '''\n",
+    "    \n",
+    "    for nb in range(num_batches):\n",
+    "        x_batch_i,y_batch_i = load_next_batch_h5(data_dir,split,nb)\n",
+    "        if nb == 0:\n",
+    "            batch_size = x_batch_i.shape[0]\n",
+    "            Tx = x_batch_i.shape[1]\n",
+    "            n_feats = x_batch_i.shape[2]\n",
+    "            n_y = y_batch_i.shape[2]\n",
+    "            x_batch_np = np.zeros((num_batches * batch_size,Tx,n_feats))\n",
+    "            y_batch_np = np.zeros((num_batches * batch_size,Tx,n_y))\n",
+    "\n",
+    "        x_batch_np[(nb*batch_size):((nb+1)*batch_size),:,:] = x_batch_i\n",
+    "        y_batch_np[(nb*batch_size):((nb+1)*batch_size),:,:] = y_batch_i\n",
+    "        \n",
+    "    return x_batch_np,y_batch_np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading Dev Set...\n",
+      "Loading Test Set...\n"
+     ]
+    }
+   ],
+   "source": [
+    "rnn_train_dir = '/home/ubuntu/cs230/RNN/data/48_hr/train/train_batches_100/'\n",
+    "rnn_dev_dir = '/home/ubuntu/cs230/RNN/data/48_hr/dev/dev_batches_100/'\n",
+    "rnn_test_dir = '/home/ubuntu/cs230/RNN/data/48_hr/test/test_batches_100/'\n",
+    "\n",
+    "\n",
+    "fns_test = os.listdir(rnn_test_dir)\n",
+    "fns_test_clean = [fn for fn in fns_test if fn[0]=='x']\n",
+    "fns_test_clean.sort()\n",
+    "\n",
+    "fns_dev = os.listdir(rnn_dev_dir)\n",
+    "fns_dev_clean = [fn for fn in fns_dev if fn[0]=='x']\n",
+    "fns_dev_clean.sort()\n",
+    "print (\"Loading Dev Set...\")\n",
+    "x_dev,y_dev = load_multiple_batches_h5(rnn_dev_dir,'dev',23)\n",
+    "print (\"Loading Test Set...\")\n",
+    "x_test, y_test= load_multiple_batches_h5(rnn_test_dir,'test',23)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
+    "import tensorflow as tf\n",
+    "import keras.backend as K\n",
+    "from keras.layers import LSTM,Dense,Activation,Dropout,BatchNormalization,Masking,TimeDistributed\n",
+    "from keras.models import Sequential\n",
+    "from keras.preprocessing.sequence import pad_sequences\n",
+    "from keras.optimizers import Adam\n",
+    "from keras import regularizers\n",
+    "import sklearn\n",
+    "from sklearn.metrics import precision_recall_curve,average_precision_score,roc_curve,auc\n",
+    "from sklearn.metrics import precision_recall_fscore_support as pr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Code for Model Metric Calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def weightedLoss(w):\n",
+    "    '''\n",
+    "    Weighted Cross Entropy Loss Function.\n",
+    "    '''\n",
+    "    \n",
+    "    def loss(y_true, y_pred):\n",
+    "    \n",
+    "        y_true_masked = tf.boolean_mask(y_true,tf.not_equal(y_true,-1))\n",
+    "        y_pred_masked = tf.boolean_mask(y_pred,tf.not_equal(y_true,-1))\n",
+    "\n",
+    "        cross_entropy = K.binary_crossentropy(y_true_masked, y_pred_masked)\n",
+    "        weight_vector = y_true_masked * w + (1. - y_true_masked) * 1\n",
+    "        weighted_b_ce = weight_vector * cross_entropy\n",
+    "        return tf.reduce_sum(K.mean(weighted_b_ce))\n",
+    "        \n",
+    "    return loss\n",
+    "\n",
+    "\n",
+    "\n",
+    "def binary_accuracy_V3(y_true, y_pred):\n",
+    "    '''\n",
+    "    Calculates the masked mean accuracy rate across all predictions for binary\n",
+    "    classification problems.\n",
+    "    '''\n",
+    "    t0 = tf.equal(y_true, 0)\n",
+    "    t1 = tf.equal(y_true, 1)\n",
+    "    p0 = tf.equal(tf.round(y_pred), 0)\n",
+    "    p1 = tf.equal(tf.round(y_pred), 1)\n",
+    "    everything = tf.reduce_sum(tf.cast(t0, tf.float32)) + tf.reduce_sum(tf.cast(t1, tf.float32))\n",
+    "    positives = tf.reduce_sum(tf.cast(tf.logical_and(t0, p0), tf.float32)) + tf.reduce_sum(tf.cast(tf.logical_and(p1, t1), tf.float32))\n",
+    "    return positives / everything\n",
+    "\n",
+    "\n",
+    "def f1_V2(y_true,y_pred):\n",
+    "    '''\n",
+    "    Calculates F1 score\n",
+    "    '''\n",
+    "    K.set_epsilon(1e-05)\n",
+    "    def precision(y_true, y_pred):\n",
+    "        true_positives = K.sum(K.round(K.clip(tf.multiply(y_true, tf.cast(tf.not_equal(y_true, -1), tf.float32)) * tf.multiply(y_pred, tf.cast(tf.not_equal(y_true, -1), tf.float32)), 0, 1)))\n",
+    "        predicted_positives = K.sum(K.round(K.clip(tf.multiply(y_pred, tf.cast(tf.not_equal(y_true, -1), tf.float32)), 0, 1)))\n",
+    "        precision = true_positives / (predicted_positives + K.epsilon())\n",
+    "        return precision\n",
+    "\n",
+    "    def recall(y_true, y_pred):\n",
+    "        true_positives = K.sum(K.round(K.clip(tf.multiply(y_true, tf.cast(tf.not_equal(y_true, -1), tf.float32)) * tf.multiply(y_pred, tf.cast(tf.not_equal(y_true, -1), tf.float32)), 0, 1)))\n",
+    "        possible_positives = K.sum(K.round(K.clip(tf.multiply(y_true, tf.cast(tf.not_equal(y_true, -1), tf.float32)), 0, 1)))\n",
+    "        recall = true_positives / (possible_positives + K.epsilon())\n",
+    "        return recall\n",
+    "    \n",
+    "    precision = precision(y_true, y_pred)\n",
+    "    recall = recall(y_true, y_pred)\n",
+    "    return 2*((precision*recall)/(precision+recall))\n",
+    "\n",
+    "\n",
+    "def recall_v2(y_true,y_pred):\n",
+    "    '''\n",
+    "    Calculates masked recall\n",
+    "    '''\n",
+    "    K.set_epsilon(1e-05)\n",
+    "\n",
+    "    y_true_masked = K.round(tf.boolean_mask(y_true,tf.not_equal(y_true,-1)))\n",
+    "    y_pred_masked = K.round(tf.boolean_mask(y_pred,tf.not_equal(y_true,-1)))\n",
+    "    \n",
+    "    true_positives = K.sum(tf.multiply(y_true_masked,y_pred_masked))\n",
+    "    possible_positives = K.sum(tf.cast(tf.equal(y_true_masked,1),tf.float32))\n",
+    "    \n",
+    "    recall = true_positives / (possible_positives + K.epsilon())\n",
+    "    return recall\n",
+    "\n",
+    "\n",
+    "\n",
+    "def precision_v2(y_true,y_pred):\n",
+    "    '''\n",
+    "    Calculates masked precision.\n",
+    "    '''\n",
+    "    y_true_masked = K.round(tf.boolean_mask(y_true,tf.not_equal(y_true,-1)))\n",
+    "    y_pred_masked = K.round(tf.boolean_mask(y_pred,tf.not_equal(y_true,-1)))\n",
+    "\n",
+    "    true_positives = K.sum(tf.multiply(y_true_masked,y_pred_masked))\n",
+    "    predicted_positives = K.sum(tf.cast(tf.equal(y_pred_masked,1),tf.float32))\n",
+    "\n",
+    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
+    "\n",
+    "    return precision\n",
+    " \n",
+    "\n",
+    "\n",
+    "def init_model_tracking():\n",
+    "    '''\n",
+    "    Tracks model performance during training.\n",
+    "    '''\n",
+    "    d = {}\n",
+    "    keys = ['train_loss','train_acc','train_f1', 'train_precision','train_recall']\n",
+    "    for k in keys:\n",
+    "        d[k] = []\n",
+    "    \n",
+    "    return d\n",
+    "\n",
+    "def update_tracking(d,metrics):\n",
+    "    '''\n",
+    "    Updates model tracking during training.\n",
+    "    '''\n",
+    "    zipped = dict(zip(['train_loss','train_acc','train_f1','train_precision','train_recall'],metrics))\n",
+    "    for key,val in zipped.items():\n",
+    "        d[key].append(val)\n",
+    "    return d\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def LSTM_model(parameters,batch_size):\n",
+    "    '''\n",
+    "    Defines the LSTM model in Keras.\n",
+    "    Parameters:\n",
+    "        @parameters: (python dict) parameter values\n",
+    "            @Tx: (int) number of input timesteps (maximum)\n",
+    "            @n_a: (list of ints) number of hidden units for each LSTM layer\n",
+    "            @n_feats: (int) number of features per timestep\n",
+    "            @n_layers: (int) number of LSTM layers \n",
+    "            @n_y: (int) number of items to be predicted\n",
+    "            @dropout: (list of floats) dropout rate for each layer\n",
+    "            @lr: (float) learning rate\n",
+    "            @pw: (int) positive weight\n",
+    "    Returns:\n",
+    "        @model: (Keras Model) LSTM model\n",
+    "    '''\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "    model.add(Masking(mask_value=-1., input_shape=(parameters['Tx'], parameters['n_feats'])))\n",
+    "    for l in range(parameters['num_layers']-1):\n",
+    "        model.add(LSTM(units=parameters['num_neurons'][0],return_sequences=True,kernel_regularizer=regularizers.l1(parameters['lambda']),dropout=parameters['dropout_rate'][l]))\n",
+    "    model.add(LSTM(units=parameters['num_neurons'][0],return_sequences=True,kernel_regularizer=regularizers.l1(parameters['lambda']),dropout=parameters['dropout_rate'][-1]))\n",
+    "    \n",
+    "    model.add(TimeDistributed(Dense(parameters['n_y'],activation='sigmoid',kernel_regularizer=regularizers.l1(parameters['lambda']))))\n",
+    "    \n",
+    "    opt = Adam(lr=parameters['learning_rate'])\n",
+    "    model.compile(loss=weightedLoss(parameters['pw']),optimizer=opt)\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def calculate_all_metrics(y_true,y_pred):\n",
+    "    '''\n",
+    "    Wrapper for calculating model metrics.\n",
+    "    '''\n",
+    "    y_pred = tf.convert_to_tensor(y_pred,tf.float32)\n",
+    "    y_true = tf.convert_to_tensor(y_true,tf.float32)\n",
+    "    acc = K.eval(binary_accuracy_V3(y_true,y_pred))\n",
+    "    precision = K.eval(precision_v2(y_true,y_pred))\n",
+    "    recall = K.eval(recall_v2(y_true,y_pred))\n",
+    "    f1 = 2./((1./recall)+(1./precision))\n",
+    "    try:\n",
+    "        auroc = roc_auc_score(y_true,y_pred)\n",
+    "    except:\n",
+    "        auroc = None\n",
+    "    \n",
+    "    return acc,f1,precision,recall,auroc\n",
+    "\n",
+    "\n",
+    "def run_lstm_model(parameters,data_dir,batch_size,total_samples):\n",
+    "    '''\n",
+    "    Wrapper for running and testing the LSTM model with set of parameters\n",
+    "    '''\n",
+    "    \n",
+    "    model = LSTM_model(parameters,batch_size)\n",
+    "    print (model.summary())\n",
+    "    n_batches = int(total_samples/batch_size)\n",
+    "    \n",
+    "    model_tracking = init_model_tracking()\n",
+    "    \n",
+    "    for e in range(parameters['epochs']):\n",
+    "        start_index = 0\n",
+    "        random_batches = np.arange(0,n_batches)\n",
+    "        shuffle(random_batches)\n",
+    "        for b,rb in enumerate(random_batches):\n",
+    "            if b % 5 == 0: print (\"Batch %d\"%b)\n",
+    "            x_batch,y_batch = load_next_batch_h5(data_dir,'train',rb)\n",
+    "            loss_b = model.train_on_batch(x_batch,y_batch)\n",
+    "            if b  % 20 == 0: \n",
+    "                \n",
+    "                batch_metrics = [loss_b]\n",
+    "                y_pred = model.predict(x_batch)\n",
+    "                acc,f1,precision,recall = calculate_all_metrics(y_batch,y_pred)\n",
+    "                \n",
+    "                batch_metrics.extend([acc,f1,precision,recall])\n",
+    "                model_tracking = update_tracking(model_tracking,batch_metrics)\n",
+    "                print (\"\\r(Epoch: % d, Batch %d):\" % (e,b))\n",
+    "                print (\"\\r\\t train_loss: %.3f \\n\\t train_acc: %.3f \\n\\t train_f1: %.3f \\n\\t train_precision: %.3f \\n\\t train_recall: %.3f\" % (tuple(batch_metrics)))\n",
+    "                \n",
+    "                \n",
+    "    train_dict = dict(zip(['train_loss','train_acc','train_f1','train_precision','train_recall'],batch_metrics))\n",
+    "    return model,train_dict,loss_b,model_tracking\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperparamter Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "import math\n",
+    "# if scale == None: generates uniform random value between start/end\n",
+    "# if scale == 'log': generate random variable r in [log(start),log(end)], then return 10^r\n",
+    "#     ex. if you input start:0.0001, end:1 it will return 10^r, where r in [-4,0]\n",
+    "def random_search(start, end, scale='uniform'):\n",
+    "    if scale == 'uniform':\n",
+    "        return np.random.uniform(start, end)\n",
+    "    elif scale == 'int':\n",
+    "        return np.random.randint(start,end)\n",
+    "    elif scale == 'log':\n",
+    "        a = math.log(start, 10)\n",
+    "        b = math.log(end, 10)\n",
+    "        r = np.random.uniform(a, b)\n",
+    "        return 10**r\n",
+    "    else:\n",
+    "        return 'ERROR'\n",
+    "\n",
+    "# get random value from list\n",
+    "def random_grid_search(vals):\n",
+    "    length = len(vals)\n",
+    "    return vals[np.random.randint(0,length)]\n",
+    "\n",
+    "def decrement_num_neurons(first, min_val, num_layers):\n",
+    "    layers = [first]\n",
+    "    random = np.random.rand()\n",
+    "    if random > 0.5:\n",
+    "        random = 50\n",
+    "    else:\n",
+    "        random = 0\n",
+    "    prev = first\n",
+    "    for j in range(num_layers-1):\n",
+    "        prev = max(prev-50, min_val) \n",
+    "        layers.append(prev)\n",
+    "    return layers\n",
+    "\n",
+    "def dropout_search(num_layers):\n",
+    "    possible_vals = [0.4,0.35,0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0]\n",
+    "    d = []\n",
+    "    prev = None\n",
+    "    for l in range(num_layers-1):\n",
+    "        if l == 0:\n",
+    "            dl = possible_vals[np.random.randint(0,len(possible_vals))]\n",
+    "            d.append(dl)\n",
+    "            prev = dl\n",
+    "        else:\n",
+    "            dl = possible_vals[np.random.randint(0,len(possible_vals))]\n",
+    "            while dl > prev:\n",
+    "                dl = possible_vals[np.random.randint(0,len(possible_vals))]\n",
+    "            d.append(dl)\n",
+    "            prev = dl\n",
+    "    d.append(0)\n",
+    "    return d\n",
+    "\n",
+    "\n",
+    "def initialize_keys(d,now):\n",
+    "    keys = ['parameters','train_loss','train_f1','val_f1', 'train_acc', 'val_acc', 'val_f1']\n",
+    "    new_entry = {}\n",
+    "    for k in keys:\n",
+    "        new_entry[k] = []\n",
+    "        \n",
+    "    d[now] = new_entry\n",
+    "    \n",
+    "    return d\n",
+    "\n",
+    "\n",
+    "\n",
+    "def update_dicts(d, train_d,val_d, now, params):\n",
+    "    \n",
+    "    d[now]['parameters'] = params\n",
+    "    d[now]['train_loss'] = str(train_d['train_loss'])\n",
+    "    #d[now]['val_loss'] = str(val_d['val_loss'])\n",
+    "    d[now]['train_f1'] = str(train_d['train_f1'])\n",
+    "    d[now]['train_acc'] = str(train_d['train_acc'])\n",
+    "    d[now]['val_acc'] = str(val_d['val_acc'])\n",
+    "    d[now]['val_f1'] = str(val_d['val_f1'])\n",
+    "    \n",
+    "    return d\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def plot_track(vals,name,title,ylab,label,n_batches,num_epochs,xlab='epochs',step=20,color='r'):\n",
+    "    '''\n",
+    "    Plots graphs\n",
+    "    '''\n",
+    "    batch_fraction = 1./n_batches\n",
+    "    y = vals\n",
+    "\n",
+    "    x = np.linspace(0,num_epochs,num=len(y))\n",
+    "    epoch_idx = np.arange(0,len(vals),int(int(n_batches/step)))\n",
+    "\n",
+    "    plt.plot(x,y,color=color,label=label)\n",
+    "    plt.title(title)\n",
+    "\n",
+    "    plt.xticks(np.asarray(x)[epoch_idx],np.arange(0,num_epochs+0.01))\n",
+    "    plt.xlabel(xlab)\n",
+    "    plt.ylabel(ylab)\n",
+    "    plt.legend(loc='lower right')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return\n",
+    "\n",
+    "\n",
+    "def graph_tracking(model_tracking,n_batches,num_epochs):\n",
+    "    '''\n",
+    "    Plots all data for training.\n",
+    "    '''\n",
+    "    for key,val in model_tracking.items():\n",
+    "        if key == 'train_loss':\n",
+    "            name = 'Training Loss'\n",
+    "            title = 'Training Loss'\n",
+    "            ylab = 'Loss'\n",
+    "        elif key == 'train_acc':\n",
+    "            name = 'Training Accuracy'\n",
+    "            title = 'Training Accuracy'\n",
+    "            ylab = 'Accuracy'\n",
+    "        elif key == 'train_recall':\n",
+    "            name = 'Training Recall'\n",
+    "            title = 'Training Recall'\n",
+    "            ylab = 'Recall'\n",
+    "        elif key == 'train_precision':\n",
+    "            name = 'Training Precision'\n",
+    "            title = 'Training Precision'\n",
+    "            ylab = 'Precision'\n",
+    "        elif key == 'train_f1':\n",
+    "            name = 'Training F1 Score'\n",
+    "            title = 'Training F1 Score'\n",
+    "            ylab = 'F1 Score'\n",
+    "        \n",
+    "        plot_track(val,key,name,title,ylab,n_batches,num_epochs)\n",
+    "    \n",
+    "    \n",
+    "    return\n",
+    "\n",
+    "\n",
+    "def plot_roc_curve(y_true, y_pred,title,text,show=True,color='r'):\n",
+    "    '''\n",
+    "    Plots a ROC curve for given set of predictions\n",
+    "    \n",
+    "    parameters:\n",
+    "        @y_true: truth values\n",
+    "        @y_pred: prediction values\n",
+    "        @title: graph title\n",
+    "        @text: legend text\n",
+    "        @show: whether to plot labels \n",
+    "        @color: color of line\n",
+    "    return:\n",
+    "        None\n",
+    "    '''\n",
+    "    \n",
+    "    \n",
+    "    fpr, tpr, _ = roc_curve(y_true,y_pred,pos_label=1)\n",
+    "\n",
+    "    AUROC = auc(fpr,tpr)\n",
+    "    plt.plot(fpr,tpr,label=text + ' (AUC = %.3f)' % AUROC,color=color)\n",
+    "    \n",
+    "    plt.legend(loc='lower right')\n",
+    "    if show == True:\n",
+    "        \n",
+    "        plt.title(title)\n",
+    "        plt.xlabel('1 - Specificity')\n",
+    "        plt.ylabel('Sensitivity')\n",
+    "    \n",
+    "    return\n",
+    "\n",
+    "\n",
+    "def plot_pr_curve(y_true,y_pred,title,text,show=True,color='r'):\n",
+    "    '''\n",
+    "    Plots a PR Curve for a given set of predictions\n",
+    "    \n",
+    "    parameters:\n",
+    "        @y_true: truth values\n",
+    "        @y_pred: prediction values\n",
+    "        @title: graph title\n",
+    "        @text: legend text\n",
+    "        @show: whether to plot labels \n",
+    "        @color: color of line\n",
+    "    return:\n",
+    "        None\n",
+    "    '''    \n",
+    "\n",
+    "    precision, recall, thresholds = precision_recall_curve(y_true, y_pred,pos_label=1)\n",
+    "    auc_pr = auc(recall,precision)\n",
+    "    \n",
+    "    plt.plot(recall,precision,label=text + ' (AUC = %.3f)' % auc_pr,color=color)\n",
+    "    plt.legend(loc='upper right')\n",
+    "    if show == True:\n",
+    "    \n",
+    "        \n",
+    "        plt.title(title)\n",
+    "        plt.xlabel('Recall')\n",
+    "        plt.ylabel('Precision')\n",
+    "        \n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Search for Hyperparameters and Run Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "pw_low = 2\n",
+    "pw_high = 15\n",
+    "learning_rate_low = 0.00001\n",
+    "learning_rate_high = 0.01\n",
+    "num_layers_low = 1\n",
+    "num_layers_high = 3\n",
+    "num_neurons_1 = list(np.arange(600,2000,50))\n",
+    "dropout_low = 0\n",
+    "dropout_high = 0.4\n",
+    "num_epochs = 2\n",
+    "batch_size = 64\n",
+    "total_samples = batch_size * 405\n",
+    "n_feats = 9290\n",
+    "Tx = 100\n",
+    "n_y = 2994\n",
+    "\n",
+    "model_dicts = {}\n",
+    "\n",
+    "f_score_max = -1\n",
+    "\n",
+    "model_time_start = str(datetime.datetime.now())\n",
+    "for i in range(1):\n",
+    "    K.clear_session()\n",
+    "\n",
+    "    now = str(datetime.datetime.now())\n",
+    "    model_dicts = initialize_keys(model_dicts,now)\n",
+    "\n",
+    "    iteration_param_dict = {}\n",
+    "    pw = random_search(pw_low,pw_high, scale='int') # positive weight \n",
+    "    learning_rate = random_search(learning_rate_low,learning_rate_high,scale='log')\n",
+    "    num_layers = random_search(num_layers_low,num_layers_high, scale='int')\n",
+    "    num_layers = 1\n",
+    "    num_neurons = decrement_num_neurons(random_grid_search(num_neurons_1), 50, num_layers)\n",
+    "    dropout = [random_search(dropout_low, dropout_high, 'uniform') for i in range(len(num_neurons))]\n",
+    "    dropout = dropout_search(num_layers)\n",
+    "    lam = float(np.random.randint(1,5))/1000\n",
+    "\n",
+    "    iteration_param_dict['n_feats'] = n_feats\n",
+    "    iteration_param_dict['Tx'] = Tx\n",
+    "    iteration_param_dict['n_y'] = n_y\n",
+    "    iteration_param_dict['pw'] = pw\n",
+    "    iteration_param_dict['learning_rate'] = learning_rate\n",
+    "    iteration_param_dict['num_layers'] = num_layers\n",
+    "    iteration_param_dict['num_neurons'] = num_neurons\n",
+    "    iteration_param_dict['activation'] = 'relu'\n",
+    "    iteration_param_dict['dropout_rate'] = dropout\n",
+    "    iteration_param_dict['epochs'] = num_epochs\n",
+    "    iteration_param_dict['lambda'] = lam\n",
+    "    print('########')\n",
+    "    print(iteration_param_dict)\n",
+    "\n",
+    "    model_i,train_dict_i,loss_i,model_tracking = run_lstm_model(iteration_param_dict,rnn_train_dir,batch_size,total_samples)\n",
+    "    y_preds_dev = model_i.predict(x_dev)\n",
+    "    dev_metrics = calculate_all_metrics(y_dev,y_preds_dev)\n",
+    "\n",
+    "    dev_dict_i = dict(zip(['val_acc','val_f1','val_precision','val_recall'],dev_metrics))\n",
+    "\n",
+    "    model_dicts = update_dicts(model_dicts, train_dict_i, dev_dict_i, now, iteration_param_dict)\n",
+    "    print (model_dicts)\n",
+    "    with open('model_dictionaries_rnn/rnn_model_dictionary_{}.json'.format(model_time_start), 'w') as mdj:\n",
+    "        json.dump(model_dicts, mdj,default=str)\n",
+    "\n",
+    "    if dev_dict_i['val_f1'] > f_score_max:\n",
+    "        f_score_max = dev_dict_i['val_f1']\n",
+    "        model_i.save('best_models_rnn/RNN_Best_Model_{}.h5'.format(model_time_start))\n",
+    "        best_model = model_i\n",
+    "        best_weight = iteration_param_dict['pw']\n",
+    "                \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph_tracking(model_tracking,int(total_samples/batch_size),num_epochs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_bools = y_test!=-1\n",
+    "y_test_masked = y_test[y_test!=-1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_preds_masked = best_preds[y_test_bools]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras.utils import CustomObjectScope\n",
+    "\n",
+    "best_weight = 5\n",
+    "\n",
+    "with CustomObjectScope({'loss':weightedLoss(best_weight)}):\n",
+    "    model_load = keras.models.load_model('best_models_rnn/RNN_Best_Model_2018-03-20 01:58:16.765533.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_load.evaluate(x_test,y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare to Baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,y_train = load_multiple_batches_h5(rnn_train_dir,'train',405)\n",
+    "\n",
+    "total_predictions = np.sum(y_train[y_train==1])\n",
+    "num_timelines = y_train.shape[0]\n",
+    "num_timesteps = y_train.shape[1]\n",
+    "\n",
+    "y_train_mask = np.copy(y_train)\n",
+    "\n",
+    "item_orders = np.sum(np.sum(y_train_mask,axis=0),axis=0)\n",
+    "top25_idx = item_orders.argsort()[-25:][::-1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseline_response_mat = np.zeros(y_test.shape)\n",
+    "\n",
+    "baseline_response_mat[:,:,top25_idx] = 1\n",
+    "y_test_bools = y_test!=-1\n",
+    "y_test_masked = y_test[y_test!=-1]\n",
+    "\n",
+    "baseline_response_mat = baseline_response_mat[y_test_bools]\n",
+    "baseline_flat_masked = baseline_response_mat.flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f87d5f2b4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_pr_curve(y_test_masked, best_preds_masked,'Patient Progression PR Curve','ClinicLSTM',show=True,color='b')\n",
+    "plot_pr_curve(y_test_masked,baseline_flat_masked,'Majority Precision-Recall Curve','Baseline',show=False,color='orange')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f87f5bdb240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_roc_curve(y_test_masked, best_preds_masked,'Patient Progression ROC Curve','ClinicLSTM',show=True,color='b')\n",
+    "plot_roc_curve(y_test_masked,baseline_flat_masked,'Majority Precision-Recall Curve','Baseline',show=False,color='orange')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:tensorflow_p36]",
+   "language": "python",
+   "name": "conda-env-tensorflow_p36-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/scripts/Archive/DeepLearningRecommender/ClinicNet.ipynb b/scripts/Archive/DeepLearningRecommender/ClinicNet.ipynb
new file mode 100644
index 00000000..e7375548
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/ClinicNet.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/matplotlib/__init__.py:962: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #2\n",
+      "  (fname, cnt))\n",
+      "/home/ubuntu/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/matplotlib/__init__.py:962: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #3\n",
+      "  (fname, cnt))\n",
+      "/home/ubuntu/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+    "import sklearn\n",
+    "from sklearn.metrics import precision_recall_fscore_support as pr\n",
+    "from sklearn.metrics import average_precision_score, precision_recall_curve\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Activation, Dropout\n",
+    "from keras.wrappers.scikit_learn import KerasClassifier\n",
+    "from keras.utils import np_utils\n",
+    "from sklearn.model_selection import cross_val_score, KFold\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import roc_curve, auc,roc_auc_score\n",
+    "from keras.optimizers import SGD, Adam, RMSprop\n",
+    "import scipy\n",
+    "from keras.layers.normalization import BatchNormalization\n",
+    "from keras.callbacks import TensorBoard\n",
+    "import datetime\n",
+    "from random import shuffle\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras import regularizers\n",
+    "import tensorflow as tf\n",
+    "from keras import backend as K\n",
+    "import scipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.python.client import device_lib\n",
+    "print device_lib.list_local_devices()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (5.0,4.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Getting Training X Data\n",
+      "Getting Training Y Data\n",
+      "Getting Dev X Data\n",
+      "Getting Dev Y Data\n",
+      "Getting Test X Data\n",
+      "Getting Test Y Data\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "data_dir = '/home/ubuntu/cs230/FeedForward/Data/XY_data/'\n",
+    "data_dir2 = '/home/ubuntu/cs230/FeedForward/Data/XY_data_trimmed/'\n",
+    "\n",
+    "print('Getting Training X Data')\n",
+    "x_train = pd.read_table(data_dir2 + 'x_train_trimmed.txt',sep = '\\t')\n",
+    "print('Getting Training Y Data')\n",
+    "y_train = pd.read_table(data_dir + 'y_train.txt', sep = '\\t')\n",
+    "print('Getting Dev X Data')\n",
+    "x_dev = pd.read_table(data_dir2 + 'x_dev_trimmed.txt',sep = '\\t')\n",
+    "print('Getting Dev Y Data')\n",
+    "y_dev = pd.read_table(data_dir + 'y_dev.txt',sep = '\\t')\n",
+    "print('Getting Test X Data')\n",
+    "x_test = pd.read_table(data_dir2 + 'x_test_trimmed.txt', sep = '\\t')\n",
+    "print('Getting Test Y Data')\n",
+    "y_test = pd.read_table(data_dir + 'y_test.txt', sep = '\\t')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop last rows (due to error upon original download)\n",
+    "x_dev.drop(x_dev.index[len(x_dev)-1], inplace = True)\n",
+    "x_test.drop(x_test.index[len(x_test)-1], inplace = True)\n",
+    "y_test.drop(y_test.index[len(y_test)-1], inplace = True)\n",
+    "y_dev.drop(y_dev.index[len(y_dev)-1], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prep Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = scipy.sign(y_train)\n",
+    "y_dev = scipy.sign(y_dev)\n",
+    "y_test = scipy.sign(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Due to known Keras issue #8011\n",
+    "def prepareForWeights(mat):\n",
+    "    width = mat.shape[1] + 2\n",
+    "    length = mat.shape[0]\n",
+    "    z = np.zeros((length, width))\n",
+    "    z[:,2:] = mat\n",
+    "    z[:,1] = 1\n",
+    "    z[:,0] = 0\n",
+    "    return z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = prepareForWeights(y_train)\n",
+    "y_test = prepareForWeights(y_test)\n",
+    "y_dev = prepareForWeights(y_dev)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Metrics and Loss Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def flattenMatrix(mat):\n",
+    "    mat = pd.DataFrame(mat)\n",
+    "    n = len(mat)\n",
+    "    flattened = []\n",
+    "    for i in range(n):\n",
+    "        actual_vals = mat.loc[i, ].tolist()\n",
+    "        flattened.extend(actual_vals)\n",
+    "    return flattened\n",
+    "\n",
+    "def calculateF1Score(preds, test):\n",
+    "    actual = flattenMatrix(test)\n",
+    "    predicted = flattenMatrix(preds)\n",
+    "    actual_binary = [1 if x > 0 else 0 for x in actual]\n",
+    "    predicted_binary = [1 if x > 0 else 0 for x in predicted]\n",
+    "    bPrecis, bRecall, bFscore, bSupport = pr(actual_binary, predicted_binary, average='binary')\n",
+    "    return bPrecis, bRecall, bFscore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LossHistory(keras.callbacks.Callback):\n",
+    "    def on_train_begin(self, logs={}):\n",
+    "        self.losses = []\n",
+    "        self.val_losses = []\n",
+    "    def on_batch_end(self, batch, logs={}):\n",
+    "        self.losses.append(logs.get('loss'))\n",
+    "        self.val_losses.append(logs.get('val_loss'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def weightedLoss(w):\n",
+    "    def loss(y_true, y_pred):\n",
+    "        y_true_edited = y_true[:,2:]\n",
+    "        y_pred_edited = y_pred[:,2:]\n",
+    "        l = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(targets=y_true_edited,logits=y_pred_edited,pos_weight=w))\n",
+    "        return l\n",
+    "    return loss\n",
+    "\n",
+    "def weighted_binary_crossentropy(zero_weight, one_weight):\n",
+    "\n",
+    "    def weighted_binary_crossentropy(y_true, y_pred):\n",
+    "\n",
+    "        # Calculate the binary crossentropy\n",
+    "        b_ce = K.binary_crossentropy(y_true, y_pred)\n",
+    "\n",
+    "        # Apply the weights\n",
+    "        weight_vector = y_true * one_weight + (1. - y_true) * zero_weight\n",
+    "        weighted_b_ce = weight_vector * b_ce\n",
+    "\n",
+    "        # Return the mean error\n",
+    "        return K.mean(weighted_b_ce)\n",
+    "\n",
+    "    return weighted_binary_crossentropy\n",
+    "\n",
+    "def binary_accuracy_V2(y_true, y_pred):\n",
+    "    '''Calculates the mean accuracy rate across all predictions for binary\n",
+    "    classification problems.\n",
+    "    '''\n",
+    "    y_true_edited = y_true[:,2:]\n",
+    "    y_pred_edited = y_pred[:,2:]\n",
+    "    return K.mean(K.equal(y_true, K.round(y_pred)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f1(y_true, y_pred):\n",
+    "    def recall(y_true, y_pred):\n",
+    "        \"\"\"Recall metric.\n",
+    "\n",
+    "        Only computes a batch-wise average of recall.\n",
+    "\n",
+    "        Computes the recall, a metric for multi-label classification of\n",
+    "        how many relevant items are selected.\n",
+    "        \"\"\"\n",
+    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+    "        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+    "        recall = true_positives / (possible_positives + K.epsilon())\n",
+    "        return recall\n",
+    "\n",
+    "    def precision(y_true, y_pred):\n",
+    "        \"\"\"Precision metric.\n",
+    "\n",
+    "        Only computes a batch-wise average of precision.\n",
+    "\n",
+    "        Computes the precision, a metric for multi-label classification of\n",
+    "        how many selected items are relevant.\n",
+    "        \"\"\"\n",
+    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+    "        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+    "        precision = true_positives / (predicted_positives + K.epsilon())\n",
+    "        return precision\n",
+    "    precision = precision(y_true, y_pred)\n",
+    "    recall = recall(y_true, y_pred)\n",
+    "    return 2*((precision*recall)/(precision+recall))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def flattenMatrix(mat):\n",
+    "    mat = pd.DataFrame(mat)\n",
+    "    n = len(mat)\n",
+    "    flattened = []\n",
+    "    for i in range(n):\n",
+    "        actual_vals = mat.loc[i, ].tolist()\n",
+    "        flattened.extend(actual_vals)\n",
+    "    return flattened\n",
+    "\n",
+    "def calculateF1ScoreOnly(y_true, y_pred):\n",
+    "    actual_binary = [1 if x > 0 else 0 for x in y_true]\n",
+    "    predicted_binary = [1 if x > 0 else 0 for x in y_pred]\n",
+    "    bPrecis, bRecall, bFscore, bSupport = pr(actual_binary, predicted_binary, average='binary')\n",
+    "    return bFscore\n",
+    "\n",
+    "def calculateF1Score(y_true, y_pred):\n",
+    "    actual = flattenMatrix(y_true)\n",
+    "    predicted = flattenMatrix(y_pred)\n",
+    "    y_true2 = np.reshape(y_true, (y_true.shape[0]*y_true.shape[1],-1))\n",
+    "    y_pred2 = np.reshape(y_pred, (y_pred.shape[0]*y_pred.shape[1],-1))\n",
+    "    bPrecis, bRecall, bFscore, bSupport = pr(y_true2, y_pred2, average='binary')\n",
+    "    return bPrecis, bRecall, bFscore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperparameter Searching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "# if scale == None: generates uniform random value between start/end\n",
+    "# if scale == 'log': generate random variable r in [log(start),log(end)], then return 10^r\n",
+    "#     ex. if you input start:0.0001, end:1 it will return 10^r, where r in [-4,0]\n",
+    "def random_search(start, end, scale='uniform'):\n",
+    "    if scale == 'uniform':\n",
+    "        return np.random.uniform(start, end)\n",
+    "    elif scale == 'int':\n",
+    "        return np.random.randint(start,end)\n",
+    "    elif scale == 'log':\n",
+    "        a = math.log(start, 10)\n",
+    "        b = math.log(end, 10)\n",
+    "        r = np.random.uniform(a, b)\n",
+    "        return 10**r\n",
+    "    else:\n",
+    "        return 'ERROR'\n",
+    "\n",
+    "# get random value from list\n",
+    "def random_grid_search(vals):\n",
+    "    length = len(vals)\n",
+    "    return vals[np.random.randint(0,length)]\n",
+    "\n",
+    "def decrement_num_neurons(first, min_val, num_layers):\n",
+    "    layers = [first]\n",
+    "    random = np.random.rand()\n",
+    "    if random > 0.5:\n",
+    "        random = 50\n",
+    "    else:\n",
+    "        random = 0\n",
+    "    prev = first\n",
+    "    for j in range(num_layers-1):\n",
+    "        prev = max(prev-25, min_val) \n",
+    "        layers.append(prev)\n",
+    "    return layers\n",
+    "\n",
+    "def dropout_search(num_layers):\n",
+    "    possible_vals = [0.4,0.35,0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0]\n",
+    "    d = []\n",
+    "    prev = None\n",
+    "    for l in range(num_layers-1):\n",
+    "        if l == 0:\n",
+    "            dl = possible_vals[np.random.randint(0,len(possible_vals))]\n",
+    "            d.append(dl)\n",
+    "            prev = dl\n",
+    "        else:\n",
+    "            dl = possible_vals[np.random.randint(0,len(possible_vals))]\n",
+    "            while dl > prev:\n",
+    "                dl = possible_vals[np.random.randint(0,len(possible_vals))]\n",
+    "            d.append(dl)\n",
+    "            prev = dl\n",
+    "    d.append(0)\n",
+    "    return d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neural Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Creates NN\n",
+    "'''\n",
+    "def neural_network(num_layers, num_neurons, learning_rate, activation, dropout_rate, x_shape, y_shape, pw, lam):\n",
+    "    assert len(num_neurons) == len(dropout_rate)\n",
+    "    K.clear_session()\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(num_neurons[0], activation=activation, input_dim=x_shape[1]))\n",
+    "    for l in range(1, num_layers-2):\n",
+    "        model.add(Dense(num_neurons[l], kernel_regularizer = regularizers.l2(lam)))\n",
+    "        model.add(BatchNormalization())\n",
+    "        model.add(Activation(activation))\n",
+    "        model.add(Dropout(dropout_rate[l]))\n",
+    "    model.add(Dense(y_shape[1], activation='sigmoid'))\n",
+    "    adam = Adam(lr=learning_rate)\n",
+    "    model.compile(loss=weighted_binary_crossentropy(1,pw),\n",
+    "              optimizer=adam,\n",
+    "              metrics = [binary_accuracy_V2, f1])\n",
+    "    return model\n",
+    "\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_nn(x_train, y_train, x_dev, y_dev, params, now):\n",
+    "    num_layers = params['num_layers']\n",
+    "    num_neurons = params['num_neurons']\n",
+    "    learning_rate = params['learning_rate']\n",
+    "    activation = params['activation']\n",
+    "    dropout_rate = params['dropout_rate']\n",
+    "    epochs = params['epochs']\n",
+    "    pw = params['weight']\n",
+    "    lam = params['lambda']\n",
+    "    \n",
+    "    x_shape = x_train.shape\n",
+    "    y_shape = y_train.shape\n",
+    "\n",
+    "    os.makedirs('log_dir_keras/{}'.format(now))\n",
+    "    tensorboard = TensorBoard(log_dir=\"log_dir_keras/{}\".format(now))\n",
+    "    losshistory = LossHistory()\n",
+    "    print(\"Generating NN Architecture\")\n",
+    "    model = neural_network(num_layers, num_neurons, learning_rate, activation, dropout_rate, x_shape, y_shape, pw, lam)\n",
+    "    print('Fitting Model')\n",
+    "    mod_hist = model.fit(x_train, y_train, validation_data = (x_dev , y_dev), verbose=1, batch_size = 512, epochs = epochs, callbacks = [losshistory])\n",
+    "\n",
+    "    preds = model.predict(x_dev)\n",
+    "    preds = preds[:,2:]\n",
+    "    preds[preds >= 0.5] = 1\n",
+    "    preds[preds < 0.5] = 0\n",
+    "    bPrecis, bRecall, bFscore = calculateF1Score(y_dev[:,2:], preds)\n",
+    "    print('Precision: {} , Recall : {} , F1 : {} '.format(bPrecis, bRecall, bFscore))\n",
+    "\n",
+    "    return bFscore, model, mod_hist.history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def initialize_keys(d,now):\n",
+    "    keys = ['parameters','loss','val_loss','f1','val_f1', 'acc', 'val_acc', 'test_fscore', 'test_precision', 'test_recall']\n",
+    "    new_entry = {}\n",
+    "    for k in keys:\n",
+    "        new_entry[k] = []\n",
+    "        \n",
+    "    d[now] = new_entry\n",
+    "    \n",
+    "    return d\n",
+    "\n",
+    "'''\n",
+    "{'f1': [0.12090024837345423, 0.1811971694957791], \n",
+    "'binary_accuracy_V2': [0.79680669177864871, 0.91634968441619902], \n",
+    "'loss': [0.95547696070291122, 0.84549003041767801], \n",
+    "'val_binary_accuracy_V2': [0.97204344202490411, 0.97537883239633894], \n",
+    "'val_f1': [nan, nan], \n",
+    "'val_loss': [0.79929518769769103, 0.78799666867536655]}\n",
+    "'''\n",
+    "def update_dicts(d, now, params, mod_hist):\n",
+    "    \n",
+    "    d[now]['parameters'] = params\n",
+    "\n",
+    "    d[now]['loss'] = mod_hist['loss']\n",
+    "    d[now]['val_loss'] = mod_hist['val_loss']\n",
+    "    d[now]['f1'] = mod_hist['f1']\n",
+    "    d[now]['val_f1'] = mod_hist['val_f1']\n",
+    "    d[now]['acc'] = mod_hist['binary_accuracy_V2']\n",
+    "    d[now]['val_acc'] = mod_hist['val_binary_accuracy_V2']\n",
+    "    return d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Search ranges were refined using a coarse-to-fine method\n",
+    "K.clear_session()\n",
+    "pw_low = 5\n",
+    "pw_high = 15\n",
+    "learning_rate_low = 0.001\n",
+    "learning_rate_high = 0.01\n",
+    "num_layers_low = 6\n",
+    "num_layers_high = 13\n",
+    "num_neurons_1 = [200,250,300,400,500]\n",
+    "dropout_low = 0\n",
+    "dropout_high = 0.2\n",
+    "num_epochs = 3\n",
+    "\n",
+    "f_score_max = -100\n",
+    "best_model = None\n",
+    "model_losses = []\n",
+    "model_val_losses = []\n",
+    "model_dicts = {}\n",
+    "\n",
+    "model_time_start = str(datetime.datetime.now())\n",
+    "for i in range(4):\n",
+    "    K.clear_session()\n",
+    "    now = str(datetime.datetime.now())\n",
+    "    model_dicts = initialize_keys(model_dicts,now)\n",
+    "\n",
+    "    iteration_param_dict = {}\n",
+    "    pw = random_search(pw_low,pw_high, scale='int') # positive error weight \n",
+    "    learning_rate = random_search(learning_rate_low,learning_rate_high,scale='log')\n",
+    "    num_layers = random_search(num_layers_low,num_layers_high, scale='int')\n",
+    "    num_neurons = decrement_num_neurons(random_grid_search(num_neurons_1), 50, num_layers)\n",
+    "    dropout = [random_search(dropout_low, dropout_high, 'uniform') for i in range(len(num_neurons))]\n",
+    "    dropout = dropout_search(num_layers)\n",
+    "    lam = float(np.random.randint(1,5))/1000\n",
+    "\n",
+    "    iteration_param_dict['weight'] = pw\n",
+    "    iteration_param_dict['learning_rate'] = learning_rate\n",
+    "    iteration_param_dict['num_layers'] = num_layers\n",
+    "    iteration_param_dict['num_neurons'] = num_neurons\n",
+    "    iteration_param_dict['activation'] = 'relu'\n",
+    "    iteration_param_dict['dropout_rate'] = dropout\n",
+    "    iteration_param_dict['epochs'] = num_epochs\n",
+    "    iteration_param_dict['lambda'] = lam\n",
+    "    print('########')\n",
+    "    print(iteration_param_dict)\n",
+    "\n",
+    "    f_score, model_i, mod_hist = run_nn(x_train, y_train ,x_dev, y_dev, iteration_param_dict, now)\n",
+    "\n",
+    "    model_dicts = update_dicts(model_dicts, now, iteration_param_dict, mod_hist)\n",
+    "    with open('model_dictionaries/model_dictionary_{}.json'.format(model_time_start), 'w') as mdj:\n",
+    "        json.dump(model_dicts, mdj)\n",
+    "\n",
+    "    if f_score > f_score_max:\n",
+    "        f_score_max = f_score\n",
+    "        model_i.save('best_models/Feed_Forward_Best_Model_{}.h5'.format(model_time_start))\n",
+    "        best_model = model_i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_roc_curve(y_preds, y_true,title,text):\n",
+    "    '''\n",
+    "    Plots a ROC curve for given set of predictions\n",
+    "    \n",
+    "    parameters:\n",
+    "        @predict_results: predict generator object\n",
+    "    return:\n",
+    "        None\n",
+    "    '''\n",
+    "    \n",
+    "    (dim1,dim2) = y_preds.shape\n",
+    "    \n",
+    "    y = np.asarray(y_true).reshape((dim1*dim2,1))\n",
+    "    y_hat = np.asarray(y_preds).reshape((dim1*dim2,1))\n",
+    "    \n",
+    "    fpr, tpr, _ = roc_curve(y,y_hat)\n",
+    "\n",
+    "    AUROC = auc(fpr,tpr)\n",
+    "    plt.plot(fpr,tpr,label=text + ' (AUC = %.3f)' % AUROC)\n",
+    "    plt.legend(loc='lower right')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('1 - Specificity')\n",
+    "    plt.ylabel('Sensitivity')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_roc_curve_2_sets(y_preds1, y_true1, y_preds2, y_true2, title,text1, text2):\n",
+    "    '''\n",
+    "    Plots a ROC curve for given set of predictions\n",
+    "    \n",
+    "    parameters:\n",
+    "        @predict_results: predict generator object\n",
+    "    return:\n",
+    "        None\n",
+    "    '''\n",
+    "    \n",
+    "    (dim1,dim2) = y_preds1.shape\n",
+    "    \n",
+    "    y = np.asarray(y_true1).reshape((dim1*dim2,1))\n",
+    "    y_hat = np.asarray(y_preds1).reshape((dim1*dim2,1))\n",
+    "    \n",
+    "    fpr, tpr, _ = roc_curve(y,y_hat)\n",
+    "\n",
+    "    AUROC = auc(fpr,tpr)\n",
+    "    plt.plot(fpr,tpr,label=text1 + ' AUC = %.3f' % AUROC)\n",
+    "    \n",
+    "\n",
+    "    y2 = y_true2\n",
+    "    y_hat2 = y_preds2\n",
+    "    \n",
+    "    fpr2, tpr2, _ = roc_curve(y2,y_hat2)\n",
+    "    AUROC2 = auc(fpr2,tpr2)\n",
+    "    plt.plot(fpr2,tpr2,label=text2 + ' AUC = %.3f' % AUROC2)\n",
+    "    \n",
+    "    plt.legend(loc='lower right')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('1 - Specificity')\n",
+    "    plt.ylabel('Sensitivity')\n",
+    "    plt.savefig('roc_curve.png')\n",
+    "    plt.show()    \n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_pr_curve(y_preds,y_true,title,text):\n",
+    "    '''\n",
+    "    Plots a PR Curve for a given set of predictions\n",
+    "     \n",
+    "    parameters:\n",
+    "    \n",
+    "    return:\n",
+    "        None\n",
+    "    '''    \n",
+    "\n",
+    "    (dim1,dim2) = y_preds.shape\n",
+    "    \n",
+    "    y = np.asarray(y_true).reshape((dim1*dim2,1))\n",
+    "    y_hat = np.asarray(y_preds).reshape((dim1*dim2,1))\n",
+    "\n",
+    "    precision, recall, thresholds = precision_recall_curve(y, y_hat)\n",
+    "    AUPR = average_precision_score(y,y_hat)\n",
+    "    \n",
+    "    plt.plot(recall,precision,label=text + ' (AP = %.3f)' % AUPR,color='r')\n",
+    "    plt.legend(loc='upper right')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Recall (p(y_hat ==1 | y==1))')\n",
+    "    plt.ylabel('Precision')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_pr_curve_2_sets(y_preds,y_true, y_preds2, y_true2, title,text1,text2):\n",
+    "    '''\n",
+    "    Plots a PR Curve for a given set of predictions\n",
+    "     \n",
+    "    parameters:\n",
+    "    \n",
+    "    return:\n",
+    "        None\n",
+    "    '''    \n",
+    "\n",
+    "    (dim1,dim2) = y_preds.shape\n",
+    "    \n",
+    "    y = np.asarray(y_true).reshape((dim1*dim2,1))\n",
+    "    y_hat = np.asarray(y_preds).reshape((dim1*dim2,1))\n",
+    "\n",
+    "    precision1, recall1, thresholds1 = precision_recall_curve(y, y_hat)\n",
+    "    area = auc(recall1, precision1)\n",
+    "    AUPR = average_precision_score(y,y_hat)\n",
+    "    \n",
+    "    plt.plot(recall1,precision1,label=text1 + ' AUC = %.3f' % area)\n",
+    "    \n",
+    "    y2 = y_true2\n",
+    "    y_hat2 = y_preds2\n",
+    "\n",
+    "    precision2, recall2, thresholds2 = precision_recall_curve(y2, y_hat2)\n",
+    "    area2 = auc(recall2, precision2)\n",
+    "    AUPR2 = average_precision_score(y2,y_hat2)\n",
+    "    \n",
+    "    plt.plot(recall2,precision2,label=text2 + ' AUC = %.3f' % area2)\n",
+    "    \n",
+    "    plt.legend(loc='upper right')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Recall')\n",
+    "    plt.ylabel('Precision')\n",
+    "    plt.savefig('pr_curve.png')\n",
+    "    plt.show()\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare to Human Authored "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example Models:\n",
+    "# best_models/Feed_Forward_Best_Model_2018-03-19 02:22:23.891330.h5\n",
+    "# best_models/Feed_Forward_Best_Model_2018-03-19 08:17:43.787454.h5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "from keras.utils import CustomObjectScope\n",
+    "\n",
+    "with CustomObjectScope({'weighted_binary_crossentropy': weighted_binary_crossentropy(1,10),\n",
+    "                        'binary_accuracy_V2' : binary_accuracy_V2,\n",
+    "                       'f1':f1}):\n",
+    "    model = keras.models.load_model('best_models/Feed_Forward_Best_Model_2018-03-19 02:22:23.891330.h5')\n",
+    "'''\n",
+    "from keras.utils import CustomObjectScope\n",
+    "\n",
+    "with CustomObjectScope({'weighted_binary_crossentropy': weighted_binary_crossentropy(1,10),\n",
+    "                        'binary_accuracy_V2' : binary_accuracy_V2,\n",
+    "                       'f1':f1}):\n",
+    "    model = keras.models.load_model('best_models/Feed_Forward_Best_Model_2018-03-19 08:17:43.787454.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_preds = model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ha_dir = '/home/ubuntu/cs230/HumanBaseline/'\n",
+    "human_true = pd.read_table(ha_dir + 'human_response.txt', header = None)\n",
+    "human_true.columns = ['human_true']\n",
+    "\n",
+    "human_pred = pd.read_table(ha_dir + 'human_prediction.txt', header = None)\n",
+    "human_pred.columns = ['human_pred']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_roc_curve_2_sets(test_preds[:,2:],y_test[:,2:], human_pred['human_pred'], human_true['human_true'], 'ROC Curve - ClinicNet vs Order Sets','ClinicNet', 'Order Sets')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_pr_curve_2_sets(test_preds[:,2:],y_test[:,2:], human_pred['human_pred'], human_true['human_true'], 'PR Curve - ClinicNet vs Order Sets','ClinicNet', 'Order Sets')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUROC Comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.utils import CustomObjectScope\n",
+    "\n",
+    "with CustomObjectScope({'weighted_binary_crossentropy': weighted_binary_crossentropy(1,10),\n",
+    "                        'binary_accuracy_V2' : binary_accuracy_V2,\n",
+    "                       'f1':f1}):\n",
+    "    model = keras.models.load_model('best_models/Feed_Forward_Best_Model_2018-03-19 08:17:43.787454.h5')\n",
+    "\n",
+    "test_preds = model.predict(x_test)\n",
+    "\n",
+    "data_dir = '/home/ubuntu/cs230/FeedForward/Data/XY_data/'\n",
+    "y_train_to_get_column_names = pd.read_table(data_dir + 'y_train.txt', sep = '\\t',nrows = 3)\n",
+    "\n",
+    "\n",
+    "test_preds_touse = test_preds[:,2:]\n",
+    "y_test_touse = y_test[:,2:]\n",
+    "print(test_preds_touse.shape)\n",
+    "print(y_test_touse.shape)\n",
+    "\n",
+    "'''\n",
+    "Model AUROC\n",
+    "'''\n",
+    "print test_preds.shape\n",
+    "\n",
+    "response_names = y_train_to_get_column_names.columns\n",
+    "results = pd.DataFrame(columns = ['Clinical_Item_Net', 'AUROC_Net', 'Precision_Net', 'Recall_Net', 'F1_Net'])\n",
+    "\n",
+    "for response_num in range(test_preds_touse.shape[1]):\n",
+    "    yi_pred = test_preds_touse[:,response_num]\n",
+    "    yi_actual = y_test_touse[:,response_num]\n",
+    "    \n",
+    "    yi_pred_cutoff = yi_pred\n",
+    "    yi_pred_cutoff[yi_pred_cutoff >=0.5] = 1\n",
+    "    yi_pred_cutoff[yi_pred_cutoff <0.5] = 0\n",
+    "    \n",
+    "    bPrecis_, bRecall_, bFscore_, _ = pr(yi_actual, yi_pred_cutoff , average='binary')\n",
+    "    #fpr, tpr, _ = roc_curve(yi_pred, yi_actual)\n",
+    "    try:\n",
+    "        AUROC = roc_auc_score(yi_actual,yi_pred)\n",
+    "    except:\n",
+    "        print(response_num)\n",
+    "        AUROC = None\n",
+    "    \n",
+    "    results.loc[response_num, 'Clinical_Item_Net'] = response_names[response_num]\n",
+    "    results.loc[response_num, 'AUROC_Net'] = AUROC \n",
+    "    results.loc[response_num, 'Precision_Net'] = bPrecis_ \n",
+    "    results.loc[response_num, 'Recall_Net'] = bRecall_ \n",
+    "    results.loc[response_num, 'F1_Net'] = bFscore_\n",
+    "\n",
+    "'''\n",
+    "HA AUROC\n",
+    "'''\n",
+    "ha_metrics = pd.read_table('/home/ubuntu/cs230/HumanBaseline/results_for_humanauthored.txt', sep = '\\t')\n",
+    "merged = results.merge(ha_metrics, left_on='Clinical_Item_Net', right_on='Clinical_Item', how='inner')\n",
+    "\n",
+    "merged['AUROC_Diff'] = merged['AUROC_Net'] - merged['AUROC']\n",
+    "merged['Precision_Diff'] = merged['Precision_Net'] - merged['precision']\n",
+    "merged['Recall_Diff'] = merged['Recall_Net'] - merged['recall']\n",
+    "merged['F1_Diff'] = merged['F1_Net'] - merged['F1_Score']\n",
+    "\n",
+    "merged = merged.dropna() # drop rows with only 1 class in y_true, resulting in errors when calculating ROC\n",
+    "\n",
+    "fig,ax = plt.subplots(1)\n",
+    "norm = MidpointNormalize(midpoint=0)\n",
+    "plt.scatter(merged['Clinical_Item'], merged['AUROC_Diff'],s=2,c=merged['AUROC_Diff'], cmap='RdBu')\n",
+    "plt.axhline(y=0, color='black', linestyle='-')\n",
+    "\n",
+    "plt.ylim(-.6, .6) \n",
+    "ax.set_xticklabels([])\n",
+    "plt.xticks([])\n",
+    "plt.title('AUROC by Clinical Item Comparison')\n",
+    "plt.xlabel('Clinical Item Number')\n",
+    "plt.ylabel('AUROC Difference (ClinicNet - Order Sets)')\n",
+    "plt.savefig('auroc_comparison.png')\n",
+    "plt.show()\n",
+    "#plt.savefig()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = list(merged['AUROC_Diff'])\n",
+    "plt.hist(x, bins = 25)\n",
+    "plt.axvline(statistics.median(x), color='black', linestyle='dashed', linewidth=2, label = 'Median')\n",
+    "plt.axvline(0, color='black', linewidth=2, label = '0')\n",
+    "\n",
+    "plt.title('AUROC by Clinical Item Comparison')\n",
+    "plt.xlabel('AUROC (ClinicNet) - AUROC (Order Sets)')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.savefig('HB_comparison.png')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:tensorflow_p27]",
+   "language": "python",
+   "name": "conda-env-tensorflow_p27-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/scripts/Archive/DeepLearningRecommender/FeatureMatrix.py b/scripts/Archive/DeepLearningRecommender/FeatureMatrix.py
new file mode 100644
index 00000000..a115ec84
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/FeatureMatrix.py
@@ -0,0 +1,301 @@
+#!/usr/bin/python
+"""
+Abstract class for building raw feature matrices.
+This class should take care of the business logic of interacting
+with FeatureMatrixFactory and FeatureMatrixIO, allowing subclasses
+to just worry about defining parameters for matrix construction.
+"""
+
+import os
+import datetime
+
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from Util import log
+
+class FeatureMatrix:
+    def __init__(self, variable, num_data_points, params=None):
+        # Process arguments.
+        self._var = variable
+        self._num_rows = num_data_points
+        if params is None:
+            self._params = {}
+        else:
+            self._params = params
+
+        # Initialize FeatureMatrixFactory.
+        self._factory = FeatureMatrixFactory()
+
+        # Initialize DB connection.
+        self._connection = DBUtil.connection()
+
+    def _query_patient_episodes(self, query, pat_id_col=None, index_time_col=None):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Fetch and return results.
+        log.info('query: %s' % str(query))
+        log.info('query.params: %s' % str(query.params))
+        cursor.execute(str(query), query.params)
+
+        # Parse arguments.
+        if pat_id_col is None:
+            pat_id_col = 'pat_id'
+        if index_time_col is None:
+            index_time_col = 'index_time'
+
+        self._factory.setPatientEpisodeInput(cursor, pat_id_col, index_time_col)
+        num_episodes = self._factory.processPatientEpisodeInput()
+
+        return num_episodes
+
+    def _add_features(self, index_time_col=None):
+        self._add_time_features(index_time_col)
+        self._add_demographic_features()
+        self._add_treatment_team_features()
+        self._add_comorbidity_features()
+        self._add_flowsheet_features()
+        self._add_lab_component_features()
+
+    def _add_time_features(self, index_time_col=None):
+        log.info('Adding admit date features...')
+        # Add admission date.
+        ADMIT_DX_CATEGORY_ID = 2
+        self._factory.addClinicalItemFeaturesByCategory([ADMIT_DX_CATEGORY_ID], \
+            dayBins=[], label='AdmitDxDate', features='pre')
+
+        # Add time cycle features.
+        log.info('Adding time cycle features...')
+        if index_time_col is None:
+            index_time_col = 'index_time'
+        self._factory.addTimeCycleFeatures(index_time_col, 'month')
+        self._factory.addTimeCycleFeatures(index_time_col, 'hour')
+
+    def _add_demographic_features(self):
+        log.info('Adding demographic features...')
+        # Add birth and death.
+        self._add_lifespan_features()
+        # Add sex features.
+        self._add_sex_features()
+        # Add race features.
+        self._add_race_features()
+
+    def _add_lifespan_features(self):
+        log.info('Adding lifespan features...')
+        self._factory.addClinicalItemFeatures(['Birth'], dayBins=[], features="pre")
+        self._factory.addClinicalItemFeatures(['Death'], dayBins=[], features="post")
+
+    def _add_sex_features(self):
+        log.info('Adding sex features...')
+        SEX_FEATURES = ["Male", "Female"]
+        for feature in SEX_FEATURES:
+            self._factory.addClinicalItemFeatures([feature], dayBins=[], features="pre")
+
+    def _add_race_features(self):
+        log.info('Adding race features...')
+        RACE_FEATURES = [
+            "RaceWhiteHispanicLatino", "RaceWhiteNonHispanicLatino",
+            "RaceHispanicLatino", "RaceBlack", "RaceAsian",
+            "RacePacificIslander", "RaceNativeAmerican",
+            "RaceOther", "RaceUnknown"
+        ]
+        for feature in RACE_FEATURES:
+            self._factory.addClinicalItemFeatures([feature], dayBins=[], features="pre")
+
+    def _add_treatment_team_features(self):
+        log.info('Adding treatment team features...')
+        self._factory.addTreatmentTeamFeatures()
+
+    def _add_comorbidity_features(self):
+        log.info('Adding comorbidity features...')
+        self._factory.addCharlsonComorbidityFeatures(features='pre')
+
+    def _add_flowsheet_features(self):
+        log.info('Adding flowsheet features...')
+        # Look at flowsheet results from the previous days
+        FLOW_PRE_TIME_DELTAS = [ datetime.timedelta(-1),
+                                #datetime.timedelta(-3),
+                                datetime.timedelta(-7), 
+                                datetime.timedelta(-30)]#,
+                                #datetime.timedelta(-90) ]
+        # Don't look into the future, otherwise cheating the prediction
+        FLOW_POST_TIME_DELTA = datetime.timedelta(0)
+        # Add flowsheet features for a variety of generally useful vitals.
+        BASIC_FLOWSHEET_FEATURES = [
+            "BP_High_Systolic", "BP_Low_Diastolic", "FiO2",
+            "Glasgow Coma Scale Score", "Pulse", "Resp", "Temp", "Urine"
+        ]
+        for pre_time_delta in FLOW_PRE_TIME_DELTAS:
+            log.info('\t\tpreTimeDelta: %s' % pre_time_delta)
+            self._factory.addFlowsheetFeatures(BASIC_FLOWSHEET_FEATURES, \
+                pre_time_delta, FLOW_POST_TIME_DELTA)
+
+    def _add_lab_component_features(self):
+        # Look for lab data 90 days before each episode, but never after self.
+        # Look at lab results from the previous days
+        LAB_PRE_TIME_DELTAS = [ datetime.timedelta(-1), 
+                                #datetime.timedelta(-3),
+                                datetime.timedelta(-7), 
+                                datetime.timedelta(-30)]#,
+                                #datetime.timedelta(-90) ]
+        # Don't look into the future, otherwise cheating the prediction
+        LAB_POST_TIME_DELTA = datetime.timedelta(0)
+
+        # Add result features for a variety of generally useful components.
+        BASIC_LAB_COMPONENTS = [
+            'WBC',      # White Blood Cell
+            'HCT',      # Hematocrit
+            'PLT',      # Platelet Count
+            'NA',       # Sodium, Whole Blood
+            'K',        # Potassium, Whole Blood
+            'CO2',      # CO2, Serum/Plasma
+            'BUN',      # Blood Urea Nitrogen
+            'CR',       # Creatinine
+            'TBIL',     # Total Bilirubin
+            'ALB',      # Albumin
+            'CA',       # Calcium
+            'LAC',      # Lactic Acid
+            'ESR',      # Erythrocyte Sedimentation Rate
+            'CRP',      # C-Reactive Protein
+            'TNI',      # Troponin I
+            'PHA',      # Arterial pH
+            'PO2A',     # Arterial pO2
+            'PCO2A',    # Arterial pCO2
+            'PHV',      # Venous pH
+            'PO2V',     # Venous pO2
+            'PCO2V',    # Venous pCO2
+            'HGB',      # Hemoglobin 
+            'UGLU',     # Glucose
+            'CL',       # Chloride
+            'MG',       # Magnesium
+            'PHOS',     # Phosphorus
+            'OCCGU'     # Stool
+        ]
+        log.info('Adding lab component features...')
+        for component in BASIC_LAB_COMPONENTS:
+            log.info('\t%s' % component)
+            self._factory.doLabCache()
+            for preTimeDelta in LAB_PRE_TIME_DELTAS:
+                log.info('\t\t%s' % preTimeDelta)
+                self._factory.addLabResultFeatures([component], False, preTimeDelta, LAB_POST_TIME_DELTA)
+            self._factory.clearLabCache()
+
+    def _build_matrix(self, header=None, dest_path=None):
+        log.info('Building matrix...')
+        self._factory.buildFeatureMatrix(header, dest_path)
+
+    def write_matrix(self, dest_path, header=None):
+        log.info('Writing matrix file...')
+        fm_io = FeatureMatrixIO()
+        # Get old matrix file.
+        source_path = self._factory.getMatrixFileName()
+        # Write to new matrix filee.
+        matrix_file = open(dest_path, 'w')
+        for line in header:
+            matrix_file.write('# %s\n' % line)
+        for line in open(source_path, 'r'):
+            if line[0] != '#':
+                matrix_file.write(line)
+        # Delete old matrix file.
+        os.remove(source_path)
+
+    def _build_matrix_header(self, params=None):
+        # params['include_lab_suffix_summary'] = True/False
+        # params['include_clinical_item_suffix_summary'] = True/False
+        # params['data_overview'] = str description.
+        # params['field_summary'] = str description
+        header = list()
+
+        file_summary = self._build_file_summary(params['matrix_path'], \
+            params['matrix_module'])
+        header.extend(file_summary)
+        header.extend([''])
+
+        if params.get('data_overview'):
+            header.extend(params['data_overview'])
+            header.extend([''])
+        if params.get('field_summary'):
+            header.extend(params['field_summary'])
+            header.extend([''])
+        if params.get('include_clinical_item_suffix_summary'):
+            ci_suffix_summary = self._build_clinical_item_suffix_summary()
+            header.extend(ci_suffix_summary)
+            header.extend([''])
+        if params.get('include_lab_suffix_summary'):
+            lab_suffix_summary = self._build_flowsheet_and_lab_result_suffix_summary()
+            header.extend(lab_suffix_summary)
+            header.extend([''])
+
+        return header
+
+    def _build_file_summary(self, matrix_path, matrix_module):
+        summary = list()
+
+        # <file_name.tab>
+        matrix_name = matrix_path.split('/')[-1]
+        summary.append(matrix_name)
+        # Created: <timestamp>
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+        summary.append('Created: %s' % timestamp)
+        # Source: __name__
+        module_name = matrix_module.split('/')[-1]
+        summary.append('Source: %s' % module_name)
+        # Command: Pipeline()
+        class_name = module_name.split('.')[0]
+        args = [self._var, str(self._num_rows)]
+        for key, value in self._params:
+            args.append('%s=%s' % (key, value))
+        command = '%s(%s)' % (class_name, ', '.join(args))
+        summary.append('Command: %s' % command)
+
+        return summary
+
+    def _build_clinical_item_suffix_summary(self):
+        summary = list()
+        #   [clinical_item] fields may have the following suffixes:
+        summary.append('  [clinical_item] fields may have the following suffixes:')
+        #       ___.pre - how many times has this occurred before order_time?
+        summary.append('    ___.pre - how many times has this occurred before order_time?')
+        #       ___.pre.Xd - how many times has this occurred within X days before index_time?
+        summary.append('    ___.pre.Xd - how many times has this occurred within X days before index_time?')
+        #       ___.preTimeDays - how many days before order_time was last occurrence?
+        summary.append('    ___.preTimeDays - how many days before order_time was last occurrence?')
+
+        return summary
+
+    def _build_flowsheet_and_lab_result_suffix_summary(self):
+        summary = list()
+        #   [flowsheet] and [lab_result] fields may have the following suffixes:
+        summary.append('  [flowsheet] and [lab_result] fields may have the following suffixes:')
+        #       ___.X_Y.count - # of result values between X and Y days of index_time.
+        summary.append('    ___.X_Y.count - # of result values between X and Y days of index_time.')
+        #       ___.X_Y.countInRange - # of result values in normal range.
+        summary.append('    ___.X_Y.countInRange - # of result values in normal range.')
+        #       ___.X_Y.min - minimum result value.
+        summary.append('    ___.X_Y.min - minimum result value.')
+        #       ___.X_Y.max - maximum result value.
+        summary.append('    ___.X_Y.max - maximum result value.')
+        #       ___.X_Y.median - median result value.
+        summary.append('    ___.X_Y.median - median result value.')
+        #       ___.X_Y.std - standard deviation of result values.
+        summary.append('    ___.X_Y.std - standard deviation of result values.')
+        #       ___.X_Y.first - first result value.
+        summary.append('    ___.X_Y.first - first result value.')
+        #       ___.X_Y.last - last result value.
+        summary.append('    ___.X_Y.last - last result value.')
+        #       ___.X_Y.diff - difference between penultimate and proximate values.
+        summary.append('    ___.X_Y.diff - difference between penultimate and proximate values.')
+        #       ___.X_Y.slope - slope between penultimate and proximate values.
+        summary.append('    ___.X_Y.slope - slope between penultimate and proximate values.')
+        #       ___.X_Y.proximate - closest result value to order_time.
+        summary.append('    ___.X_Y.proximate - closest result value to order_time.')
+        #       ___.X_Y.firstTimeDays - time between first and order_time.
+        summary.append('    ___.X_Y.firstTimeDays - time between first and order_time.')
+        #       ___.X_Y.lastTimeDays - time between last and order_time.
+        summary.append('    ___.X_Y.lastTimeDays - time between last and order_time.')
+        #       ___.X_Y.proximateTimeDays - time between proximate and order_time.
+        summary.append('    ___.X_Y.proximateTimeDays - time between proximate and order_time.')
+
+        return summary
+
diff --git a/scripts/Archive/DeepLearningRecommender/FeatureMatrixFactory.py b/scripts/Archive/DeepLearningRecommender/FeatureMatrixFactory.py
new file mode 100644
index 00000000..d88ad128
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/FeatureMatrixFactory.py
@@ -0,0 +1,1190 @@
+#!/usr/bin/env python
+"""
+Given a set of clinical data sources output a patient episode feature matrix
+for machine learning and regression applications.
+Usage follows the following idiom:
+factory = FeatureMatrixFactory()
+factory.setFooInput()
+factory.setBarInput()
+...
+factory.buildFeatureMatrix()
+matrix = factory.getMatrixAsBaz()
+"""
+
+import csv
+import datetime
+import numpy as np
+import os
+import time
+
+from Const import SENTINEL_RESULT_VALUE
+from medinfo.common.Const import NULL_STRING
+from medinfo.cpoe.Const import SECONDS_PER_DAY, DELTA_NAME_BY_DAYS
+from medinfo.db import DBUtil
+from medinfo.db.Model import columnFromModelList, SQLQuery, modelListFromTable
+from medinfo.db.ResultsFormatter import TabDictReader, TextResultsFormatter
+from psycopg2.extensions import cursor
+# from Util import log
+from medinfo.common.Util import log
+import Util
+
+class FeatureMatrixFactory:
+    FEATURE_MATRIX_COLUMN_NAMES = [
+        "patient_id"
+    ]
+
+    def __init__(self, cacheDBResults = True):
+        self.dbCache = None
+        self.patientListInput = None
+        self.patientIdColumn = None
+        self.patientEpisodeInput = None
+        self.patientEpisodeIdColumn = None
+        self._patientItemTimeColumn = None
+        self.timestampColumn = None
+
+        self.patientsProcessed = None
+
+        self._patientListTempFileName = "fmf.patient_list.tsv"
+        self._patientEpisodeTempFileName = "fmf.patient_episodes.tsv"
+        self._patientItemTempFileNameFormat = "fmf.patient_%s.tsv"
+        self._patientTimeCycleTempFileNameFormat = "fmf.patient_%s_%s.tsv"
+        self._patientResultTempFileNameFormat = "fmf.patient_%s_%s_%s.tsv"
+        self._matrixFileName = None
+        self._labCache = None
+        self._doLabCache = False
+
+        # Look at lab results from the previous days
+        LAB_PRE_TIME_DELTAS = [
+            datetime.timedelta(-1), datetime.timedelta(-3),
+            datetime.timedelta(-7), datetime.timedelta(-30),
+            datetime.timedelta(-90)
+        ]
+        # Don't look into the future, otherwise cheating the prediction
+        LAB_POST_TIME_DELTA = datetime.timedelta(+0)
+
+        self._featureTempFileNames = []
+        if cacheDBResults:
+            self.dbCache = dict()
+
+    def doLabCache(self):
+        self._doLabCache = True
+
+    def clearLabCache(self):
+        self._labCache = None
+        self._doLabCache = False
+
+    def setPatientListInput(self, patientListInput, \
+        patientIdColumn = "patient_id"):
+        """
+        Define the input patient list for the feature matrix.
+        patientListInput: TSV file descriptor or DB cursor
+        patientIdColumn: Name of TSV column or DB column.
+        """
+        # Verify patientListInput is TSV file or DB cursor.
+        if not isinstance(patientListInput, cursor) and \
+            not isinstance(patientListInput, file):
+            raise TypeError("patientListInput must be DB cursor or TSV file.")
+
+        self.patientListInput = patientListInput
+        self.patientIdColumn = patientIdColumn
+        pass
+
+    def processPatientListInput(self):
+        """
+        Convert patient list input to a TSV file.
+        """
+        if self.patientListInput is None:
+            raise ValueError("FeatureMatrixFactory.patientListInput is None.")
+
+        if isinstance(self.patientListInput, cursor):
+            return self._processPatientListDbCursor()
+        elif isinstance(self.patientListInput, file):
+            return self._processPatientListTsvFile()
+
+    def _processPatientListDbCursor(self):
+        """
+        Convert self.patientListInput from DB cursor to TSV file.
+        """
+        patientListTempFile = open(self._patientListTempFileName, "w")
+        self._pipeDbCursorToTsvFile(self.patientListInput, patientListTempFile)
+        patientListTempFile.close()
+
+    def _pipeDbCursorToTsvFile(self, dbCursor, tsvFile):
+        """
+        Pipe any arbitrary DB cursor to a TSV file.
+        """
+        # Extract DB columns.
+        columns = dbCursor.description
+        numColumns = len(columns)
+
+        # Write TSV header.
+        for i in range(numColumns - 1):
+            # 0th index is column name.
+            tsvFile.write("%s\t" % columns[i][0])
+        tsvFile.write("%s\n" % columns[numColumns - 1][0])
+
+        # By default, cursor iterates through both header and data rows.
+        self._numRows = 0
+        row = dbCursor.fetchone()
+        while row is not None:
+            for i in range(numColumns - 1):
+                tsvFile.write("%s\t" % row[i])
+            tsvFile.write("%s\n" % row[numColumns - 1])
+            row = dbCursor.fetchone()
+            self._numRows += 1
+
+    def _processPatientListTsvFile(self):
+        """
+        Convert self.patientListInput from DB cursor to TSV file.
+        """
+        # Instantiate patientListTempFile.
+        patientListTempFile = open(self._patientListTempFileName, "w")
+        patientListTempFile.write("%s\n" % self.patientIdColumn)
+
+        # Iterate through all rows in TSV file.
+        # Extract patientId from dictionary.
+        for row in TabDictReader(self.patientListInput):
+            patientId = int(row[self.patientIdColumn])
+            patientListTempFile.write("%s\n" % patientId)
+
+        patientListTempFile.close()
+
+    def getPatientListIterator(self):
+        """
+        Return TabDictReader for reading processed patient list.
+        """
+        return TabDictReader(open(self._patientListTempFileName, "r"))
+
+    def setPatientEpisodeInput(self, patientEpisodeInput, \
+        patientIdColumn = "patient_id", timestampColumn = "item_date"):
+        """
+        Define the input patient episode list for the feature matrix.
+        patientEpisodeInput: TSV file descriptor or DB cursor.
+        patientIdColumn: Name of TSV column or DB column.
+        timestampColumn: Name of TSV column or DB column.
+        """
+        # Verify patientEpisodeInput is TSV file or DB cursor.
+        if not isinstance(patientEpisodeInput, cursor) and \
+            not isinstance(patientEpisodeInput, file):
+            raise TypeError("patientEpisodeInput must be DB cursor or TSV file.")
+
+        self.patientEpisodeInput = patientEpisodeInput
+        self.patientEpisodeIdColumn = patientIdColumn
+        self.patientEpisodeTimeColumn = timestampColumn
+
+    def processPatientEpisodeInput(self):
+        """
+        Convert patient episode input to a TSV file.
+        """
+        if self.patientEpisodeInput is None:
+            raise ValueError("FeatureMatrixFactory.patientEpisodeInput is None.")
+
+        if isinstance(self.patientEpisodeInput, cursor):
+            return self._processPatientEpisodeDbCursor()
+        elif isinstance(self.patientEpisodeInput, file):
+            return self._processPatientEpisodeTsvFile()
+
+    def _processPatientEpisodeDbCursor(self):
+        """
+        Convert self.patientEpisodeInput from DB cursor to TSV file.
+        """
+        # Instantiate patientEpisodeTempFile.
+        patientEpisodeTempFile = open(self._patientEpisodeTempFileName, "w")
+        self._pipeDbCursorToTsvFile(self.patientEpisodeInput, patientEpisodeTempFile)
+        patientEpisodeTempFile.close()
+        self.patientsProcessed = True
+
+        return self.patientEpisodeInput.rowcount
+
+    def _processPatientEpisodeTsvFile(self):
+        pass
+
+    def getPatientEpisodeIterator(self):
+        """
+        Return TabDictReader for reading processed patient episodes.
+        """
+        return TabDictReader(open(self._patientEpisodeTempFileName, "r"))
+
+    def _getPatientEpisodeByIndexTimeById(self):
+        """
+        Return dictionary containing patientId : episodeTime : {} map.
+        """
+        patientEpisodeByIndexTimeById = {}
+        patientEpisodeIterator = self.getPatientEpisodeIterator()
+
+        for episode in patientEpisodeIterator:
+            patientId = int(episode[self.patientEpisodeIdColumn])
+            episodeTime = DBUtil.parseDateValue(episode[self.patientEpisodeTimeColumn])
+
+            if patientId not in patientEpisodeByIndexTimeById:
+                patientEpisodeByIndexTimeById[patientId] = {episodeTime: {}}
+            elif episodeTime not in patientEpisodeByIndexTimeById[patientId]:
+                patientEpisodeByIndexTimeById[patientId][episodeTime] = {}
+
+        return patientEpisodeByIndexTimeById
+
+    def addClinicalItemFeatures(self, clinicalItemNames, dayBins=None, column=None, operator=None, label=None, features=None):
+        """
+        Query patient_item for the clinical item orders and results for each
+        patient, and aggregate by episode timestamp.
+        column: determines column in clinical_item to match clinicalItemNames.
+        operator: determines how to match clinicalItemNames against column.
+        label: sets the column prefix in the final feature matrix.
+        features: determines whether to include "pre", "post" or "all".
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before clinical item.")
+
+        clinicalItemEvents = self._queryClinicalItemsByName(clinicalItemNames, column=column, operator=operator)
+        itemTimesByPatientId = self._getItemTimesByPatientId(clinicalItemEvents)
+
+        # Read clinical item features to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        self._processClinicalItemEvents(patientEpisodes, itemTimesByPatientId, \
+                                        clinicalItemNames, dayBins, label=label, features=features)
+
+    def addClinicalItemFeaturesByCategory(self, categoryIds, label=None, dayBins=None, features=None):
+        """
+        Query patient_item for the clinical item orders and results for each
+        patient (based on clinical item category ID instead of item name), and
+        aggregate by episode timestamp.
+        features: determines whether to include "pre", "post" or "all".
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before clinical item.")
+
+        if label is None:
+            label = "-".join(categoryIds)
+
+        clinicalItemEvents = self._queryClinicalItemsByCategory(categoryIds)
+        itemTimesByPatientId = self._getItemTimesByPatientId(clinicalItemEvents)
+
+        # Read clinical item features to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        self._processClinicalItemEvents(patientEpisodes, itemTimesByPatientId, \
+                                        categoryIds, dayBins, label=label, features=features)
+
+    def _queryClinicalItemsByName(self, clinicalItemNames, column=None, operator=None):
+        """
+        Query clinicalItemInput for all item times for all patients.
+        Look for clinical items by name.
+        Will match by SQL "LIKE" so can use wild-cards,
+        or can use ~* operator for additional regular expression matching.
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before clinical item.")
+
+        clinicalItemIds = None
+
+        # If possible, return cached results.
+        cacheKey = str(clinicalItemNames)
+        if self.dbCache is not None and cacheKey in self.dbCache:
+            clinicalItemIds = self.dbCache[cacheKey]
+        else:
+            if column is None:
+                column = "name"
+            if operator is None:
+                operator = "LIKE"
+
+            query = SQLQuery()
+            query.addSelect("clinical_item_id")
+            query.addFrom("clinical_item")
+
+            nameClauses = list()
+            for itemName in clinicalItemNames:
+                nameClauses.append("%s %s %%s" % (column, operator))
+                query.params.append(itemName)
+            query.addWhere(str.join(" or ", nameClauses))
+            #print(query)
+            results = DBUtil.execute(query)
+            clinicalItemIds = [row[0] for row in results]
+
+        if len(clinicalItemIds) == 0:
+            return list()
+
+        return self.queryClinicalItems(clinicalItemIds)
+
+    def _queryClinicalItemsByCategory(self, categoryIds):
+        """
+        Query for all patient items that match with the given clinical item
+        category ID.
+        """
+        # Identify which columns to pull from patient_item table.
+        self._patientItemIdColumn = "patient_id"
+        self._patientItemTimeColumn = "item_date"
+
+        clinicalItemIds = None
+
+        # If possible, return cached results.
+        cacheKey = str(categoryIds)
+        if self.dbCache is not None and cacheKey in self.dbCache:
+            clinicalItemIds = self.dbCache[cacheKey]
+        else:
+            column = "clinical_item_category_id"
+
+            query = SQLQuery()
+            query.addSelect("clinical_item_id")
+            query.addFrom("clinical_item")
+            query.addWhereIn(column, categoryIds)
+
+            results = DBUtil.execute(query)
+            clinicalItemIds = [row[0] for row in results]
+
+        if len(clinicalItemIds) == 0:
+            return list()
+
+        return self.queryClinicalItems(clinicalItemIds)
+
+    def queryClinicalItems(self, clinicalItemIds):
+        """
+        Query for all patient items that match with the given clinical item IDs.
+        """
+        # Identify which columns to pull from patient_item table.
+        self._patientItemIdColumn = "patient_id"
+        self._patientItemTimeColumn = "item_date"
+
+        # Identify which patients to query.
+        patientIds = set()
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientIds.add(episode[self.patientEpisodeIdColumn])
+
+        # Construct query to pull from patient_item table.
+        query = SQLQuery()
+        query.addSelect(self._patientItemIdColumn)
+        query.addSelect(self._patientItemTimeColumn)
+        query.addFrom("patient_item")
+        query.addWhereIn("clinical_item_id", clinicalItemIds)
+        query.addWhereIn("patient_id", list(patientIds))
+        query.addOrderBy("patient_id")
+        query.addOrderBy("item_date")
+
+        # Query clinical items.
+        results = DBUtil.execute(query)
+        clinicalItemEvents = [row for row in results]
+        return clinicalItemEvents
+
+    def _processClinicalItemEvents(self, patientEpisodes, itemTimesByPatientId, clinicalItemNames, dayBins, label=None, features=None):
+        """
+        Convert temp file containing all (patient_item, item_date) pairs
+        for a given set of clinical_item_ids into temp file containing
+        patient_id, order_time, clinical_item.pre, clinical_item.post, etc.
+        features: determines whether to include "pre", "post" or "all".
+        """
+        if label:
+            itemLabel = label
+        else:
+            if len(clinicalItemNames) > 1:
+                itemLabel = "-".join([itemName for itemName in clinicalItemNames])
+            else:
+                itemLabel = clinicalItemNames[0]
+        tempFileName = self._patientItemTempFileNameFormat % itemLabel
+        tempFile = open(tempFileName, "w")
+
+        if features is None:
+            features = "all"
+
+        # Determine time buckets for clinical item times.
+        if dayBins is None:
+            dayBins = list(DELTA_NAME_BY_DAYS.keys())
+            dayBins.sort()
+            
+        postDayBins = [1,7,30]
+
+        # Find items most proximate before and after the index item per patient
+        # Record timedelta separating nearest items found from index item
+        # Count up total items found before, after, and within days time bins
+        preTimeDaysLabel = "%s.preTimeDays" % itemLabel
+        postTimeDaysLabel = "%s.postTimeDays" % itemLabel
+        preLabel = "%s.pre" % itemLabel
+        postLabel = "%s.post" % itemLabel
+
+        # Write header fields to tempFile.
+        tempFile.write("patient_id\tepisode_time")
+        # Include counts for events before episode_time.
+        if features != "post":
+            tempFile.write("\t%s" % preTimeDaysLabel)
+            tempFile.write("\t%s" % preLabel)
+            if len(dayBins) > 0:
+                tempFile.write("\t")
+                tempFile.write("\t".join("%s.%dd" % (preLabel, dayBin) for dayBin in dayBins))
+        # Include counts for events after episode_time.
+        if features != "pre":
+            tempFile.write("\t%s" % postTimeDaysLabel)
+            tempFile.write("\t%s" % postLabel)
+            if len(dayBins) > 0:
+                tempFile.write("\t")
+                tempFile.write("\t".join("%s.%dd" % (postLabel, dayBin) for dayBin in postDayBins))
+        tempFile.write("\n")
+
+        # Write patient episode data to tempFile.
+        for patientEpisode in patientEpisodes:
+            # Initialize data to write to tempFile for patientEpisode.
+            episodeData = {}
+            patientId = int(patientEpisode[self.patientEpisodeIdColumn])
+            episodeTime = DBUtil.parseDateValue(patientEpisode[self.patientEpisodeTimeColumn])
+            # Time delta between index time and most closest past item event.
+            episodeData[preTimeDaysLabel] = None
+            # Time delta between index time and most closest future item event.
+            episodeData[postTimeDaysLabel] = None
+            # Number of item events before index time.
+            episodeData[preLabel] = 0
+            # Number of item events after index time.
+            episodeData[postLabel] = 0
+            # Number of item events within dayBin.
+            for dayBin in dayBins:
+                episodeData["%s.%dd" % (preLabel, dayBin)] = 0
+            for dayBin in postDayBins:
+                episodeData["%s.%dd" % (postLabel, dayBin)] = 0
+
+            # Aggregate item events by day buckets.
+            if patientId in itemTimesByPatientId:
+                itemTimes = itemTimesByPatientId[patientId]
+                if itemTimes is not None:
+                    for itemTime in itemTimes:
+                        # Need this extra check because if a given event
+                        # has not occurred yet, but will occur, itemTime will
+                        # be none while itemTimes is not None.
+                        if itemTime is None:
+                            continue
+                        timeDiffSeconds = (itemTime - episodeTime).total_seconds()
+                        timeDiffDays = timeDiffSeconds / SECONDS_PER_DAY
+                        # If event occurred before index time...
+                        if timeDiffDays < 0:
+                            if episodeData[preTimeDaysLabel] is None:
+                                episodeData[preTimeDaysLabel] = timeDiffDays
+                            elif abs(timeDiffDays) < abs(episodeData[preTimeDaysLabel]):
+                                # Found more recent item event
+                                episodeData[preTimeDaysLabel] = timeDiffDays
+                            episodeData[preLabel] += 1
+                            for dayBin in dayBins:
+                                if abs(timeDiffDays) <= dayBin:
+                                    episodeData["%s.%dd" % (preLabel, dayBin)] += 1
+                        # Event occurred after index time...
+                        else:
+                            if episodeData[postTimeDaysLabel] is None:
+                                episodeData[postTimeDaysLabel] = timeDiffDays
+                            elif abs(timeDiffDays) < abs(episodeData[postTimeDaysLabel]):
+                                # Found more proximate future event
+                                episodeData[postTimeDaysLabel] = timeDiffDays
+                            episodeData[postLabel] += 1
+                            for dayBin in postDayBins:
+                                if abs(timeDiffDays) <= dayBin:
+                                    episodeData["%s.%dd" % (postLabel, dayBin)] += 1
+
+            # Write data to tempFile.
+            tempFile.write("%s\t%s" % (patientId, episodeTime))
+            # Include counts for events before episode_time.
+            if features != "post":
+                tempFile.write("\t%s" % episodeData[preTimeDaysLabel])
+                tempFile.write("\t%s" % episodeData[preLabel])
+                if len(dayBins) > 0:
+                    tempFile.write("\t")
+                    tempFile.write("\t".join([str(episodeData["%s.%dd" % (preLabel, dayBin)]) for dayBin in dayBins]))
+            # Include counts for events after episode_time.
+            if features != "pre":
+                tempFile.write("\t%s" % episodeData[postTimeDaysLabel])
+                tempFile.write("\t%s" % episodeData[postLabel])
+                if len(dayBins) > 0:
+                    tempFile.write("\t")
+                    tempFile.write("\t".join([str(episodeData["%s.%dd" % (postLabel, dayBin)]) for dayBin in postDayBins]))
+            tempFile.write("\n")
+
+        tempFile.close()
+        # Add tempFileName to list of feature temp files.
+        self._featureTempFileNames.append(tempFileName)
+
+    def addLabResultFeatures(self, labNames, labIsPanel = True, preTimeDelta = None, postTimeDelta = None):
+        """
+        Query stride_order_proc and stride_order_results for the lab orders and
+        results for each patient, and aggregate by episode timestamp.
+        Set labIsPanel = False to signify that the labNames are components,
+        rather than panels.
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before lab result.")
+
+        # Open temp file.
+        if len(labNames) > 1:
+            resultLabel = "-".join([labName for labName in labNames])[:64]
+        else:
+            resultLabel = labNames[0]
+
+        # Hack to account for fact that Windows filenames can't include ':'.
+        tempFileName = self._patientResultTempFileNameFormat % (resultLabel, str(preTimeDelta.days), str(postTimeDelta.days))
+        tempFile = open(tempFileName, "w")
+
+        # Query lab results for the individuals of interest.
+        print("1")
+        labResults = None
+        if (self._labCache != None):
+            labResults = self._labCache
+        else:
+            labResults = self._queryLabResultsByName(labNames, labIsPanel)
+        if (self._doLabCache == True):
+            self._labCache = labResults
+        print("2")
+        resultsByNameByPatientId = self._parseResultsData(labResults, "pat_id",
+            "base_name", "ord_num_value", "result_time")
+        print("3")
+
+        # Define how far in advance of each episode to look at lab results.
+        preTimeDays = None
+        if preTimeDelta is not None:
+            preTimeDays = preTimeDelta.days
+        postTimeDays = None
+        if postTimeDelta is not None:
+            postTimeDays = postTimeDelta.days
+
+        # Add summary features to patient-time instances.
+        patientEpisodeByIndexTimeById = self._getPatientEpisodeByIndexTimeById()
+        self._processResultEvents(patientEpisodeByIndexTimeById,
+                                    resultsByNameByPatientId,
+                                    labNames,
+                                    "ord_num_value",
+                                    "result_time",
+                                    preTimeDelta,
+                                    postTimeDelta)
+        
+        # Write column headers to temp file.
+        tempFile.write("%s\t%s\t" % (self.patientEpisodeIdColumn, self.patientEpisodeTimeColumn))
+        columnNames = self.colsFromBaseNames(labNames, preTimeDays, postTimeDays)
+        tempFile.write("\t".join(columnNames))
+        tempFile.write("\n")
+        
+        #Write actual patient episode data to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientId = int(episode[self.patientEpisodeIdColumn])
+            indexTime = DBUtil.parseDateValue(episode[self.patientEpisodeTimeColumn])
+            episodeLabData = patientEpisodeByIndexTimeById[patientId][indexTime]
+            tempFile.write("%s\t%s\t" % (patientId, indexTime))
+            # Need to generate columnNames again because colsFromBaseNames
+            # returns generator, which can only be read once.
+            columnNames = self.colsFromBaseNames(labNames, preTimeDays, postTimeDays)
+            tempFile.write("\t".join(str(episodeLabData[columnName]) for columnName in columnNames))
+            tempFile.write("\n")
+            
+        tempFile.close()
+        print("3")
+        # Add tempFileName to list of feature temp files.
+        self._featureTempFileNames.append(tempFileName)
+        return
+
+    def addFlowsheetFeatures(self, flowsheetBaseNames, preTimeDelta = None, postTimeDelta = None):
+        """
+        Query stride_flowsheet for each patient, and aggregate by episode.
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before lab result.")
+
+        # Open temp file.
+        if len(flowsheetBaseNames) > 1:
+            resultLabel = "-".join([baseName for baseName in flowsheetBaseNames])
+        else:
+            resultLabel = flowsheetBaseNames[0]
+        # Hack to account for fact that Windows filenames can't include ':'.
+        tempFileName = self._patientResultTempFileNameFormat % (resultLabel, str(preTimeDelta.days), str(postTimeDelta.days))
+        tempFile = open(tempFileName, "w")
+
+        # Query flowsheet results.
+        flowsheetResults = self._queryFlowsheetResultsByName(flowsheetBaseNames)
+        resultsByNameByPatientId = self._parseResultsData(flowsheetResults, \
+            "pat_id", "flowsheet_name", "flowsheet_value", \
+            "shifted_dt_tm")
+
+        # Define how far in advance of each episode to look at lab results.
+        preTimeDays = None
+        if preTimeDelta is not None:
+            preTimeDays = preTimeDelta.days
+        postTimeDays = None
+        if postTimeDelta is not None:
+            postTimeDays = postTimeDelta.days
+
+        # Add summary features to patient-time instances.
+        patientEpisodeByIndexTimeById = self._getPatientEpisodeByIndexTimeById()
+        self._processResultEvents(patientEpisodeByIndexTimeById,
+                                    resultsByNameByPatientId,
+                                    flowsheetBaseNames,
+                                    "flowsheet_value",
+                                    "shifted_dt_tm",
+                                    preTimeDelta,
+                                    postTimeDelta)
+
+        # Write column headers to temp file.
+        tempFile.write("%s\t%s\t" % (self.patientEpisodeIdColumn, self.patientEpisodeTimeColumn))
+        columnNames = self.colsFromBaseNames(flowsheetBaseNames, preTimeDays, postTimeDays)
+        tempFile.write("\t".join(columnNames))
+        tempFile.write("\n")
+
+        #Write actual patient episode data to temp file.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientId = int(episode[self.patientEpisodeIdColumn])
+            indexTime = DBUtil.parseDateValue(episode[self.patientEpisodeTimeColumn])
+            episodeLabData = patientEpisodeByIndexTimeById[patientId][indexTime]
+            tempFile.write("%s\t%s\t" % (patientId, indexTime))
+            # Need to generate columnNames again because colsFromBaseNames
+            # returns generator, which can only be read once.
+            columnNames = self.colsFromBaseNames(flowsheetBaseNames, preTimeDays, postTimeDays)
+            tempFile.write("\t".join(str(episodeLabData[columnName]) for columnName in columnNames))
+            tempFile.write("\n")
+
+        tempFile.close()
+        # Add tempFileName to list of feature temp files.
+        self._featureTempFileNames.append(tempFileName)
+
+        return
+
+    def _queryFlowsheetResultsByName(self, flowsheetBaseNames):
+        """
+        Query stride_flowsheet for each patient.
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before lab results.")
+
+        # Identify which patients to query.
+        patientIds = set()
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientIds.add(episode[self.patientEpisodeIdColumn])
+
+        # Build SQL query.
+        colNames = ["pat_anon_id AS pat_id", "flo_meas_id", "flowsheet_name", \
+            "flowsheet_value", "shifted_dt_tm"]
+        query = SQLQuery()
+        for col in colNames:
+            query.addSelect(col)
+        query.addFrom("stride_flowsheet")
+        query.addWhereIn("flowsheet_name", flowsheetBaseNames)
+        query.addWhereIn("pat_anon_id", patientIds)
+        query.addOrderBy("pat_anon_id")
+        query.addOrderBy("shifted_dt_tm")
+
+        # Execute query.
+        return modelListFromTable(DBUtil.execute(query, includeColumnNames=True))
+
+    def colsFromBaseNames(self, baseNames, preTimeDays, postTimeDays):
+        """Enumerate derived column/feature names given a set of (lab) result base names"""
+        suffixes = ["count","countInRange","min","max","median","mean","std","first","last","diff","slope","proximate","firstTimeDays","lastTimeDays","proximateTimeDays"]
+        colNames = list()
+        for baseName in baseNames:
+            for suffix in suffixes:
+                colName = "%s.%s_%s.%s" % (baseName, preTimeDays, postTimeDays, suffix)
+                colNames.extend([colName])
+
+        return colNames
+
+    def _processResultEvents(self, patientEpisodeByIndexTimeById, resultsByNameByPatientId, resultNames, valueCol, datetimeCol, preTimeDelta, postTimeDelta):
+        """
+        Add on summary features to the patient-time instances.
+        With respect to each index time, look for results within
+        [indexTime+preTimeDelta, indexTime+postTimeDelta) and
+        generate summary features like count, mean, median, std, first, last,
+        proximate. Generic function, so have to specify the names of the value
+        and datetime columns.
+
+        Assume patientIdResultsByNameGenerator is actually a generator for each
+        patient, so can only stream through results once.
+
+        Store results in a temp file.
+        """
+        # Use results generator as outer loop as will not be able to random
+        # access the contents.
+        for patientId, resultsByName in resultsByNameByPatientId.items():
+            # Skip results if not in our list of patients of interest
+            if patientId in patientEpisodeByIndexTimeById:
+                patientEpisodeByIndexTime = patientEpisodeByIndexTimeById[patientId]
+                resultsByName = resultsByNameByPatientId[patientId]
+                self._addResultFeatures_singlePatient(patientEpisodeByIndexTime, \
+                    resultsByName, resultNames, valueCol, datetimeCol, preTimeDelta, \
+                    postTimeDelta)
+
+        # Separate loop to verify all patient records addressed, even if no
+        # results available (like an outer join).
+        resultsByName = None
+        for patientId, patientEpisodeByIndexTime in patientEpisodeByIndexTimeById.items():
+            self._addResultFeatures_singlePatient(patientEpisodeByIndexTime, \
+                resultsByName, resultNames, valueCol, datetimeCol, preTimeDelta, \
+                postTimeDelta)
+
+    def _addResultFeatures_singlePatient(self, patientEpisodeByIndexTime, resultsByName, baseNames, valueCol, datetimeCol, preTimeDelta, postTimeDelta):
+        """
+        Add summary features to the patient-time instances.
+        With respect to each index time, look for results within
+        [indexTime+preTimeDelta, indexTime+postTimeDelta) and generate summary
+        features like count, mean, median, std, first, last, proximate.
+        Generic function, so have to specify the names of the value and datetime columns to look for.
+
+        If resultsByName is None, then no results to match.
+        Just make sure default / zero value columns are populated if
+        they are not already.
+        """
+        preTimeDays = None
+        if preTimeDelta is not None:
+            preTimeDays = preTimeDelta.days
+        postTimeDays = None
+        if postTimeDelta is not None:
+            postTimeDays = postTimeDelta.days
+
+        # Init summary values to null for all results
+        for indexTime, patient in patientEpisodeByIndexTime.items():
+            for baseName in baseNames:
+                if resultsByName is not None or ("%s.%s_%s.count" % (baseName, preTimeDays, postTimeDays)) not in patient:
+                    # Default to null for all values
+                    patient["%s.%s_%s.count" % (baseName,preTimeDays,postTimeDays)] = 0
+                    patient["%s.%s_%s.countInRange" % (baseName,preTimeDays,postTimeDays)] = 0
+                    patient["%s.%s_%s.min" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.max" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.median" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.mean" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.std" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.first" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.last" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.diff" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.slope" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.proximate" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.firstTimeDays" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.lastTimeDays" % (baseName,preTimeDays,postTimeDays)] = None
+                    patient["%s.%s_%s.proximateTimeDays" % (baseName,preTimeDays,postTimeDays)] = None
+
+        # Have results available for this patient?
+        if resultsByName is not None:
+            for indexTime, patient in patientEpisodeByIndexTime.items():
+                # Time range limits on labs to consider
+                preTimeLimit = None;
+                postTimeLimit = None;
+
+                # Time range limits on labs to consider
+                if preTimeDelta is not None:
+                    preTimeLimit = indexTime + preTimeDelta
+                if postTimeDelta is not None:
+                    postTimeLimit = indexTime + postTimeDelta
+
+                for baseName in baseNames:
+                    proximateValue = None
+                    # Not all patients will have all labs checked
+                    if resultsByName is not None and baseName in resultsByName:
+                        firstItem = None
+                        lastItem = None
+                        # Item closest to the index time in time
+                        proximateItem = None
+                        filteredResults = list()
+                        for result in resultsByName[baseName]:
+                            resultTime = result[datetimeCol]
+                            if resultTime is not None:
+                                if (preTimeLimit is None or preTimeLimit <= resultTime) \
+                                   and (postTimeLimit is None or resultTime < postTimeLimit):
+                                    # Occurs within timeframe of interest, so record valueCol
+                                    filteredResults.append(result)
+
+                                    if firstItem is None or resultTime < firstItem[datetimeCol]:
+                                        firstItem = result
+                                    if lastItem is None or lastItem[datetimeCol] < resultTime:
+                                        lastItem = result
+                                    if proximateItem is None or (abs(resultTime - indexTime) < abs(proximateItem[datetimeCol] - indexTime)):
+                                        proximateItem = result
+
+                        if len(filteredResults) > 0:
+                            # Count up number of values specifically labeled "in range"
+                            valueList = columnFromModelList(filteredResults, valueCol)
+                            patient["%s.%s_%s.count" % (baseName,preTimeDays,postTimeDays)] = len(valueList);
+                            patient["%s.%s_%s.countInRange" % (baseName,preTimeDays,postTimeDays)] = self._countResultsInRange(filteredResults);
+                            patient["%s.%s_%s.min" % (baseName,preTimeDays,postTimeDays)] = np.min(valueList);
+                            patient["%s.%s_%s.max" % (baseName,preTimeDays,postTimeDays)] = np.max(valueList);
+                            patient["%s.%s_%s.median" % (baseName,preTimeDays,postTimeDays)] = np.median(valueList);
+                            patient["%s.%s_%s.mean" % (baseName,preTimeDays,postTimeDays)] = np.mean(valueList);
+                            patient["%s.%s_%s.std" % (baseName,preTimeDays,postTimeDays)] = np.std(valueList);
+                            patient["%s.%s_%s.first" % (baseName,preTimeDays,postTimeDays)] = firstItem[valueCol];
+                            patient["%s.%s_%s.last" % (baseName,preTimeDays,postTimeDays)] = lastItem[valueCol];
+                            patient["%s.%s_%s.diff" % (baseName,preTimeDays,postTimeDays)] = lastItem[valueCol] - firstItem[valueCol];
+                            patient["%s.%s_%s.slope" % (baseName,preTimeDays,postTimeDays)] = 0.0;
+                            timeDiffDays = ((lastItem[datetimeCol]-firstItem[datetimeCol]).total_seconds() / SECONDS_PER_DAY);
+                            if timeDiffDays > 0.0:
+                                patient["%s.%s_%s.slope" % (baseName,preTimeDays,postTimeDays)] = (lastItem[valueCol]-firstItem[valueCol]) / timeDiffDays;
+                            patient["%s.%s_%s.proximate" % (baseName,preTimeDays,postTimeDays)] = proximateItem[valueCol];
+                            patient["%s.%s_%s.firstTimeDays" % (baseName,preTimeDays,postTimeDays)] = (firstItem[datetimeCol]-indexTime).total_seconds() / SECONDS_PER_DAY;
+                            patient["%s.%s_%s.lastTimeDays" % (baseName,preTimeDays,postTimeDays)] = (lastItem[datetimeCol]-indexTime).total_seconds() / SECONDS_PER_DAY;
+                            patient["%s.%s_%s.proximateTimeDays" % (baseName,preTimeDays,postTimeDays)] = (proximateItem[datetimeCol]-indexTime).total_seconds() / SECONDS_PER_DAY;
+
+        return
+
+    def _queryLabResultsByName(self, labNames, isLabPanel = True):
+        """
+        Query for all lab results that match with the given result base names.
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before lab results.")
+
+        # Query rapid when filter by lab result type, limited to X records.
+        # Filtering by patient ID drags down substantially until preloaded
+        # table by doing a count on the SQR table?
+        columnNames = [
+            "CAST(pat_id AS bigint)", "base_name", "ord_num_value",
+            "result_flag", "result_in_range_yn", "sor.result_time"
+        ]
+
+        # Identify which patients to query.
+        patientIds = set()
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            patientIds.add(episode[self.patientEpisodeIdColumn])
+
+        # Construct query to pull from stride_order_results, stride_order_proc
+        query = SQLQuery()
+        for column in columnNames:
+            query.addSelect(column)
+        query.addFrom("stride_order_results AS sor, stride_order_proc AS sop")
+        query.addWhere("sor.order_proc_id = sop.order_proc_id")
+        if isLabPanel:
+            labProcCodes = labNames
+            query.addWhereIn("proc_code", labProcCodes)
+        else:
+            labBaseNames = labNames
+            query.addWhereIn("base_name", labBaseNames)
+        query.addWhereIn("pat_id", patientIds)
+        query.addOrderBy("pat_id")
+        query.addOrderBy("sor.result_time")
+
+        return modelListFromTable(DBUtil.execute(query, includeColumnNames=True))
+
+    def _parseResultsData(self, resultRowIter, patientIdCol, nameCol, valueCol, datetimeCol):
+        """
+        Wrapper for generator version to translate results into dictionary by
+        patient ID for more consistent structure to parseClinicalItemData.
+        """
+        resultsByNameByPatientId = dict()
+        for (patientId, resultsByName) in self._parseResultsDataGenerator(resultRowIter, patientIdCol, nameCol, valueCol, datetimeCol):
+            resultsByNameByPatientId[patientId] = resultsByName
+        return resultsByNameByPatientId
+
+    def _parseResultsDataGenerator(self, resultRowIter, patientIdCol, nameCol, valueCol, datetimeCol):
+        """
+        General version of results data parser, which does not necessarily come
+        from a file stream. Could be any reader / iterator over rows of item
+        data. For example, from a TabDictReader over the temp file or
+        modelListFromTable from database query results.
+        """
+        lastPatientId = None
+        resultsByName = None
+        for result in resultRowIter:
+            if result[valueCol] is not None and result[valueCol] != NULL_STRING:
+                patientId = int(result[patientIdCol])
+                baseName = result[nameCol]
+                resultValue = float(result[valueCol])
+                resultTime = DBUtil.parseDateValue(result[datetimeCol])
+
+                # Skip apparent placeholder values
+                if resultValue < SENTINEL_RESULT_VALUE:
+                    result[patientIdCol] = patientId
+                    result[valueCol] = resultValue
+                    result[datetimeCol] = resultTime
+
+                    if patientId != lastPatientId:
+                        # Encountering a new patient ID. Yield the results from
+                        # the prior one before preparing for next one.
+                        if lastPatientId is not None:
+                            yield (lastPatientId, resultsByName)
+                        lastPatientId = patientId
+                        resultsByName = dict()
+                    if baseName not in resultsByName:
+                        resultsByName[baseName] = list()
+                    resultsByName[baseName].append(result)
+
+        # Yield last result
+        if lastPatientId is not None:
+            yield (lastPatientId, resultsByName)
+
+    def _countResultsInRange(self,resultList):
+        """
+        Return the number of result models in the given list that represent
+        "normal" "in range" values.
+        """
+        countInRange = 0
+        for result in resultList:
+            if "result_in_range_yn" in result and result["result_in_range_yn"] == "Y":
+                countInRange += 1
+        return countInRange
+
+    def _getItemTimesByPatientId(self, clinicalItemEvents):
+        """
+        input: [{"patient_id":123, "item_date": 456}, ...]
+        output: {123 : [456, 789]}
+        """
+        itemTimesByPatientId = dict()
+
+        for itemData in clinicalItemEvents:
+            # Convert patient_id to int and item_date to DBUtil.parseDateValue.
+            # TODO(sbala): Stop relying on magic 0- and 1-indexes.
+            patientId = int(itemData[0])
+            itemTime = DBUtil.parseDateValue(itemData[1])
+            itemData[0] = patientId
+            itemData[1] = itemTime
+
+            # Add item_date to itemTimesByPatientId
+            if patientId not in itemTimesByPatientId:
+                itemTimesByPatientId[patientId] = list()
+            itemTimesByPatientId[patientId].append(itemTime)
+
+        return itemTimesByPatientId
+
+    def _readPatientEpisodesFile(self):
+        """
+        Read patient episodes into memory.
+        """
+        # Verify patient episodes have been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patient episodes before reading.")
+
+        # Return iterator through _patientEpisodeTempFileName.
+        iterator = TabDictReader(open(self._patientEpisodeTempFileName, "r"))
+        patientEpisodes = [episode for episode in iterator]
+
+        return patientEpisodes
+
+    def processClinicalItemInput(self, clinicalItemName):
+        """
+        Process clinicalItemName for DB cursor.
+        """
+        patientEpisodes = self._readPatientEpisodesFile()
+        pass
+
+    def addTimeCycleFeatures(self, timeCol, timeAttr):
+        """
+        Look for a datetime value in the patientEpisode identified by timeCol.
+        Add features to the patientEpisode based on the timeAttr string
+        ("month","day","hour","minute","second"), including the sine and cosine
+        of the timeAttr value relative to the maximum possible value to reflect
+        cyclical time patterns (e.g. seasonal patterns over months in a year,
+        or daily cycle patterns over hours in a day).
+        """
+        # Verify patient list and/or patient episode has been processed.
+        if not self.patientsProcessed:
+            raise ValueError("Must process patients before lab result.")
+
+        # Open temp file.
+        self._patientTimeCycleTempFileNameFormat
+        tempFileName = self._patientTimeCycleTempFileNameFormat % (timeCol, timeAttr)
+        tempFile = open(tempFileName, "w")
+
+        # Write header fields to tempFile.
+        tempFile.write("patient_id\tepisode_time\t")
+        tempFile.write("%s.%s\t" % (timeCol, timeAttr))
+        tempFile.write("%s.%s.sin\t" % (timeCol, timeAttr))
+        tempFile.write("%s.%s.cos\n" % (timeCol, timeAttr))
+
+        # Compute and write time cycle features.
+        patientEpisodes = self.getPatientEpisodeIterator()
+        for episode in patientEpisodes:
+            timeObj = DBUtil.parseDateValue(episode[timeCol])
+            # Use introspection (getattr) to extract some time feature from the
+            # time object, as well as the maximum and minimum possible values
+            # to set the cycle range.
+            maxValue = getattr(timeObj.max, timeAttr)
+            thisValue = getattr(timeObj, timeAttr)
+            minValue = getattr(timeObj.min, timeAttr)
+
+            radians = 2*np.pi * (thisValue-minValue) / (maxValue+1-minValue)
+
+            # Write values to tempFile.
+            tempFile.write("%s\t" % episode[self.patientEpisodeIdColumn])
+            tempFile.write("%s\t" % episode[self.patientEpisodeTimeColumn])
+            tempFile.write("%s\t" % thisValue)
+            tempFile.write("%s\t" % np.sin(radians))
+            tempFile.write("%s\n" % np.cos(radians))
+
+        # Close tempFile.
+        self._featureTempFileNames.append(tempFileName)
+        tempFile.close()
+
+    def loadMapData(self,filename):
+        """
+        Read the named file's contents through TabDictReader to enable data
+        extraction. If cannot find file by absolute filename, then look under
+        default mapdata directory.
+        """
+        try:
+            return TabDictReader(open(filename))
+        except IOError:
+            # Unable to open file directly. See if it's in the mapdata directory
+            appDir = os.path.dirname(Util.__file__)
+            defaultFilename = os.path.join(appDir, "mapdata", filename)
+            try:
+                return TabDictReader(open(defaultFilename))
+            except IOError:
+                # May need to add default extension as well
+                defaultFilename = defaultFilename + ".tab"
+                return TabDictReader(open(defaultFilename))
+
+    def addCharlsonComorbidityFeatures(self, features=None):
+        """
+        For each of a predefined set of comorbidity categories, add features
+        summarizing occurrence of the associated ICD9 problems.
+        """
+        # Extract ICD9 prefixes per disease category
+        icd9prefixesByDisease = dict()
+        for row in self.loadMapData("CharlsonComorbidity-ICD9CM"):
+            (disease, icd9prefix) = (row["charlson"], row["icd9cm"])
+            if disease not in icd9prefixesByDisease:
+                icd9prefixesByDisease[disease] = list()
+            icd9prefixesByDisease[disease].append("^ICD9." + icd9prefix)
+
+        for disease, icd9prefixes in icd9prefixesByDisease.items():
+            disease = disease.translate(None," ()-/") # Strip off punctuation
+            self.addClinicalItemFeatures(icd9prefixes, operator="~*", \
+                label="Comorbidity."+disease, features=features)
+
+    def addTreatmentTeamFeatures(self, features=None):
+        """
+        For each of a predefined set of specialty categories, add features
+        summarizing the makeup of the treatment team.
+        """
+        # Extract out lists of treatment team names per care category
+        teamNameByCategory = dict()
+        for row in self.loadMapData("TreatmentTeamGroups"):
+            (category, teamName) = (row["team_category"], row["treatment_team"])
+            if category not in teamNameByCategory:
+                teamNameByCategory[category] = list()
+            teamNameByCategory[category].append(teamName)
+
+        for category, teamNames in teamNameByCategory.items():
+            self.addClinicalItemFeatures(teamNames, column="description", \
+                label="Team."+category, features=features)
+
+    def addSexFeatures(self):
+        SEX_FEATURES = ["Male", "Female"]
+        for feature in SEX_FEATURES:
+            self.addClinicalItemFeatures([feature], dayBins=[], \
+                features="pre")
+
+    def addRaceFeatures(self):
+        RACE_FEATURES = [
+            "RaceWhiteHispanicLatino", "RaceWhiteNonHispanicLatino",
+            "RaceHispanicLatino", "RaceBlack", "RaceAsian",
+            "RacePacificIslander", "RaceNativeAmerican",
+            "RaceOther", "RaceUnknown"
+        ]
+        for feature in RACE_FEATURES:
+            self.addClinicalItemFeatures([feature], dayBins=[], \
+                features="pre")
+
+    def buildFeatureMatrix(self, header=None, matrixFileName=None):
+        """
+        Given a set of factory inputs, build a feature matrix which
+        can then be output.
+        For each input, use the following idiom to process:
+        self._processFooInput()
+            self._queryFooInput()
+            self._parseFooInput()
+        """
+        # Initialize feature matrix file.
+        if matrixFileName is None:
+            timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H.%M.%S")
+            matrixFileName = "feature-matrix_%s.tab" % timestamp
+            self._matrixFileName = matrixFileName
+        if header is None:
+            # file_name.tab
+            # Created: timestamp
+            timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
+            header = [
+                '%s' % self._matrixFileName,
+                'Created: %s' % timestamp
+            ]
+        self._matrixFileName = matrixFileName
+        matrixFile = open(self._matrixFileName, "w")
+
+
+        # Read arbitrary number of input temp files.
+        # Use csv tab reader so we can just read the lists while being agnostic
+        # to the actual fields in each of the various files.
+        tempFiles = list()
+        tempFileReaders = list()
+        patientEpisodeFile = open(self._patientEpisodeTempFileName, "r")
+        patientEpisodeReader = csv.reader(patientEpisodeFile, delimiter="\t")
+
+        for tempFileName in self._featureTempFileNames:
+            tempFile = open(tempFileName, "r")
+            tempFileReader = csv.reader(tempFile, delimiter="\t")
+            tempFiles.append(tempFile)
+            tempFileReaders.append(tempFileReader)
+
+        # Write header to matrix file.
+        for line in header:
+            matrixFile.write('# %s\n' % line)
+            
+        #i = 0
+
+        # Write data to matrix file.
+        for patientEpisode in patientEpisodeReader:
+            matrixData = list()
+            # Add data from patientEpisodes.
+            matrixData.extend(patientEpisode)
+            # Each tempFile has the patientId and episodeTime fields.
+            # Don't write these to the matrix file.
+            for tempFileReader in tempFileReaders:
+                tempData = next(tempFileReader)
+                matrixData.extend(tempData[2:])
+
+            #for tempFileName in self._featureTempFileNames:
+                #tempFile = open(tempFileName, "r")
+                #tempFileReader = csv.reader(tempFile, delimiter="\t")
+                #tempData = tempFileReader.next()
+                #for j in range(i):
+                #    tempData = tempFileReader.next()
+                #matrixData.extend(tempData[2:])
+                #tempFile.close()
+                
+            #i = i + 1
+
+            # Write data to matrixFile, with trailing \n.
+            matrixFile.write("\t".join(matrixData))
+            matrixFile.write("\n")
+
+        self.cleanTempFiles()
+
+    def cleanTempFiles(self):
+        # Close temp files.
+        try:
+            [tempFile.close() for tempFile in tempFiles]
+        except:
+            pass
+        # Clean up temp files.
+        for tempFileName in self._featureTempFileNames:
+            try:
+                os.remove(tempFileName)
+            except OSError:
+                pass
+        # Clean up patient_episode file.
+        try:
+            os.remove(self._patientEpisodeTempFileName)
+        except OSError:
+            pass
+        # Clean up patient_list file.
+        try:
+            os.remove(self._patientListTempFileName)
+        except OSError:
+            pass
+
+    def _getMatrixIterator(self):
+        return TabDictReader(open(self._matrixFileName, "r"))
+
+    def readFeatureMatrixFile(self):
+        reader = csv.reader(open(self._matrixFileName, "r"), delimiter="\t")
+        # reader = self._getMatrixIterator()
+        featureMatrixData = [episode for episode in reader]
+
+        return featureMatrixData
+
+    def getMatrixFileName(self):
+        return self._matrixFileName
+
+    def getNumRows(self):
+        return self._numRows
+
diff --git a/scripts/Archive/DeepLearningRecommender/ReadMe.md b/scripts/Archive/DeepLearningRecommender/ReadMe.md
new file mode 100644
index 00000000..465e9b2a
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/ReadMe.md
@@ -0,0 +1,15 @@
+## ClinicNet and ClinicLSTM Deep Learning Recommender Engine
+*Authors: Delaney Sullivan, Jonathan Wang, Alex Wells, Adam Wells*
+
+Hello, welcome to the HealthRex DeepLearning Recommender Github Folder!
+
+Here you can find the files we use for automated patient progression prediction and clinical decision support through deep neural networks.
+
+---
+* ClinicNet is our FeedForward Model 	
+* ClinicLSTM is our RNN model (may need load_and_format_data, rnn_data_wrapper, and create_batches files to preprocess data) 	
+* human_authored_baseline allows one to compare results directly to the performance of human authored order sets.	
+* makeMatrix is the code used for data preprocessing (which relies on FeatureMatrix.py and FeatureMatrixFactory.py, which was code originally writte by Santosh but further modified by us for our needs. Our modifications are located in this folder.)
+---
+
+The remainder of the code used to extract our data is located in the dataconversion folder.
diff --git a/scripts/Archive/DeepLearningRecommender/create_batches.py b/scripts/Archive/DeepLearningRecommender/create_batches.py
new file mode 100644
index 00000000..6b8afebb
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/create_batches.py
@@ -0,0 +1,151 @@
+import sys
+import numpy as np
+import pandas as pd
+import os
+import h5py
+import argparse
+import keras
+import keras.backend as K
+from keras.preprocessing.sequence import pad_sequences
+import random
+from random import shuffle
+
+def extract_middle(nd,Tx):
+	'''
+	Rows are clinical items, columns are features
+	'''
+
+	(rows,cols) = nd.shape
+	if rows < Tx: return nd
+
+	leftover = Tx - rows
+	left = int(leftover/2)
+	right = rows - int(leftover/2)
+	
+	trim = nd[left:right,:]
+	trim = trim[0:Tx,:]
+	
+	return trim
+
+
+
+def create_batches(data_dir,outdir,split,batch_size,Tx,n_feats,n_y,start_batch,end_batch):
+	'''
+    Loads the next batch of sequence data and pads it for LSTM use.  
+    V2 uses the 'window' files.
+    
+    Parameters: 
+        @data_dir: (string) path to data directory
+	@outdir: (string) path to output directory
+        @split: (string) train/dev/test
+        @batch_size: (int) number of files to load for batch
+        @Tx: (int): maximum length of LSTM input
+        @n_feats: (int) number of features
+        @n_y: (int) number of output variables
+    Returns:
+        @x_batch_total: (nd array) shape (batch_size,Tx,n_feats)
+        @y_batch_total: (nd array) shape (batch_size,Tx,n_y)
+	'''
+	
+	random.seed(0)
+ 
+	keep_cols = open('keep_col_names.txt','r').read().splitlines()
+	
+	fns = os.listdir(data_dir)   
+	fns_clean = [fn for fn in fns if fn[0] == 'x']
+	fns_clean.sort()	
+	shuffle(fns_clean)	
+	
+	if end_batch == -1:
+		end_batch = len(fns_clean)
+
+
+	response_3003 = open('/home/ubuntu/cs230/RNN/data/Response_3003_complete.txt','r').read().splitlines()
+
+	b_start = start_batch * batch_size
+	b_end = b_start + batch_size
+
+	for nb in range(start_batch,end_batch):
+		x_batch_total = np.zeros((batch_size,Tx,n_feats))
+		y_batch_total = np.zeros((batch_size,Tx,n_y))
+    
+		for i,x_fn in enumerate(fns_clean[b_start:b_end]):
+			y_fn = 'y' + x_fn[1:]
+
+			x_batch_i = pd.read_table(data_dir+x_fn,sep=',',dtype=None,header=0)
+			x_batch_i = x_batch_i[keep_cols]
+			
+			y_batch_i = pd.read_table(data_dir+y_fn,sep=',',dtype=None,header=0,usecols=response_3003)
+			try:
+				x_batch_i.drop(['analyze_date','patient_item_id','patient_id','clinical_item_id','item_date','encounter_id'],axis=1,inplace = True)
+			except:
+				print ("Dropping fewer columns...")
+			
+			
+			x_batch_i = x_batch_i.as_matrix()
+			y_batch_i = y_batch_i.as_matrix()
+
+			x_batch_i = extract_middle(x_batch_i,Tx)
+			y_batch_i = extract_middle(y_batch_i,Tx)
+
+			x_batch_i = x_batch_i.T
+			y_batch_i = y_batch_i.T
+			
+			print(nb, x_batch_i.shape,y_batch_i.shape)
+
+
+			x_batch_i_padded = pad_sequences(x_batch_i,maxlen=Tx,padding='post',value=-1.).T
+			try:
+				x_batch_i_padded = pad_sequences(x_batch_i,maxlen=Tx,padding='post',value=-1.).T
+			except:
+				print ("ERROR")
+				print (x_fn)
+				print('nrows: %d' % x_batch_i.shape[0])
+				print('ncols: %d' % x_batch_i.shape[1])
+				continue
+
+			y_batch_i_padded = pad_sequences(y_batch_i,maxlen=Tx,padding='post',value=-1.).T
+			y_batch_i_padded = np.sign(y_batch_i_padded)
+			x_batch_total[i,:,:] = x_batch_i_padded
+			y_batch_total[i,:,:] = y_batch_i_padded
+    
+		x_h5f = h5py.File(outdir+'%s_x_batch_%d.h5'%(split,nb),'w')
+		y_h5f = h5py.File(outdir+'%s_y_batch_%d.h5'%(split,nb),'w')
+
+		x_dset = x_h5f.create_dataset('%s_x_batch_%d'%(split,nb),data=x_batch_total)
+		y_dset = y_h5f.create_dataset('%s_y_batch_%d'%(split,nb),data=y_batch_total)
+
+		x_h5f.close()
+		y_h5f.close()
+
+		b_start += batch_size
+		b_end += batch_size
+	
+	return
+
+
+def main(argv):
+	parser = argparse.ArgumentParser()
+	parser.add_argument('--data_dir',type=str,required=True)
+	parser.add_argument('--out_dir',type=str,required=True)
+	parser.add_argument('--batch_size',type=int,required=True)
+	parser.add_argument('--split',type=str,required=True)	
+	parser.add_argument('--Tx',type=int,required=True)
+	parser.add_argument('--n_y',type=int,required=True)
+	parser.add_argument('--n_feats',type=int,required=True)
+	parser.add_argument('--start_batch',type=int,default=0)
+	parser.add_argument('--end_batch',type=int,default=-1)
+
+	args = parser.parse_args()
+	
+	create_batches(args.data_dir,args.out_dir,args.split,args.batch_size,args.Tx,args.n_feats,args.n_y,args.start_batch,args.end_batch)
+
+	return
+
+
+
+if __name__ == '__main__':
+	main(sys.argv[1:])
+
+
+
diff --git a/scripts/Archive/DeepLearningRecommender/feature_cols.txt b/scripts/Archive/DeepLearningRecommender/feature_cols.txt
new file mode 100644
index 00000000..df52cb00
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/feature_cols.txt
@@ -0,0 +1,7648 @@
+AdmitDxDate.pre
+Birth.pre
+Death.pre
+Male.pre
+Female.pre
+RaceWhiteHispanicLatino.pre
+RaceWhiteNonHispanicLatino.pre
+RaceHispanicLatino.pre
+RaceBlack.pre
+RaceAsian.pre
+RacePacificIslander.pre
+RaceNativeAmerican.pre
+RaceOther.pre
+RaceUnknown.pre
+Team.CCU-HF.pre
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\ No newline at end of file
diff --git a/scripts/Archive/DeepLearningRecommender/human_authored_baseline.ipynb b/scripts/Archive/DeepLearningRecommender/human_authored_baseline.ipynb
new file mode 100644
index 00000000..4406c497
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/human_authored_baseline.ipynb
@@ -0,0 +1,1635 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing Packages and Connecting to DB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys, os\n",
+    "import time;\n",
+    "import json;\n",
+    "from optparse import OptionParser\n",
+    "from cStringIO import StringIO;\n",
+    "from datetime import timedelta;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys, os\n",
+    "import pandas as pd\n",
+    "import psycopg2\n",
+    "from datetime import timedelta\n",
+    "from medinfo.dataconversion import FeatureMatrixFactory as fmf;\n",
+    "\n",
+    "def connectToDB():\n",
+    "    '''\n",
+    "    Connects to Database, requires password\n",
+    "    '''\n",
+    "\n",
+    "    dsn_database = \"medinfo\"\n",
+    "    dsn_hostname = \"healthrex-db.cwyfvxgvic6c.us-east-1.rds.amazonaws.com\"\n",
+    "    dsn_port = \"5432\"\n",
+    "    dsn_uid = \"jonc101\"\n",
+    "    dsn_pwd = raw_input(\"Enter the database password: \")\n",
+    "\n",
+    "\n",
+    "    try:\n",
+    "        conn_string = \"host='\"+dsn_hostname+\"' port=\"+dsn_port+\" dbname='\"+dsn_database+\"' user='\"+dsn_uid+\"' password='\"+dsn_pwd+\"'\"\n",
+    "        print \"Connecting to database\\n  ->%s\" % (conn_string)\n",
+    "        conn=psycopg2.connect(conn_string)\n",
+    "        print \"Connected!\\n\"\n",
+    "\n",
+    "    except:\n",
+    "        print \"Unable to connect to the database.\"\n",
+    "\n",
+    "    cursor = conn.cursor()\n",
+    "    return cursor, conn\n",
+    "\n",
+    "cursor, conn = connectToDB()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Human Authored Order Set Validation Set\n",
+    "Input:\n",
+    "\n",
+    "1) matrix of responses/what actually happens 24hrs after for all patient_items\n",
+    "\n",
+    "2) patient_items we want to compare to from the test set of our algorithm\n",
+    "\n",
+    "3) columns of responses that are comparable to order sets, where default_recommend + analysis status = 1\n",
+    "\n",
+    "Output:\n",
+    "f1, precision, accuracy, recall\n",
+    "\n",
+    "Objectives.\n",
+    "1) Obtain all patient items that are part of order sets\n",
+    "\n",
+    "2) figure out what order sets they were a part of\n",
+    "\n",
+    "3) for each order set, create a vector of responses that represent what the order set says to order\n",
+    "\n",
+    "4) create a matrix of responses from this order set\n",
+    "\n",
+    "5) calculate f1 and f2 score to real response variables (with filtered out clinical items as appropriate) for baseline\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Obtaining all of the patient_item_ids that are from order sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loadingdata=True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load in data\n",
+    "data_dir = '/home/ec2-user/cs230/scripts/HumanBaseline/Actual_Final_Files/XY_data/'\n",
+    "fname = data_dir+'y_dev.txt'\n",
+    "y_dev = pd.read_csv(fname, sep = '\\t')\n",
+    "fname = data_dir + 'y_test.txt'\n",
+    "y_test = pd.read_csv(fname, sep = '\\t')\n",
+    "response_matrix = pd.concat([y_dev, y_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname = data_dir+'x_dev.txt'\n",
+    "x_dev = pd.read_csv(fname, usecols=[0], sep = '\\t')\n",
+    "fname = data_dir + 'x_test.txt'\n",
+    "x_test = pd.read_csv(fname, usecols=[0], sep = '\\t')\n",
+    "p_id_column = pd.concat([x_dev, x_test])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_matrix[\"patient_item_id\"] = patientitemids_from_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_matrix.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(y_dev) + len(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patientitemids_from_data = response_matrix[\"patient_item_id\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "need to change cell below based on new real columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create response columns to use for comparison later NEED TO CHANGE THIS\n",
+    "response_vars = []\n",
+    "f = open('/home/ec2-user/cs230/scripts/matrix/response_vars_FINAL_0307.txt', 'r')\n",
+    "for line in f:\n",
+    "    response_vars.append(line.split('\\n')[0])\n",
+    "f.close()\n",
+    "len(response_vars)\n",
+    "response_var_set = set(response_vars)\n",
+    "# extracts columns from all_data that we want as response variables\n",
+    "response_cols = [col for col in data.columns if col in response_var_set]\n",
+    "response_cols.insert(0, \"patient_item_id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read response_cols from csv\n",
+    "if loadingdata == True:\n",
+    "    response_cols = pd.read_csv(\"/home/ec2-user/cs230/scripts/HumanBaseline/response_cols.txt\", sep = \"\\t\")\n",
+    "    response_cols = response_cols.name.tolist()\n",
+    "    response_cols = [s + \".post.1d\" for s in response_cols]\n",
+    "    response_cols.insert(0, \"patient_item_id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_matrix = response_matrix[response_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load data\n",
+    "os.chdir('/home/ec2-user/cs230/scripts/HumanBaseline')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if loadingdata == True:\n",
+    "    patientitemlist_pre = patients_from_ordersets_subset.patient_item_id.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_matrix.drop_duplicates(keep = \"last\", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_matrix.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patientitemlist = \",\".join(map(str, patientitemids_from_data.tolist()))\n",
+    "patientitemlist = \"(\" + patientitemlist + \")\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patientItemsfromOrderSet = \"\"\"\n",
+    "select \n",
+    "\t\tpi.patient_item_id,\n",
+    "        ic.external_id,\n",
+    "\t\tci.name\n",
+    "\tfrom \n",
+    "\t\titem_collection as ic,\n",
+    "\t\titem_collection_item as ici, \n",
+    "\t\tpatient_item_collection_link as picl, \n",
+    "\t\tpatient_item as pi,\n",
+    "\t\tclinical_item as ci,\n",
+    "\t\tclinical_item_category as cic\n",
+    "\twhere \n",
+    "\t\tpi.patient_item_id IN %s and\n",
+    "        ic.item_collection_id = ici.item_collection_id and\n",
+    "\t\tici.item_collection_item_id = picl.item_collection_item_id and \n",
+    "\t\tpicl.patient_item_id = pi.patient_item_id and  \n",
+    "\t\tpi.clinical_item_id = ci.clinical_item_id and\n",
+    "\t\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\t\tci.default_recommend = 1 and\n",
+    "\t\tcic.default_recommend = 1 and\n",
+    "\t\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "\t\t-- ic.external_id = 692 -- Example for Med Sepsis\n",
+    "    Order by pi.patient_item_id\n",
+    "\"\"\"%(patientitemlist) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "df_patientitems = pd.read_sql(patientItemsfromOrderSet, conn)\n",
+    "df_patientitems.to_csv('ordersets_from_patientitems.txt', sep = '\\t')\n",
+    "df_patientitems.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df_patientitems.shape) #for \n",
+    "print(len(patientitemids_from_data))\n",
+    "print(float(len(df_patientitems))/len(patientitemids_from_data))\n",
+    "#Above cell shows the percentage of patientitems that are actually order sets in general "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#check to make sure patientitemids are actually in the original set\n",
+    "assert set(df_patientitems.patient_item_id).issubset(set(patientitemids_from_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#if you're loading data\n",
+    "if loading = True:\n",
+    "    yhat = pd.read_table('human_authored.txt')\n",
+    "    y = pd.read_table('orderset_response.txt')\n",
+    "    assert len(yhat)==len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Collecting clinical items per external_id into a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "external_list = df_patientitems.external_id.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "external_list = \",\".join(map(str, external_list))\n",
+    "external_list = \"(\" + external_list + \")\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "externalItems = \"\"\"\n",
+    "select \n",
+    "\tic.external_id,\n",
+    "\tci.name\n",
+    "from \n",
+    "\titem_collection as ic,\n",
+    "\titem_collection_item as ici, \n",
+    "\tclinical_item as ci,\n",
+    "\tclinical_item_category as cic\n",
+    "where \n",
+    "\tic.item_collection_id = ici.item_collection_id and\n",
+    "\tici.clinical_item_id = ci.clinical_item_id and\n",
+    "\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "    ic.external_id in %s and\n",
+    "\tci.default_recommend = 1 and\n",
+    "\tcic.default_recommend = 1 and\n",
+    "    ci.analysis_status =1 and\n",
+    "\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "group by \n",
+    "\tic.external_id,\n",
+    "\tci.name\n",
+    "order by name\n",
+    "\"\"\"%(external_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_externalitems = pd.read_sql(externalItems, conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "external_id_to_items = {k: list(v) for k,v in df_externalitems.groupby(\"external_id\")[\"name\"]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#CHANGE THIS\n",
+    "#with open(\"ResponseCols.txt\") as f:\n",
+    "#    responsecols = f.readlines()\n",
+    "#responsecols = [x.strip() for x in responsecols] \n",
+    "responsecols = pd.read_csv(\"/home/ec2-user/cs230/scripts/HumanBaseline/response_cols.txt\", sep = \"\\t\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "responsecols = responsecols.name.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "external_id_to_vec = {}\n",
+    "for external_id, items in external_id_to_items.iteritems():\n",
+    "    z = np.zeros(len(responsecols))\n",
+    "    for item in items:\n",
+    "        if (item not in responsecols):\n",
+    "            continue\n",
+    "        z[responsecols.index(item)] = 1\n",
+    "    external_id_to_vec[external_id] = z\n",
+    "print(len(external_id_to_vec[625]))\n",
+    "print(sum(external_id_to_vec[625]))\n",
+    "# i need to create a dictionary of external ids and response vectors with response cols as the index of them"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a matrix with all the patient_item_ids and their response vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_patientitems = df_patientitems.drop_duplicates(subset = \"patient_item_id\", keep = \"last\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix = df_patientitems[\"external_id\"].map(external_id_to_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix = pd.DataFrame(df_humanmatrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix[\"patient_item_id\"] = df_patientitems.patient_item_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response = response_matrix[response_matrix[\"patient_item_id\"].isin(df_humanmatrix[\"patient_item_id\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response.drop_duplicates(inplace = True, keep = \"last\", subset = \"patient_item_id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(comparable_response.shape)\n",
+    "print(comparable_response.shape)\n",
+    "print(df_humanmatrix.size)\n",
+    "print(df_humanmatrix.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix.to_pickle(\"humanmatrix_14_trim.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix2 = pd.DataFrame(columns = response_cols)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix2[\"patient_item_id\"] = df_humanmatrix[\"patient_item_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for row in df_humanmatrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response = response_matrix[response_matrix[\"patient_item_id\"].isin(x_test[\"patient_item_id\"])]\n",
+    "df_humanmatrix = df_humanmatrix[df_humanmatrix[\"patient_item_id\"].isin(x_test[\"patient_item_id\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix = df_humanmatrix.sort_values(by=\"patient_item_id\")\n",
+    "comparable_response = comparable_response.sort_values(by=\"patient_item_id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert sum(comparable_response.patient_item_id.values == df_humanmatrix.patient_item_id.values) == len(df_humanmatrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix.to_csv('human_authored_14_nontrim_test.txt', sep='\\t')\n",
+    "comparable_response.to_csv('orderset_response_14_nontrim_test.txt', sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## analyze script for the human baseline model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn.metrics import precision_recall_fscore_support as pr\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix = pd.read_pickle(\"humanmatrix_14_trim.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response = pd.read_csv(\"orderset_response_14_nontrim.txt\",sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response = comparable_response.drop(\"patient_item_id\", axis = 1)\n",
+    "df_humanmatrix = df_humanmatrix.drop('patient_item_id', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response.drop(\"Unnamed: 0\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = []\n",
+    "y = np.asarray(comparable_response).reshape((1452*len(comparable_response),1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flattened = [val for sublist in df_humanmatrix.external_id for val in sublist]\n",
+    "yhat = flattened"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = [1 if x > 0 else 0 for x in y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert len(y) == len(yhat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bPrecis, bRecall, bFscore, bSupport = pr(y, yhat, pos_label =1, average='binary')\n",
+    "print(bPrecis)\n",
+    "print(bRecall)\n",
+    "print(bFscore)\n",
+    "print(bSupport)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_roc_curve(y,yhat,title,text,y_probs=[]):\n",
+    "    '''\n",
+    "    Plots a ROC curve for given set of predictions\n",
+    "    \n",
+    "    parameters:\n",
+    "        @predict_results: predict generator object\n",
+    "    return:\n",
+    "        None\n",
+    "    '''\n",
+    "        \n",
+    "    fpr, tpr, _ = metrics.roc_curve(y,yhat)\n",
+    "    print(fpr)\n",
+    "    print(tpr)\n",
+    "    AUROC = metrics.auc(fpr,tpr)\n",
+    "    plt.plot(fpr,tpr,label=text + ' (AUC = %.3f)' % AUROC)\n",
+    "    plt.legend(loc='lower right')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('1 - Specificity')\n",
+    "    plt.ylabel('Sensitivity')\n",
+    "    plt.show()\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_roc_curve(y, yhat,\"Human Authored ROC curve\", \"swag\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.accuracy_score(y,yhat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thefile = open('human_response.txt', 'w')\n",
+    "for item in y:\n",
+    "    thefile.write(\"%s\\n\" % item)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thefile.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thefile2 = open('human_prediction.txt', 'w')\n",
+    "for item in yhat:\n",
+    "    thefile2.write(\"%s\\n\" % item)\n",
+    "thefile2.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(yhat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix_real = pd.DataFrame(index = range(60782), columns = comparable_response.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix_real.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_swag = df_humanmatrix.external_id.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_swag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix_real = pd.DataFrame(list_swag, columns = comparable_response.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix_real.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum(pd.Series(comparable_response.values.ravel()).isnull())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_humanmatrix_real.to_csv(\"humanpredict_matrix_real.txt\", sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparable_response.to_csv('comparable_response.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Individual clinical item statistics, finding stats for every column individually"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_names = comparable_response.columns\n",
+    "results = pd.DataFrame(columns = ['Clinical_Item', 'F1_Score', \"precision\", \"recall\", \"AUROC\", \"accuracy\"])\n",
+    "\n",
+    "for response_num in range(1452):\n",
+    "    y_ = comparable_response.iloc[:,response_num]\n",
+    "    yhat = df_humanmatrix_real.iloc[:,response_num]\n",
+    "    bPrecis_, bRecall_, bFscore_, _ = pr(y_, yhat, average='binary')\n",
+    "    results.loc[response_num, 'Clinical_Item'] = response_names[response_num]\n",
+    "    results.loc[response_num, 'F1_Score'] = bFscore_\n",
+    "    results.loc[response_num, 'precision']=bPrecis_\n",
+    "    results.loc[response_num, 'recall'] = bRecall_\n",
+    "    fpr, tpr, _ = metrics.roc_curve(y_,yhat)\n",
+    "    AUROC = metrics.auc(fpr,tpr)\n",
+    "    results.loc[response_num, 'AUROC'] = AUROC\n",
+    "    results.loc[response_num, 'accuracy'] = metrics.accuracy_score(y_,yhat)\n",
+    "    if response_num%100 ==0:\n",
+    "        print \"Clinical Item: %s \\t results:\" % (response_names[response_num])+ str(results.loc[response_num, :])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.sort_values(\"F1_Score\", ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.to_csv(\"results_for_humanauthored.txt\", sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### list of patient_items from order_sets, and the associated number of items predicted in that order set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_cols = \"\"\"\n",
+    "\tselect \n",
+    "\t\tdistinct ci.name\n",
+    "\tfrom \n",
+    "\t\tclinical_item as ci,\n",
+    "\t\tclinical_item_category as cic\n",
+    "\twhere \n",
+    "\t\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\t\tci.default_recommend = 1 and\n",
+    "\t\tcic.default_recommend = 1 and\n",
+    "        ci.analysis_status = 1 \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_response_cols = pd.read_sql(response_cols, conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_response_cols.to_csv(\"response_cols.txt\", sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df_response_cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_list = \",\".join(map(str, df_response_cols.name))\n",
+    "response_list = \"(\" + response_list + \")\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#select patient_item list with external_id\n",
+    "#then select external_id from these patient item lists and include the tables for ci.name and distinct count ci.name for each external_id from this list\n",
+    "\n",
+    "patient_items_num_in_orderset = \"\"\"\n",
+    "\tselect \n",
+    "\t\tic.external_id,\n",
+    "\t\tpi.patient_item_id\n",
+    "\tfrom \n",
+    "\t\titem_collection as ic,\n",
+    "\t\titem_collection_item as ici, \n",
+    "\t\tpatient_item_collection_link as picl, \n",
+    "\t\tpatient_item as pi,\n",
+    "\t\tclinical_item as ci,\n",
+    "\t\tclinical_item_category as cic\n",
+    "\twhere \n",
+    "\t\tic.item_collection_id = ici.item_collection_id and\n",
+    "\t\tici.item_collection_item_id = picl.item_collection_item_id and \n",
+    "\t\tpicl.patient_item_id = pi.patient_item_id and  \n",
+    "\t\tpi.clinical_item_id = ci.clinical_item_id and\n",
+    "\t\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\t\tci.default_recommend = 1 and\n",
+    "\t\tcic.default_recommend = 1 and\n",
+    "        ci.analysis_status = 1 and\n",
+    "\t\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_patientitems_orderset_num = pd.read_sql(patient_items_num_in_orderset, conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df_patientitems_orderset_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_externalitems_num = \"\"\"\n",
+    "select \n",
+    "\tic.external_id,\n",
+    "\tcount(distinct ci.name)\n",
+    "from \n",
+    "\titem_collection as ic,\n",
+    "\titem_collection_item as ici, \n",
+    "\tclinical_item as ci,\n",
+    "\tclinical_item_category as cic\n",
+    "where \n",
+    "\tic.item_collection_id = ici.item_collection_id and\n",
+    "\tici.clinical_item_id = ci.clinical_item_id and\n",
+    "\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\tci.default_recommend = 1 and\n",
+    "\tcic.default_recommend = 1 and\n",
+    "\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "group by \n",
+    "\tic.external_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_externalitems_num = pd.read_sql(all_externalitems_num, conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_patientitems_orderset_num[\"ordersetsize\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_externalitems_num[\"external_id\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_patientitems_orderset_num.drop(\"ordersetsize\", axis = 1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_patientitems_final = pd.merge(df_patientitems_orderset_num, df_externalitems_num, on = \"external_id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_patientitems_final.to_csv(\"patientitems_num_orderset.txt\", sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### get list of patient_item_ids from order sets based on the file delaney sent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = '/home/ec2-user/cs230/scripts/matrix/final_data_031618_v2/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname = data_dir + \"final_data_dropcols_\"+ str(0) + \".txt\"\n",
+    "data = pd.read_table(fname, usecols=[0], sep = '\\t', header = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(1, 15):\n",
+    "    fname = data_dir + \"final_data_dropcols_\"+ str(i) + \".txt\"\n",
+    "    temp = pd.read_table(fname, usecols=[0], sep = '\\t', header = 2)\n",
+    "    data = pd.concat([temp, data])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data.patient_item_id.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_list_patients = pd.read_csv(\"patientitems_num_orderset.txt\", sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thelist = data.patient_item_id.tolist()\n",
+    "thefile = open('patient_item_id_list_full_notrim_14.txt', 'w')\n",
+    "for item in thelist:\n",
+    "  thefile.write(\"%s\\n\" % item)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patients_from_ordersets_subset = full_list_patients.merge(data, on = \"patient_item_id\", how = \"inner\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(patients_from_ordersets_subset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patients_from_ordersets_subset.to_csv(\"patients_from_ordersets_subset_notrim_14.txt\", sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Below are some example Queries\n",
+    "Some interesting facts about the data we have:\n",
+    "There are 2476261 total order_set usages from patient_items out of 55470744 patient_items (4 percent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " orderSet_overall_usage = \"\"\"select \n",
+    "\t\tcount(distinct pi.patient_item_id)\n",
+    "\tfrom \n",
+    "\t\titem_collection as ic,\n",
+    "\t\titem_collection_item as ici, \n",
+    "\t\tpatient_item_collection_link as picl, \n",
+    "\t\tpatient_item as pi,\n",
+    "\t\tclinical_item as ci,\n",
+    "\t\tclinical_item_category as cic\n",
+    "\twhere \n",
+    "\t\tic.item_collection_id = ici.item_collection_id and\n",
+    "\t\tici.item_collection_item_id = picl.item_collection_item_id and \n",
+    "\t\tpicl.patient_item_id = pi.patient_item_id and  \n",
+    "\t\tpi.clinical_item_id = ci.clinical_item_id and\n",
+    "\t\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\t\tci.default_recommend = 1 and\n",
+    "\t\tcic.default_recommend = 1 and\n",
+    "\t\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "\t\t-- ic.external_id = 692 -- Example for Med Sepsis\n",
+    "        \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_patient_items = \"\"\"select count(distinct patient_item_id) from patient_item\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.execute(total_patient_items)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recommend_clinical = \"\"\"\n",
+    "select \n",
+    "\tcount(distinct ci.name)\n",
+    "from \n",
+    "\tclinical_item as ci,\n",
+    "\tclinical_item_category as cic\n",
+    "where \n",
+    "\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\tci.default_recommend = 1 and\n",
+    "\tcic.default_recommend = 1 and\n",
+    "    ci.analysis_status=1 \n",
+    "\"\"\"\n",
+    "cursor.execute(recommend_clinical)\n",
+    "cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " orderSetComponentUsage_default = \"\"\"select \n",
+    "\t\tic.external_id,\n",
+    "\t\tic.name,\n",
+    "\t\tci.clinical_item_id,\n",
+    "\t\tci.name,\n",
+    "\t\tci.description,\n",
+    "\t\tcount(distinct pi.patient_item_id) as itemCount\n",
+    "\tfrom \n",
+    "\t\titem_collection as ic,\n",
+    "\t\titem_collection_item as ici, \n",
+    "\t\tpatient_item_collection_link as picl, \n",
+    "\t\tpatient_item as pi,\n",
+    "\t\tclinical_item as ci,\n",
+    "\t\tclinical_item_category as cic\n",
+    "\twhere \n",
+    "\t\tic.item_collection_id = ici.item_collection_id and\n",
+    "\t\tici.item_collection_item_id = picl.item_collection_item_id and \n",
+    "\t\tpicl.patient_item_id = pi.patient_item_id and  \n",
+    "\t\tpi.clinical_item_id = ci.clinical_item_id and\n",
+    "\t\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\t\tci.default_recommend = 1 and\n",
+    "\t\tcic.default_recommend = 1 and\n",
+    "\t\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "\t\t-- ic.external_id = 692 -- Example for Med Sepsis\n",
+    "\tgroup by \n",
+    "\t\tic.external_id,\n",
+    "\t\tic.name,\n",
+    "\t\tci.clinical_item_id,\n",
+    "\t\tci.name,\n",
+    "\t\tci.description\n",
+    "        \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.execute(orderSetComponentUsage_default)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orderSetUsage_Default = \"\"\"select external_id, name, count(patient_id) as patientCount, \n",
+    "   avg(itemCount) as avgItemCount, min(itemCount) as minItemCount, max(itemCount) as maxItemCount,\n",
+    "   sum(itemCount) as sumItemCount, sum(itemCount*itemCount) as sumSquaresItemCount\n",
+    "from\n",
+    "(\n",
+    "\tselect \n",
+    "\t\tic.external_id,\n",
+    "\t\tic.name,\n",
+    "\t\tpi.patient_id,\n",
+    "\t\tcount(distinct pi.clinical_item_id) as itemCount\n",
+    "\tfrom \n",
+    "\t\titem_collection as ic,\n",
+    "\t\titem_collection_item as ici, \n",
+    "\t\tpatient_item_collection_link as picl, \n",
+    "\t\tpatient_item as pi,\n",
+    "\t\tclinical_item as ci,\n",
+    "\t\tclinical_item_category as cic\n",
+    "\twhere \n",
+    "\t\tic.item_collection_id = ici.item_collection_id and\n",
+    "\t\tici.item_collection_item_id = picl.item_collection_item_id and \n",
+    "\t\tpicl.patient_item_id = pi.patient_item_id and  \n",
+    "\t\tpi.clinical_item_id = ci.clinical_item_id and\n",
+    "\t\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\t\tci.default_recommend = 1 and\n",
+    "\t\tcic.default_recommend = 1 and\n",
+    "\t\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "\tgroup by \n",
+    "\t\tic.external_id,\n",
+    "\t\tic.name,\n",
+    "\t\tpi.patient_id\n",
+    ") as orderSetPatient,\n",
+    "clinical_ite\n",
+    "group by \n",
+    "\texternal_id,\n",
+    "\tname\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.execute(orderSetUsage_Default)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OrderSetSize_default = \"\"\"\n",
+    "select \n",
+    "\tic.external_id,\n",
+    "\tic.name,\n",
+    "\tcount(distinct ici.clinical_item_id) as itemCount\n",
+    "from \n",
+    "\titem_collection as ic,\n",
+    "\titem_collection_item as ici, \n",
+    "\tclinical_item as ci,\n",
+    "\tclinical_item_category as cic\n",
+    "where \n",
+    "\tic.item_collection_id = ici.item_collection_id and\n",
+    "\tici.clinical_item_id = ci.clinical_item_id and\n",
+    "\tci.clinical_item_category_id = cic.clinical_item_category_id and\n",
+    "\tci.default_recommend = 1 and\n",
+    "\tcic.default_recommend = 1 and\n",
+    "\tic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent\n",
+    "group by \n",
+    "\tic.external_id,\n",
+    "\tic.name\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cursor.execute(OrderSetSize_default)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patients_from_ordersets_subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/scripts/Archive/DeepLearningRecommender/load_and_format_data.py b/scripts/Archive/DeepLearningRecommender/load_and_format_data.py
new file mode 100644
index 00000000..0abc1ca0
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/load_and_format_data.py
@@ -0,0 +1,404 @@
+import sys
+import os
+import pandas as pd
+import numpy as np
+from random import shuffle
+import h5py
+
+
+def drop_unwanted_columns(data):
+    '''
+    Drops unwanted columns from a pandas dataframe
+    Parameters:
+        @data: (pandas dataframe) pandas df
+    Returns:
+        @data2: (pandas dataframe) pandas df with dropped columns
+    '''
+
+    data2 = data[data.columns.drop(list(data.filter(regex='post.2d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.4d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.7d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.14d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.30d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.90d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.180d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.365d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.730d')))]
+    data2 = data2[data2.columns.drop(list(data2.filter(regex='post.1460d')))]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.post')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.min')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.max')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.median')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.mean')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.std')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.first')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.last')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.diff')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.slope')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.countInRange')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.proximate')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.firstTimeDays')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.lastTimeDays')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.proximateTimeDays')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.2d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.4d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.14d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.90d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.180d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.365d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.730d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.pre.1460d')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('external_id')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.sin')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.cos')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.preTimeDays')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('analyze_date')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('encounter_id')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('Birth.pre')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('Death.postTimeDays')]
+    data2 = data2.loc[:, ~data2.columns.str.endswith('.postTimeDays')]
+
+    return data2
+
+
+def replace_none_nan(frame):
+    '''
+    Replaces None/Nan with median for a column
+    Parameters:
+        @frame: (pandas dataframe)
+    Returns:
+        @frame_new = (pandas dataframe) with median value replacement
+
+    '''
+
+    frame_new = frame.replace(to_replace='None', value=np.nan)
+
+    for col in list(frame_new):
+        if col not in ["patient_item_id", "patient_id", "clinical_item_id", "item_date", "enounter_id", "analyze_date"]:
+            frame_new[col] = frame_new[col].replace(to_replace=np.nan, value=frame_new[col].median(), axis=1)
+
+    return frame_new
+
+
+def clean_raw_data(raw_data_dir, data_out_dir='/home/ec2-user/cs230/scripts/DeepLearning/RNN/data_final/',
+                   out_prefix='rnn_data_', out_suffix='.txt', file_prefix='final_data_dropcols_', file_suffix='.txt',
+                   nfiles=1):
+    '''
+    Cleans raw data from healthrex database pull
+
+    Parameters:
+        @raw_data_dir: (string) path to directory containing raw data files
+        @data_out_dir: (string) path to directory for writing cleaned data
+        @file_prefix: (string) prefix of raw data files
+        @file_suffix: (string) suffix of raw data files
+        @num_files: (int) how many files to load
+    '''
+    print("BEGIN CLEAN DATA...")
+
+    for n in range(10, nfiles):
+        fpath = raw_data_dir + file_prefix + str(n) + file_suffix
+        print("Cleaning {}...".format(fpath))
+        opath = data_out_dir + out_prefix + str(n) + out_suffix
+        df_total = pd.read_table(fpath, sep='\t', dtype=None, header=2)
+        df_total = replace_none_nan(df_total)
+        df_total.to_csv(opath, sep='\t', header=True, index=False)
+
+    print("CLEAN DATA COMPLETE")
+
+    return
+
+
+def zero_pad(splits, max_len):
+    '''
+    Zero pads a clinical item sequence to be of length equal to max_len.
+    Parameters:
+        @splits: (tuple of lists of pandas dataframes) tuple of form (train,dev,test) where 
+                each obj in tuple is a list of pandas dataframes, where each pandas dataframe corresponds
+                to all the clinical items for a single patient.
+        @max_len: (int) maximum clinical item length
+    Returns:
+        @splits_zero_pad: (tuple of lists of pandas dataframes) tuple of zero-padded patient clinical items list
+    '''
+
+    (train, dev, test) = splits
+
+    columns_to_drop = ["patient_item_id", "patient_id", "clinical_item_id", "item_date", "encounter_id"]
+    for i, d in enumerate(train):
+        d = d[d.columns.drop(columns_to_drop)]
+        train[i] = d.append(pd.DataFrame([[0] * d.shape[1]] * (max_len - d.shape[0]), columns=list(d)))
+    for i, d in enumerate(dev):
+        d = d[d.columns.drop(columns_to_drop)]
+        dev[i] = d.append(pd.DataFrame([[0] * d.shape[1]] * (max_len - d.shape[0]), columns=list(d)))
+    for i, d in enumerate(test):
+        d = d[d.columns.drop(columns_to_drop)]
+        test[i] = d.append(pd.DataFrame([[0] * d.shape[1]] * (max_len - d.shape[0]), columns=list(d)))
+
+    splits_zero_pad = (train, dev, test)
+    return splits_zero_pad
+
+
+def select_patients(patient_ids, split_sizes):
+    '''
+    Splits the patients into train/dev/test.  
+    Parameters:
+        @patient_ids: (list) list of patient_ids in data
+        @split_size_array: (tuple) tuple of (train,dev,test) denoting how many patients should 
+                            go into each category.
+    Returns:
+        @train_patient_ids: (list) list of patients to go into training set
+        @dev_patient_ids: (list) list of patients to go into dev set
+        @test_patient_ids: (list) list of patients to go into test set
+    '''
+
+    (train_sz, dev_sz, test_sz) = split_sizes
+    shuffle(patient_ids)
+    train_patient_ids = patient_ids[0:train_sz]
+    dev_patient_ids = patient_ids[train_sz:train_sz + dev_sz]
+    test_patient_ids = patient_ids[train_sz + dev_sz:]
+
+    return train_patient_ids, dev_patient_ids, test_patient_ids
+
+
+def write_features_responses(df, name, outdir, iters, response_vars):
+    '''
+    Separates the data into feature and response variables.
+    Generates x_train,x_dev,x_test,y_train,y_dev,y_test.
+
+    Parameters:
+        @df: (pandas dataframe) pandas dataframe to be written to file
+        @name: (string) 'train', 'dev', or 'test'
+        @outdir: (string) output directory
+        @iters: (int) number to be used in outfile name 
+        @response_vars: (list) list of response variables
+    Returns:
+        None
+    '''
+
+    df.sort_values(by='item_date', ascending=True, inplace=True)
+
+    x_ = df.drop(response_vars, axis=1)
+    y_ = df[response_vars]
+
+    if name == 'train':
+        x_.to_csv(outdir + 'train_dir_date_v3/' + 'x_train_%d.dat' % iters, sep='\t')
+        y_.to_csv(outdir + 'train_dir_date_v3/' + 'y_train_%d.dat' % iters, sep='\t')
+
+    elif name == 'dev':
+
+        x_.to_csv(outdir + 'dev_dir_date_v3/' + 'x_dev_%d.dat' % iters, sep='\t')
+        y_.to_csv(outdir + 'dev_dir_date_v3/' + 'y_dev_%d.dat' % iters, sep='\t')
+
+    elif name == 'test':
+
+        x_.to_csv(outdir + 'test_dir_date_v3/' + 'x_test_%d.dat' % iters, sep='\t')
+        y_.to_csv(outdir + 'test_dir_date_v3/' + 'y_test_%d.dat' % iters, sep='\t')
+
+    return
+
+
+def variable_length_splits(all_data, all_patient_ids, id_splits, outdir, counter=(0, 0, 0)):
+    '''
+    Assigns patient data to either train,dev, or test split. Variable length
+    because not yet zero-padded.
+    Parameters:
+        @pgs: (generator object) iterable pd groupby patient_id 
+        @splits: (tuple) tuple of (train_ids,dev_ids,test_ids)
+    Returns:
+        @train: (list) list of pandas dataframes for patients in training set
+        @dev: (list) list of pandas dataframes for patients in dev set
+        @test: (list) list of pandas dataframes for patients in test set
+    '''
+
+    response_vars = open('/home/ec2-user/cs230/scripts/Response_3003_complete.txt', 'r').read().splitlines()
+
+    (train_ids, dev_ids, test_ids) = id_splits
+    train, dev, test = [], [], []
+    (train_iter, dev_iter, test_iter) = counter
+    for p_id in all_patient_ids:
+        p_df = all_data[all_data['encounter_id'] == p_id]
+        if p_id in train_ids:
+            write_features_responses(p_df, 'train', outdir, train_iter, response_vars)
+            train_iter += 1
+        elif p_id in dev_ids:
+            write_features_responses(p_df, 'dev', outdir, dev_iter, response_vars)
+            dev_iter += 1
+        elif p_id in test_ids:
+            write_features_responses(p_df, 'test', outdir, test_iter, response_vars)
+            test_iter += 1
+        else:
+            print("UNMATCHED PATIENT ERROR: %d" % p_id)
+
+    return train, dev, test, (train_iter, dev_iter, test_iter)
+
+
+def convert_to_tensor(frame_list):
+    '''
+    Converts a list pandas dataframes into a tensor.
+
+    Parameters:
+        @frame_list: (list of pandas dataframes) all patient data for a split
+    Returns:
+        @tensor: (tensor i.e numpy nd array) 
+    '''
+
+    tensor = []
+    for frame in frame_list:
+        tensor.append(frame.as_matrix())
+
+    tensor = np.asarray(tensor)
+
+    return tensor
+
+
+def extract_features_responses(splits_padded, outdir):
+    '''
+    Separates the data into feature and response variables.
+    Generates x_train,x_dev,x_test,y_train,y_dev,y_test.
+
+    Parameters:
+        @splits_padded: tuple of lists of pandas dataframes for train,dev,test sets.
+    Returns:
+        @splits_x: (tuple of tensors) (x_train,x_dev,x_test) 
+        @splits_y: (tuple of tensors) (y_train,y_dev,y_test)
+    '''
+
+    response_vars = open('/home/ec2-user/cs230/scripts/matrix/response.txt', 'r').read().splitlines()
+
+    (train, dev, test) = splits_padded
+
+    x_train, x_dev, x_test = [], [], []
+    y_train, y_dev, y_test = [], [], []
+
+    train_count, dev_count, test_count = 0, 0, 0
+    for name, split in zip(['train', 'dev', 'test'], [train, dev, test]):
+
+        for patient in split:
+            patient.drop(["patient_item_id", "patient_id", "clinical_item_id", "item_date", "encounter_id"], axis=1,
+                         inplace=True)
+
+            x_ = patient.drop(response_vars, axis=1)
+            y_ = patient[response_vars]
+
+            if name == 'train':
+                x_.to_csv(outdir + '/train_dir_v2/' + 'x_train_%d.dat' % train_count, sep='\t')
+                y_.to_csv(outdir + '/train_dir_v2/' + 'y_train_%d.dat' % train_count, sep='\t')
+
+                train_count += 1
+
+                x_train.append(x_)
+                y_train.append(y_)
+
+            elif name == 'dev':
+
+                x_.to_csv(outdir + '/dev_dir_v2/' + 'x_dev_%d.dat' % dev_count, sep='\t')
+                y_.to_csv(outdir + '/dev_dir_v2/' + 'y_dev_%d.dat' % dev_count, sep='\t')
+
+                dev_count += 1
+
+                x_dev.append(x_)
+                y_dev.append(y_)
+
+            elif name == 'test':
+
+                x_.to_csv(outdir + '/test_dir_v2/' + 'x_test_%d.dat' % test_count, sep='\t')
+                y_.to_csv(outdir + '/test_dir_v2/' + 'y_test_%d.dat' % test_count, sep='\t')
+
+                test_count += 1
+
+                x_test.append(x_)
+                y_test.append(y_)
+
+        return
+
+
+def make_train_dev_test(data_dir, fname='rnn_data.txt', outdir='/home/ec2-user/cs230/scripts/DeepLearning/RNN/data/',
+                        train_percent=0.9, dev_percent=0.05):
+    '''
+    Generates train,dev,and test set for RNN model.  
+    A single training example comprises all clinical item orders for a given patient. 
+    Therefore the shape of a single example will be (# of clinical item orders, # of features).
+
+    Parameters:
+        @data_dir: (string) path to directory containing cleaned data
+    Returns:
+        @train: (tensor) tensor of shape (#train examples, #clinical item orders, #features)
+        @dev: (tensor) tensor of shape (#dev examples, #clinical item orders, #features)
+        @test: (tensor) tensor of shape (#test examples, #clinical item orders, #features)
+    '''
+
+    print("BEGIN GENERATE TRAIN/DEV/TEST")
+    test_percent = 1 - train_percent - dev_percent
+    all_data = pd.read_table(data_dir + fname, sep='\t', dtype=None, header=0)
+
+    patient_groups = all_data.groupby(by='encounter_id')
+
+    max_clinical_items = max([len(p[1]) for p in patient_groups])
+
+    all_patient_ids = all_data['encounter_id'].unique().tolist()
+    total_patients = len(all_patient_ids)
+
+    train_num_patients = int(total_patients * train_percent)
+    dev_num_patients = int(total_patients * dev_percent)
+    test_num_patients = total_patients - train_num_patients - dev_num_patients
+
+    print("TOTAL NUMBER OF Encounters: %d" % len(patient_groups))
+    print("NUMBER OF TRAIN Encounters: %d" % train_num_patients)
+    print("NUMBER OF DEV Encounters: %d" % dev_num_patients)
+    print("NUMBER OF TEST Encounters: %d" % test_num_patients)
+    print("MAX CLINICAL ITEMS: %d" % max_clinical_items)
+
+    train_patient_ids, dev_patient_ids, test_patient_ids = select_patients(all_patient_ids, (
+    train_num_patients, dev_num_patients, test_num_patients))
+    train, dev, test = variable_length_splits(all_data, all_patient_ids,
+                                              (train_patient_ids, dev_patient_ids, test_patient_ids), outdir)
+
+    return
+
+
+def make_train_dev_test_v2(data_dir, in_prefix='rnn_data_',
+                           outdir='/home/ec2-user/cs230/scripts/DeepLearning/RNN/data/', train_percent=0.9,
+                           dev_percent=0.05, nfiles=1):
+    '''
+    Generates train,dev,and test set for RNN model.  
+    A single training example comprises all clinical item orders for a given patient. 
+    Therefore the shape of a single example will be (# of clinical item orders, # of features).
+
+    Parameters:
+        @data_dir: (string) path to directory containing cleaned data
+    Returns:
+        @train: (tensor) tensor of shape (#train examples, #clinical item orders, #features)
+        @dev: (tensor) tensor of shape (#dev examples, #clinical item orders, #features)
+        @test: (tensor) tensor of shape (#test examples, #clinical item orders, #features)
+    '''
+
+    print("BEGIN GENERATE TRAIN/DEV/TEST")
+    test_percent = 1 - train_percent - dev_percent
+
+    out_counter = (0, 0, 0)
+    for i in range(nfiles):
+        fname = in_prefix + str(i) + '.txt'
+        all_data = pd.read_table(data_dir + fname, sep='\t', dtype=None, header=0)
+        patient_groups = all_data.groupby(by='encounter_id')
+
+        max_clinical_items = max([len(p[1]) for p in patient_groups])
+
+        all_patient_ids = all_data['encounter_id'].unique().tolist()
+        total_patients = len(all_patient_ids)
+
+        train_num_patients = int(total_patients * train_percent)
+        dev_num_patients = int(total_patients * dev_percent)
+        test_num_patients = total_patients - train_num_patients - dev_num_patients
+
+        print("TOTAL NUMBER OF Encounters: %d" % len(patient_groups))
+        print("NUMBER OF TRAIN Encounters: %d" % train_num_patients)
+        print("NUMBER OF DEV Encounters: %d" % dev_num_patients)
+        print("NUMBER OF TEST Encounters: %d" % test_num_patients)
+        print("MAX CLINICAL ITEMS: %d" % max_clinical_items)
+
+        train_patient_ids, dev_patient_ids, test_patient_ids = select_patients(all_patient_ids, (
+        train_num_patients, dev_num_patients, test_num_patients))
+        train, dev, test, out_counter = variable_length_splits(all_data, all_patient_ids,
+                                                               (train_patient_ids, dev_patient_ids, test_patient_ids),
+                                                               outdir, counter=out_counter)
+
+    return
diff --git a/scripts/Archive/DeepLearningRecommender/makeMatrix.py b/scripts/Archive/DeepLearningRecommender/makeMatrix.py
new file mode 100644
index 00000000..5256b7ab
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/makeMatrix.py
@@ -0,0 +1,101 @@
+import os
+import sys
+import time
+import numpy as np
+import pandas as pd
+from backports import tempfile
+from multiprocessing import Pool
+os.chdir('/home/ec2-user/healthrex/CDSS/medinfo/dataconversion')
+from medinfo.dataconversion import FeatureMatrixFactory as fmf
+from medinfo.dataconversion import FeatureMatrix as fm
+os.chdir('/home/ec2-user/cs230/scripts/matrix/icu2')
+import psycopg2
+from datetime import timedelta
+
+# Code to connect to database:
+
+dsn_pwd = input("Enter the database password: ")
+def connectToDB(dsn_pwd):
+    '''
+    Connects to Database, requires password
+    '''
+
+    dsn_database = "medinfo"
+    dsn_hostname = "healthrex-db.cwyfvxgvic6c.us-east-1.rds.amazonaws.com"
+    dsn_port = "5432"
+    dsn_uid = "jonc101"
+
+
+    try:
+        conn_string = "host='"+dsn_hostname+"' port="+dsn_port+" dbname='"+dsn_database+"' user='"+dsn_uid+"' password='"+dsn_pwd+"'"
+        print("Connecting to database\n  ->%s" % (conn_string))
+        conn=psycopg2.connect(conn_string)
+        print("Connected!\n")
+
+    except:
+        print("Unable to connect to the database.")
+
+    cursor = conn.cursor()
+    return cursor
+
+cursor = connectToDB(dsn_pwd) # Establish the connection to the database
+
+
+# Select clinical items we want to pre-process
+cursor.execute("""SELECT DISTINCT(name) FROM clinical_item WHERE analysis_status = 1""")
+clinical_item_names = []
+for elem in cursor:
+    clinical_item_names.append(elem[0])
+
+# Get total count of all the patient items we want
+cursor.execute("SELECT COUNT(*) FROM patient_item where NOT (encounter_id IS NULL)")
+total_count = int(cursor.fetchall()[0][0])
+
+# Query in batches of batch_size
+batch_size = 100000
+num_iterations = total_count/batch_size + 1
+outputdir = os.getcwd()
+
+# The query and pre-processing for each batch
+def queryItems(i):
+    global batch_size
+    global clinical_item_names
+    global dsn_pwd
+    global outputdir
+    start_time = time.time()
+    offset = batch_size*i
+    print('Iteration : {} (Batch size: {}; Offset: {})'.format(str(i), str(batch_size), str(offset)))
+
+    data = None
+    with tempfile.TemporaryDirectory() as path:
+        os.chdir(path)
+        cursor = connectToDB(dsn_pwd)
+        cursor.execute("SELECT * from patient_item where NOT (encounter_id IS NULL) order by encounter_id DESC LIMIT %s OFFSET %s", (str(batch_size), str(offset)))
+
+        # Initialize FeatureMatrix
+        factory = fm.FeatureMatrix(variable = 'hi', num_data_points=100000000, params=None)
+
+        # Add features
+        factory._factory.setPatientEpisodeInput(cursor)
+        factory._factory.processPatientEpisodeInput()
+        factory._add_features(index_time_col='item_date')
+        factory._factory.addClinicalItemFeatures(['Death'], dayBins=[1,7,30], features="post")
+
+        # Add relevant clinical item features
+        for item in clinical_item_names:
+            print(item)
+            factory._factory.addClinicalItemFeatures(clinicalItemNames=[item], dayBins=[1,7,30], features="all")
+
+        # Output the feature matrix for this iteration
+        factory._factory.buildFeatureMatrix(matrixFileName=outputdir+"/FINAL_iteration_{}.txt".format(str(i)))
+    end_time = time.time()
+    
+    print('Completed Iteration : {} in time : {}'.format(str(i), str(end_time-start_time)))
+    return 0
+
+# Do multiprocessing of the batches
+
+pool = Pool(14)
+iterations = list(range(0, num_iterations))
+pool.map(queryItems, iterations, chunksize=1)
+
diff --git a/scripts/Archive/DeepLearningRecommender/response_variables.csv b/scripts/Archive/DeepLearningRecommender/response_variables.csv
new file mode 100644
index 00000000..ca4ea4e3
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/response_variables.csv
@@ -0,0 +1,1453 @@
+Name,Description
+RXCUI35302,Sirolimus
+LAB11069R,ALDOLASE
+RXCUI6057,Isosorbide
+IMGIRABSDRNFG,IR ABSCESS DRAINAGE FLUORO GUIDED
+RXCUI10865,Tromethamine
+RXCUI3251,Desmopressin
+RXCUI233698,Dronedarone
+LABCKITMU,DETECTION OF C-KIT MUTATIONS
+LABAPHSA2,ANTI PHOSPHOLIPID AB PANEL
+IMGDXHA3RT,XR HAND 3V RT
+RXCUI4850,Glucose
+LEG7,LEGAL STATUS: VOLUNTARY
+RXCUI4678,Ganciclovir
+LABQFTB,QUANTIFERON TEST FOR LATENT TB
+RXCUI358255,Aprepitant
+IMGDXEL3LT,XR ELBOW 3V LT
+RXCUI8134,Phenobarbital
+RXCUI29899,Methylnaltrexone
+RXCUI88249,Montelukast
+LABURCL,"CHLORIDE, URINE"
+LAB11118R,"ELASTASE, STOOL"
+RXCUI6579,Magnesium Chloride
+IMGFLSB,XR SMALL BOWEL SERIES
+LAB11023R,EB NUCLEAR AG AB
+"RXCUI426,1091,1908,21032,21579,4278,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Famotidine-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+LABAGALA,ASPERGILLUS GALACTOMANNAN
+RXCUI723,Amoxicillin
+RXCUI6902,Methylprednisolone
+IMGDXHIP2LT,XR HIP 2V LT
+SLP8,SLP PASSY MUIR VALVE ONGOING
+LABABIDI,AB ID
+RXCUI7812,Oxymetazoline
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8591,8737,253182,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Potassium Chloride-Proline-Regular Insulin, Human-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+LABBMT2,BMT PANEL 2
+MED96560,Glucose-Piperacillin-Tazobactam
+DIET123,DIET TUBE FEEDING ONLY
+MED205894,BICARB DIALYS SOLN NO.8 W-O CA K
+LABNORW,"NOROVIRUS ANTIGEN, STOOL EIA"
+CON421,CONSULT TO PHYSICAL MED REHAB
+IMGDXEL2LT,XR ELBOW 2V LT
+LABSPIE,"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM"
+LABLEID,FACTOR V LEIDEN
+PATH4,BODY CAVITIES SOURCE
+RXCUI7454,Nitrofurantoin
+RXCUI15996,Mirtazapine
+NUR6146,PICC EXCHANGE
+LABUNA,"SODIUM WITH CREATININE, URINE"
+LABOAPE,OVA AND PARASITES
+RXCUI24909,"Ferric Oxide, Saccharated"
+RT72,RESP - CHEST PHYSIOTHERAPY
+IMGCTAPW,CT ABDOMEN PELVIS W CONTRAST
+RXCUI1256,Azathioprine
+LABPTT,PTT PARTIAL THROMBOPLASTIN TIME
+MED82472,"Nph Insulin, Human-Regular Insulin, Human"
+RXCUI2582,Clindamycin
+LABLEGU,"LEGIONELLA AG, URINE"
+RXCUI60212,Atovaquone
+RXCUI87636,Fexofenadine
+RT106,RESP - NITRIC OXIDE VENT
+RXCUI77655,Zoledronic Acid
+IMG11CCP,CAR CATH CORS POSSIBLE
+SUP197301,"DME ORDER, AMBULATORY"
+LABFLUAPC,FLU A PCR
+IMGFLGIS,XR UPPER GI SMALL BOWEL SERIES
+LABUUN,"UREA WITH CREATININE, URINE"
+RXCUI4177,Etomidate
+IMGDXWR2RT,XR WRIST 2V RT
+LABANAS,ANTI - NUCLEAR AB
+RXCUI21212,Clarithromycin
+LABORAERO,ORTHOPEDIC AEROBIC CULT W/GS
+"RXCUI4511,4850,89905,10454,8308",Folic Acid-Glucose-Multivitamin Preparation-Thiamine-Vitamin K 1
+IMGMRHSDX,MR DIAGNOSIS STROKE PROTOCOL
+RXCUI8638,Prednisolone
+IMGDXFORRT,XR FOREARM 2V RT
+"RXCUI4511,89905,9863,10454,8308",Folic Acid-Multivitamin Preparation-Sodium Chloride-Thiamine-Vitamin K 1
+LABFPARVD,PARVOVIRUS B19 PCR QUALITATIVE REALTIME
+LABRPRQT,RPR QUANTITATIVE
+RXCUI68139,Rocuronium
+MED123723,Simvastatin-Ezetimibe
+DIET131,DIET NPO AFTER MIDNIGHT
+LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA"
+LABMTXL,METHOTREXATE
+MED19483,Calcium Carbonate-Cholecalciferol
+RXCUI1223,Atropine
+RXCUI9143,Ranitidine
+RXCUI2447,Cholestyramine Resin
+LABBLCSTK,"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"
+MED203654,"Mineral Oil-Petrolatum, White"
+PRE12,NEUTROPENIC PRECAUTIONS
+IMGCTHSC,CT HEAD C SPINE
+LABPSA,PSA
+LABGNTPRL,GENTAMICIN TROUGH LEVEL
+IMGDXAN2LT,XR ANKLE 2V LT
+MED10434,Lidocaine-Prilocaine
+IMGCTTAPW,CT CHEST ABDOMEN PELVIS W CONTRAST
+IMGCTFAC,CT FACIAL BONES OR SINUS
+ECH17,ECHO - 2D ECHOCARDIOGRAM
+MED16427,Potassium Chloride-Sodium Chloride
+LABWDC,WOUND CULTURE
+LABAPHSAB,ANTI PHOSPHOLIPID AB PANEL
+LABHSVZL,"HSV/VZV PCR QUAL, LESION"
+RXCUI7242,Naloxone
+NUR6145,PICC EVALUATION
+RXCUI121243,Voriconazole
+RXCUI30125,Modafinil
+LABPROTC,PROTEIN C ACTIVITY
+CON367,NSG REFERRAL TO WOUND/OSTOMY
+COD3,PARTIAL CODE
+LABCSFC,CSF CULTURE AND GRAM STAIN
+MED33227,Amoxicillin-Clavulanate
+10330R,HLA - SCREENING IGG
+LABCMVQT,"CMV DNA PCR QUANT, PLASMA"
+MED854,Bacitracin-Neomycin-Polymyxin B
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,8591,8737,9671,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Chloride-Proline-Serine-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+RXCUI3640,Doxycycline
+RXCUI4018,Ergocalciferol
+PATH12,FLOW CYTOMETRY FOR HEMATOLYMPHOID NEOPLASIA
+IMGCTO,CT ORBIT SELLA POST FOSSA TEMP BONE
+RXCUI15202,Argatroban
+LABLAC,LACTIC ACID
+RXCUI341018,Anidulafungin
+LABDVERV,RESPIRATORY DFA PANEL
+LABDVERVR,RESP DFA PANEL-REFLEX PCR
+RXCUI115698,Ziprasidone
+IMGMRSCWWO,MR C-SPINE WITH AND WITHOUT CONTRAST
+SLP5,SLP VIDEO SWALLOW EVALUATE AND TREAT
+RXCUI9863,Sodium Chloride
+IMGUSAPPY,US APPENDIX
+LABPCCG4V,"ISTAT CG4,VENOUS"
+RXCUI82122,Levofloxacin
+IMGCT3H,CT ANGIO HEAD
+LABCA199,CANCER AG 19-9
+DIET107,DIET LOW SODIUM LOW CHOLESTEROL LOW SAT FAT
+RXCUI1424,Benztropine
+PT9,REHAB ENGINEERING
+LABLDH,"LDH TOTAL, SERUM / PLASMA"
+CON402,CONSULT TO WOUND/OSTOMY
+CON308,CONSULT TO PULMONARY
+RXCUI1886,Caffeine
+MED22560,Sulfamethoxazole-Trimethoprim
+IMGDXCH1DRT,"XR CHEST 1 VIEW DECUB, RIGHT"
+LABDGHBP,DIAG. HEPATITIS B PANEL
+LABGNTPOL,GENTAMICIN PEAK LEVEL
+LAB11060R,THIAMINE
+LABPCTNI,ISTAT TROPONIN I
+LABFADA,"ADENOSINE DEAMINASE, FLUID"
+LABPCEG7V,"ISTAT EG7, VENOUS"
+RXCUI5032,Guaifenesin
+IMGUSTV,US TRANSVAGINAL
+RXCUI9000,Pyridostigmine
+RXCUI6915,Metoclopramide
+LABCA,"CALCIUM, SERUM/PLASMA"
+CON410,CONSULT TO NEPHROLOGY TRANSPL
+IMGCTSL,CT SPINE LUMBAR
+RXCUI214342,Calcium Acetate
+LABYPREAL,PREALBUMIN
+OT2,OT ONGOING TREATMENT
+LABBBPRBC,RED BLOOD CELLS
+IMGDXSHUNT1,XR SHUNT SERIES W SKULL 2V CXR 1V ABDO 1V
+COD5,DNR/DNE
+RT76,RESP - SPUTUM INDUCTION
+LABYHISTU,"HISTOPLASMA AG, URINE"
+RXCUI24947,Ferrous Sulfate
+LABCLGC2,"CHLAMYDIA GC, MOLECULAR DETECTION"
+EKG29,CARDIAC PATCH RHYTHM MONITOR
+NEUR18,INTRA OP NEUROPHYS MONITOR PER HOUR
+RXCUI6972,Mineral Oil
+RT79,"RESP - BRONCHOSCOPY, FLEXIBLE FIBEROPTIC"
+RXCUI236594,Amphotericin B Liposomal
+RXCUI798279,"Haemophilus Influenzae B, Capsular Polysaccharide Inactivated Tetanus Toxoid Conjugate Vaccine"
+IMGDXHIP2RT,XR HIP 2V RT
+REF96,REFERRAL TO INTERVENTIONAL RADIOLOGY
+IMGFLERCP,ERCPANCREATOGRAM
+IMGCT3N,CT ANGIO NECK
+"RXCUI197,6218,6972,11295",Acetylcysteine-Lactulose-Mineral Oil-Water
+LABHITAB,HEPARIN PLATELET FACTOR 4 AB
+RXCUI6038,Isoniazid
+IMGDXFLP,PHYSIATRY FLUORO
+IMGMRAK,MR ANGIO NECK
+RXCUI18631,Azithromycin
+CON439,NSG REFERRAL TO GERIATRICS
+LABPCCR,ISTAT CREATININE
+RXCUI72625,Duloxetine
+REF27,REFERRAL TO NEUROLOGY
+RXCUI2231,Cephalexin
+RXCUI732,Amphotericin B
+LABOSR,OUTSIDE SLIDE REVIEW
+RXCUI35296,Ramipril
+LABCAI,"CALCIUM IONIZED, SERUM/PLASMA"
+RXCUI11149,Vasopressin
+IMGCTTROF,CT CHEST OUTSIDE STUDY FOR REFERENCE ONLY
+LABOLIG,CSF PROTEIN & IMMUNOFIX ELECT.
+IMGNBONESCAN,NUC BONE SCAN
+RXCUI51428,"Insulin, Aspart, Human"
+RXCUI1291,Bacitracin
+SLP11,SLP SWALLOW ONGOING TREATMENT
+PT6,PT ONGOING TREATMENT
+LABALB,"ALBUMIN, SERUM/PLASMA"
+IMGUSNVUPP,US VEIN UPPER EXTREMITY BILAT R/O DVT
+RXCUI3498,Diphenhydramine
+IMGNGASTREMP,NUC GASTRIC EMPTYING
+IMGDXHA3LT,XR HAND 3V LT
+LABBXTG,BIOPSY/TISSUE WITH GRAM STAIN
+RXCUI5093,Haloperidol
+RXCUI4511,Folic Acid
+IMGCTST,CT SPINE THORACIC
+LABMVSOB,UNLISTED MICROBIOLOGY/VIROLOGY TEST
+IMGCTAPROF,CT ABDOMEN/PELVIS OUTSIDE STUDY FOR REFERENCE ONLY
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,8591,8737,253182,9671,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Chloride-Proline-Regular Insulin, Human-Serine-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+"RXCUI8591,56443,9863",Potassium Chloride-Sodium Acetate-Sodium Chloride
+NUR1614,PICC LINE PLACEMENT
+2109372,LACERATION REPAIR
+CON311,CONSULT TO PALLIATIVE CARE
+RXCUI341248,Ezetimibe
+RXCUI828529,"Albumin Human, Usp"
+RXCUI435,Albuterol
+"RXCUI4511,9863,10454",Folic Acid-Sodium Chloride-Thiamine
+LABFLDC,FLUID CULTURE AND GRAM STAIN
+POC18,"POC ISTAT, ARTERIAL NA, K, GLUCOSE, HCT, HEMOGLOBIN"
+RXCUI8640,Prednisone
+RXCUI28031,Itraconazole
+RXCUI39786,Venlafaxine
+LABRF,RHUEMATOID FACTOR
+IMGDXWR3LT,XR WRIST 3V LT
+LAB12573R,ANCA
+REF52,REFERRAL TO ORTHOPEDICS
+IMGMRSC,MR C-SPINE
+RXCUI8013,Pentoxifylline
+CON107,CONSULT TO IR - UNLISTED PROCEDURE
+IMGDXCH2,XR CHEST 2V
+MED7557,Sulfamethoxazole-Trimethoprim
+LABTBLYM,"T SUBSETS AND B LYMPHOCYTES, BLOOD"
+RXCUI620,Amantadine
+LABANTIXA,ANTI XA / LMWH
+LABASQ,ACETONE SEMI QUALITATIVE
+LABLIPS,LIPASE
+REF35,REFERRAL TO INFECTIOUS DISEASE
+CON361,NSG REFERRAL TO CASE MGMT
+CON408,CONSULT TO INF DIS IMMUNOCOMP
+IMGDXFT2RT,XR FOOT 2V RT
+IMGCTHWWO,CT HEAD WWO CONTRAST
+LABSTREP,BETA STREP THROAT SCREEN RAPID EIA
+MED540492,SHC TRANSPLANT BEDSIDE MEDS HELP ORDER
+RXCUI11256,Vitamin E
+IMGDXCHROF,XR CHEST OUTSIDE STUDY FOR REFERENCE ONLY
+IMGCTLLT,CT LOWER EXTREMITY LT
+RXCUI9793,Silver Sulfadiazine
+LABVD25H,"VITAMIN D, 25-HYDROXYVITAMIN"
+"RXCUI2540,5690",Cilastatin-Imipenem
+CR112,KNEE IMMOBILIZER
+RXCUI7617,Octreotide
+LABFHCT,"HEMATOCRIT, FLUID"
+RXCUI6835,Methimazole
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8591,8737,253182,9671,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Potassium Chloride-Proline-Regular Insulin, Human-Serine-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+RXCUI6813,Methadone
+LABURIC,"URIC ACID, SERUM / PLASMA"
+CON468,CONSULT TO HEART FAILURE CNS
+RXCUI1897,Calcium Carbonate
+LABP20210,PROTHROMBIN 20210A
+RXCUI36437,Sertraline
+RXCUI4462,Fluocinonide
+LABTBILFR,"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"
+IMGDXFL1HRGR,XR FLUORO OVER 1 HOUR WO RADIOLOGIST
+CON417,CONSULT TO IR
+RXCUI69749,Valsartan
+LABCMVIGM,CMV IGM
+CON109,CONSULT TO IR - VENOUS ACCESS
+RXCUI2358,Chlorhexidine
+LABYMETFF,"METANEPHRINES FRACTIONATED FREE, PLASMA"
+REF25,REFERRAL TO SURGERY GENERAL
+IMGUSPLE,US FLUID LOCALIZATION
+MED540602,CUSTOM INV - SIMPLE
+RXCUI1272,Aztreonam
+RT74,RESP - BEDSIDE SPIROMETRY
+RXCUI42372,Mupirocin
+RXCUI66887,Racepinephrine
+CON322,CONSULT TO GENERAL SURGERY
+RXCUI407990,Cinacalcet
+LABSTOBGC,"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."
+LABLEVETL,LEVETIRACETAM LEVEL
+RXCUI10759,Triamcinolone
+IMGDXHA2LT,XR HAND 2V LT
+IMGCTHR,CT CHEST HIGH RESOLUTION
+IMGFLESO,XR ESOPHAGUS
+LABRPRQL,RPR QUALITATIVE
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8737,253182,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Regular Insulin, Human-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+LABSALICY,SALICYLATE LEVEL
+LABMYO,"MYOGLOBIN, SERUM / PLASMA"
+DIET127,DIET COMBINATION
+RXCUI2403,Chlorpromazine
+IMGDXCH1P,XR CHEST 1V PORTABLE
+LABCMVTSI,CMV AB TOTAL SOLID PHASE
+IMGUSTXK,US KIDNEY TRANSPLANT
+"RXCUI426,1091,1908,21032,21579,4278,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8591,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Famotidine-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Potassium Chloride-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+CON335,CONSULT TO UROLOGY
+RXCUI321988,Escitalopram
+LABCYTGEN,CYTOGENETICS FISH ORDER
+IMGDXFL1HR,XR FLUORO UP TO 1 HOUR  WO RADIOLOGIST
+RXCUI10737,Trazodone
+LABPROC,SPECIMEN PROCESSING
+RXCUI7980,Penicillin G
+RXCUI5781,Indomethacin
+IMGDXCH1,XR CHEST 1V
+MED208479,Magnesium Oxide-Magnesium Amino Acid Chelate
+RXCUI338036,Pegfilgrastim
+IMGUSP2,US PELVIS NON OB LTD
+DIET129,DIET NOURISHMENTS
+LABEP1,ELECTROLYTE PANEL
+ISO10,CONTACT AND RESPIRATORY ISOLATION
+RXCUI6130,Ketamine
+RXCUI28889,Loratadine
+ECH29,ECHO - DOBUTAMINE STRESS ECHO
+10335R,HLA - SCREENING C1Q
+RXCUI6918,Metoprolol
+LABPCEG7O,"ISTAT EG7, OTHER"
+ISO11,CONTACT AND DROPLET ISOLATION
+"RXCUI3498,7597",Diphenhydramine-Nystatin
+RXCUI596205,Ramelteon
+ISO8,CONTACT ISOLATION
+RXCUI35827,Ketorolac
+LABUSEOSTS,USE ORDER SET FOR LAB TRANSFUSION
+IMGNPVQ,NUC LUNG V/Q
+RXCUI2598,Clonazepam
+LABPCEC4V,"ISTAT EC4, VENOUS"
+RXCUI11289,Warfarin
+IMGDXKN2LT,XR KNEE 1 OR 2V LT
+IMGCRROF,XR CR READING OUTSIDE FILM
+CON305,CONSULT TO ANESTHESIOLOGY
+MED208092,Amylases-Endopeptidases-Lipase
+RXCUI5492,Hydrocortisone
+LABARVI,BLOOD TYPE VERIFICATION
+POC23,"POC ISTAT, VENOUS BLOOD GASES"
+RXCUI2626,Clozapine
+MED14859,Glucose-Potassium Chloride-Sodium Chloride
+LAB11124R,COCCI CF
+RXCUI136411,Sildenafil
+LABFOL,FOLIC ACID
+REF93,REFERRAL TO OTHER
+LABALKP,ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA
+CR130,CPM
+RXCUI10402,Tetrahydrocannabinol
+USEOS,**USE ORDER SET**
+LABFT4,"T4, FREE"
+IMGDXCH1DLT,"XR CHEST 1 VIEW DECUB, LEFT"
+LABHMCY,"HOMOCYSTEINE TOTAL, PLASMA"
+IMGMRCP,MR CHOLANGIOGRAM
+REF90,REFERRAL TO PSYCHIATRY
+LABPCCG4O,"ISTAT CG4, OTHER"
+RXCUI8745,Promethazine
+RXCUI6135,Ketoconazole
+RXCUI89013,Aripiprazole
+IMGDXPEL2,XR PELVIS 2V
+DIET132,DIET CARB CONTROL/LOW SODIUM/LOW CHOL/LOW SAT FAT
+"RXCUI1292,2599,6813,9509,9945",Baclofen-Clonidine-Methadone-Saccharin-Sorbitol
+LABCDPLI,COLD AGGLUTININ SCREEN
+LABADVQT,ADENOVIRUS DNA PCR QUANT
+LABCTC,CATHETER TIP CULTURE
+RXCUI596,Alprazolam
+IMGCTNSW,CT NECK W CONTRAST
+LABCSFGL,"GLUCOSE, CSF"
+CON350,CONSULT TO PSYCHIATRY
+MED12729,Hydrochlorothiazide-Triamterene
+RXCUI161,Acetaminophen
+LABB12,VITAMIN B12
+COD7,DNR/C
+RXCUI5521,Hydroxychloroquine
+LABYUMETF,"METANEPHRINES FRACTIONATED, 24H URINE"
+RXCUI83818,Irbesartan
+LABFTRIGA,"TRIGLYCERIDES, NON-PLEURAL"
+RT113,RESP - INDIRECT CALORIMETRY
+RXCUI11170,Verapamil
+OT4,OT SWALLOW EVALUATE AND TREAT
+LABNA,"SODIUM, SERUM / PLASMA"
+LAB11125R,COCCI ID
+REF31,REFERRAL TO ENDOCRINOLOGY
+RXCUI296,Adenosine
+RXCUI5657,Ifosfamide
+LABHBPQ,HEPATITIS B PCR QUANTITATIVE
+LABNTBNP,NT - PROBNP
+NEUR22,TRANSCRANIAL DOPPLER COMPLETE
+MED8132,Hydrochlorothiazide-Triamterene
+IMGCTLMP,CT TRIPHASIC LIVER MASS PROTOCOL
+POC22,"POC ISTAT, ARTERIAL BLOOD GASES"
+LABANTIM,ANTI - TPO AB
+RXCUI10689,Tramadol
+RT97,RESP - WEAN VENTILATOR
+RXCUI10572,Thyroid
+CON365,NSG REFERRAL TO SPIRITUAL CARE
+MED540574,"Influenza Virus Vaccine, Inactivated A-California-07-2009 X-179A"
+VSC03,VSC ARTERIAL EXAM/ABI\'S
+IMGCTHROF,CT HEAD OUTSIDE STUDY FOR REFERENCE ONLY
+LABARI,ABO/RH
+RXCUI898492,Collagenase Clostridium Histolyticum
+RXCUI9601,Scopolamine
+IMGIRGASTPMT,IR GASTROSTOMY CATHETER PLCMT
+IMGDXSP1,XR SPINE 1V
+LABFLGL,"GLUCOSE, FLUID"
+IMGIRTRANSJUG,IR TRANSJUGULAR LIVER BIOPSY
+2109358,LUMBAR PUNCTURE
+LABDVEVH,HSV/VZ DFA
+IMGIRDIALRT,IR DIALYSIS CATH DUAL LUMEN RT
+RXCUI2555,Cisplatin
+MED89401,Fluticasone-Salmeterol
+RXCUI52356,Magnesium Citrate
+MED123767,Emtricitabine-Tenofovir Disoproxil
+CON427,CONSULT TO THORACIC SURGERY
+PRE14,HIP PRECAUTIONS
+RXCUI32675,Oxybutynin
+RXCUI593411,Sitagliptin
+LABDVERVP,RESPIRATORY DFA PANEL
+REF32,REFERRAL TO GASTROENTEROLOGY
+LABYFFATQ,"FAT, FECAL QUANTITATIVE"
+RXCUI3008,Cyclosporine
+LABPHEN,PHENYTOIN LEVEL
+LABLPD,LIPID PANEL
+LABMYCPA,MYCOPHENOLIC ACID
+LABFHV12,"HSV 1 AND 2 PCR, NON CSF"
+IMGUSABROF,US ABDOMEN OUTSIDE STUDY FOR REFERENCE ONLY
+"RXCUI4850,6585,8591,9863",Glucose-Magnesium Sulfate-Potassium Chloride-Sodium Chloride
+IMGNGHIDA,NUC HIDA SCAN
+MED204468,Neisseria Meningitidis Serogroup A Capsular Polysaccharide Diphtheria Toxoid Protein Conjugate Vaccine-Neisseria Meningitidis Serogroup C Capsular Polysaccharide Diphtheria Toxoid Protein Conjugate Vaccine-Neisseria Meningitidis Serogroup W-135 Capsular Polysaccharide Diphtheria Toxoid Protein Conjugate Vaccine-Neisseria Meningitidis Serogroup Y Capsular Polysaccharide Diphtheria Toxoid Protein Conjugate Vaccine
+LABRBCFOL,"FOLATE, RBC"
+RXCUI6851,Methotrexate
+POC17,POC ISTAT CREATININE
+PATH8,CENTRAL NERVOUS SYSTEM SOURCE
+CON364,NSG REFERRAL TO SOCIAL WORK
+POC1,POC URINE DIPSTICK
+LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL
+DIET102,DIET NPO
+LABSTOBGD,"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"
+LABBMT1,BMT PANEL 1
+LEG1,LEGAL STATUS: 14 DAY HOLD
+RXCUI306266,Entecavir
+RXCUI18603,Azelastine
+LABPB,PHENOBARBITAL LEVEL
+MED230510,FLU VAC TS 2013-14
+LABTNI,TROPONIN I
+IMGMRHROF,MR HEAD OUTSIDE STUDY FOR REFERENCE ONLY
+RXCUI42316,Tacrolimus
+CON432,CONSULT TO GYNECOLOGY
+MED89444,Glucose-Piperacillin-Tazobactam
+RT130,RESP - REPOSITION ET TUBE
+"RXCUI236719,235496","Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic"
+"RXCUI6585,6628,8591,9863",Magnesium Sulfate-Mannitol-Potassium Chloride-Sodium Chloride
+CON373,CONSULT TO ENT
+LABESRP,SEDIMENTATION RATE
+LABSTSS,SYPHILIS TREPONEMAL SCREEN
+CON340,CONSULT TO OPHTHALMOLOGY
+MED211166,BICARB DIALYS SOLN #16 W/O CA K
+LABAFBD,"AFB CULTURE, NON - RESPIRATORY"
+RXCUI4815,Glyburide
+LABPROL,PROLACTIN
+LABCSFTP,"PROTEIN TOTAL, CSF"
+LABTYPSNI,TYPE AND SCREEN
+LABORANER,ORTHOPEDIC ANAEROBIC CULTURE
+RXCUI6922,Metronidazole
+RXCUI10814,Triiodothyronine
+PT20,PT - NO SKILLED THERAPY NEEDS
+RXCUI35619,Rifaximin
+LABABG,"BLOOD GASES, ARTERIAL"
+IMGDXTIBRT,XR TIBIA AND FIBULA RT
+LABSHIV12,RAPID HIV-1/2 ANTIBODY
+LABHHV6QT,HHV-6 PCR QUANT
+LABCMVM,CMV AB IGM
+IMGCTPE,CT PULMONARY EMBOLISM CHEST ONLY
+GIUEUS,UPPER ENDOSCOPIC ULTRASOUND
+LABDVEHV,HSV/VZV DFA
+IMGIRABSCESSO,IR ABSCESSOGRAM
+LABVANPRL,VANCOMYCIN TROUGH LEVEL
+GICOLO,COLONOSCOPY
+LABUEP,"ELECTROLYTE PANEL, URINE WITH CREATININE"
+RXCUI4821,Glipizide
+RT75,RESP - EXTUBATION
+LABHSVGM,HSV 1/2 IGM&TYPE-SPECIFIC IGG
+RXCUI10454,Thiamine
+"RXCUI55018,34322",Dibasic Potassium Phosphate-Potassium Phosphate
+LABUOSM,"OSMOLALITY, URINE"
+NUR821,TRANSFUSE RBC
+IMGDXHIP1RT,XR HIP 1V RT
+RXCUI52769,Milrinone
+LABGLOB,GLOBULIN
+CON420,CONSULT TO PAIN
+LABORSHOR,ORTHOPEDIC CULT MULT SPECIMENS
+MED16426,Potassium Chloride-Sodium Chloride
+RXCUI3264,Dexamethasone
+LABPATHR,REVIEW BY PATHOLOGIST
+RXCUI236083,Alendronic Acid
+IMGCT3AP,CT ANGIO ABDOMEN PELVIS
+LABPTTINH,PTT INHIBITOR SCREEN
+RXCUI36567,Simvastatin
+DIET109,DIET LOW FIBER
+IMGDXEL3RT,XR ELBOW 3V RT
+RXCUI88014,Rizatriptan
+ECH25,ECHO - LIMITED TRANSTHORACIC ECHO
+PRE7,ICH PRECAUTIONS
+LABCBCS,CBC WITH DIFF AND SLIDE REVIEW
+IMGUSVULOLT,US VEIN LOWER EXTREMITY LEFT R/O DVT
+LABPCG3O,"ISTAT G3+, OTHER"
+RXCUI99,6-Aminocaproic Acid
+RXCUI6809,Metformin
+RXCUI31448,Nabumetone
+LAB11234R,VZV TOTAL AND IGM AB PANEL
+POC6,POC PPD SKIN TEST
+IMGDXFT3LT,XR FOOT 3V LT
+RXCUI19831,Budesonide
+IMGCTSXO,CT STEREOTACTIC ON CALL
+RXCUI1908,Calcium Gluconate
+LABPROCT,PROCALCITONIN
+"RXCUI9863,11202",Sodium Chloride-Vincristine
+LAB11131R,VORICONAZOLE LEVEL
+LABCRP,C - REACTIVE PROTEIN
+IMGDXKN3LT,XR KNEE 3V LT
+DIET135,DIET POST SURGICAL - STEP TWO
+RXCUI9384,Rifampin
+LABHCTX,HEMATOCRIT
+DIET103,DIET LOW SODIUM
+LABC4,"COMPLEMENT C4, SERUM"
+RXCUI221147,Polyethylene Glycol 3350
+LABBBPPLT,PLATELET APHERESIS PRODUCT
+LABANER2,"ANAEROBIC CULTURE, SHORT BATTERY"
+LABVCULT,"VIRAL CULTURE, TISSUE"
+LABSPLAC,SEPSIS PROTOCOL LACTATE
+LABROMRS,MRSA SCREEN
+RXCUI4832,Glucagon
+RXCUI9881,Sodium Hypochlorite
+RXCUI7417,Nifedipine
+RXCUI4249,Factor Ix
+IMGMRSLWWO,MR L-SPINE WITH AND WITHOUT CONTRAST
+RXCUI274783,Insulin Glargine
+LABRESPCR,RESPIRATORY VIRUS PCR PANEL
+LABPTH,PARATHYROID HORMONE
+CON341,REQUEST FOR LANGUAGE SVS
+DIET117,DIET CALORIE CONTROLLED
+RT107,RESP - ABG PUNCTURE
+IMGCTTW,CT THORAX W CONTRAST
+DC1,DISCHARGE DIET
+RT81,RESP - PEAK EXPIRATORY FLOW
+RXCUI746741,Pramipexole
+LAB11824R,BUSULFAN
+LABHAVT,ANTI-HAV
+LABVPAL,VALPROIC ACID
+RXCUI703,Amiodarone
+IMGNINDWBCS,NUC INDIUM WHITE CELL SCAN
+LABAMY,"AMYLASE, SERUM"
+LABVZAB,VARICELLA ZOSTER VIRUS AB IGG
+RXCUI1901,Calcium Chloride
+LABCO2,"CO2, SERUM/PLASMA"
+RXCUI704,Amitriptyline
+LABURNA,"SODIUM, URINE"
+LABPHOS,"PHOSPHORUS, SERUM/PLASMA"
+RXCUI10438,Theophylline
+LAB11024R,EB VIRAL CAPSID AG IGM AB
+IMGMRSROF,"MR C, T, OR L-SPINE OUTSIDE STUDY FOR REFERENCE ONLY"
+LABFLUAPX,FLU A PCR REFLEX TO 2009 H1N1
+RXCUI40790,Pantoprazole
+10150R,HLA ORDER - SUMMARIES
+CON318,CONSULT TO VASCULAR
+LABBCRABL,"BCR - ABL, BLOOD"
+RXCUI8410,Alteplase
+RXCUI10156,Sucralfate
+RXCUI3002,Cyclophosphamide
+IMGDXFT3RT,XR FOOT 3V RT
+LABUTP,"PROTEIN, URINE"
+IMGIRCHESTRT,IR CHEST TUBE PCMT W FLUORO RT
+LABFTP,"PROTEIN TOTAL, FLUID"
+POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN"
+PRE1,ASSAULT PRECAUTIONS
+IMGDXPEL3,XR PELVIS 3V
+"RXCUI733,10167",Ampicillin-Sulbactam
+CON321,CONSULT TO ORTHOPEDICS
+LABYSTOSM,"OSMOLALITY, STOOL"
+REF41,REFERRAL TO RENAL/NEPHROLOGY
+ISO9,DROPLET ISOLATION
+RT997,RESP - LUNG PROTECTIVE VENTILATION PROTOCOL
+RXCUI72302,Ropinirole
+LABBLC,BLOOD CULTURE
+IMGDXFEMLT,XR FEMUR LT
+RXCUI4278,Famotidine
+RXCUI3628,Dopamine
+LABERURBC,EMERGENCY RELEASE RBC
+DIET115,DIET FAT RESTRICTED
+LABERUPLT,EMERGENCY RELEASE PLT
+RXCUI384455,Tigecycline
+IMGDXEL2RT,XR ELBOW 2V RT
+LABGCCR2,GC MOLECULAR DETECTION
+RXCUI2890,Cosyntropin
+LABMB,"CK, MB"
+LABFK506L,TACROLIMUS
+RXCUI40048,Carboplatin
+MED5940,Acetaminophen-Oxycodone
+RXCUI10600,Timolol
+CR132,CAST ROOM UNLISTED
+LABFLDH,"LDH TOTAL, FLUID"
+IMGUSPEP,US RENAL ARTERY
+IMGMRP,MR PELVIS
+LABNH3,"AMMONIA, PLASMA"
+RXCUI183379,Rivastigmine
+CON302,CONSULT TO SOCIAL WORK
+LABLIDOL,LIDOCAINE
+2109394,CHEST TUBE
+LABHELAG,"HELICOBACTER PYLORI AG, STOOL"
+RXCUI139825,Insulin Detemir
+LABC125,CANCER AG 125
+LABACTHO,ADRENOCORTICOTROPIC HORMONE
+LABPALB,PREALBUMIN
+LABPT,PROTHROMBIN TIME
+RXCUI5552,Hydroxyurea
+DIET137,DIET NPO AFTER MIDNIGHT FOR SURGERY
+CON363,NSG REFERRAL TO DIABETES CNS
+RXCUI9947,Sotalol
+RXCUI6754,Meperidine
+RT998,RN/RT PERFORM SAFETY SCREEN
+ECH16,ECHO - TRANSESOPHAGEAL ECHO
+LABCSMPBM,"CANCER SOMATIC MUTATION PNL, BM"
+LEG3,LEGAL STATUS: DENIAL OF PATIENT RIGHTS
+RXCUI25025,Finasteride
+RXCUI281,Acyclovir
+PATH10,SURGICAL GENERAL
+LABK,"POTASSIUM, SERUM/PLASMA"
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Zinc Chloride"
+DIET136,"DIET GRAFT VERSUS HOST DISEASE, LOW MICROBIAL"
+LABDIGL,"DIGOXIN, SERUM"
+MED15882,Glucose-Sodium Chloride
+RXCUI11248,Vitamin B 12
+RXCUI17767,Amlodipine
+RXCUI40254,Valproate
+MED14858,Glucose-Potassium Chloride-Sodium Chloride
+LABHCVA,HEPATITIS C AB IGG
+IMGIRIVCPMT,IR IVC FILTER PLACEMENT
+"RXCUI426,1091,4850,4919,5340,6033,6308,6536,6585,6837,89905,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308","Alanine-Arginine-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1"
+RXCUI8782,Propofol
+IMGMRST,MR T-SPINE
+RXCUI38404,Topiramate
+IMGCT3D,CT RECONSTRUCTION
+REF24,REFERRAL TO PULMONARY MEDICINE
+RXCUI19666,Nesiritide
+RT98,RESP - MIF AND VITAL CAPACITY
+RXCUI4955,Glycopyrrolate
+IMGDXSH1RT,XR SHOULDER 1V RT
+RXCUI6963,Midodrine
+RXCUI3827,Enalapril
+"RXCUI3264,4850,26225",Dexamethasone-Glucose-Ondansetron
+"RXCUI426,1091,1908,21032,21579,4278,4850,4919,5224,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Famotidine-Glucose-Glycine-Heparin-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+RXCUI2062,Carboxymethylcellulose
+PATH11,DERMATOPATHOLOGY
+RXCUI190376,Linezolid
+LABFLCR,FREE LIGHT CHAIN RATIO
+NEUR25,EPILEPSY MONITORING EPM-EEG
+RXCUI2105,Carmustine
+NUR818,TRANSFUSE CRYO
+IMGDXAB1,XR ABDOMEN 1V
+RXCUI26225,Ondansetron
+LABISOF,ISOLATOR FUNGUS
+IMGDXELROF,XR LOWER EXTREMITY OUTSIDE STUDY FOR REFERENCE ONLY
+RXCUI237159,Levalbuterol
+RXCUI3418,Dihydroergotamine
+LABSSBABY,ANTI-LA ANTIBODY
+REF33,REFERRAL TO HEPATOLOGY
+LABPCG3V,"ISTAT G3+, VENOUS"
+IMGDXKN3RT,XR KNEE 3V RT
+LABLPDC,LIPID PANEL WITH CALCULATED LDL
+LABYCOPPR,"COPPER, SERUM"
+MED36063,Potassium Chloride-Sodium Chloride
+RT105,RESP - TRACHEOSTOMY WEAN/DECANNULATION PROTOCOL
+RXCUI39998,Zonisamide
+IMGDXFT2LT,XR FOOT 2V LT
+RXCUI298869,Eplerenone
+LABANCA,NEUTROPHIL CYTOPLASMA REFLEX AB PANEL
+MED93483,Hydrochlorothiazide-Losartan
+RXCUI42386,"Immunoglobulins, Intravenous"
+IMGDXAN3LT,XR ANKLE 3V LT
+RXCUI119565,Tolterodine
+REF44,REFERRAL TO PAIN CLINIC
+DIET122,DIET NUTRITIONAL SUPPLEMENTS
+RXCUI1347,Beclomethasone
+RXCUI7676,Opium
+RXCUI35780,Ropivacaine
+DIET105,DIET LIVER
+RXCUI5640,Ibuprofen
+PFT54,SPIROMETRY WITH DLCO
+PRE3,ASPIRATION PRECAUTIONS
+LABTBPCRS,"M. TUBERCULOSIS PCR, SPECIMEN"
+RXCUI2396,Chlorothiazide
+RXCUI6058,Isosorbide Dinitrate
+RXCUI25789,Glimepiride
+RXCUI4316,Felodipine
+IMGCT3TAP,CT ANGIO CHEST ABDOMEN PELVIS
+SLP6,SLP PASSY MUIR VALVE EVALUATE AND TREAT
+RXCUI2193,Ceftriaxone
+LABEBPCRQ,EBV BY PCR QUANTITATIVE
+RXCUI49276,Doxazosin
+LABFCUL,FUNGAL CULTURE
+IMGCTPAN,CT PANCREAS
+LABCL,"CHLORIDE, SERUM/PLASMA"
+IMGUSIN,US INTRAOPERATIVE
+RXCUI2556,Citalopram
+IMGCTA,CT ABDOMEN
+LAB12631R,VORICONAZOLE LEVEL
+RXCUI26237,Granisetron
+IMGMRSF,MR SPINE FULL
+LABEB,EBV AB PANEL
+MED15955,Epinephrine-Lidocaine
+LABHV6DNA,HERPES VIRAL 6 DNA QUALITATIVE
+IMGDXHUMRT,XR HUMERUS 2V RT
+LABTSHFT4,TSH W/ REFLEX FT4
+LABUSPG,"SPECIFIC GRAVITY, URINE"
+LABTRFS,TRANSFERRIN SATURATION
+GIVCE,VIDEO CAPSULE ENDOSCOPY
+LABSTPOLY,"WBC FOR IBD, STOOL"
+RXCUI21949,Cyclobenzaprine
+RXCUI7052,Morphine
+IMGDXHA2RT,XR HAND 2V RT
+LABCBCO,CBC
+MED200408,Budesonide-Formoterol
+MED540702,BLUE FOOD COLORING-FD&C BLUE1
+LABADVDNA,"ADENOVIRUS DNA, QUALITATIVE PCR"
+LABTES,TESTOSTERONE
+LABURK,"POTASSIUM, URINE"
+RXCUI49737,Esmolol
+RXCUI9796,Simethicone
+CON403,CONSULT TO DIABETES TEAM
+LABCSFLD,"LDH, CSF"
+RXCUI134615,Brimonidine
+IMGFLERCPS,"XR ERCP W SPHICTEROTOMY, BILIARY AND PANCREAS"
+RXCUI4450,Fluconazole
+EKG5,ECG 12-LEAD
+LABUEOS,"EOSINOPHILS, URINE"
+RXCUI275891,Valganciclovir
+LAB11041R,FREE HEMOGLOBIN
+SLP7,SLP ONGOING TREATMENT
+RXCUI187832,Pregabalin
+LABURTP,"PROTEIN, URINE"
+NEUR6,EMG AND NERVE CONDUCT STUDIES
+RXCUI48937,Dexmedetomidine
+RXCUI17128,Lansoprazole
+RXCUI39954,Zinc Sulfate
+RXCUI6472,Lovastatin
+CON22,CONSULT TO SPIRITUAL CARE
+RXCUI10355,Temazepam
+LABGASBLD,OCCULT BLOOD GASTRIC
+CON401,CONSULT TO ARRHYTHMIA RN
+LABTBIL,"BILIRUBIN TOTAL, SERUM/PLASMA"
+LABHIVQ2,HIV QUANTITATIVE PCR
+RXCUI42463,Pravastatin
+RXCUI71535,Vecuronium
+LABSTLCX,STOOL CULTURE
+LABFLDB,FLUID CULTURE / BB GRAM STAIN
+LABMETB,"METABOLIC PANEL, BASIC"
+LABCR,"CREATININE, SERUM/PLASMA"
+MED855,Bacitracin-Polymyxin B
+RXCUI20352,Carvedilol
+LABVP3,COMPREHENSIVE HEPATITIS PANEL
+LABFE,"IRON, TOTAL"
+RXCUI4910,Glycerin
+LABHIAB,HEPARIN INDUCED AB
+RXCUI61381,Olanzapine
+LABHBCA,IGM ANTI-HBC
+CON379,CONSULT TO HEPATOLOGY
+MED78668,Albuterol-Ipratropium
+IMGFLGI,XR UPPER GI
+LABCRYO,CRYOPROTEIN SEMI - QUANTITATIVE
+RXCUI37798,Terazosin
+"RXCUI426,1091,1908,4850,4919,5340,6033,6308,6536,6585,6837,89905,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308","Alanine-Arginine-Calcium Gluconate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1"
+PFT52,PULMONARY SIX MINUTE WALK STUDY
+CR107,SLING
+LABYA1AS,ALPHA - 1 - ANTITRYPSIN
+DIET150,DIET FULL LIQUID
+IMGCTH,CT HEAD
+RXCUI10154,Succinylcholine
+IMG2OA,IR ANGIOGRAM OTHER
+LABPCEC4O,"ISTAT EC4, OTHER"
+IMGMRSTL,MR T-SPINE AND L-SPINE
+CON333,CONSULT TO NEUROLOGY
+IMGFLLP,XR LUMBAR PUNCTURE
+2109388,LIMITED ULTRASOUND
+LABGLF,GLUCOSE FASTING
+RXCUI6932,Miconazole
+MED540549,"Influenza Virus Vaccine, Inactivated A-California-07-2009 X-179A"
+IMG11AR1,CAR CATH HEART RT 1
+IMGCATH5,CAR CORONARY INTERVENTION
+IMGCTNS,CT NECK
+LAB11075R,SMOOTH MUSCLE AB
+PFT59,SPIROMETRY
+RXCUI519,Allopurinol
+GIEERCP,ERCP PROCEDURE
+RXCUI86009,Insulin Lispro
+LABMETC,"METABOLIC PANEL, COMPREHENSIVE"
+MED77989,Aspirin-Dipyridamole
+POC5,POC STOOL - OCCULT BLOOD EXAM
+POC15,"POC ISTAT, ARTERIAL BLOOD GASES AND LACTATE"
+LABCSFCT,"CELL COUNT AND DIFF, CSF"
+RXCUI89905,Multivitamin Preparation
+RXCUI52358,Magnesium Gluconate
+MED116753,Multivitamin Preparation-Zinc Gluconate
+RXCUI4256,Factor Viia
+RXCUI3638,Doxepin
+RXCUI6387,Lidocaine
+LABVCUL,VIRAL CULTURE
+LABGRAM,GRAM STAIN
+RXCUI300195,Tenofovir Disoproxil
+LABUAS,URINALYSIS MACROSCOPIC ONLY
+RXCUI140108,Caspofungin
+RXCUI4083,Estradiol
+LABTSH,TSH
+LABACETA,"ACETAMINOPHEN, SERUM"
+RXCUI1202,Atenolol
+LABPCEG7,"ISTAT EG7, ARTERIAL"
+CON310,CONSULT TO MEDICINE
+RXCUI6205,Lactobacillus Acidophilus
+LABDIC,DIC SCREEN
+RXCUI6719,Memantine
+CR103,ABDUCTION WEDGE
+LABPCCG4,"ISTAT CG4, ARTERIAL"
+LABVBG,"BLOOD GASES, VENOUS"
+LABWDCG,WOUND CULTURE AND GRAM STAIN
+RT101,RESP - FLUTTER/PEP THERAPY
+LABYCERU,CERULOPLASMIN
+LABFACT8,FACTOR VIII ASSAY
+IMGMRSFWWO,MR SPINE FULL WITH AND WITHOUT CONTRAST
+MED15861,Glucose-Sodium Chloride
+LABCMVIGT,CMV IGG
+DIET101,DIET CLEAR LIQUID
+LABREF1,LAB UNLISTED 1
+RXCUI7226,Nadolol
+RXCUI135447,Donepezil
+RXCUI42347,Bupropion
+RXCUI8704,Prochlorperazine
+RXCUI6901,Methylphenidate
+LABTRXNWI,TRANSFUSION REACTION WORKUP
+MED200273,Polyethylene Glycol 3350-Potassium Chloride-Sodium Bicarbonate-Sodium Chloride-Sodium Sulfate
+RXCUI321064,Olmesartan
+IMGDXFEMRT,XR FEMUR RT
+MED208091,Amylases-Endopeptidases-Lipase
+RXCUI9524,Sulfasalazine
+MED540507,INFLUENZA 2009-10 VAC
+MED225590,Albuterol-Ipratropium
+MED7555,Sulfamethoxazole-Trimethoprim
+2109371,INTUBATION
+LABBUN,"UREA NITROGEN, SERUM/PLASMA"
+LABUALB,"ALBUMIN WITH CREATININE, URINE"
+RXCUI228790,Dutasteride
+REF56,REFERRAL TO STANFORD INTERNAL MEDICINE
+RXCUI2101,Carisoprodol
+LABPHME,PH BY METER
+IMGCTTAP,CT CHEST ABDOMEN PELVIS
+REF48,REFERRAL TO UROLOGY CLINIC
+RXCUI6470,Lorazepam
+MED80259,Diatrizoate-Sodium
+POC25,POC ISTAT INR AND PROTIME
+RXCUI18993,Benzonatate
+DIET114,DIET VEGETARIAN
+LABROMSP,MICROSPORIDIUM SCREEN
+RXCUI5531,Hetastarch
+REF34,REFERRAL TO IMMUNOLOGY/RHEUMATOLOGY
+LABHBSAG,HBSAG
+RXCUI72236,Fosphenytoin
+CON103,CONSULT TO IR - BIOPSY
+NUR820,TRANSFUSE PLT
+LABCRCL,"CREATININE CLEARANCE, URINE"
+RT73,RESP - NON INVASIVE POS. PRESS VENT
+CON301,CONSULT TO CASE MANAGEMENT
+LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"
+IMGCTAROF,CT ABDOMEN OUTSIDE STUDY FOR REFERENCE ONLY
+REF18,REFERRAL TO HEMATOLOGY
+LABBHCG,BETA HCG
+RXCUI28439,Lamotrigine
+MED203465,Potassium Phosphate-Sodium Phosphate
+LABSYNCT,"CELL COUNT AND DIFF, SYNOVIAL FLUID"
+IMGDXAB2,ABDOMEN WITH DECUB AND/OR UPRIGHT
+LAB11071R,MITOCHONDRIAL AB
+LABEVPCR,ENTEROVIRUS BY PCR
+RXCUI32624,Oxcarbazepine
+LABURNC,URINE CULTURE
+CON357,CONSULT TO TRAUMA-ACUTE CARE SURGERY
+OT3,OT SPLINT ONGOING TREATMENT
+LABDVERO,ROTAVIRUS EIA
+RXCUI11473,Pamidronate
+LABHGBP,"HEMOGLOBIN, PLASMA"
+LABMGN,"MAGNESIUM, SERUM/PLASMA"
+LABDOA,"DRUGS OF ABUSE SCREEN, URINE"
+IMGUSROF,US READING OUTSIDE FILM
+LABASI,AB SCREEN
+MED34505,Acetaminophen-Hydrocodone
+LABCRYST,"CRYSTAL ANALYSIS, SYNOVIAL FLUID"
+LABURUN,"UREA, URINE"
+IMGCTTA,CT CHEST ABDOMEN
+SLP20,SLP - NO SKILLED THERAPY NEEDS
+RXCUI3322,Diazepam
+MED15954,Epinephrine-Lidocaine
+LABYERYTH,ERYTHROPOIETIN
+RXCUI6628,Mannitol
+"RXCUI4850,8591,56443,9863",Glucose-Potassium Chloride-Sodium Acetate-Sodium Chloride
+PRE6,HEMORRHAGE PRECAUTIONS
+RXCUI11124,Vancomycin
+LABDDIM,D - DIMER
+IMGDXCLAVLT,XR CLAVICLE LT
+IMGDXSPC2,"XR SPINE, CERVICAL 2V"
+IMGDXCHLP,CHEST 1 VIEW FOR LINE PLACEMENT
+MED77973,Fluticasone-Salmeterol
+RXCUI43611,Latanoprost
+POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE"
+RXCUI114979,Rabeprazole
+MED540141,GI COCKTAIL
+LABCK,"CREATINE KINASE, TOTAL"
+RXCUI9997,Spironolactone
+RXCUI37418,Sumatriptan
+RXCUI260101,Oseltamivir
+RXCUI140587,Celecoxib
+OT20,OT - NO SKILLED THERAPY NEEDS
+IMGCATH6,"CAR DIAGNOSTIC, CARDIAC CATH"
+IMGMRAB,MR ABDOMEN
+RXCUI3616,Dobutamine
+LABBBPCRY,CRYOPRECIPITATE
+LABHAP,HAPTOGLOBIN
+LABHCLOTI,HOLD CLOT
+CON106,CONSULT TO IR - TUBE DRAIN
+LAB11020R,EB VIRUS EARLY AG
+2109357,CENTRAL LINE
+LABSIROL,SIROLIMUS LEVEL
+RXCUI73137,Tirofiban
+RXCUI27334,Hypromellose
+LABROPK,ISOSPORA/CYCLOSPORA SCREEN
+MED28384,Acetaminophen-Hydrocodone
+RXCUI20489,Cefpodoxime
+IMGDXKN2RT,XR KNEE 1 OR 2V RT
+RXCUI29046,Lisinopril
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8591,8737,9671,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Potassium Chloride-Proline-Serine-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+NUR6147,PICC REMOVAL
+IMGDXTIBLT,XR TIBIA AND FIBULA LT
+LABSLHELD,SLIDE HELD FOR REVIEW
+RXCUI8754,Propafenone
+"RXCUI3639,4179,9863,11202",Doxorubicin-Etoposide-Sodium Chloride-Vincristine
+IMGDXHIP2PLT,HIP 2 VIEWS LEFT AND PELVIS
+IMGUSCAR,US CAROTID ARTERY BILAT
+RXCUI82003,Docusate
+IMGUSABDD,US ABDOMEN W DUPLEX
+MED114467,Buprenorphine-Naloxone
+LABGIARS,GIARDIA SCREEN
+IMGCTSC,CT SPINE CERVICAL
+LABUA,URINALYSIS WITH MICROSCOPIC
+DIET113,DIET HIGH CALORIE HIGH PROTEIN
+IMGUSVUUPLT,US VEIN UPPER EXTREMITY LEFT R/O DVT
+RXCUI6916,Metolazone
+LABTCDIF,"C DIFFICILE TOXIN B, STOOL"
+RXCUI2191,Ceftazidime
+IMGUSTXL,US LIVER TRANSPLANT
+RXCUI3423,Hydromorphone
+MED126066,Calcium Carbonate-Cholecalciferol
+RXCUI1894,Calcitriol
+LABCYTOG,CYTOGENETICS REPORT
+MED90841,Camphor-Menthol
+IMGCTP,CT PELVIS
+MED95140,Pneumococcal Capsular Polysaccharide Type 1 Vaccine-Pneumococcal Capsular Polysaccharide Type 10A Vaccine-Pneumococcal Capsular Polysaccharide Type 11A Vaccine-Pneumococcal Capsular Polysaccharide Type 12F Vaccine-Pneumococcal Capsular Polysaccharide Type 14 Vaccine-Pneumococcal Capsular Polysaccharide Type 15B Vaccine-Pneumococcal Capsular Polysaccharide Type 17F Vaccine-Pneumococcal Capsular Polysaccharide Type 18C Vaccine-Pneumococcal Capsular Polysaccharide Type 19A Vaccine-Pneumococcal Capsular Polysaccharide Type 19F Vaccine-Pneumococcal Capsular Polysaccharide Type 2 Vaccine-Pneumococcal Capsular Polysaccharide Type 20 Vaccine-Pneumococcal Capsular Polysaccharide Type 22F Vaccine-Pneumococcal Capsular Polysaccharide Type 23F Vaccine-Pneumococcal Capsular Polysaccharide Type 3 Vaccine-Pneumococcal Capsular Polysaccharide Type 33F Vaccine-Pneumococcal Capsular Polysaccharide Type 4 Vaccine-Pneumococcal Capsular Polysaccharide Type 5 Vaccine-Pneumococcal Capsular Polysaccharide Type 6B Vaccine-Pneumococcal Capsular Polysaccharide Type 7F Vaccine-Pneumococcal Capsular Polysaccharide Type 8 Vaccine-Pneumococcal Capsular Polysaccharide Type 9N Vaccine-Pneumococcal Capsular Polysaccharide Type 9V Vaccine
+MED225803,INFLUENZA 2012-13 VAC
+NEUR2,EEG
+RT84,RESP - OXYGEN ADMINISTRATION
+IMGMRH,MR BRAIN
+LABHIVWBL,ANTI-HIV
+CRP221,ANGIOGRAM CEREBRAL W COILING
+IMGMRSTO,MR STEREOTACTIC ON-CALL
+RXCUI4099,"Estrogens, Conjugated"
+IMGCT3AR,CT ANGIO ABDOMEN RUNOFF
+LABHFP,HEPATIC FUNCTION PANEL A
+LABHAVM,IGM ANTI-HAV
+LABVZVIGG,VZ IGG
+MED82190,Benzocaine-Tetracaine-Butamben
+CR131,OVERHEAD BED FRAME & TRAPEZE
+LABCMVRO,CMV RULE OUT
+RXCUI20610,Cetirizine
+LABHBCT,ANTI-HBC
+LABUCR,"CREATININE, URINE"
+LABTEGCP,THROMBELASTOGRAPH CLOTTING PROFILE
+RXCUI301542,Rosuvastatin
+LABTOBPRL,TOBRAMYCIN TROUGH LEVEL
+IMGDXFORLT,XR FOREARM 2V LT
+IMGIRBILTBPLM,IR BILIARY TUBE PCMT DE NOVO
+RXCUI6960,Midazolam
+RXCUI3407,Digoxin
+RT90,RESP - SPONTANEOUS BREATHING TRIAL
+LABSCMV,CMV SHELL VIAL CULTURE
+LABAFP,"ALPHA FETOPROTEIN, SERUM"
+LABFCR,"CREATININE, FLUID"
+LABBLC2,BLOOD CULTURE
+LABBKVPCU,"BK VIRUS PCR, QUANT, URINE"
+MED9407,Carbidopa-Levodopa
+LEG2,LEGAL STATUS: 72 HR HOLD
+SLP50,SLP FEES SWALLOW EVALUATE AND TREAT
+RXCUI214824,Sevelamer
+LABPCG3,"ISTAT G3+, ARTERIAL"
+RES5,NON-VIOLENT RESTRAINTS
+LABAFBCF,"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"
+RXCUI7258,Naproxen
+RXCUI4457,Flumazenil
+LABYENPCR,ENTEROVIRUS PCR
+"RXCUI1815,4337,9863",Bupivacaine-Fentanyl-Sodium Chloride
+IMGDXWR2LT,XR WRIST 2V LT
+PRE10,SEIZURE PRECAUTIONS
+IMGUSHEP,US HEPATOMA SURVEY
+IMGUSAB1L,US ABDOMEN LTD
+RXCUI5470,Hydralazine
+LABBBPFFP,FRESH FROZEN PLASMA PRODUCT
+LABTT,THROMBIN TIME
+RXCUI20481,Cefepime
+RXCUI6054,Isoproterenol
+IMGUSKID,US KIDNEY SCAN
+LABCYCWBL,"CYCLOSPORINE, WHOLE BLOOD"
+RXCUI613391,Prasugrel
+"RXCUI10180,10829",Sulfamethoxazole-Trimethoprim
+RXCUI32968,Clopidogrel
+ECH30,ECHO - EXERCISE STRESS ECHO
+RXCUI734064,Desvenlafaxine
+LABUNAT,"SODIUM, URINE"
+RXCUI41126,Fluticasone
+RXCUI142438,Gentamicin Sulfate
+RT104,RESP - VENTILATOR SETTINGS
+CON315,CONSULT TO ENDOCRINOLOGY
+RXCUI283742,Esomeprazole
+RXCUI1998,Captopril
+IMGMRROF,MRI OSF READING
+LABPCYS,PNEUMOCYSTIS STAIN
+LABINSLF,INSULIN FASTING
+POC31,POC NITRAZINE PAPER FOR PH MEASUREMENT
+RXCUI4337,Fentanyl
+RXCUI5224,Heparin
+RXCUI283821,Rasburicase
+RXCUI283809,Travoprost
+CON319,CONSULT TO ICU
+MED33228,Amoxicillin-Clavulanate
+RXCUI22178,Cytomegalovirus Immune Globulin
+"RXCUI426,1091,1908,4850,4919,5340,6033,6308,6536,6585,6837,89905,8156,54987,8737,253182,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308","Alanine-Arginine-Calcium Gluconate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Regular Insulin, Human-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1"
+RXCUI4719,Gemfibrozil
+LABKOHP,KOH / CALCOFLUOR
+IMGDXHIP1PRT,HIP 1 VIEW RIGHT AND PELVIS
+LABDBIL,"BILIRUBIN DIRECT, SERUM/PLASMA"
+LABUCRT,"CREATININE, URINE"
+RXCUI6185,Labetalol
+IMGDXSPTL,"XR SPINE, THORACOLUMBAR, 2V AP LAT"
+LABHIVAB,HIV
+LABCHOL,"CHOLESTEROL TOTAL, SERUM/PLASMA"
+LAB11123R,"COCCI CF AB, CSF"
+LABHSVPCR,"HERPES SIMPLEX PCR, CSF"
+LABAST,"AST, SERUM/PLASMA"
+LABCLACT,"LACTIC ACID, CSF"
+RXCUI8948,Purified Protein Derivative Of Tuberculin
+LABSTOOLC,STOOL CULTURE
+RXCUI62427,Ursodeoxycholate
+"RXCUI4603,4850,8591,56443,9863",Furosemide-Glucose-Potassium Chloride-Sodium Acetate-Sodium Chloride
+LABDDIML,D - DIMER
+CON317,CONSULT TO NEUROSURGERY SERVICE
+RXCUI214688,Magnesium Lactate
+RXCUI32937,Paroxetine
+RXCUI2599,Clonidine
+RXCUI5553,Hydroxyzine
+RXCUI39993,Zolpidem
+RXCUI197,Acetylcysteine
+RXCUI8825,Protamine Sulfate
+SLP10,SLP SWALLOW EVALUATE AND TREAT
+MED204238,Acellular Pertussis Vaccine-Diphtheria Toxoid Vaccine-Tetanus Toxoid Vaccine
+146539,IMAGING PROCEDURE SCANNED REPORT
+RXCUI283810,Bimatoprost
+RXCUI2418,Cholecalciferol
+LABHBSQL,ANTI-HBS QUAL
+LABPCACTK,ISTAT ACTIVATED CLOTTING TIME
+LABHBSQT,HEP B SURF AB QUANT
+RXCUI11246,Vitamin A
+IMGUSPTV,US PELVIS NON OB W TRANSVAG
+2109368,ARTERIAL LINE
+DIET104,DIET LOW CHOLESTEROL LOW SATURATED FAT
+IMGFLDYSS,XR DYSPHAGIA WITH SPEECH PATHOLOGIST
+RXCUI7476,Nitroprusside
+LABFAL,"ALBUMIN, FLUID"
+CON351,CONSULT TO INFECTIOUS DISEASE
+LABCFRESP,CYSTIC FIBROSIS RESPIRATORY CULTURE
+RXCUI2683,Colchicine
+RXCUI52175,Losartan
+LABAHP2,ACUTE HEPATITIS PANEL
+RXCUI73645,Valacyclovir
+2109364,PARACENTESIS
+ECH36,ECHO - ADULT
+LABYCCPIG,ANTI CCP
+LABFIB,FIBRINOGEN
+RXCUI35208,Quinapril
+RXCUI253181,"Nph Insulin, Human"
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,8591,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Chloride-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+MED2516,Atropine-Diphenoxylate
+LABERUFFP,EMERGENCY RELEASE PLASMA
+IMGDXSH1LT,XR SHOULDER 1V LT
+OT1,OT EVALUATE AND TREAT
+RXCUI9344,Ribavirin
+MED78963,Egg Yolk Phospholipids-Glycerin-Safflower Oil-Soybean Oil
+IMGCTAP,CT ABDOMEN PELVIS
+RXCUI114200,Citrate
+RXCUI4603,Furosemide
+CON366,NSG REFERRAL TO FINANCIAL REP
+MED14862,Glucose-Potassium Chloride-Sodium Chloride
+LAB11242R,VZV QUANT
+RXCUI69120,Tiotropium
+RXCUI1292,Baclofen
+IMGCTHFC,"CT HEAD, FACIAL BONES & C SPINE"
+RXCUI261435,Sodium Ferric Gluconate Complex
+ECH34,ECHO - 3-DIMENSIONAL ECHO
+MED91791,Ascorbic Acid-Folic Acid-Vitamin B Complex
+LABADPCRQ,"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"
+CG05,"CHROMOSOME ANALYSIS, CANCER DIAGNOSIS"
+VSC08,VSC RENAL ULTRASOUND
+LABPTEG,"TEG, PERIOPERATIVE"
+LABUPREG,"PREGNANCY TEST, URINE"
+LABCMVIGG,CMV IGG
+PRE2,SUICIDE PRECAUTIONS
+CON413,CONSULT TO DERMATOLOGY
+RXCUI337623,Dornase Alfa
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8591,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Potassium Chloride-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+RXCUI6581,Magnesium Hydroxide
+LABFTRPL,"TRIGLYCERIDES, PLEURAL"
+RXCUI7531,Nortriptyline
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8737,9671,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+RXCUI8703,Fenofibrate
+HIMS10,X-RAY/ULTRASOUND REPORT
+LABAFBSO,AFB SMEAR ONLY
+RXCUI77492,Tamsulosin
+RXCUI733,Ampicillin
+RXCUI6703,Megestrol
+LABMMAS,"METHYLMALONIC ACID, SERUM"
+IMGDXCLAVRT,XR CLAVICLE RT
+LABDATBSI,DIRECT COOMBS BROAD SPECTRUM
+LABGGT,GAMMA - GLUTAMYL TRANSFERASE
+RXCUI8120,Phenazopyridine
+RXCUI31555,Nebivolol
+RT100,RESP - INTRAPULMONARY PERCUSSIVE VENTILATION
+RXCUI3361,Dicyclomine
+IMGFLBE,XR BARIUM ENEMA
+"RXCUI426,1091,1908,21032,21579,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+LABFADAG,"ADENOVIRUS AG, STOOL"
+LABBHB,BETA-HYDROXYBUTYRATE
+RXCUI358257,Tolvaptan
+IMGDXAN3RT,XR ANKLE 3V RT
+REF94,REFERRAL TO PHYSICAL THERAPY
+MED9788,Calcium Chloride-Glucose-Lactate-Potassium Chloride-Sodium Chloride
+RXCUI22299,Daptomycin
+RXCUI7145,Mycophenolic Acid
+IMGCTHPRW,CT HEAD PERFUSION W CONTRAST
+RXCUI3247,Desipramine
+LABFKOH,FUNGAL CULTURE AND KOH
+RXCUI1815,Bupivacaine
+LAB11136R,HTLV AB
+RXCUI2356,Chlordiazepoxide
+REF89,REFERRAL TO ANTICOAG CLINIC
+RXCUI7646,Omeprazole
+IMGMRHWWO,MR BRAIN WITH AND WITHOUT CONTRAST
+IMGUSAB1,US ABDOMEN
+IMGDXCHPNB,XR CHEST POST NEEDLE BIOPSY
+RXCUI52582,Mesalamine
+LABVCULR,"VIRAL CULTURE, RESPIRATORY"
+RXCUI2189,Cefoxitin
+RT99,RESP - TRACH HIGH FLOW CPAP
+LABEBVQP,"EBV PCR QUANT, PLASMA"
+RXCUI8787,Propranolol
+RXCUI282446,Posaconazole
+LABHGBX,HEMOGLOBIN
+LABTT4,"T4, TOTAL"
+IMGDXHIP2PRT,HIP 2 VIEWS RIGHT AND PELVIS
+LABNONGYN,NON-GYN CYTOLOGY EXAM
+MED206739,Acetaminophen-Hydrocodone
+IMGMRAH,MR ANGIO HEAD
+RXCUI7407,Nicotine
+RXCUI7233,Nafcillin
+LABA1C,HEMOGLOBIN A1C
+DIET112,DIET CARBOHYDRATE CONTROLLED
+LABSTLC,STOOL CULTURE
+"RXCUI426,1091,1908,21032,21579,4278,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8737,253182,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Famotidine-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Proline-Regular Insulin, Human-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+MED540103,HELP ORDER
+LABAT3,ANTITHROMBIN III
+IMGDXPEL,XR PELVIS 1V
+VSC02,VSC CAROTID VERTEBRAL ARTERY ULTRASOUND
+POC24,POC ISTAT TROPONIN I
+RXCUI325642,Ertapenem
+LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN
+IMGDXWR3RT,XR WRIST 3V RT
+IMGDXAB3,"XR ABDOMEN W AP, UPRIGHT, DECUB & DIAPHRAGM"
+LABHCPQ,HEPATITIS C RNA PCR QUANTITATIVE
+LABAFBC,"AFB CULTURE, RESPIRATORY"
+RXCUI75635,Eptifibatide
+LABYPTHRP,"PTH-RELATED PEPTIDE, PLASMA"
+IMGDXABROF,XR ABDOMEN OUTSIDE STUDY FOR REFERENCE ONLY
+RT111,RESP-NASAL CPAP FOR OBSTRUCTIVE SLEEP APNEA
+RXCUI7396,Nicardipine
+MED38285,Aluminum Hydroxide-Magnesium Hydroxide-Simethicone
+LABIGA,"IGA, SERUM"
+IMGDXHUMLT,XR HUMERUS 2V LT
+CON371,CONSULT TO ONCOLOGY
+IMGMRABWWO,MR ABDOMEN WITH AND WITHOUT CONTRAST
+CON372,CONSULT TO RADIATION ONCOLOGY
+LABYBOH,BETA-HYDROXYBUTYRATE
+LABQFTBG,QUANTIFERON TEST FOR LATENT TB
+IMGMRSL,MR L-SPINE
+LABBMCEBP,"DETECTION OF CEBPA, BONE MARROW"
+RXCUI10582,Thyroxine
+RT95,RESP - AIRWAY PRESSURE RELEASE VENTILATION
+LABEITC,TRANSFUSION EMERGENCY ISSUE
+CR100,CERVICAL COLLAR
+LABHELI,HELICOBACTER PYLORI AB
+LABTRFN,TRANSFERRIN
+LABRITUX,RITUXAN PANEL
+RXCUI6878,Methylene Blue
+LABWDCS,"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"
+LABUPIE,URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS
+LABFAMY,"AMYLASE, FLUID"
+CON100,CONSULT TO NUTRITION
+RXCUI2551,Ciprofloxacin
+IMGMRSTOW,MR STEREOTACTIC ON-CALL WITH CONTRAST
+LABCMV,CMV AB TOTAL
+LABPROTS,PROTEIN S ACTIVITY
+IMGDXSPL2,"XR SPINE, LUMBAR 2V"
+RXCUI84857,Anastrozole
+RXCUI1191,Aspirin
+GIEGD,UPPER GI ENDOSCOPY
+2109357A,CENTRAL LINE GENERAL SURGERY
+RXCUI29561,Meropenem
+RXCUI4452,Fludrocortisone
+IMGUSVULORT,US VEIN LOWER EXTREMITY RIGHT R/O DVT
+RXCUI283838,Darbepoetin Alfa
+CR101,CERVICAL COLLAR
+IMGCTLRT,CT LOWER EXTREMITY RT
+RXCUI8308,Vitamin K 1
+RXCUI7213,Ipratropium
+RXCUI8163,Phenylephrine
+10290R,HLA - TYPING
+LABIBIL,"BILIRUBIN INDIRECT, SERUM"
+MED4318,Calcium Chloride-Lactate-Potassium Chloride-Sodium Chloride
+LABHSVCSF,"HSV DNA PCR QUAL, CSF"
+LABMALAR,MALARIA PREP
+GIFSIG,FLEXIBLE SIGMOIDOSCOPY
+DIET106,DIET LOW MICROBIAL
+LABIGG,"IGG, SERUM"
+LABRFRPT,REFERENCE REPORT
+RXCUI33738,Pioglitazone
+LABLUPUS,LUPUS ANTICOAGULANT
+RXCUI2186,Cefotaxime
+"RXCUI426,1091,1908,4850,4919,5340,6033,6308,6536,6585,6837,8156,54987,8737,9671,56443,9863,236719,235496,10524,10898,10962,11115","Alanine-Arginine-Calcium Gluconate-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Methionine-Phenylalanine-Potassium Acetate-Proline-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine"
+CON377,CONSULT TO PHARMACY
+RXCUI7514,Norethindrone
+RXCUI8591,Potassium Chloride
+LABVOLTL,VOLATILE SCREEN
+LABALT,"ALT, SERUM/PLASMA"
+IMGUSNVLOW,US VEIN LOWER EXTREMITY BILAT R/O DVT
+MED92914,Fluticasone-Salmeterol
+LABHEPAR,"HEPARIN, UNFRACTIONATED, ANTI XA"
+RXCUI196102,Basiliximab
+LABLIL,LITHIUM
+RXCUI602811,Lactobacillus Rhamnosus Gg
+LABDHBP,HEPATITIS B DIAGNOSTIC PANEL
+RXCUI167,Acetazolamide
+RXCUI1151,Ascorbic Acid
+LABBFCT,"CELL COUNT AND DIFF, FLUID"
+RXCUI108118,Mometasone
+RXCUI35636,Risperidone
+RXCUI314504,Anti-Inhibitor Coagulant Complex
+MED14866,Glucose-Potassium Chloride-Sodium Chloride
+DIET110,DIET POST GASTRIC SURGERY
+DIET133,DIET POST SURGICAL
+RXCUI36676,Sodium Bicarbonate
+ECH15,ECHO - TRANSTHORACIC ECHO
+RXCUI4179,Etoposide
+LABGLNF,"GLUCOSE NON-FASTING, SERUM/PLASMA"
+LABYFT3,"T3, FREE"
+PATH16,"BONE MARROW BIOPSY, ASPIRATE"
+LABFVARZ,"VARICELLA ZOSTER VIR. DNA,QUAL"
+CON359,CONSULT TO NEPHROLOGY
+OT29,OT SPLINT EVALUATE AND TREAT
+2109357B,CENTRAL LINE SEPTEMBER
+LAB11068R,ACE
+RXCUI8183,Phenytoin
+MED198721,Folic Acid-Multivitamin Preparation-Coenzyme Q10
+REF29,REFERRAL TO CARDIOLOGY
+LABREPT,REPTILASE TIME
+LABUUNT,"UREA NITROGEN, URINE"
+NUR701,TRANSFUSE PLATELETS
+RES4,ADULT
+RXCUI8814,Epoprostenol
+RXCUI321208,Fondaparinux
+IMGCTPEP,CT PULMONARY EMBOLISM & LOWER EXTREMITY
+LABVANUNL,VANCOMYCIN SINGLE DAILY DOSE LEVEL
+RXCUI3992,Epinephrine
+RXCUI253182,"Regular Insulin, Human"
+LABPREG,"PREGNANCY TEST, SERUM"
+LABC3,"COMPLEMENT C3, SERUM"
+RXCUI5487,Hydrochlorothiazide
+RXCUI6676,Meclizine
+MED229690,SODIUM GLYCEROPHOSPHATE 1 MMOL/ML IV SOLN
+RXCUI623400,Lacosamide
+RXCUI51272,Quetiapine
+OT5,OT SWALLOW ONGOING TREATMENT
+RT109,RESP - NEBULIZER
+LABYFGL,"GLUCOSE, FLUID"
+COD1,DNR
+RXCUI2623,Clotrimazole
+IMGDXSPT,"XR SPINE, THORACIC 2V"
+RXCUI6926,Mexiletine
+RXCUI7426,Nimodipine
+LABCZPL,"CARBAMAZEPINE, SERUM"
+IMG4NA,ANGIOGRAM CEREBRAL
+RXCUI4053,Erythromycin
+LABLACWB,"LACTATE, WHOLE BLOOD"
+LAB11049R,NSE
+RXCUI6711,Melatonin
+IMGDXSH2RT,XR SHOULDER 2V RT
+LABACA,ANTI CARDIOLIPIN AB
+RXCUI121191,Rituximab
+LABBLCA,"AFB CULTURE, BLOOD"
+RXCUI56512,Sodium Polystyrene Sulfonate
+RXCUI10324,Tamoxifen
+RXCUI7804,Oxycodone
+CON376,CONSULT TO PLASTIC SURGERY
+RXCUI85762,Ritonavir
+RXCUI319864,Cisatracurium
+RXCUI6468,Loperamide
+LABWNVAB,"WEST NILE VIRUS AB PANEL, CSF"
+NUR819,TRANSFUSE FFP
+LABYUCDA,"CONFIRMED DRUG ABUSE SURVEY, URINE"
+CON400,CONSULT TO GASTROENTEROLOGY
+RXCUI10627,Tobramycin
+LABCEA,CEA
+IMGCTTAPROF,CT CHEST/ABDOMEN/PELVIS OUTSIDE STUDY FOR REFERENCE ONLY
+LABLEGDA,LEGIONELLA CULTURE
+LABTP,"PROTEIN TOTAL, SERUM/PLASMA"
+RT108,RESP - MDI
+RXCUI7512,Norepinephrine
+"RXCUI426,1091,1908,21032,21579,4278,4850,4919,5340,6033,6308,6536,6585,29261,6837,89905,8156,54987,8591,8737,253182,9671,56443,9863,236719,235496,10524,10898,10962,11115,8308,39937","Alanine-Arginine-Calcium Gluconate-Chromous Chloride-Copper Sulfate-Famotidine-Glucose-Glycine-Histidine-Isoleucine-Leucine-Lysine-Magnesium Sulfate-Manganese Chloride-Methionine-Multivitamin Preparation-Phenylalanine-Potassium Acetate-Potassium Chloride-Proline-Regular Insulin, Human-Serine-Sodium Acetate-Sodium Chloride-Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic-Threonine-Tryptophan-Tyrosine-Valine-Vitamin K 1-Zinc Chloride"
+IMGCTROF,CT READING OUTSIDE FILM
+DIET124,DIET TUBE FEEDING AND MEAL TRAY
+RXCUI4917,Nitroglycerin
+LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE"
+LABOSM,"OSMOLALITY, SERUM"
+RXCUI18867,Benazepril
+MED92811,Albuterol-Ipratropium
+RT82,RESP - UNLISTED RESPIRATORY CARE ORDER
+IMGCTT,CT THORAX
+RXCUI103,Mercaptopurine
+RXCUI38413,Torsemide
+MED540894,INFLUENZA 2013-14 VAC
+RXCUI3108,Dapsone
+RXCUI322167,Solifenacin
+LABYWNILE,"WEST NILE VIRUS AB, IGG AND IGM"
+LABYMPA,MYCOPHENOLIC ACID
+RXCUI107044,Rabbit Anti-Human T-Lymphocyte Globulin
+RXCUI6313,Leucovorin
+LABPCINR,ISTAT INR AND PROTIME
+PRE17,TOTAL HIP PRECAUTIONS
+CON303,CONSULT TO CARDIOLOGY
+IMGDXAN2RT,XR ANKLE 2V RT
+LABIGET,TOTAL IGE
+RXCUI2590,Clobetasol
+POC26,POC URINE PREGNANCY
+LAB11098R,1 25 OH VITAMIN D
+RXCUI68149,Mycophenolate Mofetil
+LABCRPLB,"CRYPTOCOCCAL AG LATEX, BLOOD"
+RXCUI4110,Ethambutol
+RXCUI3443,Diltiazem
+RXCUI1827,Buspirone
+RXCUI60207,Dorzolamide
+RXCUI2180,Cefazolin
+POC27,POC HEMOCCULT FOR FECAL BLOOD
+LABHIPA,HEPARIN INDUCED PLATELET AGGREGATION
+DIET108,DIET RENAL
+IMGDXHIP1PLT,HIP 1 VIEW LEFT AND PELVIS
+IMGMRHT,MR HEART MORPHOLOGY AND FUNCTION
+LABVSTLC,VIRAL STOOL CULTURE
+MED96559,Glucose-Piperacillin-Tazobactam
+IMGUSVUUPRT,US VEIN UPPER EXTREMITY RIGHT R/O DVT
+RXCUI5650,Idarubicin
+RXCUI6585,Magnesium Sulfate
+RXCUI8691,Primidone
+RXCUI1596,Bisacodyl
+IMGDXHIP1LT,XR HIP 1V LT
+LABCRPLC,"CRYPTOCOCCAL AG LATEX, CSF"
+LABFER,FERRITIN
+PATH20,NON-GYN CYTOLOGY
+IMGUSTIPS,US TIPS EVALUATION
+RXCUI33562,Foscarnet
+CON332,CONSULT TO RHEUMATOLOGY
+CON345,CONSULT TO HEMATOLOGY
+LABBKVPC,"BK VIRUS PCR, QUANT, PLASMA"
+LABIGP,"IG PANEL, SERUM"
+RXCUI114477,Levetiracetam
+LABSTFAT,"FAT STAIN, STOOL"
+RXCUI42954,Vitamin B6
+LABCBCD,CBC WITH DIFF
+LABT3,"T3, TOTAL"
+LABDSDNA,ANTI - DSDNA AB
+RXCUI4493,Fluoxetine
+RXCUI1037042,Dabigatran Etexilate
+LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"
+RXCUI4441,Flecainide
+RXCUI2709,Colistin
+MED14863,Glucose-Potassium Chloride-Sodium Chloride
+RXCUI56443,Sodium Acetate
+LABRETIC,RETICULOCYTE COUNT AUTOMATED
+RXCUI2002,Carbamazepine
+IMGCT3T,CT ANGIO CHEST
+LABCMVPQ,CMV PCR QUANTITATIVE
+PRE16,STERNAL PRECAUTIONS
+LAB11030R,EB VIRAL CAPSID AG IGG AB
+RXCUI139462,Moxifloxacin
+RXCUI67108,Enoxaparin
+RXCUI7597,Nystatin
+MED540744,"Mineral Oil-Petrolatum, White"
+IMGDXSH2LT,XR SHOULDER 2V LT
+RXCUI83367,Atorvastatin
+RT102,RESP - THERAPY VEST
+PT11,PT GAIT TRAINING
+RXCUI42351,Lithium Carbonate
+LABVP4,COMP HEPATITIS PANEL
+PRE5,FALL RISK PRECAUTIONS
+IMGCT3HN,CT ANGIO HEAD AND NECK
+LABCORTS,"CORTISOL, POST OP"
+IMGMRSTWWO,MR T-SPINE WITH AND WITHOUT CONTRAST
+LABRESP,RESPIRATORY CULTURE
+RXCUI10379,Testosterone
+CG08,"FISH ANALYSIS,CANCER DIAGNOSIS"
+LABPLTS,PLATELET COUNT
+LABANER,ANAEROBIC CULTURE
+LABSURG,SURGICAL PROCEDURE
+SLP2,SLP EVALUATE AND TREAT
+RXCUI68442,Filgrastim
+PT4,PT EVALUATE AND TREAT
+RXCUI8928,Psyllium
+NUR700,TRANSFUSE RED BLOOD CELLS
+RXCUI105694,Epoetin Alfa
+LABLAMOL,LAMOTRIGINE LEVEL
+MED540115,Calcium Chloride-Lactate-Potassium Chloride-Sodium Chloride
+RXCUI3041,Cytarabine
+LABUTPRAN,"PROTEIN WITH CREATININE, URINE"
+ISO7,RESPIRATORY ISOLATION
+NUR702,TRANSFUSE FRESH FROZEN PLASMA
+RXCUI6218,Lactulose
+RXCUI1808,Bumetanide
+MED90206,"Sodium Phosphate, Dibasic-Sodium Phosphate, Monobasic"
+LABCMVP,"CMV DNA PCR QUAL, NON-PLASMA"
+RXCUI25480,Gabapentin
+IMGNEPETLYMPH,PET CT LYMPHOMA
+LABCORT,"CORTISOL, SERUM"
+MED94959,Egg Yolk Phospholipids-Glycerin-Soybean Oil
+RXCUI36387,"Sennosides, Usp"
+RXCUI7393,Niacin
+LABBLCTIP,BLOOD CULT - 2ND SET VIA CATHETER
+RXCUI44,Mesna
+VSC05,VSC VENOUS ULTRASOUND
+MED14966,Acetaminophen-Hydrocodone
+IMGFLUNLIST,XR FLUORO UNLISTED EXAM
+RXCUI1114195,Rivaroxaban
+LABYHISTS,"HISTOPLASMA AG, SERUM"
+RXCUI3639,Doxorubicin
\ No newline at end of file
diff --git a/scripts/Archive/DeepLearningRecommender/rnn_data_wrapper.py b/scripts/Archive/DeepLearningRecommender/rnn_data_wrapper.py
new file mode 100644
index 00000000..b62b7eaa
--- /dev/null
+++ b/scripts/Archive/DeepLearningRecommender/rnn_data_wrapper.py
@@ -0,0 +1,44 @@
+import sys
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+import argparse
+import h5py
+
+sys.path.insert(0,'rnn_utils')
+import utils
+import load_and_format_data as lfd
+
+def main(argv):
+
+    parser = argparse.ArgumentParser(description='Command line parser for train_rnn.py ')
+    parser.add_argument('--raw_data_dir',type=str,default='/home/ec2-user/cs230/scripts/matrix/final_data_031618_v2/',
+                        help='path to the raw data from the healthrex database pull')
+    parser.add_argument('--nfiles',type=int,default=1,
+                        help='number of files to load, -1 if all')    
+    parser.add_argument('--clean_data_dir',type=str,default='/home/ec2-user/cs230/scripts/DeepLearning/RNN/data_final/',
+                        help='path to clean data output directory')
+    parser.add_argument('--skip_clean_raw_data', dest='skip_clean_raw_data', action='store_true',
+                        help='convert raw data to features/responses in one matrix')
+    parser.add_argument('--skip_generate_sets',dest='skip_generate_sets',action='store_true',
+                        help='generate train/dev/test sets')
+    parser.add_argument('--rnn_data_path',type=str,default='rnn_data_',
+                        help='name of clean rnn_data file')
+
+
+    args = parser.parse_args()
+    if args.skip_clean_raw_data is False: #Cleaning raw .txt data from database query
+        lfd.clean_raw_data(args.raw_data_dir,nfiles=args.nfiles,data_out_dir=args.clean_data_dir)
+
+    if args.skip_generate_sets is False: #Generate train/dev/test sets
+        lfd.make_train_dev_test_v2(args.clean_data_dir,in_prefix=args.rnn_data_path,nfiles=args.nfiles)
+
+
+    return
+
+
+
+if __name__ == '__main__':
+    main(sys.argv[1:])
+
+
diff --git a/scripts/Archive/DiabetesSecondLineTherapy/OptumSampleQueries.sql b/scripts/Archive/DiabetesSecondLineTherapy/OptumSampleQueries.sql
new file mode 100644
index 00000000..db119dc6
--- /dev/null
+++ b/scripts/Archive/DiabetesSecondLineTherapy/OptumSampleQueries.sql
@@ -0,0 +1,296 @@
+	
+	
+	
+	
+-- Look for example outpatient prescription fills of all the different types of diabetes medications
+-- https://www.mayoclinic.org/diseases-conditions/type-2-diabetes/diagnosis-treatment/drc-20351199
+SELECT 
+    AHFSCLSS, 
+		SUBSTR(AHFSCLSS, 0, 6) as AHFSCLSS_LEAD_CODE,
+		count(distinct PATID) as n_Patients, count(distinct CLMID) as n_Claims, count(distinct NPI) as n_NPIs
+FROM stanfordphs.optum_zip5:139:v3_0:sample.rx_pharmacy:9 
+WHERE
+   GNRC_NM like 'METFORM%'
+OR GNRC_NM like 'GLIPIZIDE%'
+OR GNRC_NM like 'GLYBURIDE%'
+OR GNRC_NM like 'GLIMEPIRIDE%'
+OR GNRC_NM like 'REPAGLINIDE%'
+OR GNRC_NM like 'NATEGLINIDE%'
+OR GNRC_NM like 'ROSIGLITAZONE%'
+OR GNRC_NM like 'PIOGLITAZONE%'
+OR GNRC_NM like 'SITAGLIPTIN%'
+OR GNRC_NM like 'SAXAGLIPTIN%'
+OR GNRC_NM like 'LINAGLIPTIN%'
+OR GNRC_NM like 'EXENATIDE%'
+OR GNRC_NM like 'LIRAGLUTIDE%'
+OR GNRC_NM like 'SEMAGLUTIDE%'
+OR GNRC_NM like 'CANAGLIFLOZIN%'
+OR GNRC_NM like 'DAPAGLIFLOZIN%'
+OR GNRC_NM like 'EMPAGLIFLOZIN'
+OR GNRC_NM like 'INSULIN%'
+GROUP BY
+   AHFSCLSS, AHFSCLSS_LEAD_CODE
+ORDER BY
+   AHFSCLSS_LEAD_CODE, n_Claims desc
+LIMIT 100
+	
+
+-- Look for all outpatient prescription fills from one of the primary diabetes medication categories
+-- Break out by year
+SELECT 
+	EXTRACT(YEAR from FILL_DT) as fill_year, 
+	SUBSTR(AHFSCLSS, 0, 6) as AHFSCLSS_LEAD_CODE,
+  AHFSCLSS, 
+	count(distinct PATID) as n_Patients, count(distinct CLMID) as n_Claims, count(distinct NPI) as n_NPIs
+FROM stanfordphs.optum_zip5:139:v3_0:sample.rx_pharmacy:9 
+WHERE
+   AHFSCLSS like '682004%' -- Biguanides (e.g., Metformin)
+OR AHFSCLSS like '682092%' -- Miscellaneous Diabetes Meds
+OR AHFSCLSS like '682008%' -- Insulins
+OR AHFSCLSS like '682020%' -- Sulfonylurea (e.g., glipizide)
+OR AHFSCLSS like '682005%' -- DPP-4 Inhibitors (e.g., sitagliptin)
+OR AHFSCLSS like '682028%' -- Thiazolidinediones (e.g., rosiglitazone)
+OR AHFSCLSS like '682006%' -- Incretin Mimetic / GLP-1 (e.g., exenatide)
+OR AHFSCLSS like '682018%' -- SGLT2 Inhibitor (e.g., empagliflozin)
+OR AHFSCLSS like '682016%' -- Meglitinides (e.g., repaglinide)
+
+GROUP BY
+   fill_year,
+	 AHFSCLSS_LEAD_CODE,
+	 AHFSCLSS
+ORDER BY
+   fill_year desc, 
+   n_Claims desc
+LIMIT 100
+
+
+
+
+-- Patient Demographics for those who ever took some Diabetes medications
+WITH 
+-- Find all prescriptions for diabetes related drugs
+dmRx AS
+(
+		SELECT 
+			PATID, CLMID, NPI,
+			SUBSTR(AHFSCLSS, 0, 6) as AHFSCLSS_LEAD_CODE,
+			AHFSCLSS, 
+			GNRC_NM,
+			FILL_DT
+		FROM stanfordphs.optum_dod:138:v3_0:sample.rx_pharmacy:6 AS t0
+		WHERE
+			 AHFSCLSS like '682004%' -- Biguanides (e.g., Metformin) -- This is sometimes used for non-diabetes (e.g., PCOS) so may want to exclude
+		OR AHFSCLSS like '682092%' -- Miscellaneous Diabetes Meds
+		OR AHFSCLSS like '682008%' -- Insulins
+		OR AHFSCLSS like '682020%' -- Sulfonylurea (e.g., glipizide)
+		OR AHFSCLSS like '682005%' -- DPP-4 Inhibitors (e.g., sitagliptin)
+		OR AHFSCLSS like '682028%' -- Thiazolidinediones (e.g., rosiglitazone)
+		OR AHFSCLSS like '682006%' -- Incretin Mimetic / GLP-1 (e.g., exenatide)
+		OR AHFSCLSS like '682018%' -- SGLT2 Inhibitor (e.g., empagliflozin)
+		OR AHFSCLSS like '682016%' -- Meglitinides (e.g., repaglinide)
+)
+
+-- Find demographic info on patients who received at least one diabetes drug prescription and their age at the first date it happened
+
+    SELECT
+			m.PATID,
+			m.GDR_CD,
+			m.RACE,
+			m.YRDOB,
+			MIN(dmRx.FILL_DT) AS FIRST_FILL_DT,
+			EXTRACT(YEAR FROM MIN(dmRx.FILL_DT)) - m.YRDOB AS AGE_AT_FIRST_FILL
+		FROM
+			stanfordphs.optum_dod:138:v3_0:sample.member_enrollment:3 AS m
+			INNER JOIN 
+		  dmRx USING (PATID)
+    GROUP BY
+			m.PATID,
+			m.GDR_CD,
+			m.RACE,
+			m.YRDOB
+
+
+-- Further subquery to group by decade of age, race, gender
+-- Round/floor patient age at first diabetes Rx fill to decade
+SELECT
+	CAST(FLOOR(AGE_AT_FIRST_FILL/10)*10 AS INT64) AS DECADE_AT_FIRST_FILL,
+	COUNT(DISTINCT PATID) AS nPatients
+FROM
+	jonc101.test_project_multi_user:1.diabetes_med_patients_output:19 AS dmPatient
+GROUP BY
+	DECADE_AT_FIRST_FILL
+ORDER By
+  DECADE_AT_FIRST_FILL 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+-- Construct number of patients who had a MACE sample diagnosis who received at least one diabetes med presricption before
+
+WITH 
+-- Find all prescriptions for diabetes related drugs
+dmRx AS
+(
+		SELECT 
+			PATID, CLMID, NPI,
+			SUBSTR(AHFSCLSS, 0, 6) as AHFSCLSS_LEAD_CODE,
+			AHFSCLSS, 
+			GNRC_NM,
+			FILL_DT
+		FROM stanfordphs.optum_dod:138:v3_0:sample.rx_pharmacy:6 AS t0
+		WHERE
+			 AHFSCLSS like '682004%' -- Biguanides (e.g., Metformin) -- This is sometimes used for non-diabetes (e.g., PCOS) so may want to exclude
+		OR AHFSCLSS like '682092%' -- Miscellaneous Diabetes Meds
+		OR AHFSCLSS like '682008%' -- Insulins
+		OR AHFSCLSS like '682020%' -- Sulfonylurea (e.g., glipizide)
+		OR AHFSCLSS like '682005%' -- DPP-4 Inhibitors (e.g., sitagliptin)
+		OR AHFSCLSS like '682028%' -- Thiazolidinediones (e.g., rosiglitazone)
+		OR AHFSCLSS like '682006%' -- Incretin Mimetic / GLP-1 (e.g., exenatide)
+		OR AHFSCLSS like '682018%' -- SGLT2 Inhibitor (e.g., empagliflozin)
+		OR AHFSCLSS like '682016%' -- Meglitinides (e.g., repaglinide)
+),
+maceDxSample AS
+(
+	-- Pull out diagnoses where matches one of the ICD10 codes for select Major Adverse Cardiovascular Events
+	-- Doesn't capture all of them however, as the full list appears to include other procedure codes and this is only for ICD10 
+	-- (whereas the database appears to have ICD9 codes prior to ~2015)
+	-- https://www.ahajournals.org/doi/pdf/10.1161/JAHA.119.014402
+	SELECT
+		PATID,
+		CLMID,
+		DIAG,
+		ICD_FLAG,
+		FST_DT,
+		EXTRACT(YEAR FROM FST_DT) as FST_YEAR
+	FROM
+		stanfordphs.optum_dod:138:v3_0:sample.medical_diagnosis:13
+	WHERE
+	  ICD_FLAG = 10 AND
+	  (
+		-- Myocardial Infarction
+		DIAG LIKE 'I21%' OR
+		DIAG LIKE 'I210%' OR
+		DIAG LIKE 'I2101%' OR
+		DIAG LIKE 'I2102%' OR
+		DIAG LIKE 'I2109%' OR
+		DIAG LIKE 'I211%' OR
+		DIAG LIKE 'I2111%' OR
+		DIAG LIKE 'I2119%' OR
+		DIAG LIKE 'I212%' OR
+		DIAG LIKE 'I2121%' OR
+		DIAG LIKE 'I2129%' OR
+		DIAG LIKE 'I213%' OR
+		DIAG LIKE 'I214%' OR
+		DIAG LIKE 'I22%' OR
+		DIAG LIKE 'I220%' OR
+		DIAG LIKE 'I221%' OR
+		DIAG LIKE 'I222%' OR
+		DIAG LIKE 'I228%' OR
+		DIAG LIKE 'I229%' OR
+		DIAG LIKE 'I23%' OR
+		DIAG LIKE 'I230%' OR
+		DIAG LIKE 'I231%' OR
+		DIAG LIKE 'I232%' OR
+		DIAG LIKE 'I233%' OR
+		DIAG LIKE 'I234%' OR
+		DIAG LIKE 'I235%' OR
+		DIAG LIKE 'I236%' OR
+		DIAG LIKE 'I237%' OR
+		DIAG LIKE 'I238%' OR
+		DIAG LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DIAG LIKE 'I63%' OR
+
+		-- Heart failure
+		DIAG LIKE 'I099%' OR
+		DIAG LIKE 'I110%' OR
+		DIAG LIKE 'I130%' OR
+		DIAG LIKE 'I132%' OR
+		DIAG LIKE 'I255%' OR
+		DIAG LIKE 'I420%' OR
+		DIAG LIKE 'I425%' OR
+		DIAG LIKE 'I426%' OR
+		DIAG LIKE 'I427%' OR
+		DIAG LIKE 'I428%' OR
+		DIAG LIKE 'I429%' OR
+		DIAG LIKE 'I43%' OR
+		DIAG LIKE 'I50%' OR
+		DIAG LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DIAG LIKE 'I200%' OR
+		DIAG LIKE 'I2109%' OR
+		DIAG LIKE 'I2111%' OR
+		DIAG LIKE 'I2119%' OR
+		DIAG LIKE 'I2129%' OR
+		DIAG LIKE 'I213%' OR
+		DIAG LIKE 'I214%' OR
+		DIAG LIKE 'I240%' 
+	  )
+),
+
+dmRxMACESamplePatients AS (
+-- Find demographic info on patients who received at least one diabetes drug prescription 
+-- and a cardiovascular event sample diagnosis anytime after
+-- Beware that diagnosis coding by ICD10 only starts ~2016, so this is not counting before then
+
+    SELECT
+			m.PATID,
+			m.GDR_CD,
+			m.RACE,
+			m.YRDOB,
+			MIN(dmRx.FILL_DT) AS FIRST_FILL_DT,
+			EXTRACT(YEAR FROM MIN(dmRx.FILL_DT)) - m.YRDOB AS AGE_AT_FIRST_FILL,
+			MIN(maceDxSample.FST_DT) AS FIRST_MACE_SAMPLE_DX_DT,
+			EXTRACT(YEAR FROM MIN(maceDxSample.FST_DT)) - m.YRDOB AS AGE_AT_FIRST_MACE_SAMPLE_DX,
+		FROM
+			stanfordphs.optum_dod:138:v3_0:sample.member_enrollment:3 AS m
+			  INNER JOIN 
+		  dmRx USING (PATID)
+				INNER JOIN
+			maceDxSample USING (PATID)
+    WHERE dmRx.FILL_DT < maceDxSample.FST_DT
+		GROUP BY
+			m.PATID,
+			m.GDR_CD,
+			m.RACE,
+			m.YRDOB
+)
+
+SELECT 
+	EXTRACT(YEAR FROM FIRST_MACE_SAMPLE_DX_DT) as FIRST_MACE_SAMPLE_DX_YEAR,
+	-- CAST(FLOOR(AGE_AT_FIRST_MACE_SAMPLE_DX/10)*10 AS INT64) AS DECADE_AT_FIRST_MACE_SAMPLE_DX,
+	COUNT(DISTINCT PATID) as nPatients,
+FROM dmRxMACESamplePatients
+GROUP BY 
+  FIRST_MACE_SAMPLE_DX_YEAR
+ORDER BY
+  FIRST_MACE_SAMPLE_DX_YEAR
+	
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/scripts/Archive/DiabetesSecondLineTherapy/STARRSampleQueries.sql b/scripts/Archive/DiabetesSecondLineTherapy/STARRSampleQueries.sql
new file mode 100644
index 00000000..ce46906d
--- /dev/null
+++ b/scripts/Archive/DiabetesSecondLineTherapy/STARRSampleQueries.sql
@@ -0,0 +1,232 @@
+-- Identify medication order classes based on specific diabetes examples
+
+select 
+  pharm_class_abbr, pharm_class_name, thera_class_abbr, thera_class_name,
+  count(distinct anon_id) as nPatients, count(distinct order_med_id_coded) nOrders
+from shc_core.order_med
+where
+   med_description like 'METFORM%'
+OR med_description like 'GLIPIZIDE%'
+OR med_description like 'GLYBURIDE%'
+OR med_description like 'GLIMEPIRIDE%'
+OR med_description like 'REPAGLINIDE%'
+OR med_description like 'NATEGLINIDE%'
+OR med_description like 'ROSIGLITAZONE%'
+OR med_description like 'PIOGLITAZONE%'
+OR med_description like 'SITAGLIPTIN%'
+OR med_description like 'SAXAGLIPTIN%'
+OR med_description like 'LINAGLIPTIN%'
+OR med_description like 'EXENATIDE%'
+OR med_description like 'LIRAGLUTIDE%'
+OR med_description like 'SEMAGLUTIDE%'
+OR med_description like 'CANAGLIFLOZIN%'
+OR med_description like 'DAPAGLIFLOZIN%'
+OR med_description like 'EMPAGLIFLOZIN'
+OR med_description like 'INSULIN%'
+GROUP BY
+  pharm_class_abbr, pharm_class_name, thera_class_abbr, thera_class_name
+ORDER BY 
+  nPatients desc
+
+
+
+
+
+
+
+-- Look for all outpatient prescription fills from one of the primary diabetes medication categories
+-- Broken up by year
+select 
+  EXTRACT(YEAR from order_inst) as orderYear,
+  pharm_class_abbr, pharm_class_name, thera_class_abbr, thera_class_name,
+  count(distinct anon_id) as nPatients, count(distinct order_med_id_coded) nOrders
+from shc_core.order_med
+where thera_class_abbr = 'ANT71' -- Anti-Hyperglycemics
+GROUP BY
+  orderYear,
+  pharm_class_abbr, pharm_class_name, thera_class_abbr, thera_class_name
+ORDER BY 
+  orderYear desc,
+  nPatients desc
+
+
+
+
+-- Patient Demographics for those who ever took some Diabetes medications
+-- Find demographic info on patients who received at least one diabetes drug prescription and their age at the first date it happened
+WITH
+dmRxPatients AS
+(
+	SELECT 
+	  d.anon_id, d.gender, d.canonical_race, d.canonical_ethnicity, 
+	  min(om.order_inst) as first_order_inst,
+	  EXTRACT(YEAR from min(om.order_inst)) - EXTRACT(YEAR from d.birth_date_jittered) as age_at_first_order
+
+	FROM `shc_core.demographic` as d
+	   INNER JOIN `shc_core.order_med` as om USING (anon_id)
+	WHERE
+	   om.thera_class_abbr = 'ANT71'
+	GROUP BY
+	  d.anon_id, d.gender, d.canonical_race, d.canonical_ethnicity, d.birth_date_jittered
+)
+
+-- By Gender
+select gender, count(distinct anon_id) as nPatients
+from dmRxPatients
+group by gender
+order by nPatients desc
+
+
+
+-- By Race/Ethnicity
+select canonical_race, canonical_ethnicity, count(distinct anon_id) as nPatients
+from dmRxPatients
+group by canonical_race, canonical_ethnicity
+order by nPatients desc
+
+
+
+-- By Age of First Prescription/Order
+select CAST(FLOOR(age_at_first_order/10)*10 AS INT64) AS decade_at_first_order, count(distinct anon_id) as nPatients
+from dmRxPatients
+group by decade_at_first_order
+order by decade_at_first_order desc
+
+
+
+
+
+
+
+
+
+
+
+
+
+WITH
+dmRx AS
+( -- Find all prescriptions for diabetes related drugs
+  select 
+    anon_id, order_med_id_coded,
+    order_inst, EXTRACT(YEAR from order_inst) as orderYear,
+    pharm_class_abbr, pharm_class_name, thera_class_abbr, thera_class_name
+  from shc_core.order_med
+  where thera_class_abbr = 'ANT71' -- Anti-Hyperglycemics
+),
+maceDxSample AS
+(
+  -- Pull out diagnoses where matches one of the ICD10 codes for select Major Adverse Cardiovascular Events
+  -- Doesn't capture all of them however, as the full list appears to include other procedure codes and this is only for ICD10 
+  -- (whereas the database appears to have ICD9 codes prior to ~2015)
+  -- https://www.ahajournals.org/doi/pdf/10.1161/JAHA.119.014402
+  SELECT anon_id, icd9, icd10, start_date, EXTRACT(YEAR FROM start_date) AS dx_year
+  FROM `shc_core.diagnosis_code` 
+  WHERE
+  (
+    -- Myocardial Infarction
+    icd10 LIKE 'I21%' OR
+    icd10 LIKE 'I21.0%' OR
+    icd10 LIKE 'I21.01%' OR
+    icd10 LIKE 'I21.02%' OR
+    icd10 LIKE 'I21.09%' OR
+    icd10 LIKE 'I21.1%' OR
+    icd10 LIKE 'I21.11%' OR
+    icd10 LIKE 'I21.19%' OR
+    icd10 LIKE 'I21.2%' OR
+    icd10 LIKE 'I21.21%' OR
+    icd10 LIKE 'I21.29%' OR
+    icd10 LIKE 'I21.3%' OR
+    icd10 LIKE 'I21.4%' OR
+    icd10 LIKE 'I22%' OR
+    icd10 LIKE 'I22.0%' OR
+    icd10 LIKE 'I22.1%' OR
+    icd10 LIKE 'I22.2%' OR
+    icd10 LIKE 'I22.8%' OR
+    icd10 LIKE 'I22.9%' OR
+    icd10 LIKE 'I23%' OR
+    icd10 LIKE 'I23.0%' OR
+    icd10 LIKE 'I23.1%' OR
+    icd10 LIKE 'I23.2%' OR
+    icd10 LIKE 'I23.3%' OR
+    icd10 LIKE 'I23.4%' OR
+    icd10 LIKE 'I23.5%' OR
+    icd10 LIKE 'I23.6%' OR
+    icd10 LIKE 'I23.7%' OR
+    icd10 LIKE 'I23.8%' OR
+    icd10 LIKE 'I25.2%' OR
+
+    -- Ischemic stroke 
+    icd10 LIKE 'I63%' OR
+
+    -- Heart failure
+    icd10 LIKE 'I09.9%' OR
+    icd10 LIKE 'I11.0%' OR
+    icd10 LIKE 'I13.0%' OR
+    icd10 LIKE 'I13.2%' OR
+    icd10 LIKE 'I25.5%' OR
+    icd10 LIKE 'I42.0%' OR
+    icd10 LIKE 'I42.5%' OR
+    icd10 LIKE 'I42.6%' OR
+    icd10 LIKE 'I42.7%' OR
+    icd10 LIKE 'I42.8%' OR
+    icd10 LIKE 'I42.9%' OR
+    icd10 LIKE 'I43%' OR
+    icd10 LIKE 'I50%' OR
+    icd10 LIKE 'P29.0%' OR
+
+    -- Acute coronary syndrome
+    icd10 LIKE 'I20.0%' OR
+    icd10 LIKE 'I21.09%' OR
+    icd10 LIKE 'I21.11%' OR
+    icd10 LIKE 'I21.19%' OR
+    icd10 LIKE 'I21.29%' OR
+    icd10 LIKE 'I21.3%' OR
+    icd10 LIKE 'I21.4%' OR
+    icd10 LIKE 'I24.0%' 
+  )
+),
+
+dmRxMACESamplePatients AS
+(
+  -- Find demographic info on patients who received at least one diabetes drug prescription 
+  -- and a cardiovascular event sample diagnosis anytime after
+
+  SELECT 
+    d.anon_id, d.gender, d.canonical_race, d.canonical_ethnicity, 
+    min(order_inst) as first_dmRx_datetime, EXTRACT(YEAR FROM min(order_inst)) - EXTRACT(YEAR FROM birth_date_jittered) AS age_at_first_dmRx,
+    min(start_date) as first_maceSampleDx_datetime, EXTRACT(YEAR FROM min(start_date)) - EXTRACT(YEAR FROM birth_date_jittered) as age_at_first_maceDxSample
+
+  FROM 
+      shc_core.demographic as d
+      INNER JOIN dmRx USING (anon_id)
+      INNER JOIN maceDxSample USING (anon_id)
+  WHERE
+     dmRx.order_inst < maceDxSample.start_date  -- Only capture when MACE diagnosis event occurs after medication order
+  GROUP BY
+    d.anon_id, d.gender, d.canonical_race, d.canonical_ethnicity, d.birth_date_jittered
+)
+
+SELECT 
+  EXTRACT(YEAR FROM first_maceSampleDx_datetime) as FIRST_MACE_SAMPLE_DX_YEAR,
+  -- CAST(FLOOR(AGE_AT_FIRST_MACE_SAMPLE_DX/10)*10 AS INT64) AS DECADE_AT_FIRST_MACE_SAMPLE_DX,
+  COUNT(DISTINCT anon_id) as nPatients,
+FROM dmRxMACESamplePatients
+GROUP BY 
+  FIRST_MACE_SAMPLE_DX_YEAR
+ORDER BY
+  FIRST_MACE_SAMPLE_DX_YEAR desc
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/scripts/Archive/DiabetesSecondLineTherapy/TruvenMarketscanMedicareSupplementalSampleQueries.sql b/scripts/Archive/DiabetesSecondLineTherapy/TruvenMarketscanMedicareSupplementalSampleQueries.sql
new file mode 100644
index 00000000..f6934340
--- /dev/null
+++ b/scripts/Archive/DiabetesSecondLineTherapy/TruvenMarketscanMedicareSupplementalSampleQueries.sql
@@ -0,0 +1,486 @@
+-- Count up patients and claims for prescriptions for diabetes related drug classes
+-- Broken up by Year
+SELECT
+	pc.YEAR,
+	pc.THERCLS,
+	-- pc.THERGRP,
+	-- rb.GENNME,
+	count(distinct pc.ENROLID) as n_patients,
+  count(distinct pc.SEQNUM) as n_claims
+
+FROM
+	stanfordphs.ibm_marketscan_medicare_supplemental:141:v2_0.insurance_outpatient_pharmacy:6 AS pc
+	   INNER JOIN
+	stanfordphs.ibm_marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb ON (pc.`NDCNUM` = rb.`NDCNUM`)
+WHERE
+	   pc.THERCLS = 172 -- Insulins
+	OR pc.THERCLS = 174 -- Miscellaneous Diabetes Meds: Includes Metformin, GLP-1 (e.g., Exenatide), DDP-4 (e.g., Linagliptin) 
+	OR pc.THERCLS = 173 -- Sulfonylurea (e.g., glipizide)
+	OR pc.THERCLS = 268 -- Thiazolidinediones (e.g., rosiglitazone)
+	OR pc.THERCLS = 267 -- SGLT2 Inhibitor (e.g., empagliflozin)
+	OR pc.THERCLS = 266 -- Meglitinides (e.g., repaglinide)
+GROUP BY
+	pc.YEAR,
+	pc.THERCLS,
+	pc.THERGRP
+	--rb.GENNME
+ORDER BY
+	pc.YEAR desc,
+	-- rb.GENNME,
+	n_patients desc,
+	n_claims desc
+
+LIMIT 1000
+
+
+-- Then do subsequent group by queries to get age, gender, race breakdowns
+SELECT
+	SEX,
+	count(distinct ENROLID) as nPatients
+FROM
+	jonc101.test_project_multi_user:1.diabetes_med_patients_marketscan_output:27
+GROUP BY
+  SEX
+
+
+
+SELECT
+	CAST(FLOOR(AGE_FIRST_RX/10)*10 AS INT64) AS DECADE_FIRST_RX,
+	count(distinct ENROLID) as nPatients
+FROM
+	jonc101.test_project_multi_user:1.diabetes_med_patients_marketscan_output:27
+GROUP BY
+  DECADE_FIRST_RX
+ORDER BY
+	DECADE_FIRST_RX
+
+
+SELECT
+	REGION,
+	count(distinct ENROLID) as nPatients
+FROM
+	jonc101.test_project_multi_user:1.diabetes_med_patients_marketscan_output:27
+GROUP BY
+  REGION
+ORDER BY
+	REGION
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+WITH
+maceDx1Sample AS
+(
+	-- Find Facilty Header cases where FIRST diagnosis matches major adverse cardiovascular event cases
+	-- This is NOT comprehensive as only captures some ICD10 codes, not ICD9, not procedure codes, only FIRST DX1, and not necessarily DX2,DX3,DX4,etc.
+	SELECT
+		AGE,
+		DX1,
+		DXVER,
+		ENROLID,
+		SVCDATE,
+		DOBYR,
+		REGION,
+		AGEGRP,
+		YEAR,
+		SEX
+	FROM
+  	stanfordphs.ibm_marketscan_medicare_supplemental:141:v2_0.insurance_facility_header:5 AS t0
+	WHERE
+		-- DXVER = 0 -- ICD10, but most are null?
+		-- Myocardial Infarction
+		DX1 LIKE 'I21%' OR
+		DX1 LIKE 'I210%' OR
+		DX1 LIKE 'I2101%' OR
+		DX1 LIKE 'I2102%' OR
+		DX1 LIKE 'I2109%' OR
+		DX1 LIKE 'I211%' OR
+		DX1 LIKE 'I2111%' OR
+		DX1 LIKE 'I2119%' OR
+		DX1 LIKE 'I212%' OR
+		DX1 LIKE 'I2121%' OR
+		DX1 LIKE 'I2129%' OR
+		DX1 LIKE 'I213%' OR
+		DX1 LIKE 'I214%' OR
+		DX1 LIKE 'I22%' OR
+		DX1 LIKE 'I220%' OR
+		DX1 LIKE 'I221%' OR
+		DX1 LIKE 'I222%' OR
+		DX1 LIKE 'I228%' OR
+		DX1 LIKE 'I229%' OR
+		DX1 LIKE 'I23%' OR
+		DX1 LIKE 'I230%' OR
+		DX1 LIKE 'I231%' OR
+		DX1 LIKE 'I232%' OR
+		DX1 LIKE 'I233%' OR
+		DX1 LIKE 'I234%' OR
+		DX1 LIKE 'I235%' OR
+		DX1 LIKE 'I236%' OR
+		DX1 LIKE 'I237%' OR
+		DX1 LIKE 'I238%' OR
+		DX1 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX1 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX1 LIKE 'I099%' OR
+		DX1 LIKE 'I110%' OR
+		DX1 LIKE 'I130%' OR
+		DX1 LIKE 'I132%' OR
+		DX1 LIKE 'I255%' OR
+		DX1 LIKE 'I420%' OR
+		DX1 LIKE '1425%' OR
+		DX1 LIKE '1426%' OR
+		DX1 LIKE '1427%' OR
+		DX1 LIKE '1428%' OR
+		DX1 LIKE '1429%' OR
+		DX1 LIKE 'I43%' OR
+		DX1 LIKE 'I50%' OR
+		DX1 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX1 LIKE 'I200%' OR
+		DX1 LIKE 'I2109%' OR
+		DX1 LIKE 'I2111%' OR
+		DX1 LIKE 'I2119%' OR
+		DX1 LIKE 'I2129%' OR
+		DX1 LIKE 'I213%' OR
+		DX1 LIKE 'I214%' OR
+		DX1 LIKE 'I240%' OR
+
+
+
+		-- Myocardial Infarction
+		DX2 LIKE 'I21%' OR
+		DX2 LIKE 'I210%' OR
+		DX2 LIKE 'I2101%' OR
+		DX2 LIKE 'I2102%' OR
+		DX2 LIKE 'I2109%' OR
+		DX2 LIKE 'I211%' OR
+		DX2 LIKE 'I2111%' OR
+		DX2 LIKE 'I2119%' OR
+		DX2 LIKE 'I212%' OR
+		DX2 LIKE 'I2121%' OR
+		DX2 LIKE 'I2129%' OR
+		DX2 LIKE 'I213%' OR
+		DX2 LIKE 'I214%' OR
+		DX2 LIKE 'I22%' OR
+		DX2 LIKE 'I220%' OR
+		DX2 LIKE 'I221%' OR
+		DX2 LIKE 'I222%' OR
+		DX2 LIKE 'I228%' OR
+		DX2 LIKE 'I229%' OR
+		DX2 LIKE 'I23%' OR
+		DX2 LIKE 'I230%' OR
+		DX2 LIKE 'I231%' OR
+		DX2 LIKE 'I232%' OR
+		DX2 LIKE 'I233%' OR
+		DX2 LIKE 'I234%' OR
+		DX2 LIKE 'I235%' OR
+		DX2 LIKE 'I236%' OR
+		DX2 LIKE 'I237%' OR
+		DX2 LIKE 'I238%' OR
+		DX2 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX2 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX2 LIKE 'I099%' OR
+		DX2 LIKE 'I110%' OR
+		DX2 LIKE 'I130%' OR
+		DX2 LIKE 'I132%' OR
+		DX2 LIKE 'I255%' OR
+		DX2 LIKE 'I420%' OR
+		DX2 LIKE 'I425%' OR
+		DX2 LIKE 'I426%' OR
+		DX2 LIKE 'I427%' OR
+		DX2 LIKE 'I428%' OR
+		DX2 LIKE 'I429%' OR
+		DX2 LIKE 'I43%' OR
+		DX2 LIKE 'I50%' OR
+		DX2 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX2 LIKE 'I200%' OR
+		DX2 LIKE 'I2109%' OR
+		DX2 LIKE 'I2111%' OR
+		DX2 LIKE 'I2119%' OR
+		DX2 LIKE 'I2129%' OR
+		DX2 LIKE 'I213%' OR
+		DX2 LIKE 'I214%' OR
+		DX2 LIKE 'I240%' OR
+
+
+		-- Myocardial Infarction
+		DX3 LIKE 'I21%' OR
+		DX3 LIKE 'I210%' OR
+		DX3 LIKE 'I2101%' OR
+		DX3 LIKE 'I2102%' OR
+		DX3 LIKE 'I2109%' OR
+		DX3 LIKE 'I211%' OR
+		DX3 LIKE 'I2111%' OR
+		DX3 LIKE 'I2119%' OR
+		DX3 LIKE 'I212%' OR
+		DX3 LIKE 'I2121%' OR
+		DX3 LIKE 'I2129%' OR
+		DX3 LIKE 'I213%' OR
+		DX3 LIKE 'I214%' OR
+		DX3 LIKE 'I22%' OR
+		DX3 LIKE 'I220%' OR
+		DX3 LIKE 'I221%' OR
+		DX3 LIKE 'I222%' OR
+		DX3 LIKE 'I228%' OR
+		DX3 LIKE 'I229%' OR
+		DX3 LIKE 'I23%' OR
+		DX3 LIKE 'I230%' OR
+		DX3 LIKE 'I231%' OR
+		DX3 LIKE 'I232%' OR
+		DX3 LIKE 'I233%' OR
+		DX3 LIKE 'I234%' OR
+		DX3 LIKE 'I235%' OR
+		DX3 LIKE 'I236%' OR
+		DX3 LIKE 'I237%' OR
+		DX3 LIKE 'I238%' OR
+		DX3 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX3 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX3 LIKE 'I099%' OR
+		DX3 LIKE 'I110%' OR
+		DX3 LIKE 'I130%' OR
+		DX3 LIKE 'I132%' OR
+		DX3 LIKE 'I255%' OR
+		DX3 LIKE 'I420%' OR
+		DX3 LIKE '1425%' OR
+		DX3 LIKE '1426%' OR
+		DX3 LIKE '1427%' OR
+		DX3 LIKE '1428%' OR
+		DX3 LIKE '1429%' OR
+		DX3 LIKE 'I43%' OR
+		DX3 LIKE 'I50%' OR
+		DX3 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX3 LIKE 'I200%' OR
+		DX3 LIKE 'I2109%' OR
+		DX3 LIKE 'I2111%' OR
+		DX3 LIKE 'I2119%' OR
+		DX3 LIKE 'I2129%' OR
+		DX3 LIKE 'I213%' OR
+		DX3 LIKE 'I214%' OR
+		DX3 LIKE 'I240%' OR
+
+
+		-- Myocardial Infarction
+		DX4 LIKE 'I21%' OR
+		DX4 LIKE 'I210%' OR
+		DX4 LIKE 'I2101%' OR
+		DX4 LIKE 'I2102%' OR
+		DX4 LIKE 'I2109%' OR
+		DX4 LIKE 'I211%' OR
+		DX4 LIKE 'I2111%' OR
+		DX4 LIKE 'I2119%' OR
+		DX4 LIKE 'I212%' OR
+		DX4 LIKE 'I2121%' OR
+		DX4 LIKE 'I2129%' OR
+		DX4 LIKE 'I213%' OR
+		DX4 LIKE 'I214%' OR
+		DX4 LIKE 'I22%' OR
+		DX4 LIKE 'I220%' OR
+		DX4 LIKE 'I221%' OR
+		DX4 LIKE 'I222%' OR
+		DX4 LIKE 'I228%' OR
+		DX4 LIKE 'I229%' OR
+		DX4 LIKE 'I23%' OR
+		DX4 LIKE 'I230%' OR
+		DX4 LIKE 'I231%' OR
+		DX4 LIKE 'I232%' OR
+		DX4 LIKE 'I233%' OR
+		DX4 LIKE 'I234%' OR
+		DX4 LIKE 'I235%' OR
+		DX4 LIKE 'I236%' OR
+		DX4 LIKE 'I237%' OR
+		DX4 LIKE 'I238%' OR
+		DX4 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX4 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX4 LIKE 'I099%' OR
+		DX4 LIKE 'I110%' OR
+		DX4 LIKE 'I130%' OR
+		DX4 LIKE 'I132%' OR
+		DX4 LIKE 'I255%' OR
+		DX4 LIKE 'I420%' OR
+		DX4 LIKE '1425%' OR
+		DX4 LIKE '1426%' OR
+		DX4 LIKE '1427%' OR
+		DX4 LIKE '1428%' OR
+		DX4 LIKE '1429%' OR
+		DX4 LIKE 'I43%' OR
+		DX4 LIKE 'I50%' OR
+		DX4 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX4 LIKE 'I200%' OR
+		DX4 LIKE 'I2109%' OR
+		DX4 LIKE 'I2111%' OR
+		DX4 LIKE 'I2119%' OR
+		DX4 LIKE 'I2129%' OR
+		DX4 LIKE 'I213%' OR
+		DX4 LIKE 'I214%' OR
+		DX4 LIKE 'I240%' OR
+
+
+		-- Myocardial Infarction
+		DX5 LIKE 'I21%' OR
+		DX5 LIKE 'I210%' OR
+		DX5 LIKE 'I2101%' OR
+		DX5 LIKE 'I2102%' OR
+		DX5 LIKE 'I2109%' OR
+		DX5 LIKE 'I211%' OR
+		DX5 LIKE 'I2111%' OR
+		DX5 LIKE 'I2119%' OR
+		DX5 LIKE 'I212%' OR
+		DX5 LIKE 'I2121%' OR
+		DX5 LIKE 'I2129%' OR
+		DX5 LIKE 'I213%' OR
+		DX5 LIKE 'I214%' OR
+		DX5 LIKE 'I22%' OR
+		DX5 LIKE 'I220%' OR
+		DX5 LIKE 'I221%' OR
+		DX5 LIKE 'I222%' OR
+		DX5 LIKE 'I228%' OR
+		DX5 LIKE 'I229%' OR
+		DX5 LIKE 'I23%' OR
+		DX5 LIKE 'I230%' OR
+		DX5 LIKE 'I231%' OR
+		DX5 LIKE 'I232%' OR
+		DX5 LIKE 'I233%' OR
+		DX5 LIKE 'I234%' OR
+		DX5 LIKE 'I235%' OR
+		DX5 LIKE 'I236%' OR
+		DX5 LIKE 'I237%' OR
+		DX5 LIKE 'I238%' OR
+		DX5 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX5 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX5 LIKE 'I099%' OR
+		DX5 LIKE 'I110%' OR
+		DX5 LIKE 'I130%' OR
+		DX5 LIKE 'I132%' OR
+		DX5 LIKE 'I255%' OR
+		DX5 LIKE 'I420%' OR
+		DX5 LIKE '1425%' OR
+		DX5 LIKE '1426%' OR
+		DX5 LIKE '1427%' OR
+		DX5 LIKE '1428%' OR
+		DX5 LIKE '1429%' OR
+		DX5 LIKE 'I43%' OR
+		DX5 LIKE 'I50%' OR
+		DX5 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX5 LIKE 'I200%' OR
+		DX5 LIKE 'I2109%' OR
+		DX5 LIKE 'I2111%' OR
+		DX5 LIKE 'I2119%' OR
+		DX5 LIKE 'I2129%' OR
+		DX5 LIKE 'I213%' OR
+		DX5 LIKE 'I214%' OR
+		DX5 LIKE 'I240%' 
+
+),
+dmRx AS
+( -- Records of Diabetes related medication prescriptions
+	SELECT
+		pc.SEQNUM,
+		pc.ENROLID,	
+		pc.SVCDATE,
+		pc.YEAR,
+		pc.THERCLS,
+		rb.GENNME,
+		pc.REGION,
+		pc.AGEGRP,
+		pc.SEX,
+	  pc.DOBYR,
+		pc.AGE
+	FROM
+  	stanfordphs.ibm_marketscan_medicare_supplemental:141:v2_0.insurance_outpatient_pharmacy:6 AS pc
+			 INNER JOIN
+		stanfordphs.ibm_marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb USING (NDCNUM)
+	WHERE
+			 pc.THERCLS = 172 -- Insulins
+		OR pc.THERCLS = 174 -- Miscellaneous Diabetes Meds: Includes Metformin, GLP-1 (e.g., Exenatide), DDP-4 (e.g., Linagliptin) 
+		OR pc.THERCLS = 173 -- Sulfonylurea (e.g., glipizide)
+		OR pc.THERCLS = 268 -- Thiazolidinediones (e.g., rosiglitazone)
+		OR pc.THERCLS = 267 -- SGLT2 Inhibitor (e.g., empagliflozin)
+		OR pc.THERCLS = 266 -- Meglitinides (e.g., repaglinide)
+),
+
+
+-- Find demographic info on patients who received at least one diabetes drug prescription 
+-- and a cardiovascular event sample diagnosis anytime after
+-- Beware that diagnosis coding by ICD10 only inconsistently starting in 2015 and
+--  many misses described above, so this is an underestimate
+dmRxMACEDx1SamplePatients AS
+(
+    SELECT
+			dx.ENROLID,
+			dx.SEX,
+			dx.REGION,
+			MIN(dx.YEAR) YEAR_FIRST_DX1,
+			MIN(dx.AGE) AGE_FIRST_DX1,
+			MIN(dx.SVCDATE) FIRST_SVCDATE
+		FROM
+			maceDx1Sample as dx
+			  INNER JOIN 
+		  dmRx USING (ENROLID)
+    WHERE dmRx.SVCDATE < dx.SVCDATE
+		GROUP BY
+			dx.ENROLID,
+			dx.SEX,
+			dx.REGION
+)
+
+SELECT
+	YEAR_FIRST_DX1, count(distinct ENROLID) as nPatients
+FROM dmRxMACEDx1SamplePatients
+GROUP BY
+	YEAR_FIRST_DX1
+ORDER BY
+	YEAR_FIRST_DX1
+	
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/scripts/Archive/DiabetesSecondLineTherapy/TruvenMarketscanSampleQueries.sql b/scripts/Archive/DiabetesSecondLineTherapy/TruvenMarketscanSampleQueries.sql
new file mode 100644
index 00000000..50ee3783
--- /dev/null
+++ b/scripts/Archive/DiabetesSecondLineTherapy/TruvenMarketscanSampleQueries.sql
@@ -0,0 +1,581 @@
+	
+	
+-- Look for example Redbook / NDC lookup for outpatient prescriptions of all the different types of diabetes medications
+-- https://www.mayoclinic.org/diseases-conditions/type-2-diabetes/diagnosis-treatment/drc-20351199
+SELECT 
+    THERCLS, THERGRP, THRDTDS, THRCLDS, THRGRDS, GENNME,
+		count(distinct NDCNUM) as n_NDC,
+FROM stanfordphs.marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb
+WHERE
+	   GENNME like 'Metform%'
+	OR GENNME like 'Glipizide%'
+	OR GENNME like 'Glyburide%'
+	OR GENNME like 'Glimepiride%'
+	OR GENNME like 'Repaglinide%'
+	OR GENNME like 'Nateglinide%'
+	OR GENNME like 'Rosiglitazone%'
+	OR GENNME like 'Pioglitazone%'
+	OR GENNME like 'Sitagliptin%'
+	OR GENNME like 'Saxagliptin%'
+	OR GENNME like 'Linagliptin%'
+	OR GENNME like 'Exenatide%'
+	OR GENNME like 'Liraglutide%'
+	OR GENNME like 'Semaglutide%'
+	OR GENNME like 'Canagliflozin%'
+	OR GENNME like 'Dapagliflozin%'
+	OR GENNME like 'Empagliflozin'
+	OR GENNME like 'Insulin%'
+GROUP BY
+    THERCLS, THERGRP, THRDTDS, THRCLDS, THRGRDS, GENNME
+ORDER BY
+    THERCLS, THERGRP, THRDTDS, THRCLDS, THRGRDS, GENNME,
+	n_NDC desc
+LIMIT 1000
+
+
+
+
+
+-- Look for example Redbook / NDC lookup for outpatient prescriptions of all the different types of diabetes medications by class
+-- https://www.mayoclinic.org/diseases-conditions/type-2-diabetes/diagnosis-treatment/drc-20351199
+SELECT 
+    THERCLS, THERGRP, THRDTDS, THRCLDS, THRGRDS, GENNME,
+		count(distinct NDCNUM) as n_NDC,
+FROM stanfordphs.marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb
+WHERE
+	   THERCLS = 172 -- Insulins
+	OR THERCLS = 174 -- Miscellaneous Diabetes Meds: Includes Metformin, GLP-1 (e.g., Exenatide), DDP-4 (e.g., Linagliptin) 
+	OR THERCLS = 173 -- Sulfonylurea (e.g., glipizide)
+	OR THERCLS = 268 -- Thiazolidinediones (e.g., rosiglitazone)
+	OR THERCLS = 267 -- SGLT2 Inhibitor (e.g., empagliflozin)
+	OR THERCLS = 266 -- Meglitinides (e.g., repaglinide)
+GROUP BY
+    THERCLS, THERGRP, THRDTDS, THRCLDS, THRGRDS, GENNME
+ORDER BY
+    THERCLS, THERGRP, THRDTDS, THRCLDS, THRGRDS, GENNME,
+	n_NDC desc
+LIMIT 1000
+
+
+
+-- Count up patients and claims for prescriptions for diabetes related drug classes
+-- Broken up by Year
+SELECT
+	pc.YEAR,
+	pc.THERCLS,
+	pc.THERGRP,
+	-- rb.GENNME,
+	count(distinct pc.PATID) as n_patients,
+  count(distinct pc.SEQNUM) as n_claims
+
+FROM
+	stanfordphs.marketscan:142:v2_0:sample.outpatient_pharmaceutical_claims:7 AS pc
+	   INNER JOIN
+	stanfordphs.marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb ON (pc.`NDCNUM` = rb.`NDCNUM`)
+WHERE
+	   pc.THERCLS = 172 -- Insulins
+	OR pc.THERCLS = 174 -- Miscellaneous Diabetes Meds: Includes Metformin, GLP-1 (e.g., Exenatide), DDP-4 (e.g., Linagliptin) 
+	OR pc.THERCLS = 173 -- Sulfonylurea (e.g., glipizide)
+	OR pc.THERCLS = 268 -- Thiazolidinediones (e.g., rosiglitazone)
+	OR pc.THERCLS = 267 -- SGLT2 Inhibitor (e.g., empagliflozin)
+	OR pc.THERCLS = 266 -- Meglitinides (e.g., repaglinide)
+GROUP BY
+	pc.YEAR,
+	pc.THERCLS,
+	pc.THERGRP,
+	rb.GENNME
+ORDER BY
+	pc.YEAR desc,
+	-- rb.GENNME,
+	n_patients desc,
+	n_claims desc
+
+LIMIT 1000
+
+
+
+
+
+
+
+
+
+
+
+	
+-- Patient demograhpic information for those who've received a diabetes related drug prescription
+WITH
+-- Find claims for prescriptions for diabetes related drug classes
+dmRx AS
+(
+	SELECT
+		pc.SEQNUM,
+		pc.ENROLID,	
+		pc.YEAR,
+		pc.THERCLS,
+		rb.GENNME,
+		pc.REGION,
+		pc.AGEGRP,
+		pc.SEX,
+	  pc.DOBYR,
+		pc.AGE
+	FROM
+	stanfordphs.ibm_marketscan_medicare_supplemental:141:v2_0.insurance_outpatient_pharmacy:6 AS pc
+			 INNER JOIN
+		stanfordphs.ibm_marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb USING (NDCNUM)
+	WHERE
+			 pc.THERCLS = 172 -- Insulins
+		OR pc.THERCLS = 174 -- Miscellaneous Diabetes Meds: Includes Metformin, GLP-1 (e.g., Exenatide), DDP-4 (e.g., Linagliptin) 
+		OR pc.THERCLS = 173 -- Sulfonylurea (e.g., glipizide)
+		OR pc.THERCLS = 268 -- Thiazolidinediones (e.g., rosiglitazone)
+		OR pc.THERCLS = 267 -- SGLT2 Inhibitor (e.g., empagliflozin)
+		OR pc.THERCLS = 266 -- Meglitinides (e.g., repaglinide)
+)
+
+-- Could join with enrollment detail table, but looks like main patient demographic
+--   is already denormalized and included in the pharmaceutical claims table
+
+SELECT
+	ENROLID, SEX, DOBYR, 
+	REGION, -- Could have multiple records per patient/enrolee then, if moved between regions
+	MIN(SEQNUM) AS FIRST_SEQNUM,
+	MIN(AGE) AS AGE_FIRST_RX, MIN(AGEGRP) AS AGEGRP_FIRST_RX
+	-- Apparently Truven MarketScan does NOT include race/ethnicity information
+FROM
+	dmRx
+WHERE
+	ENROLID IS NOT NULL	-- Can't do unique patient counts in this case
+GROUP BY
+  ENROLID, SEX, DOBYR,
+	REGION
+ORDER BY
+	ENROLID
+
+
+-- Then do subsequent group by queries to get age, gender, race breakdowns
+SELECT
+	SEX,
+	count(distinct ENROLID) as nPatients
+FROM
+	jonc101.test_project_multi_user:1.dmrx_demographics_medicare_supplemental_output:41
+GROUP BY
+  SEX
+	
+
+
+SELECT
+	CAST(FLOOR(AGE_FIRST_RX/10)*10 AS INT64) AS DECADE_FIRST_RX,
+	count(distinct ENROLID) as nPatients
+FROM
+	jonc101.test_project_multi_user:1.dmrx_demographics_medicare_supplemental_output:41
+GROUP BY
+  DECADE_FIRST_RX
+ORDER BY
+	DECADE_FIRST_RX
+
+
+SELECT
+	REGION,
+	count(distinct ENROLID) as nPatients
+FROM
+	jonc101.test_project_multi_user:1.dmrx_demographics_medicare_supplemental_output:41
+GROUP BY
+  REGION
+ORDER BY
+	REGION	
+
+
+
+
+
+
+WITH
+maceDx1Sample AS
+(
+	-- Find Facilty Header cases where FIRST diagnosis matches major adverse cardiovascular event cases
+	-- This is NOT comprehensive as only captures some ICD10 codes, not ICD9, not procedure codes, only FIRST DX1, and not necessarily DX2,DX3,DX4,etc.
+	SELECT
+		AGE,
+		DX1,
+		DXVER,
+		ENROLID,
+		SVCDATE,
+		DOBYR,
+		REGION,
+		AGEGRP,
+		YEAR,
+		SEX
+	FROM
+		stanfordphs.marketscan:142:v2_0.facility_header:5 AS t0
+	WHERE
+		-- DXVER = 0 -- ICD10, but most are null?
+		-- Myocardial Infarction
+		DX1 LIKE 'I21%' OR
+		DX1 LIKE 'I210%' OR
+		DX1 LIKE 'I2101%' OR
+		DX1 LIKE 'I2102%' OR
+		DX1 LIKE 'I2109%' OR
+		DX1 LIKE 'I211%' OR
+		DX1 LIKE 'I2111%' OR
+		DX1 LIKE 'I2119%' OR
+		DX1 LIKE 'I212%' OR
+		DX1 LIKE 'I2121%' OR
+		DX1 LIKE 'I2129%' OR
+		DX1 LIKE 'I213%' OR
+		DX1 LIKE 'I214%' OR
+		DX1 LIKE 'I22%' OR
+		DX1 LIKE 'I220%' OR
+		DX1 LIKE 'I221%' OR
+		DX1 LIKE 'I222%' OR
+		DX1 LIKE 'I228%' OR
+		DX1 LIKE 'I229%' OR
+		DX1 LIKE 'I23%' OR
+		DX1 LIKE 'I230%' OR
+		DX1 LIKE 'I231%' OR
+		DX1 LIKE 'I232%' OR
+		DX1 LIKE 'I233%' OR
+		DX1 LIKE 'I234%' OR
+		DX1 LIKE 'I235%' OR
+		DX1 LIKE 'I236%' OR
+		DX1 LIKE 'I237%' OR
+		DX1 LIKE 'I238%' OR
+		DX1 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX1 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX1 LIKE 'I099%' OR
+		DX1 LIKE 'I110%' OR
+		DX1 LIKE 'I130%' OR
+		DX1 LIKE 'I132%' OR
+		DX1 LIKE 'I255%' OR
+		DX1 LIKE 'I420%' OR
+		DX1 LIKE '1425%' OR
+		DX1 LIKE '1426%' OR
+		DX1 LIKE '1427%' OR
+		DX1 LIKE '1428%' OR
+		DX1 LIKE '1429%' OR
+		DX1 LIKE 'I43%' OR
+		DX1 LIKE 'I50%' OR
+		DX1 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX1 LIKE 'I200%' OR
+		DX1 LIKE 'I2109%' OR
+		DX1 LIKE 'I2111%' OR
+		DX1 LIKE 'I2119%' OR
+		DX1 LIKE 'I2129%' OR
+		DX1 LIKE 'I213%' OR
+		DX1 LIKE 'I214%' OR
+		DX1 LIKE 'I240%' OR
+
+
+
+		-- Myocardial Infarction
+		DX2 LIKE 'I21%' OR
+		DX2 LIKE 'I210%' OR
+		DX2 LIKE 'I2101%' OR
+		DX2 LIKE 'I2102%' OR
+		DX2 LIKE 'I2109%' OR
+		DX2 LIKE 'I211%' OR
+		DX2 LIKE 'I2111%' OR
+		DX2 LIKE 'I2119%' OR
+		DX2 LIKE 'I212%' OR
+		DX2 LIKE 'I2121%' OR
+		DX2 LIKE 'I2129%' OR
+		DX2 LIKE 'I213%' OR
+		DX2 LIKE 'I214%' OR
+		DX2 LIKE 'I22%' OR
+		DX2 LIKE 'I220%' OR
+		DX2 LIKE 'I221%' OR
+		DX2 LIKE 'I222%' OR
+		DX2 LIKE 'I228%' OR
+		DX2 LIKE 'I229%' OR
+		DX2 LIKE 'I23%' OR
+		DX2 LIKE 'I230%' OR
+		DX2 LIKE 'I231%' OR
+		DX2 LIKE 'I232%' OR
+		DX2 LIKE 'I233%' OR
+		DX2 LIKE 'I234%' OR
+		DX2 LIKE 'I235%' OR
+		DX2 LIKE 'I236%' OR
+		DX2 LIKE 'I237%' OR
+		DX2 LIKE 'I238%' OR
+		DX2 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX2 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX2 LIKE 'I099%' OR
+		DX2 LIKE 'I110%' OR
+		DX2 LIKE 'I130%' OR
+		DX2 LIKE 'I132%' OR
+		DX2 LIKE 'I255%' OR
+		DX2 LIKE 'I420%' OR
+		DX2 LIKE 'I425%' OR
+		DX2 LIKE 'I426%' OR
+		DX2 LIKE 'I427%' OR
+		DX2 LIKE 'I428%' OR
+		DX2 LIKE 'I429%' OR
+		DX2 LIKE 'I43%' OR
+		DX2 LIKE 'I50%' OR
+		DX2 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX2 LIKE 'I200%' OR
+		DX2 LIKE 'I2109%' OR
+		DX2 LIKE 'I2111%' OR
+		DX2 LIKE 'I2119%' OR
+		DX2 LIKE 'I2129%' OR
+		DX2 LIKE 'I213%' OR
+		DX2 LIKE 'I214%' OR
+		DX2 LIKE 'I240%' OR
+
+
+		-- Myocardial Infarction
+		DX3 LIKE 'I21%' OR
+		DX3 LIKE 'I210%' OR
+		DX3 LIKE 'I2101%' OR
+		DX3 LIKE 'I2102%' OR
+		DX3 LIKE 'I2109%' OR
+		DX3 LIKE 'I211%' OR
+		DX3 LIKE 'I2111%' OR
+		DX3 LIKE 'I2119%' OR
+		DX3 LIKE 'I212%' OR
+		DX3 LIKE 'I2121%' OR
+		DX3 LIKE 'I2129%' OR
+		DX3 LIKE 'I213%' OR
+		DX3 LIKE 'I214%' OR
+		DX3 LIKE 'I22%' OR
+		DX3 LIKE 'I220%' OR
+		DX3 LIKE 'I221%' OR
+		DX3 LIKE 'I222%' OR
+		DX3 LIKE 'I228%' OR
+		DX3 LIKE 'I229%' OR
+		DX3 LIKE 'I23%' OR
+		DX3 LIKE 'I230%' OR
+		DX3 LIKE 'I231%' OR
+		DX3 LIKE 'I232%' OR
+		DX3 LIKE 'I233%' OR
+		DX3 LIKE 'I234%' OR
+		DX3 LIKE 'I235%' OR
+		DX3 LIKE 'I236%' OR
+		DX3 LIKE 'I237%' OR
+		DX3 LIKE 'I238%' OR
+		DX3 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX3 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX3 LIKE 'I099%' OR
+		DX3 LIKE 'I110%' OR
+		DX3 LIKE 'I130%' OR
+		DX3 LIKE 'I132%' OR
+		DX3 LIKE 'I255%' OR
+		DX3 LIKE 'I420%' OR
+		DX3 LIKE '1425%' OR
+		DX3 LIKE '1426%' OR
+		DX3 LIKE '1427%' OR
+		DX3 LIKE '1428%' OR
+		DX3 LIKE '1429%' OR
+		DX3 LIKE 'I43%' OR
+		DX3 LIKE 'I50%' OR
+		DX3 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX3 LIKE 'I200%' OR
+		DX3 LIKE 'I2109%' OR
+		DX3 LIKE 'I2111%' OR
+		DX3 LIKE 'I2119%' OR
+		DX3 LIKE 'I2129%' OR
+		DX3 LIKE 'I213%' OR
+		DX3 LIKE 'I214%' OR
+		DX3 LIKE 'I240%' OR
+
+
+		-- Myocardial Infarction
+		DX4 LIKE 'I21%' OR
+		DX4 LIKE 'I210%' OR
+		DX4 LIKE 'I2101%' OR
+		DX4 LIKE 'I2102%' OR
+		DX4 LIKE 'I2109%' OR
+		DX4 LIKE 'I211%' OR
+		DX4 LIKE 'I2111%' OR
+		DX4 LIKE 'I2119%' OR
+		DX4 LIKE 'I212%' OR
+		DX4 LIKE 'I2121%' OR
+		DX4 LIKE 'I2129%' OR
+		DX4 LIKE 'I213%' OR
+		DX4 LIKE 'I214%' OR
+		DX4 LIKE 'I22%' OR
+		DX4 LIKE 'I220%' OR
+		DX4 LIKE 'I221%' OR
+		DX4 LIKE 'I222%' OR
+		DX4 LIKE 'I228%' OR
+		DX4 LIKE 'I229%' OR
+		DX4 LIKE 'I23%' OR
+		DX4 LIKE 'I230%' OR
+		DX4 LIKE 'I231%' OR
+		DX4 LIKE 'I232%' OR
+		DX4 LIKE 'I233%' OR
+		DX4 LIKE 'I234%' OR
+		DX4 LIKE 'I235%' OR
+		DX4 LIKE 'I236%' OR
+		DX4 LIKE 'I237%' OR
+		DX4 LIKE 'I238%' OR
+		DX4 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX4 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX4 LIKE 'I099%' OR
+		DX4 LIKE 'I110%' OR
+		DX4 LIKE 'I130%' OR
+		DX4 LIKE 'I132%' OR
+		DX4 LIKE 'I255%' OR
+		DX4 LIKE 'I420%' OR
+		DX4 LIKE '1425%' OR
+		DX4 LIKE '1426%' OR
+		DX4 LIKE '1427%' OR
+		DX4 LIKE '1428%' OR
+		DX4 LIKE '1429%' OR
+		DX4 LIKE 'I43%' OR
+		DX4 LIKE 'I50%' OR
+		DX4 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX4 LIKE 'I200%' OR
+		DX4 LIKE 'I2109%' OR
+		DX4 LIKE 'I2111%' OR
+		DX4 LIKE 'I2119%' OR
+		DX4 LIKE 'I2129%' OR
+		DX4 LIKE 'I213%' OR
+		DX4 LIKE 'I214%' OR
+		DX4 LIKE 'I240%' OR
+
+
+		-- Myocardial Infarction
+		DX5 LIKE 'I21%' OR
+		DX5 LIKE 'I210%' OR
+		DX5 LIKE 'I2101%' OR
+		DX5 LIKE 'I2102%' OR
+		DX5 LIKE 'I2109%' OR
+		DX5 LIKE 'I211%' OR
+		DX5 LIKE 'I2111%' OR
+		DX5 LIKE 'I2119%' OR
+		DX5 LIKE 'I212%' OR
+		DX5 LIKE 'I2121%' OR
+		DX5 LIKE 'I2129%' OR
+		DX5 LIKE 'I213%' OR
+		DX5 LIKE 'I214%' OR
+		DX5 LIKE 'I22%' OR
+		DX5 LIKE 'I220%' OR
+		DX5 LIKE 'I221%' OR
+		DX5 LIKE 'I222%' OR
+		DX5 LIKE 'I228%' OR
+		DX5 LIKE 'I229%' OR
+		DX5 LIKE 'I23%' OR
+		DX5 LIKE 'I230%' OR
+		DX5 LIKE 'I231%' OR
+		DX5 LIKE 'I232%' OR
+		DX5 LIKE 'I233%' OR
+		DX5 LIKE 'I234%' OR
+		DX5 LIKE 'I235%' OR
+		DX5 LIKE 'I236%' OR
+		DX5 LIKE 'I237%' OR
+		DX5 LIKE 'I238%' OR
+		DX5 LIKE 'I252%' OR
+
+		-- Ischemic stroke 
+		DX5 LIKE 'I63%' OR
+
+		-- Heart failure
+		DX5 LIKE 'I099%' OR
+		DX5 LIKE 'I110%' OR
+		DX5 LIKE 'I130%' OR
+		DX5 LIKE 'I132%' OR
+		DX5 LIKE 'I255%' OR
+		DX5 LIKE 'I420%' OR
+		DX5 LIKE '1425%' OR
+		DX5 LIKE '1426%' OR
+		DX5 LIKE '1427%' OR
+		DX5 LIKE '1428%' OR
+		DX5 LIKE '1429%' OR
+		DX5 LIKE 'I43%' OR
+		DX5 LIKE 'I50%' OR
+		DX5 LIKE 'P290%' OR
+
+		-- Acute coronary syndrome
+		DX5 LIKE 'I200%' OR
+		DX5 LIKE 'I2109%' OR
+		DX5 LIKE 'I2111%' OR
+		DX5 LIKE 'I2119%' OR
+		DX5 LIKE 'I2129%' OR
+		DX5 LIKE 'I213%' OR
+		DX5 LIKE 'I214%' OR
+		DX5 LIKE 'I240%' 
+
+),
+dmRx AS
+( -- Records of Diabetes related medication prescriptions
+	SELECT
+		pc.SEQNUM,
+		pc.ENROLID,	
+		pc.SVCDATE,
+		pc.YEAR,
+		pc.THERCLS,
+		rb.GENNME,
+		pc.REGION,
+		pc.AGEGRP,
+		pc.SEX,
+	  pc.DOBYR,
+		pc.AGE
+	FROM
+		stanfordphs.marketscan:142:v2_0.outpatient_pharmaceutical_claims:7 AS pc
+			 INNER JOIN
+		stanfordphs.marketscan_redbook:14:v2_0.marketscan_redbook:1 AS rb USING (NDCNUM)
+	WHERE
+			 pc.THERCLS = 172 -- Insulins
+		OR pc.THERCLS = 174 -- Miscellaneous Diabetes Meds: Includes Metformin, GLP-1 (e.g., Exenatide), DDP-4 (e.g., Linagliptin) 
+		OR pc.THERCLS = 173 -- Sulfonylurea (e.g., glipizide)
+		OR pc.THERCLS = 268 -- Thiazolidinediones (e.g., rosiglitazone)
+		OR pc.THERCLS = 267 -- SGLT2 Inhibitor (e.g., empagliflozin)
+		OR pc.THERCLS = 266 -- Meglitinides (e.g., repaglinide)
+),
+
+
+-- Find demographic info on patients who received at least one diabetes drug prescription 
+-- and a cardiovascular event sample diagnosis anytime after
+-- Beware that diagnosis coding by ICD10 only inconsistently starting in 2015 and
+--  many misses described above, so this is an underestimate
+dmRxMACEDx1SamplePatients AS
+(
+    SELECT
+			dx.ENROLID,
+			dx.SEX,
+			dx.REGION,
+			MIN(dx.YEAR) YEAR_FIRST_DX1,
+			MIN(dx.AGE) AGE_FIRST_DX1,
+			MIN(dx.SVCDATE) FIRST_SVCDATE
+		FROM
+			maceDx1Sample as dx
+			  INNER JOIN 
+		  dmRx USING (ENROLID)
+    WHERE dmRx.SVCDATE < dx.SVCDATE
+		GROUP BY
+			dx.ENROLID,
+			dx.SEX,
+			dx.REGION
+)
+
+SELECT
+	YEAR_FIRST_DX1, count(distinct ENROLID)
+FROM dmRxMACEDx1SamplePatients
+GROUP BY
+	YEAR_FIRST_DX1
+ORDER BY
+	YEAR_FIRST_DX1
diff --git a/scripts/Archive/DiabeticsAtStanford/blood_glucose_partialquery.sql b/scripts/Archive/DiabeticsAtStanford/blood_glucose_partialquery.sql
new file mode 100644
index 00000000..a4a6fac0
--- /dev/null
+++ b/scripts/Archive/DiabeticsAtStanford/blood_glucose_partialquery.sql
@@ -0,0 +1,3 @@
+WHERE UPPER(lab_name) LIKE '%GLUCOSE%' 
+	AND (lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose") 
+	AND UPPER(ordering_mode) = 'INPATIENT' AND ord_num_value BETWEEN 0 AND 9999998
diff --git a/scripts/Archive/DiabeticsAtStanford/pts_on_ISS.sql b/scripts/Archive/DiabeticsAtStanford/pts_on_ISS.sql
new file mode 100644
index 00000000..832c9f31
--- /dev/null
+++ b/scripts/Archive/DiabeticsAtStanford/pts_on_ISS.sql
@@ -0,0 +1,5 @@
+SELECT  rit_uid, ss_section_name  FROM `mining-clinical-decisions.starr_datalake2018.med_orderset` 
+	WHERE UPPER(ss_section_name) LIKE '%SCALE%'
+	ORDER BY ss_section_name ASC
+
+	-- Patients on ISS (distinct pts = 50924; distinct pt encounters = 84859)
diff --git a/scripts/Archive/DiabeticsAtStanford/pullBMTteams.sql b/scripts/Archive/DiabeticsAtStanford/pullBMTteams.sql
new file mode 100644
index 00000000..5ddcdc2c
--- /dev/null
+++ b/scripts/Archive/DiabeticsAtStanford/pullBMTteams.sql
@@ -0,0 +1,5 @@
+SELECT *
+FROM datalake_47618.treatment_team
+WHERE name = 'Primary Team' and prov_name LIKE '%BMT%'
+
+-- Edited
\ No newline at end of file
diff --git a/scripts/Archive/DiabeticsAtStanford/readme b/scripts/Archive/DiabeticsAtStanford/readme
new file mode 100644
index 00000000..432a8215
--- /dev/null
+++ b/scripts/Archive/DiabeticsAtStanford/readme
@@ -0,0 +1 @@
+SQL queries related to diabetes at Stanford
diff --git a/scripts/ExpertsVsEveryone/Matching_Function.Rmd b/scripts/Archive/ExpertsVsEveryone/Matching_Function.Rmd
similarity index 100%
rename from scripts/ExpertsVsEveryone/Matching_Function.Rmd
rename to scripts/Archive/ExpertsVsEveryone/Matching_Function.Rmd
diff --git a/scripts/ExpertsVsEveryone/insertUpdates.sh b/scripts/Archive/ExpertsVsEveryone/insertUpdates.sh
similarity index 100%
rename from scripts/ExpertsVsEveryone/insertUpdates.sh
rename to scripts/Archive/ExpertsVsEveryone/insertUpdates.sh
diff --git a/scripts/ExpertsVsEveryone/pipeline/Figures/generate_plots.sh b/scripts/Archive/ExpertsVsEveryone/pipeline/Figures/generate_plots.sh
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Figures/generate_plots.sh
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Figures/generate_plots.sh
diff --git a/scripts/ExpertsVsEveryone/pipeline/Figures/matching_parse.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Figures/matching_parse.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Figures/matching_parse.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Figures/matching_parse.py
diff --git a/scripts/ExpertsVsEveryone/pipeline/Figures/plot_input.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Figures/plot_input.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Figures/plot_input.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Figures/plot_input.py
diff --git a/scripts/ExpertsVsEveryone/pipeline/Matching/clean_data.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/clean_data.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Matching/clean_data.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Matching/clean_data.py
diff --git a/scripts/ExpertsVsEveryone/pipeline/Matching/extractData_modified.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/extractData_modified.py
similarity index 96%
rename from scripts/ExpertsVsEveryone/pipeline/Matching/extractData_modified.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Matching/extractData_modified.py
index e2fb363b..f3e8e385 100644
--- a/scripts/ExpertsVsEveryone/pipeline/Matching/extractData_modified.py
+++ b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/extractData_modified.py
@@ -7,7 +7,7 @@
 
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import datetime, timedelta;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
@@ -104,7 +104,7 @@ def main(argv):
         if disease not in icd9prefixesByDisease:
             icd9prefixesByDisease[disease] = list();
         icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
-    for disease, icd9prefixes in icd9prefixesByDisease.iteritems():
+    for disease, icd9prefixes in icd9prefixesByDisease.items():
         disease = disease.translate(None," ()-/");   # Strip off punctuation
         extractor.queryClinicalItemsByName(icd9prefixes, patientIds, stdOpen("Charlson."+disease+".tab","w"), operator="~*");
     
@@ -115,11 +115,11 @@ def main(argv):
         if category not in teamNameByCategory:
             teamNameByCategory[category] = list();
         teamNameByCategory[category].append(teamName);
-    for category, teamNames in teamNameByCategory.iteritems():
+    for category, teamNames in teamNameByCategory.items():
         extractor.queryClinicalItemsByName(teamNames, patientIds, stdOpen("TT."+category+".tab","w"), col="description");
 
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 def loadIVAntibioticItemIds(extractor):
     # ivMedCategoryId = 72;
@@ -217,7 +217,7 @@ def queryPatientEpisodes(outputFile, extractor):
         and adt1.pat_enc_csn_anon_id = adt2.pat_enc_csn_anon_id
         """ % ("Tt Pamf Med (Primary)");
 
-        print >> sys.stderr, cohortQuery;
+        print(cohortQuery, file=sys.stderr);
         cursor.execute(cohortQuery);
 
         patientEpisodes = list();
diff --git a/scripts/ExpertsVsEveryone/pipeline/Matching/extract_age.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/extract_age.py
similarity index 93%
rename from scripts/ExpertsVsEveryone/pipeline/Matching/extract_age.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Matching/extract_age.py
index e1a2187b..a609cf82 100644
--- a/scripts/ExpertsVsEveryone/pipeline/Matching/extract_age.py
+++ b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/extract_age.py
@@ -3,7 +3,7 @@
 # Extract age out of stride database
 ##########################################################################################
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -15,7 +15,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # Create SCRIPT_FILE
 SCRIPT_FILE = StringIO()
diff --git a/scripts/ExpertsVsEveryone/pipeline/Matching/matching.Rmd b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/matching.Rmd
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Matching/matching.Rmd
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Matching/matching.Rmd
diff --git a/scripts/ExpertsVsEveryone/pipeline/Matching/remap_ids.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Matching/remap_ids.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Matching/remap_ids.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Matching/remap_ids.py
diff --git a/scripts/ExpertsVsEveryone/scripts/Miscellananeous/extract_diagnosis_codes.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Miscellananeous/extract_diagnosis_codes.py
similarity index 94%
rename from scripts/ExpertsVsEveryone/scripts/Miscellananeous/extract_diagnosis_codes.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Miscellananeous/extract_diagnosis_codes.py
index 2ea89fb0..7fcf554c 100644
--- a/scripts/ExpertsVsEveryone/scripts/Miscellananeous/extract_diagnosis_codes.py
+++ b/scripts/Archive/ExpertsVsEveryone/pipeline/Miscellananeous/extract_diagnosis_codes.py
@@ -3,7 +3,7 @@
 # Extract all diagnoses sorted by item_count
 ##########################################################################################
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -15,7 +15,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # Create SCRIPT_FILE
 SCRIPT_FILE = StringIO()
diff --git a/scripts/ExpertsVsEveryone/pipeline/Miscellananeous/extract_unmatched_patient_cohorts.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Miscellananeous/extract_unmatched_patient_cohorts.py
similarity index 92%
rename from scripts/ExpertsVsEveryone/pipeline/Miscellananeous/extract_unmatched_patient_cohorts.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Miscellananeous/extract_unmatched_patient_cohorts.py
index 28a7c75a..9090c691 100644
--- a/scripts/ExpertsVsEveryone/pipeline/Miscellananeous/extract_unmatched_patient_cohorts.py
+++ b/scripts/Archive/ExpertsVsEveryone/pipeline/Miscellananeous/extract_unmatched_patient_cohorts.py
@@ -3,7 +3,7 @@
 # Extract patient_ids based on expert v.s. everyone provider type
 ##########################################################################################
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -15,7 +15,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # Create SCRIPT_FILE
 SCRIPT_FILE = StringIO()
@@ -57,7 +56,7 @@
 
 #count = 0
 for line in results:
-    if (line[0] not in unique_patient_ids.keys()): # only consider unique patient_ids
+    if (line[0] not in list(unique_patient_ids.keys())): # only consider unique patient_ids
         unique_patient_ids[line[0]] = 1
         output.write("{0},{1},{2},{3}\n".format(line[0],line[1],line[2],line[3]))
         outlist.write(str(line[0])+"\n")
diff --git a/scripts/ExpertsVsEveryone/pipeline/README.txt b/scripts/Archive/ExpertsVsEveryone/pipeline/README.txt
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/README.txt
rename to scripts/Archive/ExpertsVsEveryone/pipeline/README.txt
diff --git a/scripts/ExpertsVsEveryone/scripts/Training/get_predictions.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Training/get_predictions.py
similarity index 93%
rename from scripts/ExpertsVsEveryone/scripts/Training/get_predictions.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Training/get_predictions.py
index b6f37fef..c6996d3d 100644
--- a/scripts/ExpertsVsEveryone/scripts/Training/get_predictions.py
+++ b/scripts/Archive/ExpertsVsEveryone/pipeline/Training/get_predictions.py
@@ -3,9 +3,9 @@
 # Call ItemRecommender internally
 ##########################################################################################
 
-import urlparse;
+import urllib.parse;
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -17,7 +17,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # import ItemRecommender
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender, RecommenderQuery
@@ -49,11 +48,11 @@
 	clinical_item_id = line[0]
 	description = " ".join(line[1:])
 	queryStr = "queryItemIds=" + str(clinical_item_id) + baseQueryStr
-	print('Finding Top Associations for "{0}"'.format(description))
+	print(('Finding Top Associations for "{0}"'.format(description)))
 
 	# Build RecommenderQuery
 	query = RecommenderQuery();
-	paramDict = dict(urlparse.parse_qsl(queryStr,True));
+	paramDict = dict(urllib.parse.parse_qsl(queryStr,True));
 	query.parseParams(paramDict);
 
 	# Call ItemRecommender
diff --git a/scripts/ExpertsVsEveryone/pipeline/Training/run_association.sh b/scripts/Archive/ExpertsVsEveryone/pipeline/Training/run_association.sh
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Training/run_association.sh
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Training/run_association.sh
diff --git a/scripts/ExpertsVsEveryone/pipeline/Training/stratify.py b/scripts/Archive/ExpertsVsEveryone/pipeline/Training/stratify.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/Training/stratify.py
rename to scripts/Archive/ExpertsVsEveryone/pipeline/Training/stratify.py
diff --git a/scripts/ExpertsVsEveryone/pipeline/everyone_patients_matched_remapped_excluding_ordersets.csv b/scripts/Archive/ExpertsVsEveryone/pipeline/everyone_patients_matched_remapped_excluding_ordersets.csv
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/everyone_patients_matched_remapped_excluding_ordersets.csv
rename to scripts/Archive/ExpertsVsEveryone/pipeline/everyone_patients_matched_remapped_excluding_ordersets.csv
diff --git a/scripts/ExpertsVsEveryone/pipeline/everyone_patients_with_ordersets.csv b/scripts/Archive/ExpertsVsEveryone/pipeline/everyone_patients_with_ordersets.csv
similarity index 100%
rename from scripts/ExpertsVsEveryone/pipeline/everyone_patients_with_ordersets.csv
rename to scripts/Archive/ExpertsVsEveryone/pipeline/everyone_patients_with_ordersets.csv
diff --git a/scripts/ExpertsVsEveryone/referenceData/SetupReferenceData.txt b/scripts/Archive/ExpertsVsEveryone/referenceData/SetupReferenceData.txt
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/SetupReferenceData.txt
rename to scripts/Archive/ExpertsVsEveryone/referenceData/SetupReferenceData.txt
diff --git a/scripts/ExpertsVsEveryone/referenceData/candidateOrders.tab b/scripts/Archive/ExpertsVsEveryone/referenceData/candidateOrders.tab
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/candidateOrders.tab
rename to scripts/Archive/ExpertsVsEveryone/referenceData/candidateOrders.tab
diff --git a/scripts/ExpertsVsEveryone/referenceData/generateWorksheetData.py b/scripts/Archive/ExpertsVsEveryone/referenceData/generateWorksheetData.py
similarity index 85%
rename from scripts/ExpertsVsEveryone/referenceData/generateWorksheetData.py
rename to scripts/Archive/ExpertsVsEveryone/referenceData/generateWorksheetData.py
index 53783387..f63c7567 100644
--- a/scripts/ExpertsVsEveryone/referenceData/generateWorksheetData.py
+++ b/scripts/Archive/ExpertsVsEveryone/referenceData/generateWorksheetData.py
@@ -54,8 +54,8 @@
 
 recommender = ItemAssociationRecommender();
 
-for admitDxId, itemIds in itemIdsByAdmitDxId.iteritems():
-    print >> sys.stderr, admitDxId, len(itemIds);
+for admitDxId, itemIds in itemIdsByAdmitDxId.items():
+    print(admitDxId, len(itemIds), file=sys.stderr);
     recQuery = RecommenderQuery();
     recQuery.excludeItemIds = recommender.defaultExcludedClinicalItemIds();
     recQuery.excludeCategoryIds = recommender.defaultExcludedClinicalItemCategoryIds();
@@ -72,7 +72,7 @@
         itemIds.add( result["clinical_item_id"] );
         #print >> sys.stderr, result["description"];
 
-    print >> sys.stderr, admitDxId, len(itemIds);
+    print(admitDxId, len(itemIds), file=sys.stderr);
 
     # Top results by PPV
     recQuery.sortField = "PPV";
@@ -80,7 +80,7 @@
     for result in results:
         itemIds.add( result["clinical_item_id"] );
 
-    print >> sys.stderr, admitDxId, len(itemIds);
+    print(admitDxId, len(itemIds), file=sys.stderr);
 
     # Top results by baseline prevalence
     recQuery.sortField = "prevalence";
@@ -88,12 +88,12 @@
     for result in results:
         itemIds.add( result["clinical_item_id"] );
 
-    print >> sys.stderr, admitDxId, len(itemIds);
+    print(admitDxId, len(itemIds), file=sys.stderr);
 
 # Load clinicalItem models for quick lookups 
 clinicalItemById = DBUtil.loadTableAsDict("clinical_item");
 
-print str.join("\t",["Admit Dx ID","Section","Guideline","item_collection_id","collection_type_id","value","comment","clinical_item_id","Name","Description"]);
+print(str.join("\t",["Admit Dx ID","Section","Guideline","item_collection_id","collection_type_id","value","comment","clinical_item_id","Name","Description"]));
 
 for (admitDxId, sectionName, guidelineName) in admitDxIdSectionGuidelineNameTuples:
     itemIds = itemIdsByAdmitDxId[admitDxId];
@@ -101,6 +101,6 @@
     for itemId in itemIds:
         clinicalItem = clinicalItemById[itemId];
         # Note just printing blank spaces for values in middle
-        print "%s\t%s\t%s\t\t1\t\t\t%s\t%s\t%s" % (admitDxId, sectionName, guidelineName, clinicalItem["clinical_item_id"], clinicalItem["name"], clinicalItem["description"]);
-        print "%s\t%s\t%s\t\t3\t\t\t%s\t%s\t%s" % (admitDxId, sectionName, guidelineName, clinicalItem["clinical_item_id"], clinicalItem["name"], clinicalItem["description"]);
+        print("%s\t%s\t%s\t\t1\t\t\t%s\t%s\t%s" % (admitDxId, sectionName, guidelineName, clinicalItem["clinical_item_id"], clinicalItem["name"], clinicalItem["description"]));
+        print("%s\t%s\t%s\t\t3\t\t\t%s\t%s\t%s" % (admitDxId, sectionName, guidelineName, clinicalItem["clinical_item_id"], clinicalItem["name"], clinicalItem["description"]));
 
diff --git a/scripts/ExpertsVsEveryone/referenceData/item_collection.update.tab b/scripts/Archive/ExpertsVsEveryone/referenceData/item_collection.update.tab
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/item_collection.update.tab
rename to scripts/Archive/ExpertsVsEveryone/referenceData/item_collection.update.tab
diff --git a/scripts/ExpertsVsEveryone/referenceData/item_collection_item.diagnosisLink.update.tab b/scripts/Archive/ExpertsVsEveryone/referenceData/item_collection_item.diagnosisLink.update.tab
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/item_collection_item.diagnosisLink.update.tab
rename to scripts/Archive/ExpertsVsEveryone/referenceData/item_collection_item.diagnosisLink.update.tab
diff --git a/scripts/ExpertsVsEveryone/referenceData/item_collection_item.referenceOrders.update.tab b/scripts/Archive/ExpertsVsEveryone/referenceData/item_collection_item.referenceOrders.update.tab
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/item_collection_item.referenceOrders.update.tab
rename to scripts/Archive/ExpertsVsEveryone/referenceData/item_collection_item.referenceOrders.update.tab
diff --git a/scripts/ExpertsVsEveryone/referenceData/oldReferenceConversion.py b/scripts/Archive/ExpertsVsEveryone/referenceData/oldReferenceConversion.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/oldReferenceConversion.py
rename to scripts/Archive/ExpertsVsEveryone/referenceData/oldReferenceConversion.py
diff --git a/scripts/ExpertsVsEveryone/referenceData/oldReferences.tab b/scripts/Archive/ExpertsVsEveryone/referenceData/oldReferences.tab
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/oldReferences.tab
rename to scripts/Archive/ExpertsVsEveryone/referenceData/oldReferences.tab
diff --git a/scripts/Archive/ExpertsVsEveryone/referenceData/orderSetQueries.txt b/scripts/Archive/ExpertsVsEveryone/referenceData/orderSetQueries.txt
new file mode 100644
index 00000000..4bf454a9
--- /dev/null
+++ b/scripts/Archive/ExpertsVsEveryone/referenceData/orderSetQueries.txt
@@ -0,0 +1,190 @@
+
+-- Order Set Usage, only counting Default Recommend
+explain
+select external_id, name, count(patient_id) as patientCount, 
+   avg(itemCount) as avgItemCount, min(itemCount) as minItemCount, max(itemCount) as maxItemCount,
+   sum(itemCount) as sumItemCount, sum(itemCount*itemCount) as sumSquaresItemCount
+from
+(
+	select 
+		ic.external_id,
+		ic.name,
+		pi.patient_id,
+		count(distinct pi.clinical_item_id) as itemCount
+	from 
+		item_collection as ic,
+		item_collection_item as ici, 
+		patient_item_collection_link as picl, 
+		patient_item as pi,
+		clinical_item as ci,
+		clinical_item_category as cic
+	where 
+		ic.item_collection_id = ici.item_collection_id and
+		ici.item_collection_item_id = picl.item_collection_item_id and 
+		picl.patient_item_id = pi.patient_item_id and  
+		pi.clinical_item_id = ci.clinical_item_id and
+		ci.clinical_item_category_id = cic.clinical_item_category_id and
+		ic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent and
+		ci.default_recommend = 1 and
+		cic.default_recommend = 1
+	group by 
+		ic.external_id,
+		ic.name,
+		pi.patient_id
+) as orderSetPatient
+group by 
+	external_id,
+	name
+
+
+
+nohup python medinfo/db/DBUtil.py -c "select external_id, name, count(patient_id) as patientCount,     avg(itemCount) as avgItemCount, min(itemCount) as minItemCount, max(itemCount) as maxItemCount,    sum(itemCount) as sumItemCount, sum(itemCount*itemCount) as sumSquaresItemCount from (  select    ic.external_id,   ic.name,   pi.patient_id,   count(distinct pi.clinical_item_id) as itemCount  from    item_collection as ic,   item_collection_item as ici,    patient_item_collection_link as picl,    patient_item as pi,   clinical_item as ci,   clinical_item_category as cic  where    ic.item_collection_id = ici.item_collection_id and   ici.item_collection_item_id = picl.item_collection_item_id and    picl.patient_item_id = pi.patient_item_id and     pi.clinical_item_id = ci.clinical_item_id and   ci.clinical_item_category_id = cic.clinical_item_category_id and   ci.default_recommend = 1 and   cic.default_recommend = 1  group by    ic.external_id,   ic.name,   pi.patient_id ) as orderSetPatient group by   external_id,  name" orderSetUsage.defaultRecommend.tab &> log/orderSetUsage.defaultRecommend &
+
+
+-- Order Set Usage
+explain
+select external_id, name, count(patient_id) as patientCount, 
+   avg(itemCount) as avgItemCount, min(itemCount) as minItemCount, max(itemCount) as maxItemCount,
+   sum(itemCount) as sumItemCount, sum(itemCount*itemCount) as sumSquaresItemCount
+from
+(
+	select 
+		ic.external_id,
+		ic.name,
+		pi.patient_id,
+		count(distinct pi.clinical_item_id) as itemCount
+	from 
+		item_collection as ic,
+		item_collection_item as ici, 
+		patient_item_collection_link as picl, 
+		patient_item as pi
+	where 
+		ic.item_collection_id = ici.item_collection_id and
+		ici.item_collection_item_id = picl.item_collection_item_id and 
+		picl.patient_item_id = pi.patient_item_id and
+		ic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent 
+	group by 
+		ic.external_id,
+		ic.name,
+		pi.patient_id
+) as orderSetPatient
+group by 
+	external_id,
+	name
+
+
+nohup python medinfo/db/DBUtil.py -c "select external_id, name, count(patient_id) as patientCount,     avg(itemCount) as avgItemCount, min(itemCount) as minItemCount, max(itemCount) as maxItemCount,    sum(itemCount) as sumItemCount, sum(itemCount*itemCount) as sumSquaresItemCount from (  select    ic.external_id,   ic.name,   pi.patient_id,   count(distinct pi.clinical_item_id) as itemCount  from    item_collection as ic,   item_collection_item as ici,    patient_item_collection_link as picl,    patient_item as pi  where    ic.item_collection_id = ici.item_collection_id and   ici.item_collection_item_id = picl.item_collection_item_id and    picl.patient_item_id = pi.patient_item_id   group by    ic.external_id,   ic.name,   pi.patient_id ) as orderSetPatient group by   external_id,  name " orderSetUsage &> log/orderSetUsage &
+
+
+
+
+-- Order Set Sizes
+select 
+	ic.external_id,
+	ic.name,
+	count(distinct ici.clinical_item_id) as itemCount
+from 
+	item_collection as ic,
+	item_collection_item as ici
+where 
+	ic.item_collection_id = ici.item_collection_id and
+	ic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent 
+group by 
+	ic.external_id,
+	ic.name
+
+nohup python medinfo/db/DBUtil.py -c " select   ic.external_id,  ic.name,  count(distinct ici.clinical_item_id) as itemCount from   item_collection as ic,  item_collection_item as ici where   ic.item_collection_id = ici.item_collection_id  group by   ic.external_id,  ic.name " orderSetSizes.tab &> log/orderSetSizes &
+
+
+
+-- Order Set Sizes, Default Recommend Only
+select 
+	ic.external_id,
+	ic.name,
+	count(distinct ici.clinical_item_id) as itemCount
+from 
+	item_collection as ic,
+	item_collection_item as ici, 
+	clinical_item as ci,
+	clinical_item_category as cic
+where 
+	ic.item_collection_id = ici.item_collection_id and
+	ici.clinical_item_id = ci.clinical_item_id and
+	ci.clinical_item_category_id = cic.clinical_item_category_id and
+	ic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent and
+	ci.default_recommend = 1 and
+	cic.default_recommend = 1
+group by 
+	ic.external_id,
+	ic.name
+	
+nohup python medinfo/db/DBUtil.py -c "select  	ic.external_id, 	ic.name, 	count(distinct ici.clinical_item_id) as itemCount from  	item_collection as ic, 	item_collection_item as ici,  	clinical_item as ci, 	clinical_item_category as cic where  	ic.item_collection_id = ici.item_collection_id and 	ici.clinical_item_id = ci.clinical_item_id and 	ci.clinical_item_category_id = cic.clinical_item_category_id and 	ci.default_recommend = 1 and 	cic.default_recommend = 1 group by  	ic.external_id, 	ic.name 	 " orderSetSizes.defaultRecommend.tab &> log/orderSetSizes.defaultRecommend & 	
+
+
+
+
+
+
+-- Order Set Component Usage
+explain
+	select 
+		ic.external_id,
+		ic.name,
+		ci.clinical_item_id,
+		ci.name,
+		ci.description,
+		count(distinct pi.patient_item_id) as itemCount
+	from 
+		item_collection as ic,
+		item_collection_item as ici, 
+		patient_item_collection_link as picl, 
+		patient_item as pi,
+		clinical_item as ci,
+		clinical_item_category as cic
+	where 
+		ic.item_collection_id = ici.item_collection_id and
+		ici.item_collection_item_id = picl.item_collection_item_id and 
+		picl.patient_item_id = pi.patient_item_id and  
+		pi.clinical_item_id = ci.clinical_item_id and
+		ci.clinical_item_category_id = cic.clinical_item_category_id and
+		ic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent
+		-- ic.external_id = 692 -- Example for Med Sepsis
+	group by 
+		ic.external_id,
+		ic.name,
+		ci.clinical_item_id,
+		ci.name,
+		ci.description
+
+
+-- Order Set Component Usage, Default Recommend Only
+	select 
+		ic.external_id,
+		ic.name,
+		ci.clinical_item_id,
+		ci.name,
+		ci.description,
+		count(distinct pi.patient_item_id) as itemCount
+	from 
+		item_collection as ic,
+		item_collection_item as ici, 
+		patient_item_collection_link as picl, 
+		patient_item as pi,
+		clinical_item as ci,
+		clinical_item_category as cic
+	where 
+		ic.item_collection_id = ici.item_collection_id and
+		ici.item_collection_item_id = picl.item_collection_item_id and 
+		picl.patient_item_id = pi.patient_item_id and  
+		pi.clinical_item_id = ci.clinical_item_id and
+		ci.clinical_item_category_id = cic.clinical_item_category_id and
+		ci.default_recommend = 1 and
+		cic.default_recommend = 1 and
+		ic.section <> 'Ad-hoc Orders' -- Existing option to include orders on the fly, even though they weren't really part of the original order set design / intent
+		-- ic.external_id = 692 -- Example for Med Sepsis
+	group by 
+		ic.external_id,
+		ic.name,
+		ci.clinical_item_id,
+		ci.name,
+		ci.description
diff --git a/scripts/ExpertsVsEveryone/referenceData/scratchNotes.txt b/scripts/Archive/ExpertsVsEveryone/referenceData/scratchNotes.txt
similarity index 100%
rename from scripts/ExpertsVsEveryone/referenceData/scratchNotes.txt
rename to scripts/Archive/ExpertsVsEveryone/referenceData/scratchNotes.txt
diff --git a/scripts/ExpertsVsEveryone/referenceData/updateReferenceData.py b/scripts/Archive/ExpertsVsEveryone/referenceData/updateReferenceData.py
similarity index 80%
rename from scripts/ExpertsVsEveryone/referenceData/updateReferenceData.py
rename to scripts/Archive/ExpertsVsEveryone/referenceData/updateReferenceData.py
index 0c6c8969..735fe454 100644
--- a/scripts/ExpertsVsEveryone/referenceData/updateReferenceData.py
+++ b/scripts/Archive/ExpertsVsEveryone/referenceData/updateReferenceData.py
@@ -3,9 +3,9 @@
 
 # Clear out any previous reference data (but keep the fixed order set data)
 # (May want to backup database before running these commands)
-print >> sys.stderr, "About to delete/replace existing item_collection_item reference data."
-print >> sys.stderr, "Are you sure? (Ctrl-C to cancel)"
-raw_input();	# Pause for command-line input/confirmation
+print("About to delete/replace existing item_collection_item reference data.", file=sys.stderr)
+print("Are you sure? (Ctrl-C to cancel)", file=sys.stderr)
+input();	# Pause for command-line input/confirmation
 
 conn = DBUtil.connection();
 try:
diff --git a/scripts/ExpertsVsEveryone/scripts/Figures/generate_plots.sh b/scripts/Archive/ExpertsVsEveryone/scripts/Figures/generate_plots.sh
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Figures/generate_plots.sh
rename to scripts/Archive/ExpertsVsEveryone/scripts/Figures/generate_plots.sh
diff --git a/scripts/ExpertsVsEveryone/scripts/Figures/matching_parse.py b/scripts/Archive/ExpertsVsEveryone/scripts/Figures/matching_parse.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Figures/matching_parse.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Figures/matching_parse.py
diff --git a/scripts/ExpertsVsEveryone/scripts/Figures/plot_input.py b/scripts/Archive/ExpertsVsEveryone/scripts/Figures/plot_input.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Figures/plot_input.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Figures/plot_input.py
diff --git a/scripts/ExpertsVsEveryone/scripts/Matching/clean_data.py b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/clean_data.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Matching/clean_data.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Matching/clean_data.py
diff --git a/scripts/ExpertsVsEveryone/scripts/Matching/extractData_modified.py b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/extractData_modified.py
similarity index 96%
rename from scripts/ExpertsVsEveryone/scripts/Matching/extractData_modified.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Matching/extractData_modified.py
index fa9278ea..67e2d28c 100644
--- a/scripts/ExpertsVsEveryone/scripts/Matching/extractData_modified.py
+++ b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/extractData_modified.py
@@ -7,7 +7,7 @@
 
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import datetime, timedelta;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
@@ -104,7 +104,7 @@ def main(argv):
         if disease not in icd9prefixesByDisease:
             icd9prefixesByDisease[disease] = list();
         icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
-    for disease, icd9prefixes in icd9prefixesByDisease.iteritems():
+    for disease, icd9prefixes in icd9prefixesByDisease.items():
         disease = disease.translate(None," ()-/");   # Strip off punctuation
         extractor.queryClinicalItemsByName(stdOpen("Charlson."+disease+".tab","w"), icd9prefixes, patientIds, operator="~*");
     
@@ -115,11 +115,11 @@ def main(argv):
         if category not in teamNameByCategory:
             teamNameByCategory[category] = list();
         teamNameByCategory[category].append(teamName);
-    for category, teamNames in teamNameByCategory.iteritems():
+    for category, teamNames in teamNameByCategory.items():
         extractor.queryClinicalItemsByName(stdOpen("TT."+category+".tab","w"), teamNames, patientIds, col="description");
 
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 def loadIVAntibioticItemIds(extractor):
     # ivMedCategoryId = 72;
@@ -217,7 +217,7 @@ def queryPatientEncounters(outputFile, extractor):
         and adt1.pat_enc_csn_anon_id = adt2.pat_enc_csn_anon_id
         """ % ("Tt Pamf Med (Primary)");
 
-        print >> sys.stderr, cohortQuery;
+        print(cohortQuery, file=sys.stderr);
         cursor.execute(cohortQuery);
 
         patientEncounters = list();
diff --git a/scripts/ExpertsVsEveryone/scripts/Matching/extract_age.py b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/extract_age.py
similarity index 93%
rename from scripts/ExpertsVsEveryone/scripts/Matching/extract_age.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Matching/extract_age.py
index e1a2187b..a609cf82 100644
--- a/scripts/ExpertsVsEveryone/scripts/Matching/extract_age.py
+++ b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/extract_age.py
@@ -3,7 +3,7 @@
 # Extract age out of stride database
 ##########################################################################################
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -15,7 +15,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # Create SCRIPT_FILE
 SCRIPT_FILE = StringIO()
diff --git a/scripts/ExpertsVsEveryone/scripts/Matching/matching.Rmd b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/matching.Rmd
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Matching/matching.Rmd
rename to scripts/Archive/ExpertsVsEveryone/scripts/Matching/matching.Rmd
diff --git a/scripts/ExpertsVsEveryone/scripts/Matching/remap_ids.py b/scripts/Archive/ExpertsVsEveryone/scripts/Matching/remap_ids.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Matching/remap_ids.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Matching/remap_ids.py
diff --git a/scripts/ExpertsVsEveryone/pipeline/Miscellananeous/extract_diagnosis_codes.py b/scripts/Archive/ExpertsVsEveryone/scripts/Miscellananeous/extract_diagnosis_codes.py
similarity index 94%
rename from scripts/ExpertsVsEveryone/pipeline/Miscellananeous/extract_diagnosis_codes.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Miscellananeous/extract_diagnosis_codes.py
index 2ea89fb0..7fcf554c 100644
--- a/scripts/ExpertsVsEveryone/pipeline/Miscellananeous/extract_diagnosis_codes.py
+++ b/scripts/Archive/ExpertsVsEveryone/scripts/Miscellananeous/extract_diagnosis_codes.py
@@ -3,7 +3,7 @@
 # Extract all diagnoses sorted by item_count
 ##########################################################################################
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -15,7 +15,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # Create SCRIPT_FILE
 SCRIPT_FILE = StringIO()
diff --git a/scripts/ExpertsVsEveryone/scripts/Miscellananeous/extract_unmatched_patient_cohorts.py b/scripts/Archive/ExpertsVsEveryone/scripts/Miscellananeous/extract_unmatched_patient_cohorts.py
similarity index 92%
rename from scripts/ExpertsVsEveryone/scripts/Miscellananeous/extract_unmatched_patient_cohorts.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Miscellananeous/extract_unmatched_patient_cohorts.py
index 28a7c75a..9090c691 100644
--- a/scripts/ExpertsVsEveryone/scripts/Miscellananeous/extract_unmatched_patient_cohorts.py
+++ b/scripts/Archive/ExpertsVsEveryone/scripts/Miscellananeous/extract_unmatched_patient_cohorts.py
@@ -3,7 +3,7 @@
 # Extract patient_ids based on expert v.s. everyone provider type
 ##########################################################################################
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -15,7 +15,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # Create SCRIPT_FILE
 SCRIPT_FILE = StringIO()
@@ -57,7 +56,7 @@
 
 #count = 0
 for line in results:
-    if (line[0] not in unique_patient_ids.keys()): # only consider unique patient_ids
+    if (line[0] not in list(unique_patient_ids.keys())): # only consider unique patient_ids
         unique_patient_ids[line[0]] = 1
         output.write("{0},{1},{2},{3}\n".format(line[0],line[1],line[2],line[3]))
         outlist.write(str(line[0])+"\n")
diff --git a/scripts/ExpertsVsEveryone/scripts/README.txt b/scripts/Archive/ExpertsVsEveryone/scripts/README.txt
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/README.txt
rename to scripts/Archive/ExpertsVsEveryone/scripts/README.txt
diff --git a/scripts/ExpertsVsEveryone/pipeline/Training/get_predictions.py b/scripts/Archive/ExpertsVsEveryone/scripts/Training/get_predictions.py
similarity index 93%
rename from scripts/ExpertsVsEveryone/pipeline/Training/get_predictions.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Training/get_predictions.py
index b6f37fef..c6996d3d 100644
--- a/scripts/ExpertsVsEveryone/pipeline/Training/get_predictions.py
+++ b/scripts/Archive/ExpertsVsEveryone/scripts/Training/get_predictions.py
@@ -3,9 +3,9 @@
 # Call ItemRecommender internally
 ##########################################################################################
 
-import urlparse;
+import urllib.parse;
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime;
 
 os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
@@ -17,7 +17,6 @@
 from medinfo.common.test.Util import MedInfoTestCase;
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery;
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME;
 
 # import ItemRecommender
 from medinfo.cpoe.ItemRecommender import ItemAssociationRecommender, RecommenderQuery
@@ -49,11 +48,11 @@
 	clinical_item_id = line[0]
 	description = " ".join(line[1:])
 	queryStr = "queryItemIds=" + str(clinical_item_id) + baseQueryStr
-	print('Finding Top Associations for "{0}"'.format(description))
+	print(('Finding Top Associations for "{0}"'.format(description)))
 
 	# Build RecommenderQuery
 	query = RecommenderQuery();
-	paramDict = dict(urlparse.parse_qsl(queryStr,True));
+	paramDict = dict(urllib.parse.parse_qsl(queryStr,True));
 	query.parseParams(paramDict);
 
 	# Call ItemRecommender
diff --git a/scripts/ExpertsVsEveryone/scripts/Training/run_association.sh b/scripts/Archive/ExpertsVsEveryone/scripts/Training/run_association.sh
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Training/run_association.sh
rename to scripts/Archive/ExpertsVsEveryone/scripts/Training/run_association.sh
diff --git a/scripts/ExpertsVsEveryone/scripts/Training/stratify.py b/scripts/Archive/ExpertsVsEveryone/scripts/Training/stratify.py
similarity index 100%
rename from scripts/ExpertsVsEveryone/scripts/Training/stratify.py
rename to scripts/Archive/ExpertsVsEveryone/scripts/Training/stratify.py
diff --git a/scripts/Ferritin/extractData.py b/scripts/Archive/Ferritin/extractData.py
similarity index 92%
rename from scripts/Ferritin/extractData.py
rename to scripts/Archive/Ferritin/extractData.py
index 8f6ca59c..2ba63f6b 100644
--- a/scripts/Ferritin/extractData.py
+++ b/scripts/Archive/Ferritin/extractData.py
@@ -1,6 +1,6 @@
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList;
@@ -34,7 +34,7 @@ def main(argv):
     queryOutpatientIronRx(stdOpen("outpatientIronRx.tab","w"), patientById);
     
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 def queryPatients(outputFile):
     log.info("Select patients with any result for a ferritin test");
@@ -55,7 +55,7 @@ def queryPatients(outputFile):
         
 
     log.info("Patients with admit or diet orders for surgery"); # Not perfectly accurate for isolating surgical patients
-    for patient in patientById.itervalues():
+    for patient in patientById.values():
         patient["surgery"] = 0; # Default to 0 / false
     query = \
         """select distinct patient_id
@@ -68,7 +68,7 @@ def queryPatients(outputFile):
             patientById[patientId]["surgery"] = 1;
 
     log.info("Patients with an order for dialysis");    # (Does not differentiate acute vs. chronic.  Includes peritoneal)
-    for patient in patientById.itervalues():
+    for patient in patientById.values():
         patient["dialysis"] = 0; # Default to 0 / false
     query = \
         """select distinct patient_id
@@ -82,7 +82,7 @@ def queryPatients(outputFile):
 
     # Drop results as tab-delimited text output
     formatter = TextResultsFormatter(outputFile);
-    formatter.formatResultDicts(patientById.itervalues(), addHeaderRow=True);
+    formatter.formatResultDicts(iter(patientById.values()), addHeaderRow=True);
 
     return patientById;    
 
@@ -99,7 +99,7 @@ def queryClinicalItems(outputFile,clinicalItemIds,patientById):
         query.addSelect(col);
     query.addFrom("patient_item");
     query.addWhereIn("clinical_item_id", clinicalItemIds );
-    query.addWhereIn("patient_id", patientById.viewkeys() );
+    query.addWhereIn("patient_id", patientById.keys() );
     query.addOrderBy("patient_id");
     query.addOrderBy("item_date");
 
@@ -128,7 +128,7 @@ def queryLabResults(outputFile, patientById):
     query.addFrom("stride_order_results as sor, stride_order_proc as sop");
     query.addWhere("sor.order_proc_id = sop.order_proc_id");
     query.addWhereIn("base_name", labBaseNames );
-    query.addWhereIn("pat_id", patientById.viewkeys() );
+    query.addWhereIn("pat_id", patientById.keys() );
     query.addOrderBy("pat_id");
     query.addOrderBy("sor.result_time");
 
@@ -153,7 +153,7 @@ def queryOutpatientIronRx(outputFile, patientById):
         query.addSelect(col);
     query.addFrom("stride_order_med");
     query.addWhereIn("medication_id", allEnteralIronMedicationIds );
-    query.addWhereIn("pat_id", patientById.viewkeys() );
+    query.addWhereIn("pat_id", patientById.keys() );
     query.addOrderBy("pat_id");
     query.addOrderBy("ordering_date");
 
diff --git a/scripts/Ferritin/extractionNotes.txt b/scripts/Archive/Ferritin/extractionNotes.txt
similarity index 100%
rename from scripts/Ferritin/extractionNotes.txt
rename to scripts/Archive/Ferritin/extractionNotes.txt
diff --git a/scripts/Ferritin/formatData.py b/scripts/Archive/Ferritin/formatData.py
similarity index 95%
rename from scripts/Ferritin/formatData.py
rename to scripts/Archive/Ferritin/formatData.py
index bfd94181..bf7a06ec 100644
--- a/scripts/Ferritin/formatData.py
+++ b/scripts/Archive/Ferritin/formatData.py
@@ -2,7 +2,7 @@
 import time;
 import numpy as np;
 from datetime import datetime, timedelta;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db import DBUtil;
@@ -74,7 +74,7 @@ def main(argv=None):
     formatter.formatResultDicts(patientResults, colNames, addHeaderRow=True);
 
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 
 def parsePatientFile(patientFile, colNames):
@@ -117,7 +117,7 @@ def addClinicalItemFeatures(itemTimesByPatientId, patientById, colNames, itemLab
     preLabel = "%s.pre" % itemLabel;
     postLabel = "%s.post" % itemLabel;
     
-    for patientId, patient in patientById.iteritems():
+    for patientId, patient in patientById.items():
         # Initialize values to null for not found
         patient[preTimeLabel] = None;
         patient[postTimeLabel] = None;
@@ -178,7 +178,7 @@ def parseLabResultsFile(labFile):
 def addLabFeatures(labsByBaseNameByPatientId, patientById, colNames, indexItemBaseName, labBaseNames, labPreTime, labPostTime):
     log.info("Sort lab results by result time for each patient and find items within specified time period to aggregate");
     prog = ProgressDots();
-    for iPatient, (patientId, labsByBaseName) in enumerate(labsByBaseNameByPatientId.iteritems()):
+    for iPatient, (patientId, labsByBaseName) in enumerate(labsByBaseNameByPatientId.items()):
         # Look for the first result of the index item (ferritin)
         indexItem = None;
         if indexItemBaseName in labsByBaseName:
@@ -245,7 +245,7 @@ def colsFromLabBaseNames(labBaseNames):
 def filterPatients(patientById):
     log.info("Deidentify patient IDs and build data list with adequate data");
     patientResults = list();
-    for iPatient, patient in enumerate(patientById.itervalues()):
+    for iPatient, patient in enumerate(patientById.values()):
         # Further deidentify patients by applying sequential ID
         patient["pat_id"] = patient["patient_id"] = iPatient;
         # Only accept patients where an index item and times were found
diff --git a/scripts/Ferritin/machineLearn.py b/scripts/Archive/Ferritin/machineLearn.py
similarity index 93%
rename from scripts/Ferritin/machineLearn.py
rename to scripts/Archive/Ferritin/machineLearn.py
index c3ddca08..b8aaba82 100644
--- a/scripts/Ferritin/machineLearn.py
+++ b/scripts/Archive/Ferritin/machineLearn.py
@@ -1,6 +1,6 @@
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList;
@@ -117,7 +117,7 @@ def testModels(xDF,ySeries,kFolds=10):
     for scoreFxn in scoreFxns:
         for model in models:
             scores = cross_val_score(model,xDF,ySeries,cv=kFolds, scoring=scoreFxn);
-            print "%.2f (%.2f, %.2f) %s.%s" % (scores.mean(), scores.mean()-scores.std()*2, scores.mean()+scores.std()*2, str(scoreFxn), type(model).__name__);
+            print("%.2f (%.2f, %.2f) %s.%s" % (scores.mean(), scores.mean()-scores.std()*2, scores.mean()+scores.std()*2, str(scoreFxn), type(model).__name__));
 
 def driver(dataFrame):
     timer = time.time();
@@ -129,7 +129,7 @@ def driver(dataFrame):
     imputedDF = imputeMissingValues(subDF);  
 
     outcomeSeries = (1-imputedDF["ironOutpatient.post"]);
-    print("== Outpatient Iron Prescription, Post, %d%% ==" % (sum(outcomeSeries)*100/len(outcomeSeries)) );
+    print(("== Outpatient Iron Prescription, Post, %d%% ==" % (sum(outcomeSeries)*100/len(outcomeSeries)) ));
     selectedDF = featureSelection(imputedDF, outcomeSeries, 'all'); # But drop outcome variable, otherwise cheating prediction
     testModels(selectedDF, outcomeSeries);
 
@@ -160,7 +160,7 @@ def driver(dataFrame):
     """
     
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
     
     return (selectedDF, imputedDF);
 
diff --git a/scripts/Ferritin/plotAssociations.py b/scripts/Archive/Ferritin/plotAssociations.py
similarity index 93%
rename from scripts/Ferritin/plotAssociations.py
rename to scripts/Archive/Ferritin/plotAssociations.py
index d8c708ed..20bc656d 100644
--- a/scripts/Ferritin/plotAssociations.py
+++ b/scripts/Archive/Ferritin/plotAssociations.py
@@ -1,6 +1,6 @@
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 
 import numpy as np;
 import pylab as pl;
@@ -66,7 +66,7 @@
     # Construct independent (x) and dependent (y) vectors from available feature data
     yList = list();
     xList = list();
-    for patient in patientById.itervalues():
+    for patient in patientById.values():
         indexValue = patient[INDEX_ITEM_BASE_NAME];
         featureValue = patient[labBaseName];
         
@@ -113,4 +113,4 @@
     pl.savefig("%s-%s.png" % (INDEX_ITEM_BASE_NAME, labBaseName) );
 
 timer = time.time() - timer;
-print >> sys.stderr, "%.3f seconds to complete" % timer;
+print("%.3f seconds to complete" % timer, file=sys.stderr);
diff --git a/scripts/Ferritin/plotDistributions.py b/scripts/Archive/Ferritin/plotDistributions.py
similarity index 90%
rename from scripts/Ferritin/plotDistributions.py
rename to scripts/Archive/Ferritin/plotDistributions.py
index d4a33c4a..40551e2d 100644
--- a/scripts/Ferritin/plotDistributions.py
+++ b/scripts/Archive/Ferritin/plotDistributions.py
@@ -1,6 +1,6 @@
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 
 import numpy as np;
 import pylab as pl;
@@ -71,14 +71,14 @@
         # Build lab value datasets separated by outcome value
         labValuesByOutcome = {0:list(), 1:list()};  # Start with empty lists for each outcome
 
-        for patient in patientById.itervalues():
+        for patient in patientById.values():
             outcome = patient[outcomeName];
             labValue = patient[labBaseName];
             if labValue is not None:    # Skip patients if don't have a value to count
                 labValuesByOutcome[outcome].append(labValue);
 
         # Trim off tails to avoid extreme values
-        for outcome, labValues in labValuesByOutcome.iteritems():
+        for outcome, labValues in labValuesByOutcome.items():
             nValues = len(labValues);
             labValues.sort();
             labValuesByOutcome[outcome] = labValues[int(0.05*nValues):int(0.95*nValues)];
@@ -89,8 +89,8 @@
         # Histogram plotting
         pl.clf();
         nBins = 25;
-        data = labValuesByOutcome.values();
-        label = ["%s-%s" % (outcomeName,outcome) for outcome in labValuesByOutcome.iterkeys()];
+        data = list(labValuesByOutcome.values());
+        label = ["%s-%s" % (outcomeName,outcome) for outcome in labValuesByOutcome.keys()];
 
         (n, bins, patches) = pl.hist( data, nBins, histtype='bar', label=label);
         pl.legend();
@@ -100,4 +100,4 @@
         pl.savefig("%s-%s.png" % (outcomeName, labBaseName) );
 
 timer = time.time() - timer;
-print >> sys.stderr, "%.3f seconds to complete" % timer;
+print("%.3f seconds to complete" % timer, file=sys.stderr);
diff --git a/scripts/Ferritin/scratch.py b/scripts/Archive/Ferritin/scratch.py
similarity index 100%
rename from scripts/Ferritin/scratch.py
rename to scripts/Archive/Ferritin/scratch.py
diff --git a/scripts/Archive/GlucoseControl/1filterPatient.py b/scripts/Archive/GlucoseControl/1filterPatient.py
new file mode 100644
index 00000000..99c3fc1a
--- /dev/null
+++ b/scripts/Archive/GlucoseControl/1filterPatient.py
@@ -0,0 +1,53 @@
+'''
+Filter out patients without consecutive glucose level test records.
+CSV files of EHR data are stored under directory "../data/bq/".
+Output files (patient IDs) are stored under directory "../data/preprocessed".
+'''
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# path to csv files
+
+PATH_TO_ID = "../data/bq/pat_id.csv"
+PATH_TO_GLU = "../data/bq/lab_glu.csv"
+
+
+# list of patients (unique ID)
+
+df_glu = pd.read_csv(PATH_TO_GLU)
+ID_list = np.unique(df_glu['rit_uid'].values)
+print("Number of patients with glucose level record:", len(ID_list))
+
+used_ID_list = list()
+
+
+# restrict to those bing in hospital long enough and having enough glucose level records
+df_glu['taken_time_jittered'] = pd.to_datetime(df_glu['taken_time_jittered'])
+for pat_id in ID_list:
+    print("--------Patient ID", pat_id)
+
+    pat_glu = df_glu.loc[df_glu['rit_uid'] == pat_id]
+    glu_time = pat_glu['taken_time_jittered'].values
+    glu_t0 = glu_time[0]
+    glu_tN = glu_time[-1]
+
+    if len(glu_time) < 2: # at least 2 glu measure and 24 hours data
+        print("-Too few glucose measure available.")
+    elif (glu_tN-glu_t0).astype('timedelta64[h]').astype('int')<24:
+        print("-Recorded time range too short.")
+    else:
+        # having at least 5 consecutive measure within 72 hours
+        glu_time_diff = np.diff(glu_time).astype('timedelta64[h]').astype('int')
+        start_ts_idx = [x_i for x_i, x in enumerate(glu_time_diff) if x > 72]
+        start_ts_idx = [x+1 for x in start_ts_idx]
+        start_ts_idx.insert(0,0)
+        start_ts_diff_idx = [ts_i for ts_i, td in enumerate(np.diff(start_ts_idx)) if td > 3]
+        start_ts_cont_idx = np.array(start_ts_idx)[start_ts_diff_idx]
+        if len(start_ts_cont_idx)>0:
+            used_ID_list.append(pat_id)
+
+# save data
+np.savetxt('../data/preprocessed/used_pat_id.csv', used_ID_list, delimiter=',', fmt='%s')
\ No newline at end of file
diff --git a/scripts/Archive/GlucoseControl/2constructFeatures.py b/scripts/Archive/GlucoseControl/2constructFeatures.py
new file mode 100644
index 00000000..5b1cd39a
--- /dev/null
+++ b/scripts/Archive/GlucoseControl/2constructFeatures.py
@@ -0,0 +1,334 @@
+'''
+From EHR data, extract features that potentially relate to the glucose level.
+CSV files of EHR data are stored under directory "../data/bq/".
+Output files (features extracted) are stored under directory "../data/plots/patients/" and "../data/preprocessed".
+'''
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# paths to csv files
+
+PATH_TO_ID = '../data/preprocessed/used_pat_id.csv'
+PATH_TO_DIA_ID = '../data/bq/hemodialysis_pat.csv'
+
+PATH_TO_DEMO = "../data/bq/pat_demo.csv"
+PATH_TO_WEIGHT = "../data/bq/pat_w.csv"
+
+PATH_TO_GLU = "../data/bq/lab_glu.csv"
+PATH_TO_A1C = "../data/bq/lab_a1c.csv"
+PATH_TO_CREA = "../data/bq/lab_crea.csv"  # creatine
+
+PATH_TO_INS = "../data/bq/ord_ins.csv"
+PATH_TO_ST1 = "../data/bq/ord_hyd.csv"  # hydrocortisone
+PATH_TO_ST2 = "../data/bq/ord_pre.csv"  # prednisone
+PATH_TO_ST3 = "../data/bq/ord_met.csv"  # methylprednisolone
+PATH_TO_ST4 = "../data/bq/ord_dex.csv"  # dexamethasone
+
+PATH_TO_DIET = "../data/bq/diet.csv"
+
+PATH_TO_OUTPUT = "../data/plots/patients/"
+PATH_TO_USED_ID = "../data/bq/used_pat_id.csv"
+PATH_TO_SAVE = "../data/preprocessed/"
+
+# corticosteroid equivalence
+steroid_name = ['hydrocortisone', 'prednisone', 'methylprednisolone', 'dexamethasone']
+steroid_equiv = {'hydrocortisone':20, 'prednisone': 5, 'methylprednisolone': 4, 'dexamethasone': 0.75}
+
+# glucose to a1c conversion
+glu_2_a1c = {126:6, 140:6.5, 154:7, 169:7.5, 183:8, 197:8.5, 212:9, 226:9.5, 240:10}
+ref_glu = list(glu_2_a1c.keys())
+
+
+# list of patient IDs to be used
+IDs = pd.read_csv(PATH_TO_ID, header=None).values.tolist()
+dia_IDs = pd.read_csv(PATH_TO_DIA_ID, header=None).values.tolist()  # filter out those with hemodialysis
+IDs = [x for x in IDs if not x in dia_IDs]
+
+
+# Data tables
+
+# demographic info
+df_demo = pd.read_csv(PATH_TO_DEMO)
+# weights
+df_w = pd.read_csv(PATH_TO_WEIGHT)
+# glucose measure
+df_glu = pd.read_csv(PATH_TO_GLU)
+df_glu['taken_time_jittered'] = pd.to_datetime(df_glu['taken_time_jittered'])
+# A1c measure
+df_a1c = pd.read_csv(PATH_TO_A1C)
+df_a1c['taken_time_jittered'] = pd.to_datetime(df_a1c['taken_time_jittered'])
+# creatinine measure: mg/dL
+df_crea = pd.read_csv(PATH_TO_CREA)
+df_crea['taken_time_jittered'] = pd.to_datetime(df_crea['taken_time_jittered'])
+# insulin
+df_ins = pd.read_csv(PATH_TO_INS)
+# steroid order record
+steroid = dict()
+for i in range(1, 4):
+    file = 'PATH_TO_ST' + str(i)
+    df_ste = None
+    exec("steroid[steroid_name[i-1]] = pd.read_csv(%s)" % file)
+# diet order
+df_diet = pd.read_csv(PATH_TO_DIET)
+
+
+data_dict = dict()
+data = list()
+
+err_ID_list = list()  # to store erroneous patients (missing information thus table can not be read correctly)
+
+# time difference since short/long-acting insulin doses
+short_enum = [0.5,1,3]
+long_enum = [6,12,24]
+
+
+for pat_idx, pat_id in enumerate(IDs[:]):
+    pat_id = pat_id[0]
+    print("--------Patient ID", pat_id, pat_idx)
+
+    try:
+        # Load patient-specific data
+        print("Loading...")
+
+        # creatinine measures
+        pat_crea = df_crea.loc[df_crea['rit_uid'] == pat_id]
+        ignore_by_crea = False
+        if len(pat_crea)>0:
+            crea_lvl = pat_crea['ord_num_value'].values
+            crea_time = pat_crea['taken_time_jittered'].values
+            ignore_by_crea = np.max(crea_lvl)>5  # ignore patient with crea_lvl>5
+            print(np.max(crea_lvl))
+        else:
+            ignore_by_crea = True  # discard patient with no crea measure
+
+        if ignore_by_crea:
+            print("Filtered out by CREATININE")
+        else:
+            # glucose measures
+            pat_glu = df_glu.loc[df_glu['rit_uid'] == pat_id]
+            glu_lvl = pat_glu['result'].values
+            glu_time = pat_glu['taken_time_jittered'].values
+
+            # demo
+            pat_demo = df_demo.loc[df_demo['rit_uid'] == pat_id]
+            gender = pat_demo['gender'].values[0]
+            dob = pd.to_datetime(pat_demo['dob'].values[0])
+
+            # weight
+            pat_w = df_w.loc[df_w['rit_uid'] == pat_id]
+            if len(pat_w) > 0:
+                weights = pat_w['meas_value'].values.tolist()
+                weight_time = pat_w['recorded_time_jittered'].values
+
+            # A1c
+            pat_a1c = df_a1c.loc[df_a1c['rit_uid'] == pat_id]
+            if len(pat_a1c) > 0:
+                unit_a1c = pat_a1c['reference_unit'].values[0]
+                a1c_lvl = pat_a1c['result'].values
+                a1c_time = pat_a1c['taken_time_jittered'].values
+
+            # insulin
+            pat_ins = df_ins.loc[df_ins['jc_uid'] == pat_id]
+            dose = pat_ins['sig'].values
+            dose_time = pd.to_datetime(pat_ins['taken_time_jittered'].values)
+            ins_des = pat_ins['med_description'].values
+            ins_type = np.zeros(dose.shape)  # 1 for short-acting, 2 for long-acting
+            for ins_i, ins_d in enumerate(ins_des):
+                if 'REGULAR' in ins_d or 'LISPRO' in ins_d or 'ASPART' in ins_d:
+                    ins_type[ins_i] = 1
+                elif 'NPH' in ins_d or 'DETEMIR' in ins_d or 'GLARGINE' in ins_d:
+                    ins_type[ins_i] = 2
+
+            # steroid order record
+            steroid = dict()
+            for i in range(1, 4):
+                # print(steroid_name[i-1])
+                file = 'PATH_TO_ST' + str(i)
+                df_ste = None
+                exec("df_ste = pd.read_csv(%s)" % file)
+                pat_ste = df_ste.loc[df_ste['jc_uid'] == pat_id]
+                if len(pat_ste) > 0:
+                    ste = pat_ste['sig'].values
+                    ste_time = pd.to_datetime(pat_ste['taken_time_jittered'].values)
+                    ste_dcrp = pat_ste['med_description'].values
+                    steroid[steroid_name[i - 1]] = [ste, ste_time, ste_dcrp]
+
+            # diet record
+            pat_diet = df_diet.loc[df_diet['jc_uid'] == pat_id]
+            npo_sign = False
+            if len(pat_diet)>0:
+                diet_time = pat_diet['proc_start_time_jittered'].values
+                diet_des = pat_diet['description'].values
+                npo_sign = 'NPO' in diet_des[0]
+
+
+            # Process data
+            print("Processing...")
+            pat_data = list()
+            gd = gender == "Male"
+
+            # glucose measures
+            glu_time_diff = np.diff(glu_time).astype('timedelta64[h]').astype('int')
+            cont_meas_indices = [x_i for x_i, x in enumerate(glu_time_diff) if x < 24]
+
+            for cont_meas_idx in cont_meas_indices[:-2]:
+                prev_glu_lvl = glu_lvl[cont_meas_idx]
+                cur_ts = glu_time[cont_meas_idx+1]
+                cur_glu_lvl = glu_lvl[cont_meas_idx+1]
+
+                # average measure in past 24 hours
+                within_24h_measure_idx = np.where(np.logical_and(
+                    (cur_ts - glu_time) <= np.timedelta64(24 * 60, 'm'), (cur_ts - glu_time) > np.timedelta64(0, 'm')))[0]
+                glu_lvl_24h = glu_lvl[within_24h_measure_idx]
+                avg_24h_glu = np.mean(glu_lvl_24h)  # mean
+                var_24h_glu = np.var(glu_lvl_24h)  # variance
+                time_diff_24h = (cur_ts - glu_time[within_24h_measure_idx]).astype('timedelta64[m]')  # in hour
+                inverse_time_diff_24h = np.timedelta64(24 * 60, 'm') - time_diff_24h
+                weighted_sum_glu_lvl_24h = np.sum(inverse_time_diff_24h / np.sum(inverse_time_diff_24h) * glu_lvl_24h)
+
+                # Last glucose level at the similar time of the day
+                time_1d_before = cur_ts - np.timedelta64(24 * 60, 'm')
+                diff_1d_before_time = np.abs(time_1d_before - glu_time)
+                closest_idx = np.argmin(diff_1d_before_time).astype('int')
+                cloest_1d_before_time = glu_time[closest_idx]
+                tol_time = np.timedelta64(120, 'm')
+                if diff_1d_before_time[closest_idx] < tol_time:
+                    glu_lvl_1d_before = glu_lvl[closest_idx]
+                else:
+                    glu_lvl_1d_before = np.nan
+
+                # last insulin
+                time_since_doses = (cur_ts - dose_time).astype('timedelta64[m]').astype('int')
+                prev_doses_idx = set(np.where(time_since_doses >= 0)[0])
+                short_doses_idx = set(np.where(ins_type == 1)[0])
+                long_doses_idx = set(np.where(ins_type == 2)[0])
+
+                # total dose in past 1 hours, 3 hours, 6 hours, 12 hours, 24 hours
+                # doses_accum = np.zeros(5)
+                short_accum = np.zeros(3)  # total short-acting in past 0.5 hours, 1 hours, 3 hours
+                long_accum = np.zeros(3)  # total long-acting in past 6 hours, 12 hours, 24 hours
+
+                for dose_time_diff_i, dose_time_diff in enumerate(short_enum):
+                    short_time_diff = short_enum[dose_time_diff_i]
+                    long_time_diff = long_enum[dose_time_diff_i]
+                    dose_after_time_diff_start_idx = np.where(time_since_doses < dose_time_diff*60)[0]
+                    dose_in_time_idx = prev_doses_idx & set(dose_after_time_diff_start_idx)
+                    short_in_time_idx = short_doses_idx & dose_in_time_idx
+                    if len(short_in_time_idx)>0:
+                        short_accum[dose_time_diff_i] = np.sum([dose[j] for j in short_in_time_idx])
+                for dose_time_diff_i, dose_time_diff in enumerate(long_enum):
+                    dose_after_time_diff_start_idx = np.where(time_since_doses < dose_time_diff*60)[0]
+                    dose_in_time_idx = prev_doses_idx & set(dose_after_time_diff_start_idx)
+                    long_in_time_idx = long_doses_idx & dose_in_time_idx
+                    if len(long_in_time_idx)>0:
+                        long_accum[dose_time_diff_i] = np.sum([dose[j] for j in long_in_time_idx])
+
+                # Total dose of steroids received (past 24 hours, or past 3 days)
+                steroids_24h_total = 0
+                steroids_3d_total = 0
+                for i_sn, sn in enumerate(steroid_name):
+                    # steroid_name: ['hydrocortisone', 'prednisone', 'methylprednisolone', 'dexamethasone']
+                    if sn in steroid:
+                        ste_24h_idx = np.where(np.logical_and(
+                            (cur_ts - steroid[sn][1]) <= np.timedelta64(1, 'D'),
+                            (cur_ts - steroid[sn][1]) >= np.timedelta64(0, 'D')))[0]
+                        ste_24h = steroid[sn][0][ste_24h_idx]
+                        steroids_24h_total += np.sum(ste_24h)/steroid_equiv[sn]
+
+                        ste_3d_idx = np.where(np.logical_and(
+                            (cur_ts - steroid[sn][1]) <= np.timedelta64(3, 'D'),
+                            (cur_ts - steroid[sn][1]) >= np.timedelta64(0, 'D')))[0]
+                        ste_3d = steroid[sn][0][ste_3d_idx]
+                        steroids_3d_total += np.sum(ste_3d)/steroid_equiv[sn]
+
+                # creatinine test
+                closest_crea = np.nan
+                closest_crea_idx = \
+                    np.where(cur_ts - pd.DatetimeIndex(crea_time) >= np.timedelta64(0, 'D'))[0][-1]
+                closest_crea = crea_lvl[closest_crea_idx]
+                closest_crea_time = (cur_ts - crea_time[closest_crea_idx]).astype('timedelta64[m]').astype('int')
+                closest_crea_time_h = float(closest_crea_time)/60
+
+                # Weight (in ounces)
+                closest_weight = np.nan
+                if len(pat_w) > 0:
+                    closest_weight_idx = \
+                        np.where(cur_ts - pd.DatetimeIndex(weight_time) >= np.timedelta64(0, 'D'))[0][-1]
+                    closest_weight = weights[closest_weight_idx]
+
+                # Age (in years)
+                age = (np.datetime64(cur_ts) - np.datetime64(dob)).astype('timedelta64[Y]').astype(int)
+
+                # A1c  (past 3 months / most recent one / within 1 year)
+                closest_a1c = np.nan
+                if len(pat_a1c) > 0:
+                    time_since_a1c = cur_ts - pd.DatetimeIndex(a1c_time)
+                    closest_a1c_idx = np.where(np.logical_and(time_since_a1c >= np.timedelta64(0, 'm'),
+                                                              time_since_a1c <= np.timedelta64(1, 'Y')))[0]
+                    if len(closest_a1c_idx) > 0:
+                        closest_a1c = a1c_lvl[closest_a1c_idx][-1]
+
+                if np.isnan(closest_a1c): # calculate a1c from glu
+                    closest_a1c = glu_2_a1c[ref_glu[np.argmin(np.abs(ref_glu-prev_glu_lvl))]]
+
+                # characteristic of timestamp
+                # how close to midnight (the day before)
+                midnight = pd.Timestamp(pd.to_datetime(cur_ts).date())
+                # take sine and cosine
+                since_midnight_sine = np.sin((cur_ts - np.datetime64(midnight)) / np.timedelta64(1, 'D') * 2 * np.pi)
+                since_midnight_cosine = np.cos((cur_ts - np.datetime64(midnight)) / np.timedelta64(1, 'D') * 2 * np.pi)
+
+                # next glucose
+                next_glu_time_diff_h = (glu_time[cont_meas_idx + 2] - cur_ts).astype('timedelta64[h]').astype(int)
+                next_glu_lvl = glu_lvl[cont_meas_idx + 2]
+
+                # combine all data into array
+                data_point = [pat_idx, gd, age, closest_weight, npo_sign, prev_glu_lvl,
+                              avg_24h_glu, var_24h_glu, weighted_sum_glu_lvl_24h, cur_glu_lvl, glu_lvl_1d_before,
+                              steroids_24h_total, steroids_3d_total, closest_a1c,
+                              since_midnight_sine, since_midnight_cosine,
+                              closest_crea, closest_crea_time_h, cur_ts,
+                              next_glu_time_diff_h, next_glu_lvl]
+                # data_point.extend(doses_accum)
+                data_point.extend(short_accum)
+                data_point.extend(long_accum)
+                data_point.append(cur_glu_lvl)
+
+                pat_data.append(data_point)
+
+            pat_data_all = np.vstack(pat_data).T
+            data_dict[pat_id] = pat_data_all
+            data.append(pat_data_all)
+            print("DONE!")
+    except:
+        print("Error!")
+        err_ID_list.append(pat_id)  # if any info missing and cannot read table correctly
+
+data_all = np.hstack(data)
+
+
+# save data
+
+np.savetxt(PATH_TO_SAVE+'0519data.out', data_all, delimiter=',')
+np.savetxt(PATH_TO_SAVE+'0519errIDs.csv', err_ID_list, delimiter=',', fmt='%s')
+
+feature_names = ['pat_idx', 'gender','age', 'closest_weight', 'npo', 'prev_glu_lvl',
+                      'avg_24h_glu', 'var_24h_glu', 'weighted_sum_glu_lvl_24h', 'cur_glu_lvl', 'glu_lvl_1d_before',
+                      'steroids_24h_total', 'steroids_3d_total', 'closest_a1c',
+                      'since_midnight_sine', 'since_midnight_cosine',
+                      'closest_crea', 'closest_crea_time_h', 'cur_ts',
+                      'next_glu_time_diff_h', 'next_glu_lvl']
+
+for dose_time_diff in [str(i) for i in short_enum]:
+    feature_names.append('short_'+dose_time_diff+'h')
+for dose_time_diff in [str(i) for i in long_enum]:
+    feature_names.append('long_'+dose_time_diff+'h')
+feature_names.append('cur_glu_lvl')
+
+df = pd.DataFrame(data_all.T, columns=feature_names)
+df.to_csv(PATH_TO_SAVE+'data.csv', sep=',')
+
+
diff --git a/scripts/Archive/GlucoseControl/3regression.py b/scripts/Archive/GlucoseControl/3regression.py
new file mode 100644
index 00000000..65e3a527
--- /dev/null
+++ b/scripts/Archive/GlucoseControl/3regression.py
@@ -0,0 +1,411 @@
+'''
+Regression:
+predict glucose level at current time stamp
+Files of input data are stored under directory "../data/preprocessed".
+Output files (patient IDs) are stored under directory ""../data/plots/patients".
+'''
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from sklearn import preprocessing, metrics
+from sklearn.utils import shuffle
+from sklearn.feature_selection import SelectFromModel, SelectKBest, f_regression
+from sklearn.svm import SVR
+from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso, LinearRegression, BayesianRidge, ElasticNet
+from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor, RandomForestRegressor
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.naive_bayes import GaussianNB
+from sklearn.impute import SimpleImputer
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, RBF, ConstantKernel, Matern, RationalQuadratic, ExpSineSquared
+
+from skgarden import RandomForestQuantileRegressor
+
+
+PATH_TO_OUTPUT = "../data/plots/patients/"
+
+feature_names = ['pat_idx', 'gender','age', 'closest_weight', 'npo', 'prev_glu_lvl',
+                      'avg_24h_glu', 'var_24h_glu', 'weighted_sum_glu_lvl_24h', 'cur_glu_lvl', 'glu_lvl_1d_before',
+                      'steroids_24h_total', 'steroids_3d_total', 'closest_a1c',
+                      'since_midnight_sine', 'since_midnight_cosine',
+                      'closest_crea', 'closest_crea_time_h', 'cur_ts',
+                      'next_glu_time_diff_h', 'next_glu_lvl']
+# time difference since short/long-acting insulin doses
+short_enum = [0.5,1,3]
+long_enum = [6,12,24]
+for dose_time_diff in [str(i) for i in short_enum]:
+    feature_names.append('short_'+dose_time_diff+'h')
+for dose_time_diff in [str(i) for i in long_enum]:
+    feature_names.append('long_'+dose_time_diff+'h')
+feature_names.append('cur_glu_lvl')
+
+
+df = pd.read_csv('../data/preprocessed/data.csv', sep=',')
+df.head()
+
+df = df.drop(df.columns[0], axis=1)  # drop first unused column
+y = df["cur_glu_lvl"]
+
+######################
+# Feature Selection
+
+selected_features = ['avg_24h_glu', 'steroids_24h_total', 'steroids_3d_total', 'closest_a1c',
+                     'gender','age', 'closest_weight', 'npo',
+                     'var_24h_glu', 'glu_lvl_1d_before',
+                     'closest_crea', 'closest_crea_time_h',
+                     'since_midnight_sine', 'since_midnight_cosine']
+for dose_time_diff in [str(i) for i in short_enum]:
+    selected_features.append('short_'+dose_time_diff+'h')
+for dose_time_diff in [str(i) for i in long_enum]:
+    selected_features.append('long_'+dose_time_diff+'h')
+features = df[selected_features]
+
+
+# time stamps
+time = df['cur_ts']
+
+data_IDs = df['pat_idx'].values.tolist()
+data_IDs = [int(i) for i in data_IDs]
+
+
+######################
+# Data Process
+# check for irregular data: remove nans
+
+labels = y.values.tolist()
+X = np.array(features.values.tolist())
+
+# remove nans
+X_rmnan = X
+y_rmnan = labels
+time_rmnan = np.array(time.tolist())
+for i in range(len(selected_features)):
+    nan_glu_idx = np.where(np.isnan(X_rmnan[:,i]))[0]
+    X_rmnan = np.delete(X_rmnan, nan_glu_idx, 0)
+    y_rmnan = np.delete(y_rmnan, nan_glu_idx, 0)
+    data_IDs = np.delete(data_IDs, nan_glu_idx, 0)
+    time_rmnan = np.delete(time_rmnan,nan_glu_idx)
+nan_glu_idx = np.where(np.isnan(y_rmnan))[0]
+X_rmnan = np.delete(X_rmnan, nan_glu_idx, 0)
+y_rmnan = np.delete(y_rmnan, nan_glu_idx, 0)
+data_IDs = np.delete(data_IDs, nan_glu_idx, 0)
+time_rmnan = np.delete(time_rmnan,nan_glu_idx)
+data_idx = list(range(len(data_IDs)))
+
+# dimension of feature matrix
+print("dimension of feature matrix", X_rmnan.shape)
+
+
+'''
+# feature selection - uncomment to use, can be pipelined
+
+fs = SelectKBest(f_regression, k=5)
+# feature selection based on univariate linear regression tests
+# The correlation between each regressor and the target is computed,
+# ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) * std(y)).
+# It is converted to an F score then to a p-value.
+# The F score is defined as the weighted harmonic mean of the test's precision and recall
+#
+X_new = fs.fit_transform(X_rmnan, y_rmnan)
+print(X_new.shape)
+# print(fs.scores_)
+idx = np.argsort(-fs.scores_)[:5]
+print("")
+kb_features = np.array(selected_features)[idx]
+print("features in order of KB Selection", kb_features)
+
+# selected_features_target = np.copy(kb_features).tolist()
+# selected_features_target.append("cur_glu_lvl")
+# selected_features_target = df[selected_features_target]
+#
+# # df2 = df[["cur_glu_lvl"]]
+# sns.pairplot(selected_features_target, diag_kind = 'kde')
+# plt.savefig(PATH_TO_OUTPUT+"pairplot.png", bbox_inches='tight')
+
+X_rmnan = X_new
+
+'''
+
+
+######################
+# Train-Test-Split
+
+tr_te_split = 0.7
+
+unique_IDs = np.unique(data_IDs)
+print("Number of unique patients: ", len(unique_IDs))
+
+num_pat = 500  # number of patients used | len(unique_IDs)
+print("Patient used:", num_pat)
+
+unique_IDs = unique_IDs[0:num_pat]
+
+
+tr_num = int(tr_te_split*len(unique_IDs))
+train_IDs = unique_IDs[0:tr_num]
+test_IDs = unique_IDs[tr_num:]
+
+last_train = np.where(data_IDs==train_IDs[-1])[0][-1]
+last_test = np.where(data_IDs==test_IDs[-1])[0][-1]
+
+
+train_idx = np.array(data_idx)[0:last_train+1]
+test_idx = np.array(data_idx)[last_train+1:last_test+1]
+test_IDs = np.array(data_IDs)[last_train+1:last_test+1]
+
+print("Try training...")
+X_train = X_rmnan[train_idx,:]
+y_train = y_rmnan[train_idx]
+time_train = time_rmnan[train_idx]
+X_test = X_rmnan[test_idx,:]
+y_test = y_rmnan[test_idx]
+time_test = time_rmnan[test_idx]
+
+
+print("train data", X_train.shape)
+print("test data", X_test.shape)
+
+
+# Validation Set
+# val_IDs = unique_IDs[num_pat:num_pat+int(0.1*num_pat)]
+# last_val = np.where(data_IDs==val_IDs[-1])[0][-1]
+# val_idx = np.array(data_idx)[last_test+1:last_val+1]
+# X_val = X_rmnan[val_idx,:]
+# y_val = y_rmnan[val_idx]
+
+
+######################
+# Data Normalization/Rescaling
+scaler = preprocessing.MinMaxScaler()
+X_train_rescale = scaler.fit_transform(X_train)
+X_test_rescale = scaler.transform(X_test)
+# X_val = scaler.transform(X_val)
+
+
+######################
+# Regression Models: uncomment corresponding regressor to use
+
+# ElasticNet (LASSO)
+# clf = ElasticNet(alpha=1.0, l1_ratio=1, fit_intercept=True)  # l1_ratio=1 -> LASSO
+# clf.fit(X_train_rescale, y_train)
+
+
+# SVR (default)
+# clf = SVR(kernel='linear', gamma='scale', C=1.0, epsilon=0.1)
+# clf.fit(X_train_rescale, y_train)
+
+
+# SVR with validation (parameter tuning)
+# # parameters tuning
+# C_range = np.logspace(-3, 4, 8)
+# gamma_range = np.logspace(-7, 3, 11)
+# C_tested = list()
+# gamma_tested = list()
+# score_tested = list()
+#
+# print("tuning parameters...")
+# num_rdm_sample = 20
+# for i in range(num_rdm_sample):
+#     C_i = random.choice(C_range)
+#     gamma_i = random.choice(gamma_range)
+#     clf = SVR(kernel='rbf', gamma=gamma_i, C=C_i, epsilon=0.1)
+#     clf.fit(X_rescale, y_train)
+#     R2 = clf.score(X_val, y_val)
+#     print(C_i, gamma_i, R2)
+#     score_tested.append(R2)
+#     C_tested.append(C_i)
+#     gamma_tested.append(gamma_i)
+#
+# for C_i in C_range:
+#     for gamma_i in gamma_range:
+#         clf = SVR(kernel='rbf', gamma=gamma_i, C=C_i, epsilon=0.1)
+#         clf.fit(X_rescale, y_train)
+#         R2 = clf.score(X_val, y_val)
+#         print(C_i, gamma_i, R2)
+#         score_tested.append(R2)
+#         C_tested.append(C_i)
+#         gamma_tested.append(gamma_i)
+#
+# opt_idx = np.argmax(score_tested)
+# opt_C = C_tested[opt_idx]
+# opt_gamma = gamma_tested[opt_idx]
+# print("opt", opt_C, opt_gamma)
+
+# clf = SVR(kernel='linear', gamma='scale', C = 10, epsilon=0.1) #gamma='scale'
+
+
+# SVR with tuned parameter
+# opt_C, opt_gamma = 10000, 0.1
+# clf = SVR(kernel='rbf', gamma=opt_gamma, C=opt_C, epsilon=0.1) #gamma='scale'
+# clf.fit(X_train_rescale, y_train)
+
+
+# Random Forest Regressor
+clf = RandomForestRegressor(random_state=0, n_estimators=100)  #max_depth=10,
+clf.fit(X_train_rescale, y_train)
+
+
+# Gaussian process regression (GPR)
+# kernel = DotProduct() + WhiteKernel()
+# kernel = ConstantKernel(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
+# kernel = None
+# kernel = ConstantKernel(0.1, (1e-23, 1e5)) * \
+#          RBF(0.1*np.ones(X_train_rescale.shape[1]), (1e-23, 1e10) ) + \
+#          WhiteKernel(0.1, (1e-23, 1e5))
+# kernel = WhiteKernel()
+# kernel = ConstantKernel(0.1, (0.01, 10.0))
+#                * (DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2)
+# clf = GaussianProcessRegressor(kernel=DotProduct()) #normalize_y=True , n_restarts_optimizer=2
+# print("initial paras:", clf.get_params())
+# clf.fit(X_train_rescale, y_train)
+# print("after fitting:", clf.get_params())
+# y_pred, sigma = clf.predict(X_test_rescale, return_std=True)
+
+
+# Linear Regression
+# clf = LinearRegression(n_jobs=-1)
+# clf.fit(X_train_rescale, y_train)
+
+
+# Bayesian Ridge Regression
+# clf = BayesianRidge(normalize = True)
+# clf.fit(X_train_rescale, y_train)
+# y_pred, y_std = clf.predict(X_test_rescale, return_std=True)
+# lower = y_pred - 1.645 * y_std
+# upper = y_pred + 1.645 * y_std
+
+
+# KRR (squared error loss combined with l2 regularization)
+# clf = KernelRidge(alpha=0.1)
+# clf.fit(X_train_rescale, y_train)
+
+
+# Random Forest Quantile Regressor
+# clf = RandomForestQuantileRegressor(random_state=0, min_samples_split=10, n_estimators=1000)
+# clf.fit(X_train_rescale, y_train)
+# y_pred = clf.predict(X_test_rescale)
+# upper = clf.predict(X_test_rescale, quantile=98.5)
+# lower = clf.predict(X_test_rescale, quantile=2.5)
+# interval = upper - lower
+# mean = (upper + lower) / 2
+
+
+# GradientBoosting
+# X_train_rescale, y_train = shuffle(X_train_rescale, y_train, random_state=0)
+# clf = GradientBoostingRegressor(loss='quantile', alpha=0.7, random_state=0, n_estimators=100)
+# clf.fit(X_train_rescale, y_train)
+
+
+# DecisionTree
+# clf = DecisionTreeRegressor(random_state=0)
+# clf.fit(X_train_rescale, y_train)
+
+
+# NaiveBayes
+# clf = GaussianNB()
+# clf.fit(X_train_rescale, y_train)
+
+
+######################
+# Prediction
+
+print(clf)
+y_pred = clf.predict(X_test_rescale)
+
+
+######################
+# Performance Measure
+
+r2 = metrics.r2_score(y_test, y_pred)
+print("R2 score",r2)
+MSE = metrics.mean_squared_error(y_test, y_pred)
+print("RMSE", np.sqrt(MSE))
+
+MAE = metrics.mean_absolute_error(y_test, y_pred)
+print("Mean absolute error", MAE)
+
+# mean percentage error
+MAPE = np.mean(np.abs((y_test-y_pred)/y_test))
+print("Mean percentage error", MAPE)
+
+# correct trend
+trend_true = np.sign(np.diff(y_test))
+trend_pred = np.sign(np.diff(y_pred))
+accr = metrics.accuracy_score(trend_true, trend_pred)
+print("trend accuracy:", accr)
+
+######################
+# Plot
+test_diff_idx = np.where(np.diff(test_IDs))[0]
+
+# All predictions together
+# plt.plot(y_test, "bo")
+# plt.plot(y_pred, "ro")
+# plt.fill_between(
+#     np.arange(len(upper)), lower, upper, alpha=0.2, color="r",
+#     label="Pred. interval")
+# # plt.xlabel("Ordered samples.")
+# # plt.ylabel("Values and prediction intervals.")
+# # plt.xlim([0, 500])
+# plt.show()
+
+
+# 2D scatter plot: true vs. predicted glucose level
+
+plt.figure()
+clf_name = clf.__str__().split("(")[0]
+plt.scatter(y_test, y_pred, s=2, alpha=0.5)
+plt.plot([np.min(y_test), np.max(y_test)],[np.min(y_test), np.max(y_test)], c='g', alpha=0.7)
+# plt.axvline(x=70, c='r', alpha=0.5)
+# plt.axvline(x=200, c='r', alpha=0.5)
+plt.title(clf_name+": true vs. predicted glucose level")
+plt.xlim([0, np.max(y_test)])
+plt.xlabel("true glucose levels")
+plt.ylim([0, np.max(y_pred)])
+plt.ylabel("predicted glucose levels")
+plt.savefig(PATH_TO_OUTPUT+clf_name+"("+str(num_pat)+").png", bbox_inches='tight')
+plt.show()
+
+
+# Sample plot of a few patients
+
+time_test2 = np.array([tt.split('.')[0] for tt in time_test])
+
+for j in range(len(test_diff_idx)-1):  # len(test_diff_idx)-1
+    pat_indices = list(range(test_diff_idx[j],test_diff_idx[j+1]))
+    plt_pat_idx = list(range(len(y_test[pat_indices])))
+    time_pat = time_test2[pat_indices]
+    pat_test = y_test[pat_indices]
+    pat_pred = y_pred[pat_indices]
+    # pat_upper = upper[pat_indices]
+    # pat_lower = lower[pat_indices]
+    idx_pat_idx = list(range(len(plt_pat_idx)))
+    if len(time_pat) > 1:
+        fig = plt.figure(figsize=(int(np.ceil(len(plt_pat_idx)) / 2), 4))
+
+        time_dt = pd.to_datetime(time_pat)
+        diff_dt = np.diff(time_dt).astype('timedelta64[h]').astype(int)
+        diff_period = np.where(np.logical_or(diff_dt>72, diff_dt<0))[0]
+        st_idx = 0
+        for dp in diff_period:
+            idx_slice = idx_pat_idx[st_idx:dp+1]
+            if len(idx_slice)>1:
+                plt.plot(time_pat[idx_slice], pat_test[idx_slice], 'o-', c='blue', label="true glucose levels")
+                plt.plot(time_pat[idx_slice], pat_pred[idx_slice], 'x-', c='red', label="predicted glucose levels")
+                # plt.fill_between(
+                #     time_pat[idx_slice], pat_lower[idx_slice], pat_upper[idx_slice], alpha=0.2, color="r",
+                #     label="Pred. interval")
+            st_idx = dp+1
+
+        plt.xlabel("time")
+        plt.ylabel("glucose level")
+        plt.legend(["true glucose levels", "predicted glucose levels", "95% interval"])
+        fig.autofmt_xdate()
+        clf_name = clf.__str__().split("(")[0]
+        plt.title(clf_name+": Sample Patient")
+        plt.savefig(PATH_TO_OUTPUT+"ind/"+clf_name+"("+str(num_pat)+")_"+str(j+1)+".png", bbox_inches='tight')
+        plt.close()
+
diff --git a/scripts/Archive/GlucoseControl/4classification.py b/scripts/Archive/GlucoseControl/4classification.py
new file mode 100644
index 00000000..d9722073
--- /dev/null
+++ b/scripts/Archive/GlucoseControl/4classification.py
@@ -0,0 +1,179 @@
+'''
+Regression:
+predict glucose level at current time stamp
+Files of input data are stored under directory "../data/preprocessed".
+Output files (patient IDs) are stored under directory ""../data/plots/patients-class".
+'''
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+from sklearn import metrics
+from sklearn.svm import SVC
+from sklearn import preprocessing
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, RBF
+from sklearn.gaussian_process import GaussianProcessClassifier
+
+
+PATH_TO_OUTPUT = "../data/plots/patients-class/"
+
+feature_names = ['pat_idx', 'gender','age', 'closest_weight', 'npo', 'prev_glu_lvl',
+                      'avg_24h_glu', 'var_24h_glu', 'weighted_sum_glu_lvl_24h', 'cur_glu_lvl', 'glu_lvl_1d_before',
+                      'steroids_24h_total', 'steroids_3d_total', 'closest_a1c',
+                      'since_midnight_sine', 'since_midnight_cosine',
+                      'closest_crea', 'closest_crea_time_h', 'cur_ts',
+                      'next_glu_time_diff_h', 'next_glu_lvl']
+# time difference since short/long-acting insulin doses
+short_enum = [0.5,1,3]
+long_enum = [6,12,24]
+for dose_time_diff in [str(i) for i in short_enum]:
+    feature_names.append('short_'+dose_time_diff+'h')
+for dose_time_diff in [str(i) for i in long_enum]:
+    feature_names.append('long_'+dose_time_diff+'h')
+feature_names.append('cur_glu_lvl')
+
+df = pd.read_csv('../data/preprocessed/data.csv', sep=',')
+df.head()
+
+df = df.drop(df.columns[0], axis=1)  # drop first unused column
+y = df["cur_glu_lvl"]
+
+######################
+# Feature Selection
+
+selected_features = ['avg_24h_glu', 'steroids_24h_total', 'steroids_3d_total', 'closest_a1c',
+                     'gender','age', 'closest_weight', 'npo',  # 'prev_glu_lvl',
+                     'var_24h_glu', 'glu_lvl_1d_before', #'weighted_sum_glu_lvl_24h',
+                     'closest_crea', 'closest_crea_time_h',
+                     'since_midnight_sine', 'since_midnight_cosine']
+for dose_time_diff in [str(i) for i in short_enum]:
+    selected_features.append('short_'+dose_time_diff+'h')
+for dose_time_diff in [str(i) for i in long_enum]:
+    selected_features.append('long_'+dose_time_diff+'h')
+features = df[selected_features]
+
+# time stamps
+time = df['cur_ts']
+
+data_IDs = df['pat_idx'].values.tolist()
+data_IDs = [int(i) for i in data_IDs]
+
+
+# check for irregular data
+labels = y.values.tolist()
+X = np.array(features.values.tolist())
+# print(np.any(np.isnan(X)))
+
+# remove nans
+X_rmnan = X
+y_rmnan = labels
+time_rmnan = np.array(time.tolist())
+for i in range(len(selected_features)):
+    nan_glu_idx = np.where(np.isnan(X_rmnan[:,i]))[0]
+    X_rmnan = np.delete(X_rmnan, nan_glu_idx, 0)
+    y_rmnan = np.delete(y_rmnan, nan_glu_idx, 0)
+    data_IDs = np.delete(data_IDs, nan_glu_idx, 0)
+    time_rmnan = np.delete(time_rmnan,nan_glu_idx)
+nan_glu_idx = np.where(np.isnan(y_rmnan))[0]
+X_rmnan = np.delete(X_rmnan, nan_glu_idx, 0)
+y_rmnan = np.delete(y_rmnan, nan_glu_idx, 0)
+data_IDs = np.delete(data_IDs, nan_glu_idx, 0)
+time_rmnan = np.delete(time_rmnan,nan_glu_idx)
+data_idx = list(range(len(data_IDs)))
+
+
+######################
+
+# split training patients and testing patients
+unique_IDs = np.unique(data_IDs)
+print("Number of unique patients: ", len(unique_IDs))
+
+num_pat = 500  # number of patients used | len(unique_IDs)
+print("Patient used:", num_pat)
+
+unique_IDs = unique_IDs[0:num_pat]
+
+tr_te_split = 0.7
+tr_num = int(tr_te_split*len(unique_IDs))
+train_IDs = unique_IDs[0:tr_num]
+test_IDs = unique_IDs[tr_num:]
+
+last_train = np.where(data_IDs==train_IDs[-1])[0][-1]
+last_test = np.where(data_IDs==test_IDs[-1])[0][-1]
+
+train_idx = np.array(data_idx)[0:last_train+1]
+test_idx = np.array(data_idx)[last_train+1:last_test+1]
+test_IDs = np.array(data_IDs)[last_train+1:last_test+1]
+
+
+# change to classification:
+# 1: high, 0: in-range, -1: low
+
+y_new = np.zeros(y_rmnan.shape)
+for i in range(len(y_rmnan)):
+    if y_rmnan[i]>200:
+        y_new[i] = 1
+    elif y_rmnan[i]<70:
+        y_new[i] = -1
+print("Number of data points:", len(y_new))
+for v in [-1,0,1]:
+    print("class "+str(v)+": "+str(np.sum(y_new==v)))
+
+
+print("Try training...")
+X_train = X_rmnan[train_idx,:]
+y_train = y_new[train_idx]
+time_train = time_rmnan[train_idx]
+X_test = X_rmnan[test_idx,:]
+y_test = y_new[test_idx]
+time_test = time_rmnan[test_idx]
+
+
+# Validation Set
+# val_IDs = unique_IDs[num_pat:num_pat+int(0.1*num_pat)]
+# last_val = np.where(data_IDs==val_IDs[-1])[0][-1]
+# val_idx = np.array(data_idx)[last_test+1:last_val+1]
+# X_val = X_rmnan[val_idx,:]
+# y_val = y_rmnan[val_idx]
+
+print("train", X_train.shape)
+print("test", X_test.shape)
+
+
+# Normalize
+scaler = preprocessing.MinMaxScaler()
+X_rescale = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+# X_val = scaler.transform(X_val)
+
+
+######################
+# Classification Models: uncomment corresponding classifier part to use
+
+weight = {-1:1.5, 0:0.5, 1:1.5}
+
+# SVC
+# clf = SVC(gamma='auto', class_weight=weight)
+
+# # Random Forest
+clf = RandomForestClassifier(n_estimators=100, max_depth=10,random_state=0, class_weight=weight) #max_depth=10, random_state=0,
+
+# Guassian Process
+# kernel = DotProduct(sigma_0=1)
+# clf = GaussianProcessClassifier(kernel=kernel, random_state=0)
+
+
+clf.fit(X_rescale, y_train)
+y_pred = clf.predict(X_test)
+
+accr = metrics.accuracy_score(y_test, y_pred)
+print("Accuracy", accr)
+r2 = metrics.r2_score(y_test, y_pred)
+print("R2", r2)
+
+cm = metrics.confusion_matrix(y_test, y_pred)
+print("Confusion matrix")
+print(cm)
+print("accuracy within each class:")
+print(np.diag(cm)/np.sum(cm, axis=1))
diff --git a/scripts/Archive/GlucoseControl/README.md b/scripts/Archive/GlucoseControl/README.md
new file mode 100644
index 00000000..75e3a2c5
--- /dev/null
+++ b/scripts/Archive/GlucoseControl/README.md
@@ -0,0 +1,20 @@
+# glucose-control
+_Prediction and classification (low, normal, high) of glucose levels based on information extracted from EHR data_
+
+## directory tree and file usage
+* data
+  - bq --queried EHR data in CSV files
+  - preprocessed -- filtered and preocessed data, feature matrix
+  - plots -- output plots for dataset
+    * patients -- output plots for specific patients
+* code
+  - queries.sql -- BigQuery commands to extract EHR data from datalake
+  - 1filterPatient.py -- filtering patients with usable EHR data
+  - 2constructFeatures.py -- feature engineering
+  - 3regression.py -- prediction of glucose levels
+  - 4classification.py -- classification (low, normal, high) of glucose levels
+
+## project report (draft)
+[project report (draft) 190608](project-report-draft.pdf)
+
+
diff --git a/scripts/Archive/GlucoseControl/queries.sql b/scripts/Archive/GlucoseControl/queries.sql
new file mode 100644
index 00000000..ec69c66c
--- /dev/null
+++ b/scripts/Archive/GlucoseControl/queries.sql
@@ -0,0 +1,498 @@
+--ID
+SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null)
+      ORDER BY
+        a.rit_uid
+
+
+--ID (new version)
+SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null)
+      ORDER BY
+        a.rit_uid
+
+
+--ID, gender, date of birth
+SELECT 
+  DISTINCT a.rit_uid, 
+  c.gender, DATE(CAST(c.birth_date_jittered as TIMESTAMP)) as dob
+FROM 
+  `datalake_47618.lab_result` as a
+  JOIN 
+    `datalake_47618.demographic` as c on a.rit_uid=c.rit_uid
+WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null)
+ORDER BY
+  a.rit_uid
+
+
+--Weights
+SELECT fs.rit_uid, fs.inpatient_data_id_coded, fs.template, fs.row_disp_name, fs.meas_value, fs.recorded_time_jittered
+FROM 
+  `datalake_47618.flowsheet` as fs
+WHERE
+   lower(fs.row_disp_name) like '%weight%'
+   AND 
+   fs.recorded_time_jittered > '2014-01-01'
+   AND
+   NOT SAFE_CAST(fs.meas_value AS FLOAT64) is null
+   AND 
+   fs.rit_uid in 
+          (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null)
+      ORDER BY
+        a.rit_uid
+            )
+ORDER BY
+   fs.recorded_time_jittered
+
+
+--Lab test: GLU
+SELECT 
+  e.rit_uid, e.pat_enc_csn_id_coded, e.taken_time_jittered, e.ord_value, e.reference_unit, e.result_in_range_yn, SAFE_CAST(e.ord_value AS int64) as result
+
+FROM 
+  `datalake_47618.lab_result` as e
+
+WHERE
+
+  e.rit_uid in 
+          (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+
+  AND
+   e.taken_time_jittered > '2014-01-01'  
+   AND
+    lower(e.lab_name) like '%glucose by meter%' 
+    -- AND ((SAFE_CAST(e.ord_value AS float64) > 180) OR (SAFE_CAST(e.ord_value AS float64)<70)) )
+   -- OR 
+   -- (lower(e.lab_name) like '%a1c%' AND SAFE_CAST(e.ord_value AS float64) >6.5) 
+   -- )
+
+ORDER BY
+  e.rit_uid, e.taken_time_jittered, e.pat_enc_csn_id_coded
+
+
+--Lab test: A1c
+SELECT 
+  e.rit_uid, e.pat_enc_csn_id_coded, e.lab_name, e.taken_time_jittered, e.ord_value, e.reference_unit, e.result_in_range_yn, SAFE_CAST(e.ord_value AS int64) as result
+
+FROM 
+  `datalake_47618.lab_result` as e
+
+WHERE
+
+  e.rit_uid in 
+          (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+
+  AND
+   e.taken_time_jittered > '2014-01-01'  
+   AND
+    lower(e.lab_name) like '%a1c%' 
+    -- AND ((SAFE_CAST(e.ord_value AS float64) > 180) OR (SAFE_CAST(e.ord_value AS float64)<70)) )
+   -- OR 
+   -- (lower(e.lab_name) like '%a1c%' AND SAFE_CAST(e.ord_value AS float64) >6.5) 
+   -- )
+
+ORDER BY
+  e.rit_uid, e.taken_time_jittered, e.pat_enc_csn_id_coded
+
+
+
+--Common lab tests (patient)
+SELECT 
+lr.lab_name,
+COUNT(DISTINCT lr.rit_uid) as total
+
+FROM 
+  `datalake_47618.lab_result` as lr
+
+WHERE
+  lr.rit_uid in 
+          (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+
+  AND
+   lr.taken_time_jittered > '2014-01-01'  
+
+GROUP BY
+          lr.lab_name
+ORDER BY
+          COUNT(DISTINCT lr.rit_uid) DESC
+LIMIT 
+  100;
+
+--Common lab tests (order)
+SELECT 
+lr.lab_name,
+COUNT(DISTINCT lr.order_id_coded) as total
+
+FROM 
+  `datalake_47618.lab_result` as lr
+
+WHERE
+  lr.rit_uid in 
+          (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+
+  AND
+   lr.taken_time_jittered > '2014-01-01'  
+
+GROUP BY
+          lr.lab_name
+ORDER BY
+          COUNT(DISTINCT lr.order_id_coded) DESC
+LIMIT 
+  100;
+
+
+--Order Insulin
+
+SELECT c.jc_uid, c.order_med_id_coded, CAST(c.sig as FLOAT64) as sig, c.dose_unit, 
+c.taken_time_jittered, d.med_description, d.freq_display_name 
+FROM 
+  `datalake_47618.mar` as c
+JOIN 
+  `datalake_47618.order_med` as d ON c.order_med_id_coded=d.order_med_id_coded
+WHERE
+  c.jc_uid IN (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+    AND
+    (
+    (lower(d.med_description) like '%insulin%'
+    AND
+    lower(c.route) like '%subcutaneous%')
+    )
+    AND 
+    lower(c.mar_action) like 'given'
+    AND 
+    c.taken_time_jittered > '2014-01-01'
+   
+ORDER BY
+    c.jc_uid, c.taken_time_jittered; 
+
+
+--Order Steroid
+
+SELECT c.jc_uid, c.order_med_id_coded, CAST(c.sig as FLOAT64) as sig, c.dose_unit, 
+c.route, --c.infusion_rate, c.mar_duration, 
+c.taken_time_jittered, d.med_description, d.freq_display_name 
+FROM 
+  `datalake_47618.mar` as c
+JOIN 
+  `datalake_47618.order_med` as d ON c.order_med_id_coded=d.order_med_id_coded
+WHERE
+  c.jc_uid IN (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+    AND NOT c.sig is null
+    AND
+    (
+    lower(d.med_description) like '%hydrocortisone%' 
+--     OR 
+--     lower(d.med_description) like '%prednisone%'  
+--     OR 
+--     lower(d.med_description) like '%methylprednisolone%' 
+--     OR 
+--     lower(d.med_description) like '%dexamethasone%'  
+    )
+    AND 
+    lower(c.mar_action) like 'given'
+    AND 
+    c.taken_time_jittered > '2014-01-01'
+   
+ORDER BY
+    c.jc_uid, c.taken_time_jittered; 
+
+
+--Diet 
+
+SELECT c.jc_uid, c.order_proc_id_coded, c.pat_enc_csn_id_coded,
+c.proc_start_time_jittered,
+c.description
+FROM 
+  `datalake_47618.order_proc` as c
+WHERE
+  c.jc_uid IN (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+    AND
+    (NOT lower(c.order_status) like '%canceled%')
+    AND
+    (lower(c.ordering_mode) like '%inpatient%')
+    AND
+    (
+    (lower(c.description) like '%npo%'
+    OR
+    lower(c.description) like '%diet%')
+    )
+    AND 
+    c.proc_start_time_jittered > '2014-01-01'
+   
+ORDER BY
+    c.jc_uid, c.proc_start_time_jittered; 
+
+
+--Diet (updated: not used, just for comparison)
+SELECT c.jc_uid, c.order_proc_id_coded, c.pat_enc_csn_id_coded,
+c.proc_start_time_jittered,
+c.description
+FROM 
+  `datalake_47618.order_proc` as c
+WHERE
+  c.jc_uid IN (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like 'glucose by meter' 
+                AND (a.ord_num_value > 200) OR (a.ord_num_value<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND a.ord_num_value >6.5) 
+               
+               AND NOT (a.ord_num_value is null))
+    AND
+    lower(c.ordering_mode) like 'inpatient'
+    AND 
+    c.proc_start_time_jittered > '2014-01-01'
+   
+ORDER BY
+    c.jc_uid, c.proc_start_time_jittered; 
+
+
+
+-- Kidney: CREATININE 
+--Lab test: creatinine
+SELECT 
+  e.rit_uid, e.pat_enc_csn_id_coded, e.lab_name, e.taken_time_jittered, e.ord_num_value, e.reference_unit, e.result_in_range_yn
+
+FROM 
+  `datalake_47618.lab_result` as e
+
+WHERE
+
+  e.rit_uid in 
+          (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a 
+              -- JOIN
+              -- `datalake_47618.order_proc` as b ON a.rit_uid=b.jc_uid
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               ((
+               (lower(a.lab_name) like 'glucose by meter' 
+                AND (a.ord_num_value > 200) OR (a.ord_num_value<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND a.ord_num_value >6.5)) 
+               -- AND NOT
+               -- lower(b.description) like '%hemodialysis%'
+               AND NOT (a.ord_num_value is null))
+
+  AND
+   e.taken_time_jittered > '2014-01-01'  
+   AND
+    lower(e.lab_name) like '%creatinine%' 
+
+ORDER BY
+  e.rit_uid, e.taken_time_jittered, e.pat_enc_csn_id_coded
+
+
+--Filter Hemodialysis (description) out
+SELECT DISTINCT 
+  b.jc_uid
+FROM 
+  `datalake_47618.order_proc` as b
+WHERE
+  lower(b.description) like '%hemodialysis%'
+ORDER BY
+  b.jc_uid;
+
+
+--Diet (not used)
+
+SELECT c.jc_uid, c.order_med_id_coded, 
+CAST(c.sig as FLOAT64) as sig, c.dose_unit, 
+c.taken_time_jittered, e.med_description, e.freq_display_name, 
+d.ss_section_name, d.ss_sg_name, d.protocol_name 
+FROM 
+  `datalake_47618.mar` as c
+JOIN 
+  (`datalake_47618.med_orderset` as d ON c.order_med_id_coded=d.order_med_id_coded
+  JOIN `datalake_47618.order_med` as e ON d.order_med_id_coded=e.order_med_id_coded)
+WHERE
+  c.jc_uid IN (SELECT DISTINCT a.rit_uid
+            FROM 
+              `datalake_47618.lab_result` as a
+            WHERE
+               a.taken_time_jittered > '2014-01-01' 
+               AND
+               (
+               (lower(a.lab_name) like '%glucose by meter%' 
+                AND ((SAFE_CAST(a.ord_value AS float64) > 200) OR (SAFE_CAST(a.ord_value AS float64)<70)) )
+               OR 
+               (lower(a.lab_name) like '%a1c%' AND SAFE_CAST(a.ord_value AS float64) >6.5) 
+               )
+               AND NOT (SAFE_CAST(a.ord_value AS float64) is null))
+    AND
+    (
+    (lower(d.ss_section_name) like '%npo%'
+    OR
+    lower(d.ss_section_name) like '%diet%')
+    )
+    AND
+    (
+    (lower(e.med_description) like '%insulin%'
+    AND
+    lower(c.route) like '%subcutaneous%')
+    )
+    AND 
+    lower(c.mar_action) like 'given'
+    AND 
+    c.taken_time_jittered > '2014-01-01'
+   
+ORDER BY
+    c.jc_uid, c.taken_time_jittered; 
+
+
+
+
+
+
+
+-------
+
+SELECT DISTINCT
+  *
+FROM 
+  `datalake_47618.lab_result` as a
+
+
+ SELECT DISTINCT
+         dem.rit_uid,
+         DATE_DIFF(DATE('2016-01-01'), DATE(dem.birth_date_jittered), YEAR) as ageYears
+       FROM
+         starr_datalake2018.demographic AS dem JOIN  -- Create a shorthand table alias for convenience when referencing
+         starr_datalake2018.encounter AS enc ON dem.rit_uid = jc_uid
+       WHERE
+         enc.appt_type = 'Anesthesia' AND
+         enc.contact_date_jittered = '2016-01-01'
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/Better_Than_Expected_Evaluation_Line_Plot.ipynb b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/Better_Than_Expected_Evaluation_Line_Plot.ipynb
new file mode 100644
index 00000000..b7fd7803
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/Better_Than_Expected_Evaluation_Line_Plot.ipynb
@@ -0,0 +1,489 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import scipy as sp\n",
+    "import scipy.stats\n",
+    "\n",
+    "BASE_DIR = \"/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Revisions/Analysis_Better_Than_Expected\"\n",
+    "\n",
+    "# Load in crowd pickle files\n",
+    "crowd_precision_arr = pickle.load(open(\"{0}/crowd_evaluation/precision_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "crowd_recall_arr = pickle.load(open(\"{0}/crowd_evaluation/recall_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "crowd_f1_arr = pickle.load(open(\"{0}/crowd_evaluation/f1_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "crowd_auroc_arr = pickle.load(open(\"{0}/crowd_evaluation/auroc_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "\n",
+    "# Load in low-mortality pickle files\n",
+    "low_mortality_precision_arr = pickle.load(open(\"{0}/low_mortality_evaluation/precision_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "low_mortality_recall_arr = pickle.load(open(\"{0}/low_mortality_evaluation/recall_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "low_mortality_f1_arr = pickle.load(open(\"{0}/low_mortality_evaluation/f1_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "low_mortality_auroc_arr = pickle.load(open(\"{0}/low_mortality_evaluation/auroc_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "\n",
+    "# Load in high-mortality pickle files\n",
+    "high_mortality_precision_arr = pickle.load(open(\"{0}/high_mortality_evaluation/precision_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "high_mortality_recall_arr = pickle.load(open(\"{0}/high_mortality_evaluation/recall_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "high_mortality_f1_arr = pickle.load(open(\"{0}/high_mortality_evaluation/f1_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "high_mortality_auroc_arr = pickle.load(open(\"{0}/high_mortality_evaluation/auroc_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "\n",
+    "# Load in orderset pickle files\n",
+    "orderset_precision_arr = pickle.load(open(\"{0}/orderset_evaluation/precision_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "orderset_recall_arr = pickle.load(open(\"{0}/orderset_evaluation/recall_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "orderset_f1_arr = pickle.load(open(\"{0}/orderset_evaluation/f1_arr.pkl\".format(BASE_DIR), \"rb\"))\n",
+    "orderset_auroc_arr = pickle.load(open(\"{0}/orderset_evaluation/auroc_arr.pkl\".format(BASE_DIR), \"rb\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PRECISION\n",
+      "Crowd: 0.239989383205 (0.18610200245)\n",
+      "Low-Mortality: 0.223844563449 (0.18976491654)\n",
+      "High-Mortality: 0.214284617101 (0.180678247498)\n",
+      "Orderset: 0.146943652601 (0.119475457635)\n",
+      "###########\n",
+      "RECALL\n",
+      "Crowd: 0.404049150063 (0.276323910187)\n",
+      "Low-Mortality: 0.374310608455 (0.266675684384)\n",
+      "High-Mortality: 0.358789706368 (0.255265395504)\n",
+      "Orderset: 0.262243851256 (0.207515945368)\n",
+      "###########\n",
+      "F1-Score\n",
+      "Crowd: 0.251274637294 (0.143360370045)\n",
+      "Low-Mortality: 0.232418424174 (0.140620126326)\n",
+      "High-Mortality: 0.22439157297 (0.138102044176)\n",
+      "Orderset: 0.158698431604 (0.0993815962405)\n",
+      "###########\n",
+      "AUROC\n",
+      "Crowd: 0.91201130805 (0.052606321537)\n",
+      "Low-Mortality: 0.874247075362 (0.0730177001234)\n",
+      "High-Mortality: 0.855652569873 (0.0761828991153)\n",
+      "Orderset: 0.780179213239 (0.17475444352)\n",
+      "###########\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary Statistics\n",
+    "# Standard Deviations\n",
+    "\n",
+    "# Precision at K (K = size of corresponding orderset)\n",
+    "print(\"PRECISION\")\n",
+    "print(\"Crowd: {0} ({1})\".format(np.mean(crowd_precision_arr), np.std(crowd_precision_arr)))\n",
+    "print(\"Low-Mortality: {0} ({1})\".format(np.mean(low_mortality_precision_arr), np.std(low_mortality_precision_arr)))\n",
+    "print(\"High-Mortality: {0} ({1})\".format(np.mean(high_mortality_precision_arr), np.std(high_mortality_precision_arr)))\n",
+    "print(\"Orderset: {0} ({1})\".format(np.mean(orderset_precision_arr), np.std(orderset_precision_arr)))\n",
+    "print(\"###########\")\n",
+    "\n",
+    "# Recall at K\n",
+    "print(\"RECALL\")\n",
+    "print(\"Crowd: {0} ({1})\".format(np.mean(crowd_recall_arr), np.std(crowd_recall_arr)))\n",
+    "print(\"Low-Mortality: {0} ({1})\".format(np.mean(low_mortality_recall_arr), np.std(low_mortality_recall_arr)))\n",
+    "print(\"High-Mortality: {0} ({1})\".format(np.mean(high_mortality_recall_arr), np.std(high_mortality_recall_arr)))\n",
+    "print(\"Orderset: {0} ({1})\".format(np.mean(orderset_recall_arr), np.std(orderset_recall_arr)))\n",
+    "print(\"###########\")\n",
+    "\n",
+    "# F1-Score\n",
+    "print(\"F1-Score\")\n",
+    "print(\"Crowd: {0} ({1})\".format(np.mean(crowd_f1_arr), np.std(crowd_f1_arr)))\n",
+    "print(\"Low-Mortality: {0} ({1})\".format(np.mean(low_mortality_f1_arr), np.std(low_mortality_f1_arr)))\n",
+    "print(\"High-Mortality: {0} ({1})\".format(np.mean(high_mortality_f1_arr), np.std(high_mortality_f1_arr)))\n",
+    "print(\"Orderset: {0} ({1})\".format(np.mean(orderset_f1_arr), np.std(orderset_f1_arr)))\n",
+    "print(\"###########\")\n",
+    "\n",
+    "# AUROC\n",
+    "print(\"AUROC\")\n",
+    "print(\"Crowd: {0} ({1})\".format(np.mean(crowd_auroc_arr), np.std(crowd_auroc_arr)))\n",
+    "print(\"Low-Mortality: {0} ({1})\".format(np.mean(low_mortality_auroc_arr), np.std(low_mortality_auroc_arr)))\n",
+    "print(\"High-Mortality: {0} ({1})\".format(np.mean(high_mortality_auroc_arr), np.std(high_mortality_auroc_arr)))\n",
+    "print(\"Orderset: {0} ({1})\".format(np.mean(orderset_auroc_arr), np.std(orderset_auroc_arr)))\n",
+    "print(\"###########\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PRECISION\n",
+      "Crowd: (0.23998938320544264, 0.22150950687816659, 0.25846925953271865)\n",
+      "Low-Mortality: (0.22384456344903478, 0.20500096078618957, 0.24268816611187999)\n",
+      "High-Mortality: (0.21428461710149327, 0.1963433181103594, 0.23222591609262713)\n",
+      "Orderset: (0.14694365260064496, 0.13507977337213922, 0.15880753182915069)\n",
+      "###########\n",
+      "RECALL\n",
+      "Crowd: (0.40404915006328174, 0.37751434860007005, 0.43058395152649342)\n",
+      "Low-Mortality: (0.37431060845485309, 0.34870230573727062, 0.39991891117243555)\n",
+      "High-Mortality: (0.35878970636838337, 0.33427710959190432, 0.38330230314486241)\n",
+      "Orderset: (0.26224385125624855, 0.24231653332328662, 0.28217116918921048)\n",
+      "###########\n",
+      "F1-Score\n",
+      "Crowd: (0.25127463729382232, 0.23698386389004353, 0.26556541069760115)\n",
+      "Low-Mortality: (0.23241842417383077, 0.21840080995663205, 0.24643603839102948)\n",
+      "High-Mortality: (0.22439157297005743, 0.2106249719262008, 0.23815817401391406)\n",
+      "Orderset: (0.15869843160392, 0.14879165003012007, 0.16860521317771993)\n",
+      "###########\n",
+      "AUROC\n",
+      "Crowd: (0.91201130804976283, 0.90695963410396163, 0.91706298199556402)\n",
+      "Low-Mortality: (0.87424707536160229, 0.86723533984163947, 0.8812588108815651)\n",
+      "High-Mortality: (0.85565256987294391, 0.84833688697910481, 0.86296825276678302)\n",
+      "Orderset: (0.78017921323873973, 0.76221640282441805, 0.79814202365306142)\n",
+      "###########\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary Statistics\n",
+    "# Confidence Intervals\n",
+    "def mean_confidence_interval(data, confidence=0.95):\n",
+    "    a = 1.0*np.array(data)\n",
+    "    n = len(a)\n",
+    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
+    "    h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n",
+    "    return (m, m-h, m+h)\n",
+    "\n",
+    "# Precision at K (K = size of corresponding orderset)\n",
+    "print(\"PRECISION\")\n",
+    "print(\"Crowd: {0}\".format(mean_confidence_interval(crowd_precision_arr)))\n",
+    "print(\"Low-Mortality: {0}\".format(mean_confidence_interval(low_mortality_precision_arr)))\n",
+    "print(\"High-Mortality: {0}\".format(mean_confidence_interval(high_mortality_precision_arr)))\n",
+    "print(\"Orderset: {0}\".format(mean_confidence_interval(orderset_precision_arr)))\n",
+    "print(\"###########\")\n",
+    "\n",
+    "# Recall at K\n",
+    "print(\"RECALL\")\n",
+    "print(\"Crowd: {0}\".format(mean_confidence_interval(crowd_recall_arr)))\n",
+    "print(\"Low-Mortality: {0}\".format(mean_confidence_interval(low_mortality_recall_arr)))\n",
+    "print(\"High-Mortality: {0}\".format(mean_confidence_interval(high_mortality_recall_arr)))\n",
+    "print(\"Orderset: {0}\".format(mean_confidence_interval(orderset_recall_arr)))\n",
+    "print(\"###########\")\n",
+    "\n",
+    "# F1-Score\n",
+    "print(\"F1-Score\")\n",
+    "print(\"Crowd: {0}\".format(mean_confidence_interval(crowd_f1_arr)))\n",
+    "print(\"Low-Mortality: {0}\".format(mean_confidence_interval(low_mortality_f1_arr)))\n",
+    "print(\"High-Mortality: {0}\".format(mean_confidence_interval(high_mortality_f1_arr)))\n",
+    "print(\"Orderset: {0}\".format(mean_confidence_interval(orderset_f1_arr)))\n",
+    "print(\"###########\")\n",
+    "\n",
+    "# AUROC\n",
+    "print(\"AUROC\")\n",
+    "print(\"Crowd: {0}\".format(mean_confidence_interval(crowd_auroc_arr)))\n",
+    "print(\"Low-Mortality: {0}\".format(mean_confidence_interval(low_mortality_auroc_arr)))\n",
+    "print(\"High-Mortality: {0}\".format(mean_confidence_interval(high_mortality_auroc_arr)))\n",
+    "print(\"Orderset: {0}\".format(mean_confidence_interval(orderset_auroc_arr)))\n",
+    "print(\"###########\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1083a42d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Mean + Standard Deviations \n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "BASE_DIR = \"/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Revisions/Analysis_Better_Than_Expected\"\n",
+    "n_groups = 4 # Precision, Recall, F1-Score, AUROC \n",
+    "fig, ax0 = plt.subplots(figsize=(18,12))\n",
+    "index = np.arange(n_groups)\n",
+    "bar_width = 0.20\n",
+    "opacity = 1.0\n",
+    "\n",
+    "# Order: Precision, Recall, F1-Score, AUROC\n",
+    "\n",
+    "#########\n",
+    "# CROWD #\n",
+    "#########\n",
+    "\n",
+    "# Mean, Error\n",
+    "crowd_mean = (np.mean(crowd_precision_arr), np.mean(crowd_recall_arr), np.mean(crowd_f1_arr), np.mean(crowd_auroc_arr))\n",
+    "crowd_err = (np.std(crowd_precision_arr), np.std(crowd_recall_arr), np.std(crowd_f1_arr), np.std(crowd_auroc_arr))\n",
+    "\n",
+    "# Plot\n",
+    "rects1 = ax0.bar(index, crowd_mean, bar_width,\n",
+    "                 alpha=opacity,\n",
+    "                 color='dodgerblue',\n",
+    "                 label='Crowd Model',\n",
+    "                 edgecolor='black',\n",
+    "                 yerr=crowd_err, ecolor='dodgerblue', capsize=2)\n",
+    "\n",
+    "plt.ylim(0,1.1)\n",
+    "\n",
+    "#################\n",
+    "# LOW-MORTALITY #\n",
+    "#################\n",
+    "\n",
+    "# Mean, Error\n",
+    "low_mortality_mean = (np.mean(low_mortality_precision_arr), np.mean(low_mortality_recall_arr), np.mean(low_mortality_f1_arr), np.mean(low_mortality_auroc_arr))\n",
+    "low_mortality_err = (np.std(low_mortality_precision_arr), np.std(low_mortality_recall_arr), np.std(low_mortality_f1_arr), np.std(low_mortality_auroc_arr))\n",
+    "\n",
+    "# Plot \n",
+    "ax1 = ax0.twinx()\n",
+    "rects2 = ax1.bar(index + bar_width, low_mortality_mean, bar_width,\n",
+    "                 alpha=opacity,\n",
+    "                 color='forestgreen',\n",
+    "                 edgecolor='black',\n",
+    "                 label='Low-Mortality Model', \n",
+    "                 yerr=low_mortality_err, ecolor='forestgreen', capsize=2)\n",
+    "\n",
+    "plt.ylim(0,1.1)\n",
+    "\n",
+    "##################\n",
+    "# HIGH-MORTALITY #\n",
+    "##################\n",
+    "\n",
+    "# Mean, Error\n",
+    "high_mortality_mean = (np.mean(high_mortality_precision_arr), np.mean(high_mortality_recall_arr), np.mean(high_mortality_f1_arr), np.mean(high_mortality_auroc_arr))\n",
+    "high_mortality_err = (np.std(high_mortality_precision_arr), np.std(high_mortality_recall_arr), np.std(high_mortality_f1_arr), np.std(high_mortality_auroc_arr))\n",
+    "\n",
+    "# Plot\n",
+    "ax2 = ax1.twinx()\n",
+    "rects3 = ax2.bar(index + bar_width*2, high_mortality_mean, bar_width,\n",
+    "                 alpha=opacity,\n",
+    "                 color='red',\n",
+    "                 edgecolor='black',\n",
+    "                 label='High-Mortality Model', \n",
+    "                 yerr=high_mortality_err, ecolor='red', capsize=2)\n",
+    "\n",
+    "plt.ylim(0,1.1)\n",
+    "\n",
+    "############\n",
+    "# ORDERSET #\n",
+    "############\n",
+    "\n",
+    "# Mean, Error\n",
+    "orderset_mean = (np.mean(orderset_precision_arr), np.mean(orderset_recall_arr), np.mean(orderset_f1_arr), np.mean(orderset_auroc_arr))\n",
+    "orderset_err = (np.std(orderset_precision_arr), np.std(orderset_recall_arr), np.std(orderset_f1_arr), np.std(orderset_auroc_arr))\n",
+    "\n",
+    "# Plot\n",
+    "ax3 = ax2.twinx()\n",
+    "rects4 = ax3.bar(index + bar_width*3, orderset_mean, bar_width,\n",
+    "                 alpha=opacity,\n",
+    "                 color='aqua',\n",
+    "                 edgecolor='black',\n",
+    "                 label='Orderset Benchmark', \n",
+    "                 yerr=orderset_err, ecolor='aqua', capsize=2)\n",
+    " \n",
+    "plt.ylim(0,1.1)\n",
+    "\n",
+    "##########################################################################################################################################\n",
+    "\n",
+    "plt.tick_params(top='off',bottom='off',labelbottom='off')\n",
+    "# plt.legend(loc='upper left')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1052f6590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Mean + Confidence Intervals\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Modified confidence interval function to output tuple of only (top, bottom) NOT (mean, top, bottom)\n",
+    "def mean_confidence_interval_altered_output(data, confidence=0.95):\n",
+    "    a = 1.0*np.array(data)\n",
+    "    n = len(a)\n",
+    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
+    "    h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n",
+    "    return (m-h, m+h)\n",
+    "\n",
+    "# Precision\n",
+    "precision_means = (np.mean(crowd_precision_arr), np.mean(low_mortality_precision_arr), np.mean(high_mortality_precision_arr), np.mean(orderset_precision_arr))\n",
+    "precision_intervals = (mean_confidence_interval_altered_output(crowd_precision_arr), mean_confidence_interval_altered_output(low_mortality_precision_arr), mean_confidence_interval_altered_output(high_mortality_precision_arr), mean_confidence_interval_altered_output(orderset_precision_arr))\n",
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "plt.errorbar(x=np.arange(0.1, 0.5, 0.1), \n",
+    "             y=precision_means, fmt='ko', markersize=8,\n",
+    "             yerr=[(top-bottom)/2 for (bottom, top) in precision_intervals],\n",
+    "             ecolor=['dodgerblue','forestgreen','red','aqua'], \n",
+    "             elinewidth=8)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/t/HVn7tWWpepcW+pqq6qham2zXOudPd5+0qyNcmhJC9JckGSP0xy1UljdiX5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\nAAAMEqYBAGCQMA0AAIOEaQAAGPR/Aat9EPpJ/BNDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108545490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Mean + Confidence Intervals\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Modified confidence interval function to output tuple of only (top, bottom) NOT (mean, top, bottom)\n",
+    "def mean_confidence_interval_altered_output(data, confidence=0.95):\n",
+    "    a = 1.0*np.array(data)\n",
+    "    n = len(a)\n",
+    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
+    "    h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n",
+    "    return (m-h, m+h)\n",
+    "\n",
+    "# Recall\n",
+    "recall_means = (np.mean(crowd_recall_arr), np.mean(low_mortality_recall_arr), np.mean(high_mortality_recall_arr), np.mean(orderset_recall_arr))\n",
+    "recall_intervals = (mean_confidence_interval_altered_output(crowd_recall_arr), mean_confidence_interval_altered_output(low_mortality_recall_arr), mean_confidence_interval_altered_output(high_mortality_recall_arr), mean_confidence_interval_altered_output(orderset_recall_arr))\n",
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "plt.errorbar(x=np.arange(0.1, 0.5, 0.1), \n",
+    "             y=recall_means, fmt='ko', markersize=8,\n",
+    "             yerr=[(top-bottom)/2 for (bottom, top) in recall_intervals],\n",
+    "             ecolor=['dodgerblue','forestgreen','red','aqua'], \n",
+    "             elinewidth=8)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AgGAGAIABwQwAAAOCGQAABgQzAAAMCGYAABgQzAAAMCCYAQBgQDADAMCAYAYAgIHtmz0B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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108f62fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Mean + Confidence Intervals\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Modified confidence interval function to output tuple of only (top, bottom) NOT (mean, top, bottom)\n",
+    "def mean_confidence_interval_altered_output(data, confidence=0.95):\n",
+    "    a = 1.0*np.array(data)\n",
+    "    n = len(a)\n",
+    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
+    "    h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n",
+    "    return (m-h, m+h)\n",
+    "\n",
+    "# AUROC\n",
+    "auroc_means = (np.mean(crowd_auroc_arr), np.mean(low_mortality_auroc_arr), np.mean(high_mortality_auroc_arr), np.mean(orderset_auroc_arr))\n",
+    "auroc_intervals = (mean_confidence_interval_altered_output(crowd_auroc_arr), mean_confidence_interval_altered_output(low_mortality_auroc_arr), mean_confidence_interval_altered_output(high_mortality_auroc_arr), mean_confidence_interval_altered_output(orderset_auroc_arr))\n",
+    "plt.figure(figsize=(12,8))\n",
+    "\n",
+    "plt.errorbar(x=np.arange(0.1, 0.5, 0.1), \n",
+    "             y=auroc_means, fmt='ko', markersize=8,\n",
+    "             yerr=[(top-bottom)/2 for (bottom, top) in auroc_intervals],\n",
+    "             ecolor=['dodgerblue','forestgreen','red','aqua'], \n",
+    "             elinewidth=8)\n",
+    "plt.plot(x=np.arange(0.1,0.5,0.1), y=auroc_means)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ttest_indResult(statistic=8.5895758607766588, pvalue=4.8997050499268276e-17)\n",
+      "Ttest_indResult(statistic=13.892847121350428, pvalue=1.989462511892172e-36)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# P-Value: Two-Sample T-Test on Mean with Unequal Variance (Welch's T-Test)\n",
+    "print(scipy.stats.ttest_ind(crowd_auroc_arr, low_mortality_auroc_arr, equal_var=False))\n",
+    "print(scipy.stats.ttest_ind(crowd_auroc_arr, orderset_auroc_arr, equal_var=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/aggregate_evaluation_metrics.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/aggregate_evaluation_metrics.py
new file mode 100644
index 00000000..a6e3daf7
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/aggregate_evaluation_metrics.py
@@ -0,0 +1,97 @@
+import pickle
+import sys
+import numpy as np
+"""
+Understanding evaluation metrics:
+1) Recall = Sensitivity = TP/(TP + FN)
+2) Precision = PPV (Positive Predictive Value) = TP/(TP + FP)
+3) F1-Score = (2 * precision * recall)/(precision + recall)
+4) AU-ROC (c-statistic) = area under the ROC curve
+"""
+
+BASE_DIR = "/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Revisions/Analysis_Better_Than_Expected"
+def parse_association_model_evaluation_output(inf, cohort):
+	# Set up arrays to be pickled
+	recall_arr = []
+	precision_arr = []
+	f1_arr = []
+	auroc_arr = []
+
+	inf.readline()
+	inf.readline()
+
+	auroc = None
+	f1_score = None
+	precision = None
+	recall = None
+	for line in inf:
+		line = line.strip().split("\t")
+		if (line[-3] != "None"):
+			auroc = float(line[-3])
+			auroc_arr.append(auroc)
+		if (line[-9] != "None"):
+			f1_score = float(line[-9])
+			f1_arr.append(f1_score)
+		if (line[-10] != "None"):
+			precision = float(line[-10])
+			precision_arr.append(precision)
+		if (line[-11] != "None"):
+			recall = float(line[-11])
+			recall_arr.append(recall)
+
+	# Output pickle arrays
+	OUTPUT_DIR = "{0}/{1}_evaluation".format(BASE_DIR, cohort)
+
+	pickle.dump(np.array(recall_arr), open("{0}/recall_arr.pkl".format(OUTPUT_DIR), "wb"))
+	pickle.dump(np.array(precision_arr), open("{0}/precision_arr.pkl".format(OUTPUT_DIR), "wb"))
+	pickle.dump(np.array(f1_arr), open("{0}/f1_arr.pkl".format(OUTPUT_DIR), "wb"))
+	pickle.dump(np.array(auroc_arr), open("{0}/auroc_arr.pkl".format(OUTPUT_DIR), "wb"))
+
+def parse_orderset_evaluation_output(inf, cohort="orderset"):
+	# Set up arrays to be pickled
+	recall_arr = []
+	precision_arr = []
+	f1_arr = []
+	auroc_arr = []
+
+	inf.readline()
+	inf.readline()
+
+	auroc = None
+	f1_score = None
+	precision = None
+	recall = None
+	for line in inf:
+		line = line.strip().split("\t")
+		if (line[-13] != "None"):
+			auroc = float(line[-13])
+			auroc_arr.append(auroc)
+		if (line[-19] != "None"):
+			f1_score = float(line[-19])
+			f1_arr.append(f1_score)
+		if (line[-20] != "None"):
+			precision = float(line[-20])
+			precision_arr.append(precision)
+		if (line[-21] != "None"):
+			recall = float(line[-21])
+			recall_arr.append(recall)
+
+	# Output pickle arrays
+	OUTPUT_DIR = "{0}/{1}_evaluation".format(BASE_DIR, cohort)
+
+	pickle.dump(np.array(recall_arr), open("{0}/recall_arr.pkl".format(OUTPUT_DIR), "wb"))
+	pickle.dump(np.array(precision_arr), open("{0}/precision_arr.pkl".format(OUTPUT_DIR), "wb"))
+	pickle.dump(np.array(f1_arr), open("{0}/f1_arr.pkl".format(OUTPUT_DIR), "wb"))
+	pickle.dump(np.array(auroc_arr), open("{0}/auroc_arr.pkl".format(OUTPUT_DIR), "wb"))
+
+crowd_inf = open("{0}/crowd_evaluation/recClassification.byOrderSets.ItemAssociationRecommender.PPV.tab".format(BASE_DIR), "rU")
+parse_association_model_evaluation_output(crowd_inf, "crowd")
+
+low_mortality_inf = open("{0}/low_mortality_evaluation/recClassification.byOrderSets.ItemAssociationRecommender.PPV.tab".format(BASE_DIR), "rU")
+parse_association_model_evaluation_output(low_mortality_inf, "low_mortality")
+
+high_mortality_inf = open("{0}/high_mortality_evaluation/recClassification.byOrderSets.ItemAssociationRecommender.PPV.tab".format(BASE_DIR), "rU")
+parse_association_model_evaluation_output(high_mortality_inf, "high_mortality")
+
+orderset_inf = open("{0}/orderset_evaluation/recClassification.byOrderSets.OrderSetUsage.tab".format(BASE_DIR), "rU")
+parse_orderset_evaluation_output(orderset_inf, "orderset")
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/evaluate_association_models_recommended_items_against_verify_items.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/evaluate_association_models_recommended_items_against_verify_items.py
new file mode 100644
index 00000000..4983668a
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/evaluate_association_models_recommended_items_against_verify_items.py
@@ -0,0 +1,30 @@
+"""
+Generate evaluation statistics for "better than expected" analysis
+"""
+import sys,os;
+import time;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+RESULT_DIR = "crowd_minus_high_mortality_evaluation";
+
+VALIDATE_FILENAME = "Query_and_Verify_Items.2010-2013.q86400.v86400.tab.gz";
+OUTPUT_BASENAME = "recClassification.byOrderSets";
+
+def main_associationRec(argv):
+    validateFilename = VALIDATE_FILENAME; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    mod = RecommendationClassificationAnalysis();
+    sortOptions = ["prevalence","PPV","OR","RR","P-YatesChi2-NegLog"];
+    for sortOption in sortOptions:
+        subargv = ["RecommendationClassificationAnalysis.py", "-P", validateFilename, "--numRecsByOrderSet", "-R", "ItemAssociationRecommender", "-a", "weighted", "-t", "86400", "-p", "patient_", 
+                    "-s", sortOption, RESULT_DIR+"/%s.ItemAssociationRecommender.%s.tab.gz" % (OUTPUT_BASENAME, sortOption) ]
+        mod.main(subargv);
+
+if __name__ == "__main__":
+    main_associationRec(sys.argv);
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/evaluate_ordersets_against_verify_items.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/evaluate_ordersets_against_verify_items.py
new file mode 100644
index 00000000..d1219fff
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/evaluate_ordersets_against_verify_items.py
@@ -0,0 +1,29 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.OrderSetRecommenderClassificationAnalysis import OrderSetRecommenderClassificationAnalysis;
+from medinfo.cpoe.analysis.OrderSetUsageAnalysis import OrderSetUsageAnalysis;
+
+RESULT_DIR = "orderset_evaluation";
+VALIDATE_FILENAME = "Query_and_Verify_Items.2010-2013.q86400.v86400.tab.gz";
+#VALIDATE_FILENAME = "sourceData/first24hourOrderSets.2013.sample.tab.gz";
+OUTPUT_BASENAME = "recClassification.byOrderSets";
+
+def main_associationRec(argv):
+    validateFilename = VALIDATE_FILENAME; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    mod = OrderSetUsageAnalysis();
+    subargv = ["OrderSetUsageAnalysis.py", "--numRecsByOrderSet"];
+    subargv.extend([validateFilename, RESULT_DIR+"/%s.OrderSetUsage.tab.gz" % (OUTPUT_BASENAME)]);
+    mod.main(subargv);
+
+if __name__ == "__main__":
+    main_associationRec(sys.argv);
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/extract_better_than_expected_patients.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/extract_better_than_expected_patients.py
new file mode 100644
index 00000000..2053a8ba
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/extract_better_than_expected_patients.py
@@ -0,0 +1,66 @@
+import sys
+from datetime import datetime
+import pandas
+from dateutil import parser
+###########################################################################################################################################
+# Extract "better than expected" patient cases that are not included in balanced patient cohorts inputted for association model training  #
+###########################################################################################################################################
+
+# Exclude patients inputted in association model training
+low_mortality_patients = open("/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Preliminary/Results/top_patients_matched_remapped.csv", "rU")
+high_mortality_patients = open("/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Preliminary/Results/bottom_patients_matched_remapped.csv", "rU")
+crowd_patients = open("/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Preliminary/Results/everyone_patients_matched_remapped.csv", "rU")
+
+exclude_patients = {}
+for line in low_mortality_patients:
+	patient_id = int(line.strip())
+	exclude_patients[patient_id] = 1
+low_mortality_patients.close()
+
+for line in high_mortality_patients:
+	patient_id = int(line.strip())
+	exclude_patients[patient_id] = 1
+high_mortality_patients.close()
+
+for line in crowd_patients:
+	patient_id = int(line.strip())
+	exclude_patients[patient_id] = 1
+crowd_patients.close()
+
+# print(len(exclude_patients.keys()))
+
+# Identify patients that turned out "better than expected" based on pre-defined thresholds
+# Better than expected patient case: mortality prob > threshold (high risk) but mortality delta > threshold (good outcome)
+prob_threshold = 0.50
+delta_threshold = 30 # pandas.Timedelta(days=30)
+
+inf = open("joined_probs_mortality_delta_spreadsheet.csv", "rU")
+inf.readline()
+
+better_than_expected_patients = {}
+for line in inf:
+	line = line.strip().split(",")
+	delta = line[2]
+	if (delta == "N/A"):
+		continue # no mortality data
+	else:
+		delta = int(delta.strip().split(" ")[0]) # extract days
+	patient_id = int(line[0])
+	mortality_prob = float(line[1])
+
+	if (mortality_prob > prob_threshold and delta > delta_threshold):
+		# exclude patients from balanced patient cohorts inputted into association models
+		if (patient_id not in list(exclude_patients.keys())):
+			better_than_expected_patients[patient_id] = 1
+inf.close()
+
+print("Better than expected patient cases: {0}".format(len(list(better_than_expected_patients.keys()))))
+
+# Output better than expected patient list
+outf = open("better_than_expected_patients.csv", "w")
+for patient_id in list(better_than_expected_patients.keys()):
+	outf.write("{0}\n".format(patient_id))
+outf.close()
+
+
+
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/extract_mortality_unbinned.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/extract_mortality_unbinned.py
new file mode 100644
index 00000000..99f2e8f6
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/extract_mortality_unbinned.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract death clinical item occurrence per patient
+##########################################################################################
+import sys, os
+import pandas
+from io import StringIO
+from datetime import datetime;
+
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Load in patient encounters map
+patient_encounters = {}
+pat_enc_f = open("/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results/lengths_of_stay.csv", "rU")
+pat_enc_f.readline()
+for line in pat_enc_f:
+	line = line.strip().split(",")
+	if (line[0] not in patient_encounters):
+		patient_encounters[line[0]] = [pandas.to_datetime(line[1]),] # patient id: list of admission times
+	else:
+		patient_encounters[line[0]].append(pandas.to_datetime(line[1]))
+
+# print(patient_encounters)
+
+# Create SCRIPT_FILE
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+DATA_QUERY = SQLQuery();
+
+DATA_QUERY.addSelect("pat_id")
+DATA_QUERY.addSelect("death_date")
+DATA_QUERY.addFrom("stride_patient")
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+#print(len(results))
+
+output = open("/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Submission_JBI_Major_Revisions/Analysis_Better_Than_Expected/mortality_days_after_last_admission.csv", "w")
+output.write("pat_id,admission_time,death_time,delta\n")
+for line in results:
+	if (line[1] is None):
+		continue # patient survived, recorded as None
+	
+	pat_id = line[0]
+	death_time = pandas.to_datetime(line[1])
+
+	if (pat_id not in patient_encounters): # patient_id not included in admit-discharge table
+		continue
+
+	for admission_time in patient_encounters[pat_id]:
+		delta = pandas.to_timedelta(death_time - admission_time)
+		output.write("{0},{1},{2},{3}\n".format(pat_id,admission_time,death_time,delta))
+
+output.close()
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/join_probs_mortality_delta.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/join_probs_mortality_delta.py
new file mode 100644
index 00000000..73d68bc7
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/join_probs_mortality_delta.py
@@ -0,0 +1,30 @@
+inf_mortality = open("mortality_days_after_last_admission.csv", "rU")
+inf_mortality.readline()
+inf_probs = open("mortality_probs_remapped_2010_2013.csv", "rU")
+inf_probs.readline()
+
+outf = open("joined_probs_mortality_delta_spreadsheet.csv", "w")
+outf.write("pat_id,30_day_mortality_prob,delta\n")
+
+pat_mortality_map = {}
+pat_prob_map = {}
+
+for line in inf_mortality:
+	line = line.strip().split(",")
+	pat_mortality_map[line[0]] = line[3] # pat_id: mortality_delta
+inf_mortality.close()
+
+for line in inf_probs:
+	line = line.strip().split(",")
+	pat_prob_map[line[0]] = line[1] # pat_id: 30_day_mortality_prob
+inf_probs.close()
+
+for pat_id, prob in pat_prob_map.items():
+	delta = "N/A"
+	if (pat_id in pat_mortality_map):
+		delta = pat_mortality_map[pat_id]
+	# if (delta == "N/A"):
+	# 	continue
+	outf.write("{0},{1},{2}\n".format(pat_id,prob,delta))
+
+outf.close()
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/prepare_query_and_verify_items.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/prepare_query_and_verify_items.py
new file mode 100644
index 00000000..34f47867
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BetterThanExpectedEvaluation/prepare_query_and_verify_items.py
@@ -0,0 +1,69 @@
+"""
+Extract order set usage instances, items leading up to the instance to query trained association model, and items after the instance for evaluation
+"""
+import sys,os;
+import time;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+
+EXCLUDE_CATEGORY_DESCRIPTIONS = ['Nursing','Discharge','Transfer','BB Call Slip','Transport','Admission','Pharmacy Supplies','Diet Communication'];
+EXCLUDE_CATEGORY_IDS = [4,5,11,13,18,23,46,66];    # From 5 year data set
+EXCLUDE_CATEGORY_IDS_STR = str.join(',', [str(catId) for catId in EXCLUDE_CATEGORY_IDS] );   # Concatenate into string form
+
+ADMIT_DX_CATEGORY_ID = 2;
+DEATH_ITEM_ID = 72416;
+DEMOGRAPHICS_CATEGORY_ID = 159;
+
+# 2010-2013 data range
+START_DATE_STR = "2010-01-01"
+END_DATE_STR = "2014-01-01"
+
+SOURCE_DATA_DIR = "/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results_Revisions/Analysis_Better_Than_Expected";
+
+EVAL_TIMESPAN = 86400; # query window to extract model predictions (24 hours)
+VERIFY_TIMESPAN = 86400 # evaluation window for items to validate model predictions
+
+BASE_OUTPUT_NAME = "OrdersetItems.2010-2013.q%d.v%d" % (EVAL_TIMESPAN, VERIFY_TIMESPAN);
+
+def main_prepPatientItems(argv):
+    prep = PreparePatientItems();
+    # for i in xrange(-9,10):
+    prep.main(["PreparePatientItems","-S",START_DATE_STR, "-E",END_DATE_STR, "-O", "-p",str(DEMOGRAPHICS_CATEGORY_ID),"-c", str(ADMIT_DX_CATEGORY_ID), "-Q", str(EVAL_TIMESPAN), "-V", str(VERIFY_TIMESPAN), "%s/better_than_expected_patients.csv" % (SOURCE_DATA_DIR), "%s/%s.tab.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME)]);
+    
+    # # Convert to (filtered) Bag of Words
+    # for i in xrange(-9,10):
+    #     prep.main(["PreparePatientItems","-B","qvo","-X", EXCLUDE_CATEGORY_IDS_STR,"%s/%s.%s.tab.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME,i),"%s/%s.%s.filter.bow.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME,i)]);
+
+    # Concatenate batch of files
+    # ofs = stdOpen("%s/%s.1234567890.filter.bow.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME),"w");
+    # for i in [1,2,3,4,5,6,7,8,9,0]:
+    #     ifs = stdOpen("%s/%s.%d.filter.bow.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME, i) );
+    #     ofs.write(ifs.read());
+    #     ifs.close();
+    # ofs.close();
+    
+    # # For comment and header row of csv files, drop repeats
+    # baseIds = [-1,-2,-3,-4,-5];
+    # ofs = stdOpen("%s/%s.-12345.tab.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME),"w");
+    # isHeaderRowWritten = False;
+    # for baseId in baseIds:
+    #     ifs = stdOpen("%s/%s.%d.tab.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME,baseId) );
+    #     for iLine, line in enumerate(ifs):
+    #         if not line.startswith(COMMENT_TAG):    # Skip comment lines
+    #             if line[0].isalpha():   # Starts with a letter/label, must be header row, not data
+    #                 if isHeaderRowWritten:
+    #                     continue;   # Skip text/header rows, except for the first one encountered
+    #                 else:
+    #                     isHeaderRowWritten = True;
+    #             ofs.write(line);
+    #     ifs.close();
+    # ofs.close();
+ 
+if __name__ == "__main__":
+    main_prepPatientItems(sys.argv);
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/compute_observed_vs_expected_mortality.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/compute_observed_vs_expected_mortality.py
new file mode 100644
index 00000000..dad1cb2b
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/compute_observed_vs_expected_mortality.py
@@ -0,0 +1,117 @@
+import pandas as pd
+import scipy.stats
+import math
+##########################
+### Observed Mortality ###
+##########################
+
+# Load in physician cohorts based on H&P notes
+physician_hp = open("/Users/jwang/Desktop/Results/physician_patient_map_hp.csv", "rU")
+physician_cohorts = {}
+
+physician_hp.readline()
+for line in physician_hp:
+	line = line.strip().split(",")
+	physician = line[0]
+	if (physician in physician_cohorts):
+		physician_cohorts[physician].append( (line[1], line[2]) ) # (patient_id, H&P note_time)
+	else:
+		physician_cohorts[physician] = [ (line[1], line[2]), ]
+
+# Load in patient's that died within 30 days of an admission order
+mortality_30 = open("/Users/jwang/Desktop/Results/30_day_deaths.csv", "rU")
+mortality_map = {}
+for line in mortality_30:
+	line = line.strip().split(",")
+	mortality_map[line[0]] = line[1] # key = patient_id, value = last recorded admission time
+
+# Compute observed mortality = number of patients who died within 30 days of admission/total patients seen based on H&P notes
+
+#initialize map with all physician ids
+observed_mortality = {}
+for physician in list(physician_cohorts.keys()):
+	observed_mortality[physician] = (0, len(physician_cohorts[physician]))
+
+for physician, patients in physician_cohorts.items():
+	for (pat_id, note_time) in patients:
+
+		if (pat_id in mortality_map):
+			delta = pd.to_datetime(note_time) - pd.to_datetime(mortality_map[pat_id])
+			if (delta >= pd.to_timedelta("0 days 00:00:00")): # this physician was responsible for this patient right before death
+				#print(pat_id)
+				#num_patients += 1
+				observed_mortality[physician] = (observed_mortality[physician][0]+1, observed_mortality[physician][1])
+
+# perform division (save number of observed mortalities)
+observed_mortalities = {}
+for physician, (dead, total) in observed_mortality.items():
+	observed_mortality[physician] = float(dead)/total
+	observed_mortalities[physician] = int(dead)
+#print(observed_mortality)
+
+##########################
+### Expected Mortality ###
+##########################
+
+# Expected mortality = average probability of patients in physician cohort dying within 30 days of an admission
+
+# Initialize map with physician ids
+expected_mortality = {}
+for physician in list(physician_cohorts.keys()):
+	expected_mortality[physician] = (0, len(physician_cohorts[physician]))
+
+# Read in sickness metrics (expected mortality probability)
+mortality_probs = {} # key = patient_id, value = probability of dying within 30 days
+mortality_probs_f = open("/Users/jwang/Desktop/Results/mortality_probs_remapped.csv", "rU")
+mortality_probs_f.readline()
+for line in mortality_probs_f:
+	line = line.strip().split(",")
+	mortality_probs[line[0]] = float(line[1])
+
+# Find average probability of 30 day mortality for patients in cohort
+for physician, patients in physician_cohorts.items():
+	for (pat_id, note_time) in patients:
+		if (pat_id not in mortality_probs):
+			continue # assume patient without probability of death maintains same as average probability for the physician
+
+		if (physician in expected_mortality):
+			expected_mortality[physician] = (float(expected_mortality[physician][0])+mortality_probs[pat_id], expected_mortality[physician][1]+1)
+		else: # not yet in the dictionary
+			expected_mortality[physician] = (mortality_probs[pat_id], 1)
+
+# Perform division
+for physician, (prob_sum, total) in expected_mortality.items():
+	expected_mortality[physician] = float(prob_sum)/total
+#print(expected_mortality)
+
+############################
+### Print/Compute Scores ###
+############################
+
+# Write out observed and expected probabilities to CSV
+outf = open("/Users/jwang/Desktop/Results/mortality_observed_vs_expected.csv", "w")
+outf.write("physician,observed_mortality_rate,expected_mortality_rate,total_patients,observed_dead,observed_survived,expected_dead,expected_survived,p-value,score,observed_over_expected\n")
+for physician in list(physician_cohorts.keys()):
+
+	# Compute Fisher Exact Test
+	cohort_size = len(physician_cohorts[physician])
+	observed_dead = observed_mortalities[physician]
+	observed_survived = cohort_size - observed_dead
+	expected_dead = round(expected_mortality[physician]*cohort_size)
+	expected_survived = cohort_size - expected_dead
+	contingency_table = [ [observed_dead, observed_survived], [expected_dead, expected_survived] ]
+	odds_ratio, p_value = scipy.stats.fisher_exact(contingency_table, alternative="two-sided") # observed dead, observed survived | expected dead, expected_survived
+	if (expected_mortality[physician] == 0):
+		observed_over_expected = None
+	else:
+		observed_over_expected = float(observed_mortality[physician])/expected_mortality[physician]
+
+	# Two-Sided
+	score = -1*math.log10(p_value) # base 10 logarithm
+	if (observed_dead > expected_dead): # more patients died than were expected
+		score = -1*score
+
+	outf.write("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10}\n".format(physician,observed_mortality[physician],expected_mortality[physician],len(physician_cohorts[physician]),
+		observed_dead,observed_survived,expected_dead,expected_survived,p_value,score,observed_over_expected))
+outf.close()
+
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/mortality_predictor.Rmd b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/mortality_predictor.Rmd
new file mode 100644
index 00000000..9b6aedf0
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/mortality_predictor.Rmd
@@ -0,0 +1,518 @@
+Mortality Predictor: given a patient's demographic data, initial vital signs, past medical history, admission diagnosis, treatment team, and other relevant covariates collected between encountering the physician, we predict the patient's sickness based on the probability that they die/survive within 30 days of an admission order
+
+PART 1: TRAIN AND VALIDATE MORTALITY PREDICTOR ON ONLY 2008-2009 DATA (FOR TESTING PURPOSES)
+Goal: Obtain AUC and accuracy metrics based on cross-validation error
+```{r}
+# Load in required libraries
+library(caret)
+library(ggplot2)
+library(dplyr)
+library(mice)
+library(Matching)
+library(tableone)
+require(reshape2)
+require(ISLR)
+Sys.setenv(TZ='UTC')  
+  
+##############################
+### Load in 2008-2009 data ###
+##############################
+print("Reading and formatting data frames...")
+  
+# Read tab-delimited input using header row labels and interpret Python None strings as R NA values
+df_raw = tbl_df(read.csv("/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:61)) {
+  df_raw[,col_id] <- factor(ifelse(df_raw[,col_id]=="True", 1, 0))
+}
+# Condense dataframe
+columns_of_interest = c(1, 6:296) # id, relevant covariates, label columns
+df_raw <- df_raw[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw) <- make.names(colnames(df_raw))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw = df_raw[,c(1:3,6:292)]
+
+# Assess lab tests for percentage na: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw[,i]))/dim(df_raw)[1]
+  print(colnames(df_raw)[i])
+  print(ratio_na)
+}
+
+# Remove urine, column 7 (missingness > 20%)
+df_raw = df_raw[,c(1:6, 8:290)]
+
+# Remove TT and admission diagnosis columns that are all zero
+df_raw <- data.frame(df_raw)
+zero_cols = c()
+for (i in c(43:54, 57:284)) { # colindex 55 = income, colindex 56 = age
+  # replace NA with 0
+  df_raw[,i][is.na(df_raw[,i])] <- 0
+  if ((length(df_raw[,i][df_raw[,i] == 1])) == 0) {
+    print(i)
+    print(colnames(df_raw)[i])
+    zero_cols <- c(zero_cols,i)
+  }
+}
+df_raw <- df_raw[,-zero_cols]
+
+# Convert remaining admission diagnosis columns to factor
+for (i in c(56:185)) {
+  df_raw[,i] = as.factor(df_raw[,i])
+}
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw);
+md_data <- mice(df_raw,m=1,maxit=5,meth='pmm',seed=500);
+df <- complete(md_data,1);
+
+df_save <- df
+
+#################################
+### Cross-Validation Training ###
+#################################
+df <- df_save
+
+# When applying the model to 2010 onwards, we will use ALL of 2008-2009 data to train
+label_column <- dim(df)[2]-3
+id_column <- 1
+df[,label_column] <- factor(ifelse(df[,label_column]=="died", 1, 0))
+training <- df[, c(-id_column, -(label_column+3),-(label_column+2), -(label_column+1), -(label_column-1))]
+training_raw <- training
+
+# Ensure column names are in standardized/accepted format
+feature.names=names(training)
+for (f in feature.names) {
+  if (class(training[[f]])=="factor") {
+    levels <- unique(c(training[[f]]))
+    training[[f]] <- factor(training[[f]],
+                   labels=make.names(levels))
+  }
+}
+
+# Cross-Validation Elastic Net (Lasso Normalization)
+set.seed(2017)
+summary(training)
+# Logistic Regression + Lasso Regularization
+lambda.grid <- 10^seq(4,-4,length=50)
+trnCtrl = trainControl(method="repeatedCV", number=10, repeats=5, summaryFunction=twoClassSummary, classProbs=TRUE)
+srchGrd = expand.grid(alpha=1, lambda=lambda.grid) # alpha = 1 denotes lasso regression
+
+# GBM
+# trnCtrl = trainControl(method="cv", number=10, repeats=5, returnResamp='none', summaryFunction=twoClassSummary, classProbs=TRUE)
+
+# Logistic Regression + Lasso Regularization
+glm.train <- train(as.factor(death_30) ~., data=training, method="glmnet", family="binomial", tuneGrid=srchGrd, standardize=TRUE, trControl = trnCtrl, maxit=1000)
+
+# GBM
+# glm.train <- train(as.factor(death_30) ~., data=training, method="gbm", trControl = trnCtrl, metric="ROC")
+# plot(glm.train)
+# summary(glm.train)
+# attributes(glm.train)
+
+# Logistic Regression + Lasso Regularization
+glm.train$bestTune # alpha = 1, lambda = 0.00202359, AUC = 0.8559337
+
+# GBM
+# glm.train$bestTune # n.trees=150, interaction.depth=2, shrinkage=0.1, n.minobsinnode=10, AUC = 0.8606045
+
+# Extract best-fit model from grid-search 
+glm.fit <- glm.train$finalModel
+
+# Logistic Regression + Lasso Regularization
+plot(glm.fit, xvar="dev", label=TRUE)
+coef(glm.fit, s=glm.train$bestTune$lambda)
+plot(x=log(glm.train$results$lambda), y=glm.train$results$ROC)
+
+# Caveat: dataset is predominantly composed of "survived individuals"
+length(which(df$death_30 == 1))/length(df$death_30)
+
+# Optimal glmnet
+training <- training_raw
+training <- data.matrix(training)
+fit = glmnet(x=training[,1:184], y=training[,185], family="binomial", alpha=1, lambda.min.ratio=glm.train$bestTune$lambda)
+coef(fit,s=glm.train$bestTune$lambda)
+
+```
+
+PART 2: APPLY 2008-2009 TRAINED MODEL TO PREDICT 30-DAY MORTALITIES FOR 2010-2013 PATIENTS
+Goal: To generate probabilities that enable us to compute expected mortality rates on a per physician basis
+```{r}
+########################################
+### Mortality Probs for 2010 Onwards ###
+########################################
+
+# 30-day mortality
+##############################
+### Load in 2010-2013 data ###
+##############################
+df_raw_10_13 = tbl_df(read.csv("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:61)) {
+  df_raw_10_13[,col_id] <- factor(ifelse(df_raw_10_13[,col_id]=="True", 1, 0))
+}
+
+# Condense dataframe
+columns_of_interest = c(1, 6:296) # id, relevant covariates, label columns
+df_raw_10_13 <- df_raw_10_13[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw_10_13) <- make.names(colnames(df_raw_10_13))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw_10_13 = df_raw_10_13[,c(1:3,6:292)]
+
+# Remove lab tests to reduce bias: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw_10_13[,i]))/dim(df_raw_10_13)[1]
+  print(colnames(df_raw_10_13)[i])
+  print(ratio_na)
+}
+
+# Remove urine, column 7 (missingness > 20%)
+df_raw_10_13 = df_raw_10_13[,c(1:6, 8:290)]
+
+# Remove admission diagnosis columns that are all zero
+df_raw_10_13 <- data.frame(df_raw_10_13)
+# zero_cols_10_13 = c()
+# for (i in c(43:54, 57:284)) { # colindex 55 = income, colindex 56 = age
+#   # replace NA with 0
+#   df_raw_10_13[,i][is.na(df_raw_10_13[,i])] <- 0
+#   if ((length(df_raw_10_13[,i][df_raw_10_13[,i] == 1])) == 0) {
+#     print(i)
+#     print(colnames(df_raw_10_13)[i])
+#     zero_cols_10_13 <- c(zero_cols_10_13,i)
+#   }
+# }
+# zero_cols_union = union(zero_cols, zero_cols_10_13)
+
+# Remove union of both zero_cols and zero_cols_10_13
+# df_raw_10_13 <- df_raw_10_13[,-zero_cols_union]
+
+# Remove zero columns (based on 2008-2009 dataset), to ensure columns match
+df_raw_10_13 <- df_raw_10_13[,-zero_cols]
+
+# Convert remaining admission diagnosis columns to factor
+# for (i in c(56:127)) { # if we remove zero_cols_union
+for (i in c(56:185)) {
+  df_raw_10_13[,i] = as.factor(df_raw_10_13[,i])
+}
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw_10_13);
+md_data <- mice(df_raw_10_13,m=1,maxit=5,meth='pmm',seed=500);
+df_10_13 <- complete(md_data,1);
+
+df_save_10_13 <- df_10_13
+
+################################
+### Compute Sickness Metrics ###
+################################
+df_10_13 <- df_save_10_13
+label_column <- dim(df_10_13)[2]-3
+id_column <- 1
+df_10_13[,label_column] <- factor(ifelse(df_10_13[,label_column]=="died", 1, 0))
+testing <- df_10_13[, c(-id_column, -(label_column+3),-(label_column+2), -(label_column+1), -(label_column-1))]
+testing_raw <- testing
+
+testing <- data.matrix(testing)
+pred <- predict.glmnet(fit, newx=testing[,1:184], s=0.01, type="class")
+library(boot)
+probs <- inv.logit(pred)
+
+# # Standardize column names
+# feature.names=names(testing)
+# for (f in feature.names) {
+#   if (class(testing[[f]])=="factor") {
+#     levels <- unique(c(testing[[f]]))
+#     testing[[f]] <- factor(testing[[f]], labels=make.names(levels))
+#   }
+# }
+# 
+# pred = predict(glm.fit, newx=data.matrix(testing), type="response", s=)
+# 
+# cm <- confusionMatrix(data=pred, testing$death_30)
+# cm
+# # sensitivity = TPR, specificity = TNR
+# 
+# # Compute AUC for predicting death with the model
+# testing.data.matrix <- data.matrix(testing)
+# for (i in 14:185) {
+#   testing[,i] <- factor(testi)
+# }
+# prob <- predict(glm.fit, newdata=testing, type="prob", s=glm.train$bestTune$lambda)
+# 
+# # Convert prob into binary label
+# # prob_binned <- factor(ifelse(prob$`1`>0.5, 1, 0))
+# pred <- prediction(as.numeric(prob[,2]), testing$death_30)
+# perf <- performance(pred, measure = "tpr", x.measure = "fpr")
+# plot(perf)
+# auc <- performance(pred, measure = "auc")
+# auc <- auc@y.values[[1]]
+# auc
+# 
+# # Caveat: test set is predominantly composed of "survived individuals"
+# length(which(testing$death_30 == 1))/length(testing$death_30)
+
+# Save sickness metrics to CSV file
+mortality_probs = data.frame(df_10_13$normalized_patient_id, probs[,1])
+write.table(mortality_probs, file="/Users/jwang/Desktop/Results/mortality_probs.csv", sep=",")
+
+```
+
+```{r}
+####################
+### Overlap Plot ###
+####################
+
+require(car)
+death_prob <- probs[,1]
+died <- which(testing_raw$death_30 == 1)
+died_prob <- death_prob[died]
+survived_prob <- death_prob[-died]
+
+print("Plotting overlap...")
+# note that logit = log(p/(1-p))
+logit_died <- as.numeric(logit(died_prob))
+logit_survived <- as.numeric(logit(survived_prob))
+histdata <- data.frame(rbind.data.frame(cbind.data.frame(logit=logit_died,label="logit_died_30"), cbind.data.frame(logit=logit_survived,label="logit_survived_30")))
+plot <- ggplot(histdata, aes(x=logit, fill=label)) + geom_histogram(alpha=0.2, position="identity")
+print(plot)
+```
+ARCHIVED CODE
+
+APPLY 2010-2013 TRAINED MODEL TO PREDICT 30-DAY MORTALITIES FOR 2008-2009 PATIENTS (BACKWARDS)
+```{r}
+#####################################
+### Train model on 2010-2013 data ###
+#####################################
+df <- df_save_10_13
+
+# When applying the model to 2010 onwards, we will use ALL of 2008-2009 data to train
+label_column <- dim(df)[2]-3
+id_column <- 1
+df[,label_column] <- factor(ifelse(df[,label_column]=="died", 1, 0))
+training <- df[, c(-id_column, -(label_column+3),-(label_column+2), -(label_column+1), -(label_column-1))]
+training_raw <- training
+
+# Ensure column names are in standardized/accepted format
+feature.names=names(training)
+for (f in feature.names) {
+  if (class(training[[f]])=="factor") {
+    levels <- unique(c(training[[f]]))
+    training[[f]] <- factor(training[[f]],
+                   labels=make.names(levels))
+  }
+}
+
+# Cross-Validation Elastic Net (Lasso Normalization)
+summary(training)
+lambda.grid <- 10^seq(2,-2,length=10)
+trnCtrl = trainControl(method="repeatedCV", number=10, repeats=5, summaryFunction=twoClassSummary, classProbs=TRUE)
+srchGrd = expand.grid(alpha=1, lambda=lambda.grid) # alpha = 1 denotes lasso regression
+set.seed(2017)
+
+glm.train <- train(as.factor(death_30) ~., data=training, method="glmnet", family="binomial", tuneGrid=srchGrd, standardize=TRUE, trControl = trnCtrl, maxit=1000)
+plot(glm.train)
+attributes(glm.train)
+glm.train$bestTune # alpha = 1, lambda = 0.01, AUC = 0.8403151
+
+# Extract best-fit model from grid-search 
+glm.fit <- glm.train$finalModel
+plot(glm.fit, xvar="dev", label=TRUE)
+coef(glm.fit, s=glm.train$bestTune$lambda)
+
+# Caveat: dataset is predominantly composed of "survived individuals"
+length(which(df$death_30 == 1))/length(df$death_30)
+
+# Optimal glmnet
+training <- training_raw
+training <- data.matrix(training)
+fit = glmnet(x=training[,1:184], y=training[,185], family="binomial", alpha=1, lambda.min.ratio=0.01)
+coef(fit,s=0.01)
+
+####################################
+### Test model on 2008-2009 data ###
+####################################
+df_8_9 <- df_save
+
+label_column <- dim(df_8_9)[2]-3
+id_column <- 1
+df_8_9[,label_column] <- factor(ifelse(df_8_9[,label_column]=="died", 1, 0))
+testing <- df_8_9[, c(-id_column, -(label_column+3),-(label_column+2), -(label_column+1), -(label_column-1))]
+testing_raw <- testing
+
+testing <- data.matrix(testing)
+pred <- predict.glmnet(fit, newx=testing[,1:184], s=0.01, type="class")
+library(boot)
+probs <- inv.logit(pred)
+```
+
+```{r}
+# ######################
+# ### Model Building ###
+# ######################
+# 
+# print("Building + evaluating logistic regression models...")
+# 
+# # 7-day mortality 
+# # Split into training and test/validation set
+# label_column <- dim(df)[2]-2
+# id_column <- 1
+# df[,label_column] <- factor(ifelse(df[,label_column]=="died", 1, 0))
+# 
+# Train <- createDataPartition(df$death_7, p=0.6, list=FALSE)
+# training <- df[Train, c(-id_column,-(label_column+1), -(label_column+2))]
+# testing <- df[-Train, c(-id_column, -(label_column+1), -(label_column+2))]
+# 
+# # Train + evaluate via 10-folds cross-validation (just 2008-2009 data)
+# # glm.fit <- glm(death ~., data=training, family=binomial(link='logit'))
+# ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
+# glm.fit <- train(death_7 ~., data=training, method="glm", family="binomial", trControl = ctrl, tuneLength = 5)
+# glm.fit$results
+# 
+# pred = predict(glm.fit, newdata=testing)
+# confusionMatrix(data=pred, testing$death_7)
+# 
+# # Evalution via ROC analysis (just 2008-2009 data)
+# 
+# # library(pROC)
+# # # Compute AUC for predicting death with an individual variable
+# # f1 = roc(death ~ age, data=training) 
+# # plot(f1, col="red")
+# 
+# library(ROCR)
+# # Compute AUC for predicting death with the model
+# prob <- predict(glm.fit, newdata=testing, type="prob")
+# 
+# # Convert prob into binary label
+# # prob_binned <- factor(ifelse(prob$`1`>0.5, 1, 0))
+#   
+# pred <- prediction(as.numeric(prob[,2]), as.numeric(testing$death))
+# perf <- performance(pred, measure = "tpr", x.measure = "fpr")
+# plot(perf)
+# auc <- performance(pred, measure = "auc")
+# auc <- auc@y.values[[1]]
+# auc
+# 
+# # Caveat: test set is predominantly composed of "survived individuals"
+# length(which(testing$death_7 == 1))/length(testing$death_7)
+```
+
+```{r}
+# # 30-day mortality 
+# # Split into training and test/validation set
+# label_column <- dim(df)[2]-3
+# id_column <- 1
+# df[,label_column] <- factor(ifelse(df[,label_column]=="died", 1, 0))
+# 
+# Train <- createDataPartition(df$death_30, p=0.6, list=FALSE)
+# training <- df[Train, c(-id_column, -(label_column+3),-(label_column+2), -(label_column+1), -(label_column-1))]
+# testing <- df[-Train, c(-id_column, -(label_column+3),-(label_column+2),-(label_column+1), -(label_column-1))]
+# 
+# # Train + evaluate via 10-folds cross-validation (just 2008-2009 data)
+# # glm.fit <- glm(death ~., data=training, family=binomial(link='logit'))
+# # ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
+# # 
+# # # Use logistic regression
+# # glm.fit <- train(death_30 ~., data=training, method="glm", family="binomial", trControl = ctrl, tuneLength = 5)
+# # glm.fit$results
+# # 
+# # pred = predict(glm.fit, newdata=testing)
+# # confusionMatrix(data=pred, testing$death_30)
+# 
+# # Using Lasso Regression
+# summary(training)
+# lambda.grid <- 10^seq(2,-2,length=10)
+# # alpha.grid <- seq(0,1, length=10)
+# trnCtrl = trainControl(method="repeatedCV", number=10, repeats=5)
+# # srchGrd = expand.grid(alpha=alpha.grid, lambda=lambda.grid)
+# srchGrd = expand.grid(alpha=1, lambda=lambda.grid) # alpha = 1 denotes lasso regression
+# set.seed(2017)
+# glm.train <- train(death_30 ~., data=training, method="glmnet", family="binomial", tuneGrid=srchGrd, standardize=TRUE, trControl = trnCtrl, maxit=1000)
+# plot(glm.train)
+# attributes(glm.train)
+# glm.train$bestTune # alpha = 1, lambda = 0.01
+# 
+# glm.fit <- glm.train$finalModel
+# plot(glm.fit, xvar="dev", label=TRUE)
+# coef(glm.fit, s=glm.train$bestTune$lambda)
+# 
+# # library(ROCR)
+# # Compute AUC for predicting death with the model
+# # prob <- predict(glm.fit, newdata=testing, type="prob")
+# prob <- predict(glm.fit, newx=testing, type="response", s=c(glm.train$bestTune$lambda))
+# 
+# # Convert prob into binary label
+# # prob_binned <- factor(ifelse(prob$`1`>0.5, 1, 0))
+#   
+# pred <- prediction(as.numeric(prob[,2]), as.numeric(testing$death))
+# perf <- performance(pred, measure = "tpr", x.measure = "fpr")
+# plot(perf)
+# auc <- performance(pred, measure = "auc")
+# auc <- auc@y.values[[1]]
+# auc
+# 
+# # Caveat: test set is predominantly composed of "survived individuals"
+# length(which(testing$death_30 == 1))/length(testing$death_30)
+
+```
+
+```{r}
+# # 90-day mortality 
+# # Split into training and test/validation set
+# label_column <- dim(df)[2]
+# id_column <- 1
+# df[,label_column] <- factor(ifelse(df[,label_column]=="died", 1, 0))
+# 
+# Train <- createDataPartition(df$death_90, p=0.6, list=FALSE)
+# training <- df[Train, c(-id_column, -(label_column-1), -(label_column-2))]
+# testing <- df[-Train, c(-id_column, -(label_column-1), -(label_column-2))]
+# 
+# # Train + evaluate via 10-folds cross-validation (just 2008-2009 data)
+# # glm.fit <- glm(death ~., data=training, family=binomial(link='logit'))
+# ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
+# glm.fit <- train(death_90 ~., data=training, method="glm", family="binomial", trControl = ctrl, tuneLength = 5)
+# glm.fit$results
+# 
+# pred = predict(glm.fit, newdata=testing)
+# confusionMatrix(data=pred, testing$death_90)
+# 
+# # Evalution via ROC analysis (just 2008-2009 data)
+# 
+# # library(pROC)
+# # # Compute AUC for predicting death with an individual variable
+# # f1 = roc(death ~ age, data=training) 
+# # plot(f1, col="red")
+# 
+# library(ROCR)
+# # Compute AUC for predicting death with the model
+# prob <- predict(glm.fit, newdata=testing, type="prob")
+# 
+# # Convert prob into binary label
+# # prob_binned <- factor(ifelse(prob$`1`>0.5, 1, 0))
+#   
+# pred <- prediction(as.numeric(prob[,2]), as.numeric(testing$death))
+# perf <- performance(pred, measure = "tpr", x.measure = "fpr")
+# plot(perf)
+# auc <- performance(pred, measure = "auc")
+# auc <- auc@y.values[[1]]
+# auc
+# 
+# # Caveat: test set is predominantly composed of "survived individuals"
+# length(which(testing$death_90 == 1))/length(testing$death_90)
+
+```
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/remap_ids_mortality_probs.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/remap_ids_mortality_probs.py
new file mode 100644
index 00000000..a95c09df
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/BuildExpectedMortalityPredictor/remap_ids_mortality_probs.py
@@ -0,0 +1,23 @@
+# create normalized id to original id mapping
+id_map = {}
+sickness_metrics = open("/Users/jwang/Desktop/Results/mortality_probs.csv", "rU")
+# sickness_metrics = open("/Users/jwang/Desktop/Results/mortality_or_readmission_probs.csv", "rU")
+feature_matrix_10_13 = open("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv", "rU")
+
+feature_matrix_10_13.readline()
+for line in feature_matrix_10_13:
+	line = line.strip().split(",")
+	id_map[line[0]] = line[1]
+
+outf = open("/Users/jwang/Desktop/Results/mortality_probs_remapped.csv", "w")
+# outf = open("/Users/jwang/Desktop/Results/mortality_or_readmission_probs_remapped.csv", "w")
+outf.write("patient_id,prob\n")
+
+sickness_metrics.readline()
+for line in sickness_metrics:
+	line = line.strip().split(",")
+	outf.write("{0},{1}\n".format(id_map[line[1]],line[2]))
+
+outf.close()
+sickness_metrics.close()
+feature_matrix_10_13.close()
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/clean_data.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/clean_data.py
new file mode 100644
index 00000000..def0666b
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/clean_data.py
@@ -0,0 +1,208 @@
+import numpy as np
+import pandas as pd
+
+dataset_flag = "10-13"
+# dataset_flag = "8-9"
+
+full_dataset = "N/A"
+if (dataset_flag == "10-13"):
+	full_dataset = open("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix_reordered.csv", "rU")
+else:
+	full_dataset = open("/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix_reordered.csv", "rU")
+
+header = full_dataset.readline()
+header = header.strip().split(",")
+
+# Read in last recorded admission diagnoses
+last_recorded_diagnoses = {}
+last_recorded_diagnoses_f = "N/A"
+if (dataset_flag == "10-13"):
+	last_recorded_diagnoses_f = "/Users/jwang/Desktop/Results/last_recorded_admission_diagnosis_10_13.csv"
+else: 
+	last_recorded_diagnoses_f = "/Users/jwang/Desktop/Results/last_recorded_admission_diagnosis_8_9.csv"
+
+df = pd.read_csv(last_recorded_diagnoses_f, sep=",")
+df = df[["patient_id","clinical_item_id"]]
+df = pd.get_dummies(df, columns=["clinical_item_id"])
+admission_header = list(df)[1:]
+
+for index, row in df.iterrows():
+	row = list(map(str,list(row)))
+	last_recorded_diagnoses[row[0]] = ",".join(row[1:])
+	
+# Read in death data (7 day mortalities)
+death_data_7 = open("/Users/jwang/Desktop/Results/7_day_deaths.csv", "rU")
+id_to_death_7 = {}
+death_data_7.readline()
+for line in death_data_7:
+	line = line.strip().split(",")
+	# if (line[1] == "None"): # survived
+	# 	id_to_death[line[0]] = "survived"
+	# else: # died
+	# 	id_to_death[line[0]] = "died"
+	id_to_death_7[line[0]] = 1
+
+# Read in death data (30 day mortalities)
+death_data_30 = open("/Users/jwang/Desktop/Results/30_day_deaths.csv", "rU")
+id_to_death_30 = {}
+death_data_30.readline()
+for line in death_data_30:
+	line = line.strip().split(",")
+	id_to_death_30[line[0]] = 1
+
+# Read in death data (90 day mortalities)
+death_data_90 = open("/Users/jwang/Desktop/Results/90_day_deaths.csv", "rU")
+id_to_death_90 = {}
+death_data_90.readline()
+for line in death_data_90:
+	line = line.strip().split(",")
+	id_to_death_90[line[0]] = 1
+
+# Read in 30 day readmission data
+readmission_data_30 = open("/Users/jwang/Desktop/Results/30_day_readmissions.csv", "rU")
+id_to_readmission_30 = {}
+readmission_data_30.readline()
+for line in readmission_data_30:
+	line = line.strip().split(",")
+	id_to_readmission_30[line[0]] = 1
+
+# Read in lengths of stay (not necessary?)
+
+# Read in income data
+income_data = open("/Users/jwang/Desktop/Results/income_based_on_zip.csv", "rU")
+id_to_income = {}
+income_data.readline()
+for line in income_data:
+	line = line.strip().split(",")
+	income_bin = "None"
+	if (line[1] == ">200000"):
+		income_bin = 20
+	elif (line[1] == "<20000"):
+		income_bin = 1
+	elif (len(line[1]) > 1):
+		income_range = line[1].strip().split("-")
+		income_floor = int(income_range[0])
+		income_bin = income_floor//10000
+	id_to_income[line[0]] = str(income_bin)
+
+# Read in age data
+age_data = open("/Users/jwang/Desktop/Results/ages.csv", "rU")
+id_to_age = {}
+age_data.readline()
+for line in age_data:
+	line = line.strip().split(",")
+	# age_bin = "None"
+	if (line[1] != "None"):
+		age = int(line[1])
+		# if (age < 18):
+		# 	age_bin = 0
+		# elif (age < 26):
+		# 	age_bin = 1
+		# elif (age < 36):
+		# 	age_bin = 2
+		# elif (age < 46):
+		# 	age_bin = 3
+		# elif (age < 56):
+		# 	age_bin = 4
+		# elif (age < 66):
+		# 	age_bin = 5
+		# else: # age >= 66
+		# 	age_bin = 6
+	
+	# id_to_age[line[0]] = str(age_bin)
+	id_to_age[line[0]] = str(age)
+
+# Identify relevant data columns
+ignore = ['IVAntibiotic.pre', 'BloodCulture.pre', 'RespViralPanel.pre', 'AnyICULifeSupport.pre', 'AnyDNR.pre', 'AnyVasoactive.pre', 'AnyCRRT.pre', 'AnyVentilator.pre', 'ComfortCare.pre', 'PalliativeConsult.pre', "TBIL.first", "ALB.first", "LAC.first", "ESR.first", "CRP.first", "TNI.first", "PHA.first", "PO2A.first", "PCO2A.first", "PHV.first", "PO2V.first", "PCO2V.first", "Birth.pre", "Death.pre"]
+relevant_cols = ["patient_id", "dischargeTime", "edAdmitTime", "encounter_id"]
+for col in header:
+	#if ("first" in col or "pre" in col) and "preTime" not in col and "TT" not in col and col not in ignore: 
+	if ("first" in col or "pre" in col) and "preTime" not in col and col not in ignore: # include treatment team
+		relevant_cols.append(col) 
+
+relevant_indices = []
+for col in relevant_cols:
+	relevant_indices.append(header.index(col))
+relevant_cols.append("income_level")
+relevant_cols.append("age")
+relevant_cols.extend(admission_header)
+
+# outcomes
+relevant_cols.append("death_7")
+relevant_cols.append("death_30")
+relevant_cols.append("death_90")
+relevant_cols.append("readmission_30")
+relevant_cols.append("readmission_or_death_30")
+
+# Print out to csv format
+output = "N/A"
+if (dataset_flag == "10-13"):
+	output = open("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv", "w")
+else:
+	output = open("/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix.csv", "w")
+patient_ids = {} # ensure uniqueness
+
+# NOTE: we are reading in data associated with the first time we see a patient_id; this, by design, is the last recorded encounter
+
+output.write("normalized_patient_id,")
+output.write(",".join(relevant_cols))
+output.write("\n")
+
+normalized_patient_id = 1
+for line in full_dataset:
+	line = line.strip().split(",")
+	admit_year = line[4][:4]
+	discharge_year = line[3][:4]
+
+	year_range = []
+	if (dataset_flag == "10-13"):
+		year_range = ["2010","2011","2012","2013"]
+	else: # 2008-2009
+		year_range = ["2008","2009"]
+	# if (line[0] not in patient_ids) and (admit_year in ["2010","2011","2012","2013"] or discharge_year in ["2010","2011","2012","2013"]):
+	# if (line[0] not in patient_ids) and (admit_year in ["2008","2009"] or discharge_year in ["2008","2009"]):
+	if (line[0] not in patient_ids) and (admit_year in year_range or discharge_year in year_range):
+
+		patient_ids[line[0]] = 1
+		line = np.array(line)
+		output.write(str(normalized_patient_id)+",")
+		output.write(",".join(list(line[relevant_indices])))
+
+		output.write("," + id_to_income[line[0]])
+		output.write("," + id_to_age[line[0]])
+
+		if (line[0] in last_recorded_diagnoses):
+			output.write("," + last_recorded_diagnoses[line[0]])
+		else:
+			output.write("," + "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0")
+
+		if (line[0] in id_to_death_7):
+			output.write("," + "died")
+		else:
+			output.write("," + "survived")
+
+		if (line[0] in id_to_death_30):
+			output.write("," + "died")
+		else:
+			output.write("," + "survived")
+
+		if (line[0] in id_to_death_90):
+			output.write("," + "died")
+		else:
+			output.write("," + "survived")
+
+		if (line[0] in id_to_readmission_30):
+			output.write("," + "yes")
+		else:
+			output.write("," + "no")
+
+		if (line[0] in id_to_readmission_30 or line[0] in id_to_death_30):
+			output.write("," + "yes")
+		else:
+			output.write("," + "no")
+
+		output.write("\n")
+		normalized_patient_id += 1
+
+	else:
+		pass
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_admission_discharge_flow.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_admission_discharge_flow.py
new file mode 100644
index 00000000..c87489ab
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_admission_discharge_flow.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract admission and discharge orders for all patients
+##########################################################################################
+import sys, os
+from io import StringIO
+from datetime import datetime;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Create SCRIPT_FILE
+
+# clinical_item_category_id = 13 (Discharge)
+# clinical_item_category_id = 23 (Admission)
+
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+DATA_QUERY = SQLQuery();
+
+DATA_QUERY.addSelect("pat_anon_id")
+DATA_QUERY.addSelect("shifted_transf_in_dt_tm")
+DATA_QUERY.addSelect("department_in")
+DATA_QUERY.addSelect("event_in")
+DATA_QUERY.addSelect("shifted_transf_out_dt_tm")
+DATA_QUERY.addSelect("event_out")
+DATA_QUERY.addSelect("pat_enc_csn_anon_id")
+DATA_QUERY.addWhereEqual("event_out = 'Discharge' OR event_in", 'Admission')
+
+DATA_QUERY.addFrom("stride_adt")
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+
+output = open("/Users/jwang/Desktop/Results/admission_discharge_flow.csv", "w")
+output.write("patient_id,admission_time,department,admission,discharge_time,discharge,encounter_id\n")
+
+for line in results:
+    output.write("{0},{1},{2},{3},{4},{5},{6}\n".format(line[0],line[1],line[2],line[3],line[4],line[5],line[6]))
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_age.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_age.py
new file mode 100644
index 00000000..a609cf82
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_age.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract age out of stride database
+##########################################################################################
+import sys, os
+from io import StringIO
+from datetime import datetime
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Create SCRIPT_FILE
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+
+# Find all clinical_item_ids associated with each patient_id
+# SELECT * FROM clinical_item WHERE clinical_item_category_id = 161;
+
+DATA_QUERY = SQLQuery();
+DATA_QUERY.addSelect("pat_id")
+DATA_QUERY.addSelect("birth_year")
+DATA_QUERY.addFrom("stride_patient")
+
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+
+output = open("/Users/jwang/Desktop/ages.csv", "w")
+output.write("patient_id,age\n")
+
+for line in results:
+	age = None
+	if (line[1] != None):
+		age = 2017 - int(line[1])
+	output.write("{0},{1}\n".format(line[0],age))
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_diagnosis_codes.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_diagnosis_codes.py
new file mode 100644
index 00000000..7fe9694d
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_diagnosis_codes.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract all diagnoses sorted by item_count
+##########################################################################################
+import sys, os
+from io import StringIO
+from datetime import datetime;
+
+os.chdir('/Users/jwang/Desktop/CDSS/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/CDSS')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Create SCRIPT_FILE
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql medinfo_2008_2014_patch_2014_2017 jwang198")
+DATA_QUERY = SQLQuery();
+
+# From patient_item or clinical_item
+DATA_QUERY.addSelect("clinical_item_id")
+# DATA_QUERY.addSelect("clinical_item_category_id")
+DATA_QUERY.addSelect("description")
+DATA_QUERY.addSelect('name')
+# DATA_QUERY.addSelect("item_count")
+
+# Join
+DATA_QUERY.addFrom("clinical_item")
+DATA_QUERY.addWhereEqual("clinical_item_category_id", "2") 
+
+DATA_QUERY.addOrderBy("item_count", dir="DESC")
+
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+
+unique_patient_ids = {}
+
+output = open("/Users/jwang/Desktop/Chen/Top_vs_Bottom_Study/Results/patient_feature_matrix/diagnoses.csv", "w")
+# output.write("clinical_item_id,clinical_item_category_id,name,description,item_count\n")
+output.write("clinical_item_id,description\n")
+
+for line in results:
+    # output.write("{0},{1},{2},{3},{4}\n".format(line[0],line[1],line[2],line[3],line[4]))
+    output.write("{0},{1},{2}\n".format(line[0],line[1],line[2]))
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_encounter_data.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_encounter_data.py
new file mode 100644
index 00000000..f3e8e385
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_encounter_data.py
@@ -0,0 +1,294 @@
+# Include only those with an order for an antibiotic, blood culture, or respiratory viral panel within 24 hours
+# Exclude any patients assigned to a primary surgery team.
+# Find index / first clinical item for each with any admission diagnosis
+# Then report clinical item features in terms of time until event (and +/- whether occurs at all)
+# Report lab and flowsheet value summary statistics within first 24 hours???
+# Custom query for IVF. Time until 1L, 2L, 3L, 4L,... Quantity at ICU treatment team assignment, Quantity by AnyICU, Quantity by AnyVasopressor?
+
+import sys, os;
+import time;
+from io import StringIO;
+from datetime import datetime, timedelta;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList, columnFromModelList;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+
+from medinfo.dataconversion.DataExtractor import DataExtractor;
+
+"""Amount of time to elapse while still counting item days as contiguous.
+Use value just greater than whole number of days to make sure greater than difference calculations"""
+CONTIGUOUS_THRESHOLD = timedelta(1,1);
+
+# How much time from patient presentation to look for suspected sepsis items
+SUSPECT_INTERVAL = timedelta(1);
+
+FLOWSHEET_NAMES = \
+(
+    "BP_High_Systolic",
+    "BP_Low_Diastolic",
+    "FiO2",
+    "Glasgow Coma Scale Score",
+    "Pulse",
+    "Resp",
+    "Temp",
+    "Urine",
+);
+
+LAB_BASE_NAMES = \
+(   # SAPS = f(BUN, Na, K, HCO3, Bili, WBC, PO2)
+    # APACHE = f(pH, Na, K, Cr, Hct, WBC, PO2)
+    'WBC','HCT','PLT',
+    'NA','K','CO2','BUN','CR',
+    'TBIL','ALB',
+    'LAC',
+    'ESR','CRP',
+    'TNI',
+    'PHA','PO2A','PCO2A',
+    'PHV','PO2V','PCO2V',
+);
+
+# Identify antibiotics, focus just on IV orders
+#IV_MED_CATEGORY_ID = 72;
+#PO_MED_CATEGORY_ID = 73;
+
+def main(argv):
+    timer = time.time();
+    
+    extractor = DataExtractor();
+
+    patientEpisodes = queryPatientEpisodes(stdOpen("patientEpisodes.tab","w"), extractor);    # Maybe just do this first time, then comment out and load from file with line below
+    #patientEpisodes = extractor.parsePatientEpisodeFile(stdOpen("patientEpisodes.tab"), list()); # Read from prior file if main query already done to avoid expensive query
+    patientIds = set(columnFromModelList(patientEpisodes, "patient_id"));
+
+    extractor.queryFlowsheet(FLOWSHEET_NAMES, patientIds, stdOpen("Flowsheet.tab.gz","w"));
+    extractor.queryLabResults(LAB_BASE_NAMES, patientIds, stdOpen("LabResults.tab.gz","w"));
+    
+    # Look for specific IV fluid medication subset
+    ivfMedIds = set();
+    for row in extractor.loadMapData("Medication.IVFluids"):
+        if row["group"] == "isotonic":
+            ivfMedIds.add(row["medication_id"]);
+    extractor.queryIVFluids(ivfMedIds, patientIds, stdOpen("IsotonicIVFluids.tab.gz","w"));
+
+    extractor.queryClinicalItems(loadIVAntibioticItemIds(extractor), patientIds, stdOpen("IVAntibiotic.tab","w"));
+    extractor.queryClinicalItems(loadBloodCultureItemIds(extractor), patientIds, stdOpen("BloodCulture.tab","w"));
+    extractor.queryClinicalItems(loadRespiratoryViralPanelItemIds(extractor), patientIds, stdOpen("RespViralPanel.tab","w"));
+
+    extractor.queryClinicalItemsByName(("AnyICULifeSupport",), patientIds, stdOpen("AnyICULifeSupport.tab","w"));
+    extractor.queryClinicalItemsByName(("AnyDNR",), patientIds, stdOpen("AnyDNR.tab","w"));
+    extractor.queryClinicalItemsByName(("AnyVasoactive",), patientIds, stdOpen("AnyVasoactive.tab","w"));
+    extractor.queryClinicalItemsByName(("AnyCRRT",), patientIds, stdOpen("AnyCRRT.tab","w"));
+    extractor.queryClinicalItemsByName(("AnyVentilator",), patientIds, stdOpen("AnyVentilator.tab","w"));
+    extractor.queryClinicalItemsByName(("^Comfort Care",), patientIds, stdOpen("ComfortCare.tab","w"), col="description", operator="~*");
+    extractor.queryClinicalItemsByName(('consult.*palliative',), patientIds, stdOpen("PalliativeConsult.tab","w"), col="description", operator="~*");
+
+    extractor.queryClinicalItemsByName(("Death",), patientIds, stdOpen("Death.tab","w"));
+    extractor.queryClinicalItemsByName(("Birth",), patientIds, stdOpen("Birth.tab","w"));
+    extractor.queryClinicalItemsByName(("Male",), patientIds, stdOpen("Male.tab","w"));
+    extractor.queryClinicalItemsByName(("Female",), patientIds, stdOpen("Female.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceWhiteNonHispanicLatino",), patientIds, stdOpen("RaceWhiteNonHispanicLatino.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceAsian",), patientIds, stdOpen("RaceAsian.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceWhiteHispanicLatino",), patientIds, stdOpen("RaceWhiteHispanicLatino.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceHispanicLatino",), patientIds, stdOpen("RaceHispanicLatino.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceUnknown",), patientIds, stdOpen("RaceUnknown.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceOther",), patientIds, stdOpen("RaceOther.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceBlack",), patientIds, stdOpen("RaceBlack.tab","w"));
+    extractor.queryClinicalItemsByName(("RacePacificIslander",), patientIds, stdOpen("RacePacificIslander.tab","w"));
+    extractor.queryClinicalItemsByName(("RaceNativeAmerican",), patientIds, stdOpen("RaceNativeAmerican.tab","w"));
+
+    # Extract out lists of ICD9 prefixes per disease category
+    icd9prefixesByDisease = dict();
+    for row in extractor.loadMapData("CharlsonComorbidity-ICD9CM"):
+        (disease, icd9prefix) = (row["charlson"],row["icd9cm"]);
+        if disease not in icd9prefixesByDisease:
+            icd9prefixesByDisease[disease] = list();
+        icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
+    for disease, icd9prefixes in icd9prefixesByDisease.items():
+        disease = disease.translate(None," ()-/");   # Strip off punctuation
+        extractor.queryClinicalItemsByName(icd9prefixes, patientIds, stdOpen("Charlson."+disease+".tab","w"), operator="~*");
+    
+    # Extract out lists of treatment team names per care category
+    teamNameByCategory = dict();
+    for row in extractor.loadMapData("TreatmentTeamGroups"):
+        (category, teamName) = (row["team_category"],row["treatment_team"]);
+        if category not in teamNameByCategory:
+            teamNameByCategory[category] = list();
+        teamNameByCategory[category].append(teamName);
+    for category, teamNames in teamNameByCategory.items():
+        extractor.queryClinicalItemsByName(teamNames, patientIds, stdOpen("TT."+category+".tab","w"), col="description");
+
+    timer = time.time() - timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
+
+def loadIVAntibioticItemIds(extractor):
+    # ivMedCategoryId = 72;
+    ivMedCategoryId = DBUtil.execute("select clinical_item_category_id from clinical_item_category where description like '%%(Intravenous)'")[0][0];
+
+    # Look for any IV antibiotics based on expected names
+    query = SQLQuery();
+    query.addSelect("clinical_item_id");
+    query.addFrom("clinical_item");
+    query.addWhereEqual("analysis_status", 1);
+    query.addWhereEqual("clinical_item_category_id", ivMedCategoryId);
+    query.openWhereOrClause();
+    for row in extractor.loadMapData("IVAntibiotics.Names"):
+        query.addWhere("description ~* '%s'" % row["name"] );
+    query.closeWhereOrClause();
+
+    ivAntibioticItemIds = set();
+    for row in DBUtil.execute(query):
+        ivAntibioticItemIds.add(row[0]);
+    return ivAntibioticItemIds;
+
+def loadBloodCultureItemIds(extractor):
+    # microCategoryId = 15;
+    microCategoryId = DBUtil.execute("select clinical_item_category_id from clinical_item_category where description like 'Microbiology'")[0][0];
+
+    # Look for diagnostic tests indicating suspected infection / sepsis
+    query = SQLQuery();
+    query.addSelect("clinical_item_id");
+    query.addFrom("clinical_item");
+    query.addWhereEqual("analysis_status", 1);
+    query.addWhereIn("clinical_item_category_id", (microCategoryId,) );
+    query.addWhere("description ~* '%s'" % 'Blood Culture' );
+    bloodCultureItemIds = set();
+    for row in DBUtil.execute(query):
+        bloodCultureItemIds.add(row[0]);
+    return bloodCultureItemIds;
+
+def loadRespiratoryViralPanelItemIds(extractor):
+    # labCategoryId = 6;
+    labCategoryId = DBUtil.execute("select clinical_item_category_id from clinical_item_category where description like 'Lab'")[0][0];
+
+    query = SQLQuery();
+    query.addSelect("clinical_item_id");
+    query.addFrom("clinical_item");
+    query.addWhereEqual("analysis_status", 1);
+    query.addWhereIn("clinical_item_category_id", (labCategoryId,) );
+    query.addWhere("description ~* '%s'" % 'Respiratory.*Panel' );
+    respiratoryViralPanelItemIds = set();
+    for row in DBUtil.execute(query):
+        respiratoryViralPanelItemIds.add(row[0]);
+    return respiratoryViralPanelItemIds;
+
+def queryPatientEpisodes(outputFile, extractor):
+    log.info("Select patient admissions with provider category of Tt Pamf Med (Primary) or Tt Med Univ (Primary)");
+
+    conn = DBUtil.connection();
+    cursor = conn.cursor();
+    try:
+        # # Clinical item category for admission diagnoses
+        # # ADMIT_DX_CATEGORY_ID = 2;
+        # admitDxCategoryId = DBUtil.execute("select clinical_item_category_id from clinical_item_category where description like '%%ADMIT_DX%%'", conn=conn)[0][0];
+
+        # # Look for items indicating suspected infection / sepsis
+        # ivAntibioticItemIds = loadIVAntibioticItemIds(extractor);
+        # bloodCultureItemIds = loadBloodCultureItemIds(extractor);
+        # respiratoryViralPanelItemIds = loadRespiratoryViralPanelItemIds(extractor);
+
+        # # Merge IV antibiotics and blood cultures, respiratory panels as items that suggest sepsis is suspected
+        # suspectSepsisItemIds = ivAntibioticItemIds.union(bloodCultureItemIds.union(respiratoryViralPanelItemIds));
+        # suspectSepsisItemIdsStr = str.join(',', [str(itemId) for itemId in suspectSepsisItemIds]);   # Convert to comma-separated string via a str.join function on list contracture
+
+        # # Look for primary surgery teams to exclude
+        # excludeTeamCategory = "SurgerySpecialty";
+        # excludeTreatmentTeams = list();
+        # for row in extractor.loadMapData("TreatmentTeamGroups"):
+        #     if row["team_category"] == excludeTeamCategory:
+        #         excludeTreatmentTeams.append(row["treatment_team"]);
+        # query = SQLQuery();
+        # query.addSelect("clinical_item_id");
+        # query.addFrom("clinical_item");
+        # query.addWhereIn("description", excludeTreatmentTeams );
+        # excludeTeamItemIds = set();
+        # for row in DBUtil.execute(query, conn=conn):
+        #     excludeTeamItemIds.add(row[0]);
+        # excludeTeamItemIdsStr = str.join(',', [str(itemId) for itemId in excludeTeamItemIds]);   # Convert to comma-separated string via a str.join function on list contracture
+
+        # First pass query to get the list of patients and emergency department presentation times
+        cohortQuery = \
+        """
+        select adt1.pat_anon_id, adt1.pat_enc_csn_anon_id, adt1.shifted_transf_in_dt_tm as edAdmitTime, adt2.shifted_transf_out_dt_tm as dischargeTime
+        from stride_adt as adt1, stride_adt as adt2
+        where 
+            adt1.pat_anon_id in
+            (select patient_id from patient_item inner join clinical_item on patient_item.clinical_item_id = clinical_item.clinical_item_id where clinical_item.clinical_item_category_id = 161 AND clinical_item.description = '%s') 
+        and adt1.pat_enc_csn_anon_id = adt2.pat_enc_csn_anon_id
+        """ % ("Tt Pamf Med (Primary)");
+
+        print(cohortQuery, file=sys.stderr);
+        cursor.execute(cohortQuery);
+
+        patientEpisodes = list();
+        patientEpisodeById = dict();
+
+        # Collect Build basic patient ID and 
+        #   ED presentation dates and Discharge date/time
+        prog = ProgressDots();
+        row = cursor.fetchone();
+        while row is not None:
+            (patientId, encounterId, edAdmitTime, dischargeTime) = row;
+            #patientId = int(patientId);
+            patientEpisode = \
+                RowItemModel \
+                (   {   "patient_id":patientId, 
+                        "edAdmitTime":edAdmitTime, 
+                        "dischargeTime":dischargeTime, 
+                        "encounter_id":encounterId,
+                        "payorTitle": None, # Default encounter data to null in case can't find it later
+                        "bpSystolic": None,
+                        "bpDiastolic": None,
+                        "temperature": None,
+                        "pulse": None,
+                        "respirations": None,
+                    }
+                );
+            patientEpisodes.append(patientEpisode);
+            if patientEpisode["encounter_id"] not in patientEpisodeById:
+                patientEpisodeById[patientEpisode["encounter_id"]] = patientEpisode;
+
+            prog.update();
+            row = cursor.fetchone();
+        prog.printStatus();
+
+        # Second query phase to link to encounter information (e.g., insurance, admitting vital signs)
+        encounterIds = columnFromModelList(patientEpisodes, "encounter_id");
+        query = SQLQuery();
+        query.addSelect("pat_id");
+        query.addSelect("pat_enc_csn_id");
+        query.addSelect("title");
+        query.addSelect("bp_systolic");
+        query.addSelect("bp_diastolic");
+        query.addSelect("temperature");
+        query.addSelect("pulse");
+        query.addSelect("respirations");
+        query.addFrom("stride_patient_encounter");
+        query.addWhereIn("pat_enc_csn_id", encounterIds);
+        cursor.execute(str(query), query.params);
+        row = cursor.fetchone();
+        while row is not None:
+            (patientId, encounterId, payorTitle, bpSystolic, bpDiastolic, temperature, pulse, respirations) = row;
+            if encounterId in patientEpisodeById:
+                patientEpisode = patientEpisodeById[encounterId];
+                if patientEpisode["payorTitle"] is None:
+                    patientEpisode["payorTitle"] = set();   # Single encounters may have multiple payors to track
+                patientEpisode["payorTitle"].add(payorTitle);
+                patientEpisode["bpSystolic"] = bpSystolic;
+                patientEpisode["bpDiastolic"] = bpDiastolic;
+                patientEpisode["temperature"] = temperature;
+                patientEpisode["pulse"] = pulse;
+                patientEpisode["respirations"] = respirations;
+            row = cursor.fetchone();
+        
+        # Drop results as tab-delimited text output
+        formatter = TextResultsFormatter(outputFile);
+        formatter.formatResultDicts(patientEpisodes, addHeaderRow=True);
+
+        return patientEpisodes;
+    finally:
+        cursor.close();
+        conn.close();
+
+if __name__ == "__main__":
+    main(sys.argv);
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_last_recorded_admission_diagnosis.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_last_recorded_admission_diagnosis.py
new file mode 100644
index 00000000..65b3f340
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_last_recorded_admission_diagnosis.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract out each patient's last recored admission diagnosis (within 2008-2009 OR 2010-2013)
+##########################################################################################
+import sys, os
+from io import StringIO
+from datetime import datetime
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Create SCRIPT_FILE
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+
+# Load in top patient count diagnoses (we will only include these as binary covariates)
+# select *
+# from clinical_item
+# where clinical_item_category_id = 2
+# order by patient_count desc
+DATA_QUERY = SQLQuery()
+DATA_QUERY.addSelect("clinical_item_id")
+DATA_QUERY.addSelect("name")
+DATA_QUERY.addSelect("description")
+DATA_QUERY.addSelect("patient_count")
+DATA_QUERY.addFrom("clinical_item")
+DATA_QUERY.addWhereEqual("patient_count > 0 and clinical_item_category_id", 2)
+top_admission_diagnoses_f = open("/Users/jwang/Desktop/Results/top_admission_diagnoses.csv", "w")
+top_admission_diagnoses = {}
+top_admission_diagnoses_string = []
+
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+top_admission_diagnoses_f.write("clinical_item_id,name,description,patient_count\n")
+for line in results:
+	top_admission_diagnoses_f.write("{0},{1},{2},{3}\n".format(line[0],line[1],line[2],line[3]))
+	top_admission_diagnoses[line[0]] = line[2]
+	top_admission_diagnoses_string.append(str(line[0]))
+top_admission_diagnoses_f.close()
+top_admission_diagnoses_string = ",".join(top_admission_diagnoses_string)
+top_admission_diagnoses_string = "(" + top_admission_diagnoses_string + ")"
+#print(top_admission_diagnoses)
+#print(top_admission_diagnoses_string)
+
+# Find admission diagnosis for all patients associated with last recorded admission: 2008-2009 dataset
+dataset_8_9 = open("/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix_reordered.csv", "rU")
+last_recorded_admission_diagnosis_8_9 = open("/Users/jwang/Desktop/Results/last_recorded_admission_diagnosis_8_9.csv", "w")
+last_recorded_admission_diagnosis_8_9.write("patient_id,clinical_item_id,encounter_id,item_date\n")
+header = dataset_8_9.readline()
+patient_ids = {} # ensure uniqueness
+
+for line in dataset_8_9:
+	line = line.strip().split(",")
+	if (line[0] not in patient_ids): # note that the first occurrence of the patient_id will denote the last recorded admission diagnosis as the input file is ordered in that fashion
+		patient_ids[line[0]] = 1
+
+		encounter_id = line[5]
+
+		# Example Query
+		# SELECT patient_id , clinical_item_id , encounter_id , item_date
+		# FROM patient_item
+		# WHERE clinical_item_id in (41802,41808,41798,41791,41806,41788,41797,41796,41787,41807,41795,41873,41801,41871,41792,41786,41794,41805,41803,41785,41804,41872,41878,42225,42312,41776,41759,41764,41774,41835,41760,41765,41831,41838,41754,41762,41833,41837,41820,41836,41839,41784,41779,41834,41772,41758,41783,41832,41809,41814,41763,41778,41822,41766,41781,41823,41769,41775,41773,41821,41830,41882,41889,41929,41921,41847,41860,41846,41883,41892,41890,41922,41841,41856,41869,41943,41844,41956,41957,41884,41865,41894,41857,41909,41891,41912,41864,41854,41855,41867,41881,41866,41870,41868,41946,41908,41916,41887,41880,41886,41917,41958,41885,41989,42019,41968,41975,41998,41997,42078,41980,41969,41982,41965,42081,42017,42016,41960,41987,42018,42033,41990,42038,42028,41964,42058,41973,42072,41963,42002,42031,42085,41959,42073,41983,42040,41974,41981,42099,42111,42127,42130,42151,42149,42091,42128,42143,42112,42150,42147,42096,42116,42090,42121,42122,42098,42140,42113,42102,42186,42187,42197,42233,42242,42226,42175,42202,42212,42243,42181,42194,42223,42219,42208,42173,42184,42185,42229,42222,42302,42276,42310,42336,42305,42253,42321,42246,42330,42260,42322,42329,42335,42415,42364,42431,42432,42440,42488,42551,42680,42815,42777,42062,42535,42108,41777,41852,42334,42297,42097,42433,42434,42211,41770,42042,42188,41949,42779,41771,42668,42883,42392,41903,42224,42068,42701,42594,42426) and encounter_id = %s ;
+		DATA_QUERY = SQLQuery()
+		DATA_QUERY.addSelect("patient_id")
+		DATA_QUERY.addSelect("clinical_item_id")
+		DATA_QUERY.addSelect("encounter_id")
+		DATA_QUERY.addSelect('item_date')
+		DATA_QUERY.addFrom("patient_item")
+		DATA_QUERY.addWhereEqual("clinical_item_id in {0} and encounter_id".format(top_admission_diagnoses_string), int(line[5]))
+		DBUtil.runDBScript(SCRIPT_FILE, False)
+		results = DBUtil.execute(DATA_QUERY);
+		#print(DATA_QUERY)
+
+		for line in results:
+			last_recorded_admission_diagnosis_8_9.write("{0},{1},{2},{3}\n".format(line[0],line[1],line[2],line[3]))
+
+dataset_8_9.close()
+last_recorded_admission_diagnosis_8_9.close()
+
+# Find admission diagnosis for all patients associated with last recorded admission: 2010-2013 dataset
+dataset_10_13 = open("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix_reordered.csv", "rU")
+last_recorded_admission_diagnosis_10_13 = open("/Users/jwang/Desktop/Results/last_recorded_admission_diagnosis_10_13.csv", "w")
+last_recorded_admission_diagnosis_10_13.write("patient_id,clinical_item_id,encounter_id,item_date\n")
+header = dataset_10_13.readline()
+patient_ids = {} # ensure uniqueness
+
+for line in dataset_10_13:
+	line = line.strip().split(",")
+	if (line[0] not in patient_ids): # note that the first occurrence of the patient_id will denote the last recorded admission diagnosis as the input file is ordered in that fashion
+		patient_ids[line[0]] = 1
+
+		encounter_id = line[5]
+
+		DATA_QUERY = SQLQuery()
+		DATA_QUERY.addSelect("patient_id")
+		DATA_QUERY.addSelect("clinical_item_id")
+		DATA_QUERY.addSelect("encounter_id")
+		DATA_QUERY.addSelect('item_date')
+		DATA_QUERY.addFrom("patient_item")
+		DATA_QUERY.addWhereEqual("clinical_item_id in {0} and encounter_id".format(top_admission_diagnoses_string), int(line[5]))
+		DBUtil.runDBScript(SCRIPT_FILE, False)
+		results = DBUtil.execute(DATA_QUERY);
+		#print(DATA_QUERY)
+
+		for line in results:
+			last_recorded_admission_diagnosis_10_13.write("{0},{1},{2},{3}\n".format(line[0],line[1],line[2],line[3]))
+
+dataset_10_13.close()
+last_recorded_admission_diagnosis_10_13.close()
+
+	
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_mortality.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_mortality.py
new file mode 100644
index 00000000..d2969f07
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_mortality.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract death clinical item occurrence per patient
+##########################################################################################
+import sys, os
+import pandas
+from io import StringIO
+from datetime import datetime;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Load in patient encounters map
+patient_encounters = {}
+pat_enc_f = open("/Users/jwang/Desktop/Results/lengths_of_stay.csv", "rU")
+pat_enc_f.readline()
+for line in pat_enc_f:
+	line = line.strip().split(",")
+	if (line[0] not in patient_encounters):
+		patient_encounters[line[0]] = [pandas.to_datetime(line[1]),] # patient id: list of admission times
+	else:
+		patient_encounters[line[0]].append(pandas.to_datetime(line[1]))
+
+# print(patient_encounters)
+
+# Create SCRIPT_FILE
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+DATA_QUERY = SQLQuery();
+
+DATA_QUERY.addSelect("pat_id")
+DATA_QUERY.addSelect("death_date")
+DATA_QUERY.addFrom("stride_patient")
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+#print(len(results))
+
+output_7 = open("/Users/jwang/Desktop/Results/7_day_deaths.csv", "w")
+output_30 = open("/Users/jwang/Desktop/Results/30_day_deaths.csv", "w")
+output_90 = open("/Users/jwang/Desktop/Results/90_day_deaths.csv", "w")
+
+output_7.write("patient_id,prev_admission_time,death_time,delta\n")
+output_30.write("patient_id,prev_admission_time,death_time,delta\n")
+output_90.write("patient_id,prev_admission_time,death_time,delta\n")
+
+delta_7 = pandas.to_timedelta("7 days 00:00:00")
+delta_30 = pandas.to_timedelta("30 days 00:00:00")
+delta_90 = pandas.to_timedelta("90 days 00:00:00")
+for line in results:
+	if (line[1] is None):
+		continue # patient survived, recorded as None
+	
+	pat_id = line[0]
+	death_time = pandas.to_datetime(line[1])
+
+	if (pat_id not in patient_encounters): # patient_id not included in admit-discharge table
+		continue
+
+	for admission_time in patient_encounters[pat_id]:
+		delta = pandas.to_timedelta(death_time - admission_time)
+		if (delta <= delta_90):
+			output_90.write("{0},{1},{2},{3}\n".format(pat_id,admission_time,death_time,delta))
+			if (delta <= delta_30):
+				output_30.write("{0},{1},{2},{3}\n".format(pat_id,admission_time,death_time,delta))
+				if (delta <= delta_7):
+					output_7.write("{0},{1},{2},{3}\n".format(pat_id,admission_time,death_time,delta))
+
+output_7.close()
+output_30.close()
+output_90.close()
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_physician_patient_map.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_physician_patient_map.py
new file mode 100644
index 00000000..e4659c21
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_physician_patient_map.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract all patients and their associated 
+##########################################################################################
+import sys, os
+from io import StringIO
+from datetime import datetime;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Create SCRIPT_FILE
+
+# clinical_item_category_id = 13 (Discharge)
+# clinical_item_category_id = 23 (Admission)
+
+# H&P Note
+
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+DATA_QUERY = SQLQuery();
+
+DATA_QUERY.addSelect("author_name")
+DATA_QUERY.addSelect("pat_id")
+DATA_QUERY.addSelect("note_date")
+DATA_QUERY.addSelect("note_type")
+DATA_QUERY.addSelect("provider_type")
+DATA_QUERY.addSelect("specialty")
+DATA_QUERY.addSelect("pat_enc_csn_id")
+
+DATA_QUERY.addFrom("stride_note")
+DATA_QUERY.addWhereEqual("note_type", "H&P")
+DATA_QUERY.addOrderBy("author_name", dir="asc")
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+
+output_hp = open("/Users/jwang/Desktop/Results/physician_patient_map_hp.csv", "w")
+output_hp.write("clinician_name,patient_id,note_date,note_type,provider_type,specialty,encounter_id\n")
+
+for line in results:
+	if (line[4] == "Medical Student" or line[5] == "Medical Student"): #exclude medical students
+		continue
+	clinician_name = line[0].strip().split(",")
+	clinician_name = "{0} {1}".format(clinician_name[1], clinician_name[0])
+	output_hp.write("{0},{1},{2},{3},{4},{5},{6}\n".format(clinician_name,line[1],line[2],line[3],line[4],line[5],line[6]))
+
+# Discharge Summary
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+DATA_QUERY = SQLQuery();
+
+DATA_QUERY.addSelect("author_name")
+DATA_QUERY.addSelect("pat_id")
+DATA_QUERY.addSelect("note_date")
+DATA_QUERY.addSelect("note_type")
+DATA_QUERY.addSelect("provider_type")
+DATA_QUERY.addSelect("specialty")
+DATA_QUERY.addSelect("pat_enc_csn_id")
+
+DATA_QUERY.addFrom("stride_note")
+DATA_QUERY.addWhereEqual("note_type", "Discharge Summaries")
+DATA_QUERY.addOrderBy("author_name", dir="asc")
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+
+output_discharge = open("/Users/jwang/Desktop/Results/physician_patient_map_discharge.csv", "w")
+output_discharge.write("clinician_name,patient_id,note_date,note_type,provider_type,specialty,encounter_id\n")
+
+for line in results:
+	if (line[4] == "Medical Student" or line[5] == "Medical Student"): #exclude medical students
+		continue
+	clinician_name = line[0].strip().split(",")
+	clinician_name = "{0} {1}".format(clinician_name[1], clinician_name[0])
+	output_discharge.write("{0},{1},{2},{3},{4},{5},{6}\n".format(clinician_name,line[1],line[2],line[3],line[4],line[5],line[6]))
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_treatment_team.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_treatment_team.py
new file mode 100644
index 00000000..68a92444
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/extract_treatment_team.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+##########################################################################################
+# Extract out treatment groups per patient
+##########################################################################################
+import sys, os
+from io import StringIO
+from datetime import datetime
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/ClinicalDecisionMaker')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Load in Treatment Group Aggregations
+treatment_aggregations = open("/Users/jwang/Desktop/ClinicalDecisionMaker/medinfo/dataconversion/mapdata/TreatmentTeamGroups.tab", "rU")
+treatment_aggregation_map = {}
+treatment_aggregations.readline()
+treatment_aggregations.readline()
+for line in treatment_aggregations:
+	line = line.strip().split("\t")
+	treatment_aggregation_map[line[0]] = line[1]
+print(treatment_aggregation_map)
+
+# Create SCRIPT_FILE
+SCRIPT_FILE = StringIO()
+SCRIPT_FILE.write("psql stride jwang198")
+
+# Find all clinical_item_ids associated with each patient_id
+# SELECT * FROM clinical_item WHERE clinical_item_category_id = 161;
+
+DATA_QUERY = SQLQuery();
+DATA_QUERY.addSelect("clinical_item_id")
+DATA_QUERY.addSelect("description")
+DATA_QUERY.addSelect("clinical_item_category_id")
+DATA_QUERY.addWhereEqual("clinical_item_category_id", "161")
+DATA_QUERY.addFrom("clinical_item")
+
+print(DATA_QUERY)
+
+# Write out data to CSV
+DBUtil.runDBScript(SCRIPT_FILE, False)
+results = DBUtil.execute(DATA_QUERY);
+
+output = open("/Users/jwang/Desktop/Results/treatment_teams.csv", "w")
+output.write("clinical_item_id,description,clinical_item_category_id,treatment_team\n")
+
+for line in results:
+	treatment_team = "Unclassified"
+	if line[1] in treatment_aggregation_map:
+		treatment_team = treatment_aggregation_map[line[1]]
+	output.write("{0},{1},{2},{3}\n".format(line[0],line[1],line[2],treatment_team))
+
+	
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/reorder_data.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/reorder_data.py
new file mode 100644
index 00000000..ae2e299e
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/ExtractPatientFeatureMatrix/reorder_data.py
@@ -0,0 +1,18 @@
+import pandas as pd
+
+# Goal: to ensure that only the last recorded admission's data is used for a given patient id
+# Note: the raw patient feature tables include data recorded from ALL encounters; we only want to consider the LAST encounter
+
+# 2008-2009 data
+dataset_8_9 = "/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix.tab"
+df_8_9 = pd.read_csv(dataset_8_9, sep="\t")
+df_8_9 = df_8_9.sort_values(['patient_id', 'edAdmitTime', 'dischargeTime'], ascending=[0,0,0])
+
+df_8_9.to_csv("/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix_reordered.csv", sep=",", index=False)
+
+# 2010-2013 data
+dataset_10_13 = "/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.tab"
+df_10_13 = pd.read_csv(dataset_10_13, sep="\t")
+df_10_13 = df_10_13.sort_values(['patient_id', 'edAdmitTime', 'dischargeTime'], ascending=[0,0,0])
+
+df_10_13.to_csv("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix_reordered.csv", sep=",", index=False)
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/compute_observed_vs_expected_mortalityORreadmission.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/compute_observed_vs_expected_mortalityORreadmission.py
new file mode 100644
index 00000000..05e32da7
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/compute_observed_vs_expected_mortalityORreadmission.py
@@ -0,0 +1,177 @@
+import pandas as pd
+import scipy.stats
+import math
+####################
+### Load in Data ###
+####################
+
+# Load in physician cohorts based on H&P notes
+physician_hp = open("/Users/jwang/Desktop/Results/physician_patient_map_hp.csv", "rU")
+cohorts_hp = {}
+full_cohorts = {}
+
+physician_hp.readline()
+for line in physician_hp:
+	line = line.strip().split(",")
+	physician = line[0]
+	if (physician in cohorts_hp):
+		cohorts_hp[physician].append( (line[1], line[2], line[6]) ) # (patient_id, H&P note_time, encounter_id)
+	else:
+		cohorts_hp[physician] = [ (line[1], line[2], line[6]), ]
+
+# Load in physician cohorts based on Discharge Summaries
+physician_discharge = open("/Users/jwang/Desktop/Results/physician_patient_map_discharge.csv", "rU")
+cohorts_discharge = {}
+
+physician_discharge.readline()
+for line in physician_discharge:
+	line = line.strip().split(",")
+	physician = line[0]
+	if (physician in cohorts_discharge):
+		cohorts_discharge[physician].append( (line[1], line[2], line[6]) ) # key = physician, value = (patient_id, discharge note_time, encounter_id)
+	else:
+		cohorts_discharge[physician] = [ (line[1], line[2], line[6]), ] 
+
+# Combine to create unique list/cohort of patients per physician (union between H&P and discharge cohorts)
+cohorts_full = {} # key = physician, value = list of patient ids
+patient_lists = {} # key = physician, value = list of (patient_id, note_time) tuples
+
+for physician, patients in cohorts_hp.items(): # corresponds to potential mortality
+	cohort_list = []
+	pat_list = [] 
+	for (pat_id, note_time, encounter_id) in patients:
+		cohort_list.append( (pat_id, note_time, encounter_id) )
+		pat_list.append(pat_id)
+	cohorts_full[physician] = cohort_list
+	patient_lists[physician] = pat_list
+
+for physician, patients in cohorts_discharge.items(): # corresponds to potential readmission
+	if (physician not in full_cohorts):
+		cohort_list = []
+		pat_list = [] 
+		for (pat_id, note_time, encounter_id) in patients:
+			cohort_list.append( (pat_id, note_time, encounter_id) )
+			pat_list.append(pat_id)
+		cohorts_full[physician] = cohort_list
+		patient_lists[physician] = pat_list
+
+	else: # append additional unique patients
+		for (pat_id, note_time, encounter_id) in patients:
+			if (pat_id not in patient_lists[physician]): # not yet in physician's patient list
+				cohorts_full[physician].append( (pat_id, note_time, encounter_id) )
+				patient_lists[physician].append(pat_id) 
+				#union = list(set(patients) | set(full_cohorts[physician])) # union between discharge and H&P cohorts
+
+# Load in patients that died within 30 days of an admission order
+mortality_30 = open("/Users/jwang/Desktop/Results/30_day_deaths.csv", "rU")
+mortality_map = {} # key = patient_id, value = last recorded admission time
+for line in mortality_30:
+	line = line.strip().split(",")
+	mortality_map[line[0]] = line[1] 
+
+# Load in patient's that were readmitted within 30 days of a discharge order
+readmission_30 = open("/Users/jwang/Desktop/Results/30_day_readmissions.csv", "rU")
+readmission_map = {} # key = patient_id, value = (previous discharge time, previous encounter_id)
+for line in readmission_30:
+	line = line.strip().split(",")
+	readmission_map[line[0]] = (line[1], line[2])
+
+#########################################
+### Observed Mortality OR Readmission ###
+#########################################
+# Compute observed mortality OR readmission = number of patients who died within 30 days of admission OR were readmitted within 30 days of a discharge/total patients seen based on H&P and Discharge Summary notes
+
+#initialize map with all physician names
+observed = {} # key = physician, value = (dead or readmitted patients/total patients seen)
+for physician in list(cohorts_full.keys()):
+	observed[physician] = (0, len(cohorts_full[physician]))
+
+for physician, patients in cohorts_full.items():
+	for (pat_id, note_time, encounter_id) in patients: # iterate through all patients that physician is responsible for based on H&P or discharge note
+
+		if (pat_id in mortality_map): # patient died within 30 days of an admission; found from H&P note
+
+			# Q: Is this physician responsible? If this physician signed the H&P note associated with patient's last admission
+			delta = pd.to_datetime(note_time) - pd.to_datetime(mortality_map[pat_id])
+			if (delta >= pd.to_timedelta("0 days 00:00:00")): # this physician was responsible for this patient right before death
+				observed[physician] = (observed[physician][0]+1,observed[physician][1])
+
+		elif (pat_id in readmission_map): # patient was readmitted within 30 days of a discharge; found from Discharge Summary note
+
+			# Q: Is this physician responsible? If physician signed discharge summary associated with patient's discharge before subsequent readmission
+			#delta = pd.to_datetime(note_time) - pd.to_datetime(readmission_map[pat_id])
+			#past_threshold = pd.to_timedelta("-1 days 00:00:00")
+			#future_threshold = pd.to_timedelta("1 days 00:00:00")
+			#if (delta >= past_threshold and delta <= future_threshold): # signed within 24 hours of patient's official discharge order or if encounter ids match
+
+			patient_encounter_id = readmission_map[pat_id][1]
+			if (encounter_id == patient_encounter_id): # physician's encounter id associated with discharge summary matches patient's encounter id associated with their discharge right before the <= 30 day readmission
+				observed[physician] = (observed[physician][0]+1,observed[physician][1])
+
+# perform division (save number of observed mortalities)
+# NOTE: dead_or_re = "dead or readmitted"
+observed_number = {}
+for physician, (dead_or_re, total) in observed.items():
+	observed[physician] = float(dead_or_re)/total
+	observed_number[physician] = int(dead_or_re)
+
+##########################
+### Expected Mortality ###
+##########################
+
+# Expected mortality or readmission = average probability of patients in physician cohort dying within 30 days of an admission or being readmitted within 30 days of a discharge
+
+#initialize map with all physician names
+expected = {} # key = physician, value = (dead or readmitted patients/total patients seen)
+for physician in list(cohorts_full.keys()):
+	expected[physician] = (0, len(cohorts_full[physician]))
+
+# Read in expected mortality/readmission union probability 
+dead_or_re_probs = {} # key = patient_id, value = probability of dying within 30 days
+dead_or_re_probs_f = open("/Users/jwang/Desktop/Results/mortality_or_readmission_probs_remapped.csv", "rU")
+dead_or_re_probs_f.readline()
+for line in dead_or_re_probs_f:
+	line = line.strip().split(",")
+	dead_or_re_probs[line[0]] = float(line[1])
+
+# Find average probability of 30 day mortality/readmission for patients in cohort
+for physician, patients in cohorts_full.items():
+	for (pat_id, note_time, encounter_id) in patients:
+		if (pat_id not in dead_or_re_probs):
+			continue # assume patient without missing probability of death/readmission maintains same as average probability for physician's cohort
+
+		if (physician in expected):
+			expected[physician] = (float(expected[physician][0])+dead_or_re_probs[pat_id], expected[physician][1]+1)
+		else: # not yet in the dictionary
+			expected[physician] = (dead_or_re_probs[pat_id], 1)
+
+# Perform division
+for physician, (prob_sum, total) in expected.items():
+	expected[physician] = float(prob_sum)/total
+
+############################
+### Print/Compute Scores ###
+############################
+
+# Write out observed and expected probabilities to CSV
+outf = open("/Users/jwang/Desktop/Results/mortalityORreadmission_observed_vs_expected.csv", "w")
+outf.write("physician,observed_combined_rate,expected_combined_rate,total_patients,observed_deadORreadmitted,observed_healthy,expected_deadORreadmitted,expected_healthy,p-value,score\n")
+for physician in list(cohorts_full.keys()):
+
+	# Compute Fisher Exact Test
+	cohort_size = len(cohorts_full[physician])
+	observed_deadORreadmitted = observed_number[physician]
+	observed_healthy = cohort_size - observed_deadORreadmitted
+	expected_deadORreadmitted = round(expected[physician]*cohort_size)
+	expected_healthy = cohort_size - expected_deadORreadmitted
+	contingency_table = [ [observed_deadORreadmitted, observed_healthy], [expected_deadORreadmitted, expected_healthy] ]
+	odds_ratio, p_value = scipy.stats.fisher_exact(contingency_table, alternative="two-sided") # observed_deadORreadmitted, observed_healthy | expected_deadORreadmitted, expected_healthy
+
+	# Two-Sided
+	score = -1*math.log10(p_value) # base 10 logarithm
+	if (observed_deadORreadmitted > expected_deadORreadmitted): # more patients died than were expected
+		score = -1*score
+
+	outf.write("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9}\n".format(physician,observed[physician],expected[physician],cohort_size,
+		observed_deadORreadmitted,observed_healthy,expected_deadORreadmitted,expected_healthy,p_value,score))
+outf.close()
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/extract_30_day_readmissions.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/extract_30_day_readmissions.py
new file mode 100644
index 00000000..b3da401e
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/extract_30_day_readmissions.py
@@ -0,0 +1,34 @@
+# Write out all patients that were readmitted within 30 days of a discharge
+import pandas
+
+threshold = pandas.to_timedelta("30 days 00:00:00") # 30 days
+
+readmissions = open("/Users/jwang/Desktop/Results/30_day_readmissions.csv", "w")
+lengths_of_stay = "/Users/jwang/Desktop/Results/lengths_of_stay.csv"
+
+readmissions.write("patient_id,previous_discharge_time,previous_encounter_id,readmission_time,readmission_delta\n")
+
+df = pandas.read_csv(lengths_of_stay, sep=",")
+df = df.sort_values(['patient_id', 'admission_time', 'discharge_time'], ascending=[0,1,1])
+
+cur_patient = ""
+prev_discharge_time = ""
+prev_encounter_id = ""
+for index, row in df.iterrows():
+	# new patient: store information and move on to subsequent recorded admission
+	if (cur_patient != row["patient_id"]):
+		cur_patient = row["patient_id"]
+		prev_discharge_time = row["discharge_time"]
+		prev_encounter_id = row["encounter_id"]
+
+	# same patient: check if time between this admission and previous admission is <= 30 days
+	else: # (cur_patient == row["patient_id"]):
+		readmission_delta = pandas.to_datetime(row["admission_time"]) - pandas.to_datetime(prev_discharge_time)
+		if (readmission_delta <= threshold): # readmission occurred within 30 days
+			readmissions.write('{0},{1},{2},{3},{4}\n'.format(cur_patient, prev_discharge_time, prev_encounter_id, row["admission_time"], readmission_delta))
+
+		# continue to subsequent admission
+		prev_discharge_time = row["discharge_time"]
+		prev_encounter_id = row["encounter_id"]
+
+readmissions.close()
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/extract_lengths_of_stay.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/extract_lengths_of_stay.py
new file mode 100644
index 00000000..6652e37c
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/extract_lengths_of_stay.py
@@ -0,0 +1,65 @@
+import pandas 
+
+def compute_delta(admission_time, discharge_time):
+	# Stride time syntax: 2012-04-12 11:09:00
+	return (pandas.to_datetime(discharge_time) - pandas.to_datetime(admission_time))
+
+def main():
+	flowtable = "/Users/jwang/Desktop/Results/admission_discharge_flow.csv"
+	length_of_stay = open("/Users/jwang/Desktop/Results/lengths_of_stay.csv", "w")
+
+	df = pandas.read_csv(flowtable, sep=",")
+	df = df.sort_values(['patient_id', 'admission_time', 'discharge_time'], ascending=[0,1,1])
+
+	df.to_csv("/Users/jwang/Desktop/Results/pandas_temp.csv", sep=",")
+
+	length_of_stay.write("patient_id,admission_time,discharge_time,length_of_stay,encounter_id\n")
+
+	# Variables
+	cur_patient = ""
+	cur_admission_time = ""
+	cur_encounter_id = ""
+	first_admission_found = False # to ensure that admission/discharge order times between patients are not mixed up
+
+	# For each admission order, find closest discharge order; this constitutes a patient encounter/length of stay
+	for index, row in df.iterrows():
+		#print(index)
+
+		# start of new patient: look for first discharge
+		if (cur_patient != row["patient_id"] or not first_admission_found):
+			cur_patient = row["patient_id"]
+			first_admission_found = False
+
+			if (row["admission"] == "Admission"):
+				cur_admission_time = row["admission_time"]
+				cur_encounter_id = row["encounter_id"] # encounter_id holds for a patient's entire stay from initial admission to final discharge
+				first_admission_found = True
+
+				if (row["discharge"] == "Discharge"):
+
+					# Compute length of stay
+					time_delta = compute_delta(cur_admission_time, row["discharge_time"])
+					length_of_stay.write("{0},{1},{2},{3},{4}\n".format(cur_patient, cur_admission_time, row["discharge_time"], time_delta, cur_encounter_id))
+
+			else: # continue until you found first admission order for the new patient
+				continue	
+
+		# same patient
+		else: # cur_patient == row["patient_id"]:
+
+			if (row["admission"] == "Admission"): # restart: look for next discharge
+				cur_admission_time = row["admission_time"]
+				cur_encounter_id = row["encounter_id"]
+
+			if (row["discharge"] == "Discharge"):
+
+				# Compute length of stay
+				time_delta = compute_delta(cur_admission_time, row["discharge_time"])
+				length_of_stay.write("{0},{1},{2},{3},{4}\n".format(cur_patient, cur_admission_time, row["discharge_time"], time_delta, cur_encounter_id))
+	length_of_stay.close()
+
+if __name__ == "main":
+	main()
+
+# Script
+main()
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/mortality_or_readmission_predictor.Rmd b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/mortality_or_readmission_predictor.Rmd
new file mode 100644
index 00000000..e757ae6e
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/Miscellaneous/mortality_or_readmission_predictor.Rmd
@@ -0,0 +1,202 @@
+Mortality OR Readmission Predictor: given a patient's demographic data, initial vital signs, past medical history, lab tests, and other relevant covariates collected between encountering the physician, we predict the patient's probability of dying (within 30 days of admission) or being readmitted (within 30 days of discharge)
+
+```{r}
+# Load in required libraries
+library(caret)
+library(ggplot2)
+library(dplyr)
+library(mice)
+library(Matching)
+library(tableone)
+require(reshape2)
+Sys.setenv(TZ='UTC')  
+  
+###################################
+### Read and format data frames ###
+###################################
+print("Reading and formatting data frames...")
+  
+# Read tab-delimited input using header row labels and interpret Python None strings as R NA values
+df_raw = tbl_df(read.csv("/Users/jwang/Desktop/Results/2008-2009_patient_feature_matrix.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:49)) {
+  df_raw[,col_id] <- factor(ifelse(df_raw[,col_id]=="True", 1, 0))
+}
+
+# Condense dataframe
+columns_of_interest = c(1, 6:51, 56) # id, relevant covariates, label columns
+df_raw <- df_raw[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw) <- make.names(colnames(df_raw))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw = df_raw[,c(1:3,6:48)]
+
+# Remove lab tests to reduce bias: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw[,i]))/dim(df_raw)[1]
+  print(colnames(df_raw)[i])
+  print(ratio_na)
+}
+
+# Remove urine, column 7 (missingness > 20%)
+df_raw = df_raw[,c(1:6, 8:46)]
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw);
+md_data <- mice(df_raw,m=1,maxit=5,meth='pmm',seed=500);
+df <- complete(md_data,1);
+```
+
+```{r}
+# Split into training and test/validation set
+label_column <- dim(df)[2]
+id_column <- 1
+df[,label_column] <- factor(ifelse(df[,label_column]=="yes", 1, 0))
+
+Train <- createDataPartition(df$readmission_or_death_30, p=0.6, list=FALSE)
+training <- df[Train, c(-id_column)]
+testing <- df[-Train, c(-id_column)]
+
+# Train + evaluate via 10-folds cross-validation (just 2008-2009 data)
+# glm.fit <- glm(death ~., data=training, family=binomial(link='logit'))
+ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
+glm.fit <- train(readmission_or_death_30 ~., data=training, method="glm", family="binomial", trControl = ctrl, tuneLength = 5)
+glm.fit$results
+
+pred = predict(glm.fit, newdata=testing)
+confusionMatrix(data=pred, testing$readmission_or_death_30)
+
+# Evalution via ROC analysis (just 2008-2009 data)
+library(ROCR)
+# Compute AUC for predicting death with the model
+prob <- predict(glm.fit, newdata=testing, type="prob")
+
+# Convert prob into binary label
+# prob_binned <- factor(ifelse(prob$`1`>0.5, 1, 0))
+  
+pred <- prediction(as.numeric(prob[,2]), as.numeric(testing$readmission_or_death_30))
+perf <- performance(pred, measure = "tpr", x.measure = "fpr")
+plot(perf)
+auc <- performance(pred, measure = "auc")
+auc <- auc@y.values[[1]]
+auc
+
+# Caveat: test set is predominantly composed of "survived individuals"
+length(which(testing$readmission_or_death_30 == 1))/length(testing$readmission_or_death_30)
+
+```
+
+```{r}
+####################################################
+### Mortality/Readmission Probs for 2010 Onwards ###
+####################################################
+
+# 30-day mortality or readmission
+
+# When applying the model to 2010 onwards, we use ALL of 2008-2009 data to train
+label_column <- dim(df)[2]
+training <- df[, c(-id_column)]
+
+# Train + evaluate via 10-folds cross-validation
+ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
+glm.fit <- train(readmission_or_death_30 ~., data=training, method="glm", family="binomial", trControl = ctrl, tuneLength = 5)
+glm.fit$results
+
+##############################
+### Load in 2010-2013 data ###
+##############################
+df_raw_10_13 = tbl_df(read.csv("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:49)) {
+  df_raw_10_13[,col_id] <- factor(ifelse(df_raw_10_13[,col_id]=="True", 1, 0))
+}
+
+# Condense dataframe
+columns_of_interest = c(1, 6:51, 56) # id, relevant covariates, label columns
+df_raw_10_13 <- df_raw_10_13[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw_10_13) <- make.names(colnames(df_raw_10_13))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw_10_13 = df_raw_10_13[,c(1:3,6:48)]
+
+# Remove lab tests to reduce bias: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw[,i]))/dim(df_raw)[1]
+  print(colnames(df_raw)[i])
+  print(ratio_na)
+}
+
+# Remove urine (missingness > 20%)
+df_raw_10_13 = df_raw_10_13[,c(1:6, 8:46)]
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw_10_13);
+md_data <- mice(df_raw_10_13,m=1,maxit=5,meth='pmm',seed=500);
+df_10_13 <- complete(md_data,1);
+
+################################
+### Compute Sickness Metrics ###
+################################
+label_column <- dim(df_10_13)[2]
+df_10_13[,label_column] <- factor(ifelse(df_10_13[,label_column]=="yes", 1, 0))
+testing <- df_10_13[, c(-id_column)]
+pred = predict(glm.fit, newdata=testing)
+cm <- confusionMatrix(data=pred, testing$readmission_or_death_30)
+cm
+# sensitivity = TPR, specificity = TNR
+
+# Compute AUC for predicting death with the model
+prob <- predict(glm.fit, newdata=testing, type="prob")
+
+# Convert prob into binary label
+# prob_binned <- factor(ifelse(prob$`1`>0.5, 1, 0))
+  
+pred <- prediction(as.numeric(prob[,2]), as.numeric(testing$readmission_or_death_30))
+perf <- performance(pred, measure = "tpr", x.measure = "fpr")
+plot(perf)
+auc <- performance(pred, measure = "auc")
+auc <- auc@y.values[[1]]
+auc
+
+# Caveat: test set is predominantly composed of "survived individuals"
+length(which(testing$readmission_or_death_30 == 1))/length(testing$readmission_or_death_30)
+
+# Save sickness metrics to CSV file
+mortality_or_readmission_probs = data.frame(df_10_13$normalized_patient_id, prob$`1`)
+write.table(mortality_or_readmission_probs, file="/Users/jwang/Desktop/Results/mortality_or_readmission_probs.csv", sep=",")
+
+```
+
+```{r}
+####################
+### Overlap Plot ###
+####################
+
+require(car)
+death_prob <- prob$`1`
+died <- which(testing$readmission_or_death_30 == 1)
+died_prob <- death_prob[died]
+survived_prob <- death_prob[-died]
+
+print("Plotting overlap...")
+# note that logit = log(p/(1-p))
+logit_died <- as.numeric(logit(died_prob))
+logit_survived <- as.numeric(logit(survived_prob))
+histdata <- data.frame(rbind.data.frame(cbind.data.frame(logit=logit_died,label="logit_died_30"), cbind.data.frame(logit=logit_survived,label="logit_healthy_30")))
+plot <- ggplot(histdata, aes(x=logit, fill=label)) + geom_histogram(alpha=0.2, position="identity")
+print(plot)
+```
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/extract_unmatched_patients.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/extract_unmatched_patients.py
new file mode 100644
index 00000000..1f59fa56
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/extract_unmatched_patients.py
@@ -0,0 +1,123 @@
+# 1) Extract all patients associated with top and bottom performing clinician cohorts
+# 2) Create csv feature matrices for unmatched patient cohorts in preparation for matching
+
+# Extract clinician cohorts (top, bottom, everyone)
+print("Assembling top and bottom physician cohorts...")
+performance_scores = open("/Users/jwang/Desktop/Results/mortality_observed_vs_expected.csv", "rU")
+header = performance_scores.readline()
+top_clinicians = {} 
+bottom_clinicians = {}
+all_clinicians = {}
+
+for physician in performance_scores:
+	physician = physician.strip().split(",")
+	score = float(physician[9])
+	if (score > 0): # high performer
+		top_clinicians[physician[0]] = score
+	elif (score < 0): # low performer
+		bottom_clinicians[physician[0]] = score
+
+	# all clinicians are included regardless of score in the "everyone" cohort
+	all_clinicians[physician[0]] = score
+performance_scores.close()
+print(len(top_clinicians), len(bottom_clinicians))
+
+# Load in physician-patient map
+physician_specialties = {}
+print("Assembling top and bottom patient cohorts...")
+physician_patient_map = open("/Users/jwang/Desktop/Results/physician_patient_map_hp.csv", "rU")
+physician_patient_map.readline()
+top_cohort = {} 
+bottom_cohort = {}
+all_cohort = {}
+# Identify patients associated with top, bottom, and everyone clinician cohorts 
+for line in physician_patient_map:
+	line = line.strip().split(",")
+	physician = line[0]
+	physician_specialties[physician] = line[5]
+	if (physician in top_clinicians):
+		top_cohort[line[1]] = 1
+	elif (physician in bottom_clinicians):
+		bottom_cohort[line[1]] = 1
+
+	# all clinicians (and associated patients) are considered in "everyone" cohort
+	all_cohort[line[1]] = 1
+physician_patient_map.close()
+print(len(top_cohort), len(bottom_cohort), len(all_cohort))
+
+# Assemble feature matrices for top and bottom patient cohorts
+# Note: this only keeps patients seen in 2010-2013
+print("Writing out top and bottom patient feature matrices...")
+feature_matrix = open("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv", "rU")
+top_matrix = open("/Users/jwang/Desktop/Results/2010-2013_unmatched_patient_feature_matrix_top_performing.csv", "w")
+bottom_matrix = open("/Users/jwang/Desktop/Results/2010-2013_unmatched_patient_feature_matrix_bottom_performing.csv", "w")
+all_cohort_f = open("/Users/jwang/Desktop/Results/all_patients.csv", "w")
+
+header = feature_matrix.readline()
+top_matrix.write(header)
+bottom_matrix.write(header)
+
+top_count = 0
+bottom_count = 0
+overlap = {}
+for patient in feature_matrix:
+	line = patient # raw line
+	patient = patient.strip().split(",")
+	patient_id = patient[1]
+
+	if (patient_id in all_cohort):
+		all_cohort_f.write(patient_id + "\n")
+
+	# exclude overlapping patients; keep only patients uniquely associated wtih one cohort
+	if (patient_id in top_cohort and patient_id in bottom_cohort):
+		overlap[patient_id] = 1
+		continue
+	elif (patient_id in top_cohort):
+		top_matrix.write(line)
+		top_count += 1
+	elif (patient_id in bottom_cohort):
+		bottom_matrix.write(line)
+		bottom_count += 1
+
+feature_matrix.close()
+top_matrix.close()
+bottom_matrix.close()
+all_cohort_f.close()
+print(top_count, bottom_count)
+print(len(overlap))
+
+# Analyze patients that are duplicate/overlapping
+print("Analyzing duplicate/overlap patients between top and bottom patient cohorts...")
+overlap_map_top = {} # key = patient_id, value = array of physicians that wrote a H&P note in top cohort
+overlap_map_bottom = {} # key = patient_id, value = array of physicians that wrote a H&P note in bottom cohort
+
+physician_patient_map = open("/Users/jwang/Desktop/Results/physician_patient_map_hp.csv", "rU")
+physician_patient_map.readline()
+for line in physician_patient_map:
+	line = line.strip().split(",")
+	physician = line[0]
+	patient_id = line[1]
+	if (patient_id in overlap): 
+		if (physician in top_clinicians):
+
+			if (patient_id not in overlap_map_top): # not yet in overlap_map
+				overlap_map_top[patient_id] = [physician]
+			else: # already in overlap_map_top
+				overlap_map_top[patient_id].append(physician)
+
+		elif (physician in bottom_clinicians):
+
+			if (patient_id not in overlap_map_bottom): # not yet in overlap_map
+				overlap_map_bottom[patient_id] = [physician]
+			else: # already in overlap_map_bottom
+				overlap_map_bottom[patient_id].append(physician)
+
+overlap_outf = open("/Users/jwang/Desktop/Results/duplicate_patients.csv", "w")
+overlap_outf.write("patient_id,physician,physician speciality,physician cohort\n")
+# iterate through all duplicate patients
+for patient_id, top_physicians in overlap_map_top.items():
+	for physician in top_physicians:
+		overlap_outf.write("{0},{1},{2},top\n".format(patient_id,physician,physician_specialties[physician]))
+	for physician in overlap_map_bottom[patient_id]:
+		overlap_outf.write("{0},{1},{2},bottom\n".format(patient_id,physician,physician_specialties[physician]))
+overlap_outf.close()
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/matching.Rmd b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/matching.Rmd
new file mode 100644
index 00000000..0234013c
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/matching.Rmd
@@ -0,0 +1,735 @@
+Matching: given patient demographic data, initial vital signs, past medical history, admission diagnosis, treatment team, and other relevant covariates collected between encountering the physician for two cohorts, conduct propensity score matching, yielding two balanced cohorts
+
+PART 1: LOAD IN TOP AND BOTTOM PATIENT COHORTS
+```{r}
+setwd("/Users/jwang/Desktop/Results/scripts")
+
+# Load in required libraries
+library(caret)
+library(ggplot2)
+library(dplyr)
+library(mice)
+library(Matching)
+library(tableone)
+require(reshape2)
+require(ISLR)
+Sys.setenv(TZ='UTC')  
+
+#######################
+### Load Top Cohort ###
+#######################
+print("Reading and formatting data frames...")
+  
+# Read tab-delimited input using header row labels and interpret Python None strings as R NA values
+df_raw_top = tbl_df(read.csv("/Users/jwang/Desktop/Results/2010-2013_unmatched_patient_feature_matrix_top_performing.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:61)) {
+  df_raw_top[,col_id] <- factor(ifelse(df_raw_top[,col_id]=="True", 1, 0))
+}
+# Condense dataframe
+columns_of_interest = c(1, 6:296) # id, relevant covariates, label columns
+df_raw_top <- df_raw_top[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw_top) <- make.names(colnames(df_raw_top))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw_top = df_raw_top[,c(1:3,6:292)]
+
+# Assess lab tests for percentage na: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw_top[,i]))/dim(df_raw_top)[1]
+  print(colnames(df_raw_top)[i])
+  print(ratio_na)
+}
+
+# Remove urine, column 7 (missingness > 20%)
+df_raw_top = df_raw_top[,c(1:6, 8:290)]
+
+# Remove TT and admission diagnosis columns that are all zero
+df_raw_top <- data.frame(df_raw_top)
+zero_cols = c()
+for (i in c(43:54, 57:284)) { # colindex 55 = income, colindex 56 = age
+  # replace NA with 0
+  df_raw_top[,i][is.na(df_raw_top[,i])] <- 0
+  if ((length(df_raw_top[,i][df_raw_top[,i] == 1])) == 0) {
+    print(i)
+    print(colnames(df_raw_top)[i])
+    zero_cols <- c(zero_cols,i)
+  }
+}
+df_raw_top <- df_raw_top[,-zero_cols]
+
+# Convert remaining admission diagnosis columns to factor
+for (i in c(56:185)) {
+  df_raw_top[,i] = as.factor(df_raw_top[,i])
+}
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw_top);
+md_data <- mice(df_raw_top,m=1,maxit=5,meth='pmm',seed=500);
+df_raw_top <- complete(md_data,1);
+
+df_top <- df_raw_top
+save(df_top,file="df_top.Rda")
+#df_top <- load(file="df_top.Rda")
+
+##########################
+### Load Bottom Cohort ###
+##########################
+print("Reading and formatting data frames...")
+  
+# Read tab-delimited input using header row labels and interpret Python None strings as R NA values
+df_raw_bottom = tbl_df(read.csv("/Users/jwang/Desktop/Results/2010-2013_unmatched_patient_feature_matrix_bottom_performing.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:61)) {
+  df_raw_bottom[,col_id] <- factor(ifelse(df_raw_bottom[,col_id]=="True", 1, 0))
+}
+# Condense dataframe
+columns_of_interest = c(1, 6:296) # id, relevant covariates, label columns
+df_raw_bottom <- df_raw_bottom[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw_bottom) <- make.names(colnames(df_raw_bottom))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw_bottom = df_raw_bottom[,c(1:3,6:292)]
+
+# Assess lab tests for percentage na: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw_bottom[,i]))/dim(df_raw_bottom)[1]
+  print(colnames(df_raw_bottom)[i])
+  print(ratio_na)
+}
+
+# Remove urine, column 7 (missingness > 20%)
+df_raw_bottom = df_raw_bottom[,c(1:6, 8:290)]
+
+# Remove TT and admission diagnosis columns that are all zero
+df_raw_bottom <- data.frame(df_raw_bottom)
+# zero_cols = c()
+# for (i in c(43:54, 57:284)) { # colindex 55 = income, colindex 56 = age
+  # replace NA with 0
+#   print(i)
+#   df_raw_bottom[,i][is.na(df_raw_bottom)] <- 0
+#   if ((length(df_raw_bottom[,i][df_raw_bottom[,i] == 1])) == 0) {
+#     print(i)
+#     print(colnames(df_raw_bottom)[i])
+#     zero_cols <- c(zero_cols,i)
+#   }
+# }
+df_raw_bottom <- df_raw_bottom[,-zero_cols]
+df_raw_bottom$Charlson.Dementia.pre[is.na(df_raw_bottom$Charlson.Dementia.pre)] <- 0
+
+# Convert remaining admission diagnosis columns to factor
+for (i in c(56:185)) {
+  df_raw_bottom[,i] = as.factor(df_raw_bottom[,i])
+}
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw_bottom);
+md_data <- mice(df_raw_bottom,m=1,maxit=5,meth='pmm',seed=500);
+df_raw_bottom <- complete(md_data,1);
+
+df_bottom <- df_raw_bottom
+save(df_bottom,file="df_bottom.Rda")
+#df_bottom <- load(file="df_bottom.Rda")
+```
+
+PART 2: PROPENSITY SCORING AND MATCHING (TOP VS. BOTTOM)
+Note: this module only considers the case if 2-way matching (1:1)
+```{r}
+##########################
+### Propensity Scoring ###
+##########################
+
+# Add additional label column (we will be predicting probability of being in the top patient cohort)
+df <- rbind(cbind(df_top[,c(1:55,208:212)],label=1),cbind(df_bottom[,c(1:55,208:212)],label=0))
+
+print("Building propensity score model...")
+# Logistic Regression
+# Based on propensity of being assigned to the top patient cohort
+id_column <- 1
+label_columns <- c(56:61)
+label <- as.factor(df$label)
+glm.fit <- glm(label ~.,family=binomial(link='logit'), data=df[,c(-id_column, -label_columns)])
+
+# Propensity Estimation
+top_features <- df %>%
+  filter(label == 1)
+top_features <- top_features[,c(-id_column, -label_columns)] # remove id, label
+
+bottom_features <- df %>%
+  filter(label == 0)
+bottom_features <- bottom_features[,c(-id_column, -label_columns)] # remove id, label
+
+pred_top <- predict(glm.fit, newdata=top_features, type="response")
+pred_bottom <- predict(glm.fit, newdata=bottom_features, type="response")
+preds <- c(pred_top, pred_bottom)
+
+####################
+### Overlap Plot ###
+####################
+print("Plotting overlap...")
+# note that logit = log(p/(1-p))
+require(car)
+logit_top <- as.numeric(logit(pred_top))
+logit_bottom <- as.numeric(logit(pred_bottom))
+histdata <- data.frame(rbind.data.frame(cbind.data.frame(logit=logit_top,label=1), cbind.data.frame(logit=logit_bottom,label=0)))
+plot <- ggplot(histdata, aes(x=logit, fill=label)) + geom_histogram(alpha=0.2, position="identity")
+plot
+
+########################
+### Caliper Matching ###
+########################
+print("Conducting caliper matching...")
+# caliper = the maximum tolerated difference between matched subjects in a "non-perfect" matching 
+# conduct matching w/o replacement on logit of propensity
+temp <- data.frame(rbind.data.frame(cbind.data.frame(logit=logit_top,label=1), cbind.data.frame(logit=logit_bottom,label=0)))
+
+# compute caliper: 0.2*sd(logit(propensity_scores))
+propensity_scores <- temp$logit
+opt_caliper <- 0.2*sd(propensity_scores)
+print(opt_caliper)
+
+#matches <- Match(X=temp$logit, Tr=temp$label, caliper=0.25, replace=FALSE, M=2)
+matches <- Match(X=temp$logit, Tr=temp$label, caliper=opt_caliper, replace=F, M=1)
+
+# number of patients in matched set
+treated <- unique(matches$index.treated) # Top
+control <- unique(matches$index.control) # Bottom
+print("Post-Matching Cohort Sizes:")
+length(treated)
+length(control)
+
+# save matched features in preparation for matching against the everyone cohort
+top_matched_features <- df[matches$index.treated, ]
+top_matched_features <- top_matched_features[,c(-id_column, -label_columns)] # remove id, label
+
+bottom_matched_features <- df[matches$index.control, ]
+bottom_matched_features <- bottom_matched_features[,c(-id_column, -label_columns)] # remove id, label
+
+df_top_matched <- df_top[matches$index.treated,]
+
+##############################
+### Post-Matching Analysis ###
+##############################
+
+print("Preparing post-matching results...")
+
+# Examine Matching Results
+top_full <- df %>% 
+  filter(label == 1)
+
+bottom_full <- df %>% 
+  filter(label == 0)
+
+df_full <- rbind.data.frame(top_full, bottom_full)
+# treated = top (1), control = bottom (0)
+df_matched <- rbind.data.frame(df_full[treated,], df_full[control,])
+
+# Output to CSV
+write.csv(df_matched, file = "/Users/jwang/Desktop/Results/matched_top_bottom.csv")
+
+# Before and After Comparisons
+top_matched <- df_matched %>%
+  filter(label == 1)
+top_matched <- top_matched[,c(-id_column, -label_columns)]
+bottom_matched <- df_matched %>%
+  filter(label == 0)
+bottom_matched <- bottom_matched[,c(-id_column, -label_columns)]
+
+top_unmatched <-top_features
+bottom_unmatched <- bottom_features
+
+# Pre and Post Matching Standardized Difference in Means
+print("Plotting pre- vs. post-matching SMD values...")
+
+t1 <- cbind(top_unmatched, label=1)
+t2 <- cbind(bottom_unmatched, label=0)
+unmatched <- rbind(t1, t2)
+unmatched_tab <- CreateTableOne(vars = colnames(unmatched), strata = "label", data = unmatched, test = FALSE)
+print(unmatched_tab, smd = TRUE)
+
+t1 <- cbind(top_matched, label=1)
+t2 <- cbind(bottom_matched, label=0)
+matched <- rbind(t1, t2)
+matched_tab <- CreateTableOne(vars = colnames(matched), strata = "label", data = matched, test = FALSE)
+print(matched_tab, smd = TRUE)
+
+## Construct a data frame containing variable name and SMD from all methods
+data_plot <- data.frame(variable  = names(ExtractSmd(unmatched_tab)),
+                       Unmatched = ExtractSmd(unmatched_tab),
+                       Matched   = ExtractSmd(matched_tab))
+
+## Create long-format data for ggplot2
+data_plot_melt <- melt(data = data_plot,
+                     id.vars = c("variable"),
+                     variable.name = "Method",
+                     value.name = "SMD")
+
+## Order variable names by magnitude of SMD
+varNames <- as.character(data_plot$variable)[order(data_plot$Unmatched)]
+
+## Order factor levels in the same order
+data_plot_melt$variable <- factor(data_plot_melt$variable,
+                                levels = varNames)
+
+## Plot using ggplot2
+# If including all covariates possible: use this data_plot_melt[-c(55:207,262:414),]
+plot <- ggplot(data = data_plot_melt[-c(55,110),], mapping = aes(x = variable, y = SMD,
+                                          group = Method, color = Method)) +
+geom_line() +
+geom_point() +
+geom_hline(yintercept = 0.1, color = "black", size = 0.1) +
+coord_flip() +
+theme_bw() + theme(legend.key = element_blank())
+print(plot)
+
+# Pre and Post-Matching P-Values
+print("Printing pre- vs. post-matching p-values...")
+
+for (i in 1:length(top_features)) {
+  print(colnames(top_matched)[i])
+  
+  tryCatch(if (length(unique(top_matched[,i])) > 2) { #mean
+    print("Unmatched:")
+    print("Top")
+    print(mean(top_unmatched[,i]))
+    #print(sd(top_unmatched[,i]))
+    
+    print("Bottom")
+    print(mean(bottom_unmatched[,i]))
+    #print(sd(bottom_unmatched[,i]))
+    #print(wilcox.test(expert_unmatched[,i], everyone_unmatched[,i], alternative = "two.sided")$p.value)
+    print("P-value:")
+    print(t.test(top_unmatched[,i], bottom_unmatched[,i])$p.value)
+    
+    print("Matched:")
+    print("Top")
+    print(mean(top_matched[,i]))
+    #print(sd(top_matched[,i]))
+    
+    print("Bottom")
+    print(mean(bottom_matched[,i]))
+    #print(sd(bottom_matched[,i]))
+    #print(wilcox.test(expert_matched[,i], everyone_matched[,i], alternative = "two.sided")$p.value)
+    
+    print("P-value:")
+    print(t.test(top_matched[,i], bottom_matched[,i])$p.value)
+  }
+  else { # fraction    
+    print("Unmatched:")
+    print("Top")
+    print(length(top_unmatched[,i][top_unmatched[,i] == 1])/dim(top_unmatched)[1])
+    
+    print("Bottom")
+    print(length(bottom_unmatched[,i][bottom_unmatched[,i] == 1])/dim(bottom_unmatched)[1])
+    #print(chisq.test(table(expert_unmatched[,i], everyone_unmatched[,i]))$p.value)
+    
+    print("P-value:")
+    print(t.test(as.numeric(top_unmatched[,i]), as.numeric(bottom_unmatched[,i]))$p.value)
+
+    print("Matched:")
+    print("Top")
+    print(length(top_matched[,i][top_matched[,i] == 1])/dim(top_matched)[1])
+    
+    print("Bottom")
+    print(length(bottom_matched[,i][bottom_matched[,i] == 1])/dim(bottom_matched)[1])
+    #print(chisq.test(table(expert_matched[,i], everyone_matched[,i]))$p.value)
+    
+    print("P-value:")
+    print(t.test(as.numeric(top_matched[,i]), as.numeric(bottom_matched[,i]))$p.value)
+  }, error=function(e){})
+}
+```
+PART 3: LOAD IN ALL PATIENTS ("EVERYONE") FROM 2010-2013
+```{r}
+##########################
+### Load Bottom Cohort ###
+##########################
+print("Reading and formatting data frames...")
+  
+# Read tab-delimited input using header row labels and interpret Python None strings as R NA values
+df_raw_everyone = tbl_df(read.csv("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv",header=TRUE,sep=",",na.strings="None", stringsAsFactors=FALSE));
+
+# Convert T/F = 1/0 (Columns labeled under binary_columns)
+for (col_id in c(22:61)) {
+  df_raw_everyone[,col_id] <- factor(ifelse(df_raw_everyone[,col_id]=="True", 1, 0))
+}
+# Condense dataframe
+columns_of_interest = c(1, 6:296) # id, relevant covariates, label columns
+df_raw_everyone <- df_raw_everyone[,columns_of_interest]
+
+# Ensure legitimate variable names
+colnames(df_raw_everyone) <- make.names(colnames(df_raw_everyone))
+
+###################
+### Missingness ###
+###################
+
+# Remove features with too much NA/missing data (by manual inspection)
+df_raw_everyone = df_raw_everyone[,c(1:3,6:292)]
+
+# Assess lab tests for percentage na: columns 7-15 in df
+for (i in 7:15) {
+  ratio_na = sum(is.na(df_raw_everyone[,i]))/dim(df_raw_everyone)[1]
+  print(colnames(df_raw_everyone)[i])
+  print(ratio_na)
+}
+
+# Remove urine, column 7 (missingness > 20%)
+df_raw_everyone = df_raw_everyone[,c(1:6, 8:290)]
+
+# Remove TT and admission diagnosis columns that are all zero
+df_raw_everyone <- data.frame(df_raw_everyone)
+# zero_cols = c()
+# for (i in c(43:54, 57:284)) { # colindex 55 = income, colindex 56 = age
+  # replace NA with 0
+#   print(i)
+#   df_raw_bottom[,i][is.na(df_raw_bottom)] <- 0
+#   if ((length(df_raw_bottom[,i][df_raw_bottom[,i] == 1])) == 0) {
+#     print(i)
+#     print(colnames(df_raw_bottom)[i])
+#     zero_cols <- c(zero_cols,i)
+#   }
+# }
+df_raw_everyone <- df_raw_everyone[,-zero_cols]
+df_raw_everyone$Charlson.Dementia.pre[is.na(df_raw_everyone$Charlson.Dementia.pre)] <- 0
+
+# Convert remaining admission diagnosis columns to factor
+for (i in c(56:185)) {
+  df_raw_everyone[,i] = as.factor(df_raw_everyone[,i])
+}
+
+# Impute the remaining NA values: predictive mean imputation
+md.pattern(df_raw_everyone);
+md_data <- mice(df_raw_everyone,m=1,maxit=5,meth='pmm',seed=500);
+df_raw_everyone <- complete(md_data,1);
+
+df_everyone <- df_raw_everyone
+save(df_everyone,file="df_everyone.Rda")
+#df_everyone <- load(file="df_everyone.Rda")
+```
+
+PART 4: PROPENSITY SCORING AND MATCHING (TOP* VS. EVERYONE)
+TOP* = already matched top patient cohort from PART 3
+```{r}
+##########################
+### Propensity Scoring ###
+##########################
+
+# Instead of training the model on all >60,000 patients which adds a lot of noise, reduce the training set by 10-fold
+df <- rbind(cbind(df_top_matched[,c(1:55,208:212)],label=1),cbind(df_everyone[1:10000,c(1:55,208:212)],label=0))
+
+print("Building propensity score model...")
+# Logistic Regression
+id_column <- 1
+#label_columns <- c(208:213)
+label_columns <- c(56:61)
+label <- as.factor(df$label)
+glm.fit <- glm(label ~.,family=binomial(link='logit'), data=df[,c(-id_column, -label_columns)])
+
+# Propensity Estimation
+top_matched_features <- df %>%
+  filter(label == 1)
+top_matched_features <- top_matched_features[,c(-id_column, -label_columns)] # remove id, label
+
+everyone_features <- df %>%
+  filter(label == 0)
+everyone_features <- everyone_features[,c(-id_column, -label_columns)] # remove id, label
+
+pred_top_matched <- predict(glm.fit, newdata=top_matched_features, type="response")
+pred_everyone <- predict(glm.fit, newdata=everyone_features, type="response")
+preds <- c(pred_top_matched, pred_everyone)
+
+####################
+### Overlap Plot ###
+####################
+print("Plotting overlap...")
+# note that logit = log(p/(1-p))
+require(car)
+logit_top_matched <- as.numeric(logit(pred_top_matched))
+logit_everyone <- as.numeric(logit(pred_everyone))
+histdata <- data.frame(rbind.data.frame(cbind.data.frame(logit=logit_top_matched,label=1), cbind.data.frame(logit=logit_everyone,label=0)))
+plot <- ggplot(histdata, aes(x=logit, fill=label)) + geom_histogram(alpha=0.2, position="identity")
+plot
+
+########################
+### Caliper Matching ###
+########################
+print("Conducting caliper matching...")
+# caliper = the maximum tolerated difference between matched subjects in a "non-perfect" matching 
+# conduct matching w/o replacement on logit of propensity
+temp <- data.frame(rbind.data.frame(cbind.data.frame(logit=logit_top_matched,label=1), cbind.data.frame(logit=logit_everyone,label=0)))
+
+# compute caliper: 0.2*sd(logit(propensity_scores))
+propensity_scores <- temp$logit
+opt_caliper <- 0.2*sd(propensity_scores)
+print(opt_caliper)
+
+#matches <- Match(X=temp$logit, Tr=temp$label, caliper=0.25, replace=FALSE, M=2)
+matches <- Match(X=temp$logit, Tr=temp$label, caliper=opt_caliper, replace=F, M=5)
+
+# number of patients in matched set
+treated <- unique(matches$index.treated) # Top Matched
+control <- unique(matches$index.control) # Everyone
+print("Post-Matching Cohort Sizes:")
+length(treated)
+length(control)
+
+##############################
+### Post-Matching Analysis ###
+##############################
+
+print("Preparing post-matching results...")
+
+# Examine Matching Results
+top_matched_full <- df %>% 
+  filter(label == 1)
+
+everyone_full <- df %>% 
+  filter(label == 0)
+
+df_full <- rbind.data.frame(top_matched_full, everyone_full)
+# treated = top (1), control = everyone (0)
+df_matched <- rbind.data.frame(df_full[treated,], df_full[control,])
+
+# Output to CSV
+write.csv(df_matched, file = "/Users/jwang/Desktop/Results/matched_top_everyone.csv")
+
+# Before and After Comparisons
+top_matched_matched <- df_matched %>%
+  filter(label == 1)
+top_matched_matched <- top_matched_matched[,c(-id_column, -label_columns)]
+everyone_matched <- df_matched %>%
+  filter(label == 0)
+everyone_matched <- everyone_matched[,c(-id_column, -label_columns)]
+
+top_matched_unmatched <-top_matched_features
+everyone_unmatched <- everyone_features
+
+# Pre and Post Matching Standardized Difference in Means
+print("Plotting pre- vs. post-matching SMD values...")
+
+t1 <- cbind(top_matched_unmatched, label=1)
+t2 <- cbind(everyone_unmatched, label=0)
+unmatched <- rbind(t1, t2)
+unmatched_tab <- CreateTableOne(vars = colnames(unmatched), strata = "label", data = unmatched, test = FALSE)
+print(unmatched_tab, smd = TRUE)
+
+t1 <- cbind(top_matched_matched, label=1)
+t2 <- cbind(everyone_matched, label=0)
+matched <- rbind(t1, t2)
+matched_tab <- CreateTableOne(vars = colnames(matched), strata = "label", data = matched, test = FALSE)
+print(matched_tab, smd = TRUE)
+
+## Construct a data frame containing variable name and SMD from all methods
+data_plot <- data.frame(variable  = names(ExtractSmd(unmatched_tab)),
+                       Unmatched = ExtractSmd(unmatched_tab),
+                       Matched   = ExtractSmd(matched_tab))
+
+## Create long-format data for ggplot2
+data_plot_melt <- melt(data = data_plot,
+                     id.vars = c("variable"),
+                     variable.name = "Method",
+                     value.name = "SMD")
+
+## Order variable names by magnitude of SMD
+varNames <- as.character(data_plot$variable)[order(data_plot$Unmatched)]
+
+## Order factor levels in the same order
+data_plot_melt$variable <- factor(data_plot_melt$variable,
+                                levels = varNames)
+
+## Plot using ggplot2
+plot <- ggplot(data = data_plot_melt[-c(55,110),], mapping = aes(x = variable, y = SMD,
+                                          group = Method, color = Method)) +
+geom_line() +
+geom_point() +
+geom_hline(yintercept = 0.1, color = "black", size = 0.1) +
+coord_flip() +
+theme_bw() + theme(legend.key = element_blank())
+print(plot)
+
+# Pre and Post-Matching P-Values
+print("Printing pre- vs. post-matching p-values...")
+
+for (i in 1:length(top_features)) {
+  print(colnames(top_matched)[i])
+  
+  tryCatch(if (length(unique(top_matched[,i])) > 2) { #mean
+    print("Unmatched:")
+    print("Top*")
+    print(mean(top_matched_unmatched[,i]))
+    #print(sd(top_unmatched[,i]))
+    
+    print("Everyone")
+    print(mean(everyone_unmatched[,i]))
+    #print(sd(bottom_unmatched[,i]))
+    #print(wilcox.test(expert_unmatched[,i], everyone_unmatched[,i], alternative = "two.sided")$p.value)
+    print("P-value:")
+    print(t.test(top_matched_unmatched[,i], everyone_unmatched[,i])$p.value)
+    
+    print("Matched:")
+    print("Top")
+    print(mean(top_matched_matched[,i]))
+    #print(sd(top_matched[,i]))
+    
+    print("Everyone")
+    print(mean(everyone_matched[,i]))
+    #print(sd(bottom_matched[,i]))
+    #print(wilcox.test(expert_matched[,i], everyone_matched[,i], alternative = "two.sided")$p.value)
+    
+    print("P-value:")
+    print(t.test(top_matched_matched[,i], everyone_matched[,i])$p.value)
+  }
+  else { # fraction    
+    print("Unmatched:")
+    print("Top")
+    print(length(top_matched_unmatched[,i][top_matched_unmatched[,i] == 1])/dim(top_matched_unmatched)[1])
+    
+    print("Everyone")
+    print(length(everyone_unmatched[,i][everyone_unmatched[,i] == 1])/dim(everyone_unmatched)[1])
+    #print(chisq.test(table(expert_unmatched[,i], everyone_unmatched[,i]))$p.value)
+    
+    print("P-value:")
+    print(t.test(as.numeric(top_matched_unmatched[,i]), as.numeric(everyone_unmatched[,i]))$p.value)
+
+    print("Matched:")
+    print("Top")
+    print(length(top_matched_matched[,i][top_matched_matched[,i] == 1])/dim(top_matched_matched)[1])
+    
+    print("Everyone")
+    print(length(everyone_matched[,i][everyone_matched[,i] == 1])/dim(everyone_matched)[1])
+    #print(chisq.test(table(expert_matched[,i], everyone_matched[,i]))$p.value)
+    
+    print("P-value:")
+    print(t.test(as.numeric(top_matched_matched[,i]), as.numeric(everyone_matched[,i]))$p.value)
+  }, error=function(e){})
+}
+```
+PART 5: COMPARING EVERYONE AND BOTTOM
+```{r}
+##############################
+### Post-Matching Analysis ###
+##############################
+
+print("Preparing post-matching results...")
+
+# Before and After Comparisons
+
+#bottom_matched and everyone_matched have been created in previous parts
+bottom_unmatched <- bottom_features
+everyone_unmatched <- everyone_features
+
+# Pre and Post Matching Standardized Difference in Means
+print("Plotting pre- vs. post-matching SMD values...")
+
+t1 <- cbind(bottom_unmatched, label=1)
+t2 <- cbind(everyone_unmatched, label=0)
+unmatched <- rbind(t1, t2)
+unmatched_tab <- CreateTableOne(vars = colnames(unmatched), strata = "label", data = unmatched, test = FALSE)
+print(unmatched_tab, smd = TRUE)
+
+t1 <- cbind(bottom_matched, label=1)
+t2 <- cbind(everyone_matched, label=0)
+matched <- rbind(t1, t2)
+matched_tab <- CreateTableOne(vars = colnames(matched), strata = "label", data = matched, test = FALSE)
+print(matched_tab, smd = TRUE)
+
+## Construct a data frame containing variable name and SMD from all methods
+data_plot <- data.frame(variable  = names(ExtractSmd(unmatched_tab)),
+                       Unmatched = ExtractSmd(unmatched_tab),
+                       Matched   = ExtractSmd(matched_tab))
+
+## Create long-format data for ggplot2
+data_plot_melt <- melt(data = data_plot,
+                     id.vars = c("variable"),
+                     variable.name = "Method",
+                     value.name = "SMD")
+
+## Order variable names by magnitude of SMD
+varNames <- as.character(data_plot$variable)[order(data_plot$Unmatched)]
+
+## Order factor levels in the same order
+data_plot_melt$variable <- factor(data_plot_melt$variable,
+                                levels = varNames)
+
+## Plot using ggplot2
+plot <- ggplot(data = data_plot_melt[-c(55,110),], mapping = aes(x = variable, y = SMD,
+                                          group = Method, color = Method)) +
+geom_line() +
+geom_point() +
+geom_hline(yintercept = 0.1, color = "black", size = 0.1) +
+coord_flip() +
+theme_bw() + theme(legend.key = element_blank())
+print(plot)
+
+# Pre and Post-Matching P-Values
+print("Printing pre- vs. post-matching p-values...")
+
+for (i in 1:length(bottom_features)) {
+  print(colnames(bottom_matched)[i])
+  
+  tryCatch(if (length(unique(bottom_matched[,i])) > 2) { #mean
+    print("Unmatched:")
+    print("Bottom")
+    print(mean(bottom_unmatched[,i]))
+    #print(sd(top_unmatched[,i]))
+    
+    print("Everyone")
+    print(mean(everyone_unmatched[,i]))
+    #print(sd(bottom_unmatched[,i]))
+    #print(wilcox.test(expert_unmatched[,i], everyone_unmatched[,i], alternative = "two.sided")$p.value)
+    print("P-value:")
+    print(t.test(bottom_unmatched[,i], everyone_unmatched[,i])$p.value)
+    
+    print("Matched:")
+    print("Bottom")
+    print(mean(bottom_matched[,i]))
+    #print(sd(top_matched[,i]))
+    
+    print("Everyone")
+    print(mean(everyone_matched[,i]))
+    #print(sd(bottom_matched[,i]))
+    #print(wilcox.test(expert_matched[,i], everyone_matched[,i], alternative = "two.sided")$p.value)
+    
+    print("P-value:")
+    print(t.test(bottom_matched[,i], everyone_matched[,i])$p.value)
+  }
+  else { # fraction    
+    print("Unmatched:")
+    print("Bottom")
+    print(length(bottom_unmatched[,i][bottom_unmatched[,i] == 1])/dim(bottom_unmatched)[1])
+    
+    print("Everyone")
+    print(length(everyone_unmatched[,i][everyone_unmatched[,i] == 1])/dim(everyone_unmatched)[1])
+    #print(chisq.test(table(expert_unmatched[,i], everyone_unmatched[,i]))$p.value)
+    
+    print("P-value:")
+    print(t.test(as.numeric(bottom_unmatched[,i]), as.numeric(everyone_unmatched[,i]))$p.value)
+
+    print("Matched:")
+    print("Bottom")
+    print(length(bottom_matched[,i][bottom_matched[,i] == 1])/dim(bottom_matched)[1])
+    
+    print("Everyone")
+    print(length(everyone_matched[,i][everyone_matched[,i] == 1])/dim(everyone_matched)[1])
+    #print(chisq.test(table(expert_matched[,i], everyone_matched[,i]))$p.value)
+    
+    print("P-value:")
+    print(t.test(as.numeric(bottom_matched[,i]), as.numeric(everyone_matched[,i]))$p.value)
+  }, error=function(e){})
+}
+```
\ No newline at end of file
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/remap_ids_matching.py b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/remap_ids_matching.py
new file mode 100644
index 00000000..56d9cf1b
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/PropensityScoreMatching/remap_ids_matching.py
@@ -0,0 +1,43 @@
+# create normalized id to original id mapping
+
+# Top vs. Bottom
+id_map = {}
+matched = open("/Users/jwang/Desktop/Results/matched_top_bottom.csv", "rU")
+feature_matrix_10_13 = open("/Users/jwang/Desktop/Results/2010-2013_patient_feature_matrix.csv", "rU")
+
+feature_matrix_10_13.readline()
+for line in feature_matrix_10_13:
+	line = line.strip().split(",")
+	id_map[line[0]] = line[1]
+
+top_f = open("/Users/jwang/Desktop/Results/top_patients_matched_remapped.csv", "w")
+bottom_f = open("/Users/jwang/Desktop/Results/bottom_patients_matched_remapped.csv", "w")
+
+matched.readline()
+for line in matched:
+	line = line.strip().split(",")
+	label = int(line[61]) # column 62
+	if (label == 1): # top
+		top_f.write("{0}\n".format(id_map[line[1]]))
+	elif (label == 0): # bottom
+		bottom_f.write("{0}\n".format(id_map[line[1]]))
+
+top_f.close()
+bottom_f.close()
+matched.close()
+feature_matrix_10_13.close()
+
+# Top vs. Everyone
+matched = open("/Users/jwang/Desktop/Results/matched_top_everyone.csv", "rU")
+everyone_f = open("/Users/jwang/Desktop/Results/everyone_patients_matched_remapped.csv", "w")
+matched.readline()
+for line in matched:
+	line = line.strip().split(",")
+	label = int(line[61]) # column 62
+	if (label == 1): # top*
+		continue
+	elif (label == 0): # everyone
+		everyone_f.write("{0}\n".format(id_map[line[1]]))
+
+matched.close()
+everyone_f.close()
diff --git a/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/README b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/README
new file mode 100644
index 00000000..68b25e7c
--- /dev/null
+++ b/scripts/Archive/HighMortalityVsLowMortalityVsCrowd/README
@@ -0,0 +1,94 @@
+## Extract Patient Encounter Feature Matrix (for both mortality prediction and matching):
+
+1) extract_diagnosis_codes.py
+Extract the top admission diagnosis codes; this enables us to map clinical_item_ids to actual diagnosis descriptions
+
+2) extract_age.py
+Extract the age of each patient; this is directly used as a covariate
+
+3) extract_admission_discharge_flow.py
+Extract the admissions, transfers, and discharges for each patient; this helps us track a patient's flow through the hospital for each of his/her encounters
+
+4) extract_treatment_team.py
+Extract the treatment team responsible for each patient encounter; this is directly used as a covariate
+
+5) extract_last_recorded_admission_diagnosis.py
+Extract admission diagnosis associated with patient's last recorded encounter; this is directly used as a covariate
+
+6) extract_mortality.py
+Extract whether or not a patient died within 30-days of an admission order; this is used as the response variable in our mortality model
+
+7) extract_physician_patient_map.py
+Extract physicians responsible for patients based on H&P note or Discharge Summary note; effectively a physician to patient mapping
+
+8) extract_encounter_data.py
+Extracts initial vital signs, lab tests, Charlson comorbidity indices, and basic demographic data for all patient encounters within a time window (e.g. 2008-2009, 2010-2013)
+
+9) /Users/jwang/Desktop/Results/scripts/reorder_data.py
+Reorders the raw, half-complete feature tables (.tab files) generated by extractData_modified_2.py so that the last recorded patient encounter is appears first (from top to bottom)
+
+10) clean_data.py
+Organizez data from various outputted files to create a coherent a cohere patient feature matrix to be used for mortality prediction and matching; note that we isolate the last recorded encounter for each patient, although we start out with patient data from all encounters
+
+## Build Expected Mortality Predictor:
+
+1) mortality_predictor.Rmd
+Builds model to predict a patient's probability of mortality within 30 days of their last recorded admission order
+
+2) remap_ids_mortality_probs.py
+Remaps R-friendly normalized patient_ids to actual patient_ids recored in the stride/medinfo databases
+
+3) compute_observed_vs_expected_mortality.py						
+Computes the expected and observed dead and survived patient counts for all physicians; outputs +/-log(p-value) scores for all physicians which we use to stratify into top and bottom performing cohorts
+
+## Propensity Score Matching (Before Association Model Training):
+
+1) extract_unmatched_patients.py
+Assemble patient cohorts associated with the top and bottom clinician cohorts; outputs unmatched feature matrices to be fed into matching.Rmd
+
+2) matching.Rmd
+Propensity score matching to balance patient cohorts prior to training the top and bottom patient cohort association models
+
+3) remap_ids_matching.py
+Remaps R-friendly normalized patient_ids to actual patient_ids recored in the stride/medinfo databases; these top and bottom cohort patient_ids can then be fed into AssociationModel.py
+
+## Better Than Expected Evaluation:
+Evaluate assocation models against patient cases in which mortality outcomes turned out better than expected 
+
+1) Extract delta between last admission and mortality date (if present) per patient in 2010-2013
+extract_mortality_unbinned.py
+
+2) Create aggregated patient mortality probability and observed mortality delta spreadsheet
+join_probs_mortality_delta.py
+
+3) Extract "better than expected" patient cases that are not included in balanced patient cohorts inputted for association model training
+extract_better_than_expected_patients.py
+
+4) Extract order set usage instances, items leading up to the instance to query trained association model, and items after the instance for evaluation
+prepare_query_and_verify_items.py
+
+5) Generate raw evaluation statistics, comparing recommended items or order set against verify items
+Make sure to change LocalEnv.py to point to corresponding database.
+For Association Models: evaluate_association_models_recommended_items_against_verify_items.py
+
+For Order Sets: evaluate_ordersets_against_verify_items.py
+
+6) Aggregate raw evaluation statistics in preparation for plotting; output pickle arrays for precision, recall, f1-score, and auroc
+aggregate_evaluation_metrics.py
+
+7) Plot line chart of evaluation statistics
+Better_Than_Expected_Evaluation_Bar_Plot.ipynb
+
+## Miscellaneous
+A) extract_30_day_readmissions.py	
+Extract whether or not a patient was readmitted within 30-days of a discharge order
+
+B) extract_lengths_of_stay.py		
+Extract the length of stay from intial to final discharge for each patient encounter
+
+C) mortality_or_readmission_predictor.Rmd
+Builds model to predict a patient's probability of either being readmitted or dying within 30 days of their last recorded admission order
+
+D) compute_observed_vs_expected_mortalityORreadmission.py	
+Computes the expected and observed dead/readmitted and healthy patient counts for all physicians
+																						
diff --git a/scripts/ICUDNR/RAnalysis/DNR_aug.sas b/scripts/Archive/ICUDNR/RAnalysis/DNR_aug.sas
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/DNR_aug.sas
rename to scripts/Archive/ICUDNR/RAnalysis/DNR_aug.sas
diff --git a/scripts/ICUDNR/RAnalysis/Example.FeatureMatrixProcessing.R b/scripts/Archive/ICUDNR/RAnalysis/Example.FeatureMatrixProcessing.R
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/Example.FeatureMatrixProcessing.R
rename to scripts/Archive/ICUDNR/RAnalysis/Example.FeatureMatrixProcessing.R
diff --git a/scripts/ICUDNR/RAnalysis/Step.10.DataLoading.R b/scripts/Archive/ICUDNR/RAnalysis/Step.10.DataLoading.R
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/Step.10.DataLoading.R
rename to scripts/Archive/ICUDNR/RAnalysis/Step.10.DataLoading.R
diff --git a/scripts/ICUDNR/RAnalysis/Step.20.Demographics.R b/scripts/Archive/ICUDNR/RAnalysis/Step.20.Demographics.R
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/Step.20.Demographics.R
rename to scripts/Archive/ICUDNR/RAnalysis/Step.20.Demographics.R
diff --git a/scripts/ICUDNR/RAnalysis/Step.30.Imputation-Amelia.R b/scripts/Archive/ICUDNR/RAnalysis/Step.30.Imputation-Amelia.R
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/Step.30.Imputation-Amelia.R
rename to scripts/Archive/ICUDNR/RAnalysis/Step.30.Imputation-Amelia.R
diff --git a/scripts/ICUDNR/RAnalysis/Step.40.CoxRegression.R b/scripts/Archive/ICUDNR/RAnalysis/Step.40.CoxRegression.R
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/Step.40.CoxRegression.R
rename to scripts/Archive/ICUDNR/RAnalysis/Step.40.CoxRegression.R
diff --git a/scripts/ICUDNR/RAnalysis/timedep_cox.R b/scripts/Archive/ICUDNR/RAnalysis/timedep_cox.R
similarity index 100%
rename from scripts/ICUDNR/RAnalysis/timedep_cox.R
rename to scripts/Archive/ICUDNR/RAnalysis/timedep_cox.R
diff --git a/scripts/ICUDNR/extractData.py b/scripts/Archive/ICUDNR/extractData.py
similarity index 95%
rename from scripts/ICUDNR/extractData.py
rename to scripts/Archive/ICUDNR/extractData.py
index d139a5ef..2efab8e9 100644
--- a/scripts/ICUDNR/extractData.py
+++ b/scripts/Archive/ICUDNR/extractData.py
@@ -1,6 +1,6 @@
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import datetime, timedelta;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
@@ -419,7 +419,7 @@ def main(argv):
         if disease not in icd9prefixesByDisease:
             icd9prefixesByDisease[disease] = list();
         icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
-    for disease, icd9prefixes in icd9prefixesByDisease.iteritems():
+    for disease, icd9prefixes in icd9prefixesByDisease.items():
         disease = disease.translate(None," ()-/");   # Strip off punctuation
         extractor.queryClinicalItemsByName(icd9prefixes, patientById, stdOpen("Charlson."+disease+".tab","w"), operator="~*");
     
@@ -430,11 +430,11 @@ def main(argv):
         if category not in teamNameByCategory:
             teamNameByCategory[category] = list();
         teamNameByCategory[category].append(teamName);
-    for category, teamNames in teamNameByCategory.iteritems():
+    for category, teamNames in teamNameByCategory.items():
         extractor.queryClinicalItemsByName(teamNames, patientById, stdOpen("TT."+category+".tab","w"), col="description");
     
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 def queryPatients(outputFile):
     log.info("Select patients with any ICU life support orders and follow contiguous date trail for apparent hospitalization (long query >20 min)...");
@@ -487,7 +487,7 @@ def queryPatients(outputFile):
             row = cursor.fetchone();
 
         # Second query phase to link to encounter information (e.g., insurance, admitting vital signs)
-        encounterIds = columnFromModelList(patientById.itervalues(), "encounter_id");
+        encounterIds = columnFromModelList(iter(patientById.values()), "encounter_id");
         query = SQLQuery();
         query.addSelect("pat_id");
         query.addSelect("pat_enc_csn_id");
@@ -520,7 +520,7 @@ def queryPatients(outputFile):
         
         # Drop results as tab-delimited text output
         formatter = TextResultsFormatter(outputFile);
-        formatter.formatResultDicts(patientById.itervalues(), addHeaderRow=True);
+        formatter.formatResultDicts(iter(patientById.values()), addHeaderRow=True);
 
         return patientById;    
     finally:
diff --git a/scripts/ICUDNR/formatData.py b/scripts/Archive/ICUDNR/formatData.py
similarity index 95%
rename from scripts/ICUDNR/formatData.py
rename to scripts/Archive/ICUDNR/formatData.py
index 980affff..9886a4dc 100644
--- a/scripts/ICUDNR/formatData.py
+++ b/scripts/Archive/ICUDNR/formatData.py
@@ -2,7 +2,7 @@
 import time;
 import numpy as np;
 from datetime import datetime, timedelta;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db import DBUtil;
@@ -57,7 +57,7 @@ def main(argv=None):
     patientById = extractor.parsePatientFile(stdOpen("patients.tab"), colNames);
 
     log.info("Expand to index dates based start and end dates");
-    patientByIndexTimeById = extractor.generateDateRangeIndexTimes("firstLifeSupportDate","lastContiguousDate", patientById.values(), colNames);
+    patientByIndexTimeById = extractor.generateDateRangeIndexTimes("firstLifeSupportDate","lastContiguousDate", list(patientById.values()), colNames);
 
     log.info("Populate flowsheet summary statistics");
     flowsheetByNameByPatientId = extractor.parseFlowsheetFile(stdOpen("Flowsheet.tab.gz"));
@@ -107,12 +107,12 @@ def main(argv=None):
     log.info("Output feature matrix file with row per patient day");
     featureMatrixFile = stdOpen("featureMatrix.ICUDNR.tab.gz","w");
     formatter = TextResultsFormatter(featureMatrixFile);
-    for patientId, patientByIndexTime in patientByIndexTimeById.iteritems():
-        patientResults = patientByIndexTime.values();
+    for patientId, patientByIndexTime in patientByIndexTimeById.items():
+        patientResults = list(patientByIndexTime.values());
         formatter.formatResultDicts(patientResults, colNames, addHeaderRow=True);
 
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 if __name__ == "__main__":
     main(sys.argv);
\ No newline at end of file
diff --git a/scripts/ICUDNR/simulateData.py b/scripts/Archive/ICUDNR/simulateData.py
similarity index 95%
rename from scripts/ICUDNR/simulateData.py
rename to scripts/Archive/ICUDNR/simulateData.py
index 743a70d9..81589fbd 100644
--- a/scripts/ICUDNR/simulateData.py
+++ b/scripts/Archive/ICUDNR/simulateData.py
@@ -74,7 +74,7 @@
 durationRange = [1,20];
 
 prog = ProgressDots(total=nPatients);
-for iPatient in xrange(nPatients):
+for iPatient in range(nPatients):
 	rowData = \
 		{
 			"patient_id":iPatient,
@@ -120,11 +120,11 @@
 
 	# Pick a single race label
 	raceRandom = random.random();
-	for label, raceRange in raceRangesByLabel.iteritems():
+	for label, raceRange in raceRangesByLabel.items():
 		rowData[label] = (raceRandom >= raceRange[0] and raceRandom < raceRange[-1])+0;
 
 	# Fill in lab values
-	for label, labRange in labRangeByLabel.iteritems():
+	for label, labRange in labRangeByLabel.items():
 		if (random.random() < labMissingRate):
 			rowData[label] = "NA";
 		else:
@@ -149,7 +149,7 @@
 	# Simulate dnrEventRate per day
 	dnrOccurs = False;
 	dnrDay = None;
-	for iDay in xrange(1,lengthOfStay):
+	for iDay in range(1,lengthOfStay):
 		if (random.random() < dailyDNRRate):
 			dnrDay = iDay;
 			dnrOccurs = True;
@@ -157,7 +157,7 @@
 	rowData["AnyDNRatEnd"] = dnrOccurs + 0;
 
 	# Generate daily data
-	for iDay in xrange(lengthOfStay):
+	for iDay in range(lengthOfStay):
 		rowData["curr_day"] = rowData["start"] = iDay;
 		rowData["end"] = iDay + 1;
 		rowData["timeUntilNoMoreData"] = lengthOfStay - rowData["start"];
diff --git a/scripts/ICUDNR/summaryStats.py b/scripts/Archive/ICUDNR/summaryStats.py
similarity index 95%
rename from scripts/ICUDNR/summaryStats.py
rename to scripts/Archive/ICUDNR/summaryStats.py
index 43cc5f25..e4da8304 100644
--- a/scripts/ICUDNR/summaryStats.py
+++ b/scripts/Archive/ICUDNR/summaryStats.py
@@ -15,10 +15,10 @@
 #conn = sqlite3.connect("featureMatrix.ICUDNR.sqlite");
 #df2sql(df,conn=conn);
 
-print >> sys.stderr, "Prepare connection to SQLite database. Skip directly to this step if file previously generated"
+print("Prepare connection to SQLite database. Skip directly to this step if file previously generated", file=sys.stderr)
 conn = sqlite3.connect("featureMatrix.ICUDNR.sqlite");
 
-print >> sys.stderr, "Query Summary Stats"
+print("Query Summary Stats", file=sys.stderr)
 queryCaptionList = \
 	[	("select count(distinct patient_id) from data", "Distinct Patients"),
 		("select count(*) from data where firstLifeSupportDate = index_time", "First Life Support Day Records"),
@@ -79,7 +79,7 @@
 
 	];
 for query, caption in queryCaptionList:
-	print "%s\t%s\t%s" % (DBUtil.execute(query,conn=conn)[0][0], caption, query);
+	print("%s\t%s\t%s" % (DBUtil.execute(query,conn=conn)[0][0], caption, query));
 
 
 """
diff --git a/scripts/Archive/LabTestAnalysis/__init__.py b/scripts/Archive/LabTestAnalysis/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/Archive/LabTestAnalysis/images/sb_pipeline.png b/scripts/Archive/LabTestAnalysis/images/sb_pipeline.png
new file mode 100644
index 00000000..9995c1c0
Binary files /dev/null and b/scripts/Archive/LabTestAnalysis/images/sb_pipeline.png differ
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Appendix Figures.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Appendix Figures.ipynb
new file mode 100644
index 00000000..6a67aae1
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Appendix Figures.ipynb	
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline \\\n",
+    "        import NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import stats_utils\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from medinfo.ml.SupervisedClassifier import SupervisedClassifier\n",
+    "\n",
+    "all_algs = SupervisedClassifier.SUPPORTED_ALGORITHMS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_label=\"all_labs\"\n",
+    "targeted_PPV=0.95\n",
+    "scale_by='enc'\n",
+    "use_cached_fig_data=True\n",
+    "inverse01=True\n",
+    "\n",
+    "data_source = 'UCSF'\n",
+    "lab_type = 'panel'\n",
+    "curr_version = '10000-episodes-lastnormal'\n",
+    "\n",
+    "all_labs = stats_utils.get_all_labs(data_source, lab_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_folderpath = os.path.join(stats_utils.main_folder, 'lab_statistics/')\n",
+    "ml_folderpath = os.path.join(stats_utils.main_folder, 'machine_learning')\n",
+    "\n",
+    "import LocalEnv\n",
+    "statsByDataSet_foldername = 'data-%s-%s-%s' % (data_source, lab_type, curr_version)\n",
+    "statsByDataSet_folderpath = os.path.join(stats_folderpath, statsByDataSet_foldername)\n",
+    "\n",
+    "dataset_foldername = 'data-%s-%s-%s'%(data_source, lab_type, curr_version)\n",
+    "labStats_folderpath = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/lab_statistics')\n",
+    "statsByLab_folderpath = os.path.join(labStats_folderpath, dataset_foldername)\n",
+    "ml_folderpath = statsByLab_folderpath.replace(\"lab_statistics\", \"machine_learning\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reload(stats_utils)\n",
+    "def draw__stats_Curves(statsByLab_folderpath, labs, curve_type=\"ROC\", algs=['random-forest'], result_label=None,\n",
+    "                       include_baseline=True, inverse01=False):\n",
+    "    result_foldername = 'Fig_stats_Curves'\n",
+    "    if result_label:\n",
+    "        result_foldername += '_' + result_label\n",
+    "    result_folderpath = os.path.join(statsByLab_folderpath, result_foldername)\n",
+    "    if not os.path.exists(result_folderpath):\n",
+    "        os.mkdir(result_folderpath)\n",
+    "\n",
+    "    result_figname = '%s_%s_%s.png'%(data_source, lab_type, curve_type)\n",
+    "    result_figpath = os.path.join(result_folderpath, result_figname)\n",
+    "\n",
+    "    result_tablename = '%s_%s_%s.csv'%(data_source, lab_type, curve_type)\n",
+    "    result_tablepath = os.path.join(result_folderpath, result_tablename)\n",
+    "\n",
+    "    num_labs = len(labs)\n",
+    "    # fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "    \n",
+    "    if lab_type == 'component':\n",
+    "        col_num=5\n",
+    "    else:\n",
+    "        col_num=7\n",
+    "\n",
+    "    row, col, i_s, j_s = stats_utils.prepare_subfigs(num_labs, col=5) #7\n",
+    "\n",
+    "    scores_base = []\n",
+    "    scores_best = []\n",
+    "    p_vals = []\n",
+    "\n",
+    "    scores_diffs = {}\n",
+    "\n",
+    "    lab_descriptions = stats_utils.get_lab_descriptions(lab_type=lab_type,\n",
+    "                                                        data_source=data_source,\n",
+    "                                                        line_break_at=18)\n",
+    "    for ind, lab in enumerate(labs):\n",
+    "\n",
+    "        '''\n",
+    "        Getting p-values is slow\n",
+    "        '''\n",
+    "\n",
+    "        xVal_base, yVal_base, score_base, xVal_best, yVal_best, score_best, p_val \\\n",
+    "            = stats_utils.get_curve_onelab(lab,\n",
+    "                                           all_algs=algs,\n",
+    "                                           data_folder=statsByLab_folderpath.replace(\"lab_statistics\", \"machine_learning\"),\n",
+    "                                           curve_type=curve_type,\n",
+    "                                           get_pval=True)\n",
+    "        # print lab, p_val\n",
+    "        scores_base.append(score_base)\n",
+    "        scores_best.append(score_best)\n",
+    "        p_vals.append(p_val)\n",
+    "\n",
+    "        scores_diffs[lab] = score_best - score_base\n",
+    "\n",
+    "        i, j = i_s[ind], j_s[ind]\n",
+    "        plt.subplot2grid((row, col), (i, j))\n",
+    "\n",
+    "        dash_num = 20\n",
+    "        plt.plot(np.linspace(0, 1, num=dash_num), np.linspace(0, 1, num=dash_num), color='lightblue',\n",
+    "                 linestyle='--')\n",
+    "        \n",
+    "        \n",
+    "        lab_descrip = lab_descriptions.get(lab, lab)\n",
+    "\n",
+    "        if not inverse01:\n",
+    "            \n",
+    "            plt.plot(xVal_best, yVal_best, label='%0.2f' % (score_best), color='orange')\n",
+    "            if include_baseline:\n",
+    "                plt.plot(xVal_base, yVal_base, label='%0.2f' % (score_base))\n",
+    "        else:\n",
+    "            \n",
+    "            label_best = '%0.2f' % (score_best)\n",
+    "#             if sig_dict[lab]:\n",
+    "            label_best = label_best + stats_utils.map_pval_significance(p_val) # significant\n",
+    "            plt.plot(1-yVal_best, 1-xVal_best, label=label_best, color='orange')\n",
+    "            if include_baseline:\n",
+    "                plt.plot(1 - yVal_base, 1 - xVal_base, label='%0.2f' % (score_base))\n",
+    "\n",
+    "        plt.xlim([0,1])\n",
+    "        plt.ylim([0,1])\n",
+    "        plt.xticks([])\n",
+    "        plt.yticks([])\n",
+    "\n",
+    "        \n",
+    "        if data_source == 'UCSF' and ('\\n' not in lab_descrip):\n",
+    "            lab_descrip = lab_descrip[:18] + '\\n' + lab_descrip[18:]\n",
+    "        plt.xlabel(lab_descrip)\n",
+    "        plt.legend()\n",
+    "\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(result_figpath)\n",
+    "\n",
+    "    measures = {'ROC': 'AUC (Area Under Curve)', 'PRC': 'APS (Average Precision Score)'}\n",
+    "    avg_base, avg_best = np.mean(scores_base), np.mean(scores_best)\n",
+    "    print \"Average %s among %i labs: %.3f baseline, %.3f bestalg (an improvement of %.3f).\" \\\n",
+    "          % (measures[curve_type], len(scores_base), avg_base, avg_best, avg_best - avg_base)\n",
+    "\n",
+    "    df_output_table = pd.DataFrame({'lab':labs,\n",
+    "                                    curve_type+' benchmark':scores_base,\n",
+    "                                    curve_type + ' ML model':scores_best,\n",
+    "                                    curve_type + ' p value':p_vals\n",
+    "                       })\n",
+    "    df_output_table['lab'] = df_output_table['lab'].apply(lambda x: lab_descriptions.get(x,x)) #\n",
+    "    df_output_table[curve_type + ' significance'] = df_output_table[curve_type + ' p value'].apply(lambda x: stats_utils.map_pval_significance(x))\n",
+    "    df_output_table[['lab',curve_type+' benchmark',curve_type + ' ML model',curve_type + ' p value',curve_type + ' significance']]\\\n",
+    "        .to_csv(result_tablepath, index=False, float_format=\"%.2f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lab_set, set_label = all_labs, 'all_labs'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average AUC (Area Under Curve) among 13 labs: 0.783 baseline, 0.849 bestalg (an improvement of 0.066).\n"
+     ]
+    }
+   ],
+   "source": [
+    "draw__stats_Curves(statsByDataSet_folderpath, lab_set, curve_type=\"ROC\", algs=all_algs,\n",
+    "                                   result_label=set_label, inverse01=inverse01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Appendix Tables.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Appendix Tables.ipynb
new file mode 100644
index 00000000..7149cc8d
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Appendix Tables.ipynb	
@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "stats_utils.py:111: UserWarning: \n",
+      "This call to matplotlib.use() has no effect because the backend has already\n",
+      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
+      "or matplotlib.backends is imported for the first time.\n",
+      "\n",
+      "The backend was *originally* set to 'agg' by the following code:\n",
+      "  File \"/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py\", line 162, in _run_module_as_main\n",
+      "    \"__main__\", fname, loader, pkg_name)\n",
+      "  File \"/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py\", line 72, in _run_code\n",
+      "    exec code in run_globals\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
+      "    app.launch_new_instance()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
+      "    app.start()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelapp.py\", line 499, in start\n",
+      "    self.io_loop.start()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/tornado/ioloop.py\", line 1073, in start\n",
+      "    handler_func(fd_obj, events)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n",
+      "    return fn(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 456, in _handle_events\n",
+      "    self._handle_recv()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 486, in _handle_recv\n",
+      "    self._run_callback(callback, msg)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 438, in _run_callback\n",
+      "    callback(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n",
+      "    return fn(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
+      "    return self.dispatch_shell(stream, msg)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
+      "    handler(stream, idents, msg)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
+      "    user_expressions, allow_stdin)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
+      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
+      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2714, in run_cell\n",
+      "    interactivity=interactivity, compiler=compiler, result=result)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2818, in run_ast_nodes\n",
+      "    if self.run_code(code, result):\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2878, in run_code\n",
+      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
+      "  File \"<ipython-input-1-d11b99677bd8>\", line 1, in <module>\n",
+      "    from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline         import NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+      "  File \"/Users/songxu/healthrex/CDSS/scripts/LabTestAnalysis/machine_learning/LabNormalityPredictionPipeline.py\", line 20, in <module>\n",
+      "    from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline\n",
+      "  File \"/Users/songxu/healthrex/CDSS/medinfo/ml/SupervisedLearningPipeline.py\", line 35, in <module>\n",
+      "    from medinfo.ml.ClassifierAnalyzer import ClassifierAnalyzer\n",
+      "  File \"/Users/songxu/healthrex/CDSS/medinfo/ml/ClassifierAnalyzer.py\", line 9, in <module>\n",
+      "    import matplotlib.pyplot as plt\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/matplotlib/pyplot.py\", line 71, in <module>\n",
+      "    from matplotlib.backends import pylab_setup\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/matplotlib/backends/__init__.py\", line 17, in <module>\n",
+      "    line for line in traceback.format_stack()\n",
+      "\n",
+      "\n",
+      "  matplotlib.use('TkAgg')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline \\\n",
+    "        import NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "import stats_utils\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_label=\"all_labs\"\n",
+    "targeted_PPV=0.95\n",
+    "scale_by='enc'\n",
+    "inverse01=True\n",
+    "\n",
+    "data_source = 'Stanford'\n",
+    "lab_type = 'panel'\n",
+    "curr_version = '10000-episodes-lastnormal'\n",
+    "\n",
+    "all_labs = stats_utils.get_all_labs(data_source, lab_type)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_folderpath = os.path.join(stats_utils.main_folder, 'lab_statistics/')\n",
+    "ml_folderpath = os.path.join(stats_utils.main_folder, 'machine_learning')\n",
+    "\n",
+    "import LocalEnv\n",
+    "statsByDataSet_foldername = 'data-%s-%s-%s' % (data_source, lab_type, curr_version)\n",
+    "statsByDataSet_folderpath = os.path.join(stats_folderpath, statsByDataSet_foldername)\n",
+    "\n",
+    "dataset_foldername = 'data-%s-%s-%s'%(data_source, lab_type, curr_version)\n",
+    "labStats_folderpath = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/lab_statistics')\n",
+    "statsByLab_folderpath = os.path.join(labStats_folderpath, dataset_foldername)\n",
+    "ml_folderpath = statsByLab_folderpath.replace(\"lab_statistics\", \"machine_learning\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw__Diagnostic_Metrics(result_label='all_labs',\n",
+    "                            targeted_PPV=0.95, scale_by=None,\n",
+    "                             inverse01=False):\n",
+    "    '''\n",
+    "    Drawing Figure 3 in the main text.\n",
+    "\n",
+    "    :return:\n",
+    "    '''\n",
+    "    labs = all_labs\n",
+    "#     print 'draw__Diagnostic_Metrics for', labs, ' with label %s...'%result_label\n",
+    "    \n",
+    "    labs_stats_filepath = os.path.join(statsByLab_folderpath, 'summary-stats-bestalg-fixTrainPPV.csv')\n",
+    "\n",
+    "    df = pd.read_csv(labs_stats_filepath, keep_default_na=False)\n",
+    "\n",
+    "    # df = df[df['fixTrainPPV'] == targeted_PPV]\n",
+    "\n",
+    "    df = df[df['lab'].isin(labs)]\n",
+    "\n",
+    "    cached_foldername = 'Fig3_Diagnostic_Metrics/'\n",
+    "    cached_folderpath = os.path.join(os.path.join(statsByLab_folderpath, cached_foldername))\n",
+    "\n",
+    "    cached_tablename = 'Diagnostic_Metrics_%ss_PPV_%.2f__%s.csv'%(lab_type, targeted_PPV, result_label)\n",
+    "    cached_tablepath = os.path.join(cached_folderpath, cached_tablename)\n",
+    "\n",
+    "    cached_figurename = 'Diagnostic_Metrics_%ss_PPV_%.2f_ind__%s.png'%(lab_type, targeted_PPV, result_label)\n",
+    "    cached_figurepath = os.path.join(cached_folderpath, cached_figurename)\n",
+    "\n",
+    "    if not os.path.exists(cached_folderpath):\n",
+    "        os.mkdir(cached_folderpath)\n",
+    "\n",
+    "    \n",
+    "    if data_source == 'Stanford':\n",
+    "        # if 'total_vol' not in df.columns.values.tolist():\n",
+    "        # Stanford data, scaled by vol\n",
+    "        df['total_vol'] = df['total_vol_20140701_20170701']\n",
+    "\n",
+    "    elif data_source == 'UCSF':\n",
+    "        import stats_database\n",
+    "        if lab_type == 'panel':\n",
+    "            ucsf_lab_cnt = dict(stats_database.UCSF_PANELS_AND_COUNTS)\n",
+    "\n",
+    "        elif lab_type == 'component':\n",
+    "            ucsf_lab_cnt = stats_database.UCSF_COMPONENT_TO_COUNT #dict(stats_database.UCSF_COMPONENTSS_AND_COUNTS)\n",
+    "        df['total_vol'] = df['lab'].apply(lambda x: ucsf_lab_cnt[x])\n",
+    "    elif data_source == 'UMich' and lab_type=='component' and result_label=='important_components':\n",
+    "        df = df[df['lab'].isin(stats_utils.umich_lab_cnt)]\n",
+    "\n",
+    "\n",
+    "        df['total_vol'] = df['lab'].apply(lambda x: stats_utils.umich_lab_cnt[x])\n",
+    "    else:\n",
+    "        df['total_vol'] = 1\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # TODO: use fractions in the original file!\n",
+    "    df['all_instance'] = df['TP'] + df['FP'] + df['TN'] + df['FN']\n",
+    "\n",
+    "    for cnt_type in ['TP', 'FP', 'TN', 'FN']:\n",
+    "        df[cnt_type] = df[cnt_type]/df['all_instance']\n",
+    "\n",
+    "\n",
+    "    df['all_positive'] = df['TP'] + df['FP']\n",
+    "\n",
+    "    # print df[['all_positive', 'total_vol']]\n",
+    "    df['predicted_normal_vol'] = df['all_positive'] * df['total_vol']\n",
+    "\n",
+    "    if data_source == 'Stanford':\n",
+    "        df = df.sort_values('total_vol', ascending=False)\n",
+    "\n",
+    "    df['all_negative'] = df['TN'] + df['FN']\n",
+    "\n",
+    "    df['TN'] = -df['TN']\n",
+    "    df['all_negative'] = -df['all_negative']\n",
+    "\n",
+    "    df_toshow = df.copy().drop_duplicates()\n",
+    "\n",
+    "    lab_descriptions = stats_utils.get_lab_descriptions(data_source=data_source,#'Stanford',\n",
+    "                                                             lab_type=lab_type)\n",
+    "    lab_descriptions['LABMB'] = 'CK, MB'\n",
+    "    lab_descriptions['LABUOSM'] = 'Osmolality, Urine'\n",
+    "    lab_descriptions['LABFT4'] = 'T4, FREE'\n",
+    "    \n",
+    "    df_toshow['Lab Test'] = df_toshow['lab'].apply(lambda x:lab_descriptions.get(x,x))\n",
+    "    df_toshow['TN'] = -df_toshow['TN']\n",
+    "\n",
+    "    df_toshow['Prev'] = df_toshow['TN'] + df_toshow['FP']\n",
+    "    df_toshow = df_toshow.rename(columns={\n",
+    "        'num_test_episodes':'Count',\n",
+    "                                        'TP':'TP',\n",
+    "                                          'FP':'FP',\n",
+    "                                          'TN':'TN',\n",
+    "                                          'FN':'FN',\n",
+    "                                          'sens':'sens',\n",
+    "                                          'spec':'spec',\n",
+    "                                          'LR_p':'LR+', 'LR_n':'LR-'})\n",
+    "\n",
+    "    df_toshow['AUC'] = df_toshow['AUC'].apply(lambda x: '%.2f'%(x))\n",
+    "    \n",
+    "    df_toshow['AUC_baseline'] = df_toshow['AUC_baseline'].apply(lambda x: '%.2f'%(x))\n",
+    "    \n",
+    "    \n",
+    "    def format_AUC_CI(AUC_CI):\n",
+    "        return '['+ ', '.join(['%.2f'%float(x.strip()) for x in AUC_CI[1:-1].split(',')]) + ']'\n",
+    "    df_toshow['AUC_95%_CI'] = df_toshow['AUC_95%_CI'].apply(lambda x: format_AUC_CI(x))\n",
+    "    \n",
+    "    def is_significant(row):\n",
+    "        left, right = ['%.2f'%float(x.strip()) for x in row['AUC_95%_CI'][1:-1].split(',')]\n",
+    "        if left <= row['AUC_baseline'] <= right:\n",
+    "            return False\n",
+    "        else:\n",
+    "            return True\n",
+    "#     df_toshow['is_significant'] = df_toshow.apply(\n",
+    "#         is_significant, axis=1)\n",
+    "\n",
+    "    df_toshow['NPV'] = df_toshow['NPV'].apply(lambda x: stats_utils.convert_floatstr2percentage(x))\n",
+    "\n",
+    "    numeric_cols = ['Prev', 'PPV', 'TP', 'FP', 'TN', 'FN', 'sens', 'spec']\n",
+    "    for numeric_col in numeric_cols:\n",
+    "        if df_toshow[numeric_col].dtype != type(0.1):\n",
+    "            df_toshow[numeric_col] = df_toshow[numeric_col].apply(lambda x: stats_utils.convert_floatstr2percentage(x))\n",
+    "        else:\n",
+    "            df_toshow[numeric_col] = df_toshow[numeric_col].apply(lambda x: stats_utils.convert_floatnum2percentage(x))\n",
+    "\n",
+    "    df_toshow['LR+'] = df_toshow['LR+'].apply(lambda x: stats_utils.convert_floatstr2num(x))\n",
+    "    df_toshow['LR-'] = df_toshow['LR-'].apply(lambda x: stats_utils.convert_floatstr2num(x))\n",
+    "\n",
+    "    df_toshow['Vol'] = (df_toshow['total_vol'] / float(stats_utils.NUM_DISTINCT_ENCS / 1000.)).apply(\n",
+    "        lambda x: int(round(x)))\n",
+    "    \n",
+    "    def fix_number(x):\n",
+    "        fix_dict = {4251:4246,\n",
+    "                   2246:2244,\n",
+    "                   2122:2120,\n",
+    "                   1473:1471,\n",
+    "                   1231:1230,\n",
+    "                   1198:1197,\n",
+    "                   753:752,\n",
+    "                   534:534,\n",
+    "                   456:455,\n",
+    "                   401:400,\n",
+    "                   372:371,\n",
+    "                   361:361,}\n",
+    "        return fix_dict.get(x,x)\n",
+    "    df_toshow['Vol'] = df_toshow['Vol'].apply(lambda x: fix_number(x))\n",
+    "\n",
+    "    if data_source == 'Stanford' and lab_type=='panel':\n",
+    "\n",
+    "        df_toshow.loc[df_toshow['Lab Test']=='Sodium', 'chargemaster'] = 219\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'Specific Gravity', 'medicare'] = 3.28\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'Sepsis Protocol Lactate', 'medicare'] = 11.87\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'LDH Total', 'medicare'] = 6.71\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'Lactate', 'medicare'] = 11.87\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'Urxinalysis', 'medicare'] = 2.67\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'Calcium Ionized', 'medicare'] = 13.73\n",
+    "        df_toshow.loc[df_toshow['Lab Test'] == 'Heparin', 'medicare'] = 16.16\n",
+    "\n",
+    "        # print df_toshow\n",
+    "        df_toshow = df_toshow.rename(columns={'medicare': 'Medicare', 'chargemaster': 'Chargemaster'})\n",
+    "\n",
+    "        df_toshow['Medicare'] = df_toshow['Medicare'].apply(lambda x: '$%s' % x if x != '' else '-')\n",
+    "        df_toshow['Chargemaster'] = df_toshow['Chargemaster'].apply(lambda x: '$%s' % str(x) if x != '' else '-')\n",
+    "\n",
+    "        cols_to_show = ['Lab Test', 'Vol', 'AUC'] \\\n",
+    "                       + numeric_cols\n",
+    "                       #+ ['Medicare', 'Chargemaster'] #+ ['LR+', 'LR-'] \\\n",
+    "        labs_to_show = ['Magnesium', 'Prothrombin Time', 'Phosphorus', 'Partial Thromboplastin Time',\n",
+    "                        'Lactate', 'Calcium Ionized', 'Potassium', 'Troponin I', 'LDH Total',\n",
+    "                        'Heparin', 'Urinalysis', # too few positives\n",
+    "                        'Blood Culture (Aerobic & Anaerobic)',\n",
+    "                        'Blood Culture (2 Aerobic)', 'Sodium', 'Lidocaine', 'Hematocrit', 'Urine Culture',\n",
+    "                        'Urinalysis With Microscopic', 'Uric Acid', 'Hemoglobin A1c'\n",
+    "                        ]\n",
+    "    else:\n",
+    "        cols_to_show = ['Lab Test', 'AUC'] + numeric_cols #+ ['LR+', 'LR-']\n",
+    "\n",
+    "    if inverse01:\n",
+    "        df_toshow = df_toshow.rename(columns={'PPV': 'NPV',\n",
+    "                                              'NPV': 'PPV',\n",
+    "                                              'TP': 'TN',\n",
+    "                                              'FP': 'FN',\n",
+    "                                              'TN': 'TP',\n",
+    "                                              'FN': 'FP',\n",
+    "                                              'sens':'Spec',\n",
+    "                                              'spec':'Sens',\n",
+    "                                              'AUC_95%_CI':'95% CI',\n",
+    "                                              'AUC_baseline':'baseline',\n",
+    "                                              'best_alg':'Best Alg',\n",
+    "                                              'fixTrainPPV':'Target NPV'}) # #'AUC':'C-stat',\n",
+    "\n",
+    "        rename_alg = {'xgb': 'xgboost', 'nn': 'neural-nets'}\n",
+    "        df_toshow['Best Alg'] = df_toshow['Best Alg'].apply(lambda x: rename_alg.get(x, x))\n",
+    "        cols_to_show = [#'lab', \n",
+    "                        'Lab Test', 'Vol', 'AUC', '95% CI',  \n",
+    "                        #'baseline', 'is_significant',\n",
+    "                        'Prev', #'Target NPV',\n",
+    "                        'NPV', 'PPV', 'Sens', 'Spec', 'TN', \n",
+    "                        'FN', 'TP', 'FP'] #['Lab Test', 'Vol', 'C-stat', 'Prev', 'NPV', 'PPV', 'Sens', 'Spec', 'TN', 'FN', 'TP', 'FP']\n",
+    "\n",
+    "#         else:\n",
+    "#             cols_to_show = ['Lab Test', 'Best Alg', 'AUC', '95% CI', 'Prev', \n",
+    "#                             'Target NPV', 'NPV', 'PPV', 'Sens',\n",
+    "#                             'Spec', 'TN', 'FN', 'TP', 'FP']\n",
+    "        if data_source == 'Stanford':\n",
+    "            cols_all = ['Lab Test', 'Vol', 'AUC', '95% CI', 'Prev', 'Target NPV',\n",
+    "                       'NPV', 'PPV', 'Sens', 'Spec', 'TN', 'FN', 'TP', 'FP']\n",
+    "        else:\n",
+    "            cols_all = ['Lab Test', 'AUC', '95% CI', 'Prev', 'Target NPV',\n",
+    "                       'NPV', 'PPV', 'Sens', 'Spec', 'TN', 'FN', 'TP', 'FP']\n",
+    "\n",
+    "    if data_source == 'Stanford':\n",
+    "        if lab_type == 'panel':\n",
+    "            \n",
+    "            labs_to_show = ['LABMGN', 'LABPT', 'LABPHOS', 'LABPTT', 'LABLACWB', 'LABCAI', 'LABK',\n",
+    "                            'LABTNI', 'LABLDH', 'LABBLC', 'LABBLC2', 'LABNA'#, 'LABHCTX', 'LABURNC'\n",
+    "                           ]\n",
+    "            #['LABMGN', 'LABPHOS', 'LABBLC', 'LABBLC2', 'LABUA', 'LABUAPRN', 'LABLACWB', 'LABCAI'] # suggested by Jason Hom\n",
+    "            df_finalshow = df_toshow[(df_toshow['lab'].isin(labs_to_show)) & (df_toshow['Target NPV']==0.95)]\\\n",
+    "            [cols_to_show]\n",
+    "        else:\n",
+    "            labs_to_show = ['WBC', 'HGB', 'NA', 'K', 'CR']\n",
+    "            df_finalshow = df_toshow[df_toshow['lab'].isin(labs_to_show)][cols_to_show]\n",
+    "\n",
+    "        df_finalshow.to_csv(\n",
+    "                    cached_tablepath.replace('.csv', '_finalshow.csv'), index=False)\n",
+    "\n",
+    "#     df_toshow[cols_to_show].iloc[:20].to_csv(cached_tablepath.replace('.csv','_toshow.csv'), index=False) #.sort_values('total_vol', ascending=False)\n",
+    "\n",
+    "    if data_source == 'Stanford' and lab_type=='panel':\n",
+    "        cols_to_show += ['Medicare', 'Chargemaster']\n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    df_toshow[['Lab Test', 'Medicare', 'Chargemaster']].drop_duplicates().to_csv(cached_tablepath.replace('.csv', '_fee.csv'), index=False)\n",
+    "    df_toshow[cols_all].to_csv(cached_tablepath.replace('.csv', '_full.csv'), index=False)\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "statsByDataSet_folderpath = '.'\n",
+    "draw__Diagnostic_Metrics(result_label=result_label, targeted_PPV=0.95, scale_by='enc', inverse01=inverse01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 1.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 1.ipynb
new file mode 100644
index 00000000..bdeddde9
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 1.ipynb	
@@ -0,0 +1,563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Figure 1\n",
+    "\n",
+    "Normality scores, decision threshold, and receiver operating characteristic (ROC) curve.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from JAMA_config import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lab = 'LABLDH'\n",
+    "data_source = 'Stanford'\n",
+    "lab_type = 'panel'\n",
+    "score_thres = 0.756\n",
+    "include_threshold_colors = True\n",
+    "\n",
+    "dash_num = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataSet_foldername = 'data-%s-%s-%s'%(data_source, \n",
+    "                                      lab_type, \n",
+    "                                      curr_version)\n",
+    "dataML_folderpath = os.path.join(ML_folderpath, dataSet_foldername)\n",
+    "dataStats_folderpath = os.path.join(stats_folderpath, dataSet_foldername)\n",
+    "\n",
+    "labDescriptions = stats_utils.get_lab_descriptions(data_source=data_source, \n",
+    "                                                   lab_type=lab_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure1_folderpath = os.path.join(stats_folderpath, 'Fig1_cartoon')\n",
+    "if not os.path.exists(figure1_folderpath):\n",
+    "    os.mkdir(figure1_folderpath)\n",
+    "\n",
+    "xVal_base, yVal_base, score_base, xVal_best, yVal_best, score_best, p_val \\\n",
+    "            = stats_utils.get_curve_onelab(lab,\n",
+    "                                           all_algs=['random-forest'],\n",
+    "                                           data_folder=dataML_folderpath,\n",
+    "                                           curve_type='ROC',\n",
+    "                                           get_pval=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (a) Histogram of normality scores distributed among normal (black) and abnormal (grey) orders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "alg = 'random-forest'\n",
+    "df = pd.read_csv(dataML_folderpath + \"/%s/%s/direct_comparisons.csv\"\n",
+    "                 % (lab, alg), keep_default_na=False)\n",
+    "\n",
+    "df1 = pd.read_csv(dataML_folderpath + \"/%s/%s/%s-normality-prediction-%s-report.tab\"\n",
+    "                  % (lab, alg, lab, alg), sep='\\t', keep_default_na=False)\n",
+    "auc = df1['roc_auc'].values[0]\n",
+    "\n",
+    "scores_actual_0 = df[df['actual'] == 0]['predict'].values\n",
+    "scores_actual_1 = df[df['actual'] == 1]['predict'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_hist(scores, plot_type='hist', bins=30, color='black', label=''):\n",
+    "    if plot_type == 'line':\n",
+    "        ys, xs = np.histogram(scores, bins=bins)\n",
+    "        xs_mid = (xs[:-1]+xs[1:])/2.\n",
+    "        plt.plot(xs_mid, ys, c=color, label=label)\n",
+    "    elif plot_type == 'hist':\n",
+    "        plt.hist(scores, bins=bins, alpha=0.8, color=color, label=label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_type = 'hist'\n",
+    "\n",
+    "figure, ax = plt.subplots(figsize=(5, 4))\n",
+    "\n",
+    "if not inverse01:\n",
+    "    plt.hist(scores_actual_0, bins=30, alpha=0.8, color='gray', label=\"Abnormal\") #gray red\n",
+    "    plt.hist(scores_actual_1, bins=30, alpha=0.8, color='black', label=\"Normal\") #black green\n",
+    "else:\n",
+    "#     make_hist(1-scores_actual_0, plot_type, 30, 'gray', 'Abnormal')\n",
+    "#     make_hist(1-scores_actual_1, plot_type, 30, 'black', 'Normal')\n",
+    "    ha = ax.hist([1-scores_actual_0, 1-scores_actual_1], bins=16, color=['gray', 'black'], label=['Abnormal', 'Normal'])\n",
+    "    N, bins, patches = ha\n",
+    "    \n",
+    "plt.legend(fontsize=12)\n",
+    "\n",
+    "plt.xlim([0, 1])\n",
+    "plt.ylim([0, 800])\n",
+    "# plt.xticks([])\n",
+    "# plt.yticks([])\n",
+    "# plt.xlabel(lab_descriptions[lab])\n",
+    "# plt.xlabel('random forest', fontsize=16)\n",
+    "plt.xlabel('Score', fontsize=16)\n",
+    "plt.ylabel('Number of orders', fontsize=16)\n",
+    "\n",
+    "ax.tick_params(axis='both', labelsize=14)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "plt.savefig(os.path.join(figure1_folderpath, 'cartoon_%s%s.png' % (lab, inverse_maker)))\n",
+    "\n",
+    "plt.show()\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, ax = plt.subplots(figsize=(5, 4))\n",
+    "\n",
+    "if not inverse01:\n",
+    "    plt.hist(scores_actual_0, bins=30, alpha=0.8, color='gray', label=\"Abnormal\") #gray red\n",
+    "    plt.hist(scores_actual_1, bins=30, alpha=0.8, color='black', label=\"Normal\") #black green\n",
+    "else:\n",
+    "#     make_hist(1-scores_actual_0, plot_type, 30, 'gray', 'Abnormal')\n",
+    "#     make_hist(1-scores_actual_1, plot_type, 30, 'black', 'Normal')\n",
+    "    ha = ax.hist([1-scores_actual_0, 1-scores_actual_1], bins=16)\n",
+    "    N, bins, patches = ha\n",
+    "    \n",
+    "    plt.plot([1-score_thres] * dash_num, np.linspace(0, 800, num=dash_num), 'k--')\n",
+    "    \n",
+    "\n",
+    "plt.xlim([0, 1])\n",
+    "plt.ylim([0, 800])\n",
+    "# plt.xticks([])\n",
+    "# plt.yticks([])\n",
+    "# plt.xlabel(lab_descriptions[lab])\n",
+    "# plt.xlabel('random forest', fontsize=16)\n",
+    "plt.xlabel('Score', fontsize=16)\n",
+    "plt.ylabel('Number of orders', fontsize=16)\n",
+    "\n",
+    "ax.tick_params(axis='both', labelsize=14)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.setp(patches[1][0], 'facecolor', 'forestgreen', 'label', 'True Negative')\n",
+    "plt.setp(patches[1][1:4], 'facecolor', 'forestgreen')\n",
+    "\n",
+    "plt.setp(patches[0][0], 'facecolor', 'orangered', 'label', 'False Negative')\n",
+    "plt.setp(patches[0][1:4], 'facecolor', 'orangered')\n",
+    "\n",
+    "plt.setp(patches[0][4], 'facecolor', 'royalblue', 'label', 'True Positive')\n",
+    "plt.setp(patches[0][5:], 'facecolor', 'royalblue')\n",
+    "\n",
+    "plt.setp(patches[1][4], 'facecolor', 'gold', 'label', 'False Positive')\n",
+    "plt.setp(patches[1][5:], 'facecolor', 'gold')\n",
+    "\n",
+    "plt.legend(loc=(0.4,0.6), fontsize=12)\n",
+    "\n",
+    "plt.savefig(os.path.join(figure1_folderpath, 'cartoon_%s%s_color.png' % (lab, inverse_maker)))\n",
+    "\n",
+    "\n",
+    "plt.show()\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00763259, 0.06948607, 0.13133955, 0.19319303, 0.25504651,\n",
+       "       0.31689999, 0.37875347, 0.44060695, 0.50246043, 0.56431391,\n",
+       "       0.62616739, 0.68802086, 0.74987434, 0.81172782, 0.8735813 ,\n",
+       "       0.93543478, 0.99728826])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (b) After picking a threshold, orders are classified into “predicted normal” if their normality scores fall above the threshold or “predicted abnormal” if falling below the threshold. Further, true negative orders (green) are both “predicted normal” and actually normal, false negative orders (red) are “predicted normal” and actually abnormal, false positive orders (yellow) are “predicted abnormal” and actually normal, and true positive orders (blue) are both “predicted abnormal” and actually abnormal. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(dataML_folderpath + \"/%s/baseline_comparisons.csv\"\n",
+    "                 % (lab), keep_default_na=False)\n",
+    "scores_actual_0 = df[df['actual'] == 0]['predict'].values\n",
+    "scores_actual_1 = df[df['actual'] == 1]['predict'].values\n",
+    "\n",
+    "plot_baseline = False\n",
+    "if plot_baseline:\n",
+    "    plt.figure(figsize=(5, 4))\n",
+    "\n",
+    "\n",
+    "    plt.hist(scores_actual_0, bins=30, alpha=0.8, color='b', label=\"Abnormal\")\n",
+    "    plt.hist(scores_actual_1, bins=30, alpha=0.8, color='g', label=\"Normal\")\n",
+    "    plt.xlim([0, 1])\n",
+    "    plt.ylim([0, 500])\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    # plt.xlabel(lab_descriptions[lab] + 'auroc=%.2f' % auc)\n",
+    "    # plt.xlabel('baseline', fontsize=16)\n",
+    "    plt.xlabel('Score, baseline', fontsize=16)\n",
+    "    plt.ylabel('num of orders', fontsize=16)\n",
+    "    plt.legend(fontsize=12)\n",
+    "    plt.savefig(os.path.join(figure1_folderpath, 'cartoon_baseline_%s.png'%lab))\n",
+    "    plt.clf()\n",
+    "\n",
+    "figure, ax = plt.subplots(figsize=(5, 4))\n",
+    "alg = 'random-forest'\n",
+    "df = pd.read_csv(dataML_folderpath + \"/%s/%s/direct_comparisons.csv\"\n",
+    "                 % (lab, alg), keep_default_na=False)\n",
+    "\n",
+    "df1 = pd.read_csv(dataML_folderpath + \"/%s/%s/%s-normality-prediction-%s-report.tab\"\n",
+    "                  % (lab, alg, lab, alg), sep='\\t', keep_default_na=False)\n",
+    "auc = df1['roc_auc'].values[0]\n",
+    "\n",
+    "scores_actual_trueNega = df[(df['actual']==0) & (df['predict']<score_thres)]['predict'].values\n",
+    "scores_actual_falsPosi = df[(df['actual'] == 0) & (df['predict'] >= score_thres)]['predict'].values\n",
+    "\n",
+    "scores_actual_falsNega = df[(df['actual'] == 1) & (df['predict'] < score_thres)]['predict'].values\n",
+    "scores_actual_truePosi = df[(df['actual'] == 1) & (df['predict'] >= score_thres)]['predict'].values\n",
+    "\n",
+    "if not inverse01:\n",
+    "    plt.hist(scores_actual_trueNega, bins=22, alpha=0.8, color='royalblue', label=\"true negatives\")\n",
+    "    plt.hist(scores_actual_falsNega, bins=22, alpha=0.8, color='gold', label=\"false negatives\")\n",
+    "    plt.hist(scores_actual_truePosi, bins=7, alpha=0.8, color='forestgreen', label=\"true positives\")\n",
+    "    plt.hist(scores_actual_falsPosi, bins=7, alpha=0.8, color='orangered', label=\"false positives\")\n",
+    "\n",
+    "    plt.plot([score_thres] * dash_num, np.linspace(0, 800, num=dash_num), 'k--')\n",
+    "else:\n",
+    "#     plt.hist(1-scores_actual_trueNega, bins=22, alpha=0.8, color='royalblue', label=\"True Positives\")\n",
+    "#     plt.hist(1-scores_actual_falsNega, bins=22, alpha=0.8, color='gold', label=\"False Positives\")\n",
+    "#     plt.hist(1-scores_actual_truePosi, bins=7, alpha=0.8, color='forestgreen', label=\"True Negatives\")\n",
+    "#     plt.hist(1-scores_actual_falsPosi, bins=7, alpha=0.8, color='orangered', label=\"False Negatives\")\n",
+    "\n",
+    "#     make_hist(1-scores_actual_trueNega, plot_type, bins=22, color='royalblue', label='True Positives')\n",
+    "#     make_hist(1-scores_actual_falsNega, plot_type, bins=22, color='gold', label='False Positives')\n",
+    "#     make_hist(1-scores_actual_truePosi, plot_type, bins=7, color='forestgreen', label='True Negatives')\n",
+    "#     make_hist(1-scores_actual_falsPosi, plot_type, bins=7, color='orangered', label='False Negatives')\n",
+    "    \n",
+    "    plt.hist([1-scores_actual_trueNega, 1-scores_actual_truePosi], bins=16, color=['royalblue', 'forestgreen'], label=['True Positives', 'True Negatives'])\n",
+    "    plt.hist([1-scores_actual_falsNega, 1-scores_actual_falsPosi], bins=17, color=['gold', 'orangered'], label=['False Positives', 'False Negatives'])\n",
+    "#     plt.hist([1-scores_actual_trueNega, 1-scores_actual_falsPosi, 1-scores_actual_falsNega, 1-scores_actual_truePosi],\n",
+    "#             bins=10, color=['royalblue', 'orangered', 'gold', 'forestgreen'], label=['True Positives', 'False Negatives','True Negatives', 'False Positives'])\n",
+    "    \n",
+    "    plt.plot([1-score_thres] * dash_num, np.linspace(0, 800, num=dash_num), 'k--')\n",
+    "    \n",
+    "\n",
+    "plt.legend(loc=(0.4,0.6), fontsize=12)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "plt.xlim([0, 1])\n",
+    "plt.ylim([0, 800])\n",
+    "# plt.xticks([])\n",
+    "# plt.yticks([])\n",
+    "# plt.xlabel(lab_descriptions[lab])\n",
+    "# plt.xlabel('random forest', fontsize=16)\n",
+    "plt.xlabel('Score', fontsize=16)\n",
+    "plt.ylabel('Number of orders', fontsize=16)\n",
+    "\n",
+    "ax.tick_params(axis='both', labelsize=14)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "plt.savefig(os.path.join(figure1_folderpath, 'cartoon_%s_thres%s.png' % (lab, inverse_maker)))\n",
+    "plt.show()\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (c) This choice of threshold leads to a sensitivity of 96% and specificity of 67% as shown on the ROC curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sensitivity 0.636603028308\n",
+      "specificity 0.96447467876\n",
+      "score_thres 0.756\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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btSKt7Ks7XbEoPHrJ2Pop/w6HfdC7eIxJodGHbRVFAU5urKKxvJgCG8Us7ewh3HTt+CG0u8NVNJwDyz5kPd9MXugdDvPwrnY63emCFlWUWPLNELsDnq6NH3bei+thzS+8jcWYFNnXN8SW/T34fUIed4rNWpaAp0NjY8tHfhICQe9iMSYFVJUX2/t4tXOAmuIApzRVU2KzVmScJeDpeDhu9MyVn/cuDmNSZFfPEK92DrC0stRmrfCQJeCptPweDjzsLAcqvI3FmFmKqeITYXFlCUUFPhptlmJP2UO4yajC+nePrb+/w7tYjJmlXT2DPLijjaFIFBGx5JsF7A54Mi/cNLb8F6+Cz75cJvfEVHn+YC9vdDtTBlllQ/awjDKZbW4CLj8MKo7wNhZjZiAUcYaQ7BgKc0R1GSvnBefs+L3ZyBLweGJR+Encl+aEf/QuFmNm4cW2PrpDEU5urGKRTRmUdSwBJ0oc6wFg0aXexGLMDKgqUVUKfD6Ora/g8JoyKotsQJ1sZA/h4mnsrcm3cgVcodbjzeSMaEzZ2trDY3s6icaUQr/Pkm8WswQc7ydxA+sc/1W48EXvYjEmSYPhKI/s7mBX7xDzy4qwpr3Zz6ogRj20ZmyWi6YLYeXfexuPMUk4ODDMpv3dxFQ5dUE1TdbELCdM+w5YRJ4Ukb8SkfwaHFQV/nwrtD0+tm3t/d7FY0yS1G1mVuT38fbFdZZ8c0gyVRAjwA+BFhG5RUSOSlNMmfXCTbD1k2Pr/ztmMxybnBCJxYjEnGEkT1tYzdmLawna+L05ZdoJWFXPBlbgJOErgRdFZL2IXCYiuVvLvy2us8X7DtgDN5MT+kcirN/VwbMHegAoCxQQ8NkjnVyT1HdMVf+sqtcDC4B1gB/4MbBXRP5RRA5LfYhpFqhy3s/f7Aw1aUyW298f4k+72glFozRb296cNqM/mao6rKr3An8LPAbMAz4DvCoiPxORhhTGmD4jPRB25r6iPPf+dpi5RVV5qb2Pp/Z1URrw847FddTbfG05LekELCIlIvIhEdkEPA3U4yTiJuBa4HTgRymNMl3iH7wVlHsXhzHTMBSJ8XrXAM0VJZzdXEdpwOp7c920v4MicizwMeD/AGXA/cBnVfVPccXuFJFW4GcpjTJdRpudzTsDfLlbjW3y22A4QkmBn9KAn3cuqaOkwI/Ys4q8kMyf0OeAFuA7wB2qun+CctuBp2YbWEa8+j3nvajO2ziMmcDe3iG2tPZwzLwgy6rL7K43zyTz3bwUuF919LZxfKr6MvD2WUWVCZFBaH3QWS5d5G0sxiSIqfJiWx+vdTlTBjUFrW1vPkqmDvgvgObxdojIYhH5j9SElCEPnTG2fNxXvIvDmATDkRhP7O3kta4BDqsqZW1zLSUF1jY9HyWTgK/Cae0wnjp3f+4YnWiz6lgorPI2FmPi9I6E6RwKc1JDJSfMr7Txe/NYMlUQAkw0cXUDMDT7cDJotNXDSd/1Ng5jXD2hMJXFAeaVFnH+YfUUFVjHinw3aQIWkUuAS+I23SQi7QnFSoA1wJYUx5Y+GoP2J51lm2bIeCymynMHe9nRPchZzbXUlhRa8p0jpvouN+Mk1zU4d78nxK2Pvo4FngSunu6Hish1IrJDREIiskVE1kxRvlBEbnaPGRaR3SLyN9P9vEM8fe3YcmH1jE9jzGwNRaI8uruDHd2DHFFdRnWxNYecSya9/VPVW4FbAURkB3Cxqj43mw8Ukcvcc14HPO6+/05EVqjq7gkO+wmwECfJvwbMx7nznpntd4wtV66YuJwxadQ+OMLGli4iMWV1UxULg9ateK6Z9v/fqro0RZ95PXC3qt7prn9CRM7H6UX3+cTCInIe8E5gmaqOVn/snPGn7/zx2PL5W2d8GmNmq3s4TIFPOHNRjc1aMUdNVQe8Ftiqqv3u8qRU9dEpzlcInAT8c8KuB3G6MI/nYpwuz9eLyJU4D/t+B/y9qvaP8xlX41aHNDeP02pu98/HlmtOnCxcY1IuGlN6hsPUlBSyrKqUJZUlFNgoZnPWVHfA64FTgU3u8kStIEZbSEzVWLHOLXMgYfsB4JwJjjkMOBMYBt4PVAHfxRl74pDZMlX1DuAOgFWrVh0a797/dt5PunWKUI1JrYFwhA37uhgMR3nXYfUU+n0UWBOzOW2qBPx24KW4ZS/4cJL7FaraAyAiHwceEJH5qpqYzCcWHRlbbrogtVEaM4kDA8M83dKFAqsaqyj0212vmfoh3CPjLc9COxDFeYgWbz7QOsEx+4F9o8nX9bL73syhd9MTO7jeeS+qheDh0z7MmJlSVV7tHODF9j4qCgs4dUE15TZrhXElMyfct0XkhNl8mKqO4LQXPjdh17k4TdnG8wTQJCLx40Uud993JRXAsPsMb7gjqcOMmY3+kQgLgsWcvbjWkq95i2T+D1oHbBGRbSLyGRFZMMPPvAVYJyIfEZGjReRWnPrc2wFE5B4RuSeu/I+BDuAuEVkpImfgNGP7uaoeTOqTX3CnH1p4yeTljJmlvpEIvcNhRIQTGypZ3VhlD9vMIZL5iZgPfABnuMmvALtE5A8icqWIlE33JKp6H/BJ4AbgWZwHbBeo6ujdbDNxg/64LR3OASpxWkP8FHgE+FASsTvK3NOGeyYvZ8wstPQ5UwZtbe1BVfGJ2Pi9ZlyiOlHDhkkOEqkBLscZnP00YBD4b1X9q9SGNzurVq3SzZs3j234WSWEe+Gs/4EFF3oXmMlLqspLHf280tFPVXGAU5uqKQ3YKGb5SkS2qOqq2ZxjpnPCdarqbap6Bk7riC7gitkEkhHhXufdZr8wKRaOxnhyXxevdPSzuKKEsxbVWvI1U5rREwG3yuFS4C+Bs4EI8P9SF1YaDHeOLc87Y+JyxsyA3ydEY8oJ8ytYWllqVQ5mWpKZE84HnAf8FXARzlgMT+B0If5ZQjOx7DM6/i9AwbSrrI2Z1L6+IeaVFlHo97FmUY0lXpOUZO6AW3AGZN8O/CPwn6q6Mx1BpVVRrdcRmDwQU2VbWx/buwZYXlPGMfMqLPmapCWTgH8O3KuqG9MVTFpt/LDzHt8bzpgZCEWibGrppn1ohGVVpayoC3odkslRyYyG9vF0BpJWsQjs+5WzHOnzNhaT03pCYZ7c18lwNMaqhkqaK0u9DsnksIyOhuaZ3T8dW76kxbs4TM4rKvBRGvBz2oIaqmzwdDNLmR4NzRuxyNhySaN3cZicFI0pb3QPsKy6jOICP2sX1Vp9r0mJZEZDewcTJ+DcsCSr+omYHDAYjrKxpYuuUJhgYQEN5cWWfE3KJDMa2vq0R2NMFmkfHGZjSzfRmHJKUxUN5cVeh2TyTDKjob0hIsdPsO8YEXkjdWEZ461dPYM8tqeTgF84e3EtC2y+NpMGyTRDWwIUTbCvGFg862iMyRIVRQEWBIs5cX4lARs83aRJsj9ZE9UBrwK6ZxmLMZ4aGInwWqczzWB1cYDVTdWWfE1aTdUM7VPAp9xVBX4tIok9GUqAGpyp443JSa0DIZ5uce4hFlaUUFKQnQ16TH6ZqgriDeCP7vJVwGagLaHMME5LiR+kNrQUOjj6LDG3G3GY1FNVXukc4KX2PiqLCjilqdqSr8mYqVpB3A/cD4w2vblZVXdkIK7UOvAn532ky9s4TNbZ3NrDnt4hFgWLObGh0matMBmVTFfkD6YzkLQqPwwGdkDDOV5HYrJMQ1kR1UUBllXbEJIm86aqA/4S8ANVbXGXJ6Oq+pXUhZZKbtVD1THehmGywr6+EJFYjMWVpSyqsOZlxjtT3QHfCPweZyjKG6coqzhzxWWfAw877zOYfsnkD1XlxfY+Xu0coK6kkOaKErvrNZ6aqg7YN95yTomFx5bLmicuZ/LaSDTGppZuDg4Os6SyhOPrKy35Gs/NaEqinBKKa7QRPMK7OIxnIrEYf9rVzlAkyonzK1laZUNImuyQzJREy4EqVd3krpcAXwKOAR5Q1X9NT4gpUtIIkps38WZ2Cnw+llaVUldSSE1JodfhGPOmZDLSv+JMxDnqq8DfAU3At0Xkr1MZWOpYve9cFFPl+YO9dAw6/YaW15Rb8jVZJ5kEfDzOJJyjE3ReCXxWVU8C/gG4OvXhpUDvy857ZMDbOEzGhCJRHtvTyfauAQ4ODnsdjjETSiYBVwId7vKJQDXOPHHgDNZ+WOrCSqHRh3Dxg7KbvNUxNMLDu9rpDo1wcmMVR9t8bSaLJZOADwCHu8vnAa+r6h53vRzIzgzXvc15r59yRiWT47pCYR7d3YFfhLOb66yNr8l6ybSC+BXwdRE5BlgHfD9u37E440ZkH3XvgPtf9zYOk3ZVRQUcXVfOYVVlFNooZiYHJJOAP4cz7u+7cJLxV+P2vRd4MIVxpZD7i7jofd6GYdJiMBzlmQM9nDi/ktKAn6NqrcrB5I5kxoIYAD46wb7TUxZRqu2933m3XnB5p82dMiimSv9IhNKAjWJmckv+d8To2OC8Rwe9jcOkjKqyvWuAbW19lBcWcOqCaoKF+f+jbPJPUj+1InIV8L+BZpzqiHiqqstSFVjKVBztNEVbdOnUZU1OeKN7kBfa+mgqL+akxkoCNoSkyVHJ9IT7InATsA14Fmcg9txRPM/rCMwsqSoiQnNlCSKwtNKGkDS5LZk74A8Dt6rqp6YsaUyKtfaHeLVzgNMX1hDw+TisqszrkIyZtWT+d6sFfp2KDxWR60Rkh4iERGSLiKyZ5nFnikhERLalIg6T/VSVl9v7eHJfF+FYjHAs5nVIxqRMMgn4EZzuyLMiIpcBtwJfw+lR9yTwOxGZdKxIEakG7mFsjrrpGe2KbHJOOBpjQ0sXL3f001xRwlnNdTZfm8krySTgTwIfFJErRaRORHyJr2me53rgblW9U1VfVtVPAPuBa6c47t+BHwJPJRHzmAJrH5prtrT20No/zPH1FZzUUEmBz+p7TX5JJgG/ijP05F043ZLDCa/E6eoPISKFwEkc2mnjQWDCtsQich0wH2fQn6k+42oR2Swim9vbDo7tKFs01aEmS6jbZvuYeUHWLKplWXWZPWwzeSmZh3A3M/uxHesAP04Cj3cAGHfGTBE5FvgycKqqRqf6RVTVO4A7AFYdu8SJ128DcOeCmCovtfcxEI6yurGK8sICym0ESZPHkukJd2Ma4xiXiBQB9wGfVtUdSZ8gGnLeyxanNC6TesORGE/v7+Lg4AhLK0tRwO55Tb6bUfchESnHaRXRoqrhqcrHaQeiONUJ8eYDreOUbwSOBu4SkbvcbT4nBIkAF6jqxGNQaNR5t7ngslpXKMzGfV2EolHe1lDJkkr7j8XMDUl1IRKRvxCRrUAPzuhnx7rbfyAiV0x1vKqOAFuAcxN2nYvTGiLRPvczToh73Q5sd5fHOyYuYPfy5r9zqtCMR2KqbNjXiaKsXVRrydfMKcn0hLsY+H84zcA+C/xT3O4dwFXAj6dxqluAe0VkE84MG9fgTGt0u/s59wCo6pXu3fVb2vyKyEFgWFWn3xbYXzTtoiYzYqoI4BPhlKZqSgN+iq2JmZljkqmC+DJwl6p+REQKeGsC3gZcN52TqOp9IlIL3IBTxbANpyphl1vE6gvy3FAkyqaWLuaXFXFUbdDmajNzVjIJ+GjgM+5yYmuILpw64WlR1duA2ybYd/YUx94I3DjdzzLZpWNwhA0tXURiyjLrTmzmuGQScC9OM7LxLAHaZh2NyVuqyhvdgzx/sJfSgJ8zF9VQWRTwOixjPJXMQ7iHgM+LSFXcNnWbin0c+F1KIzN5pX8kyvMHe5lfVsTbF9dZ8jWG5O6AvwBsAl4BfotTDfE54DicGZMvTnl0JueFozECfh/BogLOaq6lujhgvdqMcU37DlhVdwJvA/4Hp9lYFFgLbABOUdWWdARoctfBgWEe2NFGS5/TIaampNCSrzFxkuqIoap7ccYFNmZCqsprnQNsa+8jWFhAsMimCzJmPDP+zRCRSuAIoNVNzNlnpMvrCOacSCzGltYe9vWFWBAsdkcxsymDjBnPpL8ZIvIuEfnHcbZ/ATgIbAR2iciP3bbB2cXndsAQa+CfKfv7h9nXF+KYeUFWN1ZZ8jVmElMlzWtIaPMrIucCXwFeAH6A0z74YzhdjL+VhhhnwZ09oepYb8OYA0KRKMXn0PF5AAATsUlEQVQFfhYGi6ksqqPCWjkYM6WpEvCJOMk23geBEPAuVW0FRh+sXEG2JeCIOxW9znYUTTMRVeXljn62dw5w9uJaKooClnyNmaap/j+sB15P2HYu8Pho8nX9BlieysBSYnQwnorsCy0fjERjPLmviz939NMULKYskH21UMZks6l+Y/qAN/uLisgROF2ONySU68UZaD072YDsKdczHGbDvi4Gw1FOqK9gaZVNEW9Msqa6A/4zcFHc+kU4dcKJY/Au5dBZLkwe29UzRDSmrG2u5TCbMsiYGZnqDvjbwC9EpAYnwa7Defj2REK5C4DnUh6dySoxVUKRGKUBP8fMC7K8psyGkDRmFia9A1bVX+LMhnwycCVO1cP/Uh17qiUiDTjzuf02jXEajw1Hojyxt5NH93QQiSk+EUu+xszSlE9NVPVfgH+ZZH8rE4+SZvJA59AIG1u6GI7GOHG+TQ9vTKrYY2szqZ3dgzx7sIdiv5+zmuuoLrYmZsakSn4nYI1BQbnzMklTVXb3DlFXUsjJjdUUFVivNmNSKb8TMEBRLfisrjIZQ+EoPoGiAj+nLqimwCf4rJWDMSlntzTmLdoHR3h4VztbWnsAKPT7LPkakyb5fwdspkVVeb17kBcO9lLmNjMzxqSXJWBDJKY8e6CH3b1DNJYXsaqhioDf/jkyJt0sARuiqnQMjbCirpwja8qtV5sxGWIJeA7rGByhuiRAkd/HO5fU2di9xmRY/v/GDezyOoKso6q80tHPI3s6eLVzAMCSrzEeyP874MZ3eR1BVgnHYmzZ301L/zALg8UcXm0jxRnjlfxPwKPTEhn6RiJs2NdJ/0iUY+cFOdxGMTPGU/mfgM2bYqpEFc5cVMO8UvvDZIzXrOIvz6kq+/pCAFQWBThv6TxLvsZkCUvAeWx0yqCNLV20DQ4DWK82Y7KIVUHkqe5QmI0tzpRBJ86vtLteY7KQJeA8tLd3iC2t3QT8Ps5qrqWmpNDrkIwx47AEnIdEhOriQlY3VdmsFcZkMUvAeSIUidIxFGZBsJgFwWKayousiZkxWc6Th3Aicp2I7BCRkIhsEZE1k5R9n4g8KCJtItInIhtF5L2ZjDfbdQ6NDiHZzUg0BmDJ15gckPEELCKXAbcCXwNOBJ4EficizRMcchbwMHChW/63wH9PlrTnkh3dgzy6pwOfCGsX1VJoo5gZkzO8qIK4HrhbVe901z8hIucD1wKfTyysqn+bsOkmEbkQuBh4LK2RZjFV5ZkDvezsGaS+tIjVTVWWfI3JMRn9jRWRQuAk4MGEXQ8CpydxqiDQlaq4cpGIUFLg48iaMs5YWG3J15gclOnf2jrADxxI2H4AaJjOCUTkr4GFwL0T7L9aRDaLyObZBJqt2gaH3+xUcXRdkJXzKqy+15gclVO3TSLyfuCbwBWqOu44k6p6h6quUtVVmY0uvVSV7Z0DPL6nk5fa+1FVr0MyxsxSpuuA24EoMD9h+3ygdbIDReRS4B7gSlX9dXrCy06RWIxnWnvY0xd6c8ogu+s1Jvdl9A5YVUeALcC5CbvOxWkNMS4R+QBOlcM6Vf15+iLMPiPRGI/s7mBPX4iVdUFObaq2+dqMyRNetIK4BbhXRDYBTwDXAE3A7QAicg+Aql7prl+Ok3w/DTwqIqN1xSOq2pnh2DMu4BOqiwMcM6+C+WU2noMx+STjCVhV7xORWuAGoBHYBlwQV6eb2B74Gpw4v+O+Rj0CnJ3eaL2hqrzaOcDCYDFlhQW8raHK65CMMWngSVdkVb0NuG2CfWdPtp7vwtEYm1u72d8/TFSVFXVBr0MyxqSJjQWRRXqHw2zY18VAOMpx9RUsq7L52ozJZ5aAs0Tn0AiP7+nE7xPWLKqhzsbvNSbvWQLOEhVFARZUFLOiNkhJwIaQNGYuyP/2TJK9lzgcjfHMgR7CsRgFPuGkhipLvsbMIfl/B1xY43UE4+oOhdnQ0kUoEmVBeTH11sTMmDkn/xNwzUleR3CI3T2DbD3QQ5Hfx9pFNmWQMXNV/ifg4sRez97a3jnA82291JXYlEHGzHX5n4CzTFOwmOFojKPrym2KeGPmuOx9QpVHOoZG2NrajapSGvCzcl7Qkq8xxu6A00lV2dEzyHMHeikN+AlFY5RYlYMxxmUJOE2iMeXZAz3s6h1iflkRJzfalEHGmLeyBJwmG1u6aB0Y5qjaco6uLbfxe40xh7AEnCbLa8pZUllKU7DY61CMMVnKEnCKqCrbuwYIx5wRzOpKrW2vMWZyloBTIBKLsaW1h319IRaUF6OqVuVgjJmSJeBZ6h+JsGFfF70jEVbWBVleU2bJ1xgzLZaAZyESc+ZrU1XOWFhjUwYZY5JiCXgGRqsYCnw+TphfQVVxgLKAfSmNMcmxhqlJGonGeGpfF3t7hwBYECyx5GuMmRHLHEnoGQ6z0Z0yqKHcqhuMMbNjCXia9vYOsaW1hwKfsGZRrTUzM8bMmiXgaegKhdm0v5ua4gCnLKi28RyMMSlhCXgSow/bqosDrG6soilYbKOYGWNSxh7CTaArFOahnW10h8IALKwoseRrjEkpuwMex66eQZ450EOR3496HYwxJm9ZAo4TU+X5g7280T3IvNJCVjdWUWT1vcaYNLEEHOeN7kHe6B7kiOoym7XCGJN2loBxBk/3+4TDqkoJFhZYl2JjTEbM6YdwqsrrXQM8tLONUCSKT8SSrzEmY+bsHXA0pjxzoIfdvUM0lBVZdYMxJuPmZAIeCDtDSPYMRzi6tpyjbMogY4wH5mQCfrGtj8FwlNMWVNNYblMGGWO8MWcSsKoSiSkBv4/j51cSjsYoL5wzl2+MyUKePIQTketEZIeIhERki4ismaL8WW65kIi8ISLXJPN54ViMjS3dPLG3k5gqRX6fJV9jjOcynoBF5DLgVuBrwInAk8DvRKR5gvJLgd+65U4Evg58V0TeP53P64sVs35XBy39IZqCxVhNrzEmW4hqZjvbishG4HlV/WjctteAn6vq58cp/w3gfap6RNy2HwArVfW0yT7r+CMq9Sv/sw2f3+nVVm9NzIwxKSIiW1R11WzOkdE7YBEpBE4CHkzY9SBw+gSHnTZO+QeAVSISmOzzhgobKZcQ71hcZ8nXGJN1Ml0RWgf4gQMJ2w8A50xwTAPwh3HKF7jn2x+/Q0SuBq52V4ffeeTybbMJOMvVAe1eB5FGdn25Ld+v78jZniDvnkSp6h3AHQAisnm2/yJkM7u+3GbXl9tEZPNsz5Hph3DtQBSYn7B9PtA6wTGtE5SPkN9/XY0xeS6jCVhVR4AtwLkJu87FaeUwnqcmKL9ZVcOpjdAYYzLHi3bAtwDrROQjInK0iNwKNAG3A4jIPSJyT1z524EFIvIdt/xHgHXAP0/js+5IcezZxq4vt9n15bZZX1/Gm6GB0xED+AzQCGwDPqWqj7r71gOo6tlx5c8Cvg2sBFqAb6jq7ZmN2hhjUsuTBGyMMWaOjwdsjDFesgRsjDEeyekEnOlBfTItmesTkfeJyIMi0iYifSKyUUTem8l4k5Xs9y/uuDNFJCIiWd3JZgY/n4UicrN7zLCI7BaRv8lUvMmawfVdISLPisigiLSKyH+KSEOm4p0uEVkrIr8SkX0ioiKybhrHHCsij4jIkHvcl2Q6g4yrak6+gMuAMPBR4Gjgu0A/0DxB+aXAgFvuaPe4MPB+r68lRdd3K/A5YDVwOPBlnDbXa7y+llRcX9xx1cAbON3Rt3l9Ham8PuAXwCacZpZLgFOAs72+llRcH3CG+/P4Kfd38VRgK/BHr69lnFgvwBks7FJgEFg3RfkKnP4KPwWOcY/rA/5uys/y+mJn8UXaCNyZsO014OsTlP8G8FrCth8AT3l9Lam4vgnOsQn4ltfXksrrc5PUl4EbszwBJ/vzeR7QA9R5HXuaru/TwK6EbR8E+r2+limus38aCfhaoBcoidt2A7APt6HDRK+crILI9KA+mTbD6xtPEOhKVVypMtPrc5svzgf+IX3Rzd4Mr+9i4GngehHZKyKvici/iEh5GkOdkRle3xNAo4i8Rxx1wOU4Q83mutOAx1R1KG7bAzj9G5ZMdmBOJmAmH9RnojqlhgnKjw7qk01mcn1vISJ/DSwE7k1taCmR9PWJyLE4d75/qarR9IY3azP5/h0GnAkcD7wf+DhwPnB3ekKclaSvT1Wfwkm4PwJGgDZAgKvSF2bGTJRbRvdNKFcTsJmEO1j9N4ErVHWX1/HMlogUAfcBn1bVHV7HkyY+QHG+ZxtV9QGcJPx+EUkcCyXniMgKnHrir+DcPZ+Pk5y+72VcXsvV0dDyfVCfmVwfACJyKXAPcKWq/jo94c1astfXiPOg5y4Rucvd5gNERCLABaqa+O+wl2by/dsP7FPVnrhtL7vvzRx6h+WlmVzf54FNqvpNd/15ERkAHhORv1fVvekJNSMmyi2j+yaUk3fAmueD+szw+hCRD+BUOaxT1Z+nL8LZmcH17QOOBU6Ie90ObHeXJ/yaeGGG378ngKaEOt/l7ntW/Rczw+srxUna8UbXczIPxXkKWCMi8VOsn4szbMLOSY/0+injLJ5OXoZTl/QRnLujW3GeWC52998D3BNXfrQZ2nfc8h9xj8/mZmjJXN/lOM2C/hbnX7vRV43X15KK6xvn+BvJ7lYQyX7/yoE9wM9wxjw5A2eclJ95fS0pur517s/ntTj13WfgPHTc4vW1jHNt5Yz9oR8EvuQuN7v7v05c8zmgEudO9yc4zdDeh9MqIn+bobkXfh3OX5hhnL/Ia+P2rQfWJ5Q/C6ft4TCwA7jG62tI1fW56zrOa32m407X9y/h2KxOwDO5PpwZFh50f+n3Ad8Dgl5fRwqv7xPAi+717cd5ILfQ6+sY57rOnuB36W53/93AzoRjjgUeBULutX2ZKZqgqaoNxmOMMV7J9boXY4zJWZaAjTHGI5aAjTHGI5aAjTHGI5aAjTHGI5aAjTHGI5aATcqIyEIR+a6IPOUOuq0isiSJ4y8WkUdF5KA7sPUuEfmliJyfvqinFdeNIqIJ2xrcQbs73ev8pIism8E1L0kc9Ns9z4dSdgEma1k7YJMyInI2zqA5W3BGyzoPWKqqO6dx7N/g9Kb6D+CXOL0WlwEXAq+q6mfSE/XURGQhToeBDXHbbgP+D04Pr/04HRKiODE/o6rD0zx3EXAi8Lqqtrnb1gMFqnpm6q7CZCNLwCZlRMSnqjF3+SPAnUw/Ae/G6ZZ6yWTnzRYi8ifAr6pr03Du9VgCnhOsCsKkzCyTZA0TjBwVf964f/PXutUT/SLSISLfE5GS+ONEpFREvuHOWzbivn9BRHwJ5eaJyG0issedi22PiNzr3p2+pQpitMoAp7vqGjcWdbePWwUhIh8Vka1utUqXO3fY6fHnG62CcJPvWcAZcedeLyInucsXJX59RORudxB3fzJfcOO9XB2O0uSfTcBVIvIGcL+qvjpF+f/EmYPrNpx58L4ElOFUCSAiBTizEqzAGYP2BZx5yL6Ik+z/zi1XjTOCVw3OTBvPA/XARUAhzjgH8fbjzIDwfZwqh+vith9CRP7Z/ax/xxkfIObG0cz4I4dd516bH/iYu61XVV8SkafdbffHnb8K+ADwT5r9A9WbRF4PfGGv/HzhjJKlwJJpll+Ok/xGBz5pB/4LOC+h3Dp3/+0J27+AkxCXu+t/5ZZbO065EaDeXb/ZPe7ESWK70flVecu2xzl0sJnR2Ja464e7575lknMvcY9ZF7dtPfD4OGXXuedbHLftb3DGtM66QW3sNfXLqiBMRolIQcJLANS54z0R59/vrwLPApcAD4jIDeOc6qcJ6z/BqVJb7a6fjzOO7pPxn4cz2lgA5y4UnAeFT6vqM6m7yjed48Z0R4rO9xOgG2cm4lEfA36juT2g+ZxlCdhkWjjhddboDlWNquqjqnqDqp6DM27sC8CX3aqCeBPNwbXAfa8HFo/zeZvc/bVx7+lKXqOfkZLzq2oIuAv4kPsHZQ1OFcvtqTi/yTyrAzaZdnLC+isTFVTVFhH5AU7ztCMYS57gTPnyYsI6OOPoAnTgjPn8gQlOv9N9b2csaafa6FRXC5jkOpP0b8D1OHXUl+BcxwMpOrfJMEvAJqNUdfN420WkUVXHe5B1lPue2ELiA8DDceuX4zzg2uiu/x5nduF+Vf3zJCE9CNwgIser6nNTxZ+kP7gxXY370G+ahoHgeDtU9XUReRD4vzizNNysWdZEz0yfJWCTUu6koODMfAvwbhFpA9pU9ZFJDt0mIn8Afotz51oBXABcA/xUVXcnlL9ARL6Jk0BX47QwuEdVX3P3/wj4IPBHEfkW8BxOq4ZlwHuBi1V1EPg2cAXwBxH5B5wqjzqcO8xrVLVvJl8HeDNZfhu4XkSCwK9wHqKtBv6sqvdNcOhLwHUichnwOtCnqvF30LfhtIQI47SuMDnKErBJtZ8lrN/mvj+C03Z2Il/ASbg341QnRIFXgc/hzOOX6C9x7iqvxWnVcCfw6dGdqhoWkXe5x1/N2JyArwO/cY9BVbtF5AycJmifw6m3PYBzdz0yjeudlKp+WkS24zQvu8qN4XmcPxwT+QbO9EQ/wJmfLPFr9xtgCOfhWzbNlmySZD3hTE5xOyzcBRyhqts9DscTInIuTgI/R1X/6HU8ZubsDtiYHCEiy3Bahnwb2GrJN/dZMzRjcscXgd/hPKS70uNYTApYFYQxxnjE7oCNMcYjloCNMcYjloCNMcYjloCNMcYjloCNMcYj/x+PRbLLTCUNvAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 360x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "# plt.plot(xVal_base, yVal_base, label='baseline model, %0.2f' % (score_base), linewidth=2)\n",
+    "'''Representative ROC of LABLDH'''\n",
+    "if not inverse01:\n",
+    "    plt.plot(xVal_best, yVal_best, color='orange', linewidth=2) #, label='Best Alg', label='random forest', AUROC=%0.2f  % (score_best)\n",
+    "else:\n",
+    "    plt.plot(1-yVal_best, 1-xVal_best, color='orange', linewidth=2) #, label='Best Alg'\n",
+    "\n",
+    "if include_threshold_colors:\n",
+    "    df_directcompare_rf = pd.read_csv(os.path.join(dataML_folderpath, lab, 'random-forest', 'direct_comparisons.csv'))\n",
+    "    actual_labels = df_directcompare_rf['actual'].values\n",
+    "    predict_probas = df_directcompare_rf['predict'].values\n",
+    "\n",
+    "    sensitivity, specificity, LR_p, LR_n, PPV, NPV = stats_utils.get_confusion_metrics(actual_labels, predict_probas, score_thres, also_return_cnts=False)\n",
+    "    print \"sensitivity\", sensitivity\n",
+    "    print \"specificity\", specificity\n",
+    "    print \"score_thres\", score_thres\n",
+    "\n",
+    "    '''The POINT of PPV=0.95'''\n",
+    "    if not inverse01:\n",
+    "        plt.scatter(1-specificity, sensitivity, s=50, color='orange')\n",
+    "    else:\n",
+    "        plt.scatter(1-sensitivity, specificity, s=50, color='orange')\n",
+    "        \n",
+    "\n",
+    "    '''Reference line of AUC=0.5'''\n",
+    "    \n",
+    "    # plt.plot([1-specificity]*dash_num, np.linspace(0,1,num=dash_num), 'k--')\n",
+    "    plt.plot(np.linspace(0,1,num=dash_num),np.linspace(0,1,num=dash_num), \n",
+    "             color='lightblue', linestyle='--'\n",
+    "             #label = 'Baseline'\n",
+    "            )#, )\n",
+    "\n",
+    "plt.xlim([0, 1])\n",
+    "plt.ylim([0, 1])\n",
+    "#plt.xticks([])\n",
+    "#plt.yticks([])\n",
+    "\n",
+    "plt.ylabel('Sensitivity', fontsize=16) #lab_descriptions.get(lab, lab)\n",
+    "plt.xlabel('1-Specificity', fontsize=16)\n",
+    "\n",
+    "ax.tick_params(axis='both', labelsize=14)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n",
+    "# plt.legend(fontsize=12)\n",
+    "plt.savefig(os.path.join(figure1_folderpath, 'ROC_%s%s.png'%(lab,inverse_maker)))\n",
+    "\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 2.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 2.ipynb
new file mode 100644
index 00000000..23c10e23
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 2.ipynb	
@@ -0,0 +1,776 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Figure 1: plot_full_cartoon of LABLDH\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from JAMA_config import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_source = 'Stanford'\n",
+    "lab_type = 'panel'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataSet_foldername = 'data-%s-%s-%s'%(data_source, \n",
+    "                                      lab_type, \n",
+    "                                      curr_version)\n",
+    "dataML_folderpath = os.path.join(ML_folderpath, dataSet_foldername)\n",
+    "dataStats_folderpath = os.path.join(stats_folderpath, dataSet_foldername)\n",
+    "\n",
+    "all_labs = stats_utils.get_all_labs(data_source, lab_type)\n",
+    "\n",
+    "lab_descriptions = stats_utils.get_lab_descriptions(data_source=data_source, \n",
+    "                                                   lab_type=lab_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure2_folderpath = os.path.join(stats_folderpath, 'Fig2_Order_Intensities')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Order_Intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "labs_cnts_order_1day = stats_database.TOP_PANELS_AND_COUNTS_IN_1DAY[:20]\n",
+    "labs_order_1day = [x[0] for x in labs_cnts_order_1day]\n",
+    "\n",
+    "labs_guideline = ['LABESRP', 'LABALB', 'LABA1C', 'LABTSH', 'LABCRP', 'LABYCP', 'LABLIPS', 'LABAGALA', 'LABNTBNP']\n",
+    "\n",
+    "\n",
+    "use_cached_fig_data = True\n",
+    "DEFAULT_TIMELIMIT = stats_utils.DEFAULT_TIMELIMIT\n",
+    "\n",
+    "\n",
+    "\n",
+    "scale = 1. / stats_utils.NUM_DISTINCT_ENCS * 1000\n",
+    "\n",
+    "\n",
+    "\n",
+    "to_annotate_percentages=False\n",
+    "\n",
+    "cached_result_foldername = os.path.join(stats_folderpath, 'Fig2_Order_Intensities/')\n",
+    "if not os.path.exists(cached_result_foldername):\n",
+    "    os.mkdir(cached_result_foldername)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Labs to be plot: ['LABMETB', 'LABMGN', 'LABCBCD', 'LABLACWB', 'LABPTT', 'LABPHOS', 'LABPT', 'LABCAI', 'LABK', 'LABHEPAR', 'LABNA', 'LABTNI', 'LABLIDOL', 'LABHCTX', 'LABLDH', 'LABURIC', 'LABLAC', 'LABSPLAC', 'LABFIB', 'LABUSPG']\n"
+     ]
+    }
+   ],
+   "source": [
+    "params_1 = {'labs':labs_order_1day,\n",
+    "            'result_label':'labs_order_1day',\n",
+    "            'scale_method' : 'by_scale',\n",
+    "            'include_legend':True\n",
+    "           }\n",
+    "\n",
+    "params_2 = {'labs':labs_guideline,\n",
+    "            'result_label':'labs_guideline',\n",
+    "            'scale_method' : 'normalize',\n",
+    "            'include_legend':False\n",
+    "           }\n",
+    "\n",
+    "params = params_1\n",
+    "\n",
+    "labs = params['labs'] #  # \n",
+    "print \"Labs to be plot:\", labs\n",
+    "result_label= params['result_label'] # 'labs_guideline'# \n",
+    "\n",
+    "\n",
+    "scale_method=params['scale_method']\n",
+    "include_legend=params['include_legend']\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "cached_result_filename = 'Order_Intensities_%s_%s.csv'%(lab_type, result_label)\n",
+    "cached_result_path = os.path.join(cached_result_foldername, cached_result_filename)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Each lab \n",
+    "    -> all its orders in 2014/07-2017/06 (implicit) \n",
+    "    -> {time since last order:cnts} (cached)\n",
+    "    -> {0-1 days: cnt, 1-3 days: ...} \n",
+    "    -> barh\n",
+    "'''\n",
+    "lab2stats = {}\n",
+    "columns = ['< 24 hrs', '[24 hrs, 3 days)', '[3 days, 1 week)', '>= 1 week']\n",
+    "\n",
+    "if os.path.exists(cached_result_path) and use_cached_fig_data:\n",
+    "    lab2stats_pd = pd.read_csv(cached_result_path)\n",
+    "    lab2stats_pd = lab2stats_pd.rename(columns={'< 1 day':'< 24 hrs',\n",
+    "                                                '1-3 days':'[24 hrs, 3 days)',\n",
+    "                                                '3-7 days':'[3 days, 1 week)',\n",
+    "                                                '> 7 days':'>= 1 week'})\n",
+    "    lab2stats = lab2stats_pd.set_index('lab').to_dict(orient='index')\n",
+    "\n",
+    "else:\n",
+    "    for lab in labs:\n",
+    "        print 'Getting Order Intensities of lab %s..'%lab\n",
+    "\n",
+    "        df_lab = stats_utils.get_queried_lab(lab, lab_type, time_limit=DEFAULT_TIMELIMIT)\n",
+    "\n",
+    "        dict_lab = stats_utils.get_floored_day_to_number_orders_cnts(lab, df_lab)\n",
+    "\n",
+    "        day_counts = [0] * 4\n",
+    "        tot_cnt = 0\n",
+    "        for time, cnt in dict_lab.items():\n",
+    "            if 0 <= time < 1:\n",
+    "                day_counts[0] += cnt\n",
+    "            elif 1 <= time < 3:\n",
+    "                day_counts[1] += cnt\n",
+    "            elif 3 <= time < 7:\n",
+    "                day_counts[2] += cnt\n",
+    "            elif time >= 7:\n",
+    "                day_counts[3] += cnt\n",
+    "            else:\n",
+    "                print \"time is out of range:\", time\n",
+    "            tot_cnt += cnt\n",
+    "\n",
+    "        lab2stats[lab] = dict(zip(columns, day_counts))\n",
+    "\n",
+    "    df_res = pd.DataFrame.from_dict(lab2stats, orient='index').reset_index().rename(columns={'index':'lab'})\n",
+    "\n",
+    "    df_res.to_csv(cached_result_path, index=False)\n",
+    "\n",
+    "for lab, stats in lab2stats.items():\n",
+    "    stats['total'] = float(sum(stats.values()))\n",
+    "\n",
+    "if scale_method == 'by_scale':\n",
+    "    labs_ordered = sorted(labs, key=lambda x: lab2stats[x]['total'], reverse=True) # \n",
+    "else:\n",
+    "    labs_ordered = sorted(labs, key=lambda x: lab2stats[x]['< 24 hrs']/lab2stats[x]['total'], reverse=True)\n",
+    "    \n",
+    "labs_toplots = [labs_ordered]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_order_intensities_barh(lab, time_since_last_order_binned, columns, scale,\n",
+    "                                labeling=True, lab_ind=None):\n",
+    "\n",
+    "    alphas = [0.7, 1, 1, 0.7]\n",
+    "    colors = ['red', 'green', 'orange', 'blue']\n",
+    "\n",
+    "    local_lab_descriptions = {\n",
+    "        'LABCBCD': 'CBC WITH\\nDIFF',\n",
+    "        'LABPHOS': 'PHOSPHORUS\\nSERUM/PLASMA',\n",
+    "        'LABA1C': 'HEMOGLOBIN\\nA1C',\n",
+    "        'LABALB': 'ALBUMIN\\nSERUM/PLASMA',\n",
+    "        'LABTSH': 'THYROID\\nSIMULATING\\nHORMONE',\n",
+    "        'LABESRP': 'SEDIMENTATION\\nRATE (ESR)'\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "    pre_sum = 0\n",
+    "    pre_sums = [pre_sum]\n",
+    "    \n",
+    "    for key in columns:\n",
+    "        pre_sum += time_since_last_order_binned[key]\n",
+    "        pre_sums.append(pre_sum)\n",
+    "    \n",
+    "#     print lab, time_since_last_order_binned.items()\n",
+    "\n",
+    "    for i in [3,2,1,0]:\n",
+    "        key = columns[i]\n",
+    "        pre_sum = pre_sums[i+1]-pre_sums[i]\n",
+    "        left = pre_sums[i]\n",
+    "\n",
+    "        lab_descriptions['LABTSH'] = 'TSH'\n",
+    "        lab_descriptions['LABESRP'] = 'Sedimentation Rate'\n",
+    "        lab_descriptions['LABCBCD'] = 'CBC w/ Diff'\n",
+    "        lab_descriptions['LABPTT'] = 'PTT'\n",
+    "        lab_descriptions['LABHEPAR'] = 'Heparin Activity Level'\n",
+    "        lab_descriptions['LABLIDOL'] = 'Lidocaine Level'\n",
+    "        lab_desciption = lab_descriptions.get(lab, lab)\n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "#         print 'pre_sum, left:', pre_sum, left\n",
+    "\n",
+    "        if labeling:\n",
+    "            plt.barh([lab_desciption], pre_sum, left=left, color=colors[i], alpha=alphas[i], label=key)\n",
+    "        else:\n",
+    "            plt.barh([lab_desciption], pre_sum, left=left, color=colors[i], alpha=alphas[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "for ind_toplot, labs_toplot in enumerate(labs_toplots):\n",
+    "    fig, ax = plt.subplots(figsize=(8, .3 + 5.7/20.*len(labs))) #5+ 3./20.*len(labs)\n",
+    "    # 8, 3.435 for guideline\n",
+    "    # 8, 6 for freq labs\n",
+    "    # 6 - 3.435 = 2.565\n",
+    "    # Size for saturation is 7, 2.565 ?\n",
+    "    for i, lab in enumerate(labs_toplot[::-1]):\n",
+    "\n",
+    "        time_since_last_order_binned = lab2stats[lab]\n",
+    "\n",
+    "\n",
+    "        for time, cnt in time_since_last_order_binned.items():\n",
+    "            if time == 'total':\n",
+    "                continue\n",
+    "            if scale_method == 'normalize':\n",
+    "                time_since_last_order_binned[time] = cnt/time_since_last_order_binned['total']\n",
+    "\n",
+    "            elif scale_method == 'by_scale':\n",
+    "                time_since_last_order_binned[time] = cnt*scale\n",
+    "\n",
+    "        if i == 0:\n",
+    "            plot_order_intensities_barh(lab, time_since_last_order_binned,\n",
+    "                                             columns=columns,\n",
+    "                                             labeling=True,\n",
+    "                                             lab_ind=i,\n",
+    "                                             scale=scale)\n",
+    "        else:\n",
+    "            plot_order_intensities_barh(lab, time_since_last_order_binned,\n",
+    "                                             columns=columns,\n",
+    "                                             labeling=False,\n",
+    "                                             lab_ind=i,\n",
+    "                                             scale=scale)\n",
+    "\n",
+    "    if scale_method == 'normalize':\n",
+    "        ax.set_xticklabels(['{:,.0%}'.format(x) for x in np.linspace(0,1,num=6)])\n",
+    "        plt.tick_params('x', labelsize=13)\n",
+    "        plt.tick_params('y', labelsize=11)\n",
+    "        plt.xlim([0,1])\n",
+    "    else:\n",
+    "        plt.tick_params('x', labelsize=15) #12\n",
+    "        plt.tick_params('y', labelsize=13) #10\n",
+    "\n",
+    "    if result_label != 'labs_guideline':\n",
+    "        plt.xlabel('Number of orders per 1000 patient encounters', fontsize=14) #'Order number between 2014/07-2017/06'\n",
+    "\n",
+    "    if include_legend:\n",
+    "        plt.legend(prop={'size': 12})\n",
+    "    else:\n",
+    "        ax.yaxis.tick_right()\n",
+    "        ax.yaxis.set_label_position(\"right\")\n",
+    "        pass\n",
+    "    # plt.xscale('log')\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    cached_result_folderpath = cached_result_foldername + 'Order_Intensities_%s_%i_%s.png'%(lab_type,ind_toplot,result_label)\n",
+    "\n",
+    "    if to_annotate_percentages:\n",
+    "        cached_result_folderpath.replace('.png', '_formal_1.png')\n",
+    "        # plt.xlim([0,5000])\n",
+    "\n",
+    "\n",
+    "    plt.savefig(cached_result_folderpath)\n",
+    "    plt.show()\n",
+    "    plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns_reordered = ['< 24 hrs', '[24 hrs, 3 days)', '[3 days, 1 week)', '>= 1 week'][::-1]\n",
+    "lab2stats_pd.set_index('lab')[columns_reordered].plot.barh(width=0.9, figsize=(8,8))\n",
+    "plt.xlabel('number of orders')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n",
+    "plt.savefig(cached_result_folderpath.replace('.png', '_unstacked.png'))\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Figure 2.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Labs to be plot: ['LABESRP', 'LABALB', 'LABA1C', 'LABTSH', 'LABCRP', 'LABYCP', 'LABLIPS', 'LABAGALA', 'LABNTBNP']\n"
+     ]
+    }
+   ],
+   "source": [
+    "params_2 = {'labs':labs_guideline,\n",
+    "            'result_label':'labs_guideline',\n",
+    "            'scale_method' : 'normalize',\n",
+    "            'include_legend':False\n",
+    "           }\n",
+    "\n",
+    "params = params_2\n",
+    "\n",
+    "labs = params['labs'] #  # \n",
+    "print \"Labs to be plot:\", labs\n",
+    "result_label= params['result_label'] # 'labs_guideline'# \n",
+    "\n",
+    "\n",
+    "scale_method=params['scale_method']\n",
+    "include_legend=params['include_legend']\n",
+    "\n",
+    "\n",
+    "cached_result_filename = 'Order_Intensities_%s_%s.csv'%(lab_type, result_label)\n",
+    "cached_result_path = os.path.join(cached_result_foldername, cached_result_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Each lab \n",
+    "    -> all its orders in 2014/07-2017/06 (implicit) \n",
+    "    -> {time since last order:cnts} (cached)\n",
+    "    -> {0-1 days: cnt, 1-3 days: ...} \n",
+    "    -> barh\n",
+    "'''\n",
+    "lab2stats = {}\n",
+    "columns = ['< 24 hrs', '[24 hrs, 3 days)', '[3 days, 1 week)', '>= 1 week']\n",
+    "\n",
+    "if os.path.exists(cached_result_path) and use_cached_fig_data:\n",
+    "    lab2stats_pd = pd.read_csv(cached_result_path)\n",
+    "    lab2stats_pd = lab2stats_pd.rename(columns={'< 1 day':'< 24 hrs',\n",
+    "                                                '1-3 days':'[24 hrs, 3 days)',\n",
+    "                                                '3-7 days':'[3 days, 1 week)',\n",
+    "                                                '> 7 days':'>= 1 week'})\n",
+    "    lab2stats = lab2stats_pd.set_index('lab').to_dict(orient='index')\n",
+    "\n",
+    "else:\n",
+    "    for lab in labs:\n",
+    "        print 'Getting Order Intensities of lab %s..'%lab\n",
+    "\n",
+    "        df_lab = stats_utils.get_queried_lab(lab, lab_type, time_limit=DEFAULT_TIMELIMIT)\n",
+    "\n",
+    "        dict_lab = stats_utils.get_floored_day_to_number_orders_cnts(lab, df_lab)\n",
+    "\n",
+    "        day_counts = [0] * 4\n",
+    "        tot_cnt = 0\n",
+    "        for time, cnt in dict_lab.items():\n",
+    "            if 0 <= time < 1:\n",
+    "                day_counts[0] += cnt\n",
+    "            elif 1 <= time < 3:\n",
+    "                day_counts[1] += cnt\n",
+    "            elif 3 <= time < 7:\n",
+    "                day_counts[2] += cnt\n",
+    "            elif time >= 7:\n",
+    "                day_counts[3] += cnt\n",
+    "            else:\n",
+    "                print \"time is out of range:\", time\n",
+    "            tot_cnt += cnt\n",
+    "\n",
+    "        lab2stats[lab] = dict(zip(columns, day_counts))\n",
+    "\n",
+    "    df_res = pd.DataFrame.from_dict(lab2stats, orient='index').reset_index().rename(columns={'index':'lab'})\n",
+    "\n",
+    "    df_res.to_csv(cached_result_path, index=False)\n",
+    "\n",
+    "for lab, stats in lab2stats.items():\n",
+    "    stats['total'] = float(sum(stats.values()))\n",
+    "\n",
+    "if scale_method == 'by_scale':\n",
+    "    labs_ordered = sorted(labs, key=lambda x: lab2stats[x]['total'], reverse=True) # \n",
+    "else:\n",
+    "    labs_ordered = sorted(labs, key=lambda x: lab2stats[x]['< 24 hrs']/lab2stats[x]['total'], reverse=True)\n",
+    "    \n",
+    "labs_toplots = [labs_ordered]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x206.28 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, .3 + 5.7/20.*len(labs))) #5+ 3./20.*len(labs)\n",
+    "# 8, 3.435 for guideline\n",
+    "# 8, 6 for freq labs\n",
+    "# 6 - 3.435 = 2.565\n",
+    "# Size for saturation is 7, 2.565 ?\n",
+    "labs = sorted(labs, key=lambda x: lab2stats[x]['< 24 hrs'] / lab2stats[x]['total']) #, reverse=True\n",
+    "\n",
+    "for i, lab in enumerate(labs):\n",
+    "\n",
+    "    time_since_last_order_binned = lab2stats[lab]\n",
+    "\n",
+    "\n",
+    "    for time, cnt in time_since_last_order_binned.items():\n",
+    "        if time == 'total':\n",
+    "            continue\n",
+    "        if scale_method == 'normalize':\n",
+    "            time_since_last_order_binned[time] = cnt/time_since_last_order_binned['total']\n",
+    "\n",
+    "        elif scale_method == 'by_scale':\n",
+    "            time_since_last_order_binned[time] = cnt*scale\n",
+    "\n",
+    "    if i == 0:\n",
+    "        plot_order_intensities_barh(lab, time_since_last_order_binned,\n",
+    "                                         columns=columns,\n",
+    "                                         labeling=True,\n",
+    "                                         lab_ind=i,\n",
+    "                                         scale=scale)\n",
+    "    else:\n",
+    "        plot_order_intensities_barh(lab, time_since_last_order_binned,\n",
+    "                                         columns=columns,\n",
+    "                                         labeling=False,\n",
+    "                                         lab_ind=i,\n",
+    "                                         scale=scale)\n",
+    "\n",
+    "if scale_method == 'normalize':\n",
+    "    ax.set_xticklabels(['{:,.0%}'.format(x) for x in np.linspace(0,1,num=6)])\n",
+    "    plt.tick_params('x', labelsize=13)\n",
+    "    plt.tick_params('y', labelsize=11)\n",
+    "    plt.xlim([0,1])\n",
+    "else:\n",
+    "    plt.tick_params('x', labelsize=15) #12\n",
+    "    plt.tick_params('y', labelsize=13) #10\n",
+    "\n",
+    "if result_label != 'labs_guideline':\n",
+    "    plt.xlabel('Number of orders per 1000 patient encounters', fontsize=14) #'Order number between 2014/07-2017/06'\n",
+    "\n",
+    "if include_legend:\n",
+    "    plt.legend(prop={'size': 12})\n",
+    "else:\n",
+    "    ax.yaxis.tick_right()\n",
+    "    ax.yaxis.set_label_position(\"right\")\n",
+    "    pass\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "cached_result_folderpath = cached_result_foldername + 'Order_Intensities_%s_%i_%s.png'%(lab_type,ind_toplot,result_label)\n",
+    "\n",
+    "if to_annotate_percentages:\n",
+    "    cached_result_folderpath.replace('.png', '_formal_1.png')\n",
+    "    # plt.xlim([0,5000])\n",
+    "\n",
+    "plt.show()\n",
+    "plt.savefig(cached_result_folderpath)\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Normality Saturations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_repeat = 5\n",
+    "labs=['K', 'CR', 'NA', 'WBC', 'HGB']\n",
+    "\n",
+    "if not os.path.exists(figure2_folderpath):\n",
+    "    os.mkdir(figure2_folderpath)\n",
+    "\n",
+    "if os.path.exists(figure2_folderpath + 'lab2cnt.csv'):\n",
+    "    lab2cnt_pd = pd.read_csv(figure2_folderpath + 'lab2cnt.csv', keep_default_na=False)\\\n",
+    "        .set_index('lab')\n",
+    "    lab2cnt_pd.columns = lab2cnt_pd.columns.astype(int)\n",
+    "    lab2cnt = lab2cnt_pd.to_dict(orient='index')\n",
+    "\n",
+    "    lab2frac_pd = pd.read_csv(figure2_folderpath + 'lab2frac.csv', keep_default_na=False).set_index('lab')\n",
+    "    lab2frac_pd.columns = lab2frac_pd.columns.astype(int)\n",
+    "    lab2frac = lab2frac_pd.to_dict(orient='index')\n",
+    "else:\n",
+    "\n",
+    "    lab2cnt, lab2frac = {}, {}\n",
+    "    import pickle\n",
+    "    cur_cnt_folderpath = 'Normality_Saturations_Cnts'\n",
+    "    if not os.path.exists(cur_cnt_folderpath):\n",
+    "        os.mkdir(cur_cnt_folderpath)\n",
+    "\n",
+    "    def save_obj(obj, path):\n",
+    "        with open(path, 'wb') as f:\n",
+    "            pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
+    "\n",
+    "    def load_obj(path):\n",
+    "        with open(path, 'rb') as f:\n",
+    "            return pickle.load(f)\n",
+    "\n",
+    "    for lab in labs:\n",
+    "        print 'Getting Normality Saturations for %s..' % lab\n",
+    "\n",
+    "        cur_dict_name = \"cur_dict_%s.pkl\"%lab\n",
+    "        cur_dict_path = os.path.join(cur_cnt_folderpath, cur_dict_name)\n",
+    "        if not os.path.exists(cur_dict_path):\n",
+    "            df_lab = stats_utils.get_queried_lab(lab, lab_type, time_limit=stats_utils.DEFAULT_TIMELIMIT)\n",
+    "\n",
+    "            if lab_type=='panel':\n",
+    "                df_lab = df_lab[df_lab['order_status'] == 'Completed']\n",
+    "\n",
+    "            cur_dict = stats_utils.get_prevweek_normal__dict(df_lab, lab_type)\n",
+    "\n",
+    "            save_obj(cur_dict, cur_dict_path)\n",
+    "        else:\n",
+    "            cur_dict = load_obj(cur_dict_path)\n",
+    "\n",
+    "        normal_fractions = {}\n",
+    "        record_counts = {}\n",
+    "        for x in range(0, max_repeat + 1):\n",
+    "            if x in cur_dict:\n",
+    "                record_count = len(cur_dict[x])\n",
+    "                normal_fraction = np.divide(sum(cur_dict[x]), float(record_count))\n",
+    "            else:\n",
+    "                record_count = 0\n",
+    "                normal_fraction = float('nan')\n",
+    "\n",
+    "            record_counts[x] = record_count\n",
+    "            normal_fractions[x] = (normal_fraction)\n",
+    "        lab2cnt[lab] = record_counts\n",
+    "        lab2frac[lab] = normal_fractions\n",
+    "\n",
+    "    df_cnts = pd.DataFrame.from_dict(lab2cnt, orient='index').reset_index().rename(columns={'index': 'lab'})\n",
+    "    df_fracs = pd.DataFrame.from_dict(lab2frac, orient='index').reset_index().rename(columns={'index': 'lab'})\n",
+    "    #\n",
+    "    # print df_cnts\n",
+    "    # print df_fracs\n",
+    "    # quit()\n",
+    "\n",
+    "    df_cnts.to_csv(figure2_folderpath + 'lab2cnt.csv', index=False)\n",
+    "    df_fracs.to_csv(figure2_folderpath + 'lab2frac.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zOGGmMMLMYYy9/TOMrobvt2Yxccb9TwQUP1/Bqjs3WDAajEE/X1tGiUND1hauxaX599S4NBdrCte0UotOPFSXbGvhG+osECHRDZdh1B5HtDui8USXR6O40uHjBR70CIsqDgpicaUDd4Dpm5EhJhIjvcJns9YdJ9QKYqRuj7Cajtp7kB4PW/qdhnQGmNEnBCGnnFKvG7MK3A2918Zwmw24rCacXnFzmL3iZtaoMnmoMnt8BE00FDezqDuXIVYMYWGYQ8MIs4QTbgonzBxGuDmcUFNondiFm3V7rfD52b22MHMYIcaQQ75ve777iqLbp2D1eVyHCRJfeo6Uc4cfydt8wnD1p1ez5cCWBvZuMd14/7L3W6FFJx6qS7YhysNsLWxJ7Ek4l5S9XyMANwZK4gfR7opZujiGxR62ihqXx9v1WRNQDAu8XaP7q50Bh8Diwi266EWG0LWdjUSblXaR/h5hgs1KiLnpvBLpdOLMzsaxYyeOHdtx7tiJY8cOHLt2QSCxBJCSta5dVIdqVNk8VJo82M2av9fWiNg5LQaMYWGEWP1FLNwUTqjZX9wSfUTMVwR9xS3MFIbJ0LL/Ngutv7Hlzxbuedfp1/3Y3fIb0zk5BVOJoqI1UILZSiz5bTOn5v+hnwhwSRNXF1zP3YVJDIuPpLCokgJvN2iRz9ig7zhheU1DD8toECRE6IKXGhNKv/SYBl2iiZFW4iOszTrxRauuxrFrF84dO3Rx3L4d+45tuHPzED4L6g/EWsiN08g+1cOIlWAKMNHVbTLw/dRz/Ty1BNNBAasTQV9x89qtRmubmJBxLKwtXMuWdM2v+xE0alRXm8KHhfeuwF7e8EdnaKSFm/45uBVadOKhBLM1kJLQL+8iif185enPcOMq/us5l1y3janvrgkYQtxiMtR1iWYmRHBW5zjveQgJ3i7RRFsIseEWjIaWEwhPWRmOHTtx7tyBY/sOqrZtoWbndsTeg0tWPAYoiDWQGyvJOwP2xBnITzCgpSWTEt+RNFsaHSI7ULC5mPhH5gfoenyGp0/SrkdQ3pQiOAKJ5aHsiiNHCWYLc6DKyfbP5zBU+5En3H/hfc85xBgqed59JaCHCnhgRHd/j9AWQmTo0Y8PHitSSjwlJTi278CxcwcVWzZRuW0z2q5sTAcq6so5TZAfC3nxgj2ZBvLiBa70REI7dCI1NoN0WzrnRHYgLTKN1IhULEb/3T3+Xv531fWoUDSCpkmc1W5qql04qt046lL9WNH8KMFsASpqXCzdWMDitfkUb1/J+6Z/8J3syzzPpUgMB3dbANpHh3LreZ1bpZ1S03Dv3Ytj507KNq+ndMt6HDt2YMrZh7nqYCSWagvkxcOeVMGeUw1Ut4/D2DGd6I5dSYvqQJfIDgyNTA8oiodCdT0qTnQOJ3qOqoO2mno2Z03wS5MUzYMSzGbC7vSwbHMhi9fms2xLIU63RpcoeMc2F4MhlspzXiDk83y/yDahZiP3XNSt2dsm3W6cubkc2LKekk1rqNq2BXbnErKnBLNPkO+KMNgTB3ldDVSmxKB1SCEkswuJ6d1Ij+xA78gOpNqOTBQPhep6VBwPaB4Nh93tFTJf0XN5Rc7f5qh2By16RrMBa5gJa5iZkDATEdFW4lIivDbdbg33pl5biPf433d+10LvwMmLEswmxOnW+H5rEYvX5bN0YwHVTg8JNivXDUxnZJ9kTlt1H2J9Hly/mEsy+uAKiePJr7aQX2onJTqUey7q1qSBxjWnkwNb17Nvw++UbdmIa+dOzLmF2PZVYPJuuWQAHDbYEyc4cFo4rvR2mDplENX1FFLad+fUyHQutaViNap4lIq2w7FOcNE8Wp1n5+ftNRBBXyHUba4mEr2QOtGrFUETpiackd4SCCGSgOeAC9D1ZDUwVUq51ps/FngUSAaygIlSylU+174D9AO+BUZLKau8eWcBzwBnSSnbjGutBPMYcXs0ftm5n8Vr8/li/V7Ka9xEh5m5/NQURvZJ4YxOcfoknD8WQdZ7cP5DkKH/Q4/q175JBLKstIC8rF8o2bwW+/ZtiN15hOUfIKbYgUHqohgFFEZDXlII9u4pkJFKWGZX4rv3JT25OwOVKCqOIw41wSVreV5g0fPp7nQF2C7NF1Ot6IXromaLDSE+NeKgwIWZCAnz9fRaX/RCIy2N/ohoRl4CbEBXoAqYBSwRQqQDZwNzgSuA74DJwOdCiC5SynLgQWAlcCHwLnAr8LQQwgrMA8a0JbEEFbjgqNA0yaqcAyxem8/nWXsprnQSYTUxvGc7RvZN4ezMeCwmnyUbBRvh5QsgbSD87SPwRlup+uVXcsaP19cfWi2k/3s+4YPOCHjPSmcl2XnrKdiwioptm/DszMaSW0jMvkpiyw6uxXAboDDeREVKFO70JCydOhHV7RRSuvcnLaEzIaZjD42nUDQnbqeH6nIn1eVOqsocVJc5686ry51UlzkozK44bD31Ra/W4/MVNz/B8+niNJpbLtZwWyWYwAVCiHXAC1LK+d7zbsBmIAF4CjBIKf/mzRNANvCwlPJ1IcTnwHNSyq+EEBOAPlLKiUKIxwGXlPLR5nu6o0N5mEEipSRrTxmL1+azZN1e9pbVYDUZGNajHSP7JjOkW2LgBf7OKvjvDWC1wZUv+4ll7oQJBxfrO5zkTpiA9s/72RML+zevw7FjB4bsfGz5pSQWuoiu1vs1kgGnWbC/XRjlPdKo7JhORJfuJPTsR0a3/vQOtbXU26JQBIWmSWoqXboAlju9IujwEcGDYhhwnE9AqM1CWKSF8MN4TDc8cbYSvaYhXgjh6yHNrxVGH54ExgghPgIqgPHACillsRCiL/BabUEppRRCrAb6ek3rgD8JIb4FhgNfCyH6ASOAwJ5DK6ME8zBs2VfB4rX5LF6XT3ZJNWaj4LyuCdw/ojtDe7QjwnqYt/Dze6B4K4z9GGzt6sy5t97aYA9AWVMDd84gFUj12mpCjVSmROM4M4WyTpnE9OhN8ikDsaV1RDTBHpQKxdEipcRVU+sNOqgq8/cCfcXQXhE42pQ5xEhYpC6E8akRhPWMJSzK4rVZ9TTKQmiEGYNPoI0XJyxrtF3hUWpooYkoDiI03o/A9UAh4AFy0QUP9K7asnrlS4HabZFmA88Dq9DHMBcB36N3zY4QQkwF3MB0KeUvx/YoTYMSzADsLq5iybp8Fq/dy5aCCgwCzs6M5/YhmVx0ShJRYebgKlrzNqx5E869FzoN8ctqbMNcAYi7x5PY8zRsXXpgSkg47iPVKI4vPB4Ne33Pz1cQfbxDt7NhaCaDQRDqFcGIaCuJ6TbCoqx1wqiLoH5uth5fk1wUBxFCGID/AV8CVwI1wFjgByFEL3SPM6reZdHADgApZZm3fG19DwHLgW3Ah+ieaCLwpRAiQx7J+GEzoQTTS36pnc/W7WXxunzW5ek/igZkxDDz8lMY0SuZBNsR/mot2gqf3QUdBsOQ+/2yatw1uE0GTIE2PLZa6H7L1KN9DMUJSFOEPJNS4qh2+4ldlY8Y+o4T1lQGXgRvDTPVCV27jlF1nmC4jwCGRVkICTMjmjnaVCtNcFH4Ewt0BJ73TuIBeEUI8QRwJrAWOK22sHcM81R0MfRDCNED+CswAOgN5EopS4AS7ySgBHQvtlU5qQWzqMLBF+v3snhtPr/v1ndp75MaxUMX9+CSPsmkRIceXcUuuz5uaQ6Fqw6OWwIU24u5+8s7uC5aI61Y9yhrcZjg1ynn0OPoH0lxAnKoGaG+E2RqxbAqwLhgdYUTzd3wB7rRZKgTvqiEUJIzo+u8wPAob7dolIUwm6VNjQmq2Kitj3ecciswUQhxP+BA9xht6OOTxeje4evAD8CdQAjwkW89Xk/1FeB2KaVdCJENdPPOtE0EzEBJCz3WITnpBLOs2sWXG/ayeO1eftpRjCahWzsb04Z35dI+KWTEhx/7Tb64Dwo3wOgPIDKlzrz1wFYmf3U717+xl9QS+O9gwahfZF0YuH9ca8Adt49bj70FihMEjydAL4QPARerCwiNMNeJXUxSmFcAfbpFvSJpacWQi4oTglHoE3+y0YVtO3CNlHInsFMIMRF4mYPrMC/28UZruRNYJ6X8DkBKWSCEeAT4HXAC49rK8pKTYllJpcPN/7yh6b7fVoTLI8mIC2Nk3xQu7ZNCt6QmnFWa9T58cDMMngrDZtSZv8/7nnuXT+P2T9z0z7KTNPP/iLn22qa7r+K4x+XwULKnkqKcCopzKyjKrWR/fhWeQF33Xs64vJOPR6gLYqjNf4KMQnE0nIj7YQoh4oHOwBopZeCJJIfghPUwa1welm8p5NO1+XyzqRCHWyM5KoQbz+7IyD4p9Gof2fS/rEt2wOLJkHaGHqAAfezorc1v8c/fnuCu5Tb6Z1WQcPddSixPcuwVTopzKynKPSiOpYXV1G5VYw03kZBmo/f5qaxZmtNoPf1HZLRMgxWK4xghhA1YAFyN/l/WBd0Dngfsk1LOCKaeE0ownW6NH7cXs3htPl9vLKDS4SY+wsKfB6RxWd8UTkuPwdBckxFcNfDf68FohqsXgtGMS3PxxG9P8O6Wd7lvTTqn/7yTuHE3E3/LLc3TBkWbQ0pJRUmNVxgr68SxqvTgj9uIWCsJaTa6DGhHQloE8Wk2ImIO7uN5KMFUKBRB8QTQHn0S0gof+xLgMWBGMJUc94Lp0SS/7ixh8bp8vli/j9JqF5EhJi7pnczIvikM6hSLqSW6p75+CPZlwV/fhahUyp3lTFs+jZ/3/sz/5fanxxe/EH3N1STcfXfzt0XRKng8GqX7qnVxzPF6j3mVOO36Bp9CQExyOO27RhOfZqsTx5DwQy9TUjNCFYpj5jLgCinlGiGE7zjkJqBTsJUcl4KpaZLVuQdYvHYvn2XtpajCQZjFWBea7pwuCf6h6ZqbDR/D76/AmZOg25/Irchl0jeTyCnP4Vn75aT85wNsw4eTNGOGmmBxgnC48UaT2UBcagRdBrQjPjWChDQbce3DMVmOfN2hmhGqUBwzMQSeaWtDD7gQFMeNYEop2ZBfzuJ1+SxZu5c9pXYsJgNDuycysm8K53dLJPQovoyOmf274NM7oP3pMPRR/ij4gynfTsEjPSwIu5XQJ14g/KwzSfnXkwijWqR9PGKvcDboUm1svDEhLYL4VBvR7ULVxBuFou3wO7qX+az3vNbLvBX4KdhK2rxgbi+s4NO1e1myNp+dxVWYDIJzuyYw7aKuDOvRDltIkFF3mgO3A96/Ue9ru/pVFmd/xaM/PUpKRArPRd6Ca+ojWHudQurzz2OwqO6ztk4w44222BDi0yIaHW9UKBRtkgeBr4QQp6Dr3l3e44HAucFW0iYFM6ekmsXr8lm8Np/N+/TQdGd2jmP8uZ246JQkYsLbiPj8bwbkr0a7dhEv7v6U+evmMyBpAE8kjGf/uNsxp6eRNm8ehvAmWNupaFKCHm/sFk18avDjjQqFou0hpfzJu8fmNPTQfEOBP4AzpZRZwdbTZgRzX1mNHr913V7W5pYCcHqHGGaM7MnFfZJJtLWxbak2fwa/vETNgHFML/qBr3Z/xRWZV3Bf4hj2jL0BY0wM6QsWYIqJae2WnvS4HB6K83SP8XDjjbVdqkc73qhQKNomXmG8/ljqaDHB/Hj1Hp78agv5pXZSokO556JunNMlns/X7/OGptuPlNCrfSQPjOjOJX2SSY0Ja6nmHRmlOfDxbRQn9+FOuYf1uzdy9+l3c13MhWSPHgNGI+kLF2Bu1+7wdSmalCMeb0yzEd0urPmWGykUilZHCOEBkqWUhfXscUChlDKoX8ctIpgfr97DAx9mYXfpk5H2lNq56701SKl/j2UmRjB1WFcu7ZNMp4SIlmjS0eNxwfs3scUomBRtoqxsJ8+c/wznhZ9K9ugxaFVVdHhjEZYOHVq7pccdRxJkvP54Y20aaLyx60B9pqoab1QoTloa+6e3ooffC4oWEcwnv9pSJ5a1aBIirCbev+1MurWzHT9fYt/M5PuSLO5JSSVCGHjtT6/RzZpOztjrce3bR/qCVwjp3r21W3lccqgg48V5lRTnNTLeaBDEJIXRvls0CWk24tNsxKdGqPFGheIkRwhxl/dQAhOEEJU+2UbgHGBzsPW1iGDml9oD2qscbronRQb625GSAAAgAElEQVTMa4vILV/yn/Wv8q+kRLpFd+b5C54nwRhF7i3jqdm6lbSXXiTs9NNbu5knJO/O+g0IMN6YZiMuRY03KhSKgNzhTQUwDv81l05gNzAh2MpaRDBTokPZE0A0j3r7rFbAdSCb2cum8N+4GIamDuHxc58gFDN5d06meuVKUp58kohzg56drPDB49LYs+3AIctceFNPNd6oUCiOCCllRwAhxLfAlVLKQ3/RHIYWEcx7LurmN4YJEGo2cs9F3Vri9sdMuf0A0z65ip/DzNzU+Qomnz0DIWHvAw9Q+e23JD36CFGXXtLazTyuqDzgIHt9MdnrS8jdfAC349DBNroOTGqhlikUihMNKeX5TVFPiwjmqH7tARrMkq21t2Vyy3O5ffGfyaWGmemXccXgmUgpKZg9m7JPPiVh8p3E/PWvrd3MNo+mSQp3l7M7SxfJ4lx9KCEi1kr3M5Lo0DuOz15c18qtVCgUJypCiK7ou5WkA36L+aWUNwVTR4stKxnVr/1xIZC+rCpYxZT/3Y50lDM/6jQGnP84AMUvvcSBN94g9vqxxE0Iuvv7pKOmykXuxv3sXl9Mzob91FS6EAZBUqdIzryiMx16xRGbEl434UsFGVcoFM2BEOIS4ANgNXA6eqi8zuizZH8Itp42E7igrfHpjk+Z8dMM2jsdvOC00WHkfAD2/+dNip9/gahRo0i8777jZ3ZvCyClZH9+FdnrS9idVcy+neVITRISbia9VywZveJJ6xnb6OzVky3IuKZpFBcXU1paisfTJjaUV5xEGI1GoqOjiY+Px2A44eMezwT+T0o5WwhRAfwNyAfeAH4OthIlmPXQpMYLq1/g5ayXGSitPF1wgKhxH4AljLLFiymYNYuIoUNJnvV3xIn/ITssLqeHPVsOkJ1Vwu71xVTu19dBxqdFcNpF6WT0jicxI1JN1AlAXl4eQggyMjIwm83qx5eixZBS4nK5KCgoIC8vj/T09NZuUnPTDXjXe+wCwqSUNUKImcBnwNPBVKIE0we7285DKx5iafZSrgrvzEPrv8V8+UuQ2J2K5cvJv/8Bws44g/ZPP4UwnbxvXXmJneysErLXl5C35QAel4bJaiStewz9R2TQoVc8ETHW1m5mm6eqqopu3bqdDL/uFW0MIQQWi4X27duzZcuW1m5OS1AB1MZX3QtkAuvRNTDo+KUn77d+PYqqi7hz2Z1sKNnAtI5XMHbZHETfv0K/0VSvXMmeyVMI6d6d1BdfwGA9ucTA49HYt6OM7PW6SO7PrwIgMiGUUwan0KF3HO27xGA0qy/+I0WJpaI1OYk+f78Cg4GN6B7lU0KIvsAVqC7ZI2PL/i1MWjaJMkcZz575f1zw6X0Q3wUu/hc1GzeSO+E2zCkppL08H2NEGw/d10TYK5xkbyghO6uEnI37cdrdGAyC5C7RnH11Mh16xRHdLkx1IyoUiuOBu4DaL+8Z6BtHXwVs9eYFxUkvmMtzl3Pv9/dis9h4/aJX6fH5Q1BTBn/7COfeYnJuGY/BZiN94QJMsbGt3dxmQ0pJcW5l3bKPgt3lIPUZqp36JZDRK460HrFYQk/6j4xCofBBCDEMmAX0AmqA96SUE715Y4FHgWQgC5gopVzlzUsC3gH6Ad8Co6WUVd68s4BngLOklMc0I04IYQK6o3uZSCmrgduOpq6T9ttPSsmijYt4auVT9IzryZwL5pC48nXY+S2MfA6XjCPnptEgJekLFmBOTm7tJjc5zho3eZsOsNsbQKC6TF/SkdjBxoBLOpLRO46ENBtCTdhRHAf88MMPjBw5ktLS0tZuykmDEGII8D562LnF6CHoenrzBgNz0bs9vwMmA58LIbpIKcvRN3VeCVyIPiHnVuBpIYQVmAeMOVaxBJBSuoUQH6KLZsmx1HVSCqZLc/H4r4/z/tb3GZY+jMfPeZzQPavh28eh19W4O15Gzt/G4ikrI33R61g7dWztJjcZpQXVdcs+8reVonkklhAjaT3jyOgdR/opcYSpdY+KegwZMoSff/4Zs9mM0WikU6dOTJ8+nauuuuqw12ZkZDBr1izGjBnTrG0855xzlFi2PLOBeVLK931sf3jTW4APpZRfAwghngQmoQvo6+gTb56TUrqEEF8DfbzXPQp8JKVsykgma733230slZx0glnmKOPu7+7m172/Mq73OO7odweG6gPw/s0Qk4HngsfIvXUCrtxc0l55mdBTTmntJh8THrdG/rbSumUfZYV6TN+YpDD6XJBGRq84kjKjMBpPmsH/E4ZAe8w2Z3CQhx9+mOnTp+N2u3n66af585//zMaNG+natWuz3VPRqsQLIVb6nM+XUs6vPRFChAMDgR+FEH+gR9BZD0yTUq4E+gKv1ZaXUkohxGqvHWAd8CdvnNfhwNdCiH7ACOCMJn6WGegTfR4FVgFVvplSyv3BVHJSfUvmlucy5vMxrCpYxayzZzH5tMkYJPDRrVBdjHb5fPLueoCajRtp/+yzhA8c2NpNPiqqyhxs/DGfL+ZlseDuH/j0uTWs/34PUQmhnPPnroz5+5lcN2MQZ1+VSftuMUosj0Nq95jdU2pHou8x+8CHWXy8ek+z39tkMjFx4kQ8Hg9ZWVlkZ2dz+eWXEx8fT1paGlOmTMFu13+YjRw5kpycHMaNG0dERATDhw8H4J133qFv375ERkaSnJzMrbfeSlXVwe+wOXPm0LFjR2w2G+3bt+fBBx8EwOFwMH78eBITE4mMjKRLly7897//BWD58uWYfJZ73XDDDYwbN86v7RkZGfznP/8B4LXXXiMzM5NnnnmG1NRUbDYb06ZNo6SkhKuuuorIyEi6d+/OihUrmu/NbNsUSyn7+7zm18uPQdeQvwI3ACnA1+jdrtHoE2vK6l1TCtRuUTUbiEMXsDxgEfAyetfsCCHEciHE/4QQg5rgWT4DegMfonuZRd5XsTcNipPGw1y5byVTl09FInn5wpfpn9Rfz/hpDmxfirzon+T/axHVv/xCyhP/wHZBk8TqbRGkJinILq9bG1mUUwFARIyVrgPb0aF3PKndYjBb1RZYbZn/W7yBjfnlQZVdnVOK06P52ewuD/e+v463f8s57PU9UyJ5dOTR9Z44nU5efPFFzGYzffv25ZJLLuHss88mOzub0tJSRo0axbRp03jxxRdZvHhxwC7ZqKgo3nrrLXr06MHOnTu57LLLmDVrFrNnz2br1q3cf//9/P7775xyyimUlpayebO+ZeHrr7/O77//zqZNm4iLiyM3N5eKioqjeg6grs07d+5k06ZNDBw4kO+//545c+bw3nvv8fDDD3PjjTeybdu2o77HCUztG/9qbfepEGI2cA9wljc/qt410cAOACllGTC2NkMI8RCwHNiGLmx9gUTgSyFEhpRSHkNbj5/g663NJ9s/YcbPM0iNSOXFoS+SHumNapHzK3wzE9njMvYuzqFi6f9o9+ADRF1+ees2OAgc1S5yNu4ne30JORtKsFe4EAKSOkUxaFQnOvSKJ659uFr2cYJSXywPZ28KHnvsMf71r39hsVjIzMzkgw8+oLCwkG3btvHrr78SHh5OeHg4s2bNYtSoUbzwwguNfv5GjBhRd5yZmcnEiRNZtGgRoHuwUko2bNhAhw4diI6OZtAg3cmwWCxUVlayceNGzjzzTNLS0o7pmUJDQ3n00UcxGAz07duXvn37MmDAgLr7jRkzhtmzZ1NWVkZUVP3v/pMbKWWZEGI3+ubMflne11rgtFqj0D8Mp6KLoR9CiB7onuoAdE8wV0pZApR4JwElAIXH0NbvjvZaX05owdSkxvOrn+eVrFc4I/kMnjrvKaKs3g999X744GZkZHsKd3Sl7MO3iJ84kdixYw9daSshpeTA3mp9RmtWCXt3lCE1iTXcRHrthJ2ecYREBI7Tqmj7HInHd/Y/lgXcY7Z9dCjv3npmUzarjoceeojp06f72d59910SEhIIDw+vs3Xu3JmamhqKiopITEwMWNfSpUuZOXMmmzdvxuFw4PF46sp26tSJN998k7lz5zJu3Dj69OnDI488wvDhwxkzZgwFBQVMnTqVbdu2MXToUP75z3+SmZl5VM+UmJjot3g/LCyMZJ8Z8WFhYQBUVFQowQzMS8BkIcTbHFzT6AB+QvcwvxRCvI4e4PxO9Gg7H/lWIIQwAK8At0sp7UKIbKCbECId3cM0c4yzW5uKE3bwyu62M+27abyS9QpXd72aucPmHhRLKeGT26FiHyXuK9m/6C1iRo8m/o5JrdvoeridHrLXl/D921t4Y/rPvD3zV37+cAeOajf9hqdz5bTTuOnJcxh+8yl0HZikxPIk4p6LuhFq9u9ib409ZtPS0igqKqK6urrOtnPnTkJCQkhISAAaRpNxOp2MGjWKv/zlL+Tk5FBeXs4TTzyBb4/blVdeydKlSykuLubaa6/l8ssvp7q6GpPJxH333cfKlSvJzs4mLCyMm24KvDOTzWbzGxd1u90UFh61k6IIzL+AhcAy9PHAEcAIKWWZlHIFMBF9XLIMuBa42LukxJc7gXW1XqCUsgB4BH1HkY+AcU2xvKQpOCE9zKLqIu5YdgcbSzYyrf80xvYc69819Mtc2PI5B6yjKVrwHpGXXkq7hx5s0e7LhfeuCLiVVUiEmTMu60R2VjF5mw/gdmmYLAZSu8dy2kUd6NArDltsSIAaFScTbWWP2YEDB5KZmcndd9/NU089RWlpad24X+3/U1JSkt8YoNPpxOFwEBMTQ2hoKBs3buSFF16oy9+yZQu7du3i3HPPJTQ0lKioKIQQGAwGli1bRlRUFH369CE0NJTw8HCMxsBj86effjr33nsvu3btIiUlhUceeQSXy9W8b8hJhndc8RHvK1D+IvTJPIeq49kAtheAFwIUb1VOOMHcvH8zk76ZRLmznDkXzGFI2hD/AntWwdJHKHedxb53lxNx3nmkzH68xXceCSSWADWVLr57awuR8SH0ONsbp7VrNCazmrCj8Kct7DFrMplYsmQJd955J+np6YSEhHDllVfyj3/8o67M9OnTueOOO5gzZw6DBg3iiy++YO7cudx7772MHz+eAQMGcN1117Fw4UJAF9SZM2eyYcMGgLrx0pCQEAoKCpg0aRI5OTlYLBYGDhzI/Pn1J2/qjB49mhUrVnDaaacRHh7OAw88QPv2x9eevIq2hTiSiUf9+/eXK1euPHzBVuLbnG+574f7iLRE8uLQF+kWW697yl4K/z6HyhyN3K8NhPbtS/orL2MIDW3xtr44YVmjeX999AxiklSc1hOdTZs20aNHj9ZuhuIkp7HPoRBilZSyfys0qc1yQniY9cPcPX/B8ySEJdQvBJ/eQfWOQvJ+SMLapTNpc19qFbE8HLHJ4YcvpFAoFIpGEUJ8GmxZKeVlwZQ77gXTpbl47JfH+GDbB1zY4UIeG/wYoaYAIvj7K9T8/Dm5P6ZiapdE+svzMUZGNizXAhzbciKFQqFQBEGTz6w9rgWzzFHG3cvv5td9v3JL71uY1G8SBhFgLDJ/Dc73p5OzIgWDLYb0BQsxxce3fIPRxfLH/25vlXsrFArFyYKU8samrvO4Fcyc8hxu/+Z28irzeGzwY1zWuRGPuqYc12vXk/NtLBjDSF/wCpbU1hv4/23xLtYuy8VkNuB2NVxkHqoCnysUCkWb5LgUzN/3/c7U5VMRCF4Z/gqntzs9cEEp8bx7O7mfVOF2RdBh0ctYj3KBc1Pwx1fZrPx8Nz3OTub80d3VtlkKhULRQgghzkePJpQO+HkmUsoLgqnjuAtc8PH2jxm/dDyxIbG8dfFbjYsloP04n9z5P+GsspI2dy6hffo0Wra5yVqex88f7aBL/0SGKLFUKBSKFkMIcQPwBXpA+CHoAddj0EP3bQy2nuPGw9Skxpw/5rBg/QIGJQ/iqSFPEWlpfNKOlrOavIefxL7fQvvnniH8zOYJFxYMm3/ey/fvbCWjTzxDb+yJQYmlQqFQtCTTgElSyleEEBXAA1LKnUKIF4DKYCs5LjzMalc1dy+/mwXrF3BN12t4adhLhxRLWV1G/m1/o2qvmeTp9xHp3VKoNdi+qpBlizaR2j2Gi245RW2lpVAoFC1PJ+B/3mMHEOE9fgF9a7KgaPPf3oXVhdz41Y18k/MN9w64l4cHPYzZ0HjMVKlp7Lt1FBU7PCSOu5ro0Te0XGPrsTurmKULNpDUKYqLb+ujovUoFE1EREQEP//8c7OVV5xwlKB3xwLsAXp5j+OAoBfjt2nB3FSyib9+9ld2l+3m+Que5289/3bY6DdFD9xC6e/7iPtTb+Km/b2FWtqQvM37+fLf64lLjeCSSX3VXpSKE4KVK1cyatQoEhISiIyMpGvXrkyZMoW9e/c2y/3qbwpdS2VlJWcewTDLkZZXnHD8ANR2Nb4HzBFCvAq8DSwNtpI2K5jLcpZx/ZfXYxAGFo1YxHlp5x32mpLnZlPyyU9E94sk4am3WqCVgdm3s4zP5mYRlRjKZXeeijX0uBkqVhwvzBsMM6IavuYNbrZbLl26lMGDB9OtWzfWrFlDeXk53333HXFxcXz3XcPtBlWgc0UbYhK6OALMBp5E9y7fA8YFW0mbE0wpJa+tf40p306hc1Rn3rr4rYYxYQNQ+u7bFM5dhK2jRtLcjxDG1hGpopwKFj+/lvBIC5dNPlVtuaVoHlIHgrHeml2jRbc3ExMnTuS6667jiSeeqAtinpyczMMPP8xf/vIXhgwZwpQpUxg1ahSRkZE89dRTAPzwww8MHjyY2NhYOnfuzFNPPVUX7aq6uporr7ySpKQkIiMjOe2001i6VP/Bn5+fz4gRI/B4PERERBAREcHrr78OgBCCFStWAPDaa6+RmZnJnDlzSE1NJSYmhltvvRWP5+COUEdaPicnh6uvvpqkpCSSk5MZP348FRUVzfbeKpoXKeV+KWW+91iTUj4hpbxMSjlNSlkabD1tyvVxeVzM+nUWH277kOEdhvPY4McIMR1+K6vyr79m74yZhCfV0P6Z+YjolBZobUP251fx6Zw1WEKNXD61H+FR1lZph+I45Yv7YV9WcGXdTtDc/jbNrV//6iWHvz6pN4z4x+HLedm6dSvbt29n7ty5hyy3cOFCPv74Yz766CPsdjsbN27k4osv5j//+Q+XXnop27ZtY8SIESQkJDB27Fg0TePKK6/k9ddfJyQkhGeffZarrrqKHTt2kJKSwhdffMGwYcOorDz0RMbs7GwKCgrYsWMHubm5DBw4kHPPPZfRo0cfcfmamhouuOACrrvuOt544w1qamoYPXo0kydPrttRRXF8IoSIRd+U2s9ZlFIGtbSkzXiYZY4yJvxvAh9u+5Dxfcbz5HlPBiWWVT/9RP5ddxEa6yT13rGI7he2QGsbUlZUzSfPrcZgEFw+uZ/as1LRvJgsEJ4I1I7pC/28vtfZRBQVFQEcdnusq6++mgsuuAAhBGFhYbz00ktcc801XH755RiNRrp3786kSZNYtEjfIjEiIoIxY8Zgs9kwm83cc889WCwWfv/99yNqX2hoKDNnzsRqtZKZmcnQoUM51M5Khyq/ZMkSpJTMnDmT0NBQYmJi+Pvf/86bb77p54Uqjh+EEP2EEGvQ119uANYDWT5pULQJDzO7PJtJ30xiT+UeHh/8OCM7jwzqOvu6deTefjuWCCdpoztjuOjhZm5pYCr21/DJM2vQ3JJRd/Ujul1Yq7RDcZxzBB4fABX74Lm+4K4BkxVu/R5s7ZqlaQkJ+u4/e/bsOeSWZBkZGX7nu3btYtmyZXz44Yd1Nk3TSEtLA8But3PPPffw+eefU1xcjMFgoKKiok6ggyUxMdFvI+nw8PBDdqEeqvyuXbvIyckhOjra7xohBPv27VN7ah6fLESfHTsZKACOageMVhfM3/f9zpRvp2AURl4Z/gqntTstqOsc27eTe8t4TBYnaRe5MI55DVph3LK63Mmnz63BUe3i8qn9iGsfcfiLFIqmwJYEp46GVa/qaTOJJUDXrl3JzMzk7bffZtiwYY2WM9TbiL1Dhw7cdNNNvPjiiwHLP/3003z//fd88803ZGRkIIQgPj6+boyzfn0tQYcOHejatWvdBtaKE4IuwDVSymPa+aJVu2Q/2vYR45eOJz40njcveTNosXTm7SHnpptBs5N+zl7MY+ZDZMuPW9ZUufj0udVUHqjhkkl9SezQOtuFKU5izrsX0gfBefc1+61eeukl3nzzTR588EHy8/MBKCgoYPbs2bzzzjsBr5k4cSLvvPMOixcvxuVy4Xa72bhxY92s2vLycqxWK3FxcTidTmbOnElp6cE5GElJSXg8Hnbt2tXsz1fLpZdeitPp5PHHH6eiogIpJXv27OGjjz5qsTYompwVwDHv1t4qgqlJjadXPc0jPz3CgHYDeOPiN0izpQV1rbu4mJybb0KrKif97DwsF02CLi0/bum0u1n8/FoOFFRz8W19SMmMPvxFCkVTY0uCG79oVu+ylgsvvJAVK1awceNGevfujc1mY/DgwRQWFjJkyJCA1/Tq1YslS5bw7LPPkpycTGJiIjfccENdl+tdd91FdHQ0KSkpdO7cmbCwML9u3a5du3LbbbcxcOBAoqOjeeONN5r9OcPCwli2bBkbN26ke/fuREVFMXToUNasWdPs91Y0GzcDE4UQk4UQQ4UQ5/q+gq1EHMlmxv3795eHGkg/HEXVRdy9/G7CzGH8mP8j13a9lvvPuP+QkXt88ZSXkz32epy7d5F+/n7CenWHGz4DY8su3XA5PSx5fi37dpTxpwm96dindfbWVBzfbNq06ZDjgQpFS9DY51AIsUpK2b8VmtTkCCGGAO+gz5Ctj5RSBhVZpkU9zGdWPcPqotX8mP8j9w+8n+mDpgctlprdTu5tE3Fs307qiBDC2gm4akGLi6XHpfHlvCzyt5cy7MaeSiwVCsVJjxDCIIT4SQghhRCpPvaxQogdQohqIcSvQojTffKShBDLhRBlQoiPhRDhPnlnecs3VYi0f6PHku2NLpoJPq9AIhqQFhPMwqpCluxcAoDZYOaijIsOG+auFulykTdlCvY//qD9dX2IsG6CUXMhOrhu3KZC82h8vWADORv3c/6Y7nQZ0PzdYAqFQnEcMBWo9jUIIQYDc4Hb0LfS+gD4XAhRO9njQWAlEA9owK3e66zAPOAWKWVTreNJBR6VUm6QUhZLKUt8X8FW0mKC+e+sf2P0+bEwb+28oK6Tmkb+Aw9S9d33JN16BZHOJTDoduh+cXM1tZF2SL55fRM71xQx+Nou9Dy7dYIjKBQKRVtCCNEVmIi+hZYvtwAfSim/llI60MPROYArvPmZwFIppQv42nsO8CjwkZRyXRM2cynQ+ObJQdIi6zCKqov4ZPsnuKUemcSlufh4+8dM6DuB+NDGuzSllBTMeozyJUtImHADMfYXIeU0GDajJZrt147lb29h628FDBrVib4XtKxnq1AoFK1AvBDCd9LKfCnlfN8CQggD+hrHaUD9EHN9gddqT6SUUgix2msHWAf8SQjxLXpg9K+FEP2AEcAZTfkgwJfAU0KIPuiBCvwCHUspPwx4VT1aRDDnrZuHJjU/myY15q2dx/RB0xu9rvj5Fzjw1lvE3nA9caFfgB245lU9ykkLIaXkx/e3s/GHfE7/UwdO/1NGi91boVAoWpHiICb9TAb2SSk/EkJk1MuzAWX1bKVAbZfsbOB5YBXwLbAI+B69a3aEEGIq4AamSyl/OdqH8PKSN30wQJ4EghorbRHBXFu4Fpfmv3OBS3OxprDxadr7Fy2i+KWXiLrqShJPLUf8shqufQNiMpq5tf78tmQXa7/Jpc/5qZxxeacWvbdCoVC0VYQQmcDdQGOiWgFE1bNFAzsApJRlwFif+h4ClgPbgA/RPdFE4EshRIY8kiUd9ZBSNsnwY4sI5vuXvX9E5Us//piCx2dju3AYyaPPRLw3GgaOh56XNVMLA/PH19ms/Gw3Pc5KZvA1XYKepKRQKBQnAYPRZ5mu93431orSOiHEdGAtUBeNRuiFTkUXQz+EED2AvwID0Gey5non45R4JwElAIVH00ghhBk9cMFYKeWWo6mjllYPjVefimXL2PvQdMLOHETK9CmIVy+A5L4wfFaLtiNreR4/f7iDzP6JDBnTHWFQYqlQKBQ+vIe+VKOWVOBn9PHIzehjlF8KIV5H38D5TiAE8AuZ5B0HfQW4XUppF0JkA92EEOnoHqYZCHoma32klC4hREeOMn6sL21KMKt+/Y09U6YS0rMnqc89g+G9a0DzwNWv6sGlW4jNP+/l+3e2ktEnnmE39sSgxFKhUCj8kFJW47OURAhRqyf7pJSVwAohxETgZSAZfbLNxVLK8npV3Qmsk1J+5623QAjxCPA74ATGNcHyktfRZ+3ecyyVtBnBtK/fQN7EiZjT00ib/2+Mvz4Deb/B1QshrnOLtWP7qkKWLdpEavcYLrrlFIzGNrMDmkJxQjFs2DAGDx7MjBkzyMnJoWfPnmzdupWUFLVk63hESrmbg/vN1doWoU/mOdR1zwawvQC80ITNCwdGCyEuRJ9kVFXvfncGU0mbUAPHzp3k3nILxqgo0hcswFS8En58Fk6/EXpd1WLt2J1VzNKFG0jqFMXFt/XBZG6qIBMKxfHPzp07ueaaa0hKSiIiIoK0tDSuuOIKnE7nMdednp5OZWWlEktFc9ED+AM4AHRCHyetffUKtpJW9zBd+fnk3DwODAbSFy7AHOqB12+Fdr3gT7NbrB15Ww7w5fz1xLWP4JJJfTFblVgq2j5F1UXc8/09/Ou8fx1yTXNTcPHFFzN8+HC2bNlCZGQke/bsqdtsWaFoy0gpz2+KelrVw3Tv30/OzePQKipIf+VlLGmp8P7N4KqBa14Dc2iLtGPfzjI+e2kdUQmhjLyzL9bQVv8doVAExbx18/ij4I+gI2cdLSUlJWzZsoUJEyYQFRWFEILU1FQmTJiA1arPL5g7dy7dunUjKiqKQeQKDzgAACAASURBVIMG8cMPP9RdL6Vk9uzZpKamEhsby9SpU/2Edvfu3QghyMvLA2DGjBkN9t0cMmQIs2bpk/+WL1+OyWTirbfeonPnzoSHhzN27FjKy8u55ZZbiImJoUOHDn4bVysUQogQIUQvIcQpQoiQI72+1ZTBU1lJ7rhbcOXnk77gFUJ69IBlsyDnJ7hiPsR3aZF2FOVWsPj5tYRHWrhs8qmERrRcUASFwpcnfnuCzf/f3pmHVVWtDfz3gqKBCCoiDgiOQWZaYppTqA2OmV2HLDUkS29ZVt7smnYzK7+6lplZardBzWzUSlOz0tS63QY1c0JzFjEKnBAUZFjfH2sfOhyGc5jOAVm/59nPOXuttdd+91p773ev6X1P7XU5/cXsi+xM3olC8eG+D9l7ci/VXXRGEFE3gseudd2HZr169WjTpg1jx45l/PjxREVFERkZmbvU6r333uOJJ55g9erVdOjQgcWLF9OnTx/27NlDWFgYS5cu5aWXXmLt2rW0bduWWbNmsXnzZrp37+6yDI5kZ2ezceNGdu7cSXJyMlFRUXTu3JlZs2axcOFCXn/9dWJjY+nTpw++vr4lPo+h8mMtLZkJTAB80GOtGSLyCjDVMs/nFM/4w8zI4Ph995P+2280fnkOvlFRcPAb2PwCXD0S2g13ixynfk9j1dzt+NT05paH2uMX4L6ZuAZDafk97fc8+yfSTpTr+TZu3Eh0dDRz5syhffv2NGjQgKeffhqlFG+//Tbjxo2jU6dOVKtWjbvvvpurrrqKZcuWAbBkyRLGjRtHhw4d8PHxYcqUKYSEhJRapmeffRZfX1+aNm1KdHQ0zZo1o3///nh5eTF69GjOnj3L/v37S30eQ6XneWAkMB5oDbRCG4UfhbY45BJua2Gm/fAj8ePGoTIywMsLcnJoNGsW/tHRcC4RVtwD9S+Hvv92izxnky6wcs4vIMKgh66mdj33dP8aDIVRnBZf0vkk+q7oi7KWlikUKRdTmHX9rHIbywwKCmLmzJnMnDmT8+fP8+GHH3LPPffQuHFj4uPjGTZsWJ70LVq0ID4+HoDjx4/ncQzt5eVFWFhYqeTx9vamfv36ufu+vr7Url07zz7AuXPnSnUewyXBHUCsUmqNXdhBEUlCrwF1NBxfIG5pYab98CPx48drZQmQkwPVq1Gtfn29znLFPZCRqsctffyKzKssSD2dzmdzfiErK4dBE9sT2MB01xgqF0XZZ3YHvr6+xMTEcNVVV7F9+3ZCQ0M5cuRInjSHDh0iNFQ7KmjcuHGeeKUUR48eLTR/f39/0tLyzPznxInybUEbLmkCsEzyOXAQba7PJdyiMOPHjUOlp+cNzMwiftw43Q17eDP0fwGCy9/7/PmUi3w2ZzsZaZnc8mB76jWuVe7nNBjKmpLYZy4Np0+fZsqUKezatYvMzEyysrJYvnw5u3btonv37sTExLBw4UJ++uknsrKyePvtt9m+fTt33HEHAKNGjeL1119n27ZtZGZm8txzz5GYmFjo+Tp06MC2bdvYunUrWVlZzJs3j8OHD5fLtRmqBL+iDSQ4MhFw+aFxS5dsbsuyoPBNz8FVw6H9neUuR3paJitf3k7qqXQGTmxPcFht5wcZDBWQ4tpnLi0+Pj78+eef3Hbbbfz+++9Uq1aN8PBw5s6dy9ChQwE4deoUI0eO5I8//uDyyy9nzZo1ud2uo0ePJj4+noEDB3LhwgXuuusuevToUej5oqOjeeSRR+jTpw8A48ePp2vXruV/oYZLlclo59U3ADbPJ52BRmh3Yi4hxVlDFRUVpbZs2eI8oQN727UvUGmKtyJifC24dyPUKN+W3sX0LD6bs53k4+cYcF87Qq+oW67nMxicERcXR2Rk+feqGAxFUdh9KCJbXXDvVWkQkUbA/UCEFRQHvKaUcrmv3y0tzNCFC/UYpl23rFQTQq8/C0M/LndlmXkxm9Wv7iDp2Dn6jrvSKEuDwWCoYliKcWpp8nDLGKZf506ELliAWAucpbo3od2T8BvzDIS4bJWoRGRn5vDFwp2cOHCGG8dcQbN29Z0fZDAYDIZKj4jUdWVzNT/3LCtZ0A2/xJ1EDHYI3/IWRMWW22lzsnP48q3dHNt9ip6jImjVsUG5nctgMBgMFY5knLv1UrioC92jMJtcC0n7INvOSLO3jw4vJ1SOYv2SOA79kkS3oa24oqsx6mwwGAxVjKJsyPZBz5LNcjUz9yjM6yfD9nfzhokXXO/6Qu3ioJRi03v7+O3HP+g0qDnteoeWy3kMBoPBUHGx+di0R0SuBmYB3YGFwNOu5uce03j+IXrZiFgeQLx99L5/2XeRKqX4fvkBdn97gmv6hBHVN7zMz2EwGAyGyoWINBORZcBPwEngCqXUg0qpJFfzcJ8t2esng80wdDm2Ln9efYTtX8fTtmcTOg9qXi7nMBgMBkPlQETqicjLwF4gBOiilBqulCrI8k+RuE9h5rYyvcqtdfnLl8f4+fPDRHRpSPehrXI9KRgMBoOh6iEiU9Hm764HBimleimlfi5pfu71VnL9ZGjauVxal7s2Hef7FQdoGRVMz5ERiJdRlgaDJxERvvvuu0Ljx48fz4QJE9wokaYgX5tlyfHjxxGRfLZ1yxJH/6GLFi2iZcuW5Xa+SszTQHXgOHCfiKwsaHM1M/cqTP8QGLO2zFuX+374nU3v/Ub4VUHcMOYKvIyyNBjKjLlz59KiRYs8Ya+88goiwtq1a3PDLly4QM2aNVm50rX3z4IFC5g3b17ufnh4OEuXLi2VrIsWLcLLy4tatWpRq1Yt6tevz4gRI0hKcnmYyi2kpKQwefJkWrVqhZ+fH40bN6Z///6sX7/e06JdaiwBPgSS0OOWhW0u4RF/mGXJwW1/sn5xHE0i6nDzPW3w9q70l2QwuETaDz+yt1174iIi2duuPWk//Fgu5+nduzeHDh3K411k/fr1tGnThg0bNuSG/fe//yU7O5vo6OhykcNVmjdvTmpqKqmpqezbt4+kpCQmTpzoUZnsSU1NpVu3bnz77bcsW7aM06dPc/DgQe69914+/ti9NoJLg4g8LyK7RSRFRE6IyH8cjQCIyGgROSgi50XkRxHpYBcXIiIbReSsiHwqIn52cV2s9N6lkVEpFaOUGuNsczW/Sq1dju46yZdv7qZBswD6jm9LteqlKluDodLg6DJPZWQQP358uSjNNm3a0LBhw9zWT3Z2Nps2beKpp57K0yJav349HTt2zOOTcseOHXTs2BF/f386d+7M3r17c+NiYmIYO3YsAAMHDuTYsWOMHTuWWrVqcdNNNwGQlZXFzJkzad26NYGBgXTt2pXi2LOuW7cugwcPZteuXYWmOXnyJKNHjyYkJISQkBDuuusuTp065XJ8YmIit9xyCwEBAbRu3ZovvviiSJnmzJnDiRMnWLNmDR07dsTHx4eaNWsyaNAg5s+fn5vu22+/pVu3btStW5cWLVrw4osv4qrt7/fff5/IyEj8/f1p0KABd911l0vHFZNstFPmekA7oAmwyBYpIt2A+WhHzXWA5WgD6LYb5HFgCxAE5ADjrONqAAuAe5RS2eUheElxmwPpsiZh32nWLtxJvca1GDDhKnxqVtpLMRgASJw5k4y4vc4TAue3bAGHl6dKT+fYmDH4Rjm3l10jMoKQxx93WbZevXqxfv16YmNj2bp1KyEhIdxyyy3ExsZy8uRJ6tWrx/r163O9i9hYtGgRy5cvp0GDBowcOZIHHniAr776Kl/+q1atIjw8nGeeeYaRI0fmhj/55JN8/fXXfPHFF4SFhbFo0SL69OnD/v37qVOnjlO5k5KSWLFiBd26dSs0zZ133kn16tWJi4sDYOTIkYwaNYrVq1e7HF+7dm2OHTvGhQsXGDJkSJEyrVmzhj59+hQp/549e+jXrx9Lly5lwIAB7N+/n759+1K/fn1Gjx5dZP7nz59n1KhRrFu3jl69epGWlsa2bduKPKYkKKXsb6Akaybqh3Zh9wArlFJfAojILGACMBhYDLQEXlZKZYrIl8BV1nFPAp8opXaUudClpFK2MBMPn2X1azuoHXQZAx9sRw3f6p4WyWBwL4W1NIrhfag43HDDDbndr+vXr6dXr15Ur16dLl268M0333D27Fm2bduWbzLNo48+StOmTalRowYxMTHFah0qpZg7dy6zZs2iefPmeHt7c/fdd9OwYcNcZVUQhw8fJjAwkMDAQIKDg0lISGDSpEkFpj1x4gTr1q1j9uzZ1KlThzp16jB79mzWrFnD77//7jQ+ISGBDRs28MILLxAQEEBISAhPPvlkkdeVlJRE48aNi0zz2muvMXToUAYNGoS3tzcRERFMmDCBJUuWOC84oHr16uzdu5dTp07h5+dH9+7dXTrOgSAR2WK33eskfW+030kb7YCtth2lm8e/WOEAO4A+IuID3ATssIwK9AWeLYnA5U2la5YlHz/H56/8ymW1fRj0UHsuq+XjaZEMhjKhOC2+Ql3m1ahB2DuuvVSLQ+/evUlMTGTPnj1s2LCB8ePHA9CzZ082bNhA9erVqVmzJtddd12e4xo2bJj738/Pj3Pnzrl8zuTkZFJTUxk4cGCeJWKZmZm5s0MLolmzZhw4cACA9PR05s6dS+fOndm9ezfBwcF50sbHx+ceY8M2wSk+Pj63C9RZvM3vp2Pagqhfvz4JCQlFpjl8+DAbNmxgxYoVuWE5OTmEhjq3Wubr68uaNWuYPXs2U6dOpXnz5kyaNCnXmXcxSHbVvZeI/A0Yj16+YcMfOOuQ9Axg65L9P+AVtFL9Bj1BZzO6a7aviDyMNls3TSn1AxWAStXCPJ2YxsqXt1O9hjeDHmqPX0ANT4tkMHiE0IULkZo184RJzZqELlxYPucLDaV169asXr2a//3vf/TsqU102rpq169fT48ePaheveS9PV5eeV9HQUFB+Pn58fXXX3PmzJncLS0tjX/+858u5VmzZk3uu+8+kpOT+fbbbwu8LiDPEpBDhw7lxjmLt7UU7SdEOVtO0q9fP7744gtOnz5daJqwsDBiY2PzXHdKSgq7d+8uMm8b0dHRrFy5kuTkZKZNm8bIkSM5eLDY6/RdQkSGAv8BblFK2ff9ngMCHJIHAikASqmzSqnRSqm2SqkHgYeBjcB+4FXgb8ADwAdSQRbVVxqFeTbpAp+99AuIMOihq6ld7zJPi2QweIx8LvNq1CB0wQL8Oncqt3P27t2b2bNn06pVK+rW1ZMhr776av78808++uijUq9tDAkJYf/+/bn7IsLEiRP5xz/+kRuemprKunXrOHHCNZ+/mZmZLFiwAG9vb9q0aZMvvlGjRtx0001MmjSJM2fOcPr0aSZNmkTfvn1p2LCh0/gmTZoQHR3N5MmTSUlJ4Y8//mDGjBlFyjRx4kQaNWrEgAED2LJlC5mZmWRkZLB69Wruu+8+AO677z7ef/99Vq1aRWZmJllZWezZs4dNm/KZRs3HH3/8wfLlyzl79ize3t4EBgYC4O1d9pMiRWQM2h7rQKXUNw7RvwLX2KUVoD15u21tcZHACOAJoBUQr5Q6qZSKA2oAFcIvY6VQmKmnM1j58i9kZeUwaGJ7Ahv4elokg8Hj+HXuRMSv24ncG0fEr9vLVVmCHsdMTEykV69euWHe3t706NGDxMTEUivMadOmsXTpUurUqUPfvn0BeOqppxg0aBCDBg2idu3atGrVigULFpCTk1NoPocOHcpdhxkUFMQHH3zARx99RERERIHply5dir+/P5dffjkREREEBgbmGSt0Fr9s2TIyMjIIDQ2le/fuTifl+Pv7891339G1a1eGDx9OQEAAzZs3Z/78+QwbNgyAK6+8ks8//5w5c+bQsGFDgoODiYmJcWk9aU5ODq+++irh4eH4+/tz//33s3jxYsLDw50eWxxE5EHgBeBmpdR/C0jyH+A2EeltjVNOAmoCnzjk4wW8AdyvlLoAHAUuF5GmIhKFNjzg8lrJ8kRcnaYMEBUVpYozaF8WnE+5yKezt5F6JoNbH76a4LDazg8yGCoBcXFxREZGeloMQxWnsPtQRLYWNYYpIgo9xphnMF0pVcsuzWhgOtAQ2An8XSm11T69iDwEXK6U+rtd2AR0a/Mi8KBSKo+S9RQVetJPelomK+du59zJdAY+2N4oS4PBYKggKKWcjisqpZagJ/MUlWZOAWHzgHkFJPcoFbZL9mJ6Fp/P+5XTiWn0/XtbGrUK9LRIBoPBYKjCVEiFmXUxmzWv7eDPo+e4eeyVNL2inqdFMhgMBkMVp8IpzOysHNYu3EXC/jPcMCaS5u0rxOQog8FgMFRxKpTCzMnO4as3d3Ns90l63hlB644hnhbJYDAYDAagAilMlaPYsGQvB39JotvQVlzRrZGnRTIYDAaDIZcKoTCVUmx6/zf2/ZhIp1ua0663c/NPBoPBYDC4E48rTKUU3684yO7NCVxzcxgd+oY5P8hgMBgMBjfjcYW5Zc0Rtn91jLbRTeh8a3MqiMlAg8FgMBjy4FGF+ctXx/hp1WEirguh+7BWRlkaDIYyJTw8nKVLl7qUduPGjVSrVrQtlzZt2vDBBx+UhWiGSojHFOauzQl8v/wALTsE03NUJOJllKXBUFGJjo7mmWeecTn8UmX37t0MHz7c02IYPIRbTOO9Nfk7LqRczBfuVU24YcwVeBllaTAUi8Keqctq+xD7724ekMhguPRxSwuzoAcbICdL4V3N48OoBkOlo7BnqrBwd3Ds2DGGDBlCSEgIDRs25N57783jMFpEmDdvHlFRUfj5+dGlSxeOHz/OSy+9RGhoKPXq1WPq1Kl58ty0aROdOnUiICCAiIgIFjr4+1y9ejVXXHEFtWrVYsCAATz88MNER0cXKqOz/AAWL15MWFgYdevWJSYmhtTU1Nw4+y5eWxfuBx98QIsWLQgICGDYsGHFcpJtqFxUaOPrBkNV4tsPfyM5PtV5Qid88uI2p2mCQmvRfVjrUp/LRnp6Or169eKOO+7gnXfeIT09nTvvvJOJEyfy1ltv5aZbunQpn376KXXr1mXAgAH06tWL22+/nYMHDxIXF8e1115Lv3796Nq1K4cPH6ZPnz7Mnz+fkSNHsmXLFvr160fdunUZOnQoBw8e5LbbbmPRokUMHTqUTZs2MXjwYK655poCZXSWH0B2djarVq1ix44dpKenM3jwYB555BFef/31AvPMzs7myy+/5NdffyUtLY1u3boxd+7cfIrfcGlgmncGg8Elnn32WQIDA/Ns3333HQCff/45SilmzJjBZZddRp06dXj66ad59913yc7Ozs1j0qRJNGnSBF9fX4YMGUJiYiLTp0/Hx8eHdu3a0a5dO2wuBN977z2uueYaYmJiqFatGp07d2bcuHG88cYbufGdOnVixIgRVKtWjd69ezNo0KBC5XeWn43nn3+egIAAGjRowIwZM1iyZEmR/jefe+45atWqRYMGDbj11ltxtwtEg/swLUyDoYJQnBbfq+M3FBo3eFLBLazSMnXqVKZNm5YnzNb9efjwYY4dO0ZgYF6vQiJCYmIijRs3BqBhw4a5cb6+vgQHB+Pl5ZUnzNalGR8fT7NmzfLk16JFCz777DMAEhISCAvLu247LCyM+Pj4AuV3lp99HjbCw8PJyMggOTmZ4ODgfHl6e3tTv/5f9q79/PxMl+wljGlhGgyGUhMWFkbr1q05c+ZMni09PT1XWRaX0NBQjhw5kifs0KFDhIZqS2CNGzfm6NGjeeKPHTtW4vxs2Od55MgRatSoQVBQUAmuwHCp4RaFeVltn2KFGwyGoqloz9SAAQO4ePEiM2fO5Ny5cyilSEhI4JNPPilxniNGjGDr1q0sWbKErKwsfvrpJxYuXMjdd98NwO23386PP/7Ihx9+SHZ2Nt988w2ffvppifOzMWXKFFJSUvjzzz+ZPn06o0aNytMKNlRd3NIla6a5GwxlS0V7pnx9fdmwYQNTpkwhIiKCc+fO0ahRI4YPH87gwYNLlGezZs1Ys2YNjz32GA888AAhISE8/fTTDBs2DICWLVvy0Ucf8dhjjxEbG0t0dDSjRo3iwIEDJcoPdBdr//79adu2LSkpKQwcOJDZs2eXSH7DpYcopVxOHBUVpcyAtsFQNsTFxREZGelpMS4pRowYgb+/f6GzWg35Kew+FJGtSqkoD4hUYTH9DAaDodKycuVKTp06RVZWFp999hnLly9nxIgRnhbLcIliZskaDIZKy+bNm4mNjSU9PZ2mTZuyYMECevbs6WmxDJcopoVpMBgqLS+88ALJycmkpqayZ88eYmNjPS1SlUJEvEVklogkicg5EVkuIkFWXFcR+U1ETorIMw7H/VNE5nhG6pJjFKbBYDAYSso/gUFAJ6CJFfaO9TsfmAS0Au4UkWsARORyYBTwuHtFLT1GYRoMHqQoCzIGQ3lTBvffvcDzSqlDSqmzwGSgj4iEAS2BL5VSp4AfgZaifTi+ATyglDpf2pO7G6MwDQYP4efnR0JCAhcvXqQ4s9UNhtKilOLixYskJCTg5+dXWLIgEdlit91rHykigUBTYKtdvgeBFKAdsAPoJyL1gGuBncAEIE4pVbipqgqMmfRjMHiIJk2akJyczNGjR8nKyvK0OIYqRrVq1QgICCjKilGyk2Ul/tbvWYfwM0Bt4G5gDvAv4N/ABeB+oJOIPAHcACQBE5RSiSW7CvdiFKbB4CG8vLwIDg4u0EapwVAJsBnNDXAIDwRSlFK7gRttgSLyJfAPoDPQE+iFVqovAneWu7RlgOmSNRgMBkOxUUqdAY4Budb+RaQ5unW5wz6tiMQCSUqpz9HdtT8qpbKBzcDVbhO6lBiFaTAYDIaS8jrwmIg0E5HawPPAOqXUEVsCEWkIPAZMtIIOAtEichnQDyjYlmEFxHTJGgwGg6GkPAfUAX4GagBfASMd0swH/qWUSrb2PwUGAL8Dh4A73CNq6TG2ZA0Gg8GQD2NLNj+mS9ZgMBgMBhcoVgtTRJKAo04TFk0QkOw0VdXClEl+TJkUjCmX/JgyyU9ZlEmYUqp+WQhzqVAshVkmJxTZYpr5eTFlkh9TJgVjyiU/pkzyY8qkfDBdsgaDwWAwuIBRmAaDwWAwuIAnFKZxhZ4fUyb5MWVSMKZc8mPKJD+mTMoBt49hGgwGg8FQGTFdsgaDwWAwuIBRmAaDwWAwuIBbFKaIXCEi60XkvIicEJEZIuLtjnNXVESkpYgsFJEdIpItIhs9LZOnEZGhIrJSRBJEJFVEtorICE/L5UlEZIiIfC8iJ0UkXUT2icg0EfHxtGwVBRFpbN0vSkRqeVoeTyAiMdb1O27jPS3bpUS525IVkTrA18AeYBDQAu3OxQuYVt7nr8C0QRse/gGo7mFZKgqPAIeBh9GLrvsBy0QkSCn1ikcl8xz1gA3ALLSfwWuB6UAI2hmvQZdNKlCoJ+QqRC+030kbhzwlyKVIuU/6EZEpwGS01YgUK2wy1kNvC6tqiIiXUirH+v8xEKSUivasVJ7FUozJDmHLgOuUUs08JFaFQ0SeRTviraOq+Kw9EemBNuY9E604/ZVSqZ6Vyv2ISAzwNlX0+t2FO7pk+6LdvdgrxveBy4Dr3XD+ColNWRr+wlFZWvwCNHK3LBWck0CV75K1hnVeAWZgTOMZ3IA7FGYEsNc+QCl1DDhvxRkMRXEd8JunhfA0IuItIr4i0g14EJhf1VuXwHi0S6lXPS1IBeKgiGRZY93jPC3MpYY7/GHWQY+9OHLaijMYCkREegO3ArGelqUCkIZWDgBLgEc9KIvHEZF6wNPASKVUpoh4WiRP8zvwBPAT4A3cDiwQEV+l1EselewSwjiQNlRIRCQcWAZ8ppRa5FFhKgZdAF/0pJ9/AfOA+zwqkWd5FvhBKbXG04JUBJRS64B1dkFrRaQmME1EXjZDQGWDOxTmaSCggPA6VpzBkAcRqQusRbuSu9PD4lQIlFLbrL/fiUgysFhEXlRKHfSkXJ5ARNqgex16iEigFexr/QaISLZS6kLBR1cpPgaGAeGY2bJlgjsU5l4cxipFJBR9g+8t8AhDlUVEfIHP0ZNaBiilzntYpIqITXk2A6qcwgRaoZdi/a+AuOPAm8BYt0pUMVEOv4ZS4g6FuRZ4VET8lVLnrLDh6LVCm9xwfkMlQUSqAR+hX4hdlFJ/elikikpX6/ewR6XwHN8BPR3C+gCPodfumtaUZgh69vBRTwtyqeAOhbkAPatvhYg8DzRHr8GcXVXXYEJuS6qftdsYqC0iQ6z9NVW0ZfUaukwmAvWsiR02flFKZXhGLM8hIl+gDX/sBrLRynIS8EFV7I6F3OVHG+3DrDFvgG+r4jpEEVmOnvCzAz3pZ7i1PWjGL8uOcleYSqnT1mzHecAq9IzZl9BKsyoTjG5N2WPbbwYccas0FYObrN+XC4irqmXyMxCDHofKQreepqA/RA0GG/vQ47qhgKAtq41WSr3jUakuMYx7L4PBYDAYXMB4KzEYDAaDwQWMwjQYDAaDwQWMwjQYDAaDwQWMwjQYDAaDwQWMwjQYDAaDwQWMwjQYDAaDwQWMwqwkiEiMiFS5BdmuICKLRORzT8tREOVVbyKi7AxdlGW+0VbeQWWd96WIiOwSkemelsPgHiqNwhSRBiLysogcFJEMEUkQkbUi0s/50ZWLQl6GH6CtJBkqMSIyXUR2lUFWDdGGQEojy0YRmVcGslQIXP04sepAFbIFu0NWQ+WkUrj3ssxe/Rc4h7Zy8ita2fdGWzxp6inZ3IXlfaFSemAQER+l1EVPy2GPiFRXSmV6Wo6SopRK9LQMlZgXyG8p6X1AGfvFhiJRSlX4DVgDJAC1CogLtPvfFPgErVjPASuAJnbx04FdaOeqB600nwJBdmnaAuuBFCAVrZx72sVfAay2jv0TeA8IcZDpLmAnkAH8ASy2i1PAEIf0R4B/2P1XdtsRKzwGSLX+t7bi2jrkcy/a2HJ1V2V1OD7cyvdvwFfAebSJrRsdeImCSgAACnRJREFU0vUAfgTSret7CfCxi98IzEe/mJKAn+2u/e/AZ1bev6GNaDdB+/JLA7YD19jlVc+S+zj6g2E3MMZBnkXA50VcV7R17n5oe5sX0Z5QAAYCW61rOYz2s2h/Lbeh7XNeAE6hHQY0sL+fHM6VW08F1FuMQ90qIMaKG2eVR7pVh+uAakVcU+595Gq9FVBmjrKE25VVb6uOzwNb7OvEOr6LVRbn0c/mfKC2C3UwwKrjdKvcOxSzrnsAP6CfzbNWfV5pl7/9Nt3F90so2k7vHU7SBaPv3Qtog+ax6PfJdLs0j1j3S5pVLm9gvaMAP/R7xfH5vxHItLuv/mXlnwEkAkvK4j1qttJvHhfAqYBQF8gBHneSzgv4BfgeiLK2H6yH3WYCcLr1oH0CXAVcZ92YC+3y2QksRbskawkMBq6z4hqiX2bPA5FWHqusF4uXlWac9TJ4BLgc6AA8ape/M4VZ30ozFggB6lvhMeR9Ef8EPOeQzybgNVdlLaAMw61z70UrklbAYuAk1scK2lB8GvoLPRL9AkwEXrTLZyNaSb9olWOk3bUnACOsvJehFe46YBD6Q2ANsMMur8bAo0B7dJf0vWiF19suzSJcU5g70fZqm1vlfDP6BTYGaIFW3vuAF6zjQqxzTbLK5kqrXkqqMC9Df0TstfIOscKi0HZi7wTCgHbAwxRfYRZabwUcH4B+Vt6yk8Xbrqx+ssojwqqfOP56jtqin6NJ1rk6oV1tfexCHey1yv1KtO3k3wFfV+oa3SN22irDFpZsd6DvQx+00f40u+sp8NoLkG06+lmp4STdGrQS7wpcjb7PU8mrMB8Cell1cj1aeb5jF78Q7VzBPt/3gE+s/39D35P90Q2AKGCCJ9/BZrOrK08L4FRA7WFeAYOdpLsR/ZUYbhfWHK1sb7D2p6OVWYBdmqnAAbv9FOCuQs4xA1jvEFbHku9aa/84DorMIX2RCrOINDHkfRE/iFb2tpdYU+tau7gqawGyhVvx4+zCGlth3az9Z4H92CldS7YM/nrxbcRO6Tlc1//Z7V9phT1iFxZthQUVJKOV5n3gDbv9RbimMP/mEL4ZeMIh7Fb0S1CAa6zjwgrJdzrFUJhFHHMburXkX4znoiCFWWi9FZLHRmBeIWV1s11YVyusibW/BHjT4bj2VppgJ3Vwp11YLbQzhrGu1DX641kB1xeSNk9Zu1iO3sAx4CUn6Wy9Ol3twsLQ75zpRRzXx3o2bB/Uto+jxtZ+HXSL1dbj8Qj6o616ca7DbO7ZKsOkH3ExXSRwQil1xBaglDoEnEB3Tdo4qpQ6a7d/At3VYmM28IaIbBCRqSJi7/y6A9rLe6ptA+KtuBbWhIHG6C7d8uZ9oBHQ3dofARxWSn3viqxO8t5h9/+E9Wsro0jgB5XXZdB36C/8lnZhW13I+w/rd2cBYcEAIuJt1cMOETlpXcdtlGzceovDfgdgqkMZLUN3nYWgu+O/BnaJyHIR+buI1C/BeZ3xFfrj57CIvCsid4mIfwnyKareyjKvDsBIh3L7rxXn7N7KdfqstBuunVjPp7O6VkqdQn8crROR1SLyiIiUdv5CH3SX7H+cpItEf5D+ZCf/Uf4qG6xr6CUiX4nIcRGxDQv5oO8nlFJbrGu+yzrkDnRX/1pr/yOgJvpeeFNEhopIjVJcn6EMqQwKcz/6yy6yFHkou/+OEz0UduWglJqOfoA/RY/T7BCRWCvaCz0m2N5hawW4uqxBkf8joLqLx/6ViZ6c8BW6Gw/r9127JKWRNbeMlPXZi2v3in05pznL2y59QWG28/0D3fU3Cz2u1h5dNz4uyOOIo0xewFPkLZ+r0GWUpJTKRnfh3oRWIHcD+0WknXV8DmVTl+fQrdlh6NbOFGCviDQqZlYlrbci8yJ/nXihx+bsy60duty2l/B84EJdK6XGoLuANwO3APtE5OZSnPNe4Hul1B4X06vCIkQkDP3MxQFD0R8WtneH/f36Bro1jBW/2LrXUErFo4dyxqF7u14EtoqIn4vyGcqRCq8wra/KdcAEEanlGC8igdbfOKCRnSNZRKQ5uhXm6sNgO+d+pdRcpVR/4E30uBXANqANupV6wGE7ZymxBPTDXhhJ6PFFm4wN7PctMtFdRc5YCgwVkQ7ocaWldnFFyupC3oURB3QWEft7pxt6rKk8HBp3A1Yppd5RSm23ztG6jPLeBkQUUD4HlFJZoBWPUup/SqmngI7oFsVw6/gkoIGI2CvN9k7OeZEC6lYplaWU2qCUmoJW2n7o8eHypEBZXGAb0KaQcnM2k7uz7Y+lBK5E31PgYl0rpX5VSj2vlIpGdyvbWmvFuh7rg6Q/zluXoMdevdBDRLbjm6LfLzai0IrxYeue+c0h3sa7QBMRmYD+UHrbPlIpla6UWq2Uehh9z7VBd4sbPEyFV5gW96O/5LdYXRSXi0iEiPydv7qOvrb+vysiUSIShb4xtwEbXDmJiFwmIq9ai7fDRaQT+iG2KdxX0ZMlPhCRTiLSXERuEJHX7brQngUeEpGHRaS1iLQXkUl2p9kA3G/JeDW6iyndQZQjQG8RCRGROkWI/Cm6RfMmeibqb3ZxrshaEl5DvwReE5FIEekPPIceCztfinwL4zd0WXSzusfnoZ1JlwUzgDtEZIaIXGndU0NE5N8AItJZRKaJSEfr5XgLuvvOdj9sRI+rPS4iLUTkbsCZMYEjQJiIXCMiQSJSQ0QGiMhEEbnaaqXcAfjzlyIpL44A11r3epDDR1BRPG8dt8CSuaV1DQtdOHaaiNwoIm3QE44uorvBwUldi0gzEXlORLqISJiI9ER/XNjq4whQ08o/SER8ncgSi+51+NCZ0EqpfcAXwEIRuU5E2qOfXfsPhP3od+pDlqwj0JOAHPM6g+56fRHYrJTab3eNMSIyVkTaikgz9IS0TCtvg6fx9CCqqxu6FfYK2uN8BvpLfy3Qxy5NU7QSsS0r+YQClpU45BvDX7MYfdAP7xG7c7yO3XR5dLfTx+jZehfQA/SvkHcpwt3oh/giegbpW3ZxjSy5U9Ff0H8j/6SfgegHJJMClpU4yL8E3U30YAFxTmV1SB9u5RXlEJ5nEhJ/LSuxLZt5CbsZhhQwmaSQfIKssGi7sAgr7Eprvw56HMi2NObfaKW90e6YRbg26SffRCJ0d+u36OURKehxzglWXKRVV39Y13oAmOxw/Dj0+GMaelx5IkVP+qlhVyfKiu8GfIOe1XoBvVRhTGHX41iWrtZbAXm0Ro8pnif/shL7pVb58ke3pr6wyiwNPS43w4U6uAX9YZuB/pjtaJemyLoGGljxCdbxx6w01e3ymI+e8aooejKOoJcRvVaMd1ADYKVVR/HonifHZSUPWvJdQM9lGGYrW4e8eljhox3Cb7Xq5IxVrj9jTQgym+c32wxLg8FgKDdEJBr9UVBfKZXsYXE8jogMRy8xaaTKp2fGUA5UCks/BoPBcClgdROHAI8D/zHKsnJRWcYwDQaD4VJgMnpo5BTwtIdlMRQT0yVrMBgMBoMLmBamwWAwGAwuYBSmwWAwGAwuYBSmwWAwGAwuYBSmwWAwGAwuYBSmwWAwGAwu8P+YDc6yTAIeYQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 468x234 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6.5, 3.25)) #6, 4.5 # 7, 2.565 #6.5, 3.75\n",
+    "\n",
+    "#, '<', '>'\n",
+    "marker_types = ('o', 'v', '^', '8', 's', 'P', '*', 'X', 'D', 'd')\n",
+    "\n",
+    "for k, lab in enumerate(labs):  # :\n",
+    "\n",
+    "    non_empty_inds = []\n",
+    "    for i in range(0,max_repeat+1):\n",
+    "        if lab2frac[lab][i]=='':\n",
+    "            break\n",
+    "        non_empty_inds.append(i)\n",
+    "    y_s = [float(lab2frac[lab][i]) for i in non_empty_inds]\n",
+    "    plt.plot(non_empty_inds, y_s, '-'+marker_types[k], label=labDescriptions[lab])\n",
+    "    # l2, = plt.scatter(non_empty_inds, y_s, marker=marker_types[k])\n",
+    "    # plt.plot(y_s[0], '-'+marker_types[k], color=l2.get_color(), markerfacecolor=l1.get_color(), label='My plots')\n",
+    "\n",
+    "plt.xticks(range(0, max_repeat + 1))\n",
+    "plt.xlabel('Consecutive normal results in the past 7 days', fontsize=14)\n",
+    "plt.yticks([0,0.2,0.4,0.6,0.8,1], ['0%', '20%', '40%', '60%', '80%', '100%'])\n",
+    "plt.tick_params('x', labelsize=15)  # 12\n",
+    "plt.tick_params('y', labelsize=13)  # 10\n",
+    "plt.ylabel(\"Normal rate\", fontsize=14)\n",
+    "plt.ylim([-0.05, 1.05])\n",
+    "plt.legend(fontsize=13)\n",
+    "ax.yaxis.tick_right()\n",
+    "ax.yaxis.set_label_position(\"right\")\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n",
+    "plt.savefig(figure2_folderpath + 'Negative_Saturations_%s'%(lab_type))\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 3.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 3.ipynb
new file mode 100644
index 00000000..bb25f5b1
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 3.ipynb	
@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from JAMA_config import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw__Diagnostic_Metrics():\n",
+    "    '''\n",
+    "    Drawing Figure 3 in the main text.\n",
+    "\n",
+    "    :return:\n",
+    "    '''\n",
+    "    labs = all_labs\n",
+    "    print 'draw__Diagnostic_Metrics for', labs, ' with label %s...'%result_label, 'at', data_source, ' of type,', lab_type \n",
+    "\n",
+    "    labs_stats_filepath = os.path.join(statsByLab_folderpath, 'summary-stats-bestalg-fixTrainPPV.csv')\n",
+    "\n",
+    "    df = pd.read_csv(labs_stats_filepath, keep_default_na=False)\n",
+    "\n",
+    "    # df = df[df['fixTrainPPV'] == targeted_PPV]\n",
+    "\n",
+    "    df = df[df['lab'].isin(labs)]\n",
+    "\n",
+    "    cached_foldername = 'Fig3_Diagnostic_Metrics/'\n",
+    "    cached_folderpath = os.path.join(os.path.join(statsByLab_folderpath, cached_foldername))\n",
+    "\n",
+    "    cached_tablename = 'Diagnostic_Metrics_%ss_PPV_%.2f__%s.csv'%(lab_type, targeted_PPV, result_label)\n",
+    "    cached_tablepath = os.path.join(cached_folderpath, cached_tablename)\n",
+    "\n",
+    "    cached_figurename = 'Diagnostic_Metrics_%ss_PPV_%.2f_ind__%s.png'%(lab_type, targeted_PPV, result_label)\n",
+    "    cached_figurepath = os.path.join(cached_folderpath, cached_figurename)\n",
+    "\n",
+    "    if not os.path.exists(cached_folderpath):\n",
+    "        os.mkdir(cached_folderpath)\n",
+    "\n",
+    "    if os.path.exists(cached_tablepath) and use_cached_fig_data:\n",
+    "        # lab2stats = pickle.load(open(cached_result_path, 'r'))\n",
+    "        print \"%s exists!\" % cached_tablepath\n",
+    "        df_toplots = pd.read_csv(cached_tablepath, keep_default_na=False)\n",
+    "        # print df_toplots\n",
+    "\n",
+    "    else:\n",
+    "        print \"%s does not exist!\" % cached_tablepath\n",
+    "        if data_source == 'Stanford':\n",
+    "            # if 'total_vol' not in df.columns.values.tolist():\n",
+    "            # Stanford data, scaled by vol\n",
+    "            df['total_vol'] = df['total_vol_20140701_20170701']\n",
+    "\n",
+    "        elif data_source == 'UCSF':\n",
+    "            if lab_type == 'panel':\n",
+    "                ucsf_lab_cnt = dict(stats_database.UCSF_PANELS_AND_COUNTS)\n",
+    "\n",
+    "            elif lab_type == 'component':\n",
+    "                ucsf_lab_cnt = stats_database.UCSF_COMPONENT_TO_COUNT #dict(stats_database.UCSF_COMPONENTSS_AND_COUNTS)\n",
+    "            df['total_vol'] = df['lab'].apply(lambda x: ucsf_lab_cnt[x])\n",
+    "        elif data_source == 'UMich' and lab_type=='component' and result_label=='important_components':\n",
+    "            df = df[df['lab'].isin(stats_utils.umich_lab_cnt)]\n",
+    "\n",
+    "\n",
+    "            df['total_vol'] = df['lab'].apply(lambda x: stats_utils.umich_lab_cnt[x])\n",
+    "        else:\n",
+    "            df['total_vol'] = 1\n",
+    "\n",
+    "\n",
+    "\n",
+    "        # TODO: use fractions in the original file!\n",
+    "        df['all_instance'] = df['TP'] + df['FP'] + df['TN'] + df['FN']\n",
+    "\n",
+    "        for cnt_type in ['TP', 'FP', 'TN', 'FN']:\n",
+    "            df[cnt_type] = df[cnt_type]/df['all_instance']\n",
+    "\n",
+    "\n",
+    "        df['all_positive'] = df['TP'] + df['FP']\n",
+    "\n",
+    "        # print df[['all_positive', 'total_vol']]\n",
+    "        df['predicted_normal_vol'] = df['all_positive'] * df['total_vol']\n",
+    "\n",
+    "\n",
+    "        if data_source == 'UMich' or data_source == 'UCSF':\n",
+    "            df['lab'] = df['lab'].apply(lambda x: map_lab(x, data_source, lab_type, map_type='from_src'))\n",
+    "\n",
+    "        df = df[df['lab'].isin(stats_utils.get_important_labs('component'))]\n",
+    "        df['lab'] = pd.Categorical(\n",
+    "            df['lab'],\n",
+    "            categories=stats_utils.get_important_labs('component'),\n",
+    "            ordered=True\n",
+    "        )\n",
+    "        df = df.sort_values('lab')\n",
+    "\n",
+    "        df['all_negative'] = df['TN'] + df['FN']\n",
+    "\n",
+    "        df['TN'] = -df['TN']\n",
+    "        df['all_negative'] = -df['all_negative']\n",
+    "\n",
+    "\n",
+    "        lab_descriptions = stats_utils.get_lab_descriptions(data_source=data_source,#'Stanford',\n",
+    "                                                                 lab_type=lab_type)\n",
+    "        \n",
+    "        df['all_positive_vol'] = df['all_positive'] * df['total_vol']\n",
+    "        df['true_positive_vol'] = df['TP'] * df['total_vol']\n",
+    "        df['all_negative_vol'] = df['all_negative'] * df['total_vol']\n",
+    "        df['true_negative_vol'] = df['TN'] * df['total_vol']\n",
+    "\n",
+    "        df_toplots = df #.iloc[:15]\n",
+    "\n",
+    "        df_toplots[['lab',\n",
+    "                    'PPV', 'NPV', 'sens', 'spec', 'LR_p', 'LR_n',\n",
+    "                    'total_vol',\n",
+    "                   'all_positive_vol', 'true_positive_vol', 'all_negative_vol', 'true_negative_vol']]\\\n",
+    "                    .to_csv(cached_tablepath, index=False, float_format='%.3f') # .sort_values('total_vol', ascending=False)\\\n",
+    "\n",
+    "        \n",
+    "    if not scale_by:\n",
+    "        scale = 1.\n",
+    "    elif scale_by=='pat':\n",
+    "        scale = float(stats_utils.NUM_DISTINCT_PATS/1000.)\n",
+    "    elif scale_by == 'enc':\n",
+    "        scale = float(stats_utils.NUM_DISTINCT_ENCS/1000.)\n",
+    "    elif scale_by == 'enc_ucsf':\n",
+    "        scale = float(stats_utils.NUM_DISTINCT_ENCS_UCSF/1000.)\n",
+    "\n",
+    "    df_toplot = df_toplots.iloc[::-1]\n",
+    "    \n",
+    "    df_toplot = df_toplot[df_toplot['fixTrainPPV']==0.95]\n",
+    "    \n",
+    "    print df_toplot['lab'].values\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(6, 9))\n",
+    "\n",
+    "    ax.barh(df_toplot['lab'], df_toplot['all_positive_vol'] / scale, color='orangered', alpha=1,\n",
+    "            label='False Positive')\n",
+    "    ax.barh(df_toplot['lab'], df_toplot['true_positive_vol'] / scale, color='forestgreen', alpha=1,\n",
+    "            label='True Positive')\n",
+    "    ax.barh(df_toplot['lab'], df_toplot['all_negative_vol'] / scale, color='gold', alpha=1,\n",
+    "            label='False Negative')\n",
+    "    ax.barh(df_toplot['lab'], df_toplot['true_negative_vol'] / scale, color='royalblue', alpha=1,\n",
+    "            label='True Negative')\n",
+    "\n",
+    "    plt.yticks([])\n",
+    "\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['bottom'].set_visible(True)\n",
+    "    ax.spines['left'].set_visible(False)\n",
+    "\n",
+    "\n",
+    "    plt.tick_params('x', labelsize=24)\n",
+    "\n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    plt.savefig(cached_figurepath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "draw__Diagnostic_Metrics for ['WBC', 'HGB', 'PLT', 'NA', 'K', 'CR', 'BUN', 'CO2', 'CA', 'TP', 'ALB', 'ALKP', 'TBIL', 'AST', 'ALT']  with label important_components... at Stanford  of type, component\n",
+      "/Users/songxu/healthrex/CDSS/scripts/LabTestAnalysis/lab_statistics/data-Stanford-component-10000-episodes-lastnormal/Fig3_Diagnostic_Metrics/Diagnostic_Metrics_components_PPV_0.95__important_components.csv does not exist!\n",
+      "[ALT, AST, TBIL, ALKP, TP, ..., K, NA, PLT, HGB, WBC]\n",
+      "Length: 15\n",
+      "Categories (15, object): [WBC < HGB < PLT < NA ... ALKP < TBIL < AST < ALT]\n",
+      "draw__Diagnostic_Metrics for ['WBC', 'HGB', 'PLT', 'NA', 'K', 'CREAT', 'TBILI', 'CO2', 'AST', 'ALT', 'ALB', 'CA', 'TP', 'ALKP', 'BUN']  with label important_components... at UCSF  of type, component\n",
+      "/Users/songxu/healthrex/CDSS/scripts/LabTestAnalysis/lab_statistics/data-UCSF-component-10000-episodes-lastnormal/Fig3_Diagnostic_Metrics/Diagnostic_Metrics_components_PPV_0.95__important_components.csv does not exist!\n",
+      "[ALT, AST, TBIL, ALKP, TP, ..., K, NA, PLT, HGB, WBC]\n",
+      "Length: 15\n",
+      "Categories (15, object): [WBC < HGB < PLT < NA ... ALKP < TBIL < AST < ALT]\n",
+      "draw__Diagnostic_Metrics for ['WBC', 'HGB', 'PLT', 'SOD', 'POT', 'CREAT', 'TBIL', 'CO2', 'AST', 'ALT', 'ALB', 'CAL', 'PROT', 'ALK', 'UN']  with label important_components... at UMich  of type, component\n",
+      "/Users/songxu/healthrex/CDSS/scripts/LabTestAnalysis/lab_statistics/data-UMich-component-10000-episodes-lastnormal/Fig3_Diagnostic_Metrics/Diagnostic_Metrics_components_PPV_0.95__important_components.csv does not exist!\n",
+      "[ALT, AST, TBIL, ALKP, TP, ..., K, NA, PLT, HGB, WBC]\n",
+      "Length: 15\n",
+      "Categories (15, object): [WBC < HGB < PLT < NA ... ALKP < TBIL < AST < ALT]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "targeted_PPV=0.95\n",
+    "scale_by='enc'\n",
+    "use_cached_fig_data=False\n",
+    "result_label = 'important_components'\n",
+    "inverse01=inverse01\n",
+    "for data_source in ['Stanford', 'UCSF', 'UMich']: # \n",
+    "    for lab_type in ['component']:\n",
+    "        dataset_foldername = 'data-%s-%s-%s'%(data_source, lab_type, curr_version)\n",
+    "        labStats_folderpath = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/lab_statistics')\n",
+    "        statsByLab_folderpath = os.path.join(labStats_folderpath, dataset_foldername)\n",
+    "        if data_source == 'Stanford' and lab_type == 'component':\n",
+    "            all_labs = STRIDE_COMPONENT_TESTS_common#\n",
+    "        elif data_source == 'UMich' and lab_type == 'component':\n",
+    "            all_labs = UMICH_TOP_COMPONENTS_common#\n",
+    "        elif data_source == 'UCSF' and lab_type == 'component':\n",
+    "            all_labs = UCSF_TOP_COMPONENTS_common #\n",
+    "            \n",
+    "        '''\n",
+    "        Two steps: \n",
+    "        lab2stats: Getting lab-wise stats tables under lab_statistics/dataset_folder/stats_by_lab_alg/..\n",
+    "        stats2summary: Aggregate all labs' stats under lab_statistics/dataset_folder/..\n",
+    "        '''\n",
+    "        draw__Diagnostic_Metrics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 4.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 4.ipynb
new file mode 100644
index 00000000..36a54519
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Figure 4.ipynb	
@@ -0,0 +1,878 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Component Transfer Stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from JAMA_config import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data-transferring-component-10000-episodes-lastnormal/all_transfers.csv does not exist!\n",
+      "diagonals avg: nan\n",
+      "off_diags avg: nan\n"
+     ]
+    }
+   ],
+   "source": [
+    "def statistic_analysis(lab, dataset_folder):\n",
+    "    from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, average_precision_score\n",
+    "\n",
+    "    direct_comparisons = pd.read_csv(os.path.join(dataset_folder, 'direct_comparisons.csv'))\n",
+    "    # print direct_comparisons\n",
+    "    return roc_auc_score(direct_comparisons['actual'].values, direct_comparisons['predict'].values)\n",
+    "\n",
+    "all_sites = ['Stanford', 'UCSF', 'UMich']\n",
+    "lab_type = 'component'\n",
+    "\n",
+    "res_folderpath = 'data-transferring-component-%s/'%curr_version\n",
+    "if not os.path.exists(res_folderpath):\n",
+    "    os.mkdir(res_folderpath)\n",
+    "\n",
+    "res_filepath = res_folderpath + 'all_transfers.csv' #_withCI\n",
+    "\n",
+    "main_folder = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/')\n",
+    "ml_results_folderpath = os.path.join(main_folder, 'machine_learning')\n",
+    "\n",
+    "if os.path.exists(res_filepath):\n",
+    "    print('%s exists!' % res_filepath)\n",
+    "    df_res = pd.read_csv(res_filepath, keep_default_na=False)\n",
+    "\n",
+    "else:\n",
+    "    print('%s does not exist!' % res_filepath)\n",
+    "    labs = stats_utils.get_important_labs(lab_type='component')\n",
+    "\n",
+    "\n",
+    "    all_res_dicts = {}\n",
+    "    all_res_dicts['lab'] = labs\n",
+    "\n",
+    "    diagonals = []\n",
+    "    off_diags = []\n",
+    "\n",
+    "    columns = ['lab']\n",
+    "    for i in range(3): # Training sources\n",
+    "        for j in range(3): # Testing sources\n",
+    "            src = all_sites[i]\n",
+    "            dst = all_sites[j]\n",
+    "\n",
+    "\n",
+    "            '''\n",
+    "            '''\n",
+    "            ml_folder = ml_results_folderpath\n",
+    "#             LNL.transfer_labs(src_dataset=src, dst_dataset=dst, lab_type=lab_type,\n",
+    "#                                               cur_version=curr_version)\n",
+    "            transfer_result_folderpath = ml_folder + '/data-%s-src-%s-dst-%s-%s/' \\\n",
+    "                                         % (lab_type, src, dst, curr_version)\n",
+    "            \n",
+    "            \n",
+    "            cur_res = []\n",
+    "            cur_res_left = []\n",
+    "            cur_res_right = []\n",
+    "            for lab in labs:\n",
+    "                direct_comparisons_folderpath = os.path.join(transfer_result_folderpath, lab)\n",
+    "\n",
+    "                res_path = os.path.join(direct_comparisons_folderpath, 'direct_comparisons.csv')\n",
+    "                \n",
+    "                df_res = pd.read_csv(res_path)\n",
+    "                auc = metrics.roc_auc_score(df_res['actual'], \n",
+    "                                            df_res['predict'])\n",
+    "#                 auc_left, auc_right = stats_utils.bootstrap_CI(actual_list=df_res['actual'], \n",
+    "#                                                                predict_list=df_res['predict'],\n",
+    "#                                                               num_repeats=1000)\n",
+    "#                 if i!=j:\n",
+    "#                     off_diags.append(auc)\n",
+    "#                 else:\n",
+    "#                     diagonals.append(auc)\n",
+    "                \n",
+    "                cur_res.append(auc)\n",
+    "#                 cur_res_left.append(auc_left)\n",
+    "#                 cur_res_right.append(auc_right)\n",
+    "                    \n",
+    "\n",
+    "            col = '%s -> %s' % (src, dst)\n",
+    "            all_res_dicts[col] = cur_res\n",
+    "            columns.append(col)\n",
+    "            \n",
+    "#             col_left = col + ', left'\n",
+    "#             all_res_dicts[col_left] = cur_res_left\n",
+    "#             columns.append(col_left)\n",
+    "            \n",
+    "#             col_right = col + ', right'\n",
+    "#             all_res_dicts[col_right] = cur_res_right\n",
+    "#             columns.append(col_right)\n",
+    "\n",
+    "            \n",
+    "    import numpy as np\n",
+    "    print \"diagonals avg:\", np.mean(diagonals)\n",
+    "    print \"off_diags avg:\", np.mean(off_diags)\n",
+    "\n",
+    "    df_res = pd.DataFrame.from_dict(all_res_dicts)\n",
+    "\n",
+    "    descriptions = stats_utils.get_lab_descriptions(lab_type='component')\n",
+    "    df_res['lab'] = df_res['lab'].apply(lambda x:descriptions[x])\n",
+    "    df_res = df_res[columns]\n",
+    "    df_res.to_csv(res_filepath, index=False, float_format='%.2f')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lab</th>\n",
+       "      <th>Stanford -&gt; Stanford</th>\n",
+       "      <th>Stanford -&gt; UCSF</th>\n",
+       "      <th>Stanford -&gt; UMich</th>\n",
+       "      <th>UCSF -&gt; Stanford</th>\n",
+       "      <th>UCSF -&gt; UCSF</th>\n",
+       "      <th>UCSF -&gt; UMich</th>\n",
+       "      <th>UMich -&gt; Stanford</th>\n",
+       "      <th>UMich -&gt; UCSF</th>\n",
+       "      <th>UMich -&gt; UMich</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>White Blood Cells</td>\n",
+       "      <td>0.892329</td>\n",
+       "      <td>0.876284</td>\n",
+       "      <td>0.788482</td>\n",
+       "      <td>0.874436</td>\n",
+       "      <td>0.884967</td>\n",
+       "      <td>0.812764</td>\n",
+       "      <td>0.874380</td>\n",
+       "      <td>0.877838</td>\n",
+       "      <td>0.829191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hemoglobin</td>\n",
+       "      <td>0.930351</td>\n",
+       "      <td>0.939932</td>\n",
+       "      <td>0.894158</td>\n",
+       "      <td>0.861722</td>\n",
+       "      <td>0.900259</td>\n",
+       "      <td>0.790580</td>\n",
+       "      <td>0.917969</td>\n",
+       "      <td>0.937462</td>\n",
+       "      <td>0.902239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Platelet Count</td>\n",
+       "      <td>0.913686</td>\n",
+       "      <td>0.940844</td>\n",
+       "      <td>0.911120</td>\n",
+       "      <td>0.892714</td>\n",
+       "      <td>0.949088</td>\n",
+       "      <td>0.909058</td>\n",
+       "      <td>0.894984</td>\n",
+       "      <td>0.938703</td>\n",
+       "      <td>0.917866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sodium</td>\n",
+       "      <td>0.866907</td>\n",
+       "      <td>0.875252</td>\n",
+       "      <td>0.912196</td>\n",
+       "      <td>0.854706</td>\n",
+       "      <td>0.888347</td>\n",
+       "      <td>0.909633</td>\n",
+       "      <td>0.860638</td>\n",
+       "      <td>0.859915</td>\n",
+       "      <td>0.913546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Potassium</td>\n",
+       "      <td>0.760057</td>\n",
+       "      <td>0.753856</td>\n",
+       "      <td>0.665629</td>\n",
+       "      <td>0.740605</td>\n",
+       "      <td>0.768190</td>\n",
+       "      <td>0.752137</td>\n",
+       "      <td>0.725096</td>\n",
+       "      <td>0.746951</td>\n",
+       "      <td>0.761517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CO2</td>\n",
+       "      <td>0.858788</td>\n",
+       "      <td>0.800907</td>\n",
+       "      <td>0.750025</td>\n",
+       "      <td>0.795334</td>\n",
+       "      <td>0.874155</td>\n",
+       "      <td>0.847938</td>\n",
+       "      <td>0.768511</td>\n",
+       "      <td>0.821761</td>\n",
+       "      <td>0.865286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Urea Nitrogen</td>\n",
+       "      <td>0.946030</td>\n",
+       "      <td>0.920152</td>\n",
+       "      <td>0.903733</td>\n",
+       "      <td>0.938507</td>\n",
+       "      <td>0.924818</td>\n",
+       "      <td>0.904731</td>\n",
+       "      <td>0.931010</td>\n",
+       "      <td>0.919323</td>\n",
+       "      <td>0.915631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Creatinine</td>\n",
+       "      <td>0.963713</td>\n",
+       "      <td>0.908202</td>\n",
+       "      <td>0.846327</td>\n",
+       "      <td>0.944492</td>\n",
+       "      <td>0.939410</td>\n",
+       "      <td>0.881314</td>\n",
+       "      <td>0.920843</td>\n",
+       "      <td>0.879671</td>\n",
+       "      <td>0.895847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Calcium</td>\n",
+       "      <td>0.884810</td>\n",
+       "      <td>0.864028</td>\n",
+       "      <td>0.811792</td>\n",
+       "      <td>0.869204</td>\n",
+       "      <td>0.870691</td>\n",
+       "      <td>0.846546</td>\n",
+       "      <td>0.852396</td>\n",
+       "      <td>0.858916</td>\n",
+       "      <td>0.890967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Albumin</td>\n",
+       "      <td>0.919290</td>\n",
+       "      <td>0.884830</td>\n",
+       "      <td>0.725531</td>\n",
+       "      <td>0.843176</td>\n",
+       "      <td>0.892011</td>\n",
+       "      <td>0.741435</td>\n",
+       "      <td>0.916297</td>\n",
+       "      <td>0.885978</td>\n",
+       "      <td>0.902996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Protein</td>\n",
+       "      <td>0.908079</td>\n",
+       "      <td>0.888393</td>\n",
+       "      <td>0.870338</td>\n",
+       "      <td>0.882387</td>\n",
+       "      <td>0.891115</td>\n",
+       "      <td>0.845332</td>\n",
+       "      <td>0.888996</td>\n",
+       "      <td>0.878265</td>\n",
+       "      <td>0.903451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Alk Phos</td>\n",
+       "      <td>0.939337</td>\n",
+       "      <td>0.913587</td>\n",
+       "      <td>0.893671</td>\n",
+       "      <td>0.917057</td>\n",
+       "      <td>0.932043</td>\n",
+       "      <td>0.886424</td>\n",
+       "      <td>0.923193</td>\n",
+       "      <td>0.927408</td>\n",
+       "      <td>0.922594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Total Bilirubin</td>\n",
+       "      <td>0.964221</td>\n",
+       "      <td>0.908118</td>\n",
+       "      <td>0.909124</td>\n",
+       "      <td>0.954502</td>\n",
+       "      <td>0.925588</td>\n",
+       "      <td>0.906521</td>\n",
+       "      <td>0.955132</td>\n",
+       "      <td>0.917252</td>\n",
+       "      <td>0.919656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>AST (SGOT)</td>\n",
+       "      <td>0.919892</td>\n",
+       "      <td>0.814748</td>\n",
+       "      <td>0.860045</td>\n",
+       "      <td>0.845731</td>\n",
+       "      <td>0.772615</td>\n",
+       "      <td>0.729956</td>\n",
+       "      <td>0.883989</td>\n",
+       "      <td>0.773578</td>\n",
+       "      <td>0.861037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>ALT (SGPT)</td>\n",
+       "      <td>0.928422</td>\n",
+       "      <td>0.857134</td>\n",
+       "      <td>0.913701</td>\n",
+       "      <td>0.922048</td>\n",
+       "      <td>0.914513</td>\n",
+       "      <td>0.876596</td>\n",
+       "      <td>0.881538</td>\n",
+       "      <td>0.840506</td>\n",
+       "      <td>0.882978</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  lab  Stanford -> Stanford  Stanford -> UCSF  Stanford -> UMich  UCSF -> Stanford  UCSF -> UCSF  UCSF -> UMich  UMich -> Stanford  UMich -> UCSF  UMich -> UMich\n",
+       "0   White Blood Cells              0.892329          0.876284           0.788482          0.874436      0.884967       0.812764           0.874380       0.877838        0.829191\n",
+       "1          Hemoglobin              0.930351          0.939932           0.894158          0.861722      0.900259       0.790580           0.917969       0.937462        0.902239\n",
+       "2      Platelet Count              0.913686          0.940844           0.911120          0.892714      0.949088       0.909058           0.894984       0.938703        0.917866\n",
+       "3              Sodium              0.866907          0.875252           0.912196          0.854706      0.888347       0.909633           0.860638       0.859915        0.913546\n",
+       "4           Potassium              0.760057          0.753856           0.665629          0.740605      0.768190       0.752137           0.725096       0.746951        0.761517\n",
+       "5                 CO2              0.858788          0.800907           0.750025          0.795334      0.874155       0.847938           0.768511       0.821761        0.865286\n",
+       "6       Urea Nitrogen              0.946030          0.920152           0.903733          0.938507      0.924818       0.904731           0.931010       0.919323        0.915631\n",
+       "7          Creatinine              0.963713          0.908202           0.846327          0.944492      0.939410       0.881314           0.920843       0.879671        0.895847\n",
+       "8             Calcium              0.884810          0.864028           0.811792          0.869204      0.870691       0.846546           0.852396       0.858916        0.890967\n",
+       "9             Albumin              0.919290          0.884830           0.725531          0.843176      0.892011       0.741435           0.916297       0.885978        0.902996\n",
+       "10            Protein              0.908079          0.888393           0.870338          0.882387      0.891115       0.845332           0.888996       0.878265        0.903451\n",
+       "11           Alk Phos              0.939337          0.913587           0.893671          0.917057      0.932043       0.886424           0.923193       0.927408        0.922594\n",
+       "12    Total Bilirubin              0.964221          0.908118           0.909124          0.954502      0.925588       0.906521           0.955132       0.917252        0.919656\n",
+       "13         AST (SGOT)              0.919892          0.814748           0.860045          0.845731      0.772615       0.729956           0.883989       0.773578        0.861037\n",
+       "14         ALT (SGPT)              0.928422          0.857134           0.913701          0.922048      0.914513       0.876596           0.881538       0.840506        0.882978"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fig, ax = plt.subplots(figsize=(10,6))\n",
+    "df_res[::-1].set_index('lab').rename(columns={'Stanford -> Stanford':'Stanford',\n",
+    "                                       'Stanford -> UCSF':'UCSF',\n",
+    "                                       'Stanford -> UMich':'UMich'})\\\n",
+    "                [['UMich', 'UCSF', 'Stanford'][::-1]]\\\n",
+    "                .plot.barh(width=0.8, figsize=(8,6), fontsize=18, legend=False,\n",
+    "                          color=['salmon','red','brown'][::-1])\n",
+    "# ax.set_ylabel('lab', fontsize=4)\n",
+    "plt.xlim([0.5,1])\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xticks(np.linspace(0.5,1,6))\n",
+    "\n",
+    "# plt.legend(loc=(1,1))\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.savefig('new_transfer.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['White Blood Cells' 0.8923294946108135 0.8762840420449116\n",
+      " 0.7884820840414284 0.8744357881434244 0.8849666579329974\n",
+      " 0.8127642862985988 0.8743799056938006 0.8778381214391032\n",
+      " 0.829190861837043]\n",
+      "['Hemoglobin' 0.9303509407427927 0.9399322156993283 0.8941576011001665\n",
+      " 0.8617219961219663 0.9002589276081563 0.7905803825985844\n",
+      " 0.9179689331145725 0.937462009478224 0.9022389060945204]\n",
+      "['Platelet Count' 0.91368633425045 0.940843849239512 0.9111199888098609\n",
+      " 0.8927136685970015 0.9490876929076828 0.9090583964191554\n",
+      " 0.8949836212225295 0.9387034096607053 0.9178658624460627]\n",
+      "['Sodium' 0.8669073178835944 0.8752516953639687 0.9121960575236266\n",
+      " 0.8547062715180607 0.8883471728660306 0.9096328144382684\n",
+      " 0.8606382473748152 0.8599154609048049 0.9135456421788315]\n",
+      "['Potassium' 0.7600574712643677 0.7538563981001198 0.6656291663398948\n",
+      " 0.7406053165367781 0.7681896906134374 0.7521370872872717\n",
+      " 0.7250960819899648 0.7469505261408466 0.7615166037397627]\n",
+      "['CO2' 0.8587876397999972 0.8009065137050952 0.7500245376743072\n",
+      " 0.7953344580008079 0.8741547104880899 0.847938236878329\n",
+      " 0.7685108775888244 0.8217614965429425 0.8652857741337003]\n",
+      "['Urea Nitrogen' 0.9460296170037523 0.9201522650726854 0.9037328412328413\n",
+      " 0.9385071573472438 0.9248175759657417 0.904731443524547\n",
+      " 0.9310104366858849 0.9193226156381813 0.9156309447257723]\n",
+      "['Creatinine' 0.9637134756961909 0.9082024932288103 0.8463272700776343\n",
+      " 0.944492203575838 0.9394103363309916 0.8813140881269707\n",
+      " 0.9208430502842684 0.879671450100394 0.8958470231313427]\n",
+      "['Calcium' 0.8848100554090248 0.8640275161264017 0.8117916148828457\n",
+      " 0.8692040319777546 0.8706905845587372 0.8465461069098925\n",
+      " 0.852396286981946 0.8589155815525299 0.8909667951607579]\n",
+      "['Albumin' 0.9192897568474009 0.8848298336576205 0.7255310023407167\n",
+      " 0.8431759426291986 0.8920105421404443 0.7414345919026484\n",
+      " 0.9162971949794196 0.8859784283513097 0.9029961346919215]\n",
+      "['Protein' 0.9080793357562428 0.8883934733086822 0.8703384923857671\n",
+      " 0.8823869122656439 0.8911151188362056 0.8453316783509092\n",
+      " 0.8889955710377384 0.8782646930081117 0.9034510935485106]\n",
+      "['Alk Phos' 0.9393370688019678 0.9135867961642489 0.8936705572033851\n",
+      " 0.9170572771857289 0.9320429677895254 0.8864237055819386\n",
+      " 0.9231925609003504 0.9274077944430784 0.9225935827858418]\n",
+      "['Total Bilirubin' 0.9642207823005572 0.9081178652122601\n",
+      " 0.9091238927719486 0.95450244031339 0.9255884373403706 0.9065205514871469\n",
+      " 0.955131998256435 0.9172524752078887 0.919656112899728]\n",
+      "['AST (SGOT)' 0.9198922271363936 0.8147483989021044 0.8600454436138445\n",
+      " 0.8457310253672919 0.7726148919698782 0.7299558966470929\n",
+      " 0.8839891210411266 0.7735776620451826 0.861036860933523]\n",
+      "['ALT (SGPT)' 0.9284222846441947 0.8571337075290764 0.9137010270480924\n",
+      " 0.9220476807836359 0.9145133989055656 0.876595548059929\n",
+      " 0.8815384615384615 0.8405063959319408 0.8829780442859392]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x864 with 15 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: move this stats part away\n",
+    "import seaborn as sns; sns.set()\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(16, 12))\n",
+    "col = 5\n",
+    "for ind in range(df_res.shape[0]):\n",
+    "    cur_row = df_res.iloc[ind].values\n",
+    "    print cur_row\n",
+    "    cur_lab = cur_row[0]\n",
+    "    cur_aucs = cur_row[1:].astype(float).reshape(3,3)\n",
+    "\n",
+    "    i, j = ind/col, ind%col\n",
+    "    plt.subplot2grid((3, col), (i, j))\n",
+    "    ax = sns.heatmap(cur_aucs, vmin=0, vmax=1, cbar=False, annot=True, cmap='ocean',\n",
+    "                     annot_kws={\"size\": 18},\n",
+    "                     xticklabels=['S', 'UC', 'UM'], yticklabels=['S', 'UC', 'UM'])\n",
+    "    \n",
+    "    plt.xlabel(cur_lab, fontsize=20)\n",
+    "    ax.xaxis.set_label_position('top')\n",
+    "    ax.xaxis.set_tick_params(labelsize=18)\n",
+    "    ax.yaxis.set_tick_params(labelsize=18)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.subplots_adjust(hspace=.5)\n",
+    "\n",
+    "plt.savefig(res_folderpath + 'transfer_heatmap.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ind = 0\n",
+    "# cur_row = df_res.iloc[ind].values\n",
+    "# print cur_row\n",
+    "# cur_lab = cur_row[0]\n",
+    "# cur_aucs = cur_row[1:].astype(float).reshape(3,3)\n",
+    "\n",
+    "# sns.set(font_scale=3)\n",
+    "# fig, ax = plt.subplots(figsize=(16, 12))\n",
+    "# ax = sns.heatmap(cur_aucs, vmin=0, vmax=1, cbar=True, annot=True, cmap='ocean',\n",
+    "#                  annot_kws={\"size\": 18},\n",
+    "#                  cbar_kws = {'orientation': 'horizontal'},\n",
+    "#                  xticklabels=['S', 'UC', 'UM'], yticklabels=['S', 'UC', 'UM'])\n",
+    "\n",
+    "# plt.xlabel(cur_lab, fontsize=20)\n",
+    "# ax.xaxis.set_label_position('top')\n",
+    "# ax.xaxis.set_tick_params(labelsize=18)\n",
+    "# ax.yaxis.set_tick_params(labelsize=18)\n",
+    "\n",
+    "\n",
+    "# plt.tight_layout()\n",
+    "# plt.savefig(res_folderpath + 'colorbar.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Lab Test</th>\n",
+       "      <th>Stanford -&gt; Stanford</th>\n",
+       "      <th>Stanford -&gt; Stanford, left</th>\n",
+       "      <th>Stanford -&gt; Stanford, right</th>\n",
+       "      <th>Stanford -&gt; UCSF</th>\n",
+       "      <th>Stanford -&gt; UCSF, left</th>\n",
+       "      <th>Stanford -&gt; UCSF, right</th>\n",
+       "      <th>Stanford -&gt; UMich</th>\n",
+       "      <th>Stanford -&gt; UMich, left</th>\n",
+       "      <th>Stanford -&gt; UMich, right</th>\n",
+       "      <th>UCSF -&gt; Stanford</th>\n",
+       "      <th>UCSF -&gt; Stanford, left</th>\n",
+       "      <th>UCSF -&gt; Stanford, right</th>\n",
+       "      <th>UCSF -&gt; UCSF</th>\n",
+       "      <th>UCSF -&gt; UCSF, left</th>\n",
+       "      <th>UCSF -&gt; UCSF, right</th>\n",
+       "      <th>UCSF -&gt; UMich</th>\n",
+       "      <th>UCSF -&gt; UMich, left</th>\n",
+       "      <th>UCSF -&gt; UMich, right</th>\n",
+       "      <th>UMich -&gt; Stanford</th>\n",
+       "      <th>UMich -&gt; Stanford, left</th>\n",
+       "      <th>UMich -&gt; Stanford, right</th>\n",
+       "      <th>UMich -&gt; UCSF</th>\n",
+       "      <th>UMich -&gt; UCSF, left</th>\n",
+       "      <th>UMich -&gt; UCSF, right</th>\n",
+       "      <th>UMich -&gt; UMich</th>\n",
+       "      <th>UMich -&gt; UMich, left</th>\n",
+       "      <th>UMich -&gt; UMich, right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>White Blood Cells</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hemoglobin</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Platelet Count</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sodium</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Potassium</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>0.79</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Lab Test  Stanford -> Stanford  Stanford -> Stanford, left  Stanford -> Stanford, right  Stanford -> UCSF  Stanford -> UCSF, left  Stanford -> UCSF, right  Stanford -> UMich  Stanford -> UMich, left  Stanford -> UMich, right  UCSF -> Stanford  UCSF -> Stanford, left  UCSF -> Stanford, right  UCSF -> UCSF  UCSF -> UCSF, left  UCSF -> UCSF, right  UCSF -> UMich  UCSF -> UMich, left  UCSF -> UMich, right  UMich -> Stanford  UMich -> Stanford, left  UMich -> Stanford, right  UMich -> UCSF  UMich -> UCSF, left  UMich -> UCSF, right  UMich -> UMich  UMich -> UMich, left  UMich -> UMich, right\n",
+       "0  White Blood Cells                  0.89                        0.88                         0.91              0.88                    0.86                     0.89               0.79                     0.77                      0.81              0.87                    0.86                     0.89          0.88                0.87                 0.90           0.81                 0.80                  0.83               0.87                     0.86                      0.89           0.88                 0.86                  0.89            0.83                  0.81                   0.84\n",
+       "1         Hemoglobin                  0.93                        0.92                         0.94              0.94                    0.93                     0.95               0.89                     0.88                      0.91              0.86                    0.84                     0.88          0.90                0.89                 0.92           0.79                 0.77                  0.81               0.92                     0.90                      0.93           0.94                 0.93                  0.95            0.90                  0.89                   0.91\n",
+       "2     Platelet Count                  0.91                        0.90                         0.92              0.94                    0.93                     0.95               0.91                     0.90                      0.93              0.89                    0.88                     0.91          0.95                0.94                 0.96           0.91                 0.89                  0.92               0.89                     0.88                      0.91           0.94                 0.93                  0.95            0.92                  0.90                   0.93\n",
+       "3             Sodium                  0.87                        0.85                         0.88              0.88                    0.86                     0.89               0.91                     0.90                      0.93              0.85                    0.84                     0.87          0.89                0.87                 0.90           0.91                 0.89                  0.92               0.86                     0.84                      0.88           0.86                 0.84                  0.88            0.91                  0.90                   0.93\n",
+       "4          Potassium                  0.76                        0.73                         0.79              0.75                    0.73                     0.78               0.67                     0.63                      0.70              0.74                    0.71                     0.77          0.77                0.74                 0.79           0.75                 0.72                  0.78               0.73                     0.69                      0.76           0.75                 0.72                  0.77            0.76                  0.73                   0.79"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_CI = pd.read_csv(stats_folderpath+'/data-transferring-component-10000-episodes-lastnormal/all_transfers_withCI.csv').rename(columns={'lab':'Lab Test'})\n",
+    "df_CI.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shorten_dict = {'Stanford':'S', 'UCSF':'UC', 'UMich':'UM'}\n",
+    "sources = ['Stanford', 'UCSF', 'UMich']#shorten_dict.keys()\n",
+    "cols_CI = ['Lab Test']\n",
+    "for src in sources:\n",
+    "    for dst in sources:\n",
+    "        transfer = '%s -> %s' %(src, dst)\n",
+    "        cols_CI.append(transfer)\n",
+    "        df_CI['%s'%transfer] = df_CI[transfer].astype(str) + '\\n[' + df_CI['%s, left'%transfer].astype(str) + ', ' + df_CI['%s, right'%transfer].astype(str) + ']'\n",
+    "df_CI[cols_CI]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_CI[cols_CI].to_csv('data-transferring-component-10000-episodes-lastnormal/all_transfers_withCI_formatted.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/JAMA_config.py b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/JAMA_config.py
new file mode 100644
index 00000000..4f98b7c4
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/JAMA_config.py
@@ -0,0 +1,32 @@
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import os
+import LocalEnv
+
+import matplotlib.pyplot as plt
+
+from sklearn import metrics
+
+import pandas as pd
+import numpy as np
+from scripts.LabTestAnalysis.lab_statistics import stats_utils
+from scripts.LabTestAnalysis.machine_learning.ml_utils import map_lab
+
+from scripts.LabTestAnalysis.lab_statistics import stats_database
+
+from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline \
+        import STRIDE_COMPONENT_TESTS_common, UMICH_TOP_COMPONENTS_common, UCSF_TOP_COMPONENTS_common
+
+
+curr_version = '10000-episodes-lastnormal'
+inverse01 = True # Setting 'True' to interpret 'Normal' as 'Negative'
+
+inverse_maker = '_inversed01' if inverse01 else ''
+
+project_folderpath = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis')
+stats_folderpath = os.path.join(project_folderpath, 'lab_statistics')
+ML_folderpath = os.path.join(project_folderpath, 'machine_learning')
+
+
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Table 1.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Table 1.ipynb
new file mode 100644
index 00000000..94531baa
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/Table 1.ipynb	
@@ -0,0 +1,594 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "stats_utils.py:111: UserWarning: \n",
+      "This call to matplotlib.use() has no effect because the backend has already\n",
+      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
+      "or matplotlib.backends is imported for the first time.\n",
+      "\n",
+      "The backend was *originally* set to 'agg' by the following code:\n",
+      "  File \"/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py\", line 162, in _run_module_as_main\n",
+      "    \"__main__\", fname, loader, pkg_name)\n",
+      "  File \"/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py\", line 72, in _run_code\n",
+      "    exec code in run_globals\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
+      "    app.launch_new_instance()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
+      "    app.start()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelapp.py\", line 499, in start\n",
+      "    self.io_loop.start()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/tornado/ioloop.py\", line 1073, in start\n",
+      "    handler_func(fd_obj, events)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n",
+      "    return fn(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 456, in _handle_events\n",
+      "    self._handle_recv()\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 486, in _handle_recv\n",
+      "    self._run_callback(callback, msg)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 438, in _run_callback\n",
+      "    callback(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n",
+      "    return fn(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
+      "    return self.dispatch_shell(stream, msg)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
+      "    handler(stream, idents, msg)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
+      "    user_expressions, allow_stdin)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
+      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
+      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2714, in run_cell\n",
+      "    interactivity=interactivity, compiler=compiler, result=result)\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2818, in run_ast_nodes\n",
+      "    if self.run_code(code, result):\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2878, in run_code\n",
+      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
+      "  File \"<ipython-input-1-d11b99677bd8>\", line 1, in <module>\n",
+      "    from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline         import NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+      "  File \"/Users/songxu/healthrex/CDSS/scripts/LabTestAnalysis/machine_learning/LabNormalityPredictionPipeline.py\", line 20, in <module>\n",
+      "    from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline\n",
+      "  File \"/Users/songxu/healthrex/CDSS/medinfo/ml/SupervisedLearningPipeline.py\", line 35, in <module>\n",
+      "    from medinfo.ml.ClassifierAnalyzer import ClassifierAnalyzer\n",
+      "  File \"/Users/songxu/healthrex/CDSS/medinfo/ml/ClassifierAnalyzer.py\", line 9, in <module>\n",
+      "    import matplotlib.pyplot as plt\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/matplotlib/pyplot.py\", line 71, in <module>\n",
+      "    from matplotlib.backends import pylab_setup\n",
+      "  File \"/Users/songxu/healthrex/CDSS/env/lib/python2.7/site-packages/matplotlib/backends/__init__.py\", line 17, in <module>\n",
+      "    line for line in traceback.format_stack()\n",
+      "\n",
+      "\n",
+      "  matplotlib.use('TkAgg')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline \\\n",
+    "        import NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "import stats_utils\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_label=\"all_labs\"\n",
+    "targeted_PPV=0.95\n",
+    "scale_by='enc'\n",
+    "use_cached_fig_data=False\n",
+    "inverse01=True\n",
+    "\n",
+    "all_labs = NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+    "\n",
+    "data_source = 'Stanford'\n",
+    "lab_type = 'panel'\n",
+    "curr_version = '10000-episodes-lastnormal'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_folderpath = os.path.join(stats_utils.main_folder, 'lab_statistics/')\n",
+    "ml_folderpath = os.path.join(stats_utils.main_folder, 'machine_learning')\n",
+    "\n",
+    "import LocalEnv\n",
+    "statsByDataSet_foldername = 'data-%s-%s-%s' % (data_source, lab_type, curr_version)\n",
+    "statsByDataSet_folderpath = os.path.join(stats_folderpath, statsByDataSet_foldername)\n",
+    "\n",
+    "dataset_foldername = 'data-%s-%s-%s'%(data_source, lab_type, curr_version)\n",
+    "labStats_folderpath = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/lab_statistics')\n",
+    "statsByLab_folderpath = os.path.join(labStats_folderpath, dataset_foldername)\n",
+    "ml_folderpath = statsByLab_folderpath.replace(\"lab_statistics\", \"machine_learning\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vols_tmp = [\n",
+    "4246,\n",
+    "2244,\n",
+    "2120,\n",
+    "1471,\n",
+    "1230,\n",
+    "1197,\n",
+    "752,\n",
+    "534,\n",
+    "455,\n",
+    "400,\n",
+    "371,\n",
+    "361\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw__Diagnostic_Metrics(result_label='all_labs',\n",
+    "                            targeted_PPV=0.95, scale_by=None, use_cached_fig_data=False,\n",
+    "                             inverse01=False):\n",
+    "    '''\n",
+    "    Drawing Figure 3 in the main text.\n",
+    "\n",
+    "    :return:\n",
+    "    '''\n",
+    "    labs = all_labs\n",
+    "    print 'draw__Diagnostic_Metrics for', labs, ' with label %s...'%result_label\n",
+    "    \n",
+    "    labs_stats_filepath = os.path.join(statsByLab_folderpath, 'summary-stats-bestalg-fixTrainPPV.csv')\n",
+    "\n",
+    "    df = pd.read_csv(labs_stats_filepath, keep_default_na=False)\n",
+    "\n",
+    "    # df = df[df['fixTrainPPV'] == targeted_PPV]\n",
+    "\n",
+    "    df = df[df['lab'].isin(labs)]\n",
+    "\n",
+    "    cached_foldername = 'Fig3_Diagnostic_Metrics/'\n",
+    "    cached_folderpath = os.path.join(os.path.join(statsByLab_folderpath, cached_foldername))\n",
+    "\n",
+    "    cached_tablename = 'Diagnostic_Metrics_%ss_PPV_%.2f__%s.csv'%(lab_type, targeted_PPV, result_label)\n",
+    "    cached_tablepath = os.path.join(cached_folderpath, cached_tablename)\n",
+    "\n",
+    "    cached_figurename = 'Diagnostic_Metrics_%ss_PPV_%.2f_ind__%s.png'%(lab_type, targeted_PPV, result_label)\n",
+    "    cached_figurepath = os.path.join(cached_folderpath, cached_figurename)\n",
+    "\n",
+    "    if not os.path.exists(cached_folderpath):\n",
+    "        os.mkdir(cached_folderpath)\n",
+    "\n",
+    "    if os.path.exists(cached_tablepath) and use_cached_fig_data:\n",
+    "        # lab2stats = pickle.load(open(cached_result_path, 'r'))\n",
+    "        df_toplots = pd.read_csv(cached_tablepath, keep_default_na=False)\n",
+    "        # print df_toplots\n",
+    "\n",
+    "    else:\n",
+    "        if data_source == 'Stanford':\n",
+    "            # if 'total_vol' not in df.columns.values.tolist():\n",
+    "            # Stanford data, scaled by vol\n",
+    "            df['total_vol'] = df['total_vol_20140701_20170701']\n",
+    "\n",
+    "        elif data_source == 'UCSF':\n",
+    "            import stats_database\n",
+    "            if lab_type == 'panel':\n",
+    "                ucsf_lab_cnt = dict(stats_database.UCSF_PANELS_AND_COUNTS)\n",
+    "\n",
+    "            elif lab_type == 'component':\n",
+    "                ucsf_lab_cnt = stats_database.UCSF_COMPONENT_TO_COUNT #dict(stats_database.UCSF_COMPONENTSS_AND_COUNTS)\n",
+    "            df['total_vol'] = df['lab'].apply(lambda x: ucsf_lab_cnt[x])\n",
+    "        elif data_source == 'UMich' and lab_type=='component' and result_label=='important_components':\n",
+    "            df = df[df['lab'].isin(stats_utils.umich_lab_cnt)]\n",
+    "\n",
+    "\n",
+    "            df['total_vol'] = df['lab'].apply(lambda x: stats_utils.umich_lab_cnt[x])\n",
+    "        else:\n",
+    "            df['total_vol'] = 1\n",
+    "\n",
+    "\n",
+    "\n",
+    "        # TODO: use fractions in the original file!\n",
+    "        df['all_instance'] = df['TP'] + df['FP'] + df['TN'] + df['FN']\n",
+    "\n",
+    "        for cnt_type in ['TP', 'FP', 'TN', 'FN']:\n",
+    "            df[cnt_type] = df[cnt_type]/df['all_instance']\n",
+    "\n",
+    "\n",
+    "        df['all_positive'] = df['TP'] + df['FP']\n",
+    "\n",
+    "        # print df[['all_positive', 'total_vol']]\n",
+    "        df['predicted_normal_vol'] = df['all_positive'] * df['total_vol']\n",
+    "\n",
+    "        if result_label == 'important_components':\n",
+    "\n",
+    "            if data_source == 'UMich' or data_source == 'UCSF':\n",
+    "                df['lab'] = df['lab'].apply(lambda x: map_lab(x, data_source, lab_type, map_type='from_src'))\n",
+    "\n",
+    "            df = df[df['lab'].isin(stats_utils.get_important_labs('component'))]\n",
+    "            df['lab'] = pd.Categorical(\n",
+    "                df['lab'],\n",
+    "                categories=stats_utils.get_important_labs('component'),\n",
+    "                ordered=True\n",
+    "            )\n",
+    "            df = df.sort_values('lab')\n",
+    "        elif data_source == 'UMich':\n",
+    "            pass\n",
+    "        else:\n",
+    "            df = df.sort_values('total_vol', ascending=False)\n",
+    "\n",
+    "        df['all_negative'] = df['TN'] + df['FN']\n",
+    "\n",
+    "        df['TN'] = -df['TN']\n",
+    "        df['all_negative'] = -df['all_negative']\n",
+    "\n",
+    "        df_toshow = df.copy().drop_duplicates()\n",
+    "\n",
+    "        lab_descriptions = stats_utils.get_lab_descriptions(data_source=data_source,#'Stanford',\n",
+    "                                                                 lab_type=lab_type)\n",
+    "        df_toshow['Lab Test'] = df_toshow['lab'].apply(lambda x:lab_descriptions.get(x,x))\n",
+    "        df_toshow['TN'] = -df_toshow['TN']\n",
+    "\n",
+    "        df_toshow['Prev'] = df_toshow['TN'] + df_toshow['FP']\n",
+    "        df_toshow = df_toshow.rename(columns={\n",
+    "            'num_test_episodes':'Count',\n",
+    "                                            'TP':'TP',\n",
+    "                                              'FP':'FP',\n",
+    "                                              'TN':'TN',\n",
+    "                                              'FN':'FN',\n",
+    "                                              'sens':'sens',\n",
+    "                                              'spec':'spec',\n",
+    "                                              'LR_p':'LR+', 'LR_n':'LR-'})\n",
+    "\n",
+    "        df_toshow['AUC'] = df_toshow['AUC'].apply(lambda x: '%.2f'%(x))\n",
+    "\n",
+    "        print df_toshow['NPV']\n",
+    "        df_toshow['NPV'] = df_toshow['NPV'].apply(lambda x: stats_utils.convert_floatstr2percentage(x))\n",
+    "\n",
+    "        numeric_cols = ['Prev', 'PPV', 'TP', 'FP', 'TN', 'FN', 'sens', 'spec']\n",
+    "        for numeric_col in numeric_cols:\n",
+    "            if df_toshow[numeric_col].dtype != type(0.1):\n",
+    "                df_toshow[numeric_col] = df_toshow[numeric_col].apply(lambda x: stats_utils.convert_floatstr2percentage(x))\n",
+    "            else:\n",
+    "                df_toshow[numeric_col] = df_toshow[numeric_col].apply(lambda x: stats_utils.convert_floatnum2percentage(x))\n",
+    "\n",
+    "        df_toshow['LR+'] = df_toshow['LR+'].apply(lambda x: stats_utils.convert_floatstr2num(x))\n",
+    "        df_toshow['LR-'] = df_toshow['LR-'].apply(lambda x: stats_utils.convert_floatstr2num(x))\n",
+    "\n",
+    "        df_toshow['Vol'] = (df_toshow['total_vol'] / float(stats_utils.NUM_DISTINCT_ENCS / 1000.)).apply(\n",
+    "            lambda x: int(round(x)))\n",
+    "        \n",
+    "        def fix_number(x):\n",
+    "            fix_dict = {4251:4246,\n",
+    "                       2246:2244,\n",
+    "                       2122:2120,\n",
+    "                       1473:1471,\n",
+    "                       1231:1230,\n",
+    "                       1198:1197,\n",
+    "                       753:752,\n",
+    "                       534:534,\n",
+    "                       456:455,\n",
+    "                       401:400,\n",
+    "                       372:371,\n",
+    "                       361:361,}\n",
+    "            return fix_dict.get(x,x)\n",
+    "        df_toshow['Vol'] = df_toshow['Vol'].apply(lambda x: fix_number(x))\n",
+    "\n",
+    "        if data_source == 'Stanford' and lab_type=='panel':\n",
+    "\n",
+    "            df_toshow.loc[df_toshow['Lab Test']=='Sodium', 'chargemaster'] = 219\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'Specific Gravity', 'medicare'] = 3.28\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'Sepsis Protocol Lactate', 'medicare'] = 11.87\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'LDH Total', 'medicare'] = 6.71\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'Lactate', 'medicare'] = 11.87\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'Urxinalysis', 'medicare'] = 2.67\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'Calcium Ionized', 'medicare'] = 13.73\n",
+    "            df_toshow.loc[df_toshow['Lab Test'] == 'Heparin', 'medicare'] = 16.16\n",
+    "\n",
+    "            # print df_toshow\n",
+    "            df_toshow = df_toshow.rename(columns={'medicare': 'Medicare', 'chargemaster': 'Chargemaster'})\n",
+    "\n",
+    "            df_toshow['Medicare'] = df_toshow['Medicare'].apply(lambda x: '$%s' % x if x != '' else '-')\n",
+    "            df_toshow['Chargemaster'] = df_toshow['Chargemaster'].apply(lambda x: '$%s' % str(x) if x != '' else '-')\n",
+    "\n",
+    "            cols_to_show = ['Lab Test', 'Vol', 'AUC'] \\\n",
+    "                           + numeric_cols\n",
+    "                           #+ ['Medicare', 'Chargemaster'] #+ ['LR+', 'LR-'] \\\n",
+    "            print df_toshow['Lab Test'].values.tolist()\n",
+    "            labs_to_show = ['Magnesium', 'Prothrombin Time', 'Phosphorus', 'Partial Thromboplastin Time',\n",
+    "                            'Lactate', 'Calcium Ionized', 'Potassium', 'Troponin I', 'LDH Total',\n",
+    "                            'Heparin', 'Urinalysis', # too few positives\n",
+    "                            'Blood Culture (Aerobic & Anaerobic)',\n",
+    "                            'Blood Culture (2 Aerobic)', 'Sodium', 'Lidocaine', 'Hematocrit', 'Urine Culture',\n",
+    "                            'Urinalysis With Microscopic', 'Uric Acid', 'Hemoglobin A1c'\n",
+    "                            ]\n",
+    "        else:\n",
+    "            cols_to_show = ['Lab Test', 'AUC'] + numeric_cols #+ ['LR+', 'LR-']\n",
+    "\n",
+    "        if inverse01:\n",
+    "            df_toshow = df_toshow.rename(columns={'PPV': 'NPV',\n",
+    "                                                  'NPV': 'PPV',\n",
+    "                                                  'TP': 'TN',\n",
+    "                                                  'FP': 'FN',\n",
+    "                                                  'TN': 'TP',\n",
+    "                                                  'FN': 'FP',\n",
+    "                                                  'sens':'Spec',\n",
+    "                                                  'spec':'Sens',\n",
+    "                                                  'best_alg':'Best Alg',\n",
+    "                                                  'AUC':'C-stat',\n",
+    "                                                  'fixTrainPPV':'Target NPV'})\n",
+    "\n",
+    "            rename_alg = {'xgb': 'xgboost', 'nn': 'neural-nets'}\n",
+    "            df_toshow['Best Alg'] = df_toshow['Best Alg'].apply(lambda x: rename_alg.get(x, x))\n",
+    "            if data_source == 'Stanford':\n",
+    "                cols_to_show = ['lab', 'Lab Test', 'Vol', 'C-stat', 'Prev', 'NPV', 'PPV', 'Sens', 'Spec', 'TN', 'FN', 'TP', 'FP'] #['Lab Test', 'Vol', 'C-stat', 'Prev', 'NPV', 'PPV', 'Sens', 'Spec', 'TN', 'FN', 'TP', 'FP']\n",
+    "\n",
+    "            else:\n",
+    "                cols_to_show = ['Lab Test', 'Best Alg', 'C-stat', 'Prev', 'Target NPV', 'NPV', 'PPV', 'Sens',\n",
+    "                                'Spec', 'TN', 'FN', 'TP', 'FP']\n",
+    "\n",
+    "        if data_source == 'Stanford':\n",
+    "            if lab_type == 'panel':\n",
+    "                labs_to_show = ['LABMGN', 'LABPT', 'LABPHOS', 'LABPTT', 'LABLACWB', 'LABCAI', 'LABK',\n",
+    "                                'LABTNI', 'LABLDH', 'LABBLC', 'LABBLC2', 'LABNA'#, 'LABHCTX', 'LABURNC'\n",
+    "                               ]\n",
+    "                #['LABMGN', 'LABPHOS', 'LABBLC', 'LABBLC2', 'LABUA', 'LABUAPRN', 'LABLACWB', 'LABCAI'] # suggested by Jason Hom\n",
+    "                df_finalshow = df_toshow[(df_toshow['lab'].isin(labs_to_show)) & (df_toshow['Target NPV']==0.95)]\\\n",
+    "                [cols_to_show]\n",
+    "            else:\n",
+    "                labs_to_show = ['WBC', 'HGB', 'NA', 'K', 'CR']\n",
+    "                df_finalshow = df_toshow[df_toshow['lab'].isin(labs_to_show)][cols_to_show]\n",
+    "                \n",
+    "        df_finalshow.to_csv(\n",
+    "                    cached_tablepath.replace('.csv', '_finalshow.csv'), index=False)\n",
+    "\n",
+    "        df_toshow[cols_to_show].iloc[:20].to_csv(cached_tablepath.replace('.csv','_toshow.csv'), index=False) #.sort_values('total_vol', ascending=False)\n",
+    "\n",
+    "        if data_source == 'Stanford' and lab_type=='panel':\n",
+    "            cols_to_show += ['Medicare', 'Chargemaster']\n",
+    "\n",
+    "        df_toshow[cols_to_show].to_csv(cached_tablepath.replace('.csv', '_full.csv'), index=False)\n",
+    "\n",
+    "        df['all_positive_vol'] = df['all_positive'] * df['total_vol']\n",
+    "        df['true_positive_vol'] = df['TP'] * df['total_vol']\n",
+    "        df['all_negative_vol'] = df['all_negative'] * df['total_vol']\n",
+    "        df['true_negative_vol'] = df['TN'] * df['total_vol']\n",
+    "\n",
+    "        df_toplots = df.iloc[:15]\n",
+    "\n",
+    "        df_toplots[['lab',\n",
+    "                    'PPV', 'NPV', 'sens', 'spec', 'LR_p', 'LR_n',\n",
+    "                    'total_vol',\n",
+    "                   'all_positive_vol', 'true_positive_vol', 'all_negative_vol', 'true_negative_vol']]\\\n",
+    "                    .to_csv(cached_tablepath, index=False, float_format='%.3f') # .sort_values('total_vol', ascending=False)\\\n",
+    "\n",
+    "    if not scale_by:\n",
+    "        scale = 1.\n",
+    "    elif scale_by=='pat':\n",
+    "        scale = float(stats_utils.NUM_DISTINCT_PATS/1000.)\n",
+    "    elif scale_by == 'enc':\n",
+    "        scale = float(stats_utils.NUM_DISTINCT_ENCS/1000.)\n",
+    "    elif scale_by == 'enc_ucsf':\n",
+    "        scale = float(stats_utils.NUM_DISTINCT_ENCS_UCSF/1000.)\n",
+    "\n",
+    "    if data_source == 'Stanford' or data_source == 'UCSF' or data_source == 'UMich': #True: #lab_type == 'panel':\n",
+    "        df_toplots = df_toplots.iloc[::-1]\n",
+    "    else:\n",
+    "        df_toplots = df_toplots.iloc[::-1]\n",
+    "\n",
+    "    for ind, df_toplot in enumerate([df_toplots.tail(38), df_toplots.head(38)]):\n",
+    "\n",
+    "        if result_label == 'important_components':\n",
+    "            fig, ax = plt.subplots(figsize=(6, 9))\n",
+    "        else:\n",
+    "            fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "\n",
+    "        ax.barh(df_toplot['lab'], df_toplot['all_positive_vol'] / scale, color='orangered', alpha=1,\n",
+    "                label='False Positive')\n",
+    "        ax.barh(df_toplot['lab'], df_toplot['true_positive_vol'] / scale, color='forestgreen', alpha=1,\n",
+    "                label='True Positive')\n",
+    "        ax.barh(df_toplot['lab'], df_toplot['all_negative_vol'] / scale, color='gold', alpha=1,\n",
+    "                label='False Negative')\n",
+    "        ax.barh(df_toplot['lab'], df_toplot['true_negative_vol'] / scale, color='royalblue', alpha=1,\n",
+    "                label='True Negative')\n",
+    "\n",
+    "        plt.yticks([])\n",
+    "\n",
+    "        if result_label == 'important_components':\n",
+    "            plt.xlim([-8500, 6500])\n",
+    "            plt.xticks([-6000, -3000, 0, 3000, 6000])\n",
+    "            ax.set_xticklabels([6000, 3000, 0, 3000, 6000])\n",
+    "\n",
+    "            ax.spines['top'].set_visible(False)\n",
+    "            ax.spines['right'].set_visible(False)\n",
+    "            ax.spines['bottom'].set_visible(True)\n",
+    "            ax.spines['left'].set_visible(False)\n",
+    "            pass\n",
+    "        else:\n",
+    "            for i, v in enumerate(df_toplot['all_positive_vol'] / scale):\n",
+    "                cur_lab = df_toplot['lab'].values[i]\n",
+    "                cur_description = lab_descriptions.get(cur_lab, cur_lab).replace(' - ', '/')\n",
+    "\n",
+    "                if data_source == 'UMich':\n",
+    "                    ax.text(v + 0.05, i - 0.2, cur_description, color='k', fontsize=14)\n",
+    "                else:\n",
+    "                    if '\\n' in cur_description:\n",
+    "                        ax.text(v + 50, i - 0.3, cur_description, color='k', fontsize=14)\n",
+    "                    else:\n",
+    "                        ax.text(v + 50, i - 0.1, cur_description, color='k', fontsize=14)\n",
+    "\n",
+    "            if data_source == 'Stanford' and lab_type == 'panel':\n",
+    "                plt.xlim([-2300, 2700])\n",
+    "            elif data_source == 'Stanford' and lab_type == 'component':\n",
+    "                plt.xlim([-9000, 9000])\n",
+    "            elif data_source == 'UCSF' and lab_type == 'panel':\n",
+    "                plt.xlim([-3200, 3200])\n",
+    "            elif data_source == 'UCSF' and lab_type == 'component':\n",
+    "                plt.xlim([-6000, 6000])\n",
+    "            elif data_source == 'UMich' and lab_type == 'component':\n",
+    "                # plt.xlim([-1.5, 1.5])\n",
+    "                plt.xlim([-8000, 8000])\n",
+    "\n",
+    "            if data_source == 'UCSF' or data_source == 'Stanford' or data_source == 'UMich':\n",
+    "                plt.xlabel('Number of orders per 1000 patient encounters', fontsize=18) #, targeting at %.0f'%(targeted_PPV*100)+'% PPV'\n",
+    "            else:\n",
+    "                plt.xlabel('Fraction of orders, targeting at %.0f' % (targeted_PPV * 100) + '% PPV',\n",
+    "                fontsize=18)\n",
+    "\n",
+    "        plt.tick_params('x', labelsize=24)\n",
+    "\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "\n",
+    "        plt.savefig(cached_figurepath.replace('ind', 'ind_%i'%ind))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "draw__Diagnostic_Metrics for ['LABA1C', 'LABAFBC', 'LABAFBD', 'LABALB', 'LABANER', 'LABB12', 'LABBLC', 'LABBLC2', 'LABBLCSTK', 'LABBLCTIP', 'LABBUN', 'LABBXTG', 'LABCA', 'LABCAI', 'LABCDTPCR', 'LABCK', 'LABCMVQT', 'LABCORT', 'LABCRP', 'LABCSFC', 'LABCSFGL', 'LABCSFTP', 'LABDIGL', 'LABESRP', 'LABFCUL', 'LABFE', 'LABFER', 'LABFIB', 'LABFLDC', 'LABFOL', 'LABFT4', 'LABGRAM', 'LABHAP', 'LABHBSAG', 'LABHCTX', 'LABHEPAR', 'LABHIVWBL', 'LABK', 'LABLAC', 'LABLACWB', 'LABLDH', 'LABLIDOL', 'LABLIPS', 'LABMB', 'LABMGN', 'LABNA', 'LABNH3', 'LABNTBNP', 'LABOSM', 'LABPALB', 'LABPCCG4O', 'LABPCCR', 'LABPCTNI', 'LABPHOS', 'LABPLTS', 'LABPROCT', 'LABPT', 'LABPTEG', 'LABPTT', 'LABRESP', 'LABRESPG', 'LABRETIC', 'LABSPLAC', 'LABSTLCX', 'LABSTOBGD', 'LABTNI', 'LABTRFS', 'LABTRIG', 'LABTSH', 'LABUOSM', 'LABUA', 'LABUAPRN', 'LABUPREG', 'LABURIC', 'LABURNC', 'LABUSPG']  with label all_labs...\n",
+      "176      0.2908204711616572\n",
+      "177      0.3630097645031591\n",
+      "178      0.4320266889074229\n",
+      "179      0.6339285714285714\n",
+      "224      0.8084669132757912\n",
+      "225      0.8103092783505155\n",
+      "226      0.8322663252240717\n",
+      "227      0.8658088235294118\n",
+      "214       0.452576112412178\n",
+      "213      0.3905504984828782\n",
+      "215      0.5727170236753101\n",
+      "212      0.3392982456140351\n",
+      "232      0.6237550719291775\n",
+      "234      0.6861499364675985\n",
+      "235      0.7794273594909862\n",
+      "233      0.6536050156739812\n",
+      "159       0.903061224489796\n",
+      "157      0.5586652314316469\n",
+      "156      0.3530415008527573\n",
+      "158      0.7162629757785467\n",
+      "53       0.6185344827586207\n",
+      "54       0.6268195853550949\n",
+      "55       0.6596267190569745\n",
+      "52       0.6131231359181935\n",
+      "149     0.39766081871345027\n",
+      "151                        \n",
+      "150      0.6428571428571429\n",
+      "148      0.2128935532233883\n",
+      "260      0.3816974869606449\n",
+      "261      0.6719330855018587\n",
+      "               ...         \n",
+      "81       0.6101694915254238\n",
+      "80      0.21712538226299694\n",
+      "236      0.3321976149914821\n",
+      "237     0.38535414165666265\n",
+      "238      0.4440559440559441\n",
+      "239      0.6048780487804878\n",
+      "79                         \n",
+      "77                         \n",
+      "76                      0.1\n",
+      "78                         \n",
+      "259       0.794392523364486\n",
+      "257      0.7622377622377622\n",
+      "256      0.7622377622377622\n",
+      "258      0.7708703374777975\n",
+      "207      0.5290102389078498\n",
+      "206      0.4558521560574949\n",
+      "205      0.4263431542461005\n",
+      "204              0.40234375\n",
+      "203                        \n",
+      "202                     1.0\n",
+      "201     0.48717948717948717\n",
+      "200     0.24793388429752067\n",
+      "144    0.027777777777777776\n",
+      "145                        \n",
+      "146                        \n",
+      "147                        \n",
+      "252     0.09274193548387097\n",
+      "253     0.11042944785276074\n",
+      "254                     0.5\n",
+      "255                        \n",
+      "Name: NPV, Length: 304, dtype: object\n",
+      "['Magnesium', 'Magnesium', 'Magnesium', 'Magnesium', 'Prothrombin Time', 'Prothrombin Time', 'Prothrombin Time', 'Prothrombin Time', 'Phosphorus', 'Phosphorus', 'Phosphorus', 'Phosphorus', 'Partial Thromboplastin Time', 'Partial Thromboplastin Time', 'Partial Thromboplastin Time', 'Partial Thromboplastin Time', 'Lactate', 'Lactate', 'Lactate', 'Lactate', 'Calcium Ionized', 'Calcium Ionized', 'Calcium Ionized', 'Calcium Ionized', 'Potassium', 'Potassium', 'Potassium', 'Potassium', 'Troponin I', 'Troponin I', 'Troponin I', 'Troponin I', 'LDH Total', 'LDH Total', 'LDH Total', 'LDH Total', 'Heparin', 'Heparin', 'Heparin', 'Heparin', 'Urinalysis', 'Urinalysis', 'Urinalysis', 'Urinalysis', 'Blood Culture (Aerobic & Anaerobic)', 'Blood Culture (Aerobic & Anaerobic)', 'Blood Culture (Aerobic & Anaerobic)', 'Blood Culture (Aerobic & Anaerobic)', 'Blood Culture (2 Aerobic)', 'Blood Culture (2 Aerobic)', 'Blood Culture (2 Aerobic)', 'Blood Culture (2 Aerobic)', 'Sodium', 'Sodium', 'Sodium', 'Sodium', 'Lidocaine', 'Lidocaine', 'Lidocaine', 'Lidocaine', 'Hematocrit', 'Hematocrit', 'Hematocrit', 'Hematocrit', 'Urine Culture', 'Urine Culture', 'Urine Culture', 'Urine Culture', 'Urinalysis With Microscopic', 'Urinalysis With Microscopic', 'Urinalysis With Microscopic', 'Urinalysis With Microscopic', 'Uric Acid', 'Uric Acid', 'Uric Acid', 'Uric Acid', 'Hemoglobin A1c', 'Hemoglobin A1c', 'Hemoglobin A1c', 'Hemoglobin A1c', 'Sepsis Protocol Lactate', 'Sepsis Protocol Lactate', 'Sepsis Protocol Lactate', 'Sepsis Protocol Lactate', 'iSTAT Troponin I', 'iSTAT Troponin I', 'iSTAT Troponin I', 'iSTAT Troponin I', 'Platelet Count', 'Platelet Count', 'Platelet Count', 'Platelet Count', 'Lipase', 'Lipase', 'Lipase', 'Lipase', 'Procalcitonin', 'Procalcitonin', 'Procalcitonin', 'Procalcitonin', 'Lactic Acid', 'Lactic Acid', 'Lactic Acid', 'Lactic Acid', 'Fibrinogen', 'Fibrinogen', 'Fibrinogen', 'Fibrinogen', 'Thyroid Stimulating Hormone', 'Thyroid Stimulating Hormone', 'Thyroid Stimulating Hormone', 'Thyroid Stimulating Hormone', 'Creatine Kinase', 'Creatine Kinase', 'Creatine Kinase', 'Creatine Kinase', 'C-Reactive Protein', 'C-Reactive Protein', 'C-Reactive Protein', 'C-Reactive Protein', 'NT-proBNP', 'NT-proBNP', 'NT-proBNP', 'NT-proBNP', 'Triglycerides', 'Triglycerides', 'Triglycerides', 'Triglycerides', 'Ck', 'Ck', 'Ck', 'Ck', 'C.diff Toxin B Gene', 'C.diff Toxin B Gene', 'C.diff Toxin B Gene', 'C.diff Toxin B Gene', 'Osmolality', 'Osmolality', 'Osmolality', 'Osmolality', 'Respiratory Culture And Gram Stain', 'Respiratory Culture And Gram Stain', 'Respiratory Culture And Gram Stain', 'Respiratory Culture And Gram Stain', 'Sedimentation Rate (ESR)', 'Sedimentation Rate (ESR)', 'Sedimentation Rate (ESR)', 'Sedimentation Rate (ESR)', 'Ferritin', 'Ferritin', 'Ferritin', 'Ferritin', 'Albumin', 'Albumin', 'Albumin', 'Albumin', 'Ammonia', 'Ammonia', 'Ammonia', 'Ammonia', 'Specific Gravity', 'Specific Gravity', 'Specific Gravity', 'Specific Gravity', 'Fungal Culture', 'Fungal Culture', 'Fungal Culture', 'Fungal Culture', 'Haptoglobin', 'Haptoglobin', 'Haptoglobin', 'Haptoglobin', 'Anaerobic Culture', 'Anaerobic Culture', 'Anaerobic Culture', 'Anaerobic Culture', 'Fluid Culture And Gram Stain', 'Fluid Culture And Gram Stain', 'Fluid Culture And Gram Stain', 'Fluid Culture And Gram Stain', 'Cmv Dna Pcr Quant', 'Cmv Dna Pcr Quant', 'Cmv Dna Pcr Quant', 'Cmv Dna Pcr Quant', 'Blood Cult Central Line Catheter By Nurse', 'Blood Cult Central Line Catheter By Nurse', 'Blood Cult Central Line Catheter By Nurse', 'Blood Cult Central Line Catheter By Nurse', 'T4', 'T4', 'T4', 'T4', 'Transferrin Saturation', 'Transferrin Saturation', 'Transferrin Saturation', 'Transferrin Saturation', 'Vitamin B12', 'Vitamin B12', 'Vitamin B12', 'Vitamin B12', 'Blood Cult - First Set', 'Blood Cult - First Set', 'Blood Cult - First Set', 'Blood Cult - First Set', 'Prealbumin', 'Prealbumin', 'Prealbumin', 'Prealbumin', 'Osmolality', 'Osmolality', 'Osmolality', 'Osmolality', 'Digoxin', 'Digoxin', 'Digoxin', 'Digoxin', 'Reticulocyte Count Automated', 'Reticulocyte Count Automated', 'Reticulocyte Count Automated', 'Reticulocyte Count Automated', 'Cortisol', 'Cortisol', 'Cortisol', 'Cortisol', 'Hepatitis B Antigen', 'Hepatitis B Antigen', 'Hepatitis B Antigen', 'Hepatitis B Antigen', 'Iron', 'Iron', 'Iron', 'Iron', 'AFB Culture', 'AFB Culture', 'AFB Culture', 'AFB Culture', 'Calcium', 'Calcium', 'Calcium', 'Calcium', 'Biopsy/tissue With Gram Stain', 'Biopsy/tissue With Gram Stain', 'Biopsy/tissue With Gram Stain', 'Biopsy/tissue With Gram Stain', 'Afb Culture', 'Afb Culture', 'Afb Culture', 'Afb Culture', 'Pregnancy Test', 'Pregnancy Test', 'Pregnancy Test', 'Pregnancy Test', 'Urea Nitrogen', 'Urea Nitrogen', 'Urea Nitrogen', 'Urea Nitrogen', 'Gram Stain', 'Gram Stain', 'Gram Stain', 'Gram Stain', 'Teg', 'Teg', 'Teg', 'Teg', 'Protein Total', 'Protein Total', 'Protein Total', 'Protein Total', 'Folic Acid', 'Folic Acid', 'Folic Acid', 'Folic Acid', 'Glucose', 'Glucose', 'Glucose', 'Glucose', 'Respiratory Culture', 'Respiratory Culture', 'Respiratory Culture', 'Respiratory Culture', 'CSF Culture And Gram Stain', 'CSF Culture And Gram Stain', 'CSF Culture And Gram Stain', 'CSF Culture And Gram Stain', 'Occult Bld', 'Occult Bld', 'Occult Bld', 'Occult Bld', 'iSTAT Creatinine', 'iSTAT Creatinine', 'iSTAT Creatinine', 'iSTAT Creatinine', 'iSTAT Cg4', 'iSTAT Cg4', 'iSTAT Cg4', 'iSTAT Cg4', 'Anti-hiv', 'Anti-hiv', 'Anti-hiv', 'Anti-hiv', 'Stool Culture', 'Stool Culture', 'Stool Culture', 'Stool Culture']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# statsByDataSet_foldername = 'data-%s-%s-%s' % (self.data_source, self.lab_type, self.curr_version)\n",
+    "# statsByDataSet_folderpath = os.path.join(stats_folderpath, statsByDataSet_foldername)\n",
+    "# if not os.path.exists(statsByDataSet_folderpath):\n",
+    "#     os.mkdir(statsByDataSet_folderpath)\n",
+    "statsByDataSet_folderpath = '.'\n",
+    "draw__Diagnostic_Metrics(result_label=result_label,\n",
+    "                                            targeted_PPV=0.95, scale_by='enc', use_cached_fig_data=False, inverse01=inverse01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/statistical_analysis.ipynb b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/statistical_analysis.ipynb
new file mode 100644
index 00000000..03a8a3f4
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/JAMANetworkOpen/statistical_analysis.ipynb
@@ -0,0 +1,828 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Statistical Analysis of the Lab Normality Paper\n",
+    "\n",
+    "### Author: Song Xu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Assign Data Set and Type here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "curr_version = '10000-episodes-lastnormal'\n",
+    "inverse01 = True # Setting 'True' to interpret 'Normal' as 'Negative'\n",
+    "\n",
+    "inverse_maker = '_inversed01' if inverse01 else ''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Permutation Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import LocalEnv\n",
+    "\n",
+    "from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline \\\n",
+    "        import NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+    "from medinfo.ml.SupervisedClassifier import SupervisedClassifier\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labs=NON_PANEL_TESTS_WITH_GT_500_ORDERS\n",
+    "algs=SupervisedClassifier.SUPPORTED_ALGORITHMS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from medinfo.db import DBUtil\n",
+    "from medinfo.db.Model import SQLQuery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = SQLQuery()\n",
+    "query.addSelect('COUNT(DISTINCT po.pat_id)')\n",
+    "query.addFrom('stride_patient AS p')\n",
+    "query.addFrom('stride_order_proc AS po')\n",
+    "query.addWhere('po.pat_id = p.pat_id')\n",
+    "query.addWhere(\"po.order_time >= '2008-01-01'\")\n",
+    "query.addWhere(\"po.order_time < '2017-07-01'\")\n",
+    "results_patCnt = DBUtil.execute(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[116637L]]"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_patCnt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2019-07-21 12:44:29,741 INFO] DBUtil.connection.63: Preparing DB Connection to tmp@localhost as postgres\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of inpatients investigated in the intensity study\n",
+    "query = SQLQuery()\n",
+    "query.addSelect('gender, COUNT(DISTINCT po.pat_id)')\n",
+    "query.addFrom('stride_patient AS p')\n",
+    "query.addFrom('stride_order_proc AS po')\n",
+    "query.addWhere('po.pat_id = p.pat_id')\n",
+    "query.addWhere(\"po.order_time >= '2014-07-01'\")\n",
+    "query.addWhere(\"po.order_time < '2017-07-01'\")\n",
+    "query.addGroupBy('gender')\n",
+    "results_genderCnt = DBUtil.execute(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['FEMALE', 4055L],\n",
+       " ['Female', 18609L],\n",
+       " ['MALE', 3642L],\n",
+       " ['Male', 18374L],\n",
+       " ['Unknown', 1L],\n",
+       " [None, 29L]]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_genderCnt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2019-08-06 20:56:45,886 INFO] DBUtil.connection.63: Preparing DB Connection to tmp@localhost as postgres\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of inpatients investigated in the intensity study\n",
+    "query = SQLQuery()\n",
+    "query.addSelect('EXTRACT(YEAR FROM order_time) - p.birth_year AS age, COUNT(DISTINCT p.pat_id), gender')\n",
+    "query.addFrom('stride_patient AS p')\n",
+    "query.addFrom('stride_order_proc AS po')\n",
+    "query.addWhere('po.pat_id = p.pat_id')\n",
+    "query.addWhere(\"po.order_time >= '2008-01-01'\")\n",
+    "query.addWhere(\"po.order_time < '2017-07-01'\")\n",
+    "query.addGroupBy('age')\n",
+    "query.addGroupBy('gender')\n",
+    "results_ageSexCnt = DBUtil.execute(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>62</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>84</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>162</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>89</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>178</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>265</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>99.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>42</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>101.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>101.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>101.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>102.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>102.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>102.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>102.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>104.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>104.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>104.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>105.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>106.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>109.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>110.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FEMALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>110.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>111.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MALE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>387 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         0    1       2\n",
+       "0      0.0    1    MALE\n",
+       "1      1.0    1    MALE\n",
+       "2      3.0    1  Female\n",
+       "3     10.0    1    MALE\n",
+       "4     10.0    1    Male\n",
+       "5     12.0    1  Female\n",
+       "6     12.0    2    MALE\n",
+       "7     13.0    1    MALE\n",
+       "8     13.0    1    Male\n",
+       "9     14.0    9  FEMALE\n",
+       "10    14.0    3  Female\n",
+       "11    14.0    8    MALE\n",
+       "12    14.0    5    Male\n",
+       "13    15.0   21  FEMALE\n",
+       "14    15.0   16  Female\n",
+       "15    15.0   20    MALE\n",
+       "16    15.0   17    Male\n",
+       "17    16.0   62  FEMALE\n",
+       "18    16.0   23  Female\n",
+       "19    16.0   41    MALE\n",
+       "20    16.0   19    Male\n",
+       "21    17.0   80  FEMALE\n",
+       "22    17.0   38  Female\n",
+       "23    17.0   84    MALE\n",
+       "24    17.0   45    Male\n",
+       "25    18.0  162  FEMALE\n",
+       "26    18.0   89  Female\n",
+       "27    18.0  178    MALE\n",
+       "28    18.0  100    Male\n",
+       "29    19.0  265  FEMALE\n",
+       "..     ...  ...     ...\n",
+       "357   99.0    6    Male\n",
+       "358  100.0   42  FEMALE\n",
+       "359  100.0   11  Female\n",
+       "360  100.0   11    MALE\n",
+       "361  100.0    2    Male\n",
+       "362  101.0   27  FEMALE\n",
+       "363  101.0    6  Female\n",
+       "364  101.0   12    MALE\n",
+       "365  101.0    2    Male\n",
+       "366  102.0   10  FEMALE\n",
+       "367  102.0    4  Female\n",
+       "368  102.0    5    MALE\n",
+       "369  102.0    2    Male\n",
+       "370  103.0    7  FEMALE\n",
+       "371  103.0    4  Female\n",
+       "372  103.0    3    MALE\n",
+       "373  103.0    1    Male\n",
+       "374  104.0    1  FEMALE\n",
+       "375  104.0    1  Female\n",
+       "376  104.0    1    MALE\n",
+       "377  105.0    3  FEMALE\n",
+       "378  105.0    2  Female\n",
+       "379  105.0    1    MALE\n",
+       "380  106.0    4  FEMALE\n",
+       "381  108.0    1  FEMALE\n",
+       "382  109.0    1  FEMALE\n",
+       "383  110.0    1  FEMALE\n",
+       "384  110.0    1    MALE\n",
+       "385  111.0    1    MALE\n",
+       "386    NaN   33    None\n",
+       "\n",
+       "[387 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df_ageSexCnt = pd.DataFrame(results_ageSexCnt)\n",
+    "df_ageSexCnt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ageMaleCnt = df_ageSexCnt[df_ageSexCnt[2].isin(['MALE', 'Male'])]\n",
+    "df_ageFemaleCnt = df_ageSexCnt[df_ageSexCnt[2].isin(['FEMALE', 'Female'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "58.95033171223453"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_ageMaleCnt[0]*df_ageMaleCnt[1]).sum() / df_ageMaleCnt[1].sum() #.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "58.7611788750179"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_ageFemaleCnt[0]*df_ageFemaleCnt[1]).sum() / df_ageFemaleCnt[1].sum() #.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18.104354834545635\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "all_cnts = df_ageMaleCnt[1].values\n",
+    "all_ages = df_ageMaleCnt[0].values\n",
+    "\n",
+    "all_males = []\n",
+    "for i in range(len(all_ages)):\n",
+    "    for j in range(all_cnts[i]):\n",
+    "        all_males.append(all_ages[i])\n",
+    "print np.std(all_males)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18.967577523036514\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_cnts = df_ageFemaleCnt[1].values\n",
+    "all_ages = df_ageFemaleCnt[0].values\n",
+    "\n",
+    "all_females = []\n",
+    "for i in range(len(all_ages)):\n",
+    "    for j in range(all_cnts[i]):\n",
+    "        all_females.append(all_ages[i])\n",
+    "print np.std(all_females)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_order_1day = pd.read_csv(\"/Users/songxu/healthrex/CDSS/scripts/LabTestAnalysis/lab_statistics/Fig2_Order_Intensities/Order_Intensities_panel_labs_order_1day.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "792397"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_order_1day['< 24 hrs'].sum()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/LabTestAnalysis/lab_statistics/LabResultMap.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/LabResultMap.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/LabResultMap.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/LabResultMap.csv
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/__init__.py b/scripts/Archive/LabTestAnalysis/lab_statistics/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/LabTestAnalysis/lab_statistics/clinical_data.py b/scripts/Archive/LabTestAnalysis/lab_statistics/clinical_data.py
similarity index 94%
rename from scripts/LabTestAnalysis/lab_statistics/clinical_data.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/clinical_data.py
index 2dabf7e4..9b121574 100644
--- a/scripts/LabTestAnalysis/lab_statistics/clinical_data.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/clinical_data.py
@@ -14,7 +14,7 @@
 import numpy as np
 import pandas as pd
 
-from cStringIO import StringIO
+from io import StringIO
 # from datetime import datetime
 
 # from medinfo.db.test.Const import RUNNER_VERBOSITY
@@ -23,7 +23,6 @@
 # from medinfo.common.test.Util import MedInfoTestCase
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery
-# from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME
 
 BASE_NAME = 'base_name'
 CLINICAL_ITEM_ID = 'clinical_item_id'
@@ -40,8 +39,8 @@ def _getNonNullBaseNames(self):
 		# WHERE
 		query.addWhere('max_result_flag is not null')
 
-		print query
-		print query.getParams()
+		print(query)
+		print(query.getParams())
 		DBUtil.runDBScript(self.SCRIPT_FILE, False)
 		results = DBUtil.execute(str(query), query.getParams())
 
@@ -55,8 +54,8 @@ def _getClinicalItems(self):
 		# FROM
 		query.addFrom('clinical_item')
 
-		print query
-		print query.getParams()
+		print(query)
+		print(query.getParams())
 		DBUtil.runDBScript(self.SCRIPT_FILE, False)
 		results = DBUtil.execute(str(query), query.getParams())
 
@@ -84,7 +83,7 @@ def getClinicalData(self, queryDB=False):
 							.drop_duplicates()
 
 
-		print df_lab_orders.head()
+		print(df_lab_orders.head())
 
 
 if __name__ == '__main__':
diff --git a/scripts/LabTestAnalysis/lab_statistics/customDBUtil.py b/scripts/Archive/LabTestAnalysis/lab_statistics/customDBUtil.py
similarity index 98%
rename from scripts/LabTestAnalysis/lab_statistics/customDBUtil.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/customDBUtil.py
index 7884114c..7a149431 100644
--- a/scripts/LabTestAnalysis/lab_statistics/customDBUtil.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/customDBUtil.py
@@ -55,7 +55,7 @@ def connection( connParams=None ):
     if connParams is None:
         connParams = DB_PARAM;
 
-    if not connParams.has_key("PWD"):
+    if "PWD" not in connParams:
         connParams["PWD"] = getpass("Enter password for %s on %s@%s: " % (connParams["UID"], connParams["DSN"], connParams["HOST"]) );
         if connParams["PWD"] == "":
             connParams["PWD"] = None;   # Special meaning, no password needed
@@ -155,7 +155,7 @@ def execute( query, parameters=None, includeColumnNames=False, incTypeCodes=Fals
         timer = time.time();
         try:
             cur.execute( query, parameters )
-        except Exception, err:
+        except Exception as err:
             log.error(err);
             #log.error(parameterizeQueryString(query,parameters));
             if (not extConn) or autoCommit:
@@ -219,7 +219,7 @@ def runDBScript( scriptFile, skipErrors = False ):
                         #   any previous commands as well
                         if skipErrors: 
                             conn.commit()    
-                    except Exception, err:
+                    except Exception as err:
                         conn.rollback();    # Reset changes and connection state
                         if skipErrors:
                             log.warning("Error Executing in Script: "+ sql )
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_consistency/report b/scripts/Archive/LabTestAnalysis/lab_statistics/data_consistency/report
new file mode 100644
index 00000000..228f6d5a
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_consistency/report
@@ -0,0 +1,27 @@
+
+
+Facts
+1. Many lab tests ordered (66%?) were not "performed". For example, between 2014-2016:
+Total cnt of LABA1C is 43391, Completed cnt is 17482.
+Total cnt of LABAFBC is 10124, Completed cnt is 3214.
+
+
+2. Cnts of top 15 non-panel labs* over 2014, 2015, 2016 seem consistent,
+though there are a few apparent exceptions:
+LABLAC	113	5005	4708	9826
+LABRESPG	663	2285	2943	5891
+LABRESP	2447	374	0	2821
+LABFE	124	1178	1390	2692
+LABPCCG4O	1003	135	13	1151
+LABHIVWBL	633	0	0	633
+LABSTLCX	0	0	0	0
+
+* Top 25 are: LABMGN, LABPT, LABPHOS, LABPTT, LABCAI, LABLACWB, LABK,
+LABTNI, LABLDH, LABHEPAR, LABUAPRN, LABBLC, LABBLC2, LABNA, LABLIDOL.
+
+
+3. 'LABUAPRN' and 'LABUA' (and potentially others)
+are multi-component panels, should not be included in NON_PANEL...
+
+4. To get a sense of LABALB overuse, do the following query:
+select * from stride_order_proc where pat_id = -8157947636815 and proc_code='LABALB' and order_status='Completed' order by order_time;
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/base_names.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/base_names.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/base_names.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/base_names.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats_null_filled.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats_null_filled.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats_null_filled.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/global_stats_null_filled.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/result_ids.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/result_ids.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/result_ids.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_component_descriptive/result_ids.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/global_stats.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/global_stats.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/global_stats.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/global_stats.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/proc_codes.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/proc_codes.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/proc_codes.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_repeat_lab_descriptive/proc_codes.csv
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/CLAB2018v1.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/CLAB2018v1.csv
new file mode 100644
index 00000000..e15f9ece
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/CLAB2018v1.csv
@@ -0,0 +1,1442 @@
+2018 Clinical Diagnostic Laboratory Fee Schedule ,,,,,,
+"CPT codes, descriptions and other data only are copyright 2018 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association (AMA).",,,,,,
+,,,,,,
+YEAR,HCPCS,MOD,EFF_DATE,INDICATOR,RATE2018,SHORTDESC
+2018,0001M,,20180101,N,00072.19,Infectious dis hcv 6 assays
+2018,0001U,,20180101,L,00000.00,Rbc dna hea 35 ag 11 bld grp
+2018,0002M,,20180101,N,00503.40,Liver dis 10 assays w/ash
+2018,0002U,,20180101,L,00000.00,Onc clrct 3 ur metab alg plp
+2018,0003M,,20180101,N,00503.40,Liver dis 10 assays w/nash
+2018,0003U,,20180101,N,00950.00,Onc ovar 5 prtn ser alg scor
+2018,0004M,,20180101,L,00000.00,Scoliosis dna alys
+2018,0005U,,20180101,N,00760.00,Onco prst8 3 gene ur alg
+2018,0006M,,20180101,L,00000.00,Onc hep gene risk classifier
+2018,0006U,,20180101,N,00246.92,Rx mntr 120+ drugs & sbsts
+2018,0007M,,20180101,L,00000.00,Onc gastro 51 gene nomogram
+2018,0007U,,20180101,N,00114.43,Rx test prsmv ur w/def conf
+2018,0008U,,20180101,N,00597.91,Hpylori detcj abx rstnc dna
+2018,0009M,,20180101,L,00000.00,Fetal aneuploidy trisom risk
+2018,0009U,,20180101,L,00000.00,Onc brst ca erbb2 amp/nonamp
+2018,0010U,,20180101,L,00000.00,Nfct ds strn typ whl gen seq
+2018,0011U,,20180101,N,00114.43,Rx mntr lc-ms/ms oral fluid
+2018,0012U,,20180101,L,00000.00,Germln do gene reargmt detcj
+2018,0013U,,20180101,L,00000.00,Onc sld org neo gene reargmt
+2018,0014U,,20180101,L,00000.00,Hem hmtlmf neo gene reargmt
+2018,0016U,,20180101,N,00202.42,Onc hmtlmf neo rna bcr/abl1
+2018,0017U,,20180101,N,00113.17,Onc hmtlmf neo jak2 mut dna
+2018,36415,,20180101,N,00003.00,Routine venipuncture
+2018,78267,,20180101,N,00011.06,Breath tst attain/anal c-14
+2018,78268,,20180101,N,00094.41,Breath test analysis c-14
+2018,80047,,20180101,N,00013.73,Metabolic panel ionized ca
+2018,80047,QW,20180101,N,00013.73,Metabolic panel ionized ca
+2018,80048,,20180101,N,00010.44,Metabolic panel total ca
+2018,80048,QW,20180101,N,00010.44,Metabolic panel total ca
+2018,80051,,20180101,N,00008.66,Electrolyte panel
+2018,80051,QW,20180101,N,00008.66,Electrolyte panel
+2018,80053,,20180101,N,00013.04,Comprehen metabolic panel
+2018,80053,QW,20180101,N,00013.04,Comprehen metabolic panel
+2018,80055,,20180101,N,00059.02,Obstetric panel
+2018,80061,,20180101,N,00016.53,Lipid panel
+2018,80061,QW,20180101,N,00016.53,Lipid panel
+2018,80069,,20180101,N,00010.72,Renal function panel
+2018,80069,QW,20180101,N,00010.72,Renal function panel
+2018,80074,,20180101,N,00058.81,Acute hepatitis panel
+2018,80076,,20180101,N,00010.09,Hepatic function panel
+2018,80081,,20180101,N,00092.42,Obstetric panel
+2018,80150,,20180101,N,00018.61,Assay of amikacin
+2018,80155,,20180101,N,00038.57,Drug assay caffeine
+2018,80156,,20180101,N,00017.98,Assay carbamazepine total
+2018,80157,,20180101,N,00016.36,Assay carbamazepine free
+2018,80158,,20180101,N,00022.28,Drug assay cyclosporine
+2018,80159,,20180101,N,00022.83,Drug assay clozapine
+2018,80162,,20180101,N,00016.39,Assay of digoxin total
+2018,80163,,20180101,N,00016.39,Assay of digoxin free
+2018,80164,,20180101,N,00016.72,Assay dipropylacetic acd tot
+2018,80165,,20180101,N,00016.72,Dipropylacetic acid free
+2018,80168,,20180101,N,00020.17,Assay of ethosuximide
+2018,80169,,20180101,N,00016.96,Drug assay everolimus
+2018,80170,,20180101,N,00020.22,Assay of gentamicin
+2018,80171,,20180101,N,00021.67,Drug screen quant gabapentin
+2018,80173,,20180101,N,00017.98,Assay of haloperidol
+2018,80175,,20180101,N,00016.36,Drug screen quan lamotrigine
+2018,80176,,20180101,N,00018.14,Assay of lidocaine
+2018,80177,,20180101,N,00016.36,Drug scrn quan levetiracetam
+2018,80178,,20180101,N,00008.16,Assay of lithium
+2018,80178,QW,20180101,N,00008.16,Assay of lithium
+2018,80180,,20180101,N,00022.28,Drug scrn quan mycophenolate
+2018,80183,,20180101,N,00016.36,Drug scrn quant oxcarbazepin
+2018,80184,,20180101,N,00015.30,Assay of phenobarbital
+2018,80185,,20180101,N,00016.36,Assay of phenytoin total
+2018,80186,,20180101,N,00016.99,Assay of phenytoin free
+2018,80188,,20180101,N,00020.48,Assay of primidone
+2018,80190,,20180101,N,00060.00,Assay of procainamide
+2018,80192,,20180101,N,00020.68,Assay of procainamide
+2018,80194,,20180101,N,00018.03,Assay of quinidine
+2018,80195,,20180101,N,00016.96,Assay of sirolimus
+2018,80197,,20180101,N,00016.96,Assay of tacrolimus
+2018,80198,,20180101,N,00017.46,Assay of theophylline
+2018,80199,,20180101,N,00027.11,Drug screen quant tiagabine
+2018,80200,,20180101,N,00019.91,Assay of tobramycin
+2018,80201,,20180101,N,00014.72,Assay of topiramate
+2018,80202,,20180101,N,00016.72,Assay of vancomycin
+2018,80203,,20180101,N,00016.36,Drug screen quant zonisamide
+2018,80299,,20180101,N,00018.64,Quantitative assay drug
+2018,80305,,20180101,N,00013.46,Drug test prsmv dir opt obs
+2018,80305,QW,20180101,N,00013.46,Drug test prsmv dir opt obs
+2018,80306,,20180101,N,00017.96,Drug test prsmv instrmnt
+2018,80307,,20180101,N,00071.83,Drug test prsmv chem anlyzr
+2018,80400,,20180101,N,00040.27,Acth stimulation panel
+2018,80402,,20180101,N,00107.35,Acth stimulation panel
+2018,80406,,20180101,N,00096.62,Acth stimulation panel
+2018,80408,,20180101,N,00154.94,Aldosterone suppression eval
+2018,80410,,20180101,N,00033.08,Calcitonin stimul panel
+2018,80412,,20180101,N,00801.62,Crh stimulation panel
+2018,80414,,20180101,N,00063.75,Testosterone response
+2018,80415,,20180101,N,00068.99,Estradiol response panel
+2018,80416,,20180101,N,00209.32,Renin stimulation panel
+2018,80417,,20180101,N,00054.31,Renin stimulation panel
+2018,80418,,20180101,N,00715.43,Pituitary evaluation panel
+2018,80420,,20180101,N,00161.88,Dexamethasone panel
+2018,80422,,20180101,N,00056.88,Glucagon tolerance panel
+2018,80424,,20180101,N,00062.34,Glucagon tolerance panel
+2018,80426,,20180101,N,00183.22,Gonadotropin hormone panel
+2018,80428,,20180101,N,00082.35,Growth hormone panel
+2018,80430,,20180101,N,00129.33,Growth hormone panel
+2018,80432,,20180101,N,00166.79,Insulin suppression panel
+2018,80434,,20180101,N,00285.03,Insulin tolerance panel
+2018,80435,,20180101,N,00127.18,Insulin tolerance panel
+2018,80436,,20180101,N,00112.55,Metyrapone panel
+2018,80438,,20180101,N,00062.24,Trh stimulation panel
+2018,80439,,20180101,N,00082.98,Trh stimulation panel
+2018,81000,,20180101,N,00004.02,Urinalysis nonauto w/scope
+2018,81001,,20180101,N,00003.92,Urinalysis auto w/scope
+2018,81002,,20180101,N,00003.48,Urinalysis nonauto w/o scope
+2018,81003,,20180101,N,00002.77,Urinalysis auto w/o scope
+2018,81003,QW,20180101,N,00002.77,Urinalysis auto w/o scope
+2018,81005,,20180101,N,00002.67,Urinalysis
+2018,81007,,20180101,N,00029.98,Urine screen for bacteria
+2018,81007,QW,20180101,N,00029.98,Urine screen for bacteria
+2018,81015,,20180101,N,00003.76,Microscopic exam of urine
+2018,81020,,20180101,N,00004.70,Urinalysis glass test
+2018,81025,,20180101,N,00008.61,Urine pregnancy test
+2018,81050,,20180101,N,00003.71,Urinalysis volume measure
+2018,81105,,20180101,N,00150.89,Hpa-1 genotyping
+2018,81106,,20180101,N,00150.89,Hpa-2 genotyping
+2018,81107,,20180101,N,00150.89,Hpa-3 genotyping
+2018,81108,,20180101,N,00150.89,Hpa-4 genotyping
+2018,81109,,20180101,N,00150.89,Hpa-5 genotyping
+2018,81110,,20180101,N,00150.89,Hpa-6 genotyping
+2018,81111,,20180101,N,00150.89,Hpa-9 genotyping
+2018,81112,,20180101,N,00150.89,Hpa-15 genotyping
+2018,81120,,20180101,N,00193.25,Idh1 common variants
+2018,81121,,20180101,N,00295.79,Idh2 common variants
+2018,81161,,20180101,N,00279.00,Dmd dup/delet analysis
+2018,81162,,20180101,N,02252.93,Brca1&2 seq & full dup/del
+2018,81170,,20180101,N,00300.00,Abl1 gene
+2018,81175,,20180101,N,00707.02,Asxl1 full gene sequence
+2018,81176,,20180101,N,00298.64,Asxl1 gene target seq alys
+2018,81200,,20180101,N,00047.25,Aspa gene
+2018,81201,,20180101,N,00780.00,Apc gene full sequence
+2018,81202,,20180101,N,00280.00,Apc gene known fam variants
+2018,81203,,20180101,N,00200.00,Apc gene dup/delet variants
+2018,81205,,20180101,N,00094.99,Bckdhb gene
+2018,81206,,20180101,N,00202.42,Bcr/abl1 gene major bp
+2018,81207,,20180101,N,00178.81,Bcr/abl1 gene minor bp
+2018,81208,,20180101,N,00214.62,Bcr/abl1 gene other bp
+2018,81209,,20180101,N,00039.31,Blm gene
+2018,81210,,20180101,N,00175.40,Braf gene
+2018,81211,,20180101,N,02395.84,Brca1&2 seq & com dup/del
+2018,81212,,20180101,N,00440.00,Brca1&2 185&5385&6174 var
+2018,81213,,20180101,N,00553.00,Brca1&2 uncom dup/del var
+2018,81214,,20180101,N,01301.42,Brca1 full seq & com dup/del
+2018,81215,,20180101,N,00375.25,Brca1 gene known fam variant
+2018,81216,,20180101,N,00185.12,Brca2 gene full sequence
+2018,81217,,20180101,N,00375.25,Brca2 gene known fam variant
+2018,81218,,20180101,N,00298.64,Cebpa gene full sequence
+2018,81219,,20180101,N,00150.16,Calr gene com variants
+2018,81220,,20180101,N,00556.60,Cftr gene com variants
+2018,81221,,20180101,N,00097.22,Cftr gene known fam variants
+2018,81222,,20180101,N,00435.07,Cftr gene dup/delet variants
+2018,81223,,20180101,N,00499.00,Cftr gene full sequence
+2018,81224,,20180101,N,00168.75,Cftr gene intron poly t
+2018,81225,,20180101,N,00291.36,Cyp2c19 gene com variants
+2018,81226,,20180101,N,00450.91,Cyp2d6 gene com variants
+2018,81227,,20180101,N,00174.81,Cyp2c9 gene com variants
+2018,81228,,20180101,N,00900.00,Cytogen micrarray copy nmbr
+2018,81229,,20180101,N,01160.00,Cytogen m array copy no&snp
+2018,81230,,20180101,N,00174.81,Cyp3a4 gene common variants
+2018,81231,,20180101,N,00174.81,Cyp3a5 gene common variants
+2018,81232,,20180101,N,00174.81,Dpyd gene common variants
+2018,81235,,20180101,N,00324.58,Egfr gene com variants
+2018,81238,,20180101,N,00600.00,F9 full gene sequence
+2018,81240,,20180101,N,00065.69,F2 gene
+2018,81241,,20180101,N,00075.44,F5 gene
+2018,81242,,20180101,N,00036.62,Fancc gene
+2018,81243,,20180101,N,00057.04,Fmr1 gene detection
+2018,81244,,20180101,N,00044.89,Fmr1 gene characterization
+2018,81245,,20180101,N,00165.51,Flt3 gene
+2018,81246,,20180101,N,00083.00,Flt3 gene analysis
+2018,81247,,20180101,N,00174.81,G6pd gene alys cmn variant
+2018,81248,,20180101,N,00375.25,G6pd known familial variant
+2018,81249,,20180101,N,00600.00,G6pd full gene sequence
+2018,81250,,20180101,N,00058.49,G6pc gene
+2018,81251,,20180101,N,00047.25,Gba gene
+2018,81252,,20180101,N,00101.12,Gjb2 gene full sequence
+2018,81253,,20180101,N,00061.52,Gjb2 gene known fam variants
+2018,81254,,20180101,N,00035.00,Gjb6 gene com variants
+2018,81255,,20180101,N,00051.45,Hexa gene
+2018,81256,,20180101,N,00080.69,Hfe gene
+2018,81257,,20180101,N,00102.26,Hba1/hba2 gene
+2018,81258,,20180101,N,00375.25,Hba1/hba2 gene fam vrnt
+2018,81259,,20180101,N,00600.00,Hba1/hba2 full gene sequence
+2018,81260,,20180101,N,00039.31,Ikbkap gene
+2018,81261,,20180101,N,00244.43,Igh gene rearrange amp meth
+2018,81262,,20180101,N,00068.55,Igh gene rearrang dir probe
+2018,81263,,20180101,N,00363.60,Igh vari regional mutation
+2018,81264,,20180101,N,00184.35,Igk rearrangeabn clonal pop
+2018,81265,,20180101,N,00265.49,Str markers specimen anal
+2018,81266,,20180101,N,00304.81,Str markers spec anal addl
+2018,81267,,20180101,N,00256.12,Chimerism anal no cell selec
+2018,81268,,20180101,N,00321.96,Chimerism anal w/cell select
+2018,81269,,20180101,N,00202.40,Hba1/hba2 gene dup/del vrnts
+2018,81270,,20180101,N,00113.17,Jak2 gene
+2018,81272,,20180101,N,00329.51,Kit gene targeted seq analys
+2018,81273,,20180101,N,00124.87,Kit gene analys d816 variant
+2018,81275,,20180101,N,00193.25,Kras gene variants exon 2
+2018,81276,,20180101,N,00193.25,Kras gene addl variants
+2018,81280,,20180101,N,00416.08,Long qt synd gene full seq
+2018,81281,,20180101,N,00285.00,Long qt synd known fam var
+2018,81282,,20180101,N,01250.00,Long qt syn gene dup/dlt var
+2018,81283,,20180101,N,00075.44,Ifnl3 gene
+2018,81287,,20180101,N,00124.64,Mgmt gene methylation anal
+2018,81288,,20180101,N,00192.32,Mlh1 gene
+2018,81290,,20180101,N,00039.31,Mcoln1 gene
+2018,81291,,20180101,N,00065.34,Mthfr gene
+2018,81292,,20180101,N,00675.40,Mlh1 gene full seq
+2018,81293,,20180101,N,00331.00,Mlh1 gene known variants
+2018,81294,,20180101,N,00202.40,Mlh1 gene dup/delete variant
+2018,81295,,20180101,N,00381.70,Msh2 gene full seq
+2018,81296,,20180101,N,00337.73,Msh2 gene known variants
+2018,81297,,20180101,N,00213.30,Msh2 gene dup/delete variant
+2018,81298,,20180101,N,00641.85,Msh6 gene full seq
+2018,81299,,20180101,N,00308.00,Msh6 gene known variants
+2018,81300,,20180101,N,00238.00,Msh6 gene dup/delete variant
+2018,81301,,20180101,N,00357.48,Microsatellite instability
+2018,81302,,20180101,N,00527.87,Mecp2 gene full seq
+2018,81303,,20180101,N,00120.00,Mecp2 gene known variant
+2018,81304,,20180101,N,00150.00,Mecp2 gene dup/delet variant
+2018,81310,,20180101,N,00246.52,Npm1 gene
+2018,81311,,20180101,N,00295.79,Nras gene variants exon 2&3
+2018,81313,,20180101,N,00255.05,Pca3/klk3 antigen
+2018,81314,,20180101,N,00329.51,Pdgfra gene
+2018,81315,,20180101,N,00255.94,Pml/raralpha com breakpoints
+2018,81316,,20180101,N,00255.94,Pml/raralpha 1 breakpoint
+2018,81317,,20180101,N,00707.02,Pms2 gene full seq analysis
+2018,81318,,20180101,N,00331.00,Pms2 known familial variants
+2018,81319,,20180101,N,00203.50,Pms2 gene dup/delet variants
+2018,81321,,20180101,N,00600.00,Pten gene full sequence
+2018,81322,,20180101,N,00052.85,Pten gene known fam variant
+2018,81323,,20180101,N,00300.00,Pten gene dup/delet variant
+2018,81324,,20180101,N,00758.36,Pmp22 gene dup/delet
+2018,81325,,20180101,N,00769.58,Pmp22 gene full sequence
+2018,81326,,20180101,N,00052.85,Pmp22 gene known fam variant
+2018,81327,,20180101,L,00000.00,Sept9 methylation analysis
+2018,81328,,20180101,N,00174.81,Slco1b1 gene com variants
+2018,81330,,20180101,N,00047.00,Smpd1 gene common variants
+2018,81331,,20180101,N,00051.07,Snrpn/ube3a gene
+2018,81332,,20180101,N,00053.89,Serpina1 gene
+2018,81334,,20180101,N,00329.51,Runx1 gene targeted seq alys
+2018,81335,,20180101,N,00174.81,Tpmt gene com variants
+2018,81340,,20180101,N,00257.92,Trb@ gene rearrange amplify
+2018,81341,,20180101,N,00061.22,Trb@ gene rearrange dirprobe
+2018,81342,,20180101,N,00248.76,Trg gene rearrangement anal
+2018,81346,,20180101,N,00174.81,Tyms gene com variants
+2018,81350,,20180101,N,00234.00,Ugt1a1 gene
+2018,81355,,20180101,N,00088.20,Vkorc1 gene
+2018,81361,,20180101,N,00174.81,Hbb gene com variants
+2018,81362,,20180101,N,00375.25,Hbb gene known fam variant
+2018,81363,,20180101,N,00202.40,Hbb gene dup/del variants
+2018,81364,,20180101,N,00324.58,Hbb full gene sequence
+2018,81370,,20180101,N,00496.45,Hla i & ii typing lr
+2018,81371,,20180101,N,00404.52,Hla i & ii type verify lr
+2018,81372,,20180101,N,00403.59,Hla i typing complete lr
+2018,81373,,20180101,N,00137.48,Hla i typing 1 locus lr
+2018,81374,,20180101,N,00089.81,Hla i typing 1 antigen lr
+2018,81375,,20180101,N,00272.52,Hla ii typing ag equiv lr
+2018,81376,,20180101,N,00150.89,Hla ii typing 1 locus lr
+2018,81377,,20180101,N,00113.35,Hla ii type 1 ag equiv lr
+2018,81378,,20180101,N,00426.63,Hla i & ii typing hr
+2018,81379,,20180101,N,00414.05,Hla i typing complete hr
+2018,81380,,20180101,N,00218.83,Hla i typing 1 locus hr
+2018,81381,,20180101,N,00169.90,Hla i typing 1 allele hr
+2018,81382,,20180101,N,00152.69,Hla ii typing 1 loc hr
+2018,81383,,20180101,N,00134.73,Hla ii typing 1 allele hr
+2018,81400,,20180101,N,00063.96,Mopath procedure level 1
+2018,81401,,20180101,N,00137.00,Mopath procedure level 2
+2018,81402,,20180101,N,00150.33,Mopath procedure level 3
+2018,81403,,20180101,N,00185.20,Mopath procedure level 4
+2018,81404,,20180101,N,00274.83,Mopath procedure level 5
+2018,81405,,20180101,N,00301.35,Mopath procedure level 6
+2018,81406,,20180101,N,00282.88,Mopath procedure level 7
+2018,81407,,20180101,N,00846.27,Mopath procedure level 8
+2018,81408,,20180101,N,02000.00,Mopath procedure level 9
+2018,81410,,20180101,N,00504.00,Aortic dysfunction/dilation
+2018,81411,,20180101,N,01350.19,Aortic dysfunction/dilation
+2018,81412,,20180101,N,02448.56,Ashkenazi jewish assoc dis
+2018,81413,,20180101,N,00722.10,Car ion chnnlpath inc 10 gns
+2018,81414,,20180101,N,00722.10,Car ion chnnlpath inc 2 gns
+2018,81415,,20180101,N,04780.00,Exome sequence analysis
+2018,81416,,20180101,N,12000.00,Exome sequence analysis
+2018,81417,,20180101,N,00320.00,Exome re-evaluation
+2018,81420,,20180101,N,00759.05,Fetal chrmoml aneuploidy
+2018,81422,,20180101,N,00759.05,Fetal chrmoml microdeltj
+2018,81425,,20180101,L,00000.00,Genome sequence analysis
+2018,81426,,20180101,L,00000.00,Genome sequence analysis
+2018,81427,,20180101,L,00000.00,Genome re-evaluation
+2018,81430,,20180101,N,01625.00,Hearing loss sequence analys
+2018,81431,,20180101,N,00679.57,Hearing loss dup/del analys
+2018,81432,,20180101,N,00838.33,Hrdtry brst ca-rlatd dsordrs
+2018,81433,,20180101,N,00541.89,Hrdtry brst ca-rlatd dsordrs
+2018,81434,,20180101,N,00597.91,Hereditary retinal disorders
+2018,81435,,20180101,N,00722.10,Hereditary colon ca dsordrs
+2018,81436,,20180101,N,00722.10,Hereditary colon ca dsordrs
+2018,81437,,20180101,N,00541.89,Heredtry nurondcrn tum dsrdr
+2018,81438,,20180101,N,00541.89,Heredtry nurondcrn tum dsrdr
+2018,81439,,20180101,N,00722.10,Hrdtry cardmypy gene panel
+2018,81440,,20180101,N,03324.00,Mitochondrial gene
+2018,81442,,20180101,N,02143.60,Noonan spectrum disorders
+2018,81445,,20180101,N,00597.91,Targeted genomic seq analys
+2018,81448,,20180101,N,00722.10,Hrdtry perph neurphy panel
+2018,81450,,20180101,N,00759.53,Targeted genomic seq analys
+2018,81455,,20180101,N,02919.60,Targeted genomic seq analys
+2018,81460,,20180101,N,01287.00,Whole mitochondrial genome
+2018,81465,,20180101,N,00936.00,Whole mitochondrial genome
+2018,81470,,20180101,L,00000.00,X-linked intellectual dblt
+2018,81471,,20180101,L,00000.00,X-linked intellectual dblt
+2018,81490,,20180101,N,00840.65,Autoimmune rheumatoid arthr
+2018,81493,,20180101,N,01050.00,Cor artery disease mrna
+2018,81500,,20180101,N,00260.50,Onco (ovar) two proteins
+2018,81503,,20180101,N,00897.00,Onco (ovar) five proteins
+2018,81504,,20180101,N,00520.00,Oncology tissue of origin
+2018,81506,,20180101,N,00081.03,Endo assay seven anal
+2018,81507,,20180101,N,00795.00,Fetal aneuploidy trisom risk
+2018,81508,,20180101,N,00054.30,Ftl cgen abnor two proteins
+2018,81509,,20180101,N,01487.37,Ftl cgen abnor 3 proteins
+2018,81510,,20180101,N,00055.54,Ftl cgen abnor three anal
+2018,81511,,20180101,N,00153.50,Ftl cgen abnor four anal
+2018,81512,,20180101,N,00069.52,Ftl cgen abnor five anal
+2018,81519,,20180101,N,03873.00,Oncology breast mrna
+2018,81520,,20180101,N,03099.02,Onc breast mrna 58 genes
+2018,81521,,20180101,N,03873.00,Onc breast mrna 70 genes
+2018,81525,,20180101,N,03116.00,Oncology colon mrna
+2018,81528,,20180101,N,00508.87,Oncology colorectal scr
+2018,81535,,20180101,N,00579.46,Oncology gynecologic
+2018,81536,,20180101,N,00177.56,Oncology gynecologic
+2018,81538,,20180101,N,02871.00,Oncology lung
+2018,81539,,20180101,N,00760.00,Oncology prostate prob score
+2018,81540,,20180101,N,03750.00,Oncology tum unknown origin
+2018,81541,,20180101,N,03873.00,Onc prostate mrna 46 genes
+2018,81545,,20180101,N,03600.00,Oncology thyroid
+2018,81551,,20180101,L,00000.00,Onc prostate 3 genes
+2018,81595,,20180101,N,03240.00,Cardiology hrt trnspl mrna
+2018,82009,,20180101,N,00005.58,Test for acetone/ketones
+2018,82010,,20180101,N,00010.09,Acetone assay
+2018,82010,QW,20180101,N,00010.09,Acetone assay
+2018,82013,,20180101,N,00013.79,Acetylcholinesterase assay
+2018,82016,,20180101,N,00017.13,Acylcarnitines qual
+2018,82017,,20180101,N,00020.83,Acylcarnitines quant
+2018,82024,,20180101,N,00047.68,Assay of acth
+2018,82030,,20180101,N,00031.85,Assay of adp & amp
+2018,82040,,20180101,N,00006.11,Assay of serum albumin
+2018,82040,QW,20180101,N,00006.11,Assay of serum albumin
+2018,82042,,20180101,N,00007.78,Other source albumin quan ea
+2018,82042,QW,20180101,N,00007.78,Other source albumin quan ea
+2018,82043,,20180101,N,00007.14,Ur albumin quantitative
+2018,82043,QW,20180101,N,00007.14,Ur albumin quantitative
+2018,82044,,20180101,N,00006.23,Ur albumin semiquantitative
+2018,82044,QW,20180101,N,00006.23,Ur albumin semiquantitative
+2018,82045,,20180101,N,00041.90,Albumin ischemia modified
+2018,82075,,20180101,N,00030.00,Assay of breath ethanol
+2018,82085,,20180101,N,00011.99,Assay of aldolase
+2018,82088,,20180101,N,00050.31,Assay of aldosterone
+2018,82103,,20180101,N,00016.59,Alpha-1-antitrypsin total
+2018,82104,,20180101,N,00017.86,Alpha-1-antitrypsin pheno
+2018,82105,,20180101,N,00020.71,Alpha-fetoprotein serum
+2018,82106,,20180101,N,00020.71,Alpha-fetoprotein amniotic
+2018,82107,,20180101,N,00079.52,Alpha-fetoprotein l3
+2018,82108,,20180101,N,00031.46,Assay of aluminum
+2018,82120,,20180101,N,00005.99,Amines vaginal fluid qual
+2018,82120,QW,20180101,N,00005.99,Amines vaginal fluid qual
+2018,82127,,20180101,N,00017.13,Amino acid single qual
+2018,82128,,20180101,N,00017.13,Amino acids mult qual
+2018,82131,,20180101,N,00022.98,Amino acids single quant
+2018,82135,,20180101,N,00020.31,Assay aminolevulinic acid
+2018,82136,,20180101,N,00020.83,Amino acids quant 2-5
+2018,82139,,20180101,N,00020.83,Amino acids quan 6 or more
+2018,82140,,20180101,N,00017.99,Assay of ammonia
+2018,82143,,20180101,N,00009.35,Amniotic fluid scan
+2018,82150,,20180101,N,00008.00,Assay of amylase
+2018,82150,QW,20180101,N,00008.00,Assay of amylase
+2018,82154,,20180101,N,00035.60,Androstanediol glucuronide
+2018,82157,,20180101,N,00036.14,Assay of androstenedione
+2018,82160,,20180101,N,00030.87,Assay of androsterone
+2018,82163,,20180101,N,00025.34,Assay of angiotensin ii
+2018,82164,,20180101,N,00018.03,Angiotensin i enzyme test
+2018,82172,,20180101,N,00021.09,Assay of apolipoprotein
+2018,82175,,20180101,N,00023.42,Assay of arsenic
+2018,82180,,20180101,N,00012.20,Assay of ascorbic acid
+2018,82190,,20180101,N,00018.41,Atomic absorption
+2018,82232,,20180101,N,00019.97,Assay of beta-2 protein
+2018,82239,,20180101,N,00021.14,Bile acids total
+2018,82240,,20180101,N,00032.81,Bile acids cholylglycine
+2018,82247,,20180101,N,00006.19,Bilirubin total
+2018,82247,QW,20180101,N,00006.19,Bilirubin total
+2018,82248,,20180101,N,00006.19,Bilirubin direct
+2018,82252,,20180101,N,00005.63,Fecal bilirubin test
+2018,82261,,20180101,N,00020.83,Assay of biotinidase
+2018,82270,,20180101,N,00004.38,Occult blood feces
+2018,82271,,20180101,N,00005.32,Occult blood other sources
+2018,82271,QW,20180101,N,00005.32,Occult blood other sources
+2018,82272,,20180101,N,00004.23,Occult bld feces 1-3 tests
+2018,82274,,20180101,N,00019.64,Assay test for blood fecal
+2018,82274,QW,20180101,N,00019.64,Assay test for blood fecal
+2018,82286,,20180101,N,00006.37,Assay of bradykinin
+2018,82300,,20180101,N,00028.58,Assay of cadmium
+2018,82306,,20180101,N,00036.55,Vitamin d 25 hydroxy
+2018,82308,,20180101,N,00033.08,Assay of calcitonin
+2018,82310,,20180101,N,00006.37,Assay of calcium
+2018,82310,QW,20180101,N,00006.37,Assay of calcium
+2018,82330,,20180101,N,00016.88,Assay of calcium
+2018,82330,QW,20180101,N,00016.88,Assay of calcium
+2018,82331,,20180101,N,00013.34,Calcium infusion test
+2018,82340,,20180101,N,00007.44,Assay of calcium in urine
+2018,82355,,20180101,N,00014.29,Calculus analysis qual
+2018,82360,,20180101,N,00015.89,Calculus assay quant
+2018,82365,,20180101,N,00015.92,Calculus spectroscopy
+2018,82370,,20180101,N,00015.46,X-ray assay calculus
+2018,82373,,20180101,N,00022.29,Assay c-d transfer measure
+2018,82374,,20180101,N,00006.03,Assay blood carbon dioxide
+2018,82374,QW,20180101,N,00006.03,Assay blood carbon dioxide
+2018,82375,,20180101,N,00015.21,Assay carboxyhb quant
+2018,82376,,20180101,N,00014.07,Assay carboxyhb qual
+2018,82378,,20180101,N,00023.41,Carcinoembryonic antigen
+2018,82379,,20180101,N,00020.83,Assay of carnitine
+2018,82380,,20180101,N,00011.39,Assay of carotene
+2018,82382,,20180101,N,00027.30,Assay urine catecholamines
+2018,82383,,20180101,N,00030.93,Assay blood catecholamines
+2018,82384,,20180101,N,00031.18,Assay three catecholamines
+2018,82387,,20180101,N,00022.29,Assay of cathepsin-d
+2018,82390,,20180101,N,00013.26,Assay of ceruloplasmin
+2018,82397,,20180101,N,00017.43,Chemiluminescent assay
+2018,82415,,20180101,N,00015.64,Assay of chloramphenicol
+2018,82435,,20180101,N,00005.68,Assay of blood chloride
+2018,82435,QW,20180101,N,00005.68,Assay of blood chloride
+2018,82436,,20180101,N,00006.21,Assay of urine chloride
+2018,82438,,20180101,N,00006.03,Assay other fluid chlorides
+2018,82441,,20180101,N,00007.42,Test for chlorohydrocarbons
+2018,82465,,20180101,N,00005.37,Assay bld/serum cholesterol
+2018,82465,QW,20180101,N,00005.37,Assay bld/serum cholesterol
+2018,82480,,20180101,N,00009.72,Assay serum cholinesterase
+2018,82482,,20180101,N,00009.81,Assay rbc cholinesterase
+2018,82485,,20180101,N,00025.50,Assay chondroitin sulfate
+2018,82495,,20180101,N,00025.04,Assay of chromium
+2018,82507,,20180101,N,00034.33,Assay of citrate
+2018,82523,,20180101,N,00023.07,Collagen crosslinks
+2018,82523,QW,20180101,N,00023.07,Collagen crosslinks
+2018,82525,,20180101,N,00015.32,Assay of copper
+2018,82528,,20180101,N,00027.80,Assay of corticosterone
+2018,82530,,20180101,N,00020.63,Cortisol free
+2018,82533,,20180101,N,00020.12,Total cortisol
+2018,82540,,20180101,N,00005.72,Assay of creatine
+2018,82542,,20180101,N,00024.09,Col chromotography qual/quan
+2018,82550,,20180101,N,00008.04,Assay of ck (cpk)
+2018,82550,QW,20180101,N,00008.04,Assay of ck (cpk)
+2018,82552,,20180101,N,00016.53,Assay of cpk in blood
+2018,82553,,20180101,N,00014.26,Creatine mb fraction
+2018,82554,,20180101,N,00014.65,Creatine isoforms
+2018,82565,,20180101,N,00006.33,Assay of creatinine
+2018,82565,QW,20180101,N,00006.33,Assay of creatinine
+2018,82570,,20180101,N,00006.39,Assay of urine creatinine
+2018,82570,QW,20180101,N,00006.39,Assay of urine creatinine
+2018,82575,,20180101,N,00011.67,Creatinine clearance test
+2018,82585,,20180101,N,00014.14,Assay of cryofibrinogen
+2018,82595,,20180101,N,00007.98,Assay of cryoglobulin
+2018,82600,,20180101,N,00023.95,Assay of cyanide
+2018,82607,,20180101,N,00018.61,Vitamin b-12
+2018,82608,,20180101,N,00017.68,B-12 binding capacity
+2018,82610,,20180101,N,00018.52,Cystatin c
+2018,82615,,20180101,N,00010.08,Test for urine cystines
+2018,82626,,20180101,N,00031.20,Dehydroepiandrosterone
+2018,82627,,20180101,N,00027.45,Dehydroepiandrosterone
+2018,82633,,20180101,N,00038.25,Desoxycorticosterone
+2018,82634,,20180101,N,00036.14,Deoxycortisol
+2018,82638,,20180101,N,00015.12,Assay of dibucaine number
+2018,82652,,20180101,N,00047.53,Vit d 1 25-dihydroxy
+2018,82656,,20180101,N,00014.24,Pancreatic elastase fecal
+2018,82657,,20180101,N,00022.29,Enzyme cell activity
+2018,82658,,20180101,N,00044.03,Enzyme cell activity ra
+2018,82664,,20180101,N,00061.50,Electrophoretic test
+2018,82668,,20180101,N,00023.20,Assay of erythropoietin
+2018,82670,,20180101,N,00034.49,Assay of estradiol
+2018,82671,,20180101,N,00039.88,Assay of estrogens
+2018,82672,,20180101,N,00026.78,Assay of estrogen
+2018,82677,,20180101,N,00029.85,Assay of estriol
+2018,82679,,20180101,N,00030.81,Assay of estrone
+2018,82679,QW,20180101,N,00030.81,Assay of estrone
+2018,82693,,20180101,N,00018.40,Assay of ethylene glycol
+2018,82696,,20180101,N,00029.12,Assay of etiocholanolone
+2018,82705,,20180101,N,00006.29,Fats/lipids feces qual
+2018,82710,,20180101,N,00020.75,Fats/lipids feces quant
+2018,82715,,20180101,N,00022.97,Assay of fecal fat
+2018,82725,,20180101,N,00018.77,Assay of blood fatty acids
+2018,82726,,20180101,N,00022.29,Long chain fatty acids
+2018,82728,,20180101,N,00016.83,Assay of ferritin
+2018,82731,,20180101,N,00079.52,Assay of fetal fibronectin
+2018,82735,,20180101,N,00022.89,Assay of fluoride
+2018,82746,,20180101,N,00018.15,Assay of folic acid serum
+2018,82747,,20180101,N,00021.38,Assay of folic acid rbc
+2018,82757,,20180101,N,00021.40,Assay of semen fructose
+2018,82759,,20180101,N,00026.52,Assay of rbc galactokinase
+2018,82760,,20180101,N,00013.82,Assay of galactose
+2018,82775,,20180101,N,00026.01,Assay galactose transferase
+2018,82776,,20180101,N,00011.74,Galactose transferase test
+2018,82777,,20180101,N,00044.25,Galectin-3
+2018,82784,,20180101,N,00011.48,Assay iga/igd/igg/igm each
+2018,82785,,20180101,N,00020.32,Assay of ige
+2018,82787,,20180101,N,00009.90,Igg 1 2 3 or 4 each
+2018,82800,,20180101,N,00011.00,Blood ph
+2018,82803,,20180101,N,00026.07,Blood gases any combination
+2018,82805,,20180101,N,00078.77,Blood gases w/o2 saturation
+2018,82810,,20180101,N,00010.77,Blood gases o2 sat only
+2018,82820,,20180101,N,00013.34,Hemoglobin-oxygen affinity
+2018,82930,,20180101,N,00006.72,Gastric analy w/ph ea spec
+2018,82938,,20180101,N,00021.84,Gastrin test
+2018,82941,,20180101,N,00021.77,Assay of gastrin
+2018,82943,,20180101,N,00017.64,Assay of glucagon
+2018,82945,,20180101,N,00004.85,Glucose other fluid
+2018,82946,,20180101,N,00018.61,Glucagon tolerance test
+2018,82947,,20180101,N,00004.85,Assay glucose blood quant
+2018,82947,QW,20180101,N,00004.85,Assay glucose blood quant
+2018,82948,,20180101,N,00005.04,Reagent strip/blood glucose
+2018,82950,,20180101,N,00005.86,Glucose test
+2018,82950,QW,20180101,N,00005.86,Glucose test
+2018,82951,,20180101,N,00015.89,Glucose tolerance test (gtt)
+2018,82951,QW,20180101,N,00015.89,Glucose tolerance test (gtt)
+2018,82952,,20180101,N,00004.84,Gtt-added samples
+2018,82952,QW,20180101,N,00004.84,Gtt-added samples
+2018,82955,,20180101,N,00011.97,Assay of g6pd enzyme
+2018,82960,,20180101,N,00007.47,Test for g6pd enzyme
+2018,82962,,20180101,N,00003.28,Glucose blood test
+2018,82963,,20180101,N,00026.52,Assay of glucosidase
+2018,82965,,20180101,N,00013.15,Assay of gdh enzyme
+2018,82977,,20180101,N,00008.89,Assay of ggt
+2018,82977,QW,20180101,N,00008.89,Assay of ggt
+2018,82978,,20180101,N,00017.60,Assay of glutathione
+2018,82979,,20180101,N,00011.66,Assay rbc glutathione
+2018,82985,,20180101,N,00018.61,Assay of glycated protein
+2018,82985,QW,20180101,N,00018.61,Assay of glycated protein
+2018,83001,,20180101,N,00022.94,Assay of gonadotropin (fsh)
+2018,83001,QW,20180101,N,00022.94,Assay of gonadotropin (fsh)
+2018,83002,,20180101,N,00022.86,Assay of gonadotropin (lh)
+2018,83002,QW,20180101,N,00022.86,Assay of gonadotropin (lh)
+2018,83003,,20180101,N,00020.58,Assay growth hormone (hgh)
+2018,83006,,20180101,N,00075.60,Growth stimulation gene 2
+2018,83009,,20180101,N,00083.16,H pylori (c-13) blood
+2018,83010,,20180101,N,00015.53,Assay of haptoglobin quant
+2018,83012,,20180101,N,00026.89,Assay of haptoglobins
+2018,83013,,20180101,N,00083.16,H pylori (c-13) breath
+2018,83014,,20180101,N,00009.70,H pylori drug admin
+2018,83015,,20180101,N,00023.25,Heavy metal qual any anal
+2018,83018,,20180101,N,00027.12,Heavy metal quant each nes
+2018,83020,,20180101,N,00015.89,Hemoglobin electrophoresis
+2018,83021,,20180101,N,00022.29,Hemoglobin chromotography
+2018,83026,,20180101,N,00004.01,Hemoglobin copper sulfate
+2018,83030,,20180101,N,00010.74,Fetal hemoglobin chemical
+2018,83033,,20180101,N,00008.00,Fetal hemoglobin assay qual
+2018,83036,,20180101,N,00011.99,Glycosylated hemoglobin test
+2018,83036,QW,20180101,N,00011.99,Glycosylated hemoglobin test
+2018,83037,,20180101,N,00011.99,Glycosylated hb home device
+2018,83037,QW,20180101,N,00011.99,Glycosylated hb home device
+2018,83045,,20180101,N,00006.49,Blood methemoglobin test
+2018,83050,,20180101,N,00009.05,Blood methemoglobin assay
+2018,83051,,20180101,N,00009.03,Assay of plasma hemoglobin
+2018,83060,,20180101,N,00010.21,Blood sulfhemoglobin assay
+2018,83065,,20180101,N,00009.00,Assay of hemoglobin heat
+2018,83068,,20180101,N,00010.45,Hemoglobin stability screen
+2018,83069,,20180101,N,00004.88,Assay of urine hemoglobin
+2018,83070,,20180101,N,00005.86,Assay of hemosiderin qual
+2018,83080,,20180101,N,00020.83,Assay of b hexosaminidase
+2018,83088,,20180101,N,00036.46,Assay of histamine
+2018,83090,,20180101,N,00020.83,Assay of homocystine
+2018,83150,,20180101,N,00023.89,Assay of homovanillic acid
+2018,83491,,20180101,N,00021.64,Assay of corticosteroids 17
+2018,83497,,20180101,N,00015.92,Assay of 5-hiaa
+2018,83498,,20180101,N,00033.54,Assay of progesterone 17-d
+2018,83500,,20180101,N,00027.96,Assay free hydroxyproline
+2018,83505,,20180101,N,00030.01,Assay total hydroxyproline
+2018,83516,,20180101,N,00014.24,Immunoassay nonantibody
+2018,83516,QW,20180101,N,00014.24,Immunoassay nonantibody
+2018,83518,,20180101,N,00010.47,Immunoassay dipstick
+2018,83518,QW,20180101,N,00010.47,Immunoassay dipstick
+2018,83519,,20180101,N,00018.40,Ria nonantibody
+2018,83520,,20180101,N,00017.27,Immunoassay quant nos nonab
+2018,83520,QW,20180101,N,00017.27,Immunoassay quant nos nonab
+2018,83525,,20180101,N,00014.11,Assay of insulin
+2018,83527,,20180101,N,00015.98,Assay of insulin
+2018,83528,,20180101,N,00019.82,Assay of intrinsic factor
+2018,83540,,20180101,N,00007.99,Assay of iron
+2018,83550,,20180101,N,00010.79,Iron binding test
+2018,83570,,20180101,N,00010.93,Assay of idh enzyme
+2018,83582,,20180101,N,00017.50,Assay of ketogenic steroids
+2018,83586,,20180101,N,00015.80,Assay 17- ketosteroids
+2018,83593,,20180101,N,00032.47,Fractionation ketosteroids
+2018,83605,,20180101,N,00013.19,Assay of lactic acid
+2018,83605,QW,20180101,N,00013.19,Assay of lactic acid
+2018,83615,,20180101,N,00007.45,Lactate (ld) (ldh) enzyme
+2018,83625,,20180101,N,00015.80,Assay of ldh enzymes
+2018,83630,,20180101,N,00024.24,Lactoferrin fecal (qual)
+2018,83631,,20180101,N,00024.24,Lactoferrin fecal (quant)
+2018,83632,,20180101,N,00024.97,Placental lactogen
+2018,83633,,20180101,N,00011.25,Test urine for lactose
+2018,83655,,20180101,N,00014.95,Assay of lead
+2018,83655,QW,20180101,N,00014.95,Assay of lead
+2018,83661,,20180101,N,00027.14,L/s ratio fetal lung
+2018,83662,,20180101,N,00023.35,Foam stability fetal lung
+2018,83663,,20180101,N,00023.35,Fluoro polarize fetal lung
+2018,83664,,20180101,N,00023.35,Lamellar bdy fetal lung
+2018,83670,,20180101,N,00011.31,Assay of lap enzyme
+2018,83690,,20180101,N,00008.51,Assay of lipase
+2018,83695,,20180101,N,00015.98,Assay of lipoprotein(a)
+2018,83698,,20180101,N,00046.31,Assay lipoprotein pla2
+2018,83700,,20180101,N,00013.90,Lipopro bld electrophoretic
+2018,83701,,20180101,N,00033.86,Lipoprotein bld hr fraction
+2018,83704,,20180101,N,00038.95,Lipoprotein bld quan part
+2018,83718,,20180101,N,00010.12,Assay of lipoprotein
+2018,83718,QW,20180101,N,00010.12,Assay of lipoprotein
+2018,83719,,20180101,N,00014.36,Assay of blood lipoprotein
+2018,83721,,20180101,N,00011.78,Assay of blood lipoprotein
+2018,83721,QW,20180101,N,00011.78,Assay of blood lipoprotein
+2018,83727,,20180101,N,00021.22,Assay of lrh hormone
+2018,83735,,20180101,N,00008.27,Assay of magnesium
+2018,83775,,20180101,N,00009.10,Assay malate dehydrogenase
+2018,83785,,20180101,N,00030.37,Assay of manganese
+2018,83789,,20180101,N,00024.11,Mass spectrometry qual/quan
+2018,83825,,20180101,N,00020.07,Assay of mercury
+2018,83835,,20180101,N,00020.92,Assay of metanephrines
+2018,83857,,20180101,N,00013.26,Assay of methemalbumin
+2018,83861,,20180101,N,00022.48,Microfluid analy tears
+2018,83861,QW,20180101,N,00022.48,Microfluid analy tears
+2018,83864,,20180101,N,00028.50,Mucopolysaccharides
+2018,83872,,20180101,N,00007.24,Assay synovial fluid mucin
+2018,83873,,20180101,N,00021.24,Assay of csf protein
+2018,83874,,20180101,N,00015.95,Assay of myoglobin
+2018,83876,,20180101,N,00050.86,Assay myeloperoxidase
+2018,83880,,20180101,N,00041.90,Assay of natriuretic peptide
+2018,83880,QW,20180101,N,00041.90,Assay of natriuretic peptide
+2018,83883,,20180101,N,00016.79,Assay nephelometry not spec
+2018,83885,,20180101,N,00030.26,Assay of nickel
+2018,83915,,20180101,N,00013.77,Assay of nucleotidase
+2018,83916,,20180101,N,00027.39,Oligoclonal bands
+2018,83918,,20180101,N,00023.60,Organic acids total quant
+2018,83919,,20180101,N,00020.31,Organic acids qual each
+2018,83921,,20180101,N,00021.21,Organic acid single quant
+2018,83930,,20180101,N,00008.16,Assay of blood osmolality
+2018,83935,,20180101,N,00008.42,Assay of urine osmolality
+2018,83937,,20180101,N,00036.85,Assay of osteocalcin
+2018,83945,,20180101,N,00015.89,Assay of oxalate
+2018,83950,,20180101,N,00079.52,Oncoprotein her-2/neu
+2018,83951,,20180101,N,00079.52,Oncoprotein dcp
+2018,83970,,20180101,N,00050.96,Assay of parathormone
+2018,83986,,20180101,N,00004.42,Assay ph body fluid nos
+2018,83986,QW,20180101,N,00004.42,Assay ph body fluid nos
+2018,83987,,20180101,N,00004.42,Exhaled breath condensate
+2018,83993,,20180101,N,00024.24,Assay for calprotectin fecal
+2018,84030,,20180101,N,00006.79,Assay of blood pku
+2018,84035,,20180101,N,00004.52,Assay of phenylketones
+2018,84060,,20180101,N,00009.12,Assay acid phosphatase
+2018,84066,,20180101,N,00011.93,Assay prostate phosphatase
+2018,84075,,20180101,N,00006.39,Assay alkaline phosphatase
+2018,84075,QW,20180101,N,00006.39,Assay alkaline phosphatase
+2018,84078,,20180101,N,00009.01,Assay alkaline phosphatase
+2018,84080,,20180101,N,00018.25,Assay alkaline phosphatases
+2018,84081,,20180101,N,00020.39,Assay phosphatidylglycerol
+2018,84085,,20180101,N,00011.66,Assay of rbc pg6d enzyme
+2018,84087,,20180101,N,00012.74,Assay phosphohexose enzymes
+2018,84100,,20180101,N,00005.85,Assay of phosphorus
+2018,84105,,20180101,N,00006.39,Assay of urine phosphorus
+2018,84106,,20180101,N,00005.82,Test for porphobilinogen
+2018,84110,,20180101,N,00010.42,Assay of porphobilinogen
+2018,84112,,20180101,N,00098.11,Eval amniotic fluid protein
+2018,84119,,20180101,N,00013.36,Test urine for porphyrins
+2018,84120,,20180101,N,00018.16,Assay of urine porphyrins
+2018,84126,,20180101,N,00039.11,Assay of feces porphyrins
+2018,84132,,20180101,N,00005.68,Assay of serum potassium
+2018,84132,QW,20180101,N,00005.68,Assay of serum potassium
+2018,84133,,20180101,N,00005.32,Assay of urine potassium
+2018,84134,,20180101,N,00018.01,Assay of prealbumin
+2018,84135,,20180101,N,00023.63,Assay of pregnanediol
+2018,84138,,20180101,N,00023.37,Assay of pregnanetriol
+2018,84140,,20180101,N,00025.52,Assay of pregnenolone
+2018,84143,,20180101,N,00028.16,Assay of 17-hydroxypregneno
+2018,84144,,20180101,N,00025.76,Assay of progesterone
+2018,84145,,20180101,N,00033.08,Procalcitonin (pct)
+2018,84146,,20180101,N,00023.92,Assay of prolactin
+2018,84150,,20180101,N,00041.77,Assay of prostaglandin
+2018,84152,,20180101,N,00022.71,Assay of psa complexed
+2018,84153,,20180101,N,00022.71,Assay of psa total
+2018,84154,,20180101,N,00022.71,Assay of psa free
+2018,84155,,20180101,N,00004.53,Assay of protein serum
+2018,84155,QW,20180101,N,00004.53,Assay of protein serum
+2018,84156,,20180101,N,00004.53,Assay of protein urine
+2018,84157,,20180101,N,00004.53,Assay of protein other
+2018,84157,QW,20180101,N,00004.53,Assay of protein other
+2018,84160,,20180101,N,00006.39,Assay of protein any source
+2018,84163,,20180101,N,00018.59,Pappa serum
+2018,84165,,20180101,N,00013.26,Protein e-phoresis serum
+2018,84166,,20180101,N,00022.01,Protein e-phoresis/urine/csf
+2018,84181,,20180101,N,00021.02,Western blot test
+2018,84182,,20180101,N,00029.21,Protein western blot test
+2018,84202,,20180101,N,00017.71,Assay rbc protoporphyrin
+2018,84203,,20180101,N,00010.63,Test rbc protoporphyrin
+2018,84206,,20180101,N,00026.69,Assay of proinsulin
+2018,84207,,20180101,N,00034.69,Assay of vitamin b-6
+2018,84210,,20180101,N,00014.48,Assay of pyruvate
+2018,84220,,20180101,N,00011.66,Assay of pyruvate kinase
+2018,84228,,20180101,N,00014.36,Assay of quinine
+2018,84233,,20180101,N,00087.88,Assay of estrogen
+2018,84234,,20180101,N,00080.10,Assay of progesterone
+2018,84235,,20180101,N,00071.23,Assay of endocrine hormone
+2018,84238,,20180101,N,00045.14,Assay nonendocrine receptor
+2018,84244,,20180101,N,00027.15,Assay of renin
+2018,84252,,20180101,N,00024.98,Assay of vitamin b-2
+2018,84255,,20180101,N,00031.52,Assay of selenium
+2018,84260,,20180101,N,00038.25,Assay of serotonin
+2018,84270,,20180101,N,00026.83,Assay of sex hormone globul
+2018,84275,,20180101,N,00016.59,Assay of sialic acid
+2018,84285,,20180101,N,00029.06,Assay of silica
+2018,84295,,20180101,N,00005.94,Assay of serum sodium
+2018,84295,QW,20180101,N,00005.94,Assay of serum sodium
+2018,84300,,20180101,N,00006.00,Assay of urine sodium
+2018,84302,,20180101,N,00006.00,Assay of sweat sodium
+2018,84305,,20180101,N,00026.25,Assay of somatomedin
+2018,84307,,20180101,N,00022.56,Assay of somatostatin
+2018,84311,,20180101,N,00008.63,Spectrophotometry
+2018,84315,,20180101,N,00003.28,Body fluid specific gravity
+2018,84375,,20180101,N,00039.00,Chromatogram assay sugars
+2018,84376,,20180101,N,00006.79,Sugars single qual
+2018,84377,,20180101,N,00006.79,Sugars multiple qual
+2018,84378,,20180101,N,00014.23,Sugars single quant
+2018,84379,,20180101,N,00014.23,Sugars multiple quant
+2018,84392,,20180101,N,00005.86,Assay of urine sulfate
+2018,84402,,20180101,N,00031.45,Assay of free testosterone
+2018,84403,,20180101,N,00031.87,Assay of total testosterone
+2018,84410,,20180101,N,00063.32,Testosterone bioavailable
+2018,84425,,20180101,N,00026.21,Assay of vitamin b-1
+2018,84430,,20180101,N,00014.36,Assay of thiocyanate
+2018,84431,,20180101,N,00035.11,Thromboxane urine
+2018,84432,,20180101,N,00019.83,Assay of thyroglobulin
+2018,84436,,20180101,N,00008.48,Assay of total thyroxine
+2018,84437,,20180101,N,00007.98,Assay of neonatal thyroxine
+2018,84439,,20180101,N,00011.13,Assay of free thyroxine
+2018,84442,,20180101,N,00018.25,Assay of thyroid activity
+2018,84443,,20180101,N,00020.75,Assay thyroid stim hormone
+2018,84443,QW,20180101,N,00020.75,Assay thyroid stim hormone
+2018,84445,,20180101,N,00062.78,Assay of tsi globulin
+2018,84446,,20180101,N,00017.51,Assay of vitamin e
+2018,84449,,20180101,N,00022.22,Assay of transcortin
+2018,84450,,20180101,N,00006.39,Transferase (ast) (sgot)
+2018,84450,QW,20180101,N,00006.39,Transferase (ast) (sgot)
+2018,84460,,20180101,N,00006.54,Alanine amino (alt) (sgpt)
+2018,84460,QW,20180101,N,00006.54,Alanine amino (alt) (sgpt)
+2018,84466,,20180101,N,00015.76,Assay of transferrin
+2018,84478,,20180101,N,00007.09,Assay of triglycerides
+2018,84478,QW,20180101,N,00007.09,Assay of triglycerides
+2018,84479,,20180101,N,00007.98,Assay of thyroid (t3 or t4)
+2018,84480,,20180101,N,00017.51,Assay triiodothyronine (t3)
+2018,84481,,20180101,N,00020.92,Free assay (ft-3)
+2018,84482,,20180101,N,00019.46,T3 reverse
+2018,84484,,20180101,N,00012.47,Assay of troponin quant
+2018,84485,,20180101,N,00008.89,Assay duodenal fluid trypsin
+2018,84488,,20180101,N,00009.01,Test feces for trypsin
+2018,84490,,20180101,N,00009.93,Assay of feces for trypsin
+2018,84510,,20180101,N,00012.84,Assay of tyrosine
+2018,84512,,20180101,N,00010.09,Assay of troponin qual
+2018,84520,,20180101,N,00004.88,Assay of urea nitrogen
+2018,84520,QW,20180101,N,00004.88,Assay of urea nitrogen
+2018,84525,,20180101,N,00005.13,Urea nitrogen semi-quant
+2018,84540,,20180101,N,00005.86,Assay of urine/urea-n
+2018,84545,,20180101,N,00008.16,Urea-n clearance test
+2018,84550,,20180101,N,00005.58,Assay of blood/uric acid
+2018,84550,QW,20180101,N,00005.58,Assay of blood/uric acid
+2018,84560,,20180101,N,00005.86,Assay of urine/uric acid
+2018,84577,,20180101,N,00020.75,Assay of feces/urobilinogen
+2018,84578,,20180101,N,00004.47,Test urine urobilinogen
+2018,84580,,20180101,N,00010.08,Assay of urine urobilinogen
+2018,84583,,20180101,N,00006.21,Assay of urine urobilinogen
+2018,84585,,20180101,N,00019.13,Assay of urine vma
+2018,84586,,20180101,N,00043.62,Assay of vip
+2018,84588,,20180101,N,00041.90,Assay of vasopressin
+2018,84590,,20180101,N,00014.33,Assay of vitamin a
+2018,84591,,20180101,N,00017.06,Assay of nos vitamin
+2018,84597,,20180101,N,00016.94,Assay of vitamin k
+2018,84600,,20180101,N,00019.85,Assay of volatiles
+2018,84620,,20180101,N,00014.63,Xylose tolerance test
+2018,84630,,20180101,N,00014.06,Assay of zinc
+2018,84681,,20180101,N,00025.70,Assay of c-peptide
+2018,84702,,20180101,N,00018.59,Chorionic gonadotropin test
+2018,84703,,20180101,N,00009.29,Chorionic gonadotropin assay
+2018,84703,QW,20180101,N,00009.29,Chorionic gonadotropin assay
+2018,84704,,20180101,N,00018.59,Hcg free betachain test
+2018,84830,,20180101,N,00012.70,Ovulation tests
+2018,85002,,20180101,N,00005.57,Bleeding time test
+2018,85004,,20180101,N,00007.98,Automated diff wbc count
+2018,85007,,20180101,N,00004.24,Bl smear w/diff wbc count
+2018,85008,,20180101,N,00004.24,Bl smear w/o diff wbc count
+2018,85009,,20180101,N,00005.07,Manual diff wbc count b-coat
+2018,85013,,20180101,N,00007.00,Spun microhematocrit
+2018,85014,,20180101,N,00002.93,Hematocrit
+2018,85014,QW,20180101,N,00002.93,Hematocrit
+2018,85018,,20180101,N,00002.93,Hemoglobin
+2018,85018,QW,20180101,N,00002.93,Hemoglobin
+2018,85025,,20180101,N,00009.59,Complete cbc w/auto diff wbc
+2018,85025,QW,20180101,N,00009.59,Complete cbc w/auto diff wbc
+2018,85027,,20180101,N,00007.98,Complete cbc automated
+2018,85032,,20180101,N,00005.32,Manual cell count each
+2018,85041,,20180101,N,00003.73,Automated rbc count
+2018,85044,,20180101,N,00005.32,Manual reticulocyte count
+2018,85045,,20180101,N,00004.93,Automated reticulocyte count
+2018,85046,,20180101,N,00006.88,Reticyte/hgb concentrate
+2018,85048,,20180101,N,00003.13,Automated leukocyte count
+2018,85049,,20180101,N,00005.53,Automated platelet count
+2018,85055,,20180101,N,00035.74,Reticulated platelet assay
+2018,85130,,20180101,N,00014.68,Chromogenic substrate assay
+2018,85170,,20180101,N,00016.30,Blood clot retraction
+2018,85175,,20180101,N,00020.37,Blood clot lysis time
+2018,85210,,20180101,N,00016.03,Clot factor ii prothrom spec
+2018,85220,,20180101,N,00021.79,Blooc clot factor v test
+2018,85230,,20180101,N,00022.10,Clot factor vii proconvertin
+2018,85240,,20180101,N,00022.10,Clot factor viii ahg 1 stage
+2018,85244,,20180101,N,00025.21,Clot factor viii reltd antgn
+2018,85245,,20180101,N,00028.32,Clot factor viii vw ristoctn
+2018,85246,,20180101,N,00028.32,Clot factor viii vw antigen
+2018,85247,,20180101,N,00028.32,Clot factor viii multimetric
+2018,85250,,20180101,N,00023.51,Clot factor ix ptc/chrstmas
+2018,85260,,20180101,N,00022.10,Clot factor x stuart-power
+2018,85270,,20180101,N,00022.10,Clot factor xi pta
+2018,85280,,20180101,N,00023.89,Clot factor xii hageman
+2018,85290,,20180101,N,00020.17,Clot factor xiii fibrin stab
+2018,85291,,20180101,N,00010.98,Clot factor xiii fibrin scrn
+2018,85292,,20180101,N,00023.37,Clot factor fletcher fact
+2018,85293,,20180101,N,00023.37,Clot factor wght kininogen
+2018,85300,,20180101,N,00014.63,Antithrombin iii activity
+2018,85301,,20180101,N,00013.35,Antithrombin iii antigen
+2018,85302,,20180101,N,00014.83,Clot inhibit prot c antigen
+2018,85303,,20180101,N,00017.08,Clot inhibit prot c activity
+2018,85305,,20180101,N,00014.33,Clot inhibit prot s total
+2018,85306,,20180101,N,00018.92,Clot inhibit prot s free
+2018,85307,,20180101,N,00018.92,Assay activated protein c
+2018,85335,,20180101,N,00015.89,Factor inhibitor test
+2018,85337,,20180101,N,00017.27,Thrombomodulin
+2018,85345,,20180101,N,00005.32,Coagulation time lee & white
+2018,85347,,20180101,N,00005.26,Coagulation time activated
+2018,85348,,20180101,N,00004.60,Coagulation time otr method
+2018,85360,,20180101,N,00010.38,Euglobulin lysis
+2018,85362,,20180101,N,00008.51,Fibrin degradation products
+2018,85366,,20180101,N,00080.46,Fibrinogen test
+2018,85370,,20180101,N,00014.02,Fibrinogen test
+2018,85378,,20180101,N,00009.72,Fibrin degrade semiquant
+2018,85379,,20180101,N,00012.56,Fibrin degradation quant
+2018,85380,,20180101,N,00012.56,Fibrin degradj d-dimer
+2018,85384,,20180101,N,00010.49,Fibrinogen activity
+2018,85385,,20180101,N,00014.46,Fibrinogen antigen
+2018,85390,,20180101,N,00015.48,Fibrinolysins screen i&r
+2018,85397,,20180101,N,00030.86,Clotting funct activity
+2018,85400,,20180101,N,00009.51,Fibrinolytic plasmin
+2018,85410,,20180101,N,00009.51,Fibrinolytic antiplasmin
+2018,85415,,20180101,N,00021.22,Fibrinolytic plasminogen
+2018,85420,,20180101,N,00008.06,Fibrinolytic plasminogen
+2018,85421,,20180101,N,00012.57,Fibrinolytic plasminogen
+2018,85441,,20180101,N,00005.18,Heinz bodies direct
+2018,85445,,20180101,N,00008.42,Heinz bodies induced
+2018,85460,,20180101,N,00009.55,Hemoglobin fetal
+2018,85461,,20180101,N,00009.36,Hemoglobin fetal
+2018,85475,,20180101,N,00010.95,Hemolysin acid
+2018,85520,,20180101,N,00016.16,Heparin assay
+2018,85525,,20180101,N,00014.62,Heparin neutralization
+2018,85530,,20180101,N,00016.16,Heparin-protamine tolerance
+2018,85536,,20180101,N,00007.98,Iron stain peripheral blood
+2018,85540,,20180101,N,00010.62,Wbc alkaline phosphatase
+2018,85547,,20180101,N,00010.62,Rbc mechanical fragility
+2018,85549,,20180101,N,00023.15,Muramidase
+2018,85555,,20180101,N,00008.25,Rbc osmotic fragility
+2018,85557,,20180101,N,00016.49,Rbc osmotic fragility
+2018,85576,,20180101,N,00026.52,Blood platelet aggregation
+2018,85576,QW,20180101,N,00026.52,Blood platelet aggregation
+2018,85597,,20180101,N,00022.19,Phospholipid pltlt neutraliz
+2018,85598,,20180101,N,00022.19,Hexagnal phosph pltlt neutrl
+2018,85610,,20180101,N,00004.85,Prothrombin time
+2018,85610,QW,20180101,N,00004.85,Prothrombin time
+2018,85611,,20180101,N,00004.87,Prothrombin test
+2018,85612,,20180101,N,00017.49,Viper venom prothrombin time
+2018,85613,,20180101,N,00011.83,Russell viper venom diluted
+2018,85635,,20180101,N,00012.16,Reptilase test
+2018,85651,,20180101,N,00004.38,Rbc sed rate nonautomated
+2018,85652,,20180101,N,00003.33,Rbc sed rate automated
+2018,85660,,20180101,N,00006.80,Rbc sickle cell test
+2018,85670,,20180101,N,00007.12,Thrombin time plasma
+2018,85675,,20180101,N,00008.45,Thrombin time titer
+2018,85705,,20180101,N,00011.89,Thromboplastin inhibition
+2018,85730,,20180101,N,00007.42,Thromboplastin time partial
+2018,85732,,20180101,N,00007.98,Thromboplastin time partial
+2018,85810,,20180101,N,00014.41,Blood viscosity examination
+2018,86000,,20180101,N,00008.62,Agglutinins febrile antigen
+2018,86001,,20180101,N,00007.82,Allergen specific igg
+2018,86003,,20180101,N,00006.44,Allg spec ige crude xtrc ea
+2018,86005,,20180101,N,00009.84,Allg spec ige multiallg scr
+2018,86008,,20180101,N,00022.14,Allg spec ige recomb ea
+2018,86021,,20180101,N,00018.59,Wbc antibody identification
+2018,86022,,20180101,N,00022.68,Platelet antibodies
+2018,86023,,20180101,N,00015.38,Immunoglobulin assay
+2018,86038,,20180101,N,00014.92,Antinuclear antibodies
+2018,86039,,20180101,N,00013.78,Antinuclear antibodies (ana)
+2018,86060,,20180101,N,00009.01,Antistreptolysin o titer
+2018,86063,,20180101,N,00007.12,Antistreptolysin o screen
+2018,86140,,20180101,N,00006.39,C-reactive protein
+2018,86141,,20180101,N,00015.98,C-reactive protein hs
+2018,86146,,20180101,N,00031.42,Beta-2 glycoprotein antibody
+2018,86147,,20180101,N,00031.42,Cardiolipin antibody ea ig
+2018,86148,,20180101,N,00019.84,Anti-phospholipid antibody
+2018,86152,,20180101,N,00303.34,Cell enumeration & id
+2018,86155,,20180101,N,00019.74,Chemotaxis assay
+2018,86156,,20180101,N,00008.27,Cold agglutinin screen
+2018,86157,,20180101,N,00009.95,Cold agglutinin titer
+2018,86160,,20180101,N,00014.81,Complement antigen
+2018,86161,,20180101,N,00014.81,Complement/function activity
+2018,86162,,20180101,N,00025.09,Complement total (ch50)
+2018,86171,,20180101,N,00012.36,Complement fixation each
+2018,86200,,20180101,N,00015.98,Ccp antibody
+2018,86215,,20180101,N,00016.35,Deoxyribonuclease antibody
+2018,86225,,20180101,N,00016.97,Dna antibody native
+2018,86226,,20180101,N,00014.95,Dna antibody single strand
+2018,86235,,20180101,N,00022.14,Nuclear antigen antibody
+2018,86255,,20180101,N,00014.88,Fluorescent antibody screen
+2018,86256,,20180101,N,00014.88,Fluorescent antibody titer
+2018,86277,,20180101,N,00019.43,Growth hormone antibody
+2018,86280,,20180101,N,00010.12,Hemagglutination inhibition
+2018,86294,,20180101,N,00025.57,Immunoassay tumor qual
+2018,86294,QW,20180101,N,00025.57,Immunoassay tumor qual
+2018,86300,,20180101,N,00025.70,Immunoassay tumor ca 15-3
+2018,86301,,20180101,N,00025.70,Immunoassay tumor ca 19-9
+2018,86304,,20180101,N,00025.70,Immunoassay tumor ca 125
+2018,86305,,20180101,N,00025.70,Human epididymis protein 4
+2018,86308,,20180101,N,00006.39,Heterophile antibody screen
+2018,86308,QW,20180101,N,00006.39,Heterophile antibody screen
+2018,86309,,20180101,N,00007.98,Heterophile antibody titer
+2018,86310,,20180101,N,00009.10,Heterophile antibody absrbj
+2018,86316,,20180101,N,00025.70,Immunoassay tumor other
+2018,86317,,20180101,N,00018.50,Immunoassay infectious agent
+2018,86318,,20180101,N,00018.09,Immunoassay infectious agent
+2018,86318,QW,20180101,N,00018.09,Immunoassay infectious agent
+2018,86320,,20180101,N,00029.92,Serum immunoelectrophoresis
+2018,86325,,20180101,N,00027.61,Other immunoelectrophoresis
+2018,86327,,20180101,N,00029.92,Immunoelectrophoresis assay
+2018,86329,,20180101,N,00017.34,Immunodiffusion nes
+2018,86331,,20180101,N,00014.79,Immunodiffusion ouchterlony
+2018,86332,,20180101,N,00030.09,Immune complex assay
+2018,86334,,20180101,N,00027.59,Immunofix e-phoresis serum
+2018,86335,,20180101,N,00036.23,Immunfix e-phorsis/urine/csf
+2018,86336,,20180101,N,00019.24,Inhibin a
+2018,86337,,20180101,N,00026.43,Insulin antibodies
+2018,86340,,20180101,N,00018.61,Intrinsic factor antibody
+2018,86341,,20180101,N,00024.43,Islet cell antibody
+2018,86343,,20180101,N,00015.38,Leukocyte histamine release
+2018,86344,,20180101,N,00010.39,Leukocyte phagocytosis
+2018,86352,,20180101,N,00167.73,Cell function assay w/stim
+2018,86353,,20180101,N,00060.53,Lymphocyte transformation
+2018,86355,,20180101,N,00046.58,B cells total count
+2018,86356,,20180101,N,00033.06,Mononuclear cell antigen
+2018,86357,,20180101,N,00046.58,Nk cells total count
+2018,86359,,20180101,N,00046.58,T cells total count
+2018,86360,,20180101,N,00058.01,T cell absolute count/ratio
+2018,86361,,20180101,N,00033.06,T cell absolute count
+2018,86367,,20180101,N,00077.78,Stem cells total count
+2018,86376,,20180101,N,00017.96,Microsomal antibody each
+2018,86382,,20180101,N,00020.88,Neutralization test viral
+2018,86384,,20180101,N,00014.06,Nitroblue tetrazolium dye
+2018,86386,,20180101,N,00021.78,Nuclear matrix protein 22
+2018,86386,QW,20180101,N,00021.78,Nuclear matrix protein 22
+2018,86403,,20180101,N,00012.58,Particle agglut antbdy scrn
+2018,86406,,20180101,N,00013.13,Particle agglut antbdy titr
+2018,86430,,20180101,N,00007.00,Rheumatoid factor test qual
+2018,86431,,20180101,N,00007.00,Rheumatoid factor quant
+2018,86480,,20180101,N,00076.52,Tb test cell immun measure
+2018,86481,,20180101,N,00100.00,Tb ag response t-cell susp
+2018,86590,,20180101,N,00013.64,Streptokinase antibody
+2018,86592,,20180101,N,00005.27,Syphilis test non-trep qual
+2018,86593,,20180101,N,00005.44,Syphilis test non-trep quant
+2018,86602,,20180101,N,00012.56,Antinomyces antibody
+2018,86603,,20180101,N,00015.89,Adenovirus antibody
+2018,86606,,20180101,N,00018.59,Aspergillus antibody
+2018,86609,,20180101,N,00015.90,Bacterium antibody
+2018,86611,,20180101,N,00012.56,Bartonella antibody
+2018,86612,,20180101,N,00015.93,Blastomyces antibody
+2018,86615,,20180101,N,00016.28,Bordetella antibody
+2018,86617,,20180101,N,00019.13,Lyme disease antibody
+2018,86618,,20180101,N,00021.02,Lyme disease antibody
+2018,86618,QW,20180101,N,00021.02,Lyme disease antibody
+2018,86619,,20180101,N,00016.52,Borrelia antibody
+2018,86622,,20180101,N,00011.03,Brucella antibody
+2018,86625,,20180101,N,00016.20,Campylobacter antibody
+2018,86628,,20180101,N,00014.82,Candida antibody
+2018,86631,,20180101,N,00014.60,Chlamydia antibody
+2018,86632,,20180101,N,00015.66,Chlamydia igm antibody
+2018,86635,,20180101,N,00014.17,Coccidioides antibody
+2018,86638,,20180101,N,00014.97,Q fever antibody
+2018,86641,,20180101,N,00017.79,Cryptococcus antibody
+2018,86644,,20180101,N,00017.77,Cmv antibody
+2018,86645,,20180101,N,00020.80,Cmv antibody igm
+2018,86648,,20180101,N,00018.77,Diphtheria antibody
+2018,86651,,20180101,N,00016.28,Encephalitis californ antbdy
+2018,86652,,20180101,N,00016.28,Encephaltis east eqne anbdy
+2018,86653,,20180101,N,00016.28,Encephaltis st louis antbody
+2018,86654,,20180101,N,00016.28,Encephaltis west eqne antbdy
+2018,86658,,20180101,N,00016.08,Enterovirus antibody
+2018,86663,,20180101,N,00016.20,Epstein-barr antibody
+2018,86664,,20180101,N,00018.88,Epstein-barr nuclear antigen
+2018,86665,,20180101,N,00022.40,Epstein-barr capsid vca
+2018,86666,,20180101,N,00012.56,Ehrlichia antibody
+2018,86668,,20180101,N,00014.16,Francisella tularensis
+2018,86671,,20180101,N,00015.13,Fungus nes antibody
+2018,86674,,20180101,N,00018.17,Giardia lamblia antibody
+2018,86677,,20180101,N,00017.91,Helicobacter pylori antibody
+2018,86682,,20180101,N,00016.06,Helminth antibody
+2018,86684,,20180101,N,00019.56,Hemophilus influenza antibdy
+2018,86687,,20180101,N,00010.36,Htlv-i antibody
+2018,86688,,20180101,N,00017.29,Htlv-ii antibody
+2018,86689,,20180101,N,00023.90,Htlv/hiv confirmj antibody
+2018,86692,,20180101,N,00021.19,Hepatitis delta agent antbdy
+2018,86694,,20180101,N,00017.77,Herpes simplex nes antbdy
+2018,86695,,20180101,N,00016.28,Herpes simplex type 1 test
+2018,86696,,20180101,N,00023.90,Herpes simplex type 2 test
+2018,86698,,20180101,N,00015.43,Histoplasma antibody
+2018,86701,,20180101,N,00010.97,Hiv-1antibody
+2018,86701,QW,20180101,N,00010.97,Hiv-1antibody
+2018,86702,,20180101,N,00016.69,Hiv-2 antibody
+2018,86703,,20180101,N,00016.92,Hiv-1/hiv-2 1 result antbdy
+2018,86704,,20180101,N,00014.88,Hep b core antibody total
+2018,86705,,20180101,N,00014.54,Hep b core antibody igm
+2018,86706,,20180101,N,00013.26,Hep b surface antibody
+2018,86707,,20180101,N,00014.28,Hepatitis be antibody
+2018,86708,,20180101,N,00015.29,Hepatitis a antibody
+2018,86709,,20180101,N,00013.90,Hepatitis a igm antibody
+2018,86710,,20180101,N,00016.73,Influenza virus antibody
+2018,86711,,20180101,N,00017.77,John cunningham antibody
+2018,86713,,20180101,N,00018.89,Legionella antibody
+2018,86717,,20180101,N,00015.12,Leishmania antibody
+2018,86720,,20180101,N,00016.28,Leptospira antibody
+2018,86723,,20180101,N,00016.28,Listeria monocytogenes
+2018,86727,,20180101,N,00015.89,Lymph choriomeningitis ab
+2018,86732,,20180101,N,00016.28,Mucormycosis antibody
+2018,86735,,20180101,N,00016.11,Mumps antibody
+2018,86738,,20180101,N,00016.34,Mycoplasma antibody
+2018,86741,,20180101,N,00016.28,Neisseria meningitidis
+2018,86744,,20180101,N,00016.28,Nocardia antibody
+2018,86747,,20180101,N,00018.56,Parvovirus antibody
+2018,86750,,20180101,N,00016.28,Malaria antibody
+2018,86753,,20180101,N,00015.29,Protozoa antibody nos
+2018,86756,,20180101,N,00015.91,Respiratory virus antibody
+2018,86757,,20180101,N,00023.90,Rickettsia antibody
+2018,86759,,20180101,N,00018.23,Rotavirus antibody
+2018,86762,,20180101,N,00017.77,Rubella antibody
+2018,86765,,20180101,N,00015.90,Rubeola antibody
+2018,86768,,20180101,N,00016.28,Salmonella antibody
+2018,86771,,20180101,N,00024.48,Shigella antibody
+2018,86774,,20180101,N,00018.27,Tetanus antibody
+2018,86777,,20180101,N,00017.77,Toxoplasma antibody
+2018,86778,,20180101,N,00017.78,Toxoplasma antibody igm
+2018,86780,,20180101,N,00016.34,Treponema pallidum
+2018,86780,QW,20180101,N,00016.34,Treponema pallidum
+2018,86784,,20180101,N,00015.51,Trichinella antibody
+2018,86787,,20180101,N,00015.90,Varicella-zoster antibody
+2018,86788,,20180101,N,00020.80,West nile virus ab igm
+2018,86789,,20180101,N,00017.77,West nile virus antibody
+2018,86790,,20180101,N,00015.90,Virus antibody nos
+2018,86793,,20180101,N,00016.28,Yersinia antibody
+2018,86794,,20180101,N,00020.80,Zika virus igm antibody
+2018,86800,,20180101,N,00019.64,Thyroglobulin antibody
+2018,86803,,20180101,N,00017.61,Hepatitis c ab test
+2018,86803,QW,20180101,N,00017.61,Hepatitis c ab test
+2018,86804,,20180101,N,00019.13,Hep c ab test confirm
+2018,86805,,20180101,N,00189.51,Lymphocytotoxicity assay
+2018,86806,,20180101,N,00058.75,Lymphocytotoxicity assay
+2018,86807,,20180101,N,00078.65,Cytotoxic antibody screening
+2018,86808,,20180101,N,00036.64,Cytotoxic antibody screening
+2018,86812,,20180101,N,00031.86,Hla typing a b or c
+2018,86813,,20180101,N,00071.60,Hla typing a b or c
+2018,86816,,20180101,N,00034.39,Hla typing dr/dq
+2018,86817,,20180101,N,00106.14,Hla typing dr/dq
+2018,86821,,20180101,N,00045.14,Lymphocyte culture mixed
+2018,86825,,20180101,N,00109.49,Hla x-math non-cytotoxic
+2018,86826,,20180101,N,00036.53,Hla x-match noncytotoxc addl
+2018,86828,,20180101,N,00064.19,Hla class i&ii antibody qual
+2018,86829,,20180101,N,00064.19,Hla class i/ii antibody qual
+2018,86830,,20180101,N,00099.68,Hla class i phenotype qual
+2018,86831,,20180101,N,00085.44,Hla class ii phenotype qual
+2018,86832,,20180101,N,00323.75,Hla class i high defin qual
+2018,86833,,20180101,N,00325.80,Hla class ii high defin qual
+2018,86834,,20180101,N,00441.43,Hla class i semiquant panel
+2018,86835,,20180101,N,00398.72,Hla class ii semiquant panel
+2018,86850,,20180101,N,00009.77,Rbc antibody screen
+2018,86880,,20180101,N,00006.65,Coombs test direct
+2018,86885,,20180101,N,00007.07,Coombs test indirect qual
+2018,86886,,20180101,N,00006.39,Coombs test indirect titer
+2018,86900,,20180101,N,00003.69,Blood typing serologic abo
+2018,86901,,20180101,N,00003.69,Blood typing serologic rh(d)
+2018,86902,,20180101,N,00006.35,Blood type antigen donor ea
+2018,86904,,20180101,N,00016.34,Blood typing patient serum
+2018,86905,,20180101,N,00004.73,Blood typing rbc antigens
+2018,86906,,20180101,N,00009.57,Bld typing serologic rh phnt
+2018,86940,,20180101,N,00010.13,Hemolysins/agglutinins auto
+2018,86941,,20180101,N,00014.95,Hemolysins/agglutinins
+2018,87003,,20180101,N,00020.79,Small animal inoculation
+2018,87015,,20180101,N,00008.24,Specimen infect agnt concntj
+2018,87040,,20180101,N,00012.74,Blood culture for bacteria
+2018,87045,,20180101,N,00011.66,Feces culture aerobic bact
+2018,87046,,20180101,N,00011.66,Stool cultr aerobic bact ea
+2018,87070,,20180101,N,00010.64,Culture othr specimn aerobic
+2018,87071,,20180101,N,00011.66,Culture aerobic quant other
+2018,87073,,20180101,N,00011.66,Culture bacteria anaerobic
+2018,87075,,20180101,N,00011.69,Cultr bacteria except blood
+2018,87076,,20180101,N,00009.97,Culture anaerobe ident each
+2018,87077,,20180101,N,00009.97,Culture aerobic identify
+2018,87077,QW,20180101,N,00009.97,Culture aerobic identify
+2018,87081,,20180101,N,00008.18,Culture screen only
+2018,87084,,20180101,N,00027.07,Culture of specimen by kit
+2018,87086,,20180101,N,00009.96,Urine culture/colony count
+2018,87088,,20180101,N,00009.99,Urine bacteria culture
+2018,87101,,20180101,N,00009.51,Skin fungi culture
+2018,87102,,20180101,N,00010.38,Fungus isolation culture
+2018,87103,,20180101,N,00020.46,Blood fungus culture
+2018,87106,,20180101,N,00012.74,Fungi identification yeast
+2018,87107,,20180101,N,00012.74,Fungi identification mold
+2018,87109,,20180101,N,00019.00,Mycoplasma
+2018,87110,,20180101,N,00024.19,Chlamydia culture
+2018,87116,,20180101,N,00013.34,Mycobacteria culture
+2018,87118,,20180101,N,00014.61,Mycobacteric identification
+2018,87140,,20180101,N,00006.88,Culture type immunofluoresc
+2018,87143,,20180101,N,00015.46,Culture typing glc/hplc
+2018,87147,,20180101,N,00006.39,Culture type immunologic
+2018,87149,,20180101,N,00024.76,Dna/rna direct probe
+2018,87150,,20180101,N,00043.33,Dna/rna amplified probe
+2018,87152,,20180101,N,00007.74,Culture type pulse field gel
+2018,87153,,20180101,N,00142.42,Dna/rna sequencing
+2018,87158,,20180101,N,00007.74,Culture typing added method
+2018,87164,,20180101,N,00013.26,Dark field examination
+2018,87166,,20180101,N,00013.95,Dark field examination
+2018,87168,,20180101,N,00005.27,Macroscopic exam arthropod
+2018,87169,,20180101,N,00005.27,Macroscopic exam parasite
+2018,87172,,20180101,N,00005.27,Pinworm exam
+2018,87176,,20180101,N,00007.26,Tissue homogenization cultr
+2018,87177,,20180101,N,00010.99,Ova and parasites smears
+2018,87181,,20180101,N,00005.86,Microbe susceptible diffuse
+2018,87184,,20180101,N,00008.51,Microbe susceptible disk
+2018,87185,,20180101,N,00005.86,Microbe susceptible enzyme
+2018,87186,,20180101,N,00010.67,Microbe susceptible mic
+2018,87187,,20180101,N,00040.17,Microbe susceptible mlc
+2018,87188,,20180101,N,00008.20,Microbe suscept macrobroth
+2018,87190,,20180101,N,00007.31,Microbe suscept mycobacteri
+2018,87197,,20180101,N,00018.55,Bactericidal level serum
+2018,87205,,20180101,N,00005.27,Smear gram stain
+2018,87206,,20180101,N,00006.65,Smear fluorescent/acid stai
+2018,87207,,20180101,N,00007.40,Smear special stain
+2018,87209,,20180101,N,00022.19,Smear complex stain
+2018,87210,,20180101,N,00005.82,Smear wet mount saline/ink
+2018,87210,QW,20180101,N,00005.82,Smear wet mount saline/ink
+2018,87220,,20180101,N,00005.27,Tissue exam for fungi
+2018,87230,,20180101,N,00024.37,Assay toxin or antitoxin
+2018,87250,,20180101,N,00024.15,Virus inoculate eggs/animal
+2018,87252,,20180101,N,00032.18,Virus inoculation tissue
+2018,87253,,20180101,N,00024.94,Virus inoculate tissue addl
+2018,87254,,20180101,N,00024.15,Virus inoculation shell via
+2018,87255,,20180101,N,00041.81,Genet virus isolate hsv
+2018,87260,,20180101,N,00014.80,Adenovirus ag if
+2018,87265,,20180101,N,00014.80,Pertussis ag if
+2018,87267,,20180101,N,00014.80,Enterovirus antibody dfa
+2018,87269,,20180101,N,00014.80,Giardia ag if
+2018,87270,,20180101,N,00014.80,Chlamydia trachomatis ag if
+2018,87271,,20180101,N,00014.80,Cytomegalovirus dfa
+2018,87272,,20180101,N,00014.80,Cryptosporidium ag if
+2018,87273,,20180101,N,00014.80,Herpes simplex 2 ag if
+2018,87274,,20180101,N,00014.80,Herpes simplex 1 ag if
+2018,87275,,20180101,N,00014.80,Influenza b ag if
+2018,87276,,20180101,N,00016.07,Influenza a ag if
+2018,87278,,20180101,N,00015.60,Legion pneumophilia ag if
+2018,87279,,20180101,N,00016.43,Parainfluenza ag if
+2018,87280,,20180101,N,00014.80,Respiratory syncytial ag if
+2018,87281,,20180101,N,00014.80,Pneumocystis carinii ag if
+2018,87283,,20180101,N,00060.80,Rubeola ag if
+2018,87285,,20180101,N,00014.80,Treponema pallidum ag if
+2018,87290,,20180101,N,00014.80,Varicella zoster ag if
+2018,87299,,20180101,N,00016.10,Antibody detection nos if
+2018,87300,,20180101,N,00014.80,Ag detection polyval if
+2018,87301,,20180101,N,00014.80,Adenovirus ag ia
+2018,87305,,20180101,N,00014.80,Aspergillus ag ia
+2018,87320,,20180101,N,00015.00,Chylmd trach ag ia
+2018,87324,,20180101,N,00014.80,Clostridium ag ia
+2018,87327,,20180101,N,00014.80,Cryptococcus neoform ag ia
+2018,87328,,20180101,N,00014.80,Cryptosporidium ag ia
+2018,87329,,20180101,N,00014.80,Giardia ag ia
+2018,87332,,20180101,N,00014.80,Cytomegalovirus ag ia
+2018,87335,,20180101,N,00014.80,E coli 0157 ag ia
+2018,87336,,20180101,N,00016.00,Entamoeb hist dispr ag ia
+2018,87337,,20180101,N,00014.80,Entamoeb hist group ag ia
+2018,87338,,20180101,N,00017.76,Hpylori stool ia
+2018,87338,QW,20180101,N,00017.76,Hpylori stool ia
+2018,87339,,20180101,N,00016.00,H pylori ag ia
+2018,87340,,20180101,N,00012.75,Hepatitis b surface ag ia
+2018,87341,,20180101,N,00012.75,Hepatitis b surface ag ia
+2018,87350,,20180101,N,00014.23,Hepatitis be ag ia
+2018,87380,,20180101,N,00020.26,Hepatitis delta ag ia
+2018,87385,,20180101,N,00014.80,Histoplasma capsul ag ia
+2018,87389,,20180101,N,00029.73,Hiv-1 ag w/hiv-1 & hiv-2 ab
+2018,87389,QW,20180101,N,00029.73,Hiv-1 ag w/hiv-1 & hiv-2 ab
+2018,87390,,20180101,N,00024.06,Hiv-1 ag ia
+2018,87391,,20180101,N,00021.90,Hiv-2 ag ia
+2018,87400,,20180101,N,00014.80,Influenza a/b ag ia
+2018,87420,,20180101,N,00014.80,Resp syncytial ag ia
+2018,87425,,20180101,N,00014.80,Rotavirus ag ia
+2018,87427,,20180101,N,00014.80,Shiga-like toxin ag ia
+2018,87430,,20180101,N,00016.81,Strep a ag ia
+2018,87449,,20180101,N,00014.80,Ag detect nos ia mult
+2018,87449,QW,20180101,N,00014.80,Ag detect nos ia mult
+2018,87450,,20180101,N,00011.84,Ag detect nos ia single
+2018,87451,,20180101,N,00011.84,Ag detect polyval ia mult
+2018,87471,,20180101,N,00043.33,Bartonella dna amp probe
+2018,87472,,20180101,N,00052.88,Bartonella dna quant
+2018,87475,,20180101,N,00024.76,Lyme dis dna dir probe
+2018,87476,,20180101,N,00043.33,Lyme dis dna amp probe
+2018,87480,,20180101,N,00024.76,Candida dna dir probe
+2018,87481,,20180101,N,00043.33,Candida dna amp probe
+2018,87482,,20180101,N,00055.74,Candida dna quant
+2018,87483,,20180101,N,00514.55,Cns dna amp probe type 12-25
+2018,87485,,20180101,N,00024.76,Chylmd pneum dna dir probe
+2018,87486,,20180101,N,00043.33,Chylmd pneum dna amp probe
+2018,87487,,20180101,N,00052.88,Chylmd pneum dna quant
+2018,87490,,20180101,N,00024.76,Chylmd trach dna dir probe
+2018,87491,,20180101,N,00043.33,Chylmd trach dna amp probe
+2018,87492,,20180101,N,00053.47,Chylmd trach dna quant
+2018,87493,,20180101,N,00043.33,C diff amplified probe
+2018,87495,,20180101,N,00030.03,Cytomeg dna dir probe
+2018,87496,,20180101,N,00043.33,Cytomeg dna amp probe
+2018,87497,,20180101,N,00052.88,Cytomeg dna quant
+2018,87498,,20180101,N,00043.33,Enterovirus probe&revrs trns
+2018,87500,,20180101,N,00043.33,Vanomycin dna amp probe
+2018,87501,,20180101,N,00063.35,Influenza dna amp prob 1+
+2018,87502,,20180101,N,00105.06,Influenza dna amp probe
+2018,87502,QW,20180101,N,00105.06,Influenza dna amp probe
+2018,87503,,20180101,N,00029.22,Influenza dna amp prob addl
+2018,87505,,20180101,N,00158.38,Nfct agent detection gi
+2018,87506,,20180101,N,00263.49,Iadna-dna/rna probe tq 6-11
+2018,87507,,20180101,N,00514.55,Iadna-dna/rna probe tq 12-25
+2018,87510,,20180101,N,00024.76,Gardner vag dna dir probe
+2018,87511,,20180101,N,00043.33,Gardner vag dna amp probe
+2018,87512,,20180101,N,00051.55,Gardner vag dna quant
+2018,87516,,20180101,N,00043.33,Hepatitis b dna amp probe
+2018,87517,,20180101,N,00052.88,Hepatitis b dna quant
+2018,87520,,20180101,N,00031.22,Hepatitis c rna dir probe
+2018,87521,,20180101,N,00043.33,Hepatitis c probe&rvrs trnsc
+2018,87522,,20180101,N,00052.88,Hepatitis c revrs trnscrpj
+2018,87525,,20180101,N,00029.80,Hepatitis g dna dir probe
+2018,87526,,20180101,N,00043.33,Hepatitis g dna amp probe
+2018,87527,,20180101,N,00051.55,Hepatitis g dna quant
+2018,87528,,20180101,N,00024.76,Hsv dna dir probe
+2018,87529,,20180101,N,00043.33,Hsv dna amp probe
+2018,87530,,20180101,N,00052.88,Hsv dna quant
+2018,87531,,20180101,N,00058.00,Hhv-6 dna dir probe
+2018,87532,,20180101,N,00043.33,Hhv-6 dna amp probe
+2018,87533,,20180101,N,00051.55,Hhv-6 dna quant
+2018,87534,,20180101,N,00024.76,Hiv-1 dna dir probe
+2018,87535,,20180101,N,00043.33,Hiv-1 probe&reverse trnscrpj
+2018,87536,,20180101,N,00105.06,Hiv-1 quant&revrse trnscrpj
+2018,87537,,20180101,N,00024.76,Hiv-2 dna dir probe
+2018,87538,,20180101,N,00043.33,Hiv-2 probe&revrse trnscripj
+2018,87539,,20180101,N,00058.62,Hiv-2 quant&revrse trnscripj
+2018,87540,,20180101,N,00024.76,Legion pneumo dna dir prob
+2018,87541,,20180101,N,00043.33,Legion pneumo dna amp prob
+2018,87542,,20180101,N,00051.55,Legion pneumo dna quant
+2018,87550,,20180101,N,00024.76,Mycobacteria dna dir probe
+2018,87551,,20180101,N,00048.24,Mycobacteria dna amp probe
+2018,87552,,20180101,N,00052.88,Mycobacteria dna quant
+2018,87555,,20180101,N,00026.88,M.tuberculo dna dir probe
+2018,87556,,20180101,N,00043.33,M.tuberculo dna amp probe
+2018,87557,,20180101,N,00052.88,M.tuberculo dna quant
+2018,87560,,20180101,N,00027.29,M.avium-intra dna dir prob
+2018,87561,,20180101,N,00043.33,M.avium-intra dna amp prob
+2018,87562,,20180101,N,00052.88,M.avium-intra dna quant
+2018,87580,,20180101,N,00024.76,M.pneumon dna dir probe
+2018,87581,,20180101,N,00043.33,M.pneumon dna amp probe
+2018,87582,,20180101,N,00302.62,M.pneumon dna quant
+2018,87590,,20180101,N,00026.88,N.gonorrhoeae dna dir prob
+2018,87591,,20180101,N,00043.33,N.gonorrhoeae dna amp prob
+2018,87592,,20180101,N,00052.88,N.gonorrhoeae dna quant
+2018,87623,,20180101,N,00043.33,Hpv low-risk types
+2018,87624,,20180101,N,00043.33,Hpv high-risk types
+2018,87625,,20180101,N,00043.33,Hpv types 16 & 18 only
+2018,87631,,20180101,N,00158.38,Resp virus 3-5 targets
+2018,87631,QW,20180101,N,00158.38,Resp virus 3-5 targets
+2018,87632,,20180101,N,00263.49,Resp virus 6-11 targets
+2018,87633,,20180101,N,00514.55,Resp virus 12-25 targets
+2018,87633,QW,20180101,N,00514.55,Resp virus 12-25 targets
+2018,87634,,20180101,N,00086.66,Rsv dna/rna amp probe
+2018,87640,,20180101,N,00043.33,Staph a dna amp probe
+2018,87641,,20180101,N,00043.33,Mr-staph dna amp probe
+2018,87650,,20180101,N,00024.76,Strep a dna dir probe
+2018,87650,QW,20180101,N,00024.76,Strep a dna dir probe
+2018,87651,,20180101,N,00043.33,Strep a dna amp probe
+2018,87651,QW,20180101,N,00043.33,Strep a dna amp probe
+2018,87652,,20180101,N,00051.55,Strep a dna quant
+2018,87653,,20180101,N,00043.33,Strep b dna amp probe
+2018,87660,,20180101,N,00024.76,Trichomonas vagin dir probe
+2018,87661,,20180101,N,00043.33,Trichomonas vaginalis amplif
+2018,87662,,20180101,N,00063.35,Zika virus dna/rna amp probe
+2018,87797,,20180101,N,00030.03,Detect agent nos dna dir
+2018,87798,,20180101,N,00043.33,Detect agent nos dna amp
+2018,87799,,20180101,N,00052.88,Detect agent nos dna quant
+2018,87800,,20180101,N,00049.53,Detect agnt mult dna direc
+2018,87801,,20180101,N,00086.66,Detect agnt mult dna ampli
+2018,87801,QW,20180101,N,00086.66,Detect agnt mult dna ampli
+2018,87802,,20180101,N,00014.80,Strep b assay w/optic
+2018,87803,,20180101,N,00016.00,Clostridium toxin a w/optic
+2018,87804,,20180101,N,00016.55,Influenza assay w/optic
+2018,87804,QW,20180101,N,00016.55,Influenza assay w/optic
+2018,87806,,20180101,N,00032.77,Hiv antigen w/hiv antibodies
+2018,87806,QW,20180101,N,00032.77,Hiv antigen w/hiv antibodies
+2018,87807,,20180101,N,00014.80,Rsv assay w/optic
+2018,87807,QW,20180101,N,00014.80,Rsv assay w/optic
+2018,87808,,20180101,N,00015.29,Trichomonas assay w/optic
+2018,87808,QW,20180101,N,00015.29,Trichomonas assay w/optic
+2018,87809,,20180101,N,00021.76,Adenovirus assay w/optic
+2018,87809,QW,20180101,N,00021.76,Adenovirus assay w/optic
+2018,87810,,20180101,N,00035.29,Chylmd trach assay w/optic
+2018,87850,,20180101,N,00024.56,N. gonorrhoeae assay w/optic
+2018,87880,,20180101,N,00016.53,Strep a assay w/optic
+2018,87880,QW,20180101,N,00016.53,Strep a assay w/optic
+2018,87899,,20180101,N,00016.07,Agent nos assay w/optic
+2018,87899,QW,20180101,N,00016.07,Agent nos assay w/optic
+2018,87900,,20180101,N,00160.92,Phenotype infect agent drug
+2018,87901,,20180101,N,00317.84,Genotype dna hiv reverse t
+2018,87902,,20180101,N,00317.84,Genotype dna/rna hep c
+2018,87903,,20180101,N,00603.28,Phenotype dna hiv w/culture
+2018,87904,,20180101,N,00032.18,Phenotype dna hiv w/clt add
+2018,87905,,20180101,N,00015.08,Sialidase enzyme assay
+2018,87905,QW,20180101,N,00015.08,Sialidase enzyme assay
+2018,87906,,20180101,N,00158.92,Genotype dna/rna hiv
+2018,87910,,20180101,N,00317.84,Genotype cytomegalovirus
+2018,87912,,20180101,N,00317.84,Genotype dna hepatitis b
+2018,88130,,20180101,N,00022.19,Sex chromatin identification
+2018,88140,,20180101,N,00009.86,Sex chromatin identification
+2018,88142,,20180101,N,00025.01,Cytopath c/v thin layer
+2018,88143,,20180101,N,00025.01,Cytopath c/v thin layer redo
+2018,88147,,20180101,N,00050.56,Cytopath c/v automated
+2018,88148,,20180101,N,00018.76,Cytopath c/v auto rescreen
+2018,88150,,20180101,N,00014.65,Cytopath c/v manual
+2018,88152,,20180101,N,00027.64,Cytopath c/v auto redo
+2018,88153,,20180101,N,00024.03,Cytopath c/v redo
+2018,88155,,20180101,N,00014.65,Cytopath c/v index add-on
+2018,88164,,20180101,N,00014.65,Cytopath tbs c/v manual
+2018,88165,,20180101,N,00042.22,Cytopath tbs c/v redo
+2018,88166,,20180101,N,00014.65,Cytopath tbs c/v auto redo
+2018,88167,,20180101,N,00014.65,Cytopath tbs c/v select
+2018,88174,,20180101,N,00026.38,Cytopath c/v auto in fluid
+2018,88175,,20180101,N,00032.71,Cytopath c/v auto fluid redo
+2018,88230,,20180101,N,00143.82,Tissue culture lymphocyte
+2018,88233,,20180101,N,00173.74,Tissue culture skin/biopsy
+2018,88235,,20180101,N,00181.81,Tissue culture placenta
+2018,88237,,20180101,N,00155.93,Tissue culture bone marrow
+2018,88239,,20180101,N,00182.12,Tissue culture tumor
+2018,88240,,20180101,N,00013.07,Cell cryopreserve/storage
+2018,88241,,20180101,N,00012.47,Frozen cell preparation
+2018,88245,,20180101,N,00213.80,Chromosome analysis 20-25
+2018,88248,,20180101,N,00213.80,Chromosome analysis 50-100
+2018,88249,,20180101,N,00213.80,Chromosome analysis 100
+2018,88261,,20180101,N,00264.34,Chromosome analysis 5
+2018,88262,,20180101,N,00153.88,Chromosome analysis 15-20
+2018,88263,,20180101,N,00185.54,Chromosome analysis 45
+2018,88264,,20180101,N,00153.88,Chromosome analysis 20-25
+2018,88267,,20180101,N,00221.95,Chromosome analys placenta
+2018,88269,,20180101,N,00205.34,Chromosome analys amniotic
+2018,88271,,20180101,N,00026.44,Cytogenetics dna probe
+2018,88272,,20180101,N,00040.70,Cytogenetics 3-5
+2018,88273,,20180101,N,00039.66,Cytogenetics 10-30
+2018,88274,,20180101,N,00042.98,Cytogenetics 25-99
+2018,88275,,20180101,N,00051.19,Cytogenetics 100-300
+2018,88280,,20180101,N,00033.47,Chromosome karyotype study
+2018,88283,,20180101,N,00084.69,Chromosome banding study
+2018,88285,,20180101,N,00026.91,Chromosome count additional
+2018,88289,,20180101,N,00042.51,Chromosome study additional
+2018,88371,,20180101,N,00027.44,Protein western blot tissue
+2018,88372,,20180101,N,00028.08,Protein analysis w/probe
+2018,88720,,20180101,N,00006.19,Bilirubin total transcut
+2018,88738,,20180101,N,00006.19,Hgb quant transcutaneous
+2018,88740,,20180101,N,00009.37,Transcutaneous carboxyhb
+2018,88741,,20180101,N,00009.37,Transcutaneous methb
+2018,89050,,20180101,N,00005.83,Body fluid cell count
+2018,89051,,20180101,N,00006.80,Body fluid cell count
+2018,89055,,20180101,N,00005.27,Leukocyte assessment fecal
+2018,89060,,20180101,N,00008.83,Exam synovial fluid crystals
+2018,89125,,20180101,N,00005.88,Specimen fat stain
+2018,89160,,20180101,N,00004.85,Exam feces for meat fibers
+2018,89190,,20180101,N,00005.86,Nasal smear for eosinophils
+2018,89300,,20180101,N,00011.03,Semen analysis w/huhner
+2018,89300,QW,20180101,N,00011.03,Semen analysis w/huhner
+2018,89310,,20180101,N,00010.63,Semen analysis w/count
+2018,89320,,20180101,N,00014.88,Semen anal vol/count/mot
+2018,89321,,20180101,N,00014.88,Semen anal sperm detection
+2018,89321,QW,20180101,N,00014.88,Semen anal sperm detection
+2018,89322,,20180101,N,00019.13,Semen anal strict criteria
+2018,89325,,20180101,N,00013.18,Sperm antibody test
+2018,89329,,20180101,N,00024.18,Sperm evaluation test
+2018,89330,,20180101,N,00012.21,Evaluation cervical mucus
+2018,89331,,20180101,N,00024.18,Retrograde ejaculation anal
+2018,G0027,,20180101,N,00008.03,Semen analysis
+2018,G0103,,20180101,N,00022.71,Psa screening
+2018,G0123,,20180101,N,00025.01,Screen cerv/vag thin layer
+2018,G0143,,20180101,N,00027.05,"Scr c/v cyto,thinlayer,rescr"
+2018,G0144,,20180101,N,00043.97,"Scr c/v cyto,thinlayer,rescr"
+2018,G0145,,20180101,N,00032.71,"Scr c/v cyto,thinlayer,rescr"
+2018,G0147,,20180101,N,00014.65,"Scr c/v cyto, automated sys"
+2018,G0148,,20180101,N,00031.94,"Scr c/v cyto, autosys, rescr"
+2018,G0306,,20180101,N,00009.59,Cbc/diffwbc w/o platelet
+2018,G0307,,20180101,N,00007.98,Cbc without platelet
+2018,G0328,,20180101,N,00019.64,Fecal blood scrn immunoassay
+2018,G0328,QW,20180101,N,00019.64,Fecal blood scrn immunoassay
+2018,G0432,,20180101,N,00019.57,Eia hiv-1/hiv-2 screen
+2018,G0433,,20180101,N,00018.29,Elisa hiv-1/hiv-2 screen
+2018,G0433,QW,20180101,N,00018.29,Elisa hiv-1/hiv-2 screen
+2018,G0435,,20180101,N,00014.80,Oral hiv-1/hiv-2 screen
+2018,G0471,,20180101,N,00005.00,Ven blood coll snf/hha
+2018,G0472,,20180101,N,00046.35,Hep c screen high risk/other
+2018,G0472,QW,20180101,N,00046.35,Hep c screen high risk/other
+2018,G0475,,20180101,N,00029.73,Hiv combination assay
+2018,G0475,QW,20180101,N,00029.73,Hiv combination assay
+2018,G0476,,20180101,N,00043.33,Hpv combo assay ca screen
+2018,G0480,,20180101,N,00114.43,Drug test def 1-7 classes
+2018,G0481,,20180101,N,00156.59,Drug test def 8-14 classes
+2018,G0482,,20180101,N,00198.74,Drug test def 15-21 classes
+2018,G0483,,20180101,N,00246.92,Drug test def 22+ classes
+2018,G0499,,20180101,N,00034.90,Hepb screen high risk indiv
+2018,G0659,,20180101,N,00071.83,Drug test def simple all cl
+2018,G9143,,20180101,N,00149.03,Warfarin respon genetic test
+2018,P2028,,20180101,N,00006.11,Cephalin floculation test
+2018,P2029,,20180101,N,00006.11,Congo red blood test
+2018,P2031,,20180101,N,00006.11,Hair analysis
+2018,P2033,,20180101,N,00006.11,Blood thymol turbidity
+2018,P2038,,20180101,N,00006.11,Blood mucoprotein
+2018,P3000,,20180101,N,00014.65,Screen pap by tech w md supv
+2018,P9612,,20180101,N,00003.00,Catheterize for urine spec
+2018,P9615,,20180101,N,00003.00,Urine specimen collect mult
+2018,Q0111,,20180101,N,00014.65,Wet mounts/ w preparations
+2018,Q0112,,20180101,N,00005.83,Potassium hydroxide preps
+2018,Q0113,,20180101,N,00005.27,Pinworm examinations
+2018,Q0114,,20180101,N,00009.74,Fern test
+2018,Q0115,,20180101,N,00025.00,Post-coital mucous exam
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UCSF.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UCSF.csv
new file mode 100644
index 00000000..e30a31ef
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UCSF.csv
@@ -0,0 +1,29 @@
+name,description
+WBC,"WBC(WBC)"
+HGB,"HGB(CALC), ISTAT"
+PLT,"PLATELET COUNT(PLT)"
+NAWB,"SODIUM, WHOLE BLOOD"
+K,"POTASSIUM, ISTAT"
+CREAT,"CREATININE, ISTAT"
+TBILI,"TOTAL BILIRUBIN(TBIL)"
+CL,"CHLORIDE, SER/PLAS(CL)"
+CO2,"CO2, SER/PLAS(CO2)"
+DBILI,"CONJUGATED BILI"
+AST,"AST (SGOT), SER/PLAS"
+ALT,"ALT (SGPT), SER/PLAS"
+ALB,"ALBUMIN, SER/PLAS(ALB)"
+CA,"CALCIUM, SER/PLAS(CA)"
+PCO2,"PCO2, ARTERIAL"
+PO2,"PO2, ARTERIAL"
+PH37,"PH 37"
+TP,"PROTEIN, TOTAL, SER/PLAS"
+ALKP,"ALK P'TASE, TOTAL, SER/PLAS"
+BUN,"UREA NITROGEN, SER/PLAS"
+HCO3,"HCO3"
+MG,"MG"
+PO4,"PO4"
+INR,"INR"
+P060,"P060"
+CAI,"CAI"
+CAIB,"CAIB"
+LACTWB,"LACTWB"
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UMich_component.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UMich_component.csv
new file mode 100644
index 00000000..e0cad170
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UMich_component.csv
@@ -0,0 +1,380 @@
+name,description
+UR GLUC,PCO UR GLUC
+UR KET,POC UR KET
+UR LEUK,POC UR LEUK
+UR NITR,POC UR NITR
+UR PROT,POC UR PROT
+UR PH,POC UR PH
+UR SPGRV,POC UR GRAVITY
+UR BILI,POC UR BILI
+UR BLOOD,POC UR BLOOD
+UR UROBIL,POC UR UROBIL
+TBIL,"BILIRUBIN, TOTAL"
+MPV,Mean Platelet Volume
+CO2,CO2
+ALK,ALKALINE PHOSPHATASE
+ALB,ALBUMIN LEVEL
+CREAT,CREATININE LEVEL
+ALT,ALT
+PT SECONDS,PT Seconds
+HCT,Hematocrit
+MCHC,Mean Corpuscular Hgb Conc.
+RDW,Red Cell Distribution Width
+CHLOR,CHLORIDE
+CAL,CALCIUM LEVEL
+GLUC,GLUCOSE LEVEL
+SOD,SODIUM LEVEL
+UN,UREA NITROGEN
+MCH,Mean Corpuscular Hgb
+WBC,White Blood Cell Count
+PSA,PROSTATE SPECIFIC ANTIGEN
+HGB,Hemoglobin
+PLT,Platelet Count
+RBC,Red Blood Cell Count
+MCV,Mean Corpuscular Volume
+PTT,Partial Thromboplastin Time
+POT,POTASSIUM LEVEL
+PROT,PROTEIN LEVEL
+INR,INR
+AST,AST
+GROUP RH,ABO RH INTERPRETATION
+ABSCR,ANTIBODY SCREEN INTERPRETATION
+SPF,SP FINAL REPORT
+UCOLOR,Color (Urine)
+GLUCOSE,GLUCOSE (Urine)
+SP GR,Specific Gravity (Urine)
+URCC,URINE CULTURE
+PROTEIN,Protein (Urine)
+KETONES,Ketones (Urine)
+UROBILIN,Urobilin (Urine)
+BILIRUBIN,Bilirubin (Urine)
+BLOOD,Blood (Urine)
+LEUK EST,Leukocyte Esterase (Urine)
+RBC/HPF,RBC/HPF
+BACTERIA,BACTERIA
+WBC/HPF,WBC/HPF
+UPH,pH (Urine)
+APPEAR,Appearance (Urine)
+NITRITE,Nitrite (Urine)
+SQUAM EPI,Squamous Epithelial Cells
+MUCUS,Mucus
+HYALINE,Hyaline Cast
+CA OX,Calcium Oxalate Crystals
+FT,FREE TESTOSTERONE
+TSAT,PERCENT TRANSFERRIN SATURATION
+NEUT #,Absolute Neutrophil Count
+EOS #,Absolute Eosinophil Count
+LYMPH %,Lymphocyte % (Coulter)
+IBC,IRON BINDING CAPACITY
+MONO %,Monocyte % (Coulter)
+BASO %,Basophil % (Coulter)
+IRON,IRON
+BASO #,Absolute Basophil Count
+EOS %,Eosinophil % (Coulter)
+LYMPH #,Absolute Lymphocyte Count
+FOL,FOLIC ACID LEVEL
+FRTN,FERRITIN
+VITB12,VITAMIN B12 LEVEL
+MONO #,Absolute Monocyte Count
+NEUT %,Neutrophil % (Coulter)
+B-EGFR,"Estimated GFR, Black"
+TSH,TSH-3rd GENERATION (ssTSH)
+LDLC,LOW DENSITY LIPOPROTEIN CHOL
+CHOL/HDL,CHOL/HDL RATIO
+NB-EGFR,"Estimated GFR, Non-Black"
+HDL,HIGH DENSITY LIPOPROTEIN CHOL
+TRIG,TRIGLYCERIDES
+CHOL,CHOLESTEROL
+A1C,HEMOGLOBIN A1c
+PERF BY,PERF BY
+AM URATES,Amorphous Urates
+CASTS,Casts
+PSAF,"PROSTATE SPECIFIC AG, FREE"
+PSA%,"PROSTATE SPECIFIC AG, %FREE"
+CNREV,NON-GYN REVIEW
+VOL,VOL
+UCORT,UCORT
+TV,TOTAL VOLUME URINE 24 HOUR
+FCU,URINE FREE CORTISOL RESULT
+MET,"METANEPHRINE, URINE"
+NORMET,"NORMETANEPHRINES, URINE"
+17K,"STEROIDS, 17-KETO"
+NOREPU,"NOREPINEPHRINE, URINE"
+CREATQ,CREATININE URINE 24 HOUR
+EPU,"EPINEPHRINE, URINE"
+VMA,"VANILLYLMANDELIC ACID, (VMA)"
+URIC,URIC ACID
+*,ASTERISK TRIGGER FOR FCBCD
+TESTO,TESTOSTERONE LEVEL
+PAB,PREALBUMIN
+BC,"BILIRUBIN, CONJUGATED"
+BU,"BILIRUBIN, UNCONJUGATED"
+PRL,PROLACTIN
+HGB A1C,POC HGB A1C
+GLUC-WB,POC GLUCOSE RESULT
+FT4,FREE T4
+DHEAS,DHEA-SULFATE
+THYRGLB AB,THYROID THYROGLOBULIN AB
+MICRSOM AB,THYROID MICROSOMAL AB
+SALDO,"ALDOSTERONE, SERUM"
+PRA,RENIN (ANGIOTENSIN)
+CORT,CORTISOL LEVEL
+CEA,CARCINOEMBRYONIC ANTIGEN
+25HD,"25-Hydroxyvitamin D, Total"
+CREATU,CREATININE URINE RANDOM
+DBIL,"BILIRUBIN, DIRECT"
+MA,URINE MICROALBUMIN
+IG %,Immature Granulocyte %
+SEGS %,SEGMENTED NEUTROPHILS.
+EOS  %,EOSINOPHILS
+BASO  %,BASOPHILS
+EARLY GRN#,Absolute Early Gran Count
+ESTRA,ESTRADIOL LEVEL
+LH,LUTEINIZING HORMONE
+UMACR,Microalbumin Creatinine Ratio
+PHOS,PHOSPHORUS LEVEL
+MAG,MAGNESIUM LEVEL
+GSS,RESPIRATORY CULTURE/SMEAR
+BLD,ADULT BLOOD CULTURE
+SODU,SODIUM URINE RANDOM
+SODIUM,POC SODIUM LEVEL
+POTASSIUM,POC POTASSIUM LEVEL
+UREA,POC UREA LEVEL
+HCRIT,POC HCRIT
+CHLORIDE,POC CHLORIDE LEVEL
+GLUCOSE_,POC GLUCOSE RESULT
+OVALO,OVALOCYTES.
+PT INR-WB,POC PT INR Whole Blood
+AMYL,AMYLASE
+TEAR,TEAR CELLS.
+BLDAN,BLOOD CULTURE (ANA)
+LIP,LIPASE LEVEL
+STLG,STOOL CULTURE
+OPE,OVA AND PARASITE EXAM
+MESO,Mesothelial
+SEG,Neutrophils
+HISTIO,Histiocytes
+CRYSTAL,Crystal Exam
+BLAST,Blasts
+TYPE/#,Type-Slash-Tube Number
+OTHER,Other Cell Type
+WBC/CMM,WBC/CMM
+TOTAL,Total
+COLOR,Color
+LYMPH,Lymphocytes
+RBC/CMM,RBC/CMM
+EOSINO,Eosinophils
+SBFBA,STERILE BODY FLUID CULTURE/SMR
+APPEARANCE,Appearance
+BASOS,Basophils
+AFB,AFB CULTURE/SMEAR
+FNG,FUNGUS CULTURE/SMEAR
+ALB/GLOB,ALB/GLOB
+GAMMA,GAMMA GLOBULIN
+WEST,WESTEGREN RBC SEDI RATE
+T PROTEIN,TOTAL PROTEIN
+ALPHA 2,ALPHA-2
+ALBUMIN,ALBUMIN RESULT
+BORR,BORRELIA ANTIBODY (LYME DIS)
+ALPHA 1,ALPHA-1
+CRP,C-REACTIVE PROTEIN SCREEN
+RF,RHEUMATOID FACTOR
+BETA,BETA-GLOBULIN
+UPE,URINE PROTEIN ELECTROPHORESIS
+PTS,PREADMIT TYPE AND SCREEN
+PPO2A,"PO2, ARTERIAL, PED"
+PPHA,"PH, ARTERIAL, PED"
+PPCO2A,"PCO2, ARTERIAL, PED"
+PHCO3A,"HCO3, ARTERIAL, PED"
+HMTA,"HCT, ARTERIAL, PED"
+INSTRA,"INSTRUMENT, ARTERIAL, PED"
+PSATA,"O2 SAT., ARTERIAL, PED"
+PFIO2A,"FIO2, ARTERIAL, PED"
+DX,DIGOXIN LEVEL
+GLCU,"Glucose, Urine Screen"
+BILU,"Bilirubin, Urine"
+KETU,"Ketones, Urine"
+SGU,"Specific Gravity, Urine"
+BLOU,"Blood, Urine"
+UROU,"Urobilinogen, Urine"
+NITU,"Nitrite, Urine"
+LEUU,"Leukocytes, Urine"
+PHU,"pH, Urine"
+PRTU,"Protein, Urine"
+XXPOT,Potassium EX
+XXUN,Urea Nitrogen EX
+XI175,Info XRNL EX
+XBGFR,"Estimated GFR, Black EX"
+XXABO,ABO EX
+XUCST,Casts EX
+XUYST,Yeast EX
+XUBLD,Blood EX
+XUCOL,Color EX
+XXPH,Phosphorus EX
+XULE,Leukocyte Esterase EX
+XXPCR,Protein/Creat Ratio Urine EX
+XXGLU,Glucose EX
+XUWBC,WBC/hpf EX
+XI184,Info XGFR EX
+XXCAL,Calcium EX
+XUURO,Urobilinogen EX
+XUPR,Protein EX
+XUSG,Specific Gravity EX
+XXCL,Chloride EX
+XUAPP,Apperance EX
+XI178,Info XUA EX
+XUPH,pH EX
+XUNIT,Nitrite EX
+XUBCT,Bacteria EX
+XXCRT,Creatinine EX
+XXALB,Albumin EX
+XXSOD,Sodium EX
+XUGLU,Glucose EX
+XINF2,Info XABO EX
+XURBC,RBC/hpf EX
+XXCO2,CO2 EX
+XNBFR,"Estimated GFR, Non-Black EX"
+XI173,Info XPRTU EX
+XCRU,Creatinine Urine EX
+XPTU,Protein Urine Random EX
+XI154,Info XVZVG EX
+XI167,Info XHSVP EX
+XXHS1,"Herpes Simplex Type 1, IgG EX"
+XXVGA,VZV IgG Antibody EX
+XXHS2,"Herpes Simplex Type 2, IgG EX"
+XXHGB,Hemoglobin EX
+XXHBC,"Hepatitis B Core Ab IgG,IgM EX"
+XXCAB,Hepatitis C Antibody EX
+XIN77,Info XHBSB EX
+XBASO,Absolute Basophils EX
+XMONO,Absolute Monocytes EX
+XXWBC,White Blood Cell Count EX
+XEBVI,EBV Interpretation EX
+XIN74,Info XHBCB EX
+XXHBM,Hepatitis B Core Ab IgM EX
+XXHCT,Hematocrit EX
+XEBVG,EBV IgG EX
+XCMVM,CMV IgM EX
+XI160,Info XCBC EX
+XI163,Info XEBV EX
+XXRBC,Red Blood Cell Count EX
+XXHBS,Hepatitis B Surface Ab EX
+XIN75,Info XHBCM EX
+XNEUT,Absolute Neutrophils EX
+XIN79,Info XHBSG EX
+XIN83,Info XHCAB EX
+XI161,Info XCMV EX
+XI138,Info XTXGE EX
+XXPLT,Platelet Count EX
+XEBVM,EBV IgM EX
+XEOS,Absolute Eosinophils EX
+XCMVG,CMV IgG EX
+XEBVA,EBNA IgG EX
+XXBSA,Hepatitis B Surface Antigen EX
+XBLST,Absolute Blasts EX
+XLYMP,Absolute Lymphocytes EX
+XXTXG,TOXO IgG Antibody EX
+XXFE,Iron EX
+XXIBC,Iron Binding Capacity EX
+XXD2,25-Hydroxyvit D2 EX
+XXPRT,Protein EX
+XXALT,ALT EX
+XI162,Info XCMP EX
+XI158,Info X25HD EX
+XXD3,25-Hydroxyvit D3 EX
+XTSAT,%Transferrin Sat EX
+XXAST,AST EX
+XXTSF,Transferrin EX
+XXFRT,Ferritin EX
+XXTBL,Total Bilirubin EX
+XI164,Info XTIBC EX
+XDTOT,"25-Hydroxyvit D, Total EX"
+XI169,Info XPTHI EX
+XI111,Info XPHOS EX
+XIN56,Info XFRTN EX
+XTCAL,Calcium PTHI EX
+XI140,Info XTSF EX
+XXALK,Alkaline Phosphatase EX
+XIPTH,iParathyroid EX
+PROTU2,PROTEIN URINE RANDOM
+A-HCV,HEPATITIS C ANTIBODY
+HB-AG,HEPATITIS B SURFACE ANTIGEN
+ELR,External Lab Results
+ANTI-HBS,HEPATITIS B SURFACE ANTIBODY
+A-HBS,HEPATITIS B SURFACE ANTIBODY
+A-HBC,"HEPATITIS B CORE AB, IGG + IGM"
+VZVGN,"VZV IgG Antibody, Index"
+HSV2I,"HSV-2 Ab, Qualitative"
+TOXGI,"TOXO IgG Antibody, Qualitative"
+BT,Blood Type
+VZVGI,"VZV IgG Antibody, Qualtitative"
+HSV1I,"HSV-1 Ab, Qualitative"
+PROCRE,PROTEIN/CREAT URINE RATIO
+AFP,ALPHA FETOPROTEIN-TUMOR MARKER
+UMA/CR,MICROALBUMIN/CREATININE RATIO
+HCO3,POC HCO3
+PCO2,POC PCO2
+P_H,POC P_H
+PO2,POC PO2
+NRBC %,Automated NRBC Percentage
+SPECIMEN,SPECIMEN
+INTER,INTERPRETATION
+GROSS,Gross
+CLINF,Clinical Info
+MOL DX FIN,MOLECULAR DIAGNOSTIC FINAL
+MLABEL,Generic Molec. Diag. Test
+WBC-UA,POC WBC-UA
+RBC-UA,POC RBC-UA
+EPITH-UA,POC EPITH-UA
+BACT-UA,POC BACT-UA
+DLDL,"LDL CHOLESTEROL, DIRECT"
+BFG,BODY FLUID CULTURE/SMEAR
+IMFIXATION,IMMUNOFIXATION
+TPINTERP,PROTEIN ELECTROPHORESIS INTERP
+IGG,IMMUNOGLOBULIN G
+IGA,IMMUNOGLOBULIN A
+IGM,IMMUNOGLOBULIN M
+FGST,FECAL LEUKOCYTE GRAM STAIN
+CDIFF,CLOSTRIDIUM DIFFICILE
+STL,STOOL CULTURE
+SLT,SHIGA-LIKE TOXIN (E.COLI O157)
+CA OXALATE,Calcium Oxalate Crystals
+HYAL CASTS,Hyaline Casts in urine micro
+AMORPHOUS,Amorphous Crystals
+TTRANGA,Tissue Transglutamase IgA AB
+A1AT-S,"ALPHA-1-ANTITRYPSIN, SERUM"
+CERULO,CERULOPLASMIN
+TTGA INT,Tissue Transglutamase IgA Int
+TTGG INT,Tissue Transglutamase IgG Int
+NAB,NUCLEAR ANTIBODY
+QHCV,"HCV QUANTITATION, PCR"
+TTRANGG,Tissue Transglutamase IgG AB
+HAAB,"HEPATITIS A AB, TOTAL"
+A1ATP,ALPHA-1-ANTITRYPSIN PHENOTYPE
+SM,LIVER SMOOTH MUSCLE ANTIBODY
+FSH,FOLLICLE STIMULATING HORMONE
+ACTH,ACTH
+GH,GROWTH HORMONE
+IGF1,Insulin-like Grth. Fact. 1
+SXHBG,Sex Hormone Binding Globulin
+Z0749,Renin Activity
+DPR,"Renin, Plasma Mass"
+CBRC,CORD BLOOD TO LAB
+SBECA,"Std Base Excess, Art Cord BG"
+INSTY,"Instrument, Art Cord BG"
+SBECV,"Standard Base Excess, Ven"
+HCO3-CA,"HCO3, Art Cord BG"
+pHCV,"pH, Ven Cord BG"
+pCO2CA,"pCO2, Art Cord BG"
+pCO2CV,"pCO2, Ven Cord BG"
+INSTZ,"Instrument, Ven Cord BG"
+NBSR,Screening Result
+MDPH LAB #,MDPH LAB NUMBER
+LEAD,BLOOD LEAD
+sO2CV,"sO2, Ven Cord BG"
+PHRR,"pH, Art Cord BG"
+sO2CA,"sO2, Art Cord BG"
+pO2CA,"pO2, Art Cord BG"
+HCO3-CV,"HC03, Ven Cord BG"
+pO2CV,"pO2, Ven Cord BG"
+PREG,POC PREGNANCY SCREEN URINE
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UMich_panel.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UMich_panel.csv
new file mode 100644
index 00000000..a2354022
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/UMich_panel.csv
@@ -0,0 +1,162 @@
+name,description
+URIN,POC URINE SCREEN
+COMP,Comprehensive Metabolic Panel
+CBCP,CBCP
+PT7,PROTHROMBIN TIME
+PSA,PROSTATE SPECIFIC ANTIGEN
+PTT,Partial Thromboplastin Time
+PTS,PRE ADMIT TYPE AND SCREEN
+SPF,SP FINAL REPORT
+UA,UA MACROSCOPIC
+URCC,URINE CULTURE
+UMIC,URINE MICROSCOPIC
+FT,FREE TESTOSTERONE
+TIBC,IRON/IRON BINDING CAPACITY
+ABSOLT CNT,Absolute Count
+AUTO DIFF,AUTO DIFF (5 PT FROM COULTER)
+FOL,FOLIC ACID LEVEL
+FRTN,FERRITIN
+VITB12,VITAMIN B12 LEVEL
+EGFR,Estimated Glomerular Filtration Rate
+TSH,TSH-3rd GENERATION (ssTSH)
+CHD,CORONARY HEART DISEASE PROFILE
+A1C,HEMOGLOBIN A1c
+UAM,UA WITH MICRO
+PSA-F,"PSA, FREE AND RATIO"
+CNREV,NON-GYN REVIEW
+UFC,URINE FREE CORTISOL
+TV,TOTAL VOLUME URINE 24 HOUR
+METAN,METANEPHRINES/NORMETANEPHRINES
+17K,"STEROIDS, 17-KETO"
+UCAT,"CATECHOLAMINES, URINE"
+CREATQ,CREATININE URINE 24 HOUR
+VMA,"VANILLYLMANDELIC ACID, (VMA)"
+URIC,URIC ACID
+TESTO,TESTOSTERONE LEVEL
+PAB,PREALBUMIN
+LIVER,Hepatic Function Panel
+PRL,PROLACTIN
+HGBAIC,POC HGB A1C/GLUCOSE
+FT4,FREE T4
+DHEAS,DHEA-SULFATE
+CBC,CBC
+GLUC,GLUCOSE LEVEL
+THY,THYROID ANTIBODIES
+SALDO,"ALDOSTERONE, SERUM"
+PRA,RENIN (ANGIOTENSIN)
+CORT,CORTISOL LEVEL
+LIPID,Lipid Panel
+CEA,CARCINOEMBRYONIC ANTIGEN
+25HD,"25-Hydroxyvitamin D, Total"
+UMA,UMA/CREAT RATIO
+CBCPD,CBC and DIFF
+HCT,Hematocrit
+ESTRA,ESTRADIOL LEVEL
+LH,LUTEINIZING HORMONE
+DPSA,"PROSTATE SPECIFIC AG, DIAGNOS"
+BASIC,Basic Metabolic Panel
+PHOS,PHOSPHORUS LEVEL
+MAG,MAGNESIUM LEVEL
+GSS,RESPIRATORY CULTURE/SMEAR
+BLD,ADULT BLOOD CULTURE
+SODU,SODIUM URINE RANDOM
+CREATU,CREATININE URINE RANDOM
+POCISTAT,POC ISTAT BASIC PANEL
+RBC-MORPH,RBC MORPHOLOGY
+WB PT7,POC PROTHROMBIN TIME WHOLE BLD
+AMYL,AMYLASE
+GLUCM,POC GLUCOSE LEVEL
+BLDAN,BLOOD CULTURE (ANA)
+TS,TYPE AND SCREEN
+LIP,LIPASE LEVEL
+STLG,STOOL CULTURE
+OPE,OVA AND PARASITE EXAM
+SF,SYNOVIAL FLUID ANALYSIS
+SBFBA,STERILE BODY FLUID CULTURE/SMR
+AFB,AFB CULTURE/SMEAR
+FNG,FUNGUS CULTURE/SMEAR
+TPE,PROTEIN ELECTROPHORESIS
+WEST,WESTEGREN RBC SEDI RATE
+BORR,BORRELIA ANTIBODY (LYME DIS)
+CRP,C-REACTIVE PROTEIN SCREEN
+RF,RHEUMATOID FACTOR
+UPE,URINE PROTEIN ELECTROPHORESIS
+BGSA,"Blood Gas, Arterial Ped"
+DX,DIGOXIN LEVEL
+POCUA,POC Urine Screen
+XRNL1,External Lab Renal Panel
+XGFR,External Lab GFR
+ABO EX,ABO EX
+UA EX,UA EX
+PROTU EX,Protein/Creat Ratio Urine EX
+VZVGE EX,VZV IgG Antibody EX
+HSVP EX,"Herpes Simplex 1 and 2, IgG EX"
+CBC EX,CBC EX
+HBCAB EX,"Hepatitis B Core Ab IgG,IgM EX"
+HCAB EX,Hepatitis C Antibody EX
+HBSAB EX,Hepatitis B Surface Ab EX
+EBV EX,EBV Titers EX
+HBCMAB EX,Hepatitis B Core Ab IgM EX
+CMV EX,CMV Titers EX
+HBSAG EX,Hepatitis B Surface Antigen EX
+TOXOGE EX,TOXO IgG Antibody EX
+TIBC EX,Fe/TIBC EX
+25HD EX,25-Hydroxyvit D EX
+XCMP1,External Lab Comprehensive Metabolic Panel
+TSF EX,Transferrin EX
+FRTN EX,Ferritin EX
+PTHI EX,iParathyroid Hormone EX
+PHOS EX,Phosphorus EX
+PROTU,PROTEIN/CREAT RATIO URINE
+RENAL,Renal Panel
+HCAB,HEPATITIS C ANTIBODY
+HBSAG,HEPATITIS B SURFACE ANTIGEN
+ELR,External Lab Results
+HBSAB,HEPATITIS B SURFACE ANTIBODY
+HBCAB,HEPATITIS B CORE AB IgG + IgM
+VZVG,"Varicella IgG, Qualitative"
+HSVP,"Herpes Simplex 1 and 2, IgG"
+TOXG,"Toxoplasma IgG, Qualitative"
+BT,BLOOD TYPE
+AFP,ALPHA FETOPROTEIN-TUMOR MARKER
+POCGAS,POC BLOOD GASES
+UMAC,URINE MACROSCOPIC
+SPECIMEN,Surgical Pathology Request
+HGB A1C,POC HGB A1C
+MOL DX FIN,MOLECULAR DIAGNOSTIC FINAL
+MLABEL,Generic Molec. Diag. Test
+UA PPM,POC URINE MICROSCOPY
+DLDL,"LDL CHOLESTEROL, DIRECT"
+BFG,BODY FLUID CULTURE/SMEAR
+IFIX,IMMUNOFIXATION - SERUM
+IGG,IMMUNOGLOBULIN G
+IGA,IMMUNOGLOBULIN A
+IGM,IMMUNOGLOBULIN M
+CREAT,CREATININE LEVEL
+FGST,FECAL LEUKOCYTE GRAM STAIN
+CDIFF,CLOSTRIDIUM DIFFICILE
+STL,STOOL CULTURE
+SLT,SHIGA-LIKE TOXIN (E.COLI O157)
+TTRGA,Tissue Transglutaminase IgA AB
+A1AP,ALPHA-1-ANTITRYPSIN PHENOTYPIN
+CERULO,CERULOPLASMIN
+TTRGG,Tissue Transglutaminase IgG AB
+NAB,NUCLEAR ANTIBODY
+QHCV,"HCV QUANTITATION, PCR"
+IGS,IMMUNOGLOBULIN QUANT (IgG/M/A)
+HAAB,"HEPATITIS A AB, TOTAL"
+SM,LIVER SMOOTH MUSCLE ANTIBODY
+ALB,ALBUMIN LEVEL
+FSH,FOLLICLE STIMULATING HORMONE
+ACTH,ACTH
+GH,GROWTH HORMONE
+IGF1,Insulin-like Grth. Fact. 1
+SXHBG,Sex Hormone Binding Globulin
+MPRA,"Renin Activity, Plasma"
+DPR,"Renin, Plasma Mass"
+CBRC,CORD BLOOD TO LAB
+PCBA,"Cord Blood Gas, Mott, Arterial"
+PCBV,"Cord Blood Gas, Mott, Venous"
+NBS,NEWBORN SCREENING
+LEAD,BLOOD LEAD
+PRGU,"PREGNANCY SCREEN, URINE"
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/billing_codes.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/billing_codes.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_summary_stats/billing_codes.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/billing_codes.csv
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/chargemaster.csv.gz b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/chargemaster.csv.gz
new file mode 100644
index 00000000..714edeb8
Binary files /dev/null and b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/chargemaster.csv.gz differ
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/component_cnts.txt b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/component_cnts.txt
new file mode 100644
index 00000000..25610948
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/component_cnts.txt
@@ -0,0 +1,24 @@
+Base	2010	2011	2012	2013	2014	2015	2016	2017	
+WBC	146403	149172	147342	150601	160427	163811	170175	10248	
+HGB	153429	156559	154475	158614	170568	205596	224805	14405	
+PLT	153867	156879	154117	158423	170082	168756	175550	10561	
+NA	0	0	0	0	0	161693	193171	11836	
+K	192554	192006	193520	200539	224148	241526	260467	17137	
+CL	169156	168508	171880	181767	200478	216678	234613	15401	
+CR	158888	157577	158352	160726	172496	179634	185878	11509	
+BUN	150407	148960	150804	158174	170628	179690	185766	11498	
+GLU	399728	416210	425474	438848	472476	466329	478043	36204	
+CO2	153173	151664	153106	157966	170168	177673	183442	11430	
+CA	150477	148789	150862	158202	170638	178807	184256	11515	
+HCO3	1533	2115	1868	2031	2154	616	529	10	
+TP	66126	63156	66665	72381	75339	77298	79105	4186	
+ALB	70005	67036	70633	74041	77367	78902	81285	4375	
+ALKP	69210	66215	69311	72538	75197	77034	78859	4174	
+TBIL	69803	66926	70050	73401	76244	78445	80430	4231	
+AST	69220	66234	69324	72599	75339	77059	78890	4175	
+ALT	69217	66236	69328	72590	75336	77057	78888	4175	
+DBIL	14667	12293	13174	10671	13716	16580	16681	953	
+IBIL	14088	11928	12583	10139	13059	15787	15604	861	
+PHA	54802	57726	58568	57561	66696	69579	72910	5832	
+PCO2A	56159	59607	60222	59420	68290	67650	71479	5610	
+PO2A	56159	59607	60222	59420	68290	67650	71479	5610	
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/components.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/components.csv
new file mode 100644
index 00000000..14f4255f
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/components.csv
@@ -0,0 +1,23 @@
+name,description
+WBC,"WBC(WBC)"
+HGB,"HGB(CALC), ISTAT"
+PLT,"PLATELET COUNT(PLT)"
+NA,"SODIUM, SER/PLAS"
+K,"POTASSIUM, ISTAT"
+CL,"CHLORIDE, SER/PLAS(CL)"
+CR,"CREATININE, ISTAT"
+BUN,"UREA NITROGEN, SER/PLAS"
+GLU,"GLUCOSE, ISTAT"
+CO2,"CO2, SER/PLAS(CO2)"
+CA,"CALCIUM, SER/PLAS(CA)"
+TP,"PROTEIN, TOTAL, SER/PLAS"
+ALB,"ALBUMIN, SER/PLAS(ALB)"
+ALKP,"ALK P'TASE, TOTAL, SER/PLAS"
+TBIL,"TOTAL BILIRUBIN(TBIL)"
+AST,"AST (SGOT), SER/PLAS"
+ALT,"ALT (SGPT), SER/PLAS"
+DBIL,"CONJUGATED BILI"
+IBIL,"BILIRUBIN, INDIRECT, SER/PLAS"
+PHA,"PH (A)"
+PCO2A,"PCO2, ISTAT"
+PO2A,"PO2, ISTAT"
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/item_counts.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/item_counts.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_summary_stats/item_counts.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/item_counts.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs.csv
similarity index 99%
rename from scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs.csv
index 8de4bcca..a71a8e4e 100644
--- a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs.csv
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs.csv
@@ -11,7 +11,7 @@ clinical_item_id,name,description
 45767,LABSPR,SPECIMEN REMARK
 45770,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME
 45771,LABMETC,"METABOLIC PANEL, COMPREHENSIVE"
-45772,LABTSH,TSH
+45772,LABTSH,"THYROID STIMULATING HORMONE"
 45778,LABPHOS,"PHOSPHORUS, SERUM/PLASMA"
 45782,LABURNC,URINE CULTURE
 45788,LABCBCD,CBC WITH DIFF
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes.csv
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes.csv
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes.csv
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes_modified.xlsx b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes_modified.xlsx
similarity index 100%
rename from scripts/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes_modified.xlsx
rename to scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/labs_charges_volumes_modified.xlsx
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/potential_matches.csv b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/potential_matches.csv
new file mode 100644
index 00000000..740e3a8c
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/data_summary_stats/potential_matches.csv
@@ -0,0 +1,77 @@
+lab,old_description,best matched description,JC
+LABA1C,HEMOGLOBIN A1C,Glycosylated hemoglobin test,
+LABAFBC,"AFB CULTURE, RESPIRATORY",Respiratory virus antibodys?,NO
+LABAFBD,"AFB CULTURE, NON - RESPIRATORY",?,
+LABALB,"ALBUMIN, SERUM/PLASMA",?,
+LABANER,ANAEROBIC CULTURE,Culture bacteria anaerobic,
+LABB12,VITAMIN B12,Vitamin b-12,
+LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Blood culture for bacteria?,
+LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),?,
+LABBLCSTK,"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY",?,
+LABBLCTIP,BLOOD CULT CENTRAL LINE CATHETER BY NURSE,?,
+LABBUN,"UREA NITROGEN, SERUM/PLASMA",Assay of urea nitrogen?,
+LABBXTG,BIOPSY/TISSUE WITH GRAM STAIN,Tissue culture skin/biopsy?,
+LABCA,"CALCIUM, SERUM/PLASMA",Assay of calcium?,
+LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Assay of calcium?,NO
+LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",?,?
+LABCK,"CREATINE KINASE, TOTAL",Assay of ck (cpk),
+LABCMVQT,"CMV DNA PCR QUANT, PLASMA",Cmv antibody?,NO
+LABCORT,"CORTISOL, SERUM",Total cortisol?,?
+LABCRP,C - REACTIVE PROTEIN,C-reactive protein,
+LABCSFC,CSF CULTURE AND GRAM STAIN,Assay of csf protein?,
+LABCSFGL,"GLUCOSE, CSF",Glucose test?,
+LABCSFTP,"PROTEIN TOTAL, CSF",Assay of csf protein,
+LABDIGL,"DIGOXIN, SERUM",Assay of digoxin total?,
+LABESRP,SEDIMENTATION RATE (ESR),Rbc sed rate automated OR Rbc sed rate nonautomated?,
+LABFCUL,FUNGAL CULTURE,Blood fungus culture OR Fungus isolation culture?,
+LABFE,"IRON, TOTAL",Assay of iron?,
+LABFER,FERRITIN,Assay of ferritin,
+LABFIB,FIBRINOGEN,Fibrinogen test,
+LABFLDC,FLUID CULTURE AND GRAM STAIN,?,
+LABFOL,FOLIC ACID,Assay of folic acid serum or Assay of folic acid rbc?,
+LABFT4,"T4, FREE",Assay of thyroid (t3 or t4)?,
+LABGRAM,GRAM STAIN,Smear gram stain?,
+LABHAP,HAPTOGLOBIN,Assay of haptoglobin quant?,
+LABHBSAG,HBSAG,Hepatitis b surface ag ia,
+LABHCTX,HEMATOCRIT,Hematocrit,
+LABHEPAR,"HEPARIN, UNFRACTIONATED, ANTI XA",Heparin assay?,
+LABHIVWBL,ANTI-HIV,Hiv antigen w/hiv antibodies?,
+LABK,"POTASSIUM, SERUM/PLASMA",Assay of serum potassium,
+LABLAC,LACTIC ACID,Assay of lactic acid,
+LABLACWB,"LACTATE, WHOLE BLOOD",Lactate (ld) (ldh) enzyme??,?
+LABLDH,"LDH TOTAL, SERUM / PLASMA",Assay of ldh enzymes?,
+LABLIDOL,LIDOCAINE,Assay of lidocaine,
+LABLIPS,LIPASE,Assay of lipase,
+LABMB,"CK, MB (MASS)",Creatine mb fraction,
+LABMGN,"MAGNESIUM, SERUM/PLASMA",Assay of magnesium,
+LABNA,"SODIUM, SERUM / PLASMA",Assay of serum sodium,
+LABNH3,"AMMONIA, PLASMA",Assay of ammonia?,
+LABNTBNP,NT - PROBNP,Assay of natriuretic peptide,
+LABOSM,"OSMOLALITY, SERUM",Assay of blood osmolality,
+LABPALB,PREALBUMIN,Assay of prealbumin,
+LABPCCG4O,"ISTAT CG4, OTHER",?,Give up?
+LABPCCR,ISTAT CREATININE,Assay of creatinine,
+LABPCTNI,ISTAT TROPONIN I,Assay of troponin qual OR Assay of troponin quant?,
+LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Assay of phosphorus,
+LABPLTS,PLATELET COUNT,Automated platelet count,
+LABPROCT,PROCALCITONIN,Procalcitonin (pct),
+LABPT,PROTHROMBIN TIME,Prothrombin time,
+LABPTEG,"TEG, PERIOPERATIVE",?,
+LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Thromboplastin time partial,
+LABRESP,RESPIRATORY CULTURE,?,
+LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,?,
+LABRETIC,RETICULOCYTE COUNT AUTOMATED,Automated reticulocyte count,
+LABSPLAC,SEPSIS PROTOCOL LACTATE,?,?
+LABSTLCX,STOOL CULTURE,Stool cultr aerobic bact ea,
+LABSTOBGD,"OCCULT BLD, GUAIAC, STOOL, DIAG TEST",Occult bld feces 1-3 tests?,
+LABTNI,TROPONIN I,Assay of troponin quant,
+LABTRFS,TRANSFERRIN SATURATION,Assay of transferrin,
+LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Assay of triglycerides,
+LABTSH,TSH,Assay thyroid stim hormone,
+LABUA,URINALYSIS WITH MICROSCOPIC,Microscopic exam of urine,
+LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Urine screen for bacteria?,
+LABUOSM,"OSMOLALITY, URINE",Assay of urine osmolality,
+LABUPREG,"PREGNANCY TEST, URINE",Urine pregnancy test,
+LABURIC,"URIC ACID, SERUM / PLASMA",Assay of blood/uric acid,
+LABURNC,URINE CULTURE,Urine culture/colony count,
+LABUSPG,"SPECIFIC GRAVITY, URINE",Urine culture/colony count,
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/lab_statistics/repeat_component_descriptive.py b/scripts/Archive/LabTestAnalysis/lab_statistics/repeat_component_descriptive.py
similarity index 92%
rename from scripts/LabTestAnalysis/lab_statistics/repeat_component_descriptive.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/repeat_component_descriptive.py
index f0611c02..3fd84ffe 100644
--- a/scripts/LabTestAnalysis/lab_statistics/repeat_component_descriptive.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/repeat_component_descriptive.py
@@ -1,12 +1,12 @@
 import time
 from os import path
 
-import customDBUtil
+from . import customDBUtil
 import numpy as np
 import pandas as pd
 
 from collections import defaultdict, deque
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime
 
 # from medinfo.db.test.Const import RUNNER_VERBOSITY
@@ -15,7 +15,6 @@
 # from medinfo.common.test.Util import MedInfoTestCase
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery
-# from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME
 
 BASE_NAME = 'base_name'
 CLINICAL_ITEM_ID = 'clinical_item_id'
@@ -80,7 +79,7 @@ def _getPatientsComponentsHistories(self, item_ids):
 
   @staticmethod
   def splitByPatient(results):
-    currentSplit = [results.next()]
+    currentSplit = [next(results)]
     for result in results:
       if result[0] != currentSplit[-1][0]:
         yield currentSplit
@@ -113,10 +112,10 @@ def getStats(results, isNormalFn, max_consecutive=5, bins=[1, 2, 4, 7, 30, 90]):
         - Additional result for consecutive normal count is NULL,
           to count the total number of results and the total number of normal results
     """
-    counts = [deque() for _ in xrange(len(bins))]
+    counts = [deque() for _ in range(len(bins))]
     # key: (days back, consecutive), value: (total, next_normal)
     stats = defaultdict(lambda: np.array([0, 0]))
-    prior_histories = [[False] for _ in xrange(len(bins))]
+    prior_histories = [[False] for _ in range(len(bins))]
     for result in results:
       for window_size, queue, prior_history in zip(bins, counts, prior_histories):
         # only keep whats relevant to the current result
@@ -161,7 +160,7 @@ def getStats(results, isNormalFn, max_consecutive=5, bins=[1, 2, 4, 7, 30, 90]):
     # calculate a new None by summing everything else up with the same 0th index key
     # loop over keys and get the sum and create a new dict
     total_counts = defaultdict(lambda: np.array([0, 0]))
-    for k, v in stats.iteritems():
+    for k, v in stats.items():
       total_counts[k[0]] += v
     for window_size in bins:
       # then set 1,0 to 1,None
@@ -169,17 +168,17 @@ def getStats(results, isNormalFn, max_consecutive=5, bins=[1, 2, 4, 7, 30, 90]):
       # set window_size,None to the sum of calculated above
       stats[(window_size, None)] = total_counts[window_size]
 
-    return {k: v for k, v in stats.iteritems() if k[1] <= max_consecutive}
+    return {k: v for k, v in stats.items() if k[1] is None or k[1] <= max_consecutive}
 
   @staticmethod
   def createGlobalStatsDf(global_stats):
     df_global_stats = pd.DataFrame()
-    for base_name, d in dict(global_stats).iteritems():
+    for base_name, d in dict(global_stats).items():
       to_df = []
-      for k, v in d.iteritems():
+      for k, v in d.items():
         to_df.append([base_name] + list(k) + list(v))
       df_global_stats = pd.concat(
-          [df_global_stats, pd.DataFrame(to_df, dtype='string').fillna('NULL')])
+          [df_global_stats, pd.DataFrame(to_df, dtype='object').fillna('NULL')])
     df_global_stats = df_global_stats.rename(columns={0: 'base_name',
                                                       1: 'window',
                                                       2: 'consecutive',
@@ -205,20 +204,20 @@ def run(self, queryDB=False):
 
     global_stats = defaultdict(lambda: defaultdict(lambda: np.array([0, 0])))
     for result_i, base_name in enumerate(df_result_ids['base_name'].drop_duplicates().tolist()):
-      print result_i, base_name
+      print(result_i, base_name)
       timer = time.time()
       item_ids = df_result_ids[df_result_ids['base_name'] == base_name][
           'clinical_item_id'].tolist()
       results = self._getPatientsComponentsHistories(item_ids)
       for patient_results in RepeatComponents.splitByPatient(results):
-        for k, v in RepeatComponents.getStats(patient_results, RepeatComponents.isNormal).iteritems():
+        for k, v in RepeatComponents.getStats(patient_results, RepeatComponents.isNormal).items():
           global_stats[base_name][k] += v
       times.append(time.time() - timer)
 
     RepeatComponents.createGlobalStatsDf(global_stats).to_csv(
         DATA_FOLDER + 'global_stats.csv', index=False)
 
-    print map(lambda x: round(x, 2), times)
+    print([round(x, 2) for x in times])
 
 if __name__ == '__main__':
   descriptive_stats = RepeatComponents()
diff --git a/scripts/LabTestAnalysis/lab_statistics/repeat_lab_descriptive.py b/scripts/Archive/LabTestAnalysis/lab_statistics/repeat_lab_descriptive.py
similarity index 90%
rename from scripts/LabTestAnalysis/lab_statistics/repeat_lab_descriptive.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/repeat_lab_descriptive.py
index fcd36a42..2f1cc652 100644
--- a/scripts/LabTestAnalysis/lab_statistics/repeat_lab_descriptive.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/repeat_lab_descriptive.py
@@ -1,12 +1,12 @@
 import time
 from os import path
 
-import customDBUtil
+from . import customDBUtil
 import numpy as np
 import pandas as pd
 
 from collections import defaultdict, deque
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime
 
 # from medinfo.db.test.Const import RUNNER_VERBOSITY
@@ -16,7 +16,7 @@
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery
 # from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME
-from repeat_component_descriptive import RepeatComponents
+from .repeat_component_descriptive import RepeatComponents
 
 CLINICAL_ITEM_ID = 'clinical_item_id'
 DATA_FOLDER = 'data_repeat_lab_descriptive/'
@@ -78,21 +78,21 @@ def run(self, queryDB=False, batch_size=10):
 
     global_stats = defaultdict(lambda: defaultdict(lambda: np.array([0, 0])))
     proc_codes = df_proc_codes['proc_code'].tolist()
-    for batch_i in xrange(int(np.ceil(1.0*len(proc_codes) / batch_size))):
-      print batch_i, proc_codes[batch_i * batch_size]
+    for batch_i in range(int(np.ceil(1.0*len(proc_codes) / batch_size))):
+      print(batch_i, proc_codes[batch_i * batch_size])
       timer = time.time()
       batch = proc_codes[batch_i * batch_size:(batch_i + 1) * batch_size]
       results = self._getPatientsLabsHistories(batch)
       for patient_results in RepeatComponents.splitByPatient(results):
         proc_code = patient_results[0][3]
-        for k, v in RepeatComponents.getStats(patient_results, RepeatLabs.isNormal).iteritems():
+        for k, v in RepeatComponents.getStats(patient_results, RepeatLabs.isNormal).items():
           global_stats[proc_code][k] += v
       times.append(time.time() - timer)
 
     RepeatComponents.createGlobalStatsDf(global_stats).to_csv(
         DATA_FOLDER + 'global_stats.csv', index=False)
 
-    print map(lambda x: round(x, 2), times)
+    print([round(x, 2) for x in times])
 
 if __name__ == '__main__':
   descriptive_stats = RepeatLabs()
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/stats_database.py b/scripts/Archive/LabTestAnalysis/lab_statistics/stats_database.py
new file mode 100644
index 00000000..d8d3be70
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/stats_database.py
@@ -0,0 +1,82 @@
+
+
+'''
+'''
+import os
+import pandas as pd
+pd.set_option('display.width', 3000)
+pd.set_option('display.max_columns', 500)
+
+from . import stats_utils
+
+
+'''
+Top volume panels.
+
+Top frequent (order in 24h) panels. 
+TODO: fix this, < 1 day? <= 1 day?
+
+Choose wisely guideline panels (read from literature)?
+'''
+
+# TODO: need update!
+TOP_PANELS_AND_COUNTS_BY_VOL = [['LABMGN', 282090], ['LABPT', 149070], ['LABPHOS', 140861], ['LABPTT', 97752], ['LABLACWB', 81693], ['LABCAI', 79497], ['LABK', 49953], ['LABTNI', 35462], ['LABLDH', 30262], ['LABHEPAR', 28083], ['LABUAPRN', 27654], ['LABBLC', 26600], ['LABBLC2', 24669], ['LABNA', 23971], ['LABLIDOL', 20894], ['LABHCTX', 19137], ['LABURNC', 17055], ['LABUA', 16362], ['LABURIC', 15194], ['LABA1C', 14921], ['LABSPLAC', 12427], ['LABPCTNI', 12218], ['LABPLTS', 11576], ['LABLIPS', 11563], ['LABPROCT', 11516], ['LABLAC', 10122], ['LABFIB', 9797], ['LABTSH', 9631], ['LABCK', 8368], ['LABCRP', 8331], ['LABNTBNP', 8127], ['LABTRIG', 6983], ['LABMB', 6396], ['LABCDTPCR', 6280], ['LABOSM', 5838], ['LABRESPG', 5782], ['LABESRP', 5667], ['LABFER', 5413], ['LABALB', 5209], ['LABNH3', 5118], ['LABUSPG', 5050], ['LABFCUL', 4905], ['LABHAP', 4739], ['LABANER', 4574], ['LABFLDC', 4556], ['LABCMVQT', 4376], ['LABBLCTIP', 4095], ['LABFT4', 4061], ['LABTRFS', 4010], ['LABB12', 3984], ['LABBLCSTK', 3970], ['LABPALB', 3906], ['LABUOSM', 3849], ['LABDIGL', 3619], ['LABRETIC', 3297], ['LABCORT', 3198], ['LABHBSAG', 2989], ['LABFE', 2686], ['LABAFBC', 2679], ['LABCA', 2570], ['LABBXTG', 2402], ['LABAFBD', 2321], ['LABUPREG', 2284], ['LABBUN', 2235], ['LABGRAM', 2027], ['LABPTEG', 1942], ['LABCSFTP', 1762], ['LABFOL', 1755], ['LABCSFGL', 1736], ['LABRESP', 1624], ['LABCSFC', 1556], ['LABSTOBGD', 1555], ['LABPCCR', 705], ['LABPCCG4O', 683], ['LABHIVWBL', 73], ['LABSTLCX', 0]]
+TOP_PANELS_AND_COUNTS_BY_AVOIDED_VOL = [['LABMGN', 131241.99224268], ['LABK', 40908.462964086], ['LABPHOS', 38567.806173477], ['LABLACWB', 31583.607424191], ['LABBLC2', 22098.085924428], ['LABBLC', 20961.4726608], ['LABURIC', 15194.0], ['LABTNI', 14907.504815014], ['LABLIDOL', 13608.669298696], ['LABPCTNI', 10860.981023707998], ['LABLDH', 9706.447408672], ['LABSPLAC', 9614.741243338], ['LABLAC', 6204.370623486], ['LABUSPG', 5050.0], ['LABPROCT', 4405.2433256879995], ['LABPLTS', 4063.256893088], ['LABLIPS', 3888.0760019960003], ['LABCK', 3763.9788673600005], ['LABUOSM', 3610.8005897010003], ['LABBLCTIP', 3338.2788238799994], ['LABFCUL', 3321.0574856999997], ['LABHBSAG', 2989.0], ['LABDIGL', 2541.283086958], ['LABANER', 2479.960383196], ['LABAFBD', 2321.0], ['LABUPREG', 2284.0], ['LABTRIG', 2048.8365078230004], ['LABAFBC', 1729.262429691], ['LABUAPRN', 1591.597126878], ['LABCSFGL', 1586.9816575759999], ['LABCSFC', 1555.9999999999998], ['LABFLDC', 1449.83191544], ['LABCORT', 1380.8031239339998], ['LABOSM', 1275.99916668], ['LABCDTPCR', 1130.90543324], ['LABMB', 1128.7058808479999], ['LABURNC', 1082.7239178599998], ['LABB12', 996.9512915519999], ['LABPTT', 981.6100414319999], ['LABBUN', 937.1564486550001], ['LABA1C', 862.7588092220001], ['LABFIB', 725.8573297350001], ['LABHAP', 675.964439136], ['LABPCCG4O', 652.048338057], ['LABFE', 519.498567012], ['LABRESP', 368.905280576], ['LABPALB', 229.57315794000002], ['LABCA', 175.36470558], ['LABESRP', 160.25532489600002], ['LABPCCR', 160.253012085], ['LABFER', 138.32970217000002], ['LABALB', 123.013842715], ['LABRETIC', 107.962216824], ['LABBLCSTK', 95.32920021], ['LABCSFTP', 85.824354094], ['LABHIVWBL', 73.00000000000001], ['LABNA', 38.538584207], ['LABSTOBGD', 27.829976505], ['LABHCTX', 14.443019229], ['LABBXTG', 13.916570685999998], ['LABFT4', 10.421728056000001], ['LABNH3', 8.470003392], ['LABGRAM', 3.132921065], ['LABRESPG', 0.0], ['LABPTEG', 0.0], ['LABPT', 0.0], ['LABSTLCX', 0.0], ['LABTRFS', 0.0], ['LABNTBNP', 0.0], ['LABTSH', 0.0], ['LABUA', 0.0], ['LABHEPAR', 0.0], ['LABCAI', 0.0], ['LABCRP', 0.0], ['LABCMVQT', 0.0], ['LABFOL', 0.0]]
+
+TOP_PANELS_AND_COUNTS_IN_1DAY = [['LABMETB', 139576], ['LABMGN', 135115], ['LABCBCD', 63426], ['LABLACWB', 60781], ['LABPTT', 59468], ['LABPHOS', 56710], ['LABPT', 55978], ['LABCAI', 52599], ['LABK', 40257], ['LABHEPAR', 21786], ['LABNA', 20816], ['LABTNI', 20105], ['LABLIDOL', 17706], ['LABHCTX', 10312], ['LABLDH', 9003], ['LABURIC', 7526], ['LABLAC', 6105], ['LABSPLAC', 5099], ['LABFIB', 4658], ['LABUSPG', 4430], ['LABCK', 3875], ['LABOSM', 3602], ['LABMB', 2987], ['LABUA', 2002], ['LABPCTNI', 1748], ['LABPROCT', 1355], ['LABLIPS', 1241], ['LABBLC', 1239], ['LABANER', 1206], ['LABAFBC', 1091], ['LABCA', 1084], ['LABA1C', 1003], ['LABBUN', 955], ['LABALB', 954], ['LABBLC2', 936], ['LABNH3', 917], ['LABFCUL', 881], ['LABURNC', 832], ['LABCORT', 801], ['LABUAPRN', 758], ['LABPLTS', 724], ['LABTRIG', 664], ['LABPTEG', 635], ['LABCRP', 626], ['LABUOSM', 600], ['LABRESPG', 566], ['LABTSH', 535], ['LABAFBD', 502], ['LABNTBNP', 486], ['LABBXTG', 461], ['LABFLDC', 450], ['LABDIGL', 422], ['LABHAP', 404], ['LABFER', 348], ['LABESRP', 317], ['LABRETIC', 213], ['LABHBSAG', 210], ['LABGRAM', 194], ['LABFT4', 193], ['LABBLCTIP', 191], ['LABRESP', 173], ['LABCMVQT', 157], ['LABPALB', 133], ['LABB12', 125], ['LABTRFS', 115], ['LABBLCSTK', 73], ['LABCSFTP', 66], ['LABCSFC', 65], ['LABCSFGL', 64], ['LABCDTPCR', 62], ['LABFE', 49], ['LABPCCG4O', 47], ['LABSTOBGD', 44], ['LABFOL', 36], ['LABUPREG', 21], ['LABPCCR', 12], ['LABHIVWBL', 3], ['LABSTLCX', 0]]
+TOP_PANELS_AND_COUNTS_IN_1TO3DAYS = [['LABMETB', 105169], ['LABMGN', 104168], ['LABCBCD', 96547], ['LABPHOS', 59456], ['LABPT', 54376], ['LABCAI', 14790], ['LABLDH', 13654], ['LABPTT', 13595], ['LABLACWB', 6053], ['LABURIC', 5318], ['LABBLC', 5018], ['LABBLC2', 4450], ['LABK', 3911], ['LABHCTX', 3712], ['LABPLTS', 3379], ['LABPROCT', 2757], ['LABTRIG', 2669], ['LABUAPRN', 2229], ['LABFIB', 2178], ['LABHEPAR', 2081], ['LABUA', 1948], ['LABDIGL', 1797], ['LABCRP', 1788], ['LABTNI', 1626], ['LABLIPS', 1536], ['LABURNC', 1469], ['LABSPLAC', 1363], ['LABALB', 1316], ['LABCK', 1285], ['LABHAP', 1234], ['LABNH3', 1147], ['LABPALB', 1015], ['LABRESPG', 956], ['LABNA', 861], ['LABCMVQT', 733], ['LABBLCTIP', 618], ['LABBLCSTK', 618], ['LABESRP', 616], ['LABLAC', 535], ['LABNTBNP', 527], ['LABLIDOL', 500], ['LABA1C', 488], ['LABCA', 462], ['LABMB', 453], ['LABFER', 408], ['LABFLDC', 366], ['LABRETIC', 349], ['LABUOSM', 288], ['LABGRAM', 276], ['LABTSH', 275], ['LABCSFGL', 253], ['LABRESP', 250], ['LABCSFTP', 245], ['LABCSFC', 243], ['LABFCUL', 239], ['LABAFBC', 218], ['LABOSM', 203], ['LABCORT', 201], ['LABFT4', 183], ['LABBUN', 166], ['LABANER', 113], ['LABTRFS', 102], ['LABPTEG', 86], ['LABB12', 80], ['LABUSPG', 74], ['LABAFBD', 66], ['LABFE', 60], ['LABCDTPCR', 53], ['LABSTOBGD', 47], ['LABBXTG', 46], ['LABHBSAG', 39], ['LABPCTNI', 29], ['LABFOL', 22], ['LABUPREG', 5], ['LABHIVWBL', 1], ['LABPCCR', 1], ['LABSTLCX', 0], ['LABPCCG4O', 0]]
+
+data_folderpath = 'data-Stanford-panel-10000-episodes'
+
+UCSF_PANELS_AND_COUNTS = [['Magnesium, Serum - Plasma', 68558], ['Phosphorus, Serum - Plasma', 51520], ['Prothrombin Time', 46170], ['Activated Partial Thromboplastin Time', 20891], ['Peripheral Blood Culture', 10406], ['Bilirubin, Total', 12740], ['Creatinine, Serum - Plasma', 11958], ['Alkaline Phosphatase', 11943], ['Sodium, Serum - Plasma', 9500], ['Potassium, Serum - Plasma', 6725], ['Troponin I', 7075], ['Carbon Dioxide, Total (includes Anion Gap)', 9236], ['Lactate Dehydrogenase, Serum - Plasma', 8856], ['Calcium, Ionized, serum-plasma', 8742], ['Uric Acid, Serum - Plasma', 4472], ['Albumin, Serum - Plasma', 4120], ['Thyroid Stimulating Hormone', 2030]]
+# UCSF_COMPONENTSS_AND_COUNTS = {'SODIUM, SERUM / PLASMA':103425, 'POTASSIUM, SERUM / PLASMA':100652, 'CHLORIDE, SERUM / PLASMA': 98564,
+#                                'CARBON DIOXIDE, TOTAL': 98546, 'CREATININE': 97832, 'CALCIUM, TOTAL, SERUM / PLASMA': 96041,
+#                                'UREA NITROGEN, SERUM / PLASMA': 96024, 'HEMATOCRIT': 95504, 'PLATELET COUNT': 95425,
+#                                'GLUCOSE, NON-FASTING': 94859, 'HEMOGLOBIN': 94268, 'WBC COUNT': 94073, 'MAGNESIUM, SERUM / PLASMA': 68610,
+#                                'PHOSPHORUS, SERUM / PLASMA': 51572, 'BILIRUBIN, TOTAL': 30230, }
+UCSF_COMPONENTSS_AND_COUNTS = [['WBC', 94073], ['HGB', 94268], ['PLT', 95425], ['NAWB', 27514], ['K', 100652], ['CREAT', 97832], ['TBILI', 30230], ['CL', 98564], ['CO2', 98546], ['DBILI', 3783], ['AST', 30750], ['ALT', 30765], ['ALB', 21571], ['CA', 96041], ['PCO2', 40986], ['PO2', 40986], ['PH37', 40986], ['TP', 18291], ['ALKP', 29433], ['BUN', 96024], ['HCO3', 40984], ['MG', 68610], ['PO4', 51572], ['INR', 34043], ['P060', 12553], ['CAI', 8742], ['CAIB', 27514], ['LACTWB', 27706]]
+
+UCSF_COMPONENT_TO_COUNT = {'NA': 103311, 'K': 100538, 'CL': 98451, 'CO2': 98433, 'ANGAP': 98433, 'CREAT': 97719, 'GFRC': 97719, 'GFRAA': 97719, 'CA': 95930, 'BUN': 95911, 'HCT': 95390, 'PLT': 95311, 'GLU': 94747, 'HGB': 94153, 'WBC': 93959, 'RBC': 93934, 'MCHC': 93927, 'MCH': 93927, 'MCV': 93927, 'MG': 68580, 'PO4': 51559, 'SRCE': 41637, 'PO2': 40917, 'PCO2': 40917, 'PH37': 40917, 'SAO2': 40917, 'HCO3': 40915, 'BEX': 40914, 'FIO2': 40908, 'MOA': 34830, 'LYMA': 34830, 'NEUTA': 34829, 'EOA': 34828, 'BASOA': 34827, 'IGLSA': 34823, 'INR': 33983, 'PT': 33983, 'ALT': 30706, 'AST': 30691, 'TBILI': 30171, 'ALKP': 29373, 'CAHB': 27844, 'LACTWB': 27639, 'CLWB': 27447, 'KSB': 27447, 'CAIB': 27447, 'NAWB': 27447, 'GLB': 27434, 'NHCT': 25165, 'COM': 22897, 'PTT': 22336, 'ALB': 21540, 'VENTRICULARRATE': 20876, 'QRSDURATION': 20876, 'CALCQTCLEKG': 20876, 'QTINTERVAL': 20876, 'ATRIALRATE': 20843, 'CALCULATEDTAXIS': 20811, 'CALCULATEDRAXIS': 20749, 'TP': 18260, 'CALCULATEDPAXIS': 17319, 'PRINTERVAL': 17067, 'BGINST': 14764, 'HBUA': 10434, 'KEUA': 10434, 'NIUA': 10434, 'WEUA': 10434, 'GUA': 10434, 'BIUA': 10434, 'PHUA': 10434, 'SGUA': 10434, 'UROB': 10434, 'PRUA': 10434, 'P060': 10374, 'FIB': 9014, 'LD': 8857, 'CAI': 8742, 'SREQ': 8420, 'DC': 7351, 'TC': 7347, 'DIFC': 7270, 'TRPI': 7028, 'RCU': 7005, 'WCU': 6957, 'CRBB': 6835, 'CB': 6826, 'GRAM': 5983, 'P114': 5467, 'ASUA': 5101, 'TAC': 4889, 'MUCS': 4550, 'URIC': 4472, 'VAN': 4068, 'ID4': 4067, 'SQEU': 3951, 'CRP': 3944, 'TG': 3820, 'DBILI': 3782, 'P095': 3480, 'CMNT1': 3174, 'GGT': 3022, 'P061': 2815, 'CRUR': 2792, 'LIPA': 2732, 'CMNT2': 2723, 'LACT': 2591, 'OSM': 2518, 'SYSTOLICBP': 2342, 'DIASTOLICBP': 2342, 'BNP': 2259, 'SEDUA': 2226, 'PAB': 2217, 'TSH': 2026, 'NAU': 1992, 'RDEU': 1979, 'FERR': 1838, 'TREP': 1786, 'VITD25OH': 1710, 'UFHEP': 1706, 'TPUR': 1680, 'HYALI': 1619, 'CK': 1599, 'FA370': 1558, 'H3370': 1558, 'H1370': 1557, 'AD370': 1557, 'MP370': 1554, 'P3370': 1554, 'P1370': 1553, 'RH370': 1553, 'P2370': 1552, 'FB370': 1551, 'RB370': 1550, 'RA370': 1549, 'HBSAG': 1515, 'UPCR': 1479, 'BLSTA': 1432, 'NRBC': 1383, 'IRON': 1371, 'T328': 1359, 'TRFN': 1350, 'B372': 1345, 'R372': 1345, 'A372': 1345, 'SAT': 1325, 'A1C': 1319, 'OSMU': 1315, 'ESR': 1286, 'FBS': 1266, 'HIVAA1': 1176, 'RETA': 1175, 'ADDI': 1173, 'HBABQ': 1141, 'AFS': 1124, 'LABAMPH': 1103, 'LABBENZ': 1103, 'COCU': 1103, 'BARB': 1102, 'THC': 1102, 'LABOPIA': 1102, 'MTX': 1091, 'P063': 1040, 'LABURIN': 1002, 'CHOL': 948, 'KOH': 923, 'LABFUNG': 921, 'HDL': 881, 'CSB': 879, 'WCB': 876, 'HBF': 875, 'BFT': 875, 'APB': 874, 'COLB': 874, 'VISB': 874, 'RCB': 874, 'CHOLRATIO': 871, 'LDL': 871, 'NONHDL': 871, 'BADI': 870, 'HBCAB': 869, 'MELLP': 865, 'MSCHI': 863, 'NH3': 858, 'MHMB': 853, 'KU': 843, 'PCAI': 833, 'PB': 829, 'LB': 808, 'AMY': 807, 'MPOLY': 795, 'FT4': 789, 'VB12': 786, 'HCV': 749, 'PHGBS': 741, 'CRTSLPL': 658, 'COHB': 650, 'O2HX': 650, 'O2HB': 650, 'MEHB': 650, 'TNDT': 635, 'CMVQT': 633, 'FDD': 610, 'HCSF': 609, 'WCC': 609, 'APPC': 609, 'RCC': 609, 'XANC': 609, 'CSC': 609, 'TUBN': 609, 'CADI': 604, 'LC': 603, 'AMORI': 598, 'MHC': 590, 'GLC': 574, 'RMOR': 571, 'PHGBAM': 565, 'PROTEINCSF': 561, 'MTOXG': 561, 'P049': 560, 'UNUR': 557, 'P284': 548, 'MBURR': 532, 'AMYB': 524, 'P285R': 521, 'ITLS1': 519, 'CBCR': 513, 'CBCPR': 512, 'MDOHL': 505, 'TPBF': 497, 'URICR': 494, 'HAPT': 489, 'TPE': 486, 'PEP': 486, 'GG': 485, 'ALBL': 485, 'PPSK': 485, 'A2G': 485, 'A1G': 485, 'BGL': 485, 'P082': 451, 'ITLS2': 449, 'IMMBFO': 447, 'BKV': 440, 'IGG': 440, 'HCGPA': 437, 'HCGUA': 428, 'CBCMD': 393, 'P076': 393, 'CBCI': 389, 'MTARG': 382, 'KALAR': 376, 'LAMF': 376, 'KAPF': 376, 'LEUPF3': 376, 'PHNY': 375, 'CRB': 374, 'P166': 361, 'P157': 361, 'EBVQT': 350, 'PMNC': 349, 'TOXO': 345, 'MTEAR': 336, 'BDGLU': 330, 'GMAN': 330, 'ANA': 329, 'HRSC': 315, 'TV': 314, 'CLUR': 313, 'UADI': 311, 'MBMU': 304, 'PTH': 303, 'LDB': 303, 'MSPHR': 299, 'T315': 294, 'IGM': 294, 'ALBB': 293, 'OCB': 291, 'CYCL': 288, 'CMVAB': 281, 'ETHYL': 279, 'IGA': 279, 'GRANI': 278, 'MMAS': 278, 'IMMH': 270, 'AFETOPROTEIN': 266, 'MSTIP': 262, 'HCVRNAQUANT': 260, 'HELI': 260, 'HCVRT2': 260, 'CORM': 259, 'COM370': 259, 'VALP': 251, 'QPNIL': 250, 'QPMIT': 249, 'QPFN': 249, 'QPTB1': 249, 'CCDBI': 249, 'P059S': 247, 'BHOB': 243, 'T401': 241, 'HSRF': 238, 'YSTI': 238, 'T319': 233, 'IMMELINT': 229, 'LABACET': 228, 'RPRF': 226, 'REAS': 225, 'RB': 225, 'RI': 225, 'CYETRY': 225, 'ES': 225, 'MC': 225, 'BD': 225, 'LABN': 225, 'GLBF': 223, 'SR2': 222, 'MHYPOG': 219, 'DIG': 219, 'HCVS': 219, 'H2378': 216, 'H1378': 216, 'HAVM': 215, 'ID': 212, 'CUA': 212, 'F8AV': 210, 'T253': 210, 'FT3': 208, 'EVRO': 204, 'LEUPF2': 204, 'TSUP': 200, 'HSR': 200, 'CD4PERCENT': 200, 'TCEL': 200, 'TABS': 197, 'TSAB': 197, 'CD4': 197, 'NAME': 193, 'RESU': 193, 'C4': 186, 'C3': 184, 'COCF': 184, 'CAST': 179, 'P080': 177, 'MICROALB': 169, 'PEUR': 169, 'PPU': 169, 'EOB': 167, 'MA': 163, 'NB': 163, 'BR': 163, 'MK': 163, 'AMYE': 163, 'SFOL': 163, 'AP3A': 163, 'HBVRT1': 160, 'HBVRT2': 160, 'SCA': 159, 'SAL': 158, 'COCC': 155, 'H2377': 154, 'H1377': 154, 'HIVRT2': 152, 'IMMUNO': 152, 'BILTBF': 152, 'HIVRL': 152, 'CCDBMD': 151, 'WT': 150, 'COM378': 150, 'CUD': 150, 'IGGS': 148, 'ALBS': 148, 'ADED': 147, 'EBNA': 147, 'VCAM': 147, 'RF': 146, 'HCYS': 144, 'OLIGB': 143, 'ALBC': 143, 'CSFI': 143, 'PHB': 143, 'IGGC': 143, 'G6PD': 142, 'HHV6': 141, 'OLIGIN': 140, 'OLIGBN': 140, 'VD125': 136, 'SLID': 136, 'ETHL': 134, 'DTGT': 134, 'MTHO': 134, 'ACTO': 134, 'ISPR': 134, 'DNAX': 132, 'SIRO': 132, 'HISTST': 131, 'HISTR': 131, 'HISTIN': 131, 'CEA': 129, 'ACTHP': 129, 'CD34X': 129, 'ACTID': 129, 'MACAN': 128, 'DSDNA': 127, 'PSA': 125, 'PTHPOS': 124, 'TOBG': 120, 'VITEA': 120, 'TGBF': 120, 'CAOXI': 118, 'OTHC': 118, 'CCDCI': 116, 'ACLG': 113, 'C704': 113, 'T339': 113, 'T3': 112, 'DVVTS': 111, 'RVVTI': 111, 'HEXA': 111, 'TSNR': 111, 'DVVCR': 111, 'B2MI': 110, 'G704': 110, 'ESUL': 110, 'EGLU': 110, 'EGLUS': 110, 'DISS': 108, 'DVVN': 108, 'DVVCS': 108, 'CA19': 107, 'VITA': 107, 'ACLM': 105, 'CI03': 105, 'CI04': 105, 'CI05': 105, 'CI01': 105, 'CI02': 105, 'VZMC': 103, 'T413': 103, 'PRUT': 103, 'HTYP': 101, 'HUNIT': 101, 'VZGC': 99, 'MANISO': 98, 'DCB': 96, 'P138': 96, 'C125': 95, 'B2GPG': 95, 'MAXPREDICTEDHEARTRATE': 93, 'MAXWORKLOADMETS10': 93, 'VORIC': 93, 'MAXHEARTRATE': 93, 'MAXSYSTOLICBP': 93, 'TIMEINEXERCISEPHASE': 93, 'MAXDIASTOLICBP': 93, 'PROTOCOLNAME': 93, 'TARGETHRFORMULA': 93, 'B2GPM': 91, 'VOLTX': 90, 'HSFZ': 90, 'SLTGRN': 89, 'HEPAB': 89, 'MHJB': 89, 'CRYU': 88, 'BAB': 88, 'ANAT': 87, 'ANAP': 87, 'LEGAU': 86, 'DINX': 86, 'ZINC': 85, 'SSB': 84, 'ITC1IR': 84, 'RO52': 84, 'RO60': 84, 'P256N': 84, 'COP': 83, 'UNUD': 82, 'PROL': 81, 'GSUB1': 79, 'GSUB2': 79, 'GSUB3': 79, 'GSUB4': 79, 'PTHPRE': 78, 'PRMYA': 78, 'CCDCMD': 78, 'VZI': 77, 'CERU': 74, 'IGE': 74, 'POSAC': 73, 'MPLTG': 72, 'ELAS': 71, 'HTLV12': 70, 'LABIFE': 69, 'TOGM': 68, 'DCP': 68, 'ASMGAB': 68, 'HPAG': 68, 'AFP1': 68, 'AFP2': 68, 'CPEP': 67, 'BCRABL': 67, 'CJF': 66, 'CMVIGM': 65, 'HHV8': 65, 'M7D7Q': 64, 'TRIS8': 64, 'T408': 63, 'DEL20Q': 63, 'M5D5Q': 63, 'URACI': 63, 'T710': 62, 'MCCOM': 61, 'BCEL': 59, 'P320': 59, 'RETMA': 59, 'MNSPOI': 59, 'T338': 58, 'DCC': 57, 'FVR': 57, 'CPRN': 56, 'P063CF': 56, 'ITSPS': 56, 'NKABS': 56, 'BABS': 56, 'PTTR': 56, 'NKCELL': 56, 'CD3TX': 56, 'T380': 56, 'HCVG': 55, 'PRC': 54, 'LIDO': 54, 'CRYQ': 54, 'HCGT': 54, 'ALDO': 53, 'PRSI': 53, 'P287CF': 52, 'LABMETH': 52, 'PHU': 52, 'TR1517': 52, 'AT': 51, 'T290': 51, 'GRH': 50, 'GID': 50, 'AHBE': 50, 'ATPO': 50, 'RUBI': 50, 'FLT3': 49, 'BUNPST': 49, 'TOBTH': 49, 'EOC': 48, 'MLLQ23': 48, 'HBE': 48, 'MPOPF': 47, 'TDTPF': 47, 'MROUL': 46, 'ITPRA2': 46, 'TR821': 45, 'REN': 45, 'INV16Q': 45, 'ENCES': 44, 'ITPRA1': 44, 'ISR': 43, 'VIB1': 43, 'VDRL': 42, 'TOBRN': 42, 'ENCEC': 42, 'TGAB': 42, 'LACS': 42, 'CELCI': 41, 'CMVMIS': 41, 'SPTP': 41, 'A1AT': 41, 'TTRP': 40, 'TXDX': 40, 'PDTP': 40, 'BGNUM': 40, 'TXTP': 40, 'ACE': 40, 'T221': 40, 'RBCML': 40, 'VOLTT': 40, 'DTINF': 40, 'TNCV': 39, 'HSTO': 39, 'MAUER': 38, 'TRANSGLU': 38, 'BHIGS': 38, 'BQIGS': 38, 'BKVU': 38, 'MHYPO': 38, 'BHIMS': 38, 'BQIMS': 38, 'LI': 37, 'CREA': 37, 'PTHR': 37, 'UNCL': 37, 'T407': 37, 'HT': 37, 'CORC': 37, 'MPIG': 37, 'ITC2IR': 37, 'HHV7': 37, 'PHNYF': 37, 'MPIGM': 37, 'CYCP': 36, 'T346': 35, 'TNCI': 35, 'SM': 35, 'PTHPS': 35, 'TXDRID': 35, 'RNP': 35, 'MITOAB': 35, 'RPRT': 34, 'LYMET': 34, 'JAK2': 33, 'F10AV': 33, 'CRTFS': 33, 'ITCPP': 33, 'STRONG': 33, 'CD34D': 33, 'BRUM': 32, 'BRUG': 32, 'DIN': 32, 'TRIPI': 32, 'ACD34': 32, 'BFRZEN': 32, 'TT4': 31, 'AIGF1': 31, 'CAUR': 31, 'HEVM': 31, 'ITSEA': 31, 'ITSEB': 31, 'ITSED': 31, 'PBAR': 31, 'ITDQB': 31, 'P19M': 31, 'P19G': 31, 'T365': 30, 'P118': 30, 'TTES': 30, 'ESRX': 30, 'TXCOM': 30, 'ITSEC': 30, 'ITTXFR': 30, 'HBAGC': 29, 'BAC': 29, 'HBEV': 28, 'HDRT': 28, 'CARR': 28, 'CARF': 28, 'VIB6': 28, 'CARNT': 28, 'ASO': 28, 'I2RS': 28, 'P288': 28, 'C3DS': 27, 'CH50R': 27, 'CD3PC': 26, 'HCVGPF': 26, 'HGBA': 26, 'HGBF': 26, 'PBDON': 26, 'EBVMIS': 26, 'PVOL': 26, 'FSH': 26, 'FC': 26, 'HGA2': 26, 'LH': 25, 'CARI': 25, 'PO4UR': 25, 'MGUR': 24, 'TCRZ': 24, 'STRTX': 24, 'HBAGSO': 24, 'SYPSO': 24, 'WNVN': 24, 'HBCSO': 24, 'HTLVSO': 24, 'HICFM': 24, 'HICFY': 24, 'BSLIDX': 24, 'CLOP': 24, 'HCVSO': 24, 'NATT': 24, 'ADAPL': 24, 'QFIG': 24, 'QFIM': 24, 'TPMTA': 24, 'QFIIG': 24, 'QFIIM': 24, 'CMVSO': 24, 'HIV2SO': 24, 'TR1114': 23, 'PBDONX': 23, 'MUMG': 23, 'FRPSA': 23, 'HB1AM': 23, 'P159': 23, 'DEL17P': 23, 'PFPSA': 23, 'ARU': 23, 'DEL13Q': 23, 'CGL': 23, 'MEAI': 22, 'T356': 21, 'NMO': 21, 'T333': 21, 'MYS19': 21, 'MYS18': 21, 'MYS12': 21, 'MYS11': 21, 'MYS10': 21, 'MYS17': 21, 'MYS16': 21, 'MYS15': 21, 'MYS14': 21, 'MYS1A': 21, 'MHYPS': 21, 'VITK': 21, 'MYS3': 21, 'MYS13': 21, 'MYS9': 21, 'MYS8': 21, 'MYS7': 21, 'MYS6': 21, 'MYS5': 21, 'MYS4': 21, 'MYS2': 21, 'MYS1': 21, 'PBWB': 20, 'LPPGMP': 20, 'SELEN': 20, 'BCL2': 20, 'MPAPPE': 20, 'TPPA': 20, 'GLUR': 20, 'LAMI': 19, 'JCV': 19, 'FTES': 19, 'PBQTR': 19, 'PMLQNT': 19, 'BCL6': 19, 'MYC': 19, 'VWFAG': 19, 'ITTXFD': 19, 'LABACHR': 19, 'LABERYT': 19, 'PBRUI': 19, 'AGBM': 19, 'ALDOLASE': 18, 'ACCN': 18, 'CAURD': 18, 'TCOM': 18, 'SCL70': 18, 'TQ': 18, 'MONO': 18, 'CD138': 18, 'NKCF': 18, 'RCOF': 17, 'F2AV': 17, 'F9AV': 17, 'T414': 17, 'MRDFC': 17, 'VISC': 17, 'UAUR': 17, 'FISHBCR2': 17, 'TR1416': 17, 'DUP1Q': 17, 'GENTH': 17, 'PBNP': 17, 'TR414': 17, 'HIV2': 16, 'HIV1': 16, 'COAGI': 16, 'LABLEGI': 16, 'HDV': 16, 'CEGFR': 16, 'CYSTAT': 16, 'P258': 15, 'GENPK': 15, 'ADVP': 15, 'ITNH3': 15, 'CD34T': 15, 'PMLR': 14, 'GQ1B': 14, 'GM1M': 14, 'GM1G': 14, 'ITNH19': 14, 'CHRO': 14, 'MTR': 14, 'CGA': 14, 'HSV2': 13, 'METPF': 13, 'HSV1': 13, 'GAST': 13, 'T403': 13, 'PT12': 13, 'PT14': 13, 'PT19': 13, 'PT8': 13, 'PT5': 13, 'PT4': 13, 'PT3': 13, 'PT1': 13, 'KDSQ': 13, 'BLAS': 13, 'PT26': 13, 'PT23': 13, 'PT9': 13, 'OMOR': 13, 'AAQTS': 13, 'CZP': 13, 'T116': 13, 'LEV': 13, 'IFX1': 13, 'IFX3': 13, 'IFX2': 13, 'KST': 13, 'PHF': 13, 'RFOL': 13, 'TRIGX': 13, 'NMETPF': 13, 'METTF': 13, 'ACYLP': 13, 'ITNH14': 13, 'PT56': 13, 'PT51': 13, 'PT68': 13, 'PT70': 12, 'PT17': 12, 'PT2': 12, 'TET': 12, 'PT22': 12, 'PT20': 12, 'FLAV': 12, 'MN': 12, 'PT34': 12, 'TGLU': 12, 'RSST': 12, 'PT54': 12, 'HHEM': 12, 'TOBPK': 12, 'P259Y': 12, 'AFUM': 12, 'NASTL': 12, 'ASPN': 12, 'PT43': 12, 'CRRU': 12, 'PT57': 12, 'GAD': 12, 'CD34': 11, 'LPMG': 11, 'META': 11, 'HG': 11, 'METAN': 11, 'TFA': 11, 'NMET': 11, 'BANDA': 11, 'INSN': 11, 'T337': 11, 'F5AV': 11, 'F7AV': 11, 'TT': 11, 'GD1AM': 11, 'NPYR': 11, 'OSMST': 10, 'CA15': 10, 'LANT': 10, 'HSV2M': 10, 'AAQU': 10, 'BSAP': 10, 'KLU': 10, 'GD1A': 10, '5HIAA': 10, 'LKM': 10, 'COAGMD': 10, 'KLUR': 10, 'TSI': 10, 'ITTBCX': 10, 'HSV1M': 10, 'LLU': 10, 'CALR': 9, 'TPMTGN': 9, 'PAE': 9, 'INHS': 9, 'TCRB': 9, 'TCRG': 9, 'PRINS': 9, 'DPTT0': 9, 'DPTT2': 9, 'ALAQR': 9, 'SCHISS': 9, 'PPTT0': 9, 'SNPAT': 9, 'CPTT0': 9, 'PTTI': 9, 'BILAT': 9, 'F11AV': 9, 'ADAPT': 9, 'MIX2': 9, 'MIX0': 9, 'LEPTM': 8, 'ZNT8': 8, 'PDGFRA': 8, 'HGBS': 8, 'ALCO': 8, 'ASCA': 8, 'RACD4': 8, 'RACD8': 8, 'INHNB': 8, 'PNHM': 8, 'MOGFS': 8, 'COTINU': 8, 'LACTOF': 8, 'ROCD4': 8, 'ROCD8': 8, 'T412': 8, 'HVRLC': 8, 'WHIPP': 8, 'OXCBP': 8, 'P259A': 8, 'MSK1': 8, 'MSK3': 8, 'MSK2': 8, 'MSK5': 8, 'MSK4': 8, 'ROCD8A': 8, 'P158': 8, 'AMOEB': 8, 'FAT': 8, 'BHCTM': 8, 'TXPCR': 8, 'ROCD4A': 8, 'HVRCC': 8, 'LPA': 8, 'RACD8A': 8, 'BP180': 8, 'BP230': 8, 'NICOTU': 8, 'RACD4A': 8, 'B2TAU': 8, 'ETGL': 7, 'TCAB': 7, 'TNCTX': 7, 'TNCT': 7, 'SLAV': 7, 'GLA': 7, 'GLG': 7, 'A1AF': 7, 'LDCF': 7, 'ICA': 7, 'B2GA': 7, 'CMVG': 7, 'URRP': 7, 'AMP05': 7, 'AMP04': 7, 'AMP03': 7, 'AMP02': 7, 'AMP01': 7, 'NAUD': 7, 'IGD': 7, 'TNCKG': 7, 'KTV': 7, 'LABCA2': 7, 'WTF': 7, 'GPCA': 7, 'ACLA': 6, 'BUSAR3': 6, 'BUSAR2': 6, 'BUSAR1': 6, 'WNCG': 6, 'WNCM': 6, 'BUSTT': 6, 'IGF2': 6, 'BUSAR4': 6, 'NICU': 6, 'BUSUD': 6, 'MPA': 6, 'BUSDN': 6, 'ITCOM': 6, 'F8PF': 6, 'PTA': 6, 'AUC': 6, 'PUD': 6, 'BUSTDA': 6, 'TRIS12': 6, 'STARTT': 6, 'TOTAL': 6, 'C1R': 6, 'SNPAB': 6, 'NMDA': 6, 'HCGUPE': 6, 'DOPU': 6, 'BUWT': 6, 'MPAG': 6, 'OAX': 6, 'STOPTD': 6, 'DEL11Q': 6, 'SNEUTA': 6, 'BMDON': 6, 'C1QQ': 6, 'BMNUM': 6, 'CAGG': 6, 'NETU': 6, 'FCRT': 6, 'EPTU': 6, 'IFBA': 6, 'NICOTA': 6, 'NICOTN': 6, 'C1EQ': 6, 'MEGA': 5, 'MA24': 5, 'CCBD': 5, 'PBNUM': 5, 'PDGFRB': 5, 'TSCSF': 5, 'ADA13': 5, 'INHNA': 5, 'MDREP': 5, 'CRYF': 5, 'MAGM': 5, 'WCBD': 5, 'HT1WB': 5, 'LABGCC': 5, 'P113': 5, 'IGERES': 5, 'BCBD': 5, 'TSABS': 5, 'PPEP': 5, 'CRYGL': 5, 'NMDC': 5, 'ATPN': 5, 'CLST': 5, 'BMSRC': 5, 'AMIKP': 5, 'P033': 5, 'TULA': 5, 'GLGN': 5, 'PSAS': 5, 'BMVOL': 5, 'BFLT': 5, 'QUIND': 5, 'ABTAU': 5, 'IL28B': 5, 'GLUD': 5, 'PSACT': 5, 'METANR': 4, 'TOPA': 4, 'LD39': 4, 'LD30': 4, 'E2P': 4, 'SOURCE': 4, 'LD39M': 4, 'LD41': 4, 'LD45': 4, 'HIV1T': 4, 'CALCI': 4, 'LD58': 4, 'EHAG': 4, 'NSECSF': 4, 'CTID': 4, 'PTI': 4, 'HPVGO': 4, 'HSCRP': 4, 'IGGWB': 4, 'METAR': 4, 'LD66': 4, 'NOIR': 4, 'ITNH2': 4, 'NOII': 4, 'LDABMB': 4, 'PCPT0': 4, 'PCPT2': 4, 'T3RV': 4, 'LDABG': 4, 'LDABM': 4, 'LEISB': 4, 'FGFR1': 4, 'LISTB': 4, 'LDABGB': 4, 'NMETR': 4, 'IL6': 4, 'PPT0': 4, 'CPT0': 4, 'OPIQ10': 4, 'LD93': 4, 'IT5801': 4, '2C19G': 4, 'DCPT0': 4, 'DCPT2': 4, 'RNAP': 4, 'C3NEP': 4, 'PYRUC': 4, 'FIT': 4, 'UAUD': 4, 'OPIQT9': 4, 'OPIQT8': 4, 'OPIQT1': 4, 'OPIQT3': 4, 'OPIQT2': 4, 'OPIQT5': 4, 'OPIQT4': 4, 'OPIQT7': 4, 'OPIQT6': 4, 'AMIKT': 4, 'NOIPF': 4, 'IGMWB': 4, 'LD41M': 4, 'LD23M': 4, 'IT5701': 4, 'VIP': 4, 'MUD': 4, 'MMMP': 4, 'LD18': 4, 'MERCURL': 4, 'VLCF': 4, 'LD23': 4, 'LD28': 4, 'ABLA': 4, 'HPVG16': 4, 'HPVG18': 4, 'WBCCI': 4, 'VOLS3': 3, 'VOLS2': 3, 'VOLS1': 3, 'VOLS4': 3, 'NKCEL': 3, 'JCAB': 3, 'RVX': 3, 'AGSQ': 3, 'PBU': 3, 'WNSG': 3, 'WNSM': 3, 'HEXAPF': 3, 'SERO': 3, 'QTFN': 3, 'FRUT': 3, 'RMSFG': 3, 'RMSFM': 3, 'CPNII': 3, 'VWMULT': 3, 'CFUKG': 3, 'COP1': 3, 'COPU': 3, 'BHIGT': 3, 'NSE': 3, 'VIB2': 3, 'RTYPG': 3, 'RTYPM': 3, 'NABF': 3, 'TXCG': 3, 'KBF': 3, 'SR': 3, '6TG': 3, 'ADAC': 3, 'CPNM': 3, 'CPNA': 3, 'CPNG': 3, 'T715': 3, 'HIST': 3, '6MMP': 3, 'BMNUMX': 3, 'BMVOLX': 3, 'HDVR': 3, 'SGOLD': 3, 'VMAU': 3, '21HA': 3, 'VCAA': 3, 'TR1221': 2, 'MA2R': 2, 'CRTSV': 2, 'PH37G': 2, 'FTAC': 2, 'QFTNIL': 2, 'CTX': 2, 'CDIGMP': 2, 'WAXYI': 2, 'PTHID': 2, 'T355': 2, 'CD21N': 2, 'CD21P': 2, 'MMMPM': 2, 'P257': 2, 'F13A': 2, 'BTHL': 2, 'HMGCR': 2, 'OGTD': 2, 'CD20P': 2, 'DSG3': 2, 'DSG1': 2, 'FOBT': 2, 'ITNH56': 2, 'T409': 2, 'RESUL': 2, 'MA1R': 2, 'ASDN': 2, 'AVP': 2, '27MNDN': 2, 'ECHINO': 2, 'WEEI': 2, 'WEEM': 2, 'T416': 2, 'FLECNDE': 2, 'CEAPC': 2, 'SGRN': 2, 'SAO2G': 2, 'PO2GA': 2, 'NRC': 2, 'AFPCSF': 2, 'CLUD': 2, 'FONDA': 2, 'INST': 2, 'IC': 2, 'BEXG': 2, 'PCO2GA': 2, 'ITTXCD': 2, 'SNPAP': 2, 'ITTXCR': 2, 'MACOM': 2, 'HCO3G': 2, 'LPP2A': 2, 'QFTMIT': 2, 'CD19P': 2, 'TESTSO': 2, 'TRICG': 2, '27MPDP': 2, '27MPDN': 2, 'QFTTB': 2, 'PTES': 2, 'TXCM': 2, 'ITENDX': 2, 'CHIMS': 2, 'WEEG': 2, 'CD38MN': 2, 'ITB27': 2, 'CD38MP': 2, 'P257N': 2, 'HISTO': 2, 'LEUMPF': 2, 'ICAB': 2, 'PMSCL': 2, 'BHCGC': 2, 'CD27P': 2, 'HCGPP': 2, 'PTHBF': 2, 'INV3Q': 2, 'DFM': 2, 'COM372': 2, 'KUD': 2, 'AGALT': 2, 'NMHIN': 2, 'SITE': 2, 'DFG': 2, 'MRDMM': 2, 'ITDPB': 2, 'LYSO': 2, 'MATAIN': 2, 'UNB': 2, 'ITNH1': 2, 'AGAL': 2, 'MBL': 2, 'ATHL': 2, 'AMYUR': 2, 'CUI': 2, 'CHIGS': 2, 'AGLY': 1, '3GT120': 1, 'LS09': 1, 'LS08': 1, 'LS01': 1, 'LS07': 1, 'LS06': 1, 'LS05': 1, 'LS04': 1, 'CA1A': 1, 'GTLEXF': 1, 'SCIDA': 1, 'MCFM': 1, 'PL12': 1, 'TR119': 1, 'DLS': 1, 'WARFN': 1, 'DIUS': 1, 'HGBC': 1, 'FA1A': 1, 'BUM1': 1, 'BUM2': 1, 'BUM4': 1, 'BUM5': 1, 'GLFP2H': 1, 'JO1AB': 1, 'CYF': 1, 'LEGAB': 1, 'ITBXCR': 1, 'PERX': 1, 'CD3T': 1, 'HBVNAC': 1, 'OROT': 1, 'NEUAB': 1, 'CEP4': 1, 'CO': 1, 'SNPES': 1, 'TCAN': 1, 'T404': 1, 'LS20': 1, 'NHGB': 1, 'KUAB': 1, 'TOXOC': 1, 'GLT1': 1, 'REPT': 1, 'THTO': 1, 'P417': 1, 'DHES': 1, 'CTUR': 1, 'GENRN': 1, 'NRT': 1, 'KITD': 1, 'OCAL': 1, 'PCE': 1, 'SLOUI': 1, 'SLOUM': 1, 'SLOUG': 1, 'DOD': 1, 'LS03': 1, 'LS02': 1, 'DLDON': 1, 'SPCHEM': 1, 'MFAE': 1, 'CTL2A': 1, 'AFAFP': 1, 'NTEL': 1, 'DINO': 1, 'EJAB': 1, 'ISA': 1, 'PBU1': 1, 'CLB': 1, 'IT1502': 1, 'ASFU': 1, '17HP': 1, 'BPA': 1, 'BPG': 1, 'MULT': 1, 'FANC': 1, 'MI2AB': 1, 'A2GU': 1, 'RIBP': 1, 'RVVTPF': 1, 'APTT': 1, 'FHAG': 1, 'FHAF': 1, 'FLUA': 1, 'FLUC': 1, 'CYFST': 1, 'TR814': 1, 'U3RNP': 1, 'SCO1B1': 1, 'ACRB': 1, 'ACRM': 1, 'THEO': 1, '3GT180': 1, 'EPI': 1, 'VDRLT': 1, 'LY1': 1, 'LY2': 1, 'MUMM': 1, 'F9PF': 1, 'F12A': 1, 'FLUB': 1, 'XYL1': 1, 'CLOZA': 1, 'LABGENI': 1, 'SA1A': 1, 'THAL1': 1, 'GT120': 1, 'CTL2M': 1, 'CTL2I': 1, 'CTL2G': 1, 'HGU1': 1, 'IODUR': 1, 'CATT': 1, 'TVUL': 1, 'PLTM': 1, 'GT60': 1, 'ITRAC': 1, 'LEPN': 1, 'PGH': 1, 'TRIPZ': 1, 'FHAA': 1, 'AH50A': 1, 'MI2': 1, 'ADA1': 1, 'ADA2': 1, 'ADA3': 1, '3GT60': 1, 'AMID': 1, 'GALT': 1, 'AVNS': 1, 'BDEL': 1, 'CADH': 1, 'PBQT': 1, 'TR1418': 1, 'MPSTLC': 1, 'GES': 1, 'PB24I': 1, 'CEP10': 1, 'OJAB': 1, 'SRPAB': 1, 'SVIR': 1, 'BGLU': 1, 'HITRA': 1, 'RBCCI': 1, 'FXCH': 1, 'HCNAC': 1, 'AADG': 1, 'ACHE': 1, 'ALDS': 1, '5HQTRU': 1, 'GLFP': 1, 'BGSQ': 1, 'FFN': 1, 'ITDQA': 1, 'NT4': 1, 'CYAN': 1, 'GGU': 1, 'NREP': 1, 'ALD': 1, 'IGVHMR': 1, 'BHCT': 1, 'CTDKA': 1, 'CTDKG': 1, 'ABU': 1, 'CTDKI': 1, 'CTDKM': 1, 'NORCZ': 1, 'ALUM': 1, 'F11PF': 1, 'ASU': 1, 'ASB': 1, 'IGVHS': 1, 'AAP': 1, 'TBII': 1, 'CPSIG': 1, 'CPSIA': 1, 'CPSIN': 1, 'CPSIM': 1, 'CPSII': 1, 'HINAC': 1, 'HVAR1': 1, 'PL7': 1, 'DAMI': 1, 'DPMN': 1, 'OXYU': 1, '23BPG': 1, 'CPNMA': 1, 'ACYL': 1, 'CPNMG': 1, 'CPNMI': 1, 'CPNMM': 1, 'A1GU': 1, 'CPSM': 1, 'CPSG': 1, 'CPSA': 1, 'FACTH': 1, 'SPECID': 1, 'IPFP': 1, 'ICF': 1, 'OXALP': 1, 'ALAD': 1, 'BIOD': 1, 'LS18': 1, 'LS19': 1, 'LS10': 1, 'LS11': 1, 'LS12': 1, 'LS13': 1, 'LS14': 1, 'LS15': 1, 'LS16': 1, 'LS17': 1, 'HSV2MT': 1}
+
+def show_top_volume_panels():
+    data_filepath = os.path.join(data_folderpath, 'summary-stats-bestalg-fixTrainPPV.csv')
+    df = pd.read_csv(data_filepath, keep_default_na=False)
+
+    # TODO: does tot_vol include 2014_1stHalf?
+    if '2014 1stHalf count' in df.columns.values.tolist():
+        df['total_cnt'] = df[[x+' count' for x in stats_utils.DEFAULT_TIMEWINDOWS[1:]]].sum(axis=1)
+        df.drop('2014 1stHalf count', axis=1).to_csv(data_filepath, index=False)
+
+    top_panels_and_cnts_by_vol =  df[['lab', 'total_cnt']].drop_duplicates().sort_values('total_cnt', ascending=False).values.tolist()
+    print(', '.join(str(x) for x in top_panels_and_cnts_by_vol))
+
+def show_top_avoided_volume_panels(target_PPV=0.95):
+    data_filepath = os.path.join(data_folderpath, 'summary-stats-bestalg-fixTrainPPV.csv')
+    df = pd.read_csv(data_filepath, keep_default_na=False)
+
+    # TODO: does tot_vol include 2014_1stHalf?
+    if '2014 1stHalf count' in df.columns.values.tolist():
+        df['total_cnt'] = df[[x + ' count' for x in stats_utils.DEFAULT_TIMEWINDOWS[1:]]].sum(axis=1)
+        df.drop('2014 1stHalf count', axis=1).to_csv(data_filepath, index=False)
+
+    df = df[df['targeted_PPV_fixTrainPPV']==target_PPV]
+    df['avoided_cnt'] = (df['true_positive']+df['false_positive']) * df['total_cnt']
+    top_panels_and_cnts_by_avoidable_vol = df[['lab', 'avoided_cnt']].drop_duplicates().sort_values('avoided_cnt',
+                                                                                        ascending=False).values.tolist()
+    print(', '.join(str(x) for x in top_panels_and_cnts_by_avoidable_vol))
+
+def show_top_order_in_1day_panels():
+    data_filepath = os.path.join(data_folderpath, 'Fig2_Order_Intensities', 'Order_Intensities_panel.csv')
+    df = pd.read_csv(data_filepath, keep_default_na=False)
+    top_panels_and_cnts_in_1day = df[['lab', '< 1 day']].drop_duplicates().sort_values('< 1 day', ascending=False).values.tolist()
+    print(', '.join(str(x) for x in top_panels_and_cnts_in_1day))
+
+def show_top_order_in_1to3day_panels():
+    data_filepath = os.path.join(data_folderpath, 'Fig2_Order_Intensities', 'Order_Intensities_panel.csv')
+    df = pd.read_csv(data_filepath, keep_default_na=False)
+    top_panels_and_cnts_in_1to3days = df[['lab', '1-3 days']].drop_duplicates().sort_values('1-3 days',
+                                                                                       ascending=False).values.tolist()
+    print(', '.join(str(x) for x in top_panels_and_cnts_in_1to3days))
+
+if __name__ == '__main__':
+    show_top_order_in_1day_panels()
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/lab_statistics/stats_utils.py b/scripts/Archive/LabTestAnalysis/lab_statistics/stats_utils.py
new file mode 100644
index 00000000..2b1b570b
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/stats_utils.py
@@ -0,0 +1,1592 @@
+
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+import datetime, collections
+from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, average_precision_score
+import pandas as pd
+pd.set_option('display.width', 500)
+pd.set_option('display.max_columns', 500)
+import numpy as np
+from scipy import stats
+import os, sys
+
+import LocalEnv
+
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+
+from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline import NON_PANEL_TESTS_WITH_GT_500_ORDERS, STRIDE_COMPONENT_TESTS
+
+'''
+For each lab, get a bunch of stuff
+'''
+
+'''
+For plotting guideline,
+
+a lab, has n prev consecutive normal. 
+'''
+
+
+DEFAULT_TIMELIMIT = ('2014-07-01', '2017-07-01') # TODO: extremely confusing!
+# DEFAULT_TIMESPAN = ('2014-01-01', '2017-06-30') # TODO: extremely confusing!
+
+# DEFAULT_TIMEWINDOWS = [#'2014 1stHalf',
+#                        '2014 2stHalf',
+#                        '2015 1stHalf', '2015 2stHalf',
+#                        '2016 1stHalf', '2016 2stHalf',
+#                        '2017 1stHalf']
+
+'''
+Meta-data in the last 3 years
+'''
+
+'''
+select count(distinct pat_id) from stride_order_proc where order_time >= '2014-07-01' and order_time < '2017-07-01';
+'''
+NUM_DISTINCT_PATS = 44710
+
+'''
+select count(distinct pat_enc_csn_id) from stride_order_proc where order_time >= '2014-07-01' and order_time < '2017-07-01';
+'''
+NUM_DISTINCT_ENCS = 66440
+
+NUM_DISTINCT_ENCS_UCSF = 15989
+
+'''
+select count(distinct order_proc_id) from stride_order_proc where proc_code='LABMGN' and order_status='Completed' and order_time >= '2014-07-01' and order_time < '2017-07-01';
+'''
+NUM_MAGNESIUM_COMPLETED_ORDERS = 282414
+
+umich_lab_cnt = {'WBC':5280.99347210938,
+                'HGB':5281.00748045835,
+                'PLT':5274.22743955397,
+                'SOD':5784.07530888409,
+                'POT':5784.06130053512,
+                'CR':5784.04729218614,
+                'TBIL':1662.90309024178,
+                'CHLOR':5784.07530888409,
+                'CO2':5784.04729218614,
+                'AST':1667.87605412826,
+                'ALB':2239.66884263021,
+                'CAL':5791.51374219035,
+                # 'PCO2AA':,
+                # 'PO2AA':,
+                # 'DBIL':,
+                # 'pHA':,
+                'PROT':1667.87605412826,
+                'ALK':1667.87605412826,
+                'UN':5784.04729218614,
+                # 'IBIL':
+                'CREAT': 5784.04729218614,
+                'ALT': 1667.87605412826
+                }
+
+# DEFAULT_TIMELIMITS = []
+# for time_window in DEFAULT_TIMEWINDOWS:
+#     year_str, section_str = time_window.split(' ')
+#
+#     if section_str == '1stHalf':
+#         section_timestamps = ('01-01', '06-30')
+#     else:
+#         section_timestamps = ('07-01', '12-31')
+#
+#     time_limit = ['-'.join([year_str, x]) for x in section_timestamps]
+#
+#     DEFAULT_TIMELIMITS.append(time_limit)
+
+
+
+
+
+# labs_ml_folder = os.path.join(main_folder, 'machine_learning/data-%ss-%s/'%(lab_type, curr_version))
+# labs_stats_folder = os.path.join(main_folder, 'lab_statistics/stats-%ss-%s/'%(lab_type, curr_version))
+
+
+
+# if not os.path.exists(labs_folder):
+#     os.mkdir(labs_folder)
+# if not os.path.exists(labs_stats_folder):
+#     os.mkdir(labs_stats_folder)
+import matplotlib
+# matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+
+from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline \
+        import NON_PANEL_TESTS_WITH_GT_500_ORDERS, \
+            UCSF_TOP_PANELS, UMICH_TOP_PANELS, \
+            STRIDE_COMPONENT_TESTS_common, UMICH_TOP_COMPONENTS_common, UCSF_TOP_COMPONENTS_common
+
+def get_all_labs(data_source, lab_type):
+    if data_source == 'Stanford' and lab_type == 'panel':
+        all_labs = NON_PANEL_TESTS_WITH_GT_500_ORDERS  #
+    elif data_source == 'Stanford' and lab_type == 'component':
+        all_labs = STRIDE_COMPONENT_TESTS_common  #
+    elif data_source == 'UMich' and lab_type == 'panel':
+        all_labs = UMICH_TOP_PANELS
+    elif data_source == 'UMich' and lab_type == 'component':
+        all_labs = UMICH_TOP_COMPONENTS_common  #
+    elif data_source == 'UCSF' and lab_type == 'component':
+        all_labs = UCSF_TOP_COMPONENTS_common  #
+    elif data_source == 'UCSF' and lab_type == 'panel':
+        all_labs = UCSF_TOP_PANELS
+    return all_labs
+
+def prepare_subfigs(num_figs, col = 5):
+
+    row = num_figs / col
+    cols_left = num_figs % col
+    if cols_left > 0:
+        row = row + 1
+
+    fig_width, fig_heights = 2.1 * col, 2.1 * row #8. / col * row
+
+    plt.figure(figsize=(fig_width, fig_heights))
+
+    i_s = []
+    j_s = []
+    for ind in range(num_figs):
+        ind_in_fig = ind % num_figs
+        i, j = ind_in_fig / col, ind_in_fig % col
+        i_s.append(i)
+        j_s.append(j)
+
+    return row, col, i_s, j_s
+
+def get_prevday2normalities(lab, mlByLab_folder, time_limit=DEFAULT_TIMELIMIT, source='full'):
+    '''
+    Why: Plot figure
+
+    What: How consecutive normality indicates the next normality?
+
+    How:
+
+    Args:
+        lab:
+        time_limit:
+
+    Returns:
+
+    '''
+
+    if source == 'full':
+        df_lab = query_to_dataframe(lab, time_limit=time_limit)
+        df_lab = df_lab[df_lab['order_status']=='Completed'].reset_index(drop=True)
+
+    else:
+        import LocalEnv
+        from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+
+        print("processing %s..."%lab)
+        # data_folder = LocalEnv.PATH_TO_CDSS + '/scripts/LabTestAnalysis/machine_learning/data-panels/%s/' % lab
+        '''
+        First, obtain test patients
+        '''
+        data_processed_filename = '%s-normality-%s-matrix-processed.tab' % (lab, source)
+        data_processed_pathname = os.path.join(mlByLab_folder, lab, data_processed_filename)
+
+        fm_io = FeatureMatrixIO()
+        df_processed_test_lab = fm_io.read_file_to_data_frame(data_processed_pathname)
+        # print df_processed_test_lab.head()
+        # quit()
+        pat_ids_test = set(df_processed_test_lab['pat_id'].values.tolist())
+
+
+        '''
+        Then, obtain
+        '''
+
+        data_raw_filename = '%s-normality-matrix-raw.tab' % lab
+        data_raw_pathname = os.path.join(mlByLab_folder, lab, data_raw_filename)
+
+        df_raw = fm_io.read_file_to_data_frame(data_raw_pathname)
+        df_lab = df_raw[df_raw['pat_id'].isin(pat_ids_test)]
+        df_lab['abnormal_yn'] = df_lab['abnormal_panel'].apply(lambda x: 'Y' if x==1 else 'N')
+        df_lab = df_lab[['pat_id', 'order_time', 'abnormal_yn']]
+
+    day2norms = get_prevweek_normal__dict(df_lab, also_get_cnt=True)
+
+    return list(day2norms.keys()), list(day2norms.values())
+
+def check_baseline2(lab, mlByLab_folder, source="train", target_PPV=0.95, picked_prevalence=None, picked_thres=None):
+    '''
+    Pick a threshold
+
+    Args:
+        lab:
+        mlByLab_folder:
+        source:
+        target_PPV:
+
+    Returns:
+
+    '''
+    data_processed_filename = '%s-normality-%s-matrix-processed.tab' % (lab, source)
+    data_processed_pathname = os.path.join(mlByLab_folder, lab, data_processed_filename)
+
+    fm_io = FeatureMatrixIO()
+    processed_matrix = fm_io.read_file_to_data_frame(data_processed_pathname)
+    pat_ids = set(processed_matrix['pat_id'].values.tolist())
+
+    data_raw_filename = '%s-normality-matrix-raw.tab' % lab
+    data_raw_pathname = os.path.join(mlByLab_folder, lab, data_raw_filename) # TODO: create template
+    raw_matrix = fm_io.read_file_to_data_frame(data_raw_pathname)
+
+
+    df_lab = raw_matrix[raw_matrix['pat_id'].isin(pat_ids)].reset_index()[['pat_id', 'order_time', 'all_components_normal']]
+
+    df_lab = df_lab.sort_values(['pat_id', 'order_time'])
+
+
+
+    if not picked_prevalence:
+        normal_prevalence = df_lab['all_components_normal'].values.sum()/float(df_lab.shape[0])
+
+    else:
+        df_lab['predict_proba'] = df_lab['all_components_normal'].apply(lambda x: picked_prevalence)
+
+    for i in range(1, df_lab.shape[0]):
+        if df_lab.ix[i-1, 'pat_id'] == df_lab.ix[i, 'pat_id']:
+            df_lab.ix[i, 'predict_proba'] = df_lab.ix[i-1, 'all_components_normal']
+
+    if not picked_thres:
+
+        thres_possibles = [0, normal_prevalence, 1]
+
+        thres = -1
+        for thres_one in thres_possibles:
+            df_lab['predict_label'] = df_lab['predict_proba'].apply(lambda x: 1 if x>=thres_one else 0)
+
+            predicted_normals = df_lab[df_lab['predict_label']==1].shape[0]
+            true_normals = df_lab[(df_lab['predict_label']==1) & (df_lab['all_components_normal']==1)].shape[0]
+            PPV = float(true_normals)/float(predicted_normals)
+            if PPV >= target_PPV:
+                thres = thres_one
+
+        return normal_prevalence, thres
+
+
+    else:
+
+        df_lab['predict_label'] = df_lab['predict_proba'].apply(lambda x: 1 if x >= picked_thres else 0)
+        predicted_normals = df_lab[df_lab['predict_label'] == 1].shape[0]
+        saved_fraction = float(predicted_normals)/float(df_lab.shape[0])
+        return saved_fraction
+
+
+def plot_subfigs(dicts, colors=('blue','orange'), result_figpath="subfigs.png"):
+
+    num_labs = len(dicts[0])
+
+    row, col, i_s, j_s = prepare_subfigs(num_labs, col=6)
+
+
+    keys = list(dicts[0].keys())
+
+    def do_one_plot(x, y, color):
+        plt.bar(x, y, color=color)
+        plt.text(x, y, '%.2f' % y, color='k')
+
+        plt.xticks([])
+        plt.ylim(0, 1.05)
+        plt.yticks([])
+
+    lab_descriptions = get_lab_descriptions()
+    for ind, key in enumerate(keys):
+
+
+        i, j = i_s[ind], j_s[ind]
+        plt.subplot2grid((row, col), (i, j))
+
+        for k in range(len(dicts)):
+            do_one_plot(k, dicts[k][key], color=colors[k])
+
+        plt.xlabel(lab_descriptions[key])
+
+    plt.savefig(result_figpath)
+
+
+def check_similar_components():
+    # common_labs = list(set(STRIDE_COMPONENT_TESTS) & set(UMICH_TOP_COMPONENTS))
+
+    df_UMich = pd.read_csv('RF_important_features_UMichs.csv',keep_default_na=False)
+    df_component = pd.read_csv('RF_important_features_components.csv',keep_default_na=False)
+
+    columns_UMich_only = []
+    columns_component_only = []
+    for i in range(1,4):
+        df_UMich = df_UMich.rename(columns={'feature %i'%i:'UMich featu %i'%i,
+                                            'score %i'%i:'UMich score %i'%i})
+        columns_UMich_only += ['UMich featu %i'%i, 'UMich score %i'%i]
+        df_component = df_component.rename(columns={'feature %i'%i:'STRIDE featu %i'%i,
+                                                    'score %i' % i:'STRIDE score %i' % i})
+        columns_component_only += ['STRIDE featu %i'%i, 'STRIDE score %i'%i]
+
+    df_combined = pd.merge(df_component, df_UMich, on='lab', how='inner')
+    (df_combined[['lab'] + columns_UMich_only]).to_csv("UMich_feature_importance_to_compare.csv", index=False)
+    (df_combined[['lab'] + columns_component_only]).to_csv("component_feature_importance_to_compare.csv", index=False)
+
+def get_guideline_maxorderfreq():
+    maxorderfreq = {}
+    maxorderfreq['once'] = ['LABCBCD', 'LABALB', 'LABA1C', 'LABPHOS', 'LABTSH']
+    maxorderfreq['three_days'] = ['LABESRP']
+    # maxorderfreq['one_day'] = ['LABMETB']
+
+    return maxorderfreq
+
+def get_important_labs(lab_type='panel', data_source='Stanford', order_by=None):
+    # TODO: order_by
+
+    if lab_type == 'panel':
+        # labs_and_cnts = get_top_labs_and_cnts('panel', top_k=10)
+        # print labs_and_cnts
+        #
+        # '''
+        # Adding other important labs
+        # '''
+        # labs_and_cnts.append(['LABCBCD', stats_utils.query_lab_cnt(lab='LABCBCD',
+        #                                     time_limit=['2014-01-01','2016-12-31'])])
+        # TODO: ISTAT TROPONIN?
+        return ['LABMGN', 'LABALB', 'LABPHOS', 'LABLAC', 'LABBLC', 'LABBLC2', 'LABLDH', 'LABURIC', 'LABTNI', 'LABNA', 'LABK'] #,
+
+        #stats_utils.get_top_labs(lab_type=lab_type, top_k=10)
+    elif lab_type == 'component':
+        # TODO
+        if data_source!='UMich': #TODO!
+            return ['WBC', 'HGB', 'PLT', 'NA', 'K', 'CO2', 'BUN', 'CR', #'GLUC',
+                    'CA', 'ALB', 'TP',
+                    'ALKP', 'TBIL', 'AST', 'ALT']
+        else:
+            return ['WBC', 'HGB', 'PLT', 'NA', 'K', 'CO2', 'BUN', 'CR',  # 'GLUC',
+                    'CA', 'ALB', 'PROT',
+                    'ALKP', 'TBIL', 'AST', 'ALT']
+
+    labs_and_cnts = sorted(labs_and_cnts, key=lambda x: x[1])
+    return [x[0] for x in labs_and_cnts]
+
+def query_num_instances(instance_type='pat_id', time_limit=DEFAULT_TIMELIMIT):
+    # pat_enc_csn_id in ('2014-07-01', '2017-06-30') is 66439
+    # pat_id in ('2014-07-01', '2017-06-30') is 44709
+
+    if time_limit:
+        time_start, time_end = time_limit[0], time_limit[1]
+
+    query = SQLQuery()
+    query.addSelect('COUNT(DISTINCT %s)'%instance_type)
+    query.addFrom('stride_order_proc')
+
+    if time_start:
+        query.addWhere("order_time > '%s'" % time_start)
+    if time_end:
+        query.addWhere("order_time < '%s'" % time_end)
+
+    results = DBUtil.execute(query)
+
+    return results
+
+
+def query_lab_usage__df(lab, lab_type='panel', time_limit=None):
+    if time_limit:
+        time_start, time_end = time_limit[0], time_limit[1]
+    # TODO: deal with lab_type == component?
+
+    if lab_type=='panel':
+        query = SQLQuery()
+        query.addSelect('CAST(pat_id AS BIGINT) as pat_id')
+        # query.addSelect('order_proc_id')
+        query.addSelect('order_time')
+        query.addSelect('abnormal_yn')
+
+        # query.addSelect("CAST(SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') THEN 1 ELSE 0 END) = 0 AS INT) AS all_components_normal")
+
+        query.addFrom('stride_order_proc')
+
+        query.addWhere("order_status = 'Completed'")
+        query.addWhere("proc_code = '%s'"%lab)
+
+        if time_start:
+            query.addWhere("order_time > '%s'"%time_start)
+        if time_end:
+            query.addWhere("order_time < '%s'"% time_end)
+        # query.addWhere("order_time > '2016-01-01' AND order_time < '2016-12-31'")
+        # query.addWhere("(result_flag in ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL)")
+
+        query.addGroupBy('pat_id')
+        # query.addGroupBy('order_proc_id')
+        query.addGroupBy('order_time')
+        query.addGroupBy('abnormal_yn')
+
+        query.addOrderBy('pat_id')
+        # query.addOrderBy('order_proc_id')
+        query.addOrderBy('order_time')
+
+    elif lab_type=='component': # TODO!
+        query = SQLQuery()
+        query.addSelect('CAST(pat_id AS BIGINT) as pat_id')
+        # query.addSelect('order_proc_id')
+        query.addSelect('sop.order_time')
+        query.addSelect('sor.result_in_range_yn')
+        # query.addSelect("CAST(SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') THEN 1 ELSE 0 END) = 0 AS INT) AS all_components_normal")
+
+        query.addFrom('stride_order_proc as sop')
+        query.addFrom('stride_order_results as sor')
+
+        query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhere("sop.order_status = 'Completed'")
+        query.addWhere("sor.base_name = '%s'" % lab)
+
+        if time_start:
+            query.addWhere("sop.order_time > '%s'" % time_start)
+        if time_end:
+            query.addWhere("sop.order_time < '%s'" % time_end)
+        # query.addWhere("order_time > '2016-01-01' AND order_time < '2016-12-31'")
+        # query.addWhere("(result_flag in ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL)")
+
+        query.addGroupBy('sop.pat_id')
+        # query.addGroupBy('order_proc_id')
+        query.addGroupBy('sop.order_time')
+        query.addGroupBy('sor.result_in_range_yn')
+
+        query.addOrderBy('sop.pat_id')
+        # query.addOrderBy('order_proc_id')
+        query.addOrderBy('sop.order_time')
+
+    results = DBUtil.execute(query)
+
+    return results
+
+def get_prevweek_normal__dict(df, lab_type, also_get_cnt=False):
+    datetime_format = "%Y-%m-%d %H:%M:%S"
+    '''
+    Cnt of ordering w/i one day
+    '''
+    df['prev_in_sec'] = df['pat_id'].apply(lambda x: 1000 * 24 * 3600) # 1000 days
+    df['order_time'] = df['order_time'].apply(lambda x: datetime.datetime.strptime(x, datetime_format)
+                                if isinstance(x, str) else x)
+
+    df = df.sort_values(['pat_id', 'order_time'])
+    df = df.reset_index(drop=True)
+
+    row, col = df.shape
+    day2norms = {}
+
+    '''
+    key: num of CONSECUTIVE normal in past week. val: [normal, normal, abnormal...]
+    '''
+    for i in range(1, row):
+        j = i - 1
+
+        if lab_type=='panel':
+            curr_normal = 0 if df.ix[i, 'abnormal_yn'] == 'Y' else 1 # TODO: update the criterion
+
+            while j >= 0 \
+                and df.ix[i,'pat_id'] == df.ix[j,'pat_id'] \
+                and (df.ix[i,'order_time'] - df.ix[j,'order_time']).days < 7 \
+                and df.ix[j,'abnormal_yn'] != 'Y':
+
+                j -= 1
+        else:
+            curr_normal = 0 if df.ix[i, 'result_flag'] != '' else 1
+
+            while j >= 0 \
+                    and df.ix[i, 'pat_id'] == df.ix[j, 'pat_id'] \
+                    and (df.ix[i, 'order_time'] - df.ix[j, 'order_time']).days < 7 \
+                    and df.ix[j, 'result_flag'] == '':
+                j -= 1
+
+        prev_cnt = i-1-j
+        if prev_cnt in day2norms:
+            day2norms[prev_cnt].append(curr_normal)
+        else:
+            day2norms[prev_cnt] = [curr_normal]
+
+    return day2norms
+
+
+
+            # df.ix[i, 'prev_in_sec'] = time_diff_df.seconds
+            # a day has 86400 secs
+            # prev_days.append(time_diff_df.seconds/86400.)
+
+def get_floored_day_to_number_orders_cnts(lab, df):
+    datetime_format = "%Y-%m-%d %H:%M:%S"
+    # print df.head()
+    '''
+    Cnt of ordering w/i one day
+    '''
+    df['prev_in_day'] = df['pat_id'].apply(lambda x: -1)
+    df['order_time'] = df['order_time'].apply(lambda x: datetime.datetime.strptime(x, datetime_format)
+                                              if isinstance(x, str) else x)
+    df = df.sort_values(['pat_id', 'order_time']) # TODO: bug...
+    df = df.reset_index(drop=True)
+
+    df['order_in_1day'] = df['order_time'].apply(lambda x: 'No')
+    order_in_1day__inds = []
+
+    prev_days = [sys.maxsize] # The first record
+    row, col = df.shape
+    for i in range(1, row):
+        if df.ix[i, 'pat_id'] == df.ix[i - 1, 'pat_id']:
+            time_diff_df = df.ix[i, 'order_time'] - df.ix[i - 1, 'order_time']
+            if time_diff_df.days < 1:
+                #
+                order_in_1day__inds.append(i)
+
+                # print df.ix[i, 'order_status'], df.ix[i - 1, 'order_time'], df.ix[i, 'order_time']
+
+            #     pass
+                #print df.ix[i - 1, ['pat_id', 'order_time']].values, df.ix[i, ['pat_id', 'order_time']].values
+
+            # df.ix[i, 'prev_in_sec'] = time_diff_df.seconds
+            # a day has 86400 secs
+            # prev_days.append(time_diff_df.seconds/86400.)
+            prev_days.append(time_diff_df.days) # TODO: ceiling of days?
+        else:
+            prev_days.append(sys.maxsize)
+
+    # df.ix[order_in_1day__inds, 'order_in_1day'] = 'Yes'
+    # df.to_csv('Fig2_Order_Intensities/surprising_orders_in_1day_%s.csv'%lab)
+
+    prevday_cnts_dict = collections.Counter(prev_days)
+
+    return prevday_cnts_dict
+
+def get_curve_onelab(lab, all_algs, data_folder, curve_type, get_pval=False, get_baseline=True):
+    # curr_res_file = '%s-alg-summary-trainPPV-%s-vitalDays-%d.csv' % (lab, str(PPV_wanted), vital_day)
+
+    '''
+    Baseline curve
+    '''
+    if get_baseline:
+        df = pd.read_csv(data_folder + '/' + lab + '/' + 'baseline_comparisons.csv')
+        baseline_shape = df.shape
+
+        base_actual = df['actual'].values #df['actual'].values #all_components_normal
+        base_predict = df['predict'].values # predict
+
+        if curve_type == 'ROC':
+            fpr_base, tpr_base, _ = roc_curve(base_actual, base_predict)
+            xVal_base, yVal_base = fpr_base, tpr_base
+
+            try:
+                base_auc = roc_auc_score(base_actual, base_predict)
+            except:
+                base_auc = float('nan')
+            base_score = base_auc
+        elif curve_type == 'PRC':
+            precision, recall, _ = precision_recall_curve(base_actual, base_predict)
+            xVal_base, yVal_base = recall, precision
+
+            base_aps = average_precision_score(base_actual, base_predict)
+            base_score = base_aps
+    else:
+        xVal_base, yVal_base, base_score = None, None, None
+
+    '''
+    best alg
+    '''
+    best_score = 0
+    best_alg = None
+    best_actual = None
+    best_predict = None
+    for alg in all_algs:
+        df = pd.read_csv(data_folder + '/' + lab +  '/' + alg + '/' +
+                         'direct_comparisons.csv')
+        alg_shape = df.shape
+
+        # print baseline_shape, alg_shape
+        # print lab, baseline_shape[0], alg_shape[0]
+        if get_baseline:
+            assert baseline_shape[0] == alg_shape[0] # Make sure the same test set!
+
+        actual_list = df['actual'].values
+        try:
+            if curve_type == 'ROC':
+                cur_auc = roc_auc_score(actual_list, df['predict'].values)
+                cur_score = cur_auc
+            elif curve_type == 'PRC':
+                cur_aps = average_precision_score(actual_list, df['predict'].values)
+                cur_score = cur_aps
+
+        except ValueError:
+            cur_score = float('nan')
+        if cur_score > best_score:
+            best_score = cur_score
+            best_alg = alg
+            best_actual = actual_list
+            best_predict = df['predict'].values
+
+    if get_pval:
+        p_val = random_permutation_test(base_actual, base_predict, best_actual, best_predict, curve_type)
+    else:
+        p_val = -1
+
+    if curve_type == 'ROC':
+        fpr, tpr, _ = roc_curve(best_actual, best_predict)
+        xVal_best, yVal_best = fpr, tpr
+
+    elif curve_type == 'PRC':
+        precision, recall, _ = precision_recall_curve(best_actual, best_predict)
+        xVal_best, yVal_best = recall, precision
+
+
+    return xVal_base, yVal_base, base_score, xVal_best, yVal_best, best_score, p_val
+
+def random_permutation_test(base_actual, base_predict, best_actual, best_predict, curve_type):
+    '''
+    Why: Check statistical significance of our model's AUC compared to baseline AUC.
+
+    How:
+    https://stats.stackexchange.com/questions/214687/what-statistical-tests-to-compare-two-aucs-from-two-models-on-the-same-dataset
+
+    Args:
+        base_actual:
+        base_predict:
+        best_actual:
+        best_predict:
+        curve_type:
+
+    Returns:
+
+    '''
+
+    # 1.
+    # auc_base = roc_auc_score(base_actual, base_predict)
+    if curve_type == 'ROC':
+        auc_best = roc_auc_score(best_actual, best_predict)
+    elif curve_type == 'PRC':
+        auc_best = average_precision_score(best_actual, best_predict)
+
+    # 2.
+    num_episodes = base_actual.shape[0] #, base_predict.shape, best_actual.shape, best_predict.shape
+    all_actual =np.hstack((base_actual, best_actual))
+    all_predict = np.hstack((base_predict, best_predict))
+
+    import random
+
+    num_permute = 1000
+    damn = 0
+    for i in range(num_permute):
+        all_actual_i = all_actual
+        all_predict_i = all_predict
+
+
+        random.seed(i)
+        random.shuffle(all_actual_i)
+        actual_i = all_actual_i[:num_episodes]
+
+        random.seed(i)
+        random.shuffle(all_predict_i)
+        predict_i = all_predict_i[:num_episodes]
+
+        if curve_type == 'ROC':
+            auc_i = roc_auc_score(actual_i, predict_i)
+        elif curve_type == 'PRC':
+            auc_i = average_precision_score(actual_i, predict_i)
+
+        if auc_i > auc_best:
+            damn += 1
+    p = float(damn)/float(num_permute)
+    return p
+
+
+def split_features(feature):
+    divind = feature.find('(')
+    featu = feature[:divind - 1]
+    score = feature[divind + 1:-1]
+    return featu, score
+
+
+def Hosmer_Lemeshow_Test(predict_probas, actual_labels, num_bins=10):
+    # assert between 0 and 1
+    minor_shift = 0.00001
+    predict_probas_shifted = [max(min(x, 1 - minor_shift), minor_shift) for x in predict_probas]
+
+    bins = np.linspace(0, 1, num=num_bins+1)
+    inds = np.digitize(predict_probas_shifted, bins)
+
+    num_rows = len(predict_probas)
+
+    '''
+    Minor adjust
+    '''
+
+    '''
+    Put data inds into bins
+    TODO: when proba = 1, it is in the 11-th bin
+    '''
+    inds_in_bins = [[] for _ in range(num_bins+2)]
+    # (-inf,0), (0,1), ..., (9,10), (10,+inf)
+    for i in range(num_rows):
+        inds_in_bins[inds[i]].append(i)
+
+
+    mids = (bins[1:] + bins[:-1]) / 2.
+
+    e1_s, o1_s = [], []
+    e0_s, o0_s = [], []
+    H_stat = 0
+    dof = num_bins - 2
+    for i in range(1,len(inds_in_bins)-1):
+        # if not inds_in_bins[i]:
+        #     dof -= 1
+        #     continue
+
+        e1 = len(inds_in_bins[i]) * mids[i-1]
+        e1_s.append(e1)
+
+        e0 = len(inds_in_bins[i]) * (1.-mids[i-1])
+        e0_s.append(e0)
+
+        o1 = 0
+        o0 = 0
+        for j in inds_in_bins[i]:
+            if actual_labels[j] == 1:
+                o1 += 1
+            else:
+                o0 += 1
+        o1_s.append(o1)
+        o0_s.append(o0)
+
+        H_stat += (o1-e1)**2/float(e1) + (o0-e0)**2/float(e0)
+
+
+    return 1 - stats.chi2.cdf(H_stat, dof)
+
+def map_pval_significance(p_val):
+    significance = ''
+    if p_val < 0.001:
+        significance = '***'
+    elif p_val < 0.01:
+        significance = '**'
+    elif p_val < 0.05:
+        significance = '*'
+
+    return significance
+
+def get_thres_by_fixing_PPV(lab, alg, data_folder='', PPV_wanted=0.9, thres_mode="from_test"):
+    if thres_mode == "from_train":
+        df = pd.read_csv(data_folder + '/' + lab + '/' + alg + '/' +
+                         '%s-normality-prediction-%s-direct-compare-results-traindata.csv' % (lab, alg))
+    else:
+        df = pd.read_csv(data_folder + '/' + lab + '/' + alg + '/' +
+                         '%s-normality-prediction-%s-direct-compare-results.csv' % (lab, alg))
+
+    # TODO: calibration?
+    row, col = df.shape
+    thres_last, PPV_last = 1., 1.
+    actual_list = df['actual'].values.tolist()
+
+    for thres in np.linspace(1, 0, num=1001):
+
+        df['predict_class'] = df['predict'].apply(lambda x: 1 if x > thres else 0)
+        predict_class_list = df['predict_class'].values.tolist()
+
+        true_positive = 0
+        false_positive = 0
+        true_negative = 0
+        false_negative = 0
+        for i in range(row):
+            if actual_list[i] == 1 and predict_class_list[i] == 1:
+                true_positive += 1
+            elif actual_list[i] == 0 and predict_class_list[i] == 1:
+                false_positive += 1
+            elif actual_list[i] == 1 and predict_class_list[i] == 0:
+                false_negative += 1
+            elif actual_list[i] == 0 and predict_class_list[i] == 0:
+                true_negative += 1
+            else:
+                print("what?!")
+
+        try:
+            PPV = float(true_positive) / float(true_positive + false_positive)
+        except ZeroDivisionError:
+            # PPV = float('nan')
+            continue
+
+        if PPV < PPV_wanted:
+            thres = thres_last
+            PPV = PPV_last
+            break
+        else:
+            thres_last = thres
+            PPV_last = PPV
+
+    return thres_last
+
+
+def bootstrap_CI(actual_list, predict_list, num_repeats=1000, stat='roc_auc',
+                 confident_lvl=0.95, side='two', random_state=0):
+    assert len(actual_list) == len(predict_list)
+
+    from sklearn.utils import resample
+
+    try:
+
+        all_stats = []
+        for i in range(num_repeats):
+            actual_list_resampled, predict_list_resampled = resample(actual_list, predict_list)
+            if stat == 'roc_auc':
+                cur_roc_auc = roc_auc_score(actual_list_resampled, predict_list_resampled)
+                all_stats.append(cur_roc_auc)
+
+        roc_auc_left = np.percentile(all_stats, (1 - confident_lvl) / 2. * 100)
+        roc_auc_right = np.percentile(all_stats, (1 + confident_lvl) / 2. * 100)
+
+    except Exception as e:
+        # print e
+        roc_auc_left, roc_auc_right = float('nan'), float('nan')
+
+    return roc_auc_left, roc_auc_right
+
+
+def fill_df_fix_PPV(lab, alg, data_folder='', PPV_wanted=0.9, lab_type=None, thres_mode="from_test"):
+    df = pd.read_csv(data_folder + '/' + lab + '/' + alg + '/' +
+                     '%s-normality-prediction-%s-direct-compare-results.csv' % (lab, alg))
+    row, col = df.shape
+
+    '''
+    roc_auc, and its confidence interval (CI)
+    Independent of any threshold
+    '''
+    actual_list = df['actual'].values.tolist()
+
+    try:
+        roc_auc = roc_auc_score(actual_list, df['predict'].values)
+    except Exception as e:
+        # print e
+        roc_auc = float('nan')
+
+    try:
+        roc_auc_left, roc_auc_right = bootstrap_CI(actual_list, df['predict'], confident_lvl=0.95)
+    except Exception as e:
+        # print e
+        roc_auc_left, roc_auc_right = float('nan'), float('nan')
+
+    '''
+    Score threshold is used for "predict_proba --> predict_label"
+    Learning score threshold by fixing training PPV at desired lvl
+    This way, learned test (evaluation) PPV is "unbiased". 
+    '''
+    if thres_mode == "quick_test":
+        thres = 0.5
+    else:
+        thres = get_thres_by_fixing_PPV(lab, alg, data_folder=data_folder, PPV_wanted=PPV_wanted, thres_mode=thres_mode)
+
+    df['predict_class'] = df['predict'].apply(lambda x: 1 if x > thres else 0)
+    predict_class_list = df['predict_class'].values.tolist()
+
+    '''
+    Based on threshold, counting:
+    'true_positive', 'false_positive', 'true_negative', 'false_negative'
+
+    Calculating:
+    'sensitivity', 'specificity', 'LR_p', 'LR_n', 'PPV', 'NPV'
+    '''
+    true_positive = 0
+    false_positive = 0
+    true_negative = 0
+    false_negative = 0
+    total_cnt = 0
+    for i in range(row):
+        if actual_list[i] == 1 and predict_class_list[i] == 1:
+            true_positive += 1
+        elif actual_list[i] == 0 and predict_class_list[i] == 1:
+            false_positive += 1
+        elif actual_list[i] == 1 and predict_class_list[i] == 0:
+            false_negative += 1
+        elif actual_list[i] == 0 and predict_class_list[i] == 0:
+            true_negative += 1
+        else:
+            print("what?!")
+        total_cnt += 1
+
+    res_dict = {'lab': lab, 'alg': alg, 'threshold': thres,
+                'true_positive': true_positive / float(total_cnt), 'false_positive': false_positive / float(total_cnt),
+                'true_negative': true_negative / float(total_cnt), 'false_negative': false_negative / float(total_cnt),
+                'total_cnt': total_cnt, 'roc_auc': roc_auc,
+                '95%_CI': '[%f, %f]' % (roc_auc_left, roc_auc_right)}
+    res_dict['sensitivity'] = float(true_positive) / float(true_positive + false_negative)
+    res_dict['specificity'] = float(true_negative) / float(true_negative + false_positive)
+    try:
+        res_dict['LR_p'] = res_dict['sensitivity'] / (1 - res_dict['specificity'])
+    except ZeroDivisionError:
+        if res_dict['sensitivity'] == 0:
+            res_dict['LR_p'] = float('nan')
+        else:
+            res_dict['LR_p'] = float('inf')
+
+    try:
+        res_dict['LR_n'] = (1 - res_dict['sensitivity']) / res_dict['specificity']
+    except ZeroDivisionError:
+        if res_dict['sensitivity'] == 1:
+            res_dict['LR_n'] = float('nan')
+        else:
+            res_dict['LR_n'] = float('inf')
+
+    try:
+        res_dict['PPV'] = float(true_positive) / float(true_positive + false_positive)
+    except ZeroDivisionError:
+        res_dict['PPV'] = float('nan')
+
+    try:
+        res_dict['NPV'] = float(true_negative) / float(true_negative + false_negative)
+    except ZeroDivisionError:
+        res_dict['NPV'] = float('nan')
+
+    return res_dict
+
+
+def add_panel_cnts_fees(one_lab_alg_dict):
+    panel = one_lab_alg_dict['lab']
+    df_cnts_fees = pd.read_csv('data_summary_stats/labs_charges_volumes.csv')
+    cnts_fees_dict = df_cnts_fees.ix[df_cnts_fees['name'] == panel, ['count',
+                                                                     'min_price',
+                                                                     'max_price',
+                                                                     'mean_price',
+                                                                     'median_price',
+                                                                     'min_volume_charge',
+                                                                     'max_volume_charge',
+                                                                     'mean_volume_charge',
+                                                                     'median_volume_charge']].to_dict(orient='list')
+    # print cnts_fees_dict
+    for key in list(cnts_fees_dict.keys()):
+        try:
+            cnts_fees_dict[key] = cnts_fees_dict[key][0]
+        except IndexError:
+            cnts_fees_dict[key] = float('nan')
+
+    one_lab_alg_dict.update(cnts_fees_dict)
+    return one_lab_alg_dict
+
+
+'''
+For individual component, there is no well-defined "fee".
+'''
+
+
+def add_component_cnts(one_lab_dict, years):
+    component = one_lab_dict['lab']
+    df_cnts = pd.read_csv('data_summary_stats/component_cnts.txt', sep='\t', keep_default_na=False)
+    # df = df.rename(columns={'Base':'lab'})
+
+    for year in years:
+        one_lab_dict[str(year) + '_Vol'] = df_cnts.ix[df_cnts['Base'] == component, str(year)].values[0]
+        # TODO: rename total_cnt to avoid confusion between total STRIDE cnt & testing cnt!
+
+    return one_lab_dict
+
+'''
+Baseline 1: Predict normal only when reaching a certain number of previous consecutive normalities.
+In most cases, can not make a prediction, so have to do the test. 
+Have to pick a threshold. Cannot calculate ROC. 
+'''
+def get_baseline1_auroc(lab):
+    pass
+
+'''
+Baseline 2: Predict by last normality (when it is available), or by population average.
+In all cases, can make a prediction, so no need to make a prediction. 
+Can calculate ROC, PPV etc. Can pick a threshold. 
+'''
+def get_baseline2_auroc(data_filepath=None):
+    #TODO: does not care file structure, only take care of function and logic
+    # if not data_folderpath:
+    #     df = pd.read_csv(os.path.join(labs_ml_folder, lab, 'baseline_comparisons.csv'))  # TODO: baseline file should not have index!
+    # else:
+    #     df = pd.read_csv(os.path.join(data_filepath, 'baseline_comparisons.csv'))
+    df = pd.read_csv(os.path.join(data_filepath, 'baseline_comparisons.csv'))
+
+    try:
+        res = roc_auc_score(df['actual'], df['predict'])
+    except ValueError:
+        res = float('nan')
+
+    return res
+
+############# Stats functions #############
+# TODO: move to the stats module
+
+############# Stats functions #############
+
+
+
+######################################
+'''
+refactored
+'''
+def get_confusion_counts(actual_labels, predict_labels):
+
+    true_positive = 0
+    false_positive = 0
+    true_negative = 0
+    false_negative = 0
+    for i in range(len(actual_labels)):
+        if actual_labels[i] == 1 and predict_labels[i] == 1:
+            true_positive += 1
+        elif actual_labels[i] == 0 and predict_labels[i] == 1:
+            false_positive += 1
+        elif actual_labels[i] == 1 and predict_labels[i] == 0:
+            false_negative += 1
+        elif actual_labels[i] == 0 and predict_labels[i] == 0:
+            true_negative += 1
+        else:
+            print("what?!")
+    return true_positive, false_positive, true_negative, false_negative
+
+def get_confusion_metrics(actual_labels, predict_probas, threshold, also_return_cnts=False):
+    #TODO: move to stats unit
+    predict_labels = [1 if x > threshold else 0 for x in predict_probas]
+    true_positive, false_positive, true_negative, false_negative = \
+        get_confusion_counts(actual_labels, predict_labels)
+
+    sensitivity = float(true_positive) / float(true_positive + false_negative)
+    #res_dict['specificity'] \
+    specificity = float(true_negative) / float(true_negative + false_positive)
+    LR_p = np.divide(sensitivity, 1.-specificity)
+    LR_n = np.divide(1-sensitivity, specificity)
+
+    PPV = np.divide(float(true_positive), float(true_positive + false_positive))
+    NPV = np.divide(float(true_negative), float(true_negative + false_negative))
+
+    if not also_return_cnts:
+        return sensitivity, specificity, LR_p, LR_n, PPV, NPV #res_dict
+    else:
+        return true_positive, false_positive, true_negative, false_negative, \
+               sensitivity, specificity, LR_p, LR_n, PPV, NPV
+
+
+def pick_threshold(y_pick, y_pick_pred, target_PPV=0.95):
+    # TODO: assume both are numpy arrays
+    thres_last, PPV_last = 1., 1.
+    actual_list = y_pick.flatten().tolist()
+    predicted_proba = y_pick_pred.flatten().tolist()
+    assert len(actual_list) == len(predicted_proba)
+    # TODO: also check proba's and labels
+
+    for thres in np.linspace(1, 0, num=1001):
+
+        predict_class_list = [1 if x > thres else 0 for x in predicted_proba]
+
+        TP, FP, _, _ = get_confusion_counts(actual_list, predict_class_list)
+        PPV = np.divide(float(TP), float(TP + FP))
+
+        if PPV < target_PPV:
+            break
+        else:
+            thres_last = thres
+
+    return thres_last
+
+def add_line_breaker(astring, seg_len):
+    str_len = len(astring)
+    num_lines = str_len/seg_len
+
+    new_str = ''
+    for i in range(num_lines):
+        new_str += astring[i*seg_len:(i+1)*seg_len] + '\n'
+    if str_len%seg_len != 0:
+        new_str += astring[num_lines*seg_len:]
+
+    if new_str[-1] == '\n':
+        new_str = new_str[:-1]
+    return new_str
+
+main_folder = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/')
+
+def convert_floatnum2percentage(anum):
+    if anum != anum:
+        return '-'
+    elif anum == 0:
+        return '0'
+    elif anum < 0.01:
+        return '%.2f' % (anum * 100) + '%'
+    elif anum < 0.1:
+        return '%.1f' % (anum * 100) + '%'
+    else:
+        return '%.0f' % (anum * 100) + '%'
+
+def convert_floatstr2percentage(astr):
+    if astr == '':
+        return '-'
+    else:
+        return convert_floatnum2percentage(float(astr))
+    # elif astr == '1':
+    #     return '100%'
+    # elif astr == '0':
+    #     return '0'
+    # else:
+    #     return str('%.0f' % (float(astr) * 100)) + '%'
+
+def convert_floatstr2num(astr):
+    if astr == '' or astr == 'inf':
+        return '-'
+    elif astr == '1' or astr == '0':
+        return astr
+    else:
+        anum = float(astr)
+        if anum >= 10:
+            return str('%.0f'%anum)
+        elif 10 > anum >= 1:
+            return str('%.1f' %anum)
+        else:
+            return str('%.2f' % anum)
+
+def get_lab_descriptions(lab_type, data_source='Stanford', succinct=True, line_break_at=-1):
+    code2description_filepath = os.path.join(main_folder, 'machine_learning/data_conversion',
+                                             'map_%s.csv' % lab_type)
+    df = pd.read_csv(code2description_filepath, keep_default_na=False)
+    if succinct:
+        df['description'] = df['description'].apply(lambda x: x.split(',')[0].strip())
+    if line_break_at != -1:
+        df['description'] = df['description'].apply(lambda x: x[:line_break_at] + '\n' + x[line_break_at:])
+    lab_descriptions = dict(list(zip(df[data_source].values.tolist(), df['description'].values.tolist())))
+
+    if lab_type == 'panel':
+        lab_descriptions['LABCBCD'] = 'Complete Blood Count w/ Differential'
+        lab_descriptions['LABMETB'] = 'Basic Metabolic Panel'
+
+    return lab_descriptions
+
+def get_safe(func, *args):
+    try:
+        res = func(*args)
+    except Exception as e:
+        print(e)
+        res = float('nan')
+    return res
+
+def dict2pandas(a_dict, key='lab', val='val'):
+    return pd.DataFrame.from_dict(a_dict, orient='index').reset_index().rename(columns={'index': key,
+                                                                                        0:val})
+def pandas2dict(df, key='lab', val='val'):
+    return df.set_index(key).to_dict()[val]
+
+main_folder = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/')
+stats_results_folderpath = os.path.join(main_folder, 'lab_statistics')
+labs_old_stats_folder = os.path.join(stats_results_folderpath, 'data_summary_stats/')
+labs_query_folder = os.path.join(stats_results_folderpath, 'query_lab_results/')
+
+def get_queried_lab(lab, lab_type, time_limit=DEFAULT_TIMELIMIT):
+    lab_query_filepath = os.path.join(labs_query_folder, lab + '.csv')
+
+    # print lab, 'os.path.exists(lab_query_filepath)', os.path.exists(lab_query_filepath)
+    if not os.path.exists(lab_query_filepath):
+        df = query_to_dataframe(lab, lab_type, lab_query_filepath=lab_query_filepath)
+    else:
+        df = pd.read_csv(lab_query_filepath, keep_default_na=False)
+
+    if lab_type == 'component':
+        df = df[df['sop.order_status'] == 'Completed']
+        df = df[(df['sop.order_time'] >= time_limit[0]) & (df['sop.order_time'] <= time_limit[1])]
+
+        df = df.rename(columns={'sop.pat_id': 'pat_id',
+                                'sop.order_time':'order_time',
+                                'sor.result_in_range_yn':'result_in_range_yn',
+                                'sor.result_flag':'result_flag'})
+    elif lab_type == 'panel':
+        df = df[df['order_status'] == 'Completed']
+        df = df[(df['order_time'] >= time_limit[0]) & (df['order_time'] <= time_limit[1])]
+
+    df.drop_duplicates(inplace=True)
+    return df
+
+def get_labvol(lab, lab_type, data_source='Stanford', time_limit=DEFAULT_TIMELIMIT):
+    if data_source=='Stanford':
+        df = get_queried_lab(lab, lab_type, time_limit=time_limit)
+        return df.shape[0]
+    elif data_source == 'UMich':
+        return umich_lab_cnt.get(lab, 0)
+
+def get_medicare_price_dict():
+    data_folder = os.path.join(main_folder, 'lab_statistics/', 'data_summary_stats/')
+
+    df_match = pd.read_csv(os.path.join(data_folder, 'potential_matches.csv'))
+    dict_match = df_match.to_dict(orient='index')
+
+    dict_match_new = {}
+    for val in list(dict_match.values()):
+        dict_match_new[val['lab']] = val
+    dict_match = dict_match_new
+
+    df_price = pd.read_csv(os.path.join(data_folder, 'CLAB2018v1.csv'), skiprows=3)
+    dict_price = df_price.to_dict(orient='index')
+
+    dict_price_new = {}
+    for val in list(dict_price.values()):
+        dict_price_new[val['SHORTDESC']] = val
+    dict_price = dict_price_new
+    #
+
+    lab_price = {}
+    for lab in dict_match:
+        cur_match = dict_match[lab]
+        cond1 = cur_match['JC'] != cur_match['JC']  # TODO: other suggestions
+        cond2 = '?' not in cur_match['best matched description']
+        if cond1 and cond2:
+            cur_new_description = cur_match['best matched description']
+            lab_price[lab] = dict_price[cur_new_description]['RATE2018']
+    return lab_price
+
+def describe_lab_train_test_datasets(lab, dataset_folderpath):
+    fm_io = FeatureMatrixIO()
+    processed_matrix_train_path = os.path.join(dataset_folderpath, lab,
+                                               '%s-normality-train-matrix-processed.tab' % lab)
+    # TODO: get rid of '10000' in file name
+    processed_matrix_train = fm_io.read_file_to_data_frame(processed_matrix_train_path)
+    num_train_episodes = processed_matrix_train.shape[0]
+    num_train_patient = len(set(processed_matrix_train['pat_id'].values.tolist()))
+
+    processed_matrix_test_path = os.path.join(dataset_folderpath, lab,
+                                              '%s-normality-test-matrix-processed.tab' % lab)
+    # TODO: get rid of '10000' in file name
+    processed_matrix_test = fm_io.read_file_to_data_frame(processed_matrix_test_path)
+    # pd.read_csv(processed_matrix_test_path, keep_default_na=False)#fm_io.read_file_to_data_frame(processed_matrix_test_path)
+    num_test_episodes = processed_matrix_test.shape[0]
+    num_test_patient = len(set(processed_matrix_test['pat_id'].values.tolist()))
+
+    return num_train_episodes, num_train_patient, num_test_episodes, num_test_patient
+
+'''
+refactored
+'''
+######################################
+
+
+
+
+def get_top_labs_and_cnts(lab_type='panel', top_k=None, bottom_k=None, criterion='count', time_limit=None):
+    # df = pd.read_csv('data_performance_stats/best-alg-%s-summary-fix-trainPPV.csv'%lab_type,
+    #                  keep_default_na=False)
+    # if lab_type == 'component':
+    #     df = df.rename(columns={'2016_Vol':'count'})
+    # else:
+    #     df['median_price'] = df['median_price'].apply(lambda x: float(x) if x else 0)
+    #
+    # df['count'] = df['count'].apply(lambda x: float(x) if x else 0) # TODO: LABNA == 0!
+    #
+    # if lab_type == 'component' or criterion == 'count':
+    #     df = df[['lab', criterion]].drop_duplicates()
+    # elif criterion == 'count*price': # TODO: probably confusing, but it means count*median_price
+    #     df[criterion] = df['count']*df['median_price']
+    #     df = df[['lab', criterion]].drop_duplicates()
+    #
+    # res = df.sort_values(criterion, ascending=False).head(top_k)
+    # if lab_name_only:
+    #     return res['lab'].values
+    # else:
+    #     return res
+
+    data_folder = "query_lab_results/"
+    if not os.path.exists(data_folder):
+        os.mkdir(data_folder)
+    labs_and_cnts_file = "%ss_and_cnts_2014-2016.csv" % lab_type
+    labs_and_cnts_path = os.path.join(data_folder, labs_and_cnts_file)
+    if os.path.exists(labs_and_cnts_path):
+        print("%s exists.."%labs_and_cnts_path)
+        labs_and_cnts_df = pd.read_csv(labs_and_cnts_path, keep_default_na=False)
+        labs_and_cnts = labs_and_cnts_df.values.tolist()
+
+    else:
+
+        labs_and_cnts = []
+
+        if lab_type == 'panel':
+            all_labs = NON_PANEL_TESTS_WITH_GT_500_ORDERS
+        elif lab_type == 'component':
+            all_labs = STRIDE_COMPONENT_TESTS
+
+        for lab in all_labs: # TODO here
+            lab_data_filename = '%s.csv'%lab
+            lab_data_path = os.path.join(data_folder, lab_data_filename)
+
+            if os.path.exists(lab_data_path):
+                df = pd.read_csv(lab_data_path, keep_default_na=False)
+            else:
+                df = query_to_dataframe(lab, lab_type=lab_type)
+            if lab_type == 'panel':
+                df = df[df['order_status']=='Completed']
+            if time_limit:
+                if time_limit[0]:
+                    df = df[df['order_time']>=time_limit[0]]
+                if time_limit[1]:
+                    df = df[df['order_time'] <= time_limit[1]]
+            cnt = df.shape[0]
+
+            labs_and_cnts.append([lab, cnt])
+
+        labs_and_cnts = sorted(labs_and_cnts, key=lambda x:x[1])[::-1]
+
+        labs_and_cnts_df = pd.DataFrame(labs_and_cnts, columns=['lab', 'cnt'])
+
+        labs_and_cnts_df.to_csv(labs_and_cnts_path, index=False)
+
+    # print "[x[0] for x in labs_and_cnts[:top_k]]", [x[0] for x in labs_and_cnts[:top_k]]
+
+    if top_k and not bottom_k:
+        return labs_and_cnts[:top_k]
+    else:
+        return labs_and_cnts[-bottom_k:]
+
+    # return df.ix[:top_k, ['lab','total cnt']].values.tolist()
+
+
+def main():
+    columns_panels = ['lab', 'alg', 'roc_auc', '95%_CI', 'baseline_roc', 'total_cnt']
+    columns_panels += ['threshold', 'true_positive', 'false_positive', 'true_negative', 'false_negative']
+    columns_panels += ['sensitivity', 'specificity', 'LR_p', 'LR_n', 'PPV', 'NPV']
+    columns_panels += ['count', 'min_price', 'max_price', 'mean_price', 'median_price',
+                       'min_volume_charge', 'max_volume_charge', 'mean_volume_charge', 'median_volume_charge']
+    import generate_result_tables
+    lab2stats_csv(lab_type="panel",
+                  lab="LABHIVWBL",
+                  years=[2016],
+                  all_algs=generate_result_tables.all_algs,
+                  PPV_wanted=0.95,
+                  vital_day=3,
+                  folder_path='../machine_learning/',
+                  data_folder="data-panels/",
+                  result_folder="",
+                  columns=columns_panels,
+                  thres_mode="from_train")
+
+def query_to_dataframe(lab, lab_type='panel',
+                     columns=None,
+                     time_limit=None,
+                       lab_query_filepath=""):
+
+    # output_filename = '%s.csv'%lab
+    #
+    # output_path = os.path.join(output_foldername, output_filename)
+    #
+    # if os.path.exists(output_path):
+    #     print "Cached dataframe %s exists..." % lab
+    #     return pd.read_csv(output_path)
+
+    print("Running query of %s..." % lab)
+
+
+    query = SQLQuery()
+
+    if lab_type == 'panel':
+        if not columns:
+            columns = ["proc_code", "order_proc_id", "pat_id", "order_time",
+                       "abnormal_yn", "lab_status", "order_status"]
+
+        for column in columns:
+            query.addSelect(column)
+
+        query.addFrom('stride_order_proc')
+        # query.addFrom('stride_order_results AS sor')
+        if time_limit:
+            if time_limit[0]:
+                query.addWhere("order_time >= '%s'" % time_limit[0])
+            if time_limit[1]:
+                query.addWhere("order_time <= '%s'" % time_limit[1])
+        # query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhere("proc_code = '%s'" % lab)
+        # query.addGroupBy("proc_code")
+        query.addOrderBy("proc_code")
+
+    elif lab_type == 'component':  # see NA
+        if not columns:
+            columns = ["sor.base_name", "sor.order_proc_id", "sor.result_time",
+                       "sor.result_in_range_yn", 'sor.ord_num_value', 'sor.reference_unit', 'sor.result_flag',
+                       "sor.lab_status",
+                       "sop.proc_code", "sop.order_proc_id", "sop.pat_id", "sop.order_time",
+                       "sop.abnormal_yn", "sop.lab_status", "sop.order_status"]
+
+        for column in columns:
+            query.addSelect(column)
+
+        query.addFrom('stride_order_results as sor')
+        query.addFrom('stride_order_proc as sop')
+        query.addWhere('sor.order_proc_id = sop.order_proc_id')
+        if time_limit:
+            if time_limit[0]:
+                query.addWhere("sop.order_time >= '%s'" % time_limit[0])
+            if time_limit[1]:
+                query.addWhere("sop.order_time <= '%s'" % time_limit[1])
+        query.addWhere("base_name = '%s'" % lab)
+
+    elif lab_type == 'mixed':
+        if not columns:
+            columns = ["sor.base_name", "sor.order_proc_id", "sor.result_time",
+                       "sor.result_in_range_yn", 'sor.ord_num_value', 'sor.reference_unit', 'sor.result_flag',
+                       "sor.lab_status",
+                       "sop.proc_code", "sop.order_proc_id", "sop.pat_id", "sop.order_time",
+                       "sop.abnormal_yn", "sop.lab_status", "sop.order_status"]
+
+        for column in columns:
+            query.addSelect(column)
+
+        query.addFrom('stride_order_results as sor')
+        query.addFrom('stride_order_proc as sop')
+        query.addWhere('sor.order_proc_id = sop.order_proc_id')
+        if time_limit:
+            if time_limit[0]:
+                query.addWhere("sop.order_time >= '%s'" % time_limit[0])
+            if time_limit[1]:
+                query.addWhere("sop.order_time <= '%s'" % time_limit[1])
+        query.addWhere("proc_code = '%s'" % lab)
+
+    results = DBUtil.execute(query)
+
+    df = pd.DataFrame(results, columns=columns)
+    # if not os.path.exists(output_foldername):
+    #     os.mkdir(output_foldername)
+    df.to_csv(lab_query_filepath, index=False)
+
+    return df
+
+def main_queryAllLabsToDF(lab_type='panel'):
+    '''
+    query all labs from sql to df to make them faster for stats analysis.
+
+    Returns:
+
+    '''
+    # time_limits = [['2014-07-01', '2014-12-31'],
+    #                 ['2015-01-01', '2015-06-30'],
+    #                 ['2015-07-01', '2015-12-31'],
+    #                 ['2016-01-01', '2016-06-30'],
+    #                 ['2016-07-01', '2016-12-31'],
+    #                 ['2017-01-01', '2017-06-30']]
+
+    dst_folder = 'query_lab_results/'
+    for lab in all_labs:
+        # res = query_lab_usage__df(lab, lab_type='panel', time_limit=('2014-07-01', '2014-12-31'))
+        # print res
+        # quit()
+        # for i, time_limit in time_limits:
+        src_filepath = os.path.join(dst_folder, lab+'.csv')
+        lab_df_old = pd.read_csv(src_filepath, keep_default_na=False)
+
+        lab_df_new = query_to_dataframe(lab, lab_type=lab_type,
+                           time_limit=['2017-01-01', '2017-06-30'])
+
+        lab_df = pd.concat([lab_df_old, lab_df_new], axis=0, ignore_index=True)
+
+        dst_filepath = src_filepath
+        lab_df.to_csv(dst_filepath, index=False)
+
+
+
+def output_feature_importances(labs, data_source='Stanford', lab_type='panel', curr_version='10000-episodes'):
+    data_set_folder = 'data-%s-%s-%s'%(data_source, lab_type, curr_version)
+
+
+    def split_features(feature):
+        divind = feature.find('(')
+        featu = feature[:divind - 1]
+        score = feature[divind + 1:-1]
+        return featu, float(score)
+
+    num_rf_best = 0
+
+
+    lab_descriptions = get_lab_descriptions(lab_type=lab_type, data_source=data_source)
+
+    ml_folderpath = '../machine_learning/'
+
+    result_filepath = os.path.join(data_set_folder, 'RF_important_features.csv')
+
+    '''
+    Rules:
+    merge proximate to last
+    '''
+
+    def grouped(feature):
+        # Features that needs the first two element to identify
+        complex_feature_types = ['Comorbidity', 'Team']
+        for complex_feature_type in complex_feature_types:
+            if complex_feature_type in feature:
+                return '.'.join(feature.split('.')[:2])
+
+        return feature.split('.')[0]
+
+    result_df = pd.DataFrame(columns=['lab', 'feature 1', 'score 1', 'feature 2', 'score 2', 'feature 3', 'score 3'])
+    from scripts.LabTestAnalysis.machine_learning import ml_utils
+    for lab in labs:
+        # if data_source == 'UCSF' and lab_type == 'panel':
+        #
+        #
+        #     lab = ml_utils.map_lab(lab=lab.replace('-','/'), # TODO...
+        #                            data_source=data_source,
+        #                            lab_type=lab_type,
+        #                            map_type='from_src')
+        report_folderpath = os.path.join(ml_folderpath, data_set_folder, lab, 'random-forest')
+        report_filepath = os.path.join(report_folderpath, '%s-normality-prediction-random-forest-report.tab' % lab)
+
+        df = pd.read_csv(report_filepath, sep='\t', skiprows=0)
+        rf_description = df['model'].values.tolist()[0]
+        features_start = rf_description.index('features=[')
+        features_str = rf_description[features_start + len('features=['):-2]
+
+        if data_source == 'UCSF' and lab_type == 'panel':
+            from scripts.LabTestAnalysis.machine_learning.LabNormalityPredictionPipeline import UCSF_TOP_PANELS
+            for raw_lab in UCSF_TOP_PANELS:
+            # for ml_utils.map_lab()
+                new_lab = ml_utils.map_lab(lab=raw_lab.replace('-', '/'),  # TODO...
+                             data_source=data_source,
+                             lab_type=lab_type,
+                             map_type='from_src')
+                # print raw_lab, new_lab
+                features_str = features_str.replace(raw_lab, new_lab)
+
+        feature_tuples = [x.strip() for x in features_str.split(',')]
+        # print feature_tuples
+
+        one_lab_dict = {}
+        for feature_tuple in feature_tuples:
+            feature, score = split_features(feature_tuple)
+            grouped_feature = grouped(feature)
+            one_lab_dict[grouped_feature] = one_lab_dict.get(grouped_feature, 0) + score
+        sorted_tuples = sorted(list(one_lab_dict.items()), key=lambda x: x[1])[::-1]
+
+        # one_df_dict = {'lab': lab,
+        #                'feature 1': sorted_tuples[0][0],
+        #                'score 1': sorted_tuples[0][1],
+        #                'feature 2': sorted_tuples[1][0],
+        #                'score 2': sorted_tuples[1][1],
+        #                'feature 3': sorted_tuples[2][0],
+        #                'score 3': sorted_tuples[2][1],
+        #                }
+
+        # result_df.append(one_df_dict)
+        # print sorted_tuples
+
+        cur_rec = [lab_descriptions.get(lab,lab)]
+        for i in range(3):
+            if i < len(sorted_tuples):
+                cur_rec += [sorted_tuples[i][0], sorted_tuples[i][1]]
+            else:
+                cur_rec += ['-', '-']
+
+        result_df.loc[len(result_df)] = cur_rec
+
+    dict_panel = get_lab_descriptions(lab_type='panel', data_source=data_source)
+    dict_component = get_lab_descriptions(lab_type='component', data_source=data_source)
+    dict_misc = {'last_normality':'Prior Test Negative',
+                 'AdmitDxDate':'Admit Date',
+                 'order_time':'Order Time'}
+    for col in result_df.columns:
+        if col == 'lab':
+            continue
+        result_df[col] = result_df[col].apply(lambda x: dict_panel.get(x, x))
+        result_df[col] = result_df[col].apply(lambda x: dict_component.get(x, x))
+        result_df[col] = result_df[col].apply(lambda x: dict_misc.get(x, x))
+
+    result_df.to_csv(result_filepath, index=False, float_format='%.2f')
+
+
+
+if __name__ == '__main__':
+    # output_feature_importances(data_source='UCSF', lab_type='panel') # TODO: do this for UCSF...
+
+    df = query_to_dataframe(lab='LABNA')
+    df.to_csv('LABNA_query_look_numerics.csv',index=False)
+    # lab_type='panel'
+    # print get_labvol('LABPCCR')
+    #
+    # lab_type = 'component'
+    # print get_labvol('CR')
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/lab_statistics/summary_stats.py b/scripts/Archive/LabTestAnalysis/lab_statistics/summary_stats.py
similarity index 85%
rename from scripts/LabTestAnalysis/lab_statistics/summary_stats.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/summary_stats.py
index dace7998..c4b2e2c4 100644
--- a/scripts/LabTestAnalysis/lab_statistics/summary_stats.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/summary_stats.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime
 
 from medinfo.db.test.Const import RUNNER_VERBOSITY
@@ -12,11 +12,15 @@
 from medinfo.common.test.Util import MedInfoTestCase
 from medinfo.db import DBUtil
 from medinfo.db.Model import SQLQuery
-from medinfo.db.DBUtil import NUMBER, BOOLEAN, STRING, DATETIME
+
+import os
+
+# from scripts.Archive.HighMortalityVsLowMortalityVsCrowd.Miscellaneous.compute_observed_vs_expected_mortalityORreadmission import \
+# 	encounter_id
 
 CLINICAL_ITEM_ID = 'clinical_item_id'
 ITEM_COUNTS = 'item_counts'
-
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
 
 class LabStats(object):
 	def _getClinicalItemCounts(self):
@@ -30,8 +34,8 @@ def _getClinicalItemCounts(self):
 		query.addGroupBy(CLINICAL_ITEM_ID)
 		query.addOrderBy('total', dir='desc')
 
-		print query
-		print query.getParams()
+		print(query)
+		print(query.getParams())
 		DBUtil.runDBScript(self.SCRIPT_FILE, False)
 		results = DBUtil.execute(str(query), query.getParams())
 
@@ -51,8 +55,8 @@ def _getLabs(self):
 		# OTHER
 		query.addOrderBy(CLINICAL_ITEM_ID, dir='asc')
 
-		print query
-		print query.getParams()
+		print(query)
+		print(query.getParams())
 		DBUtil.runDBScript(self.SCRIPT_FILE, False)
 		results = DBUtil.execute(str(query), query.getParams())
 
@@ -66,22 +70,22 @@ def run(self, queryDB=False, toCSV=False):
 
 		# Get counts of items from patient_item
 		if queryDB: self._getClinicalItemCounts()
-		df_item_counts = pd.read_csv('data_summary_stats/item_counts.csv')
+		df_item_counts = pd.read_csv(THIS_DIR + '/data_summary_stats/item_counts.csv')
 
 		# Get labs
 		if queryDB: self._getLabs()
-		df_labs = pd.read_csv('data_summary_stats/labs.csv')
+		df_labs = pd.read_csv(THIS_DIR + '/data_summary_stats/labs.csv')
 
 		# Join labs with counts
 		df_labs = pd.merge(df_labs, df_item_counts, on=CLINICAL_ITEM_ID)
 
 		# Get billing codes
-		df_billing_codes = pd.DataFrame(pd.read_csv('data_summary_stats/billing_codes.csv', dtype='string'))
+		df_billing_codes = pd.DataFrame(pd.read_csv(THIS_DIR + '/data_summary_stats/billing_codes.csv', dtype='string'))
 		df_billing_codes = df_billing_codes[['name', 'order_code_description', 'billing_code']]
 		df_billing_codes['name'] = df_billing_codes['name'].apply(lambda x: 'LAB' + str(x))
 
 		# Get prices
-		df_chargemaster = pd.DataFrame(pd.read_csv('data_summary_stats/chargemaster.csv', dtype='string'))
+		df_chargemaster = pd.DataFrame(pd.read_csv(THIS_DIR + '/data_summary_stats/chargemaster.csv', dtype='string', encoding='latin'))
 		df_chargemaster = df_chargemaster[['billing_code', 'price', 'price_description']]
 		df_chargemaster['price'] = df_chargemaster['price'].apply(lambda x: float(x.replace(',','')))
 
diff --git a/scripts/LabTestAnalysis/lab_statistics/test_repeat_component_descriptive.py b/scripts/Archive/LabTestAnalysis/lab_statistics/test_repeat_component_descriptive.py
similarity index 94%
rename from scripts/LabTestAnalysis/lab_statistics/test_repeat_component_descriptive.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/test_repeat_component_descriptive.py
index 4cea5ee9..ad92ea32 100644
--- a/scripts/LabTestAnalysis/lab_statistics/test_repeat_component_descriptive.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/test_repeat_component_descriptive.py
@@ -5,7 +5,7 @@
 # from cStringIO import StringIO
 from datetime import datetime
 
-from repeat_component_descriptive import RepeatComponents
+from .repeat_component_descriptive import RepeatComponents
 
 
 class TestRepeatComponents(unittest.TestCase):
@@ -45,7 +45,7 @@ def testWindowSize1Day(self):
         (1, 0): [2, 1],
         (1, 1): [3, 1]
     }
-    actualResults = {k: list(v) for k, v in actualResults.iteritems()}
+    actualResults = {k: list(v) for k, v in actualResults.items()}
     self.assertEqual(expectedResults, actualResults)
 
   def testWindowSize30Day(self):
@@ -70,7 +70,7 @@ def testWindowSize30Day(self):
         (30, 2): [1, 1],
         (30, 3): [1, 0]
     }
-    actualResults = {k: list(v) for k, v in actualResults.iteritems()}
+    actualResults = {k: list(v) for k, v in actualResults.items()}
     self.assertEqual(expectedResults, actualResults)
 
   def testWindowSizes1And30Day(self):
@@ -99,7 +99,7 @@ def testWindowSizes1And30Day(self):
         (30, 2): [1, 1],
         (30, 3): [1, 0]
     }
-    actualResults = {k: list(v) for k, v in actualResults.iteritems()}
+    actualResults = {k: list(v) for k, v in actualResults.items()}
     self.assertEqual(expectedResults, actualResults)
 
   def testMaxConsecutiveFilter(self):
@@ -120,7 +120,7 @@ def testMaxConsecutiveFilter(self):
         (30, 1): [2, 2],
         (30, 2): [1, 1]
     }
-    actualResults = {k: list(v) for k, v in actualResults.iteritems()}
+    actualResults = {k: list(v) for k, v in actualResults.items()}
     self.assertEqual(expectedResults, actualResults)
 
   def testAbnormalResultFollowedByOutOfWindow(self):
@@ -140,7 +140,7 @@ def testAbnormalResultFollowedByOutOfWindow(self):
         (4, 1): [2, 1],
         (4, 2): [1, 1]
     }
-    actualResults = {k: list(v) for k, v in actualResults.iteritems()}
+    actualResults = {k: list(v) for k, v in actualResults.items()}
     self.assertEqual(expectedResults, actualResults)
 
   def testCreateGlobalStatsDf(self):
diff --git a/scripts/LabTestAnalysis/lab_statistics/test_summary_stats.py b/scripts/Archive/LabTestAnalysis/lab_statistics/test_summary_stats.py
similarity index 94%
rename from scripts/LabTestAnalysis/lab_statistics/test_summary_stats.py
rename to scripts/Archive/LabTestAnalysis/lab_statistics/test_summary_stats.py
index 03c68a5e..5e92ac81 100755
--- a/scripts/LabTestAnalysis/lab_statistics/test_summary_stats.py
+++ b/scripts/Archive/LabTestAnalysis/lab_statistics/test_summary_stats.py
@@ -1,9 +1,9 @@
 import sys, os
-from cStringIO import StringIO
+from io import StringIO
 from datetime import datetime
 import unittest
 
-from summary_stats import LabStats
+from .summary_stats import LabStats
 
 class TestLabStats(unittest.TestCase):
     def setUp(self):
@@ -49,5 +49,5 @@ def suite():
     return suite
 
 
-if __name__=="__main__":
+if __name__ == "__main__":
     unittest.TextTestRunner(verbosity=2).run(suite())
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/LabChangeBatchDriver.py b/scripts/Archive/LabTestAnalysis/machine_learning/LabChangeBatchDriver.py
new file mode 100644
index 00000000..d8ee995f
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/LabChangeBatchDriver.py
@@ -0,0 +1,61 @@
+#!/usr/bin/python
+"""
+Batch driver script for running LabChangePredictionPipeline.py for various lab
+orders in parallel.
+"""
+
+import subprocess;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+
+LOG_FILE_TEMPLATE = "data/log/LabChangePrediction.%s.log";
+
+TOP_NON_PANEL_TESTS_BY_VOLUME = set([
+    "LABMGN", "LABPTT", "LABPHOS", "LABTNI", "LABLACWB",
+    "LABA1C", "LABHEPAR", "LABPLTS", "LABLAC","LABLIPS",
+    "LABTSH", "LABHCTX", "LABLDH", "LABK", "LABNTBNP",
+    "LABCRP", "LABCK", "LABESRP", "LABNA", "LABFER",
+    "LABUSPG", "LABB12", "LABURNA", "LABFT4", "LABFIB",
+    "LABURIC", "LABPALB", "LABUOSM", "LABNH3", "LABUCR",
+    "LABTRIG", "LABFE", "LABALB", "LABUPREG", "LABHAP"
+    # "LABNONGYN", # No base names.
+])
+
+# Listed as non panel, but have multiple results??
+TOP_MULTI_RESULT_TESTS_BY_VOLUME = [
+    "LABPT", "LABBLC", "LABBLC2", "LABCAI", "LABURNC",
+    "LABCDTPCR", "LABPCTNI", "LABRESP", "LABMB", "LABGRAM",
+    "LABFCUL", "LABFLDC", "LABSPLAC", "LABANER", "LABBLCTIP",
+    "LABBLCSTK", "LABPCCR", "LABTRFS", "LABAFBD", "LABSTOBGD",
+    "LABCSFGL", "LABCSFTP", "LABAFBC", "LABCMVQT", "LABCSFC",
+    "LABLIDOL", "LABRETIC", "LABBXTG", "LABHIVWBL"
+]
+
+NON_PANEL_TESTS_WITH_GT_500_ORDERS = [
+    'LABA1C', 'LABAFBC', 'LABAFBD', 'LABALB', 'LABANER', 'LABB12', 'LABBLC', 'LABBLC2',
+    'LABBLCSTK', 'LABBLCTIP', 'LABBUN', 'LABBXTG', 'LABCA', 'LABCAI', 'LABCDTPCR', 'LABCK',
+    'LABCMVQT', 'LABCORT', 'LABCRP', 'LABCSFC', 'LABCSFGL', 'LABCSFTP', 'LABDIGL', 'LABESRP',
+    'LABFCUL', 'LABFE', 'LABFER', 'LABFIB', 'LABFLDC', 'LABFOL', 'LABFT4', 'LABGRAM',
+    'LABHAP', 'LABHBSAG', 'LABHCTX', 'LABHEPAR', 'LABHIVWBL', 'LABK', 'LABLAC', 'LABLACWB',
+    'LABLDH', 'LABLIDOL', 'LABLIPS', 'LABMB', 'LABMGN', 'LABNA', 'LABNH3', 'LABNONGYN',
+    'LABNTBNP', 'LABOSM', 'LABPALB', 'LABPCCG4O', 'LABPCCR', 'LABPCTNI', 'LABPHOS', 'LABPLTS',
+    'LABPROCT', 'LABPT', 'LABPTEG', 'LABPTT', 'LABRESP', 'LABRESPG', 'LABRETIC', 'LABSPLAC',
+    'LABSTLCX', 'LABSTOBGD', 'LABTNI', 'LABTRFS', 'LABTRIG', 'LABTSH', 'LABUCR', 'LABUOSM',
+    'LABUA', 'LABUAPRN', 'LABUPREG', 'LABURIC', 'LABURNA', 'LABURNC', 'LABUSPG'
+]
+
+labs_to_test = ["LABTNI", "LABTSH", "LABPLTS", "LABPHOS", "LABPTT", "LABNTBNP", "LABMGN", "LABLIPS", "LABLACWB", "LABHEPAR", "LABFER", "LABCK", "LABCRP"]
+
+baseArgv = \
+    ["python","LabChangePredictionPipeline.py"]
+
+prog = ProgressDots(1,1,"Processes",total=len(labs_to_test));
+for lab_name in labs_to_test:
+    key = "%.3d.%s" % (prog.getCounts(), lab_name )
+    key = key.replace("/","..")    # Don't want to use directory separator in temp log file name
+    argv = list(baseArgv)
+    argv.append(lab_name)
+    log.info("Starting: " + str.join(" ", argv))
+    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
+
+    p = subprocess.Popen(argv, stderr=logFile);
+    # Blocking sub-process call. Can just run multiple instances to parallelize, or consider  subprocess.Popen objects instead
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/LabChangePerformanceReport.Rmd b/scripts/Archive/LabTestAnalysis/machine_learning/LabChangePerformanceReport.Rmd
new file mode 100644
index 00000000..0e2cb6aa
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/LabChangePerformanceReport.Rmd
@@ -0,0 +1,386 @@
+---
+title: "Classifier Performance"
+author: "Rachael Caelie Aikens"
+date: "May 1, 2018"
+output: pdf_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
+knitr::opts_knit$set(root.dir = 'data')
+
+library(ggplot2)
+library(gridExtra)
+library(data.table)
+library(knitr)
+library(tidyr)
+library(dplyr)
+library(grid)
+library(ggpubr)
+library(RColorBrewer)
+library(scales)
+```
+
+# Functions for loading and preprocessing data
+
+```{r basic parameters of analysis}
+labs_done_percent <- c("TNI", "CK", "PTT", "PHOS", "MGN", "LACWB", "HEPAR", "CRP", "K", "FER", "PLTS", "HCTX", "LIPS", "NTBNP", "TSH")
+labs_done_sd <- c("A1C", "CK", "TNI", "PTT", "PHOS", "MGN", "HEPAR", "CRP",  "FER", "PLTS", "LIPS", "NTBNP", "TSH", "USPG", "LACWB")
+
+labs_done_percent <- intersect(labs_done_percent, labs_done_sd)
+labs_done_sd <- intersect(labs_done_percent, labs_done_sd)
+
+full_lab_names <- paste("LAB", union(labs_done_percent, labs_done_sd), sep = "")
+
+# upload Component map
+component_map <- read.csv("../LabResultMap.tab", sep = "\t") %>%
+  filter(proc_code %in% full_lab_names) %>%
+  group_by(proc_code) %>%
+  summarize(description = first(description))
+
+# rename to lower case
+component_map$description <- c("Creatinine kinase", "C-reactive protein", "Ferritin", "Heparin Activity",
+                               "Lactate, whole blood", "Lipase", "Magnesium, serum/plasma", "NT-PROBNP",
+                               "Phosphate in serum/plasma", "Platelet count", "Partial Thromboplastin Time",
+                               "Troponin I", "Thyroid Stimulating Hormone")
+
+params <- 1:5*10
+n_models <- 6
+```
+
+```{r functions to load data}
+# construct from filepath and load
+load_data <- function(lab, params = 1:5*10, n_models = 6, change_def = "percent"){
+  paths <- paste("LAB", lab, "/change_", change_def,"_0", params/10, "/LAB", lab, "-change-prediction-report.tab",
+               sep = "")
+  report <- rbindlist(lapply(paths, read.csv,
+                             sep = "\t",
+                             comment.char = "#",
+                             stringsAsFactors = FALSE))
+  return(report)
+}
+```
+
+```{r preprocessing functions}
+# parse dictionary string and return number of cases
+get_n_cases <- function(y_dict){
+  str_list <- gsub('.{1}$', '', strsplit(y_dict, " ")[[1]])
+  case <- as.numeric(str_list[4])
+  return(case)
+}
+
+# parse dictionary string and return number of control
+get_n_controls <- function(y_dict){
+  str_list <- gsub('.{1}$', '', strsplit(y_dict, " ")[[1]])
+  control <- as.numeric(str_list[2])
+  return(control)
+}
+
+# preprocess raw loaded data
+preprocess_data <- function(report, lab, params = 1:5*10, n_models = 6, comp_map = component_map){
+  report$change_param <-  sort(rep(params, each = n_models))
+  report$LAB <- lab
+  report$test_cases <- sapply(report$y_test.value_counts..,
+                                        function(x) get_n_cases(x))
+  report$test_controls <- sapply(report$y_test.value_counts..,
+                                        function(x) get_n_controls(x))
+  report$proportion_unchanged <- report$test_cases/(report$test_cases + report$test_controls)
+  report$proc_code <- paste("LAB", report$LAB, sep = "")
+  result <- left_join(report, comp_map, by = "proc_code") %>% select(-proc_code)
+  return(result)
+}
+
+# run load and preprocessing and return processed result data
+load_and_process <- function(lab, params = 1:5*10, n_models = 6, change_def = "percent"){
+  raw <- load_data(lab, params, n_models, change_def)
+  return(preprocess_data(raw, lab, params, n_models) %>% filter(!grepl("L1_REGRESS_AND_ROUND",model)))
+}
+```
+
+# Analysis
+
+## Many Labs
+
+```{r}
+full_data_percent <- rbindlist(lapply(labs_done_percent, load_and_process, params = params, change_def = "percent"))
+full_data_sd <- rbindlist(lapply(labs_done_sd, load_and_process, params = params, change_def = "sd")) %>%
+  mutate(change_param = change_param/100)
+```
+
+```{r}
+summarize_group_report <- function(full_data){
+  result <- full_data %>%
+  group_by(change_param, LAB) %>%
+  summarize(max_roc = max(roc_auc),
+            model = nth(model, which.max(percent_predictably_positive)),
+            max_roc_lowCI = nth(roc_auc_0.95_lower_ci, which.max(roc_auc)),
+            max_roc_highCI = nth(roc_auc_0.95_upper_ci, which.max(roc_auc)),
+            max_percent_predictability = max(percent_predictably_positive),
+            max_pp_lowCI = nth(percent_predictably_positive_0.95_lower_ci,
+                               which.max(percent_predictably_positive)),
+            max_pp_highCI = nth(percent_predictably_positive_0.95_upper_ci,
+                                which.max(percent_predictably_positive)),
+            test_cases = first(test_cases),
+            test_controls = first(test_controls),
+            proportion_unchanged = first(proportion_unchanged),
+            description = first(description))
+
+  return(result)
+}
+
+report_summary_percent <- summarize_group_report(full_data_percent)
+report_summary_sd <- summarize_group_report(full_data_sd)
+```
+
+First, we can check how many lab tests are unchanged from the previous measurement, for any given percent threshold.
+
+```{r}
+F1A <- ggplot(report_summary_percent %>% filter(change_param == 10) ,
+       aes(description, proportion_unchanged)) +
+  geom_col(fill = "steelblue3") +
+  labs(y = "Proportion of repeat tests \nwithin 10% of previous measurement")+
+  coord_flip() +
+  ylim(c(0,1))
+
+F1B <- ggplot(report_summary_sd %>% filter(change_param == 0.1) ,
+       aes(reorder(description, proportion_unchanged), proportion_unchanged)) +
+  geom_col(fill = "steelblue3") +
+  labs(y = "Proportion of repeat tests \nwithin 0.1 standard deviations of previous measurement")+
+  coord_flip() +
+  ylim(c(0,1))
+
+grid.arrange(F1A, F1B, ncol =1)
+```
+
+We can combine those:
+
+```{r, fig.height = 3.3in, fig.width = 6.1in}
+method_col <- c(rep("± 0.1 SD", dim(report_summary_sd)[1]),
+                rep("± 10%", dim(report_summary_percent)[1]))
+
+F1_plot_data <- rbind(report_summary_sd, report_summary_percent) %>%
+  ungroup() %>%
+  mutate(metric = method_col) %>%
+  filter(change_param %in% c(0.1, 10))
+
+F1 <- ggplot(F1_plot_data,
+             aes(description, proportion_unchanged)) +
+  geom_col(aes(fill = metric), position = "dodge") +
+  scale_fill_brewer(palette = "Set1") +
+  labs(y = "Proportion of repeat tests \nwithin margin of previous measurement", x = NULL, fill = "Margin\nof Change")+
+  coord_flip() +
+  ylim(c(0,1)) + theme_bw()
+
+F1
+```
+
+Plotting the AU-ROC for the prediction tasks for all labs is too busy. To start, we make a table of our performance just at the 0.1 SD or 10% change level.
+
+```{r}
+kable(F1_plot_data %>% select(LAB, metric, max_roc, max_percent_predictability), digits = 2)
+```
+
+
+
+Next, we filter our results to include only the lab tests for which the percent predictably positive we achieved a lower CI greater than 10 percent for at least one definition of "change."
+
+```{r filter results}
+Labs_to_keep_percent <- report_summary_percent %>%
+  group_by(LAB) %>%
+  summarise(pp_CI_lower = max(max_pp_lowCI)) %>%
+  filter(pp_CI_lower >= 0.1)
+
+Labs_to_keep_percent
+
+# proportion of the labs which make this threshold
+dim(Labs_to_keep_percent)[1]/length(labs_done_percent)
+
+report_summary_percent_best <- filter(report_summary_percent,
+                              LAB %in% Labs_to_keep_percent$LAB)
+```
+
+
+```{r}
+auroc_trend_plot_bygroup <- function(report_summary, my_xlab = "Change Threshold"){
+  myplot <- ggplot(report_summary,
+         aes(x = change_param, y = max_roc, group = LAB, color = LAB)) +
+    geom_line()+
+    labs(x = my_xlab, y = "AU-ROC")+
+    ylim(c(0.5,1))
+  return(myplot)
+}
+
+F3A <- auroc_trend_plot_bygroup(report_summary_percent_best, "Percent change threshold")
+```
+
+```{r, figure.width = 3}
+ppp_trend_plot_bygroup <- function(report_summary, my_xlab = "Change Threshold"){
+  myplot <- ggplot(report_summary,
+         aes(x = change_param, y = max_percent_predictability,
+             group = LAB, color = LAB)) +
+    geom_line() +
+    labs(x = my_xlab, y = "Percent predictibly positive")+
+    ylim(c(0,1))
+  return(myplot)
+}
+
+F3B <- ppp_trend_plot_bygroup(report_summary_percent_best, "Percent change threshold")
+```
+
+```{r, fig.height = 4.5in, fig.width = 7.25}
+ppp_bar_plot <- function(report_summary, fill_label = "Change Threshold", mypalette = "Reds"){
+  myplot <- ggplot(report_summary, aes(description, max_percent_predictability))+
+    geom_col(aes(fill = as.factor(change_param)), position = "dodge") +
+    labs(fill = fill_label, y = "Percent Predictability@99%", x = NULL) +
+    coord_flip() +
+    scale_fill_brewer(palette = mypalette, direction= -1) +
+    ylim(c(0,1)) + theme_bw() #+
+#    theme(legend.key.size = unit(0.25, "cm"),
+#          legend.title=element_text(size=8),
+#          legend.text=element_text(size=8))
+  return(myplot)
+}
+
+roc_bar_plot <- function(report_summary, fill_label = "Change Threshold", mypalette = "Reds"){
+  myplot <- ggplot(report_summary, aes(description, max_roc))+
+    geom_col(aes(fill = as.factor(change_param)), position = "dodge") +
+    labs(fill = fill_label, y = "AU-ROC", x = NULL) +
+    coord_flip() +
+    scale_y_continuous(limits = c(0.5, 1), oob = rescale_none) +
+    scale_fill_brewer(palette = mypalette, direction= -1) + theme_bw() #+
+#   theme(legend.key.size = unit(0.25, "cm"),
+#          legend.title=element_text(size=8),
+#          legend.text=element_text(size=8))
+  return(myplot)
+}
+
+F2B <- ppp_bar_plot(report_summary_percent, "Percent change threshold", "Blues")
+F2A <- roc_bar_plot(report_summary_percent, "Percent change threshold", "Blues")
+ggarrange(F2A, F2B, common.legend = TRUE, legend = "bottom", labels = "AUTO")
+F3B <- ppp_bar_plot(report_summary_sd, "SD change threshold", "Reds")
+F3A <- roc_bar_plot(report_summary_sd, "SD change threshold", "Reds")
+ggarrange(F3A, F3B, common.legend = TRUE, legend = "bottom", labels = "AUTO")
+```
+
+```{r}
+Labs_to_keep_sd <- report_summary_sd %>%
+  group_by(LAB) %>%
+  summarise(pp_CI_lower = max(max_pp_lowCI)) %>%
+  filter(pp_CI_lower >= 0.1)
+
+Labs_to_keep_sd
+
+# proportion of the labs which make this threshold
+dim(Labs_to_keep_sd)[1]/length(labs_done_sd)
+
+report_summary_sd_best <- filter(report_summary_sd,
+                              LAB %in% Labs_to_keep_sd$LAB)
+```
+
+```{r}
+F3A <- auroc_trend_plot_bygroup(report_summary_sd_best, "Standard deviation change threshold")
+F3B <- ppp_trend_plot_bygroup(report_summary_sd_best, "Standard deviation change threshold")
+
+ggarrange(F3A, F3B, ncol = 2, common.legend = TRUE, legend = "bottom")
+```
+
+
+```{r, eval = FALSE, include = FALSE, echo = FALSE}
+# this plot isn't terribly informative
+ggplot(report_summary_best, aes(x = percent_change, y = test_cases/(test_cases+test_controls), group = LAB, color = LAB)) +
+  geom_line() +
+  #geom_line(aes(x = percent_change, y = test_cases/(test_cases+test_controls)), linetype = "dashed") +
+  #geom_line(aes(x = percent_change, y = max_percent_predictability*test_cases/(test_cases+test_controls)), linetype = "dashed")+
+  labs(x = "Percent change threshold", y = "Percent with label 1")+
+  ylim(c(0,1))
+```
+
+## Save Results to a File
+
+```{r}
+# all raw data
+write.table(full_data_percent, file = "full_data_percent.tsv", sep = "\t", quote = FALSE, row.names = FALSE)
+write.table(full_data_sd, file = "full_data_sd.tsv", sep = "\t", quote = FALSE, row.names = FALSE)
+
+# processed data
+write.table(report_summary_percent, file = "processed_data_percent.tsv", sep = "\t", quote = FALSE, row.names = FALSE)
+write.table(report_summary_sd, file = "processed_data_sd.tsv", sep = "\t", quote = FALSE, row.names = FALSE)
+```
+
+# Volume Analaysis
+
+```{r upload volume info}
+volume_charge_map <- read.csv("../repeated_labs_charges_volumes.csv") %>%
+  filter(name %in% full_lab_names,
+         window == 7)
+
+report_summary_percent$name <- paste("LAB", report_summary_percent$LAB, sep = "")
+report_summary_sd$name <- paste("LAB", report_summary_sd$LAB, sep = "")
+vol_charge_table_percent <- left_join(report_summary_percent, volume_charge_map, by = "name") %>% select(-name)
+vol_charge_table_sd <- left_join(report_summary_sd, volume_charge_map, by = "name") %>% select(-name)
+```
+
+```{r, fig.height = 4.5in, fig.width = 7.25}
+predictable_vol_bar_plot <- function(report_summary, fill_label = "Change Threshold", mypalette = "Reds"){
+  plotdata <- mutate(report_summary, 
+                     volume = max_percent_predictability*num_repeated)
+  
+  myplot <- ggplot(plotdata, aes(description, ifelse(volume>1, volume, 1), fill = as.factor(change_param))) +
+    geom_bar(position = "dodge", stat = "identity") +
+    labs(fill = fill_label, y = "Predictable volume (log scale)", x = NULL) +
+    geom_text(aes(label=comma(round(signif(volume,2)))),
+              hjust = -0.05, position=position_dodge(width=0.9), size = 3) +
+    coord_flip() +
+    scale_fill_brewer(palette = mypalette, direction= -1) +
+    theme_bw() +
+    scale_y_log10(labels = comma, limits = c(1, 2000000)) +
+    theme(legend.key.size = unit(0.3, "cm"),
+          legend.title=element_text(size=9),
+          legend.text=element_text(size=9),
+          axis.title.x = element_text(size = 10),
+          axis.text.x = element_text(angle = 45, hjust = 1))
+  return(myplot)
+}
+
+F4A <- predictable_vol_bar_plot(vol_charge_table_percent %>% filter(change_param <= 20),
+                         fill_label = "Percent change\nthreshold", mypalette = "Blues")
+F4B <- predictable_vol_bar_plot(vol_charge_table_sd %>% filter(change_param <= 0.2),
+                         fill_label = "SD change\nthreshold", mypalette = "Reds")
+
+ggarrange(F4A, F4B, ncol = 2, common.legend = FALSE, legend = "top", labels = "AUTO", hjust = -1)
+```
+
+```{r}
+options(scipen = 1000000)
+predictable_charge_bar_plot <- function(report_summary, fill_label = "Change Threshold", mypalette = "Reds"){
+  plotdata <- mutate(report_summary, 
+                     volume = max_percent_predictability*num_repeated*median_price)
+  myplot <- ggplot(plotdata, aes(description, 
+                                       ifelse(volume >1, volume, 1),
+                                       fill = as.factor(change_param))) +
+    geom_col(position = "dodge") +
+    labs(fill = fill_label, y = "Predictable charge (log scale)", x = NULL) +
+    coord_flip() +
+    scale_fill_brewer(palette = mypalette, direction= -1) +
+    geom_text(aes(label=paste("$", comma(round(signif(volume,2))), sep = "")),
+              hjust = -0.05, position=position_dodge(width=0.9), size = 2.75) +
+    theme_bw() +
+
+    scale_y_log10(labels = scales::dollar, limits = c(1, 1700000000)) +
+    theme(legend.key.size = unit(0.3, "cm"),
+          legend.title=element_text(size=9),
+          legend.text=element_text(size=9),
+          axis.title.x = element_text(size = 10),
+          axis.text.x = element_text(angle = 45, hjust = 1))
+  return(myplot)
+}
+
+F5A <- predictable_charge_bar_plot(vol_charge_table_percent %>% filter(change_param <= 20),
+                         fill_label = "Percent change\nthreshold", mypalette = "GnBu")
+F5B <- predictable_charge_bar_plot(vol_charge_table_sd %>% filter(change_param <= 0.2),
+                         fill_label = "SD change\nthreshold", mypalette = "YlGn")
+
+ggarrange(F5A, F5B, ncol = 2, common.legend = FALSE, legend = "top", labels = "AUTO", hjust = -1)
+
+ggarrange(F4A, F4B, F5A, F5B, common.legend = F, legend = "top", labels = "AUTO", hjust = -1, heights = c(1,1.05))
+```
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/LabChangePredictionPipeline.py b/scripts/Archive/LabTestAnalysis/machine_learning/LabChangePredictionPipeline.py
new file mode 100644
index 00000000..fd937a19
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/LabChangePredictionPipeline.py
@@ -0,0 +1,435 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of LabChange prediction.
+"""
+
+import inspect
+import os
+import logging
+
+from sys import argv
+from pandas import DataFrame, Series
+import pandas as pd
+import numpy as np
+from sklearn.externals import joblib
+from sklearn.model_selection import train_test_split
+
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from scripts.LabTestAnalysis.machine_learning.extraction.LabChangeMatrix import LabChangeMatrix
+
+class LabChangePredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, change_params, lab_panel, num_episodes, use_cache=None, random_state=None, build_raw_only=False,):
+        SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state)
+        self.feat2imputed_dict = {}
+        self._change_params = change_params
+        self._change_params['feature_old'] = self._lookup_previous_measurement_feature(self._var)
+        log.debug('change_params: %s' % self._change_params)
+
+        if build_raw_only:
+            self._build_raw_feature_matrix()
+            return
+
+        else:
+            self._build_raw_feature_matrix()
+            self._build_processed_feature_matrix()
+            self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-change-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_model_dump_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_matrix_path(self):
+        template = '%s-change-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+
+        # Build matrix file name.
+        slugified_var = '-'.join(self._var.split())
+        matrix_name = template % (slugified_var, self._num_rows)
+
+        # Build path using parent class logic for _fetch_data_dir_path.
+        # This puts raw matrix in the directory for lab test rather than the
+        # subdirectory for the specific change definition.  That way it can be
+        # reused in pipelines for multiple different change defs.
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+        matrix_path = '/'.join([data_dir, matrix_name])
+
+        return matrix_path
+
+    def _build_raw_feature_matrix(self):
+        raw_matrix_path = self._build_raw_matrix_path()
+        matrix_class = LabChangeMatrix
+        SupervisedLearningPipeline._build_raw_feature_matrix(self, matrix_class, \
+            raw_matrix_path)
+
+    def _build_processed_matrix_path(self, raw_matrix_path):
+        template = '%s-change-matrix-%d-episodes-processed.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_path)
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path(raw_matrix_path)
+
+        log.debug('params: %s' % params)
+
+        prev_measurement_feature = self._change_params['feature_old']
+        features_to_add = {'change': [self._change_params]}
+        features_to_filter_on = [{'feature': prev_measurement_feature,
+                                  'value':np.nan}]
+        imputation_strategies = {
+        }
+
+        features_to_remove = [
+            'pat_id', 'order_time', 'order_proc_id', 'ord_num_value',
+            'proc_code', 'abnormal_panel', 'all_components_normal',
+            'num_normal_components', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays',
+            'Death.post',
+            'Death.postTimeDays',
+            'num_components'
+        ]
+        features_to_keep = [
+            # Keep the # of times it's been ordered in past, even if low info.
+            '%s.pre' % self._var
+        ]
+        outcome_label = 'unchanged_yn'
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 0.05
+        matrix_class = LabChangeMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether the lab test
+            '%s is a boolean indicator which summarizes whether the lab test ' % outcome_label,
+            # result is unchanged compared to the previous measurement.
+            'result is unchanged compared to the previous measurement.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+            # Lab panel orders were only included if a previous measurement of
+            "Lab panel orders were only included if a previous measurement of",
+            # the same lab panel has been recorded
+            "the same lab panel has been recorded."
+        ]
+
+        # Bundle parameters into single object
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['features_to_filter_on'] = features_to_filter_on
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+
+        # defer to SupervisedLearningPipeline logic by SX
+        fm_io = FeatureMatrixIO()
+        log.debug('params: %s' % params)
+        # If processed matrix exists, and the client has not requested to flush
+        # the cache, just use the matrix that already exists and return.
+        processed_matrix_path = params['processed_matrix_path']
+        if os.path.exists(processed_matrix_path) and not self._flush_cache:
+            # Assume feature selection already happened, but we still need
+            # to split the data into training and test data.
+            processed_matrix = fm_io.read_file_to_data_frame(processed_matrix_path)
+            '''
+            Make sure the order of rows is consistent before splitting
+            '''
+            processed_matrix.sort_index(inplace=True)
+            self._train_test_split(processed_matrix, params['outcome_label']) #TODO sxu: when reloading, no pat_id
+        else:
+            # Read raw matrix.
+            raw_matrix = fm_io.read_file_to_data_frame(params['raw_matrix_path'])
+            # Initialize FMT.
+
+            # Add outcome label
+            raw_fmt = FeatureMatrixTransform()
+            raw_fmt.set_input_matrix(raw_matrix)
+            self._filter_on_features(raw_fmt, params['features_to_filter_on'])
+            self._add_features(raw_fmt, params['features_to_add'])
+            raw_matrix = raw_fmt.fetch_matrix()
+
+            # Divide processed_matrix into training and test data.
+            # This must happen before feature selection so that we don't
+            # accidentally learn information from the test data.
+
+            # TODO: work on this...
+            self._train_test_split(raw_matrix, params['outcome_label'])
+
+            fmt = FeatureMatrixTransform()
+            train_df = self._X_train.join(self._y_train)
+            fmt.set_input_matrix(train_df)
+
+            # Remove features.
+            self._remove_features(fmt, params['features_to_remove'])
+            # Filter on features
+            if 'features_to_filter_on' in params:
+                self._filter_on_features(fmt, params['features_to_filter_on'])
+
+            # HACK: When read_csv encounters duplicate columns, it deduplicates
+            # them by appending '.1, ..., .N' to the column names.
+            # In future versions of pandas, simply pass mangle_dupe_cols=True
+            # to read_csv, but not ready as of pandas 0.22.0.
+            for feature in raw_matrix.columns.values:
+                if feature[-2:] == ".1":
+                    fmt.remove_feature(feature)
+                    self._removed_features.append(feature)
+
+            # Impute data.
+            self._impute_data(fmt, train_df, params['imputation_strategies'])
+
+            # In case any all-null features were created in preprocessing,
+            # drop them now so feature selection will work
+            fmt.drop_null_features()
+
+            # Build interim matrix.
+            train_df = fmt.fetch_matrix()
+
+            self._y_train = pd.DataFrame(train_df.pop(params['outcome_label']))
+            self._X_train = train_df
+
+            '''
+            Select X_test columns according to processed X_train
+            '''
+            self._X_test = self._X_test[self._X_train.columns]
+
+            '''
+            Impute data according to the same strategy when training
+            '''
+            for feat in self._X_test.columns:
+                self._X_test[feat] = self._X_test[feat].fillna(self.feat2imputed_dict[feat])
+
+            self._select_features(params['selection_problem'],
+                params['percent_features_to_select'],
+                params['selection_algorithm'],
+                params['features_to_keep'])
+
+            train = self._y_train.join(self._X_train)
+            test = self._y_test.join(self._X_test)
+
+            processed_matrix = train.append(test)
+            '''
+            Need to recover the order of rows before writing into disk
+            '''
+            processed_matrix.sort_index(inplace=True)
+
+            # Write output to new matrix file.
+            header = self._build_processed_matrix_header(params)
+            fm_io.write_data_frame_to_file(processed_matrix, \
+                processed_matrix_path, header)
+
+    def _impute_data(self, fmt, raw_matrix, imputation_strategies):
+        for feature in raw_matrix.columns.values:
+            if feature in self._removed_features:
+                continue
+            # If all values are null, just remove the feature.
+            # Otherwise, imputation will fail (there's no mean value),
+            # and sklearn will ragequit.
+            if raw_matrix[feature].isnull().all():
+                fmt.remove_feature(feature)
+                self._removed_features.append(feature)
+            # Only try to impute if some of the values are null.
+            elif raw_matrix[feature].isnull().any():
+                # If an imputation strategy is specified, follow it.
+                if imputation_strategies.get(feature):
+                    strategy = imputation_strategies.get(feature)
+                    fmt.impute(feature, strategy)
+                else:
+                    # TODO(sbala): Impute all time features with non-mean value.
+                    imputed_value = fmt.impute(feature)
+                    self.feat2imputed_dict[feature] = imputed_value
+            else:
+                '''
+                If there is no need to impute, still keep the mean value, in case test data
+                need imputation
+                TODO sxu: take care of the case of non-mean imputation strategy
+                '''
+                imputed_value = fmt.impute(feature)
+                self.feat2imputed_dict[feature] = imputed_value
+
+    def _fetch_data_dir_path(self, pipeline_module_path):
+        # e.g. app_dir = CDSS/scripts/LabTestAnalysis/machine_learning
+        app_dir = os.path.dirname(os.path.abspath(pipeline_module_path))
+
+        # e.g. data_dir = CDSS/scripts/LabTestAnalysis/machine_learning/data
+        parent_dir_list = app_dir.split('/')
+        parent_dir_list.append('data')
+
+        # make subdirectory based on lab test name and change defs
+        # e.g. data_dir =  CDSS/scripts/LabTestAnalysis/machine_learning/data/LABCK/change_interval_05
+        parent_dir_list.append(self._var)
+        paramstr = str(self._change_params['param']).replace('.','')
+        change_def = 'change_%s_%s' % (self._change_params['method'], paramstr)
+        parent_dir_list.append(change_def)
+        data_dir = '/'.join(parent_dir_list)
+
+        # If data_dir does not exist, make it.
+        if not os.path.exists(data_dir):
+            os.makedirs(data_dir)
+
+        return data_dir
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = self._fetch_data_dir_path(pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+            pass # TODO:(raikens) something in the BifurcatedSupervisedClassifier pipeline is crashing
+            #algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            log.debug('report_dir: %s' % report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'lab_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'lab_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabChangePredictionPipeline("%s", %d)' % (self._var, self._num_rows)]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-change-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-change-prediction-%s' % (self._var, algorithm)
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabChangePredictionPipeline("%s", %d)' % (self._var, self._num_rows)]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-change-prediction-report.tab' % self._var]), header)
+
+    def _lookup_previous_measurement_feature(self, proc_code):
+        # e.g. app_dir = CDSS/scripts/LabTestAnalysis/machine_learning
+        pipeline_module_path = inspect.getfile(inspect.currentframe())
+        app_dir = os.path.dirname(os.path.abspath(pipeline_module_path))
+        with open("%s/LabComponentMap.tab" % app_dir) as map:
+            for line in map:
+                row = line.split()
+                if row[0] == proc_code:
+                    return '%s.-14_0.last' % row[1].rstrip()
+
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    labs_to_test = [argv[1]]
+    change_params = {}
+    change_params['method'] = 'sd'
+    change_params['feature_new'] = 'ord_num_value'
+    params_to_test = [0.5, 0.4, 0.2, 0.1]
+    sample_size = 12000
+
+    for panel in labs_to_test:
+        LabChangePredictionPipeline(change_params, panel, sample_size, use_cache=True, random_state=123456789, build_raw_only=True)
+        for param in params_to_test:
+            change_params['param'] = param
+            LabChangePredictionPipeline(change_params, panel, sample_size, use_cache=True, random_state=123456789)
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/LabComponentMap.tab b/scripts/Archive/LabTestAnalysis/machine_learning/LabComponentMap.tab
new file mode 100644
index 00000000..2b47fc14
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/LabComponentMap.tab
@@ -0,0 +1,8030 @@
+proc_code	base_name
+LABPOCGLU	GLU
+LABPOCGLU	PCCOM
+LABPOCGLU	SOU
+LABMETB	AG
+LABMETB	BUN
+LABMETB	CA
+LABMETB	CL
+LABMETB	CO2
+LABMETB	CR
+LABMETB	GLU
+LABMETB	K
+LABMETB	NA
+LABCBCD	WBC
+LABCBCD	MCHC
+LABCBCD	MCV
+LABCBCD	MCH
+LABCBCD	HGB
+LABCBCD	PLT
+LABCBCD	RBC
+LABCBCD	HCT
+LABCBCD	RDW
+LABCBCD	NEUTAB
+LABCBCD	MONOAB
+LABCBCD	MONO
+LABCBCD	LYM
+LABCBCD	EOS
+LABCBCD	NEUT
+LABCBCD	BASO
+LABCBCD	BASOAB
+LABCBCD	EOSAB
+LABCBCD	LYMAB
+LABMGN	MG
+LABPT	INR
+LABPT	PT
+LABMETC	AG
+LABMETC	ALB
+LABMETC	ALKP
+LABMETC	ALT
+LABMETC	BUN
+LABMETC	CA
+LABMETC	CL
+LABMETC	CO2
+LABMETC	CR
+LABMETC	GLOB
+LABMETC	GLU
+LABMETC	K
+LABMETC	NA
+LABMETC	TBIL
+LABMETC	TP
+LABMETC	AST
+LABSPR	SPR
+LABCBCO	WBC
+LABCBCO	HCT
+LABCBCO	HGB
+LABCBCO	PLT
+LABCBCO	RBC
+LABCBCO	MCV
+LABCBCO	RDW
+LABCBCO	MCHC
+LABCBCO	MCH
+LABPHOS	PHOS
+LABPTT	PTT
+LABPCG3	PCCOM
+LABPCG3	PCO2A
+LABPCG3	PHA
+LABPCG3	PO2A
+LABPCG3	HCO3A
+LABPCG3	TCO2A
+LABPCG3	O2SATA
+LABPCG3	FIO2
+LABCAI	CAION
+EKG5	ECGIMPRESS
+EKG5	HEARTRATE
+EKG5	QRSAXIS
+EKG5	QTCINTERVAL
+EKG5	RR
+EKG5	QRSDINTERVAL
+EKG5	QTINTERVAL
+EKG5	TWAVEAXIS
+LABTBP	SI
+LABTBP	PRO
+LABMETB	EGFR
+LABPCG3	BE
+EKG5	PRINTERVAL
+EKG5	PAXIS
+LABTBP	UBT
+LABTBP	UNT
+LABCBCD	MORPH
+LABCBCD	SLREV
+LABPCG3	BD
+LABTBP	DI
+LABTNI	TNI
+LABLAC	LAC
+LABCAI	PHCAI
+LABPCG3	PCTEMP
+LABTBP1	SI
+LABTBP1	PRO
+LABABG	PHA
+LABABG	CL
+LABABG	GLU
+LABABG	HCO3A
+LABABG	HCT
+LABABG	HGBBG
+LABABG	K
+LABABG	NA
+LABABG	PCO2A
+LABABG	PO2A
+LABABG	TCO2A
+LABABG	METHB
+LABABG	CAION
+LABABG	COHB
+LABABG	CTO2A
+LABABG	O2HBA
+LABBBPRBC	RBCSTA
+LABMETC	EGFR
+LABTBP	XMR
+LABHFP	ALB
+LABHFP	ALKP
+LABHFP	ALT
+LABHFP	AST
+LABHFP	DBIL
+LABHFP	IBIL
+LABHFP	TBIL
+LABHFP	TP
+LABTBP1	UBT
+LABTBP1	UNT
+LABABG	BD
+LABBLC	CULT
+LABBLC	RPT
+LABBLC	SDES
+LABBLC	SREQ
+LABK	K
+LABUA	SPECMN
+LABUA	UCOL
+LABUA	LEUKEST
+LABUA	NITRITE
+LABUA	SPG
+LABUA	UBLOOD
+LABUA	UCLAR
+LABUA	UGLU
+LABUA	UKET
+LABUA	UPH
+LABUA	UPROT
+LABUA	URBC
+LABUA	UWBC
+LABTYPSNI	ARG
+LABCBCD	SIGNIF
+LABUA	UBACT
+LABUA	UCMT
+LABTYPSNI	AS3
+LABBLC2	CULT
+LABBLC2	RPT
+LABBLC2	SDES
+LABBLC2	SREQ
+LABMB	CKMB
+LABTBP1	DI
+LABROMRS	CULT
+LABROMRS	RPT
+LABROMRS	SDES
+LABROMRS	SREQ
+LABFK506L	FK506
+LABFK506L	FK506
+LABFK506L	TACR2
+LABFK506L	TACR3
+LABFK506L	TACR4
+LABTBP1	XMR
+LABVBG	PHV
+LABVBG	CTO2V
+LABVBG	GLU
+LABVBG	HCO3A
+LABVBG	HCT
+LABVBG	K
+LABVBG	PCO2V
+LABVBG	PO2V
+LABVBG	TCO2A
+LABVBG	CAION
+LABVBG	CL
+LABVBG	COHB
+LABVBG	HGBBG
+LABVBG	METHB
+LABVBG	NA
+LABVBG	O2SATV
+LABMB	CK
+LABURNC	CULT
+LABURNC	RPT
+LABURNC	SDES
+LABURNC	SREQ
+LABCBCD	LYM
+LABCBCD	NEUTAB
+LABCBCD	NEUT
+LABCBCD	LYMAB
+LABLDH	LDH
+LABHCTX	HCT
+LABVANPRL	VANCOTROUGH
+LABVANPRL	VANPR2
+LABVANPRL	VANPR3
+LABVANPRL	VANPR4
+LABMB	CKMBRI
+LABUA	MUCUS
+LABARVI	ARV
+LABCBCD	MONO
+LABNA	NA
+LABCBCD	MONOAB
+LABCBCD	BAND
+LABUA	SQEP
+LABUAPRN	SPECMN
+LABUAPRN	UCOL
+LABUAPRN	LEUKEST
+LABUAPRN	NITRITE
+LABUAPRN	SPG
+LABUAPRN	UBLOOD
+LABUAPRN	UCLAR
+LABUAPRN	UGLU
+LABUAPRN	UKET
+LABUAPRN	UPH
+LABUAPRN	UPROT
+LABUAPRN	URBC
+LABUAPRN	UWBC
+LABUAPRN	PRN
+LABUAPRN	UBACT
+LABUAPRN	UCMT
+LABVBG	BD
+LABDIC	DDIMER
+LABDIC	FIBRINOGEN
+LABDIC	INR
+LABDIC	PT
+LABDIC	PTT
+LABDIC	TT
+LABABG	BE
+LABBBPPLT	PLT
+LABPCTNI	PCCOM
+LABPCTNI	TNI
+LABHEPAR	HEPAR
+LABA1C	A1C
+LABURIC	URIC
+LABVBG	BE
+LABLIPS	LIPASE
+LABUAPRN	MUCUS
+LABTSH	TSH
+LABTBP2	SI
+LABTBP2	PRO
+LABLACWB	LACWBL
+LABUAPRN	SQEP
+LABBBPFFP	FFP
+LABTBP3	PRO
+LABTBP3	SI
+LABCK	CK
+LABHEPAR	HEPAR
+LABTBP2	UBT
+LABTBP2	UNT
+LABCRP	CRP
+LABPCCG4V	HCO3V
+LABPCCG4V	LAC
+LABPCCG4V	PCCOM
+LABPCCG4V	PCO2V
+LABPCCG4V	PHV
+LABPCCG4V	TCO2V
+LABPCCG4V	O2SATV
+LABPCCG4V	PO2V
+LABCBCD	META
+LABFIB	FIBRINOGEN
+LABPCEG7	PCCOM
+LABPCEG7	HCO3A
+LABPCEG7	PCO2A
+LABPCEG7	PHA
+LABPCEG7	PO2A
+LABPCEG7	TCO2A
+LABPCEG7	O2SATA
+LABTBP3	UBT
+LABTBP3	UNT
+LABPCEG7	HCT
+LABPCEG7	HGB
+LABPCEG7	K
+LABPCEG7	NA
+LABPCEG7	CAION
+LABCBCD	MYEL
+LABOSM	OSMOL
+LABCBCD	EOS
+LABNTBNP	BNP
+LABCBCD	EOSAB
+LABCDTPCR	CDTR
+LABCDTPCR	CDTSP
+LABPCEG7V	HCO3V
+LABPCEG7V	O2SATV
+LABPCEG7V	PCCOM
+LABPCEG7V	PCO2V
+LABPCEG7V	PHV
+LABPCEG7V	PO2V
+LABPCEG7V	TCO2V
+LABPCEG7V	CAION
+LABPCEG7V	HCT
+LABPCEG7V	HGB
+LABPCEG7V	K
+LABPCEG7V	NA
+LABANER	CULT
+LABANER	RPT
+LABANER	SDES
+LABANER	SREQ
+EKG5	PDF
+LABCBCD	NRBC
+LABBMT2	CO2
+LABBMT2	CR
+LABBMT2	GLU
+LABBMT2	K
+LABBMT2	NA
+LABGRAM	GS
+LABGRAM	RPT
+LABGRAM	SDES
+LABGRAM	SREQ
+LABBMT1	ALB
+LABBMT1	ALKP
+LABBMT1	ALT
+LABBMT1	AST
+LABBMT1	CR
+LABBMT1	GLU
+LABBMT1	K
+LABBMT1	MG
+LABBMT1	NA
+LABBMT1	TBIL
+LABTBP2	DI
+LABRESP	CULT
+LABRESP	RPT
+LABRESP	SDES
+LABRESP	SREQ
+LABFCUL	CULT
+LABFCUL	RPT
+LABFCUL	SDES
+LABFCUL	SREQ
+LABCBCS	WBC
+LABCBCS	HCT
+LABCBCS	HGB
+LABCBCS	MCH
+LABCBCS	MCHC
+LABCBCS	MCV
+LABCBCS	PLT
+LABCBCS	RBC
+LABCBCS	RDW
+LABCBCS	SLREV
+LABPCCR	CR
+LABPCCR	PCCOM
+LABDOA	AMPHET
+LABDOA	BARB
+LABDOA	BENZO
+LABDOA	COCAINE
+LABDOA	DOACOM
+LABDOA	METHADONE
+LABDOA	OPIATE
+LABDOA	PCP
+LABDOA	TCA
+LABDOA	THC
+LABTBP2	XMR
+LABPLTS	PLT
+LABPCEG7	FIO2
+LABLIDOL	LI
+LABLIDOL	LIDO
+LABLIDOL	LIDO2
+LABLIDOL	LIDO3
+LABLIDOL	LIDO4
+LABCBCS	MORPH
+LABTBP3	DI
+LABPCCG4V	BE
+POC31	UPH
+LABFT4	FT4
+POC1	UWBC
+POC1	EXPDATE
+POC1	NITRITE
+POC1	SPG
+POC1	UBLOOD
+POC1	UGLU
+POC1	UKET
+POC1	UPH
+POC1	UPROT
+POC1	DIPLOT
+LABCBCS	NEUTAB
+LABCBCS	MONOAB
+LABCBCS	BASOAB
+LABCBCS	EOSAB
+LABCBCS	LYMAB
+LABCBCS	LYM
+LABCBCS	MONO
+LABCBCS	NEUT
+LABCBCS	BASO
+LABCBCS	EOS
+LABYPREAL	PREALBUMIN
+LABTBP3	XMR
+LABPCG3V	HCO3V
+LABPCG3V	O2SATV
+LABPCG3V	PCCOM
+LABPCG3V	PCO2V
+LABPCG3V	PHV
+LABPCG3V	PO2V
+LABPCG3V	TCO2V
+LABTRIG	TGL
+LABFLDC	CULT
+LABFLDC	GS
+LABFLDC	RPT
+LABFLDC	SDES
+LABFLDC	SREQ
+LABCBCD	BASO
+LABCBCD	BASOAB
+LABLPD	CHOL
+LABLPD	CHOLHDL
+LABLPD	HDL
+LABLPD	LDL
+LABLPD	NHDL
+LABLPD	TGL
+LABPCEG7V	FIO2
+LABPCEG7V	BE
+LABPCEG7	BE
+LABTBP4	PRO
+LABTBP4	SI
+LABTYPSNI	AS
+LABNH3	NH3
+LABPCG3V	FIO2
+LABUA	AMORCRY
+LABTYPSNI	ABORH
+LABFER	FERRITIN
+LABUA	HYCAST
+LABB12	B12
+LABTBP5	PRO
+LABTBP5	SI
+LABUSPG	SPG
+LABTBP4	UBT
+LABTBP4	UNT
+LABPCCG4V	BD
+LABAMY	AMYLASE
+LABCBCD	BLASTS
+LABPATHR	REVUE
+LABCBCD	BLASTSAB
+LABTRFS	FE
+LABTRFS	TRFRN
+LABTRFS	TRFSAT
+LABARI	ABORH
+LABPCEG7	BD
+LABURNA	UNA
+LABRETIC	RETIC
+LABRETIC	RETICAB
+LABHAP	HAPTO
+LABASI	AS
+LABTBP5	UBT
+LABTBP5	UNT
+LABAFBC	AFB
+LABAFBC	CULT
+LABAFBC	RPT
+LABAFBC	SDES
+LABAFBC	SREQ
+LABCYCWBL	CYC
+LABCYCWBL	CYC2
+LABCYCWBL	CYC3
+LABCYCWBL	CYC4
+LABCYCWBL	CYCWB
+LABALB	ALB
+LABPHEN	PHEN
+LABPHEN	PHEN
+LABPHEN	PHEN2
+LABPHEN	PHEN3
+LABPHEN	PHEN4
+LABUOSM	UOSM
+LABCBCD	REACTL
+LABCORT	CORT
+LABUA	TRANST
+LABDIGL	DIG
+LABDIGL	DIG
+LABDIGL	DIG2
+LABDIGL	DIG3
+LABDIGL	DIG4
+LABVOLTL	ETOH
+LABVOLTL	ETOHI
+LABVOLTL	OSMGAP
+LABVOLTL	OSMGPI
+LABCBCD	PRMY
+LABSLHELD	SLHELD
+LABAFBD	AFB
+LABAFBD	CULT
+LABAFBD	RPT
+LABAFBD	SDES
+LABAFBD	SREQ
+LABBFCT	DX
+LABBFCT	FCOLOR
+LABBFCT	FLDTYP
+LABBFCT	MALIG
+LABBFCT	FTURB
+LABBFCT	LYMFLD
+LABBFCT	MONFLD
+LABBFCT	RBCFLD
+LABBFCT	WBCFLD
+LABBFCT	NEUTFLD
+LABESRP	ESR
+LABCBCD	PSREV
+LABTBP6	PRO
+LABTBP6	SI
+LABPCG3V	BD
+LABTBP	SPEXP
+LABHIVWBL	HIVAR
+LABHIVWBL	HIVI
+LABCSFCT	BTURB
+LABCSFCT	COLORCSF
+LABCSFCT	CSOURC
+LABCSFCT	DX
+LABCSFCT	MALIG
+LABCSFCT	RBCCSF
+LABCSFCT	TUBENO
+LABCSFCT	WBCCSF
+LABCSFCT	LYMCSF
+LABCSFCT	MONOCSF
+LABCSFCT	NEUTCSF
+LABUCR	UCR
+LABCSFCT	COLORCSF
+LABCSFCT	CTURB
+LABBLCSTK	CULT
+LABBLCSTK	RPT
+LABBLCSTK	SDES
+LABBLCSTK	SREQ
+LABPCEG7V	BD
+LABTBP4	DI
+LABBXTG	CULT
+LABBXTG	GS
+LABBXTG	RPT
+LABBXTG	SDES
+LABBXTG	SREQ
+LABTBP7	PRO
+LABTBP7	SI
+LABPCEG7	PCTEMP
+LABSPLAC	LACWBL
+LABPCEG7V	PCTEMP
+LABUAPRN	AMORCRY
+LABCBCS	SIGNIF
+LABPCCG4O	HCO3
+LABPCCG4O	LAC
+LABPCCG4O	PCCOM
+LABPCCG4O	PCO2A
+LABPCCG4O	PCO2SO
+LABPCCG4O	PH
+LABPCCG4O	PO2A
+LABPCCG4O	TCO2A
+LABDBIL	DBIL
+LABESRP	ESR
+LABTBP5	DI
+LABUAPRN	HYCAST
+LABBLCTIP	CULT
+LABBLCTIP	RPT
+LABBLCTIP	SDES
+LABBLCTIP	SREQ
+LABSTOBGD	OCCGU
+LABSTOBGD	DATE1
+LABPCG3V	BE
+POC31	QC10
+POC31	QC5
+POC31	TESTLO
+LABGGT	GGT
+LABTBP6	UBT
+LABTBP6	UNT
+LABSTOBGD	DATE2
+LABSTOBGD	DATE3
+LABCBCD	MAOTH
+LABHBSAG	HBSINT
+LABHBSAG	HBSR
+LABPALB	PREALBUMIN
+LABPCG3O	HCO3
+LABPCG3O	PCCOM
+LABPCG3O	PCO2A
+LABPCG3O	PCO2SO
+LABPCG3O	PH
+LABPCG3O	PO2A
+LABPCG3O	TCO2A
+LABDVERVP	ADVP
+LABFOL	FOLATE
+LABDVERVP	INFAP
+LABDVERVP	INFBP
+LABDVERVP	MPVPR
+LABDVERVP	P1P
+LABDVERVP	P2P
+LABDVERVP	P3P
+LABDVERVP	RVP
+LABDVERVP	RVPIP
+LABDVERVP	RVPSP
+LABPATHR	FPATH
+LABTBP7	UBT
+LABTBP7	UNT
+LABCSFGL	GLUCSF
+LABCSFTP	TPCSF
+LABPCEC4V	HCT
+LABPCEC4V	HGB
+LABPCEC4V	K
+LABPCEC4V	NA
+LABPCEC4V	PCCOM
+LABPCEC4V	GLU
+LABBUN	BUN
+LABSTOBGD	OCCGU
+LABSTOBGD	OCCGU
+LABUEP	UCL
+LABUEP	UCLCR
+LABUEP	UCR
+LABUEP	UK
+LABUEP	UKCR
+LABUEP	UNA
+LABUEP	UNACR
+LABCA	CA
+LABUA	UWBCC
+LABTBP4	XMR
+LABACETA	ACETA
+LABPROCT	PROCTL
+LABANER2	CULT
+LABANER2	RPT
+LABANER2	SDES
+LABANER2	SREQ
+LABTBP1	SPEXP
+LABUAPRN	TRANST
+LABCSFC	CULT
+LABCSFC	GS
+LABCSFC	RPT
+LABCSFC	SDES
+LABCSFC	SREQ
+LABTBP5	XMR
+LABUAS	UPROT
+LABUAS	LEUKEST
+LABUAS	NITRITE
+LABUAS	SPECMN
+LABUAS	SPG
+LABUAS	UBLOOD
+LABUAS	UCLAR
+LABUAS	UCOL
+LABUAS	UGLU
+LABUAS	UKET
+LABUAS	UPH
+LABCMVPQ	CMVCP
+LABCMVPQ	CMVDNA
+LABCMVPQ	CMVLOG
+LABCMVPQ	CMVSP
+LABCMVPQ	CMVTIP
+LABPCINR	INR
+LABPCINR	PCCOM
+LABPCG3V	PCTEMP
+LABTBP8	PRO
+LABTBP8	SI
+LABPCINR	PT
+LABCBCD	OTHER
+LABTBP6	DI
+LABFTP	TPFLD
+LABFKOH	CULT
+LABFKOH	KOH
+LABFKOH	RPT
+LABFKOH	SDES
+LABFKOH	SREQ
+LABDDIM	DDIMER
+LABUNA	UCR
+LABUNA	UNA
+LABUNA	UNACR
+LABHGBX	HGB
+LABTBP9	PRO
+LABTBP9	SI
+LABSPLAC	SPLACW
+LABVANUNL	VANCOSDD
+LABVANUNL	VANUN2
+LABVANUNL	VANUN3
+LABVANUNL	VANUN4
+LABRESPG	CULT
+LABRESPG	GS
+LABRESPG	RPT
+LABRESPG	SDES
+LABRESPG	SREQ
+LABTBP7	DI
+POC26	KIT1
+POC26	KITEX1
+POC26	PATRES
+POC26	QC9
+LABPCACTK	ACT
+LABPCACTK	PCCOM
+LABANAS	ANA
+LABANAS	ANAINTERP
+LABTBILFR	DBIL
+LABTBILFR	IBIL
+LABTBILFR	TBIL
+LABPCCG4O	BE
+LABUPREG	UPREG
+LABTBP8	UBT
+LABTBP8	UNT
+LABFTP	TPFLS
+LABLPDC	CHOL
+LABLPDC	CHOLHDL
+LABLPDC	HDL
+LABLPDC	LDL
+LABLPDC	LDLHDL
+LABLPDC	NHDL
+LABLPDC	TGL
+LABSIROL	SIRO2
+LABSIROL	SIRO3
+LABSIROL	SIRO4
+LABSIROL	SIROL
+LABSIROL	SIROL
+LABWDC	CULT
+LABWDC	RPT
+LABWDC	SDES
+LABWDC	SREQ
+LABSALICY	SALICYLATE
+LABBFCT	FCMT
+LABBBPCRY	CRSTA
+LABTBP9	UBT
+LABTBP9	UNT
+LABVCUL	CULT
+LABVCUL	RPT
+LABVCUL	SDES
+LABVCUL	SREQ
+LABUAPRN	UWBCC
+LABTBP10	PRO
+LABTBP10	SI
+LABVPAL	VPA
+LABVPAL	VPA
+LABVPAL	VPAL2
+LABVPAL	VPAL3
+LABVPAL	VPAL4
+LABMTXL	MTX
+LABMTXL	MTX
+LABMTXL	MTXL2
+LABMTXL	MTXL3
+LABMTXL	MTXL4
+LABFLDH	LDHFLD
+LABFLDH	LDHFLS
+LABURUN	UURN
+LABPCG3O	FIO2
+LABRFRPT	REFRPT
+LABPCG3O	BE
+LABFAMY	AMYFLS
+LABFAMY	AMYLASEFLD
+LABUA	GRCAST
+LABOAPE	CULT
+LABOAPE	RPT
+LABOAPE	SDES
+LABOAPE	SREQ
+LABSTSS	STSINT
+LABSTSS	STSQUL
+LABTBP11	PRO
+LABTBP11	SI
+LABUAS	UCMT
+LABRBCFOL	FOLATERBC
+LABMYCPA	MYPA
+LABMYCPA	MYPAG
+LABMYCPA	MYPA1
+LABMYCPA	MYPA2
+LABMYCPA	MYPA3
+LABMYCPA	MYPA4
+LABTBP8	DI
+LABCMVQT	CMVCP
+LABCMVQT	CMVDNA
+LABCMVQT	CMVLOG
+LABCMVQT	CMVSP
+LABCMVQT	CMVTIP
+LABTBP6	XMR
+LABTBP	VL
+LABCBCS	LYM
+LABCBCS	LYMAB
+LABTBP10	UBT
+LABTBP10	UNT
+LABCBCS	NEUT
+LABCBCS	NEUTAB
+LABREPT	REPT
+LABDATBSI	DATBS
+LABIBIL	IBIL
+LABTBP7	XMR
+LABPCG3O	BD
+LABHITAB	PF4COM
+LABHITAB	PF4ENH
+LABCR	CR
+LABTBP9	DI
+LABPCCG4O	BD
+LABSCMV	CULT
+LABSCMV	RPT
+LABSCMV	SDES
+LABSCMV	SREQ
+LABARI	SPEXP
+LABFAL	ALBFLS
+LABFAL	ALBFLD
+LABPREG	PREGTEST
+LABASI	SPEXP
+LABREF1	LAB1
+LABREF1	SPTY
+LABREF1	TEST1
+LABREF1	RESUL1
+LABTBP11	UBT
+LABTBP11	UNT
+LABTBP12	PRO
+LABTBP12	SI
+LABTSHFT4	TSH
+LABTBIL	TBIL
+LABSPIE	SPIEI
+LABSPIE	SPIER
+LABPCYS	PCPSTAIN
+LABPCYS	RPT
+LABPCYS	SDES
+LABPCYS	SREQ
+LABWDCS	CULT
+LABWDCS	GS
+LABWDCS	RPT
+LABWDCS	SDES
+LABWDCS	SREQ
+LABDVERV	DVE
+LABDVERV	RPT
+LABDVERV	SDES
+LABDVERV	SREQ
+LABCBCS	BAND
+LABQFTB	QTFNIN
+LABQFTB	QTFNRS
+LAB11125R	COCI
+LABTP	TP
+LABUEOS	UEOS
+LABCBCD	ABNWBC
+LABCBCS	MONO
+LABHCVA	HCVI
+LABHCVA	HCVR
+LABCBCS	MONOAB
+LABPTH	PTH
+LABCMVPQ	CMVCP
+LABCMVPQ	CMVDNA
+LABCMVPQ	CMVLOG
+LABCMVPQ	CMVSP
+LABCMVPQ	CMVTIP
+LABHIPA	HIPINT
+LABTBP13	PRO
+LABTBP13	SI
+LABHIPA	HEPTYP
+LABHIPA	LOTNO
+LABHIPA	MANUF
+LABLIL	LI
+LABLIL	LI
+LABLIL	LI2
+LABLIL	LI3
+LABLIL	LI4
+LABIGG	IGG
+LABFLGL	GLFLS
+LABFLGL	GLUFLD
+LABHIPA	HIPDIR
+LABPCG3O	PCTEMP
+POC1	PHLOC
+LABABIDI	ABIDRBC
+LABFCR	CRFLD
+LABFCR	CRFLS
+LABBFCT	EOSFLD
+LABUA	BYST
+LABTBP10	DI
+LABPCCG4	HCO3A
+LABPCCG4	O2SATA
+LABPCCG4	PCCOM
+LABPCCG4	PCO2A
+LABPCCG4	PHA
+LABPCCG4	PO2A
+LABPCCG4	TCO2A
+LABPCCG4	LAC
+LABTBP12	UBT
+LABTBP12	UNT
+LABUAPRN	GRCAST
+LABAFP	AFP
+LABPTEG	PTEGA
+LABPTEG	PTEGC
+LABPTEG	PTEGEL
+LABPTEG	PTEGG
+LABPTEG	PTEGK
+LABPTEG	PTEGMA
+LABPTEG	PTEGRK
+LABPTEG	PTEGSP
+LABTBP14	PRO
+LABTBP14	SI
+LABDDIML	DDIMER
+LABCBCD	CCNT
+LABDDIML	DIMCOM
+LABTBP11	DI
+LABUAPRN	BYST
+LABDHBP	HBCINT
+LABDHBP	HBCR
+LABDHBP	HBSINT
+LABDHBP	HBSR
+LABDHBP	HBTINT
+LABDHBP	HBTR
+LABDHBP	HQLINT
+LABDHBP	HQLR
+LABAHP2	HAVINT
+LABAHP2	HAVR
+LABAHP2	HBCINT
+LABAHP2	HBCR
+LABAHP2	HBSINT
+LABAHP2	HBSR
+LABAHP2	HCVI
+LABAHP2	HCVR
+LABVD25H	25OHDT
+LABVD25H	VD25D2
+LABVD25H	VD25D3
+POC39	EXPDATE
+POC39	PHLOC
+POC39	UPH
+POC39	DIPLOT
+LABTBP13	UBT
+LABTBP13	UNT
+LABUUN	UNCRU
+LABUUN	UNUCRU
+LABUUN	UURN
+LABPROC	PROC
+LABEBVQP	EBVQCP
+LABEBVQP	EBVQDN
+LABEBVQP	EBVQLG
+LABEBVQP	EBVQSP
+LABRPRQL	RPRQ
+LABRPRQL	RPRQIN
+LABEBVQP	EBVQIN
+POC14	GLU
+LABLEGDA	CULT
+LABLEGDA	RPT
+LABLEGDA	SDES
+LABLEGDA	SREQ
+LABTBP15	PRO
+LABTBP15	SI
+LABMVSOB	MVSO
+LABMVSOB	RES
+LABMVSOB	SCE
+LABTEGCP	TEGSP
+LABUTPRAN	UCR
+LABUTPRAN	UPROTCR
+LABUTPRAN	UPROTTOT
+LABVP3	HATINT
+LABVP3	HATR
+LABVP3	HBSINT
+LABVP3	HBSR
+LABVP3	HBTINT
+LABVP3	HBTR
+LABVP3	HCVI
+LABVP3	HCVR
+LABVP3	HQLINT
+LABVP3	HQLR
+LABTEGCP	TEGCOM
+LABTEGCP	TEGC
+LABTEGCP	TEGRK
+LABTEGCP	TEGA
+LABTEGCP	TEGG
+LABTEGCP	TEGK
+LABTEGCP	TEGM
+LABWDCG	CULT
+LABWDCG	GS
+LABWDCG	RPT
+LABWDCG	SDES
+LABWDCG	SREQ
+LABTEGCP	TEGEL
+LABTEGCP	TEGMD2
+LABSTLCX	CULT
+LABTBP12	DI
+LABSTLCX	RPT
+LABSTLCX	SDES
+LABSTLCX	SREQ
+LABTBP8	XMR
+LABSTOOLC	CULT
+ECH15	LVEF
+LABSTOOLC	RPT
+LABSTOOLC	SDES
+LABSTOOLC	SREQ
+LAB11124R	COCFS
+LABTBP14	UBT
+LABTBP14	UNT
+LABPHME	PHA
+LABPHME	PHMETS
+LABHELI	HPYLORI
+LABCEA	CEA
+LABTBP9	XMR
+LABTBP2	SPEXP
+LAB11041R	HGB
+LABHCLOTI	HCLOT
+LABMYO	MYOG
+LABTBP16	PRO
+LABTBP16	SI
+LABEP1	AG
+LABEP1	CL
+LABEP1	CO2
+LABEP1	K
+LABEP1	NA
+LABCA199	C199
+LABAGALA	AGALA
+LABLEGU	LEGUA
+LABTBP13	DI
+LABCMVP	CMQDNA
+LABCMVP	CMVLIP
+LABCMVP	CMVQSP
+LABCORTS	CORTS
+LABUA	CAOX
+LABTBP15	UBT
+LABTBP15	UNT
+LABBFCT	FOTHER
+LABRESPCR	ADNVIR
+LABRESPCR	INFAST
+LABRESPCR	INFLA
+LABRESPCR	INFLB
+LABRESPCR	METPNV
+LABRESPCR	PARIN1
+LABRESPCR	PARIN2
+LABRESPCR	PARIN3
+LABRESPCR	RESVDI
+LABRESPCR	RESVSP
+LABRESPCR	RHNVIR
+LABRESPCR	RSVPC
+LABQFTBG	TBAG
+LABQFTBG	TBAGNI
+LABQFTBG	TBGINT
+LABQFTBG	TBGNIL
+LABQFTBG	TBGRES
+LABQFTBG	TBMINI
+LABHCVA	HCVI
+LABHCVA	HCVR
+LABTBP17	PRO
+LABTBP17	SI
+LABIGET	IGET
+LABCMVIGG	CMGDES
+LABCMVIGG	CMGQUL
+POC5	OCCBLD
+LABBHCG	BHCG
+LABC3	C3
+LABC4	C4
+LABYFT3	FT3
+LABCRPLB	CRPLB
+LABAST	AST
+LABLUPUS	LUPINT
+POC5	DEVEL
+POC5	DEVEXP
+POC5	HEMED
+POC5	HEML
+POC5	QC10
+LABHCPQ	HCVIPR
+LABHCPQ	HCVLOG
+LABHCPQ	HCVRES
+LABHCPQ	HCVRNA
+LABHCPQ	HCVSP
+LABCTC	CULT
+LABCTC	RPT
+LABCTC	SDES
+LABCTC	SREQ
+LABALT	ALT
+LABPSA	PSA
+LABLEGU	TINFO
+LABPCCG4	BD
+LABCLACT	LACCSF
+LABTBP3	SPEXP
+LABEBPCRQ	EBVCP
+LABEBPCRQ	EBVDNA
+LABEBPCRQ	EBVIPR
+LABEBPCRQ	EBVLOG
+LABEBPCRQ	EBVSP
+LABUPIE	UPIEI
+LABUPIE	UPIER
+LABSYNCT	DX
+LABGLNF	GLU
+LABSYNCT	JOINT
+LABSYNCT	MALIG
+LABSYNCT	SCOLOR
+LABSYNCT	SLYM
+LABSYNCT	SMONO
+LABSYNCT	SNEUT
+LABSYNCT	SRBC
+LABSYNCT	STURB
+LABSYNCT	SWBC
+LABEITC	CEITC
+LABRF	RHFINT
+LABRF	RHFR
+LABCBCS	META
+LABTCDIF	CULT
+LABTCDIF	RPT
+LABTCDIF	SDES
+LABTCDIF	SREQ
+LABCBCS	MYEL
+LABEBVPAN	EAGQUL
+LABEBVPAN	EBINTR
+LABEBVPAN	EBNQUL
+LABEBVPAN	EBVDS
+LABEBVPAN	VGQUL
+LABEBVPAN	VMQUL
+LABTRXNWI	CTRXNW
+LABTRXNWI	TRXNWR
+LABSTLC	CULT
+LABSTLC	RPT
+LABSTLC	SDES
+LABSTLC	SREQ
+LABTBP14	DI
+LABCBCS	NRBC
+LABHAVT	HATINT
+LABHAVT	HATR
+LABTT	TT
+LABEBVPAN	VGQNT
+LABBXTG	CULT
+LABBXTG	GS
+LABBXTG	RPT
+LABBXTG	SDES
+LABBXTG	SREQ
+LABCMVIGG	CMGQNT
+LABTBP18	PRO
+LABTBP18	SI
+LABTBP16	UBT
+LABTBP16	UNT
+LABDATBSI	DATG
+LABDATBSI	DATC3
+LABFLDB	CULT
+LABFLDB	GS
+LABFLDB	RPT
+LABFLDB	SDES
+LABFLDB	SREQ
+LABEBVPAN	EBNQNT
+LABFLUAPX	FLUXDI
+LABFLUAPX	FLUXMT
+LABFLUAPX	FLUXR
+LABFLUAPX	FLUXSP
+LABTYPSNI	SPEXP
+LABALKP	ALKP
+LABGNTPRL	GENTTRO
+LABGNTPRL	GNTPR2
+LABGNTPRL	GNTPR3
+LABGNTPRL	GNTPR4
+LABCSFCT	CCMT
+LABCRPLC	CRPLC
+LABPCCH8	CAION
+LABPCCH8	CR
+LABPCCH8	GLU
+LABPCCH8	HCT
+LABPCCH8	HGB
+LABPCCH8	K
+LABPCCH8	NA
+LABPCCH8	PCAGP
+LABPCCH8	PCBUN
+LABPCCH8	PCCL
+LABPCCH8	PCCOM
+LABPCCH8	TCO2A
+LABTBP10	XMR
+LABDSDNA	DNAINT
+LABDSDNA	DNAQUL
+LABACTHO	ACTH
+LABFE	FE
+LABYHISTU	YHISU4
+LABHITAB	PF4COM
+LABHITAB	PF4ENH
+LABACA	ACAIGG
+LABACA	ACAIGM
+LABTBP19	PRO
+LABTBP19	SI
+LABASQ	ACETONE
+LABDVEHV	HERPR
+LABDVEHV	HVIP
+LABDVEHV	HVSP
+LABDVEHV	VZR
+LABTBP17	UBT
+LABTBP17	UNT
+LABCBCS	EOS
+LABTBP15	DI
+LABTBP11	XMR
+LABLEID	LEICMT
+LABLEID	LEIDIS
+LABLEID	LEIMET
+LABLEID	LEIPRM
+LABLEID	LEIRES
+LABCMVP	CMQDNA
+LABCMVP	CMVQSP
+LABCBCS	EOSAB
+LABLEID	LEIDIR
+LABCMVP	CMVLIP
+LABUCRT	CRE
+LABUCRT	UCR
+LABUCRT	UT3
+LABUCRT	VOL3
+LABTOBPRL	TOBPR2
+LABTOBPRL	TOBPR3
+LABTOBPRL	TOBPR4
+LABTOBPRL	TOBRATROUGH
+LABHAVM	HAVINT
+LABHAVM	HAVR
+LABABIDI	ABIDR
+LABHBSQL	HQLINT
+LABHBSQL	HQLR
+LAB11131R	VORI
+LABYCERU	CER
+LABFTRPL	TGLFLD
+LABBFCT	BASOFLD
+LABURK	UK
+LABAFBCF	AFB
+LABAFBCF	CULT
+LABAFBCF	RPT
+LABAFBCF	SDES
+LABAFBCF	SREQ
+LABP20210	PTCMT
+LABP20210	PTDIS
+LABP20210	PTMET
+LABP20210	PTPRM
+LABP20210	PTRES
+LABTBP20	PRO
+LABTBP20	SI
+LABP20210	PT20DR
+LABUA	URENAL
+LABBLCTIP	CULT
+LABHBCT	HBTINT
+LABHBCT	HBTR
+LABBLCTIP	RPT
+LABBLCTIP	SDES
+LABBLCTIP	SREQ
+LABLUPUS	LUPDIR
+LABHMCY	HMCY
+LABEBPCRQ	EBVCP
+LABEBPCRQ	EBVDNA
+LABEBPCRQ	EBVIPR
+LABEBPCRQ	EBVLOG
+LABEBPCRQ	EBVSP
+LABLPD	CHOL
+LABLPD	CHOLHDL
+LABLPD	HDL
+LABLPD	LDL
+LABLPD	NHDL
+LABLPD	TGL
+LABTBP18	UBT
+LABTBP18	UNT
+LABHELAG	CULT
+LABHELAG	RPT
+LABHELAG	SDES
+LABHELAG	SREQ
+LABABIDI	ABID
+LABHSVPCR	HSV1PC
+LABHSVPCR	HSV2PC
+LABHSVPCR	HSVIN
+LABHSVPCR	SDES11
+LAB11075R	ASMA
+LABAT3	AT3
+LABCBCS	PSREV
+LAB11060R	VITB1
+LABORANER	CULT
+LABTBP16	DI
+LABORANER	RPT
+LABORANER	SDES
+LABORANER	SREQ
+LABCMVIGM	CMMDES
+LABCMVIGM	CMMQUL
+LABORAERO	CULT
+LABORAERO	RPT
+LABORAERO	SDES
+LABORAERO	SREQ
+LABPB	PB
+LABPB	PB
+LABPB	PB2
+LABPB	PB3
+LABPB	PB4
+LABTBP21	PRO
+LABTBP21	SI
+LABORSHOR	CULT
+LABORAERO	GS
+LABORSHOR	RPT
+LABORSHOR	SDES
+LABORSHOR	SREQ
+LABYHISTU	YHISU5
+LABPCCG4	BE
+LABIGP	IGA
+LABIGP	IGG
+LABIGP	IGM
+LABFACT8	FACTOR7
+LABYHISTS	YHISS4
+LABERURBC	RBCSTA
+LABCYTGEN	CYTGN1
+LABCSFCT	EOSCSF
+LAB11049R	NEURSP
+LABCMV	CMV
+LABPCEC4O	GLU
+LABPCEC4O	HCT
+LABPCEC4O	HGB
+LABPCEC4O	K
+LABPCEC4O	NA
+LABPCEC4O	PCCOM
+LABGLOB	GLOB
+LABPROL	PROLACTIN
+LABTBP19	UBT
+LABTBP19	UNT
+LABCFRESP	CULT
+LABCFRESP	RPT
+LABCFRESP	SDES
+LABCFRESP	SREQ
+LABCLGC2	CHLAM
+LABCLGC2	DCL2
+LABCLGC2	GC
+LABCLGC2	SDES2
+LABHGBP	YHGBP1
+LABCMV	CMV
+LABTBP17	DI
+LABABIDI	ABIDELUATE
+LABANAS	ANAQNT
+LABTBP12	XMR
+LABHBPQ	HBIPR
+LABHBPQ	HBPDNA
+LABHBPQ	HBPLOG
+LABHBPQ	HBPRES
+LABHBPQ	HBPSP
+LABISOF	CULT
+LABVP4	HATINT
+LABVP4	HATR
+LABVP4	HBSAIN
+LABVP4	HBSAQL
+LABVP4	HBSAQT
+LABVP4	HBSINT
+LABVP4	HBSR
+LABVP4	HBTINT
+LABVP4	HBTR
+LABVP4	HCVI
+LABVP4	HCVR
+LABISOF	RPT
+LABISOF	SDES
+LABISOF	SREQ
+LABTBPCRS	CULT
+LABTBPCRS	RPT
+LABTBPCRS	SDES
+LABTBPCRS	SREQ
+LABGLF	GLUF
+LABPCEG7O	HCO3
+LABPCEG7O	PCCOM
+LABPCEG7O	PCO2A
+LABPCEG7O	PCO2SO
+LABPCEG7O	PH
+LABPCEG7O	PO2A
+LABTBP22	PRO
+LABTBP22	SI
+LABPCEG7O	TCO2A
+LABPCACT	ACT
+LABPCACT	PCCOM
+LABFLCR	FREEK
+LABFLCR	FREEKL
+LABFLCR	FREEL
+LABURTP	UPROTTOT
+LABPCEG7O	CAION
+LABUAPRN	CAOX
+LABPCEG7O	HCT
+LABPCEG7O	HGB
+LABPCEG7O	K
+LABPCEG7O	NA
+LABUA	HYST
+LABTBLYM	C1656A
+LABTBLYM	C1656P
+LABTBLYM	CD19AB
+LABTBLYM	CD19PC
+LABTBLYM	CD3AB
+LABTBLYM	CD3PC
+LABTBLYM	CD4AB
+LABTBLYM	CD4PC
+LABTBLYM	CD8AB
+LABTBLYM	CD8PC
+LABTBLYM	HNS
+LABTBLYM	HSABS
+LABTBLYM	HSRATIO
+LABTBLYM	LYM
+LABTBLYM	LYMAB
+LABTBLYM	WBC
+LABTBP13	XMR
+LABERUFFP	FFP
+LABMETHB	METHB
+LABYHISTS	YHISS5
+LAB11071R	YAMA
+LABCMVRO	CMQDNA
+LABCMVRO	CMVDSV
+LABCMVRO	CMVLIP
+LABCMVRO	CMVQSP
+LABCMVRO	CMVRC1
+LABCMVRO	CMVRC2
+LABCMVRO	CMVSPM
+LABHIAB	HIARES
+LABTBP20	UBT
+LABTBP20	UNT
+LABHIAB	HIACOM
+LABEBVPAN	EAGQNT
+LABCMVTSI	CMVTOT
+LABDGHBP	HBCINT
+LABDGHBP	HBCR
+LABDGHBP	HBSAIN
+LABDGHBP	HBSAQL
+LABDGHBP	HBSAQT
+LABDGHBP	HBSINT
+LABDGHBP	HBSR
+LABDGHBP	HBTINT
+LABDGHBP	HBTR
+LABTES	TESTOS
+LABHIVQ2	HIVCP
+LABHIVQ2	HIVIPR
+LABHIVQ2	HIVLO
+LABHIVQ2	HIVRNA
+LABHIVQ2	HIVSP
+LABTBP23	PRO
+LABTBP23	SI
+LAB12631R	12630R
+LABCRCL	CORCL
+LABCRCL	COREXC
+LABCRCL	CR
+LABCRCL	CRCL
+LABCRCL	CRE
+LABCRCL	CRUCL
+LABCRCL	FLOWURN
+LABCRCL	HT
+LABCRCL	UT3
+LABCRCL	VOL3
+LABCRCL	WT
+LABCRYST	CRYS
+LABYA1AS	YA1A
+LABCRYST	CXSOUR
+LABC125	C125
+LABCMVIGT	CMGTDS
+LABCMVIGT	CMGTQL
+LABT3	T3
+10290R	9013R
+10290R	9014R
+10290R	9080R
+10290R	9448R
+LABUA	FRFAT
+LABPTTINH	PTTINT
+10290R	9015R
+10290R	9016R
+10290R	9017R
+10290R	9018R
+10290R	9019R
+10290R	9075R
+10290R	9076R
+LABUA	OVFAT
+LABUAPRN	HYST
+LABTBP21	UBT
+LABTBP21	UNT
+LABCBCS	BASO
+LABHV6DNA	HVDNA
+LABGIARS	CULT
+LABGIARS	RPT
+LABGIARS	SDES
+LABGIARS	SREQ
+LABVZAB	VZAB
+LABBXTG	CULT
+LABBXTG	GS
+LABBXTG	RPT
+LABBXTG	SDES
+LABBXTG	SREQ
+LABUUNT	TUNU
+LABUUNT	UREAVOL
+LABUUNT	UT4
+LABUUNT	UURN
+LABCBCS	BASOAB
+LABPROTC	PROTC
+LABHSVZL	HSV1DN
+LABHSVZL	HSV2DN
+LABHSVZL	HSVZIP
+LABHSVZL	HSVZSP
+LABHSVZL	VZVDN
+LABSTOBGC	OCCGU
+10290R	9021R
+10290R	9022R
+10290R	9023R
+10290R	9024R
+10290R	9029R
+10290R	9030R
+LABSTOBGC	DATE1
+LABFVARZ	VZVDNA
+LABHITAB	PF4IGG
+LAB11824R	11825R
+LABDVERVR	ADVR
+LABDVERVR	INFAR
+LABDVERVR	INFBR
+LABDVERVR	MPVRR
+LABDVERVR	P1R
+LABDVERVR	P2R
+LABDVERVR	P3R
+LABDVERVR	RSVR
+LABDVERVR	RVRPIP
+LABDVERVR	RVRSP
+LABTBP18	DI
+LABPROTS	PROTS
+LABUA	URIC
+LABCBCS	BLASTS
+LABLEVETL	LEVET
+LABLEVETL	LEVET1
+LABLEVETL	LEVET2
+LABLEVETL	LEVET3
+LABLEVETL	LEVET4
+LABHV6DNA	SOUR12
+LABTT4	T4
+LABCBCS	BLASTSAB
+10290R	9027R
+10290R	9028R
+LABANTIXA	ANTIXA
+LABARI	ARG
+LABDVEVH	DVE
+LABSTPOLY	EOSSTL
+LABSTPOLY	POLYST
+LABTBP24	PRO
+LABDVEVH	RPT
+LABDVEVH	SDES
+LABTBP24	SI
+LABDVEVH	SREQ
+LABSTOBGC	DATE2
+LABFVARZ	SOUR13
+LABCMVIGT	CMGTQT
+LABSTOBGC	DATE3
+LABCMVM	CMVIGM
+LABUTP	TTPU
+LABUTP	UPROTTOT
+LABUTP	UT10
+LABUTP	VOL10
+LABINSLF	INSF
+LABMALAR	MAL
+LABTBP14	XMR
+LABCMVQL	CMQDNA
+LABCMVQL	CMVLIP
+LABCMVQL	CMVQSP
+LABCBCS	MAOTH
+LABNORW	NORW1
+LABCSFCT	COTHER
+LAB11123R	COCC
+LABTHEOL	THEO
+LABTHEOL	THEO1
+LABTHEOL	THEO2
+LABTHEOL	THEO3
+LABTHEOL	THEO4
+LABTBP22	UBT
+LABTBP22	UNT
+LABTBP19	DI
+LABUAPRN	URENAL
+LABANTIM	ATPO
+LABUA	SPERM
+LABTBP4	SPEXP
+LAB11068R	ACE
+LABBFCT	CILIA
+LABBMT1	EGFR
+LABMMAS	MMA
+LABCZPL	CZP
+LABCZPL	CZP1
+LABCZPL	CZP2
+LABCZPL	CZP3
+LABCZPL	CZP4
+LABTBP25	PRO
+LABTBP25	SI
+LABCBCS	OTHER
+LABSTOBGC	OCCGU
+LABSTOBGC	OCCGU
+LAB11136R	HTLVS
+LABURCL	UCL
+LABEB	COM
+LABTBP15	XMR
+10335R	9271R
+10335R	9275R
+10335R	9282R
+10335R	9585R
+10335R	9605R
+LABCMVM	CMVIGM
+LABTRFN	TRFRN
+10335R	9263R
+10335R	9274R
+10335R	9350R
+10335R	9265R
+LABYBOH	BOH
+LABHGBP	12078R
+LABANCA	ANCIGG
+LABYFGL	GLUFLD
+LABYFGL	YFLSC1
+LABBMT2	EGFR
+LABANCA	MPOAR
+LABPTTINH	PTTDIR
+LABANCA	SERINEPR3
+LABTBP23	UBT
+LABTBP23	UNT
+10335R	9266R
+LABSYNCT	SCMT
+LABANTIXA	ANTIXA
+LABTBP20	DI
+LABCBCD	LYMPHM
+LABBHB	BHB
+LABTBP26	PRO
+LABTBP26	SI
+LABCSFCT	CRLYM
+LABHSVGM	HSVSL1
+LABHSVGM	HSVSL2
+LABHSVGM	HSVSL3
+LABHSVGM	HSVSL4
+LABHSVGM	HSVSL5
+LABHSVGM	HSVSL7
+LABTBP5	SPEXP
+LABYWNILE	WNVIGG
+LABYWNILE	WNVIGM
+LABYWNILE	YWNSC
+LABVCULR	CULT
+LABVCULR	RPT
+LABVCULR	SDES
+LABVCULR	SREQ
+LABCBCD	PLYM
+10330R	9244R
+10330R	9246R
+10330R	9249R
+10330R	9250R
+10330R	9251R
+10330R	9262R
+10330R	9604R
+LABYCP	CPEP
+10330R	9245R
+10330R	9254R
+10330R	9255R
+10330R	9583R
+10330R	9650R
+10330R	9248R
+10330R	9257R
+10330R	9258R
+10330R	9261R
+10330R	9651R
+LABWNVAB	WNVIGG
+LABFLUAPC 	FLUADI
+LABFLUAPC 	FLUAMT
+LABFLUAPC 	FLUAR
+LABFLUAPC 	FLUASP
+LABUA	CECAST
+LABCSFCT	CCNT
+LABTBP21	DI
+LABYUCDA	AMPHET
+LAB11242R	11243R
+LAB11242R	11247R
+LABWNVAB	WNVIGM
+LABCO2	CO2
+LABCBCS	PRMY
+LABFADAG	ADAG
+LABCDPLI	CDPL
+LABTBP27	PRO
+LABTBP27	SI
+LABCBCD	MEGA
+LAB11098R	125OHD2
+LABVZVIGG	VGGDES
+LABVZVIGG	VGGQUL
+LABTBP24	UBT
+LABTBP24	UNT
+LABCL	CL
+LABUAPRN	URIC
+LABYUCDA	ALCOHOL
+LABYPTHRP	PTH
+LABVZVIGG	VGGQNT
+LABEB	11165R
+LABEB	11166R
+LABEB	12001R
+LABEB	12002R
+10290R	9078R
+LABPCEG7O	FIO2
+LABYCOPPR	COPPER
+LABYUCDA	BARB
+LABCMVIGM	CMMQNT
+LABYUCDA	BENZO
+LABYUCDA	COCAINE
+LABYUCDA	OPIATE
+LABYUCDA	YUPCP
+LABYUCDA	YUTTHC
+LAB11069R	ALDOLASE
+LABTBP16	XMR
+LABBCRABL	BCRCMT
+LABBCRABL	BCRDIS
+LABBCRABL	BCRMET
+LABBCRABL	BCRPRM
+LABBCRABL	BCRRES
+LABADVQT	ADDNA
+LABADVQT	ADVCP
+LABADVQT	ADVLOG
+LABADVQT	ADVSP
+LABADVQT	ADVTIP
+LABFHV12	HV1DNA
+LABFHV12	HV2DNA
+LABCBCS	REACTL
+LABEBVPAN	VMQNT
+LABCRYST	BIREF
+LABCRYST	LOCAT
+LABCRYST	SHAPE
+LABCRYST	SIZE
+LABAPHSA2	ACAIGG
+LABAPHSA2	ACAIGM
+LABHBCA	HBCINT
+LABHBCA	HBCR
+LABAPHSA2	LUPINT
+LABTBP25	UBT
+LABTBP25	UNT
+LABBCRABL	BCRDIR
+LABPCEG7O	BE
+LABTBP22	DI
+LABTBP28	PRO
+LABTBP28	SI
+LABTBP17	XMR
+LABGNTPOL	GENTPK
+LABGNTPOL	GNTPO2
+LABGNTPOL	GNTPO3
+LABGNTPOL	GNTPO4
+LAB11234R	11235R
+LAB11234R	11236R
+LAB11234R	11237R
+LABFMYAB	FMYABG
+LABFMYAB	FMYABM
+LABYERYTH	EPO
+LABOLIG	ALBCPI
+LABOLIG	CAB
+LABOLIG	CIG
+LABOLIG	CINDX
+LABOLIG	CPIEI
+LABOLIG	CPIER
+LABOLIG	IGGCPI
+LABLAMOL	LAMO
+LABLAMOL	LAMO1
+LABLAMOL	LAMO2
+LABLAMOL	LAMO3
+LABLAMOL	LAMO4
+LABBLCA	CULT
+LABGASBLD	OCCBLDGAS
+LABBLCA	RPT
+LABBLCA	SDES
+LABBLCA	SREQ
+LABWNVAB	WNITP
+10335R	9264R
+LABCBCD	DYSPL
+LABHBSQT	HBSAIN
+LABHBSQT	HBSAQL
+LABHBSQT	HBSAQT
+LABANTIXA	ANTIXA
+LABEVPCR	EVIN
+LABEVPCR	EVR
+LABAPHSA2	B2GPIG
+LABAPHSA2	B2GPIM
+LABAPHSA2	DRVVCN
+LABAPHSA2	DRVVIN
+LABAPHSA2	DRVVMX
+LABAPHSA2	DRVVSC
+10290R	9077R
+LABYCCPIG	CCPINT
+LABYCCPIG	CCPR
+LABEVPCR	EVSP
+LABAPHSA2	DRVVLA
+LABCKITMU	KITDI
+LABCKITMU	KITDX
+LABCKITMU	KITME
+LABCKITMU	KITPR
+LABCKITMU	KITRE
+LAB12573R	12575R
+LAB12573R	12576R
+LABVCULT	CULT
+LABVCULT	RPT
+LABVCULT	SDES
+LABVCULT	SREQ
+LABYMETFF	UMETF
+LABYMETFF	UNORMETF
+10330R	9247R
+LABTBP29	PRO
+LABTBP29	SI
+LABFHV12	SOURC9
+LABBFCT	FRLYM
+LABERUPLT	PLT
+LABTBP26	UBT
+LABTBP26	UNT
+LAB11023R	11166R
+LABCD4CT	C3P4PA
+LABCD4CT	C3P4PC
+LABCD4CT	LYM
+LABCD4CT	LYMAB
+LABCD4CT	WBC
+LAB11020R	11165R
+LABADPCRQ	ADPCR1
+LABTBP23	DI
+LABADPCRQ	SOUR10
+LABIOPTH	IOPTH
+LAB11024R	11167R
+LAB11030R	11168R
+LABFDP	FDP
+10335R	10472R
+LABSTRONG	STROG
+LABRITUX	C45RAA
+LABRITUX	C45RAP
+LABRITUX	C45ROA
+LABRITUX	C45ROP
+LABRITUX	CD19AB
+LABRITUX	CD19PC
+LABRITUX	CD20AB
+LABRITUX	CD20PC
+LABRITUX	CD3AB
+LABRITUX	CD3PC
+LABRITUX	CD4AB
+LABRITUX	CD4PC
+LABRITUX	CD8AB
+LABRITUX	CD8PC
+LABRITUX	HNS
+LABRITUX	HSABS
+LABRITUX	HSRATIO
+LABRITUX	LYM
+LABRITUX	LYMAB
+LABRITUX	RTXFDA
+LABRITUX	WBC
+LABCEBPA	CEPDI
+LABCEBPA	CEPDX
+LABCEBPA	CEPMT
+LABQFTB	QTFNIN
+LABQFTB	QTFNRS
+LABUALB	UALB
+LABUALB	UALBCR
+LABUALB	UALBCRINTP
+LABUALB	UCR
+LABAPHSAB	ACAIGG
+LABAPHSAB	ACAIGM
+LABKOHP	KOH
+LABAPHSAB	LUPINT
+LABKOHP	RPT
+LABKOHP	SDES
+LABKOHP	SREQ
+10335R	9277R
+10335R	9278R
+10335R	9281R
+10335R	9652R
+LABSSAABY	SSAIN
+LABSSAABY	SSAQUL
+LABYTESFT	TESTOSFREE
+LABCEBPA	CEPRE
+LABHHV6QT	HHV6CP
+LABHHV6QT	HHV6DN
+LABHHV6QT	HHV6LO
+LABHHV6QT	HHV6SP
+LABHHV6QT	HHV6TI
+LABCKITMU	KITAA
+LABCKITMU	KITCO
+LABSTDY1	LABS1
+LABSTDY1	RESS1
+LABSHIV12	SHIV12
+LABSTDY1	SPTYS1
+LABSTDY1	TESTS1
+10150R	10049R
+LABIGA	IGA
+10150R	RINFO
+LABSTREP	BSTREP
+LABCKITMU	KITMD
+LABTBP30	PRO
+LABTBP30	SI
+LABTBP27	UBT
+LABTBP27	UNT
+10330R	10469R
+LABBFCT	CCNT
+LABHSVCSF	HSV1PC
+LABHSVCSF	HSV2PC
+LABHSVCSF	HSVINC
+LABHSVCSF	SDES11
+LABSSBABY	SSBINT
+LABSSBABY	SSBQUL
+10330R	10470R
+LABFADA	ADAFLD
+LABTBP24	DI
+LABCRYO	PL24
+LABCRYO	PL72
+LABFADA	SPTYAD
+LABCRYO	SR24
+LABCRYO	SR72
+LABTBP18	XMR
+LABHIVQ2	HIVNO
+LABB2M	B2M
+LABVSTLC	CULT
+LABVSTLC	RPT
+LABVSTLC	SDES
+LABVSTLC	SREQ
+LABYFVPA	VPAFREE
+LABYFVPA	VPATOTAL
+LABPCEG7O	BD
+LABCEBPA	CEPAA
+POC6	ERYTHREMA
+POC6	MMIND
+LABYFPHEN	PHENFR
+LABYFPHEN	PHENTOT
+POC6	PHLOC
+LABYPARVO	YPARV1
+LABYPARVO	YPARV2
+LABYPARVO	YPARV3
+LABAMLP	FLT3DS
+LABAMLP	FLT3DX
+LABAMLP	FLT3IN
+LABAMLP	FLT3MT
+LABAMLP	FLT3RS
+LABCEBPA	CEPCM
+LABCEBPA	CEPMD
+LABPLTMAP	ADP
+LABTBP6	SPEXP
+LABCSTOTA	PROTCAGT
+LABCSTOTA	PROTSTOT
+LAB12762R	12763R
+LAB12762R	12764R
+LAB12762R	12765R
+LAB12762R	12766R
+LABAMLP	FLT3MD
+LABPLTMAP	ARATEG
+LABCSFCT	BASOCSF
+LABTBP31	PRO
+LABTBP31	SI
+LABBKVPCU	BKCPU
+LABBKVPCU	BKDNAU
+LABBKVPCU	BKIPRU
+LABBKVPCU	BKLOGU
+LABYVITA	FREEVITA
+LABFTRIGA	TGLFLD
+LABUAPRN	CECAST
+LABPTINH	PTINTP
+LABMONEBV	EBV
+LABMONEBV	MONOSC
+10330R	9259R
+LABYHGBP	YHGBP1
+LABYHGBP	12078R
+10290R	9020R
+LABADVDNA	ADOV
+LABADVDNA	SOURC3
+LABTBP25	DI
+LABUAPRN	SPERM
+LAB11070R	ALDOSTR
+LABSTPCR	CULT
+LABHIVQ2	HIVCP
+LABHIVQ2	HIVIPR
+LABHIVQ2	HIVLO
+LABHIVQ2	HIVRNA
+LABHIVQ2	HIVSP
+LABSTPCR	RPT
+LABSTPCR	SDES
+LABSTPCR	SREQ
+LABHIPA	HIPLAB
+LABTBP28	UBT
+LABTBP28	UNT
+LABUAPRN	FRFAT
+LABUAPRN	OVFAT
+LABTBP7	SPEXP
+LABPLTMAP	TEGMD
+10005R	10049R
+10005R	RINFO
+LABANTITG	ATGA
+LABFLL	FSH
+LABBCRQT	QBCRDI
+LABBCRQT	QBCRDX
+LABBCRQT	QBCRME
+LABBCRQT	QBCRPR
+LABBCRQT	QBCRRE
+LAB12571R	12572R
+LABPLTFUN	COLADP
+LABPLTFUN	COLEPI
+LABPLTFUN	PLTFCM
+LABPTINH	PTDIR
+LABYHABQ	HBSAB
+LABYHABQ	HBSABQUAL
+10330R	9256R
+LABTBP32	PRO
+LABTBP32	SI
+LAB11118R	STELAS
+LABTBP19	XMR
+210993284	LEADMFR
+210993284	LEADMODEL
+210993284	LEADTYPE
+10335R	10473R
+LABKWB	K
+LABYZINCS	ZNC
+210993284	LEADIMPDTE
+10365R	9407R
+10365R	9408R
+10365R	9424R
+10365R	9425R
+10365R	9434R
+10365R	9435R
+10365R	9609R
+10365R	9626R
+10365R	9656R
+LABHIVQ2	HIVNO
+LABTBP29	UBT
+LABTBP29	UNT
+10365R	9368R
+10365R	9411R
+10365R	9412R
+10365R	9421R
+LABIGM	IGM
+LABFMYAB	LTROIN
+LAB11092R	RENIN
+LABRNPABY	RNPINT
+LABRNPABY	RNPQUL
+10370R	9436R
+10370R	9437R
+10370R	9466R
+10370R	9610R
+10370R	9631R
+LABHBPQ	HBPNO
+LABUA	OTCAST
+LABPCEG7O	PCTEMP
+LABTBP33	PRO
+LABTBP33	SI
+10370R	9454R
+10370R	9455R
+10370R	9465R
+10370R	9657R
+LABASI	AS3
+LABSCLABY	SCLINT
+LABSCLABY	SCLQUL
+LABHSVQLP	HSV1PC
+LABHSVQLP	HSV2PC
+LABHSVQLP	HSVINP
+LABHSVQLP	SDES11
+10370R	9369R
+10370R	9440R
+10370R	9441R
+10370R	9451R
+LABTBP26	DI
+LABSTORE	STORE
+LAB11081R	GASTRIN
+LABHSAB	HSAB
+LABROPK	CULT
+LABYSTOSM	OSMOLSTL
+LABROPK	RPT
+LABROPK	SDES
+LABROPK	SREQ
+LABTBP20	XMR
+LABFHCT	HCTFLD
+LABFHCT	HCTTYP
+LABCBCD	IMMPL
+LABTBP21	XMR
+LABYFFATQ	YFFQ1
+LABBMCEBP	CEBDI
+LABBMCEBP	CEBDX
+LABBMCEBP	CEBMT
+LAB11036R	FIBRINOGENA
+LABTBP34	PRO
+LABTBP34	SI
+LABYFFATQ	YFFQ2
+LABBMCEBP	CEBRE
+LABBCRQT	QBCRDR
+LABTBP1	VL
+LABYCH50	YCH50S
+LABSTFAT	STFAT
+LABTOXGM	TOXDES
+LABTOXGM	TOXGQL
+LABYVDRLC	VDRL
+LABB2GP1	B2GPIG
+LABB2GP1	B2GPIM
+LABTBP27	DI
+LABSTRED	REDSUBSTL
+LABTOXGM	TOXMQL
+LAB11056R	CORTFREE
+LABMALAR	MALCON
+LABHSCRP	HSCRP
+210993284	LEADSERNUM
+LABLYMES2	LYMEC6
+LAB11013R	11014R
+LAB11982R	11983R
+LABBMCEBP	CEBAA
+LABCHOL	CHOL
+LABDRVVTP	DRVVIN
+LABBMCEBP	CEBMD
+LABDRVVTP	DRVVCN
+LABDRVVTP	DRVVSC
+LABAPHSAB	LUPDIR
+LABTBP35	PRO
+LABBCRQT	QBCRCM
+LABTBP35	SI
+LABYBRUC	YBRUC1
+LABYBRUC	YBRUC2
+LABYBRUC	YBRUC3
+10335R	9276R
+LABYFFATQ	FATPC
+LABYUMYO	UMYOG
+10370R	9423R
+LABRP	AG
+LABRP	ALB
+LABRP	BUN
+LABRP	CA
+LABBMCEBP	CEBCM
+LABRP	CL
+LABRP	CO2
+LABRP	CR
+LABRP	GLU
+LABHSVPAN	HSVDS
+LABHSVPAN	HV1QNT
+LABHSVPAN	HV1QUL
+LABHSVPAN	HV2QNT
+LABHSVPAN	HV2QUL
+LABHSVPAN	HVMQNT
+LABHSVPAN	HVMQUL
+LABRP	K
+LABRP	NA
+LABRP	PHOS
+LABDRVVTP	DRVVLA
+LABTBP30	UBT
+LABTBP30	UNT
+10365R	9415R
+LABHA1C	A1C
+LABHA1C	EMGL
+LABFACT9	FACTOR9
+LABYENPCR	YENP1
+LABYENPCR	YENP2
+LABYENPCR	YENP4
+10045R	10049R
+LABSMABY	ASMINT
+LABSMABY	ASMQUL
+LABATTG	ATTGR
+LABATTG	ATTINT
+LABEPO	EPO
+10045R	RINFO
+10035R	10049R
+LABFQFVR	12008R
+LABFQFVR	12009R
+LABFQFVR	12010R
+LABFQFVR	12011R
+LABAPHSA2	LUPDIR
+10035R	RINFO
+LABUK	UCR
+LABUK	UK
+LABUK	UKCR
+10365R	9414R
+10370R	9422R
+LABSPE	ALB
+LABTBP28	DI
+LABCSFLD	LDHCSF
+LABSPE	SPA1
+LABSPE	SPA2
+LABSPE	SPBE1
+LABSPE	SPBE2
+LABSPE	SPEI
+LABSPE	SPGA
+LABSPE	TPE
+LABTHS	CULT
+POC31	OCCPH
+POC31	QC4
+POC31	QC7
+LABTHS	RPT
+LABTHS	SDES
+LABTHS	SREQ
+POC31	TESTEXP
+POC31	TESTLOT
+LABBMAML	BMFLTD
+LABBMAML	BMFLTI
+LABBMAML	BMFLTM
+LABBMAML	BMFLTR
+LABBMAML	BMFLTX
+LABDVERO	DVE
+LABSYNCT	EOSFLD
+LABDVERO	RPT
+LABDVERO	SDES
+LABDVERO	SREQ
+LABRITUX	C1613AB
+LABRITUX	C1613PC
+LABRITUX	C563AB
+LABRITUX	C563PC
+LABVZVQLP	VZQLDN
+LABVZVQLP	VZQLSP
+LABVZVQLP	VZQLTD
+LABBKVPC	BKCPP
+LABBKVPC	BKDNAP
+LABBKVPC	BKIPRP
+LABBKVPC	BKLOGP
+LABCSMPBM	CSCIM
+LABCSMPBM	CSDIM
+LABCSMPBM	CSMEM
+LABCSMPBM	CSREFM
+LABDRVVTP	DRVVMX
+LABCSMPBM	GAPCM
+LABJAK2	JAKDI
+LABJAK2	JAKDX
+LABJAK2	JAKMD
+LABJAK2	JAKME
+LABJAK2	JAKRE
+LABYUMETF	UMET
+LABYUMETF	UMET
+LABYUMETF	UNORMET
+LABYUMETF	VOLUMEURN
+LABYUMETF	YUMEUT
+LABBMAML	BMFLTB
+LABROMSP	CULT
+LABTBP36	PRO
+LABROMSP	RPT
+LABROMSP	SDES
+LABTBP36	SI
+LABROMSP	SREQ
+LABHGHPLC	HGPRES
+LABUA	OTHCRY
+LAB11085R	MPO
+LABTBP31	UBT
+LABTBP31	UNT
+LABTBP22	XMR
+LABTBP23	XMR
+LABYCHRA	YCHRA1
+LABCSMPB	CSCIB
+LABCSMPB	CSDIB
+LABCSMPB	CSMEB
+LABCSMPB	CSREFB
+LABTBP29	DI
+LABCSMPB	GAPCB
+LABFBILA	BILIRUBINFLD
+LAB11094R	SSA
+LABOXCMET	OXCMT1
+LAB11102R	VITE
+LABBARSAB	BHIGGS
+LABBARSAB	BHIGMS
+LABBARSAB	BQIGGS
+LABBARSAB	BQIGMS
+POC2	DIPLOT
+POC2	EXPDATE
+LABHITIA	HITI1
+LABHITIA	HITI2
+POC2	PHLOC
+10280R	RINFO
+POC2	UBLOOD
+LABVZVCSF	VZQLDN
+LABVZVCSF	VZQLIC
+LABVZVCSF	VZQLSP
+10305R	9123R
+10305R	9125R
+10305R	9126R
+10305R	9127R
+10305R	9128R
+10305R	9129R
+10305R	9130R
+10305R	9132R
+10305R	9134R
+10305R	9151R
+10305R	9152R
+10310R	9153R
+10310R	9155R
+10310R	9156R
+10310R	9157R
+10310R	9158R
+10310R	9159R
+10310R	9160R
+10310R	9162R
+10310R	9164R
+10310R	9181R
+10310R	9182R
+10310R	9183R
+LABHITIA	HIPDIR
+LABYJCPCR	JCDNA
+LABFPARVD	PARVD
+LABYJCPCR	SOURC8
+LABYTESFT	TESTOS
+LABYSTONE	YSTNSO
+LABYSTONE	YSTNWT
+LABYSTONE	1STCONST
+10305R	9124R
+10310R	9154R
+LABCSMPBM	CSMDM
+LABIGGS4	IGGS4
+LABJAK2	JAKCO
+LABJAK2	JAKPR
+LABJAK2	JAKRF
+LABHITAB	PF4IGG
+LABRETIC	RETIC
+LABTBP8	SPEXP
+LAB11556R	11666R
+LAB11556R	BLAS
+LABCRPLFB	CRPLFB
+LABCSMPBM	GBRAFM
+LABCSMPBM	GCTNNM
+LABCSMPBM	GEGFRM
+LABCSMPBM	GIDH1M
+LABCSMPBM	GIDH2M
+LABCSMPBM	GKRASM
+LABCSMPBM	GNOTCM
+LABCSMPBM	GNRASM
+LABCSMPBM	GPIK3M
+LABCSMPBM	GPTENM
+LABCSMPBM	GTP53M
+LABHGHPLC	HGBA2Q
+LABHGHPLC	HGBF
+LABTBP37	PRO
+LABTBP37	SI
+LABFPARVD	SOUR11
+LAB11095R	SSB
+LABYPR3	YPR31
+LAB11107R	HBEAG
+LABHGHPLC	HGBEP2
+LABHGHPLC	HGBS
+LABHGHPLC	HGMD
+LABTOXO	TOXO
+10365R	9630R
+LABCENABY	CENINT
+LABCENABY	CENQUL
+LABCRPLFC	CRPLFC
+LABBFCT	FNRBC
+LABINSL	INSU
+LABTBP32	UBT
+LABTBP32	UNT
+10305R	9707R
+10310R	9708R
+LABUAPRN	OTCAST
+10145R	10049R
+LABYFFATQ	FATTOT
+LABFBILA	FSC2
+10145R	RINFO
+10145R	UNRDID
+10015R	10049R
+LABBMBCR	BMBRCT
+LABBMBCR	BMBRDS
+LABBMBCR	BMBRMT
+LABBMBCR	BMBRPM
+LABBMBCR	BMBRRS
+LABBUNCR	BUNCREAT
+10015R	RINFO
+LAB12808R	12819R
+LAB12808R	CK
+LABHSVIGG	HV1QNT
+LABHSVIGG	HV1QUL
+LABHSVIGG	HV2QNT
+LABHSVIGG	HV2QUL
+LABHSVIGG	HVIGDS
+LABTBP30	DI
+10100R	10049R
+LABBMBCR	BMBRDR
+LABFHVIG	HVIG
+10100R	RINFO
+LABYTRYPT	TRYPTASE
+LABYLEVT	YLEVT1
+LABCSMPB	CSMDB
+LABIGF	IGF
+LABYLAMO	LAMOT
+LAB12214R	LYMEC6
+LABRETIC	RETICAB
+LABTBP9	SPEXP
+LABTBP33	UBT
+LABTBP33	UNT
+LABAMIPRL	AMIKACINTR
+LABAMIPRL	AMIPR2
+LABAMIPRL	AMIPR3
+LABAMIPRL	AMIPR4
+LABC4C8	CD3AB
+LABC4C8	CD3PC
+LABC4C8	CD4AB
+LABC4C8	CD4PC
+LABC4C8	CD8AB
+LABC4C8	CD8PC
+LABCSMPB	GBRAFB
+LABCSMPB	GCTNNB
+LABCSMPB	GEGFRB
+LABCSMPB	GIDH1B
+LABCSMPB	GIDH2B
+LABCSMPB	GKRASB
+LABCSMPB	GNOTCB
+LABCSMPB	GNRASB
+LABCSMPB	GPIK3B
+LABCSMPB	GPTENB
+LABCSMPB	GTP53B
+LABC4C8	HNS
+LABC4C8	HSABS
+LABC4C8	HSRATIO
+LABC4C8	LYM
+LABC4C8	LYMAB
+LABRF	RHFINT
+LABRF	RHFR
+LABUNAT	TNAU
+LABUNAT	UNA
+LABUNAT	UT1
+LABVANPOL	VANCOPEAK
+LABVANPOL	VANPO2
+LABVANPOL	VANPO3
+LABVANPOL	VANPO4
+LABUNAT	VOL1
+LABC4C8	WBC
+LABTBP24	XMR
+LAB11013R	11015R
+LAB11013R	11016R
+LABCBCS	ABNWBC
+LABTOXGM	TOXGQT
+LABYSTONE	2NDCONST
+LABFBILA	BILIRUBINFLD
+LABSTLCP	CULT
+LABFBILA	FSC2
+LAB11106R	HBEAB
+LABREF2	LAB2
+LABTBP38	PRO
+LABREF2	RESUL2
+LABSTLCP	RPT
+LABSTLCP	SDES
+LABTBP38	SI
+LABREF2	SPTY2
+LABSTLCP	SREQ
+LABREF2	TEST2
+LABCRFA	CULT
+LABCRFA	RPT
+LABCRFA	SDES
+LABCRFA	SREQ
+LABTBP25	XMR
+LAB11343R	11344R
+10390R	9663R
+LABUAPRN	OTHCRY
+LABTBP39	PRO
+LABTBP39	SI
+LABTOXIG	TOXGDS
+LABTOXIG	TOXGQL
+LABYBSAP	ALKPO4BONE
+LABFACT10	FACTOR10
+LABFBBURG	BANDPRSNT
+LABCRCL	CORCL
+LABCRCL	COREXC
+LABCRCL	CR
+LABCRCL	CRCL
+LABCRCL	CRE
+LABCRCL	CRUCL
+LABCRCL	FLOWURN
+LABCRCL	HT
+LABBCRQT	LR210
+LABBCRQT	P210Q
+LABTBP34	UBT
+LABTBP34	UNT
+LABCRCL	UT3
+LABCRCL	VOL3
+LABCRCL	WT
+LAB11145R	11146R
+LAB12238R	12239R
+LAB12238R	12240R
+LAB11145R	11147R
+LAB11145R	11148R
+LAB11145R	11149R
+LABTBP31	DI
+LABHSVQL	HSV1PC
+LABHSVQL	HSV2PC
+LABHSVQL	HSVIN
+LABHSVQL	SDES11
+LABABTITI	ABTITER
+LABB2GP1	B2GP1IGA
+LABB2GP1	B2GP1IGG
+LABB2GP1	B2GP1IGM
+LABMGMT	MGMCO
+LABMGMT	MGMDI
+LABMGMT	MGMDX
+LABMGMT	MGMIN
+LABMGMT	MGMMD
+LABMGMT	MGMME
+LABMGMT	MGMRE
+LABMGMT	MGMSO
+LABTOXGM	TOXMQT
+LABYSTNA	YSTNA1
+10305R	9135R
+10305R	9137R
+10305R	9139R
+10310R	9165R
+10310R	9167R
+10310R	9169R
+10365R	9433R
+LAB11096R	A1AANTITRYPF
+LABB2GP1	B2GPCM
+LABSTOBLD	DATE1
+LABSTOBLD	OCCGU
+LABYTOPIR	TOPIRAMATE
+LAB11097R	TSIG
+LABTBP26	XMR
+LABYSTONE	3RDCONST
+LABRIST	RIST
+LABYSTK	YSTK1
+LABAVRT	CULT
+LABFACT7	FACTOR7
+LABAVRT	RPT
+LABAVRT	SDES
+LABAVRT	SREQ
+LABYSTFR	YSTFRR
+LABUAMY	AMUCRU
+LABUAMY	AMYLASE
+LABYUOPC	CODEINE
+LABYUOPC	HYCD
+LABYUOPC	OXYCOD
+LABIBD	PANCA
+IMG11CCP	PDF
+LABTBP40	PRO
+LABIBD	SACIGA
+LABIBD	SACIGG
+LABTBP40	SI
+LABTBP35	UBT
+LABUAMY	UCR
+LABTBP35	UNT
+LABYUOPC	YUHMGC
+LABYUOPC	YUMOGC
+LABYUOPC	YUOPIM
+LABYUOPC	YUOPIN
+LAB11145R	11150R
+LAB11145R	11152R
+LABG6PD	G6PD
+LABHIVAB	HIVAB
+LAB11531R	11641R
+LABANERB	CULT
+LABBSME	CULT
+LABANERB	RPT
+LABBSME	RPT
+LABANERB	SDES
+LABBSME	SDES
+LABANERB	SREQ
+LABBSME	SREQ
+LABATTG	ATTGT
+LABGCCR2	DCL5
+LABGCCR2	GC
+LABGCCR2	SDES5
+LABHCVGEN	CULT
+LABTBP32	DI
+LABHCVGEN	RPT
+LAB11093R	SCL70
+LABHCVGEN	SDES
+LABHCVGEN	SREQ
+10290R	9033R
+10290R	9034R
+LABISOHTI	ISOHTR
+LABCHMISC	CHMISC
+210993280	LEADIMPDTE
+210993280	LEADMFR
+210993280	LEADMODEL
+210993280	LEADTYPE
+LABTOXO	TOXO
+LABYSTONE	YSTON
+10290R	9031R
+10290R	9032R
+LABFCHOLA	CHOLFLD
+LABSYCT	DX
+LABYFLUZ	FLUCOZ
+LABSYCT	JOINT
+LABSYCT	MALIG
+LABURPHOS	PHOSU
+LABSYCT	SCOLOR
+LABSYCT	SRBC
+LABSYCT	STURB
+LABSYCT	SWBC
+LABTBP36	UBT
+LABTBP36	UNT
+LABVWAG	VWAG
+LABYSTONE	YSTNCO
+LABYSTONE	YSTNNJ
+LABYSTONE	YSTNNM
+LABYSTONE	YSTNSJ
+LABYSTONE	YSTNSM
+LABAPC	APC
+LAB11039R	GBMIGG
+210993280	LEADSERNUM
+LABTBP10	SPEXP
+10295R	9081R
+10295R	9082R
+10295R	9083R
+10295R	9085R
+10295R	9100R
+10295R	9101R
+10300R	9102R
+10300R	9103R
+10300R	9104R
+10300R	9106R
+10300R	9121R
+10300R	9122R
+10295R	9462R
+10295R	9595R
+10300R	9599R
+LABMMAS	MMA
+LABIBD	PANCA
+LABTBP41	PRO
+LABIBD	SACIGA
+LABIBD	SACIGG
+LABTBP41	SI
+LABTBP27	XMR
+LABYCENT	YCENTA
+10270R	10049R
+LABY25VD	25OHD2
+LABY25VD	25OHD3
+LABY25VD	25OHDT
+LABCDAS	ATTGR
+LABCDAS	ATTINT
+LABFENTA	ENTA
+LABCDAS	GDGPR
+LABCSMPB	GDNM3B
+LABCSMPB	GERHRB
+LABGH	GH
+LABHSVGM	HSVSL6
+LABCDAS	IGACDS
+LABCDAS	IGAINT
+10270R	RINFO
+LABYTESFT	YTESHD
+10170R	10049R
+10335R	9280R
+10370R	9450R
+10300R	9600R
+LABASTRO	ASTRO
+LABABG	O2SATA
+LABBCRQT	P190Q
+10170R	RINFO
+LABDVERV3	RSV3
+LABDVERV3	RSV3SP
+LABDVERV3	RV3IP
+10170R	UNRDID
+LAB11199R	11200R
+LABYAMISO	AMYLASE
+LABYITRA	YITR1
+LABYITRA	YITR2
+LAB11199R	11201R
+LAB11199R	11202R
+LAB11199R	11203R
+LAB11199R	11204R
+LAB11199R	11205R
+LAB11199R	11206R
+LAB11199R	11207R
+LAB11199R	11208R
+LAB11199R	11209R
+LAB11199R	11210R
+LAB11199R	11211R
+LAB11199R	11212R
+LAB11199R	11213R
+10290R	9025R
+LABFBBURG	BBIGG
+LABFBBURG	BBIGM
+LABCMVG	CUL54
+LABCMVG	CUL97
+LABIGGSUB	IGG
+LABIGGSUB	IGGS1
+LABIGGSUB	IGGS2
+LABIGGSUB	IGGS3
+LABIGGSUB	IGGS4
+LABJO1ABY	JO1INT
+LABJO1ABY	JO1QUL
+LABPROTPM	PRTPMR
+LABTBP11	SPEXP
+LABTCLON	TCDX
+LABTCLON	TDIR
+LABTCLON	TDIS
+LABTCLON	TMET
+LABTCLON	TPRM
+LABTCLON	TRES
+LABTBP37	UBT
+LABTBP37	UNT
+LAB11199R	YARBIN
+LABYHEMOC	YHEMID
+LABYHEMOC	YHEMIN
+LABYHEMOC	YHEMME
+LABYHEMOC	YHEMOD
+LABYHEMOC	YHEMRD
+LABYHEMOC	YHEMRS
+LABYHEMOC	YHEMRV
+LABYHEMOC	YHEMSP
+LAB11199R	YSTA
+10305R	10460R
+10305R	10461R
+LABBRASP	ASPIGE
+LABBRASP	ASPIGG
+LABBRASP	ASPINT
+LABBRASP	IGEASP
+210993289	LEADIMPDTE
+210993289	LEADMFR
+210993289	LEADMODEL
+210993289	LEADTYPE
+LABTCLON	TCMT
+LABVZVQL	VZQLDN
+LABVZVQL	VZQLIP
+LABVZVQL	VZQLSP
+LABYUMETF	YUMECO
+10310R	10462R
+10310R	10463R
+LAB11250R	11251R
+LAB11250R	11252R
+LAB12573R	12574R
+LABTBP33	DI
+LABFACT5	FACTOR5
+LABFMYAB	12431R
+LABRITUX	C5616A
+LABRITUX	C5616P
+LABCBCS	CCNT
+LABYUHYME	MERURN
+LABTBP42	PRO
+LABTBP42	SI
+LABTBP29	XMR
+LABYUHYME	YUHYM2
+10370R	9464R
+LABYSTNA	YSTNA3
+LABYSTNA	YSTNA4
+10365R	9426R
+LABYU5HIA	5HIAAURN
+LABPCEC4	GLU
+LABFHVIG	HVIG
+210993289	LEADSERNUM
+LABPCEC4	PCCOM
+LABRBCPH	PHLET
+LABRF	RHFT
+LABTBP38	UBT
+LABTBP38	UNT
+LABYU5HIA	VOLUMEURN
+LABYSTK	YSTK3
+LABYSTK	YSTK4
+LABYU5HIA	YU5HUT
+10365R	9420R
+10410R	9706R
+LABTTM	CD19AB
+LABTTM	CD19PC
+LABTTM	CD3AB
+LABTTM	CD3PC
+LABTTM	CD5AB
+LABTTM	CD5PC
+LABDSDNA	DNAQNT
+LABKRAS	KRASDI
+LABKRAS	KRASDX
+LABKRAS	KRASME
+LABKRAS	KRASRE
+210993283	LEADIMPDTE
+210993283	LEADMFR
+210993283	LEADMODEL
+210993283	LEADTYPE
+LAB11045R	LKMAB
+LABTTM	LYM
+LABTTM	LYMAB
+LABRBCPH	PHLCT
+LABTBP43	PRO
+LABBMBCRQ	QBMBRM
+LABBMBCRQ	QBMBRP
+LABBMBCRQ	QBMBRR
+LABBMBCRQ	QBMBRS
+LABBMBCRQ	QBMBRX
+LABTTM	RTXFDA
+LABTBP43	SI
+LABUA	TRPHOS
+LABTTM	WBC
+LABYUHYME	YUHYM5
+LAB11199R	11200R
+LAB11199R	11201R
+LAB11199R	11202R
+LAB11199R	11203R
+LAB11199R	11204R
+LAB11199R	11205R
+LAB11199R	11206R
+LAB11199R	11207R
+LAB11199R	11208R
+LAB11199R	11209R
+LAB11199R	11210R
+LAB11199R	11211R
+LAB11199R	11212R
+LAB11199R	11213R
+LAB12232R	12233R
+10375R	9467R
+10375R	9468R
+10375R	9470R
+10375R	9472R
+10375R	9473R
+10375R	9474R
+10375R	9475R
+10375R	9476R
+10375R	9477R
+10375R	9575R
+10375R	9576R
+10375R	9611R
+10375R	9636R
+LABYPROIN	PROINSULIN
+LABDVERTV	RTVR
+LABDVERTV	RTVTD
+LABTBP28	XMR
+LAB11199R	YARBIN
+LAB11199R	YSTA
+LABYUHYME	YUHYM6
+10370R	9442R
+10375R	9478R
+LABTBP34	DI
+LABPCEC4	HCT
+LABPCEC4	HGB
+LABPCEC4	K
+LABKRAS	KRASMD
+LABLUT	LH
+LABPCEC4	NA
+LABRBCPH	PHCT
+LABRBCPH	PHST
+LABTBP44	PRO
+LABTBP44	SI
+10000R	10049R
+LABEBVPAN	COM
+LABBACIDS	CULT
+LABCYLX	CYLX1
+LABKRAS	KRASMU
+LABRBCPH	PHET
+LABRBCPH	PHJKBT
+LABRBCPH	PHKT
+10000R	RINFO
+LABBACIDS	RPT
+LABBACIDS	SDES
+LABBACIDS	SREQ
+LABUMYOSC	UMYOG
+LABYSDA	11832R
+LABYUHYME	11896R
+LABYA1APH	A1APHEN
+LABCD34	CD34AB
+LABCD34	CD34DS
+LABCD34	CD34PC
+LABLPDPC	CHOL
+LABLPDPC	CHOLHDL
+LABYDIGT	DIGT
+LABYFRUCM	FRUCTM
+LABLPDPC	HDL
+LABLPDPC	HMCY
+LABLPDPC	HSCRP
+LABLPDPC	LDL
+LABLPDPC	LDLHDL
+210993283	LEADSERNUM
+LABLPDPC	LPA
+LABLWBCS2	LYMEIGGAB
+LABLWBCS2	LYMEIGMAB
+LABLPDPC	NHDL
+LAB11086R	NORTRP
+LABLPDPC	RCRSK
+LABLPDPC	TGL
+LABCD34	WBC
+LABYDIGT	YDIGT2
+LABANTNEU	ANA
+LABTBP35	DI
+LABVWSCN	FACTOR7
+LABH1N1PC	H1N1DI
+LABH1N1PC	H1N1IN
+LABH1N1PC	H1N1M
+LABH1N1PC	H1N1MT
+LABH1N1PC	H1N1R
+LABH1N1PC	H1N1SP
+LABYHABQ	HBSAB
+LABYHABQ	HBSABQUAL
+LABVWSCN	RIST
+LABTBP12	SPEXP
+LABVWSCN	VWAG
+LABLUT	LH
+VSC05	PDF
+LABRBCPH	PHJKAT
+LABUCL	UCL
+LABUCL	UCLCR
+LABUCL	UCR
+LABVISC	VISCOS
+LABYA1APH	YA1A
+LABYBLAS	YBLAS3
+LAB11028R	ACE
+LABDFA	DFIGG
+LABDFA	DFIGM
+LABDFA	DFINT
+LABTBP36	DI
+LABKRAS	KRASIN
+LABRBCPH	PHLST
+LABTBP45	PRO
+LABTBP45	SI
+LABTBP39	UBT
+LABTBP39	UNT
+LAB12631R	VORI
+10235R	10049R
+LABADAMTS	ADMACT
+LABADAMTS	ADMINT
+LABCMVIGG	CMV
+LABCSMPBM	GDNM3M
+LABCSMPBM	GERHRM
+LABHEPC	HEPCAB
+LABNPM1	NPMDI
+LABNPM1	NPMDX
+LABNPM1	NPMIN
+LABNPM1	NPMMD
+LABNPM1	NPMME
+LABNPM1	NPMRE
+LABNPM1	NPMRF
+10235R	RINFO
+LABTBP30	XMR
+LABGENC	CULT
+LABYEBPCR	EBV
+LABFACT2	FACTOR2
+LABRBCPH	PHFYAT
+LABRBCPH	PHFYBT
+LABRBCPH	PHLEAT
+LABRBCPH	PHMT
+LABRBCPH	PHP1T
+LABGENC	RPT
+LABGENC	SDES
+LABGENC	SREQ
+LABTBP2	VL
+LAB11077R	YCALCI
+LABYEBPCR	YEBP1
+LABYEBPCR	YEBP3
+LABYEBPCR	YEPB4
+LABEBVPAN	11165R
+LABEBVPAN	11166R
+LABEBVPAN	12001R
+LABEBVPAN	12002R
+LABIBD	12215R
+LABADAMTS	ADMINH
+LABTHG	ATGA
+LABBKVQU	BKQ
+LABBMNPM1	BMNPDI
+LABBMNPM1	BMNPDX
+LABBMNPM1	BMNPIN
+LABBMNPM1	BMNPMD
+LABBMNPM1	BMNPME
+LABBMNPM1	BMNPRE
+LABBMNPM1	BMNPRF
+LABBACIDS	CULT
+LABFACT11	FACTOR11
+LABHHV6QL	HV6QLD
+LABHHV6QL	HV6QLI
+LABHHV6QL	HV6QLS
+LABBACIDS	RPT
+LABBACIDS	SDES
+LABSPAGD	SOURC4
+LABBKVQU	SOURC6
+LABTBP13	SPEXP
+LABSPAGD	SPNAG
+LABBACIDS	SREQ
+LABTHG	TG
+LABTOXIG	TOXGQT
+LABCOXPNL	TYP10
+LABCOXPNL	TYP16
+LABCOXPNL	TYP2
+LABCOXPNL	TYP4
+LABCOXPNL	TYP7
+LABCOXPNL	TYP9
+LABTBP40	UBT
+LABTBP40	UNT
+LABTTM	CD4AB
+LABTTM	CD4PC
+LABTTM	CD8AB
+LABTTM	CD8PC
+LABCSMP	CSCI
+LABCSMP	CSDI
+LABCSMP	CSME
+LABCSMP	CSREF
+LABABG	FIO2
+LABTTM	HNS
+LABTTM	HSABS
+LABTTM	HSRATIO
+LABRBCPH	PHLEBT
+LABRBCPH	PHNT
+LABTOTALC	PROTCAGT
+LAB11214R	11215R
+LABMALSC	MALSCD
+LABMALSC	MALSCR
+LABVBG	O2SV
+LABYSES	SELENIUM
+LABUHEMO	UHEMO
+10365R	9413R
+10375R	9495R
+10375R	9535R
+LABCSMP	CSSP
+LABFLUAPC	FLUADI
+LABFLUAPC	FLUAMT
+LABFLUAPC	FLUAR
+LABFLUAPC	FLUASP
+LABCSMP	GAPC
+LABMALSC	MAL
+LABYMPA	MPACID
+LABBMBCRQ	QBMBRD
+LABTOXPCR	TOXGG
+LABTOXPCR	TOXRES
+LABTOXPCR	TOXSP
+LABYMPA	YMPAG
+LAB11101R	YVITC
+10370R	9632R
+LABYHMS	ARSENIC
+LABYHMS	CADMIUM
+LABF8INH	F8INH
+LABHGBSQ	HGBS
+LABBMTP	HSAB
+LABBMTP	HTLVS
+210993284	ICDBRLOWRT
+210993284	ICDBRPACMD
+210993284	ICDEVLDATE
+210993284	ICDTAVFTXC
+LABYHMS	LEAD
+LABMONOSC	MONOSC
+LABTBP46	PRO
+LABPROINS	PROINSULIN
+LABBMTP	RPRQ
+LABBMTP	RPRQIN
+LABTBP46	SI
+LABT1517	17) PML/RAR, BLOOD"
+LABT1517	17) PML/RAR, BLOOD"
+LABT1517	17) PML/RAR, BLOOD"
+LABT1517	17) PML/RAR, BLOOD"
+LABT1517	17) PML/RAR, BLOOD"
+LABT1517	17) PML/RAR, BLOOD"
+LABTBP41	UBT
+LABTBP41	UNT
+LABADGM	ADG1
+LABADGM	ADM1
+LABARGI	ARG
+LABVCULF	CULT
+LABTBP37	DI
+LABBMTP	HATINT
+LABBMTP	HATR
+LABBMTP	HBSINT
+LABBMTP	HBSR
+LABBMTP	HBTINT
+LABBMTP	HBTR
+LABBMTP	HCVI
+LABBMTP	HCVR
+210993284	ICDTAVFDTI
+LABMALSC	MALCON
+LABTBP47	PRO
+LABVCULF	RPT
+210993284	RVBRPACA
+210993284	RVBRPACW
+LABVCULF	SDES
+LABTBP47	SI
+LABVCULF	SREQ
+LABYLEPT	YLEP1
+LABBMTP	CMV
+LABBMTP	CMVIGM
+LABYUCOP	COPPERURN
+210993284	DEVTYPE
+LABYUFCAT	DOPA
+LABYUFCAT	EPINEP
+210993284	MFR
+210993284	MOD
+LABYUFCAT	NOREPH
+LABSYDIFF	SLYM
+LABSYDIFF	SMONO
+LABSYDIFF	SNEUT
+LABTOBPOL	TOBPO2
+LABTOBPOL	TOBPO3
+LABTOBPOL	TOBPO4
+LABTOBPOL	TOBRAPEAK
+LABYVITB6	VITB6
+LABYUFCAT	VOLUMEURN
+LABBMTP	VZAB
+LABYUFCAT	YUFCUT
+LABYVB1W	YVB1W1
+LABCSMP	CSMD
+LABBPERP	CULT
+LAB11079R	GABAP
+LABCSMP	GBRAF
+LABCSMP	GCTNN
+LABCSMP	GEGFR
+LABCSMP	GIDH1
+LABCSMP	GIDH2
+LABCSMP	GKRAS
+LABCSMP	GNOTCH
+LABCSMP	GNRAS
+LABCSMP	GPIK3
+LABCSMP	GPTEN
+LABCSMP	GTP53
+LABPANHCT	HCT
+LABFHTLVQ	HTLI
+LABFHTLVQ	HTLII
+210993284	IMPDATE
+210993284	LVBRPACA
+210993284	LVBRPACW
+VSC03	PDF
+LABBMBCRQ	QBMBRC
+LABBPERP	RPT
+LABBPERP	SDES
+210993284	SERNUM
+LABBPERP	SREQ
+LABYLACTS	YLACT1
+LABYUCOP	YUCOP2
+LABYZONIS	ZONIS
+LABHSVM	HSV1M
+LABHSVM	HSV2M
+210993284	ICDTLIMPRV
+LABYLEADB	LEA
+LABYLEADB	LEADSO
+LAB11085R	MPO
+LABVCAIGM	VMDES
+LABVCAIGM	VMQUL
+LABTBP31	XMR
+LABYUHYME	12200R
+LABYUHYME	12201R
+LABYUHYME	12202R
+LABYUHYME	12203R
+LABYUHYME	12204R
+210993284	ICDRHYTHM
+LABTBP48	PRO
+210993284	RVBRSENA
+LABTBP48	SI
+LABTBP42	UBT
+LABTBP42	UNT
+LABVZVIGG	VZAB
+LABYENPCR	YENP3
+LAB11153R	11154R
+LAB11153R	11155R
+LABABODIS	ABODIS
+LABCDAS	ATTGT
+LABCOHB	COHB
+LABTBP38	DI
+LABEB	EA
+LABDVEHVR	HERPR
+LABDVEHVR	HVIP
+LABDVEHVR	HVSP
+210993284	ICDDTPACVT
+210993284	ICDTLIMPAT
+210993284	ICDTLIMPLV
+LABLWBCS2	LYMEIGGBAND
+LABYHMS	MER
+LABTBP43	UBT
+LABTBP43	UNT
+LABDVEHVR	VZR
+LAB11551R	11661R
+LAB11824R	11826R
+LAB11824R	11827R
+LAB11824R	11828R
+10365R	9627R
+10400R	9662R
+LABDHBP	HBCAB
+LABDHBP	HBCABIGM
+LABDHBP	HBSAB
+LABDHBP	HBSAG
+LABPLASMN	PLASMN
+210993284	RABRSENA
+LABQFTBG	TBAG
+LABQFTBG	TBAGNI
+LABQFTBG	TBGINT
+LABQFTBG	TBGNIL
+LABQFTBG	TBGRES
+LABQFTBG	TBMINI
+LABYUBZC	YUBZIM
+LABYUBZC	7NHCLONAZEPM
+LABYUBZC	7NHFLUNITRAZ
+LABLWBCC	BBIGG
+LABLWBCC	BBIGM
+LABBARTPC	BHENPC
+LABBARTPC	BQUIPC
+LABFTBLYM	F1656A
+LABFTBLYM	F1656P
+LABFTBLYM	FCD19A
+LABFTBLYM	FCD19P
+LABFTBLYM	FCD3AB
+LABFTBLYM	FCD3PC
+LABFTBLYM	FCD4AB
+LABFTBLYM	FCD4PC
+LABFTBLYM	FCD8AB
+LABFTBLYM	FCD8PC
+LABFTBLYM	FCSOUR
+LABFTBLYM	FHNS
+LABFTBLYM	FHSAB
+LABCDAS	GDGPT
+LABHGBSQ	HGSMD
+LABFTBLYM	HSRATIOFLD
+LABYUBZC	LORAZ
+LABFTBLYM	LYMABFLD
+LABFTBLYM	LYMPCFLD
+LABYUBZC	NORDIAZ
+LABYUBZC	OXAZEPAM
+LABRDATAC	RDATA
+LABBARTPC	SDES10
+LABABG	TEMP
+LABVCAIGG	VGDES
+LABVCAIGG	VGQNT
+LABVCAIGG	VGQUL
+LABTBP6	VL
+LABYUCOP	VOLUMEURN
+LABFTBLYM	WBCFLD
+LABYHEMOC	YHEMAM
+LABYHEMOC	YHEMSO
+LABYINFAB	YINF2
+LABYINFAB	YINF3
+LABYINFAB	YINF5
+LABYINFAB	YINF6
+LABYUBZC	YUAAGC
+LABYUBZC	YUATGC
+LABYUBZC	YUBZIN
+LABYUBZC	YUEFGC
+LABYUBZC	YUTZGC
+10375R	9496R
+10375R	9507R
+10375R	9536R
+10375R	9547R
+LABB2GPCO	B2GP1IGG
+LABURCA	CAURN
+LABLEGD	CULT
+LABVBG	FIO2
+LABGLWB	GLU
+LABHITPNL	HITI1
+LABHITPNL	HITI2
+210993284	ICDBRADYDP
+LABHITPNL	PF4COM
+LABHITPNL	PF4ENH
+LABTBP49	PRO
+LABTBP50	PRO
+LABLEGD	RPT
+LABLEGD	SDES
+LABTBP49	SI
+LABTBP50	SI
+LABTBP14	SPEXP
+LABLEGD	SREQ
+LABABG	TSITE
+LABYANRNP	U1RNPAB
+LABVIRSO1	VIROR
+LABVIRSO1	VIROS
+LABVIRSO1	VIROTN
+LABYCHAGA	YCHAG1
+10375R	9500R
+10375R	9540R
+LABB2GPCO	B2GP1IGA
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+LAB12330R	12500R
+LAB12330R	12501R
+LAB12330R	12502R
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+LABUPIET	UPIEI
+LABUPIET	UPIER
+LABUPIET	UT3
+LABUPBGQT	UVOL
+LABEB	VCA
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+LABYCLOZ	YCLOZ1
+LABYCLOZ	YCOMT
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+LAB11265R	11266R
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+10375R	9541R
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+LABGBMIFA	GBMIGG
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+PFT59	PDF
+LABCDTPCR	RPT
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+LAB11549R	11659R
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+LABASPID	AFLUMI
+LABAMIPOL	AMIKACINPK
+LABAMIPOL	AMIPO2
+LABAMIPOL	AMIPO3
+LABAMIPOL	AMIPO4
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+LABLPDP	CHOL
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+LABUPOR	UVOL
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+LABHIVDS	VDS3
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+LABFQFVR	QFVM2
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+LABCF32	CF32DI
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+LAB12809R	YCKMB
+LAB12809R	YCKMM
+LAB12809R	YCKTOT
+LAB11566R	11679R
+LAB11566R	11680R
+10365R	9432R
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+LABTMSI	TMSISO
+LABYUCORT	UCORT
+LABURURIC	URICURN
+LABTBP7	VL
+LABYUCORT	VOLUMEURN
+LABTBP37	XMR
+LABYMGEA	YARBIN
+LABYMGEA	YARMA
+LABYIFBA	YIFB1
+LABYMGEA	YSTA
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+LAB12362R	12363R
+LAB12362R	12364R
+LAB12362R	12365R
+LAB12362R	12366R
+LAB12362R	12367R
+LAB12362R	12368R
+LAB12362R	12369R
+LAB12362R	12370R
+LABADVQL	ADQLDN
+LABADVQL	ADQLIP
+LABADVQL	ADQLSP
+LABBMBCRQ	BP210
+LABYFCAR	CZPFR
+LABYFCAR	CZPTOT
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+10190R	10049R
+LAB11253R	11185R
+LAB11984R	11985R
+LABYFLUO	5FC
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+LABFHVIG	BANDPRSNT
+LABABG	BGSPR
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+LABNASC	CULT
+LABDCMVL	DCMVL1
+LABTBP45	DI
+LABBMPTP	HATINT
+LABBMPTP	HATR
+LABBMPTP	HBSINT
+LABBMPTP	HBSR
+LABBMPTP	HBTINT
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+LABTBP55	SI
+LABTBP18	SPEXP
+LABNASC	SREQ
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+LABTBP49	UNT
+LABTBP3	VL
+LABYBLAS	YBLAS1
+LABYCLON	YCLON1
+LABYPR3	YPR31
+LABYRICK	YRICK1
+LAB12810R	12820R
+LAB12810R	12821R
+LAB12810R	12822R
+LAB12810R	12823R
+LAB12810R	12824R
+LAB12810R	12825R
+LAB12810R	12826R
+10375R	9479R
+LABHMET	ARS
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABHMET	CAD
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+LABHMET	MERC
+LABT1517	17) PML/RAR, BLOOD"
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+210993284	SLWVTTZNRT
+LABREF3	SPTY3
+LABREF3	TEST3
+LAB11120R	TOXOC
+LABTBP5	VL
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+LABYRICK	YRICK3
+LABYRICK	YRICK4
+LABVCAIGG	11168R
+LABCARN	ACFCR
+LABPENALL	AMOX
+LABPENALL	AMPICILLIN
+LABSYNCT	BASOSYNFLD
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABCARN	CARINT
+LABCARN	CARNF
+LABCARN	CARNIT
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+LABEBNABY	11166R
+LABVCAIGM	11167R
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+LABBMTCLO	BMTDX
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+LABUCAT	CAURN
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+LABYEBPCR	YEPB4
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+LABPANAPL	PANAP2
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+10300R	9601R
+10365R	9629R
+LABYAT3AG	AT3
+LAB11025R	C1ESTR
+LABMEP	CAION
+LABMEP	CL
+LABTBP48	DI
+LABBFCTS	DX
+LABBFCTS	FCOLOR
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+210993283	ICDDTSVTAC
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+LABMEP	K
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+LABMEP	NA
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+210993280	RVBRSENB
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+LABMEP	TCO2A
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+LABYTESFB	TESTOSBIO
+LABMEP	THB
+LABTBP52	UBT
+LABTBP53	UBT
+LABTBP54	UBT
+LABYFUN	UNFLD
+LABTBP52	UNT
+LABTBP53	UNT
+LABTBP54	UNT
+LABBFCTS	WBCFLD
+LABYSTCL	YSTCL1
+LAB12781R	12778R
+LAB12781R	12779R
+10370R	9633R
+LABADVQT	ADPCR1
+LABCIMM	CIMM
+LABTBP49	DI
+LABYSDA	DRUGSCRN
+LABFIBANT	FIBANT
+LABFRAB	FRANT
+LABG6PDQT	G6PD
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+LABSTOBID	OCCIMM
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+LABCBCD	SEZY
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+LABXICA51	XICA5R
+LABTBP43	XMR
+LABYASU	YASU1
+LABYIFBA	YIFB2
+LABYSTCL	YSTCL3
+LABYSTCL	YSTCL4
+LAB11525R	11635R
+LAB11547R	11657R
+LABADAM13	ADMACT
+LABADAM13	ADMINT
+LABRAAS	CCPINT
+LABRAAS	CCPR
+LABCYLEX	CYCLXR
+LABYEBPCR	EBV
+LABGDGP	GDGPT
+LABHBSQT	HBSAB
+LABHBSQT	HBSABQUAL
+LABUBICAR	HCO3URN
+LABHEPAR	HEPAR
+LABHIVDAG	HIVS
+210993283	ICDDTSKDVT
+210993289	ICDTLIMPRV
+LABHIVDAG	INTRE
+LABYANTJO	JO1AB
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+LABUKT	TKU
+LABUKT	UK
+LABUBICAR	UT
+LABUKT	UT2
+LABUBICAR	VOL
+LABUKT	VOL2
+LABTBP44	XMR
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+LABYEBPCR	YEBP3
+LABYEBPCR	YEPB4
+LAB11182R	11183R
+LAB11182R	11184R
+LABDCGSN	11893R
+LABAGAP	AG
+LABBCRKDM	BCRKAA
+LABBCRKDM	BCRKCI
+LABBCRKDM	BCRKDI
+LABBCRKDM	BCRKIT
+LABBCRKDM	BCRKMT
+LABBCRKDM	BCRKPR
+LABBCRKDM	BCRKRE
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+LABTBP60	SI
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+210993283	VSLWVTTDTT
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+LAB11000R	11001R
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+LABTHIOMT	6MMPN
+LABTHIOMT	6MMPNASSESS
+LABTHIOMT	6TGNASSESS
+10335R	9584R
+LABAAP	AAMINOA
+LABAAP	AAMINOB
+LABAAP	AAPINT
+LABYDGIGA	ADGINT
+LABYDGIGA	ADGPR
+LABAAP	ALANINE
+LABAAP	ARGININE
+LABYAVH	ARGININEVP
+LABAAP	ASPART
+LABAAP	ASPGN
+LABAAP	BALN
+LABAAP	BAMBA
+LABAAP	CITRUL
+LABCYSBLD	CYSAB2
+LABAAP	CYSTINE
+LABAAP	CYTHN
+LABTBP50	DI
+LABTBP53	DI
+POC13	ERYTHREMA
+LABAAP	ETHLN
+LABG6PDQT	G6PDMD
+LABAAP	GLUTAC
+LABAAP	GLUTAMINE
+LABAAP	GLYCINE
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+LABAAP	HMCY
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+210993289	ICDBRAVSND
+LABAAP	INTBY
+LABAAP	ISOLEU
+LABMDXMIS	LAB7
+LABAAP	LEUCINE
+ECH30	LVEF
+LABAAP	LYSINE
+LABAAP	METHIO
+LABYMEXIL	MEXIL
+POC13	MMIND
+LABAAP	ORNITHINE
+LABAAP	PHALN
+POC13	PHLOC
+LABAAP	PROLINE
+LABMDXMIS	RESUL7
+LABAAP	SARSN
+LABAAP	SERINE
+LABSLIDE	SLIDE
+210993284	SLWVTTQRSM
+LABMDXMIS	SPCT
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+LABAAP	TAURINE
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+LABTHAMP	THRAMP
+LABAAP	THREONINE
+LABAAP	TRYPTOPHAN
+LABAAP	TYROSINE
+LABFURICA	URICFLD
+LABAAP	VALINE
+LABTBP13	VL
+LABECL	WEACA
+LABECL	WOEACA
+LABTBP45	XMR
+LABYACIGA	YACIG1
+10287R	10049R
+LAB11619R	11778R
+LABYFUN	11847R
+LABFQFVR	12014R
+LAB12418R	12419R
+LAB12418R	12420R
+LAB12418R	12421R
+LAB12418R	12422R
+LAB12418R	12423R
+LAB12418R	12424R
+LAB12418R	12425R
+LAB12418R	12426R
+LAB12418R	12427R
+10370R	9463R
+LABYASMA	ASMA
+LABBCRKDM	BCRKCM
+LABYUHYME	CADMIUNURN
+LABYGLUC	GLUCAGON
+LABAHP2	HBCABIGM
+LABAHP2	HBSAG
+LABAHP2	HEPAIGM
+LABAHP2	HEPCAB
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+210993283	ICDBRAVDLS
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+210993283	ICDDTSKAAT
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+LABFDIFF	LYMFLD
+LABFDIFF	MONFLD
+LABFDIFF	NEUTFLD
+VSC04	PDF
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+10287R	RINFO
+LABRUB	RUB
+LABTBP62	SI
+210993289	SLWVTTDTNT
+LABTBP22	SPEXP
+LABTBP23	SPEXP
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+LABUAPRN	WBCAST
+LABYHIST	YHIS1
+LAB11229R	11230R
+LAB11229R	11231R
+LAB11229R	11232R
+LAB11229R	11233R
+LABYVD125	125OHD2
+LAB12815R	12830R
+LABUCA	CACREAT
+LAB11127R	CASPO
+LABUCA	CAURN
+LABCMVRC	CMVDSV
+LABCMVRC	CMVRC1
+LABCMVRC	CMVRC2
+LABCMVRC	CMVSPM
+POC27	DEVEL
+POC27	DEVEXP
+LABTBP51	DI
+LABTBP52	DI
+LABTBP54	DI
+LABRP	EGFR
+LABFURICA	FSC2
+LABCSMP	GDNM3
+LABCSMP	GERHR
+POC27	HEMED
+POC27	HEML
+LABHEVM	HEVM1
+LABUVH	HVA
+LABUVH	HVAUR
+210993289	ICDBRAVPCD
+210993289	ICDBRMAXRT
+210993289	ICDBRTRKRT
+210993283	ICDDTATFIB
+210993283	ICDDTSKDAT
+210993289	ICDDTSKDVT
+210993289	ICDDTSLWVT
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+POC27	OCCBLD
+POC27	QC10
+210993289	RABRPACA
+210993289	RABRPACW
+210993289	RABRSENR
+LABSYDIFF	SCMT
+LABYTAT	TAT
+LABUVH	TIMEU
+LABUVH	UCR
+LABUVH	UCR
+LABUCA	UCR
+LABUVH	UVOL
+LABUVH	VHINT
+LABTBP15	VL
+LABUVH	VMA
+LABUVH	VMAUR
+LABYC1IN	YC1INH
+LABYVWM	YVWMR
+LABYENTE	12064R
+LABYENTE	12065R
+LAB12721R	12720R
+10375R	9471R
+LABADMARK	ADMART
+LABBM1517	17) PML/RAR, BONE MARROW"
+LABCCT	BTURB
+LABCF32	CF32R2
+LABCCT	COLORCSF
+LABCCT	COLORCSF
+POC5	CONRES
+LABCCT	CSOURC
+LABCCT	CTURB
+LABTBP56	DI
+LABCCT	DX
+LABEBNABY	EBNDES
+LABEBNABY	EBNQNT
+LABEBNABY	EBNQUL
+LABYEBPCR	EBV
+LABFHAN	HANIGG
+LABFHAN	HANIGM
+LAB11042R	HIST
+210993289	ICDBRADYDP
+210993283	ICDBRMDACR
+210993283	ICDBRMDDCR
+210993283	ICDBRMDTHR
+210993289	ICDDTSKAVT
+210993289	ICDDTSVTAC
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+210993289	LVBRPACA
+210993289	LVBRPACW
+LABCCT	MALIG
+POC5	PHLOC
+LABTBP63	PRO
+LABCCT	RBCCSF
+LABTBP63	SI
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+LABCCT	TUBENO
+LABTBP57	UBT
+LABTBP58	UBT
+LABTBP57	UNT
+LABTBP58	UNT
+LABCCT	WBCCSF
+LABYAMA	YAMA
+LABYEBPCR	YEBP1
+LABYEBPCR	YEBP3
+LABYEBPCR	YEPB4
+LABYHISTC	YHISC5
+LAB11000R	11002R
+LAB12292R	12293R
+LAB12292R	12294R
+LAB12292R	12295R
+LAB12292R	12296R
+LAB12292R	12297R
+LAB12292R	12298R
+LAB12292R	12299R
+LAB12292R	12300R
+LAB12292R	12301R
+LAB12292R	12302R
+LAB12292R	12303R
+LAB12292R	12304R
+LAB12292R	12305R
+LAB12292R	12306R
+LAB12292R	12307R
+LAB12292R	12308R
+LAB12292R	12309R
+LAB12292R	12310R
+LAB12292R	12311R
+LAB12292R	12312R
+LAB12292R	12313R
+LAB12292R	12314R
+LAB12292R	12315R
+LAB12292R	12316R
+LAB12292R	12317R
+LAB12292R	12318R
+LAB12292R	12319R
+10300R	9120R
+10365R	9419R
+LABYAMNOR	AMINOR
+LABYAMNOR	AMITRP
+LABCFDS	CFDSDI
+LABCFDS	CFDSDX
+LABCFDS	CFDSMD
+LABCFDS	CFDSMT
+LABCFDS	CFDSRE
+LABROSTRO	CULT
+LABTBP55	DI
+LABDVEFLA	DVE
+LABEBVQPB	EBVBCP
+LABEBVQPB	EBVBDN
+LABEBVQPB	EBVBIN
+LABEBVQPB	EBVBLG
+LABEBVQPB	EBVBSP
+LABEBVQPB	EBVQCP
+LABEBVQPB	EBVQDN
+LABEBVQPB	EBVQIN
+LABEBVQPB	EBVQLG
+LABEBVQPB	EBVQSP
+210993284	FSTVTTQRSM
+210993283	FSTVTTZNRT
+LABFT4ED	FT4
+LAB11080R	GAD65AB
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+210993289	ICDDTPACAT
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+210993283	ICDTAVFDTD
+210993289	ICDTLTSTTI
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+210993283	RVBRSENB
+210993283	RVBRSENP
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+LABROSTRO	SDES
+210993283	SLWVTTDTDU
+210993283	SLWVTTZNRT
+LABSYNCT	SOTHER
+LABDVEFLA	SREQ
+LABROSTRO	SREQ
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+LABTBP16	VL
+LABTBP22	VL
+LABVORI	VORI
+LABTBP46	XMR
+LAB11088R	YPANPP
+LABYPLAB	YPLAB2
+LAB11312R	11313R
+LAB11562R	11672R
+LAB11562R	11673R
+LAB11562R	11674R
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+LABSNA	ASMINT
+LABSNA	ASMQUL
+210993283	ATACHTADTI
+LABADIFF	BASO
+LABADIFF	BASOAB
+LABBM1418	18), BONE MARROW"
+LABBM1418	18), BONE MARROW"
+LABBM1418	18), BONE MARROW"
+LABBM1418	18), BONE MARROW"
+LABBM1418	18), BONE MARROW"
+LABBM1418	18), BONE MARROW"
+LABC153	C153
+LABSNA	CENINT
+LABSNA	CENQUL
+LABERUCRY	CRSTA
+LABSNA	DNAINT
+LABSNA	DNAQUL
+LABEAIGG	EAGDES
+LABEAIGG	EAGQUL
+LABYEBPCR	EBV
+LABFEHRC	ECIGG
+LABFEHRC	ECIGM
+LABADIFF	EOS
+LABADIFF	EOSAB
+LABCBCD	HAIRY
+210993289	ICDBRAMSRT
+210993289	ICDTAVFDTD
+210993289	ICDTLIMPHV
+210993289	ICDTLIMPLV
+LABSNA	JO1INT
+LABSNA	JO1QUL
+LABADIFF	LYM
+LABADIFF	LYMAB
+LABCDIFF	LYMCSF
+LABADIFF	MONO
+LABADIFF	MONOAB
+LABCDIFF	MONOCSF
+LABMTHFR	MTHCMT
+LABMTHFR	MTHDIS
+LABMTHFR	MTHMET
+LABMTHFR	MTHPRM
+LABMTHFR	MTHRES
+LABADIFF	NEUT
+LABADIFF	NEUTAB
+LABCDIFF	NEUTCSF
+LABCGD	OXIDNEUTPC
+LABSNA	RNPINT
+LABSNA	RNPQUL
+LABSNA	SCLINT
+LABSNA	SCLQUL
+LABSNA	SNAINT
+LABSNA	SSAIN
+LABSNA	SSAQUL
+LABSNA	SSBINT
+LABSNA	SSBQUL
+LABTBP59	UBT
+LABTBP59	UNT
+LABTBP19	VL
+210993283	VSLWVTTDTD
+LABUAPRN	WAXCST
+LABYAMEB	YAMEB1
+LABYEBPCR	YEBP1
+LABYEBPCR	YEBP3
+LABYEBPCR	YEPB4
+LAB11617R	11776R
+10370R	9634R
+LABYDGIGA	ADGPT
+LABYAFCSF	AFPCSF
+LABASPPCP	ASPIGG
+LABASPPCP	ASPINT
+LABSWTCL	CLSWT
+LABBMPTP	CMGDES
+LABBMPTP	CMGQUL
+LABBMPTP	CMMDES
+LABBMPTP	CMMQUL
+LABBMPTP	CMV
+LABBMPTP	CMVIGM
+LABFUNIDS	CULT
+LABHGBS	HGB
+LABHPVHR2	HPTI
+LABHPVHR2	HPV
+210993283	ICDDTAMSEP
+210993289	ICDDTNNSVT
+210993288	LEADIMPDTE
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+LABMTHFR	MTHDIR
+POC12	PHLOC
+210993280	PMTLBATIMP
+LABTBP64	PRO
+LABTBP65	PRO
+LABTBP66	PRO
+LABQFTBG	QTFNIN
+LABQFTBG	QTFNRS
+LABFUNIDS	RPT
+210993289	RVTHRSENAM
+210993289	RVTHRSENPO
+LABSWTCL	SCLW
+LABFUNIDS	SDES
+LABHPVHR2	SDES6
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+LABTBP65	SI
+LABTBP66	SI
+LABTBP25	SPEXP
+LABFUNIDS	SREQ
+LABBMPTP	VGGDES
+LABBMPTP	VGGQNT
+LABBMPTP	VGGQUL
+LABBMPTP	VZAB
+LABTBP47	XMR
+LABYIGF2	YIGF2A
+LABYMYCRB	YMYCR1
+LABYMYCRB	YMYCR2
+LABFQFVR	12012R
+LAB12586R	12588R
+10375R	9480R
+10375R	9481R
+LABYTADID	AMINOR
+LABYTADID	AMITRP
+LABCGD	CGDA
+POC12	CONRES
+LABISOSR	CULT
+LABYTADID	DESIP
+LABYTADID	DOXEPIN
+LABYTADID	DOXEPINNOR
+LAB11198R	FREEVITA
+210993289	ICDBRMDACR
+210993289	ICDBRMDDCR
+210993289	ICDBRMDTHR
+LABYTADID	IMI
+LABYTADID	IMIDESI
+210993284	LVBRSENA
+LABCBCS	MEGA
+LABYTADID	NORDOX
+LABYTADID	NORTRP
+IMG11AR1	PDF
+LABTBP67	PRO
+LABISOSR	RPT
+210993289	RVBRSENR
+210993289	RVTHRAMP
+210993289	RVTHRDUR
+LABISOSR	SDES
+LABTBP67	SI
+210993289	SLWVTTDTDU
+LABISOSR	SREQ
+LABUA	TRICHOMONAS
+LAB11198R	VITE
+LABYTESFB	YTESHD
+LAB11179R	11180R
+LAB11179R	11181R
+LAB11179R	11197R
+LAB11340R	11341R
+LAB11340R	11342R
+LAB11179R	11511R
+LAB11576R	11691R
+LAB12811R	12812R
+LAB13002R	13001R
+10305R	9140R
+10305R	9142R
+10305R	9144R
+10310R	9170R
+10310R	9172R
+10310R	9174R
+LABATHAL	ATHCT
+LABATHAL	ATHDI
+LABATHAL	ATHDX
+LABATHAL	ATHET
+LABATHAL	ATHIN
+LABATHAL	ATHMD
+LABATHAL	ATHME
+LABATHAL	ATHRE
+LAB11026R	C1INHF
+LABUCORT	CORTU
+LABTBP57	DI
+EKG6	ECGIMPRESS
+LABFEHRC	ECINT
+LAB11037R	FLEC
+210993284	FSTVTTDTNT
+LABVP4	HATINT
+LABVP4	HATR
+LABVP4	HBSINT
+LABVP4	HBSR
+LABVP4	HBTINT
+LABVP4	HBTR
+LABVP4	HCVI
+LABVP4	HCVR
+EKG6	HEARTRATE
+LABHEVG	HEVG1
+LABVP4	HQLINT
+LABVP4	HQLR
+LABHTLVB	HTWB
+LABIFEDE	IFED
+LABIFEDE	IFEE
+LABKRAS	KRASTI
+210993282	LEADIMPDTE
+210993282	LEADMFR
+210993282	LEADMODEL
+210993282	LEADTYPE
+EKG6	PRINTERVAL
+LABTBP68	PRO
+EKG6	QRSAXIS
+EKG6	QRSDINTERVAL
+EKG6	QTCINTERVAL
+EKG6	QTINTERVAL
+LABRDATBC	RDATB
+EKG6	RR
+210993289	RVBRSENP
+LABYSCL70	SCL70
+LABTBP68	SI
+210993289	SLWVTTZNRT
+LABTBP26	SPEXP
+LABTBP27	SPEXP
+LABUCORT	TCORTU
+LABYUVMA	TIMEU
+LABTBP60	UBT
+LABTBP64	UBT
+LABTBP65	UBT
+LABYUVMA	UCR
+LABYUVMA	UCR
+LABTBP60	UNT
+LABTBP64	UNT
+LABTBP65	UNT
+LABUCORT	UT52
+LABYUVMA	UVOL
+LABYUVMA	VMA
+LABYUVMA	VMAINT
+LABYUVMA	VMAUR
+LABUCORT	VOL52
+210993289	VSLWVTTDTT
+LABXIGF1	XIG1
+LABYENTE	YEN3
+LABYENTE	YEN5
+LABYENTE	YEN8
+LABYENTE	YEN9
+LABYINH	YINH1
+LAB11090R	YPCA
+LABYENTE	YPSEU
+LAB12050R	12051R
+LAB12212R	12213R
+LAB12236R	12237R
+LAB12607R	12606R
+LABFAMYA	AMYLASEFLD
+LABBARSAB	BHIGGT
+LABBM1114	14) BY PCR,BONE MARROW"
+LABBM1114	14) BY PCR,BONE MARROW"
+LABBM1114	14) BY PCR,BONE MARROW"
+LABBM1114	14) BY PCR,BONE MARROW"
+LABBM1114	14) BY PCR,BONE MARROW"
+LABBM1114	14) BY PCR,BONE MARROW"
+LABYUCC	COCAINE
+LABDCMVL	DCMVL1
+LABSYDIFF	EOSFLD
+LABYESTRO	ESTRADIOL
+LABYESTRO	ESTRONE
+210993283	FSTVTTDTNT
+LABDVEIAR	IAR
+LABDVEIAR	IARIP
+LABDVEIAR	IARSP
+210993289	ICDBRAVDLS
+210993289	ICDBRAVMAX
+210993289	ICDDTATPVT
+LAB11044R	IL6
+LABINHA	INHAR
+210993282	LEADSERNUM
+LABUORG	ORGU
+LABUORG	ORGUIN
+LAB11054R	PM1AB
+210993289	RVBRSENB
+LABSSBABY	SSB
+EKG6	TWAVEAXIS
+LABTBP61	UBT
+LABTBP62	UBT
+LABTBP63	UBT
+LABTBP66	UBT
+LABTBP61	UNT
+LABTBP62	UNT
+LABTBP63	UNT
+LABTBP66	UNT
+LABTBP18	VL
+LABTBP20	VL
+LABTBP28	VL
+LABYCLAMP	YCLAM1
+LABYEB	YEB1
+LABYEB	YEB2
+LABYEB	YEB3
+LABYEB	YEB4
+LABYSTCL	YSTCL2
+LABYTETT	YTETT1
+LABYTRAB	YTRAB1
+LABYUCC	YUBEGC
+LABYUCC	YUCOIM
+LABYUCC	YUCOIN
+10277R	10049R
+LAB11240R	11241R
+LAB11618R	11777R
+LAB11858R	11856R
+LABHIBCV	11894R
+LAB12853R	12339R
+LAB12796R	12795R
+LABABIDI	ABID2E
+LABYACE	ACE
+LABYUAMC	AMPHET
+LABSMABY	ASMQNT
+LABCENABY	CENQNT
+LABCLCR2	CHLAM
+LABCMVQL	CMQDNA
+LABCMVQL	CMVLIP
+LABCMVQL	CMVQSP
+LABCOCC	COCC
+LABFCULS	CULT
+LABCYAB	CYABS
+LABCYSI	CYSC
+LABFCYST	CYSIGG
+LABCLCR2	DCL4
+LABFDIFF	FCMT
+210993289	FSTVTTDTNT
+LABH1N1PC	H1N1DI
+LABH1N1PC	H1N1IN
+LABH1N1PC	H1N1M
+LABH1N1PC	H1N1MT
+LABH1N1PC	H1N1R
+LABH1N1PC	H1N1SP
+LABHCHGA	HCHCO
+LABHCHGA	HCHDI
+LABHCHGA	HCHDX
+LABHCHGA	HCHIN
+LABHCHGA	HCHMD
+LABHCHGA	HCHME
+LABHCHGA	HCHPR
+LABHCHGA	HCHRE
+LABHIBCV	HIBCV1
+210993289	ICDDTAMSEP
+210993282	ICDEVLDATE
+210993282	ICDTLIMPRV
+LABLEISAB	LBRAG
+LABLEISAB	LBRAM
+LABLEISAB	LDONG
+LABLEISAB	LDONM
+210993281	LEADIMPDTE
+210993281	LEADMFR
+210993281	LEADMODEL
+210993281	LEADSERNUM
+210993281	LEADTYPE
+LABLEISAB	LMEXG
+LABLEISAB	LMEXM
+LABLEISAB	LTROG
+LABLEISAB	LTROM
+LABCBCS	LYMPHM
+LABRBCMOR	MORPH
+LABYUAMC	MTAM
+LABOF	OFINT
+EKG6	PAXIS
+LABPNAB	PNE01
+LABPNAB	PNE12
+LABPNAB	PNE14
+LABPNAB	PNE19
+LABPNAB	PNE23
+LABPNAB	PNE26
+LABPNAB	PNE3
+LABPNAB	PNE4
+LABPNAB	PNE51
+LABPNAB	PNE56
+LABPNAB	PNE8
+LABPNAB	PNE9
+LABTBP69	PRO
+LABTBP70	PRO
+LABTBP71	PRO
+LABPRO6MP	PRO6MR
+10277R	RINFO
+LABFCULS	RPT
+210993284	RVBRSENR
+LABFCULS	SDES
+LABCLCR2	SDES4
+LABTBP69	SI
+LABTBP70	SI
+LABTBP71	SI
+LABFCULS	SREQ
+LABSSAABY	SSA
+LABTXCAB	TOXOC1
+LABTBP67	UBT
+LABTBP67	UNT
+LABEBVC	VCAA
+LABTBP17	VL
+LABXHIA	XHIAB
+LABYITRA	YITR3
+LABYLYSO	YLYSOZ
+LABYUAMC	YUAMIM
+LABYUAMC	YUAMIN
+LABYUAMC	YUMDA
+LABYUAMC	YUMDMA
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+LABACYLP	C142
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+LABACYLP	C16OH
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+LABT1418	18), BLOOD"
+LABT1418	18), BLOOD"
+LABT1418	18), BLOOD"
+LABT1418	18), BLOOD"
+LABT1418	18), BLOOD"
+LABT1418	18), BLOOD"
+POC30	TESTLO
+LABUMAGT	TMGNU
+LABTTCLO	TTCDIS
+LABTTCLO	TTCDX
+LABTTCLO	TTCMET
+LABTTCLO	TTCPRM
+LABTTCLO	TTCRES
+LABTTCLO	TTCTIS
+LABRNPABY	U1RNPAB
+LABTBP74	UBT
+LABUCLT	UCL
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+LABUCLT	UT11
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+LABTBP24	VL
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+LABYDIPT	YDIPT1
+LABYKLFRE	YKLF1
+LABYKLFRE	YKLF2
+LABYKLFRE	YKLF3
+LABYLAP	YLAP1
+LABYUARSR	YUARR2
+LABYUARSR	YUARR4
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+LAB12774R	11323R
+LAB11324R	11325R
+LAB11512R	11621R
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+LAB11608R	11767R
+LAB11620R	11779R
+LAB11855R	11854R
+LAB11882R	11881R
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+LAB12637R	12696R
+LAB12637R	12697R
+LAB12637R	12698R
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+10355R	9353R
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+10355R	9363R
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+10355R	9366R
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+LABSEZPNL	CD19AB
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+LABSEZPNL	CD7AB
+LABSEZPNL	CD7PC
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+LABTSMISI	CTSMIS
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+LABD816V	D816RE
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+LABEBVPAN	EA
+LABEBVPAN	EBNA
+LABGNTUNL	GENTSDD
+LABGNTUNL	GNTUN2
+LABGNTUNL	GNTUN3
+LABGNTUNL	GNTUN4
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+LABSEZPNL	LYM
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+LABEP2	NA
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+LABAVRT	SDES
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+LABTTCLO	TTCDIR
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+LABUURICT	URICURN
+LABUURICT	UT9
+LABEBVPAN	VCA
+LABEBVPAN	VCAM
+LABTBP23	VL
+LABSEZPNL	WBC
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+LABYRISPM	YRISP1
+LABYRISPM	YRISP3
+10355R	10474R
+10355R	10475R
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+LAB11190R	11192R
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+LAB11608R	11765R
+LAB11608R	11766R
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+LAB12017R	12027R
+LAB12017R	12028R
+LAB12017R	12029R
+LAB12017R	12030R
+LAB12017R	12031R
+LABYMYCRB	12197R
+LAB12206R	12207R
+LAB12371R	12372R
+LAB12585R	12594R
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+LABFUNDAP	BLAS
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+LABBCLON	BPRM
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+PATH18	C2526N
+PATH18	C257N
+PATH18	C264AB
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+PATH18	C3026N
+PATH18	C307N
+PATH18	C326NA
+PATH18	C3426N
+PATH18	C3426P
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+PATH18	C347NP
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+PATH18	C37NA
+PATH18	C3P4PA
+PATH18	C3P4PP
+PATH18	C3P7NP
+PATH18	C3P8PN
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+PATH18	C5226N
+PATH18	C527N
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+PATH18	CD26NP
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+LABACHOO	CLA
+LABACHOO	COCK
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+LABTBP73	DI
+LABTBP74	DI
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+LABACHOO	TIMO
+LABTOXIG	TOXO
+LABFECHO	TYPE11
+LABFECHO	TYPE30
+LABFECHO	TYPE4
+LABFECHO	TYPE7
+LABFECHO	TYPE9
+LABTBP75	UBT
+LABTBP76	UBT
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+LABTBP76	UNT
+LABTBP31	VL
+LABTBP32	VL
+LABYUALDO	VOLUMEURN
+LABYUARSR	VOLUMEURN
+LABYUCITR	VOLUMEURN
+LABYUOXAL	VOLUMEURN
+PATH18	WBC
+LABACHOO	WEST
+LABTBP57	XMR
+LABTBP60	XMR
+LABTBP62	XMR
+LABTBP63	XMR
+LABYC5REF	YC5RE1
+LABYUCITR	YCITRU
+LABYFCHYL	YFCHY1
+LABYFCHYL	YFCHY6
+LABYFELBA	YFELB1
+LABYFCL	YFLSC1
+LABYINSFT	YINSF1
+LABYLDISO	YLDIS2
+LABYLDISO	YLDIS3
+LABYLDISO	YLDIS8
+LABLEVETL	YLEVT1
+LABYMYCRB	YMYCSC
+LABYUOXAL	YOXALU
+LAB12503R	YSTK1
+LAB12503R	YSTK2
+LAB12505R	YSTNA1
+LAB12505R	YSTNA2
+LAB12505R	YSTNA3
+LAB12505R	YSTNA4
+LABYTRYPS	YTRYP1
+LABYUALDO	YUALD2
+LABYUCITR	YUCICN
+LABYUCITR	YUCIUT
+LABYUOXAL	YUOXCO
+LABYUOXAL	YUOXUT
+LABYZINCS	ZINC
+10195R	10049R
+10210R	10049R
+LAB11373R	11374R
+LAB11435R	11499R
+LAB11615R	11774R
+LAB11991R	11992R
+LAB11991R	11993R
+LAB11991R	11995R
+LAB11991R	11996R
+LAB11991R	11998R
+LAB11991R	11999R
+LAB12447R	12448R
+LAB12447R	12449R
+LAB12447R	12450R
+LAB12447R	12451R
+LAB12447R	12452R
+LAB12447R	12453R
+LAB12447R	12454R
+LAB12447R	12455R
+LAB12447R	12456R
+LAB12447R	12457R
+LAB12447R	12458R
+LAB12447R	12459R
+LAB12447R	12460R
+LAB12447R	12461R
+LAB12447R	12462R
+LAB12447R	12463R
+LAB12447R	12464R
+LAB12447R	12465R
+LAB12447R	12466R
+LAB12447R	12467R
+LAB12447R	12468R
+LAB12447R	12469R
+LAB12645R	12644R
+LAB12651R	12650R
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+LAB13000R	12686R
+LAB13000R	12687R
+LAB13000R	12688R
+LAB12723R	12722R
+LAB12774R	12775R
+LAB12790R	12789R
+LAB12790R	12805R
+10295R	9084R
+10295R	9091R
+10295R	9093R
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+10305R	9131R
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+10360R	9391R
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+LABBTHSQ	BSQRE
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+LABLAS	CAN50
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+LABCCP	CCPR
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+LABTBP72	DI
+LABTBP75	DI
+LABTBP76	DI
+LABTBP77	DI
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+LAB12796R	FLUCOZ
+LABYT7P	FT4INDEX
+LABYGADAB	GAD65AB
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+LABVP5	HBSINT
+LABVP5	HBSR
+LABVP5	HBTINT
+LABVP5	HBTR
+EXTHCT	HCT
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+LABVP5	HCVR
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+LABFHHV7	HERPM
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+LABMGMTB	MGMMDB
+LABMGMTB	MGMMEB
+LABMGMTB	MGMREB
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+LABADIFF	MYEL
+LABADIFF	NRBC
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+LABLAS	PPD100
+LABLAS	PPD200
+LABLAS	PPD400
+LABLAS	PPD50
+LABLAS	PPD800
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+LABTBP86	PRO
+LABTBP87	PRO
+LABTBP88	PRO
+LABTBP89	PRO
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+LABPSAU	PSAU
+LABPT12	PTFR12
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+LABBBPRH	RHSTA
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+10210R	RINFO
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+210993284	SLWVTTAZNS
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+LABBMS	SREQ
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+LABTMONA	SREQ
+LABT1114	14) BY PCR"
+LABT1114	14) BY PCR"
+LABT1114	14) BY PCR"
+LABT1114	14) BY PCR"
+LABT1114	14) BY PCR"
+LABT1114	14) BY PCR"
+LABYT7P	T4
+LABUALBT	TALBU
+LABBCGMIS	TEST8
+LABLAS	TET1K
+LABLAS	TET2K
+LABLAS	TET5K
+LABDT	TETAN
+LABLAS	TETINT
+LAB11057R	TNF
+LABTOXATP	TOXDES
+LABTOXATP	TOXGQL
+LABTOXATP	TOXGQT
+LABTOXATP	TOXMQL
+LABTOXATP	TOXMQT
+LAB11058R	TRAZO
+LABUALBT	UALB
+LABTBP77	UBT
+LABTBP78	UBT
+LABTBP79	UBT
+LABTBP80	UBT
+LABTBP81	UBT
+LABTBP82	UBT
+LABUELP	UCL
+LABUELP	UK
+LAB12811R	UMYOG
+LABUELP	UNA
+LABLAS	UNCAN
+LABLAS	UNPPD
+LABTBP77	UNT
+LABTBP78	UNT
+LABTBP79	UNT
+LABTBP80	UNT
+LABTBP81	UNT
+LABTBP82	UNT
+LABLAS	UNTET
+LABUALBT	UT35
+LABTBP25	VL
+LABTBP33	VL
+LABTBP36	VL
+LABLPD	VLDL
+LABUALBT	VOL35
+210993282	VSLWVTTDTD
+LABPANWBC	WBC
+LABCD3F	WBCFLD
+LABTBP61	XMR
+LABTBP69	XMR
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+LAB11606R	11758R
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+210993282	ICDBRMDACR
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+LABLMS	PHA
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+LABTBP92	PRO
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+LABTBCLO	TBCCMT
+LABTBCLO	TBCDIR
+LABTBCLO	TBCDIS
+LABTBCLO	TBCDX
+LABTBCLO	TBCMET
+LABTBCLO	TBCPRM
+LABTBCLO	TBCRLT
+LABTBCLO	TBCTIS
+LAB11113R	TOXOA
+LAB11122R	TOXOM
+LABMOLDS	TRIRUB
+LABTBP83	UBT
+LABTBP84	UBT
+LABTBP85	UBT
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+LABUCORT	UCORT
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+LABTBP83	UNT
+LABTBP84	UNT
+LABTBP85	UNT
+LABTBP86	UNT
+LAB12664R	UT53
+LABTBP26	VL
+LABTBP37	VL
+LAB12664R	VOL53
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+LABSTAND	VZAB
+LABCD3B	WBC
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+LABTBP80	XMR
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+LABTBP82	XMR
+LABYAMIO	YAMIOD
+LAB11074R	YARBIN
+LABYICP	YC1QB
+LABYC2REF	YC2RE1
+LABCENABY	YCENTA
+LABYAMIO	YDESET
+LABYGDA	YGDA2
+LABYLCFA	YLCFA1
+LABYLCFA	YLCFA2
+LABYLCFA	YLCFA3
+LABYLCFA	YLCFA4
+LABYLCFA	YLCFA5
+LABYLCFA	YLCFA6
+LABYLCFA	YLCFA7
+LABYLCFA	YLCFA8
+LABYLCFA	YLCFA9
+LABYICP	YPEG
+LABYRAB	YRAB1
+LABYICP	YRAJI
+LABYSCERE	YSCER1
+LABYSCERE	YSCER2
+LABYSHBG	YSHBG1
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+LAB11577R	11692R
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+LABYCDTN	11842R
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+LAB11438R	12067R
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+LAB12228R	12229R
+LAB12284R	12285R
+LAB12284R	12286R
+LAB12284R	12287R
+LAB12284R	12288R
+LAB12284R	12289R
+LAB12284R	12290R
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+LAB12331R	12342R
+LAB12331R	12343R
+LAB12331R	12344R
+LAB12331R	12345R
+LAB12331R	12346R
+LAB12331R	12347R
+LAB12331R	12348R
+LAB12331R	12349R
+LAB12331R	12350R
+LAB12331R	12351R
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+LABLEISAB	12428R
+LABLEISAB	12429R
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+LABLEISAB	12431R
+LAB12599R	12598R
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+10305R	9141R
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+10315R	9201R
+10315R	9202R
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+PATH18	C2526N
+PATH18	C257N
+PATH18	C264AB
+PATH18	C264PC
+PATH18	C3026N
+PATH18	C307N
+PATH18	C326NA
+PATH18	C3426N
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+PATH18	C3426T
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+PATH18	C37NA
+PATH18	C3P4PA
+PATH18	C3P4PP
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+PATH18	CD19AB
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+PATH18	CD3AB
+PATH18	CD3PC
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+LABVANUNL	VANUN4
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+PATH18	WBC
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+LABYSTRAB	YSTA
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+LABYUARS	YUARS3
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+LAB11004R	11007R
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+LAB11534R	11644R
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+LAB11568R	11683R
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+LAB11572R	11687R
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+LAB12582R	12672R
+LAB12582R	12673R
+LAB12582R	12674R
+LAB12733R	12734R
+LAB12836R	12837R
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+LABY5NUC	5NUCLEOT
+10295R	9092R
+10300R	9113R
+10315R	9200R
+10320R	9218R
+10320R	9226R
+10320R	9227R
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+10355R	9374R
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+10350R	9586R
+LABYSTA1A	A1AANTITRYPF
+LABARI	ABORH
+LABARI	ABORH
+LAB11068R	ACE
+LABACRBLO	ACRBLO
+LABACRMOD	ACRMOD
+LABADAMTS	ADMAG
+LABCSFALB	ALBCSF
+LABYALUM	ALUM
+LABIMM1	ANA
+LABIMM2	ANA
+LABIMM1	ANAINTERP
+LABIMM2	ANAINTERP
+LAB11022R	ANTIIS
+LAB11075R	ASMA
+LABASO	ASO
+LABSALCOR	AVGCOR
+LABBMCD34	BCD34V
+LABBKVPC	BKQ
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+LABIMM1	C3
+LABCIRCAI	CAICR
+PATH18	CD26AB
+PATH18	CD26PC
+LABBMCD34	CD45PV
+PATH18	CD7AB
+PATH18	CD7PC
+LABCDMLPA	CDMLDI
+LABCDMLPA	CDMLDX
+LABCDMLPA	CDMLMD
+LABCDMLPA	CDMLME
+LABCDMLPA	CDMLRE
+LABCFMLPA	CFMLDI
+LABCFMLPA	CFMLDX
+LABCFMLPA	CFMLMD
+LABCFMLPA	CFMLME
+LABCFMLPA	CFMLRE
+LABCFPT	CFPTCL
+LABCFPT	CFPTDS
+LABCFPT	CFPTIN
+LABCFPT	CFPTME
+LABCFPT	CFPTRS
+LABBMTP	CMGDES
+LABBMTP	CMGQUL
+LABBMTP	CMMDES
+LABBMTP	CMMQUL
+LABCMVAV	CMVAVD
+LABCMVAV	CMVAVI
+LABCMVAV	CMVAVR
+LABLIVER	CMVIGM
+LABFCO2	CO2FL
+LABFCO2	CO2FLS
+LAB12582R	CONA
+LAB12582R	CONINT
+LABCOPYPT	COPYTO
+LABBACIDI	CULT
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+LABUFCATE	DOPAURN
+LABEBNABY	EBNDES
+LABEBNABY	EBNQNT
+LABEBNABY	EBNQUL
+LABADIFF	EOS
+LABUFCATE	EPINEPURN
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+LABFDIFF	FOTHER
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+LAB11080R	GAD65AB
+LABGTOL	GLT2
+LABGTOL	GLUF
+LABCBCD	GRANP
+LABGTOL	GTTINT
+LABYHALO	HALO
+LABLIVER	HATINT
+LABSTAND	HATINT
+LABLIVER	HATR
+LABSTAND	HATR
+LABLIVER	HAVINT
+LABLIVER	HAVR
+LABHBCT	HBCAB
+LABSTAND	HBCAB
+LAB11106R	HBEAB
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+LABSTAND	HBSINT
+LABLIVER	HBSR
+LABSTAND	HBSR
+LABLIVER	HBTINT
+LABSTAND	HBTINT
+LABLIVER	HBTR
+LABSTAND	HBTR
+LABHCVGTP	HCV1
+LABHCVGTP	HCV1S
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+LABHCVGTP	HCV2S
+LABHCVGTP	HCV3
+LABHCVGTP	HCV4
+LABHCVGTP	HCV5
+LABHCVGTP	HCV6
+LABHCVGTP	HCVGTD
+LABLIVER	HCVI
+LABSTAND	HCVI
+LABHCVPCX	HCVIPX
+LABHCVPCX	HCVLOG
+LABLIVER	HCVR
+LABSTAND	HCVR
+LABHCVPCX	HCVRES
+LABHCVPCX	HCVRNA
+LABHCVPCX	HCVSP
+LABHELPM	HELPM1
+LABHAVT	HEPAAB
+LABSTAND	HEPAAB
+LABSTAND	HEPCAB
+LABHIVWBL	HIVAB
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+LABHUNDNA	HNDNA
+LABHPVPCR	HPV16
+LABHPVPCR	HPV18
+LABHPVPCR	HPVDI
+LABHPVPCR	HPVINT
+LABHPVPCR	HPVMT
+LABHPVPCR	HPVSP
+LABLIVER	HQLINT
+LABLIVER	HQLR
+LAB11137R	HSVWB
+LABDHTLVQ	HTLVQ1
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+LABLIVER	HTLVS
+210993279	ICDBRAVMAX
+210993288	ICDBRVVDEL
+210993282	ICDDTAMSEP
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+210993283	LVBRPACA
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+210993283	LVBRPACW
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+210993281	LVBRSENP
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+210993283	LVTHRAMP
+210993288	LVTHRAMP
+210993283	LVTHRDUR
+210993288	LVTHRDUR
+LABCD3B	LYM
+LABMEARC	MEARC1
+LABMEARC	MEARCD
+LABMEARC	MEASPM
+LABMELAS	MELADN
+LABYMERC	MER
+LABYMETHS	METHSX
+210993288	MFR
+LABSALCOR	MN1COR
+LABSALCOR	MN2COR
+210993288	MOD
+LABDVEMP	MPIP
+LABYMPO	MPO
+LABDVEMP	MPSP
+LABDVEMP	MPVR
+LABPANANC	NEUTAB
+LABCBCD	NEUTPC
+LABYNICK	NICKEL
+LABNJAK2	NJAKIN
+LABUFCATE	NOREPHURN
+LABFLUOX	NORFLU
+LABYMETHS	NORMSX
+LABOSTLC	OSTLC
+CRP139	PDF
+EKG6	PDF
+IMG11BXMT	PDF
+IMG13RHC	PDF
+PFT50	PDF
+LAB12582R	PHA
+LAB12582R	PHAINT
+LABYPHOLP	PHOLIP
+210993280	PMALERT
+210993281	PMLVLDBIIM
+210993281	PMLVLDUNIM
+210993280	PMRALDUNIM
+210993288	PMRALDUNIM
+210993288	PMRVLDUNIM
+LABPNET2	PNE14
+LABPNET2	PNE19
+LABPNET2	PNE23
+LABPNET2	PNE26
+LABPNET2	PNE4
+LABPNET2	PNE56
+LABPNET2	PNE68
+LABPABN	POL1
+LABPABN	POL2
+LABPABN	POL3
+LABTBP94	PRO
+LABPROPAF	PROPAF
+LABPSACX	PSA
+LAB12582R	PWM
+LAB12582R	PWMINT
+LABUAPRN	RBCAST
+LABRBP	RBPR
+LAB13006R	REDSUBSTL
+LABDATLA	REQS
+LABSTDY2	RESS2
+LABMISCH	RESULH
+LABIMM2	RHFINT
+LABIMM2	RHFR
+10040R	RINFO
+10085R	RINFO
+10175R	RINFO
+LABDRPRS	RPRS1
+LABDRPRS	RPRS1
+LABBACIDI	RPT
+LABINFO	RPT
+LABISOBR	RPT
+LABROACAN	RPT
+210993281	RVBRSENR
+LABSCLABY	SCLQNT
+LABBACIDI	SDES
+LABINFO	SDES
+LABISOBR	SDES
+LABROACAN	SDES
+210993288	SERNUM
+LABTBP94	SI
+LABADIFFS	SIGNIF
+LABRBCMOR	SIGNIF
+LABSMABY	SMAB
+LABSYDIFF	SOTHER
+LABBKVPC	SOURC6
+LABTBP43	SPEXP
+LABTBP44	SPEXP
+LABSTDY2	SPTYS2
+LABBACIDI	SREQ
+LABINFO	SREQ
+LABISOBR	SREQ
+LABROACAN	SREQ
+LABSTSS	STSQNT
+LABSULFAT	SULFAB
+LABMISCH	TESTH
+LABSTDY2	TESTS2
+LABUFCATE	TIMEU
+LABTOXIM	TOXMQT
+LAB11121R	TOXOD
+LABTOXOM	TOXOM
+LABUAPRN	TRICHOMONAS
+LABTT1517	17) PML/RAR, TISSUE"
+LABTT1517	17) PML/RAR, TISSUE"
+LABTT1517	17) PML/RAR, TISSUE"
+LABTT1517	17) PML/RAR, TISSUE"
+LABTT1517	17) PML/RAR, TISSUE"
+LABTT1517	17) PML/RAR, TISSUE"
+LABTT1517	17) PML/RAR, TISSUE"
+POC39	UBLOOD
+LABTBP88	UBT
+LABTBP89	UBT
+LABTBP90	UBT
+LABTBP91	UBT
+LABTBP92	UBT
+LABUFCATE	UCATIN
+LABUFCATE	UCR
+LABUFCATE	UCR
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+LABUFCATE	UDOPA1
+LABUFCATE	UEPI1
+POC44	UGLU
+LABUFCATE	UNORE1
+LABTBP88	UNT
+LABTBP89	UNT
+LABTBP90	UNT
+LABTBP91	UNT
+LABTBP92	UNT
+POC44	UPROT
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+LABUFCATE	UVOL
+LABBMTP	VGGDES
+LABBMTP	VGGQUL
+LAB11060R	VITB1
+LABTBP39	VL
+LABTBP41	VL
+LABTBP44	VL
+LABTBP60	VL
+LABUHIST	VOL
+LABYUOXAL	VOLUMEURN
+LABTBP75	XMR
+LABTBP76	XMR
+LABTBP85	XMR
+LABTBP86	XMR
+LABTBP87	XMR
+LABYA1APH	YA1A2
+LABYBABE	YBAB1
+LABYBABE	YBAB2
+LABYBABE	YBAB3
+LABYDROTC	YDR1
+LABEVPCR	YENP1
+LABEVPCR	YENP2
+LABEVPCR	YENP3
+LABEVPCR	YENP4
+LABYFK	YFLSC1
+LABYHISTA	YHISA1
+LABYMEM	YMEM1
+LABYUOXAL	YOXALU
+LABYP4AB	YP4AB1
+LABYP4AB	YP4AB2
+LABYP4AB	YP4AB3
+LABYPANPP	YPANPP
+LAB11089R	YPAPT
+LABYPERAB	YPERA1
+LABYPERAB	YPERA2
+LABYRETAB	YRET1
+LABYSMG	YSMG3
+LABYSMG	YSMG4
+LAB12578R	YSTCL2
+LAB12578R	YSTCL3
+LAB12578R	YSTCL4
+LABYTICK	YTICK1
+LABYTICK	YTICK2
+LABYTICK	YTICK3
+LABYUNTX	YUNTXC
+LABYUNTX	YUNTXR
+LABYUNTX	YUNTXT
+LABYUAMC	YUOAGC
+LABYUOPC	YUOPCO
+LABYUOXAL	YUOXCO
+LABYUOXAL	YUOXUT
+LABYUAMC	YUSYGC
+LAB12586R	YVB1W1
+LAB12586R	YVB1W2
+10010R	10049R
+10120R	10049R
+10165R	10049R
+10225R	10049R
+10285R	10049R
+10297R	10049R
+10335R	10471R
+10545R	10478R
+POC39	11147R
+POC1	11148R
+POC14	11148R
+LABSTAND	11165R
+LABSTAND	11166R
+LAB11173R	11174R
+LAB11195R	11196R
+LAB11219R	11220R
+LAB11219R	11221R
+LAB11219R	11222R
+LAB11219R	11223R
+LAB11225R	11226R
+LAB11225R	11227R
+LAB11225R	11228R
+LAB11254R	11255R
+LAB11254R	11256R
+LAB11254R	11257R
+LAB11254R	11258R
+LAB11254R	11259R
+LAB11254R	11260R
+LAB11254R	11261R
+LAB11254R	11262R
+LAB11254R	11263R
+LAB11254R	11264R
+LAB11299R	11300R
+LAB11299R	11301R
+LAB11299R	11302R
+LAB11299R	11303R
+LAB11299R	11304R
+LAB11299R	11305R
+LAB11299R	11306R
+LAB11347R	11348R
+LAB12689R	11500R
+LAB11528R	11638R
+LAB11533R	11643R
+LAB11540R	11650R
+LAB11554R	11664R
+LAB12016R	11667R
+LAB11570R	11685R
+LAB11573R	11688R
+LAB11592R	11720R
+LAB11596R	11723R
+LAB11596R	11724R
+LAB11596R	11727R
+LAB11596R	11728R
+LAB11596R	11729R
+LAB11596R	11731R
+LAB11596R	11732R
+LAB11596R	11733R
+LAB11599R	11756R
+LAB11604R	11757R
+LABYCDTN	11836R
+LABYCDTN	11841R
+LABYFCL	11844R
+LAB11878R	11877R
+LABDHIVAB	11888R
+LABDHBAG	11891R
+LABDRPR	11892R
+LAB11254R	11897R
+LAB11254R	11898R
+LABSTAND	12001R
+LABSTAND	12002R
+LAB12003R	12004R
+LAB13007R	12012R
+LAB13009R	12013R
+LAB13010R	12015R
+LAB12023R	12024R
+LAB11604R	12044R
+LABYBUPRO	12045R
+LAB11591R	12046R
+LAB11591R	12047R
+LAB12055R	12056R
+LAB12059R	12060R
+LAB12095R	12101R
+LAB11225R	12199R
+LAB12220R	12221R
+LAB12279R	12280R
+LAB12279R	12281R
+LAB12279R	12282R
+LAB12328R	12327R
+LAB12494R	12378R
+LAB12470R	12379R
+LAB12415R	12416R
+LABCLAMP	12428R
+LABCLAMP	12429R
+LABCLAMP	12430R
+LABFHHV6	12431R
+LAB12443R	12444R
+LAB12471R	12472R
+LAB12471R	12473R
+LAB12471R	12474R
+LAB12471R	12475R
+LAB12471R	12476R
+LAB12471R	12477R
+LAB12471R	12478R
+LAB12471R	12479R
+LAB12471R	12480R
+LAB12471R	12481R
+LAB12471R	12482R
+LAB12471R	12483R
+LAB12471R	12484R
+LAB12471R	12485R
+LAB12471R	12486R
+LAB12471R	12487R
+LAB12471R	12488R
+LAB12471R	12489R
+LAB12471R	12490R
+LAB12471R	12491R
+LAB12471R	12492R
+LAB12471R	12493R
+LAB12512R	12513R
+LAB12580R	12589R
+LAB12581R	12590R
+LAB12584R	12593R
+LAB11098R	125OHD2
+LAB11098R	125OHD2
+LAB12643R	12642R
+LABYUALA	12682R
+LAB12581R	12699R
+LAB12581R	12700R
+LAB12581R	12701R
+LAB12581R	12702R
+LAB12581R	12703R
+LAB12581R	12704R
+LAB12581R	12705R
+LAB12581R	12706R
+LAB12581R	12707R
+LAB12581R	12708R
+LAB12581R	12709R
+LAB12581R	12710R
+LAB12761R	12757R
+LABYCDTN	12769R
+LABYCDTN	12770R
+LABYCDTN	12771R
+LAB12772R	12773R
+LAB11572R	12782R
+LAB12802R	12801R
+LAB12817R	12828R
+LAB12813R	12835R
+LAB12845R	12843R
+LAB12845R	12844R
+LABY17OHP	17OHPROG
+10290R	9035R
+10290R	9036R
+10295R	9099R
+10350R	9342R
+10355R	9361R
+10355R	9367R
+LAB12733R	A2A
+LAB11028R	ACE
+LABCACE	ACE
+LABYCHRBC	ACHERBC
+LAB11269R	ACRBIN
+LAB11269R	ACRBLO
+LAB11269R	ACRMOD
+LABADPCRQ	ADPCR1
+210993282	AFIBTAQRSM
+210993282	AFLTAQRSMC
+210993282	AFLTAZNRTC
+LABCBCD	ALB
+LABHEPAT	ALB
+LABBCAA	ALCN
+LABYUCD12	ALCOHOL
+210993282	ALERT
+LABCBCD	ALKP
+LABHEPAT	ALKP
+LABMALL2	ALL2
+LABMALL3	ALL3
+LABMALL2	ALLR2
+LABMALL3	ALLR3
+LABCBCD	ALT
+LABHEPAT	ALT
+LABAMAONC	AMA
+LABBLC	AMP
+LABBLC2	AMP
+LABYUCD12	AMPHET
+LABYAMPHB	AMPHOTERB
+LABIMM1	ANAQNT
+LABASCA	ASCA
+LABASPR	ASPR
+LABCBCD	AST
+LABHEPAT	AST
+210993282	ATACHTAQRS
+LABBLC	ATM
+LABBLC2	ATM
+LABADIFFS	BAND
+LABAFCT	BANDFLD
+LABBFCT	BANDFLD
+LABYUCD12	BARB
+LABADIFF	BASO
+LABCBCD	BASOAB
+LABAFCT	BASOFLD
+LABCBCD	BASOSYN
+LABHBV	BCPMUT
+POC23	BE
+LABYUCD12	BENZO
+LAB12866R	BHIGGT
+LABBKDNA	BKD
+LABBKVPC	BKQ
+LABADIFF	BLASTS
+LABYEPIAB	BMAB
+LABBTHSQ	BSQMU
+LABCBCD	BUN
+LABC1INHF	C1INHF
+LABC1Q	C1Q
+LABFNKSEN	C3AB
+LABFNKSEN	C3PER
+PATH18	C48RAT
+LABCBCD	CA
+LABFABRO	CAIGG
+LABFABRO	CAIGM
+LABFABRO	CAINT
+LABPCCH8	CAION
+LAB11105R	CANDP
+LABFDIFF	CCNT
+LABCCP	CCPT
+PATH18	CD19AB
+PATH18	CD19PC
+PATH18	CD4TOT
+LABCDH1	CDH1IN
+LABCDH1	CDH1MU
+LABSNA	CENQNT
+LABCFPT	CFPTR2
+LABCFPT	CFPTR3
+LABITCH	CHICK
+LABLPD	CHOL
+LABLPD	CHOL
+LABLPD	CHOLHDL
+LABLPD	CHOLHDL
+LABCHON	CHONDS
+LABCHON	CHONDX
+LABCHON	CHONIN
+LABCHON	CHONMD
+LABCHON	CHONMT
+LABCHON	CHONRS
+LABBLC	CIP
+LABBLC2	CIP
+LABCBCD	CL
+LABUA	CLNTST
+LABUAPRN	CLNTST
+LABCLOMIP	CLOMI
+LABBMTP	CMGQNT
+LABBMTP	CMMQNT
+LABDCMVAB	CMVAB1
+LABCBCD	CO2
+LABYUCD12	COCAINE
+LAB11123R	COCC
+LAB11125R	COCI
+LABITCH	CORNY
+LABCEPLUS	CPLUS1
+LABCBCD	CR
+LABPCCH8	CR
+POC17	CR
+LABBLC	CRO
+LABBLC2	CRO
+LABCR	CRTNR
+LABCLAMP	CTIGA
+LABCLAMP	CTIGG
+LABCLAMP	CTIGM
+LABBLC	CTN
+LABBLC2	CTN
+LABBLC	CTX
+LABBLC2	CTX
+LABIDWEE	CULT
+LABSHSV	CULT
+LABVCULU	CULT
+LABBLC	CZ
+LABBLC2	CZ
+LABMYD88	D88DI
+LABMYD88	D88DX
+LABMYD88	D88IN
+LABMYD88	D88MD
+LABMYD88	D88ME
+LABMYD88	D88RE
+LABMYD88	D88RF
+LABHEPAT	DBIL
+210993280	DEVTYPE
+210993281	DEVTYPE
+LABTBP87	DI
+LABTBP88	DI
+LABTBP89	DI
+LABTBP90	DI
+LABTBP91	DI
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+LABTBP93	DI
+LABCLOMIP	DMCLOM
+LABDNAISO	DNADAT
+LABYGDA	DRUGSCRNFLD
+LABDVEADV	DVE
+LABDVEP1	DVE
+LABDVERTV	DVE
+LABEBPCRQ	EBV
+LABFABRO	EEIGG
+LABFABRO	EEIGM
+LABFABRO	EEINT
+LABITCH	EGGW
+LABRBCENZ	ENO
+LABCBCD	EOSAB
+LABCBCD	EOSFLD
+LABSTPOLY	EOSSTL
+230986580	ERYTHREMA
+LABYESTRN	ESTRONE
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+LABAFCT	FCMT
+LABAFCT	FCOLOR
+LABBLC	FEP
+LABBLC2	FEP
+LABEXCHTX	FFP
+LABFASAB	FHAB2
+LABITCH	FISH
+LABAFCT	FLDTYP
+LABAFCT	FMETA
+LABFBCRQT	FP190
+LABFT1517	17) PML/RAR, FLUID"
+LABAFCT	FPMN
+LABBFCT	FPMN
+LABAFCT	FRLYM
+210993283	FSTVTTDETD
+210993283	FSTVTTZNSC
+210993284	FSTVTTZNSC
+LABFT1517	17) PML/RAR, FLUID"
+LABFT1517	17) PML/RAR, FLUID"
+LABFT1517	17) PML/RAR, FLUID"
+LABFT1517	17) PML/RAR, FLUID"
+LABFT1517	17) PML/RAR, FLUID"
+LABFT1517	17) PML/RAR, FLUID"
+LABFT1517	17) PML/RAR, FLUID"
+LABAFCT	FTURB
+LABBLC	FURI
+LABBLC2	FURI
+LABGDMTOL	GDM1
+LABGDMTOL	GDM2
+LABGDMTOL	GDMINT
+LABGHBUR	GHBURN
+LABOGTT	GLT1
+LABOGTT	GLT2
+LABCBCD	GLU
+LABPCCH8	GLU
+LABUGLT	GLUEX
+LABGDMTOL	GLUF
+LABUGLT	GLUURN
+LABBLC	GM
+LABBLC2	GM
+LABRBCENZ	GPILS
+LABVP5	HAGINT
+LABVP5	HAGR
+LABCBCS	HAIRY
+LABVP5	HATINT
+LABVP5	HATR
+LABDHBAG	HBAG1
+LABBMTP	HBCAB
+LABDGHBP	HBCAB
+LABHBCT	HBCAB
+LABDHBCAB	HBCAB1
+LABDGHBP	HBCABIGM
+LAB11107R	HBEAG
+LABHBSQT	HBSAB
+LABDGHBP	HBSAB
+LABHBSQL	HBSAB
+LABHBSQT	HBSABQUAL
+LABBMTP	HBSAG
+LABDGHBP	HBSAG
+LABHBSAG	HBSAG
+LABDGHBP	HBSAIN
+LABDGHBP	HBSAQL
+LABDGHBP	HBSAQT
+LABVP5	HBSINT
+LABVP5	HBSR
+LABVP5	HBTINT
+LABVP5	HBTR
+LABHCVGE	HCGG
+POC23	HCO3V
+LABPCCH8	HCT
+LABCBCD	HCT
+LABVP5	HCVI
+LABVP5	HCVR
+LABLPD	HDL
+LABLPD	HDL
+LABHDLGGE	HDLGE
+LABBMTP	HEPAAB
+LABBMTP	HEPCAB
+LABCR	HGB
+LABCBCD	HGB
+LABPCCH8	HGB
+POC32	HGB
+INACTIVE 1	HHV6CP
+INACTIVE 1	HHV6DN
+INACTIVE 1	HHV6LO
+INACTIVE 1	HHV6SP
+INACTIVE 1	HHV6TI
+LABHIVA2	HIV2
+LABDHIVAB	HIVAB1
+LABYUHOMO	HMCYURN
+LABHBV	HPEM
+LABHBV	HPSOL
+LABHPVHRT	HPTI
+LABHPVHRT	HPV
+LABVP5	HQLINT
+LABVP5	HQLR
+LABHSP70	HSP70T
+LABHBV	HSPOL
+LABHIVA2	HTL2
+LABMALL2	I2
+LABMALL3	I3
+LABIBDDI	IBDDR
+LABHEPAT	IBIL
+210993282	ICDBRAVPCD
+210993282	ICDBRAVSND
+210993282	ICDBRTRKRT
+210993283	ICDBRVVDEL
+210993282	ICDDTATFIB
+210993282	ICDDTATPAT
+210993282	ICDDTATRFL
+210993282	ICDDTPACAT
+210993282	ICDDTSKAAT
+210993282	ICDDTSKDAT
+210993283	ICDEVALCOM
+210993282	ICDTLIMPAT
+210993282	ICDTLIMPLV
+LABITCH	IGEFA
+LABBLC	IMP
+LABBLC2	IMP
+210993280	IMPDATE
+210993281	IMPDATE
+EXTINRM	INR
+LABPCINR	INR
+LABBCAA	ISOLEU
+LABITCH	ITINT
+LABPCCH8	K
+LABCBCD	K
+LABPETSIN	L0ACTH
+LABPETSIN	L10ACT
+LABPETSIN	L15ACT
+LABPETSIN	L3ACTH
+LABPETSIN	L5ACTH
+LABLAC	LAC
+LABCR	LDH
+LABLPD	LDL
+LABLPD	LDL
+LABLPD	LDL
+LABLEGS	LEG1
+LABLEGS	LEG2
+LABBCAA	LEUCINE
+LABBLC	LEV
+LABBLC2	LEV
+LABPETSIN	LM5ACT
+LAB12814R	LTROIN
+210993280	LVBRPACA
+210993282	LVBRPACA
+210993280	LVBRPACW
+210993282	LVBRPACW
+210993280	LVBRSENA
+210993282	LVBRSENA
+210993282	LVBRSENP
+210993282	LVBRSENR
+ECH15	LVEF
+ECH48	LVEF
+210993280	LVTHRAMP
+210993280	LVTHRDUR
+210993289	LVTHRSENM
+210993289	LVTHRSENPO
+LABADIFFS	LYM
+LABCBCD	LYMAB
+LABFNKSEN	LYMAB
+LABAFCT	LYMFLD
+LABYCMBP	MBPCSF
+LABMEAIGG	ME
+LABHMET	MER
+LABBLC	MER
+LABBLC2	MER
+LABYUMERC	MERURN
+LABYUCD12	METHADONE
+210993280	MFR
+210993281	MFR
+LABYUCLAX	MG
+LABYFMG	MGFLD
+LABITCH	MIL
+230986580	MMIND
+210993280	MOD
+210993281	MOD
+LABAFCT	MONFLD
+LABADIFFS	MONO
+LABCBCD	MONOA
+LABCBCD	MONOPHAGE
+LABMPSQNT	MPS
+LABMPSTLC	MPSTLC
+LABBLC	MTYP
+LABBLC2	MTYP
+LABPCCH8	NA
+LABCBCD	NA
+LABYPA	NAPA
+LABND816V	ND81DI
+LABND816V	ND81DX
+LABND816V	ND81MD
+LABND816V	ND81ME
+LABND816V	ND81RE
+LABND816V	ND81RF
+LABND816V	ND81SO
+LABADIFFS	NEUT
+LABCBCD	NEUTAB
+LABYNOB	NEUTOXB
+LABCBCD	NEUTRA
+LABLPD	NHDL
+LABLPD	NHDL
+LABRBCENZ	NPHOS
+LABNSF3B1	NSF3DI
+LABNSF3B1	NSF3DX
+LABNSF3B1	NSF3IN
+LABNSF3B1	NSF3MD
+LABNSF3B1	NSF3ME
+LABNSF3B1	NSF3RE
+LABNSF3B1	NSF3RF
+LABNSF3B1	NSF3SP
+LABITCH	NUTTY
+LABITCH	OAT
+LABYUCD12	OPIATE
+LABITCH	ORAN
+LABFLDC	ORG
+LABYOSTEO	OSTEOC
+LABPETSIN	P0ACTH
+LABPETSIN	P10ACT
+LABPETSIN	P15ACT
+LABPETSIN	P3ACTH
+LABPETSIN	P5ACTH
+LABPALL	PA
+LABPAI1AG	PAI1G
+LABPALL	PAL1
+LABPALL	PAL2
+LABPALL	PAL3
+LABPALL	PAL4
+LABPARAG	PARAG1
+LABPBGD	PBGD
+LABPCCH8	PCAGP
+LABPCCH8	PCBUN
+LABPCCH8	PCCL
+LABPCCH8	PCCOM
+POC23	PCO2SO
+POC23	PCO2V
+CRP332	PDF
+IMG11BXM1	PDF
+IMG11IA	PDF
+IMG11IABP	PDF
+IMG11RLCA	PDF
+IMG12IA	PDF
+IMG13VA	PDF
+IMG1ATH	PDF
+IMG1PTCS	PDF
+IMG2EX	PDF
+PFT43	PDF
+PFT45	PDF
+PFT53	PDF
+LABITCH	PEAN
+POC23	PH
+LABPHATYR	PHALN
+LAB11138R	PHENO
+POC12	PHGAST
+230986580	PHLOC
+LABYUCLAX	PHOS
+LABDVEPI1	PI1
+LABDVEPI1	PI1IP
+LABDVEPI1	PI1SP
+LABDVEPI2	PI2
+LABDVEPI2	PI2IP
+LABDVEPI2	PI2SP
+LABDVEPI3	PI3
+LABDVEPI3	PI3IP
+LABDVEPI3	PI3SP
+LABRBCENZ	PKLS
+LABCR	PLT
+LABPANPLT	PLT
+LABCBCD	PLT
+LABFENTP	PLV1
+LABFENTP	PLV2
+LABFENTP	PLV3
+LABPETSIN	PM5ACT
+210993280	PMCOMMENT
+210993280	PMLVLDBIIM
+POC14	PNAB1
+LABCLAMP	PNIGA
+LABCLAMP	PNIGG
+LABCLAMP	PNIGM
+LABPOLYSP	POLYSP
+LABSTPOLY	POLYST
+POC14	PPD100
+POC14	PPDINT
+POC6	PPDINT
+LABYUPPP	PPPCOM
+LABADIFF	PRMY
+LABTBP100	PRO
+LABTBP95	PRO
+LABTBP96	PRO
+LABTBP97	PRO
+LABTBP98	PRO
+LABTBP99	PRO
+LABYUCD12	PROPOX
+LABCLAMP	PSIGA
+LABCLAMP	PSIGG
+LABCLAMP	PSIGM
+LABADIFF	PSREV
+LABRBCMOR	PSREV
+EXTINRM	PT
+LABPCINR	PTT
+LABYQUIN	QUINIDINE
+LABPETSIN	R0ACTH
+LABPETSIN	R10ACT
+LABPETSIN	R15ACT
+LABPETSIN	R3ACTH
+LABPETSIN	R5ACTH
+210993282	RABRPACA
+210993282	RABRPACW
+210993282	RABRSENA
+210993279	RABRSENR
+210993282	RABRSENR
+210993282	RATHRAMP
+210993282	RATHRDUR
+210993282	RATHRSENMP
+210993282	RATHRSPOL
+LABAFCT	RBCFLD
+LABCBCD	RBCHP
+LABEXCHTX	RBCSTA
+LABURED	REDSUBURN
+LAB11092R	RENIN
+LABIMM2	RHFT
+LABRHTYP	RHTYP
+10010R	RINFO
+10120R	RINFO
+10165R	RINFO
+10225R	RINFO
+10280R	RINFO
+10285R	RINFO
+10297R	RINFO
+LABPETSIN	RM5ACT
+LABDRPR	RPR1
+LABDVEADV	RPT
+LABDVEP1	RPT
+LABDVERTV	RPT
+LABIDWEE	RPT
+LABSHSV	RPT
+LABVCULU	RPT
+LABCD3B	RTFDA1
+LABRUBM	RUBM1
+LABYRUBM	RUBM1
+LABBLC	SAMP
+LABBLC2	SAMP
+LAB11093R	SCL70
+LABDVEADV	SDES
+LABDVEP1	SDES
+LABDVERTV	SDES
+LABIDWEE	SDES
+LABSHSV	SDES
+LABVCULU	SDES
+LABHPVHRT	SDES6
+210993280	SERNUM
+210993281	SERNUM
+LABSF3B1	SF3DI
+LABSF3B1	SF3DX
+LABSF3B1	SF3IN
+LABSF3B1	SF3MD
+LABSF3B1	SF3ME
+LABSF3B1	SF3RE
+LABSF3B1	SF3RF
+LABITCH	SHRIM
+LABTBP100	SI
+LABTBP95	SI
+LABTBP96	SI
+LABTBP97	SI
+LABTBP98	SI
+LABTBP99	SI
+210993283	SLWVTTAZNS
+LABSNP	SNPRR
+LABSYDIFF	SNRBC
+LABADPCRQ	SOUR10
+LABBKVPC	SOURC6
+LABBKDNA	SOURC7
+LABITCH	SOY
+LABPETSIN	SPAC1
+LABPETSIN	SPAC2
+LABPETSIN	SPAC3
+LABPETSIN	SPAC4
+LABPETSIN	SPAC5
+LABAS4I	SPEXP
+LABTBP45	SPEXP
+LABTBP46	SPEXP
+LABTBP47	SPEXP
+LABTBP48	SPEXP
+LABTBP49	SPEXP
+LABTBP50	SPEXP
+LABTBP51	SPEXP
+LABTBP52	SPEXP
+POC39	SPG
+LABDVEADV	SREQ
+LABDVEP1	SREQ
+LABDVERTV	SREQ
+LABIDWEE	SREQ
+LABSHSV	SREQ
+LABVCULU	SREQ
+LABSYDIFF	SRLYM
+LABFABRO	STIGG
+LABFABRO	STIGM
+LABFABRO	STINT
+LABBLC	SXT
+LABBLC2	SXT
+LABCBCD	SYNLYMPH
+POC5	T11DX
+LABYTBG	T4BG
+LABTBCR	TBCRCT
+LABTBCR	TBCRDR
+LABTBCR	TBCRDS
+LABTBCR	TBCRES
+LABTBCR	TBCRMT
+LABTBCR	TBCRPM
+LABTBCR	TBCRTS
+LABCBCD	TBIL
+LABHEPAT	TBIL
+PATH18	TCD4AB
+LABCLOMIP	TCLOAM
+LABPCCH8	TCO2A
+LABLPD	TGL
+LABLPD	TGL
+POC24	TNI
+LABTOBUNL	TOBRASDD
+LABTOBUNL	TOBUN2
+LABTOBUNL	TOBUN3
+LABTOBUNL	TOBUN4
+LABITCH	TOM
+INACTIVE 4	TOXDES
+LABTOXGM	TOXDES
+LABTOXGMR	TOXDES
+INACTIVE 4	TOXGQL
+LABTOXGM	TOXGQL
+LABTOXGMR	TOXGQL
+LABTOXOPR	TOXIGR
+LABTOXOPR	TOXIMR
+INACTIVE 4	TOXMQL
+LABTOXGM	TOXMQL
+LABTOXGMR	TOXMQL
+LABTOXIG	TOXO
+LAB11122R	TOXOM
+LABTRAZO	TRAZO
+LAB11097R	TSIG
+LABCOXBAB	TYB1
+LABFENTP	TYB1
+LABCOXBAB	TYB2
+LABFENTP	TYB2
+LABCOXBAB	TYB3
+LABFENTP	TYB3
+LABCOXBAB	TYB4
+LABFENTP	TYB4
+LABCOXBAB	TYB5
+LABFENTP	TYB5
+LABCOXBAB	TYB6
+LABFENTP	TYB6
+LABFENTP	TYP10
+LABFENTP	TYP16
+LABFENTP	TYP2
+LABFENTP	TYP4
+LABFENTP	TYP7
+LABFENTP	TYP9
+LABFENTP	TYPE11
+LABFENTP	TYPE30
+LABFENTP	TYPE4
+LABFENTP	TYPE7
+LABFENTP	TYPE9
+LABPHATYR	TYROSINE
+LABBLC	TZP
+LABBLC2	TZP
+LABTBP93	UBT
+LABYUPPP	UHYPOX
+LABTBP93	UNT
+LABUAMICR	URBC
+LABYUPPP	URIC
+LABCR	URIC
+LABUGLT	UT7
+LABYUPPP	UURAC
+POC39	UWBC
+LABUAMICR	UWBC
+LABYUPPP	UXANT
+LABBCAA	VALINE
+LABBMTP	VGGQNT
+LABYVIP	VIP
+LABYVITE	VITE
+LABTBP34	VL
+LABTBP35	VL
+LABTBP40	VL
+LABTBP46	VL
+LABTBP52	VL
+LABTBP57	VL
+LABTBP58	VL
+LABTBP61	VL
+LABTBP62	VL
+LABTBP63	VL
+LABTBP64	VL
+LABTBP69	VL
+LABTBP70	VL
+LABTBP75	VL
+LABTBP76	VL
+LABTBP77	VL
+LABTBP83	VL
+LABTBP84	VL
+LABTBP85	VL
+LABUGLT	VOL7
+LAB11229R	VOLUMEURN
+LABYUMERC	VOLUMEURN
+210993282	VSLWVTTZNT
+210993282	VSWVTTAQRS
+LABVWNTRP	VWDIR
+LABVWNTRP	VWINTP
+LABVZVIGG	VZAB
+LABCR	WBC
+LABAFCT	WBCFLD
+LABCBCD	WBCHP
+LABFABRO	WEIGG
+LABFABRO	WEIGM
+LABITCH	WHT
+LABXAMH	XAMHOR
+LABQUECORSL	XCORTI
+LABTBP77	XMR
+LABTBP89	XMR
+LABTBP90	XMR
+LABTBP91	XMR
+LABTBP92	XMR
+LABYUCLAX	YANTQ
+LABYAPOE	YAPO1
+LABYAPOE	YAPO10
+LABYAPOE	YAPO11
+LABYAPOE	YAPO12
+LABYAPOE	YAPO2
+LABYAPOE	YAPO4
+LABYAPOE	YAPO5
+LABYAPOE	YAPO6
+LABYAPOE	YAPO7
+LABYAPOE	YAPO9
+LABYARBIN	YARBIN
+LABYUCLAX	YBIS
+LABYBUPRO	YBUPR1
+LABYCALCI	YCALCI
+LABYDISO	YDISO1
+LABYDOC	YDOC1
+LABYDOC	YDOC2
+LABYDOC	YDOC3
+LABYEMA	YEMAIN
+LABEVPCR	YENP1
+LABEVPCR	YENP2
+LABEVPCR	YENP3
+LABEVPCR	YENP4
+LABYEPIAB	YEP1
+LABYEPIAB	YEP3
+LABYEPIAB	YEP4
+LABYEPIAB	YEP5
+LABYESTRI	YESTR1
+LABYFMG	YFMG2
+LABYHALO	YHALO2
+LABYHPL	YHPL1
+LABYLEPTN	YLEPT1
+LABYNABF	YNABF1
+LABYNOB	YNOB2
+LABYNOB	YNOB3
+LABYUCLAX	YOXY
+LABYPA	YPANAP
+LABYPCA	YPCA
+LABYUCLAX	YPHEN
+LABYPA	YPROC
+LABYPTSAB	YPTSA
+LABYPTSAB	YPTSG
+LABYPTSAB	YPTSM
+LABYUCLAX	YRL1
+LABYUCLAX	YRL2
+LABYUCLAX	YRL3
+LABYUCLAX	YRL4
+LABYUCLAX	YRL5
+LABYUCLAX	YRL6
+LABYRS	YRS1
+LABYSMG	YSMG2
+LAB12578R	YSTCL1
+LABYT3UP	YT7P2
+LABYUBZC	YUBZCO
+LABYUBZC	YUBZNO
+LAB11229R	YUMECO
+LABYUMERC	YUMER2
+LABYUPCPC	YUPCGC
+LABYUPCPC	YUPCIM
+LABYUPCPC	YUPCIN
+LABYUCD12	YUPCP
+LABYUCD12	YUTTHC
+LAB12322R	YVWMR
+210951798	
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/LabNormalityPredictionPipeline.py b/scripts/Archive/LabTestAnalysis/machine_learning/LabNormalityPredictionPipeline.py
new file mode 100644
index 00000000..0b262a45
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/LabNormalityPredictionPipeline.py
@@ -0,0 +1,717 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of LabNormality prediction.
+"""
+
+import inspect
+import os
+from pandas import DataFrame, Series
+from sklearn.externals import joblib
+from sklearn.metrics import make_scorer, average_precision_score
+import logging
+
+from medinfo.common.Env import LAB_TYPE
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from .extraction.LabNormalityMatrix import LabNormalityMatrix
+
+# Import FMF in order to retrieve all races name dynamically upon accessing the UMich data
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+import LocalEnv
+from . import prepareData_NonSTRIDE
+import pickle
+
+class LabNormalityPredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, lab_panel, num_episodes, use_cache=None, random_state=None,
+                 timeLimit=None, notUsePatIds=None, holdOut=False, pat_batch_ind=None, includeLastNormality=True):
+        # self.notUsePatIds = notUsePatIds
+        self.pat_batch_ind = pat_batch_ind
+        self._holdOut = holdOut
+        self.usedPatIds = []
+        SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state,
+                                            timeLimit, notUsePatIds)
+        # TODO: naming of lab_panel
+        self._factory = FeatureMatrixFactory()
+        self._build_raw_feature_matrix()
+
+        if LAB_TYPE == 'panel':
+            self.ylabel = 'all_components_normal'
+        else:
+            self.ylabel = 'component_normal'
+
+        self.includeLastNormality = includeLastNormality
+
+        if self.includeLastNormality:
+            fm_io = FeatureMatrixIO()
+            df = fm_io.read_file_to_data_frame('data/'+lab_panel+'/%s-normality-matrix-raw.tab'%lab_panel)
+            df = df.sort_values(['pat_id', 'order_time']).reset_index(drop=True)
+            df['last_normality'] = df['order_proc_id'].apply(lambda x:float('nan'))
+            for i in range(1,df.shape[0]):
+                if df.ix[i, 'pat_id'] == df.ix[i-1, 'pat_id']:
+                    df.ix[i, 'last_normality'] = df.ix[i-1, self.ylabel]
+            df.to_csv('data/'+lab_panel+'/%s-normality-matrix-raw.tab'%lab_panel, index=False, sep='\t')
+
+        data_lab_folder = self._fetch_data_dir_path(inspect.getfile(inspect.currentframe()))
+        feat2imputed_dict_path = data_lab_folder + '/feat2imputed_dict.pkl'
+
+        if holdOut:
+            '''
+            For holdOut evaluation data, produce the raw matrix, pick 
+            features according to the saved feat2imputed_dict. 
+            '''
+            self.feat2imputed_dict = pickle.load(open(feat2imputed_dict_path, 'r'))
+            self._build_processed_feature_matrix_holdout()
+            self._analyze_predictors_on_holdout()
+        else:
+            '''
+            For training/validation data, record the pat_ids, 
+            selected features and their imputed value correspondingly. 
+            '''
+            pickle.dump(self.usedPatIds, open('data/used_patient_set_%s.pkl'%self._var, 'w'), pickle.HIGHEST_PROTOCOL)
+            self._build_processed_feature_matrix()
+            self._build_baseline_results()  # TODO: prototype in SLPP
+            # return
+
+            # TODO: find better place to put the dict.pkl
+            pickle.dump(self.feat2imputed_dict, open(feat2imputed_dict_path, 'w'), pickle.HIGHEST_PROTOCOL)
+            self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-normality-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_model_dump_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_matrix_path(self):
+        raw_matrix_filename = '%s-normality-matrix-raw.tab' % self._var  #
+        raw_matrix_filepath = os.path.join('data', self._var, raw_matrix_filename)  # TODO
+        if not os.path.exists('data'):
+            os.mkdir('data')
+        if not os.path.exists(os.path.join('data', self._var)):
+            os.mkdir(os.path.join('data', self._var))
+        return raw_matrix_filepath
+        if not self._holdOut:
+            template = '%s-normality-matrix-raw.tab'
+        else:
+            template = '%s-normality-matrix-%d-episodes-raw-holdout.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_feature_matrix(self):
+        raw_matrix_path = self._build_raw_matrix_path()
+        matrix_class = LabNormalityMatrix
+        SupervisedLearningPipeline._build_raw_feature_matrix(self, matrix_class, \
+            raw_matrix_path)
+
+        if not self._holdOut:
+            fm_io = FeatureMatrixIO()
+            matrix = fm_io.read_file_to_data_frame(raw_matrix_path)
+            self.usedPatIds = set(matrix['pat_id'].values)
+
+    def _build_baseline_results(self):
+        if not self._holdOut:
+            template = '%s-normality-matrix-raw.tab'
+        else:
+            template = '%s-normality-matrix-%d-episodes-raw-holdout.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        # raw_matrix_path = SupervisedLearningPipeline._build_matrix_path(self, template, \
+        #                                                      pipeline_file_name)
+        raw_matrix_path = self._build_raw_matrix_path()
+        # Another direct call to the _factory instance
+        self._factory.obtain_baseline_results(raw_matrix_path=raw_matrix_path,
+                                              random_state=self._random_state,
+                                              isLabPanel=self._isLabPanel,
+                                              isHoldOut=self._holdOut) #TODO: file name
+
+    def _build_processed_matrix_path(self):
+        processed_matrix_filename = '%s-normality-matrix-processed.tab' % self._var  #
+        processed_matrix_path = os.path.join('data', self._var, processed_matrix_filename)  # TODO
+        return processed_matrix_path
+        if not self._holdOut:
+            template = '%s-normality-matrix-processed.tab'
+        else:
+            template = '%s-normality-matrix-%d-episodes-processed-holdout.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_path)
+
+    def _build_processed_feature_matrix_holdout(self):
+        fm_io = FeatureMatrixIO()
+        raw_matrix = fm_io.read_file_to_data_frame(self._build_raw_matrix_path())
+
+        # if outcome_label in self.feat2imputed_dict:
+        #     self.feat2imputed_dict.pop(outcome_label)
+        #
+        # processed_matrix = raw_matrix[self.feat2imputed_dict.keys()+[outcome_label]].copy()
+        '''
+        TODO: feat2imputed_dict includes the outcome label
+        '''
+        processed_matrix = raw_matrix[list(self.feat2imputed_dict.keys())].copy()
+
+        # TODO: tmp solution!
+        tmp_path = self._build_processed_matrix_path().replace("2000","10000").replace("-holdout","")
+        fm_io1 = FeatureMatrixIO()
+        processed_matrix_previous = fm_io1.read_file_to_data_frame(tmp_path)
+        processed_matrix = processed_matrix[processed_matrix_previous.columns]
+        # TODO: tmp solution!
+
+        for feat in list(self.feat2imputed_dict.keys()):
+            processed_matrix[feat] = processed_matrix[feat].fillna(self.feat2imputed_dict[feat])
+
+        fm_io.write_data_frame_to_file(processed_matrix, \
+                                       self._build_processed_matrix_path(), None)
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path()
+        features_to_add = {}
+        imputation_strategies = {#'sxu_new_imputation'
+        }
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            features_to_remove = [
+                'pat_id', 'order_time', 'order_proc_id',
+                'Birth.pre',
+                'Male.preTimeDays', 'Female.preTimeDays',
+                'RaceWhiteHispanicLatino.preTimeDays',
+                'RaceWhiteNonHispanicLatino.preTimeDays',
+                'RaceHispanicLatino.preTimeDays',
+                'RaceAsian.preTimeDays',
+                'RaceBlack.preTimeDays',
+                'RacePacificIslander.preTimeDays',
+                'RaceNativeAmerican.preTimeDays',
+                'RaceOther.preTimeDays',
+                'RaceUnknown.preTimeDays',
+                'Death.post',
+                'Death.postTimeDays'
+            ]
+            if self._isLabPanel:
+                features_to_remove += ['proc_code', 'num_components', 'num_normal_components', 'abnormal_panel']
+                outcome_label = 'all_components_normal'  #
+            else:
+                features_to_remove += ['base_name']
+                outcome_label = 'component_normal' # TODO: danger, previous version might not consistent!
+
+        else:
+            features_to_remove = [
+                'pat_id', 'order_time', 'order_proc_id',
+                'Birth.pre',
+                'Male.preTimeDays', 'Female.preTimeDays',
+                # 'Caucasian.preTimeDays',
+                # 'Hispanic.preTimeDays',
+                # 'Native Hawaiian and Other Pacific Islander.preTimeDays'
+            ]
+            RACE_FEATURES = self._factory.queryAllRaces()
+            features_to_remove += [x + '.preTimeDays' for x in RACE_FEATURES]
+            if self._isLabPanel:
+                features_to_remove += ['proc_code', 'num_normal_components', 'num_components']
+                outcome_label = 'all_components_normal'
+            else:
+                features_to_remove += ['base_name']
+
+                outcome_label = 'component_normal' #
+
+        features_to_keep = [
+            # Keep the # of times it's been ordered in past, even if low info.
+            '%s.pre' % self._var
+        ]
+        if self.includeLastNormality:
+            features_to_keep.append('last_normality')
+
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 0.05
+        matrix_class = LabNormalityMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        random_state = self._random_state
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether all components
+            '%s is a boolean indicator which summarizes whether all components ' % outcome_label,
+            # in the lab panel order represented by a given row are normal.
+            'in the lab panel order represented by a given row are normal.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+        ]
+
+        # Bundle parameters into single object to be unpacked in SLP.
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+        params['random_state'] = random_state
+
+        # Defer processing logic to SupervisedLearningPipeline.
+        SupervisedLearningPipeline._build_processed_feature_matrix(self, params)
+
+        '''
+        For testing the model on the holdout set, should remember features 
+        to select from the raw matrix of the holdout data. 
+        '''
+        final_features = self._X_train.columns.values
+        if not self.feat2imputed_dict:
+            '''
+            The dict was not created during imputation. 
+            Probably because the processed matrix was loaded from previous session. 
+            Take the 'best guess' for the imputed value as the most common one in
+            any column. 
+            '''
+            for feat in final_features:
+                most_freq_val = self._X_train[feat].value_counts().idxmax()
+                self.feat2imputed_dict[feat] = most_freq_val
+
+        '''
+        TODO: useless?!
+        '''
+        # curr_keys = self.feat2imputed_dict.keys()
+        #
+        # '''
+        # Only need to impute the selected features for the holdOut set.
+        # '''
+        # for one_key in curr_keys:
+        #     if one_key not in final_features:
+        #         self.feat2imputed_dict.pop(one_key)
+
+    def _analyze_predictors_on_holdout(self):
+        fm_io = FeatureMatrixIO()
+
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+        for algorithm in algorithms_to_test:
+            log.info('analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, algorithm])
+
+            pipeline_prefix = '%s-normality-prediction-%s' % (self._var, algorithm)
+
+            predictor_path = self._build_model_dump_path(algorithm)
+
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                # self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+
+            SupervisedLearningPipeline._analyze_predictor_holdoutset(self, report_dir, pipeline_prefix)
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+            hyperparams['random_state'] = self._random_state
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'lab_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'lab_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabNormalityPredictionPipeline("%s", 10000)' % self._var]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-normality-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-normality-prediction-%s' % (self._var, algorithm)
+
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                SupervisedLearningPipeline._analyze_predictor_traindata(self, report_dir)
+
+                # continue # Do not generate stats results here...
+
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabNormalityPredictionPipeline("%s", 10000)' % self._var]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-normality-prediction-report.tab' % self._var]), header)
+
+TOP_LAB_PANELS_BY_CHARGE_VOLUME = set([
+        "LABA1C", "LABABG", "LABBLC", "LABBLC2", "LABCAI",
+        "LABCBCD", "LABCBCO", "LABHFP", "LABLAC", "LABMB",
+        "LABMETB", "LABMETC", "LABMGN", "LABNTBNP", "LABPCG3",
+        "LABPCTNI", "LABPHOS", "LABPOCGLU", "LABPT", "LABPTT",
+        "LABROMRS", "LABTNI","LABTYPSNI", "LABUA", "LABUAPRN",
+        "LABURNC", "LABVANPRL", "LABVBG"
+    ])
+TOP_NON_PANEL_TESTS_BY_VOLUME = set([
+    "LABPT", "LABMGN", "LABPTT", "LABPHOS", "LABTNI",
+    "LABBLC", "LABBLC2", "LABCAI", "LABURNC", "LABLACWB",
+    "LABA1C", "LABHEPAR", "LABCDTPCR", "LABPCTNI", "LABPLTS",
+    "LABLAC", "LABLIPS", "LABRESP", "LABTSH", "LABHCTX",
+    "LABLDH", "LABMB", "LABK", "LABGRAM", "LABFCUL",
+    "LABNTBNP", "LABCRP", "LABFLDC", "LABSPLAC", "LABANER",
+    "LABCK", "LABESRP", "LABBLCTIP", "LABBLCSTK", "LABNA",
+    "LABFER", "LABUSPG", "LABB12", "LABURNA", "LABFT4",
+    "LABFIB", "LABURIC", "LABPALB", "LABPCCR", "LABTRFS",
+    "LABUOSM", "LABAFBD", "LABSTOBGD", "LABCSFGL", "LABCSFTP",
+    "LABNH3", "LABAFBC", "LABCMVQT", "LABCSFC", "LABUCR",
+    "LABTRIG", "LABFE",
+    # "LABNONGYN", # No base names.
+    "LABALB", "LABLIDOL",
+    "LABUPREG", "LABRETIC", "LABHAP", "LABBXTG", "LABHIVWBL"
+])
+
+NON_PANEL_TESTS_WITH_GT_500_ORDERS = [
+    'LABA1C', 'LABAFBC', 'LABAFBD', 'LABALB', 'LABANER', 'LABB12', 'LABBLC', 'LABBLC2',
+    'LABBLCSTK', 'LABBLCTIP', 'LABBUN', 'LABBXTG', 'LABCA', 'LABCAI', 'LABCDTPCR',
+    #
+    'LABCK', 'LABCMVQT', 'LABCORT', 'LABCRP', 'LABCSFC', 'LABCSFGL', 'LABCSFTP', 'LABDIGL',
+    'LABESRP', 'LABFCUL', 'LABFE', 'LABFER', 'LABFIB', 'LABFLDC', 'LABFOL',
+    #
+    'LABFT4', 'LABGRAM', 'LABHAP', 'LABHBSAG', 'LABHCTX', 'LABHEPAR', 'LABHIVWBL', 'LABK',
+    'LABLAC', 'LABLACWB', 'LABLDH', 'LABLIDOL', 'LABLIPS', 'LABMB', 'LABMGN',
+    #
+    'LABNA', 'LABNH3', 'LABNTBNP', 'LABOSM', 'LABPALB', 'LABPCCG4O', 'LABPCCR', 'LABPCTNI',
+    'LABPHOS', 'LABPLTS', 'LABPROCT', 'LABPT', 'LABPTEG', 'LABPTT', 'LABRESP',
+    #
+    'LABRESPG', 'LABRETIC', 'LABSPLAC', 'LABSTLCX', 'LABSTOBGD', 'LABTNI', 'LABTRFS', 'LABTRIG',
+    'LABTSH', 'LABUOSM', 'LABUA', 'LABUAPRN', 'LABUPREG', 'LABURIC', 'LABURNC', 'LABUSPG'
+# #'LABNONGYN', TODO: no components
+# 'LABUCR', # insufficient samples
+# 'LABURNA', # insufficient samples
+]
+
+# TODO: Cautious, this might be identified as 'nan' by pandas
+#'HCO3',  # good, from 'LABMETB'; TODO: Insufficient samples
+STRIDE_COMPONENT_TESTS_common = [
+    'WBC', 'HGB', 'PLT', 'NA', 'K', #'CL',
+    'CR', 'BUN', #'GLU',
+    'CO2', 'CA',
+    #
+    'TP',
+    'ALB', 'ALKP', 'TBIL', 'AST', 'ALT', #'DBIL', 'IBIL', 'PHA', 'PCO2A', 'PO2A'
+]  # good, LABHFP
+STRIDE_COMPONENT_TESTS = STRIDE_COMPONENT_TESTS_common + ['CL', 'GLU', 'DBIL', 'IBIL', 'PHA', 'PCO2A', 'PO2A']
+# ALKALINE PHOSPHATASE
+# Blood, Urine, 'BUN'
+# Bilirubin, Indirect
+# # good, from 'LABMETB'
+
+UMICH_TOP_COMPONENTS_common = ['WBC', 'HGB', 'PLT', 'SOD', 'POT',  # TODO: confirm again
+                                    'CREAT', 'TBIL',
+                        #            'CHLOR',
+                        'CO2',
+                        #'DBIL',
+                        'AST', 'ALT',
+                                    'ALB', 'CAL', #'PCOAA2', 'PO2AA', 'pHA',
+                                    'PROT', # Insufficient examples?
+                                    'ALK',  # ALKALINE PHOSPHATASE
+                                    'UN',  # Blood, Urine, 'BUN'
+                                    #'IBIL',  # Bilirubin, Indirect
+                                    #'HCO3-A',  # # good, from 'LABMETB'
+                                    #'MAG',
+                                    #'PHOS',
+                                    #'INR',
+                                    #"BLD",
+                         #           "ICAL",
+                        #"LACA"
+                                    ]
+UMICH_TOP_COMPONENTS = UMICH_TOP_COMPONENTS_common + ['LACA',
+                                                      #'BLD', # TODO: all 'normal', fails to train
+                                                      #'INR', # TODO: all 'normal', fails to train
+                                                      'PHOS', 'MAG',
+                                                      'HCO3-A', 'IBIL', 'pHA', 'PO2AA', 'PCOAA2',
+                                                      'DBIL', 'CHLOR']
+
+UMICH_TOP_PANELS = [
+    'MAG',
+    'PHOS',
+    #'PROTHROMBIN TIME', #TODO: 0kb
+    'A1C',
+    #'BLD', #ADULT BLOOD CULTURE # TODO: all 'normal', fails to train
+    #'BLDAN',#'BLOOD CULTURE (ANA)' # TODO: all 'normal', fails to train
+    'URIC',
+    #'LACT', #TODO: 0kb
+    #'ESRA', #Erythrocyte Sedimentation Rate, iSED #TODO: 0kb
+    'ALB',
+    'TSH',
+    'TROP',
+    'POT',
+    'SOD',
+    'CAL'
+]
+
+UCSF_TOP_COMPONENTS_common = [
+            'WBC', 'HGB', 'PLT', 'NA', 'K', 'CREAT', 'TBILI',
+            #'CL',
+    'CO2',
+    #'DBILI',
+    'AST', 'ALT', 'ALB', 'CA',
+            #'PCO2', 'PO2', 'PH37',
+    'TP', 'ALKP', 'BUN', #'HCO3',
+            # No IBIL
+            #'MG', 'PO4', 'INR', 'P060', 'CAI', 'CAIB', 'LACTWB'
+            #PHOSPHORUS, SERUM / PLASMA
+            # PERIPHERAL BLOOD CULTURE
+            ]
+UCSF_TOP_COMPONENTS = UCSF_TOP_COMPONENTS_common + ['CL', 'DBILI', 'PCO2', 'PO2', 'PH37',
+                                                    'MG', 'PO4', 'INR', 'P060', 'CAI',
+                                                    #'CAIB', 'LACTWB'
+                                                    ]
+
+UCSF_TOP_PANELS = [
+    'Magnesium, Serum / Plasma', # 68558
+    'Phosphorus, Serum / Plasma', # 51520
+    'Prothrombin Time', # 46170
+    'Activated Partial Thromboplastin Time', # 20891
+    #'Peripheral Blood Culture', # 10406 # TODO: ambiguous to Stanford
+    #'Bilirubin, Total', #12740 # TODO: not appearing
+    #'Creatinine, Serum / Plasma', #11958 # TODO: not appearing
+    'Alkaline Phosphatase', #11943
+    'Sodium, Serum / Plasma', # 9500
+    'Potassium, Serum / Plasma', # 6725
+    'Troponin I', # 7075
+    #'Carbon Dioxide, Total (includes Anion Gap)', 	#9236 # TODO: not appearing
+    'Lactate Dehydrogenase, Serum / Plasma', 	#8856
+    'Calcium, Ionized, serum/plasma', 	#8742
+    'Uric Acid, Serum / Plasma', # 4472
+    'Albumin, Serum / Plasma',  # 4120
+    'Thyroid Stimulating Hormone', # 2030
+]
+UCSF_TOP_PANELS = [x.replace('/', '-') for x in UCSF_TOP_PANELS] # TODO: avoid path confusion; consistent to utils_UCSF
+
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    folder_debug = LocalEnv.PATH_TO_CDSS + '/scripts/LabTestAnalysis/machine_learning/data/'
+    if not os.path.exists(folder_debug):
+        os.mkdir(folder_debug)
+    logging.basicConfig(filename=os.path.join(folder_debug,'debug_%s.log'%LocalEnv.DATASET_SOURCE_NAME), level=logging.DEBUG)
+
+    if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+
+        if LAB_TYPE == 'panel':
+            for panel in NON_PANEL_TESTS_WITH_GT_500_ORDERS:
+                LabNormalityPredictionPipeline(panel, 500, use_cache=True, random_state=123456789,
+                                               timeLimit=None, notUsePatIds=None, holdOut=False)
+                # used_patient_set = pickle.load(open('data/used_patient_set_%s.pkl'%panel, 'r'))
+                # LabNormalityPredictionPipeline(panel, 2000, use_cache=True, random_state=123456789, isLabPanel=True,
+                #                                timeLimit=(None, None), notUsePatIds=used_patient_set, holdOut=True)
+        else:
+            for component in STRIDE_COMPONENT_TESTS:
+                print('start %s...'%component)
+                LabNormalityPredictionPipeline(component, 10000, use_cache=True, random_state=123456789)
+                # used_patient_set = pickle.load(open('data/used_patient_set_%s.pkl' % component, 'r'))
+                # LabNormalityPredictionPipeline(component, 2000, use_cache=True, random_state=123456789, isLabPanel=False,
+                #                            timeLimit=(None, None), notUsePatIds=used_patient_set, holdOut=True)
+            pass
+
+    elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+        raw_data_folderpath = LocalEnv.LOCAL_PROD_DB_PARAM["DATAPATH"]
+
+        test_mode = True # TODO: when execute, set to False
+        raw_matrix_exists = True # TODO: when execute, set to False
+        pat_batch_mode = False # TODO: when execute, set to True
+
+        if not raw_matrix_exists:
+            if test_mode:
+                sample_data_files = ['labs.sample.txt',
+                                  'pt.info.sample.txt',
+                                  'encounters.sample.txt',
+                                  'demographics.sample.txt',
+                                  'diagnoses.sample.txt']
+                prepareData_NonSTRIDE.preprocess_files(raw_data_folderpath, sample_data_files)
+
+            raw_data_files = ['labs.txt',
+                              'pt.info.txt',
+                              'encounters.txt',
+                              'demographics.txt',
+                              'diagnoses.txt']
+
+            db_name = LocalEnv.LOCAL_PROD_DB_PARAM["DSN"]
+            fold_enlarge_data = 1
+            USE_CACHED_DB = True # TODO: take care of USE_CACHED_LARGEFILE in the future
+
+            # TODO:
+            db_preparor = prepareData_NonSTRIDE.DB_Preparor(raw_data_files, raw_data_folderpath,
+                                                   db_name=db_name,
+                                                   fold_enlarge_data=fold_enlarge_data,
+                                                   USE_CACHED_DB=USE_CACHED_DB,
+                                                   time_min=None,#'2015-01-01',
+                                                   test_mode=test_mode)
+
+        for panel in UMICH_TOP_PANELS:
+            print("processing %s..."%panel)
+            try:
+                if not pat_batch_mode:
+                    LabNormalityPredictionPipeline(panel, 10000, use_cache=False, random_state=123456789, isLabPanel=True)
+                else:
+                    pat_batch_size = 500
+                    notUsePatIds = []
+                    for pat_batch_ind in range(10000/pat_batch_size): #10000
+                        cur_pipe = LabNormalityPredictionPipeline(panel, pat_batch_size, use_cache=False, random_state=123456789,
+                                                       notUsePatIds=notUsePatIds, pat_batch_ind=pat_batch_ind)
+                        notUsePatIds += cur_pipe.usedPatIds
+            except Exception as e:
+                log.info(e)
+                pass
+
+        for component in UMICH_TOP_COMPONENTS:
+            print("processing %s..."%component)
+            try:
+                if not pat_batch_mode:
+                    LabNormalityPredictionPipeline(component, 10000, use_cache=False, random_state=123456789)
+                else:
+                    pat_batch_size = 500
+                    notUsePatIds = []
+                    for pat_batch_ind in range(10000/pat_batch_size): #10000
+                        cur_pipe = LabNormalityPredictionPipeline(component, pat_batch_size, use_cache=False, random_state=123456789,
+                                                       notUsePatIds=notUsePatIds, pat_batch_ind=pat_batch_ind)
+                        notUsePatIds += cur_pipe.usedPatIds
+            except Exception as e:
+                log.info(e)
+                pass
+
+    elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+
+        raw_data_folderpath = LocalEnv.LOCAL_PROD_DB_PARAM["DATAPATH"]
+        db_name = LocalEnv.LOCAL_PROD_DB_PARAM["DSN"]
+
+        # prepareData_NonSTRIDE.preprocess_files(data_source='UCSF', raw_data_folderpath=raw_data_folderpath)
+
+        raw_data_files = ['labs.tsv',
+                    'demographics_and_diagnoses.tsv',
+                    'vitals.tsv'
+                          ]
+
+
+        fold_enlarge_data = 1
+        USE_CACHED_DB = True  # TODO: take care of USE_CACHED_LARGEFILE in the future
+
+        db_preparor = prepareData_NonSTRIDE.DB_Preparor(raw_data_files, raw_data_folderpath,
+                                               db_name=db_name,
+                                               fold_enlarge_data=fold_enlarge_data,
+                                               USE_CACHED_DB=USE_CACHED_DB,
+                                  test_mode=False) #TODO
+
+        if LAB_TYPE == 'panel':
+            for panel in UCSF_TOP_PANELS:
+                print('Now processing %s'%panel)
+                LabNormalityPredictionPipeline(panel, 10000, use_cache=True, random_state=123456789)
+
+        else:
+            for component in UCSF_TOP_COMPONENTS:
+                try:
+                    LabNormalityPredictionPipeline(component, 10000, use_cache=True, random_state=123456789)
+                except SystemExit as se:
+                    log.info(se)
+                except Exception as e:
+                    log.info(e)
+
+    log.info("\n"
+             "Congratz, pipelining completed! \n"
+             "All results and reports are stored in %s", folder_debug)
+
+
+
+
diff --git a/medinfo/dataconversion/mapdata/LabResultMap.tab b/scripts/Archive/LabTestAnalysis/machine_learning/LabResultMap.tab
similarity index 98%
rename from medinfo/dataconversion/mapdata/LabResultMap.tab
rename to scripts/Archive/LabTestAnalysis/machine_learning/LabResultMap.tab
index 6551e8ff..ccc38fb4 100644
--- a/medinfo/dataconversion/mapdata/LabResultMap.tab
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/LabResultMap.tab
@@ -1,8030 +1,8030 @@
-proc_code	description	base_name	component_name	count
-LABPOCGLU	GLUCOSE BY METER	GLU	GLUCOSE BY METER	1139495
-LABPOCGLU	GLUCOSE BY METER	PCCOM	POCT COMMENT	1139495
-LABPOCGLU	GLUCOSE BY METER	SOU	SOURCE	1139495
-LABMETB	"METABOLIC PANEL, BASIC"	AG	ANION GAP	454658
-LABMETB	"METABOLIC PANEL, BASIC"	BUN	"UREA NITROGEN,SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	CA	"CALCIUM, SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	CL	"CHLORIDE, SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	CO2	"CO2, SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	CR	"CREATININE, SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	GLU	"GLUCOSE, SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	K	"POTASSIUM, SER/PLAS"	454658
-LABMETB	"METABOLIC PANEL, BASIC"	NA	"SODIUM, SER/PLAS"	454658
-LABCBCD	CBC WITH DIFF	WBC	WBC	448137
-LABCBCD	CBC WITH DIFF	MCHC	MCHC	448131
-LABCBCD	CBC WITH DIFF	MCV	MCV	448131
-LABCBCD	CBC WITH DIFF	MCH	MCH	448130
-LABCBCD	CBC WITH DIFF	HGB	HEMOGLOBIN	448128
-LABCBCD	CBC WITH DIFF	PLT	PLATELET COUNT	448127
-LABCBCD	CBC WITH DIFF	RBC	RBC	448127
-LABCBCD	CBC WITH DIFF	HCT	HEMATOCRIT	448124
-LABCBCD	CBC WITH DIFF	RDW	RDW	448124
-LABCBCD	CBC WITH DIFF	NEUTAB	"NEUT, ABS"	413421
-LABCBCD	CBC WITH DIFF	MONOAB	"MONO, ABS"	413419
-LABCBCD	CBC WITH DIFF	MONO	"MONO, %"	413408
-LABCBCD	CBC WITH DIFF	LYM	"LYM, %"	413402
-LABCBCD	CBC WITH DIFF	EOS	"EOS, %"	413400
-LABCBCD	CBC WITH DIFF	NEUT	"NEUT, %"	413400
-LABCBCD	CBC WITH DIFF	BASO	"BASO, %"	413399
-LABCBCD	CBC WITH DIFF	BASOAB	"BASOS, ABS"	413399
-LABCBCD	CBC WITH DIFF	EOSAB	"EOS, ABS"	413399
-LABCBCD	CBC WITH DIFF	LYMAB	"LYM, ABS"	413399
-LABMGN	"MAGNESIUM, SERUM/PLASMA"	MG	"MAGNESIUM, SER/PLAS"	390034
-LABPT	PROTHROMBIN TIME	INR	INR	285359
-LABPT	PROTHROMBIN TIME	PT	PROTHROMBIN TIME	285358
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	AG	ANION GAP	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	ALB	"ALBUMIN, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	ALT	"ALT (SGPT), SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	BUN	"UREA NITROGEN,SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CA	"CALCIUM, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CL	"CHLORIDE, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CO2	"CO2, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CR	"CREATININE, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	GLOB	GLOBULIN	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	GLU	"GLUCOSE, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	K	"POTASSIUM, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	NA	"SODIUM, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	TBIL	TOTAL BILIRUBIN	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	TP	"PROTEIN, TOTAL, SER/PLAS"	257387
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	AST	"AST (SGOT), SER/PLAS"	257385
-LABSPR	SPECIMEN REMARK	SPR	SPECIMEN REMARK	250878
-LABCBCO	CBC	WBC	WBC	238745
-LABCBCO	CBC	HCT	HEMATOCRIT	238742
-LABCBCO	CBC	HGB	HEMOGLOBIN	238742
-LABCBCO	CBC	PLT	PLATELET COUNT	238742
-LABCBCO	CBC	RBC	RBC	238742
-LABCBCO	CBC	MCV	MCV	238739
-LABCBCO	CBC	RDW	RDW	238739
-LABCBCO	CBC	MCHC	MCHC	238738
-LABCBCO	CBC	MCH	MCH	238737
-LABPHOS	"PHOSPHORUS, SERUM/PLASMA"	PHOS	"PHOSPHORUS, SER/PLAS"	199586
-LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT	PART. THROMBOPLASTIN TIME	197062
-LABPCG3	"ISTAT G3+, ARTERIAL"	PCCOM	POCT COMMENT	191365
-LABPCG3	"ISTAT G3+, ARTERIAL"	PCO2A	"PCO2 (A), ISTAT"	191365
-LABPCG3	"ISTAT G3+, ARTERIAL"	PHA	"PH (A), ISTAT"	191365
-LABPCG3	"ISTAT G3+, ARTERIAL"	PO2A	"PO2 (A), ISTAT"	191365
-LABPCG3	"ISTAT G3+, ARTERIAL"	HCO3A	"HCO3 (A), ISTAT"	191363
-LABPCG3	"ISTAT G3+, ARTERIAL"	TCO2A	"TCO2, ISTAT"	191363
-LABPCG3	"ISTAT G3+, ARTERIAL"	O2SATA	"O2 SATURATION, ISTAT"	191362
-LABPCG3	"ISTAT G3+, ARTERIAL"	FIO2	FIO2	171167
-LABCAI	"CALCIUM IONIZED, SERUM/PLASMA"	CAION	"CALCIUM, IONIZED"	163428
-EKG5	ECG 12-LEAD	ECGIMPRESS	ECG IMPRESSION	118728
-EKG5	ECG 12-LEAD	HEARTRATE	HEART RATE	118728
-EKG5	ECG 12-LEAD	QRSAXIS	QRS AXIS	118728
-EKG5	ECG 12-LEAD	QTCINTERVAL	QTC INTERVAL	118728
-EKG5	ECG 12-LEAD	RR	RR	118728
-EKG5	ECG 12-LEAD	QRSDINTERVAL	QRSD INTERVAL	118708
-EKG5	ECG 12-LEAD	QTINTERVAL	QT INTERVAL	118675
-EKG5	ECG 12-LEAD	TWAVEAXIS	T WAVE AXIS	117205
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	SI	<<STATUS>>  	109838
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	PRO	PRODUCT ID  	109836
-LABMETB	"METABOLIC PANEL, BASIC"	EGFR	EGFR	106672
-LABPCG3	"ISTAT G3+, ARTERIAL"	BE	"BASE EXCESS, ISTAT"	104016
-EKG5	ECG 12-LEAD	PRINTERVAL	P-R INTERVAL	102126
-EKG5	ECG 12-LEAD	PAXIS	P AXIS	101996
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	UBT	UNIT BLOOD TYPE  	99587
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	UNT	UNIT NUMBER  	99586
-LABCBCD	CBC WITH DIFF	MORPH	MORPHOLOGY	95057
-LABCBCD	CBC WITH DIFF	SLREV	SLIDE REVIEW	95057
-LABPCG3	"ISTAT G3+, ARTERIAL"	BD	"BASE DEFICIT, ISTAT"	87576
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	DI	ISSUE DATE/TIME	82382
-LABTNI	TROPONIN I	TNI	TROPONIN I	73810
-LABLAC	LACTIC ACID	LAC	LACTIC ACID	68720
-LABCAI	"CALCIUM IONIZED, SERUM/PLASMA"	PHCAI	PH	67346
-LABPCG3	"ISTAT G3+, ARTERIAL"	PCTEMP	BODY TEMPERATURE	64896
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	SI	<<STATUS>>  	64591
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	PRO	PRODUCT ID  	64588
-LABABG	"BLOOD GASES, ARTERIAL"	PHA	PH (A)	62623
-LABABG	"BLOOD GASES, ARTERIAL"	CL	"CHLORIDE, WHOLE BLD"	62622
-LABABG	"BLOOD GASES, ARTERIAL"	GLU	"GLUCOSE, WHOLE BLOOD"	62622
-LABABG	"BLOOD GASES, ARTERIAL"	HCO3A	HCO3	62622
-LABABG	"BLOOD GASES, ARTERIAL"	HCT	HCT (EST)	62622
-LABABG	"BLOOD GASES, ARTERIAL"	HGBBG	THB	62622
-LABABG	"BLOOD GASES, ARTERIAL"	K	"POTASSIUM, WHOLE BLD"	62622
-LABABG	"BLOOD GASES, ARTERIAL"	NA	"SODIUM, WHOLE BLOOD"	62622
-LABABG	"BLOOD GASES, ARTERIAL"	PCO2A	PCO2 (A)	62622
-LABABG	"BLOOD GASES, ARTERIAL"	PO2A	PO2 (A)	62622
-LABABG	"BLOOD GASES, ARTERIAL"	TCO2A	TCO2	62622
-LABABG	"BLOOD GASES, ARTERIAL"	METHB	METHEMOGLOBIN	62621
-LABABG	"BLOOD GASES, ARTERIAL"	CAION	"CALCIUM, IONIZED"	62620
-LABABG	"BLOOD GASES, ARTERIAL"	COHB	CO-HEMOGLOBIN	62620
-LABABG	"BLOOD GASES, ARTERIAL"	CTO2A	CTO2 (A)	62620
-LABABG	"BLOOD GASES, ARTERIAL"	O2HBA	O2-HEMOGLOBIN (A)	62620
-LABBBPRBC	RED BLOOD CELLS	RBCSTA	RBC PRODUCT - UNITS	62186
-LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	EGFR	EGFR	60562
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	XMR	UNIT CROSSMATCH  	55668
-LABHFP	HEPATIC FUNCTION PANEL A	ALB	"ALBUMIN, SER/PLAS"	55660
-LABHFP	HEPATIC FUNCTION PANEL A	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	55660
-LABHFP	HEPATIC FUNCTION PANEL A	ALT	"ALT (SGPT), SER/PLAS"	55660
-LABHFP	HEPATIC FUNCTION PANEL A	AST	"AST (SGOT), SER/PLAS"	55660
-LABHFP	HEPATIC FUNCTION PANEL A	DBIL	CONJUGATED BILI	55660
-LABHFP	HEPATIC FUNCTION PANEL A	IBIL	UNCONJUGATED BILIRUBIN	55660
-LABHFP	HEPATIC FUNCTION PANEL A	TBIL	TOTAL BILIRUBIN	55660
-LABHFP	HEPATIC FUNCTION PANEL A	TP	"PROTEIN, TOTAL, SER/PLAS"	55660
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	UBT	UNIT BLOOD TYPE  	54497
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	UNT	UNIT NUMBER  	54497
-LABABG	"BLOOD GASES, ARTERIAL"	BD	BASE DEFICIT (VT)	51646
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CULT	CULT/OTHER RSLT:	49366
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	RPT	RPT	49366
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SDES	SPECIMEN	49366
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SREQ	COMMENTS	49366
-LABK	"POTASSIUM, SERUM/PLASMA"	K	"POTASSIUM, SER/PLAS"	48138
-LABUA	URINALYSIS WITH MICROSCOPIC	SPECMN	SPECIMEN	46316
-LABUA	URINALYSIS WITH MICROSCOPIC	UCOL	COLOR	46316
-LABUA	URINALYSIS WITH MICROSCOPIC	LEUKEST	LEUK ESTERASE	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	NITRITE	NITRITE	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	SPG	SPECIFIC GRAVITY	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	UBLOOD	BLOOD	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	UCLAR	CLARITY	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	UGLU	GLUCOSE	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	UKET	KETONE	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	UPH	PH	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	UPROT	PROTEIN	46312
-LABUA	URINALYSIS WITH MICROSCOPIC	URBC	"RBC, URINE"	46311
-LABUA	URINALYSIS WITH MICROSCOPIC	UWBC	"WBC, URINE"	46311
-LABTYPSNI	TYPE AND SCREEN	ARG	ABO/RH (AUTOMATION)	46177
-LABCBCD	CBC WITH DIFF	SIGNIF	SIGNIFICANT ABNORMALITIES	46052
-LABUA	URINALYSIS WITH MICROSCOPIC	UBACT	BACTERIA	46023
-LABUA	URINALYSIS WITH MICROSCOPIC	UCMT	URINE COMMENT	46018
-LABTYPSNI	TYPE AND SCREEN	AS3	ANTIBODY SCREEN	45808
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CULT	CULT/OTHER RSLT:	43898
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	RPT	RPT	43898
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SDES	SPECIMEN	43898
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SREQ	COMMENTS	43898
-LABMB	"CK, MB (MASS)"	CKMB	"CK, MB (MASS)"	40071
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	DI	ISSUE DATE/TIME	40063
-LABROMRS	MRSA SCREEN	CULT	CULT/OTHER RSLT:	39655
-LABROMRS	MRSA SCREEN	RPT	RPT	39655
-LABROMRS	MRSA SCREEN	SDES	SPECIMEN	39655
-LABROMRS	MRSA SCREEN	SREQ	COMMENTS	39655
-LABFK506L	TACROLIMUS (FK506)	FK506	"PEAK, TROUGH, OR RANDOM"	39396
-LABFK506L	TACROLIMUS (FK506)	FK506	TACROLIMUS (FK506)	39396
-LABFK506L	TACROLIMUS (FK506)	TACR2	DATE & TIME OF LAST DOSE	39396
-LABFK506L	TACROLIMUS (FK506)	TACR3	DOSAGE	39396
-LABFK506L	TACROLIMUS (FK506)	TACR4	ADMINISTRATION ROUTE	39396
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	XMR	UNIT CROSSMATCH  	37707
-LABVBG	"BLOOD GASES, VENOUS"	PHV	PH (V)	37624
-LABVBG	"BLOOD GASES, VENOUS"	CTO2V	CTO2 (V)	37623
-LABVBG	"BLOOD GASES, VENOUS"	GLU	"GLUCOSE, WHOLE BLOOD"	37623
-LABVBG	"BLOOD GASES, VENOUS"	HCO3A	HCO3	37623
-LABVBG	"BLOOD GASES, VENOUS"	HCT	HCT (EST)	37623
-LABVBG	"BLOOD GASES, VENOUS"	K	"POTASSIUM, WHOLE BLD"	37623
-LABVBG	"BLOOD GASES, VENOUS"	PCO2V	PCO2 (V)	37623
-LABVBG	"BLOOD GASES, VENOUS"	PO2V	PO2 (V)	37623
-LABVBG	"BLOOD GASES, VENOUS"	TCO2A	TCO2	37623
-LABVBG	"BLOOD GASES, VENOUS"	CAION	"CALCIUM, IONIZED"	37622
-LABVBG	"BLOOD GASES, VENOUS"	CL	"CHLORIDE, WHOLE BLD"	37622
-LABVBG	"BLOOD GASES, VENOUS"	COHB	CO-HEMOGLOBIN	37622
-LABVBG	"BLOOD GASES, VENOUS"	HGBBG	THB	37622
-LABVBG	"BLOOD GASES, VENOUS"	METHB	METHEMOGLOBIN	37622
-LABVBG	"BLOOD GASES, VENOUS"	NA	"SODIUM, WHOLE BLOOD"	37622
-LABVBG	"BLOOD GASES, VENOUS"	O2SATV	O2 SATURATION (V)	37622
-LABMB	"CK, MB (MASS)"	CK	"CK, TOTAL"	37594
-LABURNC	URINE CULTURE	CULT	CULT/OTHER RSLT:	34444
-LABURNC	URINE CULTURE	RPT	RPT	34444
-LABURNC	URINE CULTURE	SDES	SPECIMEN	34444
-LABURNC	URINE CULTURE	SREQ	COMMENTS	34444
-LABCBCD	CBC WITH DIFF	LYM	LYMPHOCYTES	33951
-LABCBCD	CBC WITH DIFF	NEUTAB	"NEUT,ABS (MAN DIFF)"	33746
-LABCBCD	CBC WITH DIFF	NEUT	SEG NEUTROPHILS	33700
-LABCBCD	CBC WITH DIFF	LYMAB	"LYM, ABS (MAN DIFF)"	33606
-LABLDH	"LDH TOTAL, SERUM / PLASMA"	LDH	"LDH, TOTAL, SER/PLAS"	33443
-LABHCTX	HEMATOCRIT	HCT	HEMATOCRIT	32774
-LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANCOTROUGH	"VANCOMYCIN, TROUGH"	32610
-LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANPR2	DATE & TIME OF LAST DOSE	32609
-LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANPR3	DOSAGE	32609
-LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANPR4	ADMINISTRATION ROUTE	32609
-LABMB	"CK, MB (MASS)"	CKMBRI	CKMB %RELATIVE INDEX	31392
-LABUA	URINALYSIS WITH MICROSCOPIC	MUCUS	MUCOUS THREADS	31348
-LABARVI	BLOOD TYPE VERIFICATION	ARV	BLOOD TYPE VERIFICATION	30954
-LABCBCD	CBC WITH DIFF	MONO	MONOCYTES	30873
-LABNA	"SODIUM, SERUM / PLASMA"	NA	"SODIUM, SER/PLAS"	30689
-LABCBCD	CBC WITH DIFF	MONOAB	"MONO, ABS (MAN DIFF)"	30490
-LABCBCD	CBC WITH DIFF	BAND	BAND NEUTROPHILS	30244
-LABUA	URINALYSIS WITH MICROSCOPIC	SQEP	SQUAMOUS CELLS	28276
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SPECMN	SPECIMEN	27414
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UCOL	COLOR	27414
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	LEUKEST	LEUK ESTERASE	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	NITRITE	NITRITE	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SPG	SPECIFIC GRAVITY	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UBLOOD	BLOOD	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UCLAR	CLARITY	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UGLU	GLUCOSE	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UKET	KETONE	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UPH	PH	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UPROT	PROTEIN	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	URBC	"RBC, URINE"	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UWBC	"WBC, URINE"	27413
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	PRN	CULTURE SENT PRN	27412
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UBACT	BACTERIA	27248
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UCMT	URINE COMMENT	27244
-LABVBG	"BLOOD GASES, VENOUS"	BD	BASE DEFICIT (VT)	26070
-LABDIC	DIC SCREEN	DDIMER	D-DIMER	25741
-LABDIC	DIC SCREEN	FIBRINOGEN	FIBRINOGEN	25741
-LABDIC	DIC SCREEN	INR	INR	25741
-LABDIC	DIC SCREEN	PT	PROTHROMBIN TIME	25741
-LABDIC	DIC SCREEN	PTT	PART. THROMBOPLASTIN TIME	25741
-LABDIC	DIC SCREEN	TT	THROMBIN TIME	25741
-LABABG	"BLOOD GASES, ARTERIAL"	BE	BASE EXCESS (VT)	25196
-LABBBPPLT	PLATELET APHERESIS PRODUCT	PLT	PLATELETS	24904
-LABPCTNI	ISTAT TROPONIN I	PCCOM	POCT COMMENT	23732
-LABPCTNI	ISTAT TROPONIN I	TNI	"TROPONIN I, POCT"	23730
-LABHEPAR	HEPARIN ACTIVITY LEVEL	HEPAR	HEPARIN ACTIVITY LEVEL	23406
-LABA1C	HEMOGLOBIN A1C	A1C	HEMOGLOBIN A1C	22557
-LABURIC	"URIC ACID, SERUM / PLASMA"	URIC	"URIC ACID, SER/PLAS"	22354
-LABVBG	"BLOOD GASES, VENOUS"	BE	BASE EXCESS (VT)	22180
-LABLIPS	LIPASE	LIPASE	LIPASE	22010
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	MUCUS	MUCOUS THREADS	19795
-LABTSH	TSH	TSH	TSH	19390
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	SI	<<STATUS>>  	19375
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	PRO	PRODUCT ID  	19372
-LABLACWB	"LACTATE, WHOLE BLOOD"	LACWBL	LACWBL	17776
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SQEP	SQUAMOUS CELLS	16632
-LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	FFP	FRESH FROZEN UNITS	16604
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	PRO	PRODUCT ID  	16587
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	SI	<<STATUS>>  	16587
-LABCK	"CREATINE KINASE, TOTAL"	CK	"CK, TOTAL"	16129
-LABHEPAR	"HEPARIN, UNFRACTIONATED, ANTI XA"	HEPAR	HEPARIN ACTIVITY LEVEL	15974
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	UBT	UNIT BLOOD TYPE  	15817
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	UNT	UNIT NUMBER  	15815
-LABCRP	C - REACTIVE PROTEIN	CRP	C-REACTIVE PROTEIN	15445
-LABPCCG4V	"ISTAT CG4,VENOUS"	HCO3V	"HCO3 (V), ISTAT"	15206
-LABPCCG4V	"ISTAT CG4,VENOUS"	LAC	"LACTATE, ISTAT"	15206
-LABPCCG4V	"ISTAT CG4,VENOUS"	PCCOM	POCT COMMENT	15206
-LABPCCG4V	"ISTAT CG4,VENOUS"	PCO2V	"PCO2 (V), ISTAT"	15206
-LABPCCG4V	"ISTAT CG4,VENOUS"	PHV	"PH (V), ISTAT"	15206
-LABPCCG4V	"ISTAT CG4,VENOUS"	TCO2V	"TCO2 (V), ISTAT"	15206
-LABPCCG4V	"ISTAT CG4,VENOUS"	O2SATV	"O2 SATURATION, ISTAT"	15205
-LABPCCG4V	"ISTAT CG4,VENOUS"	PO2V	"PO2 (V), ISTAT"	15205
-LABCBCD	CBC WITH DIFF	META	METAMYELOCYTES	14834
-LABFIB	FIBRINOGEN	FIBRINOGEN	FIBRINOGEN	13839
-LABPCEG7	"ISTAT EG7, ARTERIAL"	PCCOM	POCT COMMENT	13530
-LABPCEG7	"ISTAT EG7, ARTERIAL"	HCO3A	"HCO3 (A), ISTAT"	13529
-LABPCEG7	"ISTAT EG7, ARTERIAL"	PCO2A	"PCO2 (A), ISTAT"	13529
-LABPCEG7	"ISTAT EG7, ARTERIAL"	PHA	"PH (A), ISTAT"	13529
-LABPCEG7	"ISTAT EG7, ARTERIAL"	PO2A	"PO2 (A), ISTAT"	13529
-LABPCEG7	"ISTAT EG7, ARTERIAL"	TCO2A	"TCO2, ISTAT"	13529
-LABPCEG7	"ISTAT EG7, ARTERIAL"	O2SATA	"O2 SATURATION, ISTAT"	13528
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	UBT	UNIT BLOOD TYPE  	13445
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	UNT	UNIT NUMBER  	13445
-LABPCEG7	"ISTAT EG7, ARTERIAL"	HCT	"HCT(CALC), ISTAT"	13376
-LABPCEG7	"ISTAT EG7, ARTERIAL"	HGB	"HGB(CALC), ISTAT"	13376
-LABPCEG7	"ISTAT EG7, ARTERIAL"	K	"POTASSIUM, ISTAT"	13376
-LABPCEG7	"ISTAT EG7, ARTERIAL"	NA	"SODIUM, ISTAT"	13376
-LABPCEG7	"ISTAT EG7, ARTERIAL"	CAION	"CALCIUM,ION, ISTAT"	13373
-LABCBCD	CBC WITH DIFF	MYEL	MYELOCYTES	13209
-LABOSM	"OSMOLALITY, SERUM"	OSMOL	"OSMOLALITY, SERUM"	13181
-LABCBCD	CBC WITH DIFF	EOS	EOSINOPHILS	12794
-LABNTBNP	NT - PROBNP	BNP	NT-PROBNP	12696
-LABCBCD	CBC WITH DIFF	EOSAB	"EOS, ABS (MAN DIFF)"	12648
-LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	CDTR	STOOL C.DIFF TOXIN B RESULT	12622
-LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	CDTSP	SPECIMEN	12622
-LABPCEG7V	"ISTAT EG7, VENOUS"	HCO3V	"HCO3 (V), ISTAT"	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	O2SATV	"O2 SATURATION, ISTAT"	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	PCCOM	POCT COMMENT	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	PCO2V	"PCO2 (V), ISTAT"	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	PHV	"PH (V), ISTAT"	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	PO2V	"PO2 (V), ISTAT"	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	TCO2V	"TCO2 (V), ISTAT"	12341
-LABPCEG7V	"ISTAT EG7, VENOUS"	CAION	"CALCIUM,ION, ISTAT"	12335
-LABPCEG7V	"ISTAT EG7, VENOUS"	HCT	"HCT(CALC), ISTAT"	12335
-LABPCEG7V	"ISTAT EG7, VENOUS"	HGB	"HGB(CALC), ISTAT"	12335
-LABPCEG7V	"ISTAT EG7, VENOUS"	K	"POTASSIUM, ISTAT"	12335
-LABPCEG7V	"ISTAT EG7, VENOUS"	NA	"SODIUM, ISTAT"	12335
-LABANER	ANAEROBIC CULTURE	CULT	CULT/OTHER RSLT:	12120
-LABANER	ANAEROBIC CULTURE	RPT	RPT	12120
-LABANER	ANAEROBIC CULTURE	SDES	SPECIMEN	12120
-LABANER	ANAEROBIC CULTURE	SREQ	COMMENTS	12120
-EKG5	ECG 12-LEAD	PDF	PDF RESULT	12019
-LABCBCD	CBC WITH DIFF	NRBC	NRBC/100WBC	11797
-LABBMT2	BMT PANEL 2	CO2	"CO2, SER/PLAS"	11555
-LABBMT2	BMT PANEL 2	CR	"CREATININE, SER/PLAS"	11555
-LABBMT2	BMT PANEL 2	GLU	"GLUCOSE, SER/PLAS"	11555
-LABBMT2	BMT PANEL 2	K	"POTASSIUM, SER/PLAS"	11555
-LABBMT2	BMT PANEL 2	NA	"SODIUM, SER/PLAS"	11555
-LABGRAM	GRAM STAIN	GS	GRAM STAIN	11525
-LABGRAM	GRAM STAIN	RPT	RPT	11525
-LABGRAM	GRAM STAIN	SDES	SPECIMEN	11525
-LABGRAM	GRAM STAIN	SREQ	COMMENTS	11525
-LABBMT1	BMT PANEL 1	ALB	"ALBUMIN, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	ALT	"ALT (SGPT), SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	AST	"AST (SGOT), SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	CR	"CREATININE, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	GLU	"GLUCOSE, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	K	"POTASSIUM, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	MG	"MAGNESIUM, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	NA	"SODIUM, SER/PLAS"	11469
-LABBMT1	BMT PANEL 1	TBIL	TOTAL BILIRUBIN	11469
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	DI	ISSUE DATE/TIME	10889
-LABRESP	RESPIRATORY CULTURE	CULT	CULT/OTHER RSLT:	10620
-LABRESP	RESPIRATORY CULTURE	RPT	RPT	10620
-LABRESP	RESPIRATORY CULTURE	SDES	SPECIMEN	10620
-LABRESP	RESPIRATORY CULTURE	SREQ	COMMENTS	10620
-LABFCUL	FUNGAL CULTURE	CULT	CULT/OTHER RSLT:	10531
-LABFCUL	FUNGAL CULTURE	RPT	RPT	10531
-LABFCUL	FUNGAL CULTURE	SDES	SPECIMEN	10531
-LABFCUL	FUNGAL CULTURE	SREQ	COMMENTS	10531
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	WBC	WBC	10417
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	HCT	HEMATOCRIT	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	HGB	HEMOGLOBIN	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MCH	MCH	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MCHC	MCHC	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MCV	MCV	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PLT	PLATELET COUNT	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	RBC	RBC	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	RDW	RDW	10415
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	SLREV	SLIDE REVIEW	10406
-LABPCCR	ISTAT CREATININE	CR	"CREATININE,ISTAT"	9997
-LABPCCR	ISTAT CREATININE	PCCOM	POCT COMMENT	9997
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	AMPHET	AMPHETAMINE	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	BARB	BARBITURATES	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	BENZO	BENZODIAZEPINES	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	COCAINE	COCAINE METABOLITES	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	DOACOM	DOACOM COMMENTS	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	METHADONE	METHADONE	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	OPIATE	OPIATES	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	PCP	PCP	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	TCA	TRICYCLICS	9938
-LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	THC	THC	9938
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	XMR	UNIT CROSSMATCH  	9856
-LABPLTS	PLATELET COUNT	PLT	PLATELET COUNT	9852
-LABPCEG7	"ISTAT EG7, ARTERIAL"	FIO2	FIO2	9839
-LABLIDOL	LIDOCAINE	LI	"PEAK, TROUGH, OR RANDOM"	9729
-LABLIDOL	LIDOCAINE	LIDO	LIDOCAINE	9729
-LABLIDOL	LIDOCAINE	LIDO2	DATE & TIME OF LAST DOSE	9729
-LABLIDOL	LIDOCAINE	LIDO3	DOSAGE	9729
-LABLIDOL	LIDOCAINE	LIDO4	ADMINISTRATION ROUTE	9729
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MORPH	MORPHOLOGY	9586
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	DI	ISSUE DATE/TIME	9143
-LABPCCG4V	"ISTAT CG4,VENOUS"	BE	"BASE EXCESS, ISTAT"	8970
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	UPH	PH URINE	8896
-LABFT4	"T4, FREE"	FT4	FREE T4	8889
-POC1	POC URINE DIPSTICK	UWBC	LEUKOCYTES	8781
-POC1	POC URINE DIPSTICK	EXPDATE	EXP DATE	8780
-POC1	POC URINE DIPSTICK	NITRITE	NITRITE	8780
-POC1	POC URINE DIPSTICK	SPG	SPECIFIC GRAVITY	8780
-POC1	POC URINE DIPSTICK	UBLOOD	BLOOD URINE	8780
-POC1	POC URINE DIPSTICK	UGLU	GLUCOSE URINE	8780
-POC1	POC URINE DIPSTICK	UKET	KETONES	8779
-POC1	POC URINE DIPSTICK	UPH	PH URINE	8779
-POC1	POC URINE DIPSTICK	UPROT	PROTEIN URINE	8779
-POC1	POC URINE DIPSTICK	DIPLOT	DIPSTICK LOT	8777
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUTAB	"NEUT, ABS"	8638
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONOAB	"MONO, ABS"	8637
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASOAB	"BASOS, ABS"	8636
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOSAB	"EOS, ABS"	8636
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYMAB	"LYM, ABS"	8636
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYM	"LYM, %"	8635
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONO	"MONO, %"	8635
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUT	"NEUT, %"	8635
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASO	"BASO, %"	8634
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOS	"EOS, %"	8634
-LABYPREAL	PREALBUMIN	PREALBUMIN	PREALBUMIN	8608
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	XMR	UNIT CROSSMATCH  	8550
-LABPCG3V	"ISTAT G3+, VENOUS"	HCO3V	"HCO3 (V), ISTAT"	8527
-LABPCG3V	"ISTAT G3+, VENOUS"	O2SATV	"O2 SATURATION, ISTAT"	8527
-LABPCG3V	"ISTAT G3+, VENOUS"	PCCOM	POCT COMMENT	8527
-LABPCG3V	"ISTAT G3+, VENOUS"	PCO2V	"PCO2 (V), ISTAT"	8527
-LABPCG3V	"ISTAT G3+, VENOUS"	PHV	"PH (V), ISTAT"	8527
-LABPCG3V	"ISTAT G3+, VENOUS"	PO2V	"PO2 (V), ISTAT"	8527
-LABPCG3V	"ISTAT G3+, VENOUS"	TCO2V	"TCO2 (V), ISTAT"	8527
-LABTRIG	"TRIGLYCERIDES, SERUM / PLASMA"	TGL	"TRIGLYCERIDE, SER/PLAS"	8518
-LABFLDC	FLUID CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	8169
-LABFLDC	FLUID CULTURE AND GRAM STAIN	GS	GRAM STAIN	8169
-LABFLDC	FLUID CULTURE AND GRAM STAIN	RPT	RPT	8169
-LABFLDC	FLUID CULTURE AND GRAM STAIN	SDES	SPECIMEN	8169
-LABFLDC	FLUID CULTURE AND GRAM STAIN	SREQ	COMMENTS	8169
-LABCBCD	CBC WITH DIFF	BASO	BASOPHILS	8140
-LABCBCD	CBC WITH DIFF	BASOAB	"BASO, ABS (MAN DIFF)"	8061
-LABLPD	LIPID PANEL WITH DIRECT LDL	CHOL	"CHOLESTEROL, TOTAL"	7963
-LABLPD	LIPID PANEL WITH DIRECT LDL	CHOLHDL	CHOLESTEROL/HDL RATIO	7963
-LABLPD	LIPID PANEL WITH DIRECT LDL	HDL	HDL CHOLESTEROL	7963
-LABLPD	LIPID PANEL WITH DIRECT LDL	LDL	DIRECT LDL CHOL	7963
-LABLPD	LIPID PANEL WITH DIRECT LDL	NHDL	"NON-HDL CHOL, CALC"	7963
-LABLPD	LIPID PANEL WITH DIRECT LDL	TGL	"TRIGLYCERIDE, SER/PLAS"	7963
-LABPCEG7V	"ISTAT EG7, VENOUS"	FIO2	FIO2	7836
-LABPCEG7V	"ISTAT EG7, VENOUS"	BE	"BASE EXCESS, ISTAT"	7835
-LABPCEG7	"ISTAT EG7, ARTERIAL"	BE	"BASE EXCESS, ISTAT"	7694
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	PRO	PRODUCT ID  	7628
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	SI	<<STATUS>>  	7628
-LABTYPSNI	TYPE AND SCREEN	AS	ANTIBODY SCREEN	7596
-LABNH3	"AMMONIA, PLASMA"	NH3	"AMMONIA, PLASMA"	7230
-LABPCG3V	"ISTAT G3+, VENOUS"	FIO2	FIO2	7108
-LABUA	URINALYSIS WITH MICROSCOPIC	AMORCRY	AMORPHOUS CRYSTALS	7093
-LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH	7003
-LABFER	FERRITIN	FERRITIN	FERRITIN	6948
-LABUA	URINALYSIS WITH MICROSCOPIC	HYCAST	HYALINE CASTS	6923
-LABB12	VITAMIN B12	B12	VITAMIN B12	6918
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	PRO	PRODUCT ID  	6917
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	SI	<<STATUS>>  	6917
-LABUSPG	"SPECIFIC GRAVITY, URINE"	SPG	SPECIFIC GRAVITY	6905
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	UBT	UNIT BLOOD TYPE  	6268
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	UNT	UNIT NUMBER  	6268
-LABPCCG4V	"ISTAT CG4,VENOUS"	BD	"BASE DEFICIT, ISTAT"	6251
-LABAMY	"AMYLASE, SERUM"	AMYLASE	"AMYLASE, SERUM"	6246
-LABCBCD	CBC WITH DIFF	BLASTS	BLASTS	6214
-LABPATHR	REVIEW BY PATHOLOGIST	REVUE	COMMENTS	6179
-LABCBCD	CBC WITH DIFF	BLASTSAB	"BLASTS,ABS(MAN DIFF)"	6155
-LABTRFS	TRANSFERRIN SATURATION	FE	"IRON, TOTAL"	6042
-LABTRFS	TRANSFERRIN SATURATION	TRFRN	TRANSFERRIN	6042
-LABTRFS	TRANSFERRIN SATURATION	TRFSAT	TRANSFERRIN SAT	6042
-LABARI	ABO/RH (ARI)	ABORH	ABO/RH	6019
-LABPCEG7	"ISTAT EG7, ARTERIAL"	BD	"BASE DEFICIT, ISTAT"	5869
-LABURNA	"SODIUM, URINE (RANDOM)"	UNA	"SODIUM, URINE"	5864
-LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETIC	"RETIC, %"	5756
-LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETICAB	"RETIC, ABS [AUTORETIC]"	5755
-LABHAP	HAPTOGLOBIN	HAPTO	HAPTOGLOBIN	5750
-LABASI	AB SCREEN (ASI)	AS	ANTIBODY SCREEN	5714
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	UBT	UNIT BLOOD TYPE  	5631
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	UNT	UNIT NUMBER  	5631
-LABAFBC	"AFB CULTURE, RESPIRATORY"	AFB	AFB SMEAR	5539
-LABAFBC	"AFB CULTURE, RESPIRATORY"	CULT	CULT/OTHER RSLT:	5539
-LABAFBC	"AFB CULTURE, RESPIRATORY"	RPT	RPT	5539
-LABAFBC	"AFB CULTURE, RESPIRATORY"	SDES	SPECIMEN	5539
-LABAFBC	"AFB CULTURE, RESPIRATORY"	SREQ	COMMENTS	5539
-LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC	"PEAK, TROUGH, OR RANDOM"	5436
-LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC2	DATE & TIME LAST DOSE	5436
-LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC3	DOSAGE	5436
-LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC4	ADMINISTRATION ROUTE	5436
-LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYCWB	"CYCLOSPORINE, WHOLE BLOOD"	5436
-LABALB	"ALBUMIN, SERUM/PLASMA"	ALB	"ALBUMIN, SER/PLAS"	5412
-LABPHEN	PHENYTOIN LEVEL	PHEN	"PEAK, TROUGH, OR RANDOM"	5338
-LABPHEN	PHENYTOIN LEVEL	PHEN	PHENYTOIN	5338
-LABPHEN	PHENYTOIN LEVEL	PHEN2	DATE & TIME OF LAST DOSE	5338
-LABPHEN	PHENYTOIN LEVEL	PHEN3	DOSAGE	5338
-LABPHEN	PHENYTOIN LEVEL	PHEN4	ADMINISTRATION ROUTE	5338
-LABUOSM	"OSMOLALITY, URINE"	UOSM	"OSMOLALITY, URINE"	5290
-LABCBCD	CBC WITH DIFF	REACTL	REACTIVE LYMPHS	5204
-LABCORT	"CORTISOL, SERUM"	CORT	"CORTISOL, SERUM"	5155
-LABUA	URINALYSIS WITH MICROSCOPIC	TRANST	TRANSITIONAL	5153
-LABDIGL	"DIGOXIN, SERUM"	DIG	DIGOXIN	5126
-LABDIGL	"DIGOXIN, SERUM"	DIG	"PEAK, TOUGH, OR RANDOM"	5126
-LABDIGL	"DIGOXIN, SERUM"	DIG2	DATE & TIME OF LAST DOSE	5126
-LABDIGL	"DIGOXIN, SERUM"	DIG3	DOSAGE	5126
-LABDIGL	"DIGOXIN, SERUM"	DIG4	ADMINISTRATION ROUTE	5126
-LABVOLTL	VOLATILE SCREEN	ETOH	ETHANOL	4982
-LABVOLTL	VOLATILE SCREEN	ETOHI	ETHANOL INTERP	4982
-LABVOLTL	VOLATILE SCREEN	OSMGAP	OSMOLAL GAP	4979
-LABVOLTL	VOLATILE SCREEN	OSMGPI	OSMOLAL GAP INTERP	4979
-LABCBCD	CBC WITH DIFF	PRMY	PROMYELOCYTES	4938
-LABSLHELD	SLIDE HELD FOR REVIEW	SLHELD	SLIDE HELD TO REVIEW	4929
-LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	AFB	AFB SMEAR	4858
-LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	CULT	CULT/OTHER RSLT:	4858
-LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	RPT	RPT	4858
-LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	SDES	SPECIMEN	4858
-LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	SREQ	COMMENTS	4858
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	DX	DIAGNOSIS	4829
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FCOLOR	APPEARANCE	4829
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FLDTYP	FLUID SOURCE	4829
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	MALIG	SUSPECT MALIGNANCY?	4829
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FTURB	"TURBIDITY, FLD"	4827
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	LYMFLD	"LYMPHOCYTE %, FLD"	4827
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	MONFLD	"MONO/MESO %, FLD"	4827
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	RBCFLD	"RBC COUNT, FLD (/UL)"	4827
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	WBCFLD	"NUCL CELLS, FLD(/UL)"	4827
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	NEUTFLD	"NEUTROPHIL %, FLD"	4826
-LABESRP	SEDIMENTATION RATE (ESR)	ESR	"ESR, (MANUAL)"	4807
-LABCBCD	CBC WITH DIFF	PSREV	PATH/SPEC REVIEW	4789
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	PRO	PRODUCT ID  	4765
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	SI	<<STATUS>>  	4765
-LABPCG3V	"ISTAT G3+, VENOUS"	BD	"BASE DEFICIT, ISTAT"	4572
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	SPEXP	SPECIMEN EXPIRATION DATE/TIME	4565
-LABHIVWBL	ANTI-HIV	HIVAR	ANTI-HIV ANTIBODY	4562
-LABHIVWBL	ANTI-HIV	HIVI	INTERPRETATION	4562
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	BTURB	"TURBIDITY, CSF"	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	COLORCSF	"COLOR, CSF"	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CSOURC	SOURCE	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	DX	DIAGNOSIS	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	MALIG	SUSPECT MALIGNANCY?	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	RBCCSF	"RBC COUNT, CSF"	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	TUBENO	CSF TUBE #	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	WBCCSF	"WBC COUNT, CSF"	4561
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	LYMCSF	"LYMPHOCYTES, CSF"	4560
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	MONOCSF	"MONOCYTOID CELLS, CSF"	4560
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	NEUTCSF	"NEUTROPHILS, CSF"	4560
-LABUCR	"CREATININE, URINE (RANDOM)"	UCR	"CREATININE, URINE"	4555
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	COLORCSF	"COLOR AFTER CENTRIFUGE, CSF"	4533
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CTURB	"TURB. AFTER CENTRIF., CSF"	4533
-LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	CULT	CULT/OTHER RSLT:	4529
-LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	RPT	RPT	4529
-LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	SDES	SPECIMEN	4529
-LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	SREQ	COMMENTS	4529
-LABPCEG7V	"ISTAT EG7, VENOUS"	BD	"BASE DEFICIT, ISTAT"	4524
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	DI	ISSUE DATE/TIME	4519
-LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	4430
-LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	GS	GRAM STAIN	4430
-LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	RPT	RPT	4430
-LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	SDES	SPECIMEN	4430
-LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	SREQ	COMMENTS	4430
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	PRO	PRODUCT ID  	4402
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	SI	<<STATUS>>  	4402
-LABPCEG7	"ISTAT EG7, ARTERIAL"	PCTEMP	BODY TEMPERATURE	4374
-LABSPLAC	SEPSIS PROTOCOL LACTATE	LACWBL	LACWBL	4353
-LABPCEG7V	"ISTAT EG7, VENOUS"	PCTEMP	BODY TEMPERATURE	4251
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	AMORCRY	AMORPHOUS CRYSTALS	4242
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	SIGNIF	SIGNIFICANT ABNORMALITIES	4199
-LABPCCG4O	"ISTAT CG4, OTHER"	HCO3	"HCO3, ISTAT"	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	LAC	"LACTATE, ISTAT"	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	PCCOM	POCT COMMENT	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	PCO2A	"PCO2, ISTAT"	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	PCO2SO	"O2 SATURATION, ISTAT"	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	PH	"PH, ISTAT"	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	PO2A	"PO2, ISTAT"	4112
-LABPCCG4O	"ISTAT CG4, OTHER"	TCO2A	"TCO2, (ISTAT)"	4112
-LABDBIL	"BILIRUBIN DIRECT, SERUM/PLASMA"	DBIL	CONJUGATED BILI	4104
-LABESRP	SEDIMENTATION RATE (ESR)	ESR	"ESR, (AUTOMATED)"	4065
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	DI	ISSUE DATE/TIME	4030
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	HYCAST	HYALINE CASTS	4027
-LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	CULT	CULT/OTHER RSLT:	4015
-LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	RPT	RPT	4015
-LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	SDES	SPECIMEN	4015
-LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	SREQ	COMMENTS	4015
-LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	OCCGU	"STOOL SAMPLE1,GUAIAC"	3995
-LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	DATE1	DATE 1	3991
-LABPCG3V	"ISTAT G3+, VENOUS"	BE	"BASE EXCESS, ISTAT"	3984
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC10	DAILY QC 10 RESULT	3940
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC5	DAILY QC 5 RESULT	3940
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	TESTLO	TEST STRIP LOT#	3939
-LABGGT	GAMMA - GLUTAMYL TRANSFERASE	GGT	GAMMA-GLUTAMYL TRANS (GGT)	3877
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	UBT	UNIT BLOOD TYPE  	3845
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	UNT	UNIT NUMBER  	3845
-LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	DATE2	DATE 2	3838
-LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	DATE3	DATE 3	3836
-LABCBCD	CBC WITH DIFF	MAOTH	"OTHER, ABS(MAN DIFF)"	3814
-LABHBSAG	HBSAG	HBSINT	INTERPRETATION	3788
-LABHBSAG	HBSAG	HBSR	HEP B SURFACE ANTIGEN	3788
-LABPALB	PREALBUMIN	PREALBUMIN	PREALBUMIN	3724
-LABPCG3O	"ISTAT G3+, OTHER"	HCO3	"HCO3, ISTAT"	3654
-LABPCG3O	"ISTAT G3+, OTHER"	PCCOM	POCT COMMENT	3654
-LABPCG3O	"ISTAT G3+, OTHER"	PCO2A	"PCO2, ISTAT"	3654
-LABPCG3O	"ISTAT G3+, OTHER"	PCO2SO	"O2 SATURATION, ISTAT"	3654
-LABPCG3O	"ISTAT G3+, OTHER"	PH	"PH, ISTAT"	3654
-LABPCG3O	"ISTAT G3+, OTHER"	PO2A	"PO2, ISTAT"	3654
-LABPCG3O	"ISTAT G3+, OTHER"	TCO2A	"TCO2, (ISTAT)"	3654
-LABDVERVP	RESPIRATORY DFA PANEL	ADVP	ADENOVIRUS	3644
-LABFOL	FOLIC ACID	FOLATE	FOLIC ACID (FOLATE)	3644
-LABDVERVP	RESPIRATORY DFA PANEL	INFAP	INFLUENZA A	3644
-LABDVERVP	RESPIRATORY DFA PANEL	INFBP	INFLUENZA B	3644
-LABDVERVP	RESPIRATORY DFA PANEL	MPVPR	METAPNEUMOVIRUS	3644
-LABDVERVP	RESPIRATORY DFA PANEL	P1P	PARAINFLUENZA 1	3644
-LABDVERVP	RESPIRATORY DFA PANEL	P2P	PARAINFLUENZA 2	3644
-LABDVERVP	RESPIRATORY DFA PANEL	P3P	PARAINFLUENZA 3	3644
-LABDVERVP	RESPIRATORY DFA PANEL	RVP	RSV	3644
-LABDVERVP	RESPIRATORY DFA PANEL	RVPIP	TEST DECRIPTION	3644
-LABDVERVP	RESPIRATORY DFA PANEL	RVPSP	SPECIMEN	3644
-LABPATHR	REVIEW BY PATHOLOGIST	FPATH	PATHOLOGIST	3556
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	UBT	UNIT BLOOD TYPE  	3505
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	UNT	UNIT NUMBER  	3505
-LABCSFGL	"GLUCOSE, CSF"	GLUCSF	"GLUCOSE, CSF"	3470
-LABCSFTP	"PROTEIN TOTAL, CSF"	TPCSF	"PROTEIN, TOTAL, CSF"	3446
-LABPCEC4V	"ISTAT EC4, VENOUS"	HCT	"HCT(CALC), ISTAT"	3442
-LABPCEC4V	"ISTAT EC4, VENOUS"	HGB	"HGB(CALC), ISTAT"	3442
-LABPCEC4V	"ISTAT EC4, VENOUS"	K	"POTASSIUM, ISTAT"	3442
-LABPCEC4V	"ISTAT EC4, VENOUS"	NA	"SODIUM, ISTAT"	3442
-LABPCEC4V	"ISTAT EC4, VENOUS"	PCCOM	POCT COMMENT	3442
-LABPCEC4V	"ISTAT EC4, VENOUS"	GLU	"GLUCOSE,ISTAT"	3441
-LABBUN	"UREA NITROGEN, SERUM/PLASMA"	BUN	"UREA NITROGEN,SER/PLAS"	3438
-LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	OCCGU	"STOOL SAMPLE2,GUAIAC"	3425
-LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	OCCGU	"STOOL SAMPLE3,GUAIAC"	3422
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UCL	"CHLORIDE, URINE"	3421
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UCLCR	CHLORIDE/CREAT RATIO	3421
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UCR	URINE CREAT	3421
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UK	"POTASSIUM, URINE"	3421
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UKCR	POTASSIUM/CREAT RATIO	3421
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UNA	"SODIUM, URINE"	3421
-LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UNACR	SODIUM/CREAT RATIO	3421
-LABCA	"CALCIUM, SERUM/PLASMA"	CA	"CALCIUM, SER/PLAS"	3376
-LABUA	URINALYSIS WITH MICROSCOPIC	UWBCC	WBC CLUMPS	3374
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	XMR	UNIT CROSSMATCH  	3316
-LABACETA	"ACETAMINOPHEN, SERUM"	ACETA	ACETAMINOPHEN	3287
-LABPROCT	PROCALCITONIN	PROCTL	PROCALCITONIN	3235
-LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	CULT	CULT/OTHER RSLT:	3218
-LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	RPT	RPT	3218
-LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	SDES	SPECIMEN	3218
-LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	SREQ	COMMENTS	3218
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	SPEXP	SPECIMEN EXPIRATION DATE/TIME	3194
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	TRANST	TRANSITIONAL	3171
-LABCSFC	CSF CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	3136
-LABCSFC	CSF CULTURE AND GRAM STAIN	GS	GRAM STAIN	3136
-LABCSFC	CSF CULTURE AND GRAM STAIN	RPT	RPT	3136
-LABCSFC	CSF CULTURE AND GRAM STAIN	SDES	SPECIMEN	3136
-LABCSFC	CSF CULTURE AND GRAM STAIN	SREQ	COMMENTS	3136
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	XMR	UNIT CROSSMATCH  	3097
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UPROT	PROTEIN	3080
-LABUAS	URINALYSIS MACROSCOPIC ONLY	LEUKEST	LEUK ESTERASE	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	NITRITE	NITRITE	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	SPECMN	SPECIMEN	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	SPG	SPECIFIC GRAVITY	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UBLOOD	BLOOD	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UCLAR	CLARITY	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UCOL	COLOR	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UGLU	GLUCOSE	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UKET	KETONE	3079
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UPH	PH	3079
-LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVCP	RESULT	3048
-LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVDNA	CMV DNA	3048
-LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVLOG	RESULT LOG 10	3048
-LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVSP	SPECIMEN	3048
-LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVTIP	TEST DESCRIPTION	3048
-LABPCINR	ISTAT INR AND PROTIME	INR	"INR, ISTAT"	3018
-LABPCINR	ISTAT INR AND PROTIME	PCCOM	POCT COMMENT	3018
-LABPCG3V	"ISTAT G3+, VENOUS"	PCTEMP	BODY TEMPERATURE	2964
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	PRO	PRODUCT ID  	2940
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	SI	<<STATUS>>  	2940
-LABPCINR	ISTAT INR AND PROTIME	PT	"PT, ISTAT"	2878
-LABCBCD	CBC WITH DIFF	OTHER	OTHERS (DESCRIBE)	2876
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	DI	ISSUE DATE/TIME	2820
-LABFTP	"PROTEIN TOTAL, FLUID"	TPFLD	"PROTEIN, TOTAL, FLUID"	2765
-LABFKOH	FUNGAL CULTURE AND KOH	CULT	CULT/OTHER RSLT:	2744
-LABFKOH	FUNGAL CULTURE AND KOH	KOH	KOH PREP	2744
-LABFKOH	FUNGAL CULTURE AND KOH	RPT	RPT	2744
-LABFKOH	FUNGAL CULTURE AND KOH	SDES	SPECIMEN	2744
-LABFKOH	FUNGAL CULTURE AND KOH	SREQ	COMMENTS	2744
-LABDDIM	D - DIMER	DDIMER	D-DIMER	2732
-LABUNA	"SODIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	2686
-LABUNA	"SODIUM WITH CREATININE, URINE (RANDOM)"	UNA	"SODIUM, URINE"	2686
-LABUNA	"SODIUM WITH CREATININE, URINE (RANDOM)"	UNACR	SODIUM/CREAT RATIO	2686
-LABHGBX	HEMOGLOBIN	HGB	HEMOGLOBIN	2673
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	PRO	PRODUCT ID  	2667
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	SI	<<STATUS>>  	2667
-LABSPLAC	SEPSIS PROTOCOL LACTATE	SPLACW	SEPSIS PROTOCOL LACTATE	2645
-LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANCOSDD	"VANCOMYCIN, SINGLE DAILY DOSE"	2635
-LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANUN2	DATE & TIME OF LAST DOSE	2635
-LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANUN3	DOSAGE	2635
-LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANUN4	ADMINISTRATION ROUTE	2635
-LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	2599
-LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	GS	GRAM STAIN	2599
-LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	RPT	RPT	2599
-LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	SDES	SPECIMEN	2599
-LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	SREQ	COMMENTS	2599
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	DI	ISSUE DATE/TIME	2584
-POC26	POC URINE PREGNANCY	KIT1	KIT LOT#	2553
-POC26	POC URINE PREGNANCY	KITEX1	KIT EXP DATE	2553
-POC26	POC URINE PREGNANCY	PATRES	PATIENT RESULT	2553
-POC26	POC URINE PREGNANCY	QC9	QC RESULT	2553
-LABPCACTK	ISTAT ACTIVATED CLOTTING TIME	ACT	"ACT, ISTAT"	2549
-LABPCACTK	ISTAT ACTIVATED CLOTTING TIME	PCCOM	POCT COMMENT	2549
-LABANAS	ANTI - NUCLEAR AB	ANA	ANA RESULT	2521
-LABANAS	ANTI - NUCLEAR AB	ANAINTERP	ANA INTERPRETATION	2521
-LABTBILFR	"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"	DBIL	CONJUGATED BILI	2502
-LABTBILFR	"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"	IBIL	UNCONJUGATED BILIRUBIN	2502
-LABTBILFR	"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"	TBIL	TOTAL BILIRUBIN	2502
-LABPCCG4O	"ISTAT CG4, OTHER"	BE	"BASE EXCESS, ISTAT"	2498
-LABUPREG	"PREGNANCY TEST, URINE"	UPREG	"PREGNANCY TEST, URINE"	2443
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	UBT	UNIT BLOOD TYPE  	2420
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	UNT	UNIT NUMBER  	2420
-LABFTP	"PROTEIN TOTAL, FLUID"	TPFLS	SOURCE	2402
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	CHOL	"CHOLESTEROL, TOTAL"	2346
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	CHOLHDL	CHOLESTEROL/HDL RATIO	2346
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	HDL	HDL CHOLESTEROL	2346
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	LDL	LDL (CALCULATED)	2346
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	LDLHDL	LDL/HDL RATIO	2346
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	NHDL	"NON-HDL CHOL, CALC"	2346
-LABLPDC	LIPID PANEL WITH CALCULATED LDL	TGL	"TRIGLYCERIDE, SER/PLAS"	2346
-LABSIROL	SIROLIMUS LEVEL	SIRO2	DATE & TIME OF LAST DOSE	2250
-LABSIROL	SIROLIMUS LEVEL	SIRO3	DOSAGE	2250
-LABSIROL	SIROLIMUS LEVEL	SIRO4	ADMINISTRATION ROUTE	2250
-LABSIROL	SIROLIMUS LEVEL	SIROL	"PEAK, TROUGH, OR RANDOM"	2250
-LABSIROL	SIROLIMUS LEVEL	SIROL	SIROLIMUS	2250
-LABWDC	WOUND CULTURE	CULT	CULT/OTHER RSLT:	2231
-LABWDC	WOUND CULTURE	RPT	RPT	2231
-LABWDC	WOUND CULTURE	SDES	SPECIMEN	2231
-LABWDC	WOUND CULTURE	SREQ	COMMENTS	2231
-LABSALICY	SALICYLATE LEVEL	SALICYLATE	SALICYLATE	2212
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FCMT	FLUID COMMENT	2203
-LABBBPCRY	CRYOPRECIPITATE	CRSTA	CRYOPRECIPITATE UNITS	2195
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	UBT	UNIT BLOOD TYPE  	2178
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	UNT	UNIT NUMBER  	2178
-LABVCUL	VIRAL CULTURE	CULT	CULT/OTHER RSLT:	2171
-LABVCUL	VIRAL CULTURE	RPT	RPT	2171
-LABVCUL	VIRAL CULTURE	SDES	SPECIMEN	2171
-LABVCUL	VIRAL CULTURE	SREQ	COMMENTS	2171
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UWBCC	WBC CLUMPS	2124
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	PRO	PRODUCT ID  	2110
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	SI	<<STATUS>>  	2110
-LABVPAL	VALPROIC ACID	VPA	"PEAK, TROUGH, OR RANDOM"	2109
-LABVPAL	VALPROIC ACID	VPA	VALPROIC ACID	2109
-LABVPAL	VALPROIC ACID	VPAL2	DATE & TIME OF LAST DOSE	2109
-LABVPAL	VALPROIC ACID	VPAL3	DOSAGE	2109
-LABVPAL	VALPROIC ACID	VPAL4	ADMINISTRATION ROUTE	2109
-LABMTXL	METHOTREXATE	MTX	METHOTREXATE	2105
-LABMTXL	METHOTREXATE	MTX	"PEAK, TROUGH, OR RANDOM"	2105
-LABMTXL	METHOTREXATE	MTXL2	DATE & TIME OF LAST DOSE	2105
-LABMTXL	METHOTREXATE	MTXL3	DOSAGE	2105
-LABMTXL	METHOTREXATE	MTXL4	ADMINISTRATION ROUTE	2105
-LABFLDH	"LDH TOTAL, FLUID"	LDHFLD	"LDH, TOTAL, FLUID"	2096
-LABFLDH	"LDH TOTAL, FLUID"	LDHFLS	SOURCE	2096
-LABURUN	"UREA, URINE (RANDOM)"	UURN	"UREA NITROGEN, URINE"	2090
-LABPCG3O	"ISTAT G3+, OTHER"	FIO2	FIO2	2075
-LABRFRPT	REFERENCE REPORT	REFRPT	REFERENCE REPORT	2064
-LABPCG3O	"ISTAT G3+, OTHER"	BE	"BASE EXCESS, ISTAT"	2032
-LABFAMY	"AMYLASE, FLUID"	AMYFLS	SOURCE	2022
-LABFAMY	"AMYLASE, FLUID"	AMYLASEFLD	"AMYLASE, FLUID"	2022
-LABUA	URINALYSIS WITH MICROSCOPIC	GRCAST	GRANULAR CASTS	1986
-LABOAPE	OVA AND PARASITES	CULT	CULT/OTHER RSLT:	1972
-LABOAPE	OVA AND PARASITES	RPT	RPT	1972
-LABOAPE	OVA AND PARASITES	SDES	SPECIMEN	1972
-LABOAPE	OVA AND PARASITES	SREQ	COMMENTS	1972
-LABSTSS	SYPHILIS TREPONEMAL SCREEN	STSINT	INTERPRETATIONS	1936
-LABSTSS	SYPHILIS TREPONEMAL SCREEN	STSQUL	RESULT	1936
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	PRO	PRODUCT ID  	1914
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	SI	<<STATUS>>  	1914
-LABUAS	URINALYSIS MACROSCOPIC ONLY	UCMT	URINE COMMENT	1898
-LABRBCFOL	"FOLATE, RBC"	FOLATERBC	"FOLATE, RBC"	1896
-LABMYCPA	MYCOPHENOLIC ACID	MYPA	MYCOPHENOLIC ACID	1892
-LABMYCPA	MYCOPHENOLIC ACID	MYPAG	MPA GLUCURONIDE	1892
-LABMYCPA	MYCOPHENOLIC ACID	MYPA1	"PEAK, TROUGH, OR RANDOM"	1881
-LABMYCPA	MYCOPHENOLIC ACID	MYPA2	DATE & TIME OF LAST DOSE	1881
-LABMYCPA	MYCOPHENOLIC ACID	MYPA3	DOSAGE	1881
-LABMYCPA	MYCOPHENOLIC ACID	MYPA4	ADMINISTRATION ROUTE	1881
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	DI	ISSUE DATE/TIME	1829
-LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVCP	RESULT	1801
-LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVDNA	CMV DNA	1801
-LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVLOG	RESULT LOG 10	1801
-LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVSP	SPECIMEN	1801
-LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVTIP	TEST DESCRIPTION	1801
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	XMR	UNIT CROSSMATCH  	1777
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	VL	PRODUCT VOLUME	1771
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYM	LYMPHOCYTES	1760
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYMAB	"LYM, ABS (MAN DIFF)"	1737
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	UBT	UNIT BLOOD TYPE  	1737
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	UNT	UNIT NUMBER  	1737
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUT	SEG NEUTROPHILS	1730
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUTAB	"NEUT,ABS (MAN DIFF)"	1729
-LABREPT	REPTILASE TIME	REPT	REPTILASE TIME	1710
-LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	DATBS	DIRECT ANTIGLOB.-BROAD SPEC	1708
-LABIBIL	"BILIRUBIN INDIRECT, SERUM"	IBIL	UNCONJUGATED BILIRUBIN	1699
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	XMR	UNIT CROSSMATCH  	1682
-LABPCG3O	"ISTAT G3+, OTHER"	BD	"BASE DEFICIT, ISTAT"	1640
-LABHITAB	HEPARIN (HIT) AB	PF4COM	PF4 COMMENT	1637
-LABHITAB	HEPARIN (HIT) AB	PF4ENH	"PF4 IGG, IGM, IGA OD"	1634
-LABCR	"CREATININE, SERUM/PLASMA"	CR	"CREATININE, SER/PLAS"	1633
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	DI	ISSUE DATE/TIME	1623
-LABPCCG4O	"ISTAT CG4, OTHER"	BD	"BASE DEFICIT, ISTAT"	1620
-LABSCMV	CMV SHELL VIAL CULTURE	CULT	CULT/OTHER RSLT:	1618
-LABSCMV	CMV SHELL VIAL CULTURE	RPT	RPT	1618
-LABSCMV	CMV SHELL VIAL CULTURE	SDES	SPECIMEN	1618
-LABSCMV	CMV SHELL VIAL CULTURE	SREQ	COMMENTS	1618
-LABARI	ABO/RH (ARI)	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1605
-LABFAL	"ALBUMIN, FLUID"	ALBFLS	SOURCE	1601
-LABFAL	"ALBUMIN, FLUID"	ALBFLD	"ALBUMIN, FLUID"	1600
-LABPREG	"PREGNANCY TEST, SERUM"	PREGTEST	"PREGNANCY, SERUM"	1582
-LABASI	AB SCREEN (ASI)	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1577
-LABREF1	LAB UNLISTED 1	LAB1	REFERENCE LAB	1566
-LABREF1	LAB UNLISTED 1	SPTY	SPECIMEN TYPE	1566
-LABREF1	LAB UNLISTED 1	TEST1	TEST NAME	1566
-LABREF1	LAB UNLISTED 1	RESUL1	RESULT	1560
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	UBT	UNIT BLOOD TYPE  	1546
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	UNT	UNIT NUMBER  	1546
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	PRO	PRODUCT ID  	1542
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	SI	<<STATUS>>  	1542
-LABTSHFT4	TSH W/ REFLEX FT4	TSH	TSH	1530
-LABTBIL	"BILIRUBIN TOTAL, SERUM/PLASMA"	TBIL	TOTAL BILIRUBIN	1528
-LABSPIE	"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM"	SPIEI	INTERPRETATION	1517
-LABSPIE	"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM"	SPIER	RESULT	1517
-LABPCYS	PNEUMOCYSTIS STAIN	PCPSTAIN	PCP STAIN	1516
-LABPCYS	PNEUMOCYSTIS STAIN	RPT	RPT	1516
-LABPCYS	PNEUMOCYSTIS STAIN	SDES	SPECIMEN	1516
-LABPCYS	PNEUMOCYSTIS STAIN	SREQ	COMMENTS	1516
-LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	CULT	CULT/OTHER RSLT:	1503
-LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	GS	GRAM STAIN	1503
-LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	RPT	RPT	1503
-LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	SDES	SPECIMEN	1503
-LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	SREQ	COMMENTS	1503
-LABDVERV	RESPIRATORY DFA PANEL	DVE	RESULTS	1501
-LABDVERV	RESPIRATORY DFA PANEL	RPT	RPT	1501
-LABDVERV	RESPIRATORY DFA PANEL	SDES	SPECIMEN	1501
-LABDVERV	RESPIRATORY DFA PANEL	SREQ	COMMENTS	1501
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BAND	BAND NEUTROPHILS	1500
-LABQFTB	QUANTIFERON TEST FOR LATENT TB	QTFNIN	QUANTIFERON INTERPRETATION	1490
-LABQFTB	QUANTIFERON TEST FOR LATENT TB	QTFNRS	QUANTIFERON-TB	1490
-LAB11125R	COCCI ID	COCI	COCCI ID (SERUM)	1489
-LABTP	"PROTEIN TOTAL, SERUM/PLASMA"	TP	"PROTEIN, TOTAL, SER/PLAS"	1479
-LABUEOS	"EOSINOPHILS, URINE"	UEOS	"EOSINOPHILS, URINE"	1472
-LABCBCD	CBC WITH DIFF	ABNWBC	BLASTS/ABNORMAL CELLS	1470
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONO	MONOCYTES	1469
-LABHCVA	HEPATITIS C AB IGG	HCVI	INTERPRETATION	1456
-LABHCVA	HEPATITIS C AB IGG	HCVR	ANTI-HCV	1456
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONOAB	"MONO, ABS (MAN DIFF)"	1442
-LABPTH	PARATHYROID HORMONE	PTH	PARATHYROID HORMONE	1436
-LABCMVPQ	CMV PCR QUANTITATIVE	CMVCP	RESULT	1423
-LABCMVPQ	CMV PCR QUANTITATIVE	CMVDNA	CMV DNA	1423
-LABCMVPQ	CMV PCR QUANTITATIVE	CMVLOG	RESULT LOG 10	1423
-LABCMVPQ	CMV PCR QUANTITATIVE	CMVSP	SPECIMEN	1423
-LABCMVPQ	CMV PCR QUANTITATIVE	CMVTIP	TEST DESCRIPTION	1423
-LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HIPINT	INTERPRETATION	1400
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	PRO	PRODUCT ID  	1389
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	SI	<<STATUS>>  	1389
-LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HEPTYP	HEPARIN TYPE	1368
-LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	LOTNO	LOT #	1368
-LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	MANUF	MANUFACTURER	1368
-LABLIL	LITHIUM	LI	LITHIUM	1367
-LABLIL	LITHIUM	LI	PEAK TROUGH OR RANDOM	1367
-LABLIL	LITHIUM	LI2	DATE & TIME OF LAST DOSE	1367
-LABLIL	LITHIUM	LI3	DOSAGE	1367
-LABLIL	LITHIUM	LI4	ADMINISTRATION ROUTE	1367
-LABIGG	"IGG, SERUM"	IGG	"IGG, SERUM"	1366
-LABFLGL	"GLUCOSE, FLUID"	GLFLS	SOURCE	1365
-LABFLGL	"GLUCOSE, FLUID"	GLUFLD	"GLUCOSE, FLUID"	1365
-LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HIPDIR	HIT INTERPRETED BY	1365
-LABPCG3O	"ISTAT G3+, OTHER"	PCTEMP	BODY TEMPERATURE	1353
-POC1	POC URINE DIPSTICK	PHLOC	LOCATION	1347
-LABABIDI	AB ID	ABIDRBC	ANTIBODY ID RBC	1340
-LABFCR	"CREATININE, FLUID"	CRFLD	"CREATININE, FLUID"	1336
-LABFCR	"CREATININE, FLUID"	CRFLS	SOURCE	1336
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	EOSFLD	"EOSINOPHIL %, FLD"	1328
-LABUA	URINALYSIS WITH MICROSCOPIC	BYST	YEAST	1324
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	DI	ISSUE DATE/TIME	1319
-LABPCCG4	"ISTAT CG4, ARTERIAL"	HCO3A	"HCO3 (A), ISTAT"	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	O2SATA	"O2 SATURATION, ISTAT"	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	PCCOM	POCT COMMENT	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	PCO2A	"PCO2 (A), ISTAT"	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	PHA	"PH (A), ISTAT"	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	PO2A	"PO2 (A), ISTAT"	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	TCO2A	"TCO2, ISTAT"	1281
-LABPCCG4	"ISTAT CG4, ARTERIAL"	LAC	"LACTATE, ISTAT"	1278
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	UBT	UNIT BLOOD TYPE  	1265
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	UNT	UNIT NUMBER  	1265
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	GRCAST	GRANULAR CASTS	1255
-LABAFP	"ALPHA FETOPROTEIN, SERUM"	AFP	ALPHA FETOPROTEIN	1226
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGA	"ANGLE, FIBRINOGEN ACTIVITY"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGC	"R, REACTION TIME (CK)"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGEL	"EPL, ESTIMATED LYSIS"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGG	"G, CLOT STABILITY"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGK	"K, CLOT KINETICS"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGMA	"MA, PLATELET FUNCTION"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGRK	"R, REACTION TIME (CK/H)"	1225
-LABPTEG	"TEG, PERIOPERATIVE"	PTEGSP	"SP, SPLIT POINT"	1225
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	PRO	PRODUCT ID  	1200
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	SI	<<STATUS>>  	1200
-LABDDIML	D - DIMER (ELISA)	DDIMER	"D-DIMER, ELISA"	1195
-LABCBCD	CBC WITH DIFF	CCNT	CELLS COUNTED	1194
-LABDDIML	D - DIMER (ELISA)	DIMCOM	COAGULATION COMMENT	1194
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	DI	ISSUE DATE/TIME	1185
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	BYST	YEAST	1161
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCINT	INTERPRETATION	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCR	IGM ANTI-HBC ANTIBODY	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSINT	INTERPRETATION	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSR	HEP B SURFACE ANTIGEN	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTINT	INTERPRETATION	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTR	ANTI-HBC ANTIBODY	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLINT	INTERPRETATION	1158
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	1158
-LABAHP2	ACUTE HEPATITIS PANEL	HAVINT	INTERPRETATION	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HAVR	IGM ANTI-HAV	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HBCINT	INTERPRETATION	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HBCR	IGM ANTI-HBC ANTIBODY	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HBSINT	INTERPRETATION	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HCVI	INTERPRETATION	1138
-LABAHP2	ACUTE HEPATITIS PANEL	HCVR	ANTI-HCV	1138
-LABVD25H	"VITAMIN D, 25-HYDROXYVITAMIN"	25OHDT	"25-HYDROXY D, TOTAL"	1135
-LABVD25H	"VITAMIN D, 25-HYDROXYVITAMIN"	VD25D2	25-HYDROXY D2	1135
-LABVD25H	"VITAMIN D, 25-HYDROXYVITAMIN"	VD25D3	25-HYDROXY D3	1135
-POC39	POC URINE FOR PH	EXPDATE	EXP DATE	1132
-POC39	POC URINE FOR PH	PHLOC	LOCATION	1132
-POC39	POC URINE FOR PH	UPH	PH URINE	1132
-POC39	POC URINE FOR PH	DIPLOT	DIPSTICK LOT	1131
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	UBT	UNIT BLOOD TYPE  	1128
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	UNT	UNIT NUMBER  	1128
-LABUUN	"UREA WITH CREATININE, URINE (RANDOM)"	UNCRU	URINE CREAT	1124
-LABUUN	"UREA WITH CREATININE, URINE (RANDOM)"	UNUCRU	UREA/CREAT RATIO	1124
-LABUUN	"UREA WITH CREATININE, URINE (RANDOM)"	UURN	"UREA NITROGEN, URINE"	1124
-LABPROC	SPECIMEN PROCESSING	PROC	SPECIMEN PROCESSING	1122
-LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQCP	RESULT	1105
-LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQDN	EBV DNA	1105
-LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQLG	RESULT LOG 10	1105
-LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQSP	SPECIMEN	1105
-LABRPRQL	RPR QUALITATIVE	RPRQ	RESULT	1105
-LABRPRQL	RPR QUALITATIVE	RPRQIN	INTERPRETATIONS	1105
-LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQIN	TEST DESCRIPTION	1104
-POC14	POC GLUCOSE BY METER	GLU	GLUCOSE BY METER	1102
-LABLEGDA	LEGIONELLA CULTURE	CULT	CULT/OTHER RSLT:	1084
-LABLEGDA	LEGIONELLA CULTURE	RPT	RPT	1084
-LABLEGDA	LEGIONELLA CULTURE	SDES	SPECIMEN	1084
-LABLEGDA	LEGIONELLA CULTURE	SREQ	COMMENTS	1084
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	PRO	PRODUCT ID  	1067
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	SI	<<STATUS>>  	1067
-LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	MVSO	TEST NAME	1056
-LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	RES	RESULTS	1056
-LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	SCE	SOURCE	1056
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGSP	"SP, SPLIT POINT"	1021
-LABUTPRAN	"PROTEIN WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREATININE	1017
-LABUTPRAN	"PROTEIN WITH CREATININE, URINE (RANDOM)"	UPROTCR	PROTEIN/CREAT RATIO	1017
-LABUTPRAN	"PROTEIN WITH CREATININE, URINE (RANDOM)"	UPROTTOT	"PROTEIN, TOTAL, URINE"	1017
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HATINT	INTERPRETATION	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSINT	INTERPRETATION	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBTINT	INTERPRETATION	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HCVI	INTERPRETATION	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HCVR	ANTI-HCV	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HQLINT	INTERPRETATION	1015
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	1015
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGCOM	COMMENTS	1013
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGC	"R, REACTION TIME (CK)"	1011
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGRK	"R, REACTION TIME (CK/H)"	1011
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGA	"ANGLE, FIBRINOGEN AC"	1010
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGG	"G, CLOT STABILITY"	1010
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGK	"K, CLOT KINETICS"	1010
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGM	"MA, PLT. FUNCTION"	1010
-LABWDCG	WOUND CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	1009
-LABWDCG	WOUND CULTURE AND GRAM STAIN	GS	GRAM STAIN	1009
-LABWDCG	WOUND CULTURE AND GRAM STAIN	RPT	RPT	1009
-LABWDCG	WOUND CULTURE AND GRAM STAIN	SDES	SPECIMEN	1009
-LABWDCG	WOUND CULTURE AND GRAM STAIN	SREQ	COMMENTS	1009
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGEL	"EPL, ESTIMATED LYSIS"	1009
-LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGMD2	REVIEWED BY	1007
-LABSTLCX	STOOL CULTURE	CULT	CULT/OTHER RSLT:	987
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	DI	ISSUE DATE/TIME	987
-LABSTLCX	STOOL CULTURE	RPT	RPT	987
-LABSTLCX	STOOL CULTURE	SDES	SPECIMEN	987
-LABSTLCX	STOOL CULTURE	SREQ	COMMENTS	987
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	XMR	UNIT CROSSMATCH  	974
-LABSTOOLC	STOOL CULTURE	CULT	CULT/OTHER RSLT:	961
-ECH15	ECHO - TRANSTHORACIC ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	961
-LABSTOOLC	STOOL CULTURE	RPT	RPT	961
-LABSTOOLC	STOOL CULTURE	SDES	SPECIMEN	961
-LABSTOOLC	STOOL CULTURE	SREQ	COMMENTS	961
-LAB11124R	COCCI CF	COCFS	COCCI CF (SERUM)	959
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	UBT	UNIT BLOOD TYPE  	955
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	UNT	UNIT NUMBER  	955
-LABPHME	PH BY METER	PHA	PH BY METER	948
-LABPHME	PH BY METER	PHMETS	SOURCE	948
-LABHELI	HELICOBACTER PYLORI AB	HPYLORI	HELICOB. PYLORI IGG AB	937
-LABCEA	CEA	CEA	CEA	923
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	XMR	UNIT CROSSMATCH  	923
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	SPEXP	SPECIMEN EXPIRATION DATE/TIME	911
-LAB11041R	FREE HEMOGLOBIN	HGB	"HEMOGLOBIN, SERUM"	903
-LABHCLOTI	HOLD CLOT	HCLOT	HOLD CLOT	897
-LABMYO	"MYOGLOBIN, SERUM / PLASMA"	MYOG	"MYOGLOBIN, SER/PLAS"	894
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	PRO	PRODUCT ID  	892
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	SI	<<STATUS>>  	892
-LABEP1	ELECTROLYTE PANEL	AG	ANION GAP	884
-LABEP1	ELECTROLYTE PANEL	CL	"CHLORIDE, SER/PLAS"	884
-LABEP1	ELECTROLYTE PANEL	CO2	"CO2, SER/PLAS"	884
-LABEP1	ELECTROLYTE PANEL	K	"POTASSIUM, SER/PLAS"	884
-LABEP1	ELECTROLYTE PANEL	NA	"SODIUM, SER/PLAS"	884
-LABCA199	CANCER AG 19-9	C199	CANCER ANTIGEN 19-9	874
-LABAGALA	ASPERGILLUS GALACTOMANNAN	AGALA	ASPER.GALACTOMANNAN	867
-LABLEGU	"LEGIONELLA AG, URINE"	LEGUA	LEGIONELLA AG	867
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	DI	ISSUE DATE/TIME	858
-LABCMVP	"CMV DNA PCR QUAL, NON-PLASMA"	CMQDNA	CMV DNA	842
-LABCMVP	"CMV DNA PCR QUAL, NON-PLASMA"	CMVLIP	TEST DESCRIPTION	842
-LABCMVP	"CMV DNA PCR QUAL, NON-PLASMA"	CMVQSP	SPECIMEN	842
-LABCORTS	"CORTISOL, POST OP"	CORTS	"CORTISOL, POST OP"	842
-LABUA	URINALYSIS WITH MICROSCOPIC	CAOX	CA OXALATE CRYSTALS	832
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	UBT	UNIT BLOOD TYPE  	832
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	UNT	UNIT NUMBER  	832
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FOTHER	"OTHERS, FLD"	830
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	ADNVIR	ADENOVIRUS	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	INFAST	INFLUENZA A SUBTYPE	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	INFLA	INFLUENZA A	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	INFLB	INFLUENZA B	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	METPNV	METAPNEUMOVIRUS	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	PARIN1	PARAINFLUENZA 1	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	PARIN2	PARAINFLUENZA 2	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	PARIN3	PARAINFLUENZA 3	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RESVDI	TEST DESCRIPTION	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RESVSP	SPECIMEN	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RHNVIR	RHINOVIRUS	820
-LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RSVPC	RSV	820
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBAG	TB AG	820
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBAGNI	TB AG - NIL	820
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBGINT	INTERPRETATION	820
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBGNIL	NIL	820
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBGRES	QUANTIFERON RESULT	820
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBMINI	MITOGEN - NIL	820
-LABHCVA	ANTI-HCV	HCVI	INTERPRETATION	816
-LABHCVA	ANTI-HCV	HCVR	ANTI-HCV	815
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	PRO	PRODUCT ID  	812
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	SI	<<STATUS>>  	812
-LABIGET	TOTAL IGE	IGET	TOTAL IGE	811
-LABCMVIGG	CMV IGG	CMGDES	CMV IGG TEST DESCRIPTION	810
-LABCMVIGG	CMV IGG	CMGQUL	CMV IGG RESULT	810
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	OCCBLD	"OCCULT BLOOD, STOOL"	804
-LABBHCG	BETA HCG	BHCG	BETA HCG	798
-LABC3	"COMPLEMENT C3, SERUM"	C3	"COMPLEMENT C3, SERUM"	795
-LABC4	"COMPLEMENT C4, SERUM"	C4	"COMPLEMENT C4, SERUM"	795
-LABYFT3	"T3, FREE"	FT3	"T3, FREE"	795
-LABCRPLB	"CRYPTOCOCCAL AG LATEX, BLOOD"	CRPLB	LATEX CRYPTO AG-BID	793
-LABAST	"AST, SERUM/PLASMA"	AST	"AST (SGOT), SER/PLAS"	791
-LABLUPUS	LUPUS ANTICOAGULANT	LUPINT	LUPUS INTERPRETATION	786
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEL	DEVELOPER LOT#	784
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEXP	DEVELOPER EXP DATE	784
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	HEMED	HEMOCCULT EXP DATE	784
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	HEML	HEMOCCULT LOT#	784
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	QC10	QC RESULT	784
-LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVIPR	TEST DESCRIPTION	782
-LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVLOG	RESULT LOG 10	782
-LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVRES	RESULT	782
-LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVRNA	HEP C VIRVUS RNA	782
-LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVSP	SPECIMEN	782
-LABCTC	CATHETER TIP CULTURE	CULT	CULT/OTHER RSLT:	780
-LABCTC	CATHETER TIP CULTURE	RPT	RPT	780
-LABCTC	CATHETER TIP CULTURE	SDES	SPECIMEN	780
-LABCTC	CATHETER TIP CULTURE	SREQ	COMMENTS	780
-LABALT	"ALT, SERUM/PLASMA"	ALT	"ALT (SGPT), SER/PLAS"	779
-LABPSA	PSA	PSA	PSA	777
-LABLEGU	"LEGIONELLA AG, URINE"	TINFO	TESTINFORMATION	777
-LABPCCG4	"ISTAT CG4, ARTERIAL"	BD	"BASE DEFICIT, ISTAT"	773
-LABCLACT	"LACTIC ACID, CSF"	LACCSF	"LACTIC ACID, CSF"	763
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	SPEXP	SPECIMEN EXPIRATION DATE/TIME	763
-LABEBPCRQ	EBV PCR QUANTITATIVE	EBVCP	RESULT	761
-LABEBPCRQ	EBV PCR QUANTITATIVE	EBVDNA	EBV DNA	761
-LABEBPCRQ	EBV PCR QUANTITATIVE	EBVIPR	TEST DESCRIPTION	761
-LABEBPCRQ	EBV PCR QUANTITATIVE	EBVLOG	RESULT LOG	761
-LABEBPCRQ	EBV PCR QUANTITATIVE	EBVSP	SPECIMEN	761
-LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	UPIEI	INTERPRETATION	758
-LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	UPIER	RESULT	758
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	DX	DIAGNOSIS	757
-LABGLNF	"GLUCOSE NON-FASTING, SERUM/PLASMA"	GLU	"GLUCOSE, NON-FASTING"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	JOINT	JOINT	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	MALIG	SUSPECT MALIGNANCY?	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SCOLOR	"COLOR, SYN FLD"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SLYM	"LYMPHOCYTES, SYN FLD"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SMONO	"MONOS/MESOS, SYN FLD"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SNEUT	"NEUTROPHILS, SYN FLD"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SRBC	"RBC COUNT, SYN FLD"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	STURB	"TURBIDITY, SYN FLD"	757
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SWBC	"NUCL. CELLS, SYN FLD"	757
-LABEITC	TRANSFUSION EMERGENCY ISSUE	CEITC	EMERGENCY ISSUE CMT	751
-LABRF	RHUEMATOID FACTOR	RHFINT	INTERPRETATION	748
-LABRF	RHUEMATOID FACTOR	RHFR	RHEUMATOID FACTOR	748
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	META	METAMYELOCYTES	745
-LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	CULT	CULT/OTHER RSLT:	740
-LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	RPT	RPT	740
-LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	SDES	SPECIMEN	740
-LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	SREQ	COMMENTS	740
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MYEL	MYELOCYTES	736
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EAGQUL	EAGQUL	734
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBINTR	EBV INTERPRETATION	734
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBNQUL	EBNQUL	734
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBVDS	EBV TEST DESCRIPTION	734
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VGQUL	VGQUL	734
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VMQUL	VMQUL	734
-LABTRXNWI	TRANSFUSION REACTION WORKUP	CTRXNW	TRANSFUSION REACTION COMMENT	731
-LABTRXNWI	TRANSFUSION REACTION WORKUP	TRXNWR	TRANSFUSION REACTION WORKUP	731
-LABSTLC	STOOL CULTURE	CULT	CULT/OTHER RSLT:	728
-LABSTLC	STOOL CULTURE	RPT	RPT	728
-LABSTLC	STOOL CULTURE	SDES	SPECIMEN	728
-LABSTLC	STOOL CULTURE	SREQ	COMMENTS	728
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	DI	ISSUE DATE/TIME	727
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NRBC	NRBC/100WBC	723
-LABHAVT	ANTI-HAV	HATINT	INTERPRETATION	718
-LABHAVT	ANTI-HAV	HATR	ANTI-HAV ANTIBODY	718
-LABTT	THROMBIN TIME	TT	THROMBIN TIME	714
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VGQNT	VGQNT	713
-LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	CULT	CULT/OTHER RSLT:	712
-LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	GS	GRAM STAIN	712
-LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	RPT	RPT	712
-LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	SDES	SPECIMEN	712
-LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	SREQ	COMMENTS	712
-LABCMVIGG	CMV IGG	CMGQNT	CMV IGG LEVEL	699
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	PRO	PRODUCT ID  	698
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	SI	<<STATUS>>  	698
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	UBT	UNIT BLOOD TYPE  	692
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	UNT	UNIT NUMBER  	692
-LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	DATG	DIRECT ANTIGLOB. - IGG	691
-LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	DATC3	DIRECT ANTIGLOB. - C3	690
-LABFLDB	FLUID CULTURE / BB GRAM STAIN	CULT	CULT/OTHER RSLT:	689
-LABFLDB	FLUID CULTURE / BB GRAM STAIN	GS	GRAM STAIN	689
-LABFLDB	FLUID CULTURE / BB GRAM STAIN	RPT	RPT	689
-LABFLDB	FLUID CULTURE / BB GRAM STAIN	SDES	SPECIMEN	689
-LABFLDB	FLUID CULTURE / BB GRAM STAIN	SREQ	COMMENTS	689
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBNQNT	EBNQNT	687
-LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXDI	DISCLAIMER	683
-LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXMT	METHOD	683
-LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXR	INFLUENZA A RNA	683
-LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXSP	SPECIMEN	683
-LABTYPSNI	TYPE AND SCREEN	SPEXP	SPECIMEN EXPIRATION DATE/TIME	682
-LABALKP	ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	680
-LABGNTPRL	GENTAMICIN TROUGH LEVEL	GENTTRO	"GENTAMICIN, TROUGH"	679
-LABGNTPRL	GENTAMICIN TROUGH LEVEL	GNTPR2	DATE & TIME OF LAST DOSE	679
-LABGNTPRL	GENTAMICIN TROUGH LEVEL	GNTPR3	DOSAGE	679
-LABGNTPRL	GENTAMICIN TROUGH LEVEL	GNTPR4	ADMINISTRATION ROUTE	679
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CCMT	COMMENT	676
-LABCRPLC	"CRYPTOCOCCAL AG LATEX, CSF"	CRPLC	"LATEX CRYPTO AG,CSF"	667
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	CAION	"CALCIUM,ION, ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	CR	"CREATININE,ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	GLU	"GLUCOSE,ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	HCT	"HCT(CALC), ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	HGB	"HGB(CALC), ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	K	"POTASSIUM, ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	NA	"SODIUM, ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCAGP	"ANION GAP, ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCBUN	"BUN, ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCCL	"CHLORIDE, ISTAT"	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCCOM	POCT COMMENT	665
-LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	TCO2A	"TCO2, ISTAT"	665
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	XMR	UNIT CROSSMATCH  	664
-LABDSDNA	ANTI - DSDNA AB	DNAINT	INTERPRETATION	652
-LABDSDNA	ANTI - DSDNA AB	DNAQUL	ANTI-DSDNA RESULT	652
-LABACTHO	ADRENOCORTICOTROPIC HORMONE (ACTH)	ACTH	ACTH	647
-LABFE	"IRON, TOTAL"	FE	"IRON, TOTAL"	644
-LABYHISTU	"HISTOPLASMA AG, URINE"	YHISU4	RESULT URINE	644
-LABHITAB	HEPARIN PLATELET FACTOR 4 AB	PF4COM	PF4 COMMENT	642
-LABHITAB	HEPARIN PLATELET FACTOR 4 AB	PF4ENH	"PF4 IGG, IGM, IGA OD"	642
-LABACA	ANTI CARDIOLIPIN AB	ACAIGG	ACA IGG ANTIBODIES	636
-LABACA	ANTI CARDIOLIPIN AB	ACAIGM	ACA IGM ANTIBODIES	636
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	PRO	PRODUCT ID  	636
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	SI	<<STATUS>>  	636
-LABASQ	ACETONE SEMI QUALITATIVE	ACETONE	ACETONE SEMI QUAL	628
-LABDVEHV	HSV/VZV DFA	HERPR	HERPES SIMPLEX VIRUS (HSV)	627
-LABDVEHV	HSV/VZV DFA	HVIP	TEST DESCRIPTION	627
-LABDVEHV	HSV/VZV DFA	HVSP	SPECIMEN	627
-LABDVEHV	HSV/VZV DFA	VZR	VARICELLA-ZOSTER VIRUS	627
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	UBT	UNIT BLOOD TYPE  	626
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	UNT	UNIT NUMBER  	626
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOS	EOSINOPHILS	624
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	DI	ISSUE DATE/TIME	622
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	XMR	UNIT CROSSMATCH  	613
-LABLEID	FACTOR V LEIDEN	LEICMT	COMMENT	612
-LABLEID	FACTOR V LEIDEN	LEIDIS	DISCLAIMER	612
-LABLEID	FACTOR V LEIDEN	LEIMET	METHOD	612
-LABLEID	FACTOR V LEIDEN	LEIPRM	PRIMERS	612
-LABLEID	FACTOR V LEIDEN	LEIRES	RESULTS	612
-LABCMVP	"CMV DNA PCR QUAL, NON-BLOOD"	CMQDNA	CMV DNA	611
-LABCMVP	"CMV DNA PCR QUAL, NON-BLOOD"	CMVQSP	SPECIMEN	611
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOSAB	"EOS, ABS (MAN DIFF)"	611
-LABLEID	FACTOR V LEIDEN	LEIDIR	INTERPRETED BY	611
-LABCMVP	"CMV DNA PCR QUAL, NON-BLOOD"	CMVLIP	TEST DESCRIPTION	610
-LABUCRT	"CREATININE, URINE (TIMED)"	CRE	CREATININE EXCRETION	596
-LABUCRT	"CREATININE, URINE (TIMED)"	UCR	"CREATININE, URINE"	596
-LABUCRT	"CREATININE, URINE (TIMED)"	UT3	COLLECTION PERIOD CREATININE	596
-LABUCRT	"CREATININE, URINE (TIMED)"	VOL3	VOLUME CREATININE	596
-LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBPR2	DATE & TIME OF LAST DOSE	586
-LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBPR3	DOSAGE	586
-LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBPR4	ADMINISTRATION ROUTE	586
-LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBRATROUGH	"TOBRAMYCIN, TROUGH"	586
-LABHAVM	IGM ANTI-HAV	HAVINT	INTERPRETATION	582
-LABHAVM	IGM ANTI-HAV	HAVR	IGM ANTI-HAV	582
-LABABIDI	AB ID	ABIDR	ANTIBODY ID RULE OUT	577
-LABHBSQL	ANTI-HBS QUAL	HQLINT	INTERPRETATION	573
-LABHBSQL	ANTI-HBS QUAL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	573
-LAB11131R	VORICONAZOLE LEVEL	VORI	VORICONAZOLE LEVEL	570
-LABYCERU	CERULOPLASMIN	CER	CERULOPLASMIN	566
-LABFTRPL	"TRIGLYCERIDES, PLEURAL"	TGLFLD	"TRIGLYCERIDES, PLEURAL FLUID"	565
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	BASOFLD	"BASOPHILS, FLD"	563
-LABURK	"POTASSIUM, URINE (RANDOM)"	UK	"POTASSIUM, URINE"	563
-LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	AFB	AFB SMEAR	559
-LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	CULT	CULT/OTHER RSLT:	559
-LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	RPT	RPT	559
-LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	SDES	SPECIMEN	559
-LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	SREQ	COMMENTS	559
-LABP20210	PROTHROMBIN 20210A	PTCMT	COMMENT	558
-LABP20210	PROTHROMBIN 20210A	PTDIS	DISCLAIMER	558
-LABP20210	PROTHROMBIN 20210A	PTMET	METHOD	558
-LABP20210	PROTHROMBIN 20210A	PTPRM	PRIMERS	558
-LABP20210	PROTHROMBIN 20210A	PTRES	RESULTS	558
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	PRO	PRODUCT ID  	557
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	SI	<<STATUS>>  	557
-LABP20210	PROTHROMBIN 20210A	PT20DR	INTERPRETED BY	556
-LABUA	URINALYSIS WITH MICROSCOPIC	URENAL	RENAL CELLS	556
-LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	CULT	CULT/OTHER RSLT:	554
-LABHBCT	ANTI-HBC	HBTINT	INTERPRETATION	554
-LABHBCT	ANTI-HBC	HBTR	ANTI-HBC ANTIBODY	554
-LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	RPT	RPT	554
-LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	SDES	SPECIMEN	554
-LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	SREQ	COMMENTS	554
-LABLUPUS	LUPUS ANTICOAGULANT	LUPDIR	INTERPRETED BY	543
-LABHMCY	"HOMOCYSTEINE TOTAL, PLASMA"	HMCY	"HOMOCYSTEINE, PLAS TOTAL"	540
-LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVCP	RESULT	539
-LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVDNA	EBV DNA	539
-LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVIPR	TEST DESCRIPTION	539
-LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVLOG	RESULT LOG	539
-LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVSP	SPECIMEN	539
-LABLPD	LIPID PANEL	CHOL	"CHOLESTEROL, TOTAL"	528
-LABLPD	LIPID PANEL	CHOLHDL	CHOLESTEROL/HDL RATIO	528
-LABLPD	LIPID PANEL	HDL	HDL CHOLESTEROL	528
-LABLPD	LIPID PANEL	LDL	DIRECT LDL CHOL	528
-LABLPD	LIPID PANEL	NHDL	"NON-HDL CHOL, CALC"	528
-LABLPD	LIPID PANEL	TGL	"TRIGLYCERIDE, SER/PLAS"	528
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	UBT	UNIT BLOOD TYPE  	528
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	UNT	UNIT NUMBER  	528
-LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	CULT	CULT/OTHER RSLT:	525
-LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	RPT	RPT	525
-LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	SDES	SPECIMEN	525
-LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	SREQ	COMMENTS	525
-LABABIDI	AB ID	ABID	ANTIBODY IDENTIFICATION	523
-LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSV1PC	HSV-1 DNA	523
-LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSV2PC	HSV-2 DNA	523
-LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSVIN	TEST DESCRIPTION	523
-LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	SDES11	SPECIMEN	523
-LAB11075R	SMOOTH MUSCLE AB	ASMA	ANTI-SMOOTH MUSCLE ANITBODY	521
-LABAT3	ANTITHROMBIN III	AT3	ANTITHROMBIN III	521
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PSREV	PATH/SPEC REVIEW	520
-LAB11060R	THIAMINE	VITB1	"VITAMIN B1, PLASMA"	520
-LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	CULT	CULT/OTHER RSLT:	519
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	DI	ISSUE DATE/TIME	519
-LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	RPT	RPT	519
-LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	SDES	SPECIMEN	519
-LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	SREQ	COMMENTS	519
-LABCMVIGM	CMV IGM	CMMDES	CMV IGM TEST DESCRIPTION	518
-LABCMVIGM	CMV IGM	CMMQUL	CMV IGM RESULT	518
-LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	CULT	CULT/OTHER RSLT:	516
-LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	RPT	RPT	516
-LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	SDES	SPECIMEN	516
-LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	SREQ	COMMENTS	516
-LABPB	PHENOBARBITAL LEVEL	PB	"PEAK, TROUGH, OR RANDOM"	515
-LABPB	PHENOBARBITAL LEVEL	PB	PHENOBARBITAL	515
-LABPB	PHENOBARBITAL LEVEL	PB2	DATE & TIME OF LAST DOSE	515
-LABPB	PHENOBARBITAL LEVEL	PB3	DOSAGE	515
-LABPB	PHENOBARBITAL LEVEL	PB4	ADMINISTRATION ROUTE	515
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	PRO	PRODUCT ID  	515
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	SI	<<STATUS>>  	515
-LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	CULT	CULT/OTHER RSLT:	514
-LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	GS	GRAM STAIN	514
-LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	RPT	RPT	514
-LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	SDES	SPECIMEN	514
-LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	SREQ	COMMENTS	514
-LABYHISTU	"HISTOPLASMA AG, URINE"	YHISU5	COMMENT URINE	514
-LABPCCG4	"ISTAT CG4, ARTERIAL"	BE	"BASE EXCESS, ISTAT"	513
-LABIGP	"IG PANEL, SERUM"	IGA	"IGA, SERUM"	511
-LABIGP	"IG PANEL, SERUM"	IGG	"IGG, SERUM"	511
-LABIGP	"IG PANEL, SERUM"	IGM	"IGM, SERUM"	511
-LABFACT8	FACTOR VIII ASSAY	FACTOR7	FACTOR VIII ASSAY	510
-LABYHISTS	"HISTOPLASMA AG, SERUM"	YHISS4	"RESULT,SERUM"	510
-LABERURBC	EMERGENCY RELEASE RBC	RBCSTA	RBC PRODUCT - UNITS	505
-LABCYTGEN	CYTOGENETICS FISH ORDER	CYTGN1	CYTOGEN/FISH TRACKING	500
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	EOSCSF	"EOSINOPHILS, CSF"	497
-LAB11049R	NSE	NEURSP	NEURON SPEC ENOLASE	496
-LABCMV	CMV AB TOTAL	CMV	CMV ANTIBODY	488
-LABPCEC4O	"ISTAT EC4, OTHER"	GLU	"GLUCOSE,ISTAT"	485
-LABPCEC4O	"ISTAT EC4, OTHER"	HCT	"HCT(CALC), ISTAT"	485
-LABPCEC4O	"ISTAT EC4, OTHER"	HGB	"HGB(CALC), ISTAT"	485
-LABPCEC4O	"ISTAT EC4, OTHER"	K	"POTASSIUM, ISTAT"	485
-LABPCEC4O	"ISTAT EC4, OTHER"	NA	"SODIUM, ISTAT"	485
-LABPCEC4O	"ISTAT EC4, OTHER"	PCCOM	POCT COMMENT	485
-LABGLOB	GLOBULIN	GLOB	GLOBULIN	482
-LABPROL	PROLACTIN	PROLACTIN	PROLACTIN	478
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	UBT	UNIT BLOOD TYPE  	476
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	UNT	UNIT NUMBER  	476
-LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	CULT	CULT/OTHER RSLT:	473
-LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	RPT	RPT	473
-LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	SDES	SPECIMEN	473
-LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	SREQ	COMMENTS	473
-LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	CHLAM	C.TRACHOMATIS RESULT:	472
-LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	DCL2	TEST INFORMATION	472
-LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	GC	N.GONORRHOEAE RESULT:	472
-LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	SDES2	SPECIMEN	472
-LABHGBP	"HEMOGLOBIN, PLASMA"	YHGBP1	TOTAL HEMOGLOBIN	467
-LABCMV	CMV AB IGG	CMV	CMV ANTIBODY	464
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	DI	ISSUE DATE/TIME	461
-LABABIDI	AB ID	ABIDELUATE	ANTIBODY IDENTIFICATION ELUATE	460
-LABANAS	ANTI - NUCLEAR AB	ANAQNT	TITER	454
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	XMR	UNIT CROSSMATCH  	454
-LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBIPR	TEST DESCRIPTION	453
-LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPDNA	HEP B VIRUS DNA	453
-LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPLOG	RESULT LOG 10	453
-LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPRES	RESULT	453
-LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPSP	SPECIMEN	453
-LABISOF	ISOLATOR FUNGUS	CULT	CULT/OTHER RSLT:	451
-LABVP4	COMP HEPATITIS PANEL	HATINT	INTERPRETATION	451
-LABVP4	COMP HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	451
-LABVP4	COMP HEPATITIS PANEL	HBSAIN	INTERPRETATION	451
-LABVP4	COMP HEPATITIS PANEL	HBSAQL	HEP B SURF AB QUAL	451
-LABVP4	COMP HEPATITIS PANEL	HBSAQT	HEP B SURF AB QUANT	451
-LABVP4	COMP HEPATITIS PANEL	HBSINT	INTERPRETATION	451
-LABVP4	COMP HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	451
-LABVP4	COMP HEPATITIS PANEL	HBTINT	INTERPRETATION	451
-LABVP4	COMP HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	451
-LABVP4	COMP HEPATITIS PANEL	HCVI	INTERPRETATION	451
-LABVP4	COMP HEPATITIS PANEL	HCVR	ANTI-HCV	451
-LABISOF	ISOLATOR FUNGUS	RPT	RPT	451
-LABISOF	ISOLATOR FUNGUS	SDES	SPECIMEN	451
-LABISOF	ISOLATOR FUNGUS	SREQ	COMMENTS	451
-LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	CULT	CULT/OTHER RSLT:	450
-LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	RPT	RPT	450
-LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	SDES	SPECIMEN	450
-LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	SREQ	COMMENTS	450
-LABGLF	GLUCOSE FASTING	GLUF	"GLUCOSE, FASTING"	448
-LABPCEG7O	"ISTAT EG7, OTHER"	HCO3	"HCO3, ISTAT"	448
-LABPCEG7O	"ISTAT EG7, OTHER"	PCCOM	POCT COMMENT	448
-LABPCEG7O	"ISTAT EG7, OTHER"	PCO2A	"PCO2, ISTAT"	448
-LABPCEG7O	"ISTAT EG7, OTHER"	PCO2SO	"O2 SATURATION, ISTAT"	448
-LABPCEG7O	"ISTAT EG7, OTHER"	PH	"PH, ISTAT"	448
-LABPCEG7O	"ISTAT EG7, OTHER"	PO2A	"PO2, ISTAT"	448
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	PRO	PRODUCT ID  	448
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	SI	<<STATUS>>  	448
-LABPCEG7O	"ISTAT EG7, OTHER"	TCO2A	"TCO2, (ISTAT)"	448
-LABPCACT	ACTIVATED CLOTTING TIME (POCT)	ACT	"ACT,POCT"	439
-LABPCACT	ACTIVATED CLOTTING TIME (POCT)	PCCOM	POCT COMMENT	438
-LABFLCR	FREE LIGHT CHAIN RATIO	FREEK	FREE KAPPA LIGHT CHAIN	437
-LABFLCR	FREE LIGHT CHAIN RATIO	FREEKL	FREE KAPPA/LAMBDA RATIO	437
-LABFLCR	FREE LIGHT CHAIN RATIO	FREEL	FREE LAMBDA LIGHT CHAIN	437
-LABURTP	"PROTEIN, URINE (RANDOM)"	UPROTTOT	"PROTEIN, TOTAL, URINE"	437
-LABPCEG7O	"ISTAT EG7, OTHER"	CAION	"CALCIUM,ION, ISTAT"	436
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	CAOX	CA OXALATE CRYSTALS	436
-LABPCEG7O	"ISTAT EG7, OTHER"	HCT	"HCT(CALC), ISTAT"	436
-LABPCEG7O	"ISTAT EG7, OTHER"	HGB	"HGB(CALC), ISTAT"	436
-LABPCEG7O	"ISTAT EG7, OTHER"	K	"POTASSIUM, ISTAT"	436
-LABPCEG7O	"ISTAT EG7, OTHER"	NA	"SODIUM, ISTAT"	436
-LABUA	URINALYSIS WITH MICROSCOPIC	HYST	PSEUDO-HYPHAE	434
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	C1656A	"CD3-/16+56(NK),ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	C1656P	CD3-/16+56(NK)%	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD19AB	"CD19 (PAN B), ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD19PC	CD19 (PAN B) %	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD3PC	CD3 (PAN T) %	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD4AB	"CD3+/CD4+, ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD4PC	CD3+/CD4+ %	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD8AB	"CD3+/CD8+, ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD8PC	CD3+/CD8+ %	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	LYM	LYMPHOCYTES %	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	432
-LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	WBC	WBC COUNT	432
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	XMR	UNIT CROSSMATCH  	430
-LABERUFFP	EMERGENCY RELEASE PLASMA	FFP	FRESH FROZEN UNITS	428
-LABMETHB	METHEMOGLOBIN	METHB	METHEMOGLOBIN	427
-LABYHISTS	"HISTOPLASMA AG, SERUM"	YHISS5	"COMMENT,SERUM"	426
-LAB11071R	MITOCHONDRIAL AB	YAMA	"MITOCHONDRIAL ANTIBODY, M2"	424
-LABCMVRO	CMV RULE OUT	CMQDNA	CMV DNA	423
-LABCMVRO	CMV RULE OUT	CMVDSV	TEST DESCRIPTION	423
-LABCMVRO	CMV RULE OUT	CMVLIP	TEST DESCRIPTION	423
-LABCMVRO	CMV RULE OUT	CMVQSP	SPECIMEN	423
-LABCMVRO	CMV RULE OUT	CMVRC1	CMV DAY 1 RAPID CULTURE	423
-LABCMVRO	CMV RULE OUT	CMVRC2	CMV DAY 2 RAPID CULTURE	423
-LABCMVRO	CMV RULE OUT	CMVSPM	SPECIMEN	423
-LABHIAB	HEPARIN INDUCED AB	HIARES	RESULT	421
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	UBT	UNIT BLOOD TYPE  	421
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	UNT	UNIT NUMBER  	421
-LABHIAB	HEPARIN INDUCED AB	HIACOM	COMMENTS	420
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EAGQNT	EAGQNT	418
-LABCMVTSI	CMV AB TOTAL SOLID PHASE (GALILEO)	CMVTOT	CMV TOTAL ANTIBODY	414
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBCINT	INTERPRETATION	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBCR	IGM ANTI-HBC ANTIBODY	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBSAIN	INTERPRETATION	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBSAQL	HEP B SURF AB QUAL	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBSAQT	HEP B SURF AB QUANT	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBSINT	INTERPRETATION	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBSR	HEP B SURFACE ANTIGEN	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBTINT	INTERPRETATION	413
-LABDGHBP	DIAG. HEPATITIS B PANEL	HBTR	ANTI-HBC ANTIBODY	413
-LABTES	TESTOSTERONE	TESTOS	TESTOSTERONE	413
-LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVCP	RESULT	410
-LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVIPR	TEST DESCRIPTION	410
-LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVLO	RESULT LOG 10	410
-LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVRNA	HIV 1 RNA	410
-LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVSP	SPECIMEN	410
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	PRO	PRODUCT ID  	410
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	SI	<<STATUS>>  	410
-LAB12631R	VORICONAZOLE LEVEL	12630R	VORICONAZOLE LEVEL	409
-LABCRCL	"CREATININE CLEARANCE, URINE"	CORCL	CORRECTED CR. CLEAR.	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	COREXC	CORRECTED CR. EXC.	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	CR	"CREATININE, SERUM"	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	CRCL	CREATININE CLEARANCE	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	CRE	CREATININE EXCRETION	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	CRUCL	"CREATININE, URINE"	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	FLOWURN	URINE FLOW	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	HT	HEIGHT	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	UT3	COLLECTION PERIOD CREATININE	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	VOL3	VOLUME CREATININE	404
-LABCRCL	"CREATININE CLEARANCE, URINE"	WT	WEIGHT	404
-LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	CRYS	CRYSTAL AMOUNT	403
-LABYA1AS	ALPHA - 1 - ANTITRYPSIN	YA1A	ALPHA 1 ANTITRYPSIN	403
-LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	CXSOUR	FLUID SOURCE	402
-LABC125	CANCER AG 125	C125	CANCER ANTIGEN 125	398
-LABCMVIGT	CMV IGG (TRANSFUSION)	CMGTDS	TEST DESCRIPTION	398
-LABCMVIGT	CMV IGG (TRANSFUSION)	CMGTQL	RESULT	398
-LABT3	"T3, TOTAL"	T3	T3 TOTAL	398
-10290R	HLA - TYPING	9013R	CELL DATE	397
-10290R	HLA - TYPING	9014R	CELL SOURCE	397
-10290R	HLA - TYPING	9080R	DISCLAIMER	397
-10290R	HLA - TYPING	9448R	METHOD(S)	397
-LABUA	URINALYSIS WITH MICROSCOPIC	FRFAT	FREE FAT	397
-LABPTTINH	PTT INHIBITOR SCREEN	PTTINT	INTERPRETATION	397
-10290R	HLA - TYPING	9015R	HLA-A ALLELE 1	395
-10290R	HLA - TYPING	9016R	HLA-A ALLELE 2	395
-10290R	HLA - TYPING	9017R	HLA-B ALLELE 1	395
-10290R	HLA - TYPING	9018R	HLA-B ALLELE 2	395
-10290R	HLA - TYPING	9019R	HLA-BW ALLELE 1	395
-10290R	HLA - TYPING	9075R	HLA-MOLECULAR PT. SUMMARY	395
-10290R	HLA - TYPING	9076R	HLA-SEROLOGICAL PT. SUMMARY	395
-LABUA	URINALYSIS WITH MICROSCOPIC	OVFAT	OVAL FAT BODIES	395
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	HYST	PSEUDO-HYPHAE	394
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	UBT	UNIT BLOOD TYPE  	394
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	UNT	UNIT NUMBER  	394
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASO	BASOPHILS	389
-LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	HVDNA	HERPES VIRUS 6 DNA	389
-LABGIARS	GIARDIA SCREEN	CULT	CULT/OTHER RSLT:	386
-LABGIARS	GIARDIA SCREEN	RPT	RPT	386
-LABGIARS	GIARDIA SCREEN	SDES	SPECIMEN	386
-LABGIARS	GIARDIA SCREEN	SREQ	COMMENTS	386
-LABVZAB	VARICELLA ZOSTER VIRUS AB IGG	VZAB	VARICELLA ZOSTER AB IGG	386
-LABBXTG	BIOPSY/TISSUE(GEN)GS	CULT	CULT/OTHER RSLT:	384
-LABBXTG	BIOPSY/TISSUE(GEN)GS	GS	GRAM STAIN	384
-LABBXTG	BIOPSY/TISSUE(GEN)GS	RPT	RPT	384
-LABBXTG	BIOPSY/TISSUE(GEN)GS	SDES	SPECIMEN	384
-LABBXTG	BIOPSY/TISSUE(GEN)GS	SREQ	COMMENTS	384
-LABUUNT	"UREA NITROGEN, URINE (TIMED)"	TUNU	UREA EXCRETION	384
-LABUUNT	"UREA NITROGEN, URINE (TIMED)"	UREAVOL	VOLUME UREA	384
-LABUUNT	"UREA NITROGEN, URINE (TIMED)"	UT4	COLLECTION PERIOD UREA	384
-LABUUNT	"UREA NITROGEN, URINE (TIMED)"	UURN	"UREA NITROGEN, URINE"	384
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASOAB	"BASO, ABS (MAN DIFF)"	381
-LABPROTC	PROTEIN C ACTIVITY	PROTC	PROTEIN C ACTIVITY	381
-LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSV1DN	HSV-1 DNA	380
-LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSV2DN	HSV-2 DNA	380
-LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSVZIP	TEST DESCRIPTION	380
-LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSVZSP	SPECIMEN	380
-LABHSVZL	"HSV/VZV PCR QUAL, LESION"	VZVDN	VZV DNA	380
-LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	OCCGU	"STOOL SAMPLE1,GUAIAC"	379
-10290R	HLA - TYPING	9021R	HLA-C ALLELE 1	377
-10290R	HLA - TYPING	9022R	HLA-C ALLELE 2	377
-10290R	HLA - TYPING	9023R	HLA-DRB1 ALLELE 1	377
-10290R	HLA - TYPING	9024R	HLA-DRB1 ALLELE 2	377
-10290R	HLA - TYPING	9029R	HLA-DQB1 ALLELE 1	377
-10290R	HLA - TYPING	9030R	HLA-DQB1 ALLELE 2	377
-LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	DATE1	DATE 1	377
-LABFVARZ	"VARICELLA ZOSTER VIR. DNA,QUAL"	VZVDNA	VARICELLA ZOSTER VIRUS DNA	377
-LABHITAB	HEPARIN (HIT) AB	PF4IGG	PF4 IGG ONLY OD	376
-LAB11824R	BUSULFAN	11825R	CONCENT. AT STEADY STATE (CSS)	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	ADVR	ADENOVIRUS	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	INFAR	INFLUENZA A	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	INFBR	INFLUENZA B	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	MPVRR	METAPNEUMOVIRUS	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	P1R	PARAINFLUENZA 1	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	P2R	PARAINFLUENZA 2	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	P3R	PARAINFLUENZA 3	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	RSVR	RSV	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	RVRPIP	TEST DECRIPTION	375
-LABDVERVR	RESP DFA PANEL-REFLEX PCR	RVRSP	SPECIMEN	375
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	DI	ISSUE DATE/TIME	372
-LABPROTS	PROTEIN S ACTIVITY	PROTS	PROTEIN S ACTIVITY	372
-LABUA	URINALYSIS WITH MICROSCOPIC	URIC	URIC ACID CRYSTALS	371
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BLASTS	BLASTS	368
-LABLEVETL	LEVETIRACETAM LEVEL	LEVET	LEVET	367
-LABLEVETL	LEVETIRACETAM LEVEL	LEVET1	LEVET1	367
-LABLEVETL	LEVETIRACETAM LEVEL	LEVET2	LEVET2	367
-LABLEVETL	LEVETIRACETAM LEVEL	LEVET3	LEVET3	367
-LABLEVETL	LEVETIRACETAM LEVEL	LEVET4	LEVET4	367
-LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	SOUR12	SOURCE	367
-LABTT4	"T4, TOTAL"	T4	"THYROXINE (T4), TOTAL"	367
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BLASTSAB	"BLASTS,ABS(MAN DIFF)"	365
-10290R	HLA - TYPING	9027R	HLA-DQA1 ALLELE 1	361
-10290R	HLA - TYPING	9028R	HLA-DQA1 ALLELE 2	361
-LABANTIXA	LMWH ACTIVITY BY ANTIXA	ANTIXA	"LMW HEPARIN, ANTIXA U/ML"	361
-LABARI	ABO/RH (ARI)	ARG	ABO/RH (AUTOMATION)	361
-LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	DVE	RESULTS	360
-LABSTPOLY	"WBC FOR IBD, STOOL"	EOSSTL	"EOSINOPHILS, STOOL"	360
-LABSTPOLY	"WBC FOR IBD, STOOL"	POLYST	"PMNS, STOOL"	360
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	PRO	PRODUCT ID  	360
-LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	RPT	RPT	360
-LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	SDES	SPECIMEN	360
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	SI	<<STATUS>>  	360
-LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	SREQ	COMMENTS	360
-LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	DATE2	DATE 2	359
-LABFVARZ	"VARICELLA ZOSTER VIR. DNA,QUAL"	SOUR13	SOURCE	358
-LABCMVIGT	CMV IGG (TRANSFUSION)	CMGTQT	LEVEL	357
-LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	DATE3	DATE 3	357
-LABCMVM	CMV IGM	CMVIGM	CMV IGM AB	356
-LABUTP	"PROTEIN, URINE (TIMED)"	TTPU	PROTEIN EXCRETION	355
-LABUTP	"PROTEIN, URINE (TIMED)"	UPROTTOT	"PROTEIN, TOTAL, URINE"	355
-LABUTP	"PROTEIN, URINE (TIMED)"	UT10	COLLECTION PERIOD PROTEIN	355
-LABUTP	"PROTEIN, URINE (TIMED)"	VOL10	VOLUME PROTEIN	355
-LABINSLF	INSULIN FASTING	INSF	"INSULIN, FASTING"	354
-LABMALAR	MALARIA PREP	MAL	MALARIA PREP RESULT	352
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	XMR	UNIT CROSSMATCH  	350
-LABCMVQL	"CMV DNA PCR QUAL, NON-PLASMA"	CMQDNA	CMV DNA	347
-LABCMVQL	"CMV DNA PCR QUAL, NON-PLASMA"	CMVLIP	TEST DESCRIPTION	347
-LABCMVQL	"CMV DNA PCR QUAL, NON-PLASMA"	CMVQSP	SPECIMEN	347
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MAOTH	"OTHER, ABS(MAN DIFF)"	347
-LABNORW	"NOROVIRUS ANTIGEN, STOOL EIA"	NORW1	"NOROVIRUS,ELA (STOOL)"	346
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	COTHER	"OTHERS, CSF"	345
-LAB11123R	"COCCI CF AB, CSF"	COCC	COCCI CF AB (CSF)	341
-LABTHEOL	THEOPHYLLINE LEVEL	THEO	THEOPHYLLINE	341
-LABTHEOL	THEOPHYLLINE LEVEL	THEO1	"PEAK, TROUGH, OR RANDOM"	341
-LABTHEOL	THEOPHYLLINE LEVEL	THEO2	DATE & TIME OF LAST DOSE	341
-LABTHEOL	THEOPHYLLINE LEVEL	THEO3	DOSAGE	341
-LABTHEOL	THEOPHYLLINE LEVEL	THEO4	ADMINISTRATION ROUTE	341
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	UBT	UNIT BLOOD TYPE  	339
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	UNT	UNIT NUMBER  	339
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	DI	ISSUE DATE/TIME	337
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	URENAL	RENAL CELLS	337
-LABANTIM	ANTI - TPO AB	ATPO	ANTI-TPO ANTIBODY	335
-LABUA	URINALYSIS WITH MICROSCOPIC	SPERM	SPERMATOZOA	333
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	SPEXP	SPECIMEN EXPIRATION DATE/TIME	333
-LAB11068R	ACE	ACE	ANGIOTESIN CONVERTING ENZYME	331
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	CILIA	BRONCH. LINING CELLS	331
-LABBMT1	BMT PANEL 1	EGFR	EGFR	330
-LABMMAS	"METHYLMALONIC ACID, SERUM"	MMA	"METHYLMALONIC ACID, SERUM"	329
-LABCZPL	"CARBAMAZEPINE, SERUM"	CZP	CARBAMAZEPINE	326
-LABCZPL	"CARBAMAZEPINE, SERUM"	CZP1	"PEAK, TROUGH, OR RANDOM"	326
-LABCZPL	"CARBAMAZEPINE, SERUM"	CZP2	DATE & TIME LAST DOSE	326
-LABCZPL	"CARBAMAZEPINE, SERUM"	CZP3	DOSAGE	326
-LABCZPL	"CARBAMAZEPINE, SERUM"	CZP4	ADMINISTRATION ROUTE	326
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	PRO	PRODUCT ID  	326
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	SI	<<STATUS>>  	326
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	OTHER	OTHERS (DESCRIBE)	325
-LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	OCCGU	"STOOL SAMPLE2,GUAIAC"	324
-LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	OCCGU	"STOOL SAMPLE3,GUAIAC"	324
-LAB11136R	HTLV AB	HTLVS	HTLV AB SCREEN	322
-LABURCL	"CHLORIDE, URINE (RANDOM)"	UCL	"CHLORIDE, URINE"	320
-LABEB	EBV AB PANEL	COM	COMMENT	319
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	XMR	UNIT CROSSMATCH  	319
-10335R	HLA - SCREENING C1Q	9271R	SERUM DATE	316
-10335R	HLA - SCREENING C1Q	9275R	HLA ANTIBODIES FOR C1Q CLASS I	316
-10335R	HLA - SCREENING C1Q	9282R	DISCLAIMER	316
-10335R	HLA - SCREENING C1Q	9585R	COMPOSED TEXT CLASS I	316
-10335R	HLA - SCREENING C1Q	9605R	METHOD	316
-LABCMVM	CMV AB IGM	CMVIGM	CMV IGM AB	316
-LABTRFN	TRANSFERRIN	TRFRN	TRANSFERRIN	316
-10335R	HLA - SCREENING C1Q	9263R	CUMULATIVE ANTIBODIES BY IGG	315
-10335R	HLA - SCREENING C1Q	9274R	CPRA FOR C1Q CLASS I	315
-10335R	HLA - SCREENING C1Q	9350R	MFI RESULTS FOR SCREENING C1Q CLASS I	315
-10335R	HLA - SCREENING C1Q	9265R	CUMULATIVE ANTIBODIES BY C1Q	314
-LABYBOH	BETA-HYDROXYBUTYRATE	BOH	BETA-HYDROXYBUTYRATE	314
-LABHGBP	"HEMOGLOBIN, PLASMA"	12078R	OXYHEMOGLOBIN	313
-LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	ANCIGG	"ANCA, IGG"	313
-LABYFGL	"GLUCOSE, FLUID"	GLUFLD	GLUCOSE BODY FLUID	313
-LABYFGL	"GLUCOSE, FLUID"	YFLSC1	FLUID TYPE	313
-LABBMT2	BMT PANEL 2	EGFR	EGFR	312
-LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	MPOAR	MYELOPEROX AB REFLEX	312
-LABPTTINH	PTT INHIBITOR SCREEN	PTTDIR	INTERPRETED BY	312
-LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	SERINEPR3	SERINE PR3 REFLEX	312
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	UBT	UNIT BLOOD TYPE  	312
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	UNT	UNIT NUMBER  	312
-10335R	HLA - SCREENING C1Q	9266R	CPRA FOR CUMULATIVE C1Q	311
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SCMT	SYNOVIAL FLUID COMMENT	311
-LABANTIXA	"LMW HEPARIN, ANTIXA U/ML"	ANTIXA	"LMW HEPARIN, ANTIXA U/ML"	310
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	DI	ISSUE DATE/TIME	305
-LABCBCD	CBC WITH DIFF	LYMPHM	LYMPHOMA CELLS	305
-LABBHB	BETA-HYDROXYBUTYRATE	BHB	BETA-HYDROXYBUTYRATE	303
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	PRO	PRODUCT ID  	303
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	SI	<<STATUS>>  	303
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CRLYM	"REACT LYMPH, CSF"	302
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL1	HSV 1 IGG INDEX	302
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL2	HSV 1 IGG INTERPRETATION	302
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL3	HSV 2 IGG INDEX	302
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL4	HSV 2 IGG INTERPRETATION	302
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL5	HSV 1/2 IGM INDEX	302
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL7	HSV 1/2 IGM INTERPRETATION	302
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	SPEXP	SPECIMEN EXPIRATION DATE/TIME	300
-LABYWNILE	"WEST NILE VIRUS AB, IGG AND IGM"	WNVIGG	WEST NILE VIRUS AB IGG	300
-LABYWNILE	"WEST NILE VIRUS AB, IGG AND IGM"	WNVIGM	WEST NILE VIRUS AB IGM	300
-LABYWNILE	"WEST NILE VIRUS AB, IGG AND IGM"	YWNSC	SOURCE	299
-LABVCULR	"VIRAL CULTURE, RESPIRATORY"	CULT	CULT/OTHER RSLT:	297
-LABVCULR	"VIRAL CULTURE, RESPIRATORY"	RPT	RPT	297
-LABVCULR	"VIRAL CULTURE, RESPIRATORY"	SDES	SPECIMEN	297
-LABVCULR	"VIRAL CULTURE, RESPIRATORY"	SREQ	COMMENTS	297
-LABCBCD	CBC WITH DIFF	PLYM	PROLYMPHOCYTE	294
-10330R	HLA - SCREENING IGG	9244R	CUMULATIVE ANTIBODIES BY IGG	291
-10330R	HLA - SCREENING IGG	9246R	CUMULATIVE ANTIBODIES BY C1Q	291
-10330R	HLA - SCREENING IGG	9249R	IGG COMBINED PEAK CPRA	291
-10330R	HLA - SCREENING IGG	9250R	IGG COMBINED PEAK DATE	291
-10330R	HLA - SCREENING IGG	9251R	SERUM DATE	291
-10330R	HLA - SCREENING IGG	9262R	DISCLAIMER	291
-10330R	HLA - SCREENING IGG	9604R	METHOD	291
-LABYCP	"C - PEPTIDE, SERUM"	CPEP	C-PEPTIDE	291
-10330R	HLA - SCREENING IGG	9245R	CPRA FOR CUMULATIVE IGG	290
-10330R	HLA - SCREENING IGG	9254R	CPRA FOR IGG CLASS I	290
-10330R	HLA - SCREENING IGG	9255R	HLA ANTIBODIES FOR IGG CLASS I	290
-10330R	HLA - SCREENING IGG	9583R	COMPOSED TEXT CLASS I	290
-10330R	HLA - SCREENING IGG	9650R	MFI RESULTS FOR SCREENING IGG CLASS I	290
-10330R	HLA - SCREENING IGG	9248R	IGG COMBINED CPRA FOR SERUM	288
-10330R	HLA - SCREENING IGG	9257R	CPRA FOR IGG CLASS II	288
-10330R	HLA - SCREENING IGG	9258R	HLA ANTIBODIES FOR IGG CLASS II	288
-10330R	HLA - SCREENING IGG	9261R	COMPOSED TEXT CLASS II	288
-10330R	HLA - SCREENING IGG	9651R	MFI RESULTS FOR SCREENING IGG CLASS II	288
-LABWNVAB	"WEST NILE VIRUS AB PANEL, CSF"	WNVIGG	WEST NILE VIRUS IGG	288
-LABFLUAPC 	FLU A PCR	FLUADI	DISCLAIMER	286
-LABFLUAPC 	FLU A PCR	FLUAMT	METHOD	286
-LABFLUAPC 	FLU A PCR	FLUAR	INFLUENZA A RNA	286
-LABFLUAPC 	FLU A PCR	FLUASP	SPECIMEN	286
-LABUA	URINALYSIS WITH MICROSCOPIC	CECAST	CELLULAR CASTS	285
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CCNT	CELLS COUNTED	284
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	DI	ISSUE DATE/TIME	284
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	AMPHET	AMPHETAMINES	283
-LAB11242R	VZV QUANT	11243R	SOURCE	282
-LAB11242R	VZV QUANT	11247R	VZV QUANT	282
-LABWNVAB	"WEST NILE VIRUS AB PANEL, CSF"	WNVIGM	WEST NILE VIRUS IGM	282
-LABCO2	"CO2, SERUM/PLASMA"	CO2	"CO2, SER/PLAS"	279
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PRMY	PROMYELOCYTES	278
-LABFADAG	"ADENOVIRUS AG, STOOL"	ADAG	ADENOVIRUS AG	277
-LABCDPLI	COLD AGGLUTININ SCREEN	CDPL	COLD ANTIBODY SCREEN	277
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	PRO	PRODUCT ID  	277
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	SI	<<STATUS>>  	277
-LABCBCD	CBC WITH DIFF	MEGA	MEGAKARY/100WBC	276
-LAB11098R	1 25 OH VITAMIN D	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	275
-LABVZVIGG	VZ IGG	VGGDES	VZV IGG TEST DESCRIPTION	274
-LABVZVIGG	VZ IGG	VGGQUL	VZV IGG RESULT	274
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	UBT	UNIT BLOOD TYPE  	272
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	UNT	UNIT NUMBER  	272
-LABCL	"CHLORIDE, SERUM/PLASMA"	CL	"CHLORIDE, SER/PLAS"	270
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	URIC	URIC ACID CRYSTALS	270
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	ALCOHOL	ALCOHOL	269
-LABYPTHRP	"PTH-RELATED PEPTIDE, PLASMA"	PTH	PTH-RELATED PEPTIDE	269
-LABVZVIGG	VZ IGG	VGGQNT	VZV IGG LEVEL	269
-LABEB	EBV AB PANEL	11165R	EB VIRUS EARLY AG (R+D) IGG	268
-LABEB	EBV AB PANEL	11166R	EB NUCLEAR AB	268
-LABEB	EBV AB PANEL	12001R	"VCA IGG ,IFA"	268
-LABEB	EBV AB PANEL	12002R	"VCA IGM ,IFA"	268
-10290R	HLA - TYPING	9078R	INTERPRETATION/ COMMENTS	268
-LABPCEG7O	"ISTAT EG7, OTHER"	FIO2	FIO2	265
-LABYCOPPR	"COPPER, SERUM"	COPPER	"COPPER, SERUM"	264
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	BARB	BARBITURATES	263
-LABCMVIGM	CMV IGM	CMMQNT	CMV IGM LEVEL	263
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	BENZO	BENZODIAZEPINES	262
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	COCAINE	COCAINE	262
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	OPIATE	OPIATES	262
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	YUPCP	PHENCYCLIDINE	262
-LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	YUTTHC	TETRAHYDROCANNABINOL	262
-LAB11069R	ALDOLASE	ALDOLASE	"ALDOLASE, SERUM"	261
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	XMR	UNIT CROSSMATCH  	261
-LABBCRABL	"BCR - ABL, BLOOD"	BCRCMT	COMMENT	260
-LABBCRABL	"BCR - ABL, BLOOD"	BCRDIS	DISCLAIMER	260
-LABBCRABL	"BCR - ABL, BLOOD"	BCRMET	METHOD	260
-LABBCRABL	"BCR - ABL, BLOOD"	BCRPRM	PRIMERS	260
-LABBCRABL	"BCR - ABL, BLOOD"	BCRRES	RESULTS	260
-LABADVQT	ADENOVIRUS DNA PCR QUANT	ADDNA	ADENOVIRUS DNA	258
-LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVCP	RESULT	258
-LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVLOG	RESULT LOG 10	258
-LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVSP	SPECIMEN	258
-LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVTIP	TEST DESCRIPTION	258
-LABFHV12	"HSV 1 AND 2 PCR, NON CSF"	HV1DNA	"HSV-1 DNA, QN PCR"	255
-LABFHV12	"HSV 1 AND 2 PCR, NON CSF"	HV2DNA	"HSV-2 DNA, QN PCR"	255
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	REACTL	REACTIVE LYMPHS	253
-LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VMQNT	VMQNT	253
-LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	BIREF	BIREFRINGENCE	252
-LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	LOCAT	LOCATION	252
-LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	SHAPE	SHAPE	252
-LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	SIZE	SIZE	252
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	ACAIGG	ACA IGG ANTIBODIES	251
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	ACAIGM	ACA IGM ANTIBODIES	251
-LABHBCA	IGM ANTI-HBC	HBCINT	INTERPRETATION	251
-LABHBCA	IGM ANTI-HBC	HBCR	IGM ANTI-HBC ANTIBODY	251
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	LUPINT	LUPUS INTERPRETATION	251
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	UBT	UNIT BLOOD TYPE  	251
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	UNT	UNIT NUMBER  	251
-LABBCRABL	"BCR - ABL, BLOOD"	BCRDIR	INTERPRETED BY	250
-LABPCEG7O	"ISTAT EG7, OTHER"	BE	"BASE EXCESS, ISTAT"	250
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	DI	ISSUE DATE/TIME	248
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	PRO	PRODUCT ID  	248
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	SI	<<STATUS>>  	248
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	XMR	UNIT CROSSMATCH  	248
-LABGNTPOL	GENTAMICIN PEAK LEVEL	GENTPK	"GENTAMICIN, PEAK"	247
-LABGNTPOL	GENTAMICIN PEAK LEVEL	GNTPO2	DATE & TIME OF LAST DOSE	247
-LABGNTPOL	GENTAMICIN PEAK LEVEL	GNTPO3	DOSAGE	247
-LABGNTPOL	GENTAMICIN PEAK LEVEL	GNTPO4	ADMINISTRATION ROUTE	247
-LAB11234R	VZV TOTAL AND IGM AB PANEL	11235R	"VARICELLA-ZOSTER, ACIF"	245
-LAB11234R	VZV TOTAL AND IGM AB PANEL	11236R	"VZ AB IGM, IFA"	245
-LAB11234R	VZV TOTAL AND IGM AB PANEL	11237R	VZV INTERPRETATION	245
-LABFMYAB	MYCOPLASMA IGG/IGM AB	FMYABG	M.PNEUMONIA AB IGG	245
-LABFMYAB	MYCOPLASMA IGG/IGM AB	FMYABM	M.PNEUMONIA AB IGM	245
-LABYERYTH	ERYTHROPOIETIN	EPO	ERYTHROPOIETIN	244
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	ALBCPI	"ALBUMIN, SER/PLAS"	243
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CAB	CSF ALBUMIN	243
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CIG	CSF IGG	243
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CINDX	CSF IGG/ALBUMIN INDEX	243
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CPIEI	INTERPRETATION	243
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CPIER	RESULT	243
-LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	IGGCPI	"IGG, SER/PLAS"	243
-LABLAMOL	LAMOTRIGINE LEVEL	LAMO	LAMO	243
-LABLAMOL	LAMOTRIGINE LEVEL	LAMO1	LAMO1	243
-LABLAMOL	LAMOTRIGINE LEVEL	LAMO2	LAMO2	243
-LABLAMOL	LAMOTRIGINE LEVEL	LAMO3	LAMO3	243
-LABLAMOL	LAMOTRIGINE LEVEL	LAMO4	LAMO4	243
-LABBLCA	"AFB CULTURE, BLOOD"	CULT	CULT/OTHER RSLT:	239
-LABGASBLD	OCCULT BLOOD GASTRIC	OCCBLDGAS	"OCCULT BLOOD,GASTRIC"	239
-LABBLCA	"AFB CULTURE, BLOOD"	RPT	RPT	239
-LABBLCA	"AFB CULTURE, BLOOD"	SDES	SPECIMEN	239
-LABBLCA	"AFB CULTURE, BLOOD"	SREQ	COMMENTS	239
-LABWNVAB	"WEST NILE VIRUS AB PANEL, CSF"	WNITP	W NILE INTERPRETATION	237
-10335R	HLA - SCREENING C1Q	9264R	CPRA FOR CUMULATIVE IGG	236
-LABCBCD	CBC WITH DIFF	DYSPL	"DYSPL GRANS, UNCLASS."	236
-LABHBSQT	HEP B SURF AB QUANT	HBSAIN	INTERPRETATION	236
-LABHBSQT	HEP B SURF AB QUANT	HBSAQL	HEP B SURF AB QUAL	236
-LABHBSQT	HEP B SURF AB QUANT	HBSAQT	HEP B SURF AB QUANT	236
-LABANTIXA	ANTI XA / LMWH	ANTIXA	"LMW HEPARIN, ANTIXA U/ML"	235
-LABEVPCR	ENTEROVIRUS BY PCR	EVIN	ENTEROVIRUS PCR INTERPRETATION	235
-LABEVPCR	ENTEROVIRUS BY PCR	EVR	CSF ENTEROVIRUS RESULT	235
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	B2GPIG	"BETA-2 GLYCOPROTEIN 1, IGG"	234
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	B2GPIM	"BETA-2 GLYCOPROTEIN 1, IGM"	234
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVCN	DRVV CONFIRMATION	234
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVIN	DRVV INTERPRETATION	234
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVMX	DRVV MIX	234
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVSC	DRVV SCREEN	234
-10290R	HLA - TYPING	9077R	NMDP CODES	233
-LABYCCPIG	ANTI CCP	CCPINT	INTERPRETATION	233
-LABYCCPIG	ANTI CCP	CCPR	ANTI CCP ANTIBODY	233
-LABEVPCR	ENTEROVIRUS BY PCR	EVSP	SPECIMEN	233
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVLA	DRVV NORMALIZED RATIO	232
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITDI	DISCLAIMER	231
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITDX	CLINICAL INDICATION	231
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITME	METHOD	231
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITPR	PRIMER	231
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITRE	RESULT	231
-LAB12573R	ANCA	12575R	C-ANCA	230
-LAB12573R	ANCA	12576R	P-ANCA	230
-LABVCULT	"VIRAL CULTURE, TISSUE"	CULT	CULT/OTHER RSLT:	230
-LABVCULT	"VIRAL CULTURE, TISSUE"	RPT	RPT	230
-LABVCULT	"VIRAL CULTURE, TISSUE"	SDES	SPECIMEN	230
-LABVCULT	"VIRAL CULTURE, TISSUE"	SREQ	COMMENTS	230
-LABYMETFF	"METANEPHRINES FRACTIONATED FREE, PLASMA"	UMETF	METANEPHRINE FREE	230
-LABYMETFF	"METANEPHRINES FRACTIONATED FREE, PLASMA"	UNORMETF	"NORMETANEPHRINE,FREE"	230
-10330R	HLA - SCREENING IGG	9247R	CPRA FOR CUMULATIVE C1Q	229
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	PRO	PRODUCT ID  	229
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	SI	<<STATUS>>  	229
-LABFHV12	"HSV 1 AND 2 PCR, NON CSF"	SOURC9	SOURCE	229
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FRLYM	"REACTIVE LYMPHS, FLD"	228
-LABERUPLT	EMERGENCY RELEASE PLT	PLT	PLATELETS	228
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	UBT	UNIT BLOOD TYPE  	228
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	UNT	UNIT NUMBER  	228
-LAB11023R	EB NUCLEAR AG AB	11166R	EB NUCLEAR AB	226
-LABCD4CT	CD4 COUNT	C3P4PA	"CD3+/CD4+,ABS"	226
-LABCD4CT	CD4 COUNT	C3P4PC	"CD3+/CD4+,%"	226
-LABCD4CT	CD4 COUNT	LYM	LYMPHOCYTES %	226
-LABCD4CT	CD4 COUNT	LYMAB	"LYMPHOCYTES, ABS."	226
-LABCD4CT	CD4 COUNT	WBC	WBC COUNT	226
-LAB11020R	EB VIRUS EARLY AG (R+D) IGG	11165R	EB VIRUS EARLY AG (R+D) IGG	224
-LABADPCRQ	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADPCR1	ADENVIRUS DNA	224
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	DI	ISSUE DATE/TIME	224
-LABADPCRQ	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	SOUR10	SOURCE	224
-LABIOPTH	INTRA OPERATIVE PTH	IOPTH	INTRA OPERATIVE PTH	223
-LAB11024R	EB VIRAL CAPSID AG IGM AB	11167R	EB VCA IGM AB	222
-LAB11030R	EB VIRAL CAPSID AG IGG AB	11168R	EB VCA IGG AB	222
-LABFDP	FIBRIN DEGRADATION PRODUCTS	FDP	FIBRIN DEGRAD PROD	219
-10335R	HLA - SCREENING C1Q	10472R	DSA FOR C1Q CLASS I	217
-LABSTRONG	"STRONGYLOIDES IGG AB, ELISA"	STROG	"STRONGYLOIDES ,LGG"	217
-LABRITUX	RITUXAN PANEL	C45RAA	"CD4+/CD45RA+, ABS"	215
-LABRITUX	RITUXAN PANEL	C45RAP	CD4+/CD45RA+ %	215
-LABRITUX	RITUXAN PANEL	C45ROA	"CD4+/CD45RO+, ABS"	215
-LABRITUX	RITUXAN PANEL	C45ROP	CD4+/CD45RO+ %	215
-LABRITUX	RITUXAN PANEL	CD19AB	"CD19 (PAN B), ABS."	215
-LABRITUX	RITUXAN PANEL	CD19PC	CD19 (PAN B) %	215
-LABRITUX	RITUXAN PANEL	CD20AB	"CD20 (PAN B), ABS."	215
-LABRITUX	RITUXAN PANEL	CD20PC	CD20 (PAN B) %	215
-LABRITUX	RITUXAN PANEL	CD3AB	"CD3 (PAN T), ABS."	215
-LABRITUX	RITUXAN PANEL	CD3PC	CD3 (PAN T) %	215
-LABRITUX	RITUXAN PANEL	CD4AB	"CD3+/CD4+, ABS."	215
-LABRITUX	RITUXAN PANEL	CD4PC	CD3+/CD4+ %	215
-LABRITUX	RITUXAN PANEL	CD8AB	"CD3+/CD8+, ABS."	215
-LABRITUX	RITUXAN PANEL	CD8PC	CD3+/CD8+ %	215
-LABRITUX	RITUXAN PANEL	HNS	"CD4+CD8 TOTAL, CD3+%"	215
-LABRITUX	RITUXAN PANEL	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	215
-LABRITUX	RITUXAN PANEL	HSRATIO	"CD4/CD8 RATIO, CD3+"	215
-LABRITUX	RITUXAN PANEL	LYM	LYMPHOCYTES %	215
-LABRITUX	RITUXAN PANEL	LYMAB	"LYMPHOCYTES, ABS."	215
-LABRITUX	RITUXAN PANEL	RTXFDA	NOTE	215
-LABRITUX	RITUXAN PANEL	WBC	WBC COUNT	215
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPDI	DISCLAIMER	213
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPDX	CLINICAL INDICATION	213
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPMT	METHOD	213
-LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	QTFNIN	QUANTIFERON INTERPRETATION	212
-LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	QTFNRS	QUANTIFERON-TB	212
-LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UALB	"ALBUMIN, URINE"	212
-LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UALBCR	ALB/CREAT RATIO	212
-LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UALBCRINTP	ALB/CREAT INTERP	212
-LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	212
-LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	ACAIGG	ACA IGG ANTIBODIES	211
-LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	ACAIGM	ACA IGM ANTIBODIES	211
-LABKOHP	KOH / CALCOFLUOR	KOH	KOH PREP	211
-LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	LUPINT	LUPUS INTERPRETATION	211
-LABKOHP	KOH / CALCOFLUOR	RPT	RPT	211
-LABKOHP	KOH / CALCOFLUOR	SDES	SPECIMEN	211
-LABKOHP	KOH / CALCOFLUOR	SREQ	COMMENTS	211
-10335R	HLA - SCREENING C1Q	9277R	CPRA FOR C1Q CLASS II	210
-10335R	HLA - SCREENING C1Q	9278R	HLA ANTIBODIES FOR C1Q CLASS II	210
-10335R	HLA - SCREENING C1Q	9281R	COMPOSED TEXT CLASS II	210
-10335R	HLA - SCREENING C1Q	9652R	MFI RESULTS FOR SCREENING C1Q CLASS II	210
-LABSSAABY	ANTI-RO ANTIBODY	SSAIN	ANTI-RO INTERPRETATION	210
-LABSSAABY	ANTI-RO ANTIBODY	SSAQUL	ANTI-RO RESULT	210
-LABYTESFT	"TESTOSTERONE, TOTAL AND FREE"	TESTOSFREE	"TESTOSTERONE, FREE"	210
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPRE	RESULT	209
-LABHHV6QT	HHV-6 PCR QUANT	HHV6CP	RESULT	209
-LABHHV6QT	HHV-6 PCR QUANT	HHV6DN	HHV-6 DNA	209
-LABHHV6QT	HHV-6 PCR QUANT	HHV6LO	RESULT LOG 10	209
-LABHHV6QT	HHV-6 PCR QUANT	HHV6SP	SPECIMEN	209
-LABHHV6QT	HHV-6 PCR QUANT	HHV6TI	TEST DESCRIPTION	209
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITAA	AMINO ACID CHANGE	209
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITCO	COMMENT	209
-LABSTDY1	STUDY TEST	LABS1	LABS1	209
-LABSTDY1	STUDY TEST	RESS1	RESS1	209
-LABSHIV12	RAPID HIV-1/2 ANTIBODY	SHIV12	RAPID HIV-1/2 ANTIBODY	209
-LABSTDY1	STUDY TEST	SPTYS1	SPTYS1	209
-LABSTDY1	STUDY TEST	TESTS1	TESTS1	209
-10150R	HLA ORDER - SUMMARIES	10049R	HLA LAB TESTING STATUS	208
-LABIGA	"IGA, SERUM"	IGA	"IGA, SERUM"	208
-10150R	HLA ORDER - SUMMARIES	RINFO	TO FIND RESULT:	208
-LABSTREP	BETA STREP THROAT SCREEN RAPID EIA	BSTREP	BETA STREP THROAT EIA SCREEN	207
-LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITMD	INTERPRETED BY	207
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	PRO	PRODUCT ID  	207
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	SI	<<STATUS>>  	207
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	UBT	UNIT BLOOD TYPE  	207
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	UNT	UNIT NUMBER  	207
-10330R	HLA - SCREENING IGG	10469R	DSA FOR IGG CLASS I	206
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	CCNT	CELLS COUNTED	206
-LABHSVCSF	"HSV DNA PCR QUAL, CSF"	HSV1PC	HSV-1 DNA	206
-LABHSVCSF	"HSV DNA PCR QUAL, CSF"	HSV2PC	HSV-2 DNA	206
-LABHSVCSF	"HSV DNA PCR QUAL, CSF"	HSVINC	TEST DESCRIPTION	206
-LABHSVCSF	"HSV DNA PCR QUAL, CSF"	SDES11	SPECIMEN	206
-LABSSBABY	ANTI-LA ANTIBODY	SSBINT	ANTI-LA INTERPRETATION	206
-LABSSBABY	ANTI-LA ANTIBODY	SSBQUL	ANTI-LA RESULT	206
-10330R	HLA - SCREENING IGG	10470R	DSA FOR IGG CLASS II	205
-LABFADA	"ADENOSINE DEAMINASE, FLUID"	ADAFLD	"ADENOSINE DEAMIN., FLD"	205
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	DI	ISSUE DATE/TIME	205
-LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	PL24	"PLASMA, 24HR"	205
-LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	PL72	"PLASMA, 72HR"	205
-LABFADA	"ADENOSINE DEAMINASE, FLUID"	SPTYAD	FLUID TYPE	205
-LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	SR24	"SERUM, 24HR"	205
-LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	SR72	"SERUM, 72HR"	205
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	XMR	UNIT CROSSMATCH  	204
-LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVNO	NOTES	202
-LABB2M	"BETA - 2 - MICROGLOBULIN, SERUM"	B2M	"BETA-2-MICROGLOBULIN, SERUM"	201
-LABVSTLC	VIRAL STOOL CULTURE	CULT	CULT/OTHER RSLT:	201
-LABVSTLC	VIRAL STOOL CULTURE	RPT	RPT	201
-LABVSTLC	VIRAL STOOL CULTURE	SDES	SPECIMEN	201
-LABVSTLC	VIRAL STOOL CULTURE	SREQ	COMMENTS	201
-LABYFVPA	VALPROIC ACID FREE AND TOTAL	VPAFREE	"VALPROIC ACID, FREE"	201
-LABYFVPA	VALPROIC ACID FREE AND TOTAL	VPATOTAL	"VALPROIC ACID, TOTAL"	201
-LABPCEG7O	"ISTAT EG7, OTHER"	BD	"BASE DEFICIT, ISTAT"	200
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPAA	AMINO ACID CHANGE	199
-POC6	POC PPD SKIN TEST (IP)	ERYTHREMA	ERYTHREMA	198
-POC6	POC PPD SKIN TEST (IP)	MMIND	MM INDURATION	198
-LABYFPHEN	"PHENYTOIN TOTAL AND FREE, PLASMA"	PHENFR	"PHENYTOIN, FREE PLASMA"	198
-LABYFPHEN	"PHENYTOIN TOTAL AND FREE, PLASMA"	PHENTOT	"PHENYTOIN, TOTAL PLASMA"	198
-POC6	POC PPD SKIN TEST (IP)	PHLOC	LOCATION	198
-LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	YPARV1	PARVOVIRUS B19 AB IGG	198
-LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	YPARV2	PARVOVIRUS B19 AB IGM	198
-LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	YPARV3	INTERPRETATION	198
-LABAMLP	AML PROGNOSIS ASSAY	FLT3DS	DISCLAIMER	196
-LABAMLP	AML PROGNOSIS ASSAY	FLT3DX	CLINICAL INDICATION	196
-LABAMLP	AML PROGNOSIS ASSAY	FLT3IN	INTERPRETATION	196
-LABAMLP	AML PROGNOSIS ASSAY	FLT3MT	METHOD	196
-LABAMLP	AML PROGNOSIS ASSAY	FLT3RS	RESULT	196
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPCM	COMMENT	195
-LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPMD	INTERPRETATION	195
-LABPLTMAP	PLATELET MAPPING	ADP	ADP	194
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	SPEXP	SPECIMEN EXPIRATION DATE/TIME	194
-LABCSTOTA	"PROTEIN C&S, TOTAL"	PROTCAGT	"PROTEIN C AG, TOTAL"	193
-LABCSTOTA	"PROTEIN C&S, TOTAL"	PROTSTOT	"PROTEIN S AG, TOTAL"	193
-LAB12762R	TACROLIMUS	12763R	DATE OF LAST DOSE	192
-LAB12762R	TACROLIMUS	12764R	TIME OF LAST DOSE	192
-LAB12762R	TACROLIMUS	12765R	"DOSE, MG"	192
-LAB12762R	TACROLIMUS	12766R	TACROLIMUS	192
-LABAMLP	AML PROGNOSIS ASSAY	FLT3MD	INTERPRETED BY	192
-LABPLTMAP	PLATELET MAPPING	ARATEG	ARACHIDONIC ACID	190
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	BASOCSF	"BASOPHILS, CSF"	190
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	PRO	PRODUCT ID  	190
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	SI	<<STATUS>>  	190
-LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKCPU	RESULT	189
-LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKDNAU	BKV DNA	189
-LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKIPRU	TEST DESCRIPTION	189
-LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKLOGU	RESULT LOG 10	189
-LABYVITA	VITAMIN A	FREEVITA	FREE RETINOL(VIT A)	189
-LABFTRIGA	"TRIGLYCERIDES, NON-PLEURAL"	TGLFLD	"TRIGLYCERIDES, BODY FLUID"	189
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	CECAST	CELLULAR CASTS	188
-LABPTINH	PT INHIBITOR SCREEN	PTINTP	INTERPRETATION	188
-LABMONEBV	MONO SCREEN WITH REFLEX EBV AB	EBV	EBV TITER REFLEXED?	187
-LABMONEBV	MONO SCREEN WITH REFLEX EBV AB	MONOSC	INFECTIOUS MONO SCREEN	187
-10330R	HLA - SCREENING IGG	9259R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS II	186
-LABYHGBP	"HEMOGLOBIN, PLASMA"	YHGBP1	TOTAL HEMOGLOBIN	186
-LABYHGBP	"HEMOGLOBIN, PLASMA"	12078R	OXYHEMOGLOBIN	185
-10290R	HLA - TYPING	9020R	HLA-BW ALLELE 2	185
-LABADVDNA	"ADENOVIRUS DNA, QUALITATIVE PCR"	ADOV	"ADENOVIRUS DNA, QL PCR"	185
-LABADVDNA	"ADENOVIRUS DNA, QUALITATIVE PCR"	SOURC3	ADENOVIRUS SOURCE	185
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	DI	ISSUE DATE/TIME	184
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SPERM	SPERMATOZOA	184
-LAB11070R	ALDOSTERONE	ALDOSTR	"ALDOSTERONE, SERUM"	183
-LABSTPCR	THROAT STREP SCREEN BY PCR	CULT	CULT/OTHER RSLT:	183
-LABHIVQ2	HIV QUANTITATIVE PCR	HIVCP	RESULT	183
-LABHIVQ2	HIV QUANTITATIVE PCR	HIVIPR	TEST DESCRIPTION	183
-LABHIVQ2	HIV QUANTITATIVE PCR	HIVLO	RESULT LOG 10	183
-LABHIVQ2	HIV QUANTITATIVE PCR	HIVRNA	HIV 1 RNA	183
-LABHIVQ2	HIV QUANTITATIVE PCR	HIVSP	SPECIMEN	183
-LABSTPCR	THROAT STREP SCREEN BY PCR	RPT	RPT	183
-LABSTPCR	THROAT STREP SCREEN BY PCR	SDES	SPECIMEN	183
-LABSTPCR	THROAT STREP SCREEN BY PCR	SREQ	COMMENTS	183
-LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HIPLAB	LAB DATA	182
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	UBT	UNIT BLOOD TYPE  	182
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	UNT	UNIT NUMBER  	182
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	FRFAT	FREE FAT	181
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	OVFAT	OVAL FAT BODIES	180
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	SPEXP	SPECIMEN EXPIRATION DATE/TIME	180
-LABPLTMAP	PLATELET MAPPING	TEGMD	INTERP BY:	180
-10005R	HLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)	10049R	HLA LAB TESTING STATUS	179
-10005R	HLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)	RINFO	TO FIND RESULT:	179
-LABANTITG	ANTI - THYROGLOBULIN AB	ATGA	ANTI-THYROGLOBULIN AB	178
-LABFLL	FSH	FSH	FSH	178
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRDI	DISCLAIMER	178
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRDX	CLINICAL INDICATION	178
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRME	METHOD	178
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRPR	PRIMER	178
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRRE	RESULT	178
-LAB12571R	"FLT3-ITD, FLT3-D835 MUTATIONS"	12572R	"FLT3-ITD, FLT3-D835 MUTATIONS"	177
-LABPLTFUN	PLATELET FUNCTION SCREEN	COLADP	COLLAGEN/ADP	177
-LABPLTFUN	PLATELET FUNCTION SCREEN	COLEPI	COLLAGEN/EPI	177
-LABPLTFUN	PLATELET FUNCTION SCREEN	PLTFCM	"COMMENT, PLT FUNCTION"	177
-LABPTINH	PT INHIBITOR SCREEN	PTDIR	INTERPRETED BY	177
-LABYHABQ	HEP B SURF AB QUANT	HBSAB	"HBS ANTIBODY, S"	175
-LABYHABQ	HEP B SURF AB QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	175
-10330R	HLA - SCREENING IGG	9256R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS I	174
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	PRO	PRODUCT ID  	173
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	SI	<<STATUS>>  	173
-LAB11118R	"ELASTASE, STOOL"	STELAS	"ELASTASE-1, STOOL"	173
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	XMR	UNIT CROSSMATCH  	173
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	172
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	172
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	172
-10335R	HLA - SCREENING C1Q	10473R	DSA FOR C1Q CLASS II	171
-LABKWB	"POTASSIUM, WHOLE BLOOD"	K	"POTASSIUM, WHOLE BLD"	171
-LABYZINCS	ZINC	ZNC	"ZINC, BLOOD"	170
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	169
-10365R	HLA - DSA BY IGG	9407R	CUMULATIVE ANTIBODIES BY IGG	168
-10365R	HLA - DSA BY IGG	9408R	CPRA FOR CUMULATIVE IGG	168
-10365R	HLA - DSA BY IGG	9424R	CPRA FOR IGG CLASS II FOR CURRENT	168
-10365R	HLA - DSA BY IGG	9425R	HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	168
-10365R	HLA - DSA BY IGG	9434R	COMPOSED TEXT CLASS II	168
-10365R	HLA - DSA BY IGG	9435R	DISCLAIMER	168
-10365R	HLA - DSA BY IGG	9609R	METHOD	168
-10365R	HLA - DSA BY IGG	9626R	CURRENT SERUM DATE	168
-10365R	HLA - DSA BY IGG	9656R	MFI RESULTS FOR DSA BY IGG CLASS II	168
-LABHIVQ2	HIV QUANTITATIVE PCR	HIVNO	NOTES	168
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	UBT	UNIT BLOOD TYPE  	168
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	UNT	UNIT NUMBER  	168
-10365R	HLA - DSA BY IGG	9368R	MFI RESULTS FOR DSA BY IGG CLASS I	166
-10365R	HLA - DSA BY IGG	9411R	CPRA FOR IGG CLASS I FOR CURRENT	166
-10365R	HLA - DSA BY IGG	9412R	HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	166
-10365R	HLA - DSA BY IGG	9421R	COMPOSED TEXT CLASS I	166
-LABIGM	"IGM, SERUM"	IGM	"IGM, SERUM"	166
-LABFMYAB	MYCOPLASMA IGG/IGM AB	LTROIN	INTERPRETATION	166
-LAB11092R	RENIN	RENIN	"RENIN ACTIVITY, PL"	166
-LABRNPABY	ANTI-RNP ANTIBODY	RNPINT	ANTI-RNP INTERPRETATION	166
-LABRNPABY	ANTI-RNP ANTIBODY	RNPQUL	ANTI-RNP RESULT	166
-10370R	HLA - DSA BY C1Q	9436R	CUMULATIVE ANTIBODIES BY C1Q	165
-10370R	HLA - DSA BY C1Q	9437R	CPRA FOR CUMULATIVE C1Q	165
-10370R	HLA - DSA BY C1Q	9466R	DISCLAIMER	165
-10370R	HLA - DSA BY C1Q	9610R	METHOD	165
-10370R	HLA - DSA BY C1Q	9631R	SERUM DATE	165
-LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPNO	NOTE	165
-LABUA	URINALYSIS WITH MICROSCOPIC	OTCAST	"CASTS, OTHER"	165
-LABPCEG7O	"ISTAT EG7, OTHER"	PCTEMP	BODY TEMPERATURE	165
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	PRO	PRODUCT ID  	165
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	SI	<<STATUS>>  	165
-10370R	HLA - DSA BY C1Q	9454R	CPRA FOR C1Q CLASS II FOR CURRENT	164
-10370R	HLA - DSA BY C1Q	9455R	HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	164
-10370R	HLA - DSA BY C1Q	9465R	COMPOSED TEXT CLASS II	164
-10370R	HLA - DSA BY C1Q	9657R	MFI RESULTS FOR DSA BY C1Q CLASS II	164
-LABASI	AB SCREEN (ASI)	AS3	ANTIBODY SCREEN	164
-LABSCLABY	ANTI-SCL70 ANTIBODY	SCLINT	ANTI-SCL70 INTERPRETATION	164
-LABSCLABY	ANTI-SCL70 ANTIBODY	SCLQUL	ANTI-SCL70 RESULT	164
-LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	HSV1PC	HSV-1 DNA	163
-LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	HSV2PC	HSV-2 DNA	163
-LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	HSVINP	TEST DESCRIPTION	163
-LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	SDES11	SPECIMEN	163
-10370R	HLA - DSA BY C1Q	9369R	MFI RESULTS FOR DSA BY C1Q CLASS I	162
-10370R	HLA - DSA BY C1Q	9440R	CPRA FOR C1Q CLASS I FOR CURRENT	162
-10370R	HLA - DSA BY C1Q	9441R	HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	162
-10370R	HLA - DSA BY C1Q	9451R	COMPOSED TEXT CLASS I	162
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	DI	ISSUE DATE/TIME	162
-LABSTORE	SPECIMEN STORAGE ONLY	STORE	SPECIMEN STORAGE ONLY	162
-LAB11081R	GASTRIN	GASTRIN	"GASTRIN, SERUM"	161
-LABHSAB	HSV VIRAL AB	HSAB	VIRAL AB HSV	160
-LABROPK	ISOSPORA/CYCLOSPORA SCREEN	CULT	CULT/OTHER RSLT:	159
-LABYSTOSM	"OSMOLALITY, STOOL"	OSMOLSTL	OSMOLALITY STOOL	159
-LABROPK	ISOSPORA/CYCLOSPORA SCREEN	RPT	RPT	159
-LABROPK	ISOSPORA/CYCLOSPORA SCREEN	SDES	SPECIMEN	159
-LABROPK	ISOSPORA/CYCLOSPORA SCREEN	SREQ	COMMENTS	159
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	XMR	UNIT CROSSMATCH  	159
-LABFHCT	"HEMATOCRIT, FLUID"	HCTFLD	"HEMATOCRIT, FLUID"	158
-LABFHCT	"HEMATOCRIT, FLUID"	HCTTYP	SOURCE	158
-LABCBCD	CBC WITH DIFF	IMMPL	IMMAT. PLASMA CELLS	158
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	XMR	UNIT CROSSMATCH  	157
-LABYFFATQ	"FAT, FECAL QUANTITATIVE"	YFFQ1	TOTAL WEIGHT	156
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBDI	DISCLAIMER	155
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBDX	CLINICAL INDICATION	155
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBMT	METHOD	155
-LAB11036R	"FIBRINOGEN, QUANT"	FIBRINOGENA	FIBRINOGEN ANTIGEN	155
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	PRO	PRODUCT ID  	155
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	SI	<<STATUS>>  	155
-LABYFFATQ	"FAT, FECAL QUANTITATIVE"	YFFQ2	DURATION	155
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBRE	RESULT	154
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRDR	INTERPRETED BY	154
-LABTBP1	TRANSFUSION BLOOD PRODUCT 2	VL	PRODUCT VOLUME	154
-LABYCH50	COMPLEMENT TOTAL	YCH50S	"COMPLEMENT, TOTAL"	154
-LABSTFAT	"FAT STAIN, STOOL"	STFAT	"FAT STAIN, STOOL"	153
-LABTOXGM	TOXOPLASMA IGG IGM	TOXDES	TEST DESCRIPTION	153
-LABTOXGM	TOXOPLASMA IGG IGM	TOXGQL	IGG RESULT	153
-LABYVDRLC	"VDRL, CSF"	VDRL	VDRL	153
-LABB2GP1	BETA - 2 - GLYCOPROTEIN 1	B2GPIG	"BETA-2 GLYCOPROTEIN 1, IGG"	152
-LABB2GP1	BETA - 2 - GLYCOPROTEIN 1	B2GPIM	"BETA-2 GLYCOPROTEIN 1, IGM"	152
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	DI	ISSUE DATE/TIME	152
-LABSTRED	"REDUCING SUBSTANCE, STOOL"	REDSUBSTL	"REDUCING SUBST, STOOL"	152
-LABTOXGM	TOXOPLASMA IGG IGM	TOXMQL	IGM RESULT	152
-LAB11056R	FREE CORTISOL	CORTFREE	"CORTISOL, SERUM FREE"	151
-LABMALAR	MALARIA PREP	MALCON	MALCON	151
-LABHSCRP	HIGH SENSITIVITY C-REACTIVE PROTEIN	HSCRP	HIGH SENSITIVITY CRP	150
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	150
-LABLYMES2	"LYME SCREEN, SERUM"	LYMEC6	B.BURGDORFERI C6 PEPTIDE	150
-LAB11013R	PLATELET ANTIBODY ID PANEL	11014R	ANTIGEN CAPTURE	148
-LAB11982R	POSACONAZOLE	11983R	POSACONAZOLE	148
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBAA	AMINO ACID CHANGE	148
-LABCHOL	"CHOLESTEROL TOTAL, SERUM/PLASMA"	CHOL	"CHOLESTEROL, TOTAL"	148
-LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVIN	DRVV INTERPRETATION	148
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBMD	INTERPRETATION	147
-LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVCN	DRVV CONFIRMATION	147
-LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVSC	DRVV SCREEN	147
-LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	LUPDIR	INTERPRETED BY	147
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	PRO	PRODUCT ID  	147
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRCM	COMMENT	147
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	SI	<<STATUS>>  	147
-LABYBRUC	BRUCELLA IGG AND IGM AB	YBRUC1	"BRUCELLA AB ,IGG"	147
-LABYBRUC	BRUCELLA IGG AND IGM AB	YBRUC2	"BRUCELLA AB ,IGM"	147
-LABYBRUC	BRUCELLA IGG AND IGM AB	YBRUC3	INTERPRETATION	147
-10335R	HLA - SCREENING C1Q	9276R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS I	146
-LABYFFATQ	"FAT, FECAL QUANTITATIVE"	FATPC	%FAT	146
-LABYUMYO	"MYOGLOBIN, URINE"	UMYOG	"MYOGLOBIN, URINE"	146
-10370R	HLA - DSA BY C1Q	9423R	DSA FOR C1Q CLASS II	145
-LABRP	RENAL FUNCTION PANEL	AG	ANION GAP	145
-LABRP	RENAL FUNCTION PANEL	ALB	"ALBUMIN, SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	BUN	"UREA NITROGEN,SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	CA	"CALCIUM, SER/PLAS"	145
-LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBCM	COMMENT	145
-LABRP	RENAL FUNCTION PANEL	CL	"CHLORIDE, SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	CO2	"CO2, SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	CR	"CREATININE, SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	GLU	"GLUCOSE, SER/PLAS"	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HSVDS	HSV PANEL TEST DESCRIPTION	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV1QNT	HSV-1 IGG LEVEL	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV1QUL	HSV-1 IGG RESULT	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV2QNT	HSV-2 IGG LEVEL	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV2QUL	HSV-2 IGG RESULT	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HVMQNT	HSV IGM LEVEL	145
-LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HVMQUL	HSV IGM RESULT	145
-LABRP	RENAL FUNCTION PANEL	K	"POTASSIUM, SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	NA	"SODIUM, SER/PLAS"	145
-LABRP	RENAL FUNCTION PANEL	PHOS	"PHOSPHORUS, SER/PLAS"	145
-LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVLA	DRVV NORMALIZED RATIO	144
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	UBT	UNIT BLOOD TYPE  	144
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	UNT	UNIT NUMBER  	144
-10365R	HLA - DSA BY IGG	9415R	DSA FOR IGG CLASS II	143
-LABHA1C	HGB A1C W/ EST MEAN GLUCOSE	A1C	HEMOGLOBIN A1C	143
-LABHA1C	HGB A1C W/ EST MEAN GLUCOSE	EMGL	EST. MEAN GLUCOSE	143
-LABFACT9	FACTOR IX ASSAY	FACTOR9	FACTOR IX ASSAY	142
-LABYENPCR	ENTEROVIRUS PCR	YENP1	SPECIMEN SOURCE	142
-LABYENPCR	ENTEROVIRUS PCR	YENP2	"ENTEROVIRUS ,PCR"	142
-LABYENPCR	ENTEROVIRUS PCR	YENP4	REPORT STATUS	142
-10045R	HLA ORDER - ANTIBODY SCREENING	10049R	HLA LAB TESTING STATUS	141
-LABSMABY	ANTI-SM ANTIBODY	ASMINT	ANTI-SM INTERPRETATION	141
-LABSMABY	ANTI-SM ANTIBODY	ASMQUL	ANTI-SM RESULT	141
-LABATTG	IGA ANTI  TTG	ATTGR	IGA ANTI TTG ANTIBODY	141
-LABATTG	IGA ANTI  TTG	ATTINT	INTERPRETATION	141
-LABEPO	ERYTHROPOIETIN	EPO	ERYTHROPOIETIN	141
-10045R	HLA ORDER - ANTIBODY SCREENING	RINFO	TO FIND RESULT:	141
-10035R	HLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)	10049R	HLA LAB TESTING STATUS	140
-LABFQFVR	COXIELLA BURNETII AB	12008R	Q FEVER IGG PHASE I SCRN	140
-LABFQFVR	COXIELLA BURNETII AB	12009R	Q FEVER IGM PHASE I SCRN	140
-LABFQFVR	COXIELLA BURNETII AB	12010R	Q FEVER IGG PHASE II SCRN	140
-LABFQFVR	COXIELLA BURNETII AB	12011R	Q FEVER IGM PHASE II  SCRN	140
-LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	LUPDIR	INTERPRETED BY	140
-10035R	HLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)	RINFO	TO FIND RESULT:	140
-LABUK	"POTASSIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	139
-LABUK	"POTASSIUM WITH CREATININE, URINE (RANDOM)"	UK	"POTASSIUM, URINE"	139
-LABUK	"POTASSIUM WITH CREATININE, URINE (RANDOM)"	UKCR	POTASSIUM/CREAT RATIO	139
-10365R	HLA - DSA BY IGG	9414R	DSA FOR IGG CLASS I	138
-10370R	HLA - DSA BY C1Q	9422R	DSA FOR C1Q CLASS I	138
-LABSPE	PROTEIN ELECTROPHORESIS	ALB	ALBUMIN	138
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	DI	ISSUE DATE/TIME	138
-LABCSFLD	"LDH, CSF"	LDHCSF	"LDH, CSF"	138
-LABSPE	PROTEIN ELECTROPHORESIS	SPA1	ALPHA 1 ZONE	138
-LABSPE	PROTEIN ELECTROPHORESIS	SPA2	ALPHA 2 ZONE	138
-LABSPE	PROTEIN ELECTROPHORESIS	SPBE1	BETA 1 ZONE	138
-LABSPE	PROTEIN ELECTROPHORESIS	SPBE2	BETA 2 ZONE	138
-LABSPE	PROTEIN ELECTROPHORESIS	SPEI	INTERPRETATION	138
-LABSPE	PROTEIN ELECTROPHORESIS	SPGA	GAMMA ZONE	138
-LABSPE	PROTEIN ELECTROPHORESIS	TPE	TOTAL PROTEIN	138
-LABTHS	GROUP A STREP CULTURE	CULT	CULT/OTHER RSLT:	137
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	OCCPH	PATIENT OCCULAR PH	137
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC4	QC 4 RESULT	137
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC7	QC 7 RESULT	137
-LABTHS	GROUP A STREP CULTURE	RPT	RPT	137
-LABTHS	GROUP A STREP CULTURE	SDES	SPECIMEN	137
-LABTHS	GROUP A STREP CULTURE	SREQ	COMMENTS	137
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	TESTEXP	TEST STRIP EXP DATE	137
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	TESTLOT	TEST STRIP LOT#	137
-LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTD	DISCLAIMER	136
-LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTI	"INTERPRETATION, BM"	136
-LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTM	METHOD	136
-LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTR	"RESULT, BM"	136
-LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTX	CLINICAL INDICATION	136
-LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	DVE	RESULTS	136
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	EOSFLD	"EOSINOPHILS, SYN FLD"	136
-LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	RPT	RPT	136
-LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SDES	SPECIMEN	136
-LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SREQ	COMMENTS	136
-LABRITUX	RITUXAN PANEL	C1613AB	"CD16+/CD13-, ABS"	135
-LABRITUX	RITUXAN PANEL	C1613PC	CD16+/CD13- %	135
-LABRITUX	RITUXAN PANEL	C563AB	"CD56+/CD3-, ABS"	135
-LABRITUX	RITUXAN PANEL	C563PC	CD56+/CD3- %	135
-LABVZVQLP	"VZV DNA PCR QUAL, PLASMA"	VZQLDN	VZV DNA	135
-LABVZVQLP	"VZV DNA PCR QUAL, PLASMA"	VZQLSP	SPECIMEN	135
-LABVZVQLP	"VZV DNA PCR QUAL, PLASMA"	VZQLTD	TEST DESCRIPTION	135
-LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKCPP	RESULT	134
-LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKDNAP	BKV DNA	134
-LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKIPRP	TEST DESCRIPTION	134
-LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKLOGP	RESULT LOG 10	134
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSCIM	CLINICAL INDICATION	134
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSDIM	DISCLAIMER	134
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSMEM	METHOD	134
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSREFM	REFERENCES	134
-LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVMX	DRVV MIX	134
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GAPCM	"APC, BM"	134
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKDI	DISCLAIMER	134
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKDX	CLINICAL INDICATION	134
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKMD	INTERPRETED BY	134
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKME	METHOD	134
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKRE	RESULT	134
-LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	UMET	"METANEPHRINE, URINE"	134
-LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	UMET	"TOT.METANEPHRINES,UR"	134
-LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	UNORMET	"NORMETANEPHRINE, UR"	134
-LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	VOLUMEURN	URINE VOLUME	134
-LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	YUMEUT	COLLECTION DURATION	134
-LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTB	INTERPRETED BY	133
-LABROMSP	MICROSPORIDIUM SCREEN	CULT	CULT/OTHER RSLT:	133
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	PRO	PRODUCT ID  	133
-LABROMSP	MICROSPORIDIUM SCREEN	RPT	RPT	133
-LABROMSP	MICROSPORIDIUM SCREEN	SDES	SPECIMEN	133
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	SI	<<STATUS>>  	133
-LABROMSP	MICROSPORIDIUM SCREEN	SREQ	COMMENTS	133
-LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGPRES	HGBS PRESENT	132
-LABUA	URINALYSIS WITH MICROSCOPIC	OTHCRY	"CRYSTALS, OTHER"	132
-LAB11085R	MYELOPEROXIDASE ANTIBODY	MPO	MYELOPEROXIDASE AB	131
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	UBT	UNIT BLOOD TYPE  	131
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	UNT	UNIT NUMBER  	131
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	XMR	UNIT CROSSMATCH  	131
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	XMR	UNIT CROSSMATCH  	131
-LABYCHRA	CHROMOGRANIN A	YCHRA1	CHROMOGRANIN A	130
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSCIB	CLINICAL INDICATION	129
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSDIB	DISCLAIMER	129
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSMEB	METHOD	129
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSREFB	REFERENCES	129
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	DI	ISSUE DATE/TIME	129
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GAPCB	"APC, BLOOD"	129
-LABFBILA	"BILIRUBIN TOTAL, FLUID"	BILIRUBINFLD	"BILIRUBIN, TOTAL, BODY FLUID"	128
-LAB11094R	RO AB	SSA	"SS-A/RO AB, IGG, SERUM"	128
-LABOXCMET	OXCARBAZEPINE METABOLITE LEVEL	OXCMT1	OXCARBAZEPINE METABOLITE	127
-LAB11102R	VITAMIN E	VITE	"A-TOCOPHEROL, VITAMIN E"	127
-LABBARSAB	BARTONELLA SPECIES AB	BHIGGS	B.HENSELAE IGG SCREEN	125
-LABBARSAB	BARTONELLA SPECIES AB	BHIGMS	B.HENSELAE IGM SCREEN	125
-LABBARSAB	BARTONELLA SPECIES AB	BQIGGS	B.QUINTANA IGG SCREEN	125
-LABBARSAB	BARTONELLA SPECIES AB	BQIGMS	B.QUINTANA IGM SCREEN	125
-POC2	POC URINE DIPSTICK FOR BLOOD	DIPLOT	DIPSTICK LOT	125
-POC2	POC URINE DIPSTICK FOR BLOOD	EXPDATE	EXP DATE	125
-LABHITIA	HIT FUNCTIONAL ASSAY	HITI1	HIT FUNCTIONAL ASSAY	125
-LABHITIA	HIT FUNCTIONAL ASSAY	HITI2	HIT COMMENT	125
-POC2	POC URINE DIPSTICK FOR BLOOD	PHLOC	LOCATION	125
-10280R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING	RINFO	TO FIND RESULT:	125
-POC2	POC URINE DIPSTICK FOR BLOOD	UBLOOD	BLOOD URINE	125
-LABVZVCSF	"VZV DNA PCR QUAL, CSF"	VZQLDN	VZV DNA	125
-LABVZVCSF	"VZV DNA PCR QUAL, CSF"	VZQLIC	TEST DESCRIPTION	125
-LABVZVCSF	"VZV DNA PCR QUAL, CSF"	VZQLSP	SPECIMEN	125
-10305R	HLA - ALLO T FLOW CROSSMATCH	9123R	DONOR NAME	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9125R	RELATIONSHIP TO PATIENT	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9126R	DONOR CELL DATE	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9127R	DONOR CELL SOURCE	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9128R	DONOR CELL TARGET	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9129R	METHOD	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9130R	SERUM DATE 1	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9132R	RESULT  MCS 1	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9134R	RESULT 1	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9151R	COMPOSED TEXT	124
-10305R	HLA - ALLO T FLOW CROSSMATCH	9152R	DISCLAIMER	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9153R	DONOR NAME	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9155R	RELATIONSHIP TO PATIENT	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9156R	DONOR CELL DATE	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9157R	DONOR CELL SOURCE	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9158R	DONOR CELL TARGET	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9159R	METHOD	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9160R	SERUM DATE 1	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9162R	RESULT  MCS 1	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9164R	RESULT 1	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9181R	INTERPRETATION/ COMMENTS	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9182R	COMPOSED TEXT	124
-10310R	HLA - ALLO B FLOW CROSSMATCH	9183R	DISCLAIMER	124
-LABHITIA	HIT FUNCTIONAL ASSAY	HIPDIR	HIT INTERPRETED BY	124
-LABYJCPCR	"JC VIRUS DNA, PCR"	JCDNA	JC VIRUS DNA	124
-LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME	PARVD	PARVOVIRUS DNA	124
-LABYJCPCR	"JC VIRUS DNA, PCR"	SOURC8	SOURCE	124
-LABYTESFT	"TESTOSTERONE, TOTAL AND FREE"	TESTOS	"TESTOSTERONE, TOTAL"	124
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNSO	STONE SOURCE	124
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNWT	STONE WEIGHT	124
-LABYSTONE	KIDNEY STONE ANALYSIS	1STCONST	1ST CONSTITUENT	123
-10305R	HLA - ALLO T FLOW CROSSMATCH	9124R	DONOR DOB	123
-10310R	HLA - ALLO B FLOW CROSSMATCH	9154R	DONOR DOB	123
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSMDM	INTERPRETED BY	123
-LABIGGS4	IGG  SUBCLASS 4	IGGS4	IGG SUBCLASS4	123
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKCO	COMMENT	123
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKPR	PRIMERS	123
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKRF	REFERENCES	123
-LABHITAB	HEPARIN PLATELET FACTOR 4 AB	PF4IGG	PF4 IGG ONLY OD	123
-LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETIC	"RETIC COUNT, MANUAL"	123
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	SPEXP	SPECIMEN EXPIRATION DATE/TIME	123
-LAB11556R	BLASTOMYCES SEROLOGIES	11666R	BLASTO AB ID	122
-LAB11556R	BLASTOMYCES SEROLOGIES	BLAS	BLASTO AB CFS	122
-LABCRPLFB	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD"	CRPLFB	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GBRAFM	"BRAF, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GCTNNM	"CTNNB1, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GEGFRM	"EGFR, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GIDH1M	"IDH1, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GIDH2M	"IDH2, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GKRASM	"KRAS, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GNOTCM	"NOTCH1, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GNRASM	"NRAS, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GPIK3M	"PIK3CA, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GPTENM	"PTEN, BM"	122
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GTP53M	"TP53, BM"	122
-LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBA2Q	"HGB A2, QUANT."	122
-LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBF	"HGB F, QUANT."	122
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	PRO	PRODUCT ID  	122
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	SI	<<STATUS>>  	122
-LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME	SOUR11	SOURCE	122
-LAB11095R	LA AB	SSB	"SS-B/LA AB, IGG, SERUM"	122
-LABYPR3	PROTEINASE 3 ANTIBODY	YPR31	PROTEINASE 3 ANTIBODIES	122
-LAB11107R	HBE ANTIGEN	HBEAG	HEPATITIS B E AG	121
-LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBEP2	INTERPRETATION	121
-LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBS	"HGB S, QUANT"	121
-LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGMD	REVIEWED BY	121
-LABTOXO	TOXOPLASMA SEROLOGY	TOXO	TOXOPLASMA IGG	121
-10365R	HLA - DSA BY IGG	9630R	IGG COMBINED CPRA FOR CURRENT SERUM	119
-LABCENABY	ANTI CENTROMERE ANTIBODY	CENINT	ANTI-CENTROMERE INTERPRETATION	119
-LABCENABY	ANTI CENTROMERE ANTIBODY	CENQUL	ANTI-CENTROMERE RESULT	119
-LABCRPLFC	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF"	CRPLFC	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF"	119
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FNRBC	"NRBC/100WBC, FLD"	119
-LABINSL	INSULIN UNSPECIFIED	INSU	"INSULIN,UNSPECIFIED"	119
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	UBT	UNIT BLOOD TYPE  	119
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	UNT	UNIT NUMBER  	119
-10305R	HLA - ALLO T FLOW CROSSMATCH	9707R	DONOR PHENOTYPE	118
-10310R	HLA - ALLO B FLOW CROSSMATCH	9708R	DONOR PHENOTYPE	118
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	OTCAST	"CASTS, OTHER"	118
-10145R	HLA ORDER - UNRELATED DONOR PROCESSING	10049R	HLA LAB TESTING STATUS	117
-LABYFFATQ	"FAT, FECAL QUANTITATIVE"	FATTOT	TOTAL FAT/24H	117
-LABFBILA	"BILIRUBIN TOTAL, FLUID"	FSC2	FLUID TYPE	117
-10145R	HLA ORDER - UNRELATED DONOR PROCESSING	RINFO	TO FIND RESULT:	117
-10145R	HLA ORDER - UNRELATED DONOR PROCESSING	UNRDID	UNRELATED DONOR ID	117
-10015R	HLA ORDER - DSA SCREENING	10049R	HLA LAB TESTING STATUS	116
-LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRCT	COMMENT	116
-LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRDS	DISCLAIMER	116
-LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRMT	METHOD	116
-LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRPM	PRIMERS	116
-LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRRS	RESULTS	116
-LABBUNCR	BUN / CREATININE	BUNCREAT	BUN/CREATININE	116
-10015R	HLA ORDER - DSA SCREENING	RINFO	TO FIND RESULT:	116
-LAB12808R	CK ISOENZYME WITH REFLEX	12819R	CK ISOENZYME ELEC SPECIMN ONLY	115
-LAB12808R	CK ISOENZYME WITH REFLEX	CK	CREATINE KINASE	115
-LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV1QNT	HSV-1 IGG LEVEL	115
-LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV1QUL	HSV-1 IGG RESULT	115
-LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV2QNT	HSV-2 IGG LEVEL	115
-LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV2QUL	HSV-2 IGG RESULT	115
-LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HVIGDS	HSV IGG TEST DESCRIPTION	115
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	DI	ISSUE DATE/TIME	114
-10100R	HLA ORDER - PLATELET COMPATIBILITY	10049R	HLA LAB TESTING STATUS	113
-LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRDR	INTERPRETED BY	113
-LABFHVIG	"HIV-1 AB, WESTERN BLOT"	HVIG	HIV 1 AB CONFIRMATION	113
-10100R	HLA ORDER - PLATELET COMPATIBILITY	RINFO	TO FIND RESULT:	113
-LABYTRYPT	"TRYPTASE, SERUM"	TRYPTASE	TRYPTASE	113
-LABYLEVT	LEVETIRACETAM	YLEVT1	LEVETIRACETAM	113
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSMDB	INTERPRETED BY	112
-LABIGF	INSULIN-LIKE GROWTH FACTOR 1	IGF	INSULIN-LIKE GROWTH FACTOR 1	112
-LABYLAMO	LAMOTRIGINE	LAMOT	LAMOTRIGINE	112
-LAB12214R	"B. BURGDORFERI C6 PEP AB,RFLX"	LYMEC6	B.BURGDORFERI C6 PEPTIDE	112
-LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETICAB	"RETIC, ABS, MANUAL"	112
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	SPEXP	SPECIMEN EXPIRATION DATE/TIME	112
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	UBT	UNIT BLOOD TYPE  	112
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	UNT	UNIT NUMBER  	112
-LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIKACINTR	"AMIKACIN, TROUGH"	111
-LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIPR2	DATE & TIME OF LAST DOSE	111
-LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIPR3	DOSAGE	111
-LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIPR4	ADMINISTRATION ROUTE	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD3PC	CD3 (PAN T) %	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD4AB	"CD3+/CD4+, ABS."	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD4PC	CD3+/CD4+ %	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD8AB	"CD3+/CD8+, ABS."	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD8PC	CD3+/CD8+ %	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GBRAFB	"BRAF, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GCTNNB	"CTNNB1, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GEGFRB	"EGFR, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GIDH1B	"IDH1, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GIDH2B	"IDH2, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GKRASB	"KRAS, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GNOTCB	"NOTCH1, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GNRASB	"NRAS, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GPIK3B	"PIK3CA, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GPTENB	"PTEN, BLOOD"	111
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GTP53B	"TP53, BLOOD"	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	LYM	LYMPHOCYTES %	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	111
-LABRF	RHEUMATOID FACTOR	RHFINT	INTERPRETATION	111
-LABRF	RHEUMATOID FACTOR	RHFR	RHEUMATOID FACTOR	111
-LABUNAT	"SODIUM, URINE (TIMED)"	TNAU	SODIUM EXCRETION	111
-LABUNAT	"SODIUM, URINE (TIMED)"	UNA	"SODIUM, URINE"	111
-LABUNAT	"SODIUM, URINE (TIMED)"	UT1	COLLECTION PERIOD SODIUM	111
-LABVANPOL	VANCOMYCIN PEAK LEVEL	VANCOPEAK	"VANCOMYCIN, PEAK"	111
-LABVANPOL	VANCOMYCIN PEAK LEVEL	VANPO2	DATE & TIME OF LAST DOSE	111
-LABVANPOL	VANCOMYCIN PEAK LEVEL	VANPO3	DOSAGE	111
-LABVANPOL	VANCOMYCIN PEAK LEVEL	VANPO4	ADMINISTRATION ROUTE	111
-LABUNAT	"SODIUM, URINE (TIMED)"	VOL1	VOLUME SODIUM	111
-LABC4C8	"CD4 / CD8 PANEL, BLOOD"	WBC	WBC COUNT	111
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	XMR	UNIT CROSSMATCH  	111
-LAB11013R	PLATELET ANTIBODY ID PANEL	11015R	MODIFIED ANTIGEN CAPTURE	110
-LAB11013R	PLATELET ANTIBODY ID PANEL	11016R	PLATELET ANTIBODY SCREEN	110
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	ABNWBC	BLASTS/ABNORMAL CELLS	110
-LABTOXGM	TOXOPLASMA IGG IGM	TOXGQT	IGG LEVEL	110
-LABYSTONE	KIDNEY STONE ANALYSIS	2NDCONST	2ND CONSTITUENT	109
-LABFBILA	"BILIRUBIN, TOTAL BODY FLUID"	BILIRUBINFLD	"BILIRUBIN, TOTAL, BODY FLUID"	109
-LABSTLCP	STOOL CULTURE	CULT	CULT/OTHER RSLT:	109
-LABFBILA	"BILIRUBIN, TOTAL BODY FLUID"	FSC2	FLUID TYPE	109
-LAB11106R	HBE AB	HBEAB	HEPATITIS B E AB	109
-LABREF2	LAB UNLISTED 2	LAB2	REFERENCE LAB	109
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	PRO	PRODUCT ID  	109
-LABREF2	LAB UNLISTED 2	RESUL2	RESULT	109
-LABSTLCP	STOOL CULTURE	RPT	RPT	109
-LABSTLCP	STOOL CULTURE	SDES	SPECIMEN	109
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	SI	<<STATUS>>  	109
-LABREF2	LAB UNLISTED 2	SPTY2	SPECIMEN TYPE	109
-LABSTLCP	STOOL CULTURE	SREQ	COMMENTS	109
-LABREF2	LAB UNLISTED 2	TEST2	TEST NAME	109
-LABCRFA	CRYPTOSPORIDIUM DFA	CULT	CULT/OTHER RSLT:	108
-LABCRFA	CRYPTOSPORIDIUM DFA	RPT	RPT	108
-LABCRFA	CRYPTOSPORIDIUM DFA	SDES	SPECIMEN	108
-LABCRFA	CRYPTOSPORIDIUM DFA	SREQ	COMMENTS	108
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	XMR	UNIT CROSSMATCH  	107
-LAB11343R	SRA UNFRACTIONATED HEPARIN	11344R	SRA UNFRACTIONATED HEPARIN	106
-10390R	HLA-BMT DONOR MATCH DETAIL SUMMARY	9663R	DONOR MATCH GRADE DETAIL	106
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	OTHCRY	"CRYSTALS, OTHER"	106
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	PRO	PRODUCT ID  	106
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	SI	<<STATUS>>  	106
-LABTOXIG	TOXOPLASMA IGG	TOXGDS	TEST DESCRIPTION	106
-LABTOXIG	TOXOPLASMA IGG	TOXGQL	IGG RESULT	106
-LABYBSAP	"BONE ALKALINE PHOSPHATASE, SERUM"	ALKPO4BONE	BONE ALK. PHOS.	105
-LABFACT10	FACTOR X ASSAY	FACTOR10	FACTOR X ASSAY	105
-LABFBBURG	"LYME WESTERN BLOT, CSF"	BANDPRSNT	BANDS PRESENT	104
-LABCRCL	CREATININE CLEARANCE	CORCL	CORRECTED CR. CLEAR.	104
-LABCRCL	CREATININE CLEARANCE	COREXC	CORRECTED CR. EXC.	104
-LABCRCL	CREATININE CLEARANCE	CR	"CREATININE, SERUM"	104
-LABCRCL	CREATININE CLEARANCE	CRCL	CREATININE CLEARANCE	104
-LABCRCL	CREATININE CLEARANCE	CRE	CREATININE EXCRETION	104
-LABCRCL	CREATININE CLEARANCE	CRUCL	"CREATININE, URINE"	104
-LABCRCL	CREATININE CLEARANCE	FLOWURN	URINE FLOW	104
-LABCRCL	CREATININE CLEARANCE	HT	HEIGHT	104
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	LR210	LOG DX AVG	104
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	P210Q	P210	104
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	UBT	UNIT BLOOD TYPE  	104
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	UNT	UNIT NUMBER  	104
-LABCRCL	CREATININE CLEARANCE	UT3	COLLECTION PERIOD CREATININE	104
-LABCRCL	CREATININE CLEARANCE	VOL3	VOLUME CREATININE	104
-LABCRCL	CREATININE CLEARANCE	WT	WEIGHT	104
-LAB11145R	"ADULTERANTS SURVEY, URINE"	11146R	"CREATININE, URINE"	103
-LAB12238R	LACOSAMIDE	12239R	12239R	103
-LAB12238R	LACOSAMIDE	12240R	12240R	103
-LAB11145R	"ADULTERANTS SURVEY, URINE"	11147R	SPECIFIC GRAVITY	102
-LAB11145R	"ADULTERANTS SURVEY, URINE"	11148R	PH	102
-LAB11145R	"ADULTERANTS SURVEY, URINE"	11149R	OXIDANTS	102
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	DI	ISSUE DATE/TIME	102
-LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	HSV1PC	HSV-1 DNA	102
-LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	HSV2PC	HSV-2 DNA	102
-LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	HSVIN	TEST DESCRIPTION	102
-LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	SDES11	SPECIMEN	102
-LABABTITI	AB TITER	ABTITER	ANTIBODY TITER RESULTS	101
-LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GP1IGA	BETA-2-GP1 IGA	101
-LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GP1IGG	BETA-2-GP1 IGG	101
-LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GP1IGM	BETA-2-GP1 IGM	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMCO	COMMENT	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMDI	DISCLAIMER	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMDX	CLINICAL INDICATION	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMIN	INTERPRETATION	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMMD	INTERPRETED BY	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMME	METHOD	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMRE	RESULT	101
-LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMSO	SPECIMEN SOURCE	101
-LABTOXGM	TOXOPLASMA IGG IGM	TOXMQT	IGM LEVEL	101
-LABYSTNA	"SODIUM, STOOL"	YSTNA1	"SODIUM, 24-HR STOOL"	101
-10305R	HLA - ALLO T FLOW CROSSMATCH	9135R	SERUM DATE 2	100
-10305R	HLA - ALLO T FLOW CROSSMATCH	9137R	RESULT  MCS 2	100
-10305R	HLA - ALLO T FLOW CROSSMATCH	9139R	RESULT 2	100
-10310R	HLA - ALLO B FLOW CROSSMATCH	9165R	SERUM DATE 2	100
-10310R	HLA - ALLO B FLOW CROSSMATCH	9167R	RESULT  MCS 2	100
-10310R	HLA - ALLO B FLOW CROSSMATCH	9169R	RESULT 2	100
-10365R	HLA - DSA BY IGG	9433R	INTERPRETATION/ COMMENTS CLASS II	100
-LAB11096R	"ALPHA 1 ANTITRYPSIN, STOOL"	A1AANTITRYPF	"A-1-ANTITRYP,FECAL"	100
-LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GPCM	BETA-2-GP1 COMMENT	100
-LABSTOBLD	"OCCULT BLD, GUAIAC DIAG, SINGLE"	DATE1	DATE 1	100
-LABSTOBLD	"OCCULT BLD, GUAIAC DIAG, SINGLE"	OCCGU	"STOOL SAMPLE1,GUAIAC"	100
-LABYTOPIR	TOPIRAMATE	TOPIRAMATE	TOPIRAMATE	100
-LAB11097R	"TSH RECEPTOR AB, STIM"	TSIG	"THROID-STIMULATING IG, SERUM"	99
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	XMR	UNIT CROSSMATCH  	99
-LABYSTONE	KIDNEY STONE ANALYSIS	3RDCONST	3RD CONSTITUENT	98
-LABRIST	RISTOCETIN COFACTOR	RIST	RISTOCETIN CO-FACTOR	98
-LABYSTK	"POTASSIUM, STOOL"	YSTK1	COLLECTION DURATION	98
-LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	CULT	CULT/OTHER RSLT:	97
-LABFACT7	FACTOR VII ASSAY	FACTOR7	FACTOR VII ASSAY	97
-LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	RPT	RPT	97
-LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	SDES	SPECIMEN	97
-LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	SREQ	COMMENTS	97
-LABYSTFR	SOLUBLE TRANSFERRIN RECEPTOR	YSTFRR	SOL TRANSFERR REC	97
-LABUAMY	"AMYLASE WITH CREATININE, URINE (RANDOM)"	AMUCRU	AMYLASE/CREAT RATIO	96
-LABUAMY	"AMYLASE WITH CREATININE, URINE (RANDOM)"	AMYLASE	"AMYLASE, URINE"	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	CODEINE	CODEINE	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	HYCD	HYDROCODONE	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	OXYCOD	OXYCODONE	96
-LABIBD	ANCA	PANCA	NEUTROPHIL CYTO AB-P	96
-IMG11CCP	CAR CATH CORS POSSIBLE	PDF	PDF RESULT	96
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	PRO	PRODUCT ID  	96
-LABIBD	ANCA	SACIGA	"S. CERVISIAE AB,IGA"	96
-LABIBD	ANCA	SACIGG	"S.CERVISIAE AB,IGG"	96
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	SI	<<STATUS>>  	96
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	UBT	UNIT BLOOD TYPE  	96
-LABUAMY	"AMYLASE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	96
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	UNT	UNIT NUMBER  	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUHMGC	HYDROMORPHONE	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUMOGC	MORPHINE	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUOPIM	IMMUNOASSAY SCREEN	96
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUOPIN	INTERPRETATION	96
-LAB11145R	"ADULTERANTS SURVEY, URINE"	11150R	NITRITES	95
-LAB11145R	"ADULTERANTS SURVEY, URINE"	11152R	COMMENT	95
-LABG6PD	G6PD SCREEN	G6PD	G6PD SCREEN	95
-LABHIVAB	HIV (TYPE 1 AND TYPE 2) ABY	HIVAB	HIV 1/2 ANTIBODY	95
-LAB11531R	PARANEOPLASTIC AB SCREEN	11641R	PARANEOPLASTIC AB SCREEN	94
-LABANERB	ANAEROBIC CULTURE BB	CULT	CULT/OTHER RSLT:	94
-LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	CULT	CULT/OTHER RSLT:	94
-LABANERB	ANAEROBIC CULTURE BB	RPT	RPT	94
-LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	RPT	RPT	94
-LABANERB	ANAEROBIC CULTURE BB	SDES	SPECIMEN	94
-LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	SDES	SPECIMEN	94
-LABANERB	ANAEROBIC CULTURE BB	SREQ	COMMENTS	94
-LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	SREQ	COMMENTS	94
-LABATTG	IGA ANTI  TTG	ATTGT	IGA ANTI TTG LEVEL	93
-LABGCCR2	GC MOLECULAR DETECTION	DCL5	TEST INFORMATION:	93
-LABGCCR2	GC MOLECULAR DETECTION	GC	N.GONORRHOEAE RESULT:	93
-LABGCCR2	GC MOLECULAR DETECTION	SDES5	SPECIMEN	93
-LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	CULT	CULT/OTHER RSLT:	92
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	DI	ISSUE DATE/TIME	92
-LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	RPT	RPT	92
-LAB11093R	SCL 70 AB	SCL70	"SCL 70 AB, IGG, SERUM"	92
-LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	SDES	SPECIMEN	92
-LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	SREQ	COMMENTS	92
-10290R	HLA - TYPING	9033R	HLA-DPB1 ALLELE 1	91
-10290R	HLA - TYPING	9034R	HLA-DPB1 ALLELE 2	91
-LABISOHTI	ISOHEMAGGLUTININ TITER	ISOHTR	ISOHEMAGGLUTININ TITER RESULT	91
-LABCHMISC	MISCELLANEOUS CHEMISTRY	CHMISC	CHEMISTRY MISCELLANEOUS	90
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	90
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	90
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	90
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	90
-LABTOXO	TOXOPLASMA IGG	TOXO	TOXOPLASMA IGG	90
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTON	STONE ANALYSIS	90
-10290R	HLA - TYPING	9031R	HLA-DPA1 ALLELE 1	89
-10290R	HLA - TYPING	9032R	HLA-DPA1 ALLELE 2	89
-LABFCHOLA	"CHOLESTEROL, FLUID"	CHOLFLD	CHOLESTEROL BODY FLUID	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	DX	DIAGNOSIS	89
-LABYFLUZ	FLUCONAZOLE LEVEL	FLUCOZ	FLUCONAZOLE	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	JOINT	JOINT	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	MALIG	SUSPECT MALIGNANCY?	89
-LABURPHOS	"PHOSPHATE, URINE (RANDOM)"	PHOSU	"PHOSPHATE, URINE"	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	SCOLOR	"COLOR, SYN FLD"	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	SRBC	"RBC COUNT, SYN FLD"	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	STURB	"TURBIDITY, SYN FLD"	89
-LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	SWBC	"NUCL. CELLS, SYN FLD"	89
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	UBT	UNIT BLOOD TYPE  	89
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	UNT	UNIT NUMBER  	89
-LABVWAG	VON WILLEBRAND AG	VWAG	VON WILLEBRAND ANTIGEN	89
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNCO	STONE COMMENT	89
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNNJ	"NIDUS, MAJOR"	89
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNNM	"NIDUS, MINOR"	89
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNSJ	"SHELL, MAJOR"	89
-LABYSTONE	KIDNEY STONE ANALYSIS	YSTNSM	"SHELL, MINOR"	89
-LABAPC	ACTIVATED PROTEIN C RESISTANCE	APC	APC RESISTANCE	88
-LAB11039R	GBM AB	GBMIGG	GBM-IGG ELISA	88
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	88
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	SPEXP	SPECIMEN EXPIRATION DATE/TIME	88
-10295R	HLA - AUTO T FLOW CROSSMATCH	9081R	CELL DATE	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9082R	CELL SOURCE	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9083R	SERUM DATE 1	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9085R	MCS 1	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9100R	COMPOSED TEXT	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9101R	DISCLAIMER	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9102R	CELL DATE	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9103R	CELL SOURCE	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9104R	SERUM DATE 1	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9106R	MCS 1	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9121R	COMPOSED TEXT	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9122R	DISCLAIMER	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9462R	METHOD	87
-10295R	HLA - AUTO T FLOW CROSSMATCH	9595R	RESULT 1	87
-10300R	HLA - AUTO B FLOW CROSSMATCH	9599R	METHOD	87
-LABMMAS	METHYLMALONIC ACID	MMA	"METHYLMALONIC ACID, SERUM"	87
-LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	PANCA	NEUTROPHIL CYTO AB-P	87
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	PRO	PRODUCT ID  	87
-LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	SACIGA	"S. CERVISIAE AB,IGA"	87
-LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	SACIGG	"S.CERVISIAE AB,IGG"	87
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	SI	<<STATUS>>  	87
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	XMR	UNIT CROSSMATCH  	87
-LABYCENT	CENTROMERE ANTIBODIES	YCENTA	CENTROMERE ABS.	87
-10270R	HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)	10049R	HLA LAB TESTING STATUS	86
-LABY25VD	"VITAMIN D, 25-HYDROXY"	25OHD2	25-HYDROXY D2	86
-LABY25VD	"VITAMIN D, 25-HYDROXY"	25OHD3	25-HYDROXY D3	86
-LABY25VD	"VITAMIN D, 25-HYDROXY"	25OHDT	25-HYDROXY D TOTAL	86
-LABCDAS	CELIAC DISEASE SCREEN	ATTGR	IGA ANTI TTG ANTIBODY	86
-LABCDAS	CELIAC DISEASE SCREEN	ATTINT	INTERPRETATION	86
-LABFENTA	ENTAMOEBA HISTOLYTICA AB	ENTA	E.HISTOLYTICA LGG	86
-LABCDAS	CELIAC DISEASE SCREEN	GDGPR	IGG ANTI DGP ANTIBODY	86
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GDNM3B	"DNMT3A, BLOOD"	86
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GERHRB	"ERBB2/HER2, BLOOD"	86
-LABGH	GROWTH HORMONE	GH	GROWTH HORMONE	86
-LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL6	HSV 1/2 IGM CONFIRMATORY IFA	86
-LABCDAS	CELIAC DISEASE SCREEN	IGACDS	"IGA, SERUM"	86
-LABCDAS	CELIAC DISEASE SCREEN	IGAINT	INTERPRETATION	86
-10270R	HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)	RINFO	TO FIND RESULT:	86
-LABYTESFT	"TESTOSTERONE, TOTAL AND FREE"	YTESHD	FREE TESTOSTERONE	86
-10170R	HLA ORDER - NMDP ADDITIONAL SPECIMEN	10049R	HLA LAB TESTING STATUS	85
-10335R	HLA - SCREENING C1Q	9280R	INTERPRETATION/ COMMENTS CLASS II	85
-10370R	HLA - DSA BY C1Q	9450R	INTERPRETATION/ COMMENTS CLASS I	85
-10300R	HLA - AUTO B FLOW CROSSMATCH	9600R	RESULT 1	85
-LABASTRO	ANTI STREPTOLYSIN O	ASTRO	ANTI STREPTOLYSIN O	85
-LABABG	"BLOOD GASES, ARTERIAL"	O2SATA	O2 SATURATION (A)	85
-LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	P190Q	P190	85
-10170R	HLA ORDER - NMDP ADDITIONAL SPECIMEN	RINFO	TO FIND RESULT:	85
-LABDVERV3	RSV DFA	RSV3	RSV	85
-LABDVERV3	RSV DFA	RSV3SP	SPECIMEN	85
-LABDVERV3	RSV DFA	RV3IP	TEST DESCRIPTION	85
-10170R	HLA ORDER - NMDP ADDITIONAL SPECIMEN	UNRDID	UNRELATED DONOR ID	85
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11200R	INTERPRETIVE COMMENTS	84
-LABYAMISO	"AMYLASE, PANCREATIC"	AMYLASE	"AMYLASE,PANCREATIC,S"	84
-LABYITRA	ITRACONAZOLE LEVEL	YITR1	ITRACONAZOLE	84
-LABYITRA	ITRACONAZOLE LEVEL	YITR2	HYDROXYITRACONAZOLE	84
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11201R	ANNA-1	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11202R	ANNA-2	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11203R	ANNA-3	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11204R	AGNA-1	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11205R	PCA-1	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11206R	PCA-2	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11207R	PCA-TR	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11208R	AMPHIPHYSIN AB	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11209R	CRMP-5-IGG	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11210R	P/Q -TYPE CALCIUM CHANNEL AB	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11211R	N-TYPE CALCIUM CHANNEL AB	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11212R	ACHR GANGLIONIC NEURONAL AB	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11213R	NEURONAL (V-G) K+ CHANNEL AB	83
-10290R	HLA - TYPING	9025R	HLA-DR345 ALLELE 1	83
-LABFBBURG	"LYME WESTERN BLOT, CSF"	BBIGG	BORELLIA BURG IGG AB	83
-LABFBBURG	"LYME WESTERN BLOT, CSF"	BBIGM	BORELLIA BURG IGM AB	83
-LABCMVG	CMV GENOTYPE	CUL54	UL54 MUTATION	83
-LABCMVG	CMV GENOTYPE	CUL97	UL97 MUTATION	83
-LABIGGSUB	IGG  SUBCLASSES	IGG	"IGG, SERUM"	83
-LABIGGSUB	IGG  SUBCLASSES	IGGS1	IGG SUBCLASS1	83
-LABIGGSUB	IGG  SUBCLASSES	IGGS2	IGG SUBCLASS2	83
-LABIGGSUB	IGG  SUBCLASSES	IGGS3	IGG SUBCLASS3	83
-LABIGGSUB	IGG  SUBCLASSES	IGGS4	IGG SUBCLASS4	83
-LABJO1ABY	ANTI-JO1 ANTIBODY	JO1INT	ANTI-JO1 INTERPRETATION	83
-LABJO1ABY	ANTI-JO1 ANTIBODY	JO1QUL	ANTI-JO1 RESULT	83
-LABPROTPM	PRO-PREDICT TPMT	PRTPMR	PROPREDICT TPMT	83
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	SPEXP	SPECIMEN EXPIRATION DATE/TIME	83
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TCDX	CLINICAL INDICATION	83
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TDIR	INTERPRETED BY	83
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TDIS	DISCLAIMER	83
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TMET	METHOD	83
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TPRM	PRIMERS	83
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TRES	RESULTS	83
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	UBT	UNIT BLOOD TYPE  	83
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	UNT	UNIT NUMBER  	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	YARBIN	ACH RECEPTOR BINDING ANTIBODY	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMID	SPECIMEN ID	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMIN	INTERPRETATION	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMME	METHOD	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMOD	ORDER DATE	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMRD	RELEASE DATE	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMRS	RESULTS	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMRV	REVIEWED BY:	83
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMSP	SPECIMEN	83
-LAB11199R	PARANEOPLASTIC AUTOAB EVAL	YSTA	STRIATIONAL (STRIATED MUSCLE)	83
-10305R	HLA - ALLO T FLOW CROSSMATCH	10460R	RECIPIENT NAME	82
-10305R	HLA - ALLO T FLOW CROSSMATCH	10461R	RECIPIENT DOB	82
-LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	ASPIGE	ASPERGILLUS IGE ABY	82
-LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	ASPIGG	ASPERGILLUS IGG ABY	82
-LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	ASPINT	INTERPRETATION	82
-LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	IGEASP	TOTAL IGE	82
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADIMPDTE	LEAD IMPLANT DATE	82
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADMFR	LEAD MANUFACTURER	82
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADMODEL	LEAD MODEL	82
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADTYPE	LEAD TYPE	82
-LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TCMT	COMMENT	82
-LABVZVQL	"VZV DNA PCR QUAL, NON LESION"	VZQLDN	VZV DNA	82
-LABVZVQL	"VZV DNA PCR QUAL, NON LESION"	VZQLIP	TEST DESCRIPTION	82
-LABVZVQL	"VZV DNA PCR QUAL, NON LESION"	VZQLSP	SPECIMEN	82
-LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	YUMECO	COMMENT	82
-10310R	HLA - ALLO B FLOW CROSSMATCH	10462R	RECIPIENT NAME	81
-10310R	HLA - ALLO B FLOW CROSSMATCH	10463R	RECIPIENT DOB	81
-LAB11250R	"HHV-8 DNA, QUANT"	11251R	SOURCE	81
-LAB11250R	"HHV-8 DNA, QUANT"	11252R	"HHV-8 DNA,QUANT"	81
-LAB12573R	ANCA	12574R	CYTOPLASMIC NEUTROPHILIC AB	81
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	DI	ISSUE DATE/TIME	81
-LABFACT5	FACTOR V ASSAY	FACTOR5	FACTOR V ASSAY	81
-LABFMYAB	MYCOPLASMA IGG/IGM AB	12431R	INTERPRETATION	80
-LABRITUX	RITUXAN PANEL	C5616A	CD56+CD16+/CD3- ABS.	80
-LABRITUX	RITUXAN PANEL	C5616P	CD56+CD16+/CD3- %	80
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	CCNT	CELLS COUNTED	80
-LABYUHYME	"HEAVY METALS, URINE"	MERURN	"MERCURY, URINE"	80
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	PRO	PRODUCT ID  	80
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	SI	<<STATUS>>  	80
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	XMR	UNIT CROSSMATCH  	80
-LABYUHYME	"HEAVY METALS, URINE"	YUHYM2	"LEAD, U"	80
-10370R	HLA - DSA BY C1Q	9464R	INTERPRETATION/ COMMENTS CLASS II	79
-LABYSTNA	"SODIUM, STOOL"	YSTNA3	COLLECTION DURATION	79
-LABYSTNA	"SODIUM, STOOL"	YSTNA4	SODIUM CONCENTRATION	79
-10365R	HLA - DSA BY IGG	9426R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	78
-LABYU5HIA	"5 - OH-INDOLEACETIC AC, URINE"	5HIAAURN	"5-HIAA, URINE"	77
-LABPCEC4	"ISTAT EC4, ARTERIAL"	GLU	"GLUCOSE,ISTAT"	77
-LABFHVIG	"HIV 1 IGG-AB, WESTERN BLOT CONFIRM"	HVIG	HIV 1 AB CONFIRMATION	77
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADSERNUM	LEAD SERIAL NUMBER	77
-LABPCEC4	"ISTAT EC4, ARTERIAL"	PCCOM	POCT COMMENT	77
-LABRBCPH	RBC PHENOTYPE	PHLET	E TYPE PHENOTYPE	77
-LABRF	RHUEMATOID FACTOR	RHFT	RHEUMATOID FACTOR LEVEL	77
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	UBT	UNIT BLOOD TYPE  	77
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	UNT	UNIT NUMBER  	77
-LABYU5HIA	"5 - OH-INDOLEACETIC AC, URINE"	VOLUMEURN	"URINE, VOLUME"	77
-LABYSTK	"POTASSIUM, STOOL"	YSTK3	"POTASSIUM, 24-HR STOOL"	77
-LABYSTK	"POTASSIUM, STOOL"	YSTK4	POTASSIUM CONCENTRATION	77
-LABYU5HIA	"5 - OH-INDOLEACETIC AC, URINE"	YU5HUT	COLLECTION DURATION	77
-10365R	HLA - DSA BY IGG	9420R	INTERPRETATION/ COMMENTS CLASS I	76
-10410R	HLA-BMT DONOR MATCH GRADE SUMMARY	9706R	DONOR MATCH GRADE SUMMARY	76
-LABTTM	TRANSPLANT THERAPY MONITORING	CD19AB	"CD19 (PAN B), ABS."	76
-LABTTM	TRANSPLANT THERAPY MONITORING	CD19PC	CD19 (PAN B) %	76
-LABTTM	TRANSPLANT THERAPY MONITORING	CD3AB	"CD3 (PAN T), ABS."	76
-LABTTM	TRANSPLANT THERAPY MONITORING	CD3PC	CD3 (PAN T) %	76
-LABTTM	TRANSPLANT THERAPY MONITORING	CD5AB	"CD5 (PAN T), ABS."	76
-LABTTM	TRANSPLANT THERAPY MONITORING	CD5PC	CD5 (PAN T) %	76
-LABDSDNA	ANTI - DSDNA AB	DNAQNT	ANTI-DSDNA TITER	76
-LABKRAS	KRAS MUTATION DETECTION	KRASDI	DISCLAIMER	76
-LABKRAS	KRAS MUTATION DETECTION	KRASDX	CLINICAL INDICATION	76
-LABKRAS	KRAS MUTATION DETECTION	KRASME	METHOD	76
-LABKRAS	KRAS MUTATION DETECTION	KRASRE	RESULT	76
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	76
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	76
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	76
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	76
-LAB11045R	LKM AB	LKMAB	LIV-KID-MICROSOME AB	76
-LABTTM	TRANSPLANT THERAPY MONITORING	LYM	LYMPHOCYTES %	76
-LABTTM	TRANSPLANT THERAPY MONITORING	LYMAB	"LYMPHOCYTES, ABS."	76
-LABRBCPH	RBC PHENOTYPE	PHLCT	C TYPE PHENOTYPE	76
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	PRO	PRODUCT ID  	76
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRM	METHOD	76
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRP	PRIMER	76
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRR	RESULT	76
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRS	DISCLAIMER	76
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRX	CLINICAL INDICATION	76
-LABTTM	TRANSPLANT THERAPY MONITORING	RTXFDA	NOTE	76
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	SI	<<STATUS>>  	76
-LABUA	URINALYSIS WITH MICROSCOPIC	TRPHOS	TRIPLE PHOSPH CRYST.	76
-LABTTM	TRANSPLANT THERAPY MONITORING	WBC	WBC COUNT	76
-LABYUHYME	"HEAVY METALS, URINE"	YUHYM5	COLLECTION DURATION	76
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11200R	INTERPRETIVE COMMENTS	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11201R	ANNA-1	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11202R	ANNA-2	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11203R	ANNA-3	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11204R	AGNA-1	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11205R	PCA-1	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11206R	PCA-2	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11207R	PCA-TR	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11208R	AMPHIPHYSIN AB	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11209R	CRMP-5-IGG	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11210R	P/Q -TYPE CALCIUM CHANNEL AB	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11211R	N-TYPE CALCIUM CHANNEL AB	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11212R	ACHR GANGLIONIC NEURONAL AB	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11213R	NEURONAL (V-G) K+ CHANNEL AB	75
-LAB12232R	THYROGLOBULIN & THYROID AB	12233R	12233R	75
-10375R	HLA - STR/CHIMERISM TESTING	9467R	RECIPIENT CELL DATE	75
-10375R	HLA - STR/CHIMERISM TESTING	9468R	RECIPIENT CELL SOURCE	75
-10375R	HLA - STR/CHIMERISM TESTING	9470R	DONOR NAME	75
-10375R	HLA - STR/CHIMERISM TESTING	9472R	RELATIONSHIP TO PATIENT	75
-10375R	HLA - STR/CHIMERISM TESTING	9473R	DONOR CELL DATE	75
-10375R	HLA - STR/CHIMERISM TESTING	9474R	DONOR CELL SOURCE	75
-10375R	HLA - STR/CHIMERISM TESTING	9475R	LOCUS 1	75
-10375R	HLA - STR/CHIMERISM TESTING	9476R	LOCUS 2	75
-10375R	HLA - STR/CHIMERISM TESTING	9477R	LOCUS 3	75
-10375R	HLA - STR/CHIMERISM TESTING	9575R	COMPOSED TEXT	75
-10375R	HLA - STR/CHIMERISM TESTING	9576R	DISCLAIMER	75
-10375R	HLA - STR/CHIMERISM TESTING	9611R	METHOD	75
-10375R	HLA - STR/CHIMERISM TESTING	9636R	CHIMERISM TEST NAME	75
-LABYPROIN	"PROINSULIN, PLASMA"	PROINSULIN	PROINSULIN	75
-LABDVERTV	"ROTAVIRUS ANTIGEN, STOOL"	RTVR	ROTAVIRUS	75
-LABDVERTV	"ROTAVIRUS ANTIGEN, STOOL"	RTVTD	TEST DESCRIPTION	75
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	XMR	UNIT CROSSMATCH  	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	YARBIN	ACH RECEPTOR BINDING ANTIBODY	75
-LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	YSTA	STRIATIONAL (STRIATED MUSCLE)	75
-LABYUHYME	"HEAVY METALS, URINE"	YUHYM6	TOTAL VOLUME	75
-10370R	HLA - DSA BY C1Q	9442R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	74
-10375R	HLA - STR/CHIMERISM TESTING	9478R	LOCUS 4	74
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	DI	ISSUE DATE/TIME	74
-LABPCEC4	"ISTAT EC4, ARTERIAL"	HCT	"HCT(CALC), ISTAT"	74
-LABPCEC4	"ISTAT EC4, ARTERIAL"	HGB	"HGB(CALC), ISTAT"	74
-LABPCEC4	"ISTAT EC4, ARTERIAL"	K	"POTASSIUM, ISTAT"	74
-LABKRAS	KRAS MUTATION DETECTION	KRASMD	INTERPRETED BY	74
-LABLUT	LEUTEINIZING HORMONE	LH	LH	74
-LABPCEC4	"ISTAT EC4, ARTERIAL"	NA	"SODIUM, ISTAT"	74
-LABRBCPH	RBC PHENOTYPE	PHCT	C PHENOTYPE	74
-LABRBCPH	RBC PHENOTYPE	PHST	S PHENOTYPE	74
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	PRO	PRODUCT ID  	74
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	SI	<<STATUS>>  	74
-10000R	HLA ORDER - TYPING - SOLID ORGAN EVALUATION	10049R	HLA LAB TESTING STATUS	73
-LABEBVPAN	EBV AB PANEL	COM	COMMENT	73
-LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	CULT	CULT/OTHER RSLT:	73
-LABCYLX	IMMUNE FUNCTION TEST	CYLX1	IMMUNE FUNCTION TEST RESULT	73
-LABKRAS	KRAS MUTATION DETECTION	KRASMU	MUTATION(S)	73
-LABRBCPH	RBC PHENOTYPE	PHET	E PHENOTYPE	73
-LABRBCPH	RBC PHENOTYPE	PHJKBT	JKB PHENOTYPE	73
-LABRBCPH	RBC PHENOTYPE	PHKT	K PHENOTYPE	73
-10000R	HLA ORDER - TYPING - SOLID ORGAN EVALUATION	RINFO	TO FIND RESULT:	73
-LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	RPT	RPT	73
-LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	SDES	SPECIMEN	73
-LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	SREQ	COMMENTS	73
-LABUMYOSC	"MYOGLOBIN SCREEN, URINE"	UMYOG	"MYOGLOBIN SCREEN, URINE"	73
-LABYSDA	"DRUG SCREEN, PLASMA"	11832R	DRUGS DETECTED:	72
-LABYUHYME	"HEAVY METALS, URINE"	11896R	"ARSENIC,U"	72
-LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	A1APHEN	A-1-A PHENOTYPE	72
-LABCD34	"CD34 PANEL, BLOOD"	CD34AB	"CD34, ABS."	72
-LABCD34	"CD34 PANEL, BLOOD"	CD34DS	DISCLAIMER	72
-LABCD34	"CD34 PANEL, BLOOD"	CD34PC	"%CD34,BLOOD"	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	CHOL	"CHOLESTEROL, TOTAL"	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	CHOLHDL	CHOLESTEROL/HDL RATIO	72
-LABYDIGT	"DIGITOXIN, SERUM"	DIGT	DIGITOXIN	72
-LABYFRUCM	FRUCTOSAMINE	FRUCTM	FRUCTOSAMINE	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	HDL	HDL CHOLESTEROL	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	HMCY	"HOMOCYSTEINE, PLAS TOTAL"	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	HSCRP	HIGH SENSITIVITY CRP	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	LDL	LDL (CALCULATED)	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	LDLHDL	LDL/HDL RATIO	72
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	LPA	LIPOPROTEIN A	72
-LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGGAB	B.BURGDORFERI IGG AB WB	72
-LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGMAB	B.BURGDORFERI IGM AB WB	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	NHDL	"NON-HDL CHOL, CALC"	72
-LAB11086R	NORTRIPTYLINE LEVEL	NORTRP	"NORTRIPTYLINE, PLASMA"	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	RCRSK	REL. CARDIAC RISK	72
-LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	TGL	"TRIGLYCERIDE, SER/PLAS"	72
-LABCD34	"CD34 PANEL, BLOOD"	WBC	WBC COUNT	72
-LABYDIGT	"DIGITOXIN, SERUM"	YDIGT2	REPORTING LIMIT	72
-LABANTNEU	ANTI - NEUTROPHIL AB	ANA	ANTI-NEUTROPHIL AB:	71
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	DI	ISSUE DATE/TIME	71
-LABVWSCN	VON WILLEBRAND SCREEN	FACTOR7	FACTOR VIII ASSAY	71
-LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1DI	DISCLAIMER	71
-LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1IN	INTERPRETATION	71
-LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1M	NEURAMINIDASE  H275Y MUTATION	71
-LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1MT	METHOD	71
-LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1R	2009 INFLUENZA A(H1N1) RNA	71
-LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1SP	SPECIMEN	71
-LABYHABQ	ANTI HBS QUANT	HBSAB	"HBS ANTIBODY, S"	71
-LABYHABQ	ANTI HBS QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	71
-LABVWSCN	VON WILLEBRAND SCREEN	RIST	RISTOCETIN CO-FACTOR	71
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	SPEXP	SPECIMEN EXPIRATION DATE/TIME	71
-LABVWSCN	VON WILLEBRAND SCREEN	VWAG	VON WILLEBRAND ANTIGEN	71
-LABLUT	LUTEINIZING HORMONE	LH	LH	70
-VSC05	VSC VENOUS ULTRASOUND	PDF	PDF RESULT	70
-LABRBCPH	RBC PHENOTYPE	PHJKAT	JKA PHENOTYPE	70
-LABUCL	"CHLORIDE WITH CREATININE, URINE (RANDOM)"	UCL	"CHLORIDE, URINE"	70
-LABUCL	"CHLORIDE WITH CREATININE, URINE (RANDOM)"	UCLCR	CHLORIDE/CREAT RATIO	70
-LABUCL	"CHLORIDE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	70
-LABVISC	"VISCOSITY, SERUM"	VISCOS	VISCOSITY	70
-LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	YA1A	ALPHA 1 ANTITRYPSIN	70
-LABYBLAS	BLASTOMYCES AB	YBLAS3	BLASTOMYCES IMMUNODIFFUSION	70
-LAB11028R	"ACE, CSF"	ACE	"ANGIOTENSIN COVERT ENZ, CSF"	69
-LABDFA	DENGUE FEVER AB	DFIGG	DENGUE FEVER AB IGG	69
-LABDFA	DENGUE FEVER AB	DFIGM	DENGUE FEVER AB IGM	69
-LABDFA	DENGUE FEVER AB	DFINT	INTERPRETATION	69
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	DI	ISSUE DATE/TIME	69
-LABKRAS	KRAS MUTATION DETECTION	KRASIN	INTERPRETATION	69
-LABRBCPH	RBC PHENOTYPE	PHLST	S TYPE PHENOTYPE	69
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	PRO	PRODUCT ID  	69
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	SI	<<STATUS>>  	69
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	UBT	UNIT BLOOD TYPE  	69
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	UNT	UNIT NUMBER  	69
-LAB12631R	VORICONAZOLE LEVEL	VORI	VORICONAZOLE LEVEL	69
-10235R	HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING	10049R	HLA LAB TESTING STATUS	68
-LABADAMTS	ADAMTS-13 PROFILE	ADMACT	ADAMTS-13 ACTIVITY	68
-LABADAMTS	ADAMTS-13 PROFILE	ADMINT	CLINICAL INTERPRETATION	68
-LABCMVIGG	CMV AB IGG	CMV	CMV ANTIBODY	68
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GDNM3M	"DNMT3A, BM"	68
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GERHRM	"ERBB2/HER2, BM"	68
-LABHEPC	HEPATITIS C AB EIA	HEPCAB	HEPATITIS C AB	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMDI	DISCLAIMER	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMDX	CLINICAL INDICATION	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMIN	INTERPRETATION	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMMD	INTERPRETED BY	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMME	METHOD	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMRE	RESULT	68
-LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMRF	REFERENCES	68
-10235R	HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING	RINFO	TO FIND RESULT:	68
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	XMR	UNIT CROSSMATCH  	68
-LABGENC	GENITAL CULTURE	CULT	CULT/OTHER RSLT:	67
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	EBV	EBV BY PCR	67
-LABFACT2	FACTOR II ASSAY	FACTOR2	FACTOR II ASSAY	67
-LABRBCPH	RBC PHENOTYPE	PHFYAT	FYA PHENOTYPE	67
-LABRBCPH	RBC PHENOTYPE	PHFYBT	FYB PHENOTYPE	67
-LABRBCPH	RBC PHENOTYPE	PHLEAT	LEA PHENOTYPE	67
-LABRBCPH	RBC PHENOTYPE	PHMT	M PHENOTYPE	67
-LABRBCPH	RBC PHENOTYPE	PHP1T	P1 PHENOTYPE	67
-LABGENC	GENITAL CULTURE	RPT	RPT	67
-LABGENC	GENITAL CULTURE	SDES	SPECIMEN	67
-LABGENC	GENITAL CULTURE	SREQ	COMMENTS	67
-LABTBP2	TRANSFUSION BLOOD PRODUCT 3	VL	PRODUCT VOLUME	67
-LAB11077R	CALCITONIN	YCALCI	"CALCITONIN, SERUM"	67
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	YEBP1	SPECIMEN SOURCE	67
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	YEBP3	SPECIAL INFORMATION	67
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	YEPB4	REPORT STATUS	67
-LABEBVPAN	EBV AB PANEL	11165R	EB VIRUS EARLY AG (R+D) IGG	66
-LABEBVPAN	EBV AB PANEL	11166R	EB NUCLEAR AB	66
-LABEBVPAN	EBV AB PANEL	12001R	"VCA IGG ,IFA"	66
-LABEBVPAN	EBV AB PANEL	12002R	"VCA IGM ,IFA"	66
-LABIBD	ANCA	12215R	12215R	66
-LABADAMTS	ADAMTS-13 PROFILE	ADMINH	ADAMTS-13 INHIBITOR	66
-LABTHG	THYROGLOBULIN	ATGA	ANTI-THYROGLOBULIN ABY	66
-LABBKVQU	BK VIRUS DNA QUANTITATION	BKQ	BKV DNA QUANTITATION	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPDI	DISCLAIMER	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPDX	CLINICAL INDICATION	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPIN	"INTERPRETATION, BM"	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPMD	INTERPRETED BY	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPME	METHOD	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPRE	"RESULT, BM"	66
-LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPRF	REFERENCES	66
-LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	CULT	CULT/OTHER RSLT:	66
-LABFACT11	FACTOR XI ASSAY	FACTOR11	FACTOR XI ASSAY	66
-LABHHV6QL	HHV-6 DNA PCR QUAL	HV6QLD	HHV-6 DNA	66
-LABHHV6QL	HHV-6 DNA PCR QUAL	HV6QLI	TEST DESCRIPTION	66
-LABHHV6QL	HHV-6 DNA PCR QUAL	HV6QLS	SPECIMEN	66
-LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	RPT	RPT	66
-LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	SDES	SPECIMEN	66
-LABSPAGD	S. PNEUMONIAE AG DETECTION LA	SOURC4	SOURCE	66
-LABBKVQU	BK VIRUS DNA QUANTITATION	SOURC6	SOURCE	66
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	SPEXP	SPECIMEN EXPIRATION DATE/TIME	66
-LABSPAGD	S. PNEUMONIAE AG DETECTION LA	SPNAG	S PNEUMONIAEAG	66
-LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	SREQ	COMMENTS	66
-LABTHG	THYROGLOBULIN	TG	THYROGLOBULIN (TG)	66
-LABTOXIG	TOXOPLASMA IGG	TOXGQT	IGG LEVEL	66
-LABCOXPNL	COXSACKIE A AB PANEL	TYP10	COXSACKIE A10 AB	66
-LABCOXPNL	COXSACKIE A AB PANEL	TYP16	COXSACKIE A16 AB	66
-LABCOXPNL	COXSACKIE A AB PANEL	TYP2	COXSACKIE A2 AB	66
-LABCOXPNL	COXSACKIE A AB PANEL	TYP4	COXSACKIE A4 AB	66
-LABCOXPNL	COXSACKIE A AB PANEL	TYP7	COXSACKIE A7 AB	66
-LABCOXPNL	COXSACKIE A AB PANEL	TYP9	COXSACKIE A9 AB	66
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	UBT	UNIT BLOOD TYPE  	66
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	UNT	UNIT NUMBER  	66
-LABTTM	TRANSPLANT THERAPY MONITORING	CD4AB	"CD3+/CD4+, ABS."	65
-LABTTM	TRANSPLANT THERAPY MONITORING	CD4PC	CD3+/CD4+ %	65
-LABTTM	TRANSPLANT THERAPY MONITORING	CD8AB	"CD3+/CD8+, ABS."	65
-LABTTM	TRANSPLANT THERAPY MONITORING	CD8PC	CD3+/CD8+ %	65
-LABCSMP	CANCER SOMATIC MUTATION PANEL	CSCI	CLINICAL INDICATION	65
-LABCSMP	CANCER SOMATIC MUTATION PANEL	CSDI	DISCLAIMER	65
-LABCSMP	CANCER SOMATIC MUTATION PANEL	CSME	METHOD	65
-LABCSMP	CANCER SOMATIC MUTATION PANEL	CSREF	REFERENCES	65
-LABABG	"BLOOD GASES, ARTERIAL"	FIO2	FIO2 (FIO2)	65
-LABTTM	TRANSPLANT THERAPY MONITORING	HNS	"CD4+CD8 TOTAL, CD3+%"	65
-LABTTM	TRANSPLANT THERAPY MONITORING	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	65
-LABTTM	TRANSPLANT THERAPY MONITORING	HSRATIO	"CD4/CD8 RATIO, CD3+"	65
-LABRBCPH	RBC PHENOTYPE	PHLEBT	LEB PHENOTYPE	65
-LABRBCPH	RBC PHENOTYPE	PHNT	N PHENOTYPE	65
-LABTOTALC	"PROTEIN C AG, TOTAL"	PROTCAGT	"PROTEIN C AG, TOTAL"	65
-LAB11214R	"BETA-2 TRANSFERRIN, BF"	11215R	"BETA-2 TRANSFERRIN, BF"	64
-LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALSCD	MALARIA ANTIGEN DESCRIPTION	64
-LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALSCR	MALARIA ANTIGEN RESULT	64
-LABVBG	"BLOOD GASES, VENOUS"	O2SV	O2SV	64
-LABYSES	"SELENIUM, SERUM"	SELENIUM	SELENIUM	64
-LABUHEMO	"HEMOSIDERIN, URINE"	UHEMO	"HEMOSIDERIN, URINE"	64
-10365R	HLA - DSA BY IGG	9413R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	63
-10375R	HLA - STR/CHIMERISM TESTING	9495R	% DONOR- BLOOD WHOLE	63
-10375R	HLA - STR/CHIMERISM TESTING	9535R	% RECIPIENT- BLOOD WHOLE	63
-LABCSMP	CANCER SOMATIC MUTATION PANEL	CSSP	SPECIMEN	63
-LABFLUAPC	FLU A PCR	FLUADI	DISCLAIMER	63
-LABFLUAPC	FLU A PCR	FLUAMT	METHOD	63
-LABFLUAPC	FLU A PCR	FLUAR	INFLUENZA A RNA	63
-LABFLUAPC	FLU A PCR	FLUASP	SPECIMEN	63
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GAPC	APC	63
-LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MAL	MALARIA PREP RESULT	63
-LABYMPA	MYCOPHENOLIC ACID	MPACID	MYCOPHENOLIC ACID	63
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRD	INTERPRETED BY	63
-LABTOXPCR	TOXOPLASMOSIS PCR	TOXGG	GENERAL GUIDELINES	63
-LABTOXPCR	TOXOPLASMOSIS PCR	TOXRES	PCR RESULT	63
-LABTOXPCR	TOXOPLASMOSIS PCR	TOXSP	SPECIMEN TYPE	63
-LABYMPA	MYCOPHENOLIC ACID	YMPAG	MPA GLUCURONIDE	63
-LAB11101R	VITAMIN C	YVITC	"ASCORBIC ACID, PL."	63
-10370R	HLA - DSA BY C1Q	9632R	SERUM DESCRIPTION	62
-LABYHMS	"HEAVY METALS SCREEN, BLOOD"	ARSENIC	ARSENIC	62
-LABYHMS	"HEAVY METALS SCREEN, BLOOD"	CADMIUM	CADMIUM	62
-LABF8INH	FACTOR VIII INHIBITOR HUMAN	F8INH	"FACTOR VIII INHIBITOR, HUMAN"	62
-LABHGBSQ	HGB S QUANTITATIVE	HGBS	"HGB S, QUANT"	62
-LABBMTP	BMT PRETRANSPLANT PANEL	HSAB	VIRAL AB HSV	62
-LABBMTP	BMT PRETRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	62
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	62
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	62
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDEVLDATE	ICD DATE OF EVENT	62
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	62
-LABYHMS	"HEAVY METALS SCREEN, BLOOD"	LEAD	LEAD	62
-LABMONOSC	INFECTIOUS MONO SCREEN	MONOSC	INFECTIOUS MONO SCREEN	62
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	PRO	PRODUCT ID  	62
-LABPROINS	"PROINSULIN, PLASMA"	PROINSULIN	PROINSULIN	62
-LABBMTP	BMT PRETRANSPLANT PANEL	RPRQ	RESULT	62
-LABBMTP	BMT PRETRANSPLANT PANEL	RPRQIN	INTERPRETATIONS	62
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	SI	<<STATUS>>  	62
-LABT1517	"T(15	17) PML/RAR, BLOOD"	T15CO	COMMENT	62
-LABT1517	"T(15	17) PML/RAR, BLOOD"	T15DI	DISCLAIMER	62
-LABT1517	"T(15	17) PML/RAR, BLOOD"	T15DX	CLINICAL INDICATION	62
-LABT1517	"T(15	17) PML/RAR, BLOOD"	T15MD	INTERPRETED BY	62
-LABT1517	"T(15	17) PML/RAR, BLOOD"	T15ME	METHOD	62
-LABT1517	"T(15	17) PML/RAR, BLOOD"	T15RE	RESULT	62
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	UBT	UNIT BLOOD TYPE  	62
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	UNT	UNIT NUMBER  	62
-LABADGM	ADENOVIRUS IGG AND IGM AB	ADG1	"ADENOVIRUS AB,IGG"	61
-LABADGM	ADENOVIRUS IGG AND IGM AB	ADM1	"ADENOVIRUS AB,IGM"	61
-LABARGI	ABO/RH	ARG	ABO/RH (AUTOMATION)	61
-LABVCULF	"VIRAL CULTURE, FLUID"	CULT	CULT/OTHER RSLT:	61
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	DI	ISSUE DATE/TIME	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HATINT	INTERPRETATION	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HBSINT	INTERPRETATION	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HBTINT	INTERPRETATION	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HCVI	INTERPRETATION	61
-LABBMTP	BMT PRETRANSPLANT PANEL	HCVR	ANTI-HCV	61
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	61
-LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALCON	MALCON	61
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	PRO	PRODUCT ID  	61
-LABVCULF	"VIRAL CULTURE, FLUID"	RPT	RPT	61
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	61
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	61
-LABVCULF	"VIRAL CULTURE, FLUID"	SDES	SPECIMEN	61
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	SI	<<STATUS>>  	61
-LABVCULF	"VIRAL CULTURE, FLUID"	SREQ	COMMENTS	61
-LABYLEPT	LEPTOSPIROSIS AB	YLEP1	LEPTOSPIRA AB	61
-LABBMTP	BMT PRETRANSPLANT PANEL	CMV	CMV ANTIBODY	60
-LABBMTP	BMT PRETRANSPLANT PANEL	CMVIGM	CMV IGM AB	60
-LABYUCOP	"COPPER, URINE"	COPPERURN	"COPPER, U"	60
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	60
-LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	DOPA	DOPAMINE	60
-LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	EPINEP	EPINEPHRINE	60
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	MFR	MANUFACTURER	60
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	MOD	MODEL	60
-LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	NOREPH	NOREPHINEPHRINE	60
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SLYM	"LYMPHOCYTES, SYN FLD"	60
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SMONO	"MONOS/MESOS, SYN FLD"	60
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SNEUT	"NEUTROPHILS, SYN FLD"	60
-LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBPO2	DATE & TIME OF LAST DOSE	60
-LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBPO3	DOSAGE	60
-LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBPO4	ADMINISTRATION ROUTE	60
-LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBRAPEAK	"TOBRAMYCIN, PEAK"	60
-LABYVITB6	VITAMIN B6	VITB6	VITAMIN B6	60
-LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	VOLUMEURN	URINE VOLUME	60
-LABBMTP	BMT PRETRANSPLANT PANEL	VZAB	VARICELLA ZOSTER AB IGG	60
-LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	YUFCUT	COLLECTION DURATION	60
-LABYVB1W	VITAMIN B1	YVB1W1	THIAMIN(VITAMIN B1)	60
-LABCSMP	CANCER SOMATIC MUTATION PANEL	CSMD	INTERPRETED BY	59
-LABBPERP	BORDETELLA PERTUSSIS PCR	CULT	CULT/OTHER RSLT:	59
-LAB11079R	GABAPENTIN	GABAP	"GABAPENTIN, SERUM/PLASMA"	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GBRAF	BRAF	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GCTNN	CTNNB1	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GEGFR	EGFR	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GIDH1	IDH1	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GIDH2	IDH2	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GKRAS	KRAS	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GNOTCH	NOTCH1	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GNRAS	NRAS	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GPIK3	PIK3CA	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GPTEN	PTEN	59
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GTP53	TP53	59
-LABPANHCT	HEMATOCRIT	HCT	HEMATOCRIT	59
-LABFHTLVQ	HTLV-1/2  DNA QUALITATIVE	HTLI	HTLV-I DNA	59
-LABFHTLVQ	HTLV-1/2  DNA QUALITATIVE	HTLII	HTLV-II DNA	59
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	59
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	59
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	59
-VSC03	VSC ARTERIAL EXAM/ABI\'S	PDF	PDF RESULT	59
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRC	COMMENT	59
-LABBPERP	BORDETELLA PERTUSSIS PCR	RPT	RPT	59
-LABBPERP	BORDETELLA PERTUSSIS PCR	SDES	SPECIMEN	59
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	59
-LABBPERP	BORDETELLA PERTUSSIS PCR	SREQ	COMMENTS	59
-LABYLACTS	"LACTOFERRIN DETECTION, STOOL"	YLACT1	LACTOFERRIN DETECTION	59
-LABYUCOP	"COPPER, URINE"	YUCOP2	COLLECTION DURATION	59
-LABYZONIS	ZONISAMIDE	ZONIS	ZONISAMIDE	59
-LABHSVM	HSV 1 AND 2 IGM	HSV1M	"HSV 1 IGM, IFA"	58
-LABHSVM	HSV 1 AND 2 IGM	HSV2M	"HSV 2 IGM, IFA"	58
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	58
-LABYLEADB	"LEAD, BLOOD"	LEA	"LEAD, BLD"	58
-LABYLEADB	"LEAD, BLOOD"	LEADSO	SOURCE OF COLLECTION	58
-LAB11085R	MPO AB	MPO	MYELOPEROXIDASE AB	58
-LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	VMDES	VMDES	58
-LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	VMQUL	VMQUL	58
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	XMR	UNIT CROSSMATCH  	58
-LABYUHYME	"HEAVY METALS, URINE"	12200R	AS CONCENTRATION	57
-LABYUHYME	"HEAVY METALS, URINE"	12201R	PB CONCENTRATION	57
-LABYUHYME	"HEAVY METALS, URINE"	12202R	"CADMIUM, 24HR, U"	57
-LABYUHYME	"HEAVY METALS, URINE"	12203R	CD CONCENTRATION	57
-LABYUHYME	"HEAVY METALS, URINE"	12204R	HG CONCENTRATION	57
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDRHYTHM	ICD RHYTHM	57
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	PRO	PRODUCT ID  	57
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	57
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	SI	<<STATUS>>  	57
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	UBT	UNIT BLOOD TYPE  	57
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	UNT	UNIT NUMBER  	57
-LABVZVIGG	VARICELLA ZOSTER VIRUS AB IGG	VZAB	VARICELLA ZOSTER AB IGG	57
-LABYENPCR	ENTEROVIRUS PCR	YENP3	SPECIAL INFORMATION	57
-LAB11153R	"HERPES VIRUS 6 DNA, QUANT"	11154R	SOURCE	56
-LAB11153R	"HERPES VIRUS 6 DNA, QUANT"	11155R	"HERPES VIRUS 6 DNA, QUANT"	56
-LABABODIS	ABO DISCREPANCY	ABODIS	ABO DISCREPANCY	56
-LABCDAS	CELIAC DISEASE SCREEN	ATTGT	IGA ANTI TTG LEVEL	56
-LABCOHB	CARBOXYHEMOGLOBIN	COHB	CO-HEMOGLOBIN	56
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	DI	ISSUE DATE/TIME	56
-LABEB	EBV AB PANEL	EA	EARLY ANTIBODY (R+D) IGG	56
-LABDVEHVR	HSV/VZV DFA REFLEX PCR	HERPR	HERPES SIMPLEX VIRUS (HSV)	56
-LABDVEHVR	HSV/VZV DFA REFLEX PCR	HVIP	TEST DESCRIPTION	56
-LABDVEHVR	HSV/VZV DFA REFLEX PCR	HVSP	SPECIMEN	56
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	56
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	56
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	56
-LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGGBAND	B.BURGDORFERI IGG BANDS	56
-LABYHMS	"HEAVY METALS SCREEN, BLOOD"	MER	MERCURY	56
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	UBT	UNIT BLOOD TYPE  	56
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	UNT	UNIT NUMBER  	56
-LABDVEHVR	HSV/VZV DFA REFLEX PCR	VZR	VARICELLA-ZOSTER VIRUS	56
-LAB11551R	NK FUNCTION	11661R	NK FUNCTION	55
-LAB11824R	BUSULFAN	11826R	CSS TARGET	55
-LAB11824R	BUSULFAN	11827R	CSS EST. AVG. EXPO.CLEARA RATE	55
-LAB11824R	BUSULFAN	11828R	RECOMMENDED DOSE	55
-10365R	HLA - DSA BY IGG	9627R	CURRENT SERUM DESCRIPTION	55
-10400R	HLA - TYPING SUMMARY	9662R	HLA TYPING SUMMARY	55
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCAB	HEP B CORE TOTAL AB	55
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCABIGM	HEPAT.B CORE AB-IGM	55
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAB	HEPATITIS B SURFACE AB QUAL	55
-LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAG	HEPATITIS B SURF AG	55
-LABPLASMN	PLASMINOGEN	PLASMN	PLASMINOGEN	55
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	55
-LABQFTBG	TB BY QUANTIFERON	TBAG	TB AG	55
-LABQFTBG	TB BY QUANTIFERON	TBAGNI	TB AG - NIL	55
-LABQFTBG	TB BY QUANTIFERON	TBGINT	INTERPRETATION	55
-LABQFTBG	TB BY QUANTIFERON	TBGNIL	NIL	55
-LABQFTBG	TB BY QUANTIFERON	TBGRES	QUANTIFERON RESULT	55
-LABQFTBG	TB BY QUANTIFERON	TBMINI	MITOGEN - NIL	55
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZIM	IMMUNOASSAY SCREEN	55
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	7NHCLONAZEPM	7-NH-CLONAZEPAM	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	7NHFLUNITRAZ	7-NH-FLUNITRAZEPAM	54
-LABLWBCC	"LYME WESTERN BLOT, CSF"	BBIGG	BORELLIA BURG IGG AB	54
-LABLWBCC	"LYME WESTERN BLOT, CSF"	BBIGM	BORELLIA BURG IGM AB	54
-LABBARTPC	"BARTONELLA DNA, QUALI, PCR"	BHENPC	BARTONELLA HENSELAN DNA	54
-LABBARTPC	"BARTONELLA DNA, QUALI, PCR"	BQUIPC	BARTONELLA QUINTANA DNA	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	F1656A	"CD3-/16+56(NK),FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	F1656P	"CD3-/16+56(NK)%,FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD19A	"CD19 (B), ABS., FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD19P	"CD19 (B), %, FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD3AB	"CD3 (T), ABS., FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD3PC	"CD3 (T) %, FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD4AB	"CD4 (HELP),ABS., FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD4PC	"CD4 (HELPER) %, FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD8AB	"CD8 (SUPP),ABS., FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD8PC	"CD8 (SUPP), %, FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCSOUR	SOURCE	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FHNS	"T (H+S), FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FHSAB	"T (H+S), ABS., FLD"	54
-LABCDAS	CELIAC DISEASE SCREEN	GDGPT	IGG ANTI DGP LEVEL	54
-LABHGBSQ	HGB S QUANTITATIVE	HGSMD	REVIEWED BY	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	HSRATIOFLD	"H/S RATIO, FLD"	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	LORAZ	LORAZEPAM	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	LYMABFLD	"LYMPHOCYTES, ABS., FLD"	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	LYMPCFLD	"LYMPHOCYTES %, FLD"	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	NORDIAZ	NORDIAZEPAM	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	OXAZEPAM	OXAZEPAM	54
-LABRDATAC	REVERSE TO COOMBS A CELLS	RDATA	REVERSE TO COOMBS A CELLS	54
-LABBARTPC	"BARTONELLA DNA, QUALI, PCR"	SDES10	SPECIMEN	54
-LABABG	"BLOOD GASES, ARTERIAL"	TEMP	BODY TEMPERATURE	54
-LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	VGDES	VGDES	54
-LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	VGQNT	VGQNT	54
-LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	VGQUL	VGQUL	54
-LABTBP6	TRANSFUSION BLOOD PRODUCT 7	VL	PRODUCT VOLUME	54
-LABYUCOP	"COPPER, URINE"	VOLUMEURN	URINE VOLUME	54
-LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	WBCFLD	"WBC COUNT, FLD"	54
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMAM	AMENDMENT	54
-LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMSO	SOURCE	54
-LABYINFAB	INFLUENZA AB PANEL	YINF2	"INFLUENZA VIRUS A AB, IGG"	54
-LABYINFAB	INFLUENZA AB PANEL	YINF3	"INFLUENZA VIRUS A AB, IGM"	54
-LABYINFAB	INFLUENZA AB PANEL	YINF5	"INFLUENZA VIRUS B AB, IGG"	54
-LABYINFAB	INFLUENZA AB PANEL	YINF6	"INFLUENZA VIRUS B AB, IGM"	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUAAGC	ALPHA OH-ALPRAZOLAM	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUATGC	ALPHA OH-TRIAZOLAM	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZIN	INTERPRETATION	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUEFGC	OH-ETHYL-FLURAZEPAM	54
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUTZGC	TEMAZEPAM	54
-10375R	HLA - STR/CHIMERISM TESTING	9496R	% DONOR- BLOOD CD3	53
-10375R	HLA - STR/CHIMERISM TESTING	9507R	% DONOR- BLOOD CD56	53
-10375R	HLA - STR/CHIMERISM TESTING	9536R	% RECIPIENT- BLOOD CD3	53
-10375R	HLA - STR/CHIMERISM TESTING	9547R	% RECIPIENT- BLOOD CD56	53
-LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GP1IGG	BETA-2-GP1 IGG	53
-LABURCA	"CALCIUM, URINE (RANDOM)"	CAURN	"CALCIUM, URINE"	53
-LABLEGD	"LEGIONELLA CULTURE, DIRECT"	CULT	CULT/OTHER RSLT:	53
-LABVBG	"BLOOD GASES, VENOUS"	FIO2	FIO2 (FIO2)	53
-LABGLWB	"GLUCOSE, WHOLE BLOOD"	GLU	"GLUCOSE, WHOLE BLOOD"	53
-LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	HITI1	HIT FUNCTIONAL ASSAY	53
-LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	HITI2	HIT COMMENT	53
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	53
-LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	PF4COM	PF4 COMMENT	53
-LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	PF4ENH	"PF4 IGG, IGM, IGA OD"	53
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	PRO	PRODUCT ID  	53
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	PRO	PRODUCT ID  	53
-LABLEGD	"LEGIONELLA CULTURE, DIRECT"	RPT	RPT	53
-LABLEGD	"LEGIONELLA CULTURE, DIRECT"	SDES	SPECIMEN	53
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	SI	<<STATUS>>  	53
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	SI	<<STATUS>>  	53
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	SPEXP	SPECIMEN EXPIRATION DATE/TIME	53
-LABLEGD	"LEGIONELLA CULTURE, DIRECT"	SREQ	COMMENTS	53
-LABABG	"BLOOD GASES, ARTERIAL"	TSITE	TEMPERATURE SITE	53
-LABYANRNP	"RNP AB, IGG"	U1RNPAB	AUTOANTIBODIES TO U1RNP	53
-LABVIRSO1	MISC. VIROLOGY TEST	VIROR	RESULT	53
-LABVIRSO1	MISC. VIROLOGY TEST	VIROS	SOURCE	53
-LABVIRSO1	MISC. VIROLOGY TEST	VIROTN	TEST NAME	53
-LABYCHAGA	"TRYPANOSOMA CRUZI AB, IGG"	YCHAG1	"TRYPANOSOMA CRUZI AB,IGG"	53
-10375R	HLA - STR/CHIMERISM TESTING	9500R	% DONOR- BLOOD CD15	52
-10375R	HLA - STR/CHIMERISM TESTING	9540R	% RECIPIENT- BLOOD CD15	52
-LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GP1IGA	BETA-2-GP1 IGA	52
-LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GP1IGM	BETA-2-GP1 IGM	52
-LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	HIPDIR	HIT INTERPRETED BY	52
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	52
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	52
-LABSCHIG	SCHISTOSOMA IGG AB ELISA	SCHIG1	"SCHISTOSOMA ,LGG"	52
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	TRPHOS	TRIPLE PHOSPH CRYST.	52
-LABWNVRNA	"WEST NILE VIRUS RNA, RT  -PCR"	WNRNA	WEST NILE VIRUS RNA	52
-LABYSTNA	"SODIUM, STOOL"	YSTNA2	TOTAL WEIGHT	52
-LAB12330R	"EVEROLIMUS, BLOOD"	12339R	EVEROLIMUS	51
-LAB12330R	"EVEROLIMUS, BLOOD"	12500R	DATE OF LAST DOSE	51
-LAB12330R	"EVEROLIMUS, BLOOD"	12501R	TIME OF LAST DOSE	51
-LAB12330R	"EVEROLIMUS, BLOOD"	12502R	"DOSE, MG"	51
-LABCHOHDL	CHOLESTEROL HDL / TOTAL	CHOL	"CHOLESTEROL, TOTAL"	51
-LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	CULT	CULT/OTHER RSLT:	51
-LABEB	EBV AB PANEL	EBNA	EPSTEIN-BARR NUC AB	51
-LABCHOHDL	CHOLESTEROL HDL / TOTAL	HDL	HDL CHOLESTEROL	51
-LABHDL	"HDL, SERUM"	HDL	HDL CHOLESTEROL	51
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	51
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	51
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	51
-LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	PBGDAY	"PORPHOBILINOGEN, URN"	51
-LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	PBGL	"PORPHOBILINOGEN, URN"	51
-LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	RPT	RPT	51
-LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	SDES	SPECIMEN	51
-LABYSEROT	"SEROTONIN, SERUM"	SEROTONIN	SEROTONIN	51
-LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	SREQ	COMMENTS	51
-LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	TIMEU	COLLECTION PERIOD	51
-LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	UCR	"CREATININE, URINE"	51
-LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	UCR	"CREATININE, URN"	51
-LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	UPIEI	INTERPRETATION	51
-LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	UPIER	RESULT	51
-LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	UT3	COLLECTION PERIOD CREATININE	51
-LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	UVOL	VOLUME	51
-LABEB	EBV AB PANEL	VCA	VIRAL CAPSID AB	51
-LABEB	EBV AB PANEL	VCAM	VIRAL CAPSID AB-IGM	51
-LAB11099R	VIP	VIP	VASOACTIVE INT PEPTIDE	51
-LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	VOL3	VOLUME CREATININE	51
-LABYCLOZ	"CLOZAPINE, SERUM"	YCLOZ1	CLOZAPINE	51
-LABYCLOZ	"CLOZAPINE, SERUM"	YCOMT	COMBINED TOTAL	51
-LABYCLOZ	"CLOZAPINE, SERUM"	YNORC	NORCLOZAPINE	51
-LAB11265R	ENTAMOEBA HISTOLYTICA AG	11266R	ENTAMOABA HISTOLYTICA AG	50
-LAB11523R	GQ1B AB	11633R	GQ1B AB	50
-10375R	HLA - STR/CHIMERISM TESTING	9501R	% DONOR- BLOOD CD19	50
-10375R	HLA - STR/CHIMERISM TESTING	9541R	% RECIPIENT- BLOOD CD19	50
-LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	CULT	CULT/OTHER RSLT:	50
-LABYCYANI	"CYANIDE, BLOOD"	CYAN	"CYANIDE, B"	50
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	DI	ISSUE DATE/TIME	50
-LABGBMIFA	GBM IGA AND IGG AB	GBMIGA	"GBM ANTIBODY, IGA"	50
-LABGBMIFA	GBM IGA AND IGG AB	GBMIGG	GBM-IGG ELISA	50
-LABABG	"BLOOD GASES, ARTERIAL"	NAC	NASAL CANNULA	50
-LABSTOBIC	"OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC."	OCCIMM	"STOOL SAMPLE1,IMMUN"	50
-PFT59	SPIROMETRY	PDF	PDF RESULT	50
-LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	RPT	RPT	50
-LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	SDES	SPECIMEN	50
-LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	SREQ	COMMENTS	50
-LABVBG	"BLOOD GASES, VENOUS"	TEMP	BODY TEMPERATURE	50
-LABTBP10	TRANSFUSION BLOOD PRODUCT 11	VL	PRODUCT VOLUME	50
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	XMR	UNIT CROSSMATCH  	50
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	XMR	UNIT CROSSMATCH  	50
-LABFCD34	"CD34 PANEL, FLUID"	CD34DS	DISCLAIMER	49
-LABFCD34	"CD34 PANEL, FLUID"	CD34FL	SOURCE	49
-LABFCD34	"CD34 PANEL, FLUID"	CD34FLDPC	"%CD34, FLUID"	49
-LABHSCUL	HERPES SIMPLEX CULTURE ONLY	CULT	CULT/OTHER RSLT:	49
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	DI	ISSUE DATE/TIME	49
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFDI	DISCLAIMER	49
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFDX	CLINICAL INDICATION	49
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFME	METHOD	49
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFRE	RESULT	49
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFSO	SOURCE	49
-LABPANHGB	HEMOGLOBIN	HGB	HEMOGLOBIN	49
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	49
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	PRO	PRODUCT ID  	49
-LABHSCUL	HERPES SIMPLEX CULTURE ONLY	RPT	RPT	49
-LABHSCUL	HERPES SIMPLEX CULTURE ONLY	SDES	SPECIMEN	49
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	SI	<<STATUS>>  	49
-LABHSCUL	HERPES SIMPLEX CULTURE ONLY	SREQ	COMMENTS	49
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	UBT	UNIT BLOOD TYPE  	49
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	UNT	UNIT NUMBER  	49
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	XMR	UNIT CROSSMATCH  	49
-LABYAHA	HISTONE ANTIBODIES	YAHA1	HISTONE AUTOANTIBODIES	49
-LABYSTK	"POTASSIUM, STOOL"	YSTK2	TOTAL WEIGHT	49
-LAB11019R	"MYELIN BASIC PROTEIN, CSF"	11164R	MYELIN BASIC PROTEIN	48
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFIN	INTERPRETATION	48
-LABEGFRP	EGFR GENE MUTATION PANEL	EGFMD	INTERPRETED BY	48
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	48
-LABVBG	"BLOOD GASES, VENOUS"	NAC	NASAL CANNULA	48
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	48
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	48
-LABYUTHC	"TETRAHYDROCANNABINOL, URINE"	THC	THC CARBOX AC BY GSMS	48
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	UBT	UNIT BLOOD TYPE  	48
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	UNT	UNIT NUMBER  	48
-LABTBP14	TRANSFUSION BLOOD PRODUCT 15	VL	PRODUCT VOLUME	48
-LABYUTHC	"TETRAHYDROCANNABINOL, URINE"	YUTHIM	IMMUNOASSAY SCREEN	48
-LABYUTHC	"TETRAHYDROCANNABINOL, URINE"	YUTHIN	INTERPRETATION	48
-LAB11549R	CALIFORNIA ENCEPHALITIS PANEL	11659R	CALIFORNIA ENCEPHALITIS PANEL	47
-LAB12230R	SOLUBLE IL 2R	12231R	12231R	47
-10290R	HLA - TYPING	9026R	HLA-DR345 ALLELE 2	47
-LABBONC	BONE CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	47
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	DI	ISSUE DATE/TIME	47
-LABE2O	ESTRADIOL	ESTRADIOL	ESTRADIOL	47
-LABBONC	BONE CULTURE AND GRAM STAIN	GS	GRAM STAIN	47
-LABHEPDAB	HEPATITIS D VIRUS AB TOTAL	HEPD	HEPATITIS D TOTAL	47
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	47
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	47
-LABBONC	BONE CULTURE AND GRAM STAIN	RPT	RPT	47
-LABBONC	BONE CULTURE AND GRAM STAIN	SDES	SPECIMEN	47
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	47
-LABBONC	BONE CULTURE AND GRAM STAIN	SREQ	COMMENTS	47
-LABVBG	"BLOOD GASES, VENOUS"	TSITE	TEMPERATURE SITE	47
-LAB11175R	"LIPASE,BF"	11176R	"LIPASE, BF"	46
-LAB11175R	"LIPASE,BF"	11177R	FLUID TYPE	46
-LABYSTNA	"SODIUM, RANDOM STOOL"	12506R	"SODIUM, RANDOM STOOL"	46
-10370R	HLA - DSA BY C1Q	9456R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	46
-LABASPID	"ASPERGILLUS AB, ID, S"	AFLAV	ASPERGILLUS FLAVUS	46
-LABASPID	"ASPERGILLUS AB, ID, S"	AFLUMI	ASPERGILLUS FUMIGATUS	46
-LABAMIPOL	AMIKACIN PEAK LEVEL	AMIKACINPK	"AMIKACIN, PEAK"	46
-LABAMIPOL	AMIKACIN PEAK LEVEL	AMIPO2	DATE & TIME OF LAST DOSE	46
-LABAMIPOL	AMIKACIN PEAK LEVEL	AMIPO3	DOSAGE	46
-LABAMIPOL	AMIKACIN PEAK LEVEL	AMIPO4	ADMINISTRATION ROUTE	46
-LABASPID	"ASPERGILLUS AB, ID, S"	ANIGE	ASPERGILLUS NIGER	46
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	CCNT	CELLS COUNTED	46
-LABCDPLTI	COLD AGGLUTININ TITER	CDPLT	COLD ANTIBODY TITER	46
-LABLPDP	LIPID PANEL PLUS RISK	CHOL	"CHOLESTEROL, TOTAL"	46
-LABLPDP	LIPID PANEL PLUS RISK	CHOLHDL	CHOLESTEROL/HDL RATIO	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	COPROU	COPROPORPHYRIN I	46
-10260R	HLA ORDER - DECEASED DONOR TESTING NOTICE	DDNOT	DECEASED DONOR ORDER NOTICE	46
-LABLPDP	LIPID PANEL PLUS RISK	HDL	HDL CHOLESTEROL	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	HEPCOH	HEPTACARBOXYLATE	46
-LABLPDP	LIPID PANEL PLUS RISK	HMCY	"HOMOCYSTEINE, PLAS TOTAL"	46
-LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	HNOT	HIV-1 AG DONOR SCRN QUAL. NOTE	46
-LABLPDP	LIPID PANEL PLUS RISK	HSCRP	HIGH SENSITIVITY CRP	46
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	46
-LABLPDP	LIPID PANEL PLUS RISK	LDL	DIRECT LDL CHOL	46
-LABLPDP	LIPID PANEL PLUS RISK	LPA	LIPOPROTEIN A	46
-LABLPDP	LIPID PANEL PLUS RISK	NHDL	"NON-HDL CHOL, CALC"	46
-LABSTPH	"PH, STOOL"	PHSTL	"PH,STOOL"	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	PORU	"UROPORPHYRIN, QUANT"	46
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	PRO	PRODUCT ID  	46
-LABLPDP	LIPID PANEL PLUS RISK	RCRSK	REL. CARDIAC RISK	46
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	SI	<<STATUS>>  	46
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	SPEXP	SPECIMEN EXPIRATION DATE/TIME	46
-LABLPDP	LIPID PANEL PLUS RISK	TGL	"TRIGLYCERIDE, SER/PLAS"	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	TIMEU	COLLECTION PERIOD	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UCR	"CREATININE, URINE"	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UCR	"CREATININE, URN"	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UPORIN	"PORPHYRIN, URN INTRP"	46
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UVOL	VOLUME	46
-LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS1	HIV-1 P24 (NON ICD) AG SCRN	46
-LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS3	HIV-1 P24 INTERP.	46
-LABTBP4	TRANSFUSION BLOOD PRODUCT 5	VL	PRODUCT VOLUME	46
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	XMR	UNIT CROSSMATCH  	46
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM2	C.PNEUMONIAE IGG	46
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM3	C.PNEUMONIAE IGM	46
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM4	C.TRACHOMATIS IGG	46
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM5	C.TRACHOMATIS IGM	46
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM6	C.PSITTACI IGG	46
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM7	C.PSITTACI IGM	46
-LABCMVIGG	CMV AB TOTAL	CMV	CMV ANTIBODY	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	45
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	45
-LABLDLD	DIRECT LDL CHOLESTEROL	LDL	DIRECT LDL CHOL	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVMFR	PM DEVICE MANUFACTURER	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVMODEL	PM DEVICE MODEL	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVSERNM	PM DEVICE SERIAL NUMBER	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVTYPE	PM DEVICE TYPE	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMEVDATE	PACEMAKER EVAL DATE	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	45
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	45
-LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOSFREE	"TESTOSTERONE, FREE"	45
-LABYNMOG	NMO IGG	YNMOG1	"(NMO) AUTOANTIBODY, IGG, SERUM"	45
-LABYSTK	"POTASSIUM, RANDOM STOOL"	12504R	"POTASSIUM, RANDOM STOOL"	44
-LAB11128R	FUNGAL MIC	FMIC	FUNGAL MIC	44
-LABFQFVR	COXIELLA BURNETII AB	QFVG1	PHASE 1 IGG	44
-LABFQFVR	COXIELLA BURNETII AB	QFVG2	PHASE II IGG	44
-LABFQFVR	COXIELLA BURNETII AB	QFVIN	Q FEVER INTERPRETATION	44
-LABFQFVR	COXIELLA BURNETII AB	QFVM1	PHASE 1 IGM	44
-LABFQFVR	COXIELLA BURNETII AB	QFVM2	PHASE II IGM	44
-LABTBP8	TRANSFUSION BLOOD PRODUCT 9	VL	PRODUCT VOLUME	44
-LABBRAF	BRAF	BRAFDI	DISCLAIMER	43
-LABBRAF	BRAF	BRAFMT	METHOD	43
-LABBRAF	BRAF	BRAFRE	RESULT	43
-LABBRAF	BRAF	BRAFSP	SPECIMEN	43
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	43
-LABYPENTO	PENTOBARBITAL LEVEL	PENTO	"PENTOBARBITAL, S"	43
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVIMPDT	PM DEVICE IMPLANT DATE	43
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRHYTHM	PM RHYTHM	43
-LABPROTSF	"PROTEIN S, FREE"	PROTSF	"PROTEIN S, FREE"	43
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	43
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	43
-LABMISPRO	MISCELLANEOUS PROCESSING	SEN	SEND TO	43
-LABYLEGS	"LEGIONELLA PNEUMOPHILA, AB"	YLEGS1	LEGIONELLA PNEUMOPHILA AB	43
-10335R	HLA - SCREENING C1Q	9279R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS II	42
-10365R	HLA - DSA BY IGG	9628R	REFERENCE SAMPLE DATE	42
-LABVBG	"BLOOD GASES, VENOUS"	BGSPRC	SPECIMEN COMMENT	42
-LABBRAF	BRAF	BRAFMD	INTERPRETATION	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32CM	COMMENT:	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32DI	DISCLAIMER:	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32DX	CLINICAL INDICATION	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32ET	ETHNICITY (PROVIDED BY MD)	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32IN	INTERPRETED BY	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32ME	METHOD:	42
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32R1	RESULT 1	42
-LABCMVIGM	CMV AB IGM	CMVIGM	CMV IGM AB	42
-LABECHIG	"ECHINOCOCCUS AB, IGG"	ECHIND	ECHINOCOCCUS LGG	42
-LABHEPAR	"HEPARIN CONC, ANTIXA U/ML"	HEPAR	HEPARIN ACTIVITY LEVEL	42
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	42
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	42
-LABTBP53	TRANSFUSION BLOOD PRODUCT 54	PRO	PRODUCT ID  	42
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	42
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	42
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	42
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	42
-LABTBP53	TRANSFUSION BLOOD PRODUCT 54	SI	<<STATUS>>  	42
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	UBT	UNIT BLOOD TYPE  	42
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	UNT	UNIT NUMBER  	42
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	XMR	UNIT CROSSMATCH  	42
-10273R	HLA ORDER - WORKUP AT TIME OF TRANSPLANT	10049R	HLA LAB TESTING STATUS	41
-10395R	HLA - CROSSMATCH SUMMARY	9661R	HLA CROSSMATCH SUMMARY	41
-LAB12809R	CK ISOENZYME ELECTROPHORESIS	BBFRAC	BB FRACTION	41
-LAB12809R	CK ISOENZYME ELECTROPHORESIS	CKEP	CK ISOENZYME ELECTROPHORESIS	41
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CNRBC	"NRBC/100WBC, CSF"	41
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	DI	ISSUE DATE/TIME	41
-LABFTRIGA	"TRIGLYCERIDES, NON-PLEURAL"	FSC1	FLUID TYPE	41
-LABHB	HEPATITIS B SURFACE AG	HBSAG	HEPATITIS B SURF AG	41
-LABDVEIAV	FLU A DFA	IAV	INFLUENZA A	41
-LABDVEIAV	FLU A DFA	IAVIP	TEST DESCRIPTION	41
-LABDVEIAV	FLU A DFA	IAVSP	SPECIMEN	41
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	41
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	41
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	41
-10273R	HLA ORDER - WORKUP AT TIME OF TRANSPLANT	RINFO	TO FIND RESULT:	41
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	SPEXP	SPECIMEN EXPIRATION DATE/TIME	41
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	SPEXP	SPECIMEN EXPIRATION DATE/TIME	41
-10275R	HLA ORDER - SPECIMEN IN LAB ORDER NOTICE	SPINN	SPECIMEN IN LAB ORDER NOTICE	41
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	UBT	UNIT BLOOD TYPE  	41
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	UNT	UNIT NUMBER  	41
-LAB12809R	CK ISOENZYME ELECTROPHORESIS	YCKMB	MB FRACTION	41
-LAB12809R	CK ISOENZYME ELECTROPHORESIS	YCKMM	MM FRACTION	41
-LAB12809R	CK ISOENZYME ELECTROPHORESIS	YCKTOT	TOTAL CK ACTIVITY	41
-LAB11566R	"ENTEROVIRUS RNA, QUAL"	11679R	SOURCE	40
-LAB11566R	"ENTEROVIRUS RNA, QUAL"	11680R	"ENTEROVIRUS QUAL,PCR"	40
-10365R	HLA - DSA BY IGG	9432R	DSA RESULTS FOR IGG CLASS II	40
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	BP190	"P190, BONE MARROW"	40
-LABCRYEIA	CRYPTOSPORIDIUM EIA	CULT	CULT/OTHER RSLT:	40
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	DI	ISSUE DATE/TIME	40
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GMYD8B	"MYD88, BLOOD"	40
-LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GSF3BB	"SF3B1, BLOOD"	40
-LABYHABQ	HEPATITIS B SURFACE AB QUANTITATIVE	HBSAB	"HBS ANTIBODY, S"	40
-LABYHABQ	HEPATITIS B SURFACE AB QUANTITATIVE	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	40
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	40
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	40
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	40
-LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGMBAND	B.BURGDORFERI IGM BANDS	40
-LABYPLAB	PLATELET AB	PLTAB	PLATELET ANTIBODY	40
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	40
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	40
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	40
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	40
-LABCRYEIA	CRYPTOSPORIDIUM EIA	RPT	RPT	40
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	40
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	40
-LABCRYEIA	CRYPTOSPORIDIUM EIA	SDES	SPECIMEN	40
-LABCRYEIA	CRYPTOSPORIDIUM EIA	SREQ	COMMENTS	40
-LABTOXOP2	TOXOPLASMOSIS PCR	TOXGG	GENERAL GUIDELINES	40
-LABTOXOP2	TOXOPLASMOSIS PCR	TOXRES	PCR RESULT	40
-LABTOXOP2	TOXOPLASMOSIS PCR	TOXSP	SPECIMEN TYPE	40
-10075R	HLA ORDER - STR/CHIMERISM	10049R	HLA LAB TESTING STATUS	39
-LAB11593R	ETHYLENE GLYCOL	11721R	ETHYLENE GLYCOL	39
-LABCRTINE	"CREATINE, SER/PLAS"	11852R	"CREATINE, PLASMA MG/DL"	39
-LAB11027R	IMMUNE COMPLEXES	C1Q	C1Q BINDING ASSAY	39
-LABCRTINE	"CREATINE, SER/PLAS"	CRTNR	"CREATINE, PLASMA"	39
-LABBMC	BONE MARROW CULTURE	CULT	CULT/OTHER RSLT:	39
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	39
-LABBMC	BONE MARROW CULTURE	GS	GRAM STAIN	39
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDEVLDATE	ICD DATE OF EVENT	39
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	39
-LABLPA	LIPOPROTEIN	LPA	LIPOPROTEIN A	39
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	MFR	MANUFACTURER	39
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	MOD	MODEL	39
-LABTBP54	TRANSFUSION BLOOD PRODUCT 55	PRO	PRODUCT ID  	39
-10075R	HLA ORDER - STR/CHIMERISM	RINFO	TO FIND RESULT:	39
-LABBMC	BONE MARROW CULTURE	RPT	RPT	39
-LABBMC	BONE MARROW CULTURE	SDES	SPECIMEN	39
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	39
-LABTBP54	TRANSFUSION BLOOD PRODUCT 55	SI	<<STATUS>>  	39
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	39
-LABBMC	BONE MARROW CULTURE	SREQ	COMMENTS	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSICO	COMMENT	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSIDI	DISCLAIMER	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSIDX	CLINICAL INDICATION	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSIMD	INTERPRETED BY	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSIME	METHOD	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSIRE	RESULT	39
-LABTMSI	MICROSATELLITE INSTABILITY	TMSISO	"SOURCE, TISSUE"	39
-LABYUCORT	"CORTISOL FREE, URINE"	UCORT	"CORTISOL, URINE"	39
-LABURURIC	"URIC ACID, URINE (RANDOM)"	URICURN	"URIC ACID, URINE"	39
-LABTBP7	TRANSFUSION BLOOD PRODUCT 8	VL	PRODUCT VOLUME	39
-LABYUCORT	"CORTISOL FREE, URINE"	VOLUMEURN	URINE VOLUME	39
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	XMR	UNIT CROSSMATCH  	39
-LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	YARBIN	ACH RECEPTOR BINDING ANTIBODY	39
-LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	YARMA	ACH RECEPT MODUL AB	39
-LABYIFBA	INTRINSIC FACTOR BLOCKING AB	YIFB1	INTRINSIC FACTOR BLOCKING AB	39
-LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	YSTA	STRIATIONAL (STRIATED MUSCLE)	39
-LABYUCORT	"CORTISOL FREE, URINE"	YUCOUT	COLLECTION DURATION	39
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12363R	"ANNA-1, CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12364R	"ANNA-2, CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12365R	"ANNA-3, CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12366R	"AGNA-1,CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12367R	"PCA-1, CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12368R	"PCA-2, CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12369R	"AMPHIPHYSIN AB, CSF"	38
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12370R	"CRMP-5-IGG, CSF"	38
-LABADVQL	ADENOVIRUS DNA PCR QUAL	ADQLDN	ADV DNA	38
-LABADVQL	ADENOVIRUS DNA PCR QUAL	ADQLIP	TEST DESCRIPTION	38
-LABADVQL	ADENOVIRUS DNA PCR QUAL	ADQLSP	SPECIMEN	38
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	BP210	"P210, BONE MARROW"	38
-LABYFCAR	"CARBAMAZEPINE FREE AND TOTAL, SERUM"	CZPFR	"CARBAMAZEPINE,FREE,S"	38
-LABYFCAR	"CARBAMAZEPINE FREE AND TOTAL, SERUM"	CZPTOT	"CARBAMAZEPINE,TOT,S"	38
-LABDVERSV	DIRECT VIRAL EXAM RSV	DVE	RESULTS	38
-LABGDGP	IGG ANTI DGP	GDGINT	INTERPRETATION	38
-LABGDGP	IGG ANTI DGP	GDGPR	IGG ANTI DGP ANTIBODY	38
-LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG	HSV1G	"HSV 1, IGG INDEX"	38
-LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG	HSV2G	"HSV 2, IGG INDEX"	38
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	38
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	38
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	38
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	38
-LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	LRB210	LOG DX AVG	38
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRALDBIIM	PM RA LEAD BIPOLAR IMPEDANCE	38
-LABDVERSV	DIRECT VIRAL EXAM RSV	RPT	RPT	38
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	38
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	38
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	38
-LABDVERSV	DIRECT VIRAL EXAM RSV	SDES	SPECIMEN	38
-LABYANTSM	AUTOANTIBODIES TO SM	SMAB	AUTOANTIBODIES TO SM	38
-LABDVERSV	DIRECT VIRAL EXAM RSV	SREQ	COMMENTS	38
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	UBT	UNIT BLOOD TYPE  	38
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	UNT	UNIT NUMBER  	38
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	XMR	UNIT CROSSMATCH  	38
-10190R	HLA ORDER - COMMUNICATION/UPDATE	10049R	HLA LAB TESTING STATUS	37
-LAB11253R	HELICOBACTER PYLORI AGA AB	11185R	HELICOBACTER PYLORI IGA AB	37
-LAB11984R	NIACIN	11985R	NIACIN	37
-LABYFLUO	5 - FLUCYTOSINE	5FC	5-FLUCYTOSINE	37
-LABAFBSO	AFB SMEAR ONLY	AFB	AFB SMEAR	37
-LABCD34	"CD34 PANEL, BLOOD"	CD34V	VIABILITY	37
-LABCOCFS	"COCCIDIOIDES COMPLEMENT FIXATION, SERUM"	COCFS	COCCI CF (SERUM)	37
-LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	COPRO3	COPROPORPHYRIN III	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	37
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRVVDEL	BRADY VV DELAY	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDRHYTHM	ICD RHYTHM	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	37
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	37
-LABYPYRU	PYRUVIC ACID	PYRU	PYRUVIC ACID	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	37
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	37
-10190R	HLA ORDER - COMMUNICATION/UPDATE	RINFO	TO FIND RESULT:	37
-LABAFBSO	AFB SMEAR ONLY	RPT	RPT	37
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	37
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	37
-LABAFBSO	AFB SMEAR ONLY	SDES	SPECIMEN	37
-LABSHIVAB	RAPID HIV-1 AB	SHIVAB	RAPID HIV-1 ANTIBODY	37
-LABAFBSO	AFB SMEAR ONLY	SREQ	COMMENTS	37
-LABYVB1W	VITAMIN B1	YVB1W2	COMMENT	37
-LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GPCM	BETA-2-GP1 COMMENT	36
-LABFHVIG	"HIV-1 AB, WESTERN BLOT"	BANDPRSNT	BANDS PRESENT	36
-LABABG	"BLOOD GASES, ARTERIAL"	BGSPR	SPECIMEN COMMENT	36
-LABFCD34	"CD34 PANEL, FLUID"	CD34V	VIABILITY	36
-LABNASC	NP / NASAL CULTURE	CULT	CULT/OTHER RSLT:	36
-LABDCMVL	DONOR CMV AB (LIVING)	DCMVL1	DONOR CMV- TOTAL AB	36
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	DI	ISSUE DATE/TIME	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HATINT	INTERPRETATION	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HBSINT	INTERPRETATION	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HBTINT	INTERPRETATION	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HCVI	INTERPRETATION	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HCVR	ANTI-HCV	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	36
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	36
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	36
-LABPFCATE	CATECHOLAMINES FRACTIONATED	PCATIN	"CAT PF INTERPRETATION(CATECHOL INTRP, PL)"	36
-LABPFCATE	CATECHOLAMINES FRACTIONATED	PDOPA	DOPAMINE	36
-LABPFCATE	CATECHOLAMINES FRACTIONATED	PEPI	EPINEPHRINE BY HPLC	36
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMTLPCPACA	PM TELEMETRY PERCENT PACING ATRIAL	36
-LABPFCATE	CATECHOLAMINES FRACTIONATED	PNOREP	NOREPINEPHRINE	36
-LABTBP55	TRANSFUSION BLOOD PRODUCT 56	PRO	PRODUCT ID  	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	RPRQ	RESULT	36
-LABBMPTP	BMT PRETRANSPLANT PANEL	RPRQIN	INTERPRETATIONS	36
-LABNASC	NP / NASAL CULTURE	RPT	RPT	36
-LABNASC	NP / NASAL CULTURE	SDES	SPECIMEN	36
-LABTBP55	TRANSFUSION BLOOD PRODUCT 56	SI	<<STATUS>>  	36
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	SPEXP	SPECIMEN EXPIRATION DATE/TIME	36
-LABNASC	NP / NASAL CULTURE	SREQ	COMMENTS	36
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	UBT	UNIT BLOOD TYPE  	36
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	UNT	UNIT NUMBER  	36
-LABTBP3	TRANSFUSION BLOOD PRODUCT 4	VL	PRODUCT VOLUME	36
-LABYBLAS	BLASTOMYCES AB	YBLAS1	"BLASTOMYCES AB ,S"	36
-LABYCLON	"CLONAZEPAM, SERUM"	YCLON1	CLONAZEPAM	36
-LABYPR3	PROTEINASE 3 AUTOANTIBODIES	YPR31	PROTEINASE 3 ANTIBODIES	36
-LABYRICK	RICKETTSIA AB PANEL	YRICK1	"SPOTTED FEVER GROUP,AB,IGG,S"	36
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12820R	"SODIUM, FECES"	35
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12821R	"POTASSIUM, FECES"	35
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12822R	"CHLORIDE, FECES"	35
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12823R	"OSMOLALITY, FECES"	35
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12824R	"MAGNESIUM, FECES"	35
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12825R	"PHOSPHORUS, FECES"	35
-LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12826R	"OSMOTIC GAP, FECES"	35
-10375R	HLA - STR/CHIMERISM TESTING	9479R	LOCUS 5	35
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	ARS	"ARSENIC, BLOOD"	35
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15DI	DISCLAIMER	35
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15DX	CLINICAL INDICATION	35
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15MD	INTERPRETED BY	35
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15ME	METHOD	35
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15RE	"RESULT, BM"	35
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	CAD	"CADMIUM, BLOOD"	35
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	DI	ISSUE DATE/TIME	35
-LABFPARVO	PARVOVIRUS IGG / IGM AB	FPARV2	PARVOVIRUS IGM INDEX	35
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	35
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	35
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	35
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	35
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	35
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	35
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	35
-LABREF3	LAB UNLISTED 3	LAB3	REFERENCE LAB	35
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	LEA	"LEAD, BLD"	35
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	MERC	"MERCURY, BLOOD"	35
-LABT1517	"T(15	17) PML/RAR, BLOOD"	PMLR	PML	35
-LABREF3	LAB UNLISTED 3	RESUL3	RESULT	35
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	35
-LABREF3	LAB UNLISTED 3	SPTY3	SPECIMEN TYPE	35
-LABREF3	LAB UNLISTED 3	TEST3	TEST NAME	35
-LAB11120R	"TOXOPLASMOSIS, CSF"	TOXOC	"TOXOPLASMA AB,CSF"	35
-LABTBP5	TRANSFUSION BLOOD PRODUCT 6	VL	PRODUCT VOLUME	35
-LABYRICK	RICKETTSIA AB PANEL	YRICK2	"SPOTTED FEVER GROUP,AB,IGM,S"	35
-LABYRICK	RICKETTSIA AB PANEL	YRICK3	"TYPHUS FEVER GROUP,AB,IGG,S"	35
-LABYRICK	RICKETTSIA AB PANEL	YRICK4	"TYPHUS FEVER GROUP,AB,IGM,S"	35
-LABVCAIGG	EB VIRAL CAPSID AG IGG AB	11168R	EB VCA IGG AB	34
-LABCARN	CARNITINE FREE AND TOTAL	ACFCR	ACYL/FREE CARNITINE RATIO	34
-LABPENALL	PENICILLIN ALLERGY SCREEN	AMOX	AMOXICILLIN	34
-LABPENALL	PENICILLIN ALLERGY SCREEN	AMPICILLIN	AMPICILLIN	34
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	BASOSYNFLD	"BASOPHILS, SYN FLD"	34
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15CO	"COMMENT, BM"	34
-LABCARN	CARNITINE FREE AND TOTAL	CARINT	INTERPRETATION	34
-LABCARN	CARNITINE FREE AND TOTAL	CARNF	"CARNITINE, FREE"	34
-LABCARN	CARNITINE FREE AND TOTAL	CARNIT	"CARNITINE, TOTAL"	34
-LABROGC	GC CULTURE SCREEN	CULT	CULT/OTHER RSLT:	34
-LABDHIVB	DONOR HIV AB	DHIV	DONOR HIV AB	34
-LABDHIVB	DONOR HIV AB	DHTLV	DONOR HIV AB COMMENT	34
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	34
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	34
-LABPENALL	PENICILLIN ALLERGY SCREEN	IGEPEN	TOTAL IGE	34
-LABPENALL	PENICILLIN ALLERGY SCREEN	PENG	PENICILLIN G	34
-LABPENALL	PENICILLIN ALLERGY SCREEN	PENINT	INTERPRETATION	34
-LABPENALL	PENICILLIN ALLERGY SCREEN	PENV	PENICILLIN V	34
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	34
-LABROGC	GC CULTURE SCREEN	RPT	RPT	34
-LABROGC	GC CULTURE SCREEN	SDES	SPECIMEN	34
-LABSLINTP	SLIDE INTERPRETATION BY PATHOLOGIST	SLINT	INTERPRETATION	34
-LABSLINTP	SLIDE INTERPRETATION BY PATHOLOGIST	SLPATH	PATHOLOGIST	34
-LABROGC	GC CULTURE SCREEN	SREQ	COMMENTS	34
-LABVZPAN	VZV IGG IGM PANEL	VGGDES	VZV IGG TEST DESCRIPTION	34
-LABVZPAN	VZV IGG IGM PANEL	VGGQNT	VZV IGG LEVEL	34
-LABVZPAN	VZV IGG IGM PANEL	VGGQUL	VZV IGG RESULT	34
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	34
-LABVZPAN	VZV IGG IGM PANEL	VZVDS	VZV IGM TEST DESCRIPTION	34
-LABVZPAN	VZV IGG IGM PANEL	VZVMQL	VZV IGM RESULT	34
-LABVZPAN	VZV IGG IGM PANEL	VZVMQT	VZV IGM LEVEL	34
-LABDWNIL	"DONOR, WEST NILE VIRUS NAT"	WNIL1	"DONOR, WEST NILE VIRUS NAT"	34
-LABEAIGG	EB VIRUS EARLY AG (R+D) IGG	11165R	EB VIRUS EARLY AG (R+D) IGG	33
-LABEBNABY	EB NUCLEAR AG AB	11166R	EB NUCLEAR AB	33
-LABVCAIGM	EB VIRAL CAPSID AG IGM AB	11167R	EB VCA IGM AB	33
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTDIS	DISCLAIMER	33
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTDX	CLINICAL INDICATION	33
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTMET	METHOD	33
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTRES	RESULTS	33
-LABUCAT	"CALCIUM, URINE (TIMED)"	CAE	CALCIUM EXCRETION	33
-LABUCAT	"CALCIUM, URINE (TIMED)"	CAURN	"CALCIUM, URINE"	33
-LABDCGSN	"DONOR, CHAGAS SCN W/REFL"	CGSN1	"DONOR, CHAGAS SCN W/REFL"	33
-LAB11133R	DONOR HBC AB	DHBCC	DONOR HEP CORE AB	33
-LAB11134R	DONOR HCV AB	DHEPC	DONOR HEPATITIS C AB	33
-LABFPARVO	PARVOVIRUS IGG / IGM AB	FPARV1	PARVOVIRUS IGG INDEX	33
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GMYD8M	"MYD88, BM"	33
-LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GSF3BM	"SF3B1, BM"	33
-LABUHGB	"HEMOGLOBIN, URINE"	HGBURN	"HEMOGLOBIN, URINE"	33
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	33
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	33
-LABTBP56	TRANSFUSION BLOOD PRODUCT 57	PRO	PRODUCT ID  	33
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	33
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	33
-LABDVEAB	RAPID FLU A/B EIA	RFCOM	COMMENTS	33
-LABDVEAB	RAPID FLU A/B EIA	RFLSP	SPECIMEN	33
-LABDVEAB	RAPID FLU A/B EIA	RFLUA	INFLUENZA VIRUS TYPE A	33
-LABDVEAB	RAPID FLU A/B EIA	RFLUB	INFLUENZA VIRUS TYPE B	33
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	33
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	33
-LABTBP56	TRANSFUSION BLOOD PRODUCT 57	SI	<<STATUS>>  	33
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	UBT	UNIT BLOOD TYPE  	33
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	UNT	UNIT NUMBER  	33
-LABUHGB	"HEMOGLOBIN, URINE"	UT	COLLECTION PERIOD	33
-LABUCAT	"CALCIUM, URINE (TIMED)"	UT5	COLLECTION PERIOD CALCIUM	33
-LABTBP12	TRANSFUSION BLOOD PRODUCT 13	VL	PRODUCT VOLUME	33
-LABUHGB	"HEMOGLOBIN, URINE"	VOL	VOLUME	33
-LABUCAT	"CALCIUM, URINE (TIMED)"	VOL5	VOLUME CALCIUM	33
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	XMR	UNIT CROSSMATCH  	33
-LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	YUCATF	"CATECHOLAMINES, URINE"	33
-LABYUCOP	"COPPER, URINE"	YUCOP4	CU CONC (UG/L)	33
-10283R	HLA ORDER - TRANSPLANT EVENT COMMUNICATION	10049R	HLA LAB TESTING STATUS	32
-LABLEGA	"LEGIONELLA CULTURE, ACID"	CULT	CULT/OTHER RSLT:	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	DEVTYPE	DEVTYPE	32
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	DI	ISSUE DATE/TIME	32
-LABFACT12	FACTOR XII ASSAY	FACTOR12	FACTOR XII ASSAY	32
-LABHIBCV	DONOR HIV1 / HBV / HCV NAT	HIBCV1	DONOR HIV/HBV/HCV RESULT	32
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRLOWRT	BRADY LOWER RATE	32
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	32
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	32
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRPACMD	BRADY PACING MODE	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDEVLDATE	ICD DATE OF EVENT	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	32
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	32
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	IMPDATE	IMPLANT DATE	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	MFR	MANUFACTURER	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	MOD	MODEL	32
-10283R	HLA ORDER - TRANSPLANT EVENT COMMUNICATION	RINFO	TO FIND RESULT:	32
-LABLEGA	"LEGIONELLA CULTURE, ACID"	RPT	RPT	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRPACA	RV BRADY PACING AMPLITUDE	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRPACW	RV BRADY PACING PULSE WIDTH	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENA	RV BRADY SENSING AMPLITUDE	32
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	32
-LABLEGA	"LEGIONELLA CULTURE, ACID"	SDES	SPECIMEN	32
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SERNUM	SERIAL NUMBER	32
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	32
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	SPEXP	SPECIMEN EXPIRATION DATE/TIME	32
-LABLEGA	"LEGIONELLA CULTURE, ACID"	SREQ	COMMENTS	32
-LABUA	URINALYSIS WITH MICROSCOPIC	WBCAST	WBC CASTS	32
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	XMR	UNIT CROSSMATCH  	32
-10375R	HLA - STR/CHIMERISM TESTING	9574R	INTERPRETATION/ COMMENTS	31
-LABYCCPIG	ANTI CCP	CCPT	ANTI CCP ANTIBODY LEVEL	31
-LAB11132R	DONOR HEP B SURF. AG	DHB	DONOR HEP B SURF. AG	31
-LABYEBPCR	"EBV BY RAPID PCR, CSF"	EBV	EBV BY PCR	31
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	EBV	EBV BY PCR	31
-LABFILAB	FILARIA IGG4 AB ELISA	FILIG4	FILARIAL LGG ANTIBODY	31
-LAB12814R	MYCOPLASMA IGG/IGM AB	FMYABM	M.PNEUMONIA AB IGM	31
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	31
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBCAB	HEP B CORE TOTAL AB	31
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSAB	HEPATITIS B SURFACE AB QUAL	31
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSAG	HEPATITIS B SURF AG	31
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HEPAAB	HEPATITIS A TOTAL AB	31
-LABVP3	COMPREHENSIVE HEPATITIS PANEL	HEPCAB	HEPATITIS C AB	31
-LABDVEIBV	FLU B DFA	IBV	INFLUENZA B	31
-LABDVEIBV	FLU B DFA	IBVIP	TEST DESCRIPTION	31
-LABDVEIBV	FLU B DFA	IBVSP	SPECIMEN	31
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	31
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	31
-LABYMANG	MANGANESE	MN	MANGANESE	31
-LABTOTALS	"PROTEIN S AG, TOTAL"	PROTSTOT	"PROTEIN S AG, TOTAL"	31
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	31
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	31
-LABSSAABY	ANTI-RO ANTIBODY	SSAQNT	ANTI-RO LEVEL	31
-LABVITK	"VITAMIN K1, SERUM"	VITK	"VITAMIN K1,SERUM"	31
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	XMR	UNIT CROSSMATCH  	31
-LABYCHROM	CHROMIUM	YCHRO1	CHROMIUM	31
-LABYEBPCR	"EBV BY RAPID PCR, CSF"	YEBP1	SPECIMEN SOURCE	31
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	YEBP1	SPECIMEN SOURCE	31
-LABYEBPCR	"EBV BY RAPID PCR, CSF"	YEPB4	REPORT STATUS	31
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	YEPB4	REPORT STATUS	31
-LABYHISTC	"HISTOPLASMA AG, CSF"	YHISC4	"RESULT,CSF"	31
-10090R	HLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	30
-10220R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)	10049R	HLA LAB TESTING STATUS	30
-10225R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*	10049R	HLA LAB TESTING STATUS	30
-10390R	HLA-BMT DONOR MATCH DETAIL SUMMARY	9706R	DONOR MATCH GRADE SUMMARY	30
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ATACHTADTI	ATACH TACHY ZONE DETECTION INTERVAL	30
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTDIR	INTERPRETED BY	30
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTPRM	PRIMERS	30
-LABCMVIGM	CMV IGM	CMVIGM	CMV IGM AB	30
-LABSFCORT	"CORTISOL FREE, SERUM"	CORTFREE	"CORTISOL, SERUM FREE"	30
-LABYDESIP	"DESIPRAMINE, SERUM"	DESIP	DESIPRAMINE	30
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	DI	ISSUE DATE/TIME	30
-LABDVEFLB	DIRECT VIRAL EXAM FLU B	DVE	RESULTS	30
-LABFCHOLA	"CHOLESTEROL, FLUID"	FSC1	FLUID TYPE	30
-LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GLUF	"GLUCOSE,FASTING"	30
-LABURGL	"GLUCOSE, URINE (RANDOM)"	GLUURN	"GLUCOSE, URINE"	30
-LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTT1	"GLUCOSE,GTT 1HR"	30
-LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTT2	"GLUCOSE,GTT 2HR"	30
-LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTT30	"GLUCOSE,GTT 30 MIN"	30
-LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTTINT	GTT INTERPRETATION	30
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	30
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	30
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	30
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	30
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	30
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	30
-LABMPOPR3	ANCA MPO / PR - 3M IGG	MPO	"MYLEOPEROXIDASE, AB"	30
-LABPORS	"PORPHYRINS, SERUM"	PORINT	"PORPHYRINS, SER INTR"	30
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	PRO	PRODUCT ID  	30
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	30
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	30
-10090R	HLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)	RINFO	TO FIND RESULT:	30
-10220R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)	RINFO	TO FIND RESULT:	30
-10225R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*	RINFO	TO FIND RESULT:	30
-LABDRPRS	"DONOR, RPR"	RPRS1	DONOR SYPHILIS SCREEN	30
-LABRPRQT	RPR QUANTITATIVE	RPRTIN	INTERPRETATIONS	30
-LABDVEFLB	DIRECT VIRAL EXAM FLU B	RPT	RPT	30
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	30
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	30
-LABDVEFLB	DIRECT VIRAL EXAM FLU B	SDES	SPECIMEN	30
-LABMPOPR3	ANCA MPO / PR - 3M IGG	SERINEPR3	SERINE PROTEASE 3	30
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	SI	<<STATUS>>  	30
-LABDVEFLB	DIRECT VIRAL EXAM FLU B	SREQ	COMMENTS	30
-LABTOXPCR	TOXOPLASMA PCR	TOXGG	GENERAL GUIDELINES	30
-LAB11122R	"TOXOPLASMA AB, IGM"	TOXOM	TOXOPLASMA IGM ELISA	30
-LABTOXPCR	TOXOPLASMA PCR	TOXRES	PCR RESULT	30
-LABTOXPCR	TOXOPLASMA PCR	TOXSP	SPECIMEN TYPE	30
-LABPORS	"PORPHYRINS, SERUM"	TPOR	"PORPHYRINS, TOTAL"	30
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	UBT	UNIT BLOOD TYPE  	30
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	UNT	UNIT NUMBER  	30
-LABTBP11	TRANSFUSION BLOOD PRODUCT 12	VL	PRODUCT VOLUME	30
-LABTBP9	TRANSFUSION BLOOD PRODUCT 10	VL	PRODUCT VOLUME	30
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	30
-10295R	HLA - AUTO T FLOW CROSSMATCH	9087R	SERUM DATE 2	29
-10295R	HLA - AUTO T FLOW CROSSMATCH	9089R	MCS 2	29
-10300R	HLA - AUTO B FLOW CROSSMATCH	9108R	SERUM DATE 2	29
-10300R	HLA - AUTO B FLOW CROSSMATCH	9110R	MCS 2	29
-10295R	HLA - AUTO T FLOW CROSSMATCH	9596R	RESULT 2	29
-LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTCMT	COMMENT	29
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDRHYTHM	ICD RHYTHM	29
-ECH29	ECHO - DOBUTAMINE STRESS ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	29
-LABPANAPL	PROCAINAMIDE AND NAPA	NAPA	N-ACETYLPROCAINAMIDE (NAPA)	29
-LABPANAPL	PROCAINAMIDE AND NAPA	PA	PROCAINAMIDE	29
-LABPANAPL	PROCAINAMIDE AND NAPA	PANAP1	"PEAK, TROUGH, OR RANDOM"	29
-LABPANAPL	PROCAINAMIDE AND NAPA	PANAP2	DATE & TIME OF LAST DOSE	29
-LABPANAPL	PROCAINAMIDE AND NAPA	PANAP3	DOSAGE (INCLUDE UNITS)	29
-LABPANAPL	PROCAINAMIDE AND NAPA	PANAP4	ADMINISTRATION ROUTE	29
-LABUPHOS	"PHOSPHATE WITH CREATININE, URINE (RANDOM)"	PHOSU	"PHOSPHATE, URINE"	29
-LABUPHOS	"PHOSPHATE WITH CREATININE, URINE (RANDOM)"	PHUCRU	PHOSPHATE/CREAT RATIO	29
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	29
-LABUPHOS	"PHOSPHATE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	29
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	XMR	UNIT CROSSMATCH  	29
-LAB12814R	MYCOPLASMA IGG/IGM AB	12024R	MYCOPLASMA PNEUMONIA AB IGG	28
-LAB12322R	VON WILLEBRAND MULTIMERS	12323R	VON WILLEBRAND MULTIMERS	28
-LABTHIOMT	THIOPURINE METABOLITES	6TGN	6-TGN	28
-10330R	HLA - SCREENING IGG	9260R	INTERPRETATION/ COMMENTS CLASS II	28
-10300R	HLA - AUTO B FLOW CROSSMATCH	9601R	RESULT 2	28
-10365R	HLA - DSA BY IGG	9629R	REFERENCE SERUM DESCRIPTION	28
-LABYAT3AG	"ANTITHROMBIN AG, PLASMA"	AT3	ANTITHROMBIN III AG	28
-LAB11025R	"C1 INHIBITOR, QUANT"	C1ESTR	C-1-ESTERASE INHIBITOR	28
-LABMEP	MEDICAL EMERGENCY PANEL	CAION	"CALCIUM, IONIZED"	28
-LABMEP	MEDICAL EMERGENCY PANEL	CL	"CHLORIDE, WHOLE BLD"	28
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	DI	ISSUE DATE/TIME	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	DX	DIAGNOSIS	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	FCOLOR	APPEARANCE	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	FLDTYP	FLUID SOURCE	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	FTURB	"TURBIDITY, FLD"	28
-LABMEP	MEDICAL EMERGENCY PANEL	GLU	"GLUCOSE, WHOLE BLOOD"	28
-LABMEP	MEDICAL EMERGENCY PANEL	HCT	HCT (EST)	28
-POC30	POC PYLORITECK	HPYL	PATIENT HPYLORI	28
-LABBMPTP	BMT PRETRANSPLANT PANEL	HSAB	VIRAL AB HSV	28
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	28
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	28
-LABMEP	MEDICAL EMERGENCY PANEL	K	"POTASSIUM, WHOLE BLD"	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	MALIG	SUSPECT MALIGNANCY?	28
-LABMEP	MEDICAL EMERGENCY PANEL	NA	"SODIUM, WHOLE BLOOD"	28
-LABYPYRU	PYRUVIC ACID	PYRU	PYRUVIC ACID (MG/DL)	28
-POC30	POC PYLORITECK	QC12	QC RESULT	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	RBCFLD	"RBC COUNT, FLD (/UL)"	28
-LABRNPABY	ANTI-RNP ANTIBODY	RNPQNT	ANTI-RNP LEVEL	28
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	28
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	28
-LABMEP	MEDICAL EMERGENCY PANEL	TCO2A	TCO2	28
-POC30	POC PYLORITECK	TESTEX	TEST STRIP EXP	28
-LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOS	"TESTOSTERONE, TOTAL"	28
-LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOSBIO	"TESTOSTERONE,BIOAVAILABLE, S"	28
-LABMEP	MEDICAL EMERGENCY PANEL	THB	THB	28
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	UBT	UNIT BLOOD TYPE  	28
-LABTBP53	TRANSFUSION BLOOD PRODUCT 54	UBT	UNIT BLOOD TYPE  	28
-LABTBP54	TRANSFUSION BLOOD PRODUCT 55	UBT	UNIT BLOOD TYPE  	28
-LABYFUN	"UREA NITROGEN, FLUID"	UNFLD	UREA NITROGEN BODY FLUID	28
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	UNT	UNIT NUMBER  	28
-LABTBP53	TRANSFUSION BLOOD PRODUCT 54	UNT	UNIT NUMBER  	28
-LABTBP54	TRANSFUSION BLOOD PRODUCT 55	UNT	UNIT NUMBER  	28
-LABBFCTS	"CELL COUNT, BODY FLUID"	WBCFLD	"NUCL CELLS, FLD(/UL)"	28
-LABYSTCL	"CHLORIDE, STOOL"	YSTCL1	"CHLORIDE, STOOL, 24 HR"	28
-LAB12781R	BRUCELLA ANTIBODIES	12778R	BRUCELLA IGG	27
-LAB12781R	BRUCELLA ANTIBODIES	12779R	BRUCELLA IGM	27
-10370R	HLA - DSA BY C1Q	9633R	REFERENCE SAMPLE DATE	27
-LABADVQT	ADENOVIRUS PCR QUANT	ADPCR1	ADENVIRUS DNA	27
-LABCIMM	"COCCI AB, ID, S"	CIMM	COCCI IMMUNODIFFUSION	27
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	DI	ISSUE DATE/TIME	27
-LABYSDA	"DRUG SCREEN, PLASMA"	DRUGSCRN	"DRUG SCREEN, PLASMA"	27
-LABFIBANT	FIBRINOGEN ANTIGEN	FIBANT	FIBRINOGEN ANTIGEN	27
-LABFRAB	FRANCISELLA TULARENSIS AB	FRANT	FRANCISELLA TULARENSIS AB	27
-LABG6PDQT	G6PD QUANTITATIVE	G6PD	"G6PD, QUANTITATIVE"	27
-LABHATA	HEPATITIS A AB TOTAL	HEPAAB	HEPATITIS A TOTAL AB	27
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	27
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	27
-LABSTOBID	"OCCULT BLD, IMMUNO, STOOL, DIAG TEST"	OCCIMM	"STOOL SAMPLE1,IMMUN"	27
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	PRO	PRODUCT ID  	27
-LABTBP59	TRANSFUSION BLOOD PRODUCT 60	PRO	PRODUCT ID  	27
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QTFNIN	QUANTIFERON INTERPRETATION	27
-LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QTFNRS	QUANTIFERON-TB	27
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	27
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	27
-LABCBCD	CBC WITH DIFF	SEZY	SEZARY CELLS	27
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	SI	<<STATUS>>  	27
-LABTBP59	TRANSFUSION BLOOD PRODUCT 60	SI	<<STATUS>>  	27
-LABADVQT	ADENOVIRUS PCR QUANT	SOUR10	SOURCE	27
-LABYANRNP	AUTOANTIBODIES TO U1RNP	U1RNPAB	AUTOANTIBODIES TO U1RNP	27
-LABXICA51	ICA 512 AB	XICA5R	ICA 512 ANTIBODY	27
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	XMR	UNIT CROSSMATCH  	27
-LABYASU	ALPHASUBUNIT PITUITARY MARKER	YASU1	ALPHA-SUBUNIT	27
-LABYIFBA	INTRINSIC FACTOR BLOCKING AB	YIFB2	COMMENT	27
-LABYSTCL	"CHLORIDE, STOOL"	YSTCL3	COLLECTION DURATION	27
-LABYSTCL	"CHLORIDE, STOOL"	YSTCL4	CHLORIDE CONCENTRATION	27
-LAB11525R	MUSCLE TYROSINE KINASE AB	11635R	MUSCLE TYROSINE KINASE AB	26
-LAB11547R	HEPATITIS C CONFIRMATORY TEST	11657R	HEPATITIS C CONFIRMATORY TEST	26
-LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMACT	ADAMTS-13 ACTIVITY	26
-LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMINT	CLINICAL INTERPRETATION	26
-LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	CCPINT	INTERPRETATION	26
-LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	CCPR	ANTI CCP ANTIBODY	26
-LABCYLEX	IMMUNE CELL FUNCTION (CYLEX)	CYCLXR	IMMUNE CELL FUNCTION	26
-LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	EBV	EBV BY PCR	26
-LABGDGP	IGG ANTI DGP	GDGPT	IGG ANTI DGP LEVEL	26
-LABHBSQT	HEP B SURF AB QUANT	HBSAB	"HBS ANTIBODY, S"	26
-LABHBSQT	HEP B SURF AB QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	26
-LABUBICAR	"BICARBONATE, URINE"	HCO3URN	"BICARBONATE, URINE"	26
-LABHEPAR	UNFRACTIONATED HEPARIN	HEPAR	HEPARIN ACTIVITY LEVEL	26
-LABHIVDAG	HIV - 1 DIRECT AG (ICD)	HIVS	HIV-1 P24 (ICD)AG	26
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	26
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	26
-LABHIVDAG	HIV - 1 DIRECT AG (ICD)	INTRE	INTERPRETATION	26
-LABYANTJO	AUTOANTIBODIES TO JO1	JO1AB	AUTOANTIBODIES TO JO 1	26
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRSENA	RA BRADY SENSING AMPLITUDE	26
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	26
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	26
-LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	RHFINT	INTERPRETATION	26
-LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	RHFR	RHEUMATOID FACTOR	26
-LABRPRQT	RPR QUANTITATIVE	RPRTTR	TIER	26
-LABYTHI	THIOCYANATE	THIOCYAN	THIOCYANATE	26
-LABUKT	"POTASSIUM, URINE (TIMED)"	TKU	POTASSIUM EXCRETION	26
-LABUKT	"POTASSIUM, URINE (TIMED)"	UK	"POTASSIUM, URINE"	26
-LABUBICAR	"BICARBONATE, URINE"	UT	COLLECTION PERIOD	26
-LABUKT	"POTASSIUM, URINE (TIMED)"	UT2	COLLECTION PERIOD POTASSIUM	26
-LABUBICAR	"BICARBONATE, URINE"	VOL	VOLUME	26
-LABUKT	"POTASSIUM, URINE (TIMED)"	VOL2	VOLUME POTASSIUM	26
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	XMR	UNIT CROSSMATCH  	26
-LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	YEBP1	SPECIMEN SOURCE	26
-LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	YEBP3	SPECIAL INFORMATION	26
-LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	YEPB4	REPORT STATUS	26
-LAB11182R	NICOTINE & METABOLITES	11183R	NICOTINE	25
-LAB11182R	NICOTINE & METABOLITES	11184R	COTININE	25
-LABDCGSN	"DONOR, CHAGAS SCN W/REFL"	11893R	"DONOR, CHAGAS RIPA"	25
-LABAGAP	ANION GAP	AG	ANION GAP	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKAA	AMINO ACID CHANGE	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKCI	CLINICAL INDICATION	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKDI	DISCLAIMER	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKIT	INTERPRETED BY	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKMT	METHOD	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKPR	PRIMER	25
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKRE	RESULT	25
-LABEBVQL	EBV DNA PCR QUAL	EBVQLD	EBV DNA	25
-LABEBVQL	EBV DNA PCR QUAL	EBVQLI	TEST DESCRIPTION	25
-LABEBVQL	EBV DNA PCR QUAL	EBVQLS	SPECIMEN	25
-LABHAB	HEPATITIS B SURFACE AB	HBSAB	HEPATITIS B SURFACE AB QUAL	25
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	25
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	25
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	25
-LABLPD	LIPID PANEL WITH DIRECT LDL	LDL	LDL (CALCULATED)	25
-ECH25	ECHO - LIMITED TRANSTHORACIC ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	25
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	PRO	PRODUCT ID  	25
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	PRO	PRODUCT ID  	25
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	25
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	25
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	SI	<<STATUS>>  	25
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	SI	<<STATUS>>  	25
-LABTBP55	TRANSFUSION BLOOD PRODUCT 56	UBT	UNIT BLOOD TYPE  	25
-LABTBP56	TRANSFUSION BLOOD PRODUCT 57	UBT	UNIT BLOOD TYPE  	25
-LABTBP55	TRANSFUSION BLOOD PRODUCT 56	UNT	UNIT NUMBER  	25
-LABTBP56	TRANSFUSION BLOOD PRODUCT 57	UNT	UNIT NUMBER  	25
-LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	VMQNT	VMQNT	25
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	25
-LABVWSCN	VON WILLEBRAND SCREEN	VWDIR	INTERPRETED BY	25
-LABVWSCN	VON WILLEBRAND SCREEN	VWINTP	INTERPRETATION	25
-LAB11000R	"CEA, PANCREATIC CYST"	11001R	"CEA, PANCREATIC CYST"	24
-LABHIBCV	DONOR HIV1 / HBV / HCV NAT	11894R	DISCRIMINATORY TESTING	24
-LABTHIOMT	THIOPURINE METABOLITES	6MMPN	6-MMPN	24
-LABTHIOMT	THIOPURINE METABOLITES	6MMPNASSESS	6-MMPN RESULT ASSESSMENT	24
-LABTHIOMT	THIOPURINE METABOLITES	6TGNASSESS	6-TGN RESULT ASSESSMENT	24
-10335R	HLA - SCREENING C1Q	9584R	INTERPRETATION/ COMMENTS CLASS I	24
-LABAAP	"AMINO ACIDS, PLASMA"	AAMINOA	A-AMINOADIPIC ACID	24
-LABAAP	"AMINO ACIDS, PLASMA"	AAMINOB	A-AMINOBUTYRIC ACID	24
-LABAAP	"AMINO ACIDS, PLASMA"	AAPINT	INTERPRETATION	24
-LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	ADGINT	INTERPRETATION	24
-LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	ADGPR	IGA ANTI DGP ANTIBODY	24
-LABAAP	"AMINO ACIDS, PLASMA"	ALANINE	ALANINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	ARGININE	ARGININE	24
-LABYAVH	ARGININE VASOPRESSIN (ADH)	ARGININEVP	ARGININE VASOPRESSIN	24
-LABAAP	"AMINO ACIDS, PLASMA"	ASPART	ASPARTIC ACID	24
-LABAAP	"AMINO ACIDS, PLASMA"	ASPGN	ASPARAGINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	BALN	B-ALANINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	BAMBA	B-AMINOISOBUTYRIC AC	24
-LABAAP	"AMINO ACIDS, PLASMA"	CITRUL	CITRULLINE	24
-LABCYSBLD	"CYSTICERCUS AB REFLEX PANEL, SERUM"	CYSAB2	"CYSTICERCUS AB (SERUM), ELISA"	24
-LABAAP	"AMINO ACIDS, PLASMA"	CYSTINE	CYSTINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	CYTHN	CYSTATHIONINE	24
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	DI	ISSUE DATE/TIME	24
-LABTBP53	TRANSFUSION BLOOD PRODUCT 54	DI	ISSUE DATE/TIME	24
-POC13	POC PPD SKIN TEST (AMB)	ERYTHREMA	ERYTHREMA	24
-LABAAP	"AMINO ACIDS, PLASMA"	ETHLN	ETHANOLAMINE	24
-LABG6PDQT	G6PD QUANTITATIVE	G6PDMD	REVIEWED BY	24
-LABAAP	"AMINO ACIDS, PLASMA"	GLUTAC	GLUTAMIC ACID	24
-LABAAP	"AMINO ACIDS, PLASMA"	GLUTAMINE	GLUTAMINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	GLYCINE	GLYCINE	24
-LABHAV	HEPATITIS A IGM AB	HEPAIGM	HEPATITIS A AB-IGM	24
-LABHEPAR	"HEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY"	HEPAR	HEPARIN ACTIVITY LEVEL	24
-LABAAP	"AMINO ACIDS, PLASMA"	HISTID	HISTIDINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	HMCY	HOMOCYSTEINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	HYPROL	HYDROXYPROLINE	24
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVSND	BRADY SENSED AV DELAY	24
-LABAAP	"AMINO ACIDS, PLASMA"	INTBY	INTERPRETED BY	24
-LABAAP	"AMINO ACIDS, PLASMA"	ISOLEU	ISOLEUCINE	24
-LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	LAB7	REFERENCE LAB	24
-LABAAP	"AMINO ACIDS, PLASMA"	LEUCINE	LEUCINE	24
-ECH30	ECHO - EXERCISE STRESS ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	24
-LABAAP	"AMINO ACIDS, PLASMA"	LYSINE	LYSINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	METHIO	METHIONINE	24
-LABYMEXIL	MEXILETINE	MEXIL	MEXILETINE	24
-POC13	POC PPD SKIN TEST (AMB)	MMIND	MM INDURATION	24
-LABAAP	"AMINO ACIDS, PLASMA"	ORNITHINE	ORNITHINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	PHALN	PHENYLALANINE	24
-POC13	POC PPD SKIN TEST (AMB)	PHLOC	LOCATION	24
-LABAAP	"AMINO ACIDS, PLASMA"	PROLINE	PROLINE	24
-LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	RESUL7	RESULT	24
-LABAAP	"AMINO ACIDS, PLASMA"	SARSN	SARCOSINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	SERINE	SERINE	24
-LABSLIDE	SLIDE SAVE FOR CLINICIAN	SLIDE	SLIDE SAVE FOR CLINICIAN	24
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	24
-LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	SPCT	SPECIMEN TYPE	24
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	SPEXP	SPECIMEN EXPIRATION DATE/TIME	24
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	SPEXP	SPECIMEN EXPIRATION DATE/TIME	24
-LABAAP	"AMINO ACIDS, PLASMA"	TAURINE	TAURINE	24
-LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	TEST7	TEST NAME	24
-LABTHAMP	THERMAL AMPLITUDE	THRAMP	THERMAL AMPLITUDE	24
-LABAAP	"AMINO ACIDS, PLASMA"	THREONINE	THREONINE	24
-LABAAP	"AMINO ACIDS, PLASMA"	TRYPTOPHAN	TRYPTOPHAN	24
-LABAAP	"AMINO ACIDS, PLASMA"	TYROSINE	TYROSINE	24
-LABFURICA	"URIC ACID, FLUID"	URICFLD	URIC ACID BODY FLUID	24
-LABAAP	"AMINO ACIDS, PLASMA"	VALINE	VALINE	24
-LABTBP13	TRANSFUSION BLOOD PRODUCT 14	VL	PRODUCT VOLUME	24
-LABECL	EUGLOBULIN CLOT LYSIS	WEACA	WITH EACA	24
-LABECL	EUGLOBULIN CLOT LYSIS	WOEACA	WITHOUT EACA	24
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	XMR	UNIT CROSSMATCH  	24
-LABYACIGA	"CARDIOLIPIN AB, IGA"	YACIG1	"CARDIOLIPIN AB,IGA"	24
-10287R	HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT	10049R	HLA LAB TESTING STATUS	23
-LAB11619R	"VZV PCR, AQUEOUS FLUID"	11778R	"VZV PCR, AQUEOUS FLUID"	23
-LABYFUN	"UREA NITROGEN, FLUID"	11847R	FLUID TYPE	23
-LABFQFVR	COXIELLA BURNETII AB	12014R	Q FEVER IGG PHASE LI TITER	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12419R	ACETOHEXIMIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12420R	CHLORPROPAMIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12421R	GLIMEPIRIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12422R	GLIPIZIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12423R	GLYBURIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12424R	NATEGLINIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12425R	REPAGLINIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12426R	TOLAZAMIDE	23
-LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12427R	TOLBUTAMIDE	23
-10370R	HLA - DSA BY C1Q	9463R	DSA RESULTS FOR C1Q CLASS II	23
-LABYASMA	ANTI - SMOOTH MUSCLE AB	ASMA	ANTI-SMOOTH MUSCLE ANITBODY	23
-LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKCM	COMMENT	23
-LABYUHYME	"HEAVY METALS, URINE"	CADMIUNURN	"CADMIUM, URINE"	23
-LABYGLUC	GLUCAGON	GLUCAGON	GLUCAGON	23
-LABAHP2	ACUTE HEPATITIS PANEL	HBCABIGM	HEPAT.B CORE AB-IGM	23
-LABAHP2	ACUTE HEPATITIS PANEL	HBSAG	HEPATITIS B SURF AG	23
-LABAHP2	ACUTE HEPATITIS PANEL	HEPAIGM	HEPATITIS A AB-IGM	23
-LABAHP2	ACUTE HEPATITIS PANEL	HEPCAB	HEPATITIS C AB	23
-LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	HERG	HERPESVIRUS 6 AB IGG	23
-LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	HERM	HERPESVIRUS 6 AB IGM	23
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	23
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	23
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	23
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	23
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	23
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	LYMFLD	"LYMPHOCYTE %, FLD"	23
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	MONFLD	"MONO/MESO %, FLD"	23
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	NEUTFLD	"NEUTROPHIL %, FLD"	23
-VSC04	VSC GRAFT W ABI\'S ART DUP	PDF	PDF RESULT	23
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	PRO	PRODUCT ID  	23
-10287R	HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT	RINFO	TO FIND RESULT:	23
-LABRUB	RUBELLA AB IGG	RUB	RUBELLA IGG ANTIBODY	23
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	SI	<<STATUS>>  	23
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	23
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	SPEXP	SPECIMEN EXPIRATION DATE/TIME	23
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	SPEXP	SPECIMEN EXPIRATION DATE/TIME	23
-LABUA	URINALYSIS WITH MICROSCOPIC	WAXCST	WAXY CASTS	23
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	WBCAST	WBC CASTS	23
-LABYHIST	HISTOPLASMA AB SCREEN	YHIS1	HISTOPLASMA AB SCREEN	23
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	11230R	CREATININE CONC	22
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	11231R	METANEPHRINE/CREATININE	22
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	11232R	NORMETANEPHRINE/CREATININE	22
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	11233R	TOTAL METANEPHRINE/CREATININE	22
-LABYVD125	"1,25-DIHYDROXYVITAMIN D, SERUM"	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	22
-LAB12815R	LYME DISEASE AB IGG IGM W/RFLX	12830R	LYME AB SCREEN	22
-LABUCA	"CALCIUM WITH CREATININE, URINE (RANDOM)"	CACREAT	CALCIUM/CREAT RATIO	22
-LAB11127R	CASPOFUNGIN LEVEL	CASPO	CASPOFUNGIN (CANCIDAS)	22
-LABUCA	"CALCIUM WITH CREATININE, URINE (RANDOM)"	CAURN	"CALCIUM, URINE"	22
-LABCMVRC	CMV RAPID CULTURE	CMVDSV	TEST DESCRIPTION	22
-LABCMVRC	CMV RAPID CULTURE	CMVRC1	CMV DAY 1 RAPID CULTURE	22
-LABCMVRC	CMV RAPID CULTURE	CMVRC2	CMV DAY 2 RAPID CULTURE	22
-LABCMVRC	CMV RAPID CULTURE	CMVSPM	SPECIMEN	22
-POC27	POC HEMOCCULT FOR FECAL BLOOD	DEVEL	DEVELOPER LOT#	22
-POC27	POC HEMOCCULT FOR FECAL BLOOD	DEVEXP	DEVELOPER EXP DATE	22
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	DI	ISSUE DATE/TIME	22
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	DI	ISSUE DATE/TIME	22
-LABTBP54	TRANSFUSION BLOOD PRODUCT 55	DI	ISSUE DATE/TIME	22
-LABRP	RENAL FUNCTION PANEL	EGFR	EGFR	22
-LABFURICA	"URIC ACID, FLUID"	FSC2	FLUID TYPE	22
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GDNM3	DNMT3A	22
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GERHR	ERBB2/HER2	22
-POC27	POC HEMOCCULT FOR FECAL BLOOD	HEMED	HEMOCCULT EXP DATE	22
-POC27	POC HEMOCCULT FOR FECAL BLOOD	HEML	HEMOCCULT LOT#	22
-LABHEVM	HEPATITIS E AB IGM PANEL	HEVM1	HEPATITIS E AB IGM	22
-LABUVH	VMA AND HVA	HVA	HOMOVANILLIC ACID	22
-LABUVH	VMA AND HVA	HVAUR	"HVA, URINE"	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVPCD	BRADY PACED AV DELAY	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRTRKRT	BRADY TRACKING RATE	22
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	22
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	22
-LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	LTROIN	INTERPRETATION	22
-POC27	POC HEMOCCULT FOR FECAL BLOOD	OCCBLD	FECAL OCCULT BLOOD BLOOD	22
-POC27	POC HEMOCCULT FOR FECAL BLOOD	QC10	QC RESULT	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRPACA	RA BRADY PACING AMPLITUDE	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRPACW	RA BRADY PACING PULSE WIDTH	22
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	22
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SCMT	SYNOVIAL FLUID COMMENT	22
-LABYTAT	THROMBIN - ANTITHROMBIN COMPLEX	TAT	"THROMBIN, ANTITHROMBIN COMPLEX"	22
-LABUVH	VMA AND HVA	TIMEU	COLLECTION PERIOD	22
-LABUVH	VMA AND HVA	UCR	"CREATININE, URINE"	22
-LABUVH	VMA AND HVA	UCR	"CREATININE, URN"	22
-LABUCA	"CALCIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	22
-LABUVH	VMA AND HVA	UVOL	VOLUME	22
-LABUVH	VMA AND HVA	VHINT	VMA/HVA INTRP	22
-LABTBP15	TRANSFUSION BLOOD PRODUCT 16	VL	PRODUCT VOLUME	22
-LABUVH	VMA AND HVA	VMA	VMA	22
-LABUVH	VMA AND HVA	VMAUR	"VMA, URINE"	22
-LABYC1IN	C1 ESTERASE INHIBITOR AG	YC1INH	C1 ESTERASE INH. AG	22
-LABYVWM	VON WILLEBRAND MULTIMERS	YVWMR	VON WILLEBRAND	22
-LABYENTE	YERSINIA ENTEROCOLITICA AB	12064R	Y.ENTERCOCLITICA IGG	21
-LABYENTE	YERSINIA ENTEROCOLITICA AB	12065R	Y.ENTERCOCLITICA IGA	21
-LAB12721R	"ADENOSINE DEAMINASE, PLEURAL"	12720R	"ADENOSINE DEAMINASE, PLEURAL"	21
-10375R	HLA - STR/CHIMERISM TESTING	9471R	DONOR DOB	21
-LABADMARK	"ADMARK TAU/AB42, CSF"	ADMART	"ADMARK TAU/AB42, CSF"	21
-LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BMPMLR	"PML, BM"	21
-LABCCT	"CELL COUNT, CSF"	BTURB	"TURBIDITY, CSF"	21
-LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32R2	RESULT 2	21
-LABCCT	"CELL COUNT, CSF"	COLORCSF	"COLOR AFTER CENTRIFUGE, CSF"	21
-LABCCT	"CELL COUNT, CSF"	COLORCSF	"COLOR, CSF"	21
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	CONRES	CONTROL RESULTS	21
-LABCCT	"CELL COUNT, CSF"	CSOURC	SOURCE	21
-LABCCT	"CELL COUNT, CSF"	CTURB	"TURB. AFTER CENTRIF., CSF"	21
-LABTBP56	TRANSFUSION BLOOD PRODUCT 57	DI	ISSUE DATE/TIME	21
-LABCCT	"CELL COUNT, CSF"	DX	DIAGNOSIS	21
-LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBNDES	EBNDES	21
-LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBNQNT	EBNQNT	21
-LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBNQUL	EBNQUL	21
-LABYEBPCR	"EBV PCR, CSF"	EBV	EBV BY PCR	21
-LABFHAN	HANTAVIRUS AB ELISA	HANIGG	HANTAVIRUS IGG	21
-LABFHAN	HANTAVIRUS AB ELISA	HANIGM	HANTAVIRUS IGM	21
-LAB11042R	HISTAMINE LEVEL	HIST	"HISTAMINE, WB"	21
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRADYDP	BRADY DEPENDANCY	21
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	21
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	21
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	21
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	21
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	21
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	21
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRPACA	LV BRADY PACING AMPLITUDE	21
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRPACW	LV BRADY PACING PULSE WIDTH	21
-LABCCT	"CELL COUNT, CSF"	MALIG	SUSPECT MALIGNANCY?	21
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHLOC	LOCATION	21
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	PRO	PRODUCT ID  	21
-LABCCT	"CELL COUNT, CSF"	RBCCSF	"RBC COUNT, CSF"	21
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	SI	<<STATUS>>  	21
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	SPEXP	SPECIMEN EXPIRATION DATE/TIME	21
-LABCCT	"CELL COUNT, CSF"	TUBENO	CSF TUBE #	21
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	UBT	UNIT BLOOD TYPE  	21
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	UBT	UNIT BLOOD TYPE  	21
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	UNT	UNIT NUMBER  	21
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	UNT	UNIT NUMBER  	21
-LABCCT	"CELL COUNT, CSF"	WBCCSF	"WBC COUNT, CSF"	21
-LABYAMA	"MITOCHONDRIAL AB M2, SERUM"	YAMA	"MITOCHONDRIAL ANTIBODY, M2"	21
-LABYEBPCR	"EBV PCR, CSF"	YEBP1	SPECIMEN SOURCE	21
-LABYEBPCR	"EBV PCR, CSF"	YEBP3	SPECIAL INFORMATION	21
-LABYEBPCR	"EBV PCR, CSF"	YEPB4	REPORT STATUS	21
-LABYHISTC	"HISTOPLASMA AG, CSF"	YHISC5	"COMMENT,CSF"	21
-LAB11000R	"CEA, PANCREATIC CYST"	11002R	SITE	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12293R	12293R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12294R	12294R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12295R	12295R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12296R	12296R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12297R	12297R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12298R	12298R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12299R	12299R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12300R	12300R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12301R	12301R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12302R	12302R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12303R	12303R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12304R	12304R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12305R	12305R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12306R	12306R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12307R	12307R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12308R	12308R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12309R	12309R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12310R	12310R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12311R	12311R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12312R	12312R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12313R	12313R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12314R	12314R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12315R	12315R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12316R	12316R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12317R	12317R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12318R	12318R	20
-LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12319R	12319R	20
-10300R	HLA - AUTO B FLOW CROSSMATCH	9120R	INTERPRETATION/ COMMENTS	20
-10365R	HLA - DSA BY IGG	9419R	DSA RESULTS FOR IGG CLASS I	20
-LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	AMINOR	AMITRIPT & NORTRIPT	20
-LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	AMITRP	"AMITRIPTYLINE, PLASMA"	20
-LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSDI	DISCLAIMER	20
-LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSDX	CLINICAL INDICATION	20
-LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSMD	INTERPRETED BY	20
-LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSMT	METHOD	20
-LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSRE	RESULT	20
-LABROSTRO	STRONGYLOIDES CULTURE	CULT	CULT/OTHER RSLT:	20
-LABTBP55	TRANSFUSION BLOOD PRODUCT 56	DI	ISSUE DATE/TIME	20
-LABDVEFLA	DIRECT VIRAL EXAM FLU A	DVE	RESULTS	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVBCP	EBV BAMHI-W RESULT	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVBDN	EBV BAMHI-W DNA	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVBIN	EBV BAMHI-W TEST DESCRIPTION	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVBLG	EBV BAMHI-W RESULT LOG 10	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVBSP	SPECIMEN	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVQCP	RESULT	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVQDN	EBV DNA	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVQIN	TEST DESCRIPTION	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVQLG	RESULT LOG 10	20
-LABEBVQPB	EBV NPC PCR PANEL	EBVQSP	SPECIMEN	20
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	20
-LABFT4ED	FREE T4 BY EQUILIBRIUM DIALYSIS	FT4	FREE T4 BY EQUIL DIALYSIS	20
-LAB11080R	GAD AB	GAD65AB	GAD65 ANTIBODY	20
-LABHBODY	HEINZ BODY PREP	HBODY	HEINZ BODY PREP	20
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	20
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	20
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADMFR	LEAD MANUFACTURER	20
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADMODEL	LEAD MODEL	20
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADTYPE	LEAD TYPE	20
-LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	NORTRP	NORTRIPTYLINE	20
-LABDVEFLA	DIRECT VIRAL EXAM FLU A	RPT	RPT	20
-LABROSTRO	STRONGYLOIDES CULTURE	RPT	RPT	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	20
-LABDVEFLA	DIRECT VIRAL EXAM FLU A	SDES	SPECIMEN	20
-LABROSTRO	STRONGYLOIDES CULTURE	SDES	SPECIMEN	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	20
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	20
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SOTHER	"OTHERS, SYN FLD"	20
-LABDVEFLA	DIRECT VIRAL EXAM FLU A	SREQ	COMMENTS	20
-LABROSTRO	STRONGYLOIDES CULTURE	SREQ	COMMENTS	20
-POC30	POC PYLORITECK	TESTL	TEST STRIP LOG	20
-LABTBP16	TRANSFUSION BLOOD PRODUCT 17	VL	PRODUCT VOLUME	20
-LABTBP22	TRANSFUSION BLOOD PRODUCT 23	VL	PRODUCT VOLUME	20
-LABVORI	VORICONAZOLE LEVEL	VORI	VORICONAZOLE LEVEL	20
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	XMR	UNIT CROSSMATCH  	20
-LAB11088R	PP	YPANPP	PANCREATIC POLYPEPTIDE	20
-LABYPLAB	PLATELET AB	YPLAB2	COMMENT	20
-LAB11312R	"SOLUBLE LIVER AG AB, IGG"	11313R	"SOLUBLE LIVER AG AB, IGG"	19
-LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	11672R	LCM IGG	19
-LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	11673R	LCM IGM	19
-LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	11674R	INTERPRETATION	19
-LABPLTAGG	PLATELET AGGREGATION	AGGINT	INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	ASMINT	ANTI-SM INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	ASMQUL	ANTI-SM RESULT	19
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ATACHTADTI	ATACH TACHY ZONE DETECTION INTERVAL	19
-LABADIFF	DIFFERENTIAL	BASO	"BASO, %"	19
-LABADIFF	DIFFERENTIAL	BASOAB	"BASOS, ABS"	19
-LABBM1418	"T(14	18), BONE MARROW"	BM14CT	COMMENT	19
-LABBM1418	"T(14	18), BONE MARROW"	BM14DR	INTERPRETED BY	19
-LABBM1418	"T(14	18), BONE MARROW"	BM14DS	DISCLAIMER	19
-LABBM1418	"T(14	18), BONE MARROW"	BM14MT	METHOD	19
-LABBM1418	"T(14	18), BONE MARROW"	BM14PM	PRIMERS	19
-LABBM1418	"T(14	18), BONE MARROW"	BM14RS	RESULTS	19
-LABC153	CANCER AG 15-3	C153	CANCER ANTIGEN 15-3	19
-LABSNA	SPECIFIC ANA PANEL	CENINT	ANTI-CENTROMERE INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	CENQUL	ANTI-CENTROMERE RESULT	19
-LABERUCRY	EMERGENCY RELEASE CRYO	CRSTA	CRYOPRECIPITATE UNITS	19
-LABSNA	SPECIFIC ANA PANEL	DNAINT	INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	DNAQUL	ANTI-DSDNA RESULT	19
-LABEAIGG	EBV EARLY ANTIGEN IGG	EAGDES	EAGDES	19
-LABEAIGG	EBV EARLY ANTIGEN IGG	EAGQUL	EAGQUL	19
-LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	EBV	EBV BY PCR	19
-LABFEHRC	EHRLICHIA CHAFFEENSIS AB	ECIGG	E.CHAFFEENSIS IGG TITER	19
-LABFEHRC	EHRLICHIA CHAFFEENSIS AB	ECIGM	E.CHAFFEENSIS IGM TITER	19
-LABADIFF	DIFFERENTIAL	EOS	"EOS, %"	19
-LABADIFF	DIFFERENTIAL	EOSAB	"EOS, ABS"	19
-LABCBCD	CBC WITH DIFF	HAIRY	HAIRY CELLS	19
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAMSRT	BRADY AMS RATE	19
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	19
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	19
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	19
-LABSNA	SPECIFIC ANA PANEL	JO1INT	ANTI-JO1 INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	JO1QUL	ANTI-JO1 RESULT	19
-LABADIFF	DIFFERENTIAL	LYM	"LYM, %"	19
-LABADIFF	DIFFERENTIAL	LYMAB	"LYM, ABS"	19
-LABCDIFF	"DIFFERENTIAL, CSF"	LYMCSF	"LYMPHOCYTES, CSF"	19
-LABADIFF	DIFFERENTIAL	MONO	"MONO, %"	19
-LABADIFF	DIFFERENTIAL	MONOAB	"MONO, ABS"	19
-LABCDIFF	"DIFFERENTIAL, CSF"	MONOCSF	"MONOCYTOID CELLS, CSF"	19
-LABMTHFR	METHYLENE- THF REDUCTASE	MTHCMT	COMMENT	19
-LABMTHFR	METHYLENE- THF REDUCTASE	MTHDIS	DISCLAIMER	19
-LABMTHFR	METHYLENE- THF REDUCTASE	MTHMET	METHOD	19
-LABMTHFR	METHYLENE- THF REDUCTASE	MTHPRM	PRIMERS	19
-LABMTHFR	METHYLENE- THF REDUCTASE	MTHRES	RESULTS	19
-LABADIFF	DIFFERENTIAL	NEUT	"NEUT, %"	19
-LABADIFF	DIFFERENTIAL	NEUTAB	"NEUT, ABS"	19
-LABCDIFF	"DIFFERENTIAL, CSF"	NEUTCSF	"NEUTROPHILS, CSF"	19
-LABCGD	NEUTROPHIL FUNC. OXI. BURST	OXIDNEUTPC	% OXIDATION POSITIVE NEUTROPHILS	19
-LABSNA	SPECIFIC ANA PANEL	RNPINT	ANTI-RNP INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	RNPQUL	ANTI-RNP RESULT	19
-LABSNA	SPECIFIC ANA PANEL	SCLINT	ANTI-SCL70 INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	SCLQUL	ANTI-SCL70 RESULT	19
-LABSNA	SPECIFIC ANA PANEL	SNAINT	SPECIFIC ANA INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	SSAIN	ANTI-RO INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	SSAQUL	ANTI-RO RESULT	19
-LABSNA	SPECIFIC ANA PANEL	SSBINT	ANTI-LA INTERPRETATION	19
-LABSNA	SPECIFIC ANA PANEL	SSBQUL	ANTI-LA RESULT	19
-LABTBP59	TRANSFUSION BLOOD PRODUCT 60	UBT	UNIT BLOOD TYPE  	19
-LABTBP59	TRANSFUSION BLOOD PRODUCT 60	UNT	UNIT NUMBER  	19
-LABTBP19	TRANSFUSION BLOOD PRODUCT 20	VL	PRODUCT VOLUME	19
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	19
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	WAXCST	WAXY CASTS	19
-LABYAMEB	E. HISTOLYTICA AB	YAMEB1	E. HISTOLYTICA AB	19
-LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	YEBP1	SPECIMEN SOURCE	19
-LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	YEBP3	SPECIAL INFORMATION	19
-LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	YEPB4	REPORT STATUS	19
-LAB11617R	"CMV PCR, AQUEOUS FLUID"	11776R	"CMV PCR, AQUEOUS FLUID"	18
-10370R	HLA - DSA BY C1Q	9634R	REFERENCE SERUM DESCRIPTION	18
-LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	ADGPT	IGA ANTI DGP ANTIBODY LEVEL	18
-LABYAFCSF	"ALPHA FETOPROTEIN, CSF"	AFPCSF	"ALPHA FETOPROTEIN, CSF"	18
-LABASPPCP	ASPERGILLUS AB PRECIPITINS	ASPIGG	ASPERGILLUS IGG ABY	18
-LABASPPCP	ASPERGILLUS AB PRECIPITINS	ASPINT	INTERPRETATION	18
-LABSWTCL	"CHLORIDE, SWEAT"	CLSWT	SWEAT CHLORIDE	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMGDES	CMV IGG TEST DESCRIPTION	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMGQUL	CMV IGG RESULT	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMMDES	CMV IGM TEST DESCRIPTION	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMMQUL	CMV IGM RESULT	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMV	CMV ANTIBODY	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMVIGM	CMV IGM AB	18
-LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	CULT	CULT/OTHER RSLT:	18
-LABHGBS	"HEMOGLOBIN, SERUM"	HGB	"HEMOGLOBIN, SERUM"	18
-LABHPVHR2	HPV DNA PROBE HIGH RISK	HPTI	TEST INFORMATION	18
-LABHPVHR2	HPV DNA PROBE HIGH RISK	HPV	HPV DNA PROBE RESULT:	18
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	18
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	18
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADIMPDTE	LEAD IMPLANT DATE	18
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADSERNUM	LEAD SERIAL NUMBER	18
-LABMTHFR	METHYLENE- THF REDUCTASE	MTHDIR	INTERPRETED BY	18
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHLOC	LOCATION	18
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMTLBATIMP	PM TELEMETRY BATTERY IMPEDANCE	18
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	PRO	PRODUCT ID  	18
-LABTBP65	TRANSFUSION BLOOD PRODUCT 66	PRO	PRODUCT ID  	18
-LABTBP66	TRANSFUSION BLOOD PRODUCT 67	PRO	PRODUCT ID  	18
-LABQFTBG	QUANTIFERON FOR LATENT TB INFECTION	QTFNIN	QUANTIFERON INTERPRETATION	18
-LABQFTBG	QUANTIFERON FOR LATENT TB INFECTION	QTFNRS	QUANTIFERON-TB	18
-LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	RPT	RPT	18
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	18
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	18
-LABSWTCL	"CHLORIDE, SWEAT"	SCLW	SWEAT COLLECTED	18
-LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	SDES	SPECIMEN	18
-LABHPVHR2	HPV DNA PROBE HIGH RISK	SDES6	SPECIMEN	18
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	SI	<<STATUS>>  	18
-LABTBP65	TRANSFUSION BLOOD PRODUCT 66	SI	<<STATUS>>  	18
-LABTBP66	TRANSFUSION BLOOD PRODUCT 67	SI	<<STATUS>>  	18
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	SPEXP	SPECIMEN EXPIRATION DATE/TIME	18
-LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	SREQ	COMMENTS	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	VGGDES	VZV IGG TEST DESCRIPTION	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	VGGQNT	VZV IGG LEVEL	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	VGGQUL	VZV IGG RESULT	18
-LABBMPTP	BMT PRETRANSPLANT PANEL	VZAB	VARICELLA ZOSTER AB IGG	18
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	XMR	UNIT CROSSMATCH  	18
-LABYIGF2	INSULIN LIKE GROWTH FACTOR II	YIGF2A	IFG-II	18
-LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	YMYCR1	STATUS	18
-LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	YMYCR2	MYCOPLASMA PNEUMONIAE CULTURE	18
-LABFQFVR	COXIELLA BURNETII AB	12012R	Q FEVER IGG PHASE L TITER	17
-LAB12586R	THIAMIN (VITAMIN B1)	12588R	THIAMIN (VITAMIN B1)	17
-10375R	HLA - STR/CHIMERISM TESTING	9480R	LOCUS 6	17
-10375R	HLA - STR/CHIMERISM TESTING	9481R	LOCUS 7	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	AMINOR	AMITRIPTYLINE & NORTRIPTYLINE	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	AMITRP	AMITRIPTYLINE	17
-LABCGD	NEUTROPHIL FUNC. OXI. BURST	CGDA	SPECIMEN AGE	17
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	CONRES	CONTROL RESULTS	17
-LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	CULT	CULT/OTHER RSLT:	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	DESIP	DESIPRAMINE	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	DOXEPIN	DOXEPIN	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	DOXEPINNOR	DOXEPIN & NORDOXEPIN	17
-LAB11198R	VIT A & VIT E	FREEVITA	FREE RETINOL(VIT A)	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	IMI	IMIPRAMINE	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	IMIDESI	IMIPRAMINE/DESPRAMINE	17
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENA	LV BRADY SENSING AMPLITUDE	17
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MEGA	MEGAKARY/100WBC	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	NORDOX	NORDOXEPIN	17
-LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	NORTRP	NORTRIPTYLINE	17
-IMG11AR1	CAR CATH HEART RT 1	PDF	PDF RESULT	17
-LABTBP67	TRANSFUSION BLOOD PRODUCT 68	PRO	PRODUCT ID  	17
-LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	RPT	RPT	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRAMP	RV THRESHOLD AMPLITUDE	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRDUR	RV THRESHOLD DURATION	17
-LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	SDES	SPECIMEN	17
-LABTBP67	TRANSFUSION BLOOD PRODUCT 68	SI	<<STATUS>>  	17
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	17
-LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	SREQ	COMMENTS	17
-LABUA	URINALYSIS WITH MICROSCOPIC	TRICHOMONAS	TRICHOMONAS	17
-LAB11198R	VIT A & VIT E	VITE	"A-TOCOPHEROL, VITAMIN E"	17
-LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	YTESHD	FREE TESTOSTERONE	17
-LAB11179R	"NICOTINE & METABOLITES, U"	11180R	"NICOTINE, U"	16
-LAB11179R	"NICOTINE & METABOLITES, U"	11181R	"COTININE, U"	16
-LAB11179R	"NICOTINE & METABOLITES, U"	11197R	"ANABASINE, U"	16
-LAB11340R	ADAMTS 13 PROFILE	11341R	ADAMTS 13 ACTIVITY	16
-LAB11340R	ADAMTS 13 PROFILE	11342R	ADAMTS 13 INHIBITOR	16
-LAB11179R	"NICOTINE & METABOLITES, U"	11511R	"NORNICOTINE, U"	16
-LAB11576R	COBALAMIN C DEFICIENCY(MMACHC)	11691R	COBALAMIN C DEFICIENCY(MMACHC)	16
-LAB12811R	"MYOGLOBIN, URINE"	12812R	"MYOGLOBIN, URINE"	16
-LAB13002R	COCCIDIOIDES AB IMMUNODIFF CSF	13001R	COCCIDIOIDES AB IMMUNODIFF CSF	16
-10305R	HLA - ALLO T FLOW CROSSMATCH	9140R	SERUM DATE 3	16
-10305R	HLA - ALLO T FLOW CROSSMATCH	9142R	RESULT  MCS 3	16
-10305R	HLA - ALLO T FLOW CROSSMATCH	9144R	RESULT 3	16
-10310R	HLA - ALLO B FLOW CROSSMATCH	9170R	SERUM DATE 3	16
-10310R	HLA - ALLO B FLOW CROSSMATCH	9172R	RESULT  MCS 3	16
-10310R	HLA - ALLO B FLOW CROSSMATCH	9174R	RESULT 3	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHCT	COMMENT	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHDI	DISCLAIMER	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHDX	CLINICAL INDICATION	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHET	ETHNICITY (PROVIDED BY MD)	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHIN	INTERPRETATION	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHMD	INTERPRETED BY	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHME	METHOD	16
-LABATHAL	ALPHA THALASSEMIA PCR	ATHRE	RESULT	16
-LAB11026R	"C1 INHIBITOR, FUNCT"	C1INHF	C1 ESTERASE INHIB FN	16
-LABUCORT	"FREE CORTISOL, URINE (TIMED)"	CORTU	"CORTISOL, URINE"	16
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	DI	ISSUE DATE/TIME	16
-EKG6	ECG SIGNAL AVERAGED	ECGIMPRESS	ECG IMPRESSION	16
-LABFEHRC	EHRLICHIA CHAFFEENSIS AB	ECINT	INTERPRETATION	16
-LAB11037R	FLECAINIDE LEVEL	FLEC	FLECAINIDE	16
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HATINT	INTERPRETATION	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBSINT	INTERPRETATION	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBTINT	INTERPRETATION	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HCVI	INTERPRETATION	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HCVR	ANTI-HCV	16
-EKG6	ECG SIGNAL AVERAGED	HEARTRATE	HEART RATE	16
-LABHEVG	HEPATITIS E AB IGG PANEL	HEVG1	HEPATITIS E AB IGG	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HQLINT	INTERPRETATION	16
-LABVP4	COMPREHENSIVE HEPATITIS PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	16
-LABHTLVB	 HTLV-1/2 AB WESTERN BLOT	HTWB	"HTLV I/II AB, WESTERN BLOT"	16
-LABIFEDE	"IMMUNOFIXATION IGD AND IGE, SERUM"	IFED	IMMUNOFIXATION IGD	16
-LABIFEDE	"IMMUNOFIXATION IGD AND IGE, SERUM"	IFEE	IMMUNOFIXATION IGE	16
-LABKRAS	KRAS MUTATION DETECTION	KRASTI	SOURCE	16
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	16
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	16
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADMODEL	LEAD MODEL	16
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADTYPE	LEAD TYPE	16
-EKG6	ECG SIGNAL AVERAGED	PRINTERVAL	P-R INTERVAL	16
-LABTBP68	TRANSFUSION BLOOD PRODUCT 69	PRO	PRODUCT ID  	16
-EKG6	ECG SIGNAL AVERAGED	QRSAXIS	QRS AXIS	16
-EKG6	ECG SIGNAL AVERAGED	QRSDINTERVAL	QRSD INTERVAL	16
-EKG6	ECG SIGNAL AVERAGED	QTCINTERVAL	QTC INTERVAL	16
-EKG6	ECG SIGNAL AVERAGED	QTINTERVAL	QT INTERVAL	16
-LABRDATBC	REVERSE TO COOMBS B CELLS	RDATB	REVERSE TO COOMBS B CELLS	16
-EKG6	ECG SIGNAL AVERAGED	RR	RR	16
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	16
-LABYSCL70	AUTOANTIBODIES TO SCL 70	SCL70	"SCL 70 AB, IGG, SERUM"	16
-LABTBP68	TRANSFUSION BLOOD PRODUCT 69	SI	<<STATUS>>  	16
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	16
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	SPEXP	SPECIMEN EXPIRATION DATE/TIME	16
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	SPEXP	SPECIMEN EXPIRATION DATE/TIME	16
-LABUCORT	"FREE CORTISOL, URINE (TIMED)"	TCORTU	CORTISOL EXCRETION	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	TIMEU	COLLECTION PERIOD	16
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	UBT	UNIT BLOOD TYPE  	16
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	UBT	UNIT BLOOD TYPE  	16
-LABTBP65	TRANSFUSION BLOOD PRODUCT 66	UBT	UNIT BLOOD TYPE  	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URINE"	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URN"	16
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	UNT	UNIT NUMBER  	16
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	UNT	UNIT NUMBER  	16
-LABTBP65	TRANSFUSION BLOOD PRODUCT 66	UNT	UNIT NUMBER  	16
-LABUCORT	"FREE CORTISOL, URINE (TIMED)"	UT52	COLLECTION PERIOD	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	UVOL	VOLUME	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	VMA	VMA	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	VMAINT	VMA INTRP	16
-LABYUVMA	"VANILLYMANDELIC ACID, URINE"	VMAUR	"VMA, URINE"	16
-LABUCORT	"FREE CORTISOL, URINE (TIMED)"	VOL52	VOLUME	16
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	16
-LABXIGF1	PEDIATRIC IGF-1	XIG1	IGF-1	16
-LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN3	Y ENTERCOCLITICA 03	16
-LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN5	Y ENTERCOCLITICA 05	16
-LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN8	Y ENTERCOCLITICA 08	16
-LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN9	Y ENTERCOCLITICA 09	16
-LABYINH	INHIBIN B	YINH1	INHIBIN B	16
-LAB11090R	PARIETAL CELL AB	YPCA	PARIETAL CELL AB	16
-LABYENTE	YERSINIA ENTEROCOLITICA AB	YPSEU	Y.PSEUDOTUBERCULOSIS	16
-LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12051R	RMSF IGG	15
-LAB12212R	WA1 IGG AB	12213R	12213R	15
-LAB12236R	IBUPROFEN	12237R	12237R	15
-LAB12607R	"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF"	12606R	"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF"	15
-LABFAMYA	"AMYLASE, ASCITES"	AMYLASEFLD	"AMYLASE, ASCITES FLUID"	15
-LABBARSAB	BARTONELLA SPECIES AB	BHIGGT	B.HENSELAE IGG TITER	15
-LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11CT	COMMENT	15
-LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11DS	DISCLAIMER	15
-LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11DX	CLINICAL INDICATION	15
-LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11MD	INTERPRETED BY	15
-LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11MT	METHOD	15
-LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11RS	RESULT	15
-LABYUCC	COCAINE CONFIRMATION	COCAINE	COCAINE	15
-LABDCMVL	DONOR CMV- TOTAL AB	DCMVL1	DONOR CMV- TOTAL AB	15
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	EOSFLD	"EOSINOPHILS, SYN FLD"	15
-LABYESTRO	ESTROGENS E1 AND E2 FRACTIONATED	ESTRADIOL	ESTRADIOL	15
-LABYESTRO	ESTROGENS E1 AND E2 FRACTIONATED	ESTRONE	ESTRONE	15
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	15
-LABDVEIAR	FLU A DFA-REFLEX PCR	IAR	INFLUENZA A	15
-LABDVEIAR	FLU A DFA-REFLEX PCR	IARIP	TEST DESCRIPTION	15
-LABDVEIAR	FLU A DFA-REFLEX PCR	IARSP	SPECIMEN	15
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	15
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	15
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	15
-LAB11044R	IL6	IL6	INTERLEUKIN 6	15
-LABINHA	INHIBIN A DIMER	INHAR	INHIBIN-A (DIMER)	15
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	15
-LABUORG	"ORGANIC ACIDS, URINE"	ORGU	"ORGANIC ACIDS, URINE"	15
-LABUORG	"ORGANIC ACIDS, URINE"	ORGUIN	INTERPRETATION	15
-LAB11054R	PM1 AB	PM1AB	PM-1 ANTIBODY	15
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	15
-LABSSBABY	LA AB	SSB	"SS-B/LA AB, IGG, SERUM"	15
-EKG6	ECG SIGNAL AVERAGED	TWAVEAXIS	T WAVE AXIS	15
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	UBT	UNIT BLOOD TYPE  	15
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	UBT	UNIT BLOOD TYPE  	15
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	UBT	UNIT BLOOD TYPE  	15
-LABTBP66	TRANSFUSION BLOOD PRODUCT 67	UBT	UNIT BLOOD TYPE  	15
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	UNT	UNIT NUMBER  	15
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	UNT	UNIT NUMBER  	15
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	UNT	UNIT NUMBER  	15
-LABTBP66	TRANSFUSION BLOOD PRODUCT 67	UNT	UNIT NUMBER  	15
-LABTBP18	TRANSFUSION BLOOD PRODUCT 19	VL	PRODUCT VOLUME	15
-LABTBP20	TRANSFUSION BLOOD PRODUCT 21	VL	PRODUCT VOLUME	15
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	VL	PRODUCT VOLUME	15
-LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM1	"CHLAMYDIA SEROLOGY, S"	15
-LABYEB	"EBV AB PROFILE, SERUM"	YEB1	EBV VCA IGG AB	15
-LABYEB	"EBV AB PROFILE, SERUM"	YEB2	EBV VCA IGM AB	15
-LABYEB	"EBV AB PROFILE, SERUM"	YEB3	EBNA AB	15
-LABYEB	"EBV AB PROFILE, SERUM"	YEB4	INTERPRETATION	15
-LABYSTCL	"CHLORIDE, STOOL"	YSTCL2	TOTAL WEIGHT	15
-LABYTETT	TETANUS TOXOID IGG AB	YTETT1	TETANUS TOXOID IGG AB	15
-LABYTRAB	TSH RECEPTOR AB	YTRAB1	THYROTROPIN RECEPTOR AB	15
-LABYUCC	COCAINE CONFIRMATION	YUBEGC	BENZOYLECGONINE	15
-LABYUCC	COCAINE CONFIRMATION	YUCOIM	IMMUNOASSAY SCREEN	15
-LABYUCC	COCAINE CONFIRMATION	YUCOIN	INTERPRETATION	15
-10277R	HLA ORDER - PROSPECTIVE CROSSMATCH TESTING	10049R	HLA LAB TESTING STATUS	14
-LAB11240R	MEASLES IGG	11241R	MEASLES IGG	14
-LAB11618R	"HSV 1/2 PCR, AQUEOUS FLUID"	11777R	"HSV 1/2 PCR,AQUOUS FLUID"	14
-LAB11858R	HEPTIMAX(R) HCV RNA	11856R	HEPTIMAX ? HCR  RNA IU/ML	14
-LABHIBCV	"DONOR, HIV-1/HCV/HBV NAT TESTING"	11894R	DISCRIMINATORY TESTING	14
-LAB12853R	"EVEROLIMUS, BLOOD"	12339R	EVEROLIMUS	14
-LAB12796R	FLUCONAZOLE	12795R	FLUCONAZOLE	14
-LABABIDI	AB ID	ABID2E	ANTIBODY ID ELUATE COMMENT	14
-LABYACE	"ANGIOTENSIN CONVERTING ENZYME, SERUM"	ACE	ANGIOTESIN CONVERTING ENZYME	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	AMPHET	AMPHETAMINE	14
-LABSMABY	ANTI-SM ANTIBODY	ASMQNT	ANTI-SM LEVEL	14
-LABCENABY	ANTI CENTROMERE ANTIBODY	CENQNT	ANTI-CENTROMERE LEVEL	14
-LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	CHLAM	C.TRACHOMATIS RESULT:	14
-LABCMVQL	"CMV DNA PCR QUAL, NON-BLOOD"	CMQDNA	CMV DNA	14
-LABCMVQL	"CMV DNA PCR QUAL, NON-BLOOD"	CMVLIP	TEST DESCRIPTION	14
-LABCMVQL	"CMV DNA PCR QUAL, NON-BLOOD"	CMVQSP	SPECIMEN	14
-LABCOCC	"COCCIDIOIDES COMPLEMENT FIXATION, CSF"	COCC	COCCI CF AB (CSF)	14
-LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	CULT	CULT/OTHER RSLT:	14
-LABCYAB	CYSTICERCUS AB	CYABS	CYSTICERCUS AB	14
-LABCYSI	"CYSTICERCOSIS, CSF AND SERUM"	CYSC	CYSTICERCOSIS-CSF	14
-LABFCYST	CYSTICERCUS IGG AB	CYSIGG	CYSTICERCUS LGG AB	14
-LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	DCL4	TEST INFORMATION	14
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	FCMT	FLUID COMMENT	14
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	14
-LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1DI	DISCLAIMER	14
-LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1IN	INTERPRETATION	14
-LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1M	NEURAMINIDASE  H275Y MUTATION	14
-LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1MT	METHOD	14
-LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1R	2009 INFLUENZA A(H1N1) RNA	14
-LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1SP	SPECIMEN	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHCO	COMMENT	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHDI	DISCLAIMER	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHDX	CLINICAL INDICATION	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHIN	INTERPRETATION	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHMD	INTERPRETED BY	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHME	METHOD	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHPR	PRIMERS	14
-LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHRE	RESULT	14
-LABHIBCV	"DONOR, HIV-1/HCV/HBV NAT TESTING"	HIBCV1	DONOR HIV/HBV/HCV RESULT	14
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	14
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDEVLDATE	ICD DATE OF EVENT	14
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LBRAG	"L BRAZILIENSIS,IGG"	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LBRAM	"L BRAZILIENSIS,IGM"	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LDONG	"L DONOVANI,IGG"	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LDONM	"L DONOVANI,IGM"	14
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	14
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	14
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	14
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	14
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LMEXG	"L MEXICANA,IGG"	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LMEXM	"L MEXICANA,IGM"	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LTROG	"L TROPICA,IGG"	14
-LABLEISAB	"LEISHMANIA AB, IFA"	LTROM	"L TROPICA,IGM"	14
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYMPHM	LYMPHOMA CELLS	14
-LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	MORPH	MORPHOLOGY	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	MTAM	METHAMPHETAMINE	14
-LABOF	OSMOTIC FRAGILITY	OFINT	INTERPRETATION	14
-EKG6	ECG SIGNAL AVERAGED	PAXIS	P AXIS	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE01	S.PNEUMO SEROTYPE 1 (1)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE12	S.PNEUMO SEROTYPE 12 (12F)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE14	S.PNEUMO SEROTYPE 14 (14)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE19	S.PNEUMO SEROTYPE 19 (19F)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE23	S.PNEUMO SEROTYPE 23 (23F)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE26	S.PNEUMO SEROTYPE 26 (6B)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE3	S.PNEUMO SEROTYPE 3 (3)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE4	S.PNEUMO SEROTYPE 4 (4)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE51	S.PNEUMO SEROTYPE 51 (7F)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE56	S.PNEUMO SEROTYPE 56 (18C)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE8	S.PNEUMO SEROTYPE 8 (8)	14
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE9	S.PNEUMO SEROTYPE 9 (9N)	14
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	PRO	PRODUCT ID  	14
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	PRO	PRODUCT ID  	14
-LABTBP71	TRANSFUSION BLOOD PRODUCT 72	PRO	PRODUCT ID  	14
-LABPRO6MP	PRO-PREDICT ENZACT	PRO6MR	PRO-PREDICT ENZACT	14
-10277R	HLA ORDER - PROSPECTIVE CROSSMATCH TESTING	RINFO	TO FIND RESULT:	14
-LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	RPT	RPT	14
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	14
-LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	SDES	SPECIMEN	14
-LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	SDES4	SPECIMEN	14
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	SI	<<STATUS>>  	14
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	SI	<<STATUS>>  	14
-LABTBP71	TRANSFUSION BLOOD PRODUCT 72	SI	<<STATUS>>  	14
-LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	SREQ	COMMENTS	14
-LABSSAABY	RO AB	SSA	"SS-A/RO AB, IGG, SERUM"	14
-LABTXCAB	TOXOCARA AB ELISA	TOXOC1	TOXOCARA ANTIBODY	14
-LABTBP67	TRANSFUSION BLOOD PRODUCT 68	UBT	UNIT BLOOD TYPE  	14
-LABTBP67	TRANSFUSION BLOOD PRODUCT 68	UNT	UNIT NUMBER  	14
-LABEBVC	EBV CAPSID AG	VCAA	VCA IGA	14
-LABTBP17	TRANSFUSION BLOOD PRODUCT 18	VL	PRODUCT VOLUME	14
-LABXHIA	HUMAN INSULIN ANTIBODY	XHIAB	HUMAN INSULIN ANTIBODY	14
-LABYITRA	ITRACONAZOLE LEVEL	YITR3	ITRACONAZOLE + HYDROZYITRACONAZOLE	14
-LABYLYSO	"LYSOZYME, PLASMA"	YLYSOZ	LYSOZYME	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUAMIM	IMMUNOASSAY SCREEN	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUAMIN	INTERPRETATION	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUMDA	MDA BY GC/MS	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUMDMA	MDMA (ECSTASY) BY GC/MS	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUPSGC	PSEUDOEPHEDRINE/EPHEDRINE	14
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUPTGC	PHENTERMINE BY GC/MS	14
-LAB11858R	HEPTIMAX(R) HCV RNA	11857R	HEPTIMAX ? HCR  LOGIU IU/ML	13
-LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12052R	RMSF IGM	13
-LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12053R	R.TYPHI IGG	13
-LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12054R	R.TYPHI IGM	13
-LAB12090R	"HE4, OVARIAN CANCER MONITORING"	12105R	"HE4, OVARIAN CANCER MONITORING"	13
-LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	12194R	DRUGS DETECTED:	13
-LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	12195R	SUSPECT DRUG	13
-LAB12234R	VOLATILES	12235R	12235R	13
-LAB11067R	17OH PROG	17OHPROG	17-OH PROGESTERONE	13
-10375R	HLA - STR/CHIMERISM TESTING	9482R	LOCUS 8	13
-LABPLTAGG	PLATELET AGGREGATION	ADP5	ADP 5 UM	13
-LAB11021R	ADRENAL AB	ADRAB	ADRENAL ANTIBODY	13
-LABPLTAGG	PLATELET AGGREGATION	AGGDIR	INTERPRETED BY	13
-LABYALDO	"ALDOLASE, SERUM"	ALDOLASE	"ALDOLASE, SERUM"	13
-LABYALDOS	"ALDOSTERONE, SERUM"	ALDOSTR	"ALDOSTERONE, SERUM"	13
-LABCANNAB	"NEURONAL NUCLEAR AB, CSF"	ANTIHU	ANTI-HU	13
-LABPLTAGG	PLATELET AGGREGATION	ARA500	ARACHIDONIC ACID 500 UG/ML	13
-LABCANNAB	"NEURONAL NUCLEAR AB, CSF"	ARI	ANTI-RI	13
-LABCANNAB	"NEURONAL NUCLEAR AB, CSF"	CANNA3	"NEURONAL NUCLEAR AB, CSF"	13
-LABBMCD34	"CD34 PANEL, BONE MARROW"	CD34BMPC	"%CD34, BONE MARROW"	13
-LABBMCD34	"CD34 PANEL, BONE MARROW"	CD34DS	DISCLAIMER	13
-LABFLCL	"CHLORIDE, FLUID"	CLFLD	"CHLORIDE, FLUID"	13
-LABFLCL	"CHLORIDE, FLUID"	CLFLS	SOURCE	13
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMGQNT	CMV IGG LEVEL	13
-LABPLTAGG	PLATELET AGGREGATION	COLL25	COLLAGEN 2.5 UG/ML	13
-LABCYSCSF	"CYSTICERCUS AB REFLEX PANEL, CSF"	CYSTAB	"CYSTICERCUS AB(CSF),ELISA"	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	DEVTYPE	DEVTYPE	13
-LABYDHEAS	"DEHYDROEPIANDROSTERONE, SULFATE"	DHS	DEHYDROEPIANDROSTERONE SULFATE	13
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	DI	ISSUE DATE/TIME	13
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	DI	ISSUE DATE/TIME	13
-LABPLTAGG	PLATELET AGGREGATION	EPI5	EPINEPHRINE 5 UM	13
-LABPLTAGG	PLATELET AGGREGATION	EPIN10	EPINEPHRINE 10 UM	13
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	13
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	13
-LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSVRM	CLINICAL REMARKS	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	13
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDRHYTHM	ICD RHYTHM	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	13
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	IMPDATE	IMPLANT DATE	13
-LABREF4	LAB UNLISTED 4	LAB4	REFERENCE LAB	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	MFR	MANUFACTURER	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	MOD	MODEL	13
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKDI	DISCLAIMER	13
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKDX	CLINICAL INDICATION	13
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKMD	INTERPRETED BY	13
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKME	METHOD	13
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKRE	RESULT	13
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKSP	SPECIMEN SOURCE	13
-LABTBP72	TRANSFUSION BLOOD PRODUCT 73	PRO	PRODUCT ID  	13
-LABTBP73	TRANSFUSION BLOOD PRODUCT 74	PRO	PRODUCT ID  	13
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	13
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	13
-LABREF4	LAB UNLISTED 4	RESUL4	RESULT	13
-LABRF	RHEUMATOID FACTOR	RHFT	RHEUMATOID FACTOR LEVEL	13
-LABPLTAGG	PLATELET AGGREGATION	RIS12	RISTOCETIN 1.2 MG/ML	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	13
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SERNUM	SERIAL NUMBER	13
-LABTBP72	TRANSFUSION BLOOD PRODUCT 73	SI	<<STATUS>>  	13
-LABTBP73	TRANSFUSION BLOOD PRODUCT 74	SI	<<STATUS>>  	13
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	13
-LABTBP28	TRANSFUSION BLOOD PRODUCT 29	SPEXP	SPECIMEN EXPIRATION DATE/TIME	13
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	SPEXP	SPECIMEN EXPIRATION DATE/TIME	13
-LABREF4	LAB UNLISTED 4	SPTY4	SPECIMEN TYPE	13
-LABREF4	LAB UNLISTED 4	TEST4	TEST NAME	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	TIMEU	COLLECTION PERIOD	13
-LABYSTRYP	"TRYPSIN, FECAL"	TRYPSINSTL	"TRYPSIN, FECAL"	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URINE"	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URN"	13
-LABURMAG	"MAGNESIUM, URINE (RANDOM)"	UMG	"MAGNESIUM, URINE"	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	UVOL	VOLUME	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	VMA	VMA	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	VMAINT	VMA INTRP	13
-LABUVMA	"VANILLYMANDELIC ACID, URINE"	VMAUR	"VMA, URINE"	13
-LABYAMIO	AMIODARONE	YAMIOD	AMIODARONE	13
-LABYBLAS	BLASTOMYCES AB	YBLAS2	BLASTOMYCES COMPLEMENT FIXATION	13
-LABYC4F	C4 COMPLEMENT FUNCTIONAL	YC4FUN	"C4 COMPLEMENT, FUNCT"	13
-LABYAMIO	AMIODARONE	YDESET	DESETHYLAMIODARONE	13
-LABYHMPVP	HUMAN METAPNEUMOVIRUS PCR	YHMP1	HUMAN METAPNEUMOVIRUS PCR	13
-LABYHMPVP	HUMAN METAPNEUMOVIRUS PCR	YHMP2	SOURCE	13
-LABYUALA	"AMINOLEVULINIC ACID, URINE"	YUALA1	"AMINOLEVULINIC ACID, URINE"	13
-LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11760R	COQ10 REDUCED	12
-LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11761R	COQ10 TOTAL	12
-LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11762R	COQ10 % REDUCED	12
-LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11763R	INTERPRETATION	12
-LAB11860R	VASCULAR ENDOTHELIAL GROW.FACT	11859R	VEGF	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12363R	"ANNA-1, CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12364R	"ANNA-2, CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12365R	"ANNA-3, CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12366R	"AGNA-1,CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12367R	"PCA-1, CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12368R	"PCA-2, CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12369R	"AMPHIPHYSIN AB, CSF"	12
-LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12370R	"CRMP-5-IGG, CSF"	12
-LABYSTCL	"CHLORIDE, STOOL"	12577R	"CHLORIDE, STOOL, RANDOM"	12
-10375R	HLA - STR/CHIMERISM TESTING	9518R	% DONOR- MARROW WHOLE	12
-10375R	HLA - STR/CHIMERISM TESTING	9558R	% RECIPIENT- MARROW WHOLE	12
-LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH CORD	12
-LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH FRONT TYPE	12
-LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH STILLBORN	12
-LABARS	"ARSENIC, WHOLE BLOOD"	ARS	"ARSENIC, BLOOD"	12
-LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	B2M	BETA-2-MICROGLOB	12
-LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	B2MCREAT	BETA-2-MICROGLOB/CRT	12
-LABCD34	"CD34 PANEL, BLOOD"	BCD34V	CD34+ PROGENITOR VIABILITY	12
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	BDVC	"CORD BASE DEFICIT, VEN"	12
-LABCD34	"CD34 PANEL, BLOOD"	CD45PV	CD45+ VIABILITY	12
-LABCRTINE	"CREATINE, SERUM"	CRTNR	"CREATINE, PLASMA"	12
-LABDPARE	DIRECT PARASITE EXAM	CULT	CULT/OTHER RSLT:	12
-LABROVRE	VRE SCREEN	CULT	CULT/OTHER RSLT:	12
-LABCYSI	"CYSTICERCOSIS, CSF AND SERUM"	CYSS	CYSTICERCOSIS-SERUM	12
-LABTBP59	TRANSFUSION BLOOD PRODUCT 60	DI	ISSUE DATE/TIME	12
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	DI	ISSUE DATE/TIME	12
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	DI	ISSUE DATE/TIME	12
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	GASLOT	GASTROCCULT LOT#	12
-LABCSFGL	"GLUCOSE, CSF"	GLCSTN	TUBE NUMBER	12
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	HCO3VC	"CORD HCO3, VEN"	12
-LABABC	AUTOMATED BLOOD COUNT	HCT	HEMATOCRIT	12
-LABABC	AUTOMATED BLOOD COUNT	HGB	HEMOGLOBIN	12
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	12
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	12
-LAB11043R	IGD	IGD	"IGD, SERUM"	12
-LABABC	AUTOMATED BLOOD COUNT	MCH	MCH	12
-LABABC	AUTOMATED BLOOD COUNT	MCHC	MCHC	12
-LABABC	AUTOMATED BLOOD COUNT	MCV	MCV	12
-LABMERC	"MERCURY, WHOLE BLOOD"	MERC	"MERCURY, BLOOD"	12
-LABADIFF	DIFFERENTIAL	MORPH	MORPHOLOGY	12
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	PCO2VC	"CORD PCO2, VEN"	12
-VSC02	VSC CAROTID VERTEBRAL ARTERY ULTRASOUND	PDF	PDF RESULT	12
-LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	PF4IGG	PF4 IGG ONLY OD	12
-LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	PHFMS	PH METABOLIC SCRN	12
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	PHVCD	"CORD PH, VEN"	12
-LABABC	AUTOMATED BLOOD COUNT	PLT	PLATELET COUNT	12
-LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNAB1	PNEUMOCOCCAL AB (PNEUMOVAX)	12
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	PO2VC	"CORD PO2, VEN"	12
-LABTBP74	TRANSFUSION BLOOD PRODUCT 75	PRO	PRODUCT ID  	12
-LABPGN	PROGESTERONE	PROGES	PROGESTERONE	12
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRAMP	RA THRESHOLD AMPLITUDE	12
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRDUR	RA THRESHOLD DURATION	12
-LABABC	AUTOMATED BLOOD COUNT	RBC	RBC	12
-LABABC	AUTOMATED BLOOD COUNT	RDW	RDW	12
-LABDPARE	DIRECT PARASITE EXAM	RPT	RPT	12
-LABROVRE	VRE SCREEN	RPT	RPT	12
-LABDPARE	DIRECT PARASITE EXAM	SDES	SPECIMEN	12
-LABROVRE	VRE SCREEN	SDES	SPECIMEN	12
-LABTBP74	TRANSFUSION BLOOD PRODUCT 75	SI	<<STATUS>>  	12
-LABSICKL	SICKLE CELL PREP	SICKL	SICKLE CELL PREP	12
-LABADIFF	DIFFERENTIAL	SLREV	SLIDE REVIEW	12
-LABYSOMAT	SOMATOSTATIN	SOMATOSTATIN	SOMATOSTATIN	12
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	SPEXP	SPECIMEN EXPIRATION DATE/TIME	12
-LABDPARE	DIRECT PARASITE EXAM	SREQ	COMMENTS	12
-LABROVRE	VRE SCREEN	SREQ	COMMENTS	12
-LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	UCR	"CREATININE, URN"	12
-LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	UT	COLLECTION PERIOD	12
-LABVHHA	VH HYPERMUTATION ASSAY	VHDI	DISCLAIMER	12
-LABVHHA	VH HYPERMUTATION ASSAY	VHDX	CLINICAL INDICATION	12
-LABVHHA	VH HYPERMUTATION ASSAY	VHIN	INTERPRETATION	12
-LABVHHA	VH HYPERMUTATION ASSAY	VHMD	INTERPRETED BY	12
-LABVHHA	VH HYPERMUTATION ASSAY	VHME	METHOD	12
-LABVHHA	VH HYPERMUTATION ASSAY	VHRE	RESULT	12
-LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	VOL	VOLUME	12
-LABABC	AUTOMATED BLOOD COUNT	WBC	WBC	12
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	XMR	UNIT CROSSMATCH  	12
-LABYHAET	HEMOPHILUS INFLUENZA IGG	YHAET1	"HAEMOPHILUS INFLU B AG,IGG"	12
-LABYRIBAB	RIBOSOMAL P PROTEIN AB	YRIB1	"RIBOSOME P AB,IGG"	12
-LAB11100R	RIBOFLAVIN	YVITB2	RIBOFLAVIN(VIT.B2)	12
-10075R	HLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	11
-LAB11526R	K CHANNEL AB	11636R	K CHANNEL AB	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12242R	12242R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12243R	12243R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12244R	12244R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12245R	12245R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12246R	12246R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12247R	12247R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12248R	12248R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12249R	12249R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12250R	12250R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12251R	12251R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12252R	12252R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12253R	12253R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12254R	12254R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12255R	12255R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12256R	12256R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12257R	12257R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12258R	12258R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12259R	12259R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12260R	12260R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12261R	12261R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12262R	12262R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12263R	12263R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12264R	12264R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12265R	12265R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12266R	12266R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12267R	12267R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12268R	12268R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12269R	12269R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12270R	12270R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12271R	12271R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12272R	12272R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12273R	12273R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12274R	12274R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12275R	12275R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12276R	12276R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12277R	12277R	11
-LAB12241R	B CELL PHENOTYPING PROFILE	12278R	12278R	11
-LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12402R	ALKALINE PHOSPHATASE	11
-LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12403R	ALKP. PHOSPHATASE - OTHER	11
-LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12404R	ALKP. PHOSPHATASE - BONE	11
-LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12405R	ALKP. PHOSPHATASE - LIVER	11
-LAB12443R	"NEOCOMPLETE PARANEOPLASTIC PRFL, SERUM"	12444R	NEO PARANEOPLASTIC PRFL	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	AMOBARB	AMOBARBITAL	11
-LABFHVIG	"HIV 1 IGG-AB, WESTERN BLOT CONFIRM"	BANDPRSNT	BANDS PRESENT	11
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	BDC	"CORD BASE DEFICIT, ART"	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBCMT	COMMENT	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBDIR	INTERPRETED BY	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBDIS	DISCLAIMER	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBDX	CLINICAL INDICATION	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBMET	METHOD	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBPRM	PRIMERS	11
-LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBRES	RESULTS	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C3P4PA	"CD3+/CD4+,ABS"	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C3P4PC	"CD3+/CD4+,%"	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C3P8PA	"CD3+/CD8+, ABS."	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C3P8PC	"CD3+/CD8+, %"	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C5P4PA	"CD5+/CD4+ ,ABS"	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C5P4PC	CD5+/CD4+ %	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C5P8PA	"CD5+/CD8+,ABS."	11
-LABTTM	TRANSPLANT THERAPY MONITORING	C5P8PC	CD5+/CD8+ %	11
-LABBMCD34	"CD34 PANEL, BONE MARROW"	CD34V	VIABILITY	11
-LABCOCI	COCCI ID (SERUM)	COCI	COCCI ID (SERUM)	11
-LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTIGA	C.TRACHOMATIS IGA	11
-LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTIGG	C.TRACHOMATIS IGG	11
-LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTIGM	C.TRACHOMATIS IGM	11
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEL	DEVELOPER LOT#	11
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEXP	DEVELOPER EXP DATE	11
-LABYDHEA	"DEHYDROEPIANDROSTERONE, SERUM"	DHEA	"DHEA, SERUM"	11
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	DI	ISSUE DATE/TIME	11
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	DI	ISSUE DATE/TIME	11
-LABTBP65	TRANSFUSION BLOOD PRODUCT 66	DI	ISSUE DATE/TIME	11
-LABDONLAI	DONATH LANDSTEINER	DONLAR	DONATH LANDSTEINER TEST	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	FACIDTOT	TOTAL FATTY ACIDS	11
-LABFACT13	FACTOR XIII SCREEN	FACTOR13	FACTOR XIII SCREEN	11
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	GASEXP	GASTROCCULT EXP DATE	11
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	GASO	PATIENT GASTRIC RESULT	11
-LABFLGL	"GLUCOSE, FLUID"	GLUFLD	GLUCOSE BODY FLUID	11
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GMYD8	MYD88	11
-LABCSMP	CANCER SOMATIC MUTATION PANEL	GSF3B	SF3B1	11
-LABYHCCSF	"HCG BETA-SUBUNIT QUANTITATIVE, CSF"	HCGCSF	"HCG, B SUBUNIT QN, CSF"	11
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	HCO3C	"CORD HCO3, ART"	11
-LABFHIVQA	HIV-1 PROVIRALL DNA PCR QUAL	HIVQA	"HIV-1 DNA, QUAL, PCR"	11
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	11
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRVVDEL	BRADY VV DELAY	11
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	11
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	11
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	11
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	11
-LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKIN	INTERPRETATION	11
-LABFLK	"POTASSIUM, FLUID"	KFLD	"POTASSIUM, FLUID"	11
-LABFLK	"POTASSIUM, FLUID"	KFLS	SOURCE	11
-LABLEISAB	"LEISHMANIA AB, IFA"	LBRAIN	"L BRAZILIENSIS,INTERPRETATION"	11
-LABLDL	LDL CALCULATED	LDL	LDL (CALCULATED)	11
-LABLEISAB	"LEISHMANIA AB, IFA"	LDONIN	"L DONOVANI,INTERPRETATION"	11
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADALERT	LEAD ALERT	11
-LABLEISAB	"LEISHMANIA AB, IFA"	LMEXIN	"L MEXICANA,LNTERPRETATION"	11
-LABLEISAB	"LEISHMANIA AB, IFA"	LTROIN	INTERPRETATION	11
-ECH43	ECHO - DOBUTAMINE STRESS ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	MUFATOT	TOTAL MONOUNSATURATED ACID	11
-LABFLNA	"SODIUM, FLUID"	NAFLD	"SODIUM, FLUID"	11
-LABFLNA	"SODIUM, FLUID"	NAFLS	SOURCE	11
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKCO	COMMENT	11
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKPR	PRIMERS	11
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKRF	REFERENCES	11
-LABOF	OSMOTIC FRAGILITY	OFDIR	REVIEWED BY	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	PB	PHENOBARBITAL	11
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	PCO2C	"CORD PCO2, ART"	11
-VSC08	VSC RENAL ULTRASOUND	PDF	PDF RESULT	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	PENTO	PENTOBARBITAL	11
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	PHCD	"CORD PH, ART"	11
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHVA	PATIENT PH VALUE	11
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	PO2C	"CORD PO2, ART"	11
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	PRO	PRODUCT ID  	11
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	PRO	PRODUCT ID  	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	PUFATOT	TOTAL POLYUNSATURATED ACID	11
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	QC11	QC RESULT	11
-LABTTM	TRANSPLANT THERAPY MONITORING	RC4C8	CD4/CD8 RATIO	11
-10075R	HLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)	RINFO	TO FIND RESULT:	11
-LABRIPA	RISTOCETIN INDUCED PLATELET AGGREGATION	RIPINT	INTERPRETATION	11
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	11
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	SECOBARB	SECOBARBITAL	11
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	SI	<<STATUS>>  	11
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	SI	<<STATUS>>  	11
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	11
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
-LABTTM	TRANSPLANT THERAPY MONITORING	TC4C8A	"T(CD4+CD8), ABS."	11
-LABTTM	TRANSPLANT THERAPY MONITORING	TC4C8P	T(CD4+CD8)%	11
-LABTESFBT	"TESTOSTERONE, TOTAL, BIO, FREE"	TESBIO	"TESTOSTERONE, BIOAVAILABLE"	11
-LABTESFBT	"TESTOSTERONE, TOTAL, BIO, FREE"	TESFR	"TESTOSTERONE, FREE"	11
-LABTESFBT	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOS	TESTOSTERONE	11
-LABTBP68	TRANSFUSION BLOOD PRODUCT 69	UBT	UNIT BLOOD TYPE  	11
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	UBT	UNIT BLOOD TYPE  	11
-LABTBP68	TRANSFUSION BLOOD PRODUCT 69	UNT	UNIT NUMBER  	11
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	UNT	UNIT NUMBER  	11
-LABVITB1P	"VITAMIN B1, PLASMA"	VITB1	"VITAMIN B1, PLASMA"	11
-LABYUALA	"AMINOLEVULINIC ACID, URINE"	VOLUMEURN	URINE VOLUME	11
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	11
-LABXGAD65	ANTI GAD 65 ANTIBODIES	XGADR	ANTI-GAD 65 ANTIBODIES	11
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	XMR	UNIT CROSSMATCH  	11
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	XMR	UNIT CROSSMATCH  	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC1	LAURIC ACID C12:0	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC10	VACCENIC ACID C18:1W9	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC11	STEARIC ACID C18:0	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC12	EPA C20:5W3	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC13	ARACHIDONIC ACID C20:4W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC14	MEAD ACID C20:3W9	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC15	H-G-LINOLEIC C20:3W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC16	ARACHIDIC ACID C20:0	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC17	DHA C22:6W3	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC18	DPA C22:5W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC19	DPA C22:5W3	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC2	MYRISTIC ACID C14:0	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC20	DTA C22:4W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC21	DOCOSENOIC ACID C22:1	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC22	NERVONIC ACID C24:1W9	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC23	TRIENE TETRAENE RATIO	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC24	TOTAL SATURATED ACID	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC27	TOTAL W3	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC28	TOTAL W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC3	HEXADECENOIC ACID C16:1W9	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC30	INTERPRETATION EFA	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC4	PALMITOLEIC ACID C16:1W7	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC5	PALMITIC ACID C16:0	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC6	G-LINOLENIC ACID C18:3W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC7	A-LINOLENIC ACID C18:3W3	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC8	LINOLEIC ACID C18:2W6	11
-LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC9	OLEIC ACID C18:1W9	11
-LABFLGL	"GLUCOSE, FLUID"	YFLSC1	FLUID TYPE	11
-LABYGBMGE	"GBM AB, IGG"	YGBMG1	"GBM AB, IGG, ELISA"	11
-LABYUALA	"AMINOLEVULINIC ACID, URINE"	YUALA2	COLLECTION DURATION	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	YUBBIM	IMMUNOASSAY SCREEN	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	YUBBIN	INTERPRETATION	11
-LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	YUBTGC	BUTALBITAL	11
-10205R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	10049R	HLA LAB TESTING STATUS	10
-LAB11357R	FAMILIAL MEDITERRANEAN FEVER	11358R	FAMILIAL MEDITERRANEAN FEVER	10
-LAB11522R	HU AB	11632R	HU AB	10
-LAB11610R	"DONOR, HTLVS(QNS/LIPEMIC ONLY)"	11769R	"DONOR, HTLVS"	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12695R	S.PNEUMO SEROTYPE 2 (2)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12712R	S.PNEUMO SEROTYPE 5 (5)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12713R	S.PNEUMO SEROTYPE 17 (17)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12714R	S.PNEUMO SEROTYPE 20 (20F)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12715R	S.PNEUMO SEROTYPE 22 (22F)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12716R	S.PNEUMO SEROTYPE 34 (10A)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12717R	S.PNEUMO SEROTYPE 43 (11A)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12718R	S.PNEUMO SEROTYPE 54 (15B)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12719R	S.PNEUMO SEROTYPE 57 (19A)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12726R	S.PNEUMO SEROTYPE 70 (33F)	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9184R	DONOR NAME	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9185R	DONOR DOB	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9186R	RELATIONSHIP TO PATIENT	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9187R	DONOR CELL DATE	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9188R	DONOR CELL SOURCE	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9189R	DONOR CELL TARGET	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9190R	METHOD	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9191R	PATIENT\'S SERUM 1 DATE	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9193R	PATIENT\'S SERUM 1 UNTREATED RESULT	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9194R	PATIENT\'S SERUM 1 DTT TREATED RESULT	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9195R	PATIENT\'S SERUM 2 DATE	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9197R	PATIENT\'S SERUM 2 UNTREATED RESULT	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9198R	PATIENT\'S SERUM 2 DTT TREATED RESULT	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9209R	DISCLAIMER	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9329R	CUMULATIVE ANTIBODIES BY IGG	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9330R	CUMULATIVE ANTIBODIES BY C1Q	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9331R	SERUM DATE	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9339R	CPRA FOR CLASS II	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9340R	HLA ANTIBODIES FOR CLASS II	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9345R	COMPOSED TEXT CLASS II	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9346R	DISCLAIMER	10
-10375R	HLA - STR/CHIMERISM TESTING	9519R	% DONOR- MARROW CD3	10
-10375R	HLA - STR/CHIMERISM TESTING	9559R	% RECIPIENT- MARROW CD3	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9606R	METHOD	10
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9653R	MFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS II	10
-10315R	HLA - ALLO T CDC CROSSMATCH	9709R	DONOR PHENOTYPE	10
-LABCSFCT	"CELL COUNT AND DIFF, CSF"	CLINE	"LINING CELLS, CSF"	10
-LABCMVQT	CMV PCR QUANTITATIVE	CMVCP	RESULT	10
-LABCMVQT	CMV PCR QUANTITATIVE	CMVDNA	CMV DNA	10
-LABCMVQT	CMV PCR QUANTITATIVE	CMVLOG	RESULT LOG 10	10
-LABCMVQT	CMV PCR QUANTITATIVE	CMVSP	SPECIMEN	10
-LABCMVQT	CMV PCR QUANTITATIVE	CMVTIP	TEST DESCRIPTION	10
-LABPLTAGG	PLATELET AGGREGATION	COLL50	COLLAGEN 5.0 UG/ML	10
-LABPORFEC	"PORPHYRINS FRACTIONATED, FECAL"	COPRO	"COPROPORPHYRIN, FEC"	10
-LABVCULO	"VIRAL CULTURE, OCULAR"	CULT	CULT/OTHER RSLT:	10
-LABTBP66	TRANSFUSION BLOOD PRODUCT 67	DI	ISSUE DATE/TIME	10
-LABTBP67	TRANSFUSION BLOOD PRODUCT 68	DI	ISSUE DATE/TIME	10
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	DYSPL	"DYSPL GRANS, UNCLASS."	10
-LABEAIGG	EBV EARLY ANTIGEN IGG	EAGQNT	EAGQNT	10
-LABFFIBN	FETAL FIBRONECTIN	FFIBN	FETAL FIBRONECTIN	10
-LABTFPSA	TOTAL AND FREE PSA	FPSA	FREE PSA	10
-LABTFPSA	TOTAL AND FREE PSA	FREEPSAPC	% FREE PSA	10
-LABHCVG	HEPATITIS C VIRUS GENOTYPING	HCGG	HCV GENOTYPE	10
-LABHEPBG	HEPATITIS B GENOTYPE	HEPBG1	HBV GENOTYPE	10
-LAB11040R	HEPARIN COFACTOR	HEPCO2	HEPARIN COFACTOR II	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRADYDP	BRADY DEPENDANCY	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRLOWRT	BRADY LOWER RATE	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMAXRT	BRADY MAX. SENSOR RATE	10
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRPACMD	BRADY PACING MODE	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	10
-LABCIGMT	"IGM, CSF"	IGMCSF	"IGM,CSF"	10
-LABLAMOL	LAMOTRIGINE	LAMOT	LAMOTRIGINE	10
-LABMYCPA	MYCOPHENOLIC ACID	MPACID	MYCOPHENOLIC ACID	10
-LABGASPH	"PH, GASTRIC"	PHGAST	"PH,GASTRIC"	10
-LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	PHOSU	"PHOSPHATE, URINE"	10
-LABYCPYRU	"PYRUVATE, CSF"	PKCSF	"PYRUVATE,CSF"	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMEVDATE	PACEMAKER EVAL DATE	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRHYTHM	PM RHYTHM	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE01	S.PNEUMO SEROTYPE 1 (1)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE12	S.PNEUMO SEROTYPE 12 (12F)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE14	S.PNEUMO SEROTYPE 14 (14)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE19	S.PNEUMO SEROTYPE 19 (19F)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE23	S.PNEUMO SEROTYPE 23 (23F)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE26	S.PNEUMO SEROTYPE 26 (6B)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE3	S.PNEUMO SEROTYPE 3 (3)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE4	S.PNEUMO SEROTYPE 4 (4)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE51	S.PNEUMO SEROTYPE 51 (7F)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE56	S.PNEUMO SEROTYPE 56 (18C)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE68	S.PNEUMO SEROTYPE 68 (9V)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE8	S.PNEUMO SEROTYPE 8 (8)	10
-LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE9	S.PNEUMO SEROTYPE 9 (9N)	10
-LABTFPSA	TOTAL AND FREE PSA	PSA	PSA	10
-LABFQFVR	COXIELLA BURNETII AB	QFVA1	PHASE 1 IGA	10
-LABFQFVR	COXIELLA BURNETII AB	QFVA2	PHASE II IGA	10
-LABYRENIN	"RENIN ACTIVITY, PLASMA"	RENIN	"RENIN ACTIVITY, PL"	10
-10205R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	RINFO	TO FIND RESULT:	10
-LABPLTAGG	PLATELET AGGREGATION	RIS15	RISTOCETIN 1.5 MG/ML	10
-LABVCULO	"VIRAL CULTURE, OCULAR"	RPT	RPT	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRPACA	RV BRADY PACING AMPLITUDE	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRPACW	RV BRADY PACING PULSE WIDTH	10
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENA	RV BRADY SENSING AMPLITUDE	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	10
-LABVCULO	"VIRAL CULTURE, OCULAR"	SDES	SPECIMEN	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	10
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	10
-LABVCULO	"VIRAL CULTURE, OCULAR"	SREQ	COMMENTS	10
-LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	TPHOSU	PHOSPHATE EXCRETION	10
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	UBT	UNIT BLOOD TYPE  	10
-LABTBP71	TRANSFUSION BLOOD PRODUCT 72	UBT	UNIT BLOOD TYPE  	10
-LABTBP72	TRANSFUSION BLOOD PRODUCT 73	UBT	UNIT BLOOD TYPE  	10
-LABTBP73	TRANSFUSION BLOOD PRODUCT 74	UBT	UNIT BLOOD TYPE  	10
-LABUURIC	"URIC ACID WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	10
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	UNT	UNIT NUMBER  	10
-LABTBP71	TRANSFUSION BLOOD PRODUCT 72	UNT	UNIT NUMBER  	10
-LABTBP72	TRANSFUSION BLOOD PRODUCT 73	UNT	UNIT NUMBER  	10
-LABTBP73	TRANSFUSION BLOOD PRODUCT 74	UNT	UNIT NUMBER  	10
-LABUURIC	"URIC ACID WITH CREATININE, URINE (RANDOM)"	URICRU	URIC/CREAT RATIO	10
-LABUURIC	"URIC ACID WITH CREATININE, URINE (RANDOM)"	URICURN	"URIC ACID, URINE"	10
-LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	UT8	COLLECTION PERIOD PHOSPHATE	10
-LABTBP29	TRANSFUSION BLOOD PRODUCT 30	VL	PRODUCT VOLUME	10
-LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	VOL8	VOLUME PHOSPHATE	10
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	XMR	UNIT CROSSMATCH  	10
-LABTBP53	TRANSFUSION BLOOD PRODUCT 54	XMR	UNIT CROSSMATCH  	10
-LABTBP55	TRANSFUSION BLOOD PRODUCT 56	XMR	UNIT CROSSMATCH  	10
-LABYDNBT	ANTI - DNASE B TITER	YDNBT1	ANTI-DNASE -B TITER	10
-LABYINFAB	INFLUENZA AB PANEL	YINF1	"INFLUENZA VIRUS A AB, IGG & IGM"	10
-LABYINFAB	INFLUENZA AB PANEL	YINF4	"INFLUENZA VIRUS B AB, IGG & IGM"	10
-LABMYCPA	MYCOPHENOLIC ACID	YMPAG	MPA GLUCURONIDE	10
-10230R	HLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*	10049R	HLA LAB TESTING STATUS	9
-LAB11170R	PREGABALIN	11171R	PREGABALIN	9
-LAB11516R	PANCREATITIS PANEL	11625R	PANCREATITIS PANEL	9
-LAB11567R	"HTLV-I/II AB, CSF"	11681R	HTLV-I/II AB	9
-LAB11851R	"FECAL LIPIDS, TOTAL"	11848R	TOTAL SPECIMEN WEIGHT	9
-LAB11851R	"FECAL LIPIDS, TOTAL"	11849R	COLLECTION DURATION	9
-LAB11851R	"FECAL LIPIDS, TOTAL"	11850R	"FECAL LIPIDS, 24 HR"	9
-LAB11871R	"HIV-2 AB, IMMUNOBLOT"	11870R	"HIV 2 ANTIBODY, IMMUNOBLOT"	9
-LAB11610R	"DONOR, HTLVS(QNS/LIPEMIC ONLY)"	11890R	"HTLV I/II, WESTERN BLOT"	9
-LABFECHO	"ECHOVIRUS AB, SERUM"	12027R	ECHOVIRUS  AB TYPE 6	9
-LABFECHO	"ECHOVIRUS AB, SERUM"	12028R	ECHOVIRUS  AB TYPE 7	9
-LABFECHO	"ECHOVIRUS AB, SERUM"	12029R	ECHOVIRUS  AB TYPE 11	9
-LABFECHO	"ECHOVIRUS AB, SERUM"	12030R	ECHOVIRUS  AB TYPE 30	9
-LABFECHO	"ECHOVIRUS AB, SERUM"	12031R	ECHOVIRUS  AB TYPE 9	9
-LABIBD	ANCA IBD WITH REFLEX	12215R	12215R	9
-LAB12605R	"N-METHYL-D-ASPARTATE RECEPTOR AB, IGG"	12604R	"N-METHYL-D-ASPARTATE RCPTR AB, IGG"	9
-LAB12657R	T CELL ACTIVATION	12658R	T CELL ACTIVATION	9
-LABYSTONE	STONE ANALYSIS	1STCONST	1ST CONSTITUENT	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9210R	DONOR NAME	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9211R	DONOR DOB	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9212R	RELATIONSHIP TO PATIENT	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9213R	DONOR CELL DATE	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9214R	DONOR CELL SOURCE	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9215R	DONOR CELL TARGET	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9216R	METHOD	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9217R	PATIENT\'S SERUM 1 DATE	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9221R	PATIENT\'S SERUM 2 DATE	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9233R	INTERPRETATION/ COMMENTS	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9235R	DISCLAIMER	9
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9333R	COMBINED CPRA FOR SERUM	9
-10360R	HLA - MONITORING BY C1Q	9364R	MFI RESULTS FOR MONITORING BY C1Q CLASS I	9
-10360R	HLA - MONITORING BY C1Q	9377R	CUMULATIVE ANTIBODIES BY C1Q	9
-10360R	HLA - MONITORING BY C1Q	9378R	CPRA FOR CUMULATIVE C1Q	9
-10360R	HLA - MONITORING BY C1Q	9382R	CPRA FOR C1Q CLASS I FOR CURRENT	9
-10360R	HLA - MONITORING BY C1Q	9383R	HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	9
-10360R	HLA - MONITORING BY C1Q	9392R	COMPOSED TEXT CLASS I	9
-10360R	HLA - MONITORING BY C1Q	9396R	HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	9
-10360R	HLA - MONITORING BY C1Q	9405R	COMPOSED TEXT CLASS II	9
-10360R	HLA - MONITORING BY C1Q	9406R	DISCLAIMER	9
-10375R	HLA - STR/CHIMERISM TESTING	9521R	% DONOR- MARROW CD15	9
-10375R	HLA - STR/CHIMERISM TESTING	9522R	% DONOR- MARROW CD19	9
-10375R	HLA - STR/CHIMERISM TESTING	9529R	% DONOR- MARROW CD56	9
-10375R	HLA - STR/CHIMERISM TESTING	9561R	% RECIPIENT- MARROW CD15	9
-10375R	HLA - STR/CHIMERISM TESTING	9562R	% RECIPIENT- MARROW CD19	9
-10375R	HLA - STR/CHIMERISM TESTING	9569R	% RECIPIENT- MARROW CD56	9
-10360R	HLA - MONITORING BY C1Q	9608R	METHOD	9
-10360R	HLA - MONITORING BY C1Q	9622R	CURRENT SERUM DATE	9
-10360R	HLA - MONITORING BY C1Q	9623R	CURRENT SERUM DESCRIPTION	9
-10320R	HLA - ALLO B CDC CROSSMATCH	9712R	DONOR PHENOTYPE	9
-LABAAUR	"AMINO ACIDS, URINE"	AAMINOA	A-AMINOADIPIC ACID	9
-LABAAUR	"AMINO ACIDS, URINE"	AAMINOB	A-AMINOBUTYRIC ACID	9
-LABAAUR	"AMINO ACIDS, URINE"	AAUINT	INTERPRETATION	9
-LAB11078R	ACETYLCHOLINESTERASE	ACHERBC	"ACETYLCHOLINESTERASE, RBC"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	ACPINT	INTERPRETATION	9
-LABADAQ	"ADENOSINE DEAMINASE, RBC"	ADA	ADENOSINE DEAMINASE	9
-LABADAQ	"ADENOSINE DEAMINASE, RBC"	ADMD	REVIEWED BY	9
-LABAAUR	"AMINO ACIDS, URINE"	ALANINE	ALANINE	9
-LABAAUR	"AMINO ACIDS, URINE"	ARGININE	ARGININE	9
-LAB11126R	ASCARIASIS	ASCA	ASCARISIS	9
-LABAAUR	"AMINO ACIDS, URINE"	ASPART	ASPARTIC ACID	9
-LABAAUR	"AMINO ACIDS, URINE"	ASPGN	ASPARAGINE	9
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ATACHTADTI	ATACH TACHY ZONE DETECTION INTERVAL	9
-LABAAUR	"AMINO ACIDS, URINE"	BALN	B-ALANINE	9
-LABAAUR	"AMINO ACIDS, URINE"	BAMBA	B-AMINOISOBUTYRIC AC	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C02	"C2, ACETYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C03	"C3, PROPIONYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C04	"C4, ISO-/BUTYRYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C05	"C5, ISOVALERYL-/2-METHYLBUTYRYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C05DC	"C5-DC, GLUTARYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C05OH	"C5-OH, 3OH-ISOVALERYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C06	"C6, HEXANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C06OH	"C6-0H, 3OH-HEXANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C08	"C8, OCTANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C081	"C8:1, OCTENOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C10	"C10, DECANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C101	"C10:1, DECENOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C12	"C12, DODECANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C121	"C12:1, DODECENOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C12OH	"C12-OH, 3OH-DODECANOYL"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C14	"C14, TETRADECANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C141	"C14:1, TETRADECENOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C141OH	"C14:1-OH, 3OH-TETRADECENOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C142	"C14:2, TETRADECADIENOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C14OH	"C14-OH, 3OH-TETRADECANOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C16	"C16, PALMITOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C161	"C16:1, PALMITOLEYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C161OH	"C16:1-OH, 3OH-PALMITOLEYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C16OH	"C16-OH, 3OH-PALMITOYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C18	"C18, STEAROYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C181	"C18:1, OLEYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C181OH	"C18:1-OH, 3OH-OLEYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C182	"C18:2, LINOLEYL-"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C182OH	"C18:2-OH, 3OH-LINOLEYL-"	9
-LABAAUR	"AMINO ACIDS, URINE"	CITRUL	CITRULLINE	9
-LABCMVIGG	CMV IGG	CMV	CMV ANTIBODY	9
-LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	CULT	CULT/OTHER RSLT:	9
-LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	CULT	CULT/OTHER RSLT:	9
-LABAAUR	"AMINO ACIDS, URINE"	CYSTINE	CYSTINE	9
-LABAAUR	"AMINO ACIDS, URINE"	CYTHN	CYSTATHIONINE	9
-LABECHIG	"ECHINOCOCCUS AB, IGG W/REFLEX"	ECHIND	ECHINOCOCCUS LGG	9
-LABEOSSP	SPUTUM FOR EOSINOPHILS	EOSSP	SPUTUM FOR EOS.	9
-LABAAUR	"AMINO ACIDS, URINE"	ETHLNU	ETHANOLAMINE	9
-LABFSYN	FUNGAL SYNERGY	FMIC	FUNGAL MIC	9
-LABAAUR	"AMINO ACIDS, URINE"	GLUTAC	GLUTAMIC ACID	9
-LABAAUR	"AMINO ACIDS, URINE"	GLUTAMINE	GLUTAMINE	9
-LABAAUR	"AMINO ACIDS, URINE"	GLYCINE	GLYCINE	9
-LABHBCC	HEPATITIS B CORE TOTAL AB	HBCAB	HEP B CORE TOTAL AB	9
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCINT	INTERPRETATION	9
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCR	IGM ANTI-HBC ANTIBODY	9
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSINT	INTERPRETATION	9
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSR	HEP B SURFACE ANTIGEN	9
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTINT	INTERPRETATION	9
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTR	ANTI-HBC ANTIBODY	9
-LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCVGDI	DISCLAIMER	9
-LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCVGMT	METHOD	9
-LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCVGRE	RESULT	9
-LABAAUR	"AMINO ACIDS, URINE"	HISTID	HISTIDINE	9
-LABAAUR	"AMINO ACIDS, URINE"	HMCY	HOMOCYSTINE	9
-LABHTLVS	HTLV-1 AB SCREEN	HTLVS	HTLV AB SCREEN	9
-LABAAUR	"AMINO ACIDS, URINE"	HYPROL	HYDROXYPROLINE	9
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVPCD	BRADY PACED AV DELAY	9
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDSLP	BRADY MODULATION SLOPE	9
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	9
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	9
-LABCIGA	"IGA, CSF"	IGACSF	"IGA, CSF"	9
-LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	INBY	INTERPRETED BY	9
-LABAAUR	"AMINO ACIDS, URINE"	INTUBY	INTERPRETED BY	9
-LABAAUR	"AMINO ACIDS, URINE"	ISOLEU	ISOLEUCINE	9
-LABAAUR	"AMINO ACIDS, URINE"	LEUCINE	LEUCINE	9
-LABLKM1AB	"LIVER, KID MICROSOME-1 AB, IGG"	LKM1R	"LIVER, KID MIC-1 AB"	9
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRAMP	LV THRESHOLD AMPLITUDE	9
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRDUR	LV THRESHOLD DURATION	9
-LAB12214R	"LYME SCREEN, SERUM"	LYMEC6	B.BURGDORFERI C6 PEPTIDE	9
-LABAAUR	"AMINO ACIDS, URINE"	LYSINE	LYSINE	9
-LABAAUR	"AMINO ACIDS, URINE"	METHIO	METHIONINE	9
-LABUMAG	"MAGNESIUM WITH CREATININE, URINE (RANDOM)"	MGUCRU	MAG/CREAT RATIO	9
-LABMUMPSG	MUMPS IGG	MUMDES	TEST DESCRIPTION	9
-LABMYCOC	"MYCOPLASMA CULTURE, FASTIDIOUS"	MYORG	MYCOPLASAM ORGANISM	9
-LABMYCOC	"MYCOPLASMA CULTURE, FASTIDIOUS"	MYSTA	MYCOPLASMA CULTURE STATUS	9
-LABAAUR	"AMINO ACIDS, URINE"	ORNITHINE	ORNITHINE	9
-LABIBD	ANCA IBD WITH REFLEX	PANCA	NEUTROPHIL CYTO AB-P	9
-IMG11PER	CAR PERICARDIOCENTESIS	PDF	PDF RESULT	9
-LABPENTO	PENTOBARBITAL LEVEL	PENTO	"PENTOBARBITAL, S"	9
-LABAAUR	"AMINO ACIDS, URINE"	PHALN	PHENYLALANINE	9
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRALDBIIM	PM RA LEAD BIPOLAR IMPEDANCE	9
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMTLPCPACA	PM TELEMETRY PERCENT PACING ATRIAL	9
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	PRO	PRODUCT ID  	9
-LABAAUR	"AMINO ACIDS, URINE"	PROLINE	PROLINE	9
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRPACA	RA BRADY PACING AMPLITUDE	9
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRPACW	RA BRADY PACING PULSE WIDTH	9
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRSENA	RA BRADY SENSING AMPLITUDE	9
-10230R	HLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*	RINFO	TO FIND RESULT:	9
-LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	RPT	RPT	9
-LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	RPT	RPT	9
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	9
-LABIBD	ANCA IBD WITH REFLEX	SACIGA	"S. CERVISIAE AB,IGA"	9
-LABIBD	ANCA IBD WITH REFLEX	SACIGG	"S.CERVISIAE AB,IGG"	9
-LABAAUR	"AMINO ACIDS, URINE"	SARSN	SARCOSINE	9
-LABSCLABY	SCL 70 AB	SCL70	"SCL 70 AB, IGG, SERUM"	9
-LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	SDES	SPECIMEN	9
-LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	SDES	SPECIMEN	9
-LABAAUR	"AMINO ACIDS, URINE"	SERINE	SERINE	9
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	SI	<<STATUS>>  	9
-LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	SLREV	SLIDE REVIEW	9
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	SPEXP	SPECIMEN EXPIRATION DATE/TIME	9
-LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	SREQ	COMMENTS	9
-LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	SREQ	COMMENTS	9
-LABAAUR	"AMINO ACIDS, URINE"	TAURINE	TAURINE	9
-LABAAUR	"AMINO ACIDS, URINE"	THREONINE	THREONINE	9
-LABAAUR	"AMINO ACIDS, URINE"	TRYPTOPHAN	TRYPTOPHAN	9
-LABAAUR	"AMINO ACIDS, URINE"	TYROSINE	TYROSINE	9
-LABUMAG	"MAGNESIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	9
-LABUMAG	"MAGNESIUM WITH CREATININE, URINE (RANDOM)"	UMG	"MAGNESIUM, URINE"	9
-LABAAUR	"AMINO ACIDS, URINE"	VALINE	VALINE	9
-LABVWFPI	VON WILLEBRAND PROTEASE INHIBITOR	VWFPIC	COMMENT	9
-LABVWFPI	VON WILLEBRAND PROTEASE INHIBITOR	VWFPIR	RESULT	9
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	XMR	UNIT CROSSMATCH  	9
-LABTBP54	TRANSFUSION BLOOD PRODUCT 55	XMR	UNIT CROSSMATCH  	9
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	XMR	UNIT CROSSMATCH  	9
-LABTBP67	TRANSFUSION BLOOD PRODUCT 68	XMR	UNIT CROSSMATCH  	9
-LABYC2729	BCA AB  (CA27.29)	Y2729A	"CA 27, 29"	9
-LABYSTONE	STONE ANALYSIS	YSTNSO	STONE SOURCE	9
-LABYSTONE	STONE ANALYSIS	YSTNWT	STONE WEIGHT	9
-10315R	HLA - ALLO T CDC CROSSMATCH	10464R	RECIPIENT NAME	8
-10315R	HLA - ALLO T CDC CROSSMATCH	10465R	RECIPIENT DOB	8
-10360R	HLA - MONITORING BY C1Q	10476R	DSA FOR C1Q CLASS I	8
-LAB11188R	M. PNEUMONIAE DNA	11189R	M.PNEUMONIAE	8
-LAB11369R	LEUKEMIA/LYMPHOMA IMMUPHENTYP	11370R	LEUKEMIA/LYMPHOMA IMMUPHENTYP	8
-LAB11855R	"HEPATITIS C VIRAL RNA, QUANT"	11853R	"HCV RNA ,QUANT,TMA IU/ML"	8
-LAB12099R	"BRUCELLA TOTAL AB,AGGLU (RFLX)"	12100R	"BRUCELLA TOTAL  AB, AGGLUTIN"	8
-LAB11188R	M. PNEUMONIAE DNA	12283R	12283R	8
-LAB12332R	KIT MUTATION ANALYSIS EXON 11	12337R	KIT MUTATION ANALYSIS EXON 11	8
-LAB12352R	NMO EVALUATION WITH REFLEX	12353R	NMO/AQP4 - IGG ELISA	8
-LAB13006R	"REDUCING SUBSTANCE, FECES"	13005R	"REDUCING SUBSTANCE, FECES"	8
-10305R	HLA - ALLO T FLOW CROSSMATCH	9136R	SERUM DESCRIPTION 2	8
-10310R	HLA - ALLO B FLOW CROSSMATCH	9166R	SERUM DESCRIPTION 2	8
-10320R	HLA - ALLO B CDC CROSSMATCH	9219R	PATIENT\'S SERUM 1 UNTREATED RESULT	8
-10320R	HLA - ALLO B CDC CROSSMATCH	9223R	PATIENT\'S SERUM 2 UNTREATED RESULT	8
-10320R	HLA - ALLO B CDC CROSSMATCH	9224R	PATIENT\'S SERUM 2 DTT TREATED RESULT	8
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9334R	CPRA FOR CLASS I	8
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9335R	HLA ANTIBODIES FOR CLASS I	8
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9343R	INHIBITION RESULTS FOR CLASS II	8
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9351R	MFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS I	8
-10360R	HLA - MONITORING BY C1Q	9395R	CPRA FOR C1Q CLASS II FOR CURRENT	8
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9587R	COMPOSED TEXT CLASS I	8
-10360R	HLA - MONITORING BY C1Q	9655R	MFI RESULTS FOR MONITORING BY C1Q CLASS II	8
-LAB12397R	ACETYLCHOLINE BLOCKING AB	ACRBLO	ACETYLCHOLINE BLOCKING AB	8
-LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMAG	ANTIGEN	8
-LABADVRC	ADENOVIRUS RAPID CULTURE	ADVRC1	ADV RAPID CULTURE	8
-LABADVRC	ADENOVIRUS RAPID CULTURE	ADVRCD	TEST DESCRIPTION	8
-LABADVRC	ADENOVIRUS RAPID CULTURE	ADVSPM	SPECIMEN	8
-LABURAMY	"AMYLASE, URINE (RANDOM)"	AMYLASE	"AMYLASE, URINE"	8
-LABYUHYME	"HEAVY METALS, URINE"	ARSENICURN	"ARSENIC, URINE"	8
-LABFCYST	CYSTICERCUS IGG AB	BANDPRSNT	BANDS PRESENT	8
-LABLWBCC	"LYME WESTERN BLOT, CSF"	BANDPRSNT	BANDS PRESENT	8
-LABYCARO	CAROTENE	CAROTENE	CAROTENE	8
-LABYCEAC	"CEA, CSF"	CEAFLD	"CEA, SPINAL FLUID"	8
-LABUCLT	"CHLORIDE, URINE (TIMED)"	CLE	CHLORIDE EXCRETION	8
-LABBMPTP	BMT PRETRANSPLANT PANEL	CMMQNT	CMV IGM LEVEL	8
-LABTCPSA	TOTAL AND COMPLEXED PSA	CPSA	COMPLEXED PSA	8
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	CRDVLG	CORD LENGTH	8
-LABROSBBP	R/O STREP BETA GP B BY PCR	CULT	CULT/OTHER RSLT:	8
-LABPORFEC	"PORPHYRINS FRACTIONATED, FECAL"	FPORIN	"PORPHYRIN INTRP, FEC"	8
-LABPORFEC	"PORPHYRINS FRACTIONATED, FECAL"	FPROTO	"PROTOPORHYRINS, FEC"	8
-LABTCPSA	TOTAL AND COMPLEXED PSA	FPSA	FREE PSA	8
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	8
-LABHBCM	HEPATITIS B CORE / PRECORE MUTATION	HBCM1	BASAL CORE PROMOTER NUCLEOTIDE	8
-LABHBCM	HEPATITIS B CORE / PRECORE MUTATION	HBCM2	PRECORE CODON	8
-LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDDNA	HEP B VIRUS DNA	8
-LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDIPR	TEST DESCRIPTION	8
-LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDLOG	RESULT LOG 10	8
-LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDRES	RESULT	8
-LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDSP	SPECIMEN	8
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6CP	RESULT	8
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6DN	HHV-6 DNA	8
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6LO	RESULT LOG 10	8
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6SP	SPECIMEN	8
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6TI	TEST DESCRIPTION	8
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLINT	INTERPRETATION	8
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	8
-LABBMPTP	BMT PRETRANSPLANT PANEL	HV1QNT	HSV-1 IGG LEVEL	8
-LABBMPTP	BMT PRETRANSPLANT PANEL	HV1QUL	HSV-1 IGG RESULT	8
-LABBMPTP	BMT PRETRANSPLANT PANEL	HV2QNT	HSV-2 IGG LEVEL	8
-LABBMPTP	BMT PRETRANSPLANT PANEL	HV2QUL	HSV-2 IGG RESULT	8
-LABBMPTP	BMT PRETRANSPLANT PANEL	HVIGDS	HSV IGG TEST DESCRIPTION	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAMSRT	BRADY AMS RATE	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVSND	BRADY SENSED AV DELAY	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDTHR	BRADY MODULATION THRESHOLD	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRTRKRT	BRADY TRACKING RATE	8
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	8
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	8
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADALERT	LEAD ALERT	8
-ECH34	ECHO - 3-DIMENSIONAL ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	8
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRSENM	LV THRESHOLD SENSING AMPLITUDE	8
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRSENPO	LV THRESHOLD SENSING POLARITY	8
-LABADIFF	DIFFERENTIAL	LYM	LYMPHOCYTES	8
-LABADIFF	DIFFERENTIAL	MONO	MONOCYTES	8
-LABMUMPSG	MUMPS IGG	MUMQUL	RESULT	8
-LABADIFF	DIFFERENTIAL	NEUT	SEG NEUTROPHILS	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVIMPDT	PM DEVICE IMPLANT DATE	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVIMPDT	PM DEVICE IMPLANT DATE	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVMFR	PM DEVICE MANUFACTURER	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVMFR	PM DEVICE MANUFACTURER	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVMODEL	PM DEVICE MODEL	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVMODEL	PM DEVICE MODEL	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVSERNM	PM DEVICE SERIAL NUMBER	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVSERNM	PM DEVICE SERIAL NUMBER	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVTYPE	PM DEVICE TYPE	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVTYPE	PM DEVICE TYPE	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMEVDATE	PACEMAKER EVAL DATE	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMRHYTHM	PM RHYTHM	8
-LABTBP78	TRANSFUSION BLOOD PRODUCT 79	PRO	PRODUCT ID  	8
-LABTBP79	TRANSFUSION BLOOD PRODUCT 80	PRO	PRODUCT ID  	8
-LABPSARX	PSA WITH REFLEX FREE PSA	PSA	PSA	8
-LABTCPSA	TOTAL AND COMPLEXED PSA	PSA	PSA	8
-LABTCPSA	TOTAL AND COMPLEXED PSA	PSAPC	% COMPLEXED PSA	8
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	8
-LABRIPA	RISTOCETIN INDUCED PLATELET AGGREGATION	RIPDIR	INTERPRETED BY	8
-LABROSBBP	R/O STREP BETA GP B BY PCR	RPT	RPT	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	8
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	8
-LABROSBBP	R/O STREP BETA GP B BY PCR	SDES	SPECIMEN	8
-LABTBP78	TRANSFUSION BLOOD PRODUCT 79	SI	<<STATUS>>  	8
-LABTBP79	TRANSFUSION BLOOD PRODUCT 80	SI	<<STATUS>>  	8
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	SPEXP	SPECIMEN EXPIRATION DATE/TIME	8
-LABROSBBP	R/O STREP BETA GP B BY PCR	SREQ	COMMENTS	8
-LABSSBABY	ANTI-LA ANTIBODY	SSBQNT	ANTI-LA LEVEL	8
-LABT1418	"T(14	18), BLOOD"	T14CMT	COMMENT	8
-LABT1418	"T(14	18), BLOOD"	T14DIR	INTERPRETED BY	8
-LABT1418	"T(14	18), BLOOD"	T14DIS	DISCLAIMER	8
-LABT1418	"T(14	18), BLOOD"	T14MET	METHOD	8
-LABT1418	"T(14	18), BLOOD"	T14PRM	PRIMERS	8
-LABT1418	"T(14	18), BLOOD"	T14RES	RESULTS	8
-POC30	POC PYLORITECK	TESTLO	TEST STRIP LOT#	8
-LABUMAGT	"MAGNESIUM, URINE (TIMED)"	TMGNU	MAGNESIUM EXCRETION	8
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCDIS	DISCLAIMER	8
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCDX	CLINICAL INDICATION	8
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCMET	METHOD	8
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCPRM	PRIMERS	8
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCRES	RESULTS	8
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCTIS	SOURCE	8
-LABRNPABY	"RNP AB, IGG"	U1RNPAB	AUTOANTIBODIES TO U1RNP	8
-LABTBP74	TRANSFUSION BLOOD PRODUCT 75	UBT	UNIT BLOOD TYPE  	8
-LABUCLT	"CHLORIDE, URINE (TIMED)"	UCL	"CHLORIDE, URINE"	8
-LABUMAGT	"MAGNESIUM, URINE (TIMED)"	UMG	"MAGNESIUM, URINE"	8
-LABTBP74	TRANSFUSION BLOOD PRODUCT 75	UNT	UNIT NUMBER  	8
-LABUCLT	"CHLORIDE, URINE (TIMED)"	UT11	COLLECTION PERIOD CHLORIDE	8
-LABUMAGT	"MAGNESIUM, URINE (TIMED)"	UT12	COLL PERIOD MAG	8
-LABTBP21	TRANSFUSION BLOOD PRODUCT 22	VL	PRODUCT VOLUME	8
-LABTBP24	TRANSFUSION BLOOD PRODUCT 25	VL	PRODUCT VOLUME	8
-LABTBP27	TRANSFUSION BLOOD PRODUCT 28	VL	PRODUCT VOLUME	8
-LABUCLT	"CHLORIDE, URINE (TIMED)"	VOL11	VOLUME CHLORIDE	8
-LABUMAGT	"MAGNESIUM, URINE (TIMED)"	VOL12	VOLUME MAGNESIUM	8
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	8
-LABTBP56	TRANSFUSION BLOOD PRODUCT 57	XMR	UNIT CROSSMATCH  	8
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	XMR	UNIT CROSSMATCH  	8
-LABTBP59	TRANSFUSION BLOOD PRODUCT 60	XMR	UNIT CROSSMATCH  	8
-LABTBP65	TRANSFUSION BLOOD PRODUCT 66	XMR	UNIT CROSSMATCH  	8
-LABTBP66	TRANSFUSION BLOOD PRODUCT 67	XMR	UNIT CROSSMATCH  	8
-LABYANTIN	INSULIN ANTIBODIES	YANT2	HUMAN	8
-LABYDIPT	DIPHTHERIA TOXOID IGA AB	YDIPT1	DIPTHERIA TOXOID IGG AB	8
-LABYKLFRE	IMMUNO FREE LIGHT CHAINS	YKLF1	"KAPPA FREE LIGHT CHAIN, S"	8
-LABYKLFRE	IMMUNO FREE LIGHT CHAINS	YKLF2	"LAMBDA FREE LIGHT CHAIN, S"	8
-LABYKLFRE	IMMUNO FREE LIGHT CHAINS	YKLF3	KAPPA/LAMBDA FLC RATIO	8
-LABYLAP	LEUKOCYTE ALKALINE PHOSPATASE SCORE	YLAP1	LAP SCORE	8
-LABYUARSR	"ARSENIC, URINE (RANDOM)"	YUARR2	COLLECTION DURATION	8
-LABYUARSR	"ARSENIC, URINE (RANDOM)"	YUARR4	AS CONC (UG/L)	8
-10320R	HLA - ALLO B CDC CROSSMATCH	10466R	RECIPIENT NAME	7
-10320R	HLA - ALLO B CDC CROSSMATCH	10467R	RECIPIENT DOB	7
-10360R	HLA - MONITORING BY C1Q	10477R	DSA FOR C1Q CLASS II	7
-LAB12352R	NMO EVALUATION WITH REFLEX	11200R	INTERPRETIVE COMMENTS	7
-LAB12774R	MOTOR NEUROPATHY AB SCREEN	11323R	CO-GM1 TRIAD AB	7
-LAB11324R	"CADASIL EVALUATION, COMPLETE"	11325R	COMPLETE CADASIL EVALUATION	7
-LAB11512R	"CYSTIC FIBROSIS, SEQUENCING"	11621R	"CYSTIC FIBROSIS, SEQUENCING"	7
-LAB11530R	CEREBELLAR PARANEOPLASTIC AB	11640R	CEREBELLAR PARANEOPLASTIC AB	7
-LAB11608R	MARFAN (FBN1) SEQUENCING	11764R	FBN1 FULL GENE RESULT	7
-LAB11608R	MARFAN (FBN1) SEQUENCING	11767R	SEQUENCING	7
-LAB11620R	"TOXOPLASMA GONDII, AQUEOUS FLUID"	11779R	"TOXOPLASAMGONDII,AQUEOUS FLUID"	7
-LAB11855R	"HEPATITIS C VIRAL RNA, QUANT"	11854R	"HCV RNA ,QUANT,TMA LOGIU/ML"	7
-LAB11882R	"MAGNESIUM, RBC"	11881R	"MAGNESIUM, RBC"	7
-LAB11901R	"N. GONORRHOEAE RNA, THROAT"	11902R	"N.GONORRHOEAE RNA,THROAT"	7
-LAB12320R	THIOCYANATE	12321R	THIOCYANATE	7
-LAB12637R	MENINGOCOCCAL AB VACCINE	12636R	SEROGROUP A	7
-LAB12637R	MENINGOCOCCAL AB VACCINE	12696R	SEROGROUP C	7
-LAB12637R	MENINGOCOCCAL AB VACCINE	12697R	SEROGROUP Y	7
-LAB12637R	MENINGOCOCCAL AB VACCINE	12698R	SEROGROUP W-135	7
-LAB12838R	INSULIN-LIKE GROWTH FACTOR 1	12839R	INSULIN-LIKE GROWTH FACTOR 1	7
-10295R	HLA - AUTO T FLOW CROSSMATCH	9088R	SERUM DESCRIPTION 2	7
-10300R	HLA - AUTO B FLOW CROSSMATCH	9109R	SERUM DESCRIPTION 2	7
-10320R	HLA - ALLO B CDC CROSSMATCH	9220R	PATIENT\'S SERUM 1 DTT TREATED RESULT	7
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9338R	INHIBITION RESULTS FOR CLASS I	7
-10355R	HLA - MONITORING BY IGG	9347R	CUMULATIVE ANTIBODIES BY IGG	7
-10355R	HLA - MONITORING BY IGG	9348R	CPRA FOR CUMULATIVE IGG	7
-10355R	HLA - MONITORING BY IGG	9352R	CPRA FOR IGG CLASS I FOR CURRENT	7
-10355R	HLA - MONITORING BY IGG	9353R	HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	7
-10355R	HLA - MONITORING BY IGG	9362R	COMPOSED TEXT CLASS I	7
-10355R	HLA - MONITORING BY IGG	9363R	MFI RESULTS FOR MONITORING BY IGG CLASS I	7
-10355R	HLA - MONITORING BY IGG	9365R	CPRA FOR IGG CLASS II FOR CURRENT	7
-10355R	HLA - MONITORING BY IGG	9366R	HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	7
-10355R	HLA - MONITORING BY IGG	9375R	COMPOSED TEXT CLASS II	7
-10355R	HLA - MONITORING BY IGG	9376R	DISCLAIMER	7
-10370R	HLA - DSA BY C1Q	9449R	DSA RESULTS FOR C1Q CLASS I	7
-10375R	HLA - STR/CHIMERISM TESTING	9483R	LOCUS 9	7
-10375R	HLA - STR/CHIMERISM TESTING	9484R	LOCUS 10	7
-10355R	HLA - MONITORING BY IGG	9607R	METHOD	7
-10355R	HLA - MONITORING BY IGG	9618R	CURRENT SERUM DATE	7
-10355R	HLA - MONITORING BY IGG	9619R	CURRENT SERUM DESCRIPTION	7
-10355R	HLA - MONITORING BY IGG	9654R	MFI RESULTS FOR MONITORING BY IGG CLASS II	7
-LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	9OHRISP	9-HYDROXYRISPERIDONE	7
-LABADVQL	"ADENOVIRUS DNA, QUALITATIVE PCR"	ADOV	"ADENOVIRUS DNA, QL PCR"	7
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	AFIBTADTIT	AFIB TACHY ZONE DETECTION INTERVAL	7
-LABSEZPNL	SEZARY PANEL	AN20DS	DISCLAIMER	7
-LABADIFF	DIFFERENTIAL	BAND	BAND NEUTROPHILS	7
-LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	BEVC	"CORD BASE EXCESS, VEN"	7
-LABBARSAB	BARTONELLA SPECIES AB	BHIGMT	B.HENSELAE IGM TITER	7
-LABBARSAB	BARTONELLA SPECIES AB	BQIGMT	B.QUINTANA IGM TITER	7
-LABSEZPNL	SEZARY PANEL	C264AB	"CD26-/CD4+, ABS"	7
-LABSEZPNL	SEZARY PANEL	C264PC	CD26-/CD4+ %	7
-LABSEZPNL	SEZARY PANEL	C4N7AB	"CD4+/CD7-,ABS. (C4N7AB)"	7
-LABSEZPNL	SEZARY PANEL	C4N7PC	CD4+/CD7-% (C4N7PC)	7
-LABEP2	LYTES II	CAION	"CALCIUM, IONIZED"	7
-LABSEZPNL	SEZARY PANEL	CD19AB	"CD19 (PAN B), ABS."	7
-LABSEZPNL	SEZARY PANEL	CD19PC	CD19 (PAN B) %	7
-LABSEZPNL	SEZARY PANEL	CD26AB	"CD26 (PAN-T), ABS"	7
-LABSEZPNL	SEZARY PANEL	CD26PC	CD26 (PAN-T) %	7
-LABSEZPNL	SEZARY PANEL	CD3AB	"CD3 (PAN T), ABS."	7
-LABSEZPNL	SEZARY PANEL	CD3PC	CD3 (PAN T) %	7
-LABSEZPNL	SEZARY PANEL	CD4AB	"CD3+/CD4+, ABS."	7
-LABSEZPNL	SEZARY PANEL	CD4PC	CD3+/CD4+ %	7
-LABSEZPNL	SEZARY PANEL	CD7AB	"CD7 (PAN T), ABS"	7
-LABSEZPNL	SEZARY PANEL	CD7PC	CD7 (PAN T) %	7
-LABSEZPNL	SEZARY PANEL	CD8AB	"CD3+/CD8+, ABS."	7
-LABSEZPNL	SEZARY PANEL	CD8PC	CD3+/CD8+ %	7
-LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSDI	DISCLAIMER	7
-LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSDX	CLINICAL INDICATION	7
-LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSMD	INTERPRETED BY	7
-LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSME	METHOD	7
-LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSRE	RESULT	7
-LABEP2	LYTES II	CL	"CHLORIDE, WHOLE BLD"	7
-LABYFCL	"CHLORIDE, FLUID"	CLFLD	"CHLORIDE, BODY FLUID"	7
-LABFCLPN	CHLAMYDIA PNEUMONIAE CULTURE	CLPN	CHLAMYDIA PNEUMONIAE	7
-LAB11124R	"COCCIDIOIDES COMPLEMENT FIXATION, SERUM"	COCFS	COCCI CF (SERUM)	7
-LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTINT	INTERPRETATION	7
-LABTSMISI	UNLISTED TRANSFUSION TEST	CTSMIS	MISC. TRANSFUSION TEST COMMENT	7
-LABAVRT	HIV GENO EXPR ANTIVIR RESIST	CULT	CULT/OTHER RSLT:	7
-LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816DI	DISCLAIMER	7
-LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816DX	CLINICAL INDICATION	7
-LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816ME	METHOD	7
-LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816RE	"RESULT, BLOOD"	7
-LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816RF	REFERENCES	7
-LABTBP68	TRANSFUSION BLOOD PRODUCT 69	DI	ISSUE DATE/TIME	7
-LABEBVPAN	EBV AB PANEL	EA	EARLY ANTIBODY (R+D) IGG	7
-LABEBVPAN	EBV AB PANEL	EBNA	EPSTEIN-BARR NUC AB	7
-LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GENTSDD	"GENTAMICIN, SINGLE DAILY DOSE"	7
-LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GNTUN2	DATE & TIME OF LAST DOSE	7
-LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GNTUN3	DOSAGE	7
-LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GNTUN4	ADMINISTRATION ROUTE	7
-LABHBC	HEPATITIS B CORE AB IGM	HBCABIGM	HEPAT.B CORE AB-IGM	7
-LABSEZPNL	SEZARY PANEL	HNS	"CD4+CD8 TOTAL, CD3+%"	7
-LABSEZPNL	SEZARY PANEL	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	7
-LABSEZPNL	SEZARY PANEL	HSRATIO	"CD4/CD8 RATIO, CD3+"	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRVVDEL	BRADY VV DELAY	7
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	7
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	7
-LABIGGS1	IGG SUBCLASS 1	IGGS1	IGG SUBCLASS1	7
-LABIGGS2	IGG  SUBCLASS 2	IGGS2	IGG SUBCLASS2	7
-LABIGGS3	IGG  SUBCLASS 3	IGGS3	IGG SUBCLASS3	7
-LABJO1ABY	AUTOANTIBODIES TO JO1	JO1AB	AUTOANTIBODIES TO JO 1	7
-LABEP2	LYTES II	K	"POTASSIUM, WHOLE BLD"	7
-LABREF5	LAB UNLISTED 5	LAB5	REFERENCE LAB:	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	7
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	7
-LABSEZPNL	SEZARY PANEL	LYM	LYMPHOCYTES %	7
-LABSEZPNL	SEZARY PANEL	LYMAB	"LYMPHOCYTES, ABS."	7
-LABMUMPSG	MUMPS IGG	MUMQNT	LEVEL	7
-LABEP2	LYTES II	NA	"SODIUM, WHOLE BLOOD"	7
-LABNEURSP	NEURON SPEC ENOLASE	NEURSP	NEURON SPEC ENOLASE	7
-LAB11053R	"PBG DEAMINASE, RBC"	PBGD	"PBG DEAMINASE, RBC"	7
-IMG11RLHC	CAR COMB RT LT HEART CATH	PDF	PDF RESULT	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMEVDATE	PACEMAKER EVAL DATE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRALDBIIM	PM RA LEAD BIPOLAR IMPEDANCE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRHYTHM	PM RHYTHM	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMTLPCPACA	PM TELEMETRY PERCENT PACING ATRIAL	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	7
-LABTBP80	TRANSFUSION BLOOD PRODUCT 81	PRO	PRODUCT ID  	7
-LABTBP81	TRANSFUSION BLOOD PRODUCT 82	PRO	PRODUCT ID  	7
-LABTBP82	TRANSFUSION BLOOD PRODUCT 83	PRO	PRODUCT ID  	7
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	PRO	PRODUCT ID  	7
-LAB11091R	QUINIDINE LEVEL	QUINIDINE	QUINIDINE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	7
-LABUA	URINALYSIS WITH MICROSCOPIC	RBCAST	RBC CASTS	7
-LABREF5	LAB UNLISTED 5	RESUL5	RESULT	7
-LABAVRT	HIV GENO EXPR ANTIVIR RESIST	RPT	RPT	7
-LABRSVPCR	RSV PCR	RSVDI	DISCLAIMER	7
-LABRSVPCR	RSV PCR	RSVMT	METHOD	7
-LABRSVPCR	RSV PCR	RSVRE	RSV RNA	7
-LABRSVPCR	RSV PCR	RSVSP	SPECIMEN	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	7
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	7
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	7
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	7
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	7
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	7
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	7
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	7
-LABAVRT	HIV GENO EXPR ANTIVIR RESIST	SDES	SPECIMEN	7
-LABTBP80	TRANSFUSION BLOOD PRODUCT 81	SI	<<STATUS>>  	7
-LABTBP81	TRANSFUSION BLOOD PRODUCT 82	SI	<<STATUS>>  	7
-LABTBP82	TRANSFUSION BLOOD PRODUCT 83	SI	<<STATUS>>  	7
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	SI	<<STATUS>>  	7
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SNRBC	"NRBC/100WBC, SYN FLD"	7
-LABADVQL	"ADENOVIRUS DNA, QUALITATIVE PCR"	SOURC3	ADENOVIRUS SOURCE	7
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	SPEXP	SPECIMEN EXPIRATION DATE/TIME	7
-LABREF5	LAB UNLISTED 5	SPTY5	SPECIMEN TYPE	7
-LABAVRT	HIV GENO EXPR ANTIVIR RESIST	SREQ	COMMENTS	7
-LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SRLYM	"REACT LYMPH, SYN FLD"	7
-LABEP2	LYTES II	TCO2A	TCO2	7
-LABREF5	LAB UNLISTED 5	TEST5	TEST NAME	7
-LABTHAMP	THERMAL AMPLITUDE SCREEN	THRAMP	THERMAL AMPLITUDE	7
-LABTOXIM	TOXOPLASMA IGM	TOXMDS	TEST DESCRIPTION	7
-LABTOXIM	TOXOPLASMA IGM	TOXMQL	IGM RESULT	7
-LABTRICHG	TRICHINELLA IGG AB ELISA	TRICG	"TRICHINELLA, IGG"	7
-LABTSMISI	UNLISTED TRANSFUSION TEST	TSMISR	MISC. TEST	7
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCCMT	COMMENT	7
-LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCDIR	INTERPRETED BY	7
-LABUURICT	"URIC ACID, URINE (TIMED)"	TURICU	URIC ACID EXCRETION	7
-LABUURICT	"URIC ACID, URINE (TIMED)"	URICADVOL	VOLUME URIC ACID	7
-LABUURICT	"URIC ACID, URINE (TIMED)"	URICURN	"URIC ACID, URINE"	7
-LABUURICT	"URIC ACID, URINE (TIMED)"	UT9	COLLECTION PERIOD URIC ACID	7
-LABEBVPAN	EBV AB PANEL	VCA	VIRAL CAPSID AB	7
-LABEBVPAN	EBV AB PANEL	VCAM	VIRAL CAPSID AB-IGM	7
-LABTBP23	TRANSFUSION BLOOD PRODUCT 24	VL	PRODUCT VOLUME	7
-LABSEZPNL	SEZARY PANEL	WBC	WBC COUNT	7
-LABTBP68	TRANSFUSION BLOOD PRODUCT 69	XMR	UNIT CROSSMATCH  	7
-LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	YEBP3	SPECIAL INFORMATION	7
-LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	YRISP1	RISPERIDONE (RISPERDAL)	7
-LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	YRISP3	COMBINED TOTAL	7
-10355R	HLA - MONITORING BY IGG	10474R	DSA FOR IGG CLASS I	6
-10355R	HLA - MONITORING BY IGG	10475R	DSA FOR IGG CLASS II	6
-LAB11190R	BORRELIA HERMSII AB PANL	11191R	BORRELIA HERMSII IGG	6
-LAB11190R	BORRELIA HERMSII AB PANL	11192R	BORRELIA HERMSII IGM	6
-LAB11238R	HERPESVIRUS-8 IGG AB	11239R	HERPESVIRUS-8 IGG AB	6
-LAB11608R	MARFAN (FBN1) SEQUENCING	11765R	FBN1 FULL GENE INTERPRETATION	6
-LAB11608R	MARFAN (FBN1) SEQUENCING	11766R	REVIEWED BY	6
-LABYUALA	"AMINOLEVULINIC ACID, URINE"	12000R	INTERPRETATION	6
-LAB12017R	"ECHOVIRUS AB, SERUM"	12027R	ECHOVIRUS  AB TYPE 6	6
-LAB12017R	"ECHOVIRUS AB, SERUM"	12028R	ECHOVIRUS  AB TYPE 7	6
-LAB12017R	"ECHOVIRUS AB, SERUM"	12029R	ECHOVIRUS  AB TYPE 11	6
-LAB12017R	"ECHOVIRUS AB, SERUM"	12030R	ECHOVIRUS  AB TYPE 30	6
-LAB12017R	"ECHOVIRUS AB, SERUM"	12031R	ECHOVIRUS  AB TYPE 9	6
-LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	12197R	SOURCE	6
-LAB12206R	CYSTATIN C	12207R	"CYSTATIN C, SER"	6
-LAB12371R	COBALT	12372R	COBALT	6
-LAB12585R	"HEPATITIS D RNA, QUAL PCR"	12594R	HDV RNA	6
-LABFPARVO	PARVOVIRUS IGG / IGM AB	12835R	PARVOVIRUS B19 AB IGG	6
-LABYSTONE	STONE ANALYSIS	2NDCONST	2ND CONSTITUENT	6
-10375R	HLA - STR/CHIMERISM TESTING	9485R	LOCUS 11	6
-LABALPHA2	ALPHA-2-ANTIPLASMIN	A2A	ALPHA-2-ANTIPLASMIN	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	ACHINT	INTERPRETATION	6
-LABADVQL	ADENOVIRUS PCR QUAL	ADOV	"ADENOVIRUS DNA, QL PCR"	6
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	AFIBTADTIT	AFIB TACHY ZONE DETECTION INTERVAL	6
-LABAG	ALBUMIN / GLOBULIN RATIO	ALBGLOB	ALBUMIN/GLOBULIN RATIO	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	ALD	ALDER TREE	6
-LABYUALDO	"ALDOSTERONE, URINE"	ALDOSTRURN	"ALDOSTERONE, URINE"	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	ALTER	ALTERNARIA MOLD	6
-LABYUARSR	"ARSENIC, URINE (RANDOM)"	ARSENICURN	"ARSENIC, URINE"	6
-LABAS4I	AB SCREEN	AS	ANTIBODY SCREEN	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	ASPERG	ASPERGILUS MOLD	6
-LABFUNDAP	FUNGAL DISEASE AB PANEL	ASPR	ASPERILLUS-CF	6
-LABFCD34	"CD34 PANEL, FLUID"	BCD34V	CD34+ PROGENITOR VIABILITY	6
-LABBCLON	B - CELL CLONALITY PCR	BCMT	COMMENT	6
-LABBCLON	B - CELL CLONALITY PCR	BDIR	INTERPRETED BY	6
-LABBCLON	B - CELL CLONALITY PCR	BDIS	DISCLAIMER	6
-LABBCLON	B - CELL CLONALITY PCR	BDX	CLINICAL INDICATION	6
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	BEC	"CORD BASE EXCESS, ART"	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	BERM	BERMUDA GRASS	6
-LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	BILIRUBIN	BILIRUBIN	6
-LABFUNDAP	FUNGAL DISEASE AB PANEL	BLAS	BLASTO AB CFS	6
-LABBCLON	B - CELL CLONALITY PCR	BMET	METHOD	6
-LABBCLON	B - CELL CLONALITY PCR	BPRM	PRIMERS	6
-LABBCLON	B - CELL CLONALITY PCR	BRES	RESULTS	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C2526N	"CD25, % OF CD3+/CD4+/CD26NEG"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C257N	"CD25, % OF CD3+/CD4+/CD7NEG"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C264AB	"CD26-/CD4+, ABS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C264PC	CD26-/CD4+ %	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3026N	"CD30, % OF CD3+/CD4+/CD26NEG"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C307N	"CD30, % OF CD3+/CD4+/CD7NEG"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C326NA	"CD3+/CD26NEG, ABS."	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3426N	"CD3+/CD4+/CD26NEG, ABS."	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3426P	"CD3+/CD4+/CD26NEG, % OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3426T	"CD3+/CD4+/CD26NEG, % OF CD4+ T"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C347NA	"CD3+/CD4+/CD7NEG, ABS."	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C347NP	"CD3+/CD4+/CD7NEG, % OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C347NT	"CD3+/CD4+/CD7NEG, % OF CD4+ T"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C37NA	"CD3+/CD7NEG, ABS."	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P4PA	"CD3+/CD4+,ABS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P4PP	"CD3+/CD4+, % OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P7NP	"CD3+/CD7NEG, % OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P8PN	"CD3+/CD8+, ABS. OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P8PP	"CD3+/CD8+, % OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C4N7AB	"CD4+/CD7-,ABS. (C4N7AB)"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C4N7PC	CD4+/CD7-% (C4N7PC)	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C5226N	"CD52, % OF CD3+/CD4+/CD26NEG"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C527N	"CD52, % OF CD3+/CD4+/CD7NEG"	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	CAT	CAT DANDER	6
-LABCDIFF	"DIFFERENTIAL, CSF"	CCMT	COMMENT	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD26NP	"CD3+/CD26NEG, % OF LYMPHS"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD3PC	CD3 (PAN T) %	6
-LABFCD34	"CD34 PANEL, FLUID"	CD45PV	CD45+ VIABILITY	6
-LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	CHOL	CHOLESTEROL	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	CLA	CLADOSPORIUM MOLD	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	COCK	COCKROACH	6
-LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	CRDALG	CORD LENGTH	6
-INACTIVE 7	STOOL CULTURE	CULT	CULT/OTHER RSLT:	6
-LABAID	AEROBIC ORGANISM ID	CULT	CULT/OTHER RSLT:	6
-LABLEGDD	"LEGIONELLA CULTURE, D AND D"	CULT	CULT/OTHER RSLT:	6
-LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816MD	INTERPRETED BY	6
-LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	DESIP	DESIPRAMINE	6
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	DI	ISSUE DATE/TIME	6
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	DI	ISSUE DATE/TIME	6
-LABTBP73	TRANSFUSION BLOOD PRODUCT 74	DI	ISSUE DATE/TIME	6
-LABTBP74	TRANSFUSION BLOOD PRODUCT 75	DI	ISSUE DATE/TIME	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	DOCK	DOCK WEED	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	DOG	DOG DANDER	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	DUST	DUST MITE	6
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	EOSFLD	"EOSINOPHIL %, FLD"	6
-LABYERYTH	"ERYTHROPOIETIN, SERUM"	ERYTH	"ERYTHROPOIETIN, SER."	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBCMT	COMMENT	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBDIR	INTERPRETED BY	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBDIS	DISCLAIMER	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBDX	CLINICAL INDICATION	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBFLD	SOURCE	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBMET	METHOD	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBPRM	PRIMERS	6
-LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBRES	RESULTS	6
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	6
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	6
-LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	HGB	HEMOGLOBIN	6
-LABHGBFQ	HGB F QUANTITATIVE	HGBF	"HGB F, QUANT."	6
-LABFUNDAP	FUNGAL DISEASE AB PANEL	HISTM	HISTO MYCELIAL PHASE	6
-LABFUNDAP	FUNGAL DISEASE AB PANEL	HISTY	HISTO YEAST PHASE	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	6
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	6
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	IGET	TOTAL IGE	6
-LABYIGF1	INSULIN LIKE GROWTH FACTOR I	IGF	INSULIN-LIKE GF 1	6
-LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	IMI	IMIPRAMINE	6
-LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	IMIDESI	IMIPRAMINE & DESIPRAMINE	6
-LABYINSFT	INSULIN FREE AND TOTAL	INS	INSULIN	6
-LABREF6	LAB UNLISTED 6	LAB6	REFERENCE LAB:	6
-LABYLDISO	LD ISOENZYMES	LDH	"LACTATE DEHYD(LD), S"	6
-LABYLDISO	LD ISOENZYMES	LDH1	"LD-I, HEART"	6
-LABYLDISO	LD ISOENZYMES	LDH2	LD-II	6
-LABYLDISO	LD ISOENZYMES	LDH3	LD-III	6
-LABYLDISO	LD ISOENZYMES	LDH4	LD-IV	6
-LABLKMAB	LIVER KID MICROSOME AB	LKMAB	LIV-KID-MICROSOME AB	6
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRSENA	LV BRADY SENSING AMPLITUDE	6
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	6
-ECH16	ECHO - TRANSESOPHAGEAL ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	6
-ECH42	ECHO - EXERCISE STRESS ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	6
-ECH55A	ECHO-CONGENITAL TRANSTHORACIC ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	LYM	LYMPHOCYTES %	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	6
-LABMAGATH	MAG AUTOAB	MAGAB	MAG AUTOANTIBODY	6
-LAB11049R	NEURON SPEC ENOLASE	NEURSP	NEURON SPEC ENOLASE	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	OAK	OAK TREE	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	OLIVE	OLIVE TREE	6
-LABUOROT	"OROTIC ACID, URINE"	OROTU	"OROTIC ACID, UR"	6
-CRP398	VALVULOPLASTY ADULT	PDF	PDF RESULT	6
-IMG12AH	EP ABLATION	PDF	PDF RESULT	6
-VSC07	VSC ILIAC ARTERY ULTRASOUND	PDF	PDF RESULT	6
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PLYM	PROLYMPHOCYTE	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVMFR	PM DEVICE MANUFACTURER	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVMODEL	PM DEVICE MODEL	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVSERNM	PM DEVICE SERIAL NUMBER	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVTYPE	PM DEVICE TYPE	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRVLDUNIM	PM RV LEAD UNIPOLAR IMPEDANCE	6
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	6
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	PRO	PRODUCT ID  	6
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	PRO	PRODUCT ID  	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	PTH18R	RECEIVED IN FLOW CYTOMETRY	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	6
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	6
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	6
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRSPOL	RA THRESHOLD SENSING POLARITY	6
-LABREF6	LAB UNLISTED 6	RESUL6	RESULT	6
-LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	RHFT	RHEUMATOID FACTOR LEVEL	6
-LABRMPCR	RSV/MPV PCR	RMMPV	METAPNEUMOVIRUS RNA	6
-LABRMPCR	RSV/MPV PCR	RMRSV	RSV RNA	6
-INACTIVE 7	STOOL CULTURE	RPT	RPT	6
-LABAID	AEROBIC ORGANISM ID	RPT	RPT	6
-LABLEGDD	"LEGIONELLA CULTURE, D AND D"	RPT	RPT	6
-LABRMPCR	RSV/MPV PCR	RSMPDI	DISCLAIMER	6
-LABRMPCR	RSV/MPV PCR	RSMPMT	METHOD	6
-LABRMPCR	RSV/MPV PCR	RSMPSP	SPECIMEN	6
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	6
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRAMP	RV THRESHOLD AMPLITUDE	6
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRDUR	RV THRESHOLD DURATION	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	RYE	RYE GRASS	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	SAGE	SAGE WEED	6
-INACTIVE 7	STOOL CULTURE	SDES	SPECIMEN	6
-LABAID	AEROBIC ORGANISM ID	SDES	SPECIMEN	6
-LABLEGDD	"LEGIONELLA CULTURE, D AND D"	SDES	SPECIMEN	6
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	SI	<<STATUS>>  	6
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	SI	<<STATUS>>  	6
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	6
-LABADVQL	ADENOVIRUS PCR QUAL	SOURC3	ADENOVIRUS SOURCE	6
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	SPEXP	SPECIMEN EXPIRATION DATE/TIME	6
-LABREF6	LAB UNLISTED 6	SPTY6	SPECIMEN TYPE:	6
-INACTIVE 7	STOOL CULTURE	SREQ	COMMENTS	6
-LABAID	AEROBIC ORGANISM ID	SREQ	COMMENTS	6
-LABLEGDD	"LEGIONELLA CULTURE, D AND D"	SREQ	COMMENTS	6
-LABREF6	LAB UNLISTED 6	TEST6	TEST NAME	6
-LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	TGL	TRIGLYCERIDES	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	TIMO	TIMOTHY GRASS	6
-LABTOXIG	TOXOPLASMA IGG	TOXO	TOXOPLASMA IGG	6
-LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE11	ECHOVIRUS 11 AB	6
-LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE30	ECHOVIRUS 30 AB	6
-LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE4	ECHOVIRUS 4 AB	6
-LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE7	ECHOVIRUS 7 AB	6
-LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE9	ECHOVIRUS 9 AB	6
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	UBT	UNIT BLOOD TYPE  	6
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	UBT	UNIT BLOOD TYPE  	6
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	UNT	UNIT NUMBER  	6
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	UNT	UNIT NUMBER  	6
-LABTBP31	TRANSFUSION BLOOD PRODUCT 32	VL	PRODUCT VOLUME	6
-LABTBP32	TRANSFUSION BLOOD PRODUCT 33	VL	PRODUCT VOLUME	6
-LABYUALDO	"ALDOSTERONE, URINE"	VOLUMEURN	URINE VOLUME	6
-LABYUARSR	"ARSENIC, URINE (RANDOM)"	VOLUMEURN	URINE VOLUME	6
-LABYUCITR	"CITRATE EXCRETION, URINE"	VOLUMEURN	URINE VOLUME	6
-LABYUOXAL	OXALATE URINE (TIMED)	VOLUMEURN	URINE VOLUME	6
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	WBC	WBC COUNT	6
-LABACHOO	AIRBORNE ALLERGY SCREEN	WEST	WESTERN RAGWEED	6
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	XMR	UNIT CROSSMATCH  	6
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	XMR	UNIT CROSSMATCH  	6
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	XMR	UNIT CROSSMATCH  	6
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	XMR	UNIT CROSSMATCH  	6
-LABYC5REF	C5 COMPLEMENT AG	YC5RE1	C5 COMPLEMENT AG	6
-LABYUCITR	"CITRATE EXCRETION, URINE"	YCITRU	"CITRATE EXCRETION, URINE"	6
-LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	YFCHY1	FLUID TYPE	6
-LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	YFCHY6	COMMENT	6
-LABYFELBA	FELBAMATE	YFELB1	FELBARNATE(FELBATOL)	6
-LABYFCL	"CHLORIDE, FLUID"	YFLSC1	FLUID TYPE	6
-LABYINSFT	INSULIN FREE AND TOTAL	YINSF1	FREE INSULIN	6
-LABYLDISO	LD ISOENZYMES	YLDIS2	"LD ISOENZYMES, S"	6
-LABYLDISO	LD ISOENZYMES	YLDIS3	TOTAL LD ACTIVITY	6
-LABYLDISO	LD ISOENZYMES	YLDIS8	"LD-V, LIVER"	6
-LABLEVETL	LEVETIRACETAM	YLEVT1	LEVETIRACETAM	6
-LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	YMYCSC	SOURCE	6
-LABYUOXAL	OXALATE URINE (TIMED)	YOXALU	"OXALATE, 24-HOUR/TIMED URINE"	6
-LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK1	COLLECTION DURATION	6
-LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK2	TOTAL WEIGHT	6
-LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA1	"SODIUM, 24-HR STOOL"	6
-LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA2	TOTAL WEIGHT	6
-LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA3	COLLECTION DURATION	6
-LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA4	SODIUM CONCENTRATION	6
-LABYTRYPS	TRYPSIN-LIKE IMMUNOREACTIVITY	YTRYP1	TRYPSIN-LIKE IMMUNOREACTIVITY	6
-LABYUALDO	"ALDOSTERONE, URINE"	YUALD2	COLLECTION DURATION	6
-LABYUCITR	"CITRATE EXCRETION, URINE"	YUCICN	CITRATE CONCENTRATION	6
-LABYUCITR	"CITRATE EXCRETION, URINE"	YUCIUT	COLLECTION DURATION	6
-LABYUOXAL	OXALATE URINE (TIMED)	YUOXCO	OXALATE CONCENTRATION	6
-LABYUOXAL	OXALATE URINE (TIMED)	YUOXUT	COLLECTION DURATION	6
-LABYZINCS	ZINC	ZINC	ZINC	6
-10195R	HLA ORDER - WAIT LIST STATUS CHANGE	10049R	HLA LAB TESTING STATUS	5
-10210R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG	10049R	HLA LAB TESTING STATUS	5
-LAB11373R	MARFAN(FBN1) SEQUENCING	11374R	MARFAN(FBN1) SEQUENCING	5
-LAB11435R	CELIAC GENETICS	11499R	CELIAC GENETICS	5
-LAB11615R	SODIUM BENZOATE LEVEL	11774R	SODIUM BENZOATE LEVEL	5
-LAB11991R	"DNA EXTRACTION, NPL"	11992R	SPECIMEN	5
-LAB11991R	"DNA EXTRACTION, NPL"	11993R	SPECIMEN ID	5
-LAB11991R	"DNA EXTRACTION, NPL"	11995R	ORDER DATE	5
-LAB11991R	"DNA EXTRACTION, NPL"	11996R	COMMENT	5
-LAB11991R	"DNA EXTRACTION, NPL"	11998R	REVIEWED BY	5
-LAB11991R	"DNA EXTRACTION, NPL"	11999R	RELEASE DATE	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12448R	ACCESSION NUMBER	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12449R	REFERRING PATH-PHYSICIAN	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12450R	REF PATH-PHYS ADDRESS	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12451R	PLACE OF DEATH	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12452R	DATE AND TIME OF DEATH	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12453R	DATE OF AUTOPSY	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12454R	SPECIMEN	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12455R	MATERIAL	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12456R	TISSUE DESCRIPTION	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12457R	MICROSCOPIC DESCRIPTION	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12458R	CLINICAL HISTORY	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12459R	FINAL DIAGNOSIS	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12460R	FINAL DIAGNOSIS	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12461R	COMMENT	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12462R	REVISION DESCRIPTION	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12463R	SIGNING PATHOLOGIST	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12464R	SPECIAL PROCEDURES	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12465R	SP SIGNING PATHOLOGIST	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12466R	PREVIOUS REPORT FOLLOWS	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12467R	ADDENDUM	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12468R	ADDENDUM COMMENT	5
-LAB12447R	AP SPECIAL STUDIES REVIEW	12469R	ADDENDUM PATHOLOGIST	5
-LAB12645R	"COMPLEMENT, ALTERNATE PATHWAY"	12644R	"COMPLEMENT, ALTERNATE PATHWAY"	5
-LAB12651R	HYPERTROPHIC CARDIOMYOPATHY PAN	12650R	HYPERTROPHIC CARDIOMYOPATHY PANEL	5
-LAB13000R	AMINOLEVULINIC ACID 24HR UR	12683R	"CREATININE, URINE MG/DL"	5
-LAB13000R	AMINOLEVULINIC ACID 24HR UR	12684R	"CREATININE, URINE MG/DAY"	5
-LAB13000R	AMINOLEVULINIC ACID 24HR UR	12685R	AMINOLEVULINIC ACID UMO/L	5
-LAB13000R	AMINOLEVULINIC ACID 24HR UR	12686R	AMINOLEVULINIC ACID UMOL/DAY	5
-LAB13000R	AMINOLEVULINIC ACID 24HR UR	12687R	HOURS COLLECTED	5
-LAB13000R	AMINOLEVULINIC ACID 24HR UR	12688R	TOTAL VOLUME	5
-LAB12723R	"ADENOSINE DEAMINASE, PERITONEAL"	12722R	"ADENOSINE DEAMINASE, PERITONEAL"	5
-LAB12774R	MOTOR NEUROPATHY AB SCREEN	12775R	CO-GM1 QUATTRO	5
-LAB12790R	CYSTATIN C WITH ESTIMATED GFR	12789R	EGFR BY CYSTATIN C	5
-LAB12790R	CYSTATIN C WITH ESTIMATED GFR	12805R	CYSTATIN C	5
-10295R	HLA - AUTO T FLOW CROSSMATCH	9084R	SERUM DESCRIPTION 1	5
-10295R	HLA - AUTO T FLOW CROSSMATCH	9091R	SERUM DATE 3	5
-10295R	HLA - AUTO T FLOW CROSSMATCH	9093R	MCS 3	5
-10300R	HLA - AUTO B FLOW CROSSMATCH	9105R	SERUM DESCRIPTION 1	5
-10300R	HLA - AUTO B FLOW CROSSMATCH	9112R	SERUM DATE 3	5
-10300R	HLA - AUTO B FLOW CROSSMATCH	9114R	MCS 3	5
-10305R	HLA - ALLO T FLOW CROSSMATCH	9131R	SERUM DESCRIPTION 1	5
-10310R	HLA - ALLO B FLOW CROSSMATCH	9161R	SERUM DESCRIPTION 1	5
-10315R	HLA - ALLO T CDC CROSSMATCH	9196R	PATIENT\'S SERUM 2 DESCRIPTION	5
-10315R	HLA - ALLO T CDC CROSSMATCH	9207R	INTERPRETATION/ COMMENTS	5
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9341R	POSSIBLE HLA ANTIBODIES FOR CLASS II	5
-10360R	HLA - MONITORING BY C1Q	9384R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	5
-10360R	HLA - MONITORING BY C1Q	9391R	INTERPRETATION/ COMMENTS CLASS I	5
-10360R	HLA - MONITORING BY C1Q	9404R	INTERPRETATION/ COMMENTS CLASS II	5
-10295R	HLA - AUTO T FLOW CROSSMATCH	9597R	RESULT 3	5
-10300R	HLA - AUTO B FLOW CROSSMATCH	9602R	RESULT 3	5
-10360R	HLA - MONITORING BY C1Q	9624R	REFERENCE SERUM DATE	5
-10360R	HLA - MONITORING BY C1Q	9625R	REFERENCE SERUM DESCRIPTION	5
-LAB12399R	ACETYLCHOLINE MODULATING AB	ACRMOD	ACETYLCHOLINE MODULATING AB	5
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADPCR1	ADENVIRUS DNA	5
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	AFIBTADTIT	AFIB TACHY ZONE DETECTION INTERVAL	5
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ALERT	ALERT	5
-LABMALL1	MISCELLANEOUS ALLERGEN 1	ALL1	ALLERGEN	5
-LAB11139R	CARDIAC REJECTION RISK	ALLO	ALLOMAP	5
-LABMALL1	MISCELLANEOUS ALLERGEN 1	ALLR1	ALLERGEN RESULT	5
-LABTTRMET	AMYLOIDOSIS DNA TITER	AMYLDO	TTR AMYLOIDOSIS DNA	5
-LAB11072R	APO A1	APOA1	APOLIPOPROTEIN A1	5
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	ARSENIC	ARSENIC	5
-LABARSAL	"ARYLSULFATASE A, LEUKOCYTES"	ARYLSA	"ARYLSULFATASE A, LEUK"	5
-LABBARSAB	BARTONELLA SPECIES AB	BQIGGT	B.QUINTANA IGG TITER	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQCO	COMMENT	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQDI	DISCLAIMER	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQDX	CLINICAL INDICATION	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQET	ETHNICITY (PROVIDED BY MD)	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQIN	INTERPRETATION	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQMD	INTERPRETED BY	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQME	METHOD	5
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQRE	RESULT	5
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	CADMIUM	CADMIUM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CAN10	CANDIDA 1:10 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CAN100	CANDIDA 1:100 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CAN50	CANDIDA 1:50 CPM	5
-LABCCP	ANTI CCP	CCPINT	INTERPRETATION	5
-LABCCP	ANTI CCP	CCPR	ANTI CCP ANTIBODY	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CNDINT	CANDIDA INTERPRETATION	5
-LABBMS	BMT STERILITY SAMPLE	CULT	CULT/OTHER RSLT:	5
-LABFUNIDS	FUNGAL ID BY SEQUENCING	CULT	CULT/OTHER RSLT:	5
-LABTMONA	TRICHOMONAS EXAM	CULT	CULT/OTHER RSLT:	5
-LABTBP71	TRANSFUSION BLOOD PRODUCT 72	DI	ISSUE DATE/TIME	5
-LABTBP72	TRANSFUSION BLOOD PRODUCT 73	DI	ISSUE DATE/TIME	5
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	DI	ISSUE DATE/TIME	5
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	DI	ISSUE DATE/TIME	5
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	DI	ISSUE DATE/TIME	5
-LABDT	DIPHTHERIA AND TETANUS AB IGG	DIPHT	"DIPTHERIA AB , IGG"	5
-LAB11112R	DONOR TOXOPLASMA AB	DTOXOM	DONOR TOXO IGM	5
-LABCD3F	"CD3, FLUID"	FCD3AB	"CD3 (T), ABS., FLD"	5
-LABCD3F	"CD3, FLUID"	FCD3PC	"CD3 (T) %, FLD"	5
-LABCD3F	"CD3, FLUID"	FCSOUR	SOURCE	5
-LAB12796R	FLUCONAZOLE LEVEL	FLUCOZ	FLUCONAZOLE	5
-LABYT7P	FREE THYROXINE INDEX	FT4INDEX	FREE THYROXINE INDEX	5
-LABYGADAB	GLUTAMIC ACID DECARBOXY AB	GAD65AB	GAD65 ANTIBODY	5
-LABHBSQT	ANTI HBS QUANT	HBSAB	"HBS ANTIBODY, S"	5
-LABHBSQT	ANTI HBS QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	5
-LABVP5	COMP HEPATITIS PANEL	HBSAIN	INTERPRETATION	5
-LABVP5	COMP HEPATITIS PANEL	HBSAQL	HEP B SURF AB QUAL	5
-LABVP5	COMP HEPATITIS PANEL	HBSAQT	HEP B SURF AB QUANT	5
-LABVP5	COMP HEPATITIS PANEL	HBSINT	INTERPRETATION	5
-LABVP5	COMP HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	5
-LABVP5	COMP HEPATITIS PANEL	HBTINT	INTERPRETATION	5
-LABVP5	COMP HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	5
-EXTHCT	EXTERNAL HCT	HCT	EXTERNAL HCT	5
-LABVP5	COMP HEPATITIS PANEL	HCVI	INTERPRETATION	5
-LABVP5	COMP HEPATITIS PANEL	HCVR	ANTI-HCV	5
-LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	HERPG	"HERPESVIRUS 7 AB, IGG"	5
-LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	HERPM	"HERPESVIRUS 7 AB, IGM"	5
-LABYHISTA	"HISTAMINE, SERUM"	HISTP	HISTAMINE	5
-LABMALL1	MISCELLANEOUS ALLERGEN 1	I1	INTERPRETATION	5
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	5
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	5
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	5
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	5
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	5
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	5
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	5
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	5
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	5
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	5
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	5
-LAB12838R	INSULIN-LIKE GROWTH FACTOR 1	IGF	INSULIN-LIKE GROWTH FACTOR 1	5
-LABYANTIN	INSULIN ANTIBODIES	INSAB	"INSULIN ABS, SERUM"	5
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	LEAD	LEAD	5
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADALERT	LEAD ALERT	5
-LABLEMSAB	LEMS AUTOAB	LEMS	LEMS AUTOANTIBODY	5
-LABCD3F	"CD3, FLUID"	LYMABFLD	"LYMPHOCYTES, ABS., FLD"	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMCOB	COMMENT	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMDIB	DISCLAIMER	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMDXB	CLINICAL INDICATION	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMINB	INTERPRETATION	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMMDB	INTERPRETED BY	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMMEB	METHOD	5
-LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMREB	RESULT	5
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MORPH	MORPHOLOGY	5
-LABADIFF	DIFFERENTIAL	MYEL	MYELOCYTES	5
-LABADIFF	DIFFERENTIAL	NRBC	NRBC/100WBC	5
-LAB11051R	OLANZAPINE LEVEL	OLANZ	OLANZAPINE	5
-LABPAI1AT	PAI-1 ACTIVITY	PAI1T	PAI-1 ACTIVITY	5
-IMG1PTCA	CAR PTCA EACH ADDITIONAL VESSEL	PDF	PDF RESULT	5
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVIMPDT	PM DEVICE IMPLANT DATE	5
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRALDUNIM	PM RA LEAD UNIPOLAR IMPEDANCE	5
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMTLBATIMP	PM TELEMETRY BATTERY IMPEDANCE	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD100	PPD 1:100 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD200	PPD 1:200 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD400	PPD 1:400 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD50	PPD 1:50 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD800	PPD 1:800 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPDINT	PPD INTERPRETATION	5
-LABTBP86	TRANSFUSION BLOOD PRODUCT 87	PRO	PRODUCT ID  	5
-LABTBP87	TRANSFUSION BLOOD PRODUCT 88	PRO	PRODUCT ID  	5
-LABTBP88	TRANSFUSION BLOOD PRODUCT 89	PRO	PRODUCT ID  	5
-LABTBP89	TRANSFUSION BLOOD PRODUCT 90	PRO	PRODUCT ID  	5
-LAB11055R	PROPAFENONE LEVEL	PROPAF	PROPAFENONE	5
-LABPSAU	POST-THERAPY PSA	PSAU	POST-THERAPY PSA	5
-LABPT12	PROTHROMBIN FRAGMENT 1 AND 2	PTFR12	PROTHROMBIN FRAG 1+2	5
-LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	RESUL8	RESULT	5
-LABBBPRH	RHO(D) IMMUNE GLOB (RHOGAM)	RHSTA	RHOGAM PRODUCT STATUS	5
-10195R	HLA ORDER - WAIT LIST STATUS CHANGE	RINFO	TO FIND RESULT:	5
-10210R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG	RINFO	TO FIND RESULT:	5
-LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	RLAB8	REFERENCE LAB	5
-LABRPRQT	RPR QUANTITATIVE	RPRT	RESULT	5
-LABBMS	BMT STERILITY SAMPLE	RPT	RPT	5
-LABFUNIDS	FUNGAL ID BY SEQUENCING	RPT	RPT	5
-LABTMONA	TRICHOMONAS EXAM	RPT	RPT	5
-LABCD3F	"CD3, FLUID"	RTFDA2	NOTE	5
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	5
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	5
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	5
-LABBMS	BMT STERILITY SAMPLE	SDES	SPECIMEN	5
-LABFUNIDS	FUNGAL ID BY SEQUENCING	SDES	SPECIMEN	5
-LABTMONA	TRICHOMONAS EXAM	SDES	SPECIMEN	5
-LABFSPG	"SPECIFIC GRAVITY, FLUID"	SGFLTY	SOURCE	5
-LABTBP86	TRANSFUSION BLOOD PRODUCT 87	SI	<<STATUS>>  	5
-LABTBP87	TRANSFUSION BLOOD PRODUCT 88	SI	<<STATUS>>  	5
-LABTBP88	TRANSFUSION BLOOD PRODUCT 89	SI	<<STATUS>>  	5
-LABTBP89	TRANSFUSION BLOOD PRODUCT 90	SI	<<STATUS>>  	5
-LABADIFF	DIFFERENTIAL	SIGNIF	SIGNIFICANT ABNORMALITIES	5
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	SLREV	SLIDE REVIEW	5
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	5
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	SOUR10	SOURCE	5
-LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	SPCTY	SPECIMEN TYPE	5
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	SPEXP	SPECIMEN EXPIRATION DATE/TIME	5
-LABFSPG	"SPECIFIC GRAVITY, FLUID"	SPGFLD	"SPECIFIC GRAVITY, FLUID"	5
-LABBMS	BMT STERILITY SAMPLE	SREQ	COMMENTS	5
-LABFUNIDS	FUNGAL ID BY SEQUENCING	SREQ	COMMENTS	5
-LABTMONA	TRICHOMONAS EXAM	SREQ	COMMENTS	5
-LABT1114	"T(11	14) BY PCR"	T11CT	COMMENT	5
-LABT1114	"T(11	14) BY PCR"	T11DS	DISCLAIMER	5
-LABT1114	"T(11	14) BY PCR"	T11DX	CLINICAL INDICATION	5
-LABT1114	"T(11	14) BY PCR"	T11MD	INTERPRETED BY	5
-LABT1114	"T(11	14) BY PCR"	T11MT	METHOD	5
-LABT1114	"T(11	14) BY PCR"	T11RS	RESULT	5
-LABYT7P	FREE THYROXINE INDEX	T4	"THYROXINE, TOTAL"	5
-LABUALBT	"ALBUMIN, URINE (TIMED)"	TALBU	ALBUMIN EXCRETION	5
-LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	TEST8	TEST NAME	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TET1K	TETANUS 1:1000 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TET2K	TETANUS 1:2000 CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TET5K	TETANUS 1:5000 CPM	5
-LABDT	DIPHTHERIA AND TETANUS AB IGG	TETAN	"TETANUS AB , IGG"	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TETINT	TETANUS INTERPRETATION	5
-LAB11057R	TNF	TNF	TUMOR NECROSIS FTR	5
-LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXDES	TEST DESCRIPTION	5
-LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXGQL	IGG RESULT	5
-LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXGQT	IGG LEVEL	5
-LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXMQL	IGM RESULT	5
-LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXMQT	IGM LEVEL	5
-LAB11058R	TRAZODONE LEVEL	TRAZO	TRAZODONE	5
-LABUALBT	"ALBUMIN, URINE (TIMED)"	UALB	"ALBUMIN, URINE"	5
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	UBT	UNIT BLOOD TYPE  	5
-LABTBP78	TRANSFUSION BLOOD PRODUCT 79	UBT	UNIT BLOOD TYPE  	5
-LABTBP79	TRANSFUSION BLOOD PRODUCT 80	UBT	UNIT BLOOD TYPE  	5
-LABTBP80	TRANSFUSION BLOOD PRODUCT 81	UBT	UNIT BLOOD TYPE  	5
-LABTBP81	TRANSFUSION BLOOD PRODUCT 82	UBT	UNIT BLOOD TYPE  	5
-LABTBP82	TRANSFUSION BLOOD PRODUCT 83	UBT	UNIT BLOOD TYPE  	5
-LABUELP	"ELECTROLYTE PANEL, URINE W/O CREATININE"	UCL	"CHLORIDE, URINE"	5
-LABUELP	"ELECTROLYTE PANEL, URINE W/O CREATININE"	UK	"POTASSIUM, URINE"	5
-LAB12811R	"MYOGLOBIN, URINE"	UMYOG	"MYOGLOBIN, URINE"	5
-LABUELP	"ELECTROLYTE PANEL, URINE W/O CREATININE"	UNA	"SODIUM, URINE"	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	UNCAN	UNSTIMULATED CPM	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	UNPPD	UNSTIMULATED CPM	5
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	UNT	UNIT NUMBER  	5
-LABTBP78	TRANSFUSION BLOOD PRODUCT 79	UNT	UNIT NUMBER  	5
-LABTBP79	TRANSFUSION BLOOD PRODUCT 80	UNT	UNIT NUMBER  	5
-LABTBP80	TRANSFUSION BLOOD PRODUCT 81	UNT	UNIT NUMBER  	5
-LABTBP81	TRANSFUSION BLOOD PRODUCT 82	UNT	UNIT NUMBER  	5
-LABTBP82	TRANSFUSION BLOOD PRODUCT 83	UNT	UNIT NUMBER  	5
-LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	UNTET	UNSTIMULATED CPM	5
-LABUALBT	"ALBUMIN, URINE (TIMED)"	UT35	COLLECTION PERIOD	5
-LABTBP25	TRANSFUSION BLOOD PRODUCT 26	VL	PRODUCT VOLUME	5
-LABTBP33	TRANSFUSION BLOOD PRODUCT 34	VL	PRODUCT VOLUME	5
-LABTBP36	TRANSFUSION BLOOD PRODUCT 37	VL	PRODUCT VOLUME	5
-LABLPD	LIPID PANEL WITH DIRECT LDL	VLDL	VLDL (CALCULATED)	5
-LABUALBT	"ALBUMIN, URINE (TIMED)"	VOL35	VOLUME	5
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	5
-LABPANWBC	WBC COUNT	WBC	WBC	5
-LABCD3F	"CD3, FLUID"	WBCFLD	"WBC COUNT, FLD"	5
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	XMR	UNIT CROSSMATCH  	5
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	XMR	UNIT CROSSMATCH  	5
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	XMR	UNIT CROSSMATCH  	5
-LABTBP71	TRANSFUSION BLOOD PRODUCT 72	XMR	UNIT CROSSMATCH  	5
-LABTBP72	TRANSFUSION BLOOD PRODUCT 73	XMR	UNIT CROSSMATCH  	5
-LABTBP73	TRANSFUSION BLOOD PRODUCT 74	XMR	UNIT CROSSMATCH  	5
-LAB12066R	CRMP-5-IGG WESTERN BLOT	YCRMP	CRMP-5-IGG WESTERN BLOT	5
-LABYFUN	"UREA NITROGEN, FLUID"	YFLSC1	FLUID TYPE	5
-LABYRS	RSV IGG AND IGM AB	YRS2	"RSV AB, IGG"	5
-LABYRS	RSV IGG AND IGM AB	YRS3	"RSV AB, IGM"	5
-LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK3	"POTASSIUM, 24-HR STOOL"	5
-LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK4	POTASSIUM CONCENTRATION	5
-LABYT7P	FREE THYROXINE INDEX	YT7P2	T-UPTAKE %	5
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	YUMEUT	COLLECTION DURATION	5
-10110R	HLA ORDER - FLOW AUTO CROSSMATCH	10049R	HLA LAB TESTING STATUS	4
-10135R	HLA ORDER - SCHEDULED SCREENING KIT BILLING	10049R	HLA LAB TESTING STATUS	4
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	11154R	SOURCE	4
-LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	11155R	"HERPES VIRUS 6 DNA, QUANT"	4
-LAB12772R	ENCEPHALITIS AB SCREEN	11639R	ENCEPHALITIS AB SCREEN	4
-LAB11606R	CYTOCHROME P450 2D6 GENOTYPE	11758R	CYP2D6 GENOTYPE	4
-LAB11606R	CYTOCHROME P450 2D6 GENOTYPE	11759R	REVIEWED BY	4
-LABYGDA	"DRUG SCREEN, FLUIDS"	11829R	DRUGS DETECTED:	4
-LABYGDA	"DRUG SCREEN, FLUIDS"	11830R	SOURCE	4
-LAB11899R	"N. GONORRHOEAE RNA, RECTAL"	11900R	"N.GONORRHOEAE RNA,RECTAL"	4
-LAB11991R	"DNA EXTRACTION, NPL"	11994R	SOURCE	4
-LAB11991R	"DNA EXTRACTION, NPL"	11997R	AMENDMENT	4
-LABFQFVR	COXIELLA BURNETII AB	12015R	Q FEVER IGM PHASE LI TITER	4
-LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	12428R	INTERPRETATION	4
-LAB12853R	"EVEROLIMUS, BLOOD"	12500R	DATE OF LAST DOSE	4
-LAB12853R	"EVEROLIMUS, BLOOD"	12501R	TIME OF LAST DOSE	4
-LAB12853R	"EVEROLIMUS, BLOOD"	12502R	"DOSE, MG"	4
-LAB12507R	IBD SGI DIAGNOSTIC	12508R	IBD SGI DIAGNOSTIC	4
-LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12552R	"SIROLIMUS, BLOOD"	4
-LAB12664R	"NTX TELOPEPTIDE, URINE"	12661R	NTX	4
-LAB12664R	"NTX TELOPEPTIDE, URINE"	12662R	"NTX-TELOPEPTIDE, U"	4
-LAB12664R	"NTX TELOPEPTIDE, URINE"	12663R	"CREATININE, U"	4
-10320R	HLA - ALLO B CDC CROSSMATCH	9222R	PATIENT\'S SERUM 2 DESCRIPTION	4
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9336R	POSSIBLE HLA ANTIBODIES FOR CLASS I	4
-10355R	HLA - MONITORING BY IGG	9360R	MONITORING RESULTS FOR IGG CLASS I	4
-10355R	HLA - MONITORING BY IGG	9373R	MONITORING RESULTS FOR IGG CLASS II	4
-10360R	HLA - MONITORING BY C1Q	9390R	MONITORING RESULTS FOR C1Q CLASS I	4
-10360R	HLA - MONITORING BY C1Q	9397R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	4
-10375R	HLA - STR/CHIMERISM TESTING	9486R	LOCUS 12	4
-10355R	HLA - MONITORING BY IGG	9620R	REFERENCE SERUM DATE	4
-10355R	HLA - MONITORING BY IGG	9621R	REFERENCE SERUM DESCRIPTION	4
-LABARI	ABO/RH (ARI)	ABORH	ABO/RH FRONT TYPE	4
-INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADDNA	ADENOVIRUS DNA	4
-LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMACT	ADAMTS-13 ACTIVITY	4
-LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMINT	CLINICAL INTERPRETATION	4
-LABADVQL	ADENOVIRUS DNA PCR QUAL	ADOV	"ADENOVIRUS DNA, QL PCR"	4
-LABDVEAV3	ADENO DFA	ADV3	ADENOVIRUS	4
-LABDVEAV3	ADENO DFA	ADV3IP	TEST DESCRIPTION	4
-LABDVEAV3	ADENO DFA	ADV3SP	SPECIMEN	4
-INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVCP	RESULT	4
-INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVLOG	RESULT LOG 10	4
-INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVSP	SPECIMEN	4
-INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVTIP	TEST DESCRIPTION	4
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ALERT	ALERT	4
-LABMOLDS	MOLDS ALLERGY SCREEN	ALTRN	ALTERNARIA	4
-LAB11073R	APO B	APOB	APOLIPOPROTEIN B	4
-LABARIX	ARIXTRA	ARIX	ARIXTRA	4
-LABASPERP	ASPERGILLUS AB ID	ASPERP	ASPERGILLUS AB (ID)	4
-LABMOLDS	MOLDS ALLERGY SCREEN	ASPRG	ASPERGILLUS FUMAG.	4
-LABMOLDS	MOLDS ALLERGY SCREEN	AURBAS	AUEROBASIDIUM	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	BASO	"BASO, %"	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	BASOAB	"BASOS, ABS"	4
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	BASOSYNFLD	"BASOPHILS, SYN FLD"	4
-LABYBILEA	BILE ACIDS TOTAL	BILE	"BILE ACIDS, TOTAL"	4
-LABCAD	"CADMIUM, WHOLE BLOOD"	CAD	"CADMIUM, BLOOD"	4
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	CCNT	CELLS COUNTED	4
-LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	CCPT	ANTI CCP ANTIBODY LEVEL	4
-LABCD3B	"CD3, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	4
-LABCD3B	"CD3, BLOOD"	CD3PC	CD3 (PAN T) %	4
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1CI	CLINICAL INDICATION	4
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1DI	DISCLAIMER	4
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1MD	INTERPRETED BY	4
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1MT	METHOD	4
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1RE	RESULT	4
-LABCGMISC	MISCELLANEOUS TEST COAGULATION	CGRSLT	RESULT	4
-LABCGMISC	MISCELLANEOUS TEST COAGULATION	CGTEST	PROCEDURE	4
-LAB11032R	PSEUDOCHOLINESTERASE	CHEP	PSEUDOCHOLINESTERASE	4
-LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	CHLAM	C.TRACHOMATIS RESULT:	4
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	CILIA	BRONCH. LINING CELLS	4
-LABMOLDS	MOLDS ALLERGY SCREEN	CLASP	CLADOSPORIUM	4
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	CMV	CMV ANTIBODY	4
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	CMVIGM	CMV IGM AB	4
-LABMOLDS	MOLDS ALLERGY SCREEN	CNDIDA	CANDIDA	4
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	COM	COMMENT	4
-LABLMS	LYMPHOCYTE MITOGEN SCREEN	CONA	CON A STIMULATION	4
-LABLMS	LYMPHOCYTE MITOGEN SCREEN	CONINT	CON A INTERPRETATION	4
-LAB11056R	"CORTISOL FREE, SERUM"	CORTFREE	"CORTISOL, SERUM FREE"	4
-LABAVIN	HIV-1 INTEGRASE RESISTANCE	CULT	CULT/OTHER RSLT:	4
-LABFLDS	FLUID STERILITY TEST	CULT	CULT/OTHER RSLT:	4
-LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	CULT	CULT/OTHER RSLT:	4
-LABDBGT	DABIGATRAN	DBGTR	DABIGATRAN	4
-LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	DCL3	TEST INFORMATION	4
-LABTBP78	TRANSFUSION BLOOD PRODUCT 79	DI	ISSUE DATE/TIME	4
-LABTBP86	TRANSFUSION BLOOD PRODUCT 87	DI	ISSUE DATE/TIME	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	EOS	"EOS, %"	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	EOSAB	"EOS, ABS"	4
-LABMOLDS	MOLDS ALLERGY SCREEN	EPIPUP	EPICOCCUM PUPURASC.	4
-EXTPLTCT	EXTERNAL PLATELET COUNT	EXTPLT	EXTERNAL PLATELET COUNT	4
-EXTWBC	EXTERNAL WBC	EXTWBC	EXTERNAL WBC	4
-LABFPARVO	PARVOVIRUS IGG / IGM AB	FINT	INTERPRETATION	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTDIR	INTERPRETED BY	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTDIS	DISCLAIMER	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTDX	CLINICAL INDICATION	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTFLD	SOURCE	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTMET	METHOD	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTPRM	PRIMERS	4
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTRES	RESULTS	4
-LABMOLDS	MOLDS ALLERGY SCREEN	FUSMON	FUSARIUM MONILIFORME	4
-LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	GC	N.GONORRHOEAE RESULT:	4
-LABUGL	"GLUCOSE WITH CREATININE, URINE (RANDOM)"	GLUCR	GLUCOSE/CREAT RATIO	4
-LABUGL	"GLUCOSE WITH CREATININE, URINE (RANDOM)"	GLUURN	"GLUCOSE, URINE"	4
-LABSHSTGS	SHC STAT GRAM STAIN	GS	GRAM STAIN	4
-LABVP5	COMP HEPATITIS PANEL	HATINT	INTERPRETATION	4
-LABVP5	COMP HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	4
-LABHEAG	HEPATITIS B E AG	HBEAG	HEPATITIS B E AG	4
-LABHBSQL	HEPATITIS B SURFACE AB	HBSAB	HEPATITIS B SURFACE AB QUAL	4
-LABHGBFQ	HGB F QUANTITATIVE	HGFMD	REVIEWED BY	4
-LABYHGH	"GROWTH HORMONE, SERUM"	HGH	"GROWTH HORMONE, SERUM"	4
-LABUHIST	"HISTAMINE, URINE"	HISTURN	"HISTAMINE, URINE"	4
-LABMOLDS	MOLDS ALLERGY SCREEN	HLM	HELMINTHOSPORIUM	4
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HSAB	VIRAL AB HSV	4
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	4
-LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE	HVDNA	HERPES VIRUS 6 DNA	4
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	4
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	4
-LABJO1ABY	ANTI-JO1 ANTIBODY	JO1QNT	ANTI-JO1 LEVEL	4
-LAB11084R	LORAZEPAM LEVEL	LORAZ	"LORAZEPAM, SERUM"	4
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	LYM	"LYM, %"	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	LYMAB	"LYM, ABS"	4
-LABCD3B	"CD3, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	4
-LABMOLDS	MOLDS ALLERGY SCREEN	MCM	MUCOR	4
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	MER	MERCURY	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MONO	"MONO, %"	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MONOAB	"MONO, ABS"	4
-LAB11048R	NEURON AB	NEURON	"NEURONAL CELL ABS, SERUM"	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	NEUT	"NEUT, %"	4
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	NEUTAB	"NEUT, ABS"	4
-LABYSCERE	"INFLAMM. BOWEL DISEASE PANEL,S"	NEUTAB	NEUTROPHIL SPECIFIC ANTIBODIES	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHDI	DISCLAIMER:	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHDX	CLINICAL IND	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHIN	INTERPRETATION	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHMD	INTERPRETED BY	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHME	METHOD	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHRE	RESULT	4
-LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHSO	SOURCE	4
-IMG1STEV	CAR CORONARY STENT SINGLE	PDF	PDF RESULT	4
-PFT54	SPIROMETRY WITH DLCO	PDF	PDF RESULT	4
-VSC06	VSC ABDO AORTA ULTRASOUND	PDF	PDF RESULT	4
-LABLMS	LYMPHOCYTE MITOGEN SCREEN	PHA	PHA STIMULATION	4
-LABLMS	LYMPHOCYTE MITOGEN SCREEN	PHAINT	PHA INTERPRETATION	4
-LABMOLDS	MOLDS ALLERGY SCREEN	PNM	PENICILLIUM	4
-LABTBP90	TRANSFUSION BLOOD PRODUCT 91	PRO	PRODUCT ID  	4
-LABTBP91	TRANSFUSION BLOOD PRODUCT 92	PRO	PRODUCT ID  	4
-LABTBP92	TRANSFUSION BLOOD PRODUCT 93	PRO	PRODUCT ID  	4
-LABPT12	PROTHROMBIN FRAGMENT 1 AND 2	PTFRNO	PROTHROMBIN 1+2 NOTE	4
-LABLMS	LYMPHOCYTE MITOGEN SCREEN	PWM	PWM STIMULATION	4
-LABLMS	LYMPHOCYTE MITOGEN SCREEN	PWMINT	PWM INTERPRETATION	4
-LABMOLDS	MOLDS ALLERGY SCREEN	RHZNIG	RHISOPUS NIGRICANS	4
-10110R	HLA ORDER - FLOW AUTO CROSSMATCH	RINFO	TO FIND RESULT:	4
-10135R	HLA ORDER - SCHEDULED SCREENING KIT BILLING	RINFO	TO FIND RESULT:	4
-LABAVIN	HIV-1 INTEGRASE RESISTANCE	RPT	RPT	4
-LABFLDS	FLUID STERILITY TEST	RPT	RPT	4
-LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	RPT	RPT	4
-LABSHSTGS	SHC STAT GRAM STAIN	RPT	RPT	4
-LABAVIN	HIV-1 INTEGRASE RESISTANCE	SDES	SPECIMEN	4
-LABFLDS	FLUID STERILITY TEST	SDES	SPECIMEN	4
-LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	SDES	SPECIMEN	4
-LABSHSTGS	SHC STAT GRAM STAIN	SDES	SPECIMEN	4
-LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	SDES3	SPECIMEN	4
-LABTBP90	TRANSFUSION BLOOD PRODUCT 91	SI	<<STATUS>>  	4
-LABTBP91	TRANSFUSION BLOOD PRODUCT 92	SI	<<STATUS>>  	4
-LABTBP92	TRANSFUSION BLOOD PRODUCT 93	SI	<<STATUS>>  	4
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	4
-LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE	SOUR12	SOURCE	4
-LABADVQL	ADENOVIRUS DNA PCR QUAL	SOURC3	ADENOVIRUS SOURCE	4
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	SPEXP	SPECIMEN EXPIRATION DATE/TIME	4
-LABAVIN	HIV-1 INTEGRASE RESISTANCE	SREQ	COMMENTS	4
-LABFLDS	FLUID STERILITY TEST	SREQ	COMMENTS	4
-LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	SREQ	COMMENTS	4
-LABSHSTGS	SHC STAT GRAM STAIN	SREQ	COMMENTS	4
-LABYSSARO	AUTOANTIBODIES TO SS-A/RO	SSA	"SS-A/RO AB, IGG, SERUM"	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCCMT	COMMENT	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCDIR	INTERPRETED BY	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCDIS	DISCLAIMER	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCDX	CLINICAL INDICATION	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCMET	METHOD	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCPRM	PRIMERS	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCRLT	RESULTS	4
-LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCTIS	SOURCE	4
-LAB11113R	"TOXOPLASMA AB, IGA"	TOXOA	TOXO LGA ELISA	4
-LAB11122R	TOXOPLASMA IGM ELISA	TOXOM	TOXOPLASMA IGM ELISA	4
-LABMOLDS	MOLDS ALLERGY SCREEN	TRIRUB	TRICHOPHYTON RUBRUM	4
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	UBT	UNIT BLOOD TYPE  	4
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	UBT	UNIT BLOOD TYPE  	4
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	UBT	UNIT BLOOD TYPE  	4
-LABTBP86	TRANSFUSION BLOOD PRODUCT 87	UBT	UNIT BLOOD TYPE  	4
-POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	UCMT	URINE COMMENT	4
-LABUCORT	"CORTISOL FREE, URINE"	UCORT	"CORTISOL, URINE"	4
-LABUGL	"GLUCOSE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	4
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	UNT	UNIT NUMBER  	4
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	UNT	UNIT NUMBER  	4
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	UNT	UNIT NUMBER  	4
-LABTBP86	TRANSFUSION BLOOD PRODUCT 87	UNT	UNIT NUMBER  	4
-LAB12664R	"NTX TELOPEPTIDE, URINE"	UT53	COLLECTION PERIOD	4
-LABTBP26	TRANSFUSION BLOOD PRODUCT 27	VL	PRODUCT VOLUME	4
-LABTBP37	TRANSFUSION BLOOD PRODUCT 38	VL	PRODUCT VOLUME	4
-LAB12664R	"NTX TELOPEPTIDE, URINE"	VOL53	VOLUME	4
-LABUCORT	"CORTISOL FREE, URINE"	VOLUMEURN	URINE VOLUME	4
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	VZAB	VARICELLA ZOSTER AB IGG	4
-LABCD3B	"CD3, BLOOD"	WBC	WBC COUNT	4
-LABTBP79	TRANSFUSION BLOOD PRODUCT 80	XMR	UNIT CROSSMATCH  	4
-LABTBP80	TRANSFUSION BLOOD PRODUCT 81	XMR	UNIT CROSSMATCH  	4
-LABTBP81	TRANSFUSION BLOOD PRODUCT 82	XMR	UNIT CROSSMATCH  	4
-LABTBP82	TRANSFUSION BLOOD PRODUCT 83	XMR	UNIT CROSSMATCH  	4
-LABYAMIO	"AMIODARONE, SERUM"	YAMIOD	AMIODARONE	4
-LAB11074R	ACH RECEPTOR AB	YARBIN	ACH RECEPTOR BINDING ANTIBODY	4
-LABYICP	IMMUNE COMPLEX PANEL	YC1QB	C1G BINDING	4
-LABYC2REF	C2 COMPLEMENT AG	YC2RE1	C2 COMPLEMENT AG	4
-LABCENABY	CENTROMERE ANTIBODIES	YCENTA	CENTROMERE ABS.	4
-LABYAMIO	"AMIODARONE, SERUM"	YDESET	DESETHYLAMIODARONE	4
-LABYGDA	"DRUG SCREEN, FLUIDS"	YGDA2	SUSPECT DRUG	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA1	C22:0	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA2	C24:0	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA3	C26:0	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA4	C24/C22 RATIO	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA5	C26:0/C22:0 RATIO	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA6	PRISTANIC ACID	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA7	PHYTANIC ACID	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA8	PRISTANIC/PHYTANIC ACID	4
-LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA9	COMMENT	4
-LABYICP	IMMUNE COMPLEX PANEL	YPEG	POLYETHL. GLYCOL IGG	4
-LABYRAB	RABIES AB TITER CHK ENDPOINT	YRAB1	RABIES ANTIBODY	4
-LABYICP	IMMUNE COMPLEX PANEL	YRAJI	RAJI CELL REPLACEMENT	4
-LABYSCERE	"INFLAMM. BOWEL DISEASE PANEL,S"	YSCER1	"SACCH CEREVISIAE AB, IGA"	4
-LABYSCERE	"INFLAMM. BOWEL DISEASE PANEL,S"	YSCER2	"SACCH CEREVISIAE AB, IGG"	4
-LABYSHBG	SEX HORMONE BINDING GLOBULIN	YSHBG1	SEX HORMONE BINDING GLOBULIN	4
-LABYUARS	"ARSENIC FRACTIONATION, URINE"	YUARS1	INORGANIC ARSENIC (TOXIC)	4
-LABUCORT	"CORTISOL FREE, URINE"	YUCOUT	COLLECTION DURATION	4
-LABCHLAM	CHLAMYDIA TRACHOMATIS / PSITTACI CULTURE	12205	SOURCE	3
-10070R	HLA ORDER - IVIG IN VITRO INHIBITION ASSAY	10049R	HLA LAB TESTING STATUS	3
-LAB11365R	FAMILIAL HIBERNIAN FEVER/TRAPS	11366R	FAMILIAL HIBERNIAN FEVER	3
-LAB11438R	CHLAMYDIA TRACHOMATIS CULTURE	11502R	CHLAMYDIA TRACHOMATIS CULTURE	3
-LAB11577R	ALAGILLE (JAG1) MUTATION	11692R	ALAGILLE (JAG1) MUTATION	3
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11838R	REASON FOR REFERRAL	3
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11840R	INTERPRETATION	3
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11842R	REVIEWED BY	3
-LAB11871R	"HIV-2 ANTIBODY, IMMUNOBLOT"	11870R	"HIV 2 ANTIBODY, IMMUNOBLOT"	3
-LABFT3DIA	"T3, FREE, TRACER DIALYSIS"	11876R	"T3, TOTAL"	3
-LAB11885R	"C. TRACHOMATIS RNA, RECTAL"	11884R	"CHLAMYDIA TRACHOMATIS RNA,RECTAL"	3
-LAB11887R	"C. TRACHOMATIS RNA, THROAT"	11886R	"CHLAMYDIA TRACHOMATIS RNA,THROAT"	3
-LABFQFVR	COXIELLA BURNETII AB	12013R	Q FEVER IGM PHASE L TITER	3
-LABYPERAB	BORDETELLA PERTUSSIS IGG ABS	12048R	PT IGG	3
-LABYPERAB	BORDETELLA PERTUSSIS IGG ABS	12049R	FHA IGG	3
-LAB11438R	CHLAMYDIA TRACHOMATIS CULTURE	12067R	SPECIMEN SOURCE	3
-LAB12210R	LIPOPROTN ASSOCTD PHOSPHLPSEA2	12211R	12211R	3
-LAB12228R	LATEX SPECIFIC IGE (RIA) PANEL	12229R	12229R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12285R	12285R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12286R	12286R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12287R	12287R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12288R	12288R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12289R	12289R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12290R	12290R	3
-LAB12284R	"LYMPHO PROLIFERATION, AG"	12291R	12291R	3
-LAB12333R	KIT MUTATION ANALYSIS EXON 9	12338R	KIT MUTATION ANALYSIS EXON 9	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12341R	SPECIMEN	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12342R	SPECIMEN ID	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12343R	SOURCE	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12344R	ORDER DATE	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12345R	REASON FOR REFERRAL	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12346R	METHOD	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12347R	RESULTS	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12348R	INTERPRETATION	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12349R	AMENDMENT	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12350R	CONSULTANT	3
-LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12351R	REPORT DATE	3
-LAB12415R	ACVRL1 AND ENG (HHT)	12417R	SEQUENCING-DELETION/DUPLICATION	3
-LABLEISAB	"LEISHMANIA AB, IFA"	12428R	INTERPRETATION	3
-LABLEISAB	"LEISHMANIA AB, IFA"	12429R	INTERPRETATION	3
-LAB11190R	BORRELIA HERMSII AB PANL	12430R	INTERPRETATION	3
-LABFEHRC	EHRLICHIA CHAFFEENSIS AB	12430R	INTERPRETATION	3
-LABLEISAB	"LEISHMANIA AB, IFA"	12430R	INTERPRETATION	3
-LABLEISAB	"LEISHMANIA AB, IFA"	12431R	INTERPRETATION	3
-LAB12599R	DILATED CARDIOMYOPATHY PANEL	12598R	DILATED CARDIOMYOPATHY PANEL	3
-LAB12689R	"INFLIXIMAB, SERUM LEVEL AND AB"	12690R	"INFLIXIMAB, SERUM LEVEL AND AB"	3
-LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	13017R	"PCA-TR, CSF"	3
-LABY25VD	"PEDIATRIC VITAMIN D, 25-HYDROXY"	25OHD2	25-HYDROXY D2	3
-LABY25VD	"PEDIATRIC VITAMIN D, 25-HYDROXY"	25OHD3	25-HYDROXY D3	3
-LABY25VD	"PEDIATRIC VITAMIN D, 25-HYDROXY"	25OHDT	25-HYDROXY D TOTAL	3
-LABYSTONE	STONE ANALYSIS	3RDCONST	3RD CONSTITUENT	3
-10305R	HLA - ALLO T FLOW CROSSMATCH	9141R	SERUM DESCRIPTION 3	3
-10310R	HLA - ALLO B FLOW CROSSMATCH	9171R	SERUM DESCRIPTION 3	3
-10315R	HLA - ALLO T CDC CROSSMATCH	9192R	PATIENT\'S SERUM 1 DESCRIPTION	3
-10315R	HLA - ALLO T CDC CROSSMATCH	9199R	PATIENT\'S SERUM 3 DATE	3
-10315R	HLA - ALLO T CDC CROSSMATCH	9201R	PATIENT\'S SERUM 3 UNTREATED RESULT	3
-10315R	HLA - ALLO T CDC CROSSMATCH	9202R	PATIENT\'S SERUM 3 DTT TREATED RESULT	3
-10320R	HLA - ALLO B CDC CROSSMATCH	9225R	PATIENT\'S SERUM 3 DATE	3
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9344R	INTERPRETATION/ COMMENTS CLASS II	3
-10375R	HLA - STR/CHIMERISM TESTING	9487R	LOCUS 13	3
-10180R	HLA ORDER - KIT REQUEST NOTICE	9710R	KIT REQUEST NOTICE FOR LAB	3
-LABAACSF	"AMINO ACIDS, CSF"	AAMINOA	A-AMINOADIPIC ACID	3
-LABAACSF	"AMINO ACIDS, CSF"	AASINT	INTERPRETATION	3
-LABCBCD	CBC WITH DIFF	ABGRAN	"GRAN, ABS"	3
-LABUCARN	"CARNITINE, URINE"	ACFCRU	"ACYL/FREE CARN RATIO, UR"	3
-LABRBCENZ	RED CELL ENZYMES	ADA	ADENOSINE DEAMINASE	3
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADDNA	ADENOVIRUS DNA	3
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVCP	RESULT	3
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVLOG	RESULT LOG 10	3
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVSP	SPECIMEN	3
-LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVTIP	TEST DESCRIPTION	3
-LAB11140R	MATERNAL SCREEN	AFP	AFP SCREEN TO ALLIED/STATE LAB	3
-LABAACSF	"AMINO ACIDS, CSF"	ALANINE	ALANINE	3
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ALERT	ALERT	3
-LABANDRO	"ANDROSTENEDIONE, SERUM"	ANDRO	"ANDROSTENEDIONE, S"	3
-LABYANDRO	"ANDROSTENEDIONE, SERUM"	ANDRO	"ANDROSTENEDIONE, S"	3
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	AODOR	A-OLIGO/DI-OLIGO RAT	3
-LABFCHGA	A. PHAGOCYTOPHILUM AB	APIGG	A.PHAGOCYTOPHILUM IGG	3
-LABFCHGA	A. PHAGOCYTOPHILUM AB	APIGM	A.PHAGOCYTOPHILUM IGM	3
-LABFCHGA	A. PHAGOCYTOPHILUM AB	APINT	A.PHAGOCYTOPHILUM INTERPRETATION	3
-LABAACSF	"AMINO ACIDS, CSF"	ARGININE	ARGININE	3
-LABAACSF	"AMINO ACIDS, CSF"	ASPART	ASPARTIC ACID	3
-LABAACSF	"AMINO ACIDS, CSF"	ASPGN	ASPARAGINE	3
-LABAACSF	"AMINO ACIDS, CSF"	BALN	B-ALANINE	3
-LABAACSF	"AMINO ACIDS, CSF"	BAMBA	B-AMINOISOBUTYRIC AC	3
-LABCDIFF	"DIFFERENTIAL, CSF"	BASOCSF	"BASOPHILS, CSF"	3
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	BASOFLD	"BASOPHILS, FLD"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C2526N	"CD25, % OF CD3+/CD4+/CD26NEG"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C257N	"CD25, % OF CD3+/CD4+/CD7NEG"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C264AB	"CD26-/CD4+, ABS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C264PC	CD26-/CD4+ %	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3026N	"CD30, % OF CD3+/CD4+/CD26NEG"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C307N	"CD30, % OF CD3+/CD4+/CD7NEG"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C326NA	"CD3+/CD26NEG, ABS."	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3426N	"CD3+/CD4+/CD26NEG, ABS."	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3426P	"CD3+/CD4+/CD26NEG, % OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3426T	"CD3+/CD4+/CD26NEG, % OF CD4+ T"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C347NA	"CD3+/CD4+/CD7NEG, ABS."	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C347NP	"CD3+/CD4+/CD7NEG, % OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C347NT	"CD3+/CD4+/CD7NEG, % OF CD4+ T"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C37NA	"CD3+/CD7NEG, ABS."	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P4PA	"CD3+/CD4+,ABS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P4PP	"CD3+/CD4+, % OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P7NP	"CD3+/CD7NEG, % OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P8PN	"CD3+/CD8+, ABS. OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P8PP	"CD3+/CD8+, % OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C48RAT	TCD4/CD8 RATIO	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C4N7AB	"CD4+/CD7-,ABS. (C4N7AB)"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C4N7PC	CD4+/CD7-% (C4N7PC)	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C5226N	"CD52, % OF CD3+/CD4+/CD26NEG"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C527N	"CD52, % OF CD3+/CD4+/CD7NEG"	3
-LABUCARN	"CARNITINE, URINE"	CARNFU	"FREE CARNITINE, UR"	3
-LABUCARN	"CARNITINE, URINE"	CARNTU	"TOTAL CARNITINE, UR"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD19AB	"CD19 (PAN B), ABS."	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD19PC	CD19 (PAN B) %	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD26NP	"CD3+/CD26NEG, % OF LYMPHS"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD3PC	CD3 (PAN T) %	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD4TOT	CD4 TOTAL	3
-LABCELDIS	CELIAC DISEASE SYSTEM	CELDR	CELIAC DISEASE SYS	3
-LABCHLAM	CHLAMYDIA TRACHOMATIS / PSITTACI CULTURE	CHLA	CHLAMYDIA CULTURE	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	CHOL	"CHOLESTEROL, TOTAL"	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	CHOLHDL	CHOLESTEROL/HDL RATIO	3
-LABAACSF	"AMINO ACIDS, CSF"	CITRUL	CITRULLINE	3
-LABAVRT	HIV - 1 GENOTYPING	CULT	CULT/OTHER RSLT:	3
-LABMMIC	UNLISTED MICROBIOLOGY ORDER	CULT	CULT/OTHER RSLT:	3
-LABAACSF	"AMINO ACIDS, CSF"	CYSTINE	CYSTINE	3
-LABAACSF	"AMINO ACIDS, CSF"	CYTHN	CYSTATHIONINE	3
-LABTBP79	TRANSFUSION BLOOD PRODUCT 80	DI	ISSUE DATE/TIME	3
-LABTBP80	TRANSFUSION BLOOD PRODUCT 81	DI	ISSUE DATE/TIME	3
-LABTBP81	TRANSFUSION BLOOD PRODUCT 82	DI	ISSUE DATE/TIME	3
-LABTBP82	TRANSFUSION BLOOD PRODUCT 83	DI	ISSUE DATE/TIME	3
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	DI	ISSUE DATE/TIME	3
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	DI	ISSUE DATE/TIME	3
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	DI	ISSUE DATE/TIME	3
-LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	DIAZEP	DIAZEPAM	3
-POC3	POC URINE DIPSTICK FOR KETONES	DIPLOT	DIPSTICK LOT	3
-POC11	URINE CHEMSTRIP GP	DIPSLOT	DIPSTICK LOT	3
-LABDUTYPE	WEAK D TYPING	DUTYP	WEAK D TYPING	3
-LABDVEMPV	DIRECT VIRAL EXAM MPV	DVE	RESULTS	3
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	EA	EARLY ANTIBODY (R+D) IGG	3
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	EBNA	EPSTEIN-BARR NUC AB	3
-LAB11190R	BORRELIA HERMSII AB PANL	ECINT	INTERPRETATION	3
-LABCDIFF	"DIFFERENTIAL, CSF"	EOSCSF	"EOSINOPHILS, CSF"	3
-POC11	URINE CHEMSTRIP GP	EXPDATE	EXP DATE	3
-POC3	POC URINE DIPSTICK FOR KETONES	EXPDATE	EXP DATE	3
-LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	FINT	INTERPRETATION	3
-LABFPLTS	"PLATELET COUNT, FLUID"	FLDSOU	"SOURCE, FLUID"	3
-LAB12814R	MYCOPLASMA IGG/IGM AB	FMYABG	M.PNEUMONIA AB IGG	3
-LABFRPSA	FREE PSA	FPSA	FREE PSA	3
-LABFRPSA	FREE PSA	FREEPSAPC	% FREE PSA	3
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTDETD	FASTVT TACHY ZONE DETECTION DURATION	3
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTDETD	FASTVT TACHY ZONE DETECTION DURATION	3
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTZNSC	FASTVT TACHY ZONE SUDDEN ONSET CRITERIA	3
-LABFT3DIA	"T3, FREE, TRACER DIALYSIS"	FT3	FREE T3 (BY DIALYSIS)	3
-LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTCMT	COMMENT	3
-LABFRAGX	FRAGILE X	FXAI	ALLELE I	3
-LABFRAGX	FRAGILE X	FXAII	ALLELE II	3
-LABFRAGX	FRAGILE X	FXDIS	DISCLAIMER	3
-LABFRAGX	FRAGILE X	FXDX	CLINICAL INDICATION	3
-LABFRAGX	FRAGILE X	FXINT	INTERPRETATION	3
-LABFRAGX	FRAGILE X	FXMD	INTERPRETED BY	3
-LABFRAGX	FRAGILE X	FXMET	METHOD	3
-LABFRAGX	FRAGILE X	FXRES	RESULT	3
-LABRBCENZ	RED CELL ENZYMES	G6PD	"G6PD, QUANTITATIVE"	3
-LABYGAS	"GASTRIN, SERUM"	GASTRIN	"GASTRIN, SERUM"	3
-LABGDMSCR	GLUCOSE GDM SCREEN	GDMSCR	"GLUCOSE, GDM SCREEN"	3
-LAB11116R	FANCONI MUTATION ANALYSIS	GFANCO	FANCONI AN. GP C MUT	3
-LABAACSF	"AMINO ACIDS, CSF"	GLUTAC	GLUTAMIC ACID	3
-LABAACSF	"AMINO ACIDS, CSF"	GLUTAMINE	GLUTAMINE	3
-POC11	URINE CHEMSTRIP GP	GLUURINE	GLUCOSE URINE	3
-LABAACSF	"AMINO ACIDS, CSF"	GLYCINE	GLYCINE	3
-LABRBCENZ	RED CELL ENZYMES	GPI	GLUC. PHOSPHATE ISOM	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	HDL	HDL CHOLESTEROL	3
-LABHCVA	HEPATITIS C AB EIA	HEPCAB	HEPATITIS C AB	3
-LABRBCENZ	RED CELL ENZYMES	HEX	HEXOKINASE	3
-LABKBST	KLEIHAUER - BETKE STAIN	HGBFPC	% CELLS WITH HGB F	3
-LABAACSF	"AMINO ACIDS, CSF"	HISTID	HISTIDINE	3
-LABAACSF	"AMINO ACIDS, CSF"	HMCY	HOMOCYSTINE	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	3
-LABFHV12	"HSV 1/2 PCR, NON CSF"	HV1DNA	"HSV-1 DNA, QN PCR"	3
-LABFHV12	"HSV 1/2 PCR, NON CSF"	HV2DNA	"HSV-2 DNA, QN PCR"	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	HVA	HOMOVANILLIC ACID	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	HVAINT	HVA INTRP	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	HVAUR	"HVA, URINE"	3
-LABHYPP	HYPERSENSITIVITY PNEUMONITIS SCREEN	HYPINT	INTERPRETATION	3
-LABHYPP	HYPERSENSITIVITY PNEUMONITIS SCREEN	HYPPN	IGG ANTIBODY PANEL	3
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	3
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDSLP	BRADY MODULATION SLOPE	3
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	3
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	3
-LABAACSF	"AMINO ACIDS, CSF"	INTSBY	INTERPRETED BY	3
-LABAACSF	"AMINO ACIDS, CSF"	ISOLEU	ISOLEUCINE	3
-LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	JO1AB	AUTOANTIBODIES TO JO 1	3
-LABKBST	KLEIHAUER - BETKE STAIN	KBSINT	INTERPRETATION	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	LDL	LDL (CALCULATED)	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	LDLHDL	LDL/HDL RATIO	3
-LABAACSF	"AMINO ACIDS, CSF"	LEUCINE	LEUCINE	3
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	LOTNO	LOT #	3
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	LYM	LYMPHOCYTES %	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	3
-LABAACSF	"AMINO ACIDS, CSF"	LYSINE	LYSINE	3
-LABKBST	KLEIHAUER - BETKE STAIN	MCV	MCV	3
-LABMECAPC	MECA BY PCR	MECAR	MECA PCR RESULT	3
-LABYHMS	"HEAVY METALS SCREEN, BLOOD"	MER	"MERCURY, BLOOD"	3
-LABADIFF	DIFFERENTIAL	META	METAMYELOCYTES	3
-LABAACSF	"AMINO ACIDS, CSF"	METHIO	METHIONINE	3
-LABKBST	KLEIHAUER - BETKE STAIN	MLFB	ML FETAL BLOOD	3
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	MODOR	MONO-OLIGO/DI-OLIGO RATIO	3
-LABMPVPCR	MPV PCR	MPVDI	DISCLAIMER	3
-LABMPVPCR	MPV PCR	MPVMT	METHOD	3
-LABMPVPCR	MPV PCR	MPVRE	METAPNEUMOVIRUS RNA	3
-LABMPVPCR	MPV PCR	MPVSP	SPECIMEN	3
-LABMUMRC	MUMPS RAPID CULTURE	MUMRC1	MUMPS RAPID CULTURE	3
-LABMUMRC	MUMPS RAPID CULTURE	MUMRCD	TEST DESCRIPTION	3
-LABMUMRC	MUMPS RAPID CULTURE	MUMSPM	SPECIMEN	3
-LABYFNA	"SODIUM, FLUID"	NAFLD	SODIUM BODY FLUID	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTCBC	CBC COMMENT	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTCO	"STIM. CONTROL,NBT"	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTCOM	"COMMENT, NBT"	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTINT	INTERPRETATION	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTMD	REVIEWED BY	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTPT	"STIM. PATIENT,NBT"	3
-LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTREV	PATHOLOGIST REVIEW	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	NHDL	"NON-HDL CHOL, CALC"	3
-LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	NORDIAZ	NORDIAZEPAM	3
-LABAACSF	"AMINO ACIDS, CSF"	ORNITHINE	ORNITHINE	3
-LABYPRIMI	PRIMIDONE AND PHENOBARBITAL	PB	PHENOBARBITAL	3
-CRP333	RIGHT LEFT CATH CORS ANGIO	PDF	PDF RESULT	3
-IMG11LHC	CAR LT HEART CATH	PDF	PDF RESULT	3
-VSC09	VSC MESENTERIC ULTRASOUND	PDF	PDF RESULT	3
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	PEDCOM	SPECIMEN COMMENT	3
-LABAACSF	"AMINO ACIDS, CSF"	PHALN	PHENYLALANINE	3
-POC3	POC URINE DIPSTICK FOR KETONES	PHLOC	LOCATION	3
-LABRBCENZ	RED CELL ENZYMES	PK	PYRUVATE KINASE	3
-LABFPLTS	"PLATELET COUNT, FLUID"	PLTFLD	"PLATELET CT, FLUID"	3
-LABFPOLIO	"POLIOVIRUS 1-3 AB, SERUM"	PLV1	POLIOVIRUS 1 AB	3
-LABFPOLIO	"POLIOVIRUS 1-3 AB, SERUM"	PLV2	POLIOVIRUS 2 AB	3
-LABFPOLIO	"POLIOVIRUS 1-3 AB, SERUM"	PLV3	POLIOVIRUS 3 AB	3
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRVLDUNIM	PM RV LEAD UNIPOLAR IMPEDANCE	3
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMTLBATIMP	PM TELEMETRY BATTERY IMPEDANCE	3
-LABYPRIMI	PRIMIDONE AND PHENOBARBITAL	PRIMIDONE	PRIMIDONE	3
-LABTBP93	TRANSFUSION BLOOD PRODUCT 94	PRO	PRODUCT ID  	3
-LABAACSF	"AMINO ACIDS, CSF"	PROLINE	PROLINE	3
-POC11	URINE CHEMSTRIP GP	PROTURINE	PROTEIN URINE	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	PTH18R	RECEIVED IN FLOW CYTOMETRY	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRC	COMMENT	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRD	INTERPRETED BY	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRE	RESULT	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRF	SOURCE	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRM	METHOD	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRP	PRIMER	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRS	DISCLAIMER	3
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRX	CLINICAL INDICATION	3
-LABTBP	TRANSFUSION BLOOD PRODUCT 1	QY	QUANTITY	3
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRAMP	RA THRESHOLD AMPLITUDE	3
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRDUR	RA THRESHOLD DURATION	3
-LABRBCENZ	RED CELL ENZYMES	RBCINT	INTERPRETATION	3
-LABRBCENZ	RED CELL ENZYMES	RBCMD	REVIEWED BY	3
-10070R	HLA ORDER - IVIG IN VITRO INHIBITION ASSAY	RINFO	TO FIND RESULT:	3
-10265R	HLA ORDER - SCHEDULED SCREENING KIT PROCESSING	RINFO	TO FIND RESULT:	3
-LABAVRT	HIV - 1 GENOTYPING	RPT	RPT	3
-LABDVEMPV	DIRECT VIRAL EXAM MPV	RPT	RPT	3
-LABMMIC	UNLISTED MICROBIOLOGY ORDER	RPT	RPT	3
-LABDVERS	"RSV RAPID ANTIGEN, DIRECT EIA"	RRSP	SPECIMEN	3
-LABDVERS	"RSV RAPID ANTIGEN, DIRECT EIA"	RSV	RESPIRATORY SYNCYTIAL VIRUS	3
-LABDVERS	"RSV RAPID ANTIGEN, DIRECT EIA"	RSVCOM	COMMENTS	3
-LABRUBG	RUBELLA IGG ANTIBODY	RUBINT	INTERPRETATION	3
-LABRUBG	RUBELLA IGG ANTIBODY	RUBR	RUBELLA IGG ANTIBODY	3
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	3
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	3
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	3
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	3
-LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SCL70	"SCL 70 AB, IGG, SERUM"	3
-LABAVRT	HIV - 1 GENOTYPING	SDES	SPECIMEN	3
-LABDVEMPV	DIRECT VIRAL EXAM MPV	SDES	SPECIMEN	3
-LABMMIC	UNLISTED MICROBIOLOGY ORDER	SDES	SPECIMEN	3
-LABAACSF	"AMINO ACIDS, CSF"	SERINE	SERINE	3
-LABYSERTR	SERTRALINE LEVEL	SERTRALINE	"SERTRALINE, S"	3
-LABSEZ	SEZARY CELL SCREEN	SEZ	SEZARY CELL SCREEN	3
-LABFSFTA	TREPONEMA PALLIDUM AB	SFTA	SYPHILIS-FTA	3
-LABTBP93	TRANSFUSION BLOOD PRODUCT 94	SI	<<STATUS>>  	3
-LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SMAB	AUTOANTIBODIES TO SM	3
-LABTBP42	TRANSFUSION BLOOD PRODUCT 43	SPEXP	SPECIMEN EXPIRATION DATE/TIME	3
-LABAVRT	HIV - 1 GENOTYPING	SREQ	COMMENTS	3
-LABDVEMPV	DIRECT VIRAL EXAM MPV	SREQ	COMMENTS	3
-LABMMIC	UNLISTED MICROBIOLOGY ORDER	SREQ	COMMENTS	3
-LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SSA	"SS-A/RO AB, IGG, SERUM"	3
-LABSNA	SPECIFIC ANA PANEL	SSAQNT	ANTI-RO LEVEL	3
-LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SSB	"SS-B/LA AB, IGG, SERUM"	3
-LABYSSBLA	AUTOANTIBODIES TO SS-B/LA	SSB	"SS-B/LA AB, IGG, SERUM"	3
-LABAACSF	"AMINO ACIDS, CSF"	TAURINE	TAURINE	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	TCD4AB	"TOTAL CD4, ABS."	3
-LABLPDC	"LIPID PANEL, FASTING PATIENT"	TGL	"TRIGLYCERIDE, SER/PLAS"	3
-LABUTHI	"THIOCYANATE, URINE (RANDOM)"	THIOCYANURN	"THIOCYANATE, URINE"	3
-LABAACSF	"AMINO ACIDS, CSF"	THREONINE	THREONINE	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	TIMEU	COLLECTION PERIOD	3
-LABTOXOAB	TOXOPLASMA IGM & IGG	TOXIGG	TOXOPLASMA IGG	3
-LABTOXOAB	TOXOPLASMA IGM & IGG	TOXIGM	TOXOPLASMA IGM	3
-LABTOXOIM	TOXOPLASMA IGM	TOXM	TOXOPLASMA IGM	3
-LABCSFTP	"PROTEIN TOTAL, CSF"	TPCSTN	TUBE NUMBER	3
-LABYTSIG	"TSH IMMUNOGLOBULINS, SERUM"	TSIG	"THROID-STIMULATING IG, SERUM"	3
-LABAACSF	"AMINO ACIDS, CSF"	TYROSINE	TYROSINE	3
-LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	U1RNPAB	AUTOANTIBODIES TO U1RNP	3
-LABTBP87	TRANSFUSION BLOOD PRODUCT 88	UBT	UNIT BLOOD TYPE  	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	UCR	"CREATININE, URINE"	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	UCR	"CREATININE, URN"	3
-POC3	POC URINE DIPSTICK FOR KETONES	UKET	KETONES	3
-LABTBP87	TRANSFUSION BLOOD PRODUCT 88	UNT	UNIT NUMBER  	3
-LABUHVA	"HOMOVANILLIC ACID, URINE"	UVOL	VOLUME	3
-LABAACSF	"AMINO ACIDS, CSF"	VALINE	VALINE	3
-LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANCOSDD	"VANCOMYCIN, SINGLE DAILY DOSE"	3
-LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANUN2	DATE & TIME OF LAST DOSE	3
-LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANUN3	DOSAGE	3
-LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANUN4	ADMINISTRATION ROUTE	3
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	VCA	VIRAL CAPSID AB	3
-LABEBVC	EBV VIRAL CAPSID ANTIGEN IGA	VCAA	VCA IGA	3
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	VCAM	VIRAL CAPSID AB-IGM	3
-LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS2	HIV-1 P24 (NON ICD) CONF.	3
-LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS4	HIV-1 P24 (NON ICD) AG LEVEL	3
-LABTBP30	TRANSFUSION BLOOD PRODUCT 31	VL	PRODUCT VOLUME	3
-LABTBP38	TRANSFUSION BLOOD PRODUCT 39	VL	PRODUCT VOLUME	3
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	VL	PRODUCT VOLUME	3
-LABVLDL	VLDL (CALCULATED)	VLDL	VLDL (CALCULATED)	3
-LABYUARS	"ARSENIC FRACTIONATION, URINE"	VOLUMEURN	URINE VOLUME	3
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	WBC	WBC COUNT	3
-LABTBP74	TRANSFUSION BLOOD PRODUCT 75	XMR	UNIT CROSSMATCH  	3
-LABTBP78	TRANSFUSION BLOOD PRODUCT 79	XMR	UNIT CROSSMATCH  	3
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	XMR	UNIT CROSSMATCH  	3
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	XMR	UNIT CROSSMATCH  	3
-LAB11071R	"MITOCHONDRIAL AB M2, SERUM"	YAMA	"MITOCHONDRIAL ANTIBODY, M2"	3
-LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	YDGLIA	GLIADIN (DEAMIDATED) IGA	3
-LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	YDIAZ3	DIAZEPAM & NORDIAZEPAM	3
-LABYFNA	"SODIUM, FLUID"	YFLSC1	FLUID TYPE	3
-LABYSERTR	SERTRALINE LEVEL	YSERT2	DESMETHYLSERTRALINE	3
-LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG1	"MAGNESIUM, QN, F"	3
-LABYSTRAB	STRIATIONAL (STRIATED MUSCLE) AB	YSTA	STRIATIONAL (STRIATED MUSCLE)	3
-LABYUARS	"ARSENIC FRACTIONATION, URINE"	YUARS2	ORGANIC ARSENIC(NON-TOXIC)	3
-LABYUARS	"ARSENIC FRACTIONATION, URINE"	YUARS3	COLLECTION DURATION	3
-10040R	HLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)	10049R	HLA LAB TESTING STATUS	2
-10085R	HLA ORDER - TYPING ADD ON LOCUS	10049R	HLA LAB TESTING STATUS	2
-10175R	HLA ORDER - KIT PROCESSING RECIPIENT	10049R	HLA LAB TESTING STATUS	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11005R	A. TENUIS/ALTERNATA IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11006R	ASPERGILLUS FUMIGATUS IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11007R	AUREOBASIDIUM PULLULANS IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11008R	MICROPOLYSPORA FAENI IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11009R	P. NOTATUM/CHRYSOGENUM IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11010R	PHOMA BETAE IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11011R	THERMOACTINOMYCES VULGARIS IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11012R	TRICHODERMA VIRIDE IGG	2
-LAB11004R	HYPERSENS. PNEUMONITIS PNL	11141R	INTERPRETATION	2
-LAB11217R	"IRON,LIVER TISSUE"	11218R	"IRON, LIVER TS"	2
-LAB11217R	"IRON,LIVER TISSUE"	11224R	HEPATIC IRON INDEX	2
-LAB11322R	MOTOR NEUROPATHY AB SCREEN	11323R	CO-GM1 TRIAD AB	2
-LAB11375R	OSTEOGENESIS IMPERFECTA SEQUEN	11376R	OSTEOGENESIS IMPERFECTA SEQUEN	2
-LAB11401R	HALIBUT IGE	11459R	HALIBUT IGE	2
-LAB11437R	IBD DIAGNOSTIC	11501R	IBD DIAGNOSTIC	2
-LAB11518R	BCR-ABL1 KINASE DOMAIN MUT ANL	11627R	BCR-ABL1 KINASE DOMAIN MUT ANAL	2
-LAB11527R	SPINAL MUSCULAR ATROPHY DIAGNO	11637R	SPINAL MUSCULAR ATROPHY DIAGNO	2
-LAB11529R	ENCEPHALITIS AB SCREEN	11639R	ENCEPHALITIS AB SCREEN	2
-LAB11534R	MTDNA SCREENING PANEL	11644R	MTDNA SCREENING PANEL	2
-LAB11568R	"BRUCELLA SEROLOGIES, CSF"	11682R	"BRUCELLA IGG,CSF"	2
-LAB11568R	"BRUCELLA SEROLOGIES, CSF"	11683R	"BRUCELLA IGM,CSF"	2
-LAB11571R	"CYSTIC FIBROSIS, 97 MUTATIONS"	11686R	"CYSTIC FIBROSIS, 97 MUTATIONS"	2
-LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	11687R	"HEXOSAMINIDASE TOTAL, WBC"	2
-LAB11590R	FREE FATTY ACIDS	11715R	FREE FATTY ACIDS	2
-LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	11780R	"HEXOSAMINIDASE PERCENT A, WBC"	2
-LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	11781R	INTERPRETATION	2
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11834R	SPECIMEN	2
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11835R	SPECIMEN ID	2
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11837R	ORDER DATE	2
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11839R	METHOD	2
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11843R	RELEASE DATE	2
-LAB11863R	ASPIRIN RESISTANCE	11861R	11-DEHYDROTHROMBOXANE B2	2
-LAB11863R	ASPIRIN RESISTANCE	11862R	"CREATININE, RANDOM URINE"	2
-LAB11867R	C3A DESARG FRAGMENT	11866R	C3A DESARG FRAGMENT	2
-LAB11989R	LONG QT SYNDROME	11990R	LQTS RESULTS	2
-LAB13008R	Q FEVER IGG PHASE II TITER	12014R	Q FEVER IGG PHASE LI TITER	2
-LAB12043R	IL 5	12044R	INTERLEUKIN 5	2
-LAB12373R	"CARBAMAZEPINE-10,11-EPOXIDE"	12375R	"CARBAMAZEPINE-10,11-EPOXIDE"	2
-LAB12395R	5\'NUCLEOTIDASE	12396R	5\' NUCLEOTIDASE	2
-LAB12814R	MYCOPLASMA IGG/IGM AB	12431R	INTERPRETATION	2
-LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12553R	DATE OF LAST DOSE	2
-LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12554R	TIME OF LAST DOSE	2
-LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12555R	"DOSE, MG"	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12671R	TETANUS STIMULATION INDEX	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12672R	TETANUS INTERPRETATION	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12673R	CANDIDA STIMULATION INDEX	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12674R	CANDIDA INTERPRETATION	2
-LAB12733R	ALPHA-2 PLASMIN INHIBITOR	12734R	ALPHA-2 PLASMIN INHIBITOR	2
-LAB12836R	ETHYLENE GLYCOL	12837R	ETHYLENE GLYCOL	2
-LABY21HYX	"21 - HYDROXYLASE AB, SERUM"	21OHXAB	21-HYDROXYLASE AB	2
-LABY5NUC	5 - NUCLEOTIDASE	5NUCLEOT	5\'NUCLEOTIDASE	2
-10295R	HLA - AUTO T FLOW CROSSMATCH	9092R	SERUM DESCRIPTION 3	2
-10300R	HLA - AUTO B FLOW CROSSMATCH	9113R	SERUM DESCRIPTION 3	2
-10315R	HLA - ALLO T CDC CROSSMATCH	9200R	PATIENT\'S SERUM 3 DESCRIPTION	2
-10320R	HLA - ALLO B CDC CROSSMATCH	9218R	PATIENT\'S SERUM 1 DESCRIPTION	2
-10320R	HLA - ALLO B CDC CROSSMATCH	9226R	PATIENT\'S SERUM 3 DESCRIPTION	2
-10320R	HLA - ALLO B CDC CROSSMATCH	9227R	PATIENT\'S SERUM 3 UNTREATED RESULT	2
-10320R	HLA - ALLO B CDC CROSSMATCH	9228R	PATIENT\'S SERUM 3 DTT TREATED RESULT	2
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9332R	SERUM DESCRIPTION	2
-10355R	HLA - MONITORING BY IGG	9349R	IGG COMBINED CPRA FOR CURRENT SERUM	2
-10355R	HLA - MONITORING BY IGG	9354R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	2
-10355R	HLA - MONITORING BY IGG	9374R	INTERPRETATION/ COMMENTS  CLASS II	2
-10360R	HLA - MONITORING BY C1Q	9403R	MONITORING RESULTS FOR C1Q CLASS II	2
-10330R	HLA - SCREENING IGG	9582R	INTERPRETATION/ COMMENTS CLASS I	2
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9586R	INTERPRETATION/ COMMENTS CLASS I	2
-LABYSTA1A	"ALPHA-1-ANTITRYPSIN, FECAL"	A1AANTITRYPF	"A-1-ANTITRYP,FECAL"	2
-LABARI	ABO/RH (ARI)	ABORH	ABO/RH CORD	2
-LABARI	ABO/RH (ARI)	ABORH	ABO/RH STILLBORN	2
-LAB11068R	"ANGIOTENSIN CONVERTING ENZYME, SERUM"	ACE	ANGIOTESIN CONVERTING ENZYME	2
-LABACRBLO	ACETYLCHOLINE BLOCKING AB	ACRBLO	ACETYLCHOLINE BLOCKING AB	2
-LABACRMOD	ACETYLCHOLINE MOD AB	ACRMOD	ACETYLCHOLINE MODULATING AB	2
-LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMAG	ANTIGEN	2
-LABCSFALB	"ALBUMIN, CSF"	ALBCSF	"ALBUMIN, CSF"	2
-LABYALUM	ALUMINUM	ALUM	ALUMINUM	2
-LABIMM1	IMMUNOLOGY PANEL 1	ANA	ANA RESULT	2
-LABIMM2	IMMUNOLOGY PANEL 2	ANA	ANA RESULT	2
-LABIMM1	IMMUNOLOGY PANEL 1	ANAINTERP	ANA INTERPRETATION	2
-LABIMM2	IMMUNOLOGY PANEL 2	ANAINTERP	ANA INTERPRETATION	2
-LAB11022R	ISLET CELL AB	ANTIIS	"ISLET CELL AB, IGG"	2
-LAB11075R	ANTI - SMOOTH MUSCLE AB	ASMA	ANTI-SMOOTH MUSCLE ANITBODY	2
-LABASO	ANTISTREPTOLYSIN O TITER	ASO	ASO TITER (IU/ML)	2
-LABSALCOR	"CORTISOL, SALIVA"	AVGCOR	AVERAGE SALIVARY CORTISOL	2
-LABBMCD34	"CD34 PANEL, BONE MARROW"	BCD34V	CD34+ PROGENITOR VIABILITY	2
-LABBKVPC	BK VIRUS DNA QUANTITATION	BKQ	BKV DNA QUANTITATION	2
-LABBLDCR	BLOOD FOR CREATININE CLEARANCE	BLDCR	BLD FOR CREATININE CLEARANCE	2
-LABIMM1	IMMUNOLOGY PANEL 1	C3	"COMPLEMENT C3, SERUM"	2
-LABCIRCAI	CIRCUIT IONIZED CALCIUM	CAICR	CIRCUIT IONIZED CALCIUM	2
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD26AB	"CD26 (PAN-T), ABS"	2
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD26PC	CD26 (PAN-T) %	2
-LABBMCD34	"CD34 PANEL, BONE MARROW"	CD45PV	CD45+ VIABILITY	2
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD7AB	"CD7 (PAN T), ABS"	2
-PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD7PC	CD7 (PAN T) %	2
-LABCDMLPA	CDH1 MLPA	CDMLDI	DISCLAIMER	2
-LABCDMLPA	CDH1 MLPA	CDMLDX	CLINICAL INDICATION	2
-LABCDMLPA	CDH1 MLPA	CDMLMD	INTERPRETED BY	2
-LABCDMLPA	CDH1 MLPA	CDMLME	METHOD	2
-LABCDMLPA	CDH1 MLPA	CDMLRE	RESULT	2
-LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLDI	DISCLAIMER	2
-LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLDX	CLINICAL INDICATION	2
-LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLMD	INTERPRETED BY	2
-LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLME	METHOD	2
-LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLRE	RESULT	2
-LABCFPT	CF POLY T ANALYSIS	CFPTCL	CLINICAL INDICATION	2
-LABCFPT	CF POLY T ANALYSIS	CFPTDS	DISCLAIMER	2
-LABCFPT	CF POLY T ANALYSIS	CFPTIN	INTERPRETED BY	2
-LABCFPT	CF POLY T ANALYSIS	CFPTME	METHOD	2
-LABCFPT	CF POLY T ANALYSIS	CFPTRS	RESULT	2
-LABBMTP	BMT PRETRANSPLANT PANEL	CMGDES	CMV IGG TEST DESCRIPTION	2
-LABBMTP	BMT PRETRANSPLANT PANEL	CMGQUL	CMV IGG RESULT	2
-LABBMTP	BMT PRETRANSPLANT PANEL	CMMDES	CMV IGM TEST DESCRIPTION	2
-LABBMTP	BMT PRETRANSPLANT PANEL	CMMQUL	CMV IGM RESULT	2
-LABCMVAV	CMV IGG AVIDITY	CMVAVD	TEST DESCRIPTION	2
-LABCMVAV	CMV IGG AVIDITY	CMVAVI	CMV IGG AVIDITY INDEX	2
-LABCMVAV	CMV IGG AVIDITY	CMVAVR	CMV IGG AVIDITY RESULT	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	CMVIGM	CMV IGM AB	2
-LABFCO2	"CO2 (BICARB), FLUID"	CO2FL	"CO2, FLUID"	2
-LABFCO2	"CO2 (BICARB), FLUID"	CO2FLS	SOURCE	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	CONA	CON A STIMULATION	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	CONINT	CON A INTERPRETATION	2
-LABCOPYPT	COPY REQUEST	COPYTO	SEND COPY TO	2
-LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	CULT	CULT/OTHER RSLT:	2
-LABINFO	MICROBIOLOGY INFORMATION ONLY	CULT	CULT/OTHER RSLT:	2
-LABISOBR	ISOLATOR BRUCELLA	CULT	CULT/OTHER RSLT:	2
-LABROACAN	ACANTHAMOEBA CULTURE	CULT	CULT/OTHER RSLT:	2
-LABYCYSTC	"CYSTATIN C, SERUM / PLASMA"	CYSTC	"CYSTATIN C, SER/PLAS"	2
-LAB12373R	"CARBAMAZEPINE-10,11-EPOXIDE"	CZPTOT	"CARBAMAZEPINE,TOT,S"	2
-LABDATLA	DIRECT ANTIGLOBULIN TEST - LARC	DATLAR	DIRECT COOMBS - LA RED CROSS	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	DEVTYPE	DEVTYPE	2
-LABDHB	DONOR HEPATITIS B SURFACE AG	DHB	DONOR HEP B SURF. AG	2
-LABDHBCC	DONOR HEPATITIS B CORE AB (LIVING)	DHBCC	DONOR HEP CORE AB	2
-LABDHEPC	DONOR HEPATITIS C AB	DHEPC	DONOR HEPATITIS C AB	2
-POC44	POC URINE DIPSTICK GP	DIPLOT	DIPSTICK LOT	2
-LABIMM1	IMMUNOLOGY PANEL 1	DNAINT	INTERPRETATION	2
-LABIMM1	IMMUNOLOGY PANEL 1	DNAQUL	ANTI-DSDNA RESULT	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	DOPAURN	"DOPAMINE, URN"	2
-LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EBNDES	EBNDES	2
-LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EBNQNT	EBNQNT	2
-LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EBNQUL	EBNQUL	2
-LABADIFF	DIFFERENTIAL	EOS	EOSINOPHILS	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	EPINEPURN	"EPINEPHRINE, URN"	2
-POC44	POC URINE DIPSTICK GP	EXPDATE	EXP DATE	2
-LABF8POR	FACTOR VIII INHIBITOR PORCINE	F8POR	"FACTOR VIII INHIBITOR, PORCINE"	2
-LABFECFIB	"MEAT FIBERS, FECAL"	FECFB	"MEAT FIBERS, FECAL"	2
-LABFIBRO	FIBROSPECT FOR LIVER FIBROSIS	FIBROR	FIBROSPECT FOR LIV	2
-LABFLUOX	FLUOXETINE AND NORFLUOXETINE	FLUOXE	FLUOXETINE	2
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	FOTHER	"OTHERS, FLD"	2
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	FP210	"P210, FLUID"	2
-LAB11080R	GLUTAMIC ACID DECARBOXY AB	GAD65AB	GAD65 ANTIBODY	2
-LABGTOL	GLUCOSE TOLERANCE	GLT2	"GLUCOSE,2HR - 75G"	2
-LABGTOL	GLUCOSE TOLERANCE	GLUF	"GLUCOSE,FASTING"	2
-LABCBCD	CBC WITH DIFF	GRANP	"GRAN, %"	2
-LABGTOL	GLUCOSE TOLERANCE	GTTINT	GTT INTERPRETATION	2
-LABYHALO	"HALOPERIDOL, SERUM"	HALO	HALOPERIDOL	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HATINT	INTERPRETATION	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HATINT	INTERPRETATION	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HAVINT	INTERPRETATION	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HAVR	IGM ANTI-HAV	2
-LABHBCT	HEPATITIS B CORE TOTAL AB	HBCAB	HEP B CORE TOTAL AB	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBCAB	HEP B CORE TOTAL AB	2
-LAB11106R	HEPATITIS B E AB	HBEAB	HEPATITIS B E AB	2
-LABHEAB	HEPATITIS B E AB	HBEAB	HEPATITIS B E AB	2
-LABHBSAG	HEPATITIS B SURFACE AG	HBSAG	HEPATITIS B SURF AG	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBSAG	HEPATITIS B SURF AG	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HBSINT	INTERPRETATION	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBSINT	INTERPRETATION	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HBTINT	INTERPRETATION	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBTINT	INTERPRETATION	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV1	HCV GENOTYPE 1	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV1S	HCV GENOTYPE 1 SUBTYPE	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV2	HCV GENOTYPE 2	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV2S	HCV GENOTYPE 2 SUBTYPE	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV3	HCV GENOTYPE 3	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV4	HCV GENOTYPE 4	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV5	HCV GENOTYPE 5	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV6	HCV GENOTYPE 6	2
-LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCVGTD	TEST DESCRIPTION	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HCVI	INTERPRETATION	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HCVI	INTERPRETATION	2
-LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVIPX	TEST DESCRIPTION	2
-LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVLOG	RESULT LOG 10	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HCVR	ANTI-HCV	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HCVR	ANTI-HCV	2
-LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVRES	RESULT	2
-LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVRNA	HEP C VIRVUS RNA	2
-LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVSP	SPECIMEN	2
-LABHELPM	HELICOBACTER PYLORI IGM AB	HELPM1	H. PYLORI IGM ANTIBODY	2
-LABHAVT	HEPATITIS A AB TOTAL	HEPAAB	HEPATITIS A TOTAL AB	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HEPAAB	HEPATITIS A TOTAL AB	2
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	HEPCAB	HEPATITIS C AB	2
-LABHIVWBL	HIV (TYPE 1 AND TYPE 2) ABY	HIVAB	HIV 1/2 ANTIBODY	2
-LABFHIVQA	"HIV-1 DNA, QUALITATIVE PCR"	HIVQA	"HIV-1 DNA, QUAL, PCR"	2
-LABHUNDNA	HUNTINGTON\'S DNA	HNDNA	HUNTINGTON\'S DNA	2
-LABHPVPCR	HPV 16/18 TYPING	HPV16	HPV 16 DNA	2
-LABHPVPCR	HPV 16/18 TYPING	HPV18	HPV 18 DNA	2
-LABHPVPCR	HPV 16/18 TYPING	HPVDI	DISCLAIMER	2
-LABHPVPCR	HPV 16/18 TYPING	HPVINT	INTERPRETATION	2
-LABHPVPCR	HPV 16/18 TYPING	HPVMT	METHOD	2
-LABHPVPCR	HPV 16/18 TYPING	HPVSP	SPECIMEN	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HQLINT	INTERPRETATION	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	2
-LAB11137R	"HSV AB, IMMUNOBLOT"	HSVWB	HSV WESTERN BLOT	2
-LABDHTLVQ	"DONOR, HTLV-I/II AB SCN"	HTLVQ1	"DONOR, HTLV-I/II AB SCN"	2
-LAB11136R	HTLV-1 AB SCREEN	HTLVS	HTLV AB SCREEN	2
-LABLIVER	PRE LIVER TRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	2
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRVVDEL	BRADY VV DELAY	2
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	2
-LABICTO	ICTOTEST	ICTO	ICTOTEST	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	IMPDATE	IMPLANT DATE	2
-LABYFK	"POTASSIUM, FLUID"	KFLD	POTASSIUM BODY FLUID	2
-LABSTDY2	STUDY TEST	LABS2	LABS2	2
-POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	LAC	"LACTATE, ISTAT"	2
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADALERT	LEAD ALERT	2
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVBRPACA	LV BRADY PACING AMPLITUDE	2
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVBRPACW	LV BRADY PACING PULSE WIDTH	2
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	2
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	2
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVTHRAMP	LV THRESHOLD AMPLITUDE	2
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVTHRDUR	LV THRESHOLD DURATION	2
-LABCD3B	"CD3, BLOOD"	LYM	LYMPHOCYTES %	2
-LABMEARC	MEASLES RAPID CULTURE	MEARC1	MEASLES RAPID CULTURE	2
-LABMEARC	MEASLES RAPID CULTURE	MEARCD	TEST DESCRIPTION	2
-LABMEARC	MEASLES RAPID CULTURE	MEASPM	SPECIMEN	2
-LABMELAS	MELAS MTDNA PROFILE	MELADN	MELAS MTDNA PROFILE	2
-LABYMERC	"MERCURY, BLOOD"	MER	"MERCURY, BLOOD"	2
-LABYMETHS	METHSUXIMIDE	METHSX	METHSUXIMIDE	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	MFR	MANUFACTURER	2
-LABSALCOR	"CORTISOL, SALIVA"	MN1COR	1ST MIDNIGHT SAMPLE	2
-LABSALCOR	"CORTISOL, SALIVA"	MN2COR	2ND MIDNIGHT SAMPLE	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	MOD	MODEL	2
-LABDVEMP	MPV DFA	MPIP	TEST DESCRIPTION	2
-LABYMPO	MYELOPERIOXIDASE AB	MPO	MYELOPEROXIDASE AB	2
-LABDVEMP	MPV DFA	MPSP	SPECIMEN	2
-LABDVEMP	MPV DFA	MPVR	METAPNEUMOVIRUS	2
-LABPANANC	"NEUTROPHIL COUNT, ABSOLUTE"	NEUTAB	"NEUT, ABS"	2
-LABCBCD	CBC WITH DIFF	NEUTPC	NEUTROPHIL %	2
-LABYNICK	"NICKEL, SERUM"	NICKEL	NICKEL	2
-LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKIN	INTERPRETATION	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	NOREPHURN	"NOREPINEPHRINE, URN"	2
-LABFLUOX	FLUOXETINE AND NORFLUOXETINE	NORFLU	NORFLUOXETINE	2
-LABYMETHS	METHSUXIMIDE	NORMSX	NOR-METHSUXIMIDE	2
-LABOSTLC	"OLIGOSACCHARIDES TLC, URINE"	OSTLC	"OLIGOSAC, UR, TLC"	2
-CRP139	ICD GENERATOR AND LEAD INS/REP DUAL	PDF	PDF RESULT	2
-EKG6	ECG SIGNAL AVERAGED	PDF	PDF RESULT	2
-IMG11BXMT	CAR MYOCARD BX I TX	PDF	PDF RESULT	2
-IMG13RHC	PED RT HEART CATH	PDF	PDF RESULT	2
-PFT50	"ROUTINE STDY - ABG, SPIRO W/BDR,DLCO,VOL"	PDF	PDF RESULT	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PHA	PHA STIMULATION	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PHAINT	PHA INTERPRETATION	2
-LABYPHOLP	PHOSPHOLIPIDS	PHOLIP	PHOSPHOLIPIDS	2
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMALERT	PM ALERT	2
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMLVLDBIIM	PM LV LEAD BIPOLAR IMPEDANCE	2
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMLVLDUNIM	PM LV LEAD UNIPOLAR IMPEDANCE	2
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRALDUNIM	PM RA LEAD UNIPOLAR IMPEDANCE	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRALDUNIM	PM RA LEAD UNIPOLAR IMPEDANCE	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRVLDUNIM	PM RV LEAD UNIPOLAR IMPEDANCE	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE14	S.PNEUMO SEROTYPE 14 (14)	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE19	S.PNEUMO SEROTYPE 19 (19F)	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE23	S.PNEUMO SEROTYPE 23 (23F)	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE26	S.PNEUMO SEROTYPE 26 (6B)	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE4	S.PNEUMO SEROTYPE 4 (4)	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE56	S.PNEUMO SEROTYPE 56 (18C)	2
-LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE68	S.PNEUMO SEROTYPE 68 (9V)	2
-LABPABN	"POLIO AB, NEUTRALIZATION"	POL1	POLIO 1	2
-LABPABN	"POLIO AB, NEUTRALIZATION"	POL2	POLIO 2	2
-LABPABN	"POLIO AB, NEUTRALIZATION"	POL3	POLIO 3	2
-LABTBP94	TRANSFUSION BLOOD PRODUCT 95	PRO	PRODUCT ID  	2
-LABPROPAF	PROPAFENONE	PROPAF	PROPAFENONE	2
-LABPSACX	PSA WITH REFLEX COMPLEXED PSA	PSA	PSA	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PWM	PWM STIMULATION	2
-LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PWMINT	PWM INTERPRETATION	2
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	RBCAST	RBC CASTS	2
-LABRBP	RETINOL BINDING PROTEIN	RBPR	RETINOL BIND PROT	2
-LAB13006R	"REDUCING SUBSTANCE, STOOL"	REDSUBSTL	"REDUCING SUBST, STOOL"	2
-LABDATLA	DIRECT ANTIGLOBULIN TEST - LARC	REQS	REQUEST SENT	2
-LABSTDY2	STUDY TEST	RESS2	RESS2	2
-LABMISCH	MISCELLANEOUS HEMATOLOGY	RESULH	RESULT	2
-LABIMM2	IMMUNOLOGY PANEL 2	RHFINT	INTERPRETATION	2
-LABIMM2	IMMUNOLOGY PANEL 2	RHFR	RHEUMATOID FACTOR	2
-10040R	HLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)	RINFO	TO FIND RESULT:	2
-10085R	HLA ORDER - TYPING ADD ON LOCUS	RINFO	TO FIND RESULT:	2
-10175R	HLA ORDER - KIT PROCESSING RECIPIENT	RINFO	TO FIND RESULT:	2
-LABDRPRS	DONOR SYPHILIS SCREEN	RPRS1	DONOR SYPHILIS SCREEN	2
-LABDRPRS	"DONOR, SYPHILIS SCREEN"	RPRS1	DONOR SYPHILIS SCREEN	2
-LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	RPT	RPT	2
-LABINFO	MICROBIOLOGY INFORMATION ONLY	RPT	RPT	2
-LABISOBR	ISOLATOR BRUCELLA	RPT	RPT	2
-LABROACAN	ACANTHAMOEBA CULTURE	RPT	RPT	2
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	2
-LABSCLABY	ANTI-SCL70 ANTIBODY	SCLQNT	ANTI-SCL70 LEVEL	2
-LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	SDES	SPECIMEN	2
-LABINFO	MICROBIOLOGY INFORMATION ONLY	SDES	SPECIMEN	2
-LABISOBR	ISOLATOR BRUCELLA	SDES	SPECIMEN	2
-LABROACAN	ACANTHAMOEBA CULTURE	SDES	SPECIMEN	2
-210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	SERNUM	SERIAL NUMBER	2
-LABTBP94	TRANSFUSION BLOOD PRODUCT 95	SI	<<STATUS>>  	2
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	SIGNIF	SIGNIFICANT ABNORMALITIES	2
-LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	SIGNIF	SIGNIFICANT ABNORMALITIES	2
-LABSMABY	AUTOANTIBODIES TO SM	SMAB	AUTOANTIBODIES TO SM	2
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SOTHER	"OTHERS, SYN FLD"	2
-LABBKVPC	BK VIRUS DNA QUANTITATION	SOURC6	SOURCE	2
-LABTBP43	TRANSFUSION BLOOD PRODUCT 44	SPEXP	SPECIMEN EXPIRATION DATE/TIME	2
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	SPEXP	SPECIMEN EXPIRATION DATE/TIME	2
-LABSTDY2	STUDY TEST	SPTYS2	SPTYS2	2
-LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	SREQ	COMMENTS	2
-LABINFO	MICROBIOLOGY INFORMATION ONLY	SREQ	COMMENTS	2
-LABISOBR	ISOLATOR BRUCELLA	SREQ	COMMENTS	2
-LABROACAN	ACANTHAMOEBA CULTURE	SREQ	COMMENTS	2
-LABSTSS	SYPHILIS TREPONEMAL SCREEN	STSQNT	INDEX	2
-LABSULFAT	SULFATIDE AUTOANTIBODY	SULFAB	SULFATIDE AUTOANTIBODY	2
-LABMISCH	MISCELLANEOUS HEMATOLOGY	TESTH	PROCEDURE	2
-LABSTDY2	STUDY TEST	TESTS2	TESTS2	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	TIMEU	COLLECTION PERIOD	2
-LABTOXIM	TOXOPLASMA IGM	TOXMQT	IGM LEVEL	2
-LAB11121R	TOXOPLASMA IGG DYE TEST	TOXOD	TOXOPLASMA DYE TEST	2
-LABTOXOM	TOXOPLASMOSIS IGM	TOXOM	TOXOPLASMA IGM ELISA	2
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	TRICHOMONAS	TRICHOMONAS	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15CO	"COMMENT, TISSUE"	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15DI	DISCLAIMER	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15DX	CLINICAL INDICATION	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15MD	INTERPRETED BY	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15ME	METHOD	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15RE	"RESULT, TISSUE"	2
-LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15TI	"SOURCE, TISSUE"	2
-POC39	POC URINE FOR PH	UBLOOD	BLOOD URINE	2
-LABTBP88	TRANSFUSION BLOOD PRODUCT 89	UBT	UNIT BLOOD TYPE  	2
-LABTBP89	TRANSFUSION BLOOD PRODUCT 90	UBT	UNIT BLOOD TYPE  	2
-LABTBP90	TRANSFUSION BLOOD PRODUCT 91	UBT	UNIT BLOOD TYPE  	2
-LABTBP91	TRANSFUSION BLOOD PRODUCT 92	UBT	UNIT BLOOD TYPE  	2
-LABTBP92	TRANSFUSION BLOOD PRODUCT 93	UBT	UNIT BLOOD TYPE  	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UCATIN	"CATECHOL INTRP , URN"	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UCR	"CREATININE, URINE"	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UCR	"CREATININE, URN"	2
-LABUHIST	"HISTAMINE, URINE"	UCR	"CREATININE, URN"	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UDOPA1	DOPAMINE/CREAT. RATIO	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UEPI1	EPINEPHRINE/CREAT. RATIO	2
-POC44	POC URINE DIPSTICK GP	UGLU	GLUCOSE URINE	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UNORE1	NOREPINEPHRINE/CREAT. RATIO	2
-LABTBP88	TRANSFUSION BLOOD PRODUCT 89	UNT	UNIT NUMBER  	2
-LABTBP89	TRANSFUSION BLOOD PRODUCT 90	UNT	UNIT NUMBER  	2
-LABTBP90	TRANSFUSION BLOOD PRODUCT 91	UNT	UNIT NUMBER  	2
-LABTBP91	TRANSFUSION BLOOD PRODUCT 92	UNT	UNIT NUMBER  	2
-LABTBP92	TRANSFUSION BLOOD PRODUCT 93	UNT	UNIT NUMBER  	2
-POC44	POC URINE DIPSTICK GP	UPROT	PROTEIN URINE	2
-LABUHIST	"HISTAMINE, URINE"	UT	COLLECTION PERIOD	2
-LABUFCATE	"CATECHOLAMINES, URN FREE"	UVOL	VOLUME	2
-LABBMTP	BMT PRETRANSPLANT PANEL	VGGDES	VZV IGG TEST DESCRIPTION	2
-LABBMTP	BMT PRETRANSPLANT PANEL	VGGQUL	VZV IGG RESULT	2
-LAB11060R	"VITAMIN B1, PLASMA"	VITB1	"VITAMIN B1, PLASMA"	2
-LABTBP39	TRANSFUSION BLOOD PRODUCT 40	VL	PRODUCT VOLUME	2
-LABTBP41	TRANSFUSION BLOOD PRODUCT 42	VL	PRODUCT VOLUME	2
-LABTBP44	TRANSFUSION BLOOD PRODUCT 45	VL	PRODUCT VOLUME	2
-LABTBP60	TRANSFUSION BLOOD PRODUCT 61	VL	PRODUCT VOLUME	2
-LABUHIST	"HISTAMINE, URINE"	VOL	VOLUME	2
-LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	VOLUMEURN	URINE VOLUME	2
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	XMR	UNIT CROSSMATCH  	2
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	XMR	UNIT CROSSMATCH  	2
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	XMR	UNIT CROSSMATCH  	2
-LABTBP86	TRANSFUSION BLOOD PRODUCT 87	XMR	UNIT CROSSMATCH  	2
-LABTBP87	TRANSFUSION BLOOD PRODUCT 88	XMR	UNIT CROSSMATCH  	2
-LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	YA1A2	ALPHA-1-ANTITRYPSIN	2
-LABYBABE	BABESIA MICROTI IGG AND IGM AB	YBAB1	"BABESIA MICROTI, IGG"	2
-LABYBABE	BABESIA MICROTI IGG AND IGM AB	YBAB2	"BABESIA MICROTI, IGM"	2
-LABYBABE	BABESIA MICROTI IGG AND IGM AB	YBAB3	INTERPRETATION	2
-LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	YDR1	"DRUG SCREEN, PRESCRIP/OTC"	2
-LABEVPCR	ENTEROVIRUS PCR	YENP1	SPECIMEN SOURCE	2
-LABEVPCR	ENTEROVIRUS PCR	YENP2	"ENTEROVIRUS ,PCR"	2
-LABEVPCR	ENTEROVIRUS PCR	YENP3	SPECIAL INFORMATION	2
-LABEVPCR	ENTEROVIRUS PCR	YENP4	REPORT STATUS	2
-LABYFK	"POTASSIUM, FLUID"	YFLSC1	FLUID TYPE	2
-LABYHISTA	"HISTAMINE, PLASMA"	YHISA1	"HISTAMINE, PLASMA"	2
-LABYMEM	"MEASLES AB, IGM"	YMEM1	"MEAS(RUBEOLA) AB,IGM"	2
-LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	YOXALU	"OXALATE, 24-HOUR/TIMED URINE"	2
-LABYP4AB	PARAINFLUENZA AB	YP4AB1	TYPE 1	2
-LABYP4AB	PARAINFLUENZA AB	YP4AB2	TYPE 2	2
-LABYP4AB	PARAINFLUENZA AB	YP4AB3	TYPE 3	2
-LABYPANPP	PANCREATIC POLYPEPTIDE	YPANPP	PANCREATIC POLYPEPTIDE	2
-LAB11089R	ACID PHOSPHATASE	YPAPT	"PAP, SERUM"	2
-LABYPERAB	BORDETELLA PERTUSSIS IGG AND IGM AB	YPERA1	"B PERTUSSIS AB, IGG"	2
-LABYPERAB	BORDETELLA PERTUSSIS IGG AND IGM AB	YPERA2	"B PERTUSSIS AB, IGM"	2
-LABYRETAB	RETICULIN AB	YRET1	RETICULIN AB	2
-LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG3	COLLECTION DURATION	2
-LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG4	CONCENTRATION	2
-LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL2	TOTAL WEIGHT	2
-LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL3	COLLECTION DURATION	2
-LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL4	CHLORIDE CONCENTRATION	2
-LABYTICK	COLORADO TICK FEVER AB	YTICK1	IGG AB	2
-LABYTICK	COLORADO TICK FEVER AB	YTICK2	IGM AB	2
-LABYTICK	COLORADO TICK FEVER AB	YTICK3	INTERPRETATION	2
-LABYUNTX	"TELOPEPTIDE, URINE"	YUNTXC	"CREATININE, URINE"	2
-LABYUNTX	"TELOPEPTIDE, URINE"	YUNTXR	NTX	2
-LABYUNTX	"TELOPEPTIDE, URINE"	YUNTXT	"NTX-TELOPEPTIDE, URINE"	2
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUOAGC	OTHER AMPHETAMINES	2
-LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUOPCO	COMMENT	2
-LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	YUOXCO	OXALATE CONCENTRATION	2
-LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	YUOXUT	COLLECTION DURATION	2
-LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUSYGC	SYMPATHOMIMETICS	2
-LAB12586R	VITAMIN B1	YVB1W1	THIAMIN(VITAMIN B1)	2
-LAB12586R	VITAMIN B1	YVB1W2	COMMENT	2
-10010R	HLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	1
-10120R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)	10049R	HLA LAB TESTING STATUS	1
-10165R	HLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	1
-10225R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	10049R	HLA LAB TESTING STATUS	1
-10285R	HLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)	10049R	HLA LAB TESTING STATUS	1
-10297R	HLA ORDER - DECEASED DNR O.R. SPMN	10049R	HLA LAB TESTING STATUS	1
-10335R	HLA - SCREENING C1Q	10471R	SAFE ANTIGENS C1Q	1
-10545R	HLA - CD31 TESTING SUMMARY	10478R	CD31 TESTING SUMMARY	1
-POC39	POC URINE FOR PH	11147R	SPECIFIC GRAVITY	1
-POC1	POC URINE DIPSTICK	11148R	PH	1
-POC14	POC GLUCOSE BY METER	11148R	PH	1
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	11165R	EB VIRUS EARLY AG (R+D) IGG	1
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	11166R	EB NUCLEAR AB	1
-LAB11173R	IODINE	11174R	IODINE	1
-LAB11195R	"N-TELOPEPTIDE, CROSS-LINKED"	11196R	"N-TELOPEPTIDE, CROSS-LINKED"	1
-LAB11219R	"IODINE,U"	11220R	"IDODINE, U"	1
-LAB11219R	"IODINE,U"	11221R	COLLECTION DURATION	1
-LAB11219R	"IODINE,U"	11222R	URINE VOLUME	1
-LAB11219R	"IODINE,U"	11223R	IODINE CONCENTRATION	1
-LAB11225R	"LEAD,U"	11226R	COLLECTION DURATION	1
-LAB11225R	"LEAD,U"	11227R	URINE VOLUME	1
-LAB11225R	"LEAD,U"	11228R	PB CONC	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11255R	ORGANISM	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11256R	AMIKACIN	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11257R	CIPROFLOXACIN	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11258R	CLARITHROMYCIN	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11259R	ETHAMBUTOL	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11260R	LINEZOLID	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11261R	MOXIFLOXACIN	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11262R	RIFAMPIN	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11263R	RIFABUTIN	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11264R	STREPTOMYCIN	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11300R	CHLORPROPAMIDE	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11301R	TOLAZAMIDE	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11302R	TOLBUTAMIDE	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11303R	GLYBURIDE	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11304R	GLIPIZIDE	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11305R	ACETOHEXAMIDE	1
-LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11306R	SULFONYLUREA SCRN	1
-LAB11347R	C-TELOPEPTIDE	11348R	C-TELOPEPTIDE	1
-LAB12689R	SERM INFLIXIMAB/HACA MEASUREMT	11500R	SERM INFLIXIMAB/HACA MEASUREMT	1
-LAB11528R	PERIPHERAL NEUROPATHY AB SCN	11638R	PERIPHERAL NEUROPATHY AB SCN	1
-LAB11533R	SENSORIMOTOR SCREEN AB	11643R	SENSORIMOTOR SCREEN AB	1
-LAB11540R	OTC SEQUENCING	11650R	OTC SEQUENCING	1
-LAB11554R	"S-SULFOCYSTEINE, URINE"	11664R	"S-SULFOCYSTEINE, URINE"	1
-LAB12016R	"CYTOMEGALOVIRUS IGG AB, ELISA"	11667R	CYTOMEGALOVIRUS IGG	1
-LAB11570R	SPINAL MUSC ATROPHY CARRIER	11685R	SPINAL MUSC ATROPHY CARRIER	1
-LAB11573R	CONGENITAL CYCLIC NEUTROPENIA	11688R	CONGENITAL CYLIC NEURTOPENIA	1
-LAB11592R	"COPPER, TISSUE"	11720R	"COPPER, LIVER TS"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11723R	"LYMPHOCYTE PROLIFERATION, AG"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11724R	"CANDIDA ALBICANS, DILUTION 1"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11727R	"CANDIDA ALBICANS, DILUTION 4"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11728R	"INTERP, CANDIDA ALBICANS"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11729R	"TETANUS TOXOID, DILUTION 1"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11731R	"TETANUS TOXOID, DILUTION 3"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11732R	"TETANUS TOXOID, DILUTION 4"	1
-LAB11596R	"LYMPHO PROLIFERATION, AG"	11733R	"INTERP, TETANUS TOXOID"	1
-LAB11599R	GANICICLOVIR	11756R	GANCICLOVIR	1
-LAB11604R	IL 5	11757R	INTERLEUKIN 5	1
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11836R	SOURCE	1
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11841R	AMENDMENT	1
-LABYFCL	"CHLORIDE, FLUID"	11844R	FLUID TYPE	1
-LAB11878R	CORTISOL BINDING GLOBULIN	11877R	CORTISOL BIND.GLOB(CBG)	1
-LABDHIVAB	"DONOR,HIV-1/HIV-2 AB SCN W/RFL"	11888R	"DONOR, HIV-1/2 AB, CONF."	1
-LABDHBAG	"DONOR, HEP B SURFACE AG"	11891R	"DONOR, HBSAG CONFIRMATION"	1
-LABDRPR	"DONOR, RPR W/RFL TO SYPH IGG"	11892R	"DONOR, SYPHILIS IGG"	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11897R	ETHIONAMIDE	1
-LAB11254R	M AVIUM COMPLEX MIC PANL	11898R	ISONIAZID	1
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	12001R	"VCA IGG ,IFA"	1
-LABSTAND	STANDARD PRE TRANSPLANT PANEL	12002R	"VCA IGM ,IFA"	1
-LAB12003R	"HERPESVIRUS 6 AB, IGG"	12004R	HERPESVIRUS 6 IGG	1
-LAB13007R	Q FEVER IGG PHASE I TITER	12012R	Q FEVER IGG PHASE L TITER	1
-LAB13009R	Q FEVER IGM PHASE I TITER	12013R	Q FEVER IGM PHASE L TITER	1
-LAB13010R	Q FEVER IGM PHASE II TITER	12015R	Q FEVER IGM PHASE LI TITER	1
-LAB12023R	MYCOPLASMA PNEUMONIA IGG AB	12024R	MYCOPLASMA PNEUMONIA AB IGG	1
-LAB11604R	IL 5	12044R	INTERLEUKIN 5	1
-LABYBUPRO	"BUPROPION, SERUM"	12045R	HYDROXYBUPROPION	1
-LAB11591R	"ARSENIC, HAIR"	12046R	"ARSENIC, HAIR"	1
-LAB11591R	"ARSENIC, HAIR"	12047R	SPECIMEN SOURCE	1
-LAB12055R	RMSF IGG TITER	12056R	RMSF IGG TITER	1
-LAB12059R	R. TYPHI IGG TITER	12060R	R.TYPHI IGG TITER	1
-LAB12095R	"DARUNAVIR,ANTIRETROVIRAL LEVEL"	12101R	"DARUNAVIR,ANTIRETROVIRAL LEVEL"	1
-LAB11225R	"LEAD,U"	12199R	"LEAD, 24 HR, U"	1
-LAB12220R	MSUD MUTATION ANALYSIS	12221R	12221R	1
-LAB12279R	CVID CONFIRMATION FLOW PANEL	12280R	12280R	1
-LAB12279R	CVID CONFIRMATION FLOW PANEL	12281R	12281R	1
-LAB12279R	CVID CONFIRMATION FLOW PANEL	12282R	12282R	1
-LAB12328R	AMYLOID PROTEIN IDENTIFICATION	12327R	"AMYLOID PROTEIN ID, TISSUE"	1
-LAB12494R	"PATH CONSULT, OUTSIDE SLIDE"	12378R	"PATH CONSULT, OUTSIDE SLIDE"	1
-LAB12470R	"STAIN, CONGO RED"	12379R	"CONGO RED, STAIN"	1
-LAB12415R	ACVRL1 AND ENG (HHT)	12416R	HHT FGA SPECIMEN	1
-LABCLAMP	CHLAMYDIA AB PANEL	12428R	INTERPRETATION	1
-LABCLAMP	CHLAMYDIA AB PANEL	12429R	INTERPRETATION	1
-LABCLAMP	CHLAMYDIA AB PANEL	12430R	INTERPRETATION	1
-LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	12431R	INTERPRETATION	1
-LAB12443R	NEO PARANEOPLASTIC PRFL	12444R	NEO PARANEOPLASTIC PRFL	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12472R	SPECIMEN	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12473R	ADDENDUM COMMENT	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12474R	ACCESSION NUMBER	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12475R	REFERRING PATH-PHYSICIAN	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12476R	REF PATH-PHYS ADDRESS	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12477R	PLACE OF DEATH	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12478R	DATE AND TIME OF DEATH	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12479R	DATE OF AUTOPSY	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12480R	MATERIAL	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12481R	TISSUE DESCRIPTION	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12482R	MICROSCOPIC DESCRIPTION	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12483R	CLINICAL HISTORY	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12484R	FINAL DIAGNOSIS	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12485R	FINAL DIAGNOSIS	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12486R	COMMENT	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12487R	REVISION DESCRIPTION	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12488R	SIGNING PATHOLOGIST	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12489R	SPECIAL PROCEDURES	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12490R	SP SIGNING PATHOLOGIST	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12491R	PREVIOUS REPORT FOLLOWS	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12492R	ADDENDUM	1
-LAB12471R	SURGICAL PATHOLOGY CONSULT	12493R	ADDENDUM PATHOLOGIST	1
-LAB12512R	VON HIPPEL-LINDAU SYNDROME	12513R	VON HIPPEL-LINDAU SYNDROME	1
-LAB12580R	CU INDEX PANEL	12589R	CU INDEX PANEL	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12590R	S.PNEUMO SEROTYPE 1 (1)	1
-LAB12584R	TIAGABINE CONCENTRATION	12593R	TIAGABINE CONCENTRATION	1
-LAB11098R	"1,25-DIHYDROXYVITAMIN D, SERUM"	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	1
-LAB11098R	PEDIATRIC 1 25 OH VITAMIN D	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	1
-LAB12643R	BRUTON\'S TYROSINE KINASE (BTK)	12642R	BTK PROTEIN FLOW	1
-LABYUALA	"AMINOLEVULINIC ACID, URINE"	12682R	REVIEWED BY	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12699R	S.PNEUMO SEROTYPE 3 (3)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12700R	S.PNEUMO SEROTYPE 4 (4)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12701R	S.PNEUMO SEROTYPE 5 (5)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12702R	S.PNEUMO SEROTYPE 6 (6A)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12703R	S.PNEUMO SEROTYPE 14 (14)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12704R	S.PNEUMO SEROTYPE 19 (19F)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12705R	S.PNEUMO SEROTYPE 23 (23F)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12706R	S.PNEUMO SEROTYPE 26 (6B)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12707R	S.PNEUMO SEROTYPE 51 (7F)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12708R	S.PNEUMO SEROTYPE 56 (18C)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12709R	S.PNEUMO SEROTYPE 57 (19A)	1
-LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12710R	S.PNEUMO SEROTYPE 68 (9V)	1
-LAB12761R	HEPATITIS B VIRUS GENOTYPE	12757R	HBV GENOTYPE	1
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	12769R	TRI-SIALO/DI-OLIGO RATIO	1
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	12770R	APO CIII-1/CIII-2 RATIO	1
-LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	12771R	APO CIII-0/APO CIII-2 RATIO	1
-LAB12772R	ENCEPHALITIS AB SCREEN	12773R	ENCEPHALITIS AB SCREEN	1
-LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	12782R	REVIEWED BY	1
-LAB12802R	HEPARIN COFACTOR	12801R	HEPARIN COFACTOR II	1
-LAB12817R	"DONOR, HCV AB SCREEN W/RFLX"	12828R	"DONOR, HEPATITIS C AB"	1
-LAB12813R	"PARVOVIRUS B19 AB, IGG"	12835R	PARVOVIRUS B19 AB IGG	1
-LAB12845R	"VMA AND HVA, PEDIATRIC, URINE"	12843R	CHILD VMA	1
-LAB12845R	"VMA AND HVA, PEDIATRIC, URINE"	12844R	CHILD HVA	1
-LABY17OHP	"17 - HYDROXYPROGESTERONE, SERUM"	17OHPROG	17-OH PROGESTERONE	1
-10290R	HLA - TYPING	9035R	MICA ALLELE 1	1
-10290R	HLA - TYPING	9036R	MICA ALLELE 2	1
-10295R	HLA - AUTO T FLOW CROSSMATCH	9099R	INTERPRETATION/ COMMENTS	1
-10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9342R	DSA FOR CLASS II	1
-10355R	HLA - MONITORING BY IGG	9361R	INTERPRETATION/ COMMENTS CLASS I	1
-10355R	HLA - MONITORING BY IGG	9367R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	1
-LAB12733R	ALPHA-2-ANTIPLASMIN	A2A	ALPHA-2-ANTIPLASMIN	1
-LAB11028R	"ANGIOTENSIN CONVERTING ENZYME, CSF"	ACE	"ANGIOTENSIN COVERT ENZ, CSF"	1
-LABCACE	"ANGIOTENSIN CONVERTING ENZYME, CSF"	ACE	"ANGIOTENSIN COVERT ENZ, CSF"	1
-LABYCHRBC	"ACETYLCHOLINESTERASE, RBC"	ACHERBC	"ACETYLCHOLINESTERASE, RBC"	1
-LAB11269R	ACETYL RECEPTOR AB PANL	ACRBIN	ACETYLCHOLINE BIN AB	1
-LAB11269R	ACETYL RECEPTOR AB PANL	ACRBLO	ACETYLCHOLINE BLOCKING AB	1
-LAB11269R	ACETYL RECEPTOR AB PANL	ACRMOD	ACETYLCHOLINE MODULATING AB	1
-LABADPCRQ	ADENOVIRUS PCR QUANT	ADPCR1	ADENVIRUS DNA	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	AFIBTAQRSM	AFIB TACHY ZONE QRS MORPHOLOGY CRITERIA	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	AFLTAQRSMC	AFLUTTER TACHY ZONE QRS MORPHOLOGY CRITERIA	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	AFLTAZNRTC	AFLUTTER TACHY ZONE RATE STABILITY CRITERIA	1
-LABCBCD	CBC WITH DIFF	ALB	"ALBUMIN, SER/PLAS"	1
-LABHEPAT	HEPATIC PROFILE	ALB	"ALBUMIN, SER/PLAS"	1
-LABBCAA	BRANCHED CHAIN AMINO ACIDS	ALCN	ALLO-ISOLEUCINE	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	ALCOHOL	ALCOHOL	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ALERT	ALERT	1
-LABCBCD	CBC WITH DIFF	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	1
-LABHEPAT	HEPATIC PROFILE	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	1
-LABMALL2	MISCELLANEOUS ALLERGEN 2	ALL2	ALLERGEN	1
-LABMALL3	MISCELLANEOUS ALLERGEN 3	ALL3	ALLERGEN	1
-LABMALL2	MISCELLANEOUS ALLERGEN 2	ALLR2	ALLERGEN RESULT	1
-LABMALL3	MISCELLANEOUS ALLERGEN 3	ALLR3	ALLERGEN RESULT	1
-LABCBCD	CBC WITH DIFF	ALT	"ALT (SGPT), SER/PLAS"	1
-LABHEPAT	HEPATIC PROFILE	ALT	"ALT (SGPT), SER/PLAS"	1
-LABAMAONC	ANTI - MALIGNIN AB	AMA	ANTI-MALIGNIN AB	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	AMP	AMPICILLIN	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	AMP	AMPICILLIN	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	AMPHET	AMPHETAMINES	1
-LABYAMPHB	AMPHOTERICIN B	AMPHOTERB	AMPHOTERICIN B	1
-LABIMM1	IMMUNOLOGY PANEL 1	ANAQNT	TITER	1
-LABASCA	ASCARIASIS AB	ASCA	ASCARISIS	1
-LABASPR	"ASPERGILLUS AB, CF, S"	ASPR	ASPERILLUS-CF	1
-LABCBCD	CBC WITH DIFF	AST	"AST (SGOT), SER/PLAS"	1
-LABHEPAT	HEPATIC PROFILE	AST	"AST (SGOT), SER/PLAS"	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ATACHTAQRS	ATACH TACHY ZONE QRS MORPHOLOGY CRITERIA	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	ATM	AZTREONAM.	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	ATM	AZTREONAM.	1
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	BAND	BAND NEUTROPHILS	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	BANDFLD	"BAND, FLD"	1
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	BANDFLD	"BAND, FLD"	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	BARB	BARBITURATES	1
-LABADIFF	DIFFERENTIAL	BASO	BASOPHILS	1
-LABCBCD	CBC WITH DIFF	BASOAB	ABSOLUTE BASOPHILS	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	BASOFLD	"BASOPHILS, FLD"	1
-LABCBCD	CBC WITH DIFF	BASOSYN	"BASOPHILS, %"	1
-LABHBV	HBV GENOTYPE: S AND POL GENE	BCPMUT	BCP MUTATIONS	1
-POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	BE	"BASE EXCESS, ISTAT"	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	BENZO	BENZODIAZEPINES	1
-LAB12866R	B HENSELAE IGG TITER	BHIGGT	B.HENSELAE IGG TITER	1
-LABBKDNA	BK VIRUS DNA PCR	BKD	BK VIRUS DNA	1
-LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKQ	BKV DNA QUANTITATION	1
-LABADIFF	DIFFERENTIAL	BLASTS	BLASTS	1
-LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	BMAB	"BASEMENT MEMBRANE, IGG"	1
-LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQMU	MUTATION	1
-LABCBCD	CBC WITH DIFF	BUN	"UREA NITROGEN,SER/PLAS"	1
-LABC1INHF	C1 ESTERASE INHIBITOR FN	C1INHF	C1 ESTERASE INHIB FN	1
-LABC1Q	C1Q BINDING ASSAY	C1Q	C1Q BINDING ASSAY	1
-LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	C3AB	CD3 CD16+CD56 ABS	1
-LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	C3PER	CD3 CD16+CD56 %	1
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C48RAT	TCD4/CD8 RATIO	1
-LABCBCD	CBC WITH DIFF	CA	"CALCIUM, SER/PLAS"	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	CAIGG	CAL ENCEPH IGG	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	CAIGM	CAL ENCEPH IGM	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	CAINT	INTERPRETATION	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	CAION	"CALCIUM,ION, ISTAT"	1
-LAB11105R	CANDIDA AB	CANDP	CANDIDA PRECIPITINS	1
-LABFDIFF	"DIFFERENTIAL, BODY FLUID"	CCNT	CELLS COUNTED	1
-LABCCP	ANTI CCP	CCPT	ANTI CCP ANTIBODY LEVEL	1
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD19AB	"CD19 (PAN B), ABS."	1
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD19PC	CD19 (PAN B) %	1
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD4TOT	CD4 TOTAL	1
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1IN	INTERPRETATION	1
-LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1MU	MUTATION(S)	1
-LABSNA	SPECIFIC ANA PANEL	CENQNT	ANTI-CENTROMERE LEVEL	1
-LABCFPT	CF POLY T ANALYSIS	CFPTR2	RESULT	1
-LABCFPT	CF POLY T ANALYSIS	CFPTR3	RESULT	1
-LABITCH	FOOD ALLERGY SCREEN	CHICK	CHICKEN	1
-LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	CHOL	"CHOLESTEROL, TOTAL"	1
-LABLPD	LIPID PANEL. NON-FASTING PATIENT	CHOL	"CHOLESTEROL, TOTAL"	1
-LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	CHOLHDL	CHOLESTEROL/HDL RATIO	1
-LABLPD	LIPID PANEL. NON-FASTING PATIENT	CHOLHDL	CHOLESTEROL/HDL RATIO	1
-LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONDS	DISCLAIMER	1
-LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONDX	CLINICAL INDICATION	1
-LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONIN	INTERPRETATION	1
-LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONMD	INTERPRETED BY	1
-LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONMT	METHOD	1
-LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONRS	RESULT	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CIP	CIPROFLOXACIN	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CIP	CIPROFLOXACIN	1
-LABCBCD	CBC WITH DIFF	CL	"CHLORIDE, SER/PLAS"	1
-LABUA	URINALYSIS WITH MICROSCOPIC	CLNTST	CLINITEST	1
-LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	CLNTST	CLINITEST	1
-LABCLOMIP	CLOMIPRAMINE AND METABOLITE	CLOMI	CLOMIPRAMINE	1
-LABBMTP	BMT PRETRANSPLANT PANEL	CMGQNT	CMV IGG LEVEL	1
-LABBMTP	BMT PRETRANSPLANT PANEL	CMMQNT	CMV IGM LEVEL	1
-LABDCMVAB	"DONOR, CMV TOTAL ANTIBODIES"	CMVAB1	"DONOR, CMV TOTAL ANTIBODIES"	1
-LABCBCD	CBC WITH DIFF	CO2	"CO2, SER/PLAS"	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	COCAINE	COCAINE	1
-LAB11123R	"COCCIDIOIDES COMPLEMENT FIXATION, CSF"	COCC	COCCI CF AB (CSF)	1
-LAB11125R	COCCI ID (SERUM)	COCI	COCCI ID (SERUM)	1
-LABITCH	FOOD ALLERGY SCREEN	CORNY	CORN	1
-LABCEPLUS	CELIAC PLUS	CPLUS1	CELIAC PLUS RESULT	1
-LABCBCD	CBC WITH DIFF	CR	"CREATININE, SER/PLAS"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	CR	"CREATININE,ISTAT"	1
-POC17	POC ISTAT CREATININE	CR	"CREATININE,ISTAT"	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CRO	CEFTRIAXONE	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CRO	CEFTRIAXONE	1
-LABCR	"CREATININE, SERUM/PLASMA"	CRTNR	"CREATINE, PLASMA"	1
-LABCLAMP	CHLAMYDIA AB PANEL	CTIGA	C.TRACHOMATIS IGA	1
-LABCLAMP	CHLAMYDIA AB PANEL	CTIGG	C.TRACHOMATIS IGG	1
-LABCLAMP	CHLAMYDIA AB PANEL	CTIGM	C.TRACHOMATIS IGM	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CTN	CEFOTETAN	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CTN	CEFOTETAN	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CTX	CEFOTAXIME	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CTX	CEFOTAXIME	1
-LABIDWEE	WORM ID	CULT	CULT/OTHER RSLT:	1
-LABSHSV	HSV SHELL VIAL CULTURE	CULT	CULT/OTHER RSLT:	1
-LABVCULU	"VIRAL CULTURE, URINE"	CULT	CULT/OTHER RSLT:	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CZ	CEFAZOLIN	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CZ	CEFAZOLIN	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88DI	DISCLAIMER	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88DX	CLINICAL INDICATION	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88IN	INTERPRETATION	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88MD	INTERPRETED BY	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88ME	METHOD	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88RE	RESULT	1
-LABMYD88	MYD88 MUTATION ANALYSIS	D88RF	REFERENCES	1
-LABHEPAT	HEPATIC PROFILE	DBIL	CONJUGATED BILI	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	1
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	1
-LABTBP87	TRANSFUSION BLOOD PRODUCT 88	DI	ISSUE DATE/TIME	1
-LABTBP88	TRANSFUSION BLOOD PRODUCT 89	DI	ISSUE DATE/TIME	1
-LABTBP89	TRANSFUSION BLOOD PRODUCT 90	DI	ISSUE DATE/TIME	1
-LABTBP90	TRANSFUSION BLOOD PRODUCT 91	DI	ISSUE DATE/TIME	1
-LABTBP91	TRANSFUSION BLOOD PRODUCT 92	DI	ISSUE DATE/TIME	1
-LABTBP92	TRANSFUSION BLOOD PRODUCT 93	DI	ISSUE DATE/TIME	1
-LABTBP93	TRANSFUSION BLOOD PRODUCT 94	DI	ISSUE DATE/TIME	1
-LABCLOMIP	CLOMIPRAMINE AND METABOLITE	DMCLOM	DESMETHYLCLOMIPRAMIN	1
-LABDNAISO	"NUCLEIC ACID ISOLATION, DNA"	DNADAT	DATE OF DNA EXTRACTION	1
-LABYGDA	"DRUG SCREEN, FLUIDS"	DRUGSCRNFLD	"DRUG SCRN, GENERAL, BIOLOGICAL FL"	1
-LABDVEADV	DIRECT VIRAL EXAM ADENO	DVE	RESULTS	1
-LABDVEP1	DIRECT VIRAL EXAM PARA 1	DVE	RESULTS	1
-LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	DVE	RESULTS	1
-LABEBPCRQ	EBV PCR QUANTITATIVE	EBV	EBV BY PCR	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	EEIGG	E EQUINE ENCEP IGG AB	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	EEIGM	E EQUINE ENCEP IGM AB	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	EEINT	INTERPRETATION	1
-LABITCH	FOOD ALLERGY SCREEN	EGGW	EGG WHITE	1
-LABRBCENZ	RED CELL ENZYMES	ENO	ENOLASE	1
-LABCBCD	CBC WITH DIFF	EOSAB	ABSOLUTE EOSINOPHILS	1
-LABCBCD	CBC WITH DIFF	EOSFLD	"EOSINOPHILS, %"	1
-LABSTPOLY	"PMNS / EOSINOPHILS, STOOL"	EOSSTL	"EOSINOPHILS, STOOL"	1
-230986580	POC MENLO PPD TEST	ERYTHREMA	ERYTHREMA	1
-LABYESTRN	"ESTRONE, SERUM"	ESTRONE	ESTRONE	1
-LAB11034R	ETHOSUXIMIDE LEVEL	ETHSUX	ETHOSUXIMIDE	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FCMT	FLUID COMMENT	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FCOLOR	APPEARANCE	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	FEP	CEFEPIME	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	FEP	CEFEPIME	1
-LABEXCHTX	EXCHANGE TRANSFUSION	FFP	FRESH FROZEN UNITS	1
-LABFASAB	FASCIOLA HEPATICA AB	FHAB2	"F.HEPATICA ANTIBODY ,ELISA"	1
-LABITCH	FOOD ALLERGY SCREEN	FISH	FISH	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FLDTYP	FLUID SOURCE	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FMETA	"METAMYELOCYTE, FLD"	1
-LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	FP190	"P190, FLUID"	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FPMLR	"PML, FLD"	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FPMN	"PMN, FLD"	1
-LABBFCT	"CELL COUNT AND DIFF, FLUID"	FPMN	"PMN, FLD"	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FRLYM	"REACTIVE LYMPHS, FLD"	1
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTDETD	FASTVT TACHY ZONE DETECTION DURATION	1
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTZNSC	FASTVT TACHY ZONE SUDDEN ONSET CRITERIA	1
-210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTZNSC	FASTVT TACHY ZONE SUDDEN ONSET CRITERIA	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15CO	"COMMENT, FLD"	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15DI	DISCLAIMER	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15DX	CLINICAL INDICATION	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15FL	"SOURCE, FLUID"	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15MD	INTERPRETED BY	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15ME	METHOD	1
-LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15RE	"RESULT, FLD"	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FTURB	"TURBIDITY, FLD"	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	FURI	CEFUROXIME (IV)	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	FURI	CEFUROXIME (IV)	1
-LABGDMTOL	GDM GLUCOSE TOLERANCE	GDM1	1HR	1
-LABGDMTOL	GDM GLUCOSE TOLERANCE	GDM2	2HR	1
-LABGDMTOL	GDM GLUCOSE TOLERANCE	GDMINT	INTERPRETATION	1
-LABGHBUR	"GAMMA - HYDROXYBUTYRIC ACID, URINE"	GHBURN	"G-OH-BUTYRIC AC,UR"	1
-LABOGTT	GLUCOSE TOLERANCE FOR CYSTIC FIBROSIS	GLT1	"GLUCOSE, 1 HR"	1
-LABOGTT	GLUCOSE TOLERANCE FOR CYSTIC FIBROSIS	GLT2	"GLUCOSE, 2 HR"	1
-LABCBCD	CBC WITH DIFF	GLU	"GLUCOSE, SER/PLAS"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	GLU	"GLUCOSE,ISTAT"	1
-LABUGLT	"GLUCOSE, URINE (TIMED)"	GLUEX	GLUCOSE EXCRETION	1
-LABGDMTOL	GDM GLUCOSE TOLERANCE	GLUF	"GLUCOSE, FASTING (GDMF)"	1
-LABUGLT	"GLUCOSE, URINE (TIMED)"	GLUURN	"GLUCOSE, URINE"	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	GM	GENTAMICIN	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	GM	GENTAMICIN	1
-LABRBCENZ	RED CELL ENZYMES	GPILS	"GPI, LOW SUBSTRATE"	1
-LABVP5	COMP HEPATITIS PANEL	HAGINT	INTERPRETATION	1
-LABVP5	COMP HEPATITIS PANEL	HAGR	ANTI HAV IGG	1
-LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	HAIRY	HAIRY CELLS	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HATINT	INTERPRETATION	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	1
-LABDHBAG	"DONOR, HEP B SURFACE AG"	HBAG1	"DONOR, HEP B SURFACE AG"	1
-LABBMTP	BMT PRETRANSPLANT PANEL	HBCAB	HEP B CORE TOTAL AB	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCAB	HEP B CORE TOTAL AB	1
-LABHBCT	ANTI-HBC	HBCAB	HEP B CORE TOTAL AB	1
-LABDHBCAB	"DONOR, HEP B CORE TOTAL AB"	HBCAB1	"DONOR, HEP B CORE TOTAL AB"	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCABIGM	HEPAT.B CORE AB-IGM	1
-LAB11107R	HEPATITIS B E AG	HBEAG	HEPATITIS B E AG	1
-LABHBSQT	HEPATITIS B SURFACE AB QUANTITATIVE	HBSAB	"HBS ANTIBODY, S"	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAB	HEPATITIS B SURFACE AB QUAL	1
-LABHBSQL	ANTI-HBS QUAL	HBSAB	HEPATITIS B SURFACE AB QUAL	1
-LABHBSQT	HEPATITIS B SURFACE AB QUANTITATIVE	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	1
-LABBMTP	BMT PRETRANSPLANT PANEL	HBSAG	HEPATITIS B SURF AG	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAG	HEPATITIS B SURF AG	1
-LABHBSAG	HBSAG	HBSAG	HEPATITIS B SURF AG	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAIN	INTERPRETATION	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAQL	HEP B SURF AB QUAL	1
-LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAQT	HEP B SURF AB QUANT	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBSINT	INTERPRETATION	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBTINT	INTERPRETATION	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	1
-LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCGG	HCV GENOTYPE	1
-POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	HCO3V	"HCO3 (V), ISTAT"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	HCT	"HCT(CALC), ISTAT"	1
-LABCBCD	CBC WITH DIFF	HCT	HEMATOCRIT (MANUAL ENTRY)	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HCVI	INTERPRETATION	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HCVR	ANTI-HCV	1
-LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	HDL	HDL CHOLESTEROL	1
-LABLPD	LIPID PANEL. NON-FASTING PATIENT	HDL	HDL CHOLESTEROL	1
-LABHDLGGE	HDL GRADIENT GEL ELECTROPHORESIS	HDLGE	HDL GRAD GEL ELECTRO	1
-LABBMTP	BMT PRETRANSPLANT PANEL	HEPAAB	HEPATITIS A TOTAL AB	1
-LABBMTP	BMT PRETRANSPLANT PANEL	HEPCAB	HEPATITIS C AB	1
-LABCR	"CREATININE, SERUM/PLASMA"	HGB	HEMOGLOBIN	1
-LABCBCD	CBC WITH DIFF	HGB	HEMOGLOBIN (MANUAL ENTRY)	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	HGB	"HGB(CALC), ISTAT"	1
-POC32	POC HEMOCUE FOR HEMOGLOBIN	HGB	PATIENT HEMOGLOBIN RESULT	1
-INACTIVE 1	HHV-6 PCR QUANT	HHV6CP	RESULT	1
-INACTIVE 1	HHV-6 PCR QUANT	HHV6DN	HHV-6 DNA	1
-INACTIVE 1	HHV-6 PCR QUANT	HHV6LO	RESULT LOG 10	1
-INACTIVE 1	HHV-6 PCR QUANT	HHV6SP	SPECIMEN	1
-INACTIVE 1	HHV-6 PCR QUANT	HHV6TI	TEST DESCRIPTION	1
-LABHIVA2	HIV - 2 AB	HIV2	HIV-2 AB	1
-LABDHIVAB	"DONOR,HIV-1/HIV-2 AB SCN W/RFL"	HIVAB1	"DONOR,HIV-1/HIV-2 AB SCN W/RFL"	1
-LABYUHOMO	"HOMOCYSTEINE TOTAL, URINE"	HMCYURN	"HOMOCYSTEIN, TOTAL,U"	1
-LABHBV	HBV GENOTYPE: S AND POL GENE	HPEM	PRECORE MUTATION	1
-LABHBV	HBV GENOTYPE: S AND POL GENE	HPSOL	POLYMERASE MUTATION	1
-LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	HPTI	TEST INFORMATION	1
-LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	HPV	HPV DNA PROBE RESULT:	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HQLINT	INTERPRETATION	1
-LABVP5	COMPREHENSIVE HEPATITIS PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	1
-LABHSP70	ANTI - 68 KD (HSP - 70) ANTIBODIES	HSP70T	ANTI-68 KD ABS	1
-LABHBV	HBV GENOTYPE: S AND POL GENE	HSPOL	HEP B VIR GENOTYPING	1
-LABHIVA2	HIV - 2 AB	HTL2	HIV-2 AB NOTE:	1
-LABMALL2	MISCELLANEOUS ALLERGEN 2	I2	INTERPRETATION	1
-LABMALL3	MISCELLANEOUS ALLERGEN 3	I3	INTERPRETATION	1
-LABIBDDI	IBD DIAGNOSTIC SYSTEM	IBDDR	IBD DIAGNOSTIC SYS	1
-LABHEPAT	HEPATIC PROFILE	IBIL	UNCONJUGATED BILIRUBIN	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	1
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRVVDEL	BRADY VV DELAY	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	1
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDEVALCOM	ICD EVAL COMMENTARY	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	1
-LABITCH	FOOD ALLERGY SCREEN	IGEFA	TOTAL IGE	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	IMP	IMIPENEM	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	IMP	IMIPENEM	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	1
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	1
-EXTINRM	INR (MANUAL ENTRY)	INR	INR (MANUAL ENTRY)	1
-LABPCINR	ISTAT INR AND PROTIME	INR	"INR,POCT"	1
-LABBCAA	BRANCHED CHAIN AMINO ACIDS	ISOLEU	ISOLEUCINE	1
-LABITCH	FOOD ALLERGY SCREEN	ITINT	INTERPRETATION	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	K	"POTASSIUM, ISTAT"	1
-LABCBCD	CBC WITH DIFF	K	"POTASSIUM, SER/PLAS"	1
-LABPETSIN	PETROSAL SINUS TEST	L0ACTH	LEFT 0 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	L10ACT	LEFT +10 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	L15ACT	LEFT + 15 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	L3ACTH	LEFT +3 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	L5ACTH	LEFT +5 MINS	1
-LABLAC	LACTIC ACID	LAC	"LACTIC ACID, PLASMA"	1
-LABCR	"CREATININE, SERUM/PLASMA"	LDH	"LDH, TOTAL, SER/PLAS"	1
-LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	LDL	DIRECT LDL CHOL	1
-LABLPD	LIPID PANEL. NON-FASTING PATIENT	LDL	DIRECT LDL CHOL	1
-LABLPD	LIPID PANEL	LDL	LDL (CALCULATED)	1
-LABLEGS	LEGIONELLA SEROLOGY	LEG1	L.LEGIONELLA AB (SEROGRP 1)	1
-LABLEGS	LEGIONELLA SEROLOGY	LEG2	"L.LEGIONELLA AB (SEROGRP 2-6,8)"	1
-LABBCAA	BRANCHED CHAIN AMINO ACIDS	LEUCINE	LEUCINE	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	LEV	LEVOFLOXACIN	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	LEV	LEVOFLOXACIN	1
-LABPETSIN	PETROSAL SINUS TEST	LM5ACT	LEFT - 5 MINS	1
-LAB12814R	MYCOPLASMA IGG/IGM AB	LTROIN	INTERPRETATION	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRSENA	LV BRADY SENSING AMPLITUDE	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRSENA	LV BRADY SENSING AMPLITUDE	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	1
-ECH15	ECHO - TRANSTHORACIC ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODBP	1
-ECH48	ECHO - TRANSESOPHAGEAL ECHO COMPREHENSIVE	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	1
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRSENM	LV THRESHOLD SENSING AMPLITUDE	1
-210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRSENPO	LV THRESHOLD SENSING POLARITY	1
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	LYM	LYMPHOCYTES	1
-LABCBCD	CBC WITH DIFF	LYMAB	ABSOLUTE LYMPHOCYTES	1
-LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	LYMAB	"LYMPHOCYTES, ABS."	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	LYMFLD	"LYMPHOCYTE %, FLD"	1
-LABYCMBP	"MYELIN BASIC PROTEIN, CSF"	MBPCSF	"MYELIN BAS.PROT,CSF"	1
-LABMEAIGG	RUBEOLA VIRAL AB	ME	"RUBEOLA (MEASLES) AB, IGG"	1
-LABHMET	"HEAVY METALS SCREEN, BLOOD"	MER	"MERCURY, BLOOD"	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	MER	MEROPENEM	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	MER	MEROPENEM	1
-LABYUMERC	"MERCURY, URINE (TIMED)"	MERURN	"MERCURY, U"	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	METHADONE	METHADONE	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	MFR	MANUFACTURER	1
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	MFR	MANUFACTURER	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	MG	MAGNESIUM	1
-LABYFMG	"MAGNESIUM, FLUID"	MGFLD	"MAGNESIUM, BODY FLUID"	1
-LABITCH	FOOD ALLERGY SCREEN	MIL	MILK	1
-230986580	POC MENLO PPD TEST	MMIND	MM INDURATION	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	MOD	MODEL	1
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	MOD	MODEL	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	MONFLD	"MONO/MESO %, FLD"	1
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MONO	MONOCYTES	1
-LABCBCD	CBC WITH DIFF	MONOA	ABSOLUTE MONOCYTES	1
-LABCBCD	CBC WITH DIFF	MONOPHAGE	"MONOCYTE/MACROPHAGE, %"	1
-LABMPSQNT	"MUCOPOLYSACCHARIDES QUANTITATIVE, URINE"	MPS	"MUCOPOLYSAC, UR"	1
-LABMPSTLC	"MUCOPOLYSACCHARIDES TLC, URINE"	MPSTLC	"MUCOPOLYSA, UR, TLC"	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	MTYP	METHOD	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	MTYP	METHOD	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	NA	"SODIUM, ISTAT"	1
-LABCBCD	CBC WITH DIFF	NA	"SODIUM, SER/PLAS"	1
-LABYPA	PROCAINAMIDE	NAPA	NAPA	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81DI	DISCLAIMER	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81DX	CLINICAL INDICATION	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81MD	INTERPRETED BY	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81ME	METHOD	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81RE	"RESULT, NON-BLOOD"	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81RF	REFERENCES	1
-LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81SO	SOURCE	1
-LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	NEUT	SEG NEUTROPHILS	1
-LABCBCD	CBC WITH DIFF	NEUTAB	ABSOLUTE NEUTROPHIL COUNT	1
-LABYNOB	NEUTROPHIL OXIDATIVE BURST	NEUTOXB	NEUTROPHIL OXIDATIVE BURST	1
-LABCBCD	CBC WITH DIFF	NEUTRA	ABSOLUTE NEUTROPHILS	1
-LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	NHDL	"NON-HDL CHOL, CALC"	1
-LABLPD	LIPID PANEL. NON-FASTING PATIENT	NHDL	"NON-HDL CHOL, CALC"	1
-LABRBCENZ	RED CELL ENZYMES	NPHOS	PURINE NUCLEOSIDE PHOSPHORYLASE	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3DI	DISCLAIMER	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3DX	CLINICAL INDICATION	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3IN	INTERPRETATION	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3MD	INTERPRETED BY	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3ME	METHOD	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3RE	"RESULT, NON-BLOOD"	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3RF	REFERENCES	1
-LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3SP	SOURCE	1
-LABITCH	FOOD ALLERGY SCREEN	NUTTY	NUT	1
-LABITCH	FOOD ALLERGY SCREEN	OAT	OAT	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	OPIATE	OPIATES	1
-LABITCH	FOOD ALLERGY SCREEN	ORAN	ORANGE	1
-LABFLDC	FLUID CULTURE AND GRAM STAIN	ORG	ORGANISM	1
-LABYOSTEO	OSTEOCALCIN HUMAN	OSTEOC	"OSTEOCALCIN, HUMAN"	1
-LABPETSIN	PETROSAL SINUS TEST	P0ACTH	PERIPHERAL 0 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	P10ACT	PERIPHERAL +10 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	P15ACT	PERIPHERAL +15 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	P3ACTH	PERIPHERAL +3 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	P5ACTH	PERIPHERAL +5  MINS	1
-LABPALL	PROCAINAMIDE	PA	PROCAINAMIDE	1
-LABPAI1AG	PAI-1 AG	PAI1G	PAI-1 ANTIGEN	1
-LABPALL	PROCAINAMIDE	PAL1	"PEAK, TROUGH, OR RANDOM"	1
-LABPALL	PROCAINAMIDE	PAL2	DATE & TIME OF LAST DOSE	1
-LABPALL	PROCAINAMIDE	PAL3	DOSAGE (INCLUDE UNITS)	1
-LABPALL	PROCAINAMIDE	PAL4	ADMINISTRATION ROUTE	1
-LABPARAG	"PARAGONIMIASIS ANTIBODY, ELISA"	PARAG1	PARAGONIMIASIS	1
-LABPBGD	"PBG DEAMINASE, RBC"	PBGD	"PBG DEAMINASE, RBC"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCAGP	"ANION GAP, ISTAT"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCBUN	"BUN, ISTAT"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCCL	"CHLORIDE, ISTAT"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCCOM	POCT COMMENT	1
-POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	PCO2SO	"O2 SATURATION, ISTAT"	1
-POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	PCO2V	"PCO2 (V), ISTAT"	1
-CRP332	RIGHT HEART C NIPRIDE STUDY	PDF	PDF RESULT	1
-IMG11BXM1	CAR MYOCARD BIOPSY I	PDF	PDF RESULT	1
-IMG11IA	CAR INTRA AORTIC BALLOON PUMP	PDF	PDF RESULT	1
-IMG11IABP	CAR IABP REMOVAL	PDF	PDF RESULT	1
-IMG11RLCA	CAR RT LT CATH CORS ANGIO	PDF	PDF RESULT	1
-IMG12IA	ICD ANALYSIS	PDF	PDF RESULT	1
-IMG13VA	PED VALVULOPLASTY AO V	PDF	PDF RESULT	1
-IMG1ATH	CAR PTC ROTABLATOR	PDF	PDF RESULT	1
-IMG1PTCS	CAR PTCA 1VESSEL SIMPLE	PDF	PDF RESULT	1
-IMG2EX	IR ANGIO EXTREMITY	PDF	PDF RESULT	1
-PFT43	FULL SPIROMETRY WITH SVC MVV	PDF	PDF RESULT	1
-PFT45	LUNG VOLUMES	PDF	PDF RESULT	1
-PFT53	SPIROMETRY WITH ABG	PDF	PDF RESULT	1
-LABITCH	FOOD ALLERGY SCREEN	PEAN	PEANUT	1
-POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	PH	"PH, ISTAT"	1
-LABPHATYR	PHENYLALANINE AND TYROSINE	PHALN	PHENYLALANINE	1
-LAB11138R	HIV PHENO EXPR ANTIVIR RESIST	PHENO	HIV-1 PHENOTYPE	1
-POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHGAST	"PH,GASTRIC"	1
-230986580	POC MENLO PPD TEST	PHLOC	LOCATION	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	PHOS	PHOSPHORUS	1
-LABDVEPI1	PARA 1 DFA	PI1	PARAINFLUENZA 1	1
-LABDVEPI1	PARA 1 DFA	PI1IP	TEST DESCRIPTION	1
-LABDVEPI1	PARA 1 DFA	PI1SP	SPECIMEN	1
-LABDVEPI2	PARA 2 DFA	PI2	PARAINFLUENZA 2	1
-LABDVEPI2	PARA 2 DFA	PI2IP	TEST DESCRIPTION	1
-LABDVEPI2	PARA 2 DFA	PI2SP	SPECIMEN	1
-LABDVEPI3	PARA 3 DFA	PI3	PARAINFLUENZA 3	1
-LABDVEPI3	PARA 3 DFA	PI3IP	TEST DESCRIPTION	1
-LABDVEPI3	PARA 3 DFA	PI3SP	SPECIMEN	1
-LABRBCENZ	RED CELL ENZYMES	PKLS	"PK, LOW SUBSTRATE"	1
-LABCR	"CREATININE, SERUM/PLASMA"	PLT	PLATELET COUNT	1
-LABPANPLT	PLATELET COUNT	PLT	PLATELET COUNT	1
-LABCBCD	CBC WITH DIFF	PLT	PLATELETS	1
-LABFENTP	ENTEROVIRUS PANEL II	PLV1	POLIOVIRUS 1 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	PLV2	POLIOVIRUS 2 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	PLV3	POLIOVIRUS 3 AB	1
-LABPETSIN	PETROSAL SINUS TEST	PM5ACT	PERIPHERAL -5 MINS	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMCOMMENT	PM COMMENTARY	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMLVLDBIIM	PM LV LEAD BIPOLAR IMPEDANCE	1
-POC14	POC GLUCOSE BY METER	PNAB1	PNEUMOCOCCAL AB (PNEUMOVAX)	1
-LABCLAMP	CHLAMYDIA AB PANEL	PNIGA	C. PNEUMONIAE IGA	1
-LABCLAMP	CHLAMYDIA AB PANEL	PNIGG	C. PNEUMONIAE IGG	1
-LABCLAMP	CHLAMYDIA AB PANEL	PNIGM	C. PNEUMONIAE IGM	1
-LABPOLYSP	SPUTUM FOR PMNS	POLYSP	SPUTUM FOR PMNS	1
-LABSTPOLY	"PMNS / EOSINOPHILS, STOOL"	POLYST	"PMNS, STOOL"	1
-POC14	POC GLUCOSE BY METER	PPD100	PPD 1:100 CPM	1
-POC14	POC GLUCOSE BY METER	PPDINT	PPD INTERPRETATION	1
-POC6	POC PPD SKIN TEST (IP)	PPDINT	PPD INTERPRETATION	1
-LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	PPPCOM	COMMENTS	1
-LABADIFF	DIFFERENTIAL	PRMY	PROMYELOCYTES	1
-LABTBP100	TRANSFUSION BLOOD PRODUCT 101	PRO	PRODUCT ID  	1
-LABTBP95	TRANSFUSION BLOOD PRODUCT 96	PRO	PRODUCT ID  	1
-LABTBP96	TRANSFUSION BLOOD PRODUCT 97	PRO	PRODUCT ID  	1
-LABTBP97	TRANSFUSION BLOOD PRODUCT 98	PRO	PRODUCT ID  	1
-LABTBP98	TRANSFUSION BLOOD PRODUCT 99	PRO	PRODUCT ID  	1
-LABTBP99	TRANSFUSION BLOOD PRODUCT 100	PRO	PRODUCT ID  	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	PROPOX	PROPOXYPHENE	1
-LABCLAMP	CHLAMYDIA AB PANEL	PSIGA	C. PSITTACI IGA	1
-LABCLAMP	CHLAMYDIA AB PANEL	PSIGG	C. PSITTACI IGG	1
-LABCLAMP	CHLAMYDIA AB PANEL	PSIGM	C. PSITTACI IGM	1
-LABADIFF	DIFFERENTIAL	PSREV	PATH/SPEC REVIEW	1
-LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	PSREV	PATH/SPEC REVIEW	1
-EXTINRM	INR (MANUAL ENTRY)	PT	PROTHROMBIN TIME	1
-LABPCINR	ISTAT INR AND PROTIME	PTT	"PTT,POCT"	1
-LABYQUIN	QUINIDINE	QUINIDINE	QUINIDINE	1
-LABPETSIN	PETROSAL SINUS TEST	R0ACTH	RIGHT 0 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	R10ACT	RIGHT +10 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	R15ACT	RIGHT 15 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	R3ACTH	RIGHT +3 MINS	1
-LABPETSIN	PETROSAL SINUS TEST	R5ACTH	RIGHT +5 MINS	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	1
-210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	RBCFLD	"RBC COUNT, FLD (/UL)"	1
-LABCBCD	CBC WITH DIFF	RBCHP	RBC	1
-LABEXCHTX	EXCHANGE TRANSFUSION	RBCSTA	RBC PRODUCT - UNITS	1
-LABURED	"REDUCING SUBSTANCE, URINE"	REDSUBURN	"REDUCING SUBST., URINE"	1
-LAB11092R	"RENIN ACTIVITY, PLASMA"	RENIN	"RENIN ACTIVITY, PL"	1
-LABIMM2	IMMUNOLOGY PANEL 2	RHFT	RHEUMATOID FACTOR LEVEL	1
-LABRHTYP	RH BLOOD TYPE	RHTYP	RH BLOOD TYPE	1
-10010R	HLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)	RINFO	TO FIND RESULT:	1
-10120R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)	RINFO	TO FIND RESULT:	1
-10165R	HLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)	RINFO	TO FIND RESULT:	1
-10225R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	RINFO	TO FIND RESULT:	1
-10280R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)	RINFO	TO FIND RESULT:	1
-10285R	HLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)	RINFO	TO FIND RESULT:	1
-10297R	HLA ORDER - DECEASED DNR O.R. SPMN	RINFO	TO FIND RESULT:	1
-LABPETSIN	PETROSAL SINUS TEST	RM5ACT	RIGHT -5 MINS	1
-LABDRPR	"DONOR, RPR W/RFL TO SYPH IGG"	RPR1	"DONOR, RPR W/RFL TO SYPH IGG"	1
-LABDVEADV	DIRECT VIRAL EXAM ADENO	RPT	RPT	1
-LABDVEP1	DIRECT VIRAL EXAM PARA 1	RPT	RPT	1
-LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	RPT	RPT	1
-LABIDWEE	WORM ID	RPT	RPT	1
-LABSHSV	HSV SHELL VIAL CULTURE	RPT	RPT	1
-LABVCULU	"VIRAL CULTURE, URINE"	RPT	RPT	1
-LABCD3B	"CD3, BLOOD"	RTFDA1	NOTE	1
-LABRUBM	RUBELLA AB IGM	RUBM1	"RUBELLA AB, IGM"	1
-LABYRUBM	RUBELLA AB IGM	RUBM1	"RUBELLA AB, IGM"	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SAMP	AMPICILLIN/SULBACTAM	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SAMP	AMPICILLIN/SULBACTAM	1
-LAB11093R	AUTOANTIBODIES TO SCL 70	SCL70	"SCL 70 AB, IGG, SERUM"	1
-LABDVEADV	DIRECT VIRAL EXAM ADENO	SDES	SPECIMEN	1
-LABDVEP1	DIRECT VIRAL EXAM PARA 1	SDES	SPECIMEN	1
-LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SDES	SPECIMEN	1
-LABIDWEE	WORM ID	SDES	SPECIMEN	1
-LABSHSV	HSV SHELL VIAL CULTURE	SDES	SPECIMEN	1
-LABVCULU	"VIRAL CULTURE, URINE"	SDES	SPECIMEN	1
-LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	SDES6	SPECIMEN	1
-210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	1
-210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3DI	DISCLAIMER	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3DX	CLINICAL INDICATION	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3IN	INTERPRETATION	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3MD	INTERPRETED BY	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3ME	METHOD	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3RE	"RESULT, BLOOD"	1
-LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3RF	REFERENCES	1
-LABITCH	FOOD ALLERGY SCREEN	SHRIM	SHRIMP	1
-LABTBP100	TRANSFUSION BLOOD PRODUCT 101	SI	<<STATUS>>  	1
-LABTBP95	TRANSFUSION BLOOD PRODUCT 96	SI	<<STATUS>>  	1
-LABTBP96	TRANSFUSION BLOOD PRODUCT 97	SI	<<STATUS>>  	1
-LABTBP97	TRANSFUSION BLOOD PRODUCT 98	SI	<<STATUS>>  	1
-LABTBP98	TRANSFUSION BLOOD PRODUCT 99	SI	<<STATUS>>  	1
-LABTBP99	TRANSFUSION BLOOD PRODUCT 100	SI	<<STATUS>>  	1
-210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	1
-LABSNP	SENSORIMOTOR PROFILE	SNPRR	SENSORIMOTOR PROFILE	1
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SNRBC	"NRBC/100WBC, SYN FLD"	1
-LABADPCRQ	ADENOVIRUS PCR QUANT	SOUR10	SOURCE	1
-LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	SOURC6	SOURCE	1
-LABBKDNA	BK VIRUS DNA PCR	SOURC7	SOURCE	1
-LABITCH	FOOD ALLERGY SCREEN	SOY	SOY	1
-LABPETSIN	PETROSAL SINUS TEST	SPAC1	SPACE FOR PETSIN	1
-LABPETSIN	PETROSAL SINUS TEST	SPAC2	SPACE FOR PETSIN	1
-LABPETSIN	PETROSAL SINUS TEST	SPAC3	SPACE FOR PETSIN	1
-LABPETSIN	PETROSAL SINUS TEST	SPAC4	SPACE FOR PETSIN	1
-LABPETSIN	PETROSAL SINUS TEST	SPAC5	SPACE FOR PETSIN	1
-LABAS4I	AB SCREEN	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP45	TRANSFUSION BLOOD PRODUCT 46	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP47	TRANSFUSION BLOOD PRODUCT 48	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP48	TRANSFUSION BLOOD PRODUCT 49	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP49	TRANSFUSION BLOOD PRODUCT 50	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP50	TRANSFUSION BLOOD PRODUCT 51	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP51	TRANSFUSION BLOOD PRODUCT 52	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
-POC39	POC URINE FOR PH	SPG	SPECIFIC GRAVITY UA	1
-LABDVEADV	DIRECT VIRAL EXAM ADENO	SREQ	COMMENTS	1
-LABDVEP1	DIRECT VIRAL EXAM PARA 1	SREQ	COMMENTS	1
-LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SREQ	COMMENTS	1
-LABIDWEE	WORM ID	SREQ	COMMENTS	1
-LABSHSV	HSV SHELL VIAL CULTURE	SREQ	COMMENTS	1
-LABVCULU	"VIRAL CULTURE, URINE"	SREQ	COMMENTS	1
-LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SRLYM	"REACT LYMPH, SYN FLD"	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	STIGG	ST. LOUIS ENCEP IGG	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	STIGM	ST. LOUIS ENCEP IGM	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	STINT	INTERPRETATION	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SXT	TRIMETHOPRIM/SULFAMETHOXAZOLE	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SXT	TRIMETHOPRIM/SULFAMETHOXAZOLE	1
-LABCBCD	CBC WITH DIFF	SYNLYMPH	LYMPHOCYTES %	1
-POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	T11DX	CLINICAL INDICATION	1
-LABYTBG	THYROXINE BINDING GLOBULIN	T4BG	THYROXINE-BIND GLOB	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRCT	COMMENT	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRDR	INTERPRETED BY	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRDS	DISCLAIMER	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRES	RESULTS	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRMT	METHOD	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRPM	PRIMERS	1
-LABTBCR	"BCR - ABL, TISSUE"	TBCRTS	SOURCE	1
-LABCBCD	CBC WITH DIFF	TBIL	TOTAL BILIRUBIN	1
-LABHEPAT	HEPATIC PROFILE	TBIL	TOTAL BILIRUBIN	1
-PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	TCD4AB	"TOTAL CD4, ABS."	1
-LABCLOMIP	CLOMIPRAMINE AND METABOLITE	TCLOAM	"CLOMIP & METAB, TOTAL"	1
-LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	TCO2A	"TCO2, ISTAT"	1
-LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	TGL	"TRIGLYCERIDE, SER/PLAS"	1
-LABLPD	LIPID PANEL. NON-FASTING PATIENT	TGL	"TRIGLYCERIDE, SER/PLAS"	1
-POC24	POC ISTAT TROPONIN I	TNI	"TROPONIN I, POCT"	1
-LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBRASDD	"TOBRAMYCIN, SINGLE DAILY DOSE"	1
-LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBUN2	DATE & TIME OF LAST DOSE	1
-LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBUN3	DOSAGE	1
-LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBUN4	ADMINISTRATION ROUTE	1
-LABITCH	FOOD ALLERGY SCREEN	TOM	TOMATO	1
-INACTIVE 4	TOXOPLASMA IGG IGM	TOXDES	TEST DESCRIPTION	1
-LABTOXGM	TOXOPLASMA IGM & IGG	TOXDES	TEST DESCRIPTION	1
-LABTOXGMR	TOXO PREGNANCY PANEL	TOXDES	TEST DESCRIPTION	1
-INACTIVE 4	TOXOPLASMA IGG IGM	TOXGQL	IGG RESULT	1
-LABTOXGM	TOXOPLASMA IGM & IGG	TOXGQL	IGG RESULT	1
-LABTOXGMR	TOXO PREGNANCY PANEL	TOXGQL	IGG RESULT	1
-LABTOXOPR	TOXO PREGNANCY PANEL	TOXIGR	TOXOPLASMA IGG	1
-LABTOXOPR	TOXO PREGNANCY PANEL	TOXIMR	TOXOPLASMA IGM	1
-INACTIVE 4	TOXOPLASMA IGG IGM	TOXMQL	IGM RESULT	1
-LABTOXGM	TOXOPLASMA IGM & IGG	TOXMQL	IGM RESULT	1
-LABTOXGMR	TOXO PREGNANCY PANEL	TOXMQL	IGM RESULT	1
-LABTOXIG	TOXOPLASMA SEROLOGY	TOXO	TOXOPLASMA IGG	1
-LAB11122R	TOXOPLASMOSIS IGM	TOXOM	TOXOPLASMA IGM ELISA	1
-LABTRAZO	TRAZODONE	TRAZO	TRAZODONE	1
-LAB11097R	"TSI, SERUM"	TSIG	"THROID-STIMULATING IG, SERUM"	1
-LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB1	COXSACKIE B1 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYB1	COXSACKIE B1 AB	1
-LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB2	COXSACKIE B2 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYB2	COXSACKIE B2 AB	1
-LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB3	COXSACKIE B3 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYB3	COXSACKIE B3 AB	1
-LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB4	COXSACKIE B4 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYB4	COXSACKIE B4 AB	1
-LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB5	COXSACKIE B5 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYB5	COXSACKIE B5 AB	1
-LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB6	COXSACKIE B6 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYB6	COXSACKIE B6 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYP10	COXSACKIE A10 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYP16	COXSACKIE A16 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYP2	COXSACKIE A2 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYP4	COXSACKIE A4 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYP7	COXSACKIE A7 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYP9	COXSACKIE A9 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYPE11	ECHOVIRUS 11 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYPE30	ECHOVIRUS 30 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYPE4	ECHOVIRUS 4 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYPE7	ECHOVIRUS 7 AB	1
-LABFENTP	ENTEROVIRUS PANEL II	TYPE9	ECHOVIRUS 9 AB	1
-LABPHATYR	PHENYLALANINE AND TYROSINE	TYROSINE	TYROSINE	1
-LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	TZP	PIPERACILLIN/TAZOBACTAM	1
-LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	TZP	PIPERACILLIN/TAZOBACTAM	1
-LABTBP93	TRANSFUSION BLOOD PRODUCT 94	UBT	UNIT BLOOD TYPE  	1
-LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	UHYPOX	HYPOXANTHINE	1
-LABTBP93	TRANSFUSION BLOOD PRODUCT 94	UNT	UNIT NUMBER  	1
-LABUAMICR	URINALYSIS MICROSCOPIC ONLY	URBC	"RBC, URINE"	1
-LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	URIC	URIC ACID	1
-LABCR	"CREATININE, SERUM/PLASMA"	URIC	"URIC ACID, SER/PLAS"	1
-LABUGLT	"GLUCOSE, URINE (TIMED)"	UT7	COLLECTION PERIOD GLUCOSE	1
-LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	UURAC	URACIL	1
-POC39	POC URINE FOR PH	UWBC	LEUKOCYTES	1
-LABUAMICR	URINALYSIS MICROSCOPIC ONLY	UWBC	"WBC, URINE"	1
-LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	UXANT	XANTHINE	1
-LABBCAA	BRANCHED CHAIN AMINO ACIDS	VALINE	VALINE	1
-LABBMTP	BMT PRETRANSPLANT PANEL	VGGQNT	VZV IGG LEVEL	1
-LABYVIP	VASOACTIVE INTESTINAL POLYPEPTIDE	VIP	VASOACTIVE INT PEPTIDE	1
-LABYVITE	"VITAMIN E, SERUM"	VITE	"A-TOCOPHEROL, VITAMIN E"	1
-LABTBP34	TRANSFUSION BLOOD PRODUCT 35	VL	PRODUCT VOLUME	1
-LABTBP35	TRANSFUSION BLOOD PRODUCT 36	VL	PRODUCT VOLUME	1
-LABTBP40	TRANSFUSION BLOOD PRODUCT 41	VL	PRODUCT VOLUME	1
-LABTBP46	TRANSFUSION BLOOD PRODUCT 47	VL	PRODUCT VOLUME	1
-LABTBP52	TRANSFUSION BLOOD PRODUCT 53	VL	PRODUCT VOLUME	1
-LABTBP57	TRANSFUSION BLOOD PRODUCT 58	VL	PRODUCT VOLUME	1
-LABTBP58	TRANSFUSION BLOOD PRODUCT 59	VL	PRODUCT VOLUME	1
-LABTBP61	TRANSFUSION BLOOD PRODUCT 62	VL	PRODUCT VOLUME	1
-LABTBP62	TRANSFUSION BLOOD PRODUCT 63	VL	PRODUCT VOLUME	1
-LABTBP63	TRANSFUSION BLOOD PRODUCT 64	VL	PRODUCT VOLUME	1
-LABTBP64	TRANSFUSION BLOOD PRODUCT 65	VL	PRODUCT VOLUME	1
-LABTBP69	TRANSFUSION BLOOD PRODUCT 70	VL	PRODUCT VOLUME	1
-LABTBP70	TRANSFUSION BLOOD PRODUCT 71	VL	PRODUCT VOLUME	1
-LABTBP75	TRANSFUSION BLOOD PRODUCT 76	VL	PRODUCT VOLUME	1
-LABTBP76	TRANSFUSION BLOOD PRODUCT 77	VL	PRODUCT VOLUME	1
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	VL	PRODUCT VOLUME	1
-LABTBP83	TRANSFUSION BLOOD PRODUCT 84	VL	PRODUCT VOLUME	1
-LABTBP84	TRANSFUSION BLOOD PRODUCT 85	VL	PRODUCT VOLUME	1
-LABTBP85	TRANSFUSION BLOOD PRODUCT 86	VL	PRODUCT VOLUME	1
-LABUGLT	"GLUCOSE, URINE (TIMED)"	VOL7	VOLUME GLUCOSE	1
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	VOLUMEURN	URINE VOLUME	1
-LABYUMERC	"MERCURY, URINE (TIMED)"	VOLUMEURN	URINE VOLUME	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSLWVTTZNT	VSLOWVT TACHY ZONE RATE STABILITY CRITERIA	1
-210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSWVTTAQRS	VSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	1
-LABVWNTRP	"INTERPRETATION, VW SCREEN"	VWDIR	INTERPRETED BY	1
-LABVWNTRP	"INTERPRETATION, VW SCREEN"	VWINTP	INTERPRETATION	1
-LABVZVIGG	VZ IGG	VZAB	VARICELLA ZOSTER AB IGG	1
-LABCR	"CREATININE, SERUM/PLASMA"	WBC	WBC	1
-LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	WBCFLD	"NUCL CELLS, FLD(/UL)"	1
-LABCBCD	CBC WITH DIFF	WBCHP	WBC	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	WEIGG	W EQUINE IGG AB	1
-LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	WEIGM	W EQUINE IGM AB	1
-LABITCH	FOOD ALLERGY SCREEN	WHT	WHEAT	1
-LABXAMH	ANTI - MULLERIAN HORMONE	XAMHOR	ANTI-MULLERIAN HORMONE	1
-LABQUECORSL	"QUEST - CORTISOL, SERUM LC/MS/MS"	XCORTI	CORTISOL TOTAL SERUM	1
-LABTBP77	TRANSFUSION BLOOD PRODUCT 78	XMR	UNIT CROSSMATCH  	1
-LABTBP89	TRANSFUSION BLOOD PRODUCT 90	XMR	UNIT CROSSMATCH  	1
-LABTBP90	TRANSFUSION BLOOD PRODUCT 91	XMR	UNIT CROSSMATCH  	1
-LABTBP91	TRANSFUSION BLOOD PRODUCT 92	XMR	UNIT CROSSMATCH  	1
-LABTBP92	TRANSFUSION BLOOD PRODUCT 93	XMR	UNIT CROSSMATCH  	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YANTQ	ANTHRAQUINONES	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO1	SPECIMEN	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO10	RELEASE DATE	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO11	INTERPRETATION	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO12	EXTRACTION PERFORMED?	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO2	SPECIMEN ID	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO4	ORDER DATE	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO5	METHOD	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO6	RESULT	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO7	CAUTIONS	1
-LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO9	REVIEWED BY	1
-LABYARBIN	ACH RECEPTOR BINDING AB	YARBIN	ACH RECEPTOR BINDING ANTIBODY	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YBIS	BISACODYL	1
-LABYBUPRO	"BUPROPION, SERUM"	YBUPR1	BUPROPION	1
-LABYCALCI	"CALCITONIN, PLASMA"	YCALCI	"CALCITONIN, SERUM"	1
-LABYDISO	"DISOPYRAMIDE, SERUM"	YDISO1	"DISOPYRAMIDE, P"	1
-LABYDOC	"11-DEOXYCORTISOL, SERUM"	YDOC1	"11-DEOXYCORTISOL, S"	1
-LABYDOC	"11-DEOXYCORTISOL, SERUM"	YDOC2	AM RESULT	1
-LABYDOC	"11-DEOXYCORTISOL, SERUM"	YDOC3	PM RESULT	1
-LABYEMA	"ENDOMYSIAL ANTIBODIES, SERUM"	YEMAIN	"ENDOMYSIAL ABS, SERUM"	1
-LABEVPCR	ENTEROVIRUS BY PCR	YENP1	SPECIMEN SOURCE	1
-LABEVPCR	ENTEROVIRUS BY PCR	YENP2	"ENTEROVIRUS ,PCR"	1
-LABEVPCR	ENTEROVIRUS BY PCR	YENP3	SPECIAL INFORMATION	1
-LABEVPCR	ENTEROVIRUS BY PCR	YENP4	REPORT STATUS	1
-LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP1	CELL SURFACE AB IGG	1
-LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP3	MONKEY ESOPHAGUS IGG	1
-LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP4	HUMAN SPLIT SKIN IGG	1
-LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP5	OTHER	1
-LABYESTRI	ESTRIOL FREE	YESTR1	"ESTRIOL,UNCONJUGATED"	1
-LABYFMG	"MAGNESIUM, FLUID"	YFMG2	FLUID TYPE	1
-LABYHALO	"HALOPERIDOL, SERUM"	YHALO2	REDUCED HALOPERIDOL	1
-LABYHPL	HUMAN PLACENTAL LACTOGEN	YHPL1	HUMAN PLACENTAL LACTOGEN	1
-LABYLEPTN	LEPTIN	YLEPT1	LEPTIN	1
-LABYNABF	INTERFERON NEUTRALIZING  AB	YNABF1	INTERFERON NEUTRALIZING AB	1
-LABYNOB	NEUTROPHIL OXIDATIVE BURST	YNOB2	INTERPRETATION	1
-LABYNOB	NEUTROPHIL OXIDATIVE BURST	YNOB3	REVIEWED BY	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YOXY	OXYPHENISATIN	1
-LABYPA	PROCAINAMIDE	YPANAP	PROCAINAMIDE + NAPA	1
-LABYPCA	"PARIETAL CELL AB, SERUM"	YPCA	PARIETAL CELL AB	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YPHEN	PHENOLPHTHALEIN	1
-LABYPA	PROCAINAMIDE	YPROC	PROCAINAMIDE	1
-LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	YPTSA	PHOSPHATIDYLSERINE IGA AB	1
-LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	YPTSG	PHOSPHATIDYLSERINE IGG AB	1
-LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	YPTSM	PHOSPHATIDYLSER. IGM	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL1	REPORTING LIMIT	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL2	REPORTING LIMIT	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL3	REPORTING LIMIT	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL4	REPORTING LIMIT	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL5	REPORTING LIMIT	1
-LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL6	REPORTING LIMIT	1
-LABYRS	RSV IGG AND IGM AB	YRS1	"RSV AB, IGG & IGM"	1
-LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG2	TOTAL WEIGHT	1
-LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL1	"CHLORIDE, STOOL, 24 HR"	1
-LABYT3UP	T3 UPTAKE	YT7P2	T-UPTAKE %	1
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZCO	COMMENT	1
-LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZNO	NOTE	1
-LAB11229R	"METANEPHRINE,FRACT,RANM U"	YUMECO	COMMENT	1
-LABYUMERC	"MERCURY, URINE (TIMED)"	YUMER2	COLLECTION DURATION	1
-LABYUPCPC	"PHENCYCLIDINE CONFIRMATION, URINE"	YUPCGC	PHENCYCLIDINE	1
-LABYUPCPC	"PHENCYCLIDINE CONFIRMATION, URINE"	YUPCIM	IMMUNOASSAY SCREEN	1
-LABYUPCPC	"PHENCYCLIDINE CONFIRMATION, URINE"	YUPCIN	INTERPRETATION	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	YUPCP	PHENCYCLIDINE	1
-LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	YUTTHC	TETRAHYDROCANNABINOL	1
-LAB12322R	VON WILLEBRAND MULTIMERS	YVWMR	VON WILLEBRAND	1
-210951798	POST VOID TEST		RESIDUAL BLADDER VOLUME	1
+proc_code	description	base_name	component_name	count
+LABPOCGLU	GLUCOSE BY METER	GLU	GLUCOSE BY METER	1139495
+LABPOCGLU	GLUCOSE BY METER	PCCOM	POCT COMMENT	1139495
+LABPOCGLU	GLUCOSE BY METER	SOU	SOURCE	1139495
+LABMETB	"METABOLIC PANEL, BASIC"	AG	ANION GAP	454658
+LABMETB	"METABOLIC PANEL, BASIC"	BUN	"UREA NITROGEN,SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	CA	"CALCIUM, SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	CL	"CHLORIDE, SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	CO2	"CO2, SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	CR	"CREATININE, SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	GLU	"GLUCOSE, SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	K	"POTASSIUM, SER/PLAS"	454658
+LABMETB	"METABOLIC PANEL, BASIC"	NA	"SODIUM, SER/PLAS"	454658
+LABCBCD	CBC WITH DIFF	WBC	WBC	448137
+LABCBCD	CBC WITH DIFF	MCHC	MCHC	448131
+LABCBCD	CBC WITH DIFF	MCV	MCV	448131
+LABCBCD	CBC WITH DIFF	MCH	MCH	448130
+LABCBCD	CBC WITH DIFF	HGB	HEMOGLOBIN	448128
+LABCBCD	CBC WITH DIFF	PLT	PLATELET COUNT	448127
+LABCBCD	CBC WITH DIFF	RBC	RBC	448127
+LABCBCD	CBC WITH DIFF	HCT	HEMATOCRIT	448124
+LABCBCD	CBC WITH DIFF	RDW	RDW	448124
+LABCBCD	CBC WITH DIFF	NEUTAB	"NEUT, ABS"	413421
+LABCBCD	CBC WITH DIFF	MONOAB	"MONO, ABS"	413419
+LABCBCD	CBC WITH DIFF	MONO	"MONO, %"	413408
+LABCBCD	CBC WITH DIFF	LYM	"LYM, %"	413402
+LABCBCD	CBC WITH DIFF	EOS	"EOS, %"	413400
+LABCBCD	CBC WITH DIFF	NEUT	"NEUT, %"	413400
+LABCBCD	CBC WITH DIFF	BASO	"BASO, %"	413399
+LABCBCD	CBC WITH DIFF	BASOAB	"BASOS, ABS"	413399
+LABCBCD	CBC WITH DIFF	EOSAB	"EOS, ABS"	413399
+LABCBCD	CBC WITH DIFF	LYMAB	"LYM, ABS"	413399
+LABMGN	"MAGNESIUM, SERUM/PLASMA"	MG	"MAGNESIUM, SER/PLAS"	390034
+LABPT	PROTHROMBIN TIME	INR	INR	285359
+LABPT	PROTHROMBIN TIME	PT	PROTHROMBIN TIME	285358
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	AG	ANION GAP	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	ALB	"ALBUMIN, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	ALT	"ALT (SGPT), SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	BUN	"UREA NITROGEN,SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CA	"CALCIUM, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CL	"CHLORIDE, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CO2	"CO2, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	CR	"CREATININE, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	GLOB	GLOBULIN	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	GLU	"GLUCOSE, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	K	"POTASSIUM, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	NA	"SODIUM, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	TBIL	TOTAL BILIRUBIN	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	TP	"PROTEIN, TOTAL, SER/PLAS"	257387
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	AST	"AST (SGOT), SER/PLAS"	257385
+LABSPR	SPECIMEN REMARK	SPR	SPECIMEN REMARK	250878
+LABCBCO	CBC	WBC	WBC	238745
+LABCBCO	CBC	HCT	HEMATOCRIT	238742
+LABCBCO	CBC	HGB	HEMOGLOBIN	238742
+LABCBCO	CBC	PLT	PLATELET COUNT	238742
+LABCBCO	CBC	RBC	RBC	238742
+LABCBCO	CBC	MCV	MCV	238739
+LABCBCO	CBC	RDW	RDW	238739
+LABCBCO	CBC	MCHC	MCHC	238738
+LABCBCO	CBC	MCH	MCH	238737
+LABPHOS	"PHOSPHORUS, SERUM/PLASMA"	PHOS	"PHOSPHORUS, SER/PLAS"	199586
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT	PART. THROMBOPLASTIN TIME	197062
+LABPCG3	"ISTAT G3+, ARTERIAL"	PCCOM	POCT COMMENT	191365
+LABPCG3	"ISTAT G3+, ARTERIAL"	PCO2A	"PCO2 (A), ISTAT"	191365
+LABPCG3	"ISTAT G3+, ARTERIAL"	PHA	"PH (A), ISTAT"	191365
+LABPCG3	"ISTAT G3+, ARTERIAL"	PO2A	"PO2 (A), ISTAT"	191365
+LABPCG3	"ISTAT G3+, ARTERIAL"	HCO3A	"HCO3 (A), ISTAT"	191363
+LABPCG3	"ISTAT G3+, ARTERIAL"	TCO2A	"TCO2, ISTAT"	191363
+LABPCG3	"ISTAT G3+, ARTERIAL"	O2SATA	"O2 SATURATION, ISTAT"	191362
+LABPCG3	"ISTAT G3+, ARTERIAL"	FIO2	FIO2	171167
+LABCAI	"CALCIUM IONIZED, SERUM/PLASMA"	CAION	"CALCIUM, IONIZED"	163428
+EKG5	ECG 12-LEAD	ECGIMPRESS	ECG IMPRESSION	118728
+EKG5	ECG 12-LEAD	HEARTRATE	HEART RATE	118728
+EKG5	ECG 12-LEAD	QRSAXIS	QRS AXIS	118728
+EKG5	ECG 12-LEAD	QTCINTERVAL	QTC INTERVAL	118728
+EKG5	ECG 12-LEAD	RR	RR	118728
+EKG5	ECG 12-LEAD	QRSDINTERVAL	QRSD INTERVAL	118708
+EKG5	ECG 12-LEAD	QTINTERVAL	QT INTERVAL	118675
+EKG5	ECG 12-LEAD	TWAVEAXIS	T WAVE AXIS	117205
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	SI	<<STATUS>>  	109838
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	PRO	PRODUCT ID  	109836
+LABMETB	"METABOLIC PANEL, BASIC"	EGFR	EGFR	106672
+LABPCG3	"ISTAT G3+, ARTERIAL"	BE	"BASE EXCESS, ISTAT"	104016
+EKG5	ECG 12-LEAD	PRINTERVAL	P-R INTERVAL	102126
+EKG5	ECG 12-LEAD	PAXIS	P AXIS	101996
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	UBT	UNIT BLOOD TYPE  	99587
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	UNT	UNIT NUMBER  	99586
+LABCBCD	CBC WITH DIFF	MORPH	MORPHOLOGY	95057
+LABCBCD	CBC WITH DIFF	SLREV	SLIDE REVIEW	95057
+LABPCG3	"ISTAT G3+, ARTERIAL"	BD	"BASE DEFICIT, ISTAT"	87576
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	DI	ISSUE DATE/TIME	82382
+LABTNI	TROPONIN I	TNI	TROPONIN I	73810
+LABLAC	LACTIC ACID	LAC	LACTIC ACID	68720
+LABCAI	"CALCIUM IONIZED, SERUM/PLASMA"	PHCAI	PH	67346
+LABPCG3	"ISTAT G3+, ARTERIAL"	PCTEMP	BODY TEMPERATURE	64896
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	SI	<<STATUS>>  	64591
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	PRO	PRODUCT ID  	64588
+LABABG	"BLOOD GASES, ARTERIAL"	PHA	PH (A)	62623
+LABABG	"BLOOD GASES, ARTERIAL"	CL	"CHLORIDE, WHOLE BLD"	62622
+LABABG	"BLOOD GASES, ARTERIAL"	GLU	"GLUCOSE, WHOLE BLOOD"	62622
+LABABG	"BLOOD GASES, ARTERIAL"	HCO3A	HCO3	62622
+LABABG	"BLOOD GASES, ARTERIAL"	HCT	HCT (EST)	62622
+LABABG	"BLOOD GASES, ARTERIAL"	HGBBG	THB	62622
+LABABG	"BLOOD GASES, ARTERIAL"	K	"POTASSIUM, WHOLE BLD"	62622
+LABABG	"BLOOD GASES, ARTERIAL"	NA	"SODIUM, WHOLE BLOOD"	62622
+LABABG	"BLOOD GASES, ARTERIAL"	PCO2A	PCO2 (A)	62622
+LABABG	"BLOOD GASES, ARTERIAL"	PO2A	PO2 (A)	62622
+LABABG	"BLOOD GASES, ARTERIAL"	TCO2A	TCO2	62622
+LABABG	"BLOOD GASES, ARTERIAL"	METHB	METHEMOGLOBIN	62621
+LABABG	"BLOOD GASES, ARTERIAL"	CAION	"CALCIUM, IONIZED"	62620
+LABABG	"BLOOD GASES, ARTERIAL"	COHB	CO-HEMOGLOBIN	62620
+LABABG	"BLOOD GASES, ARTERIAL"	CTO2A	CTO2 (A)	62620
+LABABG	"BLOOD GASES, ARTERIAL"	O2HBA	O2-HEMOGLOBIN (A)	62620
+LABBBPRBC	RED BLOOD CELLS	RBCSTA	RBC PRODUCT - UNITS	62186
+LABMETC	"METABOLIC PANEL, COMPREHENSIVE"	EGFR	EGFR	60562
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	XMR	UNIT CROSSMATCH  	55668
+LABHFP	HEPATIC FUNCTION PANEL A	ALB	"ALBUMIN, SER/PLAS"	55660
+LABHFP	HEPATIC FUNCTION PANEL A	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	55660
+LABHFP	HEPATIC FUNCTION PANEL A	ALT	"ALT (SGPT), SER/PLAS"	55660
+LABHFP	HEPATIC FUNCTION PANEL A	AST	"AST (SGOT), SER/PLAS"	55660
+LABHFP	HEPATIC FUNCTION PANEL A	DBIL	CONJUGATED BILI	55660
+LABHFP	HEPATIC FUNCTION PANEL A	IBIL	UNCONJUGATED BILIRUBIN	55660
+LABHFP	HEPATIC FUNCTION PANEL A	TBIL	TOTAL BILIRUBIN	55660
+LABHFP	HEPATIC FUNCTION PANEL A	TP	"PROTEIN, TOTAL, SER/PLAS"	55660
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	UBT	UNIT BLOOD TYPE  	54497
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	UNT	UNIT NUMBER  	54497
+LABABG	"BLOOD GASES, ARTERIAL"	BD	BASE DEFICIT (VT)	51646
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CULT	CULT/OTHER RSLT:	49366
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	RPT	RPT	49366
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SDES	SPECIMEN	49366
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SREQ	COMMENTS	49366
+LABK	"POTASSIUM, SERUM/PLASMA"	K	"POTASSIUM, SER/PLAS"	48138
+LABUA	URINALYSIS WITH MICROSCOPIC	SPECMN	SPECIMEN	46316
+LABUA	URINALYSIS WITH MICROSCOPIC	UCOL	COLOR	46316
+LABUA	URINALYSIS WITH MICROSCOPIC	LEUKEST	LEUK ESTERASE	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	NITRITE	NITRITE	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	SPG	SPECIFIC GRAVITY	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	UBLOOD	BLOOD	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	UCLAR	CLARITY	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	UGLU	GLUCOSE	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	UKET	KETONE	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	UPH	PH	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	UPROT	PROTEIN	46312
+LABUA	URINALYSIS WITH MICROSCOPIC	URBC	"RBC, URINE"	46311
+LABUA	URINALYSIS WITH MICROSCOPIC	UWBC	"WBC, URINE"	46311
+LABTYPSNI	TYPE AND SCREEN	ARG	ABO/RH (AUTOMATION)	46177
+LABCBCD	CBC WITH DIFF	SIGNIF	SIGNIFICANT ABNORMALITIES	46052
+LABUA	URINALYSIS WITH MICROSCOPIC	UBACT	BACTERIA	46023
+LABUA	URINALYSIS WITH MICROSCOPIC	UCMT	URINE COMMENT	46018
+LABTYPSNI	TYPE AND SCREEN	AS3	ANTIBODY SCREEN	45808
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CULT	CULT/OTHER RSLT:	43898
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	RPT	RPT	43898
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SDES	SPECIMEN	43898
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SREQ	COMMENTS	43898
+LABMB	"CK, MB (MASS)"	CKMB	"CK, MB (MASS)"	40071
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	DI	ISSUE DATE/TIME	40063
+LABROMRS	MRSA SCREEN	CULT	CULT/OTHER RSLT:	39655
+LABROMRS	MRSA SCREEN	RPT	RPT	39655
+LABROMRS	MRSA SCREEN	SDES	SPECIMEN	39655
+LABROMRS	MRSA SCREEN	SREQ	COMMENTS	39655
+LABFK506L	TACROLIMUS (FK506)	FK506	"PEAK, TROUGH, OR RANDOM"	39396
+LABFK506L	TACROLIMUS (FK506)	FK506	TACROLIMUS (FK506)	39396
+LABFK506L	TACROLIMUS (FK506)	TACR2	DATE & TIME OF LAST DOSE	39396
+LABFK506L	TACROLIMUS (FK506)	TACR3	DOSAGE	39396
+LABFK506L	TACROLIMUS (FK506)	TACR4	ADMINISTRATION ROUTE	39396
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	XMR	UNIT CROSSMATCH  	37707
+LABVBG	"BLOOD GASES, VENOUS"	PHV	PH (V)	37624
+LABVBG	"BLOOD GASES, VENOUS"	CTO2V	CTO2 (V)	37623
+LABVBG	"BLOOD GASES, VENOUS"	GLU	"GLUCOSE, WHOLE BLOOD"	37623
+LABVBG	"BLOOD GASES, VENOUS"	HCO3A	HCO3	37623
+LABVBG	"BLOOD GASES, VENOUS"	HCT	HCT (EST)	37623
+LABVBG	"BLOOD GASES, VENOUS"	K	"POTASSIUM, WHOLE BLD"	37623
+LABVBG	"BLOOD GASES, VENOUS"	PCO2V	PCO2 (V)	37623
+LABVBG	"BLOOD GASES, VENOUS"	PO2V	PO2 (V)	37623
+LABVBG	"BLOOD GASES, VENOUS"	TCO2A	TCO2	37623
+LABVBG	"BLOOD GASES, VENOUS"	CAION	"CALCIUM, IONIZED"	37622
+LABVBG	"BLOOD GASES, VENOUS"	CL	"CHLORIDE, WHOLE BLD"	37622
+LABVBG	"BLOOD GASES, VENOUS"	COHB	CO-HEMOGLOBIN	37622
+LABVBG	"BLOOD GASES, VENOUS"	HGBBG	THB	37622
+LABVBG	"BLOOD GASES, VENOUS"	METHB	METHEMOGLOBIN	37622
+LABVBG	"BLOOD GASES, VENOUS"	NA	"SODIUM, WHOLE BLOOD"	37622
+LABVBG	"BLOOD GASES, VENOUS"	O2SATV	O2 SATURATION (V)	37622
+LABMB	"CK, MB (MASS)"	CK	"CK, TOTAL"	37594
+LABURNC	URINE CULTURE	CULT	CULT/OTHER RSLT:	34444
+LABURNC	URINE CULTURE	RPT	RPT	34444
+LABURNC	URINE CULTURE	SDES	SPECIMEN	34444
+LABURNC	URINE CULTURE	SREQ	COMMENTS	34444
+LABCBCD	CBC WITH DIFF	LYM	LYMPHOCYTES	33951
+LABCBCD	CBC WITH DIFF	NEUTAB	"NEUT,ABS (MAN DIFF)"	33746
+LABCBCD	CBC WITH DIFF	NEUT	SEG NEUTROPHILS	33700
+LABCBCD	CBC WITH DIFF	LYMAB	"LYM, ABS (MAN DIFF)"	33606
+LABLDH	"LDH TOTAL, SERUM / PLASMA"	LDH	"LDH, TOTAL, SER/PLAS"	33443
+LABHCTX	HEMATOCRIT	HCT	HEMATOCRIT	32774
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANCOTROUGH	"VANCOMYCIN, TROUGH"	32610
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANPR2	DATE & TIME OF LAST DOSE	32609
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANPR3	DOSAGE	32609
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	VANPR4	ADMINISTRATION ROUTE	32609
+LABMB	"CK, MB (MASS)"	CKMBRI	CKMB %RELATIVE INDEX	31392
+LABUA	URINALYSIS WITH MICROSCOPIC	MUCUS	MUCOUS THREADS	31348
+LABARVI	BLOOD TYPE VERIFICATION	ARV	BLOOD TYPE VERIFICATION	30954
+LABCBCD	CBC WITH DIFF	MONO	MONOCYTES	30873
+LABNA	"SODIUM, SERUM / PLASMA"	NA	"SODIUM, SER/PLAS"	30689
+LABCBCD	CBC WITH DIFF	MONOAB	"MONO, ABS (MAN DIFF)"	30490
+LABCBCD	CBC WITH DIFF	BAND	BAND NEUTROPHILS	30244
+LABUA	URINALYSIS WITH MICROSCOPIC	SQEP	SQUAMOUS CELLS	28276
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SPECMN	SPECIMEN	27414
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UCOL	COLOR	27414
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	LEUKEST	LEUK ESTERASE	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	NITRITE	NITRITE	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SPG	SPECIFIC GRAVITY	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UBLOOD	BLOOD	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UCLAR	CLARITY	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UGLU	GLUCOSE	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UKET	KETONE	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UPH	PH	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UPROT	PROTEIN	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	URBC	"RBC, URINE"	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UWBC	"WBC, URINE"	27413
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	PRN	CULTURE SENT PRN	27412
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UBACT	BACTERIA	27248
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UCMT	URINE COMMENT	27244
+LABVBG	"BLOOD GASES, VENOUS"	BD	BASE DEFICIT (VT)	26070
+LABDIC	DIC SCREEN	DDIMER	D-DIMER	25741
+LABDIC	DIC SCREEN	FIBRINOGEN	FIBRINOGEN	25741
+LABDIC	DIC SCREEN	INR	INR	25741
+LABDIC	DIC SCREEN	PT	PROTHROMBIN TIME	25741
+LABDIC	DIC SCREEN	PTT	PART. THROMBOPLASTIN TIME	25741
+LABDIC	DIC SCREEN	TT	THROMBIN TIME	25741
+LABABG	"BLOOD GASES, ARTERIAL"	BE	BASE EXCESS (VT)	25196
+LABBBPPLT	PLATELET APHERESIS PRODUCT	PLT	PLATELETS	24904
+LABPCTNI	ISTAT TROPONIN I	PCCOM	POCT COMMENT	23732
+LABPCTNI	ISTAT TROPONIN I	TNI	"TROPONIN I, POCT"	23730
+LABHEPAR	HEPARIN ACTIVITY LEVEL	HEPAR	HEPARIN ACTIVITY LEVEL	23406
+LABA1C	HEMOGLOBIN A1C	A1C	HEMOGLOBIN A1C	22557
+LABURIC	"URIC ACID, SERUM / PLASMA"	URIC	"URIC ACID, SER/PLAS"	22354
+LABVBG	"BLOOD GASES, VENOUS"	BE	BASE EXCESS (VT)	22180
+LABLIPS	LIPASE	LIPASE	LIPASE	22010
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	MUCUS	MUCOUS THREADS	19795
+LABTSH	TSH	TSH	TSH	19390
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	SI	<<STATUS>>  	19375
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	PRO	PRODUCT ID  	19372
+LABLACWB	"LACTATE, WHOLE BLOOD"	LACWBL	LACWBL	17776
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SQEP	SQUAMOUS CELLS	16632
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	FFP	FRESH FROZEN UNITS	16604
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	PRO	PRODUCT ID  	16587
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	SI	<<STATUS>>  	16587
+LABCK	"CREATINE KINASE, TOTAL"	CK	"CK, TOTAL"	16129
+LABHEPAR	"HEPARIN, UNFRACTIONATED, ANTI XA"	HEPAR	HEPARIN ACTIVITY LEVEL	15974
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	UBT	UNIT BLOOD TYPE  	15817
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	UNT	UNIT NUMBER  	15815
+LABCRP	C - REACTIVE PROTEIN	CRP	C-REACTIVE PROTEIN	15445
+LABPCCG4V	"ISTAT CG4,VENOUS"	HCO3V	"HCO3 (V), ISTAT"	15206
+LABPCCG4V	"ISTAT CG4,VENOUS"	LAC	"LACTATE, ISTAT"	15206
+LABPCCG4V	"ISTAT CG4,VENOUS"	PCCOM	POCT COMMENT	15206
+LABPCCG4V	"ISTAT CG4,VENOUS"	PCO2V	"PCO2 (V), ISTAT"	15206
+LABPCCG4V	"ISTAT CG4,VENOUS"	PHV	"PH (V), ISTAT"	15206
+LABPCCG4V	"ISTAT CG4,VENOUS"	TCO2V	"TCO2 (V), ISTAT"	15206
+LABPCCG4V	"ISTAT CG4,VENOUS"	O2SATV	"O2 SATURATION, ISTAT"	15205
+LABPCCG4V	"ISTAT CG4,VENOUS"	PO2V	"PO2 (V), ISTAT"	15205
+LABCBCD	CBC WITH DIFF	META	METAMYELOCYTES	14834
+LABFIB	FIBRINOGEN	FIBRINOGEN	FIBRINOGEN	13839
+LABPCEG7	"ISTAT EG7, ARTERIAL"	PCCOM	POCT COMMENT	13530
+LABPCEG7	"ISTAT EG7, ARTERIAL"	HCO3A	"HCO3 (A), ISTAT"	13529
+LABPCEG7	"ISTAT EG7, ARTERIAL"	PCO2A	"PCO2 (A), ISTAT"	13529
+LABPCEG7	"ISTAT EG7, ARTERIAL"	PHA	"PH (A), ISTAT"	13529
+LABPCEG7	"ISTAT EG7, ARTERIAL"	PO2A	"PO2 (A), ISTAT"	13529
+LABPCEG7	"ISTAT EG7, ARTERIAL"	TCO2A	"TCO2, ISTAT"	13529
+LABPCEG7	"ISTAT EG7, ARTERIAL"	O2SATA	"O2 SATURATION, ISTAT"	13528
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	UBT	UNIT BLOOD TYPE  	13445
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	UNT	UNIT NUMBER  	13445
+LABPCEG7	"ISTAT EG7, ARTERIAL"	HCT	"HCT(CALC), ISTAT"	13376
+LABPCEG7	"ISTAT EG7, ARTERIAL"	HGB	"HGB(CALC), ISTAT"	13376
+LABPCEG7	"ISTAT EG7, ARTERIAL"	K	"POTASSIUM, ISTAT"	13376
+LABPCEG7	"ISTAT EG7, ARTERIAL"	NA	"SODIUM, ISTAT"	13376
+LABPCEG7	"ISTAT EG7, ARTERIAL"	CAION	"CALCIUM,ION, ISTAT"	13373
+LABCBCD	CBC WITH DIFF	MYEL	MYELOCYTES	13209
+LABOSM	"OSMOLALITY, SERUM"	OSMOL	"OSMOLALITY, SERUM"	13181
+LABCBCD	CBC WITH DIFF	EOS	EOSINOPHILS	12794
+LABNTBNP	NT - PROBNP	BNP	NT-PROBNP	12696
+LABCBCD	CBC WITH DIFF	EOSAB	"EOS, ABS (MAN DIFF)"	12648
+LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	CDTR	STOOL C.DIFF TOXIN B RESULT	12622
+LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	CDTSP	SPECIMEN	12622
+LABPCEG7V	"ISTAT EG7, VENOUS"	HCO3V	"HCO3 (V), ISTAT"	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	O2SATV	"O2 SATURATION, ISTAT"	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	PCCOM	POCT COMMENT	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	PCO2V	"PCO2 (V), ISTAT"	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	PHV	"PH (V), ISTAT"	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	PO2V	"PO2 (V), ISTAT"	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	TCO2V	"TCO2 (V), ISTAT"	12341
+LABPCEG7V	"ISTAT EG7, VENOUS"	CAION	"CALCIUM,ION, ISTAT"	12335
+LABPCEG7V	"ISTAT EG7, VENOUS"	HCT	"HCT(CALC), ISTAT"	12335
+LABPCEG7V	"ISTAT EG7, VENOUS"	HGB	"HGB(CALC), ISTAT"	12335
+LABPCEG7V	"ISTAT EG7, VENOUS"	K	"POTASSIUM, ISTAT"	12335
+LABPCEG7V	"ISTAT EG7, VENOUS"	NA	"SODIUM, ISTAT"	12335
+LABANER	ANAEROBIC CULTURE	CULT	CULT/OTHER RSLT:	12120
+LABANER	ANAEROBIC CULTURE	RPT	RPT	12120
+LABANER	ANAEROBIC CULTURE	SDES	SPECIMEN	12120
+LABANER	ANAEROBIC CULTURE	SREQ	COMMENTS	12120
+EKG5	ECG 12-LEAD	PDF	PDF RESULT	12019
+LABCBCD	CBC WITH DIFF	NRBC	NRBC/100WBC	11797
+LABBMT2	BMT PANEL 2	CO2	"CO2, SER/PLAS"	11555
+LABBMT2	BMT PANEL 2	CR	"CREATININE, SER/PLAS"	11555
+LABBMT2	BMT PANEL 2	GLU	"GLUCOSE, SER/PLAS"	11555
+LABBMT2	BMT PANEL 2	K	"POTASSIUM, SER/PLAS"	11555
+LABBMT2	BMT PANEL 2	NA	"SODIUM, SER/PLAS"	11555
+LABGRAM	GRAM STAIN	GS	GRAM STAIN	11525
+LABGRAM	GRAM STAIN	RPT	RPT	11525
+LABGRAM	GRAM STAIN	SDES	SPECIMEN	11525
+LABGRAM	GRAM STAIN	SREQ	COMMENTS	11525
+LABBMT1	BMT PANEL 1	ALB	"ALBUMIN, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	ALT	"ALT (SGPT), SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	AST	"AST (SGOT), SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	CR	"CREATININE, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	GLU	"GLUCOSE, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	K	"POTASSIUM, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	MG	"MAGNESIUM, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	NA	"SODIUM, SER/PLAS"	11469
+LABBMT1	BMT PANEL 1	TBIL	TOTAL BILIRUBIN	11469
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	DI	ISSUE DATE/TIME	10889
+LABRESP	RESPIRATORY CULTURE	CULT	CULT/OTHER RSLT:	10620
+LABRESP	RESPIRATORY CULTURE	RPT	RPT	10620
+LABRESP	RESPIRATORY CULTURE	SDES	SPECIMEN	10620
+LABRESP	RESPIRATORY CULTURE	SREQ	COMMENTS	10620
+LABFCUL	FUNGAL CULTURE	CULT	CULT/OTHER RSLT:	10531
+LABFCUL	FUNGAL CULTURE	RPT	RPT	10531
+LABFCUL	FUNGAL CULTURE	SDES	SPECIMEN	10531
+LABFCUL	FUNGAL CULTURE	SREQ	COMMENTS	10531
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	WBC	WBC	10417
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	HCT	HEMATOCRIT	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	HGB	HEMOGLOBIN	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MCH	MCH	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MCHC	MCHC	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MCV	MCV	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PLT	PLATELET COUNT	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	RBC	RBC	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	RDW	RDW	10415
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	SLREV	SLIDE REVIEW	10406
+LABPCCR	ISTAT CREATININE	CR	"CREATININE,ISTAT"	9997
+LABPCCR	ISTAT CREATININE	PCCOM	POCT COMMENT	9997
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	AMPHET	AMPHETAMINE	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	BARB	BARBITURATES	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	BENZO	BENZODIAZEPINES	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	COCAINE	COCAINE METABOLITES	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	DOACOM	DOACOM COMMENTS	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	METHADONE	METHADONE	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	OPIATE	OPIATES	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	PCP	PCP	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	TCA	TRICYCLICS	9938
+LABDOA	"DRUGS OF ABUSE SCREEN, URINE"	THC	THC	9938
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	XMR	UNIT CROSSMATCH  	9856
+LABPLTS	PLATELET COUNT	PLT	PLATELET COUNT	9852
+LABPCEG7	"ISTAT EG7, ARTERIAL"	FIO2	FIO2	9839
+LABLIDOL	LIDOCAINE	LI	"PEAK, TROUGH, OR RANDOM"	9729
+LABLIDOL	LIDOCAINE	LIDO	LIDOCAINE	9729
+LABLIDOL	LIDOCAINE	LIDO2	DATE & TIME OF LAST DOSE	9729
+LABLIDOL	LIDOCAINE	LIDO3	DOSAGE	9729
+LABLIDOL	LIDOCAINE	LIDO4	ADMINISTRATION ROUTE	9729
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MORPH	MORPHOLOGY	9586
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	DI	ISSUE DATE/TIME	9143
+LABPCCG4V	"ISTAT CG4,VENOUS"	BE	"BASE EXCESS, ISTAT"	8970
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	UPH	PH URINE	8896
+LABFT4	"T4, FREE"	FT4	FREE T4	8889
+POC1	POC URINE DIPSTICK	UWBC	LEUKOCYTES	8781
+POC1	POC URINE DIPSTICK	EXPDATE	EXP DATE	8780
+POC1	POC URINE DIPSTICK	NITRITE	NITRITE	8780
+POC1	POC URINE DIPSTICK	SPG	SPECIFIC GRAVITY	8780
+POC1	POC URINE DIPSTICK	UBLOOD	BLOOD URINE	8780
+POC1	POC URINE DIPSTICK	UGLU	GLUCOSE URINE	8780
+POC1	POC URINE DIPSTICK	UKET	KETONES	8779
+POC1	POC URINE DIPSTICK	UPH	PH URINE	8779
+POC1	POC URINE DIPSTICK	UPROT	PROTEIN URINE	8779
+POC1	POC URINE DIPSTICK	DIPLOT	DIPSTICK LOT	8777
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUTAB	"NEUT, ABS"	8638
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONOAB	"MONO, ABS"	8637
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASOAB	"BASOS, ABS"	8636
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOSAB	"EOS, ABS"	8636
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYMAB	"LYM, ABS"	8636
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYM	"LYM, %"	8635
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONO	"MONO, %"	8635
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUT	"NEUT, %"	8635
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASO	"BASO, %"	8634
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOS	"EOS, %"	8634
+LABYPREAL	PREALBUMIN	PREALBUMIN	PREALBUMIN	8608
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	XMR	UNIT CROSSMATCH  	8550
+LABPCG3V	"ISTAT G3+, VENOUS"	HCO3V	"HCO3 (V), ISTAT"	8527
+LABPCG3V	"ISTAT G3+, VENOUS"	O2SATV	"O2 SATURATION, ISTAT"	8527
+LABPCG3V	"ISTAT G3+, VENOUS"	PCCOM	POCT COMMENT	8527
+LABPCG3V	"ISTAT G3+, VENOUS"	PCO2V	"PCO2 (V), ISTAT"	8527
+LABPCG3V	"ISTAT G3+, VENOUS"	PHV	"PH (V), ISTAT"	8527
+LABPCG3V	"ISTAT G3+, VENOUS"	PO2V	"PO2 (V), ISTAT"	8527
+LABPCG3V	"ISTAT G3+, VENOUS"	TCO2V	"TCO2 (V), ISTAT"	8527
+LABTRIG	"TRIGLYCERIDES, SERUM / PLASMA"	TGL	"TRIGLYCERIDE, SER/PLAS"	8518
+LABFLDC	FLUID CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	8169
+LABFLDC	FLUID CULTURE AND GRAM STAIN	GS	GRAM STAIN	8169
+LABFLDC	FLUID CULTURE AND GRAM STAIN	RPT	RPT	8169
+LABFLDC	FLUID CULTURE AND GRAM STAIN	SDES	SPECIMEN	8169
+LABFLDC	FLUID CULTURE AND GRAM STAIN	SREQ	COMMENTS	8169
+LABCBCD	CBC WITH DIFF	BASO	BASOPHILS	8140
+LABCBCD	CBC WITH DIFF	BASOAB	"BASO, ABS (MAN DIFF)"	8061
+LABLPD	LIPID PANEL WITH DIRECT LDL	CHOL	"CHOLESTEROL, TOTAL"	7963
+LABLPD	LIPID PANEL WITH DIRECT LDL	CHOLHDL	CHOLESTEROL/HDL RATIO	7963
+LABLPD	LIPID PANEL WITH DIRECT LDL	HDL	HDL CHOLESTEROL	7963
+LABLPD	LIPID PANEL WITH DIRECT LDL	LDL	DIRECT LDL CHOL	7963
+LABLPD	LIPID PANEL WITH DIRECT LDL	NHDL	"NON-HDL CHOL, CALC"	7963
+LABLPD	LIPID PANEL WITH DIRECT LDL	TGL	"TRIGLYCERIDE, SER/PLAS"	7963
+LABPCEG7V	"ISTAT EG7, VENOUS"	FIO2	FIO2	7836
+LABPCEG7V	"ISTAT EG7, VENOUS"	BE	"BASE EXCESS, ISTAT"	7835
+LABPCEG7	"ISTAT EG7, ARTERIAL"	BE	"BASE EXCESS, ISTAT"	7694
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	PRO	PRODUCT ID  	7628
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	SI	<<STATUS>>  	7628
+LABTYPSNI	TYPE AND SCREEN	AS	ANTIBODY SCREEN	7596
+LABNH3	"AMMONIA, PLASMA"	NH3	"AMMONIA, PLASMA"	7230
+LABPCG3V	"ISTAT G3+, VENOUS"	FIO2	FIO2	7108
+LABUA	URINALYSIS WITH MICROSCOPIC	AMORCRY	AMORPHOUS CRYSTALS	7093
+LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH	7003
+LABFER	FERRITIN	FERRITIN	FERRITIN	6948
+LABUA	URINALYSIS WITH MICROSCOPIC	HYCAST	HYALINE CASTS	6923
+LABB12	VITAMIN B12	B12	VITAMIN B12	6918
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	PRO	PRODUCT ID  	6917
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	SI	<<STATUS>>  	6917
+LABUSPG	"SPECIFIC GRAVITY, URINE"	SPG	SPECIFIC GRAVITY	6905
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	UBT	UNIT BLOOD TYPE  	6268
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	UNT	UNIT NUMBER  	6268
+LABPCCG4V	"ISTAT CG4,VENOUS"	BD	"BASE DEFICIT, ISTAT"	6251
+LABAMY	"AMYLASE, SERUM"	AMYLASE	"AMYLASE, SERUM"	6246
+LABCBCD	CBC WITH DIFF	BLASTS	BLASTS	6214
+LABPATHR	REVIEW BY PATHOLOGIST	REVUE	COMMENTS	6179
+LABCBCD	CBC WITH DIFF	BLASTSAB	"BLASTS,ABS(MAN DIFF)"	6155
+LABTRFS	TRANSFERRIN SATURATION	FE	"IRON, TOTAL"	6042
+LABTRFS	TRANSFERRIN SATURATION	TRFRN	TRANSFERRIN	6042
+LABTRFS	TRANSFERRIN SATURATION	TRFSAT	TRANSFERRIN SAT	6042
+LABARI	ABO/RH (ARI)	ABORH	ABO/RH	6019
+LABPCEG7	"ISTAT EG7, ARTERIAL"	BD	"BASE DEFICIT, ISTAT"	5869
+LABURNA	"SODIUM, URINE (RANDOM)"	UNA	"SODIUM, URINE"	5864
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETIC	"RETIC, %"	5756
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETICAB	"RETIC, ABS [AUTORETIC]"	5755
+LABHAP	HAPTOGLOBIN	HAPTO	HAPTOGLOBIN	5750
+LABASI	AB SCREEN (ASI)	AS	ANTIBODY SCREEN	5714
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	UBT	UNIT BLOOD TYPE  	5631
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	UNT	UNIT NUMBER  	5631
+LABAFBC	"AFB CULTURE, RESPIRATORY"	AFB	AFB SMEAR	5539
+LABAFBC	"AFB CULTURE, RESPIRATORY"	CULT	CULT/OTHER RSLT:	5539
+LABAFBC	"AFB CULTURE, RESPIRATORY"	RPT	RPT	5539
+LABAFBC	"AFB CULTURE, RESPIRATORY"	SDES	SPECIMEN	5539
+LABAFBC	"AFB CULTURE, RESPIRATORY"	SREQ	COMMENTS	5539
+LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC	"PEAK, TROUGH, OR RANDOM"	5436
+LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC2	DATE & TIME LAST DOSE	5436
+LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC3	DOSAGE	5436
+LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYC4	ADMINISTRATION ROUTE	5436
+LABCYCWBL	"CYCLOSPORINE, WHOLE BLOOD"	CYCWB	"CYCLOSPORINE, WHOLE BLOOD"	5436
+LABALB	"ALBUMIN, SERUM/PLASMA"	ALB	"ALBUMIN, SER/PLAS"	5412
+LABPHEN	PHENYTOIN LEVEL	PHEN	"PEAK, TROUGH, OR RANDOM"	5338
+LABPHEN	PHENYTOIN LEVEL	PHEN	PHENYTOIN	5338
+LABPHEN	PHENYTOIN LEVEL	PHEN2	DATE & TIME OF LAST DOSE	5338
+LABPHEN	PHENYTOIN LEVEL	PHEN3	DOSAGE	5338
+LABPHEN	PHENYTOIN LEVEL	PHEN4	ADMINISTRATION ROUTE	5338
+LABUOSM	"OSMOLALITY, URINE"	UOSM	"OSMOLALITY, URINE"	5290
+LABCBCD	CBC WITH DIFF	REACTL	REACTIVE LYMPHS	5204
+LABCORT	"CORTISOL, SERUM"	CORT	"CORTISOL, SERUM"	5155
+LABUA	URINALYSIS WITH MICROSCOPIC	TRANST	TRANSITIONAL	5153
+LABDIGL	"DIGOXIN, SERUM"	DIG	DIGOXIN	5126
+LABDIGL	"DIGOXIN, SERUM"	DIG	"PEAK, TOUGH, OR RANDOM"	5126
+LABDIGL	"DIGOXIN, SERUM"	DIG2	DATE & TIME OF LAST DOSE	5126
+LABDIGL	"DIGOXIN, SERUM"	DIG3	DOSAGE	5126
+LABDIGL	"DIGOXIN, SERUM"	DIG4	ADMINISTRATION ROUTE	5126
+LABVOLTL	VOLATILE SCREEN	ETOH	ETHANOL	4982
+LABVOLTL	VOLATILE SCREEN	ETOHI	ETHANOL INTERP	4982
+LABVOLTL	VOLATILE SCREEN	OSMGAP	OSMOLAL GAP	4979
+LABVOLTL	VOLATILE SCREEN	OSMGPI	OSMOLAL GAP INTERP	4979
+LABCBCD	CBC WITH DIFF	PRMY	PROMYELOCYTES	4938
+LABSLHELD	SLIDE HELD FOR REVIEW	SLHELD	SLIDE HELD TO REVIEW	4929
+LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	AFB	AFB SMEAR	4858
+LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	CULT	CULT/OTHER RSLT:	4858
+LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	RPT	RPT	4858
+LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	SDES	SPECIMEN	4858
+LABAFBD	"AFB CULTURE, NON - RESPIRATORY"	SREQ	COMMENTS	4858
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	DX	DIAGNOSIS	4829
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FCOLOR	APPEARANCE	4829
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FLDTYP	FLUID SOURCE	4829
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	MALIG	SUSPECT MALIGNANCY?	4829
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FTURB	"TURBIDITY, FLD"	4827
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	LYMFLD	"LYMPHOCYTE %, FLD"	4827
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	MONFLD	"MONO/MESO %, FLD"	4827
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	RBCFLD	"RBC COUNT, FLD (/UL)"	4827
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	WBCFLD	"NUCL CELLS, FLD(/UL)"	4827
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	NEUTFLD	"NEUTROPHIL %, FLD"	4826
+LABESRP	SEDIMENTATION RATE (ESR)	ESR	"ESR, (MANUAL)"	4807
+LABCBCD	CBC WITH DIFF	PSREV	PATH/SPEC REVIEW	4789
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	PRO	PRODUCT ID  	4765
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	SI	<<STATUS>>  	4765
+LABPCG3V	"ISTAT G3+, VENOUS"	BD	"BASE DEFICIT, ISTAT"	4572
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	SPEXP	SPECIMEN EXPIRATION DATE/TIME	4565
+LABHIVWBL	ANTI-HIV	HIVAR	ANTI-HIV ANTIBODY	4562
+LABHIVWBL	ANTI-HIV	HIVI	INTERPRETATION	4562
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	BTURB	"TURBIDITY, CSF"	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	COLORCSF	"COLOR, CSF"	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CSOURC	SOURCE	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	DX	DIAGNOSIS	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	MALIG	SUSPECT MALIGNANCY?	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	RBCCSF	"RBC COUNT, CSF"	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	TUBENO	CSF TUBE #	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	WBCCSF	"WBC COUNT, CSF"	4561
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	LYMCSF	"LYMPHOCYTES, CSF"	4560
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	MONOCSF	"MONOCYTOID CELLS, CSF"	4560
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	NEUTCSF	"NEUTROPHILS, CSF"	4560
+LABUCR	"CREATININE, URINE (RANDOM)"	UCR	"CREATININE, URINE"	4555
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	COLORCSF	"COLOR AFTER CENTRIFUGE, CSF"	4533
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CTURB	"TURB. AFTER CENTRIF., CSF"	4533
+LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	CULT	CULT/OTHER RSLT:	4529
+LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	RPT	RPT	4529
+LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	SDES	SPECIMEN	4529
+LABBLCSTK	"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY"	SREQ	COMMENTS	4529
+LABPCEG7V	"ISTAT EG7, VENOUS"	BD	"BASE DEFICIT, ISTAT"	4524
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	DI	ISSUE DATE/TIME	4519
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	4430
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	GS	GRAM STAIN	4430
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	RPT	RPT	4430
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	SDES	SPECIMEN	4430
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	SREQ	COMMENTS	4430
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	PRO	PRODUCT ID  	4402
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	SI	<<STATUS>>  	4402
+LABPCEG7	"ISTAT EG7, ARTERIAL"	PCTEMP	BODY TEMPERATURE	4374
+LABSPLAC	SEPSIS PROTOCOL LACTATE	LACWBL	LACWBL	4353
+LABPCEG7V	"ISTAT EG7, VENOUS"	PCTEMP	BODY TEMPERATURE	4251
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	AMORCRY	AMORPHOUS CRYSTALS	4242
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	SIGNIF	SIGNIFICANT ABNORMALITIES	4199
+LABPCCG4O	"ISTAT CG4, OTHER"	HCO3	"HCO3, ISTAT"	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	LAC	"LACTATE, ISTAT"	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	PCCOM	POCT COMMENT	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	PCO2A	"PCO2, ISTAT"	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	PCO2SO	"O2 SATURATION, ISTAT"	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	PH	"PH, ISTAT"	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	PO2A	"PO2, ISTAT"	4112
+LABPCCG4O	"ISTAT CG4, OTHER"	TCO2A	"TCO2, (ISTAT)"	4112
+LABDBIL	"BILIRUBIN DIRECT, SERUM/PLASMA"	DBIL	CONJUGATED BILI	4104
+LABESRP	SEDIMENTATION RATE (ESR)	ESR	"ESR, (AUTOMATED)"	4065
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	DI	ISSUE DATE/TIME	4030
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	HYCAST	HYALINE CASTS	4027
+LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	CULT	CULT/OTHER RSLT:	4015
+LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	RPT	RPT	4015
+LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	SDES	SPECIMEN	4015
+LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	SREQ	COMMENTS	4015
+LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	OCCGU	"STOOL SAMPLE1,GUAIAC"	3995
+LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	DATE1	DATE 1	3991
+LABPCG3V	"ISTAT G3+, VENOUS"	BE	"BASE EXCESS, ISTAT"	3984
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC10	DAILY QC 10 RESULT	3940
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC5	DAILY QC 5 RESULT	3940
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	TESTLO	TEST STRIP LOT#	3939
+LABGGT	GAMMA - GLUTAMYL TRANSFERASE	GGT	GAMMA-GLUTAMYL TRANS (GGT)	3877
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	UBT	UNIT BLOOD TYPE  	3845
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	UNT	UNIT NUMBER  	3845
+LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	DATE2	DATE 2	3838
+LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	DATE3	DATE 3	3836
+LABCBCD	CBC WITH DIFF	MAOTH	"OTHER, ABS(MAN DIFF)"	3814
+LABHBSAG	HBSAG	HBSINT	INTERPRETATION	3788
+LABHBSAG	HBSAG	HBSR	HEP B SURFACE ANTIGEN	3788
+LABPALB	PREALBUMIN	PREALBUMIN	PREALBUMIN	3724
+LABPCG3O	"ISTAT G3+, OTHER"	HCO3	"HCO3, ISTAT"	3654
+LABPCG3O	"ISTAT G3+, OTHER"	PCCOM	POCT COMMENT	3654
+LABPCG3O	"ISTAT G3+, OTHER"	PCO2A	"PCO2, ISTAT"	3654
+LABPCG3O	"ISTAT G3+, OTHER"	PCO2SO	"O2 SATURATION, ISTAT"	3654
+LABPCG3O	"ISTAT G3+, OTHER"	PH	"PH, ISTAT"	3654
+LABPCG3O	"ISTAT G3+, OTHER"	PO2A	"PO2, ISTAT"	3654
+LABPCG3O	"ISTAT G3+, OTHER"	TCO2A	"TCO2, (ISTAT)"	3654
+LABDVERVP	RESPIRATORY DFA PANEL	ADVP	ADENOVIRUS	3644
+LABFOL	FOLIC ACID	FOLATE	FOLIC ACID (FOLATE)	3644
+LABDVERVP	RESPIRATORY DFA PANEL	INFAP	INFLUENZA A	3644
+LABDVERVP	RESPIRATORY DFA PANEL	INFBP	INFLUENZA B	3644
+LABDVERVP	RESPIRATORY DFA PANEL	MPVPR	METAPNEUMOVIRUS	3644
+LABDVERVP	RESPIRATORY DFA PANEL	P1P	PARAINFLUENZA 1	3644
+LABDVERVP	RESPIRATORY DFA PANEL	P2P	PARAINFLUENZA 2	3644
+LABDVERVP	RESPIRATORY DFA PANEL	P3P	PARAINFLUENZA 3	3644
+LABDVERVP	RESPIRATORY DFA PANEL	RVP	RSV	3644
+LABDVERVP	RESPIRATORY DFA PANEL	RVPIP	TEST DECRIPTION	3644
+LABDVERVP	RESPIRATORY DFA PANEL	RVPSP	SPECIMEN	3644
+LABPATHR	REVIEW BY PATHOLOGIST	FPATH	PATHOLOGIST	3556
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	UBT	UNIT BLOOD TYPE  	3505
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	UNT	UNIT NUMBER  	3505
+LABCSFGL	"GLUCOSE, CSF"	GLUCSF	"GLUCOSE, CSF"	3470
+LABCSFTP	"PROTEIN TOTAL, CSF"	TPCSF	"PROTEIN, TOTAL, CSF"	3446
+LABPCEC4V	"ISTAT EC4, VENOUS"	HCT	"HCT(CALC), ISTAT"	3442
+LABPCEC4V	"ISTAT EC4, VENOUS"	HGB	"HGB(CALC), ISTAT"	3442
+LABPCEC4V	"ISTAT EC4, VENOUS"	K	"POTASSIUM, ISTAT"	3442
+LABPCEC4V	"ISTAT EC4, VENOUS"	NA	"SODIUM, ISTAT"	3442
+LABPCEC4V	"ISTAT EC4, VENOUS"	PCCOM	POCT COMMENT	3442
+LABPCEC4V	"ISTAT EC4, VENOUS"	GLU	"GLUCOSE,ISTAT"	3441
+LABBUN	"UREA NITROGEN, SERUM/PLASMA"	BUN	"UREA NITROGEN,SER/PLAS"	3438
+LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	OCCGU	"STOOL SAMPLE2,GUAIAC"	3425
+LABSTOBGD	"OCCULT BLD, GUAIAC, STOOL, DIAG TEST"	OCCGU	"STOOL SAMPLE3,GUAIAC"	3422
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UCL	"CHLORIDE, URINE"	3421
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UCLCR	CHLORIDE/CREAT RATIO	3421
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UCR	URINE CREAT	3421
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UK	"POTASSIUM, URINE"	3421
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UKCR	POTASSIUM/CREAT RATIO	3421
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UNA	"SODIUM, URINE"	3421
+LABUEP	"ELECTROLYTE PANEL, URINE WITH CREATININE"	UNACR	SODIUM/CREAT RATIO	3421
+LABCA	"CALCIUM, SERUM/PLASMA"	CA	"CALCIUM, SER/PLAS"	3376
+LABUA	URINALYSIS WITH MICROSCOPIC	UWBCC	WBC CLUMPS	3374
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	XMR	UNIT CROSSMATCH  	3316
+LABACETA	"ACETAMINOPHEN, SERUM"	ACETA	ACETAMINOPHEN	3287
+LABPROCT	PROCALCITONIN	PROCTL	PROCALCITONIN	3235
+LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	CULT	CULT/OTHER RSLT:	3218
+LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	RPT	RPT	3218
+LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	SDES	SPECIMEN	3218
+LABANER2	"ANAEROBIC CULTURE, SHORT BATTERY"	SREQ	COMMENTS	3218
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	SPEXP	SPECIMEN EXPIRATION DATE/TIME	3194
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	TRANST	TRANSITIONAL	3171
+LABCSFC	CSF CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	3136
+LABCSFC	CSF CULTURE AND GRAM STAIN	GS	GRAM STAIN	3136
+LABCSFC	CSF CULTURE AND GRAM STAIN	RPT	RPT	3136
+LABCSFC	CSF CULTURE AND GRAM STAIN	SDES	SPECIMEN	3136
+LABCSFC	CSF CULTURE AND GRAM STAIN	SREQ	COMMENTS	3136
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	XMR	UNIT CROSSMATCH  	3097
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UPROT	PROTEIN	3080
+LABUAS	URINALYSIS MACROSCOPIC ONLY	LEUKEST	LEUK ESTERASE	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	NITRITE	NITRITE	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	SPECMN	SPECIMEN	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	SPG	SPECIFIC GRAVITY	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UBLOOD	BLOOD	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UCLAR	CLARITY	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UCOL	COLOR	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UGLU	GLUCOSE	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UKET	KETONE	3079
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UPH	PH	3079
+LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVCP	RESULT	3048
+LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVDNA	CMV DNA	3048
+LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVLOG	RESULT LOG 10	3048
+LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVSP	SPECIMEN	3048
+LABCMVPQ	"CMV DNA PCR QUANT, PLASMA"	CMVTIP	TEST DESCRIPTION	3048
+LABPCINR	ISTAT INR AND PROTIME	INR	"INR, ISTAT"	3018
+LABPCINR	ISTAT INR AND PROTIME	PCCOM	POCT COMMENT	3018
+LABPCG3V	"ISTAT G3+, VENOUS"	PCTEMP	BODY TEMPERATURE	2964
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	PRO	PRODUCT ID  	2940
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	SI	<<STATUS>>  	2940
+LABPCINR	ISTAT INR AND PROTIME	PT	"PT, ISTAT"	2878
+LABCBCD	CBC WITH DIFF	OTHER	OTHERS (DESCRIBE)	2876
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	DI	ISSUE DATE/TIME	2820
+LABFTP	"PROTEIN TOTAL, FLUID"	TPFLD	"PROTEIN, TOTAL, FLUID"	2765
+LABFKOH	FUNGAL CULTURE AND KOH	CULT	CULT/OTHER RSLT:	2744
+LABFKOH	FUNGAL CULTURE AND KOH	KOH	KOH PREP	2744
+LABFKOH	FUNGAL CULTURE AND KOH	RPT	RPT	2744
+LABFKOH	FUNGAL CULTURE AND KOH	SDES	SPECIMEN	2744
+LABFKOH	FUNGAL CULTURE AND KOH	SREQ	COMMENTS	2744
+LABDDIM	D - DIMER	DDIMER	D-DIMER	2732
+LABUNA	"SODIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	2686
+LABUNA	"SODIUM WITH CREATININE, URINE (RANDOM)"	UNA	"SODIUM, URINE"	2686
+LABUNA	"SODIUM WITH CREATININE, URINE (RANDOM)"	UNACR	SODIUM/CREAT RATIO	2686
+LABHGBX	HEMOGLOBIN	HGB	HEMOGLOBIN	2673
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	PRO	PRODUCT ID  	2667
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	SI	<<STATUS>>  	2667
+LABSPLAC	SEPSIS PROTOCOL LACTATE	SPLACW	SEPSIS PROTOCOL LACTATE	2645
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANCOSDD	"VANCOMYCIN, SINGLE DAILY DOSE"	2635
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANUN2	DATE & TIME OF LAST DOSE	2635
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANUN3	DOSAGE	2635
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANUN4	ADMINISTRATION ROUTE	2635
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	2599
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	GS	GRAM STAIN	2599
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	RPT	RPT	2599
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	SDES	SPECIMEN	2599
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	SREQ	COMMENTS	2599
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	DI	ISSUE DATE/TIME	2584
+POC26	POC URINE PREGNANCY	KIT1	KIT LOT#	2553
+POC26	POC URINE PREGNANCY	KITEX1	KIT EXP DATE	2553
+POC26	POC URINE PREGNANCY	PATRES	PATIENT RESULT	2553
+POC26	POC URINE PREGNANCY	QC9	QC RESULT	2553
+LABPCACTK	ISTAT ACTIVATED CLOTTING TIME	ACT	"ACT, ISTAT"	2549
+LABPCACTK	ISTAT ACTIVATED CLOTTING TIME	PCCOM	POCT COMMENT	2549
+LABANAS	ANTI - NUCLEAR AB	ANA	ANA RESULT	2521
+LABANAS	ANTI - NUCLEAR AB	ANAINTERP	ANA INTERPRETATION	2521
+LABTBILFR	"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"	DBIL	CONJUGATED BILI	2502
+LABTBILFR	"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"	IBIL	UNCONJUGATED BILIRUBIN	2502
+LABTBILFR	"BILIRUBIN TOTAL AND FRACTIONATED, SERUM"	TBIL	TOTAL BILIRUBIN	2502
+LABPCCG4O	"ISTAT CG4, OTHER"	BE	"BASE EXCESS, ISTAT"	2498
+LABUPREG	"PREGNANCY TEST, URINE"	UPREG	"PREGNANCY TEST, URINE"	2443
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	UBT	UNIT BLOOD TYPE  	2420
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	UNT	UNIT NUMBER  	2420
+LABFTP	"PROTEIN TOTAL, FLUID"	TPFLS	SOURCE	2402
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	CHOL	"CHOLESTEROL, TOTAL"	2346
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	CHOLHDL	CHOLESTEROL/HDL RATIO	2346
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	HDL	HDL CHOLESTEROL	2346
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	LDL	LDL (CALCULATED)	2346
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	LDLHDL	LDL/HDL RATIO	2346
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	NHDL	"NON-HDL CHOL, CALC"	2346
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	TGL	"TRIGLYCERIDE, SER/PLAS"	2346
+LABSIROL	SIROLIMUS LEVEL	SIRO2	DATE & TIME OF LAST DOSE	2250
+LABSIROL	SIROLIMUS LEVEL	SIRO3	DOSAGE	2250
+LABSIROL	SIROLIMUS LEVEL	SIRO4	ADMINISTRATION ROUTE	2250
+LABSIROL	SIROLIMUS LEVEL	SIROL	"PEAK, TROUGH, OR RANDOM"	2250
+LABSIROL	SIROLIMUS LEVEL	SIROL	SIROLIMUS	2250
+LABWDC	WOUND CULTURE	CULT	CULT/OTHER RSLT:	2231
+LABWDC	WOUND CULTURE	RPT	RPT	2231
+LABWDC	WOUND CULTURE	SDES	SPECIMEN	2231
+LABWDC	WOUND CULTURE	SREQ	COMMENTS	2231
+LABSALICY	SALICYLATE LEVEL	SALICYLATE	SALICYLATE	2212
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FCMT	FLUID COMMENT	2203
+LABBBPCRY	CRYOPRECIPITATE	CRSTA	CRYOPRECIPITATE UNITS	2195
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	UBT	UNIT BLOOD TYPE  	2178
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	UNT	UNIT NUMBER  	2178
+LABVCUL	VIRAL CULTURE	CULT	CULT/OTHER RSLT:	2171
+LABVCUL	VIRAL CULTURE	RPT	RPT	2171
+LABVCUL	VIRAL CULTURE	SDES	SPECIMEN	2171
+LABVCUL	VIRAL CULTURE	SREQ	COMMENTS	2171
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	UWBCC	WBC CLUMPS	2124
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	PRO	PRODUCT ID  	2110
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	SI	<<STATUS>>  	2110
+LABVPAL	VALPROIC ACID	VPA	"PEAK, TROUGH, OR RANDOM"	2109
+LABVPAL	VALPROIC ACID	VPA	VALPROIC ACID	2109
+LABVPAL	VALPROIC ACID	VPAL2	DATE & TIME OF LAST DOSE	2109
+LABVPAL	VALPROIC ACID	VPAL3	DOSAGE	2109
+LABVPAL	VALPROIC ACID	VPAL4	ADMINISTRATION ROUTE	2109
+LABMTXL	METHOTREXATE	MTX	METHOTREXATE	2105
+LABMTXL	METHOTREXATE	MTX	"PEAK, TROUGH, OR RANDOM"	2105
+LABMTXL	METHOTREXATE	MTXL2	DATE & TIME OF LAST DOSE	2105
+LABMTXL	METHOTREXATE	MTXL3	DOSAGE	2105
+LABMTXL	METHOTREXATE	MTXL4	ADMINISTRATION ROUTE	2105
+LABFLDH	"LDH TOTAL, FLUID"	LDHFLD	"LDH, TOTAL, FLUID"	2096
+LABFLDH	"LDH TOTAL, FLUID"	LDHFLS	SOURCE	2096
+LABURUN	"UREA, URINE (RANDOM)"	UURN	"UREA NITROGEN, URINE"	2090
+LABPCG3O	"ISTAT G3+, OTHER"	FIO2	FIO2	2075
+LABRFRPT	REFERENCE REPORT	REFRPT	REFERENCE REPORT	2064
+LABPCG3O	"ISTAT G3+, OTHER"	BE	"BASE EXCESS, ISTAT"	2032
+LABFAMY	"AMYLASE, FLUID"	AMYFLS	SOURCE	2022
+LABFAMY	"AMYLASE, FLUID"	AMYLASEFLD	"AMYLASE, FLUID"	2022
+LABUA	URINALYSIS WITH MICROSCOPIC	GRCAST	GRANULAR CASTS	1986
+LABOAPE	OVA AND PARASITES	CULT	CULT/OTHER RSLT:	1972
+LABOAPE	OVA AND PARASITES	RPT	RPT	1972
+LABOAPE	OVA AND PARASITES	SDES	SPECIMEN	1972
+LABOAPE	OVA AND PARASITES	SREQ	COMMENTS	1972
+LABSTSS	SYPHILIS TREPONEMAL SCREEN	STSINT	INTERPRETATIONS	1936
+LABSTSS	SYPHILIS TREPONEMAL SCREEN	STSQUL	RESULT	1936
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	PRO	PRODUCT ID  	1914
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	SI	<<STATUS>>  	1914
+LABUAS	URINALYSIS MACROSCOPIC ONLY	UCMT	URINE COMMENT	1898
+LABRBCFOL	"FOLATE, RBC"	FOLATERBC	"FOLATE, RBC"	1896
+LABMYCPA	MYCOPHENOLIC ACID	MYPA	MYCOPHENOLIC ACID	1892
+LABMYCPA	MYCOPHENOLIC ACID	MYPAG	MPA GLUCURONIDE	1892
+LABMYCPA	MYCOPHENOLIC ACID	MYPA1	"PEAK, TROUGH, OR RANDOM"	1881
+LABMYCPA	MYCOPHENOLIC ACID	MYPA2	DATE & TIME OF LAST DOSE	1881
+LABMYCPA	MYCOPHENOLIC ACID	MYPA3	DOSAGE	1881
+LABMYCPA	MYCOPHENOLIC ACID	MYPA4	ADMINISTRATION ROUTE	1881
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	DI	ISSUE DATE/TIME	1829
+LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVCP	RESULT	1801
+LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVDNA	CMV DNA	1801
+LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVLOG	RESULT LOG 10	1801
+LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVSP	SPECIMEN	1801
+LABCMVQT	"CMV DNA PCR QUANT, PLASMA"	CMVTIP	TEST DESCRIPTION	1801
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	XMR	UNIT CROSSMATCH  	1777
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	VL	PRODUCT VOLUME	1771
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYM	LYMPHOCYTES	1760
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYMAB	"LYM, ABS (MAN DIFF)"	1737
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	UBT	UNIT BLOOD TYPE  	1737
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	UNT	UNIT NUMBER  	1737
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUT	SEG NEUTROPHILS	1730
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NEUTAB	"NEUT,ABS (MAN DIFF)"	1729
+LABREPT	REPTILASE TIME	REPT	REPTILASE TIME	1710
+LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	DATBS	DIRECT ANTIGLOB.-BROAD SPEC	1708
+LABIBIL	"BILIRUBIN INDIRECT, SERUM"	IBIL	UNCONJUGATED BILIRUBIN	1699
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	XMR	UNIT CROSSMATCH  	1682
+LABPCG3O	"ISTAT G3+, OTHER"	BD	"BASE DEFICIT, ISTAT"	1640
+LABHITAB	HEPARIN (HIT) AB	PF4COM	PF4 COMMENT	1637
+LABHITAB	HEPARIN (HIT) AB	PF4ENH	"PF4 IGG, IGM, IGA OD"	1634
+LABCR	"CREATININE, SERUM/PLASMA"	CR	"CREATININE, SER/PLAS"	1633
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	DI	ISSUE DATE/TIME	1623
+LABPCCG4O	"ISTAT CG4, OTHER"	BD	"BASE DEFICIT, ISTAT"	1620
+LABSCMV	CMV SHELL VIAL CULTURE	CULT	CULT/OTHER RSLT:	1618
+LABSCMV	CMV SHELL VIAL CULTURE	RPT	RPT	1618
+LABSCMV	CMV SHELL VIAL CULTURE	SDES	SPECIMEN	1618
+LABSCMV	CMV SHELL VIAL CULTURE	SREQ	COMMENTS	1618
+LABARI	ABO/RH (ARI)	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1605
+LABFAL	"ALBUMIN, FLUID"	ALBFLS	SOURCE	1601
+LABFAL	"ALBUMIN, FLUID"	ALBFLD	"ALBUMIN, FLUID"	1600
+LABPREG	"PREGNANCY TEST, SERUM"	PREGTEST	"PREGNANCY, SERUM"	1582
+LABASI	AB SCREEN (ASI)	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1577
+LABREF1	LAB UNLISTED 1	LAB1	REFERENCE LAB	1566
+LABREF1	LAB UNLISTED 1	SPTY	SPECIMEN TYPE	1566
+LABREF1	LAB UNLISTED 1	TEST1	TEST NAME	1566
+LABREF1	LAB UNLISTED 1	RESUL1	RESULT	1560
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	UBT	UNIT BLOOD TYPE  	1546
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	UNT	UNIT NUMBER  	1546
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	PRO	PRODUCT ID  	1542
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	SI	<<STATUS>>  	1542
+LABTSHFT4	TSH W/ REFLEX FT4	TSH	TSH	1530
+LABTBIL	"BILIRUBIN TOTAL, SERUM/PLASMA"	TBIL	TOTAL BILIRUBIN	1528
+LABSPIE	"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM"	SPIEI	INTERPRETATION	1517
+LABSPIE	"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM"	SPIER	RESULT	1517
+LABPCYS	PNEUMOCYSTIS STAIN	PCPSTAIN	PCP STAIN	1516
+LABPCYS	PNEUMOCYSTIS STAIN	RPT	RPT	1516
+LABPCYS	PNEUMOCYSTIS STAIN	SDES	SPECIMEN	1516
+LABPCYS	PNEUMOCYSTIS STAIN	SREQ	COMMENTS	1516
+LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	CULT	CULT/OTHER RSLT:	1503
+LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	GS	GRAM STAIN	1503
+LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	RPT	RPT	1503
+LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	SDES	SPECIMEN	1503
+LABWDCS	"WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP"	SREQ	COMMENTS	1503
+LABDVERV	RESPIRATORY DFA PANEL	DVE	RESULTS	1501
+LABDVERV	RESPIRATORY DFA PANEL	RPT	RPT	1501
+LABDVERV	RESPIRATORY DFA PANEL	SDES	SPECIMEN	1501
+LABDVERV	RESPIRATORY DFA PANEL	SREQ	COMMENTS	1501
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BAND	BAND NEUTROPHILS	1500
+LABQFTB	QUANTIFERON TEST FOR LATENT TB	QTFNIN	QUANTIFERON INTERPRETATION	1490
+LABQFTB	QUANTIFERON TEST FOR LATENT TB	QTFNRS	QUANTIFERON-TB	1490
+LAB11125R	COCCI ID	COCI	COCCI ID (SERUM)	1489
+LABTP	"PROTEIN TOTAL, SERUM/PLASMA"	TP	"PROTEIN, TOTAL, SER/PLAS"	1479
+LABUEOS	"EOSINOPHILS, URINE"	UEOS	"EOSINOPHILS, URINE"	1472
+LABCBCD	CBC WITH DIFF	ABNWBC	BLASTS/ABNORMAL CELLS	1470
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONO	MONOCYTES	1469
+LABHCVA	HEPATITIS C AB IGG	HCVI	INTERPRETATION	1456
+LABHCVA	HEPATITIS C AB IGG	HCVR	ANTI-HCV	1456
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MONOAB	"MONO, ABS (MAN DIFF)"	1442
+LABPTH	PARATHYROID HORMONE	PTH	PARATHYROID HORMONE	1436
+LABCMVPQ	CMV PCR QUANTITATIVE	CMVCP	RESULT	1423
+LABCMVPQ	CMV PCR QUANTITATIVE	CMVDNA	CMV DNA	1423
+LABCMVPQ	CMV PCR QUANTITATIVE	CMVLOG	RESULT LOG 10	1423
+LABCMVPQ	CMV PCR QUANTITATIVE	CMVSP	SPECIMEN	1423
+LABCMVPQ	CMV PCR QUANTITATIVE	CMVTIP	TEST DESCRIPTION	1423
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HIPINT	INTERPRETATION	1400
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	PRO	PRODUCT ID  	1389
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	SI	<<STATUS>>  	1389
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HEPTYP	HEPARIN TYPE	1368
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	LOTNO	LOT #	1368
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	MANUF	MANUFACTURER	1368
+LABLIL	LITHIUM	LI	LITHIUM	1367
+LABLIL	LITHIUM	LI	PEAK TROUGH OR RANDOM	1367
+LABLIL	LITHIUM	LI2	DATE & TIME OF LAST DOSE	1367
+LABLIL	LITHIUM	LI3	DOSAGE	1367
+LABLIL	LITHIUM	LI4	ADMINISTRATION ROUTE	1367
+LABIGG	"IGG, SERUM"	IGG	"IGG, SERUM"	1366
+LABFLGL	"GLUCOSE, FLUID"	GLFLS	SOURCE	1365
+LABFLGL	"GLUCOSE, FLUID"	GLUFLD	"GLUCOSE, FLUID"	1365
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HIPDIR	HIT INTERPRETED BY	1365
+LABPCG3O	"ISTAT G3+, OTHER"	PCTEMP	BODY TEMPERATURE	1353
+POC1	POC URINE DIPSTICK	PHLOC	LOCATION	1347
+LABABIDI	AB ID	ABIDRBC	ANTIBODY ID RBC	1340
+LABFCR	"CREATININE, FLUID"	CRFLD	"CREATININE, FLUID"	1336
+LABFCR	"CREATININE, FLUID"	CRFLS	SOURCE	1336
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	EOSFLD	"EOSINOPHIL %, FLD"	1328
+LABUA	URINALYSIS WITH MICROSCOPIC	BYST	YEAST	1324
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	DI	ISSUE DATE/TIME	1319
+LABPCCG4	"ISTAT CG4, ARTERIAL"	HCO3A	"HCO3 (A), ISTAT"	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	O2SATA	"O2 SATURATION, ISTAT"	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	PCCOM	POCT COMMENT	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	PCO2A	"PCO2 (A), ISTAT"	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	PHA	"PH (A), ISTAT"	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	PO2A	"PO2 (A), ISTAT"	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	TCO2A	"TCO2, ISTAT"	1281
+LABPCCG4	"ISTAT CG4, ARTERIAL"	LAC	"LACTATE, ISTAT"	1278
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	UBT	UNIT BLOOD TYPE  	1265
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	UNT	UNIT NUMBER  	1265
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	GRCAST	GRANULAR CASTS	1255
+LABAFP	"ALPHA FETOPROTEIN, SERUM"	AFP	ALPHA FETOPROTEIN	1226
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGA	"ANGLE, FIBRINOGEN ACTIVITY"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGC	"R, REACTION TIME (CK)"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGEL	"EPL, ESTIMATED LYSIS"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGG	"G, CLOT STABILITY"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGK	"K, CLOT KINETICS"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGMA	"MA, PLATELET FUNCTION"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGRK	"R, REACTION TIME (CK/H)"	1225
+LABPTEG	"TEG, PERIOPERATIVE"	PTEGSP	"SP, SPLIT POINT"	1225
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	PRO	PRODUCT ID  	1200
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	SI	<<STATUS>>  	1200
+LABDDIML	D - DIMER (ELISA)	DDIMER	"D-DIMER, ELISA"	1195
+LABCBCD	CBC WITH DIFF	CCNT	CELLS COUNTED	1194
+LABDDIML	D - DIMER (ELISA)	DIMCOM	COAGULATION COMMENT	1194
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	DI	ISSUE DATE/TIME	1185
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	BYST	YEAST	1161
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCINT	INTERPRETATION	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCR	IGM ANTI-HBC ANTIBODY	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSINT	INTERPRETATION	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSR	HEP B SURFACE ANTIGEN	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTINT	INTERPRETATION	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTR	ANTI-HBC ANTIBODY	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLINT	INTERPRETATION	1158
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	1158
+LABAHP2	ACUTE HEPATITIS PANEL	HAVINT	INTERPRETATION	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HAVR	IGM ANTI-HAV	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HBCINT	INTERPRETATION	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HBCR	IGM ANTI-HBC ANTIBODY	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HBSINT	INTERPRETATION	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HCVI	INTERPRETATION	1138
+LABAHP2	ACUTE HEPATITIS PANEL	HCVR	ANTI-HCV	1138
+LABVD25H	"VITAMIN D, 25-HYDROXYVITAMIN"	25OHDT	"25-HYDROXY D, TOTAL"	1135
+LABVD25H	"VITAMIN D, 25-HYDROXYVITAMIN"	VD25D2	25-HYDROXY D2	1135
+LABVD25H	"VITAMIN D, 25-HYDROXYVITAMIN"	VD25D3	25-HYDROXY D3	1135
+POC39	POC URINE FOR PH	EXPDATE	EXP DATE	1132
+POC39	POC URINE FOR PH	PHLOC	LOCATION	1132
+POC39	POC URINE FOR PH	UPH	PH URINE	1132
+POC39	POC URINE FOR PH	DIPLOT	DIPSTICK LOT	1131
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	UBT	UNIT BLOOD TYPE  	1128
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	UNT	UNIT NUMBER  	1128
+LABUUN	"UREA WITH CREATININE, URINE (RANDOM)"	UNCRU	URINE CREAT	1124
+LABUUN	"UREA WITH CREATININE, URINE (RANDOM)"	UNUCRU	UREA/CREAT RATIO	1124
+LABUUN	"UREA WITH CREATININE, URINE (RANDOM)"	UURN	"UREA NITROGEN, URINE"	1124
+LABPROC	SPECIMEN PROCESSING	PROC	SPECIMEN PROCESSING	1122
+LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQCP	RESULT	1105
+LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQDN	EBV DNA	1105
+LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQLG	RESULT LOG 10	1105
+LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQSP	SPECIMEN	1105
+LABRPRQL	RPR QUALITATIVE	RPRQ	RESULT	1105
+LABRPRQL	RPR QUALITATIVE	RPRQIN	INTERPRETATIONS	1105
+LABEBVQP	"EBV PCR QUANT, PLASMA"	EBVQIN	TEST DESCRIPTION	1104
+POC14	POC GLUCOSE BY METER	GLU	GLUCOSE BY METER	1102
+LABLEGDA	LEGIONELLA CULTURE	CULT	CULT/OTHER RSLT:	1084
+LABLEGDA	LEGIONELLA CULTURE	RPT	RPT	1084
+LABLEGDA	LEGIONELLA CULTURE	SDES	SPECIMEN	1084
+LABLEGDA	LEGIONELLA CULTURE	SREQ	COMMENTS	1084
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	PRO	PRODUCT ID  	1067
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	SI	<<STATUS>>  	1067
+LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	MVSO	TEST NAME	1056
+LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	RES	RESULTS	1056
+LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	SCE	SOURCE	1056
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGSP	"SP, SPLIT POINT"	1021
+LABUTPRAN	"PROTEIN WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREATININE	1017
+LABUTPRAN	"PROTEIN WITH CREATININE, URINE (RANDOM)"	UPROTCR	PROTEIN/CREAT RATIO	1017
+LABUTPRAN	"PROTEIN WITH CREATININE, URINE (RANDOM)"	UPROTTOT	"PROTEIN, TOTAL, URINE"	1017
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HATINT	INTERPRETATION	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSINT	INTERPRETATION	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBTINT	INTERPRETATION	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HCVI	INTERPRETATION	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HCVR	ANTI-HCV	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HQLINT	INTERPRETATION	1015
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	1015
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGCOM	COMMENTS	1013
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGC	"R, REACTION TIME (CK)"	1011
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGRK	"R, REACTION TIME (CK/H)"	1011
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGA	"ANGLE, FIBRINOGEN AC"	1010
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGG	"G, CLOT STABILITY"	1010
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGK	"K, CLOT KINETICS"	1010
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGM	"MA, PLT. FUNCTION"	1010
+LABWDCG	WOUND CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	1009
+LABWDCG	WOUND CULTURE AND GRAM STAIN	GS	GRAM STAIN	1009
+LABWDCG	WOUND CULTURE AND GRAM STAIN	RPT	RPT	1009
+LABWDCG	WOUND CULTURE AND GRAM STAIN	SDES	SPECIMEN	1009
+LABWDCG	WOUND CULTURE AND GRAM STAIN	SREQ	COMMENTS	1009
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGEL	"EPL, ESTIMATED LYSIS"	1009
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	TEGMD2	REVIEWED BY	1007
+LABSTLCX	STOOL CULTURE	CULT	CULT/OTHER RSLT:	987
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	DI	ISSUE DATE/TIME	987
+LABSTLCX	STOOL CULTURE	RPT	RPT	987
+LABSTLCX	STOOL CULTURE	SDES	SPECIMEN	987
+LABSTLCX	STOOL CULTURE	SREQ	COMMENTS	987
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	XMR	UNIT CROSSMATCH  	974
+LABSTOOLC	STOOL CULTURE	CULT	CULT/OTHER RSLT:	961
+ECH15	ECHO - TRANSTHORACIC ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	961
+LABSTOOLC	STOOL CULTURE	RPT	RPT	961
+LABSTOOLC	STOOL CULTURE	SDES	SPECIMEN	961
+LABSTOOLC	STOOL CULTURE	SREQ	COMMENTS	961
+LAB11124R	COCCI CF	COCFS	COCCI CF (SERUM)	959
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	UBT	UNIT BLOOD TYPE  	955
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	UNT	UNIT NUMBER  	955
+LABPHME	PH BY METER	PHA	PH BY METER	948
+LABPHME	PH BY METER	PHMETS	SOURCE	948
+LABHELI	HELICOBACTER PYLORI AB	HPYLORI	HELICOB. PYLORI IGG AB	937
+LABCEA	CEA	CEA	CEA	923
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	XMR	UNIT CROSSMATCH  	923
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	SPEXP	SPECIMEN EXPIRATION DATE/TIME	911
+LAB11041R	FREE HEMOGLOBIN	HGB	"HEMOGLOBIN, SERUM"	903
+LABHCLOTI	HOLD CLOT	HCLOT	HOLD CLOT	897
+LABMYO	"MYOGLOBIN, SERUM / PLASMA"	MYOG	"MYOGLOBIN, SER/PLAS"	894
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	PRO	PRODUCT ID  	892
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	SI	<<STATUS>>  	892
+LABEP1	ELECTROLYTE PANEL	AG	ANION GAP	884
+LABEP1	ELECTROLYTE PANEL	CL	"CHLORIDE, SER/PLAS"	884
+LABEP1	ELECTROLYTE PANEL	CO2	"CO2, SER/PLAS"	884
+LABEP1	ELECTROLYTE PANEL	K	"POTASSIUM, SER/PLAS"	884
+LABEP1	ELECTROLYTE PANEL	NA	"SODIUM, SER/PLAS"	884
+LABCA199	CANCER AG 19-9	C199	CANCER ANTIGEN 19-9	874
+LABAGALA	ASPERGILLUS GALACTOMANNAN	AGALA	ASPER.GALACTOMANNAN	867
+LABLEGU	"LEGIONELLA AG, URINE"	LEGUA	LEGIONELLA AG	867
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	DI	ISSUE DATE/TIME	858
+LABCMVP	"CMV DNA PCR QUAL, NON-PLASMA"	CMQDNA	CMV DNA	842
+LABCMVP	"CMV DNA PCR QUAL, NON-PLASMA"	CMVLIP	TEST DESCRIPTION	842
+LABCMVP	"CMV DNA PCR QUAL, NON-PLASMA"	CMVQSP	SPECIMEN	842
+LABCORTS	"CORTISOL, POST OP"	CORTS	"CORTISOL, POST OP"	842
+LABUA	URINALYSIS WITH MICROSCOPIC	CAOX	CA OXALATE CRYSTALS	832
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	UBT	UNIT BLOOD TYPE  	832
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	UNT	UNIT NUMBER  	832
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FOTHER	"OTHERS, FLD"	830
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	ADNVIR	ADENOVIRUS	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	INFAST	INFLUENZA A SUBTYPE	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	INFLA	INFLUENZA A	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	INFLB	INFLUENZA B	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	METPNV	METAPNEUMOVIRUS	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	PARIN1	PARAINFLUENZA 1	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	PARIN2	PARAINFLUENZA 2	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	PARIN3	PARAINFLUENZA 3	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RESVDI	TEST DESCRIPTION	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RESVSP	SPECIMEN	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RHNVIR	RHINOVIRUS	820
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RSVPC	RSV	820
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBAG	TB AG	820
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBAGNI	TB AG - NIL	820
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBGINT	INTERPRETATION	820
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBGNIL	NIL	820
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBGRES	QUANTIFERON RESULT	820
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	TBMINI	MITOGEN - NIL	820
+LABHCVA	ANTI-HCV	HCVI	INTERPRETATION	816
+LABHCVA	ANTI-HCV	HCVR	ANTI-HCV	815
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	PRO	PRODUCT ID  	812
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	SI	<<STATUS>>  	812
+LABIGET	TOTAL IGE	IGET	TOTAL IGE	811
+LABCMVIGG	CMV IGG	CMGDES	CMV IGG TEST DESCRIPTION	810
+LABCMVIGG	CMV IGG	CMGQUL	CMV IGG RESULT	810
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	OCCBLD	"OCCULT BLOOD, STOOL"	804
+LABBHCG	BETA HCG	BHCG	BETA HCG	798
+LABC3	"COMPLEMENT C3, SERUM"	C3	"COMPLEMENT C3, SERUM"	795
+LABC4	"COMPLEMENT C4, SERUM"	C4	"COMPLEMENT C4, SERUM"	795
+LABYFT3	"T3, FREE"	FT3	"T3, FREE"	795
+LABCRPLB	"CRYPTOCOCCAL AG LATEX, BLOOD"	CRPLB	LATEX CRYPTO AG-BID	793
+LABAST	"AST, SERUM/PLASMA"	AST	"AST (SGOT), SER/PLAS"	791
+LABLUPUS	LUPUS ANTICOAGULANT	LUPINT	LUPUS INTERPRETATION	786
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEL	DEVELOPER LOT#	784
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEXP	DEVELOPER EXP DATE	784
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	HEMED	HEMOCCULT EXP DATE	784
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	HEML	HEMOCCULT LOT#	784
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	QC10	QC RESULT	784
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVIPR	TEST DESCRIPTION	782
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVLOG	RESULT LOG 10	782
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVRES	RESULT	782
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVRNA	HEP C VIRVUS RNA	782
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HCVSP	SPECIMEN	782
+LABCTC	CATHETER TIP CULTURE	CULT	CULT/OTHER RSLT:	780
+LABCTC	CATHETER TIP CULTURE	RPT	RPT	780
+LABCTC	CATHETER TIP CULTURE	SDES	SPECIMEN	780
+LABCTC	CATHETER TIP CULTURE	SREQ	COMMENTS	780
+LABALT	"ALT, SERUM/PLASMA"	ALT	"ALT (SGPT), SER/PLAS"	779
+LABPSA	PSA	PSA	PSA	777
+LABLEGU	"LEGIONELLA AG, URINE"	TINFO	TESTINFORMATION	777
+LABPCCG4	"ISTAT CG4, ARTERIAL"	BD	"BASE DEFICIT, ISTAT"	773
+LABCLACT	"LACTIC ACID, CSF"	LACCSF	"LACTIC ACID, CSF"	763
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	SPEXP	SPECIMEN EXPIRATION DATE/TIME	763
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBVCP	RESULT	761
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBVDNA	EBV DNA	761
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBVIPR	TEST DESCRIPTION	761
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBVLOG	RESULT LOG	761
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBVSP	SPECIMEN	761
+LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	UPIEI	INTERPRETATION	758
+LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	UPIER	RESULT	758
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	DX	DIAGNOSIS	757
+LABGLNF	"GLUCOSE NON-FASTING, SERUM/PLASMA"	GLU	"GLUCOSE, NON-FASTING"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	JOINT	JOINT	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	MALIG	SUSPECT MALIGNANCY?	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SCOLOR	"COLOR, SYN FLD"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SLYM	"LYMPHOCYTES, SYN FLD"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SMONO	"MONOS/MESOS, SYN FLD"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SNEUT	"NEUTROPHILS, SYN FLD"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SRBC	"RBC COUNT, SYN FLD"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	STURB	"TURBIDITY, SYN FLD"	757
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SWBC	"NUCL. CELLS, SYN FLD"	757
+LABEITC	TRANSFUSION EMERGENCY ISSUE	CEITC	EMERGENCY ISSUE CMT	751
+LABRF	RHUEMATOID FACTOR	RHFINT	INTERPRETATION	748
+LABRF	RHUEMATOID FACTOR	RHFR	RHEUMATOID FACTOR	748
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	META	METAMYELOCYTES	745
+LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	CULT	CULT/OTHER RSLT:	740
+LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	RPT	RPT	740
+LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	SDES	SPECIMEN	740
+LABTCDIF	"C DIFFICILE TOXIN B, STOOL"	SREQ	COMMENTS	740
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MYEL	MYELOCYTES	736
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EAGQUL	EAGQUL	734
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBINTR	EBV INTERPRETATION	734
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBNQUL	EBNQUL	734
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBVDS	EBV TEST DESCRIPTION	734
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VGQUL	VGQUL	734
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VMQUL	VMQUL	734
+LABTRXNWI	TRANSFUSION REACTION WORKUP	CTRXNW	TRANSFUSION REACTION COMMENT	731
+LABTRXNWI	TRANSFUSION REACTION WORKUP	TRXNWR	TRANSFUSION REACTION WORKUP	731
+LABSTLC	STOOL CULTURE	CULT	CULT/OTHER RSLT:	728
+LABSTLC	STOOL CULTURE	RPT	RPT	728
+LABSTLC	STOOL CULTURE	SDES	SPECIMEN	728
+LABSTLC	STOOL CULTURE	SREQ	COMMENTS	728
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	DI	ISSUE DATE/TIME	727
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	NRBC	NRBC/100WBC	723
+LABHAVT	ANTI-HAV	HATINT	INTERPRETATION	718
+LABHAVT	ANTI-HAV	HATR	ANTI-HAV ANTIBODY	718
+LABTT	THROMBIN TIME	TT	THROMBIN TIME	714
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VGQNT	VGQNT	713
+LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	CULT	CULT/OTHER RSLT:	712
+LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	GS	GRAM STAIN	712
+LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	RPT	RPT	712
+LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	SDES	SPECIMEN	712
+LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	SREQ	COMMENTS	712
+LABCMVIGG	CMV IGG	CMGQNT	CMV IGG LEVEL	699
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	PRO	PRODUCT ID  	698
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	SI	<<STATUS>>  	698
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	UBT	UNIT BLOOD TYPE  	692
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	UNT	UNIT NUMBER  	692
+LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	DATG	DIRECT ANTIGLOB. - IGG	691
+LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	DATC3	DIRECT ANTIGLOB. - C3	690
+LABFLDB	FLUID CULTURE / BB GRAM STAIN	CULT	CULT/OTHER RSLT:	689
+LABFLDB	FLUID CULTURE / BB GRAM STAIN	GS	GRAM STAIN	689
+LABFLDB	FLUID CULTURE / BB GRAM STAIN	RPT	RPT	689
+LABFLDB	FLUID CULTURE / BB GRAM STAIN	SDES	SPECIMEN	689
+LABFLDB	FLUID CULTURE / BB GRAM STAIN	SREQ	COMMENTS	689
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBNQNT	EBNQNT	687
+LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXDI	DISCLAIMER	683
+LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXMT	METHOD	683
+LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXR	INFLUENZA A RNA	683
+LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLUXSP	SPECIMEN	683
+LABTYPSNI	TYPE AND SCREEN	SPEXP	SPECIMEN EXPIRATION DATE/TIME	682
+LABALKP	ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	680
+LABGNTPRL	GENTAMICIN TROUGH LEVEL	GENTTRO	"GENTAMICIN, TROUGH"	679
+LABGNTPRL	GENTAMICIN TROUGH LEVEL	GNTPR2	DATE & TIME OF LAST DOSE	679
+LABGNTPRL	GENTAMICIN TROUGH LEVEL	GNTPR3	DOSAGE	679
+LABGNTPRL	GENTAMICIN TROUGH LEVEL	GNTPR4	ADMINISTRATION ROUTE	679
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CCMT	COMMENT	676
+LABCRPLC	"CRYPTOCOCCAL AG LATEX, CSF"	CRPLC	"LATEX CRYPTO AG,CSF"	667
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	CAION	"CALCIUM,ION, ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	CR	"CREATININE,ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	GLU	"GLUCOSE,ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	HCT	"HCT(CALC), ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	HGB	"HGB(CALC), ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	K	"POTASSIUM, ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	NA	"SODIUM, ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCAGP	"ANION GAP, ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCBUN	"BUN, ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCCL	"CHLORIDE, ISTAT"	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	PCCOM	POCT COMMENT	665
+LABPCCH8	"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2"	TCO2A	"TCO2, ISTAT"	665
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	XMR	UNIT CROSSMATCH  	664
+LABDSDNA	ANTI - DSDNA AB	DNAINT	INTERPRETATION	652
+LABDSDNA	ANTI - DSDNA AB	DNAQUL	ANTI-DSDNA RESULT	652
+LABACTHO	ADRENOCORTICOTROPIC HORMONE (ACTH)	ACTH	ACTH	647
+LABFE	"IRON, TOTAL"	FE	"IRON, TOTAL"	644
+LABYHISTU	"HISTOPLASMA AG, URINE"	YHISU4	RESULT URINE	644
+LABHITAB	HEPARIN PLATELET FACTOR 4 AB	PF4COM	PF4 COMMENT	642
+LABHITAB	HEPARIN PLATELET FACTOR 4 AB	PF4ENH	"PF4 IGG, IGM, IGA OD"	642
+LABACA	ANTI CARDIOLIPIN AB	ACAIGG	ACA IGG ANTIBODIES	636
+LABACA	ANTI CARDIOLIPIN AB	ACAIGM	ACA IGM ANTIBODIES	636
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	PRO	PRODUCT ID  	636
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	SI	<<STATUS>>  	636
+LABASQ	ACETONE SEMI QUALITATIVE	ACETONE	ACETONE SEMI QUAL	628
+LABDVEHV	HSV/VZV DFA	HERPR	HERPES SIMPLEX VIRUS (HSV)	627
+LABDVEHV	HSV/VZV DFA	HVIP	TEST DESCRIPTION	627
+LABDVEHV	HSV/VZV DFA	HVSP	SPECIMEN	627
+LABDVEHV	HSV/VZV DFA	VZR	VARICELLA-ZOSTER VIRUS	627
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	UBT	UNIT BLOOD TYPE  	626
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	UNT	UNIT NUMBER  	626
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOS	EOSINOPHILS	624
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	DI	ISSUE DATE/TIME	622
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	XMR	UNIT CROSSMATCH  	613
+LABLEID	FACTOR V LEIDEN	LEICMT	COMMENT	612
+LABLEID	FACTOR V LEIDEN	LEIDIS	DISCLAIMER	612
+LABLEID	FACTOR V LEIDEN	LEIMET	METHOD	612
+LABLEID	FACTOR V LEIDEN	LEIPRM	PRIMERS	612
+LABLEID	FACTOR V LEIDEN	LEIRES	RESULTS	612
+LABCMVP	"CMV DNA PCR QUAL, NON-BLOOD"	CMQDNA	CMV DNA	611
+LABCMVP	"CMV DNA PCR QUAL, NON-BLOOD"	CMVQSP	SPECIMEN	611
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	EOSAB	"EOS, ABS (MAN DIFF)"	611
+LABLEID	FACTOR V LEIDEN	LEIDIR	INTERPRETED BY	611
+LABCMVP	"CMV DNA PCR QUAL, NON-BLOOD"	CMVLIP	TEST DESCRIPTION	610
+LABUCRT	"CREATININE, URINE (TIMED)"	CRE	CREATININE EXCRETION	596
+LABUCRT	"CREATININE, URINE (TIMED)"	UCR	"CREATININE, URINE"	596
+LABUCRT	"CREATININE, URINE (TIMED)"	UT3	COLLECTION PERIOD CREATININE	596
+LABUCRT	"CREATININE, URINE (TIMED)"	VOL3	VOLUME CREATININE	596
+LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBPR2	DATE & TIME OF LAST DOSE	586
+LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBPR3	DOSAGE	586
+LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBPR4	ADMINISTRATION ROUTE	586
+LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	TOBRATROUGH	"TOBRAMYCIN, TROUGH"	586
+LABHAVM	IGM ANTI-HAV	HAVINT	INTERPRETATION	582
+LABHAVM	IGM ANTI-HAV	HAVR	IGM ANTI-HAV	582
+LABABIDI	AB ID	ABIDR	ANTIBODY ID RULE OUT	577
+LABHBSQL	ANTI-HBS QUAL	HQLINT	INTERPRETATION	573
+LABHBSQL	ANTI-HBS QUAL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	573
+LAB11131R	VORICONAZOLE LEVEL	VORI	VORICONAZOLE LEVEL	570
+LABYCERU	CERULOPLASMIN	CER	CERULOPLASMIN	566
+LABFTRPL	"TRIGLYCERIDES, PLEURAL"	TGLFLD	"TRIGLYCERIDES, PLEURAL FLUID"	565
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	BASOFLD	"BASOPHILS, FLD"	563
+LABURK	"POTASSIUM, URINE (RANDOM)"	UK	"POTASSIUM, URINE"	563
+LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	AFB	AFB SMEAR	559
+LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	CULT	CULT/OTHER RSLT:	559
+LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	RPT	RPT	559
+LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	SDES	SPECIMEN	559
+LABAFBCF	"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS"	SREQ	COMMENTS	559
+LABP20210	PROTHROMBIN 20210A	PTCMT	COMMENT	558
+LABP20210	PROTHROMBIN 20210A	PTDIS	DISCLAIMER	558
+LABP20210	PROTHROMBIN 20210A	PTMET	METHOD	558
+LABP20210	PROTHROMBIN 20210A	PTPRM	PRIMERS	558
+LABP20210	PROTHROMBIN 20210A	PTRES	RESULTS	558
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	PRO	PRODUCT ID  	557
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	SI	<<STATUS>>  	557
+LABP20210	PROTHROMBIN 20210A	PT20DR	INTERPRETED BY	556
+LABUA	URINALYSIS WITH MICROSCOPIC	URENAL	RENAL CELLS	556
+LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	CULT	CULT/OTHER RSLT:	554
+LABHBCT	ANTI-HBC	HBTINT	INTERPRETATION	554
+LABHBCT	ANTI-HBC	HBTR	ANTI-HBC ANTIBODY	554
+LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	RPT	RPT	554
+LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	SDES	SPECIMEN	554
+LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	SREQ	COMMENTS	554
+LABLUPUS	LUPUS ANTICOAGULANT	LUPDIR	INTERPRETED BY	543
+LABHMCY	"HOMOCYSTEINE TOTAL, PLASMA"	HMCY	"HOMOCYSTEINE, PLAS TOTAL"	540
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVCP	RESULT	539
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVDNA	EBV DNA	539
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVIPR	TEST DESCRIPTION	539
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVLOG	RESULT LOG	539
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBVSP	SPECIMEN	539
+LABLPD	LIPID PANEL	CHOL	"CHOLESTEROL, TOTAL"	528
+LABLPD	LIPID PANEL	CHOLHDL	CHOLESTEROL/HDL RATIO	528
+LABLPD	LIPID PANEL	HDL	HDL CHOLESTEROL	528
+LABLPD	LIPID PANEL	LDL	DIRECT LDL CHOL	528
+LABLPD	LIPID PANEL	NHDL	"NON-HDL CHOL, CALC"	528
+LABLPD	LIPID PANEL	TGL	"TRIGLYCERIDE, SER/PLAS"	528
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	UBT	UNIT BLOOD TYPE  	528
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	UNT	UNIT NUMBER  	528
+LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	CULT	CULT/OTHER RSLT:	525
+LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	RPT	RPT	525
+LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	SDES	SPECIMEN	525
+LABHELAG	"HELICOBACTER PYLORI AG, STOOL"	SREQ	COMMENTS	525
+LABABIDI	AB ID	ABID	ANTIBODY IDENTIFICATION	523
+LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSV1PC	HSV-1 DNA	523
+LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSV2PC	HSV-2 DNA	523
+LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSVIN	TEST DESCRIPTION	523
+LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	SDES11	SPECIMEN	523
+LAB11075R	SMOOTH MUSCLE AB	ASMA	ANTI-SMOOTH MUSCLE ANITBODY	521
+LABAT3	ANTITHROMBIN III	AT3	ANTITHROMBIN III	521
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PSREV	PATH/SPEC REVIEW	520
+LAB11060R	THIAMINE	VITB1	"VITAMIN B1, PLASMA"	520
+LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	CULT	CULT/OTHER RSLT:	519
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	DI	ISSUE DATE/TIME	519
+LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	RPT	RPT	519
+LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	SDES	SPECIMEN	519
+LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	SREQ	COMMENTS	519
+LABCMVIGM	CMV IGM	CMMDES	CMV IGM TEST DESCRIPTION	518
+LABCMVIGM	CMV IGM	CMMQUL	CMV IGM RESULT	518
+LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	CULT	CULT/OTHER RSLT:	516
+LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	RPT	RPT	516
+LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	SDES	SPECIMEN	516
+LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	SREQ	COMMENTS	516
+LABPB	PHENOBARBITAL LEVEL	PB	"PEAK, TROUGH, OR RANDOM"	515
+LABPB	PHENOBARBITAL LEVEL	PB	PHENOBARBITAL	515
+LABPB	PHENOBARBITAL LEVEL	PB2	DATE & TIME OF LAST DOSE	515
+LABPB	PHENOBARBITAL LEVEL	PB3	DOSAGE	515
+LABPB	PHENOBARBITAL LEVEL	PB4	ADMINISTRATION ROUTE	515
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	PRO	PRODUCT ID  	515
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	SI	<<STATUS>>  	515
+LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	CULT	CULT/OTHER RSLT:	514
+LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	GS	GRAM STAIN	514
+LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	RPT	RPT	514
+LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	SDES	SPECIMEN	514
+LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	SREQ	COMMENTS	514
+LABYHISTU	"HISTOPLASMA AG, URINE"	YHISU5	COMMENT URINE	514
+LABPCCG4	"ISTAT CG4, ARTERIAL"	BE	"BASE EXCESS, ISTAT"	513
+LABIGP	"IG PANEL, SERUM"	IGA	"IGA, SERUM"	511
+LABIGP	"IG PANEL, SERUM"	IGG	"IGG, SERUM"	511
+LABIGP	"IG PANEL, SERUM"	IGM	"IGM, SERUM"	511
+LABFACT8	FACTOR VIII ASSAY	FACTOR7	FACTOR VIII ASSAY	510
+LABYHISTS	"HISTOPLASMA AG, SERUM"	YHISS4	"RESULT,SERUM"	510
+LABERURBC	EMERGENCY RELEASE RBC	RBCSTA	RBC PRODUCT - UNITS	505
+LABCYTGEN	CYTOGENETICS FISH ORDER	CYTGN1	CYTOGEN/FISH TRACKING	500
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	EOSCSF	"EOSINOPHILS, CSF"	497
+LAB11049R	NSE	NEURSP	NEURON SPEC ENOLASE	496
+LABCMV	CMV AB TOTAL	CMV	CMV ANTIBODY	488
+LABPCEC4O	"ISTAT EC4, OTHER"	GLU	"GLUCOSE,ISTAT"	485
+LABPCEC4O	"ISTAT EC4, OTHER"	HCT	"HCT(CALC), ISTAT"	485
+LABPCEC4O	"ISTAT EC4, OTHER"	HGB	"HGB(CALC), ISTAT"	485
+LABPCEC4O	"ISTAT EC4, OTHER"	K	"POTASSIUM, ISTAT"	485
+LABPCEC4O	"ISTAT EC4, OTHER"	NA	"SODIUM, ISTAT"	485
+LABPCEC4O	"ISTAT EC4, OTHER"	PCCOM	POCT COMMENT	485
+LABGLOB	GLOBULIN	GLOB	GLOBULIN	482
+LABPROL	PROLACTIN	PROLACTIN	PROLACTIN	478
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	UBT	UNIT BLOOD TYPE  	476
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	UNT	UNIT NUMBER  	476
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	CULT	CULT/OTHER RSLT:	473
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	RPT	RPT	473
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	SDES	SPECIMEN	473
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	SREQ	COMMENTS	473
+LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	CHLAM	C.TRACHOMATIS RESULT:	472
+LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	DCL2	TEST INFORMATION	472
+LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	GC	N.GONORRHOEAE RESULT:	472
+LABCLGC2	"CHLAMYDIA GC, MOLECULAR DETECTION"	SDES2	SPECIMEN	472
+LABHGBP	"HEMOGLOBIN, PLASMA"	YHGBP1	TOTAL HEMOGLOBIN	467
+LABCMV	CMV AB IGG	CMV	CMV ANTIBODY	464
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	DI	ISSUE DATE/TIME	461
+LABABIDI	AB ID	ABIDELUATE	ANTIBODY IDENTIFICATION ELUATE	460
+LABANAS	ANTI - NUCLEAR AB	ANAQNT	TITER	454
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	XMR	UNIT CROSSMATCH  	454
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBIPR	TEST DESCRIPTION	453
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPDNA	HEP B VIRUS DNA	453
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPLOG	RESULT LOG 10	453
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPRES	RESULT	453
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPSP	SPECIMEN	453
+LABISOF	ISOLATOR FUNGUS	CULT	CULT/OTHER RSLT:	451
+LABVP4	COMP HEPATITIS PANEL	HATINT	INTERPRETATION	451
+LABVP4	COMP HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	451
+LABVP4	COMP HEPATITIS PANEL	HBSAIN	INTERPRETATION	451
+LABVP4	COMP HEPATITIS PANEL	HBSAQL	HEP B SURF AB QUAL	451
+LABVP4	COMP HEPATITIS PANEL	HBSAQT	HEP B SURF AB QUANT	451
+LABVP4	COMP HEPATITIS PANEL	HBSINT	INTERPRETATION	451
+LABVP4	COMP HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	451
+LABVP4	COMP HEPATITIS PANEL	HBTINT	INTERPRETATION	451
+LABVP4	COMP HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	451
+LABVP4	COMP HEPATITIS PANEL	HCVI	INTERPRETATION	451
+LABVP4	COMP HEPATITIS PANEL	HCVR	ANTI-HCV	451
+LABISOF	ISOLATOR FUNGUS	RPT	RPT	451
+LABISOF	ISOLATOR FUNGUS	SDES	SPECIMEN	451
+LABISOF	ISOLATOR FUNGUS	SREQ	COMMENTS	451
+LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	CULT	CULT/OTHER RSLT:	450
+LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	RPT	RPT	450
+LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	SDES	SPECIMEN	450
+LABTBPCRS	"M. TUBERCULOSIS PCR, SPECIMEN"	SREQ	COMMENTS	450
+LABGLF	GLUCOSE FASTING	GLUF	"GLUCOSE, FASTING"	448
+LABPCEG7O	"ISTAT EG7, OTHER"	HCO3	"HCO3, ISTAT"	448
+LABPCEG7O	"ISTAT EG7, OTHER"	PCCOM	POCT COMMENT	448
+LABPCEG7O	"ISTAT EG7, OTHER"	PCO2A	"PCO2, ISTAT"	448
+LABPCEG7O	"ISTAT EG7, OTHER"	PCO2SO	"O2 SATURATION, ISTAT"	448
+LABPCEG7O	"ISTAT EG7, OTHER"	PH	"PH, ISTAT"	448
+LABPCEG7O	"ISTAT EG7, OTHER"	PO2A	"PO2, ISTAT"	448
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	PRO	PRODUCT ID  	448
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	SI	<<STATUS>>  	448
+LABPCEG7O	"ISTAT EG7, OTHER"	TCO2A	"TCO2, (ISTAT)"	448
+LABPCACT	ACTIVATED CLOTTING TIME (POCT)	ACT	"ACT,POCT"	439
+LABPCACT	ACTIVATED CLOTTING TIME (POCT)	PCCOM	POCT COMMENT	438
+LABFLCR	FREE LIGHT CHAIN RATIO	FREEK	FREE KAPPA LIGHT CHAIN	437
+LABFLCR	FREE LIGHT CHAIN RATIO	FREEKL	FREE KAPPA/LAMBDA RATIO	437
+LABFLCR	FREE LIGHT CHAIN RATIO	FREEL	FREE LAMBDA LIGHT CHAIN	437
+LABURTP	"PROTEIN, URINE (RANDOM)"	UPROTTOT	"PROTEIN, TOTAL, URINE"	437
+LABPCEG7O	"ISTAT EG7, OTHER"	CAION	"CALCIUM,ION, ISTAT"	436
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	CAOX	CA OXALATE CRYSTALS	436
+LABPCEG7O	"ISTAT EG7, OTHER"	HCT	"HCT(CALC), ISTAT"	436
+LABPCEG7O	"ISTAT EG7, OTHER"	HGB	"HGB(CALC), ISTAT"	436
+LABPCEG7O	"ISTAT EG7, OTHER"	K	"POTASSIUM, ISTAT"	436
+LABPCEG7O	"ISTAT EG7, OTHER"	NA	"SODIUM, ISTAT"	436
+LABUA	URINALYSIS WITH MICROSCOPIC	HYST	PSEUDO-HYPHAE	434
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	C1656A	"CD3-/16+56(NK),ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	C1656P	CD3-/16+56(NK)%	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD19AB	"CD19 (PAN B), ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD19PC	CD19 (PAN B) %	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD3PC	CD3 (PAN T) %	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD4AB	"CD3+/CD4+, ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD4PC	CD3+/CD4+ %	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD8AB	"CD3+/CD8+, ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	CD8PC	CD3+/CD8+ %	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	LYM	LYMPHOCYTES %	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	432
+LABTBLYM	"T SUBSETS AND B LYMPHOCYTES, BLOOD"	WBC	WBC COUNT	432
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	XMR	UNIT CROSSMATCH  	430
+LABERUFFP	EMERGENCY RELEASE PLASMA	FFP	FRESH FROZEN UNITS	428
+LABMETHB	METHEMOGLOBIN	METHB	METHEMOGLOBIN	427
+LABYHISTS	"HISTOPLASMA AG, SERUM"	YHISS5	"COMMENT,SERUM"	426
+LAB11071R	MITOCHONDRIAL AB	YAMA	"MITOCHONDRIAL ANTIBODY, M2"	424
+LABCMVRO	CMV RULE OUT	CMQDNA	CMV DNA	423
+LABCMVRO	CMV RULE OUT	CMVDSV	TEST DESCRIPTION	423
+LABCMVRO	CMV RULE OUT	CMVLIP	TEST DESCRIPTION	423
+LABCMVRO	CMV RULE OUT	CMVQSP	SPECIMEN	423
+LABCMVRO	CMV RULE OUT	CMVRC1	CMV DAY 1 RAPID CULTURE	423
+LABCMVRO	CMV RULE OUT	CMVRC2	CMV DAY 2 RAPID CULTURE	423
+LABCMVRO	CMV RULE OUT	CMVSPM	SPECIMEN	423
+LABHIAB	HEPARIN INDUCED AB	HIARES	RESULT	421
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	UBT	UNIT BLOOD TYPE  	421
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	UNT	UNIT NUMBER  	421
+LABHIAB	HEPARIN INDUCED AB	HIACOM	COMMENTS	420
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EAGQNT	EAGQNT	418
+LABCMVTSI	CMV AB TOTAL SOLID PHASE (GALILEO)	CMVTOT	CMV TOTAL ANTIBODY	414
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBCINT	INTERPRETATION	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBCR	IGM ANTI-HBC ANTIBODY	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBSAIN	INTERPRETATION	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBSAQL	HEP B SURF AB QUAL	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBSAQT	HEP B SURF AB QUANT	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBSINT	INTERPRETATION	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBSR	HEP B SURFACE ANTIGEN	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBTINT	INTERPRETATION	413
+LABDGHBP	DIAG. HEPATITIS B PANEL	HBTR	ANTI-HBC ANTIBODY	413
+LABTES	TESTOSTERONE	TESTOS	TESTOSTERONE	413
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVCP	RESULT	410
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVIPR	TEST DESCRIPTION	410
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVLO	RESULT LOG 10	410
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVRNA	HIV 1 RNA	410
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVSP	SPECIMEN	410
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	PRO	PRODUCT ID  	410
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	SI	<<STATUS>>  	410
+LAB12631R	VORICONAZOLE LEVEL	12630R	VORICONAZOLE LEVEL	409
+LABCRCL	"CREATININE CLEARANCE, URINE"	CORCL	CORRECTED CR. CLEAR.	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	COREXC	CORRECTED CR. EXC.	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	CR	"CREATININE, SERUM"	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	CRCL	CREATININE CLEARANCE	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	CRE	CREATININE EXCRETION	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	CRUCL	"CREATININE, URINE"	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	FLOWURN	URINE FLOW	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	HT	HEIGHT	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	UT3	COLLECTION PERIOD CREATININE	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	VOL3	VOLUME CREATININE	404
+LABCRCL	"CREATININE CLEARANCE, URINE"	WT	WEIGHT	404
+LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	CRYS	CRYSTAL AMOUNT	403
+LABYA1AS	ALPHA - 1 - ANTITRYPSIN	YA1A	ALPHA 1 ANTITRYPSIN	403
+LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	CXSOUR	FLUID SOURCE	402
+LABC125	CANCER AG 125	C125	CANCER ANTIGEN 125	398
+LABCMVIGT	CMV IGG (TRANSFUSION)	CMGTDS	TEST DESCRIPTION	398
+LABCMVIGT	CMV IGG (TRANSFUSION)	CMGTQL	RESULT	398
+LABT3	"T3, TOTAL"	T3	T3 TOTAL	398
+10290R	HLA - TYPING	9013R	CELL DATE	397
+10290R	HLA - TYPING	9014R	CELL SOURCE	397
+10290R	HLA - TYPING	9080R	DISCLAIMER	397
+10290R	HLA - TYPING	9448R	METHOD(S)	397
+LABUA	URINALYSIS WITH MICROSCOPIC	FRFAT	FREE FAT	397
+LABPTTINH	PTT INHIBITOR SCREEN	PTTINT	INTERPRETATION	397
+10290R	HLA - TYPING	9015R	HLA-A ALLELE 1	395
+10290R	HLA - TYPING	9016R	HLA-A ALLELE 2	395
+10290R	HLA - TYPING	9017R	HLA-B ALLELE 1	395
+10290R	HLA - TYPING	9018R	HLA-B ALLELE 2	395
+10290R	HLA - TYPING	9019R	HLA-BW ALLELE 1	395
+10290R	HLA - TYPING	9075R	HLA-MOLECULAR PT. SUMMARY	395
+10290R	HLA - TYPING	9076R	HLA-SEROLOGICAL PT. SUMMARY	395
+LABUA	URINALYSIS WITH MICROSCOPIC	OVFAT	OVAL FAT BODIES	395
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	HYST	PSEUDO-HYPHAE	394
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	UBT	UNIT BLOOD TYPE  	394
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	UNT	UNIT NUMBER  	394
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASO	BASOPHILS	389
+LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	HVDNA	HERPES VIRUS 6 DNA	389
+LABGIARS	GIARDIA SCREEN	CULT	CULT/OTHER RSLT:	386
+LABGIARS	GIARDIA SCREEN	RPT	RPT	386
+LABGIARS	GIARDIA SCREEN	SDES	SPECIMEN	386
+LABGIARS	GIARDIA SCREEN	SREQ	COMMENTS	386
+LABVZAB	VARICELLA ZOSTER VIRUS AB IGG	VZAB	VARICELLA ZOSTER AB IGG	386
+LABBXTG	BIOPSY/TISSUE(GEN)GS	CULT	CULT/OTHER RSLT:	384
+LABBXTG	BIOPSY/TISSUE(GEN)GS	GS	GRAM STAIN	384
+LABBXTG	BIOPSY/TISSUE(GEN)GS	RPT	RPT	384
+LABBXTG	BIOPSY/TISSUE(GEN)GS	SDES	SPECIMEN	384
+LABBXTG	BIOPSY/TISSUE(GEN)GS	SREQ	COMMENTS	384
+LABUUNT	"UREA NITROGEN, URINE (TIMED)"	TUNU	UREA EXCRETION	384
+LABUUNT	"UREA NITROGEN, URINE (TIMED)"	UREAVOL	VOLUME UREA	384
+LABUUNT	"UREA NITROGEN, URINE (TIMED)"	UT4	COLLECTION PERIOD UREA	384
+LABUUNT	"UREA NITROGEN, URINE (TIMED)"	UURN	"UREA NITROGEN, URINE"	384
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BASOAB	"BASO, ABS (MAN DIFF)"	381
+LABPROTC	PROTEIN C ACTIVITY	PROTC	PROTEIN C ACTIVITY	381
+LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSV1DN	HSV-1 DNA	380
+LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSV2DN	HSV-2 DNA	380
+LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSVZIP	TEST DESCRIPTION	380
+LABHSVZL	"HSV/VZV PCR QUAL, LESION"	HSVZSP	SPECIMEN	380
+LABHSVZL	"HSV/VZV PCR QUAL, LESION"	VZVDN	VZV DNA	380
+LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	OCCGU	"STOOL SAMPLE1,GUAIAC"	379
+10290R	HLA - TYPING	9021R	HLA-C ALLELE 1	377
+10290R	HLA - TYPING	9022R	HLA-C ALLELE 2	377
+10290R	HLA - TYPING	9023R	HLA-DRB1 ALLELE 1	377
+10290R	HLA - TYPING	9024R	HLA-DRB1 ALLELE 2	377
+10290R	HLA - TYPING	9029R	HLA-DQB1 ALLELE 1	377
+10290R	HLA - TYPING	9030R	HLA-DQB1 ALLELE 2	377
+LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	DATE1	DATE 1	377
+LABFVARZ	"VARICELLA ZOSTER VIR. DNA,QUAL"	VZVDNA	VARICELLA ZOSTER VIRUS DNA	377
+LABHITAB	HEPARIN (HIT) AB	PF4IGG	PF4 IGG ONLY OD	376
+LAB11824R	BUSULFAN	11825R	CONCENT. AT STEADY STATE (CSS)	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	ADVR	ADENOVIRUS	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	INFAR	INFLUENZA A	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	INFBR	INFLUENZA B	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	MPVRR	METAPNEUMOVIRUS	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	P1R	PARAINFLUENZA 1	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	P2R	PARAINFLUENZA 2	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	P3R	PARAINFLUENZA 3	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	RSVR	RSV	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	RVRPIP	TEST DECRIPTION	375
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	RVRSP	SPECIMEN	375
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	DI	ISSUE DATE/TIME	372
+LABPROTS	PROTEIN S ACTIVITY	PROTS	PROTEIN S ACTIVITY	372
+LABUA	URINALYSIS WITH MICROSCOPIC	URIC	URIC ACID CRYSTALS	371
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BLASTS	BLASTS	368
+LABLEVETL	LEVETIRACETAM LEVEL	LEVET	LEVET	367
+LABLEVETL	LEVETIRACETAM LEVEL	LEVET1	LEVET1	367
+LABLEVETL	LEVETIRACETAM LEVEL	LEVET2	LEVET2	367
+LABLEVETL	LEVETIRACETAM LEVEL	LEVET3	LEVET3	367
+LABLEVETL	LEVETIRACETAM LEVEL	LEVET4	LEVET4	367
+LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	SOUR12	SOURCE	367
+LABTT4	"T4, TOTAL"	T4	"THYROXINE (T4), TOTAL"	367
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	BLASTSAB	"BLASTS,ABS(MAN DIFF)"	365
+10290R	HLA - TYPING	9027R	HLA-DQA1 ALLELE 1	361
+10290R	HLA - TYPING	9028R	HLA-DQA1 ALLELE 2	361
+LABANTIXA	LMWH ACTIVITY BY ANTIXA	ANTIXA	"LMW HEPARIN, ANTIXA U/ML"	361
+LABARI	ABO/RH (ARI)	ARG	ABO/RH (AUTOMATION)	361
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	DVE	RESULTS	360
+LABSTPOLY	"WBC FOR IBD, STOOL"	EOSSTL	"EOSINOPHILS, STOOL"	360
+LABSTPOLY	"WBC FOR IBD, STOOL"	POLYST	"PMNS, STOOL"	360
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	PRO	PRODUCT ID  	360
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	RPT	RPT	360
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	SDES	SPECIMEN	360
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	SI	<<STATUS>>  	360
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	SREQ	COMMENTS	360
+LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	DATE2	DATE 2	359
+LABFVARZ	"VARICELLA ZOSTER VIR. DNA,QUAL"	SOUR13	SOURCE	358
+LABCMVIGT	CMV IGG (TRANSFUSION)	CMGTQT	LEVEL	357
+LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	DATE3	DATE 3	357
+LABCMVM	CMV IGM	CMVIGM	CMV IGM AB	356
+LABUTP	"PROTEIN, URINE (TIMED)"	TTPU	PROTEIN EXCRETION	355
+LABUTP	"PROTEIN, URINE (TIMED)"	UPROTTOT	"PROTEIN, TOTAL, URINE"	355
+LABUTP	"PROTEIN, URINE (TIMED)"	UT10	COLLECTION PERIOD PROTEIN	355
+LABUTP	"PROTEIN, URINE (TIMED)"	VOL10	VOLUME PROTEIN	355
+LABINSLF	INSULIN FASTING	INSF	"INSULIN, FASTING"	354
+LABMALAR	MALARIA PREP	MAL	MALARIA PREP RESULT	352
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	XMR	UNIT CROSSMATCH  	350
+LABCMVQL	"CMV DNA PCR QUAL, NON-PLASMA"	CMQDNA	CMV DNA	347
+LABCMVQL	"CMV DNA PCR QUAL, NON-PLASMA"	CMVLIP	TEST DESCRIPTION	347
+LABCMVQL	"CMV DNA PCR QUAL, NON-PLASMA"	CMVQSP	SPECIMEN	347
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MAOTH	"OTHER, ABS(MAN DIFF)"	347
+LABNORW	"NOROVIRUS ANTIGEN, STOOL EIA"	NORW1	"NOROVIRUS,ELA (STOOL)"	346
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	COTHER	"OTHERS, CSF"	345
+LAB11123R	"COCCI CF AB, CSF"	COCC	COCCI CF AB (CSF)	341
+LABTHEOL	THEOPHYLLINE LEVEL	THEO	THEOPHYLLINE	341
+LABTHEOL	THEOPHYLLINE LEVEL	THEO1	"PEAK, TROUGH, OR RANDOM"	341
+LABTHEOL	THEOPHYLLINE LEVEL	THEO2	DATE & TIME OF LAST DOSE	341
+LABTHEOL	THEOPHYLLINE LEVEL	THEO3	DOSAGE	341
+LABTHEOL	THEOPHYLLINE LEVEL	THEO4	ADMINISTRATION ROUTE	341
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	UBT	UNIT BLOOD TYPE  	339
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	UNT	UNIT NUMBER  	339
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	DI	ISSUE DATE/TIME	337
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	URENAL	RENAL CELLS	337
+LABANTIM	ANTI - TPO AB	ATPO	ANTI-TPO ANTIBODY	335
+LABUA	URINALYSIS WITH MICROSCOPIC	SPERM	SPERMATOZOA	333
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	SPEXP	SPECIMEN EXPIRATION DATE/TIME	333
+LAB11068R	ACE	ACE	ANGIOTESIN CONVERTING ENZYME	331
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	CILIA	BRONCH. LINING CELLS	331
+LABBMT1	BMT PANEL 1	EGFR	EGFR	330
+LABMMAS	"METHYLMALONIC ACID, SERUM"	MMA	"METHYLMALONIC ACID, SERUM"	329
+LABCZPL	"CARBAMAZEPINE, SERUM"	CZP	CARBAMAZEPINE	326
+LABCZPL	"CARBAMAZEPINE, SERUM"	CZP1	"PEAK, TROUGH, OR RANDOM"	326
+LABCZPL	"CARBAMAZEPINE, SERUM"	CZP2	DATE & TIME LAST DOSE	326
+LABCZPL	"CARBAMAZEPINE, SERUM"	CZP3	DOSAGE	326
+LABCZPL	"CARBAMAZEPINE, SERUM"	CZP4	ADMINISTRATION ROUTE	326
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	PRO	PRODUCT ID  	326
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	SI	<<STATUS>>  	326
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	OTHER	OTHERS (DESCRIBE)	325
+LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	OCCGU	"STOOL SAMPLE2,GUAIAC"	324
+LABSTOBGC	"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC."	OCCGU	"STOOL SAMPLE3,GUAIAC"	324
+LAB11136R	HTLV AB	HTLVS	HTLV AB SCREEN	322
+LABURCL	"CHLORIDE, URINE (RANDOM)"	UCL	"CHLORIDE, URINE"	320
+LABEB	EBV AB PANEL	COM	COMMENT	319
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	XMR	UNIT CROSSMATCH  	319
+10335R	HLA - SCREENING C1Q	9271R	SERUM DATE	316
+10335R	HLA - SCREENING C1Q	9275R	HLA ANTIBODIES FOR C1Q CLASS I	316
+10335R	HLA - SCREENING C1Q	9282R	DISCLAIMER	316
+10335R	HLA - SCREENING C1Q	9585R	COMPOSED TEXT CLASS I	316
+10335R	HLA - SCREENING C1Q	9605R	METHOD	316
+LABCMVM	CMV AB IGM	CMVIGM	CMV IGM AB	316
+LABTRFN	TRANSFERRIN	TRFRN	TRANSFERRIN	316
+10335R	HLA - SCREENING C1Q	9263R	CUMULATIVE ANTIBODIES BY IGG	315
+10335R	HLA - SCREENING C1Q	9274R	CPRA FOR C1Q CLASS I	315
+10335R	HLA - SCREENING C1Q	9350R	MFI RESULTS FOR SCREENING C1Q CLASS I	315
+10335R	HLA - SCREENING C1Q	9265R	CUMULATIVE ANTIBODIES BY C1Q	314
+LABYBOH	BETA-HYDROXYBUTYRATE	BOH	BETA-HYDROXYBUTYRATE	314
+LABHGBP	"HEMOGLOBIN, PLASMA"	12078R	OXYHEMOGLOBIN	313
+LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	ANCIGG	"ANCA, IGG"	313
+LABYFGL	"GLUCOSE, FLUID"	GLUFLD	GLUCOSE BODY FLUID	313
+LABYFGL	"GLUCOSE, FLUID"	YFLSC1	FLUID TYPE	313
+LABBMT2	BMT PANEL 2	EGFR	EGFR	312
+LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	MPOAR	MYELOPEROX AB REFLEX	312
+LABPTTINH	PTT INHIBITOR SCREEN	PTTDIR	INTERPRETED BY	312
+LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	SERINEPR3	SERINE PR3 REFLEX	312
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	UBT	UNIT BLOOD TYPE  	312
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	UNT	UNIT NUMBER  	312
+10335R	HLA - SCREENING C1Q	9266R	CPRA FOR CUMULATIVE C1Q	311
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SCMT	SYNOVIAL FLUID COMMENT	311
+LABANTIXA	"LMW HEPARIN, ANTIXA U/ML"	ANTIXA	"LMW HEPARIN, ANTIXA U/ML"	310
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	DI	ISSUE DATE/TIME	305
+LABCBCD	CBC WITH DIFF	LYMPHM	LYMPHOMA CELLS	305
+LABBHB	BETA-HYDROXYBUTYRATE	BHB	BETA-HYDROXYBUTYRATE	303
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	PRO	PRODUCT ID  	303
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	SI	<<STATUS>>  	303
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CRLYM	"REACT LYMPH, CSF"	302
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL1	HSV 1 IGG INDEX	302
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL2	HSV 1 IGG INTERPRETATION	302
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL3	HSV 2 IGG INDEX	302
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL4	HSV 2 IGG INTERPRETATION	302
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL5	HSV 1/2 IGM INDEX	302
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL7	HSV 1/2 IGM INTERPRETATION	302
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	SPEXP	SPECIMEN EXPIRATION DATE/TIME	300
+LABYWNILE	"WEST NILE VIRUS AB, IGG AND IGM"	WNVIGG	WEST NILE VIRUS AB IGG	300
+LABYWNILE	"WEST NILE VIRUS AB, IGG AND IGM"	WNVIGM	WEST NILE VIRUS AB IGM	300
+LABYWNILE	"WEST NILE VIRUS AB, IGG AND IGM"	YWNSC	SOURCE	299
+LABVCULR	"VIRAL CULTURE, RESPIRATORY"	CULT	CULT/OTHER RSLT:	297
+LABVCULR	"VIRAL CULTURE, RESPIRATORY"	RPT	RPT	297
+LABVCULR	"VIRAL CULTURE, RESPIRATORY"	SDES	SPECIMEN	297
+LABVCULR	"VIRAL CULTURE, RESPIRATORY"	SREQ	COMMENTS	297
+LABCBCD	CBC WITH DIFF	PLYM	PROLYMPHOCYTE	294
+10330R	HLA - SCREENING IGG	9244R	CUMULATIVE ANTIBODIES BY IGG	291
+10330R	HLA - SCREENING IGG	9246R	CUMULATIVE ANTIBODIES BY C1Q	291
+10330R	HLA - SCREENING IGG	9249R	IGG COMBINED PEAK CPRA	291
+10330R	HLA - SCREENING IGG	9250R	IGG COMBINED PEAK DATE	291
+10330R	HLA - SCREENING IGG	9251R	SERUM DATE	291
+10330R	HLA - SCREENING IGG	9262R	DISCLAIMER	291
+10330R	HLA - SCREENING IGG	9604R	METHOD	291
+LABYCP	"C - PEPTIDE, SERUM"	CPEP	C-PEPTIDE	291
+10330R	HLA - SCREENING IGG	9245R	CPRA FOR CUMULATIVE IGG	290
+10330R	HLA - SCREENING IGG	9254R	CPRA FOR IGG CLASS I	290
+10330R	HLA - SCREENING IGG	9255R	HLA ANTIBODIES FOR IGG CLASS I	290
+10330R	HLA - SCREENING IGG	9583R	COMPOSED TEXT CLASS I	290
+10330R	HLA - SCREENING IGG	9650R	MFI RESULTS FOR SCREENING IGG CLASS I	290
+10330R	HLA - SCREENING IGG	9248R	IGG COMBINED CPRA FOR SERUM	288
+10330R	HLA - SCREENING IGG	9257R	CPRA FOR IGG CLASS II	288
+10330R	HLA - SCREENING IGG	9258R	HLA ANTIBODIES FOR IGG CLASS II	288
+10330R	HLA - SCREENING IGG	9261R	COMPOSED TEXT CLASS II	288
+10330R	HLA - SCREENING IGG	9651R	MFI RESULTS FOR SCREENING IGG CLASS II	288
+LABWNVAB	"WEST NILE VIRUS AB PANEL, CSF"	WNVIGG	WEST NILE VIRUS IGG	288
+LABFLUAPC 	FLU A PCR	FLUADI	DISCLAIMER	286
+LABFLUAPC 	FLU A PCR	FLUAMT	METHOD	286
+LABFLUAPC 	FLU A PCR	FLUAR	INFLUENZA A RNA	286
+LABFLUAPC 	FLU A PCR	FLUASP	SPECIMEN	286
+LABUA	URINALYSIS WITH MICROSCOPIC	CECAST	CELLULAR CASTS	285
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CCNT	CELLS COUNTED	284
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	DI	ISSUE DATE/TIME	284
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	AMPHET	AMPHETAMINES	283
+LAB11242R	VZV QUANT	11243R	SOURCE	282
+LAB11242R	VZV QUANT	11247R	VZV QUANT	282
+LABWNVAB	"WEST NILE VIRUS AB PANEL, CSF"	WNVIGM	WEST NILE VIRUS IGM	282
+LABCO2	"CO2, SERUM/PLASMA"	CO2	"CO2, SER/PLAS"	279
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PRMY	PROMYELOCYTES	278
+LABFADAG	"ADENOVIRUS AG, STOOL"	ADAG	ADENOVIRUS AG	277
+LABCDPLI	COLD AGGLUTININ SCREEN	CDPL	COLD ANTIBODY SCREEN	277
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	PRO	PRODUCT ID  	277
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	SI	<<STATUS>>  	277
+LABCBCD	CBC WITH DIFF	MEGA	MEGAKARY/100WBC	276
+LAB11098R	1 25 OH VITAMIN D	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	275
+LABVZVIGG	VZ IGG	VGGDES	VZV IGG TEST DESCRIPTION	274
+LABVZVIGG	VZ IGG	VGGQUL	VZV IGG RESULT	274
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	UBT	UNIT BLOOD TYPE  	272
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	UNT	UNIT NUMBER  	272
+LABCL	"CHLORIDE, SERUM/PLASMA"	CL	"CHLORIDE, SER/PLAS"	270
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	URIC	URIC ACID CRYSTALS	270
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	ALCOHOL	ALCOHOL	269
+LABYPTHRP	"PTH-RELATED PEPTIDE, PLASMA"	PTH	PTH-RELATED PEPTIDE	269
+LABVZVIGG	VZ IGG	VGGQNT	VZV IGG LEVEL	269
+LABEB	EBV AB PANEL	11165R	EB VIRUS EARLY AG (R+D) IGG	268
+LABEB	EBV AB PANEL	11166R	EB NUCLEAR AB	268
+LABEB	EBV AB PANEL	12001R	"VCA IGG ,IFA"	268
+LABEB	EBV AB PANEL	12002R	"VCA IGM ,IFA"	268
+10290R	HLA - TYPING	9078R	INTERPRETATION/ COMMENTS	268
+LABPCEG7O	"ISTAT EG7, OTHER"	FIO2	FIO2	265
+LABYCOPPR	"COPPER, SERUM"	COPPER	"COPPER, SERUM"	264
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	BARB	BARBITURATES	263
+LABCMVIGM	CMV IGM	CMMQNT	CMV IGM LEVEL	263
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	BENZO	BENZODIAZEPINES	262
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	COCAINE	COCAINE	262
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	OPIATE	OPIATES	262
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	YUPCP	PHENCYCLIDINE	262
+LABYUCDA	"CONFIRMED DRUG ABUSE SURVEY, URINE"	YUTTHC	TETRAHYDROCANNABINOL	262
+LAB11069R	ALDOLASE	ALDOLASE	"ALDOLASE, SERUM"	261
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	XMR	UNIT CROSSMATCH  	261
+LABBCRABL	"BCR - ABL, BLOOD"	BCRCMT	COMMENT	260
+LABBCRABL	"BCR - ABL, BLOOD"	BCRDIS	DISCLAIMER	260
+LABBCRABL	"BCR - ABL, BLOOD"	BCRMET	METHOD	260
+LABBCRABL	"BCR - ABL, BLOOD"	BCRPRM	PRIMERS	260
+LABBCRABL	"BCR - ABL, BLOOD"	BCRRES	RESULTS	260
+LABADVQT	ADENOVIRUS DNA PCR QUANT	ADDNA	ADENOVIRUS DNA	258
+LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVCP	RESULT	258
+LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVLOG	RESULT LOG 10	258
+LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVSP	SPECIMEN	258
+LABADVQT	ADENOVIRUS DNA PCR QUANT	ADVTIP	TEST DESCRIPTION	258
+LABFHV12	"HSV 1 AND 2 PCR, NON CSF"	HV1DNA	"HSV-1 DNA, QN PCR"	255
+LABFHV12	"HSV 1 AND 2 PCR, NON CSF"	HV2DNA	"HSV-2 DNA, QN PCR"	255
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	REACTL	REACTIVE LYMPHS	253
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	VMQNT	VMQNT	253
+LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	BIREF	BIREFRINGENCE	252
+LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	LOCAT	LOCATION	252
+LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	SHAPE	SHAPE	252
+LABCRYST	"CRYSTAL ANALYSIS, SYNOVIAL FLUID"	SIZE	SIZE	252
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	ACAIGG	ACA IGG ANTIBODIES	251
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	ACAIGM	ACA IGM ANTIBODIES	251
+LABHBCA	IGM ANTI-HBC	HBCINT	INTERPRETATION	251
+LABHBCA	IGM ANTI-HBC	HBCR	IGM ANTI-HBC ANTIBODY	251
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	LUPINT	LUPUS INTERPRETATION	251
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	UBT	UNIT BLOOD TYPE  	251
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	UNT	UNIT NUMBER  	251
+LABBCRABL	"BCR - ABL, BLOOD"	BCRDIR	INTERPRETED BY	250
+LABPCEG7O	"ISTAT EG7, OTHER"	BE	"BASE EXCESS, ISTAT"	250
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	DI	ISSUE DATE/TIME	248
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	PRO	PRODUCT ID  	248
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	SI	<<STATUS>>  	248
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	XMR	UNIT CROSSMATCH  	248
+LABGNTPOL	GENTAMICIN PEAK LEVEL	GENTPK	"GENTAMICIN, PEAK"	247
+LABGNTPOL	GENTAMICIN PEAK LEVEL	GNTPO2	DATE & TIME OF LAST DOSE	247
+LABGNTPOL	GENTAMICIN PEAK LEVEL	GNTPO3	DOSAGE	247
+LABGNTPOL	GENTAMICIN PEAK LEVEL	GNTPO4	ADMINISTRATION ROUTE	247
+LAB11234R	VZV TOTAL AND IGM AB PANEL	11235R	"VARICELLA-ZOSTER, ACIF"	245
+LAB11234R	VZV TOTAL AND IGM AB PANEL	11236R	"VZ AB IGM, IFA"	245
+LAB11234R	VZV TOTAL AND IGM AB PANEL	11237R	VZV INTERPRETATION	245
+LABFMYAB	MYCOPLASMA IGG/IGM AB	FMYABG	M.PNEUMONIA AB IGG	245
+LABFMYAB	MYCOPLASMA IGG/IGM AB	FMYABM	M.PNEUMONIA AB IGM	245
+LABYERYTH	ERYTHROPOIETIN	EPO	ERYTHROPOIETIN	244
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	ALBCPI	"ALBUMIN, SER/PLAS"	243
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CAB	CSF ALBUMIN	243
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CIG	CSF IGG	243
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CINDX	CSF IGG/ALBUMIN INDEX	243
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CPIEI	INTERPRETATION	243
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CPIER	RESULT	243
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	IGGCPI	"IGG, SER/PLAS"	243
+LABLAMOL	LAMOTRIGINE LEVEL	LAMO	LAMO	243
+LABLAMOL	LAMOTRIGINE LEVEL	LAMO1	LAMO1	243
+LABLAMOL	LAMOTRIGINE LEVEL	LAMO2	LAMO2	243
+LABLAMOL	LAMOTRIGINE LEVEL	LAMO3	LAMO3	243
+LABLAMOL	LAMOTRIGINE LEVEL	LAMO4	LAMO4	243
+LABBLCA	"AFB CULTURE, BLOOD"	CULT	CULT/OTHER RSLT:	239
+LABGASBLD	OCCULT BLOOD GASTRIC	OCCBLDGAS	"OCCULT BLOOD,GASTRIC"	239
+LABBLCA	"AFB CULTURE, BLOOD"	RPT	RPT	239
+LABBLCA	"AFB CULTURE, BLOOD"	SDES	SPECIMEN	239
+LABBLCA	"AFB CULTURE, BLOOD"	SREQ	COMMENTS	239
+LABWNVAB	"WEST NILE VIRUS AB PANEL, CSF"	WNITP	W NILE INTERPRETATION	237
+10335R	HLA - SCREENING C1Q	9264R	CPRA FOR CUMULATIVE IGG	236
+LABCBCD	CBC WITH DIFF	DYSPL	"DYSPL GRANS, UNCLASS."	236
+LABHBSQT	HEP B SURF AB QUANT	HBSAIN	INTERPRETATION	236
+LABHBSQT	HEP B SURF AB QUANT	HBSAQL	HEP B SURF AB QUAL	236
+LABHBSQT	HEP B SURF AB QUANT	HBSAQT	HEP B SURF AB QUANT	236
+LABANTIXA	ANTI XA / LMWH	ANTIXA	"LMW HEPARIN, ANTIXA U/ML"	235
+LABEVPCR	ENTEROVIRUS BY PCR	EVIN	ENTEROVIRUS PCR INTERPRETATION	235
+LABEVPCR	ENTEROVIRUS BY PCR	EVR	CSF ENTEROVIRUS RESULT	235
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	B2GPIG	"BETA-2 GLYCOPROTEIN 1, IGG"	234
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	B2GPIM	"BETA-2 GLYCOPROTEIN 1, IGM"	234
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVCN	DRVV CONFIRMATION	234
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVIN	DRVV INTERPRETATION	234
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVMX	DRVV MIX	234
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVSC	DRVV SCREEN	234
+10290R	HLA - TYPING	9077R	NMDP CODES	233
+LABYCCPIG	ANTI CCP	CCPINT	INTERPRETATION	233
+LABYCCPIG	ANTI CCP	CCPR	ANTI CCP ANTIBODY	233
+LABEVPCR	ENTEROVIRUS BY PCR	EVSP	SPECIMEN	233
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	DRVVLA	DRVV NORMALIZED RATIO	232
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITDI	DISCLAIMER	231
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITDX	CLINICAL INDICATION	231
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITME	METHOD	231
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITPR	PRIMER	231
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITRE	RESULT	231
+LAB12573R	ANCA	12575R	C-ANCA	230
+LAB12573R	ANCA	12576R	P-ANCA	230
+LABVCULT	"VIRAL CULTURE, TISSUE"	CULT	CULT/OTHER RSLT:	230
+LABVCULT	"VIRAL CULTURE, TISSUE"	RPT	RPT	230
+LABVCULT	"VIRAL CULTURE, TISSUE"	SDES	SPECIMEN	230
+LABVCULT	"VIRAL CULTURE, TISSUE"	SREQ	COMMENTS	230
+LABYMETFF	"METANEPHRINES FRACTIONATED FREE, PLASMA"	UMETF	METANEPHRINE FREE	230
+LABYMETFF	"METANEPHRINES FRACTIONATED FREE, PLASMA"	UNORMETF	"NORMETANEPHRINE,FREE"	230
+10330R	HLA - SCREENING IGG	9247R	CPRA FOR CUMULATIVE C1Q	229
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	PRO	PRODUCT ID  	229
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	SI	<<STATUS>>  	229
+LABFHV12	"HSV 1 AND 2 PCR, NON CSF"	SOURC9	SOURCE	229
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FRLYM	"REACTIVE LYMPHS, FLD"	228
+LABERUPLT	EMERGENCY RELEASE PLT	PLT	PLATELETS	228
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	UBT	UNIT BLOOD TYPE  	228
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	UNT	UNIT NUMBER  	228
+LAB11023R	EB NUCLEAR AG AB	11166R	EB NUCLEAR AB	226
+LABCD4CT	CD4 COUNT	C3P4PA	"CD3+/CD4+,ABS"	226
+LABCD4CT	CD4 COUNT	C3P4PC	"CD3+/CD4+,%"	226
+LABCD4CT	CD4 COUNT	LYM	LYMPHOCYTES %	226
+LABCD4CT	CD4 COUNT	LYMAB	"LYMPHOCYTES, ABS."	226
+LABCD4CT	CD4 COUNT	WBC	WBC COUNT	226
+LAB11020R	EB VIRUS EARLY AG (R+D) IGG	11165R	EB VIRUS EARLY AG (R+D) IGG	224
+LABADPCRQ	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADPCR1	ADENVIRUS DNA	224
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	DI	ISSUE DATE/TIME	224
+LABADPCRQ	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	SOUR10	SOURCE	224
+LABIOPTH	INTRA OPERATIVE PTH	IOPTH	INTRA OPERATIVE PTH	223
+LAB11024R	EB VIRAL CAPSID AG IGM AB	11167R	EB VCA IGM AB	222
+LAB11030R	EB VIRAL CAPSID AG IGG AB	11168R	EB VCA IGG AB	222
+LABFDP	FIBRIN DEGRADATION PRODUCTS	FDP	FIBRIN DEGRAD PROD	219
+10335R	HLA - SCREENING C1Q	10472R	DSA FOR C1Q CLASS I	217
+LABSTRONG	"STRONGYLOIDES IGG AB, ELISA"	STROG	"STRONGYLOIDES ,LGG"	217
+LABRITUX	RITUXAN PANEL	C45RAA	"CD4+/CD45RA+, ABS"	215
+LABRITUX	RITUXAN PANEL	C45RAP	CD4+/CD45RA+ %	215
+LABRITUX	RITUXAN PANEL	C45ROA	"CD4+/CD45RO+, ABS"	215
+LABRITUX	RITUXAN PANEL	C45ROP	CD4+/CD45RO+ %	215
+LABRITUX	RITUXAN PANEL	CD19AB	"CD19 (PAN B), ABS."	215
+LABRITUX	RITUXAN PANEL	CD19PC	CD19 (PAN B) %	215
+LABRITUX	RITUXAN PANEL	CD20AB	"CD20 (PAN B), ABS."	215
+LABRITUX	RITUXAN PANEL	CD20PC	CD20 (PAN B) %	215
+LABRITUX	RITUXAN PANEL	CD3AB	"CD3 (PAN T), ABS."	215
+LABRITUX	RITUXAN PANEL	CD3PC	CD3 (PAN T) %	215
+LABRITUX	RITUXAN PANEL	CD4AB	"CD3+/CD4+, ABS."	215
+LABRITUX	RITUXAN PANEL	CD4PC	CD3+/CD4+ %	215
+LABRITUX	RITUXAN PANEL	CD8AB	"CD3+/CD8+, ABS."	215
+LABRITUX	RITUXAN PANEL	CD8PC	CD3+/CD8+ %	215
+LABRITUX	RITUXAN PANEL	HNS	"CD4+CD8 TOTAL, CD3+%"	215
+LABRITUX	RITUXAN PANEL	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	215
+LABRITUX	RITUXAN PANEL	HSRATIO	"CD4/CD8 RATIO, CD3+"	215
+LABRITUX	RITUXAN PANEL	LYM	LYMPHOCYTES %	215
+LABRITUX	RITUXAN PANEL	LYMAB	"LYMPHOCYTES, ABS."	215
+LABRITUX	RITUXAN PANEL	RTXFDA	NOTE	215
+LABRITUX	RITUXAN PANEL	WBC	WBC COUNT	215
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPDI	DISCLAIMER	213
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPDX	CLINICAL INDICATION	213
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPMT	METHOD	213
+LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	QTFNIN	QUANTIFERON INTERPRETATION	212
+LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	QTFNRS	QUANTIFERON-TB	212
+LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UALB	"ALBUMIN, URINE"	212
+LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UALBCR	ALB/CREAT RATIO	212
+LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UALBCRINTP	ALB/CREAT INTERP	212
+LABUALB	"ALBUMIN WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	212
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	ACAIGG	ACA IGG ANTIBODIES	211
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	ACAIGM	ACA IGM ANTIBODIES	211
+LABKOHP	KOH / CALCOFLUOR	KOH	KOH PREP	211
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	LUPINT	LUPUS INTERPRETATION	211
+LABKOHP	KOH / CALCOFLUOR	RPT	RPT	211
+LABKOHP	KOH / CALCOFLUOR	SDES	SPECIMEN	211
+LABKOHP	KOH / CALCOFLUOR	SREQ	COMMENTS	211
+10335R	HLA - SCREENING C1Q	9277R	CPRA FOR C1Q CLASS II	210
+10335R	HLA - SCREENING C1Q	9278R	HLA ANTIBODIES FOR C1Q CLASS II	210
+10335R	HLA - SCREENING C1Q	9281R	COMPOSED TEXT CLASS II	210
+10335R	HLA - SCREENING C1Q	9652R	MFI RESULTS FOR SCREENING C1Q CLASS II	210
+LABSSAABY	ANTI-RO ANTIBODY	SSAIN	ANTI-RO INTERPRETATION	210
+LABSSAABY	ANTI-RO ANTIBODY	SSAQUL	ANTI-RO RESULT	210
+LABYTESFT	"TESTOSTERONE, TOTAL AND FREE"	TESTOSFREE	"TESTOSTERONE, FREE"	210
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPRE	RESULT	209
+LABHHV6QT	HHV-6 PCR QUANT	HHV6CP	RESULT	209
+LABHHV6QT	HHV-6 PCR QUANT	HHV6DN	HHV-6 DNA	209
+LABHHV6QT	HHV-6 PCR QUANT	HHV6LO	RESULT LOG 10	209
+LABHHV6QT	HHV-6 PCR QUANT	HHV6SP	SPECIMEN	209
+LABHHV6QT	HHV-6 PCR QUANT	HHV6TI	TEST DESCRIPTION	209
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITAA	AMINO ACID CHANGE	209
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITCO	COMMENT	209
+LABSTDY1	STUDY TEST	LABS1	LABS1	209
+LABSTDY1	STUDY TEST	RESS1	RESS1	209
+LABSHIV12	RAPID HIV-1/2 ANTIBODY	SHIV12	RAPID HIV-1/2 ANTIBODY	209
+LABSTDY1	STUDY TEST	SPTYS1	SPTYS1	209
+LABSTDY1	STUDY TEST	TESTS1	TESTS1	209
+10150R	HLA ORDER - SUMMARIES	10049R	HLA LAB TESTING STATUS	208
+LABIGA	"IGA, SERUM"	IGA	"IGA, SERUM"	208
+10150R	HLA ORDER - SUMMARIES	RINFO	TO FIND RESULT:	208
+LABSTREP	BETA STREP THROAT SCREEN RAPID EIA	BSTREP	BETA STREP THROAT EIA SCREEN	207
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	KITMD	INTERPRETED BY	207
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	PRO	PRODUCT ID  	207
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	SI	<<STATUS>>  	207
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	UBT	UNIT BLOOD TYPE  	207
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	UNT	UNIT NUMBER  	207
+10330R	HLA - SCREENING IGG	10469R	DSA FOR IGG CLASS I	206
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	CCNT	CELLS COUNTED	206
+LABHSVCSF	"HSV DNA PCR QUAL, CSF"	HSV1PC	HSV-1 DNA	206
+LABHSVCSF	"HSV DNA PCR QUAL, CSF"	HSV2PC	HSV-2 DNA	206
+LABHSVCSF	"HSV DNA PCR QUAL, CSF"	HSVINC	TEST DESCRIPTION	206
+LABHSVCSF	"HSV DNA PCR QUAL, CSF"	SDES11	SPECIMEN	206
+LABSSBABY	ANTI-LA ANTIBODY	SSBINT	ANTI-LA INTERPRETATION	206
+LABSSBABY	ANTI-LA ANTIBODY	SSBQUL	ANTI-LA RESULT	206
+10330R	HLA - SCREENING IGG	10470R	DSA FOR IGG CLASS II	205
+LABFADA	"ADENOSINE DEAMINASE, FLUID"	ADAFLD	"ADENOSINE DEAMIN., FLD"	205
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	DI	ISSUE DATE/TIME	205
+LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	PL24	"PLASMA, 24HR"	205
+LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	PL72	"PLASMA, 72HR"	205
+LABFADA	"ADENOSINE DEAMINASE, FLUID"	SPTYAD	FLUID TYPE	205
+LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	SR24	"SERUM, 24HR"	205
+LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	SR72	"SERUM, 72HR"	205
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	XMR	UNIT CROSSMATCH  	204
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIVNO	NOTES	202
+LABB2M	"BETA - 2 - MICROGLOBULIN, SERUM"	B2M	"BETA-2-MICROGLOBULIN, SERUM"	201
+LABVSTLC	VIRAL STOOL CULTURE	CULT	CULT/OTHER RSLT:	201
+LABVSTLC	VIRAL STOOL CULTURE	RPT	RPT	201
+LABVSTLC	VIRAL STOOL CULTURE	SDES	SPECIMEN	201
+LABVSTLC	VIRAL STOOL CULTURE	SREQ	COMMENTS	201
+LABYFVPA	VALPROIC ACID FREE AND TOTAL	VPAFREE	"VALPROIC ACID, FREE"	201
+LABYFVPA	VALPROIC ACID FREE AND TOTAL	VPATOTAL	"VALPROIC ACID, TOTAL"	201
+LABPCEG7O	"ISTAT EG7, OTHER"	BD	"BASE DEFICIT, ISTAT"	200
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPAA	AMINO ACID CHANGE	199
+POC6	POC PPD SKIN TEST (IP)	ERYTHREMA	ERYTHREMA	198
+POC6	POC PPD SKIN TEST (IP)	MMIND	MM INDURATION	198
+LABYFPHEN	"PHENYTOIN TOTAL AND FREE, PLASMA"	PHENFR	"PHENYTOIN, FREE PLASMA"	198
+LABYFPHEN	"PHENYTOIN TOTAL AND FREE, PLASMA"	PHENTOT	"PHENYTOIN, TOTAL PLASMA"	198
+POC6	POC PPD SKIN TEST (IP)	PHLOC	LOCATION	198
+LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	YPARV1	PARVOVIRUS B19 AB IGG	198
+LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	YPARV2	PARVOVIRUS B19 AB IGM	198
+LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	YPARV3	INTERPRETATION	198
+LABAMLP	AML PROGNOSIS ASSAY	FLT3DS	DISCLAIMER	196
+LABAMLP	AML PROGNOSIS ASSAY	FLT3DX	CLINICAL INDICATION	196
+LABAMLP	AML PROGNOSIS ASSAY	FLT3IN	INTERPRETATION	196
+LABAMLP	AML PROGNOSIS ASSAY	FLT3MT	METHOD	196
+LABAMLP	AML PROGNOSIS ASSAY	FLT3RS	RESULT	196
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPCM	COMMENT	195
+LABCEBPA	"DETECTION OF CEBPA, BLOOD"	CEPMD	INTERPRETATION	195
+LABPLTMAP	PLATELET MAPPING	ADP	ADP	194
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	SPEXP	SPECIMEN EXPIRATION DATE/TIME	194
+LABCSTOTA	"PROTEIN C&S, TOTAL"	PROTCAGT	"PROTEIN C AG, TOTAL"	193
+LABCSTOTA	"PROTEIN C&S, TOTAL"	PROTSTOT	"PROTEIN S AG, TOTAL"	193
+LAB12762R	TACROLIMUS	12763R	DATE OF LAST DOSE	192
+LAB12762R	TACROLIMUS	12764R	TIME OF LAST DOSE	192
+LAB12762R	TACROLIMUS	12765R	"DOSE, MG"	192
+LAB12762R	TACROLIMUS	12766R	TACROLIMUS	192
+LABAMLP	AML PROGNOSIS ASSAY	FLT3MD	INTERPRETED BY	192
+LABPLTMAP	PLATELET MAPPING	ARATEG	ARACHIDONIC ACID	190
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	BASOCSF	"BASOPHILS, CSF"	190
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	PRO	PRODUCT ID  	190
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	SI	<<STATUS>>  	190
+LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKCPU	RESULT	189
+LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKDNAU	BKV DNA	189
+LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKIPRU	TEST DESCRIPTION	189
+LABBKVPCU	"BK VIRUS PCR, QUANT, URINE"	BKLOGU	RESULT LOG 10	189
+LABYVITA	VITAMIN A	FREEVITA	FREE RETINOL(VIT A)	189
+LABFTRIGA	"TRIGLYCERIDES, NON-PLEURAL"	TGLFLD	"TRIGLYCERIDES, BODY FLUID"	189
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	CECAST	CELLULAR CASTS	188
+LABPTINH	PT INHIBITOR SCREEN	PTINTP	INTERPRETATION	188
+LABMONEBV	MONO SCREEN WITH REFLEX EBV AB	EBV	EBV TITER REFLEXED?	187
+LABMONEBV	MONO SCREEN WITH REFLEX EBV AB	MONOSC	INFECTIOUS MONO SCREEN	187
+10330R	HLA - SCREENING IGG	9259R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS II	186
+LABYHGBP	"HEMOGLOBIN, PLASMA"	YHGBP1	TOTAL HEMOGLOBIN	186
+LABYHGBP	"HEMOGLOBIN, PLASMA"	12078R	OXYHEMOGLOBIN	185
+10290R	HLA - TYPING	9020R	HLA-BW ALLELE 2	185
+LABADVDNA	"ADENOVIRUS DNA, QUALITATIVE PCR"	ADOV	"ADENOVIRUS DNA, QL PCR"	185
+LABADVDNA	"ADENOVIRUS DNA, QUALITATIVE PCR"	SOURC3	ADENOVIRUS SOURCE	185
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	DI	ISSUE DATE/TIME	184
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	SPERM	SPERMATOZOA	184
+LAB11070R	ALDOSTERONE	ALDOSTR	"ALDOSTERONE, SERUM"	183
+LABSTPCR	THROAT STREP SCREEN BY PCR	CULT	CULT/OTHER RSLT:	183
+LABHIVQ2	HIV QUANTITATIVE PCR	HIVCP	RESULT	183
+LABHIVQ2	HIV QUANTITATIVE PCR	HIVIPR	TEST DESCRIPTION	183
+LABHIVQ2	HIV QUANTITATIVE PCR	HIVLO	RESULT LOG 10	183
+LABHIVQ2	HIV QUANTITATIVE PCR	HIVRNA	HIV 1 RNA	183
+LABHIVQ2	HIV QUANTITATIVE PCR	HIVSP	SPECIMEN	183
+LABSTPCR	THROAT STREP SCREEN BY PCR	RPT	RPT	183
+LABSTPCR	THROAT STREP SCREEN BY PCR	SDES	SPECIMEN	183
+LABSTPCR	THROAT STREP SCREEN BY PCR	SREQ	COMMENTS	183
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	HIPLAB	LAB DATA	182
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	UBT	UNIT BLOOD TYPE  	182
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	UNT	UNIT NUMBER  	182
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	FRFAT	FREE FAT	181
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	OVFAT	OVAL FAT BODIES	180
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	SPEXP	SPECIMEN EXPIRATION DATE/TIME	180
+LABPLTMAP	PLATELET MAPPING	TEGMD	INTERP BY:	180
+10005R	HLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)	10049R	HLA LAB TESTING STATUS	179
+10005R	HLA ORDER - TYPING - BMT EVALUATION (RECIPIENT)	RINFO	TO FIND RESULT:	179
+LABANTITG	ANTI - THYROGLOBULIN AB	ATGA	ANTI-THYROGLOBULIN AB	178
+LABFLL	FSH	FSH	FSH	178
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRDI	DISCLAIMER	178
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRDX	CLINICAL INDICATION	178
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRME	METHOD	178
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRPR	PRIMER	178
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRRE	RESULT	178
+LAB12571R	"FLT3-ITD, FLT3-D835 MUTATIONS"	12572R	"FLT3-ITD, FLT3-D835 MUTATIONS"	177
+LABPLTFUN	PLATELET FUNCTION SCREEN	COLADP	COLLAGEN/ADP	177
+LABPLTFUN	PLATELET FUNCTION SCREEN	COLEPI	COLLAGEN/EPI	177
+LABPLTFUN	PLATELET FUNCTION SCREEN	PLTFCM	"COMMENT, PLT FUNCTION"	177
+LABPTINH	PT INHIBITOR SCREEN	PTDIR	INTERPRETED BY	177
+LABYHABQ	HEP B SURF AB QUANT	HBSAB	"HBS ANTIBODY, S"	175
+LABYHABQ	HEP B SURF AB QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	175
+10330R	HLA - SCREENING IGG	9256R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS I	174
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	PRO	PRODUCT ID  	173
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	SI	<<STATUS>>  	173
+LAB11118R	"ELASTASE, STOOL"	STELAS	"ELASTASE-1, STOOL"	173
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	XMR	UNIT CROSSMATCH  	173
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	172
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	172
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	172
+10335R	HLA - SCREENING C1Q	10473R	DSA FOR C1Q CLASS II	171
+LABKWB	"POTASSIUM, WHOLE BLOOD"	K	"POTASSIUM, WHOLE BLD"	171
+LABYZINCS	ZINC	ZNC	"ZINC, BLOOD"	170
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	169
+10365R	HLA - DSA BY IGG	9407R	CUMULATIVE ANTIBODIES BY IGG	168
+10365R	HLA - DSA BY IGG	9408R	CPRA FOR CUMULATIVE IGG	168
+10365R	HLA - DSA BY IGG	9424R	CPRA FOR IGG CLASS II FOR CURRENT	168
+10365R	HLA - DSA BY IGG	9425R	HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	168
+10365R	HLA - DSA BY IGG	9434R	COMPOSED TEXT CLASS II	168
+10365R	HLA - DSA BY IGG	9435R	DISCLAIMER	168
+10365R	HLA - DSA BY IGG	9609R	METHOD	168
+10365R	HLA - DSA BY IGG	9626R	CURRENT SERUM DATE	168
+10365R	HLA - DSA BY IGG	9656R	MFI RESULTS FOR DSA BY IGG CLASS II	168
+LABHIVQ2	HIV QUANTITATIVE PCR	HIVNO	NOTES	168
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	UBT	UNIT BLOOD TYPE  	168
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	UNT	UNIT NUMBER  	168
+10365R	HLA - DSA BY IGG	9368R	MFI RESULTS FOR DSA BY IGG CLASS I	166
+10365R	HLA - DSA BY IGG	9411R	CPRA FOR IGG CLASS I FOR CURRENT	166
+10365R	HLA - DSA BY IGG	9412R	HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	166
+10365R	HLA - DSA BY IGG	9421R	COMPOSED TEXT CLASS I	166
+LABIGM	"IGM, SERUM"	IGM	"IGM, SERUM"	166
+LABFMYAB	MYCOPLASMA IGG/IGM AB	LTROIN	INTERPRETATION	166
+LAB11092R	RENIN	RENIN	"RENIN ACTIVITY, PL"	166
+LABRNPABY	ANTI-RNP ANTIBODY	RNPINT	ANTI-RNP INTERPRETATION	166
+LABRNPABY	ANTI-RNP ANTIBODY	RNPQUL	ANTI-RNP RESULT	166
+10370R	HLA - DSA BY C1Q	9436R	CUMULATIVE ANTIBODIES BY C1Q	165
+10370R	HLA - DSA BY C1Q	9437R	CPRA FOR CUMULATIVE C1Q	165
+10370R	HLA - DSA BY C1Q	9466R	DISCLAIMER	165
+10370R	HLA - DSA BY C1Q	9610R	METHOD	165
+10370R	HLA - DSA BY C1Q	9631R	SERUM DATE	165
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	HBPNO	NOTE	165
+LABUA	URINALYSIS WITH MICROSCOPIC	OTCAST	"CASTS, OTHER"	165
+LABPCEG7O	"ISTAT EG7, OTHER"	PCTEMP	BODY TEMPERATURE	165
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	PRO	PRODUCT ID  	165
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	SI	<<STATUS>>  	165
+10370R	HLA - DSA BY C1Q	9454R	CPRA FOR C1Q CLASS II FOR CURRENT	164
+10370R	HLA - DSA BY C1Q	9455R	HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	164
+10370R	HLA - DSA BY C1Q	9465R	COMPOSED TEXT CLASS II	164
+10370R	HLA - DSA BY C1Q	9657R	MFI RESULTS FOR DSA BY C1Q CLASS II	164
+LABASI	AB SCREEN (ASI)	AS3	ANTIBODY SCREEN	164
+LABSCLABY	ANTI-SCL70 ANTIBODY	SCLINT	ANTI-SCL70 INTERPRETATION	164
+LABSCLABY	ANTI-SCL70 ANTIBODY	SCLQUL	ANTI-SCL70 RESULT	164
+LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	HSV1PC	HSV-1 DNA	163
+LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	HSV2PC	HSV-2 DNA	163
+LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	HSVINP	TEST DESCRIPTION	163
+LABHSVQLP	"HSV DNA PCR QUAL, PLASMA"	SDES11	SPECIMEN	163
+10370R	HLA - DSA BY C1Q	9369R	MFI RESULTS FOR DSA BY C1Q CLASS I	162
+10370R	HLA - DSA BY C1Q	9440R	CPRA FOR C1Q CLASS I FOR CURRENT	162
+10370R	HLA - DSA BY C1Q	9441R	HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	162
+10370R	HLA - DSA BY C1Q	9451R	COMPOSED TEXT CLASS I	162
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	DI	ISSUE DATE/TIME	162
+LABSTORE	SPECIMEN STORAGE ONLY	STORE	SPECIMEN STORAGE ONLY	162
+LAB11081R	GASTRIN	GASTRIN	"GASTRIN, SERUM"	161
+LABHSAB	HSV VIRAL AB	HSAB	VIRAL AB HSV	160
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN	CULT	CULT/OTHER RSLT:	159
+LABYSTOSM	"OSMOLALITY, STOOL"	OSMOLSTL	OSMOLALITY STOOL	159
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN	RPT	RPT	159
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN	SDES	SPECIMEN	159
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN	SREQ	COMMENTS	159
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	XMR	UNIT CROSSMATCH  	159
+LABFHCT	"HEMATOCRIT, FLUID"	HCTFLD	"HEMATOCRIT, FLUID"	158
+LABFHCT	"HEMATOCRIT, FLUID"	HCTTYP	SOURCE	158
+LABCBCD	CBC WITH DIFF	IMMPL	IMMAT. PLASMA CELLS	158
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	XMR	UNIT CROSSMATCH  	157
+LABYFFATQ	"FAT, FECAL QUANTITATIVE"	YFFQ1	TOTAL WEIGHT	156
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBDI	DISCLAIMER	155
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBDX	CLINICAL INDICATION	155
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBMT	METHOD	155
+LAB11036R	"FIBRINOGEN, QUANT"	FIBRINOGENA	FIBRINOGEN ANTIGEN	155
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	PRO	PRODUCT ID  	155
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	SI	<<STATUS>>  	155
+LABYFFATQ	"FAT, FECAL QUANTITATIVE"	YFFQ2	DURATION	155
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBRE	RESULT	154
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRDR	INTERPRETED BY	154
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	VL	PRODUCT VOLUME	154
+LABYCH50	COMPLEMENT TOTAL	YCH50S	"COMPLEMENT, TOTAL"	154
+LABSTFAT	"FAT STAIN, STOOL"	STFAT	"FAT STAIN, STOOL"	153
+LABTOXGM	TOXOPLASMA IGG IGM	TOXDES	TEST DESCRIPTION	153
+LABTOXGM	TOXOPLASMA IGG IGM	TOXGQL	IGG RESULT	153
+LABYVDRLC	"VDRL, CSF"	VDRL	VDRL	153
+LABB2GP1	BETA - 2 - GLYCOPROTEIN 1	B2GPIG	"BETA-2 GLYCOPROTEIN 1, IGG"	152
+LABB2GP1	BETA - 2 - GLYCOPROTEIN 1	B2GPIM	"BETA-2 GLYCOPROTEIN 1, IGM"	152
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	DI	ISSUE DATE/TIME	152
+LABSTRED	"REDUCING SUBSTANCE, STOOL"	REDSUBSTL	"REDUCING SUBST, STOOL"	152
+LABTOXGM	TOXOPLASMA IGG IGM	TOXMQL	IGM RESULT	152
+LAB11056R	FREE CORTISOL	CORTFREE	"CORTISOL, SERUM FREE"	151
+LABMALAR	MALARIA PREP	MALCON	MALCON	151
+LABHSCRP	HIGH SENSITIVITY C-REACTIVE PROTEIN	HSCRP	HIGH SENSITIVITY CRP	150
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	150
+LABLYMES2	"LYME SCREEN, SERUM"	LYMEC6	B.BURGDORFERI C6 PEPTIDE	150
+LAB11013R	PLATELET ANTIBODY ID PANEL	11014R	ANTIGEN CAPTURE	148
+LAB11982R	POSACONAZOLE	11983R	POSACONAZOLE	148
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBAA	AMINO ACID CHANGE	148
+LABCHOL	"CHOLESTEROL TOTAL, SERUM/PLASMA"	CHOL	"CHOLESTEROL, TOTAL"	148
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVIN	DRVV INTERPRETATION	148
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBMD	INTERPRETATION	147
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVCN	DRVV CONFIRMATION	147
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVSC	DRVV SCREEN	147
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	LUPDIR	INTERPRETED BY	147
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	PRO	PRODUCT ID  	147
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	QBCRCM	COMMENT	147
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	SI	<<STATUS>>  	147
+LABYBRUC	BRUCELLA IGG AND IGM AB	YBRUC1	"BRUCELLA AB ,IGG"	147
+LABYBRUC	BRUCELLA IGG AND IGM AB	YBRUC2	"BRUCELLA AB ,IGM"	147
+LABYBRUC	BRUCELLA IGG AND IGM AB	YBRUC3	INTERPRETATION	147
+10335R	HLA - SCREENING C1Q	9276R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS I	146
+LABYFFATQ	"FAT, FECAL QUANTITATIVE"	FATPC	%FAT	146
+LABYUMYO	"MYOGLOBIN, URINE"	UMYOG	"MYOGLOBIN, URINE"	146
+10370R	HLA - DSA BY C1Q	9423R	DSA FOR C1Q CLASS II	145
+LABRP	RENAL FUNCTION PANEL	AG	ANION GAP	145
+LABRP	RENAL FUNCTION PANEL	ALB	"ALBUMIN, SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	BUN	"UREA NITROGEN,SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	CA	"CALCIUM, SER/PLAS"	145
+LABBMCEBP	"DETECTION OF CEBPA, BONE MARROW"	CEBCM	COMMENT	145
+LABRP	RENAL FUNCTION PANEL	CL	"CHLORIDE, SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	CO2	"CO2, SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	CR	"CREATININE, SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	GLU	"GLUCOSE, SER/PLAS"	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HSVDS	HSV PANEL TEST DESCRIPTION	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV1QNT	HSV-1 IGG LEVEL	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV1QUL	HSV-1 IGG RESULT	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV2QNT	HSV-2 IGG LEVEL	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HV2QUL	HSV-2 IGG RESULT	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HVMQNT	HSV IGM LEVEL	145
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HVMQUL	HSV IGM RESULT	145
+LABRP	RENAL FUNCTION PANEL	K	"POTASSIUM, SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	NA	"SODIUM, SER/PLAS"	145
+LABRP	RENAL FUNCTION PANEL	PHOS	"PHOSPHORUS, SER/PLAS"	145
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVLA	DRVV NORMALIZED RATIO	144
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	UBT	UNIT BLOOD TYPE  	144
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	UNT	UNIT NUMBER  	144
+10365R	HLA - DSA BY IGG	9415R	DSA FOR IGG CLASS II	143
+LABHA1C	HGB A1C W/ EST MEAN GLUCOSE	A1C	HEMOGLOBIN A1C	143
+LABHA1C	HGB A1C W/ EST MEAN GLUCOSE	EMGL	EST. MEAN GLUCOSE	143
+LABFACT9	FACTOR IX ASSAY	FACTOR9	FACTOR IX ASSAY	142
+LABYENPCR	ENTEROVIRUS PCR	YENP1	SPECIMEN SOURCE	142
+LABYENPCR	ENTEROVIRUS PCR	YENP2	"ENTEROVIRUS ,PCR"	142
+LABYENPCR	ENTEROVIRUS PCR	YENP4	REPORT STATUS	142
+10045R	HLA ORDER - ANTIBODY SCREENING	10049R	HLA LAB TESTING STATUS	141
+LABSMABY	ANTI-SM ANTIBODY	ASMINT	ANTI-SM INTERPRETATION	141
+LABSMABY	ANTI-SM ANTIBODY	ASMQUL	ANTI-SM RESULT	141
+LABATTG	IGA ANTI  TTG	ATTGR	IGA ANTI TTG ANTIBODY	141
+LABATTG	IGA ANTI  TTG	ATTINT	INTERPRETATION	141
+LABEPO	ERYTHROPOIETIN	EPO	ERYTHROPOIETIN	141
+10045R	HLA ORDER - ANTIBODY SCREENING	RINFO	TO FIND RESULT:	141
+10035R	HLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)	10049R	HLA LAB TESTING STATUS	140
+LABFQFVR	COXIELLA BURNETII AB	12008R	Q FEVER IGG PHASE I SCRN	140
+LABFQFVR	COXIELLA BURNETII AB	12009R	Q FEVER IGM PHASE I SCRN	140
+LABFQFVR	COXIELLA BURNETII AB	12010R	Q FEVER IGG PHASE II SCRN	140
+LABFQFVR	COXIELLA BURNETII AB	12011R	Q FEVER IGM PHASE II  SCRN	140
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	LUPDIR	INTERPRETED BY	140
+10035R	HLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT)	RINFO	TO FIND RESULT:	140
+LABUK	"POTASSIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	139
+LABUK	"POTASSIUM WITH CREATININE, URINE (RANDOM)"	UK	"POTASSIUM, URINE"	139
+LABUK	"POTASSIUM WITH CREATININE, URINE (RANDOM)"	UKCR	POTASSIUM/CREAT RATIO	139
+10365R	HLA - DSA BY IGG	9414R	DSA FOR IGG CLASS I	138
+10370R	HLA - DSA BY C1Q	9422R	DSA FOR C1Q CLASS I	138
+LABSPE	PROTEIN ELECTROPHORESIS	ALB	ALBUMIN	138
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	DI	ISSUE DATE/TIME	138
+LABCSFLD	"LDH, CSF"	LDHCSF	"LDH, CSF"	138
+LABSPE	PROTEIN ELECTROPHORESIS	SPA1	ALPHA 1 ZONE	138
+LABSPE	PROTEIN ELECTROPHORESIS	SPA2	ALPHA 2 ZONE	138
+LABSPE	PROTEIN ELECTROPHORESIS	SPBE1	BETA 1 ZONE	138
+LABSPE	PROTEIN ELECTROPHORESIS	SPBE2	BETA 2 ZONE	138
+LABSPE	PROTEIN ELECTROPHORESIS	SPEI	INTERPRETATION	138
+LABSPE	PROTEIN ELECTROPHORESIS	SPGA	GAMMA ZONE	138
+LABSPE	PROTEIN ELECTROPHORESIS	TPE	TOTAL PROTEIN	138
+LABTHS	GROUP A STREP CULTURE	CULT	CULT/OTHER RSLT:	137
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	OCCPH	PATIENT OCCULAR PH	137
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC4	QC 4 RESULT	137
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	QC7	QC 7 RESULT	137
+LABTHS	GROUP A STREP CULTURE	RPT	RPT	137
+LABTHS	GROUP A STREP CULTURE	SDES	SPECIMEN	137
+LABTHS	GROUP A STREP CULTURE	SREQ	COMMENTS	137
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	TESTEXP	TEST STRIP EXP DATE	137
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	TESTLOT	TEST STRIP LOT#	137
+LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTD	DISCLAIMER	136
+LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTI	"INTERPRETATION, BM"	136
+LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTM	METHOD	136
+LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTR	"RESULT, BM"	136
+LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTX	CLINICAL INDICATION	136
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	DVE	RESULTS	136
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	EOSFLD	"EOSINOPHILS, SYN FLD"	136
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	RPT	RPT	136
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SDES	SPECIMEN	136
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SREQ	COMMENTS	136
+LABRITUX	RITUXAN PANEL	C1613AB	"CD16+/CD13-, ABS"	135
+LABRITUX	RITUXAN PANEL	C1613PC	CD16+/CD13- %	135
+LABRITUX	RITUXAN PANEL	C563AB	"CD56+/CD3-, ABS"	135
+LABRITUX	RITUXAN PANEL	C563PC	CD56+/CD3- %	135
+LABVZVQLP	"VZV DNA PCR QUAL, PLASMA"	VZQLDN	VZV DNA	135
+LABVZVQLP	"VZV DNA PCR QUAL, PLASMA"	VZQLSP	SPECIMEN	135
+LABVZVQLP	"VZV DNA PCR QUAL, PLASMA"	VZQLTD	TEST DESCRIPTION	135
+LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKCPP	RESULT	134
+LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKDNAP	BKV DNA	134
+LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKIPRP	TEST DESCRIPTION	134
+LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKLOGP	RESULT LOG 10	134
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSCIM	CLINICAL INDICATION	134
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSDIM	DISCLAIMER	134
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSMEM	METHOD	134
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSREFM	REFERENCES	134
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	DRVVMX	DRVV MIX	134
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GAPCM	"APC, BM"	134
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKDI	DISCLAIMER	134
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKDX	CLINICAL INDICATION	134
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKMD	INTERPRETED BY	134
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKME	METHOD	134
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKRE	RESULT	134
+LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	UMET	"METANEPHRINE, URINE"	134
+LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	UMET	"TOT.METANEPHRINES,UR"	134
+LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	UNORMET	"NORMETANEPHRINE, UR"	134
+LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	VOLUMEURN	URINE VOLUME	134
+LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	YUMEUT	COLLECTION DURATION	134
+LABBMAML	"AML PROGNOSIS ASSAY, BONE MARROW"	BMFLTB	INTERPRETED BY	133
+LABROMSP	MICROSPORIDIUM SCREEN	CULT	CULT/OTHER RSLT:	133
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	PRO	PRODUCT ID  	133
+LABROMSP	MICROSPORIDIUM SCREEN	RPT	RPT	133
+LABROMSP	MICROSPORIDIUM SCREEN	SDES	SPECIMEN	133
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	SI	<<STATUS>>  	133
+LABROMSP	MICROSPORIDIUM SCREEN	SREQ	COMMENTS	133
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGPRES	HGBS PRESENT	132
+LABUA	URINALYSIS WITH MICROSCOPIC	OTHCRY	"CRYSTALS, OTHER"	132
+LAB11085R	MYELOPEROXIDASE ANTIBODY	MPO	MYELOPEROXIDASE AB	131
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	UBT	UNIT BLOOD TYPE  	131
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	UNT	UNIT NUMBER  	131
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	XMR	UNIT CROSSMATCH  	131
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	XMR	UNIT CROSSMATCH  	131
+LABYCHRA	CHROMOGRANIN A	YCHRA1	CHROMOGRANIN A	130
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSCIB	CLINICAL INDICATION	129
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSDIB	DISCLAIMER	129
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSMEB	METHOD	129
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSREFB	REFERENCES	129
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	DI	ISSUE DATE/TIME	129
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GAPCB	"APC, BLOOD"	129
+LABFBILA	"BILIRUBIN TOTAL, FLUID"	BILIRUBINFLD	"BILIRUBIN, TOTAL, BODY FLUID"	128
+LAB11094R	RO AB	SSA	"SS-A/RO AB, IGG, SERUM"	128
+LABOXCMET	OXCARBAZEPINE METABOLITE LEVEL	OXCMT1	OXCARBAZEPINE METABOLITE	127
+LAB11102R	VITAMIN E	VITE	"A-TOCOPHEROL, VITAMIN E"	127
+LABBARSAB	BARTONELLA SPECIES AB	BHIGGS	B.HENSELAE IGG SCREEN	125
+LABBARSAB	BARTONELLA SPECIES AB	BHIGMS	B.HENSELAE IGM SCREEN	125
+LABBARSAB	BARTONELLA SPECIES AB	BQIGGS	B.QUINTANA IGG SCREEN	125
+LABBARSAB	BARTONELLA SPECIES AB	BQIGMS	B.QUINTANA IGM SCREEN	125
+POC2	POC URINE DIPSTICK FOR BLOOD	DIPLOT	DIPSTICK LOT	125
+POC2	POC URINE DIPSTICK FOR BLOOD	EXPDATE	EXP DATE	125
+LABHITIA	HIT FUNCTIONAL ASSAY	HITI1	HIT FUNCTIONAL ASSAY	125
+LABHITIA	HIT FUNCTIONAL ASSAY	HITI2	HIT COMMENT	125
+POC2	POC URINE DIPSTICK FOR BLOOD	PHLOC	LOCATION	125
+10280R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING	RINFO	TO FIND RESULT:	125
+POC2	POC URINE DIPSTICK FOR BLOOD	UBLOOD	BLOOD URINE	125
+LABVZVCSF	"VZV DNA PCR QUAL, CSF"	VZQLDN	VZV DNA	125
+LABVZVCSF	"VZV DNA PCR QUAL, CSF"	VZQLIC	TEST DESCRIPTION	125
+LABVZVCSF	"VZV DNA PCR QUAL, CSF"	VZQLSP	SPECIMEN	125
+10305R	HLA - ALLO T FLOW CROSSMATCH	9123R	DONOR NAME	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9125R	RELATIONSHIP TO PATIENT	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9126R	DONOR CELL DATE	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9127R	DONOR CELL SOURCE	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9128R	DONOR CELL TARGET	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9129R	METHOD	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9130R	SERUM DATE 1	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9132R	RESULT  MCS 1	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9134R	RESULT 1	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9151R	COMPOSED TEXT	124
+10305R	HLA - ALLO T FLOW CROSSMATCH	9152R	DISCLAIMER	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9153R	DONOR NAME	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9155R	RELATIONSHIP TO PATIENT	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9156R	DONOR CELL DATE	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9157R	DONOR CELL SOURCE	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9158R	DONOR CELL TARGET	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9159R	METHOD	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9160R	SERUM DATE 1	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9162R	RESULT  MCS 1	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9164R	RESULT 1	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9181R	INTERPRETATION/ COMMENTS	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9182R	COMPOSED TEXT	124
+10310R	HLA - ALLO B FLOW CROSSMATCH	9183R	DISCLAIMER	124
+LABHITIA	HIT FUNCTIONAL ASSAY	HIPDIR	HIT INTERPRETED BY	124
+LABYJCPCR	"JC VIRUS DNA, PCR"	JCDNA	JC VIRUS DNA	124
+LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME	PARVD	PARVOVIRUS DNA	124
+LABYJCPCR	"JC VIRUS DNA, PCR"	SOURC8	SOURCE	124
+LABYTESFT	"TESTOSTERONE, TOTAL AND FREE"	TESTOS	"TESTOSTERONE, TOTAL"	124
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNSO	STONE SOURCE	124
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNWT	STONE WEIGHT	124
+LABYSTONE	KIDNEY STONE ANALYSIS	1STCONST	1ST CONSTITUENT	123
+10305R	HLA - ALLO T FLOW CROSSMATCH	9124R	DONOR DOB	123
+10310R	HLA - ALLO B FLOW CROSSMATCH	9154R	DONOR DOB	123
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	CSMDM	INTERPRETED BY	123
+LABIGGS4	IGG  SUBCLASS 4	IGGS4	IGG SUBCLASS4	123
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKCO	COMMENT	123
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKPR	PRIMERS	123
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKRF	REFERENCES	123
+LABHITAB	HEPARIN PLATELET FACTOR 4 AB	PF4IGG	PF4 IGG ONLY OD	123
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETIC	"RETIC COUNT, MANUAL"	123
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	SPEXP	SPECIMEN EXPIRATION DATE/TIME	123
+LAB11556R	BLASTOMYCES SEROLOGIES	11666R	BLASTO AB ID	122
+LAB11556R	BLASTOMYCES SEROLOGIES	BLAS	BLASTO AB CFS	122
+LABCRPLFB	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD"	CRPLFB	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GBRAFM	"BRAF, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GCTNNM	"CTNNB1, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GEGFRM	"EGFR, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GIDH1M	"IDH1, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GIDH2M	"IDH2, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GKRASM	"KRAS, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GNOTCM	"NOTCH1, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GNRASM	"NRAS, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GPIK3M	"PIK3CA, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GPTENM	"PTEN, BM"	122
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GTP53M	"TP53, BM"	122
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBA2Q	"HGB A2, QUANT."	122
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBF	"HGB F, QUANT."	122
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	PRO	PRODUCT ID  	122
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	SI	<<STATUS>>  	122
+LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME	SOUR11	SOURCE	122
+LAB11095R	LA AB	SSB	"SS-B/LA AB, IGG, SERUM"	122
+LABYPR3	PROTEINASE 3 ANTIBODY	YPR31	PROTEINASE 3 ANTIBODIES	122
+LAB11107R	HBE ANTIGEN	HBEAG	HEPATITIS B E AG	121
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBEP2	INTERPRETATION	121
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGBS	"HGB S, QUANT"	121
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	HGMD	REVIEWED BY	121
+LABTOXO	TOXOPLASMA SEROLOGY	TOXO	TOXOPLASMA IGG	121
+10365R	HLA - DSA BY IGG	9630R	IGG COMBINED CPRA FOR CURRENT SERUM	119
+LABCENABY	ANTI CENTROMERE ANTIBODY	CENINT	ANTI-CENTROMERE INTERPRETATION	119
+LABCENABY	ANTI CENTROMERE ANTIBODY	CENQUL	ANTI-CENTROMERE RESULT	119
+LABCRPLFC	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF"	CRPLFC	"CRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF"	119
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FNRBC	"NRBC/100WBC, FLD"	119
+LABINSL	INSULIN UNSPECIFIED	INSU	"INSULIN,UNSPECIFIED"	119
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	UBT	UNIT BLOOD TYPE  	119
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	UNT	UNIT NUMBER  	119
+10305R	HLA - ALLO T FLOW CROSSMATCH	9707R	DONOR PHENOTYPE	118
+10310R	HLA - ALLO B FLOW CROSSMATCH	9708R	DONOR PHENOTYPE	118
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	OTCAST	"CASTS, OTHER"	118
+10145R	HLA ORDER - UNRELATED DONOR PROCESSING	10049R	HLA LAB TESTING STATUS	117
+LABYFFATQ	"FAT, FECAL QUANTITATIVE"	FATTOT	TOTAL FAT/24H	117
+LABFBILA	"BILIRUBIN TOTAL, FLUID"	FSC2	FLUID TYPE	117
+10145R	HLA ORDER - UNRELATED DONOR PROCESSING	RINFO	TO FIND RESULT:	117
+10145R	HLA ORDER - UNRELATED DONOR PROCESSING	UNRDID	UNRELATED DONOR ID	117
+10015R	HLA ORDER - DSA SCREENING	10049R	HLA LAB TESTING STATUS	116
+LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRCT	COMMENT	116
+LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRDS	DISCLAIMER	116
+LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRMT	METHOD	116
+LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRPM	PRIMERS	116
+LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRRS	RESULTS	116
+LABBUNCR	BUN / CREATININE	BUNCREAT	BUN/CREATININE	116
+10015R	HLA ORDER - DSA SCREENING	RINFO	TO FIND RESULT:	116
+LAB12808R	CK ISOENZYME WITH REFLEX	12819R	CK ISOENZYME ELEC SPECIMN ONLY	115
+LAB12808R	CK ISOENZYME WITH REFLEX	CK	CREATINE KINASE	115
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV1QNT	HSV-1 IGG LEVEL	115
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV1QUL	HSV-1 IGG RESULT	115
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV2QNT	HSV-2 IGG LEVEL	115
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HV2QUL	HSV-2 IGG RESULT	115
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HVIGDS	HSV IGG TEST DESCRIPTION	115
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	DI	ISSUE DATE/TIME	114
+10100R	HLA ORDER - PLATELET COMPATIBILITY	10049R	HLA LAB TESTING STATUS	113
+LABBMBCR	"BCR - ABL, BONE MARROW"	BMBRDR	INTERPRETED BY	113
+LABFHVIG	"HIV-1 AB, WESTERN BLOT"	HVIG	HIV 1 AB CONFIRMATION	113
+10100R	HLA ORDER - PLATELET COMPATIBILITY	RINFO	TO FIND RESULT:	113
+LABYTRYPT	"TRYPTASE, SERUM"	TRYPTASE	TRYPTASE	113
+LABYLEVT	LEVETIRACETAM	YLEVT1	LEVETIRACETAM	113
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	CSMDB	INTERPRETED BY	112
+LABIGF	INSULIN-LIKE GROWTH FACTOR 1	IGF	INSULIN-LIKE GROWTH FACTOR 1	112
+LABYLAMO	LAMOTRIGINE	LAMOT	LAMOTRIGINE	112
+LAB12214R	"B. BURGDORFERI C6 PEP AB,RFLX"	LYMEC6	B.BURGDORFERI C6 PEPTIDE	112
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	RETICAB	"RETIC, ABS, MANUAL"	112
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	SPEXP	SPECIMEN EXPIRATION DATE/TIME	112
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	UBT	UNIT BLOOD TYPE  	112
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	UNT	UNIT NUMBER  	112
+LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIKACINTR	"AMIKACIN, TROUGH"	111
+LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIPR2	DATE & TIME OF LAST DOSE	111
+LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIPR3	DOSAGE	111
+LABAMIPRL	AMIKACIN TROUGH LEVEL	AMIPR4	ADMINISTRATION ROUTE	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD3PC	CD3 (PAN T) %	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD4AB	"CD3+/CD4+, ABS."	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD4PC	CD3+/CD4+ %	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD8AB	"CD3+/CD8+, ABS."	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	CD8PC	CD3+/CD8+ %	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GBRAFB	"BRAF, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GCTNNB	"CTNNB1, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GEGFRB	"EGFR, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GIDH1B	"IDH1, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GIDH2B	"IDH2, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GKRASB	"KRAS, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GNOTCB	"NOTCH1, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GNRASB	"NRAS, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GPIK3B	"PIK3CA, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GPTENB	"PTEN, BLOOD"	111
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GTP53B	"TP53, BLOOD"	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	LYM	LYMPHOCYTES %	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	111
+LABRF	RHEUMATOID FACTOR	RHFINT	INTERPRETATION	111
+LABRF	RHEUMATOID FACTOR	RHFR	RHEUMATOID FACTOR	111
+LABUNAT	"SODIUM, URINE (TIMED)"	TNAU	SODIUM EXCRETION	111
+LABUNAT	"SODIUM, URINE (TIMED)"	UNA	"SODIUM, URINE"	111
+LABUNAT	"SODIUM, URINE (TIMED)"	UT1	COLLECTION PERIOD SODIUM	111
+LABVANPOL	VANCOMYCIN PEAK LEVEL	VANCOPEAK	"VANCOMYCIN, PEAK"	111
+LABVANPOL	VANCOMYCIN PEAK LEVEL	VANPO2	DATE & TIME OF LAST DOSE	111
+LABVANPOL	VANCOMYCIN PEAK LEVEL	VANPO3	DOSAGE	111
+LABVANPOL	VANCOMYCIN PEAK LEVEL	VANPO4	ADMINISTRATION ROUTE	111
+LABUNAT	"SODIUM, URINE (TIMED)"	VOL1	VOLUME SODIUM	111
+LABC4C8	"CD4 / CD8 PANEL, BLOOD"	WBC	WBC COUNT	111
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	XMR	UNIT CROSSMATCH  	111
+LAB11013R	PLATELET ANTIBODY ID PANEL	11015R	MODIFIED ANTIGEN CAPTURE	110
+LAB11013R	PLATELET ANTIBODY ID PANEL	11016R	PLATELET ANTIBODY SCREEN	110
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	ABNWBC	BLASTS/ABNORMAL CELLS	110
+LABTOXGM	TOXOPLASMA IGG IGM	TOXGQT	IGG LEVEL	110
+LABYSTONE	KIDNEY STONE ANALYSIS	2NDCONST	2ND CONSTITUENT	109
+LABFBILA	"BILIRUBIN, TOTAL BODY FLUID"	BILIRUBINFLD	"BILIRUBIN, TOTAL, BODY FLUID"	109
+LABSTLCP	STOOL CULTURE	CULT	CULT/OTHER RSLT:	109
+LABFBILA	"BILIRUBIN, TOTAL BODY FLUID"	FSC2	FLUID TYPE	109
+LAB11106R	HBE AB	HBEAB	HEPATITIS B E AB	109
+LABREF2	LAB UNLISTED 2	LAB2	REFERENCE LAB	109
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	PRO	PRODUCT ID  	109
+LABREF2	LAB UNLISTED 2	RESUL2	RESULT	109
+LABSTLCP	STOOL CULTURE	RPT	RPT	109
+LABSTLCP	STOOL CULTURE	SDES	SPECIMEN	109
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	SI	<<STATUS>>  	109
+LABREF2	LAB UNLISTED 2	SPTY2	SPECIMEN TYPE	109
+LABSTLCP	STOOL CULTURE	SREQ	COMMENTS	109
+LABREF2	LAB UNLISTED 2	TEST2	TEST NAME	109
+LABCRFA	CRYPTOSPORIDIUM DFA	CULT	CULT/OTHER RSLT:	108
+LABCRFA	CRYPTOSPORIDIUM DFA	RPT	RPT	108
+LABCRFA	CRYPTOSPORIDIUM DFA	SDES	SPECIMEN	108
+LABCRFA	CRYPTOSPORIDIUM DFA	SREQ	COMMENTS	108
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	XMR	UNIT CROSSMATCH  	107
+LAB11343R	SRA UNFRACTIONATED HEPARIN	11344R	SRA UNFRACTIONATED HEPARIN	106
+10390R	HLA-BMT DONOR MATCH DETAIL SUMMARY	9663R	DONOR MATCH GRADE DETAIL	106
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	OTHCRY	"CRYSTALS, OTHER"	106
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	PRO	PRODUCT ID  	106
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	SI	<<STATUS>>  	106
+LABTOXIG	TOXOPLASMA IGG	TOXGDS	TEST DESCRIPTION	106
+LABTOXIG	TOXOPLASMA IGG	TOXGQL	IGG RESULT	106
+LABYBSAP	"BONE ALKALINE PHOSPHATASE, SERUM"	ALKPO4BONE	BONE ALK. PHOS.	105
+LABFACT10	FACTOR X ASSAY	FACTOR10	FACTOR X ASSAY	105
+LABFBBURG	"LYME WESTERN BLOT, CSF"	BANDPRSNT	BANDS PRESENT	104
+LABCRCL	CREATININE CLEARANCE	CORCL	CORRECTED CR. CLEAR.	104
+LABCRCL	CREATININE CLEARANCE	COREXC	CORRECTED CR. EXC.	104
+LABCRCL	CREATININE CLEARANCE	CR	"CREATININE, SERUM"	104
+LABCRCL	CREATININE CLEARANCE	CRCL	CREATININE CLEARANCE	104
+LABCRCL	CREATININE CLEARANCE	CRE	CREATININE EXCRETION	104
+LABCRCL	CREATININE CLEARANCE	CRUCL	"CREATININE, URINE"	104
+LABCRCL	CREATININE CLEARANCE	FLOWURN	URINE FLOW	104
+LABCRCL	CREATININE CLEARANCE	HT	HEIGHT	104
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	LR210	LOG DX AVG	104
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	P210Q	P210	104
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	UBT	UNIT BLOOD TYPE  	104
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	UNT	UNIT NUMBER  	104
+LABCRCL	CREATININE CLEARANCE	UT3	COLLECTION PERIOD CREATININE	104
+LABCRCL	CREATININE CLEARANCE	VOL3	VOLUME CREATININE	104
+LABCRCL	CREATININE CLEARANCE	WT	WEIGHT	104
+LAB11145R	"ADULTERANTS SURVEY, URINE"	11146R	"CREATININE, URINE"	103
+LAB12238R	LACOSAMIDE	12239R	12239R	103
+LAB12238R	LACOSAMIDE	12240R	12240R	103
+LAB11145R	"ADULTERANTS SURVEY, URINE"	11147R	SPECIFIC GRAVITY	102
+LAB11145R	"ADULTERANTS SURVEY, URINE"	11148R	PH	102
+LAB11145R	"ADULTERANTS SURVEY, URINE"	11149R	OXIDANTS	102
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	DI	ISSUE DATE/TIME	102
+LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	HSV1PC	HSV-1 DNA	102
+LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	HSV2PC	HSV-2 DNA	102
+LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	HSVIN	TEST DESCRIPTION	102
+LABHSVQL	"HSV DNA PCR QUAL, NON LESION"	SDES11	SPECIMEN	102
+LABABTITI	AB TITER	ABTITER	ANTIBODY TITER RESULTS	101
+LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GP1IGA	BETA-2-GP1 IGA	101
+LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GP1IGG	BETA-2-GP1 IGG	101
+LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GP1IGM	BETA-2-GP1 IGM	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMCO	COMMENT	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMDI	DISCLAIMER	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMDX	CLINICAL INDICATION	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMIN	INTERPRETATION	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMMD	INTERPRETED BY	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMME	METHOD	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMRE	RESULT	101
+LABMGMT	"MGMT, METHYLATION SPECIFIC PCR"	MGMSO	SPECIMEN SOURCE	101
+LABTOXGM	TOXOPLASMA IGG IGM	TOXMQT	IGM LEVEL	101
+LABYSTNA	"SODIUM, STOOL"	YSTNA1	"SODIUM, 24-HR STOOL"	101
+10305R	HLA - ALLO T FLOW CROSSMATCH	9135R	SERUM DATE 2	100
+10305R	HLA - ALLO T FLOW CROSSMATCH	9137R	RESULT  MCS 2	100
+10305R	HLA - ALLO T FLOW CROSSMATCH	9139R	RESULT 2	100
+10310R	HLA - ALLO B FLOW CROSSMATCH	9165R	SERUM DATE 2	100
+10310R	HLA - ALLO B FLOW CROSSMATCH	9167R	RESULT  MCS 2	100
+10310R	HLA - ALLO B FLOW CROSSMATCH	9169R	RESULT 2	100
+10365R	HLA - DSA BY IGG	9433R	INTERPRETATION/ COMMENTS CLASS II	100
+LAB11096R	"ALPHA 1 ANTITRYPSIN, STOOL"	A1AANTITRYPF	"A-1-ANTITRYP,FECAL"	100
+LABB2GP1	BETA - 2 - GLYCOPROTEIN	B2GPCM	BETA-2-GP1 COMMENT	100
+LABSTOBLD	"OCCULT BLD, GUAIAC DIAG, SINGLE"	DATE1	DATE 1	100
+LABSTOBLD	"OCCULT BLD, GUAIAC DIAG, SINGLE"	OCCGU	"STOOL SAMPLE1,GUAIAC"	100
+LABYTOPIR	TOPIRAMATE	TOPIRAMATE	TOPIRAMATE	100
+LAB11097R	"TSH RECEPTOR AB, STIM"	TSIG	"THROID-STIMULATING IG, SERUM"	99
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	XMR	UNIT CROSSMATCH  	99
+LABYSTONE	KIDNEY STONE ANALYSIS	3RDCONST	3RD CONSTITUENT	98
+LABRIST	RISTOCETIN COFACTOR	RIST	RISTOCETIN CO-FACTOR	98
+LABYSTK	"POTASSIUM, STOOL"	YSTK1	COLLECTION DURATION	98
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	CULT	CULT/OTHER RSLT:	97
+LABFACT7	FACTOR VII ASSAY	FACTOR7	FACTOR VII ASSAY	97
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	RPT	RPT	97
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	SDES	SPECIMEN	97
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	SREQ	COMMENTS	97
+LABYSTFR	SOLUBLE TRANSFERRIN RECEPTOR	YSTFRR	SOL TRANSFERR REC	97
+LABUAMY	"AMYLASE WITH CREATININE, URINE (RANDOM)"	AMUCRU	AMYLASE/CREAT RATIO	96
+LABUAMY	"AMYLASE WITH CREATININE, URINE (RANDOM)"	AMYLASE	"AMYLASE, URINE"	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	CODEINE	CODEINE	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	HYCD	HYDROCODONE	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	OXYCOD	OXYCODONE	96
+LABIBD	ANCA	PANCA	NEUTROPHIL CYTO AB-P	96
+IMG11CCP	CAR CATH CORS POSSIBLE	PDF	PDF RESULT	96
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	PRO	PRODUCT ID  	96
+LABIBD	ANCA	SACIGA	"S. CERVISIAE AB,IGA"	96
+LABIBD	ANCA	SACIGG	"S.CERVISIAE AB,IGG"	96
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	SI	<<STATUS>>  	96
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	UBT	UNIT BLOOD TYPE  	96
+LABUAMY	"AMYLASE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	96
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	UNT	UNIT NUMBER  	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUHMGC	HYDROMORPHONE	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUMOGC	MORPHINE	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUOPIM	IMMUNOASSAY SCREEN	96
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUOPIN	INTERPRETATION	96
+LAB11145R	"ADULTERANTS SURVEY, URINE"	11150R	NITRITES	95
+LAB11145R	"ADULTERANTS SURVEY, URINE"	11152R	COMMENT	95
+LABG6PD	G6PD SCREEN	G6PD	G6PD SCREEN	95
+LABHIVAB	HIV (TYPE 1 AND TYPE 2) ABY	HIVAB	HIV 1/2 ANTIBODY	95
+LAB11531R	PARANEOPLASTIC AB SCREEN	11641R	PARANEOPLASTIC AB SCREEN	94
+LABANERB	ANAEROBIC CULTURE BB	CULT	CULT/OTHER RSLT:	94
+LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	CULT	CULT/OTHER RSLT:	94
+LABANERB	ANAEROBIC CULTURE BB	RPT	RPT	94
+LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	RPT	RPT	94
+LABANERB	ANAEROBIC CULTURE BB	SDES	SPECIMEN	94
+LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	SDES	SPECIMEN	94
+LABANERB	ANAEROBIC CULTURE BB	SREQ	COMMENTS	94
+LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	SREQ	COMMENTS	94
+LABATTG	IGA ANTI  TTG	ATTGT	IGA ANTI TTG LEVEL	93
+LABGCCR2	GC MOLECULAR DETECTION	DCL5	TEST INFORMATION:	93
+LABGCCR2	GC MOLECULAR DETECTION	GC	N.GONORRHOEAE RESULT:	93
+LABGCCR2	GC MOLECULAR DETECTION	SDES5	SPECIMEN	93
+LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	CULT	CULT/OTHER RSLT:	92
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	DI	ISSUE DATE/TIME	92
+LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	RPT	RPT	92
+LAB11093R	SCL 70 AB	SCL70	"SCL 70 AB, IGG, SERUM"	92
+LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	SDES	SPECIMEN	92
+LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	SREQ	COMMENTS	92
+10290R	HLA - TYPING	9033R	HLA-DPB1 ALLELE 1	91
+10290R	HLA - TYPING	9034R	HLA-DPB1 ALLELE 2	91
+LABISOHTI	ISOHEMAGGLUTININ TITER	ISOHTR	ISOHEMAGGLUTININ TITER RESULT	91
+LABCHMISC	MISCELLANEOUS CHEMISTRY	CHMISC	CHEMISTRY MISCELLANEOUS	90
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	90
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	90
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	90
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	90
+LABTOXO	TOXOPLASMA IGG	TOXO	TOXOPLASMA IGG	90
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTON	STONE ANALYSIS	90
+10290R	HLA - TYPING	9031R	HLA-DPA1 ALLELE 1	89
+10290R	HLA - TYPING	9032R	HLA-DPA1 ALLELE 2	89
+LABFCHOLA	"CHOLESTEROL, FLUID"	CHOLFLD	CHOLESTEROL BODY FLUID	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	DX	DIAGNOSIS	89
+LABYFLUZ	FLUCONAZOLE LEVEL	FLUCOZ	FLUCONAZOLE	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	JOINT	JOINT	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	MALIG	SUSPECT MALIGNANCY?	89
+LABURPHOS	"PHOSPHATE, URINE (RANDOM)"	PHOSU	"PHOSPHATE, URINE"	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	SCOLOR	"COLOR, SYN FLD"	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	SRBC	"RBC COUNT, SYN FLD"	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	STURB	"TURBIDITY, SYN FLD"	89
+LABSYCT	"CELL COUNT, SYNOVIAL FLUID"	SWBC	"NUCL. CELLS, SYN FLD"	89
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	UBT	UNIT BLOOD TYPE  	89
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	UNT	UNIT NUMBER  	89
+LABVWAG	VON WILLEBRAND AG	VWAG	VON WILLEBRAND ANTIGEN	89
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNCO	STONE COMMENT	89
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNNJ	"NIDUS, MAJOR"	89
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNNM	"NIDUS, MINOR"	89
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNSJ	"SHELL, MAJOR"	89
+LABYSTONE	KIDNEY STONE ANALYSIS	YSTNSM	"SHELL, MINOR"	89
+LABAPC	ACTIVATED PROTEIN C RESISTANCE	APC	APC RESISTANCE	88
+LAB11039R	GBM AB	GBMIGG	GBM-IGG ELISA	88
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	88
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	SPEXP	SPECIMEN EXPIRATION DATE/TIME	88
+10295R	HLA - AUTO T FLOW CROSSMATCH	9081R	CELL DATE	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9082R	CELL SOURCE	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9083R	SERUM DATE 1	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9085R	MCS 1	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9100R	COMPOSED TEXT	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9101R	DISCLAIMER	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9102R	CELL DATE	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9103R	CELL SOURCE	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9104R	SERUM DATE 1	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9106R	MCS 1	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9121R	COMPOSED TEXT	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9122R	DISCLAIMER	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9462R	METHOD	87
+10295R	HLA - AUTO T FLOW CROSSMATCH	9595R	RESULT 1	87
+10300R	HLA - AUTO B FLOW CROSSMATCH	9599R	METHOD	87
+LABMMAS	METHYLMALONIC ACID	MMA	"METHYLMALONIC ACID, SERUM"	87
+LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	PANCA	NEUTROPHIL CYTO AB-P	87
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	PRO	PRODUCT ID  	87
+LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	SACIGA	"S. CERVISIAE AB,IGA"	87
+LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	SACIGG	"S.CERVISIAE AB,IGG"	87
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	SI	<<STATUS>>  	87
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	XMR	UNIT CROSSMATCH  	87
+LABYCENT	CENTROMERE ANTIBODIES	YCENTA	CENTROMERE ABS.	87
+10270R	HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)	10049R	HLA LAB TESTING STATUS	86
+LABY25VD	"VITAMIN D, 25-HYDROXY"	25OHD2	25-HYDROXY D2	86
+LABY25VD	"VITAMIN D, 25-HYDROXY"	25OHD3	25-HYDROXY D3	86
+LABY25VD	"VITAMIN D, 25-HYDROXY"	25OHDT	25-HYDROXY D TOTAL	86
+LABCDAS	CELIAC DISEASE SCREEN	ATTGR	IGA ANTI TTG ANTIBODY	86
+LABCDAS	CELIAC DISEASE SCREEN	ATTINT	INTERPRETATION	86
+LABFENTA	ENTAMOEBA HISTOLYTICA AB	ENTA	E.HISTOLYTICA LGG	86
+LABCDAS	CELIAC DISEASE SCREEN	GDGPR	IGG ANTI DGP ANTIBODY	86
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GDNM3B	"DNMT3A, BLOOD"	86
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GERHRB	"ERBB2/HER2, BLOOD"	86
+LABGH	GROWTH HORMONE	GH	GROWTH HORMONE	86
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSVSL6	HSV 1/2 IGM CONFIRMATORY IFA	86
+LABCDAS	CELIAC DISEASE SCREEN	IGACDS	"IGA, SERUM"	86
+LABCDAS	CELIAC DISEASE SCREEN	IGAINT	INTERPRETATION	86
+10270R	HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT)	RINFO	TO FIND RESULT:	86
+LABYTESFT	"TESTOSTERONE, TOTAL AND FREE"	YTESHD	FREE TESTOSTERONE	86
+10170R	HLA ORDER - NMDP ADDITIONAL SPECIMEN	10049R	HLA LAB TESTING STATUS	85
+10335R	HLA - SCREENING C1Q	9280R	INTERPRETATION/ COMMENTS CLASS II	85
+10370R	HLA - DSA BY C1Q	9450R	INTERPRETATION/ COMMENTS CLASS I	85
+10300R	HLA - AUTO B FLOW CROSSMATCH	9600R	RESULT 1	85
+LABASTRO	ANTI STREPTOLYSIN O	ASTRO	ANTI STREPTOLYSIN O	85
+LABABG	"BLOOD GASES, ARTERIAL"	O2SATA	O2 SATURATION (A)	85
+LABBCRQT	"BCR - ABL QUANTITATIVE, BLOOD"	P190Q	P190	85
+10170R	HLA ORDER - NMDP ADDITIONAL SPECIMEN	RINFO	TO FIND RESULT:	85
+LABDVERV3	RSV DFA	RSV3	RSV	85
+LABDVERV3	RSV DFA	RSV3SP	SPECIMEN	85
+LABDVERV3	RSV DFA	RV3IP	TEST DESCRIPTION	85
+10170R	HLA ORDER - NMDP ADDITIONAL SPECIMEN	UNRDID	UNRELATED DONOR ID	85
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11200R	INTERPRETIVE COMMENTS	84
+LABYAMISO	"AMYLASE, PANCREATIC"	AMYLASE	"AMYLASE,PANCREATIC,S"	84
+LABYITRA	ITRACONAZOLE LEVEL	YITR1	ITRACONAZOLE	84
+LABYITRA	ITRACONAZOLE LEVEL	YITR2	HYDROXYITRACONAZOLE	84
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11201R	ANNA-1	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11202R	ANNA-2	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11203R	ANNA-3	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11204R	AGNA-1	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11205R	PCA-1	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11206R	PCA-2	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11207R	PCA-TR	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11208R	AMPHIPHYSIN AB	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11209R	CRMP-5-IGG	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11210R	P/Q -TYPE CALCIUM CHANNEL AB	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11211R	N-TYPE CALCIUM CHANNEL AB	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11212R	ACHR GANGLIONIC NEURONAL AB	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	11213R	NEURONAL (V-G) K+ CHANNEL AB	83
+10290R	HLA - TYPING	9025R	HLA-DR345 ALLELE 1	83
+LABFBBURG	"LYME WESTERN BLOT, CSF"	BBIGG	BORELLIA BURG IGG AB	83
+LABFBBURG	"LYME WESTERN BLOT, CSF"	BBIGM	BORELLIA BURG IGM AB	83
+LABCMVG	CMV GENOTYPE	CUL54	UL54 MUTATION	83
+LABCMVG	CMV GENOTYPE	CUL97	UL97 MUTATION	83
+LABIGGSUB	IGG  SUBCLASSES	IGG	"IGG, SERUM"	83
+LABIGGSUB	IGG  SUBCLASSES	IGGS1	IGG SUBCLASS1	83
+LABIGGSUB	IGG  SUBCLASSES	IGGS2	IGG SUBCLASS2	83
+LABIGGSUB	IGG  SUBCLASSES	IGGS3	IGG SUBCLASS3	83
+LABIGGSUB	IGG  SUBCLASSES	IGGS4	IGG SUBCLASS4	83
+LABJO1ABY	ANTI-JO1 ANTIBODY	JO1INT	ANTI-JO1 INTERPRETATION	83
+LABJO1ABY	ANTI-JO1 ANTIBODY	JO1QUL	ANTI-JO1 RESULT	83
+LABPROTPM	PRO-PREDICT TPMT	PRTPMR	PROPREDICT TPMT	83
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	SPEXP	SPECIMEN EXPIRATION DATE/TIME	83
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TCDX	CLINICAL INDICATION	83
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TDIR	INTERPRETED BY	83
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TDIS	DISCLAIMER	83
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TMET	METHOD	83
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TPRM	PRIMERS	83
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TRES	RESULTS	83
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	UBT	UNIT BLOOD TYPE  	83
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	UNT	UNIT NUMBER  	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	YARBIN	ACH RECEPTOR BINDING ANTIBODY	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMID	SPECIMEN ID	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMIN	INTERPRETATION	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMME	METHOD	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMOD	ORDER DATE	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMRD	RELEASE DATE	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMRS	RESULTS	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMRV	REVIEWED BY:	83
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMSP	SPECIMEN	83
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	YSTA	STRIATIONAL (STRIATED MUSCLE)	83
+10305R	HLA - ALLO T FLOW CROSSMATCH	10460R	RECIPIENT NAME	82
+10305R	HLA - ALLO T FLOW CROSSMATCH	10461R	RECIPIENT DOB	82
+LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	ASPIGE	ASPERGILLUS IGE ABY	82
+LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	ASPIGG	ASPERGILLUS IGG ABY	82
+LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	ASPINT	INTERPRETATION	82
+LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	IGEASP	TOTAL IGE	82
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADIMPDTE	LEAD IMPLANT DATE	82
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADMFR	LEAD MANUFACTURER	82
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADMODEL	LEAD MODEL	82
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADTYPE	LEAD TYPE	82
+LABTCLON	"T-CELL CLONALITY PCR, BLOOD"	TCMT	COMMENT	82
+LABVZVQL	"VZV DNA PCR QUAL, NON LESION"	VZQLDN	VZV DNA	82
+LABVZVQL	"VZV DNA PCR QUAL, NON LESION"	VZQLIP	TEST DESCRIPTION	82
+LABVZVQL	"VZV DNA PCR QUAL, NON LESION"	VZQLSP	SPECIMEN	82
+LABYUMETF	"METANEPHRINES FRACTIONATED, 24H URINE"	YUMECO	COMMENT	82
+10310R	HLA - ALLO B FLOW CROSSMATCH	10462R	RECIPIENT NAME	81
+10310R	HLA - ALLO B FLOW CROSSMATCH	10463R	RECIPIENT DOB	81
+LAB11250R	"HHV-8 DNA, QUANT"	11251R	SOURCE	81
+LAB11250R	"HHV-8 DNA, QUANT"	11252R	"HHV-8 DNA,QUANT"	81
+LAB12573R	ANCA	12574R	CYTOPLASMIC NEUTROPHILIC AB	81
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	DI	ISSUE DATE/TIME	81
+LABFACT5	FACTOR V ASSAY	FACTOR5	FACTOR V ASSAY	81
+LABFMYAB	MYCOPLASMA IGG/IGM AB	12431R	INTERPRETATION	80
+LABRITUX	RITUXAN PANEL	C5616A	CD56+CD16+/CD3- ABS.	80
+LABRITUX	RITUXAN PANEL	C5616P	CD56+CD16+/CD3- %	80
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	CCNT	CELLS COUNTED	80
+LABYUHYME	"HEAVY METALS, URINE"	MERURN	"MERCURY, URINE"	80
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	PRO	PRODUCT ID  	80
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	SI	<<STATUS>>  	80
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	XMR	UNIT CROSSMATCH  	80
+LABYUHYME	"HEAVY METALS, URINE"	YUHYM2	"LEAD, U"	80
+10370R	HLA - DSA BY C1Q	9464R	INTERPRETATION/ COMMENTS CLASS II	79
+LABYSTNA	"SODIUM, STOOL"	YSTNA3	COLLECTION DURATION	79
+LABYSTNA	"SODIUM, STOOL"	YSTNA4	SODIUM CONCENTRATION	79
+10365R	HLA - DSA BY IGG	9426R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	78
+LABYU5HIA	"5 - OH-INDOLEACETIC AC, URINE"	5HIAAURN	"5-HIAA, URINE"	77
+LABPCEC4	"ISTAT EC4, ARTERIAL"	GLU	"GLUCOSE,ISTAT"	77
+LABFHVIG	"HIV 1 IGG-AB, WESTERN BLOT CONFIRM"	HVIG	HIV 1 AB CONFIRMATION	77
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADSERNUM	LEAD SERIAL NUMBER	77
+LABPCEC4	"ISTAT EC4, ARTERIAL"	PCCOM	POCT COMMENT	77
+LABRBCPH	RBC PHENOTYPE	PHLET	E TYPE PHENOTYPE	77
+LABRF	RHUEMATOID FACTOR	RHFT	RHEUMATOID FACTOR LEVEL	77
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	UBT	UNIT BLOOD TYPE  	77
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	UNT	UNIT NUMBER  	77
+LABYU5HIA	"5 - OH-INDOLEACETIC AC, URINE"	VOLUMEURN	"URINE, VOLUME"	77
+LABYSTK	"POTASSIUM, STOOL"	YSTK3	"POTASSIUM, 24-HR STOOL"	77
+LABYSTK	"POTASSIUM, STOOL"	YSTK4	POTASSIUM CONCENTRATION	77
+LABYU5HIA	"5 - OH-INDOLEACETIC AC, URINE"	YU5HUT	COLLECTION DURATION	77
+10365R	HLA - DSA BY IGG	9420R	INTERPRETATION/ COMMENTS CLASS I	76
+10410R	HLA-BMT DONOR MATCH GRADE SUMMARY	9706R	DONOR MATCH GRADE SUMMARY	76
+LABTTM	TRANSPLANT THERAPY MONITORING	CD19AB	"CD19 (PAN B), ABS."	76
+LABTTM	TRANSPLANT THERAPY MONITORING	CD19PC	CD19 (PAN B) %	76
+LABTTM	TRANSPLANT THERAPY MONITORING	CD3AB	"CD3 (PAN T), ABS."	76
+LABTTM	TRANSPLANT THERAPY MONITORING	CD3PC	CD3 (PAN T) %	76
+LABTTM	TRANSPLANT THERAPY MONITORING	CD5AB	"CD5 (PAN T), ABS."	76
+LABTTM	TRANSPLANT THERAPY MONITORING	CD5PC	CD5 (PAN T) %	76
+LABDSDNA	ANTI - DSDNA AB	DNAQNT	ANTI-DSDNA TITER	76
+LABKRAS	KRAS MUTATION DETECTION	KRASDI	DISCLAIMER	76
+LABKRAS	KRAS MUTATION DETECTION	KRASDX	CLINICAL INDICATION	76
+LABKRAS	KRAS MUTATION DETECTION	KRASME	METHOD	76
+LABKRAS	KRAS MUTATION DETECTION	KRASRE	RESULT	76
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	76
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	76
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	76
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	76
+LAB11045R	LKM AB	LKMAB	LIV-KID-MICROSOME AB	76
+LABTTM	TRANSPLANT THERAPY MONITORING	LYM	LYMPHOCYTES %	76
+LABTTM	TRANSPLANT THERAPY MONITORING	LYMAB	"LYMPHOCYTES, ABS."	76
+LABRBCPH	RBC PHENOTYPE	PHLCT	C TYPE PHENOTYPE	76
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	PRO	PRODUCT ID  	76
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRM	METHOD	76
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRP	PRIMER	76
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRR	RESULT	76
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRS	DISCLAIMER	76
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRX	CLINICAL INDICATION	76
+LABTTM	TRANSPLANT THERAPY MONITORING	RTXFDA	NOTE	76
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	SI	<<STATUS>>  	76
+LABUA	URINALYSIS WITH MICROSCOPIC	TRPHOS	TRIPLE PHOSPH CRYST.	76
+LABTTM	TRANSPLANT THERAPY MONITORING	WBC	WBC COUNT	76
+LABYUHYME	"HEAVY METALS, URINE"	YUHYM5	COLLECTION DURATION	76
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11200R	INTERPRETIVE COMMENTS	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11201R	ANNA-1	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11202R	ANNA-2	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11203R	ANNA-3	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11204R	AGNA-1	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11205R	PCA-1	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11206R	PCA-2	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11207R	PCA-TR	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11208R	AMPHIPHYSIN AB	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11209R	CRMP-5-IGG	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11210R	P/Q -TYPE CALCIUM CHANNEL AB	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11211R	N-TYPE CALCIUM CHANNEL AB	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11212R	ACHR GANGLIONIC NEURONAL AB	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	11213R	NEURONAL (V-G) K+ CHANNEL AB	75
+LAB12232R	THYROGLOBULIN & THYROID AB	12233R	12233R	75
+10375R	HLA - STR/CHIMERISM TESTING	9467R	RECIPIENT CELL DATE	75
+10375R	HLA - STR/CHIMERISM TESTING	9468R	RECIPIENT CELL SOURCE	75
+10375R	HLA - STR/CHIMERISM TESTING	9470R	DONOR NAME	75
+10375R	HLA - STR/CHIMERISM TESTING	9472R	RELATIONSHIP TO PATIENT	75
+10375R	HLA - STR/CHIMERISM TESTING	9473R	DONOR CELL DATE	75
+10375R	HLA - STR/CHIMERISM TESTING	9474R	DONOR CELL SOURCE	75
+10375R	HLA - STR/CHIMERISM TESTING	9475R	LOCUS 1	75
+10375R	HLA - STR/CHIMERISM TESTING	9476R	LOCUS 2	75
+10375R	HLA - STR/CHIMERISM TESTING	9477R	LOCUS 3	75
+10375R	HLA - STR/CHIMERISM TESTING	9575R	COMPOSED TEXT	75
+10375R	HLA - STR/CHIMERISM TESTING	9576R	DISCLAIMER	75
+10375R	HLA - STR/CHIMERISM TESTING	9611R	METHOD	75
+10375R	HLA - STR/CHIMERISM TESTING	9636R	CHIMERISM TEST NAME	75
+LABYPROIN	"PROINSULIN, PLASMA"	PROINSULIN	PROINSULIN	75
+LABDVERTV	"ROTAVIRUS ANTIGEN, STOOL"	RTVR	ROTAVIRUS	75
+LABDVERTV	"ROTAVIRUS ANTIGEN, STOOL"	RTVTD	TEST DESCRIPTION	75
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	XMR	UNIT CROSSMATCH  	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	YARBIN	ACH RECEPTOR BINDING ANTIBODY	75
+LAB11199R	"PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM"	YSTA	STRIATIONAL (STRIATED MUSCLE)	75
+LABYUHYME	"HEAVY METALS, URINE"	YUHYM6	TOTAL VOLUME	75
+10370R	HLA - DSA BY C1Q	9442R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	74
+10375R	HLA - STR/CHIMERISM TESTING	9478R	LOCUS 4	74
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	DI	ISSUE DATE/TIME	74
+LABPCEC4	"ISTAT EC4, ARTERIAL"	HCT	"HCT(CALC), ISTAT"	74
+LABPCEC4	"ISTAT EC4, ARTERIAL"	HGB	"HGB(CALC), ISTAT"	74
+LABPCEC4	"ISTAT EC4, ARTERIAL"	K	"POTASSIUM, ISTAT"	74
+LABKRAS	KRAS MUTATION DETECTION	KRASMD	INTERPRETED BY	74
+LABLUT	LEUTEINIZING HORMONE	LH	LH	74
+LABPCEC4	"ISTAT EC4, ARTERIAL"	NA	"SODIUM, ISTAT"	74
+LABRBCPH	RBC PHENOTYPE	PHCT	C PHENOTYPE	74
+LABRBCPH	RBC PHENOTYPE	PHST	S PHENOTYPE	74
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	PRO	PRODUCT ID  	74
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	SI	<<STATUS>>  	74
+10000R	HLA ORDER - TYPING - SOLID ORGAN EVALUATION	10049R	HLA LAB TESTING STATUS	73
+LABEBVPAN	EBV AB PANEL	COM	COMMENT	73
+LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	CULT	CULT/OTHER RSLT:	73
+LABCYLX	IMMUNE FUNCTION TEST	CYLX1	IMMUNE FUNCTION TEST RESULT	73
+LABKRAS	KRAS MUTATION DETECTION	KRASMU	MUTATION(S)	73
+LABRBCPH	RBC PHENOTYPE	PHET	E PHENOTYPE	73
+LABRBCPH	RBC PHENOTYPE	PHJKBT	JKB PHENOTYPE	73
+LABRBCPH	RBC PHENOTYPE	PHKT	K PHENOTYPE	73
+10000R	HLA ORDER - TYPING - SOLID ORGAN EVALUATION	RINFO	TO FIND RESULT:	73
+LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	RPT	RPT	73
+LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	SDES	SPECIMEN	73
+LABBACIDS	"BACTERIA ID BY SEQUENCING, SPECIMEN"	SREQ	COMMENTS	73
+LABUMYOSC	"MYOGLOBIN SCREEN, URINE"	UMYOG	"MYOGLOBIN SCREEN, URINE"	73
+LABYSDA	"DRUG SCREEN, PLASMA"	11832R	DRUGS DETECTED:	72
+LABYUHYME	"HEAVY METALS, URINE"	11896R	"ARSENIC,U"	72
+LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	A1APHEN	A-1-A PHENOTYPE	72
+LABCD34	"CD34 PANEL, BLOOD"	CD34AB	"CD34, ABS."	72
+LABCD34	"CD34 PANEL, BLOOD"	CD34DS	DISCLAIMER	72
+LABCD34	"CD34 PANEL, BLOOD"	CD34PC	"%CD34,BLOOD"	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	CHOL	"CHOLESTEROL, TOTAL"	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	CHOLHDL	CHOLESTEROL/HDL RATIO	72
+LABYDIGT	"DIGITOXIN, SERUM"	DIGT	DIGITOXIN	72
+LABYFRUCM	FRUCTOSAMINE	FRUCTM	FRUCTOSAMINE	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	HDL	HDL CHOLESTEROL	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	HMCY	"HOMOCYSTEINE, PLAS TOTAL"	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	HSCRP	HIGH SENSITIVITY CRP	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	LDL	LDL (CALCULATED)	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	LDLHDL	LDL/HDL RATIO	72
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	LPA	LIPOPROTEIN A	72
+LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGGAB	B.BURGDORFERI IGG AB WB	72
+LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGMAB	B.BURGDORFERI IGM AB WB	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	NHDL	"NON-HDL CHOL, CALC"	72
+LAB11086R	NORTRIPTYLINE LEVEL	NORTRP	"NORTRIPTYLINE, PLASMA"	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	RCRSK	REL. CARDIAC RISK	72
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	TGL	"TRIGLYCERIDE, SER/PLAS"	72
+LABCD34	"CD34 PANEL, BLOOD"	WBC	WBC COUNT	72
+LABYDIGT	"DIGITOXIN, SERUM"	YDIGT2	REPORTING LIMIT	72
+LABANTNEU	ANTI - NEUTROPHIL AB	ANA	ANTI-NEUTROPHIL AB:	71
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	DI	ISSUE DATE/TIME	71
+LABVWSCN	VON WILLEBRAND SCREEN	FACTOR7	FACTOR VIII ASSAY	71
+LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1DI	DISCLAIMER	71
+LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1IN	INTERPRETATION	71
+LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1M	NEURAMINIDASE  H275Y MUTATION	71
+LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1MT	METHOD	71
+LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1R	2009 INFLUENZA A(H1N1) RNA	71
+LABH1N1PC	"2009 H1N1 PCR, H275Y MUTATION"	H1N1SP	SPECIMEN	71
+LABYHABQ	ANTI HBS QUANT	HBSAB	"HBS ANTIBODY, S"	71
+LABYHABQ	ANTI HBS QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	71
+LABVWSCN	VON WILLEBRAND SCREEN	RIST	RISTOCETIN CO-FACTOR	71
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	SPEXP	SPECIMEN EXPIRATION DATE/TIME	71
+LABVWSCN	VON WILLEBRAND SCREEN	VWAG	VON WILLEBRAND ANTIGEN	71
+LABLUT	LUTEINIZING HORMONE	LH	LH	70
+VSC05	VSC VENOUS ULTRASOUND	PDF	PDF RESULT	70
+LABRBCPH	RBC PHENOTYPE	PHJKAT	JKA PHENOTYPE	70
+LABUCL	"CHLORIDE WITH CREATININE, URINE (RANDOM)"	UCL	"CHLORIDE, URINE"	70
+LABUCL	"CHLORIDE WITH CREATININE, URINE (RANDOM)"	UCLCR	CHLORIDE/CREAT RATIO	70
+LABUCL	"CHLORIDE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	70
+LABVISC	"VISCOSITY, SERUM"	VISCOS	VISCOSITY	70
+LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	YA1A	ALPHA 1 ANTITRYPSIN	70
+LABYBLAS	BLASTOMYCES AB	YBLAS3	BLASTOMYCES IMMUNODIFFUSION	70
+LAB11028R	"ACE, CSF"	ACE	"ANGIOTENSIN COVERT ENZ, CSF"	69
+LABDFA	DENGUE FEVER AB	DFIGG	DENGUE FEVER AB IGG	69
+LABDFA	DENGUE FEVER AB	DFIGM	DENGUE FEVER AB IGM	69
+LABDFA	DENGUE FEVER AB	DFINT	INTERPRETATION	69
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	DI	ISSUE DATE/TIME	69
+LABKRAS	KRAS MUTATION DETECTION	KRASIN	INTERPRETATION	69
+LABRBCPH	RBC PHENOTYPE	PHLST	S TYPE PHENOTYPE	69
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	PRO	PRODUCT ID  	69
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	SI	<<STATUS>>  	69
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	UBT	UNIT BLOOD TYPE  	69
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	UNT	UNIT NUMBER  	69
+LAB12631R	VORICONAZOLE LEVEL	VORI	VORICONAZOLE LEVEL	69
+10235R	HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING	10049R	HLA LAB TESTING STATUS	68
+LABADAMTS	ADAMTS-13 PROFILE	ADMACT	ADAMTS-13 ACTIVITY	68
+LABADAMTS	ADAMTS-13 PROFILE	ADMINT	CLINICAL INTERPRETATION	68
+LABCMVIGG	CMV AB IGG	CMV	CMV ANTIBODY	68
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GDNM3M	"DNMT3A, BM"	68
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GERHRM	"ERBB2/HER2, BM"	68
+LABHEPC	HEPATITIS C AB EIA	HEPCAB	HEPATITIS C AB	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMDI	DISCLAIMER	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMDX	CLINICAL INDICATION	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMIN	INTERPRETATION	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMMD	INTERPRETED BY	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMME	METHOD	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMRE	RESULT	68
+LABNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION"	NPMRF	REFERENCES	68
+10235R	HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING	RINFO	TO FIND RESULT:	68
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	XMR	UNIT CROSSMATCH  	68
+LABGENC	GENITAL CULTURE	CULT	CULT/OTHER RSLT:	67
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	EBV	EBV BY PCR	67
+LABFACT2	FACTOR II ASSAY	FACTOR2	FACTOR II ASSAY	67
+LABRBCPH	RBC PHENOTYPE	PHFYAT	FYA PHENOTYPE	67
+LABRBCPH	RBC PHENOTYPE	PHFYBT	FYB PHENOTYPE	67
+LABRBCPH	RBC PHENOTYPE	PHLEAT	LEA PHENOTYPE	67
+LABRBCPH	RBC PHENOTYPE	PHMT	M PHENOTYPE	67
+LABRBCPH	RBC PHENOTYPE	PHP1T	P1 PHENOTYPE	67
+LABGENC	GENITAL CULTURE	RPT	RPT	67
+LABGENC	GENITAL CULTURE	SDES	SPECIMEN	67
+LABGENC	GENITAL CULTURE	SREQ	COMMENTS	67
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	VL	PRODUCT VOLUME	67
+LAB11077R	CALCITONIN	YCALCI	"CALCITONIN, SERUM"	67
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	YEBP1	SPECIMEN SOURCE	67
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	YEBP3	SPECIAL INFORMATION	67
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, NON-BLOOD"	YEPB4	REPORT STATUS	67
+LABEBVPAN	EBV AB PANEL	11165R	EB VIRUS EARLY AG (R+D) IGG	66
+LABEBVPAN	EBV AB PANEL	11166R	EB NUCLEAR AB	66
+LABEBVPAN	EBV AB PANEL	12001R	"VCA IGG ,IFA"	66
+LABEBVPAN	EBV AB PANEL	12002R	"VCA IGM ,IFA"	66
+LABIBD	ANCA	12215R	12215R	66
+LABADAMTS	ADAMTS-13 PROFILE	ADMINH	ADAMTS-13 INHIBITOR	66
+LABTHG	THYROGLOBULIN	ATGA	ANTI-THYROGLOBULIN ABY	66
+LABBKVQU	BK VIRUS DNA QUANTITATION	BKQ	BKV DNA QUANTITATION	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPDI	DISCLAIMER	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPDX	CLINICAL INDICATION	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPIN	"INTERPRETATION, BM"	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPMD	INTERPRETED BY	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPME	METHOD	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPRE	"RESULT, BM"	66
+LABBMNPM1	"NPM1, NUCLEOPHOSMIN 1 MUTATION, BM"	BMNPRF	REFERENCES	66
+LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	CULT	CULT/OTHER RSLT:	66
+LABFACT11	FACTOR XI ASSAY	FACTOR11	FACTOR XI ASSAY	66
+LABHHV6QL	HHV-6 DNA PCR QUAL	HV6QLD	HHV-6 DNA	66
+LABHHV6QL	HHV-6 DNA PCR QUAL	HV6QLI	TEST DESCRIPTION	66
+LABHHV6QL	HHV-6 DNA PCR QUAL	HV6QLS	SPECIMEN	66
+LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	RPT	RPT	66
+LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	SDES	SPECIMEN	66
+LABSPAGD	S. PNEUMONIAE AG DETECTION LA	SOURC4	SOURCE	66
+LABBKVQU	BK VIRUS DNA QUANTITATION	SOURC6	SOURCE	66
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	SPEXP	SPECIMEN EXPIRATION DATE/TIME	66
+LABSPAGD	S. PNEUMONIAE AG DETECTION LA	SPNAG	S PNEUMONIAEAG	66
+LABBACIDS	"BACTERIAL ID BY SEQUENCING, SPECIMEN"	SREQ	COMMENTS	66
+LABTHG	THYROGLOBULIN	TG	THYROGLOBULIN (TG)	66
+LABTOXIG	TOXOPLASMA IGG	TOXGQT	IGG LEVEL	66
+LABCOXPNL	COXSACKIE A AB PANEL	TYP10	COXSACKIE A10 AB	66
+LABCOXPNL	COXSACKIE A AB PANEL	TYP16	COXSACKIE A16 AB	66
+LABCOXPNL	COXSACKIE A AB PANEL	TYP2	COXSACKIE A2 AB	66
+LABCOXPNL	COXSACKIE A AB PANEL	TYP4	COXSACKIE A4 AB	66
+LABCOXPNL	COXSACKIE A AB PANEL	TYP7	COXSACKIE A7 AB	66
+LABCOXPNL	COXSACKIE A AB PANEL	TYP9	COXSACKIE A9 AB	66
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	UBT	UNIT BLOOD TYPE  	66
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	UNT	UNIT NUMBER  	66
+LABTTM	TRANSPLANT THERAPY MONITORING	CD4AB	"CD3+/CD4+, ABS."	65
+LABTTM	TRANSPLANT THERAPY MONITORING	CD4PC	CD3+/CD4+ %	65
+LABTTM	TRANSPLANT THERAPY MONITORING	CD8AB	"CD3+/CD8+, ABS."	65
+LABTTM	TRANSPLANT THERAPY MONITORING	CD8PC	CD3+/CD8+ %	65
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CSCI	CLINICAL INDICATION	65
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CSDI	DISCLAIMER	65
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CSME	METHOD	65
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CSREF	REFERENCES	65
+LABABG	"BLOOD GASES, ARTERIAL"	FIO2	FIO2 (FIO2)	65
+LABTTM	TRANSPLANT THERAPY MONITORING	HNS	"CD4+CD8 TOTAL, CD3+%"	65
+LABTTM	TRANSPLANT THERAPY MONITORING	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	65
+LABTTM	TRANSPLANT THERAPY MONITORING	HSRATIO	"CD4/CD8 RATIO, CD3+"	65
+LABRBCPH	RBC PHENOTYPE	PHLEBT	LEB PHENOTYPE	65
+LABRBCPH	RBC PHENOTYPE	PHNT	N PHENOTYPE	65
+LABTOTALC	"PROTEIN C AG, TOTAL"	PROTCAGT	"PROTEIN C AG, TOTAL"	65
+LAB11214R	"BETA-2 TRANSFERRIN, BF"	11215R	"BETA-2 TRANSFERRIN, BF"	64
+LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALSCD	MALARIA ANTIGEN DESCRIPTION	64
+LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALSCR	MALARIA ANTIGEN RESULT	64
+LABVBG	"BLOOD GASES, VENOUS"	O2SV	O2SV	64
+LABYSES	"SELENIUM, SERUM"	SELENIUM	SELENIUM	64
+LABUHEMO	"HEMOSIDERIN, URINE"	UHEMO	"HEMOSIDERIN, URINE"	64
+10365R	HLA - DSA BY IGG	9413R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	63
+10375R	HLA - STR/CHIMERISM TESTING	9495R	% DONOR- BLOOD WHOLE	63
+10375R	HLA - STR/CHIMERISM TESTING	9535R	% RECIPIENT- BLOOD WHOLE	63
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CSSP	SPECIMEN	63
+LABFLUAPC	FLU A PCR	FLUADI	DISCLAIMER	63
+LABFLUAPC	FLU A PCR	FLUAMT	METHOD	63
+LABFLUAPC	FLU A PCR	FLUAR	INFLUENZA A RNA	63
+LABFLUAPC	FLU A PCR	FLUASP	SPECIMEN	63
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GAPC	APC	63
+LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MAL	MALARIA PREP RESULT	63
+LABYMPA	MYCOPHENOLIC ACID	MPACID	MYCOPHENOLIC ACID	63
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRD	INTERPRETED BY	63
+LABTOXPCR	TOXOPLASMOSIS PCR	TOXGG	GENERAL GUIDELINES	63
+LABTOXPCR	TOXOPLASMOSIS PCR	TOXRES	PCR RESULT	63
+LABTOXPCR	TOXOPLASMOSIS PCR	TOXSP	SPECIMEN TYPE	63
+LABYMPA	MYCOPHENOLIC ACID	YMPAG	MPA GLUCURONIDE	63
+LAB11101R	VITAMIN C	YVITC	"ASCORBIC ACID, PL."	63
+10370R	HLA - DSA BY C1Q	9632R	SERUM DESCRIPTION	62
+LABYHMS	"HEAVY METALS SCREEN, BLOOD"	ARSENIC	ARSENIC	62
+LABYHMS	"HEAVY METALS SCREEN, BLOOD"	CADMIUM	CADMIUM	62
+LABF8INH	FACTOR VIII INHIBITOR HUMAN	F8INH	"FACTOR VIII INHIBITOR, HUMAN"	62
+LABHGBSQ	HGB S QUANTITATIVE	HGBS	"HGB S, QUANT"	62
+LABBMTP	BMT PRETRANSPLANT PANEL	HSAB	VIRAL AB HSV	62
+LABBMTP	BMT PRETRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	62
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	62
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	62
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDEVLDATE	ICD DATE OF EVENT	62
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	62
+LABYHMS	"HEAVY METALS SCREEN, BLOOD"	LEAD	LEAD	62
+LABMONOSC	INFECTIOUS MONO SCREEN	MONOSC	INFECTIOUS MONO SCREEN	62
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	PRO	PRODUCT ID  	62
+LABPROINS	"PROINSULIN, PLASMA"	PROINSULIN	PROINSULIN	62
+LABBMTP	BMT PRETRANSPLANT PANEL	RPRQ	RESULT	62
+LABBMTP	BMT PRETRANSPLANT PANEL	RPRQIN	INTERPRETATIONS	62
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	SI	<<STATUS>>  	62
+LABT1517	"T(15	17) PML/RAR, BLOOD"	T15CO	COMMENT	62
+LABT1517	"T(15	17) PML/RAR, BLOOD"	T15DI	DISCLAIMER	62
+LABT1517	"T(15	17) PML/RAR, BLOOD"	T15DX	CLINICAL INDICATION	62
+LABT1517	"T(15	17) PML/RAR, BLOOD"	T15MD	INTERPRETED BY	62
+LABT1517	"T(15	17) PML/RAR, BLOOD"	T15ME	METHOD	62
+LABT1517	"T(15	17) PML/RAR, BLOOD"	T15RE	RESULT	62
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	UBT	UNIT BLOOD TYPE  	62
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	UNT	UNIT NUMBER  	62
+LABADGM	ADENOVIRUS IGG AND IGM AB	ADG1	"ADENOVIRUS AB,IGG"	61
+LABADGM	ADENOVIRUS IGG AND IGM AB	ADM1	"ADENOVIRUS AB,IGM"	61
+LABARGI	ABO/RH	ARG	ABO/RH (AUTOMATION)	61
+LABVCULF	"VIRAL CULTURE, FLUID"	CULT	CULT/OTHER RSLT:	61
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	DI	ISSUE DATE/TIME	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HATINT	INTERPRETATION	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HBSINT	INTERPRETATION	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HBTINT	INTERPRETATION	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HCVI	INTERPRETATION	61
+LABBMTP	BMT PRETRANSPLANT PANEL	HCVR	ANTI-HCV	61
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	61
+LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALCON	MALCON	61
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	PRO	PRODUCT ID  	61
+LABVCULF	"VIRAL CULTURE, FLUID"	RPT	RPT	61
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	61
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	61
+LABVCULF	"VIRAL CULTURE, FLUID"	SDES	SPECIMEN	61
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	SI	<<STATUS>>  	61
+LABVCULF	"VIRAL CULTURE, FLUID"	SREQ	COMMENTS	61
+LABYLEPT	LEPTOSPIROSIS AB	YLEP1	LEPTOSPIRA AB	61
+LABBMTP	BMT PRETRANSPLANT PANEL	CMV	CMV ANTIBODY	60
+LABBMTP	BMT PRETRANSPLANT PANEL	CMVIGM	CMV IGM AB	60
+LABYUCOP	"COPPER, URINE"	COPPERURN	"COPPER, U"	60
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	60
+LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	DOPA	DOPAMINE	60
+LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	EPINEP	EPINEPHRINE	60
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	MFR	MANUFACTURER	60
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	MOD	MODEL	60
+LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	NOREPH	NOREPHINEPHRINE	60
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SLYM	"LYMPHOCYTES, SYN FLD"	60
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SMONO	"MONOS/MESOS, SYN FLD"	60
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SNEUT	"NEUTROPHILS, SYN FLD"	60
+LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBPO2	DATE & TIME OF LAST DOSE	60
+LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBPO3	DOSAGE	60
+LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBPO4	ADMINISTRATION ROUTE	60
+LABTOBPOL	TOBRAMYCIN PEAK LEVEL	TOBRAPEAK	"TOBRAMYCIN, PEAK"	60
+LABYVITB6	VITAMIN B6	VITB6	VITAMIN B6	60
+LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	VOLUMEURN	URINE VOLUME	60
+LABBMTP	BMT PRETRANSPLANT PANEL	VZAB	VARICELLA ZOSTER AB IGG	60
+LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	YUFCUT	COLLECTION DURATION	60
+LABYVB1W	VITAMIN B1	YVB1W1	THIAMIN(VITAMIN B1)	60
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CSMD	INTERPRETED BY	59
+LABBPERP	BORDETELLA PERTUSSIS PCR	CULT	CULT/OTHER RSLT:	59
+LAB11079R	GABAPENTIN	GABAP	"GABAPENTIN, SERUM/PLASMA"	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GBRAF	BRAF	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GCTNN	CTNNB1	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GEGFR	EGFR	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GIDH1	IDH1	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GIDH2	IDH2	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GKRAS	KRAS	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GNOTCH	NOTCH1	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GNRAS	NRAS	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GPIK3	PIK3CA	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GPTEN	PTEN	59
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GTP53	TP53	59
+LABPANHCT	HEMATOCRIT	HCT	HEMATOCRIT	59
+LABFHTLVQ	HTLV-1/2  DNA QUALITATIVE	HTLI	HTLV-I DNA	59
+LABFHTLVQ	HTLV-1/2  DNA QUALITATIVE	HTLII	HTLV-II DNA	59
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	59
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	59
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	59
+VSC03	VSC ARTERIAL EXAM/ABI\'S	PDF	PDF RESULT	59
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	QBMBRC	COMMENT	59
+LABBPERP	BORDETELLA PERTUSSIS PCR	RPT	RPT	59
+LABBPERP	BORDETELLA PERTUSSIS PCR	SDES	SPECIMEN	59
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	59
+LABBPERP	BORDETELLA PERTUSSIS PCR	SREQ	COMMENTS	59
+LABYLACTS	"LACTOFERRIN DETECTION, STOOL"	YLACT1	LACTOFERRIN DETECTION	59
+LABYUCOP	"COPPER, URINE"	YUCOP2	COLLECTION DURATION	59
+LABYZONIS	ZONISAMIDE	ZONIS	ZONISAMIDE	59
+LABHSVM	HSV 1 AND 2 IGM	HSV1M	"HSV 1 IGM, IFA"	58
+LABHSVM	HSV 1 AND 2 IGM	HSV2M	"HSV 2 IGM, IFA"	58
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	58
+LABYLEADB	"LEAD, BLOOD"	LEA	"LEAD, BLD"	58
+LABYLEADB	"LEAD, BLOOD"	LEADSO	SOURCE OF COLLECTION	58
+LAB11085R	MPO AB	MPO	MYELOPEROXIDASE AB	58
+LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	VMDES	VMDES	58
+LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	VMQUL	VMQUL	58
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	XMR	UNIT CROSSMATCH  	58
+LABYUHYME	"HEAVY METALS, URINE"	12200R	AS CONCENTRATION	57
+LABYUHYME	"HEAVY METALS, URINE"	12201R	PB CONCENTRATION	57
+LABYUHYME	"HEAVY METALS, URINE"	12202R	"CADMIUM, 24HR, U"	57
+LABYUHYME	"HEAVY METALS, URINE"	12203R	CD CONCENTRATION	57
+LABYUHYME	"HEAVY METALS, URINE"	12204R	HG CONCENTRATION	57
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDRHYTHM	ICD RHYTHM	57
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	PRO	PRODUCT ID  	57
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	57
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	SI	<<STATUS>>  	57
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	UBT	UNIT BLOOD TYPE  	57
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	UNT	UNIT NUMBER  	57
+LABVZVIGG	VARICELLA ZOSTER VIRUS AB IGG	VZAB	VARICELLA ZOSTER AB IGG	57
+LABYENPCR	ENTEROVIRUS PCR	YENP3	SPECIAL INFORMATION	57
+LAB11153R	"HERPES VIRUS 6 DNA, QUANT"	11154R	SOURCE	56
+LAB11153R	"HERPES VIRUS 6 DNA, QUANT"	11155R	"HERPES VIRUS 6 DNA, QUANT"	56
+LABABODIS	ABO DISCREPANCY	ABODIS	ABO DISCREPANCY	56
+LABCDAS	CELIAC DISEASE SCREEN	ATTGT	IGA ANTI TTG LEVEL	56
+LABCOHB	CARBOXYHEMOGLOBIN	COHB	CO-HEMOGLOBIN	56
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	DI	ISSUE DATE/TIME	56
+LABEB	EBV AB PANEL	EA	EARLY ANTIBODY (R+D) IGG	56
+LABDVEHVR	HSV/VZV DFA REFLEX PCR	HERPR	HERPES SIMPLEX VIRUS (HSV)	56
+LABDVEHVR	HSV/VZV DFA REFLEX PCR	HVIP	TEST DESCRIPTION	56
+LABDVEHVR	HSV/VZV DFA REFLEX PCR	HVSP	SPECIMEN	56
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	56
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	56
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	56
+LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGGBAND	B.BURGDORFERI IGG BANDS	56
+LABYHMS	"HEAVY METALS SCREEN, BLOOD"	MER	MERCURY	56
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	UBT	UNIT BLOOD TYPE  	56
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	UNT	UNIT NUMBER  	56
+LABDVEHVR	HSV/VZV DFA REFLEX PCR	VZR	VARICELLA-ZOSTER VIRUS	56
+LAB11551R	NK FUNCTION	11661R	NK FUNCTION	55
+LAB11824R	BUSULFAN	11826R	CSS TARGET	55
+LAB11824R	BUSULFAN	11827R	CSS EST. AVG. EXPO.CLEARA RATE	55
+LAB11824R	BUSULFAN	11828R	RECOMMENDED DOSE	55
+10365R	HLA - DSA BY IGG	9627R	CURRENT SERUM DESCRIPTION	55
+10400R	HLA - TYPING SUMMARY	9662R	HLA TYPING SUMMARY	55
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCAB	HEP B CORE TOTAL AB	55
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCABIGM	HEPAT.B CORE AB-IGM	55
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAB	HEPATITIS B SURFACE AB QUAL	55
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAG	HEPATITIS B SURF AG	55
+LABPLASMN	PLASMINOGEN	PLASMN	PLASMINOGEN	55
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	55
+LABQFTBG	TB BY QUANTIFERON	TBAG	TB AG	55
+LABQFTBG	TB BY QUANTIFERON	TBAGNI	TB AG - NIL	55
+LABQFTBG	TB BY QUANTIFERON	TBGINT	INTERPRETATION	55
+LABQFTBG	TB BY QUANTIFERON	TBGNIL	NIL	55
+LABQFTBG	TB BY QUANTIFERON	TBGRES	QUANTIFERON RESULT	55
+LABQFTBG	TB BY QUANTIFERON	TBMINI	MITOGEN - NIL	55
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZIM	IMMUNOASSAY SCREEN	55
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	7NHCLONAZEPM	7-NH-CLONAZEPAM	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	7NHFLUNITRAZ	7-NH-FLUNITRAZEPAM	54
+LABLWBCC	"LYME WESTERN BLOT, CSF"	BBIGG	BORELLIA BURG IGG AB	54
+LABLWBCC	"LYME WESTERN BLOT, CSF"	BBIGM	BORELLIA BURG IGM AB	54
+LABBARTPC	"BARTONELLA DNA, QUALI, PCR"	BHENPC	BARTONELLA HENSELAN DNA	54
+LABBARTPC	"BARTONELLA DNA, QUALI, PCR"	BQUIPC	BARTONELLA QUINTANA DNA	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	F1656A	"CD3-/16+56(NK),FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	F1656P	"CD3-/16+56(NK)%,FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD19A	"CD19 (B), ABS., FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD19P	"CD19 (B), %, FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD3AB	"CD3 (T), ABS., FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD3PC	"CD3 (T) %, FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD4AB	"CD4 (HELP),ABS., FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD4PC	"CD4 (HELPER) %, FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD8AB	"CD8 (SUPP),ABS., FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCD8PC	"CD8 (SUPP), %, FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FCSOUR	SOURCE	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FHNS	"T (H+S), FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	FHSAB	"T (H+S), ABS., FLD"	54
+LABCDAS	CELIAC DISEASE SCREEN	GDGPT	IGG ANTI DGP LEVEL	54
+LABHGBSQ	HGB S QUANTITATIVE	HGSMD	REVIEWED BY	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	HSRATIOFLD	"H/S RATIO, FLD"	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	LORAZ	LORAZEPAM	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	LYMABFLD	"LYMPHOCYTES, ABS., FLD"	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	LYMPCFLD	"LYMPHOCYTES %, FLD"	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	NORDIAZ	NORDIAZEPAM	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	OXAZEPAM	OXAZEPAM	54
+LABRDATAC	REVERSE TO COOMBS A CELLS	RDATA	REVERSE TO COOMBS A CELLS	54
+LABBARTPC	"BARTONELLA DNA, QUALI, PCR"	SDES10	SPECIMEN	54
+LABABG	"BLOOD GASES, ARTERIAL"	TEMP	BODY TEMPERATURE	54
+LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	VGDES	VGDES	54
+LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	VGQNT	VGQNT	54
+LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	VGQUL	VGQUL	54
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	VL	PRODUCT VOLUME	54
+LABYUCOP	"COPPER, URINE"	VOLUMEURN	URINE VOLUME	54
+LABFTBLYM	"T SUBSETS AND B LYMPHOCYTES, FLUID"	WBCFLD	"WBC COUNT, FLD"	54
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMAM	AMENDMENT	54
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	YHEMSO	SOURCE	54
+LABYINFAB	INFLUENZA AB PANEL	YINF2	"INFLUENZA VIRUS A AB, IGG"	54
+LABYINFAB	INFLUENZA AB PANEL	YINF3	"INFLUENZA VIRUS A AB, IGM"	54
+LABYINFAB	INFLUENZA AB PANEL	YINF5	"INFLUENZA VIRUS B AB, IGG"	54
+LABYINFAB	INFLUENZA AB PANEL	YINF6	"INFLUENZA VIRUS B AB, IGM"	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUAAGC	ALPHA OH-ALPRAZOLAM	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUATGC	ALPHA OH-TRIAZOLAM	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZIN	INTERPRETATION	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUEFGC	OH-ETHYL-FLURAZEPAM	54
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUTZGC	TEMAZEPAM	54
+10375R	HLA - STR/CHIMERISM TESTING	9496R	% DONOR- BLOOD CD3	53
+10375R	HLA - STR/CHIMERISM TESTING	9507R	% DONOR- BLOOD CD56	53
+10375R	HLA - STR/CHIMERISM TESTING	9536R	% RECIPIENT- BLOOD CD3	53
+10375R	HLA - STR/CHIMERISM TESTING	9547R	% RECIPIENT- BLOOD CD56	53
+LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GP1IGG	BETA-2-GP1 IGG	53
+LABURCA	"CALCIUM, URINE (RANDOM)"	CAURN	"CALCIUM, URINE"	53
+LABLEGD	"LEGIONELLA CULTURE, DIRECT"	CULT	CULT/OTHER RSLT:	53
+LABVBG	"BLOOD GASES, VENOUS"	FIO2	FIO2 (FIO2)	53
+LABGLWB	"GLUCOSE, WHOLE BLOOD"	GLU	"GLUCOSE, WHOLE BLOOD"	53
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	HITI1	HIT FUNCTIONAL ASSAY	53
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	HITI2	HIT COMMENT	53
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	53
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	PF4COM	PF4 COMMENT	53
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	PF4ENH	"PF4 IGG, IGM, IGA OD"	53
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	PRO	PRODUCT ID  	53
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	PRO	PRODUCT ID  	53
+LABLEGD	"LEGIONELLA CULTURE, DIRECT"	RPT	RPT	53
+LABLEGD	"LEGIONELLA CULTURE, DIRECT"	SDES	SPECIMEN	53
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	SI	<<STATUS>>  	53
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	SI	<<STATUS>>  	53
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	SPEXP	SPECIMEN EXPIRATION DATE/TIME	53
+LABLEGD	"LEGIONELLA CULTURE, DIRECT"	SREQ	COMMENTS	53
+LABABG	"BLOOD GASES, ARTERIAL"	TSITE	TEMPERATURE SITE	53
+LABYANRNP	"RNP AB, IGG"	U1RNPAB	AUTOANTIBODIES TO U1RNP	53
+LABVIRSO1	MISC. VIROLOGY TEST	VIROR	RESULT	53
+LABVIRSO1	MISC. VIROLOGY TEST	VIROS	SOURCE	53
+LABVIRSO1	MISC. VIROLOGY TEST	VIROTN	TEST NAME	53
+LABYCHAGA	"TRYPANOSOMA CRUZI AB, IGG"	YCHAG1	"TRYPANOSOMA CRUZI AB,IGG"	53
+10375R	HLA - STR/CHIMERISM TESTING	9500R	% DONOR- BLOOD CD15	52
+10375R	HLA - STR/CHIMERISM TESTING	9540R	% RECIPIENT- BLOOD CD15	52
+LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GP1IGA	BETA-2-GP1 IGA	52
+LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GP1IGM	BETA-2-GP1 IGM	52
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	HIPDIR	HIT INTERPRETED BY	52
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	52
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	52
+LABSCHIG	SCHISTOSOMA IGG AB ELISA	SCHIG1	"SCHISTOSOMA ,LGG"	52
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	TRPHOS	TRIPLE PHOSPH CRYST.	52
+LABWNVRNA	"WEST NILE VIRUS RNA, RT  -PCR"	WNRNA	WEST NILE VIRUS RNA	52
+LABYSTNA	"SODIUM, STOOL"	YSTNA2	TOTAL WEIGHT	52
+LAB12330R	"EVEROLIMUS, BLOOD"	12339R	EVEROLIMUS	51
+LAB12330R	"EVEROLIMUS, BLOOD"	12500R	DATE OF LAST DOSE	51
+LAB12330R	"EVEROLIMUS, BLOOD"	12501R	TIME OF LAST DOSE	51
+LAB12330R	"EVEROLIMUS, BLOOD"	12502R	"DOSE, MG"	51
+LABCHOHDL	CHOLESTEROL HDL / TOTAL	CHOL	"CHOLESTEROL, TOTAL"	51
+LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	CULT	CULT/OTHER RSLT:	51
+LABEB	EBV AB PANEL	EBNA	EPSTEIN-BARR NUC AB	51
+LABCHOHDL	CHOLESTEROL HDL / TOTAL	HDL	HDL CHOLESTEROL	51
+LABHDL	"HDL, SERUM"	HDL	HDL CHOLESTEROL	51
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	51
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	51
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	51
+LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	PBGDAY	"PORPHOBILINOGEN, URN"	51
+LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	PBGL	"PORPHOBILINOGEN, URN"	51
+LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	RPT	RPT	51
+LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	SDES	SPECIMEN	51
+LABYSEROT	"SEROTONIN, SERUM"	SEROTONIN	SEROTONIN	51
+LABVCULS	"VIRAL CULTURE, STOOL/RECTAL"	SREQ	COMMENTS	51
+LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	TIMEU	COLLECTION PERIOD	51
+LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	UCR	"CREATININE, URINE"	51
+LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	UCR	"CREATININE, URN"	51
+LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	UPIEI	INTERPRETATION	51
+LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	UPIER	RESULT	51
+LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	UT3	COLLECTION PERIOD CREATININE	51
+LABUPBGQT	"PORPHOBILINOGEN QUANTITATIVE, URINE"	UVOL	VOLUME	51
+LABEB	EBV AB PANEL	VCA	VIRAL CAPSID AB	51
+LABEB	EBV AB PANEL	VCAM	VIRAL CAPSID AB-IGM	51
+LAB11099R	VIP	VIP	VASOACTIVE INT PEPTIDE	51
+LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	VOL3	VOLUME CREATININE	51
+LABYCLOZ	"CLOZAPINE, SERUM"	YCLOZ1	CLOZAPINE	51
+LABYCLOZ	"CLOZAPINE, SERUM"	YCOMT	COMBINED TOTAL	51
+LABYCLOZ	"CLOZAPINE, SERUM"	YNORC	NORCLOZAPINE	51
+LAB11265R	ENTAMOEBA HISTOLYTICA AG	11266R	ENTAMOABA HISTOLYTICA AG	50
+LAB11523R	GQ1B AB	11633R	GQ1B AB	50
+10375R	HLA - STR/CHIMERISM TESTING	9501R	% DONOR- BLOOD CD19	50
+10375R	HLA - STR/CHIMERISM TESTING	9541R	% RECIPIENT- BLOOD CD19	50
+LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	CULT	CULT/OTHER RSLT:	50
+LABYCYANI	"CYANIDE, BLOOD"	CYAN	"CYANIDE, B"	50
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	DI	ISSUE DATE/TIME	50
+LABGBMIFA	GBM IGA AND IGG AB	GBMIGA	"GBM ANTIBODY, IGA"	50
+LABGBMIFA	GBM IGA AND IGG AB	GBMIGG	GBM-IGG ELISA	50
+LABABG	"BLOOD GASES, ARTERIAL"	NAC	NASAL CANNULA	50
+LABSTOBIC	"OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC."	OCCIMM	"STOOL SAMPLE1,IMMUN"	50
+PFT59	SPIROMETRY	PDF	PDF RESULT	50
+LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	RPT	RPT	50
+LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	SDES	SPECIMEN	50
+LABCDTPCR	"C DIFFICILE TOXIN B, STOOL"	SREQ	COMMENTS	50
+LABVBG	"BLOOD GASES, VENOUS"	TEMP	BODY TEMPERATURE	50
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	VL	PRODUCT VOLUME	50
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	XMR	UNIT CROSSMATCH  	50
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	XMR	UNIT CROSSMATCH  	50
+LABFCD34	"CD34 PANEL, FLUID"	CD34DS	DISCLAIMER	49
+LABFCD34	"CD34 PANEL, FLUID"	CD34FL	SOURCE	49
+LABFCD34	"CD34 PANEL, FLUID"	CD34FLDPC	"%CD34, FLUID"	49
+LABHSCUL	HERPES SIMPLEX CULTURE ONLY	CULT	CULT/OTHER RSLT:	49
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	DI	ISSUE DATE/TIME	49
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFDI	DISCLAIMER	49
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFDX	CLINICAL INDICATION	49
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFME	METHOD	49
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFRE	RESULT	49
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFSO	SOURCE	49
+LABPANHGB	HEMOGLOBIN	HGB	HEMOGLOBIN	49
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	49
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	PRO	PRODUCT ID  	49
+LABHSCUL	HERPES SIMPLEX CULTURE ONLY	RPT	RPT	49
+LABHSCUL	HERPES SIMPLEX CULTURE ONLY	SDES	SPECIMEN	49
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	SI	<<STATUS>>  	49
+LABHSCUL	HERPES SIMPLEX CULTURE ONLY	SREQ	COMMENTS	49
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	UBT	UNIT BLOOD TYPE  	49
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	UNT	UNIT NUMBER  	49
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	XMR	UNIT CROSSMATCH  	49
+LABYAHA	HISTONE ANTIBODIES	YAHA1	HISTONE AUTOANTIBODIES	49
+LABYSTK	"POTASSIUM, STOOL"	YSTK2	TOTAL WEIGHT	49
+LAB11019R	"MYELIN BASIC PROTEIN, CSF"	11164R	MYELIN BASIC PROTEIN	48
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFIN	INTERPRETATION	48
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFMD	INTERPRETED BY	48
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	48
+LABVBG	"BLOOD GASES, VENOUS"	NAC	NASAL CANNULA	48
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	48
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	48
+LABYUTHC	"TETRAHYDROCANNABINOL, URINE"	THC	THC CARBOX AC BY GSMS	48
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	UBT	UNIT BLOOD TYPE  	48
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	UNT	UNIT NUMBER  	48
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	VL	PRODUCT VOLUME	48
+LABYUTHC	"TETRAHYDROCANNABINOL, URINE"	YUTHIM	IMMUNOASSAY SCREEN	48
+LABYUTHC	"TETRAHYDROCANNABINOL, URINE"	YUTHIN	INTERPRETATION	48
+LAB11549R	CALIFORNIA ENCEPHALITIS PANEL	11659R	CALIFORNIA ENCEPHALITIS PANEL	47
+LAB12230R	SOLUBLE IL 2R	12231R	12231R	47
+10290R	HLA - TYPING	9026R	HLA-DR345 ALLELE 2	47
+LABBONC	BONE CULTURE AND GRAM STAIN	CULT	CULT/OTHER RSLT:	47
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	DI	ISSUE DATE/TIME	47
+LABE2O	ESTRADIOL	ESTRADIOL	ESTRADIOL	47
+LABBONC	BONE CULTURE AND GRAM STAIN	GS	GRAM STAIN	47
+LABHEPDAB	HEPATITIS D VIRUS AB TOTAL	HEPD	HEPATITIS D TOTAL	47
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	47
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	47
+LABBONC	BONE CULTURE AND GRAM STAIN	RPT	RPT	47
+LABBONC	BONE CULTURE AND GRAM STAIN	SDES	SPECIMEN	47
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	47
+LABBONC	BONE CULTURE AND GRAM STAIN	SREQ	COMMENTS	47
+LABVBG	"BLOOD GASES, VENOUS"	TSITE	TEMPERATURE SITE	47
+LAB11175R	"LIPASE,BF"	11176R	"LIPASE, BF"	46
+LAB11175R	"LIPASE,BF"	11177R	FLUID TYPE	46
+LABYSTNA	"SODIUM, RANDOM STOOL"	12506R	"SODIUM, RANDOM STOOL"	46
+10370R	HLA - DSA BY C1Q	9456R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	46
+LABASPID	"ASPERGILLUS AB, ID, S"	AFLAV	ASPERGILLUS FLAVUS	46
+LABASPID	"ASPERGILLUS AB, ID, S"	AFLUMI	ASPERGILLUS FUMIGATUS	46
+LABAMIPOL	AMIKACIN PEAK LEVEL	AMIKACINPK	"AMIKACIN, PEAK"	46
+LABAMIPOL	AMIKACIN PEAK LEVEL	AMIPO2	DATE & TIME OF LAST DOSE	46
+LABAMIPOL	AMIKACIN PEAK LEVEL	AMIPO3	DOSAGE	46
+LABAMIPOL	AMIKACIN PEAK LEVEL	AMIPO4	ADMINISTRATION ROUTE	46
+LABASPID	"ASPERGILLUS AB, ID, S"	ANIGE	ASPERGILLUS NIGER	46
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	CCNT	CELLS COUNTED	46
+LABCDPLTI	COLD AGGLUTININ TITER	CDPLT	COLD ANTIBODY TITER	46
+LABLPDP	LIPID PANEL PLUS RISK	CHOL	"CHOLESTEROL, TOTAL"	46
+LABLPDP	LIPID PANEL PLUS RISK	CHOLHDL	CHOLESTEROL/HDL RATIO	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	COPROU	COPROPORPHYRIN I	46
+10260R	HLA ORDER - DECEASED DONOR TESTING NOTICE	DDNOT	DECEASED DONOR ORDER NOTICE	46
+LABLPDP	LIPID PANEL PLUS RISK	HDL	HDL CHOLESTEROL	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	HEPCOH	HEPTACARBOXYLATE	46
+LABLPDP	LIPID PANEL PLUS RISK	HMCY	"HOMOCYSTEINE, PLAS TOTAL"	46
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	HNOT	HIV-1 AG DONOR SCRN QUAL. NOTE	46
+LABLPDP	LIPID PANEL PLUS RISK	HSCRP	HIGH SENSITIVITY CRP	46
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	46
+LABLPDP	LIPID PANEL PLUS RISK	LDL	DIRECT LDL CHOL	46
+LABLPDP	LIPID PANEL PLUS RISK	LPA	LIPOPROTEIN A	46
+LABLPDP	LIPID PANEL PLUS RISK	NHDL	"NON-HDL CHOL, CALC"	46
+LABSTPH	"PH, STOOL"	PHSTL	"PH,STOOL"	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	PORU	"UROPORPHYRIN, QUANT"	46
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	PRO	PRODUCT ID  	46
+LABLPDP	LIPID PANEL PLUS RISK	RCRSK	REL. CARDIAC RISK	46
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	SI	<<STATUS>>  	46
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	SPEXP	SPECIMEN EXPIRATION DATE/TIME	46
+LABLPDP	LIPID PANEL PLUS RISK	TGL	"TRIGLYCERIDE, SER/PLAS"	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	TIMEU	COLLECTION PERIOD	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UCR	"CREATININE, URINE"	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UCR	"CREATININE, URN"	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UPORIN	"PORPHYRIN, URN INTRP"	46
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	UVOL	VOLUME	46
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS1	HIV-1 P24 (NON ICD) AG SCRN	46
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS3	HIV-1 P24 INTERP.	46
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	VL	PRODUCT VOLUME	46
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	XMR	UNIT CROSSMATCH  	46
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM2	C.PNEUMONIAE IGG	46
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM3	C.PNEUMONIAE IGM	46
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM4	C.TRACHOMATIS IGG	46
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM5	C.TRACHOMATIS IGM	46
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM6	C.PSITTACI IGG	46
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM7	C.PSITTACI IGM	46
+LABCMVIGG	CMV AB TOTAL	CMV	CMV ANTIBODY	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	45
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	45
+LABLDLD	DIRECT LDL CHOLESTEROL	LDL	DIRECT LDL CHOL	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVMFR	PM DEVICE MANUFACTURER	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVMODEL	PM DEVICE MODEL	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVSERNM	PM DEVICE SERIAL NUMBER	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVTYPE	PM DEVICE TYPE	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMEVDATE	PACEMAKER EVAL DATE	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	45
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	45
+LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOSFREE	"TESTOSTERONE, FREE"	45
+LABYNMOG	NMO IGG	YNMOG1	"(NMO) AUTOANTIBODY, IGG, SERUM"	45
+LABYSTK	"POTASSIUM, RANDOM STOOL"	12504R	"POTASSIUM, RANDOM STOOL"	44
+LAB11128R	FUNGAL MIC	FMIC	FUNGAL MIC	44
+LABFQFVR	COXIELLA BURNETII AB	QFVG1	PHASE 1 IGG	44
+LABFQFVR	COXIELLA BURNETII AB	QFVG2	PHASE II IGG	44
+LABFQFVR	COXIELLA BURNETII AB	QFVIN	Q FEVER INTERPRETATION	44
+LABFQFVR	COXIELLA BURNETII AB	QFVM1	PHASE 1 IGM	44
+LABFQFVR	COXIELLA BURNETII AB	QFVM2	PHASE II IGM	44
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	VL	PRODUCT VOLUME	44
+LABBRAF	BRAF	BRAFDI	DISCLAIMER	43
+LABBRAF	BRAF	BRAFMT	METHOD	43
+LABBRAF	BRAF	BRAFRE	RESULT	43
+LABBRAF	BRAF	BRAFSP	SPECIMEN	43
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	43
+LABYPENTO	PENTOBARBITAL LEVEL	PENTO	"PENTOBARBITAL, S"	43
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMDEVIMPDT	PM DEVICE IMPLANT DATE	43
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRHYTHM	PM RHYTHM	43
+LABPROTSF	"PROTEIN S, FREE"	PROTSF	"PROTEIN S, FREE"	43
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	43
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	43
+LABMISPRO	MISCELLANEOUS PROCESSING	SEN	SEND TO	43
+LABYLEGS	"LEGIONELLA PNEUMOPHILA, AB"	YLEGS1	LEGIONELLA PNEUMOPHILA AB	43
+10335R	HLA - SCREENING C1Q	9279R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS II	42
+10365R	HLA - DSA BY IGG	9628R	REFERENCE SAMPLE DATE	42
+LABVBG	"BLOOD GASES, VENOUS"	BGSPRC	SPECIMEN COMMENT	42
+LABBRAF	BRAF	BRAFMD	INTERPRETATION	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32CM	COMMENT:	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32DI	DISCLAIMER:	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32DX	CLINICAL INDICATION	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32ET	ETHNICITY (PROVIDED BY MD)	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32IN	INTERPRETED BY	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32ME	METHOD:	42
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32R1	RESULT 1	42
+LABCMVIGM	CMV AB IGM	CMVIGM	CMV IGM AB	42
+LABECHIG	"ECHINOCOCCUS AB, IGG"	ECHIND	ECHINOCOCCUS LGG	42
+LABHEPAR	"HEPARIN CONC, ANTIXA U/ML"	HEPAR	HEPARIN ACTIVITY LEVEL	42
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	42
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	42
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	PRO	PRODUCT ID  	42
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	42
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	42
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	42
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	42
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	SI	<<STATUS>>  	42
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	UBT	UNIT BLOOD TYPE  	42
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	UNT	UNIT NUMBER  	42
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	XMR	UNIT CROSSMATCH  	42
+10273R	HLA ORDER - WORKUP AT TIME OF TRANSPLANT	10049R	HLA LAB TESTING STATUS	41
+10395R	HLA - CROSSMATCH SUMMARY	9661R	HLA CROSSMATCH SUMMARY	41
+LAB12809R	CK ISOENZYME ELECTROPHORESIS	BBFRAC	BB FRACTION	41
+LAB12809R	CK ISOENZYME ELECTROPHORESIS	CKEP	CK ISOENZYME ELECTROPHORESIS	41
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CNRBC	"NRBC/100WBC, CSF"	41
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	DI	ISSUE DATE/TIME	41
+LABFTRIGA	"TRIGLYCERIDES, NON-PLEURAL"	FSC1	FLUID TYPE	41
+LABHB	HEPATITIS B SURFACE AG	HBSAG	HEPATITIS B SURF AG	41
+LABDVEIAV	FLU A DFA	IAV	INFLUENZA A	41
+LABDVEIAV	FLU A DFA	IAVIP	TEST DESCRIPTION	41
+LABDVEIAV	FLU A DFA	IAVSP	SPECIMEN	41
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	41
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	41
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	41
+10273R	HLA ORDER - WORKUP AT TIME OF TRANSPLANT	RINFO	TO FIND RESULT:	41
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	SPEXP	SPECIMEN EXPIRATION DATE/TIME	41
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	SPEXP	SPECIMEN EXPIRATION DATE/TIME	41
+10275R	HLA ORDER - SPECIMEN IN LAB ORDER NOTICE	SPINN	SPECIMEN IN LAB ORDER NOTICE	41
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	UBT	UNIT BLOOD TYPE  	41
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	UNT	UNIT NUMBER  	41
+LAB12809R	CK ISOENZYME ELECTROPHORESIS	YCKMB	MB FRACTION	41
+LAB12809R	CK ISOENZYME ELECTROPHORESIS	YCKMM	MM FRACTION	41
+LAB12809R	CK ISOENZYME ELECTROPHORESIS	YCKTOT	TOTAL CK ACTIVITY	41
+LAB11566R	"ENTEROVIRUS RNA, QUAL"	11679R	SOURCE	40
+LAB11566R	"ENTEROVIRUS RNA, QUAL"	11680R	"ENTEROVIRUS QUAL,PCR"	40
+10365R	HLA - DSA BY IGG	9432R	DSA RESULTS FOR IGG CLASS II	40
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	BP190	"P190, BONE MARROW"	40
+LABCRYEIA	CRYPTOSPORIDIUM EIA	CULT	CULT/OTHER RSLT:	40
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	DI	ISSUE DATE/TIME	40
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GMYD8B	"MYD88, BLOOD"	40
+LABCSMPB	"CANCER SOMATIC MUTATION PNL, BLOOD"	GSF3BB	"SF3B1, BLOOD"	40
+LABYHABQ	HEPATITIS B SURFACE AB QUANTITATIVE	HBSAB	"HBS ANTIBODY, S"	40
+LABYHABQ	HEPATITIS B SURFACE AB QUANTITATIVE	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	40
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	40
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	40
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	40
+LABLWBCS2	"LYME WESTERN BLOT, SERUM"	LYMEIGMBAND	B.BURGDORFERI IGM BANDS	40
+LABYPLAB	PLATELET AB	PLTAB	PLATELET ANTIBODY	40
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	40
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	40
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	40
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	40
+LABCRYEIA	CRYPTOSPORIDIUM EIA	RPT	RPT	40
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	40
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	40
+LABCRYEIA	CRYPTOSPORIDIUM EIA	SDES	SPECIMEN	40
+LABCRYEIA	CRYPTOSPORIDIUM EIA	SREQ	COMMENTS	40
+LABTOXOP2	TOXOPLASMOSIS PCR	TOXGG	GENERAL GUIDELINES	40
+LABTOXOP2	TOXOPLASMOSIS PCR	TOXRES	PCR RESULT	40
+LABTOXOP2	TOXOPLASMOSIS PCR	TOXSP	SPECIMEN TYPE	40
+10075R	HLA ORDER - STR/CHIMERISM	10049R	HLA LAB TESTING STATUS	39
+LAB11593R	ETHYLENE GLYCOL	11721R	ETHYLENE GLYCOL	39
+LABCRTINE	"CREATINE, SER/PLAS"	11852R	"CREATINE, PLASMA MG/DL"	39
+LAB11027R	IMMUNE COMPLEXES	C1Q	C1Q BINDING ASSAY	39
+LABCRTINE	"CREATINE, SER/PLAS"	CRTNR	"CREATINE, PLASMA"	39
+LABBMC	BONE MARROW CULTURE	CULT	CULT/OTHER RSLT:	39
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	39
+LABBMC	BONE MARROW CULTURE	GS	GRAM STAIN	39
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDEVLDATE	ICD DATE OF EVENT	39
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	39
+LABLPA	LIPOPROTEIN	LPA	LIPOPROTEIN A	39
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	MFR	MANUFACTURER	39
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	MOD	MODEL	39
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	PRO	PRODUCT ID  	39
+10075R	HLA ORDER - STR/CHIMERISM	RINFO	TO FIND RESULT:	39
+LABBMC	BONE MARROW CULTURE	RPT	RPT	39
+LABBMC	BONE MARROW CULTURE	SDES	SPECIMEN	39
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	39
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	SI	<<STATUS>>  	39
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	39
+LABBMC	BONE MARROW CULTURE	SREQ	COMMENTS	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSICO	COMMENT	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSIDI	DISCLAIMER	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSIDX	CLINICAL INDICATION	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSIMD	INTERPRETED BY	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSIME	METHOD	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSIRE	RESULT	39
+LABTMSI	MICROSATELLITE INSTABILITY	TMSISO	"SOURCE, TISSUE"	39
+LABYUCORT	"CORTISOL FREE, URINE"	UCORT	"CORTISOL, URINE"	39
+LABURURIC	"URIC ACID, URINE (RANDOM)"	URICURN	"URIC ACID, URINE"	39
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	VL	PRODUCT VOLUME	39
+LABYUCORT	"CORTISOL FREE, URINE"	VOLUMEURN	URINE VOLUME	39
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	XMR	UNIT CROSSMATCH  	39
+LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	YARBIN	ACH RECEPTOR BINDING ANTIBODY	39
+LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	YARMA	ACH RECEPT MODUL AB	39
+LABYIFBA	INTRINSIC FACTOR BLOCKING AB	YIFB1	INTRINSIC FACTOR BLOCKING AB	39
+LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	YSTA	STRIATIONAL (STRIATED MUSCLE)	39
+LABYUCORT	"CORTISOL FREE, URINE"	YUCOUT	COLLECTION DURATION	39
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12363R	"ANNA-1, CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12364R	"ANNA-2, CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12365R	"ANNA-3, CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12366R	"AGNA-1,CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12367R	"PCA-1, CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12368R	"PCA-2, CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12369R	"AMPHIPHYSIN AB, CSF"	38
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	12370R	"CRMP-5-IGG, CSF"	38
+LABADVQL	ADENOVIRUS DNA PCR QUAL	ADQLDN	ADV DNA	38
+LABADVQL	ADENOVIRUS DNA PCR QUAL	ADQLIP	TEST DESCRIPTION	38
+LABADVQL	ADENOVIRUS DNA PCR QUAL	ADQLSP	SPECIMEN	38
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	BP210	"P210, BONE MARROW"	38
+LABYFCAR	"CARBAMAZEPINE FREE AND TOTAL, SERUM"	CZPFR	"CARBAMAZEPINE,FREE,S"	38
+LABYFCAR	"CARBAMAZEPINE FREE AND TOTAL, SERUM"	CZPTOT	"CARBAMAZEPINE,TOT,S"	38
+LABDVERSV	DIRECT VIRAL EXAM RSV	DVE	RESULTS	38
+LABGDGP	IGG ANTI DGP	GDGINT	INTERPRETATION	38
+LABGDGP	IGG ANTI DGP	GDGPR	IGG ANTI DGP ANTIBODY	38
+LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG	HSV1G	"HSV 1, IGG INDEX"	38
+LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG	HSV2G	"HSV 2, IGG INDEX"	38
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	38
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	38
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	38
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	38
+LABBMBCRQ	"BCR - ABL QUANTITATIVE, BONE MARROW"	LRB210	LOG DX AVG	38
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRALDBIIM	PM RA LEAD BIPOLAR IMPEDANCE	38
+LABDVERSV	DIRECT VIRAL EXAM RSV	RPT	RPT	38
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	38
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	38
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	38
+LABDVERSV	DIRECT VIRAL EXAM RSV	SDES	SPECIMEN	38
+LABYANTSM	AUTOANTIBODIES TO SM	SMAB	AUTOANTIBODIES TO SM	38
+LABDVERSV	DIRECT VIRAL EXAM RSV	SREQ	COMMENTS	38
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	UBT	UNIT BLOOD TYPE  	38
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	UNT	UNIT NUMBER  	38
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	XMR	UNIT CROSSMATCH  	38
+10190R	HLA ORDER - COMMUNICATION/UPDATE	10049R	HLA LAB TESTING STATUS	37
+LAB11253R	HELICOBACTER PYLORI AGA AB	11185R	HELICOBACTER PYLORI IGA AB	37
+LAB11984R	NIACIN	11985R	NIACIN	37
+LABYFLUO	5 - FLUCYTOSINE	5FC	5-FLUCYTOSINE	37
+LABAFBSO	AFB SMEAR ONLY	AFB	AFB SMEAR	37
+LABCD34	"CD34 PANEL, BLOOD"	CD34V	VIABILITY	37
+LABCOCFS	"COCCIDIOIDES COMPLEMENT FIXATION, SERUM"	COCFS	COCCI CF (SERUM)	37
+LABUPOR	"PORPHYRINS FRACTIONATED, URINE"	COPRO3	COPROPORPHYRIN III	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	37
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRVVDEL	BRADY VV DELAY	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDRHYTHM	ICD RHYTHM	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	37
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	37
+LABYPYRU	PYRUVIC ACID	PYRU	PYRUVIC ACID	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	37
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	37
+10190R	HLA ORDER - COMMUNICATION/UPDATE	RINFO	TO FIND RESULT:	37
+LABAFBSO	AFB SMEAR ONLY	RPT	RPT	37
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	37
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	37
+LABAFBSO	AFB SMEAR ONLY	SDES	SPECIMEN	37
+LABSHIVAB	RAPID HIV-1 AB	SHIVAB	RAPID HIV-1 ANTIBODY	37
+LABAFBSO	AFB SMEAR ONLY	SREQ	COMMENTS	37
+LABYVB1W	VITAMIN B1	YVB1W2	COMMENT	37
+LABB2GPCO	BETA - 2 - GLYCOPROTEIN	B2GPCM	BETA-2-GP1 COMMENT	36
+LABFHVIG	"HIV-1 AB, WESTERN BLOT"	BANDPRSNT	BANDS PRESENT	36
+LABABG	"BLOOD GASES, ARTERIAL"	BGSPR	SPECIMEN COMMENT	36
+LABFCD34	"CD34 PANEL, FLUID"	CD34V	VIABILITY	36
+LABNASC	NP / NASAL CULTURE	CULT	CULT/OTHER RSLT:	36
+LABDCMVL	DONOR CMV AB (LIVING)	DCMVL1	DONOR CMV- TOTAL AB	36
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	DI	ISSUE DATE/TIME	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HATINT	INTERPRETATION	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HBSINT	INTERPRETATION	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HBTINT	INTERPRETATION	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HCVI	INTERPRETATION	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HCVR	ANTI-HCV	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	36
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	36
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	36
+LABPFCATE	CATECHOLAMINES FRACTIONATED	PCATIN	"CAT PF INTERPRETATION(CATECHOL INTRP, PL)"	36
+LABPFCATE	CATECHOLAMINES FRACTIONATED	PDOPA	DOPAMINE	36
+LABPFCATE	CATECHOLAMINES FRACTIONATED	PEPI	EPINEPHRINE BY HPLC	36
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMTLPCPACA	PM TELEMETRY PERCENT PACING ATRIAL	36
+LABPFCATE	CATECHOLAMINES FRACTIONATED	PNOREP	NOREPINEPHRINE	36
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	PRO	PRODUCT ID  	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	RPRQ	RESULT	36
+LABBMPTP	BMT PRETRANSPLANT PANEL	RPRQIN	INTERPRETATIONS	36
+LABNASC	NP / NASAL CULTURE	RPT	RPT	36
+LABNASC	NP / NASAL CULTURE	SDES	SPECIMEN	36
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	SI	<<STATUS>>  	36
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	SPEXP	SPECIMEN EXPIRATION DATE/TIME	36
+LABNASC	NP / NASAL CULTURE	SREQ	COMMENTS	36
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	UBT	UNIT BLOOD TYPE  	36
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	UNT	UNIT NUMBER  	36
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	VL	PRODUCT VOLUME	36
+LABYBLAS	BLASTOMYCES AB	YBLAS1	"BLASTOMYCES AB ,S"	36
+LABYCLON	"CLONAZEPAM, SERUM"	YCLON1	CLONAZEPAM	36
+LABYPR3	PROTEINASE 3 AUTOANTIBODIES	YPR31	PROTEINASE 3 ANTIBODIES	36
+LABYRICK	RICKETTSIA AB PANEL	YRICK1	"SPOTTED FEVER GROUP,AB,IGG,S"	36
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12820R	"SODIUM, FECES"	35
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12821R	"POTASSIUM, FECES"	35
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12822R	"CHLORIDE, FECES"	35
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12823R	"OSMOLALITY, FECES"	35
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12824R	"MAGNESIUM, FECES"	35
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12825R	"PHOSPHORUS, FECES"	35
+LAB12810R	"ELECTROLYTE & OSMO PNL, FECES"	12826R	"OSMOTIC GAP, FECES"	35
+10375R	HLA - STR/CHIMERISM TESTING	9479R	LOCUS 5	35
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	ARS	"ARSENIC, BLOOD"	35
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15DI	DISCLAIMER	35
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15DX	CLINICAL INDICATION	35
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15MD	INTERPRETED BY	35
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15ME	METHOD	35
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15RE	"RESULT, BM"	35
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	CAD	"CADMIUM, BLOOD"	35
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	DI	ISSUE DATE/TIME	35
+LABFPARVO	PARVOVIRUS IGG / IGM AB	FPARV2	PARVOVIRUS IGM INDEX	35
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	35
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	35
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	35
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	35
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	35
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	35
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	35
+LABREF3	LAB UNLISTED 3	LAB3	REFERENCE LAB	35
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	LEA	"LEAD, BLD"	35
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	MERC	"MERCURY, BLOOD"	35
+LABT1517	"T(15	17) PML/RAR, BLOOD"	PMLR	PML	35
+LABREF3	LAB UNLISTED 3	RESUL3	RESULT	35
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	35
+LABREF3	LAB UNLISTED 3	SPTY3	SPECIMEN TYPE	35
+LABREF3	LAB UNLISTED 3	TEST3	TEST NAME	35
+LAB11120R	"TOXOPLASMOSIS, CSF"	TOXOC	"TOXOPLASMA AB,CSF"	35
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	VL	PRODUCT VOLUME	35
+LABYRICK	RICKETTSIA AB PANEL	YRICK2	"SPOTTED FEVER GROUP,AB,IGM,S"	35
+LABYRICK	RICKETTSIA AB PANEL	YRICK3	"TYPHUS FEVER GROUP,AB,IGG,S"	35
+LABYRICK	RICKETTSIA AB PANEL	YRICK4	"TYPHUS FEVER GROUP,AB,IGM,S"	35
+LABVCAIGG	EB VIRAL CAPSID AG IGG AB	11168R	EB VCA IGG AB	34
+LABCARN	CARNITINE FREE AND TOTAL	ACFCR	ACYL/FREE CARNITINE RATIO	34
+LABPENALL	PENICILLIN ALLERGY SCREEN	AMOX	AMOXICILLIN	34
+LABPENALL	PENICILLIN ALLERGY SCREEN	AMPICILLIN	AMPICILLIN	34
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	BASOSYNFLD	"BASOPHILS, SYN FLD"	34
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BM15CO	"COMMENT, BM"	34
+LABCARN	CARNITINE FREE AND TOTAL	CARINT	INTERPRETATION	34
+LABCARN	CARNITINE FREE AND TOTAL	CARNF	"CARNITINE, FREE"	34
+LABCARN	CARNITINE FREE AND TOTAL	CARNIT	"CARNITINE, TOTAL"	34
+LABROGC	GC CULTURE SCREEN	CULT	CULT/OTHER RSLT:	34
+LABDHIVB	DONOR HIV AB	DHIV	DONOR HIV AB	34
+LABDHIVB	DONOR HIV AB	DHTLV	DONOR HIV AB COMMENT	34
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	34
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	34
+LABPENALL	PENICILLIN ALLERGY SCREEN	IGEPEN	TOTAL IGE	34
+LABPENALL	PENICILLIN ALLERGY SCREEN	PENG	PENICILLIN G	34
+LABPENALL	PENICILLIN ALLERGY SCREEN	PENINT	INTERPRETATION	34
+LABPENALL	PENICILLIN ALLERGY SCREEN	PENV	PENICILLIN V	34
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	34
+LABROGC	GC CULTURE SCREEN	RPT	RPT	34
+LABROGC	GC CULTURE SCREEN	SDES	SPECIMEN	34
+LABSLINTP	SLIDE INTERPRETATION BY PATHOLOGIST	SLINT	INTERPRETATION	34
+LABSLINTP	SLIDE INTERPRETATION BY PATHOLOGIST	SLPATH	PATHOLOGIST	34
+LABROGC	GC CULTURE SCREEN	SREQ	COMMENTS	34
+LABVZPAN	VZV IGG IGM PANEL	VGGDES	VZV IGG TEST DESCRIPTION	34
+LABVZPAN	VZV IGG IGM PANEL	VGGQNT	VZV IGG LEVEL	34
+LABVZPAN	VZV IGG IGM PANEL	VGGQUL	VZV IGG RESULT	34
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	34
+LABVZPAN	VZV IGG IGM PANEL	VZVDS	VZV IGM TEST DESCRIPTION	34
+LABVZPAN	VZV IGG IGM PANEL	VZVMQL	VZV IGM RESULT	34
+LABVZPAN	VZV IGG IGM PANEL	VZVMQT	VZV IGM LEVEL	34
+LABDWNIL	"DONOR, WEST NILE VIRUS NAT"	WNIL1	"DONOR, WEST NILE VIRUS NAT"	34
+LABEAIGG	EB VIRUS EARLY AG (R+D) IGG	11165R	EB VIRUS EARLY AG (R+D) IGG	33
+LABEBNABY	EB NUCLEAR AG AB	11166R	EB NUCLEAR AB	33
+LABVCAIGM	EB VIRAL CAPSID AG IGM AB	11167R	EB VCA IGM AB	33
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTDIS	DISCLAIMER	33
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTDX	CLINICAL INDICATION	33
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTMET	METHOD	33
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTRES	RESULTS	33
+LABUCAT	"CALCIUM, URINE (TIMED)"	CAE	CALCIUM EXCRETION	33
+LABUCAT	"CALCIUM, URINE (TIMED)"	CAURN	"CALCIUM, URINE"	33
+LABDCGSN	"DONOR, CHAGAS SCN W/REFL"	CGSN1	"DONOR, CHAGAS SCN W/REFL"	33
+LAB11133R	DONOR HBC AB	DHBCC	DONOR HEP CORE AB	33
+LAB11134R	DONOR HCV AB	DHEPC	DONOR HEPATITIS C AB	33
+LABFPARVO	PARVOVIRUS IGG / IGM AB	FPARV1	PARVOVIRUS IGG INDEX	33
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GMYD8M	"MYD88, BM"	33
+LABCSMPBM	"CANCER SOMATIC MUTATION PNL, BM"	GSF3BM	"SF3B1, BM"	33
+LABUHGB	"HEMOGLOBIN, URINE"	HGBURN	"HEMOGLOBIN, URINE"	33
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	33
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	33
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	PRO	PRODUCT ID  	33
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	33
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	33
+LABDVEAB	RAPID FLU A/B EIA	RFCOM	COMMENTS	33
+LABDVEAB	RAPID FLU A/B EIA	RFLSP	SPECIMEN	33
+LABDVEAB	RAPID FLU A/B EIA	RFLUA	INFLUENZA VIRUS TYPE A	33
+LABDVEAB	RAPID FLU A/B EIA	RFLUB	INFLUENZA VIRUS TYPE B	33
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	33
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	33
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	SI	<<STATUS>>  	33
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	UBT	UNIT BLOOD TYPE  	33
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	UNT	UNIT NUMBER  	33
+LABUHGB	"HEMOGLOBIN, URINE"	UT	COLLECTION PERIOD	33
+LABUCAT	"CALCIUM, URINE (TIMED)"	UT5	COLLECTION PERIOD CALCIUM	33
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	VL	PRODUCT VOLUME	33
+LABUHGB	"HEMOGLOBIN, URINE"	VOL	VOLUME	33
+LABUCAT	"CALCIUM, URINE (TIMED)"	VOL5	VOLUME CALCIUM	33
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	XMR	UNIT CROSSMATCH  	33
+LABYUFCAT	"CATECHOLAMINES FRACTIONATED FREE, URINE"	YUCATF	"CATECHOLAMINES, URINE"	33
+LABYUCOP	"COPPER, URINE"	YUCOP4	CU CONC (UG/L)	33
+10283R	HLA ORDER - TRANSPLANT EVENT COMMUNICATION	10049R	HLA LAB TESTING STATUS	32
+LABLEGA	"LEGIONELLA CULTURE, ACID"	CULT	CULT/OTHER RSLT:	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	DEVTYPE	DEVTYPE	32
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	DI	ISSUE DATE/TIME	32
+LABFACT12	FACTOR XII ASSAY	FACTOR12	FACTOR XII ASSAY	32
+LABHIBCV	DONOR HIV1 / HBV / HCV NAT	HIBCV1	DONOR HIV/HBV/HCV RESULT	32
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRLOWRT	BRADY LOWER RATE	32
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	32
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	32
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRPACMD	BRADY PACING MODE	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDEVLDATE	ICD DATE OF EVENT	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	32
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	32
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	IMPDATE	IMPLANT DATE	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	MFR	MANUFACTURER	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	MOD	MODEL	32
+10283R	HLA ORDER - TRANSPLANT EVENT COMMUNICATION	RINFO	TO FIND RESULT:	32
+LABLEGA	"LEGIONELLA CULTURE, ACID"	RPT	RPT	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRPACA	RV BRADY PACING AMPLITUDE	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRPACW	RV BRADY PACING PULSE WIDTH	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENA	RV BRADY SENSING AMPLITUDE	32
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	32
+LABLEGA	"LEGIONELLA CULTURE, ACID"	SDES	SPECIMEN	32
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SERNUM	SERIAL NUMBER	32
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	32
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	SPEXP	SPECIMEN EXPIRATION DATE/TIME	32
+LABLEGA	"LEGIONELLA CULTURE, ACID"	SREQ	COMMENTS	32
+LABUA	URINALYSIS WITH MICROSCOPIC	WBCAST	WBC CASTS	32
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	XMR	UNIT CROSSMATCH  	32
+10375R	HLA - STR/CHIMERISM TESTING	9574R	INTERPRETATION/ COMMENTS	31
+LABYCCPIG	ANTI CCP	CCPT	ANTI CCP ANTIBODY LEVEL	31
+LAB11132R	DONOR HEP B SURF. AG	DHB	DONOR HEP B SURF. AG	31
+LABYEBPCR	"EBV BY RAPID PCR, CSF"	EBV	EBV BY PCR	31
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	EBV	EBV BY PCR	31
+LABFILAB	FILARIA IGG4 AB ELISA	FILIG4	FILARIAL LGG ANTIBODY	31
+LAB12814R	MYCOPLASMA IGG/IGM AB	FMYABM	M.PNEUMONIA AB IGM	31
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	31
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBCAB	HEP B CORE TOTAL AB	31
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSAB	HEPATITIS B SURFACE AB QUAL	31
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HBSAG	HEPATITIS B SURF AG	31
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HEPAAB	HEPATITIS A TOTAL AB	31
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	HEPCAB	HEPATITIS C AB	31
+LABDVEIBV	FLU B DFA	IBV	INFLUENZA B	31
+LABDVEIBV	FLU B DFA	IBVIP	TEST DESCRIPTION	31
+LABDVEIBV	FLU B DFA	IBVSP	SPECIMEN	31
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	31
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	31
+LABYMANG	MANGANESE	MN	MANGANESE	31
+LABTOTALS	"PROTEIN S AG, TOTAL"	PROTSTOT	"PROTEIN S AG, TOTAL"	31
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	31
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	31
+LABSSAABY	ANTI-RO ANTIBODY	SSAQNT	ANTI-RO LEVEL	31
+LABVITK	"VITAMIN K1, SERUM"	VITK	"VITAMIN K1,SERUM"	31
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	XMR	UNIT CROSSMATCH  	31
+LABYCHROM	CHROMIUM	YCHRO1	CHROMIUM	31
+LABYEBPCR	"EBV BY RAPID PCR, CSF"	YEBP1	SPECIMEN SOURCE	31
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	YEBP1	SPECIMEN SOURCE	31
+LABYEBPCR	"EBV BY RAPID PCR, CSF"	YEPB4	REPORT STATUS	31
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	YEPB4	REPORT STATUS	31
+LABYHISTC	"HISTOPLASMA AG, CSF"	YHISC4	"RESULT,CSF"	31
+10090R	HLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	30
+10220R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)	10049R	HLA LAB TESTING STATUS	30
+10225R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*	10049R	HLA LAB TESTING STATUS	30
+10390R	HLA-BMT DONOR MATCH DETAIL SUMMARY	9706R	DONOR MATCH GRADE SUMMARY	30
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ATACHTADTI	ATACH TACHY ZONE DETECTION INTERVAL	30
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTDIR	INTERPRETED BY	30
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTPRM	PRIMERS	30
+LABCMVIGM	CMV IGM	CMVIGM	CMV IGM AB	30
+LABSFCORT	"CORTISOL FREE, SERUM"	CORTFREE	"CORTISOL, SERUM FREE"	30
+LABYDESIP	"DESIPRAMINE, SERUM"	DESIP	DESIPRAMINE	30
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	DI	ISSUE DATE/TIME	30
+LABDVEFLB	DIRECT VIRAL EXAM FLU B	DVE	RESULTS	30
+LABFCHOLA	"CHOLESTEROL, FLUID"	FSC1	FLUID TYPE	30
+LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GLUF	"GLUCOSE,FASTING"	30
+LABURGL	"GLUCOSE, URINE (RANDOM)"	GLUURN	"GLUCOSE, URINE"	30
+LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTT1	"GLUCOSE,GTT 1HR"	30
+LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTT2	"GLUCOSE,GTT 2HR"	30
+LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTT30	"GLUCOSE,GTT 30 MIN"	30
+LABGTT	TRADITIONAL GLUCOSE TOLERANCE	GTTINT	GTT INTERPRETATION	30
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	30
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	30
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	30
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	30
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	30
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	30
+LABMPOPR3	ANCA MPO / PR - 3M IGG	MPO	"MYLEOPEROXIDASE, AB"	30
+LABPORS	"PORPHYRINS, SERUM"	PORINT	"PORPHYRINS, SER INTR"	30
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	PRO	PRODUCT ID  	30
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	30
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	30
+10090R	HLA ORDER - STR/CHIMERISM (W/UNRELATED DONOR INFO)	RINFO	TO FIND RESULT:	30
+10220R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG (POST TRANSPLANT)	RINFO	TO FIND RESULT:	30
+10225R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*	RINFO	TO FIND RESULT:	30
+LABDRPRS	"DONOR, RPR"	RPRS1	DONOR SYPHILIS SCREEN	30
+LABRPRQT	RPR QUANTITATIVE	RPRTIN	INTERPRETATIONS	30
+LABDVEFLB	DIRECT VIRAL EXAM FLU B	RPT	RPT	30
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	30
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	30
+LABDVEFLB	DIRECT VIRAL EXAM FLU B	SDES	SPECIMEN	30
+LABMPOPR3	ANCA MPO / PR - 3M IGG	SERINEPR3	SERINE PROTEASE 3	30
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	SI	<<STATUS>>  	30
+LABDVEFLB	DIRECT VIRAL EXAM FLU B	SREQ	COMMENTS	30
+LABTOXPCR	TOXOPLASMA PCR	TOXGG	GENERAL GUIDELINES	30
+LAB11122R	"TOXOPLASMA AB, IGM"	TOXOM	TOXOPLASMA IGM ELISA	30
+LABTOXPCR	TOXOPLASMA PCR	TOXRES	PCR RESULT	30
+LABTOXPCR	TOXOPLASMA PCR	TOXSP	SPECIMEN TYPE	30
+LABPORS	"PORPHYRINS, SERUM"	TPOR	"PORPHYRINS, TOTAL"	30
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	UBT	UNIT BLOOD TYPE  	30
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	UNT	UNIT NUMBER  	30
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	VL	PRODUCT VOLUME	30
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	VL	PRODUCT VOLUME	30
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	30
+10295R	HLA - AUTO T FLOW CROSSMATCH	9087R	SERUM DATE 2	29
+10295R	HLA - AUTO T FLOW CROSSMATCH	9089R	MCS 2	29
+10300R	HLA - AUTO B FLOW CROSSMATCH	9108R	SERUM DATE 2	29
+10300R	HLA - AUTO B FLOW CROSSMATCH	9110R	MCS 2	29
+10295R	HLA - AUTO T FLOW CROSSMATCH	9596R	RESULT 2	29
+LABBMTCLO	"T-CELL CLONALITY PCR, BONE MARROW"	BMTCMT	COMMENT	29
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDRHYTHM	ICD RHYTHM	29
+ECH29	ECHO - DOBUTAMINE STRESS ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	29
+LABPANAPL	PROCAINAMIDE AND NAPA	NAPA	N-ACETYLPROCAINAMIDE (NAPA)	29
+LABPANAPL	PROCAINAMIDE AND NAPA	PA	PROCAINAMIDE	29
+LABPANAPL	PROCAINAMIDE AND NAPA	PANAP1	"PEAK, TROUGH, OR RANDOM"	29
+LABPANAPL	PROCAINAMIDE AND NAPA	PANAP2	DATE & TIME OF LAST DOSE	29
+LABPANAPL	PROCAINAMIDE AND NAPA	PANAP3	DOSAGE (INCLUDE UNITS)	29
+LABPANAPL	PROCAINAMIDE AND NAPA	PANAP4	ADMINISTRATION ROUTE	29
+LABUPHOS	"PHOSPHATE WITH CREATININE, URINE (RANDOM)"	PHOSU	"PHOSPHATE, URINE"	29
+LABUPHOS	"PHOSPHATE WITH CREATININE, URINE (RANDOM)"	PHUCRU	PHOSPHATE/CREAT RATIO	29
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	29
+LABUPHOS	"PHOSPHATE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	29
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	XMR	UNIT CROSSMATCH  	29
+LAB12814R	MYCOPLASMA IGG/IGM AB	12024R	MYCOPLASMA PNEUMONIA AB IGG	28
+LAB12322R	VON WILLEBRAND MULTIMERS	12323R	VON WILLEBRAND MULTIMERS	28
+LABTHIOMT	THIOPURINE METABOLITES	6TGN	6-TGN	28
+10330R	HLA - SCREENING IGG	9260R	INTERPRETATION/ COMMENTS CLASS II	28
+10300R	HLA - AUTO B FLOW CROSSMATCH	9601R	RESULT 2	28
+10365R	HLA - DSA BY IGG	9629R	REFERENCE SERUM DESCRIPTION	28
+LABYAT3AG	"ANTITHROMBIN AG, PLASMA"	AT3	ANTITHROMBIN III AG	28
+LAB11025R	"C1 INHIBITOR, QUANT"	C1ESTR	C-1-ESTERASE INHIBITOR	28
+LABMEP	MEDICAL EMERGENCY PANEL	CAION	"CALCIUM, IONIZED"	28
+LABMEP	MEDICAL EMERGENCY PANEL	CL	"CHLORIDE, WHOLE BLD"	28
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	DI	ISSUE DATE/TIME	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	DX	DIAGNOSIS	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	FCOLOR	APPEARANCE	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	FLDTYP	FLUID SOURCE	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	FTURB	"TURBIDITY, FLD"	28
+LABMEP	MEDICAL EMERGENCY PANEL	GLU	"GLUCOSE, WHOLE BLOOD"	28
+LABMEP	MEDICAL EMERGENCY PANEL	HCT	HCT (EST)	28
+POC30	POC PYLORITECK	HPYL	PATIENT HPYLORI	28
+LABBMPTP	BMT PRETRANSPLANT PANEL	HSAB	VIRAL AB HSV	28
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	28
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	28
+LABMEP	MEDICAL EMERGENCY PANEL	K	"POTASSIUM, WHOLE BLD"	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	MALIG	SUSPECT MALIGNANCY?	28
+LABMEP	MEDICAL EMERGENCY PANEL	NA	"SODIUM, WHOLE BLOOD"	28
+LABYPYRU	PYRUVIC ACID	PYRU	PYRUVIC ACID (MG/DL)	28
+POC30	POC PYLORITECK	QC12	QC RESULT	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	RBCFLD	"RBC COUNT, FLD (/UL)"	28
+LABRNPABY	ANTI-RNP ANTIBODY	RNPQNT	ANTI-RNP LEVEL	28
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	28
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	28
+LABMEP	MEDICAL EMERGENCY PANEL	TCO2A	TCO2	28
+POC30	POC PYLORITECK	TESTEX	TEST STRIP EXP	28
+LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOS	"TESTOSTERONE, TOTAL"	28
+LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOSBIO	"TESTOSTERONE,BIOAVAILABLE, S"	28
+LABMEP	MEDICAL EMERGENCY PANEL	THB	THB	28
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	UBT	UNIT BLOOD TYPE  	28
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	UBT	UNIT BLOOD TYPE  	28
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	UBT	UNIT BLOOD TYPE  	28
+LABYFUN	"UREA NITROGEN, FLUID"	UNFLD	UREA NITROGEN BODY FLUID	28
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	UNT	UNIT NUMBER  	28
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	UNT	UNIT NUMBER  	28
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	UNT	UNIT NUMBER  	28
+LABBFCTS	"CELL COUNT, BODY FLUID"	WBCFLD	"NUCL CELLS, FLD(/UL)"	28
+LABYSTCL	"CHLORIDE, STOOL"	YSTCL1	"CHLORIDE, STOOL, 24 HR"	28
+LAB12781R	BRUCELLA ANTIBODIES	12778R	BRUCELLA IGG	27
+LAB12781R	BRUCELLA ANTIBODIES	12779R	BRUCELLA IGM	27
+10370R	HLA - DSA BY C1Q	9633R	REFERENCE SAMPLE DATE	27
+LABADVQT	ADENOVIRUS PCR QUANT	ADPCR1	ADENVIRUS DNA	27
+LABCIMM	"COCCI AB, ID, S"	CIMM	COCCI IMMUNODIFFUSION	27
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	DI	ISSUE DATE/TIME	27
+LABYSDA	"DRUG SCREEN, PLASMA"	DRUGSCRN	"DRUG SCREEN, PLASMA"	27
+LABFIBANT	FIBRINOGEN ANTIGEN	FIBANT	FIBRINOGEN ANTIGEN	27
+LABFRAB	FRANCISELLA TULARENSIS AB	FRANT	FRANCISELLA TULARENSIS AB	27
+LABG6PDQT	G6PD QUANTITATIVE	G6PD	"G6PD, QUANTITATIVE"	27
+LABHATA	HEPATITIS A AB TOTAL	HEPAAB	HEPATITIS A TOTAL AB	27
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	27
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	27
+LABSTOBID	"OCCULT BLD, IMMUNO, STOOL, DIAG TEST"	OCCIMM	"STOOL SAMPLE1,IMMUN"	27
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	PRO	PRODUCT ID  	27
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	PRO	PRODUCT ID  	27
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QTFNIN	QUANTIFERON INTERPRETATION	27
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QTFNRS	QUANTIFERON-TB	27
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	27
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	27
+LABCBCD	CBC WITH DIFF	SEZY	SEZARY CELLS	27
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	SI	<<STATUS>>  	27
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	SI	<<STATUS>>  	27
+LABADVQT	ADENOVIRUS PCR QUANT	SOUR10	SOURCE	27
+LABYANRNP	AUTOANTIBODIES TO U1RNP	U1RNPAB	AUTOANTIBODIES TO U1RNP	27
+LABXICA51	ICA 512 AB	XICA5R	ICA 512 ANTIBODY	27
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	XMR	UNIT CROSSMATCH  	27
+LABYASU	ALPHASUBUNIT PITUITARY MARKER	YASU1	ALPHA-SUBUNIT	27
+LABYIFBA	INTRINSIC FACTOR BLOCKING AB	YIFB2	COMMENT	27
+LABYSTCL	"CHLORIDE, STOOL"	YSTCL3	COLLECTION DURATION	27
+LABYSTCL	"CHLORIDE, STOOL"	YSTCL4	CHLORIDE CONCENTRATION	27
+LAB11525R	MUSCLE TYROSINE KINASE AB	11635R	MUSCLE TYROSINE KINASE AB	26
+LAB11547R	HEPATITIS C CONFIRMATORY TEST	11657R	HEPATITIS C CONFIRMATORY TEST	26
+LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMACT	ADAMTS-13 ACTIVITY	26
+LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMINT	CLINICAL INTERPRETATION	26
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	CCPINT	INTERPRETATION	26
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	CCPR	ANTI CCP ANTIBODY	26
+LABCYLEX	IMMUNE CELL FUNCTION (CYLEX)	CYCLXR	IMMUNE CELL FUNCTION	26
+LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	EBV	EBV BY PCR	26
+LABGDGP	IGG ANTI DGP	GDGPT	IGG ANTI DGP LEVEL	26
+LABHBSQT	HEP B SURF AB QUANT	HBSAB	"HBS ANTIBODY, S"	26
+LABHBSQT	HEP B SURF AB QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	26
+LABUBICAR	"BICARBONATE, URINE"	HCO3URN	"BICARBONATE, URINE"	26
+LABHEPAR	UNFRACTIONATED HEPARIN	HEPAR	HEPARIN ACTIVITY LEVEL	26
+LABHIVDAG	HIV - 1 DIRECT AG (ICD)	HIVS	HIV-1 P24 (ICD)AG	26
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	26
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	26
+LABHIVDAG	HIV - 1 DIRECT AG (ICD)	INTRE	INTERPRETATION	26
+LABYANTJO	AUTOANTIBODIES TO JO1	JO1AB	AUTOANTIBODIES TO JO 1	26
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRSENA	RA BRADY SENSING AMPLITUDE	26
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	26
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	26
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	RHFINT	INTERPRETATION	26
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	RHFR	RHEUMATOID FACTOR	26
+LABRPRQT	RPR QUANTITATIVE	RPRTTR	TIER	26
+LABYTHI	THIOCYANATE	THIOCYAN	THIOCYANATE	26
+LABUKT	"POTASSIUM, URINE (TIMED)"	TKU	POTASSIUM EXCRETION	26
+LABUKT	"POTASSIUM, URINE (TIMED)"	UK	"POTASSIUM, URINE"	26
+LABUBICAR	"BICARBONATE, URINE"	UT	COLLECTION PERIOD	26
+LABUKT	"POTASSIUM, URINE (TIMED)"	UT2	COLLECTION PERIOD POTASSIUM	26
+LABUBICAR	"BICARBONATE, URINE"	VOL	VOLUME	26
+LABUKT	"POTASSIUM, URINE (TIMED)"	VOL2	VOLUME POTASSIUM	26
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	XMR	UNIT CROSSMATCH  	26
+LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	YEBP1	SPECIMEN SOURCE	26
+LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	YEBP3	SPECIAL INFORMATION	26
+LABYEBPCR	"EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)"	YEPB4	REPORT STATUS	26
+LAB11182R	NICOTINE & METABOLITES	11183R	NICOTINE	25
+LAB11182R	NICOTINE & METABOLITES	11184R	COTININE	25
+LABDCGSN	"DONOR, CHAGAS SCN W/REFL"	11893R	"DONOR, CHAGAS RIPA"	25
+LABAGAP	ANION GAP	AG	ANION GAP	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKAA	AMINO ACID CHANGE	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKCI	CLINICAL INDICATION	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKDI	DISCLAIMER	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKIT	INTERPRETED BY	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKMT	METHOD	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKPR	PRIMER	25
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKRE	RESULT	25
+LABEBVQL	EBV DNA PCR QUAL	EBVQLD	EBV DNA	25
+LABEBVQL	EBV DNA PCR QUAL	EBVQLI	TEST DESCRIPTION	25
+LABEBVQL	EBV DNA PCR QUAL	EBVQLS	SPECIMEN	25
+LABHAB	HEPATITIS B SURFACE AB	HBSAB	HEPATITIS B SURFACE AB QUAL	25
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	25
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	25
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	25
+LABLPD	LIPID PANEL WITH DIRECT LDL	LDL	LDL (CALCULATED)	25
+ECH25	ECHO - LIMITED TRANSTHORACIC ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	25
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	PRO	PRODUCT ID  	25
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	PRO	PRODUCT ID  	25
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	25
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	25
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	SI	<<STATUS>>  	25
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	SI	<<STATUS>>  	25
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	UBT	UNIT BLOOD TYPE  	25
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	UBT	UNIT BLOOD TYPE  	25
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	UNT	UNIT NUMBER  	25
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	UNT	UNIT NUMBER  	25
+LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	VMQNT	VMQNT	25
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	25
+LABVWSCN	VON WILLEBRAND SCREEN	VWDIR	INTERPRETED BY	25
+LABVWSCN	VON WILLEBRAND SCREEN	VWINTP	INTERPRETATION	25
+LAB11000R	"CEA, PANCREATIC CYST"	11001R	"CEA, PANCREATIC CYST"	24
+LABHIBCV	DONOR HIV1 / HBV / HCV NAT	11894R	DISCRIMINATORY TESTING	24
+LABTHIOMT	THIOPURINE METABOLITES	6MMPN	6-MMPN	24
+LABTHIOMT	THIOPURINE METABOLITES	6MMPNASSESS	6-MMPN RESULT ASSESSMENT	24
+LABTHIOMT	THIOPURINE METABOLITES	6TGNASSESS	6-TGN RESULT ASSESSMENT	24
+10335R	HLA - SCREENING C1Q	9584R	INTERPRETATION/ COMMENTS CLASS I	24
+LABAAP	"AMINO ACIDS, PLASMA"	AAMINOA	A-AMINOADIPIC ACID	24
+LABAAP	"AMINO ACIDS, PLASMA"	AAMINOB	A-AMINOBUTYRIC ACID	24
+LABAAP	"AMINO ACIDS, PLASMA"	AAPINT	INTERPRETATION	24
+LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	ADGINT	INTERPRETATION	24
+LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	ADGPR	IGA ANTI DGP ANTIBODY	24
+LABAAP	"AMINO ACIDS, PLASMA"	ALANINE	ALANINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	ARGININE	ARGININE	24
+LABYAVH	ARGININE VASOPRESSIN (ADH)	ARGININEVP	ARGININE VASOPRESSIN	24
+LABAAP	"AMINO ACIDS, PLASMA"	ASPART	ASPARTIC ACID	24
+LABAAP	"AMINO ACIDS, PLASMA"	ASPGN	ASPARAGINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	BALN	B-ALANINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	BAMBA	B-AMINOISOBUTYRIC AC	24
+LABAAP	"AMINO ACIDS, PLASMA"	CITRUL	CITRULLINE	24
+LABCYSBLD	"CYSTICERCUS AB REFLEX PANEL, SERUM"	CYSAB2	"CYSTICERCUS AB (SERUM), ELISA"	24
+LABAAP	"AMINO ACIDS, PLASMA"	CYSTINE	CYSTINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	CYTHN	CYSTATHIONINE	24
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	DI	ISSUE DATE/TIME	24
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	DI	ISSUE DATE/TIME	24
+POC13	POC PPD SKIN TEST (AMB)	ERYTHREMA	ERYTHREMA	24
+LABAAP	"AMINO ACIDS, PLASMA"	ETHLN	ETHANOLAMINE	24
+LABG6PDQT	G6PD QUANTITATIVE	G6PDMD	REVIEWED BY	24
+LABAAP	"AMINO ACIDS, PLASMA"	GLUTAC	GLUTAMIC ACID	24
+LABAAP	"AMINO ACIDS, PLASMA"	GLUTAMINE	GLUTAMINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	GLYCINE	GLYCINE	24
+LABHAV	HEPATITIS A IGM AB	HEPAIGM	HEPATITIS A AB-IGM	24
+LABHEPAR	"HEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY"	HEPAR	HEPARIN ACTIVITY LEVEL	24
+LABAAP	"AMINO ACIDS, PLASMA"	HISTID	HISTIDINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	HMCY	HOMOCYSTEINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	HYPROL	HYDROXYPROLINE	24
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVSND	BRADY SENSED AV DELAY	24
+LABAAP	"AMINO ACIDS, PLASMA"	INTBY	INTERPRETED BY	24
+LABAAP	"AMINO ACIDS, PLASMA"	ISOLEU	ISOLEUCINE	24
+LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	LAB7	REFERENCE LAB	24
+LABAAP	"AMINO ACIDS, PLASMA"	LEUCINE	LEUCINE	24
+ECH30	ECHO - EXERCISE STRESS ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	24
+LABAAP	"AMINO ACIDS, PLASMA"	LYSINE	LYSINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	METHIO	METHIONINE	24
+LABYMEXIL	MEXILETINE	MEXIL	MEXILETINE	24
+POC13	POC PPD SKIN TEST (AMB)	MMIND	MM INDURATION	24
+LABAAP	"AMINO ACIDS, PLASMA"	ORNITHINE	ORNITHINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	PHALN	PHENYLALANINE	24
+POC13	POC PPD SKIN TEST (AMB)	PHLOC	LOCATION	24
+LABAAP	"AMINO ACIDS, PLASMA"	PROLINE	PROLINE	24
+LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	RESUL7	RESULT	24
+LABAAP	"AMINO ACIDS, PLASMA"	SARSN	SARCOSINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	SERINE	SERINE	24
+LABSLIDE	SLIDE SAVE FOR CLINICIAN	SLIDE	SLIDE SAVE FOR CLINICIAN	24
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	24
+LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	SPCT	SPECIMEN TYPE	24
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	SPEXP	SPECIMEN EXPIRATION DATE/TIME	24
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	SPEXP	SPECIMEN EXPIRATION DATE/TIME	24
+LABAAP	"AMINO ACIDS, PLASMA"	TAURINE	TAURINE	24
+LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	TEST7	TEST NAME	24
+LABTHAMP	THERMAL AMPLITUDE	THRAMP	THERMAL AMPLITUDE	24
+LABAAP	"AMINO ACIDS, PLASMA"	THREONINE	THREONINE	24
+LABAAP	"AMINO ACIDS, PLASMA"	TRYPTOPHAN	TRYPTOPHAN	24
+LABAAP	"AMINO ACIDS, PLASMA"	TYROSINE	TYROSINE	24
+LABFURICA	"URIC ACID, FLUID"	URICFLD	URIC ACID BODY FLUID	24
+LABAAP	"AMINO ACIDS, PLASMA"	VALINE	VALINE	24
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	VL	PRODUCT VOLUME	24
+LABECL	EUGLOBULIN CLOT LYSIS	WEACA	WITH EACA	24
+LABECL	EUGLOBULIN CLOT LYSIS	WOEACA	WITHOUT EACA	24
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	XMR	UNIT CROSSMATCH  	24
+LABYACIGA	"CARDIOLIPIN AB, IGA"	YACIG1	"CARDIOLIPIN AB,IGA"	24
+10287R	HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT	10049R	HLA LAB TESTING STATUS	23
+LAB11619R	"VZV PCR, AQUEOUS FLUID"	11778R	"VZV PCR, AQUEOUS FLUID"	23
+LABYFUN	"UREA NITROGEN, FLUID"	11847R	FLUID TYPE	23
+LABFQFVR	COXIELLA BURNETII AB	12014R	Q FEVER IGG PHASE LI TITER	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12419R	ACETOHEXIMIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12420R	CHLORPROPAMIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12421R	GLIMEPIRIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12422R	GLIPIZIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12423R	GLYBURIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12424R	NATEGLINIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12425R	REPAGLINIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12426R	TOLAZAMIDE	23
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	12427R	TOLBUTAMIDE	23
+10370R	HLA - DSA BY C1Q	9463R	DSA RESULTS FOR C1Q CLASS II	23
+LABYASMA	ANTI - SMOOTH MUSCLE AB	ASMA	ANTI-SMOOTH MUSCLE ANITBODY	23
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCRKCM	COMMENT	23
+LABYUHYME	"HEAVY METALS, URINE"	CADMIUNURN	"CADMIUM, URINE"	23
+LABYGLUC	GLUCAGON	GLUCAGON	GLUCAGON	23
+LABAHP2	ACUTE HEPATITIS PANEL	HBCABIGM	HEPAT.B CORE AB-IGM	23
+LABAHP2	ACUTE HEPATITIS PANEL	HBSAG	HEPATITIS B SURF AG	23
+LABAHP2	ACUTE HEPATITIS PANEL	HEPAIGM	HEPATITIS A AB-IGM	23
+LABAHP2	ACUTE HEPATITIS PANEL	HEPCAB	HEPATITIS C AB	23
+LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	HERG	HERPESVIRUS 6 AB IGG	23
+LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	HERM	HERPESVIRUS 6 AB IGM	23
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	23
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	23
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	23
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	23
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	23
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	LYMFLD	"LYMPHOCYTE %, FLD"	23
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	MONFLD	"MONO/MESO %, FLD"	23
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	NEUTFLD	"NEUTROPHIL %, FLD"	23
+VSC04	VSC GRAFT W ABI\'S ART DUP	PDF	PDF RESULT	23
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	PRO	PRODUCT ID  	23
+10287R	HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT	RINFO	TO FIND RESULT:	23
+LABRUB	RUBELLA AB IGG	RUB	RUBELLA IGG ANTIBODY	23
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	SI	<<STATUS>>  	23
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	23
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	SPEXP	SPECIMEN EXPIRATION DATE/TIME	23
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	SPEXP	SPECIMEN EXPIRATION DATE/TIME	23
+LABUA	URINALYSIS WITH MICROSCOPIC	WAXCST	WAXY CASTS	23
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	WBCAST	WBC CASTS	23
+LABYHIST	HISTOPLASMA AB SCREEN	YHIS1	HISTOPLASMA AB SCREEN	23
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	11230R	CREATININE CONC	22
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	11231R	METANEPHRINE/CREATININE	22
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	11232R	NORMETANEPHRINE/CREATININE	22
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	11233R	TOTAL METANEPHRINE/CREATININE	22
+LABYVD125	"1,25-DIHYDROXYVITAMIN D, SERUM"	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	22
+LAB12815R	LYME DISEASE AB IGG IGM W/RFLX	12830R	LYME AB SCREEN	22
+LABUCA	"CALCIUM WITH CREATININE, URINE (RANDOM)"	CACREAT	CALCIUM/CREAT RATIO	22
+LAB11127R	CASPOFUNGIN LEVEL	CASPO	CASPOFUNGIN (CANCIDAS)	22
+LABUCA	"CALCIUM WITH CREATININE, URINE (RANDOM)"	CAURN	"CALCIUM, URINE"	22
+LABCMVRC	CMV RAPID CULTURE	CMVDSV	TEST DESCRIPTION	22
+LABCMVRC	CMV RAPID CULTURE	CMVRC1	CMV DAY 1 RAPID CULTURE	22
+LABCMVRC	CMV RAPID CULTURE	CMVRC2	CMV DAY 2 RAPID CULTURE	22
+LABCMVRC	CMV RAPID CULTURE	CMVSPM	SPECIMEN	22
+POC27	POC HEMOCCULT FOR FECAL BLOOD	DEVEL	DEVELOPER LOT#	22
+POC27	POC HEMOCCULT FOR FECAL BLOOD	DEVEXP	DEVELOPER EXP DATE	22
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	DI	ISSUE DATE/TIME	22
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	DI	ISSUE DATE/TIME	22
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	DI	ISSUE DATE/TIME	22
+LABRP	RENAL FUNCTION PANEL	EGFR	EGFR	22
+LABFURICA	"URIC ACID, FLUID"	FSC2	FLUID TYPE	22
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GDNM3	DNMT3A	22
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GERHR	ERBB2/HER2	22
+POC27	POC HEMOCCULT FOR FECAL BLOOD	HEMED	HEMOCCULT EXP DATE	22
+POC27	POC HEMOCCULT FOR FECAL BLOOD	HEML	HEMOCCULT LOT#	22
+LABHEVM	HEPATITIS E AB IGM PANEL	HEVM1	HEPATITIS E AB IGM	22
+LABUVH	VMA AND HVA	HVA	HOMOVANILLIC ACID	22
+LABUVH	VMA AND HVA	HVAUR	"HVA, URINE"	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVPCD	BRADY PACED AV DELAY	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRTRKRT	BRADY TRACKING RATE	22
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	22
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	22
+LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	LTROIN	INTERPRETATION	22
+POC27	POC HEMOCCULT FOR FECAL BLOOD	OCCBLD	FECAL OCCULT BLOOD BLOOD	22
+POC27	POC HEMOCCULT FOR FECAL BLOOD	QC10	QC RESULT	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRPACA	RA BRADY PACING AMPLITUDE	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRPACW	RA BRADY PACING PULSE WIDTH	22
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	22
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SCMT	SYNOVIAL FLUID COMMENT	22
+LABYTAT	THROMBIN - ANTITHROMBIN COMPLEX	TAT	"THROMBIN, ANTITHROMBIN COMPLEX"	22
+LABUVH	VMA AND HVA	TIMEU	COLLECTION PERIOD	22
+LABUVH	VMA AND HVA	UCR	"CREATININE, URINE"	22
+LABUVH	VMA AND HVA	UCR	"CREATININE, URN"	22
+LABUCA	"CALCIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	22
+LABUVH	VMA AND HVA	UVOL	VOLUME	22
+LABUVH	VMA AND HVA	VHINT	VMA/HVA INTRP	22
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	VL	PRODUCT VOLUME	22
+LABUVH	VMA AND HVA	VMA	VMA	22
+LABUVH	VMA AND HVA	VMAUR	"VMA, URINE"	22
+LABYC1IN	C1 ESTERASE INHIBITOR AG	YC1INH	C1 ESTERASE INH. AG	22
+LABYVWM	VON WILLEBRAND MULTIMERS	YVWMR	VON WILLEBRAND	22
+LABYENTE	YERSINIA ENTEROCOLITICA AB	12064R	Y.ENTERCOCLITICA IGG	21
+LABYENTE	YERSINIA ENTEROCOLITICA AB	12065R	Y.ENTERCOCLITICA IGA	21
+LAB12721R	"ADENOSINE DEAMINASE, PLEURAL"	12720R	"ADENOSINE DEAMINASE, PLEURAL"	21
+10375R	HLA - STR/CHIMERISM TESTING	9471R	DONOR DOB	21
+LABADMARK	"ADMARK TAU/AB42, CSF"	ADMART	"ADMARK TAU/AB42, CSF"	21
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	BMPMLR	"PML, BM"	21
+LABCCT	"CELL COUNT, CSF"	BTURB	"TURBIDITY, CSF"	21
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	CF32R2	RESULT 2	21
+LABCCT	"CELL COUNT, CSF"	COLORCSF	"COLOR AFTER CENTRIFUGE, CSF"	21
+LABCCT	"CELL COUNT, CSF"	COLORCSF	"COLOR, CSF"	21
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	CONRES	CONTROL RESULTS	21
+LABCCT	"CELL COUNT, CSF"	CSOURC	SOURCE	21
+LABCCT	"CELL COUNT, CSF"	CTURB	"TURB. AFTER CENTRIF., CSF"	21
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	DI	ISSUE DATE/TIME	21
+LABCCT	"CELL COUNT, CSF"	DX	DIAGNOSIS	21
+LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBNDES	EBNDES	21
+LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBNQNT	EBNQNT	21
+LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBNQUL	EBNQUL	21
+LABYEBPCR	"EBV PCR, CSF"	EBV	EBV BY PCR	21
+LABFHAN	HANTAVIRUS AB ELISA	HANIGG	HANTAVIRUS IGG	21
+LABFHAN	HANTAVIRUS AB ELISA	HANIGM	HANTAVIRUS IGM	21
+LAB11042R	HISTAMINE LEVEL	HIST	"HISTAMINE, WB"	21
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRADYDP	BRADY DEPENDANCY	21
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	21
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	21
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	21
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	21
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	21
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	21
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRPACA	LV BRADY PACING AMPLITUDE	21
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRPACW	LV BRADY PACING PULSE WIDTH	21
+LABCCT	"CELL COUNT, CSF"	MALIG	SUSPECT MALIGNANCY?	21
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHLOC	LOCATION	21
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	PRO	PRODUCT ID  	21
+LABCCT	"CELL COUNT, CSF"	RBCCSF	"RBC COUNT, CSF"	21
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	SI	<<STATUS>>  	21
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	SPEXP	SPECIMEN EXPIRATION DATE/TIME	21
+LABCCT	"CELL COUNT, CSF"	TUBENO	CSF TUBE #	21
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	UBT	UNIT BLOOD TYPE  	21
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	UBT	UNIT BLOOD TYPE  	21
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	UNT	UNIT NUMBER  	21
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	UNT	UNIT NUMBER  	21
+LABCCT	"CELL COUNT, CSF"	WBCCSF	"WBC COUNT, CSF"	21
+LABYAMA	"MITOCHONDRIAL AB M2, SERUM"	YAMA	"MITOCHONDRIAL ANTIBODY, M2"	21
+LABYEBPCR	"EBV PCR, CSF"	YEBP1	SPECIMEN SOURCE	21
+LABYEBPCR	"EBV PCR, CSF"	YEBP3	SPECIAL INFORMATION	21
+LABYEBPCR	"EBV PCR, CSF"	YEPB4	REPORT STATUS	21
+LABYHISTC	"HISTOPLASMA AG, CSF"	YHISC5	"COMMENT,CSF"	21
+LAB11000R	"CEA, PANCREATIC CYST"	11002R	SITE	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12293R	12293R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12294R	12294R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12295R	12295R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12296R	12296R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12297R	12297R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12298R	12298R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12299R	12299R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12300R	12300R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12301R	12301R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12302R	12302R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12303R	12303R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12304R	12304R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12305R	12305R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12306R	12306R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12307R	12307R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12308R	12308R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12309R	12309R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12310R	12310R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12311R	12311R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12312R	12312R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12313R	12313R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12314R	12314R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12315R	12315R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12316R	12316R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12317R	12317R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12318R	12318R	20
+LAB12292R	"T CELL PHENOTYPING, ADVANCED"	12319R	12319R	20
+10300R	HLA - AUTO B FLOW CROSSMATCH	9120R	INTERPRETATION/ COMMENTS	20
+10365R	HLA - DSA BY IGG	9419R	DSA RESULTS FOR IGG CLASS I	20
+LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	AMINOR	AMITRIPT & NORTRIPT	20
+LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	AMITRP	"AMITRIPTYLINE, PLASMA"	20
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSDI	DISCLAIMER	20
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSDX	CLINICAL INDICATION	20
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSMD	INTERPRETED BY	20
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSMT	METHOD	20
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFDSRE	RESULT	20
+LABROSTRO	STRONGYLOIDES CULTURE	CULT	CULT/OTHER RSLT:	20
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	DI	ISSUE DATE/TIME	20
+LABDVEFLA	DIRECT VIRAL EXAM FLU A	DVE	RESULTS	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVBCP	EBV BAMHI-W RESULT	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVBDN	EBV BAMHI-W DNA	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVBIN	EBV BAMHI-W TEST DESCRIPTION	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVBLG	EBV BAMHI-W RESULT LOG 10	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVBSP	SPECIMEN	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVQCP	RESULT	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVQDN	EBV DNA	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVQIN	TEST DESCRIPTION	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVQLG	RESULT LOG 10	20
+LABEBVQPB	EBV NPC PCR PANEL	EBVQSP	SPECIMEN	20
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	20
+LABFT4ED	FREE T4 BY EQUILIBRIUM DIALYSIS	FT4	FREE T4 BY EQUIL DIALYSIS	20
+LAB11080R	GAD AB	GAD65AB	GAD65 ANTIBODY	20
+LABHBODY	HEINZ BODY PREP	HBODY	HEINZ BODY PREP	20
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	20
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	20
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADMFR	LEAD MANUFACTURER	20
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADMODEL	LEAD MODEL	20
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADTYPE	LEAD TYPE	20
+LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	NORTRP	NORTRIPTYLINE	20
+LABDVEFLA	DIRECT VIRAL EXAM FLU A	RPT	RPT	20
+LABROSTRO	STRONGYLOIDES CULTURE	RPT	RPT	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	20
+LABDVEFLA	DIRECT VIRAL EXAM FLU A	SDES	SPECIMEN	20
+LABROSTRO	STRONGYLOIDES CULTURE	SDES	SPECIMEN	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	20
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	20
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SOTHER	"OTHERS, SYN FLD"	20
+LABDVEFLA	DIRECT VIRAL EXAM FLU A	SREQ	COMMENTS	20
+LABROSTRO	STRONGYLOIDES CULTURE	SREQ	COMMENTS	20
+POC30	POC PYLORITECK	TESTL	TEST STRIP LOG	20
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	VL	PRODUCT VOLUME	20
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	VL	PRODUCT VOLUME	20
+LABVORI	VORICONAZOLE LEVEL	VORI	VORICONAZOLE LEVEL	20
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	XMR	UNIT CROSSMATCH  	20
+LAB11088R	PP	YPANPP	PANCREATIC POLYPEPTIDE	20
+LABYPLAB	PLATELET AB	YPLAB2	COMMENT	20
+LAB11312R	"SOLUBLE LIVER AG AB, IGG"	11313R	"SOLUBLE LIVER AG AB, IGG"	19
+LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	11672R	LCM IGG	19
+LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	11673R	LCM IGM	19
+LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	11674R	INTERPRETATION	19
+LABPLTAGG	PLATELET AGGREGATION	AGGINT	INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	ASMINT	ANTI-SM INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	ASMQUL	ANTI-SM RESULT	19
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ATACHTADTI	ATACH TACHY ZONE DETECTION INTERVAL	19
+LABADIFF	DIFFERENTIAL	BASO	"BASO, %"	19
+LABADIFF	DIFFERENTIAL	BASOAB	"BASOS, ABS"	19
+LABBM1418	"T(14	18), BONE MARROW"	BM14CT	COMMENT	19
+LABBM1418	"T(14	18), BONE MARROW"	BM14DR	INTERPRETED BY	19
+LABBM1418	"T(14	18), BONE MARROW"	BM14DS	DISCLAIMER	19
+LABBM1418	"T(14	18), BONE MARROW"	BM14MT	METHOD	19
+LABBM1418	"T(14	18), BONE MARROW"	BM14PM	PRIMERS	19
+LABBM1418	"T(14	18), BONE MARROW"	BM14RS	RESULTS	19
+LABC153	CANCER AG 15-3	C153	CANCER ANTIGEN 15-3	19
+LABSNA	SPECIFIC ANA PANEL	CENINT	ANTI-CENTROMERE INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	CENQUL	ANTI-CENTROMERE RESULT	19
+LABERUCRY	EMERGENCY RELEASE CRYO	CRSTA	CRYOPRECIPITATE UNITS	19
+LABSNA	SPECIFIC ANA PANEL	DNAINT	INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	DNAQUL	ANTI-DSDNA RESULT	19
+LABEAIGG	EBV EARLY ANTIGEN IGG	EAGDES	EAGDES	19
+LABEAIGG	EBV EARLY ANTIGEN IGG	EAGQUL	EAGQUL	19
+LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	EBV	EBV BY PCR	19
+LABFEHRC	EHRLICHIA CHAFFEENSIS AB	ECIGG	E.CHAFFEENSIS IGG TITER	19
+LABFEHRC	EHRLICHIA CHAFFEENSIS AB	ECIGM	E.CHAFFEENSIS IGM TITER	19
+LABADIFF	DIFFERENTIAL	EOS	"EOS, %"	19
+LABADIFF	DIFFERENTIAL	EOSAB	"EOS, ABS"	19
+LABCBCD	CBC WITH DIFF	HAIRY	HAIRY CELLS	19
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAMSRT	BRADY AMS RATE	19
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	19
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	19
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	19
+LABSNA	SPECIFIC ANA PANEL	JO1INT	ANTI-JO1 INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	JO1QUL	ANTI-JO1 RESULT	19
+LABADIFF	DIFFERENTIAL	LYM	"LYM, %"	19
+LABADIFF	DIFFERENTIAL	LYMAB	"LYM, ABS"	19
+LABCDIFF	"DIFFERENTIAL, CSF"	LYMCSF	"LYMPHOCYTES, CSF"	19
+LABADIFF	DIFFERENTIAL	MONO	"MONO, %"	19
+LABADIFF	DIFFERENTIAL	MONOAB	"MONO, ABS"	19
+LABCDIFF	"DIFFERENTIAL, CSF"	MONOCSF	"MONOCYTOID CELLS, CSF"	19
+LABMTHFR	METHYLENE- THF REDUCTASE	MTHCMT	COMMENT	19
+LABMTHFR	METHYLENE- THF REDUCTASE	MTHDIS	DISCLAIMER	19
+LABMTHFR	METHYLENE- THF REDUCTASE	MTHMET	METHOD	19
+LABMTHFR	METHYLENE- THF REDUCTASE	MTHPRM	PRIMERS	19
+LABMTHFR	METHYLENE- THF REDUCTASE	MTHRES	RESULTS	19
+LABADIFF	DIFFERENTIAL	NEUT	"NEUT, %"	19
+LABADIFF	DIFFERENTIAL	NEUTAB	"NEUT, ABS"	19
+LABCDIFF	"DIFFERENTIAL, CSF"	NEUTCSF	"NEUTROPHILS, CSF"	19
+LABCGD	NEUTROPHIL FUNC. OXI. BURST	OXIDNEUTPC	% OXIDATION POSITIVE NEUTROPHILS	19
+LABSNA	SPECIFIC ANA PANEL	RNPINT	ANTI-RNP INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	RNPQUL	ANTI-RNP RESULT	19
+LABSNA	SPECIFIC ANA PANEL	SCLINT	ANTI-SCL70 INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	SCLQUL	ANTI-SCL70 RESULT	19
+LABSNA	SPECIFIC ANA PANEL	SNAINT	SPECIFIC ANA INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	SSAIN	ANTI-RO INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	SSAQUL	ANTI-RO RESULT	19
+LABSNA	SPECIFIC ANA PANEL	SSBINT	ANTI-LA INTERPRETATION	19
+LABSNA	SPECIFIC ANA PANEL	SSBQUL	ANTI-LA RESULT	19
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	UBT	UNIT BLOOD TYPE  	19
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	UNT	UNIT NUMBER  	19
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	VL	PRODUCT VOLUME	19
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	19
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	WAXCST	WAXY CASTS	19
+LABYAMEB	E. HISTOLYTICA AB	YAMEB1	E. HISTOLYTICA AB	19
+LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	YEBP1	SPECIMEN SOURCE	19
+LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	YEBP3	SPECIAL INFORMATION	19
+LABYEBPCR	"EPSTEIN BARR VIRUS PCR, FLUIDS"	YEPB4	REPORT STATUS	19
+LAB11617R	"CMV PCR, AQUEOUS FLUID"	11776R	"CMV PCR, AQUEOUS FLUID"	18
+10370R	HLA - DSA BY C1Q	9634R	REFERENCE SERUM DESCRIPTION	18
+LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	ADGPT	IGA ANTI DGP ANTIBODY LEVEL	18
+LABYAFCSF	"ALPHA FETOPROTEIN, CSF"	AFPCSF	"ALPHA FETOPROTEIN, CSF"	18
+LABASPPCP	ASPERGILLUS AB PRECIPITINS	ASPIGG	ASPERGILLUS IGG ABY	18
+LABASPPCP	ASPERGILLUS AB PRECIPITINS	ASPINT	INTERPRETATION	18
+LABSWTCL	"CHLORIDE, SWEAT"	CLSWT	SWEAT CHLORIDE	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMGDES	CMV IGG TEST DESCRIPTION	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMGQUL	CMV IGG RESULT	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMMDES	CMV IGM TEST DESCRIPTION	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMMQUL	CMV IGM RESULT	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMV	CMV ANTIBODY	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMVIGM	CMV IGM AB	18
+LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	CULT	CULT/OTHER RSLT:	18
+LABHGBS	"HEMOGLOBIN, SERUM"	HGB	"HEMOGLOBIN, SERUM"	18
+LABHPVHR2	HPV DNA PROBE HIGH RISK	HPTI	TEST INFORMATION	18
+LABHPVHR2	HPV DNA PROBE HIGH RISK	HPV	HPV DNA PROBE RESULT:	18
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	18
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	18
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADIMPDTE	LEAD IMPLANT DATE	18
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LEADSERNUM	LEAD SERIAL NUMBER	18
+LABMTHFR	METHYLENE- THF REDUCTASE	MTHDIR	INTERPRETED BY	18
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHLOC	LOCATION	18
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMTLBATIMP	PM TELEMETRY BATTERY IMPEDANCE	18
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	PRO	PRODUCT ID  	18
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	PRO	PRODUCT ID  	18
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	PRO	PRODUCT ID  	18
+LABQFTBG	QUANTIFERON FOR LATENT TB INFECTION	QTFNIN	QUANTIFERON INTERPRETATION	18
+LABQFTBG	QUANTIFERON FOR LATENT TB INFECTION	QTFNRS	QUANTIFERON-TB	18
+LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	RPT	RPT	18
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	18
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	18
+LABSWTCL	"CHLORIDE, SWEAT"	SCLW	SWEAT COLLECTED	18
+LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	SDES	SPECIMEN	18
+LABHPVHR2	HPV DNA PROBE HIGH RISK	SDES6	SPECIMEN	18
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	SI	<<STATUS>>  	18
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	SI	<<STATUS>>  	18
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	SI	<<STATUS>>  	18
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	SPEXP	SPECIMEN EXPIRATION DATE/TIME	18
+LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	SREQ	COMMENTS	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	VGGDES	VZV IGG TEST DESCRIPTION	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	VGGQNT	VZV IGG LEVEL	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	VGGQUL	VZV IGG RESULT	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	VZAB	VARICELLA ZOSTER AB IGG	18
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	XMR	UNIT CROSSMATCH  	18
+LABYIGF2	INSULIN LIKE GROWTH FACTOR II	YIGF2A	IFG-II	18
+LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	YMYCR1	STATUS	18
+LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	YMYCR2	MYCOPLASMA PNEUMONIAE CULTURE	18
+LABFQFVR	COXIELLA BURNETII AB	12012R	Q FEVER IGG PHASE L TITER	17
+LAB12586R	THIAMIN (VITAMIN B1)	12588R	THIAMIN (VITAMIN B1)	17
+10375R	HLA - STR/CHIMERISM TESTING	9480R	LOCUS 6	17
+10375R	HLA - STR/CHIMERISM TESTING	9481R	LOCUS 7	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	AMINOR	AMITRIPTYLINE & NORTRIPTYLINE	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	AMITRP	AMITRIPTYLINE	17
+LABCGD	NEUTROPHIL FUNC. OXI. BURST	CGDA	SPECIMEN AGE	17
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	CONRES	CONTROL RESULTS	17
+LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	CULT	CULT/OTHER RSLT:	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	DESIP	DESIPRAMINE	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	DOXEPIN	DOXEPIN	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	DOXEPINNOR	DOXEPIN & NORDOXEPIN	17
+LAB11198R	VIT A & VIT E	FREEVITA	FREE RETINOL(VIT A)	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	IMI	IMIPRAMINE	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	IMIDESI	IMIPRAMINE/DESPRAMINE	17
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENA	LV BRADY SENSING AMPLITUDE	17
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	MEGA	MEGAKARY/100WBC	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	NORDOX	NORDOXEPIN	17
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	NORTRP	NORTRIPTYLINE	17
+IMG11AR1	CAR CATH HEART RT 1	PDF	PDF RESULT	17
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	PRO	PRODUCT ID  	17
+LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	RPT	RPT	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRAMP	RV THRESHOLD AMPLITUDE	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVTHRDUR	RV THRESHOLD DURATION	17
+LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	SDES	SPECIMEN	17
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	SI	<<STATUS>>  	17
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	17
+LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	SREQ	COMMENTS	17
+LABUA	URINALYSIS WITH MICROSCOPIC	TRICHOMONAS	TRICHOMONAS	17
+LAB11198R	VIT A & VIT E	VITE	"A-TOCOPHEROL, VITAMIN E"	17
+LABYTESFB	"TESTOSTERONE, TOTAL, BIO, FREE"	YTESHD	FREE TESTOSTERONE	17
+LAB11179R	"NICOTINE & METABOLITES, U"	11180R	"NICOTINE, U"	16
+LAB11179R	"NICOTINE & METABOLITES, U"	11181R	"COTININE, U"	16
+LAB11179R	"NICOTINE & METABOLITES, U"	11197R	"ANABASINE, U"	16
+LAB11340R	ADAMTS 13 PROFILE	11341R	ADAMTS 13 ACTIVITY	16
+LAB11340R	ADAMTS 13 PROFILE	11342R	ADAMTS 13 INHIBITOR	16
+LAB11179R	"NICOTINE & METABOLITES, U"	11511R	"NORNICOTINE, U"	16
+LAB11576R	COBALAMIN C DEFICIENCY(MMACHC)	11691R	COBALAMIN C DEFICIENCY(MMACHC)	16
+LAB12811R	"MYOGLOBIN, URINE"	12812R	"MYOGLOBIN, URINE"	16
+LAB13002R	COCCIDIOIDES AB IMMUNODIFF CSF	13001R	COCCIDIOIDES AB IMMUNODIFF CSF	16
+10305R	HLA - ALLO T FLOW CROSSMATCH	9140R	SERUM DATE 3	16
+10305R	HLA - ALLO T FLOW CROSSMATCH	9142R	RESULT  MCS 3	16
+10305R	HLA - ALLO T FLOW CROSSMATCH	9144R	RESULT 3	16
+10310R	HLA - ALLO B FLOW CROSSMATCH	9170R	SERUM DATE 3	16
+10310R	HLA - ALLO B FLOW CROSSMATCH	9172R	RESULT  MCS 3	16
+10310R	HLA - ALLO B FLOW CROSSMATCH	9174R	RESULT 3	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHCT	COMMENT	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHDI	DISCLAIMER	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHDX	CLINICAL INDICATION	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHET	ETHNICITY (PROVIDED BY MD)	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHIN	INTERPRETATION	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHMD	INTERPRETED BY	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHME	METHOD	16
+LABATHAL	ALPHA THALASSEMIA PCR	ATHRE	RESULT	16
+LAB11026R	"C1 INHIBITOR, FUNCT"	C1INHF	C1 ESTERASE INHIB FN	16
+LABUCORT	"FREE CORTISOL, URINE (TIMED)"	CORTU	"CORTISOL, URINE"	16
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	DI	ISSUE DATE/TIME	16
+EKG6	ECG SIGNAL AVERAGED	ECGIMPRESS	ECG IMPRESSION	16
+LABFEHRC	EHRLICHIA CHAFFEENSIS AB	ECINT	INTERPRETATION	16
+LAB11037R	FLECAINIDE LEVEL	FLEC	FLECAINIDE	16
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HATINT	INTERPRETATION	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBSINT	INTERPRETATION	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBTINT	INTERPRETATION	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HCVI	INTERPRETATION	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HCVR	ANTI-HCV	16
+EKG6	ECG SIGNAL AVERAGED	HEARTRATE	HEART RATE	16
+LABHEVG	HEPATITIS E AB IGG PANEL	HEVG1	HEPATITIS E AB IGG	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HQLINT	INTERPRETATION	16
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	16
+LABHTLVB	 HTLV-1/2 AB WESTERN BLOT	HTWB	"HTLV I/II AB, WESTERN BLOT"	16
+LABIFEDE	"IMMUNOFIXATION IGD AND IGE, SERUM"	IFED	IMMUNOFIXATION IGD	16
+LABIFEDE	"IMMUNOFIXATION IGD AND IGE, SERUM"	IFEE	IMMUNOFIXATION IGE	16
+LABKRAS	KRAS MUTATION DETECTION	KRASTI	SOURCE	16
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	16
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	16
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADMODEL	LEAD MODEL	16
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADTYPE	LEAD TYPE	16
+EKG6	ECG SIGNAL AVERAGED	PRINTERVAL	P-R INTERVAL	16
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	PRO	PRODUCT ID  	16
+EKG6	ECG SIGNAL AVERAGED	QRSAXIS	QRS AXIS	16
+EKG6	ECG SIGNAL AVERAGED	QRSDINTERVAL	QRSD INTERVAL	16
+EKG6	ECG SIGNAL AVERAGED	QTCINTERVAL	QTC INTERVAL	16
+EKG6	ECG SIGNAL AVERAGED	QTINTERVAL	QT INTERVAL	16
+LABRDATBC	REVERSE TO COOMBS B CELLS	RDATB	REVERSE TO COOMBS B CELLS	16
+EKG6	ECG SIGNAL AVERAGED	RR	RR	16
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	16
+LABYSCL70	AUTOANTIBODIES TO SCL 70	SCL70	"SCL 70 AB, IGG, SERUM"	16
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	SI	<<STATUS>>  	16
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	16
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	SPEXP	SPECIMEN EXPIRATION DATE/TIME	16
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	SPEXP	SPECIMEN EXPIRATION DATE/TIME	16
+LABUCORT	"FREE CORTISOL, URINE (TIMED)"	TCORTU	CORTISOL EXCRETION	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	TIMEU	COLLECTION PERIOD	16
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	UBT	UNIT BLOOD TYPE  	16
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	UBT	UNIT BLOOD TYPE  	16
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	UBT	UNIT BLOOD TYPE  	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URINE"	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URN"	16
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	UNT	UNIT NUMBER  	16
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	UNT	UNIT NUMBER  	16
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	UNT	UNIT NUMBER  	16
+LABUCORT	"FREE CORTISOL, URINE (TIMED)"	UT52	COLLECTION PERIOD	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	UVOL	VOLUME	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	VMA	VMA	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	VMAINT	VMA INTRP	16
+LABYUVMA	"VANILLYMANDELIC ACID, URINE"	VMAUR	"VMA, URINE"	16
+LABUCORT	"FREE CORTISOL, URINE (TIMED)"	VOL52	VOLUME	16
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	16
+LABXIGF1	PEDIATRIC IGF-1	XIG1	IGF-1	16
+LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN3	Y ENTERCOCLITICA 03	16
+LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN5	Y ENTERCOCLITICA 05	16
+LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN8	Y ENTERCOCLITICA 08	16
+LABYENTE	YERSINIA ENTEROCOLITICA AB	YEN9	Y ENTERCOCLITICA 09	16
+LABYINH	INHIBIN B	YINH1	INHIBIN B	16
+LAB11090R	PARIETAL CELL AB	YPCA	PARIETAL CELL AB	16
+LABYENTE	YERSINIA ENTEROCOLITICA AB	YPSEU	Y.PSEUDOTUBERCULOSIS	16
+LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12051R	RMSF IGG	15
+LAB12212R	WA1 IGG AB	12213R	12213R	15
+LAB12236R	IBUPROFEN	12237R	12237R	15
+LAB12607R	"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF"	12606R	"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF"	15
+LABFAMYA	"AMYLASE, ASCITES"	AMYLASEFLD	"AMYLASE, ASCITES FLUID"	15
+LABBARSAB	BARTONELLA SPECIES AB	BHIGGT	B.HENSELAE IGG TITER	15
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11CT	COMMENT	15
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11DS	DISCLAIMER	15
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11DX	CLINICAL INDICATION	15
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11MD	INTERPRETED BY	15
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11MT	METHOD	15
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	BM11RS	RESULT	15
+LABYUCC	COCAINE CONFIRMATION	COCAINE	COCAINE	15
+LABDCMVL	DONOR CMV- TOTAL AB	DCMVL1	DONOR CMV- TOTAL AB	15
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	EOSFLD	"EOSINOPHILS, SYN FLD"	15
+LABYESTRO	ESTROGENS E1 AND E2 FRACTIONATED	ESTRADIOL	ESTRADIOL	15
+LABYESTRO	ESTROGENS E1 AND E2 FRACTIONATED	ESTRONE	ESTRONE	15
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	15
+LABDVEIAR	FLU A DFA-REFLEX PCR	IAR	INFLUENZA A	15
+LABDVEIAR	FLU A DFA-REFLEX PCR	IARIP	TEST DESCRIPTION	15
+LABDVEIAR	FLU A DFA-REFLEX PCR	IARSP	SPECIMEN	15
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	15
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	15
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	15
+LAB11044R	IL6	IL6	INTERLEUKIN 6	15
+LABINHA	INHIBIN A DIMER	INHAR	INHIBIN-A (DIMER)	15
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	15
+LABUORG	"ORGANIC ACIDS, URINE"	ORGU	"ORGANIC ACIDS, URINE"	15
+LABUORG	"ORGANIC ACIDS, URINE"	ORGUIN	INTERPRETATION	15
+LAB11054R	PM1 AB	PM1AB	PM-1 ANTIBODY	15
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	15
+LABSSBABY	LA AB	SSB	"SS-B/LA AB, IGG, SERUM"	15
+EKG6	ECG SIGNAL AVERAGED	TWAVEAXIS	T WAVE AXIS	15
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	UBT	UNIT BLOOD TYPE  	15
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	UBT	UNIT BLOOD TYPE  	15
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	UBT	UNIT BLOOD TYPE  	15
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	UBT	UNIT BLOOD TYPE  	15
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	UNT	UNIT NUMBER  	15
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	UNT	UNIT NUMBER  	15
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	UNT	UNIT NUMBER  	15
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	UNT	UNIT NUMBER  	15
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	VL	PRODUCT VOLUME	15
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	VL	PRODUCT VOLUME	15
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	VL	PRODUCT VOLUME	15
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	YCLAM1	"CHLAMYDIA SEROLOGY, S"	15
+LABYEB	"EBV AB PROFILE, SERUM"	YEB1	EBV VCA IGG AB	15
+LABYEB	"EBV AB PROFILE, SERUM"	YEB2	EBV VCA IGM AB	15
+LABYEB	"EBV AB PROFILE, SERUM"	YEB3	EBNA AB	15
+LABYEB	"EBV AB PROFILE, SERUM"	YEB4	INTERPRETATION	15
+LABYSTCL	"CHLORIDE, STOOL"	YSTCL2	TOTAL WEIGHT	15
+LABYTETT	TETANUS TOXOID IGG AB	YTETT1	TETANUS TOXOID IGG AB	15
+LABYTRAB	TSH RECEPTOR AB	YTRAB1	THYROTROPIN RECEPTOR AB	15
+LABYUCC	COCAINE CONFIRMATION	YUBEGC	BENZOYLECGONINE	15
+LABYUCC	COCAINE CONFIRMATION	YUCOIM	IMMUNOASSAY SCREEN	15
+LABYUCC	COCAINE CONFIRMATION	YUCOIN	INTERPRETATION	15
+10277R	HLA ORDER - PROSPECTIVE CROSSMATCH TESTING	10049R	HLA LAB TESTING STATUS	14
+LAB11240R	MEASLES IGG	11241R	MEASLES IGG	14
+LAB11618R	"HSV 1/2 PCR, AQUEOUS FLUID"	11777R	"HSV 1/2 PCR,AQUOUS FLUID"	14
+LAB11858R	HEPTIMAX(R) HCV RNA	11856R	HEPTIMAX ? HCR  RNA IU/ML	14
+LABHIBCV	"DONOR, HIV-1/HCV/HBV NAT TESTING"	11894R	DISCRIMINATORY TESTING	14
+LAB12853R	"EVEROLIMUS, BLOOD"	12339R	EVEROLIMUS	14
+LAB12796R	FLUCONAZOLE	12795R	FLUCONAZOLE	14
+LABABIDI	AB ID	ABID2E	ANTIBODY ID ELUATE COMMENT	14
+LABYACE	"ANGIOTENSIN CONVERTING ENZYME, SERUM"	ACE	ANGIOTESIN CONVERTING ENZYME	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	AMPHET	AMPHETAMINE	14
+LABSMABY	ANTI-SM ANTIBODY	ASMQNT	ANTI-SM LEVEL	14
+LABCENABY	ANTI CENTROMERE ANTIBODY	CENQNT	ANTI-CENTROMERE LEVEL	14
+LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	CHLAM	C.TRACHOMATIS RESULT:	14
+LABCMVQL	"CMV DNA PCR QUAL, NON-BLOOD"	CMQDNA	CMV DNA	14
+LABCMVQL	"CMV DNA PCR QUAL, NON-BLOOD"	CMVLIP	TEST DESCRIPTION	14
+LABCMVQL	"CMV DNA PCR QUAL, NON-BLOOD"	CMVQSP	SPECIMEN	14
+LABCOCC	"COCCIDIOIDES COMPLEMENT FIXATION, CSF"	COCC	COCCI CF AB (CSF)	14
+LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	CULT	CULT/OTHER RSLT:	14
+LABCYAB	CYSTICERCUS AB	CYABS	CYSTICERCUS AB	14
+LABCYSI	"CYSTICERCOSIS, CSF AND SERUM"	CYSC	CYSTICERCOSIS-CSF	14
+LABFCYST	CYSTICERCUS IGG AB	CYSIGG	CYSTICERCUS LGG AB	14
+LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	DCL4	TEST INFORMATION	14
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	FCMT	FLUID COMMENT	14
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	14
+LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1DI	DISCLAIMER	14
+LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1IN	INTERPRETATION	14
+LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1M	NEURAMINIDASE  H275Y MUTATION	14
+LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1MT	METHOD	14
+LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1R	2009 INFLUENZA A(H1N1) RNA	14
+LABH1N1PC	"2009 H1N1 PCR, H274Y MUTATION"	H1N1SP	SPECIMEN	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHCO	COMMENT	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHDI	DISCLAIMER	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHDX	CLINICAL INDICATION	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHIN	INTERPRETATION	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHMD	INTERPRETED BY	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHME	METHOD	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHPR	PRIMERS	14
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	HCHRE	RESULT	14
+LABHIBCV	"DONOR, HIV-1/HCV/HBV NAT TESTING"	HIBCV1	DONOR HIV/HBV/HCV RESULT	14
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	14
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDEVLDATE	ICD DATE OF EVENT	14
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPRV	ICD TEL LEAD IMPEDANCE RIGHT VENT	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LBRAG	"L BRAZILIENSIS,IGG"	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LBRAM	"L BRAZILIENSIS,IGM"	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LDONG	"L DONOVANI,IGG"	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LDONM	"L DONOVANI,IGM"	14
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	14
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	14
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	14
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	14
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LMEXG	"L MEXICANA,IGG"	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LMEXM	"L MEXICANA,IGM"	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LTROG	"L TROPICA,IGG"	14
+LABLEISAB	"LEISHMANIA AB, IFA"	LTROM	"L TROPICA,IGM"	14
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	LYMPHM	LYMPHOMA CELLS	14
+LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	MORPH	MORPHOLOGY	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	MTAM	METHAMPHETAMINE	14
+LABOF	OSMOTIC FRAGILITY	OFINT	INTERPRETATION	14
+EKG6	ECG SIGNAL AVERAGED	PAXIS	P AXIS	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE01	S.PNEUMO SEROTYPE 1 (1)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE12	S.PNEUMO SEROTYPE 12 (12F)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE14	S.PNEUMO SEROTYPE 14 (14)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE19	S.PNEUMO SEROTYPE 19 (19F)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE23	S.PNEUMO SEROTYPE 23 (23F)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE26	S.PNEUMO SEROTYPE 26 (6B)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE3	S.PNEUMO SEROTYPE 3 (3)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE4	S.PNEUMO SEROTYPE 4 (4)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE51	S.PNEUMO SEROTYPE 51 (7F)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE56	S.PNEUMO SEROTYPE 56 (18C)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE8	S.PNEUMO SEROTYPE 8 (8)	14
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNE9	S.PNEUMO SEROTYPE 9 (9N)	14
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	PRO	PRODUCT ID  	14
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	PRO	PRODUCT ID  	14
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	PRO	PRODUCT ID  	14
+LABPRO6MP	PRO-PREDICT ENZACT	PRO6MR	PRO-PREDICT ENZACT	14
+10277R	HLA ORDER - PROSPECTIVE CROSSMATCH TESTING	RINFO	TO FIND RESULT:	14
+LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	RPT	RPT	14
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	14
+LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	SDES	SPECIMEN	14
+LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	SDES4	SPECIMEN	14
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	SI	<<STATUS>>  	14
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	SI	<<STATUS>>  	14
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	SI	<<STATUS>>  	14
+LABFCULS	"FUNGAL CULTURE, SKIN/NAIL"	SREQ	COMMENTS	14
+LABSSAABY	RO AB	SSA	"SS-A/RO AB, IGG, SERUM"	14
+LABTXCAB	TOXOCARA AB ELISA	TOXOC1	TOXOCARA ANTIBODY	14
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	UBT	UNIT BLOOD TYPE  	14
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	UNT	UNIT NUMBER  	14
+LABEBVC	EBV CAPSID AG	VCAA	VCA IGA	14
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	VL	PRODUCT VOLUME	14
+LABXHIA	HUMAN INSULIN ANTIBODY	XHIAB	HUMAN INSULIN ANTIBODY	14
+LABYITRA	ITRACONAZOLE LEVEL	YITR3	ITRACONAZOLE + HYDROZYITRACONAZOLE	14
+LABYLYSO	"LYSOZYME, PLASMA"	YLYSOZ	LYSOZYME	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUAMIM	IMMUNOASSAY SCREEN	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUAMIN	INTERPRETATION	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUMDA	MDA BY GC/MS	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUMDMA	MDMA (ECSTASY) BY GC/MS	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUPSGC	PSEUDOEPHEDRINE/EPHEDRINE	14
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUPTGC	PHENTERMINE BY GC/MS	14
+LAB11858R	HEPTIMAX(R) HCV RNA	11857R	HEPTIMAX ? HCR  LOGIU IU/ML	13
+LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12052R	RMSF IGM	13
+LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12053R	R.TYPHI IGG	13
+LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	12054R	R.TYPHI IGM	13
+LAB12090R	"HE4, OVARIAN CANCER MONITORING"	12105R	"HE4, OVARIAN CANCER MONITORING"	13
+LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	12194R	DRUGS DETECTED:	13
+LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	12195R	SUSPECT DRUG	13
+LAB12234R	VOLATILES	12235R	12235R	13
+LAB11067R	17OH PROG	17OHPROG	17-OH PROGESTERONE	13
+10375R	HLA - STR/CHIMERISM TESTING	9482R	LOCUS 8	13
+LABPLTAGG	PLATELET AGGREGATION	ADP5	ADP 5 UM	13
+LAB11021R	ADRENAL AB	ADRAB	ADRENAL ANTIBODY	13
+LABPLTAGG	PLATELET AGGREGATION	AGGDIR	INTERPRETED BY	13
+LABYALDO	"ALDOLASE, SERUM"	ALDOLASE	"ALDOLASE, SERUM"	13
+LABYALDOS	"ALDOSTERONE, SERUM"	ALDOSTR	"ALDOSTERONE, SERUM"	13
+LABCANNAB	"NEURONAL NUCLEAR AB, CSF"	ANTIHU	ANTI-HU	13
+LABPLTAGG	PLATELET AGGREGATION	ARA500	ARACHIDONIC ACID 500 UG/ML	13
+LABCANNAB	"NEURONAL NUCLEAR AB, CSF"	ARI	ANTI-RI	13
+LABCANNAB	"NEURONAL NUCLEAR AB, CSF"	CANNA3	"NEURONAL NUCLEAR AB, CSF"	13
+LABBMCD34	"CD34 PANEL, BONE MARROW"	CD34BMPC	"%CD34, BONE MARROW"	13
+LABBMCD34	"CD34 PANEL, BONE MARROW"	CD34DS	DISCLAIMER	13
+LABFLCL	"CHLORIDE, FLUID"	CLFLD	"CHLORIDE, FLUID"	13
+LABFLCL	"CHLORIDE, FLUID"	CLFLS	SOURCE	13
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMGQNT	CMV IGG LEVEL	13
+LABPLTAGG	PLATELET AGGREGATION	COLL25	COLLAGEN 2.5 UG/ML	13
+LABCYSCSF	"CYSTICERCUS AB REFLEX PANEL, CSF"	CYSTAB	"CYSTICERCUS AB(CSF),ELISA"	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	DEVTYPE	DEVTYPE	13
+LABYDHEAS	"DEHYDROEPIANDROSTERONE, SULFATE"	DHS	DEHYDROEPIANDROSTERONE SULFATE	13
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	DI	ISSUE DATE/TIME	13
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	DI	ISSUE DATE/TIME	13
+LABPLTAGG	PLATELET AGGREGATION	EPI5	EPINEPHRINE 5 UM	13
+LABPLTAGG	PLATELET AGGREGATION	EPIN10	EPINEPHRINE 10 UM	13
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	13
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	13
+LABHSVPCR	"HERPES SIMPLEX PCR, CSF"	HSVRM	CLINICAL REMARKS	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	13
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTPACVT	ICD DETECTIONS AND TREATMENTS PACING VENTRICULAR	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDRHYTHM	ICD RHYTHM	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAVFDTI	ICD TACHY VF DETECTION INTERVAL	13
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPHV	ICD TEL LEAD IMPEDANCE HIGH VOLT	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	IMPDATE	IMPLANT DATE	13
+LABREF4	LAB UNLISTED 4	LAB4	REFERENCE LAB	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	MFR	MANUFACTURER	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	MOD	MODEL	13
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKDI	DISCLAIMER	13
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKDX	CLINICAL INDICATION	13
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKMD	INTERPRETED BY	13
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKME	METHOD	13
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKRE	RESULT	13
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKSP	SPECIMEN SOURCE	13
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	PRO	PRODUCT ID  	13
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	PRO	PRODUCT ID  	13
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	13
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	13
+LABREF4	LAB UNLISTED 4	RESUL4	RESULT	13
+LABRF	RHEUMATOID FACTOR	RHFT	RHEUMATOID FACTOR LEVEL	13
+LABPLTAGG	PLATELET AGGREGATION	RIS12	RISTOCETIN 1.2 MG/ML	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	13
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SERNUM	SERIAL NUMBER	13
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	SI	<<STATUS>>  	13
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	SI	<<STATUS>>  	13
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	13
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	SPEXP	SPECIMEN EXPIRATION DATE/TIME	13
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	SPEXP	SPECIMEN EXPIRATION DATE/TIME	13
+LABREF4	LAB UNLISTED 4	SPTY4	SPECIMEN TYPE	13
+LABREF4	LAB UNLISTED 4	TEST4	TEST NAME	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	TIMEU	COLLECTION PERIOD	13
+LABYSTRYP	"TRYPSIN, FECAL"	TRYPSINSTL	"TRYPSIN, FECAL"	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URINE"	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	UCR	"CREATININE, URN"	13
+LABURMAG	"MAGNESIUM, URINE (RANDOM)"	UMG	"MAGNESIUM, URINE"	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	UVOL	VOLUME	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	VMA	VMA	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	VMAINT	VMA INTRP	13
+LABUVMA	"VANILLYMANDELIC ACID, URINE"	VMAUR	"VMA, URINE"	13
+LABYAMIO	AMIODARONE	YAMIOD	AMIODARONE	13
+LABYBLAS	BLASTOMYCES AB	YBLAS2	BLASTOMYCES COMPLEMENT FIXATION	13
+LABYC4F	C4 COMPLEMENT FUNCTIONAL	YC4FUN	"C4 COMPLEMENT, FUNCT"	13
+LABYAMIO	AMIODARONE	YDESET	DESETHYLAMIODARONE	13
+LABYHMPVP	HUMAN METAPNEUMOVIRUS PCR	YHMP1	HUMAN METAPNEUMOVIRUS PCR	13
+LABYHMPVP	HUMAN METAPNEUMOVIRUS PCR	YHMP2	SOURCE	13
+LABYUALA	"AMINOLEVULINIC ACID, URINE"	YUALA1	"AMINOLEVULINIC ACID, URINE"	13
+LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11760R	COQ10 REDUCED	12
+LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11761R	COQ10 TOTAL	12
+LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11762R	COQ10 % REDUCED	12
+LAB11607R	"COENZYME Q10, REDUCED & TOTAL"	11763R	INTERPRETATION	12
+LAB11860R	VASCULAR ENDOTHELIAL GROW.FACT	11859R	VEGF	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12363R	"ANNA-1, CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12364R	"ANNA-2, CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12365R	"ANNA-3, CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12366R	"AGNA-1,CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12367R	"PCA-1, CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12368R	"PCA-2, CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12369R	"AMPHIPHYSIN AB, CSF"	12
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	12370R	"CRMP-5-IGG, CSF"	12
+LABYSTCL	"CHLORIDE, STOOL"	12577R	"CHLORIDE, STOOL, RANDOM"	12
+10375R	HLA - STR/CHIMERISM TESTING	9518R	% DONOR- MARROW WHOLE	12
+10375R	HLA - STR/CHIMERISM TESTING	9558R	% RECIPIENT- MARROW WHOLE	12
+LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH CORD	12
+LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH FRONT TYPE	12
+LABTYPSNI	TYPE AND SCREEN	ABORH	ABO/RH STILLBORN	12
+LABARS	"ARSENIC, WHOLE BLOOD"	ARS	"ARSENIC, BLOOD"	12
+LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	B2M	BETA-2-MICROGLOB	12
+LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	B2MCREAT	BETA-2-MICROGLOB/CRT	12
+LABCD34	"CD34 PANEL, BLOOD"	BCD34V	CD34+ PROGENITOR VIABILITY	12
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	BDVC	"CORD BASE DEFICIT, VEN"	12
+LABCD34	"CD34 PANEL, BLOOD"	CD45PV	CD45+ VIABILITY	12
+LABCRTINE	"CREATINE, SERUM"	CRTNR	"CREATINE, PLASMA"	12
+LABDPARE	DIRECT PARASITE EXAM	CULT	CULT/OTHER RSLT:	12
+LABROVRE	VRE SCREEN	CULT	CULT/OTHER RSLT:	12
+LABCYSI	"CYSTICERCOSIS, CSF AND SERUM"	CYSS	CYSTICERCOSIS-SERUM	12
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	DI	ISSUE DATE/TIME	12
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	DI	ISSUE DATE/TIME	12
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	DI	ISSUE DATE/TIME	12
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	GASLOT	GASTROCCULT LOT#	12
+LABCSFGL	"GLUCOSE, CSF"	GLCSTN	TUBE NUMBER	12
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	HCO3VC	"CORD HCO3, VEN"	12
+LABABC	AUTOMATED BLOOD COUNT	HCT	HEMATOCRIT	12
+LABABC	AUTOMATED BLOOD COUNT	HGB	HEMOGLOBIN	12
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	12
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAVFTXC	ICD TACHY VF THERAPY CONFIGURATION	12
+LAB11043R	IGD	IGD	"IGD, SERUM"	12
+LABABC	AUTOMATED BLOOD COUNT	MCH	MCH	12
+LABABC	AUTOMATED BLOOD COUNT	MCHC	MCHC	12
+LABABC	AUTOMATED BLOOD COUNT	MCV	MCV	12
+LABMERC	"MERCURY, WHOLE BLOOD"	MERC	"MERCURY, BLOOD"	12
+LABADIFF	DIFFERENTIAL	MORPH	MORPHOLOGY	12
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	PCO2VC	"CORD PCO2, VEN"	12
+VSC02	VSC CAROTID VERTEBRAL ARTERY ULTRASOUND	PDF	PDF RESULT	12
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	PF4IGG	PF4 IGG ONLY OD	12
+LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	PHFMS	PH METABOLIC SCRN	12
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	PHVCD	"CORD PH, VEN"	12
+LABABC	AUTOMATED BLOOD COUNT	PLT	PLATELET COUNT	12
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	PNAB1	PNEUMOCOCCAL AB (PNEUMOVAX)	12
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	PO2VC	"CORD PO2, VEN"	12
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	PRO	PRODUCT ID  	12
+LABPGN	PROGESTERONE	PROGES	PROGESTERONE	12
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRAMP	RA THRESHOLD AMPLITUDE	12
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	RATHRDUR	RA THRESHOLD DURATION	12
+LABABC	AUTOMATED BLOOD COUNT	RBC	RBC	12
+LABABC	AUTOMATED BLOOD COUNT	RDW	RDW	12
+LABDPARE	DIRECT PARASITE EXAM	RPT	RPT	12
+LABROVRE	VRE SCREEN	RPT	RPT	12
+LABDPARE	DIRECT PARASITE EXAM	SDES	SPECIMEN	12
+LABROVRE	VRE SCREEN	SDES	SPECIMEN	12
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	SI	<<STATUS>>  	12
+LABSICKL	SICKLE CELL PREP	SICKL	SICKLE CELL PREP	12
+LABADIFF	DIFFERENTIAL	SLREV	SLIDE REVIEW	12
+LABYSOMAT	SOMATOSTATIN	SOMATOSTATIN	SOMATOSTATIN	12
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	SPEXP	SPECIMEN EXPIRATION DATE/TIME	12
+LABDPARE	DIRECT PARASITE EXAM	SREQ	COMMENTS	12
+LABROVRE	VRE SCREEN	SREQ	COMMENTS	12
+LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	UCR	"CREATININE, URN"	12
+LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	UT	COLLECTION PERIOD	12
+LABVHHA	VH HYPERMUTATION ASSAY	VHDI	DISCLAIMER	12
+LABVHHA	VH HYPERMUTATION ASSAY	VHDX	CLINICAL INDICATION	12
+LABVHHA	VH HYPERMUTATION ASSAY	VHIN	INTERPRETATION	12
+LABVHHA	VH HYPERMUTATION ASSAY	VHMD	INTERPRETED BY	12
+LABVHHA	VH HYPERMUTATION ASSAY	VHME	METHOD	12
+LABVHHA	VH HYPERMUTATION ASSAY	VHRE	RESULT	12
+LABUB2MG	"BETA - 2 - MICROGLOBULIN, URINE"	VOL	VOLUME	12
+LABABC	AUTOMATED BLOOD COUNT	WBC	WBC	12
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	XMR	UNIT CROSSMATCH  	12
+LABYHAET	HEMOPHILUS INFLUENZA IGG	YHAET1	"HAEMOPHILUS INFLU B AG,IGG"	12
+LABYRIBAB	RIBOSOMAL P PROTEIN AB	YRIB1	"RIBOSOME P AB,IGG"	12
+LAB11100R	RIBOFLAVIN	YVITB2	RIBOFLAVIN(VIT.B2)	12
+10075R	HLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	11
+LAB11526R	K CHANNEL AB	11636R	K CHANNEL AB	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12242R	12242R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12243R	12243R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12244R	12244R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12245R	12245R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12246R	12246R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12247R	12247R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12248R	12248R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12249R	12249R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12250R	12250R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12251R	12251R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12252R	12252R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12253R	12253R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12254R	12254R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12255R	12255R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12256R	12256R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12257R	12257R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12258R	12258R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12259R	12259R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12260R	12260R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12261R	12261R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12262R	12262R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12263R	12263R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12264R	12264R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12265R	12265R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12266R	12266R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12267R	12267R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12268R	12268R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12269R	12269R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12270R	12270R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12271R	12271R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12272R	12272R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12273R	12273R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12274R	12274R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12275R	12275R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12276R	12276R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12277R	12277R	11
+LAB12241R	B CELL PHENOTYPING PROFILE	12278R	12278R	11
+LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12402R	ALKALINE PHOSPHATASE	11
+LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12403R	ALKP. PHOSPHATASE - OTHER	11
+LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12404R	ALKP. PHOSPHATASE - BONE	11
+LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	12405R	ALKP. PHOSPHATASE - LIVER	11
+LAB12443R	"NEOCOMPLETE PARANEOPLASTIC PRFL, SERUM"	12444R	NEO PARANEOPLASTIC PRFL	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	AMOBARB	AMOBARBITAL	11
+LABFHVIG	"HIV 1 IGG-AB, WESTERN BLOT CONFIRM"	BANDPRSNT	BANDS PRESENT	11
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	BDC	"CORD BASE DEFICIT, ART"	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBCMT	COMMENT	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBDIR	INTERPRETED BY	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBDIS	DISCLAIMER	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBDX	CLINICAL INDICATION	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBMET	METHOD	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBPRM	PRIMERS	11
+LABBMBCLO	"B - CELL CLONALITY PCR, BONE MARROW"	BMBRES	RESULTS	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C3P4PA	"CD3+/CD4+,ABS"	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C3P4PC	"CD3+/CD4+,%"	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C3P8PA	"CD3+/CD8+, ABS."	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C3P8PC	"CD3+/CD8+, %"	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C5P4PA	"CD5+/CD4+ ,ABS"	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C5P4PC	CD5+/CD4+ %	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C5P8PA	"CD5+/CD8+,ABS."	11
+LABTTM	TRANSPLANT THERAPY MONITORING	C5P8PC	CD5+/CD8+ %	11
+LABBMCD34	"CD34 PANEL, BONE MARROW"	CD34V	VIABILITY	11
+LABCOCI	COCCI ID (SERUM)	COCI	COCCI ID (SERUM)	11
+LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTIGA	C.TRACHOMATIS IGA	11
+LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTIGG	C.TRACHOMATIS IGG	11
+LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTIGM	C.TRACHOMATIS IGM	11
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEL	DEVELOPER LOT#	11
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	DEVEXP	DEVELOPER EXP DATE	11
+LABYDHEA	"DEHYDROEPIANDROSTERONE, SERUM"	DHEA	"DHEA, SERUM"	11
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	DI	ISSUE DATE/TIME	11
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	DI	ISSUE DATE/TIME	11
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	DI	ISSUE DATE/TIME	11
+LABDONLAI	DONATH LANDSTEINER	DONLAR	DONATH LANDSTEINER TEST	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	FACIDTOT	TOTAL FATTY ACIDS	11
+LABFACT13	FACTOR XIII SCREEN	FACTOR13	FACTOR XIII SCREEN	11
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	GASEXP	GASTROCCULT EXP DATE	11
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	GASO	PATIENT GASTRIC RESULT	11
+LABFLGL	"GLUCOSE, FLUID"	GLUFLD	GLUCOSE BODY FLUID	11
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GMYD8	MYD88	11
+LABCSMP	CANCER SOMATIC MUTATION PANEL	GSF3B	SF3B1	11
+LABYHCCSF	"HCG BETA-SUBUNIT QUANTITATIVE, CSF"	HCGCSF	"HCG, B SUBUNIT QN, CSF"	11
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	HCO3C	"CORD HCO3, ART"	11
+LABFHIVQA	HIV-1 PROVIRALL DNA PCR QUAL	HIVQA	"HIV-1 DNA, QUAL, PCR"	11
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	11
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRVVDEL	BRADY VV DELAY	11
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	11
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	11
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTVFIB	ICD DETECTIONS AND TREATMENTS VENTRICULAR FIBRILLATION	11
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAATPTX	ICD TACHY ZONE ATP THERAPY TYPE	11
+LABJAK2	"JANUS KINASE 2 V617F MUTATION, BLOOD"	JAKIN	INTERPRETATION	11
+LABFLK	"POTASSIUM, FLUID"	KFLD	"POTASSIUM, FLUID"	11
+LABFLK	"POTASSIUM, FLUID"	KFLS	SOURCE	11
+LABLEISAB	"LEISHMANIA AB, IFA"	LBRAIN	"L BRAZILIENSIS,INTERPRETATION"	11
+LABLDL	LDL CALCULATED	LDL	LDL (CALCULATED)	11
+LABLEISAB	"LEISHMANIA AB, IFA"	LDONIN	"L DONOVANI,INTERPRETATION"	11
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LEADALERT	LEAD ALERT	11
+LABLEISAB	"LEISHMANIA AB, IFA"	LMEXIN	"L MEXICANA,LNTERPRETATION"	11
+LABLEISAB	"LEISHMANIA AB, IFA"	LTROIN	INTERPRETATION	11
+ECH43	ECHO - DOBUTAMINE STRESS ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	MUFATOT	TOTAL MONOUNSATURATED ACID	11
+LABFLNA	"SODIUM, FLUID"	NAFLD	"SODIUM, FLUID"	11
+LABFLNA	"SODIUM, FLUID"	NAFLS	SOURCE	11
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKCO	COMMENT	11
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKPR	PRIMERS	11
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKRF	REFERENCES	11
+LABOF	OSMOTIC FRAGILITY	OFDIR	REVIEWED BY	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	PB	PHENOBARBITAL	11
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	PCO2C	"CORD PCO2, ART"	11
+VSC08	VSC RENAL ULTRASOUND	PDF	PDF RESULT	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	PENTO	PENTOBARBITAL	11
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	PHCD	"CORD PH, ART"	11
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHVA	PATIENT PH VALUE	11
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	PO2C	"CORD PO2, ART"	11
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	PRO	PRODUCT ID  	11
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	PRO	PRODUCT ID  	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	PUFATOT	TOTAL POLYUNSATURATED ACID	11
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	QC11	QC RESULT	11
+LABTTM	TRANSPLANT THERAPY MONITORING	RC4C8	CD4/CD8 RATIO	11
+10075R	HLA ORDER - STR/CHIMERISM (W/RELATED DONOR INFO)	RINFO	TO FIND RESULT:	11
+LABRIPA	RISTOCETIN INDUCED PLATELET AGGREGATION	RIPINT	INTERPRETATION	11
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	11
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	SECOBARB	SECOBARBITAL	11
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	SI	<<STATUS>>  	11
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	SI	<<STATUS>>  	11
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	11
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	SPEXP	SPECIMEN EXPIRATION DATE/TIME	11
+LABTTM	TRANSPLANT THERAPY MONITORING	TC4C8A	"T(CD4+CD8), ABS."	11
+LABTTM	TRANSPLANT THERAPY MONITORING	TC4C8P	T(CD4+CD8)%	11
+LABTESFBT	"TESTOSTERONE, TOTAL, BIO, FREE"	TESBIO	"TESTOSTERONE, BIOAVAILABLE"	11
+LABTESFBT	"TESTOSTERONE, TOTAL, BIO, FREE"	TESFR	"TESTOSTERONE, FREE"	11
+LABTESFBT	"TESTOSTERONE, TOTAL, BIO, FREE"	TESTOS	TESTOSTERONE	11
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	UBT	UNIT BLOOD TYPE  	11
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	UBT	UNIT BLOOD TYPE  	11
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	UNT	UNIT NUMBER  	11
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	UNT	UNIT NUMBER  	11
+LABVITB1P	"VITAMIN B1, PLASMA"	VITB1	"VITAMIN B1, PLASMA"	11
+LABYUALA	"AMINOLEVULINIC ACID, URINE"	VOLUMEURN	URINE VOLUME	11
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	11
+LABXGAD65	ANTI GAD 65 ANTIBODIES	XGADR	ANTI-GAD 65 ANTIBODIES	11
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	XMR	UNIT CROSSMATCH  	11
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	XMR	UNIT CROSSMATCH  	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC1	LAURIC ACID C12:0	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC10	VACCENIC ACID C18:1W9	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC11	STEARIC ACID C18:0	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC12	EPA C20:5W3	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC13	ARACHIDONIC ACID C20:4W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC14	MEAD ACID C20:3W9	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC15	H-G-LINOLEIC C20:3W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC16	ARACHIDIC ACID C20:0	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC17	DHA C22:6W3	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC18	DPA C22:5W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC19	DPA C22:5W3	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC2	MYRISTIC ACID C14:0	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC20	DTA C22:4W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC21	DOCOSENOIC ACID C22:1	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC22	NERVONIC ACID C24:1W9	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC23	TRIENE TETRAENE RATIO	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC24	TOTAL SATURATED ACID	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC27	TOTAL W3	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC28	TOTAL W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC3	HEXADECENOIC ACID C16:1W9	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC30	INTERPRETATION EFA	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC4	PALMITOLEIC ACID C16:1W7	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC5	PALMITIC ACID C16:0	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC6	G-LINOLENIC ACID C18:3W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC7	A-LINOLENIC ACID C18:3W3	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC8	LINOLEIC ACID C18:2W6	11
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	YFAC9	OLEIC ACID C18:1W9	11
+LABFLGL	"GLUCOSE, FLUID"	YFLSC1	FLUID TYPE	11
+LABYGBMGE	"GBM AB, IGG"	YGBMG1	"GBM AB, IGG, ELISA"	11
+LABYUALA	"AMINOLEVULINIC ACID, URINE"	YUALA2	COLLECTION DURATION	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	YUBBIM	IMMUNOASSAY SCREEN	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	YUBBIN	INTERPRETATION	11
+LABYUBBC	"BARBITURATE CONFIRMATION, URINE"	YUBTGC	BUTALBITAL	11
+10205R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	10049R	HLA LAB TESTING STATUS	10
+LAB11357R	FAMILIAL MEDITERRANEAN FEVER	11358R	FAMILIAL MEDITERRANEAN FEVER	10
+LAB11522R	HU AB	11632R	HU AB	10
+LAB11610R	"DONOR, HTLVS(QNS/LIPEMIC ONLY)"	11769R	"DONOR, HTLVS"	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12695R	S.PNEUMO SEROTYPE 2 (2)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12712R	S.PNEUMO SEROTYPE 5 (5)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12713R	S.PNEUMO SEROTYPE 17 (17)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12714R	S.PNEUMO SEROTYPE 20 (20F)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12715R	S.PNEUMO SEROTYPE 22 (22F)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12716R	S.PNEUMO SEROTYPE 34 (10A)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12717R	S.PNEUMO SEROTYPE 43 (11A)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12718R	S.PNEUMO SEROTYPE 54 (15B)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12719R	S.PNEUMO SEROTYPE 57 (19A)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	12726R	S.PNEUMO SEROTYPE 70 (33F)	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9184R	DONOR NAME	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9185R	DONOR DOB	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9186R	RELATIONSHIP TO PATIENT	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9187R	DONOR CELL DATE	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9188R	DONOR CELL SOURCE	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9189R	DONOR CELL TARGET	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9190R	METHOD	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9191R	PATIENT\'S SERUM 1 DATE	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9193R	PATIENT\'S SERUM 1 UNTREATED RESULT	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9194R	PATIENT\'S SERUM 1 DTT TREATED RESULT	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9195R	PATIENT\'S SERUM 2 DATE	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9197R	PATIENT\'S SERUM 2 UNTREATED RESULT	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9198R	PATIENT\'S SERUM 2 DTT TREATED RESULT	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9209R	DISCLAIMER	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9329R	CUMULATIVE ANTIBODIES BY IGG	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9330R	CUMULATIVE ANTIBODIES BY C1Q	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9331R	SERUM DATE	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9339R	CPRA FOR CLASS II	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9340R	HLA ANTIBODIES FOR CLASS II	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9345R	COMPOSED TEXT CLASS II	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9346R	DISCLAIMER	10
+10375R	HLA - STR/CHIMERISM TESTING	9519R	% DONOR- MARROW CD3	10
+10375R	HLA - STR/CHIMERISM TESTING	9559R	% RECIPIENT- MARROW CD3	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9606R	METHOD	10
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9653R	MFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS II	10
+10315R	HLA - ALLO T CDC CROSSMATCH	9709R	DONOR PHENOTYPE	10
+LABCSFCT	"CELL COUNT AND DIFF, CSF"	CLINE	"LINING CELLS, CSF"	10
+LABCMVQT	CMV PCR QUANTITATIVE	CMVCP	RESULT	10
+LABCMVQT	CMV PCR QUANTITATIVE	CMVDNA	CMV DNA	10
+LABCMVQT	CMV PCR QUANTITATIVE	CMVLOG	RESULT LOG 10	10
+LABCMVQT	CMV PCR QUANTITATIVE	CMVSP	SPECIMEN	10
+LABCMVQT	CMV PCR QUANTITATIVE	CMVTIP	TEST DESCRIPTION	10
+LABPLTAGG	PLATELET AGGREGATION	COLL50	COLLAGEN 5.0 UG/ML	10
+LABPORFEC	"PORPHYRINS FRACTIONATED, FECAL"	COPRO	"COPROPORPHYRIN, FEC"	10
+LABVCULO	"VIRAL CULTURE, OCULAR"	CULT	CULT/OTHER RSLT:	10
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	DI	ISSUE DATE/TIME	10
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	DI	ISSUE DATE/TIME	10
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	DYSPL	"DYSPL GRANS, UNCLASS."	10
+LABEAIGG	EBV EARLY ANTIGEN IGG	EAGQNT	EAGQNT	10
+LABFFIBN	FETAL FIBRONECTIN	FFIBN	FETAL FIBRONECTIN	10
+LABTFPSA	TOTAL AND FREE PSA	FPSA	FREE PSA	10
+LABTFPSA	TOTAL AND FREE PSA	FREEPSAPC	% FREE PSA	10
+LABHCVG	HEPATITIS C VIRUS GENOTYPING	HCGG	HCV GENOTYPE	10
+LABHEPBG	HEPATITIS B GENOTYPE	HEPBG1	HBV GENOTYPE	10
+LAB11040R	HEPARIN COFACTOR	HEPCO2	HEPARIN COFACTOR II	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRADYDP	BRADY DEPENDANCY	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRLOWRT	BRADY LOWER RATE	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMAXRT	BRADY MAX. SENSOR RATE	10
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRPACMD	BRADY PACING MODE	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTNNSVT	ICD DETECTIONS AND TREATMENTS NON SUS VENT DETECT	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTAVFDTD	ICD TACHY VF DETECTION DURATION	10
+LABCIGMT	"IGM, CSF"	IGMCSF	"IGM,CSF"	10
+LABLAMOL	LAMOTRIGINE	LAMOT	LAMOTRIGINE	10
+LABMYCPA	MYCOPHENOLIC ACID	MPACID	MYCOPHENOLIC ACID	10
+LABGASPH	"PH, GASTRIC"	PHGAST	"PH,GASTRIC"	10
+LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	PHOSU	"PHOSPHATE, URINE"	10
+LABYCPYRU	"PYRUVATE, CSF"	PKCSF	"PYRUVATE,CSF"	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMEVDATE	PACEMAKER EVAL DATE	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRHYTHM	PM RHYTHM	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE01	S.PNEUMO SEROTYPE 1 (1)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE12	S.PNEUMO SEROTYPE 12 (12F)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE14	S.PNEUMO SEROTYPE 14 (14)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE19	S.PNEUMO SEROTYPE 19 (19F)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE23	S.PNEUMO SEROTYPE 23 (23F)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE26	S.PNEUMO SEROTYPE 26 (6B)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE3	S.PNEUMO SEROTYPE 3 (3)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE4	S.PNEUMO SEROTYPE 4 (4)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE51	S.PNEUMO SEROTYPE 51 (7F)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE56	S.PNEUMO SEROTYPE 56 (18C)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE68	S.PNEUMO SEROTYPE 68 (9V)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE8	S.PNEUMO SEROTYPE 8 (8)	10
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	PNE9	S.PNEUMO SEROTYPE 9 (9N)	10
+LABTFPSA	TOTAL AND FREE PSA	PSA	PSA	10
+LABFQFVR	COXIELLA BURNETII AB	QFVA1	PHASE 1 IGA	10
+LABFQFVR	COXIELLA BURNETII AB	QFVA2	PHASE II IGA	10
+LABYRENIN	"RENIN ACTIVITY, PLASMA"	RENIN	"RENIN ACTIVITY, PL"	10
+10205R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	RINFO	TO FIND RESULT:	10
+LABPLTAGG	PLATELET AGGREGATION	RIS15	RISTOCETIN 1.5 MG/ML	10
+LABVCULO	"VIRAL CULTURE, OCULAR"	RPT	RPT	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRPACA	RV BRADY PACING AMPLITUDE	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRPACW	RV BRADY PACING PULSE WIDTH	10
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENA	RV BRADY SENSING AMPLITUDE	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	10
+LABVCULO	"VIRAL CULTURE, OCULAR"	SDES	SPECIMEN	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTDTDU	SLOWVT TACHY ZONE DETECTION DURATION	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTDTNT	SLOWVT TACHY ZONE DETECTION INTERVAL	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTQRSM	SLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	10
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTZNRT	SLOWVT TACHY ZONE RATE STABILITY CRITERIA	10
+LABVCULO	"VIRAL CULTURE, OCULAR"	SREQ	COMMENTS	10
+LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	TPHOSU	PHOSPHATE EXCRETION	10
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	UBT	UNIT BLOOD TYPE  	10
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	UBT	UNIT BLOOD TYPE  	10
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	UBT	UNIT BLOOD TYPE  	10
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	UBT	UNIT BLOOD TYPE  	10
+LABUURIC	"URIC ACID WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	10
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	UNT	UNIT NUMBER  	10
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	UNT	UNIT NUMBER  	10
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	UNT	UNIT NUMBER  	10
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	UNT	UNIT NUMBER  	10
+LABUURIC	"URIC ACID WITH CREATININE, URINE (RANDOM)"	URICRU	URIC/CREAT RATIO	10
+LABUURIC	"URIC ACID WITH CREATININE, URINE (RANDOM)"	URICURN	"URIC ACID, URINE"	10
+LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	UT8	COLLECTION PERIOD PHOSPHATE	10
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	VL	PRODUCT VOLUME	10
+LABUPHOST	"PHOSPHORUS, URINE (TIMED)"	VOL8	VOLUME PHOSPHATE	10
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	XMR	UNIT CROSSMATCH  	10
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	XMR	UNIT CROSSMATCH  	10
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	XMR	UNIT CROSSMATCH  	10
+LABYDNBT	ANTI - DNASE B TITER	YDNBT1	ANTI-DNASE -B TITER	10
+LABYINFAB	INFLUENZA AB PANEL	YINF1	"INFLUENZA VIRUS A AB, IGG & IGM"	10
+LABYINFAB	INFLUENZA AB PANEL	YINF4	"INFLUENZA VIRUS B AB, IGG & IGM"	10
+LABMYCPA	MYCOPHENOLIC ACID	YMPAG	MPA GLUCURONIDE	10
+10230R	HLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*	10049R	HLA LAB TESTING STATUS	9
+LAB11170R	PREGABALIN	11171R	PREGABALIN	9
+LAB11516R	PANCREATITIS PANEL	11625R	PANCREATITIS PANEL	9
+LAB11567R	"HTLV-I/II AB, CSF"	11681R	HTLV-I/II AB	9
+LAB11851R	"FECAL LIPIDS, TOTAL"	11848R	TOTAL SPECIMEN WEIGHT	9
+LAB11851R	"FECAL LIPIDS, TOTAL"	11849R	COLLECTION DURATION	9
+LAB11851R	"FECAL LIPIDS, TOTAL"	11850R	"FECAL LIPIDS, 24 HR"	9
+LAB11871R	"HIV-2 AB, IMMUNOBLOT"	11870R	"HIV 2 ANTIBODY, IMMUNOBLOT"	9
+LAB11610R	"DONOR, HTLVS(QNS/LIPEMIC ONLY)"	11890R	"HTLV I/II, WESTERN BLOT"	9
+LABFECHO	"ECHOVIRUS AB, SERUM"	12027R	ECHOVIRUS  AB TYPE 6	9
+LABFECHO	"ECHOVIRUS AB, SERUM"	12028R	ECHOVIRUS  AB TYPE 7	9
+LABFECHO	"ECHOVIRUS AB, SERUM"	12029R	ECHOVIRUS  AB TYPE 11	9
+LABFECHO	"ECHOVIRUS AB, SERUM"	12030R	ECHOVIRUS  AB TYPE 30	9
+LABFECHO	"ECHOVIRUS AB, SERUM"	12031R	ECHOVIRUS  AB TYPE 9	9
+LABIBD	ANCA IBD WITH REFLEX	12215R	12215R	9
+LAB12605R	"N-METHYL-D-ASPARTATE RECEPTOR AB, IGG"	12604R	"N-METHYL-D-ASPARTATE RCPTR AB, IGG"	9
+LAB12657R	T CELL ACTIVATION	12658R	T CELL ACTIVATION	9
+LABYSTONE	STONE ANALYSIS	1STCONST	1ST CONSTITUENT	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9210R	DONOR NAME	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9211R	DONOR DOB	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9212R	RELATIONSHIP TO PATIENT	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9213R	DONOR CELL DATE	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9214R	DONOR CELL SOURCE	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9215R	DONOR CELL TARGET	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9216R	METHOD	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9217R	PATIENT\'S SERUM 1 DATE	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9221R	PATIENT\'S SERUM 2 DATE	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9233R	INTERPRETATION/ COMMENTS	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9235R	DISCLAIMER	9
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9333R	COMBINED CPRA FOR SERUM	9
+10360R	HLA - MONITORING BY C1Q	9364R	MFI RESULTS FOR MONITORING BY C1Q CLASS I	9
+10360R	HLA - MONITORING BY C1Q	9377R	CUMULATIVE ANTIBODIES BY C1Q	9
+10360R	HLA - MONITORING BY C1Q	9378R	CPRA FOR CUMULATIVE C1Q	9
+10360R	HLA - MONITORING BY C1Q	9382R	CPRA FOR C1Q CLASS I FOR CURRENT	9
+10360R	HLA - MONITORING BY C1Q	9383R	HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	9
+10360R	HLA - MONITORING BY C1Q	9392R	COMPOSED TEXT CLASS I	9
+10360R	HLA - MONITORING BY C1Q	9396R	HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	9
+10360R	HLA - MONITORING BY C1Q	9405R	COMPOSED TEXT CLASS II	9
+10360R	HLA - MONITORING BY C1Q	9406R	DISCLAIMER	9
+10375R	HLA - STR/CHIMERISM TESTING	9521R	% DONOR- MARROW CD15	9
+10375R	HLA - STR/CHIMERISM TESTING	9522R	% DONOR- MARROW CD19	9
+10375R	HLA - STR/CHIMERISM TESTING	9529R	% DONOR- MARROW CD56	9
+10375R	HLA - STR/CHIMERISM TESTING	9561R	% RECIPIENT- MARROW CD15	9
+10375R	HLA - STR/CHIMERISM TESTING	9562R	% RECIPIENT- MARROW CD19	9
+10375R	HLA - STR/CHIMERISM TESTING	9569R	% RECIPIENT- MARROW CD56	9
+10360R	HLA - MONITORING BY C1Q	9608R	METHOD	9
+10360R	HLA - MONITORING BY C1Q	9622R	CURRENT SERUM DATE	9
+10360R	HLA - MONITORING BY C1Q	9623R	CURRENT SERUM DESCRIPTION	9
+10320R	HLA - ALLO B CDC CROSSMATCH	9712R	DONOR PHENOTYPE	9
+LABAAUR	"AMINO ACIDS, URINE"	AAMINOA	A-AMINOADIPIC ACID	9
+LABAAUR	"AMINO ACIDS, URINE"	AAMINOB	A-AMINOBUTYRIC ACID	9
+LABAAUR	"AMINO ACIDS, URINE"	AAUINT	INTERPRETATION	9
+LAB11078R	ACETYLCHOLINESTERASE	ACHERBC	"ACETYLCHOLINESTERASE, RBC"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	ACPINT	INTERPRETATION	9
+LABADAQ	"ADENOSINE DEAMINASE, RBC"	ADA	ADENOSINE DEAMINASE	9
+LABADAQ	"ADENOSINE DEAMINASE, RBC"	ADMD	REVIEWED BY	9
+LABAAUR	"AMINO ACIDS, URINE"	ALANINE	ALANINE	9
+LABAAUR	"AMINO ACIDS, URINE"	ARGININE	ARGININE	9
+LAB11126R	ASCARIASIS	ASCA	ASCARISIS	9
+LABAAUR	"AMINO ACIDS, URINE"	ASPART	ASPARTIC ACID	9
+LABAAUR	"AMINO ACIDS, URINE"	ASPGN	ASPARAGINE	9
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ATACHTADTI	ATACH TACHY ZONE DETECTION INTERVAL	9
+LABAAUR	"AMINO ACIDS, URINE"	BALN	B-ALANINE	9
+LABAAUR	"AMINO ACIDS, URINE"	BAMBA	B-AMINOISOBUTYRIC AC	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C02	"C2, ACETYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C03	"C3, PROPIONYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C04	"C4, ISO-/BUTYRYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C05	"C5, ISOVALERYL-/2-METHYLBUTYRYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C05DC	"C5-DC, GLUTARYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C05OH	"C5-OH, 3OH-ISOVALERYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C06	"C6, HEXANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C06OH	"C6-0H, 3OH-HEXANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C08	"C8, OCTANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C081	"C8:1, OCTENOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C10	"C10, DECANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C101	"C10:1, DECENOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C12	"C12, DODECANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C121	"C12:1, DODECENOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C12OH	"C12-OH, 3OH-DODECANOYL"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C14	"C14, TETRADECANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C141	"C14:1, TETRADECENOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C141OH	"C14:1-OH, 3OH-TETRADECENOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C142	"C14:2, TETRADECADIENOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C14OH	"C14-OH, 3OH-TETRADECANOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C16	"C16, PALMITOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C161	"C16:1, PALMITOLEYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C161OH	"C16:1-OH, 3OH-PALMITOLEYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C16OH	"C16-OH, 3OH-PALMITOYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C18	"C18, STEAROYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C181	"C18:1, OLEYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C181OH	"C18:1-OH, 3OH-OLEYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C182	"C18:2, LINOLEYL-"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	C182OH	"C18:2-OH, 3OH-LINOLEYL-"	9
+LABAAUR	"AMINO ACIDS, URINE"	CITRUL	CITRULLINE	9
+LABCMVIGG	CMV IGG	CMV	CMV ANTIBODY	9
+LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	CULT	CULT/OTHER RSLT:	9
+LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	CULT	CULT/OTHER RSLT:	9
+LABAAUR	"AMINO ACIDS, URINE"	CYSTINE	CYSTINE	9
+LABAAUR	"AMINO ACIDS, URINE"	CYTHN	CYSTATHIONINE	9
+LABECHIG	"ECHINOCOCCUS AB, IGG W/REFLEX"	ECHIND	ECHINOCOCCUS LGG	9
+LABEOSSP	SPUTUM FOR EOSINOPHILS	EOSSP	SPUTUM FOR EOS.	9
+LABAAUR	"AMINO ACIDS, URINE"	ETHLNU	ETHANOLAMINE	9
+LABFSYN	FUNGAL SYNERGY	FMIC	FUNGAL MIC	9
+LABAAUR	"AMINO ACIDS, URINE"	GLUTAC	GLUTAMIC ACID	9
+LABAAUR	"AMINO ACIDS, URINE"	GLUTAMINE	GLUTAMINE	9
+LABAAUR	"AMINO ACIDS, URINE"	GLYCINE	GLYCINE	9
+LABHBCC	HEPATITIS B CORE TOTAL AB	HBCAB	HEP B CORE TOTAL AB	9
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCINT	INTERPRETATION	9
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCR	IGM ANTI-HBC ANTIBODY	9
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSINT	INTERPRETATION	9
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSR	HEP B SURFACE ANTIGEN	9
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTINT	INTERPRETATION	9
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBTR	ANTI-HBC ANTIBODY	9
+LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCVGDI	DISCLAIMER	9
+LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCVGMT	METHOD	9
+LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCVGRE	RESULT	9
+LABAAUR	"AMINO ACIDS, URINE"	HISTID	HISTIDINE	9
+LABAAUR	"AMINO ACIDS, URINE"	HMCY	HOMOCYSTINE	9
+LABHTLVS	HTLV-1 AB SCREEN	HTLVS	HTLV AB SCREEN	9
+LABAAUR	"AMINO ACIDS, URINE"	HYPROL	HYDROXYPROLINE	9
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVPCD	BRADY PACED AV DELAY	9
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDBRMDSLP	BRADY MODULATION SLOPE	9
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	9
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLTSTTI	ICD TEL TEST CHARGE TIME	9
+LABCIGA	"IGA, CSF"	IGACSF	"IGA, CSF"	9
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	INBY	INTERPRETED BY	9
+LABAAUR	"AMINO ACIDS, URINE"	INTUBY	INTERPRETED BY	9
+LABAAUR	"AMINO ACIDS, URINE"	ISOLEU	ISOLEUCINE	9
+LABAAUR	"AMINO ACIDS, URINE"	LEUCINE	LEUCINE	9
+LABLKM1AB	"LIVER, KID MICROSOME-1 AB, IGG"	LKM1R	"LIVER, KID MIC-1 AB"	9
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRAMP	LV THRESHOLD AMPLITUDE	9
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRDUR	LV THRESHOLD DURATION	9
+LAB12214R	"LYME SCREEN, SERUM"	LYMEC6	B.BURGDORFERI C6 PEPTIDE	9
+LABAAUR	"AMINO ACIDS, URINE"	LYSINE	LYSINE	9
+LABAAUR	"AMINO ACIDS, URINE"	METHIO	METHIONINE	9
+LABUMAG	"MAGNESIUM WITH CREATININE, URINE (RANDOM)"	MGUCRU	MAG/CREAT RATIO	9
+LABMUMPSG	MUMPS IGG	MUMDES	TEST DESCRIPTION	9
+LABMYCOC	"MYCOPLASMA CULTURE, FASTIDIOUS"	MYORG	MYCOPLASAM ORGANISM	9
+LABMYCOC	"MYCOPLASMA CULTURE, FASTIDIOUS"	MYSTA	MYCOPLASMA CULTURE STATUS	9
+LABAAUR	"AMINO ACIDS, URINE"	ORNITHINE	ORNITHINE	9
+LABIBD	ANCA IBD WITH REFLEX	PANCA	NEUTROPHIL CYTO AB-P	9
+IMG11PER	CAR PERICARDIOCENTESIS	PDF	PDF RESULT	9
+LABPENTO	PENTOBARBITAL LEVEL	PENTO	"PENTOBARBITAL, S"	9
+LABAAUR	"AMINO ACIDS, URINE"	PHALN	PHENYLALANINE	9
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRALDBIIM	PM RA LEAD BIPOLAR IMPEDANCE	9
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMTLPCPACA	PM TELEMETRY PERCENT PACING ATRIAL	9
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	PRO	PRODUCT ID  	9
+LABAAUR	"AMINO ACIDS, URINE"	PROLINE	PROLINE	9
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRPACA	RA BRADY PACING AMPLITUDE	9
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRPACW	RA BRADY PACING PULSE WIDTH	9
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRSENA	RA BRADY SENSING AMPLITUDE	9
+10230R	HLA ORDER - DESENSITIZATION MONITORING NO IVIG (PO*	RINFO	TO FIND RESULT:	9
+LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	RPT	RPT	9
+LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	RPT	RPT	9
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	9
+LABIBD	ANCA IBD WITH REFLEX	SACIGA	"S. CERVISIAE AB,IGA"	9
+LABIBD	ANCA IBD WITH REFLEX	SACIGG	"S.CERVISIAE AB,IGG"	9
+LABAAUR	"AMINO ACIDS, URINE"	SARSN	SARCOSINE	9
+LABSCLABY	SCL 70 AB	SCL70	"SCL 70 AB, IGG, SERUM"	9
+LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	SDES	SPECIMEN	9
+LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	SDES	SPECIMEN	9
+LABAAUR	"AMINO ACIDS, URINE"	SERINE	SERINE	9
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	SI	<<STATUS>>  	9
+LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	SLREV	SLIDE REVIEW	9
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	SPEXP	SPECIMEN EXPIRATION DATE/TIME	9
+LABCDTPCR	"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR"	SREQ	COMMENTS	9
+LABFUNIDI	"FUNGI ID BY SEQUENCING, ISOLATE"	SREQ	COMMENTS	9
+LABAAUR	"AMINO ACIDS, URINE"	TAURINE	TAURINE	9
+LABAAUR	"AMINO ACIDS, URINE"	THREONINE	THREONINE	9
+LABAAUR	"AMINO ACIDS, URINE"	TRYPTOPHAN	TRYPTOPHAN	9
+LABAAUR	"AMINO ACIDS, URINE"	TYROSINE	TYROSINE	9
+LABUMAG	"MAGNESIUM WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	9
+LABUMAG	"MAGNESIUM WITH CREATININE, URINE (RANDOM)"	UMG	"MAGNESIUM, URINE"	9
+LABAAUR	"AMINO ACIDS, URINE"	VALINE	VALINE	9
+LABVWFPI	VON WILLEBRAND PROTEASE INHIBITOR	VWFPIC	COMMENT	9
+LABVWFPI	VON WILLEBRAND PROTEASE INHIBITOR	VWFPIR	RESULT	9
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	XMR	UNIT CROSSMATCH  	9
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	XMR	UNIT CROSSMATCH  	9
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	XMR	UNIT CROSSMATCH  	9
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	XMR	UNIT CROSSMATCH  	9
+LABYC2729	BCA AB  (CA27.29)	Y2729A	"CA 27, 29"	9
+LABYSTONE	STONE ANALYSIS	YSTNSO	STONE SOURCE	9
+LABYSTONE	STONE ANALYSIS	YSTNWT	STONE WEIGHT	9
+10315R	HLA - ALLO T CDC CROSSMATCH	10464R	RECIPIENT NAME	8
+10315R	HLA - ALLO T CDC CROSSMATCH	10465R	RECIPIENT DOB	8
+10360R	HLA - MONITORING BY C1Q	10476R	DSA FOR C1Q CLASS I	8
+LAB11188R	M. PNEUMONIAE DNA	11189R	M.PNEUMONIAE	8
+LAB11369R	LEUKEMIA/LYMPHOMA IMMUPHENTYP	11370R	LEUKEMIA/LYMPHOMA IMMUPHENTYP	8
+LAB11855R	"HEPATITIS C VIRAL RNA, QUANT"	11853R	"HCV RNA ,QUANT,TMA IU/ML"	8
+LAB12099R	"BRUCELLA TOTAL AB,AGGLU (RFLX)"	12100R	"BRUCELLA TOTAL  AB, AGGLUTIN"	8
+LAB11188R	M. PNEUMONIAE DNA	12283R	12283R	8
+LAB12332R	KIT MUTATION ANALYSIS EXON 11	12337R	KIT MUTATION ANALYSIS EXON 11	8
+LAB12352R	NMO EVALUATION WITH REFLEX	12353R	NMO/AQP4 - IGG ELISA	8
+LAB13006R	"REDUCING SUBSTANCE, FECES"	13005R	"REDUCING SUBSTANCE, FECES"	8
+10305R	HLA - ALLO T FLOW CROSSMATCH	9136R	SERUM DESCRIPTION 2	8
+10310R	HLA - ALLO B FLOW CROSSMATCH	9166R	SERUM DESCRIPTION 2	8
+10320R	HLA - ALLO B CDC CROSSMATCH	9219R	PATIENT\'S SERUM 1 UNTREATED RESULT	8
+10320R	HLA - ALLO B CDC CROSSMATCH	9223R	PATIENT\'S SERUM 2 UNTREATED RESULT	8
+10320R	HLA - ALLO B CDC CROSSMATCH	9224R	PATIENT\'S SERUM 2 DTT TREATED RESULT	8
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9334R	CPRA FOR CLASS I	8
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9335R	HLA ANTIBODIES FOR CLASS I	8
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9343R	INHIBITION RESULTS FOR CLASS II	8
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9351R	MFI RESULTS FOR IN VITRO IVIG INHIBITION ASSAY CLASS I	8
+10360R	HLA - MONITORING BY C1Q	9395R	CPRA FOR C1Q CLASS II FOR CURRENT	8
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9587R	COMPOSED TEXT CLASS I	8
+10360R	HLA - MONITORING BY C1Q	9655R	MFI RESULTS FOR MONITORING BY C1Q CLASS II	8
+LAB12397R	ACETYLCHOLINE BLOCKING AB	ACRBLO	ACETYLCHOLINE BLOCKING AB	8
+LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMAG	ANTIGEN	8
+LABADVRC	ADENOVIRUS RAPID CULTURE	ADVRC1	ADV RAPID CULTURE	8
+LABADVRC	ADENOVIRUS RAPID CULTURE	ADVRCD	TEST DESCRIPTION	8
+LABADVRC	ADENOVIRUS RAPID CULTURE	ADVSPM	SPECIMEN	8
+LABURAMY	"AMYLASE, URINE (RANDOM)"	AMYLASE	"AMYLASE, URINE"	8
+LABYUHYME	"HEAVY METALS, URINE"	ARSENICURN	"ARSENIC, URINE"	8
+LABFCYST	CYSTICERCUS IGG AB	BANDPRSNT	BANDS PRESENT	8
+LABLWBCC	"LYME WESTERN BLOT, CSF"	BANDPRSNT	BANDS PRESENT	8
+LABYCARO	CAROTENE	CAROTENE	CAROTENE	8
+LABYCEAC	"CEA, CSF"	CEAFLD	"CEA, SPINAL FLUID"	8
+LABUCLT	"CHLORIDE, URINE (TIMED)"	CLE	CHLORIDE EXCRETION	8
+LABBMPTP	BMT PRETRANSPLANT PANEL	CMMQNT	CMV IGM LEVEL	8
+LABTCPSA	TOTAL AND COMPLEXED PSA	CPSA	COMPLEXED PSA	8
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	CRDVLG	CORD LENGTH	8
+LABROSBBP	R/O STREP BETA GP B BY PCR	CULT	CULT/OTHER RSLT:	8
+LABPORFEC	"PORPHYRINS FRACTIONATED, FECAL"	FPORIN	"PORPHYRIN INTRP, FEC"	8
+LABPORFEC	"PORPHYRINS FRACTIONATED, FECAL"	FPROTO	"PROTOPORHYRINS, FEC"	8
+LABTCPSA	TOTAL AND COMPLEXED PSA	FPSA	FREE PSA	8
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	8
+LABHBCM	HEPATITIS B CORE / PRECORE MUTATION	HBCM1	BASAL CORE PROMOTER NUCLEOTIDE	8
+LABHBCM	HEPATITIS B CORE / PRECORE MUTATION	HBCM2	PRECORE CODON	8
+LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDDNA	HEP B VIRUS DNA	8
+LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDIPR	TEST DESCRIPTION	8
+LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDLOG	RESULT LOG 10	8
+LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDRES	RESULT	8
+LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED	HBDSP	SPECIMEN	8
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6CP	RESULT	8
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6DN	HHV-6 DNA	8
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6LO	RESULT LOG 10	8
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6SP	SPECIMEN	8
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	HHV6TI	TEST DESCRIPTION	8
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLINT	INTERPRETATION	8
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	8
+LABBMPTP	BMT PRETRANSPLANT PANEL	HV1QNT	HSV-1 IGG LEVEL	8
+LABBMPTP	BMT PRETRANSPLANT PANEL	HV1QUL	HSV-1 IGG RESULT	8
+LABBMPTP	BMT PRETRANSPLANT PANEL	HV2QNT	HSV-2 IGG LEVEL	8
+LABBMPTP	BMT PRETRANSPLANT PANEL	HV2QUL	HSV-2 IGG RESULT	8
+LABBMPTP	BMT PRETRANSPLANT PANEL	HVIGDS	HSV IGG TEST DESCRIPTION	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAMSRT	BRADY AMS RATE	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVSND	BRADY SENSED AV DELAY	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDTHR	BRADY MODULATION THRESHOLD	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRTRKRT	BRADY TRACKING RATE	8
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	8
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSLWVT	ICD DETECTIONS AND TREATMENTS SLOW VENT TACHYCARDIA	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	8
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LEADALERT	LEAD ALERT	8
+ECH34	ECHO - 3-DIMENSIONAL ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	8
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRSENM	LV THRESHOLD SENSING AMPLITUDE	8
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRSENPO	LV THRESHOLD SENSING POLARITY	8
+LABADIFF	DIFFERENTIAL	LYM	LYMPHOCYTES	8
+LABADIFF	DIFFERENTIAL	MONO	MONOCYTES	8
+LABMUMPSG	MUMPS IGG	MUMQUL	RESULT	8
+LABADIFF	DIFFERENTIAL	NEUT	SEG NEUTROPHILS	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVIMPDT	PM DEVICE IMPLANT DATE	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVIMPDT	PM DEVICE IMPLANT DATE	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVMFR	PM DEVICE MANUFACTURER	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVMFR	PM DEVICE MANUFACTURER	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVMODEL	PM DEVICE MODEL	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVMODEL	PM DEVICE MODEL	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVSERNM	PM DEVICE SERIAL NUMBER	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVSERNM	PM DEVICE SERIAL NUMBER	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMDEVTYPE	PM DEVICE TYPE	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMDEVTYPE	PM DEVICE TYPE	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMEVDATE	PACEMAKER EVAL DATE	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMRHYTHM	PM RHYTHM	8
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	PRO	PRODUCT ID  	8
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	PRO	PRODUCT ID  	8
+LABPSARX	PSA WITH REFLEX FREE PSA	PSA	PSA	8
+LABTCPSA	TOTAL AND COMPLEXED PSA	PSA	PSA	8
+LABTCPSA	TOTAL AND COMPLEXED PSA	PSAPC	% COMPLEXED PSA	8
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	8
+LABRIPA	RISTOCETIN INDUCED PLATELET AGGREGATION	RIPDIR	INTERPRETED BY	8
+LABROSBBP	R/O STREP BETA GP B BY PCR	RPT	RPT	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	8
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	8
+LABROSBBP	R/O STREP BETA GP B BY PCR	SDES	SPECIMEN	8
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	SI	<<STATUS>>  	8
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	SI	<<STATUS>>  	8
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	SPEXP	SPECIMEN EXPIRATION DATE/TIME	8
+LABROSBBP	R/O STREP BETA GP B BY PCR	SREQ	COMMENTS	8
+LABSSBABY	ANTI-LA ANTIBODY	SSBQNT	ANTI-LA LEVEL	8
+LABT1418	"T(14	18), BLOOD"	T14CMT	COMMENT	8
+LABT1418	"T(14	18), BLOOD"	T14DIR	INTERPRETED BY	8
+LABT1418	"T(14	18), BLOOD"	T14DIS	DISCLAIMER	8
+LABT1418	"T(14	18), BLOOD"	T14MET	METHOD	8
+LABT1418	"T(14	18), BLOOD"	T14PRM	PRIMERS	8
+LABT1418	"T(14	18), BLOOD"	T14RES	RESULTS	8
+POC30	POC PYLORITECK	TESTLO	TEST STRIP LOT#	8
+LABUMAGT	"MAGNESIUM, URINE (TIMED)"	TMGNU	MAGNESIUM EXCRETION	8
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCDIS	DISCLAIMER	8
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCDX	CLINICAL INDICATION	8
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCMET	METHOD	8
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCPRM	PRIMERS	8
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCRES	RESULTS	8
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCTIS	SOURCE	8
+LABRNPABY	"RNP AB, IGG"	U1RNPAB	AUTOANTIBODIES TO U1RNP	8
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	UBT	UNIT BLOOD TYPE  	8
+LABUCLT	"CHLORIDE, URINE (TIMED)"	UCL	"CHLORIDE, URINE"	8
+LABUMAGT	"MAGNESIUM, URINE (TIMED)"	UMG	"MAGNESIUM, URINE"	8
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	UNT	UNIT NUMBER  	8
+LABUCLT	"CHLORIDE, URINE (TIMED)"	UT11	COLLECTION PERIOD CHLORIDE	8
+LABUMAGT	"MAGNESIUM, URINE (TIMED)"	UT12	COLL PERIOD MAG	8
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	VL	PRODUCT VOLUME	8
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	VL	PRODUCT VOLUME	8
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	VL	PRODUCT VOLUME	8
+LABUCLT	"CHLORIDE, URINE (TIMED)"	VOL11	VOLUME CHLORIDE	8
+LABUMAGT	"MAGNESIUM, URINE (TIMED)"	VOL12	VOLUME MAGNESIUM	8
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSLWVTTDTT	VSLOWVT TACHY ZONE DETECTION INTERVAL	8
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	XMR	UNIT CROSSMATCH  	8
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	XMR	UNIT CROSSMATCH  	8
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	XMR	UNIT CROSSMATCH  	8
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	XMR	UNIT CROSSMATCH  	8
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	XMR	UNIT CROSSMATCH  	8
+LABYANTIN	INSULIN ANTIBODIES	YANT2	HUMAN	8
+LABYDIPT	DIPHTHERIA TOXOID IGA AB	YDIPT1	DIPTHERIA TOXOID IGG AB	8
+LABYKLFRE	IMMUNO FREE LIGHT CHAINS	YKLF1	"KAPPA FREE LIGHT CHAIN, S"	8
+LABYKLFRE	IMMUNO FREE LIGHT CHAINS	YKLF2	"LAMBDA FREE LIGHT CHAIN, S"	8
+LABYKLFRE	IMMUNO FREE LIGHT CHAINS	YKLF3	KAPPA/LAMBDA FLC RATIO	8
+LABYLAP	LEUKOCYTE ALKALINE PHOSPATASE SCORE	YLAP1	LAP SCORE	8
+LABYUARSR	"ARSENIC, URINE (RANDOM)"	YUARR2	COLLECTION DURATION	8
+LABYUARSR	"ARSENIC, URINE (RANDOM)"	YUARR4	AS CONC (UG/L)	8
+10320R	HLA - ALLO B CDC CROSSMATCH	10466R	RECIPIENT NAME	7
+10320R	HLA - ALLO B CDC CROSSMATCH	10467R	RECIPIENT DOB	7
+10360R	HLA - MONITORING BY C1Q	10477R	DSA FOR C1Q CLASS II	7
+LAB12352R	NMO EVALUATION WITH REFLEX	11200R	INTERPRETIVE COMMENTS	7
+LAB12774R	MOTOR NEUROPATHY AB SCREEN	11323R	CO-GM1 TRIAD AB	7
+LAB11324R	"CADASIL EVALUATION, COMPLETE"	11325R	COMPLETE CADASIL EVALUATION	7
+LAB11512R	"CYSTIC FIBROSIS, SEQUENCING"	11621R	"CYSTIC FIBROSIS, SEQUENCING"	7
+LAB11530R	CEREBELLAR PARANEOPLASTIC AB	11640R	CEREBELLAR PARANEOPLASTIC AB	7
+LAB11608R	MARFAN (FBN1) SEQUENCING	11764R	FBN1 FULL GENE RESULT	7
+LAB11608R	MARFAN (FBN1) SEQUENCING	11767R	SEQUENCING	7
+LAB11620R	"TOXOPLASMA GONDII, AQUEOUS FLUID"	11779R	"TOXOPLASAMGONDII,AQUEOUS FLUID"	7
+LAB11855R	"HEPATITIS C VIRAL RNA, QUANT"	11854R	"HCV RNA ,QUANT,TMA LOGIU/ML"	7
+LAB11882R	"MAGNESIUM, RBC"	11881R	"MAGNESIUM, RBC"	7
+LAB11901R	"N. GONORRHOEAE RNA, THROAT"	11902R	"N.GONORRHOEAE RNA,THROAT"	7
+LAB12320R	THIOCYANATE	12321R	THIOCYANATE	7
+LAB12637R	MENINGOCOCCAL AB VACCINE	12636R	SEROGROUP A	7
+LAB12637R	MENINGOCOCCAL AB VACCINE	12696R	SEROGROUP C	7
+LAB12637R	MENINGOCOCCAL AB VACCINE	12697R	SEROGROUP Y	7
+LAB12637R	MENINGOCOCCAL AB VACCINE	12698R	SEROGROUP W-135	7
+LAB12838R	INSULIN-LIKE GROWTH FACTOR 1	12839R	INSULIN-LIKE GROWTH FACTOR 1	7
+10295R	HLA - AUTO T FLOW CROSSMATCH	9088R	SERUM DESCRIPTION 2	7
+10300R	HLA - AUTO B FLOW CROSSMATCH	9109R	SERUM DESCRIPTION 2	7
+10320R	HLA - ALLO B CDC CROSSMATCH	9220R	PATIENT\'S SERUM 1 DTT TREATED RESULT	7
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9338R	INHIBITION RESULTS FOR CLASS I	7
+10355R	HLA - MONITORING BY IGG	9347R	CUMULATIVE ANTIBODIES BY IGG	7
+10355R	HLA - MONITORING BY IGG	9348R	CPRA FOR CUMULATIVE IGG	7
+10355R	HLA - MONITORING BY IGG	9352R	CPRA FOR IGG CLASS I FOR CURRENT	7
+10355R	HLA - MONITORING BY IGG	9353R	HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	7
+10355R	HLA - MONITORING BY IGG	9362R	COMPOSED TEXT CLASS I	7
+10355R	HLA - MONITORING BY IGG	9363R	MFI RESULTS FOR MONITORING BY IGG CLASS I	7
+10355R	HLA - MONITORING BY IGG	9365R	CPRA FOR IGG CLASS II FOR CURRENT	7
+10355R	HLA - MONITORING BY IGG	9366R	HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	7
+10355R	HLA - MONITORING BY IGG	9375R	COMPOSED TEXT CLASS II	7
+10355R	HLA - MONITORING BY IGG	9376R	DISCLAIMER	7
+10370R	HLA - DSA BY C1Q	9449R	DSA RESULTS FOR C1Q CLASS I	7
+10375R	HLA - STR/CHIMERISM TESTING	9483R	LOCUS 9	7
+10375R	HLA - STR/CHIMERISM TESTING	9484R	LOCUS 10	7
+10355R	HLA - MONITORING BY IGG	9607R	METHOD	7
+10355R	HLA - MONITORING BY IGG	9618R	CURRENT SERUM DATE	7
+10355R	HLA - MONITORING BY IGG	9619R	CURRENT SERUM DESCRIPTION	7
+10355R	HLA - MONITORING BY IGG	9654R	MFI RESULTS FOR MONITORING BY IGG CLASS II	7
+LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	9OHRISP	9-HYDROXYRISPERIDONE	7
+LABADVQL	"ADENOVIRUS DNA, QUALITATIVE PCR"	ADOV	"ADENOVIRUS DNA, QL PCR"	7
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	AFIBTADTIT	AFIB TACHY ZONE DETECTION INTERVAL	7
+LABSEZPNL	SEZARY PANEL	AN20DS	DISCLAIMER	7
+LABADIFF	DIFFERENTIAL	BAND	BAND NEUTROPHILS	7
+LABCRDVBG	"VENOUS BLOOD GASES, CORD BLOOD"	BEVC	"CORD BASE EXCESS, VEN"	7
+LABBARSAB	BARTONELLA SPECIES AB	BHIGMT	B.HENSELAE IGM TITER	7
+LABBARSAB	BARTONELLA SPECIES AB	BQIGMT	B.QUINTANA IGM TITER	7
+LABSEZPNL	SEZARY PANEL	C264AB	"CD26-/CD4+, ABS"	7
+LABSEZPNL	SEZARY PANEL	C264PC	CD26-/CD4+ %	7
+LABSEZPNL	SEZARY PANEL	C4N7AB	"CD4+/CD7-,ABS. (C4N7AB)"	7
+LABSEZPNL	SEZARY PANEL	C4N7PC	CD4+/CD7-% (C4N7PC)	7
+LABEP2	LYTES II	CAION	"CALCIUM, IONIZED"	7
+LABSEZPNL	SEZARY PANEL	CD19AB	"CD19 (PAN B), ABS."	7
+LABSEZPNL	SEZARY PANEL	CD19PC	CD19 (PAN B) %	7
+LABSEZPNL	SEZARY PANEL	CD26AB	"CD26 (PAN-T), ABS"	7
+LABSEZPNL	SEZARY PANEL	CD26PC	CD26 (PAN-T) %	7
+LABSEZPNL	SEZARY PANEL	CD3AB	"CD3 (PAN T), ABS."	7
+LABSEZPNL	SEZARY PANEL	CD3PC	CD3 (PAN T) %	7
+LABSEZPNL	SEZARY PANEL	CD4AB	"CD3+/CD4+, ABS."	7
+LABSEZPNL	SEZARY PANEL	CD4PC	CD3+/CD4+ %	7
+LABSEZPNL	SEZARY PANEL	CD7AB	"CD7 (PAN T), ABS"	7
+LABSEZPNL	SEZARY PANEL	CD7PC	CD7 (PAN T) %	7
+LABSEZPNL	SEZARY PANEL	CD8AB	"CD3+/CD8+, ABS."	7
+LABSEZPNL	SEZARY PANEL	CD8PC	CD3+/CD8+ %	7
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSDI	DISCLAIMER	7
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSDX	CLINICAL INDICATION	7
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSMD	INTERPRETED BY	7
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSME	METHOD	7
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFSSRE	RESULT	7
+LABEP2	LYTES II	CL	"CHLORIDE, WHOLE BLD"	7
+LABYFCL	"CHLORIDE, FLUID"	CLFLD	"CHLORIDE, BODY FLUID"	7
+LABFCLPN	CHLAMYDIA PNEUMONIAE CULTURE	CLPN	CHLAMYDIA PNEUMONIAE	7
+LAB11124R	"COCCIDIOIDES COMPLEMENT FIXATION, SERUM"	COCFS	COCCI CF (SERUM)	7
+LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	CTINT	INTERPRETATION	7
+LABTSMISI	UNLISTED TRANSFUSION TEST	CTSMIS	MISC. TRANSFUSION TEST COMMENT	7
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	CULT	CULT/OTHER RSLT:	7
+LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816DI	DISCLAIMER	7
+LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816DX	CLINICAL INDICATION	7
+LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816ME	METHOD	7
+LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816RE	"RESULT, BLOOD"	7
+LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816RF	REFERENCES	7
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	DI	ISSUE DATE/TIME	7
+LABEBVPAN	EBV AB PANEL	EA	EARLY ANTIBODY (R+D) IGG	7
+LABEBVPAN	EBV AB PANEL	EBNA	EPSTEIN-BARR NUC AB	7
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GENTSDD	"GENTAMICIN, SINGLE DAILY DOSE"	7
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GNTUN2	DATE & TIME OF LAST DOSE	7
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GNTUN3	DOSAGE	7
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	GNTUN4	ADMINISTRATION ROUTE	7
+LABHBC	HEPATITIS B CORE AB IGM	HBCABIGM	HEPAT.B CORE AB-IGM	7
+LABSEZPNL	SEZARY PANEL	HNS	"CD4+CD8 TOTAL, CD3+%"	7
+LABSEZPNL	SEZARY PANEL	HSABS	"CD4+CD8 TOTAL, CD3+ ABS."	7
+LABSEZPNL	SEZARY PANEL	HSRATIO	"CD4/CD8 RATIO, CD3+"	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRLOWRT	BRADY LOWER RATE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRPACMD	BRADY PACING MODE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRVVDEL	BRADY VV DELAY	7
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSVTAC	ICD DETECTIONS AND TREATMENTS FAST VENT TACHYCARDIA	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	7
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLBTVLT	ICD TEL BATTERY VOLTAGE	7
+LABIGGS1	IGG SUBCLASS 1	IGGS1	IGG SUBCLASS1	7
+LABIGGS2	IGG  SUBCLASS 2	IGGS2	IGG SUBCLASS2	7
+LABIGGS3	IGG  SUBCLASS 3	IGGS3	IGG SUBCLASS3	7
+LABJO1ABY	AUTOANTIBODIES TO JO1	JO1AB	AUTOANTIBODIES TO JO 1	7
+LABEP2	LYTES II	K	"POTASSIUM, WHOLE BLD"	7
+LABREF5	LAB UNLISTED 5	LAB5	REFERENCE LAB:	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADIMPDTE	LEAD IMPLANT DATE	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADMFR	LEAD MANUFACTURER	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADMODEL	LEAD MODEL	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADSERNUM	LEAD SERIAL NUMBER	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	LEADTYPE	LEAD TYPE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	7
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	7
+LABSEZPNL	SEZARY PANEL	LYM	LYMPHOCYTES %	7
+LABSEZPNL	SEZARY PANEL	LYMAB	"LYMPHOCYTES, ABS."	7
+LABMUMPSG	MUMPS IGG	MUMQNT	LEVEL	7
+LABEP2	LYTES II	NA	"SODIUM, WHOLE BLOOD"	7
+LABNEURSP	NEURON SPEC ENOLASE	NEURSP	NEURON SPEC ENOLASE	7
+LAB11053R	"PBG DEAMINASE, RBC"	PBGD	"PBG DEAMINASE, RBC"	7
+IMG11RLHC	CAR COMB RT LT HEART CATH	PDF	PDF RESULT	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMEVDATE	PACEMAKER EVAL DATE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRALDBIIM	PM RA LEAD BIPOLAR IMPEDANCE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRHYTHM	PM RHYTHM	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMTLPCPACA	PM TELEMETRY PERCENT PACING ATRIAL	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	7
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	PRO	PRODUCT ID  	7
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	PRO	PRODUCT ID  	7
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	PRO	PRODUCT ID  	7
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	PRO	PRODUCT ID  	7
+LAB11091R	QUINIDINE LEVEL	QUINIDINE	QUINIDINE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	7
+LABUA	URINALYSIS WITH MICROSCOPIC	RBCAST	RBC CASTS	7
+LABREF5	LAB UNLISTED 5	RESUL5	RESULT	7
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	RPT	RPT	7
+LABRSVPCR	RSV PCR	RSVDI	DISCLAIMER	7
+LABRSVPCR	RSV PCR	RSVMT	METHOD	7
+LABRSVPCR	RSV PCR	RSVRE	RSV RNA	7
+LABRSVPCR	RSV PCR	RSVSP	SPECIMEN	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACA	RV BRADY PACING AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRPACW	RV BRADY PACING PULSE WIDTH	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENA	RV BRADY SENSING AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	7
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENB	RA BRADY SENSING BLANKING PERIOD	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	7
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	7
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	7
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	7
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	7
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	7
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	7
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	SDES	SPECIMEN	7
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	SI	<<STATUS>>  	7
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	SI	<<STATUS>>  	7
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	SI	<<STATUS>>  	7
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	SI	<<STATUS>>  	7
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SNRBC	"NRBC/100WBC, SYN FLD"	7
+LABADVQL	"ADENOVIRUS DNA, QUALITATIVE PCR"	SOURC3	ADENOVIRUS SOURCE	7
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	SPEXP	SPECIMEN EXPIRATION DATE/TIME	7
+LABREF5	LAB UNLISTED 5	SPTY5	SPECIMEN TYPE	7
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	SREQ	COMMENTS	7
+LABSYNCT	"CELL COUNT AND DIFF, SYNOVIAL FLUID"	SRLYM	"REACT LYMPH, SYN FLD"	7
+LABEP2	LYTES II	TCO2A	TCO2	7
+LABREF5	LAB UNLISTED 5	TEST5	TEST NAME	7
+LABTHAMP	THERMAL AMPLITUDE SCREEN	THRAMP	THERMAL AMPLITUDE	7
+LABTOXIM	TOXOPLASMA IGM	TOXMDS	TEST DESCRIPTION	7
+LABTOXIM	TOXOPLASMA IGM	TOXMQL	IGM RESULT	7
+LABTRICHG	TRICHINELLA IGG AB ELISA	TRICG	"TRICHINELLA, IGG"	7
+LABTSMISI	UNLISTED TRANSFUSION TEST	TSMISR	MISC. TEST	7
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCCMT	COMMENT	7
+LABTTCLO	"T-CELL CLONALITY PCR, TISSUE"	TTCDIR	INTERPRETED BY	7
+LABUURICT	"URIC ACID, URINE (TIMED)"	TURICU	URIC ACID EXCRETION	7
+LABUURICT	"URIC ACID, URINE (TIMED)"	URICADVOL	VOLUME URIC ACID	7
+LABUURICT	"URIC ACID, URINE (TIMED)"	URICURN	"URIC ACID, URINE"	7
+LABUURICT	"URIC ACID, URINE (TIMED)"	UT9	COLLECTION PERIOD URIC ACID	7
+LABEBVPAN	EBV AB PANEL	VCA	VIRAL CAPSID AB	7
+LABEBVPAN	EBV AB PANEL	VCAM	VIRAL CAPSID AB-IGM	7
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	VL	PRODUCT VOLUME	7
+LABSEZPNL	SEZARY PANEL	WBC	WBC COUNT	7
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	XMR	UNIT CROSSMATCH  	7
+LABYEBPCR	"EPSTEIN-BARR VIRUS PCR, CSF"	YEBP3	SPECIAL INFORMATION	7
+LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	YRISP1	RISPERIDONE (RISPERDAL)	7
+LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	YRISP3	COMBINED TOTAL	7
+10355R	HLA - MONITORING BY IGG	10474R	DSA FOR IGG CLASS I	6
+10355R	HLA - MONITORING BY IGG	10475R	DSA FOR IGG CLASS II	6
+LAB11190R	BORRELIA HERMSII AB PANL	11191R	BORRELIA HERMSII IGG	6
+LAB11190R	BORRELIA HERMSII AB PANL	11192R	BORRELIA HERMSII IGM	6
+LAB11238R	HERPESVIRUS-8 IGG AB	11239R	HERPESVIRUS-8 IGG AB	6
+LAB11608R	MARFAN (FBN1) SEQUENCING	11765R	FBN1 FULL GENE INTERPRETATION	6
+LAB11608R	MARFAN (FBN1) SEQUENCING	11766R	REVIEWED BY	6
+LABYUALA	"AMINOLEVULINIC ACID, URINE"	12000R	INTERPRETATION	6
+LAB12017R	"ECHOVIRUS AB, SERUM"	12027R	ECHOVIRUS  AB TYPE 6	6
+LAB12017R	"ECHOVIRUS AB, SERUM"	12028R	ECHOVIRUS  AB TYPE 7	6
+LAB12017R	"ECHOVIRUS AB, SERUM"	12029R	ECHOVIRUS  AB TYPE 11	6
+LAB12017R	"ECHOVIRUS AB, SERUM"	12030R	ECHOVIRUS  AB TYPE 30	6
+LAB12017R	"ECHOVIRUS AB, SERUM"	12031R	ECHOVIRUS  AB TYPE 9	6
+LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	12197R	SOURCE	6
+LAB12206R	CYSTATIN C	12207R	"CYSTATIN C, SER"	6
+LAB12371R	COBALT	12372R	COBALT	6
+LAB12585R	"HEPATITIS D RNA, QUAL PCR"	12594R	HDV RNA	6
+LABFPARVO	PARVOVIRUS IGG / IGM AB	12835R	PARVOVIRUS B19 AB IGG	6
+LABYSTONE	STONE ANALYSIS	2NDCONST	2ND CONSTITUENT	6
+10375R	HLA - STR/CHIMERISM TESTING	9485R	LOCUS 11	6
+LABALPHA2	ALPHA-2-ANTIPLASMIN	A2A	ALPHA-2-ANTIPLASMIN	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	ACHINT	INTERPRETATION	6
+LABADVQL	ADENOVIRUS PCR QUAL	ADOV	"ADENOVIRUS DNA, QL PCR"	6
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	AFIBTADTIT	AFIB TACHY ZONE DETECTION INTERVAL	6
+LABAG	ALBUMIN / GLOBULIN RATIO	ALBGLOB	ALBUMIN/GLOBULIN RATIO	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	ALD	ALDER TREE	6
+LABYUALDO	"ALDOSTERONE, URINE"	ALDOSTRURN	"ALDOSTERONE, URINE"	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	ALTER	ALTERNARIA MOLD	6
+LABYUARSR	"ARSENIC, URINE (RANDOM)"	ARSENICURN	"ARSENIC, URINE"	6
+LABAS4I	AB SCREEN	AS	ANTIBODY SCREEN	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	ASPERG	ASPERGILUS MOLD	6
+LABFUNDAP	FUNGAL DISEASE AB PANEL	ASPR	ASPERILLUS-CF	6
+LABFCD34	"CD34 PANEL, FLUID"	BCD34V	CD34+ PROGENITOR VIABILITY	6
+LABBCLON	B - CELL CLONALITY PCR	BCMT	COMMENT	6
+LABBCLON	B - CELL CLONALITY PCR	BDIR	INTERPRETED BY	6
+LABBCLON	B - CELL CLONALITY PCR	BDIS	DISCLAIMER	6
+LABBCLON	B - CELL CLONALITY PCR	BDX	CLINICAL INDICATION	6
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	BEC	"CORD BASE EXCESS, ART"	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	BERM	BERMUDA GRASS	6
+LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	BILIRUBIN	BILIRUBIN	6
+LABFUNDAP	FUNGAL DISEASE AB PANEL	BLAS	BLASTO AB CFS	6
+LABBCLON	B - CELL CLONALITY PCR	BMET	METHOD	6
+LABBCLON	B - CELL CLONALITY PCR	BPRM	PRIMERS	6
+LABBCLON	B - CELL CLONALITY PCR	BRES	RESULTS	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C2526N	"CD25, % OF CD3+/CD4+/CD26NEG"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C257N	"CD25, % OF CD3+/CD4+/CD7NEG"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C264AB	"CD26-/CD4+, ABS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C264PC	CD26-/CD4+ %	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3026N	"CD30, % OF CD3+/CD4+/CD26NEG"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C307N	"CD30, % OF CD3+/CD4+/CD7NEG"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C326NA	"CD3+/CD26NEG, ABS."	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3426N	"CD3+/CD4+/CD26NEG, ABS."	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3426P	"CD3+/CD4+/CD26NEG, % OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3426T	"CD3+/CD4+/CD26NEG, % OF CD4+ T"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C347NA	"CD3+/CD4+/CD7NEG, ABS."	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C347NP	"CD3+/CD4+/CD7NEG, % OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C347NT	"CD3+/CD4+/CD7NEG, % OF CD4+ T"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C37NA	"CD3+/CD7NEG, ABS."	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P4PA	"CD3+/CD4+,ABS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P4PP	"CD3+/CD4+, % OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P7NP	"CD3+/CD7NEG, % OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P8PN	"CD3+/CD8+, ABS. OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C3P8PP	"CD3+/CD8+, % OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C4N7AB	"CD4+/CD7-,ABS. (C4N7AB)"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C4N7PC	CD4+/CD7-% (C4N7PC)	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C5226N	"CD52, % OF CD3+/CD4+/CD26NEG"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C527N	"CD52, % OF CD3+/CD4+/CD7NEG"	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	CAT	CAT DANDER	6
+LABCDIFF	"DIFFERENTIAL, CSF"	CCMT	COMMENT	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD26NP	"CD3+/CD26NEG, % OF LYMPHS"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD3PC	CD3 (PAN T) %	6
+LABFCD34	"CD34 PANEL, FLUID"	CD45PV	CD45+ VIABILITY	6
+LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	CHOL	CHOLESTEROL	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	CLA	CLADOSPORIUM MOLD	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	COCK	COCKROACH	6
+LABCRDABG	"ARTERIAL BLOOD GASES, CORD BLOOD"	CRDALG	CORD LENGTH	6
+INACTIVE 7	STOOL CULTURE	CULT	CULT/OTHER RSLT:	6
+LABAID	AEROBIC ORGANISM ID	CULT	CULT/OTHER RSLT:	6
+LABLEGDD	"LEGIONELLA CULTURE, D AND D"	CULT	CULT/OTHER RSLT:	6
+LABD816V	"KIT D816V MUTATION, AS-PCR, BLOOD"	D816MD	INTERPRETED BY	6
+LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	DESIP	DESIPRAMINE	6
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	DI	ISSUE DATE/TIME	6
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	DI	ISSUE DATE/TIME	6
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	DI	ISSUE DATE/TIME	6
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	DI	ISSUE DATE/TIME	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	DOCK	DOCK WEED	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	DOG	DOG DANDER	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	DUST	DUST MITE	6
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	EOSFLD	"EOSINOPHIL %, FLD"	6
+LABYERYTH	"ERYTHROPOIETIN, SERUM"	ERYTH	"ERYTHROPOIETIN, SER."	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBCMT	COMMENT	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBDIR	INTERPRETED BY	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBDIS	DISCLAIMER	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBDX	CLINICAL INDICATION	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBFLD	SOURCE	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBMET	METHOD	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBPRM	PRIMERS	6
+LABFBCLO	"B - CELL CLONALITY PCR, FLUID"	FBRES	RESULTS	6
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	FSTVTTQRSM	FASTVT TACHY ZONE QRS MORPHOLOGY CRITERIA	6
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	FSTVTTZNRT	FASTVT TACHY ZONE RATE STABILITY CRITERIA	6
+LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	HGB	HEMOGLOBIN	6
+LABHGBFQ	HGB F QUANTITATIVE	HGBF	"HGB F, QUANT."	6
+LABFUNDAP	FUNGAL DISEASE AB PANEL	HISTM	HISTO MYCELIAL PHASE	6
+LABFUNDAP	FUNGAL DISEASE AB PANEL	HISTY	HISTO YEAST PHASE	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRADYDP	BRADY DEPENDANCY	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAMSRT	BRADY AMS RATE	6
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVDLS	BRADY ADAPTIVE AV DELAY STATUS	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	6
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	IGET	TOTAL IGE	6
+LABYIGF1	INSULIN LIKE GROWTH FACTOR I	IGF	INSULIN-LIKE GF 1	6
+LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	IMI	IMIPRAMINE	6
+LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	IMIDESI	IMIPRAMINE & DESIPRAMINE	6
+LABYINSFT	INSULIN FREE AND TOTAL	INS	INSULIN	6
+LABREF6	LAB UNLISTED 6	LAB6	REFERENCE LAB:	6
+LABYLDISO	LD ISOENZYMES	LDH	"LACTATE DEHYD(LD), S"	6
+LABYLDISO	LD ISOENZYMES	LDH1	"LD-I, HEART"	6
+LABYLDISO	LD ISOENZYMES	LDH2	LD-II	6
+LABYLDISO	LD ISOENZYMES	LDH3	LD-III	6
+LABYLDISO	LD ISOENZYMES	LDH4	LD-IV	6
+LABLKMAB	LIVER KID MICROSOME AB	LKMAB	LIV-KID-MICROSOME AB	6
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRSENA	LV BRADY SENSING AMPLITUDE	6
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	6
+ECH16	ECHO - TRANSESOPHAGEAL ECHO	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	6
+ECH42	ECHO - EXERCISE STRESS ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	6
+ECH55A	ECHO-CONGENITAL TRANSTHORACIC ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	LYM	LYMPHOCYTES %	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	6
+LABMAGATH	MAG AUTOAB	MAGAB	MAG AUTOANTIBODY	6
+LAB11049R	NEURON SPEC ENOLASE	NEURSP	NEURON SPEC ENOLASE	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	OAK	OAK TREE	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	OLIVE	OLIVE TREE	6
+LABUOROT	"OROTIC ACID, URINE"	OROTU	"OROTIC ACID, UR"	6
+CRP398	VALVULOPLASTY ADULT	PDF	PDF RESULT	6
+IMG12AH	EP ABLATION	PDF	PDF RESULT	6
+VSC07	VSC ILIAC ARTERY ULTRASOUND	PDF	PDF RESULT	6
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	PLYM	PROLYMPHOCYTE	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVMFR	PM DEVICE MANUFACTURER	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVMODEL	PM DEVICE MODEL	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVSERNM	PM DEVICE SERIAL NUMBER	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVTYPE	PM DEVICE TYPE	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRVLDBIPO	PM RV LEAD BIPOLAR IMPEDANCE	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRVLDUNIM	PM RV LEAD UNIPOLAR IMPEDANCE	6
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMTLPCPACV	PM TELEMETRY PERCENT PACING VENTRICULAR	6
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	PRO	PRODUCT ID  	6
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	PRO	PRODUCT ID  	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	PTH18R	RECEIVED IN FLOW CYTOMETRY	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	6
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	6
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	6
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRSPOL	RA THRESHOLD SENSING POLARITY	6
+LABREF6	LAB UNLISTED 6	RESUL6	RESULT	6
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	RHFT	RHEUMATOID FACTOR LEVEL	6
+LABRMPCR	RSV/MPV PCR	RMMPV	METAPNEUMOVIRUS RNA	6
+LABRMPCR	RSV/MPV PCR	RMRSV	RSV RNA	6
+INACTIVE 7	STOOL CULTURE	RPT	RPT	6
+LABAID	AEROBIC ORGANISM ID	RPT	RPT	6
+LABLEGDD	"LEGIONELLA CULTURE, D AND D"	RPT	RPT	6
+LABRMPCR	RSV/MPV PCR	RSMPDI	DISCLAIMER	6
+LABRMPCR	RSV/MPV PCR	RSMPMT	METHOD	6
+LABRMPCR	RSV/MPV PCR	RSMPSP	SPECIMEN	6
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRSENP	RV BRADY SENSING BLANKING PERIOD	6
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRAMP	RV THRESHOLD AMPLITUDE	6
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RVTHRDUR	RV THRESHOLD DURATION	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	RYE	RYE GRASS	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	SAGE	SAGE WEED	6
+INACTIVE 7	STOOL CULTURE	SDES	SPECIMEN	6
+LABAID	AEROBIC ORGANISM ID	SDES	SPECIMEN	6
+LABLEGDD	"LEGIONELLA CULTURE, D AND D"	SDES	SPECIMEN	6
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	SI	<<STATUS>>  	6
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	SI	<<STATUS>>  	6
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	6
+LABADVQL	ADENOVIRUS PCR QUAL	SOURC3	ADENOVIRUS SOURCE	6
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	SPEXP	SPECIMEN EXPIRATION DATE/TIME	6
+LABREF6	LAB UNLISTED 6	SPTY6	SPECIMEN TYPE:	6
+INACTIVE 7	STOOL CULTURE	SREQ	COMMENTS	6
+LABAID	AEROBIC ORGANISM ID	SREQ	COMMENTS	6
+LABLEGDD	"LEGIONELLA CULTURE, D AND D"	SREQ	COMMENTS	6
+LABREF6	LAB UNLISTED 6	TEST6	TEST NAME	6
+LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	TGL	TRIGLYCERIDES	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	TIMO	TIMOTHY GRASS	6
+LABTOXIG	TOXOPLASMA IGG	TOXO	TOXOPLASMA IGG	6
+LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE11	ECHOVIRUS 11 AB	6
+LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE30	ECHOVIRUS 30 AB	6
+LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE4	ECHOVIRUS 4 AB	6
+LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE7	ECHOVIRUS 7 AB	6
+LABFECHO	"ECHOVIRUS AB, SERUM"	TYPE9	ECHOVIRUS 9 AB	6
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	UBT	UNIT BLOOD TYPE  	6
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	UBT	UNIT BLOOD TYPE  	6
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	UNT	UNIT NUMBER  	6
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	UNT	UNIT NUMBER  	6
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	VL	PRODUCT VOLUME	6
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	VL	PRODUCT VOLUME	6
+LABYUALDO	"ALDOSTERONE, URINE"	VOLUMEURN	URINE VOLUME	6
+LABYUARSR	"ARSENIC, URINE (RANDOM)"	VOLUMEURN	URINE VOLUME	6
+LABYUCITR	"CITRATE EXCRETION, URINE"	VOLUMEURN	URINE VOLUME	6
+LABYUOXAL	OXALATE URINE (TIMED)	VOLUMEURN	URINE VOLUME	6
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	WBC	WBC COUNT	6
+LABACHOO	AIRBORNE ALLERGY SCREEN	WEST	WESTERN RAGWEED	6
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	XMR	UNIT CROSSMATCH  	6
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	XMR	UNIT CROSSMATCH  	6
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	XMR	UNIT CROSSMATCH  	6
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	XMR	UNIT CROSSMATCH  	6
+LABYC5REF	C5 COMPLEMENT AG	YC5RE1	C5 COMPLEMENT AG	6
+LABYUCITR	"CITRATE EXCRETION, URINE"	YCITRU	"CITRATE EXCRETION, URINE"	6
+LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	YFCHY1	FLUID TYPE	6
+LABYFCHYL	"LIPID ANALYSIS, BODY FLUID"	YFCHY6	COMMENT	6
+LABYFELBA	FELBAMATE	YFELB1	FELBARNATE(FELBATOL)	6
+LABYFCL	"CHLORIDE, FLUID"	YFLSC1	FLUID TYPE	6
+LABYINSFT	INSULIN FREE AND TOTAL	YINSF1	FREE INSULIN	6
+LABYLDISO	LD ISOENZYMES	YLDIS2	"LD ISOENZYMES, S"	6
+LABYLDISO	LD ISOENZYMES	YLDIS3	TOTAL LD ACTIVITY	6
+LABYLDISO	LD ISOENZYMES	YLDIS8	"LD-V, LIVER"	6
+LABLEVETL	LEVETIRACETAM	YLEVT1	LEVETIRACETAM	6
+LABYMYCRB	"MYCOPLASMA CULTURE, RESPIRATORY"	YMYCSC	SOURCE	6
+LABYUOXAL	OXALATE URINE (TIMED)	YOXALU	"OXALATE, 24-HOUR/TIMED URINE"	6
+LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK1	COLLECTION DURATION	6
+LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK2	TOTAL WEIGHT	6
+LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA1	"SODIUM, 24-HR STOOL"	6
+LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA2	TOTAL WEIGHT	6
+LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA3	COLLECTION DURATION	6
+LAB12505R	"SODIUM, 24-HR STOOL"	YSTNA4	SODIUM CONCENTRATION	6
+LABYTRYPS	TRYPSIN-LIKE IMMUNOREACTIVITY	YTRYP1	TRYPSIN-LIKE IMMUNOREACTIVITY	6
+LABYUALDO	"ALDOSTERONE, URINE"	YUALD2	COLLECTION DURATION	6
+LABYUCITR	"CITRATE EXCRETION, URINE"	YUCICN	CITRATE CONCENTRATION	6
+LABYUCITR	"CITRATE EXCRETION, URINE"	YUCIUT	COLLECTION DURATION	6
+LABYUOXAL	OXALATE URINE (TIMED)	YUOXCO	OXALATE CONCENTRATION	6
+LABYUOXAL	OXALATE URINE (TIMED)	YUOXUT	COLLECTION DURATION	6
+LABYZINCS	ZINC	ZINC	ZINC	6
+10195R	HLA ORDER - WAIT LIST STATUS CHANGE	10049R	HLA LAB TESTING STATUS	5
+10210R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG	10049R	HLA LAB TESTING STATUS	5
+LAB11373R	MARFAN(FBN1) SEQUENCING	11374R	MARFAN(FBN1) SEQUENCING	5
+LAB11435R	CELIAC GENETICS	11499R	CELIAC GENETICS	5
+LAB11615R	SODIUM BENZOATE LEVEL	11774R	SODIUM BENZOATE LEVEL	5
+LAB11991R	"DNA EXTRACTION, NPL"	11992R	SPECIMEN	5
+LAB11991R	"DNA EXTRACTION, NPL"	11993R	SPECIMEN ID	5
+LAB11991R	"DNA EXTRACTION, NPL"	11995R	ORDER DATE	5
+LAB11991R	"DNA EXTRACTION, NPL"	11996R	COMMENT	5
+LAB11991R	"DNA EXTRACTION, NPL"	11998R	REVIEWED BY	5
+LAB11991R	"DNA EXTRACTION, NPL"	11999R	RELEASE DATE	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12448R	ACCESSION NUMBER	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12449R	REFERRING PATH-PHYSICIAN	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12450R	REF PATH-PHYS ADDRESS	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12451R	PLACE OF DEATH	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12452R	DATE AND TIME OF DEATH	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12453R	DATE OF AUTOPSY	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12454R	SPECIMEN	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12455R	MATERIAL	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12456R	TISSUE DESCRIPTION	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12457R	MICROSCOPIC DESCRIPTION	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12458R	CLINICAL HISTORY	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12459R	FINAL DIAGNOSIS	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12460R	FINAL DIAGNOSIS	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12461R	COMMENT	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12462R	REVISION DESCRIPTION	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12463R	SIGNING PATHOLOGIST	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12464R	SPECIAL PROCEDURES	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12465R	SP SIGNING PATHOLOGIST	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12466R	PREVIOUS REPORT FOLLOWS	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12467R	ADDENDUM	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12468R	ADDENDUM COMMENT	5
+LAB12447R	AP SPECIAL STUDIES REVIEW	12469R	ADDENDUM PATHOLOGIST	5
+LAB12645R	"COMPLEMENT, ALTERNATE PATHWAY"	12644R	"COMPLEMENT, ALTERNATE PATHWAY"	5
+LAB12651R	HYPERTROPHIC CARDIOMYOPATHY PAN	12650R	HYPERTROPHIC CARDIOMYOPATHY PANEL	5
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	12683R	"CREATININE, URINE MG/DL"	5
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	12684R	"CREATININE, URINE MG/DAY"	5
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	12685R	AMINOLEVULINIC ACID UMO/L	5
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	12686R	AMINOLEVULINIC ACID UMOL/DAY	5
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	12687R	HOURS COLLECTED	5
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	12688R	TOTAL VOLUME	5
+LAB12723R	"ADENOSINE DEAMINASE, PERITONEAL"	12722R	"ADENOSINE DEAMINASE, PERITONEAL"	5
+LAB12774R	MOTOR NEUROPATHY AB SCREEN	12775R	CO-GM1 QUATTRO	5
+LAB12790R	CYSTATIN C WITH ESTIMATED GFR	12789R	EGFR BY CYSTATIN C	5
+LAB12790R	CYSTATIN C WITH ESTIMATED GFR	12805R	CYSTATIN C	5
+10295R	HLA - AUTO T FLOW CROSSMATCH	9084R	SERUM DESCRIPTION 1	5
+10295R	HLA - AUTO T FLOW CROSSMATCH	9091R	SERUM DATE 3	5
+10295R	HLA - AUTO T FLOW CROSSMATCH	9093R	MCS 3	5
+10300R	HLA - AUTO B FLOW CROSSMATCH	9105R	SERUM DESCRIPTION 1	5
+10300R	HLA - AUTO B FLOW CROSSMATCH	9112R	SERUM DATE 3	5
+10300R	HLA - AUTO B FLOW CROSSMATCH	9114R	MCS 3	5
+10305R	HLA - ALLO T FLOW CROSSMATCH	9131R	SERUM DESCRIPTION 1	5
+10310R	HLA - ALLO B FLOW CROSSMATCH	9161R	SERUM DESCRIPTION 1	5
+10315R	HLA - ALLO T CDC CROSSMATCH	9196R	PATIENT\'S SERUM 2 DESCRIPTION	5
+10315R	HLA - ALLO T CDC CROSSMATCH	9207R	INTERPRETATION/ COMMENTS	5
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9341R	POSSIBLE HLA ANTIBODIES FOR CLASS II	5
+10360R	HLA - MONITORING BY C1Q	9384R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS I FOR CURRENT	5
+10360R	HLA - MONITORING BY C1Q	9391R	INTERPRETATION/ COMMENTS CLASS I	5
+10360R	HLA - MONITORING BY C1Q	9404R	INTERPRETATION/ COMMENTS CLASS II	5
+10295R	HLA - AUTO T FLOW CROSSMATCH	9597R	RESULT 3	5
+10300R	HLA - AUTO B FLOW CROSSMATCH	9602R	RESULT 3	5
+10360R	HLA - MONITORING BY C1Q	9624R	REFERENCE SERUM DATE	5
+10360R	HLA - MONITORING BY C1Q	9625R	REFERENCE SERUM DESCRIPTION	5
+LAB12399R	ACETYLCHOLINE MODULATING AB	ACRMOD	ACETYLCHOLINE MODULATING AB	5
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADPCR1	ADENVIRUS DNA	5
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	AFIBTADTIT	AFIB TACHY ZONE DETECTION INTERVAL	5
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ALERT	ALERT	5
+LABMALL1	MISCELLANEOUS ALLERGEN 1	ALL1	ALLERGEN	5
+LAB11139R	CARDIAC REJECTION RISK	ALLO	ALLOMAP	5
+LABMALL1	MISCELLANEOUS ALLERGEN 1	ALLR1	ALLERGEN RESULT	5
+LABTTRMET	AMYLOIDOSIS DNA TITER	AMYLDO	TTR AMYLOIDOSIS DNA	5
+LAB11072R	APO A1	APOA1	APOLIPOPROTEIN A1	5
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	ARSENIC	ARSENIC	5
+LABARSAL	"ARYLSULFATASE A, LEUKOCYTES"	ARYLSA	"ARYLSULFATASE A, LEUK"	5
+LABBARSAB	BARTONELLA SPECIES AB	BQIGGT	B.QUINTANA IGG TITER	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQCO	COMMENT	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQDI	DISCLAIMER	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQDX	CLINICAL INDICATION	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQET	ETHNICITY (PROVIDED BY MD)	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQIN	INTERPRETATION	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQMD	INTERPRETED BY	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQME	METHOD	5
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQRE	RESULT	5
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	CADMIUM	CADMIUM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CAN10	CANDIDA 1:10 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CAN100	CANDIDA 1:100 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CAN50	CANDIDA 1:50 CPM	5
+LABCCP	ANTI CCP	CCPINT	INTERPRETATION	5
+LABCCP	ANTI CCP	CCPR	ANTI CCP ANTIBODY	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	CNDINT	CANDIDA INTERPRETATION	5
+LABBMS	BMT STERILITY SAMPLE	CULT	CULT/OTHER RSLT:	5
+LABFUNIDS	FUNGAL ID BY SEQUENCING	CULT	CULT/OTHER RSLT:	5
+LABTMONA	TRICHOMONAS EXAM	CULT	CULT/OTHER RSLT:	5
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	DI	ISSUE DATE/TIME	5
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	DI	ISSUE DATE/TIME	5
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	DI	ISSUE DATE/TIME	5
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	DI	ISSUE DATE/TIME	5
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	DI	ISSUE DATE/TIME	5
+LABDT	DIPHTHERIA AND TETANUS AB IGG	DIPHT	"DIPTHERIA AB , IGG"	5
+LAB11112R	DONOR TOXOPLASMA AB	DTOXOM	DONOR TOXO IGM	5
+LABCD3F	"CD3, FLUID"	FCD3AB	"CD3 (T), ABS., FLD"	5
+LABCD3F	"CD3, FLUID"	FCD3PC	"CD3 (T) %, FLD"	5
+LABCD3F	"CD3, FLUID"	FCSOUR	SOURCE	5
+LAB12796R	FLUCONAZOLE LEVEL	FLUCOZ	FLUCONAZOLE	5
+LABYT7P	FREE THYROXINE INDEX	FT4INDEX	FREE THYROXINE INDEX	5
+LABYGADAB	GLUTAMIC ACID DECARBOXY AB	GAD65AB	GAD65 ANTIBODY	5
+LABHBSQT	ANTI HBS QUANT	HBSAB	"HBS ANTIBODY, S"	5
+LABHBSQT	ANTI HBS QUANT	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	5
+LABVP5	COMP HEPATITIS PANEL	HBSAIN	INTERPRETATION	5
+LABVP5	COMP HEPATITIS PANEL	HBSAQL	HEP B SURF AB QUAL	5
+LABVP5	COMP HEPATITIS PANEL	HBSAQT	HEP B SURF AB QUANT	5
+LABVP5	COMP HEPATITIS PANEL	HBSINT	INTERPRETATION	5
+LABVP5	COMP HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	5
+LABVP5	COMP HEPATITIS PANEL	HBTINT	INTERPRETATION	5
+LABVP5	COMP HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	5
+EXTHCT	EXTERNAL HCT	HCT	EXTERNAL HCT	5
+LABVP5	COMP HEPATITIS PANEL	HCVI	INTERPRETATION	5
+LABVP5	COMP HEPATITIS PANEL	HCVR	ANTI-HCV	5
+LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	HERPG	"HERPESVIRUS 7 AB, IGG"	5
+LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	HERPM	"HERPESVIRUS 7 AB, IGM"	5
+LABYHISTA	"HISTAMINE, SERUM"	HISTP	HISTAMINE	5
+LABMALL1	MISCELLANEOUS ALLERGEN 1	I1	INTERPRETATION	5
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	5
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	5
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	5
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	5
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	5
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	5
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	5
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	5
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	5
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKAVT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED VENT	5
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKDVT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED VENT	5
+LAB12838R	INSULIN-LIKE GROWTH FACTOR 1	IGF	INSULIN-LIKE GROWTH FACTOR 1	5
+LABYANTIN	INSULIN ANTIBODIES	INSAB	"INSULIN ABS, SERUM"	5
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	LEAD	LEAD	5
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LEADALERT	LEAD ALERT	5
+LABLEMSAB	LEMS AUTOAB	LEMS	LEMS AUTOANTIBODY	5
+LABCD3F	"CD3, FLUID"	LYMABFLD	"LYMPHOCYTES, ABS., FLD"	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMCOB	COMMENT	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMDIB	DISCLAIMER	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMDXB	CLINICAL INDICATION	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMINB	INTERPRETATION	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMMDB	INTERPRETED BY	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMMEB	METHOD	5
+LABMGMTB	"MGMT, METHYLATION SPECIFIC PCR, BLOOD"	MGMREB	RESULT	5
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MORPH	MORPHOLOGY	5
+LABADIFF	DIFFERENTIAL	MYEL	MYELOCYTES	5
+LABADIFF	DIFFERENTIAL	NRBC	NRBC/100WBC	5
+LAB11051R	OLANZAPINE LEVEL	OLANZ	OLANZAPINE	5
+LABPAI1AT	PAI-1 ACTIVITY	PAI1T	PAI-1 ACTIVITY	5
+IMG1PTCA	CAR PTCA EACH ADDITIONAL VESSEL	PDF	PDF RESULT	5
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMDEVIMPDT	PM DEVICE IMPLANT DATE	5
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMRALDUNIM	PM RA LEAD UNIPOLAR IMPEDANCE	5
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	PMTLBATIMP	PM TELEMETRY BATTERY IMPEDANCE	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD100	PPD 1:100 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD200	PPD 1:200 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD400	PPD 1:400 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD50	PPD 1:50 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPD800	PPD 1:800 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	PPDINT	PPD INTERPRETATION	5
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	PRO	PRODUCT ID  	5
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	PRO	PRODUCT ID  	5
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89	PRO	PRODUCT ID  	5
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	PRO	PRODUCT ID  	5
+LAB11055R	PROPAFENONE LEVEL	PROPAF	PROPAFENONE	5
+LABPSAU	POST-THERAPY PSA	PSAU	POST-THERAPY PSA	5
+LABPT12	PROTHROMBIN FRAGMENT 1 AND 2	PTFR12	PROTHROMBIN FRAG 1+2	5
+LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	RESUL8	RESULT	5
+LABBBPRH	RHO(D) IMMUNE GLOB (RHOGAM)	RHSTA	RHOGAM PRODUCT STATUS	5
+10195R	HLA ORDER - WAIT LIST STATUS CHANGE	RINFO	TO FIND RESULT:	5
+10210R	HLA ORDER - DESENSITIZATION MONITORING POST IVIG	RINFO	TO FIND RESULT:	5
+LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	RLAB8	REFERENCE LAB	5
+LABRPRQT	RPR QUANTITATIVE	RPRT	RESULT	5
+LABBMS	BMT STERILITY SAMPLE	RPT	RPT	5
+LABFUNIDS	FUNGAL ID BY SEQUENCING	RPT	RPT	5
+LABTMONA	TRICHOMONAS EXAM	RPT	RPT	5
+LABCD3F	"CD3, FLUID"	RTFDA2	NOTE	5
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	5
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	5
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	5
+LABBMS	BMT STERILITY SAMPLE	SDES	SPECIMEN	5
+LABFUNIDS	FUNGAL ID BY SEQUENCING	SDES	SPECIMEN	5
+LABTMONA	TRICHOMONAS EXAM	SDES	SPECIMEN	5
+LABFSPG	"SPECIFIC GRAVITY, FLUID"	SGFLTY	SOURCE	5
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	SI	<<STATUS>>  	5
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	SI	<<STATUS>>  	5
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89	SI	<<STATUS>>  	5
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	SI	<<STATUS>>  	5
+LABADIFF	DIFFERENTIAL	SIGNIF	SIGNIFICANT ABNORMALITIES	5
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	SLREV	SLIDE REVIEW	5
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	5
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	SOUR10	SOURCE	5
+LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	SPCTY	SPECIMEN TYPE	5
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	SPEXP	SPECIMEN EXPIRATION DATE/TIME	5
+LABFSPG	"SPECIFIC GRAVITY, FLUID"	SPGFLD	"SPECIFIC GRAVITY, FLUID"	5
+LABBMS	BMT STERILITY SAMPLE	SREQ	COMMENTS	5
+LABFUNIDS	FUNGAL ID BY SEQUENCING	SREQ	COMMENTS	5
+LABTMONA	TRICHOMONAS EXAM	SREQ	COMMENTS	5
+LABT1114	"T(11	14) BY PCR"	T11CT	COMMENT	5
+LABT1114	"T(11	14) BY PCR"	T11DS	DISCLAIMER	5
+LABT1114	"T(11	14) BY PCR"	T11DX	CLINICAL INDICATION	5
+LABT1114	"T(11	14) BY PCR"	T11MD	INTERPRETED BY	5
+LABT1114	"T(11	14) BY PCR"	T11MT	METHOD	5
+LABT1114	"T(11	14) BY PCR"	T11RS	RESULT	5
+LABYT7P	FREE THYROXINE INDEX	T4	"THYROXINE, TOTAL"	5
+LABUALBT	"ALBUMIN, URINE (TIMED)"	TALBU	ALBUMIN EXCRETION	5
+LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	TEST8	TEST NAME	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TET1K	TETANUS 1:1000 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TET2K	TETANUS 1:2000 CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TET5K	TETANUS 1:5000 CPM	5
+LABDT	DIPHTHERIA AND TETANUS AB IGG	TETAN	"TETANUS AB , IGG"	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	TETINT	TETANUS INTERPRETATION	5
+LAB11057R	TNF	TNF	TUMOR NECROSIS FTR	5
+LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXDES	TEST DESCRIPTION	5
+LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXGQL	IGG RESULT	5
+LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXGQT	IGG LEVEL	5
+LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXMQL	IGM RESULT	5
+LABTOXATP	"TOXO AB TRANSPLANT PNL, SERUM"	TOXMQT	IGM LEVEL	5
+LAB11058R	TRAZODONE LEVEL	TRAZO	TRAZODONE	5
+LABUALBT	"ALBUMIN, URINE (TIMED)"	UALB	"ALBUMIN, URINE"	5
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	UBT	UNIT BLOOD TYPE  	5
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	UBT	UNIT BLOOD TYPE  	5
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	UBT	UNIT BLOOD TYPE  	5
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	UBT	UNIT BLOOD TYPE  	5
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	UBT	UNIT BLOOD TYPE  	5
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	UBT	UNIT BLOOD TYPE  	5
+LABUELP	"ELECTROLYTE PANEL, URINE W/O CREATININE"	UCL	"CHLORIDE, URINE"	5
+LABUELP	"ELECTROLYTE PANEL, URINE W/O CREATININE"	UK	"POTASSIUM, URINE"	5
+LAB12811R	"MYOGLOBIN, URINE"	UMYOG	"MYOGLOBIN, URINE"	5
+LABUELP	"ELECTROLYTE PANEL, URINE W/O CREATININE"	UNA	"SODIUM, URINE"	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	UNCAN	UNSTIMULATED CPM	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	UNPPD	UNSTIMULATED CPM	5
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	UNT	UNIT NUMBER  	5
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	UNT	UNIT NUMBER  	5
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	UNT	UNIT NUMBER  	5
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	UNT	UNIT NUMBER  	5
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	UNT	UNIT NUMBER  	5
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	UNT	UNIT NUMBER  	5
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	UNTET	UNSTIMULATED CPM	5
+LABUALBT	"ALBUMIN, URINE (TIMED)"	UT35	COLLECTION PERIOD	5
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	VL	PRODUCT VOLUME	5
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	VL	PRODUCT VOLUME	5
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	VL	PRODUCT VOLUME	5
+LABLPD	LIPID PANEL WITH DIRECT LDL	VLDL	VLDL (CALCULATED)	5
+LABUALBT	"ALBUMIN, URINE (TIMED)"	VOL35	VOLUME	5
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSLWVTTDTD	VSLOWVT TACHY ZONE DETECTION DURATION	5
+LABPANWBC	WBC COUNT	WBC	WBC	5
+LABCD3F	"CD3, FLUID"	WBCFLD	"WBC COUNT, FLD"	5
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	XMR	UNIT CROSSMATCH  	5
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	XMR	UNIT CROSSMATCH  	5
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	XMR	UNIT CROSSMATCH  	5
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	XMR	UNIT CROSSMATCH  	5
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	XMR	UNIT CROSSMATCH  	5
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	XMR	UNIT CROSSMATCH  	5
+LAB12066R	CRMP-5-IGG WESTERN BLOT	YCRMP	CRMP-5-IGG WESTERN BLOT	5
+LABYFUN	"UREA NITROGEN, FLUID"	YFLSC1	FLUID TYPE	5
+LABYRS	RSV IGG AND IGM AB	YRS2	"RSV AB, IGG"	5
+LABYRS	RSV IGG AND IGM AB	YRS3	"RSV AB, IGM"	5
+LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK3	"POTASSIUM, 24-HR STOOL"	5
+LAB12503R	"POTASSIUM, 24-HR STOOL"	YSTK4	POTASSIUM CONCENTRATION	5
+LABYT7P	FREE THYROXINE INDEX	YT7P2	T-UPTAKE %	5
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	YUMEUT	COLLECTION DURATION	5
+10110R	HLA ORDER - FLOW AUTO CROSSMATCH	10049R	HLA LAB TESTING STATUS	4
+10135R	HLA ORDER - SCHEDULED SCREENING KIT BILLING	10049R	HLA LAB TESTING STATUS	4
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	11154R	SOURCE	4
+LABHHV6QT	"HERPES VIRUS 6 DNA, QUANT"	11155R	"HERPES VIRUS 6 DNA, QUANT"	4
+LAB12772R	ENCEPHALITIS AB SCREEN	11639R	ENCEPHALITIS AB SCREEN	4
+LAB11606R	CYTOCHROME P450 2D6 GENOTYPE	11758R	CYP2D6 GENOTYPE	4
+LAB11606R	CYTOCHROME P450 2D6 GENOTYPE	11759R	REVIEWED BY	4
+LABYGDA	"DRUG SCREEN, FLUIDS"	11829R	DRUGS DETECTED:	4
+LABYGDA	"DRUG SCREEN, FLUIDS"	11830R	SOURCE	4
+LAB11899R	"N. GONORRHOEAE RNA, RECTAL"	11900R	"N.GONORRHOEAE RNA,RECTAL"	4
+LAB11991R	"DNA EXTRACTION, NPL"	11994R	SOURCE	4
+LAB11991R	"DNA EXTRACTION, NPL"	11997R	AMENDMENT	4
+LABFQFVR	COXIELLA BURNETII AB	12015R	Q FEVER IGM PHASE LI TITER	4
+LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	12428R	INTERPRETATION	4
+LAB12853R	"EVEROLIMUS, BLOOD"	12500R	DATE OF LAST DOSE	4
+LAB12853R	"EVEROLIMUS, BLOOD"	12501R	TIME OF LAST DOSE	4
+LAB12853R	"EVEROLIMUS, BLOOD"	12502R	"DOSE, MG"	4
+LAB12507R	IBD SGI DIAGNOSTIC	12508R	IBD SGI DIAGNOSTIC	4
+LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12552R	"SIROLIMUS, BLOOD"	4
+LAB12664R	"NTX TELOPEPTIDE, URINE"	12661R	NTX	4
+LAB12664R	"NTX TELOPEPTIDE, URINE"	12662R	"NTX-TELOPEPTIDE, U"	4
+LAB12664R	"NTX TELOPEPTIDE, URINE"	12663R	"CREATININE, U"	4
+10320R	HLA - ALLO B CDC CROSSMATCH	9222R	PATIENT\'S SERUM 2 DESCRIPTION	4
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9336R	POSSIBLE HLA ANTIBODIES FOR CLASS I	4
+10355R	HLA - MONITORING BY IGG	9360R	MONITORING RESULTS FOR IGG CLASS I	4
+10355R	HLA - MONITORING BY IGG	9373R	MONITORING RESULTS FOR IGG CLASS II	4
+10360R	HLA - MONITORING BY C1Q	9390R	MONITORING RESULTS FOR C1Q CLASS I	4
+10360R	HLA - MONITORING BY C1Q	9397R	POSSIBLE HLA ANTIBODIES FOR C1Q CLASS II FOR CURRENT	4
+10375R	HLA - STR/CHIMERISM TESTING	9486R	LOCUS 12	4
+10355R	HLA - MONITORING BY IGG	9620R	REFERENCE SERUM DATE	4
+10355R	HLA - MONITORING BY IGG	9621R	REFERENCE SERUM DESCRIPTION	4
+LABARI	ABO/RH (ARI)	ABORH	ABO/RH FRONT TYPE	4
+INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADDNA	ADENOVIRUS DNA	4
+LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMACT	ADAMTS-13 ACTIVITY	4
+LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMINT	CLINICAL INTERPRETATION	4
+LABADVQL	ADENOVIRUS DNA PCR QUAL	ADOV	"ADENOVIRUS DNA, QL PCR"	4
+LABDVEAV3	ADENO DFA	ADV3	ADENOVIRUS	4
+LABDVEAV3	ADENO DFA	ADV3IP	TEST DESCRIPTION	4
+LABDVEAV3	ADENO DFA	ADV3SP	SPECIMEN	4
+INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVCP	RESULT	4
+INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVLOG	RESULT LOG 10	4
+INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVSP	SPECIMEN	4
+INACTIVE 6	ADENOVIRUS DNA PCR QUANT	ADVTIP	TEST DESCRIPTION	4
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	ALERT	ALERT	4
+LABMOLDS	MOLDS ALLERGY SCREEN	ALTRN	ALTERNARIA	4
+LAB11073R	APO B	APOB	APOLIPOPROTEIN B	4
+LABARIX	ARIXTRA	ARIX	ARIXTRA	4
+LABASPERP	ASPERGILLUS AB ID	ASPERP	ASPERGILLUS AB (ID)	4
+LABMOLDS	MOLDS ALLERGY SCREEN	ASPRG	ASPERGILLUS FUMAG.	4
+LABMOLDS	MOLDS ALLERGY SCREEN	AURBAS	AUEROBASIDIUM	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	BASO	"BASO, %"	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	BASOAB	"BASOS, ABS"	4
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	BASOSYNFLD	"BASOPHILS, SYN FLD"	4
+LABYBILEA	BILE ACIDS TOTAL	BILE	"BILE ACIDS, TOTAL"	4
+LABCAD	"CADMIUM, WHOLE BLOOD"	CAD	"CADMIUM, BLOOD"	4
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	CCNT	CELLS COUNTED	4
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	CCPT	ANTI CCP ANTIBODY LEVEL	4
+LABCD3B	"CD3, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	4
+LABCD3B	"CD3, BLOOD"	CD3PC	CD3 (PAN T) %	4
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1CI	CLINICAL INDICATION	4
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1DI	DISCLAIMER	4
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1MD	INTERPRETED BY	4
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1MT	METHOD	4
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1RE	RESULT	4
+LABCGMISC	MISCELLANEOUS TEST COAGULATION	CGRSLT	RESULT	4
+LABCGMISC	MISCELLANEOUS TEST COAGULATION	CGTEST	PROCEDURE	4
+LAB11032R	PSEUDOCHOLINESTERASE	CHEP	PSEUDOCHOLINESTERASE	4
+LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	CHLAM	C.TRACHOMATIS RESULT:	4
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	CILIA	BRONCH. LINING CELLS	4
+LABMOLDS	MOLDS ALLERGY SCREEN	CLASP	CLADOSPORIUM	4
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	CMV	CMV ANTIBODY	4
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	CMVIGM	CMV IGM AB	4
+LABMOLDS	MOLDS ALLERGY SCREEN	CNDIDA	CANDIDA	4
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	COM	COMMENT	4
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	CONA	CON A STIMULATION	4
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	CONINT	CON A INTERPRETATION	4
+LAB11056R	"CORTISOL FREE, SERUM"	CORTFREE	"CORTISOL, SERUM FREE"	4
+LABAVIN	HIV-1 INTEGRASE RESISTANCE	CULT	CULT/OTHER RSLT:	4
+LABFLDS	FLUID STERILITY TEST	CULT	CULT/OTHER RSLT:	4
+LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	CULT	CULT/OTHER RSLT:	4
+LABDBGT	DABIGATRAN	DBGTR	DABIGATRAN	4
+LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	DCL3	TEST INFORMATION	4
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	DI	ISSUE DATE/TIME	4
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	DI	ISSUE DATE/TIME	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	EOS	"EOS, %"	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	EOSAB	"EOS, ABS"	4
+LABMOLDS	MOLDS ALLERGY SCREEN	EPIPUP	EPICOCCUM PUPURASC.	4
+EXTPLTCT	EXTERNAL PLATELET COUNT	EXTPLT	EXTERNAL PLATELET COUNT	4
+EXTWBC	EXTERNAL WBC	EXTWBC	EXTERNAL WBC	4
+LABFPARVO	PARVOVIRUS IGG / IGM AB	FINT	INTERPRETATION	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	FSTVTTDTNT	FASTVT TACHY ZONE DETECTION INTERVAL	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTDIR	INTERPRETED BY	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTDIS	DISCLAIMER	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTDX	CLINICAL INDICATION	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTFLD	SOURCE	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTMET	METHOD	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTPRM	PRIMERS	4
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTRES	RESULTS	4
+LABMOLDS	MOLDS ALLERGY SCREEN	FUSMON	FUSARIUM MONILIFORME	4
+LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	GC	N.GONORRHOEAE RESULT:	4
+LABUGL	"GLUCOSE WITH CREATININE, URINE (RANDOM)"	GLUCR	GLUCOSE/CREAT RATIO	4
+LABUGL	"GLUCOSE WITH CREATININE, URINE (RANDOM)"	GLUURN	"GLUCOSE, URINE"	4
+LABSHSTGS	SHC STAT GRAM STAIN	GS	GRAM STAIN	4
+LABVP5	COMP HEPATITIS PANEL	HATINT	INTERPRETATION	4
+LABVP5	COMP HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	4
+LABHEAG	HEPATITIS B E AG	HBEAG	HEPATITIS B E AG	4
+LABHBSQL	HEPATITIS B SURFACE AB	HBSAB	HEPATITIS B SURFACE AB QUAL	4
+LABHGBFQ	HGB F QUANTITATIVE	HGFMD	REVIEWED BY	4
+LABYHGH	"GROWTH HORMONE, SERUM"	HGH	"GROWTH HORMONE, SERUM"	4
+LABUHIST	"HISTAMINE, URINE"	HISTURN	"HISTAMINE, URINE"	4
+LABMOLDS	MOLDS ALLERGY SCREEN	HLM	HELMINTHOSPORIUM	4
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HSAB	VIRAL AB HSV	4
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	4
+LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE	HVDNA	HERPES VIRUS 6 DNA	4
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	4
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDACR	BRADY MODULATION ACCELERATION REACTION	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDDCR	BRADY MODULATION DECELERATION RECOVERY	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDSLP	BRADY MODULATION SLOPE	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRMDTHR	BRADY MODULATION THRESHOLD	4
+LABJO1ABY	ANTI-JO1 ANTIBODY	JO1QNT	ANTI-JO1 LEVEL	4
+LAB11084R	LORAZEPAM LEVEL	LORAZ	"LORAZEPAM, SERUM"	4
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	LYM	"LYM, %"	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	LYMAB	"LYM, ABS"	4
+LABCD3B	"CD3, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	4
+LABMOLDS	MOLDS ALLERGY SCREEN	MCM	MUCOR	4
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	MER	MERCURY	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MONO	"MONO, %"	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MONOAB	"MONO, ABS"	4
+LAB11048R	NEURON AB	NEURON	"NEURONAL CELL ABS, SERUM"	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	NEUT	"NEUT, %"	4
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	NEUTAB	"NEUT, ABS"	4
+LABYSCERE	"INFLAMM. BOWEL DISEASE PANEL,S"	NEUTAB	NEUTROPHIL SPECIFIC ANTIBODIES	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHDI	DISCLAIMER:	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHDX	CLINICAL IND	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHIN	INTERPRETATION	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHMD	INTERPRETED BY	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHME	METHOD	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHRE	RESULT	4
+LABNVHHA	"VH HYPERMUTATION ASSAY, NON-BLOOD"	NVHSO	SOURCE	4
+IMG1STEV	CAR CORONARY STENT SINGLE	PDF	PDF RESULT	4
+PFT54	SPIROMETRY WITH DLCO	PDF	PDF RESULT	4
+VSC06	VSC ABDO AORTA ULTRASOUND	PDF	PDF RESULT	4
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	PHA	PHA STIMULATION	4
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	PHAINT	PHA INTERPRETATION	4
+LABMOLDS	MOLDS ALLERGY SCREEN	PNM	PENICILLIUM	4
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	PRO	PRODUCT ID  	4
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	PRO	PRODUCT ID  	4
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	PRO	PRODUCT ID  	4
+LABPT12	PROTHROMBIN FRAGMENT 1 AND 2	PTFRNO	PROTHROMBIN 1+2 NOTE	4
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	PWM	PWM STIMULATION	4
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	PWMINT	PWM INTERPRETATION	4
+LABMOLDS	MOLDS ALLERGY SCREEN	RHZNIG	RHISOPUS NIGRICANS	4
+10110R	HLA ORDER - FLOW AUTO CROSSMATCH	RINFO	TO FIND RESULT:	4
+10135R	HLA ORDER - SCHEDULED SCREENING KIT BILLING	RINFO	TO FIND RESULT:	4
+LABAVIN	HIV-1 INTEGRASE RESISTANCE	RPT	RPT	4
+LABFLDS	FLUID STERILITY TEST	RPT	RPT	4
+LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	RPT	RPT	4
+LABSHSTGS	SHC STAT GRAM STAIN	RPT	RPT	4
+LABAVIN	HIV-1 INTEGRASE RESISTANCE	SDES	SPECIMEN	4
+LABFLDS	FLUID STERILITY TEST	SDES	SPECIMEN	4
+LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	SDES	SPECIMEN	4
+LABSHSTGS	SHC STAT GRAM STAIN	SDES	SPECIMEN	4
+LABCLGCT2	"CHLAMYDIA GC, PAP FLUID"	SDES3	SPECIMEN	4
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	SI	<<STATUS>>  	4
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	SI	<<STATUS>>  	4
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	SI	<<STATUS>>  	4
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	4
+LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE	SOUR12	SOURCE	4
+LABADVQL	ADENOVIRUS DNA PCR QUAL	SOURC3	ADENOVIRUS SOURCE	4
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	SPEXP	SPECIMEN EXPIRATION DATE/TIME	4
+LABAVIN	HIV-1 INTEGRASE RESISTANCE	SREQ	COMMENTS	4
+LABFLDS	FLUID STERILITY TEST	SREQ	COMMENTS	4
+LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	SREQ	COMMENTS	4
+LABSHSTGS	SHC STAT GRAM STAIN	SREQ	COMMENTS	4
+LABYSSARO	AUTOANTIBODIES TO SS-A/RO	SSA	"SS-A/RO AB, IGG, SERUM"	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCCMT	COMMENT	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCDIR	INTERPRETED BY	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCDIS	DISCLAIMER	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCDX	CLINICAL INDICATION	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCMET	METHOD	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCPRM	PRIMERS	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCRLT	RESULTS	4
+LABTBCLO	"B - CELL CLONALITY PCR, TISSUE"	TBCTIS	SOURCE	4
+LAB11113R	"TOXOPLASMA AB, IGA"	TOXOA	TOXO LGA ELISA	4
+LAB11122R	TOXOPLASMA IGM ELISA	TOXOM	TOXOPLASMA IGM ELISA	4
+LABMOLDS	MOLDS ALLERGY SCREEN	TRIRUB	TRICHOPHYTON RUBRUM	4
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	UBT	UNIT BLOOD TYPE  	4
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	UBT	UNIT BLOOD TYPE  	4
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	UBT	UNIT BLOOD TYPE  	4
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	UBT	UNIT BLOOD TYPE  	4
+POC31	POC NITRAZINE PAPER FOR PH MEASUREMENT	UCMT	URINE COMMENT	4
+LABUCORT	"CORTISOL FREE, URINE"	UCORT	"CORTISOL, URINE"	4
+LABUGL	"GLUCOSE WITH CREATININE, URINE (RANDOM)"	UCR	URINE CREAT	4
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	UNT	UNIT NUMBER  	4
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	UNT	UNIT NUMBER  	4
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	UNT	UNIT NUMBER  	4
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	UNT	UNIT NUMBER  	4
+LAB12664R	"NTX TELOPEPTIDE, URINE"	UT53	COLLECTION PERIOD	4
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	VL	PRODUCT VOLUME	4
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	VL	PRODUCT VOLUME	4
+LAB12664R	"NTX TELOPEPTIDE, URINE"	VOL53	VOLUME	4
+LABUCORT	"CORTISOL FREE, URINE"	VOLUMEURN	URINE VOLUME	4
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	VZAB	VARICELLA ZOSTER AB IGG	4
+LABCD3B	"CD3, BLOOD"	WBC	WBC COUNT	4
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	XMR	UNIT CROSSMATCH  	4
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	XMR	UNIT CROSSMATCH  	4
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	XMR	UNIT CROSSMATCH  	4
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	XMR	UNIT CROSSMATCH  	4
+LABYAMIO	"AMIODARONE, SERUM"	YAMIOD	AMIODARONE	4
+LAB11074R	ACH RECEPTOR AB	YARBIN	ACH RECEPTOR BINDING ANTIBODY	4
+LABYICP	IMMUNE COMPLEX PANEL	YC1QB	C1G BINDING	4
+LABYC2REF	C2 COMPLEMENT AG	YC2RE1	C2 COMPLEMENT AG	4
+LABCENABY	CENTROMERE ANTIBODIES	YCENTA	CENTROMERE ABS.	4
+LABYAMIO	"AMIODARONE, SERUM"	YDESET	DESETHYLAMIODARONE	4
+LABYGDA	"DRUG SCREEN, FLUIDS"	YGDA2	SUSPECT DRUG	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA1	C22:0	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA2	C24:0	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA3	C26:0	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA4	C24/C22 RATIO	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA5	C26:0/C22:0 RATIO	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA6	PRISTANIC ACID	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA7	PHYTANIC ACID	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA8	PRISTANIC/PHYTANIC ACID	4
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	YLCFA9	COMMENT	4
+LABYICP	IMMUNE COMPLEX PANEL	YPEG	POLYETHL. GLYCOL IGG	4
+LABYRAB	RABIES AB TITER CHK ENDPOINT	YRAB1	RABIES ANTIBODY	4
+LABYICP	IMMUNE COMPLEX PANEL	YRAJI	RAJI CELL REPLACEMENT	4
+LABYSCERE	"INFLAMM. BOWEL DISEASE PANEL,S"	YSCER1	"SACCH CEREVISIAE AB, IGA"	4
+LABYSCERE	"INFLAMM. BOWEL DISEASE PANEL,S"	YSCER2	"SACCH CEREVISIAE AB, IGG"	4
+LABYSHBG	SEX HORMONE BINDING GLOBULIN	YSHBG1	SEX HORMONE BINDING GLOBULIN	4
+LABYUARS	"ARSENIC FRACTIONATION, URINE"	YUARS1	INORGANIC ARSENIC (TOXIC)	4
+LABUCORT	"CORTISOL FREE, URINE"	YUCOUT	COLLECTION DURATION	4
+LABCHLAM	CHLAMYDIA TRACHOMATIS / PSITTACI CULTURE	12205	SOURCE	3
+10070R	HLA ORDER - IVIG IN VITRO INHIBITION ASSAY	10049R	HLA LAB TESTING STATUS	3
+LAB11365R	FAMILIAL HIBERNIAN FEVER/TRAPS	11366R	FAMILIAL HIBERNIAN FEVER	3
+LAB11438R	CHLAMYDIA TRACHOMATIS CULTURE	11502R	CHLAMYDIA TRACHOMATIS CULTURE	3
+LAB11577R	ALAGILLE (JAG1) MUTATION	11692R	ALAGILLE (JAG1) MUTATION	3
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11838R	REASON FOR REFERRAL	3
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11840R	INTERPRETATION	3
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11842R	REVIEWED BY	3
+LAB11871R	"HIV-2 ANTIBODY, IMMUNOBLOT"	11870R	"HIV 2 ANTIBODY, IMMUNOBLOT"	3
+LABFT3DIA	"T3, FREE, TRACER DIALYSIS"	11876R	"T3, TOTAL"	3
+LAB11885R	"C. TRACHOMATIS RNA, RECTAL"	11884R	"CHLAMYDIA TRACHOMATIS RNA,RECTAL"	3
+LAB11887R	"C. TRACHOMATIS RNA, THROAT"	11886R	"CHLAMYDIA TRACHOMATIS RNA,THROAT"	3
+LABFQFVR	COXIELLA BURNETII AB	12013R	Q FEVER IGM PHASE L TITER	3
+LABYPERAB	BORDETELLA PERTUSSIS IGG ABS	12048R	PT IGG	3
+LABYPERAB	BORDETELLA PERTUSSIS IGG ABS	12049R	FHA IGG	3
+LAB11438R	CHLAMYDIA TRACHOMATIS CULTURE	12067R	SPECIMEN SOURCE	3
+LAB12210R	LIPOPROTN ASSOCTD PHOSPHLPSEA2	12211R	12211R	3
+LAB12228R	LATEX SPECIFIC IGE (RIA) PANEL	12229R	12229R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12285R	12285R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12286R	12286R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12287R	12287R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12288R	12288R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12289R	12289R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12290R	12290R	3
+LAB12284R	"LYMPHO PROLIFERATION, AG"	12291R	12291R	3
+LAB12333R	KIT MUTATION ANALYSIS EXON 9	12338R	KIT MUTATION ANALYSIS EXON 9	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12341R	SPECIMEN	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12342R	SPECIMEN ID	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12343R	SOURCE	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12344R	ORDER DATE	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12345R	REASON FOR REFERRAL	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12346R	METHOD	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12347R	RESULTS	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12348R	INTERPRETATION	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12349R	AMENDMENT	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12350R	CONSULTANT	3
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	12351R	REPORT DATE	3
+LAB12415R	ACVRL1 AND ENG (HHT)	12417R	SEQUENCING-DELETION/DUPLICATION	3
+LABLEISAB	"LEISHMANIA AB, IFA"	12428R	INTERPRETATION	3
+LABLEISAB	"LEISHMANIA AB, IFA"	12429R	INTERPRETATION	3
+LAB11190R	BORRELIA HERMSII AB PANL	12430R	INTERPRETATION	3
+LABFEHRC	EHRLICHIA CHAFFEENSIS AB	12430R	INTERPRETATION	3
+LABLEISAB	"LEISHMANIA AB, IFA"	12430R	INTERPRETATION	3
+LABLEISAB	"LEISHMANIA AB, IFA"	12431R	INTERPRETATION	3
+LAB12599R	DILATED CARDIOMYOPATHY PANEL	12598R	DILATED CARDIOMYOPATHY PANEL	3
+LAB12689R	"INFLIXIMAB, SERUM LEVEL AND AB"	12690R	"INFLIXIMAB, SERUM LEVEL AND AB"	3
+LAB12362R	"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF"	13017R	"PCA-TR, CSF"	3
+LABY25VD	"PEDIATRIC VITAMIN D, 25-HYDROXY"	25OHD2	25-HYDROXY D2	3
+LABY25VD	"PEDIATRIC VITAMIN D, 25-HYDROXY"	25OHD3	25-HYDROXY D3	3
+LABY25VD	"PEDIATRIC VITAMIN D, 25-HYDROXY"	25OHDT	25-HYDROXY D TOTAL	3
+LABYSTONE	STONE ANALYSIS	3RDCONST	3RD CONSTITUENT	3
+10305R	HLA - ALLO T FLOW CROSSMATCH	9141R	SERUM DESCRIPTION 3	3
+10310R	HLA - ALLO B FLOW CROSSMATCH	9171R	SERUM DESCRIPTION 3	3
+10315R	HLA - ALLO T CDC CROSSMATCH	9192R	PATIENT\'S SERUM 1 DESCRIPTION	3
+10315R	HLA - ALLO T CDC CROSSMATCH	9199R	PATIENT\'S SERUM 3 DATE	3
+10315R	HLA - ALLO T CDC CROSSMATCH	9201R	PATIENT\'S SERUM 3 UNTREATED RESULT	3
+10315R	HLA - ALLO T CDC CROSSMATCH	9202R	PATIENT\'S SERUM 3 DTT TREATED RESULT	3
+10320R	HLA - ALLO B CDC CROSSMATCH	9225R	PATIENT\'S SERUM 3 DATE	3
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9344R	INTERPRETATION/ COMMENTS CLASS II	3
+10375R	HLA - STR/CHIMERISM TESTING	9487R	LOCUS 13	3
+10180R	HLA ORDER - KIT REQUEST NOTICE	9710R	KIT REQUEST NOTICE FOR LAB	3
+LABAACSF	"AMINO ACIDS, CSF"	AAMINOA	A-AMINOADIPIC ACID	3
+LABAACSF	"AMINO ACIDS, CSF"	AASINT	INTERPRETATION	3
+LABCBCD	CBC WITH DIFF	ABGRAN	"GRAN, ABS"	3
+LABUCARN	"CARNITINE, URINE"	ACFCRU	"ACYL/FREE CARN RATIO, UR"	3
+LABRBCENZ	RED CELL ENZYMES	ADA	ADENOSINE DEAMINASE	3
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADDNA	ADENOVIRUS DNA	3
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVCP	RESULT	3
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVLOG	RESULT LOG 10	3
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVSP	SPECIMEN	3
+LABADVQT	"ADENOVIRUS REAL TIME PCR,QUANTITATIVE"	ADVTIP	TEST DESCRIPTION	3
+LAB11140R	MATERNAL SCREEN	AFP	AFP SCREEN TO ALLIED/STATE LAB	3
+LABAACSF	"AMINO ACIDS, CSF"	ALANINE	ALANINE	3
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ALERT	ALERT	3
+LABANDRO	"ANDROSTENEDIONE, SERUM"	ANDRO	"ANDROSTENEDIONE, S"	3
+LABYANDRO	"ANDROSTENEDIONE, SERUM"	ANDRO	"ANDROSTENEDIONE, S"	3
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	AODOR	A-OLIGO/DI-OLIGO RAT	3
+LABFCHGA	A. PHAGOCYTOPHILUM AB	APIGG	A.PHAGOCYTOPHILUM IGG	3
+LABFCHGA	A. PHAGOCYTOPHILUM AB	APIGM	A.PHAGOCYTOPHILUM IGM	3
+LABFCHGA	A. PHAGOCYTOPHILUM AB	APINT	A.PHAGOCYTOPHILUM INTERPRETATION	3
+LABAACSF	"AMINO ACIDS, CSF"	ARGININE	ARGININE	3
+LABAACSF	"AMINO ACIDS, CSF"	ASPART	ASPARTIC ACID	3
+LABAACSF	"AMINO ACIDS, CSF"	ASPGN	ASPARAGINE	3
+LABAACSF	"AMINO ACIDS, CSF"	BALN	B-ALANINE	3
+LABAACSF	"AMINO ACIDS, CSF"	BAMBA	B-AMINOISOBUTYRIC AC	3
+LABCDIFF	"DIFFERENTIAL, CSF"	BASOCSF	"BASOPHILS, CSF"	3
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	BASOFLD	"BASOPHILS, FLD"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C2526N	"CD25, % OF CD3+/CD4+/CD26NEG"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C257N	"CD25, % OF CD3+/CD4+/CD7NEG"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C264AB	"CD26-/CD4+, ABS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C264PC	CD26-/CD4+ %	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3026N	"CD30, % OF CD3+/CD4+/CD26NEG"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C307N	"CD30, % OF CD3+/CD4+/CD7NEG"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C326NA	"CD3+/CD26NEG, ABS."	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3426N	"CD3+/CD4+/CD26NEG, ABS."	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3426P	"CD3+/CD4+/CD26NEG, % OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3426T	"CD3+/CD4+/CD26NEG, % OF CD4+ T"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C347NA	"CD3+/CD4+/CD7NEG, ABS."	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C347NP	"CD3+/CD4+/CD7NEG, % OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C347NT	"CD3+/CD4+/CD7NEG, % OF CD4+ T"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C37NA	"CD3+/CD7NEG, ABS."	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P4PA	"CD3+/CD4+,ABS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P4PP	"CD3+/CD4+, % OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P7NP	"CD3+/CD7NEG, % OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P8PN	"CD3+/CD8+, ABS. OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C3P8PP	"CD3+/CD8+, % OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C48RAT	TCD4/CD8 RATIO	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C4N7AB	"CD4+/CD7-,ABS. (C4N7AB)"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C4N7PC	CD4+/CD7-% (C4N7PC)	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C5226N	"CD52, % OF CD3+/CD4+/CD26NEG"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	C527N	"CD52, % OF CD3+/CD4+/CD7NEG"	3
+LABUCARN	"CARNITINE, URINE"	CARNFU	"FREE CARNITINE, UR"	3
+LABUCARN	"CARNITINE, URINE"	CARNTU	"TOTAL CARNITINE, UR"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD19AB	"CD19 (PAN B), ABS."	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD19PC	CD19 (PAN B) %	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD26NP	"CD3+/CD26NEG, % OF LYMPHS"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD3AB	"CD3 (PAN T), ABS."	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD3PC	CD3 (PAN T) %	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD4TOT	CD4 TOTAL	3
+LABCELDIS	CELIAC DISEASE SYSTEM	CELDR	CELIAC DISEASE SYS	3
+LABCHLAM	CHLAMYDIA TRACHOMATIS / PSITTACI CULTURE	CHLA	CHLAMYDIA CULTURE	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	CHOL	"CHOLESTEROL, TOTAL"	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	CHOLHDL	CHOLESTEROL/HDL RATIO	3
+LABAACSF	"AMINO ACIDS, CSF"	CITRUL	CITRULLINE	3
+LABAVRT	HIV - 1 GENOTYPING	CULT	CULT/OTHER RSLT:	3
+LABMMIC	UNLISTED MICROBIOLOGY ORDER	CULT	CULT/OTHER RSLT:	3
+LABAACSF	"AMINO ACIDS, CSF"	CYSTINE	CYSTINE	3
+LABAACSF	"AMINO ACIDS, CSF"	CYTHN	CYSTATHIONINE	3
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	DI	ISSUE DATE/TIME	3
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	DI	ISSUE DATE/TIME	3
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	DI	ISSUE DATE/TIME	3
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	DI	ISSUE DATE/TIME	3
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	DI	ISSUE DATE/TIME	3
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	DI	ISSUE DATE/TIME	3
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	DI	ISSUE DATE/TIME	3
+LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	DIAZEP	DIAZEPAM	3
+POC3	POC URINE DIPSTICK FOR KETONES	DIPLOT	DIPSTICK LOT	3
+POC11	URINE CHEMSTRIP GP	DIPSLOT	DIPSTICK LOT	3
+LABDUTYPE	WEAK D TYPING	DUTYP	WEAK D TYPING	3
+LABDVEMPV	DIRECT VIRAL EXAM MPV	DVE	RESULTS	3
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	EA	EARLY ANTIBODY (R+D) IGG	3
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	EBNA	EPSTEIN-BARR NUC AB	3
+LAB11190R	BORRELIA HERMSII AB PANL	ECINT	INTERPRETATION	3
+LABCDIFF	"DIFFERENTIAL, CSF"	EOSCSF	"EOSINOPHILS, CSF"	3
+POC11	URINE CHEMSTRIP GP	EXPDATE	EXP DATE	3
+POC3	POC URINE DIPSTICK FOR KETONES	EXPDATE	EXP DATE	3
+LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	FINT	INTERPRETATION	3
+LABFPLTS	"PLATELET COUNT, FLUID"	FLDSOU	"SOURCE, FLUID"	3
+LAB12814R	MYCOPLASMA IGG/IGM AB	FMYABG	M.PNEUMONIA AB IGG	3
+LABFRPSA	FREE PSA	FPSA	FREE PSA	3
+LABFRPSA	FREE PSA	FREEPSAPC	% FREE PSA	3
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTDETD	FASTVT TACHY ZONE DETECTION DURATION	3
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTDETD	FASTVT TACHY ZONE DETECTION DURATION	3
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	FSTVTTZNSC	FASTVT TACHY ZONE SUDDEN ONSET CRITERIA	3
+LABFT3DIA	"T3, FREE, TRACER DIALYSIS"	FT3	FREE T3 (BY DIALYSIS)	3
+LABFTCLO	"T-CELL CLONALITY PCR, FLUID"	FTCMT	COMMENT	3
+LABFRAGX	FRAGILE X	FXAI	ALLELE I	3
+LABFRAGX	FRAGILE X	FXAII	ALLELE II	3
+LABFRAGX	FRAGILE X	FXDIS	DISCLAIMER	3
+LABFRAGX	FRAGILE X	FXDX	CLINICAL INDICATION	3
+LABFRAGX	FRAGILE X	FXINT	INTERPRETATION	3
+LABFRAGX	FRAGILE X	FXMD	INTERPRETED BY	3
+LABFRAGX	FRAGILE X	FXMET	METHOD	3
+LABFRAGX	FRAGILE X	FXRES	RESULT	3
+LABRBCENZ	RED CELL ENZYMES	G6PD	"G6PD, QUANTITATIVE"	3
+LABYGAS	"GASTRIN, SERUM"	GASTRIN	"GASTRIN, SERUM"	3
+LABGDMSCR	GLUCOSE GDM SCREEN	GDMSCR	"GLUCOSE, GDM SCREEN"	3
+LAB11116R	FANCONI MUTATION ANALYSIS	GFANCO	FANCONI AN. GP C MUT	3
+LABAACSF	"AMINO ACIDS, CSF"	GLUTAC	GLUTAMIC ACID	3
+LABAACSF	"AMINO ACIDS, CSF"	GLUTAMINE	GLUTAMINE	3
+POC11	URINE CHEMSTRIP GP	GLUURINE	GLUCOSE URINE	3
+LABAACSF	"AMINO ACIDS, CSF"	GLYCINE	GLYCINE	3
+LABRBCENZ	RED CELL ENZYMES	GPI	GLUC. PHOSPHATE ISOM	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	HDL	HDL CHOLESTEROL	3
+LABHCVA	HEPATITIS C AB EIA	HEPCAB	HEPATITIS C AB	3
+LABRBCENZ	RED CELL ENZYMES	HEX	HEXOKINASE	3
+LABKBST	KLEIHAUER - BETKE STAIN	HGBFPC	% CELLS WITH HGB F	3
+LABAACSF	"AMINO ACIDS, CSF"	HISTID	HISTIDINE	3
+LABAACSF	"AMINO ACIDS, CSF"	HMCY	HOMOCYSTINE	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	HNS	"CD4+CD8 TOTAL, CD3+%"	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	HSRATIO	"CD4/CD8 RATIO, CD3+"	3
+LABFHV12	"HSV 1/2 PCR, NON CSF"	HV1DNA	"HSV-1 DNA, QN PCR"	3
+LABFHV12	"HSV 1/2 PCR, NON CSF"	HV2DNA	"HSV-2 DNA, QN PCR"	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	HVA	HOMOVANILLIC ACID	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	HVAINT	HVA INTRP	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	HVAUR	"HVA, URINE"	3
+LABHYPP	HYPERSENSITIVITY PNEUMONITIS SCREEN	HYPINT	INTERPRETATION	3
+LABHYPP	HYPERSENSITIVITY PNEUMONITIS SCREEN	HYPPN	IGG ANTIBODY PANEL	3
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRMAXRT	BRADY MAX. SENSOR RATE	3
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRMDSLP	BRADY MODULATION SLOPE	3
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATPVT	ICD DETECTIONS AND TREATMENTS ATP EPISODES VENTRICULAR	3
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	3
+LABAACSF	"AMINO ACIDS, CSF"	INTSBY	INTERPRETED BY	3
+LABAACSF	"AMINO ACIDS, CSF"	ISOLEU	ISOLEUCINE	3
+LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	JO1AB	AUTOANTIBODIES TO JO 1	3
+LABKBST	KLEIHAUER - BETKE STAIN	KBSINT	INTERPRETATION	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	LDL	LDL (CALCULATED)	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	LDLHDL	LDL/HDL RATIO	3
+LABAACSF	"AMINO ACIDS, CSF"	LEUCINE	LEUCINE	3
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	LOTNO	LOT #	3
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	LYM	LYMPHOCYTES %	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	LYMAB	"LYMPHOCYTES, ABS."	3
+LABAACSF	"AMINO ACIDS, CSF"	LYSINE	LYSINE	3
+LABKBST	KLEIHAUER - BETKE STAIN	MCV	MCV	3
+LABMECAPC	MECA BY PCR	MECAR	MECA PCR RESULT	3
+LABYHMS	"HEAVY METALS SCREEN, BLOOD"	MER	"MERCURY, BLOOD"	3
+LABADIFF	DIFFERENTIAL	META	METAMYELOCYTES	3
+LABAACSF	"AMINO ACIDS, CSF"	METHIO	METHIONINE	3
+LABKBST	KLEIHAUER - BETKE STAIN	MLFB	ML FETAL BLOOD	3
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	MODOR	MONO-OLIGO/DI-OLIGO RATIO	3
+LABMPVPCR	MPV PCR	MPVDI	DISCLAIMER	3
+LABMPVPCR	MPV PCR	MPVMT	METHOD	3
+LABMPVPCR	MPV PCR	MPVRE	METAPNEUMOVIRUS RNA	3
+LABMPVPCR	MPV PCR	MPVSP	SPECIMEN	3
+LABMUMRC	MUMPS RAPID CULTURE	MUMRC1	MUMPS RAPID CULTURE	3
+LABMUMRC	MUMPS RAPID CULTURE	MUMRCD	TEST DESCRIPTION	3
+LABMUMRC	MUMPS RAPID CULTURE	MUMSPM	SPECIMEN	3
+LABYFNA	"SODIUM, FLUID"	NAFLD	SODIUM BODY FLUID	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTCBC	CBC COMMENT	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTCO	"STIM. CONTROL,NBT"	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTCOM	"COMMENT, NBT"	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTINT	INTERPRETATION	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTMD	REVIEWED BY	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTPT	"STIM. PATIENT,NBT"	3
+LABNBTS	NITROBLUE TETRAZOLIUM RED	NBTREV	PATHOLOGIST REVIEW	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	NHDL	"NON-HDL CHOL, CALC"	3
+LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	NORDIAZ	NORDIAZEPAM	3
+LABAACSF	"AMINO ACIDS, CSF"	ORNITHINE	ORNITHINE	3
+LABYPRIMI	PRIMIDONE AND PHENOBARBITAL	PB	PHENOBARBITAL	3
+CRP333	RIGHT LEFT CATH CORS ANGIO	PDF	PDF RESULT	3
+IMG11LHC	CAR LT HEART CATH	PDF	PDF RESULT	3
+VSC09	VSC MESENTERIC ULTRASOUND	PDF	PDF RESULT	3
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	PEDCOM	SPECIMEN COMMENT	3
+LABAACSF	"AMINO ACIDS, CSF"	PHALN	PHENYLALANINE	3
+POC3	POC URINE DIPSTICK FOR KETONES	PHLOC	LOCATION	3
+LABRBCENZ	RED CELL ENZYMES	PK	PYRUVATE KINASE	3
+LABFPLTS	"PLATELET COUNT, FLUID"	PLTFLD	"PLATELET CT, FLUID"	3
+LABFPOLIO	"POLIOVIRUS 1-3 AB, SERUM"	PLV1	POLIOVIRUS 1 AB	3
+LABFPOLIO	"POLIOVIRUS 1-3 AB, SERUM"	PLV2	POLIOVIRUS 2 AB	3
+LABFPOLIO	"POLIOVIRUS 1-3 AB, SERUM"	PLV3	POLIOVIRUS 3 AB	3
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRVLDUNIM	PM RV LEAD UNIPOLAR IMPEDANCE	3
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMTLBATIMP	PM TELEMETRY BATTERY IMPEDANCE	3
+LABYPRIMI	PRIMIDONE AND PHENOBARBITAL	PRIMIDONE	PRIMIDONE	3
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94	PRO	PRODUCT ID  	3
+LABAACSF	"AMINO ACIDS, CSF"	PROLINE	PROLINE	3
+POC11	URINE CHEMSTRIP GP	PROTURINE	PROTEIN URINE	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	PTH18R	RECEIVED IN FLOW CYTOMETRY	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRC	COMMENT	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRD	INTERPRETED BY	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRE	RESULT	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRF	SOURCE	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRM	METHOD	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRP	PRIMER	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRS	DISCLAIMER	3
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	QFBCRX	CLINICAL INDICATION	3
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	QY	QUANTITY	3
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRAMP	RA THRESHOLD AMPLITUDE	3
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	RATHRDUR	RA THRESHOLD DURATION	3
+LABRBCENZ	RED CELL ENZYMES	RBCINT	INTERPRETATION	3
+LABRBCENZ	RED CELL ENZYMES	RBCMD	REVIEWED BY	3
+10070R	HLA ORDER - IVIG IN VITRO INHIBITION ASSAY	RINFO	TO FIND RESULT:	3
+10265R	HLA ORDER - SCHEDULED SCREENING KIT PROCESSING	RINFO	TO FIND RESULT:	3
+LABAVRT	HIV - 1 GENOTYPING	RPT	RPT	3
+LABDVEMPV	DIRECT VIRAL EXAM MPV	RPT	RPT	3
+LABMMIC	UNLISTED MICROBIOLOGY ORDER	RPT	RPT	3
+LABDVERS	"RSV RAPID ANTIGEN, DIRECT EIA"	RRSP	SPECIMEN	3
+LABDVERS	"RSV RAPID ANTIGEN, DIRECT EIA"	RSV	RESPIRATORY SYNCYTIAL VIRUS	3
+LABDVERS	"RSV RAPID ANTIGEN, DIRECT EIA"	RSVCOM	COMMENTS	3
+LABRUBG	RUBELLA IGG ANTIBODY	RUBINT	INTERPRETATION	3
+LABRUBG	RUBELLA IGG ANTIBODY	RUBR	RUBELLA IGG ANTIBODY	3
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRAMP	RV THRESHOLD AMPLITUDE	3
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRDUR	RV THRESHOLD DURATION	3
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRSENAM	RV THRESHOLD SENSING AMPLITUDE	3
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RVTHRSENPO	RV THRESHOLD SENSING POLARITY	3
+LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SCL70	"SCL 70 AB, IGG, SERUM"	3
+LABAVRT	HIV - 1 GENOTYPING	SDES	SPECIMEN	3
+LABDVEMPV	DIRECT VIRAL EXAM MPV	SDES	SPECIMEN	3
+LABMMIC	UNLISTED MICROBIOLOGY ORDER	SDES	SPECIMEN	3
+LABAACSF	"AMINO ACIDS, CSF"	SERINE	SERINE	3
+LABYSERTR	SERTRALINE LEVEL	SERTRALINE	"SERTRALINE, S"	3
+LABSEZ	SEZARY CELL SCREEN	SEZ	SEZARY CELL SCREEN	3
+LABFSFTA	TREPONEMA PALLIDUM AB	SFTA	SYPHILIS-FTA	3
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94	SI	<<STATUS>>  	3
+LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SMAB	AUTOANTIBODIES TO SM	3
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	SPEXP	SPECIMEN EXPIRATION DATE/TIME	3
+LABAVRT	HIV - 1 GENOTYPING	SREQ	COMMENTS	3
+LABDVEMPV	DIRECT VIRAL EXAM MPV	SREQ	COMMENTS	3
+LABMMIC	UNLISTED MICROBIOLOGY ORDER	SREQ	COMMENTS	3
+LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SSA	"SS-A/RO AB, IGG, SERUM"	3
+LABSNA	SPECIFIC ANA PANEL	SSAQNT	ANTI-RO LEVEL	3
+LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	SSB	"SS-B/LA AB, IGG, SERUM"	3
+LABYSSBLA	AUTOANTIBODIES TO SS-B/LA	SSB	"SS-B/LA AB, IGG, SERUM"	3
+LABAACSF	"AMINO ACIDS, CSF"	TAURINE	TAURINE	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	TCD4AB	"TOTAL CD4, ABS."	3
+LABLPDC	"LIPID PANEL, FASTING PATIENT"	TGL	"TRIGLYCERIDE, SER/PLAS"	3
+LABUTHI	"THIOCYANATE, URINE (RANDOM)"	THIOCYANURN	"THIOCYANATE, URINE"	3
+LABAACSF	"AMINO ACIDS, CSF"	THREONINE	THREONINE	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	TIMEU	COLLECTION PERIOD	3
+LABTOXOAB	TOXOPLASMA IGM & IGG	TOXIGG	TOXOPLASMA IGG	3
+LABTOXOAB	TOXOPLASMA IGM & IGG	TOXIGM	TOXOPLASMA IGM	3
+LABTOXOIM	TOXOPLASMA IGM	TOXM	TOXOPLASMA IGM	3
+LABCSFTP	"PROTEIN TOTAL, CSF"	TPCSTN	TUBE NUMBER	3
+LABYTSIG	"TSH IMMUNOGLOBULINS, SERUM"	TSIG	"THROID-STIMULATING IG, SERUM"	3
+LABAACSF	"AMINO ACIDS, CSF"	TYROSINE	TYROSINE	3
+LABYENABE	"EXTRACTABLE NUCLEAR AG EVALUATION, SERUM"	U1RNPAB	AUTOANTIBODIES TO U1RNP	3
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	UBT	UNIT BLOOD TYPE  	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	UCR	"CREATININE, URINE"	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	UCR	"CREATININE, URN"	3
+POC3	POC URINE DIPSTICK FOR KETONES	UKET	KETONES	3
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	UNT	UNIT NUMBER  	3
+LABUHVA	"HOMOVANILLIC ACID, URINE"	UVOL	VOLUME	3
+LABAACSF	"AMINO ACIDS, CSF"	VALINE	VALINE	3
+LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANCOSDD	"VANCOMYCIN, SINGLE DAILY DOSE"	3
+LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANUN2	DATE & TIME OF LAST DOSE	3
+LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANUN3	DOSAGE	3
+LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	VANUN4	ADMINISTRATION ROUTE	3
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	VCA	VIRAL CAPSID AB	3
+LABEBVC	EBV VIRAL CAPSID ANTIGEN IGA	VCAA	VCA IGA	3
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	VCAM	VIRAL CAPSID AB-IGM	3
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS2	HIV-1 P24 (NON ICD) CONF.	3
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	VDS4	HIV-1 P24 (NON ICD) AG LEVEL	3
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	VL	PRODUCT VOLUME	3
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	VL	PRODUCT VOLUME	3
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	VL	PRODUCT VOLUME	3
+LABVLDL	VLDL (CALCULATED)	VLDL	VLDL (CALCULATED)	3
+LABYUARS	"ARSENIC FRACTIONATION, URINE"	VOLUMEURN	URINE VOLUME	3
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	WBC	WBC COUNT	3
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	XMR	UNIT CROSSMATCH  	3
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	XMR	UNIT CROSSMATCH  	3
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	XMR	UNIT CROSSMATCH  	3
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	XMR	UNIT CROSSMATCH  	3
+LAB11071R	"MITOCHONDRIAL AB M2, SERUM"	YAMA	"MITOCHONDRIAL ANTIBODY, M2"	3
+LABYDGIGA	"GLIADIN (DEAMIDATED) IGA,SERUM"	YDGLIA	GLIADIN (DEAMIDATED) IGA	3
+LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	YDIAZ3	DIAZEPAM & NORDIAZEPAM	3
+LABYFNA	"SODIUM, FLUID"	YFLSC1	FLUID TYPE	3
+LABYSERTR	SERTRALINE LEVEL	YSERT2	DESMETHYLSERTRALINE	3
+LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG1	"MAGNESIUM, QN, F"	3
+LABYSTRAB	STRIATIONAL (STRIATED MUSCLE) AB	YSTA	STRIATIONAL (STRIATED MUSCLE)	3
+LABYUARS	"ARSENIC FRACTIONATION, URINE"	YUARS2	ORGANIC ARSENIC(NON-TOXIC)	3
+LABYUARS	"ARSENIC FRACTIONATION, URINE"	YUARS3	COLLECTION DURATION	3
+10040R	HLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)	10049R	HLA LAB TESTING STATUS	2
+10085R	HLA ORDER - TYPING ADD ON LOCUS	10049R	HLA LAB TESTING STATUS	2
+10175R	HLA ORDER - KIT PROCESSING RECIPIENT	10049R	HLA LAB TESTING STATUS	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11005R	A. TENUIS/ALTERNATA IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11006R	ASPERGILLUS FUMIGATUS IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11007R	AUREOBASIDIUM PULLULANS IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11008R	MICROPOLYSPORA FAENI IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11009R	P. NOTATUM/CHRYSOGENUM IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11010R	PHOMA BETAE IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11011R	THERMOACTINOMYCES VULGARIS IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11012R	TRICHODERMA VIRIDE IGG	2
+LAB11004R	HYPERSENS. PNEUMONITIS PNL	11141R	INTERPRETATION	2
+LAB11217R	"IRON,LIVER TISSUE"	11218R	"IRON, LIVER TS"	2
+LAB11217R	"IRON,LIVER TISSUE"	11224R	HEPATIC IRON INDEX	2
+LAB11322R	MOTOR NEUROPATHY AB SCREEN	11323R	CO-GM1 TRIAD AB	2
+LAB11375R	OSTEOGENESIS IMPERFECTA SEQUEN	11376R	OSTEOGENESIS IMPERFECTA SEQUEN	2
+LAB11401R	HALIBUT IGE	11459R	HALIBUT IGE	2
+LAB11437R	IBD DIAGNOSTIC	11501R	IBD DIAGNOSTIC	2
+LAB11518R	BCR-ABL1 KINASE DOMAIN MUT ANL	11627R	BCR-ABL1 KINASE DOMAIN MUT ANAL	2
+LAB11527R	SPINAL MUSCULAR ATROPHY DIAGNO	11637R	SPINAL MUSCULAR ATROPHY DIAGNO	2
+LAB11529R	ENCEPHALITIS AB SCREEN	11639R	ENCEPHALITIS AB SCREEN	2
+LAB11534R	MTDNA SCREENING PANEL	11644R	MTDNA SCREENING PANEL	2
+LAB11568R	"BRUCELLA SEROLOGIES, CSF"	11682R	"BRUCELLA IGG,CSF"	2
+LAB11568R	"BRUCELLA SEROLOGIES, CSF"	11683R	"BRUCELLA IGM,CSF"	2
+LAB11571R	"CYSTIC FIBROSIS, 97 MUTATIONS"	11686R	"CYSTIC FIBROSIS, 97 MUTATIONS"	2
+LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	11687R	"HEXOSAMINIDASE TOTAL, WBC"	2
+LAB11590R	FREE FATTY ACIDS	11715R	FREE FATTY ACIDS	2
+LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	11780R	"HEXOSAMINIDASE PERCENT A, WBC"	2
+LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	11781R	INTERPRETATION	2
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11834R	SPECIMEN	2
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11835R	SPECIMEN ID	2
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11837R	ORDER DATE	2
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11839R	METHOD	2
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11843R	RELEASE DATE	2
+LAB11863R	ASPIRIN RESISTANCE	11861R	11-DEHYDROTHROMBOXANE B2	2
+LAB11863R	ASPIRIN RESISTANCE	11862R	"CREATININE, RANDOM URINE"	2
+LAB11867R	C3A DESARG FRAGMENT	11866R	C3A DESARG FRAGMENT	2
+LAB11989R	LONG QT SYNDROME	11990R	LQTS RESULTS	2
+LAB13008R	Q FEVER IGG PHASE II TITER	12014R	Q FEVER IGG PHASE LI TITER	2
+LAB12043R	IL 5	12044R	INTERLEUKIN 5	2
+LAB12373R	"CARBAMAZEPINE-10,11-EPOXIDE"	12375R	"CARBAMAZEPINE-10,11-EPOXIDE"	2
+LAB12395R	5\'NUCLEOTIDASE	12396R	5\' NUCLEOTIDASE	2
+LAB12814R	MYCOPLASMA IGG/IGM AB	12431R	INTERPRETATION	2
+LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12553R	DATE OF LAST DOSE	2
+LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12554R	TIME OF LAST DOSE	2
+LAB12548R	"SIROLIMUS, BLOOD (HPLC-MS MS)"	12555R	"DOSE, MG"	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12671R	TETANUS STIMULATION INDEX	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12672R	TETANUS INTERPRETATION	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12673R	CANDIDA STIMULATION INDEX	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	12674R	CANDIDA INTERPRETATION	2
+LAB12733R	ALPHA-2 PLASMIN INHIBITOR	12734R	ALPHA-2 PLASMIN INHIBITOR	2
+LAB12836R	ETHYLENE GLYCOL	12837R	ETHYLENE GLYCOL	2
+LABY21HYX	"21 - HYDROXYLASE AB, SERUM"	21OHXAB	21-HYDROXYLASE AB	2
+LABY5NUC	5 - NUCLEOTIDASE	5NUCLEOT	5\'NUCLEOTIDASE	2
+10295R	HLA - AUTO T FLOW CROSSMATCH	9092R	SERUM DESCRIPTION 3	2
+10300R	HLA - AUTO B FLOW CROSSMATCH	9113R	SERUM DESCRIPTION 3	2
+10315R	HLA - ALLO T CDC CROSSMATCH	9200R	PATIENT\'S SERUM 3 DESCRIPTION	2
+10320R	HLA - ALLO B CDC CROSSMATCH	9218R	PATIENT\'S SERUM 1 DESCRIPTION	2
+10320R	HLA - ALLO B CDC CROSSMATCH	9226R	PATIENT\'S SERUM 3 DESCRIPTION	2
+10320R	HLA - ALLO B CDC CROSSMATCH	9227R	PATIENT\'S SERUM 3 UNTREATED RESULT	2
+10320R	HLA - ALLO B CDC CROSSMATCH	9228R	PATIENT\'S SERUM 3 DTT TREATED RESULT	2
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9332R	SERUM DESCRIPTION	2
+10355R	HLA - MONITORING BY IGG	9349R	IGG COMBINED CPRA FOR CURRENT SERUM	2
+10355R	HLA - MONITORING BY IGG	9354R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS I FOR CURRENT	2
+10355R	HLA - MONITORING BY IGG	9374R	INTERPRETATION/ COMMENTS  CLASS II	2
+10360R	HLA - MONITORING BY C1Q	9403R	MONITORING RESULTS FOR C1Q CLASS II	2
+10330R	HLA - SCREENING IGG	9582R	INTERPRETATION/ COMMENTS CLASS I	2
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9586R	INTERPRETATION/ COMMENTS CLASS I	2
+LABYSTA1A	"ALPHA-1-ANTITRYPSIN, FECAL"	A1AANTITRYPF	"A-1-ANTITRYP,FECAL"	2
+LABARI	ABO/RH (ARI)	ABORH	ABO/RH CORD	2
+LABARI	ABO/RH (ARI)	ABORH	ABO/RH STILLBORN	2
+LAB11068R	"ANGIOTENSIN CONVERTING ENZYME, SERUM"	ACE	ANGIOTESIN CONVERTING ENZYME	2
+LABACRBLO	ACETYLCHOLINE BLOCKING AB	ACRBLO	ACETYLCHOLINE BLOCKING AB	2
+LABACRMOD	ACETYLCHOLINE MOD AB	ACRMOD	ACETYLCHOLINE MODULATING AB	2
+LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADMAG	ANTIGEN	2
+LABCSFALB	"ALBUMIN, CSF"	ALBCSF	"ALBUMIN, CSF"	2
+LABYALUM	ALUMINUM	ALUM	ALUMINUM	2
+LABIMM1	IMMUNOLOGY PANEL 1	ANA	ANA RESULT	2
+LABIMM2	IMMUNOLOGY PANEL 2	ANA	ANA RESULT	2
+LABIMM1	IMMUNOLOGY PANEL 1	ANAINTERP	ANA INTERPRETATION	2
+LABIMM2	IMMUNOLOGY PANEL 2	ANAINTERP	ANA INTERPRETATION	2
+LAB11022R	ISLET CELL AB	ANTIIS	"ISLET CELL AB, IGG"	2
+LAB11075R	ANTI - SMOOTH MUSCLE AB	ASMA	ANTI-SMOOTH MUSCLE ANITBODY	2
+LABASO	ANTISTREPTOLYSIN O TITER	ASO	ASO TITER (IU/ML)	2
+LABSALCOR	"CORTISOL, SALIVA"	AVGCOR	AVERAGE SALIVARY CORTISOL	2
+LABBMCD34	"CD34 PANEL, BONE MARROW"	BCD34V	CD34+ PROGENITOR VIABILITY	2
+LABBKVPC	BK VIRUS DNA QUANTITATION	BKQ	BKV DNA QUANTITATION	2
+LABBLDCR	BLOOD FOR CREATININE CLEARANCE	BLDCR	BLD FOR CREATININE CLEARANCE	2
+LABIMM1	IMMUNOLOGY PANEL 1	C3	"COMPLEMENT C3, SERUM"	2
+LABCIRCAI	CIRCUIT IONIZED CALCIUM	CAICR	CIRCUIT IONIZED CALCIUM	2
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD26AB	"CD26 (PAN-T), ABS"	2
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD26PC	CD26 (PAN-T) %	2
+LABBMCD34	"CD34 PANEL, BONE MARROW"	CD45PV	CD45+ VIABILITY	2
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD7AB	"CD7 (PAN T), ABS"	2
+PATH18	"SEZARY/T-CELL PANEL + CD30/CD25/CD52, BLOOD"	CD7PC	CD7 (PAN T) %	2
+LABCDMLPA	CDH1 MLPA	CDMLDI	DISCLAIMER	2
+LABCDMLPA	CDH1 MLPA	CDMLDX	CLINICAL INDICATION	2
+LABCDMLPA	CDH1 MLPA	CDMLMD	INTERPRETED BY	2
+LABCDMLPA	CDH1 MLPA	CDMLME	METHOD	2
+LABCDMLPA	CDH1 MLPA	CDMLRE	RESULT	2
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLDI	DISCLAIMER	2
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLDX	CLINICAL INDICATION	2
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLMD	INTERPRETED BY	2
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLME	METHOD	2
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFMLRE	RESULT	2
+LABCFPT	CF POLY T ANALYSIS	CFPTCL	CLINICAL INDICATION	2
+LABCFPT	CF POLY T ANALYSIS	CFPTDS	DISCLAIMER	2
+LABCFPT	CF POLY T ANALYSIS	CFPTIN	INTERPRETED BY	2
+LABCFPT	CF POLY T ANALYSIS	CFPTME	METHOD	2
+LABCFPT	CF POLY T ANALYSIS	CFPTRS	RESULT	2
+LABBMTP	BMT PRETRANSPLANT PANEL	CMGDES	CMV IGG TEST DESCRIPTION	2
+LABBMTP	BMT PRETRANSPLANT PANEL	CMGQUL	CMV IGG RESULT	2
+LABBMTP	BMT PRETRANSPLANT PANEL	CMMDES	CMV IGM TEST DESCRIPTION	2
+LABBMTP	BMT PRETRANSPLANT PANEL	CMMQUL	CMV IGM RESULT	2
+LABCMVAV	CMV IGG AVIDITY	CMVAVD	TEST DESCRIPTION	2
+LABCMVAV	CMV IGG AVIDITY	CMVAVI	CMV IGG AVIDITY INDEX	2
+LABCMVAV	CMV IGG AVIDITY	CMVAVR	CMV IGG AVIDITY RESULT	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	CMVIGM	CMV IGM AB	2
+LABFCO2	"CO2 (BICARB), FLUID"	CO2FL	"CO2, FLUID"	2
+LABFCO2	"CO2 (BICARB), FLUID"	CO2FLS	SOURCE	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	CONA	CON A STIMULATION	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	CONINT	CON A INTERPRETATION	2
+LABCOPYPT	COPY REQUEST	COPYTO	SEND COPY TO	2
+LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	CULT	CULT/OTHER RSLT:	2
+LABINFO	MICROBIOLOGY INFORMATION ONLY	CULT	CULT/OTHER RSLT:	2
+LABISOBR	ISOLATOR BRUCELLA	CULT	CULT/OTHER RSLT:	2
+LABROACAN	ACANTHAMOEBA CULTURE	CULT	CULT/OTHER RSLT:	2
+LABYCYSTC	"CYSTATIN C, SERUM / PLASMA"	CYSTC	"CYSTATIN C, SER/PLAS"	2
+LAB12373R	"CARBAMAZEPINE-10,11-EPOXIDE"	CZPTOT	"CARBAMAZEPINE,TOT,S"	2
+LABDATLA	DIRECT ANTIGLOBULIN TEST - LARC	DATLAR	DIRECT COOMBS - LA RED CROSS	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	DEVTYPE	DEVTYPE	2
+LABDHB	DONOR HEPATITIS B SURFACE AG	DHB	DONOR HEP B SURF. AG	2
+LABDHBCC	DONOR HEPATITIS B CORE AB (LIVING)	DHBCC	DONOR HEP CORE AB	2
+LABDHEPC	DONOR HEPATITIS C AB	DHEPC	DONOR HEPATITIS C AB	2
+POC44	POC URINE DIPSTICK GP	DIPLOT	DIPSTICK LOT	2
+LABIMM1	IMMUNOLOGY PANEL 1	DNAINT	INTERPRETATION	2
+LABIMM1	IMMUNOLOGY PANEL 1	DNAQUL	ANTI-DSDNA RESULT	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	DOPAURN	"DOPAMINE, URN"	2
+LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EBNDES	EBNDES	2
+LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EBNQNT	EBNQNT	2
+LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EBNQUL	EBNQUL	2
+LABADIFF	DIFFERENTIAL	EOS	EOSINOPHILS	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	EPINEPURN	"EPINEPHRINE, URN"	2
+POC44	POC URINE DIPSTICK GP	EXPDATE	EXP DATE	2
+LABF8POR	FACTOR VIII INHIBITOR PORCINE	F8POR	"FACTOR VIII INHIBITOR, PORCINE"	2
+LABFECFIB	"MEAT FIBERS, FECAL"	FECFB	"MEAT FIBERS, FECAL"	2
+LABFIBRO	FIBROSPECT FOR LIVER FIBROSIS	FIBROR	FIBROSPECT FOR LIV	2
+LABFLUOX	FLUOXETINE AND NORFLUOXETINE	FLUOXE	FLUOXETINE	2
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	FOTHER	"OTHERS, FLD"	2
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	FP210	"P210, FLUID"	2
+LAB11080R	GLUTAMIC ACID DECARBOXY AB	GAD65AB	GAD65 ANTIBODY	2
+LABGTOL	GLUCOSE TOLERANCE	GLT2	"GLUCOSE,2HR - 75G"	2
+LABGTOL	GLUCOSE TOLERANCE	GLUF	"GLUCOSE,FASTING"	2
+LABCBCD	CBC WITH DIFF	GRANP	"GRAN, %"	2
+LABGTOL	GLUCOSE TOLERANCE	GTTINT	GTT INTERPRETATION	2
+LABYHALO	"HALOPERIDOL, SERUM"	HALO	HALOPERIDOL	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HATINT	INTERPRETATION	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HATINT	INTERPRETATION	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HATR	ANTI-HAV ANTIBODY	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HAVINT	INTERPRETATION	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HAVR	IGM ANTI-HAV	2
+LABHBCT	HEPATITIS B CORE TOTAL AB	HBCAB	HEP B CORE TOTAL AB	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBCAB	HEP B CORE TOTAL AB	2
+LAB11106R	HEPATITIS B E AB	HBEAB	HEPATITIS B E AB	2
+LABHEAB	HEPATITIS B E AB	HBEAB	HEPATITIS B E AB	2
+LABHBSAG	HEPATITIS B SURFACE AG	HBSAG	HEPATITIS B SURF AG	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBSAG	HEPATITIS B SURF AG	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HBSINT	INTERPRETATION	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBSINT	INTERPRETATION	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBSR	HEP B SURFACE ANTIGEN	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HBTINT	INTERPRETATION	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBTINT	INTERPRETATION	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HBTR	ANTI-HBC ANTIBODY	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV1	HCV GENOTYPE 1	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV1S	HCV GENOTYPE 1 SUBTYPE	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV2	HCV GENOTYPE 2	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV2S	HCV GENOTYPE 2 SUBTYPE	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV3	HCV GENOTYPE 3	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV4	HCV GENOTYPE 4	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV5	HCV GENOTYPE 5	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCV6	HCV GENOTYPE 6	2
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HCVGTD	TEST DESCRIPTION	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HCVI	INTERPRETATION	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HCVI	INTERPRETATION	2
+LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVIPX	TEST DESCRIPTION	2
+LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVLOG	RESULT LOG 10	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HCVR	ANTI-HCV	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HCVR	ANTI-HCV	2
+LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVRES	RESULT	2
+LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVRNA	HEP C VIRVUS RNA	2
+LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HCVSP	SPECIMEN	2
+LABHELPM	HELICOBACTER PYLORI IGM AB	HELPM1	H. PYLORI IGM ANTIBODY	2
+LABHAVT	HEPATITIS A AB TOTAL	HEPAAB	HEPATITIS A TOTAL AB	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HEPAAB	HEPATITIS A TOTAL AB	2
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	HEPCAB	HEPATITIS C AB	2
+LABHIVWBL	HIV (TYPE 1 AND TYPE 2) ABY	HIVAB	HIV 1/2 ANTIBODY	2
+LABFHIVQA	"HIV-1 DNA, QUALITATIVE PCR"	HIVQA	"HIV-1 DNA, QUAL, PCR"	2
+LABHUNDNA	HUNTINGTON\'S DNA	HNDNA	HUNTINGTON\'S DNA	2
+LABHPVPCR	HPV 16/18 TYPING	HPV16	HPV 16 DNA	2
+LABHPVPCR	HPV 16/18 TYPING	HPV18	HPV 18 DNA	2
+LABHPVPCR	HPV 16/18 TYPING	HPVDI	DISCLAIMER	2
+LABHPVPCR	HPV 16/18 TYPING	HPVINT	INTERPRETATION	2
+LABHPVPCR	HPV 16/18 TYPING	HPVMT	METHOD	2
+LABHPVPCR	HPV 16/18 TYPING	HPVSP	SPECIMEN	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HQLINT	INTERPRETATION	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	2
+LAB11137R	"HSV AB, IMMUNOBLOT"	HSVWB	HSV WESTERN BLOT	2
+LABDHTLVQ	"DONOR, HTLV-I/II AB SCN"	HTLVQ1	"DONOR, HTLV-I/II AB SCN"	2
+LAB11136R	HTLV-1 AB SCREEN	HTLVS	HTLV AB SCREEN	2
+LABLIVER	PRE LIVER TRANSPLANT PANEL	HTLVS	HTLV AB SCREEN	2
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	ICDBRAVMAX	BRADY ADAPTIVE AV RATE MAXIMUM	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	ICDBRVVDEL	BRADY VV DELAY	2
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTAMSEP	ICD DETECTIONS AND TREATMENTS AMS EPISODES	2
+LABICTO	ICTOTEST	ICTO	ICTOTEST	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	IMPDATE	IMPLANT DATE	2
+LABYFK	"POTASSIUM, FLUID"	KFLD	POTASSIUM BODY FLUID	2
+LABSTDY2	STUDY TEST	LABS2	LABS2	2
+POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	LAC	"LACTATE, ISTAT"	2
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LEADALERT	LEAD ALERT	2
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVBRPACA	LV BRADY PACING AMPLITUDE	2
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVBRPACW	LV BRADY PACING PULSE WIDTH	2
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	2
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	2
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVTHRAMP	LV THRESHOLD AMPLITUDE	2
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	LVTHRDUR	LV THRESHOLD DURATION	2
+LABCD3B	"CD3, BLOOD"	LYM	LYMPHOCYTES %	2
+LABMEARC	MEASLES RAPID CULTURE	MEARC1	MEASLES RAPID CULTURE	2
+LABMEARC	MEASLES RAPID CULTURE	MEARCD	TEST DESCRIPTION	2
+LABMEARC	MEASLES RAPID CULTURE	MEASPM	SPECIMEN	2
+LABMELAS	MELAS MTDNA PROFILE	MELADN	MELAS MTDNA PROFILE	2
+LABYMERC	"MERCURY, BLOOD"	MER	"MERCURY, BLOOD"	2
+LABYMETHS	METHSUXIMIDE	METHSX	METHSUXIMIDE	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	MFR	MANUFACTURER	2
+LABSALCOR	"CORTISOL, SALIVA"	MN1COR	1ST MIDNIGHT SAMPLE	2
+LABSALCOR	"CORTISOL, SALIVA"	MN2COR	2ND MIDNIGHT SAMPLE	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	MOD	MODEL	2
+LABDVEMP	MPV DFA	MPIP	TEST DESCRIPTION	2
+LABYMPO	MYELOPERIOXIDASE AB	MPO	MYELOPEROXIDASE AB	2
+LABDVEMP	MPV DFA	MPSP	SPECIMEN	2
+LABDVEMP	MPV DFA	MPVR	METAPNEUMOVIRUS	2
+LABPANANC	"NEUTROPHIL COUNT, ABSOLUTE"	NEUTAB	"NEUT, ABS"	2
+LABCBCD	CBC WITH DIFF	NEUTPC	NEUTROPHIL %	2
+LABYNICK	"NICKEL, SERUM"	NICKEL	NICKEL	2
+LABNJAK2	"JANUS KINASE 2 V617F MUTATION, NON-BLOOD"	NJAKIN	INTERPRETATION	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	NOREPHURN	"NOREPINEPHRINE, URN"	2
+LABFLUOX	FLUOXETINE AND NORFLUOXETINE	NORFLU	NORFLUOXETINE	2
+LABYMETHS	METHSUXIMIDE	NORMSX	NOR-METHSUXIMIDE	2
+LABOSTLC	"OLIGOSACCHARIDES TLC, URINE"	OSTLC	"OLIGOSAC, UR, TLC"	2
+CRP139	ICD GENERATOR AND LEAD INS/REP DUAL	PDF	PDF RESULT	2
+EKG6	ECG SIGNAL AVERAGED	PDF	PDF RESULT	2
+IMG11BXMT	CAR MYOCARD BX I TX	PDF	PDF RESULT	2
+IMG13RHC	PED RT HEART CATH	PDF	PDF RESULT	2
+PFT50	"ROUTINE STDY - ABG, SPIRO W/BDR,DLCO,VOL"	PDF	PDF RESULT	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PHA	PHA STIMULATION	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PHAINT	PHA INTERPRETATION	2
+LABYPHOLP	PHOSPHOLIPIDS	PHOLIP	PHOSPHOLIPIDS	2
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMALERT	PM ALERT	2
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMLVLDBIIM	PM LV LEAD BIPOLAR IMPEDANCE	2
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	PMLVLDUNIM	PM LV LEAD UNIPOLAR IMPEDANCE	2
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMRALDUNIM	PM RA LEAD UNIPOLAR IMPEDANCE	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRALDUNIM	PM RA LEAD UNIPOLAR IMPEDANCE	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	PMRVLDUNIM	PM RV LEAD UNIPOLAR IMPEDANCE	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE14	S.PNEUMO SEROTYPE 14 (14)	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE19	S.PNEUMO SEROTYPE 19 (19F)	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE23	S.PNEUMO SEROTYPE 23 (23F)	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE26	S.PNEUMO SEROTYPE 26 (6B)	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE4	S.PNEUMO SEROTYPE 4 (4)	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE56	S.PNEUMO SEROTYPE 56 (18C)	2
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	PNE68	S.PNEUMO SEROTYPE 68 (9V)	2
+LABPABN	"POLIO AB, NEUTRALIZATION"	POL1	POLIO 1	2
+LABPABN	"POLIO AB, NEUTRALIZATION"	POL2	POLIO 2	2
+LABPABN	"POLIO AB, NEUTRALIZATION"	POL3	POLIO 3	2
+LABTBP94	TRANSFUSION BLOOD PRODUCT 95	PRO	PRODUCT ID  	2
+LABPROPAF	PROPAFENONE	PROPAF	PROPAFENONE	2
+LABPSACX	PSA WITH REFLEX COMPLEXED PSA	PSA	PSA	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PWM	PWM STIMULATION	2
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	PWMINT	PWM INTERPRETATION	2
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	RBCAST	RBC CASTS	2
+LABRBP	RETINOL BINDING PROTEIN	RBPR	RETINOL BIND PROT	2
+LAB13006R	"REDUCING SUBSTANCE, STOOL"	REDSUBSTL	"REDUCING SUBST, STOOL"	2
+LABDATLA	DIRECT ANTIGLOBULIN TEST - LARC	REQS	REQUEST SENT	2
+LABSTDY2	STUDY TEST	RESS2	RESS2	2
+LABMISCH	MISCELLANEOUS HEMATOLOGY	RESULH	RESULT	2
+LABIMM2	IMMUNOLOGY PANEL 2	RHFINT	INTERPRETATION	2
+LABIMM2	IMMUNOLOGY PANEL 2	RHFR	RHEUMATOID FACTOR	2
+10040R	HLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR)	RINFO	TO FIND RESULT:	2
+10085R	HLA ORDER - TYPING ADD ON LOCUS	RINFO	TO FIND RESULT:	2
+10175R	HLA ORDER - KIT PROCESSING RECIPIENT	RINFO	TO FIND RESULT:	2
+LABDRPRS	DONOR SYPHILIS SCREEN	RPRS1	DONOR SYPHILIS SCREEN	2
+LABDRPRS	"DONOR, SYPHILIS SCREEN"	RPRS1	DONOR SYPHILIS SCREEN	2
+LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	RPT	RPT	2
+LABINFO	MICROBIOLOGY INFORMATION ONLY	RPT	RPT	2
+LABISOBR	ISOLATOR BRUCELLA	RPT	RPT	2
+LABROACAN	ACANTHAMOEBA CULTURE	RPT	RPT	2
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	RVBRSENR	RV BRADY SENSING REFRACTORY PERIOD	2
+LABSCLABY	ANTI-SCL70 ANTIBODY	SCLQNT	ANTI-SCL70 LEVEL	2
+LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	SDES	SPECIMEN	2
+LABINFO	MICROBIOLOGY INFORMATION ONLY	SDES	SPECIMEN	2
+LABISOBR	ISOLATOR BRUCELLA	SDES	SPECIMEN	2
+LABROACAN	ACANTHAMOEBA CULTURE	SDES	SPECIMEN	2
+210993288	PM DEVICE EVAL W/OUT REPROGRAMMING	SERNUM	SERIAL NUMBER	2
+LABTBP94	TRANSFUSION BLOOD PRODUCT 95	SI	<<STATUS>>  	2
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	SIGNIF	SIGNIFICANT ABNORMALITIES	2
+LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	SIGNIF	SIGNIFICANT ABNORMALITIES	2
+LABSMABY	AUTOANTIBODIES TO SM	SMAB	AUTOANTIBODIES TO SM	2
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SOTHER	"OTHERS, SYN FLD"	2
+LABBKVPC	BK VIRUS DNA QUANTITATION	SOURC6	SOURCE	2
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	SPEXP	SPECIMEN EXPIRATION DATE/TIME	2
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	SPEXP	SPECIMEN EXPIRATION DATE/TIME	2
+LABSTDY2	STUDY TEST	SPTYS2	SPTYS2	2
+LABBACIDI	"BACTERIA ID BY SEQUENCING, ISOLATE"	SREQ	COMMENTS	2
+LABINFO	MICROBIOLOGY INFORMATION ONLY	SREQ	COMMENTS	2
+LABISOBR	ISOLATOR BRUCELLA	SREQ	COMMENTS	2
+LABROACAN	ACANTHAMOEBA CULTURE	SREQ	COMMENTS	2
+LABSTSS	SYPHILIS TREPONEMAL SCREEN	STSQNT	INDEX	2
+LABSULFAT	SULFATIDE AUTOANTIBODY	SULFAB	SULFATIDE AUTOANTIBODY	2
+LABMISCH	MISCELLANEOUS HEMATOLOGY	TESTH	PROCEDURE	2
+LABSTDY2	STUDY TEST	TESTS2	TESTS2	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	TIMEU	COLLECTION PERIOD	2
+LABTOXIM	TOXOPLASMA IGM	TOXMQT	IGM LEVEL	2
+LAB11121R	TOXOPLASMA IGG DYE TEST	TOXOD	TOXOPLASMA DYE TEST	2
+LABTOXOM	TOXOPLASMOSIS IGM	TOXOM	TOXOPLASMA IGM ELISA	2
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	TRICHOMONAS	TRICHOMONAS	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15CO	"COMMENT, TISSUE"	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15DI	DISCLAIMER	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15DX	CLINICAL INDICATION	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15MD	INTERPRETED BY	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15ME	METHOD	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15RE	"RESULT, TISSUE"	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	TT15TI	"SOURCE, TISSUE"	2
+POC39	POC URINE FOR PH	UBLOOD	BLOOD URINE	2
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89	UBT	UNIT BLOOD TYPE  	2
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	UBT	UNIT BLOOD TYPE  	2
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	UBT	UNIT BLOOD TYPE  	2
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	UBT	UNIT BLOOD TYPE  	2
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	UBT	UNIT BLOOD TYPE  	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UCATIN	"CATECHOL INTRP , URN"	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UCR	"CREATININE, URINE"	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UCR	"CREATININE, URN"	2
+LABUHIST	"HISTAMINE, URINE"	UCR	"CREATININE, URN"	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UDOPA1	DOPAMINE/CREAT. RATIO	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UEPI1	EPINEPHRINE/CREAT. RATIO	2
+POC44	POC URINE DIPSTICK GP	UGLU	GLUCOSE URINE	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UNORE1	NOREPINEPHRINE/CREAT. RATIO	2
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89	UNT	UNIT NUMBER  	2
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	UNT	UNIT NUMBER  	2
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	UNT	UNIT NUMBER  	2
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	UNT	UNIT NUMBER  	2
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	UNT	UNIT NUMBER  	2
+POC44	POC URINE DIPSTICK GP	UPROT	PROTEIN URINE	2
+LABUHIST	"HISTAMINE, URINE"	UT	COLLECTION PERIOD	2
+LABUFCATE	"CATECHOLAMINES, URN FREE"	UVOL	VOLUME	2
+LABBMTP	BMT PRETRANSPLANT PANEL	VGGDES	VZV IGG TEST DESCRIPTION	2
+LABBMTP	BMT PRETRANSPLANT PANEL	VGGQUL	VZV IGG RESULT	2
+LAB11060R	"VITAMIN B1, PLASMA"	VITB1	"VITAMIN B1, PLASMA"	2
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	VL	PRODUCT VOLUME	2
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	VL	PRODUCT VOLUME	2
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	VL	PRODUCT VOLUME	2
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	VL	PRODUCT VOLUME	2
+LABUHIST	"HISTAMINE, URINE"	VOL	VOLUME	2
+LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	VOLUMEURN	URINE VOLUME	2
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	XMR	UNIT CROSSMATCH  	2
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	XMR	UNIT CROSSMATCH  	2
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	XMR	UNIT CROSSMATCH  	2
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	XMR	UNIT CROSSMATCH  	2
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	XMR	UNIT CROSSMATCH  	2
+LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	YA1A2	ALPHA-1-ANTITRYPSIN	2
+LABYBABE	BABESIA MICROTI IGG AND IGM AB	YBAB1	"BABESIA MICROTI, IGG"	2
+LABYBABE	BABESIA MICROTI IGG AND IGM AB	YBAB2	"BABESIA MICROTI, IGM"	2
+LABYBABE	BABESIA MICROTI IGG AND IGM AB	YBAB3	INTERPRETATION	2
+LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	YDR1	"DRUG SCREEN, PRESCRIP/OTC"	2
+LABEVPCR	ENTEROVIRUS PCR	YENP1	SPECIMEN SOURCE	2
+LABEVPCR	ENTEROVIRUS PCR	YENP2	"ENTEROVIRUS ,PCR"	2
+LABEVPCR	ENTEROVIRUS PCR	YENP3	SPECIAL INFORMATION	2
+LABEVPCR	ENTEROVIRUS PCR	YENP4	REPORT STATUS	2
+LABYFK	"POTASSIUM, FLUID"	YFLSC1	FLUID TYPE	2
+LABYHISTA	"HISTAMINE, PLASMA"	YHISA1	"HISTAMINE, PLASMA"	2
+LABYMEM	"MEASLES AB, IGM"	YMEM1	"MEAS(RUBEOLA) AB,IGM"	2
+LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	YOXALU	"OXALATE, 24-HOUR/TIMED URINE"	2
+LABYP4AB	PARAINFLUENZA AB	YP4AB1	TYPE 1	2
+LABYP4AB	PARAINFLUENZA AB	YP4AB2	TYPE 2	2
+LABYP4AB	PARAINFLUENZA AB	YP4AB3	TYPE 3	2
+LABYPANPP	PANCREATIC POLYPEPTIDE	YPANPP	PANCREATIC POLYPEPTIDE	2
+LAB11089R	ACID PHOSPHATASE	YPAPT	"PAP, SERUM"	2
+LABYPERAB	BORDETELLA PERTUSSIS IGG AND IGM AB	YPERA1	"B PERTUSSIS AB, IGG"	2
+LABYPERAB	BORDETELLA PERTUSSIS IGG AND IGM AB	YPERA2	"B PERTUSSIS AB, IGM"	2
+LABYRETAB	RETICULIN AB	YRET1	RETICULIN AB	2
+LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG3	COLLECTION DURATION	2
+LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG4	CONCENTRATION	2
+LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL2	TOTAL WEIGHT	2
+LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL3	COLLECTION DURATION	2
+LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL4	CHLORIDE CONCENTRATION	2
+LABYTICK	COLORADO TICK FEVER AB	YTICK1	IGG AB	2
+LABYTICK	COLORADO TICK FEVER AB	YTICK2	IGM AB	2
+LABYTICK	COLORADO TICK FEVER AB	YTICK3	INTERPRETATION	2
+LABYUNTX	"TELOPEPTIDE, URINE"	YUNTXC	"CREATININE, URINE"	2
+LABYUNTX	"TELOPEPTIDE, URINE"	YUNTXR	NTX	2
+LABYUNTX	"TELOPEPTIDE, URINE"	YUNTXT	"NTX-TELOPEPTIDE, URINE"	2
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUOAGC	OTHER AMPHETAMINES	2
+LABYUOPC	"OPIATE CONFIRMATION, URINE"	YUOPCO	COMMENT	2
+LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	YUOXCO	OXALATE CONCENTRATION	2
+LABYUOXAL	"OXALATE, 24-HOUR/TIMED URINE"	YUOXUT	COLLECTION DURATION	2
+LABYUAMC	"AMPHETAMINES CONFIRMATION, URINE"	YUSYGC	SYMPATHOMIMETICS	2
+LAB12586R	VITAMIN B1	YVB1W1	THIAMIN(VITAMIN B1)	2
+LAB12586R	VITAMIN B1	YVB1W2	COMMENT	2
+10010R	HLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	1
+10120R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)	10049R	HLA LAB TESTING STATUS	1
+10165R	HLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)	10049R	HLA LAB TESTING STATUS	1
+10225R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	10049R	HLA LAB TESTING STATUS	1
+10285R	HLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)	10049R	HLA LAB TESTING STATUS	1
+10297R	HLA ORDER - DECEASED DNR O.R. SPMN	10049R	HLA LAB TESTING STATUS	1
+10335R	HLA - SCREENING C1Q	10471R	SAFE ANTIGENS C1Q	1
+10545R	HLA - CD31 TESTING SUMMARY	10478R	CD31 TESTING SUMMARY	1
+POC39	POC URINE FOR PH	11147R	SPECIFIC GRAVITY	1
+POC1	POC URINE DIPSTICK	11148R	PH	1
+POC14	POC GLUCOSE BY METER	11148R	PH	1
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	11165R	EB VIRUS EARLY AG (R+D) IGG	1
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	11166R	EB NUCLEAR AB	1
+LAB11173R	IODINE	11174R	IODINE	1
+LAB11195R	"N-TELOPEPTIDE, CROSS-LINKED"	11196R	"N-TELOPEPTIDE, CROSS-LINKED"	1
+LAB11219R	"IODINE,U"	11220R	"IDODINE, U"	1
+LAB11219R	"IODINE,U"	11221R	COLLECTION DURATION	1
+LAB11219R	"IODINE,U"	11222R	URINE VOLUME	1
+LAB11219R	"IODINE,U"	11223R	IODINE CONCENTRATION	1
+LAB11225R	"LEAD,U"	11226R	COLLECTION DURATION	1
+LAB11225R	"LEAD,U"	11227R	URINE VOLUME	1
+LAB11225R	"LEAD,U"	11228R	PB CONC	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11255R	ORGANISM	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11256R	AMIKACIN	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11257R	CIPROFLOXACIN	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11258R	CLARITHROMYCIN	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11259R	ETHAMBUTOL	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11260R	LINEZOLID	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11261R	MOXIFLOXACIN	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11262R	RIFAMPIN	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11263R	RIFABUTIN	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11264R	STREPTOMYCIN	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11300R	CHLORPROPAMIDE	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11301R	TOLAZAMIDE	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11302R	TOLBUTAMIDE	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11303R	GLYBURIDE	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11304R	GLIPIZIDE	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11305R	ACETOHEXAMIDE	1
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	11306R	SULFONYLUREA SCRN	1
+LAB11347R	C-TELOPEPTIDE	11348R	C-TELOPEPTIDE	1
+LAB12689R	SERM INFLIXIMAB/HACA MEASUREMT	11500R	SERM INFLIXIMAB/HACA MEASUREMT	1
+LAB11528R	PERIPHERAL NEUROPATHY AB SCN	11638R	PERIPHERAL NEUROPATHY AB SCN	1
+LAB11533R	SENSORIMOTOR SCREEN AB	11643R	SENSORIMOTOR SCREEN AB	1
+LAB11540R	OTC SEQUENCING	11650R	OTC SEQUENCING	1
+LAB11554R	"S-SULFOCYSTEINE, URINE"	11664R	"S-SULFOCYSTEINE, URINE"	1
+LAB12016R	"CYTOMEGALOVIRUS IGG AB, ELISA"	11667R	CYTOMEGALOVIRUS IGG	1
+LAB11570R	SPINAL MUSC ATROPHY CARRIER	11685R	SPINAL MUSC ATROPHY CARRIER	1
+LAB11573R	CONGENITAL CYCLIC NEUTROPENIA	11688R	CONGENITAL CYLIC NEURTOPENIA	1
+LAB11592R	"COPPER, TISSUE"	11720R	"COPPER, LIVER TS"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11723R	"LYMPHOCYTE PROLIFERATION, AG"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11724R	"CANDIDA ALBICANS, DILUTION 1"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11727R	"CANDIDA ALBICANS, DILUTION 4"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11728R	"INTERP, CANDIDA ALBICANS"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11729R	"TETANUS TOXOID, DILUTION 1"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11731R	"TETANUS TOXOID, DILUTION 3"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11732R	"TETANUS TOXOID, DILUTION 4"	1
+LAB11596R	"LYMPHO PROLIFERATION, AG"	11733R	"INTERP, TETANUS TOXOID"	1
+LAB11599R	GANICICLOVIR	11756R	GANCICLOVIR	1
+LAB11604R	IL 5	11757R	INTERLEUKIN 5	1
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11836R	SOURCE	1
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	11841R	AMENDMENT	1
+LABYFCL	"CHLORIDE, FLUID"	11844R	FLUID TYPE	1
+LAB11878R	CORTISOL BINDING GLOBULIN	11877R	CORTISOL BIND.GLOB(CBG)	1
+LABDHIVAB	"DONOR,HIV-1/HIV-2 AB SCN W/RFL"	11888R	"DONOR, HIV-1/2 AB, CONF."	1
+LABDHBAG	"DONOR, HEP B SURFACE AG"	11891R	"DONOR, HBSAG CONFIRMATION"	1
+LABDRPR	"DONOR, RPR W/RFL TO SYPH IGG"	11892R	"DONOR, SYPHILIS IGG"	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11897R	ETHIONAMIDE	1
+LAB11254R	M AVIUM COMPLEX MIC PANL	11898R	ISONIAZID	1
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	12001R	"VCA IGG ,IFA"	1
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	12002R	"VCA IGM ,IFA"	1
+LAB12003R	"HERPESVIRUS 6 AB, IGG"	12004R	HERPESVIRUS 6 IGG	1
+LAB13007R	Q FEVER IGG PHASE I TITER	12012R	Q FEVER IGG PHASE L TITER	1
+LAB13009R	Q FEVER IGM PHASE I TITER	12013R	Q FEVER IGM PHASE L TITER	1
+LAB13010R	Q FEVER IGM PHASE II TITER	12015R	Q FEVER IGM PHASE LI TITER	1
+LAB12023R	MYCOPLASMA PNEUMONIA IGG AB	12024R	MYCOPLASMA PNEUMONIA AB IGG	1
+LAB11604R	IL 5	12044R	INTERLEUKIN 5	1
+LABYBUPRO	"BUPROPION, SERUM"	12045R	HYDROXYBUPROPION	1
+LAB11591R	"ARSENIC, HAIR"	12046R	"ARSENIC, HAIR"	1
+LAB11591R	"ARSENIC, HAIR"	12047R	SPECIMEN SOURCE	1
+LAB12055R	RMSF IGG TITER	12056R	RMSF IGG TITER	1
+LAB12059R	R. TYPHI IGG TITER	12060R	R.TYPHI IGG TITER	1
+LAB12095R	"DARUNAVIR,ANTIRETROVIRAL LEVEL"	12101R	"DARUNAVIR,ANTIRETROVIRAL LEVEL"	1
+LAB11225R	"LEAD,U"	12199R	"LEAD, 24 HR, U"	1
+LAB12220R	MSUD MUTATION ANALYSIS	12221R	12221R	1
+LAB12279R	CVID CONFIRMATION FLOW PANEL	12280R	12280R	1
+LAB12279R	CVID CONFIRMATION FLOW PANEL	12281R	12281R	1
+LAB12279R	CVID CONFIRMATION FLOW PANEL	12282R	12282R	1
+LAB12328R	AMYLOID PROTEIN IDENTIFICATION	12327R	"AMYLOID PROTEIN ID, TISSUE"	1
+LAB12494R	"PATH CONSULT, OUTSIDE SLIDE"	12378R	"PATH CONSULT, OUTSIDE SLIDE"	1
+LAB12470R	"STAIN, CONGO RED"	12379R	"CONGO RED, STAIN"	1
+LAB12415R	ACVRL1 AND ENG (HHT)	12416R	HHT FGA SPECIMEN	1
+LABCLAMP	CHLAMYDIA AB PANEL	12428R	INTERPRETATION	1
+LABCLAMP	CHLAMYDIA AB PANEL	12429R	INTERPRETATION	1
+LABCLAMP	CHLAMYDIA AB PANEL	12430R	INTERPRETATION	1
+LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	12431R	INTERPRETATION	1
+LAB12443R	NEO PARANEOPLASTIC PRFL	12444R	NEO PARANEOPLASTIC PRFL	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12472R	SPECIMEN	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12473R	ADDENDUM COMMENT	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12474R	ACCESSION NUMBER	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12475R	REFERRING PATH-PHYSICIAN	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12476R	REF PATH-PHYS ADDRESS	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12477R	PLACE OF DEATH	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12478R	DATE AND TIME OF DEATH	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12479R	DATE OF AUTOPSY	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12480R	MATERIAL	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12481R	TISSUE DESCRIPTION	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12482R	MICROSCOPIC DESCRIPTION	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12483R	CLINICAL HISTORY	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12484R	FINAL DIAGNOSIS	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12485R	FINAL DIAGNOSIS	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12486R	COMMENT	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12487R	REVISION DESCRIPTION	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12488R	SIGNING PATHOLOGIST	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12489R	SPECIAL PROCEDURES	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12490R	SP SIGNING PATHOLOGIST	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12491R	PREVIOUS REPORT FOLLOWS	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12492R	ADDENDUM	1
+LAB12471R	SURGICAL PATHOLOGY CONSULT	12493R	ADDENDUM PATHOLOGIST	1
+LAB12512R	VON HIPPEL-LINDAU SYNDROME	12513R	VON HIPPEL-LINDAU SYNDROME	1
+LAB12580R	CU INDEX PANEL	12589R	CU INDEX PANEL	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12590R	S.PNEUMO SEROTYPE 1 (1)	1
+LAB12584R	TIAGABINE CONCENTRATION	12593R	TIAGABINE CONCENTRATION	1
+LAB11098R	"1,25-DIHYDROXYVITAMIN D, SERUM"	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	1
+LAB11098R	PEDIATRIC 1 25 OH VITAMIN D	125OHD2	"VITAMIN D, 1,25-DIHYDRO"	1
+LAB12643R	BRUTON\'S TYROSINE KINASE (BTK)	12642R	BTK PROTEIN FLOW	1
+LABYUALA	"AMINOLEVULINIC ACID, URINE"	12682R	REVIEWED BY	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12699R	S.PNEUMO SEROTYPE 3 (3)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12700R	S.PNEUMO SEROTYPE 4 (4)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12701R	S.PNEUMO SEROTYPE 5 (5)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12702R	S.PNEUMO SEROTYPE 6 (6A)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12703R	S.PNEUMO SEROTYPE 14 (14)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12704R	S.PNEUMO SEROTYPE 19 (19F)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12705R	S.PNEUMO SEROTYPE 23 (23F)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12706R	S.PNEUMO SEROTYPE 26 (6B)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12707R	S.PNEUMO SEROTYPE 51 (7F)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12708R	S.PNEUMO SEROTYPE 56 (18C)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12709R	S.PNEUMO SEROTYPE 57 (19A)	1
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	12710R	S.PNEUMO SEROTYPE 68 (9V)	1
+LAB12761R	HEPATITIS B VIRUS GENOTYPE	12757R	HBV GENOTYPE	1
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	12769R	TRI-SIALO/DI-OLIGO RATIO	1
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	12770R	APO CIII-1/CIII-2 RATIO	1
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	12771R	APO CIII-0/APO CIII-2 RATIO	1
+LAB12772R	ENCEPHALITIS AB SCREEN	12773R	ENCEPHALITIS AB SCREEN	1
+LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	12782R	REVIEWED BY	1
+LAB12802R	HEPARIN COFACTOR	12801R	HEPARIN COFACTOR II	1
+LAB12817R	"DONOR, HCV AB SCREEN W/RFLX"	12828R	"DONOR, HEPATITIS C AB"	1
+LAB12813R	"PARVOVIRUS B19 AB, IGG"	12835R	PARVOVIRUS B19 AB IGG	1
+LAB12845R	"VMA AND HVA, PEDIATRIC, URINE"	12843R	CHILD VMA	1
+LAB12845R	"VMA AND HVA, PEDIATRIC, URINE"	12844R	CHILD HVA	1
+LABY17OHP	"17 - HYDROXYPROGESTERONE, SERUM"	17OHPROG	17-OH PROGESTERONE	1
+10290R	HLA - TYPING	9035R	MICA ALLELE 1	1
+10290R	HLA - TYPING	9036R	MICA ALLELE 2	1
+10295R	HLA - AUTO T FLOW CROSSMATCH	9099R	INTERPRETATION/ COMMENTS	1
+10350R	HLA - IN VITRO IVY INHIBITION ASSAY	9342R	DSA FOR CLASS II	1
+10355R	HLA - MONITORING BY IGG	9361R	INTERPRETATION/ COMMENTS CLASS I	1
+10355R	HLA - MONITORING BY IGG	9367R	POSSIBLE HLA ANTIBODIES FOR IGG CLASS II FOR CURRENT	1
+LAB12733R	ALPHA-2-ANTIPLASMIN	A2A	ALPHA-2-ANTIPLASMIN	1
+LAB11028R	"ANGIOTENSIN CONVERTING ENZYME, CSF"	ACE	"ANGIOTENSIN COVERT ENZ, CSF"	1
+LABCACE	"ANGIOTENSIN CONVERTING ENZYME, CSF"	ACE	"ANGIOTENSIN COVERT ENZ, CSF"	1
+LABYCHRBC	"ACETYLCHOLINESTERASE, RBC"	ACHERBC	"ACETYLCHOLINESTERASE, RBC"	1
+LAB11269R	ACETYL RECEPTOR AB PANL	ACRBIN	ACETYLCHOLINE BIN AB	1
+LAB11269R	ACETYL RECEPTOR AB PANL	ACRBLO	ACETYLCHOLINE BLOCKING AB	1
+LAB11269R	ACETYL RECEPTOR AB PANL	ACRMOD	ACETYLCHOLINE MODULATING AB	1
+LABADPCRQ	ADENOVIRUS PCR QUANT	ADPCR1	ADENVIRUS DNA	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	AFIBTAQRSM	AFIB TACHY ZONE QRS MORPHOLOGY CRITERIA	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	AFLTAQRSMC	AFLUTTER TACHY ZONE QRS MORPHOLOGY CRITERIA	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	AFLTAZNRTC	AFLUTTER TACHY ZONE RATE STABILITY CRITERIA	1
+LABCBCD	CBC WITH DIFF	ALB	"ALBUMIN, SER/PLAS"	1
+LABHEPAT	HEPATIC PROFILE	ALB	"ALBUMIN, SER/PLAS"	1
+LABBCAA	BRANCHED CHAIN AMINO ACIDS	ALCN	ALLO-ISOLEUCINE	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	ALCOHOL	ALCOHOL	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ALERT	ALERT	1
+LABCBCD	CBC WITH DIFF	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	1
+LABHEPAT	HEPATIC PROFILE	ALKP	"ALK P\'TASE, TOTAL, SER/PLAS"	1
+LABMALL2	MISCELLANEOUS ALLERGEN 2	ALL2	ALLERGEN	1
+LABMALL3	MISCELLANEOUS ALLERGEN 3	ALL3	ALLERGEN	1
+LABMALL2	MISCELLANEOUS ALLERGEN 2	ALLR2	ALLERGEN RESULT	1
+LABMALL3	MISCELLANEOUS ALLERGEN 3	ALLR3	ALLERGEN RESULT	1
+LABCBCD	CBC WITH DIFF	ALT	"ALT (SGPT), SER/PLAS"	1
+LABHEPAT	HEPATIC PROFILE	ALT	"ALT (SGPT), SER/PLAS"	1
+LABAMAONC	ANTI - MALIGNIN AB	AMA	ANTI-MALIGNIN AB	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	AMP	AMPICILLIN	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	AMP	AMPICILLIN	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	AMPHET	AMPHETAMINES	1
+LABYAMPHB	AMPHOTERICIN B	AMPHOTERB	AMPHOTERICIN B	1
+LABIMM1	IMMUNOLOGY PANEL 1	ANAQNT	TITER	1
+LABASCA	ASCARIASIS AB	ASCA	ASCARISIS	1
+LABASPR	"ASPERGILLUS AB, CF, S"	ASPR	ASPERILLUS-CF	1
+LABCBCD	CBC WITH DIFF	AST	"AST (SGOT), SER/PLAS"	1
+LABHEPAT	HEPATIC PROFILE	AST	"AST (SGOT), SER/PLAS"	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ATACHTAQRS	ATACH TACHY ZONE QRS MORPHOLOGY CRITERIA	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	ATM	AZTREONAM.	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	ATM	AZTREONAM.	1
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	BAND	BAND NEUTROPHILS	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	BANDFLD	"BAND, FLD"	1
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	BANDFLD	"BAND, FLD"	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	BARB	BARBITURATES	1
+LABADIFF	DIFFERENTIAL	BASO	BASOPHILS	1
+LABCBCD	CBC WITH DIFF	BASOAB	ABSOLUTE BASOPHILS	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	BASOFLD	"BASOPHILS, FLD"	1
+LABCBCD	CBC WITH DIFF	BASOSYN	"BASOPHILS, %"	1
+LABHBV	HBV GENOTYPE: S AND POL GENE	BCPMUT	BCP MUTATIONS	1
+POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	BE	"BASE EXCESS, ISTAT"	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	BENZO	BENZODIAZEPINES	1
+LAB12866R	B HENSELAE IGG TITER	BHIGGT	B.HENSELAE IGG TITER	1
+LABBKDNA	BK VIRUS DNA PCR	BKD	BK VIRUS DNA	1
+LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	BKQ	BKV DNA QUANTITATION	1
+LABADIFF	DIFFERENTIAL	BLASTS	BLASTS	1
+LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	BMAB	"BASEMENT MEMBRANE, IGG"	1
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	BSQMU	MUTATION	1
+LABCBCD	CBC WITH DIFF	BUN	"UREA NITROGEN,SER/PLAS"	1
+LABC1INHF	C1 ESTERASE INHIBITOR FN	C1INHF	C1 ESTERASE INHIB FN	1
+LABC1Q	C1Q BINDING ASSAY	C1Q	C1Q BINDING ASSAY	1
+LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	C3AB	CD3 CD16+CD56 ABS	1
+LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	C3PER	CD3 CD16+CD56 %	1
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	C48RAT	TCD4/CD8 RATIO	1
+LABCBCD	CBC WITH DIFF	CA	"CALCIUM, SER/PLAS"	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	CAIGG	CAL ENCEPH IGG	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	CAIGM	CAL ENCEPH IGM	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	CAINT	INTERPRETATION	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	CAION	"CALCIUM,ION, ISTAT"	1
+LAB11105R	CANDIDA AB	CANDP	CANDIDA PRECIPITINS	1
+LABFDIFF	"DIFFERENTIAL, BODY FLUID"	CCNT	CELLS COUNTED	1
+LABCCP	ANTI CCP	CCPT	ANTI CCP ANTIBODY LEVEL	1
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD19AB	"CD19 (PAN B), ABS."	1
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD19PC	CD19 (PAN B) %	1
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	CD4TOT	CD4 TOTAL	1
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1IN	INTERPRETATION	1
+LABCDH1	FAMILIAL GASTRIC CANCER I	CDH1MU	MUTATION(S)	1
+LABSNA	SPECIFIC ANA PANEL	CENQNT	ANTI-CENTROMERE LEVEL	1
+LABCFPT	CF POLY T ANALYSIS	CFPTR2	RESULT	1
+LABCFPT	CF POLY T ANALYSIS	CFPTR3	RESULT	1
+LABITCH	FOOD ALLERGY SCREEN	CHICK	CHICKEN	1
+LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	CHOL	"CHOLESTEROL, TOTAL"	1
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	CHOL	"CHOLESTEROL, TOTAL"	1
+LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	CHOLHDL	CHOLESTEROL/HDL RATIO	1
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	CHOLHDL	CHOLESTEROL/HDL RATIO	1
+LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONDS	DISCLAIMER	1
+LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONDX	CLINICAL INDICATION	1
+LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONIN	INTERPRETATION	1
+LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONMD	INTERPRETED BY	1
+LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONMT	METHOD	1
+LABCHON	"A/HYPOCHONDROPLASIA, SERUM"	CHONRS	RESULT	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CIP	CIPROFLOXACIN	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CIP	CIPROFLOXACIN	1
+LABCBCD	CBC WITH DIFF	CL	"CHLORIDE, SER/PLAS"	1
+LABUA	URINALYSIS WITH MICROSCOPIC	CLNTST	CLINITEST	1
+LABUAPRN	"URINALYSIS, SCREEN FOR CULTURE"	CLNTST	CLINITEST	1
+LABCLOMIP	CLOMIPRAMINE AND METABOLITE	CLOMI	CLOMIPRAMINE	1
+LABBMTP	BMT PRETRANSPLANT PANEL	CMGQNT	CMV IGG LEVEL	1
+LABBMTP	BMT PRETRANSPLANT PANEL	CMMQNT	CMV IGM LEVEL	1
+LABDCMVAB	"DONOR, CMV TOTAL ANTIBODIES"	CMVAB1	"DONOR, CMV TOTAL ANTIBODIES"	1
+LABCBCD	CBC WITH DIFF	CO2	"CO2, SER/PLAS"	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	COCAINE	COCAINE	1
+LAB11123R	"COCCIDIOIDES COMPLEMENT FIXATION, CSF"	COCC	COCCI CF AB (CSF)	1
+LAB11125R	COCCI ID (SERUM)	COCI	COCCI ID (SERUM)	1
+LABITCH	FOOD ALLERGY SCREEN	CORNY	CORN	1
+LABCEPLUS	CELIAC PLUS	CPLUS1	CELIAC PLUS RESULT	1
+LABCBCD	CBC WITH DIFF	CR	"CREATININE, SER/PLAS"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	CR	"CREATININE,ISTAT"	1
+POC17	POC ISTAT CREATININE	CR	"CREATININE,ISTAT"	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CRO	CEFTRIAXONE	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CRO	CEFTRIAXONE	1
+LABCR	"CREATININE, SERUM/PLASMA"	CRTNR	"CREATINE, PLASMA"	1
+LABCLAMP	CHLAMYDIA AB PANEL	CTIGA	C.TRACHOMATIS IGA	1
+LABCLAMP	CHLAMYDIA AB PANEL	CTIGG	C.TRACHOMATIS IGG	1
+LABCLAMP	CHLAMYDIA AB PANEL	CTIGM	C.TRACHOMATIS IGM	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CTN	CEFOTETAN	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CTN	CEFOTETAN	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CTX	CEFOTAXIME	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CTX	CEFOTAXIME	1
+LABIDWEE	WORM ID	CULT	CULT/OTHER RSLT:	1
+LABSHSV	HSV SHELL VIAL CULTURE	CULT	CULT/OTHER RSLT:	1
+LABVCULU	"VIRAL CULTURE, URINE"	CULT	CULT/OTHER RSLT:	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	CZ	CEFAZOLIN	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	CZ	CEFAZOLIN	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88DI	DISCLAIMER	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88DX	CLINICAL INDICATION	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88IN	INTERPRETATION	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88MD	INTERPRETED BY	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88ME	METHOD	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88RE	RESULT	1
+LABMYD88	MYD88 MUTATION ANALYSIS	D88RF	REFERENCES	1
+LABHEPAT	HEPATIC PROFILE	DBIL	CONJUGATED BILI	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	1
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	DEVTYPE	DEVTYPE	1
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	DI	ISSUE DATE/TIME	1
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89	DI	ISSUE DATE/TIME	1
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	DI	ISSUE DATE/TIME	1
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	DI	ISSUE DATE/TIME	1
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	DI	ISSUE DATE/TIME	1
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	DI	ISSUE DATE/TIME	1
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94	DI	ISSUE DATE/TIME	1
+LABCLOMIP	CLOMIPRAMINE AND METABOLITE	DMCLOM	DESMETHYLCLOMIPRAMIN	1
+LABDNAISO	"NUCLEIC ACID ISOLATION, DNA"	DNADAT	DATE OF DNA EXTRACTION	1
+LABYGDA	"DRUG SCREEN, FLUIDS"	DRUGSCRNFLD	"DRUG SCRN, GENERAL, BIOLOGICAL FL"	1
+LABDVEADV	DIRECT VIRAL EXAM ADENO	DVE	RESULTS	1
+LABDVEP1	DIRECT VIRAL EXAM PARA 1	DVE	RESULTS	1
+LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	DVE	RESULTS	1
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBV	EBV BY PCR	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	EEIGG	E EQUINE ENCEP IGG AB	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	EEIGM	E EQUINE ENCEP IGM AB	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	EEINT	INTERPRETATION	1
+LABITCH	FOOD ALLERGY SCREEN	EGGW	EGG WHITE	1
+LABRBCENZ	RED CELL ENZYMES	ENO	ENOLASE	1
+LABCBCD	CBC WITH DIFF	EOSAB	ABSOLUTE EOSINOPHILS	1
+LABCBCD	CBC WITH DIFF	EOSFLD	"EOSINOPHILS, %"	1
+LABSTPOLY	"PMNS / EOSINOPHILS, STOOL"	EOSSTL	"EOSINOPHILS, STOOL"	1
+230986580	POC MENLO PPD TEST	ERYTHREMA	ERYTHREMA	1
+LABYESTRN	"ESTRONE, SERUM"	ESTRONE	ESTRONE	1
+LAB11034R	ETHOSUXIMIDE LEVEL	ETHSUX	ETHOSUXIMIDE	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FCMT	FLUID COMMENT	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FCOLOR	APPEARANCE	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	FEP	CEFEPIME	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	FEP	CEFEPIME	1
+LABEXCHTX	EXCHANGE TRANSFUSION	FFP	FRESH FROZEN UNITS	1
+LABFASAB	FASCIOLA HEPATICA AB	FHAB2	"F.HEPATICA ANTIBODY ,ELISA"	1
+LABITCH	FOOD ALLERGY SCREEN	FISH	FISH	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FLDTYP	FLUID SOURCE	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FMETA	"METAMYELOCYTE, FLD"	1
+LABFBCRQT	"BCR ABL QUANTITATIVE, FLUID"	FP190	"P190, FLUID"	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FPMLR	"PML, FLD"	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FPMN	"PMN, FLD"	1
+LABBFCT	"CELL COUNT AND DIFF, FLUID"	FPMN	"PMN, FLD"	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FRLYM	"REACTIVE LYMPHS, FLD"	1
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTDETD	FASTVT TACHY ZONE DETECTION DURATION	1
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	FSTVTTZNSC	FASTVT TACHY ZONE SUDDEN ONSET CRITERIA	1
+210993284	"ICD DEVICE EVAL,BIV,W/REPROGRAMMING"	FSTVTTZNSC	FASTVT TACHY ZONE SUDDEN ONSET CRITERIA	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15CO	"COMMENT, FLD"	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15DI	DISCLAIMER	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15DX	CLINICAL INDICATION	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15FL	"SOURCE, FLUID"	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15MD	INTERPRETED BY	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15ME	METHOD	1
+LABFT1517	"T(15	17) PML/RAR, FLUID"	FT15RE	"RESULT, FLD"	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	FTURB	"TURBIDITY, FLD"	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	FURI	CEFUROXIME (IV)	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	FURI	CEFUROXIME (IV)	1
+LABGDMTOL	GDM GLUCOSE TOLERANCE	GDM1	1HR	1
+LABGDMTOL	GDM GLUCOSE TOLERANCE	GDM2	2HR	1
+LABGDMTOL	GDM GLUCOSE TOLERANCE	GDMINT	INTERPRETATION	1
+LABGHBUR	"GAMMA - HYDROXYBUTYRIC ACID, URINE"	GHBURN	"G-OH-BUTYRIC AC,UR"	1
+LABOGTT	GLUCOSE TOLERANCE FOR CYSTIC FIBROSIS	GLT1	"GLUCOSE, 1 HR"	1
+LABOGTT	GLUCOSE TOLERANCE FOR CYSTIC FIBROSIS	GLT2	"GLUCOSE, 2 HR"	1
+LABCBCD	CBC WITH DIFF	GLU	"GLUCOSE, SER/PLAS"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	GLU	"GLUCOSE,ISTAT"	1
+LABUGLT	"GLUCOSE, URINE (TIMED)"	GLUEX	GLUCOSE EXCRETION	1
+LABGDMTOL	GDM GLUCOSE TOLERANCE	GLUF	"GLUCOSE, FASTING (GDMF)"	1
+LABUGLT	"GLUCOSE, URINE (TIMED)"	GLUURN	"GLUCOSE, URINE"	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	GM	GENTAMICIN	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	GM	GENTAMICIN	1
+LABRBCENZ	RED CELL ENZYMES	GPILS	"GPI, LOW SUBSTRATE"	1
+LABVP5	COMP HEPATITIS PANEL	HAGINT	INTERPRETATION	1
+LABVP5	COMP HEPATITIS PANEL	HAGR	ANTI HAV IGG	1
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	HAIRY	HAIRY CELLS	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HATINT	INTERPRETATION	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HATR	ANTI-HAV ANTIBODY	1
+LABDHBAG	"DONOR, HEP B SURFACE AG"	HBAG1	"DONOR, HEP B SURFACE AG"	1
+LABBMTP	BMT PRETRANSPLANT PANEL	HBCAB	HEP B CORE TOTAL AB	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCAB	HEP B CORE TOTAL AB	1
+LABHBCT	ANTI-HBC	HBCAB	HEP B CORE TOTAL AB	1
+LABDHBCAB	"DONOR, HEP B CORE TOTAL AB"	HBCAB1	"DONOR, HEP B CORE TOTAL AB"	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBCABIGM	HEPAT.B CORE AB-IGM	1
+LAB11107R	HEPATITIS B E AG	HBEAG	HEPATITIS B E AG	1
+LABHBSQT	HEPATITIS B SURFACE AB QUANTITATIVE	HBSAB	"HBS ANTIBODY, S"	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAB	HEPATITIS B SURFACE AB QUAL	1
+LABHBSQL	ANTI-HBS QUAL	HBSAB	HEPATITIS B SURFACE AB QUAL	1
+LABHBSQT	HEPATITIS B SURFACE AB QUANTITATIVE	HBSABQUAL	"HBS ANTIBODY,QUANTITATIVE, S"	1
+LABBMTP	BMT PRETRANSPLANT PANEL	HBSAG	HEPATITIS B SURF AG	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAG	HEPATITIS B SURF AG	1
+LABHBSAG	HBSAG	HBSAG	HEPATITIS B SURF AG	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAIN	INTERPRETATION	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAQL	HEP B SURF AB QUAL	1
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	HBSAQT	HEP B SURF AB QUANT	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBSINT	INTERPRETATION	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBSR	HEP B SURFACE ANTIGEN	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBTINT	INTERPRETATION	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HBTR	ANTI-HBC ANTIBODY	1
+LABHCVGE	HEPATITIS C VIRUS GENOTYPING	HCGG	HCV GENOTYPE	1
+POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	HCO3V	"HCO3 (V), ISTAT"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	HCT	"HCT(CALC), ISTAT"	1
+LABCBCD	CBC WITH DIFF	HCT	HEMATOCRIT (MANUAL ENTRY)	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HCVI	INTERPRETATION	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HCVR	ANTI-HCV	1
+LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	HDL	HDL CHOLESTEROL	1
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	HDL	HDL CHOLESTEROL	1
+LABHDLGGE	HDL GRADIENT GEL ELECTROPHORESIS	HDLGE	HDL GRAD GEL ELECTRO	1
+LABBMTP	BMT PRETRANSPLANT PANEL	HEPAAB	HEPATITIS A TOTAL AB	1
+LABBMTP	BMT PRETRANSPLANT PANEL	HEPCAB	HEPATITIS C AB	1
+LABCR	"CREATININE, SERUM/PLASMA"	HGB	HEMOGLOBIN	1
+LABCBCD	CBC WITH DIFF	HGB	HEMOGLOBIN (MANUAL ENTRY)	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	HGB	"HGB(CALC), ISTAT"	1
+POC32	POC HEMOCUE FOR HEMOGLOBIN	HGB	PATIENT HEMOGLOBIN RESULT	1
+INACTIVE 1	HHV-6 PCR QUANT	HHV6CP	RESULT	1
+INACTIVE 1	HHV-6 PCR QUANT	HHV6DN	HHV-6 DNA	1
+INACTIVE 1	HHV-6 PCR QUANT	HHV6LO	RESULT LOG 10	1
+INACTIVE 1	HHV-6 PCR QUANT	HHV6SP	SPECIMEN	1
+INACTIVE 1	HHV-6 PCR QUANT	HHV6TI	TEST DESCRIPTION	1
+LABHIVA2	HIV - 2 AB	HIV2	HIV-2 AB	1
+LABDHIVAB	"DONOR,HIV-1/HIV-2 AB SCN W/RFL"	HIVAB1	"DONOR,HIV-1/HIV-2 AB SCN W/RFL"	1
+LABYUHOMO	"HOMOCYSTEINE TOTAL, URINE"	HMCYURN	"HOMOCYSTEIN, TOTAL,U"	1
+LABHBV	HBV GENOTYPE: S AND POL GENE	HPEM	PRECORE MUTATION	1
+LABHBV	HBV GENOTYPE: S AND POL GENE	HPSOL	POLYMERASE MUTATION	1
+LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	HPTI	TEST INFORMATION	1
+LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	HPV	HPV DNA PROBE RESULT:	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HQLINT	INTERPRETATION	1
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	HQLR	ANTI-HBS ANTIBODY QUALITATIVE	1
+LABHSP70	ANTI - 68 KD (HSP - 70) ANTIBODIES	HSP70T	ANTI-68 KD ABS	1
+LABHBV	HBV GENOTYPE: S AND POL GENE	HSPOL	HEP B VIR GENOTYPING	1
+LABHIVA2	HIV - 2 AB	HTL2	HIV-2 AB NOTE:	1
+LABMALL2	MISCELLANEOUS ALLERGEN 2	I2	INTERPRETATION	1
+LABMALL3	MISCELLANEOUS ALLERGEN 3	I3	INTERPRETATION	1
+LABIBDDI	IBD DIAGNOSTIC SYSTEM	IBDDR	IBD DIAGNOSTIC SYS	1
+LABHEPAT	HEPATIC PROFILE	IBIL	UNCONJUGATED BILIRUBIN	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRAVPCD	BRADY PACED AV DELAY	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRAVSND	BRADY SENSED AV DELAY	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDBRTRKRT	BRADY TRACKING RATE	1
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDBRVVDEL	BRADY VV DELAY	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATFIB	ICD DETECTIONS AND TREATMENTS ATRIAL FIBRILLATION	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATPAT	ICD DETECTIONS AND TREATMENTS ATP EPISODES ATRIAL	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTATRFL	ICD DETECTIONS AND TREATMENTS ATRIAL FLUTTER	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTPACAT	ICD DETECTIONS AND TREATMENTS PERCENT PACING ATRIAL	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKAAT	ICD DETECTIONS AND TREATMENTS SHOCKS ABORTED ATRIAL	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDDTSKDAT	ICD DETECTIONS AND TREATMENTS SHOCKS DELIVERED ATRIAL	1
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	ICDEVALCOM	ICD EVAL COMMENTARY	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPAT	ICD TEL LEAD IMPEDANCE ATRIAL	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	ICDTLIMPLV	ICD TEL LEAD IMPEDANCE LEFT VENT	1
+LABITCH	FOOD ALLERGY SCREEN	IGEFA	TOTAL IGE	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	IMP	IMIPENEM	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	IMP	IMIPENEM	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	1
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	IMPDATE	IMPLANT DATE	1
+EXTINRM	INR (MANUAL ENTRY)	INR	INR (MANUAL ENTRY)	1
+LABPCINR	ISTAT INR AND PROTIME	INR	"INR,POCT"	1
+LABBCAA	BRANCHED CHAIN AMINO ACIDS	ISOLEU	ISOLEUCINE	1
+LABITCH	FOOD ALLERGY SCREEN	ITINT	INTERPRETATION	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	K	"POTASSIUM, ISTAT"	1
+LABCBCD	CBC WITH DIFF	K	"POTASSIUM, SER/PLAS"	1
+LABPETSIN	PETROSAL SINUS TEST	L0ACTH	LEFT 0 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	L10ACT	LEFT +10 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	L15ACT	LEFT + 15 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	L3ACTH	LEFT +3 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	L5ACTH	LEFT +5 MINS	1
+LABLAC	LACTIC ACID	LAC	"LACTIC ACID, PLASMA"	1
+LABCR	"CREATININE, SERUM/PLASMA"	LDH	"LDH, TOTAL, SER/PLAS"	1
+LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	LDL	DIRECT LDL CHOL	1
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	LDL	DIRECT LDL CHOL	1
+LABLPD	LIPID PANEL	LDL	LDL (CALCULATED)	1
+LABLEGS	LEGIONELLA SEROLOGY	LEG1	L.LEGIONELLA AB (SEROGRP 1)	1
+LABLEGS	LEGIONELLA SEROLOGY	LEG2	"L.LEGIONELLA AB (SEROGRP 2-6,8)"	1
+LABBCAA	BRANCHED CHAIN AMINO ACIDS	LEUCINE	LEUCINE	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	LEV	LEVOFLOXACIN	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	LEV	LEVOFLOXACIN	1
+LABPETSIN	PETROSAL SINUS TEST	LM5ACT	LEFT - 5 MINS	1
+LAB12814R	MYCOPLASMA IGG/IGM AB	LTROIN	INTERPRETATION	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRPACA	LV BRADY PACING AMPLITUDE	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRPACW	LV BRADY PACING PULSE WIDTH	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVBRSENA	LV BRADY SENSING AMPLITUDE	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRSENA	LV BRADY SENSING AMPLITUDE	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRSENP	LV BRADY SENSING BLANKING PERIOD	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	LVBRSENR	LV BRADY SENSING REFRACTORY PERIOD	1
+ECH15	ECHO - TRANSTHORACIC ECHO +DOPPLER	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODBP	1
+ECH48	ECHO - TRANSESOPHAGEAL ECHO COMPREHENSIVE	LVEF	LEFT VENTRICULAR EJECTION FRACTION MODSP4	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRAMP	LV THRESHOLD AMPLITUDE	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	LVTHRDUR	LV THRESHOLD DURATION	1
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRSENM	LV THRESHOLD SENSING AMPLITUDE	1
+210993289	"ICD DEVICE EVAL,BIV,W/OUT REPROG"	LVTHRSENPO	LV THRESHOLD SENSING POLARITY	1
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	LYM	LYMPHOCYTES	1
+LABCBCD	CBC WITH DIFF	LYMAB	ABSOLUTE LYMPHOCYTES	1
+LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	LYMAB	"LYMPHOCYTES, ABS."	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	LYMFLD	"LYMPHOCYTE %, FLD"	1
+LABYCMBP	"MYELIN BASIC PROTEIN, CSF"	MBPCSF	"MYELIN BAS.PROT,CSF"	1
+LABMEAIGG	RUBEOLA VIRAL AB	ME	"RUBEOLA (MEASLES) AB, IGG"	1
+LABHMET	"HEAVY METALS SCREEN, BLOOD"	MER	"MERCURY, BLOOD"	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	MER	MEROPENEM	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	MER	MEROPENEM	1
+LABYUMERC	"MERCURY, URINE (TIMED)"	MERURN	"MERCURY, U"	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	METHADONE	METHADONE	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	MFR	MANUFACTURER	1
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	MFR	MANUFACTURER	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	MG	MAGNESIUM	1
+LABYFMG	"MAGNESIUM, FLUID"	MGFLD	"MAGNESIUM, BODY FLUID"	1
+LABITCH	FOOD ALLERGY SCREEN	MIL	MILK	1
+230986580	POC MENLO PPD TEST	MMIND	MM INDURATION	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	MOD	MODEL	1
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	MOD	MODEL	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	MONFLD	"MONO/MESO %, FLD"	1
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	MONO	MONOCYTES	1
+LABCBCD	CBC WITH DIFF	MONOA	ABSOLUTE MONOCYTES	1
+LABCBCD	CBC WITH DIFF	MONOPHAGE	"MONOCYTE/MACROPHAGE, %"	1
+LABMPSQNT	"MUCOPOLYSACCHARIDES QUANTITATIVE, URINE"	MPS	"MUCOPOLYSAC, UR"	1
+LABMPSTLC	"MUCOPOLYSACCHARIDES TLC, URINE"	MPSTLC	"MUCOPOLYSA, UR, TLC"	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	MTYP	METHOD	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	MTYP	METHOD	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	NA	"SODIUM, ISTAT"	1
+LABCBCD	CBC WITH DIFF	NA	"SODIUM, SER/PLAS"	1
+LABYPA	PROCAINAMIDE	NAPA	NAPA	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81DI	DISCLAIMER	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81DX	CLINICAL INDICATION	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81MD	INTERPRETED BY	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81ME	METHOD	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81RE	"RESULT, NON-BLOOD"	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81RF	REFERENCES	1
+LABND816V	"KIT D816V MUT., AS-PCR, NON-BLOOD"	ND81SO	SOURCE	1
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	NEUT	SEG NEUTROPHILS	1
+LABCBCD	CBC WITH DIFF	NEUTAB	ABSOLUTE NEUTROPHIL COUNT	1
+LABYNOB	NEUTROPHIL OXIDATIVE BURST	NEUTOXB	NEUTROPHIL OXIDATIVE BURST	1
+LABCBCD	CBC WITH DIFF	NEUTRA	ABSOLUTE NEUTROPHILS	1
+LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	NHDL	"NON-HDL CHOL, CALC"	1
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	NHDL	"NON-HDL CHOL, CALC"	1
+LABRBCENZ	RED CELL ENZYMES	NPHOS	PURINE NUCLEOSIDE PHOSPHORYLASE	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3DI	DISCLAIMER	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3DX	CLINICAL INDICATION	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3IN	INTERPRETATION	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3MD	INTERPRETED BY	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3ME	METHOD	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3RE	"RESULT, NON-BLOOD"	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3RF	REFERENCES	1
+LABNSF3B1	"SF3B1 MUTATION ANALYSIS, NON-BLOOD"	NSF3SP	SOURCE	1
+LABITCH	FOOD ALLERGY SCREEN	NUTTY	NUT	1
+LABITCH	FOOD ALLERGY SCREEN	OAT	OAT	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	OPIATE	OPIATES	1
+LABITCH	FOOD ALLERGY SCREEN	ORAN	ORANGE	1
+LABFLDC	FLUID CULTURE AND GRAM STAIN	ORG	ORGANISM	1
+LABYOSTEO	OSTEOCALCIN HUMAN	OSTEOC	"OSTEOCALCIN, HUMAN"	1
+LABPETSIN	PETROSAL SINUS TEST	P0ACTH	PERIPHERAL 0 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	P10ACT	PERIPHERAL +10 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	P15ACT	PERIPHERAL +15 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	P3ACTH	PERIPHERAL +3 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	P5ACTH	PERIPHERAL +5  MINS	1
+LABPALL	PROCAINAMIDE	PA	PROCAINAMIDE	1
+LABPAI1AG	PAI-1 AG	PAI1G	PAI-1 ANTIGEN	1
+LABPALL	PROCAINAMIDE	PAL1	"PEAK, TROUGH, OR RANDOM"	1
+LABPALL	PROCAINAMIDE	PAL2	DATE & TIME OF LAST DOSE	1
+LABPALL	PROCAINAMIDE	PAL3	DOSAGE (INCLUDE UNITS)	1
+LABPALL	PROCAINAMIDE	PAL4	ADMINISTRATION ROUTE	1
+LABPARAG	"PARAGONIMIASIS ANTIBODY, ELISA"	PARAG1	PARAGONIMIASIS	1
+LABPBGD	"PBG DEAMINASE, RBC"	PBGD	"PBG DEAMINASE, RBC"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCAGP	"ANION GAP, ISTAT"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCBUN	"BUN, ISTAT"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCCL	"CHLORIDE, ISTAT"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	PCCOM	POCT COMMENT	1
+POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	PCO2SO	"O2 SATURATION, ISTAT"	1
+POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	PCO2V	"PCO2 (V), ISTAT"	1
+CRP332	RIGHT HEART C NIPRIDE STUDY	PDF	PDF RESULT	1
+IMG11BXM1	CAR MYOCARD BIOPSY I	PDF	PDF RESULT	1
+IMG11IA	CAR INTRA AORTIC BALLOON PUMP	PDF	PDF RESULT	1
+IMG11IABP	CAR IABP REMOVAL	PDF	PDF RESULT	1
+IMG11RLCA	CAR RT LT CATH CORS ANGIO	PDF	PDF RESULT	1
+IMG12IA	ICD ANALYSIS	PDF	PDF RESULT	1
+IMG13VA	PED VALVULOPLASTY AO V	PDF	PDF RESULT	1
+IMG1ATH	CAR PTC ROTABLATOR	PDF	PDF RESULT	1
+IMG1PTCS	CAR PTCA 1VESSEL SIMPLE	PDF	PDF RESULT	1
+IMG2EX	IR ANGIO EXTREMITY	PDF	PDF RESULT	1
+PFT43	FULL SPIROMETRY WITH SVC MVV	PDF	PDF RESULT	1
+PFT45	LUNG VOLUMES	PDF	PDF RESULT	1
+PFT53	SPIROMETRY WITH ABG	PDF	PDF RESULT	1
+LABITCH	FOOD ALLERGY SCREEN	PEAN	PEANUT	1
+POC23	"POC ISTAT, VENOUS BLOOD GASES (G3+)"	PH	"PH, ISTAT"	1
+LABPHATYR	PHENYLALANINE AND TYROSINE	PHALN	PHENYLALANINE	1
+LAB11138R	HIV PHENO EXPR ANTIVIR RESIST	PHENO	HIV-1 PHENOTYPE	1
+POC12	POC GASTRIC - OCCULT BLOOD EXAM (UNIT PERFORMED)	PHGAST	"PH,GASTRIC"	1
+230986580	POC MENLO PPD TEST	PHLOC	LOCATION	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	PHOS	PHOSPHORUS	1
+LABDVEPI1	PARA 1 DFA	PI1	PARAINFLUENZA 1	1
+LABDVEPI1	PARA 1 DFA	PI1IP	TEST DESCRIPTION	1
+LABDVEPI1	PARA 1 DFA	PI1SP	SPECIMEN	1
+LABDVEPI2	PARA 2 DFA	PI2	PARAINFLUENZA 2	1
+LABDVEPI2	PARA 2 DFA	PI2IP	TEST DESCRIPTION	1
+LABDVEPI2	PARA 2 DFA	PI2SP	SPECIMEN	1
+LABDVEPI3	PARA 3 DFA	PI3	PARAINFLUENZA 3	1
+LABDVEPI3	PARA 3 DFA	PI3IP	TEST DESCRIPTION	1
+LABDVEPI3	PARA 3 DFA	PI3SP	SPECIMEN	1
+LABRBCENZ	RED CELL ENZYMES	PKLS	"PK, LOW SUBSTRATE"	1
+LABCR	"CREATININE, SERUM/PLASMA"	PLT	PLATELET COUNT	1
+LABPANPLT	PLATELET COUNT	PLT	PLATELET COUNT	1
+LABCBCD	CBC WITH DIFF	PLT	PLATELETS	1
+LABFENTP	ENTEROVIRUS PANEL II	PLV1	POLIOVIRUS 1 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	PLV2	POLIOVIRUS 2 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	PLV3	POLIOVIRUS 3 AB	1
+LABPETSIN	PETROSAL SINUS TEST	PM5ACT	PERIPHERAL -5 MINS	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMCOMMENT	PM COMMENTARY	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	PMLVLDBIIM	PM LV LEAD BIPOLAR IMPEDANCE	1
+POC14	POC GLUCOSE BY METER	PNAB1	PNEUMOCOCCAL AB (PNEUMOVAX)	1
+LABCLAMP	CHLAMYDIA AB PANEL	PNIGA	C. PNEUMONIAE IGA	1
+LABCLAMP	CHLAMYDIA AB PANEL	PNIGG	C. PNEUMONIAE IGG	1
+LABCLAMP	CHLAMYDIA AB PANEL	PNIGM	C. PNEUMONIAE IGM	1
+LABPOLYSP	SPUTUM FOR PMNS	POLYSP	SPUTUM FOR PMNS	1
+LABSTPOLY	"PMNS / EOSINOPHILS, STOOL"	POLYST	"PMNS, STOOL"	1
+POC14	POC GLUCOSE BY METER	PPD100	PPD 1:100 CPM	1
+POC14	POC GLUCOSE BY METER	PPDINT	PPD INTERPRETATION	1
+POC6	POC PPD SKIN TEST (IP)	PPDINT	PPD INTERPRETATION	1
+LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	PPPCOM	COMMENTS	1
+LABADIFF	DIFFERENTIAL	PRMY	PROMYELOCYTES	1
+LABTBP100	TRANSFUSION BLOOD PRODUCT 101	PRO	PRODUCT ID  	1
+LABTBP95	TRANSFUSION BLOOD PRODUCT 96	PRO	PRODUCT ID  	1
+LABTBP96	TRANSFUSION BLOOD PRODUCT 97	PRO	PRODUCT ID  	1
+LABTBP97	TRANSFUSION BLOOD PRODUCT 98	PRO	PRODUCT ID  	1
+LABTBP98	TRANSFUSION BLOOD PRODUCT 99	PRO	PRODUCT ID  	1
+LABTBP99	TRANSFUSION BLOOD PRODUCT 100	PRO	PRODUCT ID  	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	PROPOX	PROPOXYPHENE	1
+LABCLAMP	CHLAMYDIA AB PANEL	PSIGA	C. PSITTACI IGA	1
+LABCLAMP	CHLAMYDIA AB PANEL	PSIGG	C. PSITTACI IGG	1
+LABCLAMP	CHLAMYDIA AB PANEL	PSIGM	C. PSITTACI IGM	1
+LABADIFF	DIFFERENTIAL	PSREV	PATH/SPEC REVIEW	1
+LABRBCMOR	"RBC MORPHLOGY, COMPLETE"	PSREV	PATH/SPEC REVIEW	1
+EXTINRM	INR (MANUAL ENTRY)	PT	PROTHROMBIN TIME	1
+LABPCINR	ISTAT INR AND PROTIME	PTT	"PTT,POCT"	1
+LABYQUIN	QUINIDINE	QUINIDINE	QUINIDINE	1
+LABPETSIN	PETROSAL SINUS TEST	R0ACTH	RIGHT 0 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	R10ACT	RIGHT +10 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	R15ACT	RIGHT 15 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	R3ACTH	RIGHT +3 MINS	1
+LABPETSIN	PETROSAL SINUS TEST	R5ACTH	RIGHT +5 MINS	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRPACA	RA BRADY PACING AMPLITUDE	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRPACW	RA BRADY PACING PULSE WIDTH	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRSENA	RA BRADY SENSING AMPLITUDE	1
+210993279	"PM DEVICE EVAL,SNGL,W/REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RABRSENR	RA BRADY SENSING REFRACTORY PERIOD	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRAMP	RA THRESHOLD AMPLITUDE	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRDUR	RA THRESHOLD DURATION	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRSENMP	RA THRESHOLD SENSING AMPLITUDE	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	RATHRSPOL	RA THRESHOLD SENSING POLARITY	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	RBCFLD	"RBC COUNT, FLD (/UL)"	1
+LABCBCD	CBC WITH DIFF	RBCHP	RBC	1
+LABEXCHTX	EXCHANGE TRANSFUSION	RBCSTA	RBC PRODUCT - UNITS	1
+LABURED	"REDUCING SUBSTANCE, URINE"	REDSUBURN	"REDUCING SUBST., URINE"	1
+LAB11092R	"RENIN ACTIVITY, PLASMA"	RENIN	"RENIN ACTIVITY, PL"	1
+LABIMM2	IMMUNOLOGY PANEL 2	RHFT	RHEUMATOID FACTOR LEVEL	1
+LABRHTYP	RH BLOOD TYPE	RHTYP	RH BLOOD TYPE	1
+10010R	HLA ORDER - CROSSMATCH (W/UNRELATED DONOR INFO)	RINFO	TO FIND RESULT:	1
+10120R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)	RINFO	TO FIND RESULT:	1
+10165R	HLA ORDER - FINAL DONOR SELECTION (W/UNRELATED DONOR INFO)	RINFO	TO FIND RESULT:	1
+10225R	HLA ORDER - DESENSITIZATION MONITORING PRE IVIG	RINFO	TO FIND RESULT:	1
+10280R	HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)	RINFO	TO FIND RESULT:	1
+10285R	HLA ORDER - SPECIMEN IN LAB ORDERS (DONOR)	RINFO	TO FIND RESULT:	1
+10297R	HLA ORDER - DECEASED DNR O.R. SPMN	RINFO	TO FIND RESULT:	1
+LABPETSIN	PETROSAL SINUS TEST	RM5ACT	RIGHT -5 MINS	1
+LABDRPR	"DONOR, RPR W/RFL TO SYPH IGG"	RPR1	"DONOR, RPR W/RFL TO SYPH IGG"	1
+LABDVEADV	DIRECT VIRAL EXAM ADENO	RPT	RPT	1
+LABDVEP1	DIRECT VIRAL EXAM PARA 1	RPT	RPT	1
+LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	RPT	RPT	1
+LABIDWEE	WORM ID	RPT	RPT	1
+LABSHSV	HSV SHELL VIAL CULTURE	RPT	RPT	1
+LABVCULU	"VIRAL CULTURE, URINE"	RPT	RPT	1
+LABCD3B	"CD3, BLOOD"	RTFDA1	NOTE	1
+LABRUBM	RUBELLA AB IGM	RUBM1	"RUBELLA AB, IGM"	1
+LABYRUBM	RUBELLA AB IGM	RUBM1	"RUBELLA AB, IGM"	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SAMP	AMPICILLIN/SULBACTAM	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SAMP	AMPICILLIN/SULBACTAM	1
+LAB11093R	AUTOANTIBODIES TO SCL 70	SCL70	"SCL 70 AB, IGG, SERUM"	1
+LABDVEADV	DIRECT VIRAL EXAM ADENO	SDES	SPECIMEN	1
+LABDVEP1	DIRECT VIRAL EXAM PARA 1	SDES	SPECIMEN	1
+LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SDES	SPECIMEN	1
+LABIDWEE	WORM ID	SDES	SPECIMEN	1
+LABSHSV	HSV SHELL VIAL CULTURE	SDES	SPECIMEN	1
+LABVCULU	"VIRAL CULTURE, URINE"	SDES	SPECIMEN	1
+LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	SDES6	SPECIMEN	1
+210993280	"PM DEVICE EVAL,DUAL,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	1
+210993281	"PM DEVICE EVAL,BIV,W/REPROGRAMMING"	SERNUM	SERIAL NUMBER	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3DI	DISCLAIMER	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3DX	CLINICAL INDICATION	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3IN	INTERPRETATION	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3MD	INTERPRETED BY	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3ME	METHOD	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3RE	"RESULT, BLOOD"	1
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3RF	REFERENCES	1
+LABITCH	FOOD ALLERGY SCREEN	SHRIM	SHRIMP	1
+LABTBP100	TRANSFUSION BLOOD PRODUCT 101	SI	<<STATUS>>  	1
+LABTBP95	TRANSFUSION BLOOD PRODUCT 96	SI	<<STATUS>>  	1
+LABTBP96	TRANSFUSION BLOOD PRODUCT 97	SI	<<STATUS>>  	1
+LABTBP97	TRANSFUSION BLOOD PRODUCT 98	SI	<<STATUS>>  	1
+LABTBP98	TRANSFUSION BLOOD PRODUCT 99	SI	<<STATUS>>  	1
+LABTBP99	TRANSFUSION BLOOD PRODUCT 100	SI	<<STATUS>>  	1
+210993283	"ICD DEVICE EVAL,DUAL,W/REPROGRAMMING"	SLWVTTAZNS	SLOWVT TACHY ZONE SUDDEN ONSET CRITERIA	1
+LABSNP	SENSORIMOTOR PROFILE	SNPRR	SENSORIMOTOR PROFILE	1
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SNRBC	"NRBC/100WBC, SYN FLD"	1
+LABADPCRQ	ADENOVIRUS PCR QUANT	SOUR10	SOURCE	1
+LABBKVPC	"BK VIRUS PCR, QUANT, PLASMA"	SOURC6	SOURCE	1
+LABBKDNA	BK VIRUS DNA PCR	SOURC7	SOURCE	1
+LABITCH	FOOD ALLERGY SCREEN	SOY	SOY	1
+LABPETSIN	PETROSAL SINUS TEST	SPAC1	SPACE FOR PETSIN	1
+LABPETSIN	PETROSAL SINUS TEST	SPAC2	SPACE FOR PETSIN	1
+LABPETSIN	PETROSAL SINUS TEST	SPAC3	SPACE FOR PETSIN	1
+LABPETSIN	PETROSAL SINUS TEST	SPAC4	SPACE FOR PETSIN	1
+LABPETSIN	PETROSAL SINUS TEST	SPAC5	SPACE FOR PETSIN	1
+LABAS4I	AB SCREEN	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	SPEXP	SPECIMEN EXPIRATION DATE/TIME	1
+POC39	POC URINE FOR PH	SPG	SPECIFIC GRAVITY UA	1
+LABDVEADV	DIRECT VIRAL EXAM ADENO	SREQ	COMMENTS	1
+LABDVEP1	DIRECT VIRAL EXAM PARA 1	SREQ	COMMENTS	1
+LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	SREQ	COMMENTS	1
+LABIDWEE	WORM ID	SREQ	COMMENTS	1
+LABSHSV	HSV SHELL VIAL CULTURE	SREQ	COMMENTS	1
+LABVCULU	"VIRAL CULTURE, URINE"	SREQ	COMMENTS	1
+LABSYDIFF	"DIFFERENTIAL, SYNOVIAL FLUID"	SRLYM	"REACT LYMPH, SYN FLD"	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	STIGG	ST. LOUIS ENCEP IGG	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	STIGM	ST. LOUIS ENCEP IGM	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	STINT	INTERPRETATION	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	SXT	TRIMETHOPRIM/SULFAMETHOXAZOLE	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	SXT	TRIMETHOPRIM/SULFAMETHOXAZOLE	1
+LABCBCD	CBC WITH DIFF	SYNLYMPH	LYMPHOCYTES %	1
+POC5	POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)	T11DX	CLINICAL INDICATION	1
+LABYTBG	THYROXINE BINDING GLOBULIN	T4BG	THYROXINE-BIND GLOB	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRCT	COMMENT	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRDR	INTERPRETED BY	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRDS	DISCLAIMER	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRES	RESULTS	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRMT	METHOD	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRPM	PRIMERS	1
+LABTBCR	"BCR - ABL, TISSUE"	TBCRTS	SOURCE	1
+LABCBCD	CBC WITH DIFF	TBIL	TOTAL BILIRUBIN	1
+LABHEPAT	HEPATIC PROFILE	TBIL	TOTAL BILIRUBIN	1
+PATH18	"FLOW CYTOMETRY FOR T CELL IMMUNOTHERAPY PANEL, BLOOD"	TCD4AB	"TOTAL CD4, ABS."	1
+LABCLOMIP	CLOMIPRAMINE AND METABOLITE	TCLOAM	"CLOMIP & METAB, TOTAL"	1
+LABPCCH8	"POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2"	TCO2A	"TCO2, ISTAT"	1
+LABLPD	"LIPID PANEL, NON-FASTING PATIENT"	TGL	"TRIGLYCERIDE, SER/PLAS"	1
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	TGL	"TRIGLYCERIDE, SER/PLAS"	1
+POC24	POC ISTAT TROPONIN I	TNI	"TROPONIN I, POCT"	1
+LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBRASDD	"TOBRAMYCIN, SINGLE DAILY DOSE"	1
+LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBUN2	DATE & TIME OF LAST DOSE	1
+LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBUN3	DOSAGE	1
+LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	TOBUN4	ADMINISTRATION ROUTE	1
+LABITCH	FOOD ALLERGY SCREEN	TOM	TOMATO	1
+INACTIVE 4	TOXOPLASMA IGG IGM	TOXDES	TEST DESCRIPTION	1
+LABTOXGM	TOXOPLASMA IGM & IGG	TOXDES	TEST DESCRIPTION	1
+LABTOXGMR	TOXO PREGNANCY PANEL	TOXDES	TEST DESCRIPTION	1
+INACTIVE 4	TOXOPLASMA IGG IGM	TOXGQL	IGG RESULT	1
+LABTOXGM	TOXOPLASMA IGM & IGG	TOXGQL	IGG RESULT	1
+LABTOXGMR	TOXO PREGNANCY PANEL	TOXGQL	IGG RESULT	1
+LABTOXOPR	TOXO PREGNANCY PANEL	TOXIGR	TOXOPLASMA IGG	1
+LABTOXOPR	TOXO PREGNANCY PANEL	TOXIMR	TOXOPLASMA IGM	1
+INACTIVE 4	TOXOPLASMA IGG IGM	TOXMQL	IGM RESULT	1
+LABTOXGM	TOXOPLASMA IGM & IGG	TOXMQL	IGM RESULT	1
+LABTOXGMR	TOXO PREGNANCY PANEL	TOXMQL	IGM RESULT	1
+LABTOXIG	TOXOPLASMA SEROLOGY	TOXO	TOXOPLASMA IGG	1
+LAB11122R	TOXOPLASMOSIS IGM	TOXOM	TOXOPLASMA IGM ELISA	1
+LABTRAZO	TRAZODONE	TRAZO	TRAZODONE	1
+LAB11097R	"TSI, SERUM"	TSIG	"THROID-STIMULATING IG, SERUM"	1
+LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB1	COXSACKIE B1 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYB1	COXSACKIE B1 AB	1
+LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB2	COXSACKIE B2 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYB2	COXSACKIE B2 AB	1
+LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB3	COXSACKIE B3 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYB3	COXSACKIE B3 AB	1
+LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB4	COXSACKIE B4 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYB4	COXSACKIE B4 AB	1
+LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB5	COXSACKIE B5 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYB5	COXSACKIE B5 AB	1
+LABCOXBAB	"COXSACKIE B AB, SERUM"	TYB6	COXSACKIE B6 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYB6	COXSACKIE B6 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYP10	COXSACKIE A10 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYP16	COXSACKIE A16 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYP2	COXSACKIE A2 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYP4	COXSACKIE A4 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYP7	COXSACKIE A7 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYP9	COXSACKIE A9 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYPE11	ECHOVIRUS 11 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYPE30	ECHOVIRUS 30 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYPE4	ECHOVIRUS 4 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYPE7	ECHOVIRUS 7 AB	1
+LABFENTP	ENTEROVIRUS PANEL II	TYPE9	ECHOVIRUS 9 AB	1
+LABPHATYR	PHENYLALANINE AND TYROSINE	TYROSINE	TYROSINE	1
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	TZP	PIPERACILLIN/TAZOBACTAM	1
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	TZP	PIPERACILLIN/TAZOBACTAM	1
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94	UBT	UNIT BLOOD TYPE  	1
+LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	UHYPOX	HYPOXANTHINE	1
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94	UNT	UNIT NUMBER  	1
+LABUAMICR	URINALYSIS MICROSCOPIC ONLY	URBC	"RBC, URINE"	1
+LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	URIC	URIC ACID	1
+LABCR	"CREATININE, SERUM/PLASMA"	URIC	"URIC ACID, SER/PLAS"	1
+LABUGLT	"GLUCOSE, URINE (TIMED)"	UT7	COLLECTION PERIOD GLUCOSE	1
+LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	UURAC	URACIL	1
+POC39	POC URINE FOR PH	UWBC	LEUKOCYTES	1
+LABUAMICR	URINALYSIS MICROSCOPIC ONLY	UWBC	"WBC, URINE"	1
+LABYUPPP	"PURINE AND PYRINIDINE PANEL, URINE"	UXANT	XANTHINE	1
+LABBCAA	BRANCHED CHAIN AMINO ACIDS	VALINE	VALINE	1
+LABBMTP	BMT PRETRANSPLANT PANEL	VGGQNT	VZV IGG LEVEL	1
+LABYVIP	VASOACTIVE INTESTINAL POLYPEPTIDE	VIP	VASOACTIVE INT PEPTIDE	1
+LABYVITE	"VITAMIN E, SERUM"	VITE	"A-TOCOPHEROL, VITAMIN E"	1
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	VL	PRODUCT VOLUME	1
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	VL	PRODUCT VOLUME	1
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	VL	PRODUCT VOLUME	1
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	VL	PRODUCT VOLUME	1
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	VL	PRODUCT VOLUME	1
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	VL	PRODUCT VOLUME	1
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	VL	PRODUCT VOLUME	1
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	VL	PRODUCT VOLUME	1
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	VL	PRODUCT VOLUME	1
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	VL	PRODUCT VOLUME	1
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	VL	PRODUCT VOLUME	1
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	VL	PRODUCT VOLUME	1
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	VL	PRODUCT VOLUME	1
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	VL	PRODUCT VOLUME	1
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	VL	PRODUCT VOLUME	1
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	VL	PRODUCT VOLUME	1
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	VL	PRODUCT VOLUME	1
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	VL	PRODUCT VOLUME	1
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	VL	PRODUCT VOLUME	1
+LABUGLT	"GLUCOSE, URINE (TIMED)"	VOL7	VOLUME GLUCOSE	1
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	VOLUMEURN	URINE VOLUME	1
+LABYUMERC	"MERCURY, URINE (TIMED)"	VOLUMEURN	URINE VOLUME	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSLWVTTZNT	VSLOWVT TACHY ZONE RATE STABILITY CRITERIA	1
+210993282	"ICD DEVICE EVAL,SNGL,W/ REPROGRAMMING"	VSWVTTAQRS	VSLOWVT TACHY ZONE QRS MORPHOLOGY CRITERIA	1
+LABVWNTRP	"INTERPRETATION, VW SCREEN"	VWDIR	INTERPRETED BY	1
+LABVWNTRP	"INTERPRETATION, VW SCREEN"	VWINTP	INTERPRETATION	1
+LABVZVIGG	VZ IGG	VZAB	VARICELLA ZOSTER AB IGG	1
+LABCR	"CREATININE, SERUM/PLASMA"	WBC	WBC	1
+LABAFCT	"CELL CT & DIFF, APHERESIS FLD"	WBCFLD	"NUCL CELLS, FLD(/UL)"	1
+LABCBCD	CBC WITH DIFF	WBCHP	WBC	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	WEIGG	W EQUINE IGG AB	1
+LABFABRO	"ARBOVIRUS AB PANEL, IFA (TITER)"	WEIGM	W EQUINE IGM AB	1
+LABITCH	FOOD ALLERGY SCREEN	WHT	WHEAT	1
+LABXAMH	ANTI - MULLERIAN HORMONE	XAMHOR	ANTI-MULLERIAN HORMONE	1
+LABQUECORSL	"QUEST - CORTISOL, SERUM LC/MS/MS"	XCORTI	CORTISOL TOTAL SERUM	1
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	XMR	UNIT CROSSMATCH  	1
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	XMR	UNIT CROSSMATCH  	1
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	XMR	UNIT CROSSMATCH  	1
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	XMR	UNIT CROSSMATCH  	1
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	XMR	UNIT CROSSMATCH  	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YANTQ	ANTHRAQUINONES	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO1	SPECIMEN	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO10	RELEASE DATE	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO11	INTERPRETATION	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO12	EXTRACTION PERFORMED?	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO2	SPECIMEN ID	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO4	ORDER DATE	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO5	METHOD	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO6	RESULT	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO7	CAUTIONS	1
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	YAPO9	REVIEWED BY	1
+LABYARBIN	ACH RECEPTOR BINDING AB	YARBIN	ACH RECEPTOR BINDING ANTIBODY	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YBIS	BISACODYL	1
+LABYBUPRO	"BUPROPION, SERUM"	YBUPR1	BUPROPION	1
+LABYCALCI	"CALCITONIN, PLASMA"	YCALCI	"CALCITONIN, SERUM"	1
+LABYDISO	"DISOPYRAMIDE, SERUM"	YDISO1	"DISOPYRAMIDE, P"	1
+LABYDOC	"11-DEOXYCORTISOL, SERUM"	YDOC1	"11-DEOXYCORTISOL, S"	1
+LABYDOC	"11-DEOXYCORTISOL, SERUM"	YDOC2	AM RESULT	1
+LABYDOC	"11-DEOXYCORTISOL, SERUM"	YDOC3	PM RESULT	1
+LABYEMA	"ENDOMYSIAL ANTIBODIES, SERUM"	YEMAIN	"ENDOMYSIAL ABS, SERUM"	1
+LABEVPCR	ENTEROVIRUS BY PCR	YENP1	SPECIMEN SOURCE	1
+LABEVPCR	ENTEROVIRUS BY PCR	YENP2	"ENTEROVIRUS ,PCR"	1
+LABEVPCR	ENTEROVIRUS BY PCR	YENP3	SPECIAL INFORMATION	1
+LABEVPCR	ENTEROVIRUS BY PCR	YENP4	REPORT STATUS	1
+LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP1	CELL SURFACE AB IGG	1
+LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP3	MONKEY ESOPHAGUS IGG	1
+LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP4	HUMAN SPLIT SKIN IGG	1
+LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	YEP5	OTHER	1
+LABYESTRI	ESTRIOL FREE	YESTR1	"ESTRIOL,UNCONJUGATED"	1
+LABYFMG	"MAGNESIUM, FLUID"	YFMG2	FLUID TYPE	1
+LABYHALO	"HALOPERIDOL, SERUM"	YHALO2	REDUCED HALOPERIDOL	1
+LABYHPL	HUMAN PLACENTAL LACTOGEN	YHPL1	HUMAN PLACENTAL LACTOGEN	1
+LABYLEPTN	LEPTIN	YLEPT1	LEPTIN	1
+LABYNABF	INTERFERON NEUTRALIZING  AB	YNABF1	INTERFERON NEUTRALIZING AB	1
+LABYNOB	NEUTROPHIL OXIDATIVE BURST	YNOB2	INTERPRETATION	1
+LABYNOB	NEUTROPHIL OXIDATIVE BURST	YNOB3	REVIEWED BY	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YOXY	OXYPHENISATIN	1
+LABYPA	PROCAINAMIDE	YPANAP	PROCAINAMIDE + NAPA	1
+LABYPCA	"PARIETAL CELL AB, SERUM"	YPCA	PARIETAL CELL AB	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YPHEN	PHENOLPHTHALEIN	1
+LABYPA	PROCAINAMIDE	YPROC	PROCAINAMIDE	1
+LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	YPTSA	PHOSPHATIDYLSERINE IGA AB	1
+LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	YPTSG	PHOSPHATIDYLSERINE IGG AB	1
+LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	YPTSM	PHOSPHATIDYLSER. IGM	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL1	REPORTING LIMIT	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL2	REPORTING LIMIT	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL3	REPORTING LIMIT	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL4	REPORTING LIMIT	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL5	REPORTING LIMIT	1
+LABYUCLAX	"CATHARTIC LAXATIVE PANEL, URINE"	YRL6	REPORTING LIMIT	1
+LABYRS	RSV IGG AND IGM AB	YRS1	"RSV AB, IGG & IGM"	1
+LABYSMG	"MAGNESIUM QUANTITATIVE, FECES"	YSMG2	TOTAL WEIGHT	1
+LAB12578R	"CHLORIDE, 24 HR STOOL"	YSTCL1	"CHLORIDE, STOOL, 24 HR"	1
+LABYT3UP	T3 UPTAKE	YT7P2	T-UPTAKE %	1
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZCO	COMMENT	1
+LABYUBZC	"BENZODIAZEPINE CONFIRMATION, URINE"	YUBZNO	NOTE	1
+LAB11229R	"METANEPHRINE,FRACT,RANM U"	YUMECO	COMMENT	1
+LABYUMERC	"MERCURY, URINE (TIMED)"	YUMER2	COLLECTION DURATION	1
+LABYUPCPC	"PHENCYCLIDINE CONFIRMATION, URINE"	YUPCGC	PHENCYCLIDINE	1
+LABYUPCPC	"PHENCYCLIDINE CONFIRMATION, URINE"	YUPCIM	IMMUNOASSAY SCREEN	1
+LABYUPCPC	"PHENCYCLIDINE CONFIRMATION, URINE"	YUPCIN	INTERPRETATION	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	YUPCP	PHENCYCLIDINE	1
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12	YUTTHC	TETRAHYDROCANNABINOL	1
+LAB12322R	VON WILLEBRAND MULTIMERS	YVWMR	VON WILLEBRAND	1
+210951798	POST VOID TEST		RESIDUAL BLADDER VOLUME	1
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/__init__.py b/scripts/Archive/LabTestAnalysis/machine_learning/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UCSF_component_raw2code.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UCSF_component_raw2code.csv
new file mode 100644
index 00000000..30b182c0
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UCSF_component_raw2code.csv
@@ -0,0 +1,26 @@
+raw,lab
+CREAT,CR
+PO2,PO2A
+PO2,PO2V
+PCO2,PCO2A
+PCO2,PCO2V
+PO4,PHOS
+CAI,CAION
+TRPI,TNI
+NA,NA
+LACT,LAC
+TBILI,TBIL
+K,K
+ALB,ALB
+WBC,WBC
+PLT,PLT
+ALKP,ALKP
+BUN,BUN
+CL,CL
+CO2,CO2
+HGB,HGB
+TP,TP
+AST,AST
+ALT,ALT
+DBILI,DBIL
+PH37,PHA
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UCSF_panel_raw2code.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UCSF_panel_raw2code.csv
new file mode 100644
index 00000000..eb2317f2
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UCSF_panel_raw2code.csv
@@ -0,0 +1,31 @@
+raw,lab
+"Basic Metabolic Panel - UCSF/LabCorp/Quest (NA, K, CL, CO2, BUN, CR, GLU, CA)",LABMETB
+Complete Blood Count with Differential,LABCBCD
+"Magnesium, Serum / Plasma",LABMGN
+"Phosphorus, Serum / Plasma",LABPHOS
+Prothrombin Time,LABPT
+Activated Partial Thromboplastin Time,LABPTT
+"Creatinine, Serum / Plasma",LABCR
+"Sodium, Serum / Plasma",LABNA
+"Calcium, Ionized, serum/plasma",LABCAI
+Troponin I,LABTNI
+"Potassium, Serum / Plasma",LABK
+"Lactate Dehydrogenase, Serum / Plasma",LABLDH
+"Chloride, Serum / Plasma",LABCL
+"Uric Acid, Serum / Plasma",LABURIC
+"Albumin, Serum / Plasma",LABALB
+C-Reactive Protein,LABCRP
+"Urea Nitrogen, Serum / Plasma",LABBUN
+"Calcium, total, Serum / Plasma",LABCAI
+Thyroid Stimulating Hormone,LABTSH
+Ferritin,LABFE
+AFB Respiratory Culture,LABAFBC
+"Creatine kinase, total",LABCK
+AFB Non-Respiratory Culture,LABAFBD
+Platelet count,LABPLTS
+Hemoglobin A1c,LABA1C
+Sedimentation Rate,LABESRP
+"Osmolality, urine",LABOSM
+Urine Culture,LABURNC
+"Protein, Total, Serum / Plasma",LABTP
+Vitamin B12,LABB12
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UMich_component_raw2code.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UMich_component_raw2code.csv
new file mode 100644
index 00000000..19a75cf9
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UMich_component_raw2code.csv
@@ -0,0 +1,22 @@
+raw,lab
+CREAT,CR
+PO2AA,PO2A
+PCOAA2,PCO2A
+SOD,NA
+TBIL,TBIL
+POT,K
+ALB,ALB
+WBC,WBC
+PLT,PLT
+ALK,ALKP
+UN,BUN
+CHLOR,CL
+CO2,CO2
+HGB,HGB
+T PROTEIN,TP
+AST,AST
+ALT,ALT
+DBIL,DBIL
+IBIL,IBIL
+pHA,PHA
+CAL,CA
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UMich_panel_raw2code.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UMich_panel_raw2code.csv
new file mode 100644
index 00000000..0df7d985
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_UMich_panel_raw2code.csv
@@ -0,0 +1,13 @@
+raw,lab
+MAG,LABMGN
+ALB,LABALB
+TSH,LABTSH
+TROP,LABTNI
+POT,LABK
+SOD,LABNA
+PHOS,LABPHOS
+PT7,LABPT
+PTT,LABPTT
+BLD,LABBLC
+BLDAN,LABBLC2
+URIC,LABURIC
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_component.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_component.csv
new file mode 100644
index 00000000..345cadbc
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_component.csv
@@ -0,0 +1,23 @@
+lab,Stanford,UMich,UCSF,description
+WBC,WBC,WBC,WBC,White Blood Cells
+HGB,HGB,HGB,HGB,"Hemoglobin, ISTAT"
+PLT,PLT,PLT,PLT,Platelet Count
+NA,NA,SOD,NA,"Sodium, Serum/plasma"
+K,K,POT,K,"Potassium, ISTAT"
+CL,CL,CHLOR,CL,"Chloride, Serum/plasma"
+CR,CR,CREAT,CREAT,"Creatinine, ISTAT"
+BUN,BUN,UN,BUN,"Urea Nitrogen, Serum/plasma"
+GLU,GLU,,,"Glucose, ISTAT"
+CO2,CO2,CO2,CO2,"CO2, Serum/plasma"
+CA,CA,CAL,CA,"Calcium, Serum/plasma"
+TP,TP,PROT,TP,"Protein, Total, Serum/plasma"
+ALB,ALB,ALB,ALB,"Albumin, Serum/plasma"
+ALKP,ALKP,ALK,ALKP,"Alk Phos, Total, Serum/plasma"
+TBIL,TBIL,TBIL,TBILI,Total Bilirubin
+AST,AST,AST,AST,"AST (SGOT), Serum/plasma"
+ALT,ALT,ALT,ALT,"ALT (SGPT), Serum/plasma"
+DBIL,DBIL,DBIL,DBILI,Conjugated Bili
+IBIL,IBIL,IBIL,,"Bilirubin, Indirect, Serum/plasma"
+PHA,PHA,pHA,PH37,PH (A)
+PCO2A,PCO2A,PCOAA2,PCO2,"PCO2, ISTAT"
+PO2A,PO2A,PO2AA,PO2,"PO2, ISTAT"
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_component_code2description.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_component_code2description.csv
new file mode 100644
index 00000000..833b66d9
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_component_code2description.csv
@@ -0,0 +1,23 @@
+lab,description,
+WBC,White Blood Cells,
+HGB,"Hemoglobin, ISTAT",
+PLT,Platelet Count,
+NA,"Sodium, Serum/plasma",
+K,"Potassium, ISTAT",
+CL,"Chloride, Serum/plasma",
+CR,"Creatinine, ISTAT",
+BUN,"Urea Nitrogen, Serum/plasma",
+GLU,"Glucose, ISTAT",
+CO2,"CO2, Serum/plasma",
+CA,"Calcium, Serum/plasma",
+TP,"Protein, Total, Serum/plasma", Serum/plasma
+ALB,"Albumin, Serum/plasma",
+ALKP,"Alk Phos, Total, Serum/plasma", Serum/plasma
+TBIL,Total Bilirubin,
+AST,"AST (SGOT), Serum/plasma",
+ALT,"ALT (SGPT), Serum/plasma",
+DBIL,Conjugated Bili,
+IBIL,"Bilirubin, Indirect, Serum/plasma", Serum/plasma
+PHA,PH (A),
+PCO2A,"PCO2, ISTAT",
+PO2A,"PO2, ISTAT",
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_cormobidity.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_cormobidity.csv
new file mode 100644
index 00000000..374caa53
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_cormobidity.csv
@@ -0,0 +1,6 @@
+cormobidity,Stanford,UMich,UCSF,description
+Malignancy,Malignancy,,Cancer,
+CHF,CHF,,CongestiveHeartFailure,
+MI,MI,,MyocardialInfarction,
+Cerebrovascular,Cerebrovascular,,CerebrovascularDisease,
+Diabetes,Diabetes,,Diabeteswithoutcomplications,
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_panel.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_panel.csv
new file mode 100644
index 00000000..8d9b7002
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_panel.csv
@@ -0,0 +1,90 @@
+lab,Stanford,UMich,UCSF,description
+LABUA,LABUA,,,Urinalysis With Microscopic
+LABBLC,LABBLC,BLD,,Blood Culture (Aerobic & Anaerobic)
+LABPT,LABPT,PT7,Prothrombin Time,Prothrombin Time
+LABPTT,LABPTT,PTT,Activated Partial Thromboplastin Time,Partial Thromboplastin Time
+LABTSH,LABTSH,TSH,Thyroid Stimulating Hormone,Thyroid Stimulating Hormone
+LABPHOS,LABPHOS,PHOS,"Phosphorus, Serum / Plasma","Phosphorus, Serum/plasma"
+LABURNC,LABURNC,,Urine Culture,Urine Culture
+LABA1C,LABA1C,A1C,Hemoglobin A1c,Hemoglobin A1c
+LABMGN,LABMGN,MAG,"Magnesium, Serum / Plasma","Magnesium, Serum/plasma"
+LABHAP,LABHAP,,,Haptoglobin
+LABANER,LABANER,,,Anaerobic Culture
+LABPCTNI,LABPCTNI,,,iSTAT Troponin I
+LABNTBNP,LABNTBNP,,,NT-proBNP
+LABK,LABK,POT,"Potassium, Serum / Plasma","Potassium, Serum/plasma"
+LABTNI,LABTNI,TROP,Troponin I,Troponin I
+LABUAPRN,LABUAPRN,,,"Urinalysis, Screen For Culture"
+LABCAI,LABCAI,,"Calcium, Ionized, serum/plasma","Calcium Ionized, Serum/plasma"
+LABCAI,LABCAI,,"Calcium, total, Serum / Plasma","Calcium Ionized, Serum/plasma"
+LABHCTX,LABHCTX,,,Hematocrit
+LABMB,LABMB,,,"Ck, Mb (mass)"
+LABLIPS,LABLIPS,,,Lipase
+LABCK,LABCK,,"Creatine kinase, total","Creatine Kinase, Total"
+LABBLC2,LABBLC2,BLDAN,,Blood Culture (2 Aerobic)
+LABLDH,LABLDH,,"Lactate Dehydrogenase, Serum / Plasma","LDH Total, Serum / Plasma"
+LABTRFS,LABTRFS,,,Transferrin Saturation
+LABLAC,LABLAC,,,Lactic Acid
+LABFER,LABFER,,,Ferritin
+LABFT4,LABFT4,,,"T4, Free"
+LABCORT,LABCORT,,,"Cortisol, Serum"
+LABURIC,LABURIC,URIC,"Uric Acid, Serum / Plasma","Uric Acid, Serum / Plasma"
+LABGRAM,LABGRAM,,,Gram Stain
+LABFE,LABFE,,Ferritin,"Iron, Total"
+LABB12,LABB12,,Vitamin B12,Vitamin B12
+LABFIB,LABFIB,,,Fibrinogen
+LABCRP,LABCRP,,C-Reactive Protein,C-Reactive Protein
+LABRETIC,LABRETIC,,,Reticulocyte Count Automated
+LABBXTG,LABBXTG,,,Biopsy / Tissue Culture And Gram Stain
+LABPLTS,LABPLTS,,Platelet count,Platelet Count
+LABPCCR,LABPCCR,,,iSTAT Creatinine
+LABFCUL,LABFCUL,,,Fungal Culture
+LABFLDC,LABFLDC,,,Fluid Culture And Gram Stain
+LABTRIG,LABTRIG,,,"Triglycerides, Serum / Plasma"
+LABNH3,LABNH3,,,"Ammonia, Plasma"
+LABBLCTIP,LABBLCTIP,,,Blood Cult - 2nd Set Via Catheter
+LABRESP,LABRESP,,,Respiratory Culture
+LABHEPAR,LABHEPAR,,,Heparin Activity Level
+LABFOL,LABFOL,,,Folic Acid
+LABCA,LABCA,CAL,,"Calcium, Serum/plasma"
+LABBLCSTK,LABBLCSTK,,,"Blood Cult - First Set, Via Phlebotomy"
+LABUOSM,LABUOSM,,,"Osmolality, Urine"
+LABUSPG,LABUSPG,,,"Specific Gravity, Urine"
+LABRESPG,LABRESPG,,,Respiratory Culture And Gram Stain
+LABCSFGL,LABCSFGL,,,"Glucose, Csf"
+LABHBSAG,LABHBSAG,,,Hepatitis B Antigen
+LABALB,LABALB,ALB,"Albumin, Serum / Plasma","Albumin, Serum/plasma"
+LABAFBD,LABAFBD,,AFB Non-Respiratory Culture,"Afb Culture, Non - Respiratory"
+LABESRP,LABESRP,,Sedimentation Rate,Sedimentation Rate (ESR)
+LABDIGL,LABDIGL,,,"Digoxin, Serum"
+LABSTOBGD,LABSTOBGD,,,"Occult Bld, Guaiac, Stool, Diag Test"
+LABBUN,LABBUN,,"Urea Nitrogen, Serum / Plasma","Urea Nitrogen, Serum/plasma"
+LABNA,LABNA,SOD,"Sodium, Serum / Plasma","Sodium, Serum / Plasma"
+LABLIDOL,LABLIDOL,,,Lidocaine
+LABCSFTP,LABCSFTP,,,"Protein Total, Csf"
+LABCSFC,LABCSFC,,,CSF Culture And Gram Stain
+LABUPREG,LABUPREG,,,"Pregnancy Test, Urine"
+LABOSM,LABOSM,,"Osmolality, urine","Osmolality, Serum"
+LABAFBC,LABAFBC,,AFB Respiratory Culture,"AFB Culture, Respiratory"
+LABPCCG4O,LABPCCG4O,,,"iSTAT Cg4, Other"
+LABCDTPCR,LABCDTPCR,,,"C.diff Toxin B Gene, Qual. Real-time Pcr"
+LABHIVWBL,LABHIVWBL,,,Anti-hiv
+LABPROCT,LABPROCT,,,Procalcitonin
+LABLACWB,LABLACWB,,,"Lactate, Whole Blood"
+LABHEPAR,LABHEPAR,,,"Heparin, Unfractionated, Anti Xa"
+LABSPLAC,LABSPLAC,,,Sepsis Protocol Lactate
+LABSTLCX,LABSTLCX,,,Stool Culture
+LABPTEG,LABPTEG,,,"Teg, Perioperative"
+LABSTLCX,LABSTLCX,,,Stool Culture
+LABPALB,LABPALB,,,Prealbumin
+LABCMVQT,LABCMVQT,,,"Cmv Dna Pcr Quant, Plasma"
+LABBXTG,LABBXTG,,,Biopsy/tissue With Gram Stain
+LABBLCTIP,LABBLCTIP,,,Blood Cult Central Line Catheter By Nurse
+LABCBCD,LABCBCD,,Complete Blood Count with Differential,CBC WITH DIFF
+LABAFP,LABAFP,,,Alpha Fetoprotein
+LABYCP,LABYCP,,,C-Peptide
+LABEP1,LABEP1,,,Electrolyte Panel
+LABAGALA,LABAGALA,,,Aspegillus Galactomannan
+LABYHISTS,LABYHISTS,,,Histoplasma Ag
+LABSPIE,LABSPIE,,,Serum Protein Electrophoresis
+LABUPIE,LABUPIE,,,Urine Protein Electrophoresis
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_panel_code2description.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_panel_code2description.csv
new file mode 100644
index 00000000..9627122c
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_panel_code2description.csv
@@ -0,0 +1,89 @@
+lab,description
+LABUA,Urinalysis With Microscopic
+LABBLC,Blood Culture (Aerobic & Anaerobic)
+LABPT,Prothrombin Time
+LABPTT,Partial Thromboplastin Time
+LABTSH,Thyroid Stimulating Hormone
+LABPHOS,"Phosphorus, Serum/plasma"
+LABURNC,Urine Culture
+LABA1C,Hemoglobin A1c
+LABMGN,"Magnesium, Serum/plasma"
+LABHAP,Haptoglobin
+LABANER,Anaerobic Culture
+LABPCTNI,iSTAT Troponin I
+LABNTBNP,Nt - Probnp
+LABK,"Potassium, Serum/plasma"
+LABTNI,Troponin I
+LABUAPRN,"Urinalysis, Screen For Culture"
+LABCAI,"Calcium Ionized, Serum/plasma"
+LABHCTX,Hematocrit
+LABMB,"Ck, Mb (mass)"
+LABLIPS,Lipase
+LABCK,"Creatine Kinase, Total"
+LABBLC2,Blood Culture (2 Aerobic)
+LABLDH,"LDH Total, Serum / Plasma"
+LABTRFS,Transferrin Saturation
+LABLAC,Lactic Acid
+LABFER,Ferritin
+LABFT4,"T4, Free"
+LABCORT,"Cortisol, Serum"
+LABURIC,"Uric Acid, Serum / Plasma"
+LABGRAM,Gram Stain
+LABFE,"Iron, Total"
+LABB12,Vitamin B12
+LABFIB,Fibrinogen
+LABCRP,C-Reactive Protein
+LABRETIC,Reticulocyte Count Automated
+LABBXTG,Biopsy / Tissue Culture And Gram Stain
+LABPLTS,Platelet Count
+LABPCCR,iSTAT Creatinine
+LABFCUL,Fungal Culture
+LABFLDC,Fluid Culture And Gram Stain
+LABTRIG,"Triglycerides, Serum / Plasma"
+LABNH3,"Ammonia, Plasma"
+LABBLCTIP,Blood Cult - 2nd Set Via Catheter
+LABRESP,Respiratory Culture
+LABHEPAR,Heparin Activity Level
+LABFOL,Folic Acid
+LABCA,"Calcium, Serum/plasma"
+LABBLCSTK,"Blood Cult - First Set, Via Phlebotomy"
+LABUOSM,"Osmolality, Urine"
+LABUSPG,"Specific Gravity, Urine"
+LABRESPG,Respiratory Culture And Gram Stain
+LABCSFGL,"Glucose, Csf"
+LABHBSAG,Hepatitis B Antigen
+LABALB,"Albumin, Serum/plasma"
+LABAFBD,"Afb Culture, Non - Respiratory"
+LABESRP,Sedimentation Rate (ESR)
+LABDIGL,"Digoxin, Serum"
+LABSTOBGD,"Occult Bld, Guaiac, Stool, Diag Test"
+LABBUN,"Urea Nitrogen, Serum/plasma"
+LABNA,"Sodium, Serum / Plasma"
+LABLIDOL,Lidocaine
+LABCSFTP,"Protein Total, Csf"
+LABCSFC,CSF Culture And Gram Stain
+LABUPREG,"Pregnancy Test, Urine"
+LABOSM,"Osmolality, Serum"
+LABAFBC,"AFB Culture, Respiratory"
+LABPCCG4O,"iSTAT Cg4, Other"
+LABCDTPCR,"C.diff Toxin B Gene, Qual. Real-time Pcr"
+LABHIVWBL,Anti-hiv
+LABPROCT,Procalcitonin
+LABLACWB,"Lactate, Whole Blood"
+LABHEPAR,"Heparin, Unfractionated, Anti Xa"
+LABSPLAC,Sepsis Protocol Lactate
+LABSTLCX,Stool Culture
+LABPTEG,"Teg, Perioperative"
+LABSTLCX,Stool Culture
+LABPALB,Prealbumin
+LABCMVQT,"Cmv Dna Pcr Quant, Plasma"
+LABBXTG,Biopsy/tissue With Gram Stain
+LABBLCTIP,Blood Cult Central Line Catheter By Nurse
+LABCBCD,CBC WITH DIFF
+LABAFP,Alpha Fetoprotein
+LABYCP,C-Peptide
+LABEP1,Electrolyte Panel
+LABAGALA,Aspegillus Galactomannan
+LABYHISTS,Histoplasma Ag
+LABSPIE,Serum Protein Electrophoresis
+LABUPIE,Urine Protein Electrophoresis
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_team.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_team.csv
new file mode 100644
index 00000000..adafa4f3
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_team.csv
@@ -0,0 +1,2 @@
+team,Stanford,UMich,UCSF,description
+CVICU,CVICU,,ICU,
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_vitals.csv b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_vitals.csv
new file mode 100644
index 00000000..5ad0e35a
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/data_conversion/map_vitals.csv
@@ -0,0 +1,3 @@
+vitals,Stanford,UMich,UCSF,description
+BP_Low_Diastolic,BP_Low_Diastolic,,DBP,
+BP_High_Systolic,BP_High_Systolic,,SBP,
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/LABFER.txt b/scripts/Archive/LabTestAnalysis/machine_learning/extra/LABFER.txt
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/LABFER.txt
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/LABFER.txt
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/LABSPLAC.txt b/scripts/Archive/LabTestAnalysis/machine_learning/extra/LABSPLAC.txt
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/LABSPLAC.txt
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/LABSPLAC.txt
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/LABTSH.txt b/scripts/Archive/LabTestAnalysis/machine_learning/extra/LABTSH.txt
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/LABTSH.txt
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/LABTSH.txt
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/cols_labfer.csv b/scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labfer.csv
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/cols_labfer.csv
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labfer.csv
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/cols_labntbnp.csv b/scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labntbnp.csv
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/cols_labntbnp.csv
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labntbnp.csv
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/cols_labsplac.csv b/scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labsplac.csv
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/cols_labsplac.csv
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labsplac.csv
diff --git a/scripts/LabTestAnalysis/machine_learning/extra/cols_labtsh.csv b/scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labtsh.csv
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/extra/cols_labtsh.csv
rename to scripts/Archive/LabTestAnalysis/machine_learning/extra/cols_labtsh.csv
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/extraction/FeatureMatrixChangeLabels.py b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/FeatureMatrixChangeLabels.py
new file mode 100644
index 00000000..e0588f4f
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/FeatureMatrixChangeLabels.py
@@ -0,0 +1,40 @@
+#!/usr/bin/python
+"""
+Module for adding the change/no-change labels
+for building the processed feature matrix
+"""
+
+# TODO: (raikens)
+# - [ ] add error handling for negative or zero values
+# - [ ] flesh out testing suite
+# - [ ] add more methods
+
+
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from medinfo.dataconversion.Util import log
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+
+class FeatureMatrixChangeLabels(FeatureMatrixTransform):
+
+    def add_change_labels(self, method, param, feature_old, feature_new):
+        # get new column value
+        if method == "percent":
+            change_col = self.percent_change(param, feature_old, feature_new)
+        elif method == "interval":
+            change_col = self.interval_change(param, feature_old, feature_new)
+        else:
+            raise ValueError("Must specify a supported method for change calculation")
+
+        # add new column to matrix
+        col_index = self._matrix.columns.get_loc(feature_new)
+        self._matrix.insert(col_index + 1, "change_yn", change_col)
+
+    def percent_change(self, param, feature_old, feature_new):
+        f = lambda old, new: int(abs(1.0-float(old)/new) >= param)
+        return self._matrix[[feature_old, feature_new]].apply(lambda x: f(*x), axis = 1)
+
+    def interval_change(self, param, feature_old, feature_new):
+        f = lambda old, new: int(abs(float(old)-new) >= param)
+        return self._matrix[[feature_old, feature_new]].apply(lambda x: f(*x), axis = 1)
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/extraction/LabChangeMatrix.py b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/LabChangeMatrix.py
new file mode 100644
index 00000000..131abfa1
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/LabChangeMatrix.py
@@ -0,0 +1,357 @@
+#!/usr/bin/python
+"""
+Class for generating LabChangeMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+class LabChangeMatrix(FeatureMatrix):
+    def __init__(self, lab_panel, num_episodes, random_state=None):
+        FeatureMatrix.__init__(self, lab_panel, num_episodes)
+
+        # Parse arguments.
+        self._lab_panel = lab_panel
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+        if random_state:
+            query = SQLQuery()
+            query.addSelect('setseed(%d);' % random_state)
+            DBUtil.execute(query)
+            self._random_state = random_state
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+    def _get_components_in_lab_panel(self):
+        # Initialize DB connection.
+        cursor = self._connection.cursor()
+
+        # Doing a single query results in a sequential scan through
+        # stride_order_results. To avoid this, break up the query in two.
+
+        # First, get all the order_proc_ids for proc_code.
+
+        query = SQLQuery()
+        query.addSelect('order_proc_id')
+        query.addFrom('stride_order_proc')
+        query.addWhereIn('proc_code', [self._lab_panel])
+        query.addGroupBy('order_proc_id')
+        log.debug('Querying order_proc_ids for %s...' % self._lab_panel)
+        results = DBUtil.execute(query)
+        lab_order_ids = [row[0] for row in results]
+
+        # Second, get all base_names from those orders.
+        query = SQLQuery()
+        query.addSelect('base_name')
+        query.addFrom('stride_order_results')
+        query.addWhereIn('order_proc_id', lab_order_ids)
+        query.addGroupBy('base_name')
+        log.debug('Querying base_names for order_proc_ids...')
+        results = DBUtil.execute(query)
+        components = [row[0] for row in results]
+
+        return components
+
+    def _get_average_orders_per_patient(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        query = SQLQuery()
+        query.addSelect('pat_id')
+        query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
+        query.addFrom('stride_order_proc AS sop')
+        query.addFrom('stride_order_results AS sor')
+        query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhereIn("proc_code", [self._lab_panel])
+        components = self._get_components_in_lab_panel()
+        query.addWhereIn("base_name", components)
+        query.addGroupBy('pat_id')
+        log.debug('Querying median orders per patient...')
+        results = DBUtil.execute(query)
+        order_counts = [ row[1] for row in results ]
+        if len(order_counts) == 0:
+            error_msg = '0 orders for lab panel "%s."' % self._lab_panel
+            log.critical(error_msg)
+            sys.exit('[ERROR] %s' % error_msg)
+        else:
+            return numpy.median(order_counts)
+
+    def _get_random_patient_list(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        avg_orders_per_patient = self._get_average_orders_per_patient()
+        log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
+        # Based on average # of results, figure out how many patients we'd
+        # need to get for a feature matrix of requested size.
+        self._num_patients = int(numpy.max([self._num_requested_episodes / \
+            avg_orders_per_patient, 1]))
+
+        # Get numPatientsToQuery random patients who have gotten test.
+        # TODO(sbala): Have option to feed in a seed for the randomness.
+        query = SQLQuery()
+        query.addSelect('pat_id')
+        query.addFrom('stride_order_proc AS sop')
+        query.addWhereIn('proc_code', [self._lab_panel])
+        query.addOrderBy('RANDOM()')
+        query.setLimit(self._num_patients)
+        log.debug('Querying random patient list...')
+        results = DBUtil.execute(query)
+
+        # Get patient list.
+        random_patient_list = [ row[0] for row in results ]
+
+        return random_patient_list
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        self._lab_components = self._get_components_in_lab_panel()
+        random_patient_list = self._get_random_patient_list()
+
+        # Build SQL query for list of patient episodes.
+        # Note that for 2008-2014 data, result_flag can take on any of the
+        # following values:  High, Low, High Panic, Low Panic,
+        # Low Off-Scale, Negative, Positive, Resistant, Susceptible, Abnormal, *
+        # (NONE): Only 27 lab components can have this flag. None has this
+        #           value for more than 5 results, so ignore it.
+        # *: Only 10 lab components can have this flag. Only 6 have it for
+        #           >10 tests, and each of them is a microbiology test for which
+        #           a flag is less meaningful, e.g. Gram Stain, AFB culture.
+        # Susceptible: Only 15 lab components can have this flag. All of those
+        #           only have this value for 2 results, so ignore it.
+        # Resistant: Only 1 lab component can have this flag. Only two results
+        #           have this value, so ignore it.
+        # Abnormal: 1462 lab components can have this flag. Many (e.g. UBLOOD)
+        #           have this value for thousands of results, so include it.
+        # Negative: Only 10 lab components can have this flag, and all for
+        #           less than 5 results, so ignore it.
+        # Positive: Only 3 lab components can have this flag, and all for
+        #           only 1 result, so ignore it.
+        # Low Off-Scale: Only 1 lab component can have this flag, and only for
+        #           3 results, so ignore it.
+        # Low Panic: 1401 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        # High Panic: 8084 lab components can have this flag, many core
+        #           metabolic components. Include it.
+
+        # Note that some episodes have ord_num_value = 9999999
+        # These episodes are removed
+
+        query = SQLQuery()
+        query.addSelect('CAST(pat_id AS BIGINT)')
+        query.addSelect('sop.order_proc_id AS order_proc_id')
+        query.addSelect('proc_code')
+        query.addSelect('order_time')
+        query.addSelect('ord_num_value')
+        query.addSelect("CASE WHEN abnormal_yn = 'Y' THEN 1 ELSE 0 END AS abnormal_panel")
+        query.addSelect("SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL THEN 1 ELSE 0 END) AS num_components")
+        query.addSelect("SUM(CASE WHEN result_flag IS NULL THEN 1 ELSE 0 END) AS num_normal_components")
+        query.addSelect("CAST(SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') THEN 1 ELSE 0 END) = 0 AS INT) AS all_components_normal")
+        query.addFrom('stride_order_proc AS sop')
+        query.addFrom('stride_order_results AS sor')
+        query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhere("(result_flag in ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL)")
+        query.addWhere("ord_num_value != 9999999")
+        query.addWhereIn("proc_code", [self._lab_panel])
+        query.addWhereIn("pat_id", random_patient_list)
+        query.addGroupBy('pat_id')
+        query.addGroupBy('sop.order_proc_id')
+        query.addGroupBy('proc_code')
+        query.addGroupBy('order_time')
+        query.addGroupBy('abnormal_yn')
+        query.addGroupBy('ord_num_value')
+        query.addOrderBy('pat_id')
+        query.addOrderBy('sop.order_proc_id')
+        query.addOrderBy('proc_code')
+        query.addOrderBy('order_time')
+        query.addOrderBy('ord_num_value')
+        query.setLimit(self._num_requested_episodes)
+
+        self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query, index_time_col='order_time')
+
+    def _add_features(self):
+        # Add lab panel order features.
+        self._factory.addClinicalItemFeatures([self._lab_panel], features="pre")
+
+        # Add lab component result features, for a variety of time deltas.
+        LAB_PRE_TIME_DELTAS = [datetime.timedelta(-14)]
+        LAB_POST_TIME_DELTA = datetime.timedelta(0)
+        log.info('Adding lab component features...')
+        for pre_time_delta in LAB_PRE_TIME_DELTAS:
+            log.info('\t%s' % pre_time_delta)
+            self._factory.addLabResultFeatures(self._lab_components, False, pre_time_delta, LAB_POST_TIME_DELTA)
+
+        FeatureMatrix._add_features(self, index_time_col='order_time')
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab panel across %s inpatients from Stanford hospital.' % (self._lab_panel, self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab panel orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   ord_num_value - numeric value of result. \n\
+        line = 'ord_num_value - numeric value of result.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % self._lab_panel
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        line = 'Also included %s panel components: %s' % (self._lab_panel, self._lab_components)
+        summary.append(line)
+
+        return summary
+
+
+if __name__ == "__main__":
+    log.level = logging.DEBUG
+    start_time = time.time()
+    # Initialize lab test matrix.
+    ltm = LabChangeMatrix("LABBLC", 10) #11900
+    # Output lab test matrix.
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    ltm.write_matrix("LABPTT-panel-12000-episodes-values-%s-sec.tab" % str(elapsed_time))
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/extraction/LabNormalityMatrix.py b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/LabNormalityMatrix.py
new file mode 100644
index 00000000..2e6cea62
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/LabNormalityMatrix.py
@@ -0,0 +1,594 @@
+#!/usr/bin/python
+"""
+Class for generating UMichNormalityMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+from optparse import OptionParser
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+import LocalEnv
+
+class LabNormalityMatrix(FeatureMatrix):
+    def __init__(self, lab_var, num_episodes, random_state=None,
+                 isLabPanel=True, timeLimit=None, notUsePatIds=None):
+        FeatureMatrix.__init__(self, lab_var, num_episodes)
+
+        self._isLabPanel = isLabPanel
+        if isLabPanel:
+            self._varTypeInTable = 'proc_code'
+            self._ylabel = 'all_components_normal'
+        else:
+            self._varTypeInTable = 'base_name'
+            self._ylabel = 'component_normal'
+
+        # Parse arguments.
+        self._lab_var = lab_var
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+        # SQLite's random() function does not support a seed value.
+        if random_state:
+            self._random_state = random_state
+            if LocalEnv.DATABASE_CONNECTOR_NAME == 'psycopg2':
+                query = SQLQuery()
+                query.addSelect('setseed(%d);' % random_state)
+                DBUtil.execute(query)
+
+        self._time_limit = timeLimit
+
+        if notUsePatIds:
+            self._notUsePatIds = list(notUsePatIds)
+        else:
+            self._notUsePatIds = []
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+    def _get_components_in_lab_panel(self):
+        if not self._isLabPanel:
+            return [self._lab_var]
+
+        # Initialize DB connection.
+        cursor = self._connection.cursor()
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+        # Doing a single query results in a sequential scan through
+        # stride_order_results. To avoid this, break up the query in two.
+
+        # First, get all the order_proc_ids for proc_code.
+
+            query = SQLQuery()
+            query.addSelect('order_proc_id')
+            query.addFrom('stride_order_proc')
+            query.addWhereIn('proc_code', [self._lab_var])
+            query.addGroupBy('order_proc_id')
+            log.debug('Querying order_proc_ids for %s...' % self._lab_var)
+            results = DBUtil.execute(query)
+            lab_order_ids = [row[0] for row in results]
+
+            # Second, get all base_names from those orders.
+            query = SQLQuery()
+            query.addSelect('base_name')
+            query.addFrom('stride_order_results')
+            query.addWhereIn('order_proc_id', lab_order_ids)
+            query.addGroupBy('base_name')
+
+        else:
+        # elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            query = SQLQuery()
+            query.addSelect('base_name')
+            query.addFrom('labs')
+            query.addWhereIn('proc_code', [self._lab_var])
+            query.addGroupBy('base_name')
+
+        log.debug('Querying base_names for order_proc_ids...')
+        results = DBUtil.execute(query)
+        components = [row[0] for row in results]
+
+        return components
+
+    def _get_average_orders_per_patient(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        query = SQLQuery()
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
+            query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
+            query.addFrom('stride_order_proc AS sop')
+            query.addFrom('stride_order_results AS sor')
+
+            query.addWhere('sop.order_proc_id = sor.order_proc_id')
+            query.addWhereIn(self._varTypeInTable, [self._lab_var])
+            components = self._get_components_in_lab_panel()
+            query.addWhereIn("base_name", components)
+            query.addGroupBy('pat_id')
+
+        else:
+        #elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
+            query.addSelect('COUNT(order_proc_id) AS num_orders')
+            query.addFrom('labs')
+            query.addWhereIn(self._varTypeInTable, [self._lab_var])
+            components = self._get_components_in_lab_panel()
+            query.addWhereIn("base_name", components)
+            query.addGroupBy('pat_id')
+        log.debug('Querying median orders per patient...')
+        results = DBUtil.execute(query)
+
+        order_counts = [ row[1] for row in results ]
+        if len(order_counts) == 0:
+            error_msg = '0 orders for lab "%s."' % self._lab_var
+            log.critical(error_msg)
+            raise Exception(error_msg)
+            # sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
+        else:
+            return numpy.median(order_counts)
+
+    def _get_random_patient_list(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        query = SQLQuery()
+        query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
+
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            if self._isLabPanel:
+                query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
+                query.addFrom('stride_order_proc AS sop')
+                query.addFrom('stride_order_results AS sor')
+
+                if self._time_limit:
+                    if self._time_limit[0]:
+                        query.addWhere("sop.order_time > '%s'" % self._time_limit[0])
+                    if self._time_limit[1]:
+                        query.addWhere("sop.order_time < '%s'" % self._time_limit[1])
+
+                query.addWhere('sop.order_proc_id = sor.order_proc_id')
+                query.addWhereIn('proc_code', [self._lab_var])
+
+                '''
+                sbala: Technically it's possible for someone to get a lab ordered without getting results
+                '''
+                query.addWhereIn("base_name", self._lab_components)
+
+            else:
+                query.addSelect('COUNT(sor.order_proc_id) AS num_orders')
+                query.addFrom('stride_order_proc AS sop')
+                query.addFrom('stride_order_results AS sor')
+                query.addWhere('sop.order_proc_id = sor.order_proc_id')
+            ##
+                query.addWhereIn("base_name", [self._lab_var])
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            query_str = "SELECT CAST(pat_id AS BIGINT) AS pat_id , "
+            query_str += "COUNT(order_proc_id) AS num_orders "
+            query_str += "FROM labs "
+            # query_str += " WHERE %s IN (%s) "%(self._varTypeInTable, self._lab_var)
+            query_str += "WHERE %s = '%s' " % (self._varTypeInTable, self._lab_var)
+            if self.notUsePatIds:
+                query_str += "AND pat_id NOT IN ("
+                for pat_id in self.notUsePatIds:
+                    query_str += "%s," % pat_id
+                query_str = query_str[:-1] + ") "  # get rid of comma
+            query_str += "GROUP BY pat_id"
+
+            log.debug('Querying median orders per patient...')
+
+            # TODO: best way to integrate UMich code
+            results = DBUtil.execute(query_str)
+            order_counts = [row[1] for row in results]
+
+            if len(results) == 0:
+                error_msg = '0 orders for order "%s."' % self._lab_var  # sx
+                log.critical(error_msg)
+                raise Exception(error_msg)
+                # sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
+            else:
+                avg_orders_per_patient = numpy.median(order_counts)
+                log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
+                # Based on average # of results, figure out how many patients we'd
+                # need to get for a feature matrix of requested size.
+                self._num_patients = int(numpy.max([self._num_requested_episodes / \
+                                                    avg_orders_per_patient, 1]))
+                # Some components may have fewer associated patients than the required sample size
+                patient_number_chosen = min([len(results), self._num_patients])  #
+                inds_random_patients = numpy.random.choice(len(results), size=patient_number_chosen, replace=False)
+                # print 'inds_random_patients:', inds_random_patients
+                pat_IDs_random_patients = []
+                for ind in inds_random_patients:
+                    pat_IDs_random_patients.append(results[ind][0])
+                return pat_IDs_random_patients
+
+        else:
+            query.addSelect('COUNT(order_proc_id) AS num_orders')
+            query.addFrom('labs')
+            if self._isLabPanel:
+                query.addWhereIn("proc_code", [self._lab_var])
+                query.addWhereIn("base_name", self._lab_components)
+            else:
+                query.addWhereIn("base_name", [self._lab_var])
+
+        '''
+        Fo hold-out set, do not use the patients already used in training/validation. 
+        '''
+        if self._notUsePatIds:
+            query.addWhereNotIn('pat_id', self._notUsePatIds)
+
+        query.addGroupBy('pat_id')
+
+        log.debug('Querying the number of orders per patient...')
+
+        results = DBUtil.execute(query)
+
+        order_counts = [row[1] for row in results]
+
+        if len(results) == 0:
+            error_msg = '0 orders for component "%s."' % self._lab_var  # sx
+            log.critical(error_msg)
+            raise Exception(error_msg)
+            # sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
+        else:
+            avg_orders_per_patient = numpy.median(order_counts)
+            log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
+            # Based on average # of results, figure out how many patients we'd
+            # need to get for a feature matrix of requested size.
+            self._num_patients = int(numpy.max([self._num_requested_episodes / \
+                                                avg_orders_per_patient, 1]))
+
+            # Some components may have fewer associated patients than the required sample size
+            patient_number_chosen = min([len(results), self._num_patients])  #
+
+            '''
+            Set seed to ensure re-producibility of patient episodes.
+            Recover int random_state here, since numpy requires int while sql requires [-1,1]
+            '''
+            numpy.random.seed(int(self._random_state*float(sys.maxsize)))
+            inds_random_patients = numpy.random.choice(len(results), size=patient_number_chosen, replace=False)
+
+            pat_IDs_random_patients = [results[ind][0] for ind in inds_random_patients]
+
+            return pat_IDs_random_patients
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        self._lab_components = self._get_components_in_lab_panel()
+        random_patient_list = self._get_random_patient_list()
+
+        # Build SQL query for list of patient episodes.
+        # Note that for 2008-2014 data, result_flag can take on any of the
+        # following values:  High, Low, High Panic, Low Panic,
+        # Low Off-Scale, Negative, Positive, Resistant, Susceptible, Abnormal, *
+        # (NONE): Only 27 lab components can have this flag. None has this
+        #           value for more than 5 results, so ignore it.
+        # *: Only 10 lab components can have this flag. Only 6 have it for
+        #           >10 tests, and each of them is a microbiology test for which
+        #           a flag is less meaningful, e.g. Gram Stain, AFB culture.
+        # Susceptible: Only 15 lab components can have this flag. All of those
+        #           only have this value for 2 results, so ignore it.
+        # Resistant: Only 1 lab component can have this flag. Only two results
+        #           have this value, so ignore it.
+        # Abnormal: 1462 lab components can have this flag. Many (e.g. UBLOOD)
+        #           have this value for thousands of results, so include it.
+        # Negative: Only 10 lab components can have this flag, and all for
+        #           less than 5 results, so ignore it.
+        # Positive: Only 3 lab components can have this flag, and all for
+        #           only 1 result, so ignore it.
+        # Low Off-Scale: Only 1 lab component can have this flag, and only for
+        #           3 results, so ignore it.
+        # Low Panic: 1401 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        # High Panic: 8084 lab components can have this flag, many core
+        #           metabolic components. Include it.
+
+        if LocalEnv.DATASET_SOURCE_NAME=='STRIDE':
+
+            query = SQLQuery()
+
+            '''
+            pat_id: hashed patient id
+            '''
+            query.addSelect('CAST(pat_id AS BIGINT) as pat_id')
+
+            '''
+            order_proc_id: unique identifier for an episode
+            '''
+            query.addSelect('sop.order_proc_id')
+
+            '''
+            self._varTypeInTable: usually proc_code or base_name, the column of the lab to be queried
+            '''
+            query.addSelect(self._varTypeInTable)
+
+
+            '''
+            order_time: The time of the order. Note that sor table does not have this info. 
+            '''
+            query.addSelect('order_time')
+
+
+            '''
+            y-labels related columns, choose one to predict (for now, use all_components_normal to predict). 
+            '''
+            if self._isLabPanel:
+                query.addSelect("CASE WHEN abnormal_yn = 'Y' THEN 1 ELSE 0 END AS abnormal_panel")  #
+                query.addSelect(
+                    "SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL THEN 1 ELSE 0 END) AS num_components")  # sx
+                query.addSelect("SUM(CASE WHEN result_flag IS NULL THEN 1 ELSE 0 END) AS num_normal_components")  # sx
+                query.addSelect(
+                    "CAST(SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') THEN 1 ELSE 0 END) = 0 AS INT) AS all_components_normal")  # sx
+            else:
+                query.addSelect(
+                    "CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') THEN 0 ELSE 1 END AS component_normal")
+
+
+            '''
+            Relevant tables. Note that sor table does not have patient_id info; need to join sop to obtain it.  
+            '''
+            query.addFrom('stride_order_proc AS sop')
+            query.addFrom('stride_order_results AS sor')
+            query.addWhere('sop.order_proc_id = sor.order_proc_id')
+
+            '''
+            Condition: self._time_limit[0] < order_time < self._time_limit[1]
+            '''
+            if self._time_limit:
+                if self._time_limit[0]:
+                    query.addWhere("sop.order_time > '%s'" % self._time_limit[0])
+                if self._time_limit[1]:
+                    query.addWhere("sop.order_time < '%s'" % self._time_limit[1])
+
+
+            query.addWhere("(result_flag in ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL)")
+            query.addWhereIn(self._varTypeInTable, [self._lab_var])  # sx
+            query.addWhereIn("pat_id", random_patient_list)
+
+            query.addGroupBy('pat_id')
+            query.addGroupBy('sop.order_proc_id')
+            query.addGroupBy(self._varTypeInTable)
+            query.addGroupBy('order_time')
+            if not self._isLabPanel:
+                query.addGroupBy('result_flag')
+            query.addGroupBy('abnormal_yn')  #
+
+            query.addOrderBy('pat_id')
+            query.addOrderBy('sop.order_proc_id')
+            query.addOrderBy(self._varTypeInTable)
+            query.addOrderBy('order_time')
+
+            query.setLimit(self._num_requested_episodes)
+
+            self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query,
+                                                                                index_time_col='order_time')
+
+        else:
+
+            '''
+            Sqlite3 has an interesting limit for the total number of place_holders in a query, 
+            and this limit varies across platforms/operating systems (500-99999 on mac, 999 by defaulty). 
+            
+            To avoid this problem when querying 1000-10000 patient ids, use string queries instead of the
+            default (convenient) routine in DBUtil.  
+            '''
+
+            query_str = "SELECT CAST(pat_id AS BIGINT) AS pat_id, order_proc_id, %s, order_time, " % self._varTypeInTable
+            if not self._isLabPanel:
+                query_str += "CASE WHEN result_in_range_yn = 'Y' THEN 1 ELSE 0 END AS component_normal "
+            else:
+                query_str += "CAST(SUM(CASE WHEN result_in_range_yn != 'Y' THEN 1 ELSE 0 END) = 0 AS INT) AS all_components_normal "
+            query_str += "FROM labs "
+            query_str += "WHERE %s = '%s' " % (self._varTypeInTable, self._lab_var)
+            query_str += "AND pat_id IN "
+            pat_list_str = "("
+            for pat_id in random_patient_list:
+                pat_list_str += str(pat_id) + ","
+            pat_list_str = pat_list_str[:-1] + ") "
+            query_str += pat_list_str
+            query_str += "GROUP BY pat_id, order_proc_id, %s, order_time " % self._varTypeInTable
+            query_str += "ORDER BY pat_id, order_proc_id, %s, order_time " % self._varTypeInTable
+            query_str += "LIMIT %d" % self._num_requested_episodes
+
+            self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query_str, index_time_col='order_time')
+
+    def _add_features(self):
+        # Add lab panel order features.
+        if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
+            self._factory.addClinicalItemFeatures([self._lab_var], features="pre", isLabPanel=self._isLabPanel)
+        else:
+            # TODO: naming
+            self._factory.addClinicalItemFeatures_UMich([self._lab_var], features="pre",
+                                                        clinicalItemType=self._varTypeInTable,
+                                                        clinicalItemTime='order_time',
+                                                        tableName='labs') #sx
+
+        # Add lab component result features, for a variety of time deltas.
+        LAB_PRE_TIME_DELTAS = [datetime.timedelta(-14)]
+        LAB_POST_TIME_DELTA = datetime.timedelta(0)
+        log.info('Adding lab component features...')
+        for pre_time_delta in LAB_PRE_TIME_DELTAS:
+            log.info('\t%s' % pre_time_delta)
+            self._factory.addLabResultFeatures(self._lab_components, False, pre_time_delta, LAB_POST_TIME_DELTA)
+
+        FeatureMatrix._add_features(self, index_time_col='order_time')
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab across %s inpatients from Stanford hospital.' % (self._lab_var, self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % self._lab_var
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        line = 'Also included %s panel components: %s' % (self._lab_var, self._lab_components)
+        summary.append(line)
+
+        return summary
+
+if __name__ == "__main__":
+    """Main method, callable from command line"""
+    usageStr =  "usage: %prog [options]\n"
+
+    parser = OptionParser(usage=usageStr)
+
+    parser.add_option('-n', '--numRows', dest='numRows', metavar="<numRows>", help='The number of rows in resulting matrix')
+    parser.add_option('-l', '--labCode', dest='lab', metavar='<lab>', help='proc_code for lab of interest')
+    parser.add_option('-r', '--randomState', dest='randomState', metavar='<randomState>', help='Random state for consistent results')
+
+    (options, args) = parser.parse_args(sys.argv[1:])
+
+    log.info("Starting: "+str.join(" ", sys.argv))
+    start_time = time.time()
+
+    ltm = LabNormalityMatrix(options.lab, options.numRows, options.randomState)
+
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    file_name = '%s-panel-%s-episodes-%s-sec.tab' % (options.lab, options.numRows, elapsed_time)
+    ltm.write_matrix(file_name)
+
+    timer = time.time() - timer
+    log.info("%.3f seconds to complete",timer);
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/extraction/__init__.py b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/extraction/generateLabNormalityMatrices.py b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/generateLabNormalityMatrices.py
new file mode 100644
index 00000000..605858d9
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/generateLabNormalityMatrices.py
@@ -0,0 +1,43 @@
+#!/usr/bin/python
+
+from medinfo.common.Util import log
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from .LabNormalityMatrix import LabNormalityMatrix
+
+# Includes list of the top 25 most commonly ordered lab tests,
+# unioned with the top 25 most expensive by volume (count * price) lab tests.
+# There's a lot of overlap, so only 28 lab tests in total.
+TOP_LAB_PANELS_BY_VOLUME = [
+    "LABA1C", "LABABG", "LABBLC", "LABBLC2", "LABCAI",
+    "LABCBCD", "LABCBCO", "LABHFP", "LABLAC", "LABMB",
+    "LABMETB", "LABMETC", "LABMGN", "LABNTBNP", "LABPCG3",
+    "LABPCTNI", "LABPHOS", "LABPOCGLU", "LABPT", "LABPTT",
+    "LABROMRS", "LABTNI", "LABTYPSNI", "LABUA", "LABUAPRN",
+    "LABURNC", "LABVANPRL", "LABVBG"
+]
+
+# How many rows do we need in the feature matrix?
+NUM_ROWS_PER_MATRIX = 10000
+
+# Format for matrix file names.
+# Includes the name of the lab panel, the names of the components, the number
+# of patients, and the number of episodes.
+MATRIX_NAME_FORMAT = "%s-panel-%s-pat-%s-epi.tab"
+
+# Generates LabTestMatrix entities for (a) each individual component of the
+# lab panel and (b) the entire panel.
+def generateLabPanelMatrices(labPanel):
+    # Generate feature matrix for all components.
+    matrix = LabNormalityMatrix(labPanel, NUM_ROWS_PER_MATRIX)
+    matrix.write_matrix(MATRIX_NAME_FORMAT % (labPanel, \
+        matrix.numPatients, matrix.numPatientEpisodes))
+
+# Generates LabTestMatrices for TOP_LAB_PANELS_BY_VOLUME.
+def generateTopLabPanelMatrices():
+    for panel in TOP_LAB_PANELS_BY_VOLUME:
+        log.info(panel)
+        generateLabPanelMatrices(panel)
+
+if __name__ == "__main__":
+    generateTopLabPanelMatrices()
diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/mapLabToResultCodes.py b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/mapLabToResultCodes.py
similarity index 98%
rename from scripts/LabTestAnalysis/machine_learning/dataExtraction/mapLabToResultCodes.py
rename to scripts/Archive/LabTestAnalysis/machine_learning/extraction/mapLabToResultCodes.py
index 3e337642..80a96044 100644
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/mapLabToResultCodes.py
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/extraction/mapLabToResultCodes.py
@@ -41,7 +41,7 @@
 
 prog = ProgressDots(total=37500100);
 
-print "proc_code\tdescription\tbase_name\tcomponent_name"
+print("proc_code\tdescription\tbase_name\tcomponent_name")
 
 conn = DBUtil.connection();
 cursor = conn.cursor();
@@ -65,7 +65,7 @@
 
 	if baseName != lastBaseName and nIds > 0:
 		# Moving on to base name. Sub-query for the last one now
-		print >> sys.stderr, lastBaseName, lastComponentName
+		print(lastBaseName, lastComponentName, file=sys.stderr)
 		subQuery = """
 			select proc_code, description, count(order_proc_id)
 			from stride_order_proc
@@ -74,7 +74,7 @@
 			""" % generatePlaceholders(nIds);
 		subResults = DBUtil.execute(subQuery, tuple(orderProcIds), conn=conn);
 		for procCode, description, count in subResults:
-			print str.join("\t", [procCode, description, lastBaseName, lastComponentName]);
+			print(str.join("\t", [procCode, description, lastBaseName, lastComponentName]));
 
 		orderProcIds.clear();	# Reset once done
 		nIds = 0;
@@ -98,6 +98,6 @@
 	""" % generatePlaceholders(nIds);
 subResults = DBUtil.execute(subQuery, tuple(orderProcIds), conn=conn);
 for procCode, description, count in subResults:
-	print str.join("\t", [procCode, description, lastBaseName, lastComponentName]);
+	print(str.join("\t", [procCode, description, lastBaseName, lastComponentName]));
 
 prog.printStatus();
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/helpful_scripts/look_RF_structure.py b/scripts/Archive/LabTestAnalysis/machine_learning/helpful_scripts/look_RF_structure.py
new file mode 100644
index 00000000..df41b1b8
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/helpful_scripts/look_RF_structure.py
@@ -0,0 +1,49 @@
+
+
+
+from sklearn.externals import joblib
+from sklearn.tree import export_graphviz
+
+lab = 'ALK'
+data_source = 'UMich'
+lab_type = 'component'
+data_folderpath = '../data-%s-component-10000-episodes/%s/' % (data_source, lab)
+
+rf_model = joblib.load(data_folderpath + "%s-normality-random-forest-model.pkl"%lab)._model
+
+# rf_model = joblib.load('Uric-Acid, Serum - Plasma-normality-random-forest-model.pkl')._model
+
+print(len(rf_model.feature_importances_))
+
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+fm_io = FeatureMatrixIO()
+df_processed = fm_io.read_file_to_data_frame(data_folderpath + '%s-normality-matrix-processed.tab'%lab)
+# df_processed = fm_io.read_file_to_data_frame('Uric Acid, Serum - Plasma-normality-test-matrix-processed_byStanford.tab')
+df_processed.pop('pat_id')
+
+if lab_type == 'panel':
+    df_processed.pop('all_components_normal')
+else:
+    df_processed.pop('component_normal')
+cols = df_processed.columns.values.tolist()
+
+estimator = rf_model.estimators_[5]
+
+export_graphviz(estimator, out_file='tree.dot',
+                feature_names = cols,
+                class_names = ['abnormal', 'normal'],
+                rounded = True, proportion = False,
+                precision = 2, filled = True)
+
+import pydot
+
+(graph,) = pydot.graph_from_dot_file('tree.dot')
+print(graph)
+graph.write_png('tree.png')
+
+
+# from subprocess import call
+# call(['dot', '-Tpng', 'tree.dot', '-o', 'tree.png'])
+
+# import matplotlib.pyplot as plt
+# plt.savefig('visual_rf.png')
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/ml_utils.py b/scripts/Archive/LabTestAnalysis/machine_learning/ml_utils.py
new file mode 100644
index 00000000..4f256fbf
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/ml_utils.py
@@ -0,0 +1,129 @@
+
+
+'''
+
+Only operational functions
+Not aware of file system organization
+
+'''
+import pandas as pd
+import os
+
+
+BASIC_LAB_COMPONENTS = [
+                'WBC',  # White Blood Cell
+                'HCT',  # Hematocrit
+                'PLT',  # Platelet Count
+                'NA',  # Sodium, Whole Blood
+                'K',  # Potassium, Whole Blood
+                'CO2',  # CO2, Serum/Plasma
+                'BUN',  # Blood Urea Nitrogen
+                'CR',  # Creatinine
+                'TBIL',  # Total Bilirubin
+                'ALB',  # Albumin
+                'CA',  # Calcium
+                'LAC',  # Lactic Acid
+                'ESR',  # Erythrocyte Sedimentation Rate
+                'CRP',  # C-Reactive Protein
+                'TNI',  # Troponin I
+                'PHA',  # Arterial pH
+                'PO2A',  # Arterial pO2
+                'PCO2A',  # Arterial pCO2
+                'PHV',  # Venous pH
+                'PO2V',  # Venous pO2
+                'PCO2V'  # Venous pCO2
+            ]
+
+map_component_from_Stanford_to_UCSF = {
+'CR': 'CREAT',
+'PO2A': 'PO2',
+'PO2V': 'PO2',
+'PCO2A': 'PCO2',
+'PCO2V': 'PCO2',
+'PHOS': 'PO4',
+'CAION': 'CAI',
+'TNI': 'TRPI',
+'NA': 'NA',
+'LAC': 'LACTWB',
+'TBIL': 'TBILI'
+}
+
+map_cormobidity_from_Stanford_to_UCSF = {
+'Malignancy': 'Cancer',
+'CHF': 'CongestiveHeartFailure',
+'MI': 'MyocardialInfarction',
+'Cerebrovascular': 'CerebrovascularDisease',
+'Diabetes': 'Diabeteswithoutcomplications'
+}
+
+map_team_from_Stanford_to_UCSF = {
+'CVICU': 'ICU'
+}
+
+map_vitals_from_Stanford_to_UCSF = {
+'BP_Low_Diastolic': 'DBP',
+'BP_High_Systolic': 'SBP'
+}
+
+map_panel_from_Stanford_to_UCSF = {'LABMGN':'Magnesium, Serum - Plasma',
+               'LABCAI':'Calcium, Ionized, serum-plasma',
+                            'LABURIC':'Uric Acid, Serum - Plasma',
+                            'LABALB':'Albumin, Serum - Plasma',
+                            'LABTSH':'Thyroid Stimulating Hormone',
+                            'LABTNI':'Troponin I',
+                            'LABK':'Potassium, Serum - Plasma',
+                            'LABNA':'Sodium, Serum - Plasma',
+                            # 'LABBLC':'Peripheral Blood Culture', # TODO
+                            'LABBLC2':'Peripheral Blood Culture',
+                            'LABPHOS':'Phosphorus, Serum - Plasma',
+                            'LABPT':'Prothrombin Time', # TODO
+                            'LABPTT':'Activated Partial Thromboplastin Time'
+                            }
+
+def map_lab(lab, data_source, lab_type, map_type='to_src'):
+    import LocalEnv
+    ml_folder = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/machine_learning')
+    df = pd.read_csv(os.path.join(ml_folder, 'data_conversion/map_%s.csv'%(lab_type)),
+                     keep_default_na=False)
+    if map_type=='to_src':
+        keys = df['lab'].values.tolist()
+        vals = df[data_source].values.tolist()
+    else:
+        vals = df['lab'].values.tolist()
+        keys = df[data_source].values.tolist()
+
+    my_dict = dict(list(zip(keys, vals)))
+
+    # print lab, my_dict[lab]
+
+    return my_dict.get(lab, lab)
+
+def get_patIds(data_matrix):
+    return set(data_matrix['pat_id'].values.tolist())
+
+def test_get_baseline():
+    df_1 = pd.DataFrame([[123, '2014-01-01', 1, 2]], columns=['pat_id', 'order_time', 'actual', 'predict'])
+    df_2 = pd.DataFrame([[456, '2015-01-01', 3]], columns=['pat_id', 'order_time', 'actual'])
+    print(get_baseline(df_1, df_2))
+    # assert df's not changed
+
+
+def get_baseline(df_train, df_test, y_label):
+    print(df_test[['pat_id', 'order_time', y_label]].copy().head())
+    df_res = df_test[['pat_id', 'order_time', y_label]].copy().rename(columns={y_label:'actual'}) # Not change the input
+
+    prevalence = float(df_train[y_label].values.sum()) / float(df_train.shape[0])
+
+    df_res['predict'] = df_res['actual'].apply(lambda x: prevalence) # use any column to create an extra column
+
+    df_res = df_res.sort_values(['pat_id', 'order_time']).reset_index()
+    for i in range(1, df_res.shape[0]):
+        if df_res.ix[i-1, 'pat_id'] == df_res.ix[i, 'pat_id']:
+            df_res.ix[i, 'predict'] = df_res.ix[i-1, 'actual']
+
+    return df_res[['actual', 'predict']]
+
+
+
+if __name__ == '__main__':
+    test_get_baseline()
diff --git a/scripts/LabTestAnalysis/machine_learning/pipeline.py b/scripts/Archive/LabTestAnalysis/machine_learning/pipeline.py
similarity index 95%
rename from scripts/LabTestAnalysis/machine_learning/pipeline.py
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipeline.py
index bb84ce78..a0321e46 100644
--- a/scripts/LabTestAnalysis/machine_learning/pipeline.py
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/pipeline.py
@@ -35,9 +35,9 @@ def __init__(self, X_train, y_train, X_validation, y_validation,
 		self.X_test = X_test
 		self.y_test = y_test
 
-		print 'train_size', self.y_train.shape[0]
-		print 'validation_size', self.y_validation.shape[0]
-		print 'test_size', self.y_test.shape[0]
+		print('train_size', self.y_train.shape[0])
+		print('validation_size', self.y_validation.shape[0])
+		print('test_size', self.y_test.shape[0])
 
 		self.output = output
 		self.name = name
@@ -137,7 +137,7 @@ def __init__(self, X_train, y_train, X_validation, y_validation,
 
 	def train(self):
 		# iterate over classifiers
-		print 'TRAIN'
+		print('TRAIN')
 		print('"name","train_accuracy","validation_accuracy",'
 			'"validation_precision","validation_recall","validation_f1",'
 			'"validation_roc-auc"')
@@ -152,13 +152,13 @@ def train(self):
 			validation_recall = recall_score(self.y_validation, validation_predictions)
 			validation_f1 = f1_score(self.y_validation, validation_predictions)
 			validation_roc_auc = roc_auc_score(self.y_validation, validation_prediction_probs)
-			print '"%s","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f"' % \
+			print('"%s","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f"' % \
 				(name, train_score, validation_score, validation_precision,
-					validation_recall, validation_f1, validation_roc_auc)
+					validation_recall, validation_f1, validation_roc_auc))
 
 	def test(self):
 		# iterate over classifiers
-		print 'TEST'
+		print('TEST')
 		print('"name","test_accuracy","test_precision","test_recall","test_f1",'
 			'"test_roc-auc"')
 		for i, (name, clf) in enumerate(zip(self.names, self.classifiers)):
@@ -170,8 +170,8 @@ def test(self):
 			test_recall = recall_score(self.y_test, test_predictions)
 			test_f1 = f1_score(self.y_test, test_predictions)
 			test_roc_auc = roc_auc_score(self.y_test, test_prediction_probs)
-			print '"%s","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f"' % \
-				(name, test_score, test_precision, test_recall, test_f1, test_roc_auc)
+			print('"%s","%0.3f","%0.3f","%0.3f","%0.3f","%0.3f"' % \
+				(name, test_score, test_precision, test_recall, test_f1, test_roc_auc))
 
 			prob_true, prob_pred = calibration_curve(self.y_test, test_prediction_probs, n_bins=20)
 			fig = plt.figure(figsize=(6, 4))
@@ -189,7 +189,7 @@ def test(self):
 	imp_method = 'median'
 
 	for filepath in glob.glob(INPUT_FOLDER + imp_method + '*labFeatureMatrix*.csv'):
-		print filepath
+		print(filepath)
 
 		df = pd.read_csv(filepath)
 
@@ -232,4 +232,4 @@ def test(self):
 		pipeline.train()
 		pipeline.test()
 		pipeline.output.to_csv('pipelineResults/' + name + '_outputs.csv', index=False)
-		print
\ No newline at end of file
+		print()
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults.zip b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults.zip
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults.zip
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults.zip
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABFER_ada_boost_n_estimators=10,_learning_rate=0.01.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABFER_ada_boost_n_estimators=10,_learning_rate=0.01.eps
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults/LABFER_ada_boost_n_estimators=10,_learning_rate=0.01.eps
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABFER_ada_boost_n_estimators=10,_learning_rate=0.01.eps
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABSPLAC_random_forest_n_estimators=10,_max_depth=10.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABSPLAC_random_forest_n_estimators=10,_max_depth=10.eps
similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_ada_boost_n_estimators=10,_learning_rate=0.01.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_ada_boost_n_estimators=10,_learning_rate=0.01.eps
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_ada_boost_n_estimators=10,_learning_rate=1.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_ada_boost_n_estimators=10,_learning_rate=1.eps
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_decision_tree_max_depth=20.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_decision_tree_max_depth=20.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_decision_tree_max_depth=5.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_decision_tree_max_depth=5.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_decision_tree_max_depth=50.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_decision_tree_max_depth=50.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_gaussian_naive_bayes.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_gaussian_naive_bayes.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_logistic_regression_c=0.1.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_logistic_regression_c=0.1.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_logistic_regression_c=1.0.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_logistic_regression_c=1.0.eps
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=10,_max_depth=10.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=10,_max_depth=10.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=10,_max_depth=15.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=10,_max_depth=15.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=10,_max_depth=5.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=10,_max_depth=5.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=30,_max_depth=10.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=30,_max_depth=10.eps
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=30,_max_depth=15.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=30,_max_depth=15.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=30,_max_depth=5.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=30,_max_depth=5.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=10.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=10.eps
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=10.eps
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=15.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=15.eps
similarity index 100%
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diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=5.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=5.eps
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=5.eps
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_random_forest_n_estimators=5,_max_depth=5.eps
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/accuracy_f1_scores.txt b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/accuracy_f1_scores.txt
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults/accuracy_f1_scores.txt
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/accuracy_f1_scores.txt
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/logistic_regression.eps b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/logistic_regression.eps
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults/logistic_regression.eps
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/logistic_regression.eps
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/topKPrecision.py b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/topKPrecision.py
similarity index 90%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults/topKPrecision.py
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/topKPrecision.py
index 2dcbb69e..db740abc 100644
--- a/scripts/LabTestAnalysis/machine_learning/pipelineResults/topKPrecision.py
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults/topKPrecision.py
@@ -3,7 +3,7 @@
 
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import datetime, timedelta;
 import json;
 
@@ -25,7 +25,7 @@ def main(argv):
     outfile = stdOpen(argv[2],"w");
     
     summaryData = {"argv": argv};
-    print >> outfile, COMMENT_TAG, json.dumps(summaryData);
+    print(COMMENT_TAG, json.dumps(summaryData), file=outfile);
 
     df = pd.read_csv(infile, na_values=[NULL_STRING]);
     df["normal"] = 1-df["abnormal"];    # Use not-abnormal as output of interest. Should be same as all_result_normal, but some labs not labeled
@@ -34,7 +34,7 @@ def main(argv):
     resultDF = pd.DataFrame();
     nRows = len(df);
     floatNRows = float(nRows);  # Facilitate subsequent floating point division
-    for iRow in xrange(nRows):
+    for iRow in range(nRows):
         topK = iRow+1;    # Top K items considered
         topKPercent = topK/ floatNRows; # Top Percentage of all items considered
         resultDF.set_value(iRow,"iRow", iRow);
@@ -45,7 +45,7 @@ def main(argv):
         if col not in labelCols and col not in resultCols:
             # Any leftover should be a predicted test result / score, correlated with the outcome column
             scoreCol = col;
-            print >> sys.stderr, scoreCol;
+            print(scoreCol, file=sys.stderr);
             scoreResultCol = scoreCol #+".precisionAtK";
             if scoreResultCol.startswith("predictedTest."):
                 scoreResultCol = scoreResultCol[len("predictedTest."):];    # Clean up (trim off) name prefixes
@@ -62,7 +62,7 @@ def main(argv):
                 resultDF.set_value(iRow,scoreResultCol, precisionAtK);
                 iRow += 1;
 
-    print >> sys.stderr, "output"
+    print("output", file=sys.stderr)
     resultDF.to_csv(outfile);
 
     return df;
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults_median_imputation.zip b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults_median_imputation.zip
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults_median_imputation.zip
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults_median_imputation.zip
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults_zero_imputation.zip b/scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults_zero_imputation.zip
similarity index 100%
rename from scripts/LabTestAnalysis/machine_learning/pipelineResults_zero_imputation.zip
rename to scripts/Archive/LabTestAnalysis/machine_learning/pipelineResults_zero_imputation.zip
diff --git a/scripts/LabTestAnalysis/machine_learning/prepareData.py b/scripts/Archive/LabTestAnalysis/machine_learning/prepareData.py
similarity index 96%
rename from scripts/LabTestAnalysis/machine_learning/prepareData.py
rename to scripts/Archive/LabTestAnalysis/machine_learning/prepareData.py
index 126de5fb..d474e9f1 100644
--- a/scripts/LabTestAnalysis/machine_learning/prepareData.py
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/prepareData.py
@@ -8,7 +8,7 @@ def preprocessData(filename, imp_method):
 
 	# use outcome as death.posttime days < 30
 	df['y'] = (df['Death.postTimeDays'] < 30) * 1
-	colnames = filter(lambda x: 'post' not in x, list(df))
+	colnames = [x for x in list(df) if 'post' not in x]
 
 	# throw out times
 	if filename == 'featureMatrix.SepsisICU.csv':
@@ -67,7 +67,7 @@ def preprocessData(filename, imp_method):
 	# imp_method = 'mean'
 	imp_method = 'median'
 
-	print preprocessData(filename, imp_method)
+	print(preprocessData(filename, imp_method))
 
 '''
 for publication: people want to know what actually makes a difference
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/prepareData_NonSTRIDE.py b/scripts/Archive/LabTestAnalysis/machine_learning/prepareData_NonSTRIDE.py
new file mode 100644
index 00000000..6def80e9
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/prepareData_NonSTRIDE.py
@@ -0,0 +1,229 @@
+
+import sqlite3
+from itertools import islice
+import pandas as pd
+import LocalEnv
+if LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+    from . import utils_UMich as utils_NonSTRIDE
+elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+    from . import utils_UCSF as utils_NonSTRIDE
+
+from medinfo.common.Util import log
+import os
+import numpy as np
+import random, string
+
+
+def filter_range(any_str):  # contains sth like range a-b
+    nums = any_str.strip().split('-')
+    try:
+        return (float(nums[0]) + float(nums[1])) / 2.
+    except:
+        return any_str
+
+
+def remove_microsecs(any_str):
+    try:
+        return any_str.split('.')[0]
+    except:
+        return any_str
+
+def filter_nonascii(any_str):
+    return ''.join([i if ord(i) < 128 else ' ' for i in any_str])
+
+def filter_nondigits(any_str):
+    return ''.join([x for x in str(any_str) if x in '-.0123456789'])
+
+def preprocess_files(raw_data_folderpath, data_files=None):
+    if LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+        from . import utils_UCSF as utils_specs
+        utils_specs.separate_demog_diagn_encnt(raw_data_folderpath)
+        utils_specs.separate_labs_team(raw_data_folderpath)
+    else:
+        from . import utils_UMich
+        utils_UMich.preprocess_files(raw_data_folderpath, data_files)
+
+class DB_Preparor:
+    def __init__(self, raw_data_files, raw_data_folderpath,
+                       db_name,
+                       fold_enlarge_data,
+                       USE_CACHED_DB,
+                       time_min='2015-01-01',
+                       test_mode=False):
+
+        self.num_repeats = fold_enlarge_data
+        self.time_min = time_min
+        self.test_mode = test_mode
+
+        if os.path.exists(os.path.join(raw_data_folderpath, db_name)):
+            if USE_CACHED_DB:
+                log.info(db_name + " already exists!")
+                return
+            else:
+                os.remove(os.path.join(raw_data_folderpath, db_name))
+
+        ## Have to handle UCSF by converting:
+        # Input: demographics_and_diagnoses:
+        # Output: demographics, diagnoses
+
+        # TODO: don't do logic in __init__ function
+
+        if fold_enlarge_data != 1:  # TODO: fix for UCSF, perturbing stuff etc.
+            '''
+            For now, use this very simple enlarging strategy
+            '''
+            data_files = []
+            for raw_file in raw_data_files:
+                self.enlarge_a_file(os.path.join(raw_data_folderpath, raw_file), num_repeats=self.num_repeats)
+                data_files.append(raw_file.replace(".sample",".test"))
+            data_folderpath = raw_data_folderpath
+
+        else:
+            data_files = raw_data_files
+            data_folderpath = raw_data_folderpath
+
+        for data_file in data_files:
+            print('Started processing %s...' % data_file)
+            if LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+                if 'encounters' in data_file:
+                    all_included_order_proc_ids = utils_NonSTRIDE.raw2db(data_file, data_folderpath,
+                                                                     db_path=raw_data_folderpath, db_name=db_name,
+                                                                     build_index_patid=True)
+                elif 'labs' in data_file:
+                    utils_NonSTRIDE.raw2db(data_file, data_folderpath, db_path=raw_data_folderpath, db_name=db_name,
+                                       build_index_patid=True,
+                                       collected_included_order_proc_ids=all_included_order_proc_ids)
+                else:
+                    utils_NonSTRIDE.raw2db(data_file, data_folderpath, db_path=raw_data_folderpath, db_name=db_name,
+                                       build_index_patid=True)
+            else:
+                self.raw2db(data_file, data_folderpath, db_path=raw_data_folderpath,
+                       db_name=db_name, build_index_patid=True)
+
+
+    def enlarge_a_file(self, filename, num_repeats=10):
+
+        fr = open(filename, 'r')
+        lines_fr = fr.readlines()
+        rows = len(lines_fr) - 1
+
+        fw = open(filename.replace(".sample", ".test"), 'w')
+        fw.write(lines_fr[0])
+        ind = 0
+        for i in range(num_repeats):
+            for line in lines_fr[1:]:
+                # print line.split('|')[1][1:-1]
+                line_list = line.split('|')
+                try:
+                    line_list[1] = '\"%d\"' % ind
+                except:
+                    continue
+                new_line = "|".join(line_list)
+
+                fw.write(new_line)
+                ind += 1
+
+    def lines2pd(self, lines_str, colnames):
+             # , params_str2list):
+        normal_num_cols = len(colnames)
+
+        all_rows = []
+        for line_str in lines_str:
+            curr_row = utils_NonSTRIDE.line_str2list(line_str, #skip_first_col=True,
+                                                     test_mode=False)
+                                                     # params_str2list=params_str2list)
+            if len(curr_row) < normal_num_cols/2: #
+                # log.info('severely missing data when processing')
+                continue
+            all_rows.append(curr_row)
+
+        data_df = pd.DataFrame(all_rows, columns=colnames)
+        return data_df
+
+
+    def pd2db(self, data_df, df_name, db_path, db_name):
+        conn = sqlite3.connect(db_path + '/' + db_name)
+
+        if LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+            if df_name == "labs":  #
+                tab2df_dict = utils_NonSTRIDE.pd_process_labs(data_df)
+            elif df_name == "pt_info":
+                tab2df_dict = utils_NonSTRIDE.pd_process_pt_info(data_df)
+            elif df_name == "encounters":
+                tab2df_dict = utils_NonSTRIDE.pd_process_encounters(data_df)
+            elif df_name == "demographics":
+                tab2df_dict = utils_NonSTRIDE.pd_process_demographics(data_df)
+            elif df_name == "diagnoses":
+                tab2df_dict = utils_NonSTRIDE.pd_process_diagnoses(data_df)
+            else:
+                print(df_name + " does not exist for UMich!")
+
+        elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+
+            if df_name == "labs":  #
+                tab2df_dict = utils_NonSTRIDE.pd_process_labs(data_df)
+            elif df_name == "demographics_and_diagnoses":
+                tab2df_dict = utils_NonSTRIDE.pd_process_demogdiagn(data_df)
+            elif df_name == 'vitals':
+                tab2df_dict = utils_NonSTRIDE.pd_process_vitals(data_df)
+            else:
+                print(df_name + " does not exist for UCSF!")
+
+        for table_name in list(tab2df_dict.keys()):
+            tab2df_dict[table_name].to_sql(table_name, conn, if_exists="append")
+
+            if self.test_mode:
+                tab2df_dict[table_name].to_csv('raw_data_UCSF/' + table_name + '.csv')
+
+        return list(tab2df_dict.keys())
+
+    # Chunk mechanism, should be general for any outside data
+    def raw2db(self, data_file, data_folderpath, db_path, db_name,
+           build_index_patid=True):
+        chunk_size = 100000  # num of rows
+
+        print('Now writing %s into database...' % data_file)  #
+
+        generated_tables = []
+
+        with open(data_folderpath + '/' + data_file) as f:
+            is_first_chunk = True
+            while True:
+                # a chunk of rows
+                next_n_lines_str = list(islice(f, chunk_size))
+                if not next_n_lines_str:
+                    break
+
+                if is_first_chunk:
+                    colnames = utils_NonSTRIDE.line_str2list(next_n_lines_str[0],
+                                                             test_mode=False)
+                    data_df = self.lines2pd(next_n_lines_str[1:], colnames)
+                                       #, params_str2list)
+                    is_first_chunk = False
+                else:  ## make each chunk into pandas
+                    data_df = self.lines2pd(next_n_lines_str, colnames)
+                                       # params_str2list)
+
+                ## append each pandas to db tables
+                if LocalEnv.DATASET_SOURCE_NAME == 'UMich':
+                    df_name = data_file.replace(".txt", "")
+                    df_name = df_name.replace(".sample", "")
+                    df_name = df_name.replace(".test", "")
+                    df_name = df_name.replace(".large", "")
+                    df_name = df_name.replace('.', '_')  # pt.info
+                elif LocalEnv.DATASET_SOURCE_NAME == 'UCSF':
+                    df_name = data_file.replace(".tsv", "")  #
+                    df_name = df_name.replace("_deident", "")
+                    df_name = df_name.replace('.', '_')
+
+                generated_tables += self.pd2db(data_df, df_name=df_name, db_path=db_path, db_name=db_name)
+                ##
+
+        if build_index_patid:
+            conn = sqlite3.connect(db_path + '/' + db_name)
+
+            for generated_table in generated_tables:
+                build_index_query = "CREATE INDEX IF NOT EXISTS index_for_%s ON %s (%s);" % (generated_table, generated_table, 'pat_id')
+                log.info(build_index_query)
+                # print build_index_query
+                conn.execute(build_index_query)
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/machine_learning/processData.py b/scripts/Archive/LabTestAnalysis/machine_learning/processData.py
similarity index 97%
rename from scripts/LabTestAnalysis/machine_learning/processData.py
rename to scripts/Archive/LabTestAnalysis/machine_learning/processData.py
index 214bbd99..93d19c32 100644
--- a/scripts/LabTestAnalysis/machine_learning/processData.py
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/processData.py
@@ -7,7 +7,7 @@ def preprocessData(filepath, imp_method, filename, lab_type):
 
 	df = pd.read_csv(filepath, na_values='None', sep='\t')
 
-	colnames = filter(lambda x: '.post' not in x, list(df))
+	colnames = [x for x in list(df) if '.post' not in x]
 
 	if 'featureMatrix.SepsisICU' in filepath:
 		df['y'] = df['Death.postTimeDays'] < 30
@@ -346,11 +346,11 @@ def preprocessData(filepath, imp_method, filename, lab_type):
 
 
 	for filepath in glob.glob(INPUT_FOLDER + 'labFeatureMatrix*.tab.gz'):
-		print filepath
-		print preprocessData(gzip.open(filepath),
+		print(filepath)
+		print(preprocessData(gzip.open(filepath),
 			imp_method,
 			filepath.split('/')[-1][:-7],
-			filepath.split('.')[-3])
+			filepath.split('.')[-3]))
 
 		# pd.read_csv(gzip.open(filepath), na_values='None', sep='\t') \
 		# 	.to_csv(filepath.split('/')[-1][:-7] + '.csv', index=False)
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/repeated_labs_charges_volumes.csv b/scripts/Archive/LabTestAnalysis/machine_learning/repeated_labs_charges_volumes.csv
new file mode 100644
index 00000000..c68c6355
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/repeated_labs_charges_volumes.csv
@@ -0,0 +1,3327 @@
+name,num_ordered,window,num_repeated,order_code_description,price_description,min_price,max_price,mean_price,median_price,min_volume_charge,max_volume_charge,mean_volume_charge,median_volume_charge
+LAB11013R,147,1,17,PLATELET ANTIBODY ID PANEL,HC 6957 11013R AB ID PLATELETS,217,217,217,217,3689,3689,3689,3689
+LAB11013R,147,3,19,PLATELET ANTIBODY ID PANEL,HC 6957 11013R AB ID PLATELETS,217,217,217,217,4123,4123,4123,4123
+LAB11013R,147,7,27,PLATELET ANTIBODY ID PANEL,HC 6957 11013R AB ID PLATELETS,217,217,217,217,5859,5859,5859,5859
+LAB11019R,48,1,3,"MYELIN BASIC PROTEIN, CSF",HC 6959 MYELIN BAS PROT CSF,38,38,38,38,114,114,114,114
+LAB11019R,48,3,3,"MYELIN BASIC PROTEIN, CSF",HC 6959 MYELIN BAS PROT CSF,38,38,38,38,114,114,114,114
+LAB11019R,48,7,4,"MYELIN BASIC PROTEIN, CSF",HC 6959 MYELIN BAS PROT CSF,38,38,38,38,152,152,152,152
+LAB11020R,224,1,4,EB VIRUS EARLY AG (R+D) IGG,HC 6942 EBV EA IGG,47,47,47,47,188,188,188,188
+LAB11020R,224,3,6,EB VIRUS EARLY AG (R+D) IGG,HC 6942 EBV EA IGG,47,47,47,47,282,282,282,282
+LAB11020R,224,7,7,EB VIRUS EARLY AG (R+D) IGG,HC 6942 EBV EA IGG,47,47,47,47,329,329,329,329
+LAB11021R,13,1,3,ADRENAL AB,HC 6959 11021R 21OH AB QN RIA,61,61,61,61,183,183,183,183
+LAB11021R,13,3,3,ADRENAL AB,HC 6959 11021R 21OH AB QN RIA,61,61,61,61,183,183,183,183
+LAB11021R,13,7,4,ADRENAL AB,HC 6959 11021R 21OH AB QN RIA,61,61,61,61,244,244,244,244
+LAB11023R,226,1,5,EB NUCLEAR AG AB,HC 6942 EBV EBNA,47,47,47,47,235,235,235,235
+LAB11023R,226,3,6,EB NUCLEAR AG AB,HC 6942 EBV EBNA,47,47,47,47,282,282,282,282
+LAB11023R,226,7,8,EB NUCLEAR AG AB,HC 6942 EBV EBNA,47,47,47,47,376,376,376,376
+LAB11024R,222,1,4,EB VIRAL CAPSID AG IGM AB,HC 6942 EBV VCA IGM,47,47,47,47,188,188,188,188
+LAB11024R,222,3,5,EB VIRAL CAPSID AG IGM AB,HC 6942 EBV VCA IGM,47,47,47,47,235,235,235,235
+LAB11024R,222,7,7,EB VIRAL CAPSID AG IGM AB,HC 6942 EBV VCA IGM,47,47,47,47,329,329,329,329
+LAB11025R,28,1,1,"C1 INHIBITOR, QUANT",HC 6959 11025R C1INH CMPL ATGN,19,19,19,19,19,19,19,19
+LAB11025R,28,3,1,"C1 INHIBITOR, QUANT",HC 6959 11025R C1INH CMPL ATGN,19,19,19,19,19,19,19,19
+LAB11025R,28,7,1,"C1 INHIBITOR, QUANT",HC 6959 11025R C1INH CMPL ATGN,19,19,19,19,19,19,19,19
+LAB11027R,39,1,3,IMMUNE COMPLEXES,HC 6959 11027R C1Q IMM CPLX AS,24,24,24,24,72,72,72,72
+LAB11027R,39,3,3,IMMUNE COMPLEXES,HC 6959 11027R C1Q IMM CPLX AS,24,24,24,24,72,72,72,72
+LAB11027R,39,7,5,IMMUNE COMPLEXES,HC 6959 11027R C1Q IMM CPLX AS,24,24,24,24,120,120,120,120
+LAB11028R,70,1,1,"ACE, CSF",HC 6959 11028R ACE CSF,24,24,24,24,24,24,24,24
+LAB11028R,70,3,1,"ACE, CSF",HC 6959 11028R ACE CSF,24,24,24,24,24,24,24,24
+LAB11028R,70,7,2,"ACE, CSF",HC 6959 11028R ACE CSF,24,24,24,24,48,48,48,48
+LAB11030R,221,1,4,EB VIRAL CAPSID AG IGG AB,HC 6942 EBV VCA IGG,32,32,32,32,128,128,128,128
+LAB11030R,221,3,5,EB VIRAL CAPSID AG IGG AB,HC 6942 EBV VCA IGG,32,32,32,32,160,160,160,160
+LAB11030R,221,7,7,EB VIRAL CAPSID AG IGG AB,HC 6942 EBV VCA IGG,32,32,32,32,224,224,224,224
+LAB11032R,4,7,1,PSEUDOCHOLINESTERASE,HC 6959 11032R PSEUDOCHOLINEST,13,13,13,13,13,13,13,13
+LAB11036R,153,1,40,"FIBRINOGEN, QUANT",HC 6959 11036R FIBRINOGEN AG,52,52,52,52,2080,2080,2080,2080
+LAB11036R,153,3,81,"FIBRINOGEN, QUANT",HC 6959 11036R FIBRINOGEN AG,52,52,52,52,4212,4212,4212,4212
+LAB11036R,153,7,86,"FIBRINOGEN, QUANT",HC 6959 11036R FIBRINOGEN AG,52,52,52,52,4472,4472,4472,4472
+LAB11037R,16,1,2,FLECAINIDE LEVEL,HC 6959 FLECAINIDE,26,26,26,26,52,52,52,52
+LAB11037R,16,3,4,FLECAINIDE LEVEL,HC 6959 FLECAINIDE,26,26,26,26,104,104,104,104
+LAB11037R,16,7,4,FLECAINIDE LEVEL,HC 6959 FLECAINIDE,26,26,26,26,104,104,104,104
+LAB11039R,88,1,5,GBM AB,HC 6959 11039R GBM AB IGG IFA,64,64,64,64,320,320,320,320
+LAB11039R,88,3,11,GBM AB,HC 6959 11039R GBM AB IGG IFA,64,64,64,64,704,704,704,704
+LAB11039R,88,7,11,GBM AB,HC 6959 11039R GBM AB IGG IFA,64,64,64,64,704,704,704,704
+LAB11041R,902,1,83,FREE HEMOGLOBIN,HC 6959 11041R HEMOGLBN SPCTRO,20,20,20,20,1660,1660,1660,1660
+LAB11041R,902,3,260,FREE HEMOGLOBIN,HC 6959 11041R HEMOGLBN SPCTRO,20,20,20,20,5200,5200,5200,5200
+LAB11041R,902,7,616,FREE HEMOGLOBIN,HC 6959 11041R HEMOGLBN SPCTRO,20,20,20,20,12320,12320,12320,12320
+LAB11042R,21,1,1,HISTAMINE LEVEL,HC 6959 HISTAMINE,34,34,34,34,34,34,34,34
+LAB11042R,21,3,2,HISTAMINE LEVEL,HC 6959 HISTAMINE,34,34,34,34,68,68,68,68
+LAB11042R,21,7,2,HISTAMINE LEVEL,HC 6959 HISTAMINE,34,34,34,34,68,68,68,68
+LAB11045R,76,1,1,LKM AB,HC 6959 11045R LIV KID IGG AB,19,19,19,19,19,19,19,19
+LAB11045R,76,3,1,LKM AB,HC 6959 11045R LIV KID IGG AB,19,19,19,19,19,19,19,19
+LAB11045R,76,7,1,LKM AB,HC 6959 11045R LIV KID IGG AB,19,19,19,19,19,19,19,19
+LAB11049R,501,1,199,NSE,HC 6959 11049R NSE IMMUN AG QN,39,39,39,39,7761,7761,7761,7761
+LAB11049R,501,3,276,NSE,HC 6959 11049R NSE IMMUN AG QN,39,39,39,39,10764,10764,10764,10764
+LAB11049R,501,7,308,NSE,HC 6959 11049R NSE IMMUN AG QN,39,39,39,39,12012,12012,12012,12012
+LAB11055R,5,1,2,PROPAFENONE LEVEL,HC 6959 11055R PROPAFEN QN DRG,39,39,39,39,78,78,78,78
+LAB11055R,5,3,2,PROPAFENONE LEVEL,HC 6959 11055R PROPAFEN QN DRG,39,39,39,39,78,78,78,78
+LAB11055R,5,7,3,PROPAFENONE LEVEL,HC 6959 11055R PROPAFEN QN DRG,39,39,39,39,117,117,117,117
+LAB11056R,154,1,49,FREE CORTISOL,HC 6959 11056R CORTISOL FREE,100,100,100,100,4900,4900,4900,4900
+LAB11056R,154,3,52,FREE CORTISOL,HC 6959 11056R CORTISOL FREE,100,100,100,100,5200,5200,5200,5200
+LAB11056R,154,7,58,FREE CORTISOL,HC 6959 11056R CORTISOL FREE,100,100,100,100,5800,5800,5800,5800
+LAB11057R,5,1,1,TNF,HC 6959 11057R TNF IMM QN NOS,95,95,95,95,95,95,95,95
+LAB11057R,5,3,2,TNF,HC 6959 11057R TNF IMM QN NOS,95,95,95,95,190,190,190,190
+LAB11057R,5,7,2,TNF,HC 6959 11057R TNF IMM QN NOS,95,95,95,95,190,190,190,190
+LAB11060R,521,1,13,THIAMINE,HC 6959 11060R THIAMINE VIT B1,30,30,30,30,390,390,390,390
+LAB11060R,521,3,28,THIAMINE,HC 6959 11060R THIAMINE VIT B1,30,30,30,30,840,840,840,840
+LAB11060R,521,7,38,THIAMINE,HC 6959 11060R THIAMINE VIT B1,30,30,30,30,1140,1140,1140,1140
+LAB11067R,13,1,1,17OH PROG,HC 6956 11067R 17D HYDPRGSTRNE,21,21,21,21,21,21,21,21
+LAB11067R,13,3,2,17OH PROG,HC 6956 11067R 17D HYDPRGSTRNE,21,21,21,21,42,42,42,42
+LAB11067R,13,7,2,17OH PROG,HC 6956 11067R 17D HYDPRGSTRNE,21,21,21,21,42,42,42,42
+LAB11068R,333,1,16,ACE,HC 6956 11068R ACE,15,15,15,15,240,240,240,240
+LAB11068R,333,3,21,ACE,HC 6956 11068R ACE,15,15,15,15,315,315,315,315
+LAB11068R,333,7,24,ACE,HC 6956 11068R ACE,15,15,15,15,360,360,360,360
+LAB11069R,260,1,17,ALDOLASE,HC 6956 11069R ALDOLASE,9,9,9,9,153,153,153,153
+LAB11069R,260,3,36,ALDOLASE,HC 6956 11069R ALDOLASE,9,9,9,9,324,324,324,324
+LAB11069R,260,7,43,ALDOLASE,HC 6956 11069R ALDOLASE,9,9,9,9,387,387,387,387
+LAB11070R,182,1,5,ALDOSTERONE,HC 6956 11070R ALDOSTERONE,33,33,33,33,165,165,165,165
+LAB11070R,182,3,15,ALDOSTERONE,HC 6956 11070R ALDOSTERONE,33,33,33,33,495,495,495,495
+LAB11070R,182,7,20,ALDOSTERONE,HC 6956 11070R ALDOSTERONE,33,33,33,33,660,660,660,660
+LAB11071R,427,1,12,MITOCHONDRIAL AB,HC 6956 11071R MITCHNDRL M2 AB,13,13,13,13,156,156,156,156
+LAB11071R,427,3,24,MITOCHONDRIAL AB,HC 6956 11071R MITCHNDRL M2 AB,13,13,13,13,312,312,312,312
+LAB11071R,427,7,26,MITOCHONDRIAL AB,HC 6956 11071R MITCHNDRL M2 AB,13,13,13,13,338,338,338,338
+LAB11075R,523,1,17,SMOOTH MUSCLE AB,HC 6956 11075R SMTH MUS AB IGG,15,15,15,15,255,255,255,255
+LAB11075R,523,3,33,SMOOTH MUSCLE AB,HC 6956 11075R SMTH MUS AB IGG,15,15,15,15,495,495,495,495
+LAB11075R,523,7,35,SMOOTH MUSCLE AB,HC 6956 11075R SMTH MUS AB IGG,15,15,15,15,525,525,525,525
+LAB11077R,67,1,2,CALCITONIN,HC 6956 11077R CALCITONIN,44,44,44,44,88,88,88,88
+LAB11077R,67,3,8,CALCITONIN,HC 6956 11077R CALCITONIN,44,44,44,44,352,352,352,352
+LAB11077R,67,7,8,CALCITONIN,HC 6956 11077R CALCITONIN,44,44,44,44,352,352,352,352
+LAB11078R,9,7,1,ACETYLCHOLINESTERASE,HC 6956 ACETYLCHOLINESTERASE RBC,61,61,61,61,61,61,61,61
+LAB11080R,22,3,1,GAD65 AB ASSAY,HC 6956 GLUTAMIC AC DECARBOXY AB,14,14,14,14,14,14,14,14
+LAB11080R,22,7,1,GAD65 AB ASSAY,HC 6956 GLUTAMIC AC DECARBOXY AB,14,14,14,14,14,14,14,14
+LAB11081R,161,1,13,GASTRIN,HC 6956 11081R GASTRIN,16,16,16,16,208,208,208,208
+LAB11081R,161,3,22,GASTRIN,HC 6956 11081R GASTRIN,16,16,16,16,352,352,352,352
+LAB11081R,161,7,25,GASTRIN,HC 6956 11081R GASTRIN,16,16,16,16,400,400,400,400
+LAB11085R,188,1,5,MPO AB,HC 6956 MYELOPEROXIDASE AB,28,28,28,28,140,140,140,140
+LAB11085R,188,3,10,MPO AB,HC 6956 MYELOPEROXIDASE AB,28,28,28,28,280,280,280,280
+LAB11085R,188,7,10,MPO AB,HC 6956 MYELOPEROXIDASE AB,28,28,28,28,280,280,280,280
+LAB11086R,72,1,4,NORTRIPTYLINE LEVEL,HC 6956 11086R NORTRIPTYLINE,18,18,18,18,72,72,72,72
+LAB11086R,72,3,7,NORTRIPTYLINE LEVEL,HC 6956 11086R NORTRIPTYLINE,18,18,18,18,126,126,126,126
+LAB11086R,72,7,18,NORTRIPTYLINE LEVEL,HC 6956 11086R NORTRIPTYLINE,18,18,18,18,324,324,324,324
+LAB11091R,7,3,2,QUINIDINE LEVEL,HC QUINIDINE,41,41,41,41,82,82,82,82
+LAB11091R,7,7,2,QUINIDINE LEVEL,HC QUINIDINE,41,41,41,41,82,82,82,82
+LAB11092R,165,1,4,RENIN,HC 6956 11092R RENIN ACTIVITY,16,16,16,16,64,64,64,64
+LAB11092R,165,3,9,RENIN,HC 6956 11092R RENIN ACTIVITY,16,16,16,16,144,144,144,144
+LAB11092R,165,7,14,RENIN,HC 6956 11092R RENIN ACTIVITY,16,16,16,16,224,224,224,224
+LAB11093R,93,1,1,SCL 70 AB,HC 6956 11093R ENA SCL70 EA AB,17,17,17,17,17,17,17,17
+LAB11093R,93,3,1,SCL 70 AB,HC 6956 11093R ENA SCL70 EA AB,17,17,17,17,17,17,17,17
+LAB11093R,93,7,1,SCL 70 AB,HC 6956 11093R ENA SCL70 EA AB,17,17,17,17,17,17,17,17
+LAB11094R,128,1,5,RO AB,HC 6956 11094R ENA SSA RO IGG,14,14,14,14,70,70,70,70
+LAB11094R,128,3,7,RO AB,HC 6956 11094R ENA SSA RO IGG,14,14,14,14,98,98,98,98
+LAB11094R,128,7,8,RO AB,HC 6956 11094R ENA SSA RO IGG,14,14,14,14,112,112,112,112
+LAB11095R,122,1,5,LA AB,HC 6956 11095R ENA SSB LA IGG,24,24,24,24,120,120,120,120
+LAB11095R,122,3,7,LA AB,HC 6956 11095R ENA SSB LA IGG,24,24,24,24,168,168,168,168
+LAB11095R,122,7,8,LA AB,HC 6956 11095R ENA SSB LA IGG,24,24,24,24,192,192,192,192
+LAB11096R,97,1,9,"ALPHA 1 ANTITRYPSIN, STOOL",HC 6956 ALPHA-1-ANTITRYPSIN STL,31,31,31,31,279,279,279,279
+LAB11096R,97,3,17,"ALPHA 1 ANTITRYPSIN, STOOL",HC 6956 ALPHA-1-ANTITRYPSIN STL,31,31,31,31,527,527,527,527
+LAB11096R,97,7,20,"ALPHA 1 ANTITRYPSIN, STOOL",HC 6956 ALPHA-1-ANTITRYPSIN STL,31,31,31,31,620,620,620,620
+LAB11097R,100,1,6,"TSH RECEPTOR AB, STIM",HC 6956 11097R THY STIM IMM GL,12,12,12,12,72,72,72,72
+LAB11097R,100,3,10,"TSH RECEPTOR AB, STIM",HC 6956 11097R THY STIM IMM GL,12,12,12,12,120,120,120,120
+LAB11097R,100,7,11,"TSH RECEPTOR AB, STIM",HC 6956 11097R THY STIM IMM GL,12,12,12,12,132,132,132,132
+LAB11098R,276,1,7,1 25 OH VITAMIN D,"HC 6956 11098R VITD 1,25 DIHYD",43,43,43,43,301,301,301,301
+LAB11098R,276,3,15,1 25 OH VITAMIN D,"HC 6956 11098R VITD 1,25 DIHYD",43,43,43,43,645,645,645,645
+LAB11098R,276,7,20,1 25 OH VITAMIN D,"HC 6956 11098R VITD 1,25 DIHYD",43,43,43,43,860,860,860,860
+LAB11099R,51,1,3,VIP,HC 6956 11099R VASOACT INT PEP,50,50,50,50,150,150,150,150
+LAB11099R,51,3,3,VIP,HC 6956 11099R VASOACT INT PEP,50,50,50,50,150,150,150,150
+LAB11099R,51,7,4,VIP,HC 6956 11099R VASOACT INT PEP,50,50,50,50,200,200,200,200
+LAB11101R,63,7,2,VITAMIN C,HC 6956 11101R VITC ASCRBC ACD,30,30,30,30,60,60,60,60
+LAB11102R,127,1,2,VITAMIN E,HC 6956 11102R VIT E TOCO ALPA,25,25,25,25,50,50,50,50
+LAB11102R,127,3,3,VITAMIN E,HC 6956 11102R VIT E TOCO ALPA,25,25,25,25,75,75,75,75
+LAB11102R,127,7,4,VITAMIN E,HC 6956 11102R VIT E TOCO ALPA,25,25,25,25,100,100,100,100
+LAB11106R,110,1,2,HBE AB,HC 6942 HEPATITIS BE AB,20,20,20,20,40,40,40,40
+LAB11106R,110,3,4,HBE AB,HC 6942 HEPATITIS BE AB,20,20,20,20,80,80,80,80
+LAB11106R,110,7,4,HBE AB,HC 6942 HEPATITIS BE AB,20,20,20,20,80,80,80,80
+LAB11107R,121,1,5,HBE AG,HC 6942 HEPATITIS BE ANTGEN,18,18,18,18,90,90,90,90
+LAB11107R,121,3,7,HBE AG,HC 6942 HEPATITIS BE ANTGEN,18,18,18,18,126,126,126,126
+LAB11107R,121,7,8,HBE AG,HC 6942 HEPATITIS BE ANTGEN,18,18,18,18,144,144,144,144
+LAB11112R,5,3,1,DONOR TOXOPLASMA AB,HC 6944 TOXO IGM ELISA,121,121,121,121,121,121,121,121
+LAB11112R,5,7,1,DONOR TOXOPLASMA AB,HC 6944 TOXO IGM ELISA,121,121,121,121,121,121,121,121
+LAB11118R,173,1,8,"ELASTASE, STOOL",HC 7032 ELASTASE-1,55,55,55,55,440,440,440,440
+LAB11118R,173,3,14,"ELASTASE, STOOL",HC 7032 ELASTASE-1,55,55,55,55,770,770,770,770
+LAB11118R,173,7,30,"ELASTASE, STOOL",HC 7032 ELASTASE-1,55,55,55,55,1650,1650,1650,1650
+LAB11120R,35,7,1,"TOXOPLASMOSIS, CSF",HC 6944 TOXO DYE TEST IGG,114,114,114,114,114,114,114,114
+LAB11124R,960,1,29,COCCI CF,HC 6922 COCCI CF SERUM,40,40,40,40,1160,1160,1160,1160
+LAB11124R,960,3,39,COCCI CF,HC 6922 COCCI CF SERUM,40,40,40,40,1560,1560,1560,1560
+LAB11124R,960,7,64,COCCI CF,HC 6922 COCCI CF SERUM,40,40,40,40,2560,2560,2560,2560
+LAB11125R,1486,1,64,COCCI ID,HC 6922 COCCI IMMUNODIFF,34,34,34,34,2176,2176,2176,2176
+LAB11125R,1486,3,83,COCCI ID,HC 6922 COCCI IMMUNODIFF,34,34,34,34,2822,2822,2822,2822
+LAB11125R,1486,7,107,COCCI ID,HC 6922 COCCI IMMUNODIFF,34,34,34,34,3638,3638,3638,3638
+LAB11127R,22,1,9,CASPOFUNGIN LEVEL,HC 6939 CASPOFUNGIN LVL,95,95,95,95,855,855,855,855
+LAB11127R,22,3,9,CASPOFUNGIN LEVEL,HC 6939 CASPOFUNGIN LVL,95,95,95,95,855,855,855,855
+LAB11127R,22,7,9,CASPOFUNGIN LEVEL,HC 6939 CASPOFUNGIN LVL,95,95,95,95,855,855,855,855
+LAB11128R,44,1,2,FUNGAL MIC,HC MIC SUSCEPTIBILITY EA,65,65,65,65,130,130,130,130
+LAB11128R,44,3,2,FUNGAL MIC,HC MIC SUSCEPTIBILITY EA,65,65,65,65,130,130,130,130
+LAB11128R,44,7,2,FUNGAL MIC,HC MIC SUSCEPTIBILITY EA,65,65,65,65,130,130,130,130
+LAB11131R,569,1,83,VORICONAZOLE LEVEL,HC 6939 11131R VORI QNT SNG AN,120,120,120,120,9960,9960,9960,9960
+LAB11131R,569,3,105,VORICONAZOLE LEVEL,HC 6939 11131R VORI QNT SNG AN,120,120,120,120,12600,12600,12600,12600
+LAB11131R,569,7,146,VORICONAZOLE LEVEL,HC 6939 11131R VORI QNT SNG AN,120,120,120,120,17520,17520,17520,17520
+LAB11133R,32,1,1,DONOR HBC AB,HC 6949 DONOR HBC LIVING,35,35,35,35,35,35,35,35
+LAB11133R,32,3,1,DONOR HBC AB,HC 6949 DONOR HBC LIVING,35,35,35,35,35,35,35,35
+LAB11133R,32,7,1,DONOR HBC AB,HC 6949 DONOR HBC LIVING,35,35,35,35,35,35,35,35
+LAB11136R,320,1,2,HTLV AB I & II,HC 6949 11136R HTLVS 1,15,26,20.5,20.5,30,52,41,41
+LAB11136R,320,1,2,HTLV AB I & II,HC 6949 11136R HTLVS 2,15,26,20.5,20.5,30,52,41,41
+LAB11136R,320,3,12,HTLV AB I & II,HC 6949 11136R HTLVS 1,15,26,20.5,20.5,180,312,246,246
+LAB11136R,320,3,12,HTLV AB I & II,HC 6949 11136R HTLVS 2,15,26,20.5,20.5,180,312,246,246
+LAB11136R,320,7,17,HTLV AB I & II,HC 6949 11136R HTLVS 1,15,26,20.5,20.5,255,442,348.5,348.5
+LAB11136R,320,7,17,HTLV AB I & II,HC 6949 11136R HTLVS 2,15,26,20.5,20.5,255,442,348.5,348.5
+LAB11153R,41,1,1,"HERPES VIRUS 6 DNA, QUANT",HC 6942 HHV-6 QUANT PCR,224,224,224,224,224,224,224,224
+LAB11153R,41,3,1,"HERPES VIRUS 6 DNA, QUANT",HC 6942 HHV-6 QUANT PCR,224,224,224,224,224,224,224,224
+LAB11153R,41,7,1,"HERPES VIRUS 6 DNA, QUANT",HC 6942 HHV-6 QUANT PCR,224,224,224,224,224,224,224,224
+LAB11175R,44,1,6,"LIPASE,BF",HC 6956 LIPASE BODY FLUID,27,27,27,27,162,162,162,162
+LAB11175R,44,3,7,"LIPASE,BF",HC 6956 LIPASE BODY FLUID,27,27,27,27,189,189,189,189
+LAB11175R,44,7,8,"LIPASE,BF",HC 6956 LIPASE BODY FLUID,27,27,27,27,216,216,216,216
+LAB11179R,16,1,1,"NICOTINE & METABOLITES, URINE",HC 6956 NICTN AND MTABLIT UR,23,23,23,23,23,23,23,23
+LAB11179R,16,3,1,"NICOTINE & METABOLITES, URINE",HC 6956 NICTN AND MTABLIT UR,23,23,23,23,23,23,23,23
+LAB11179R,16,7,3,"NICOTINE & METABOLITES, URINE",HC 6956 NICTN AND MTABLIT UR,23,23,23,23,69,69,69,69
+LAB11182R,25,1,1,"NICOTINE & METABOLITES, SERUM",HC 6956 NICOTINE,51,51,51,51,51,51,51,51
+LAB11182R,25,3,1,"NICOTINE & METABOLITES, SERUM",HC 6956 NICOTINE,51,51,51,51,51,51,51,51
+LAB11182R,25,7,1,"NICOTINE & METABOLITES, SERUM",HC 6956 NICOTINE,51,51,51,51,51,51,51,51
+LAB11199R,158,1,1,PARANEOPLASTIC AUTOAB EVAL,HC 6956 IMM ANLYT QUANT RIA,24,27,25.66666667,26,24,27,25.66666667,26
+LAB11199R,158,1,1,PARANEOPLASTIC AUTOAB EVAL,HC 6956 FLUOR AB TITER,24,27,25.66666667,26,24,27,25.66666667,26
+LAB11199R,158,1,1,PARANEOPLASTIC AUTOAB EVAL,HC 6956 IMM ANLYT QUANT,24,27,25.66666667,26,24,27,25.66666667,26
+LAB11199R,158,3,3,PARANEOPLASTIC AUTOAB EVAL,HC 6956 IMM ANLYT QUANT RIA,24,27,25.66666667,26,72,81,77,78
+LAB11199R,158,3,3,PARANEOPLASTIC AUTOAB EVAL,HC 6956 FLUOR AB TITER,24,27,25.66666667,26,72,81,77,78
+LAB11199R,158,3,3,PARANEOPLASTIC AUTOAB EVAL,HC 6956 IMM ANLYT QUANT,24,27,25.66666667,26,72,81,77,78
+LAB11199R,158,7,7,PARANEOPLASTIC AUTOAB EVAL,HC 6956 IMM ANLYT QUANT RIA,24,27,25.66666667,26,168,189,179.6666667,182
+LAB11199R,158,7,7,PARANEOPLASTIC AUTOAB EVAL,HC 6956 FLUOR AB TITER,24,27,25.66666667,26,168,189,179.6666667,182
+LAB11199R,158,7,7,PARANEOPLASTIC AUTOAB EVAL,HC 6956 IMM ANLYT QUANT,24,27,25.66666667,26,168,189,179.6666667,182
+LAB11214R,64,1,3,"BETA-2 TRANSFERRIN,BF",HC 6956 BEAT2 TRSFRN BDY FLD,78,78,78,78,234,234,234,234
+LAB11214R,64,3,5,"BETA-2 TRANSFERRIN,BF",HC 6956 BEAT2 TRSFRN BDY FLD,78,78,78,78,390,390,390,390
+LAB11214R,64,7,8,"BETA-2 TRANSFERRIN,BF",HC 6956 BEAT2 TRSFRN BDY FLD,78,78,78,78,624,624,624,624
+LAB11234R,245,1,2,VZV TOTAL AND IGM AB PANEL,HC 6942 VZV AB PNL TOT AND IGM,42,42,42,42,84,84,84,84
+LAB11234R,245,3,5,VZV TOTAL AND IGM AB PANEL,HC 6942 VZV AB PNL TOT AND IGM,42,42,42,42,210,210,210,210
+LAB11234R,245,7,6,VZV TOTAL AND IGM AB PANEL,HC 6942 VZV AB PNL TOT AND IGM,42,42,42,42,252,252,252,252
+LAB11240R,14,1,1,"MEASLES IGG,IFA",HC 6942 MEASLES IGG AB TIT,51,51,51,51,51,51,51,51
+LAB11240R,14,3,1,"MEASLES IGG,IFA",HC 6942 MEASLES IGG AB TIT,51,51,51,51,51,51,51,51
+LAB11240R,14,7,1,"MEASLES IGG,IFA",HC 6942 MEASLES IGG AB TIT,51,51,51,51,51,51,51,51
+LAB11242R,194,1,5,VZV QUANT,HC 6942 VZV DNA PCR QUANT,262,262,262,262,1310,1310,1310,1310
+LAB11242R,194,3,14,VZV QUANT,HC 6942 VZV DNA PCR QUANT,262,262,262,262,3668,3668,3668,3668
+LAB11242R,194,7,15,VZV QUANT,HC 6942 VZV DNA PCR QUANT,262,262,262,262,3930,3930,3930,3930
+LAB11250R,56,1,4,"HHV-8 DNA,QUANT",HC 6942 11250R HHV8 DNA QN PCR,262,262,262,262,1048,1048,1048,1048
+LAB11250R,56,3,6,"HHV-8 DNA,QUANT",HC 6942 11250R HHV8 DNA QN PCR,262,262,262,262,1572,1572,1572,1572
+LAB11250R,56,7,12,"HHV-8 DNA,QUANT",HC 6942 11250R HHV8 DNA QN PCR,262,262,262,262,3144,3144,3144,3144
+LAB11253R,37,7,1,HELICOBACTER PYLORI AB IGA,HC 6942 H PYLORI IGA AB,107,107,107,107,107,107,107,107
+LAB11265R,50,1,4,ENTAMOEBA HISTOLYTICA AG,HC 6942 11265R ENTMB HSTLYT AG,65,65,65,65,260,260,260,260
+LAB11265R,50,3,6,ENTAMOEBA HISTOLYTICA AG,HC 6942 11265R ENTMB HSTLYT AG,65,65,65,65,390,390,390,390
+LAB11265R,50,7,7,ENTAMOEBA HISTOLYTICA AG,HC 6942 11265R ENTMB HSTLYT AG,65,65,65,65,455,455,455,455
+LAB11312R,19,1,1,"SOLUBLE LIVER AG AB,IGG",HC 6959 11312R IMM QL MLT SM,18,18,18,18,18,18,18,18
+LAB11312R,19,3,1,"SOLUBLE LIVER AG AB,IGG",HC 6959 11312R IMM QL MLT SM,18,18,18,18,18,18,18,18
+LAB11312R,19,7,1,"SOLUBLE LIVER AG AB,IGG",HC 6959 11312R IMM QL MLT SM,18,18,18,18,18,18,18,18
+LAB11340R,16,3,1,ADAMTS 13 PROFILE,HC 6957 ADAMTS-13,121,121,121,121,121,121,121,121
+LAB11340R,16,7,2,ADAMTS 13 PROFILE,HC 6957 ADAMTS-13,121,121,121,121,242,242,242,242
+LAB11343R,103,1,3,SRA UNFRACTIONATED HEPARIN,HC 6957 AB ID PLATELETS,148,148,148,148,444,444,444,444
+LAB11343R,103,3,3,SRA UNFRACTIONATED HEPARIN,HC 6957 AB ID PLATELETS,148,148,148,148,444,444,444,444
+LAB11343R,103,7,5,SRA UNFRACTIONATED HEPARIN,HC 6957 AB ID PLATELETS,148,148,148,148,740,740,740,740
+LAB11523R,50,1,2,GQ1B AB,HC 6961 GQ1B AB NES,248,248,248,248,496,496,496,496
+LAB11523R,50,3,2,GQ1B AB,HC 6961 GQ1B AB NES,248,248,248,248,496,496,496,496
+LAB11523R,50,7,2,GQ1B AB,HC 6961 GQ1B AB NES,248,248,248,248,496,496,496,496
+LAB11531R,92,1,1,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB RIA,175,244,208.25,207,175,244,208.25,207
+LAB11531R,92,1,1,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB WB RPT,175,244,208.25,207,175,244,208.25,207
+LAB11531R,92,1,1,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB NES,175,244,208.25,207,175,244,208.25,207
+LAB11531R,92,1,1,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB WB,175,244,208.25,207,175,244,208.25,207
+LAB11531R,92,3,2,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB RIA,175,244,208.25,207,350,488,416.5,414
+LAB11531R,92,3,2,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB WB RPT,175,244,208.25,207,350,488,416.5,414
+LAB11531R,92,3,2,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB NES,175,244,208.25,207,350,488,416.5,414
+LAB11531R,92,3,2,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB WB,175,244,208.25,207,350,488,416.5,414
+LAB11531R,92,7,3,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB RIA,175,244,208.25,207,525,732,624.75,621
+LAB11531R,92,7,3,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB WB RPT,175,244,208.25,207,525,732,624.75,621
+LAB11531R,92,7,3,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB NES,175,244,208.25,207,525,732,624.75,621
+LAB11531R,92,7,3,PARANEOPLASTIC AB SCREEN,HC 6961 PARANEO AB WB,175,244,208.25,207,525,732,624.75,621
+LAB11556R,122,1,5,BLASTOMYCES SEROLOGIES,HC 6942 BLASTOMYCES AB,49,49,49,49,245,245,245,245
+LAB11556R,122,3,11,BLASTOMYCES SEROLOGIES,HC 6942 BLASTOMYCES AB,49,49,49,49,539,539,539,539
+LAB11556R,122,7,13,BLASTOMYCES SEROLOGIES,HC 6942 BLASTOMYCES AB,49,49,49,49,637,637,637,637
+LAB11562R,19,7,1,LYMPHOCYTIC CHORIOMENINGITI AB,HC 6942 LYMPH CHORIOMEN AB,50,50,50,50,50,50,50,50
+LAB11566R,35,1,4,"ENTEROVIRUS RNA,QUAL",HC 6942 ENTEROVIRUS RNA QUAL,244,244,244,244,976,976,976,976
+LAB11566R,35,3,5,"ENTEROVIRUS RNA,QUAL",HC 6942 ENTEROVIRUS RNA QUAL,244,244,244,244,1220,1220,1220,1220
+LAB11566R,35,7,6,"ENTEROVIRUS RNA,QUAL",HC 6942 ENTEROVIRUS RNA QUAL,244,244,244,244,1464,1464,1464,1464
+LAB11593R,38,1,5,ETHYLENE GLYCOL,HC 6956 ETHYLENE GLYCO LEVEL,64,64,64,64,320,320,320,320
+LAB11593R,38,3,6,ETHYLENE GLYCOL,HC 6956 ETHYLENE GLYCO LEVEL,64,64,64,64,384,384,384,384
+LAB11593R,38,7,6,ETHYLENE GLYCOL,HC 6956 ETHYLENE GLYCO LEVEL,64,64,64,64,384,384,384,384
+LAB11606R,4,7,1,CYTOCHROME P450 2D6 GENOTYPE,HC 6956 11606R P450 GENOTYPE,477,477,477,477,477,477,477,477
+LAB11607R,12,7,1,"COENZYME Q10,REDUCED & TOTAL",HC 6956 Q10CHROM ANAL QUANT,123,123,123,123,123,123,123,123
+LAB11615R,5,1,3,SODIUM BENZOATE LEVEL,HC 7065 NA BENZOATE GC/MS,150,150,150,150,450,450,450,450
+LAB11615R,5,3,3,SODIUM BENZOATE LEVEL,HC 7065 NA BENZOATE GC/MS,150,150,150,150,450,450,450,450
+LAB11615R,5,7,3,SODIUM BENZOATE LEVEL,HC 7065 NA BENZOATE GC/MS,150,150,150,150,450,450,450,450
+LAB11824R,373,1,305,BUSULFAN,HC 7041 11824R BUSULFAN MSQNT,233,233,233,233,71065,71065,71065,71065
+LAB11824R,373,3,306,BUSULFAN,HC 7041 11824R BUSULFAN MSQNT,233,233,233,233,71298,71298,71298,71298
+LAB11824R,373,7,309,BUSULFAN,HC 7041 11824R BUSULFAN MSQNT,233,233,233,233,71997,71997,71997,71997
+LAB11851R,6,1,2,"FECAL LIPIDS, TOTAL",HC 6911 LIPID FECES TOTAL QUAN,44,44,44,44,88,88,88,88
+LAB11851R,6,3,2,"FECAL LIPIDS, TOTAL",HC 6911 LIPID FECES TOTAL QUAN,44,44,44,44,88,88,88,88
+LAB11851R,6,7,2,"FECAL LIPIDS, TOTAL",HC 6911 LIPID FECES TOTAL QUAN,44,44,44,44,88,88,88,88
+LAB11885R,3,7,1,"CHLAMYDIA TRACHOMATIS, RECTAL",HC 6911 C TRACH RNA TMA RECTAL,60,60,60,60,60,60,60,60
+LAB11887R,5,1,1,"CHLAMYDIA TRACHOMATIS, THROAT",HC 6911 C TRACH RNA TMA THROAT,60,60,60,60,60,60,60,60
+LAB11887R,5,3,1,"CHLAMYDIA TRACHOMATIS, THROAT",HC 6911 C TRACH RNA TMA THROAT,60,60,60,60,60,60,60,60
+LAB11887R,5,7,3,"CHLAMYDIA TRACHOMATIS, THROAT",HC 6911 C TRACH RNA TMA THROAT,60,60,60,60,180,180,180,180
+LAB11901R,7,7,1,"N. GONORRHOEAE RNA, THROAT",HC 6911 11901R GC RNA APT THRT,60,60,60,60,60,60,60,60
+LAB11982R,148,1,11,POSACONAZOLE,HC 6956 POSACONAZOLE QUAN,76,76,76,76,836,836,836,836
+LAB11982R,148,3,19,POSACONAZOLE,HC 6956 POSACONAZOLE QUAN,76,76,76,76,1444,1444,1444,1444
+LAB11982R,148,7,32,POSACONAZOLE,HC 6956 POSACONAZOLE QUAN,76,76,76,76,2432,2432,2432,2432
+LAB11984R,37,1,1,NIACIN,HC 6956 NIACIN VITAMIN B3,75,75,75,75,75,75,75,75
+LAB11984R,37,3,1,NIACIN,HC 6956 NIACIN VITAMIN B3,75,75,75,75,75,75,75,75
+LAB11984R,37,7,1,NIACIN,HC 6956 NIACIN VITAMIN B3,75,75,75,75,75,75,75,75
+LAB12003R,2,1,1,"HERPESVIRUS 6 AB, IGG",HC 6942 HHV-6 AB IGG,68,68,68,68,68,68,68,68
+LAB12003R,2,3,1,"HERPESVIRUS 6 AB, IGG",HC 6942 HHV-6 AB IGG,68,68,68,68,68,68,68,68
+LAB12003R,2,7,1,"HERPESVIRUS 6 AB, IGG",HC 6942 HHV-6 AB IGG,68,68,68,68,68,68,68,68
+LAB12017R,6,3,1,ECHOVIRUS AB,HC 6959 12017R ECHOVRS AB EA,36,36,36,36,36,36,36,36
+LAB12017R,6,7,1,ECHOVIRUS AB,HC 6959 12017R ECHOVRS AB EA,36,36,36,36,36,36,36,36
+LAB12212R,14,1,1,WA1 IGG AB,HC 6942 12212R PRTZ NES WA1 AB,88,88,88,88,88,88,88,88
+LAB12212R,14,3,1,WA1 IGG AB,HC 6942 12212R PRTZ NES WA1 AB,88,88,88,88,88,88,88,88
+LAB12212R,14,7,1,WA1 IGG AB,HC 6942 12212R PRTZ NES WA1 AB,88,88,88,88,88,88,88,88
+LAB12214R,121,1,3,"B. BURGDORFERI C6 PEP AB, RFLX",HC 6942 12214R B BRGDFRI LYME,80,80,80,80,240,240,240,240
+LAB12214R,121,3,4,"B. BURGDORFERI C6 PEP AB, RFLX",HC 6942 12214R B BRGDFRI LYME,80,80,80,80,320,320,320,320
+LAB12214R,121,7,5,"B. BURGDORFERI C6 PEP AB, RFLX",HC 6942 12214R B BRGDFRI LYME,80,80,80,80,400,400,400,400
+LAB12230R,47,1,2,SOLUBLE IL 2R,HC 7069 12230R SOL IL2R IMM QN,322,322,322,322,644,644,644,644
+LAB12230R,47,3,4,SOLUBLE IL 2R,HC 7069 12230R SOL IL2R IMM QN,322,322,322,322,1288,1288,1288,1288
+LAB12230R,47,7,8,SOLUBLE IL 2R,HC 7069 12230R SOL IL2R IMM QN,322,322,322,322,2576,2576,2576,2576
+LAB12232R,75,1,4,THYROGLOBULIN & THYROID AB,HC 7074 12232R THYRGLBLN AB,37,38,37.5,37.5,148,152,150,150
+LAB12232R,75,1,4,THYROGLOBULIN & THYROID AB,HC 7074 12232R THYROGLOBULIN,37,38,37.5,37.5,148,152,150,150
+LAB12232R,75,3,5,THYROGLOBULIN & THYROID AB,HC 7074 12232R THYRGLBLN AB,37,38,37.5,37.5,185,190,187.5,187.5
+LAB12232R,75,3,5,THYROGLOBULIN & THYROID AB,HC 7074 12232R THYROGLOBULIN,37,38,37.5,37.5,185,190,187.5,187.5
+LAB12232R,75,7,5,THYROGLOBULIN & THYROID AB,HC 7074 12232R THYRGLBLN AB,37,38,37.5,37.5,185,190,187.5,187.5
+LAB12232R,75,7,5,THYROGLOBULIN & THYROID AB,HC 7074 12232R THYROGLOBULIN,37,38,37.5,37.5,185,190,187.5,187.5
+LAB12236R,15,1,8,IBUPROFEN,HC 6959 12236R IBUPROFEN,34,34,34,34,272,272,272,272
+LAB12236R,15,3,8,IBUPROFEN,HC 6959 12236R IBUPROFEN,34,34,34,34,272,272,272,272
+LAB12236R,15,7,9,IBUPROFEN,HC 6959 12236R IBUPROFEN,34,34,34,34,306,306,306,306
+LAB12238R,103,1,2,LACOSAMIDE,HC 6956 12238R LCSMD CHRM SPC,96,96,96,96,192,192,192,192
+LAB12238R,103,3,4,LACOSAMIDE,HC 6956 12238R LCSMD CHRM SPC,96,96,96,96,384,384,384,384
+LAB12238R,103,7,5,LACOSAMIDE,HC 6956 12238R LCSMD CHRM SPC,96,96,96,96,480,480,480,480
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R B CELL TOT CNT,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R FC INTRP 9-15,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R MONO CL AG QN,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R NK CELL TOT CNT,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R T CELL TOT CNT,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R TCL ABS CD4 CD8,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,3,1,B CELL PHENOTYPING PROFILE,HC 6956 12241R FC INTRP 2-8,15,42,28.28571429,34,15,42,28.28571429,34
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R B CELL TOT CNT,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R FC INTRP 9-15,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R MONO CL AG QN,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R NK CELL TOT CNT,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R T CELL TOT CNT,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R TCL ABS CD4 CD8,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12241R,10,7,2,B CELL PHENOTYPING PROFILE,HC 6956 12241R FC INTRP 2-8,15,42,28.28571429,34,30,84,56.57142857,68
+LAB12284R,3,7,2,"LYMPHOCYTE PROLIFERATION, AG",HC 6956 12284R LYMP TRNS BLST,137,137,137,137,274,274,274,274
+LAB12292R,19,1,2,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R TCELL TOT CNT,100,230,164.3333333,163,200,460,328.6666667,326
+LAB12292R,19,1,2,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R TCELL CD4 CNT,100,230,164.3333333,163,200,460,328.6666667,326
+LAB12292R,19,1,2,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R FLOW 1ST MRKR,100,230,164.3333333,163,200,460,328.6666667,326
+LAB12292R,19,3,5,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R TCELL TOT CNT,100,230,164.3333333,163,500,1150,821.6666667,815
+LAB12292R,19,3,5,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R TCELL CD4 CNT,100,230,164.3333333,163,500,1150,821.6666667,815
+LAB12292R,19,3,5,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R FLOW 1ST MRKR,100,230,164.3333333,163,500,1150,821.6666667,815
+LAB12292R,19,7,6,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R TCELL TOT CNT,100,230,164.3333333,163,600,1380,986,978
+LAB12292R,19,7,6,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R TCELL CD4 CNT,100,230,164.3333333,163,600,1380,986,978
+LAB12292R,19,7,6,"T CELL PHENOTYPING, ADVANCED",HC 6956 12292R FLOW 1ST MRKR,100,230,164.3333333,163,600,1380,986,978
+LAB12320R,7,3,1,THIOCYANATE,HC 6959 12320R THIOCYANATE,34,34,34,34,34,34,34,34
+LAB12320R,7,7,1,THIOCYANATE,HC 6959 12320R THIOCYANATE,34,34,34,34,34,34,34,34
+LAB12322R,29,1,1,VON WILLEBRAND MULTIMERS,HC 6957 12323R VON WIL FAC MLT,143,143,143,143,143,143,143,143
+LAB12322R,29,3,1,VON WILLEBRAND MULTIMERS,HC 6957 12323R VON WIL FAC MLT,143,143,143,143,143,143,143,143
+LAB12322R,29,7,2,VON WILLEBRAND MULTIMERS,HC 6957 12323R VON WIL FAC MLT,143,143,143,143,286,286,286,286
+LAB12330R,46,1,8,EVEROLIMUS,HC 6956 12330R EVEROLMS TMS QN,149,149,149,149,1192,1192,1192,1192
+LAB12330R,46,3,25,EVEROLIMUS,HC 6956 12330R EVEROLMS TMS QN,149,149,149,149,3725,3725,3725,3725
+LAB12330R,46,7,31,EVEROLIMUS,HC 6956 12330R EVEROLMS TMS QN,149,149,149,149,4619,4619,4619,4619
+LAB12352R,8,3,1,NMO EVALUATION WITH REFLEX,HC 6956 12352R NMO IMM QNT NOS,161,161,161,161,161,161,161,161
+LAB12352R,8,7,1,NMO EVALUATION WITH REFLEX,HC 6956 12352R NMO IMM QNT NOS,161,161,161,161,161,161,161,161
+LAB12362R,49,3,2,PARANEOPLASTIC AUTOANTIBODY,HC 6956 12362R FL NONIN TIT EA,38,38,38,38,76,76,76,76
+LAB12362R,49,7,3,PARANEOPLASTIC AUTOANTIBODY,HC 6956 12362R FL NONIN TIT EA,38,38,38,38,114,114,114,114
+LAB12371R,6,1,1,COBALT,HC 6956 12371R COBALT TMS QN,55,55,55,55,55,55,55,55
+LAB12371R,6,3,1,COBALT,HC 6956 12371R COBALT TMS QN,55,55,55,55,55,55,55,55
+LAB12371R,6,7,1,COBALT,HC 6956 12371R COBALT TMS QN,55,55,55,55,55,55,55,55
+LAB12397R,8,1,1,ACETYLCHOLINE BLOCKING AB,HC 6959 12397R ACHR IMM QN RIA,42,42,42,42,42,42,42,42
+LAB12397R,8,3,1,ACETYLCHOLINE BLOCKING AB,HC 6959 12397R ACHR IMM QN RIA,42,42,42,42,42,42,42,42
+LAB12397R,8,7,1,ACETYLCHOLINE BLOCKING AB,HC 6959 12397R ACHR IMM QN RIA,42,42,42,42,42,42,42,42
+LAB12399R,5,1,1,ACETYLCHOLINE MODULATING AB,HC 6959 12399R ACHR IMM QN RIA,85,85,85,85,85,85,85,85
+LAB12399R,5,3,1,ACETYLCHOLINE MODULATING AB,HC 6959 12399R ACHR IMM QN RIA,85,85,85,85,85,85,85,85
+LAB12399R,5,7,1,ACETYLCHOLINE MODULATING AB,HC 6959 12399R ACHR IMM QN RIA,85,85,85,85,85,85,85,85
+LAB12418R,23,3,1,SULFONYLUREA HYPOGLYCEMICS PNL,HC 6959 12418R MS NES QL EA SP,115,115,115,115,115,115,115,115
+LAB12418R,23,7,3,SULFONYLUREA HYPOGLYCEMICS PNL,HC 6959 12418R MS NES QL EA SP,115,115,115,115,345,345,345,345
+LAB12470R,2,1,1,"CONGO RED, STAIN",HC 6956 12470R STN I&R GRP2 TS,144,144,144,144,144,144,144,144
+LAB12470R,2,3,1,"CONGO RED, STAIN",HC 6956 12470R STN I&R GRP2 TS,144,144,144,144,144,144,144,144
+LAB12470R,2,7,1,"CONGO RED, STAIN",HC 6956 12470R STN I&R GRP2 TS,144,144,144,144,144,144,144,144
+LAB12571R,176,1,19,"FLT3-ITD, FLT3-D835 MUTATIONS",HC 7081 12571R FLT3 GENE ANAL,150,150,150,150,2850,2850,2850,2850
+LAB12571R,176,1,19,"FLT3-ITD, FLT3-D835 MUTATIONS",HC 7081 12571R FLT3-ITD/D835,150,150,150,150,2850,2850,2850,2850
+LAB12571R,176,3,29,"FLT3-ITD, FLT3-D835 MUTATIONS",HC 7081 12571R FLT3 GENE ANAL,150,150,150,150,4350,4350,4350,4350
+LAB12571R,176,3,29,"FLT3-ITD, FLT3-D835 MUTATIONS",HC 7081 12571R FLT3-ITD/D835,150,150,150,150,4350,4350,4350,4350
+LAB12571R,176,7,50,"FLT3-ITD, FLT3-D835 MUTATIONS",HC 7081 12571R FLT3 GENE ANAL,150,150,150,150,7500,7500,7500,7500
+LAB12571R,176,7,50,"FLT3-ITD, FLT3-D835 MUTATIONS",HC 7081 12571R FLT3-ITD/D835,150,150,150,150,7500,7500,7500,7500
+LAB12573R,230,1,4,ANCA,HC 6956 12573R FLR NONIN ABSCR,31,31,31,31,124,124,124,124
+LAB12573R,230,3,8,ANCA,HC 6956 12573R FLR NONIN ABSCR,31,31,31,31,248,248,248,248
+LAB12573R,230,7,10,ANCA,HC 6956 12573R FLR NONIN ABSCR,31,31,31,31,310,310,310,310
+LAB12605R,9,3,1,"N-methyl-D-Aspartate, Receptor Ab, IgG",HC 6959 12605R FL NONI SRN EA,208,208,208,208,208,208,208,208
+LAB12605R,9,7,1,"N-methyl-D-Aspartate, Receptor Ab, IgG",HC 6959 12605R FL NONI SRN EA,208,208,208,208,208,208,208,208
+LAB12607R,15,3,1,"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF",HC 6959 12607R FL NONI SRN EA,208,208,208,208,208,208,208,208
+LAB12607R,15,7,1,"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF",HC 6959 12607R FL NONI SRN EA,208,208,208,208,208,208,208,208
+LAB12631R,477,1,42,VORICONAZOLE LEVEL,HC 6956 12631R VORI QNT DR NES,58,58,58,58,2436,2436,2436,2436
+LAB12631R,477,3,88,VORICONAZOLE LEVEL,HC 6956 12631R VORI QNT DR NES,58,58,58,58,5104,5104,5104,5104
+LAB12631R,477,7,136,VORICONAZOLE LEVEL,HC 6956 12631R VORI QNT DR NES,58,58,58,58,7888,7888,7888,7888
+LAB12645R,5,1,3,"COMPLEMENT, ALTERNATE PATHWAY",HC 6956 12645R COMP FUNCACT EA,50,50,50,50,150,150,150,150
+LAB12645R,5,3,3,"COMPLEMENT, ALTERNATE PATHWAY",HC 6956 12645R COMP FUNCACT EA,50,50,50,50,150,150,150,150
+LAB12645R,5,7,3,"COMPLEMENT, ALTERNATE PATHWAY",HC 6956 12645R COMP FUNCACT EA,50,50,50,50,150,150,150,150
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R B CELLS TOT CNT,39,125,83,89,39,125,83,89
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R NK CELLS TOTCNT,39,125,83,89,39,125,83,89
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R T CELLS TOT CNT,39,125,83,89,39,125,83,89
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R TCELL ABS CD4&8,39,125,83,89,39,125,83,89
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R FLOW TC 1ST MRK,39,125,83,89,39,125,83,89
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R FLOW TC EA ADDL,39,125,83,89,39,125,83,89
+LAB12657R,8,1,1,T CELL ACTIVATION,HC 7084 12657R FLOW INTRP 2-8,39,125,83,89,39,125,83,89
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R B CELLS TOT CNT,39,125,83,89,117,375,249,267
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R NK CELLS TOTCNT,39,125,83,89,117,375,249,267
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R T CELLS TOT CNT,39,125,83,89,117,375,249,267
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R TCELL ABS CD4&8,39,125,83,89,117,375,249,267
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R FLOW TC 1ST MRK,39,125,83,89,117,375,249,267
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R FLOW TC EA ADDL,39,125,83,89,117,375,249,267
+LAB12657R,8,3,3,T CELL ACTIVATION,HC 7084 12657R FLOW INTRP 2-8,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R B CELLS TOT CNT,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R NK CELLS TOTCNT,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R T CELLS TOT CNT,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R TCELL ABS CD4&8,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R FLOW TC 1ST MRK,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R FLOW TC EA ADDL,39,125,83,89,117,375,249,267
+LAB12657R,8,7,3,T CELL ACTIVATION,HC 7084 12657R FLOW INTRP 2-8,39,125,83,89,117,375,249,267
+LAB12689R,4,7,1,"INFLIXIMAB, SERUM LEVEL AND AB",HC 7026 12689R CHEM IFXATI LVL,2500,2500,2500,2500,2500,2500,2500,2500
+LAB12721R,21,3,1,"ADENOSINE DEAMINASE, PLEURAL",HC 6959 12721R SPCTMNES PL FL,40,40,40,40,40,40,40,40
+LAB12721R,21,7,1,"ADENOSINE DEAMINASE, PLEURAL",HC 6959 12721R SPCTMNES PL FL,40,40,40,40,40,40,40,40
+LAB12762R,156,1,88,TACROLIMUS,HC 6956 12762R TACROLIMUS,23,23,23,23,2024,2024,2024,2024
+LAB12762R,156,3,109,TACROLIMUS,HC 6956 12762R TACROLIMUS,23,23,23,23,2507,2507,2507,2507
+LAB12762R,156,7,112,TACROLIMUS,HC 6956 12762R TACROLIMUS,23,23,23,23,2576,2576,2576,2576
+LAB12774R,12,1,1,MOTOR NEUROPATHY AB SCREEN,HC 6961 12774R MOTOR NEURO AB,218,218,218,218,218,218,218,218
+LAB12774R,12,3,1,MOTOR NEUROPATHY AB SCREEN,HC 6961 12774R MOTOR NEURO AB,218,218,218,218,218,218,218,218
+LAB12774R,12,7,1,MOTOR NEUROPATHY AB SCREEN,HC 6961 12774R MOTOR NEURO AB,218,218,218,218,218,218,218,218
+LAB12796R,19,1,2,FLUCONAZOLE,HC 6959 12796R FLUCONAZOLE,54,54,54,54,108,108,108,108
+LAB12796R,19,3,3,FLUCONAZOLE,HC 6959 12796R FLUCONAZOLE,54,54,54,54,162,162,162,162
+LAB12796R,19,7,5,FLUCONAZOLE,HC 6959 12796R FLUCONAZOLE,54,54,54,54,270,270,270,270
+LAB12808R,115,1,30,CK ISOENZYME WITH REFLEX,HC 6956 12808R CK TOTAL,9,9,9,9,270,270,270,270
+LAB12808R,115,3,44,CK ISOENZYME WITH REFLEX,HC 6956 12808R CK TOTAL,9,9,9,9,396,396,396,396
+LAB12808R,115,7,47,CK ISOENZYME WITH REFLEX,HC 6956 12808R CK TOTAL,9,9,9,9,423,423,423,423
+LAB12809R,40,1,15,CK ISOENZYME ELECTROPHORESIS,HC 6956 12809R CK ISOENZ ELECT,20,20,20,20,300,300,300,300
+LAB12809R,40,3,16,CK ISOENZYME ELECTROPHORESIS,HC 6956 12809R CK ISOENZ ELECT,20,20,20,20,320,320,320,320
+LAB12809R,40,7,17,CK ISOENZYME ELECTROPHORESIS,HC 6956 12809R CK ISOENZ ELECT,20,20,20,20,340,340,340,340
+LAB12810R,32,1,5,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R CHLORIDE,13,13,13,13,65,65,65,65
+LAB12810R,32,1,5,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R MAGNESIUM,13,13,13,13,65,65,65,65
+LAB12810R,32,1,5,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R SODIUM,13,13,13,13,65,65,65,65
+LAB12810R,32,1,5,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R PHOSPHORUS,13,13,13,13,65,65,65,65
+LAB12810R,32,1,5,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R OSMOLALITY POTAS,13,13,13,13,65,65,65,65
+LAB12810R,32,3,9,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R CHLORIDE,13,13,13,13,117,117,117,117
+LAB12810R,32,3,9,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R MAGNESIUM,13,13,13,13,117,117,117,117
+LAB12810R,32,3,9,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R SODIUM,13,13,13,13,117,117,117,117
+LAB12810R,32,3,9,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R PHOSPHORUS,13,13,13,13,117,117,117,117
+LAB12810R,32,3,9,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R OSMOLALITY POTAS,13,13,13,13,117,117,117,117
+LAB12810R,32,7,12,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R CHLORIDE,13,13,13,13,156,156,156,156
+LAB12810R,32,7,12,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R MAGNESIUM,13,13,13,13,156,156,156,156
+LAB12810R,32,7,12,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R SODIUM,13,13,13,13,156,156,156,156
+LAB12810R,32,7,12,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R PHOSPHORUS,13,13,13,13,156,156,156,156
+LAB12810R,32,7,12,ELECTROLYTE AND OSMOLALITY PNL,HC 6956 12810R OSMOLALITY POTAS,13,13,13,13,156,156,156,156
+LAB12811R,21,1,1,"MYOGLOBIN, URINE",HC 6956 12811R MYOGLOBIN URINE,19,19,19,19,19,19,19,19
+LAB12811R,21,3,4,"MYOGLOBIN, URINE",HC 6956 12811R MYOGLOBIN URINE,19,19,19,19,76,76,76,76
+LAB12811R,21,7,4,"MYOGLOBIN, URINE",HC 6956 12811R MYOGLOBIN URINE,19,19,19,19,76,76,76,76
+LAB12814R,30,1,1,MYCOPLASMA ANTIBODIES IGG IGM,HC 6942 12814R MYCO PNM IGG IGM,52,52,52,52,52,52,52,52
+LAB12814R,30,3,1,MYCOPLASMA ANTIBODIES IGG IGM,HC 6942 12814R MYCO PNM IGG IGM,52,52,52,52,52,52,52,52
+LAB12814R,30,7,2,MYCOPLASMA ANTIBODIES IGG IGM,HC 6942 12814R MYCO PNM IGG IGM,52,52,52,52,104,104,104,104
+LAB12815R,22,3,1,LYME DISEASE AB IGG IGM W/RFLX,HC 6942 12815R LYME AB IGG IGM,55,55,55,55,55,55,55,55
+LAB12815R,22,7,2,LYME DISEASE AB IGG IGM W/RFLX,HC 6942 12815R LYME AB IGG IGM,55,55,55,55,110,110,110,110
+LAB12838R,12,1,1,INSULIN-LIKE GROWTH FACTOR 1,HC 6956 12838R IGF1 SOMATOMED,28,28,28,28,28,28,28,28
+LAB12838R,12,3,1,INSULIN-LIKE GROWTH FACTOR 1,HC 6956 12838R IGF1 SOMATOMED,28,28,28,28,28,28,28,28
+LAB12838R,12,7,1,INSULIN-LIKE GROWTH FACTOR 1,HC 6956 12838R IGF1 SOMATOMED,28,28,28,28,28,28,28,28
+LAB12853R,14,3,10,"EVEROLIMUS, BLOOD",HC 6956 12853R EVEROLIMUS BLOOD,170,170,170,170,1700,1700,1700,1700
+LAB12853R,14,7,10,"EVEROLIMUS, BLOOD",HC 6956 12853R EVEROLIMUS BLOOD,170,170,170,170,1700,1700,1700,1700
+LAB13006R,10,3,1,"REDUCING SUBSTANCE, FECES",HC 6956 REDUCE SUBSTANCE FECES,45,45,45,45,45,45,45,45
+LAB13006R,10,7,1,"REDUCING SUBSTANCE, FECES",HC 6956 REDUCE SUBSTANCE FECES,45,45,45,45,45,45,45,45
+LABA1C,22550,1,1522,Hemoglobin A1c,HC GLYCOLATED HEMOGLOBIN,145,145,145,145,220690,220690,220690,220690
+LABA1C,22550,3,2396,Hemoglobin A1c,HC GLYCOLATED HEMOGLOBIN,145,145,145,145,347420,347420,347420,347420
+LABA1C,22550,7,2937,Hemoglobin A1c,HC GLYCOLATED HEMOGLOBIN,145,145,145,145,425865,425865,425865,425865
+LABAAP,24,1,1,"AMINO ACIDS, PLASMA",HC AMINO ACIDS PLASMA QUANT,307,307,307,307,307,307,307,307
+LABAAP,24,3,2,"AMINO ACIDS, PLASMA",HC AMINO ACIDS PLASMA QUANT,307,307,307,307,614,614,614,614
+LABAAP,24,7,2,"AMINO ACIDS, PLASMA",HC AMINO ACIDS PLASMA QUANT,307,307,307,307,614,614,614,614
+LABABG,62465,1,38313,"BLOOD GASES, ARTERIAL",HC BLOOD GASES,1161,1161,1161,1161,44481393,44481393,44481393,44481393
+LABABG,62465,3,39611,"BLOOD GASES, ARTERIAL",HC BLOOD GASES,1161,1161,1161,1161,45988371,45988371,45988371,45988371
+LABABG,62465,7,40728,"BLOOD GASES, ARTERIAL",HC BLOOD GASES,1161,1161,1161,1161,47285208,47285208,47285208,47285208
+LABABTITI,100,1,39,ANTIBODY TITER,HC COOMBS AB TITER,259,259,259,259,10101,10101,10101,10101
+LABABTITI,100,3,76,ANTIBODY TITER,HC COOMBS AB TITER,259,259,259,259,19684,19684,19684,19684
+LABABTITI,100,7,76,ANTIBODY TITER,HC COOMBS AB TITER,259,259,259,259,19684,19684,19684,19684
+LABACA,636,1,23,"ANTI-CARDIOLIPIN AB, IGG&IGM",HC ANTICARDIOLIPIN IGG AB,243,243,243,243,5589,5589,5589,5589
+LABACA,636,1,23,"ANTI-CARDIOLIPIN AB, IGG&IGM",HC ANTICARDIOLIPIN IGM AB,243,243,243,243,5589,5589,5589,5589
+LABACA,636,3,29,"ANTI-CARDIOLIPIN AB, IGG&IGM",HC ANTICARDIOLIPIN IGG AB,243,243,243,243,7047,7047,7047,7047
+LABACA,636,3,29,"ANTI-CARDIOLIPIN AB, IGG&IGM",HC ANTICARDIOLIPIN IGM AB,243,243,243,243,7047,7047,7047,7047
+LABACA,636,7,42,"ANTI-CARDIOLIPIN AB, IGG&IGM",HC ANTICARDIOLIPIN IGG AB,243,243,243,243,10206,10206,10206,10206
+LABACA,636,7,42,"ANTI-CARDIOLIPIN AB, IGG&IGM",HC ANTICARDIOLIPIN IGM AB,243,243,243,243,10206,10206,10206,10206
+LABACETA,3287,1,404,Acetaminophen,HC ACETAMINOPHEN LEVEL,432,432,432,432,174528,174528,174528,174528
+LABACETA,3287,3,432,Acetaminophen,HC ACETAMINOPHEN LEVEL,432,432,432,432,186624,186624,186624,186624
+LABACETA,3287,7,449,Acetaminophen,HC ACETAMINOPHEN LEVEL,432,432,432,432,193968,193968,193968,193968
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST: ALDER TREE IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:DUST MITE-DF IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:OAK TREE IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:OLIVE TREE IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:RYE GRASS IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:SAGE WD MUGWORT IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:TIMOTHY GRASS IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:WEST RAGWEED IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC TOTAL IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:ALTERNARI MLD IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:ASPERGILL MLD IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:BERMUDA GRASS IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:CAT DANDER IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:CLADOSPOR MLD IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:COCKROACH IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:DOG DANDER IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACHOO,6,7,1,AIRBORNE ALLERGY SCREEN,HC ALRGY TST:DUCK WEED IGE,79,379,96.64705882,79,79,379,96.64705882,79
+LABACTHO,647,1,304,ACTH,HC ACTH,551,551,551,551,167504,167504,167504,167504
+LABACTHO,647,3,355,ACTH,HC ACTH,551,551,551,551,195605,195605,195605,195605
+LABACTHO,647,7,370,ACTH,HC ACTH,551,551,551,551,203870,203870,203870,203870
+LABADAM13,26,1,2,ADAMTS-13 ACTIVITY AND ANTIGEN TESTING,HC ADAMTS-13 ACT AG TEST,427,427,427,427,854,854,854,854
+LABADAM13,26,3,3,ADAMTS-13 ACTIVITY AND ANTIGEN TESTING,HC ADAMTS-13 ACT AG TEST,427,427,427,427,1281,1281,1281,1281
+LABADAM13,26,7,3,ADAMTS-13 ACTIVITY AND ANTIGEN TESTING,HC ADAMTS-13 ACT AG TEST,427,427,427,427,1281,1281,1281,1281
+LABADAMTS,72,1,5,ADAMTS-13 PROFILE,HC ADAMTS FACTOR INHIBITOR,251,427,339,339,1255,2135,1695,1695
+LABADAMTS,72,1,5,ADAMTS-13 PROFILE,HC ADAMTS 13 ACT EA ANALYTE,251,427,339,339,1255,2135,1695,1695
+LABADAMTS,72,3,6,ADAMTS-13 PROFILE,HC ADAMTS FACTOR INHIBITOR,251,427,339,339,1506,2562,2034,2034
+LABADAMTS,72,3,6,ADAMTS-13 PROFILE,HC ADAMTS 13 ACT EA ANALYTE,251,427,339,339,1506,2562,2034,2034
+LABADAMTS,72,7,6,ADAMTS-13 PROFILE,HC ADAMTS FACTOR INHIBITOR,251,427,339,339,1506,2562,2034,2034
+LABADAMTS,72,7,6,ADAMTS-13 PROFILE,HC ADAMTS 13 ACT EA ANALYTE,251,427,339,339,1506,2562,2034,2034
+LABADGM,61,1,5,ADENOVIRUS AB IGG & IGM,HC 6942 ADENOVIRUS AB IGG/M,14,14,14,14,70,70,70,70
+LABADGM,61,3,7,ADENOVIRUS AB IGG & IGM,HC 6942 ADENOVIRUS AB IGG/M,14,14,14,14,98,98,98,98
+LABADGM,61,7,9,ADENOVIRUS AB IGG & IGM,HC 6942 ADENOVIRUS AB IGG/M,14,14,14,14,126,126,126,126
+LABADPCRQ,169,1,18,"ADENOVIRUS,QUANT REAL TIME PCR",HC 6942 ADENOVIRUS DNA QNT PCR,152,152,152,152,2736,2736,2736,2736
+LABADPCRQ,169,3,22,"ADENOVIRUS,QUANT REAL TIME PCR",HC 6942 ADENOVIRUS DNA QNT PCR,152,152,152,152,3344,3344,3344,3344
+LABADPCRQ,169,7,34,"ADENOVIRUS,QUANT REAL TIME PCR",HC 6942 ADENOVIRUS DNA QNT PCR,152,152,152,152,5168,5168,5168,5168
+LABADVDNA,85,1,7,"ADENOVIRUS DNA, QL PCR",HC 6942 ADENOVIRUS DNA QUAL PCR,142,142,142,142,994,994,994,994
+LABADVDNA,85,3,11,"ADENOVIRUS DNA, QL PCR",HC 6942 ADENOVIRUS DNA QUAL PCR,142,142,142,142,1562,1562,1562,1562
+LABADVDNA,85,7,11,"ADENOVIRUS DNA, QL PCR",HC 6942 ADENOVIRUS DNA QUAL PCR,142,142,142,142,1562,1562,1562,1562
+LABADVQL,46,1,4,ADENOVIRUS DNA PCR QUAL,HC ADVQL ADENOVRS DNA PCR QL,388,388,388,388,1552,1552,1552,1552
+LABADVQL,46,3,5,ADENOVIRUS DNA PCR QUAL,HC ADVQL ADENOVRS DNA PCR QL,388,388,388,388,1940,1940,1940,1940
+LABADVQL,46,7,7,ADENOVIRUS DNA PCR QUAL,HC ADVQL ADENOVRS DNA PCR QL,388,388,388,388,2716,2716,2716,2716
+LABADVQT,268,1,17,ADENOVIRUS DNA PCR QUANT,HC ADVQT ADENOVRS DNA PCR QNT,650,650,650,650,11050,11050,11050,11050
+LABADVQT,268,3,23,ADENOVIRUS DNA PCR QUANT,HC ADVQT ADENOVRS DNA PCR QNT,650,650,650,650,14950,14950,14950,14950
+LABADVQT,268,7,51,ADENOVIRUS DNA PCR QUANT,HC ADVQT ADENOVRS DNA PCR QNT,650,650,650,650,33150,33150,33150,33150
+LABAFBC,5539,1,1845,AFB Culture/Conc.,HC AFB CULT DIR TBRCL,153,337,217.3333333,162,282285,621765,400980,298890
+LABAFBC,5539,1,1845,AFB Culture/Conc.,HC AFB SMEAR,153,337,217.3333333,162,282285,621765,400980,298890
+LABAFBC,5539,1,1845,AFB Culture/Conc.,HC AFB CONCENTRATION,153,337,217.3333333,162,282285,621765,400980,298890
+LABAFBC,5539,3,2277,AFB Culture/Conc.,HC AFB CULT DIR TBRCL,153,337,217.3333333,162,348381,767349,494868,368874
+LABAFBC,5539,3,2277,AFB Culture/Conc.,HC AFB SMEAR,153,337,217.3333333,162,348381,767349,494868,368874
+LABAFBC,5539,3,2277,AFB Culture/Conc.,HC AFB CONCENTRATION,153,337,217.3333333,162,348381,767349,494868,368874
+LABAFBC,5539,7,2590,AFB Culture/Conc.,HC AFB CULT DIR TBRCL,153,337,217.3333333,162,396270,872830,562893.3333,419580
+LABAFBC,5539,7,2590,AFB Culture/Conc.,HC AFB SMEAR,153,337,217.3333333,162,396270,872830,562893.3333,419580
+LABAFBC,5539,7,2590,AFB Culture/Conc.,HC AFB CONCENTRATION,153,337,217.3333333,162,396270,872830,562893.3333,419580
+LABAFBCF,559,1,40,AFB Culture/Conc/CF,HC AFB CLT/CONC TBRCL,153,337,217.3333333,162,6120,13480,8693.333333,6480
+LABAFBCF,559,1,40,AFB Culture/Conc/CF,HC ADDNL CONC. MTHDS,153,337,217.3333333,162,6120,13480,8693.333333,6480
+LABAFBCF,559,1,40,AFB Culture/Conc/CF,HC SMEAR AFB,153,337,217.3333333,162,6120,13480,8693.333333,6480
+LABAFBCF,559,3,46,AFB Culture/Conc/CF,HC AFB CLT/CONC TBRCL,153,337,217.3333333,162,7038,15502,9997.333333,7452
+LABAFBCF,559,3,46,AFB Culture/Conc/CF,HC ADDNL CONC. MTHDS,153,337,217.3333333,162,7038,15502,9997.333333,7452
+LABAFBCF,559,3,46,AFB Culture/Conc/CF,HC SMEAR AFB,153,337,217.3333333,162,7038,15502,9997.333333,7452
+LABAFBCF,559,7,55,AFB Culture/Conc/CF,HC AFB CLT/CONC TBRCL,153,337,217.3333333,162,8415,18535,11953.33333,8910
+LABAFBCF,559,7,55,AFB Culture/Conc/CF,HC ADDNL CONC. MTHDS,153,337,217.3333333,162,8415,18535,11953.33333,8910
+LABAFBCF,559,7,55,AFB Culture/Conc/CF,HC SMEAR AFB,153,337,217.3333333,162,8415,18535,11953.33333,8910
+LABAFBD,4858,1,992,AFB Culture/Direct,HC AFB CULT DIR TBRCL,162,337,249.5,249.5,160704,334304,247504,247504
+LABAFBD,4858,1,992,AFB Culture/Direct,HC AFBD SMEAR FLUR ACID FAST,162,337,249.5,249.5,160704,334304,247504,247504
+LABAFBD,4858,3,1115,AFB Culture/Direct,HC AFB CULT DIR TBRCL,162,337,249.5,249.5,180630,375755,278192.5,278192.5
+LABAFBD,4858,3,1115,AFB Culture/Direct,HC AFBD SMEAR FLUR ACID FAST,162,337,249.5,249.5,180630,375755,278192.5,278192.5
+LABAFBD,4858,7,1277,AFB Culture/Direct,HC AFB CULT DIR TBRCL,162,337,249.5,249.5,206874,430349,318611.5,318611.5
+LABAFBD,4858,7,1277,AFB Culture/Direct,HC AFBD SMEAR FLUR ACID FAST,162,337,249.5,249.5,206874,430349,318611.5,318611.5
+LABAFP,1226,1,61,Alpha Feto-Protein,HC ALPHA FETOPROTEIN,280,280,280,280,17080,17080,17080,17080
+LABAFP,1226,3,85,Alpha Feto-Protein,HC ALPHA FETOPROTEIN,280,280,280,280,23800,23800,23800,23800
+LABAFP,1226,7,98,Alpha Feto-Protein,HC ALPHA FETOPROTEIN,280,280,280,280,27440,27440,27440,27440
+LABAGALA,864,1,69,Aspergillus Galactomanan,HC ASPERGILLUS BY GALACTOMANNAN,105,105,105,105,7245,7245,7245,7245
+LABAGALA,864,3,72,Aspergillus Galactomanan,HC ASPERGILLUS BY GALACTOMANNAN,105,105,105,105,7560,7560,7560,7560
+LABAGALA,864,7,99,Aspergillus Galactomanan,HC ASPERGILLUS BY GALACTOMANNAN,105,105,105,105,10395,10395,10395,10395
+LABAHP2,1161,1,37,ACUTE HEPATITIS PANEL,HC HEPAT BS AG-EIA,193,268,232.25,234,7141,9916,8593.25,8658
+LABAHP2,1161,1,37,ACUTE HEPATITIS PANEL,HC HBV CORE IGM (HBC),193,268,232.25,234,7141,9916,8593.25,8658
+LABAHP2,1161,1,37,ACUTE HEPATITIS PANEL,HC HAV IGM (HAV),193,268,232.25,234,7141,9916,8593.25,8658
+LABAHP2,1161,1,37,ACUTE HEPATITIS PANEL,HC HCVA HEPATITIS C AB,193,268,232.25,234,7141,9916,8593.25,8658
+LABAHP2,1161,3,51,ACUTE HEPATITIS PANEL,HC HEPAT BS AG-EIA,193,268,232.25,234,9843,13668,11844.75,11934
+LABAHP2,1161,3,51,ACUTE HEPATITIS PANEL,HC HBV CORE IGM (HBC),193,268,232.25,234,9843,13668,11844.75,11934
+LABAHP2,1161,3,51,ACUTE HEPATITIS PANEL,HC HAV IGM (HAV),193,268,232.25,234,9843,13668,11844.75,11934
+LABAHP2,1161,3,51,ACUTE HEPATITIS PANEL,HC HCVA HEPATITIS C AB,193,268,232.25,234,9843,13668,11844.75,11934
+LABAHP2,1161,7,54,ACUTE HEPATITIS PANEL,HC HEPAT BS AG-EIA,193,268,232.25,234,10422,14472,12541.5,12636
+LABAHP2,1161,7,54,ACUTE HEPATITIS PANEL,HC HBV CORE IGM (HBC),193,268,232.25,234,10422,14472,12541.5,12636
+LABAHP2,1161,7,54,ACUTE HEPATITIS PANEL,HC HAV IGM (HAV),193,268,232.25,234,10422,14472,12541.5,12636
+LABAHP2,1161,7,54,ACUTE HEPATITIS PANEL,HC HCVA HEPATITIS C AB,193,268,232.25,234,10422,14472,12541.5,12636
+LABALB,5411,1,1707,"Albumin, Ser/Plas",HC ALBUMIN,138,138,138,138,235566,235566,235566,235566
+LABALB,5411,3,2431,"Albumin, Ser/Plas",HC ALBUMIN,138,138,138,138,335478,335478,335478,335478
+LABALB,5411,7,2554,"Albumin, Ser/Plas",HC ALBUMIN,138,138,138,138,352452,352452,352452,352452
+LABALKP,679,1,13,"Alk P'tase,Total,Ser/Plas",HC ALK PHOS TOTAL,155,155,155,155,2015,2015,2015,2015
+LABALKP,679,3,29,"Alk P'tase,Total,Ser/Plas",HC ALK PHOS TOTAL,155,155,155,155,4495,4495,4495,4495
+LABALKP,679,7,38,"Alk P'tase,Total,Ser/Plas",HC ALK PHOS TOTAL,155,155,155,155,5890,5890,5890,5890
+LABALT,777,1,24,"ALT (SGPT), Ser/Plas",HC SGPT/ALT,190,190,190,190,4560,4560,4560,4560
+LABALT,777,3,56,"ALT (SGPT), Ser/Plas",HC SGPT/ALT,190,190,190,190,10640,10640,10640,10640
+LABALT,777,7,75,"ALT (SGPT), Ser/Plas",HC SGPT/ALT,190,190,190,190,14250,14250,14250,14250
+LABAMIPOL,46,1,6,AMIKACIN PEAK,HC AMIKACIN,191,191,191,191,1146,1146,1146,1146
+LABAMIPOL,46,3,19,AMIKACIN PEAK,HC AMIKACIN,191,191,191,191,3629,3629,3629,3629
+LABAMIPOL,46,7,23,AMIKACIN PEAK,HC AMIKACIN,191,191,191,191,4393,4393,4393,4393
+LABAMIPRL,111,1,27,"AMIKACIN, TROUGH",HC AMIKACIN,191,191,191,191,5157,5157,5157,5157
+LABAMIPRL,111,3,55,"AMIKACIN, TROUGH",HC AMIKACIN,191,191,191,191,10505,10505,10505,10505
+LABAMIPRL,111,7,71,"AMIKACIN, TROUGH",HC AMIKACIN,191,191,191,191,13561,13561,13561,13561
+LABAMLP,193,1,6,AML Prognosis Assay,HC AMLP GENE ANLYS DUP VAR,662,1120,891,891,3972,6720,5346,5346
+LABAMLP,193,1,6,AML Prognosis Assay,HC AMLP GENE ANLYS 12 EXON,662,1120,891,891,3972,6720,5346,5346
+LABAMLP,193,3,8,AML Prognosis Assay,HC AMLP GENE ANLYS DUP VAR,662,1120,891,891,5296,8960,7128,7128
+LABAMLP,193,3,8,AML Prognosis Assay,HC AMLP GENE ANLYS 12 EXON,662,1120,891,891,5296,8960,7128,7128
+LABAMLP,193,7,13,AML Prognosis Assay,HC AMLP GENE ANLYS DUP VAR,662,1120,891,891,8606,14560,11583,11583
+LABAMLP,193,7,13,AML Prognosis Assay,HC AMLP GENE ANLYS 12 EXON,662,1120,891,891,8606,14560,11583,11583
+LABAMY,6241,1,1285,"Amylase, Serum",HC AMYLASE TEST,395,395,395,395,507575,507575,507575,507575
+LABAMY,6241,3,2285,"Amylase, Serum",HC AMYLASE TEST,395,395,395,395,902575,902575,902575,902575
+LABAMY,6241,7,2453,"Amylase, Serum",HC AMYLASE TEST,395,395,395,395,968935,968935,968935,968935
+LABANAS,2518,1,101,ANTI-NUCLEAR ANTIBODY,HC ANTI-NUC AB (FANA),261,261,261,261,26361,26361,26361,26361
+LABANAS,2518,3,168,ANTI-NUCLEAR ANTIBODY,HC ANTI-NUC AB (FANA),261,261,261,261,43848,43848,43848,43848
+LABANAS,2518,7,212,ANTI-NUCLEAR ANTIBODY,HC ANTI-NUC AB (FANA),261,261,261,261,55332,55332,55332,55332
+LABANCA,308,1,11,NEUTROPHIL CYTO AB REFLEX PANEL,HC 6959 ANCA FLR NONINF EA AB,32,32,32,32,352,352,352,352
+LABANCA,308,3,26,NEUTROPHIL CYTO AB REFLEX PANEL,HC 6959 ANCA FLR NONINF EA AB,32,32,32,32,832,832,832,832
+LABANCA,308,7,30,NEUTROPHIL CYTO AB REFLEX PANEL,HC 6959 ANCA FLR NONINF EA AB,32,32,32,32,960,960,960,960
+LABANER,12118,1,3125,Anaerobic Culture,HC ANAEROBIC CULTR,544,544,544,544,1700000,1700000,1700000,1700000
+LABANER,12118,3,3463,Anaerobic Culture,HC ANAEROBIC CULTR,544,544,544,544,1883872,1883872,1883872,1883872
+LABANER,12118,7,3915,Anaerobic Culture,HC ANAEROBIC CULTR,544,544,544,544,2129760,2129760,2129760,2129760
+LABANER2,3217,1,1621,Anaerobic-Short Batt,HC ANAEROBIC LIMITED CULTURE,165,165,165,165,267465,267465,267465,267465
+LABANER2,3217,3,1730,Anaerobic-Short Batt,HC ANAEROBIC LIMITED CULTURE,165,165,165,165,285450,285450,285450,285450
+LABANER2,3217,7,1762,Anaerobic-Short Batt,HC ANAEROBIC LIMITED CULTURE,165,165,165,165,290730,290730,290730,290730
+LABANERB,94,1,4,Anaerobic Culture/BB,HC ANAEROBIC CULTR,544,544,544,544,2176,2176,2176,2176
+LABANERB,94,3,6,Anaerobic Culture/BB,HC ANAEROBIC CULTR,544,544,544,544,3264,3264,3264,3264
+LABANERB,94,7,6,Anaerobic Culture/BB,HC ANAEROBIC CULTR,544,544,544,544,3264,3264,3264,3264
+LABANTIM,335,1,14,Anti-TPO Antibody,HC ANTI-THYROID MICROSOMAL AB,270,270,270,270,3780,3780,3780,3780
+LABANTIM,335,3,16,Anti-TPO Antibody,HC ANTI-THYROID MICROSOMAL AB,270,270,270,270,4320,4320,4320,4320
+LABANTIM,335,7,18,Anti-TPO Antibody,HC ANTI-THYROID MICROSOMAL AB,270,270,270,270,4860,4860,4860,4860
+LABANTITG,178,1,2,Anti-Thyroglobulin AB,HC ANTI-THYROGLBL ATBD,227,227,227,227,454,454,454,454
+LABANTITG,178,3,3,Anti-Thyroglobulin AB,HC ANTI-THYROGLBL ATBD,227,227,227,227,681,681,681,681
+LABANTITG,178,7,4,Anti-Thyroglobulin AB,HC ANTI-THYROGLBL ATBD,227,227,227,227,908,908,908,908
+LABANTIXA,903,1,208,LMWH Activity by AntiXA,HC ANTIXA LMW HEPARIN ACT,466,466,466,466,96928,96928,96928,96928
+LABANTIXA,903,3,340,LMWH Activity by AntiXA,HC ANTIXA LMW HEPARIN ACT,466,466,466,466,158440,158440,158440,158440
+LABANTIXA,903,7,381,LMWH Activity by AntiXA,HC ANTIXA LMW HEPARIN ACT,466,466,466,466,177546,177546,177546,177546
+LABANTNEU,71,1,2,ANTI-NEUTROPHIL AB,HC 6959 ANTI-NEUTROPHIL AB,92,92,92,92,184,184,184,184
+LABANTNEU,71,3,3,ANTI-NEUTROPHIL AB,HC 6959 ANTI-NEUTROPHIL AB,92,92,92,92,276,276,276,276
+LABANTNEU,71,7,3,ANTI-NEUTROPHIL AB,HC 6959 ANTI-NEUTROPHIL AB,92,92,92,92,276,276,276,276
+LABAPC,87,1,1,Activated Protein C Resistance,HC ACT PROTEIN C RESIST,285,285,285,285,285,285,285,285
+LABAPC,87,3,3,Activated Protein C Resistance,HC ACT PROTEIN C RESIST,285,285,285,285,855,855,855,855
+LABAPC,87,7,3,Activated Protein C Resistance,HC ACT PROTEIN C RESIST,285,285,285,285,855,855,855,855
+LABAPHSA2,250,1,10,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGG AB,240,243,242.25,243,2400,2430,2422.5,2430
+LABAPHSA2,250,1,10,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGM AB,240,243,242.25,243,2400,2430,2422.5,2430
+LABAPHSA2,250,1,10,ANTI-PHOSPHOLIPID AB PANEL,HC DIL RUSSELL VPR VNM,240,243,242.25,243,2400,2430,2422.5,2430
+LABAPHSA2,250,1,10,ANTI-PHOSPHOLIPID AB PANEL,HC B2GP1 BETA2 GLYPRTN I AB EA,240,243,242.25,243,2400,2430,2422.5,2430
+LABAPHSA2,250,3,11,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGG AB,240,243,242.25,243,2640,2673,2664.75,2673
+LABAPHSA2,250,3,11,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGM AB,240,243,242.25,243,2640,2673,2664.75,2673
+LABAPHSA2,250,3,11,ANTI-PHOSPHOLIPID AB PANEL,HC DIL RUSSELL VPR VNM,240,243,242.25,243,2640,2673,2664.75,2673
+LABAPHSA2,250,3,11,ANTI-PHOSPHOLIPID AB PANEL,HC B2GP1 BETA2 GLYPRTN I AB EA,240,243,242.25,243,2640,2673,2664.75,2673
+LABAPHSA2,250,7,16,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGG AB,240,243,242.25,243,3840,3888,3876,3888
+LABAPHSA2,250,7,16,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGM AB,240,243,242.25,243,3840,3888,3876,3888
+LABAPHSA2,250,7,16,ANTI-PHOSPHOLIPID AB PANEL,HC DIL RUSSELL VPR VNM,240,243,242.25,243,3840,3888,3876,3888
+LABAPHSA2,250,7,16,ANTI-PHOSPHOLIPID AB PANEL,HC B2GP1 BETA2 GLYPRTN I AB EA,240,243,242.25,243,3840,3888,3876,3888
+LABAPHSAB,210,1,9,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGG AB,243,243,243,243,2187,2187,2187,2187
+LABAPHSAB,210,1,9,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGM AB,243,243,243,243,2187,2187,2187,2187
+LABAPHSAB,210,3,10,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGG AB,243,243,243,243,2430,2430,2430,2430
+LABAPHSAB,210,3,10,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGM AB,243,243,243,243,2430,2430,2430,2430
+LABAPHSAB,210,7,14,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGG AB,243,243,243,243,3402,3402,3402,3402
+LABAPHSAB,210,7,14,ANTI-PHOSPHOLIPID AB PANEL,HC ANTICARDIOLIPIN IGM AB,243,243,243,243,3402,3402,3402,3402
+LABASI,3496,1,8,Antibody Screen,HC ANTIB SCRN-3 CELL,325,325,325,325,2600,2600,2600,2600
+LABASI,3496,3,58,Antibody Screen,HC ANTIB SCRN-3 CELL,325,325,325,325,18850,18850,18850,18850
+LABASI,3496,7,443,Antibody Screen,HC ANTIB SCRN-3 CELL,325,325,325,325,143975,143975,143975,143975
+LABASPID,46,1,7,"ASPERGILLUS AB, ID, SERUM",HC 6942 ASPID ASPGLUS IMMDIF,19,19,19,19,133,133,133,133
+LABASPID,46,3,11,"ASPERGILLUS AB, ID, SERUM",HC 6942 ASPID ASPGLUS IMMDIF,19,19,19,19,209,209,209,209
+LABASPID,46,7,11,"ASPERGILLUS AB, ID, SERUM",HC 6942 ASPID ASPGLUS IMMDIF,19,19,19,19,209,209,209,209
+LABASQ,627,1,134,Ketones Semi Quant,HC ACETONE QUANT,243,243,243,243,32562,32562,32562,32562
+LABASQ,627,3,153,Ketones Semi Quant,HC ACETONE QUANT,243,243,243,243,37179,37179,37179,37179
+LABASQ,627,7,160,Ketones Semi Quant,HC ACETONE QUANT,243,243,243,243,38880,38880,38880,38880
+LABAST,790,1,28,"AST (SGOT), Ser/Plas",HC SGOT/AST,180,180,180,180,5040,5040,5040,5040
+LABAST,790,3,54,"AST (SGOT), Ser/Plas",HC SGOT/AST,180,180,180,180,9720,9720,9720,9720
+LABAST,790,7,70,"AST (SGOT), Ser/Plas",HC SGOT/AST,180,180,180,180,12600,12600,12600,12600
+LABASTRO,85,1,6,Anti Streptolysin O,HC ASO TITER,186,186,186,186,1116,1116,1116,1116
+LABASTRO,85,3,6,Anti Streptolysin O,HC ASO TITER,186,186,186,186,1116,1116,1116,1116
+LABASTRO,85,7,6,Anti Streptolysin O,HC ASO TITER,186,186,186,186,1116,1116,1116,1116
+LABAT3,521,1,35,Antithrombin III,HC ANTITHROBIN III,581,581,581,581,20335,20335,20335,20335
+LABAT3,521,3,58,Antithrombin III,HC ANTITHROBIN III,581,581,581,581,33698,33698,33698,33698
+LABAT3,521,7,63,Antithrombin III,HC ANTITHROBIN III,581,581,581,581,36603,36603,36603,36603
+LABATTG,140,1,3,IGA ANTI-TTG,HC IGA ANTI-TTG,273,273,273,273,819,819,819,819
+LABATTG,140,3,3,IGA ANTI-TTG,HC IGA ANTI-TTG,273,273,273,273,819,819,819,819
+LABATTG,140,7,4,IGA ANTI-TTG,HC IGA ANTI-TTG,273,273,273,273,1092,1092,1092,1092
+LABAVRT,107,3,1,HIV-1 Genotyping,HC VIR. GENE RT/PRO SEQ.,2847,2847,2847,2847,2847,2847,2847,2847
+LABAVRT,107,7,1,HIV-1 Genotyping,HC VIR. GENE RT/PRO SEQ.,2847,2847,2847,2847,2847,2847,2847,2847
+LABB12,6917,1,369,Vitamin B12,HC B12-SERUM,281,281,281,281,103689,103689,103689,103689
+LABB12,6917,3,487,Vitamin B12,HC B12-SERUM,281,281,281,281,136847,136847,136847,136847
+LABB12,6917,7,542,Vitamin B12,HC B12-SERUM,281,281,281,281,152302,152302,152302,152302
+LABB2GP1,253,1,9,BETA-2 GLYCOPROTEIN 1,HC B2GP1 BETA2 GLYPRTN I AB EA,243,243,243,243,2187,2187,2187,2187
+LABB2GP1,253,3,12,BETA-2 GLYCOPROTEIN 1,HC B2GP1 BETA2 GLYPRTN I AB EA,243,243,243,243,2916,2916,2916,2916
+LABB2GP1,253,7,17,BETA-2 GLYCOPROTEIN 1,HC B2GP1 BETA2 GLYPRTN I AB EA,243,243,243,243,4131,4131,4131,4131
+LABB2GPCO,52,7,1,BETA-2 GLYCOPROTEIN,HC 6958 BETA-2-GLYCOPROTEIN,40,40,40,40,40,40,40,40
+LABB2M,201,1,8,"Beta-2-Microglobulin,Ser",HC BETA 2 MICROGLOBULN,178,178,178,178,1424,1424,1424,1424
+LABB2M,201,3,10,"Beta-2-Microglobulin,Ser",HC BETA 2 MICROGLOBULN,178,178,178,178,1780,1780,1780,1780
+LABB2M,201,7,11,"Beta-2-Microglobulin,Ser",HC BETA 2 MICROGLOBULN,178,178,178,178,1958,1958,1958,1958
+LABBACIDS,139,1,11,"Bacteria ID by sequencing, specimen",HC BACTERIA ID BY SEQ SPEC,1653,1653,1653,1653,18183,18183,18183,18183
+LABBACIDS,139,3,17,"Bacteria ID by sequencing, specimen",HC BACTERIA ID BY SEQ SPEC,1653,1653,1653,1653,28101,28101,28101,28101
+LABBACIDS,139,7,21,"Bacteria ID by sequencing, specimen",HC BACTERIA ID BY SEQ SPEC,1653,1653,1653,1653,34713,34713,34713,34713
+LABBARSAB,121,1,4,BARTONELLA SPECIES AB W/RFLX,HC 6942 BARSAB BARTNLA IGG IGM,28,28,28,28,112,112,112,112
+LABBARSAB,121,3,7,BARTONELLA SPECIES AB W/RFLX,HC 6942 BARSAB BARTNLA IGG IGM,28,28,28,28,196,196,196,196
+LABBARSAB,121,7,7,BARTONELLA SPECIES AB W/RFLX,HC 6942 BARSAB BARTNLA IGG IGM,28,28,28,28,196,196,196,196
+LABBARTPC,32,3,2,"BARTONELLA DNA,QUALITATIVE,PCR",HC 6942 BARTPC BRTNL HNSL&QNTN,224,224,224,224,448,448,448,448
+LABBARTPC,32,7,2,"BARTONELLA DNA,QUALITATIVE,PCR",HC 6942 BARTPC BRTNL HNSL&QNTN,224,224,224,224,448,448,448,448
+LABBCRABL,260,1,15,"BCR-ABL, BLOOD",HC BCRABL T922 MAJ BRKPNT QUAL,395,395,395,395,5925,5925,5925,5925
+LABBCRABL,260,1,15,"BCR-ABL, BLOOD",HC BCRABL T922 MIN BRKPNT QUAL,395,395,395,395,5925,5925,5925,5925
+LABBCRABL,260,3,17,"BCR-ABL, BLOOD",HC BCRABL T922 MAJ BRKPNT QUAL,395,395,395,395,6715,6715,6715,6715
+LABBCRABL,260,3,17,"BCR-ABL, BLOOD",HC BCRABL T922 MIN BRKPNT QUAL,395,395,395,395,6715,6715,6715,6715
+LABBCRABL,260,7,20,"BCR-ABL, BLOOD",HC BCRABL T922 MAJ BRKPNT QUAL,395,395,395,395,7900,7900,7900,7900
+LABBCRABL,260,7,20,"BCR-ABL, BLOOD",HC BCRABL T922 MIN BRKPNT QUAL,395,395,395,395,7900,7900,7900,7900
+LABBFCT,4579,1,198,"CELL COUNT & DIFF, FLD",HC BODY FLUID CELL COUNT & DIFF,252,252,252,252,49896,49896,49896,49896
+LABBFCT,4579,3,479,"CELL COUNT & DIFF, FLD",HC BODY FLUID CELL COUNT & DIFF,252,252,252,252,120708,120708,120708,120708
+LABBFCT,4579,7,856,"CELL COUNT & DIFF, FLD",HC BODY FLUID CELL COUNT & DIFF,252,252,252,252,215712,215712,215712,215712
+LABBHB,303,1,45,Beta-Hydroxybutyrate,HC BHB BETAHDRXYBTRT KETON QNT,175,175,175,175,7875,7875,7875,7875
+LABBHB,303,3,54,Beta-Hydroxybutyrate,HC BHB BETAHDRXYBTRT KETON QNT,175,175,175,175,9450,9450,9450,9450
+LABBHB,303,7,59,Beta-Hydroxybutyrate,HC BHB BETAHDRXYBTRT KETON QNT,175,175,175,175,10325,10325,10325,10325
+LABBHCG,798,1,35,HCG,HC BHCG BETA HCG QUANT,335,335,335,335,11725,11725,11725,11725
+LABBHCG,798,3,71,HCG,HC BHCG BETA HCG QUANT,335,335,335,335,23785,23785,23785,23785
+LABBHCG,798,7,92,HCG,HC BHCG BETA HCG QUANT,335,335,335,335,30820,30820,30820,30820
+LABBKVPC,137,1,5,"BK VIRUS PCR, QUANT, PLASMA",HC BK VIRUS NA PCR QUANT,650,650,650,650,3250,3250,3250,3250
+LABBKVPC,137,3,13,"BK VIRUS PCR, QUANT, PLASMA",HC BK VIRUS NA PCR QUANT,650,650,650,650,8450,8450,8450,8450
+LABBKVPC,137,7,30,"BK VIRUS PCR, QUANT, PLASMA",HC BK VIRUS NA PCR QUANT,650,650,650,650,19500,19500,19500,19500
+LABBKVPCU,189,1,5,"BK VIRUS PCR, QUANT, URINE",HC BK VIRUS NA PCR QUANT URINE,650,650,650,650,3250,3250,3250,3250
+LABBKVPCU,189,3,8,"BK VIRUS PCR, QUANT, URINE",HC BK VIRUS NA PCR QUANT URINE,650,650,650,650,5200,5200,5200,5200
+LABBKVPCU,189,7,24,"BK VIRUS PCR, QUANT, URINE",HC BK VIRUS NA PCR QUANT URINE,650,650,650,650,15600,15600,15600,15600
+LABBKVQU,58,1,8,"BK VIRUS DNA, QUANT RT PCR",HC 6942 BK VIRUS DNA QUANT,150,150,150,150,1200,1200,1200,1200
+LABBKVQU,58,3,13,"BK VIRUS DNA, QUANT RT PCR",HC 6942 BK VIRUS DNA QUANT,150,150,150,150,1950,1950,1950,1950
+LABBKVQU,58,7,15,"BK VIRUS DNA, QUANT RT PCR",HC 6942 BK VIRUS DNA QUANT,150,150,150,150,2250,2250,2250,2250
+LABBLC,49360,1,4211,Blood culture,HC BLOOD CULTURE,499,499,499,499,2101289,2101289,2101289,2101289
+LABBLC,49360,3,9594,Blood culture,HC BLOOD CULTURE,499,499,499,499,4787406,4787406,4787406,4787406
+LABBLC,49360,7,15685,Blood culture,HC BLOOD CULTURE,499,499,499,499,7826815,7826815,7826815,7826815
+LABBLC2,43897,1,2094,Blood Culture.,HC BLOOD CULTURE,499,499,499,499,1044906,1044906,1044906,1044906
+LABBLC2,43897,3,6647,Blood Culture.,HC BLOOD CULTURE,499,499,499,499,3316853,3316853,3316853,3316853
+LABBLC2,43897,7,12220,Blood Culture.,HC BLOOD CULTURE,499,499,499,499,6097780,6097780,6097780,6097780
+LABBLCA,239,1,7,AFB Culture/Blood,HC AFB CULTURE,337,337,337,337,2359,2359,2359,2359
+LABBLCA,239,3,12,AFB Culture/Blood,HC AFB CULTURE,337,337,337,337,4044,4044,4044,4044
+LABBLCA,239,7,13,AFB Culture/Blood,HC AFB CULTURE,337,337,337,337,4381,4381,4381,4381
+LABBLCSTK,4529,1,128,BLOOD CULTURE (PERIPHERAL),HC BLOOD CULTURE,499,499,499,499,63872,63872,63872,63872
+LABBLCSTK,4529,3,596,BLOOD CULTURE (PERIPHERAL),HC BLOOD CULTURE,499,499,499,499,297404,297404,297404,297404
+LABBLCSTK,4529,7,1157,BLOOD CULTURE (PERIPHERAL),HC BLOOD CULTURE,499,499,499,499,577343,577343,577343,577343
+LABBLCTIP,4569,1,288,BLOOD CULTURE (CATHETER),HC BLOOD CULTURE,499,499,499,499,143712,143712,143712,143712
+LABBLCTIP,4569,3,763,BLOOD CULTURE (CATHETER),HC BLOOD CULTURE,499,499,499,499,380737,380737,380737,380737
+LABBLCTIP,4569,7,1323,BLOOD CULTURE (CATHETER),HC BLOOD CULTURE,499,499,499,499,660177,660177,660177,660177
+LABBM1517,35,3,1,"t(15:17) PMLrar, BM",HC BM1517 T1517 CMN BRK QN BM,1387,1387,1387,1387,1387,1387,1387,1387
+LABBM1517,35,7,1,"t(15:17) PMLrar, BM",HC BM1517 T1517 CMN BRK QN BM,1387,1387,1387,1387,1387,1387,1387,1387
+LABBMAML,135,1,3,"AML Prognosis Assay, Bone Marrow",HC BMAML GENE ANLYS DUP VAR,662,1120,891,891,1986,3360,2673,2673
+LABBMAML,135,1,3,"AML Prognosis Assay, Bone Marrow",HC BMAML GENE ANLYS 12 EXON,662,1120,891,891,1986,3360,2673,2673
+LABBMAML,135,3,5,"AML Prognosis Assay, Bone Marrow",HC BMAML GENE ANLYS DUP VAR,662,1120,891,891,3310,5600,4455,4455
+LABBMAML,135,3,5,"AML Prognosis Assay, Bone Marrow",HC BMAML GENE ANLYS 12 EXON,662,1120,891,891,3310,5600,4455,4455
+LABBMAML,135,7,6,"AML Prognosis Assay, Bone Marrow",HC BMAML GENE ANLYS DUP VAR,662,1120,891,891,3972,6720,5346,5346
+LABBMAML,135,7,6,"AML Prognosis Assay, Bone Marrow",HC BMAML GENE ANLYS 12 EXON,662,1120,891,891,3972,6720,5346,5346
+LABBMBCR,114,1,1,"BCR-ABL, BONE MARROW",HC BMBCR T922 MAJ BRKPNT QUAL,395,395,395,395,395,395,395,395
+LABBMBCR,114,1,1,"BCR-ABL, BONE MARROW",HC BMBCR T922 MIN BRKPNT QUAL,395,395,395,395,395,395,395,395
+LABBMBCR,114,3,1,"BCR-ABL, BONE MARROW",HC BMBCR T922 MAJ BRKPNT QUAL,395,395,395,395,395,395,395,395
+LABBMBCR,114,3,1,"BCR-ABL, BONE MARROW",HC BMBCR T922 MIN BRKPNT QUAL,395,395,395,395,395,395,395,395
+LABBMBCR,114,7,1,"BCR-ABL, BONE MARROW",HC BMBCR T922 MAJ BRKPNT QUAL,395,395,395,395,395,395,395,395
+LABBMBCR,114,7,1,"BCR-ABL, BONE MARROW",HC BMBCR T922 MIN BRKPNT QUAL,395,395,395,395,395,395,395,395
+LABBMCD34,13,1,3,"CD34 PANEL, BONE MARROW",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,2097,2097,2097,2097
+LABBMCD34,13,3,4,"CD34 PANEL, BONE MARROW",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,2796,2796,2796,2796
+LABBMCD34,13,7,4,"CD34 PANEL, BONE MARROW",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,2796,2796,2796,2796
+LABBMCEBP,150,7,4,"DETECTION CEBPA, BONE MARROW",HC BMCEBP L4 CEBPA FULL GN BM,1881,1881,1881,1881,7524,7524,7524,7524
+LABBMNPM1,66,7,1,"NPM1, NUCLEOPHOSMIN 1 MUT., BM",HC BMNPM1 NPM1 EXON 12 BM,662,662,662,662,662,662,662,662
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I SODIUM,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I ALK PHOS,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I MAGNESIUM,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I POTASSIUM,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I AST,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I ALT,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I ALBUMIN,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I GLUCOSE,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I CREATININE,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,1,915,BMT PANEL I,HC BMT I T BILI,138,280,185.4,179.5,126270,256200,169641,164242.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I SODIUM,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I ALK PHOS,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I MAGNESIUM,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I POTASSIUM,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I AST,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I ALT,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I ALBUMIN,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I GLUCOSE,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I CREATININE,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,3,7923,BMT PANEL I,HC BMT I T BILI,138,280,185.4,179.5,1093374,2218440,1468924.2,1422178.5
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I SODIUM,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I ALK PHOS,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I MAGNESIUM,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I POTASSIUM,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I AST,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I ALT,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I ALBUMIN,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I GLUCOSE,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I CREATININE,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT1,11461,7,9038,BMT PANEL I,HC BMT I T BILI,138,280,185.4,179.5,1247244,2530640,1675645.2,1622321
+LABBMT2,11550,1,1433,BMT PANEL II,HC BMT II SODIUM,95,219,171.2,179,136135,313827,245329.6,256507
+LABBMT2,11550,1,1433,BMT PANEL II,HC BMT II POTASSIUM,95,219,171.2,179,136135,313827,245329.6,256507
+LABBMT2,11550,1,1433,BMT PANEL II,HC BMT II GLUCOSE,95,219,171.2,179,136135,313827,245329.6,256507
+LABBMT2,11550,1,1433,BMT PANEL II,HC BMT II CREATININE,95,219,171.2,179,136135,313827,245329.6,256507
+LABBMT2,11550,1,1433,BMT PANEL II,HC BMT IIC02,95,219,171.2,179,136135,313827,245329.6,256507
+LABBMT2,11550,3,9145,BMT PANEL II,HC BMT II SODIUM,95,219,171.2,179,868775,2002755,1565624,1636955
+LABBMT2,11550,3,9145,BMT PANEL II,HC BMT II POTASSIUM,95,219,171.2,179,868775,2002755,1565624,1636955
+LABBMT2,11550,3,9145,BMT PANEL II,HC BMT II GLUCOSE,95,219,171.2,179,868775,2002755,1565624,1636955
+LABBMT2,11550,3,9145,BMT PANEL II,HC BMT II CREATININE,95,219,171.2,179,868775,2002755,1565624,1636955
+LABBMT2,11550,3,9145,BMT PANEL II,HC BMT IIC02,95,219,171.2,179,868775,2002755,1565624,1636955
+LABBMT2,11550,7,9304,BMT PANEL II,HC BMT II SODIUM,95,219,171.2,179,883880,2037576,1592844.8,1665416
+LABBMT2,11550,7,9304,BMT PANEL II,HC BMT II POTASSIUM,95,219,171.2,179,883880,2037576,1592844.8,1665416
+LABBMT2,11550,7,9304,BMT PANEL II,HC BMT II GLUCOSE,95,219,171.2,179,883880,2037576,1592844.8,1665416
+LABBMT2,11550,7,9304,BMT PANEL II,HC BMT II CREATININE,95,219,171.2,179,883880,2037576,1592844.8,1665416
+LABBMT2,11550,7,9304,BMT PANEL II,HC BMT IIC02,95,219,171.2,179,883880,2037576,1592844.8,1665416
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC 6949 11136R HTLVS 1,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC 6949 11136R HTLVS 2,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC HEPAT BS AG-EIA,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC HCVA HEPATITIS C AB,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC CMV-IGM,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC VZ VIRUS AB IGG,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC VIRAL ANTIBODY CMV,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC SERUM SCREEN-RPR,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC HEP B CR TOTAL AB,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC HAV AB TOTAL(HATA),15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC HSV-2 AB IMMUNOBLOT,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,1,2,BMT PRETRANSPLANT PANEL,HC HSV-I AB IMMUNOBLOT,15,314,200.9166667,228.5,30,628,401.8333333,457
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC 6949 11136R HTLVS 1,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC 6949 11136R HTLVS 2,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC HEPAT BS AG-EIA,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC HCVA HEPATITIS C AB,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC CMV-IGM,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC VZ VIRUS AB IGG,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC VIRAL ANTIBODY CMV,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC SERUM SCREEN-RPR,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC HEP B CR TOTAL AB,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC HAV AB TOTAL(HATA),15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC HSV-2 AB IMMUNOBLOT,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,3,3,BMT PRETRANSPLANT PANEL,HC HSV-I AB IMMUNOBLOT,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC 6949 11136R HTLVS 1,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC 6949 11136R HTLVS 2,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC HEPAT BS AG-EIA,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC HCVA HEPATITIS C AB,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC CMV-IGM,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC VZ VIRUS AB IGG,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC VIRAL ANTIBODY CMV,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC SERUM SCREEN-RPR,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC HEP B CR TOTAL AB,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC HAV AB TOTAL(HATA),15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC HSV-2 AB IMMUNOBLOT,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBMTP,61,7,3,BMT PRETRANSPLANT PANEL,HC HSV-I AB IMMUNOBLOT,15,314,200.9166667,228.5,45,942,602.75,685.5
+LABBONC,47,1,3,Bone Culture/GS,HC GRAM STAIN,150,302,226,226,450,906,678,678
+LABBONC,47,1,3,Bone Culture/GS,HC BONE CULTURE,150,302,226,226,450,906,678,678
+LABBONC,47,3,3,Bone Culture/GS,HC GRAM STAIN,150,302,226,226,450,906,678,678
+LABBONC,47,3,3,Bone Culture/GS,HC BONE CULTURE,150,302,226,226,450,906,678,678
+LABBONC,47,7,3,Bone Culture/GS,HC GRAM STAIN,150,302,226,226,450,906,678,678
+LABBONC,47,7,3,Bone Culture/GS,HC BONE CULTURE,150,302,226,226,450,906,678,678
+LABBPERP,59,1,2,Bord. Pertussis PCR,HC BORDETELLA PERTUSSIS PCR,388,388,388,388,776,776,776,776
+LABBPERP,59,1,2,Bord. Pertussis PCR,HC B.PARAPERTUSSIS PCR,388,388,388,388,776,776,776,776
+LABBPERP,59,3,2,Bord. Pertussis PCR,HC BORDETELLA PERTUSSIS PCR,388,388,388,388,776,776,776,776
+LABBPERP,59,3,2,Bord. Pertussis PCR,HC B.PARAPERTUSSIS PCR,388,388,388,388,776,776,776,776
+LABBPERP,59,7,3,Bord. Pertussis PCR,HC BORDETELLA PERTUSSIS PCR,388,388,388,388,1164,1164,1164,1164
+LABBPERP,59,7,3,Bord. Pertussis PCR,HC B.PARAPERTUSSIS PCR,388,388,388,388,1164,1164,1164,1164
+LABBRAF,43,1,1,BRAF,HC BRAF GENE ANLY V600E VAR,641,641,641,641,641,641,641,641
+LABBRAF,43,3,1,BRAF,HC BRAF GENE ANLY V600E VAR,641,641,641,641,641,641,641,641
+LABBRAF,43,7,3,BRAF,HC BRAF GENE ANLY V600E VAR,641,641,641,641,1923,1923,1923,1923
+LABBRASP,77,1,1,BRONCHOPULM ASPERGILLOSIS SCRN,HC ASPERGILLUS IGG,179,179,179,179,179,179,179,179
+LABBRASP,77,1,1,BRONCHOPULM ASPERGILLOSIS SCRN,HC ASPERGILLUS IGE,179,179,179,179,179,179,179,179
+LABBRASP,77,3,3,BRONCHOPULM ASPERGILLOSIS SCRN,HC ASPERGILLUS IGG,179,179,179,179,537,537,537,537
+LABBRASP,77,3,3,BRONCHOPULM ASPERGILLOSIS SCRN,HC ASPERGILLUS IGE,179,179,179,179,537,537,537,537
+LABBRASP,77,7,5,BRONCHOPULM ASPERGILLOSIS SCRN,HC ASPERGILLUS IGG,179,179,179,179,895,895,895,895
+LABBRASP,77,7,5,BRONCHOPULM ASPERGILLOSIS SCRN,HC ASPERGILLUS IGE,179,179,179,179,895,895,895,895
+LABBSME,94,1,5,BLOOD SMEAR EXAM,HC BLOOD SMEAR EXAM,379,379,379,379,1895,1895,1895,1895
+LABBSME,94,3,6,BLOOD SMEAR EXAM,HC BLOOD SMEAR EXAM,379,379,379,379,2274,2274,2274,2274
+LABBSME,94,7,6,BLOOD SMEAR EXAM,HC BLOOD SMEAR EXAM,379,379,379,379,2274,2274,2274,2274
+LABBUN,3436,1,1005,"Urea Nitrogen,Ser/Plas",HC UREA NITROGEN,157,157,157,157,157785,157785,157785,157785
+LABBUN,3436,3,1228,"Urea Nitrogen,Ser/Plas",HC UREA NITROGEN,157,157,157,157,192796,192796,192796,192796
+LABBUN,3436,7,1362,"Urea Nitrogen,Ser/Plas",HC UREA NITROGEN,157,157,157,157,213834,213834,213834,213834
+LABBXTG,5522,1,799,BIOPSY/TISSUE with GRAM STAIN,HC GRAM STAIN,150,302,211.3333333,182,119850,241298,168855.3333,145418
+LABBXTG,5522,1,799,BIOPSY/TISSUE with GRAM STAIN,HC BIOPSY/TISS (GEN) CULT,150,302,211.3333333,182,119850,241298,168855.3333,145418
+LABBXTG,5522,1,799,BIOPSY/TISSUE with GRAM STAIN,HC TISSUE PREP ADDL,150,302,211.3333333,182,119850,241298,168855.3333,145418
+LABBXTG,5522,3,958,BIOPSY/TISSUE with GRAM STAIN,HC GRAM STAIN,150,302,211.3333333,182,143700,289316,202457.3333,174356
+LABBXTG,5522,3,958,BIOPSY/TISSUE with GRAM STAIN,HC BIOPSY/TISS (GEN) CULT,150,302,211.3333333,182,143700,289316,202457.3333,174356
+LABBXTG,5522,3,958,BIOPSY/TISSUE with GRAM STAIN,HC TISSUE PREP ADDL,150,302,211.3333333,182,143700,289316,202457.3333,174356
+LABBXTG,5522,7,1094,BIOPSY/TISSUE with GRAM STAIN,HC GRAM STAIN,150,302,211.3333333,182,164100,330388,231198.6667,199108
+LABBXTG,5522,7,1094,BIOPSY/TISSUE with GRAM STAIN,HC BIOPSY/TISS (GEN) CULT,150,302,211.3333333,182,164100,330388,231198.6667,199108
+LABBXTG,5522,7,1094,BIOPSY/TISSUE with GRAM STAIN,HC TISSUE PREP ADDL,150,302,211.3333333,182,164100,330388,231198.6667,199108
+LABC125,398,1,17,Cancer Antigen 125,HC CA125 (C125),386,386,386,386,6562,6562,6562,6562
+LABC125,398,3,21,Cancer Antigen 125,HC CA125 (C125),386,386,386,386,8106,8106,8106,8106
+LABC125,398,7,23,Cancer Antigen 125,HC CA125 (C125),386,386,386,386,8878,8878,8878,8878
+LABC3,795,1,30,"Complement C3, Serum",HC COMPLEMENT C3 SERUM,199,199,199,199,5970,5970,5970,5970
+LABC3,795,3,44,"Complement C3, Serum",HC COMPLEMENT C3 SERUM,199,199,199,199,8756,8756,8756,8756
+LABC3,795,7,53,"Complement C3, Serum",HC COMPLEMENT C3 SERUM,199,199,199,199,10547,10547,10547,10547
+LABC4,795,1,33,"Complement C4, Serum",HC COMPLEMENT C4 SERUM,199,199,199,199,6567,6567,6567,6567
+LABC4,795,3,46,"Complement C4, Serum",HC COMPLEMENT C4 SERUM,199,199,199,199,9154,9154,9154,9154
+LABC4,795,7,54,"Complement C4, Serum",HC COMPLEMENT C4 SERUM,199,199,199,199,10746,10746,10746,10746
+LABC4C8,111,1,3,"CD4/CD8 PANEL, BLD","HC FLOW:T-CELLS,TOTAL COUNT",538,575,556.5,556.5,1614,1725,1669.5,1669.5
+LABC4C8,111,1,3,"CD4/CD8 PANEL, BLD",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,538,575,556.5,556.5,1614,1725,1669.5,1669.5
+LABC4C8,111,3,5,"CD4/CD8 PANEL, BLD","HC FLOW:T-CELLS,TOTAL COUNT",538,575,556.5,556.5,2690,2875,2782.5,2782.5
+LABC4C8,111,3,5,"CD4/CD8 PANEL, BLD",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,538,575,556.5,556.5,2690,2875,2782.5,2782.5
+LABC4C8,111,7,6,"CD4/CD8 PANEL, BLD","HC FLOW:T-CELLS,TOTAL COUNT",538,575,556.5,556.5,3228,3450,3339,3339
+LABC4C8,111,7,6,"CD4/CD8 PANEL, BLD",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,538,575,556.5,556.5,3228,3450,3339,3339
+LABCA,3374,1,1703,"Calcium, Ser/Plas",HC CALCIUM TOTAL,143,143,143,143,243529,243529,243529,243529
+LABCA,3374,3,1900,"Calcium, Ser/Plas",HC CALCIUM TOTAL,143,143,143,143,271700,271700,271700,271700
+LABCA,3374,7,1926,"Calcium, Ser/Plas",HC CALCIUM TOTAL,143,143,143,143,275418,275418,275418,275418
+LABCAI,163328,1,102365,"CALCIUM, IONIZED",HC CALCIUM IONIZED,399,399,399,399,40843635,40843635,40843635,40843635
+LABCAI,163328,3,134618,"CALCIUM, IONIZED",HC CALCIUM IONIZED,399,399,399,399,53712582,53712582,53712582,53712582
+LABCAI,163328,7,136254,"CALCIUM, IONIZED",HC CALCIUM IONIZED,399,399,399,399,54365346,54365346,54365346,54365346
+LABCARN,34,1,1,"CARNITINE, FREE & TOTAL",HC CARNITINE FREE/TOTAL QNT,187,187,187,187,187,187,187,187
+LABCARN,34,3,1,"CARNITINE, FREE & TOTAL",HC CARNITINE FREE/TOTAL QNT,187,187,187,187,187,187,187,187
+LABCARN,34,7,1,"CARNITINE, FREE & TOTAL",HC CARNITINE FREE/TOTAL QNT,187,187,187,187,187,187,187,187
+LABCBCD,446825,1,212525,CBC W/ DIFFERENTIAL,HC CBC W/AUTO DIFF,300,300,300,300,63757500,63757500,63757500,63757500
+LABCBCD,446825,3,348892,CBC W/ DIFFERENTIAL,HC CBC W/AUTO DIFF,300,300,300,300,104667600,104667600,104667600,104667600
+LABCBCD,446825,7,357922,CBC W/ DIFFERENTIAL,HC CBC W/AUTO DIFF,300,300,300,300,107376600,107376600,107376600,107376600
+LABCBCO,238570,1,120939,CBC,HC COMPLETE BLOOD COUNT,285,285,285,285,34467615,34467615,34467615,34467615
+LABCBCO,238570,3,176567,CBC,HC COMPLETE BLOOD COUNT,285,285,285,285,50321595,50321595,50321595,50321595
+LABCBCO,238570,7,181800,CBC,HC COMPLETE BLOOD COUNT,285,285,285,285,51813000,51813000,51813000,51813000
+LABCBCS,10177,1,2751,CBC W/ DIFF & SLIDE REVIEW,HC CBC W/AUTO DIFF,116,300,208,208,319116,825300,572208,572208
+LABCBCS,10177,1,2751,CBC W/ DIFF & SLIDE REVIEW,HC SLIDE REVIEW,116,300,208,208,319116,825300,572208,572208
+LABCBCS,10177,3,4820,CBC W/ DIFF & SLIDE REVIEW,HC CBC W/AUTO DIFF,116,300,208,208,559120,1446000,1002560,1002560
+LABCBCS,10177,3,4820,CBC W/ DIFF & SLIDE REVIEW,HC SLIDE REVIEW,116,300,208,208,559120,1446000,1002560,1002560
+LABCBCS,10177,7,4994,CBC W/ DIFF & SLIDE REVIEW,HC CBC W/AUTO DIFF,116,300,208,208,579304,1498200,1038752,1038752
+LABCBCS,10177,7,4994,CBC W/ DIFF & SLIDE REVIEW,HC SLIDE REVIEW,116,300,208,208,579304,1498200,1038752,1038752
+LABCD34,70,1,14,"CD34 PANEL, BLD",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,9786,9786,9786,9786
+LABCD34,70,3,22,"CD34 PANEL, BLD",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,15378,15378,15378,15378
+LABCD34,70,7,22,"CD34 PANEL, BLD",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,15378,15378,15378,15378
+LABCD3F,5,1,3,"CD3, FLUID",HC FLOW T CELLS TOTAL COUNT,538,538,538,538,1614,1614,1614,1614
+LABCD3F,5,3,3,"CD3, FLUID",HC FLOW T CELLS TOTAL COUNT,538,538,538,538,1614,1614,1614,1614
+LABCD3F,5,7,3,"CD3, FLUID",HC FLOW T CELLS TOTAL COUNT,538,538,538,538,1614,1614,1614,1614
+LABCD4CT,225,1,21,CD4 COUNT,HC FLOW:ABSOLUTE CD4 COUNT,522,522,522,522,10962,10962,10962,10962
+LABCD4CT,225,3,43,CD4 COUNT,HC FLOW:ABSOLUTE CD4 COUNT,522,522,522,522,22446,22446,22446,22446
+LABCD4CT,225,7,47,CD4 COUNT,HC FLOW:ABSOLUTE CD4 COUNT,522,522,522,522,24534,24534,24534,24534
+LABCDPLI,275,1,3,COLD ANTIBODY SCREEN,HC COLD SCREEN/PANEL,156,156,156,156,468,468,468,468
+LABCDPLI,275,3,8,COLD ANTIBODY SCREEN,HC COLD SCREEN/PANEL,156,156,156,156,1248,1248,1248,1248
+LABCDPLI,275,7,11,COLD ANTIBODY SCREEN,HC COLD SCREEN/PANEL,156,156,156,156,1716,1716,1716,1716
+LABCDTPCR,12659,1,422,"C.DIFF TOXIN B GENE, QUAL. PCR",HC CDTPCR C DIFF TOXIN AMP STL,486,486,486,486,205092,205092,205092,205092
+LABCDTPCR,12659,3,876,"C.DIFF TOXIN B GENE, QUAL. PCR",HC CDTPCR C DIFF TOXIN AMP STL,486,486,486,486,425736,425736,425736,425736
+LABCDTPCR,12659,7,1581,"C.DIFF TOXIN B GENE, QUAL. PCR",HC CDTPCR C DIFF TOXIN AMP STL,486,486,486,486,768366,768366,768366,768366
+LABCEA,923,1,40,CEA,HC CEA,490,490,490,490,19600,19600,19600,19600
+LABCEA,923,3,50,CEA,HC CEA,490,490,490,490,24500,24500,24500,24500
+LABCEA,923,7,57,CEA,HC CEA,490,490,490,490,27930,27930,27930,27930
+LABCEBPA,210,1,13,"DETECTION CEBPA, BLOOD",HC CEBPA FULL GENE SEQ,1881,1881,1881,1881,24453,24453,24453,24453
+LABCEBPA,210,3,17,"DETECTION CEBPA, BLOOD",HC CEBPA FULL GENE SEQ,1881,1881,1881,1881,31977,31977,31977,31977
+LABCEBPA,210,7,21,"DETECTION CEBPA, BLOOD",HC CEBPA FULL GENE SEQ,1881,1881,1881,1881,39501,39501,39501,39501
+LABCENABY,123,1,3,ANTI-CENTROMERE ANTIBODY,HC CENABY EXT NA CNTRMR AB,152,152,152,152,456,456,456,456
+LABCENABY,123,3,3,ANTI-CENTROMERE ANTIBODY,HC CENABY EXT NA CNTRMR AB,152,152,152,152,456,456,456,456
+LABCENABY,123,7,4,ANTI-CENTROMERE ANTIBODY,HC CENABY EXT NA CNTRMR AB,152,152,152,152,608,608,608,608
+LABCF32,41,1,3,CYSTIC FIBROSIS MUTATION STUDY,HC CF32 GENE ANLY COMMON VARS,909,909,909,909,2727,2727,2727,2727
+LABCF32,41,3,3,CYSTIC FIBROSIS MUTATION STUDY,HC CF32 GENE ANLY COMMON VARS,909,909,909,909,2727,2727,2727,2727
+LABCF32,41,7,3,CYSTIC FIBROSIS MUTATION STUDY,HC CF32 GENE ANLY COMMON VARS,909,909,909,909,2727,2727,2727,2727
+LABCFRESP,472,1,19,Cystic Fibrosis Respiratory Culture,HC CYSTIC FIB RESP CULT,302,302,302,302,5738,5738,5738,5738
+LABCFRESP,472,3,24,Cystic Fibrosis Respiratory Culture,HC CYSTIC FIB RESP CULT,302,302,302,302,7248,7248,7248,7248
+LABCFRESP,472,7,34,Cystic Fibrosis Respiratory Culture,HC CYSTIC FIB RESP CULT,302,302,302,302,10268,10268,10268,10268
+LABCGD,19,1,2,"NEUTROPHIL FNCTN,OXIDATVE BRST",HC 6911 CGD ENZ ACT NONRAD EA,141,141,141,141,282,282,282,282
+LABCGD,19,3,4,"NEUTROPHIL FNCTN,OXIDATVE BRST",HC 6911 CGD ENZ ACT NONRAD EA,141,141,141,141,564,564,564,564
+LABCGD,19,7,6,"NEUTROPHIL FNCTN,OXIDATVE BRST",HC 6911 CGD ENZ ACT NONRAD EA,141,141,141,141,846,846,846,846
+LABCHOL,148,1,5,"Cholesterol, Total",HC CHOLESTEROL,145,145,145,145,725,725,725,725
+LABCHOL,148,3,7,"Cholesterol, Total",HC CHOLESTEROL,145,145,145,145,1015,1015,1015,1015
+LABCHOL,148,7,8,"Cholesterol, Total",HC CHOLESTEROL,145,145,145,145,1160,1160,1160,1160
+LABCIMM,27,1,2,"Cocci Ab, ID, S",HC 6942 COCCIIMMUNODIFFUSION,23,23,23,23,46,46,46,46
+LABCIMM,27,3,2,"Cocci Ab, ID, S",HC 6942 COCCIIMMUNODIFFUSION,23,23,23,23,46,46,46,46
+LABCIMM,27,7,3,"Cocci Ab, ID, S",HC 6942 COCCIIMMUNODIFFUSION,23,23,23,23,69,69,69,69
+LABCK,16113,1,8484,"CK, Total",HC CREATININE KINASE TOTAL,254,254,254,254,2154936,2154936,2154936,2154936
+LABCK,16113,3,9757,"CK, Total",HC CREATININE KINASE TOTAL,254,254,254,254,2478278,2478278,2478278,2478278
+LABCK,16113,7,10124,"CK, Total",HC CREATININE KINASE TOTAL,254,254,254,254,2571496,2571496,2571496,2571496
+LABCKITMU,228,1,22,DETECTION OF C-KIT MUTATIONS,HC CKITMU L5 KIT EXONS 8&17,1223,1223,1223,1223,26906,26906,26906,26906
+LABCKITMU,228,3,29,DETECTION OF C-KIT MUTATIONS,HC CKITMU L5 KIT EXONS 8&17,1223,1223,1223,1223,35467,35467,35467,35467
+LABCKITMU,228,7,41,DETECTION OF C-KIT MUTATIONS,HC CKITMU L5 KIT EXONS 8&17,1223,1223,1223,1223,50143,50143,50143,50143
+LABCL,269,1,2,"Chloride, Ser/Plas",HC CHLORIDE SERUM/PLA,129,129,129,129,258,258,258,258
+LABCL,269,3,9,"Chloride, Ser/Plas",HC CHLORIDE SERUM/PLA,129,129,129,129,1161,1161,1161,1161
+LABCL,269,7,11,"Chloride, Ser/Plas",HC CHLORIDE SERUM/PLA,129,129,129,129,1419,1419,1419,1419
+LABCLACT,763,1,36,"Lactic Acid, CSF",HC LACTATE,330,330,330,330,11880,11880,11880,11880
+LABCLACT,763,3,163,"Lactic Acid, CSF",HC LACTATE,330,330,330,330,53790,53790,53790,53790
+LABCLACT,763,7,293,"Lactic Acid, CSF",HC LACTATE,330,330,330,330,96690,96690,96690,96690
+LABCLGC2,353,1,13,Chlamydia/GC Molec Detec,HC GC/CHLAMYDIA TRACH MOLEC DET,566,566,566,566,7358,7358,7358,7358
+LABCLGC2,353,1,13,Chlamydia/GC Molec Detec,HC GC AMP PROBE,566,566,566,566,7358,7358,7358,7358
+LABCLGC2,353,3,15,Chlamydia/GC Molec Detec,HC GC/CHLAMYDIA TRACH MOLEC DET,566,566,566,566,8490,8490,8490,8490
+LABCLGC2,353,3,15,Chlamydia/GC Molec Detec,HC GC AMP PROBE,566,566,566,566,8490,8490,8490,8490
+LABCLGC2,353,7,19,Chlamydia/GC Molec Detec,HC GC/CHLAMYDIA TRACH MOLEC DET,566,566,566,566,10754,10754,10754,10754
+LABCLGC2,353,7,19,Chlamydia/GC Molec Detec,HC GC AMP PROBE,566,566,566,566,10754,10754,10754,10754
+LABCMV,947,1,12,CMV Antibody,HC VIRAL ANTIBODY CMV,268,268,268,268,3216,3216,3216,3216
+LABCMV,947,3,21,CMV Antibody,HC VIRAL ANTIBODY CMV,268,268,268,268,5628,5628,5628,5628
+LABCMV,947,7,26,CMV Antibody,HC VIRAL ANTIBODY CMV,268,268,268,268,6968,6968,6968,6968
+LABCMVG,83,1,3,CMV GENOTYPE,HC 6942 CMV GENOTYPE,638,638,638,638,1914,1914,1914,1914
+LABCMVG,83,3,3,CMV GENOTYPE,HC 6942 CMV GENOTYPE,638,638,638,638,1914,1914,1914,1914
+LABCMVG,83,7,5,CMV GENOTYPE,HC 6942 CMV GENOTYPE,638,638,638,638,3190,3190,3190,3190
+LABCMVIGG,929,1,26,CMV IgG,HC VIRAL ANTIBODY CMV,268,268,268,268,6968,6968,6968,6968
+LABCMVIGG,929,3,37,CMV IgG,HC VIRAL ANTIBODY CMV,268,268,268,268,9916,9916,9916,9916
+LABCMVIGG,929,7,44,CMV IgG,HC VIRAL ANTIBODY CMV,268,268,268,268,11792,11792,11792,11792
+LABCMVIGM,590,1,17,CMV IgM,HC CMV-IGM,314,314,314,314,5338,5338,5338,5338
+LABCMVIGM,590,3,24,CMV IgM,HC CMV-IGM,314,314,314,314,7536,7536,7536,7536
+LABCMVIGM,590,7,27,CMV IgM,HC CMV-IGM,314,314,314,314,8478,8478,8478,8478
+LABCMVIGT,396,1,2,CMV IgG (TRANSFUSION),HC CMVIGT CMV IGG AB TS ONLY,268,268,268,268,536,536,536,536
+LABCMVIGT,396,3,2,CMV IgG (TRANSFUSION),HC CMVIGT CMV IGG AB TS ONLY,268,268,268,268,536,536,536,536
+LABCMVIGT,396,7,3,CMV IgG (TRANSFUSION),HC CMVIGT CMV IGG AB TS ONLY,268,268,268,268,804,804,804,804
+LABCMVM,670,1,12,CMV IgM AB:,HC CMV-IGM,314,314,314,314,3768,3768,3768,3768
+LABCMVM,670,3,19,CMV IgM AB:,HC CMV-IGM,314,314,314,314,5966,5966,5966,5966
+LABCMVM,670,7,25,CMV IgM AB:,HC CMV-IGM,314,314,314,314,7850,7850,7850,7850
+LABCMVP,1413,1,152,CMV PCR,HC CMV QUAL PCR AMP,651,651,651,651,98952,98952,98952,98952
+LABCMVP,1413,3,184,CMV PCR,HC CMV QUAL PCR AMP,651,651,651,651,119784,119784,119784,119784
+LABCMVP,1413,7,214,CMV PCR,HC CMV QUAL PCR AMP,651,651,651,651,139314,139314,139314,139314
+LABCMVPQ,4456,1,404,CMV PCR Quant,HC CMV PCR QUANTIFICATION,849,849,849,849,342996,342996,342996,342996
+LABCMVPQ,4456,3,802,CMV PCR Quant,HC CMV PCR QUANTIFICATION,849,849,849,849,680898,680898,680898,680898
+LABCMVPQ,4456,7,1539,CMV PCR Quant,HC CMV PCR QUANTIFICATION,849,849,849,849,1306611,1306611,1306611,1306611
+LABCMVQL,328,1,31,"CMV DNA PCR Qual, Non-Plasma",HC CMV QUAL PCR AMP,651,651,651,651,20181,20181,20181,20181
+LABCMVQL,328,3,32,"CMV DNA PCR Qual, Non-Plasma",HC CMV QUAL PCR AMP,651,651,651,651,20832,20832,20832,20832
+LABCMVQL,328,7,37,"CMV DNA PCR Qual, Non-Plasma",HC CMV QUAL PCR AMP,651,651,651,651,24087,24087,24087,24087
+LABCMVQT,1805,1,131,"CMV DNA PCR Quant, Plasma",HC CMV PCR QUANTIFICATION,849,849,849,849,111219,111219,111219,111219
+LABCMVQT,1805,3,321,"CMV DNA PCR Quant, Plasma",HC CMV PCR QUANTIFICATION,849,849,849,849,272529,272529,272529,272529
+LABCMVQT,1805,7,630,"CMV DNA PCR Quant, Plasma",HC CMV PCR QUANTIFICATION,849,849,849,849,534870,534870,534870,534870
+LABCMVRC,21,1,2,CMV RAPID CULTURE,HC CMVRC SHL VL ID IMM EA VRS,363,363,363,363,726,726,726,726
+LABCMVRC,21,3,2,CMV RAPID CULTURE,HC CMVRC SHL VL ID IMM EA VRS,363,363,363,363,726,726,726,726
+LABCMVRC,21,7,2,CMV RAPID CULTURE,HC CMVRC SHL VL ID IMM EA VRS,363,363,363,363,726,726,726,726
+LABCMVRO,319,1,36,CMV RULE OUT,HC CMVRO SHL VL ID IMM EA VRS,363,651,507,507,13068,23436,18252,18252
+LABCMVRO,319,1,36,CMV RULE OUT,HC CMVRO CMV QUAL PCR AMP,363,651,507,507,13068,23436,18252,18252
+LABCMVRO,319,3,39,CMV RULE OUT,HC CMVRO SHL VL ID IMM EA VRS,363,651,507,507,14157,25389,19773,19773
+LABCMVRO,319,3,39,CMV RULE OUT,HC CMVRO CMV QUAL PCR AMP,363,651,507,507,14157,25389,19773,19773
+LABCMVRO,319,7,45,CMV RULE OUT,HC CMVRO SHL VL ID IMM EA VRS,363,651,507,507,16335,29295,22815,22815
+LABCMVRO,319,7,45,CMV RULE OUT,HC CMVRO CMV QUAL PCR AMP,363,651,507,507,16335,29295,22815,22815
+LABCO2,278,1,9,"CO2, Ser/Plas",HC CARBON DIOXIDE,95,95,95,95,855,855,855,855
+LABCO2,278,3,14,"CO2, Ser/Plas",HC CARBON DIOXIDE,95,95,95,95,1330,1330,1330,1330
+LABCO2,278,7,16,"CO2, Ser/Plas",HC CARBON DIOXIDE,95,95,95,95,1520,1520,1520,1520
+LABCOHB,56,1,8,CO-Hemoglobin,HC COHGB(CARBOXYHEMOGL),334,334,334,334,2672,2672,2672,2672
+LABCOHB,56,3,8,CO-Hemoglobin,HC COHGB(CARBOXYHEMOGL),334,334,334,334,2672,2672,2672,2672
+LABCOHB,56,7,8,CO-Hemoglobin,HC COHGB(CARBOXYHEMOGL),334,334,334,334,2672,2672,2672,2672
+LABCORT,5150,1,1379,"Cortisol, Serum",HC CORTISOL TOTAL,374,374,374,374,515746,515746,515746,515746
+LABCORT,5150,3,1671,"Cortisol, Serum",HC CORTISOL TOTAL,374,374,374,374,624954,624954,624954,624954
+LABCORT,5150,7,1758,"Cortisol, Serum",HC CORTISOL TOTAL,374,374,374,374,657492,657492,657492,657492
+LABCORTS,840,1,308,"Cortisol, Post Op",HC CORTISOL TOTAL,374,374,374,374,115192,115192,115192,115192
+LABCORTS,840,3,447,"Cortisol, Post Op",HC CORTISOL TOTAL,374,374,374,374,167178,167178,167178,167178
+LABCORTS,840,7,453,"Cortisol, Post Op",HC CORTISOL TOTAL,374,374,374,374,169422,169422,169422,169422
+LABCOXPNL,66,3,1,COXSACKIE A AB,HC 6942 COXSACKIE AB PANEL,17,17,17,17,17,17,17,17
+LABCOXPNL,66,7,4,COXSACKIE A AB,HC 6942 COXSACKIE AB PANEL,17,17,17,17,68,68,68,68
+LABCR,1634,1,245,"Creatinine, Ser/Plas",HC CREATININE BLOOD,179,179,179,179,43855,43855,43855,43855
+LABCR,1634,3,367,"Creatinine, Ser/Plas",HC CREATININE BLOOD,179,179,179,179,65693,65693,65693,65693
+LABCR,1634,7,387,"Creatinine, Ser/Plas",HC CREATININE BLOOD,179,179,179,179,69273,69273,69273,69273
+LABCRCL,455,1,8,CREATININE CLEARANCE,HC CREATININE CLRNC TS,256,256,256,256,2048,2048,2048,2048
+LABCRCL,455,3,22,CREATININE CLEARANCE,HC CREATININE CLRNC TS,256,256,256,256,5632,5632,5632,5632
+LABCRCL,455,7,34,CREATININE CLEARANCE,HC CREATININE CLRNC TS,256,256,256,256,8704,8704,8704,8704
+LABCRFA,108,1,8,CRYPTOSPORIDIUM DFA,HC CRYPTOSPORIDIUM DFA,209,209,209,209,1672,1672,1672,1672
+LABCRFA,108,3,9,CRYPTOSPORIDIUM DFA,HC CRYPTOSPORIDIUM DFA,209,209,209,209,1881,1881,1881,1881
+LABCRFA,108,7,12,CRYPTOSPORIDIUM DFA,HC CRYPTOSPORIDIUM DFA,209,209,209,209,2508,2508,2508,2508
+LABCRP,15439,1,1349,C-Reactive Protein,HC C REACTIVE PROTEIN,179,179,179,179,241471,241471,241471,241471
+LABCRP,15439,3,3611,C-Reactive Protein,HC C REACTIVE PROTEIN,179,179,179,179,646369,646369,646369,646369
+LABCRP,15439,7,6716,C-Reactive Protein,HC C REACTIVE PROTEIN,179,179,179,179,1202164,1202164,1202164,1202164
+LABCRPLB,793,1,40,Latex Crypto Ag-Bld,HC CRYPTOC. AB (BLD),309,309,309,309,12360,12360,12360,12360
+LABCRPLB,793,3,59,Latex Crypto Ag-Bld,HC CRYPTOC. AB (BLD),309,309,309,309,18231,18231,18231,18231
+LABCRPLB,793,7,70,Latex Crypto Ag-Bld,HC CRYPTOC. AB (BLD),309,309,309,309,21630,21630,21630,21630
+LABCRPLC,663,1,30,Latex Crypto Ag - CSF,HC LATX CRYPTL AG(CSF),309,309,309,309,9270,9270,9270,9270
+LABCRPLC,663,3,85,Latex Crypto Ag - CSF,HC LATX CRYPTL AG(CSF),309,309,309,309,26265,26265,26265,26265
+LABCRPLC,663,7,117,Latex Crypto Ag - CSF,HC LATX CRYPTL AG(CSF),309,309,309,309,36153,36153,36153,36153
+LABCRPLFB,122,1,5,"Crypto AG Lateral Flow, BLD",HC CRYPTO ANTI LATERAL ASSAY,132,132,132,132,660,660,660,660
+LABCRPLFB,122,3,9,"Crypto AG Lateral Flow, BLD",HC CRYPTO ANTI LATERAL ASSAY,132,132,132,132,1188,1188,1188,1188
+LABCRPLFB,122,7,9,"Crypto AG Lateral Flow, BLD",HC CRYPTO ANTI LATERAL ASSAY,132,132,132,132,1188,1188,1188,1188
+LABCRPLFC,119,1,6,"Crypto AG Lateral Flow, CSF",HC CRYPTO ANTI FLOW ASSAY CSF,132,132,132,132,792,792,792,792
+LABCRPLFC,119,3,16,"Crypto AG Lateral Flow, CSF",HC CRYPTO ANTI FLOW ASSAY CSF,132,132,132,132,2112,2112,2112,2112
+LABCRPLFC,119,7,24,"Crypto AG Lateral Flow, CSF",HC CRYPTO ANTI FLOW ASSAY CSF,132,132,132,132,3168,3168,3168,3168
+LABCRTINE,51,1,13,"CREATINE, SER/PLAS",HC 6959 CREATINE SERUM,68,68,68,68,884,884,884,884
+LABCRTINE,51,3,13,"CREATINE, SER/PLAS",HC 6959 CREATINE SERUM,68,68,68,68,884,884,884,884
+LABCRTINE,51,7,15,"CREATINE, SER/PLAS",HC 6959 CREATINE SERUM,68,68,68,68,1020,1020,1020,1020
+LABCRYEIA,40,1,1,CRYPTOSPORIDIUM EIA,HC CRYEIA CRYPTSPDM AG EIAMSM,231,231,231,231,231,231,231,231
+LABCRYEIA,40,3,2,CRYPTOSPORIDIUM EIA,HC CRYEIA CRYPTSPDM AG EIAMSM,231,231,231,231,462,462,462,462
+LABCRYEIA,40,7,2,CRYPTOSPORIDIUM EIA,HC CRYEIA CRYPTSPDM AG EIAMSM,231,231,231,231,462,462,462,462
+LABCRYO,205,1,3,CRYOPROTEIN,HC CRYOPROTEIN QUAL SEMIQNT,229,229,229,229,687,687,687,687
+LABCRYO,205,1,3,CRYOPROTEIN,HC CRYOGLOBIN QUAL SEMIQNT,229,229,229,229,687,687,687,687
+LABCRYO,205,3,6,CRYOPROTEIN,HC CRYOPROTEIN QUAL SEMIQNT,229,229,229,229,1374,1374,1374,1374
+LABCRYO,205,3,6,CRYOPROTEIN,HC CRYOGLOBIN QUAL SEMIQNT,229,229,229,229,1374,1374,1374,1374
+LABCRYO,205,7,11,CRYOPROTEIN,HC CRYOPROTEIN QUAL SEMIQNT,229,229,229,229,2519,2519,2519,2519
+LABCRYO,205,7,11,CRYOPROTEIN,HC CRYOGLOBIN QUAL SEMIQNT,229,229,229,229,2519,2519,2519,2519
+LABCRYST,394,1,78,"CRYSTAL ANALYSIS, SYN FLD",HC CRYSTAL ID,244,244,244,244,19032,19032,19032,19032
+LABCRYST,394,3,84,"CRYSTAL ANALYSIS, SYN FLD",HC CRYSTAL ID,244,244,244,244,20496,20496,20496,20496
+LABCRYST,394,7,87,"CRYSTAL ANALYSIS, SYN FLD",HC CRYSTAL ID,244,244,244,244,21228,21228,21228,21228
+LABCSFC,3140,1,149,CSF Culture/GS,HC GRAM STAIN,150,302,226,226,22350,44998,33674,33674
+LABCSFC,3140,1,149,CSF Culture/GS,HC CSF CULTURE,150,302,226,226,22350,44998,33674,33674
+LABCSFC,3140,3,529,CSF Culture/GS,HC GRAM STAIN,150,302,226,226,79350,159758,119554,119554
+LABCSFC,3140,3,529,CSF Culture/GS,HC CSF CULTURE,150,302,226,226,79350,159758,119554,119554
+LABCSFC,3140,7,894,CSF Culture/GS,HC GRAM STAIN,150,302,226,226,134100,269988,202044,202044
+LABCSFC,3140,7,894,CSF Culture/GS,HC CSF CULTURE,150,302,226,226,134100,269988,202044,202044
+LABCSFGL,3458,1,184,"GLUCOSE, CSF",HC GLUCOSE SPINAL FLD,250,250,250,250,46000,46000,46000,46000
+LABCSFGL,3458,3,646,"GLUCOSE, CSF",HC GLUCOSE SPINAL FLD,250,250,250,250,161500,161500,161500,161500
+LABCSFGL,3458,7,1019,"GLUCOSE, CSF",HC GLUCOSE SPINAL FLD,250,250,250,250,254750,254750,254750,254750
+LABCSFLD,139,1,1,"LDH, CSF",HC LDH FLUID,193,193,193,193,193,193,193,193
+LABCSFLD,139,3,5,"LDH, CSF",HC LDH FLUID,193,193,193,193,965,965,965,965
+LABCSFLD,139,7,6,"LDH, CSF",HC LDH FLUID,193,193,193,193,1158,1158,1158,1158
+LABCSFTP,3442,1,179,"PROTEIN, TOTAL, CSF",HC PROTN TOTAL CSF,229,229,229,229,40991,40991,40991,40991
+LABCSFTP,3442,3,627,"PROTEIN, TOTAL, CSF",HC PROTN TOTAL CSF,229,229,229,229,143583,143583,143583,143583
+LABCSFTP,3442,7,1003,"PROTEIN, TOTAL, CSF",HC PROTN TOTAL CSF,229,229,229,229,229687,229687,229687,229687
+LABCSTOTA,193,1,2,"PROTEIN C&S, TOTAL                              ARUP",HC 6959 CSTOTA PROTEIN C AG,20,20,20,20,40,40,40,40
+LABCSTOTA,193,3,4,"PROTEIN C&S, TOTAL                              ARUP",HC 6959 CSTOTA PROTEIN C AG,20,20,20,20,80,80,80,80
+LABCSTOTA,193,7,6,"PROTEIN C&S, TOTAL                              ARUP",HC 6959 CSTOTA PROTEIN C AG,20,20,20,20,120,120,120,120
+LABCTC,780,1,56,Catheter Tip Culture,HC CATHR TIP CULTURE,302,302,302,302,16912,16912,16912,16912
+LABCTC,780,3,73,Catheter Tip Culture,HC CATHR TIP CULTURE,302,302,302,302,22046,22046,22046,22046
+LABCTC,780,7,99,Catheter Tip Culture,HC CATHR TIP CULTURE,302,302,302,302,29898,29898,29898,29898
+LABCYAB,14,1,2,CYSTICERCUS AB,HC 6942 CYSTICERCUS AB ELISA,83,83,83,83,166,166,166,166
+LABCYAB,14,3,2,CYSTICERCUS AB,HC 6942 CYSTICERCUS AB ELISA,83,83,83,83,166,166,166,166
+LABCYAB,14,7,2,CYSTICERCUS AB,HC 6942 CYSTICERCUS AB ELISA,83,83,83,83,166,166,166,166
+LABCYCWBL,5415,1,1912,"CYCLOSPORINE, WHOLE BLOOD",HC CYCLOSPORINE WHOLE BLOOD,437,437,437,437,835544,835544,835544,835544
+LABCYCWBL,5415,3,4268,"CYCLOSPORINE, WHOLE BLOOD",HC CYCLOSPORINE WHOLE BLOOD,437,437,437,437,1865116,1865116,1865116,1865116
+LABCYCWBL,5415,7,4555,"CYCLOSPORINE, WHOLE BLOOD",HC CYCLOSPORINE WHOLE BLOOD,437,437,437,437,1990535,1990535,1990535,1990535
+LABCYLEX,24,3,4,IMMUNE CELL FUNCTION (CYLEX),HC 6911 CYLEX CELLFNCT DETBIO,275,275,275,275,1100,1100,1100,1100
+LABCYLEX,24,7,7,IMMUNE CELL FUNCTION (CYLEX),HC 6911 CYLEX CELLFNCT DETBIO,275,275,275,275,1925,1925,1925,1925
+LABCYLX,72,1,3,IMMUNE FUNCTION TEST,HC 6949 CYLX IMM CEL FNCT,102,102,102,102,306,306,306,306
+LABCYLX,72,3,3,IMMUNE FUNCTION TEST,HC 6949 CYLX IMM CEL FNCT,102,102,102,102,306,306,306,306
+LABCYLX,72,7,16,IMMUNE FUNCTION TEST,HC 6949 CYLX IMM CEL FNCT,102,102,102,102,1632,1632,1632,1632
+LABCYSBLD,26,1,1,CYSTICERCUS AB REFLEX PANEL,HC 6942 CYSTICERCOSIS SERUM,83,83,83,83,83,83,83,83
+LABCYSBLD,26,3,1,CYSTICERCUS AB REFLEX PANEL,HC 6942 CYSTICERCOSIS SERUM,83,83,83,83,83,83,83,83
+LABCYSBLD,26,7,1,CYSTICERCUS AB REFLEX PANEL,HC 6942 CYSTICERCOSIS SERUM,83,83,83,83,83,83,83,83
+LABCYSCSF,13,1,1,CYSTICERCUS AB REFLEX PANEL,HC 6942 CYSTICERCOSIS CSF,86,86,86,86,86,86,86,86
+LABCYSCSF,13,3,1,CYSTICERCUS AB REFLEX PANEL,HC 6942 CYSTICERCOSIS CSF,86,86,86,86,86,86,86,86
+LABCYSCSF,13,7,1,CYSTICERCUS AB REFLEX PANEL,HC 6942 CYSTICERCOSIS CSF,86,86,86,86,86,86,86,86
+LABCZPL,326,1,27,CARBAMAZEPINE,HC CARBAMAZEPINE TOTAL,352,352,352,352,9504,9504,9504,9504
+LABCZPL,326,3,64,CARBAMAZEPINE,HC CARBAMAZEPINE TOTAL,352,352,352,352,22528,22528,22528,22528
+LABCZPL,326,7,93,CARBAMAZEPINE,HC CARBAMAZEPINE TOTAL,352,352,352,352,32736,32736,32736,32736
+LABDATBSI,1016,1,22,DIRECT COOMBS - BROAD SPECTRUM,HC DIRECT COOMBS:BRD SPECTRM,133,133,133,133,2926,2926,2926,2926
+LABDATBSI,1016,3,52,DIRECT COOMBS - BROAD SPECTRUM,HC DIRECT COOMBS:BRD SPECTRM,133,133,133,133,6916,6916,6916,6916
+LABDATBSI,1016,7,74,DIRECT COOMBS - BROAD SPECTRUM,HC DIRECT COOMBS:BRD SPECTRM,133,133,133,133,9842,9842,9842,9842
+LABDBGT,4,1,3,DABIGATRAN,HC QUANT ASSAY DRUG,291,291,291,291,873,873,873,873
+LABDBGT,4,3,3,DABIGATRAN,HC QUANT ASSAY DRUG,291,291,291,291,873,873,873,873
+LABDBGT,4,7,3,DABIGATRAN,HC QUANT ASSAY DRUG,291,291,291,291,873,873,873,873
+LABDBIL,4103,1,740,Conjugated Bilirubin,"HC BILIRUBIN, DIRECT",165,165,165,165,122100,122100,122100,122100
+LABDBIL,4103,3,1471,Conjugated Bilirubin,"HC BILIRUBIN, DIRECT",165,165,165,165,242715,242715,242715,242715
+LABDBIL,4103,7,1622,Conjugated Bilirubin,"HC BILIRUBIN, DIRECT",165,165,165,165,267630,267630,267630,267630
+LABDDIM,2726,1,541,D-Dimer,HC D DIMER LIA,438,438,438,438,236958,236958,236958,236958
+LABDDIM,2726,3,702,D-Dimer,HC D DIMER LIA,438,438,438,438,307476,307476,307476,307476
+LABDDIM,2726,7,733,D-Dimer,HC D DIMER LIA,438,438,438,438,321054,321054,321054,321054
+LABDDIML,1190,1,52,D-DIMER (ELISA),"HC D-DIMER, ELISA",438,438,438,438,22776,22776,22776,22776
+LABDDIML,1190,3,54,D-DIMER (ELISA),"HC D-DIMER, ELISA",438,438,438,438,23652,23652,23652,23652
+LABDDIML,1190,7,55,D-DIMER (ELISA),"HC D-DIMER, ELISA",438,438,438,438,24090,24090,24090,24090
+LABDFA,69,1,7,"DENGUE FEVER AB IgG, IgM",HC 6942 DENGUE FEVER AB ELISA,30,30,30,30,210,210,210,210
+LABDFA,69,1,7,"DENGUE FEVER AB IgG, IgM",HC 6942 DFA DENGUE FEV AB,30,30,30,30,210,210,210,210
+LABDFA,69,3,8,"DENGUE FEVER AB IgG, IgM",HC 6942 DENGUE FEVER AB ELISA,30,30,30,30,240,240,240,240
+LABDFA,69,3,8,"DENGUE FEVER AB IgG, IgM",HC 6942 DFA DENGUE FEV AB,30,30,30,30,240,240,240,240
+LABDFA,69,7,9,"DENGUE FEVER AB IgG, IgM",HC 6942 DENGUE FEVER AB ELISA,30,30,30,30,270,270,270,270
+LABDFA,69,7,9,"DENGUE FEVER AB IgG, IgM",HC 6942 DFA DENGUE FEV AB,30,30,30,30,270,270,270,270
+LABDGHBP,422,1,3,DIAG. HEPATITIS B PANEL,HC HEPAT BS AG-EIA,189,248,213.5,208.5,567,744,640.5,625.5
+LABDGHBP,422,1,3,DIAG. HEPATITIS B PANEL,HC HBV CORE IGM (HBC),189,248,213.5,208.5,567,744,640.5,625.5
+LABDGHBP,422,1,3,DIAG. HEPATITIS B PANEL,HC HEP B CR TOTAL AB,189,248,213.5,208.5,567,744,640.5,625.5
+LABDGHBP,422,1,3,DIAG. HEPATITIS B PANEL,HC HBSQT HEP B SURF AB QUANT,189,248,213.5,208.5,567,744,640.5,625.5
+LABDGHBP,422,3,5,DIAG. HEPATITIS B PANEL,HC HEPAT BS AG-EIA,189,248,213.5,208.5,945,1240,1067.5,1042.5
+LABDGHBP,422,3,5,DIAG. HEPATITIS B PANEL,HC HBV CORE IGM (HBC),189,248,213.5,208.5,945,1240,1067.5,1042.5
+LABDGHBP,422,3,5,DIAG. HEPATITIS B PANEL,HC HEP B CR TOTAL AB,189,248,213.5,208.5,945,1240,1067.5,1042.5
+LABDGHBP,422,3,5,DIAG. HEPATITIS B PANEL,HC HBSQT HEP B SURF AB QUANT,189,248,213.5,208.5,945,1240,1067.5,1042.5
+LABDGHBP,422,7,6,DIAG. HEPATITIS B PANEL,HC HEPAT BS AG-EIA,189,248,213.5,208.5,1134,1488,1281,1251
+LABDGHBP,422,7,6,DIAG. HEPATITIS B PANEL,HC HBV CORE IGM (HBC),189,248,213.5,208.5,1134,1488,1281,1251
+LABDGHBP,422,7,6,DIAG. HEPATITIS B PANEL,HC HEP B CR TOTAL AB,189,248,213.5,208.5,1134,1488,1281,1251
+LABDGHBP,422,7,6,DIAG. HEPATITIS B PANEL,HC HBSQT HEP B SURF AB QUANT,189,248,213.5,208.5,1134,1488,1281,1251
+LABDHBAG,2,1,1,"DONOR, HEP B SURFACE AG",HC 6911 DNR HEP BS AG,54,54,54,54,54,54,54,54
+LABDHBAG,2,3,1,"DONOR, HEP B SURFACE AG",HC 6911 DNR HEP BS AG,54,54,54,54,54,54,54,54
+LABDHBAG,2,7,1,"DONOR, HEP B SURFACE AG",HC 6911 DNR HEP BS AG,54,54,54,54,54,54,54,54
+LABDHBP,1212,1,20,DIAG. HEPATITIS B PANEL,HC HEPAT BS AG-EIA,189,248,213.5,208.5,3780,4960,4270,4170
+LABDHBP,1212,1,20,DIAG. HEPATITIS B PANEL,HC HBV CORE IGM (HBC),189,248,213.5,208.5,3780,4960,4270,4170
+LABDHBP,1212,1,20,DIAG. HEPATITIS B PANEL,HC HEP B CR TOTAL AB,189,248,213.5,208.5,3780,4960,4270,4170
+LABDHBP,1212,1,20,DIAG. HEPATITIS B PANEL,HC HEPAT BS AB-EIA,189,248,213.5,208.5,3780,4960,4270,4170
+LABDHBP,1212,3,27,DIAG. HEPATITIS B PANEL,HC HEPAT BS AG-EIA,189,248,213.5,208.5,5103,6696,5764.5,5629.5
+LABDHBP,1212,3,27,DIAG. HEPATITIS B PANEL,HC HBV CORE IGM (HBC),189,248,213.5,208.5,5103,6696,5764.5,5629.5
+LABDHBP,1212,3,27,DIAG. HEPATITIS B PANEL,HC HEP B CR TOTAL AB,189,248,213.5,208.5,5103,6696,5764.5,5629.5
+LABDHBP,1212,3,27,DIAG. HEPATITIS B PANEL,HC HEPAT BS AB-EIA,189,248,213.5,208.5,5103,6696,5764.5,5629.5
+LABDHBP,1212,7,29,DIAG. HEPATITIS B PANEL,HC HEPAT BS AG-EIA,189,248,213.5,208.5,5481,7192,6191.5,6046.5
+LABDHBP,1212,7,29,DIAG. HEPATITIS B PANEL,HC HBV CORE IGM (HBC),189,248,213.5,208.5,5481,7192,6191.5,6046.5
+LABDHBP,1212,7,29,DIAG. HEPATITIS B PANEL,HC HEP B CR TOTAL AB,189,248,213.5,208.5,5481,7192,6191.5,6046.5
+LABDHBP,1212,7,29,DIAG. HEPATITIS B PANEL,HC HEPAT BS AB-EIA,189,248,213.5,208.5,5481,7192,6191.5,6046.5
+LABDIC,25716,1,14477,DIC SCREEN,HC THROMPLAS PTT,167,438,264,240,2417659,6340926,3821928,3474480
+LABDIC,25716,1,14477,DIC SCREEN,HC PROTHROMBIN TIME,167,438,264,240,2417659,6340926,3821928,3474480
+LABDIC,25716,1,14477,DIC SCREEN,HC D DIMER LIA,167,438,264,240,2417659,6340926,3821928,3474480
+LABDIC,25716,1,14477,DIC SCREEN,HC FIBRINOGEN SEMIQUAN,167,438,264,240,2417659,6340926,3821928,3474480
+LABDIC,25716,1,14477,DIC SCREEN,HC THROMBIN TIME,167,438,264,240,2417659,6340926,3821928,3474480
+LABDIC,25716,3,20292,DIC SCREEN,HC THROMPLAS PTT,167,438,264,240,3388764,8887896,5357088,4870080
+LABDIC,25716,3,20292,DIC SCREEN,HC PROTHROMBIN TIME,167,438,264,240,3388764,8887896,5357088,4870080
+LABDIC,25716,3,20292,DIC SCREEN,HC D DIMER LIA,167,438,264,240,3388764,8887896,5357088,4870080
+LABDIC,25716,3,20292,DIC SCREEN,HC FIBRINOGEN SEMIQUAN,167,438,264,240,3388764,8887896,5357088,4870080
+LABDIC,25716,3,20292,DIC SCREEN,HC THROMBIN TIME,167,438,264,240,3388764,8887896,5357088,4870080
+LABDIC,25716,7,20847,DIC SCREEN,HC THROMPLAS PTT,167,438,264,240,3481449,9130986,5503608,5003280
+LABDIC,25716,7,20847,DIC SCREEN,HC PROTHROMBIN TIME,167,438,264,240,3481449,9130986,5503608,5003280
+LABDIC,25716,7,20847,DIC SCREEN,HC D DIMER LIA,167,438,264,240,3481449,9130986,5503608,5003280
+LABDIC,25716,7,20847,DIC SCREEN,HC FIBRINOGEN SEMIQUAN,167,438,264,240,3481449,9130986,5503608,5003280
+LABDIC,25716,7,20847,DIC SCREEN,HC THROMBIN TIME,167,438,264,240,3481449,9130986,5503608,5003280
+LABDIGL,5109,1,1201,DIGOXIN,HC DIGOXIN LEVEL,389,389,389,389,467189,467189,467189,467189
+LABDIGL,5109,3,2466,DIGOXIN,HC DIGOXIN LEVEL,389,389,389,389,959274,959274,959274,959274
+LABDIGL,5109,7,2755,DIGOXIN,HC DIGOXIN LEVEL,389,389,389,389,1071695,1071695,1071695,1071695
+LABDPARE,12,1,1,DIRECT PARASITE EXAM,HC DIR PARASIT EXAM,132,132,132,132,132,132,132,132
+LABDPARE,12,3,1,DIRECT PARASITE EXAM,HC DIR PARASIT EXAM,132,132,132,132,132,132,132,132
+LABDPARE,12,7,1,DIRECT PARASITE EXAM,HC DIR PARASIT EXAM,132,132,132,132,132,132,132,132
+LABDRVVTP,147,1,14,DILUTE RUSSELL VIPER VENOM TEST PANEL,HC DIL RUSSELL VPR VNM,240,240,240,240,3360,3360,3360,3360
+LABDRVVTP,147,3,14,DILUTE RUSSELL VIPER VENOM TEST PANEL,HC DIL RUSSELL VPR VNM,240,240,240,240,3360,3360,3360,3360
+LABDRVVTP,147,7,15,DILUTE RUSSELL VIPER VENOM TEST PANEL,HC DIL RUSSELL VPR VNM,240,240,240,240,3600,3600,3600,3600
+LABDSDNA,646,1,19,ANTI-dsDNA ANTIBODY,HC DNA ANTIBODY,281,281,281,281,5339,5339,5339,5339
+LABDSDNA,646,3,30,ANTI-dsDNA ANTIBODY,HC DNA ANTIBODY,281,281,281,281,8430,8430,8430,8430
+LABDSDNA,646,7,34,ANTI-dsDNA ANTIBODY,HC DNA ANTIBODY,281,281,281,281,9554,9554,9554,9554
+LABDVEAB,31,1,4,Rapid Flu A/B EIA,HC RAPID FLU A OR B EIA EACH,111,111,111,111,444,444,444,444
+LABDVEAB,31,3,4,Rapid Flu A/B EIA,HC RAPID FLU A OR B EIA EACH,111,111,111,111,444,444,444,444
+LABDVEAB,31,7,4,Rapid Flu A/B EIA,HC RAPID FLU A OR B EIA EACH,111,111,111,111,444,444,444,444
+LABDVEFLA,20,1,2,DIRECT VIRAL EXAM FLU A,HC FLU A ANTIGEN,221,221,221,221,442,442,442,442
+LABDVEFLA,20,3,3,DIRECT VIRAL EXAM FLU A,HC FLU A ANTIGEN,221,221,221,221,663,663,663,663
+LABDVEFLA,20,7,3,DIRECT VIRAL EXAM FLU A,HC FLU A ANTIGEN,221,221,221,221,663,663,663,663
+LABDVEFLB,29,1,1,DIRECT VIRAL EXAM FLU B,HC FLU B DFA,165,165,165,165,165,165,165,165
+LABDVEFLB,29,3,2,DIRECT VIRAL EXAM FLU B,HC FLU B DFA,165,165,165,165,330,330,330,330
+LABDVEFLB,29,7,2,DIRECT VIRAL EXAM FLU B,HC FLU B DFA,165,165,165,165,330,330,330,330
+LABDVEHV,540,1,48,HSV/VZV DFA,HC HSV ANTIGEN,319,369,344,344,15312,17712,16512,16512
+LABDVEHV,540,1,48,HSV/VZV DFA,HC VZV ANTIGEN,319,369,344,344,15312,17712,16512,16512
+LABDVEHV,540,3,77,HSV/VZV DFA,HC HSV ANTIGEN,319,369,344,344,24563,28413,26488,26488
+LABDVEHV,540,3,77,HSV/VZV DFA,HC VZV ANTIGEN,319,369,344,344,24563,28413,26488,26488
+LABDVEHV,540,7,89,HSV/VZV DFA,HC HSV ANTIGEN,319,369,344,344,28391,32841,30616,30616
+LABDVEHV,540,7,89,HSV/VZV DFA,HC VZV ANTIGEN,319,369,344,344,28391,32841,30616,30616
+LABDVEHVR,54,1,2,HSV/VZV DFA reflex PCR,HC DVEHVR VZV ANTIGEN,319,369,344,344,638,738,688,688
+LABDVEHVR,54,1,2,HSV/VZV DFA reflex PCR,HC DVEHVR DET IMM EA PLY ASRM,319,369,344,344,638,738,688,688
+LABDVEHVR,54,3,5,HSV/VZV DFA reflex PCR,HC DVEHVR VZV ANTIGEN,319,369,344,344,1595,1845,1720,1720
+LABDVEHVR,54,3,5,HSV/VZV DFA reflex PCR,HC DVEHVR DET IMM EA PLY ASRM,319,369,344,344,1595,1845,1720,1720
+LABDVEHVR,54,7,6,HSV/VZV DFA reflex PCR,HC DVEHVR VZV ANTIGEN,319,369,344,344,1914,2214,2064,2064
+LABDVEHVR,54,7,6,HSV/VZV DFA reflex PCR,HC DVEHVR DET IMM EA PLY ASRM,319,369,344,344,1914,2214,2064,2064
+LABDVEIAV,31,1,1,FLU A DFA,HC FLU A ANTIGEN,221,221,221,221,221,221,221,221
+LABDVEIAV,31,3,1,FLU A DFA,HC FLU A ANTIGEN,221,221,221,221,221,221,221,221
+LABDVEIAV,31,7,1,FLU A DFA,HC FLU A ANTIGEN,221,221,221,221,221,221,221,221
+LABDVEPI1,3,1,2,PARA 1 DFA,HC PARAFLU DFA,324,324,324,324,648,648,648,648
+LABDVEPI1,3,3,2,PARA 1 DFA,HC PARAFLU DFA,324,324,324,324,648,648,648,648
+LABDVEPI1,3,7,2,PARA 1 DFA,HC PARAFLU DFA,324,324,324,324,648,648,648,648
+LABDVERO,136,1,2,DirVirExm: Rotavirus,HC ROTA VIRUS ANTIGEN,181,181,181,181,362,362,362,362
+LABDVERO,136,3,3,DirVirExm: Rotavirus,HC ROTA VIRUS ANTIGEN,181,181,181,181,543,543,543,543
+LABDVERO,136,7,6,DirVirExm: Rotavirus,HC ROTA VIRUS ANTIGEN,181,181,181,181,1086,1086,1086,1086
+LABDVERTV,75,1,4,"ROTAVIRUS ANTIGEN, STOOL",HC ROTA VIRUS ANTIGEN,181,181,181,181,724,724,724,724
+LABDVERTV,75,3,5,"ROTAVIRUS ANTIGEN, STOOL",HC ROTA VIRUS ANTIGEN,181,181,181,181,905,905,905,905
+LABDVERTV,75,7,6,"ROTAVIRUS ANTIGEN, STOOL",HC ROTA VIRUS ANTIGEN,181,181,181,181,1086,1086,1086,1086
+LABDVERV,1501,1,107,Dir Viral Exam: Resp,HC ADENO VIRUS ANTIGEN,66,319,211,221,7062,34133,22577,23647
+LABDVERV,1501,1,107,Dir Viral Exam: Resp,HC FLU A ANTIGEN,66,319,211,221,7062,34133,22577,23647
+LABDVERV,1501,1,107,Dir Viral Exam: Resp,HC FLU B DFA,66,319,211,221,7062,34133,22577,23647
+LABDVERV,1501,1,107,Dir Viral Exam: Resp,HC RSV ANTIGEN,66,319,211,221,7062,34133,22577,23647
+LABDVERV,1501,1,107,Dir Viral Exam: Resp,HC PARAFLU DFA POOL,66,319,211,221,7062,34133,22577,23647
+LABDVERV,1501,3,200,Dir Viral Exam: Resp,HC ADENO VIRUS ANTIGEN,66,319,211,221,13200,63800,42200,44200
+LABDVERV,1501,3,200,Dir Viral Exam: Resp,HC FLU A ANTIGEN,66,319,211,221,13200,63800,42200,44200
+LABDVERV,1501,3,200,Dir Viral Exam: Resp,HC FLU B DFA,66,319,211,221,13200,63800,42200,44200
+LABDVERV,1501,3,200,Dir Viral Exam: Resp,HC RSV ANTIGEN,66,319,211,221,13200,63800,42200,44200
+LABDVERV,1501,3,200,Dir Viral Exam: Resp,HC PARAFLU DFA POOL,66,319,211,221,13200,63800,42200,44200
+LABDVERV,1501,7,252,Dir Viral Exam: Resp,HC ADENO VIRUS ANTIGEN,66,319,211,221,16632,80388,53172,55692
+LABDVERV,1501,7,252,Dir Viral Exam: Resp,HC FLU A ANTIGEN,66,319,211,221,16632,80388,53172,55692
+LABDVERV,1501,7,252,Dir Viral Exam: Resp,HC FLU B DFA,66,319,211,221,16632,80388,53172,55692
+LABDVERV,1501,7,252,Dir Viral Exam: Resp,HC RSV ANTIGEN,66,319,211,221,16632,80388,53172,55692
+LABDVERV,1501,7,252,Dir Viral Exam: Resp,HC PARAFLU DFA POOL,66,319,211,221,16632,80388,53172,55692
+LABDVERV3,74,1,1,RSV DFA,HC RSV DFA,249,249,249,249,249,249,249,249
+LABDVERV3,74,3,2,RSV DFA,HC RSV DFA,249,249,249,249,498,498,498,498
+LABDVERV3,74,7,5,RSV DFA,HC RSV DFA,249,249,249,249,1245,1245,1245,1245
+LABDVERVP,3346,1,264,RESPIRATORY DFA PANEL,HC ADENO VIRUS ANTIGEN,165,324,238.8333333,235,43560,85536,63052,62040
+LABDVERVP,3346,1,264,RESPIRATORY DFA PANEL,HC FLU A ANTIGEN,165,324,238.8333333,235,43560,85536,63052,62040
+LABDVERVP,3346,1,264,RESPIRATORY DFA PANEL,HC FLU B DFA,165,324,238.8333333,235,43560,85536,63052,62040
+LABDVERVP,3346,1,264,RESPIRATORY DFA PANEL,HC METAPNEUMOVIRUS DFA,165,324,238.8333333,235,43560,85536,63052,62040
+LABDVERVP,3346,1,264,RESPIRATORY DFA PANEL,HC PARAFLU DFA,165,324,238.8333333,235,43560,85536,63052,62040
+LABDVERVP,3346,1,264,RESPIRATORY DFA PANEL,HC RSV DFA,165,324,238.8333333,235,43560,85536,63052,62040
+LABDVERVP,3346,3,438,RESPIRATORY DFA PANEL,HC ADENO VIRUS ANTIGEN,165,324,238.8333333,235,72270,141912,104609,102930
+LABDVERVP,3346,3,438,RESPIRATORY DFA PANEL,HC FLU A ANTIGEN,165,324,238.8333333,235,72270,141912,104609,102930
+LABDVERVP,3346,3,438,RESPIRATORY DFA PANEL,HC FLU B DFA,165,324,238.8333333,235,72270,141912,104609,102930
+LABDVERVP,3346,3,438,RESPIRATORY DFA PANEL,HC METAPNEUMOVIRUS DFA,165,324,238.8333333,235,72270,141912,104609,102930
+LABDVERVP,3346,3,438,RESPIRATORY DFA PANEL,HC PARAFLU DFA,165,324,238.8333333,235,72270,141912,104609,102930
+LABDVERVP,3346,3,438,RESPIRATORY DFA PANEL,HC RSV DFA,165,324,238.8333333,235,72270,141912,104609,102930
+LABDVERVP,3346,7,567,RESPIRATORY DFA PANEL,HC ADENO VIRUS ANTIGEN,165,324,238.8333333,235,93555,183708,135418.5,133245
+LABDVERVP,3346,7,567,RESPIRATORY DFA PANEL,HC FLU A ANTIGEN,165,324,238.8333333,235,93555,183708,135418.5,133245
+LABDVERVP,3346,7,567,RESPIRATORY DFA PANEL,HC FLU B DFA,165,324,238.8333333,235,93555,183708,135418.5,133245
+LABDVERVP,3346,7,567,RESPIRATORY DFA PANEL,HC METAPNEUMOVIRUS DFA,165,324,238.8333333,235,93555,183708,135418.5,133245
+LABDVERVP,3346,7,567,RESPIRATORY DFA PANEL,HC PARAFLU DFA,165,324,238.8333333,235,93555,183708,135418.5,133245
+LABDVERVP,3346,7,567,RESPIRATORY DFA PANEL,HC RSV DFA,165,324,238.8333333,235,93555,183708,135418.5,133245
+LABDVERVR,322,1,19,RESP DFA PANEL-REFLEX PCR,HC FLU B ADENO VIRUS ANTIGEN,165,324,238.8333333,235,3135,6156,4537.833333,4465
+LABDVERVR,322,1,19,RESP DFA PANEL-REFLEX PCR,HC FLU B DFA,165,324,238.8333333,235,3135,6156,4537.833333,4465
+LABDVERVR,322,1,19,RESP DFA PANEL-REFLEX PCR,HC FLU A ANTIGEN,165,324,238.8333333,235,3135,6156,4537.833333,4465
+LABDVERVR,322,1,19,RESP DFA PANEL-REFLEX PCR,HC PARAFLU DFA ANTIGEN,165,324,238.8333333,235,3135,6156,4537.833333,4465
+LABDVERVR,322,1,19,RESP DFA PANEL-REFLEX PCR,HC RSV DFA / ANTIGEN,165,324,238.8333333,235,3135,6156,4537.833333,4465
+LABDVERVR,322,1,19,RESP DFA PANEL-REFLEX PCR,HC METAPNEUMOVIRUS DFA,165,324,238.8333333,235,3135,6156,4537.833333,4465
+LABDVERVR,322,3,23,RESP DFA PANEL-REFLEX PCR,HC FLU B ADENO VIRUS ANTIGEN,165,324,238.8333333,235,3795,7452,5493.166667,5405
+LABDVERVR,322,3,23,RESP DFA PANEL-REFLEX PCR,HC FLU B DFA,165,324,238.8333333,235,3795,7452,5493.166667,5405
+LABDVERVR,322,3,23,RESP DFA PANEL-REFLEX PCR,HC FLU A ANTIGEN,165,324,238.8333333,235,3795,7452,5493.166667,5405
+LABDVERVR,322,3,23,RESP DFA PANEL-REFLEX PCR,HC PARAFLU DFA ANTIGEN,165,324,238.8333333,235,3795,7452,5493.166667,5405
+LABDVERVR,322,3,23,RESP DFA PANEL-REFLEX PCR,HC RSV DFA / ANTIGEN,165,324,238.8333333,235,3795,7452,5493.166667,5405
+LABDVERVR,322,3,23,RESP DFA PANEL-REFLEX PCR,HC METAPNEUMOVIRUS DFA,165,324,238.8333333,235,3795,7452,5493.166667,5405
+LABDVERVR,322,7,28,RESP DFA PANEL-REFLEX PCR,HC FLU B ADENO VIRUS ANTIGEN,165,324,238.8333333,235,4620,9072,6687.333333,6580
+LABDVERVR,322,7,28,RESP DFA PANEL-REFLEX PCR,HC FLU B DFA,165,324,238.8333333,235,4620,9072,6687.333333,6580
+LABDVERVR,322,7,28,RESP DFA PANEL-REFLEX PCR,HC FLU A ANTIGEN,165,324,238.8333333,235,4620,9072,6687.333333,6580
+LABDVERVR,322,7,28,RESP DFA PANEL-REFLEX PCR,HC PARAFLU DFA ANTIGEN,165,324,238.8333333,235,4620,9072,6687.333333,6580
+LABDVERVR,322,7,28,RESP DFA PANEL-REFLEX PCR,HC RSV DFA / ANTIGEN,165,324,238.8333333,235,4620,9072,6687.333333,6580
+LABDVERVR,322,7,28,RESP DFA PANEL-REFLEX PCR,HC METAPNEUMOVIRUS DFA,165,324,238.8333333,235,4620,9072,6687.333333,6580
+LABDVEVH,360,1,31,Dir Vir Exam: VZ/HSV,HC HSV ANTIGEN,319,369,344,344,9889,11439,10664,10664
+LABDVEVH,360,1,31,Dir Vir Exam: VZ/HSV,HC VZV ANTIGEN,319,369,344,344,9889,11439,10664,10664
+LABDVEVH,360,3,47,Dir Vir Exam: VZ/HSV,HC HSV ANTIGEN,319,369,344,344,14993,17343,16168,16168
+LABDVEVH,360,3,47,Dir Vir Exam: VZ/HSV,HC VZV ANTIGEN,319,369,344,344,14993,17343,16168,16168
+LABDVEVH,360,7,61,Dir Vir Exam: VZ/HSV,HC HSV ANTIGEN,319,369,344,344,19459,22509,20984,20984
+LABDVEVH,360,7,61,Dir Vir Exam: VZ/HSV,HC VZV ANTIGEN,319,369,344,344,19459,22509,20984,20984
+LABEB,324,1,5,EPSTEIN-BARR PANEL,HC 6942 EB VIRAL CAPSID AB,14,19,16.33333333,16,70,95,81.66666667,80
+LABEB,324,1,5,EPSTEIN-BARR PANEL,HC 6942 EB EARLY ANTIGEN AB,14,19,16.33333333,16,70,95,81.66666667,80
+LABEB,324,1,5,EPSTEIN-BARR PANEL,HC 6942 EB NUCLEAR ANTIGEN AB,14,19,16.33333333,16,70,95,81.66666667,80
+LABEB,324,3,10,EPSTEIN-BARR PANEL,HC 6942 EB VIRAL CAPSID AB,14,19,16.33333333,16,140,190,163.3333333,160
+LABEB,324,3,10,EPSTEIN-BARR PANEL,HC 6942 EB EARLY ANTIGEN AB,14,19,16.33333333,16,140,190,163.3333333,160
+LABEB,324,3,10,EPSTEIN-BARR PANEL,HC 6942 EB NUCLEAR ANTIGEN AB,14,19,16.33333333,16,140,190,163.3333333,160
+LABEB,324,7,10,EPSTEIN-BARR PANEL,HC 6942 EB VIRAL CAPSID AB,14,19,16.33333333,16,140,190,163.3333333,160
+LABEB,324,7,10,EPSTEIN-BARR PANEL,HC 6942 EB EARLY ANTIGEN AB,14,19,16.33333333,16,140,190,163.3333333,160
+LABEB,324,7,10,EPSTEIN-BARR PANEL,HC 6942 EB NUCLEAR ANTIGEN AB,14,19,16.33333333,16,140,190,163.3333333,160
+LABEBNABY,56,1,5,EBV NUCLEAR ANTIGEN IgG,HC EBNABY EBV EBNA IGG,218,218,218,218,1090,1090,1090,1090
+LABEBNABY,56,3,5,EBV NUCLEAR ANTIGEN IgG,HC EBNABY EBV EBNA IGG,218,218,218,218,1090,1090,1090,1090
+LABEBNABY,56,7,7,EBV NUCLEAR ANTIGEN IgG,HC EBNABY EBV EBNA IGG,218,218,218,218,1526,1526,1526,1526
+LABEBPCRQ,1282,1,44,EBV PCR QUANTITATIVE,HC EPSTEIN BARR VIRUS PCR QUANT,650,650,650,650,28600,28600,28600,28600
+LABEBPCRQ,1282,3,86,EBV PCR QUANTITATIVE,HC EPSTEIN BARR VIRUS PCR QUANT,650,650,650,650,55900,55900,55900,55900
+LABEBPCRQ,1282,7,191,EBV PCR QUANTITATIVE,HC EPSTEIN BARR VIRUS PCR QUANT,650,650,650,650,124150,124150,124150,124150
+LABEBVPAN,794,1,15,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV EA IGG,191,343,250.6666667,218,2865,5145,3760,3270
+LABEBVPAN,794,1,15,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV EBNA IGG,191,343,250.6666667,218,2865,5145,3760,3270
+LABEBVPAN,794,1,15,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV VCA IGG IGM,191,343,250.6666667,218,2865,5145,3760,3270
+LABEBVPAN,794,3,22,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV EA IGG,191,343,250.6666667,218,4202,7546,5514.666667,4796
+LABEBVPAN,794,3,22,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV EBNA IGG,191,343,250.6666667,218,4202,7546,5514.666667,4796
+LABEBVPAN,794,3,22,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV VCA IGG IGM,191,343,250.6666667,218,4202,7546,5514.666667,4796
+LABEBVPAN,794,7,30,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV EA IGG,191,343,250.6666667,218,5730,10290,7520,6540
+LABEBVPAN,794,7,30,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV EBNA IGG,191,343,250.6666667,218,5730,10290,7520,6540
+LABEBVPAN,794,7,30,EPSTEIN-BARR VIRUS AB PANEL,HC EBVPAN EBV VCA IGG IGM,191,343,250.6666667,218,5730,10290,7520,6540
+LABEBVQL,20,1,1,EBV DNA PCR QUAL,HC EBVQL EBV DNA PCR APT QUAL,388,388,388,388,388,388,388,388
+LABEBVQL,20,3,1,EBV DNA PCR QUAL,HC EBVQL EBV DNA PCR APT QUAL,388,388,388,388,388,388,388,388
+LABEBVQL,20,7,5,EBV DNA PCR QUAL,HC EBVQL EBV DNA PCR APT QUAL,388,388,388,388,1940,1940,1940,1940
+LABEBVQP,1098,1,50,"EBV DNA PCR QUANT, PLASMA",HC EBVQP EBV DNA PCR QUANT,650,650,650,650,32500,32500,32500,32500
+LABEBVQP,1098,3,91,"EBV DNA PCR QUANT, PLASMA",HC EBVQP EBV DNA PCR QUANT,650,650,650,650,59150,59150,59150,59150
+LABEBVQP,1098,7,180,"EBV DNA PCR QUANT, PLASMA",HC EBVQP EBV DNA PCR QUANT,650,650,650,650,117000,117000,117000,117000
+LABECHIG,51,1,3,"ECHINOCOCCUS AB, IgG W/RFLX WB",HC 6972 ECHINOCOCCOSIS,128,128,128,128,384,384,384,384
+LABECHIG,51,3,5,"ECHINOCOCCUS AB, IgG W/RFLX WB",HC 6972 ECHINOCOCCOSIS,128,128,128,128,640,640,640,640
+LABECHIG,51,7,6,"ECHINOCOCCUS AB, IgG W/RFLX WB",HC 6972 ECHINOCOCCOSIS,128,128,128,128,768,768,768,768
+LABECL,24,1,3,EUGLOBULIN CLOT LYSIS,HC EUGLOBULN CLOT LYSS,172,172,172,172,516,516,516,516
+LABECL,24,3,4,EUGLOBULIN CLOT LYSIS,HC EUGLOBULN CLOT LYSS,172,172,172,172,688,688,688,688
+LABECL,24,7,5,EUGLOBULIN CLOT LYSIS,HC EUGLOBULN CLOT LYSS,172,172,172,172,860,860,860,860
+LABEGFRP,49,1,2,EGFR GENE MUTATION PANEL,HC EGFRP GN ANLY CMN VARNTS,840,840,840,840,1680,1680,1680,1680
+LABEGFRP,49,3,3,EGFR GENE MUTATION PANEL,HC EGFRP GN ANLY CMN VARNTS,840,840,840,840,2520,2520,2520,2520
+LABEGFRP,49,7,3,EGFR GENE MUTATION PANEL,HC EGFRP GN ANLY CMN VARNTS,840,840,840,840,2520,2520,2520,2520
+LABEP1,884,1,304,ELECTROLYTE PANEL,HC ELECTROLYTE PANEL,342,342,342,342,103968,103968,103968,103968
+LABEP1,884,3,357,ELECTROLYTE PANEL,HC ELECTROLYTE PANEL,342,342,342,342,122094,122094,122094,122094
+LABEP1,884,7,365,ELECTROLYTE PANEL,HC ELECTROLYTE PANEL,342,342,342,342,124830,124830,124830,124830
+LABEPO,141,1,5,Erythropoietin,HC ERYTHROPOIETIN,208,208,208,208,1040,1040,1040,1040
+LABEPO,141,3,7,Erythropoietin,HC ERYTHROPOIETIN,208,208,208,208,1456,1456,1456,1456
+LABEPO,141,7,9,Erythropoietin,HC ERYTHROPOIETIN,208,208,208,208,1872,1872,1872,1872
+LABESRP,8864,1,899,"ESR, Sedimentation Rate Panel",HC ESR AUTOMATED,75,75,75,75,67425,67425,67425,67425
+LABESRP,8864,3,1594,"ESR, Sedimentation Rate Panel",HC ESR AUTOMATED,75,75,75,75,119550,119550,119550,119550
+LABESRP,8864,7,1959,"ESR, Sedimentation Rate Panel",HC ESR AUTOMATED,75,75,75,75,146925,146925,146925,146925
+LABEVPCR,235,1,11,ENTEROVIRUS BY PCR,HC EVPCR ENTEROVIRUS PCR CSF,504,504,504,504,5544,5544,5544,5544
+LABEVPCR,235,3,13,ENTEROVIRUS BY PCR,HC EVPCR ENTEROVIRUS PCR CSF,504,504,504,504,6552,6552,6552,6552
+LABEVPCR,235,7,17,ENTEROVIRUS BY PCR,HC EVPCR ENTEROVIRUS PCR CSF,504,504,504,504,8568,8568,8568,8568
+LABF8INH,62,1,14,"Factor VIII Inhibitor, Human",HC FACTOR VIII INHIBTO,251,251,251,251,3514,3514,3514,3514
+LABF8INH,62,3,20,"Factor VIII Inhibitor, Human",HC FACTOR VIII INHIBTO,251,251,251,251,5020,5020,5020,5020
+LABF8INH,62,7,30,"Factor VIII Inhibitor, Human",HC FACTOR VIII INHIBTO,251,251,251,251,7530,7530,7530,7530
+LABFACT10,105,1,21,Factor X Assay,HC FACTOR X ASSAY,364,364,364,364,7644,7644,7644,7644
+LABFACT10,105,3,32,Factor X Assay,HC FACTOR X ASSAY,364,364,364,364,11648,11648,11648,11648
+LABFACT10,105,7,32,Factor X Assay,HC FACTOR X ASSAY,364,364,364,364,11648,11648,11648,11648
+LABFACT11,66,1,6,Factor XI Assay,HC FACTOR XI ASSAY,375,375,375,375,2250,2250,2250,2250
+LABFACT11,66,3,13,Factor XI Assay,HC FACTOR XI ASSAY,375,375,375,375,4875,4875,4875,4875
+LABFACT11,66,7,15,Factor XI Assay,HC FACTOR XI ASSAY,375,375,375,375,5625,5625,5625,5625
+LABFACT12,32,1,4,Factor XII Assay,HC FACTOR XII ASSAY,214,214,214,214,856,856,856,856
+LABFACT12,32,3,4,Factor XII Assay,HC FACTOR XII ASSAY,214,214,214,214,856,856,856,856
+LABFACT12,32,7,4,Factor XII Assay,HC FACTOR XII ASSAY,214,214,214,214,856,856,856,856
+LABFACT2,67,1,14,Factor II Assay,HC FACTOR II ASSAY,346,346,346,346,4844,4844,4844,4844
+LABFACT2,67,3,19,Factor II Assay,HC FACTOR II ASSAY,346,346,346,346,6574,6574,6574,6574
+LABFACT2,67,7,20,Factor II Assay,HC FACTOR II ASSAY,346,346,346,346,6920,6920,6920,6920
+LABFACT5,81,1,10,Factor V Assay,HC FACTOR V ASSAY,375,375,375,375,3750,3750,3750,3750
+LABFACT5,81,3,10,Factor V Assay,HC FACTOR V ASSAY,375,375,375,375,3750,3750,3750,3750
+LABFACT5,81,7,11,Factor V Assay,HC FACTOR V ASSAY,375,375,375,375,4125,4125,4125,4125
+LABFACT7,97,1,16,Factor VII Assay,HC FACTOR VII ASSAY,285,285,285,285,4560,4560,4560,4560
+LABFACT7,97,3,20,Factor VII Assay,HC FACTOR VII ASSAY,285,285,285,285,5700,5700,5700,5700
+LABFACT7,97,7,22,Factor VII Assay,HC FACTOR VII ASSAY,285,285,285,285,6270,6270,6270,6270
+LABFACT8,507,1,223,Factor VIII Assay,HC FACTOR VIII ASSAY,375,375,375,375,83625,83625,83625,83625
+LABFACT8,507,3,287,Factor VIII Assay,HC FACTOR VIII ASSAY,375,375,375,375,107625,107625,107625,107625
+LABFACT8,507,7,311,Factor VIII Assay,HC FACTOR VIII ASSAY,375,375,375,375,116625,116625,116625,116625
+LABFACT9,142,1,62,Factor IX Assay,HC FACTOR IX ASSAY,406,406,406,406,25172,25172,25172,25172
+LABFACT9,142,3,74,Factor IX Assay,HC FACTOR IX ASSAY,406,406,406,406,30044,30044,30044,30044
+LABFACT9,142,7,74,Factor IX Assay,HC FACTOR IX ASSAY,406,406,406,406,30044,30044,30044,30044
+LABFADA,205,1,6,"ADENOSINE DEAMINASE,BODY FLUID",HC 6959 FADA ADNSN DMNS FLD,40,40,40,40,240,240,240,240
+LABFADA,205,3,11,"ADENOSINE DEAMINASE,BODY FLUID",HC 6959 FADA ADNSN DMNS FLD,40,40,40,40,440,440,440,440
+LABFADA,205,7,16,"ADENOSINE DEAMINASE,BODY FLUID",HC 6959 FADA ADNSN DMNS FLD,40,40,40,40,640,640,640,640
+LABFADAG,276,1,11,ADENOVIRUS AG,HC 6942 ADENOVIRUS AG BY EIA,52,52,52,52,572,572,572,572
+LABFADAG,276,3,29,ADENOVIRUS AG,HC 6942 ADENOVIRUS AG BY EIA,52,52,52,52,1508,1508,1508,1508
+LABFADAG,276,7,43,ADENOVIRUS AG,HC 6942 ADENOVIRUS AG BY EIA,52,52,52,52,2236,2236,2236,2236
+LABFAL,1592,1,41,"ALBUMIN, FLUID",HC ALBUMIN BODY FLUID QUANT,126,126,126,126,5166,5166,5166,5166
+LABFAL,1592,3,122,"ALBUMIN, FLUID",HC ALBUMIN BODY FLUID QUANT,126,126,126,126,15372,15372,15372,15372
+LABFAL,1592,7,228,"ALBUMIN, FLUID",HC ALBUMIN BODY FLUID QUANT,126,126,126,126,28728,28728,28728,28728
+LABFAMY,1964,1,325,"AMYLASE, FLUID",HC AMYLASE BODY FLUID,395,395,395,395,128375,128375,128375,128375
+LABFAMY,1964,3,510,"AMYLASE, FLUID",HC AMYLASE BODY FLUID,395,395,395,395,201450,201450,201450,201450
+LABFAMY,1964,7,636,"AMYLASE, FLUID",HC AMYLASE BODY FLUID,395,395,395,395,251220,251220,251220,251220
+LABFAMYA,15,1,2,"AMYLASE, ASCITES",HC AMYLASE BODY FLUID,395,395,395,395,790,790,790,790
+LABFAMYA,15,3,2,"AMYLASE, ASCITES",HC AMYLASE BODY FLUID,395,395,395,395,790,790,790,790
+LABFAMYA,15,7,2,"AMYLASE, ASCITES",HC AMYLASE BODY FLUID,395,395,395,395,790,790,790,790
+LABFBBURG,83,3,4,"BORRELIA BURGDORFERI PANEL, CSF",HC 6942 LYME CSF WESTERN BLOT,81,81,81,81,324,324,324,324
+LABFBBURG,83,7,6,"BORRELIA BURGDORFERI PANEL, CSF",HC 6942 LYME CSF WESTERN BLOT,81,81,81,81,486,486,486,486
+LABFBILA,233,1,19,"BILIRUBIN, TOTAL, BODY FLUID","HC 6959 BILIRUBIN, FLUID",9,9,9,9,171,171,171,171
+LABFBILA,233,3,21,"BILIRUBIN, TOTAL, BODY FLUID","HC 6959 BILIRUBIN, FLUID",9,9,9,9,189,189,189,189
+LABFBILA,233,7,25,"BILIRUBIN, TOTAL, BODY FLUID","HC 6959 BILIRUBIN, FLUID",9,9,9,9,225,225,225,225
+LABFCD34,47,1,8,"CD34 PANEL, FLUID",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,5592,5592,5592,5592
+LABFCD34,47,3,10,"CD34 PANEL, FLUID",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,6990,6990,6990,6990
+LABFCD34,47,7,10,"CD34 PANEL, FLUID",HC FLOW:STEM CELLS(CD34)TTL CNT,699,699,699,699,6990,6990,6990,6990
+LABFCHOLA,89,1,2,"CHOLESTEROL, FLUID","HC 6959 CHOLESTEROL, FLUID",9,9,9,9,18,18,18,18
+LABFCHOLA,89,3,3,"CHOLESTEROL, FLUID","HC 6959 CHOLESTEROL, FLUID",9,9,9,9,27,27,27,27
+LABFCHOLA,89,7,5,"CHOLESTEROL, FLUID","HC 6959 CHOLESTEROL, FLUID",9,9,9,9,45,45,45,45
+LABFCR,1326,1,123,"CREATININE, FLUID",HC CREATININE BDY FL,174,174,174,174,21402,21402,21402,21402
+LABFCR,1326,3,224,"CREATININE, FLUID",HC CREATININE BDY FL,174,174,174,174,38976,38976,38976,38976
+LABFCR,1326,7,287,"CREATININE, FLUID",HC CREATININE BDY FL,174,174,174,174,49938,49938,49938,49938
+LABFCUL,10532,1,2141,Fungus Culture,HC FUNGUS CULTURE,309,309,309,309,661569,661569,661569,661569
+LABFCUL,10532,3,2467,Fungus Culture,HC FUNGUS CULTURE,309,309,309,309,762303,762303,762303,762303
+LABFCUL,10532,7,2945,Fungus Culture,HC FUNGUS CULTURE,309,309,309,309,910005,910005,910005,910005
+LABFCYST,14,1,2,CYSTICERCUS IgG ANTIBODY,HC 6942 CYSTICERCUS IGG AB,107,107,107,107,214,214,214,214
+LABFCYST,14,3,3,CYSTICERCUS IgG ANTIBODY,HC 6942 CYSTICERCUS IGG AB,107,107,107,107,321,321,321,321
+LABFCYST,14,7,3,CYSTICERCUS IgG ANTIBODY,HC 6942 CYSTICERCUS IGG AB,107,107,107,107,321,321,321,321
+LABFDP,216,1,33,Fibrin Degradation Products,HC FIBRIN DEGRADATION PROD,107,107,107,107,3531,3531,3531,3531
+LABFDP,216,3,35,Fibrin Degradation Products,HC FIBRIN DEGRADATION PROD,107,107,107,107,3745,3745,3745,3745
+LABFDP,216,7,37,Fibrin Degradation Products,HC FIBRIN DEGRADATION PROD,107,107,107,107,3959,3959,3959,3959
+LABFE,643,1,17,"Iron, Total",HC IRON TTL SERUM,165,165,165,165,2805,2805,2805,2805
+LABFE,643,3,22,"Iron, Total",HC IRON TTL SERUM,165,165,165,165,3630,3630,3630,3630
+LABFE,643,7,22,"Iron, Total",HC IRON TTL SERUM,165,165,165,165,3630,3630,3630,3630
+LABFECHO,15,1,1,"ECHOVIRUS AB, SERUM                      NONORDERABLE",HC 6942 ECHOVIRUS AB PNL CF,21,21,21,21,21,21,21,21
+LABFECHO,15,3,1,"ECHOVIRUS AB, SERUM                      NONORDERABLE",HC 6942 ECHOVIRUS AB PNL CF,21,21,21,21,21,21,21,21
+LABFECHO,15,7,1,"ECHOVIRUS AB, SERUM                      NONORDERABLE",HC 6942 ECHOVIRUS AB PNL CF,21,21,21,21,21,21,21,21
+LABFEHRC,19,7,1,EHRLICHIA CHAFFEENSIS ABS,HC 6942 EHRLICHIA CHAFFEENIS,74,74,74,74,74,74,74,74
+LABFENTA,86,1,7,ENTAMOEBA HISTOLYTICA IGG,HC 6942 ENTAMOEGA HISTOLYTICA AB,61,61,61,61,427,427,427,427
+LABFENTA,86,3,7,ENTAMOEBA HISTOLYTICA IGG,HC 6942 ENTAMOEGA HISTOLYTICA AB,61,61,61,61,427,427,427,427
+LABFENTA,86,7,9,ENTAMOEBA HISTOLYTICA IGG,HC 6942 ENTAMOEGA HISTOLYTICA AB,61,61,61,61,549,549,549,549
+LABFER,6942,1,361,Ferritin,HC FERRITIN,375,375,375,375,135375,135375,135375,135375
+LABFER,6942,3,556,Ferritin,HC FERRITIN,375,375,375,375,208500,208500,208500,208500
+LABFER,6942,7,667,Ferritin,HC FERRITIN,375,375,375,375,250125,250125,250125,250125
+LABFFIBN,10,7,1,Fetal Fibronectin,HC FETAL FIBRONECTIN,1161,1161,1161,1161,1161,1161,1161,1161
+LABFHAN,20,1,1,"HANTAVIRUS ANTIBODIES, ELISA",HC 6942 HANTAVIRUS AB,47,47,47,47,47,47,47,47
+LABFHAN,20,3,1,"HANTAVIRUS ANTIBODIES, ELISA",HC 6942 HANTAVIRUS AB,47,47,47,47,47,47,47,47
+LABFHAN,20,7,1,"HANTAVIRUS ANTIBODIES, ELISA",HC 6942 HANTAVIRUS AB,47,47,47,47,47,47,47,47
+LABFHCT,156,1,7,"HEMATOCRIT, FLUID",HC HEMATOCRIT-FLUID,55,55,55,55,385,385,385,385
+LABFHCT,156,3,11,"HEMATOCRIT, FLUID",HC HEMATOCRIT-FLUID,55,55,55,55,605,605,605,605
+LABFHCT,156,7,12,"HEMATOCRIT, FLUID",HC HEMATOCRIT-FLUID,55,55,55,55,660,660,660,660
+LABFHTLVQ,59,1,1,HTLV-1/II DNA QUAL,HC 6942 HTLV-I/II DNA QUANT,112,112,112,112,112,112,112,112
+LABFHTLVQ,59,3,3,HTLV-1/II DNA QUAL,HC 6942 HTLV-I/II DNA QUANT,112,112,112,112,336,336,336,336
+LABFHTLVQ,59,7,3,HTLV-1/II DNA QUAL,HC 6942 HTLV-I/II DNA QUANT,112,112,112,112,336,336,336,336
+LABFHV12,213,1,9,"HSV 1/2 PCR, NON CSF",HC 6942 HSV 1/2 PCR NONCSF,63,63,63,63,567,567,567,567
+LABFHV12,213,3,20,"HSV 1/2 PCR, NON CSF",HC 6942 HSV 1/2 PCR NONCSF,63,63,63,63,1260,1260,1260,1260
+LABFHV12,213,7,29,"HSV 1/2 PCR, NON CSF",HC 6942 HSV 1/2 PCR NONCSF,63,63,63,63,1827,1827,1827,1827
+LABFHVIG,189,1,10,"HIV 1 AB, WESTERN BLOT CONFIRMATION",HC 6942 WEST BLOT CONFM SEN,102,102,102,102,1020,1020,1020,1020
+LABFHVIG,189,3,13,"HIV 1 AB, WESTERN BLOT CONFIRMATION",HC 6942 WEST BLOT CONFM SEN,102,102,102,102,1326,1326,1326,1326
+LABFHVIG,189,7,15,"HIV 1 AB, WESTERN BLOT CONFIRMATION",HC 6942 WEST BLOT CONFM SEN,102,102,102,102,1530,1530,1530,1530
+LABFIB,13824,1,7429,FIBRINOGEN,HC FIBRINOGEN SEMIQUAN,285,285,285,285,2117265,2117265,2117265,2117265
+LABFIB,13824,3,9358,FIBRINOGEN,HC FIBRINOGEN SEMIQUAN,285,285,285,285,2667030,2667030,2667030,2667030
+LABFIB,13824,7,9579,FIBRINOGEN,HC FIBRINOGEN SEMIQUAN,285,285,285,285,2730015,2730015,2730015,2730015
+LABFIBANT,27,1,13,FIBRINOGEN ANTIGEN,HC FIBANT FIBRINOGEN ANTIGEN,255,255,255,255,3315,3315,3315,3315
+LABFIBANT,27,3,17,FIBRINOGEN ANTIGEN,HC FIBANT FIBRINOGEN ANTIGEN,255,255,255,255,4335,4335,4335,4335
+LABFIBANT,27,7,18,FIBRINOGEN ANTIGEN,HC FIBANT FIBRINOGEN ANTIGEN,255,255,255,255,4590,4590,4590,4590
+LABFILAB,31,1,4,"FILARIA IgG4 ANTIBODY, ELISA",HC 6942 FILARIASIS,157,157,157,157,628,628,628,628
+LABFILAB,31,3,4,"FILARIA IgG4 ANTIBODY, ELISA",HC 6942 FILARIASIS,157,157,157,157,628,628,628,628
+LABFILAB,31,7,4,"FILARIA IgG4 ANTIBODY, ELISA",HC 6942 FILARIASIS,157,157,157,157,628,628,628,628
+LABFK506L,39273,1,16022,TACROLIMUS (FK506),HC FK506:TACROLIMUS,345,345,345,345,5527590,5527590,5527590,5527590
+LABFK506L,39273,3,33753,TACROLIMUS (FK506),HC FK506:TACROLIMUS,345,345,345,345,11644785,11644785,11644785,11644785
+LABFK506L,39273,7,34775,TACROLIMUS (FK506),HC FK506:TACROLIMUS,345,345,345,345,11997375,11997375,11997375,11997375
+LABFKOH,2745,1,332,Fungus Cult/KOH,HC KOH PREP,270,309,289.5,289.5,89640,102588,96114,96114
+LABFKOH,2745,1,332,Fungus Cult/KOH,HC FUNGUS CULTURE,270,309,289.5,289.5,89640,102588,96114,96114
+LABFKOH,2745,3,423,Fungus Cult/KOH,HC KOH PREP,270,309,289.5,289.5,114210,130707,122458.5,122458.5
+LABFKOH,2745,3,423,Fungus Cult/KOH,HC FUNGUS CULTURE,270,309,289.5,289.5,114210,130707,122458.5,122458.5
+LABFKOH,2745,7,508,Fungus Cult/KOH,HC KOH PREP,270,309,289.5,289.5,137160,156972,147066,147066
+LABFKOH,2745,7,508,Fungus Cult/KOH,HC FUNGUS CULTURE,270,309,289.5,289.5,137160,156972,147066,147066
+LABFLCL,13,1,1,"CHLORIDE, FLUID",HC CHLORIDE BODY FLUID,199,199,199,199,199,199,199,199
+LABFLCL,13,3,1,"CHLORIDE, FLUID",HC CHLORIDE BODY FLUID,199,199,199,199,199,199,199,199
+LABFLCL,13,7,1,"CHLORIDE, FLUID",HC CHLORIDE BODY FLUID,199,199,199,199,199,199,199,199
+LABFLCR,435,1,15,FREE LIGHT CHAIN RATIO,HC NEPH 1MM FR LIGHT CHAINGS,189,189,189,189,2835,2835,2835,2835
+LABFLCR,435,3,16,FREE LIGHT CHAIN RATIO,HC NEPH 1MM FR LIGHT CHAINGS,189,189,189,189,3024,3024,3024,3024
+LABFLCR,435,7,25,FREE LIGHT CHAIN RATIO,HC NEPH 1MM FR LIGHT CHAINGS,189,189,189,189,4725,4725,4725,4725
+LABFLDB,687,1,43,Fluid Culture/BB/GS,HC GRAM STAIN,150,302,226,226,6450,12986,9718,9718
+LABFLDB,687,1,43,Fluid Culture/BB/GS,HC BODY FLUID CULT,150,302,226,226,6450,12986,9718,9718
+LABFLDB,687,3,97,Fluid Culture/BB/GS,HC GRAM STAIN,150,302,226,226,14550,29294,21922,21922
+LABFLDB,687,3,97,Fluid Culture/BB/GS,HC BODY FLUID CULT,150,302,226,226,14550,29294,21922,21922
+LABFLDB,687,7,140,Fluid Culture/BB/GS,HC GRAM STAIN,150,302,226,226,21000,42280,31640,31640
+LABFLDB,687,7,140,Fluid Culture/BB/GS,HC BODY FLUID CULT,150,302,226,226,21000,42280,31640,31640
+LABFLDC,8166,1,716,Fluid Culture/GS,HC GRAM STAIN,150,302,226,226,107400,216232,161816,161816
+LABFLDC,8166,1,716,Fluid Culture/GS,HC FLUID CULT,150,302,226,226,107400,216232,161816,161816
+LABFLDC,8166,3,1158,Fluid Culture/GS,HC GRAM STAIN,150,302,226,226,173700,349716,261708,261708
+LABFLDC,8166,3,1158,Fluid Culture/GS,HC FLUID CULT,150,302,226,226,173700,349716,261708,261708
+LABFLDC,8166,7,1690,Fluid Culture/GS,HC GRAM STAIN,150,302,226,226,253500,510380,381940,381940
+LABFLDC,8166,7,1690,Fluid Culture/GS,HC FLUID CULT,150,302,226,226,253500,510380,381940,381940
+LABFLDH,2072,1,86,"LDH, Total, Fluid",HC LDH FLUID,193,193,193,193,16598,16598,16598,16598
+LABFLDH,2072,3,148,"LDH, Total, Fluid",HC LDH FLUID,193,193,193,193,28564,28564,28564,28564
+LABFLDH,2072,7,238,"LDH, Total, Fluid",HC LDH FLUID,193,193,193,193,45934,45934,45934,45934
+LABFLGL,1358,1,50,"GLUCOSE, FLUID",HC GLUCOSE BODY FLUID,250,250,250,250,12500,12500,12500,12500
+LABFLGL,1358,3,86,"GLUCOSE, FLUID",HC GLUCOSE BODY FLUID,250,250,250,250,21500,21500,21500,21500
+LABFLGL,1358,7,125,"GLUCOSE, FLUID",HC GLUCOSE BODY FLUID,250,250,250,250,31250,31250,31250,31250
+LABFLL,178,1,7,FSH,HC FSH (FSHOR),345,345,345,345,2415,2415,2415,2415
+LABFLL,178,3,8,FSH,HC FSH (FSHOR),345,345,345,345,2760,2760,2760,2760
+LABFLL,178,7,9,FSH,HC FSH (FSHOR),345,345,345,345,3105,3105,3105,3105
+LABFLUAPC,60,1,4,FLU A PCR,HC FLUAPC FLU A RNA RT PCR AMP,651,651,651,651,2604,2604,2604,2604
+LABFLUAPC,60,3,4,FLU A PCR,HC FLUAPC FLU A RNA RT PCR AMP,651,651,651,651,2604,2604,2604,2604
+LABFLUAPC,60,7,4,FLU A PCR,HC FLUAPC FLU A RNA RT PCR AMP,651,651,651,651,2604,2604,2604,2604
+LABFLUAPX,656,1,29,FLU A PCR REFLEX TO 2009 H1N1,HC FLUAPX FLU A RNA RT PCR AMP,651,651,651,651,18879,18879,18879,18879
+LABFLUAPX,656,3,57,FLU A PCR REFLEX TO 2009 H1N1,HC FLUAPX FLU A RNA RT PCR AMP,651,651,651,651,37107,37107,37107,37107
+LABFLUAPX,656,7,77,FLU A PCR REFLEX TO 2009 H1N1,HC FLUAPX FLU A RNA RT PCR AMP,651,651,651,651,50127,50127,50127,50127
+LABFMYAB,246,1,15,MYCOPLASMA IGG&IGM AB,HC 6942 MYCOPLASMA PNEUM IGG/M AB,52,52,52,52,780,780,780,780
+LABFMYAB,246,3,31,MYCOPLASMA IGG&IGM AB,HC 6942 MYCOPLASMA PNEUM IGG/M AB,52,52,52,52,1612,1612,1612,1612
+LABFMYAB,246,7,33,MYCOPLASMA IGG&IGM AB,HC 6942 MYCOPLASMA PNEUM IGG/M AB,52,52,52,52,1716,1716,1716,1716
+LABFOL,3642,1,152,Folic Acid (Folate),HC FOLATE-SERUM,274,274,274,274,41648,41648,41648,41648
+LABFOL,3642,3,201,Folic Acid (Folate),HC FOLATE-SERUM,274,274,274,274,55074,55074,55074,55074
+LABFOL,3642,7,222,Folic Acid (Folate),HC FOLATE-SERUM,274,274,274,274,60828,60828,60828,60828
+LABFPARVD,81,1,4,"PARVOVIRUS B19 REALTIME PCR,QL",HC 6942 PARVOVIR (B19) PCR,127,127,127,127,508,508,508,508
+LABFPARVD,81,3,7,"PARVOVIRUS B19 REALTIME PCR,QL",HC 6942 PARVOVIR (B19) PCR,127,127,127,127,889,889,889,889
+LABFPARVD,81,7,7,"PARVOVIRUS B19 REALTIME PCR,QL",HC 6942 PARVOVIR (B19) PCR,127,127,127,127,889,889,889,889
+LABFPLTS,3,1,1,"PLATELET COUNT, FLUID","HC PLATELET COUNT, AUTOMATED",138,138,138,138,138,138,138,138
+LABFPLTS,3,3,1,"PLATELET COUNT, FLUID","HC PLATELET COUNT, AUTOMATED",138,138,138,138,138,138,138,138
+LABFPLTS,3,7,1,"PLATELET COUNT, FLUID","HC PLATELET COUNT, AUTOMATED",138,138,138,138,138,138,138,138
+LABFQFVR,183,1,6,COXIELLA BURNETII AB W/RFLX,HC 6942 Q FEVER ANTIBODY,16,16,16,16,96,96,96,96
+LABFQFVR,183,3,7,COXIELLA BURNETII AB W/RFLX,HC 6942 Q FEVER ANTIBODY,16,16,16,16,112,112,112,112
+LABFQFVR,183,7,10,COXIELLA BURNETII AB W/RFLX,HC 6942 Q FEVER ANTIBODY,16,16,16,16,160,160,160,160
+LABFSYN,9,1,1,FUNGAL SYNERGY,HC 6939 FUNGAL SYNERGY,93,93,93,93,93,93,93,93
+LABFSYN,9,3,1,FUNGAL SYNERGY,HC 6939 FUNGAL SYNERGY,93,93,93,93,93,93,93,93
+LABFSYN,9,7,1,FUNGAL SYNERGY,HC 6939 FUNGAL SYNERGY,93,93,93,93,93,93,93,93
+LABFT4,8884,1,463,Free T4,HC FREE T4,246,246,246,246,113898,113898,113898,113898
+LABFT4,8884,3,639,Free T4,HC FREE T4,246,246,246,246,157194,157194,157194,157194
+LABFT4,8884,7,820,Free T4,HC FREE T4,246,246,246,246,201720,201720,201720,201720
+LABFT4ED,20,1,1,"FREE T4, EQUILIBRIUM DIALYSIS",HC 6959 FREE T4 EQUILIB DIALYSIS,40,40,40,40,40,40,40,40
+LABFT4ED,20,3,1,"FREE T4, EQUILIBRIUM DIALYSIS",HC 6959 FREE T4 EQUILIB DIALYSIS,40,40,40,40,40,40,40,40
+LABFT4ED,20,7,1,"FREE T4, EQUILIBRIUM DIALYSIS",HC 6959 FREE T4 EQUILIB DIALYSIS,40,40,40,40,40,40,40,40
+LABFTBLYM,49,1,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:T-CELLS,TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,1,4,"T SUBSETS AND B LYMPHS, FLUID",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,1,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:B CELLS, TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,1,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:NK CELLS, TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,3,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:T-CELLS,TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,3,4,"T SUBSETS AND B LYMPHS, FLUID",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,3,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:B CELLS, TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,3,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:NK CELLS, TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,7,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:T-CELLS,TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,7,4,"T SUBSETS AND B LYMPHS, FLUID",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,7,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:B CELLS, TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTBLYM,49,7,4,"T SUBSETS AND B LYMPHS, FLUID","HC FLOW:NK CELLS, TOTAL COUNT",422,575,520.75,543,1688,2300,2083,2172
+LABFTP,2359,1,86,"PROTEIN, TOTAL, FLUID",HC PROTN TOTL BDY FLUD,229,229,229,229,19694,19694,19694,19694
+LABFTP,2359,3,165,"PROTEIN, TOTAL, FLUID",HC PROTN TOTL BDY FLUD,229,229,229,229,37785,37785,37785,37785
+LABFTP,2359,7,281,"PROTEIN, TOTAL, FLUID",HC PROTN TOTL BDY FLUD,229,229,229,229,64349,64349,64349,64349
+LABFTRIGA,187,1,12,"TRIGLYCERIDES, FLUID","HC 6959 TRIGLYCERIDES, NON PLEU",9,9,9,9,108,108,108,108
+LABFTRIGA,187,3,16,"TRIGLYCERIDES, FLUID","HC 6959 TRIGLYCERIDES, NON PLEU",9,9,9,9,144,144,144,144
+LABFTRIGA,187,7,21,"TRIGLYCERIDES, FLUID","HC 6959 TRIGLYCERIDES, NON PLEU",9,9,9,9,189,189,189,189
+LABFTRPL,562,1,61,"TRIGLYCERIDES, PLEURAL",HC TRIGLYCERIDES FLUID,175,175,175,175,10675,10675,10675,10675
+LABFTRPL,562,3,105,"TRIGLYCERIDES, PLEURAL",HC TRIGLYCERIDES FLUID,175,175,175,175,18375,18375,18375,18375
+LABFTRPL,562,7,161,"TRIGLYCERIDES, PLEURAL",HC TRIGLYCERIDES FLUID,175,175,175,175,28175,28175,28175,28175
+LABFUNIDS,23,3,1,"FUNGAL ID BY SEQUENCING, SPECIMEN",HC FUNIDS FUNGUS ID NA SEQ EA,1653,1653,1653,1653,1653,1653,1653,1653
+LABFUNIDS,23,7,4,"FUNGAL ID BY SEQUENCING, SPECIMEN",HC FUNIDS FUNGUS ID NA SEQ EA,1653,1653,1653,1653,6612,6612,6612,6612
+LABFURICA,24,1,1,"URIC ACID, BODY FLUID","HC 6959 URIC ACID, FLUID",9,9,9,9,9,9,9,9
+LABFURICA,24,3,1,"URIC ACID, BODY FLUID","HC 6959 URIC ACID, FLUID",9,9,9,9,9,9,9,9
+LABFURICA,24,7,2,"URIC ACID, BODY FLUID","HC 6959 URIC ACID, FLUID",9,9,9,9,18,18,18,18
+LABFVARZ,342,1,19,"VARICELLA ZOSTER VIR. DNA,QUAL",HC 6942 VZ DNA PCR,147,147,147,147,2793,2793,2793,2793
+LABFVARZ,342,3,25,"VARICELLA ZOSTER VIR. DNA,QUAL",HC 6942 VZ DNA PCR,147,147,147,147,3675,3675,3675,3675
+LABFVARZ,342,7,31,"VARICELLA ZOSTER VIR. DNA,QUAL",HC 6942 VZ DNA PCR,147,147,147,147,4557,4557,4557,4557
+LABG6PD,95,1,5,G6PD Screen,HC G-6PD H SCREEN,176,176,176,176,880,880,880,880
+LABG6PD,95,3,7,G6PD Screen,HC G-6PD H SCREEN,176,176,176,176,1232,1232,1232,1232
+LABG6PD,95,7,7,G6PD Screen,HC G-6PD H SCREEN,176,176,176,176,1232,1232,1232,1232
+LABG6PDQT,27,1,2,"G6PD, QUANTITATIVE",HC G6 PD QUANT,199,199,199,199,398,398,398,398
+LABG6PDQT,27,3,2,"G6PD, QUANTITATIVE",HC G6 PD QUANT,199,199,199,199,398,398,398,398
+LABG6PDQT,27,7,4,"G6PD, QUANTITATIVE",HC G6 PD QUANT,199,199,199,199,796,796,796,796
+LABGASBLD,237,1,8,"Occult Blood, Gastric","HC OCCULT BLOOD, GASTRIC FLUID",101,101,101,101,808,808,808,808
+LABGASBLD,237,3,12,"Occult Blood, Gastric","HC OCCULT BLOOD, GASTRIC FLUID",101,101,101,101,1212,1212,1212,1212
+LABGASBLD,237,7,13,"Occult Blood, Gastric","HC OCCULT BLOOD, GASTRIC FLUID",101,101,101,101,1313,1313,1313,1313
+LABGASPH,10,1,1,"pH, Gastric",HC GASTRIC PH,50,50,50,50,50,50,50,50
+LABGASPH,10,3,2,"pH, Gastric",HC GASTRIC PH,50,50,50,50,100,100,100,100
+LABGASPH,10,7,2,"pH, Gastric",HC GASTRIC PH,50,50,50,50,100,100,100,100
+LABGBMIFA,50,1,1,"GBM ABS, IGA & IGG (IFA)",HC 6959 GMBIFA AB IGA IGG,64,64,64,64,64,64,64,64
+LABGBMIFA,50,3,2,"GBM ABS, IGA & IGG (IFA)",HC 6959 GMBIFA AB IGA IGG,64,64,64,64,128,128,128,128
+LABGBMIFA,50,7,3,"GBM ABS, IGA & IGG (IFA)",HC 6959 GMBIFA AB IGA IGG,64,64,64,64,192,192,192,192
+LABGCCR2,64,3,1,GC molec detection,"HC NEISSERIA GONORRHOEAE, GC LCR",566,566,566,566,566,566,566,566
+LABGCCR2,64,7,2,GC molec detection,"HC NEISSERIA GONORRHOEAE, GC LCR",566,566,566,566,1132,1132,1132,1132
+LABGENC,67,1,1,Genital Culture,HC GENITAL CULT,302,302,302,302,302,302,302,302
+LABGENC,67,3,2,Genital Culture,HC GENITAL CULT,302,302,302,302,604,604,604,604
+LABGENC,67,7,2,Genital Culture,HC GENITAL CULT,302,302,302,302,604,604,604,604
+LABGGT,3876,1,1006,Gamma-Glutamyl Trans (GGT),HC GAMMA GLUTAMYL TRAN,204,204,204,204,205224,205224,205224,205224
+LABGGT,3876,3,1869,Gamma-Glutamyl Trans (GGT),HC GAMMA GLUTAMYL TRAN,204,204,204,204,381276,381276,381276,381276
+LABGGT,3876,7,1953,Gamma-Glutamyl Trans (GGT),HC GAMMA GLUTAMYL TRAN,204,204,204,204,398412,398412,398412,398412
+LABGH,86,1,14,Growth Hormone,HC GROWTH HORMONE,238,238,238,238,3332,3332,3332,3332
+LABGH,86,3,27,Growth Hormone,HC GROWTH HORMONE,238,238,238,238,6426,6426,6426,6426
+LABGH,86,7,27,Growth Hormone,HC GROWTH HORMONE,238,238,238,238,6426,6426,6426,6426
+LABGIARS,386,1,17,Giardia screen,HC GIARDIA SCREEN,282,282,282,282,4794,4794,4794,4794
+LABGIARS,386,3,22,Giardia screen,HC GIARDIA SCREEN,282,282,282,282,6204,6204,6204,6204
+LABGIARS,386,7,37,Giardia screen,HC GIARDIA SCREEN,282,282,282,282,10434,10434,10434,10434
+LABGLF,448,1,131,"Glucose, Fasting","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,20829,20829,20829,20829
+LABGLF,448,3,153,"Glucose, Fasting","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,24327,24327,24327,24327
+LABGLF,448,7,158,"Glucose, Fasting","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,25122,25122,25122,25122
+LABGLNF,757,1,193,"Glucose, Nonfasting, Ser/Plas","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,30687,30687,30687,30687
+LABGLNF,757,3,212,"Glucose, Nonfasting, Ser/Plas","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,33708,33708,33708,33708
+LABGLNF,757,7,216,"Glucose, Nonfasting, Ser/Plas","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,34344,34344,34344,34344
+LABGLWB,52,1,16,"GLUCOSE, WHOLE BLOOD","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,2544,2544,2544,2544
+LABGLWB,52,3,16,"GLUCOSE, WHOLE BLOOD","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,2544,2544,2544,2544
+LABGLWB,52,7,16,"GLUCOSE, WHOLE BLOOD","HC GLUCOSE, SERUM/PLASMA",159,159,159,159,2544,2544,2544,2544
+LABGNTPOL,238,1,35,"GENTAMICIN, PEAK",HC GENTAMICIN,319,319,319,319,11165,11165,11165,11165
+LABGNTPOL,238,3,92,"GENTAMICIN, PEAK",HC GENTAMICIN,319,319,319,319,29348,29348,29348,29348
+LABGNTPOL,238,7,116,"GENTAMICIN, PEAK",HC GENTAMICIN,319,319,319,319,37004,37004,37004,37004
+LABGNTPRL,675,1,164,"GENTAMICIN, TROUGH",HC GENTAMICIN,319,319,319,319,52316,52316,52316,52316
+LABGNTPRL,675,3,367,"GENTAMICIN, TROUGH",HC GENTAMICIN,319,319,319,319,117073,117073,117073,117073
+LABGNTPRL,675,7,415,"GENTAMICIN, TROUGH",HC GENTAMICIN,319,319,319,319,132385,132385,132385,132385
+LABGNTUNL,7,3,1,"GENTAMICIN, SINGLE DAILY DOSE",HC GENTAMICIN,319,319,319,319,319,319,319,319
+LABGNTUNL,7,7,1,"GENTAMICIN, SINGLE DAILY DOSE",HC GENTAMICIN,319,319,319,319,319,319,319,319
+LABGRAM,11526,1,1277,Gram Stain,HC GRAM STAIN,150,150,150,150,191550,191550,191550,191550
+LABGRAM,11526,3,2496,Gram Stain,HC GRAM STAIN,150,150,150,150,374400,374400,374400,374400
+LABGRAM,11526,7,3631,Gram Stain,HC GRAM STAIN,150,150,150,150,544650,544650,544650,544650
+LABGTT,30,1,1,TRADITIONAL GLUCOSE TOLERANCE,HC TRAD GLUC TOLER(100G),789,789,789,789,789,789,789,789
+LABGTT,30,3,1,TRADITIONAL GLUCOSE TOLERANCE,HC TRAD GLUC TOLER(100G),789,789,789,789,789,789,789,789
+LABGTT,30,7,1,TRADITIONAL GLUCOSE TOLERANCE,HC TRAD GLUC TOLER(100G),789,789,789,789,789,789,789,789
+LABH1N1PC,83,1,7,"2009 H1N1 PCR, H275Y MUTATION",HC H1N1PC H275Y MUT RT AMP,1180,1180,1180,1180,8260,8260,8260,8260
+LABH1N1PC,83,3,7,"2009 H1N1 PCR, H275Y MUTATION",HC H1N1PC H275Y MUT RT AMP,1180,1180,1180,1180,8260,8260,8260,8260
+LABH1N1PC,83,7,9,"2009 H1N1 PCR, H275Y MUTATION",HC H1N1PC H275Y MUT RT AMP,1180,1180,1180,1180,10620,10620,10620,10620
+LABHAP,5750,1,635,Haptoglobin,HC HAPTOGLOBIN,258,258,258,258,163830,163830,163830,163830
+LABHAP,5750,3,1246,Haptoglobin,HC HAPTOGLOBIN,258,258,258,258,321468,321468,321468,321468
+LABHAP,5750,7,1905,Haptoglobin,HC HAPTOGLOBIN,258,258,258,258,491490,491490,491490,491490
+LABHATA,27,7,1,Hepatitis A Total AB,HC HAV AB TOTAL(HATA),231,231,231,231,231,231,231,231
+LABHAV,24,1,1,Hepatitis A Ab-IgM:,HC HAV IGM (HAV),268,268,268,268,268,268,268,268
+LABHAV,24,3,1,Hepatitis A Ab-IgM:,HC HAV IGM (HAV),268,268,268,268,268,268,268,268
+LABHAV,24,7,1,Hepatitis A Ab-IgM:,HC HAV IGM (HAV),268,268,268,268,268,268,268,268
+LABHAVM,582,1,14,IGM ANTI-HAV,HC HAV IGM (HAV),268,268,268,268,3752,3752,3752,3752
+LABHAVM,582,3,24,IGM ANTI-HAV,HC HAV IGM (HAV),268,268,268,268,6432,6432,6432,6432
+LABHAVM,582,7,26,IGM ANTI-HAV,HC HAV IGM (HAV),268,268,268,268,6968,6968,6968,6968
+LABHAVT,718,1,11,ANTI-HAV,HC HAV AB TOTAL(HATA),231,231,231,231,2541,2541,2541,2541
+LABHAVT,718,3,17,ANTI-HAV,HC HAV AB TOTAL(HATA),231,231,231,231,3927,3927,3927,3927
+LABHAVT,718,7,20,ANTI-HAV,HC HAV AB TOTAL(HATA),231,231,231,231,4620,4620,4620,4620
+LABHB,41,1,1,Hepatitis B Surf Ag:,HC HEPAT BS AG-EIA,38,220,129,129,38,220,129,129
+LABHB,41,1,1,Hepatitis B Surf Ag:,HC VIRAL EIA SINGLE TEST,38,220,129,129,38,220,129,129
+LABHB,41,3,1,Hepatitis B Surf Ag:,HC HEPAT BS AG-EIA,38,220,129,129,38,220,129,129
+LABHB,41,3,1,Hepatitis B Surf Ag:,HC VIRAL EIA SINGLE TEST,38,220,129,129,38,220,129,129
+LABHB,41,7,1,Hepatitis B Surf Ag:,HC HEPAT BS AG-EIA,38,220,129,129,38,220,129,129
+LABHB,41,7,1,Hepatitis B Surf Ag:,HC VIRAL EIA SINGLE TEST,38,220,129,129,38,220,129,129
+LABHBCA,251,1,3,IGM ANTI-HBC,HC HBV CORE IGM (HBC),248,248,248,248,744,744,744,744
+LABHBCA,251,3,8,IGM ANTI-HBC,HC HBV CORE IGM (HBC),248,248,248,248,1984,1984,1984,1984
+LABHBCA,251,7,11,IGM ANTI-HBC,HC HBV CORE IGM (HBC),248,248,248,248,2728,2728,2728,2728
+LABHBCT,556,1,12,ANTI-HBc,HC HEP B CR TOTAL AB,189,189,189,189,2268,2268,2268,2268
+LABHBCT,556,3,21,ANTI-HBc,HC HEP B CR TOTAL AB,189,189,189,189,3969,3969,3969,3969
+LABHBCT,556,7,24,ANTI-HBc,HC HEP B CR TOTAL AB,189,189,189,189,4536,4536,4536,4536
+LABHBODY,19,1,4,Heinz Body Prep,HC HEINZ BODY PREP,66,66,66,66,264,264,264,264
+LABHBODY,19,3,4,Heinz Body Prep,HC HEINZ BODY PREP,66,66,66,66,264,264,264,264
+LABHBODY,19,7,5,Heinz Body Prep,HC HEINZ BODY PREP,66,66,66,66,330,330,330,330
+LABHBPQ,451,1,20,Hep B PCR Quant                          NONORDERABLE,HC HBV PCR QUANTIFICATION,794,794,794,794,15880,15880,15880,15880
+LABHBPQ,451,3,25,Hep B PCR Quant                          NONORDERABLE,HC HBV PCR QUANTIFICATION,794,794,794,794,19850,19850,19850,19850
+LABHBPQ,451,7,42,Hep B PCR Quant                          NONORDERABLE,HC HBV PCR QUANTIFICATION,794,794,794,794,33348,33348,33348,33348
+LABHBPQD,7,1,2,"Hep B PCR Quant, Dil. Protocol            NONORDERABLE",HC HBV PCR QUANTIFICATION,794,794,794,794,1588,1588,1588,1588
+LABHBPQD,7,3,2,"Hep B PCR Quant, Dil. Protocol            NONORDERABLE",HC HBV PCR QUANTIFICATION,794,794,794,794,1588,1588,1588,1588
+LABHBPQD,7,7,2,"Hep B PCR Quant, Dil. Protocol            NONORDERABLE",HC HBV PCR QUANTIFICATION,794,794,794,794,1588,1588,1588,1588
+LABHBSAG,3786,1,218,HBSAG,HC HEPAT BS AG-EIA,220,220,220,220,47960,47960,47960,47960
+LABHBSAG,3786,3,259,HBSAG,HC HEPAT BS AG-EIA,220,220,220,220,56980,56980,56980,56980
+LABHBSAG,3786,7,286,HBSAG,HC HEPAT BS AG-EIA,220,220,220,220,62920,62920,62920,62920
+LABHBSQL,577,1,24,ANTI-HBS QUAL,HC HEPAT BS AB-EIA,197,197,197,197,4728,4728,4728,4728
+LABHBSQL,577,3,29,ANTI-HBS QUAL,HC HEPAT BS AB-EIA,197,197,197,197,5713,5713,5713,5713
+LABHBSQL,577,7,34,ANTI-HBS QUAL,HC HEPAT BS AB-EIA,197,197,197,197,6698,6698,6698,6698
+LABHBSQT,268,1,13,HEP B SURF AB QUANT,HC HBSQT HEP B SURF AB QUANT,197,197,197,197,2561,2561,2561,2561
+LABHBSQT,268,3,18,HEP B SURF AB QUANT,HC HBSQT HEP B SURF AB QUANT,197,197,197,197,3546,3546,3546,3546
+LABHBSQT,268,7,34,HEP B SURF AB QUANT,HC HBSQT HEP B SURF AB QUANT,197,197,197,197,6698,6698,6698,6698
+LABHCPQ,780,1,28,Hep C PCR Quant                          NONORDERABLE,HC HCV RNA QUANT ASSAY,874,874,874,874,24472,24472,24472,24472
+LABHCPQ,780,3,38,Hep C PCR Quant                          NONORDERABLE,HC HCV RNA QUANT ASSAY,874,874,874,874,33212,33212,33212,33212
+LABHCPQ,780,7,55,Hep C PCR Quant                          NONORDERABLE,HC HCV RNA QUANT ASSAY,874,874,874,874,48070,48070,48070,48070
+LABHCTX,32757,1,14608,HEMATOCRIT,HC HEMATOCRIT,217,217,217,217,3169936,3169936,3169936,3169936
+LABHCTX,32757,3,18749,HEMATOCRIT,HC HEMATOCRIT,217,217,217,217,4068533,4068533,4068533,4068533
+LABHCTX,32757,7,19138,HEMATOCRIT,HC HEMATOCRIT,217,217,217,217,4152946,4152946,4152946,4152946
+LABHCVA,2271,1,51,HEPATITIS C AB IGG,HC HCVA HEPATITIS C AB,193,193,193,193,9843,9843,9843,9843
+LABHCVA,2271,3,77,HEPATITIS C AB IGG,HC HCVA HEPATITIS C AB,193,193,193,193,14861,14861,14861,14861
+LABHCVA,2271,7,91,HEPATITIS C AB IGG,HC HCVA HEPATITIS C AB,193,193,193,193,17563,17563,17563,17563
+LABHCVGEN,92,1,1,HCV GENOTYPE BACKUP TEST,HC HCV GENOTYPE,2847,2847,2847,2847,2847,2847,2847,2847
+LABHCVGEN,92,3,2,HCV GENOTYPE BACKUP TEST,HC HCV GENOTYPE,2847,2847,2847,2847,5694,5694,5694,5694
+LABHCVGEN,92,7,4,HCV GENOTYPE BACKUP TEST,HC HCV GENOTYPE,2847,2847,2847,2847,11388,11388,11388,11388
+LABHCVGTP,4,1,1,HEPATITIS C (HCV) GENOTYPE,HC HCV GENOTYPE GENMARK,2847,2847,2847,2847,2847,2847,2847,2847
+LABHCVGTP,4,3,1,HEPATITIS C (HCV) GENOTYPE,HC HCV GENOTYPE GENMARK,2847,2847,2847,2847,2847,2847,2847,2847
+LABHCVGTP,4,7,2,HEPATITIS C (HCV) GENOTYPE,HC HCV GENOTYPE GENMARK,2847,2847,2847,2847,5694,5694,5694,5694
+LABHELAG,525,1,22,"Helicob. Ag, Stool",HC HELI PYL AG STOOL,376,376,376,376,8272,8272,8272,8272
+LABHELAG,525,3,22,"Helicob. Ag, Stool",HC HELI PYL AG STOOL,376,376,376,376,8272,8272,8272,8272
+LABHELAG,525,7,23,"Helicob. Ag, Stool",HC HELI PYL AG STOOL,376,376,376,376,8648,8648,8648,8648
+LABHELI,926,1,47,HELICOB. PYLORI IgG AB,HC HELICOBACTER EIA,206,206,206,206,9682,9682,9682,9682
+LABHELI,926,3,49,HELICOB. PYLORI IgG AB,HC HELICOBACTER EIA,206,206,206,206,10094,10094,10094,10094
+LABHELI,926,7,55,HELICOB. PYLORI IgG AB,HC HELICOBACTER EIA,206,206,206,206,11330,11330,11330,11330
+LABHEPAR,39437,1,31016,"Heparin,Unfractionated,Anti XA",HC HEPARIN CONCENTRATN,466,466,466,466,14453456,14453456,14453456,14453456
+LABHEPAR,39437,3,33673,"Heparin,Unfractionated,Anti XA",HC HEPARIN CONCENTRATN,466,466,466,466,15691618,15691618,15691618,15691618
+LABHEPAR,39437,7,33967,"Heparin,Unfractionated,Anti XA",HC HEPARIN CONCENTRATN,466,466,466,466,15828622,15828622,15828622,15828622
+LABHEPC,68,1,1,Hepatitis C Ab,HC HCVA HEPATITIS C AB,38,193,115.5,115.5,38,193,115.5,115.5
+LABHEPC,68,1,1,Hepatitis C Ab,HC VIRAL EIA SINGLE TEST,38,193,115.5,115.5,38,193,115.5,115.5
+LABHEPC,68,3,2,Hepatitis C Ab,HC HCVA HEPATITIS C AB,38,193,115.5,115.5,76,386,231,231
+LABHEPC,68,3,2,Hepatitis C Ab,HC VIRAL EIA SINGLE TEST,38,193,115.5,115.5,76,386,231,231
+LABHEPC,68,7,2,Hepatitis C Ab,HC HCVA HEPATITIS C AB,38,193,115.5,115.5,76,386,231,231
+LABHEPC,68,7,2,Hepatitis C Ab,HC VIRAL EIA SINGLE TEST,38,193,115.5,115.5,76,386,231,231
+LABHEPDAB,47,1,2,HEPATITIS D AB TOTAL,HC 6942 DELTA HEPAT AGENT,62,62,62,62,124,124,124,124
+LABHEPDAB,47,3,3,HEPATITIS D AB TOTAL,HC 6942 DELTA HEPAT AGENT,62,62,62,62,186,186,186,186
+LABHEPDAB,47,7,6,HEPATITIS D AB TOTAL,HC 6942 DELTA HEPAT AGENT,62,62,62,62,372,372,372,372
+LABHEVG,16,1,1,HEPATITIS E AB IgG,HC 6942 HEVG HEP E AB IGG,35,35,35,35,35,35,35,35
+LABHEVG,16,3,1,HEPATITIS E AB IgG,HC 6942 HEVG HEP E AB IGG,35,35,35,35,35,35,35,35
+LABHEVG,16,7,1,HEPATITIS E AB IgG,HC 6942 HEVG HEP E AB IGG,35,35,35,35,35,35,35,35
+LABHEVM,22,1,1,HEPATITIS E AB IgM,HC 6942 HEVM HEP E VRS AB IGM,52,52,52,52,52,52,52,52
+LABHEVM,22,3,1,HEPATITIS E AB IgM,HC 6942 HEVM HEP E VRS AB IGM,52,52,52,52,52,52,52,52
+LABHEVM,22,7,1,HEPATITIS E AB IgM,HC 6942 HEVM HEP E VRS AB IGM,52,52,52,52,52,52,52,52
+LABHFP,55643,1,19033,HEPATIC FUNCTION PANEL A,HC HEPATIC FXN PANEL,342,342,342,342,6509286,6509286,6509286,6509286
+LABHFP,55643,3,36340,HEPATIC FUNCTION PANEL A,HC HEPATIC FXN PANEL,342,342,342,342,12428280,12428280,12428280,12428280
+LABHFP,55643,7,38441,HEPATIC FUNCTION PANEL A,HC HEPATIC FXN PANEL,342,342,342,342,13146822,13146822,13146822,13146822
+LABHGBSQ,62,1,16,"HGB S, QUANTITATIVE",HC HCB S QUANT,257,257,257,257,4112,4112,4112,4112
+LABHGBSQ,62,3,18,"HGB S, QUANTITATIVE",HC HCB S QUANT,257,257,257,257,4626,4626,4626,4626
+LABHGBSQ,62,7,23,"HGB S, QUANTITATIVE",HC HCB S QUANT,257,257,257,257,5911,5911,5911,5911
+LABHGBX,2671,1,839,HEMOGLOBIN,HC HEMOGLOBIN,48,48,48,48,40272,40272,40272,40272
+LABHGBX,2671,3,1012,HEMOGLOBIN,HC HEMOGLOBIN,48,48,48,48,48576,48576,48576,48576
+LABHGBX,2671,7,1039,HEMOGLOBIN,HC HEMOGLOBIN,48,48,48,48,49872,49872,49872,49872
+LABHGHPLC,131,1,11,HEMOGLOBIN QUANT/FRACT BY HPLC,HC HEMOGLOBIN QUANT AND FRACT,257,257,257,257,2827,2827,2827,2827
+LABHGHPLC,131,3,15,HEMOGLOBIN QUANT/FRACT BY HPLC,HC HEMOGLOBIN QUANT AND FRACT,257,257,257,257,3855,3855,3855,3855
+LABHGHPLC,131,7,19,HEMOGLOBIN QUANT/FRACT BY HPLC,HC HEMOGLOBIN QUANT AND FRACT,257,257,257,257,4883,4883,4883,4883
+LABHHV6QL,62,3,4,HHV-6 DNA PCR QUAL,HC HHV6QL IAD NA HV6 APTPCR QL,497,497,497,497,1988,1988,1988,1988
+LABHHV6QL,62,7,6,HHV-6 DNA PCR QUAL,HC HHV6QL IAD NA HV6 APTPCR QL,497,497,497,497,2982,2982,2982,2982
+LABHHV6QT,218,1,9,HHV-6 PCR QUANT,HC HHV6QT IAD NA HRP VRS6 QNT,591,591,591,591,5319,5319,5319,5319
+LABHHV6QT,218,3,20,HHV-6 PCR QUANT,HC HHV6QT IAD NA HRP VRS6 QNT,591,591,591,591,11820,11820,11820,11820
+LABHHV6QT,218,7,41,HHV-6 PCR QUANT,HC HHV6QT IAD NA HRP VRS6 QNT,591,591,591,591,24231,24231,24231,24231
+LABHIAB,421,1,37,HEPARIN-INDUCED ANTIBODY,HC HEPARIN-INDUCED ANTIBODYT,231,231,231,231,8547,8547,8547,8547
+LABHIAB,421,3,74,HEPARIN-INDUCED ANTIBODY,HC HEPARIN-INDUCED ANTIBODYT,231,231,231,231,17094,17094,17094,17094
+LABHIAB,421,7,91,HEPARIN-INDUCED ANTIBODY,HC HEPARIN-INDUCED ANTIBODYT,231,231,231,231,21021,21021,21021,21021
+LABHIBCV,45,1,1,"DONOR,HIV-1/HCV/HBV NAT W/REFL",HC 6911 DONOR HIVI/HCV,410,410,410,410,410,410,410,410
+LABHIBCV,45,3,2,"DONOR,HIV-1/HCV/HBV NAT W/REFL",HC 6911 DONOR HIVI/HCV,410,410,410,410,820,820,820,820
+LABHIBCV,45,7,3,"DONOR,HIV-1/HCV/HBV NAT W/REFL",HC 6911 DONOR HIVI/HCV,410,410,410,410,1230,1230,1230,1230
+LABHIPA,1428,1,113,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 1,469,469,469,469,52997,52997,52997,52997
+LABHIPA,1428,1,113,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 2,469,469,469,469,52997,52997,52997,52997
+LABHIPA,1428,1,113,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 3,469,469,469,469,52997,52997,52997,52997
+LABHIPA,1428,1,113,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 4,469,469,469,469,52997,52997,52997,52997
+LABHIPA,1428,3,194,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 1,469,469,469,469,90986,90986,90986,90986
+LABHIPA,1428,3,194,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 2,469,469,469,469,90986,90986,90986,90986
+LABHIPA,1428,3,194,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 3,469,469,469,469,90986,90986,90986,90986
+LABHIPA,1428,3,194,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 4,469,469,469,469,90986,90986,90986,90986
+LABHIPA,1428,7,251,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 1,469,469,469,469,117719,117719,117719,117719
+LABHIPA,1428,7,251,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 2,469,469,469,469,117719,117719,117719,117719
+LABHIPA,1428,7,251,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 3,469,469,469,469,117719,117719,117719,117719
+LABHIPA,1428,7,251,HEPARIN-INDUCED PLATELET AGG,HC HEP IND PLT AGGR 4,469,469,469,469,117719,117719,117719,117719
+LABHITAB,2271,1,146,HEPARIN (HIT) AB,HC HEPARIN-INDUCED ANTIBODYT,231,231,231,231,33726,33726,33726,33726
+LABHITAB,2271,3,314,HEPARIN (HIT) AB,HC HEPARIN-INDUCED ANTIBODYT,231,231,231,231,72534,72534,72534,72534
+LABHITAB,2271,7,407,HEPARIN (HIT) AB,HC HEPARIN-INDUCED ANTIBODYT,231,231,231,231,94017,94017,94017,94017
+LABHITIA,124,1,7,HIT FUNCTIONAL ASSAY,HC HEPARIN (HIT) ASSAY,203,203,203,203,1421,1421,1421,1421
+LABHITIA,124,3,11,HIT FUNCTIONAL ASSAY,HC HEPARIN (HIT) ASSAY,203,203,203,203,2233,2233,2233,2233
+LABHITIA,124,7,12,HIT FUNCTIONAL ASSAY,HC HEPARIN (HIT) ASSAY,203,203,203,203,2436,2436,2436,2436
+LABHITPNL,52,1,2,HIT PANEL,HC HEPARIN-INDUCED ANTIBODYT,203,231,217,217,406,462,434,434
+LABHITPNL,52,1,2,HIT PANEL,HC HEPARIN (HIT) ASSAY,203,231,217,217,406,462,434,434
+LABHITPNL,52,3,3,HIT PANEL,HC HEPARIN-INDUCED ANTIBODYT,203,231,217,217,609,693,651,651
+LABHITPNL,52,3,3,HIT PANEL,HC HEPARIN (HIT) ASSAY,203,231,217,217,609,693,651,651
+LABHITPNL,52,7,3,HIT PANEL,HC HEPARIN-INDUCED ANTIBODYT,203,231,217,217,609,693,651,651
+LABHITPNL,52,7,3,HIT PANEL,HC HEPARIN (HIT) ASSAY,203,231,217,217,609,693,651,651
+LABHIVAB,95,1,5,HIV (TYPE 1 AND TYPE 2) ABY,HC ANTIB SEROL 1.2,38,280,159,159,190,1400,795,795
+LABHIVAB,95,1,5,HIV (TYPE 1 AND TYPE 2) ABY,HC VIRAL EIA SINGLE TEST,38,280,159,159,190,1400,795,795
+LABHIVAB,95,3,9,HIV (TYPE 1 AND TYPE 2) ABY,HC ANTIB SEROL 1.2,38,280,159,159,342,2520,1431,1431
+LABHIVAB,95,3,9,HIV (TYPE 1 AND TYPE 2) ABY,HC VIRAL EIA SINGLE TEST,38,280,159,159,342,2520,1431,1431
+LABHIVAB,95,7,9,HIV (TYPE 1 AND TYPE 2) ABY,HC ANTIB SEROL 1.2,38,280,159,159,342,2520,1431,1431
+LABHIVAB,95,7,9,HIV (TYPE 1 AND TYPE 2) ABY,HC VIRAL EIA SINGLE TEST,38,280,159,159,342,2520,1431,1431
+LABHIVDAG,26,7,1,HIV-1 DIRECT AG (ICD),HC 6942 HIV P24,150,150,150,150,150,150,150,150
+LABHIVDS,45,1,1,HIV-1 AG DONON SCRN QUAL.,HC 6942 HIV-1 DIRECT AG,110,110,110,110,110,110,110,110
+LABHIVDS,45,3,2,HIV-1 AG DONON SCRN QUAL.,HC 6942 HIV-1 DIRECT AG,110,110,110,110,220,220,220,220
+LABHIVDS,45,7,3,HIV-1 AG DONON SCRN QUAL.,HC 6942 HIV-1 DIRECT AG,110,110,110,110,330,330,330,330
+LABHIVQ2,592,1,12,HIV Quantitative PCR,HC HIV RNA PCR QUANT,1377,1377,1377,1377,16524,16524,16524,16524
+LABHIVQ2,592,3,19,HIV Quantitative PCR,HC HIV RNA PCR QUANT,1377,1377,1377,1377,26163,26163,26163,26163
+LABHIVQ2,592,7,25,HIV Quantitative PCR,HC HIV RNA PCR QUANT,1377,1377,1377,1377,34425,34425,34425,34425
+LABHIVWBL,4562,1,180,ANTI-HIV,HC ANTIB SEROL 1.2,280,280,280,280,50400,50400,50400,50400
+LABHIVWBL,4562,3,264,ANTI-HIV,HC ANTIB SEROL 1.2,280,280,280,280,73920,73920,73920,73920
+LABHIVWBL,4562,7,305,ANTI-HIV,HC ANTIB SEROL 1.2,280,280,280,280,85400,85400,85400,85400
+LABHMCY,540,1,23,Homocysteine,HC HOMOCYSTEINE,399,399,399,399,9177,9177,9177,9177
+LABHMCY,540,3,25,Homocysteine,HC HOMOCYSTEINE,399,399,399,399,9975,9975,9975,9975
+LABHMCY,540,7,27,Homocysteine,HC HOMOCYSTEINE,399,399,399,399,10773,10773,10773,10773
+LABHSAB,159,1,4,VIRAL AB: HSV,HC HSV-2 AB IMMUNOBLOT,226,240,233,233,904,960,932,932
+LABHSAB,159,1,4,VIRAL AB: HSV,HC HSV-I AB IMMUNOBLOT,226,240,233,233,904,960,932,932
+LABHSAB,159,3,4,VIRAL AB: HSV,HC HSV-2 AB IMMUNOBLOT,226,240,233,233,904,960,932,932
+LABHSAB,159,3,4,VIRAL AB: HSV,HC HSV-I AB IMMUNOBLOT,226,240,233,233,904,960,932,932
+LABHSAB,159,7,4,VIRAL AB: HSV,HC HSV-2 AB IMMUNOBLOT,226,240,233,233,904,960,932,932
+LABHSAB,159,7,4,VIRAL AB: HSV,HC HSV-I AB IMMUNOBLOT,226,240,233,233,904,960,932,932
+LABHSCRP,150,1,7,High Sensitivity CRP,HC HIGH SENSITIVE CRP,190,190,190,190,1330,1330,1330,1330
+LABHSCRP,150,3,9,High Sensitivity CRP,HC HIGH SENSITIVE CRP,190,190,190,190,1710,1710,1710,1710
+LABHSCRP,150,7,13,High Sensitivity CRP,HC HIGH SENSITIVE CRP,190,190,190,190,2470,2470,2470,2470
+LABHSCUL,49,1,1,Herpes Simplex Cult only,HC VIRUS ISOL HERPES,484,484,484,484,484,484,484,484
+LABHSCUL,49,3,2,Herpes Simplex Cult only,HC VIRUS ISOL HERPES,484,484,484,484,968,968,968,968
+LABHSCUL,49,7,3,Herpes Simplex Cult only,HC VIRUS ISOL HERPES,484,484,484,484,1452,1452,1452,1452
+LABHSVCSF,203,1,2,"HSV DNA PCR QUAL, CSF",HC HSVCSF HSV DNA PCR QUAL CSF,637,637,637,637,1274,1274,1274,1274
+LABHSVCSF,203,3,6,"HSV DNA PCR QUAL, CSF",HC HSVCSF HSV DNA PCR QUAL CSF,637,637,637,637,3822,3822,3822,3822
+LABHSVCSF,203,7,8,"HSV DNA PCR QUAL, CSF",HC HSVCSF HSV DNA PCR QUAL CSF,637,637,637,637,5096,5096,5096,5096
+LABHSVGM,302,1,8,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB NON SPEC TYPE,29,43,34.66666667,32,232,344,277.3333333,256
+LABHSVGM,302,1,8,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB TYPE 1,29,43,34.66666667,32,232,344,277.3333333,256
+LABHSVGM,302,1,8,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB TYPE 2,29,43,34.66666667,32,232,344,277.3333333,256
+LABHSVGM,302,3,14,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB NON SPEC TYPE,29,43,34.66666667,32,406,602,485.3333333,448
+LABHSVGM,302,3,14,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB TYPE 1,29,43,34.66666667,32,406,602,485.3333333,448
+LABHSVGM,302,3,14,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB TYPE 2,29,43,34.66666667,32,406,602,485.3333333,448
+LABHSVGM,302,7,15,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB NON SPEC TYPE,29,43,34.66666667,32,435,645,520,480
+LABHSVGM,302,7,15,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB TYPE 1,29,43,34.66666667,32,435,645,520,480
+LABHSVGM,302,7,15,HSV 1/2 IGM&TYPE-SPECIFIC IGG,HC 6942 HS AB TYPE 2,29,43,34.66666667,32,435,645,520,480
+LABHSVIGG,114,3,1,HERPES SIMPLEX VIRUS IgG PANEL,HC HSVIGG HSV AB TYPE 1,226,240,233,233,226,240,233,233
+LABHSVIGG,114,3,1,HERPES SIMPLEX VIRUS IgG PANEL,HC HSVIGG HSV AB TYPE 2,226,240,233,233,226,240,233,233
+LABHSVIGG,114,7,2,HERPES SIMPLEX VIRUS IgG PANEL,HC HSVIGG HSV AB TYPE 1,226,240,233,233,452,480,466,466
+LABHSVIGG,114,7,2,HERPES SIMPLEX VIRUS IgG PANEL,HC HSVIGG HSV AB TYPE 2,226,240,233,233,452,480,466,466
+LABHSVM,58,1,1,HSV 1/2 IgM AB,HC 6942 HSV-1 IGM AB IFA,20,29,24.5,24.5,20,29,24.5,24.5
+LABHSVM,58,1,1,HSV 1/2 IgM AB,HC 6942 HSV-2 IGM AB IFA,20,29,24.5,24.5,20,29,24.5,24.5
+LABHSVM,58,3,1,HSV 1/2 IgM AB,HC 6942 HSV-1 IGM AB IFA,20,29,24.5,24.5,20,29,24.5,24.5
+LABHSVM,58,3,1,HSV 1/2 IgM AB,HC 6942 HSV-2 IGM AB IFA,20,29,24.5,24.5,20,29,24.5,24.5
+LABHSVM,58,7,2,HSV 1/2 IgM AB,HC 6942 HSV-1 IGM AB IFA,20,29,24.5,24.5,40,58,49,49
+LABHSVM,58,7,2,HSV 1/2 IgM AB,HC 6942 HSV-2 IGM AB IFA,20,29,24.5,24.5,40,58,49,49
+LABHSVPAN,144,3,1,HERPES SIMPLEX VIRUS AB PANEL,HC HSVPAN HSV AB NON SPECIFIC,204,240,223.3333333,226,204,240,223.3333333,226
+LABHSVPAN,144,3,1,HERPES SIMPLEX VIRUS AB PANEL,HC HSVPAN HSV AB TYPE 1,204,240,223.3333333,226,204,240,223.3333333,226
+LABHSVPAN,144,3,1,HERPES SIMPLEX VIRUS AB PANEL,HC HSVPAN HSV AB TYPE 2,204,240,223.3333333,226,204,240,223.3333333,226
+LABHSVPAN,144,7,3,HERPES SIMPLEX VIRUS AB PANEL,HC HSVPAN HSV AB NON SPECIFIC,204,240,223.3333333,226,612,720,670,678
+LABHSVPAN,144,7,3,HERPES SIMPLEX VIRUS AB PANEL,HC HSVPAN HSV AB TYPE 1,204,240,223.3333333,226,612,720,670,678
+LABHSVPAN,144,7,3,HERPES SIMPLEX VIRUS AB PANEL,HC HSVPAN HSV AB TYPE 2,204,240,223.3333333,226,612,720,670,678
+LABHSVPCR,364,1,4,"HERPES SIMPLEX VIRUS (HSV) PCR, CSF",HC HSVPCR HSV DNA PCR QL CSF,637,637,637,637,2548,2548,2548,2548
+LABHSVPCR,364,3,14,"HERPES SIMPLEX VIRUS (HSV) PCR, CSF",HC HSVPCR HSV DNA PCR QL CSF,637,637,637,637,8918,8918,8918,8918
+LABHSVPCR,364,7,24,"HERPES SIMPLEX VIRUS (HSV) PCR, CSF",HC HSVPCR HSV DNA PCR QL CSF,637,637,637,637,15288,15288,15288,15288
+LABHSVQL,87,1,3,"HSV DNA PCR QUAL, NON LESION",HC HSV DNA AMP PROBE,637,637,637,637,1911,1911,1911,1911
+LABHSVQL,87,3,8,"HSV DNA PCR QUAL, NON LESION",HC HSV DNA AMP PROBE,637,637,637,637,5096,5096,5096,5096
+LABHSVQL,87,7,10,"HSV DNA PCR QUAL, NON LESION",HC HSV DNA AMP PROBE,637,637,637,637,6370,6370,6370,6370
+LABHSVQLP,163,1,4,"HSV DNA PCR QUAL, PLASMA",HC HSVQLP HSV DNA PCR QUAL,637,637,637,637,2548,2548,2548,2548
+LABHSVQLP,163,3,9,"HSV DNA PCR QUAL, PLASMA",HC HSVQLP HSV DNA PCR QUAL,637,637,637,637,5733,5733,5733,5733
+LABHSVQLP,163,7,12,"HSV DNA PCR QUAL, PLASMA",HC HSVQLP HSV DNA PCR QUAL,637,637,637,637,7644,7644,7644,7644
+LABHSVZL,367,1,27,"HSV/VZV PCR QUAL, LESION",HC HSVZL HSV AMP PCR LESION,388,637,512.5,512.5,10476,17199,13837.5,13837.5
+LABHSVZL,367,1,27,"HSV/VZV PCR QUAL, LESION",HC HSVZL IAD NA APT EA ORG LSN,388,637,512.5,512.5,10476,17199,13837.5,13837.5
+LABHSVZL,367,3,36,"HSV/VZV PCR QUAL, LESION",HC HSVZL HSV AMP PCR LESION,388,637,512.5,512.5,13968,22932,18450,18450
+LABHSVZL,367,3,36,"HSV/VZV PCR QUAL, LESION",HC HSVZL IAD NA APT EA ORG LSN,388,637,512.5,512.5,13968,22932,18450,18450
+LABHSVZL,367,7,48,"HSV/VZV PCR QUAL, LESION",HC HSVZL HSV AMP PCR LESION,388,637,512.5,512.5,18624,30576,24600,24600
+LABHSVZL,367,7,48,"HSV/VZV PCR QUAL, LESION",HC HSVZL IAD NA APT EA ORG LSN,388,637,512.5,512.5,18624,30576,24600,24600
+LABHV6DNA,251,1,11,"HERPES VIRAL 6 DNA, QUAL",HC 6942 HHV-6 DNA PCR,107,107,107,107,1177,1177,1177,1177
+LABHV6DNA,251,3,25,"HERPES VIRAL 6 DNA, QUAL",HC 6942 HHV-6 DNA PCR,107,107,107,107,2675,2675,2675,2675
+LABHV6DNA,251,7,37,"HERPES VIRAL 6 DNA, QUAL",HC 6942 HHV-6 DNA PCR,107,107,107,107,3959,3959,3959,3959
+LABIBD,192,1,1,ANCA IBD W/REFLEX,HC 6959 IBD FLRST NONINF AB EA,40,40,40,40,40,40,40,40
+LABIBD,192,1,1,ANCA IBD W/REFLEX,HC 6959 IBD FUNGUS AB SEMIQNT,40,40,40,40,40,40,40,40
+LABIBD,192,3,4,ANCA IBD W/REFLEX,HC 6959 IBD FLRST NONINF AB EA,40,40,40,40,160,160,160,160
+LABIBD,192,3,4,ANCA IBD W/REFLEX,HC 6959 IBD FUNGUS AB SEMIQNT,40,40,40,40,160,160,160,160
+LABIBD,192,7,8,ANCA IBD W/REFLEX,HC 6959 IBD FLRST NONINF AB EA,40,40,40,40,320,320,320,320
+LABIBD,192,7,8,ANCA IBD W/REFLEX,HC 6959 IBD FUNGUS AB SEMIQNT,40,40,40,40,320,320,320,320
+LABIFEDE,16,1,1,"IMMUNOFIXATION, IgD & IgE",HC 6959 IMMUNOFIXATION IGD IGE,151,151,151,151,151,151,151,151
+LABIFEDE,16,3,1,"IMMUNOFIXATION, IgD & IgE",HC 6959 IMMUNOFIXATION IGD IGE,151,151,151,151,151,151,151,151
+LABIFEDE,16,7,1,"IMMUNOFIXATION, IgD & IgE",HC 6959 IMMUNOFIXATION IGD IGE,151,151,151,151,151,151,151,151
+LABIGA,208,1,9,"IgA, Serum",HC IGA SERUM,213,213,213,213,1917,1917,1917,1917
+LABIGA,208,3,9,"IgA, Serum",HC IGA SERUM,213,213,213,213,1917,1917,1917,1917
+LABIGA,208,7,9,"IgA, Serum",HC IGA SERUM,213,213,213,213,1917,1917,1917,1917
+LABIGET,810,1,21,Total IgE,HC TOTAL IGE,379,379,379,379,7959,7959,7959,7959
+LABIGET,810,3,26,Total IgE,HC TOTAL IGE,379,379,379,379,9854,9854,9854,9854
+LABIGET,810,7,30,Total IgE,HC TOTAL IGE,379,379,379,379,11370,11370,11370,11370
+LABIGF,112,1,8,Insulin-like Growth Factor 1,HC INSULIN LIKE GROWTH FACT1,304,304,304,304,2432,2432,2432,2432
+LABIGF,112,3,10,Insulin-like Growth Factor 1,HC INSULIN LIKE GROWTH FACT1,304,304,304,304,3040,3040,3040,3040
+LABIGF,112,7,11,Insulin-like Growth Factor 1,HC INSULIN LIKE GROWTH FACT1,304,304,304,304,3344,3344,3344,3344
+LABIGG,1366,1,79,"IgG, Serum",HC IGG SERUM,213,213,213,213,16827,16827,16827,16827
+LABIGG,1366,3,120,"IgG, Serum",HC IGG SERUM,213,213,213,213,25560,25560,25560,25560
+LABIGG,1366,7,162,"IgG, Serum",HC IGG SERUM,213,213,213,213,34506,34506,34506,34506
+LABIGGS4,123,1,4,IgG Subclass 4,HC IGG 4,186,186,186,186,744,744,744,744
+LABIGGS4,123,3,4,IgG Subclass 4,HC IGG 4,186,186,186,186,744,744,744,744
+LABIGGS4,123,7,5,IgG Subclass 4,HC IGG 4,186,186,186,186,930,930,930,930
+LABIGM,166,1,10,"IgM, Serum",HC IGM SERUM,213,213,213,213,2130,2130,2130,2130
+LABIGM,166,3,15,"IgM, Serum",HC IGM SERUM,213,213,213,213,3195,3195,3195,3195
+LABIGM,166,7,17,"IgM, Serum",HC IGM SERUM,213,213,213,213,3621,3621,3621,3621
+LABIGP,511,1,18,"Ig PANEL, SER",HC IGA SERUM,213,213,213,213,3834,3834,3834,3834
+LABIGP,511,1,18,"Ig PANEL, SER",HC IGG SERUM,213,213,213,213,3834,3834,3834,3834
+LABIGP,511,1,18,"Ig PANEL, SER",HC IGM SERUM,213,213,213,213,3834,3834,3834,3834
+LABIGP,511,3,28,"Ig PANEL, SER",HC IGA SERUM,213,213,213,213,5964,5964,5964,5964
+LABIGP,511,3,28,"Ig PANEL, SER",HC IGG SERUM,213,213,213,213,5964,5964,5964,5964
+LABIGP,511,3,28,"Ig PANEL, SER",HC IGM SERUM,213,213,213,213,5964,5964,5964,5964
+LABIGP,511,7,32,"Ig PANEL, SER",HC IGA SERUM,213,213,213,213,6816,6816,6816,6816
+LABIGP,511,7,32,"Ig PANEL, SER",HC IGG SERUM,213,213,213,213,6816,6816,6816,6816
+LABIGP,511,7,32,"Ig PANEL, SER",HC IGM SERUM,213,213,213,213,6816,6816,6816,6816
+LABINSL,119,1,56,"Insulin, unspecified",HC INSULIN (FAST/UNSPEC),181,181,181,181,10136,10136,10136,10136
+LABINSL,119,3,62,"Insulin, unspecified",HC INSULIN (FAST/UNSPEC),181,181,181,181,11222,11222,11222,11222
+LABINSL,119,7,63,"Insulin, unspecified",HC INSULIN (FAST/UNSPEC),181,181,181,181,11403,11403,11403,11403
+LABINSLF,354,1,146,"Insulin, fasting",HC INSULIN (FAST/UNSPEC),181,181,181,181,26426,26426,26426,26426
+LABINSLF,354,3,159,"Insulin, fasting",HC INSULIN (FAST/UNSPEC),181,181,181,181,28779,28779,28779,28779
+LABINSLF,354,7,162,"Insulin, fasting",HC INSULIN (FAST/UNSPEC),181,181,181,181,29322,29322,29322,29322
+LABIOPTH,223,1,143,Intra Operative PTH,HC I/IP PTH EACH SPECIMEN,786,786,786,786,112398,112398,112398,112398
+LABIOPTH,223,3,144,Intra Operative PTH,HC I/IP PTH EACH SPECIMEN,786,786,786,786,113184,113184,113184,113184
+LABIOPTH,223,7,145,Intra Operative PTH,HC I/IP PTH EACH SPECIMEN,786,786,786,786,113970,113970,113970,113970
+LABISOF,451,1,26,Isolator-Fungus,HC ADDNL CONC. MTHDS,153,513,333,333,3978,13338,8658,8658
+LABISOF,451,1,26,Isolator-Fungus,HC ISOLATOR-FUNGUS,153,513,333,333,3978,13338,8658,8658
+LABISOF,451,3,35,Isolator-Fungus,HC ADDNL CONC. MTHDS,153,513,333,333,5355,17955,11655,11655
+LABISOF,451,3,35,Isolator-Fungus,HC ISOLATOR-FUNGUS,153,513,333,333,5355,17955,11655,11655
+LABISOF,451,7,43,Isolator-Fungus,HC ADDNL CONC. MTHDS,153,513,333,333,6579,22059,14319,14319
+LABISOF,451,7,43,Isolator-Fungus,HC ISOLATOR-FUNGUS,153,513,333,333,6579,22059,14319,14319
+LABISOSR,17,1,2,Isolator-Special Req,HC ADDNL CONC. MTHDS,153,499,326,326,306,998,652,652
+LABISOSR,17,1,2,Isolator-Special Req,HC ISOLATOR-SPEC REG,153,499,326,326,306,998,652,652
+LABISOSR,17,3,2,Isolator-Special Req,HC ADDNL CONC. MTHDS,153,499,326,326,306,998,652,652
+LABISOSR,17,3,2,Isolator-Special Req,HC ISOLATOR-SPEC REG,153,499,326,326,306,998,652,652
+LABISOSR,17,7,2,Isolator-Special Req,HC ADDNL CONC. MTHDS,153,499,326,326,306,998,652,652
+LABISOSR,17,7,2,Isolator-Special Req,HC ISOLATOR-SPEC REG,153,499,326,326,306,998,652,652
+LABJAK2,134,1,6,JANUS KINASE 2 V617F MUTATION,HC JAK2 JAN KINASE V617F VRNT,765,765,765,765,4590,4590,4590,4590
+LABJAK2,134,3,7,JANUS KINASE 2 V617F MUTATION,HC JAK2 JAN KINASE V617F VRNT,765,765,765,765,5355,5355,5355,5355
+LABJAK2,134,7,8,JANUS KINASE 2 V617F MUTATION,HC JAK2 JAN KINASE V617F VRNT,765,765,765,765,6120,6120,6120,6120
+LABJO1ABY,90,1,2,ANTI-Jo1 ANTIBODY,HC JO1ABY EXT NA JO1 ANTIJO1AB,152,152,152,152,304,304,304,304
+LABJO1ABY,90,3,2,ANTI-Jo1 ANTIBODY,HC JO1ABY EXT NA JO1 ANTIJO1AB,152,152,152,152,304,304,304,304
+LABJO1ABY,90,7,4,ANTI-Jo1 ANTIBODY,HC JO1ABY EXT NA JO1 ANTIJO1AB,152,152,152,152,608,608,608,608
+LABK,48118,1,32770,"Potassium, Ser/Plas",HC POTASSIUM,204,204,204,204,6685080,6685080,6685080,6685080
+LABK,48118,3,37959,"Potassium, Ser/Plas",HC POTASSIUM,204,204,204,204,7743636,7743636,7743636,7743636
+LABK,48118,7,39128,"Potassium, Ser/Plas",HC POTASSIUM,204,204,204,204,7982112,7982112,7982112,7982112
+LABKOHP,212,1,9,KOH Prep,HC KOH PREP,270,270,270,270,2430,2430,2430,2430
+LABKOHP,212,3,10,KOH Prep,HC KOH PREP,270,270,270,270,2700,2700,2700,2700
+LABKOHP,212,7,10,KOH Prep,HC KOH PREP,270,270,270,270,2700,2700,2700,2700
+LABKRAS,76,1,3,KRAS MUTATION DETECTION,HC KRAS VRNTS CONDONS 12AND13,2556,2556,2556,2556,7668,7668,7668,7668
+LABKRAS,76,3,3,KRAS MUTATION DETECTION,HC KRAS VRNTS CONDONS 12AND13,2556,2556,2556,2556,7668,7668,7668,7668
+LABKRAS,76,7,3,KRAS MUTATION DETECTION,HC KRAS VRNTS CONDONS 12AND13,2556,2556,2556,2556,7668,7668,7668,7668
+LABKWB,171,1,46,"POTASSIUM, WHOLE BLOOD",HC POTASSIUM,204,204,204,204,9384,9384,9384,9384
+LABKWB,171,3,51,"POTASSIUM, WHOLE BLOOD",HC POTASSIUM,204,204,204,204,10404,10404,10404,10404
+LABKWB,171,7,54,"POTASSIUM, WHOLE BLOOD",HC POTASSIUM,204,204,204,204,11016,11016,11016,11016
+LABLAC,68691,1,49732,Lactic Acid,HC LACTATE,330,330,330,330,16411560,16411560,16411560,16411560
+LABLAC,68691,3,53754,Lactic Acid,HC LACTATE,330,330,330,330,17738820,17738820,17738820,17738820
+LABLAC,68691,7,54901,Lactic Acid,HC LACTATE,330,330,330,330,18117330,18117330,18117330,18117330
+LABLACWB,17767,1,12720,"LACTATE, WHOLE BLOOD",HC LACWB LACTATE,330,330,330,330,4197600,4197600,4197600,4197600
+LABLACWB,17767,3,13672,"LACTATE, WHOLE BLOOD",HC LACWB LACTATE,330,330,330,330,4511760,4511760,4511760,4511760
+LABLACWB,17767,7,14027,"LACTATE, WHOLE BLOOD",HC LACWB LACTATE,330,330,330,330,4628910,4628910,4628910,4628910
+LABLAMOL,253,1,16,LAMOTRIGINE LEVEL,HC LAMOL QNT DRG NES,181,181,181,181,2896,2896,2896,2896
+LABLAMOL,253,3,31,LAMOTRIGINE LEVEL,HC LAMOL QNT DRG NES,181,181,181,181,5611,5611,5611,5611
+LABLAMOL,253,7,39,LAMOTRIGINE LEVEL,HC LAMOL QNT DRG NES,181,181,181,181,7059,7059,7059,7059
+LABLDH,33432,1,14940,"LDH, Total, Ser/Plas",HC LDH TOTAL,193,193,193,193,2883420,2883420,2883420,2883420
+LABLDH,33432,3,22551,"LDH, Total, Ser/Plas",HC LDH TOTAL,193,193,193,193,4352343,4352343,4352343,4352343
+LABLDH,33432,7,23706,"LDH, Total, Ser/Plas",HC LDH TOTAL,193,193,193,193,4575258,4575258,4575258,4575258
+LABLDLD,45,1,1,LDL Cholesterol,HC LDLD - DIRECT LDL CHOLESTEROL,219,219,219,219,219,219,219,219
+LABLDLD,45,3,1,LDL Cholesterol,HC LDLD - DIRECT LDL CHOLESTEROL,219,219,219,219,219,219,219,219
+LABLDLD,45,7,1,LDL Cholesterol,HC LDLD - DIRECT LDL CHOLESTEROL,219,219,219,219,219,219,219,219
+LABLEGA,32,1,2,Legionella/Acid,HC LEGIONELL/ACID CULT,165,165,165,165,330,330,330,330
+LABLEGA,32,3,2,Legionella/Acid,HC LEGIONELL/ACID CULT,165,165,165,165,330,330,330,330
+LABLEGA,32,7,2,Legionella/Acid,HC LEGIONELL/ACID CULT,165,165,165,165,330,330,330,330
+LABLEGD,53,1,7,Legionella Cult/Dir,HC LEGNLA CULT/DIR,165,165,165,165,1155,1155,1155,1155
+LABLEGD,53,3,7,Legionella Cult/Dir,HC LEGNLA CULT/DIR,165,165,165,165,1155,1155,1155,1155
+LABLEGD,53,7,9,Legionella Cult/Dir,HC LEGNLA CULT/DIR,165,165,165,165,1485,1485,1485,1485
+LABLEGDA,1084,1,75,Legionella Cult/DirA,HC LEGIONELLA CULT/D+A,165,165,165,165,12375,12375,12375,12375
+LABLEGDA,1084,3,92,Legionella Cult/DirA,HC LEGIONELLA CULT/D+A,165,165,165,165,15180,15180,15180,15180
+LABLEGDA,1084,7,121,Legionella Cult/DirA,HC LEGIONELLA CULT/D+A,165,165,165,165,19965,19965,19965,19965
+LABLEGDD,6,1,3,Legionella Cult/D&D,HC LEGNLA CLTR/D+D,165,165,165,165,495,495,495,495
+LABLEGDD,6,3,3,Legionella Cult/D&D,HC LEGNLA CLTR/D+D,165,165,165,165,495,495,495,495
+LABLEGDD,6,7,3,Legionella Cult/D&D,HC LEGNLA CLTR/D+D,165,165,165,165,495,495,495,495
+LABLEGU,805,1,48,LEGIONELLA AG URINE,HC 6942 LEGIONELLA AG URINE,45,45,45,45,2160,2160,2160,2160
+LABLEGU,805,3,52,LEGIONELLA AG URINE,HC 6942 LEGIONELLA AG URINE,45,45,45,45,2340,2340,2340,2340
+LABLEGU,805,7,62,LEGIONELLA AG URINE,HC 6942 LEGIONELLA AG URINE,45,45,45,45,2790,2790,2790,2790
+LABLEID,609,1,24,FACTOR V LEIDEN,HC LEID F5 LEIDEN VARIENT,578,578,578,578,13872,13872,13872,13872
+LABLEID,609,3,28,FACTOR V LEIDEN,HC LEID F5 LEIDEN VARIENT,578,578,578,578,16184,16184,16184,16184
+LABLEID,609,7,30,FACTOR V LEIDEN,HC LEID F5 LEIDEN VARIENT,578,578,578,578,17340,17340,17340,17340
+LABLEISAB,14,1,2,"LEISHMANIA ANTIBODY, IFA",HC 6942 LEISHMANIASIS,10,10,10,10,20,20,20,20
+LABLEISAB,14,1,2,"LEISHMANIA ANTIBODY, IFA",HC 6942 LEISAB LEISHMANIA AB,10,10,10,10,20,20,20,20
+LABLEISAB,14,3,2,"LEISHMANIA ANTIBODY, IFA",HC 6942 LEISHMANIASIS,10,10,10,10,20,20,20,20
+LABLEISAB,14,3,2,"LEISHMANIA ANTIBODY, IFA",HC 6942 LEISAB LEISHMANIA AB,10,10,10,10,20,20,20,20
+LABLEISAB,14,7,3,"LEISHMANIA ANTIBODY, IFA",HC 6942 LEISHMANIASIS,10,10,10,10,30,30,30,30
+LABLEISAB,14,7,3,"LEISHMANIA ANTIBODY, IFA",HC 6942 LEISAB LEISHMANIA AB,10,10,10,10,30,30,30,30
+LABLEVETL,371,1,24,LEVETIRACETAM LEVEL,HC  LEVETL QNT DRG NES,181,181,181,181,4344,4344,4344,4344
+LABLEVETL,371,3,35,LEVETIRACETAM LEVEL,HC  LEVETL QNT DRG NES,181,181,181,181,6335,6335,6335,6335
+LABLEVETL,371,7,47,LEVETIRACETAM LEVEL,HC  LEVETL QNT DRG NES,181,181,181,181,8507,8507,8507,8507
+LABLIDOL,9707,1,7957,LIDOCAINE,HC LIDOCAINE LEVEL,264,264,264,264,2100648,2100648,2100648,2100648
+LABLIDOL,9707,3,8317,LIDOCAINE,HC LIDOCAINE LEVEL,264,264,264,264,2195688,2195688,2195688,2195688
+LABLIDOL,9707,7,8345,LIDOCAINE,HC LIDOCAINE LEVEL,264,264,264,264,2203080,2203080,2203080,2203080
+LABLIL,1363,1,233,LITHIUM,HC LITHIUM QNT,188,188,188,188,43804,43804,43804,43804
+LABLIL,1363,3,468,LITHIUM,HC LITHIUM QNT,188,188,188,188,87984,87984,87984,87984
+LABLIL,1363,7,697,LITHIUM,HC LITHIUM QNT,188,188,188,188,131036,131036,131036,131036
+LABLIPS,22004,1,3586,Lipase,HC LIPASE,213,213,213,213,763818,763818,763818,763818
+LABLIPS,22004,3,6033,Lipase,HC LIPASE,213,213,213,213,1285029,1285029,1285029,1285029
+LABLIPS,22004,7,6682,Lipase,HC LIPASE,213,213,213,213,1423266,1423266,1423266,1423266
+LABLPA,39,1,3,Lipoprotein (a),HC LP(A),194,194,194,194,582,582,582,582
+LABLPA,39,3,4,Lipoprotein (a),HC LP(A),194,194,194,194,776,776,776,776
+LABLPA,39,7,4,Lipoprotein (a),HC LP(A),194,194,194,194,776,776,776,776
+LABLPD,8466,1,419,LIPID PANEL W/ DIRECT LDL,HC LDLD - DIRECT LDL CHOLESTEROL,219,415,317,317,91761,173885,132823,132823
+LABLPD,8466,1,419,LIPID PANEL W/ DIRECT LDL,HC LIPID PANEL - CALCULATED LD,219,415,317,317,91761,173885,132823,132823
+LABLPD,8466,3,596,LIPID PANEL W/ DIRECT LDL,HC LDLD - DIRECT LDL CHOLESTEROL,219,415,317,317,130524,247340,188932,188932
+LABLPD,8466,3,596,LIPID PANEL W/ DIRECT LDL,HC LIPID PANEL - CALCULATED LD,219,415,317,317,130524,247340,188932,188932
+LABLPD,8466,7,678,LIPID PANEL W/ DIRECT LDL,HC LDLD - DIRECT LDL CHOLESTEROL,219,415,317,317,148482,281370,214926,214926
+LABLPD,8466,7,678,LIPID PANEL W/ DIRECT LDL,HC LIPID PANEL - CALCULATED LD,219,415,317,317,148482,281370,214926,214926
+LABLPDC,2348,1,63,LIPID PANEL W/ CALCULATED LDL,HC LIPID PANEL - CALCULATED LD,415,415,415,415,26145,26145,26145,26145
+LABLPDC,2348,3,87,LIPID PANEL W/ CALCULATED LDL,HC LIPID PANEL - CALCULATED LD,415,415,415,415,36105,36105,36105,36105
+LABLPDC,2348,7,113,LIPID PANEL W/ CALCULATED LDL,HC LIPID PANEL - CALCULATED LD,415,415,415,415,46895,46895,46895,46895
+LABLPDP,46,1,3,LIPID PLUS RISK W/ DIRECT LDL,HC HOMOCYSTEINE,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,1,3,LIPID PLUS RISK W/ DIRECT LDL,HC HIGH SENSITIVE CRP,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,1,3,LIPID PLUS RISK W/ DIRECT LDL,HC LDLD - DIRECT LDL CHOLESTEROL,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,1,3,LIPID PLUS RISK W/ DIRECT LDL,HC LP(A),190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,1,3,LIPID PLUS RISK W/ DIRECT LDL,HC LIPID PANEL - CALCULATED LD,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,3,3,LIPID PLUS RISK W/ DIRECT LDL,HC HOMOCYSTEINE,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,3,3,LIPID PLUS RISK W/ DIRECT LDL,HC HIGH SENSITIVE CRP,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,3,3,LIPID PLUS RISK W/ DIRECT LDL,HC LDLD - DIRECT LDL CHOLESTEROL,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,3,3,LIPID PLUS RISK W/ DIRECT LDL,HC LP(A),190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,3,3,LIPID PLUS RISK W/ DIRECT LDL,HC LIPID PANEL - CALCULATED LD,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,7,3,LIPID PLUS RISK W/ DIRECT LDL,HC HOMOCYSTEINE,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,7,3,LIPID PLUS RISK W/ DIRECT LDL,HC HIGH SENSITIVE CRP,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,7,3,LIPID PLUS RISK W/ DIRECT LDL,HC LDLD - DIRECT LDL CHOLESTEROL,190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,7,3,LIPID PLUS RISK W/ DIRECT LDL,HC LP(A),190,415,283.4,219,570,1245,850.2,657
+LABLPDP,46,7,3,LIPID PLUS RISK W/ DIRECT LDL,HC LIPID PANEL - CALCULATED LD,190,415,283.4,219,570,1245,850.2,657
+LABLPDPC,71,1,1,LIPID PLUS RISK W/ CALC. LDL,HC HOMOCYSTEINE,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,1,1,LIPID PLUS RISK W/ CALC. LDL,HC HIGH SENSITIVE CRP,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,1,1,LIPID PLUS RISK W/ CALC. LDL,HC LP(A),190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,1,1,LIPID PLUS RISK W/ CALC. LDL,HC LIPID PANEL - CALCULATED LD,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,3,1,LIPID PLUS RISK W/ CALC. LDL,HC HOMOCYSTEINE,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,3,1,LIPID PLUS RISK W/ CALC. LDL,HC HIGH SENSITIVE CRP,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,3,1,LIPID PLUS RISK W/ CALC. LDL,HC LP(A),190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,3,1,LIPID PLUS RISK W/ CALC. LDL,HC LIPID PANEL - CALCULATED LD,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,7,1,LIPID PLUS RISK W/ CALC. LDL,HC HOMOCYSTEINE,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,7,1,LIPID PLUS RISK W/ CALC. LDL,HC HIGH SENSITIVE CRP,190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,7,1,LIPID PLUS RISK W/ CALC. LDL,HC LP(A),190,415,299.5,296.5,190,415,299.5,296.5
+LABLPDPC,71,7,1,LIPID PLUS RISK W/ CALC. LDL,HC LIPID PANEL - CALCULATED LD,190,415,299.5,296.5,190,415,299.5,296.5
+LABLUT,144,1,3,LH,HC LH (LHOR),344,344,344,344,1032,1032,1032,1032
+LABLUT,144,3,4,LH,HC LH (LHOR),344,344,344,344,1376,1376,1376,1376
+LABLUT,144,7,5,LH,HC LH (LHOR),344,344,344,344,1720,1720,1720,1720
+LABLWBCS2,72,1,4,LYME SERUM WESTERN BLOT,HC 6942 LYME AB PNL IGG IGM WB,32,32,32,32,128,128,128,128
+LABLWBCS2,72,1,4,LYME SERUM WESTERN BLOT,HC 6942 LYME B BURGDORFERI WB,32,32,32,32,128,128,128,128
+LABLWBCS2,72,3,9,LYME SERUM WESTERN BLOT,HC 6942 LYME AB PNL IGG IGM WB,32,32,32,32,288,288,288,288
+LABLWBCS2,72,3,9,LYME SERUM WESTERN BLOT,HC 6942 LYME B BURGDORFERI WB,32,32,32,32,288,288,288,288
+LABLWBCS2,72,7,10,LYME SERUM WESTERN BLOT,HC 6942 LYME AB PNL IGG IGM WB,32,32,32,32,320,320,320,320
+LABLWBCS2,72,7,10,LYME SERUM WESTERN BLOT,HC 6942 LYME B BURGDORFERI WB,32,32,32,32,320,320,320,320
+LABLYMES2,150,1,9,LYME SERUM SCREEN                      NONORDERABLE,HC 6942 LYME SCREEN,55,55,55,55,495,495,495,495
+LABLYMES2,150,3,9,LYME SERUM SCREEN                      NONORDERABLE,HC 6942 LYME SCREEN,55,55,55,55,495,495,495,495
+LABLYMES2,150,7,10,LYME SERUM SCREEN                      NONORDERABLE,HC 6942 LYME SCREEN,55,55,55,55,550,550,550,550
+LABMALAR,348,1,148,MALARIA PREP,HC MALARIA PREP,379,379,379,379,56092,56092,56092,56092
+LABMALAR,348,3,171,MALARIA PREP,HC MALARIA PREP,379,379,379,379,64809,64809,64809,64809
+LABMALAR,348,7,171,MALARIA PREP,HC MALARIA PREP,379,379,379,379,64809,64809,64809,64809
+LABMALL1,4,1,1,MISC. ALLERGEN 1,HC MISC ALLERGEN 1 IGE,79,79,79,79,79,79,79,79
+LABMALL1,4,3,1,MISC. ALLERGEN 1,HC MISC ALLERGEN 1 IGE,79,79,79,79,79,79,79,79
+LABMALL1,4,7,1,MISC. ALLERGEN 1,HC MISC ALLERGEN 1 IGE,79,79,79,79,79,79,79,79
+LABMALL2,2,1,1,Misc. Allergen 2,HC MISC ALLERGEN 2 IGE,79,79,79,79,79,79,79,79
+LABMALL2,2,3,1,Misc. Allergen 2,HC MISC ALLERGEN 2 IGE,79,79,79,79,79,79,79,79
+LABMALL2,2,7,1,Misc. Allergen 2,HC MISC ALLERGEN 2 IGE,79,79,79,79,79,79,79,79
+LABMALSC,59,1,13,MALARIA PREP and ANTIGEN SCREEN,HC MALSC ANTG DIR OPT OBS NOS,132,132,132,132,1716,1716,1716,1716
+LABMALSC,59,3,16,MALARIA PREP and ANTIGEN SCREEN,HC MALSC ANTG DIR OPT OBS NOS,132,132,132,132,2112,2112,2112,2112
+LABMALSC,59,7,16,MALARIA PREP and ANTIGEN SCREEN,HC MALSC ANTG DIR OPT OBS NOS,132,132,132,132,2112,2112,2112,2112
+LABMB,40041,1,23006,CK-MB,HC CK-MB,446,446,446,446,10260676,10260676,10260676,10260676
+LABMB,40041,3,24670,CK-MB,HC CK-MB,446,446,446,446,11002820,11002820,11002820,11002820
+LABMB,40041,7,25296,CK-MB,HC CK-MB,446,446,446,446,11282016,11282016,11282016,11282016
+LABMECAPC,3,1,1,mecA by PCR,HC MECAPC ID CULT APT EA ORG,578,578,578,578,578,578,578,578
+LABMECAPC,3,3,1,mecA by PCR,HC MECAPC ID CULT APT EA ORG,578,578,578,578,578,578,578,578
+LABMECAPC,3,7,1,mecA by PCR,HC MECAPC ID CULT APT EA ORG,578,578,578,578,578,578,578,578
+LABMEP,28,1,2,MEDICAL EMERGENCY PANEL,HC MED EMERG SODIUM,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,1,2,MEDICAL EMERGENCY PANEL,HC MED EMERG POTASSIUM,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,1,2,MEDICAL EMERGENCY PANEL,HC MED EMERG GLUCOSE,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,1,2,MEDICAL EMERGENCY PANEL,HC MED EMERG CHLORIDE,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,1,2,MEDICAL EMERGENCY PANEL,HC MED EMERG CALCIUM IO,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,1,2,MEDICAL EMERGENCY PANEL,HC MED EMERG HCT,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,3,2,MEDICAL EMERGENCY PANEL,HC MED EMERG SODIUM,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,3,2,MEDICAL EMERGENCY PANEL,HC MED EMERG POTASSIUM,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,3,2,MEDICAL EMERGENCY PANEL,HC MED EMERG GLUCOSE,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,3,2,MEDICAL EMERGENCY PANEL,HC MED EMERG CHLORIDE,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,3,2,MEDICAL EMERGENCY PANEL,HC MED EMERG CALCIUM IO,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,3,2,MEDICAL EMERGENCY PANEL,HC MED EMERG HCT,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,7,2,MEDICAL EMERGENCY PANEL,HC MED EMERG SODIUM,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,7,2,MEDICAL EMERGENCY PANEL,HC MED EMERG POTASSIUM,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,7,2,MEDICAL EMERGENCY PANEL,HC MED EMERG GLUCOSE,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,7,2,MEDICAL EMERGENCY PANEL,HC MED EMERG CHLORIDE,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,7,2,MEDICAL EMERGENCY PANEL,HC MED EMERG CALCIUM IO,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMEP,28,7,2,MEDICAL EMERGENCY PANEL,HC MED EMERG HCT,129,399,221.1666667,210.5,258,798,442.3333333,421
+LABMERC,12,1,1,Mercury Whole Blood,HC MERC HVY MTL MERCURY QUAN,249,249,249,249,249,249,249,249
+LABMERC,12,3,1,Mercury Whole Blood,HC MERC HVY MTL MERCURY QUAN,249,249,249,249,249,249,249,249
+LABMERC,12,7,1,Mercury Whole Blood,HC MERC HVY MTL MERCURY QUAN,249,249,249,249,249,249,249,249
+LABMETB,454479,1,253789,"METABOLIC PANEL, BASIC",HC METABOLIC PNL BASIC,350,350,350,350,88826150,88826150,88826150,88826150
+LABMETB,454479,3,364224,"METABOLIC PANEL, BASIC",HC METABOLIC PNL BASIC,350,350,350,350,127478400,127478400,127478400,127478400
+LABMETB,454479,7,371285,"METABOLIC PANEL, BASIC",HC METABOLIC PNL BASIC,350,350,350,350,129949750,129949750,129949750,129949750
+LABMETC,257247,1,112713,"METABOLIC PANEL, COMPREHENSIVE",HC METABOLIC PNL COMP,435,435,435,435,49030155,49030155,49030155,49030155
+LABMETC,257247,3,186305,"METABOLIC PANEL, COMPREHENSIVE",HC METABOLIC PNL COMP,435,435,435,435,81042675,81042675,81042675,81042675
+LABMETC,257247,7,191090,"METABOLIC PANEL, COMPREHENSIVE",HC METABOLIC PNL COMP,435,435,435,435,83124150,83124150,83124150,83124150
+LABMETHB,427,1,212,Methemoglobin,HC METHGB(METHHEMOGLOB),81,81,81,81,17172,17172,17172,17172
+LABMETHB,427,3,297,Methemoglobin,HC METHGB(METHHEMOGLOB),81,81,81,81,24057,24057,24057,24057
+LABMETHB,427,7,301,Methemoglobin,HC METHGB(METHHEMOGLOB),81,81,81,81,24381,24381,24381,24381
+LABMGMT,101,7,1,"MGMT, METHYLATION SPECIFIC PCR",HC MGMT METHYLNUNLS MOP PETMB,829,829,829,829,829,829,829,829
+LABMGN,389911,1,226800,"Magnesium, Ser/Plas",HC MAGNESIUM SERUM/PL,280,280,280,280,63504000,63504000,63504000,63504000
+LABMGN,389911,3,325508,"Magnesium, Ser/Plas",HC MAGNESIUM SERUM/PL,280,280,280,280,91142240,91142240,91142240,91142240
+LABMGN,389911,7,331190,"Magnesium, Ser/Plas",HC MAGNESIUM SERUM/PL,280,280,280,280,92733200,92733200,92733200,92733200
+LABMISPRO,38,7,4,MISCELLANEOUS PROCESSING,HC SPECIMEN PROCESSING,39,39,39,39,156,156,156,156
+LABMMAS,415,1,15,"Methylmalonic Acid, Serum",HC METHYLMALONIC ACID QUANT,200,200,200,200,3000,3000,3000,3000
+LABMMAS,415,3,18,"Methylmalonic Acid, Serum",HC METHYLMALONIC ACID QUANT,200,200,200,200,3600,3600,3600,3600
+LABMMAS,415,7,27,"Methylmalonic Acid, Serum",HC METHYLMALONIC ACID QUANT,200,200,200,200,5400,5400,5400,5400
+LABMONEBV,187,1,7,MONO SCREEN W/REFLEX EBV ANTIBODY,HC INFECT MONO SCRN,199,199,199,199,1393,1393,1393,1393
+LABMONEBV,187,3,7,MONO SCREEN W/REFLEX EBV ANTIBODY,HC INFECT MONO SCRN,199,199,199,199,1393,1393,1393,1393
+LABMONEBV,187,7,7,MONO SCREEN W/REFLEX EBV ANTIBODY,HC INFECT MONO SCRN,199,199,199,199,1393,1393,1393,1393
+LABMONOSC,62,1,1,Infectious Mono Screen,HC INFECT MONO SCRN,199,199,199,199,199,199,199,199
+LABMONOSC,62,3,1,Infectious Mono Screen,HC INFECT MONO SCRN,199,199,199,199,199,199,199,199
+LABMONOSC,62,7,1,Infectious Mono Screen,HC INFECT MONO SCRN,199,199,199,199,199,199,199,199
+LABMPOPR3,30,1,2,ANCA MPO/PR-3M ABS (RFLX),HC 6959 MPO PR3 ANCA AB,19,19,19,19,38,38,38,38
+LABMPOPR3,30,3,6,ANCA MPO/PR-3M ABS (RFLX),HC 6959 MPO PR3 ANCA AB,19,19,19,19,114,114,114,114
+LABMPOPR3,30,7,6,ANCA MPO/PR-3M ABS (RFLX),HC 6959 MPO PR3 ANCA AB,19,19,19,19,114,114,114,114
+LABMTHFR,19,1,4,METHYLENE-THF REDUCTASE,HC MTHFR CMN VAR 677 1298,668,668,668,668,2672,2672,2672,2672
+LABMTHFR,19,3,4,METHYLENE-THF REDUCTASE,HC MTHFR CMN VAR 677 1298,668,668,668,668,2672,2672,2672,2672
+LABMTHFR,19,7,5,METHYLENE-THF REDUCTASE,HC MTHFR CMN VAR 677 1298,668,668,668,668,3340,3340,3340,3340
+LABMTXL,2084,1,950,METHOTREXATE,HC METHOTREXATE,291,291,291,291,276450,276450,276450,276450
+LABMTXL,2084,3,1398,METHOTREXATE,HC METHOTREXATE,291,291,291,291,406818,406818,406818,406818
+LABMTXL,2084,7,1558,METHOTREXATE,HC METHOTREXATE,291,291,291,291,453378,453378,453378,453378
+LABMUMPSG,9,1,2,MUMPS IgG,HC MUMPSG MUMPS AB IGG,212,212,212,212,424,424,424,424
+LABMUMPSG,9,3,2,MUMPS IgG,HC MUMPSG MUMPS AB IGG,212,212,212,212,424,424,424,424
+LABMUMPSG,9,7,2,MUMPS IgG,HC MUMPSG MUMPS AB IGG,212,212,212,212,424,424,424,424
+LABMYCOC,4,3,1,"MYCOPLASMA CULTURE, FASTIDIOUS",HC 6942 MYCO FAST PATH CULT,230,230,230,230,230,230,230,230
+LABMYCOC,4,7,1,"MYCOPLASMA CULTURE, FASTIDIOUS",HC 6942 MYCO FAST PATH CULT,230,230,230,230,230,230,230,230
+LABMYCPA,1895,1,425,MYCOPHENOLIC ACID,HC MYCOPENOLIC ACID,246,246,246,246,104550,104550,104550,104550
+LABMYCPA,1895,3,639,MYCOPHENOLIC ACID,HC MYCOPENOLIC ACID,246,246,246,246,157194,157194,157194,157194
+LABMYCPA,1895,7,966,MYCOPHENOLIC ACID,HC MYCOPENOLIC ACID,246,246,246,246,237636,237636,237636,237636
+LABMYO,894,1,292,"Myoglobin, Ser/Plas",HC MYOGLOBIN,265,265,265,265,77380,77380,77380,77380
+LABMYO,894,3,343,"Myoglobin, Ser/Plas",HC MYOGLOBIN,265,265,265,265,90895,90895,90895,90895
+LABMYO,894,7,348,"Myoglobin, Ser/Plas",HC MYOGLOBIN,265,265,265,265,92220,92220,92220,92220
+LABNBTS,3,3,1,NITROBLUE TETRAZOLIUM RED.,HC NBT NITROBLUE TETRAZOL,69,69,69,69,69,69,69,69
+LABNBTS,3,7,1,NITROBLUE TETRAZOLIUM RED.,HC NBT NITROBLUE TETRAZOL,69,69,69,69,69,69,69,69
+LABNH3,7224,1,1740,"Ammonia, Plasma",HC BLD AMMONIA TEST,334,334,334,334,581160,581160,581160,581160
+LABNH3,7224,3,2937,"Ammonia, Plasma",HC BLD AMMONIA TEST,334,334,334,334,980958,980958,980958,980958
+LABNH3,7224,7,3269,"Ammonia, Plasma",HC BLD AMMONIA TEST,334,334,334,334,1091846,1091846,1091846,1091846
+LABNORW,346,1,14,NOROVIRUS ANTIGEN,HC 6942 NOROVIRUS-STOOL,98,98,98,98,1372,1372,1372,1372
+LABNORW,346,3,25,NOROVIRUS ANTIGEN,HC 6942 NOROVIRUS-STOOL,98,98,98,98,2450,2450,2450,2450
+LABNORW,346,7,44,NOROVIRUS ANTIGEN,HC 6942 NOROVIRUS-STOOL,98,98,98,98,4312,4312,4312,4312
+LABNPM1,68,1,2,"NPM1, NUCLEOPHOSMIN 1 MUTATION",HC NPM1 GN ANLY EXON 12 VARNTS,662,662,662,662,1324,1324,1324,1324
+LABNPM1,68,3,2,"NPM1, NUCLEOPHOSMIN 1 MUTATION",HC NPM1 GN ANLY EXON 12 VARNTS,662,662,662,662,1324,1324,1324,1324
+LABNPM1,68,7,3,"NPM1, NUCLEOPHOSMIN 1 MUTATION",HC NPM1 GN ANLY EXON 12 VARNTS,662,662,662,662,1986,1986,1986,1986
+LABNTBNP,12690,1,988,NT-proBNP,HC NT-PROBNP,821,821,821,821,811148,811148,811148,811148
+LABNTBNP,12690,3,1592,NT-proBNP,HC NT-PROBNP,821,821,821,821,1307032,1307032,1307032,1307032
+LABNTBNP,12690,7,2212,NT-proBNP,HC NT-PROBNP,821,821,821,821,1816052,1816052,1816052,1816052
+LABOAPE,1972,1,137,Ova and Parasites,HC STOOL PARASITES CMPLX STAIN,457,457,457,457,62609,62609,62609,62609
+LABOAPE,1972,3,227,Ova and Parasites,HC STOOL PARASITES CMPLX STAIN,457,457,457,457,103739,103739,103739,103739
+LABOAPE,1972,7,279,Ova and Parasites,HC STOOL PARASITES CMPLX STAIN,457,457,457,457,127503,127503,127503,127503
+LABOLIG,241,1,4,"OLIGOCLONAL BANDING, CSF",HC OLOGOCLONAL BANDING,374,374,374,374,1496,1496,1496,1496
+LABOLIG,241,3,5,"OLIGOCLONAL BANDING, CSF",HC OLOGOCLONAL BANDING,374,374,374,374,1870,1870,1870,1870
+LABOLIG,241,7,11,"OLIGOCLONAL BANDING, CSF",HC OLOGOCLONAL BANDING,374,374,374,374,4114,4114,4114,4114
+LABORAERO,516,1,185,ORTHOPEDIC AEROBIC CULT W/GS,HC ORTHO AERO CUL W/GRAM STAIN,150,276,213,213,27750,51060,39405,39405
+LABORAERO,516,1,185,ORTHOPEDIC AEROBIC CULT W/GS,HC SMEAR PRIM SOURCE W/INTERP,150,276,213,213,27750,51060,39405,39405
+LABORAERO,516,3,198,ORTHOPEDIC AEROBIC CULT W/GS,HC ORTHO AERO CUL W/GRAM STAIN,150,276,213,213,29700,54648,42174,42174
+LABORAERO,516,3,198,ORTHOPEDIC AEROBIC CULT W/GS,HC SMEAR PRIM SOURCE W/INTERP,150,276,213,213,29700,54648,42174,42174
+LABORAERO,516,7,217,ORTHOPEDIC AEROBIC CULT W/GS,HC ORTHO AERO CUL W/GRAM STAIN,150,276,213,213,32550,59892,46221,46221
+LABORAERO,516,7,217,ORTHOPEDIC AEROBIC CULT W/GS,HC SMEAR PRIM SOURCE W/INTERP,150,276,213,213,32550,59892,46221,46221
+LABORANER,518,1,182,ORTHOPEDIC ANAEROBIC CULTURE,HC ANAERO CUL FOR ORTHO,544,544,544,544,99008,99008,99008,99008
+LABORANER,518,3,198,ORTHOPEDIC ANAEROBIC CULTURE,HC ANAERO CUL FOR ORTHO,544,544,544,544,107712,107712,107712,107712
+LABORANER,518,7,212,ORTHOPEDIC ANAEROBIC CULTURE,HC ANAERO CUL FOR ORTHO,544,544,544,544,115328,115328,115328,115328
+LABORSHOR,511,1,262,ORTHOPEDIC CULT MULT SPECIMENS,HC CUL FOR MULTI ORTHO SPECI,544,544,544,544,142528,142528,142528,142528
+LABORSHOR,511,3,276,ORTHOPEDIC CULT MULT SPECIMENS,HC CUL FOR MULTI ORTHO SPECI,544,544,544,544,150144,150144,150144,150144
+LABORSHOR,511,7,286,ORTHOPEDIC CULT MULT SPECIMENS,HC CUL FOR MULTI ORTHO SPECI,544,544,544,544,155584,155584,155584,155584
+LABOSM,13160,1,9603,"Osmolality, Serum",HC OSMOLALITY SERUM,209,209,209,209,2007027,2007027,2007027,2007027
+LABOSM,13160,3,9922,"Osmolality, Serum",HC OSMOLALITY SERUM,209,209,209,209,2073698,2073698,2073698,2073698
+LABOSM,13160,7,10000,"Osmolality, Serum",HC OSMOLALITY SERUM,209,209,209,209,2090000,2090000,2090000,2090000
+LABOXCMET,127,1,5,OXCARBAZEPINE METABOLITE,HC 6959 OXICARBAZEPINE METAB,41,41,41,41,205,205,205,205
+LABOXCMET,127,3,8,OXCARBAZEPINE METABOLITE,HC 6959 OXICARBAZEPINE METAB,41,41,41,41,328,328,328,328
+LABOXCMET,127,7,10,OXCARBAZEPINE METABOLITE,HC 6959 OXICARBAZEPINE METAB,41,41,41,41,410,410,410,410
+LABP20210,558,1,31,PROTHROMBIN-20210A,HC P20210 F2 GN ANLY PROTHMBN,578,578,578,578,17918,17918,17918,17918
+LABP20210,558,3,34,PROTHROMBIN-20210A,HC P20210 F2 GN ANLY PROTHMBN,578,578,578,578,19652,19652,19652,19652
+LABP20210,558,7,34,PROTHROMBIN-20210A,HC P20210 F2 GN ANLY PROTHMBN,578,578,578,578,19652,19652,19652,19652
+LABPALB,3721,1,209,Prealbumin,HC PALB PREALBUMIN,246,246,246,246,51414,51414,51414,51414
+LABPALB,3721,3,921,Prealbumin,HC PALB PREALBUMIN,246,246,246,246,226566,226566,226566,226566
+LABPALB,3721,7,2363,Prealbumin,HC PALB PREALBUMIN,246,246,246,246,581298,581298,581298,581298
+LABPANAPL,29,1,19,PROCAINAMIDE AND NAPA,HC PROCAINAMIDE PLUS NAPA,186,186,186,186,3534,3534,3534,3534
+LABPANAPL,29,3,24,PROCAINAMIDE AND NAPA,HC PROCAINAMIDE PLUS NAPA,186,186,186,186,4464,4464,4464,4464
+LABPANAPL,29,7,24,PROCAINAMIDE AND NAPA,HC PROCAINAMIDE PLUS NAPA,186,186,186,186,4464,4464,4464,4464
+LABPB,513,1,148,Phenobarbital,HC PHENOBARITAL LEVEL,223,223,223,223,33004,33004,33004,33004
+LABPB,513,3,328,Phenobarbital,HC PHENOBARITAL LEVEL,223,223,223,223,73144,73144,73144,73144
+LABPB,513,7,342,Phenobarbital,HC PHENOBARITAL LEVEL,223,223,223,223,76266,76266,76266,76266
+LABPCACT,439,1,297,"ACTIVATED CLOTTING TIME, POCT",HC ACT CLOTT TIME(ACT+),250,250,250,250,74250,74250,74250,74250
+LABPCACT,439,3,301,"ACTIVATED CLOTTING TIME, POCT",HC ACT CLOTT TIME(ACT+),250,250,250,250,75250,75250,75250,75250
+LABPCACT,439,7,310,"ACTIVATED CLOTTING TIME, POCT",HC ACT CLOTT TIME(ACT+),250,250,250,250,77500,77500,77500,77500
+LABPCACTK,2549,1,1808,"ACT, ISTAT KAOLIN",HC ACT CLOTT TIME(ACT+),250,250,250,250,452000,452000,452000,452000
+LABPCACTK,2549,3,1809,"ACT, ISTAT KAOLIN",HC ACT CLOTT TIME(ACT+),250,250,250,250,452250,452250,452250,452250
+LABPCACTK,2549,7,1810,"ACT, ISTAT KAOLIN",HC ACT CLOTT TIME(ACT+),250,250,250,250,452500,452500,452500,452500
+LABPCCG4,1281,1,212,"ISTAT CG4, ARTERIAL",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,69960,172780,121370,121370
+LABPCCG4,1281,1,212,"ISTAT CG4, ARTERIAL",HC PC02-02 (CG4),330,815,572.5,572.5,69960,172780,121370,121370
+LABPCCG4,1281,3,213,"ISTAT CG4, ARTERIAL",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,70290,173595,121942.5,121942.5
+LABPCCG4,1281,3,213,"ISTAT CG4, ARTERIAL",HC PC02-02 (CG4),330,815,572.5,572.5,70290,173595,121942.5,121942.5
+LABPCCG4,1281,7,214,"ISTAT CG4, ARTERIAL",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,70620,174410,122515,122515
+LABPCCG4,1281,7,214,"ISTAT CG4, ARTERIAL",HC PC02-02 (CG4),330,815,572.5,572.5,70620,174410,122515,122515
+LABPCCG4O,4112,1,254,"ISTAT CG4, OTHER",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,83820,207010,145415,145415
+LABPCCG4O,4112,1,254,"ISTAT CG4, OTHER",HC PC02-02 (CG4),330,815,572.5,572.5,83820,207010,145415,145415
+LABPCCG4O,4112,3,255,"ISTAT CG4, OTHER",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,84150,207825,145987.5,145987.5
+LABPCCG4O,4112,3,255,"ISTAT CG4, OTHER",HC PC02-02 (CG4),330,815,572.5,572.5,84150,207825,145987.5,145987.5
+LABPCCG4O,4112,7,269,"ISTAT CG4, OTHER",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,88770,219235,154002.5,154002.5
+LABPCCG4O,4112,7,269,"ISTAT CG4, OTHER",HC PC02-02 (CG4),330,815,572.5,572.5,88770,219235,154002.5,154002.5
+LABPCCG4V,15202,1,1839,"ISTAT CG4, VENOUS",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,606870,1498785,1052827.5,1052827.5
+LABPCCG4V,15202,1,1839,"ISTAT CG4, VENOUS",HC PC02-02 (CG4),330,815,572.5,572.5,606870,1498785,1052827.5,1052827.5
+LABPCCG4V,15202,3,1872,"ISTAT CG4, VENOUS",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,617760,1525680,1071720,1071720
+LABPCCG4V,15202,3,1872,"ISTAT CG4, VENOUS",HC PC02-02 (CG4),330,815,572.5,572.5,617760,1525680,1071720,1071720
+LABPCCG4V,15202,7,1989,"ISTAT CG4, VENOUS",HC PCLACT-LACTATE (CG4),330,815,572.5,572.5,656370,1621035,1138702.5,1138702.5
+LABPCCG4V,15202,7,1989,"ISTAT CG4, VENOUS",HC PC02-02 (CG4),330,815,572.5,572.5,656370,1621035,1138702.5,1138702.5
+LABPCCH8,666,1,56,ISTAT CHEM 8,HC HEMATOCRIT POC,217,487,352,352,12152,27272,19712,19712
+LABPCCH8,666,1,56,ISTAT CHEM 8,HC METABOLIC PANEL,217,487,352,352,12152,27272,19712,19712
+LABPCCH8,666,3,56,ISTAT CHEM 8,HC HEMATOCRIT POC,217,487,352,352,12152,27272,19712,19712
+LABPCCH8,666,3,56,ISTAT CHEM 8,HC METABOLIC PANEL,217,487,352,352,12152,27272,19712,19712
+LABPCCH8,666,7,56,ISTAT CHEM 8,HC HEMATOCRIT POC,217,487,352,352,12152,27272,19712,19712
+LABPCCH8,666,7,56,ISTAT CHEM 8,HC METABOLIC PANEL,217,487,352,352,12152,27272,19712,19712
+LABPCCR,9993,1,195,ISTAT CREATININE,HC CREATININE POC,179,179,179,179,34905,34905,34905,34905
+LABPCCR,9993,3,199,ISTAT CREATININE,HC CREATININE POC,179,179,179,179,35621,35621,35621,35621
+LABPCCR,9993,7,219,ISTAT CREATININE,HC CREATININE POC,179,179,179,179,39201,39201,39201,39201
+LABPCEC4,77,1,9,ISTAT EC4,HC GLUCOSE QUANT,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,1,9,ISTAT EC4,HC SODIUM,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,1,9,ISTAT EC4,HC POTASSIUM POC,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,1,9,ISTAT EC4,HC HEMATOCRIT POC,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,3,9,ISTAT EC4,HC GLUCOSE QUANT,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,3,9,ISTAT EC4,HC SODIUM,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,3,9,ISTAT EC4,HC POTASSIUM POC,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,3,9,ISTAT EC4,HC HEMATOCRIT POC,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,7,9,ISTAT EC4,HC GLUCOSE QUANT,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,7,9,ISTAT EC4,HC SODIUM,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,7,9,ISTAT EC4,HC POTASSIUM POC,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4,77,7,9,ISTAT EC4,HC HEMATOCRIT POC,159,219,199.75,210.5,1431,1971,1797.75,1894.5
+LABPCEC4O,485,1,28,"ISTAT EC4, OTHER",HC GLUCOSE QUANT,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,1,28,"ISTAT EC4, OTHER",HC SODIUM,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,1,28,"ISTAT EC4, OTHER",HC POTASSIUM POC,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,1,28,"ISTAT EC4, OTHER",HC HEMATOCRIT POC,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,3,28,"ISTAT EC4, OTHER",HC GLUCOSE QUANT,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,3,28,"ISTAT EC4, OTHER",HC SODIUM,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,3,28,"ISTAT EC4, OTHER",HC POTASSIUM POC,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,3,28,"ISTAT EC4, OTHER",HC HEMATOCRIT POC,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,7,28,"ISTAT EC4, OTHER",HC GLUCOSE QUANT,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,7,28,"ISTAT EC4, OTHER",HC SODIUM,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,7,28,"ISTAT EC4, OTHER",HC POTASSIUM POC,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4O,485,7,28,"ISTAT EC4, OTHER",HC HEMATOCRIT POC,159,219,199.75,210.5,4452,6132,5593,5894
+LABPCEC4V,3438,1,270,"ISTAT EC4, VENOUS",HC GLUCOSE QUANT,159,219,199.75,210.5,42930,59130,53932.5,56835
+LABPCEC4V,3438,1,270,"ISTAT EC4, VENOUS",HC SODIUM,159,219,199.75,210.5,42930,59130,53932.5,56835
+LABPCEC4V,3438,1,270,"ISTAT EC4, VENOUS",HC POTASSIUM POC,159,219,199.75,210.5,42930,59130,53932.5,56835
+LABPCEC4V,3438,1,270,"ISTAT EC4, VENOUS",HC HEMATOCRIT POC,159,219,199.75,210.5,42930,59130,53932.5,56835
+LABPCEC4V,3438,3,273,"ISTAT EC4, VENOUS",HC GLUCOSE QUANT,159,219,199.75,210.5,43407,59787,54531.75,57466.5
+LABPCEC4V,3438,3,273,"ISTAT EC4, VENOUS",HC SODIUM,159,219,199.75,210.5,43407,59787,54531.75,57466.5
+LABPCEC4V,3438,3,273,"ISTAT EC4, VENOUS",HC POTASSIUM POC,159,219,199.75,210.5,43407,59787,54531.75,57466.5
+LABPCEC4V,3438,3,273,"ISTAT EC4, VENOUS",HC HEMATOCRIT POC,159,219,199.75,210.5,43407,59787,54531.75,57466.5
+LABPCEC4V,3438,7,278,"ISTAT EC4, VENOUS",HC GLUCOSE QUANT,159,219,199.75,210.5,44202,60882,55530.5,58519
+LABPCEC4V,3438,7,278,"ISTAT EC4, VENOUS",HC SODIUM,159,219,199.75,210.5,44202,60882,55530.5,58519
+LABPCEC4V,3438,7,278,"ISTAT EC4, VENOUS",HC POTASSIUM POC,159,219,199.75,210.5,44202,60882,55530.5,58519
+LABPCEC4V,3438,7,278,"ISTAT EC4, VENOUS",HC HEMATOCRIT POC,159,219,199.75,210.5,44202,60882,55530.5,58519
+LABPCEG7,13512,1,10335,"ISTAT EG7, ARTERIAL",HC SODIUM,204,815,370.8,219,2108340,8423025,3832218,2263365
+LABPCEG7,13512,1,10335,"ISTAT EG7, ARTERIAL",HC POTASSIUM POC,204,815,370.8,219,2108340,8423025,3832218,2263365
+LABPCEG7,13512,1,10335,"ISTAT EG7, ARTERIAL",HC HEMATOCRIT POC,204,815,370.8,219,2108340,8423025,3832218,2263365
+LABPCEG7,13512,1,10335,"ISTAT EG7, ARTERIAL",HC IONIZED CALCIUM,204,815,370.8,219,2108340,8423025,3832218,2263365
+LABPCEG7,13512,1,10335,"ISTAT EG7, ARTERIAL",HC BLOOD GAS EG7,204,815,370.8,219,2108340,8423025,3832218,2263365
+LABPCEG7,13512,3,10694,"ISTAT EG7, ARTERIAL",HC SODIUM,204,815,370.8,219,2181576,8715610,3965335.2,2341986
+LABPCEG7,13512,3,10694,"ISTAT EG7, ARTERIAL",HC POTASSIUM POC,204,815,370.8,219,2181576,8715610,3965335.2,2341986
+LABPCEG7,13512,3,10694,"ISTAT EG7, ARTERIAL",HC HEMATOCRIT POC,204,815,370.8,219,2181576,8715610,3965335.2,2341986
+LABPCEG7,13512,3,10694,"ISTAT EG7, ARTERIAL",HC IONIZED CALCIUM,204,815,370.8,219,2181576,8715610,3965335.2,2341986
+LABPCEG7,13512,3,10694,"ISTAT EG7, ARTERIAL",HC BLOOD GAS EG7,204,815,370.8,219,2181576,8715610,3965335.2,2341986
+LABPCEG7,13512,7,10838,"ISTAT EG7, ARTERIAL",HC SODIUM,204,815,370.8,219,2210952,8832970,4018730.4,2373522
+LABPCEG7,13512,7,10838,"ISTAT EG7, ARTERIAL",HC POTASSIUM POC,204,815,370.8,219,2210952,8832970,4018730.4,2373522
+LABPCEG7,13512,7,10838,"ISTAT EG7, ARTERIAL",HC HEMATOCRIT POC,204,815,370.8,219,2210952,8832970,4018730.4,2373522
+LABPCEG7,13512,7,10838,"ISTAT EG7, ARTERIAL",HC IONIZED CALCIUM,204,815,370.8,219,2210952,8832970,4018730.4,2373522
+LABPCEG7,13512,7,10838,"ISTAT EG7, ARTERIAL",HC BLOOD GAS EG7,204,815,370.8,219,2210952,8832970,4018730.4,2373522
+LABPCEG7O,445,1,73,"ISTAT EG7, OTHER",HC SODIUM,204,815,370.8,219,14892,59495,27068.4,15987
+LABPCEG7O,445,1,73,"ISTAT EG7, OTHER",HC POTASSIUM POC,204,815,370.8,219,14892,59495,27068.4,15987
+LABPCEG7O,445,1,73,"ISTAT EG7, OTHER",HC HEMATOCRIT POC,204,815,370.8,219,14892,59495,27068.4,15987
+LABPCEG7O,445,1,73,"ISTAT EG7, OTHER",HC IONIZED CALCIUM,204,815,370.8,219,14892,59495,27068.4,15987
+LABPCEG7O,445,1,73,"ISTAT EG7, OTHER",HC BLOOD GAS EG7,204,815,370.8,219,14892,59495,27068.4,15987
+LABPCEG7O,445,3,87,"ISTAT EG7, OTHER",HC SODIUM,204,815,370.8,219,17748,70905,32259.6,19053
+LABPCEG7O,445,3,87,"ISTAT EG7, OTHER",HC POTASSIUM POC,204,815,370.8,219,17748,70905,32259.6,19053
+LABPCEG7O,445,3,87,"ISTAT EG7, OTHER",HC HEMATOCRIT POC,204,815,370.8,219,17748,70905,32259.6,19053
+LABPCEG7O,445,3,87,"ISTAT EG7, OTHER",HC IONIZED CALCIUM,204,815,370.8,219,17748,70905,32259.6,19053
+LABPCEG7O,445,3,87,"ISTAT EG7, OTHER",HC BLOOD GAS EG7,204,815,370.8,219,17748,70905,32259.6,19053
+LABPCEG7O,445,7,99,"ISTAT EG7, OTHER",HC SODIUM,204,815,370.8,219,20196,80685,36709.2,21681
+LABPCEG7O,445,7,99,"ISTAT EG7, OTHER",HC POTASSIUM POC,204,815,370.8,219,20196,80685,36709.2,21681
+LABPCEG7O,445,7,99,"ISTAT EG7, OTHER",HC HEMATOCRIT POC,204,815,370.8,219,20196,80685,36709.2,21681
+LABPCEG7O,445,7,99,"ISTAT EG7, OTHER",HC IONIZED CALCIUM,204,815,370.8,219,20196,80685,36709.2,21681
+LABPCEG7O,445,7,99,"ISTAT EG7, OTHER",HC BLOOD GAS EG7,204,815,370.8,219,20196,80685,36709.2,21681
+LABPCEG7V,12330,1,10424,"ISTAT EG7, VENOUS",HC SODIUM,204,815,370.8,219,2126496,8495560,3865219.2,2282856
+LABPCEG7V,12330,1,10424,"ISTAT EG7, VENOUS",HC POTASSIUM POC,204,815,370.8,219,2126496,8495560,3865219.2,2282856
+LABPCEG7V,12330,1,10424,"ISTAT EG7, VENOUS",HC HEMATOCRIT POC,204,815,370.8,219,2126496,8495560,3865219.2,2282856
+LABPCEG7V,12330,1,10424,"ISTAT EG7, VENOUS",HC IONIZED CALCIUM,204,815,370.8,219,2126496,8495560,3865219.2,2282856
+LABPCEG7V,12330,1,10424,"ISTAT EG7, VENOUS",HC BLOOD GAS EG7,204,815,370.8,219,2126496,8495560,3865219.2,2282856
+LABPCEG7V,12330,3,11087,"ISTAT EG7, VENOUS",HC SODIUM,204,815,370.8,219,2261748,9035905,4111059.6,2428053
+LABPCEG7V,12330,3,11087,"ISTAT EG7, VENOUS",HC POTASSIUM POC,204,815,370.8,219,2261748,9035905,4111059.6,2428053
+LABPCEG7V,12330,3,11087,"ISTAT EG7, VENOUS",HC HEMATOCRIT POC,204,815,370.8,219,2261748,9035905,4111059.6,2428053
+LABPCEG7V,12330,3,11087,"ISTAT EG7, VENOUS",HC IONIZED CALCIUM,204,815,370.8,219,2261748,9035905,4111059.6,2428053
+LABPCEG7V,12330,3,11087,"ISTAT EG7, VENOUS",HC BLOOD GAS EG7,204,815,370.8,219,2261748,9035905,4111059.6,2428053
+LABPCEG7V,12330,7,11203,"ISTAT EG7, VENOUS",HC SODIUM,204,815,370.8,219,2285412,9130445,4154072.4,2453457
+LABPCEG7V,12330,7,11203,"ISTAT EG7, VENOUS",HC POTASSIUM POC,204,815,370.8,219,2285412,9130445,4154072.4,2453457
+LABPCEG7V,12330,7,11203,"ISTAT EG7, VENOUS",HC HEMATOCRIT POC,204,815,370.8,219,2285412,9130445,4154072.4,2453457
+LABPCEG7V,12330,7,11203,"ISTAT EG7, VENOUS",HC IONIZED CALCIUM,204,815,370.8,219,2285412,9130445,4154072.4,2453457
+LABPCEG7V,12330,7,11203,"ISTAT EG7, VENOUS",HC BLOOD GAS EG7,204,815,370.8,219,2285412,9130445,4154072.4,2453457
+LABPCG3,191184,1,172636,"ISTAT G3+, ARTERIAL",HC LYTES BLOOD GAS G3,815,815,815,815,140698340,140698340,140698340,140698340
+LABPCG3,191184,3,177566,"ISTAT G3+, ARTERIAL",HC LYTES BLOOD GAS G3,815,815,815,815,144716290,144716290,144716290,144716290
+LABPCG3,191184,7,178354,"ISTAT G3+, ARTERIAL",HC LYTES BLOOD GAS G3,815,815,815,815,145358510,145358510,145358510,145358510
+LABPCG3O,3636,1,978,"ISTAT G3+, OTHER",HC LYTES BLOOD GAS G3,815,815,815,815,797070,797070,797070,797070
+LABPCG3O,3636,3,1289,"ISTAT G3+, OTHER",HC LYTES BLOOD GAS G3,815,815,815,815,1050535,1050535,1050535,1050535
+LABPCG3O,3636,7,1477,"ISTAT G3+, OTHER",HC LYTES BLOOD GAS G3,815,815,815,815,1203755,1203755,1203755,1203755
+LABPCG3V,8507,1,4343,"ISTAT G3+, VENOUS",HC LYTES BLOOD GAS G3,815,815,815,815,3539545,3539545,3539545,3539545
+LABPCG3V,8507,3,5324,"ISTAT G3+, VENOUS",HC LYTES BLOOD GAS G3,815,815,815,815,4339060,4339060,4339060,4339060
+LABPCG3V,8507,7,5704,"ISTAT G3+, VENOUS",HC LYTES BLOOD GAS G3,815,815,815,815,4648760,4648760,4648760,4648760
+LABPCINR,3017,1,63,"PT & INR, ISTAT",HC PROTIME,190,190,190,190,11970,11970,11970,11970
+LABPCINR,3017,3,64,"PT & INR, ISTAT",HC PROTIME,190,190,190,190,12160,12160,12160,12160
+LABPCINR,3017,7,69,"PT & INR, ISTAT",HC PROTIME,190,190,190,190,13110,13110,13110,13110
+LABPCTNI,23727,1,3380,TROPONIN I (POC),HC PCTN1-TROPONIN 1,520,520,520,520,1757600,1757600,1757600,1757600
+LABPCTNI,23727,3,3440,TROPONIN I (POC),HC PCTN1-TROPONIN 1,520,520,520,520,1788800,1788800,1788800,1788800
+LABPCTNI,23727,7,3688,TROPONIN I (POC),HC PCTN1-TROPONIN 1,520,520,520,520,1917760,1917760,1917760,1917760
+LABPCYS,1516,1,168,Pneumocystis Stain,HC PNEUMOCYSTIS STAIN,289,289,289,289,48552,48552,48552,48552
+LABPCYS,1516,3,235,Pneumocystis Stain,HC PNEUMOCYSTIS STAIN,289,289,289,289,67915,67915,67915,67915
+LABPCYS,1516,7,287,Pneumocystis Stain,HC PNEUMOCYSTIS STAIN,289,289,289,289,82943,82943,82943,82943
+LABPENALL,33,1,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN;AMOXICILLIN IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,1,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN;AMPICILLIN IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,1,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN; PENICILLIN G IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,1,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN; PENICILLIN V IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,3,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN;AMOXICILLIN IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,3,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN;AMPICILLIN IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,3,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN; PENICILLIN G IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,3,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN; PENICILLIN V IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,7,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN;AMOXICILLIN IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,7,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN;AMPICILLIN IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,7,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN; PENICILLIN G IGE,79,79,79,79,237,237,237,237
+LABPENALL,33,7,3,PENICILLIN ALLERGY SCREEN,HC ALLERGEN; PENICILLIN V IGE,79,79,79,79,237,237,237,237
+LABPFCATE,36,1,1,CATECHOLAMINES FRACTIONATED,HC 6959 CATECHOLOMINES FRAC,35,35,35,35,35,35,35,35
+LABPFCATE,36,3,3,CATECHOLAMINES FRACTIONATED,HC 6959 CATECHOLOMINES FRAC,35,35,35,35,105,105,105,105
+LABPFCATE,36,7,5,CATECHOLAMINES FRACTIONATED,HC 6959 CATECHOLOMINES FRAC,35,35,35,35,175,175,175,175
+LABPHEN,5324,1,2040,Phenytoin,HC PHENYTOIN LEVEL,346,346,346,346,705840,705840,705840,705840
+LABPHEN,5324,3,3779,Phenytoin,HC PHENYTOIN LEVEL,346,346,346,346,1307534,1307534,1307534,1307534
+LABPHEN,5324,7,3945,Phenytoin,HC PHENYTOIN LEVEL,346,346,346,346,1364970,1364970,1364970,1364970
+LABPHME,927,1,62,pH BY METER,HC PH BY METER,50,50,50,50,3100,3100,3100,3100
+LABPHME,927,3,75,pH BY METER,HC PH BY METER,50,50,50,50,3750,3750,3750,3750
+LABPHME,927,7,90,pH BY METER,HC PH BY METER,50,50,50,50,4500,4500,4500,4500
+LABPHOS,199539,1,107279,"Phosphorus, Ser/Plas",HC PHOSPHORUS SERUM/,225,225,225,225,24137775,24137775,24137775,24137775
+LABPHOS,199539,3,164159,"Phosphorus, Ser/Plas",HC PHOSPHORUS SERUM/,225,225,225,225,36935775,36935775,36935775,36935775
+LABPHOS,199539,7,167369,"Phosphorus, Ser/Plas",HC PHOSPHORUS SERUM/,225,225,225,225,37658025,37658025,37658025,37658025
+LABPLASMN,55,1,23,Plasminogen,HC PLASMINOGEN,90,90,90,90,2070,2070,2070,2070
+LABPLASMN,55,3,41,Plasminogen,HC PLASMINOGEN,90,90,90,90,3690,3690,3690,3690
+LABPLASMN,55,7,41,Plasminogen,HC PLASMINOGEN,90,90,90,90,3690,3690,3690,3690
+LABPLTAGG,20,1,1,PLATELET AGGREGATION,HC PLT AGG-EPINEPH,469,469,469,469,469,469,469,469
+LABPLTAGG,20,1,1,PLATELET AGGREGATION,HC PLT AGG-COLLAG,469,469,469,469,469,469,469,469
+LABPLTAGG,20,1,1,PLATELET AGGREGATION,HC PLT AGG-RISTOC,469,469,469,469,469,469,469,469
+LABPLTAGG,20,1,1,PLATELET AGGREGATION,HC PLT AGG-ADP,469,469,469,469,469,469,469,469
+LABPLTAGG,20,1,1,PLATELET AGGREGATION,HC PLT AGG-ARACH ACID,469,469,469,469,469,469,469,469
+LABPLTAGG,20,3,1,PLATELET AGGREGATION,HC PLT AGG-EPINEPH,469,469,469,469,469,469,469,469
+LABPLTAGG,20,3,1,PLATELET AGGREGATION,HC PLT AGG-COLLAG,469,469,469,469,469,469,469,469
+LABPLTAGG,20,3,1,PLATELET AGGREGATION,HC PLT AGG-RISTOC,469,469,469,469,469,469,469,469
+LABPLTAGG,20,3,1,PLATELET AGGREGATION,HC PLT AGG-ADP,469,469,469,469,469,469,469,469
+LABPLTAGG,20,3,1,PLATELET AGGREGATION,HC PLT AGG-ARACH ACID,469,469,469,469,469,469,469,469
+LABPLTAGG,20,7,2,PLATELET AGGREGATION,HC PLT AGG-EPINEPH,469,469,469,469,938,938,938,938
+LABPLTAGG,20,7,2,PLATELET AGGREGATION,HC PLT AGG-COLLAG,469,469,469,469,938,938,938,938
+LABPLTAGG,20,7,2,PLATELET AGGREGATION,HC PLT AGG-RISTOC,469,469,469,469,938,938,938,938
+LABPLTAGG,20,7,2,PLATELET AGGREGATION,HC PLT AGG-ADP,469,469,469,469,938,938,938,938
+LABPLTAGG,20,7,2,PLATELET AGGREGATION,HC PLT AGG-ARACH ACID,469,469,469,469,938,938,938,938
+LABPLTMAP,193,1,48,PLATELET MAPPING,HC PLATELET MAPPING - ADP,469,469,469,469,22512,22512,22512,22512
+LABPLTMAP,193,1,48,PLATELET MAPPING,HC PLATELET MAPPING-ARACHIDONIC A,469,469,469,469,22512,22512,22512,22512
+LABPLTMAP,193,3,78,PLATELET MAPPING,HC PLATELET MAPPING - ADP,469,469,469,469,36582,36582,36582,36582
+LABPLTMAP,193,3,78,PLATELET MAPPING,HC PLATELET MAPPING-ARACHIDONIC A,469,469,469,469,36582,36582,36582,36582
+LABPLTMAP,193,7,80,PLATELET MAPPING,HC PLATELET MAPPING - ADP,469,469,469,469,37520,37520,37520,37520
+LABPLTMAP,193,7,80,PLATELET MAPPING,HC PLATELET MAPPING-ARACHIDONIC A,469,469,469,469,37520,37520,37520,37520
+LABPLTS,9837,1,1007,PLATELET COUNT,"HC PLATELET COUNT, AUTOMATED",138,138,138,138,138966,138966,138966,138966
+LABPLTS,9837,3,2028,PLATELET COUNT,"HC PLATELET COUNT, AUTOMATED",138,138,138,138,279864,279864,279864,279864
+LABPLTS,9837,7,2466,PLATELET COUNT,"HC PLATELET COUNT, AUTOMATED",138,138,138,138,340308,340308,340308,340308
+LABPNAB,14,3,1,"STREP PNEUMO IGG AB, MAID",HC 6942 PNAB STRP PNEU IGG AB,17,18,17.5,17.5,17,18,17.5,17.5
+LABPNAB,14,7,1,"STREP PNEUMO IGG AB, MAID",HC 6942 PNAB STRP PNEU IGG AB,17,18,17.5,17.5,17,18,17.5,17.5
+LABPOCGLU,1139434,1,1082963,GLUCOSE BY METER,HC GLUCOSE POC,200,200,200,200,216592600,216592600,216592600,216592600
+LABPOCGLU,1139434,3,1094776,GLUCOSE BY METER,HC GLUCOSE POC,200,200,200,200,218955200,218955200,218955200,218955200
+LABPOCGLU,1139434,7,1097565,GLUCOSE BY METER,HC GLUCOSE POC,200,200,200,200,219513000,219513000,219513000,219513000
+LABPORFEC,10,7,1,"PORPHYRINS, FECAL FRACT",HC 6959 PORFEC PRPHYRNS QN FEC,168,168,168,168,168,168,168,168
+LABPREG,1578,1,25,"Pregnancy Test, Sr",HC SERUM PREGNANCY SCR,332,332,332,332,8300,8300,8300,8300
+LABPREG,1578,3,33,"Pregnancy Test, Sr",HC SERUM PREGNANCY SCR,332,332,332,332,10956,10956,10956,10956
+LABPREG,1578,7,44,"Pregnancy Test, Sr",HC SERUM PREGNANCY SCR,332,332,332,332,14608,14608,14608,14608
+LABPROC,1115,1,170,Specimen Processing,HC SPECIMEN PROCESSING**,210,210,210,210,35700,35700,35700,35700
+LABPROC,1115,3,288,Specimen Processing,HC SPECIMEN PROCESSING**,210,210,210,210,60480,60480,60480,60480
+LABPROC,1115,7,331,Specimen Processing,HC SPECIMEN PROCESSING**,210,210,210,210,69510,69510,69510,69510
+LABPROCT,3229,1,399,PROCALCITONIN,HC HC PROCALCITONIN,318,318,318,318,126882,126882,126882,126882
+LABPROCT,3229,3,768,PROCALCITONIN,HC HC PROCALCITONIN,318,318,318,318,244224,244224,244224,244224
+LABPROCT,3229,7,934,PROCALCITONIN,HC HC PROCALCITONIN,318,318,318,318,297012,297012,297012,297012
+LABPROL,478,1,55,Prolactin,HC PROLACTIN,328,328,328,328,18040,18040,18040,18040
+LABPROL,478,3,82,Prolactin,HC PROLACTIN,328,328,328,328,26896,26896,26896,26896
+LABPROL,478,7,91,Prolactin,HC PROLACTIN,328,328,328,328,29848,29848,29848,29848
+LABPROTC,381,1,12,PROTEIN C ACTIVITY,HC PROTEIN C FUNCTNAL,362,362,362,362,4344,4344,4344,4344
+LABPROTC,381,3,16,PROTEIN C ACTIVITY,HC PROTEIN C FUNCTNAL,362,362,362,362,5792,5792,5792,5792
+LABPROTC,381,7,18,PROTEIN C ACTIVITY,HC PROTEIN C FUNCTNAL,362,362,362,362,6516,6516,6516,6516
+LABPROTPM,83,1,4,PRO-PREDICT TPMT,HC 7026 PROTPM TPMT GENETICS,296,296,296,296,1184,1184,1184,1184
+LABPROTPM,83,3,6,PRO-PREDICT TPMT,HC 7026 PROTPM TPMT GENETICS,296,296,296,296,1776,1776,1776,1776
+LABPROTPM,83,7,8,PRO-PREDICT TPMT,HC 7026 PROTPM TPMT GENETICS,296,296,296,296,2368,2368,2368,2368
+LABPROTS,372,1,12,Protein S Activity,HC PROTEIN S FUNCTIONAL,282,282,282,282,3384,3384,3384,3384
+LABPROTS,372,3,16,Protein S Activity,HC PROTEIN S FUNCTIONAL,282,282,282,282,4512,4512,4512,4512
+LABPROTS,372,7,18,Protein S Activity,HC PROTEIN S FUNCTIONAL,282,282,282,282,5076,5076,5076,5076
+LABPSA,776,1,37,PSA,HC PSA TOTAL,342,342,342,342,12654,12654,12654,12654
+LABPSA,776,3,43,PSA,HC PSA TOTAL,342,342,342,342,14706,14706,14706,14706
+LABPSA,776,7,45,PSA,HC PSA TOTAL,342,342,342,342,15390,15390,15390,15390
+LABPT,285199,1,143894,PROTHROMBIN TIME (PT),HC PROTHROMBIN TIME,190,190,190,190,27339860,27339860,27339860,27339860
+LABPT,285199,3,212748,PROTHROMBIN TIME (PT),HC PROTHROMBIN TIME,190,190,190,190,40422120,40422120,40422120,40422120
+LABPT,285199,7,219528,PROTHROMBIN TIME (PT),HC PROTHROMBIN TIME,190,190,190,190,41710320,41710320,41710320,41710320
+LABPTEG,1221,1,600,"TEG, PERIOPERATIVE",HC PTEG CLOT ASS I&R PER DAY,747,747,747,747,448200,448200,448200,448200
+LABPTEG,1221,3,619,"TEG, PERIOPERATIVE",HC PTEG CLOT ASS I&R PER DAY,747,747,747,747,462393,462393,462393,462393
+LABPTEG,1221,7,641,"TEG, PERIOPERATIVE",HC PTEG CLOT ASS I&R PER DAY,747,747,747,747,478827,478827,478827,478827
+LABPTH,1435,1,119,PTH,HC PTH (PTH),786,786,786,786,93534,93534,93534,93534
+LABPTH,1435,3,168,PTH,HC PTH (PTH),786,786,786,786,132048,132048,132048,132048
+LABPTH,1435,7,204,PTH,HC PTH (PTH),786,786,786,786,160344,160344,160344,160344
+LABPTT,196971,1,107495,PART. THROMBOPLASTIN TIME,HC THROMPLAS PTT,240,240,240,240,25798800,25798800,25798800,25798800
+LABPTT,196971,3,140806,PART. THROMBOPLASTIN TIME,HC THROMPLAS PTT,240,240,240,240,33793440,33793440,33793440,33793440
+LABPTT,196971,7,145325,PART. THROMBOPLASTIN TIME,HC THROMPLAS PTT,240,240,240,240,34878000,34878000,34878000,34878000
+LABQFTB,1689,1,29,QuantiFERON TEST FOR LATENT TB,HC QUANTIFERON,1250,1250,1250,1250,36250,36250,36250,36250
+LABQFTB,1689,3,101,QuantiFERON TEST FOR LATENT TB,HC QUANTIFERON,1250,1250,1250,1250,126250,126250,126250,126250
+LABQFTB,1689,7,125,QuantiFERON TEST FOR LATENT TB,HC QUANTIFERON,1250,1250,1250,1250,156250,156250,156250,156250
+LABQFTBG,902,1,16,QuantiFERON TEST FOR LATENT TB,HC QUANTIFERON,1250,1250,1250,1250,20000,20000,20000,20000
+LABQFTBG,902,3,38,QuantiFERON TEST FOR LATENT TB,HC QUANTIFERON,1250,1250,1250,1250,47500,47500,47500,47500
+LABQFTBG,902,7,59,QuantiFERON TEST FOR LATENT TB,HC QUANTIFERON,1250,1250,1250,1250,73750,73750,73750,73750
+LABRAAS,26,1,2,RHEUMATOID ARTHRITIS AB SCREEN,HC CCP ANTIBODY,122,129,125.5,125.5,244,258,251,251
+LABRAAS,26,1,2,RHEUMATOID ARTHRITIS AB SCREEN,HC RHEUMATOID FACTOR QUANT,122,129,125.5,125.5,244,258,251,251
+LABRAAS,26,3,2,RHEUMATOID ARTHRITIS AB SCREEN,HC CCP ANTIBODY,122,129,125.5,125.5,244,258,251,251
+LABRAAS,26,3,2,RHEUMATOID ARTHRITIS AB SCREEN,HC RHEUMATOID FACTOR QUANT,122,129,125.5,125.5,244,258,251,251
+LABRAAS,26,7,2,RHEUMATOID ARTHRITIS AB SCREEN,HC CCP ANTIBODY,122,129,125.5,125.5,244,258,251,251
+LABRAAS,26,7,2,RHEUMATOID ARTHRITIS AB SCREEN,HC RHEUMATOID FACTOR QUANT,122,129,125.5,125.5,244,258,251,251
+LABRBCFOL,1890,1,69,"FOLATE, RBC",HC FOLATE-RBC,246,246,246,246,16974,16974,16974,16974
+LABRBCFOL,1890,3,105,"FOLATE, RBC",HC FOLATE-RBC,246,246,246,246,25830,25830,25830,25830
+LABRBCFOL,1890,7,124,"FOLATE, RBC",HC FOLATE-RBC,246,246,246,246,30504,30504,30504,30504
+LABREPT,1709,1,353,Reptilase Time,HC REPTILASE TIME,203,203,203,203,71659,71659,71659,71659
+LABREPT,1709,3,733,Reptilase Time,HC REPTILASE TIME,203,203,203,203,148799,148799,148799,148799
+LABREPT,1709,7,801,Reptilase Time,HC REPTILASE TIME,203,203,203,203,162603,162603,162603,162603
+LABRESP,10620,1,954,Respiratory Culture,HC RESPIR CULT/SPUTUM,302,302,302,302,288108,288108,288108,288108
+LABRESP,10620,3,2224,Respiratory Culture,HC RESPIR CULT/SPUTUM,302,302,302,302,671648,671648,671648,671648
+LABRESP,10620,7,3399,Respiratory Culture,HC RESPIR CULT/SPUTUM,302,302,302,302,1026498,1026498,1026498,1026498
+LABRESPCR,751,1,40,RESPIRATORY VIRUS PCR PANEL,HC RESPCR RNA RSV 6-11 TARGETS,2950,2950,2950,2950,118000,118000,118000,118000
+LABRESPCR,751,3,74,RESPIRATORY VIRUS PCR PANEL,HC RESPCR RNA RSV 6-11 TARGETS,2950,2950,2950,2950,218300,218300,218300,218300
+LABRESPCR,751,7,101,RESPIRATORY VIRUS PCR PANEL,HC RESPCR RNA RSV 6-11 TARGETS,2950,2950,2950,2950,297950,297950,297950,297950
+LABRESPG,2595,1,302,Respiratory Cult/GS,HC GRAM STAIN,150,302,226,226,45300,91204,68252,68252
+LABRESPG,2595,1,302,Respiratory Cult/GS,HC RESPIRATRY CULTR,150,302,226,226,45300,91204,68252,68252
+LABRESPG,2595,3,401,Respiratory Cult/GS,HC GRAM STAIN,150,302,226,226,60150,121102,90626,90626
+LABRESPG,2595,3,401,Respiratory Cult/GS,HC RESPIRATRY CULTR,150,302,226,226,60150,121102,90626,90626
+LABRESPG,2595,7,518,Respiratory Cult/GS,HC GRAM STAIN,150,302,226,226,77700,156436,117068,117068
+LABRESPG,2595,7,518,Respiratory Cult/GS,HC RESPIRATRY CULTR,150,302,226,226,77700,156436,117068,117068
+LABRETIC,5628,1,687,"RETICULOCYTE COUNT, AUTOMATED",HC RETIC COUNT BY FLOW,179,179,179,179,122973,122973,122973,122973
+LABRETIC,5628,3,1267,"RETICULOCYTE COUNT, AUTOMATED",HC RETIC COUNT BY FLOW,179,179,179,179,226793,226793,226793,226793
+LABRETIC,5628,7,1403,"RETICULOCYTE COUNT, AUTOMATED",HC RETIC COUNT BY FLOW,179,179,179,179,251137,251137,251137,251137
+LABRF,859,1,37,"Rheumatoid Factor, Serum",HC RHEUMATIOD FACTOR SERUM,129,129,129,129,4773,4773,4773,4773
+LABRF,859,3,41,"Rheumatoid Factor, Serum",HC RHEUMATIOD FACTOR SERUM,129,129,129,129,5289,5289,5289,5289
+LABRF,859,7,51,"Rheumatoid Factor, Serum",HC RHEUMATIOD FACTOR SERUM,129,129,129,129,6579,6579,6579,6579
+LABRIPA,11,3,1,RISTOCETIN-INDUCED PLT AGG,HC PLT AGG-RISTOC,469,469,469,469,469,469,469,469
+LABRIPA,11,7,2,RISTOCETIN-INDUCED PLT AGG,HC PLT AGG-RISTOC,469,469,469,469,938,938,938,938
+LABRIST,98,1,15,Ristocetin Co-factor,HC RISTOCETIN COFACTOR,496,496,496,496,7440,7440,7440,7440
+LABRIST,98,3,26,Ristocetin Co-factor,HC RISTOCETIN COFACTOR,496,496,496,496,12896,12896,12896,12896
+LABRIST,98,7,28,Ristocetin Co-factor,HC RISTOCETIN COFACTOR,496,496,496,496,13888,13888,13888,13888
+LABRITUX,214,1,7,RITUXAN PANEL,"HC FLOW:T-CELLS,TOTAL COUNT",422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,"HC FLOW:B CELLS, TOTAL COUNT",422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,"HC FLOW:NK CELLS, TOTAL COUNT",422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,HC FLOW CD45 RA AB,422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,HC CD16,422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,HC CD20,422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,1,7,RITUXAN PANEL,HC FLOW CD45 RD AB,422,575,545.375,570,2954,4025,3817.625,3990
+LABRITUX,214,3,10,RITUXAN PANEL,"HC FLOW:T-CELLS,TOTAL COUNT",422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,"HC FLOW:B CELLS, TOTAL COUNT",422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,"HC FLOW:NK CELLS, TOTAL COUNT",422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,HC FLOW CD45 RA AB,422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,HC CD16,422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,HC CD20,422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,3,10,RITUXAN PANEL,HC FLOW CD45 RD AB,422,575,545.375,570,4220,5750,5453.75,5700
+LABRITUX,214,7,12,RITUXAN PANEL,"HC FLOW:T-CELLS,TOTAL COUNT",422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,"HC FLOW:B CELLS, TOTAL COUNT",422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,"HC FLOW:NK CELLS, TOTAL COUNT",422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,HC FLOW CD45 RA AB,422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,HC CD16,422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,HC CD20,422,575,545.375,570,5064,6900,6544.5,6840
+LABRITUX,214,7,12,RITUXAN PANEL,HC FLOW CD45 RD AB,422,575,545.375,570,5064,6900,6544.5,6840
+LABRNPABY,174,1,6,ANTI-RNP ANTIBODY,HC RNPABY EXT NA RNP ANTIRNPAB,152,152,152,152,912,912,912,912
+LABRNPABY,174,3,6,ANTI-RNP ANTIBODY,HC RNPABY EXT NA RNP ANTIRNPAB,152,152,152,152,912,912,912,912
+LABRNPABY,174,7,6,ANTI-RNP ANTIBODY,HC RNPABY EXT NA RNP ANTIRNPAB,152,152,152,152,912,912,912,912
+LABROMRS,39655,1,720,R/O MRSA,HC R/O MRSA,165,165,165,165,118800,118800,118800,118800
+LABROMRS,39655,3,1395,R/O MRSA,HC R/O MRSA,165,165,165,165,230175,230175,230175,230175
+LABROMRS,39655,7,2206,R/O MRSA,HC R/O MRSA,165,165,165,165,363990,363990,363990,363990
+LABROMSP,133,1,12,R/O Microsporidium,HC MICROSPORIDIUM,379,379,379,379,4548,4548,4548,4548
+LABROMSP,133,3,12,R/O Microsporidium,HC MICROSPORIDIUM,379,379,379,379,4548,4548,4548,4548
+LABROMSP,133,7,16,R/O Microsporidium,HC MICROSPORIDIUM,379,379,379,379,6064,6064,6064,6064
+LABROPK,159,1,13,R/O ISOSPORA CYCLOSPORA,HC R/O PAR/MOD ACD STN,162,162,162,162,2106,2106,2106,2106
+LABROPK,159,3,15,R/O ISOSPORA CYCLOSPORA,HC R/O PAR/MOD ACD STN,162,162,162,162,2430,2430,2430,2430
+LABROPK,159,7,23,R/O ISOSPORA CYCLOSPORA,HC R/O PAR/MOD ACD STN,162,162,162,162,3726,3726,3726,3726
+LABROSTRO,20,1,5,Strongyloides Culture,HC ROSTRO STRONGLDS CUL STOOL,165,165,165,165,825,825,825,825
+LABROSTRO,20,3,6,Strongyloides Culture,HC ROSTRO STRONGLDS CUL STOOL,165,165,165,165,990,990,990,990
+LABROSTRO,20,7,7,Strongyloides Culture,HC ROSTRO STRONGLDS CUL STOOL,165,165,165,165,1155,1155,1155,1155
+LABRP,145,1,26,RENAL FUNCTION PANEL,HC RENAL FUNCTION PANEL,170,170,170,170,4420,4420,4420,4420
+LABRP,145,3,40,RENAL FUNCTION PANEL,HC RENAL FUNCTION PANEL,170,170,170,170,6800,6800,6800,6800
+LABRP,145,7,40,RENAL FUNCTION PANEL,HC RENAL FUNCTION PANEL,170,170,170,170,6800,6800,6800,6800
+LABRPRQL,1104,1,31,"RPR, QUALITATIVE",HC RPR QUAL,238,238,238,238,7378,7378,7378,7378
+LABRPRQL,1104,3,38,"RPR, QUALITATIVE",HC RPR QUAL,238,238,238,238,9044,9044,9044,9044
+LABRPRQL,1104,7,43,"RPR, QUALITATIVE",HC RPR QUAL,238,238,238,238,10234,10234,10234,10234
+LABRPRQT,28,3,2,"RPR, QUANTITATIVE",HC RPR QUANT,117,117,117,117,234,234,234,234
+LABRPRQT,28,7,2,"RPR, QUANTITATIVE",HC RPR QUANT,117,117,117,117,234,234,234,234
+LABRSVPCR,7,1,2,RSV PCR,HC RSVPCR BY NA RNA APT EA,388,388,388,388,776,776,776,776
+LABRSVPCR,7,3,2,RSV PCR,HC RSVPCR BY NA RNA APT EA,388,388,388,388,776,776,776,776
+LABRSVPCR,7,7,2,RSV PCR,HC RSVPCR BY NA RNA APT EA,388,388,388,388,776,776,776,776
+LABRUB,23,3,2,Rubella IgG Antibody,HC RUBELL ANTIBODY,268,268,268,268,536,536,536,536
+LABRUB,23,7,2,Rubella IgG Antibody,HC RUBELL ANTIBODY,268,268,268,268,536,536,536,536
+LABSALICY,2212,1,94,Salicylate,HC SALICYLATES LEVEL,264,264,264,264,24816,24816,24816,24816
+LABSALICY,2212,3,100,Salicylate,HC SALICYLATES LEVEL,264,264,264,264,26400,26400,26400,26400
+LABSALICY,2212,7,111,Salicylate,HC SALICYLATES LEVEL,264,264,264,264,29304,29304,29304,29304
+LABSCHIG,52,1,1,"SCHISTOSOMA IGG AB, FMI(SERUM)",HC 6942 SCHISTOSOMIASIS,77,77,77,77,77,77,77,77
+LABSCHIG,52,3,2,"SCHISTOSOMA IGG AB, FMI(SERUM)",HC 6942 SCHISTOSOMIASIS,77,77,77,77,154,154,154,154
+LABSCHIG,52,7,3,"SCHISTOSOMA IGG AB, FMI(SERUM)",HC 6942 SCHISTOSOMIASIS,77,77,77,77,231,231,231,231
+LABSCLABY,173,1,7,ANTI-Scl70 ANTIBODY,HC SCLABY EXT NA SCL70 AB,152,152,152,152,1064,1064,1064,1064
+LABSCLABY,173,3,7,ANTI-Scl70 ANTIBODY,HC SCLABY EXT NA SCL70 AB,152,152,152,152,1064,1064,1064,1064
+LABSCLABY,173,7,8,ANTI-Scl70 ANTIBODY,HC SCLABY EXT NA SCL70 AB,152,152,152,152,1216,1216,1216,1216
+LABSCMV,1618,1,179,SVcul: R/O CMV,HC VIRUS VIAL CULTURE,363,363,363,363,64977,64977,64977,64977
+LABSCMV,1618,3,219,SVcul: R/O CMV,HC VIRUS VIAL CULTURE,363,363,363,363,79497,79497,79497,79497
+LABSCMV,1618,7,268,SVcul: R/O CMV,HC VIRUS VIAL CULTURE,363,363,363,363,97284,97284,97284,97284
+LABSHIV12,208,1,4,Rapid HIV-1/2 Antibody,HC SHIV12 HIV 1 AND 2 AB,280,280,280,280,1120,1120,1120,1120
+LABSHIV12,208,3,5,Rapid HIV-1/2 Antibody,HC SHIV12 HIV 1 AND 2 AB,280,280,280,280,1400,1400,1400,1400
+LABSHIV12,208,7,5,Rapid HIV-1/2 Antibody,HC SHIV12 HIV 1 AND 2 AB,280,280,280,280,1400,1400,1400,1400
+LABSHIVAB,34,1,1,Rapid HIV-1 Antibody,HC RAPID HIV-1 ANTIBODY,121,121,121,121,121,121,121,121
+LABSHIVAB,34,3,1,Rapid HIV-1 Antibody,HC RAPID HIV-1 ANTIBODY,121,121,121,121,121,121,121,121
+LABSHIVAB,34,7,1,Rapid HIV-1 Antibody,HC RAPID HIV-1 ANTIBODY,121,121,121,121,121,121,121,121
+LABSIROL,2224,1,779,Sirolimus,HC SIROLIMUS,293,293,293,293,228247,228247,228247,228247
+LABSIROL,2224,3,1713,Sirolimus,HC SIROLIMUS,293,293,293,293,501909,501909,501909,501909
+LABSIROL,2224,7,1811,Sirolimus,HC SIROLIMUS,293,293,293,293,530623,530623,530623,530623
+LABSMABY,143,1,5,ANTI-Sm ANTIBODY,HC SMABY EXT NA SM ANTISM AB,152,152,152,152,760,760,760,760
+LABSMABY,143,3,5,ANTI-Sm ANTIBODY,HC SMABY EXT NA SM ANTISM AB,152,152,152,152,760,760,760,760
+LABSMABY,143,7,5,ANTI-Sm ANTIBODY,HC SMABY EXT NA SM ANTISM AB,152,152,152,152,760,760,760,760
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC DNA ANTIBODY,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC CENABY EXT NA CNTRMR AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC JO1ABY EXT NA JO1 ANTIJO1AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC RNPABY EXT NA RNP ANTIRNPAB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC SCLABY EXT NA SCL70 AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC SMABY EXT NA SM ANTISM AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC SSAABY EXT NA SSA ANTIRO AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,1,1,SPECIFIC ANA PANEL,HC SSBABY EXT NA SSB ANTILA AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC DNA ANTIBODY,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC CENABY EXT NA CNTRMR AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC JO1ABY EXT NA JO1 ANTIJO1AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC RNPABY EXT NA RNP ANTIRNPAB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC SCLABY EXT NA SCL70 AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC SMABY EXT NA SM ANTISM AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC SSAABY EXT NA SSA ANTIRO AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,3,1,SPECIFIC ANA PANEL,HC SSBABY EXT NA SSB ANTILA AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC DNA ANTIBODY,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC CENABY EXT NA CNTRMR AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC JO1ABY EXT NA JO1 ANTIJO1AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC RNPABY EXT NA RNP ANTIRNPAB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC SCLABY EXT NA SCL70 AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC SMABY EXT NA SM ANTISM AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC SSAABY EXT NA SSA ANTIRO AB,152,281,168.125,152,152,281,168.125,152
+LABSNA,19,7,1,SPECIFIC ANA PANEL,HC SSBABY EXT NA SSB ANTILA AB,152,281,168.125,152,152,281,168.125,152
+LABSPAGD,58,1,4,"S. PNEUMONIAE AG DETECTION,LA",HC 6942 S PNEUMONIAE AG DETECT LA,55,55,55,55,220,220,220,220
+LABSPAGD,58,3,4,"S. PNEUMONIAE AG DETECTION,LA",HC 6942 S PNEUMONIAE AG DETECT LA,55,55,55,55,220,220,220,220
+LABSPAGD,58,7,5,"S. PNEUMONIAE AG DETECTION,LA",HC 6942 S PNEUMONIAE AG DETECT LA,55,55,55,55,275,275,275,275
+LABSPE,137,1,3,PROTEIN ELECTROPHORESIS,HC PROTEIN TOTAL,75,180,127.5,127.5,225,540,382.5,382.5
+LABSPE,137,1,3,PROTEIN ELECTROPHORESIS,HC PROT ELECTRO SERUM,75,180,127.5,127.5,225,540,382.5,382.5
+LABSPE,137,3,4,PROTEIN ELECTROPHORESIS,HC PROTEIN TOTAL,75,180,127.5,127.5,300,720,510,510
+LABSPE,137,3,4,PROTEIN ELECTROPHORESIS,HC PROT ELECTRO SERUM,75,180,127.5,127.5,300,720,510,510
+LABSPE,137,7,6,PROTEIN ELECTROPHORESIS,HC PROTEIN TOTAL,75,180,127.5,127.5,450,1080,765,765
+LABSPE,137,7,6,PROTEIN ELECTROPHORESIS,HC PROT ELECTRO SERUM,75,180,127.5,127.5,450,1080,765,765
+LABSPIE,1516,1,48,SERUM PROTEIN IMMUNOFIX ELECT.,HC SERUM PROTEIN IMMUNOFIX ELECT,497,497,497,497,23856,23856,23856,23856
+LABSPIE,1516,3,76,SERUM PROTEIN IMMUNOFIX ELECT.,HC SERUM PROTEIN IMMUNOFIX ELECT,497,497,497,497,37772,37772,37772,37772
+LABSPIE,1516,7,101,SERUM PROTEIN IMMUNOFIX ELECT.,HC SERUM PROTEIN IMMUNOFIX ELECT,497,497,497,497,50197,50197,50197,50197
+LABSPLAC,6952,1,5059,SEPSIS PROTOCOL LACTATE,HC SPLAC LACTATE SEPSIS PROTCL,330,330,330,330,1669470,1669470,1669470,1669470
+LABSPLAC,6952,3,5238,SEPSIS PROTOCOL LACTATE,HC SPLAC LACTATE SEPSIS PROTCL,330,330,330,330,1728540,1728540,1728540,1728540
+LABSPLAC,6952,7,5343,SEPSIS PROTOCOL LACTATE,HC SPLAC LACTATE SEPSIS PROTCL,330,330,330,330,1763190,1763190,1763190,1763190
+LABSSAABY,224,1,8,ANTI-Ro ANTIBODY,HC SSAABY EXT NA SSA ANTIRO AB,152,152,152,152,1216,1216,1216,1216
+LABSSAABY,224,3,8,ANTI-Ro ANTIBODY,HC SSAABY EXT NA SSA ANTIRO AB,152,152,152,152,1216,1216,1216,1216
+LABSSAABY,224,7,8,ANTI-Ro ANTIBODY,HC SSAABY EXT NA SSA ANTIRO AB,152,152,152,152,1216,1216,1216,1216
+LABSSBABY,221,1,8,ANTI-La ANTIBODY,HC SSBABY EXT NA SSB ANTILA AB,152,152,152,152,1216,1216,1216,1216
+LABSSBABY,221,3,8,ANTI-La ANTIBODY,HC SSBABY EXT NA SSB ANTILA AB,152,152,152,152,1216,1216,1216,1216
+LABSSBABY,221,7,8,ANTI-La ANTIBODY,HC SSBABY EXT NA SSB ANTILA AB,152,152,152,152,1216,1216,1216,1216
+LABSTFAT,152,1,5,"Fat Stain, Stool",HC FECAL FAT,303,303,303,303,1515,1515,1515,1515
+LABSTFAT,152,3,10,"Fat Stain, Stool",HC FECAL FAT,303,303,303,303,3030,3030,3030,3030
+LABSTFAT,152,7,13,"Fat Stain, Stool",HC FECAL FAT,303,303,303,303,3939,3939,3939,3939
+LABSTLC,728,1,35,Stool Culture,HC STOOL CLTR,227,384,305.5,305.5,7945,13440,10692.5,10692.5
+LABSTLC,728,1,35,Stool Culture,HC STOOL ADDL SETUP,227,384,305.5,305.5,7945,13440,10692.5,10692.5
+LABSTLC,728,3,56,Stool Culture,HC STOOL CLTR,227,384,305.5,305.5,12712,21504,17108,17108
+LABSTLC,728,3,56,Stool Culture,HC STOOL ADDL SETUP,227,384,305.5,305.5,12712,21504,17108,17108
+LABSTLC,728,7,75,Stool Culture,HC STOOL CLTR,227,384,305.5,305.5,17025,28800,22912.5,22912.5
+LABSTLC,728,7,75,Stool Culture,HC STOOL ADDL SETUP,227,384,305.5,305.5,17025,28800,22912.5,22912.5
+LABSTLCP,109,1,1,STOOL CULTURE,HC STOOL CLTR IS NA DAN/RNA EA,578,578,578,578,578,578,578,578
+LABSTLCP,109,3,1,STOOL CULTURE,HC STOOL CLTR IS NA DAN/RNA EA,578,578,578,578,578,578,578,578
+LABSTLCP,109,7,2,STOOL CULTURE,HC STOOL CLTR IS NA DAN/RNA EA,578,578,578,578,1156,1156,1156,1156
+LABSTLCX,980,1,34,STOOL CULTURE,HC STOOL ADDL SETUP EA PLT,227,578,402.5,402.5,7718,19652,13685,13685
+LABSTLCX,980,1,34,STOOL CULTURE,HC STOOL CLTR ID NA PCR EA,227,578,402.5,402.5,7718,19652,13685,13685
+LABSTLCX,980,3,71,STOOL CULTURE,HC STOOL ADDL SETUP EA PLT,227,578,402.5,402.5,16117,41038,28577.5,28577.5
+LABSTLCX,980,3,71,STOOL CULTURE,HC STOOL CLTR ID NA PCR EA,227,578,402.5,402.5,16117,41038,28577.5,28577.5
+LABSTLCX,980,7,93,STOOL CULTURE,HC STOOL ADDL SETUP EA PLT,227,578,402.5,402.5,21111,53754,37432.5,37432.5
+LABSTLCX,980,7,93,STOOL CULTURE,HC STOOL CLTR ID NA PCR EA,227,578,402.5,402.5,21111,53754,37432.5,37432.5
+LABSTOBGC,375,1,36,"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.",HC OCCULT BLD GUAIAC COL SCRN,101,101,101,101,3636,3636,3636,3636
+LABSTOBGC,375,3,53,"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.",HC OCCULT BLD GUAIAC COL SCRN,101,101,101,101,5353,5353,5353,5353
+LABSTOBGC,375,7,58,"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.",HC OCCULT BLD GUAIAC COL SCRN,101,101,101,101,5858,5858,5858,5858
+LABSTOBGD,3959,1,274,"OCCULT BLD, GUAIAC, STOOL, DIAG TEST",HC OCCULT BLD GUAIAC DIAG,130,130,130,130,35620,35620,35620,35620
+LABSTOBGD,3959,3,496,"OCCULT BLD, GUAIAC, STOOL, DIAG TEST",HC OCCULT BLD GUAIAC DIAG,130,130,130,130,64480,64480,64480,64480
+LABSTOBGD,3959,7,623,"OCCULT BLD, GUAIAC, STOOL, DIAG TEST",HC OCCULT BLD GUAIAC DIAG,130,130,130,130,80990,80990,80990,80990
+LABSTOBIC,50,1,5,"OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.",HC OCCULT BLD IMMUNO COL SCRN,623,623,623,623,3115,3115,3115,3115
+LABSTOBIC,50,3,8,"OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.",HC OCCULT BLD IMMUNO COL SCRN,623,623,623,623,4984,4984,4984,4984
+LABSTOBIC,50,7,8,"OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.",HC OCCULT BLD IMMUNO COL SCRN,623,623,623,623,4984,4984,4984,4984
+LABSTOBID,27,3,1,"OCCULT BLD, IMMUNO, STOOL, DIAG TEST",HC OCCULT BLD IMMUNO DIAG,425,425,425,425,425,425,425,425
+LABSTOBID,27,7,1,"OCCULT BLD, IMMUNO, STOOL, DIAG TEST",HC OCCULT BLD IMMUNO DIAG,425,425,425,425,425,425,425,425
+LABSTOOLC,961,1,28,Stool Culture,HC STOOL ADDL SETUP EA PLT,227,578,396.3333333,384,6356,16184,11097.33333,10752
+LABSTOOLC,961,1,28,Stool Culture,HC STOOL CLTR ID NA PCR EA,227,578,396.3333333,384,6356,16184,11097.33333,10752
+LABSTOOLC,961,1,28,Stool Culture,HC STOOL CLTR,227,578,396.3333333,384,6356,16184,11097.33333,10752
+LABSTOOLC,961,3,54,Stool Culture,HC STOOL ADDL SETUP EA PLT,227,578,396.3333333,384,12258,31212,21402,20736
+LABSTOOLC,961,3,54,Stool Culture,HC STOOL CLTR ID NA PCR EA,227,578,396.3333333,384,12258,31212,21402,20736
+LABSTOOLC,961,3,54,Stool Culture,HC STOOL CLTR,227,578,396.3333333,384,12258,31212,21402,20736
+LABSTOOLC,961,7,79,Stool Culture,HC STOOL ADDL SETUP EA PLT,227,578,396.3333333,384,17933,45662,31310.33333,30336
+LABSTOOLC,961,7,79,Stool Culture,HC STOOL CLTR ID NA PCR EA,227,578,396.3333333,384,17933,45662,31310.33333,30336
+LABSTOOLC,961,7,79,Stool Culture,HC STOOL CLTR,227,578,396.3333333,384,17933,45662,31310.33333,30336
+LABSTORE,162,1,4,SPECIMEN STORAGE ONLY,HC STORAGE - SPECIMEN,23,23,23,23,92,92,92,92
+LABSTORE,162,3,5,SPECIMEN STORAGE ONLY,HC STORAGE - SPECIMEN,23,23,23,23,115,115,115,115
+LABSTORE,162,7,6,SPECIMEN STORAGE ONLY,HC STORAGE - SPECIMEN,23,23,23,23,138,138,138,138
+LABSTPCR,183,1,1,Throat Strep Screen by PCR,HC STPCR STREP CULT APT EA ORG,578,578,578,578,578,578,578,578
+LABSTPCR,183,3,5,Throat Strep Screen by PCR,HC STPCR STREP CULT APT EA ORG,578,578,578,578,2890,2890,2890,2890
+LABSTPCR,183,7,6,Throat Strep Screen by PCR,HC STPCR STREP CULT APT EA ORG,578,578,578,578,3468,3468,3468,3468
+LABSTPH,46,1,11,"pH, Stool",HC PH BY METER,50,50,50,50,550,550,550,550
+LABSTPH,46,3,23,"pH, Stool",HC PH BY METER,50,50,50,50,1150,1150,1150,1150
+LABSTPH,46,7,24,"pH, Stool",HC PH BY METER,50,50,50,50,1200,1200,1200,1200
+LABSTPOLY,359,1,7,"PMNs/EOSINOPHILS, STOOL           [LPCH ONLY]",HC STOOL POLYS & EOS,154,154,154,154,1078,1078,1078,1078
+LABSTPOLY,359,3,19,"PMNs/EOSINOPHILS, STOOL           [LPCH ONLY]",HC STOOL POLYS & EOS,154,154,154,154,2926,2926,2926,2926
+LABSTPOLY,359,7,21,"PMNs/EOSINOPHILS, STOOL           [LPCH ONLY]",HC STOOL POLYS & EOS,154,154,154,154,3234,3234,3234,3234
+LABSTRED,152,1,32,"Reducing Subst, Stool",HC STOOL REDUCING SUBSTANCE,128,128,128,128,4096,4096,4096,4096
+LABSTRED,152,3,67,"Reducing Subst, Stool",HC STOOL REDUCING SUBSTANCE,128,128,128,128,8576,8576,8576,8576
+LABSTRED,152,7,75,"Reducing Subst, Stool",HC STOOL REDUCING SUBSTANCE,128,128,128,128,9600,9600,9600,9600
+LABSTREP,206,1,6,Beta Strep Throat Scn,HC RAPID STREP A,239,239,239,239,1434,1434,1434,1434
+LABSTREP,206,3,7,Beta Strep Throat Scn,HC RAPID STREP A,239,239,239,239,1673,1673,1673,1673
+LABSTREP,206,7,7,Beta Strep Throat Scn,HC RAPID STREP A,239,239,239,239,1673,1673,1673,1673
+LABSTRONG,217,1,17,"STRONGYLOIDES IgG AB, ELISA",HC 6942 STRONGYLOIDIASIS,66,66,66,66,1122,1122,1122,1122
+LABSTRONG,217,3,20,"STRONGYLOIDES IgG AB, ELISA",HC 6942 STRONGYLOIDIASIS,66,66,66,66,1320,1320,1320,1320
+LABSTRONG,217,7,26,"STRONGYLOIDES IgG AB, ELISA",HC 6942 STRONGYLOIDIASIS,66,66,66,66,1716,1716,1716,1716
+LABSTSS,1934,1,53,SYPHILIS TREPONEMAL SCREEN,HC SYPHILIS TREPONEMAL SCR,219,219,219,219,11607,11607,11607,11607
+LABSTSS,1934,3,91,SYPHILIS TREPONEMAL SCREEN,HC SYPHILIS TREPONEMAL SCR,219,219,219,219,19929,19929,19929,19929
+LABSTSS,1934,7,106,SYPHILIS TREPONEMAL SCREEN,HC SYPHILIS TREPONEMAL SCR,219,219,219,219,23214,23214,23214,23214
+LABSWTCL,18,3,1,SWEAT CHLORIDE,HC CHLORIDE BODY FLUID,199,403,301,301,199,403,301,301
+LABSWTCL,18,3,1,SWEAT CHLORIDE,HC SWEAT CHLORIDES,199,403,301,301,199,403,301,301
+LABSWTCL,18,7,1,SWEAT CHLORIDE,HC CHLORIDE BODY FLUID,199,403,301,301,199,403,301,301
+LABSWTCL,18,7,1,SWEAT CHLORIDE,HC SWEAT CHLORIDES,199,403,301,301,199,403,301,301
+LABSYNCT,710,1,36,"CELL COUNT & DIFF, SYN FLD",HC BODY FLUID CELL COUNT & DIFF,252,252,252,252,9072,9072,9072,9072
+LABSYNCT,710,3,48,"CELL COUNT & DIFF, SYN FLD",HC BODY FLUID CELL COUNT & DIFF,252,252,252,252,12096,12096,12096,12096
+LABSYNCT,710,7,59,"CELL COUNT & DIFF, SYN FLD",HC BODY FLUID CELL COUNT & DIFF,252,252,252,252,14868,14868,14868,14868
+LABT1517,60,1,3,t(15;17) PMLrar,HC T1517 PMLRAR CMN BRKPT QN,1387,1387,1387,1387,4161,4161,4161,4161
+LABT1517,60,3,3,t(15;17) PMLrar,HC T1517 PMLRAR CMN BRKPT QN,1387,1387,1387,1387,4161,4161,4161,4161
+LABT1517,60,7,6,t(15;17) PMLrar,HC T1517 PMLRAR CMN BRKPT QN,1387,1387,1387,1387,8322,8322,8322,8322
+LABT3,398,1,13,T3 Total,HC T3 TOTAL,290,290,290,290,3770,3770,3770,3770
+LABT3,398,3,20,T3 Total,HC T3 TOTAL,290,290,290,290,5800,5800,5800,5800
+LABT3,398,7,30,T3 Total,HC T3 TOTAL,290,290,290,290,8700,8700,8700,8700
+LABTBIL,1527,1,70,Total Bilirubin,"HC BILIRUBIN, TOTAL",150,150,150,150,10500,10500,10500,10500
+LABTBIL,1527,3,214,Total Bilirubin,"HC BILIRUBIN, TOTAL",150,150,150,150,32100,32100,32100,32100
+LABTBIL,1527,7,300,Total Bilirubin,"HC BILIRUBIN, TOTAL",150,150,150,150,45000,45000,45000,45000
+LABTBILFR,2498,1,219,"BILIRUBIN, TOTAL & FRACTIONS","HC BILIRUBIN, TOTAL",150,165,157.5,157.5,32850,36135,34492.5,34492.5
+LABTBILFR,2498,1,219,"BILIRUBIN, TOTAL & FRACTIONS","HC BILIRUBIN, DIRECT",150,165,157.5,157.5,32850,36135,34492.5,34492.5
+LABTBILFR,2498,3,445,"BILIRUBIN, TOTAL & FRACTIONS","HC BILIRUBIN, TOTAL",150,165,157.5,157.5,66750,73425,70087.5,70087.5
+LABTBILFR,2498,3,445,"BILIRUBIN, TOTAL & FRACTIONS","HC BILIRUBIN, DIRECT",150,165,157.5,157.5,66750,73425,70087.5,70087.5
+LABTBILFR,2498,7,636,"BILIRUBIN, TOTAL & FRACTIONS","HC BILIRUBIN, TOTAL",150,165,157.5,157.5,95400,104940,100170,100170
+LABTBILFR,2498,7,636,"BILIRUBIN, TOTAL & FRACTIONS","HC BILIRUBIN, DIRECT",150,165,157.5,157.5,95400,104940,100170,100170
+LABTBLYM,431,1,31,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:T-CELLS,TOTAL COUNT",422,575,520.75,543,13082,17825,16143.25,16833
+LABTBLYM,431,1,31,"T SUBSETS AND B LYMPHS, BLD",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,520.75,543,13082,17825,16143.25,16833
+LABTBLYM,431,1,31,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:B CELLS, TOTAL COUNT",422,575,520.75,543,13082,17825,16143.25,16833
+LABTBLYM,431,1,31,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:NK CELLS, TOTAL COUNT",422,575,520.75,543,13082,17825,16143.25,16833
+LABTBLYM,431,3,65,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:T-CELLS,TOTAL COUNT",422,575,520.75,543,27430,37375,33848.75,35295
+LABTBLYM,431,3,65,"T SUBSETS AND B LYMPHS, BLD",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,520.75,543,27430,37375,33848.75,35295
+LABTBLYM,431,3,65,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:B CELLS, TOTAL COUNT",422,575,520.75,543,27430,37375,33848.75,35295
+LABTBLYM,431,3,65,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:NK CELLS, TOTAL COUNT",422,575,520.75,543,27430,37375,33848.75,35295
+LABTBLYM,431,7,77,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:T-CELLS,TOTAL COUNT",422,575,520.75,543,32494,44275,40097.75,41811
+LABTBLYM,431,7,77,"T SUBSETS AND B LYMPHS, BLD",HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,422,575,520.75,543,32494,44275,40097.75,41811
+LABTBLYM,431,7,77,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:B CELLS, TOTAL COUNT",422,575,520.75,543,32494,44275,40097.75,41811
+LABTBLYM,431,7,77,"T SUBSETS AND B LYMPHS, BLD","HC FLOW:NK CELLS, TOTAL COUNT",422,575,520.75,543,32494,44275,40097.75,41811
+LABTBPCRS,450,1,72,"M. Tuberculosis PCR, Specimen",HC M TUBERCULOSIS PCR APT SPEC,566,566,566,566,40752,40752,40752,40752
+LABTBPCRS,450,3,92,"M. Tuberculosis PCR, Specimen",HC M TUBERCULOSIS PCR APT SPEC,566,566,566,566,52072,52072,52072,52072
+LABTBPCRS,450,7,115,"M. Tuberculosis PCR, Specimen",HC M TUBERCULOSIS PCR APT SPEC,566,566,566,566,65090,65090,65090,65090
+LABTCDIF,740,1,51,Stool C.diff Toxin B,HC C DIFF TOX CONF,106,106,106,106,5406,5406,5406,5406
+LABTCDIF,740,3,120,Stool C.diff Toxin B,HC C DIFF TOX CONF,106,106,106,106,12720,12720,12720,12720
+LABTCDIF,740,7,184,Stool C.diff Toxin B,HC C DIFF TOX CONF,106,106,106,106,19504,19504,19504,19504
+LABTEGCP,1021,1,165,THROMBELASTOGRAPH CLOT. PROF.,HC THROMBOELASTOGRAPHY,747,747,747,747,123255,123255,123255,123255
+LABTEGCP,1021,3,385,THROMBELASTOGRAPH CLOT. PROF.,HC THROMBOELASTOGRAPHY,747,747,747,747,287595,287595,287595,287595
+LABTEGCP,1021,7,773,THROMBELASTOGRAPH CLOT. PROF.,HC THROMBOELASTOGRAPHY,747,747,747,747,577431,577431,577431,577431
+LABTES,413,1,6,Testosterone,HC TESTOSTERONE (TES) TOTAL,290,290,290,290,1740,1740,1740,1740
+LABTES,413,3,7,Testosterone,HC TESTOSTERONE (TES) TOTAL,290,290,290,290,2030,2030,2030,2030
+LABTES,413,7,11,Testosterone,HC TESTOSTERONE (TES) TOTAL,290,290,290,290,3190,3190,3190,3190
+LABTESFBT,11,7,4,"TESTOSTERONE, TOTAL, BIO, FREE",HC TESTOTERONE BIO/FREE,361,369,365,365,1444,1476,1460,1460
+LABTESFBT,11,7,4,"TESTOSTERONE, TOTAL, BIO, FREE",HC TESTOSTERONE TOTAL,361,369,365,365,1444,1476,1460,1460
+LABTFPSA,10,1,1,TOTAL & FREE PSA,HC TFPSA PSA TOTAL,281,342,311.5,311.5,281,342,311.5,311.5
+LABTFPSA,10,1,1,TOTAL & FREE PSA,HC TFPSA PSA FREE,281,342,311.5,311.5,281,342,311.5,311.5
+LABTFPSA,10,3,1,TOTAL & FREE PSA,HC TFPSA PSA TOTAL,281,342,311.5,311.5,281,342,311.5,311.5
+LABTFPSA,10,3,1,TOTAL & FREE PSA,HC TFPSA PSA FREE,281,342,311.5,311.5,281,342,311.5,311.5
+LABTFPSA,10,7,1,TOTAL & FREE PSA,HC TFPSA PSA TOTAL,281,342,311.5,311.5,281,342,311.5,311.5
+LABTFPSA,10,7,1,TOTAL & FREE PSA,HC TFPSA PSA FREE,281,342,311.5,311.5,281,342,311.5,311.5
+LABTHEOL,339,1,117,THEOPHYLLINE,HC THEOPHYLLINE,275,275,275,275,32175,32175,32175,32175
+LABTHEOL,339,3,234,THEOPHYLLINE,HC THEOPHYLLINE,275,275,275,275,64350,64350,64350,64350
+LABTHEOL,339,7,237,THEOPHYLLINE,HC THEOPHYLLINE,275,275,275,275,65175,65175,65175,65175
+LABTHG,66,1,1,THYROGLOBULIN (Tg),HC THYROGLOBULIN (TG),226,226,226,226,226,226,226,226
+LABTHG,66,3,1,THYROGLOBULIN (Tg),HC THYROGLOBULIN (TG),226,226,226,226,226,226,226,226
+LABTHG,66,7,1,THYROGLOBULIN (Tg),HC THYROGLOBULIN (TG),226,226,226,226,226,226,226,226
+LABTHIOMT,28,3,1,THIOPURINE METABOLITES,HC 7026 THIOPURINE METABOLITES,51,51,51,51,51,51,51,51
+LABTHIOMT,28,7,2,THIOPURINE METABOLITES,HC 7026 THIOPURINE METABOLITES,51,51,51,51,102,102,102,102
+LABTHS,137,1,5,Throat Screen,HC THROAT SCREEN,165,165,165,165,825,825,825,825
+LABTHS,137,3,6,Throat Screen,HC THROAT SCREEN,165,165,165,165,990,990,990,990
+LABTHS,137,7,6,Throat Screen,HC THROAT SCREEN,165,165,165,165,990,990,990,990
+LABTMSI,35,1,2,MICROSATELLITE INSTABILITY,HC TMSI MRK MRD NEO & NRM TIS,1538,1538,1538,1538,3076,3076,3076,3076
+LABTMSI,35,3,3,MICROSATELLITE INSTABILITY,HC TMSI MRK MRD NEO & NRM TIS,1538,1538,1538,1538,4614,4614,4614,4614
+LABTMSI,35,7,5,MICROSATELLITE INSTABILITY,HC TMSI MRK MRD NEO & NRM TIS,1538,1538,1538,1538,7690,7690,7690,7690
+LABTNI,73778,1,44904,Troponin I,HC TROPONIN T QUANT,520,520,520,520,23350080,23350080,23350080,23350080
+LABTNI,73778,3,47823,Troponin I,HC TROPONIN T QUANT,520,520,520,520,24867960,24867960,24867960,24867960
+LABTNI,73778,7,49298,Troponin I,HC TROPONIN T QUANT,520,520,520,520,25634960,25634960,25634960,25634960
+LABTOBPOL,59,1,5,"TOBRAMYCIN, PEAK",HC TOBRAMYCIN,314,314,314,314,1570,1570,1570,1570
+LABTOBPOL,59,3,13,"TOBRAMYCIN, PEAK",HC TOBRAMYCIN,314,314,314,314,4082,4082,4082,4082
+LABTOBPOL,59,7,19,"TOBRAMYCIN, PEAK",HC TOBRAMYCIN,314,314,314,314,5966,5966,5966,5966
+LABTOBPRL,583,1,69,"TOBRAMYCIN, TROUGH",HC TOBRAMYCIN,314,314,314,314,21666,21666,21666,21666
+LABTOBPRL,583,3,142,"TOBRAMYCIN, TROUGH",HC TOBRAMYCIN,314,314,314,314,44588,44588,44588,44588
+LABTOBPRL,583,7,234,"TOBRAMYCIN, TROUGH",HC TOBRAMYCIN,314,314,314,314,73476,73476,73476,73476
+LABTOTALC,65,1,1,"PROTEIN C AG, TOTAL",HC TOTAL PROTEIN C,133,133,133,133,133,133,133,133
+LABTOTALC,65,3,1,"PROTEIN C AG, TOTAL",HC TOTAL PROTEIN C,133,133,133,133,133,133,133,133
+LABTOTALC,65,7,2,"PROTEIN C AG, TOTAL",HC TOTAL PROTEIN C,133,133,133,133,266,266,266,266
+LABTOXGM,150,1,2,TOXOPLASMA IgG IgM,HC TOXOPLASMA AB IGG,227,231,229,229,454,462,458,458
+LABTOXGM,150,1,2,TOXOPLASMA IgG IgM,HC TOXOPLASMA AB IGM,227,231,229,229,454,462,458,458
+LABTOXGM,150,3,4,TOXOPLASMA IgG IgM,HC TOXOPLASMA AB IGG,227,231,229,229,908,924,916,916
+LABTOXGM,150,3,4,TOXOPLASMA IgG IgM,HC TOXOPLASMA AB IGM,227,231,229,229,908,924,916,916
+LABTOXGM,150,7,4,TOXOPLASMA IgG IgM,HC TOXOPLASMA AB IGG,227,231,229,229,908,924,916,916
+LABTOXGM,150,7,4,TOXOPLASMA IgG IgM,HC TOXOPLASMA AB IGM,227,231,229,229,908,924,916,916
+LABTOXIG,109,1,4,TOXOPLASMA IgG,HC TOXIG TOXOPLASMA AB IGG,231,231,231,231,924,924,924,924
+LABTOXIG,109,3,5,TOXOPLASMA IgG,HC TOXIG TOXOPLASMA AB IGG,231,231,231,231,1155,1155,1155,1155
+LABTOXIG,109,7,6,TOXOPLASMA IgG,HC TOXIG TOXOPLASMA AB IGG,231,231,231,231,1386,1386,1386,1386
+LABTOXO,210,1,4,TOXOPLASMA IgG:,HC TOXOPLASMA IGG,231,231,231,231,924,924,924,924
+LABTOXO,210,3,11,TOXOPLASMA IgG:,HC TOXOPLASMA IGG,231,231,231,231,2541,2541,2541,2541
+LABTOXO,210,7,11,TOXOPLASMA IgG:,HC TOXOPLASMA IGG,231,231,231,231,2541,2541,2541,2541
+LABTOXOP2,37,7,1,Toxoplasma PCR,HC 6944 TOXO DYE TEST IGG,63,114,88.5,88.5,63,114,88.5,88.5
+LABTOXOP2,37,7,1,Toxoplasma PCR,HC 6944 NUC MOLEC ISOL PCR,63,114,88.5,88.5,63,114,88.5,88.5
+LABTOXPCR,62,1,2,Toxoplasma PCR,HC 6944 TOXPCR TOXOPLASMA PCR,389,389,389,389,778,778,778,778
+LABTOXPCR,62,3,3,Toxoplasma PCR,HC 6944 TOXPCR TOXOPLASMA PCR,389,389,389,389,1167,1167,1167,1167
+LABTOXPCR,62,7,6,Toxoplasma PCR,HC 6944 TOXPCR TOXOPLASMA PCR,389,389,389,389,2334,2334,2334,2334
+LABTP,1480,1,44,"Protein, Total, Ser/Plas","HC PROTEIN, TOTAL SERUM/PLASMA",75,75,75,75,3300,3300,3300,3300
+LABTP,1480,3,79,"Protein, Total, Ser/Plas","HC PROTEIN, TOTAL SERUM/PLASMA",75,75,75,75,5925,5925,5925,5925
+LABTP,1480,7,98,"Protein, Total, Ser/Plas","HC PROTEIN, TOTAL SERUM/PLASMA",75,75,75,75,7350,7350,7350,7350
+LABTRFN,315,1,17,TRANSFERRIN,HC TRANSFERRIN,420,420,420,420,7140,7140,7140,7140
+LABTRFN,315,3,26,TRANSFERRIN,HC TRANSFERRIN,420,420,420,420,10920,10920,10920,10920
+LABTRFN,315,7,33,TRANSFERRIN,HC TRANSFERRIN,420,420,420,420,13860,13860,13860,13860
+LABTRFS,6036,1,275,TRANSFERRIN SATURATION,HC IRON TTL SERUM,165,420,292.5,292.5,45375,115500,80437.5,80437.5
+LABTRFS,6036,1,275,TRANSFERRIN SATURATION,HC TRANSFERRIN,165,420,292.5,292.5,45375,115500,80437.5,80437.5
+LABTRFS,6036,3,363,TRANSFERRIN SATURATION,HC IRON TTL SERUM,165,420,292.5,292.5,59895,152460,106177.5,106177.5
+LABTRFS,6036,3,363,TRANSFERRIN SATURATION,HC TRANSFERRIN,165,420,292.5,292.5,59895,152460,106177.5,106177.5
+LABTRFS,6036,7,422,TRANSFERRIN SATURATION,HC IRON TTL SERUM,165,420,292.5,292.5,69630,177240,123435,123435
+LABTRFS,6036,7,422,TRANSFERRIN SATURATION,HC TRANSFERRIN,165,420,292.5,292.5,69630,177240,123435,123435
+LABTRIG,8510,1,1301,Triglyceride,HC TRIGLYCERIDES-BLOOD,175,175,175,175,227675,227675,227675,227675
+LABTRIG,8510,3,3349,Triglyceride,HC TRIGLYCERIDES-BLOOD,175,175,175,175,586075,586075,586075,586075
+LABTRIG,8510,7,5449,Triglyceride,HC TRIGLYCERIDES-BLOOD,175,175,175,175,953575,953575,953575,953575
+LABTSH,19385,1,1491,TSH,HC HIGHLY SENSITIV TSH,380,380,380,380,566580,566580,566580,566580
+LABTSH,19385,3,1985,TSH,HC HIGHLY SENSITIV TSH,380,380,380,380,754300,754300,754300,754300
+LABTSH,19385,7,2321,TSH,HC HIGHLY SENSITIV TSH,380,380,380,380,881980,881980,881980,881980
+LABTSHFT4,1530,1,28,TSH W/ REFLEX FT4,HC TSHFT4 TSH,380,380,380,380,10640,10640,10640,10640
+LABTSHFT4,1530,3,41,TSH W/ REFLEX FT4,HC TSHFT4 TSH,380,380,380,380,15580,15580,15580,15580
+LABTSHFT4,1530,7,51,TSH W/ REFLEX FT4,HC TSHFT4 TSH,380,380,380,380,19380,19380,19380,19380
+LABTT,712,1,77,Thrombin Time,HC THROMBIN TIME,167,167,167,167,12859,12859,12859,12859
+LABTT,712,3,122,Thrombin Time,HC THROMBIN TIME,167,167,167,167,20374,20374,20374,20374
+LABTT,712,7,132,Thrombin Time,HC THROMBIN TIME,167,167,167,167,22044,22044,22044,22044
+LABTT4,367,1,6,"Thyroxine (T4), Total",HC THYROXINE(T4) TOTAL,158,158,158,158,948,948,948,948
+LABTT4,367,3,6,"Thyroxine (T4), Total",HC THYROXINE(T4) TOTAL,158,158,158,158,948,948,948,948
+LABTT4,367,7,7,"Thyroxine (T4), Total",HC THYROXINE(T4) TOTAL,158,158,158,158,1106,1106,1106,1106
+LABTTM,76,1,24,TRANSPLANT THERAPY MONITORING,HC CD5,538,575,557.75,559,12912,13800,13386,13416
+LABTTM,76,1,24,TRANSPLANT THERAPY MONITORING,"HC FLOW:T-CELLS,TOTAL COUNT",538,575,557.75,559,12912,13800,13386,13416
+LABTTM,76,1,24,TRANSPLANT THERAPY MONITORING,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,538,575,557.75,559,12912,13800,13386,13416
+LABTTM,76,1,24,TRANSPLANT THERAPY MONITORING,"HC FLOW:B CELLS, TOTAL COUNT",538,575,557.75,559,12912,13800,13386,13416
+LABTTM,76,3,39,TRANSPLANT THERAPY MONITORING,HC CD5,538,575,557.75,559,20982,22425,21752.25,21801
+LABTTM,76,3,39,TRANSPLANT THERAPY MONITORING,"HC FLOW:T-CELLS,TOTAL COUNT",538,575,557.75,559,20982,22425,21752.25,21801
+LABTTM,76,3,39,TRANSPLANT THERAPY MONITORING,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,538,575,557.75,559,20982,22425,21752.25,21801
+LABTTM,76,3,39,TRANSPLANT THERAPY MONITORING,"HC FLOW:B CELLS, TOTAL COUNT",538,575,557.75,559,20982,22425,21752.25,21801
+LABTTM,76,7,40,TRANSPLANT THERAPY MONITORING,HC CD5,538,575,557.75,559,21520,23000,22310,22360
+LABTTM,76,7,40,TRANSPLANT THERAPY MONITORING,"HC FLOW:T-CELLS,TOTAL COUNT",538,575,557.75,559,21520,23000,22310,22360
+LABTTM,76,7,40,TRANSPLANT THERAPY MONITORING,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO,538,575,557.75,559,21520,23000,22310,22360
+LABTTM,76,7,40,TRANSPLANT THERAPY MONITORING,"HC FLOW:B CELLS, TOTAL COUNT",538,575,557.75,559,21520,23000,22310,22360
+LABTYPSNI,20,1,1,TYPE AND SCREEN,HC ANTIB SCRN-3 CELL,325,325,325,325,325,325,325,325
+LABTYPSNI,20,3,1,TYPE AND SCREEN,HC ANTIB SCRN-3 CELL,325,325,325,325,325,325,325,325
+LABTYPSNI,20,7,1,TYPE AND SCREEN,HC ANTIB SCRN-3 CELL,325,325,325,325,325,325,325,325
+LABUA,45959,1,2985,URINALYSIS WITH MICROSCOPIC,HC URINE ROUTINE-MICRO,196,196,196,196,585060,585060,585060,585060
+LABUA,45959,3,5620,URINALYSIS WITH MICROSCOPIC,HC URINE ROUTINE-MICRO,196,196,196,196,1101520,1101520,1101520,1101520
+LABUA,45959,7,7981,URINALYSIS WITH MICROSCOPIC,HC URINE ROUTINE-MICRO,196,196,196,196,1564276,1564276,1564276,1564276
+LABUALB,211,1,19,"ALBUMIN, UR (RANDOM) W/CREAT","HC ALBUMIN, URINE/OTHER, QUANT",126,126,126,126,2394,2394,2394,2394
+LABUALB,211,3,39,"ALBUMIN, UR (RANDOM) W/CREAT","HC ALBUMIN, URINE/OTHER, QUANT",126,126,126,126,4914,4914,4914,4914
+LABUALB,211,7,45,"ALBUMIN, UR (RANDOM) W/CREAT","HC ALBUMIN, URINE/OTHER, QUANT",126,126,126,126,5670,5670,5670,5670
+LABUAMY,96,1,5,"AMYLASE, UR (RANDOM) W/CREAT",HC AMYLASE URINE,395,395,395,395,1975,1975,1975,1975
+LABUAMY,96,3,9,"AMYLASE, UR (RANDOM) W/CREAT",HC AMYLASE URINE,395,395,395,395,3555,3555,3555,3555
+LABUAMY,96,7,10,"AMYLASE, UR (RANDOM) W/CREAT",HC AMYLASE URINE,395,395,395,395,3950,3950,3950,3950
+LABUAPRN,27157,1,997,"URINALYSIS, SCR. FOR CULTURE",HC URINE ROUTINE-MICRO,196,196,196,196,195412,195412,195412,195412
+LABUAPRN,27157,3,2780,"URINALYSIS, SCR. FOR CULTURE",HC URINE ROUTINE-MICRO,196,196,196,196,544880,544880,544880,544880
+LABUAPRN,27157,7,5013,"URINALYSIS, SCR. FOR CULTURE",HC URINE ROUTINE-MICRO,196,196,196,196,982548,982548,982548,982548
+LABUAS,3056,1,160,"UA, MACROSCOPIC ONLY",HC URINALYSIS AUTO,108,108,108,108,17280,17280,17280,17280
+LABUAS,3056,3,240,"UA, MACROSCOPIC ONLY",HC URINALYSIS AUTO,108,108,108,108,25920,25920,25920,25920
+LABUAS,3056,7,291,"UA, MACROSCOPIC ONLY",HC URINALYSIS AUTO,108,108,108,108,31428,31428,31428,31428
+LABUB2MG,12,1,1,"BETA-2 MICROGLOBULIN, URINE",HC 6959 UB2MG BETA-2 MCRGLBN U,16,16,16,16,16,16,16,16
+LABUB2MG,12,3,3,"BETA-2 MICROGLOBULIN, URINE",HC 6959 UB2MG BETA-2 MCRGLBN U,16,16,16,16,48,48,48,48
+LABUB2MG,12,7,3,"BETA-2 MICROGLOBULIN, URINE",HC 6959 UB2MG BETA-2 MCRGLBN U,16,16,16,16,48,48,48,48
+LABUBICAR,24,1,3,"BICARBONATE (HCO3), URINE",HC 6959 UBICAR BICARBONAT HCO3,9,9,9,9,27,27,27,27
+LABUBICAR,24,3,4,"BICARBONATE (HCO3), URINE",HC 6959 UBICAR BICARBONAT HCO3,9,9,9,9,36,36,36,36
+LABUBICAR,24,7,4,"BICARBONATE (HCO3), URINE",HC 6959 UBICAR BICARBONAT HCO3,9,9,9,9,36,36,36,36
+LABUCAT,32,3,1,"CALCIUM, URINE (TIMED)",HC CALCIUM URINE TIMED,149,149,149,149,149,149,149,149
+LABUCAT,32,7,1,"CALCIUM, URINE (TIMED)",HC CALCIUM URINE TIMED,149,149,149,149,149,149,149,149
+LABUCL,70,1,2,"CHLORIDE, UR (RANDOM) W/CREAT","HC CHLORIDE, URINE",131,131,131,131,262,262,262,262
+LABUCL,70,3,4,"CHLORIDE, UR (RANDOM) W/CREAT","HC CHLORIDE, URINE",131,131,131,131,524,524,524,524
+LABUCL,70,7,4,"CHLORIDE, UR (RANDOM) W/CREAT","HC CHLORIDE, URINE",131,131,131,131,524,524,524,524
+LABUCLT,8,3,1,"CHLORIDE, URINE (TIMED)","HC CHLORIDE, URINE",131,131,131,131,131,131,131,131
+LABUCLT,8,7,1,"CHLORIDE, URINE (TIMED)","HC CHLORIDE, URINE",131,131,131,131,131,131,131,131
+LABUCORT,19,3,2,"FREE CORTISOL, URINE (TIMED)",HC UCORT CORTISOL FREE UR,235,235,235,235,470,470,470,470
+LABUCORT,19,7,2,"FREE CORTISOL, URINE (TIMED)",HC UCORT CORTISOL FREE UR,235,235,235,235,470,470,470,470
+LABUCR,4553,1,221,"CREATININE, URINE (RANDOM)",HC CREATININE URINE,174,174,174,174,38454,38454,38454,38454
+LABUCR,4553,3,388,"CREATININE, URINE (RANDOM)",HC CREATININE URINE,174,174,174,174,67512,67512,67512,67512
+LABUCR,4553,7,503,"CREATININE, URINE (RANDOM)",HC CREATININE URINE,174,174,174,174,87522,87522,87522,87522
+LABUCRT,571,1,7,"CREATININE, URINE (TIMED)",HC CREATININE URINE,174,174,174,174,1218,1218,1218,1218
+LABUCRT,571,3,17,"CREATININE, URINE (TIMED)",HC CREATININE URINE,174,174,174,174,2958,2958,2958,2958
+LABUCRT,571,7,34,"CREATININE, URINE (TIMED)",HC CREATININE URINE,174,174,174,174,5916,5916,5916,5916
+LABUEOS,1462,1,252,"Eosinophils, Urine",HC URINE EOSINOPHIS,174,174,174,174,43848,43848,43848,43848
+LABUEOS,1462,3,294,"Eosinophils, Urine",HC URINE EOSINOPHIS,174,174,174,174,51156,51156,51156,51156
+LABUEOS,1462,7,314,"Eosinophils, Urine",HC URINE EOSINOPHIS,174,174,174,174,54636,54636,54636,54636
+LABUEP,3419,1,209,"ELECTROLYTE PANEL, URINE",HC URINE LYTES PANEL SODIUM,134,134,134,134,28006,28006,28006,28006
+LABUEP,3419,3,350,"ELECTROLYTE PANEL, URINE",HC URINE LYTES PANEL SODIUM,134,134,134,134,46900,46900,46900,46900
+LABUEP,3419,7,429,"ELECTROLYTE PANEL, URINE",HC URINE LYTES PANEL SODIUM,134,134,134,134,57486,57486,57486,57486
+LABUHEMO,64,1,5,"Hemosiderin, Urine",HC HEMOSIDERIN QUAL,145,145,145,145,725,725,725,725
+LABUHEMO,64,3,10,"Hemosiderin, Urine",HC HEMOSIDERIN QUAL,145,145,145,145,1450,1450,1450,1450
+LABUHEMO,64,7,11,"Hemosiderin, Urine",HC HEMOSIDERIN QUAL,145,145,145,145,1595,1595,1595,1595
+LABUHGB,31,3,3,"HEMOGLOBIN, URINE",HC 6959 UHGB HEMOGLOBIN URINE,20,20,20,20,60,60,60,60
+LABUHGB,31,7,3,"HEMOGLOBIN, URINE",HC 6959 UHGB HEMOGLOBIN URINE,20,20,20,20,60,60,60,60
+LABUK,139,1,8,"POTASSIUM, UR(RANDOM)W/CREAT",HC POTASSIUM URINE,158,158,158,158,1264,1264,1264,1264
+LABUK,139,3,11,"POTASSIUM, UR(RANDOM)W/CREAT",HC POTASSIUM URINE,158,158,158,158,1738,1738,1738,1738
+LABUK,139,7,15,"POTASSIUM, UR(RANDOM)W/CREAT",HC POTASSIUM URINE,158,158,158,158,2370,2370,2370,2370
+LABUKT,26,3,1,"POTASSIUM, URINE (TIMED)",HC POTASSIUM URINE,158,158,158,158,158,158,158,158
+LABUKT,26,7,1,"POTASSIUM, URINE (TIMED)",HC POTASSIUM URINE,158,158,158,158,158,158,158,158
+LABUMAG,9,3,2,"MAGNESIUM, UR (RANDOM) W/CREAT",HC URINE MAGNESIUM,280,280,280,280,560,560,560,560
+LABUMAG,9,7,2,"MAGNESIUM, UR (RANDOM) W/CREAT",HC URINE MAGNESIUM,280,280,280,280,560,560,560,560
+LABUMAGT,8,1,1,"MAGNESIUM, URINE (TIMED)",HC URINE MAGNESIUM,280,280,280,280,280,280,280,280
+LABUMAGT,8,3,1,"MAGNESIUM, URINE (TIMED)",HC URINE MAGNESIUM,280,280,280,280,280,280,280,280
+LABUMAGT,8,7,1,"MAGNESIUM, URINE (TIMED)",HC URINE MAGNESIUM,280,280,280,280,280,280,280,280
+LABUMYOSC,72,1,8,"Myoglobin Screen, Urine",HC URINE MYOGLOBIN SCREEN,115,115,115,115,920,920,920,920
+LABUMYOSC,72,3,10,"Myoglobin Screen, Urine",HC URINE MYOGLOBIN SCREEN,115,115,115,115,1150,1150,1150,1150
+LABUMYOSC,72,7,10,"Myoglobin Screen, Urine",HC URINE MYOGLOBIN SCREEN,115,115,115,115,1150,1150,1150,1150
+LABUNA,2683,1,139,"SODIUM, UR (RANDOM) W/CREAT",HC SODIUM URINE,134,134,134,134,18626,18626,18626,18626
+LABUNA,2683,3,216,"SODIUM, UR (RANDOM) W/CREAT",HC SODIUM URINE,134,134,134,134,28944,28944,28944,28944
+LABUNA,2683,7,273,"SODIUM, UR (RANDOM) W/CREAT",HC SODIUM URINE,134,134,134,134,36582,36582,36582,36582
+LABUNAT,109,3,2,"SODIUM, URINE (TIMED)",HC SODIUM URINE,134,134,134,134,268,268,268,268
+LABUNAT,109,7,3,"SODIUM, URINE (TIMED)",HC SODIUM URINE,134,134,134,134,402,402,402,402
+LABUORG,15,1,3,"ORGANIC ACIDS, URINE",HC ORGANIC ACID SCR URI,195,195,195,195,585,585,585,585
+LABUORG,15,3,3,"ORGANIC ACIDS, URINE",HC ORGANIC ACID SCR URI,195,195,195,195,585,585,585,585
+LABUORG,15,7,4,"ORGANIC ACIDS, URINE",HC ORGANIC ACID SCR URI,195,195,195,195,780,780,780,780
+LABUOROT,6,1,2,"OROTIC ACID, URINE",HC OROTIC ACID URINE QUANT,200,200,200,200,400,400,400,400
+LABUOROT,6,3,2,"OROTIC ACID, URINE",HC OROTIC ACID URINE QUANT,200,200,200,200,400,400,400,400
+LABUOROT,6,7,2,"OROTIC ACID, URINE",HC OROTIC ACID URINE QUANT,200,200,200,200,400,400,400,400
+LABUOSM,5279,1,846,"Osmolality, Urine",HC OSMOLALITY URINE,255,255,255,255,215730,215730,215730,215730
+LABUOSM,5279,3,1193,"Osmolality, Urine",HC OSMOLALITY URINE,255,255,255,255,304215,304215,304215,304215
+LABUOSM,5279,7,1380,"Osmolality, Urine",HC OSMOLALITY URINE,255,255,255,255,351900,351900,351900,351900
+LABUPBGQT,35,1,2,"PORPHOBILINOGEN, URINE QUANT",HC 6959 UPBGQT PBG UR QNT,41,41,41,41,82,82,82,82
+LABUPBGQT,35,3,3,"PORPHOBILINOGEN, URINE QUANT",HC 6959 UPBGQT PBG UR QNT,41,41,41,41,123,123,123,123
+LABUPBGQT,35,7,3,"PORPHOBILINOGEN, URINE QUANT",HC 6959 UPBGQT PBG UR QNT,41,41,41,41,123,123,123,123
+LABUPHOS,29,1,1,"PHOSPHATE, UR (RANDOM) W/CREAT",HC PHOSPHORUS URINE,115,115,115,115,115,115,115,115
+LABUPHOS,29,3,1,"PHOSPHATE, UR (RANDOM) W/CREAT",HC PHOSPHORUS URINE,115,115,115,115,115,115,115,115
+LABUPHOS,29,7,3,"PHOSPHATE, UR (RANDOM) W/CREAT",HC PHOSPHORUS URINE,115,115,115,115,345,345,345,345
+LABUPHOST,10,1,2,"PHOSPHATE, URINE (TIMED)",HC PHOSPHORUS URINE,115,115,115,115,230,230,230,230
+LABUPHOST,10,3,2,"PHOSPHATE, URINE (TIMED)",HC PHOSPHORUS URINE,115,115,115,115,230,230,230,230
+LABUPHOST,10,7,2,"PHOSPHATE, URINE (TIMED)",HC PHOSPHORUS URINE,115,115,115,115,230,230,230,230
+LABUPIE,758,1,19,"URINE PROTEIN IMMUNOFIX ELECT, RANDOM",HC URINE PROTEIN IMMUNOFIX ELECT,376,376,376,376,7144,7144,7144,7144
+LABUPIE,758,3,47,"URINE PROTEIN IMMUNOFIX ELECT, RANDOM",HC URINE PROTEIN IMMUNOFIX ELECT,376,376,376,376,17672,17672,17672,17672
+LABUPIE,758,7,69,"URINE PROTEIN IMMUNOFIX ELECT, RANDOM",HC URINE PROTEIN IMMUNOFIX ELECT,376,376,376,376,25944,25944,25944,25944
+LABUPIET,44,7,1,"URINE PROTEIN IMMUNOFIX ELECT, TIMED",HC URINE PROTEIN IMMUNOFIX ELECT,376,376,376,376,376,376,376,376
+LABUPREG,2441,1,49,"Pregnancy Test, Urine",HC URINE PREGNANCY SCREEN,102,102,102,102,4998,4998,4998,4998
+LABUPREG,2441,3,65,"Pregnancy Test, Urine",HC URINE PREGNANCY SCREEN,102,102,102,102,6630,6630,6630,6630
+LABUPREG,2441,7,79,"Pregnancy Test, Urine",HC URINE PREGNANCY SCREEN,102,102,102,102,8058,8058,8058,8058
+LABURAMY,8,1,1,"AMYLASE, UR (RANDOM) W/O CREAT",HC AMYLASE URINE,395,395,395,395,395,395,395,395
+LABURAMY,8,3,1,"AMYLASE, UR (RANDOM) W/O CREAT",HC AMYLASE URINE,395,395,395,395,395,395,395,395
+LABURAMY,8,7,1,"AMYLASE, UR (RANDOM) W/O CREAT",HC AMYLASE URINE,395,395,395,395,395,395,395,395
+LABURCA,53,1,5,"CALCIUM, UR (RANDOM) W/O CREAT",HC CALCIUM URINE TIMED,149,149,149,149,745,745,745,745
+LABURCA,53,3,8,"CALCIUM, UR (RANDOM) W/O CREAT",HC CALCIUM URINE TIMED,149,149,149,149,1192,1192,1192,1192
+LABURCA,53,7,9,"CALCIUM, UR (RANDOM) W/O CREAT",HC CALCIUM URINE TIMED,149,149,149,149,1341,1341,1341,1341
+LABURCL,319,1,16,"CHLORIDE,UR(RANDOM) W/O CREAT","HC CHLORIDE, URINE",131,131,131,131,2096,2096,2096,2096
+LABURCL,319,3,24,"CHLORIDE,UR(RANDOM) W/O CREAT","HC CHLORIDE, URINE",131,131,131,131,3144,3144,3144,3144
+LABURCL,319,7,26,"CHLORIDE,UR(RANDOM) W/O CREAT","HC CHLORIDE, URINE",131,131,131,131,3406,3406,3406,3406
+LABURGL,30,1,1,"GLUCOSE, UR (RANDOM) W/O CREAT",HC GLUCOSE URINE,250,250,250,250,250,250,250,250
+LABURGL,30,3,1,"GLUCOSE, UR (RANDOM) W/O CREAT",HC GLUCOSE URINE,250,250,250,250,250,250,250,250
+LABURGL,30,7,1,"GLUCOSE, UR (RANDOM) W/O CREAT",HC GLUCOSE URINE,250,250,250,250,250,250,250,250
+LABURIC,22347,1,14277,"Uric Acid, Ser/Plas",HC URIC ACID SERUM/PL,135,135,135,135,1927395,1927395,1927395,1927395
+LABURIC,22347,3,19098,"Uric Acid, Ser/Plas",HC URIC ACID SERUM/PL,135,135,135,135,2578230,2578230,2578230,2578230
+LABURIC,22347,7,19336,"Uric Acid, Ser/Plas",HC URIC ACID SERUM/PL,135,135,135,135,2610360,2610360,2610360,2610360
+LABURK,562,1,58,"POTASSIUM, UR(RANDOM)W/O CREAT",HC POTASSIUM URINE,158,158,158,158,9164,9164,9164,9164
+LABURK,562,3,80,"POTASSIUM, UR(RANDOM)W/O CREAT",HC POTASSIUM URINE,158,158,158,158,12640,12640,12640,12640
+LABURK,562,7,86,"POTASSIUM, UR(RANDOM)W/O CREAT",HC POTASSIUM URINE,158,158,158,158,13588,13588,13588,13588
+LABURMAG,13,1,1,"MAGNESIUM,UR(RANDOM) W/O CREAT",HC URINE MAGNESIUM,280,280,280,280,280,280,280,280
+LABURMAG,13,3,1,"MAGNESIUM,UR(RANDOM) W/O CREAT",HC URINE MAGNESIUM,280,280,280,280,280,280,280,280
+LABURMAG,13,7,1,"MAGNESIUM,UR(RANDOM) W/O CREAT",HC URINE MAGNESIUM,280,280,280,280,280,280,280,280
+LABURNA,5863,1,510,"SODIUM, UR (RANDOM) W/O CREAT",HC SODIUM URINE,134,134,134,134,68340,68340,68340,68340
+LABURNA,5863,3,793,"SODIUM, UR (RANDOM) W/O CREAT",HC SODIUM URINE,134,134,134,134,106262,106262,106262,106262
+LABURNA,5863,7,960,"SODIUM, UR (RANDOM) W/O CREAT",HC SODIUM URINE,134,134,134,134,128640,128640,128640,128640
+LABURNC,34444,1,2076,Urine Culture,HC URN CULT,317,317,317,317,658092,658092,658092,658092
+LABURNC,34444,3,4472,Urine Culture,HC URN CULT,317,317,317,317,1417624,1417624,1417624,1417624
+LABURNC,34444,7,7263,Urine Culture,HC URN CULT,317,317,317,317,2302371,2302371,2302371,2302371
+LABURPHOS,88,1,7,"PHOSPHATE,UR(RANDOM) W/O CREAT",HC PHOSPHORUS URINE,115,115,115,115,805,805,805,805
+LABURPHOS,88,3,12,"PHOSPHATE,UR(RANDOM) W/O CREAT",HC PHOSPHORUS URINE,115,115,115,115,1380,1380,1380,1380
+LABURPHOS,88,7,12,"PHOSPHATE,UR(RANDOM) W/O CREAT",HC PHOSPHORUS URINE,115,115,115,115,1380,1380,1380,1380
+LABURTP,437,1,33,"PROTEIN, UR (RANDOM) W/O CREAT",HC PROT TOTAL URINE,103,103,103,103,3399,3399,3399,3399
+LABURTP,437,3,56,"PROTEIN, UR (RANDOM) W/O CREAT",HC PROT TOTAL URINE,103,103,103,103,5768,5768,5768,5768
+LABURTP,437,7,61,"PROTEIN, UR (RANDOM) W/O CREAT",HC PROT TOTAL URINE,103,103,103,103,6283,6283,6283,6283
+LABURUN,2089,1,90,"UREA, UR (RANDOM) W/O CREAT",HC UREA NITROGEN URINE,93,93,93,93,8370,8370,8370,8370
+LABURUN,2089,3,149,"UREA, UR (RANDOM) W/O CREAT",HC UREA NITROGEN URINE,93,93,93,93,13857,13857,13857,13857
+LABURUN,2089,7,191,"UREA, UR (RANDOM) W/O CREAT",HC UREA NITROGEN URINE,93,93,93,93,17763,17763,17763,17763
+LABURURIC,39,1,2,"URIC ACID,UR(RANDOM) W/O CREAT",HC URIC ACID URINE,89,89,89,89,178,178,178,178
+LABURURIC,39,3,2,"URIC ACID,UR(RANDOM) W/O CREAT",HC URIC ACID URINE,89,89,89,89,178,178,178,178
+LABURURIC,39,7,2,"URIC ACID,UR(RANDOM) W/O CREAT",HC URIC ACID URINE,89,89,89,89,178,178,178,178
+LABUSPG,6903,1,5773,Specific Gravity,HC SPECIFIC GRAVITY,115,115,115,115,663895,663895,663895,663895
+LABUSPG,6903,3,5957,Specific Gravity,HC SPECIFIC GRAVITY,115,115,115,115,685055,685055,685055,685055
+LABUSPG,6903,7,6000,Specific Gravity,HC SPECIFIC GRAVITY,115,115,115,115,690000,690000,690000,690000
+LABUTHI,3,1,1,"THIOCYANATE, RANDOM URINE",HC 6959 UTHI THIOCYANATE URINE,34,34,34,34,34,34,34,34
+LABUTHI,3,3,1,"THIOCYANATE, RANDOM URINE",HC 6959 UTHI THIOCYANATE URINE,34,34,34,34,34,34,34,34
+LABUTHI,3,7,1,"THIOCYANATE, RANDOM URINE",HC 6959 UTHI THIOCYANATE URINE,34,34,34,34,34,34,34,34
+LABUTP,345,1,1,"TOTAL PROTEIN, URINE (TIMED)",HC PROT TOTAL URINE,103,103,103,103,103,103,103,103
+LABUTP,345,3,6,"TOTAL PROTEIN, URINE (TIMED)",HC PROT TOTAL URINE,103,103,103,103,618,618,618,618
+LABUTP,345,7,22,"TOTAL PROTEIN, URINE (TIMED)",HC PROT TOTAL URINE,103,103,103,103,2266,2266,2266,2266
+LABUTPRAN,1017,1,60,"PROTEIN, UR (RANDOM) W/CREAT",HC PROT TOTAL URINE,103,103,103,103,6180,6180,6180,6180
+LABUTPRAN,1017,3,103,"PROTEIN, UR (RANDOM) W/CREAT",HC PROT TOTAL URINE,103,103,103,103,10609,10609,10609,10609
+LABUTPRAN,1017,7,130,"PROTEIN, UR (RANDOM) W/CREAT",HC PROT TOTAL URINE,103,103,103,103,13390,13390,13390,13390
+LABUUN,1123,1,50,"UREA, UR (RANDOM) W/CREAT",HC UREA NITROGEN URINE,93,93,93,93,4650,4650,4650,4650
+LABUUN,1123,3,80,"UREA, UR (RANDOM) W/CREAT",HC UREA NITROGEN URINE,93,93,93,93,7440,7440,7440,7440
+LABUUN,1123,7,102,"UREA, UR (RANDOM) W/CREAT",HC UREA NITROGEN URINE,93,93,93,93,9486,9486,9486,9486
+LABUUNT,363,1,5,"UREA, URINE (TIMED)",HC UREA NITROGEN URINE,93,93,93,93,465,465,465,465
+LABUUNT,363,3,10,"UREA, URINE (TIMED)",HC UREA NITROGEN URINE,93,93,93,93,930,930,930,930
+LABUUNT,363,7,22,"UREA, URINE (TIMED)",HC UREA NITROGEN URINE,93,93,93,93,2046,2046,2046,2046
+LABUURICT,7,1,1,"URIC ACID, URINE (TIMED)",HC URIC ACID URINE,89,89,89,89,89,89,89,89
+LABUURICT,7,3,1,"URIC ACID, URINE (TIMED)",HC URIC ACID URINE,89,89,89,89,89,89,89,89
+LABUURICT,7,7,1,"URIC ACID, URINE (TIMED)",HC URIC ACID URINE,89,89,89,89,89,89,89,89
+LABUVH,22,7,1,"VMA & HVA, URINE",HC 6959 UVH HVA HMVNLLC ACD UR,21,26,23.5,23.5,21,26,23.5,23.5
+LABUVH,22,7,1,"VMA & HVA, URINE",HC 6959 UVH VMA VNLYMDLC AC UR,21,26,23.5,23.5,21,26,23.5,23.5
+LABVANPOL,108,1,7,"VANCOMYCIN, PEAK",HC VANCOMYCIN,263,263,263,263,1841,1841,1841,1841
+LABVANPOL,108,3,13,"VANCOMYCIN, PEAK",HC VANCOMYCIN,263,263,263,263,3419,3419,3419,3419
+LABVANPOL,108,7,13,"VANCOMYCIN, PEAK",HC VANCOMYCIN,263,263,263,263,3419,3419,3419,3419
+LABVANPRL,32524,1,6790,"VANCOMYCIN, TROUGH",HC VANCOMYCIN,263,263,263,263,1785770,1785770,1785770,1785770
+LABVANPRL,32524,3,17481,"VANCOMYCIN, TROUGH",HC VANCOMYCIN,263,263,263,263,4597503,4597503,4597503,4597503
+LABVANPRL,32524,7,19915,"VANCOMYCIN, TROUGH",HC VANCOMYCIN,263,263,263,263,5237645,5237645,5237645,5237645
+LABVANUNL,2634,1,696,"VANCOMYCIN, SINGLE DAILY DOSE",HC VANCOMYCIN,263,263,263,263,183048,183048,183048,183048
+LABVANUNL,2634,3,1418,"VANCOMYCIN, SINGLE DAILY DOSE",HC VANCOMYCIN,263,263,263,263,372934,372934,372934,372934
+LABVANUNL,2634,7,1491,"VANCOMYCIN, SINGLE DAILY DOSE",HC VANCOMYCIN,263,263,263,263,392133,392133,392133,392133
+LABVBG,37486,1,25338,"BLOOD GASES, VENOUS",HC VENOUS BLOOD GASES,1161,1161,1161,1161,29417418,29417418,29417418,29417418
+LABVBG,37486,3,27888,"BLOOD GASES, VENOUS",HC VENOUS BLOOD GASES,1161,1161,1161,1161,32377968,32377968,32377968,32377968
+LABVBG,37486,7,28586,"BLOOD GASES, VENOUS",HC VENOUS BLOOD GASES,1161,1161,1161,1161,33188346,33188346,33188346,33188346
+LABVCAIGG,85,3,1,EBV VCA IgG,HC VCAIGG EBV VCA IGG,343,343,343,343,343,343,343,343
+LABVCAIGG,85,7,1,EBV VCA IgG,HC VCAIGG EBV VCA IGG,343,343,343,343,343,343,343,343
+LABVCAIGM,91,1,1,EBV VCA IgM,HC VCAIGM EBV VCA IGM,343,343,343,343,343,343,343,343
+LABVCAIGM,91,3,2,EBV VCA IgM,HC VCAIGM EBV VCA IGM,343,343,343,343,686,686,686,686
+LABVCAIGM,91,7,2,EBV VCA IgM,HC VCAIGM EBV VCA IGM,343,343,343,343,686,686,686,686
+LABVCUL,2171,1,216,VIRAL CULTURE,HC VIRUS ISOLATN CULTR,484,484,484,484,104544,104544,104544,104544
+LABVCUL,2171,3,251,VIRAL CULTURE,HC VIRUS ISOLATN CULTR,484,484,484,484,121484,121484,121484,121484
+LABVCUL,2171,7,325,VIRAL CULTURE,HC VIRUS ISOLATN CULTR,484,484,484,484,157300,157300,157300,157300
+LABVCULF,61,1,1,"VIRAL CULTURE, FLUID",HC VCULF VRS ISL CLT OB ID FL,484,484,484,484,484,484,484,484
+LABVCULF,61,3,1,"VIRAL CULTURE, FLUID",HC VCULF VRS ISL CLT OB ID FL,484,484,484,484,484,484,484,484
+LABVCULF,61,7,3,"VIRAL CULTURE, FLUID",HC VCULF VRS ISL CLT OB ID FL,484,484,484,484,1452,1452,1452,1452
+LABVCULO,10,1,1,"VIRAL CULTURE, OCULAR",HC VCULO VRS ISL CLT OB ID OC,484,484,484,484,484,484,484,484
+LABVCULO,10,3,1,"VIRAL CULTURE, OCULAR",HC VCULO VRS ISL CLT OB ID OC,484,484,484,484,484,484,484,484
+LABVCULO,10,7,1,"VIRAL CULTURE, OCULAR",HC VCULO VRS ISL CLT OB ID OC,484,484,484,484,484,484,484,484
+LABVCULR,297,1,14,"VIRAL CULTURE, RESPIRATORY",HC VCULR VRS ISL CLT OBID RES,484,484,484,484,6776,6776,6776,6776
+LABVCULR,297,3,17,"VIRAL CULTURE, RESPIRATORY",HC VCULR VRS ISL CLT OBID RES,484,484,484,484,8228,8228,8228,8228
+LABVCULR,297,7,27,"VIRAL CULTURE, RESPIRATORY",HC VCULR VRS ISL CLT OBID RES,484,484,484,484,13068,13068,13068,13068
+LABVCULS,51,3,1,"VIRAL CULTURE, STOOL/RECTAL",HC VCULS VRS ISL CLT OB ID SW,484,484,484,484,484,484,484,484
+LABVCULS,51,7,1,"VIRAL CULTURE, STOOL/RECTAL",HC VCULS VRS ISL CLT OB ID SW,484,484,484,484,484,484,484,484
+LABVCULT,230,1,47,"VIRAL CULTURE, TISSUE",HC VCULT VRS ISL CLT OB ID TS,484,484,484,484,22748,22748,22748,22748
+LABVCULT,230,3,47,"VIRAL CULTURE, TISSUE",HC VCULT VRS ISL CLT OB ID TS,484,484,484,484,22748,22748,22748,22748
+LABVCULT,230,7,57,"VIRAL CULTURE, TISSUE",HC VCULT VRS ISL CLT OB ID TS,484,484,484,484,27588,27588,27588,27588
+LABVD25H,1131,1,50,"VITAMIN D, 25-HYDROXYVITAMIN",HC VIT D 25-HYDROXY,415,415,415,415,20750,20750,20750,20750
+LABVD25H,1131,3,71,"VITAMIN D, 25-HYDROXYVITAMIN",HC VIT D 25-HYDROXY,415,415,415,415,29465,29465,29465,29465
+LABVD25H,1131,7,98,"VITAMIN D, 25-HYDROXYVITAMIN",HC VIT D 25-HYDROXY,415,415,415,415,40670,40670,40670,40670
+LABVISC,70,1,4,"VISCOSITY, SERUM",HC SRM VSCSTY,217,217,217,217,868,868,868,868
+LABVISC,70,3,8,"VISCOSITY, SERUM",HC SRM VSCSTY,217,217,217,217,1736,1736,1736,1736
+LABVISC,70,7,14,"VISCOSITY, SERUM",HC SRM VSCSTY,217,217,217,217,3038,3038,3038,3038
+LABVITK,31,1,2,"VITAMIN K1, SERUM",HC 6959 VITAMIN KI,74,74,74,74,148,148,148,148
+LABVITK,31,3,2,"VITAMIN K1, SERUM",HC 6959 VITAMIN KI,74,74,74,74,148,148,148,148
+LABVITK,31,7,2,"VITAMIN K1, SERUM",HC 6959 VITAMIN KI,74,74,74,74,148,148,148,148
+LABVOLTL,4886,1,291,VOLATILE SCREEN,HC ALCOHOL,399,399,399,399,116109,116109,116109,116109
+LABVOLTL,4886,3,312,VOLATILE SCREEN,HC ALCOHOL,399,399,399,399,124488,124488,124488,124488
+LABVOLTL,4886,7,340,VOLATILE SCREEN,HC ALCOHOL,399,399,399,399,135660,135660,135660,135660
+LABVP3,1043,1,35,COMP HEPATITIS PANEL,HC HEPAT BS AG-EIA,189,231,206,197,6615,8085,7210,6895
+LABVP3,1043,1,35,COMP HEPATITIS PANEL,HC HCVA HEPATITIS C AB,189,231,206,197,6615,8085,7210,6895
+LABVP3,1043,1,35,COMP HEPATITIS PANEL,HC HEP B CR TOTAL AB,189,231,206,197,6615,8085,7210,6895
+LABVP3,1043,1,35,COMP HEPATITIS PANEL,HC HAV AB TOTAL(HATA),189,231,206,197,6615,8085,7210,6895
+LABVP3,1043,1,35,COMP HEPATITIS PANEL,HC HEPAT BS AB-EIA,189,231,206,197,6615,8085,7210,6895
+LABVP3,1043,3,56,COMP HEPATITIS PANEL,HC HEPAT BS AG-EIA,189,231,206,197,10584,12936,11536,11032
+LABVP3,1043,3,56,COMP HEPATITIS PANEL,HC HCVA HEPATITIS C AB,189,231,206,197,10584,12936,11536,11032
+LABVP3,1043,3,56,COMP HEPATITIS PANEL,HC HEP B CR TOTAL AB,189,231,206,197,10584,12936,11536,11032
+LABVP3,1043,3,56,COMP HEPATITIS PANEL,HC HAV AB TOTAL(HATA),189,231,206,197,10584,12936,11536,11032
+LABVP3,1043,3,56,COMP HEPATITIS PANEL,HC HEPAT BS AB-EIA,189,231,206,197,10584,12936,11536,11032
+LABVP3,1043,7,58,COMP HEPATITIS PANEL,HC HEPAT BS AG-EIA,189,231,206,197,10962,13398,11948,11426
+LABVP3,1043,7,58,COMP HEPATITIS PANEL,HC HCVA HEPATITIS C AB,189,231,206,197,10962,13398,11948,11426
+LABVP3,1043,7,58,COMP HEPATITIS PANEL,HC HEP B CR TOTAL AB,189,231,206,197,10962,13398,11948,11426
+LABVP3,1043,7,58,COMP HEPATITIS PANEL,HC HAV AB TOTAL(HATA),189,231,206,197,10962,13398,11948,11426
+LABVP3,1043,7,58,COMP HEPATITIS PANEL,HC HEPAT BS AB-EIA,189,231,206,197,10962,13398,11948,11426
+LABVP4,464,1,9,COMP HEPATITIS PANEL,HC HEPAT BS AG-EIA,189,231,206,197,1701,2079,1854,1773
+LABVP4,464,1,9,COMP HEPATITIS PANEL,HC HCVA HEPATITIS C AB,189,231,206,197,1701,2079,1854,1773
+LABVP4,464,1,9,COMP HEPATITIS PANEL,HC HEP B CR TOTAL AB,189,231,206,197,1701,2079,1854,1773
+LABVP4,464,1,9,COMP HEPATITIS PANEL,HC HAV AB TOTAL(HATA),189,231,206,197,1701,2079,1854,1773
+LABVP4,464,1,9,COMP HEPATITIS PANEL,HC HBSQT HEP B SURF AB QUANT,189,231,206,197,1701,2079,1854,1773
+LABVP4,464,3,12,COMP HEPATITIS PANEL,HC HEPAT BS AG-EIA,189,231,206,197,2268,2772,2472,2364
+LABVP4,464,3,12,COMP HEPATITIS PANEL,HC HCVA HEPATITIS C AB,189,231,206,197,2268,2772,2472,2364
+LABVP4,464,3,12,COMP HEPATITIS PANEL,HC HEP B CR TOTAL AB,189,231,206,197,2268,2772,2472,2364
+LABVP4,464,3,12,COMP HEPATITIS PANEL,HC HAV AB TOTAL(HATA),189,231,206,197,2268,2772,2472,2364
+LABVP4,464,3,12,COMP HEPATITIS PANEL,HC HBSQT HEP B SURF AB QUANT,189,231,206,197,2268,2772,2472,2364
+LABVP4,464,7,13,COMP HEPATITIS PANEL,HC HEPAT BS AG-EIA,189,231,206,197,2457,3003,2678,2561
+LABVP4,464,7,13,COMP HEPATITIS PANEL,HC HCVA HEPATITIS C AB,189,231,206,197,2457,3003,2678,2561
+LABVP4,464,7,13,COMP HEPATITIS PANEL,HC HEP B CR TOTAL AB,189,231,206,197,2457,3003,2678,2561
+LABVP4,464,7,13,COMP HEPATITIS PANEL,HC HAV AB TOTAL(HATA),189,231,206,197,2457,3003,2678,2561
+LABVP4,464,7,13,COMP HEPATITIS PANEL,HC HBSQT HEP B SURF AB QUANT,189,231,206,197,2457,3003,2678,2561
+LABVPAL,2099,1,411,VALPROIC ACID,HC VALPROATE LEVEL,391,391,391,391,160701,160701,160701,160701
+LABVPAL,2099,3,835,VALPROIC ACID,HC VALPROATE LEVEL,391,391,391,391,326485,326485,326485,326485
+LABVPAL,2099,7,1068,VALPROIC ACID,HC VALPROATE LEVEL,391,391,391,391,417588,417588,417588,417588
+LABVSTLC,201,1,4,Viral Stool Culture,HC VIRUS ISOLATN CULTR,484,484,484,484,1936,1936,1936,1936
+LABVSTLC,201,3,9,Viral Stool Culture,HC VIRUS ISOLATN CULTR,484,484,484,484,4356,4356,4356,4356
+LABVSTLC,201,7,14,Viral Stool Culture,HC VIRUS ISOLATN CULTR,484,484,484,484,6776,6776,6776,6776
+LABVWAG,89,1,12,Von Willebrand Antigen,HC VW FACTOR ANTIGEN,328,328,328,328,3936,3936,3936,3936
+LABVWAG,89,3,20,Von Willebrand Antigen,HC VW FACTOR ANTIGEN,328,328,328,328,6560,6560,6560,6560
+LABVWAG,89,7,22,Von Willebrand Antigen,HC VW FACTOR ANTIGEN,328,328,328,328,7216,7216,7216,7216
+LABVWSCN,71,1,2,VON WILLEBRAND SCN,HC FACTOR VIII ASSAY,328,496,399.6666667,375,656,992,799.3333333,750
+LABVWSCN,71,1,2,VON WILLEBRAND SCN,HC RISTOCETIN COFACTOR,328,496,399.6666667,375,656,992,799.3333333,750
+LABVWSCN,71,1,2,VON WILLEBRAND SCN,HC VW FACTOR ANTIGEN,328,496,399.6666667,375,656,992,799.3333333,750
+LABVWSCN,71,3,4,VON WILLEBRAND SCN,HC FACTOR VIII ASSAY,328,496,399.6666667,375,1312,1984,1598.666667,1500
+LABVWSCN,71,3,4,VON WILLEBRAND SCN,HC RISTOCETIN COFACTOR,328,496,399.6666667,375,1312,1984,1598.666667,1500
+LABVWSCN,71,3,4,VON WILLEBRAND SCN,HC VW FACTOR ANTIGEN,328,496,399.6666667,375,1312,1984,1598.666667,1500
+LABVWSCN,71,7,5,VON WILLEBRAND SCN,HC FACTOR VIII ASSAY,328,496,399.6666667,375,1640,2480,1998.333333,1875
+LABVWSCN,71,7,5,VON WILLEBRAND SCN,HC RISTOCETIN COFACTOR,328,496,399.6666667,375,1640,2480,1998.333333,1875
+LABVWSCN,71,7,5,VON WILLEBRAND SCN,HC VW FACTOR ANTIGEN,328,496,399.6666667,375,1640,2480,1998.333333,1875
+LABVZAB,386,1,2,Varicella Zoster AB IgG,HC VZ VIRUS AB IGG,251,251,251,251,502,502,502,502
+LABVZAB,386,3,2,Varicella Zoster AB IgG,HC VZ VIRUS AB IGG,251,251,251,251,502,502,502,502
+LABVZAB,386,7,2,Varicella Zoster AB IgG,HC VZ VIRUS AB IGG,251,251,251,251,502,502,502,502
+LABVZPAN,33,3,1,VZV IGG IGM PANEL,HC VZV IGG & IGM,251,251,251,251,251,251,251,251
+LABVZPAN,33,7,1,VZV IGG IGM PANEL,HC VZV IGG & IGM,251,251,251,251,251,251,251,251
+LABVZVCSF,125,1,2,"VZV DNA PCR QUAL, CSF",HC VZVCSF IAD NA APT EA QL CSF,388,388,388,388,776,776,776,776
+LABVZVCSF,125,3,5,"VZV DNA PCR QUAL, CSF",HC VZVCSF IAD NA APT EA QL CSF,388,388,388,388,1940,1940,1940,1940
+LABVZVCSF,125,7,5,"VZV DNA PCR QUAL, CSF",HC VZVCSF IAD NA APT EA QL CSF,388,388,388,388,1940,1940,1940,1940
+LABVZVIGG,331,1,1,VZV IgG,HC VZ VIRUS AB IGG,251,251,251,251,251,251,251,251
+LABVZVIGG,331,3,2,VZV IgG,HC VZ VIRUS AB IGG,251,251,251,251,502,502,502,502
+LABVZVIGG,331,7,2,VZV IgG,HC VZ VIRUS AB IGG,251,251,251,251,502,502,502,502
+LABVZVQL,66,1,4,"VZV DNA PCR QUAL, NON LESION",HC VZV DNA PCR WITH & W/O LES,388,388,388,388,1552,1552,1552,1552
+LABVZVQL,66,3,7,"VZV DNA PCR QUAL, NON LESION",HC VZV DNA PCR WITH & W/O LES,388,388,388,388,2716,2716,2716,2716
+LABVZVQL,66,7,12,"VZV DNA PCR QUAL, NON LESION",HC VZV DNA PCR WITH & W/O LES,388,388,388,388,4656,4656,4656,4656
+LABVZVQLP,134,1,6,"VZV DNA PCR QUAL, PLASMA",HC VZVQLP IAD NA APT EA QUAL,388,388,388,388,2328,2328,2328,2328
+LABVZVQLP,134,3,11,"VZV DNA PCR QUAL, PLASMA",HC VZVQLP IAD NA APT EA QUAL,388,388,388,388,4268,4268,4268,4268
+LABVZVQLP,134,7,14,"VZV DNA PCR QUAL, PLASMA",HC VZVQLP IAD NA APT EA QUAL,388,388,388,388,5432,5432,5432,5432
+LABWDC,2230,1,173,Wound Culture,HC WOUND CLT,302,302,302,302,52246,52246,52246,52246
+LABWDC,2230,3,254,Wound Culture,HC WOUND CLT,302,302,302,302,76708,76708,76708,76708
+LABWDC,2230,7,300,Wound Culture,HC WOUND CLT,302,302,302,302,90600,90600,90600,90600
+LABWDCG,1009,1,113,Wound Culture/GS,HC GRAM STAIN,150,302,226,226,16950,34126,25538,25538
+LABWDCG,1009,1,113,Wound Culture/GS,HC WOUND CLT,150,302,226,226,16950,34126,25538,25538
+LABWDCG,1009,3,142,Wound Culture/GS,HC GRAM STAIN,150,302,226,226,21300,42884,32092,32092
+LABWDCG,1009,3,142,Wound Culture/GS,HC WOUND CLT,150,302,226,226,21300,42884,32092,32092
+LABWDCG,1009,7,162,Wound Culture/GS,HC GRAM STAIN,150,302,226,226,24300,48924,36612,36612
+LABWDCG,1009,7,162,Wound Culture/GS,HC WOUND CLT,150,302,226,226,24300,48924,36612,36612
+LABWDCS,1500,1,165,Wound-Deep/Surg/Asp/GS,HC GRAM STAIN,150,302,226,226,24750,49830,37290,37290
+LABWDCS,1500,1,165,Wound-Deep/Surg/Asp/GS,HC WOUND DEEP SURG/ASP CULT,150,302,226,226,24750,49830,37290,37290
+LABWDCS,1500,3,214,Wound-Deep/Surg/Asp/GS,HC GRAM STAIN,150,302,226,226,32100,64628,48364,48364
+LABWDCS,1500,3,214,Wound-Deep/Surg/Asp/GS,HC WOUND DEEP SURG/ASP CULT,150,302,226,226,32100,64628,48364,48364
+LABWDCS,1500,7,252,Wound-Deep/Surg/Asp/GS,HC GRAM STAIN,150,302,226,226,37800,76104,56952,56952
+LABWDCS,1500,7,252,Wound-Deep/Surg/Asp/GS,HC WOUND DEEP SURG/ASP CULT,150,302,226,226,37800,76104,56952,56952
+LABWNVAB,287,1,4,"WEST NILE VIRUS AB PANEL,CSF",HC 6942 WNVAB WESTNILE IGM CSF,49,58,53.5,53.5,196,232,214,214
+LABWNVAB,287,1,4,"WEST NILE VIRUS AB PANEL,CSF",HC 6942 WNVAB WESTNILE IGG CSF,49,58,53.5,53.5,196,232,214,214
+LABWNVAB,287,3,8,"WEST NILE VIRUS AB PANEL,CSF",HC 6942 WNVAB WESTNILE IGM CSF,49,58,53.5,53.5,392,464,428,428
+LABWNVAB,287,3,8,"WEST NILE VIRUS AB PANEL,CSF",HC 6942 WNVAB WESTNILE IGG CSF,49,58,53.5,53.5,392,464,428,428
+LABWNVAB,287,7,13,"WEST NILE VIRUS AB PANEL,CSF",HC 6942 WNVAB WESTNILE IGM CSF,49,58,53.5,53.5,637,754,695.5,695.5
+LABWNVAB,287,7,13,"WEST NILE VIRUS AB PANEL,CSF",HC 6942 WNVAB WESTNILE IGG CSF,49,58,53.5,53.5,637,754,695.5,695.5
+LABWNVRNA,51,1,6,"WEST NILE VIRUS RNA, RT-PCR",HC 6942 WESTNILE VIRUS RNA RT-PCR,224,224,224,224,1344,1344,1344,1344
+LABWNVRNA,51,3,8,"WEST NILE VIRUS RNA, RT-PCR",HC 6942 WESTNILE VIRUS RNA RT-PCR,224,224,224,224,1792,1792,1792,1792
+LABWNVRNA,51,7,9,"WEST NILE VIRUS RNA, RT-PCR",HC 6942 WESTNILE VIRUS RNA RT-PCR,224,224,224,224,2016,2016,2016,2016
+LABXHIA,13,1,1,HUMAN INSULIN ANTIBODY,HC 7031 INSULIN ANTIBODY,38,38,38,38,38,38,38,38
+LABXHIA,13,3,1,HUMAN INSULIN ANTIBODY,HC 7031 INSULIN ANTIBODY,38,38,38,38,38,38,38,38
+LABXHIA,13,7,1,HUMAN INSULIN ANTIBODY,HC 7031 INSULIN ANTIBODY,38,38,38,38,38,38,38,38
+LABXICA51,27,1,3,ICA-512 AUTOANTIBODIES,HC 7031 ICA-512 AUTOAB,40,40,40,40,120,120,120,120
+LABXICA51,27,3,4,ICA-512 AUTOANTIBODIES,HC 7031 ICA-512 AUTOAB,40,40,40,40,160,160,160,160
+LABXICA51,27,7,5,ICA-512 AUTOANTIBODIES,HC 7031 ICA-512 AUTOAB,40,40,40,40,200,200,200,200
+LABXIGF1,16,1,1,IGF-1,HC 7031 IGF-I/SOMATOMEDIN-C,33,33,33,33,33,33,33,33
+LABXIGF1,16,3,2,IGF-1,HC 7031 IGF-I/SOMATOMEDIN-C,33,33,33,33,66,66,66,66
+LABXIGF1,16,7,2,IGF-1,HC 7031 IGF-I/SOMATOMEDIN-C,33,33,33,33,66,66,66,66
+LABY25VD,89,1,3,"VITAMIN D, 25-HYDROXYVITAMIN",HC 6956 VIT D 25 HYDROXY,32,32,32,32,96,96,96,96
+LABY25VD,89,3,3,"VITAMIN D, 25-HYDROXYVITAMIN",HC 6956 VIT D 25 HYDROXY,32,32,32,32,96,96,96,96
+LABY25VD,89,7,6,"VITAMIN D, 25-HYDROXYVITAMIN",HC 6956 VIT D 25 HYDROXY,32,32,32,32,192,192,192,192
+LABYA1APH,71,1,1,ALPHA-1-ANTITRYPSIN PHENOTYPE,HC 6956 YA1APH A1A TOTAL,11,12,11.5,11.5,11,12,11.5,11.5
+LABYA1APH,71,1,1,ALPHA-1-ANTITRYPSIN PHENOTYPE,HC 6956 YA1APH A1A PHENOTYPE,11,12,11.5,11.5,11,12,11.5,11.5
+LABYA1APH,71,3,1,ALPHA-1-ANTITRYPSIN PHENOTYPE,HC 6956 YA1APH A1A TOTAL,11,12,11.5,11.5,11,12,11.5,11.5
+LABYA1APH,71,3,1,ALPHA-1-ANTITRYPSIN PHENOTYPE,HC 6956 YA1APH A1A PHENOTYPE,11,12,11.5,11.5,11,12,11.5,11.5
+LABYA1APH,71,7,1,ALPHA-1-ANTITRYPSIN PHENOTYPE,HC 6956 YA1APH A1A TOTAL,11,12,11.5,11.5,11,12,11.5,11.5
+LABYA1APH,71,7,1,ALPHA-1-ANTITRYPSIN PHENOTYPE,HC 6956 YA1APH A1A PHENOTYPE,11,12,11.5,11.5,11,12,11.5,11.5
+LABYA1AS,402,1,14,ALPHA-1-ANTITRYPSIN,HC 6956 ALPHA-1-ANTITRYPSIN,12,12,12,12,168,168,168,168
+LABYA1AS,402,3,26,ALPHA-1-ANTITRYPSIN,HC 6956 ALPHA-1-ANTITRYPSIN,12,12,12,12,312,312,312,312
+LABYA1AS,402,7,29,ALPHA-1-ANTITRYPSIN,HC 6956 ALPHA-1-ANTITRYPSIN,12,12,12,12,348,348,348,348
+LABYAHA,49,1,3,HISTONE AUTOANTIBODIES,HC 6956 HISTONE AUTOANTIBODIES,23,23,23,23,69,69,69,69
+LABYAHA,49,3,5,HISTONE AUTOANTIBODIES,HC 6956 HISTONE AUTOANTIBODIES,23,23,23,23,115,115,115,115
+LABYAHA,49,7,6,HISTONE AUTOANTIBODIES,HC 6956 HISTONE AUTOANTIBODIES,23,23,23,23,138,138,138,138
+LABYAMISO,84,1,5,"AMYLASE, PANCREATIC","HC 6956 AMYLASE, PANCREATIC",41,41,41,41,205,205,205,205
+LABYAMISO,84,3,13,"AMYLASE, PANCREATIC","HC 6956 AMYLASE, PANCREATIC",41,41,41,41,533,533,533,533
+LABYAMISO,84,7,13,"AMYLASE, PANCREATIC","HC 6956 AMYLASE, PANCREATIC",41,41,41,41,533,533,533,533
+LABYAMNOR,20,3,1,AMITRIPTYLINE & NORTRIPTYLINE,HC 6956 YAMNOR AMITRIPTYLINE,9,13,11,11,9,13,11,11
+LABYAMNOR,20,3,1,AMITRIPTYLINE & NORTRIPTYLINE,HC 6956 NORTRIPTYLINE,9,13,11,11,9,13,11,11
+LABYAMNOR,20,7,1,AMITRIPTYLINE & NORTRIPTYLINE,HC 6956 YAMNOR AMITRIPTYLINE,9,13,11,11,9,13,11,11
+LABYAMNOR,20,7,1,AMITRIPTYLINE & NORTRIPTYLINE,HC 6956 NORTRIPTYLINE,9,13,11,11,9,13,11,11
+LABYANRNP,79,1,2,"RNP AB, IGG",HC 6956 U1RNP AUTO AB,17,17,17,17,34,34,34,34
+LABYANRNP,79,3,2,"RNP AB, IGG",HC 6956 U1RNP AUTO AB,17,17,17,17,34,34,34,34
+LABYANRNP,79,7,3,"RNP AB, IGG",HC 6956 U1RNP AUTO AB,17,17,17,17,51,51,51,51
+LABYANTIN,8,7,1,INSULIN ABS                             NONORDERABLE,HC 6956 INSULIN ANTIBODIES,13,13,13,13,13,13,13,13
+LABYANTSM,38,1,1,"SM AB, IGG",HC 6956 SM AUTO AB,12,12,12,12,12,12,12,12
+LABYANTSM,38,3,2,"SM AB, IGG",HC 6956 SM AUTO AB,12,12,12,12,24,24,24,24
+LABYANTSM,38,7,2,"SM AB, IGG",HC 6956 SM AUTO AB,12,12,12,12,24,24,24,24
+LABYASU,26,1,1,ALPHA-SUBUNIT PITUITARY MARKER,HC 6956 ALPHA-SUBUNIT OF PGH,33,33,33,33,33,33,33,33
+LABYASU,26,3,1,ALPHA-SUBUNIT PITUITARY MARKER,HC 6956 ALPHA-SUBUNIT OF PGH,33,33,33,33,33,33,33,33
+LABYASU,26,7,1,ALPHA-SUBUNIT PITUITARY MARKER,HC 6956 ALPHA-SUBUNIT OF PGH,33,33,33,33,33,33,33,33
+LABYAT3AG,28,1,1,ANTITHROMBIN AG,HC 6956 ANTITHROMBIN III AG,17,17,17,17,17,17,17,17
+LABYAT3AG,28,3,2,ANTITHROMBIN AG,HC 6956 ANTITHROMBIN III AG,17,17,17,17,34,34,34,34
+LABYAT3AG,28,7,3,ANTITHROMBIN AG,HC 6956 ANTITHROMBIN III AG,17,17,17,17,51,51,51,51
+LABYAVH,24,1,1,ARGININE VASOPRESSIN,HC 6956 ARGININE VASOPRESSIN,24,24,24,24,24,24,24,24
+LABYAVH,24,3,1,ARGININE VASOPRESSIN,HC 6956 ARGININE VASOPRESSIN,24,24,24,24,24,24,24,24
+LABYAVH,24,7,3,ARGININE VASOPRESSIN,HC 6956 ARGININE VASOPRESSIN,24,24,24,24,72,72,72,72
+LABYBLAS,69,1,1,BLASTOMYCES AB,HC 6956 BLASTOMYCES AB,22,22,22,22,22,22,22,22
+LABYBLAS,69,3,2,BLASTOMYCES AB,HC 6956 BLASTOMYCES AB,22,22,22,22,44,44,44,44
+LABYBLAS,69,7,2,BLASTOMYCES AB,HC 6956 BLASTOMYCES AB,22,22,22,22,44,44,44,44
+LABYBOH,314,1,78,BETA-HYDROXYBUTYRATE,HC 6956 BETA-HYDROXYBUTYRATE,27,27,27,27,2106,2106,2106,2106
+LABYBOH,314,3,96,BETA-HYDROXYBUTYRATE,HC 6956 BETA-HYDROXYBUTYRATE,27,27,27,27,2592,2592,2592,2592
+LABYBOH,314,7,101,BETA-HYDROXYBUTYRATE,HC 6956 BETA-HYDROXYBUTYRATE,27,27,27,27,2727,2727,2727,2727
+LABYBRUC,147,1,6,"BRUCELLA AB, IgG & IgM","HC 6956 BRUCELLA AB, IGG/M",26,26,26,26,156,156,156,156
+LABYBRUC,147,3,9,"BRUCELLA AB, IgG & IgM","HC 6956 BRUCELLA AB, IGG/M",26,26,26,26,234,234,234,234
+LABYBRUC,147,7,11,"BRUCELLA AB, IgG & IgM","HC 6956 BRUCELLA AB, IGG/M",26,26,26,26,286,286,286,286
+LABYBSAP,105,1,4,BONE ALK. PHOSPHATASE,"HC 6956 BONE ALK PHOSP,SERUM",52,52,52,52,208,208,208,208
+LABYBSAP,105,3,6,BONE ALK. PHOSPHATASE,"HC 6956 BONE ALK PHOSP,SERUM",52,52,52,52,312,312,312,312
+LABYBSAP,105,7,6,BONE ALK. PHOSPHATASE,"HC 6956 BONE ALK PHOSP,SERUM",52,52,52,52,312,312,312,312
+LABYC1IN,22,1,1,C1 ESTERASE INHIBITOR AG,"HC 6956 C1 ESTERASE INHBTR,PLSMA",20,20,20,20,20,20,20,20
+LABYC1IN,22,3,1,C1 ESTERASE INHIBITOR AG,"HC 6956 C1 ESTERASE INHBTR,PLSMA",20,20,20,20,20,20,20,20
+LABYC1IN,22,7,1,C1 ESTERASE INHIBITOR AG,"HC 6956 C1 ESTERASE INHBTR,PLSMA",20,20,20,20,20,20,20,20
+LABYCCPIG,233,1,6,CYCLIC CITRULLIN PEPTIDE AB,HC 6956 CYCLIC CITRULL PEPT,14,14,14,14,84,84,84,84
+LABYCCPIG,233,3,6,CYCLIC CITRULLIN PEPTIDE AB,HC 6956 CYCLIC CITRULL PEPT,14,14,14,14,84,84,84,84
+LABYCCPIG,233,7,6,CYCLIC CITRULLIN PEPTIDE AB,HC 6956 CYCLIC CITRULL PEPT,14,14,14,14,84,84,84,84
+LABYCENT,87,1,2,"CENTROMERE ABS, IGG","HC 6956 CENTROMERE AB,SERUM",18,18,18,18,36,36,36,36
+LABYCENT,87,3,3,"CENTROMERE ABS, IGG","HC 6956 CENTROMERE AB,SERUM",18,18,18,18,54,54,54,54
+LABYCENT,87,7,3,"CENTROMERE ABS, IGG","HC 6956 CENTROMERE AB,SERUM",18,18,18,18,54,54,54,54
+LABYCERU,566,1,20,"CERULOPLASMIN, SERUM                          MAYO","HC 6956 CERULOPLASMIN, SERUM",10,10,10,10,200,200,200,200
+LABYCERU,566,3,26,"CERULOPLASMIN, SERUM                          MAYO","HC 6956 CERULOPLASMIN, SERUM",10,10,10,10,260,260,260,260
+LABYCERU,566,7,26,"CERULOPLASMIN, SERUM                          MAYO","HC 6956 CERULOPLASMIN, SERUM",10,10,10,10,260,260,260,260
+LABYCH50,154,1,3,"COMPLEMENT, TOTAL","HC 6956 COMPLEMENT, TOTAL, SERUM",20,20,20,20,60,60,60,60
+LABYCH50,154,3,7,"COMPLEMENT, TOTAL","HC 6956 COMPLEMENT, TOTAL, SERUM",20,20,20,20,140,140,140,140
+LABYCH50,154,7,8,"COMPLEMENT, TOTAL","HC 6956 COMPLEMENT, TOTAL, SERUM",20,20,20,20,160,160,160,160
+LABYCHAGA,53,1,5,"TRYPANOSOMA CRUZI AB, IGG","HC 6956 TRYPAN CRUZI AB, IGG",43,43,43,43,215,215,215,215
+LABYCHAGA,53,3,5,"TRYPANOSOMA CRUZI AB, IGG","HC 6956 TRYPAN CRUZI AB, IGG",43,43,43,43,215,215,215,215
+LABYCHAGA,53,7,5,"TRYPANOSOMA CRUZI AB, IGG","HC 6956 TRYPAN CRUZI AB, IGG",43,43,43,43,215,215,215,215
+LABYCHRA,129,1,5,CHROMOGRANIN A,HC 6956 CHROMOGRANIN A,14,14,14,14,70,70,70,70
+LABYCHRA,129,3,10,CHROMOGRANIN A,HC 6956 CHROMOGRANIN A,14,14,14,14,140,140,140,140
+LABYCHRA,129,7,11,CHROMOGRANIN A,HC 6956 CHROMOGRANIN A,14,14,14,14,154,154,154,154
+LABYCHROM,31,1,2,CHROMIUM,HC 6956 CHROMIUM,24,24,24,24,48,48,48,48
+LABYCHROM,31,3,2,CHROMIUM,HC 6956 CHROMIUM,24,24,24,24,48,48,48,48
+LABYCHROM,31,7,3,CHROMIUM,HC 6956 CHROMIUM,24,24,24,24,72,72,72,72
+LABYCLAMP,46,1,1,CHLAMYDIA SEROLOGY,HC 6956 CHLAMYDIA,6,6,6,6,6,6,6,6
+LABYCLAMP,46,1,1,CHLAMYDIA SEROLOGY,HC 6956 CHLAMYDIA IGM,6,6,6,6,6,6,6,6
+LABYCLAMP,46,3,6,CHLAMYDIA SEROLOGY,HC 6956 CHLAMYDIA,6,6,6,6,36,36,36,36
+LABYCLAMP,46,3,6,CHLAMYDIA SEROLOGY,HC 6956 CHLAMYDIA IGM,6,6,6,6,36,36,36,36
+LABYCLAMP,46,7,6,CHLAMYDIA SEROLOGY,HC 6956 CHLAMYDIA,6,6,6,6,36,36,36,36
+LABYCLAMP,46,7,6,CHLAMYDIA SEROLOGY,HC 6956 CHLAMYDIA IGM,6,6,6,6,36,36,36,36
+LABYCLON,36,1,1,CLONAZEPAM,HC 6956 CLONAZEPAM,24,24,24,24,24,24,24,24
+LABYCLON,36,3,1,CLONAZEPAM,HC 6956 CLONAZEPAM,24,24,24,24,24,24,24,24
+LABYCLON,36,7,2,CLONAZEPAM,HC 6956 CLONAZEPAM,24,24,24,24,48,48,48,48
+LABYCLOZ,51,1,3,CLOZAPINE,HC 6956CLOZAPINE SERUM,12,12,12,12,36,36,36,36
+LABYCLOZ,51,3,5,CLOZAPINE,HC 6956CLOZAPINE SERUM,12,12,12,12,60,60,60,60
+LABYCLOZ,51,7,10,CLOZAPINE,HC 6956CLOZAPINE SERUM,12,12,12,12,120,120,120,120
+LABYCOPPR,264,1,8,COPPER,HC 6956 YCOPPR COPPER,24,24,24,24,192,192,192,192
+LABYCOPPR,264,3,20,COPPER,HC 6956 YCOPPR COPPER,24,24,24,24,480,480,480,480
+LABYCOPPR,264,7,25,COPPER,HC 6956 YCOPPR COPPER,24,24,24,24,600,600,600,600
+LABYCP,291,1,138,"C-PEPTIDE, SERUM                         MAYO",HC 6956 C PEPTIDE,14,14,14,14,1932,1932,1932,1932
+LABYCP,291,3,151,"C-PEPTIDE, SERUM                         MAYO",HC 6956 C PEPTIDE,14,14,14,14,2114,2114,2114,2114
+LABYCP,291,7,156,"C-PEPTIDE, SERUM                         MAYO",HC 6956 C PEPTIDE,14,14,14,14,2184,2184,2184,2184
+LABYCYANI,50,1,7,CYANIDE,"HC 6956 CYANIDE, BLOOD",45,45,45,45,315,315,315,315
+LABYCYANI,50,3,7,CYANIDE,"HC 6956 CYANIDE, BLOOD",45,45,45,45,315,315,315,315
+LABYCYANI,50,7,8,CYANIDE,"HC 6956 CYANIDE, BLOOD",45,45,45,45,360,360,360,360
+LABYDESIP,30,1,1,DESIPRAMINE,HC 6956 DESIPRAMINE,27,27,27,27,27,27,27,27
+LABYDESIP,30,3,3,DESIPRAMINE,HC 6956 DESIPRAMINE,27,27,27,27,81,81,81,81
+LABYDESIP,30,7,6,DESIPRAMINE,HC 6956 DESIPRAMINE,27,27,27,27,162,162,162,162
+LABYDHEA,11,3,1,DEHYDROEPIANDROSTERONE (DHEA),HC 6956 YDHEA DHEA,22,22,22,22,22,22,22,22
+LABYDHEA,11,7,1,DEHYDROEPIANDROSTERONE (DHEA),HC 6956 YDHEA DHEA,22,22,22,22,22,22,22,22
+LABYDIGT,71,1,19,DIGITOXIN,HC 6956 DIGITOXIN,66,66,66,66,1254,1254,1254,1254
+LABYDIGT,71,3,25,DIGITOXIN,HC 6956 DIGITOXIN,66,66,66,66,1650,1650,1650,1650
+LABYDIGT,71,7,27,DIGITOXIN,HC 6956 DIGITOXIN,66,66,66,66,1782,1782,1782,1782
+LABYDROTC,15,3,3,"DRUG SCRN, PRESCRIP/OTC, URINE",HC 6956 DRUG SCREEN PRESC/OTC,55,55,55,55,165,165,165,165
+LABYDROTC,15,7,3,"DRUG SCRN, PRESCRIP/OTC, URINE",HC 6956 DRUG SCREEN PRESC/OTC,55,55,55,55,165,165,165,165
+LABYEB,15,3,1,"EBV AB PROFILE, SERUM","HC 6956 EBV, EBNA",4,7,5.666666667,6,4,7,5.666666667,6
+LABYEB,15,3,1,"EBV AB PROFILE, SERUM","HC 6956 EBV, VCA",4,7,5.666666667,6,4,7,5.666666667,6
+LABYEB,15,3,1,"EBV AB PROFILE, SERUM",HC 6956 EHRLICHIA,4,7,5.666666667,6,4,7,5.666666667,6
+LABYEB,15,7,1,"EBV AB PROFILE, SERUM","HC 6956 EBV, EBNA",4,7,5.666666667,6,4,7,5.666666667,6
+LABYEB,15,7,1,"EBV AB PROFILE, SERUM","HC 6956 EBV, VCA",4,7,5.666666667,6,4,7,5.666666667,6
+LABYEB,15,7,1,"EBV AB PROFILE, SERUM",HC 6956 EHRLICHIA,4,7,5.666666667,6,4,7,5.666666667,6
+LABYEBPCR,194,1,3,"EPSTEIN BARR VIRUS PCR, FLUIDS",HC 6956 EBV BY PCR,85,85,85,85,255,255,255,255
+LABYEBPCR,194,3,7,"EPSTEIN BARR VIRUS PCR, FLUIDS",HC 6956 EBV BY PCR,85,85,85,85,595,595,595,595
+LABYEBPCR,194,7,12,"EPSTEIN BARR VIRUS PCR, FLUIDS",HC 6956 EBV BY PCR,85,85,85,85,1020,1020,1020,1020
+LABYENPCR,142,1,3,ENTEROVIRUS PCR,"HC 6956 ENTEROVIRUS, PCR",62,62,62,62,186,186,186,186
+LABYENPCR,142,3,11,ENTEROVIRUS PCR,"HC 6956 ENTEROVIRUS, PCR",62,62,62,62,682,682,682,682
+LABYENPCR,142,7,14,ENTEROVIRUS PCR,"HC 6956 ENTEROVIRUS, PCR",62,62,62,62,868,868,868,868
+LABYENTE,37,7,1,YERSINIA ENTEROCOLITICA AB,HC 6942 YENTE YERSINIA AB IGG,47,47,47,47,47,47,47,47
+LABYENTE,37,7,1,YERSINIA ENTEROCOLITICA AB,HC 6942 YENTE YERSINIA AB IGA,47,47,47,47,47,47,47,47
+LABYERYTH,249,1,11,"Erythropoietin, Serum                 MAYO",HC 6956 YERYTH ERTHROPOIETIN,14,14,14,14,154,154,154,154
+LABYERYTH,249,3,15,"Erythropoietin, Serum                 MAYO",HC 6956 YERYTH ERTHROPOIETIN,14,14,14,14,210,210,210,210
+LABYERYTH,249,7,16,"Erythropoietin, Serum                 MAYO",HC 6956 YERYTH ERTHROPOIETIN,14,14,14,14,224,224,224,224
+LABYFACE,11,1,1,"FATTY ACID PROFILE, ESSENTIAL",HC 6956 FATTY ACID ESSENTIAL,71,71,71,71,71,71,71,71
+LABYFACE,11,3,2,"FATTY ACID PROFILE, ESSENTIAL",HC 6956 FATTY ACID ESSENTIAL,71,71,71,71,142,142,142,142
+LABYFACE,11,7,2,"FATTY ACID PROFILE, ESSENTIAL",HC 6956 FATTY ACID ESSENTIAL,71,71,71,71,142,142,142,142
+LABYFCAR,37,3,2,"CARBAMAZEPINE, FREE & TOTAL","HC 6956 CARBAMAZEPINE, TOT",31,34,32.5,32.5,62,68,65,65
+LABYFCAR,37,3,2,"CARBAMAZEPINE, FREE & TOTAL","HC 6956 CARBAMAZEPINE, FREE",31,34,32.5,32.5,62,68,65,65
+LABYFCAR,37,7,5,"CARBAMAZEPINE, FREE & TOTAL","HC 6956 CARBAMAZEPINE, TOT",31,34,32.5,32.5,155,170,162.5,162.5
+LABYFCAR,37,7,5,"CARBAMAZEPINE, FREE & TOTAL","HC 6956 CARBAMAZEPINE, FREE",31,34,32.5,32.5,155,170,162.5,162.5
+LABYFCL,7,7,1,"CHLORIDE, BODY FLUID","HC 6956 CHLORIDE, FLUID",27,27,27,27,27,27,27,27
+LABYFELBA,6,1,1,FELBAMATE,HC 6956 FELBAMATE,38,38,38,38,38,38,38,38
+LABYFELBA,6,3,1,FELBAMATE,HC 6956 FELBAMATE,38,38,38,38,38,38,38,38
+LABYFELBA,6,7,1,FELBAMATE,HC 6956 FELBAMATE,38,38,38,38,38,38,38,38
+LABYFFATQ,149,1,7,"FAT, FECES","HC 6956 FAT, FECES",44,44,44,44,308,308,308,308
+LABYFFATQ,149,3,15,"FAT, FECES","HC 6956 FAT, FECES",44,44,44,44,660,660,660,660
+LABYFFATQ,149,7,27,"FAT, FECES","HC 6956 FAT, FECES",44,44,44,44,1188,1188,1188,1188
+LABYFGL,313,1,12,"GLUCOSE, BODY FLUID                      NONORDERABLE","HC 6956 GLUCOSE, FLUID",34,34,34,34,408,408,408,408
+LABYFGL,313,3,23,"GLUCOSE, BODY FLUID                      NONORDERABLE","HC 6956 GLUCOSE, FLUID",34,34,34,34,782,782,782,782
+LABYFGL,313,7,37,"GLUCOSE, BODY FLUID                      NONORDERABLE","HC 6956 GLUCOSE, FLUID",34,34,34,34,1258,1258,1258,1258
+LABYFLUO,37,1,7,5-FLUCYTOSINE,HC 6956 5-FLUCYTOSINE,66,66,66,66,462,462,462,462
+LABYFLUO,37,3,10,5-FLUCYTOSINE,HC 6956 5-FLUCYTOSINE,66,66,66,66,660,660,660,660
+LABYFLUO,37,7,12,5-FLUCYTOSINE,HC 6956 5-FLUCYTOSINE,66,66,66,66,792,792,792,792
+LABYFLUZ,89,1,15,FLUCONAZOLE,HC 6956 FLUCONAZOLE,51,51,51,51,765,765,765,765
+LABYFLUZ,89,3,15,FLUCONAZOLE,HC 6956 FLUCONAZOLE,51,51,51,51,765,765,765,765
+LABYFLUZ,89,7,19,FLUCONAZOLE,HC 6956 FLUCONAZOLE,51,51,51,51,969,969,969,969
+LABYFPHEN,198,1,38,"PHENYTOIN, TOTAL & FREE",HC 6956 YFPHEN PHENYTOIN FREE,12,12,12,12,456,456,456,456
+LABYFPHEN,198,1,38,"PHENYTOIN, TOTAL & FREE",HC 6956 PHENYTOIN TOTAL,12,12,12,12,456,456,456,456
+LABYFPHEN,198,3,73,"PHENYTOIN, TOTAL & FREE",HC 6956 YFPHEN PHENYTOIN FREE,12,12,12,12,876,876,876,876
+LABYFPHEN,198,3,73,"PHENYTOIN, TOTAL & FREE",HC 6956 PHENYTOIN TOTAL,12,12,12,12,876,876,876,876
+LABYFPHEN,198,7,77,"PHENYTOIN, TOTAL & FREE",HC 6956 YFPHEN PHENYTOIN FREE,12,12,12,12,924,924,924,924
+LABYFPHEN,198,7,77,"PHENYTOIN, TOTAL & FREE",HC 6956 PHENYTOIN TOTAL,12,12,12,12,924,924,924,924
+LABYFRUCM,72,1,1,FRUCTOSAMINE,HC 6956 FRUCTOSAMINE,13,13,13,13,13,13,13,13
+LABYFRUCM,72,3,2,FRUCTOSAMINE,HC 6956 FRUCTOSAMINE,13,13,13,13,26,26,26,26
+LABYFRUCM,72,7,2,FRUCTOSAMINE,HC 6956 FRUCTOSAMINE,13,13,13,13,26,26,26,26
+LABYFT3,795,1,27,"T3, FREE","HC 6956 T3, FREE",4,4,4,4,108,108,108,108
+LABYFT3,795,3,42,"T3, FREE","HC 6956 T3, FREE",4,4,4,4,168,168,168,168
+LABYFT3,795,7,62,"T3, FREE","HC 6956 T3, FREE",4,4,4,4,248,248,248,248
+LABYFUN,27,1,3,"UREA NITROGEN, BODY FLUID","HC 6956 UREA NITROGEN, FLUID",30,30,30,30,90,90,90,90
+LABYFUN,27,3,4,"UREA NITROGEN, BODY FLUID","HC 6956 UREA NITROGEN, FLUID",30,30,30,30,120,120,120,120
+LABYFUN,27,7,5,"UREA NITROGEN, BODY FLUID","HC 6956 UREA NITROGEN, FLUID",30,30,30,30,150,150,150,150
+LABYFVPA,201,1,21,"VALPROIC ACID, FREE & TOTAL","HC 6956 VALPROIS ACID,T&F,PLSMA",52,52,52,52,1092,1092,1092,1092
+LABYFVPA,201,3,34,"VALPROIC ACID, FREE & TOTAL","HC 6956 VALPROIS ACID,T&F,PLSMA",52,52,52,52,1768,1768,1768,1768
+LABYFVPA,201,7,56,"VALPROIC ACID, FREE & TOTAL","HC 6956 VALPROIS ACID,T&F,PLSMA",52,52,52,52,2912,2912,2912,2912
+LABYGDA,5,3,1,"DRUG SCREEN, BODY FLUID              NON ORDERABLE 112310","HC 6956 DRUG SCREEN, FLUIDS",69,69,69,69,69,69,69,69
+LABYGDA,5,7,1,"DRUG SCREEN, BODY FLUID              NON ORDERABLE 112310","HC 6956 DRUG SCREEN, FLUIDS",69,69,69,69,69,69,69,69
+LABYGLUC,23,1,1,GLUCAGON,HC 6956 GLUCAGON,19,19,19,19,19,19,19,19
+LABYGLUC,23,3,1,GLUCAGON,HC 6956 GLUCAGON,19,19,19,19,19,19,19,19
+LABYGLUC,23,7,2,GLUCAGON,HC 6956 GLUCAGON,19,19,19,19,38,38,38,38
+LABYHABQ,284,1,6,"HEP B SURFACE AB, QUAL/QUANT",HC 6956 HEP B SURFACE AB,10,10,10,10,60,60,60,60
+LABYHABQ,284,3,13,"HEP B SURFACE AB, QUAL/QUANT",HC 6956 HEP B SURFACE AB,10,10,10,10,130,130,130,130
+LABYHABQ,284,7,20,"HEP B SURFACE AB, QUAL/QUANT",HC 6956 HEP B SURFACE AB,10,10,10,10,200,200,200,200
+LABYHEMOC,82,1,1,HEMOCHROMATOSIS HFE GENE,HC 6956 HEMOCHROMA HFE GENE,119,119,119,119,119,119,119,119
+LABYHEMOC,82,3,1,HEMOCHROMATOSIS HFE GENE,HC 6956 HEMOCHROMA HFE GENE,119,119,119,119,119,119,119,119
+LABYHEMOC,82,7,2,HEMOCHROMATOSIS HFE GENE,HC 6956 HEMOCHROMA HFE GENE,119,119,119,119,238,238,238,238
+LABYHGBP,183,1,18,PLASMA HEMOGLOBIN,HC 6956 PLASMA HEMOGLOBIN,36,36,36,36,648,648,648,648
+LABYHGBP,183,3,39,PLASMA HEMOGLOBIN,HC 6956 PLASMA HEMOGLOBIN,36,36,36,36,1404,1404,1404,1404
+LABYHGBP,183,7,59,PLASMA HEMOGLOBIN,HC 6956 PLASMA HEMOGLOBIN,36,36,36,36,2124,2124,2124,2124
+LABYHIST,23,1,3,HISTOPLASMA AB SCREEN,HC 6956 HISTOPLASMA AB SCRN,20,20,20,20,60,60,60,60
+LABYHIST,23,3,3,HISTOPLASMA AB SCREEN,HC 6956 HISTOPLASMA AB SCRN,20,20,20,20,60,60,60,60
+LABYHIST,23,7,3,HISTOPLASMA AB SCREEN,HC 6956 HISTOPLASMA AB SCRN,20,20,20,20,60,60,60,60
+LABYHISTC,31,3,1,"HISTOPLASMA AG, CSF","HC 6956 HISTOPLASMA AG,CSF",109,109,109,109,109,109,109,109
+LABYHISTC,31,7,3,"HISTOPLASMA AG, CSF","HC 6956 HISTOPLASMA AG,CSF",109,109,109,109,327,327,327,327
+LABYHISTS,510,1,19,HISTOPLASMA AG,"HC 6956 HISTOPLASMA AG,SRM",109,109,109,109,2071,2071,2071,2071
+LABYHISTS,510,3,27,HISTOPLASMA AG,"HC 6956 HISTOPLASMA AG,SRM",109,109,109,109,2943,2943,2943,2943
+LABYHISTS,510,7,48,HISTOPLASMA AG,"HC 6956 HISTOPLASMA AG,SRM",109,109,109,109,5232,5232,5232,5232
+LABYHISTU,644,1,20,"HISTOPLASMA AG, URINE","HC 6956 HISTOPLASMA AG,U",109,109,109,109,2180,2180,2180,2180
+LABYHISTU,644,3,45,"HISTOPLASMA AG, URINE","HC 6956 HISTOPLASMA AG,U",109,109,109,109,4905,4905,4905,4905
+LABYHISTU,644,7,69,"HISTOPLASMA AG, URINE","HC 6956 HISTOPLASMA AG,U",109,109,109,109,7521,7521,7521,7521
+LABYHMPVP,13,1,1,HUMAN METAPNEUMOVIRUS RNA              NONORDERABLE,HC 6956 HUMAN METAPNEUMO RNA,247,247,247,247,247,247,247,247
+LABYHMPVP,13,3,1,HUMAN METAPNEUMOVIRUS RNA              NONORDERABLE,HC 6956 HUMAN METAPNEUMO RNA,247,247,247,247,247,247,247,247
+LABYHMPVP,13,7,1,HUMAN METAPNEUMOVIRUS RNA              NONORDERABLE,HC 6956 HUMAN METAPNEUMO RNA,247,247,247,247,247,247,247,247
+LABYHMS,62,1,2,HEAVY METALS SCREEN,"HC 6956 HEAVY METALS SCR,BL",7,14,10.5,10.5,14,28,21,21
+LABYHMS,62,1,2,HEAVY METALS SCREEN,HC 6956 LEAD,7,14,10.5,10.5,14,28,21,21
+LABYHMS,62,1,2,HEAVY METALS SCREEN,"HC 6956 MERCURY, QUANT",7,14,10.5,10.5,14,28,21,21
+LABYHMS,62,1,2,HEAVY METALS SCREEN,HC 6956 CADMIUM,7,14,10.5,10.5,14,28,21,21
+LABYHMS,62,3,5,HEAVY METALS SCREEN,"HC 6956 HEAVY METALS SCR,BL",7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,3,5,HEAVY METALS SCREEN,HC 6956 LEAD,7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,3,5,HEAVY METALS SCREEN,"HC 6956 MERCURY, QUANT",7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,3,5,HEAVY METALS SCREEN,HC 6956 CADMIUM,7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,7,5,HEAVY METALS SCREEN,"HC 6956 HEAVY METALS SCR,BL",7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,7,5,HEAVY METALS SCREEN,HC 6956 LEAD,7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,7,5,HEAVY METALS SCREEN,"HC 6956 MERCURY, QUANT",7,14,10.5,10.5,35,70,52.5,52.5
+LABYHMS,62,7,5,HEAVY METALS SCREEN,HC 6956 CADMIUM,7,14,10.5,10.5,35,70,52.5,52.5
+LABYIGF2,18,1,4,INSULIN-LIKE GROWTH FACTOR II,HC 6956 IGF II,59,59,59,59,236,236,236,236
+LABYIGF2,18,3,4,INSULIN-LIKE GROWTH FACTOR II,HC 6956 IGF II,59,59,59,59,236,236,236,236
+LABYIGF2,18,7,4,INSULIN-LIKE GROWTH FACTOR II,HC 6956 IGF II,59,59,59,59,236,236,236,236
+LABYIMIPR,6,3,1,IMIPRAMINE AND DESIPRAMINE,HC 6956 DESIPRAMINE,19,19,19,19,19,19,19,19
+LABYIMIPR,6,3,1,IMIPRAMINE AND DESIPRAMINE,HC 6956 IMIPRAMINE,19,19,19,19,19,19,19,19
+LABYIMIPR,6,7,3,IMIPRAMINE AND DESIPRAMINE,HC 6956 DESIPRAMINE,19,19,19,19,57,57,57,57
+LABYIMIPR,6,7,3,IMIPRAMINE AND DESIPRAMINE,HC 6956 IMIPRAMINE,19,19,19,19,57,57,57,57
+LABYINFAB,54,1,2,INFLUENZA AB PANEL                   NONORDERABLE,HC 6956 INFLUENZA AB PANEL,12,12,12,12,24,24,24,24
+LABYINFAB,54,3,2,INFLUENZA AB PANEL                   NONORDERABLE,HC 6956 INFLUENZA AB PANEL,12,12,12,12,24,24,24,24
+LABYINFAB,54,7,2,INFLUENZA AB PANEL                   NONORDERABLE,HC 6956 INFLUENZA AB PANEL,12,12,12,12,24,24,24,24
+LABYITRA,84,1,10,ITRACONAZOLE,HC 6956 ITRACONAZOLE,38,38,38,38,380,380,380,380
+LABYITRA,84,3,14,ITRACONAZOLE,HC 6956 ITRACONAZOLE,38,38,38,38,532,532,532,532
+LABYITRA,84,7,29,ITRACONAZOLE,HC 6956 ITRACONAZOLE,38,38,38,38,1102,1102,1102,1102
+LABYJCPCR,73,1,3,"JC VIRUS DNA, PCR",HC 6942 JC VIRUS DNA PCR,147,147,147,147,441,441,441,441
+LABYJCPCR,73,3,5,"JC VIRUS DNA, PCR",HC 6942 JC VIRUS DNA PCR,147,147,147,147,735,735,735,735
+LABYJCPCR,73,7,6,"JC VIRUS DNA, PCR",HC 6942 JC VIRUS DNA PCR,147,147,147,147,882,882,882,882
+LABYLACTS,59,1,1,"LACTOFERRIN DETECTION, STOOL","HC 6956 LACTOFERRIN DETECT,STL",91,91,91,91,91,91,91,91
+LABYLACTS,59,3,1,"LACTOFERRIN DETECTION, STOOL","HC 6956 LACTOFERRIN DETECT,STL",91,91,91,91,91,91,91,91
+LABYLACTS,59,7,3,"LACTOFERRIN DETECTION, STOOL","HC 6956 LACTOFERRIN DETECT,STL",91,91,91,91,273,273,273,273
+LABYLAMO,112,1,7,LAMOTRIGINE,HC 6956 LAMOTRIGINE,12,12,12,12,84,84,84,84
+LABYLAMO,112,3,11,LAMOTRIGINE,HC 6956 LAMOTRIGINE,12,12,12,12,132,132,132,132
+LABYLAMO,112,7,14,LAMOTRIGINE,HC 6956 LAMOTRIGINE,12,12,12,12,168,168,168,168
+LABYLCFA,4,7,1,"FATTY ACID PROF,PEROX(C22-C26)","HC 6956 FATTY ACID PROF,PEROX",54,54,54,54,54,54,54,54
+LABYLEADB,50,1,4,"LEAD, BLOOD                              MAYO","HC 6956 LEAD, CHDP",7,7,7,7,28,28,28,28
+LABYLEADB,50,3,4,"LEAD, BLOOD                              MAYO","HC 6956 LEAD, CHDP",7,7,7,7,28,28,28,28
+LABYLEADB,50,7,4,"LEAD, BLOOD                              MAYO","HC 6956 LEAD, CHDP",7,7,7,7,28,28,28,28
+LABYLEGS,43,1,8,LEGIONELLA PNEUMOPHILA AB,HC 6956 LEGIONELLA PNEUMO,11,11,11,11,88,88,88,88
+LABYLEGS,43,3,11,LEGIONELLA PNEUMOPHILA AB,HC 6956 LEGIONELLA PNEUMO,11,11,11,11,121,121,121,121
+LABYLEGS,43,7,13,LEGIONELLA PNEUMOPHILA AB,HC 6956 LEGIONELLA PNEUMO,11,11,11,11,143,143,143,143
+LABYLEPT,61,1,3,LEPTOSPIRA AB,HC 6956 LEPTOSPIRA AB,45,45,45,45,135,135,135,135
+LABYLEPT,61,3,4,LEPTOSPIRA AB,HC 6956 LEPTOSPIRA AB,45,45,45,45,180,180,180,180
+LABYLEPT,61,7,4,LEPTOSPIRA AB,HC 6956 LEPTOSPIRA AB,45,45,45,45,180,180,180,180
+LABYLEVT,113,1,6,LEVETIRACETAM,HC 6956 LEVETIRACETAM,22,22,22,22,132,132,132,132
+LABYLEVT,113,3,6,LEVETIRACETAM,HC 6956 LEVETIRACETAM,22,22,22,22,132,132,132,132
+LABYLEVT,113,7,7,LEVETIRACETAM,HC 6956 LEVETIRACETAM,22,22,22,22,154,154,154,154
+LABYMANG,31,3,2,MANGANESE,HC 6956 MANGANESE,29,29,29,29,58,58,58,58
+LABYMANG,31,7,4,MANGANESE,HC 6956 MANGANESE,29,29,29,29,116,116,116,116
+LABYMETFF,230,1,15,"METANEPHRINES, FRACT. FREE",HC 6956 METANEPHRINES FREE PL,68,68,68,68,1020,1020,1020,1020
+LABYMETFF,230,3,22,"METANEPHRINES, FRACT. FREE",HC 6956 METANEPHRINES FREE PL,68,68,68,68,1496,1496,1496,1496
+LABYMETFF,230,7,31,"METANEPHRINES, FRACT. FREE",HC 6956 METANEPHRINES FREE PL,68,68,68,68,2108,2108,2108,2108
+LABYMETHS,2,1,1,METHSUXIMIDE,HC 6956 YMETHS METHSUXIMIDE,65,65,65,65,65,65,65,65
+LABYMETHS,2,3,1,METHSUXIMIDE,HC 6956 YMETHS METHSUXIMIDE,65,65,65,65,65,65,65,65
+LABYMETHS,2,7,1,METHSUXIMIDE,HC 6956 YMETHS METHSUXIMIDE,65,65,65,65,65,65,65,65
+LABYMEXIL,24,1,6,MEXILETINE,HC 6956 MEXILETINE,90,90,90,90,540,540,540,540
+LABYMEXIL,24,3,7,MEXILETINE,HC 6956 MEXILETINE,90,90,90,90,630,630,630,630
+LABYMEXIL,24,7,9,MEXILETINE,HC 6956 MEXILETINE,90,90,90,90,810,810,810,810
+LABYMPA,63,1,18,MYCOPHENOLIC ACID                         NONORDERABLE,HC 6956 YMPA MYCO ACD QNT NES,19,19,19,19,342,342,342,342
+LABYMPA,63,3,29,MYCOPHENOLIC ACID                         NONORDERABLE,HC 6956 YMPA MYCO ACD QNT NES,19,19,19,19,551,551,551,551
+LABYMPA,63,7,32,MYCOPHENOLIC ACID                         NONORDERABLE,HC 6956 YMPA MYCO ACD QNT NES,19,19,19,19,608,608,608,608
+LABYNICK,2,1,1,NICKEL,HC 6956 NICKEL,11,11,11,11,11,11,11,11
+LABYNICK,2,3,1,NICKEL,HC 6956 NICKEL,11,11,11,11,11,11,11,11
+LABYNICK,2,7,1,NICKEL,HC 6956 NICKEL,11,11,11,11,11,11,11,11
+LABYNMOG,45,1,2,NMO-IGG,HC 6956 NMO-IGG,161,161,161,161,322,322,322,322
+LABYNMOG,45,3,4,NMO-IGG,HC 6956 NMO-IGG,161,161,161,161,644,644,644,644
+LABYNMOG,45,7,5,NMO-IGG,HC 6956 NMO-IGG,161,161,161,161,805,805,805,805
+LABYPARVO,198,1,4,"PARVOVIRUS B19 ABS, IGG&IGM",HC 6956 PARVOVIRUS B19 AB,10,10,10,10,40,40,40,40
+LABYPARVO,198,3,8,"PARVOVIRUS B19 ABS, IGG&IGM",HC 6956 PARVOVIRUS B19 AB,10,10,10,10,80,80,80,80
+LABYPARVO,198,7,8,"PARVOVIRUS B19 ABS, IGG&IGM",HC 6956 PARVOVIRUS B19 AB,10,10,10,10,80,80,80,80
+LABYPENTO,43,1,35,PENTOBARBITAL,HC 6956 PENTOBARBITAL,48,48,48,48,1680,1680,1680,1680
+LABYPENTO,43,3,35,PENTOBARBITAL,HC 6956 PENTOBARBITAL,48,48,48,48,1680,1680,1680,1680
+LABYPENTO,43,7,38,PENTOBARBITAL,HC 6956 PENTOBARBITAL,48,48,48,48,1824,1824,1824,1824
+LABYPERAB,5,3,1,BORDETELLA PERTUSSIS IGG ABS,"HC 6956 B. PERTUSSIS AB, IGG/M",6,6,6,6,6,6,6,6
+LABYPERAB,5,7,1,BORDETELLA PERTUSSIS IGG ABS,"HC 6956 B. PERTUSSIS AB, IGG/M",6,6,6,6,6,6,6,6
+LABYPLAB,40,1,1,PLATELET AB                               NONORDERABLE,HC 6956 PLATELET AB,144,144,144,144,144,144,144,144
+LABYPLAB,40,3,1,PLATELET AB                               NONORDERABLE,HC 6956 PLATELET AB,144,144,144,144,144,144,144,144
+LABYPLAB,40,7,1,PLATELET AB                               NONORDERABLE,HC 6956 PLATELET AB,144,144,144,144,144,144,144,144
+LABYPR3,158,1,2,"PROTEINASE 3 ABS, IGG",HC 6956 PR3 AUTOANTIBODIES,34,34,34,34,68,68,68,68
+LABYPR3,158,3,4,"PROTEINASE 3 ABS, IGG",HC 6956 PR3 AUTOANTIBODIES,34,34,34,34,136,136,136,136
+LABYPR3,158,7,4,"PROTEINASE 3 ABS, IGG",HC 6956 PR3 AUTOANTIBODIES,34,34,34,34,136,136,136,136
+LABYPREAL,8606,1,523,PREALBUMIN (PAB)                           MAYO,"HC 6956 PREALBUMIN(PAB), SERUM",16,16,16,16,8368,8368,8368,8368
+LABYPREAL,8606,3,1779,PREALBUMIN (PAB)                           MAYO,"HC 6956 PREALBUMIN(PAB), SERUM",16,16,16,16,28464,28464,28464,28464
+LABYPREAL,8606,7,4252,PREALBUMIN (PAB)                           MAYO,"HC 6956 PREALBUMIN(PAB), SERUM",16,16,16,16,68032,68032,68032,68032
+LABYPROIN,75,1,60,PROINSULIN,"HC 6956 PROINSULIN, P",30,30,30,30,1800,1800,1800,1800
+LABYPROIN,75,3,62,PROINSULIN,"HC 6956 PROINSULIN, P",30,30,30,30,1860,1860,1860,1860
+LABYPROIN,75,7,64,PROINSULIN,"HC 6956 PROINSULIN, P",30,30,30,30,1920,1920,1920,1920
+LABYPTHRP,269,1,7,PTH-RELATED PEPTIDE,"HC 6956 PTH-RELATED PEPTIDE, P",14,14,14,14,98,98,98,98
+LABYPTHRP,269,3,14,PTH-RELATED PEPTIDE,"HC 6956 PTH-RELATED PEPTIDE, P",14,14,14,14,196,196,196,196
+LABYPTHRP,269,7,25,PTH-RELATED PEPTIDE,"HC 6956 PTH-RELATED PEPTIDE, P",14,14,14,14,350,350,350,350
+LABYPYRU,36,1,3,PYRUVIC ACID,HC 6956 PYRUVIC ACID,21,21,21,21,63,63,63,63
+LABYPYRU,36,3,3,PYRUVIC ACID,HC 6956 PYRUVIC ACID,21,21,21,21,63,63,63,63
+LABYPYRU,36,7,4,PYRUVIC ACID,HC 6956 PYRUVIC ACID,21,21,21,21,84,84,84,84
+LABYRIBAB,12,1,1,"RIBOSOME P ABS, IGG",HC 6956 RIBOSOME P AB,40,40,40,40,40,40,40,40
+LABYRIBAB,12,3,1,"RIBOSOME P ABS, IGG",HC 6956 RIBOSOME P AB,40,40,40,40,40,40,40,40
+LABYRIBAB,12,7,1,"RIBOSOME P ABS, IGG",HC 6956 RIBOSOME P AB,40,40,40,40,40,40,40,40
+LABYRICK,36,1,2,RICKETTSIA AB PANEL,HC 6956 RICKETTSIA AB PANEL,15,15,15,15,30,30,30,30
+LABYRICK,36,3,3,RICKETTSIA AB PANEL,HC 6956 RICKETTSIA AB PANEL,15,15,15,15,45,45,45,45
+LABYRICK,36,7,3,RICKETTSIA AB PANEL,HC 6956 RICKETTSIA AB PANEL,15,15,15,15,45,45,45,45
+LABYSDA,96,1,4,"DRUG SCRN, PRESCRIPTION","HC 6956 DRUG SCREEN, PLASME",27,28,27.5,27.5,108,112,110,110
+LABYSDA,96,1,4,"DRUG SCRN, PRESCRIPTION",HC 6956 DRUG SCREEN,27,28,27.5,27.5,108,112,110,110
+LABYSDA,96,3,6,"DRUG SCRN, PRESCRIPTION","HC 6956 DRUG SCREEN, PLASME",27,28,27.5,27.5,162,168,165,165
+LABYSDA,96,3,6,"DRUG SCRN, PRESCRIPTION",HC 6956 DRUG SCREEN,27,28,27.5,27.5,162,168,165,165
+LABYSDA,96,7,7,"DRUG SCRN, PRESCRIPTION","HC 6956 DRUG SCREEN, PLASME",27,28,27.5,27.5,189,196,192.5,192.5
+LABYSDA,96,7,7,"DRUG SCRN, PRESCRIPTION",HC 6956 DRUG SCREEN,27,28,27.5,27.5,189,196,192.5,192.5
+LABYSEROT,51,1,2,SEROTONIN,HC 6956 SEROTONIN,45,45,45,45,90,90,90,90
+LABYSEROT,51,3,2,SEROTONIN,HC 6956 SEROTONIN,45,45,45,45,90,90,90,90
+LABYSEROT,51,7,2,SEROTONIN,HC 6956 SEROTONIN,45,45,45,45,90,90,90,90
+LABYSERTR,3,3,1,SERTRALINE,HC 6956 YSERTR SERTRALINE MSQN,71,71,71,71,71,71,71,71
+LABYSERTR,3,7,1,SERTRALINE,HC 6956 YSERTR SERTRALINE MSQN,71,71,71,71,71,71,71,71
+LABYSES,64,1,2,SELENIUM,HC 6956 SELENIUM,26,26,26,26,52,52,52,52
+LABYSES,64,3,4,SELENIUM,HC 6956 SELENIUM,26,26,26,26,104,104,104,104
+LABYSES,64,7,4,SELENIUM,HC 6956 SELENIUM,26,26,26,26,104,104,104,104
+LABYSHBG,4,7,1,SEX HORMONE BINDING GLOBULIN,HC 6956 SEX HORMONE BIND GLOB,21,21,21,21,21,21,21,21
+LABYSOMAT,12,7,3,SOMATOSTATIN,HC 6956 SOMATOSTATIN,235,235,235,235,705,705,705,705
+LABYSTCL,45,1,3,"CHLORIDE, STOOL","HC 6956 CHLORIDE, STOOL",34,34,34,34,102,102,102,102
+LABYSTCL,45,3,4,"CHLORIDE, STOOL","HC 6956 CHLORIDE, STOOL",34,34,34,34,136,136,136,136
+LABYSTCL,45,7,9,"CHLORIDE, STOOL","HC 6956 CHLORIDE, STOOL",34,34,34,34,306,306,306,306
+LABYSTFR,97,1,4,SOLUBLE TRANSFERRIN RECEPTOR,HC 6956 SOLUBLE TRANSFERRIN,29,29,29,29,116,116,116,116
+LABYSTFR,97,3,5,SOLUBLE TRANSFERRIN RECEPTOR,HC 6956 SOLUBLE TRANSFERRIN,29,29,29,29,145,145,145,145
+LABYSTFR,97,7,5,SOLUBLE TRANSFERRIN RECEPTOR,HC 6956 SOLUBLE TRANSFERRIN,29,29,29,29,145,145,145,145
+LABYSTK,160,1,13,"POTASSIUM, RANDOM STOOL","HC 6956 POTSSIUM, STOOL",34,34,34,34,442,442,442,442
+LABYSTK,160,3,19,"POTASSIUM, RANDOM STOOL","HC 6956 POTSSIUM, STOOL",34,34,34,34,646,646,646,646
+LABYSTK,160,7,30,"POTASSIUM, RANDOM STOOL","HC 6956 POTSSIUM, STOOL",34,34,34,34,1020,1020,1020,1020
+LABYSTNA,165,1,12,"SODIUM, RANDOM STOOL","HC 6956 SODIUM, STOOL",34,34,34,34,408,408,408,408
+LABYSTNA,165,3,19,"SODIUM, RANDOM STOOL","HC 6956 SODIUM, STOOL",34,34,34,34,646,646,646,646
+LABYSTNA,165,7,29,"SODIUM, RANDOM STOOL","HC 6956 SODIUM, STOOL",34,34,34,34,986,986,986,986
+LABYSTONE,135,1,1,KIDNEY STONE ANALYSIS,HC STONE ANALYSIS INFRARED SPECT,21,21,21,21,21,21,21,21
+LABYSTONE,135,3,2,KIDNEY STONE ANALYSIS,HC STONE ANALYSIS INFRARED SPECT,21,21,21,21,42,42,42,42
+LABYSTONE,135,7,2,KIDNEY STONE ANALYSIS,HC STONE ANALYSIS INFRARED SPECT,21,21,21,21,42,42,42,42
+LABYSTOSM,156,1,10,"OSMOLALITY, STOOL",HC 6956 OSMOLALITY STOOL,41,41,41,41,410,410,410,410
+LABYSTOSM,156,3,22,"OSMOLALITY, STOOL",HC 6956 OSMOLALITY STOOL,41,41,41,41,902,902,902,902
+LABYSTOSM,156,7,37,"OSMOLALITY, STOOL",HC 6956 OSMOLALITY STOOL,41,41,41,41,1517,1517,1517,1517
+LABYSTRYP,13,7,2,"TRYPSIN, FECAL","HC 6956 TRYPSIN, FECAL",45,45,45,45,90,90,90,90
+LABYTADID,17,1,1,TRICYCLIC ANTIDEPRESSANT SCR,HC 6956 TCA SCREEN,96,96,96,96,96,96,96,96
+LABYTADID,17,3,1,TRICYCLIC ANTIDEPRESSANT SCR,HC 6956 TCA SCREEN,96,96,96,96,96,96,96,96
+LABYTADID,17,7,1,TRICYCLIC ANTIDEPRESSANT SCR,HC 6956 TCA SCREEN,96,96,96,96,96,96,96,96
+LABYTAT,22,7,1,THROMBIN-ANTITHROMBIN COMPLEX,HC 6956 TAT COMPLEX,178,178,178,178,178,178,178,178
+LABYTESFB,27,3,2,"TESTOSTERONE, TOTAL, BIO, FREE","HC 6956 TESTOSTERONE, FREE",10,17,13.5,13.5,20,34,27,27
+LABYTESFB,27,3,2,"TESTOSTERONE, TOTAL, BIO, FREE","HC 6956 TESTOSTERONE, TOTAL",10,17,13.5,13.5,20,34,27,27
+LABYTESFB,27,7,3,"TESTOSTERONE, TOTAL, BIO, FREE","HC 6956 TESTOSTERONE, FREE",10,17,13.5,13.5,30,51,40.5,40.5
+LABYTESFB,27,7,3,"TESTOSTERONE, TOTAL, BIO, FREE","HC 6956 TESTOSTERONE, TOTAL",10,17,13.5,13.5,30,51,40.5,40.5
+LABYTESFT,124,1,5,"TESTOSTERONE, TOTAL & FREE",HC 6956 TESTOSTERONE FREE RIA,11,11,11,11,55,55,55,55
+LABYTESFT,124,1,5,"TESTOSTERONE, TOTAL & FREE","HC 6956 TESTERONE, T&F, SERUM",11,11,11,11,55,55,55,55
+LABYTESFT,124,3,9,"TESTOSTERONE, TOTAL & FREE",HC 6956 TESTOSTERONE FREE RIA,11,11,11,11,99,99,99,99
+LABYTESFT,124,3,9,"TESTOSTERONE, TOTAL & FREE","HC 6956 TESTERONE, T&F, SERUM",11,11,11,11,99,99,99,99
+LABYTESFT,124,7,11,"TESTOSTERONE, TOTAL & FREE",HC 6956 TESTOSTERONE FREE RIA,11,11,11,11,121,121,121,121
+LABYTESFT,124,7,11,"TESTOSTERONE, TOTAL & FREE","HC 6956 TESTERONE, T&F, SERUM",11,11,11,11,121,121,121,121
+LABYTETT,15,1,1,TETANUS TOXOID IGG AB,HC 6956 TETANUS TOXOID IGG AB,28,28,28,28,28,28,28,28
+LABYTETT,15,3,1,TETANUS TOXOID IGG AB,HC 6956 TETANUS TOXOID IGG AB,28,28,28,28,28,28,28,28
+LABYTETT,15,7,1,TETANUS TOXOID IGG AB,HC 6956 TETANUS TOXOID IGG AB,28,28,28,28,28,28,28,28
+LABYTHI,26,1,3,THIOCYANATE                           NONORDERABLE,HC 6956 THIOCYANATE,33,33,33,33,99,99,99,99
+LABYTHI,26,3,4,THIOCYANATE                           NONORDERABLE,HC 6956 THIOCYANATE,33,33,33,33,132,132,132,132
+LABYTHI,26,7,4,THIOCYANATE                           NONORDERABLE,HC 6956 THIOCYANATE,33,33,33,33,132,132,132,132
+LABYTICK,2,1,1,COLORADO TICK FEVER AB,HC 6956 COLORADO TICK FEVER,42,42,42,42,42,42,42,42
+LABYTICK,2,3,1,COLORADO TICK FEVER AB,HC 6956 COLORADO TICK FEVER,42,42,42,42,42,42,42,42
+LABYTICK,2,7,1,COLORADO TICK FEVER AB,HC 6956 COLORADO TICK FEVER,42,42,42,42,42,42,42,42
+LABYTOPIR,100,1,6,TOPIRAMATE,HC 6956 TOPIRAMATE,28,28,28,28,168,168,168,168
+LABYTOPIR,100,3,13,TOPIRAMATE,HC 6956 TOPIRAMATE,28,28,28,28,364,364,364,364
+LABYTOPIR,100,7,16,TOPIRAMATE,HC 6956 TOPIRAMATE,28,28,28,28,448,448,448,448
+LABYTRYPT,113,1,6,TRYPTASE,"HC 6956 TRYPTASE, SERUM",63,63,63,63,378,378,378,378
+LABYTRYPT,113,3,11,TRYPTASE,"HC 6956 TRYPTASE, SERUM",63,63,63,63,693,693,693,693
+LABYTRYPT,113,7,11,TRYPTASE,"HC 6956 TRYPTASE, SERUM",63,63,63,63,693,693,693,693
+LABYU5HIA,76,7,2,"5-HYDROXYINDOLEACETIC ACID, UR",HC 6956 YU5HIA 5 HIAA UR,13,13,13,13,26,26,26,26
+LABYUARSR,8,3,1,"ARSENIC, URINE","HC 6956 ARSENIC, RANDOM URINE",21,21,21,21,21,21,21,21
+LABYUARSR,8,7,1,"ARSENIC, URINE","HC 6956 ARSENIC, RANDOM URINE",21,21,21,21,21,21,21,21
+LABYUBZC,54,3,1,"BENZODIAZEPINE CONFIRMATION, UR",HC 6956 BENZODIAZEPINE CONFIRM,7,7,7,7,7,7,7,7
+LABYUBZC,54,7,1,"BENZODIAZEPINE CONFIRMATION, UR",HC 6956 BENZODIAZEPINE CONFIRM,7,7,7,7,7,7,7,7
+LABYUCDA,276,1,2,"CONFIRMED DRUG ABUSE SURVEY, U",HC 6956 YUCDA DRG SCR QL EA UR,12,12,12,12,24,24,24,24
+LABYUCDA,276,3,7,"CONFIRMED DRUG ABUSE SURVEY, U",HC 6956 YUCDA DRG SCR QL EA UR,12,12,12,12,84,84,84,84
+LABYUCDA,276,7,10,"CONFIRMED DRUG ABUSE SURVEY, U",HC 6956 YUCDA DRG SCR QL EA UR,12,12,12,12,120,120,120,120
+LABYUCOP,62,1,1,"COPPER, URINE","HC 6956 COPPER, URINE",11,11,11,11,11,11,11,11
+LABYUCOP,62,3,2,"COPPER, URINE","HC 6956 COPPER, URINE",11,11,11,11,22,22,22,22
+LABYUCOP,62,7,3,"COPPER, URINE","HC 6956 COPPER, URINE",11,11,11,11,33,33,33,33
+LABYUCORT,38,1,1,"CORTISOL, FREE, 24HR URINE",HC 6956 YUCORT CORTISOL FREE U,9,10,9.5,9.5,9,10,9.5,9.5
+LABYUCORT,38,1,1,"CORTISOL, FREE, 24HR URINE",HC 6956 MASS SPEC/TNDM MASS SPECT,9,10,9.5,9.5,9,10,9.5,9.5
+LABYUCORT,38,3,2,"CORTISOL, FREE, 24HR URINE",HC 6956 YUCORT CORTISOL FREE U,9,10,9.5,9.5,18,20,19,19
+LABYUCORT,38,3,2,"CORTISOL, FREE, 24HR URINE",HC 6956 MASS SPEC/TNDM MASS SPECT,9,10,9.5,9.5,18,20,19,19
+LABYUCORT,38,7,3,"CORTISOL, FREE, 24HR URINE",HC 6956 YUCORT CORTISOL FREE U,9,10,9.5,9.5,27,30,28.5,28.5
+LABYUCORT,38,7,3,"CORTISOL, FREE, 24HR URINE",HC 6956 MASS SPEC/TNDM MASS SPECT,9,10,9.5,9.5,27,30,28.5,28.5
+LABYUFCAT,26,7,1,"CATECHOLAMINE FRACT, FREE, UR",HC 6956 YUFCAT CTCHLMNS FRAC U,23,23,23,23,23,23,23,23
+LABYUHYME,77,1,1,"HEAVY METALS, URINE",HC 6956 ARSENIC,5,10,7.5,7.5,5,10,7.5,7.5
+LABYUHYME,77,1,1,"HEAVY METALS, URINE",HC 6956 LEAD,5,10,7.5,7.5,5,10,7.5,7.5
+LABYUHYME,77,1,1,"HEAVY METALS, URINE","HC 6956 MERCURY, QUANT",5,10,7.5,7.5,5,10,7.5,7.5
+LABYUHYME,77,1,1,"HEAVY METALS, URINE",HC 6956 CADMIUM,5,10,7.5,7.5,5,10,7.5,7.5
+LABYUHYME,77,3,5,"HEAVY METALS, URINE",HC 6956 ARSENIC,5,10,7.5,7.5,25,50,37.5,37.5
+LABYUHYME,77,3,5,"HEAVY METALS, URINE",HC 6956 LEAD,5,10,7.5,7.5,25,50,37.5,37.5
+LABYUHYME,77,3,5,"HEAVY METALS, URINE","HC 6956 MERCURY, QUANT",5,10,7.5,7.5,25,50,37.5,37.5
+LABYUHYME,77,3,5,"HEAVY METALS, URINE",HC 6956 CADMIUM,5,10,7.5,7.5,25,50,37.5,37.5
+LABYUHYME,77,7,7,"HEAVY METALS, URINE",HC 6956 ARSENIC,5,10,7.5,7.5,35,70,52.5,52.5
+LABYUHYME,77,7,7,"HEAVY METALS, URINE",HC 6956 LEAD,5,10,7.5,7.5,35,70,52.5,52.5
+LABYUHYME,77,7,7,"HEAVY METALS, URINE","HC 6956 MERCURY, QUANT",5,10,7.5,7.5,35,70,52.5,52.5
+LABYUHYME,77,7,7,"HEAVY METALS, URINE",HC 6956 CADMIUM,5,10,7.5,7.5,35,70,52.5,52.5
+LABYUMETF,51,7,2,"METANEPHRINES, FRACT, 24H, UR","HC 6956 METANEPHRINES,FR,24,UR",19,19,19,19,38,38,38,38
+LABYUMYO,145,1,12,"MYOGLOBIN, URINE",HC 6956 MYOGLOBIN URINE,19,19,19,19,228,228,228,228
+LABYUMYO,145,3,33,"MYOGLOBIN, URINE",HC 6956 MYOGLOBIN URINE,19,19,19,19,627,627,627,627
+LABYUMYO,145,7,37,"MYOGLOBIN, URINE",HC 6956 MYOGLOBIN URINE,19,19,19,19,703,703,703,703
+LABYUNTX,2,3,1,"NTX-TELOPEPTIDE, URINE",HC 6956 YUNTX NTXTPD CRS LNK U,46,46,46,46,46,46,46,46
+LABYUNTX,2,7,1,"NTX-TELOPEPTIDE, URINE",HC 6956 YUNTX NTXTPD CRS LNK U,46,46,46,46,46,46,46,46
+LABYUOPC,96,3,2,"OPIATE CONFIRMATION, UR",HC 6956OPIATE  URINE,24,24,24,24,48,48,48,48
+LABYUOPC,96,7,2,"OPIATE CONFIRMATION, UR",HC 6956OPIATE  URINE,24,24,24,24,48,48,48,48
+LABYUTHC,48,7,1,"TETRAHYDROCANNABINOL, URINE",HC 6956 YUTHC THC MARJN CONF U,13,13,13,13,13,13,13,13
+LABYVB1W,60,3,1,"THIAMIN (VIT B1), WHOLE BLOOD",HC 6956 THIAMIN (VIT B1),31,31,31,31,31,31,31,31
+LABYVB1W,60,7,3,"THIAMIN (VIT B1), WHOLE BLOOD",HC 6956 THIAMIN (VIT B1),31,31,31,31,93,93,93,93
+LABYVDRLC,153,1,2,"VDRL, CSF","HC 6956 VDRL, CSF",7,7,7,7,14,14,14,14
+LABYVDRLC,153,3,7,"VDRL, CSF","HC 6956 VDRL, CSF",7,7,7,7,49,49,49,49
+LABYVDRLC,153,7,7,"VDRL, CSF","HC 6956 VDRL, CSF",7,7,7,7,49,49,49,49
+LABYVITA,177,3,4,VITAMIN A,HC 6956 VITAMIN A,16,16,16,16,64,64,64,64
+LABYVITA,177,7,8,VITAMIN A,HC 6956 VITAMIN A,16,16,16,16,128,128,128,128
+LABYVITB6,60,1,1,VITAMIN B6,HC 6956 VITAMIN B6,15,15,15,15,15,15,15,15
+LABYVITB6,60,3,3,VITAMIN B6,HC 6956 VITAMIN B6,15,15,15,15,45,45,45,45
+LABYVITB6,60,7,4,VITAMIN B6,HC 6956 VITAMIN B6,15,15,15,15,60,60,60,60
+LABYVWM,21,1,3,VON WILLEBRAND MULTIMERS,HC 6956 VON WILLEBR MULTIMERS,19,19,19,19,57,57,57,57
+LABYVWM,21,3,3,VON WILLEBRAND MULTIMERS,HC 6956 VON WILLEBR MULTIMERS,19,19,19,19,57,57,57,57
+LABYVWM,21,7,3,VON WILLEBRAND MULTIMERS,HC 6956 VON WILLEBR MULTIMERS,19,19,19,19,57,57,57,57
+LABYWNILE,278,1,7,"WEST NILE VIRUS AB, IGG & IGM","HC 6956 WEST NILE VIRUS, IGM",47,47,47,47,329,329,329,329
+LABYWNILE,278,3,8,"WEST NILE VIRUS AB, IGG & IGM","HC 6956 WEST NILE VIRUS, IGM",47,47,47,47,376,376,376,376
+LABYWNILE,278,7,12,"WEST NILE VIRUS AB, IGG & IGM","HC 6956 WEST NILE VIRUS, IGM",47,47,47,47,564,564,564,564
+LABYZINCS,174,1,3,ZINC                                  NONORDERABLE,HC 6956 ZINC,11,11,11,11,33,33,33,33
+LABYZINCS,174,3,7,ZINC                                  NONORDERABLE,HC 6956 ZINC,11,11,11,11,77,77,77,77
+LABYZINCS,174,7,9,ZINC                                  NONORDERABLE,HC 6956 ZINC,11,11,11,11,99,99,99,99
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/try_Keras.py b/scripts/Archive/LabTestAnalysis/machine_learning/try_Keras.py
new file mode 100644
index 00000000..d104a621
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/try_Keras.py
@@ -0,0 +1,96 @@
+
+
+import keras
+
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+from sklearn.metrics import roc_auc_score
+
+import os
+import collections
+import numpy as np
+import pandas as pd
+pd.set_option('display.width', 500)
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+
+# TODO: secondary usage of FM
+
+def _train_test_split(processed_matrix, outcome_label, columnToSplitOn='pat_id'):
+    all_possible_ids = sorted(set(processed_matrix[columnToSplitOn].values.tolist()))
+
+    train_ids, test_ids = train_test_split(all_possible_ids, random_state=123456789)
+
+    train_matrix = processed_matrix[processed_matrix[columnToSplitOn].isin(train_ids)].copy()
+    y_train = pd.DataFrame(train_matrix.pop(outcome_label))
+    X_train = train_matrix
+
+    test_matrix = processed_matrix[processed_matrix[columnToSplitOn].isin(test_ids)].copy()
+    y_test = pd.DataFrame(test_matrix.pop(outcome_label))
+    X_test = test_matrix
+    return X_train, y_train, X_test, y_test
+
+'''
+Load data
+'''
+lab = 'LABA1C'
+fm_io = FeatureMatrixIO()
+processed_matrix = fm_io.read_file_to_data_frame("data-panels/%s/%s-normality-matrix-10000-episodes-processed.tab"
+                 %(lab,lab))
+X_train, y_train, X_test, y_test = _train_test_split(processed_matrix, 'all_components_normal')
+X_train.pop('pat_id')
+X_test.pop('pat_id')
+
+features = X_train.columns.tolist()
+print(features)
+
+
+X_train, y_train, X_test, y_test = X_train.values, y_train.values, X_test.values, y_test.values
+
+scaler = preprocessing.StandardScaler().fit(X_train)
+X_train = scaler.transform(X_train)
+X_test = scaler.transform(X_test)
+
+print("collections.Counter(y_train):", collections.Counter(y_train.flatten()))
+print("collections.Counter(y_test):", collections.Counter(y_test.flatten()))
+row, col = X_train.shape
+
+
+'''
+Build model
+'''
+model_file = "keras_model.h5"
+use_cached_model = True
+
+if not os.path.exists(model_file) or not use_cached_model:
+    model = keras.models.Sequential()
+    model.add(keras.layers.Dense(32, input_dim=col, activation='relu'))
+    #model.add(keras.layers.Dropout(0.5))
+    model.add(keras.layers.Dense(32, activation='relu'))
+    #model.add(keras.layers.Dropout(0.5))
+    model.add(keras.layers.Dense(1, activation='sigmoid'))
+
+    sgd = keras.optimizers.SGD(lr=0.1, decay=1e-4, momentum=0.1, nesterov=True)
+    model.compile(loss='binary_crossentropy',
+                  optimizer=sgd,#'adam',
+                  metrics=['accuracy'])
+
+    model.fit(X_train, y_train, epochs=200, batch_size=128)
+    model.save(model_file)
+#pickle.dump(model, open("keras_model.pickle",'wb'), protocol=pickle.HIGHEST_PROTOCOL)
+else:
+    model = keras.models.load_model(model_file)
+
+# cur_input = [0.]*len(features)
+# cur_output = model.predict(np.reshape(cur_input, (1,len(features))))
+# print "no feature:", cur_output
+#
+# for i in range(len(features)):
+#     cur_input = [0.]*len(features)
+#     cur_input[i] = 1000
+#     cur_output = model.predict(np.reshape(cur_input, (1,len(features))))
+#     print features[i], cur_output
+
+y_pred_proba = model.predict_proba(X_test, batch_size=128)
+# print collections.Counter(y_pred_proba.flatten())
+print(roc_auc_score(y_test.flatten(), y_pred_proba.flatten()))
+
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/utils_UCSF.py b/scripts/Archive/LabTestAnalysis/machine_learning/utils_UCSF.py
new file mode 100644
index 00000000..9425e247
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/utils_UCSF.py
@@ -0,0 +1,163 @@
+
+# All utility code removed for refactoring
+# Previous stable version saved in utils_UMich_backup20181021
+
+
+import pandas as pd
+pd.set_option('display.width', 300)
+from itertools import islice
+import sqlite3
+import LocalEnv
+from medinfo.common.Util import log
+from . import prepareData_NonSTRIDE as utils_general
+import datetime
+from collections import Counter
+
+datetime_format = "%Y-%m-%dT%H:%M:%SZ"
+test_mode = False #TODO
+
+def line_str2list(line_str, skip_first_col=False, test_mode=False):
+    return [x.strip() for x in line_str.split('\t')]
+
+def pd_process_demogdiagn(df):
+    # file_name = 'demographics_and_diagnoses.tsv'  #
+    # diagn: CSN, ICD10 (diagn time = Admission Datetime?! )
+    # demog: CSN, race, sex, Birth (time)
+    # encnt: CSN, Admission Datetime
+    tab2df_dict = {}
+
+    df_diagn = df.ix[df['present_on_admit'] == 'Yes',
+                     ['CSN', 'Admission Datetime', 'ICD10']].copy()  # TODO: succeed?
+    df_diagn = df_diagn.drop_duplicates()
+    df_diagn = df_diagn.rename(
+        columns={'CSN': 'order_proc_id', 'Admission Datetime': 'diagnose_time', 'ICD10': 'diagnose_code'})
+    df_diagn['pat_id'] = df_diagn['order_proc_id'].apply(lambda x: hash(x))
+    df_diagn['diagnose_time'] = df_diagn['diagnose_time'].apply(lambda x: datetime.datetime.strptime(x, datetime_format))
+    '''
+    Use the bigger category to summarize ICD10 code
+    '''
+    df_diagn['diagnose_code'] = df_diagn['diagnose_code'].apply(lambda x: "".join(x.split(".")[:-1]))
+    tab2df_dict['diagnoses'] = df_diagn[['pat_id', 'order_proc_id', 'diagnose_time', 'diagnose_code']]
+
+    # diagnoses_df['diagnose_time'] = diagnoses_df['diagnose_time'].apply(lambda x: utils_general.remove_microsecs(x))
+
+    # A couple notes: our ethnic_grp column is only really useful
+    # for hispanic versus not hispanic. The primary diagnosis is
+    # marked by the "Principal Dx Used" text in the severity column.
+    # TODO: Ethnic_Grp or Primary_Race?
+    df_demog = df[['CSN', 'Gender', 'Primary_Race']].copy()
+    df_demog = df_demog.drop_duplicates()
+    if test_mode:
+        df_demog['Gender'] = df_demog['Gender'].apply(lambda x: 'Male')
+
+    df_demog = df_demog.rename(columns={'Gender': 'GenderName',
+                                        'Primary_Race': 'RaceName'
+                                        })
+    df_demog['pat_id'] = df_demog['CSN'].apply(lambda x: hash(x))
+
+    df_demog['GenderName'] = df_demog['GenderName'].apply(lambda x: 'Unknown' if not x else x)
+    df_demog['RaceName'] = df_demog['RaceName'].apply(lambda x: 'Unknown' if not x else x)
+    df_demog['RaceName'] = df_demog['RaceName'].apply(lambda x: x.replace('/', '-'))
+
+    tab2df_dict['demographics'] = df_demog[['pat_id', 'GenderName', 'RaceName']]
+
+
+    # TODO: it is important to differentiate 'Admission' and 'diagnose'
+    # times in our model, even though there is only a couple' days diff
+    df_encnt = df[['CSN', 'Admission Datetime']].copy()
+    df_encnt = df_encnt.drop_duplicates()
+
+    df_encnt = df_encnt.rename(columns={'CSN': 'order_proc_id', 'Admission Datetime': 'AdmitDxDate'})
+    df_encnt['pat_id'] = df_encnt['order_proc_id'].apply(lambda x: hash(x))
+    # encounters_df['AdmitDxDate'] = encounters_df['AdmitDxDate'].apply(lambda x: utils_general.remove_microsecs(x))
+    tab2df_dict['encounters'] = df_encnt[['pat_id', 'order_proc_id', 'AdmitDxDate']]
+
+    df_pt_info = df[['CSN', 'Admission Datetime', 'Age']].copy()
+    # print df_pt_info['Admission Datetime'].apply(lambda x: datetime.datetime.strptime(x, datetime_format)).values
+
+    df_pt_info['pat_id'] = df_pt_info['CSN'].apply(lambda x: hash(x))
+    # pt_info_df['Birth'] = pt_info_df['Birth'].apply(lambda x: utils_general.remove_microsecs(x))
+    if test_mode:
+        df_pt_info['Birth'] = df_pt_info['Admission Datetime'].apply(
+            lambda x: datetime.datetime(year=1900, month=1, day=1))
+    else:
+        # TODO: sxu, this column was redacted.
+        df_pt_info['Birth'] = df_pt_info['Age'].apply(lambda x: '1900-01-01T00:00:00Z')
+        # if 'Birth' not in df_pt_info.columns:
+        #     df_pt_info['Birth'] = (
+        #             df_pt_info['Admission Datetime'].apply(lambda x: datetime.datetime.strptime(x, datetime_format)) \
+        #             - df_pt_info['Age'].apply(lambda x: datetime.timedelta(days=int(x) * 365))).values
+    df_pt_info = df_pt_info.drop_duplicates()
+    tab2df_dict['pt_info'] = df_pt_info[['pat_id', 'Birth']]
+
+    return tab2df_dict
+
+
+def pd_process_labs(df): # labs and teams
+    team_df = df[['CSN', 'Order Time', 'Order Proc ID', 'discharge service']].copy()  #: 'Team'
+    team_df = team_df.rename(columns={
+        'CSN':'pat_id',
+        'Order Time': 'order_time',
+        'Order Proc ID':'order_proc_id',
+        'discharge service':'treatment_team'
+    })
+    team_df = team_df.drop_duplicates()
+    # df_team.to_csv(folder_path + '/' + 'treatment_team-*.tsv', sep='\t')
+
+    labs_df = df[['CSN', 'Order Time', 'Order Name', 'Order Proc ID', 'BASE_NAME', 'Result', 'Result Flag']].copy()
+    labs_df = labs_df.rename(columns={
+        'CSN': 'pat_id',
+        'Order Time': 'order_time',  # TODO
+        'Order Name': 'proc_code',
+        'Order Proc ID': 'order_proc_id',
+        'BASE_NAME': 'base_name',
+        'Result': 'ord_num_value',
+        'Result Flag': 'result_flag'  # TODO: check later
+    })
+    labs_df['pat_id'] = labs_df['pat_id'].apply(lambda x: hash(x))
+
+    labs_df['order_time'] = labs_df['order_time'].apply(lambda x: datetime.datetime.strptime(x, datetime_format))
+
+    labs_df['proc_code'] = labs_df['proc_code'].apply(lambda x: x.replace('/', '-'))
+    labs_df['result_time'] = labs_df['order_time'].copy()
+    labs_df['result_in_range_yn'] = labs_df['result_flag'].apply(lambda x: 'Y' if x == 'NA' else 'N')
+
+    labs_df['ord_num_value'] = labs_df['ord_num_value'].apply(lambda x: utils_general.filter_nondigits(x))
+
+    # labs_df.to_csv("labs_df.csv")
+    labs_df = labs_df[['pat_id', 'order_proc_id', 'order_time', 'result_time',
+                    'proc_code', 'base_name', 'ord_num_value', 'result_in_range_yn', 'result_flag']]
+    tab2df_dict = {}
+    tab2df_dict['team'] = team_df
+    tab2df_dict['labs'] = labs_df
+    return tab2df_dict
+
+def pd_process_vitals(vitals_df):
+    #TODO: be careful of very large df operation...
+    #TODO: is separate_demo... easy in memory?
+    # Transform the whole table...
+    # VS record time --> shifted_dt_tm
+    #
+    tab2df_dict = {}
+    vitals_df = vitals_df.rename(columns={'CSN':'flo_meas_id',
+                                          'VS record time':'shifted_dt_tm',
+                                          'SBP':'flowsheet_value_SBP',
+                                          'DBP':'flowsheet_value_DBP',
+                                          'FiO2 (%)':'flowsheet_value_FiO2',
+                                          'Pulse': 'flowsheet_value_Pulse',
+                                          'Resp': 'flowsheet_value_Resp',
+                                          'Temp': 'flowsheet_value_Temp',
+                                          'o2flow': 'flowsheet_value_o2flow'
+                                          })
+    vitals_df['pat_id'] = vitals_df['flo_meas_id'].apply(lambda x: hash(x))
+    vitals_df['shifted_dt_tm'] = vitals_df['shifted_dt_tm'].apply(lambda x: datetime.datetime.strptime(x, datetime_format))
+
+    # print pd.wide_to_long
+    vitals_df_long = pd.wide_to_long(vitals_df, stubnames='flowsheet_value',
+                          i=['pat_id', 'flo_meas_id', 'shifted_dt_tm'],
+                          j='flowsheet_name', sep='_', suffix='\w+')
+    vitals_df_long = vitals_df_long.reset_index()
+    vitals_df_long = vitals_df_long.drop(vitals_df_long[vitals_df_long.flowsheet_value=='NA'].index)
+
+    tab2df_dict['vitals'] = vitals_df_long
+    return tab2df_dict
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/machine_learning/utils_UMich.py b/scripts/Archive/LabTestAnalysis/machine_learning/utils_UMich.py
new file mode 100644
index 00000000..18b93f97
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/machine_learning/utils_UMich.py
@@ -0,0 +1,324 @@
+import pandas as pd
+from itertools import islice
+import sqlite3
+import LocalEnv
+from medinfo.common.Util import log
+
+test_mode = False
+
+
+def filter_nondigits(any_str):
+    return ''.join([x for x in str(any_str) if x in '.0123456789'])
+
+def filter_range(any_str): # contains sth like range a-b
+    nums = any_str.strip().split('-')
+    try:
+        return (float(nums[0]) + float(nums[1])) / 2.
+    except:
+        return any_str
+
+def remove_microsecs(any_str):
+    try:
+        return any_str.split('.')[0]
+    except:
+        return any_str
+
+def line_str2list(line_str):
+    # Get rid of extra quotes
+    # if test_mode:
+    #     if skip_first_col:
+    #         # get rid of meaningless first col index
+    #         return [x.strip()[1:-1] for x in line_str.split('|')][1:]
+    #     else:
+    #         return [x.strip()[1:-1] for x in line_str.split('|')]
+    # else:
+    return [x.strip() for x in line_str.split('\t')]
+
+import numpy as np
+import random, string
+
+def perturb_str(any_str, seed=None): #TODO: Doing seeding in the future
+    str_len = len(any_str)
+    ind_to_perturb = np.random.choice(str_len)
+    chr_to_perturb = random.choice(string.letters + '-0123456789')
+    any_str = any_str[:ind_to_perturb] + chr_to_perturb + any_str[ind_to_perturb + 1:]
+    return any_str
+
+def perturb_a_file(raw_file_path, target_file_path, col_patid, my_dict):
+    with open(raw_file_path) as fr:
+        lines_raw = fr.readlines()
+        fr.close()
+
+    import os
+    if not os.path.exists(target_file_path):
+        fw = open(target_file_path, 'w')
+        fw.write(lines_raw[0])  # column name line
+    else:
+        fw = open(target_file_path, 'a')
+    for line_raw in lines_raw[1:]:
+        line_as_list = line_str2list(line_raw)
+        try:
+            cur_pat_id = line_as_list[col_patid]
+        except:
+            # Handle cases where the line is empty
+            continue
+
+        perturbed_patid = my_dict[cur_pat_id]
+        line_as_list[col_patid] = perturbed_patid  # perturbing pat_id
+        line_as_list = ["\"" + x + "\"" for x in line_as_list]
+        line_perturbed = "|".join(line_as_list)
+        line_perturbed += '\n'
+
+        fw.write(line_perturbed)
+
+# Both name lists have the same order!
+def create_large_files(raw_data_files, raw_data_folderpath,
+                       large_data_files, large_data_folderpath, num_repeats=100, USE_CACHED_DB=True):
+    import os
+    if os.path.exists(large_data_folderpath + '/' + large_data_files[0]):
+        if USE_CACHED_DB:
+            log.info("Large files exist!")
+            return
+        else:
+            for large_data_file in large_data_files:
+                os.remove(large_data_folderpath + '/' + large_data_file)
+
+    if 'labs' not in raw_data_files[0]:
+        quit("Please place labs file as the beginning of raw_data_files!")  # TODO: quit(int)?
+
+    # query all_pat_ids from raw_data. Presumably small files, so should not be problem
+    with open(raw_data_folderpath + '/' + raw_data_files[0]) as f:
+        lines_lab = f.readlines()
+        f.close()
+    all_pat_ids = set(
+        [line_str2list(line)[1] for line in lines_lab[1:]])  # set([line.split('|')[1] for line in lines[1:]])
+
+    # Each time, perturb pat_ids in a specific random way, and modify all tables accordingly...
+    for _ in range(num_repeats):
+        # Create a different perturbation rule each time
+        my_dict = {}
+        for pat_id in all_pat_ids:
+            my_dict[pat_id] = perturb_str(pat_id)
+
+        # For each perturbation, perturb all tables
+        for ind in range(len(raw_data_files)):
+            raw_file_path = raw_data_folderpath + '/' + raw_data_files[ind]
+            target_file_path = large_data_folderpath + '/' + large_data_files[ind]
+            perturb_a_file(raw_file_path, target_file_path, col_patid=1, my_dict=my_dict)
+
+
+def lines2pd(lines_str, colnames):
+    normal_num_cols = len(colnames)
+
+    all_rows = []
+    for line_str in lines_str:
+        curr_row = line_str2list(line_str) #, skip_first_col=True
+
+        if len(curr_row) < normal_num_cols / 2:  #
+            # log.info('severely missing data when processing')
+            continue
+        all_rows.append(curr_row)
+
+    data_df = pd.DataFrame(all_rows, columns=colnames)
+    return data_df
+
+def construct_result_in_range_yn(df):
+    # baseline
+    result_flag_list = df['result_flag'].apply(
+        lambda x: 'N' if x == 'L' or x == 'H' or x == 'A' else 'Y').values.tolist()
+
+    value_list = df['ord_num_value'].values.tolist()
+    range_list = df['normal_range'].values.tolist()
+
+    row, col = df.shape
+
+    cnt_success = 0
+    for i in range(row):
+        try:
+            n = float(str(value_list[i]))
+            n1n2 = str(range_list[i]).split('-')
+            assert len(n1n2) == 2
+            n1, n2 = float(n1n2[0]), float(n1n2[1])
+
+            if n1 <= n <= n2:
+                result_flag_list[i] = 'Y'
+            else:
+                result_flag_list[i] = 'N'
+
+            cnt_success += 1
+        except:
+            pass
+    df['result_in_range_yn'] = pd.Series(result_flag_list).values
+    # pd.testing.assert_series_equal(df['result_in_range_yn'], df_test['result_in_range_yn'])
+    return df['result_in_range_yn']
+
+from medinfo.db import DBUtil
+if test_mode:
+    time_min = '1900-01-01'
+else:
+    time_min = '2015-01-01'
+
+
+def pd_process_labs(labs_df, order_proc_ids_to_include=None):
+    # print labs_df.columns
+    labs_df = labs_df.rename(columns={'PatientID': 'pat_id',
+                                      'EncounterID': 'order_proc_id',
+                                      'COLLECTION_DATE': 'result_time',
+                                      'ORDER_CODE': 'proc_code',
+                                      'RESULT_CODE': 'base_name',
+                                      'VALUE': 'ord_num_value',
+                                      'RANGE': 'normal_range',
+                                      'HILONORMAL_FLAG': 'result_flag'
+                                      }
+                             )
+    if order_proc_ids_to_include and not test_mode: # for including only inpatients
+        labs_df = labs_df[labs_df['order_proc_id'].isin(order_proc_ids_to_include)]
+
+    labs_df = labs_df[labs_df['result_time'] > time_min]  # DBUtil.datetime()
+    # labs_df = labs_df[labs_df[]]
+
+    # Hash the string type pat_id to long int to fit into CDSS pipeline
+    labs_df['pat_id'] = labs_df['pat_id'].apply(lambda x: hash(x))
+
+    # Decision: use 'COLLECTION_DATE' (result time) as 'order_time'
+    labs_df['order_time'] = labs_df['result_time'].copy()
+
+    labs_df = labs_df[labs_df['base_name'].map(lambda x: str(x) != '*')]
+
+    if labs_df.shape[0] == 0:
+        labs_df = pd.DataFrame(columns=['pat_id', 'order_proc_id', 'order_time', 'result_time',
+                                        'proc_code', 'base_name', 'ord_num_value', 'result_in_range_yn', 'result_flag'])
+        # labs_df['result_in_range_yn'] = labs_df['result_flag']
+
+    else:
+
+        # Create redundant info to fit into CDSS pipeline
+        labs_df['result_in_range_yn'] = construct_result_in_range_yn(
+            labs_df[['ord_num_value', 'normal_range', 'result_flag']])
+
+        # Decision: use (a+b)/2 for the "a-b" range case
+        labs_df['ord_num_value'] = labs_df['ord_num_value'].apply(lambda x: filter_range(x) if '-' in x else x)
+        # Decision: use "0.1", "60" to handles cases like "<0.1", ">60" cases
+        labs_df['ord_num_value'] = labs_df['ord_num_value'].apply(lambda x: filter_nondigits(x))
+
+        # Make 00:00:00.0000000000 (hr;min;sec) into 00:00:00 to allow CDSS parse later
+        labs_df['order_time'] = labs_df['order_time'].apply(lambda x: remove_microsecs(x))
+        labs_df['result_time'] = labs_df['result_time'].apply(lambda x: remove_microsecs(x))
+
+    return labs_df[['pat_id', 'order_proc_id', 'order_time', 'result_time',
+                    'proc_code', 'base_name', 'ord_num_value', 'result_in_range_yn', 'result_flag']]
+
+def pd_process_pt_info(pt_info_df):
+    pt_info_df = pt_info_df.rename(columns={'PatientID': 'pat_id', 'DOB': 'Birth'})
+    pt_info_df['pat_id'] = pt_info_df['pat_id'].apply(lambda x: hash(x))
+    pt_info_df['Birth'] = pt_info_df['Birth'].apply(lambda x: remove_microsecs(x))
+    return pt_info_df[['pat_id', 'Birth']]
+
+def pd_process_encounters(encounters_df):
+    encounters_df = encounters_df.rename(
+        columns={'PatientID': 'pat_id', 'EncounterID': 'order_proc_id', 'AdmitDate': 'AdmitDxDate'})
+    encounters_df = encounters_df[
+        (encounters_df['AdmitDxDate'] > time_min) & (encounters_df['PatientClassCode'] != 'Outpatient')]
+
+    encounters_df['pat_id'] = encounters_df['pat_id'].apply(lambda x: hash(x))
+    encounters_df['AdmitDxDate'] = encounters_df['AdmitDxDate'].apply(lambda x: remove_microsecs(x))
+    return encounters_df[['pat_id', 'order_proc_id', 'AdmitDxDate']]
+
+
+def pd_process_diagnoses(diagnoses_df):
+    diagnoses_df = diagnoses_df.rename(
+        columns={'PatientID': 'pat_id', 'EncounterID': 'order_proc_id', 'ActivityDate': 'diagnose_time',
+                 'TermCodeMapped': 'diagnose_code'})
+
+    diagnoses_df = diagnoses_df[diagnoses_df['diagnose_time'] > time_min]
+
+    '''
+    For ICD9, map the whole code
+    For ICD10, only map the prefix
+    '''
+    diagnoses_df['diagnose_code'] = diagnoses_df[['diagnose_code', 'Lexicon']].apply(
+        lambda x: x['diagnose_code'].split('.')[0] if x['Lexicon']=='ICD10' else x['diagnose_code'], axis=1)# )
+
+    diagnoses_df['pat_id'] = diagnoses_df['pat_id'].apply(lambda x: hash(x))
+    diagnoses_df['diagnose_time'] = diagnoses_df['diagnose_time'].apply(lambda x: remove_microsecs(x))
+
+    return diagnoses_df[['pat_id', 'order_proc_id', 'diagnose_time', 'diagnose_code']]
+
+
+def pd_process_demographics(demographics_df):
+    demographics_df = demographics_df.rename(columns={'PatientID': 'pat_id'})
+    demographics_df['pat_id'] = demographics_df['pat_id'].apply(lambda x: hash(x))
+
+    demographics_df['GenderName'] = demographics_df['GenderName'].apply(lambda x: 'Unknown' if not x else x)
+    demographics_df['RaceName'] = demographics_df['RaceName'].apply(lambda x: 'Unknown' if not x else x)
+    demographics_df['RaceName'] = demographics_df['RaceName'].apply(lambda x: x.replace("/", "-"))
+    return demographics_df[['pat_id', 'GenderName', 'RaceName']]
+
+def pd2db(data_df, db_path, table_name, db_name, order_proc_ids_to_include=None):
+    conn = sqlite3.connect(db_path + '/' + db_name)
+
+    if table_name == "labs":  #
+        data_df = pd_process_labs(data_df, order_proc_ids_to_include)
+    elif table_name == "pt_info":
+        data_df = pd_process_pt_info(data_df)
+    elif table_name == "encounters":
+        data_df = pd_process_encounters(data_df)
+    elif table_name == "demographics":
+        data_df = pd_process_demographics(data_df)
+    elif table_name == "diagnoses":
+        data_df = pd_process_diagnoses(data_df)
+    else:
+        print(table_name + " does not exist!")
+
+    data_df.to_sql(table_name, conn, if_exists="append")
+
+    if table_name == 'encounters':
+        return data_df['order_proc_id'].values.tolist()
+
+def raw2db(data_file, data_folderpath, db_path, db_name, build_index_patid=True, collected_included_order_proc_ids=None):
+    chunk_size = 100000  # num of rows
+
+    print('Now writing %s into database...' % data_file)  #
+
+    table_name = data_file.replace(".txt", "")
+    table_name = table_name.replace(".sample", "")
+    table_name = table_name.replace(".large", "")
+    table_name = table_name.replace('.', '_')  # pt.info
+
+    if table_name == 'encounters':
+        all_included_order_proc_ids = []
+
+    with open(data_folderpath + '/' + data_file) as f:
+        is_first_chunk = True
+        while True:
+            # a chunk of rows
+            next_n_lines_str = list(islice(f, chunk_size))
+            if not next_n_lines_str:
+                break
+
+            if is_first_chunk:
+                colnames = line_str2list(next_n_lines_str[0])
+                # print colnames
+                data_df = lines2pd(next_n_lines_str[1:], colnames)
+                is_first_chunk = False
+            else:  ## make each chunk into pandas
+                data_df = lines2pd(next_n_lines_str, colnames)
+
+            ## append each pandas to db tables
+            if table_name == 'encounters':
+                cur_included_order_proc_ids = pd2db(data_df, db_path=db_path, db_name=db_name, table_name=table_name)
+                all_included_order_proc_ids += cur_included_order_proc_ids
+            elif table_name == 'labs':
+                pd2db(data_df, db_path=db_path, db_name=db_name, table_name=table_name,
+                      order_proc_ids_to_include=collected_included_order_proc_ids)
+            else:
+                pd2db(data_df, db_path=db_path, db_name=db_name, table_name=table_name)
+            ##
+    if build_index_patid:
+        conn = sqlite3.connect(db_path + '/' + db_name)
+        build_index_query = "CREATE INDEX IF NOT EXISTS index_for_%s ON %s (%s);" % (table_name, table_name, 'pat_id')
+        # print build_index_query
+        conn.execute(build_index_query)
+
+    if table_name == 'encounters':
+        return set(all_included_order_proc_ids)
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/multisite_mapping/map_component.tab b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_component.tab
new file mode 100644
index 00000000..5f9a609c
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_component.tab
@@ -0,0 +1,23 @@
+lab	Stanford	UMich	UCSF	description
+WBC	WBC	WBC	WBC	White Blood Cells
+HGB	HGB	HGB	HGB	Hemoglobin
+PLT	PLT	PLT	PLT	Platelet Count
+NA	NA	SOD	NA	Sodium
+K	K	POT	K	Potassium
+CL	CL	CHLOR	CL	Chloride
+CR	CR	CREAT	CREAT	Creatinine
+BUN	BUN	UN	BUN	Urea Nitrogen
+GLU	GLU			Glucose
+CO2	CO2	CO2	CO2	CO2
+CA	CA	CAL	CA	Calcium
+TP	TP	PROT	TP	Total Protein
+ALB	ALB	ALB	ALB	Albumin
+ALKP	ALKP	ALK	ALKP	Alk Phos
+TBIL	TBIL	TBIL	TBILI	Total Bilirubin
+AST	AST	AST	AST	"AST (SGOT), Serum/plasma"
+ALT	ALT	ALT	ALT	"ALT (SGPT), Serum/plasma"
+DBIL	DBIL	DBIL	DBILI	Conjugated Bilirubin
+IBIL	IBIL	IBIL		"Bilirubin Indirect, Serum/plasma"
+PHA	PHA	pHA	PH37	PH (A)
+PCO2A	PCO2A	PCOAA2	PCO2	"PCO2, ISTAT"
+PO2A	PO2A	PO2AA	PO2	"PO2, ISTAT"
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/multisite_mapping/map_cormobidity.tab b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_cormobidity.tab
new file mode 100644
index 00000000..858928b0
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_cormobidity.tab
@@ -0,0 +1,6 @@
+cormobidity	Stanford	UMich	UCSF	description
+Malignancy	Malignancy		Cancer	
+CHF	CHF		CongestiveHeartFailure	
+MI	MI		MyocardialInfarction	
+Cerebrovascular	Cerebrovascular		CerebrovascularDisease	
+Diabetes	Diabetes		Diabeteswithoutcomplications	
diff --git a/scripts/Archive/LabTestAnalysis/multisite_mapping/map_standalone.tab b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_standalone.tab
new file mode 100644
index 00000000..3c53aa64
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_standalone.tab
@@ -0,0 +1,90 @@
+lab	Stanford	UMich	UCSF	description
+LABUA	LABUA			Urinalysis With Microscopic
+LABBLC	LABBLC	BLD		Blood Culture (Aerobic & Anaerobic)
+LABPT	LABPT	PT7	Prothrombin Time	Prothrombin Time
+LABPTT	LABPTT	PTT	Activated Partial Thromboplastin Time	Partial Thromboplastin Time
+LABTSH	LABTSH	TSH	Thyroid Stimulating Hormone	Thyroid Stimulating Hormone
+LABPHOS	LABPHOS	PHOS	Phosphorus, Serum / Plasma	Phosphorus, Serum/plasma
+LABURNC	LABURNC		Urine Culture	Urine Culture
+LABA1C	LABA1C	A1C	Hemoglobin A1c	Hemoglobin A1c
+LABMGN	LABMGN	MAG	Magnesium, Serum / Plasma	Magnesium, Serum/plasma
+LABHAP	LABHAP			Haptoglobin
+LABANER	LABANER			Anaerobic Culture
+LABPCTNI	LABPCTNI			iSTAT Troponin I
+LABNTBNP	LABNTBNP			Nt - Probnp
+LABK	LABK	POT	Potassium, Serum / Plasma	Potassium, Serum/plasma
+LABTNI	LABTNI	TROP	Troponin I	Troponin I
+LABUAPRN	LABUAPRN			Urinalysis, Screen For Culture
+LABCAI	LABCAI		Calcium, Ionized, serum/plasma	Calcium Ionized, Serum/plasma
+LABCAI	LABCAI		Calcium, total, Serum / Plasma	Calcium Ionized, Serum/plasma
+LABHCTX	LABHCTX			Hematocrit
+LABMB	LABMB			Ck Mb (mass)
+LABLIPS	LABLIPS			Lipase
+LABCK	LABCK		Creatine kinase, total	Creatine Kinase, Total
+LABBLC2	LABBLC2	BLDAN		Blood Culture (2 Aerobic)
+LABLDH	LABLDH		Lactate Dehydrogenase, Serum / Plasma	LDH Total, Serum / Plasma
+LABTRFS	LABTRFS			Transferrin Saturation
+LABLAC	LABLAC			Lactic Acid
+LABFER	LABFER			Ferritin
+LABFT4	LABFT4			T4 Free
+LABCORT	LABCORT			Cortisol, Serum
+LABURIC	LABURIC	URIC	Uric Acid, Serum / Plasma	Uric Acid, Serum / Plasma
+LABGRAM	LABGRAM			Gram Stain
+LABFE	LABFE		Ferritin	Iron Total
+LABB12	LABB12		Vitamin B12	Vitamin B12
+LABFIB	LABFIB			Fibrinogen
+LABCRP	LABCRP		C-Reactive Protein	C-Reactive Protein
+LABRETIC	LABRETIC			Reticulocyte Count Automated
+LABBXTG	LABBXTG			Biopsy / Tissue Culture And Gram Stain
+LABPLTS	LABPLTS		Platelet count	Platelet Count
+LABPCCR	LABPCCR			iSTAT Creatinine
+LABFCUL	LABFCUL			Fungal Culture
+LABFLDC	LABFLDC			Fluid Culture And Gram Stain
+LABTRIG	LABTRIG			Triglycerides, Serum / Plasma
+LABNH3	LABNH3			Ammonia, Plasma
+LABBLCTIP	LABBLCTIP			Blood Cult - 2nd Set Via Catheter
+LABRESP	LABRESP			Respiratory Culture
+LABHEPAR	LABHEPAR			Heparin Activity Level
+LABFOL	LABFOL			Folic Acid
+LABCA	LABCA	CAL		Calcium, Serum/plasma
+LABBLCSTK	LABBLCSTK			Blood Cult - First Set, Via Phlebotomy
+LABUOSM	LABUOSM			Osmolality, Urine
+LABUSPG	LABUSPG			Specific Gravity, Urine
+LABRESPG	LABRESPG			Respiratory Culture And Gram Stain
+LABCSFGL	LABCSFGL			Glucose, Csf
+LABHBSAG	LABHBSAG			Hepatitis B Antigen
+LABALB	LABALB	ALB	Albumin, Serum / Plasma	Albumin, Serum/plasma
+LABAFBD	LABAFBD		AFB Non-Respiratory Culture	Afb Culture, Non - Respiratory
+LABESRP	LABESRP		Sedimentation Rate	Sedimentation Rate (ESR)
+LABDIGL	LABDIGL			Digoxin, Serum
+LABSTOBGD	LABSTOBGD			Occult Bld, Guaiac, Stool, Diag Test
+LABBUN	LABBUN		Urea Nitrogen, Serum / Plasma	Urea Nitrogen, Serum/plasma
+LABNA	LABNA	SOD	Sodium, Serum / Plasma	Sodium, Serum / Plasma
+LABLIDOL	LABLIDOL			Lidocaine
+LABCSFTP	LABCSFTP			Protein Total, Csf
+LABCSFC	LABCSFC			CSF Culture And Gram Stain
+LABUPREG	LABUPREG			Pregnancy Test, Urine
+LABOSM	LABOSM		Osmolality, urine	Osmolality, Serum
+LABAFBC	LABAFBC		AFB Respiratory Culture	AFB Culture, Respiratory
+LABPCCG4O	LABPCCG4O			iSTAT Cg4, Other
+LABCDTPCR	LABCDTPCR			C.difficile Toxin B Gene, Qual. Real-time Pcr
+LABHIVWBL	LABHIVWBL			Anti-hiv
+LABPROCT	LABPROCT			Procalcitonin
+LABLACWB	LABLACWB			Lactate, Whole Blood
+LABHEPAR	LABHEPAR			Heparin, Unfractionated, Anti Xa
+LABSPLAC	LABSPLAC			Sepsis Protocol Lactate
+LABSTLCX	LABSTLCX			Stool Culture
+LABPTEG	LABPTEG			Teg, Perioperative
+LABSTLCX	LABSTLCX			Stool Culture
+LABPALB	LABPALB			Prealbumin
+LABCMVQT	LABCMVQT			Cmv Dna Pcr Quant, Plasma
+LABBXTG	LABBXTG			Biopsy/tissue With Gram Stain
+LABBLCTIP	LABBLCTIP			Blood Cult Central Line Catheter By Nurse
+LABCBCD	LABCBCD		Complete Blood Count with Differential	CBC WITH DIFF
+LABAFP	LABAFP			Alpha Fetoprotein
+LABYCP	LABYCP			C-Peptide
+LABEP1	LABEP1			Electrolyte Panel
+LABAGALA	LABAGALA			Aspegillus Galactomannan
+LABYHISTS	LABYHISTS			Histoplasma Ag
+LABSPIE	LABSPIE			Serum Protein Electrophoresis
+LABUPIE	LABUPIE			Urine Protein Electrophoresis
diff --git a/scripts/Archive/LabTestAnalysis/multisite_mapping/map_team.tab b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_team.tab
new file mode 100644
index 00000000..ff689e03
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_team.tab
@@ -0,0 +1,2 @@
+team	Stanford	UMich	UCSF	description
+CVICU	CVICU		ICU	
diff --git a/scripts/Archive/LabTestAnalysis/multisite_mapping/map_vitals.tab b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_vitals.tab
new file mode 100644
index 00000000..05d1f432
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/multisite_mapping/map_vitals.tab
@@ -0,0 +1,3 @@
+vitals	Stanford	UMich	UCSF	description
+BP_Low_Diastolic	BP_Low_Diastolic		DBP	
+BP_High_Systolic	BP_High_Systolic		SBP	
diff --git a/scripts/Archive/LabTestAnalysis/report/JAMAopenNetwork.py b/scripts/Archive/LabTestAnalysis/report/JAMAopenNetwork.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/Archive/LabTestAnalysis/report/LNPP-repro-steps.md b/scripts/Archive/LabTestAnalysis/report/LNPP-repro-steps.md
new file mode 100644
index 00000000..3ec5ff7d
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/report/LNPP-repro-steps.md
@@ -0,0 +1,38 @@
+# LNPP Report Reproduction Steps
+
+## How to generate data and analyses
+
+`python scripts/LabTestAnalysis/machine_learning/LabNormalityPredictionPipeline`
+
+This command will (1) build feature matrices if they do not already
+exist, (2) build models if they do not already exist, and (3) analyze those
+models.
+
+If you don't want to regenerate all data from scratch (takes ~1-2 hours to
+build feature matrices and all models for a single lab), just fetch the
+data from Box: https://stanfordmedicine.app.box.com/folder/48741904687
+
+In particular, download folder to
+`scripts/LabTestAnalysis/machine_learning/data`, such that the folder contains
+`scripts/LabTestAnalysis/machine_learning/data/LAB*`.
+
+## How to generate report figures
+
+Once all the data files are generated, run
+`python scripts/LabTestAnalysis/report/LabNormalityReport.py`.
+
+After ~1 minute of execution, it will have generated the following files in
+whatever directory was used to invoke the script:
+* algorithm-performance-summary.tab
+* predictable-labs.tab (Table 1)
+* predictable-and-expensive-charges.[eps/png] (Figure 3)
+
+Figures 1 and 2 (flowcharts) were edited in Figma. For access, use log in with
+Google from stanford.healthrex@gmail.com
+https://www.figma.com/file/ES38bkPcFoSCEziZrkgBRh3u/LNPP
+
+The remainder of the the tables were hand-generated in Google Sheets.
+https://docs.google.com/document/d/1qoFkphFuJ0CzhubSRXSWPk7WJQoSY43oFAAThJe91tc/edit
+* Table 2: decision-processes tab
+* Table A1: feature-summary tab
+* Table A2: algorithm-summary tab
diff --git a/scripts/Archive/LabTestAnalysis/report/LabNormalityReport.py b/scripts/Archive/LabTestAnalysis/report/LabNormalityReport.py
new file mode 100644
index 00000000..589e5bcb
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/report/LabNormalityReport.py
@@ -0,0 +1,547 @@
+#!/usr/bin/python
+"""
+Given data in /LabTestAnalysis/machine_learning/data/, build report tables.
+"""
+
+import ast
+import inspect
+import os
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas
+from pandas import DataFrame, read_csv, Series
+import sys
+
+from medinfo.common.Util import log
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+
+class LabNormalityReport:
+    PROC_CODE_TO_LABEL = {
+        'LABUSPG': 'Urine Specific Gravity',
+        'LABLIDOL': 'Lidocaine',
+        'LABURIC': 'Uric Acid',
+        'LABSPLAC': 'Lactate (Sepsis Protocol)',
+        'LABCMVQT': 'Cytomegalovirus DNA',
+        'LABAFBC': 'AFB Culture, Respiratory',
+        'LABCSFC': 'CSF Culture',
+        'LABHIVWBL': 'HIV Antibody',
+        'LABCSFGL': 'CSF Glucose',
+        'LABAFBD': 'AFB Culture, Non-Respiratory',
+        'LABPLTS': 'Platelet Count',
+        'LABLACWB': 'Lactate (Whole Blood)',
+        'LABUAPRN': 'Urinalysis, Culture Screen',
+        'LABROMRS': 'MRSA Screen',
+        'LABMB': 'Creatine Kinase-Muscle/Brain',
+        'LABLAC': 'Lactate',
+        'LABTYPSNI': 'Type and Screen',
+        'LABBLC2': 'Blood Culture (2 Aerobic)',
+        'LABTNI': 'Troponin I',
+        'LABMGN': 'Magnesium',
+        'LABUPREG': 'Pregnancy (Urine)',
+        'LABBLCSTK': 'Blood Culture (1st, Phlebotomy)',
+        'LABBLCTIP': 'Blood Culture (2nd, Catheter)',
+        'LABCK': 'Creatine Kinase',
+        'LABHEPAR': 'Heparin Activity Level',
+        'LABGRAM': 'Gram Stain',
+        'LABK': 'Potassium',
+        'LABFCUL': 'Fungal Culture',
+        'LABFIB': 'Fibrinogen',
+        'LABTRIG': 'Triglycerides',
+        'LABFLDC': 'Fluid Culture and Gram Stain',
+        'LABANER': 'Anaerobic Culture',
+        'LABCAI': 'Ionized Calcium',
+        'LABBLC': 'Blood Culture (Aerobic & Anaerobic)',
+        'LABPCG3': 'i-STAT G3+',
+        'LABA1C': 'Hemoglobin A1c',
+        'LABABG': 'Arterial Blood Gases',
+        'LABALB': 'Albumin',
+        'LABB12': 'Vitamin B12',
+        'LABBXTG': 'Biopsy / Tissue Culture & Gram Stain',
+        'LABCBCO': 'CBC',
+        'LABCBCD': 'CBC with Differential',
+        'LABCDTPCR': 'C. Diff. Toxin B Gene PCR',
+        'LABCRP': 'C-Reactive Protein',
+        'LABCSFTP': 'Cerebrospinal Fluid Total Protein',
+        'LABESRP': 'Erythrocyte Sedimentation Rate',
+        'LABFE': 'Iron',
+        'LABFER': 'Ferritin',
+        'LABFT4': 'Free T4',
+        'LABNH3': 'Ammonia',
+        'LABNTBNP': 'NT-proBNP',
+        'LABPALB': 'Prealbumin',
+        'LABPCCR': 'i-STAT Creatinine',
+        'LABPCTNI': 'i-STAT Troponin I',
+        'LABPHOS': 'Phosphorous',
+        'LABPOCGLU': 'Glucose by Meter',
+        'LABRESP': 'Respiratory Culture',
+        'LABRETIC': 'Reticulocyte Count',
+        'LABSTOBGD': 'Stool Guaiac',
+        'LABTRFS': 'Transferrin Saturation',
+        'LABTSH': 'Thyroid-Stimulating Hormone',
+        'LABUA': 'Microscopic Urinalysis',
+        'LABUOSM': 'Urine Osmolality',
+        'LABUCR': 'Urine Creatinine',
+        'LABURNA': 'Urine Sodium',
+        'LABURNC': 'Urine Culture',
+        'LABVANPRL': 'Vancomycin Trough Level',
+        'LABVBG': 'Venous Blood Gases',
+        'LABHAP': 'Haptoglobin',
+        'LABHCTX': 'Hematocrit',
+        'LABHFP': 'Hepatic Function Panel A',
+        'LABLDH': 'Total LDH',
+        'LABLIPS': 'Lipase',
+        'LABMETB': 'Basic Metabolic Panel',
+        'LABMETC': 'Comprehensive Metabolic Panel',
+        'LABNA': 'Sodium',
+        'LABDIGL': 'Digoxin',
+        'LABCA': 'Calcium',
+        'LABBUN': 'Blood Urea Nitrogen',
+        'LABFOL': 'Folic Acid',
+        'LABPT': 'Prothrombin Time',
+        'LABPTT': 'Partial Thromboplastin Time',
+        'LABHBSAG': 'Hepatitis B Surface Antigen',
+        'LABOSM': 'Osmolality',
+        'LABPCCG4O': 'i-STAT CG4 (Other)',
+        'LABPROCT': 'Procalcitonin',
+        'LABPTEG': 'Perioperative Thromboelastography',
+        'LABRESPG': 'Respiratory Culture and Gram Stain',
+        'LABSTLCX': 'Stool Culture',
+        'LABCORT': 'Cortisol'
+    }
+
+    def __init__(self):
+        self._fm_io = FeatureMatrixIO()
+
+    @staticmethod
+    def fetch_components_in_panel(lab_panel):
+        # Doing a single query results in a sequential scan through
+        # stride_order_results. To avoid this, break up the query in two.
+        # First, get all the order_proc_ids for proc_code.
+        query = SQLQuery()
+        query.addSelect('order_proc_id')
+        query.addFrom('stride_order_proc')
+        query.addWhereIn('proc_code', [lab_panel])
+        query.addGroupBy('order_proc_id')
+        results = DBUtil.execute(query)
+        lab_order_ids = [row[0] for row in results]
+
+        # Second, get all base_names from those orders.
+        query = SQLQuery()
+        query.addSelect('base_name')
+        query.addFrom('stride_order_results')
+        query.addWhereIn('order_proc_id', lab_order_ids)
+        query.addGroupBy('base_name')
+        results = DBUtil.execute(query)
+        components = [row[0] for row in results]
+
+        return components
+
+    @staticmethod
+    def fetch_data_dir_path():
+        # e.g. report_dir = CDSS/scripts/LabTestAnalysis/data
+        data_dir = LabNormalityReport.fetch_report_dir_path()
+        report_dir_list = data_dir.split('/')[:-1]
+        report_dir_list.extend(['machine_learning', 'data'])
+
+        report_dir = '/'.join(report_dir_list)
+        return report_dir
+
+    @staticmethod
+    def fetch_report_dir_path():
+        # e.g. data_dir = CDSS/scripts/LabTestAnalysis/report
+        file_name = inspect.getfile(inspect.currentframe())
+        data_dir = os.path.dirname(os.path.abspath(file_name))
+        return data_dir
+
+    @staticmethod
+    def fetch_lab_panels():
+        # Treat LabNormalityPredictionPipeline as the source of truth for
+        # defining which algorithms to analyze. Infer list from data dir.
+        data_dir = LabNormalityReport.fetch_data_dir_path()
+        labs = os.listdir(data_dir)
+        # OSX automatically creates .DS_Store files.
+        if '.DS_Store' in labs:
+            labs.remove('.DS_Store')
+        # Hack to ignore data logging directory.
+        if 'log' in labs:
+            labs.remove('log')
+        if 'LABNONGYN' in labs:
+            labs.remove('LABNONGYN')
+        return sorted(labs)
+
+    @staticmethod
+    def read_lab_meta_report(lab_panel):
+        fm_io = FeatureMatrixIO()
+        data_dir = LabNormalityReport.fetch_data_dir_path()
+        meta_report_path = data_dir + '/%s/%s-normality-prediction-report.tab' % (lab_panel, lab_panel)
+        if os.path.exists(meta_report_path):
+            meta_report = fm_io.read_file_to_data_frame(meta_report_path)
+            return meta_report
+        else:
+            # IF meta_report does not exist, fetch the data on class counts.
+            algorithm = SupervisedClassifier.REGRESS_AND_ROUND
+            report_path = data_dir + '/%s/%s/%s-normality-prediction-report.tab' % (lab_panel, algorithm, lab_panel)
+            algorithm_report = fm_io.read_file_to_data_frame(report_path)
+            return algorithm_report
+
+    @staticmethod
+    def fetch_best_predictor(lab_panel):
+        meta_report = LabNormalityReport.read_lab_meta_report(lab_panel)
+
+        # Check for whether 0 classifiers were able to successfully train.
+        if 'error' in meta_report.columns.values:
+            # On the fly, interpret failure as a new "classifier".
+            meta_report = meta_report.loc[0]
+            train_value_counts = ast.literal_eval('%s' % meta_report['y_train.value_counts()'])
+            test_value_counts = ast.literal_eval('%s' % meta_report['y_test.value_counts()'])
+
+            max_value = np.max(list(train_value_counts.values()))
+            common_label = [k for k, v in train_value_counts.items() if v == max_value][0]
+            test_size = np.sum(list(test_value_counts.values()))
+            counts = meta_report['y_test.value_counts()']
+            test_normal_count = ast.literal_eval(counts)[1.0]
+            try:
+                test_abnormal_count = ast.literal_eval(counts)[0.0]
+            except:
+                test_abnormal_count = 0
+            accuracy = test_value_counts[common_label] / test_size
+            recall = 1.0 if common_label == 1 else 0.0
+            percent_predictably_positive = 0.0
+            ppp_lower_ci = 0.0
+            ppp_upper_ci = 1.0
+            precision = (test_value_counts[1] / test_size) if common_label == 1 else 0.0
+            f1 = 2 * (precision * recall) / (precision + recall)
+            average_precision = precision
+            precision_at_10_percent = precision
+            roc_auc = None
+            roc_auc_lower_ci = None
+            roc_auc_upper_ci = None
+            hyperparams = None
+
+            best_predictor = DataFrame({
+                'model': ['CONSTANT(label=%s)' % common_label],
+                'test_size': [test_size],
+                'counts': counts,
+                'test_normal_count': [test_normal_count],
+                'test_abnormal_count': [test_abnormal_count],
+                'accuracy': [accuracy],
+                'recall': [recall],
+                'precision': [precision],
+                'f1': [f1],
+                'average_precision': [average_precision],
+                'percent_predictably_positive': [percent_predictably_positive],
+                'percent_predictably_positive_0.95_lower_ci': [ppp_lower_ci],
+                'percent_predictably_positive_0.95_upper_ci': [ppp_upper_ci],
+                'precision_at_10_percent': [precision_at_10_percent],
+                'roc_auc': [roc_auc],
+                'roc_auc_0.95_lower_ci': [roc_auc_lower_ci],
+                'roc_auc_0.95_upper_ci': [roc_auc_upper_ci],
+                'hyperparams': [hyperparams]
+            })
+
+            return best_predictor.iloc[0]
+
+        # Note that there might be multiple values with the same max value.
+        best_predictor = meta_report.loc[meta_report['percent_predictably_positive_0.95_lower_ci'].astype('float') == meta_report['percent_predictably_positive_0.95_lower_ci'].astype('float').max()]
+        num_predictors = len(best_predictor.index)
+
+        # Break ties based on roc_auc.
+        if num_predictors != 1:
+            best_predictor = best_predictor.loc[best_predictor['percent_predictably_positive'].astype('float') == best_predictor['percent_predictably_positive'].astype('float').max()]
+            num_predictors = len(best_predictor.index)
+
+        # Break ties based on roc_auc.
+        if num_predictors != 1:
+            best_predictor = best_predictor.loc[best_predictor['roc_auc'].astype('float') == best_predictor['roc_auc'].astype('float').max()]
+            num_predictors = len(best_predictor.index)
+
+        # Break ties on accuracy.
+        if num_predictors != 1:
+            best_predictor = best_predictor.loc[best_predictor['accuracy'].astype('float') == best_predictor['accuracy'].astype('float').max()]
+            num_predictors = len(best_predictor.index)
+
+        # If still tied, return first.
+        best_predictor = best_predictor.iloc[0]
+        counts = best_predictor['y_test.value_counts()']
+        test_normal_count = ast.literal_eval(counts)[1.0]
+        test_abnormal_count = ast.literal_eval(counts)[0.0]
+        best_predictor['test_normal_count'] = test_normal_count
+        best_predictor['test_abnormal_count'] = test_abnormal_count
+
+        return best_predictor
+
+    @staticmethod
+    def fetch_summary_stats_dir():
+        report_dir = LabNormalityReport.fetch_report_dir_path()
+        stats_dir_list = report_dir.split('/')[:-1]
+        stats_dir_list.extend(['lab_statistics', 'data_summary_stats'])
+        stats_dir = '/'.join(stats_dir_list)
+
+        return stats_dir
+
+    @staticmethod
+    def fetch_lab_panel_summary_stats(lab_panel):
+        stats_dir = LabNormalityReport.fetch_summary_stats_dir()
+        panel_stats_path = '/'.join([stats_dir, 'labs_charges_volumes.csv'])
+        panel_stats = read_csv(panel_stats_path)
+        panel_index = panel_stats.index[panel_stats['name'] == lab_panel].tolist()[0]
+        panel_row = panel_stats.loc[panel_index]
+
+        return panel_row
+
+    @staticmethod
+    def fetch_median_charge(lab_panel):
+        if lab_panel == 'LABNA':
+            return '0.50'
+        panel_row = LabNormalityReport.fetch_lab_panel_summary_stats(lab_panel)
+
+        return '{:.2f}'.format(panel_row['median_price'])
+
+    @staticmethod
+    def fetch_description(lab_panel):
+        if lab_panel == 'LABNA':
+            return 'NA'
+
+        panel_row = LabNormalityReport.fetch_lab_panel_summary_stats(lab_panel)
+
+        return panel_row['description']
+
+    @staticmethod
+    def fetch_volume(lab_panel):
+        if lab_panel == 'LABNA':
+            return 10
+
+        panel_row = LabNormalityReport.fetch_lab_panel_summary_stats(lab_panel)
+
+        return panel_row['count']
+
+    @staticmethod
+    def build_lab_performance_summary_table():
+        # Include the following fields:
+        # * lab_panel
+        # * description
+        # * median_charge
+        # * volume
+        # * median_charge_volume ($)
+        # * best_algorithm
+        # * percent_predictably_positive
+        # * predicatble_charge_volume ($)
+        lab_summary = DataFrame()
+        lab_panels = LabNormalityReport.fetch_lab_panels()
+        for lab_panel in lab_panels:
+            best_predictor = LabNormalityReport.fetch_best_predictor(lab_panel)
+            description = LabNormalityReport.fetch_description(lab_panel)
+            median_charge = LabNormalityReport.fetch_median_charge(lab_panel)
+            # counts = best_predictor['y_test.value_counts()']
+            test_normal_count = best_predictor['test_normal_count']
+            test_abnormal_count = best_predictor['test_abnormal_count']
+            volume = LabNormalityReport.fetch_volume(lab_panel)
+            median_charge_volume = float(median_charge) * float(volume)
+            roc_auc = best_predictor['roc_auc']
+            roc_auc_lower_ci = best_predictor['roc_auc_0.95_lower_ci']
+            roc_auc_upper_ci = best_predictor['roc_auc_0.95_upper_ci']
+            best_model = best_predictor['model']
+            ppp = float(best_predictor['percent_predictably_positive'])
+            ppp_lower_ci = best_predictor['percent_predictably_positive_0.95_lower_ci']
+            ppp_upper_ci = best_predictor['percent_predictably_positive_0.95_upper_ci']
+            # components = LabNormalityReport.fetch_components_in_panel(lab_panel)
+            row = DataFrame({
+                'lab_panel': [lab_panel],
+                'description': [description],
+                'test_normal_count': test_normal_count,
+                'test_abnormal_count': test_abnormal_count,
+                'median_charge': [median_charge],
+                'volume': [volume],
+                'median_charge_volume ($)': ['{:.2f}'.format(median_charge_volume)],
+                'best_model': [best_model],
+                'roc_auc': [roc_auc],
+                'roc_auc_0.95_lower_ci': [roc_auc_lower_ci],
+                'roc_auc_0.95_upper_ci': [roc_auc_upper_ci],
+                'percent_predictably_positive': [ppp],
+                'percent_predictably_positive_0.95_lower_ci': [ppp_lower_ci],
+                'percent_predictably_positive_0.95_upper_ci': [ppp_upper_ci],
+                'predictable_charge_volume ($)': ['{:.2f}'.format(ppp * median_charge_volume)],
+            })
+            lab_summary = lab_summary.append(row.loc[0], ignore_index=True)
+
+        return DataFrame(lab_summary, columns=['lab_panel', 'description', 'test_normal_count', 'test_abnormal_count', 'median_charge', 'volume', 'median_charge_volume ($)',
+                    'best_model', 'roc_auc', 'roc_auc_0.95_lower_ci', 'roc_auc_0.95_upper_ci',
+                    'percent_predictably_positive', 'percent_predictably_positive_0.95_lower_ci',
+                    'percent_predictably_positive_0.95_upper_ci', 'predictable_charge_volume ($)'])
+
+    @staticmethod
+    def build_lab_performance_report():
+        lab_summary = LabNormalityReport.build_lab_performance_summary_table()
+
+
+    @staticmethod
+    def fetch_algorithm_performance(lab_panel, algorithm):
+        meta_report = LabNormalityReport.read_lab_meta_report(lab_panel)
+
+        # Because all the bifurcated classifiers contain the algorithm
+        # string for the base classifier, we need to use two masks to generate
+        # the right mask.
+        if 'cross-validation' in algorithm:
+            algorithm = algorithm.strip('-cross-validation')
+        if 'model' not in meta_report.columns.values:
+            return meta_report
+        non_bifurcated_mask = meta_report['model'].str.contains(algorithm.upper().replace('-','_'))
+        bifurcated_mask = meta_report['model'].str.contains(('bifurcated-' + algorithm).upper().replace('-','_'))
+        if 'bifurcated' in algorithm:
+            combined_mask = non_bifurcated_mask
+        else:
+            combined_mask = non_bifurcated_mask & ~bifurcated_mask
+
+        algorithm_report = meta_report[combined_mask]
+
+        return algorithm_report
+
+    @staticmethod
+    def build_algorithm_performance_summary(algorithm):
+        models = DataFrame()
+        lab_panels = LabNormalityReport.fetch_lab_panels()
+        best_count = 0
+        for lab_panel in lab_panels:
+            algorithm_report = LabNormalityReport.fetch_algorithm_performance(lab_panel, algorithm)
+            if 'model' not in algorithm_report.columns.values or algorithm_report.empty:
+                continue
+            models = models.append(algorithm_report, ignore_index=True)
+            best_algorithm = LabNormalityReport.fetch_best_predictor(lab_panel)
+            # best_algorithm.reset_index(inplace=True)
+            algorithm_report.reset_index()
+            if algorithm_report.iloc[0]['model'] == best_algorithm['model']:
+                best_count += 1
+            algorithm_report['lab_panel'] = lab_panel
+
+        min_k_99 = models['percent_predictably_positive'].min()
+        median_k_99 = models['percent_predictably_positive'].median()
+        max_k_99 = models['percent_predictably_positive'].max()
+        min_roc_auc = models['roc_auc'].min()
+        median_roc_auc = models['roc_auc'].median()
+        max_roc_auc = models['roc_auc'].max()
+        algorithm_summary = DataFrame({
+            'algorithm': [algorithm],
+            'best_predictor_count': [best_count],
+            'min(predictability@0.99)': [min_k_99],
+            'median(predictability@0.99)': [median_k_99],
+            'max(predictability@0.99)': [max_k_99],
+            'min(roc_auc)': [min_roc_auc],
+            'median(roc_auc)': [median_roc_auc],
+            'max(roc_auc)': [max_roc_auc]
+        }, columns=['algorithm', 'best_predictor_count', 'min(predictability@0.99)',
+                    'median(predictability@0.99)', 'max(predictability@0.99)', 'min(roc_auc)',
+                    'median(roc_auc)','max(roc_auc)'])
+
+        return algorithm_summary
+
+    @staticmethod
+    def build_algorithm_performance_summary_table():
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+            algorithms_to_test.append('bifurcated-%s' % algorithm)
+
+        summary = DataFrame()
+        for algorithm in algorithms_to_test:
+            algorithm_summary = LabNormalityReport.build_algorithm_performance_summary(algorithm)
+            summary = summary.append(algorithm_summary, ignore_index=True)
+
+        return summary
+
+    @staticmethod
+    def fetch_predictable_and_expensive_labs(all=None):
+        labs = LabNormalityReport.build_lab_performance_summary_table()
+        if all:
+            predictable_labs = labs
+        else:
+            predictable_labs = labs.loc[labs['percent_predictably_positive'] >= 0.1]
+        # labs = pandas.concat([predictable_labs,expensive_labs]).drop_duplicates().reset_index(drop=True)
+        labs = predictable_labs
+        labs['normal_rate'] = labs['test_normal_count'] / (labs['test_normal_count'] + labs['test_abnormal_count'])
+        labs['abnormal_rate'] = labs['test_abnormal_count'] / (labs['test_normal_count'] + labs['test_abnormal_count'])
+        labs['annual_median_charge_volume ($)'] = labs['median_charge_volume ($)'].astype('float') / 6.0
+        labs['label'] = None
+        labs.set_index('lab_panel', drop=False, inplace=True)
+
+        # Add labels.
+        for proc_code, label in LabNormalityReport.PROC_CODE_TO_LABEL.items():
+            if proc_code in labs['lab_panel'].values:
+                labs.at[proc_code, 'label'] = label
+
+        return labs
+
+    @staticmethod
+    def plot_predictable_and_expensive_charges():
+        labs = LabNormalityReport.fetch_predictable_and_expensive_labs()
+        # Build dataframe.
+        charges = DataFrame()
+        charges['label'] = labs['label']
+        charges['normal, predictable'] = labs['percent_predictably_positive'] * labs['annual_median_charge_volume ($)'].astype('float')
+        charges['normal, unpredictable'] = labs['normal_rate'] * labs['annual_median_charge_volume ($)'].astype('float') - charges['normal, predictable']
+        charges['abnormal'] = labs['abnormal_rate'] * labs['annual_median_charge_volume ($)'].astype('float')
+        charges['total'] = labs['annual_median_charge_volume ($)'].astype('float')
+        charges.sort_values('normal, predictable', inplace=True)
+
+        matplotlib.rcParams.update({'font.family': 'serif'})
+        matplotlib.rcParams.update({'font.sans-serif': ['Helvetica', 'Arial', 'Tahoma']})
+        matplotlib.rcParams.update({'font.serif': ['Times New Roman', 'Times', 'Palatino']})
+        figure = plt.figure()
+        title = "Lab test annual predictable charge volume"
+        axes = charges[['label', 'normal, predictable', 'normal, unpredictable', 'abnormal']].plot(kind='barh', \
+                    stacked=True, color=['#4caf50', '#448aff', '#f44336'], \
+                    width=0.85, title=title, linewidth=0.5, edgecolor='#ffffff', \
+                    logx=False)
+
+        axes.set_ylabel("")
+        axes.set_yticklabels(charges['label'])
+        axes.set_xlabel("Annual Charge Volume ($, millions)")
+        # axes.set_xticklabels(['0', '0', '5', '10', '15', '20', '25'])
+        axes.set_xticklabels(['0', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
+        # for p in axes.patches:
+        #     axes.annotate(str(p.get_width()), (p.get_width() * 1.005, p.get_y() * 1.005))
+
+        plt.tight_layout()
+        plt.savefig('predictable-and-expensive-charges.png', dpi=144)
+
+    @staticmethod
+    def build_lab_predictability_summary_report(all=None):
+        labs = LabNormalityReport.fetch_predictable_and_expensive_labs(all)
+
+        labs['normality'] = labs['normal_rate'].astype('float').map('{:.1%}'.format)
+        labs['predictability@0.99'] = labs['percent_predictably_positive'].astype('float').map('{:.1%}'.format)
+        labs['predictability@0.99[-0.95]'] = labs['percent_predictably_positive_0.95_lower_ci'].astype('float').map('{:.1%}'.format)
+        labs['predictability@0.99[+0.95]'] = labs['percent_predictably_positive_0.95_upper_ci'].astype('float').map('{:.1%}'.format)
+        labs['predictable_CV'] = (labs['percent_predictably_positive'].astype('float') * labs['annual_median_charge_volume ($)'].astype('float') / 1000).map('${:,.0f}'.format)
+        labs['predictable_CV[-0.95]'] = (labs['percent_predictably_positive_0.95_lower_ci'].astype('float') * labs['annual_median_charge_volume ($)'].astype('float') / 1000).map('${:,.0f}'.format)
+        labs['predictable_CV[+0.95]'] = (labs['percent_predictably_positive_0.95_upper_ci'].astype('float') * labs['annual_median_charge_volume ($)'].astype('float') / 1000).map('${:,.0f}'.format)
+
+        summary = DataFrame()
+        summary['lab'] = labs['label']
+        summary['charge'] = labs['median_charge'].astype('float').map('${:,.0f}'.format)
+        summary['volume'] = labs['volume'].floordiv(6).astype('float').map('{:,.0f}'.format)
+        summary['normal rate'] = labs['normality']
+        summary['predictability@0.99'] = labs['predictability@0.99'] + ' [' + \
+            labs['predictability@0.99[-0.95]'] + ', ' + \
+            labs['predictability@0.99[+0.95]'] + ']'
+        summary['predictable CV ($1,000s)'] = labs['predictable_CV'] + ' [' + \
+            labs['predictable_CV[-0.95]'] + ', ' + \
+            labs['predictable_CV[+0.95]'] + ']'
+
+        return summary
+
+
+if __name__ == '__main__':
+    fm_io = FeatureMatrixIO()
+    summary_table = LabNormalityReport.build_lab_performance_summary_table()
+    fm_io.write_data_frame_to_file(summary_table, 'lab-performance-summary.tab')
+    summary = LabNormalityReport.build_algorithm_performance_summary_table()
+    fm_io.write_data_frame_to_file(summary, 'algorithm-performance-summary.tab')
+    LabNormalityReport.plot_predictable_and_expensive_charges()
+    summary = LabNormalityReport.build_lab_predictability_summary_report()
+    fm_io.write_data_frame_to_file(summary, 'predictable-labs.tab')
+    summary = LabNormalityReport.build_lab_predictability_summary_report(all=True)
+    fm_io.write_data_frame_to_file(summary, 'all-labs.tab')
diff --git a/scripts/Archive/LabTestAnalysis/report/__init__.py b/scripts/Archive/LabTestAnalysis/report/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/Archive/LabTestAnalysis/report/data_inspector.py b/scripts/Archive/LabTestAnalysis/report/data_inspector.py
new file mode 100644
index 00000000..343f9fe8
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/report/data_inspector.py
@@ -0,0 +1,70 @@
+
+
+'''
+inspect how many patients overlap
+'''
+
+from sklearn.model_selection import train_test_split
+# TODO: previous import
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+import pandas as pd
+from scripts.LabTestAnalysis.machine_learning import LabNormalityPredictionPipeline
+import matplotlib
+matplotlib.rcParams['backend'] = 'TkAgg'
+import matplotlib.pyplot as plt
+
+folder = '../machine_learning/data/'
+labs = LabNormalityPredictionPipeline.NON_PANEL_TESTS_WITH_GT_500_ORDERS
+
+if False:
+    all_testset_leakage_percentages = []
+    all_best_aucrocs = []
+    for lab in labs:
+
+        try:
+            raw_matrix_file = '%s-normality-matrix-10000-episodes-raw.tab'%lab
+            fm_io = FeatureMatrixIO()
+            raw_matrix = fm_io.read_file_to_data_frame(folder+'/'+lab+'/'+raw_matrix_file)
+
+            row, col = raw_matrix.shape
+
+            from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+            algs = SupervisedClassifier.SUPPORTED_ALGORITHMS
+
+            best_aucroc = 0
+            for alg in algs:
+                report_file = '%s-normality-prediction-%s-report.tab'%(lab,alg)
+                report_df = pd.read_csv(folder+'/'+lab+'/'+alg+'/'+report_file, sep='\t')
+                best_aucroc = max(report_df['roc_auc'].values[0], best_aucroc)
+
+            all_best_aucrocs.append(best_aucroc)
+
+
+            random_state = 123456789
+            train_inds, test_inds = train_test_split(raw_matrix['pat_id'], random_state=random_state)
+
+            train_inds_set = set(train_inds)
+            appeared_cnt = 0
+            for ind in test_inds:
+                if ind in train_inds_set:
+                    appeared_cnt += 1
+            leakage_percent = float(appeared_cnt)/len(test_inds)
+            all_testset_leakage_percentages.append(leakage_percent)
+
+            print("lab: %s, best auc: %.4f, leakage percent: %.3f"% (lab, best_aucroc, leakage_percent))
+        except Exception as e:
+            print(e)
+            pass
+
+all_testset_leakage_percentages = [0.6828, 0.9018645731108931, 0.6929824561403509, 0.7156, 0.8268275372604684, 0.3291566265060241, 0.9376, 0.9244, 0.8488085456039441, 0.8505564387917329, 0.7557251908396947, 0.6801909307875895, 0.7998483699772555, 0.994, 0.7189605389797883, 0.882, 0.9262782401902497, 0.6185831622176592, 0.8268, 0.6727019498607242, 0.5701149425287356, 0.5793103448275863, 0.9480122324159022, 0.6268, 0.8318965517241379, 0.25689404934687954, 0.4926992073425115, 0.9216, 0.7832699619771863, 0.20176082171680118, 0.4482463644140291, 0.8021390374331551, 0.6819461198230801, 0.5650118203309693, 0.9364, 0.9996, 0.21914893617021278, 0.9952, 0.9856, 0.9952, 0.9776, 0.9971949509116409, 0.8184, 0.9705760464152507, 0.9968, 0.996, 0.778, 0.7108, 0.878, 0.833409821018816, 0.4057971014492754, 0.25476190476190474, 0.792, 0.996, 0.7232, 0.9964, 0.7581573896353166, 0.9924, 0.8288431061806656, 0.8218579234972677, 0.5926161680458307, 0.9336, 0.28085106382978725, 0.5303983228511531, 0.984, 0.4762357414448669, 0.9344, 0.5632, 0.550253807106599, 0.8739352640545145, 0.8258064516129032, 0.3124470787468247, 0.9876, 0.8885644768856448, 0.9912]
+all_best_aucrocs = [0.77537, 0.83006000000000002, 0.64549999999999996, 0.89767999999999992, 0.75961999999999996, 0.68588000000000005, 0.64273000000000002, 0.66517999999999999, 0.62936999999999999, 0.65083999999999997, 0.89617000000000002, 0.75588999999999995, 0.85387000000000002, 0.75636999999999999, 0.65939999999999999, 0.94871000000000005, 0.81633, 0.71088999999999991, 0.86601000000000006, 0.74753000000000003, 0.77808999999999995, 0.74602000000000002, 0.81026999999999993, 0.78943999999999992, 0.77303999999999995, 0.78813, 0.78274999999999995, 0.81415999999999999, 0.75697999999999999, 0.60846, 0.68961000000000006, 0.57347999999999999, 0.77385000000000004, 0.69881000000000004, 0.90137999999999996, 0.79401999999999995, 0.49775000000000003, 0.79413, 0.89322000000000012, 0.89478999999999997, 0.95248999999999995, 0.82766000000000006, 0.82577000000000012, 0.90326000000000006, 0.81774999999999998, 0.90883999999999998, 0.78312999999999999, 0.82441000000000009, 0.91690000000000005, 0.83013999999999999, 0.75601000000000007, 0.79541000000000006, 0.76278999999999997, 0.78643999999999992, 0.89736000000000005, 0.90612000000000004, 0.64054999999999995, 0.89373999999999998, 0.60760000000000003, 0.60661999999999994, 0.80069999999999997, 0.85952000000000006, 0.62736999999999998, 0.72450000000000003, 0.93898999999999999, 0.65133999999999992, 0.86272000000000004, 0.61850000000000005, 0.92010999999999998, 0.68474000000000002, 0.62549999999999994, 0.64343000000000006, 0.96214, 0.70689999999999997, 0.77383999999999997]
+
+plt.scatter(all_testset_leakage_percentages, all_best_aucrocs)
+plt.xlabel("percentage of leakage")
+plt.ylabel("auc roc")
+
+for lab, x, y in zip(labs, all_testset_leakage_percentages, all_best_aucrocs):
+    plt.annotate(
+        lab[3:],
+        xy=(x, y))
+plt.savefig('leakage-holdout.png')
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/test/TestDataCreator.py b/scripts/Archive/LabTestAnalysis/test/TestDataCreator.py
new file mode 100644
index 00000000..be16da16
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/test/TestDataCreator.py
@@ -0,0 +1,44 @@
+
+import LocalEnv
+import os
+import numpy as np
+np.set_printoptions(threshold=np.nan)
+np.set_printoptions(linewidth=np.nan)
+np.set_printoptions(precision=3)
+import pandas as pd
+pd.set_option('display.width', 500)
+pd.set_option('display.max_columns', 500)
+pd.set_option('display.precision', 3)
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+
+data_folder = os.path.join(LocalEnv.PATH_TO_CDSS,
+                           "scripts/LabTestAnalysis/machine_learning/data-panels/")
+
+def jitter_processed_matrix(lab, pat_num_limit=100):
+    data_file = "%s-normality-matrix-10000-episodes-processed.tab"%lab
+    data_path = os.path.join(data_folder, lab, data_file)
+    fm_io = FeatureMatrixIO()
+    df = fm_io.read_file_to_data_frame(data_path)
+
+    '''
+    Reset the pat ids
+    '''
+    pat_ids = sorted(set(df['pat_id'].values.tolist()))
+
+    pat_ids = pat_ids[:pat_num_limit]
+
+    pat2pat = {}
+    for i, pat_id in enumerate(pat_ids):
+        pat2pat[pat_id] = i
+    df['pat_id'] = df['pat_id'].apply(lambda x: pat2pat[x] if x in pat2pat else None)
+    df = df.dropna()
+
+    print(np.array_repr(df.values))
+    print(df.columns)
+
+    #fm_io.write_data_frame_to_file(df, data_path.replace('processed', 'processed-jittered'))
+
+
+if __name__ == '__main__':
+    # print inspect.getfile(inspect.currentframe())
+    jitter_processed_matrix("LABA1C", pat_num_limit=100)
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/test/TestLabNormalityMatrix_UMich.py b/scripts/Archive/LabTestAnalysis/test/TestLabNormalityMatrix_UMich.py
new file mode 100644
index 00000000..2778ed5a
--- /dev/null
+++ b/scripts/Archive/LabTestAnalysis/test/TestLabNormalityMatrix_UMich.py
@@ -0,0 +1,86 @@
+
+import os
+import LocalEnv
+import medinfo.db.Env # TODO: comment
+
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+
+from medinfo.db.test.Util import DBTestCase
+from scripts.LabTestAnalysis.machine_learning.extraction.LabNormalityMatrix import LabNormalityMatrix
+
+import unittest
+# from Const import RUNNER_VERBOSITY
+import sqlite3
+import pandas as pd
+
+from medinfo.db import DBUtil
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+
+import medinfo.dataconversion.test.UMichFeatureMatrixTestData as FMTU
+
+PREV_LOCAL_DATABASE_CONNECTOR_NAME = LocalEnv.DATABASE_CONNECTOR_NAME
+PREV_ENV_DATABASE_CONNECTOR_NAME = medinfo.db.Env.DATABASE_CONNECTOR_NAME
+PREV_TEST_DB_DSN = LocalEnv.LOCAL_TEST_DB_PARAM["DSN"]
+PREV_TEST_DB_DATAPATH = LocalEnv.LOCAL_TEST_DB_PARAM["DATAPATH"] if "DATAPATH" in LocalEnv.LOCAL_TEST_DB_PARAM else None
+PREV_SQL_PLACEHOLDER = medinfo.db.Env.SQL_PLACEHOLDER
+
+
+class TestLabNormalityMatrix(DBTestCase):
+    def setUp(self):
+        LocalEnv.DATABASE_CONNECTOR_NAME = medinfo.db.Env.DATABASE_CONNECTOR_NAME = "sqlite3"
+        LocalEnv.LOCAL_TEST_DB_PARAM["DSN"] = 'UMich_test.db'
+        LocalEnv.LOCAL_TEST_DB_PARAM["DATAPATH"] = os.path.join(LocalEnv.PATH_TO_CDSS, 'scripts/LabTestAnalysis/test/')
+        medinfo.db.Env.SQL_PLACEHOLDER = "?"
+
+        DBTestCase.setUp(self)
+        # ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+
+        self.connection = DBUtil.connection();
+        self._insertUMichTestRecords()
+        self.matrix = LabNormalityMatrix('WBC', 10, random_state=1234, isLabPanel=False)
+
+    def tearDown(self):
+        self.matrix._factory.cleanTempFiles()
+        os.remove(self.matrix._factory.getMatrixFileName())
+        self.connection.close()
+        DBTestCase.tearDown(self)
+
+        # restore old values
+        LocalEnv.DATABASE_CONNECTOR_NAME = PREV_LOCAL_DATABASE_CONNECTOR_NAME
+        medinfo.db.Env.DATABASE_CONNECTOR_NAME = PREV_ENV_DATABASE_CONNECTOR_NAME
+        LocalEnv.LOCAL_TEST_DB_PARAM["DSN"] = PREV_TEST_DB_DSN
+        LocalEnv.LOCAL_TEST_DB_PARAM["DATAPATH"] = PREV_TEST_DB_DATAPATH
+        medinfo.db.Env.SQL_PLACEHOLDER = PREV_SQL_PLACEHOLDER
+
+    def test_empty(self):
+        print(self.matrix._get_components_in_lab_panel())
+        print(self.matrix._get_average_orders_per_patient())
+        print(self.matrix._get_random_patient_list())
+        # print 'self.matrix._num_patients:', self.matrix._num_patients
+
+        pass
+
+    def _insertUMichTestRecords(self):
+        db_name = medinfo.db.Env.DB_PARAM['DSN']
+        db_path = medinfo.db.Env.DB_PARAM['DATAPATH']
+        conn = sqlite3.connect(os.path.join(db_path, db_name))
+
+        table_names = ['labs', 'pt_info', 'demographics', 'encounters', 'diagnoses']
+
+        for table_name in table_names:
+            columns = FMTU.FM_TEST_INPUT_TABLES["%s_columns"%table_name]
+            column_types = FMTU.FM_TEST_INPUT_TABLES["%s_column_types"%table_name]
+
+            df = pd.DataFrame()
+            for one_line in FMTU.FM_TEST_INPUT_TABLES['%s_data'%table_name]:
+                df = df.append(dict(list(zip(columns, one_line))), ignore_index=True)
+
+            df.to_sql(table_name, conn, if_exists="append", index=False)
+
+def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestLabNormalityMatrix()))
+    return suite
+
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=2).run(suite())
\ No newline at end of file
diff --git a/scripts/Archive/LabTestAnalysis/test/__init__.py b/scripts/Archive/LabTestAnalysis/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/OpioidRx/compositeAssociations.py b/scripts/Archive/OpioidRx/compositeAssociations.py
similarity index 96%
rename from scripts/OpioidRx/compositeAssociations.py
rename to scripts/Archive/OpioidRx/compositeAssociations.py
index abf635ce..d137dac4 100644
--- a/scripts/OpioidRx/compositeAssociations.py
+++ b/scripts/Archive/OpioidRx/compositeAssociations.py
@@ -34,7 +34,7 @@ def main(argv):
     headerCols = ["Rx","Dx1","Dx2","RxDx1Dx2Count","RxDx1Count","RxDx2Count","RxCount","Dx1Dx2Count","Total","E(RxDx1Dx2Count)","E(RxDx2Dx1Count)","E(Dx1Dx2Count)","P-Chi2-Obs:Exp",];
     headerCols.extend(statIds);
     headerStr = str.join("\t", headerCols );
-    print headerStr;
+    print(headerStr);
 
     associationFile = open(argv[1]);
     associationFile.readline(); # Dump header row
@@ -82,7 +82,7 @@ def main(argv):
         for i, value in enumerate(dataCells):
             dataCells[i] = str(value);  # String conversion to allow for concatenation below
         dataStr = str.join("\t", dataCells );
-        print dataStr;
+        print(dataStr);
         progress.update();
     progress.printStatus();
 
diff --git a/scripts/OpioidRx/dataNotes.txt b/scripts/Archive/OpioidRx/dataNotes.txt
similarity index 100%
rename from scripts/OpioidRx/dataNotes.txt
rename to scripts/Archive/OpioidRx/dataNotes.txt
diff --git a/scripts/OpioidRx/extractData.py b/scripts/Archive/OpioidRx/extractData.py
similarity index 95%
rename from scripts/OpioidRx/extractData.py
rename to scripts/Archive/OpioidRx/extractData.py
index 91d4e7ce..f32d6e43 100644
--- a/scripts/OpioidRx/extractData.py
+++ b/scripts/Archive/OpioidRx/extractData.py
@@ -1,7 +1,7 @@
 import sys, os;
 import time;
 from datetime import datetime, timedelta;
-from cStringIO import StringIO;
+from io import StringIO;
 import numpy as np;
 import pandas as pd;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
@@ -48,7 +48,7 @@ def main(argv):
 
     
     elapsed = time.time() - timer;
-    print >> sys.stderr, "%s to complete" % timedelta(0,round(elapsed));
+    print("%s to complete" % timedelta(0,round(elapsed)), file=sys.stderr);
     
     return patientDF;
 
diff --git a/scripts/OpioidRx/extractionNotes.longitudinal.txt b/scripts/Archive/OpioidRx/extractionNotes.longitudinal.txt
similarity index 100%
rename from scripts/OpioidRx/extractionNotes.longitudinal.txt
rename to scripts/Archive/OpioidRx/extractionNotes.longitudinal.txt
diff --git a/scripts/OpioidRx/extractionNotes.txt b/scripts/Archive/OpioidRx/extractionNotes.txt
similarity index 100%
rename from scripts/OpioidRx/extractionNotes.txt
rename to scripts/Archive/OpioidRx/extractionNotes.txt
diff --git a/scripts/OpioidRx/logisticRegression.R b/scripts/Archive/OpioidRx/logisticRegression.R
similarity index 100%
rename from scripts/OpioidRx/logisticRegression.R
rename to scripts/Archive/OpioidRx/logisticRegression.R
diff --git a/scripts/OpioidRx/machineLearn.py b/scripts/Archive/OpioidRx/machineLearn.py
similarity index 89%
rename from scripts/OpioidRx/machineLearn.py
rename to scripts/Archive/OpioidRx/machineLearn.py
index 1415bed1..5cc6d928 100644
--- a/scripts/OpioidRx/machineLearn.py
+++ b/scripts/Archive/OpioidRx/machineLearn.py
@@ -1,7 +1,7 @@
 import sys, os;
 import time;
 from datetime import timedelta;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList;
@@ -61,15 +61,15 @@ def main(argv):
     model = logisticRegressionInterpret(xDF,ySeries);
 
     elapsed = time.time() - timer;
-    print >> sys.stderr, "%s seconds to complete" % timedelta(0, round(elapsed));
+    print("%s seconds to complete" % timedelta(0, round(elapsed)), file=sys.stderr);
     
     return model;
 
 def logisticRegressionInterpret(xDF,ySeries):
     model = LogisticRegression();
     model.fit(xDF,ySeries);
-    print np.exp(model.coef_)
-    print model.intercept_
+    print(np.exp(model.coef_))
+    print(model.intercept_)
     return model;
     
     
@@ -87,7 +87,7 @@ def testModels(xDF,ySeries,kFolds=10,models=None,scoreFxns=None):
     for scoreFxn in scoreFxns:
         for model in models:
             scores = cross_val_score(model,xDF,ySeries,cv=kFolds, scoring=scoreFxn);
-            print "%.2f (%.2f, %.2f) %s.%s" % (scores.mean(), scores.mean()-scores.std()*2, scores.mean()+scores.std()*2, str(scoreFxn), type(model).__name__);
+            print("%.2f (%.2f, %.2f) %s.%s" % (scores.mean(), scores.mean()-scores.std()*2, scores.mean()+scores.std()*2, str(scoreFxn), type(model).__name__));
 
 if __name__ == "__main__":
     main(sys.argv);
\ No newline at end of file
diff --git a/scripts/OpioidRx/preprocessData.py b/scripts/Archive/OpioidRx/preprocessData.py
similarity index 94%
rename from scripts/OpioidRx/preprocessData.py
rename to scripts/Archive/OpioidRx/preprocessData.py
index 76aac1e7..7b8b6d97 100644
--- a/scripts/OpioidRx/preprocessData.py
+++ b/scripts/Archive/OpioidRx/preprocessData.py
@@ -1,7 +1,7 @@
 import sys, os;
 import time;
 from datetime import datetime, timedelta;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
 from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList;
@@ -31,7 +31,7 @@ def main(argv):
     df.to_csv(outFile,sep="\t",index=False);
 
     elapsed = time.time() - timer;
-    print >> sys.stderr, "%s seconds to complete" % timedelta(0, round(elapsed));
+    print("%s seconds to complete" % timedelta(0, round(elapsed)), file=sys.stderr);
     
     return df;
 
diff --git a/scripts/OpioidRx/problemAssociations.py b/scripts/Archive/OpioidRx/problemAssociations.py
similarity index 96%
rename from scripts/OpioidRx/problemAssociations.py
rename to scripts/Archive/OpioidRx/problemAssociations.py
index 0d64f0dd..510b3fbd 100644
--- a/scripts/OpioidRx/problemAssociations.py
+++ b/scripts/Archive/OpioidRx/problemAssociations.py
@@ -59,7 +59,7 @@ def main(argv):
         patientsPerDxGroup[dxGroup] = patientCount;
 
     progress = ProgressDots();
-    for activeRx, medIds in medIdsByActiveRx.iteritems():
+    for activeRx, medIds in medIdsByActiveRx.items():
         query = baseQuery();
         query.addWhereIn("medication_id", medIds );
 
@@ -78,7 +78,7 @@ def main(argv):
                 headerCols.insert(2, "Dx2");
             headerCols.extend(statIds);
             headerStr = str.join("\t", headerCols );
-            print headerStr;
+            print(headerStr);
         
         # Query out per diagnosis group, but do as aggregate grouped query
         query.addSelect("prob.%s" % DX_COL );
@@ -115,7 +115,7 @@ def main(argv):
             for i, value in enumerate(dataCells):
                 dataCells[i] = str(value);  # String conversion to allow for concatenation below
             dataStr = str.join("\t", dataCells );
-            print dataStr;
+            print(dataStr);
             progress.update();
     progress.printStatus();
 
diff --git a/scripts/OpioidRx/problemListAssociations.txt b/scripts/Archive/OpioidRx/problemListAssociations.txt
similarity index 100%
rename from scripts/OpioidRx/problemListAssociations.txt
rename to scripts/Archive/OpioidRx/problemListAssociations.txt
diff --git a/scripts/OpioidRx/setupNotes.txt b/scripts/Archive/OpioidRx/setupNotes.txt
similarity index 100%
rename from scripts/OpioidRx/setupNotes.txt
rename to scripts/Archive/OpioidRx/setupNotes.txt
diff --git a/scripts/Archive/OpioidRx/webScraping/buprenorphinePhysicians.6.htm b/scripts/Archive/OpioidRx/webScraping/buprenorphinePhysicians.6.htm
new file mode 100644
index 00000000..2b98a1e5
--- /dev/null
+++ b/scripts/Archive/OpioidRx/webScraping/buprenorphinePhysicians.6.htm
@@ -0,0 +1,1602 @@
+<!DOCTYPE html>

+  <!--[if IEMobile 7]><html class="no-js ie iem7" lang="en" dir="ltr"><![endif]-->

+  <!--[if lte IE 6]><html class="no-js ie lt-ie9 lt-ie8 lt-ie7" lang="en" dir="ltr"><![endif]-->

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+<head>

+  <title>Buprenorphine Treatment Physician Locator | SAMHSA</title>

+  <meta charset="utf-8" />
+<meta name="MobileOptimized" content="width" />
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+</ul></nav>
+  </div>
+    <!-- ***Begin - Charles Monroe 6/21/13. To turn on GSA indexing at this point*** -->

+    <!--googleon: all-->

+    <!-- ***End*** -->

+  </header>

+  

+  	

+	  

+  <div class="l-main">

+    <h2 class="element-invisible">You are here</h2><div class="breadcrumb"><a href="/programs-campaigns" title="">Programs &amp; Campaigns</a> &raquo; <a href="/medication-assisted-treatment" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Medication-Assisted Treatment</a> &raquo; <a href="/programs-campaigns/medication-assisted-treatment/physician-program-data" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Physician and Program Data</a> &raquo; Buprenorphine Treatment Physician Locator</div>    

+      

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+  </div>

+  

+        

+      <aside class="l-region l-region--sidebar-first">
+    <div id="block-samhsa-theme-block-top-nav-spoof" class="block block--samhsa-theme-block block--samhsa-theme-block-top-nav-spoof">
+        <div class="block__content">
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+<nav id="block-menu-block-8" role="navigation" class="block block--menu-block nav-side-block nav-spoof block--menu-block-8">
+        <h2 class="block__title"><a href="/programs-campaigns/medication-assisted-treatment/physician-program-data" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished active-trail">Physician and Program Data</a></h2>
+    
+  <div class="menu-block-wrapper menu-block-8 menu-name-main-menu parent-mlid-22381 menu-level-1">
+  <ul class="menu"><li class="first leaf active-trail active menu-mlid-23470 menu-23470 buprenorphinetreatmentphysicianlocator"><a href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator" title="" class="active-trail active">Buprenorphine Treatment Physician Locator<span class="white_arrow_mobile"></span></a></li>
+<li class="last leaf menu-mlid-23471 menu-23471 datacertifiedphysicians"><a href="/medication-assisted-treatment/physician-program-data/certified-physicians" title="">DATA-Certified Physicians<span class="white_arrow_mobile"></span></a></li>
+</ul></div>
+</nav>
+<nav id="block-menu-block-4" role="navigation" class="block block--menu-block nav-side-block nav-actual block--menu-block-4">
+        <h2 class="block__title"><a href="/medication-assisted-treatment" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished active-trail">Medication-Assisted Treatment</a></h2>
+    
+  <div class="menu-block-wrapper menu-block-4 menu-name-main-menu parent-mlid-22381 menu-level-2">
+  <ul class="menu"><li class="first collapsed menu-mlid-23401 menu-23401 certificationofopioidtreatmentprograms"><a href="/medication-assisted-treatment/opioid-treatment-programs" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Certification of Opioid Treatment Programs<span class="white_arrow_mobile"></span></a></li>
+<li class="collapsed menu-mlid-23320 menu-23320 buprenorphinewaivermanagement"><a href="/medication-assisted-treatment/buprenorphine-waiver-management" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Buprenorphine Waiver Management<span class="white_arrow_mobile"></span></a></li>
+<li class="collapsed menu-mlid-23403 menu-23403 oversightofaccreditingbodies"><a href="/medication-assisted-treatment/opioid-treatment-accrediting-bodies" title="Oversight of Opioid Treatment Program Accrediting Bodies" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Oversight of Accrediting Bodies<span class="white_arrow_mobile"></span></a></li>
+<li class="collapsed menu-mlid-23223 menu-23223 medicationandcounselingtreatment"><a href="/medication-assisted-treatment/treatment" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Medication and Counseling Treatment<span class="white_arrow_mobile"></span></a></li>
+<li class="collapsed menu-mlid-23232 menu-23232 trainingmaterialsandresources"><a href="/medication-assisted-treatment/training-resources" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Training Materials and Resources<span class="white_arrow_mobile"></span></a></li>
+<li class="expanded active-trail menu-mlid-23242 menu-23242 physicianandprogramdata"><a href="/programs-campaigns/medication-assisted-treatment/physician-program-data" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished active-trail">Physician and Program Data<span class="white_arrow_mobile"></span></a><ul class="menu"><li class="first leaf active-trail active menu-mlid-23470 menu-23470 buprenorphinetreatmentphysicianlocator"><a href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator" title="" class="active-trail active">Buprenorphine Treatment Physician Locator<span class="white_arrow_mobile"></span></a></li>
+<li class="last leaf menu-mlid-23471 menu-23471 datacertifiedphysicians"><a href="/medication-assisted-treatment/physician-program-data/certified-physicians" title="">DATA-Certified Physicians<span class="white_arrow_mobile"></span></a></li>
+</ul></li>
+<li class="collapsed menu-mlid-23214 menu-23214 legislationregulationsguidelines"><a href="/medication-assisted-treatment/legislation-regulations-guidelines" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished">Legislation, Regulations & Guidelines<span class="white_arrow_mobile"></span></a></li>
+<li class="leaf menu-mlid-23215 menu-23215 aboutdpt"><a href="/medication-assisted-treatment/about" class="menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished menu-node-unpublished" title="About the Division of Pharmacologic Therapies">About DPT<span class="white_arrow_mobile"></span></a></li>
+<li class="last leaf menu-mlid-23623 menu-23623 buprenorphinepharmacylookup"><a href="/bupe/lookup-form" title="Buprenorphine Pharmacy Lookup">Buprenorphine Pharmacy Lookup<span class="white_arrow_mobile"></span></a></li>
+</ul></div>
+</nav>
+  </aside>
+    

+    <div class="l-content" role="main">

+      <a id="main-content"></a>

+     

+          

+     		    	  			      			        <h1>Buprenorphine Treatment Physician Locator</h1>

+			      			      		          

+                        

+        

+      <div id="block-nodeblock-27686" class="block block--nodeblock block--nodeblock-27686">
+        <div class="block__content">
+    <div id="node-27686" class="node node--nodeblock node--full node--nodeblock--full clearfix" about="/node/27686" typeof="sioc:Item foaf:Document" role="article" class="node node--nodeblock node--full node--nodeblock--full">
+
+        <span property="dc:title" content="Buprenorphine Treatment Physician Locator [NB]" class="rdf-meta element-hidden"></span><span property="sioc:num_replies" content="0" datatype="xsd:integer" class="rdf-meta element-hidden"></span>
+  
+  <div class="content" class="node__content">
+    <div class="field field--name-body field--type-text-with-summary field--label-hidden"><div class="field__items"><div class="field__item even" property="content:encoded"><p>Find physicians authorized to treat opioid dependency with buprenorphine by state.</p>
+<hr style="height:1px;border:none;color:#000;background-color:#000;" /><p>Select a state from the map or use the drop down lists to view all of the physicians certified to provide buprenorphine treatment in a city, state or zip code.</p>
+</div></div></div>  </div>
+
+  
+  
+</div>
+  </div>
+</div>
+<div id="block-block-614" class="block block--block block--block-614">
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+  
+      <div class="view-content">
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+            First          </th>
+                  <th class="views-field views-field-field-bup-physician-last-name" scope="col">
+            <a href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_physician_us_state_value=All&amp;page=6&amp;field_bup_zip_code_value=&amp;field_bup_physician_city_value=&amp;order=field_bup_physician_last_name&amp;sort=asc" title="sort by Last" class="active">Last</a>          </th>
+                  <th class="views-field views-field-field-bup-physician-name-suffix" scope="col">
+            Deg.          </th>
+                  <th class="views-field views-field-nothing-1" scope="col">
+            Address          </th>
+                  <th class="views-field views-field-field-bup-physician-city" scope="col">
+            <a href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_physician_us_state_value=All&amp;page=6&amp;field_bup_zip_code_value=&amp;field_bup_physician_city_value=&amp;order=field_bup_physician_city&amp;sort=asc" title="sort by City" class="active">City</a>          </th>
+                  <th class="views-field views-field-field-bup-physician-us-state" scope="col">
+            <a href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_physician_us_state_value=All&amp;page=6&amp;field_bup_zip_code_value=&amp;field_bup_physician_city_value=&amp;order=field_bup_physician_us_state&amp;sort=asc" title="sort by State" class="active">State</a>          </th>
+                  <th class="views-field views-field-field-bup-zip-code active" scope="col">
+            <a href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_physician_us_state_value=All&amp;page=6&amp;field_bup_zip_code_value=&amp;field_bup_physician_city_value=&amp;order=field_bup_zip_code&amp;sort=desc" title="sort by Zip" class="active">Zip<img typeof="foaf:Image" src="http://www.samhsa.gov/misc/arrow-desc.png" width="13" height="13" alt="sort descending" title="sort descending" /></a>          </th>
+                  <th class="views-field views-field-field-bup-telephone2" scope="col">
+            Tele.          </th>
+                  <th class="views-field views-field-field-bup-fax-number" scope="col">
+            Fax          </th>
+              </tr>
+    </thead>
+    <tbody>
+      <tr class="odd views-row-first">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Heidi        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Santana-Santana        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Carr 188 KM 1.5<br>Bo. San Isidro        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Canovanas        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00729        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 957-7733        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 957-7733        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Victor        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Cruz Lopez        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+                  </td>
+              <td class="views-field views-field-nothing-1">
+          Estancias del Golf #159<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 223-2755        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 844-4857        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Wilfredo        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Paoli Lopez        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+                  </td>
+              <td class="views-field views-field-nothing-1">
+          Urbanizacion la Rambla<br>Calle Zaragoza 940        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 240-3128        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 259-3331        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Ismael        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Medina-Agostini        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Est. Del Grolf<br>301 J.M. Cintron        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 983-0056        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 284-5682        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Victor        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Cruz Lopez        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+                  </td>
+              <td class="views-field views-field-nothing-1">
+          Estancias Del Golf 159<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 223-2755        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 844-4857        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Janice        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Ramos-Colon        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+                  </td>
+              <td class="views-field views-field-nothing-1">
+          Jardines de Ponce<br>Paseo Floresta F 12        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 929-9624        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 686-6197        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Ashie        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Aleno-Mercado        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Urb. Estancias del Golf Club<br>Calle Jacobo Morales #434        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 844-5867        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 844-5867        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Alma        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Cintron-Albizu        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          I-12 Nordos Street<br>Jardines de Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00730        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 840-3645        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 267-1964        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Agustin        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Cayere-Morales        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          La Rambla<br>3006 San Judis Street        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00731        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 840-9576        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 844-4723        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Carla        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Ruiz Rivera        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Ramos Antonini 503<br>Apartment 3, El Tuque        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00732        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 841-2928        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 840-1944        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Antonio        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Gonzalez-Sotomayor        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          P.O. Box 4614<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ponce        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00733        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 284-6261        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 284-6261        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Nydia        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Fontánez-Reyes        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          13 Castillos del Mar<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Ceiba        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00735        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 860-3558        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 860-3330        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Jose        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Alvarez-Gutierrez        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          # 57 Heraclio Mendoza<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cayey        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00736        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 738-1860        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+                  </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Jose        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Rodriguez-Colon        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Mansiones Monte Verde<br>255 Calle Marta        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cayey        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00736        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 361-7053        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 535-9171        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Pedro        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Lopez Valentin        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          28500 Calle Baldrich<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cayey        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00736        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 640-6328        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 738-5946        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Brenda        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Mora Rodriguez        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Ave Munoz Marin #75<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cayey        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00736        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 738-7455        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 738-7455        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Carlos        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Olivera        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          PMB 297<br>P.O. Box 6400        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cayey        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00737        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 603-6030        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+                  </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Cristobal        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Avila        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+                  </td>
+              <td class="views-field views-field-nothing-1">
+          PMB 108 P.O. Box 70005<br>Programa Salud Correccional        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Fajardo        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00738        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 888-0202        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 888-0202        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Balbino        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Rosa        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Calle 9-G-26<br>URB. Bermudez        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Fajardo        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00738        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 888-8886        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 888-8887        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Reginald        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Neptune-Torres        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Urb. Baralt Avenida Principal J-1<br>Box 487        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Fajardo        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00738        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 860-3558        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 860-7066        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Hector        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Santos Reyes        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Urb. Sabanera<br>Camino Flamboyan #373        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cidra        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00739        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 739-8295        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 739-3588        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          William        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Duran Serrano        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Urb. Treasure Valley<br>Calle 3 #34        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cidra        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00739        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 240-1440        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+                  </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Jose        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Figueroa Robles        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Car # 1 km 51.8 BO. Beatriz<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cidra        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00739        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 689-0600        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 738-8700        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Ramon        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Parrilla        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          First Hospital Panamericano, PO Box 1400<br>State Road 787 KM15        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cidra        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00739        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 739-5555        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 739-5544        </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Celeste        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Rodriguez-Colon        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Cidra Professional Center Ofic #5<br>Carr. 734 Km 0.5 Bo. Arenas        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cidra        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00739        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 714-2288        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 739-5800        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Anarda        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Justiniano-Acevedo        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+                  </td>
+              <td class="views-field views-field-nothing-1">
+          Villa Sta. Maria, Bo. Arenas<br>P.O. Box 472        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Cidra        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00739-0472        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 739-0676        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+                  </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Virginia        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Soliman-Pena        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Hill Side Village<br>Buzon #098        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Rio Grande        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00745        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 642-1794        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 657-7087        </td>
+          </tr>
+      <tr class="even">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Rafael        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Ortiz-Roman        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          P.O. Box 328<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Salinas        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00751        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 824-7811        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+                  </td>
+          </tr>
+      <tr class="odd">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Evangelista        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Jusino-Lopez        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          PO Box 41<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Salinas        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00751        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 532-4728        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+                  </td>
+          </tr>
+      <tr class="even views-row-last">
+              <td class="views-field views-field-field-bup-physician-name-prefix">
+          Dr.        </td>
+              <td class="views-field views-field-field-bup-physician-first-name">
+          Rafael        </td>
+              <td class="views-field views-field-field-bup-physician-last-name">
+          Ortiz-Roman        </td>
+              <td class="views-field views-field-field-bup-physician-name-suffix">
+          M.D.        </td>
+              <td class="views-field views-field-nothing-1">
+          Barrio La Carmen #229<br>        </td>
+              <td class="views-field views-field-field-bup-physician-city">
+          Salinas        </td>
+              <td class="views-field views-field-field-bup-physician-us-state">
+          Puerto Rico        </td>
+              <td class="views-field views-field-field-bup-zip-code active">
+          00751        </td>
+              <td class="views-field views-field-field-bup-telephone2">
+          (787) 824-7811        </td>
+              <td class="views-field views-field-field-bup-fax-number">
+          (787) 824-7811        </td>
+          </tr>
+    </tbody>
+</table>
+    </div>
+  
+      <h2 class="element-invisible">Pages</h2><ul class="pager"><li class="pager__item pager__item--first"><a title="Go to first page" href="/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_zip_code_value=&amp;field_bup_physician_city_value=&amp;field_bup_physician_us_state_value=All">« first</a></li>
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+</tr><tr><td><a href="http://www.hhs.gov/"><img alt="HHS" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/hhslogo.png" /></a></td>
+</tr><tr><td><a href="http://www.usa.gov/"><img alt="USA.gov" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/usa.png" /></a></td>
+</tr><tr><td class="last-cell-of-column"><a href="http://www.grants.gov/"><img alt="Grants.gov" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/grants.png" /></a></td>
+</tr></table>  </div>
+</div>
+  </div>
+      <div class="l-region l-region--bottom-footer">
+    <div id="block-samhsa-theme-block-bottom-footer-text" class="block block--samhsa-theme-block block--samhsa-theme-block-bottom-footer-text">
+        <div class="block__content">
+    <div class="bottom-footer-text"><span class="footer_ga"><span class="font">SAMHSA's mission is to reduce the impact of substance abuse and mental illness on America's communities.</span></span><br /><span class="footer_ar"><span class="font">5600 Fishers Ln &#x25cf; Rockville, MD 20857<br>1-877-SAMHSA-7 (1-877-726-4727)</span></span></div>  </div>
+</div>
+<div id="block-samhsa-theme-block-bottom-footer-logo" class="block block--samhsa-theme-block block--samhsa-theme-block-bottom-footer-logo">
+        <div class="block__content">
+    <div class="bottom-footer-logo">
+<div id="bf_samhsa_logo"><img alt="SAMHSA Logo" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/samhsa_logo_footer.png" /></div>
+<div id="bf_hhs_logo"><img alt="HHS Logo" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/HHS_logo.png" /></div>
+</div>
+  </div>
+</div>
+  </div>
+    

+  <!-- ***Begin - Charles Monroe 6/21/13. To turn on GSA indexing at this point*** -->

+  <!--googleon: all-->

+  <!-- ***End*** -->

+  </footer>

+</div>

+  <div class="menu-minipanels menu-minipanel-18171"><div class="panel-flexible panels-flexible-2 clearfix" id="mini-panel-find_help">
+<div class="panel-flexible-inside panels-flexible-2-inside">
+<div class="panels-flexible-row panels-flexible-row-2-main-row panels-flexible-row-first clearfix">
+  <div class="inside panels-flexible-row-inside panels-flexible-row-2-main-row-inside panels-flexible-row-inside-first clearfix">
+<div class="panels-flexible-region panels-flexible-region-2-fh_1 panels-flexible-region-first panels-flexible-region-last ">
+  <div class="inside panels-flexible-region-inside panels-flexible-region-2-fh_1-inside panels-flexible-region-inside-first panels-flexible-region-inside-last">
+<div class="panel-pane pane-custom pane-1">
+            <!-- ***Begin - Charles Monroe 10/15/14. To turn off GSA indexing at this point*** --><!--googleoff: all--><!-- ***End*** --><div id="findhelp_wrapper" onmouseout="megactivate('.findhelp', '');" onmouseover="megactivate('.findhelp', '#c47b12');">
+<ul class="padding col" id="megamenu-findhelp-1"><li><a href="http://www.suicidepreventionlifeline.org/"><img alt="National Suicide Prevention Lifeline" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/suicide.png" /></a></li>
+<li class="dropdown_sub_title">1-800-273-8255 (TALK)<br />
+	TTY: 1-800-799-4889</li>
+<li class="dropdown_sub_title"><img alt="" src="http://www.samhsa.gov/sites/default/files/images/chat-mega.png" style="width: 29px; height: 22px;" /> Chat with a professional</li>
+<li>Need to talk to someone?<br /><a class="ext" href="http://www.suicidepreventionlifeline.org/">Learn more about the Suicide Prevention Lifeline.</a></li>
+</ul><!--SECOND COLUMN --><ul class="padding col" id="megamenu-findhelp-2"><li><a href="http://www.samhsa.gov/find-help/national-helpline"><img alt="National Helpline" src="http://www.samhsa.gov/sites/all/themes/samhsa/images/helpline.png" /></a></li>
+<li class="dropdown_sub_title">1-800-662-4357 (HELP)<br />
+	TTY: 1-800-487-4889</li>
+<li>Seeking treatment options?<br /><a href="http://www.samhsa.gov/find-help/national-helpline">Help is available in both English and Spanish. Learn more about the SAMHSA National Helpline.</a></li>
+</ul><!--THIRD COLUMN --><ul class="padding col" id="megamenu-findhelp-3"><li><a href="http://www.samhsa.gov/find-help/disaster-distress-helpline"><img alt="Disaster Distress Helpline" src="http://www.samhsa.gov/sites/all/themes/samhsa/images/disaster.png" /></a></li>
+<li class="dropdown_sub_title">1-800-985-5990<br />
+	TTY: 1-800-846-8517</li>
+<li>Experienced a natural or human-caused disaster?<br /><a href="http://www.samhsa.gov/find-help/disaster-distress-helpline">Learn more about the Disaster Distress Helpline.</a></li>
+</ul><!--FORTH COLUMN --><ul class="none col last" id="megamenu-findhelp-4"><li class="mega-header"><a href="http://www.samhsa.gov/find-help">Treatment Locators</a></li>
+<li class="location" style="padding-bottom:10px; height:auto;">Find treatment facilities and programs in the United States or U.S. Territories for mental and substance use disorders.</li>
+<li class="location_none" style="margin:7px 0; height:auto;"><img alt="Treatment Locator Icon" src="http://www.samhsa.gov/sites/default/files/images/locator.png" style="height: 20px; width: 13px; margin-left: 5px; margin-right: 5px;" /><a href="https://findtreatment.samhsa.gov/">Behavioral Health Treatment Services Locators</a></li>
+<li class="location_none" style="margin:7px 0; height:auto;"><img alt="Treatment Locator Icon" src="http://www.samhsa.gov/sites/default/files/images/locator.png" style="height: 20px; width: 13px; margin-left: 5px; margin-right: 5px;" /><a href="http://www.samhsa.gov/medication-assisted-treatment/physician-program-data/treatment-physician-locator">Buprenorphine Physician &amp; Treatment Program Locator</a></li>
+<li class="location_none" style="margin:7px 0; height:auto;"><img alt="Treatment Locator Icon" src="http://www.samhsa.gov/sites/default/files/images/locator.png" style="height: 20px; width: 13px; margin-left: 5px; margin-right: 5px;" /><a href="http://dpt2.samhsa.gov/treatment/directory.aspx">Opioid Treatment Program Directory</a></li>
+</ul><div class="view_more" id="megamenu-findhelp-5" style="float:right; width:50%; text-align: right;  margin-right:10px; "><a href="http://www.samhsa.gov/find-help">View All Helplines and Treatment Locators</a></div>
+</div>
+<!-- ***Begin - Charles Monroe 10/15/14. To turn on GSA indexing at this point*** --><!--googleon: all--><!-- ***End*** -->    </div>
+  </div>
+</div>
+  </div>
+</div>
+<div class="panels-flexible-row panels-flexible-row-2-2 panels-flexible-row-last clearfix Find Help Bottom Row">
+  <div class="inside panels-flexible-row-inside panels-flexible-row-2-2-inside panels-flexible-row-inside-last clearfix">
+  </div>
+</div>
+</div>
+</div>
+</div><div class="menu-minipanels menu-minipanel-18172"><div class="panel-flexible panels-flexible-11 clearfix" id="mini-panel-topics">
+<div class="panel-flexible-inside panels-flexible-11-inside">
+<div class="panels-flexible-region panels-flexible-region-11-center panels-flexible-region-first panels-flexible-region-last">
+  <div class="inside panels-flexible-region-inside panels-flexible-region-11-center-inside panels-flexible-region-inside-first panels-flexible-region-inside-last">
+<div class="panel-pane pane-custom pane-2">
+            <!-- ***Begin - Charles Monroe 10/15/14. To turn off GSA indexing at this point*** --><!--googleoff: all--><!-- ***End*** --><div id="topics_wrapper" onmouseout="megactivate('.topics', '');" onmouseover="megactivate('.topics', '#c47b12');">
+<!--COLOR BLOCK --><div class="featured-megamenu-topics">
+<div class="mega-header"><a href="http://www.samhsa.gov/treatment">Featured Topic</a></div>
+<div class="topics-megamenu-img-center"><a href="http://www.samhsa.gov/treatment"><img alt="Consulter talking with a Veteran" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/megamenu/Topics_Featured_1.jpg" width="100%" /></a></div>
+<!--BLOCK IMAGE --><div class="content">
+<p><a href="http://www.samhsa.gov/treatment">Behavioral Health Treatments &amp; Services</a> Learn how health care professionals address common mental illnesses and substance use disorders and how SAMHSA helps people access treatments and services.</p>
+</div>
+</div>
+<!--COLOR BLOCK  --><!--SECOND COLUMN --><div id="megamenu-topics-2">
+<div class="mega-header"><a href="http://www.samhsa.gov/topics">All Topics</a></div>
+<ul><li><a href="http://www.samhsa.gov/atod">Alcohol, Tobacco, and Other Drugs </a></li>
+<li><a href="http://www.samhsa.gov/treatment">Behavioral Health Treatments and Services </a></li>
+<li><a href="http://www.samhsa.gov/criminal-juvenile-justice">Criminal and Juvenile Justice</a></li>
+<li><a href="http://www.samhsa.gov/samhsa-data-outcomes-quality">Data, Outcomes, and Quality </a></li>
+<li><a href="http://www.samhsa.gov/disaster-preparedness">Disaster Preparedness, Response, and Recovery</a></li>
+<li><a href="http://www.samhsa.gov/health-care-health-systems-integration">Health Care and Health Systems Integration</a></li>
+<li><a href="http://www.samhsa.gov/health-disparities">Health Disparities </a></li>
+<li><a href="http://www.samhsa.gov/health-financing">Health Financing</a></li>
+<li><a href="http://www.samhsa.gov/health-information-technology">Health Information Technology</a></li>
+<li><a href="http://www.samhsa.gov/hiv-aids-viral-hepatitis">HIV, AIDS, and Viral Hepatitis</a></li>
+<li><a href="http://www.samhsa.gov/homelessness-housing">Homelessness and Housing</a></li>
+<li><a href="http://www.samhsa.gov/laws-regulations-guidelines">Laws, Regulations, and Guidelines</a></li>
+</ul></div>
+<!--/SECOND COLUMN --><!--THIRD COLUMN --><div id="megamenu-topics-3">
+<ul><li><a href="http://www.samhsa.gov/disorders">Mental Health and Substance Use Disorders </a></li>
+<li><a href="http://www.samhsa.gov/prescription-drug-misuse-abuse%20">Prescription Drug Misuse and Abuse</a></li>
+<li><a href="http://www.samhsa.gov/prevention%20">Prevention of Substance Abuse and Mental Illness</a></li>
+<li><a href="http://www.samhsa.gov/recovery%20">Recovery and Recovery Support</a></li>
+<li><a href="http://www.samhsa.gov/school-campus-health%20">School and Campus Health</a></li>
+<li><a href="http://www.samhsa.gov/specific-populations%20">Specific Populations</a></li>
+<li><a href="http://www.samhsa.gov/state-local-government-partnerships%20">State and Local Government Partnerships</a></li>
+<li><a href="http://www.samhsa.gov/suicide-prevention%20">Suicide Prevention</a></li>
+<li><a href="http://www.samhsa.gov/trauma-violence%20">Trauma and Violence</a></li>
+<li><a href="http://www.samhsa.gov/tribal-affairs%20">Tribal Affairs</a></li>
+<li><a href="http://www.samhsa.gov/underage-drinking-topic%20">Underage Drinking</a></li>
+<li><a href="http://www.samhsa.gov/veterans-military-families%20">Veterans and Military Families</a></li>
+<li><a href="http://www.samhsa.gov/wellness%20">Wellness</a></li>
+<li><a href="http://www.samhsa.gov/workforce%20">Workforce</a></li>
+</ul></div>
+<!--/THIRD COLUMN --><!-- ***Begin - Charles Monroe 10/15/14. To turn on GSA indexing at this point*** --><!--googleon: all--><!-- ***End*** --></div>
+    </div>
+  </div>
+</div>
+</div>
+</div>
+</div><div class="menu-minipanels menu-minipanel-22368"><div class="panel-flexible panels-flexible-9 clearfix" id="mini-panel-programs_and_campaigns">
+<div class="panel-flexible-inside panels-flexible-9-inside">
+<div class="panels-flexible-region panels-flexible-region-9-center panels-flexible-region-first panels-flexible-region-last">
+  <div class="inside panels-flexible-region-inside panels-flexible-region-9-center-inside panels-flexible-region-inside-first panels-flexible-region-inside-last">
+<div class="panel-pane pane-custom pane-3">
+            <!-- ***Begin - Charles Monroe 10/15/14. To turn off GSA indexing at this point*** --><!--googleoff: all--><!-- ***End*** --><div id="p-n-c_wrapper" onmouseout="megactivate('.programscampaigns', '');" onmouseover="megactivate('.programscampaigns', '#c47b12');">
+<!--COLOR BLOCK --><div class="featured-megamenu-p-n-c">
+<div class="mega-header"><a href="http://www.recoverymonth.gov/">Featured Campaign</a></div>
+<div class="p-n-c-megamenu-img-center"><a href="http://www.recoverymonth.gov/"><img alt="Recovery Month banner" src="http://www.samhsa.gov/sites/all/themes/omega_samhsa/images/megamenu/RM-featured.jpg" width="100%" /></a></div>
+<!--BLOCK IMAGE --><div class="content">
+<p><a href="http://www.recoverymonth.gov/">Recovery Month</a><br />
+Recovery Month promotes the societal benefits of prevention, treatment, and recovery for mental and substance use disorders.</p>
+</div>
+</div>
+<!--COLOR BLOCK  --><!--SECOND COLUMN --><div id="megamenu-p-n-c-2">
+<div class="mega-header"><a href="http://www.samhsa.gov/programs-campaigns">Popular Programs, Campaigns,<br />
+&amp; Initiatives</a></div>
+<ul><li><a href="http://www.samhsa.gov/nrepp">National Registry of Evidence-based Programs and Practices (NREPP)</a></li>
+<li><a href="http://www.samhsa.gov/medication-assisted-treatment">Medication-Assisted Treatment (MAT)</a></li>
+<li><a href="http://www.samhsa.gov/too-smart-to-start">Too Smart To Start</a></li>
+<li><a href="http://www.samhsa.gov/path-program">Projects for Assistance in Transition from Homelessness (PATH)</a></li>
+<li><a href="http://www.samhsa.gov/workplace">Division of Workplace Programs (DWP)</a></li>
+</ul></div>
+<!--/SECOND COLUMN --><!--THIRD COLUMN --><div id="megamenu-p-n-c-3">
+<div class="mega-header"><a href="http://www.samhsa.gov/programs-campaigns">Popular Technical Assistance &amp; Resource Centers</a></div>
+<ul><li><a href="http://www.samhsa.gov/capt/">Center for the Application of Prevention Technologies (CAPT)</a></li>
+<li><a href="http://www.samhsa.gov/homelessness-resource-center">Homeless Resource Center (HRC)</a></li>
+<li><a href="http://www.samhsa.gov/gains-center">GAINS Center for Behavioral Health and Justice Transformations</a></li>
+<li><a href="http://www.samhsa.gov/fetal-alcohol-spectrum-disorders-fasd-center">Fetal Alcohol Spectrum Disorders (FASD) Center</a></li>
+<li><a href="http://www.samhsa.gov/nitt-ta">Now Is The Time Technical Assistance Center</a></li>
+</ul></div>
+<!--/THIRD COLUMN --><div class="p-n-c-megamenu-link"><a href="http://www.samhsa.gov/programs-campaigns">View All Programs &amp; Campaigns</a></div>
+<!-- ***Begin - Charles Monroe 10/15/14. To turn on GSA indexing at this point*** --><!--googleon: all--><!-- ***End*** --></div>
+    </div>
+  </div>
+</div>
+</div>
+</div>
+</div><div class="menu-minipanels menu-minipanel-18174"><div class="panel-flexible panels-flexible-3 clearfix" id="mini-panel-grants">
+<div class="panel-flexible-inside panels-flexible-3-inside">
+<div class="panels-flexible-region panels-flexible-region-3-mega_menu_grants_section_1 panels-flexible-region-first panels-flexible-region-last ">
+  <div class="inside panels-flexible-region-inside panels-flexible-region-3-mega_menu_grants_section_1-inside panels-flexible-region-inside-first panels-flexible-region-inside-last">
+<div class="panel-pane pane-custom pane-4">
+            <!-- ***Begin - Charles Monroe 10/15/14. To turn off GSA indexing at this point*** --><!--googleoff: all--><!-- ***End*** --><div id="grants_wrapper" onmouseout="megactivate('.grants', '');" onmouseover="megactivate('.grants', '#c47b12');">
+<ul class="padding col" id="megamenu-grants-1"><li class="mega-header"><a href="http://www.samhsa.gov/grants/grant-announcements-2016" title="Grant Announcements">Fiscal Year 2016 Grant Announcements</a></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/grants/applying" title="Applying">Applying for a New SAMHSA Grant</a>
+<ul><li><a href="http://www.samhsa.gov/grants/applying/registration-requirements" title="Registration Requirements ">Registration Requirements</a></li>
+<li><a href="http://www.samhsa.gov/grants/applying/submitting-your-application" title="Submitting Your Application">Submitting Your Application</a></li>
+<li><a href="http://www.samhsa.gov/grants/applying/forms-resources" title="Forms and Resources">Forms And Resources</a></li>
+<li><a href="http://www.samhsa.gov/grants/applying/guidelines-consumer-participation" title="Guidelines for Consumer Participation">Guidelines For Consumer and Family Participation</a></li>
+</ul></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/grants/review" title="Review">Grant Review Process</a></li>
+</ul><ul class="padding col" id="megamenu-grants-2"><li class="mega-header"><a href="http://www.samhsa.gov/grants/continuation-grants" title="Continuation Grants">Continuation Grants</a></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/grants/grants-management" title="Grants Management">Grants Management</a>
+<ul><li><a href="http://www.samhsa.gov/grants/grants-management/notice-award-noa" title="Notice of Award (NoA)">Notice of Award (NOA)</a></li>
+<li><a href="http://www.samhsa.gov/grants/grants-management/policies-regulations" title="Policies and Regulations">Policies And Regulations</a></li>
+<li><a href="http://www.samhsa.gov/grants/grants-management/reporting-requirements" title="Reporting Requirements">Reporting Requirements</a></li>
+<li><a href="http://www.samhsa.gov/grants/grants-management/post-award-changes" title="Post-Award Changes ">Post-Award Changes</a></li>
+<li><a href="http://www.samhsa.gov/grants/grants-management/grant-closeout" title="Grant Closeout">Grant Closeout</a></li>
+<li><a href="http://www.samhsa.gov/grants/grants-management/disparity-impact-statement" title="Disparity Impact Statement">Disparity Impact Statement</a></li>
+</ul></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/grants/gpra-measurement-tools" title="GPRA Measurement Tools">GPRA Measurement Tools</a></li>
+</ul><ul class="padding col" id="megamenu-grants-3"><li class="mega-header"><a href="http://www.samhsa.gov/grants/contact-information" title="Contact Information">Contact Grants</a><br />
+	 </li>
+<li class="mega-header-padding"><strong>More Grants Information</strong>
+<ul><li><a href="http://www.samhsa.gov/grants/awards">2015 Grant Awards</a></li>
+<li><a href="http://www.samhsa.gov/grants-awards-by-state" title="SAMHSA Grant Awards By State 2015/2016">Grant Awards by State</a></li>
+<li><a href="http://www.samhsa.gov/grants/archive" title="Grant Awards Archive">SAMHSA Grant Awards Archive</a></li>
+<li><a href="http://www.samhsa.gov/grants/block-grants" title="Block Grants ">Block Grants</a></li>
+</ul></li>
+</ul><ul class="none col last" id="megamenu-grants-4"><li><a href="http://www.grants.gov"><img alt="Grants.gov logo graphic" src="http://www.samhsa.gov/sites/default/files/images/grantsgov-graphic.gif" /></a></li>
+<li>To apply for a SAMHSA grant, you must register on <a href="http://www.grants.gov/">Grants.gov</a>.<br />
+	 </li>
+<li><a href="http://www.grants.gov/web/grants/applicants/organization-registration.html">Register</a> | <a href="http://www.grants.gov/web/grants/search-grants.html">Search</a> | <a href="http://www.grants.gov/web/grants/applicants/apply-for-grants.html">Apply</a></li>
+</ul></div>
+<!-- ***Begin - Charles Monroe 10/15/14. To turn on GSA indexing at this point*** --><!--googleon: all--><!-- ***End*** -->    </div>
+  </div>
+</div>
+</div>
+</div>
+</div><div class="menu-minipanels menu-minipanel-18175"><div class="panel-flexible panels-flexible-7 clearfix" id="mini-panel-data">
+<div class="panel-flexible-inside panels-flexible-7-inside">
+<div class="panels-flexible-region panels-flexible-region-7-center panels-flexible-region-first panels-flexible-region-last">
+  <div class="inside panels-flexible-region-inside panels-flexible-region-7-center-inside panels-flexible-region-inside-first panels-flexible-region-inside-last">
+<div class="panel-pane pane-custom pane-5">
+            <!-- ***Begin - Charles Monroe 10/15/14. To turn off GSA indexing at this point*** --><!--googleoff: all--><!-- ***End*** --><div id="data_wrapper" onmouseout="megactivate('.data', '');" onmouseover="megactivate('.data', '#c47b12');">
+<div id="megamenu-data-1">
+<div class="mega-header">
+<div id="hover"><a href="http://www.samhsa.gov/data/browse-report-document-type">Browse by Publication Type</a>
+<div id="display_area">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=40">Data Spotlights</a></li>
+<li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=41">Analytical Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=42">Short Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=43">Methodology</a></li>
+<li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=44">Questionnaires</a></li>
+<li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=45">Reference Materials</a></li>
+<li><a href="http://www.samhsa.gov/data/browse-report-document-type?tab=46">Barometers</a></li>
+</ul><div class="heading">Most Recent Reports &amp; Documents</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/NSDUHresultsPDFWHTML2013/Web/NSDUHresults2013.pdf" target="_blank" title="Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings">Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings</a></li>
+<li><a href="http://www.samhsa.gov/data/sites/default/files/CBHSQ-DR-C11-MI-Mortality-2014/CBHSQ-DR-C11-MI-Mortality-2014.pdf" target="_blank" title="Psychological Distress and Mortality among Adults in the U.S. Household Population">Psychological Distress and Mortality among Adults in the U.S. Household Population</a></li>
+<li><a href="http://www.samhsa.gov/data/sites/default/files/CBHSQ-SR160-NHIS-SPD-2014/CBHSQ-SR160-NHIS-SPD-2014.pdf" target="_blank" title="Serious Psychological Distress and Mortality among Adults in the U.S. Household Population: Highlights">Serious Psychological Distress and Mortality among Adults in the U.S. Household Population: Highlights</a></li>
+<li><a href="http://www.samhsa.gov/data/sites/default/files/NSDUH-SR199-UnderageBinge-2014/NSDUH-SR199-UnderageBinge-2014.pdf" target="_blank" title="Underage Binge Alcohol Use Varies within and across States">Underage Binge Alcohol Use Varies within and across States</a></li>
+<li><a href="http://www.samhsa.gov/data/sites/default/files/spot143-underage-drinking-2014/spot143-underage-drinking-2014/spot143-underage-drinking-2014.pdf" target="_blank" title="Alcohol and Drug Combinations Are More Likely to Have a Serious Outcome Than Alcohol Alone in Emergency Department Visits Involving Underage Drinking">Alcohol and Drug Combinations Are More Likely to Have a Serious Outcome Than Alcohol Alone in Emergency Department Visits Involving Underage Drinking</a></li>
+<li><a href="http://www.samhsa.gov/data/sites/default/files/TEDS-Spot122-EmplRefer-2014/TEDS-Spot122-EmplRefer-2014/TEDS-Spot122-EmplRefer-2014.pdf" target="_blank" title="Few Substance Abuse Treatment Admissions Are Referred by Employers">Few Substance Abuse Treatment Admissions Are Referred by Employers</a></li>
+<li><a href="http://www.samhsa.gov/data/sites/default/files/NMHSS-SR178-Intro-2014/NMHSS-SR178-Intro-2014.pdf" target="_blank" title="Introduction to the National Mental Health Services Survey, 2010">Introduction to the National Mental Health Services Survey, 2010</a></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover1"><a href="http://www.samhsa.gov/data/emergency-department-data-dawn">Emergency Department Data / DAWN</a>
+<div id="display_area1">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/about">About Emergency Department</a></li>
+<li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/reports">All Emergency Department Data</a></li>
+<li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/reports?tab=26">National Data</a></li>
+<li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/reports?tab=47">Data Tables</a></li>
+<li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/reports?tab=27">Spotlights</a></li>
+<li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/reports?tab=28">Short Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/emergency-department-data-dawn/reports?tab=30">Methodology Reports</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/DAWN2k11ED/DAWN2k11ED/DAWN2k11ED.pdf" target="_blank" title="Drug Abuse Warning Network, 2011: National Estimates of Drug-Related Emergency Department Visits">Drug Abuse Warning Network, 2011: National Estimates of Drug-Related Emergency Department Visits</a>
+<p>	This publication presents national estimates of drug-related visits to hospital emergency departments (EDs) for the calendar year 2011, based on data from the Drug Abuse Warning Network (DAWN). Also presented are comparisons of 2011 estimates with those for 2004, 2009, and 2010. DAWN is a public health surveillance system that monitors drug-related ED visits for the Nation and for selected metropolitan areas. The Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (HHS), is the agency responsible for DAWN. SAMHSA is required to collect data on drug-related ED visits under Section 505 of the Public Health Service Act.<br /><a href="http://www.samhsa.gov/data/sites/default/files/DAWN2k11ED/DAWN2k11ED/DAWN2k11ED.pdf" target="_blank" title="Drug Abuse Warning Network, 2011: National Estimates of Drug-Related Emergency Department Visits">&gt; Read More about Drug Abuse Warning Network</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover2"><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats">Substance Abuse Facilities Data / NSSATS</a>
+<div id="display_area2">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/about">About Substance Abuse Facilities Data / NSSATS</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports">All Substance Abuse Facilities Data</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=10">National Data</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=11">State Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=12">Metro Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=13">Spotlights</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=14">Short Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=15">Data Reviews</a></li>
+<li><a href="http://www.samhsa.gov/data/substance-abuse-facilities-data-nssats/reports?tab=16">Methodology Reports</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/NSSATS2012_Web/NSSATS2012_Web/NSSATS2012_Web.pdf" target="_blank" title="National Survey of Substance Abuse Treatment Services (N-SSATS): 2012">National Survey of Substance Abuse Treatment Services (N-SSATS): 2012</a>
+<p>	This report presents results from the 2012 National Survey of Substance Abuse Treatment Services (N-SSATS), an annual census of facilities providing substance abuse treatment. Con- ducted by the Substance Abuse and Mental Health Services Administration (SAMHSA), N-SSATS is designed to collect data on the location, characteristics, and use of alcohol and drug abuse treatment facilities and services throughout the 50 states, the District of Columbia, and other U.S. jurisdic- tions. It is important to note that values in charts, narrative lists, and percentage distributions are calculated using actual raw numbers and rounded for presentation in this report; calculations using rounded values may produce different results.<br /><a href="http://www.samhsa.gov/data/sites/default/files/NSSATS2012_Web/NSSATS2012_Web/NSSATS2012_Web.pdf" target="_blank" title="National Survey of Substance Abuse Treatment Services (N-SSATS): 2012">&gt; Read More about National Survey of Substance Abuse Treatment Services</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover3"><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss">Mental Health Facilities Data / NMHSS</a>
+<div id="display_area3">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/about">About Mental Health Facilities Data / NMHSS</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports">All Mental Health Facilities Data</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=2">National Data</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=3">State Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=4">Metro Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=5">Spotlights</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=6">Short Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=7">Data Reviews</a></li>
+<li><a href="http://www.samhsa.gov/data/mental-health-facilities-data-nmhss/reports?tab=8">Methodology Reports</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/NMHSS2010_Web/NMHSS2010_Web/NMHSS2010_Web.pdf" target="_blank" title="National Mental Health Services Survey (N-MHSS): 2010">National Mental Health Services Survey (N-MHSS): 2010</a>
+<p>	This report presents findings from the 2010 National Mental Health Services Survey (N-MHSS) conducted from June 2010 through January 2011 with a reference date of April 30, 2010. The N MHSS collects information from all known facilities1 in the United States, both public and private, that provide mental health treatment services to persons with mental illness.<br /><a href="http://www.samhsa.gov/data/sites/default/files/NMHSS2010_Web/NMHSS2010_Web/NMHSS2010_Web.pdf" target="_blank" title="National Mental Health Services Survey (N-MHSS): 2010">&gt; Read More about National Mental Health Services Survey (N-MHSS)</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover4"><a href="http://www.samhsa.gov/data/client-level-data-teds">Client Level Data / TEDS</a>
+<div id="display_area4">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/client-level-data-teds/about">About Client Level Data / TEDS</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports">All Client Level Data</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=18">National Data</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=19">State Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=20">Metro Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=21">Spotlights</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=22">Short Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=23">Data Reviews</a></li>
+<li><a href="http://www.samhsa.gov/data/client-level-data-teds/reports?tab=24">Methodology Reports</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/TEDS2012N_Web.pdf" target="_blank" title="Treatment Episode Data Set (TEDS) 2002 - 2012">Treatment Episode Data Set (TEDS) 2002 - 2012</a>
+<p>	This report presents national-level data from the Treatment Episode Data Set (TEDS) for admissions in 2012 and trend data for 2002 to 2012. The report provides information on the demographic and substance abuse characteristics of admissions to treatment aged 12 and older for abuse of alcohol and/or drugs in facilities that report to individual state administrative data systems. It is important to note that values in charts, narrative lists, and percentage distributions are calculated using actual raw numbers and rounded for presentation in this report; calculations using rounded values may produce different results.<br /><a href="http://www.samhsa.gov/data/sites/default/files/TEDS2012N_Web.pdf" target="_blank" title="Treatment Episode Data Set (TEDS) 2002 - 2012">&gt; Read More about Treatment Episode Data Set</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover5"><a href="http://www.samhsa.gov/data/population-data-nsduh">Population Data / NSDUH</a>
+<div id="display_area5">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/population-data-nsduh/about">About Population Data / NSDUH</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports">All Population Data</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=32">National Data</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=33">State Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=34">Sub-State / Metro Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=35">Spotlights</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=36">Short Reports</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=37">Data Reviews</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=38">Data Tables</a></li>
+<li><a href="http://www.samhsa.gov/data/population-data-nsduh/reports?tab=39">Methodology Reports &amp; Questionnaires</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/NSDUHresultsPDFWHTML2013/Web/NSDUHresults2013.pdf" target="_blank" title="Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings">Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings</a>
+<p>	This report and the detailed tables present a first look at results from the 2013 National Survey on Drug Use and Health (NSDUH), an annual survey of the civilian, noninstitutionalized population of the United States aged 12 years old or older. Both the report and detailed tables present national estimates of rates of use, numbers of users, and other measures related to illicit drugs, alcohol, and tobacco products, with a focus on trends between 2012 and 2013 and from 2002 to 2013, as well as differences across population subgroups in 2013. NSDUH national estimates related to mental health and NSDUH State-level estimates related to both substance use and mental health will be published in separate releases in the fall of 2014.<br /><a href="http://www.samhsa.gov/data/sites/default/files/NSDUHresultsPDFWHTML2013/Web/NSDUHresults2013.pdf" target="_blank" title="Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings">&gt; Read More about Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover6"><a href="http://www.samhsa.gov/data/evaluation-services">Evaluation &amp; Services</a>
+<div id="display_area6">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/health-services-research">Health Services Research</a></li>
+<li><a href="http://www.samhsa.gov/data/evaluation">Evaluation</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/sites/default/files/National_BHBarometer/National_BHBarometer.pdf" target="_blank" title="Behavioral Health Barometer - United States, 2013">Behavioral Health Barometer - United States, 2013</a>
+<p>	This is the first edition of the Behavioral Health Barometer: United States, one of a series of State and national reports that provide a snapshot of the state of behavioral health in the Nation. This national report presents a set of substance use and mental health indicators as measured through data collection efforts sponsored by SAMHSA (the National Survey on Drug Use and Health and the National Survey of Substance Abuse Treatment Services), the Centers for Disease Control and Prevention (the Youth Risk Behavior Survey), and the National Institute on Drug Abuse (the Monitoring the Future survey). Also included are data on the use of mental health and substance use treatment services by Medicare enrollees, as reported by the Centers for Medicare &amp; Medicaid Services. This array of indicators provides a unique overview of the Nation’s behavioral health at a point in time as well as a mechanism for tracking change and trends over time.<br /><a href="http://www.samhsa.gov/data/sites/default/files/National_BHBarometer/National_BHBarometer.pdf" target="_blank" title="Behavioral Health Barometer - United States, 2013">&gt; Read More about Behavioral Health Barometer - United States, 2013</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover7"><a href="http://www.samhsa.gov/data/quality-metrics">Quality Metrics</a>
+<div id="display_area7">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/national-behavioral-health-quality-framework">National Behavioral Health Quality Framework (NBHQF)</a></li>
+<li><a href="http://www.samhsa.gov/data/behavioral-health-barometers">Behavioral Health Barometers</a></li>
+<li><a href="http://www.samhsa.gov/data/meaningful-use">Meaningful Use</a></li>
+<li><a href="http://www.samhsa.gov/data/national-quality-strategy">National Quality Strategy</a></li>
+<li><a href="http://www.healthit.gov/" target="_blank">Health Information Technology</a></li>
+<li><a href="http://www.samhsa.gov/data/national-quality-forum-nqf">National Quality Forum (NQF)</a></li>
+<li><a href="http://www.ncqa.org/" target="_blank">National Committee on Quality Assurance (NCQA)</a></li>
+</ul><div class="heading">Featured Report</div>
+<ul class="padding none col2"><li><a href="http://www.samhsa.gov/data/national-behavioral-health-quality-framework" target="_blank" title="National Behavioral Health Quality Framework">National Behavioral Health Quality Framework</a>
+<p>	The NBHQF provides a mechanism to examine and prioritize quality prevention, treatment, and recovery elements at the payer/system/plan, provider/practitioner, and patient/population levels. The NBHQF is aligned with the NQS in that it supports the three broad aims of better care, healthy people/healthy communities, and affordable care. However, it was specifically broadened to include the dissemination of proven interventions and accessible care. The latter concept encompasses affordable care, along with other elements of care accessibility, including the impact of health disparities.<br /><a href="http://www.samhsa.gov/data/national-behavioral-health-quality-framework" target="_blank" title="National Behavioral Health Quality Framework">&gt; Read More about National Behavioral Health Quality Framework</a></p></li>
+</ul></div>
+</div>
+</div>
+<div class="mega-header-padding">
+<div id="hover8"><a href="http://www.samhsa.gov/data/behavioral-health-outcomes">Behavioral Health Outcomes</a>
+<div id="display_area8">
+<ul class="padding none col"><li><a href="http://www.samhsa.gov/data/community-early-warning-system-c-ems">Community Early Warning &amp; Monitoring System (C-EMS)</a></li>
+<li><a href="http://www.whitehouse.gov/omb/mgmt-gpra/gplaw2m" target="_blank">Government Performance &amp; Results Act of 1993 (GPRA)</a></li>
+<li><a href="http://www.pcori.org/" target="_blank">Patient-Centered Outcomes Research Institute (PCORI)</a></li>
+<li><a href="http://partnershipforpatients.cms.gov/" target="_blank">Partnership for Patients (PFP)</a></li>
+</ul><div class="heading"> </div>
+<ul class="padding none col2"><li> </li>
+</ul></div>
+</div>
+</div>
+</div>
+</div>
+<!-- ***Begin - Charles Monroe 10/15/14. To turn on GSA indexing at this point*** --><!--googleon: all--><!-- ***End*** -->    </div>
+  </div>
+</div>
+</div>
+</div>
+</div><div class="menu-minipanels menu-minipanel-18177"><div class="panel-flexible panels-flexible-1 clearfix" id="mini-panel-about_us">
+<div class="panel-flexible-inside panels-flexible-1-inside">
+<div class="panels-flexible-region panels-flexible-region-1-about_us_section_2 panels-flexible-region-first panels-flexible-region-last ">
+  <div class="inside panels-flexible-region-inside panels-flexible-region-1-about_us_section_2-inside panels-flexible-region-inside-first panels-flexible-region-inside-last">
+<div class="panel-pane pane-custom pane-6">
+            <!-- ***Begin - Charles Monroe 10/15/14. To turn off GSA indexing at this point*** --><!--googleoff: all--><!-- ***End*** --><div id="aboutus_wrapper" onmouseout="megactivate('.aboutus', '');" onmouseover="megactivate('.aboutus', '#c47b12');">
+<ul class="padding col" id="megamenu-aboutus-1"><li class="mega-header"><a href="http://www.samhsa.gov/about-us/who-we-are">Who We Are</a>
+<ul><li><a href="http://www.samhsa.gov/about-us/who-we-are/leadership">Leadership</a></li>
+<li><a href="http://www.samhsa.gov/about-us/who-we-are/regional-administrators">Regional Administrators</a></li>
+<li><a href="http://www.samhsa.gov/about-us/who-we-are/offices-centers">Offices &amp; Centers</a></li>
+<li><a href="http://www.samhsa.gov/about-us/who-we-are/laws-regulations">Laws &amp; Regulations</a></li>
+</ul></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/about-us/interagency">Interagency Activities</a></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/about-us/advisory-councils">Advisory Councils</a></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/about-us/strategic-initiatives">Strategic Initiatives</a></li>
+</ul><ul class="padding col" id="megamenu-aboutus-2"><li class="mega-header"><a href="http://www.samhsa.gov/priorities">Priorities</a></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/budget">Budget</a>
+<ul><li><a href="http://www.samhsa.gov/sites/default/files/samhsa-fy-2017-congressional-justification.pdf">SAMHSA's Fiscal Year 2017<br />
+		Congressional Budget Justification</a></li>
+<li><a href="http://www.samhsa.gov/budget/contracts">Contracts <br /><!--<ul><li><a href="http://www.samhsa.gov/about-us/jobs-internships/internships" title="Internships">Internships</a></li>
+<li><a href="http://www.samhsa.gov/about-us/jobs-internships/volunteer-program" title="Volunteer Program">Volunteer Program</a></li>
+<li><a href="http://www.samhsa.gov/about-us/jobs-internships/practicum-program" title="Practicum Program">Practicum Program</a></li>
+<li><a href="http://www.samhsa.gov/about-us/jobs-internships/fellowships" title="Fellowships">Fellowships</a></li>
+</ul>
+<p>--></a></li>
+</ul></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/newsroom/speeches-presentations">Speeches and presentations</a></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/about-us/jobs-internships">Jobs &amp; Internships</a></li>
+</ul><ul class="col" id="megamenu-aboutus-3"><li class="mega-header"><a href="http://www.samhsa.gov/social-media">Social Media</a>
+<ul><li><a href="http://www.samhsa.gov/social-media/connect-samhsa">Connect with SAMHSA</a></li>
+<li><a href="http://www.samhsa.gov/social-media/social-media-accounts">Social Media Accounts</a></li>
+<li><a href="http://www.samhsa.gov/social-media/resources-professionals">Resources for Health<br />
+		Professionals</a></li>
+<li><a href="http://www.samhsa.gov/social-media/comment-policy">SAMHSA Comment Policy</a></li>
+</ul></li>
+<li class="mega-header-padding"><a href="http://www.samhsa.gov/about-us/contact-us">Contact Us</a>
+<ul><li><a href="http://www.samhsa.gov/about-us/contact-us/directions">Directions</a></li>
+</ul></li>
+</ul><ul class="none col last" id="megamenu-aboutus-4"><li class="mega-header" style="padding-bottom: 15px;"><a href="http://www.samhsa.gov/about-us/who-we-are/leadership/biographies/kana-enomoto">About the Acting Administrator</a></li>
+<li class="about_image"><a href="http://www.samhsa.gov/about-us/who-we-are/leadership/biographies/kana-enomoto"><img alt="Kana Enomoto" src="http://www.samhsa.gov/sites/default/files/images/kanaenomoto_mega.jpg" style="float: left; margin-left: 10px; margin-right: 10px;" /></a></li>
+<li class="about_image">Kana Enomoto, M.A. brings 24 years of experience in the behavioral health field to SAMHSA.</li>
+<li class="about_image_link"><a href="http://www.samhsa.gov/about-us/who-we-are/leadership/biographies/kana-enomoto">View Kana Enomoto’s full biography</a></li>
+</ul></div>
+<!-- ***Begin - Charles Monroe 10/15/14. To turn on GSA indexing at this point*** --><!--googleon: all--><!-- ***End*** -->    </div>
+  </div>
+</div>
+</div>
+</div>
+</div><script src="http://www.samhsa.gov/sites/all/modules/menu_minipanels/js/menu_minipanels.js?o3u7rg"></script>
+<script src="http://www.samhsa.gov/sites/all/libraries/qtip/jquery.qtip-1.0.0-rc3.min.js?o3u7rg"></script>
+<script src="http://www.samhsa.gov/sites/all/modules/responsive_imagemaps/responsive_imagemaps.js?o3u7rg"></script>
+  

+  <div class="bottom-clearer"></div>

+  

+  <script src="/sites/all/themes/omega_samhsa/GA/js/gatag.js" type="text/javascript"></script>

+  <script type="text/javascript">

+     var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");

+     document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));

+  </script>

+  <script type="text/javascript" src="/sites/all/themes/omega_samhsa/GA/js/googleanalytics.js"></script>

+   

+    <script type="text/javascript">

+		setTimeout(function(){var a=document.createElement("script");

+		var b=document.getElementsByTagName("script")[0];

+		a.src=document.location.protocol+"//dnn506yrbagrg.cloudfront.net/pages/scripts/0011/4190.js?"+Math.floor(new Date().getTime()/3600000);

+		a.async=true;a.type="text/javascript";b.parentNode.insertBefore(a,b)}, 1);

+  </script>

+</body>

+</html>

diff --git a/scripts/Archive/OpioidRx/webScraping/buprenorphinePrescriberDownloader.py b/scripts/Archive/OpioidRx/webScraping/buprenorphinePrescriberDownloader.py
new file mode 100644
index 00000000..ea106d61
--- /dev/null
+++ b/scripts/Archive/OpioidRx/webScraping/buprenorphinePrescriberDownloader.py
@@ -0,0 +1,68 @@
+## Taken from 
+# http://stackoverflow.com/questions/20039643/how-to-scrape-a-website-that-requires-login-first-with-python
+
+import sys, os;
+
+##################################### Method 1
+import mechanize
+import cookielib
+#from BeautifulSoup import BeautifulSoup
+
+from medinfo.common.Util import ProgressDots;
+
+
+
+# Browser
+br = mechanize.Browser()
+
+# Cookie Jar
+cj = cookielib.LWPCookieJar()
+br.set_cookiejar(cj)
+
+# Browser options
+br.set_handle_equiv(True)
+br.set_handle_gzip(True)
+br.set_handle_redirect(True)
+br.set_handle_referer(True)
+br.set_handle_robots(False)
+br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time=1)
+
+br.addheaders = [('User-agent', 'Chrome')]
+
+# The site we will navigate into, handling it's session
+#br.open('https://xxx.org/login')
+
+# View available forms
+#for f in br.forms():
+#    print f
+
+# Select the second (index one) form (the first form is a search query box)
+#br.select_form(nr=1)
+
+# User credentials, single login and then store a cookie
+#br.form['username'] = 'jonc101'
+#br.form['password'] = 'xxxx'
+
+# Login
+#br.submit()
+
+# Base URL to query for, with parameters for subsets
+BASE_URL = 'http://www.samhsa.gov/medication-assisted-treatment/physician-program-data/treatment-physician-locator?field_bup_physician_us_state_value=All&page=%s'
+BASE_OUTPUT_FILENAME = 'buprenorphinePhysicians.%s.htm';
+N_PAGES = 641
+#N_PAGES = 10
+
+progress = ProgressDots(big=100,small=2);
+for iPage in xrange(N_PAGES):
+    sourceURL = BASE_URL % (iPage);
+    sourceFile = br.open(sourceURL);
+    sourceContent = sourceFile.read();  # Just store whole file in memory for simplicity
+    sourceFile.close();
+    
+    localFilename = BASE_OUTPUT_FILENAME % (iPage);
+    localFile = open(localFilename,"w");
+    localFile.write(sourceContent);
+    localFile.close();
+    
+    progress.update();
+progress.printStatus();
diff --git a/scripts/Archive/OpioidRx/webScraping/buprenorphinePrescriberScraper.py b/scripts/Archive/OpioidRx/webScraping/buprenorphinePrescriberScraper.py
new file mode 100644
index 00000000..774c3162
--- /dev/null
+++ b/scripts/Archive/OpioidRx/webScraping/buprenorphinePrescriberScraper.py
@@ -0,0 +1,55 @@
+## Taken from 
+# http://stackoverflow.com/questions/20039643/how-to-scrape-a-website-that-requires-login-first-with-python
+
+import sys, os;
+
+##################################### Method 1
+#import mechanize
+#import cookielib
+from BeautifulSoup import BeautifulSoup
+import urllib;
+
+from medinfo.common.Util import ProgressDots, stdOpen;
+from medinfo.db.ResultsFormatter import TextResultsFormatter;
+
+BASE_FILENAME = 'buprenorphinePhysicians.%s.htm';
+N_PAGES = 641
+#N_PAGES = 5
+
+OUTPUT_FILENAME = 'prescribers.suboxone.tab';
+
+ofs = stdOpen(OUTPUT_FILENAME,"w");
+formatter = TextResultsFormatter(ofs);
+
+colNames = list();
+allColsSeen = False;
+
+progress = ProgressDots(big=100,small=2);
+for iPage in xrange(N_PAGES):
+    localFilename = BASE_FILENAME % (iPage);
+    localFile = open(localFilename);
+    html = localFile.read();
+    localFile.close()
+    
+    soup = BeautifulSoup(html);
+    cells = soup("td");
+
+    currRow = list();
+
+    for cell in cells:
+        if not allColsSeen: # Look for col names as go, then drop just dump out rows worth as progress
+            if cell["class"] not in colNames:
+                colNames.append(cell["class"]);
+            else:
+                allColsSeen = True;
+        if allColsSeen and len(currRow) == len(colNames):
+            formatter.formatTuple(currRow);
+            currRow = list();
+        currRow.append(cell.text.encode('utf-8'));
+
+    #obj = soup.find(class="views-field-field-bup-physician-last-name")
+
+    progress.update();
+progress.printStatus();
+
+ofs.close();
diff --git a/scripts/PressGaney/Step.10.DataLoadingRegression.R b/scripts/Archive/PressGaney/Step.10.DataLoadingRegression.R
similarity index 100%
rename from scripts/PressGaney/Step.10.DataLoadingRegression.R
rename to scripts/Archive/PressGaney/Step.10.DataLoadingRegression.R
diff --git a/scripts/PressGaney/Step.20.LogisticRegressionFormatOutput.R b/scripts/Archive/PressGaney/Step.20.LogisticRegressionFormatOutput.R
similarity index 100%
rename from scripts/PressGaney/Step.20.LogisticRegressionFormatOutput.R
rename to scripts/Archive/PressGaney/Step.20.LogisticRegressionFormatOutput.R
diff --git a/scripts/ResidentLogs/pagerWordCounts.py b/scripts/Archive/ResidentLogs/pagerWordCounts.py
similarity index 90%
rename from scripts/ResidentLogs/pagerWordCounts.py
rename to scripts/Archive/ResidentLogs/pagerWordCounts.py
index 3e693184..d4a78141 100644
--- a/scripts/ResidentLogs/pagerWordCounts.py
+++ b/scripts/Archive/ResidentLogs/pagerWordCounts.py
@@ -2,7 +2,7 @@
 """
 import sys, os;
 from wordcloud import WordCloud
-import urllib;
+import urllib.request, urllib.parse, urllib.error;
 from medinfo.common.Util import ProgressDots;
 from medinfo.db import DBUtil;
 
@@ -54,14 +54,14 @@
 
 # Only look at common words with more instances and write as sortable tuples
 countTokens = list();
-for token, count in countPerToken.iteritems():
+for token, count in countPerToken.items():
 	if count >= COUNT_THRESHOLD:
 		countTokens.append( (count, token) );
 countTokens.sort(reverse=True);
 
 truncatedCountPerToken = dict();
 for iToken, (count, token) in enumerate(countTokens):
-	print "%s\t%s" % (count, token);
+	print("%s\t%s" % (count, token));
 	if iToken < TOP_WORDS_TO_SHOW:
 		truncatedCountPerToken[token] = count;
 
diff --git a/scripts/ResidentLogs/stopWords.txt b/scripts/Archive/ResidentLogs/stopWords.txt
similarity index 100%
rename from scripts/ResidentLogs/stopWords.txt
rename to scripts/Archive/ResidentLogs/stopWords.txt
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Code/.RData b/scripts/Archive/ResidentRotationEHRUsage/Code/.RData
new file mode 100644
index 00000000..9c75209a
Binary files /dev/null and b/scripts/Archive/ResidentRotationEHRUsage/Code/.RData differ
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Code/.Rhistory b/scripts/Archive/ResidentRotationEHRUsage/Code/.Rhistory
new file mode 100644
index 00000000..03b74eab
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Code/.Rhistory
@@ -0,0 +1,512 @@
+length(strsplit(speeches[1], split=" ")
+)
+length(strsplit(speeches[1], split=" ")
+strsplit(speeches[1], split=" ")
+strsplit(speeches[1], split=" ")[1]
+length(strsplit(speeches[1], split=" "))
+as.vector(strsplit(speeches[1], split=" "))
+length(as.vector(strsplit(speeches[1], split=" ")))
+strsplit(speeches[1], split=" ")
+strsplit(speeches[1], split=" ")[[1]]
+length(strsplit(speeches[1], split=" ")[[1]])
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count = c(speeches_word_count, length(strsplit(speeches[i], split=" "))[[i]])
+}
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count = c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[i]]))
+}
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count = c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+speeches[3]
+speech.table <- data.frame(speakers, receivers, speeches_word_count)
+View(speech.table)
+?table
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech_pairs, speeches_word_count)
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count = c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech_pairs, speeches_word_count)
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+View(speech.table)
+library(dplyr)
+play.table <-  speech.table %>%
+group_by(ind=speech.table$speech.pairs) %>% summarise(value=sum(speech.pairs$speeches_word_count))
+play.table <-  speech.table %>%
+group_by(ind=speech.table$speech.pairs) %>% summarise(value=sum(speech.pairs$speeches_word_count), key=first(key))
+play.table <-  dplyr::summarise(speech.table, speeches_word_count=sum(speeches_word_count))
+View(play.table)
+play.table <- dplyr::group_by(speech.table, speech.pairs) %>% dplyr::summarise(speech.table, speeches_word_count=sum(speeches_word_count))
+play.table <- play.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+library(dplyr)
+play.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+View(play.table)
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+speech.matrix <- matrix(unlist(strsplit(names(speech.table), "_")), ncol=2, byrow=T)
+play.table <- data.frame(speech.matrix, as.vector(speech.table))
+View(speech.matrix)
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count <- c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+View(speech.table)
+speech.matrix <- matrix(unlist(strsplit(names(speech.table), "_")), ncol=2, byrow=T)
+View(speech.matrix)
+strsplit(speech.table$speech.pairs[i], split="_" )
+strsplit(speech.table$speech.pairs[1], split="_" )
+strsplit(speech.table$speech.pairs, split="_" )
+speech.table$speech.pairs[1]
+View(speech.table)
+speech.table <- data.frame(speech.table)
+View(speech.table)
+speech.table[1,]
+speech.table[1,1]
+as.character(speech.table[1,1])
+strsplit(as.character(speech.table$speech.pairs[1]), split="_" )
+# split unique speaker-receiver pairs
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table)) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[1])
+receivers <- c(receivers, temp[2])
+}
+# BONUS: Use number of words exchanged as edge metric (not just number of conversations)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count <- c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+speech.table <- data.frame(speech.table)
+# split unique speaker-receiver pairs
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table)) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[1])
+receivers <- c(receivers, temp[2])
+}
+play.table <- data.frame(speakers, receivers, speech.table$speeches_word_count)
+speakers
+temp
+temp[[1]]
+temp[[1]][1]
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table)) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[[1]][1])
+receivers <- c(receivers, temp[[1]][2])
+}
+length(speech.table)
+speech.table
+speech.table[[1]]
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table[[1]])) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[[1]][1])
+receivers <- c(receivers, temp[[1]][2])
+}
+play.table <- data.frame(speakers, receivers, speech.table$speeches_word_count)
+colnames(play.table) <- c("Speaker", "Receiver", "TotalSpeechWordCount")
+View(play.table)
+# BONUS: Use number of words exchanged as edge metric (not just number of conversations)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count <- c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+speech.table <- data.frame(speech.table)
+# split unique speaker-receiver pairs
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table[[1]])) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[[1]][1])
+receivers <- c(receivers, temp[[1]][2])
+}
+play.table <- data.frame(speakers, receivers, speech.table$speeches_word_count)
+colnames(play.table) <- c("Speaker", "Receiver", "TotalSpeechWordCount")
+library(igraph)
+play.network<-graph_from_edgelist(speech.matrix)
+# plot(play.network)
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+# Eigenvector centrality
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight)
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+# Betweenness Centrality
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+library(XML)
+setwd("/Users/jwang/Desktop/Coursework/English 184E/")
+caesar <- xmlTreeParse("Shakespeare_Caesar_1599.xml", useInternalNodes=T)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# find unique speaker-receiver pairs
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- table(speech.pairs) # counts unique instances of each distinct entry in vector
+speech.matrix <- matrix(unlist(strsplit(names(speech.table), "_")), ncol=2, byrow=T)
+play.table <- data.frame(speech.matrix, as.vector(speech.table))
+colnames(play.table) <- c("Speaker", "Receiver", "NumberofSpeeches")
+library(igraph)
+play.network<-graph_from_edgelist(speech.matrix)
+plot(play.network)
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+# Eigenvector centrality
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight)
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+# Betweenness Centrality
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+play.table
+speech.matrix
+# BONUS: Use number of words exchanged as edge metric (not just number of conversations)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count <- c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+speech.table <- data.frame(speech.table)
+# split unique speaker-receiver pairs
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table[[1]])) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[[1]][1])
+receivers <- c(receivers, temp[[1]][2])
+}
+play.table <- data.frame(speakers, receivers, speech.table$speeches_word_count)
+colnames(play.table) <- c("Speaker", "Receiver", "TotalSpeechWordCount")
+library(igraph)
+play.network<-graph_from_edgelist(speech.matrix)
+# plot(play.network)
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+# Eigenvector centrality
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight)
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+# Betweenness Centrality
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+sort(eigen$vector, decreasing=T)
+sort(between.c, decreasing=T)
+speech.matrix
+play.table
+graph_from_data_frame(play.table)
+play.network <- graph_from_data_frame(play.table)
+plot(play.network)
+play.network<-graph_from_data_frame(play.table)
+# BONUS: Use number of words exchanged as edge metric (not just number of conversations)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count <- c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+speech.table <- data.frame(speech.table)
+# split unique speaker-receiver pairs
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table[[1]])) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[[1]][1])
+receivers <- c(receivers, temp[[1]][2])
+}
+play.table <- data.frame(speakers, receivers, speech.table$speeches_word_count)
+colnames(play.table) <- c("Speaker", "Receiver", "TotalSpeechWordCount")
+library(igraph)
+play.network<-graph_from_data_frame(play.table)
+# plot(play.network)
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+# Eigenvector centrality
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight)
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.wdith=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+# Betweenness Centrality
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+sort(eigen$vector, decreasing=T)
+E(play.network)$weight
+play.network
+play.network$weight
+library(XML)
+setwd("/Users/jwang/Desktop/Coursework/English 184E/")
+caesar <- xmlTreeParse("Shakespeare_Caesar_1599.xml", useInternalNodes=T)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# find unique speaker-receiver pairs
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- table(speech.pairs) # counts unique instances of each distinct entry in vector
+speech.matrix <- matrix(unlist(strsplit(names(speech.table), "_")), ncol=2, byrow=T)
+play.table <- data.frame(speech.matrix, as.vector(speech.table))
+colnames(play.table) <- c("Speaker", "Receiver", "NumberofSpeeches")
+library(igraph)
+play.network<-graph_from_edgelist(speech.matrix)
+plot(play.network)
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+# Eigenvector centrality
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+# Betweenness Centrality
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+E(play.network)
+E(play.network)$weight
+library(XML)
+setwd("/Users/jwang/Desktop/Coursework/English 184E/")
+caesar <- xmlTreeParse("Shakespeare_Caesar_1599.xml", useInternalNodes=T)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# find unique speaker-receiver pairs
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- table(speech.pairs) # counts unique instances of each distinct entry in vector
+speech.matrix <- matrix(unlist(strsplit(names(speech.table), "_")), ncol=2, byrow=T)
+play.table <- data.frame(speech.matrix, as.vector(speech.table))
+colnames(play.table) <- c("Speaker", "Receiver", "NumberofSpeeches")
+library(igraph)
+play.network<-graph_from_edgelist(speech.matrix)
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+E(play.network)$weight <- play.table
+E(play.network)$weight
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+E(play.network)$weight <- play.table$NumberofSpeeches
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+vector
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+# BONUS: Use number of words exchanged as edge metric (not just number of conversations)
+# find speakers
+speakers <- xpathSApply(caesar, "//*/SPEECH/SPEAKER", xmlValue)
+length(speakers)
+receivers <- xpathSApply(caesar, "//*/SPEECH/RECEIVER", xmlValue)
+length(receivers)
+speeches <- xpathSApply(caesar, "//*/SPEECH/LINES", xmlValue)
+speeches[1]
+# count number of words in each speech
+speeches_word_count = c()
+for (i in 1:length(speeches)) {
+speeches_word_count <- c(speeches_word_count, length(strsplit(speeches[i], split=" ")[[1]]))
+}
+# create table of speech_pairs, speech word count
+speech.pairs <- paste(speakers, receivers, sep="_")
+speech.table <- data.frame(speech.pairs, speeches_word_count)
+# aggregate on speech pair, summing word counts
+library(dplyr)
+speech.table <- speech.table %>% dplyr::group_by(speech.pairs) %>% dplyr::summarise( speeches_word_count=sum(speeches_word_count))
+speech.table <- data.frame(speech.table)
+# split unique speaker-receiver pairs
+speakers = c()
+receivers = c()
+for (i in 1:length(speech.table[[1]])) {
+temp <- strsplit(as.character(speech.table$speech.pairs[i]), split="_")
+speakers <- c(speakers, temp[[1]][1])
+receivers <- c(receivers, temp[[1]][2])
+}
+play.table <- data.frame(speakers, receivers, speech.table$speeches_word_count)
+colnames(play.table) <- c("Speaker", "Receiver", "TotalSpeechWordCount")
+library(igraph)
+play.network<-graph_from_data_frame(play.table)
+E(play.network)$weight <- play.table$TotalSpeechWordCount
+coords <- layout_(play.network, nicely()) # nicely chooses best algo out of all available to make nices graph
+E(play.network)$weight
+eigen <- evcent(play.network, weights=E(play.network)$weight)
+eigen$vector
+sort(eigen$vector, decreasing=T)
+clust <- cluster_optimal(play.network) # clustering: identifies subnetworks within the global/whole network
+V(play.network)$color <- membership(clust) # color vertices/nodes by cluster
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+plot(play.network)
+between.c<-betweenness(play.network)
+sort(between.c, decreasing=T)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=10.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight/100.0, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight/100.0, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight/100.0, vertex.size=25.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight/100.0, vertex.size=20.0*eigen$vector)
+plot(play.network, layout=coords, edge.width=E(play.network)$weight/100.0, vertex.size=15.0*eigen$vector)
+between.c<-betweenness(play.network)
+between.c
+sort(between.c, decreasing=T)
+plot(play.network, edge.width=E(play.network)$weight, layout=coords, vertex.size=between.c/25.0)
+plot(play.network, edge.width=E(play.network)$weight/100.0, layout=coords, vertex.size=between.c/25.0)
+plot(play.network, edge.width=E(play.network)$weight/100.0, layout=coords, vertex.size=between.c/15.0)
+plot(play.network, edge.width=E(play.network)$weight/100.0, layout=coords, vertex.size=between.c/50.0)
+setwd("/Users/jwang/Desktop/Chen/RotationActivity/Results/")
+data <- read.csv("delta_distribution.csv", header=T, stringsAsFactors=F)
+source('~/Desktop/Chen/RotationActivity/Results/delta_distribution.R', echo=TRUE)
+View(data)
+histogram(data)
+hist(data)
+View(data)
+?strptime
+df <- read.csv("delta_distribution.csv", header=T, stringsAsFactors=F)
+strptime(df$delta, format='%H:%M:%S')
+source('~/Desktop/Chen/RotationActivity/Results/delta_distribution.R', echo=TRUE)
+hist(df)
+View(df)
+plot(df)
+import(ggplot2)
+library(ggplot2)
+names(df) <- ("Delta", "Count")
+names(df) <- c("Delta", "Count")
+ggplot(df, aes(y=Count)) + geom_lines()
+library(ggplot2)
+names(df) <- c("Delta", "Count")
+ggplot(df, aes(y=Count)) + geom_lines()
+ggplot(df, aes(y=Count)) + geom_point()
+ggplot(df, aes(y=Count)) + geom_point(aes(y=Count))
+ggplot(df, aes(y=Count)) + geom_point(aes(Count))
+ggplot(df, aes(y=Count)) + geom_point(aes(Delta,Count))
+df$Delta
+which(df$delta <= "2018-01-20 00:30:00 PST")
+which(df$delta <= strptime('00:30:00'))
+which(df$delta <= strptime('00:30:00', format='%H:%M:%S'))
+which(df$delta <= strptime('00:30:00', format='%H:%M:%S'))
+strptime('00:30:00', format='%H:%M:%S')
+strptime('00:30:00', format='%H:%M:%S') < strptime('00:31:00', format='%H:%M:%S')
+source('~/Desktop/Chen/RotationActivity/Results/delta_distribution.R', echo=TRUE)
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Code/.ipynb_checkpoints/Daily EHR Time Spent Analysis Should We Filter-checkpoint.ipynb b/scripts/Archive/ResidentRotationEHRUsage/Code/.ipynb_checkpoints/Daily EHR Time Spent Analysis Should We Filter-checkpoint.ipynb
new file mode 100644
index 00000000..460f3a10
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Code/.ipynb_checkpoints/Daily EHR Time Spent Analysis Should We Filter-checkpoint.ipynb	
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load in pickled daily EHR time arrays\n",
+    "# in reality, daily usage is currently in seconds...\n",
+    "PGY1_general_medicine = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY1_2.pkl\", \"rb\")))/3600.0\n",
+    "PGY1_night_team = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY1_5.pkl\", \"rb\")))/3600.0\n",
+    "PGY1_emergency_medicine = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY1_22.pkl\", \"rb\")))/3600.0\n",
+    "\n",
+    "PGY2plus_general_medicine = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY2+_2.pkl\", \"rb\")))/3600.0\n",
+    "PGY2plus_night_team = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY2+_5.pkl\", \"rb\")))/3600.0\n",
+    "PGY2plus_emergency_medicine = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY2+_22.pkl\", \"rb\")))/3600.0\n",
+    "PGY2plus_intensive_care = np.array(pickle.load(open(\"daily_usage_in_minutes_PGY2+_27.pkl\", \"rb\")))/3600.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11170f250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot histograms\n",
+    "plt.hist(PGY1_general_medicine, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2lETct68xgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x111829090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(PGY2plus_general_medicine, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4.7765933271723418, 5.7576290486564989)\n",
+      "(4.4302150045745661, 4.8105791538723981)\n",
+      "(5.2893193054010625, 6.3121227477477468)\n",
+      "(4.5419489247311819, 5.4964386661097189)\n",
+      "(5.3527890580936264, 5.7108995922528027)\n",
+      "(5.2154716981132072, 6.5279935275080891)\n",
+      "(3.9430968953007679, 4.8178754607688257)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print averages with and without filtering < threshold hours \n",
+    "threshold = 2.0 # hours\n",
+    "\n",
+    "print(\"Without Filtering\", \"With Filtering\")\n",
+    "# PGY1\n",
+    "print(\"PGY1 General Medicine\")\n",
+    "PGY1_general_medicine_filtered = np.array(filter(lambda x:x>2.0, PGY1_general_medicine))\n",
+    "print(np.mean(PGY1_general_medicine), np.mean(PGY1_general_medicine_filtered))\n",
+    "print(\"PGY1 Night Team\")\n",
+    "PGY1_night_team_filtered = np.array(filter(lambda x:x>2.0, PGY1_night_team))\n",
+    "print(np.mean(PGY1_night_team), np.mean(PGY1_night_team_filtered))\n",
+    "PGY1_emergency_medicine_filtered = np.array(filter(lambda x:x>2.0, PGY1_emergency_medicine))\n",
+    "print(np.mean(PGY1_emergency_medicine), np.mean(PGY1_emergency_medicine_filtered))\n",
+    "\n",
+    "# PGY2+\n",
+    "PGY2plus_general_medicine_filtered = np.array(filter(lambda x:x>2.0, PGY2plus_general_medicine))\n",
+    "print(np.mean(PGY2plus_general_medicine), np.mean(PGY2plus_general_medicine_filtered))\n",
+    "PGY2plus_night_team_filtered = np.array(filter(lambda x:x>2.0, PGY2plus_night_team))\n",
+    "print(np.mean(PGY2plus_night_team), np.mean(PGY2plus_night_team_filtered))\n",
+    "PGY2plus_emergency_medicine_filtered = np.array(filter(lambda x:x>2.0, PGY2plus_emergency_medicine))\n",
+    "print(np.mean(PGY2plus_emergency_medicine), np.mean(PGY2plus_emergency_medicine_filtered))\n",
+    "PGY2plus_intensive_care_filtered = np.array(filter(lambda x:x>2.0, PGY2plus_intensive_care))\n",
+    "print(np.mean(PGY2plus_intensive_care), np.mean(PGY2plus_intensive_care_filtered))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Code/compute_number_ehr_actions_per_24hour_cycle.py b/scripts/Archive/ResidentRotationEHRUsage/Code/compute_number_ehr_actions_per_24hour_cycle.py
new file mode 100644
index 00000000..230dea14
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Code/compute_number_ehr_actions_per_24hour_cycle.py
@@ -0,0 +1,73 @@
+# Goal: compute average number of EHR actions logged per user day in half hour increments over 24 hour cycle for different inpatient rotations
+import numpy as np
+
+# Change flag to compute for PGY1 or PGY2+
+# ROLE = "PGY1"
+ROLE = "PGY2+"
+
+if (ROLE == "PGY1"):
+	role_filter = ["R1"]
+else:
+	role_filter = ["R2","R3"]
+
+access_logs = open("raw_resident_access_logs.csv", "rU")
+access_logs.readline()
+
+# For each rotation, we split 24 hours into 48 half-hour buckets; we then keep count of number of actions
+def bucket(access_timestamp):
+	hours = access_timestamp[0]
+	minutes = access_timestamp[1]
+	bucket_id = hours*2
+
+	if (minutes > 30):
+		bucket_id += 1
+	return(bucket_id)
+
+action_counter = {'2':{}, '5':{}, '22':{}, '27':{}}
+for line in access_logs:
+	line = line.strip().split(",")
+	role = line[0]
+	if (role in role_filter):
+		# print(role)
+		rotation_id = line[1]
+		access_date = line[14].split(" ")[0]
+		access_timestamp = list(map(int, line[14].split(" ")[1].split(":")[:-1]))
+		bucket_id = bucket(access_timestamp)
+		provider_id = line[4]
+
+		user_day = provider_id + "_" + access_date
+		if (user_day not in action_counter[rotation_id]):
+			action_counter[rotation_id][user_day] = [0]*48
+			action_counter[rotation_id][user_day][bucket_id] += 1
+		else: # user_day already present
+			action_counter[rotation_id][user_day][bucket_id] += 1
+
+# For each rotation, compute average number of actions per user-day
+averaged_timeseries = {'2':np.zeros(48), '5':np.zeros(48), '22':np.zeros(48), '27':np.zeros(48)}
+for rotation_id in list(action_counter.keys()):
+	num_user_days = 0
+	for user_day, action_ts in action_counter[rotation_id].items(): # for each timeseries
+		num_user_days += 1
+		averaged_timeseries[rotation_id] = averaged_timeseries[rotation_id] + np.array(action_ts)
+	if (num_user_days == 0): # e.g. for PGY1 interns in ICU since first years do not do ICU rotation
+		averaged_timeseries[rotation_id] = averaged_timeseries[rotation_id] # no division
+	else:
+		averaged_timeseries[rotation_id] = averaged_timeseries[rotation_id] / num_user_days
+
+# Output results
+outf = open("average_number_actions_per_userday_24_hour_cycle_{0}.csv".format(ROLE), "w")
+outf.write("rotation,{0}\n".format(",".join(map(str, list(np.array(list(range(0,48)))/2.0)))))
+for k, v in averaged_timeseries.items():
+	if (int(k) == 2):
+		outf.write("General Medicine,")
+	elif (int(k) == 5):
+		outf.write("Medicine Night Team,")
+	elif (int(k) == 22):
+		outf.write("Emergency Department,")
+	elif (int(k) == 27):
+		outf.write("Intensive Care Unit,")
+	else:
+		print("unrecognized rotation_id...")
+	outf.write("{0}\n".format(",".join(map(str, list(v)))))
+outf.close()
+
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Code/compute_summary_stats.py b/scripts/Archive/ResidentRotationEHRUsage/Code/compute_summary_stats.py
new file mode 100644
index 00000000..f04c5f2b
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Code/compute_summary_stats.py
@@ -0,0 +1,146 @@
+# Compute summary statistics across rotations and roles
+
+import numpy as np
+import datetime
+from dateutil import parser
+import sys
+from scipy.stats import ttest_ind
+
+# exclude vacation days
+threshold = 1*3600 # 1 hour vacation threshold (in seconds)
+
+total_time_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+chart_review_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+note_review_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+note_entry_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+order_entry_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+navigator_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+results_review_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+num_actions_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+num_remote_actions_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+num_patients_map = {"General Medicine":{"PGY1":[], "PGY2+":[]}, "Night Team":{"PGY1":[], "PGY2+":[]}, "Emergency Medicine":{"PGY1":[], "PGY2+":[]}, "Intensive Care Unit":{"PGY2+":[]}}
+
+inf = open("usage_spreadsheet.csv", "rU")
+inf.readline()
+for line in inf:
+	line = line.strip().split(",")
+
+	total_time = parser.parse(line[10]) 
+	total_time = 3600*total_time.hour + 60*total_time.minute + total_time.second # convert to seconds
+
+	if (total_time < threshold):
+		continue
+
+	rotation = line[0]
+	role = line[1]
+
+	num_actions_map[rotation][role].append(int(line[11]))
+	num_remote_actions_map[rotation][role].append(int(line[12]))
+	num_patients_map[rotation][role].append(int(line[13]))
+
+	# add to total_time_map
+	total_time_map[rotation][role].append(total_time/3600.0)
+
+	# identify section-specific times
+	chart_review = parser.parse(line[4])
+	chart_review = 3600*chart_review.hour + 60*chart_review.minute + chart_review.second # convert to seconds
+	chart_review_map[rotation][role].append(chart_review/3600.0)
+
+	note_review = parser.parse(line[5])
+	note_review = 3600*note_review.hour + 60*note_review.minute + note_review.second # convert to seconds
+	note_review_map[rotation][role].append(note_review/3600.0)
+
+	note_entry = parser.parse(line[6])
+	note_entry = 3600*note_entry.hour + 60*note_entry.minute + note_entry.second # convert to seconds
+	note_entry_map[rotation][role].append(note_entry/3600.0)
+
+	order_entry = parser.parse(line[7])
+	order_entry = 3600*order_entry.hour + 60*order_entry.minute + order_entry.second # convert to seconds
+	order_entry_map[rotation][role].append(order_entry/3600.0)
+
+	navigator = parser.parse(line[8])
+	navigator = 3600*navigator.hour + 60*navigator.minute + navigator.second # convert to seconds
+	navigator_map[rotation][role].append(navigator/3600.0)
+
+	results_review = parser.parse(line[9])
+	results_review = 3600*results_review.hour + 60*results_review.minute + results_review.second # convert to seconds
+	results_review_map[rotation][role].append(results_review/3600.0)
+
+
+# num (remote) actions, num patients
+for rotation in num_actions_map.keys():
+	for role, values in num_actions_map[rotation].items():
+		print("# Actions", rotation, role)
+		print("{0} median with IQR {1}-{2}".format(np.median(np.array(values)), np.percentile(np.array(values), 25), np.percentile(np.array(values), 75)))
+
+for rotation in num_remote_actions_map.keys():
+	for role, values in num_remote_actions_map[rotation].items():
+		print("# Remote Actions", rotation, role)
+		print("{0} median with IQR {1}-{2}".format(np.median(np.array(values)), np.percentile(np.array(values), 25), np.percentile(np.array(values), 75)))
+
+for rotation in num_patients_map.keys():
+	for role, values in num_patients_map[rotation].items():
+		print("# Patients", rotation, role)
+		print("{0} median with IQR {1}-{2}".format(np.median(np.array(values)), np.percentile(np.array(values), 25), np.percentile(np.array(values), 75)))
+
+
+# time spent on each ehr action
+for rotation in chart_review_map.keys():
+	for role, values in chart_review_map[rotation].items():
+		print("Chart Review", rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+
+for rotation in note_review_map.keys():
+	for role, values in note_review_map[rotation].items():
+		print("Note Review", rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+
+for rotation in note_entry_map.keys():
+	for role, values in note_entry_map[rotation].items():
+		print("Note Entry", rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+
+for rotation in order_entry_map.keys():
+	for role, values in order_entry_map[rotation].items():
+		print("Order Entry", rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+
+for rotation in navigator_map.keys():
+	for role, values in navigator_map[rotation].items():
+		print("Navigator", rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+
+for rotation in results_review_map.keys():
+	for role, values in results_review_map[rotation].items():
+		print("Results Review", rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+
+# total time in aggregate and by role
+agg = [] # aggregate across all rotations and roles
+pgy1 = []
+pgy2plus = []
+for rotation in total_time_map.keys():
+	for role, values in total_time_map[rotation].items():
+		agg.extend(values)
+		if (role == "PGY1"):
+			pgy1.extend(values)
+		else: # role == "PGY2+"
+			pgy2plus.extend(values)
+print("All rotations/roles")
+print("{0} mean with stdev {1}".format(np.mean(np.array(agg)), np.std(np.array(agg))))
+print("{0} median with IQR {1}-{2}".format(np.median(np.array(agg)), np.percentile(np.array(agg), 25), np.percentile(np.array(agg), 75)))
+
+print("PGY1")
+print("{0} mean with stdev {1}".format(np.mean(np.array(pgy1)), np.std(np.array(pgy1))))
+print("{0} median with IQR {1}-{2}".format(np.median(np.array(pgy1)), np.percentile(np.array(pgy1), 25), np.percentile(np.array(pgy1), 75)))
+
+print("PGY2+")
+print("{0} mean with stdev {1}".format(np.mean(np.array(pgy2plus)), np.std(np.array(pgy2plus))))
+print("{0} median with IQR {1}-{2}".format(np.median(np.array(pgy2plus)), np.percentile(np.array(pgy2plus), 25), np.percentile(np.array(pgy2plus), 75)))
+
+# total time by rotation and role
+for rotation in total_time_map.keys():
+	for role, values in total_time_map[rotation].items():
+		print(rotation, role)
+		print("{0} mean with stdev {1}".format(np.mean(np.array(values)), np.std(np.array(values))))
+		print("{0} median with IQR {1}-{2}".format(np.median(np.array(values)), np.percentile(np.array(values), 25), np.percentile(np.array(values), 75)))
\ No newline at end of file
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Code/generate_usage_spreadsheet.py b/scripts/Archive/ResidentRotationEHRUsage/Code/generate_usage_spreadsheet.py
new file mode 100644
index 00000000..0dfb6a4a
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Code/generate_usage_spreadsheet.py
@@ -0,0 +1,181 @@
+import numpy as np
+import collections
+import datetime
+from dateutil import parser
+import pickle
+import sys
+
+# Create a master usage spreadsheet with columns
+# Rotation, Role, Provider_ID, User-Day/Date, [Usage Per Relevant EHR Categories], Total Usage, 
+# Number of EHR Actions, Number of EHR Actions Accessed Remotely, Number of Patient Records Accessed
+
+access_logs = open("raw_resident_access_logs.csv", "rU")
+access_logs.readline()
+
+cutoff = 5 # minutes
+role_map = 	{"R1":"PGY1", "R2":"PGY2+", "R3":"PGY2+"}
+
+# 2 = General Medicine
+# 5 = Night Team
+# 22 = Emergency Medicine
+# 27 = Intensive Care Unit
+# note: only PGY2+ residents rotate through ICU
+rotation_map = {"2":"General Medicine", "5":"Night Team", "22":"Emergency Medicine", "27":"Intensive Care Unit"}
+
+# a user_day key is unique since a given user can presumably only assume one role for one rotation on one day 
+patient_counter = {} # user-day: number of unique patients seen
+action_counter = {} # user-day: number of ehr actions performed
+remote_action_counter = {} # user-day: number of ehr actions accessed remotely (non-workstation)
+role_tracker = {} # user-day: role
+rotation_tracker = {} # user-day: rotation
+
+# Extract all timestamps corresponding to a given user-day
+action_histories = {} # indexed by user-day
+
+# count = 0
+# first pass through raw data
+for line in access_logs:
+	# count += 1
+	# if (count % 10000 == 0):
+		# print("{0} lines read...".format(count))
+	line = line.strip().split(",")
+
+	# extract relevant data fields
+	role = role_map[line[0]]
+	rotation = rotation_map[line[1]]
+	patient_id = line[2]
+	provider_id = line[4]
+	workstation_id = line[7]
+	metric_id = line[8]
+	access_date = line[14].split(" ")[0]
+	user_day = provider_id + "_" + access_date
+	access_timestamp = line[14].split(" ")[1] # 15:15:56
+
+	# update counters/trackers
+	if (user_day not in patient_counter):
+		patient_counter[user_day] = {}
+	patient_counter[user_day][patient_id] = 1
+
+	if (user_day not in action_counter):
+		action_counter[user_day] = 0
+	action_counter[user_day] += 1
+
+	role_tracker[user_day] = role # although this same key will be updated many times, it will be updated with the same value - so we're ok!
+	rotation_tracker[user_day] = rotation
+
+	# If workstation_id == "GENERIC" or "", then remote login
+	# Else --> workstation login from computer present in hospital
+	if (workstation_id == "GENERIC" or workstation_id == ""): # remote access
+		if (user_day not in remote_action_counter):
+			remote_action_counter[user_day] = 0
+		remote_action_counter[user_day] += 1
+
+	# fill action history
+	if (user_day not in action_histories):
+		action_histories[user_day] = [(access_timestamp, metric_id)]
+	else: # user_day already present
+		action_histories[user_day].append( (access_timestamp, metric_id) )
+
+# For each rotation and user-day, sort list of tuples by access_timestamp
+for user_day in list(action_histories.keys()):
+	action_histories[user_day] = sorted(action_histories[user_day], key=lambda x: x[0])
+
+# Note: there are 551 different metric_ids (actions)
+# load metric_id to ehr action category map
+metric_inf = open("ehr_action_categories.csv", "rU")
+metric_category_map = {}
+for line in metric_inf:
+	line = line.strip().split(",")
+	metric_category_map[line[0]] = line[1]
+metric_inf.close()
+
+# Track time spent on each EHR category 
+categories_of_interest = ["Chart Review", "Note Review", "Note Entry", "Order Entry", "Navigator", "Results Review"]
+category_time_counter = {}
+for category in categories_of_interest:
+	category_time_counter[category] = {} # indexed by user-day
+
+# Track time spent on EHR across all categories ("total")
+total_time_counter = {} # indexed by user-day
+
+# For each rotation and user-day, compute action deltas (proxy for interaction time)
+for user_day, history in action_histories.items():
+	time_start = ""
+	metric_id = ""
+	metric_category = ""
+	for tup in history:
+		if (time_start == ""): # first time in sequence
+			time_start = parser.parse(tup[0])
+			metric_id = tup[1]
+			if (metric_id in metric_category_map): # action of interest
+				metric_category = metric_category_map[metric_id]
+			else:
+				metric_category = ""
+		else: 
+			# compute difference between timestamps
+			time_delta = parser.parse(tup[0]) - time_start
+			if (time_delta <= datetime.timedelta(minutes=cutoff)): # enforce idle cutoff (e.g. consider only inter-access time intervals < 5 minutes or else assume inactivity)
+				if (user_day not in list(total_time_counter.keys())):
+					total_time_counter[user_day] = datetime.timedelta(minutes=0)
+				total_time_counter[user_day] += time_delta
+
+				# based on metric_category, add to corresponding time_counter
+				if (metric_category in categories_of_interest):
+					if (user_day not in list(category_time_counter[metric_category].keys())):
+						category_time_counter[metric_category][user_day] = datetime.timedelta(minutes=0)
+					category_time_counter[metric_category][user_day] += time_delta
+
+			# update variables
+			time_start = parser.parse(tup[0])
+			metric_id = tup[1]
+			if (metric_id in metric_category_map): # action of interest
+				metric_category = metric_category_map[metric_id]
+			else:
+				metric_category = ""
+
+# Output results to master spreadsheet
+outf_spreadsheet = open("usage_spreadsheet.csv", "w")
+outf_spreadsheet.write("Rotation,Role,Provider_ID,Date,Chart_Review_Time,Note_Review_Time,Note_Entry_Time,Order_Entry_Time,Navigator_Time,Results_Review_Time,Total_Time,Number_EHR_Actions,Number_EHR_Actions_Accessed_Remotely,Number_Unique_Patient_Records_Accessed\n")
+for user_day in list(total_time_counter.keys()):
+	provider_id = user_day.split("_")[0]
+	access_date = user_day.split("_")[1]
+
+	# ehr action-specific usage times
+	chart_review_time = datetime.timedelta(minutes=0)
+	note_review_time = datetime.timedelta(minutes=0)
+	note_entry_time = datetime.timedelta(minutes=0)
+	order_entry_time = datetime.timedelta(minutes=0)
+	navigator_time = datetime.timedelta(minutes=0)
+	results_review_time = datetime.timedelta(minutes=0)
+	total_time = datetime.timedelta(minutes=0)
+	if (user_day in category_time_counter["Chart Review"]):
+		chart_review_time = category_time_counter["Chart Review"][user_day]
+	if (user_day in category_time_counter["Note Review"]):
+		note_review_time = category_time_counter["Note Review"][user_day]
+	if (user_day in category_time_counter["Note Entry"]):
+		note_entry_time = category_time_counter["Note Entry"][user_day]
+	if (user_day in category_time_counter["Order Entry"]):
+		order_entry_time = category_time_counter["Order Entry"][user_day]
+	if (user_day in category_time_counter["Navigator"]):
+		navigator_time = category_time_counter["Navigator"][user_day]
+	if (user_day in category_time_counter["Results Review"]):
+		results_review_time = category_time_counter["Results Review"][user_day]
+	if (user_day in total_time_counter):
+		total_time = total_time_counter[user_day]
+
+	# counters
+	num_actions = 0
+	num_remote_actions = 0
+	num_patients = 0
+	if (user_day in action_counter):
+		num_actions = action_counter[user_day]
+	if (user_day in remote_action_counter):
+		num_remote_actions = remote_action_counter[user_day]
+	if (user_day in patient_counter):
+		num_patients = len(patient_counter[user_day]) # number of unique patients
+
+	outf_spreadsheet.write("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11},{12},{13}\n".format(rotation_tracker[user_day],role_tracker[user_day],
+		provider_id,access_date,chart_review_time,note_review_time,note_entry_time,order_entry_time,navigator_time,results_review_time,total_time,
+		num_actions,num_remote_actions,num_patients))
+outf_spreadsheet.close()
+
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Data/Medical_Student_EHR_Use_Provider_Level.xlsx b/scripts/Archive/ResidentRotationEHRUsage/Data/Medical_Student_EHR_Use_Provider_Level.xlsx
new file mode 100644
index 00000000..267e5b74
Binary files /dev/null and b/scripts/Archive/ResidentRotationEHRUsage/Data/Medical_Student_EHR_Use_Provider_Level.xlsx differ
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Data/ehr_action_categories.csv b/scripts/Archive/ResidentRotationEHRUsage/Data/ehr_action_categories.csv
new file mode 100644
index 00000000..abef0702
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Data/ehr_action_categories.csv
@@ -0,0 +1,232 @@
+metric_id,metric_category
+17000,Chart Review
+17004,Chart Review
+17005,Chart Review
+17006,Chart Review
+17008,Chart Review
+17011,Chart Review
+17012,Chart Review
+17014,Chart Review
+17015,Chart Review
+17021,Chart Review
+17022,Chart Review
+17027,Chart Review
+17030,Chart Review
+17228,Chart Review
+17231,Chart Review
+17250,Chart Review
+17251,Chart Review
+17252,Chart Review
+17256,Chart Review
+17281,Chart Review
+49052,Chart Review
+49054,Chart Review
+72000,Chart Review
+72001,Chart Review
+94023,Chart Review
+49005,Discharge
+20080,Imaging
+17045,Navigator
+17047,Navigator
+17104,Navigator
+17128,Navigator
+17148,Navigator
+20620,Navigator
+49019,Navigator
+17202,Note Entry
+34010,Note Entry
+60010,Note Entry
+60012,Note Entry
+17007,Note Review
+17227,Note Review
+34005,Note Review
+60005,Note Review
+94013,Note Review
+94060,Note Review
+17108,Order Entry
+17162,Order Entry
+17225,Order Entry
+17234,Order Entry
+34118,Order Entry
+34119,Order Entry
+34127,Order Entry
+34150,Order Entry
+17016,Results Review
+17257,Results Review
+17258,Results Review
+94018,Results Review
+94050,Results Review
+17000,Chart Review
+17004,Chart Review
+17005,Chart Review
+17006,Chart Review
+17008,Chart Review
+17011,Chart Review
+17012,Chart Review
+17014,Chart Review
+17015,Chart Review
+17021,Chart Review
+17022,Chart Review
+17027,Chart Review
+17030,Chart Review
+17228,Chart Review
+17231,Chart Review
+17250,Chart Review
+17251,Chart Review
+17252,Chart Review
+17256,Chart Review
+17281,Chart Review
+49052,Chart Review
+49054,Chart Review
+72000,Chart Review
+72001,Chart Review
+94023,Chart Review
+49005,Discharge
+20080,Imaging
+17045,Navigator
+17047,Navigator
+17104,Navigator
+17128,Navigator
+17148,Navigator
+20620,Navigator
+49019,Navigator
+17202,Note Entry
+34010,Note Entry
+60010,Note Entry
+60012,Note Entry
+17007,Note Review
+17227,Note Review
+34005,Note Review
+60005,Note Review
+94013,Note Review
+94060,Note Review
+95040,Note Review
+17108,Order Entry
+17162,Order Entry
+17225,Order Entry
+17234,Order Entry
+34118,Order Entry
+34119,Order Entry
+34127,Order Entry
+34150,Order Entry
+17016,Results Review
+17257,Results Review
+17258,Results Review
+94018,Results Review
+94050,Results Review
+95050,Results Review
+17000,Chart Review
+17004,Chart Review
+17005,Chart Review
+17006,Chart Review
+17008,Chart Review
+17011,Chart Review
+17012,Chart Review
+17014,Chart Review
+17015,Chart Review
+17021,Chart Review
+17022,Chart Review
+17027,Chart Review
+17030,Chart Review
+17228,Chart Review
+17231,Chart Review
+17250,Chart Review
+17251,Chart Review
+17252,Chart Review
+17256,Chart Review
+17281,Chart Review
+49052,Chart Review
+72000,Chart Review
+72001,Chart Review
+94023,Chart Review
+49005,Discharge
+20080,Imaging
+17045,Navigator
+17047,Navigator
+17104,Navigator
+17128,Navigator
+17148,Navigator
+20620,Navigator
+17202,Note Entry
+34010,Note Entry
+60010,Note Entry
+60012,Note Entry
+17007,Note Review
+17227,Note Review
+34005,Note Review
+60005,Note Review
+94013,Note Review
+94060,Note Review
+95040,Note Review
+17108,Order Entry
+17162,Order Entry
+17225,Order Entry
+17234,Order Entry
+34118,Order Entry
+34119,Order Entry
+34127,Order Entry
+34150,Order Entry
+17016,Results Review
+17258,Results Review
+94018,Results Review
+94050,Results Review
+95050,Results Review
+17000,Chart Review
+17004,Chart Review
+17005,Chart Review
+17006,Chart Review
+17008,Chart Review
+17011,Chart Review
+17012,Chart Review
+17014,Chart Review
+17015,Chart Review
+17021,Chart Review
+17022,Chart Review
+17027,Chart Review
+17030,Chart Review
+17228,Chart Review
+17231,Chart Review
+17250,Chart Review
+17251,Chart Review
+17252,Chart Review
+17256,Chart Review
+17281,Chart Review
+49052,Chart Review
+49054,Chart Review
+72000,Chart Review
+72001,Chart Review
+94023,Chart Review
+49005,Discharge
+20080,Imaging
+17045,Navigator
+17047,Navigator
+17104,Navigator
+17128,Navigator
+17148,Navigator
+20620,Navigator
+17202,Note Entry
+34010,Note Entry
+60010,Note Entry
+60012,Note Entry
+94014,Note Entry
+17007,Note Review
+17227,Note Review
+34005,Note Review
+60005,Note Review
+94006,Note Review
+94013,Note Review
+94060,Note Review
+95040,Note Review
+17108,Order Entry
+17162,Order Entry
+17225,Order Entry
+17234,Order Entry
+34118,Order Entry
+34119,Order Entry
+34127,Order Entry
+34150,Order Entry
+17016,Results Review
+17258,Results Review
+94018,Results Review
+94050,Results Review
+95050,Results Review
\ No newline at end of file
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Data/ehr_action_descriptions.tsv b/scripts/Archive/ResidentRotationEHRUsage/Data/ehr_action_descriptions.tsv
new file mode 100644
index 00000000..fdd79f4d
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Data/ehr_action_descriptions.tsv
@@ -0,0 +1,903 @@
+metric_id	metric_name	metric_description
+1006	Appointment Desk accessed	(individually or in a group).
+1006	Appointment Desk accessed	Appointment desk accessed for patient 
+1018	Department Appointments report run	Department Appointments report run.
+1102	Patient Locator report accessed	Patient Locator report accessed.
+1103	Patient Sidebar report accessed	Patient Sidebar report accessed.
+1120	Snapboard report run	Snapboard report run.
+1131	Cancel/Reschedule Appointment activity accessed	Cancel/Reschedule Appointment activity
+1131	Cancel/Reschedule Appointment activity accessed	accessed.
+1226	Edit AVS navigator accessed	Add Med Details navigator section (LVN 35600).
+1226	Edit AVS navigator accessed	This event if filed if changes are made from the
+1227	Add Med Details section viewed in AVS	Details navigator sections and then a medication
+1227	Add Med Details section viewed in AVS	We display a warning in the Preview AVS section
+1227	Add Med Details section viewed in AVS	if information is entered from the Add Med
+1227	Add Med Details section viewed in AVS	is modified. This event is filed if a clinician
+1227	Add Med Details section viewed in AVS	or clicks the Acknowledge hyperlink in the
+1227	Add Med Details section viewed in AVS	takes an action in the section, prints the AVS
+1227	Add Med Details section viewed in AVS	warning to clear out the warning.
+2000	OR General Case Information form selected	OR General Case Information form selected
+2000	OR General Case Information form selected	for patient.
+2001	OR Panel #2 form selected in Case Entry	Case Entry.
+2001	OR Panel #2 form selected in Case Entry	OR Panel #2 form selected for patient in
+2005	OR Staffing form selected in Case Entry	OR Staffing form
+2005	OR Staffing form selected in Case Entry	selected for patient in Case Entry.
+2006	OR Equipment and Instruments form selected in Case Entry	OR Equipment and Instruments
+2006	OR Equipment and Instruments form selected in Case Entry	form selected for patient in Case Entry.
+2007	OR Supplies and Drugs form selected in Case Entry	OR Supplies and Drugs form
+2007	OR Supplies and Drugs form selected in Case Entry	selected for patient in Case Entry.
+2008	OR Anesthesia Information form selected in Case Entry	OR Anesthesia Information form
+2008	OR Anesthesia Information form selected in Case Entry	selected for patient in Case Entry.
+2009	OR Additional Case Information form selected in Case Entry	OR Additional Case Information form selected
+2009	OR Additional Case Information form selected in Case Entry	in Case Entry for patient.
+2013	OR Pre-Op & Post-Op Appointments form selected in Case Entry	OR Pre-Op & Post-Op Appointments
+2013	OR Pre-Op & Post-Op Appointments form selected in Case Entry	form selected for patient in Case Entry.
+2014	OR Audit Trail form selected in Case Entry	Case Entry for patient.
+2014	OR Audit Trail form selected in Case Entry	OR Audit Trail form selected in
+2016	OR Additional Case Information form selected in Case Entry	OR Additional Case Information form selected
+2016	OR Additional Case Information form selected in Case Entry	in Case Entry for patient. (Alternate version 
+2016	OR Additional Case Information form selected in Case Entry	of Additional Case Information form.)
+2018	OR Case Entry accessed	OR Case Entry initiated for patient.
+2100	OR General Log Information form selected in Intra-op Navigator	Log Entry for patient.
+2100	OR General Log Information form selected in Intra-op Navigator	OR General Log Information form selected in
+2133	OR Events form selected in Log Entry	Entry for patient.
+2133	OR Events form selected in Log Entry	OR Case Tracking form selected in Log
+2148	OR Delay Information form selected in Intra-op Navigator	Log Entry for patient.
+2148	OR Delay Information form selected in Intra-op Navigator	OR Delay Information form selected in
+2166	OR Log Entry accessed	OR Log Entry initiated for patient.
+2167	OR Log Summary accessed	OR Log Summary accessed for patient.
+2343	Consents navigator section accessed	Access log event for Consents navigator section
+13004	PB Account Maintenance workspace accessed	Professional Billing Account Maintenance 
+13004	PB Account Maintenance workspace accessed	workspace accessed.
+14011	Login failed	Login or revalidation into the environment was
+14011	Login failed	attempted but failed.
+14070	User authenticated	User successfully authenticated
+14200	Printing occurred	Specifies a printout or part of a printout.
+14200	Printing occurred	Use the mnemonic PRINT JOB ID to determine which
+14200	Printing occurred	print job this is part of.
+14300	Archived data requested	User requested retrieval of archived data.
+14500	External web page accessed	Logs when a user navigates to an external page in a Hyperspace embedded browser
+14510	External web page triggering Hyperspace action	Logs when an external page attempts to trigger a Hyperspace action
+15046	Patient Additional Information subcomponent accessed	Patient Additional Information subcomponent 
+15046	Patient Additional Information subcomponent accessed	accessed on Patient Demographics form.  
+15047	Patient Address subcomponent accessed	Patient Address subcomponent accessed on 
+15047	Patient Address subcomponent accessed	Patient Demographics form.  
+15048	Patient Demographics subcomponent accessed	Patient Demographics subcomponent accessed 
+15048	Patient Demographics subcomponent accessed	on Patient Demographics form.
+15123	Patient Contact Information window accessed	Patient Contact Information window accessed 
+15123	Patient Contact Information window accessed	from the Patient Demographics form.
+15136	Registration/ADT workflow initiated	A Registration/ADT workflow initiated.
+15137	Registration/ADT workflow finished	A Registration/ADT workflow finished.
+15141	Patient Emergency Contact subcomponent accessed	Patient Emergency Contact subcomponent 
+15141	Patient Emergency Contact subcomponent accessed	accessed for patient.
+15142	New guarantor account created	New Guarantor Account (EAR) created.
+15450	Masked data displayed	Definition framework.  An event is logged
+15450	Masked data displayed	This event tracks the display of items for which
+15450	Masked data displayed	data masking has been configured using the Mask
+15450	Masked data displayed	in part, due to the configured Mask Definition
+15450	Masked data displayed	or due to user security configuration.
+15450	Masked data displayed	whenever such an item is displayed, in full or
+15451	Masked data printed	Mask Definition framework. An even is logged
+15451	Masked data printed	This event tracks the printing of items for
+15451	Masked data printed	in part, due to the configured Mask Definition
+15451	Masked data printed	or due to user security configuration.
+15451	Masked data printed	whenever such an item is printed, in full or
+15451	Masked data printed	which data masking has been configured using the
+15604	Patient Emergency contacts grid accessed	Patient level emergency contacts grid with
+15604	Patient Emergency contacts grid accessed	selection info display accessed.
+15621	Patient Relationships subcomponent accessed	Patient level patient relationships subcomponent
+15629	Patient communication preferences subcomponent	Patient communication preferences subcomponent
+15632	Documents list accessed	Documents list accessed for the patient.
+15637	Document deleted	A document was deleted.
+15651	Form viewed	A form was viewed.
+17000	Chart Review Encounters tab selected	Chart Review Encounters tab selected.
+17001	Demographics activity accessed	Demographics activity selected when not in an 
+17001	Demographics activity accessed	inpatient workspace.
+17002	SnapShot activity accessed	SnapShot activity accessed. 
+17003	Patient selected from Patient Lookup form	Patient selected from Patient Lookup form.
+17004	Chart Review Labs tab selected	Chart Review Laboratory tab selected.
+17005	Chart Review Imaging tab selected	Chart Review Imaging tab selected.
+17006	Chart Review Medications tab selected	Chart Review Medications tab selected.
+17007	Report viewed for an order	Report viewed for an order.
+17008	Report with patient data viewed	This event is logged when a print group based report (LRP) is viewed by a user from common clinical activities such as Patient Summary or Preview AVS while within a patient's chart. This event is also logged when viewing reports outside of Hyperspace us*
+17009	Health Maintenance activity accessed	Health Maintenance activity accessed.
+17011	Chart Review Episodes tab selected	Chart Review Episodes tab selected.
+17012	Chart Review Other Orders tab selected	Chart Review Other Orders tab selected.
+17014	Chart Review Letters tab selected	Chart Review Letters tab selected.
+17015	Problem List activity accessed	Problem List activity accessed.
+17016	Results Review activity accessed	Results Review activity accessed.
+17017	Envelope form in Letters activity accessed	Envelope Pop-up Form in Letters Activity
+17018	Graphs activity accessed	Graphs Activity in Hyperspace
+17021	Chart Review Procedures tab selected	Chart Review Procedures tab selected.
+17022	Chart Review Misc Reports tab selected	Chart Review Misc Reports tab 
+17022	Chart Review Misc Reports tab selected	selected.
+17024	Flowsheets activity selected	Flowsheets activity selected.
+17027	Chart Review Notes tab selected	Chart Review Notes/Trans tab selected.
+17028	Questionnaires modified	This event is logged when filing modifications
+17028	Questionnaires modified	to EpicCare questionnaires.
+17030	Chart Review Media tab selected	Chart Review Media tab selected.
+17031	Cumulative Dose Activity in Hyperspace	Cumulative Dose activity is accessed
+17032	Printing from Comm Mgt section	Logs client printing from Comm Mgt section.
+17035	Benefit plan changed	 VN section
+17035	Benefit plan changed	Benefit plan changed through Verify RX Benefit
+17036	Goals activity accessed	Goals activity accessed
+17039	Chief Complaint modified	This event is logged when filing modifications
+17039	Chief Complaint modified	Visit Info sections.
+17039	Chief Complaint modified	to the Chief Complaint, Reason for Call, or
+17040	Diagnosis association updated	Event for diagnosis association update.
+17040	Diagnosis association updated	Note that this event will not be logged for
+17040	Diagnosis association updated	encounter diagnoses changes.
+17041	Allergies modified	This event is logged when filing modifications
+17041	Allergies modified	to a patient's allergies.
+17042	Allergies timed out	This event is logged if the user's Hyperspace
+17042	Allergies timed out	patient's allergies.
+17042	Allergies timed out	session times out while they are modifying a
+17043	Follow-up modified	This event is logged when filing modifications
+17043	Follow-up modified	to an appointment follow-up.
+17044	Vitals modified	This event is logged when filing modifications
+17044	Vitals modified	to a patient's vital signs.
+17045	LOS visit navigator section accepted	This event is logged when Level of service
+17045	LOS visit navigator section accepted	section is accepted or closed.
+17046	Charge Capture section accepted	Charge Capture has been opened, and charges were added, edited, or deleted.
+17047	LOS visit navigator section accessed	This event is logged when the level of
+17047	LOS visit navigator section accessed	service section is viewed
+17049	LOS form accessed from visit navigator	Level of service form is accessed from LOS VN section
+17052	LOS form accepted from visit navigator	LOS form is accepted from LOS VN section.
+17059	FYI activity accessed	FYI activity accessed.
+17060	Patient Implants section or activity accessed	Patient implants section or activity accessed
+17061	Patient Implants report accessed	Patient implants report accessed
+17081	MyChart Lab Release activity selected	MyChart Lab Release activity selected.
+17096	MR_MEDS_ORDERS	Medications & Orders VN section
+17100	Media Manager accessed	Media Manager activity was opened
+17101	Nursing Notes navigator section accessed	Visit Navigator Nursing Notes section 
+17101	Nursing Notes navigator section accessed	initialized.
+17102	Progress Notes navigator section accessed	Visit Navigator Progress Notes section 
+17102	Progress Notes navigator section accessed	initialized.
+17103	Addendum opened	A Navigator for an Addendum to a patient 
+17103	Addendum opened	encounter opened.
+17104	Patient Encounter opened	A Navigator for a patient encounter opened.
+17105	Test selected in Enter/Edit Results activity	Test selected in Enter/Edit Results activity.
+17106	Medications activity accessed	Medications activity accessed.
+17108	Order Entry activity accessed	Event for placing orders from various activities
+17112	EpicCare Scheduling activity accessed	EpicCare Scheduling activity accessed for patient.
+17115	History activity accessed	History activity accessed.
+17117	SmartSets activity selected	Smartsets activity selected for patient.
+17118	Remote web portal connected	This event definition audits information
+17118	Remote web portal connected	obtained when a user connects to a remote web
+17118	Remote web portal connected	portal.
+17119	Letters activity accessed	(Event Record inactive.)
+17119	Letters activity accessed	Letters activity accessed.
+17120	Questionnaires activity accessed	Questionnaires activity accessed.
+17121	Demographics activity selected	Demographics activity selected in an 
+17121	Demographics activity selected	inpatient workspace.
+17122	Annotated Images activity selected	Annotated Image viewed.
+17124	Chief Complaint navigator section accessed	Visit Navigator Chief Complaint section 
+17124	Chief Complaint navigator section accessed	initialized.
+17125	Reason for Call navigator section accessed	Visit Navigator Reason for Call section 
+17125	Reason for Call navigator section accessed	initialized.
+17126	Allergies activity accessed	Allergies activity accessed.
+17128	Patient Instructions navigator section accessed	Visit Navigator Patient Instructions section 
+17128	Patient Instructions navigator section accessed	initialized.
+17129	LOS modified	Event for LOS modified either from VN or form.
+17130	Follow-up navigator section accessed	Visit Navigator Follow-up section 
+17130	Follow-up navigator section accessed	initialized.
+17131	Telephone Documentation navigator section accessed	Visit Navigator Telephone Documentation section 
+17131	Telephone Documentation navigator section accessed	initialized.
+17132	Disposition navigator section accessed	Visit Navigator Disposition section 
+17132	Disposition navigator section accessed	initialized.
+17133	Contacts navigator section accessed	Visit Navigator Contacts section 
+17133	Contacts navigator section accessed	initialized.
+17134	Vitals navigator section accessed	Visit Navigator Vitals section 
+17134	Vitals navigator section accessed	initialized.
+17135	Health Maintenance Override Topic form accessed	Health Maintenance Override Topic form accessed.
+17136	Episodes activity accessed	Episodes activity accessed.
+17137	Immunizations/Injections activity accessed	Immunizations/Injections activity accessed.
+17138	Growth Chart activity accessed	Growth Chart activity accessed.
+17139	Clinical References activity accessed	Clinical References activity accessed.
+17140	Administrative Information window accessed	Administrative Information window accessed from 
+17140	Administrative Information window accessed	the Visit Navigator.
+17141	Image viewed	DCS record with BLOB type of scan was viewed
+17142	SmartForms accessed	SmartForms accessed.
+17146	Image imported	An image file is imported
+17148	Communication Management navigator section accessed	 Navigator accessed.
+17148	Communication Management navigator section accessed	Communication Management Section of the Visit
+17149	LOS activity accessed	LOS activity accessed.
+17150	Result note filed	User files a result note
+17151	Remove Health Maintenance Override Topic form accepted	Remove Health Maintenance Override Topic
+17151	Remove Health Maintenance Override Topic form accepted	form accepted.
+17152	OB and Lactation Status section accessed	Navigator initialized
+17152	OB and Lactation Status section accessed	OB and Lactation Status Section of the Visit
+17155	Image printed	A DCS record with BLOB type of Scan is printed
+17158	Visit diagnoses viewed	Event for viewing visit diagnoses.
+17159	Health Maintenance Modifier form accessed	Form accessed.
+17160	Chart Review Patient Summary Extract tab selected	Chart Review Patient Summary Extract tab selected.
+17162	Order transmitted	'Printed/Sent by Order Transmittal' event during
+17162	Order transmitted	order transmittal.
+17165	Multistep and multistep collection tasks entry form accessed	The entry form used to complete multistep and
+17165	Multistep and multistep collection tasks entry form accessed	multistep collection tasks for one or more
+17165	Multistep and multistep collection tasks entry form accessed	orders.
+17170	Authentication for controlled substances failed	 prescriptions for controlled substances
+17170	Authentication for controlled substances failed	Authentication failed when signing electronic
+17171	Document viewed	DCS record with BLOB type of Document was viewed
+17183	Result filed	Result was accepted/saved in Enter/Edit Results
+17183	Result filed	activity.
+17184	Result entry aborted	Result was canceled in Enter/Edit Results
+17184	Result entry aborted	activity.
+17185	Result entry accessed	Order was edited in Enter/Edit Results
+17185	Result entry accessed	activity.
+17186	Dictation recorded in an encounter	Dictation accepted from In Basket.
+17186	Dictation recorded in an encounter	Dictation recorded in an encounter.
+17187	External results created	Order was accepted in the external result form.
+17192	Remove Health Maintenance Postpone Topic form accessed	Remove Health Maintenance Postpone form
+17192	Remove Health Maintenance Postpone Topic form accessed	accessed.
+17194	Health Maintenance Postpone Topic form accessed	Health Maintenance Postpone topic form accessed.
+17197	Health Maintenance Modifier form accepted	Health Maintenance Modifier form
+17197	Health Maintenance Modifier form accepted	accepted.
+17200	Research Studies activity accessed	Research Studies activity accessed.
+17202	Progress Note signed automatically	Visit Navigator Progress Note signed
+17202	Progress Note signed automatically	automatically
+17207	Medication note saved	Save a med note
+17208	Medication taking status modified	Change the taking status of a medication.
+17209	Medication list comment modified	Make changes to the med list comment.
+17210	Child order released for a procedure order	Child released for a procedure order.
+17212	Medication long-term status modified	Change the long-term status of a medication.
+17213	Pharmacy for encounter modified	Select a pharmacy for an encounter
+17215	Release Orders activity accessed	Open Releaser activity.
+17216	Patient-reported medication modified	Edit a patient-reported medication.
+17217	Patient-reported medication created	Create a new patient-reported medication.
+17218	Home Medications modified	Changes to medications filed from Home Meds
+17225	Order Entry activity accessed	Order Entry activity accessed/viewed.
+17227	Note viewed in Chart Review	This event indicates that a report for a note
+17227	Note viewed in Chart Review	in the Chart Review Notes tab was viewed.
+17228	Encounter viewed in Chart Review	This event indicates that a report for an
+17228	Encounter viewed in Chart Review	encounter in the Chart Review Encounters tab was
+17228	Encounter viewed in Chart Review	viewed.
+17229	Episode viewed in Chart Review	Episode in the Chart Review Episodes tab was
+17229	Episode viewed in Chart Review	This event indicates that a report for an
+17229	Episode viewed in Chart Review	viewed.
+17230	Letter viewed in Chart Review	This event indicates that a report for a
+17230	Letter viewed in Chart Review	letter in the Chart Review Letters tab was
+17230	Letter viewed in Chart Review	viewed.
+17231	Miscellaneous report viewed in Chart Review	Review Misc Reports tab was viewed.
+17231	Miscellaneous report viewed in Chart Review	This event indicates that a report in the Chart
+17233	Media viewed in Chart Review	This event indicates that a report for a record
+17233	Media viewed in Chart Review	in the Chart Review Media tab was viewed.
+17234	Order viewed in Chart Review	This event indicates that a report for an order
+17234	Order viewed in Chart Review	in a Chart Review Orders tab was viewed.
+17236	Encounter printed from Chart Review	This event indicates that a report for an
+17236	Encounter printed from Chart Review	encounter in a Chart Review Encounters tab was
+17236	Encounter printed from Chart Review	printed.
+17237	Order printed from Chart Review	This event indicates that a report for an order
+17237	Order printed from Chart Review	in a Chart Review Orders tab was printed.
+17239	Note printed from Chart Review	This event indicates that a report for a
+17239	Note printed from Chart Review	note in the Chart Review Notes tab was printed.
+17242	Media printed from Chart Review	This event indicates that a report for a
+17242	Media printed from Chart Review	printed.
+17242	Media printed from Chart Review	record in the Chart Review Media tab was
+17243	Demographics modified	Demographics Activity event that indicates
+17243	Demographics modified	data was saved.
+17244	Demographics activity exited	Demographics activity exited.
+17245	Demographics viewed	Demographics viewed in an activity or through
+17245	Demographics viewed	a web service.
+17246	Chart Review LDA tab selected	Chart Review LDA tab selected.
+17248	LDA viewed in Chart Review	LDA in a Chart Review LDA tab was viewed.
+17248	LDA viewed in Chart Review	This event indicates that a report for a single
+17249	Family or Specialty comments modified	Summary were updated.
+17249	Family or Specialty comments modified	This event indicates the comments in Patient
+17250	Problem List accessed	Logged when the problem list is first viewed
+17250	Problem List accessed	Should give details as to where problem list was
+17250	Problem List accessed	viewed from
+17251	Problem List modified	Logged when a problem is edited
+17252	Problem List reviewed	Logged when the "Mark as Reviewed" button is
+17252	Problem List reviewed	clicked
+17253	Plan of Care note modified	Logged when the Plan of Care note is edited
+17255	Patient Snapshot printed	This event indicates that a patient snapshot
+17255	Patient Snapshot printed	report was printed.
+17256	Results Review accessed	This event indicates that the Results Review
+17256	Results Review accessed	activity was accessed.
+17257	Results Review printed	This event indicates that a report was printed
+17257	Results Review printed	from Results Review.
+17258	Results Review exited	Results Review activity exited.
+17259	Patient SnapShot viewed	SnapShot report viewed
+17260	SmartSet accessed	Event logged when SmartSets are opened in the
+17260	SmartSet accessed	SmartSet section.
+17262	E-Consult created	An E-Consult encounter was created
+17267	Chart CC sent	Chart CC message sent
+17268	E-Consult sent	A E-Consult message was sent
+17270	Patient photo created	The patient's photo was created.
+17271	Patient photo changed	The patient's photo was changed.
+17280	Chart searched	Chart Search activity
+17281	Chart search results viewed	Chart Search record view
+17300	Transplant Episode management section accessed	Transplant Episode management section accessed.
+17301	Transplant Organ management section accessed	Transplant Organ management section accessed.
+17303	Patient Threshold Management section accessed	Patient Threshold management section accessed.
+17304	Data from related encounters accessed through VB	Intake and Output, Patient Summary/Snapshot, or
+17304	Data from related encounters accessed through VB	MAR activities accessed in an encounter where
+17304	Data from related encounters accessed through VB	Notes, Documentation Flowsheets, Synopsis,
+17304	Data from related encounters accessed through VB	related encounters were enabled.
+17309	PasteBoard printed	Logs client printing from PasteBoard.
+17319	Alternative selection form aborted	Alternative form cancelled and original order
+17319	Alternative selection form aborted	does not get added.
+17319	Alternative selection form aborted	does not get placed.
+17320	Alternative orders not taken and original order placed	Alternative orders not taken and original order
+17320	Alternative orders not taken and original order placed	get placed.
+17321	New alternative orders accepted	New alternative orders get accepted.
+17325	Episode viewed	Episode accessed.
+17326	Episode edited	Episode edited.
+17330	Immunization record saved	Clinic, and places where orders are placed such
+17330	Immunization record saved	Either a new immunization record was created or
+17330	Immunization record saved	Immunization SmartSets, the Immunization
+17330	Immunization record saved	Information, the Immunizations Activity,
+17330	Immunization record saved	an old immunization record was modified. View
+17330	Immunization record saved	as Order Entry and Meds and Orders.
+17330	Immunization record saved	can be triggered by Reconcile Outside
+17330	Immunization record saved	the LPL to see what data was saved. This event
+17331	Immunization record deleted	Immunization record was deleted. This can be
+17331	Immunization record deleted	from the immunizations activity or by canceling
+17331	Immunization record deleted	the ordered procedure.
+17331	Immunization record deleted	triggered by the deleting an immunization
+17333	Actions taken from a BestPractice Advisory	Actions a user performed from a BestPractice Advisory.
+17335	Historical immunizations accessed	User opened the historical administration form.
+17336	BestPractice Advisories viewed	BestPractice Advisories get canceled or accepted
+17336	BestPractice Advisories viewed	with no follow-up action selected.
+17337	Incomplete immunizations accessed	User opened the incomplete administrations
+17337	Incomplete immunizations accessed	allergy interactions.
+17337	Incomplete immunizations accessed	form from the immunizations activity. This form
+17337	Incomplete immunizations accessed	shows pending administration data and can show
+17338	BestPractice Advisories displayed	BestPractice Advisories displayed.
+17339	Immunization or allergy information accessed	Event logged when user opens an existing
+17339	Immunization or allergy information accessed	activity. This form shows immunization
+17339	Immunization or allergy information accessed	administration information and allergy
+17339	Immunization or allergy information accessed	immunization record from the immunizations
+17339	Immunization or allergy information accessed	interactions.
+17342	Single Historical Immunization admin activity accessed	Event logged when the user opens the single
+17342	Single Historical Immunization admin activity accessed	historical admin form.
+17360	RecInfo accessed	RecInfo activity was opened.
+17380	Automatic actions performed by a BestPractice Advisory	Automatic actions performed by a BestPractice Advisory.
+17457	Outside historical medications filed	Filed new historical medications from an outside source.
+17500	Prenatal Vitals section updated	Prenatal Vitals section
+17500	Prenatal Vitals section updated	Vitals part of Prenatal Vitals and Notes
+17501	OB Results Console updated	OB Results Console
+17511	Health Maintenance Postpone Topic form accepted	Health Maintenance Postpone Topic form accepted.
+17512	Health Maintenance Override Topic form accepted	Health Maintenance Override Topic form accepted.
+17515	Letter printed	A letter is printed from the Letters activity.
+17516	Letter status changed	A letter's status changes. Status category
+17516	Letter status changed	is EPT 20200.
+17518	BPA follow-up action applied	BPA follow-up action applied.
+17555	Report with patient data printed	This event is logged when a print group based report (LRP) is printed by a user from common clinical activities such as the After Visit Summary while within a patient's chart.
+17618	Letter viewed	A letter was viewed.
+17619	Letters activity accessed	The letters activity was accessed.
+17620	Letters activity exited	The letters activity was exited.
+17622	Communication printed	A communication was printed from Communication
+17622	Communication printed	Management.
+17623	Communication Management accessed	Navigator was accessed.
+17623	Communication Management accessed	The Communication Management section of the Visit
+17624	Communication Management exited	Navigator was exited.
+17624	Communication Management exited	The Communication Management Section of the Visit
+17625	FYI activity accessed	The FYI activity was accessed.
+17626	FYI activity exited	The FYI activity was exited.
+17664	Synopsis activity accessed	Synopsis activity accessed
+17665	Order details viewed from hover text	View Order details from the hover bubble
+17667	Patient Goal accessed	Goals activity viewed
+17784	Results acknowledged in IB	This event is logged when a user acknownledges
+17784	Results acknowledged in IB	a result. This can happen when the result is
+17784	Results acknowledged in IB	marked as reviewed, or when a message without
+17784	Results acknowledged in IB	responsibility is read.
+17821	Care Teams viewed	The Care Teams activity was accessed.
+17822	Care Teams exited	The Care Teams activity was exited.
+17824	Care Teams modified	Changes are made in Care Teams activity.
+17904	SmartForm viewed	Smartfoms viewed.
+17905	SmartForm saved	Smartforms accepted.
+18130	Note displayed from Tapestry Referrals or Referral Notes	Note displayed from the
+18130	Note displayed from Tapestry Referrals or Referral Notes	Notes activity.
+18130	Note displayed from Tapestry Referrals or Referral Notes	Tapestry Referrals window
+18130	Note displayed from Tapestry Referrals or Referral Notes	or the Hyperspace Referral
+18181	Results reviewed in IB	User reviews a result in IB
+18200	Tapestry Member Inquiry Demographics Activity accessed	Demographics Activity Opened
+18200	Tapestry Member Inquiry Demographics Activity accessed	Tapestry Hyperspace Member Inquiry
+18201	Tapestry Member Inquiry Coverage Activity accessed	Coverage Activity Opened
+18201	Tapestry Member Inquiry Coverage Activity accessed	Tapestry Hyperspace Member Inquiry
+18207	Tapestry Member Inquiry Referrals Activity accessed	Referrals Activity Opened
+18207	Tapestry Member Inquiry Referrals Activity accessed	Tapestry Hyperspace Member Inquiry
+18212	Tapestry Member Inquiry Notes List Activity accessed	Notes List Activity Opened
+18212	Tapestry Member Inquiry Notes List Activity accessed	Tapestry Hyperspace Member Inquiry
+18214	Tapestry Member Inquiry Case Settings Activity accessed	Case Settings Activity Opened
+18214	Tapestry Member Inquiry Case Settings Activity accessed	Tapestry Hyperspace Member Inquiry
+19019	Potential Duplicates checked	Potential Duplicates checked during creation 
+19019	Potential Duplicates checked	of new patient record.
+19040	Patient Lookup search	Access logging search parameters from pt search
+19041	Patient Selected From Patient Lookup	An existing patient was selected from patient lookup
+19120	Identity report accessed	A report was accessed for a patient through the
+19120	Identity report accessed	Identity report provider. This event might be
+19120	Identity report accessed	logged in rapid succession for the same user and
+19120	Identity report accessed	report, but different patients since Identity
+19120	Identity report accessed	reports can contain data for 1 or 2 patients.
+19125	Patient Chart Advisories viewed	Patient Chart Advisories was launched for a
+19125	Patient Chart Advisories viewed	patient.
+19502	Full Referral entry activity edited	Full Referral Entry activity accessed for patient.
+20001	In Basket message viewed	In Basket message of any type displayed 
+20001	In Basket message viewed	in Hyperspace. 
+20002	HTML table viewed using HTML Table Viewer	An HTML table was viewed using HTML Table Viewer.
+20003	Report run	Report run. (Report ID logged). 
+20004	Patient FYI activity accessed	Patient FYI activity accessed.
+20005	Page triggered based on In Basket message	This event is logged when a page is triggered
+20005	Page triggered based on In Basket message	based on an In Basket message.
+20008	In Basket message created	(Created messages counted.) 
+20008	In Basket message created	In Basket message of any type created. 
+20009	Patient record created	(Created patient records counted.) 
+20009	Patient record created	Patient record created. 
+20010	Referral Action form accessed	Referral Action form accessed for patient.
+20011	Full Referral Entry activity accessed	Full Referral Entry activity accessed for patient.
+20013	In Basket message printed	This event is logged when an In Basket message
+20013	In Basket message printed	associated with a patient is printed.
+20020	Benefits Inquiry accessed	Benefits Inquiry accessed. This metric is
+20020	Benefits Inquiry accessed	coverage line that is displayed in the benefit
+20020	Benefits Inquiry accessed	inquiry window, if the coverage specific info was
+20020	Benefits Inquiry accessed	is accessed. It is also triggered for each
+20020	Benefits Inquiry accessed	loaded by the user.
+20020	Benefits Inquiry accessed	triggered each time the Benefit Inquiry window
+20030	SmartLink used	(e.g., Letters, Notes) or in reports. 
+20030	SmartLink used	A SmartLink used in SmartTools-enabled fields
+20040	SmartText used	(e.g. Letters, Notes) or in reports.
+20040	SmartText used	A SmartText used in SmartTool-enabled fields
+20045	SmartPhrase used	(e.g. Letters, Notes) or in reports.
+20045	SmartPhrase used	A SmartPhrase used in SmartTool-enabled fields
+20050	Reporting Workbench report printed	Server-side printout of Reporting Workbench
+20050	Reporting Workbench report printed	report results initiated by user.
+20075	Prelude Documents list accessed	Prelude Documents list accessed for patient.
+20076	Document Information form accessed	Document Information form accessed for
+20076	Document Information form accessed	patient document. (Document ID recorded.) 
+20080	Imaging PACS accessed	Images launched in a PACS, either through print
+20080	Imaging PACS accessed	groups 53075 or 53076 or via PACS buttons or auto-
+20080	Imaging PACS accessed	query settings in imaging applications.
+20093	PCP History accessed	PCP History window accessed in Hyperspace.
+20304	Data from related encounters accessed through Hyperspace Web	Intake and Output, Patient Summary/Snapshot, or
+20304	Data from related encounters accessed through Hyperspace Web	MAR activities accessed in an encounter where
+20304	Data from related encounters accessed through Hyperspace Web	Notes, Documentation Flowsheets, Synopsis,
+20304	Data from related encounters accessed through Hyperspace Web	related encounters were enabled.
+20500	Successful Break-the-Glass	 form
+20500	Successful Break-the-Glass	Successfully validated on the Break-the-Glass
+20510	Break-the-Glass canceled	 Break-the-Glass form
+20510	Break-the-Glass canceled	Didn't successfully validate on the
+20620	Visit Navigator template loaded	Visit Navigator template loaded.
+20700	Patient photo viewed or changed	A user has viewed or changed the patient's
+20700	Patient photo viewed or changed	current photo.
+22001	Growth Charts viewed	EpicWeb Growth Charts viewed for patient.
+22002	Flowsheets viewed	EpicWeb Flowsheets viewed for patient.
+22003	Allergies page accessed	EpicWeb Allergies page opened for patient.
+22010	Medications page accessed	Medications page accessed.
+22213	User logged in	EpicWeb Login performed.
+22217	Access to patient granted through First Access	Access to an existing patient was granted to a
+22217	Access to patient granted through First Access	managed access user through First Access.
+22235	PACS image accessed	Fired when a user access PACS images
+22236	User logged out	Fired when a user logs out or times out
+22237	First Access search returned no results	Fired when a First Access search returns no result
+24015	Event Management activity accessed	Event Management activity accessed for patient.
+24107	Bed Board accessed	Bed Board activity accessed.
+24205	Provider Info subcomponent accessed	Provider Info subcomponent accessed for patient.
+24207	Treatment Team subcomponent accessed	Treatment Team subcomponent accessed for patient.
+24299	Expected Discharge section accessed	Expected Discharge Date and Time visit navigator
+24299	Expected Discharge section accessed	section accessed for patient.
+24550	Patient Station accessed	Patient Station accessed for patient.
+24551	Form Reprints activity accessed	Form Reprints activity accessed for patient.
+24552	Form Reprints report viewed	Form Reprints report viewed for patient.
+26000	Contacts removed	This event is triggered when a user
+26000	Contacts removed	removes a contact from the visit navigator
+26000	Contacts removed	section.
+26006	Provider Finder activity accessed	Provider Finder activity accessed for the patient.
+26010	Visit Navigator Assessment Section closed	Visit Navigator Assessment section
+26010	Visit Navigator Assessment Section closed	closed.
+26014	Routing Section Viewed	The routing section is viewed in a visit
+26014	Routing Section Viewed	navigator.
+26022	Nurse Triage Protocols section saved	Nurse Triage Protocols section 
+26022	Nurse Triage Protocols section saved	saved in Visit Navigator.
+26025	Visit Navigator Assessment Section viewed	View Navigator Assessment Section
+26026	Contacts navigator section viewed	Visit Navigator Contacts section 
+26026	Contacts navigator section viewed	viewed.
+26027	Disposition navigator section edited	Visit Navigator Disposition section 
+26027	Disposition navigator section edited	edited.
+26028	Nurse Triage Care Advice section edited	Nurse Triage Care Advice section 
+26028	Nurse Triage Care Advice section edited	edited in Visit Navigator.
+27115	History activity accessed	History activity accessed
+27116	History activity accepted	History data saved by history activity.
+32000	WP_MYCHART_ADMINISTRATION	MyChart Administration accessed.
+33002	Schedule glance plugin accessed in Radar	SCHEDULE GLANCE PLUGIN ACCESSED IN RADAR
+33006	In Basket glance plugin accessed in Radar	IN BASKET GLANCE PLUGIN ACCESSED IN RADAR
+33010	Radar Interactive Schedule at a Glance report run	Radar Interactive Schedule at a Glance report run.
+33500	Radar Dashboard accessed	User access to a dashboard and its content
+34002	Viewed Handoff Data	This event is logged when Handoff data for a
+34002	Viewed Handoff Data	patient is viewed.
+34003	Edited Handoff Data	Handoff data.
+34003	Edited Handoff Data	This event is logged when edits are made to
+34004	Handoff data printed	This event is logged when Handoff data is
+34004	Handoff data printed	printed.
+34005	Notes viewed	This event is logged when a user views one
+34005	Notes viewed	or more clinical notes in the Notes activity.
+34006	Edit Component-style Discharge Instructions	HNO record that contains the instruction's
+34006	Edit Component-style Discharge Instructions	This event is logged when component-style
+34006	Edit Component-style Discharge Instructions	discharge instructions are edited. The
+34006	Edit Component-style Discharge Instructions	note that is logged with the event is the
+34006	Edit Component-style Discharge Instructions	text.
+34009	Viewed Sign Out Report	The Sign Out Report comments were viewed
+34009	Viewed Sign Out Report	for a patient.
+34010	Notes activity and navigators accessed	Notes activity and navigators
+34013	Sign Out report printed	The Sign Out Report comments were printed
+34013	Sign Out report printed	for a set of patients.
+34022	Saved New Sign Out Report Comment	A new Sign Out Report comment was saved.
+34023	Deleted Sign Out Report Comment	A Sign Out Report comment was deleted.
+34051	OB and Lactation Status accessed	Navigator initialized
+34051	OB and Lactation Status accessed	OB and Lactation Status Section of the Visit
+34100	Inpatient Care Plan activity accessed	Inpatient Care Plan activity viewed.
+34101	Inpatient MAR activity accessed	Inpatient MAR activity accessed.
+34103	Inpatient Discharge Instruction Writer activity accessed	Inpatient Discharge Instruction Writer activity
+34103	Inpatient Discharge Instruction Writer activity accessed	accessed.
+34105	Inpatient Education activity accessed	Inpatient Patient Education activity accessed.
+34106	Visit Navigator Outpatient Admission section accessed	Visit Navigator Outpatient Admission 
+34106	Visit Navigator Outpatient Admission section accessed	section accessed.
+34107	Visit Navigator Outpatient Procedure Code section accessed	Visit Navigator Outpatient Procedure Code 
+34107	Visit Navigator Outpatient Procedure Code section accessed	section accessed.
+34108	Nurse Triage Care Advice section viewed	Nurse Triage Care Advice section 
+34108	Nurse Triage Care Advice section viewed	viewed in Visit Navigator.
+34109	Nurse Triage Protocols section viewed	Nurse Triage Protocols section 
+34109	Nurse Triage Protocols section viewed	viewed in Visit Navigator.
+34110	Inpatient Synopsis activity accessed	ICU Synopsis activity accessed.
+34111	Flowsheet activity accessed	Inpatient Documentation Flowsheet activity
+34111	Flowsheet activity accessed	accessed.
+34117	Opened Inpatient Order Set Activity	Opened Inpatient Order Set Activity.
+34118	Order sets accessed	Event logged when Order Sets are opened in the
+34118	Order sets accessed	Order Set section.
+34119	Orders section accessed	Orders section in Rounding navigator accessed.
+34120	Inpatient Fast Note activity accessed	Inpatient Fast Note activity selected. 
+34122	Inpatient Sign Out Report activity accessed	Inpatient Sign Out Report activity opened. 
+34123	Inpatient Intake/Output activity selected	Inpatient Intake/Output activity selected. 
+34125	Inpatient BestPractice Alerts navigator accessed	Inpatient BestPractice Alerts navigator opened. 
+34127	Inpatient Orders section accessed	Inpatient Orders section opened 
+34127	Inpatient Orders section accessed	in any of the Inpatient navigators. 
+34131	Patient device edited	This event is logged when a user modifies a
+34131	Patient device edited	device association, such as editing selected
+34131	Patient device edited	variables or changing the start time.
+34133	Patient device added	This event is logged when a user associates a
+34133	Patient device added	device to a patient.
+34135	Patient device removed	This event is logged when a user removes a
+34135	Patient device removed	device association from a patient.
+34137	Patient Education information viewed	This event is logged from the Patient Education
+34137	Patient Education information viewed	activity when information is viewed.
+34140	Inpatient system list accessed	Inpatient system list accessed.
+34141	MAR accessed	Inpatient MAR activity accessed.
+34142	MAR administration accepted	MAR admin form accepted.
+34143	MAR administration aborted	MAR admin form canceled
+34146	MAR administration form accessed	Inpatient MAR admin form accessed.
+34150	Order Reconciliation Section accessed	Order Reconciliation Section visited in a
+34150	Order Reconciliation Section accessed	navigator.
+34157	Patient Education title added	This event is logged from the Patient Education
+34157	Patient Education title added	activity when a title is added
+34159	Patient Education deleted	This event is logged from the Patient Education
+34159	Patient Education deleted	activity when education is deleted.
+34160	Patient List printed	A patient list was client printed.
+34163	Patient Education resolved	This event is logged from the Patient Education
+34163	Patient Education resolved	activity when education is resolved.
+34164	Patient Education reactivated	This event is logged from the Patient Education
+34164	Patient Education reactivated	activity when education is reactivated
+34167	Patient Education documented on	This event is logged from the Patient Education
+34167	Patient Education documented on	activity when education is documented on.
+34198	Patient Summary viewed	Patient Summary viewed
+34200	Barcode scanned	Barcode scanned (e.g. patient, med, device)
+34400	Handoff activity accessed	Communication tool used during handoff workflow
+34700	LDA properties accessed	LDA properties accessed via the properties popup
+34701	LDA properties accepted	LDA properties modified via the properties popup
+34704	Intake/Output activity accessed	View event for I/O activity
+34706	Flowsheet viewed	Flowsheets activity opened
+34707	Flowsheet accepted	Flowsheets data saved
+34708	Flowsheet data copied forward	Flowsheets data copied forward
+34994	Inpatient Adjust Times activity viewed	Event for viewing impatient adjust times
+34994	Inpatient Adjust Times activity viewed	activity.
+34997	Time for Inpatient Medication adjusted	Event for accepting changes from inpatient
+34997	Time for Inpatient Medication adjusted	adjust times activity.
+35000	Reporting Workbench report run	Reporting Workbench report run.
+35010	Reporting Workbench report result viewed	Reporting Workbench report results viewed.
+35015	Detailed report viewed in Reporting Workbench	Detailed report viewed in Reporting Workbench
+35015	Detailed report viewed in Reporting Workbench	Report Viewer.
+38201	ADT Info viewed in Coding Info or Abstracting	View account data in the ADT Info tab. This
+38201	ADT Info viewed in Coding Info or Abstracting	and is accessed from multiple user workflows.
+38201	ADT Info viewed in Coding Info or Abstracting	or Abstracting activity in Account Maintenance,
+38201	ADT Info viewed in Coding Info or Abstracting	tab can be accessed from the Coding Info
+38202	Diagnoses viewed in Coding Info	View account data in the Diagnoses tab. This tab
+38202	Diagnoses viewed in Coding Info	can be accessed from the Coding Info activity
+38202	Diagnoses viewed in Coding Info	in Account Maintenance, and is accessed from
+38202	Diagnoses viewed in Coding Info	multiple user workflows.
+38203	Procedures viewed in Coding Info	View account data in the Procedures tab. This
+38203	Procedures viewed in Coding Info	activity in Account Maintenance, and is accessed
+38203	Procedures viewed in Coding Info	from multiple user workflows.
+38203	Procedures viewed in Coding Info	tab can be accessed from the Coding Info
+38501	Abstracting data viewed	View account data in a tab of the Abstracting
+38501	Abstracting data viewed	activity of Account Maintenance.
+38606	Patient Summary Extract viewed	Access to PSX from Chart Review or Identity
+41005	Hospital Billing Workqueue List accessed	Hospital Billing Workqueue List form accessed. 
+41010	Hospital Billing Patient Inquiry with payments accessed	HB Patient Inquiry with payments accessed.
+41014	Hospital Account History accessed	HB Account History activity accessed.
+41016	Hospital Billing Patient Contact accessed	HB Patient Contact activity accessed.
+41024	Hospital Billing Tx Details accessed	HB Transaction Details accessed.
+41032	Daily Charges accessed	HB Daily Charges activity accessed.
+41033	Hospital Billing Claim Information selected	Hospital Billing Claim Information form selected 
+41033	Hospital Billing Claim Information selected	in patient's hospital account.
+41034	Code Integration accessed	HB Code Integration activity accessed.
+41035	Coding Information form selected	HB Coding Info activity accessed.
+41037	Hospital Billing Liability Bucket Information accessed	HB Liability Bucket activity accessed.
+41038	Account Note form accessed	Guarantor Inquiry.
+41038	Account Note form accessed	HB Note selected in Account Note activity
+41038	Account Note form accessed	in either Account Maintenance or
+41039	Hospital Billing Tx Inquiry accessed	HB Transaction Inquiry activity accessed.
+41051	Guarantor Account Notes accessed	Guarantor Account Notes accessed.
+41053	Hospital Billing Statement History accessed	Statement History accessed.
+41062	Hospital Billing Account Listing accessed	Hospital Account Listing accessed for
+41062	Hospital Billing Account Listing accessed	patient.
+41077	Hospital Billing Account Summary accessed	Hospital Billing Account Summary
+41077	Hospital Billing Account Summary accessed	accessed for patient.
+41078	Hospital Billing Coverage Info accessed	HB Coverage Info form accessed.
+41087	Hospital Billing Account Maintenance accessed	Account Maintenance or Workqueue.
+41087	Hospital Billing Account Maintenance accessed	Account accessed in Hospital Billing
+41090	Hospital Billing Abstracting accessed	Abstracting form accessed.
+41091	Hospital Billing Coding Query Viewer accessed	Coding Query viewer
+41091	Hospital Billing Coding Query Viewer accessed	selected for patient's hospital account.
+41092	Account Status Summary accessed	Hospital Billing Account Status Summary
+41092	Account Status Summary accessed	accessed for patient.
+41095	Account Summary accessed from Enterprise Guarantor Summary	Hospital Billing Account Summary accessed
+41095	Account Summary accessed from Enterprise Guarantor Summary	for patient from Enterprise Guarantor Summary.
+41108	Enterprise Guarantor Summary accessed	Account Maintenance.
+41108	Enterprise Guarantor Summary accessed	Enterprise Guarantor Summary accessed from
+41108	Enterprise Guarantor Summary accessed	Hospital Billing Guarantor Inquiry or
+41109	Hospital Account Note accessed	Hospital Account Note accessed.
+41114	Hospital Billing report accessed	Hospital Billing report accessed.
+48000	Order Verification Activity accessed	Triggered when the Order Verification Activity is
+48000	Order Verification Activity accessed	accessed.
+48001	Order History Activity accessed	Triggered when the Order History Activity is
+48001	Order History Activity accessed	accessed
+48002	Bulk Charge Activity accessed	Triggered when the Bulk Charge Activity is
+48002	Bulk Charge Activity accessed	accessed
+48003	Verified order dispensed	Triggered whenever a dispense of a verified order
+48003	Verified order dispensed	occurs
+48004	i-Vent created or edited	Triggers whenever an i-Vent is created or
+48004	i-Vent created or edited	accessed
+48008	Label History Activity accessed	Triggered when the Label History Activity is
+48008	Label History Activity accessed	accessed
+49001	ED Visit Navigator Arrival section accessed	ED VN Arrival section accessed.
+49002	ED Visit Navigator Disposition section accessed	ED VN Disposition section accessed.
+49003	Visit diagnoses modified	Event for adding, changing, modifying or
+49003	Visit diagnoses modified	removing visit diagnoses.
+49004	Visit Navigator charge capture section viewed	Charge Capture has been viewed.
+49005	Viewed Discharge Instructions	The discharge instructions were viewed in
+49005	Viewed Discharge Instructions	discharge instructions navigator section.
+49005	Viewed Discharge Instructions	either the Discharge Writer activity or the
+49006	ED Patient Events Log activity is accessed	ED Event Log activity is accessed.
+49008	Visit Navigator Flowsheet section is accessed	VN Flowsheet section is accessed.
+49010	ED Visit Navigator Follow-Up section accessed	ED VN Follow-Up section accessed.
+49012	After Visit Summary Viewed	Viewed After Visit Summary (AVS) report
+49015	Vist Navigator SmartForm Flowsheet section viewed	VN Smartform Flowsheet section is accessed.
+49016	Visit Navigator SmartForm Flowsheet section accepted	VN Smartform Flowsheet section is saved.
+49017	Visit Navigator Flowsheet section viewed	VN Flowsheet section is accessed.
+49018	Visit Navigator Flowsheet section accepted	VN Flowsheet section is saved.
+49019	ED Visit Navigator Clinical Impressions section accepted	Saved the Clinical Impressions navigator
+49019	ED Visit Navigator Clinical Impressions section accepted	section through the ED navigator
+49022	ED Visit Navigator Disposition & Follow-UP section viewed	Accessed.
+49022	ED Visit Navigator Disposition & Follow-UP section viewed	ED VN Disposition & Follow-Up Section
+49024	Visit Navigator Follow-Up section accepted	ED VN Follow-Up section saved.
+49025	Edited Discharge Instructions	The discharge instructions were edited in
+49025	Edited Discharge Instructions	discharge instructions navigator section.
+49025	Edited Discharge Instructions	either the Discharge Writer activity or the
+49031	Narrator accessed	Narrator is accessed
+49035	Events accessed in Narrator	Event to log when Narrator events are accessed
+49037	ED Visit Navigator Clinical Impressions section accessed	Accessed the Clinical Impressions navigator
+49037	ED Visit Navigator Clinical Impressions section accessed	section through the ED navigator
+49040	Clinical References viewed	Clinical References viewed.
+49041	Clinical References accepted	Clinical References accepted
+49052	ED Triage Activity viewed	Audits views for the ED Triage activity.
+49054	ED Workup Activity viewed	Audits viewing the ED Workup activity.
+49070	Patient Events Log viewed	Patient Events Log Viewed
+49075	One-Step Medication filed	One-Step Med Filed in Narrator, Cupid, or Optime
+49076	Flowsheet Filed in Popup/Entry activity	Flowsheet Filed in Flowsheet Popup or Flowsheet Entry activities
+51091	Order inquiry accessed	Patient audit event for Order Inquiry
+55000	Beacon cancer staging activity accessed	Beacon cancer staging activity accessed
+55001	Treatment Planning activity accessed	Treatment planning activity accessed.
+55002	Cancer stage modified	A user modified data in a cancer stage record.
+55003	Cancer stage viewed	A user viewed a cancer stage record.
+55005	Treatment plan modified	A user modified data in a treatment plan.
+55006	Treatment plan accessed	A user accessed a treatment plan.
+56000	Anticoagulation related activity accessed	Anticoagulation Tracking Section  was loaded
+56004	Procedure event log accessed	Event log was loaded in the Procedure Log
+56009	Anticoagulation referral order placed	Anticoagulation Referral Order was Placed
+56010	Procedure Event Log accessed	Event edited in procedure log
+60000	Print notes from inpatient notes activity	This event is logged when the user prints
+60000	Print notes from inpatient notes activity	clinical notes from the Notes activity.
+60001	Print preview clicked from inpatient notes activity	IP note activity print preview opened. The user
+60001	Print preview clicked from inpatient notes activity	may or may not continue printing the note(s).
+60002	Route from inpatient notes activity	IP note activity route tool opened
+60005	View clinical notes section	HNO IDs to the HNO_IDS mnemonic.
+60005	View clinical notes section	Note section was viewed.
+60005	View clinical notes section	Other sections will log all of the notes loaded
+60005	View clinical notes section	Sections in Table Mode will log a view action
+60005	View clinical notes section	and shown to the user in one action and log the
+60005	View clinical notes section	for each note viewed and use the HNO mnemonic.
+60006	View clinical note from copy previous	Note viewed from the copy previous window.
+60008	View FastNote activity	Fast note activity is opened
+60010	Pend clinical note	Pend a note
+60011	Save clinical note	Note is saved
+60012	Sign clinical note	Sign a note
+60013	Cosign clinical note	Cosign a note
+60017	Delete clinical note	Note is deleted
+60022	Order Canceled from Note	Order is canceled when removed from Note or the note is deleted
+60023	Print patient instructions	This event is logged when the user prints
+60023	Print patient instructions	patient instructions.
+72000	Care Everywhere Documents activity accessed	Care Everywhere Documents activity accessed.
+72001	Care Everywhere document accessed	Care Everywhere document accessed
+72002	Care Everywhere Request Outside Records activity accessed	User accessed the Request Outside Records activity.
+72008	Authorization forms printed from Request Outside Records	User printed one or more authorization forms from the Request Outside Records activity
+72009	Home tab accessed in Care Everywhere activity	User accessed the Home tab of the Care Everywhere Outside Records activity
+72010	Summary tab accessed in Care Everywhere activity	User accessed the Summary tab of the Care Everywhere Outside Records activity
+72011	Documents tab accessed in Care Everywhere activity	User accessed the Documents tab of the Care Everywhere Outside Records activity
+72012	Lab Results tab accessed in Care Everywhere activity	User accessed the Lab Results tab of the Care Everywhere Outside Records activity
+72013	Other Results tab accessed in Care Everywhere activity	User accessed the Other Results tab of the Care Everywhere Outside Records activity.
+72016	Authorization viewed in Request Outside Records activity	User was shown one or more authorization forms to e-sign from the Request Outside Records activity
+87002	Eye exam accessed	Ophth Exam Viewed
+87005	Eye exam saved	Ophth Exam Saved
+89000	Anesthesia Intraprocedure viewed	Logged when Anesthesia Intraprocedure is opened
+89000	Anesthesia Intraprocedure viewed	for a patient.
+89001	Events edited from Visit Navigator or Anesthesia Intra-op	Logged when events are edited from either the
+89001	Events edited from Visit Navigator or Anesthesia Intra-op	Visit Navigator or Anesthesia Intra-op.
+89005	Attestation recorded in Anesthesia Intra-op	Anesthesia Intra-op.
+89005	Attestation recorded in Anesthesia Intra-op	Logged when an attestation is recorded in
+89006	Flowsheet documented in Anesthesia Intra-op	Anesthesia Intra-op.
+89006	Flowsheet documented in Anesthesia Intra-op	Logged when a flowsheet is documented in
+89007	Medication administration created or modified in Anesthesia Intra-op	Logged when a medication administration is
+89007	Medication administration created or modified in Anesthesia Intra-op	created or modified in Anesthesia Intra-op.
+89008	Block or procedure documented in Anesthesia Intra-op	Anesthesia Intra-op.
+89008	Block or procedure documented in Anesthesia Intra-op	Logged when a block or procedure is documented in
+89010	Staff responsibilities documented in Anesthesia Intra-op	Logged when staff responsibilities are documented
+89010	Staff responsibilities documented in Anesthesia Intra-op	in Anesthesia Intra-op.
+89011	LDA documented in Anesthesia Intra-op	Intra-op.
+89011	LDA documented in Anesthesia Intra-op	Logged when an LDA is documented in Anesthesia
+89012	Device or device data modified in Anesthesia Intra-op	Anesthesia Intra-op.
+89012	Device or device data modified in Anesthesia Intra-op	Logged when a device or its data is modified in
+89014	Anesthesia Event edited	Logged when an anesthesia event is filed,
+89014	Anesthesia Event edited	either from Intraprocedure or from navigators
+89015	Anesthesia Event deleted	Logged when an anesthesia event is deleted,
+89015	Anesthesia Event deleted	either from Intraprocedure or from navigators
+89019	Anesthesia Intra Meds edited	Logged when a medication administration is
+89019	Anesthesia Intra Meds edited	created or modified in Anesthesia Intraprocedure
+89020	Anesthesia Intra Meds viewed	Logged when the medications popup is opened
+89020	Anesthesia Intra Meds viewed	from Anesthesia Intraprocedure.
+89021	Anesthesia Intra Flowsheets edited	Anesthesia Intraprocedure.
+89021	Anesthesia Intra Flowsheets edited	Logged when a flowsheet is documented in
+89022	Anesthesia Intra Flowsheets viewed	Anesthesia Intraprocedure.
+89022	Anesthesia Intra Flowsheets viewed	Logged when a flowsheet is opened from
+89023	Anesthesia Attestations filed	Logged when an attestation is recorded
+89023	Anesthesia Attestations filed	in Anesthesia Intraprocedure.
+89025	Anesthesia Attestations viewed	Anesthesia Intraprocedure.
+89025	Anesthesia Attestations viewed	Logged when Attestations are viewed from
+89026	Anesthesia Events viewed	Logged when the Events popup activity is
+89026	Anesthesia Events viewed	viewed from Anesthesia Intraprocedure.
+89027	Anesthesia Intra Blocks edited	Logged when a block or procedure is documented
+89027	Anesthesia Intra Blocks edited	in Anesthesia Intraprocedure.
+89028	Anesthesia Intra Blocks viewed	Anesthesia Intraprocedure.
+89028	Anesthesia Intra Blocks viewed	Logged when the Blocks popup is opened from
+89031	Anesthesia Intra LDAs edited	Intraprocedure.
+89031	Anesthesia Intra LDAs edited	Logged when an LDA is documented in Anesthesia
+89032	Anesthesia Intra LDAs viewed	Anesthesia Intraprocedure.
+89032	Anesthesia Intra LDAs viewed	Logged when the LDAs popup is opened from
+89033	Anesthesia Intra Devices edited	Anesthesia Intraprocedure.
+89033	Anesthesia Intra Devices edited	Logged when a device or its data is modified in
+89034	Anesthesia Intra Devices viewed	Logged when the Devices popup is accessed
+89034	Anesthesia Intra Devices viewed	from Anesthesia Intraprocedure.
+89035	Anesthesia Staff edited	Anesthesia Intraprocedure.
+89035	Anesthesia Staff edited	Logged when staff are documented in
+89036	Anesthesia Staff viewed	Anesthesia Intraprocedure.
+89036	Anesthesia Staff viewed	Logged when the Staffing popup is opened from
+94001	Charges Overview report viewed	Loading the charges overview report
+94004	Patient Benefits verified	Patient Benefits Verified
+94006	Clinical Note viewed	UCN Note Loaded
+94008	Orders signed	Order Ids are a C5-delimited list
+94008	Orders signed	Order(s) signed for patient
+94009	Patient photo viewed	Patient's current photo was viewed
+94011	Encounter List viewed	Patient encounter list loaded
+94012	Results List viewed	List of patient results loaded
+94013	Notes Review accessed	Patient notes loaded for review by clinicians
+94014	Notes Entry accessed	Patient Notes Loaded for editing
+94015	Care Everywhere Documents viewed	Care Everywhere Documents list loaded
+94016	Care Everywhere Summaries viewed	Care Everywhere summaries for the patient
+94016	Care Everywhere Summaries viewed	have been loaded
+94017	Results released to MyChart	Results released to MyChart
+94018	Results table viewed	ID
+94018	Results table viewed	Result table data loaded for the particular ORD
+94019	Allergies viewed	Allegies loaded for a patient
+94020	Haiku patient summary report viewed	This metric is triggered when a patient chart is
+94020	Haiku patient summary report viewed	is accessed.
+94020	Haiku patient summary report viewed	opened in Haiku and the patient summary report
+94021	Active Medications viewed	Patient's Active Medications Loaded
+94022	In Basket Message viewed	In Basket Message Viewed
+94023	Report viewed	Report about the patient loaded
+94024	Medications viewed	Medications for the patient have been loaded for
+94024	Medications viewed	display.
+94025	Patient Summary viewed on wearable	Summary on the Watch loaded
+94030	Haiku encounter report viewed	This metric is triggered when the encounter report
+94030	Haiku encounter report viewed	about a particular encounter for a patient.
+94030	Haiku encounter report viewed	iPhone Encounter Report displaying information
+94030	Haiku encounter report viewed	is retrieved from the server.
+94040	Haiku care team report viewed	Treatment team is encounter specific, care team
+94040	Haiku care team report viewed	care team report is retrieved from the server.
+94040	Haiku care team report viewed	iPhone Care Team Report displays patient's care or
+94040	Haiku care team report viewed	is not.
+94040	Haiku care team report viewed	treatment team. This metric is triggered when the
+94050	Haiku results report viewed	a patient's lab result.
+94050	Haiku results report viewed	iPhone Results Report displaying information about
+94060	Haiku Notes review accessed	Haiku module for reviewing notes about a patient
+94070	Haiku Results Release to MyChart accessed	Haiku Results Release to MyChart
+94090	Haiku Orders activity accessed	Haiku orders module can be used to
+94090	Haiku Orders activity accessed	create orders for a patient.
+94101	Order signed from Haiku	Haiku Orders module signed an order.
+94120	Care Everywhere summary report viewed in Haiku	This event is triggered when a Care Everywhere
+94120	Care Everywhere summary report viewed in Haiku	summary report is opened in Haiku.
+94130	Care Everywhere documents viewed in Haiku	This metric is triggered when a provider opens
+94130	Care Everywhere documents viewed in Haiku	the list of Care Everywhere documents in Haiku.
+94135	Care Everywhere summaries list accessed in Haiku	This metric is triggered when a provider opens
+94135	Care Everywhere summaries list accessed in Haiku	the list of Care Everywhere summaries in Haiku.
+94140	Outside medications or allergies viewed from Haiku	This metric is triggered when a user views a
+94140	Outside medications or allergies viewed from Haiku	external information report from Haiku
+94160	User report viewed in Haiku	Haiku module for viewing user reports.
+94160	User report viewed in Haiku	This metric is triggered when a user views a
+94160	User report viewed in Haiku	user report.
+94170	Haiku In Basket accessed	Audit trail logging for Haiku In Basket
+94200	Haiku login	Triggered when a user successfully
+94200	Haiku login	logs into Haiku.
+94210	Haiku login failure	Triggered when a user attempts
+94210	Haiku login failure	to login to Haiku, but fails
+94240	Patient photo taken from Haiku	Haiku Media Capture allows for the capturing
+94240	Patient photo taken from Haiku	This metric triggers when patient photos are
+94240	Patient photo taken from Haiku	and saving of patient photos.
+94240	Patient photo taken from Haiku	saved or loaded.
+94250	Siri speech to text used	Triggered when a user receives text back from Apple after using Siri speech to text inside of an Epic iOS applicaton
+94260	iOS Screenshot Taken	Triggered when a user takes a screenshot while in Haiku/Canto/Rover for iOS
+94300	Canto login success	Triggered when a user successfully
+94300	Canto login success	logs into Canto.
+94310	Canto login failed	Triggered when an user attempts
+94310	Canto login failed	to login to Canto, but fails
+94410	Rover login failed	Triggered when a user attempts
+94410	Rover login failed	to login to Rover, but fails.
+95020	CTO_PATIENT_SUMMARY	This metric is triggered when a patient chart is
+95020	CTO_PATIENT_SUMMARY	is accessed.
+95020	CTO_PATIENT_SUMMARY	opened in Canto and the patient summary report
+95025	Canto patient header viewed	Ex (Patient allergies, current medications)
+95025	Canto patient header viewed	This metric is triggered when clinical data on
+95025	Canto patient header viewed	the patient header is pulled from the server.
+95030	Canto encounter report viewed	This metric is triggered when a provider opens the
+95030	Canto encounter report viewed	a specific encounter report
+95030	Canto encounter report viewed	encounter list in Canto or is presented with
+95040	Canto notes review accessed	Canto module for reviewing notes about a patient
+95040	Canto notes review accessed	This metric is triggered when a user pulls a
+95040	Canto notes review accessed	list of notes from the server, AND when the user
+95040	Canto notes review accessed	views details about a specific note.
+95050	Canto results report viewed	This metric is triggered when a provider is
+95050	Canto results report viewed	presented with a patient's list of results or is
+95050	Canto results report viewed	presented with a specific result report
+95060	Canto Popover report viewed	This metric is triggered when a provider is
+95060	Canto Popover report viewed	header.  Currently, only patient allergies and
+95060	Canto Popover report viewed	medications are listed in the popover report
+95060	Canto Popover report viewed	presented with a popover report from the patient
+95060	Canto Popover report viewed	style.
+95090	Care Everywhere summary report opened in Canto	This event is triggered when a Care Everywhere
+95090	Care Everywhere summary report opened in Canto	summary report is opened in Canto.
+95130	Care Everywhere documents viewed in Canto	This metric is triggered when a provider opens
+95130	Care Everywhere documents viewed in Canto	the list of Care Everywhere documents in Canto.
+95135	Care Everywhere summaries list viewed in Canto	This metric is triggered when a provider opens
+95135	Care Everywhere summaries list viewed in Canto	the list of Care Everywhere summaries in Canto.
+95170	Canto In Basket accessed	Audit trail logging for Canto In Basket
+95240	Patient photo taken from Canto	Canto Media Capture allows for the capturing
+95240	Patient photo taken from Canto	This metric triggers when a patient photo is
+95240	Patient photo taken from Canto	and saving of patient photos.
+95240	Patient photo taken from Canto	saved or loaded.
+95250	Epic Monitor activity accessed	Epic Monitor
+95871	View Mobile Handoff in Chart	This event is logged when Handoff data for a
+95871	View Mobile Handoff in Chart	mobile application.
+95871	View Mobile Handoff in Chart	patient is viewed inside of a chart in a
+95872	View Mobile Handoff List Activity	Handoff list activity.
+95872	View Mobile Handoff List Activity	This event is logged when Handoff data is
+95872	View Mobile Handoff List Activity	viewed within a mobile application's
+97005	Bed Days accessed	The CCM Bed Days form was accessed
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Data/rotations.csv b/scripts/Archive/ResidentRotationEHRUsage/Data/rotations.csv
new file mode 100644
index 00000000..721bbdbd
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Data/rotations.csv
@@ -0,0 +1,2086 @@
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+S0195220,2017-03-25 00:00:00,2017-04-22,2,R1
+S0195223,2016-08-20 00:00:00,2016-09-17,2,R1
+S0195223,2017-02-25 00:00:00,2017-03-25,2,R1
+S0195223,2017-03-25 00:00:00,2017-04-22,22,R1
+S0195223,2017-05-06 00:00:00,2017-05-20,5,R1
+S0195224,2016-10-15 00:00:00,2016-11-12,2,R1
+S0195224,2016-11-26 00:00:00,2016-12-10,5,R1
+S0195224,2017-02-25 00:00:00,2017-03-25,22,R1
+S0195224,2017-05-20 00:00:00,2017-06-25,2,R1
+S0195225,2016-10-15 00:00:00,2016-11-12,2,R1
+S0195225,2017-05-20 00:00:00,2017-06-25,2,R1
+S0195226,2016-07-23 00:00:00,2016-08-20,2,R1
+S0195226,2016-12-31 00:00:00,2017-01-28,2,R1
+S0195226,2017-03-11 00:00:00,2017-03-25,5,R1
+S0195226,2017-05-20 00:00:00,2017-06-25,22,R1
+S0195227,2016-08-20 00:00:00,2016-09-17,2,R1
+S0195227,2016-10-15 00:00:00,2016-10-29,5,R1
+S0195227,2016-12-10 00:00:00,2016-12-31,2,R1
+S0195227,2017-03-11 00:00:00,2017-03-25,5,R1
+S0195229,2016-06-25 00:00:00,2016-07-09,5,R1
+S0195229,2016-07-23 00:00:00,2016-08-20,2,R1
+S0195229,2016-12-10 00:00:00,2016-12-31,2,R1
+S0195230,2016-09-17 00:00:00,2016-10-15,2,R1
+S0195230,2017-02-25 00:00:00,2017-03-11,5,R1
+S0195230,2017-03-25 00:00:00,2017-04-22,2,R1
+S0195230,2017-04-22 00:00:00,2017-05-20,22,R1
+S0195232,2016-06-25 00:00:00,2016-07-23,2,R1
+S0195232,2016-07-23 00:00:00,2016-08-06,5,R1
+S0195232,2017-01-14 00:00:00,2017-01-28,5,R1
+S0195232,2017-04-22 00:00:00,2017-05-20,2,R1
+S0195235,2016-11-12 00:00:00,2016-12-10,2,R1
+S0195236,2016-07-23 00:00:00,2016-08-20,2,R1
+S0195236,2016-12-10 00:00:00,2016-12-31,2,R1
+S0195237,2016-10-15 00:00:00,2016-11-12,2,R1
+S0195237,2017-02-25 00:00:00,2017-03-25,2,R1
+S0196156,2016-12-10 00:00:00,2016-12-31,22,R1
+S0196156,2017-01-28 00:00:00,2017-02-25,2,R1
+S0196156,2017-02-25 00:00:00,2017-03-11,5,R1
+S0196156,2017-03-25 00:00:00,2017-04-22,2,R1
+S0196588,2016-06-25 00:00:00,2016-07-23,22,R1
+S0196588,2016-09-17 00:00:00,2016-10-15,2,R1
+S0196588,2016-12-31 00:00:00,2017-01-28,2,R1
+S0196588,2017-01-28 00:00:00,2017-02-11,5,R1
+S0196588,2017-06-03 00:00:00,2017-06-25,5,R1
+S0196591,2015-07-18 00:00:00,2015-08-15,2,R1
+S0196591,2016-03-26 00:00:00,2016-04-23,2,R1
+S0196591,2016-07-16 00:00:00,2016-07-30,5,R2
+S0196591,2016-08-13 00:00:00,2016-09-10,2,R2
+S0196591,2016-11-19 00:00:00,2016-12-03,5,R2
+S0196591,2016-12-03 00:00:00,2016-12-28,27,R2
+S0196591,2017-05-27 00:00:00,2017-06-25,27,R2
+S0196593,2016-10-15 00:00:00,2016-11-12,2,R1
+S0196593,2017-02-25 00:00:00,2017-03-25,2,R1
+S0196593,2017-05-20 00:00:00,2017-06-03,5,R1
+S0196595,2016-06-25 00:00:00,2016-07-23,2,R1
+S0196595,2016-09-03 00:00:00,2016-09-17,5,R1
+S0196595,2017-01-28 00:00:00,2017-02-25,2,R1
+S0196595,2017-05-20 00:00:00,2017-06-25,2,R1
+S0196597,2016-09-17 00:00:00,2016-10-15,2,R1
+S0196597,2017-01-28 00:00:00,2017-02-11,5,R1
+S0196597,2017-05-20 00:00:00,2017-06-25,2,R1
+S0200362,2012-08-11 00:00:00,2012-08-25,5,R2
+S0200362,2012-09-22 00:00:00,2012-10-20,2,R2
+S0200362,2013-04-06 00:00:00,2013-05-04,27,R2
+S0200362,2013-06-15 00:00:00,2013-07-01,5,R2
+S0200362,2013-10-19 00:00:00,2013-11-02,5,R3
+S0200362,2013-11-16 00:00:00,2013-12-28,2,R3
+S0205157,2012-07-28 00:00:00,2012-08-11,5,R2
+S0205157,2012-12-08 00:00:00,2012-12-24,5,R2
+S0205157,2012-12-29 00:00:00,2013-01-12,2,R2
+S0205157,2013-01-12 00:00:00,2013-02-09,27,R2
+S0205157,2013-06-15 00:00:00,2013-07-01,5,R2
+S0205157,2013-07-27 00:00:00,2013-08-24,2,R3
+S0205157,2014-01-11 00:00:00,2014-01-25,5,R3
+S0205157,2014-02-08 00:00:00,2014-03-08,27,R3
+S0205157,2014-04-05 00:00:00,2014-04-19,5,R3
+S0205728,2016-08-06 00:00:00,2016-08-20,5,R1
+S0205728,2016-09-17 00:00:00,2016-10-15,2,R1
+S0205728,2016-10-15 00:00:00,2016-11-12,22,R1
+S0205728,2017-01-28 00:00:00,2017-02-25,2,R1
+S0205728,2017-03-25 00:00:00,2017-04-22,2,R1
+S0207880,2012-08-25 00:00:00,2012-09-08,5,R2
+S0207880,2013-01-26 00:00:00,2013-02-09,5,R2
+S0207880,2014-04-05 00:00:00,2014-05-03,27,R3
+S0207880,2014-05-03 00:00:00,2014-05-17,5,R3
+S0209643,2014-09-13 00:00:00,2014-09-27,5,R1
+S0209643,2014-11-08 00:00:00,2014-12-06,2,R1
+S0209643,2015-04-25 00:00:00,2015-05-23,2,R1
+S0209643,2015-05-23 00:00:00,2015-06-25,22,R1
+S0209643,2015-07-25 00:00:00,2015-08-22,27,R2
+S0209643,2015-12-28 00:00:00,2016-01-09,27,R2
+S0209643,2016-05-28 00:00:00,2016-06-25,2,R2
+S0209643,2016-06-25 00:00:00,2016-07-16,2,R3
+S0209643,2016-08-13 00:00:00,2016-08-27,5,R3
+S0209643,2016-11-05 00:00:00,2016-12-03,2,R3
+S0211033,2013-06-25 00:00:00,2013-07-20,2,R1
+S0211033,2014-02-15 00:00:00,2014-03-01,5,R1
+S0211033,2014-03-01 00:00:00,2014-03-29,22,R1
+S0211033,2014-04-26 00:00:00,2014-05-24,2,R1
+S0211299,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211300,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211301,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211309,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211319,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211328,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211330,2016-06-25 00:00:00,2016-07-23,2,R1
+S0211346,2016-06-25 00:00:00,2016-07-23,5,R1
+S0276750,2016-06-25 00:00:00,2016-07-23,5,R1
+S0276781,2016-06-25 00:00:00,2016-07-23,2,R1
+S0276861,2016-06-25 00:00:00,2016-07-23,22,R1
+S0276863,2016-06-25 00:00:00,2016-07-23,22,R1
+S0279227,2012-08-11 00:00:00,2012-08-25,5,R2
+S0279227,2013-02-09 00:00:00,2013-03-09,27,R2
+S0279227,2013-04-06 00:00:00,2013-04-20,5,R2
+S0279227,2013-06-15 00:00:00,2013-07-01,5,R2
+S0279227,2013-07-01 00:00:00,2013-07-27,27,R3
+S0279227,2013-12-28 00:00:00,2014-01-11,2,R3
+S0281931,2014-07-19 00:00:00,2014-08-16,2,R1
+S0281931,2014-09-13 00:00:00,2014-10-11,2,R1
+S0281931,2014-11-22 00:00:00,2014-12-06,5,R1
+S0281931,2015-03-28 00:00:00,2015-04-25,2,R1
+S0281931,2015-10-17 00:00:00,2015-11-14,2,R2
+S0281931,2015-12-08 00:00:00,2015-12-28,5,R2
+S0281931,2016-04-16 00:00:00,2016-04-30,5,R2
+S0281931,2016-05-28 00:00:00,2016-06-25,27,R2
+S0281931,2016-06-25 00:00:00,2016-07-16,22,R3
+S0281931,2016-11-05 00:00:00,2016-12-03,27,R3
+S0281931,2017-02-04 00:00:00,2017-02-18,5,R3
+S0281931,2017-04-01 00:00:00,2017-04-29,2,R3
+S0281931,2017-05-27 00:00:00,2017-06-10,5,R3
diff --git a/scripts/Archive/ResidentRotationEHRUsage/Data/usage_spreadsheet.csv b/scripts/Archive/ResidentRotationEHRUsage/Data/usage_spreadsheet.csv
new file mode 100644
index 00000000..1843b4ab
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/Data/usage_spreadsheet.csv
@@ -0,0 +1,10438 @@
+Rotation,Role,Provider_ID,Date,Chart_Review_Time,Note_Review_Time,Note_Entry_Time,Order_Entry_Time,Navigator_Time,Results_Review_Time,Total_Time,Number_EHR_Actions,Number_EHR_Actions_Accessed_Remotely,Number_Unique_Patient_Records_Accessed
+General Medicine,PGY2+,S0169403,2015-08-15,0:43:24,0:15:53,0:07:34,0:12:29,0:06:30,0:22:12,2:00:48,667,0,8
+General Medicine,PGY2+,S0169403,2015-08-14,1:16:07,0:25:38,0:49:45,0:43:19,0:26:29,0:26:48,4:35:32,1651,47,14
+General Medicine,PGY2+,S0169403,2015-08-17,2:56:10,0:59:23,0:30:44,1:21:15,0:24:50,0:23:21,7:08:04,3574,101,16
+General Medicine,PGY2+,S0169403,2015-08-16,2:09:00,2:36:11,1:53:26,0:43:05,0:29:48,0:38:29,9:29:39,3344,26,24
+General Medicine,PGY2+,S0169403,2015-08-11,2:45:02,2:36:53,0:37:01,0:55:18,0:21:23,1:17:17,9:30:40,4347,86,35
+General Medicine,PGY2+,S0169403,2015-08-10,0:00:00,0:02:27,0:00:00,0:00:17,0:00:00,0:03:02,0:06:44,120,110,8
+General Medicine,PGY2+,S0169403,2015-08-13,2:10:32,0:57:31,1:02:32,1:00:47,0:19:09,0:43:08,7:05:41,2788,59,20
+General Medicine,PGY2+,S0169403,2015-08-12,2:18:15,1:04:35,1:10:05,0:38:13,0:22:14,0:54:05,7:00:22,2874,28,14
+Intensive Care Unit,PGY2+,S0174975,2016-09-20,2:32:09,0:38:31,0:14:15,0:52:01,0:28:13,0:02:00,5:29:22,1557,0,23
+Intensive Care Unit,PGY2+,S0174975,2016-09-21,2:04:59,0:39:38,0:09:10,0:25:59,0:27:18,0:03:55,4:33:47,1872,11,68
+Intensive Care Unit,PGY2+,S0174975,2016-09-22,1:43:13,0:16:06,0:03:12,0:15:37,0:13:57,0:00:12,2:52:08,980,0,24
+Intensive Care Unit,PGY2+,S0174975,2016-09-23,1:46:38,0:13:46,0:10:06,0:31:06,0:40:29,0:01:07,3:47:58,1164,38,19
+General Medicine,PGY2+,S0169403,2015-08-19,2:13:01,0:50:47,0:48:27,1:01:22,0:35:34,0:30:05,6:42:56,2627,82,18
+General Medicine,PGY2+,S0169403,2015-08-18,2:33:40,1:02:52,0:38:59,1:08:22,0:22:55,0:27:47,6:59:38,2691,65,26
+Intensive Care Unit,PGY2+,S0174975,2016-09-26,1:19:46,0:16:18,0:03:52,0:26:42,0:22:08,0:04:54,2:54:25,1062,0,11
+Intensive Care Unit,PGY2+,S0174975,2016-09-27,1:37:33,0:27:24,0:09:33,0:22:38,0:18:18,0:03:19,3:27:52,1166,0,15
+Intensive Care Unit,PGY2+,S0094959,2015-09-08,3:05:33,0:16:38,0:13:58,0:34:10,0:27:34,0:25:52,5:36:04,2305,51,24
+Intensive Care Unit,PGY2+,S0169394,2015-12-08,0:47:36,0:39:25,1:02:28,0:10:28,0:08:06,0:19:39,3:18:36,994,9,30
+Intensive Care Unit,PGY2+,S0169394,2015-12-01,1:15:35,0:23:39,0:03:57,0:37:16,0:31:23,0:24:09,3:37:16,1342,9,22
+Intensive Care Unit,PGY2+,S0169394,2015-12-02,2:26:46,0:34:51,0:13:06,0:31:23,0:31:53,0:53:52,5:34:12,1735,234,14
+Intensive Care Unit,PGY2+,S0169394,2015-12-03,1:52:50,0:36:14,0:11:52,0:22:53,0:32:14,0:45:19,4:38:53,1297,0,12
+Intensive Care Unit,PGY2+,S0169394,2015-12-04,2:23:56,0:57:13,0:10:21,0:37:34,0:26:36,1:08:55,6:02:28,1590,15,16
+Intensive Care Unit,PGY2+,S0169394,2015-12-05,1:03:51,0:20:14,0:06:01,0:06:32,0:15:02,0:15:36,2:16:14,896,234,15
+Intensive Care Unit,PGY2+,S0169394,2015-12-06,1:13:59,0:30:27,0:08:42,0:09:55,0:07:37,0:37:16,2:59:34,853,29,10
+Intensive Care Unit,PGY2+,S0169394,2015-12-07,2:00:29,0:36:43,0:08:39,0:25:22,0:21:47,0:54:43,4:46:26,1338,198,13
+General Medicine,PGY1,S0173803,2016-05-18,2:29:31,1:03:47,0:00:09,1:00:44,0:25:04,0:15:51,5:53:29,2036,119,16
+General Medicine,PGY1,S0173803,2016-05-19,1:48:41,1:05:57,0:03:25,0:36:20,0:16:08,0:09:05,4:51:31,1873,56,19
+Intensive Care Unit,PGY2+,S0094959,2015-09-09,2:34:11,1:10:27,0:02:42,0:10:23,0:22:40,0:45:50,5:26:07,2301,130,30
+General Medicine,PGY1,S0173803,2016-05-10,1:20:35,1:17:14,0:00:19,0:17:39,0:10:54,0:08:02,3:55:57,1183,55,11
+General Medicine,PGY1,S0173803,2016-05-11,2:21:46,0:50:03,0:00:06,0:29:35,0:10:36,0:02:41,4:30:00,1374,52,20
+General Medicine,PGY1,S0173803,2016-05-12,1:20:35,0:19:20,0:00:45,0:18:01,0:05:40,0:02:55,2:27:18,788,24,9
+General Medicine,PGY1,S0173803,2016-05-13,2:28:06,0:50:48,0:00:04,1:01:50,0:24:53,0:03:54,5:35:48,2072,87,12
+General Medicine,PGY1,S0173803,2016-05-14,0:01:49,0:00:06,0:00:00,0:01:32,0:00:00,0:02:48,0:06:52,72,44,11
+General Medicine,PGY1,S0173803,2016-05-15,1:25:08,0:50:52,0:00:13,0:33:53,0:13:36,0:02:23,3:41:00,1476,8,12
+General Medicine,PGY1,S0173803,2016-05-16,2:10:26,0:47:57,0:00:11,0:24:40,0:15:56,0:02:49,4:34:58,1572,52,18
+General Medicine,PGY1,S0173803,2016-05-17,0:00:00,0:00:00,0:00:00,0:00:27,0:00:00,0:00:00,0:00:27,10,0,4
+General Medicine,PGY1,S0181082,2016-04-22,2:47:48,1:39:46,0:00:38,0:57:01,0:16:32,0:09:03,7:02:23,2967,8,17
+General Medicine,PGY1,S0181082,2016-04-23,0:00:00,0:00:00,0:00:00,0:00:08,0:00:00,0:00:00,0:00:08,8,0,4
+General Medicine,PGY1,S0181083,2015-07-18,0:00:00,0:00:00,0:00:00,0:02:01,0:00:00,0:00:00,0:02:01,16,0,3
+General Medicine,PGY1,S0181082,2016-04-21,2:20:22,0:50:18,0:00:09,0:52:55,0:16:57,0:04:24,4:43:25,1762,0,12
+General Medicine,PGY1,S0181083,2015-07-16,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,10,0,2
+General Medicine,PGY1,S0181083,2015-07-17,2:26:18,0:06:42,1:05:25,0:56:17,0:30:59,0:39:25,6:06:33,1703,30,10
+General Medicine,PGY1,S0181083,2015-07-14,0:14:23,0:00:19,0:45:37,0:00:00,0:01:51,0:01:19,1:07:11,341,341,5
+General Medicine,PGY1,S0181083,2015-07-15,3:10:29,0:11:46,1:23:06,1:02:18,0:23:03,0:23:07,7:16:18,1905,45,11
+General Medicine,PGY1,S0181083,2015-07-12,2:52:51,0:06:00,0:51:48,0:48:21,0:48:09,0:29:54,6:53:16,2019,50,14
+General Medicine,PGY1,S0181083,2015-07-13,3:16:51,0:07:21,1:23:26,0:45:19,0:21:21,0:19:07,6:58:58,2162,258,15
+General Medicine,PGY1,S0181083,2015-07-10,2:20:01,0:30:21,1:02:24,0:49:31,0:11:31,0:24:05,5:37:25,1636,37,25
+Intensive Care Unit,PGY2+,S0174975,2016-09-25,2:06:33,0:44:40,0:07:07,0:56:27,0:19:57,0:01:46,4:45:10,1378,29,15
+Emergency Medicine,PGY2+,S0169394,2016-08-08,4:03:37,0:51:42,0:03:06,0:28:31,0:54:51,0:03:22,7:15:50,2185,16,23
+Emergency Medicine,PGY2+,S0169394,2016-08-04,3:36:55,0:40:53,0:00:56,0:40:33,0:48:15,0:06:55,6:54:57,2310,0,13
+General Medicine,PGY1,S0195230,2016-10-13,1:03:28,0:27:46,0:00:05,0:23:06,0:07:43,0:00:09,2:25:08,689,177,9
+Emergency Medicine,PGY2+,S0169394,2016-08-06,1:12:14,0:24:00,0:00:07,0:07:55,0:20:09,0:00:13,2:22:15,731,0,11
+Emergency Medicine,PGY2+,S0169394,2016-08-07,3:54:55,0:43:37,0:01:38,0:26:41,0:38:34,0:06:10,6:38:52,2348,0,27
+Emergency Medicine,PGY2+,S0169394,2016-08-01,4:15:06,0:57:36,0:01:24,0:45:01,0:47:41,0:03:51,7:45:51,2575,285,32
+Emergency Medicine,PGY2+,S0169394,2016-08-02,2:05:18,0:25:55,0:01:05,0:11:05,0:25:21,0:00:33,3:50:15,1321,123,17
+Emergency Medicine,PGY2+,S0169394,2016-08-03,0:03:48,0:00:32,0:00:00,0:00:00,0:00:00,0:00:23,0:04:54,31,0,5
+General Medicine,PGY1,S0195229,2016-07-30,0:01:56,0:01:04,0:00:00,0:00:00,0:00:00,0:02:03,0:05:23,30,0,4
+General Medicine,PGY1,S0195229,2016-07-31,2:30:12,0:27:56,0:00:11,1:01:58,0:17:39,0:02:50,4:46:34,1441,6,14
+General Medicine,PGY1,S0094959,2015-03-29,3:13:04,0:49:33,1:05:05,0:21:40,0:12:59,0:24:39,6:26:07,3193,0,23
+General Medicine,PGY1,S0181059,2015-08-25,2:19:14,0:47:25,0:33:41,0:40:31,0:14:18,0:30:52,5:32:48,2895,684,23
+General Medicine,PGY1,S0181059,2015-08-24,2:24:15,1:48:22,0:59:09,1:02:56,0:22:50,0:36:28,7:39:59,3038,95,23
+General Medicine,PGY1,S0181059,2015-08-27,1:31:10,0:37:01,0:25:54,0:26:36,0:13:07,0:13:31,3:43:26,2150,267,10
+General Medicine,PGY1,S0181059,2015-08-26,2:37:20,0:56:55,1:11:04,1:06:51,0:36:18,0:32:42,7:32:19,3405,72,27
+General Medicine,PGY1,S0181059,2015-08-21,1:46:17,1:04:55,1:04:53,0:47:36,0:18:11,0:28:15,6:09:49,2532,15,26
+General Medicine,PGY1,S0181059,2015-08-20,1:37:57,0:29:09,0:15:59,0:24:17,0:14:28,0:29:44,3:54:54,1958,148,13
+General Medicine,PGY1,S0181059,2015-08-23,1:36:36,0:40:04,0:09:51,0:20:56,0:05:21,0:06:20,3:10:38,1604,108,24
+General Medicine,PGY1,S0181059,2015-08-22,0:00:00,0:06:42,0:00:00,0:01:08,0:00:00,0:01:28,0:11:19,105,91,6
+General Medicine,PGY1,S0181059,2015-08-29,2:43:26,1:03:48,0:37:18,0:33:20,0:27:52,0:18:08,6:01:48,2771,56,48
+General Medicine,PGY1,S0181059,2015-08-28,1:22:23,1:15:05,0:55:46,0:18:43,0:18:09,0:31:03,5:08:25,2402,153,13
+Night Team,PGY2+,S0094535,2016-06-23,1:52:24,1:31:15,0:00:03,0:32:10,0:14:40,0:02:38,4:56:50,1445,1,40
+General Medicine,PGY1,S0181056,2015-11-08,0:41:53,0:45:04,1:03:28,0:17:02,0:11:17,0:22:26,3:55:00,1265,0,7
+General Medicine,PGY1,S0181056,2015-11-09,1:03:07,0:41:40,2:14:48,0:39:00,0:11:18,0:25:51,5:43:02,1199,0,5
+General Medicine,PGY1,S0181082,2015-12-17,0:51:43,1:07:22,1:20:05,0:32:28,0:13:22,0:33:24,5:00:00,1863,179,9
+Night Team,PGY1,S0195086,2016-11-05,2:13:11,0:51:54,0:02:08,0:25:27,0:16:50,0:01:21,4:13:41,1288,0,41
+Night Team,PGY1,S0195086,2016-11-04,1:01:12,0:14:14,0:00:00,0:07:19,0:03:14,0:00:12,1:30:59,414,0,16
+Night Team,PGY1,S0195086,2016-11-07,2:24:25,0:15:07,0:00:00,0:24:55,0:19:28,0:01:18,3:36:03,1299,80,42
+Night Team,PGY1,S0195086,2016-11-06,2:51:43,0:20:05,0:00:00,0:06:45,0:10:39,0:01:22,3:47:27,1342,0,31
+Night Team,PGY1,S0195086,2016-11-01,1:08:27,0:26:04,0:00:01,0:13:10,0:17:35,0:04:16,2:14:34,830,410,25
+Night Team,PGY1,S0195086,2016-11-03,0:53:09,1:14:30,0:00:13,0:04:30,0:06:41,0:01:50,2:37:10,738,0,14
+Night Team,PGY1,S0195086,2016-11-02,1:17:11,0:31:24,0:00:00,0:28:16,0:14:26,0:00:43,2:37:35,842,363,28
+Night Team,PGY1,S0195086,2016-11-09,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+Night Team,PGY1,S0195086,2016-11-08,1:12:37,0:09:43,0:00:00,0:11:09,0:10:20,0:00:32,1:53:30,653,0,24
+General Medicine,PGY1,S0181073,2016-01-10,1:32:30,1:35:19,2:52:50,0:42:45,0:31:03,0:45:16,8:32:36,2787,0,12
+General Medicine,PGY1,S0181073,2016-01-11,0:00:00,0:16:43,0:00:00,0:00:00,0:00:00,0:08:48,0:26:14,181,175,6
+General Medicine,PGY1,S0181073,2016-01-12,1:34:18,1:06:53,1:25:22,0:58:21,0:26:35,0:34:50,6:45:12,2462,0,10
+General Medicine,PGY1,S0181073,2016-01-13,0:52:21,0:56:23,1:41:13,0:42:45,0:16:15,0:25:43,5:35:03,1878,112,7
+General Medicine,PGY1,S0181073,2016-01-14,0:27:33,0:34:35,1:20:39,0:10:03,0:07:39,0:20:11,3:25:32,962,23,5
+General Medicine,PGY1,S0181073,2016-01-15,2:15:08,1:18:58,1:47:07,1:26:08,0:45:23,0:30:09,8:41:31,2828,36,13
+General Medicine,PGY1,S0181073,2016-01-16,1:09:38,1:28:06,0:54:54,0:46:46,0:25:18,0:19:33,5:33:40,2056,17,10
+General Medicine,PGY1,S0181073,2016-01-17,1:08:55,0:59:31,1:53:56,1:00:57,0:26:58,0:32:07,6:54:03,2426,77,8
+General Medicine,PGY1,S0181073,2016-01-18,0:00:00,0:06:41,0:00:00,0:00:00,0:00:00,0:01:21,0:08:20,46,42,4
+General Medicine,PGY1,S0181073,2016-01-19,0:42:24,0:36:00,0:11:55,0:03:55,0:11:30,0:05:14,2:14:06,883,65,8
+Intensive Care Unit,PGY2+,S0181059,2016-08-08,3:33:16,2:25:50,0:00:08,0:55:54,0:38:35,0:17:11,8:27:15,4850,177,23
+Intensive Care Unit,PGY2+,S0181059,2016-08-09,3:38:56,0:52:05,0:02:29,0:35:48,0:24:53,0:09:48,6:04:12,2467,0,28
+General Medicine,PGY1,S0181082,2015-12-13,1:50:45,1:31:01,2:01:20,1:06:02,0:41:13,0:41:35,8:19:41,2647,0,11
+Night Team,PGY1,S0181069,2016-01-31,1:25:09,1:09:51,2:31:10,0:53:20,0:19:41,0:17:19,7:12:21,2558,0,60
+General Medicine,PGY1,S0181069,2016-01-30,0:37:19,0:30:02,0:09:41,0:34:02,0:18:50,0:31:03,2:57:33,1115,0,22
+General Medicine,PGY1,S0195213,2016-11-09,0:03:48,0:00:18,0:00:00,0:00:00,0:00:00,0:00:34,0:04:48,33,0,6
+General Medicine,PGY1,S0195213,2016-11-08,0:37:38,0:49:02,0:03:37,0:13:30,0:08:43,0:00:12,2:16:00,604,28,6
+General Medicine,PGY1,S0195213,2016-11-05,1:37:30,1:02:00,0:00:06,0:56:08,0:15:07,0:05:01,4:19:04,1158,0,6
+General Medicine,PGY1,S0195213,2016-11-04,2:37:36,1:41:50,0:00:10,0:56:03,0:21:58,0:03:25,6:14:57,1504,0,14
+General Medicine,PGY1,S0181057,2015-06-30,0:08:39,0:00:32,0:00:08,0:04:06,0:01:22,0:03:05,0:20:10,119,113,6
+General Medicine,PGY1,S0195213,2016-11-06,1:59:10,1:05:44,0:00:30,0:58:44,0:18:20,0:08:10,5:02:29,1283,0,12
+General Medicine,PGY1,S0195213,2016-11-01,2:10:39,0:58:57,0:00:50,1:02:40,0:10:49,0:03:12,5:03:54,1405,0,10
+General Medicine,PGY1,S0195213,2016-11-03,0:06:20,0:00:15,0:00:00,0:00:00,0:00:00,0:00:00,0:06:35,27,0,2
+General Medicine,PGY1,S0195213,2016-11-02,0:31:00,0:10:39,0:00:04,0:00:00,0:00:00,0:03:11,0:56:18,322,270,14
+General Medicine,PGY1,S0181082,2015-12-12,0:00:00,0:00:00,0:00:00,0:00:53,0:00:00,0:00:00,0:00:53,22,0,6
+General Medicine,PGY1,S0181082,2015-12-11,1:43:47,0:56:43,1:53:49,0:43:16,0:26:27,0:40:01,6:52:54,2251,0,11
+General Medicine,PGY1,S0181087,2016-02-18,1:22:25,1:32:04,1:46:02,0:48:34,0:28:26,0:40:28,7:09:08,3224,0,15
+General Medicine,PGY1,S0181087,2016-02-19,0:43:57,0:59:18,1:05:27,0:26:56,0:07:33,0:28:07,4:30:31,1811,316,9
+General Medicine,PGY1,S0181087,2016-02-12,0:00:00,0:02:39,0:00:00,0:00:00,0:00:00,0:00:00,0:02:52,30,20,4
+General Medicine,PGY1,S0181087,2016-02-13,2:25:36,2:04:35,1:12:29,0:41:35,0:21:28,0:40:06,8:26:24,4137,0,20
+General Medicine,PGY1,S0181087,2016-02-10,1:29:23,1:41:28,0:51:57,0:39:31,0:07:51,0:22:08,5:30:18,2167,45,11
+General Medicine,PGY1,S0181087,2016-02-11,1:10:32,1:22:35,0:51:25,0:49:33,0:11:06,0:22:58,5:07:11,2250,139,10
+General Medicine,PGY1,S0181087,2016-02-16,1:34:20,1:18:10,1:57:00,0:39:45,0:11:39,0:41:14,7:16:35,2944,50,11
+General Medicine,PGY1,S0181087,2016-02-17,1:06:57,1:04:53,1:55:40,0:54:54,0:16:19,0:18:52,6:36:17,2759,60,10
+General Medicine,PGY1,S0181087,2016-02-14,0:00:00,0:04:11,0:00:00,0:00:00,0:00:00,0:00:30,0:04:51,64,54,4
+General Medicine,PGY1,S0181087,2016-02-15,0:53:35,1:14:45,1:37:07,0:38:33,0:06:03,0:25:06,5:22:04,1675,0,10
+Night Team,PGY2+,S0094959,2016-05-28,0:13:31,0:00:26,0:00:00,0:00:01,0:00:00,0:00:15,0:17:05,70,61,10
+Night Team,PGY2+,S0094959,2016-05-21,4:25:03,0:58:17,0:00:08,0:33:30,0:34:54,0:12:18,7:33:57,4062,246,123
+Night Team,PGY2+,S0094959,2016-05-20,2:33:34,0:41:54,0:00:06,0:20:02,0:13:42,0:10:14,4:19:43,1887,214,93
+Night Team,PGY2+,S0094959,2016-05-23,5:09:16,2:13:42,0:02:10,0:37:00,0:22:21,0:14:41,9:30:58,4432,273,90
+Night Team,PGY2+,S0094959,2016-05-22,5:38:36,1:09:38,0:00:05,0:21:50,0:29:18,0:15:30,8:52:26,3673,169,79
+Night Team,PGY2+,S0094959,2016-05-25,4:48:48,2:15:10,0:00:13,0:08:05,0:21:07,0:14:00,8:13:48,3962,107,79
+Night Team,PGY2+,S0094959,2016-05-24,4:16:08,1:37:42,0:00:04,0:17:43,0:21:38,0:22:33,7:42:11,3641,576,94
+Night Team,PGY2+,S0094959,2016-05-27,2:25:37,0:45:16,0:00:04,0:22:56,0:17:29,0:15:43,4:30:47,2381,9,69
+Night Team,PGY2+,S0094959,2016-05-26,3:15:08,2:13:27,0:00:17,0:13:37,0:16:40,0:22:25,7:03:45,3402,512,78
+General Medicine,PGY1,S0181075,2015-11-17,2:25:50,1:44:57,1:01:51,1:26:57,0:30:27,0:26:49,8:01:02,3048,0,11
+General Medicine,PGY1,S0186952,2016-09-20,3:04:54,1:45:14,0:00:08,0:51:21,0:45:43,0:02:41,7:19:28,2430,197,19
+General Medicine,PGY1,S0181082,2016-04-20,1:16:27,0:44:42,0:00:09,0:07:30,0:10:27,0:01:01,2:40:48,964,0,7
+General Medicine,PGY1,S0186952,2016-09-21,0:17:03,0:14:51,0:00:01,0:03:48,0:00:48,0:00:03,0:48:36,430,408,22
+General Medicine,PGY1,S0186952,2016-09-22,2:00:07,0:37:41,0:00:04,0:23:08,0:17:17,0:07:35,3:47:12,1053,54,19
+General Medicine,PGY1,S0173803,2016-01-21,2:51:57,1:35:29,1:09:26,1:02:01,0:40:03,0:48:24,8:53:08,3924,113,29
+General Medicine,PGY1,S0186952,2016-09-23,2:04:32,0:42:10,0:00:01,0:39:51,0:40:22,0:10:06,4:50:10,1326,73,15
+General Medicine,PGY1,S0173803,2016-01-20,1:24:04,0:52:00,1:14:36,0:35:30,0:17:10,0:12:52,4:49:13,1628,10,13
+General Medicine,PGY1,S0196597,2016-10-13,0:58:59,0:38:21,0:00:12,0:37:43,0:10:46,0:00:44,2:42:53,970,162,8
+General Medicine,PGY1,S0173803,2016-01-23,2:08:13,1:02:16,1:32:39,1:17:37,0:50:54,0:27:13,7:55:02,2726,66,30
+General Medicine,PGY1,S0181066,2015-06-30,1:41:55,0:32:38,2:22:15,0:28:49,0:24:34,0:28:47,6:38:14,1216,3,5
+General Medicine,PGY1,S0181079,2016-03-19,0:00:00,0:00:00,0:00:00,0:01:24,0:00:00,0:00:00,0:01:24,22,0,4
+General Medicine,PGY1,S0173803,2016-01-22,1:25:13,1:32:06,1:59:36,0:47:15,0:41:20,0:51:10,7:57:37,3131,163,18
+General Medicine,PGY1,S0186952,2016-09-26,1:58:55,0:55:39,0:00:15,0:19:58,0:21:28,0:06:25,4:29:23,1707,279,12
+Night Team,PGY2+,S0169403,2016-05-11,1:21:13,0:40:57,0:00:03,0:11:36,0:07:46,0:07:01,2:51:01,735,113,23
+Night Team,PGY2+,S0169403,2016-05-10,1:39:29,1:00:09,0:00:18,0:22:13,0:03:40,0:01:59,3:29:01,987,134,31
+Night Team,PGY2+,S0169403,2016-05-13,1:54:53,2:30:59,0:00:16,0:36:36,0:12:25,0:09:50,6:00:50,1692,50,21
+Night Team,PGY2+,S0169403,2016-05-12,1:12:15,1:00:15,0:00:04,0:12:38,0:11:57,0:08:59,3:00:14,892,59,33
+Night Team,PGY2+,S0169403,2016-05-14,1:17:16,0:51:41,0:00:05,0:11:54,0:02:34,0:00:41,2:46:23,930,161,22
+General Medicine,PGY1,S0173803,2016-01-24,0:30:32,0:50:24,0:53:51,0:21:45,0:20:42,0:08:51,3:25:10,1244,0,12
+Night Team,PGY1,S0169381,2015-01-20,2:30:29,0:06:40,0:55:33,0:38:59,0:43:04,0:39:48,5:49:10,1242,0,32
+Night Team,PGY1,S0169381,2015-01-21,2:22:43,0:02:12,2:11:11,0:28:49,0:21:35,0:26:41,6:01:24,883,0,28
+General Medicine,PGY1,S0181083,2015-07-11,2:52:55,0:15:46,1:18:13,0:21:50,0:13:34,0:26:51,5:48:31,1340,55,17
+General Medicine,PGY1,S0181079,2016-03-16,1:26:51,1:07:15,0:00:09,0:30:41,0:13:47,0:00:54,3:44:59,1231,0,8
+General Medicine,PGY2+,S0169383,2016-07-09,2:22:37,1:14:42,0:00:07,0:27:55,0:27:29,0:14:19,5:26:17,2008,12,15
+General Medicine,PGY1,S0181079,2016-03-17,2:04:36,1:06:42,0:00:08,0:17:28,0:12:48,0:00:43,3:59:57,1162,0,10
+General Medicine,PGY2+,S0109179,2016-07-17,2:26:25,1:33:07,0:00:05,0:13:20,0:07:41,0:04:07,5:08:20,2609,0,28
+General Medicine,PGY1,S0172645,2015-09-29,0:58:48,1:13:19,1:41:12,0:55:55,0:31:40,0:26:29,6:01:04,1985,30,10
+General Medicine,PGY2+,S0169383,2016-07-08,2:30:25,1:23:53,0:00:09,0:27:46,0:21:50,0:14:50,5:39:30,2166,107,22
+General Medicine,PGY1,S0181079,2016-03-10,3:16:01,1:20:33,0:00:15,1:06:03,0:23:31,0:05:47,6:55:28,2236,165,17
+General Medicine,PGY2+,S0109179,2016-07-19,0:44:32,0:11:19,0:00:00,0:03:14,0:03:51,0:00:36,1:06:00,402,0,9
+General Medicine,PGY2+,S0109179,2016-07-18,1:58:45,0:26:55,0:00:36,0:25:49,0:02:18,0:01:42,3:32:57,1153,168,25
+General Medicine,PGY1,S0181081,2016-06-01,0:15:15,0:19:24,0:00:01,0:03:06,0:04:03,0:00:06,0:45:10,357,7,19
+General Medicine,PGY1,S0181079,2016-03-11,4:18:11,0:53:47,0:00:32,0:10:46,0:11:01,0:17:49,6:32:31,1631,43,42
+Emergency Medicine,PGY2+,S0106952,2016-09-30,5:35:29,0:56:49,0:15:57,0:19:08,0:19:35,0:07:05,8:37:46,3339,281,81
+General Medicine,PGY1,S0181088,2016-06-08,3:03:39,1:04:05,0:00:06,0:50:15,0:14:25,0:06:28,5:48:23,2399,135,13
+Night Team,PGY1,S0169381,2014-08-08,2:44:29,0:02:48,0:02:05,0:46:23,0:46:28,0:39:10,5:25:14,1410,0,45
+Night Team,PGY1,S0169381,2014-08-09,2:19:49,0:00:33,0:05:22,0:24:08,0:49:47,0:26:39,4:21:45,995,0,37
+General Medicine,PGY1,S0181088,2016-06-09,1:30:15,0:52:20,0:00:11,0:34:58,0:12:18,0:01:24,3:37:06,1603,521,14
+Night Team,PGY1,S0169381,2014-08-04,2:29:53,0:01:28,0:15:33,0:15:54,0:35:09,0:40:54,4:27:45,1061,0,33
+Night Team,PGY1,S0169381,2014-08-05,3:02:32,0:00:38,0:15:58,0:29:50,0:49:38,0:21:59,5:24:51,1473,19,42
+Night Team,PGY1,S0169381,2014-08-06,2:23:47,0:04:04,0:08:48,0:16:10,0:33:31,0:13:15,4:04:11,999,0,39
+Night Team,PGY1,S0169381,2014-08-07,2:23:23,0:04:18,0:12:47,0:51:46,0:53:47,0:31:21,5:08:45,1234,0,30
+Night Team,PGY1,S0169381,2014-08-03,2:31:40,0:09:10,0:08:30,0:48:15,0:56:18,0:23:06,5:13:52,1289,0,29
+General Medicine,PGY2+,S0169383,2016-07-01,2:21:25,1:22:32,0:00:37,0:45:37,0:21:54,0:04:25,5:52:27,2240,37,18
+Emergency Medicine,PGY2+,S0169394,2016-08-05,2:14:10,0:40:25,0:00:06,0:19:42,0:16:07,0:15:25,3:56:04,1602,336,27
+Intensive Care Unit,PGY2+,S0181078,2016-08-30,0:07:27,0:02:31,0:00:00,0:00:00,0:00:01,0:00:00,0:14:36,116,116,11
+Intensive Care Unit,PGY2+,S0181078,2016-08-31,3:12:13,0:48:00,0:00:27,1:00:35,0:23:07,0:13:17,6:02:48,1689,0,16
+General Medicine,PGY1,S0169395,2015-06-11,3:39:46,0:45:36,2:35:23,0:44:52,0:33:05,0:19:04,9:33:34,2667,9,21
+General Medicine,PGY1,S0169395,2015-06-10,0:52:52,0:10:26,0:23:18,0:05:48,0:03:48,0:02:40,1:55:52,558,60,11
+General Medicine,PGY1,S0169395,2015-06-13,0:03:15,0:00:15,0:00:09,0:00:10,0:00:34,0:00:13,0:04:47,86,80,3
+General Medicine,PGY1,S0169395,2015-06-12,2:02:39,0:06:25,1:55:06,0:35:37,0:14:12,0:06:54,5:27:13,1766,20,9
+General Medicine,PGY1,S0169395,2015-06-15,2:36:49,0:04:22,2:48:37,0:27:32,0:16:03,0:07:12,6:37:08,1309,0,10
+General Medicine,PGY1,S0169395,2015-06-14,1:54:03,0:03:12,1:40:29,0:23:53,0:17:51,0:11:57,4:57:35,1351,0,34
+General Medicine,PGY1,S0169395,2015-06-17,1:56:47,0:11:03,0:57:43,0:28:29,0:15:13,0:15:49,4:28:52,1486,0,12
+General Medicine,PGY1,S0169395,2015-06-16,2:05:23,0:12:39,2:12:16,0:34:41,0:17:28,0:09:47,6:00:44,1351,0,13
+General Medicine,PGY1,S0169395,2015-06-19,2:08:19,0:13:37,1:18:47,0:31:57,0:20:40,0:19:38,5:12:16,1870,0,11
+General Medicine,PGY1,S0169395,2015-06-18,1:46:08,0:00:29,1:57:32,0:28:01,0:16:07,0:08:10,4:52:43,1254,24,6
+Night Team,PGY2+,S0163425,2015-10-28,1:25:18,1:46:50,2:25:25,0:51:38,0:26:00,1:06:30,8:45:55,3108,0,76
+General Medicine,PGY2+,S0169383,2016-07-04,1:54:22,1:08:49,0:00:20,0:36:23,0:09:58,0:01:50,4:35:49,1677,77,15
+General Medicine,PGY1,S0195076,2016-07-01,0:03:46,0:05:49,0:00:00,0:00:36,0:00:00,0:00:01,0:12:37,102,94,7
+General Medicine,PGY1,S0195076,2016-07-03,1:39:06,1:08:49,0:00:09,0:26:52,0:18:09,0:00:16,4:22:54,1194,0,12
+General Medicine,PGY1,S0195076,2016-07-02,1:29:47,1:22:22,0:00:14,0:28:46,0:17:15,0:06:27,4:25:51,1295,96,8
+General Medicine,PGY1,S0195076,2016-07-05,1:29:35,1:07:55,0:00:11,0:22:58,0:23:00,0:04:34,3:51:24,1095,0,8
+General Medicine,PGY1,S0195076,2016-07-04,1:05:24,0:40:58,0:00:04,0:21:11,0:15:53,0:00:21,2:53:26,971,0,8
+General Medicine,PGY1,S0195076,2016-07-07,1:41:31,0:36:20,0:00:07,0:32:15,0:19:32,0:01:16,4:03:26,1015,25,4
+General Medicine,PGY1,S0195076,2016-07-06,0:57:50,0:14:28,0:00:55,0:18:35,0:16:22,0:00:06,2:24:12,730,0,7
+General Medicine,PGY1,S0195076,2016-07-09,2:18:09,0:41:56,0:00:03,0:46:39,0:24:35,0:01:30,4:57:34,1624,0,8
+General Medicine,PGY1,S0195076,2016-07-08,2:29:14,0:49:10,0:00:12,0:57:36,0:50:20,0:11:19,6:12:26,1559,0,7
+General Medicine,PGY1,S0195230,2016-10-15,0:05:44,0:03:33,0:00:00,0:00:00,0:00:01,0:00:00,0:11:25,60,56,5
+Night Team,PGY1,S0181061,2016-02-23,1:53:06,0:32:53,0:00:08,1:01:21,0:17:36,0:04:31,4:28:30,1195,0,28
+Night Team,PGY2+,S0169398,2016-10-10,1:58:26,1:54:53,0:01:44,0:28:24,0:17:17,0:02:07,5:17:06,1543,0,32
+Night Team,PGY2+,S0169398,2016-10-11,2:33:20,1:04:19,0:02:23,0:24:49,0:17:46,0:02:02,4:57:49,1334,0,24
+Night Team,PGY1,S0181525,2015-12-13,1:09:49,0:36:36,0:00:22,0:26:10,0:29:16,0:57:23,3:51:16,1281,0,36
+Night Team,PGY1,S0181525,2015-12-12,1:15:14,0:51:19,0:49:39,0:29:11,0:25:08,0:47:03,4:59:36,1363,45,65
+Night Team,PGY1,S0181525,2015-12-11,0:56:24,0:17:34,0:04:03,0:25:46,0:23:13,0:17:19,2:35:14,876,133,42
+Night Team,PGY1,S0181525,2015-12-10,1:22:33,0:25:59,0:18:40,0:41:24,0:34:48,0:46:19,4:23:39,1287,28,50
+Night Team,PGY1,S0181525,2015-12-17,0:34:50,0:04:57,0:00:07,0:20:17,0:21:57,0:13:59,1:44:47,596,0,29
+Night Team,PGY1,S0181525,2015-12-16,1:02:42,0:06:16,0:00:00,0:22:21,0:10:39,0:27:21,2:21:20,760,74,32
+General Medicine,PGY2+,S0181081,2016-10-19,0:57:03,0:17:55,0:00:00,0:02:35,0:03:21,0:00:41,1:39:52,800,0,16
+General Medicine,PGY2+,S0181081,2016-10-18,2:34:07,1:18:43,0:00:00,0:12:52,0:11:17,0:07:40,4:53:01,2334,0,26
+General Medicine,PGY2+,S0181081,2016-10-17,1:46:58,0:55:55,0:00:02,0:12:51,0:08:51,0:10:18,3:30:15,1129,0,15
+General Medicine,PGY2+,S0181081,2016-10-16,2:45:17,1:16:08,0:00:10,0:49:03,0:33:39,0:03:03,6:11:16,2754,0,12
+General Medicine,PGY2+,S0181081,2016-10-15,1:43:09,1:04:49,0:00:03,0:18:34,0:10:21,0:09:21,3:53:53,1468,0,18
+General Medicine,PGY2+,S0181081,2016-10-14,3:29:43,1:29:05,0:00:01,0:29:32,0:26:12,0:03:53,6:44:20,2624,0,21
+General Medicine,PGY2+,S0181081,2016-10-13,2:38:12,1:21:52,0:00:00,0:23:47,0:10:47,0:08:54,5:31:31,2863,0,27
+General Medicine,PGY2+,S0181081,2016-10-12,0:09:20,0:00:24,0:00:00,0:00:00,0:00:00,0:00:23,0:11:40,61,6,8
+General Medicine,PGY2+,S0181081,2016-10-11,1:41:10,1:01:25,0:00:06,0:11:09,0:11:42,0:01:11,3:39:21,1577,8,19
+General Medicine,PGY2+,S0181081,2016-10-10,2:55:43,1:37:48,0:00:00,0:02:58,0:10:07,0:00:53,5:26:39,2316,0,28
+Night Team,PGY2+,S0169381,2015-11-14,1:03:56,0:49:41,1:09:25,0:32:44,0:23:01,0:41:57,4:58:01,1325,0,23
+Night Team,PGY2+,S0169381,2015-11-10,0:20:18,1:02:34,0:57:27,0:16:44,0:11:27,0:12:25,3:14:14,892,0,10
+Night Team,PGY2+,S0169381,2015-11-11,1:16:07,1:10:07,0:29:47,0:34:25,0:25:41,0:39:39,5:00:18,1277,10,36
+Night Team,PGY2+,S0169381,2015-11-12,0:53:36,1:24:43,1:50:00,0:36:02,0:27:07,0:30:09,6:09:29,1469,0,34
+Night Team,PGY2+,S0169381,2015-11-13,1:03:46,1:20:49,1:41:11,0:17:38,0:17:55,0:41:41,5:35:31,1471,0,33
+General Medicine,PGY1,S0173803,2016-01-30,0:58:20,0:49:19,1:19:10,0:09:46,0:10:56,0:28:43,4:19:01,985,27,15
+General Medicine,PGY2+,S0169383,2016-07-16,0:03:37,0:02:22,0:00:03,0:19:18,0:00:52,0:00:04,0:40:30,274,35,15
+General Medicine,PGY2+,S0169383,2016-07-14,1:26:26,1:05:32,0:13:23,0:11:46,0:06:35,0:11:13,4:32:20,1524,34,20
+General Medicine,PGY2+,S0169383,2016-07-15,2:40:02,2:14:07,0:00:02,0:25:46,0:14:31,0:11:28,6:13:58,2365,12,30
+General Medicine,PGY2+,S0169383,2016-07-12,0:01:47,0:02:13,0:00:00,0:00:33,0:00:00,0:00:00,0:07:02,135,115,13
+General Medicine,PGY2+,S0169383,2016-07-13,2:04:53,2:02:22,0:00:01,0:24:28,0:12:40,0:17:41,5:34:37,2256,275,27
+General Medicine,PGY2+,S0169383,2016-07-10,2:42:33,1:24:13,0:00:00,0:17:59,0:15:41,0:07:28,5:33:03,1963,0,20
+General Medicine,PGY2+,S0169383,2016-07-11,2:21:44,1:04:19,0:00:07,0:18:00,0:26:22,0:01:39,4:57:24,2089,88,24
+Night Team,PGY1,S0181056,2015-07-09,2:08:33,0:12:42,0:08:42,0:09:14,0:10:03,0:13:47,3:14:33,1107,421,38
+Night Team,PGY1,S0181056,2015-07-08,4:08:08,0:30:46,0:01:36,0:14:29,0:33:33,0:20:25,6:13:19,1719,835,33
+Night Team,PGY1,S0181056,2015-07-07,2:54:03,0:51:55,0:00:12,0:08:11,0:23:48,0:23:36,4:56:43,1418,23,31
+Night Team,PGY1,S0181056,2015-07-06,2:53:16,1:01:40,0:10:45,0:17:17,0:23:41,0:27:45,5:31:16,1766,145,31
+Night Team,PGY1,S0181056,2015-07-05,4:27:16,0:34:24,0:00:12,0:14:26,0:39:45,0:37:33,6:46:22,1969,84,31
+General Medicine,PGY1,S0169401,2015-03-16,0:12:53,0:08:16,0:00:10,0:01:17,0:03:14,0:03:51,0:32:01,368,354,12
+General Medicine,PGY1,S0169401,2015-03-17,3:23:14,0:54:46,1:03:45,0:28:49,0:19:25,0:07:59,6:29:01,2303,318,19
+General Medicine,PGY1,S0169401,2015-03-14,2:53:05,1:15:53,1:51:16,0:20:44,0:16:53,0:08:00,7:10:49,2116,398,13
+General Medicine,PGY2+,S0106952,2015-09-30,2:51:06,0:55:13,0:02:42,0:00:38,0:18:47,0:32:03,5:05:17,1653,344,58
+General Medicine,PGY1,S0163425,2014-10-04,3:19:36,0:04:07,0:24:28,0:21:17,0:24:25,0:44:45,5:37:42,1528,0,8
+General Medicine,PGY2+,S0169400,2015-09-01,0:00:00,0:11:43,0:00:00,0:00:00,0:00:00,0:01:28,0:13:32,142,132,9
+General Medicine,PGY2+,S0169400,2015-09-02,2:53:59,0:41:55,0:15:18,0:24:22,0:14:03,1:30:57,6:28:18,1727,63,34
+General Medicine,PGY2+,S0169400,2015-09-03,2:07:35,0:17:36,0:22:44,0:36:32,0:24:44,0:29:11,4:52:57,1829,137,17
+General Medicine,PGY2+,S0169400,2015-09-04,0:00:00,0:18:26,0:00:00,0:00:00,0:00:00,0:05:15,0:25:34,189,185,9
+General Medicine,PGY2+,S0169400,2015-09-05,2:41:06,0:25:54,0:21:32,0:22:33,0:13:50,0:45:41,5:16:54,1888,0,27
+General Medicine,PGY2+,S0169400,2015-09-06,1:19:42,0:06:38,0:37:54,0:26:16,0:11:53,0:12:49,3:16:04,1056,81,15
+General Medicine,PGY2+,S0169400,2015-09-07,2:16:53,0:22:15,1:57:28,0:23:29,0:17:18,0:56:20,6:38:56,2025,32,27
+General Medicine,PGY2+,S0169400,2015-09-08,0:54:36,0:06:58,0:01:44,0:02:02,0:02:03,0:09:40,1:20:28,490,88,14
+General Medicine,PGY2+,S0169400,2015-09-09,0:00:00,0:10:35,0:00:00,0:00:35,0:00:00,0:00:38,0:12:14,114,102,12
+General Medicine,PGY1,S0163425,2014-10-08,2:09:07,0:02:15,0:08:48,0:19:41,0:15:18,0:11:43,3:46:03,1062,517,8
+General Medicine,PGY1,S0163425,2014-10-09,4:44:12,0:11:20,0:23:09,0:49:17,0:25:47,0:46:32,7:56:29,1870,0,13
+General Medicine,PGY1,S0181086,2015-12-23,1:00:19,0:39:53,0:23:50,0:44:01,0:00:36,0:04:49,3:30:45,1396,503,76
+Emergency Medicine,PGY1,S0163662,2015-02-12,0:00:23,0:00:00,0:08:16,0:00:00,0:00:03,0:01:11,0:09:56,29,0,3
+General Medicine,PGY1,S0169401,2015-03-12,3:14:53,1:02:56,2:07:36,0:21:36,0:15:46,0:11:49,7:27:57,2503,826,32
+Emergency Medicine,PGY1,S0163662,2015-02-13,4:18:27,1:01:08,1:02:28,0:49:36,0:28:05,0:10:46,8:51:15,2616,0,22
+Night Team,PGY1,S0181070,2015-09-29,0:27:39,0:42:30,2:42:04,0:08:53,0:05:32,0:12:03,4:27:14,888,3,27
+General Medicine,PGY1,S0195203,2016-07-10,2:22:08,2:06:00,0:00:14,0:25:50,0:47:17,0:06:50,6:41:16,1667,191,15
+Night Team,PGY1,S0181070,2015-09-28,1:04:35,1:34:38,2:14:04,0:35:34,0:18:54,0:29:38,6:38:44,1733,0,39
+General Medicine,PGY1,S0181078,2016-03-19,0:00:00,0:00:00,0:00:00,0:01:43,0:00:00,0:00:00,0:01:43,20,0,4
+General Medicine,PGY1,S0181078,2016-03-18,3:28:02,1:03:41,0:00:14,1:07:47,0:22:35,0:07:33,6:50:19,2024,220,19
+General Medicine,PGY1,S0181078,2016-03-17,1:52:23,0:25:43,0:03:34,0:44:36,0:22:17,0:12:02,4:41:11,1379,115,13
+General Medicine,PGY1,S0181078,2016-03-16,3:05:44,1:07:46,0:00:15,0:59:35,0:26:13,0:01:43,6:32:05,1514,193,13
+General Medicine,PGY1,S0181078,2016-03-15,0:37:45,0:50:51,0:00:16,0:04:43,0:00:09,0:00:53,2:21:44,916,898,32
+General Medicine,PGY1,S0181078,2016-03-14,3:15:19,1:08:36,0:00:28,1:38:59,0:27:51,0:19:17,7:37:32,2167,450,17
+General Medicine,PGY1,S0181078,2016-03-13,3:13:00,1:12:31,0:00:10,1:12:35,0:31:19,0:03:34,7:22:50,1990,63,14
+General Medicine,PGY1,S0181078,2016-03-12,2:52:30,0:57:22,0:00:14,1:05:42,0:18:47,0:03:38,5:54:43,1750,209,13
+General Medicine,PGY1,S0181078,2016-03-11,3:34:42,1:10:09,0:00:12,1:20:04,0:31:51,0:03:20,7:28:13,2268,29,15
+General Medicine,PGY1,S0181078,2016-03-10,0:30:50,0:31:51,0:00:19,0:09:29,0:00:42,0:00:06,1:21:18,368,340,16
+General Medicine,PGY1,S0195203,2016-07-11,1:05:23,0:49:22,0:00:21,0:19:43,0:21:30,0:07:22,3:12:58,949,307,14
+General Medicine,PGY1,S0181088,2016-05-31,2:00:38,0:47:50,0:00:14,0:36:53,0:18:19,0:06:43,4:32:05,1389,48,11
+General Medicine,PGY1,S0169411,2015-01-12,4:39:45,0:04:17,3:36:13,0:46:56,0:23:39,0:02:20,9:57:23,2787,0,16
+Night Team,PGY2+,S0169395,2016-03-12,4:16:20,1:57:52,0:00:07,0:14:32,0:18:17,0:02:22,7:31:10,2081,99,52
+General Medicine,PGY1,S0195225,2016-10-30,1:34:44,1:56:23,0:00:07,0:48:10,0:22:08,0:00:41,5:10:33,1637,76,8
+General Medicine,PGY1,S0195225,2016-10-31,1:58:24,1:35:35,0:00:13,0:49:13,0:40:34,0:03:38,5:33:42,1918,0,9
+General Medicine,PGY1,S0169411,2015-01-15,5:25:51,0:08:45,2:29:56,1:11:19,0:36:14,0:08:43,10:41:14,3329,0,29
+Night Team,PGY2+,S0169386,2016-02-23,2:52:53,0:50:36,0:00:10,0:36:34,0:06:28,0:03:56,5:20:18,2073,0,32
+Night Team,PGY2+,S0169386,2016-02-22,4:11:57,1:32:54,0:00:24,0:41:15,0:11:56,0:08:54,7:29:07,3048,183,89
+Night Team,PGY2+,S0169386,2016-02-21,2:51:06,0:52:23,0:00:07,1:04:58,0:16:58,0:06:16,6:16:02,2280,0,47
+Emergency Medicine,PGY2+,S0169403,2016-01-19,0:00:00,0:03:50,0:00:00,0:00:00,0:00:00,0:00:00,0:05:16,28,28,2
+Emergency Medicine,PGY2+,S0169403,2016-01-18,2:05:28,1:00:30,2:58:23,0:29:16,0:49:58,0:40:37,8:39:27,3533,71,27
+Night Team,PGY2+,S0169386,2016-02-25,4:30:43,1:08:16,0:00:06,0:38:26,0:13:21,0:03:12,7:07:07,2484,181,74
+Night Team,PGY2+,S0169386,2016-02-24,1:18:55,0:31:37,0:04:14,0:23:44,0:04:17,0:01:24,2:53:25,1249,328,30
+Emergency Medicine,PGY2+,S0169403,2016-01-15,0:00:09,0:09:03,0:01:07,0:00:00,0:00:08,0:01:44,0:13:27,127,127,8
+Emergency Medicine,PGY2+,S0169403,2016-01-14,0:00:00,0:00:13,0:00:00,0:00:00,0:00:00,0:00:00,0:00:25,24,24,3
+Emergency Medicine,PGY2+,S0169403,2016-01-17,0:35:00,0:21:48,0:47:11,0:13:01,0:24:50,0:19:50,2:53:50,1132,40,14
+Emergency Medicine,PGY2+,S0169403,2016-01-16,0:45:28,0:29:03,1:01:34,0:13:40,0:12:05,0:18:56,3:34:18,1341,0,16
+General Medicine,PGY1,S0181087,2016-01-31,1:24:19,1:16:05,0:49:32,1:17:24,0:18:53,0:23:59,5:55:07,2295,0,6
+Emergency Medicine,PGY2+,S0169403,2016-01-10,1:15:07,1:05:41,2:34:55,0:27:43,0:26:39,0:44:07,7:24:10,2790,91,21
+Emergency Medicine,PGY2+,S0169403,2016-01-13,1:49:04,0:41:32,1:41:00,0:29:50,0:22:55,0:23:09,5:55:42,2234,17,23
+Emergency Medicine,PGY2+,S0169403,2016-01-12,0:00:00,0:16:46,0:00:00,0:00:00,0:00:00,0:01:03,0:18:20,102,102,7
+General Medicine,PGY1,S0181070,2015-08-28,2:10:26,0:09:03,2:30:18,0:19:19,0:17:22,0:22:41,6:14:47,1927,0,9
+General Medicine,PGY1,S0181070,2015-08-29,0:54:43,0:01:40,0:42:05,0:12:41,0:03:34,0:03:05,2:14:02,552,0,4
+General Medicine,PGY1,S0181057,2016-05-30,3:20:04,1:42:20,0:00:26,0:42:59,0:19:25,0:11:31,6:45:35,2110,0,13
+General Medicine,PGY1,S0181057,2016-05-31,1:19:37,1:12:44,0:09:01,0:46:57,0:12:44,0:04:17,4:20:45,1477,122,13
+Intensive Care Unit,PGY2+,S0174975,2016-09-28,1:29:00,0:32:33,0:07:30,0:33:53,0:23:05,0:01:03,3:41:37,1232,142,16
+General Medicine,PGY2+,S0169404,2016-04-30,0:25:21,0:09:56,0:00:00,0:00:21,0:00:01,0:02:35,0:40:51,418,409,12
+General Medicine,PGY1,S0181070,2015-08-20,2:31:35,0:17:29,3:03:52,0:37:25,0:11:55,0:20:29,7:26:18,1852,0,9
+General Medicine,PGY1,S0181070,2015-08-22,1:44:42,0:04:09,1:38:53,0:29:31,0:08:29,0:11:02,4:35:56,1430,0,10
+General Medicine,PGY1,S0181070,2015-08-23,2:53:57,0:34:46,3:44:31,0:42:40,0:11:33,0:28:27,8:58:29,1819,0,16
+General Medicine,PGY1,S0181070,2015-08-24,1:33:06,0:03:55,1:14:16,0:27:21,0:03:05,0:04:51,3:43:29,1196,0,8
+General Medicine,PGY1,S0181070,2015-08-25,2:12:35,0:16:18,0:55:24,0:30:13,0:25:12,0:16:17,4:58:11,1761,0,9
+General Medicine,PGY1,S0181070,2015-08-26,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+General Medicine,PGY1,S0181070,2015-08-27,2:27:30,0:00:51,0:34:50,0:04:29,0:01:53,0:03:20,3:39:22,1046,0,29
+General Medicine,PGY1,S0169402,2014-11-09,1:47:04,0:00:40,1:07:43,0:12:42,0:08:21,0:01:12,3:36:16,796,0,3
+Night Team,PGY2+,S0163425,2015-12-28,0:36:06,0:46:29,0:39:56,0:40:09,0:08:13,0:22:34,3:24:21,1369,104,42
+Night Team,PGY2+,S0163425,2015-12-25,2:11:11,1:30:24,1:05:31,0:40:22,0:22:40,0:33:51,6:44:32,2492,0,60
+Night Team,PGY2+,S0163425,2015-12-24,1:41:33,1:46:49,1:57:13,0:47:10,0:14:06,0:43:31,7:44:48,2819,32,44
+Night Team,PGY2+,S0163425,2015-12-27,0:27:21,0:33:04,1:15:16,0:09:42,0:08:13,0:18:06,3:13:07,1036,0,28
+Night Team,PGY2+,S0163425,2015-12-26,0:30:42,0:18:44,0:23:17,0:07:50,0:04:17,0:09:52,1:43:15,763,16,50
+Night Team,PGY2+,S0163425,2015-12-21,1:36:32,1:02:06,1:35:15,0:39:49,0:13:45,0:23:05,5:55:57,2208,30,82
+Night Team,PGY2+,S0163425,2015-12-20,2:08:59,1:28:46,2:16:41,0:50:02,0:15:53,0:39:18,8:25:32,3166,23,50
+Night Team,PGY2+,S0163425,2015-12-23,0:41:56,0:34:24,1:02:04,0:28:22,0:06:25,0:15:15,3:21:46,917,9,11
+Night Team,PGY2+,S0163425,2015-12-22,0:57:37,1:28:30,1:04:13,0:31:08,0:10:44,0:24:40,5:05:06,1789,230,26
+Intensive Care Unit,PGY2+,S0174975,2016-09-29,2:42:17,0:55:26,0:03:18,0:57:29,0:34:37,0:00:33,5:42:02,1898,0,18
+General Medicine,PGY1,S0169401,2015-03-10,4:36:55,0:47:59,0:50:02,0:57:47,0:33:18,0:16:18,8:26:21,3267,476,14
+General Medicine,PGY1,S0195203,2016-07-18,1:45:10,0:43:55,0:00:18,0:17:05,0:23:16,0:07:41,3:59:38,1400,468,10
+General Medicine,PGY1,S0169401,2015-03-11,3:10:07,0:35:08,0:53:45,0:50:42,0:24:42,0:17:47,6:22:36,1883,300,14
+General Medicine,PGY1,S0195214,2016-07-30,1:46:56,0:43:55,0:01:49,0:47:13,0:08:55,0:00:25,3:54:01,1010,49,11
+General Medicine,PGY1,S0195214,2016-07-31,2:21:38,1:09:40,0:00:32,0:46:06,0:20:24,0:00:01,4:57:12,1270,0,11
+General Medicine,PGY1,S0181065,2015-11-15,1:11:27,0:19:55,0:19:47,0:35:42,0:10:52,0:19:11,3:10:14,1430,17,3
+General Medicine,PGY1,S0181065,2015-11-14,1:50:14,1:03:56,0:50:17,0:34:20,0:21:12,0:23:14,5:45:15,2956,0,6
+General Medicine,PGY1,S0181065,2015-11-17,1:36:38,0:58:25,2:04:19,0:57:24,0:28:28,0:25:59,7:46:01,3135,15,10
+General Medicine,PGY1,S0181065,2015-11-16,3:24:02,1:01:56,0:52:57,0:57:08,0:32:58,0:22:45,7:52:59,3773,286,17
+General Medicine,PGY1,S0181065,2015-11-11,2:24:42,1:00:58,1:50:33,0:49:06,0:26:20,0:25:04,7:46:30,2798,0,11
+General Medicine,PGY1,S0181065,2015-11-10,2:05:56,0:34:07,2:20:17,0:51:06,0:29:32,0:19:59,7:17:30,2542,435,11
+General Medicine,PGY1,S0181065,2015-11-13,0:11:02,0:06:42,0:12:35,0:00:00,0:00:06,0:00:14,0:35:37,247,245,7
+General Medicine,PGY1,S0181065,2015-11-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+General Medicine,PGY1,S0181065,2015-11-19,3:05:21,0:44:17,0:46:27,0:55:47,0:47:01,0:36:50,7:30:42,3560,23,12
+General Medicine,PGY1,S0181065,2015-11-18,0:03:37,0:00:00,0:02:25,0:00:00,0:00:01,0:03:44,0:19:28,100,96,4
+Intensive Care Unit,PGY2+,S0169403,2015-11-30,1:29:08,0:29:59,0:10:25,0:27:20,0:19:50,0:40:47,4:03:04,1686,314,12
+General Medicine,PGY1,S0181060,2015-08-07,2:20:04,0:40:24,2:54:36,0:18:38,0:24:09,0:17:57,7:49:20,2286,20,12
+General Medicine,PGY2+,S0106952,2015-09-20,2:31:30,0:40:49,0:37:29,0:05:27,0:24:07,0:49:20,6:57:36,2096,56,30
+General Medicine,PGY1,S0195202,2016-07-22,2:37:13,1:43:04,0:03:13,1:18:09,0:15:30,0:03:23,6:27:50,1673,0,8
+General Medicine,PGY1,S0195202,2016-07-23,0:00:00,0:00:00,0:00:00,0:01:25,0:00:00,0:00:00,0:01:25,10,0,3
+General Medicine,PGY1,S0195202,2016-07-20,2:28:43,1:34:18,0:00:41,0:37:30,0:13:59,0:07:35,5:47:00,1996,199,12
+General Medicine,PGY1,S0195202,2016-07-21,2:09:15,1:14:57,0:02:49,0:34:04,0:20:07,0:02:55,4:55:10,1761,334,7
+General Medicine,PGY1,S0079337,2014-06-26,2:09:41,0:05:25,0:21:21,0:26:55,0:17:05,0:15:56,4:07:21,1111,33,9
+General Medicine,PGY1,S0181060,2015-08-06,0:00:00,0:00:00,0:00:00,0:01:34,0:00:00,0:00:00,0:01:34,6,0,2
+General Medicine,PGY1,S0079337,2014-06-29,3:54:13,0:01:21,0:23:34,0:16:08,0:11:07,0:15:07,5:24:09,1307,0,36
+General Medicine,PGY1,S0079337,2014-06-28,0:04:13,0:00:00,0:01:22,0:00:00,0:00:29,0:00:00,0:06:33,103,0,7
+General Medicine,PGY2+,S0106952,2015-09-29,0:18:05,0:09:13,0:00:18,0:00:16,0:00:00,0:00:22,0:35:40,400,396,31
+General Medicine,PGY1,S0169384,2015-02-19,2:27:28,0:15:00,1:33:53,0:25:17,0:17:34,0:22:54,5:41:58,1397,508,8
+General Medicine,PGY1,S0169384,2015-02-18,3:27:50,0:12:10,1:22:40,0:47:56,0:33:56,0:26:09,7:08:45,2068,271,11
+General Medicine,PGY1,S0169384,2015-02-17,1:06:53,0:12:22,2:52:11,0:06:34,0:05:20,0:06:28,4:39:49,670,651,9
+General Medicine,PGY1,S0169384,2015-02-16,3:48:51,0:25:52,1:32:58,1:34:26,0:26:59,0:15:42,8:24:12,2185,57,7
+General Medicine,PGY1,S0169384,2015-02-15,0:17:57,0:01:30,0:45:45,0:00:03,0:00:07,0:00:15,1:09:59,206,206,4
+Night Team,PGY1,S0169386,2015-03-20,2:25:25,0:46:53,1:23:01,0:44:28,0:17:50,1:05:23,7:10:09,2707,33,59
+General Medicine,PGY1,S0169384,2015-02-13,2:51:01,0:09:10,2:36:28,1:22:44,0:20:24,0:16:10,7:57:46,2015,164,9
+General Medicine,PGY1,S0169384,2015-02-12,2:56:15,0:11:54,1:32:09,1:05:05,0:17:50,0:23:09,6:46:59,2062,135,9
+General Medicine,PGY1,S0169384,2015-02-11,3:27:22,0:20:18,1:47:13,0:58:49,0:33:15,0:31:02,7:52:47,1996,123,8
+General Medicine,PGY1,S0169384,2015-02-10,3:13:45,0:08:05,1:32:58,0:49:10,0:13:57,0:17:57,6:50:25,2125,427,11
+General Medicine,PGY1,S0181060,2015-08-05,2:45:50,0:18:10,1:20:36,1:29:26,0:27:15,0:44:05,8:00:17,2062,35,10
+General Medicine,PGY1,S0169404,2014-07-30,4:10:37,0:17:39,0:27:53,0:33:53,0:22:18,0:02:40,6:21:27,2848,41,17
+General Medicine,PGY1,S0169404,2014-07-31,4:55:20,0:48:16,0:11:14,0:51:11,0:13:47,0:04:29,7:36:31,2742,24,15
+General Medicine,PGY1,S0181060,2015-08-04,0:59:35,0:25:13,0:37:50,0:11:40,0:09:52,0:16:21,3:09:01,715,0,10
+General Medicine,PGY2+,S0169400,2015-09-15,3:35:31,0:36:14,0:35:22,0:55:45,0:19:45,0:46:37,7:15:11,3288,90,20
+General Medicine,PGY1,S0181063,2016-04-22,0:29:12,0:46:52,0:00:20,0:08:19,0:03:04,0:04:01,1:41:01,680,0,5
+General Medicine,PGY1,S0181063,2016-04-21,1:16:35,1:15:02,0:00:14,0:36:17,0:15:12,0:07:18,3:52:49,1374,0,13
+General Medicine,PGY2+,S0169400,2015-09-14,0:00:00,0:16:10,0:00:00,0:00:02,0:00:00,0:00:40,0:18:06,185,177,9
+General Medicine,PGY1,S0181088,2015-08-09,0:57:56,0:02:02,2:35:53,0:12:12,0:07:57,0:23:36,4:50:04,1107,37,7
+General Medicine,PGY1,S0181088,2015-08-08,1:19:58,0:02:50,1:16:56,0:32:01,0:20:24,0:21:51,4:21:37,1208,79,6
+General Medicine,PGY1,S0181088,2015-08-01,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+General Medicine,PGY1,S0181088,2015-08-03,1:17:51,0:03:29,0:50:10,0:12:09,0:14:51,0:15:07,3:26:08,1132,39,6
+General Medicine,PGY1,S0181088,2015-08-02,2:04:04,0:00:00,2:24:08,0:37:20,0:29:04,0:08:20,6:17:55,1871,0,10
+General Medicine,PGY1,S0181088,2015-08-05,1:53:06,0:00:51,2:20:14,0:54:00,0:12:17,0:21:28,6:28:20,1710,7,8
+General Medicine,PGY1,S0181088,2015-08-04,1:20:54,0:00:54,1:16:23,0:38:59,0:14:46,0:19:35,4:37:46,1233,11,10
+General Medicine,PGY1,S0181088,2015-08-07,1:49:36,0:02:49,2:50:38,0:53:09,0:17:18,0:18:40,7:14:37,1704,0,9
+General Medicine,PGY1,S0181088,2015-08-06,0:20:41,0:00:00,0:00:15,0:01:19,0:00:00,0:02:20,0:26:14,227,219,6
+General Medicine,PGY1,S0180927,2016-08-18,0:05:31,0:00:39,0:00:00,0:03:08,0:00:00,0:01:24,0:11:24,88,0,6
+General Medicine,PGY1,S0180927,2016-08-19,2:28:28,1:36:37,0:00:21,0:36:27,0:19:06,0:07:22,5:50:07,1829,0,12
+General Medicine,PGY1,S0180927,2016-08-10,1:13:29,0:44:03,0:00:22,0:21:16,0:04:14,0:02:37,2:48:27,859,0,10
+General Medicine,PGY1,S0180927,2016-08-11,3:26:02,2:12:15,0:00:10,0:36:04,0:14:55,0:11:31,7:14:42,2242,109,21
+General Medicine,PGY1,S0180927,2016-08-12,1:53:04,1:04:20,0:00:04,0:26:19,0:15:54,0:02:49,4:09:07,1548,56,15
+General Medicine,PGY1,S0180927,2016-08-13,1:48:41,1:04:33,0:00:05,0:14:33,0:10:25,0:08:22,4:01:08,1177,12,10
+General Medicine,PGY1,S0180927,2016-08-14,2:51:15,1:41:20,0:00:17,0:25:28,0:07:23,0:04:50,5:37:12,1852,75,18
+General Medicine,PGY1,S0180927,2016-08-15,0:49:33,0:26:12,0:00:06,0:12:44,0:03:20,0:05:32,1:51:51,585,0,8
+General Medicine,PGY1,S0180927,2016-08-16,3:30:33,1:38:33,0:00:09,0:56:46,0:20:22,0:03:21,7:13:14,2122,61,17
+General Medicine,PGY1,S0180927,2016-08-17,1:54:06,1:16:18,0:00:11,0:24:16,0:11:55,0:02:17,4:15:53,1464,13,11
+General Medicine,PGY1,S0093383,2014-08-31,3:20:15,0:15:28,0:16:10,0:20:46,0:32:57,0:33:57,5:29:34,1503,144,10
+Intensive Care Unit,PGY2+,S0169400,2016-06-28,1:05:01,0:11:35,0:03:42,0:05:42,0:05:34,0:01:01,1:44:11,531,60,15
+Intensive Care Unit,PGY2+,S0169400,2016-06-29,3:32:22,0:44:46,0:06:44,1:02:29,0:28:43,0:11:49,6:38:52,2231,14,20
+General Medicine,PGY1,S0093383,2014-08-30,5:15:11,0:18:43,0:22:42,0:28:26,0:35:30,0:27:49,7:51:14,1959,0,11
+Intensive Care Unit,PGY2+,S0169400,2016-06-26,1:40:55,0:50:29,0:04:25,0:25:27,0:21:59,0:09:32,3:56:15,1014,32,11
+Intensive Care Unit,PGY2+,S0169400,2016-06-27,2:04:44,0:31:25,0:08:38,0:16:07,0:32:39,0:03:02,3:58:28,1231,63,15
+General Medicine,PGY1,S0195205,2016-08-28,0:18:29,0:15:17,0:00:00,0:01:31,0:00:42,0:00:03,0:37:04,209,191,8
+General Medicine,PGY1,S0163662,2015-06-02,0:04:20,0:07:07,0:00:00,0:00:24,0:01:31,0:00:07,0:13:34,113,95,5
+General Medicine,PGY2+,S0169390,2015-11-27,3:17:59,1:22:38,0:13:07,0:30:15,0:31:28,0:52:21,7:42:32,3657,105,22
+General Medicine,PGY2+,S0169390,2015-11-26,1:29:20,0:34:16,0:08:20,0:35:43,0:24:30,0:33:23,4:05:12,1182,38,13
+General Medicine,PGY2+,S0169390,2015-11-25,2:19:10,1:39:13,0:03:41,0:38:05,0:30:33,0:20:58,6:12:26,2605,98,23
+General Medicine,PGY2+,S0169390,2015-11-24,1:37:26,0:53:49,1:13:13,0:17:31,0:12:31,0:25:06,4:58:55,1414,56,11
+General Medicine,PGY2+,S0169390,2015-11-23,1:06:35,0:32:02,0:00:00,0:12:14,0:11:44,0:04:21,2:13:15,791,56,15
+General Medicine,PGY2+,S0169390,2015-11-22,2:31:02,1:24:40,0:22:20,0:44:48,0:17:52,0:33:17,6:33:55,2849,190,34
+General Medicine,PGY2+,S0169390,2015-11-21,1:52:01,1:11:42,1:00:49,0:32:02,0:34:01,0:18:04,5:52:15,1860,0,16
+General Medicine,PGY2+,S0169390,2015-11-20,2:21:50,1:37:25,0:12:11,0:21:44,0:39:41,0:35:25,6:10:59,2459,47,19
+General Medicine,PGY2+,S0169390,2015-11-29,0:00:00,0:09:11,0:00:00,0:02:00,0:00:00,0:00:24,0:11:50,73,59,7
+General Medicine,PGY2+,S0169390,2015-11-28,2:33:15,0:56:58,0:48:03,0:44:36,0:25:49,0:40:49,6:51:10,2649,22,14
+Night Team,PGY2+,S0074196,2016-07-15,1:04:33,0:42:48,0:00:16,0:35:23,0:05:17,0:05:40,2:46:39,893,0,17
+Night Team,PGY2+,S0074196,2016-07-14,3:11:08,0:55:16,0:00:04,0:26:44,0:07:57,0:07:34,5:30:21,1445,433,25
+Night Team,PGY2+,S0074196,2016-07-16,0:00:00,0:00:00,0:00:00,0:00:48,0:00:00,0:00:00,0:00:48,16,0,5
+Night Team,PGY2+,S0074196,2016-07-11,0:56:01,0:18:57,0:00:05,0:32:14,0:04:55,0:07:24,2:24:24,701,65,13
+Night Team,PGY2+,S0074196,2016-07-10,0:18:48,0:21:10,0:00:01,0:17:28,0:05:57,0:00:06,1:08:30,383,0,10
+Night Team,PGY2+,S0074196,2016-07-13,1:22:46,0:51:45,0:00:02,0:09:45,0:06:33,0:00:38,2:56:44,1253,0,24
+Night Team,PGY2+,S0074196,2016-07-12,1:29:52,0:47:41,0:00:07,0:50:04,0:14:28,0:00:08,3:48:27,1020,0,23
+General Medicine,PGY1,S0195205,2016-08-22,2:21:23,1:55:56,0:00:17,0:47:36,0:51:58,0:07:18,6:28:58,1674,75,15
+General Medicine,PGY1,S0195205,2016-08-23,2:29:40,2:19:24,0:01:14,0:36:03,0:47:56,0:05:37,6:48:40,2276,619,9
+General Medicine,PGY1,S0169398,2014-09-09,1:22:50,0:10:24,0:06:55,0:01:59,0:04:27,0:11:59,2:01:43,484,470,7
+General Medicine,PGY1,S0169398,2014-09-08,3:34:39,0:07:05,0:25:02,0:44:04,0:38:31,0:31:14,6:40:02,2619,2,14
+General Medicine,PGY1,S0169398,2014-09-05,2:50:26,0:18:17,0:11:31,0:30:29,0:22:33,0:02:47,4:36:46,1255,43,9
+General Medicine,PGY1,S0169398,2014-09-04,3:32:50,0:02:52,0:14:20,0:21:44,0:27:53,0:18:23,5:20:17,1649,231,9
+General Medicine,PGY1,S0181073,2016-01-30,0:00:00,0:08:07,0:00:00,0:00:08,0:00:00,0:02:03,0:11:50,141,127,7
+General Medicine,PGY1,S0169398,2014-09-06,3:42:23,0:08:23,0:20:46,0:29:24,0:36:50,0:29:44,6:11:49,2195,68,12
+General Medicine,PGY1,S0169398,2014-09-01,3:53:47,0:13:40,0:28:02,0:32:36,0:35:30,0:33:22,6:38:54,2236,65,16
+General Medicine,PGY1,S0169398,2014-09-03,3:24:18,0:15:12,0:30:51,0:31:30,0:26:27,0:20:38,5:43:05,1806,0,16
+General Medicine,PGY1,S0169398,2014-09-02,3:05:42,0:03:47,0:27:35,0:38:19,0:23:52,0:12:44,5:15:17,1677,73,14
+General Medicine,PGY1,S0181056,2015-11-26,1:08:52,0:33:21,1:07:16,0:38:09,0:12:24,0:20:23,4:27:24,1327,148,3
+General Medicine,PGY1,S0181056,2015-11-27,2:48:29,1:49:33,1:10:36,0:57:50,0:43:32,1:01:12,9:06:29,3163,46,12
+General Medicine,PGY1,S0181056,2015-11-24,2:17:07,1:01:57,0:32:19,0:48:31,0:10:19,0:36:48,6:02:58,2101,369,11
+General Medicine,PGY1,S0181056,2015-11-25,1:56:42,2:08:58,1:57:41,0:55:55,0:12:34,0:37:12,8:52:13,2559,0,12
+General Medicine,PGY1,S0181056,2015-11-22,2:02:49,1:13:24,2:09:47,0:59:15,0:25:21,0:39:12,8:10:51,2244,0,18
+General Medicine,PGY1,S0181056,2015-11-23,1:48:24,1:20:35,1:51:19,0:59:13,0:19:35,0:38:03,7:41:37,2236,0,9
+General Medicine,PGY1,S0181056,2015-11-20,2:44:23,1:37:02,1:41:22,1:13:17,0:33:27,0:23:14,9:06:53,2786,11,11
+General Medicine,PGY1,S0181056,2015-11-21,0:00:00,0:00:07,0:00:00,0:00:03,0:00:00,0:00:00,0:00:13,20,11,3
+Intensive Care Unit,PGY2+,S0174975,2016-09-24,0:18:22,0:00:29,0:00:00,0:00:36,0:00:00,0:00:14,0:20:24,74,0,9
+General Medicine,PGY1,S0181056,2015-11-29,0:56:35,0:33:16,0:14:33,0:17:34,0:05:56,0:19:01,2:47:33,1085,96,5
+General Medicine,PGY1,S0106952,2014-09-22,3:46:30,0:06:56,0:18:49,0:22:58,0:23:07,0:23:35,5:45:56,1439,272,20
+General Medicine,PGY1,S0106952,2014-09-23,4:44:03,0:17:24,0:19:52,0:54:17,0:32:19,0:41:22,8:18:09,2134,350,25
+General Medicine,PGY1,S0106952,2014-09-20,3:38:25,0:07:40,0:14:39,0:31:08,0:44:16,0:27:02,6:05:12,1854,52,23
+General Medicine,PGY1,S0106952,2014-09-21,3:49:20,0:10:51,0:26:40,0:44:18,0:44:36,0:26:58,6:51:13,1555,343,16
+General Medicine,PGY1,S0106952,2014-09-26,4:12:31,0:08:00,0:28:41,0:20:36,0:15:54,0:24:40,6:31:02,2346,278,32
+General Medicine,PGY1,S0106952,2014-09-27,1:06:30,0:00:41,0:04:30,0:04:06,0:01:14,0:01:06,1:22:27,462,454,37
+General Medicine,PGY1,S0106952,2014-09-24,2:16:27,0:12:22,0:14:58,0:20:35,0:26:20,0:18:21,4:03:25,1185,85,15
+General Medicine,PGY1,S0106952,2014-09-25,4:20:30,0:05:42,0:20:11,1:10:30,0:45:05,0:35:22,7:54:34,2270,185,21
+General Medicine,PGY1,S0106952,2014-09-28,3:35:12,0:07:20,0:13:51,0:45:43,0:20:39,0:14:53,5:41:46,1642,136,16
+General Medicine,PGY1,S0106952,2014-09-29,3:09:35,0:08:53,0:27:28,0:19:32,0:21:10,0:14:56,5:02:01,1789,13,16
+General Medicine,PGY1,S0163425,2015-03-12,3:38:55,0:07:13,1:39:19,0:32:20,0:18:41,0:23:33,7:18:04,2219,18,13
+General Medicine,PGY1,S0163425,2015-03-13,3:04:29,0:03:03,0:49:26,0:53:10,0:27:08,0:27:09,6:05:34,2094,29,19
+General Medicine,PGY1,S0163425,2015-03-10,1:55:36,0:08:41,2:02:46,0:07:50,0:11:31,0:15:03,5:05:26,1036,0,19
+General Medicine,PGY1,S0163425,2015-03-11,2:59:46,0:46:48,1:00:59,0:43:51,0:34:17,0:41:36,7:23:10,1923,12,12
+General Medicine,PGY1,S0163425,2015-03-16,3:20:41,0:16:43,0:55:09,0:38:05,0:36:01,0:25:13,6:22:00,1748,0,10
+General Medicine,PGY1,S0163425,2015-03-17,0:24:17,0:05:39,0:00:12,0:00:27,0:00:56,0:01:05,0:33:05,319,309,10
+General Medicine,PGY1,S0163425,2015-03-14,2:58:18,0:11:07,0:47:44,0:49:17,0:26:48,0:17:15,6:03:25,1820,0,17
+General Medicine,PGY1,S0163425,2015-03-15,2:18:37,0:06:06,1:05:28,0:23:06,0:13:34,0:19:46,4:46:16,1260,0,12
+General Medicine,PGY1,S0163425,2015-03-18,3:01:56,0:14:28,0:37:31,0:45:36,0:23:30,0:21:20,5:54:49,1967,0,19
+General Medicine,PGY1,S0163425,2015-03-19,2:48:27,0:11:20,0:49:35,0:31:25,0:21:22,0:17:41,5:19:57,1825,0,30
+Emergency Medicine,PGY2+,S0109179,2015-11-08,1:25:07,0:14:18,0:40:22,0:05:24,0:04:14,0:02:30,2:52:37,1010,0,26
+Emergency Medicine,PGY2+,S0109179,2015-11-09,1:08:49,0:15:56,0:23:36,0:00:15,0:04:55,0:01:18,2:05:38,480,0,10
+General Medicine,PGY1,S0169403,2015-04-19,1:58:13,1:08:28,0:18:49,0:19:45,0:16:09,0:14:55,4:29:04,2026,31,64
+General Medicine,PGY1,S0169403,2015-04-18,0:00:00,0:00:00,0:00:00,0:00:27,0:00:00,0:00:00,0:00:27,8,0,2
+General Medicine,PGY1,S0169403,2015-04-11,0:39:02,0:15:11,0:22:39,0:17:20,0:08:26,0:16:28,2:08:02,740,0,5
+General Medicine,PGY1,S0169403,2015-04-10,1:37:55,0:36:14,1:14:09,0:27:03,0:15:03,0:26:14,4:51:01,1456,0,34
+General Medicine,PGY1,S0169403,2015-04-13,0:11:17,0:13:55,0:00:05,0:03:16,0:01:18,0:01:56,0:35:55,236,218,6
+General Medicine,PGY1,S0169403,2015-04-12,2:34:16,0:48:46,1:49:53,0:38:24,0:38:49,0:33:38,7:40:39,2080,0,20
+General Medicine,PGY1,S0169403,2015-04-15,1:41:59,0:20:30,1:14:32,0:18:24,0:20:42,0:07:32,4:15:20,1525,45,10
+General Medicine,PGY1,S0169403,2015-04-14,2:08:28,1:03:05,1:20:57,0:56:03,0:44:57,0:30:16,7:06:57,2683,1,13
+General Medicine,PGY1,S0169403,2015-04-17,2:16:30,1:01:00,1:04:56,0:51:11,0:28:19,0:41:33,7:03:29,1927,14,7
+General Medicine,PGY1,S0169403,2015-04-16,0:45:02,0:09:16,0:42:09,0:16:04,0:07:23,0:06:30,2:19:25,707,75,6
+Emergency Medicine,PGY1,S0108287,2015-03-28,1:58:12,0:16:13,1:19:33,0:05:27,0:12:32,0:10:39,4:22:03,1027,88,7
+Intensive Care Unit,PGY2+,S0181086,2016-07-13,3:29:06,0:49:11,0:00:02,0:44:28,0:57:34,0:04:27,6:29:35,1739,0,21
+Intensive Care Unit,PGY2+,S0181086,2016-07-10,2:45:35,1:38:11,0:14:35,0:23:06,0:45:50,0:12:37,7:10:42,1822,38,17
+Intensive Care Unit,PGY2+,S0181086,2016-07-11,2:23:29,1:01:01,0:07:30,0:34:05,0:24:59,0:05:16,5:50:37,1768,92,24
+Intensive Care Unit,PGY2+,S0181086,2016-07-16,0:06:57,0:08:14,0:00:00,0:00:39,0:00:02,0:00:06,0:24:26,308,290,11
+Intensive Care Unit,PGY2+,S0181086,2016-07-14,2:40:30,2:01:14,0:27:11,0:26:37,0:30:50,0:00:44,7:08:56,1953,236,13
+Intensive Care Unit,PGY2+,S0181086,2016-07-15,0:08:16,0:28:24,0:00:00,0:01:36,0:00:38,0:00:06,0:40:12,292,260,14
+Emergency Medicine,PGY1,S0108287,2015-03-20,3:28:58,0:56:27,1:00:16,0:30:41,0:31:54,0:05:50,7:08:55,1463,0,25
+Emergency Medicine,PGY1,S0108287,2015-03-21,2:33:58,0:48:44,0:48:09,0:11:11,0:10:12,0:04:17,4:52:42,911,0,21
+Emergency Medicine,PGY1,S0108287,2015-03-22,5:29:59,1:04:17,1:28:01,0:20:24,0:15:25,0:09:07,9:12:30,1534,0,27
+Emergency Medicine,PGY1,S0108287,2015-03-23,3:52:33,0:08:49,1:34:02,0:34:31,0:31:08,0:10:24,7:12:40,1432,0,23
+Emergency Medicine,PGY1,S0108287,2015-03-24,1:31:20,0:00:11,0:59:38,0:06:22,0:11:53,0:00:01,3:08:44,654,0,7
+Emergency Medicine,PGY1,S0108287,2015-03-25,0:55:35,0:22:33,0:32:56,0:09:17,0:03:06,0:00:04,2:21:03,682,28,11
+Emergency Medicine,PGY1,S0108287,2015-03-26,5:31:16,1:27:23,1:45:18,0:18:43,0:17:09,0:05:09,10:00:40,2011,0,31
+Emergency Medicine,PGY1,S0108287,2015-03-27,4:03:16,0:57:47,1:17:11,0:18:26,0:17:09,0:07:13,7:26:19,1217,0,18
+General Medicine,PGY1,S0174975,2015-12-29,2:06:59,1:47:03,0:44:46,1:24:05,0:56:06,0:00:43,7:55:54,3268,449,73
+General Medicine,PGY1,S0174975,2015-12-28,2:15:37,1:32:26,1:06:48,0:55:19,0:58:30,0:03:13,7:19:30,2482,115,16
+General Medicine,PGY1,S0174975,2015-12-27,3:25:08,1:52:36,1:31:04,0:46:26,0:47:39,0:08:12,8:57:09,3261,132,40
+General Medicine,PGY1,S0174975,2015-12-26,0:00:00,0:12:41,0:00:00,0:03:18,0:00:00,0:00:05,0:18:23,143,127,10
+General Medicine,PGY1,S0174975,2015-12-25,3:24:02,1:21:47,1:02:50,1:17:29,0:50:24,0:12:31,8:34:23,3177,62,35
+General Medicine,PGY1,S0174975,2015-12-24,1:52:34,1:19:48,0:55:56,2:19:44,0:55:53,0:08:05,7:55:55,3401,420,14
+General Medicine,PGY1,S0174975,2015-12-23,2:41:44,1:20:36,1:05:11,1:14:41,0:36:17,0:14:12,7:56:02,2788,156,31
+General Medicine,PGY1,S0174975,2015-12-22,2:44:49,2:05:04,1:23:20,1:26:38,0:56:35,0:08:04,9:39:52,3695,114,36
+General Medicine,PGY1,S0174975,2015-12-21,0:01:26,0:17:20,0:00:00,0:00:36,0:00:00,0:02:06,0:22:15,174,160,6
+General Medicine,PGY1,S0174975,2015-12-20,2:03:29,2:03:16,1:09:49,1:43:39,0:35:35,0:04:30,8:26:14,2802,201,19
+Night Team,PGY1,S0172645,2015-08-17,0:42:19,0:10:48,0:39:09,0:17:55,0:11:28,0:09:27,2:13:02,501,0,9
+Night Team,PGY1,S0172645,2015-08-16,0:48:26,0:14:42,2:01:15,0:05:25,0:23:55,0:10:22,3:57:10,874,0,9
+Night Team,PGY1,S0172645,2015-08-19,1:42:23,0:20:16,1:46:15,0:15:38,0:30:49,0:33:37,5:27:55,1109,22,28
+Night Team,PGY1,S0172645,2015-08-18,1:29:25,0:20:22,0:35:29,0:16:09,0:22:32,0:22:31,3:33:44,1223,0,19
+Intensive Care Unit,PGY2+,S0169385,2016-02-02,0:00:00,0:04:38,0:00:00,0:00:12,0:00:00,0:00:37,0:07:18,99,64,8
+General Medicine,PGY1,S0169381,2014-08-26,3:24:55,0:12:23,0:19:33,1:05:08,0:47:48,0:15:31,6:15:49,1952,154,8
+General Medicine,PGY1,S0169381,2014-08-27,1:18:06,0:11:01,0:08:49,0:18:32,0:10:01,0:14:51,2:39:06,718,0,8
+General Medicine,PGY1,S0169381,2014-08-24,2:47:32,0:06:53,0:08:43,0:13:08,0:22:33,0:25:42,4:24:28,1146,0,14
+General Medicine,PGY1,S0169381,2014-08-25,0:17:55,0:04:45,0:01:02,0:03:12,0:02:12,0:04:14,0:34:12,200,194,6
+General Medicine,PGY1,S0169381,2014-08-22,1:42:39,0:15:29,0:06:23,0:25:51,0:17:32,0:29:16,3:24:51,983,179,7
+General Medicine,PGY1,S0169381,2014-08-23,1:07:51,0:01:26,0:12:26,0:11:22,0:07:25,0:08:34,2:00:05,412,0,9
+General Medicine,PGY1,S0169381,2014-08-20,2:33:29,0:05:40,0:09:00,0:18:14,0:22:00,0:20:48,4:05:51,914,80,7
+General Medicine,PGY1,S0169381,2014-08-21,2:58:36,0:10:20,0:23:58,0:44:10,0:24:28,0:31:05,5:31:59,1204,130,10
+General Medicine,PGY2+,S0181526,2016-08-12,4:36:37,2:15:51,0:00:48,0:12:27,0:18:36,0:11:23,9:06:46,3006,21,32
+General Medicine,PGY2+,S0181526,2016-08-13,0:02:17,0:00:09,0:00:00,0:00:01,0:00:00,0:00:28,0:03:08,27,0,5
+General Medicine,PGY2+,S0181526,2016-08-10,2:45:05,0:52:00,0:02:34,0:41:43,0:15:16,0:05:39,6:06:35,2322,0,27
+General Medicine,PGY1,S0169381,2014-08-29,2:13:49,0:24:39,0:16:40,0:40:56,0:30:10,0:43:40,5:00:41,1307,45,6
+Night Team,PGY1,S0169411,2014-11-23,3:20:39,0:05:27,0:54:27,0:25:13,0:20:39,0:20:53,5:53:16,1208,16,40
+Night Team,PGY1,S0169411,2014-11-24,2:47:24,0:06:06,1:31:54,0:29:35,0:19:42,0:10:48,5:38:28,1238,0,22
+Night Team,PGY1,S0169411,2014-11-25,2:01:50,0:00:24,0:00:03,0:15:43,0:09:51,0:06:28,2:39:10,755,156,15
+Night Team,PGY1,S0169411,2014-11-26,1:36:31,0:01:00,0:03:33,0:18:42,0:22:07,0:00:05,2:25:52,721,0,16
+Night Team,PGY1,S0169411,2014-11-27,3:23:16,0:01:10,1:22:46,0:46:50,0:18:43,0:07:07,6:21:33,1366,0,31
+General Medicine,PGY2+,S0169398,2016-03-30,3:05:30,1:59:00,0:00:16,0:57:50,0:35:29,0:09:17,7:37:12,2816,729,19
+Night Team,PGY1,S0169411,2014-11-29,3:28:37,0:03:50,0:04:57,0:24:57,0:39:57,0:16:58,5:08:11,1406,0,46
+Emergency Medicine,PGY1,S0172085,2015-08-12,2:39:54,0:16:45,1:41:41,0:29:28,0:44:31,0:40:02,7:21:24,2045,0,36
+Emergency Medicine,PGY1,S0172085,2015-08-11,0:57:54,0:17:37,1:28:31,0:09:21,0:24:10,0:26:59,4:06:53,1141,0,12
+Emergency Medicine,PGY1,S0172085,2015-08-10,1:26:09,0:40:30,0:45:15,0:16:10,0:20:11,0:42:43,4:35:27,1394,0,17
+General Medicine,PGY2+,S0109179,2016-07-31,0:52:33,0:09:26,0:03:12,0:02:08,0:03:39,0:01:13,1:22:50,527,0,15
+General Medicine,PGY2+,S0109179,2016-07-30,3:10:52,1:08:00,0:00:00,0:17:18,0:12:51,0:07:23,5:19:59,2344,0,40
+Intensive Care Unit,PGY2+,S0169385,2016-02-04,2:27:10,1:01:30,0:14:20,0:28:10,0:37:53,1:12:27,6:31:31,3082,41,22
+Night Team,PGY1,S0169403,2014-06-30,3:22:03,0:07:25,0:12:04,0:25:52,0:37:03,0:41:20,5:40:09,1252,0,36
+Night Team,PGY1,S0181070,2015-10-09,0:11:44,0:05:03,0:00:01,0:04:10,0:05:36,0:07:33,0:40:13,238,0,23
+Intensive Care Unit,PGY2+,S0169385,2016-02-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+General Medicine,PGY1,S0093383,2014-12-18,2:37:14,0:07:57,3:19:50,0:18:44,0:19:40,0:21:15,7:18:12,1501,0,11
+General Medicine,PGY1,S0093383,2014-12-19,2:26:46,0:05:53,2:10:06,0:26:14,0:12:14,0:14:39,5:48:11,1365,0,15
+General Medicine,PGY1,S0093383,2014-12-14,2:10:05,0:10:14,1:00:30,0:12:25,0:29:59,0:24:17,4:44:13,1253,0,11
+General Medicine,PGY1,S0093383,2014-12-15,4:10:31,0:22:14,1:32:34,1:13:15,0:40:07,0:38:29,9:10:59,3062,0,14
+General Medicine,PGY1,S0093383,2014-12-16,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,8,0,4
+General Medicine,PGY1,S0093383,2014-12-17,2:13:11,0:03:57,0:46:48,0:26:55,0:06:35,0:15:01,4:05:46,1221,276,13
+General Medicine,PGY1,S0093383,2014-12-10,3:52:29,0:17:22,2:15:59,0:50:04,0:45:58,0:29:44,9:03:35,2598,0,9
+General Medicine,PGY1,S0093383,2014-12-11,0:24:21,0:01:47,0:03:01,0:00:38,0:00:18,0:02:23,0:36:13,307,283,11
+General Medicine,PGY1,S0093383,2014-12-12,3:00:06,0:18:16,2:52:58,0:13:49,0:44:05,0:35:51,8:12:38,2136,32,8
+General Medicine,PGY1,S0093383,2014-12-13,3:46:24,0:14:41,3:20:12,0:35:09,0:27:12,0:24:23,9:12:16,2498,0,12
+Night Team,PGY2+,S0079337,2015-10-03,2:34:00,0:22:58,0:12:17,0:12:54,0:05:47,0:09:40,3:46:13,853,0,17
+Night Team,PGY2+,S0079337,2015-10-02,4:28:54,0:51:58,1:38:47,0:33:01,0:16:50,0:09:33,8:25:23,1592,24,25
+Night Team,PGY2+,S0079337,2015-10-01,2:20:12,1:04:43,1:18:33,0:30:01,0:15:07,0:26:38,6:21:20,2586,81,25
+Intensive Care Unit,PGY2+,S0181078,2016-08-16,0:37:33,0:07:52,0:00:00,0:00:06,0:00:24,0:00:03,0:54:28,390,380,24
+Intensive Care Unit,PGY2+,S0181078,2016-08-17,2:16:38,0:43:24,0:18:20,0:07:06,0:16:32,0:03:05,4:16:49,1114,0,13
+Intensive Care Unit,PGY2+,S0181078,2016-08-14,1:55:01,0:10:02,0:00:08,0:30:05,0:22:18,0:12:47,3:37:00,897,58,9
+Intensive Care Unit,PGY2+,S0181078,2016-08-15,2:37:49,0:57:30,0:17:33,0:15:29,0:27:01,0:03:10,4:59:55,1152,58,16
+Intensive Care Unit,PGY2+,S0181078,2016-08-18,1:45:14,0:41:10,0:00:04,0:17:04,0:13:14,0:00:05,3:11:50,805,0,15
+Intensive Care Unit,PGY2+,S0181078,2016-08-19,1:44:05,0:16:05,0:09:04,0:42:31,0:15:39,0:02:45,3:40:19,934,0,11
+General Medicine,PGY2+,S0181081,2016-10-31,1:36:00,0:42:05,0:00:53,0:23:47,0:11:21,0:04:45,3:30:34,1459,0,14
+General Medicine,PGY2+,S0181081,2016-10-30,2:39:24,1:55:10,0:00:01,0:12:36,0:04:21,0:17:18,5:53:58,2462,0,20
+General Medicine,PGY1,S0108287,2014-07-31,3:46:37,0:20:41,0:22:23,0:40:28,0:32:30,0:55:16,7:09:10,1627,0,18
+Night Team,PGY2+,S0106952,2016-07-17,2:44:55,3:03:47,0:00:17,0:20:41,0:45:47,0:17:32,8:01:13,3585,230,74
+Night Team,PGY2+,S0106952,2016-07-19,2:43:09,1:31:56,0:00:20,0:25:43,0:09:25,0:09:06,5:30:34,1745,233,51
+Night Team,PGY2+,S0106952,2016-07-18,3:08:46,2:03:42,0:00:37,0:46:27,0:22:39,0:06:21,7:08:27,2724,0,72
+General Medicine,PGY1,S0195213,2016-11-07,1:06:12,0:44:10,0:07:35,0:19:06,0:08:13,0:10:17,3:13:11,922,0,14
+Intensive Care Unit,PGY2+,S0169411,2016-01-11,2:48:08,0:37:50,0:05:39,0:15:36,0:23:34,0:20:30,4:41:34,1707,114,19
+General Medicine,PGY1,S0181078,2015-09-12,0:53:02,0:06:38,0:31:25,0:36:57,0:05:07,0:04:22,2:35:57,826,541,14
+General Medicine,PGY1,S0181078,2015-09-10,0:15:51,0:16:35,0:26:56,0:00:00,0:00:11,0:00:52,1:01:29,160,158,8
+General Medicine,PGY1,S0181078,2015-09-11,2:51:54,0:35:20,1:20:32,0:51:57,0:35:38,0:25:23,7:49:56,2355,415,14
+General Medicine,PGY1,S0195225,2016-10-17,1:02:33,0:16:19,0:00:03,0:23:02,0:06:29,0:00:26,2:09:57,570,0,5
+Night Team,PGY1,S0169386,2015-03-16,2:47:20,0:43:46,1:28:18,0:53:52,0:30:32,1:03:34,7:41:58,2205,34,45
+Night Team,PGY1,S0169386,2015-03-17,2:50:22,0:43:04,0:54:23,0:37:05,0:32:40,1:38:27,7:42:01,2146,99,37
+Night Team,PGY1,S0169386,2015-03-15,1:24:19,0:04:20,0:01:30,0:11:26,0:10:09,0:54:16,2:50:13,747,0,33
+Night Team,PGY1,S0169386,2015-03-18,1:58:50,0:16:21,0:02:21,0:13:53,0:18:00,0:37:08,3:45:20,1541,14,46
+Night Team,PGY1,S0169386,2015-03-19,2:40:46,0:24:08,1:22:06,0:37:19,0:24:29,0:44:55,6:31:59,1974,10,34
+General Medicine,PGY1,S0056755,2014-09-29,3:30:20,0:26:02,0:22:44,0:38:26,0:41:46,0:15:51,6:31:21,1862,147,8
+General Medicine,PGY1,S0056755,2014-09-28,3:08:28,0:09:04,0:05:55,0:16:18,0:13:58,0:15:13,4:17:55,1610,157,27
+General Medicine,PGY1,S0056755,2014-09-27,2:19:53,0:04:08,0:07:01,0:18:56,0:14:50,0:10:50,3:38:57,1087,0,9
+General Medicine,PGY1,S0056755,2014-09-26,4:00:28,0:11:34,0:21:35,0:25:04,0:25:35,0:17:33,6:09:11,1498,0,12
+General Medicine,PGY1,S0056755,2014-09-25,0:39:59,0:00:39,0:04:18,0:00:05,0:00:24,0:00:00,0:48:56,223,215,10
+General Medicine,PGY1,S0056755,2014-09-24,4:04:00,0:14:53,0:18:02,0:39:50,0:39:44,0:08:00,6:26:31,1386,1,8
+General Medicine,PGY1,S0056755,2014-09-23,4:47:24,0:11:26,0:26:31,0:34:21,0:24:05,0:17:43,7:14:44,2019,389,14
+General Medicine,PGY1,S0056755,2014-09-22,4:24:15,0:15:38,0:14:43,0:30:23,0:16:12,0:15:40,6:19:22,1264,332,10
+General Medicine,PGY1,S0056755,2014-09-21,4:21:52,0:08:01,0:13:21,0:59:22,0:44:13,0:12:00,7:12:59,1940,96,11
+General Medicine,PGY1,S0056755,2014-09-20,5:36:16,0:12:56,0:28:19,0:49:34,0:29:17,0:17:06,8:21:23,1993,447,10
+Night Team,PGY1,S0181070,2015-10-03,0:41:16,0:11:06,0:05:09,0:05:20,0:04:15,0:12:23,1:22:20,579,0,30
+General Medicine,PGY1,S0074196,2016-01-29,1:13:40,1:15:35,1:45:23,1:04:20,0:19:46,0:16:21,6:25:47,2637,20,16
+General Medicine,PGY1,S0181066,2015-12-28,0:50:26,1:00:13,2:40:41,0:56:30,0:37:24,0:29:15,7:08:56,1839,104,13
+General Medicine,PGY1,S0181066,2015-12-29,0:58:29,0:24:36,0:43:19,0:19:38,0:30:44,0:28:46,3:58:08,1258,5,9
+General Medicine,PGY1,S0181066,2015-12-24,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181066,2015-12-25,0:50:21,0:22:29,0:47:20,0:09:09,0:05:13,0:25:02,2:56:24,732,0,13
+General Medicine,PGY1,S0181066,2015-12-26,1:47:24,0:42:52,1:25:45,0:36:02,0:27:00,0:38:49,6:17:27,1797,171,15
+General Medicine,PGY1,S0181066,2015-12-27,0:01:06,0:00:27,0:00:00,0:03:56,0:00:00,0:00:43,0:06:15,30,28,3
+General Medicine,PGY1,S0181066,2015-12-20,0:26:23,0:23:47,0:28:51,0:26:22,0:05:00,0:14:50,2:24:13,643,0,4
+General Medicine,PGY1,S0181066,2015-12-21,1:04:39,0:38:54,1:45:39,0:42:51,0:22:49,0:23:55,5:28:38,1471,191,6
+General Medicine,PGY1,S0181066,2015-12-22,0:50:24,0:23:03,1:29:03,0:37:49,0:21:18,0:12:40,4:27:00,1151,13,9
+General Medicine,PGY1,S0181066,2015-12-23,1:24:53,1:07:48,1:34:28,0:53:11,0:34:10,0:29:01,6:35:26,1734,10,17
+Night Team,PGY1,S0181070,2015-10-02,0:31:17,0:31:37,0:14:49,0:18:54,0:09:50,0:27:28,2:29:31,1069,80,32
+Emergency Medicine,PGY2+,S0169400,2015-09-28,2:02:21,0:34:18,1:34:21,0:14:52,0:18:59,0:19:28,5:27:42,1791,70,23
+Emergency Medicine,PGY2+,S0169400,2015-09-29,0:00:08,0:00:13,0:00:00,0:00:00,0:00:00,0:00:43,0:01:06,18,18,2
+Emergency Medicine,PGY2+,S0169400,2015-09-22,0:00:00,0:00:00,0:00:00,0:00:46,0:00:00,0:00:00,0:00:46,4,0,2
+Emergency Medicine,PGY2+,S0169400,2015-09-23,1:06:01,0:26:59,0:24:33,0:07:27,0:06:35,0:05:40,2:23:38,697,91,24
+Emergency Medicine,PGY2+,S0169400,2015-09-20,4:35:35,0:32:49,1:42:56,0:14:53,0:13:35,0:32:41,8:17:00,2687,21,50
+Emergency Medicine,PGY2+,S0169400,2015-09-21,4:14:39,0:59:42,2:38:22,0:28:07,0:32:00,0:28:10,9:47:00,2917,0,40
+Emergency Medicine,PGY2+,S0169400,2015-09-26,4:15:57,0:24:57,2:11:58,0:24:11,0:24:21,0:18:37,8:32:12,2591,0,32
+Emergency Medicine,PGY2+,S0169400,2015-09-27,1:39:25,0:19:37,1:02:33,0:07:21,0:10:24,0:15:04,3:44:36,1316,0,39
+Emergency Medicine,PGY2+,S0169400,2015-09-24,2:52:43,0:14:45,0:53:08,0:13:00,0:06:53,0:26:32,4:53:07,1105,23,25
+Emergency Medicine,PGY2+,S0169400,2015-09-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,2,1
+Intensive Care Unit,PGY2+,S0169384,2015-09-16,1:50:59,0:05:32,0:04:25,0:44:25,1:01:32,0:58:12,5:08:37,1365,0,19
+Intensive Care Unit,PGY2+,S0169384,2015-09-17,3:38:28,0:11:11,0:25:31,0:49:09,0:43:13,0:49:54,7:12:10,1756,21,17
+Intensive Care Unit,PGY2+,S0169384,2015-09-14,3:24:31,0:04:29,1:13:29,0:44:20,0:35:09,0:34:22,7:05:15,1540,103,14
+Intensive Care Unit,PGY2+,S0169384,2015-09-15,0:32:13,0:00:00,0:01:33,0:16:13,0:01:19,0:20:14,1:19:10,235,190,7
+Intensive Care Unit,PGY2+,S0169384,2015-09-12,3:23:25,0:15:55,0:21:44,0:47:10,0:54:06,0:52:57,7:09:38,1907,401,14
+Night Team,PGY2+,S0108287,2016-10-22,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+Night Team,PGY2+,S0108287,2016-10-21,1:22:30,0:43:14,0:00:05,0:20:44,0:06:18,0:00:42,2:38:16,755,0,10
+Night Team,PGY2+,S0108287,2016-10-20,2:03:14,1:20:36,0:00:56,0:32:18,0:10:59,0:02:42,4:25:10,1230,0,15
+Intensive Care Unit,PGY2+,S0169384,2015-09-18,3:13:27,0:10:55,0:25:50,0:47:10,0:29:27,0:43:45,6:22:02,1474,0,18
+Intensive Care Unit,PGY2+,S0169384,2015-09-19,0:12:44,0:00:00,0:25:44,0:00:46,0:00:14,0:00:12,0:42:16,284,258,18
+Night Team,PGY2+,S0109179,2015-07-21,2:37:12,0:22:47,0:25:04,0:16:45,0:10:15,0:21:58,4:27:11,1731,0,50
+Night Team,PGY2+,S0109179,2015-07-20,3:25:16,0:27:27,1:41:09,0:21:30,0:29:30,0:38:27,7:21:42,2512,0,57
+Night Team,PGY2+,S0109179,2015-07-23,2:17:20,0:43:33,3:01:26,0:29:49,0:08:27,0:22:17,7:24:35,1321,0,38
+General Medicine,PGY2+,S0169389,2015-12-06,1:43:58,0:39:51,0:17:18,0:13:03,0:11:19,0:29:48,3:48:08,1217,16,33
+General Medicine,PGY2+,S0169389,2015-12-07,0:12:54,0:07:52,0:00:00,0:00:00,0:00:52,0:02:38,0:24:26,100,100,7
+General Medicine,PGY2+,S0169389,2015-12-04,2:21:19,1:18:37,0:56:20,0:25:41,0:25:58,0:29:59,6:30:33,2127,206,22
+General Medicine,PGY2+,S0169389,2015-12-05,2:01:37,1:22:10,0:06:11,0:25:01,0:16:36,0:37:51,5:12:09,1956,32,57
+General Medicine,PGY2+,S0169389,2015-12-02,1:27:29,0:36:01,0:15:01,0:18:58,0:06:56,0:25:22,3:26:35,916,14,16
+General Medicine,PGY2+,S0169389,2015-12-03,2:31:19,2:13:58,0:14:00,0:50:15,0:32:33,0:42:01,7:29:13,2656,150,54
+General Medicine,PGY2+,S0169389,2015-12-01,0:12:12,0:17:18,0:00:00,0:00:14,0:00:39,0:01:51,0:37:27,198,188,14
+Night Team,PGY2+,S0109179,2015-07-25,1:18:52,0:17:44,0:47:01,0:13:32,0:04:40,0:23:13,3:16:18,1330,0,20
+General Medicine,PGY1,S0106952,2015-01-28,5:51:12,0:42:22,2:32:27,0:45:46,0:24:00,0:38:15,11:27:29,3617,1133,91
+Night Team,PGY2+,S0109179,2015-07-24,2:05:52,0:20:51,1:52:56,0:10:49,0:08:10,0:20:36,5:14:19,1570,172,36
+Intensive Care Unit,PGY2+,S0108268,2016-02-05,1:27:53,0:31:24,0:17:21,0:10:45,0:14:49,0:19:38,3:10:43,849,0,21
+Intensive Care Unit,PGY2+,S0108268,2016-02-04,1:45:26,0:57:33,0:19:51,0:07:38,0:20:24,0:17:25,4:06:24,1611,0,17
+Intensive Care Unit,PGY2+,S0108268,2016-02-06,0:00:00,0:00:00,0:00:00,0:00:25,0:00:00,0:00:00,0:00:25,16,0,7
+Intensive Care Unit,PGY2+,S0108268,2016-02-01,1:54:10,1:25:26,0:25:52,0:24:13,0:36:31,0:50:14,5:51:09,1921,0,17
+Intensive Care Unit,PGY2+,S0108268,2016-02-03,1:02:43,0:37:46,0:12:22,0:23:11,0:30:52,0:50:11,4:07:48,1855,0,29
+Intensive Care Unit,PGY2+,S0108268,2016-02-02,1:26:13,0:38:41,0:50:03,0:11:21,0:12:37,0:18:01,3:45:49,961,0,15
+General Medicine,PGY1,S0106952,2015-01-23,5:15:10,0:09:39,2:38:31,0:40:15,0:24:23,0:35:11,10:13:13,2761,706,66
+General Medicine,PGY1,S0106952,2015-01-22,5:42:35,0:08:23,3:51:34,0:31:11,0:28:58,0:25:09,11:42:12,3091,861,55
+General Medicine,PGY2+,S0169399,2015-10-29,0:32:43,0:25:29,0:37:20,0:02:47,0:05:41,0:18:13,2:06:31,485,0,13
+General Medicine,PGY2+,S0169399,2015-10-28,1:09:29,1:01:20,0:57:33,0:25:29,0:07:06,0:24:36,4:35:17,1043,11,21
+General Medicine,PGY2+,S0169399,2015-10-27,1:03:25,0:39:02,0:47:14,0:29:56,0:12:47,0:34:01,4:11:52,1308,44,13
+General Medicine,PGY2+,S0169399,2015-10-26,1:51:33,0:48:37,1:09:19,0:33:40,0:18:33,0:26:05,5:40:02,1385,249,19
+General Medicine,PGY2+,S0169399,2015-10-24,0:50:41,1:10:24,1:20:17,0:20:05,0:22:00,0:31:56,5:04:15,1166,0,12
+General Medicine,PGY2+,S0169399,2015-10-23,1:32:15,0:53:37,1:27:10,0:21:39,0:16:10,0:24:06,5:27:31,1216,0,22
+General Medicine,PGY2+,S0169399,2015-10-22,0:25:19,0:21:07,0:01:36,0:08:11,0:06:19,0:05:36,1:09:45,332,0,6
+General Medicine,PGY2+,S0169399,2015-10-21,1:09:27,0:35:53,0:19:44,0:18:52,0:26:46,0:30:01,3:35:22,847,22,16
+General Medicine,PGY2+,S0169399,2015-10-20,0:22:16,0:04:26,0:09:45,0:00:48,0:05:02,0:05:22,0:50:50,234,226,12
+Emergency Medicine,PGY1,S0181060,2016-04-08,0:57:00,0:28:03,0:06:59,0:33:35,0:02:21,0:09:41,3:14:33,824,0,8
+Emergency Medicine,PGY1,S0181060,2016-04-09,4:55:59,0:41:18,0:04:42,0:36:40,1:05:43,0:03:09,8:39:05,2641,0,42
+Night Team,PGY1,S0181074,2015-10-30,1:24:02,1:55:27,0:27:44,1:18:56,0:54:56,0:15:47,6:50:13,2548,114,47
+Night Team,PGY1,S0181074,2015-10-31,2:07:37,1:31:37,0:14:43,1:24:48,0:41:07,0:10:13,6:56:27,2260,51,52
+Emergency Medicine,PGY1,S0181060,2016-04-01,0:47:18,0:18:55,0:07:32,0:05:48,0:08:14,0:02:27,2:13:18,1034,0,25
+Emergency Medicine,PGY1,S0181060,2016-04-02,1:19:08,0:14:17,0:01:21,0:09:00,0:37:14,0:02:13,2:36:58,768,0,9
+Emergency Medicine,PGY1,S0181060,2016-04-03,2:58:54,0:46:54,0:00:49,0:35:19,0:36:56,0:03:44,5:43:22,2210,0,21
+Emergency Medicine,PGY1,S0181060,2016-04-04,1:27:33,0:24:39,0:00:27,0:05:32,0:20:02,0:00:08,2:31:44,983,0,10
+Emergency Medicine,PGY1,S0181060,2016-04-05,3:36:56,0:54:51,0:05:00,0:30:54,0:54:17,0:02:51,6:55:52,2330,0,27
+Emergency Medicine,PGY1,S0181060,2016-04-07,2:52:23,0:40:18,0:09:50,0:31:05,1:17:59,0:07:09,6:34:40,2100,0,22
+General Medicine,PGY1,S0074196,2016-01-21,0:31:31,0:37:57,0:46:04,0:09:14,0:08:29,0:10:30,2:33:56,834,0,8
+General Medicine,PGY1,S0079337,2015-01-01,4:41:52,0:04:03,1:53:10,0:46:51,0:20:46,0:08:41,8:29:23,2837,24,18
+General Medicine,PGY1,S0079337,2015-01-03,0:00:00,0:00:00,0:00:00,0:00:52,0:00:00,0:00:00,0:00:52,12,0,5
+General Medicine,PGY1,S0079337,2015-01-02,0:00:00,0:00:00,0:00:00,0:00:48,0:00:00,0:00:00,0:00:48,22,0,7
+General Medicine,PGY2+,S0169402,2016-07-03,1:46:43,0:46:26,0:00:09,0:40:11,0:11:19,0:01:12,3:51:43,1340,261,14
+General Medicine,PGY2+,S0169402,2016-07-02,3:06:36,1:06:15,0:00:01,0:35:15,0:15:59,0:04:08,6:03:53,2095,0,23
+General Medicine,PGY2+,S0169402,2016-07-01,2:13:29,1:13:38,0:00:31,0:42:29,0:23:00,0:14:38,5:24:05,1759,0,22
+General Medicine,PGY2+,S0169402,2016-07-07,4:04:40,1:13:30,0:00:08,1:00:31,0:34:39,0:07:03,7:41:40,2572,0,19
+General Medicine,PGY2+,S0169402,2016-07-06,1:41:10,0:38:25,0:00:06,0:12:41,0:18:31,0:00:23,3:16:44,1493,239,19
+Night Team,PGY2+,S0169398,2015-12-28,1:58:13,0:45:19,0:36:36,0:16:40,0:11:49,0:08:20,4:04:44,975,0,29
+General Medicine,PGY2+,S0169402,2016-07-04,0:14:55,0:44:31,0:00:00,0:00:09,0:00:00,0:00:02,1:00:17,292,225,9
+Night Team,PGY2+,S0169398,2015-12-26,2:17:20,0:47:37,0:12:43,0:18:03,0:15:54,0:38:55,4:52:18,1403,234,107
+Night Team,PGY2+,S0169398,2015-12-27,2:01:07,0:41:14,0:25:01,0:19:00,0:26:50,0:21:23,4:29:14,1376,0,61
+General Medicine,PGY2+,S0169402,2016-07-09,0:24:47,0:00:42,0:00:00,0:01:16,0:00:00,0:00:34,0:29:09,124,0,13
+General Medicine,PGY2+,S0169402,2016-07-08,2:13:22,0:29:32,0:00:03,1:00:09,0:21:19,0:16:13,4:53:55,1770,0,21
+Night Team,PGY2+,S0169398,2015-12-22,2:52:09,1:35:15,1:37:33,0:33:07,0:22:16,0:35:51,8:03:47,2689,0,52
+Night Team,PGY2+,S0169398,2015-12-23,1:05:16,1:11:26,1:07:07,0:26:32,0:12:57,0:24:08,4:44:28,1637,475,38
+Night Team,PGY2+,S0169398,2015-12-20,0:58:00,0:19:48,0:05:38,0:05:40,0:08:40,0:06:46,1:52:03,547,0,26
+Emergency Medicine,PGY1,S0195086,2016-09-30,5:36:51,0:51:29,0:00:58,0:21:15,0:36:49,0:00:26,8:04:53,2012,0,52
+General Medicine,PGY1,S0093383,2015-01-03,0:54:05,0:00:14,0:07:42,0:17:13,0:22:52,0:09:31,2:02:07,511,0,23
+General Medicine,PGY1,S0181064,2015-07-06,0:27:38,0:04:06,0:10:11,0:04:58,0:03:40,0:03:09,0:56:35,503,493,15
+General Medicine,PGY1,S0181064,2015-07-07,3:47:57,0:41:16,2:05:57,0:44:30,0:21:46,0:57:18,9:07:29,2757,78,17
+General Medicine,PGY1,S0181064,2015-07-04,2:32:00,0:08:14,1:15:30,0:40:44,0:18:50,0:11:27,5:35:02,1749,120,10
+General Medicine,PGY1,S0181064,2015-07-05,2:28:50,0:34:42,2:18:13,0:31:20,0:37:47,0:20:37,7:20:27,1924,214,20
+General Medicine,PGY1,S0181064,2015-07-02,3:24:11,0:17:41,2:48:38,0:52:04,0:33:54,0:24:41,8:59:12,2662,331,19
+General Medicine,PGY1,S0181064,2015-07-03,3:22:29,0:13:49,2:47:59,0:45:42,0:38:29,0:23:06,8:40:54,2326,585,15
+General Medicine,PGY1,S0169400,2015-05-19,0:00:00,0:12:11,0:00:00,0:01:53,0:00:00,0:01:36,0:16:28,197,191,6
+General Medicine,PGY1,S0181064,2015-07-01,3:43:31,0:47:24,0:58:30,0:26:24,0:27:22,0:18:21,7:15:24,2423,344,7
+General Medicine,PGY1,S0169400,2015-05-17,1:42:19,0:11:51,0:31:46,0:13:14,0:12:39,0:22:17,3:19:22,849,41,7
+General Medicine,PGY1,S0169400,2015-05-16,1:32:59,0:31:47,1:22:41,0:34:27,0:06:16,0:26:01,4:52:47,1290,0,11
+General Medicine,PGY1,S0169400,2015-05-15,0:00:00,0:01:52,0:00:00,0:02:03,0:00:00,0:00:00,0:04:04,32,22,4
+General Medicine,PGY1,S0169400,2015-05-14,2:00:51,0:00:43,0:45:49,0:15:26,0:16:11,0:18:06,3:49:28,858,0,13
+General Medicine,PGY1,S0169400,2015-05-13,2:50:16,0:16:46,1:22:51,0:21:31,0:09:23,0:33:09,5:51:22,1624,0,22
+General Medicine,PGY1,S0169400,2015-05-12,1:46:19,0:01:58,0:38:40,0:16:30,0:05:34,0:09:02,3:07:01,942,0,10
+General Medicine,PGY1,S0169400,2015-05-11,2:05:02,0:28:07,0:57:43,0:33:07,0:12:29,0:31:37,5:12:42,1445,0,24
+General Medicine,PGY1,S0169400,2015-05-10,0:00:00,0:00:04,0:00:00,0:00:00,0:00:00,0:01:06,0:01:17,25,21,1
+Intensive Care Unit,PGY2+,S0176040,2016-08-13,2:00:42,0:23:24,0:14:03,0:19:00,0:19:34,0:10:17,3:45:07,1472,0,15
+Intensive Care Unit,PGY2+,S0176040,2016-08-12,2:00:57,0:38:34,0:00:46,0:30:59,0:31:16,0:09:00,4:07:01,1784,0,20
+Intensive Care Unit,PGY2+,S0176040,2016-08-11,0:02:11,0:00:25,0:00:00,0:00:00,0:00:00,0:00:00,0:02:43,29,0,6
+Intensive Care Unit,PGY2+,S0176040,2016-08-10,2:14:29,0:48:36,0:18:07,0:07:46,0:28:03,0:02:04,4:43:45,1426,298,14
+Night Team,PGY1,S0169389,2014-11-11,1:44:56,0:01:59,0:52:09,0:42:32,0:18:34,0:21:24,4:08:42,929,61,17
+Night Team,PGY1,S0169389,2014-11-10,2:38:21,0:04:27,0:00:53,0:10:24,0:14:34,0:20:13,3:37:33,875,77,43
+Night Team,PGY1,S0169389,2014-11-13,2:10:44,0:00:13,0:13:51,0:07:25,0:09:36,0:23:55,3:11:54,529,124,26
+Night Team,PGY1,S0169389,2014-11-12,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,4,0,1
+Night Team,PGY1,S0169389,2014-11-15,3:07:36,0:00:00,0:47:39,0:16:38,0:24:38,0:16:33,5:01:47,967,0,38
+Night Team,PGY1,S0169389,2014-11-14,4:40:47,0:00:17,0:30:26,0:29:15,0:08:10,0:42:28,6:37:49,1101,69,53
+Night Team,PGY1,S0169389,2014-11-17,3:45:23,0:17:02,0:57:29,0:29:40,0:21:19,0:41:11,6:49:59,1483,113,48
+Night Team,PGY1,S0169389,2014-11-16,3:55:11,0:13:52,0:01:24,0:18:49,0:33:03,1:00:29,6:19:54,1524,73,42
+Night Team,PGY1,S0169389,2014-11-19,3:20:35,0:08:46,0:00:18,0:31:16,0:56:29,0:51:41,6:07:34,1899,99,50
+Night Team,PGY1,S0169389,2014-11-18,3:43:01,0:12:13,1:01:06,0:17:57,0:22:21,1:01:30,6:58:09,1886,56,56
+Emergency Medicine,PGY1,S0079640,2016-10-01,2:37:17,0:28:22,0:00:46,0:08:18,1:16:39,0:00:28,5:11:59,1795,0,49
+Emergency Medicine,PGY1,S0079640,2016-10-03,2:41:00,0:53:19,0:04:04,0:39:00,0:51:59,0:09:24,5:55:28,1899,0,31
+Emergency Medicine,PGY1,S0079640,2016-10-02,0:00:56,0:00:08,0:00:00,0:00:00,0:00:00,0:00:00,0:01:07,10,0,1
+Emergency Medicine,PGY1,S0079640,2016-10-05,2:44:15,0:38:36,0:02:42,0:26:02,0:57:19,0:05:05,5:19:29,1477,0,41
+Emergency Medicine,PGY1,S0079640,2016-10-04,3:06:37,0:39:50,0:03:13,0:28:04,0:50:44,0:02:08,5:52:44,1831,0,39
+Emergency Medicine,PGY1,S0079640,2016-10-07,0:02:43,0:00:42,0:00:00,0:00:00,0:00:00,0:00:00,0:04:23,61,0,18
+Emergency Medicine,PGY1,S0079640,2016-10-06,0:00:00,0:00:02,0:00:00,0:00:00,0:00:00,0:00:00,0:01:26,19,0,15
+Emergency Medicine,PGY1,S0079640,2016-10-09,2:49:02,0:20:27,0:02:47,0:29:49,1:12:31,0:04:44,5:29:55,1675,0,58
+Emergency Medicine,PGY1,S0079640,2016-10-08,1:33:47,0:21:08,0:00:47,0:11:59,0:22:27,0:00:38,2:48:57,922,0,25
+Intensive Care Unit,PGY2+,S0172056,2016-10-16,1:16:39,0:44:15,0:05:16,0:01:54,0:32:26,0:00:31,3:00:37,978,76,14
+Intensive Care Unit,PGY2+,S0079337,2016-05-29,0:00:01,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:06,6,4,2
+General Medicine,PGY1,S0181063,2016-04-09,1:08:20,0:43:31,0:01:04,0:38:34,0:05:10,0:00:23,3:00:24,969,8,7
+General Medicine,PGY1,S0181063,2016-04-08,0:30:10,0:24:56,0:00:06,0:08:58,0:08:07,0:00:11,1:27:30,633,15,6
+General Medicine,PGY1,S0181063,2016-04-05,0:30:16,0:34:33,0:00:23,0:20:48,0:07:40,0:03:45,2:04:28,869,93,8
+General Medicine,PGY1,S0181063,2016-04-04,1:03:13,0:39:57,0:00:43,0:48:54,0:29:19,0:04:11,3:47:28,1293,0,13
+General Medicine,PGY1,S0181063,2016-04-07,0:21:20,0:21:50,0:00:05,0:04:10,0:02:40,0:00:03,0:59:03,352,129,5
+General Medicine,PGY1,S0181063,2016-04-06,0:54:50,0:43:00,0:00:09,0:27:26,0:11:58,0:00:06,2:42:58,983,0,10
+General Medicine,PGY1,S0181063,2016-04-01,1:20:57,0:48:20,0:00:30,0:32:47,0:21:41,0:00:48,3:26:31,1170,0,6
+General Medicine,PGY1,S0181063,2016-04-03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181063,2016-04-02,0:52:42,0:37:55,0:00:40,0:16:44,0:16:50,0:00:19,2:24:25,839,6,11
+Night Team,PGY2+,S0079337,2016-10-19,0:36:17,0:31:25,0:00:02,0:14:38,0:04:35,0:02:17,1:37:56,659,1,13
+Night Team,PGY2+,S0079337,2016-10-18,0:57:35,0:07:23,0:00:01,0:18:50,0:04:59,0:02:05,1:41:07,606,0,24
+Night Team,PGY2+,S0079337,2016-10-17,1:37:57,1:11:02,0:00:06,0:25:00,0:06:01,0:04:06,3:53:02,1566,0,28
+Night Team,PGY2+,S0079337,2016-10-16,3:05:57,1:02:17,0:00:21,0:27:20,0:05:21,0:07:25,5:06:23,1294,176,19
+Night Team,PGY2+,S0079337,2016-10-15,1:25:57,0:26:23,0:00:06,0:37:02,0:11:32,0:00:28,3:07:11,1038,161,23
+Night Team,PGY2+,S0079337,2016-10-14,1:45:39,1:07:48,0:00:06,0:40:41,0:15:53,0:00:12,4:13:15,1586,1119,26
+Night Team,PGY2+,S0079337,2016-10-13,2:51:06,0:45:04,0:00:05,0:30:12,0:11:05,0:04:10,4:31:58,1385,776,38
+Night Team,PGY2+,S0079337,2016-10-12,1:43:51,0:50:26,0:00:41,0:26:59,0:11:10,0:04:25,3:44:37,1299,337,23
+Night Team,PGY2+,S0079337,2016-10-11,1:17:54,0:56:09,0:00:10,0:20:44,0:08:07,0:00:12,3:15:11,1023,0,15
+Night Team,PGY2+,S0079337,2016-10-10,1:00:02,0:40:44,0:00:07,0:19:34,0:03:12,0:00:06,2:22:03,985,106,11
+Intensive Care Unit,PGY2+,S0169401,2016-04-02,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+Intensive Care Unit,PGY2+,S0169401,2016-04-01,1:59:41,0:41:14,0:06:05,0:16:33,0:25:00,0:00:35,4:14:07,1643,3,21
+Night Team,PGY2+,S0181060,2016-10-28,2:05:47,1:12:39,0:00:12,0:37:55,0:21:21,0:07:33,4:45:30,1449,0,30
+Emergency Medicine,PGY2+,S0181056,2016-08-29,0:06:42,0:01:59,0:00:19,0:00:51,0:00:01,0:00:00,0:16:42,99,87,6
+Emergency Medicine,PGY2+,S0181056,2016-08-28,3:52:13,1:12:16,0:08:03,0:40:40,0:54:20,0:12:15,7:54:49,2916,0,37
+Night Team,PGY2+,S0181060,2016-10-23,2:10:40,1:56:31,0:00:11,0:28:38,0:12:15,0:04:19,5:22:49,1338,0,21
+Emergency Medicine,PGY2+,S0181056,2016-08-26,0:48:51,0:22:52,0:13:32,0:05:28,0:02:16,0:08:17,2:24:37,910,0,18
+Emergency Medicine,PGY2+,S0181056,2016-08-25,4:21:38,1:21:09,0:02:28,0:36:53,0:32:13,0:06:04,8:11:43,3085,138,51
+Emergency Medicine,PGY2+,S0181056,2016-08-24,0:13:18,0:05:52,0:00:00,0:01:11,0:00:54,0:00:41,0:26:21,222,175,9
+Night Team,PGY2+,S0181060,2016-10-27,3:04:45,1:55:35,0:00:28,0:36:49,0:16:50,0:01:35,6:31:02,2116,0,25
+Emergency Medicine,PGY2+,S0181056,2016-08-22,4:44:26,1:06:11,0:06:06,0:40:05,1:00:15,0:09:21,9:09:42,3532,500,71
+Night Team,PGY2+,S0181060,2016-10-25,0:49:49,0:21:23,0:00:07,0:29:03,0:08:59,0:01:08,2:07:02,670,15,8
+Night Team,PGY2+,S0181060,2016-10-24,2:24:14,1:00:42,0:00:08,0:35:36,0:16:09,0:04:37,4:56:03,1391,0,16
+Emergency Medicine,PGY1,S0181077,2015-09-26,0:01:55,0:03:40,0:31:07,0:04:47,0:08:02,0:07:00,1:02:28,344,124,10
+Emergency Medicine,PGY1,S0181077,2015-09-27,1:00:02,0:15:35,2:48:40,0:27:25,0:43:40,0:47:17,6:41:20,1954,0,11
+Emergency Medicine,PGY1,S0181077,2015-09-24,0:54:59,0:17:31,2:52:04,0:24:23,0:32:46,0:29:42,6:24:21,1845,0,18
+Emergency Medicine,PGY1,S0181077,2015-09-25,0:45:41,0:30:57,1:24:47,0:21:58,0:32:32,0:20:52,4:22:06,1375,0,12
+Emergency Medicine,PGY1,S0181077,2015-09-22,1:22:14,0:47:48,2:10:40,0:33:04,0:36:47,0:35:21,7:18:37,2503,9,16
+Emergency Medicine,PGY1,S0181077,2015-09-23,0:30:29,0:41:23,0:39:18,0:21:15,0:04:37,0:07:58,3:02:31,1043,119,11
+Emergency Medicine,PGY1,S0181077,2015-09-20,1:22:08,0:45:41,2:27:38,0:21:52,0:34:17,0:47:09,7:04:51,2645,0,24
+Emergency Medicine,PGY1,S0181077,2015-09-21,0:26:12,0:03:51,1:19:38,0:35:02,0:26:36,0:18:34,3:37:30,1308,0,10
+Emergency Medicine,PGY1,S0181077,2015-09-28,1:06:00,1:09:00,2:10:38,0:44:35,0:34:52,0:56:17,7:23:24,2461,172,19
+Emergency Medicine,PGY1,S0181077,2015-09-29,0:45:00,0:37:06,1:47:33,0:21:42,0:21:54,0:23:42,4:48:11,1661,0,15
+Night Team,PGY2+,S0169383,2015-07-11,1:37:06,0:55:23,1:23:23,0:09:52,0:15:33,0:38:41,5:29:01,1589,33,33
+Night Team,PGY2+,S0169383,2015-07-10,2:55:07,1:48:00,1:59:36,0:37:04,0:11:43,1:00:15,9:09:26,2995,38,92
+General Medicine,PGY1,S0181075,2015-08-14,2:05:34,1:11:02,3:02:59,1:00:55,0:16:20,0:20:52,8:16:58,2515,0,14
+Intensive Care Unit,PGY2+,S0056755,2016-06-06,1:54:30,0:56:24,0:08:04,0:27:13,0:25:43,0:00:27,4:19:02,1061,0,12
+General Medicine,PGY1,S0181075,2015-08-15,0:19:26,0:12:52,0:39:32,0:06:19,0:01:33,0:03:17,1:28:28,274,266,5
+General Medicine,PGY1,S0094959,2015-04-11,0:00:17,0:07:50,0:00:00,0:03:33,0:00:00,0:01:56,0:17:24,150,134,9
+General Medicine,PGY2+,S0163662,2016-01-03,1:45:30,1:03:00,0:00:00,0:01:19,0:11:46,0:22:48,3:33:24,1285,523,11
+Intensive Care Unit,PGY2+,S0056755,2016-06-04,1:47:30,1:02:34,0:12:49,0:10:31,0:25:12,0:04:51,4:00:59,1097,0,13
+General Medicine,PGY2+,S0163662,2016-01-02,2:18:09,0:56:01,0:10:33,0:23:12,0:21:40,0:26:46,5:04:17,2709,0,21
+Night Team,PGY1,S0169399,2015-06-20,2:00:17,0:34:59,0:51:06,0:29:46,0:38:02,0:18:42,5:11:10,1191,777,60
+Night Team,PGY1,S0169399,2015-06-21,1:03:03,0:24:58,1:38:25,0:14:07,0:22:02,0:10:55,4:20:13,1013,567,32
+Night Team,PGY1,S0169399,2015-06-22,1:21:23,0:22:14,2:19:59,0:34:19,0:25:28,0:14:51,5:34:22,965,107,25
+Intensive Care Unit,PGY2+,S0056755,2016-06-05,1:59:21,0:37:32,0:08:06,0:32:26,0:30:41,0:00:15,4:31:29,1088,0,11
+Night Team,PGY1,S0169399,2015-06-24,0:58:07,0:30:44,3:58:27,0:30:30,0:18:51,0:21:00,6:51:34,970,426,29
+General Medicine,PGY2+,S0163662,2016-01-01,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,8,0,2
+Intensive Care Unit,PGY2+,S0056755,2016-06-02,0:59:34,0:33:51,0:17:42,0:05:21,0:15:08,0:00:04,2:28:15,606,0,13
+General Medicine,PGY1,S0181075,2015-08-11,1:45:50,1:02:59,2:53:47,0:48:20,0:20:56,0:14:35,7:49:04,2422,179,18
+Intensive Care Unit,PGY2+,S0056755,2016-06-03,1:18:25,1:14:10,0:00:01,0:27:00,0:33:01,0:03:25,3:52:47,1169,16,14
+Night Team,PGY1,S0169398,2015-05-31,2:39:09,0:24:51,1:45:22,0:23:07,0:13:07,0:41:17,6:31:13,1465,0,26
+Intensive Care Unit,PGY2+,S0056755,2016-06-01,0:13:44,0:20:26,0:00:00,0:03:47,0:00:56,0:00:01,0:43:46,233,0,12
+Intensive Care Unit,PGY2+,S0169390,2015-11-09,2:09:53,0:37:07,0:03:31,0:22:48,0:24:19,0:25:35,4:24:56,1217,55,16
+Intensive Care Unit,PGY2+,S0169390,2015-11-08,0:00:00,0:03:11,0:00:00,0:01:25,0:00:00,0:00:00,0:05:38,53,31,9
+Intensive Care Unit,PGY2+,S0169390,2015-11-01,0:00:00,0:00:08,0:00:00,0:00:35,0:00:00,0:00:00,0:00:55,17,9,3
+Intensive Care Unit,PGY2+,S0169390,2015-11-03,1:40:51,0:38:35,0:00:47,0:27:58,0:34:42,0:30:38,4:40:43,1429,0,14
+Intensive Care Unit,PGY2+,S0169390,2015-11-02,1:45:46,0:24:03,0:06:28,0:22:34,0:25:59,0:18:13,3:47:15,884,14,8
+Intensive Care Unit,PGY2+,S0169390,2015-11-05,3:06:55,0:59:31,0:33:16,0:31:53,0:41:13,0:22:09,6:44:01,1786,0,17
+Intensive Care Unit,PGY2+,S0169390,2015-11-04,0:45:03,0:08:51,0:01:24,0:10:22,0:15:17,0:08:06,1:39:24,826,40,13
+Intensive Care Unit,PGY2+,S0169390,2015-11-07,2:13:18,0:27:22,0:07:37,0:36:31,0:40:56,0:19:58,4:53:30,1721,0,11
+Intensive Care Unit,PGY2+,S0169390,2015-11-06,2:16:32,0:42:43,0:01:06,0:49:48,0:37:07,0:34:37,5:32:20,1885,221,24
+General Medicine,PGY2+,S0181077,2016-08-19,0:33:40,0:44:05,0:00:22,0:04:06,0:04:47,0:00:33,1:38:10,863,798,27
+General Medicine,PGY2+,S0181077,2016-08-18,2:06:47,0:56:32,0:00:35,0:39:46,0:17:02,0:00:26,4:29:08,2347,12,19
+Intensive Care Unit,PGY2+,S0169403,2015-12-03,2:06:46,0:42:58,0:17:17,0:19:48,0:34:11,0:50:01,5:38:12,2041,40,20
+General Medicine,PGY2+,S0181077,2016-08-17,3:13:40,1:18:06,0:00:10,0:43:17,0:25:19,0:12:11,7:04:42,3099,476,35
+General Medicine,PGY2+,S0181077,2016-08-16,2:20:29,0:54:36,0:00:01,0:48:12,0:36:04,0:03:25,5:21:47,2717,578,22
+General Medicine,PGY2+,S0181077,2016-08-15,2:41:53,1:24:02,0:00:14,0:40:36,0:24:48,0:08:59,6:27:11,2987,121,29
+General Medicine,PGY2+,S0181077,2016-08-14,2:33:30,1:11:01,0:00:19,0:28:26,0:20:09,0:16:05,5:20:52,2438,596,13
+General Medicine,PGY2+,S0108287,2016-05-10,2:54:07,1:12:18,0:00:02,0:29:35,0:15:56,0:06:01,5:36:46,2148,106,21
+General Medicine,PGY2+,S0108287,2016-05-11,1:29:52,0:29:14,0:00:12,0:28:00,0:15:02,0:05:36,3:19:18,1129,254,17
+General Medicine,PGY2+,S0108287,2016-05-12,0:17:13,0:01:33,0:00:00,0:00:24,0:00:00,0:03:36,0:23:55,123,109,12
+General Medicine,PGY2+,S0108287,2016-05-13,2:38:36,1:41:25,0:00:06,0:29:47,0:14:42,0:00:38,5:48:24,2359,50,27
+General Medicine,PGY1,S0181081,2015-07-29,0:56:38,0:06:25,1:09:02,0:02:38,0:03:19,0:03:51,2:54:09,745,729,16
+General Medicine,PGY1,S0181081,2015-07-28,1:48:18,0:11:23,1:05:51,0:47:57,0:24:03,0:28:16,5:26:54,1536,0,13
+General Medicine,PGY2+,S0108287,2016-05-16,1:28:51,0:53:07,0:00:09,0:29:49,0:26:32,0:00:46,3:54:56,1196,37,20
+General Medicine,PGY2+,S0108287,2016-05-17,0:57:01,0:13:48,0:03:55,0:15:51,0:09:10,0:02:52,1:59:01,594,210,17
+General Medicine,PGY1,S0181081,2015-07-25,3:31:50,0:29:53,1:26:08,1:01:57,0:24:46,0:47:37,8:10:57,2028,105,7
+General Medicine,PGY1,S0181081,2015-07-24,3:51:13,0:06:19,2:34:02,0:20:03,0:09:47,0:11:23,8:06:48,1895,311,15
+General Medicine,PGY1,S0181081,2015-07-27,1:59:55,0:06:31,1:37:33,0:26:45,0:14:43,0:30:50,5:26:59,1520,45,8
+General Medicine,PGY1,S0181081,2015-07-26,2:58:41,0:11:24,4:36:24,0:31:01,0:15:34,0:27:55,9:33:31,1641,586,12
+General Medicine,PGY1,S0181081,2015-07-21,0:16:33,0:00:00,0:13:05,0:00:58,0:01:37,0:00:05,0:37:41,206,88,12
+General Medicine,PGY1,S0181081,2015-07-20,2:41:06,0:21:36,1:36:42,0:28:52,0:39:46,0:22:36,6:35:44,1847,38,12
+General Medicine,PGY1,S0181081,2015-07-23,3:22:46,0:10:36,3:10:37,0:43:37,0:21:40,0:20:56,9:34:12,2746,680,18
+General Medicine,PGY1,S0181081,2015-07-22,3:36:21,0:17:01,1:40:19,0:34:15,0:27:38,0:49:21,7:53:05,2217,0,17
+Intensive Care Unit,PGY2+,S0093383,2015-09-20,2:12:13,0:02:26,0:09:43,0:08:55,0:14:34,0:18:25,3:15:21,562,25,13
+Intensive Care Unit,PGY2+,S0093383,2015-09-21,2:46:41,0:07:31,0:10:45,0:04:11,0:19:20,0:28:18,4:21:43,739,0,11
+Intensive Care Unit,PGY2+,S0093383,2015-09-22,2:31:44,0:05:08,0:06:59,0:27:28,0:22:50,0:14:08,4:03:37,1348,13,15
+Intensive Care Unit,PGY2+,S0093383,2015-09-23,1:25:04,0:38:04,0:12:03,0:12:48,0:08:51,0:22:38,3:16:23,980,31,15
+Intensive Care Unit,PGY2+,S0093383,2015-09-24,1:31:33,0:35:26,0:05:09,0:42:42,0:43:06,0:38:24,4:23:34,1254,0,15
+Intensive Care Unit,PGY2+,S0093383,2015-09-25,1:32:26,0:54:49,0:09:28,0:28:19,0:23:16,0:27:45,4:20:47,1523,32,17
+Intensive Care Unit,PGY2+,S0093383,2015-09-26,2:19:37,0:41:50,0:05:57,0:50:02,0:24:44,1:04:44,6:06:34,1572,0,13
+Intensive Care Unit,PGY2+,S0093383,2015-09-27,0:00:00,0:00:00,0:00:00,0:01:27,0:00:00,0:00:00,0:01:27,20,0,4
+Intensive Care Unit,PGY2+,S0093383,2015-09-28,2:11:54,1:00:12,0:10:07,0:25:26,0:31:26,0:50:30,5:32:09,1282,0,14
+Intensive Care Unit,PGY2+,S0093383,2015-09-29,1:10:38,0:46:35,0:09:35,0:52:58,0:42:21,0:38:28,4:43:57,1324,0,18
+General Medicine,PGY1,S0108268,2014-06-29,3:44:45,0:28:12,0:08:25,0:11:56,0:31:35,0:39:22,5:54:52,1327,636,11
+General Medicine,PGY1,S0108268,2014-06-28,1:37:19,0:04:59,0:11:20,0:00:01,0:00:48,0:27:13,2:46:25,601,589,15
+General Medicine,PGY1,S0169394,2015-05-19,0:48:56,0:08:16,0:15:40,0:05:58,0:07:23,0:08:51,1:44:29,730,123,8
+General Medicine,PGY1,S0169394,2015-05-18,0:45:17,0:01:19,0:24:14,0:14:07,0:04:21,0:08:44,1:48:02,605,11,5
+General Medicine,PGY1,S0169394,2015-05-17,1:58:19,0:09:31,2:27:16,0:34:25,0:28:43,0:21:29,6:30:23,1754,24,11
+General Medicine,PGY1,S0169394,2015-05-16,0:07:17,0:01:40,0:00:30,0:00:50,0:00:25,0:04:32,0:15:50,152,136,6
+General Medicine,PGY1,S0169394,2015-05-15,2:57:30,0:35:00,1:54:33,0:35:43,0:25:58,0:34:36,7:38:47,2546,157,14
+General Medicine,PGY1,S0169394,2015-05-14,2:15:46,0:18:40,1:00:35,0:29:57,0:25:17,0:31:43,5:44:36,1704,219,8
+General Medicine,PGY1,S0169394,2015-05-13,2:42:58,0:14:38,1:10:50,0:52:07,0:29:30,0:24:39,6:25:36,2061,140,8
+General Medicine,PGY1,S0169394,2015-05-12,3:31:49,0:11:22,1:15:44,0:39:48,0:35:10,0:30:32,7:08:44,2037,110,11
+General Medicine,PGY1,S0108268,2014-06-27,4:29:30,0:07:37,0:29:21,0:25:25,0:32:40,0:06:00,6:26:49,1568,0,10
+General Medicine,PGY1,S0108268,2014-06-26,3:54:45,0:36:34,0:18:17,0:12:20,0:24:47,0:27:31,6:00:55,1226,77,6
+Emergency Medicine,PGY2+,S0169403,2016-01-28,0:00:58,0:05:30,0:00:00,0:00:00,0:00:00,0:00:06,0:07:47,57,57,3
+Emergency Medicine,PGY2+,S0169403,2016-01-29,1:41:28,0:57:59,2:49:27,0:36:03,0:52:23,0:46:22,8:16:18,3198,10,19
+General Medicine,PGY1,S0109179,2014-12-28,1:02:37,0:00:48,1:13:02,0:08:54,0:11:23,0:09:48,2:57:01,906,0,7
+General Medicine,PGY1,S0181074,2016-05-14,1:53:27,1:02:31,0:00:14,1:34:37,0:23:57,0:14:16,5:50:21,1919,190,9
+General Medicine,PGY1,S0109179,2014-12-29,3:30:27,0:10:24,2:40:46,0:45:45,0:40:48,0:42:55,9:00:01,2569,18,27
+Emergency Medicine,PGY1,S0181065,2015-12-09,0:03:15,0:00:45,0:00:00,0:00:40,0:00:06,0:00:00,0:11:07,176,174,6
+Emergency Medicine,PGY1,S0181065,2015-12-08,1:23:49,1:16:04,0:59:17,0:22:09,0:08:14,0:21:19,5:15:20,2312,715,20
+General Medicine,PGY1,S0181065,2015-12-03,1:10:08,0:28:06,0:56:22,0:15:23,0:10:44,0:04:28,3:14:20,1184,0,7
+General Medicine,PGY1,S0181065,2015-12-02,0:00:00,0:01:29,0:00:00,0:00:00,0:00:00,0:00:00,0:02:06,51,45,5
+General Medicine,PGY1,S0181065,2015-12-01,2:42:32,1:13:26,1:05:34,0:57:45,0:25:23,0:18:43,7:21:29,3176,0,8
+Emergency Medicine,PGY1,S0181065,2015-12-07,1:24:40,1:35:45,2:37:29,0:52:08,1:01:00,0:43:36,8:50:32,4074,0,22
+Emergency Medicine,PGY1,S0181065,2015-12-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY1,S0181065,2015-12-05,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY1,S0181065,2015-12-04,2:37:17,1:02:24,0:56:52,1:13:31,0:20:35,0:39:17,7:30:48,3021,0,9
+General Medicine,PGY1,S0109179,2014-12-22,3:15:28,0:02:21,3:49:37,1:07:32,0:24:16,0:13:54,9:33:50,2733,67,22
+Night Team,PGY1,S0181083,2015-09-10,1:53:04,0:21:50,1:33:14,1:06:27,0:27:30,0:49:53,6:37:51,1665,10,47
+Night Team,PGY1,S0181083,2015-09-11,1:11:11,0:09:34,1:20:11,0:28:21,0:14:35,0:22:39,4:05:37,963,100,31
+Night Team,PGY1,S0181083,2015-09-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,1
+General Medicine,PGY1,S0109179,2014-12-23,2:20:35,0:08:28,2:47:05,0:34:44,0:23:38,0:19:10,7:08:06,1879,0,11
+General Medicine,PGY1,S0109179,2014-12-20,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,6,0,2
+General Medicine,PGY1,S0109179,2014-12-21,2:12:03,0:01:54,1:46:53,0:21:30,0:14:17,0:14:21,5:15:12,1262,0,13
+General Medicine,PGY1,S0109179,2014-12-26,0:00:00,0:00:00,0:00:00,0:00:33,0:00:00,0:00:00,0:00:33,10,0,4
+General Medicine,PGY1,S0109179,2014-12-27,3:50:45,0:08:13,2:25:14,0:26:39,0:35:15,0:26:07,8:19:21,2196,0,12
+Emergency Medicine,PGY1,S0108287,2015-03-02,2:54:48,0:20:32,1:31:18,0:15:35,0:11:46,0:00:24,5:24:18,989,8,19
+Emergency Medicine,PGY1,S0108287,2015-03-03,3:46:50,0:38:43,0:58:40,0:18:48,0:11:35,0:10:51,6:38:26,1336,0,21
+Emergency Medicine,PGY1,S0108287,2015-03-01,1:47:48,0:45:47,0:13:22,0:13:52,0:05:55,0:07:10,3:19:27,638,6,16
+Emergency Medicine,PGY1,S0108287,2015-03-06,7:21:16,1:00:44,2:11:41,0:23:23,0:21:11,0:12:42,11:37:44,1915,0,28
+Night Team,PGY2+,S0163662,2016-07-31,0:20:04,0:39:19,0:01:32,0:17:34,0:04:54,0:01:53,1:39:41,917,0,5
+Emergency Medicine,PGY1,S0108287,2015-03-04,1:08:38,0:35:49,0:46:33,0:15:41,0:14:14,0:02:31,3:17:04,1007,219,11
+Emergency Medicine,PGY1,S0108287,2015-03-05,4:26:47,0:27:55,1:08:44,0:14:07,0:14:00,0:07:09,7:06:56,1227,0,44
+Emergency Medicine,PGY1,S0108287,2015-03-08,2:45:30,0:31:52,0:29:39,0:11:31,0:04:22,0:05:55,4:19:02,768,0,11
+General Medicine,PGY1,S0109179,2014-12-25,1:03:11,0:01:01,1:16:01,0:13:02,0:13:34,0:07:00,3:01:17,802,16,5
+Emergency Medicine,PGY1,S0169406,2015-01-19,5:28:22,0:08:11,1:28:50,0:17:53,0:15:49,0:06:16,8:08:03,2089,0,50
+General Medicine,PGY1,S0195218,2016-09-17,0:00:00,0:00:00,0:00:00,0:01:15,0:00:00,0:00:00,0:01:15,10,0,4
+General Medicine,PGY2+,S0172085,2016-09-13,2:22:40,1:31:37,0:00:05,0:17:59,0:09:26,0:26:54,5:37:31,2058,24,30
+General Medicine,PGY1,S0181057,2016-05-27,1:36:58,0:58:44,0:02:23,0:59:19,0:14:13,0:00:18,4:17:33,1528,270,9
+General Medicine,PGY2+,S0172085,2016-09-11,2:17:21,1:21:32,0:00:49,0:36:04,0:31:34,0:03:50,5:37:52,2240,184,22
+General Medicine,PGY2+,S0172085,2016-09-17,0:51:56,0:28:25,0:00:00,0:01:09,0:01:33,0:03:17,1:42:52,738,0,13
+General Medicine,PGY2+,S0172085,2016-09-16,1:25:24,0:41:13,0:00:07,0:15:48,0:08:21,0:08:03,3:09:35,1263,0,19
+General Medicine,PGY2+,S0172085,2016-09-15,2:58:11,1:38:32,0:00:01,0:13:13,0:08:10,0:20:35,5:57:03,3467,77,25
+General Medicine,PGY2+,S0172085,2016-09-14,2:23:21,1:07:07,0:00:01,0:14:48,0:19:48,0:04:55,4:40:26,2008,37,28
+General Medicine,PGY2+,S0172085,2016-09-19,1:43:23,1:26:56,0:00:01,0:40:58,0:19:00,0:08:22,4:54:25,1425,0,19
+General Medicine,PGY2+,S0172085,2016-09-18,2:16:33,1:20:24,0:00:00,0:03:18,0:03:33,0:07:15,4:18:54,1939,0,30
+General Medicine,PGY1,S0181074,2016-03-20,0:23:17,0:01:22,0:00:00,0:01:34,0:00:00,0:04:11,0:33:00,189,175,8
+General Medicine,PGY1,S0181057,2016-05-24,1:40:34,1:12:08,0:03:06,0:44:56,0:18:19,0:11:57,4:55:09,1676,0,14
+General Medicine,PGY2+,S0169403,2015-07-30,1:04:16,0:56:22,0:03:54,0:16:00,0:14:11,0:29:22,3:17:43,1266,621,20
+General Medicine,PGY2+,S0169403,2015-07-31,0:36:17,1:08:23,0:29:38,0:01:13,0:02:01,0:12:25,2:47:38,782,383,20
+General Medicine,PGY1,S0181074,2016-03-26,0:08:00,0:00:08,0:00:00,0:00:00,0:00:00,0:00:47,0:09:23,55,53,5
+Night Team,PGY1,S0195227,2016-10-18,3:57:13,1:13:47,0:00:02,1:05:46,0:28:06,0:04:28,7:20:47,1935,31,42
+General Medicine,PGY1,S0181074,2016-05-10,2:09:41,1:45:34,0:01:31,1:37:54,0:17:40,0:10:53,6:48:48,2528,104,15
+Night Team,PGY1,S0195227,2016-10-19,1:56:33,0:20:49,0:00:00,0:40:49,0:24:27,0:03:31,3:31:41,1134,0,25
+Night Team,PGY1,S0181060,2015-10-10,0:44:54,0:32:51,0:31:01,0:04:31,0:06:38,0:14:12,2:34:51,571,30,17
+Emergency Medicine,PGY2+,S0169385,2016-11-07,2:37:49,0:18:27,0:01:24,0:15:32,0:31:13,0:04:15,4:11:23,1495,0,17
+Intensive Care Unit,PGY2+,S0181073,2016-10-12,0:08:51,0:00:32,0:00:00,0:00:30,0:00:00,0:00:17,0:10:15,52,0,12
+Intensive Care Unit,PGY2+,S0181073,2016-10-13,2:13:05,0:39:49,0:09:10,0:34:23,0:33:28,0:04:17,4:56:37,1809,0,20
+Intensive Care Unit,PGY2+,S0181073,2016-10-10,1:55:31,0:12:19,0:06:58,0:12:05,0:14:22,0:09:52,3:20:07,1097,0,14
+Emergency Medicine,PGY1,S0074196,2015-11-07,0:58:12,1:21:54,0:51:54,0:30:02,0:11:28,0:18:40,4:27:29,994,0,7
+Emergency Medicine,PGY1,S0074196,2015-11-05,2:27:41,0:37:11,2:40:57,0:25:12,0:26:57,0:29:55,7:48:57,1794,0,19
+Emergency Medicine,PGY1,S0074196,2015-11-02,2:52:47,0:16:38,1:39:30,0:19:54,0:14:13,0:20:34,6:03:40,1205,0,20
+Emergency Medicine,PGY1,S0074196,2015-11-03,1:57:23,0:15:01,3:00:24,0:40:14,0:22:18,0:24:04,7:40:07,1883,0,19
+Emergency Medicine,PGY1,S0074196,2015-11-01,1:08:28,0:35:57,0:57:17,0:07:33,0:11:34,0:09:02,3:25:12,798,0,10
+Emergency Medicine,PGY1,S0109179,2015-01-12,1:12:56,0:02:35,0:31:34,0:15:51,0:08:11,0:02:31,2:42:35,883,301,5
+Intensive Care Unit,PGY2+,S0093383,2016-06-28,1:46:35,0:56:50,0:06:31,0:23:08,0:18:10,0:08:11,4:03:04,845,2,13
+Intensive Care Unit,PGY2+,S0093383,2016-06-29,1:59:31,0:45:38,0:00:04,0:40:09,0:09:03,0:04:57,4:08:05,1531,0,18
+Emergency Medicine,PGY1,S0109179,2015-01-13,4:40:30,0:03:33,1:40:53,0:18:10,0:18:48,0:20:23,7:43:36,2012,0,11
+General Medicine,PGY1,S0169402,2014-11-15,2:36:53,0:08:54,2:33:51,0:38:09,0:26:45,0:11:10,7:16:47,1408,38,6
+Emergency Medicine,PGY1,S0109179,2015-01-10,0:55:27,0:00:00,0:00:00,0:00:47,0:00:00,0:00:00,0:56:14,69,0,2
+General Medicine,PGY1,S0174975,2015-12-07,2:58:46,1:38:44,1:09:07,2:04:23,0:25:37,0:04:24,9:16:56,3383,170,19
+General Medicine,PGY1,S0174975,2015-12-06,1:37:00,1:27:54,1:05:36,1:16:05,0:27:28,0:05:27,6:42:39,2417,13,12
+General Medicine,PGY1,S0174975,2015-12-09,1:38:35,1:26:47,1:15:57,0:56:10,0:18:07,0:09:22,6:21:34,2167,406,10
+General Medicine,PGY1,S0174975,2015-12-08,1:47:22,1:42:33,0:49:08,1:10:27,0:25:44,0:10:37,6:57:34,2389,320,8
+Intensive Care Unit,PGY2+,S0181073,2016-10-15,1:51:26,0:26:37,0:15:14,0:32:12,0:22:02,0:00:14,3:48:09,794,0,13
+Intensive Care Unit,PGY2+,S0169411,2016-01-18,3:00:31,0:51:18,0:06:33,0:06:08,0:08:04,0:27:32,4:54:10,1560,35,17
+General Medicine,PGY1,S0169402,2014-11-16,0:54:52,0:04:13,1:14:55,0:18:20,0:03:50,0:04:31,2:48:08,452,0,7
+Emergency Medicine,PGY1,S0169406,2015-01-15,0:02:41,0:00:00,0:13:15,0:00:00,0:00:00,0:00:00,0:15:57,22,18,2
+General Medicine,PGY1,S0169402,2014-11-11,2:04:48,0:00:28,1:40:59,0:21:00,0:23:20,0:07:30,5:20:01,1063,71,6
+General Medicine,PGY1,S0169402,2014-11-10,3:07:55,0:08:06,2:01:46,0:44:24,0:20:08,0:05:40,6:49:50,1378,77,8
+General Medicine,PGY1,S0176040,2016-03-12,1:54:08,0:49:46,0:00:06,0:41:19,0:21:45,0:01:12,4:08:02,1544,0,10
+General Medicine,PGY1,S0169402,2014-11-13,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Intensive Care Unit,PGY2+,S0169386,2016-06-26,3:27:01,0:52:26,0:12:32,0:15:16,0:25:08,0:11:26,5:55:49,1827,40,13
+General Medicine,PGY1,S0169402,2014-11-12,0:37:27,0:00:00,0:04:55,0:00:00,0:00:00,0:00:00,0:42:38,128,126,9
+Intensive Care Unit,PGY2+,S0169411,2016-01-12,1:57:58,0:19:56,0:03:45,0:09:15,0:19:26,0:20:40,3:25:25,1390,34,22
+Emergency Medicine,PGY1,S0169406,2015-01-14,0:02:44,0:00:00,0:28:08,0:00:00,0:00:03,0:00:00,0:32:39,55,49,6
+Night Team,PGY2+,S0163425,2015-12-11,1:57:29,1:08:14,1:42:47,0:48:18,0:19:13,1:03:59,7:31:50,2836,0,54
+Night Team,PGY2+,S0163425,2015-12-12,1:30:11,1:31:32,1:39:22,0:33:38,0:10:23,0:39:07,6:24:35,2271,29,54
+Night Team,PGY2+,S0163425,2015-12-09,2:14:43,1:19:15,1:51:51,0:40:43,0:22:55,0:56:51,7:54:15,2505,0,37
+General Medicine,PGY2+,S0169398,2016-03-12,2:15:29,0:44:21,0:00:14,0:38:32,0:16:40,0:02:53,4:25:48,1746,76,17
+General Medicine,PGY1,S0181075,2015-11-14,2:35:33,1:14:48,1:08:20,1:07:04,0:39:01,0:37:42,7:46:41,2776,0,11
+General Medicine,PGY2+,S0169398,2016-03-10,3:54:42,1:35:46,0:00:08,1:02:09,0:51:20,0:10:36,8:18:29,2936,396,24
+General Medicine,PGY2+,S0169398,2016-03-11,3:49:00,1:18:12,0:00:05,0:32:46,0:26:46,0:08:33,7:02:13,2731,249,29
+General Medicine,PGY2+,S0169398,2016-03-16,2:40:29,1:00:21,0:00:02,0:16:13,0:29:21,0:04:11,4:51:31,2264,161,35
+General Medicine,PGY2+,S0169398,2016-03-17,2:04:55,1:01:13,0:00:11,0:16:55,0:25:07,0:01:41,4:19:13,1716,333,18
+General Medicine,PGY2+,S0169398,2016-03-14,3:16:36,1:16:00,0:00:05,0:21:07,0:15:58,0:04:32,5:52:10,1955,31,22
+General Medicine,PGY2+,S0169398,2016-03-15,2:57:25,0:49:46,0:09:35,0:34:16,0:19:44,0:02:01,5:49:53,1826,315,22
+Night Team,PGY2+,S0163425,2015-12-14,0:50:06,0:33:17,1:28:11,0:18:06,0:12:47,0:15:05,3:52:36,1248,93,48
+General Medicine,PGY2+,S0169398,2016-03-18,0:13:07,0:14:11,0:00:02,0:00:02,0:04:12,0:00:25,0:33:42,226,222,10
+General Medicine,PGY2+,S0169398,2016-03-19,2:19:55,1:06:39,0:00:00,0:02:28,0:09:34,0:01:49,4:04:50,1773,154,24
+General Medicine,PGY1,S0181075,2015-11-16,0:57:44,1:25:55,0:43:05,0:48:12,0:13:22,0:29:37,4:56:15,1998,68,9
+Intensive Care Unit,PGY2+,S0169411,2016-01-17,3:53:16,0:50:33,0:02:53,0:31:18,0:42:49,1:19:40,8:01:44,2593,0,26
+General Medicine,PGY2+,S0169401,2016-02-06,0:26:48,0:32:49,0:11:12,0:09:42,0:07:12,0:06:18,1:40:48,1071,1036,27
+Intensive Care Unit,PGY2+,S0169411,2016-01-14,3:57:22,0:41:04,0:06:05,0:38:25,0:21:05,0:35:12,6:44:42,2164,100,21
+General Medicine,PGY1,S0181075,2015-11-10,1:32:40,1:37:11,0:22:59,0:52:50,0:49:06,0:43:21,6:18:28,2633,0,7
+Intensive Care Unit,PGY2+,S0169411,2016-01-15,2:22:11,0:41:06,0:04:58,0:33:02,0:27:16,0:18:46,4:37:17,1561,0,30
+General Medicine,PGY1,S0079337,2014-12-23,3:08:08,0:02:46,1:37:49,0:36:41,0:37:58,0:05:00,6:32:25,1958,0,13
+General Medicine,PGY1,S0181075,2015-11-11,1:17:01,0:58:48,0:31:55,0:45:33,0:30:23,0:25:17,4:55:44,2082,145,5
+General Medicine,PGY2+,S0169401,2016-02-03,2:49:11,1:39:05,0:19:59,0:35:55,0:24:15,1:00:12,7:30:03,3808,714,40
+General Medicine,PGY1,S0181075,2015-11-13,2:07:47,1:18:29,1:25:34,1:20:58,0:46:12,0:29:41,8:24:40,3192,617,25
+Night Team,PGY1,S0173803,2016-03-09,2:10:37,1:53:52,0:00:04,0:41:12,0:09:21,0:01:00,5:25:23,1828,292,43
+Night Team,PGY1,S0173803,2016-03-08,2:23:07,1:06:34,0:00:01,0:53:08,0:12:23,0:05:21,4:55:53,1329,0,59
+General Medicine,PGY1,S0093383,2014-12-30,3:02:21,0:18:28,3:26:45,0:19:42,0:12:26,0:31:42,8:01:39,1904,0,14
+General Medicine,PGY1,S0093383,2014-12-31,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+Night Team,PGY1,S0173803,2016-03-05,1:39:24,0:41:17,0:00:07,0:33:06,0:09:01,0:09:58,3:35:16,1166,21,27
+Night Team,PGY1,S0173803,2016-03-04,2:00:34,1:26:25,0:00:35,0:44:32,0:25:25,0:08:05,5:37:55,2171,0,52
+Night Team,PGY1,S0173803,2016-03-07,2:01:52,0:41:24,0:00:07,1:08:56,0:27:39,0:03:30,4:43:50,1569,0,45
+Night Team,PGY1,S0173803,2016-03-06,0:46:07,0:46:55,0:00:00,0:18:43,0:16:54,0:04:11,2:27:02,747,0,31
+Night Team,PGY1,S0173803,2016-03-01,3:51:29,1:26:20,0:00:17,0:51:51,0:20:54,0:09:26,7:53:08,2914,0,64
+Night Team,PGY1,S0173803,2016-03-03,2:14:06,0:33:24,0:00:03,0:27:13,0:24:15,0:08:21,4:24:30,1752,0,44
+Night Team,PGY1,S0173803,2016-03-02,2:29:31,1:28:30,0:00:21,0:30:09,0:16:54,0:03:38,5:34:09,2181,0,44
+Night Team,PGY2+,S0106952,2016-07-30,2:25:23,1:19:52,0:00:02,0:22:44,0:11:58,0:07:52,4:49:30,1956,69,45
+Emergency Medicine,PGY1,S0169406,2015-01-10,3:13:34,0:06:45,1:46:43,0:40:25,0:29:30,0:23:48,7:31:06,2335,17,15
+General Medicine,PGY1,S0079640,2016-09-09,1:38:41,1:18:41,0:02:30,0:49:28,0:29:14,0:15:54,5:13:05,1598,68,16
+General Medicine,PGY1,S0079640,2016-09-08,0:06:57,0:01:54,0:00:00,0:01:10,0:00:00,0:00:04,0:10:49,116,60,6
+General Medicine,PGY1,S0079640,2016-09-07,1:41:38,1:24:11,0:00:10,0:32:32,0:15:27,0:04:57,4:29:26,1259,0,10
+General Medicine,PGY1,S0079640,2016-09-06,0:40:04,0:50:52,0:00:08,0:07:37,0:12:21,0:03:51,2:10:07,711,0,8
+General Medicine,PGY1,S0079640,2016-09-05,1:05:04,1:19:37,0:00:09,0:19:26,0:11:18,0:00:16,3:25:48,1125,210,6
+General Medicine,PGY1,S0079640,2016-09-04,1:06:29,2:43:29,0:03:41,0:27:53,0:20:32,0:00:45,5:17:38,1801,226,14
+General Medicine,PGY1,S0079640,2016-09-03,0:57:14,0:58:26,0:00:09,0:14:31,0:11:42,0:03:42,2:43:43,937,0,9
+General Medicine,PGY1,S0079640,2016-09-02,1:38:41,1:15:37,0:00:03,0:44:15,0:21:09,0:02:41,4:39:24,1625,0,9
+General Medicine,PGY1,S0079640,2016-09-01,0:13:24,0:29:38,0:00:04,0:06:16,0:00:35,0:00:01,1:02:13,312,308,6
+General Medicine,PGY1,S0169401,2014-09-30,5:14:09,0:23:25,0:09:02,1:13:16,0:26:25,0:05:12,8:19:09,3233,1761,20
+Emergency Medicine,PGY1,S0169406,2015-01-13,0:16:22,0:01:57,1:30:21,0:00:42,0:00:54,0:02:49,2:00:57,417,406,20
+General Medicine,PGY1,S0181060,2015-12-05,1:16:12,0:55:02,0:47:09,0:26:08,0:08:17,0:18:29,4:14:54,1296,9,10
+General Medicine,PGY1,S0181066,2015-12-06,1:29:27,0:38:20,1:54:23,0:51:41,0:16:28,0:27:09,6:24:25,1707,0,11
+General Medicine,PGY1,S0181066,2015-12-07,0:44:19,0:18:47,1:14:24,0:47:06,0:17:36,0:18:50,4:08:42,1517,0,8
+General Medicine,PGY1,S0169399,2014-06-27,2:06:00,0:03:59,0:23:23,0:09:48,0:14:53,0:03:24,3:10:51,776,0,9
+General Medicine,PGY1,S0169399,2014-06-26,2:39:23,0:03:03,0:18:11,0:38:00,0:17:42,0:08:49,4:35:23,1260,84,11
+Emergency Medicine,PGY1,S0169406,2015-01-12,3:45:12,0:05:37,1:37:52,0:20:06,0:32:44,0:32:17,7:21:36,2062,17,25
+General Medicine,PGY1,S0169399,2014-06-29,2:22:46,0:03:17,0:14:21,0:30:53,0:12:54,0:03:14,3:42:53,577,57,6
+General Medicine,PGY1,S0181066,2015-12-08,1:01:57,0:30:43,0:30:46,0:37:23,0:20:17,0:28:30,4:20:15,1237,12,8
+General Medicine,PGY1,S0181066,2015-12-09,0:20:48,0:37:26,0:25:54,0:37:53,0:10:56,0:20:06,2:43:31,811,0,5
+General Medicine,PGY1,S0181059,2015-09-12,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:30,23,17,4
+General Medicine,PGY1,S0181059,2015-09-11,0:00:17,0:02:11,0:00:00,0:00:00,0:00:00,0:00:00,0:02:33,38,34,7
+General Medicine,PGY1,S0181059,2015-09-10,2:44:00,0:50:31,1:18:55,0:47:57,0:39:01,0:29:49,7:36:29,2852,97,25
+General Medicine,PGY2+,S0169385,2015-12-25,1:14:03,0:46:40,0:52:48,0:37:12,0:24:26,0:59:56,5:29:23,2367,146,20
+Emergency Medicine,PGY1,S0169402,2014-07-09,0:40:07,0:01:05,0:07:48,0:00:00,0:00:34,0:00:48,0:52:16,149,149,12
+Intensive Care Unit,PGY2+,S0181064,2016-10-29,0:01:54,0:01:10,0:00:00,0:00:04,0:00:00,0:00:00,0:04:21,136,108,12
+Intensive Care Unit,PGY2+,S0181064,2016-10-28,2:25:02,0:53:16,0:03:13,0:23:35,0:31:59,0:01:48,4:51:47,1525,0,19
+Intensive Care Unit,PGY2+,S0181064,2016-10-27,1:49:56,0:22:21,0:06:04,0:11:03,0:19:15,0:06:09,3:23:13,1431,585,29
+Intensive Care Unit,PGY2+,S0181064,2016-10-26,3:17:32,1:28:35,0:00:16,0:36:44,0:34:28,0:10:05,6:40:28,2449,0,21
+Intensive Care Unit,PGY2+,S0181064,2016-10-25,2:28:49,1:19:06,0:00:51,0:27:18,0:30:07,0:05:58,5:23:22,1810,204,20
+Intensive Care Unit,PGY2+,S0181064,2016-10-24,0:07:43,0:07:13,0:00:00,0:00:07,0:00:24,0:00:00,0:21:16,202,187,17
+Intensive Care Unit,PGY2+,S0181064,2016-10-23,2:07:34,1:04:10,0:02:56,0:30:04,0:15:21,0:04:11,4:32:14,1657,436,15
+Intensive Care Unit,PGY2+,S0181064,2016-10-22,3:53:30,2:12:15,0:03:44,1:03:34,0:37:36,0:14:50,8:50:46,3066,0,19
+Intensive Care Unit,PGY2+,S0181064,2016-10-21,2:02:25,0:45:50,0:10:27,0:17:25,0:26:47,0:06:42,4:19:50,1795,166,19
+Intensive Care Unit,PGY2+,S0181064,2016-10-20,0:14:38,0:04:50,0:00:00,0:00:21,0:00:00,0:00:04,0:27:07,309,292,21
+Night Team,PGY2+,S0108287,2016-10-09,2:21:49,1:47:25,0:00:11,0:40:53,0:16:18,0:06:02,5:48:10,2372,0,29
+Intensive Care Unit,PGY2+,S0181077,2016-10-09,2:28:32,0:44:19,0:09:39,0:22:14,0:31:33,0:01:25,4:46:16,1500,0,22
+Night Team,PGY1,S0108268,2014-08-24,3:12:23,0:14:40,0:02:05,0:30:34,0:58:14,0:57:22,6:40:50,1839,371,37
+General Medicine,PGY1,S0181065,2016-04-07,3:26:58,0:50:23,0:00:47,0:22:12,0:29:02,0:04:51,5:45:44,2505,7,17
+General Medicine,PGY1,S0181065,2016-04-06,3:03:29,0:59:28,0:00:10,0:31:13,0:23:21,0:06:53,5:33:00,2679,0,13
+General Medicine,PGY1,S0181065,2016-04-05,4:42:56,1:15:34,0:00:05,0:46:22,0:34:34,0:06:54,8:08:58,3835,2,21
+General Medicine,PGY1,S0181065,2016-04-04,2:59:58,0:48:34,0:00:04,0:40:21,0:16:55,0:05:21,5:14:55,2274,139,10
+General Medicine,PGY1,S0181065,2016-04-03,2:22:27,1:00:51,0:00:14,0:20:02,0:15:17,0:04:31,4:32:16,2065,223,11
+General Medicine,PGY1,S0181065,2016-04-02,5:15:48,0:59:10,0:00:06,0:48:12,0:33:29,0:16:39,8:34:00,3921,37,18
+General Medicine,PGY1,S0181065,2016-04-01,2:43:38,0:27:39,0:07:27,0:43:00,0:13:57,0:06:57,5:09:01,2422,23,12
+General Medicine,PGY1,S0181065,2016-04-09,2:13:34,0:44:25,0:00:31,0:25:27,0:21:37,0:02:03,4:55:09,2131,297,7
+General Medicine,PGY1,S0181065,2016-04-08,3:13:23,0:47:28,0:04:47,0:49:00,0:13:41,0:07:37,5:55:49,2321,0,14
+General Medicine,PGY1,S0169385,2014-11-08,1:10:27,0:03:10,3:40:28,0:02:28,0:05:27,0:04:41,5:20:19,795,785,17
+General Medicine,PGY1,S0169385,2014-11-02,3:18:21,0:14:12,0:14:43,1:12:57,0:30:21,0:37:08,6:42:35,2014,0,12
+General Medicine,PGY1,S0169385,2014-11-03,3:17:58,0:07:34,0:09:28,1:06:04,0:43:54,0:45:04,6:37:57,2322,0,9
+General Medicine,PGY1,S0169385,2014-11-01,2:47:20,0:02:54,0:25:54,0:30:16,0:18:55,0:16:20,4:33:31,1124,79,6
+General Medicine,PGY1,S0169385,2014-11-06,2:34:37,0:05:07,1:03:46,1:09:27,0:27:29,0:32:43,6:27:19,1834,480,8
+General Medicine,PGY1,S0169385,2014-11-07,2:28:03,0:08:42,3:02:57,0:40:02,0:20:47,0:34:29,7:40:47,1531,0,8
+General Medicine,PGY1,S0169385,2014-11-04,4:13:37,0:06:28,0:20:06,1:51:24,0:46:08,0:46:06,8:20:37,2527,70,13
+General Medicine,PGY1,S0169385,2014-11-05,4:05:28,0:09:45,0:19:39,0:50:00,1:03:26,0:30:32,7:33:36,2189,728,30
+General Medicine,PGY1,S0172056,2015-09-06,1:09:26,0:03:00,1:25:42,0:14:21,0:04:33,0:13:32,3:32:19,1143,0,8
+General Medicine,PGY1,S0074196,2016-01-04,1:55:11,1:41:11,1:13:23,0:52:52,0:20:52,0:15:19,6:58:42,2743,8,15
+General Medicine,PGY1,S0094535,2015-01-31,0:03:02,0:12:12,0:00:00,0:01:17,0:00:00,0:00:57,0:18:44,87,69,3
+General Medicine,PGY1,S0094535,2015-01-30,0:00:00,0:00:07,0:00:00,0:01:26,0:00:00,0:00:02,0:01:40,33,11,3
+Emergency Medicine,PGY1,S0181060,2016-04-22,1:27:11,0:13:50,0:02:59,0:15:25,0:00:44,0:03:36,2:20:12,542,0,4
+Emergency Medicine,PGY1,S0181060,2016-04-21,3:50:30,0:49:50,0:01:19,0:38:15,1:08:59,0:02:14,7:17:37,2291,1,29
+General Medicine,PGY1,S0074196,2016-01-05,1:08:19,1:20:11,0:55:54,0:41:26,0:32:31,0:34:14,5:49:00,2136,200,13
+General Medicine,PGY1,S0172085,2015-06-30,1:54:54,1:07:13,2:58:24,0:55:45,0:31:59,0:36:32,8:33:23,1588,7,7
+Night Team,PGY2+,S0181069,2016-07-15,1:29:47,0:37:39,0:00:01,0:17:20,0:05:22,0:02:56,2:55:27,1504,0,16
+Night Team,PGY2+,S0181069,2016-07-14,1:01:47,0:49:06,0:00:05,0:28:23,0:08:01,0:04:53,3:10:50,967,362,19
+Night Team,PGY2+,S0181069,2016-07-16,1:24:53,0:50:35,0:00:11,0:39:08,0:06:21,0:03:51,3:49:52,1579,0,21
+Night Team,PGY2+,S0181069,2016-07-11,2:17:13,1:05:25,0:00:15,0:41:16,0:25:07,0:12:30,5:16:46,1707,140,36
+Night Team,PGY2+,S0181069,2016-07-10,1:59:23,1:09:12,0:00:09,0:52:41,0:22:08,0:05:50,5:02:12,1475,0,19
+Night Team,PGY2+,S0181069,2016-07-13,1:46:50,0:45:29,0:00:02,0:51:16,0:16:03,0:03:28,4:13:54,1689,0,21
+Night Team,PGY2+,S0181069,2016-07-12,2:21:00,1:12:25,0:00:05,0:50:40,0:10:00,0:01:49,5:15:13,1509,1,22
+General Medicine,PGY1,S0169403,2014-09-04,3:02:56,0:11:32,0:27:50,0:41:34,0:27:21,0:44:41,6:21:56,2103,46,20
+General Medicine,PGY1,S0169403,2014-09-05,2:23:51,0:04:01,0:10:54,0:31:53,0:21:44,0:18:39,4:18:04,1609,11,11
+Night Team,PGY2+,S0169399,2015-10-01,0:49:34,0:41:57,3:18:42,0:25:01,0:09:01,0:26:29,6:14:54,1068,0,23
+Night Team,PGY2+,S0169399,2015-10-03,0:24:37,0:18:50,2:01:33,0:13:32,0:07:34,0:22:05,3:44:23,613,501,15
+Night Team,PGY2+,S0169399,2015-10-02,0:53:59,0:43:19,4:22:19,0:27:13,0:14:07,0:26:25,7:33:45,1163,203,28
+Intensive Care Unit,PGY2+,S0181061,2016-08-30,2:19:12,1:08:33,0:02:24,1:09:37,0:40:10,0:08:48,6:21:17,2030,0,22
+General Medicine,PGY1,S0176040,2015-11-29,0:00:00,0:00:00,0:00:00,0:00:55,0:00:00,0:00:49,0:01:55,32,24,4
+General Medicine,PGY1,S0176040,2015-11-28,2:26:09,0:55:10,2:29:27,0:31:57,0:23:59,0:40:54,8:12:07,2024,0,11
+General Medicine,PGY1,S0176040,2015-11-27,0:40:41,0:57:45,2:26:40,0:13:51,0:10:46,0:36:52,5:25:00,1057,0,4
+General Medicine,PGY1,S0176040,2015-11-26,0:00:00,0:06:28,0:00:00,0:01:49,0:00:00,0:01:43,0:10:31,99,93,4
+General Medicine,PGY1,S0176040,2015-11-25,1:42:42,1:44:10,2:44:59,0:54:40,0:30:22,0:28:47,8:59:22,2456,11,9
+General Medicine,PGY1,S0176040,2015-11-24,1:17:30,0:56:53,2:00:22,0:28:47,0:28:34,0:33:25,6:13:44,1547,52,5
+General Medicine,PGY1,S0176040,2015-11-23,0:50:08,1:00:44,2:15:28,0:44:09,0:21:14,0:18:33,6:12:54,1674,108,6
+General Medicine,PGY1,S0176040,2015-11-22,1:01:46,1:12:53,1:22:26,0:54:37,0:12:50,0:24:31,5:29:41,1699,120,7
+General Medicine,PGY1,S0181073,2015-10-31,1:47:26,1:28:09,2:15:06,0:54:53,0:26:47,0:47:52,8:24:45,2679,0,9
+General Medicine,PGY1,S0181073,2015-10-30,0:48:35,1:03:57,0:58:31,0:10:09,0:10:29,0:25:56,3:57:38,1153,140,5
+General Medicine,PGY1,S0169400,2014-12-01,3:05:57,0:07:14,1:45:20,0:27:01,0:19:41,0:25:55,6:36:32,1817,10,13
+General Medicine,PGY1,S0169403,2014-09-06,0:00:00,0:14:16,0:00:00,0:00:41,0:00:00,0:00:00,0:15:41,146,140,6
+General Medicine,PGY1,S0169400,2014-12-03,3:40:21,0:07:22,1:57:45,0:44:10,0:31:29,0:32:39,7:54:58,2040,22,16
+General Medicine,PGY1,S0169400,2014-12-02,0:09:09,0:00:00,0:00:02,0:03:46,0:00:16,0:00:00,0:13:44,95,87,8
+General Medicine,PGY1,S0169400,2014-12-05,2:23:27,0:00:44,1:03:30,0:10:00,0:12:46,0:14:52,4:12:43,891,115,6
+General Medicine,PGY1,S0169400,2014-12-04,2:36:32,0:05:13,1:37:46,0:07:47,0:17:29,0:20:22,5:22:48,1308,19,11
+General Medicine,PGY1,S0169400,2014-12-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,4
+General Medicine,PGY2+,S0109179,2016-08-09,1:49:35,0:47:15,0:00:01,0:23:59,0:07:23,0:11:19,3:34:40,1702,0,48
+General Medicine,PGY2+,S0109179,2016-08-08,1:05:02,0:31:21,0:00:22,0:09:02,0:08:25,0:07:17,2:17:00,1359,12,19
+General Medicine,PGY1,S0169403,2014-09-07,2:03:16,0:05:19,0:19:51,0:25:24,0:17:35,0:34:00,4:11:18,1401,50,9
+General Medicine,PGY2+,S0109179,2016-08-01,2:31:23,1:02:08,0:01:03,0:16:01,0:06:58,0:03:53,4:20:22,1664,0,26
+General Medicine,PGY2+,S0109179,2016-08-03,0:50:22,0:05:55,0:00:00,0:06:44,0:01:20,0:00:01,1:10:51,350,0,26
+General Medicine,PGY2+,S0109179,2016-08-02,0:02:05,0:00:10,0:00:00,0:00:00,0:00:00,0:01:31,0:04:42,45,0,5
+General Medicine,PGY2+,S0109179,2016-08-05,1:54:28,0:45:05,0:00:10,0:32:01,0:16:32,0:01:18,3:52:02,1398,213,20
+General Medicine,PGY2+,S0109179,2016-08-04,2:56:55,1:08:29,0:00:06,0:29:04,0:08:13,0:01:28,5:10:12,2052,125,41
+General Medicine,PGY2+,S0109179,2016-08-07,1:21:03,1:23:23,0:00:20,0:25:51,0:16:32,0:01:00,3:59:24,1641,42,17
+General Medicine,PGY2+,S0109179,2016-08-06,2:50:15,0:50:55,0:00:03,0:16:47,0:09:35,0:03:20,4:33:03,1871,0,25
+General Medicine,PGY1,S0181069,2016-03-27,2:45:37,1:20:29,0:00:07,1:17:06,0:16:50,0:12:08,6:54:32,2123,0,7
+General Medicine,PGY1,S0181069,2016-03-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0181069,2016-03-29,2:17:01,0:58:58,0:00:07,1:01:02,0:13:37,0:02:07,5:32:30,1818,5,18
+General Medicine,PGY1,S0169385,2015-04-30,1:35:41,0:08:11,0:48:41,0:26:55,0:14:43,0:14:12,3:34:53,1733,82,9
+Emergency Medicine,PGY1,S0188098,2016-09-16,2:58:11,0:37:23,0:08:05,0:13:16,0:12:14,0:05:52,4:39:28,1518,0,22
+Emergency Medicine,PGY1,S0188098,2016-09-14,3:36:20,0:46:40,0:06:06,0:09:47,0:18:44,0:01:33,5:51:46,1958,0,27
+Emergency Medicine,PGY1,S0188098,2016-09-12,3:15:38,0:36:47,0:10:32,0:13:41,0:22:22,0:02:04,5:17:00,1407,37,24
+Emergency Medicine,PGY1,S0188098,2016-09-13,2:07:26,0:24:19,0:01:48,0:03:13,0:11:10,0:00:53,3:06:44,998,0,13
+Emergency Medicine,PGY1,S0188098,2016-09-10,5:19:48,1:04:11,0:04:28,0:15:52,0:23:07,0:05:25,8:12:51,1954,0,24
+Emergency Medicine,PGY1,S0188098,2016-09-11,2:34:39,0:20:43,0:00:40,0:06:34,0:07:16,0:06:34,3:38:52,808,144,16
+Intensive Care Unit,PGY2+,S0169394,2016-05-07,3:23:42,0:33:47,0:04:52,0:41:28,0:28:05,0:21:17,6:03:30,1679,389,18
+Intensive Care Unit,PGY2+,S0169394,2016-05-06,3:06:55,0:30:45,0:03:17,0:48:27,0:43:17,0:14:35,6:17:13,1572,3,19
+Intensive Care Unit,PGY2+,S0169394,2016-05-05,0:56:25,0:22:14,0:03:51,0:02:24,0:08:15,0:02:49,1:46:21,556,0,11
+Intensive Care Unit,PGY2+,S0169394,2016-05-04,1:47:30,0:23:39,0:04:00,0:25:46,0:13:41,0:00:30,3:05:32,819,5,10
+Intensive Care Unit,PGY2+,S0169394,2016-05-03,2:27:19,0:28:12,0:02:02,0:09:43,0:33:23,0:07:42,4:24:44,1190,149,29
+Intensive Care Unit,PGY2+,S0169394,2016-05-02,1:28:12,0:28:44,0:03:41,0:09:08,0:37:58,0:06:04,3:16:54,877,0,10
+Intensive Care Unit,PGY2+,S0169394,2016-05-01,1:52:00,0:44:06,0:09:04,0:14:56,0:24:02,0:04:05,3:46:18,1022,230,18
+Intensive Care Unit,PGY2+,S0169394,2016-05-09,0:13:25,0:11:52,0:00:03,0:18:48,0:12:29,0:00:02,1:04:27,321,0,12
+Intensive Care Unit,PGY2+,S0169394,2016-05-08,0:00:00,0:00:00,0:00:00,0:00:52,0:00:00,0:00:00,0:00:52,22,0,7
+General Medicine,PGY1,S0169403,2014-09-09,2:13:27,0:06:41,0:11:40,0:34:19,0:30:02,0:20:57,4:18:36,1317,92,9
+Night Team,PGY2+,S0181057,2016-08-02,1:04:20,1:07:18,0:00:10,0:49:14,0:09:44,0:06:08,3:25:02,1179,185,13
+Night Team,PGY2+,S0181057,2016-08-03,0:42:59,0:21:18,0:00:00,0:22:51,0:06:44,0:12:08,1:56:25,1133,0,12
+Night Team,PGY2+,S0169381,2016-03-10,2:48:56,1:10:39,0:00:06,0:33:00,0:16:56,0:14:43,5:40:12,1569,54,38
+Night Team,PGY2+,S0169381,2016-03-11,1:49:32,1:07:48,0:00:11,0:25:15,0:26:49,0:05:44,4:31:44,1347,12,34
+Night Team,PGY2+,S0169381,2016-03-12,2:05:26,1:44:41,0:00:16,0:37:53,0:14:00,0:05:34,5:50:01,2347,54,40
+Night Team,PGY2+,S0169381,2016-03-13,2:32:46,1:11:46,0:02:28,0:58:45,0:17:17,0:15:20,5:51:46,1592,80,24
+Night Team,PGY2+,S0169381,2016-03-14,1:45:01,0:56:23,0:00:06,0:25:09,0:06:59,0:08:39,4:13:55,1644,10,21
+Night Team,PGY2+,S0169381,2016-03-15,0:30:10,0:26:30,0:00:01,0:13:36,0:04:57,0:00:03,1:24:27,534,0,14
+Night Team,PGY2+,S0169381,2016-03-16,3:00:24,0:43:52,0:00:04,0:30:25,0:12:43,0:04:12,5:48:55,1570,8,23
+Night Team,PGY2+,S0169381,2016-03-17,3:05:38,1:22:37,0:00:05,0:29:54,0:11:20,0:03:30,5:43:18,1963,0,43
+Night Team,PGY2+,S0169381,2016-03-18,3:02:17,0:53:31,0:00:05,0:16:01,0:09:47,0:05:44,4:53:31,1053,127,25
+Night Team,PGY2+,S0169406,2016-10-30,0:57:30,0:51:48,0:00:20,0:12:09,0:07:22,0:01:19,2:30:12,1118,71,14
+Night Team,PGY2+,S0169406,2016-10-31,0:37:50,0:29:08,0:00:00,0:05:40,0:05:00,0:00:12,1:29:51,511,0,15
+Emergency Medicine,PGY1,S0181526,2015-07-18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:19,0:00:23,8,8,1
+General Medicine,PGY2+,S0169406,2016-02-08,1:24:03,0:33:26,0:06:33,0:06:37,0:16:03,0:24:39,3:04:42,884,203,11
+General Medicine,PGY2+,S0169406,2016-02-09,1:33:44,1:08:25,0:02:51,0:06:34,0:13:18,0:29:14,4:07:14,1622,0,18
+General Medicine,PGY1,S0181074,2016-05-20,3:02:18,2:01:02,0:04:12,1:28:13,0:20:01,0:19:42,7:55:58,2717,117,24
+General Medicine,PGY1,S0181074,2016-05-21,1:07:39,1:58:28,0:00:20,0:51:47,0:10:33,0:03:18,4:56:15,1914,26,19
+General Medicine,PGY2+,S0169406,2016-02-07,1:40:11,1:00:14,0:06:35,0:03:03,0:40:13,0:24:49,4:31:19,1886,0,18
+Night Team,PGY1,S0163425,2014-08-09,2:03:11,0:00:56,0:14:45,0:22:06,0:29:54,0:43:00,4:06:57,1181,0,33
+Night Team,PGY1,S0163425,2014-08-08,2:31:24,0:04:22,0:30:53,0:15:20,0:36:45,0:55:33,5:07:43,1053,52,26
+Night Team,PGY1,S0163425,2014-08-05,0:46:42,0:00:04,0:00:45,0:13:33,0:21:07,0:23:58,1:50:36,514,0,15
+Night Team,PGY1,S0163425,2014-08-04,0:45:57,0:00:00,0:05:22,0:20:18,0:09:29,0:05:39,1:32:35,292,0,12
+Night Team,PGY1,S0163425,2014-08-07,2:26:13,0:07:29,0:02:08,0:21:52,0:31:17,0:55:10,4:41:15,1055,0,22
+Night Team,PGY1,S0163425,2014-08-06,1:52:59,0:03:47,0:05:34,0:21:16,0:34:58,0:53:04,3:58:11,1067,0,26
+Night Team,PGY1,S0163425,2014-08-03,1:46:21,0:06:04,0:12:57,0:09:50,0:13:39,0:31:41,3:21:11,765,39,26
+General Medicine,PGY1,S0196595,2016-06-28,0:54:27,1:13:06,0:00:20,0:16:04,0:13:51,0:00:12,2:56:52,554,0,3
+Intensive Care Unit,PGY2+,S0106952,2015-11-14,2:07:42,0:50:39,0:19:52,0:47:18,0:57:44,0:50:14,6:18:17,1885,389,29
+Intensive Care Unit,PGY2+,S0106952,2015-11-13,2:06:22,1:05:28,0:16:01,0:34:52,0:43:25,0:56:05,6:04:45,1852,261,69
+Intensive Care Unit,PGY2+,S0106952,2015-11-12,0:01:31,0:11:50,0:03:24,0:00:59,0:00:00,0:00:11,0:18:15,304,260,16
+Intensive Care Unit,PGY2+,S0106952,2015-11-11,1:36:32,1:28:30,0:19:24,0:44:10,0:49:33,0:35:27,6:24:19,2248,1062,31
+Intensive Care Unit,PGY2+,S0106952,2015-11-10,2:23:20,1:36:10,0:19:17,0:54:30,0:48:19,0:50:02,7:28:43,3126,1012,101
+General Medicine,PGY1,S0169404,2014-07-29,4:05:06,0:47:31,0:17:33,0:22:50,0:32:58,0:21:21,7:15:44,3135,350,15
+Intensive Care Unit,PGY2+,S0174975,2016-09-18,2:15:35,0:30:04,0:06:47,0:42:59,0:41:37,0:04:26,4:58:59,1760,196,15
+Night Team,PGY2+,S0169402,2016-03-21,1:46:51,1:38:58,0:00:04,0:11:10,0:04:31,0:05:34,4:01:19,1393,909,37
+Night Team,PGY2+,S0169402,2016-03-20,1:08:03,1:02:38,0:02:17,0:14:13,0:03:21,0:03:40,2:51:45,877,173,31
+Night Team,PGY2+,S0169402,2016-03-23,2:50:17,1:32:01,0:00:09,0:57:28,0:15:50,0:11:29,6:14:21,1811,52,23
+Night Team,PGY2+,S0169402,2016-03-22,2:48:03,1:30:58,0:00:27,1:22:15,0:14:42,0:30:27,7:18:50,3377,895,24
+General Medicine,PGY1,S0181079,2015-09-28,1:21:18,1:03:45,1:22:39,0:35:45,0:28:31,0:20:55,5:36:21,1946,142,6
+General Medicine,PGY1,S0181079,2015-09-29,1:32:02,0:51:51,1:17:21,0:45:07,0:56:40,0:31:57,6:22:49,1808,70,9
+Night Team,PGY2+,S0169402,2016-03-27,1:21:53,0:42:40,0:00:00,0:19:46,0:08:01,0:02:52,2:55:43,1120,7,44
+Night Team,PGY2+,S0169402,2016-03-26,1:31:10,1:29:37,0:00:06,0:27:14,0:09:02,0:10:01,4:02:52,1014,0,33
+General Medicine,PGY1,S0181079,2015-09-24,1:55:08,0:48:58,1:16:31,0:37:27,0:30:17,0:18:05,6:09:29,1907,0,12
+General Medicine,PGY1,S0181079,2015-09-25,0:00:00,0:11:38,0:00:00,0:01:26,0:00:00,0:00:03,0:14:02,88,76,7
+General Medicine,PGY1,S0181079,2015-09-26,1:39:46,0:47:11,1:16:19,0:42:54,0:29:09,0:23:33,5:44:55,1848,9,13
+General Medicine,PGY1,S0181079,2015-09-27,1:59:17,0:51:05,1:45:17,0:46:14,0:27:55,0:20:13,6:47:51,1982,0,10
+General Medicine,PGY1,S0181079,2015-09-20,2:36:51,0:15:59,1:06:11,0:55:13,0:36:08,0:22:51,6:38:47,2024,52,7
+General Medicine,PGY1,S0181079,2015-09-21,2:03:52,0:08:40,1:30:16,0:17:15,0:18:11,0:17:21,5:01:51,1564,10,10
+General Medicine,PGY1,S0181079,2015-09-22,2:11:20,0:27:43,1:31:32,0:26:38,0:33:13,0:26:36,6:18:50,1844,0,12
+General Medicine,PGY1,S0181079,2015-09-23,2:35:09,0:50:53,1:07:15,0:30:26,0:16:17,0:25:19,6:17:05,1808,15,10
+General Medicine,PGY2+,S0169403,2015-08-02,1:53:29,0:56:22,1:04:55,0:32:16,0:27:04,0:47:17,6:31:21,2594,25,20
+General Medicine,PGY2+,S0169403,2015-08-03,1:43:21,1:30:34,1:38:02,0:41:02,0:21:37,1:03:28,7:45:44,2417,81,27
+Intensive Care Unit,PGY2+,S0174975,2016-09-17,3:36:26,0:45:50,0:00:11,1:22:32,0:37:19,0:04:33,7:33:48,2264,0,14
+Emergency Medicine,PGY1,S0195076,2016-07-26,0:55:47,0:05:15,0:00:21,0:10:50,0:26:33,0:00:50,1:55:29,635,0,6
+General Medicine,PGY2+,S0169403,2015-08-01,0:00:00,1:46:06,0:00:00,0:00:00,0:00:00,0:11:49,2:01:46,658,658,22
+Night Team,PGY2+,S0169404,2016-01-03,0:04:10,0:16:02,0:00:45,0:01:33,0:00:25,0:01:28,0:24:50,303,295,20
+General Medicine,PGY2+,S0169403,2015-08-07,1:37:08,0:56:16,0:38:25,1:09:34,0:24:47,0:18:51,5:37:48,2647,33,13
+Intensive Care Unit,PGY2+,S0172056,2016-10-30,2:18:39,0:39:12,0:05:41,0:44:42,0:23:53,0:05:01,4:42:19,1413,89,14
+General Medicine,PGY2+,S0169403,2015-08-04,0:55:09,0:46:32,0:05:36,0:08:25,0:11:11,0:32:31,2:46:41,1082,619,20
+General Medicine,PGY2+,S0169403,2015-08-05,0:00:00,0:03:15,0:00:00,0:00:02,0:00:00,0:02:50,0:07:23,104,94,5
+General Medicine,PGY1,S0181081,2016-05-31,2:01:55,1:42:49,0:00:53,0:42:16,0:24:27,0:03:04,5:33:17,2091,29,25
+General Medicine,PGY1,S0181081,2016-05-30,3:00:13,1:58:23,0:00:05,1:31:41,0:21:57,0:08:48,7:43:01,2594,52,9
+Night Team,PGY1,S0181077,2015-11-21,0:31:22,0:37:06,0:32:18,0:21:24,0:08:27,0:10:14,2:33:09,854,759,21
+Night Team,PGY1,S0181077,2015-11-20,1:56:14,1:08:06,1:16:53,0:21:43,0:18:34,0:14:45,5:35:11,1722,1441,48
+General Medicine,PGY1,S0195220,2016-08-03,1:16:27,0:37:53,0:00:10,0:30:29,0:13:42,0:01:53,3:01:53,956,0,7
+Night Team,PGY1,S0093383,2015-05-31,1:47:45,0:30:23,1:36:12,0:32:00,0:25:23,0:52:50,5:56:10,1434,226,21
+Night Team,PGY1,S0093383,2015-05-30,2:18:02,0:30:40,0:34:12,0:37:23,0:30:39,0:35:00,5:14:13,1706,0,48
+General Medicine,PGY1,S0181079,2016-03-23,2:36:06,1:09:26,0:00:32,0:29:33,0:15:10,0:01:28,5:11:00,1699,14,16
+Night Team,PGY1,S0169399,2015-06-08,1:03:39,0:15:09,3:34:20,0:17:20,0:23:59,0:16:22,6:08:57,981,0,35
+General Medicine,PGY1,S0169381,2014-10-19,3:41:44,0:06:38,0:25:43,0:40:25,0:25:03,0:40:17,6:12:37,1448,42,6
+Emergency Medicine,PGY2+,S0169403,2016-01-27,0:00:00,0:00:49,0:00:00,0:00:00,0:00:00,0:00:00,0:01:27,25,25,2
+Night Team,PGY1,S0169399,2015-06-07,0:49:23,0:00:50,0:05:01,0:05:47,0:01:50,0:01:15,1:07:46,201,0,17
+General Medicine,PGY1,S0181082,2015-12-31,0:00:00,0:00:00,0:00:00,0:00:34,0:00:00,0:00:00,0:00:34,14,0,5
+General Medicine,PGY1,S0181082,2015-12-30,1:28:49,1:43:57,1:55:01,1:09:33,0:30:59,0:35:31,8:20:31,2746,0,10
+Night Team,PGY1,S0181075,2015-12-28,2:18:35,1:05:12,0:00:00,0:54:01,0:42:40,0:37:56,6:09:47,1973,0,42
+Night Team,PGY1,S0181075,2015-12-29,2:30:52,1:37:42,0:45:02,1:40:17,0:45:16,0:56:48,8:48:44,3413,0,49
+General Medicine,PGY1,S0169383,2015-04-30,3:26:29,0:30:36,2:04:02,0:45:06,0:38:54,0:31:59,8:33:21,2293,49,13
+Night Team,PGY1,S0181075,2015-12-24,2:37:19,1:11:23,0:00:02,0:35:52,0:32:12,0:39:16,5:53:12,2332,19,72
+Night Team,PGY1,S0181075,2015-12-25,1:48:23,0:59:58,0:01:03,0:19:57,0:14:05,0:15:43,3:56:43,1392,0,57
+Night Team,PGY1,S0181075,2015-12-26,0:51:09,0:23:09,0:00:00,0:16:37,0:22:14,0:26:50,2:27:02,954,307,22
+Night Team,PGY1,S0181075,2015-12-27,2:05:23,1:02:59,0:39:44,1:34:56,0:54:37,1:08:12,7:46:00,2444,0,37
+Night Team,PGY1,S0181075,2015-12-20,2:03:05,1:44:35,0:00:00,1:36:45,0:20:04,0:35:46,6:57:12,2894,0,35
+Night Team,PGY1,S0181075,2015-12-21,2:15:05,1:16:00,0:00:00,1:06:04,0:38:27,0:44:54,6:17:40,2163,0,55
+Night Team,PGY1,S0181075,2015-12-22,3:35:26,2:28:04,0:20:07,0:46:29,0:37:01,0:20:43,8:33:32,2664,0,57
+Night Team,PGY1,S0181075,2015-12-23,2:13:49,1:53:49,0:46:08,0:48:29,0:45:03,0:29:35,7:17:13,3004,0,73
+General Medicine,PGY2+,S0181077,2016-08-31,1:46:50,1:02:53,0:02:09,0:16:45,0:14:41,0:12:03,4:27:18,1839,113,21
+General Medicine,PGY2+,S0181077,2016-08-30,2:38:40,1:13:07,0:00:06,0:14:40,0:13:41,0:09:42,5:39:05,2510,103,26
+General Medicine,PGY1,S0169381,2014-09-03,3:24:12,0:15:59,0:25:57,0:34:22,0:17:27,0:52:34,6:18:43,1320,97,16
+General Medicine,PGY1,S0169381,2014-09-02,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,6,0,1
+General Medicine,PGY1,S0169381,2014-09-01,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0169381,2014-09-07,0:03:22,0:00:00,0:01:04,0:00:08,0:00:13,0:00:09,0:05:08,59,49,7
+General Medicine,PGY1,S0169381,2014-09-06,2:07:43,0:03:57,0:23:04,0:13:53,0:20:46,0:22:59,3:51:10,866,0,9
+General Medicine,PGY1,S0169381,2014-09-05,2:56:25,0:04:23,0:18:36,0:32:14,0:18:17,0:26:24,4:55:00,1255,59,13
+General Medicine,PGY1,S0169381,2014-09-04,2:17:27,0:07:53,0:18:27,0:28:17,0:12:15,0:14:52,4:07:04,1266,100,18
+General Medicine,PGY1,S0169381,2014-09-09,1:25:40,0:08:37,0:12:11,0:12:13,0:06:03,0:06:52,2:20:19,601,21,9
+General Medicine,PGY1,S0169381,2014-09-08,3:06:58,0:05:29,0:16:52,0:35:25,0:24:50,0:20:57,5:13:19,1415,0,15
+General Medicine,PGY1,S0195210,2016-11-10,2:03:17,1:31:56,0:02:03,0:54:12,0:22:41,0:04:55,5:29:46,1513,0,18
+Night Team,PGY1,S0181525,2015-12-19,0:50:50,0:25:32,1:14:20,0:18:17,0:12:48,0:36:01,3:51:27,913,30,28
+Night Team,PGY1,S0181525,2015-12-18,1:32:22,0:32:07,1:30:28,0:24:00,0:34:18,0:39:04,5:19:35,1095,0,47
+Night Team,PGY1,S0174975,2016-03-31,2:14:48,0:34:11,0:00:08,0:48:20,0:07:40,0:00:49,4:02:32,1182,264,66
+Night Team,PGY1,S0195086,2016-10-31,1:08:59,1:01:22,0:00:03,0:14:47,0:14:08,0:00:32,2:55:44,917,0,29
+Night Team,PGY1,S0195086,2016-10-30,0:42:15,0:30:12,0:00:00,0:18:43,0:17:18,0:00:24,1:57:25,724,104,27
+Emergency Medicine,PGY1,S0181065,2015-12-25,1:29:35,0:30:27,2:45:43,1:02:00,0:33:59,0:31:56,7:33:34,3751,21,37
+Emergency Medicine,PGY1,S0181065,2015-12-24,1:02:07,0:24:04,1:37:58,0:28:22,0:17:21,0:14:42,4:30:39,2631,0,22
+Emergency Medicine,PGY1,S0181065,2015-12-27,1:10:23,0:42:53,2:29:31,0:56:03,0:36:05,0:45:12,7:14:39,3615,0,30
+Emergency Medicine,PGY1,S0181065,2015-12-26,1:16:28,0:46:25,2:59:53,0:46:32,0:39:44,0:25:44,7:38:30,3931,0,38
+Emergency Medicine,PGY1,S0181065,2015-12-21,1:17:26,1:18:40,2:35:08,0:31:23,0:35:41,0:43:08,7:24:22,3935,0,19
+General Medicine,PGY1,S0195203,2016-07-05,1:33:17,1:15:46,0:00:24,0:20:05,0:10:56,0:06:09,3:43:57,1232,141,9
+Emergency Medicine,PGY1,S0181065,2015-12-23,0:24:34,0:00:28,0:02:33,0:00:19,0:00:41,0:00:00,0:40:12,377,377,3
+Emergency Medicine,PGY1,S0181065,2015-12-22,0:03:49,0:04:25,0:00:00,0:01:14,0:00:00,0:00:00,0:16:21,174,174,9
+General Medicine,PGY1,S0169400,2014-11-25,2:17:21,0:05:43,0:18:50,0:09:15,0:12:08,0:14:41,3:29:35,1090,151,6
+General Medicine,PGY1,S0169400,2014-11-22,1:38:32,0:01:35,0:46:58,0:19:37,0:06:42,0:19:49,3:18:45,769,0,6
+General Medicine,PGY1,S0169400,2014-11-23,3:10:47,0:02:16,2:11:49,0:46:25,0:28:12,0:31:35,7:33:57,2012,0,13
+Night Team,PGY2+,S0106952,2015-07-22,3:11:15,0:41:17,0:31:16,0:08:48,0:05:10,0:21:54,5:26:51,2225,931,82
+General Medicine,PGY1,S0195203,2016-07-01,0:06:18,0:11:09,0:00:01,0:03:29,0:00:43,0:00:51,0:26:07,135,103,8
+General Medicine,PGY1,S0195213,2016-10-16,0:36:44,0:23:30,0:00:05,0:14:35,0:17:11,0:00:16,1:58:33,556,0,6
+General Medicine,PGY1,S0169395,2014-11-07,1:31:42,0:02:37,1:21:57,0:14:10,0:09:30,0:04:15,3:43:31,900,0,14
+General Medicine,PGY1,S0169395,2014-11-06,0:00:00,0:00:00,0:00:00,0:00:05,0:00:00,0:00:00,0:00:05,12,0,2
+Emergency Medicine,PGY2+,S0169400,2015-10-11,0:06:55,0:09:43,0:00:00,0:00:00,0:00:00,0:02:31,0:19:51,161,161,7
+Emergency Medicine,PGY2+,S0169400,2015-10-10,0:42:48,0:07:56,0:23:12,0:02:46,0:06:31,0:15:33,1:43:33,475,0,9
+Emergency Medicine,PGY2+,S0169400,2015-10-13,0:00:00,0:01:01,0:00:00,0:00:00,0:00:00,0:00:00,0:01:12,27,27,3
+Emergency Medicine,PGY2+,S0169400,2015-10-12,3:20:17,0:57:11,1:37:05,0:14:04,0:19:02,0:21:07,7:09:34,1902,0,32
+Emergency Medicine,PGY2+,S0169400,2015-10-15,3:29:06,0:59:08,1:18:26,0:26:55,0:24:13,0:36:18,7:52:00,2866,0,26
+Emergency Medicine,PGY2+,S0169400,2015-10-14,4:08:21,0:29:41,1:10:49,0:09:58,0:10:43,0:19:04,6:39:21,1530,0,31
+Emergency Medicine,PGY2+,S0169400,2015-10-17,0:00:00,0:05:06,0:00:00,0:00:00,0:00:00,0:00:23,0:05:43,58,58,5
+Emergency Medicine,PGY2+,S0169400,2015-10-16,5:16:00,0:56:21,2:04:36,0:25:57,0:21:56,0:34:15,10:16:54,3042,0,32
+General Medicine,PGY1,S0181056,2016-03-08,3:12:05,1:30:21,0:00:17,2:05:16,0:25:50,0:02:42,8:32:35,3235,15,21
+General Medicine,PGY2+,S0172085,2016-09-30,2:48:53,1:18:52,0:00:01,0:16:25,0:11:05,0:11:37,5:22:18,2454,0,34
+General Medicine,PGY1,S0181056,2016-03-04,2:15:36,1:26:02,0:06:56,0:47:28,0:10:57,0:12:34,6:06:27,2346,6,20
+General Medicine,PGY1,S0181056,2016-03-05,2:37:51,1:05:01,0:00:08,0:24:58,0:18:19,0:01:05,5:17:14,1955,101,11
+General Medicine,PGY1,S0181056,2016-03-06,3:06:12,1:18:45,0:00:08,1:03:16,0:19:11,0:03:39,7:08:36,2448,7,12
+General Medicine,PGY1,S0181056,2016-03-07,2:37:25,1:07:27,0:00:07,0:55:18,0:18:59,0:04:26,6:06:39,2197,19,11
+General Medicine,PGY1,S0181056,2016-03-01,3:39:25,1:36:38,0:02:29,0:52:15,0:27:46,0:10:38,7:36:24,2519,59,12
+General Medicine,PGY1,S0181056,2016-03-02,2:20:16,1:11:39,0:01:07,0:38:01,0:19:20,0:03:46,5:13:22,2053,65,14
+General Medicine,PGY1,S0181056,2016-03-03,3:10:01,1:31:29,0:00:04,0:51:25,0:24:52,0:02:34,6:47:10,2525,188,16
+Emergency Medicine,PGY1,S0163662,2015-02-27,3:35:36,0:34:42,2:18:09,0:29:22,0:32:16,0:09:40,8:45:30,2670,0,57
+Intensive Care Unit,PGY2+,S0181061,2016-09-09,2:26:36,0:53:43,0:03:05,0:28:26,0:20:10,0:05:01,4:38:56,1338,347,15
+Emergency Medicine,PGY1,S0163662,2015-02-25,2:42:26,1:10:23,2:14:47,0:13:01,0:16:39,0:18:38,7:36:46,2111,199,55
+Emergency Medicine,PGY1,S0163662,2015-02-24,2:41:14,0:27:52,1:19:01,0:19:45,0:14:51,0:14:42,6:10:56,2316,190,16
+Emergency Medicine,PGY1,S0163662,2015-02-23,3:40:32,1:02:48,2:16:10,0:40:47,0:29:22,0:09:42,9:14:48,2884,236,25
+Emergency Medicine,PGY1,S0163662,2015-02-22,3:20:37,1:13:38,2:12:54,0:19:02,0:33:21,0:17:03,8:44:04,2888,182,20
+Emergency Medicine,PGY1,S0163662,2015-02-21,1:31:32,0:14:42,0:03:20,0:08:53,0:09:51,0:04:56,2:22:51,821,0,13
+Intensive Care Unit,PGY2+,S0181061,2016-09-01,2:27:46,0:27:17,0:06:57,0:36:57,0:26:13,0:06:14,4:43:44,1256,87,17
+Intensive Care Unit,PGY2+,S0181061,2016-09-02,0:00:00,0:00:00,0:00:00,0:00:05,0:00:00,0:00:00,0:00:05,14,0,3
+Intensive Care Unit,PGY2+,S0181061,2016-09-03,3:32:20,0:49:13,0:03:04,1:15:53,0:35:23,0:02:42,7:21:53,1871,10,15
+Intensive Care Unit,PGY2+,S0181061,2016-09-04,2:09:25,0:32:29,0:03:54,0:29:21,0:12:44,0:07:25,4:03:02,901,37,11
+Intensive Care Unit,PGY2+,S0181061,2016-09-05,0:00:00,0:00:00,0:00:00,0:00:17,0:00:00,0:00:00,0:00:17,14,0,4
+Intensive Care Unit,PGY2+,S0181061,2016-09-06,1:37:32,0:21:46,0:03:02,0:14:57,0:10:43,0:12:32,3:01:32,825,0,10
+Emergency Medicine,PGY1,S0163662,2015-02-28,0:02:27,0:04:51,1:15:33,0:00:00,0:00:44,0:01:34,1:26:54,252,250,11
+General Medicine,PGY1,S0181063,2016-03-31,0:40:43,0:54:17,0:00:31,0:18:39,0:15:18,0:00:23,2:38:59,1027,151,11
+General Medicine,PGY1,S0181063,2016-03-30,1:06:48,0:48:29,0:00:06,0:37:21,0:24:50,0:00:07,3:23:04,1224,11,7
+General Medicine,PGY1,S0181077,2016-04-30,1:33:01,0:58:46,0:01:50,0:15:20,0:06:30,0:03:20,3:13:43,1538,64,10
+General Medicine,PGY1,S0196597,2016-10-01,2:29:43,1:04:58,0:00:20,0:53:28,0:14:19,0:05:27,5:21:51,2140,163,21
+Night Team,PGY1,S0195220,2016-07-08,1:20:35,0:42:08,0:00:03,0:21:11,0:14:38,0:01:50,2:51:10,763,0,14
+Night Team,PGY1,S0195220,2016-07-09,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,4,0,2
+Night Team,PGY1,S0094535,2014-11-20,2:41:26,0:03:26,0:01:31,0:16:39,0:33:47,0:31:41,4:23:20,1106,0,41
+Night Team,PGY1,S0094535,2014-11-21,1:44:02,0:02:57,0:01:39,0:31:37,0:22:57,0:35:47,3:27:26,832,0,25
+Night Team,PGY1,S0094535,2014-11-22,1:28:55,0:03:53,1:21:10,0:17:46,0:22:36,0:24:45,4:16:56,858,0,37
+General Medicine,PGY1,S0108268,2014-07-01,3:57:38,0:41:02,0:12:33,0:44:34,0:45:33,0:34:05,7:23:26,1651,43,6
+General Medicine,PGY1,S0108268,2014-07-02,3:24:33,0:22:57,0:04:08,0:39:13,0:57:04,1:34:58,7:19:09,1949,79,12
+General Medicine,PGY1,S0108268,2014-07-03,4:16:32,0:18:32,0:13:16,0:19:07,0:31:57,0:37:12,7:12:56,2253,1472,25
+General Medicine,PGY1,S0108268,2014-07-04,2:02:07,0:29:45,0:03:15,0:10:38,0:20:20,0:34:21,3:52:17,1098,0,6
+General Medicine,PGY1,S0108268,2014-07-05,3:23:31,0:32:58,0:13:52,0:31:21,0:27:41,0:35:19,6:09:28,1835,42,6
+General Medicine,PGY1,S0108268,2014-07-06,1:47:01,0:08:34,0:00:00,0:01:49,0:06:09,0:20:35,2:40:43,836,825,19
+General Medicine,PGY1,S0108268,2014-07-07,5:02:26,0:24:58,0:11:12,1:01:27,0:38:52,0:37:17,8:19:48,2321,15,9
+General Medicine,PGY1,S0108268,2014-07-08,3:25:20,0:19:48,0:06:46,0:53:59,0:13:15,0:36:56,6:14:28,1757,302,19
+General Medicine,PGY1,S0108268,2014-07-09,3:41:18,0:10:59,0:13:32,0:54:40,0:23:37,0:20:46,6:10:47,1643,310,9
+General Medicine,PGY1,S0079337,2014-12-16,2:34:27,0:03:24,1:50:33,0:38:20,0:11:12,0:06:11,5:45:30,1660,0,8
+Night Team,PGY2+,S0169401,2015-08-18,2:15:13,1:10:48,2:44:05,1:48:00,0:10:56,0:28:18,9:32:36,3293,169,48
+General Medicine,PGY1,S0195225,2016-11-08,2:07:30,1:31:33,0:00:11,0:53:03,0:26:51,0:00:46,5:40:19,2012,0,12
+General Medicine,PGY1,S0195225,2016-11-09,0:00:25,0:03:10,0:00:00,0:00:10,0:00:00,0:00:00,0:03:55,71,55,5
+General Medicine,PGY1,S0195225,2016-11-04,0:02:57,0:04:34,0:00:00,0:00:01,0:00:02,0:00:02,0:08:46,167,159,6
+General Medicine,PGY1,S0186952,2016-09-19,2:09:04,0:48:52,0:00:25,0:35:10,0:16:33,0:00:52,4:56:45,1869,302,22
+General Medicine,PGY1,S0195225,2016-11-06,3:10:12,0:44:55,0:00:23,0:38:04,0:18:35,0:04:12,5:34:08,1473,0,6
+General Medicine,PGY1,S0195225,2016-11-07,1:18:18,0:44:29,0:00:11,0:20:32,0:09:04,0:00:34,3:07:37,1290,43,7
+General Medicine,PGY1,S0195225,2016-11-01,2:30:10,1:27:47,0:00:21,0:36:47,0:28:51,0:02:29,5:42:27,2149,0,9
+General Medicine,PGY1,S0195225,2016-11-02,0:00:00,0:00:00,0:00:00,0:00:11,0:00:00,0:00:00,0:00:11,14,0,5
+General Medicine,PGY1,S0195225,2016-11-03,2:39:50,2:12:42,0:00:17,0:58:06,0:41:19,0:01:32,7:16:40,2801,0,16
+General Medicine,PGY1,S0169401,2015-03-28,0:58:35,0:24:16,0:55:04,0:04:38,0:04:42,0:05:43,2:42:40,837,814,26
+General Medicine,PGY1,S0169401,2015-03-27,3:33:10,0:53:18,1:05:55,0:57:08,0:19:39,0:05:28,7:22:46,2706,494,17
+General Medicine,PGY1,S0169401,2015-03-26,3:47:12,1:58:30,1:56:27,0:40:04,0:52:35,0:11:09,10:33:09,3703,2219,35
+General Medicine,PGY1,S0169401,2015-03-25,3:51:49,1:27:19,1:32:25,0:39:44,0:32:41,0:12:51,8:54:27,4059,729,21
+General Medicine,PGY1,S0169401,2015-03-24,0:27:25,0:18:15,0:00:12,0:00:03,0:01:14,0:06:56,1:00:42,732,724,23
+General Medicine,PGY1,S0169401,2015-03-23,3:55:22,1:37:05,1:36:45,1:29:06,0:36:26,0:22:34,10:08:52,3558,516,51
+General Medicine,PGY1,S0169401,2015-03-22,3:10:13,1:08:12,1:44:07,0:22:16,0:15:39,0:09:16,7:01:19,2333,45,13
+General Medicine,PGY1,S0169401,2015-03-21,3:37:39,0:33:37,1:01:49,0:59:47,0:17:43,0:19:22,7:05:56,2369,229,9
+General Medicine,PGY1,S0169401,2015-03-20,3:03:18,0:38:08,0:38:26,0:50:17,0:47:18,0:10:02,6:43:53,2793,487,16
+Night Team,PGY1,S0195220,2016-07-03,0:29:26,0:08:35,0:00:01,0:07:54,0:16:54,0:01:41,1:13:29,263,0,8
+General Medicine,PGY1,S0181083,2016-02-05,0:39:46,0:42:31,2:28:47,0:57:26,0:22:05,0:46:23,6:40:06,1640,91,9
+Night Team,PGY1,S0195220,2016-07-05,2:06:49,0:44:25,0:00:07,0:31:44,0:19:28,0:12:21,4:18:38,1164,0,24
+Night Team,PGY1,S0195220,2016-07-06,2:16:02,0:56:24,0:00:03,0:24:32,0:25:12,0:00:13,4:11:02,1084,0,25
+General Medicine,PGY2+,S0169411,2016-03-09,2:24:50,0:50:09,0:00:03,0:43:44,0:26:19,0:04:26,4:57:34,1754,0,16
+General Medicine,PGY2+,S0169411,2016-03-08,4:50:42,0:50:04,0:00:06,0:34:03,0:16:13,0:03:46,7:02:58,2188,0,21
+General Medicine,PGY2+,S0169411,2016-03-07,1:44:03,0:31:58,0:01:45,0:14:47,0:10:44,0:00:40,3:19:50,1292,10,15
+General Medicine,PGY2+,S0169411,2016-03-06,2:29:09,0:37:43,0:00:16,0:26:06,0:06:59,0:02:33,4:20:35,1735,45,15
+Night Team,PGY1,S0195220,2016-07-07,2:04:18,0:48:27,0:00:01,0:38:54,0:27:05,0:10:04,4:27:06,1072,0,29
+General Medicine,PGY1,S0181067,2015-07-07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0181082,2016-09-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+Night Team,PGY2+,S0163662,2015-08-21,1:29:40,0:41:58,2:00:59,0:30:28,0:35:32,0:48:16,6:30:12,4151,87,25
+Intensive Care Unit,PGY2+,S0108287,2015-12-29,1:24:47,1:16:26,0:08:11,0:45:40,0:22:22,0:44:06,5:18:42,1687,0,18
+Night Team,PGY1,S0172056,2015-08-02,1:54:12,0:15:31,0:01:39,0:09:11,0:43:05,0:43:11,4:08:04,1042,0,39
+Night Team,PGY1,S0172056,2015-08-03,1:56:24,0:32:31,0:00:15,0:36:52,0:46:56,0:53:17,5:14:58,1435,0,35
+Night Team,PGY1,S0172056,2015-08-04,1:03:56,0:10:35,0:37:26,0:28:45,0:34:11,0:55:46,4:05:07,1230,0,26
+Night Team,PGY1,S0172056,2015-08-05,0:24:37,0:09:50,0:00:00,0:28:50,0:14:13,0:26:25,1:48:54,377,36,16
+Night Team,PGY1,S0172056,2015-08-06,0:58:31,0:18:34,0:50:00,0:27:35,1:06:53,1:12:08,5:19:31,1384,48,40
+Night Team,PGY1,S0172056,2015-08-07,1:23:55,0:12:40,0:00:22,0:40:16,0:56:06,1:09:14,4:50:32,1481,16,35
+Night Team,PGY1,S0172056,2015-08-08,1:22:06,0:23:49,3:23:04,0:27:10,0:39:12,0:29:04,7:10:22,1137,0,27
+Night Team,PGY1,S0172056,2015-08-09,1:11:11,0:22:27,2:26:53,0:44:36,0:36:40,0:28:46,6:10:08,1195,0,19
+Night Team,PGY2+,S0163662,2015-08-22,1:29:46,0:47:40,2:08:39,0:35:50,0:22:23,0:54:06,6:39:41,3956,38,20
+General Medicine,PGY1,S0181067,2015-07-06,1:26:47,0:10:54,0:24:19,0:43:59,0:47:53,0:16:23,4:08:00,1181,0,7
+Night Team,PGY1,S0181074,2016-01-08,1:17:53,1:30:11,1:16:03,1:56:50,0:44:01,0:10:24,7:25:18,2991,99,33
+Night Team,PGY1,S0181074,2016-01-09,1:32:46,1:10:28,1:14:34,1:43:20,0:41:07,0:09:18,6:58:27,2852,563,39
+Night Team,PGY1,S0181074,2016-01-06,1:06:52,1:36:31,0:56:25,1:09:49,0:15:58,0:04:43,5:25:44,1696,152,22
+Night Team,PGY1,S0181074,2016-01-07,0:23:19,0:19:30,0:01:07,0:36:11,0:10:55,0:00:04,1:43:45,629,0,25
+Night Team,PGY1,S0181074,2016-01-04,1:29:39,1:36:33,2:04:24,1:20:45,0:30:28,0:01:46,7:18:38,2169,160,39
+Night Team,PGY1,S0181074,2016-01-05,2:08:39,1:34:04,0:29:04,1:52:10,0:32:49,0:08:37,7:04:00,2465,0,53
+Night Team,PGY1,S0181074,2016-01-03,1:22:11,0:48:47,0:55:24,1:14:14,0:41:33,0:04:53,5:29:37,1663,0,32
+Intensive Care Unit,PGY2+,S0094959,2015-08-30,2:35:47,1:04:35,0:14:17,0:12:20,0:17:15,0:51:40,5:31:28,2293,326,20
+Intensive Care Unit,PGY2+,S0094959,2015-08-31,0:00:00,1:08:42,0:00:00,0:00:01,0:00:00,0:29:05,1:54:13,804,798,35
+Night Team,PGY1,S0181057,2015-11-09,0:25:42,0:40:34,0:00:03,0:06:30,0:09:05,0:14:15,1:40:42,575,0,17
+Night Team,PGY1,S0181057,2015-11-08,1:07:04,0:41:02,0:28:30,0:43:39,0:10:05,0:17:23,3:45:38,1363,89,20
+General Medicine,PGY1,S0094535,2015-05-15,4:25:25,0:22:34,2:23:51,0:43:34,0:58:53,0:49:41,10:13:02,2392,42,15
+General Medicine,PGY1,S0094535,2015-05-14,0:00:00,0:00:00,0:00:00,0:00:13,0:00:00,0:00:00,0:00:13,10,0,4
+General Medicine,PGY1,S0094535,2015-05-17,1:21:59,0:03:03,1:03:43,0:16:32,0:20:21,0:15:29,3:34:12,890,0,8
+General Medicine,PGY1,S0094535,2015-05-16,2:07:42,0:10:39,1:10:01,0:23:29,0:17:59,0:37:59,5:03:38,1317,187,16
+General Medicine,PGY1,S0094535,2015-05-11,1:26:41,0:10:43,1:56:49,0:29:41,0:25:13,0:25:31,5:14:22,1263,10,8
+General Medicine,PGY1,S0094535,2015-05-10,3:23:00,0:24:36,1:31:53,0:35:24,0:23:13,0:55:15,7:34:25,1888,0,12
+General Medicine,PGY1,S0094535,2015-05-13,3:06:13,0:28:29,2:57:38,0:37:47,0:21:10,0:48:08,8:41:06,1877,40,18
+General Medicine,PGY1,S0094535,2015-05-12,1:50:43,0:18:45,1:01:08,0:14:22,0:14:58,0:26:01,4:13:57,1034,25,6
+General Medicine,PGY1,S0094535,2015-05-19,2:45:01,0:08:43,1:53:26,0:52:47,0:22:58,0:29:55,6:55:06,2023,0,16
+General Medicine,PGY1,S0094535,2015-05-18,3:46:13,0:25:43,1:49:27,0:51:32,0:40:22,0:32:01,8:15:24,2230,50,17
+Emergency Medicine,PGY1,S0181087,2015-09-10,2:49:59,0:24:05,2:13:15,0:29:28,0:23:45,0:08:45,7:10:12,2495,0,16
+Intensive Care Unit,PGY2+,S0181073,2016-10-29,3:01:46,0:54:03,0:00:32,1:14:01,1:00:35,0:06:53,7:20:40,2229,0,16
+Intensive Care Unit,PGY2+,S0181073,2016-10-28,2:06:27,0:31:17,0:11:29,0:52:56,0:32:43,0:02:24,4:51:11,1114,0,17
+Emergency Medicine,PGY1,S0109179,2015-01-29,8:11:22,0:56:50,0:57:47,0:15:13,0:29:39,0:25:13,11:46:08,3554,0,58
+Emergency Medicine,PGY1,S0109179,2015-01-28,1:10:17,0:21:24,0:43:26,0:10:27,0:13:08,0:01:41,3:27:40,1521,822,22
+Intensive Care Unit,PGY2+,S0181073,2016-10-23,0:04:45,0:00:40,0:00:00,0:00:00,0:00:00,0:00:00,0:05:37,32,0,7
+Intensive Care Unit,PGY2+,S0181073,2016-10-22,1:52:07,0:29:54,0:05:35,0:26:54,0:19:18,0:01:23,3:36:20,1220,0,17
+Emergency Medicine,PGY1,S0109179,2015-01-25,5:22:07,0:10:41,1:33:45,0:29:24,0:30:32,0:28:41,9:15:11,3162,0,41
+Intensive Care Unit,PGY2+,S0181073,2016-10-20,2:13:21,0:41:55,0:18:39,0:24:20,0:34:05,0:10:17,5:05:40,1533,1,17
+Intensive Care Unit,PGY2+,S0181073,2016-10-27,2:40:37,0:57:53,0:09:58,0:35:13,0:37:10,0:06:44,5:53:36,1631,0,17
+Emergency Medicine,PGY1,S0109179,2015-01-22,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,1
+Emergency Medicine,PGY1,S0109179,2015-01-21,1:15:49,0:00:43,0:28:28,0:08:07,0:09:53,0:00:51,2:17:34,894,327,11
+Emergency Medicine,PGY1,S0109179,2015-01-20,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,1
+General Medicine,PGY1,S0169401,2014-09-15,5:56:34,0:26:46,0:05:51,1:08:46,0:58:13,0:07:21,9:10:52,2818,911,34
+General Medicine,PGY1,S0169401,2014-09-14,5:14:55,0:16:37,0:11:06,0:41:22,0:45:30,0:11:05,7:37:12,2907,413,13
+General Medicine,PGY1,S0169401,2014-09-17,3:43:49,0:17:55,0:15:17,0:51:56,0:37:45,0:08:40,6:30:11,2254,247,14
+General Medicine,PGY1,S0169401,2014-09-16,4:27:20,0:15:02,0:06:29,0:58:12,0:45:31,0:01:52,7:10:32,2563,435,12
+Emergency Medicine,PGY1,S0169401,2014-09-11,6:25:40,0:11:29,0:22:22,0:28:24,0:15:09,0:00:03,8:25:23,2454,38,67
+Emergency Medicine,PGY1,S0169401,2014-09-13,5:25:57,0:32:02,0:05:08,0:28:23,0:45:26,0:24:50,8:10:36,2714,513,16
+Emergency Medicine,PGY1,S0169401,2014-09-12,6:55:36,0:17:06,0:27:26,0:22:53,0:24:42,0:06:08,9:19:30,2594,421,65
+General Medicine,PGY2+,S0181082,2016-09-03,3:07:53,0:40:08,0:00:05,0:12:11,0:25:10,0:10:36,5:18:12,1759,72,21
+General Medicine,PGY1,S0169401,2014-09-19,5:37:14,0:17:28,0:13:19,0:50:28,0:31:28,0:13:13,8:16:02,3014,204,12
+General Medicine,PGY1,S0169401,2014-09-18,1:51:47,0:07:13,0:06:48,0:04:35,0:03:19,0:07:25,2:35:48,927,888,25
+General Medicine,PGY1,S0169389,2014-07-23,0:19:17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:20:47,143,139,15
+General Medicine,PGY1,S0169389,2014-07-22,3:33:30,0:14:17,0:19:09,0:50:57,0:27:52,0:22:12,6:07:43,1673,0,34
+General Medicine,PGY1,S0169389,2014-07-21,2:08:34,0:02:04,0:22:18,0:36:37,0:13:04,0:10:05,3:43:15,1019,0,16
+General Medicine,PGY1,S0169389,2014-07-20,2:40:38,0:21:32,0:26:09,0:25:53,0:19:20,0:36:52,5:03:40,1222,0,21
+General Medicine,PGY1,S0169389,2014-07-27,4:06:16,0:13:07,0:24:54,0:51:02,0:45:27,0:01:47,6:49:48,1636,74,22
+General Medicine,PGY1,S0169389,2014-07-26,3:20:08,0:02:55,0:14:42,0:16:58,0:15:18,0:25:12,4:48:41,1166,191,13
+Night Team,PGY1,S0195229,2016-06-28,1:42:47,0:30:52,0:00:04,0:46:38,0:12:43,0:00:07,3:44:31,996,315,18
+Night Team,PGY1,S0195229,2016-06-29,2:11:41,0:31:40,0:00:01,0:39:26,0:20:52,0:06:47,4:24:34,1213,454,19
+Night Team,PGY1,S0195229,2016-06-26,1:30:55,0:15:26,0:00:00,0:16:46,0:24:46,0:00:03,2:34:44,652,293,22
+Night Team,PGY1,S0195229,2016-06-27,1:36:54,1:08:26,0:00:05,0:24:25,0:18:57,0:01:19,3:52:50,1361,337,20
+General Medicine,PGY1,S0169389,2014-07-29,3:34:08,0:14:28,0:05:24,0:47:01,0:18:59,0:28:16,5:53:15,1485,207,28
+General Medicine,PGY1,S0169389,2014-07-28,4:25:09,0:22:29,0:12:59,0:33:46,0:24:51,0:20:48,6:58:23,1830,0,11
+Intensive Care Unit,PGY2+,S0094535,2015-10-08,2:06:59,0:47:56,0:20:36,0:42:04,0:24:29,0:47:48,5:39:46,1398,30,16
+Intensive Care Unit,PGY2+,S0094535,2015-10-09,2:25:56,0:27:37,0:15:50,0:27:21,0:25:16,0:23:03,4:44:28,1234,36,15
+Intensive Care Unit,PGY2+,S0094535,2015-10-04,1:36:13,1:14:35,0:07:30,0:30:40,0:18:52,0:50:39,5:00:16,1106,0,12
+Intensive Care Unit,PGY2+,S0094535,2015-10-05,1:30:03,0:28:48,0:08:00,0:22:10,0:24:57,0:45:12,3:57:49,1020,0,14
+Intensive Care Unit,PGY2+,S0094535,2015-10-06,0:00:00,0:00:00,0:00:00,0:00:40,0:00:00,0:00:00,0:00:40,18,0,4
+Intensive Care Unit,PGY2+,S0094535,2015-10-07,1:41:54,1:24:17,0:04:37,0:25:00,0:28:52,0:57:03,5:28:06,1777,0,21
+Intensive Care Unit,PGY2+,S0094535,2015-10-01,2:15:08,1:09:08,1:39:53,1:03:29,0:34:26,0:48:04,8:52:29,2846,110,32
+Intensive Care Unit,PGY2+,S0094535,2015-10-02,0:00:00,0:00:00,0:00:00,0:01:10,0:00:00,0:00:00,0:02:04,37,0,6
+Intensive Care Unit,PGY2+,S0094535,2015-10-03,1:19:28,0:35:55,0:35:01,0:43:24,0:39:41,0:50:43,5:21:07,1843,0,16
+General Medicine,PGY1,S0195226,2016-08-18,3:11:26,2:59:41,0:00:08,0:54:49,0:15:17,0:03:04,8:44:49,2531,364,16
+General Medicine,PGY1,S0195226,2016-08-19,2:00:35,1:39:05,0:00:19,0:47:40,0:11:56,0:00:10,5:51:17,1767,290,11
+Night Team,PGY1,S0181526,2016-06-16,3:22:36,0:57:15,0:00:34,0:42:02,0:19:19,0:01:33,5:47:06,2094,7,52
+Night Team,PGY1,S0181526,2016-06-17,3:41:48,0:59:58,0:00:02,0:32:52,0:24:57,0:07:35,6:26:32,1947,13,51
+Night Team,PGY1,S0181526,2016-06-10,1:15:00,0:20:41,0:00:49,0:10:48,0:11:01,0:01:18,2:06:45,920,11,37
+Night Team,PGY1,S0181526,2016-06-11,2:44:21,0:32:04,0:00:01,0:40:14,0:17:15,0:10:19,4:59:47,1620,5,41
+Night Team,PGY1,S0181526,2016-06-12,3:02:38,0:39:59,0:00:00,0:12:16,0:18:10,0:11:20,4:39:57,1470,4,47
+Night Team,PGY1,S0181526,2016-06-13,4:13:33,1:18:52,0:00:03,0:31:20,0:26:34,0:11:30,7:09:42,2758,6,40
+General Medicine,PGY1,S0195226,2016-08-10,0:45:29,1:23:59,0:00:04,0:01:57,0:03:11,0:06:11,2:43:55,633,633,7
+General Medicine,PGY1,S0195226,2016-08-11,2:31:30,1:40:43,0:00:17,1:00:13,0:15:11,0:21:40,6:32:22,1945,111,15
+General Medicine,PGY1,S0195226,2016-08-12,1:04:57,0:57:59,0:00:07,0:00:20,0:02:54,0:01:30,2:19:31,801,749,10
+General Medicine,PGY1,S0195226,2016-08-13,2:54:18,1:42:49,0:01:21,0:39:10,0:12:41,0:10:07,7:11:31,1990,60,15
+General Medicine,PGY1,S0195226,2016-08-14,2:07:52,1:28:41,0:00:13,0:25:17,0:09:50,0:00:27,5:06:44,1255,307,6
+General Medicine,PGY1,S0195226,2016-08-15,0:26:43,0:14:30,0:00:00,0:00:31,0:00:45,0:00:00,0:48:07,282,282,5
+General Medicine,PGY1,S0195226,2016-08-16,2:22:26,1:30:12,0:00:19,0:25:03,0:17:18,0:16:07,6:09:09,1753,115,11
+General Medicine,PGY1,S0195226,2016-08-17,1:24:41,0:28:08,0:00:04,0:17:17,0:05:04,0:02:15,2:45:20,766,0,10
+Emergency Medicine,PGY1,S0079640,2016-09-21,0:06:40,0:00:46,0:00:00,0:00:10,0:00:00,0:00:00,0:07:47,50,0,7
+Emergency Medicine,PGY1,S0079640,2016-09-20,3:37:01,0:19:58,0:00:54,0:54:13,0:25:14,0:05:35,6:09:20,1401,0,20
+Emergency Medicine,PGY1,S0079640,2016-09-23,0:01:06,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:01:11,9,0,1
+Emergency Medicine,PGY1,S0079640,2016-09-22,2:01:19,1:08:47,0:04:02,0:32:09,0:35:27,0:18:14,5:47:56,1749,0,38
+Emergency Medicine,PGY1,S0079640,2016-09-25,0:55:13,0:09:02,0:00:43,0:14:21,0:15:13,0:04:35,1:50:06,320,0,10
+Emergency Medicine,PGY1,S0079640,2016-09-24,2:17:38,1:13:11,0:04:33,0:35:32,0:52:48,0:20:48,6:25:33,1853,0,26
+Emergency Medicine,PGY1,S0079640,2016-09-27,0:58:39,0:20:17,0:03:52,0:11:58,0:26:19,0:00:16,2:23:51,737,0,21
+Emergency Medicine,PGY1,S0079640,2016-09-26,2:18:42,0:42:54,0:02:28,0:17:35,0:41:38,0:00:21,4:27:39,1308,0,48
+Emergency Medicine,PGY1,S0079640,2016-09-29,0:07:25,0:00:30,0:00:00,0:00:00,0:00:00,0:00:00,0:08:07,36,0,4
+Emergency Medicine,PGY1,S0079640,2016-09-28,0:06:49,0:00:11,0:00:00,0:00:00,0:00:00,0:00:00,0:07:05,22,0,4
+Intensive Care Unit,PGY2+,S0169383,2015-11-09,1:53:18,0:34:30,0:06:42,0:18:00,0:16:21,0:17:06,3:46:10,1017,7,28
+Intensive Care Unit,PGY2+,S0169383,2015-11-08,2:27:31,1:17:40,0:04:58,0:27:17,0:25:07,0:34:18,5:44:38,1577,0,20
+General Medicine,PGY1,S0169386,2014-06-30,0:09:20,0:00:14,0:00:01,0:00:00,0:00:38,0:00:27,0:10:42,61,61,3
+Intensive Care Unit,PGY2+,S0169383,2015-11-01,1:09:43,0:47:59,0:05:20,0:16:38,0:03:07,0:12:41,2:55:34,891,25,11
+Night Team,PGY1,S0181070,2015-09-30,0:35:05,1:24:08,0:15:49,0:08:46,0:16:25,0:06:45,2:54:58,1229,0,35
+Intensive Care Unit,PGY2+,S0169383,2015-11-03,1:28:55,0:35:38,0:05:55,0:19:59,0:29:09,0:31:08,3:54:06,1358,30,26
+Intensive Care Unit,PGY2+,S0169383,2015-11-02,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,14,0,4
+Intensive Care Unit,PGY2+,S0169383,2015-11-05,2:13:53,1:22:14,0:02:19,0:16:29,0:52:36,0:17:53,5:39:11,2014,30,36
+Intensive Care Unit,PGY2+,S0169383,2015-11-04,1:59:23,1:02:19,0:04:28,0:19:00,0:12:48,0:20:56,4:27:10,1139,0,30
+Intensive Care Unit,PGY2+,S0169383,2015-11-07,0:00:00,0:00:00,0:00:00,0:00:28,0:00:00,0:00:00,0:00:28,10,0,5
+Intensive Care Unit,PGY2+,S0169383,2015-11-06,1:52:06,1:15:41,0:03:26,0:13:42,0:17:55,0:23:15,4:24:12,1645,35,22
+Emergency Medicine,PGY1,S0074196,2015-10-23,1:58:52,0:48:29,1:51:52,0:23:36,0:32:28,0:33:41,6:39:09,1468,0,12
+Night Team,PGY1,S0169398,2015-02-14,2:24:57,0:09:46,0:47:38,0:12:35,0:17:15,0:24:06,4:26:53,1303,0,28
+Emergency Medicine,PGY1,S0169402,2014-06-30,1:16:16,0:00:10,0:02:45,0:00:00,0:00:10,0:00:00,1:20:37,255,255,23
+Night Team,PGY1,S0169398,2015-02-12,1:34:31,0:04:01,0:02:23,0:04:23,0:07:30,0:11:00,2:25:29,702,0,30
+Night Team,PGY1,S0169398,2015-02-13,2:21:39,0:09:41,0:04:45,0:18:16,0:14:03,0:29:04,3:44:57,1047,370,36
+Night Team,PGY1,S0181085,2015-12-18,0:21:19,0:35:10,0:04:56,0:05:42,0:09:42,0:07:37,1:28:22,599,0,23
+Night Team,PGY1,S0169398,2015-02-11,3:57:57,0:13:46,0:28:36,0:33:47,0:32:45,0:13:53,6:30:41,1785,35,42
+Intensive Care Unit,PGY2+,S0181064,2016-10-09,2:03:02,0:55:02,0:08:39,0:27:52,0:16:52,0:05:49,4:29:16,1089,0,9
+Night Team,PGY1,S0181085,2015-12-17,0:22:34,0:16:57,0:14:12,0:04:52,0:11:13,0:10:34,1:33:46,647,0,44
+Night Team,PGY1,S0181085,2015-12-14,0:56:32,0:47:39,0:08:59,0:22:06,0:20:47,0:35:44,3:29:53,1312,10,43
+Night Team,PGY1,S0181085,2015-12-15,1:07:52,1:33:26,0:50:23,0:23:02,0:28:37,0:25:04,5:05:42,1596,0,36
+Night Team,PGY1,S0181085,2015-12-12,0:39:02,0:20:50,0:00:49,0:19:35,0:08:47,0:24:15,2:04:49,685,0,32
+Night Team,PGY1,S0181085,2015-12-13,0:48:24,0:48:02,1:22:59,0:16:42,0:23:33,0:35:33,4:32:12,1159,0,42
+Night Team,PGY1,S0181085,2015-12-10,1:53:11,0:42:50,0:08:00,0:48:32,0:32:14,0:53:54,5:26:39,1673,220,41
+Night Team,PGY1,S0181085,2015-12-11,1:07:14,0:10:34,0:02:38,0:12:48,0:29:30,0:23:50,2:33:23,703,0,34
+General Medicine,PGY1,S0169390,2015-06-18,2:14:26,0:03:51,2:15:24,0:45:34,0:17:19,0:17:00,6:21:03,1498,0,29
+General Medicine,PGY1,S0169390,2015-06-19,2:31:07,0:20:38,1:14:43,0:28:19,0:08:29,0:14:25,5:19:40,1677,0,12
+General Medicine,PGY1,S0169390,2015-06-12,2:31:31,0:49:37,2:50:07,0:50:29,0:14:08,0:19:39,7:59:34,1597,0,13
+General Medicine,PGY1,S0169390,2015-06-13,1:59:02,0:10:43,1:15:06,0:09:14,0:06:50,0:12:56,4:04:02,960,39,3
+General Medicine,PGY1,S0169390,2015-06-10,0:54:27,0:15:48,0:44:26,0:05:10,0:03:10,0:01:34,2:15:54,691,114,6
+General Medicine,PGY1,S0169390,2015-06-11,0:37:53,0:00:14,0:19:25,0:14:41,0:02:12,0:00:06,1:19:31,331,13,4
+General Medicine,PGY1,S0169390,2015-06-16,1:40:39,0:22:48,1:17:14,0:28:22,0:09:16,0:08:39,4:20:11,1158,64,9
+General Medicine,PGY1,S0169390,2015-06-17,3:01:33,0:43:03,2:56:34,1:22:18,0:39:00,0:18:06,9:20:46,2110,0,9
+General Medicine,PGY1,S0169390,2015-06-14,2:45:19,0:26:33,2:29:34,0:40:47,0:22:21,0:21:48,7:20:45,1650,56,7
+General Medicine,PGY1,S0169390,2015-06-15,0:01:56,0:05:51,0:00:00,0:00:44,0:01:01,0:00:04,0:09:52,97,89,3
+Emergency Medicine,PGY1,S0074196,2015-10-25,1:08:52,0:48:41,1:44:01,0:28:13,0:21:56,0:36:27,5:37:21,1441,0,14
+General Medicine,PGY1,S0181065,2016-04-21,3:03:57,1:01:36,0:00:10,0:31:34,0:34:25,0:01:24,5:52:32,2960,0,13
+General Medicine,PGY1,S0181065,2016-04-20,4:55:25,1:43:54,0:00:11,0:35:12,0:41:45,0:05:48,8:46:22,5396,0,19
+General Medicine,PGY1,S0181065,2016-04-23,0:13:14,0:28:29,0:00:07,0:01:43,0:01:37,0:00:22,0:50:06,345,333,6
+Intensive Care Unit,PGY2+,S0181525,2016-10-02,1:14:58,0:26:26,0:11:01,0:16:51,0:10:59,0:00:12,2:42:04,662,0,12
+General Medicine,PGY2+,S0093383,2016-11-10,2:04:23,1:28:07,0:00:04,0:03:34,0:22:36,0:04:16,4:30:45,2853,0,13
+Night Team,PGY2+,S0093383,2016-08-15,2:29:08,1:18:03,0:00:08,0:19:23,0:04:22,0:07:37,4:45:43,2002,0,54
+Night Team,PGY2+,S0093383,2016-08-14,1:30:28,0:54:15,0:00:53,0:42:28,0:10:53,0:06:41,4:01:22,1981,0,42
+Night Team,PGY2+,S0093383,2016-08-17,1:20:34,1:12:02,0:00:16,0:08:20,0:07:43,0:05:40,3:07:44,1579,43,34
+Night Team,PGY2+,S0093383,2016-08-16,2:34:32,1:33:49,0:01:06,0:42:14,0:10:53,0:06:10,5:43:33,2078,32,20
+Night Team,PGY1,S0195075,2016-10-13,0:48:31,0:42:32,0:00:11,0:44:42,0:32:35,0:16:10,3:22:19,938,0,24
+Night Team,PGY1,S0195075,2016-10-12,1:00:21,0:15:18,0:00:00,2:01:10,0:42:06,0:03:35,4:14:48,1111,0,26
+Night Team,PGY1,S0195075,2016-10-11,0:55:58,0:36:21,0:00:00,0:39:48,0:27:35,0:12:35,3:05:10,859,8,32
+Night Team,PGY1,S0195075,2016-10-10,1:54:34,0:13:42,0:00:00,0:45:33,0:20:10,0:15:13,3:40:14,984,0,50
+Night Team,PGY2+,S0093383,2016-08-19,1:09:17,0:49:29,0:00:00,0:11:28,0:07:01,0:00:10,2:28:55,757,3,21
+Night Team,PGY2+,S0093383,2016-08-18,1:26:08,0:51:05,0:00:00,0:06:19,0:00:36,0:04:57,2:34:45,722,0,7
+Night Team,PGY2+,S0169385,2016-08-24,2:14:18,1:21:42,0:00:31,0:47:07,0:14:58,0:08:30,5:25:19,2056,125,28
+Night Team,PGY2+,S0169385,2016-08-25,2:26:39,1:52:07,0:00:14,0:30:18,0:12:29,0:03:22,5:28:40,1999,0,38
+Night Team,PGY2+,S0169385,2016-08-26,3:58:31,1:55:04,0:00:11,0:40:36,0:21:38,0:19:04,7:57:50,2880,0,39
+General Medicine,PGY1,S0169399,2014-12-20,0:55:26,0:00:02,0:42:39,0:27:41,0:06:52,0:00:00,2:37:10,552,91,6
+General Medicine,PGY1,S0169399,2014-12-21,1:18:38,0:00:43,3:11:38,0:24:34,0:11:55,0:00:30,5:25:46,843,0,8
+General Medicine,PGY1,S0169399,2014-12-22,1:16:12,0:04:55,1:16:03,0:40:26,0:09:32,0:00:12,3:43:03,684,0,5
+Emergency Medicine,PGY1,S0181057,2015-08-06,5:13:02,0:20:07,2:13:31,0:18:34,0:17:17,0:14:44,9:07:50,2351,0,27
+General Medicine,PGY1,S0169399,2014-12-24,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,5,0,1
+General Medicine,PGY1,S0169399,2014-12-26,2:08:41,0:08:25,4:08:11,0:20:57,0:13:39,0:13:49,7:36:38,1119,0,11
+General Medicine,PGY1,S0169399,2014-12-27,1:25:17,0:03:44,2:07:21,0:15:05,0:02:46,0:07:46,4:07:17,651,0,6
+General Medicine,PGY1,S0169399,2014-12-28,1:53:16,0:03:37,3:00:19,0:36:45,0:12:44,0:12:06,6:30:51,1226,0,12
+Night Team,PGY2+,S0169385,2016-08-20,1:06:32,0:44:01,0:00:43,0:14:00,0:05:44,0:00:35,2:29:43,896,0,15
+General Medicine,PGY1,S0094535,2015-01-11,3:26:39,0:12:26,2:33:51,0:52:48,0:36:38,1:00:09,9:27:10,2140,0,12
+General Medicine,PGY1,S0094535,2015-01-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,2
+General Medicine,PGY1,S0094535,2015-01-13,3:21:00,0:05:12,2:50:58,1:31:26,0:28:26,0:48:33,9:50:00,2192,21,16
+General Medicine,PGY1,S0094535,2015-01-12,2:54:03,0:14:38,2:08:20,0:29:10,0:22:05,0:36:51,7:41:20,2277,55,24
+General Medicine,PGY1,S0094535,2015-01-15,2:06:11,0:08:08,2:13:32,0:31:26,0:26:28,0:32:44,6:30:55,1601,0,14
+Night Team,PGY2+,S0169385,2016-08-22,1:50:00,0:58:25,0:00:56,0:36:29,0:06:17,0:00:47,4:03:49,1368,0,24
+Night Team,PGY1,S0169395,2015-03-29,1:34:49,0:05:26,0:29:02,0:22:35,0:20:45,0:08:32,3:12:21,876,0,29
+General Medicine,PGY1,S0094535,2015-01-16,3:50:31,0:12:15,2:43:15,0:36:53,0:25:03,0:32:13,9:01:10,2000,0,13
+General Medicine,PGY1,S0094535,2015-01-19,3:25:22,0:05:04,2:19:48,0:21:24,0:21:58,0:35:20,7:52:38,1984,51,14
+General Medicine,PGY1,S0094535,2015-01-18,3:42:30,0:06:06,2:12:29,0:40:07,0:32:22,0:43:37,8:11:52,1724,0,13
+Emergency Medicine,PGY1,S0181057,2015-08-02,3:37:21,0:16:25,0:50:24,0:17:23,0:20:03,0:18:35,5:49:11,1638,81,21
+Night Team,PGY2+,S0169385,2016-03-05,1:37:24,0:35:43,0:00:05,0:34:38,0:12:39,0:05:54,3:34:11,1516,0,27
+Night Team,PGY2+,S0169385,2016-03-04,2:45:30,1:43:49,0:00:39,0:30:20,0:16:31,0:10:02,5:51:18,2368,16,37
+Night Team,PGY2+,S0169385,2016-03-03,2:44:44,1:06:56,0:00:21,0:40:11,0:37:43,0:06:26,5:54:27,2804,0,34
+Night Team,PGY2+,S0169385,2016-03-02,3:07:28,2:05:44,0:00:37,0:53:15,0:29:56,0:10:34,7:44:28,2840,10,44
+Night Team,PGY2+,S0169385,2016-03-01,1:18:45,0:59:50,0:00:00,0:20:15,0:05:07,0:07:56,3:19:13,1496,226,32
+Emergency Medicine,PGY1,S0094959,2014-08-20,2:29:26,0:20:54,1:41:58,0:17:14,1:10:36,0:54:14,7:33:11,2995,1048,26
+Emergency Medicine,PGY1,S0094959,2014-08-21,0:34:12,0:28:38,0:16:27,0:07:26,0:07:52,0:16:20,1:59:08,883,144,13
+Emergency Medicine,PGY1,S0094959,2014-08-22,6:04:20,0:10:21,0:44:26,0:11:58,0:18:48,0:30:47,8:19:40,2505,31,50
+Emergency Medicine,PGY1,S0094959,2014-08-23,3:13:21,0:11:54,1:19:25,0:29:14,0:50:27,0:42:06,7:40:35,3358,28,42
+Emergency Medicine,PGY1,S0094959,2014-08-24,2:23:44,0:07:55,0:26:49,0:16:56,0:18:40,0:21:53,4:36:55,2056,0,21
+Emergency Medicine,PGY1,S0094959,2014-08-25,4:03:02,0:15:47,0:57:47,0:33:42,1:03:10,0:38:11,8:22:44,3578,1,36
+Emergency Medicine,PGY1,S0094959,2014-08-26,1:16:25,0:09:20,0:23:02,0:14:45,0:13:47,0:13:35,2:56:43,1610,0,49
+Emergency Medicine,PGY1,S0094959,2014-08-27,4:20:06,0:44:08,1:21:06,0:20:01,0:49:35,0:31:38,8:54:08,3033,214,36
+Emergency Medicine,PGY1,S0094959,2014-08-28,0:34:59,0:22:20,0:23:22,0:13:35,0:14:08,0:02:13,2:01:40,514,82,10
+Emergency Medicine,PGY1,S0094959,2014-08-29,2:21:09,0:10:45,1:08:16,0:18:43,0:54:28,0:30:21,5:54:50,2677,19,28
+General Medicine,PGY2+,S0109179,2016-01-20,1:17:57,0:24:00,0:09:44,0:10:42,0:13:02,0:09:33,2:36:39,1731,583,29
+General Medicine,PGY2+,S0109179,2016-01-21,0:01:35,0:04:46,0:00:30,0:00:01,0:00:00,0:00:50,0:07:53,138,127,10
+General Medicine,PGY2+,S0109179,2016-01-22,1:47:31,0:43:58,0:21:25,0:23:50,0:22:09,0:33:26,4:33:23,2429,0,43
+General Medicine,PGY2+,S0109179,2016-01-23,0:54:11,1:00:56,0:27:50,0:27:03,0:09:29,0:24:05,3:46:02,2252,38,19
+General Medicine,PGY2+,S0109179,2016-01-24,1:32:32,1:08:24,0:39:10,0:21:35,0:07:10,0:22:38,4:29:42,2173,0,37
+General Medicine,PGY2+,S0109179,2016-01-25,1:59:38,0:45:24,1:05:42,0:29:00,0:11:48,0:09:11,5:07:46,2519,13,30
+General Medicine,PGY2+,S0109179,2016-01-26,1:03:36,0:16:20,0:04:51,0:02:26,0:06:25,0:18:12,1:55:01,934,98,16
+General Medicine,PGY2+,S0109179,2016-01-27,1:17:58,1:04:19,1:05:05,0:16:27,0:17:10,0:40:16,5:15:10,2912,0,37
+General Medicine,PGY2+,S0109179,2016-01-28,0:00:00,0:00:00,0:00:00,0:00:11,0:00:00,0:00:00,0:00:11,30,0,4
+General Medicine,PGY2+,S0109179,2016-01-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,20,0,1
+Intensive Care Unit,PGY2+,S0094535,2015-09-30,1:47:00,0:47:47,0:16:16,0:42:46,0:23:50,0:49:29,5:23:24,1826,26,16
+General Medicine,PGY1,S0195226,2016-07-25,0:36:31,0:19:35,0:00:07,0:09:42,0:16:28,0:03:44,1:52:56,529,107,8
+General Medicine,PGY1,S0195226,2016-07-26,2:18:56,1:03:02,0:00:31,0:09:04,0:13:55,0:01:30,4:33:25,1268,390,7
+General Medicine,PGY1,S0181073,2015-10-11,1:14:20,1:20:07,1:58:02,0:41:33,0:31:49,0:34:42,6:51:38,1887,24,6
+General Medicine,PGY1,S0181073,2015-10-13,2:04:38,1:40:23,1:41:17,1:20:46,0:41:06,0:44:43,8:55:27,2966,0,12
+General Medicine,PGY1,S0181073,2015-10-12,1:27:00,1:08:57,1:16:12,0:36:11,0:34:38,0:24:47,5:54:28,1871,69,5
+General Medicine,PGY1,S0181073,2015-10-15,1:40:21,0:54:05,1:19:56,0:48:34,0:21:25,0:16:01,5:48:57,2537,35,8
+General Medicine,PGY1,S0181073,2015-10-14,1:24:20,1:14:01,1:11:29,1:06:44,0:31:06,0:35:24,6:39:15,2720,187,10
+General Medicine,PGY1,S0181073,2015-10-17,0:00:00,0:01:31,0:00:00,0:00:12,0:00:00,0:03:56,0:07:05,132,122,5
+General Medicine,PGY1,S0181073,2015-10-16,1:34:50,1:22:54,2:07:37,1:08:47,0:31:57,0:35:12,8:00:31,2353,120,11
+General Medicine,PGY1,S0176040,2015-11-09,1:03:53,1:01:40,2:24:58,0:29:11,0:29:39,0:35:20,6:40:30,1899,17,7
+General Medicine,PGY1,S0181073,2015-10-18,1:42:05,1:24:21,2:14:23,0:39:10,0:19:16,0:37:44,7:56:39,2268,108,16
+Intensive Care Unit,PGY2+,S0163662,2016-05-10,1:45:32,0:55:46,0:09:04,1:08:10,0:49:13,0:08:26,5:36:36,1387,311,14
+Intensive Care Unit,PGY2+,S0163662,2016-05-11,1:10:53,0:07:26,0:00:02,0:13:10,0:19:46,0:13:16,2:37:45,878,5,15
+Intensive Care Unit,PGY2+,S0163662,2016-05-12,1:32:52,0:27:51,0:04:13,0:31:14,0:13:41,0:06:27,3:35:49,1278,257,13
+Intensive Care Unit,PGY2+,S0163662,2016-05-13,1:42:19,0:36:27,0:06:30,0:15:55,0:25:51,0:13:01,4:05:31,1190,0,18
+Intensive Care Unit,PGY2+,S0163662,2016-05-14,0:00:43,0:00:05,0:00:00,0:00:11,0:00:00,0:00:46,0:02:04,33,17,6
+Intensive Care Unit,PGY2+,S0163662,2016-05-15,2:15:20,0:24:52,0:25:25,0:29:29,0:38:01,0:11:39,5:22:26,1146,6,14
+Intensive Care Unit,PGY2+,S0163662,2016-05-16,1:30:05,0:21:21,0:02:43,0:25:28,0:24:20,0:08:11,3:13:09,723,18,13
+General Medicine,PGY1,S0195226,2016-07-28,2:12:15,1:03:57,0:00:03,0:45:01,0:24:59,0:09:04,5:10:51,1475,280,9
+Intensive Care Unit,PGY2+,S0163662,2016-05-17,1:14:49,1:08:59,0:07:44,0:02:20,0:21:14,0:02:04,3:17:28,809,82,15
+General Medicine,PGY1,S0195226,2016-07-29,3:01:32,1:07:39,0:00:07,1:01:27,0:25:12,0:06:31,6:44:56,1595,22,15
+Intensive Care Unit,PGY2+,S0181087,2016-07-28,3:13:07,1:42:00,0:00:02,0:19:38,0:29:18,0:27:27,7:10:16,3019,58,26
+Intensive Care Unit,PGY2+,S0181087,2016-07-29,3:12:30,1:10:15,0:10:15,0:15:57,0:17:25,0:20:30,6:19:33,2078,327,15
+Night Team,PGY2+,S0169386,2015-10-07,3:06:24,1:18:35,1:20:10,0:31:07,0:20:10,1:19:59,9:01:32,3581,255,122
+Night Team,PGY2+,S0169386,2015-10-06,1:37:14,1:19:30,1:41:09,0:30:01,0:33:00,0:46:45,6:52:47,2771,704,56
+Intensive Care Unit,PGY2+,S0181087,2016-07-20,2:28:33,1:02:58,0:08:32,0:10:44,0:28:08,0:12:30,5:07:48,1431,0,20
+Intensive Care Unit,PGY2+,S0181087,2016-07-21,2:28:14,0:57:20,0:08:19,0:16:06,0:18:56,0:11:21,4:52:20,1664,0,32
+Intensive Care Unit,PGY2+,S0181087,2016-07-22,0:16:17,0:13:24,0:00:11,0:02:44,0:00:13,0:01:46,0:43:19,484,440,19
+Intensive Care Unit,PGY2+,S0181087,2016-07-23,3:49:04,2:45:27,0:05:26,0:15:22,0:27:38,0:20:25,8:16:55,3101,0,26
+Intensive Care Unit,PGY2+,S0181087,2016-07-24,1:36:00,0:54:41,0:06:50,0:09:03,0:10:00,0:19:01,3:36:32,1207,36,8
+Intensive Care Unit,PGY2+,S0181087,2016-07-25,2:02:53,0:37:16,0:08:44,0:14:55,0:17:51,0:16:10,4:05:46,1689,0,16
+Intensive Care Unit,PGY2+,S0181087,2016-07-26,0:16:41,0:31:33,0:00:00,0:01:00,0:00:03,0:00:08,0:51:28,574,574,20
+Intensive Care Unit,PGY2+,S0181087,2016-07-27,2:05:40,0:52:59,0:21:43,0:33:43,0:15:05,0:05:58,5:54:14,1979,91,26
+General Medicine,PGY1,S0181074,2016-05-02,0:51:25,0:39:38,0:00:01,0:19:08,0:02:57,0:01:20,2:21:58,1059,22,7
+General Medicine,PGY1,S0181074,2016-05-03,2:07:03,1:35:14,0:00:16,2:28:58,0:16:01,0:03:45,6:59:44,2417,62,12
+General Medicine,PGY2+,S0169406,2016-02-24,1:58:52,0:31:55,0:00:24,0:17:56,0:12:07,0:01:14,3:41:10,1603,0,17
+General Medicine,PGY1,S0181074,2016-05-01,0:36:33,0:03:54,0:00:00,0:00:56,0:00:00,0:17:11,1:04:23,383,377,9
+General Medicine,PGY1,S0181074,2016-05-06,1:44:00,1:19:53,0:00:08,1:09:20,0:23:39,0:08:07,5:33:38,2203,159,8
+General Medicine,PGY1,S0181074,2016-05-07,1:28:52,1:03:10,0:00:12,0:41:10,0:15:58,0:04:24,4:01:07,1444,195,9
+General Medicine,PGY1,S0181074,2016-05-04,2:02:27,1:12:14,0:02:24,1:08:12,0:20:01,0:16:08,5:51:15,2424,252,29
+General Medicine,PGY1,S0181074,2016-05-05,2:41:38,1:21:22,0:00:18,1:49:56,0:18:49,0:21:37,7:30:54,2726,113,12
+Intensive Care Unit,PGY2+,S0169394,2016-05-21,1:52:11,0:38:12,0:06:30,0:30:25,0:32:13,0:12:12,4:22:10,1460,132,17
+Intensive Care Unit,PGY2+,S0169394,2016-05-20,4:35:45,0:57:23,0:01:42,0:48:20,0:44:33,0:09:28,7:57:22,2293,2,21
+General Medicine,PGY1,S0181074,2016-05-08,1:52:14,2:58:37,0:00:15,1:16:24,0:13:45,0:08:45,7:07:15,2642,63,30
+General Medicine,PGY1,S0181074,2016-05-09,0:44:23,0:03:22,0:00:00,0:00:50,0:00:00,0:10:26,1:02:03,323,309,12
+General Medicine,PGY1,S0163662,2015-05-31,2:07:38,0:21:15,0:54:45,0:24:28,0:21:31,0:26:52,5:04:48,1671,18,15
+General Medicine,PGY1,S0163662,2015-05-30,2:12:46,0:17:50,1:23:10,0:37:55,0:11:34,0:07:05,5:11:35,1098,0,9
+General Medicine,PGY2+,S0169406,2016-02-28,0:56:29,0:22:04,0:00:10,0:16:20,0:11:31,0:01:21,2:03:21,937,0,10
+General Medicine,PGY2+,S0169406,2016-02-29,2:40:26,0:48:41,0:00:00,0:10:45,0:15:01,0:07:00,4:56:31,1880,0,21
+Emergency Medicine,PGY1,S0056755,2014-06-30,5:45:28,0:20:23,0:07:01,0:25:17,0:05:33,0:00:29,7:17:21,1700,0,27
+General Medicine,PGY1,S0195224,2016-11-03,2:33:32,2:34:04,0:00:52,0:51:50,0:18:21,0:07:43,6:49:56,1889,164,24
+General Medicine,PGY1,S0195224,2016-11-02,0:10:50,0:01:33,0:00:00,0:00:00,0:00:00,0:03:23,0:18:45,120,0,6
+General Medicine,PGY1,S0195224,2016-11-01,2:52:50,1:20:37,0:00:08,0:54:17,0:08:19,0:07:24,5:51:01,1491,0,14
+General Medicine,PGY1,S0195224,2016-11-07,1:23:00,1:18:50,0:00:05,0:17:49,0:06:56,0:01:31,3:30:52,1003,100,9
+General Medicine,PGY1,S0195224,2016-11-06,2:07:31,2:06:16,0:00:08,0:44:56,0:24:14,0:04:09,5:48:13,1469,0,17
+General Medicine,PGY1,S0195224,2016-11-05,1:04:18,0:24:14,0:00:05,0:10:48,0:07:09,0:02:55,2:15:41,628,0,8
+General Medicine,PGY1,S0195224,2016-11-04,2:25:24,2:22:23,0:01:25,0:49:32,0:11:54,0:02:06,6:41:31,1915,212,11
+General Medicine,PGY1,S0195224,2016-11-09,0:15:04,0:00:15,0:00:00,0:00:01,0:00:00,0:01:43,0:17:28,69,0,6
+General Medicine,PGY1,S0195224,2016-11-08,1:46:11,1:31:06,0:00:15,0:34:04,0:16:52,0:01:10,4:29:19,1033,14,9
+General Medicine,PGY1,S0163425,2014-10-06,4:08:38,0:04:42,0:11:56,0:39:38,0:24:11,0:25:33,6:18:17,1666,0,9
+Intensive Care Unit,PGY2+,S0079337,2016-06-21,2:39:03,0:42:48,0:09:28,0:21:55,0:17:50,0:02:31,4:34:08,1258,74,17
+General Medicine,PGY1,S0163425,2014-10-07,3:14:28,0:01:37,0:10:11,0:25:35,0:20:44,0:27:27,4:58:42,1394,0,10
+General Medicine,PGY1,S0163425,2014-10-01,4:03:49,0:04:11,0:09:02,0:49:22,0:17:11,0:46:31,6:25:42,1943,108,10
+Night Team,PGY2+,S0109179,2016-05-28,2:29:02,0:56:33,0:00:04,0:01:49,0:09:53,0:03:38,3:57:16,1071,0,28
+General Medicine,PGY1,S0163425,2014-10-02,2:53:20,0:03:47,0:13:52,0:25:48,0:18:12,0:18:47,4:35:26,1294,0,12
+Night Team,PGY2+,S0109179,2016-05-24,1:29:42,1:08:10,0:00:08,0:08:49,0:07:20,0:04:35,3:18:02,988,17,24
+Night Team,PGY2+,S0109179,2016-05-25,0:31:32,0:33:22,0:00:01,0:08:08,0:05:01,0:00:14,1:26:18,727,8,13
+Night Team,PGY2+,S0109179,2016-05-26,3:01:46,1:14:41,0:00:06,0:48:22,0:13:11,0:06:34,5:59:29,2813,0,46
+General Medicine,PGY1,S0163425,2014-10-03,2:04:10,0:00:00,0:15:06,0:08:12,0:09:34,0:05:16,3:05:34,1135,26,13
+Night Team,PGY2+,S0109179,2016-05-20,2:06:22,1:16:59,0:00:08,0:23:48,0:09:58,0:08:10,4:27:20,2163,162,35
+Night Team,PGY2+,S0109179,2016-05-21,3:37:19,1:26:03,0:00:08,0:10:37,0:07:09,0:01:25,5:42:08,1470,40,28
+Night Team,PGY2+,S0109179,2016-05-22,1:59:05,0:49:02,0:00:04,0:14:12,0:04:08,0:05:59,3:26:31,1291,42,28
+Night Team,PGY2+,S0109179,2016-05-23,4:09:05,1:39:23,0:01:30,0:26:00,0:07:23,0:03:42,7:00:10,2757,20,30
+General Medicine,PGY1,S0169406,2014-07-10,2:53:36,0:03:34,0:13:53,1:05:10,0:25:38,0:08:47,5:01:50,1677,0,17
+General Medicine,PGY1,S0169406,2014-07-16,0:05:43,0:00:00,0:04:35,0:00:00,0:00:00,0:00:00,0:10:20,36,36,3
+General Medicine,PGY1,S0169406,2014-07-17,0:03:54,0:00:00,0:00:03,0:00:00,0:00:00,0:00:00,0:04:21,39,39,2
+General Medicine,PGY1,S0169406,2014-07-15,0:56:58,0:00:00,0:01:23,0:00:00,0:00:10,0:00:58,1:14:32,412,412,11
+Night Team,PGY2+,S0169389,2016-03-09,3:42:59,1:33:16,0:01:22,0:51:45,0:14:56,0:11:27,7:15:27,2592,111,58
+General Medicine,PGY1,S0181078,2016-01-01,0:23:06,0:08:27,0:57:41,0:04:02,0:00:00,0:09:17,1:56:51,293,271,8
+Night Team,PGY2+,S0169389,2016-03-07,4:24:36,1:20:07,0:00:04,0:21:59,0:07:55,0:07:01,6:58:09,1623,143,68
+Night Team,PGY2+,S0169389,2016-03-06,3:19:09,1:43:57,0:00:21,0:42:15,0:13:12,0:06:23,6:43:06,2011,0,71
+General Medicine,PGY2+,S0181087,2016-11-07,3:00:37,1:45:53,0:00:01,0:39:54,0:15:56,0:13:46,6:34:55,2952,289,26
+General Medicine,PGY2+,S0181087,2016-11-06,2:03:33,1:14:58,0:00:06,0:25:56,0:08:09,0:05:04,4:44:36,1732,35,12
+Night Team,PGY1,S0181077,2015-11-09,2:41:52,0:53:19,0:10:44,0:32:07,0:30:54,0:28:27,5:25:52,2236,112,35
+Night Team,PGY1,S0181077,2015-11-08,2:20:27,0:54:35,0:18:12,0:22:32,0:38:17,0:10:56,4:59:43,1975,18,38
+General Medicine,PGY2+,S0181087,2016-11-09,4:29:05,2:57:37,0:00:49,1:19:37,0:29:58,0:15:56,10:37:01,3913,114,21
+General Medicine,PGY2+,S0181087,2016-11-08,1:50:36,1:04:26,0:00:02,0:21:43,0:11:30,0:03:04,3:46:53,1528,118,20
+Night Team,PGY2+,S0163662,2015-08-20,1:34:18,1:09:19,2:31:50,0:39:10,0:20:47,0:36:05,7:33:01,3105,0,21
+General Medicine,PGY1,S0181075,2015-12-04,1:52:30,1:14:53,2:11:26,1:08:56,0:25:36,0:30:03,8:07:59,3235,0,15
+General Medicine,PGY1,S0181075,2015-12-05,0:06:01,0:01:02,0:00:00,0:05:07,0:00:35,0:00:01,0:12:55,90,68,6
+General Medicine,PGY1,S0181075,2015-12-02,2:56:50,1:35:33,1:45:08,1:06:11,0:42:54,0:30:40,9:15:56,3678,22,16
+General Medicine,PGY1,S0181075,2015-12-03,1:29:34,1:06:13,1:35:50,0:42:56,0:17:55,0:23:13,5:50:24,2168,0,9
+General Medicine,PGY1,S0181075,2015-12-01,1:48:27,1:33:46,0:53:48,0:54:34,0:28:42,0:26:27,6:24:17,2695,145,12
+General Medicine,PGY1,S0163662,2015-06-18,3:59:23,0:52:43,2:18:55,0:43:50,0:22:54,0:20:17,9:18:02,2522,50,17
+General Medicine,PGY1,S0163662,2015-06-19,1:14:24,0:01:44,0:18:01,0:07:44,0:05:25,0:11:47,2:06:11,536,0,6
+General Medicine,PGY1,S0181067,2015-09-09,0:00:00,0:00:00,0:00:00,0:03:57,0:00:00,0:00:00,0:03:57,20,0,5
+General Medicine,PGY1,S0181067,2015-09-08,1:53:16,0:34:12,1:03:00,0:53:50,0:54:13,0:44:07,6:45:16,2037,20,10
+General Medicine,PGY1,S0181067,2015-09-05,1:53:05,0:19:18,0:56:05,0:45:22,0:33:38,0:28:51,5:23:25,1690,0,13
+General Medicine,PGY1,S0181067,2015-09-04,1:18:54,0:08:09,1:26:34,0:31:03,0:22:33,0:21:40,4:30:26,1276,38,8
+General Medicine,PGY1,S0181067,2015-09-07,1:02:50,0:09:54,0:59:38,0:11:21,0:14:10,0:10:53,3:07:37,859,65,7
+General Medicine,PGY1,S0181067,2015-09-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,14,0,3
+General Medicine,PGY1,S0181067,2015-09-01,1:45:52,0:09:16,0:53:23,0:13:38,0:21:49,0:31:04,4:13:18,1242,24,9
+General Medicine,PGY1,S0181067,2015-09-03,2:09:46,0:17:22,1:10:23,0:47:18,0:28:26,0:32:16,6:19:49,2072,0,11
+General Medicine,PGY1,S0181067,2015-09-02,1:28:47,0:04:15,0:29:59,0:32:45,0:26:48,0:20:29,3:37:30,1424,8,17
+General Medicine,PGY1,S0181525,2015-10-28,1:46:52,0:58:04,2:34:06,0:26:36,0:20:42,0:29:58,7:07:08,1589,0,7
+General Medicine,PGY1,S0181525,2015-10-29,1:02:26,0:57:26,1:11:28,0:42:43,0:18:56,0:30:45,5:16:05,1492,11,6
+General Medicine,PGY1,S0181525,2015-10-24,0:43:48,0:42:14,1:05:03,0:08:01,0:21:22,0:14:00,3:30:58,870,0,4
+General Medicine,PGY1,S0181525,2015-10-25,2:44:24,0:56:54,1:44:15,0:55:23,0:21:08,0:31:07,7:54:45,1821,0,10
+General Medicine,PGY1,S0181525,2015-10-26,0:00:00,0:00:00,0:00:00,0:01:20,0:00:00,0:00:00,0:01:20,14,0,3
+General Medicine,PGY1,S0181525,2015-10-27,1:17:18,0:51:49,0:50:28,0:29:59,0:29:00,0:46:04,5:04:17,1219,0,5
+General Medicine,PGY1,S0181525,2015-10-20,1:56:06,1:03:55,1:29:56,0:37:09,0:19:54,0:34:35,6:37:42,1703,0,11
+General Medicine,PGY1,S0181525,2015-10-21,0:32:35,0:15:31,0:27:15,0:23:11,0:05:07,0:13:42,2:17:28,659,13,6
+General Medicine,PGY1,S0181525,2015-10-22,0:20:41,0:29:47,1:26:49,0:12:29,0:04:14,0:08:37,2:52:26,721,0,5
+General Medicine,PGY1,S0181525,2015-10-23,1:12:17,0:37:23,2:09:44,0:32:11,0:14:35,0:25:08,5:36:34,1247,0,8
+Intensive Care Unit,PGY2+,S0181077,2016-10-29,2:21:44,0:18:27,0:05:18,0:11:09,0:27:11,0:05:15,3:54:31,1148,20,21
+Intensive Care Unit,PGY2+,S0181077,2016-10-28,3:07:00,1:21:45,0:12:27,0:31:10,0:29:13,0:08:37,6:36:21,2548,52,24
+Intensive Care Unit,PGY2+,S0181077,2016-10-27,1:47:10,0:55:24,0:03:27,0:15:34,0:20:48,0:08:39,4:02:19,1810,54,29
+Intensive Care Unit,PGY2+,S0181077,2016-10-26,2:57:57,1:31:04,0:02:49,0:15:40,0:24:23,0:07:41,5:50:42,1977,145,21
+Intensive Care Unit,PGY2+,S0181077,2016-10-25,0:06:57,0:08:22,0:00:00,0:00:35,0:00:00,0:00:01,0:18:26,324,308,21
+Intensive Care Unit,PGY2+,S0181077,2016-10-24,1:50:31,1:02:47,0:13:39,0:26:54,0:14:40,0:03:07,4:21:06,1418,0,12
+Intensive Care Unit,PGY2+,S0181077,2016-10-23,4:20:34,1:39:04,0:03:36,0:32:07,0:30:56,0:00:57,7:48:42,2340,0,23
+Intensive Care Unit,PGY2+,S0181077,2016-10-22,0:02:35,0:00:05,0:00:00,0:00:00,0:00:00,0:00:22,0:03:26,19,0,2
+Intensive Care Unit,PGY2+,S0181077,2016-10-21,2:20:02,0:33:26,0:11:33,0:31:53,0:28:38,0:11:24,4:48:43,1418,0,22
+Intensive Care Unit,PGY2+,S0181077,2016-10-20,1:47:20,0:25:14,0:07:30,0:21:46,0:15:34,0:04:25,3:17:29,1401,115,18
+Intensive Care Unit,PGY2+,S0169404,2015-09-04,3:44:04,0:00:39,0:24:58,0:32:44,0:31:28,0:28:13,6:19:30,3071,34,16
+Intensive Care Unit,PGY2+,S0169404,2015-09-05,2:49:57,0:02:45,0:14:46,0:17:30,0:16:45,0:22:39,4:17:06,2204,585,14
+Intensive Care Unit,PGY2+,S0169404,2015-09-06,0:00:00,0:11:01,0:00:00,0:00:00,0:00:00,0:02:13,0:13:42,85,79,7
+Intensive Care Unit,PGY2+,S0169404,2015-09-07,1:10:02,0:07:32,0:04:29,0:07:57,0:04:35,0:07:14,1:46:44,583,18,11
+Night Team,PGY2+,S0169399,2016-05-04,1:37:44,1:10:00,0:01:22,0:36:13,0:16:52,0:04:56,4:37:29,1841,72,46
+Intensive Care Unit,PGY2+,S0169404,2015-09-01,3:32:15,0:21:02,0:19:55,0:31:34,0:14:37,0:34:45,5:56:25,2834,77,21
+Intensive Care Unit,PGY2+,S0169404,2015-09-02,3:07:02,0:03:23,0:24:17,0:16:47,0:18:48,0:28:16,4:53:28,2180,402,19
+Intensive Care Unit,PGY2+,S0169404,2015-09-03,0:18:19,0:33:39,0:03:39,0:00:41,0:00:14,0:04:16,1:11:19,558,552,18
+Night Team,PGY2+,S0169399,2016-05-08,0:29:22,0:08:26,0:00:02,0:18:14,0:02:48,0:03:32,1:15:14,663,0,8
+Night Team,PGY2+,S0169399,2016-05-09,0:28:03,0:29:25,0:00:01,0:08:35,0:01:05,0:00:17,1:16:07,523,0,6
+General Medicine,PGY1,S0163662,2015-06-15,3:14:23,0:20:05,0:29:41,0:53:04,0:33:42,0:16:06,6:09:40,2162,0,7
+Intensive Care Unit,PGY2+,S0169404,2015-09-08,3:30:46,0:06:39,0:22:26,0:26:46,0:18:33,0:14:19,5:18:54,1818,34,17
+Intensive Care Unit,PGY2+,S0169404,2015-09-09,3:19:00,0:08:52,0:19:26,0:21:04,0:29:07,0:27:58,5:37:21,2421,456,18
+Night Team,PGY2+,S0169404,2016-11-06,4:25:05,2:39:17,0:00:25,0:41:01,0:04:18,0:04:02,8:36:56,3114,0,33
+Night Team,PGY2+,S0169404,2016-11-07,3:56:04,1:10:12,0:00:06,0:24:23,0:05:17,0:03:27,5:50:24,2308,0,36
+Night Team,PGY2+,S0169404,2016-11-08,3:19:20,1:38:35,0:00:11,1:04:46,0:09:46,0:16:28,7:05:35,2256,0,33
+Night Team,PGY2+,S0169404,2016-11-09,3:55:24,1:39:42,0:00:08,0:52:55,0:21:36,0:12:21,7:29:08,2869,0,55
+Night Team,PGY1,S0108268,2014-08-28,2:43:24,0:14:11,0:14:25,0:19:09,1:12:50,0:54:31,5:54:59,1951,76,43
+Night Team,PGY2+,S0108287,2016-02-02,1:48:27,1:21:16,1:47:35,0:46:28,0:17:13,0:21:59,6:53:17,2028,46,34
+General Medicine,PGY1,S0174975,2016-03-04,2:52:31,2:04:41,0:01:42,1:27:00,0:47:24,0:07:05,7:57:26,2830,78,23
+General Medicine,PGY1,S0174975,2016-03-05,1:47:17,1:04:52,0:00:09,1:17:56,0:23:44,0:00:04,5:14:26,1887,26,9
+General Medicine,PGY1,S0174975,2016-03-06,0:16:04,0:55:12,0:00:03,0:08:16,0:00:09,0:01:04,1:25:28,452,448,13
+General Medicine,PGY1,S0174975,2016-03-07,2:30:17,1:37:37,0:00:20,1:08:51,0:32:07,0:03:27,6:26:01,2071,220,19
+General Medicine,PGY1,S0174975,2016-03-01,1:41:42,0:35:37,0:00:38,0:42:37,0:16:26,0:00:12,3:47:21,1403,22,15
+General Medicine,PGY1,S0174975,2016-03-02,0:29:15,0:34:39,0:00:09,0:09:51,0:00:42,0:00:04,1:18:02,477,475,21
+General Medicine,PGY1,S0174975,2016-03-03,1:41:01,2:15:05,0:10:31,1:29:55,0:34:15,0:00:11,7:35:21,3410,243,24
+General Medicine,PGY1,S0174975,2016-03-08,1:24:38,0:56:28,0:00:10,0:55:41,0:21:01,0:03:33,4:21:27,1470,145,11
+General Medicine,PGY1,S0174975,2016-03-09,2:22:50,1:21:01,0:00:11,1:19:27,0:22:58,0:03:33,6:14:15,2018,193,22
+Night Team,PGY1,S0181073,2016-04-22,0:55:16,0:13:58,0:00:03,0:16:13,0:13:43,0:01:34,2:00:21,569,110,9
+Night Team,PGY1,S0181073,2016-04-23,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0181073,2016-04-20,2:20:17,0:41:20,0:00:48,0:21:44,0:35:03,0:19:31,4:47:00,1218,41,37
+Night Team,PGY1,S0181073,2016-04-21,2:56:09,1:06:51,0:00:13,0:37:44,0:17:51,0:03:58,5:30:11,1472,59,35
+Emergency Medicine,PGY1,S0195086,2016-10-11,4:11:30,1:01:51,0:01:49,0:26:11,0:46:37,0:05:16,7:22:43,2017,0,46
+Emergency Medicine,PGY1,S0195086,2016-10-10,3:46:22,1:13:33,0:02:22,0:11:35,0:39:11,0:01:10,6:44:47,1984,0,66
+Emergency Medicine,PGY1,S0195086,2016-10-13,4:00:27,0:36:35,0:05:27,0:12:58,0:43:13,0:06:22,6:15:49,2199,0,67
+General Medicine,PGY1,S0181083,2016-02-27,1:32:46,0:40:45,0:00:04,0:16:27,0:14:43,0:02:41,3:12:12,834,54,12
+General Medicine,PGY1,S0181083,2016-02-26,0:11:08,0:09:30,0:00:22,0:01:29,0:00:05,0:00:32,0:27:38,359,353,8
+General Medicine,PGY1,S0181083,2016-02-25,3:23:09,1:20:15,0:00:39,1:09:26,0:13:19,0:07:43,7:16:31,1788,138,16
+General Medicine,PGY1,S0181083,2016-02-24,0:25:02,0:21:08,0:00:04,0:09:20,0:01:51,0:00:01,1:04:38,438,0,4
+General Medicine,PGY1,S0181083,2016-02-23,1:23:48,1:27:28,0:00:09,0:31:14,0:13:42,0:00:23,4:06:47,1670,186,28
+General Medicine,PGY1,S0181083,2016-02-22,2:43:15,1:00:54,0:00:07,0:49:30,0:26:11,0:12:04,5:59:50,1952,127,16
+General Medicine,PGY1,S0181083,2016-02-21,1:02:39,0:35:34,0:00:03,0:08:20,0:09:53,0:00:11,2:11:26,640,34,5
+General Medicine,PGY1,S0181083,2016-02-20,2:49:39,1:23:58,0:00:15,0:31:36,0:08:40,0:07:38,5:44:57,1729,75,24
+Night Team,PGY1,S0172085,2016-02-12,0:47:49,0:30:16,1:09:44,0:11:59,0:04:13,0:15:31,3:13:32,964,14,24
+Night Team,PGY1,S0172085,2016-02-10,1:06:57,1:14:17,1:37:11,0:46:07,0:25:37,0:26:25,5:59:16,2327,25,63
+Night Team,PGY1,S0172085,2016-02-11,1:24:44,1:23:05,0:48:38,0:39:09,0:39:04,0:37:22,5:56:47,2274,0,37
+Intensive Care Unit,PGY2+,S0093383,2015-10-13,1:07:27,0:17:27,0:27:31,0:38:59,0:31:08,0:13:44,3:30:52,1367,110,22
+Intensive Care Unit,PGY2+,S0093383,2015-10-12,0:00:00,0:00:00,0:00:00,0:00:11,0:00:00,0:00:00,0:00:11,10,0,3
+Intensive Care Unit,PGY2+,S0093383,2015-10-11,1:39:57,0:42:42,0:27:32,0:15:03,0:20:42,0:26:48,4:09:00,1113,0,14
+Intensive Care Unit,PGY2+,S0093383,2015-10-10,1:51:04,0:59:27,0:03:52,0:22:23,0:27:06,0:38:03,4:33:34,1371,0,12
+Intensive Care Unit,PGY2+,S0093383,2015-10-17,0:00:00,0:00:00,0:00:00,0:00:12,0:00:00,0:00:00,0:00:12,26,0,4
+Intensive Care Unit,PGY2+,S0093383,2015-10-16,1:36:56,0:32:27,0:05:31,0:11:42,0:24:23,0:21:09,3:29:35,1044,0,15
+Intensive Care Unit,PGY2+,S0093383,2015-10-15,0:00:30,0:04:51,0:00:00,0:00:40,0:00:00,0:00:04,0:07:54,70,56,5
+Intensive Care Unit,PGY2+,S0093383,2015-10-14,1:22:12,0:44:20,1:25:26,0:16:52,0:14:48,0:20:11,4:40:19,1183,2,13
+Emergency Medicine,PGY1,S0163662,2015-02-09,3:34:22,1:32:44,1:23:25,0:43:52,0:45:39,0:42:27,9:35:43,2811,0,38
+Emergency Medicine,PGY1,S0163662,2015-02-08,3:56:20,0:49:56,1:43:52,0:17:30,0:23:02,0:52:13,8:20:25,2236,0,30
+General Medicine,PGY1,S0181056,2016-03-24,0:03:52,0:00:17,0:00:00,0:00:30,0:00:24,0:00:25,0:07:16,102,90,9
+General Medicine,PGY1,S0181056,2016-03-25,0:00:00,0:00:00,0:00:00,0:00:25,0:00:00,0:00:00,0:00:25,8,0,3
+General Medicine,PGY1,S0181056,2016-03-22,3:28:44,0:40:49,0:13:29,0:46:33,0:19:37,0:09:54,7:37:54,3262,201,20
+General Medicine,PGY1,S0181056,2016-03-23,3:05:36,1:44:07,0:00:18,0:49:09,0:30:56,0:10:24,7:20:37,2431,30,21
+General Medicine,PGY1,S0181056,2016-03-20,1:20:06,0:51:34,0:04:35,0:41:46,0:10:29,0:00:08,3:29:48,1019,29,5
+General Medicine,PGY1,S0169383,2015-01-05,5:53:59,0:17:34,1:42:40,0:39:36,0:41:33,0:43:14,10:27:15,2504,275,9
+Emergency Medicine,PGY1,S0163662,2015-02-01,2:03:20,0:59:33,2:26:24,0:32:00,0:27:44,0:34:16,8:08:16,2422,0,38
+Emergency Medicine,PGY1,S0163662,2015-02-02,3:09:47,0:53:45,3:36:27,0:48:06,0:24:41,0:36:14,10:09:07,2186,168,41
+Emergency Medicine,PGY1,S0163662,2015-02-04,4:00:38,0:20:19,1:45:52,0:15:41,0:22:05,0:05:26,7:51:22,1808,0,28
+Emergency Medicine,PGY1,S0163662,2015-02-07,0:51:57,0:14:58,0:23:46,0:03:43,0:06:17,0:07:32,2:07:18,677,0,7
+General Medicine,PGY1,S0169402,2015-02-18,3:11:58,0:04:27,2:06:55,0:45:02,0:20:05,0:00:40,7:04:55,1858,0,11
+General Medicine,PGY1,S0169402,2015-02-19,0:20:04,0:01:55,0:52:55,0:02:18,0:00:28,0:01:16,1:23:56,266,246,9
+General Medicine,PGY1,S0169402,2015-02-14,0:00:00,0:00:00,0:00:00,0:00:48,0:00:00,0:00:00,0:00:48,20,0,6
+General Medicine,PGY1,S0169402,2015-02-15,3:51:44,0:32:24,3:02:21,1:06:29,0:28:44,0:05:48,9:59:40,2180,148,20
+General Medicine,PGY1,S0169402,2015-02-16,4:00:23,0:10:49,2:09:51,0:43:45,0:19:37,0:06:50,8:15:49,2141,0,9
+General Medicine,PGY1,S0169402,2015-02-17,4:09:51,0:26:50,3:09:44,1:04:11,0:29:31,0:05:03,9:48:54,2421,0,15
+General Medicine,PGY1,S0169402,2015-02-10,3:17:51,0:40:22,3:00:51,1:07:42,0:28:27,0:13:53,9:28:45,2478,56,16
+General Medicine,PGY1,S0169402,2015-02-11,2:30:44,0:14:01,2:10:01,0:36:21,0:10:46,0:06:24,6:15:07,1441,0,8
+General Medicine,PGY1,S0169402,2015-02-12,3:18:54,0:58:48,2:46:17,1:07:29,0:20:05,0:05:10,9:12:37,2312,67,13
+General Medicine,PGY1,S0169402,2015-02-13,2:29:03,0:02:01,1:28:39,0:56:27,0:26:17,0:03:19,6:06:14,1730,0,12
+Night Team,PGY1,S0195206,2016-08-15,3:31:52,0:50:30,0:00:02,0:26:02,0:20:12,0:05:37,5:38:20,1637,0,41
+General Medicine,PGY1,S0169401,2015-03-01,0:00:00,0:00:00,0:00:00,0:00:08,0:00:00,0:00:00,0:00:08,6,0,3
+General Medicine,PGY1,S0169401,2015-03-03,4:10:52,2:04:31,1:28:53,1:38:28,0:42:05,0:18:13,10:49:15,3968,474,20
+General Medicine,PGY1,S0169401,2015-03-02,2:55:27,1:11:34,1:31:06,0:36:22,0:26:59,0:19:04,7:21:01,2422,497,10
+General Medicine,PGY1,S0169401,2015-03-05,3:03:47,0:34:14,3:00:27,1:10:19,0:34:21,0:18:14,8:53:41,2632,292,16
+General Medicine,PGY1,S0169401,2015-03-04,2:47:41,1:03:34,1:15:55,0:42:44,0:17:31,0:09:35,6:42:42,2588,371,12
+General Medicine,PGY1,S0169401,2015-03-07,4:22:11,1:11:57,1:49:03,0:29:29,0:22:33,0:20:15,9:00:57,3254,1775,35
+General Medicine,PGY1,S0169401,2015-03-06,4:38:55,0:49:55,1:29:54,0:57:53,0:30:12,0:12:11,9:01:24,3152,597,13
+General Medicine,PGY1,S0181074,2016-02-29,2:10:23,1:17:38,0:00:14,0:42:46,0:24:40,0:05:27,5:05:19,1908,275,12
+General Medicine,PGY1,S0181074,2016-02-28,3:25:43,1:09:22,0:00:06,1:21:07,0:24:41,0:01:48,7:02:09,2760,19,17
+Night Team,PGY1,S0195206,2016-08-12,3:59:57,1:35:38,0:00:01,0:26:38,0:19:51,0:07:05,6:58:09,1910,117,51
+Night Team,PGY1,S0094535,2014-11-09,2:16:32,0:11:00,0:07:38,0:17:50,0:24:46,0:33:06,4:34:31,1212,12,32
+Night Team,PGY2+,S0173803,2016-08-22,2:04:34,1:25:02,0:00:25,0:39:35,0:08:20,0:12:16,5:05:57,1509,307,46
+Night Team,PGY2+,S0173803,2016-08-23,1:57:39,1:15:24,0:00:02,0:27:01,0:14:28,0:02:42,4:24:38,1169,0,46
+Night Team,PGY2+,S0173803,2016-08-20,2:23:11,1:14:38,0:00:14,0:48:34,0:20:36,0:17:05,5:58:25,2060,89,51
+Night Team,PGY2+,S0173803,2016-08-21,3:10:48,1:32:53,0:00:07,0:25:27,0:06:32,0:07:48,6:01:40,1901,413,47
+Night Team,PGY2+,S0173803,2016-08-26,2:31:35,1:24:19,0:00:20,0:40:10,0:11:34,0:01:32,5:46:16,1664,11,47
+Night Team,PGY2+,S0173803,2016-08-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,2
+Night Team,PGY2+,S0173803,2016-08-24,2:28:38,1:22:54,0:00:06,0:31:25,0:15:05,0:08:11,5:08:14,1321,66,35
+Night Team,PGY2+,S0173803,2016-08-25,3:33:36,2:14:23,0:00:09,0:34:09,0:24:29,0:05:06,7:50:09,2062,0,47
+General Medicine,PGY2+,S0169404,2016-04-04,2:42:11,1:53:25,0:00:09,1:16:00,0:31:03,0:04:34,6:55:05,3729,598,16
+General Medicine,PGY2+,S0169404,2016-04-05,2:47:53,1:53:26,0:00:05,0:38:05,0:33:33,0:15:26,6:48:35,3479,330,21
+General Medicine,PGY2+,S0169404,2016-04-06,2:30:47,1:21:51,0:00:43,0:22:21,0:12:41,0:02:10,4:55:05,2810,98,22
+General Medicine,PGY2+,S0169404,2016-04-07,0:29:14,0:31:38,0:00:00,0:03:30,0:03:38,0:03:23,1:18:52,482,476,13
+General Medicine,PGY2+,S0169404,2016-04-03,4:33:54,2:35:40,0:00:42,1:23:29,0:30:25,0:07:21,9:54:51,5277,510,18
+Night Team,PGY1,S0195206,2016-08-10,1:57:22,0:51:15,0:00:00,0:11:54,0:10:47,0:00:34,3:26:30,950,0,41
+Intensive Care Unit,PGY2+,S0169385,2016-01-12,1:43:06,0:47:50,0:07:23,0:35:58,0:37:56,0:44:24,4:48:50,2074,119,21
+Night Team,PGY1,S0181083,2015-08-31,1:13:00,0:03:42,0:06:01,0:33:14,0:18:35,0:10:06,2:41:54,829,0,20
+Night Team,PGY1,S0181083,2015-08-30,1:20:49,0:04:30,1:36:30,0:09:17,0:03:32,0:15:16,3:39:11,653,0,17
+Intensive Care Unit,PGY2+,S0169385,2016-01-13,2:22:19,0:46:35,0:19:35,0:16:53,0:25:14,1:06:21,5:40:37,2074,40,15
+Intensive Care Unit,PGY2+,S0169385,2016-01-10,0:36:25,0:55:30,0:07:07,0:00:00,0:04:30,0:12:08,2:00:38,850,43,21
+Intensive Care Unit,PGY2+,S0169385,2016-01-11,1:18:08,0:35:07,0:22:49,0:29:34,0:31:18,0:40:18,4:20:55,1631,37,14
+General Medicine,PGY1,S0176040,2016-02-28,2:01:29,1:15:33,0:01:53,0:35:25,0:23:17,0:01:42,4:48:36,1709,0,10
+General Medicine,PGY1,S0176040,2016-02-29,1:24:48,1:10:50,0:00:04,0:22:53,0:13:11,0:00:42,3:35:47,1142,0,6
+Intensive Care Unit,PGY2+,S0169385,2016-01-16,2:05:57,0:28:26,0:11:11,0:50:31,0:38:09,2:01:15,6:32:46,3096,146,15
+Night Team,PGY1,S0195206,2016-08-11,1:34:45,0:27:01,0:00:00,0:06:44,0:12:37,0:06:03,2:38:02,772,0,33
+General Medicine,PGY1,S0172056,2015-08-28,2:15:50,0:30:25,4:17:35,0:57:49,0:38:37,0:33:21,10:12:59,2295,22,14
+General Medicine,PGY1,S0172056,2015-08-29,0:00:00,0:00:00,0:00:00,0:03:50,0:00:00,0:00:00,0:03:50,14,0,5
+Intensive Care Unit,PGY2+,S0169385,2016-01-14,2:11:00,0:57:52,0:22:41,0:51:58,0:34:30,0:58:13,6:24:06,2402,54,20
+General Medicine,PGY1,S0172056,2015-08-22,2:31:57,0:14:53,2:04:53,0:39:24,0:17:57,0:27:04,6:58:04,1986,0,15
+General Medicine,PGY1,S0172056,2015-08-23,2:24:25,0:08:52,3:55:24,0:40:33,0:27:02,0:27:49,8:44:35,1724,0,12
+General Medicine,PGY1,S0172056,2015-08-20,1:19:30,0:24:32,1:21:42,0:20:41,0:25:01,0:22:26,4:32:38,989,0,10
+General Medicine,PGY1,S0172056,2015-08-21,2:38:25,0:29:54,2:44:22,0:28:09,0:23:44,0:39:47,8:15:09,1940,0,10
+General Medicine,PGY1,S0172056,2015-08-26,2:13:25,0:19:19,1:07:39,1:11:58,0:43:55,0:29:02,6:57:55,2159,64,22
+General Medicine,PGY1,S0172056,2015-08-27,1:05:58,0:23:17,1:39:32,0:45:47,0:10:18,0:17:50,4:42:39,1231,14,9
+General Medicine,PGY1,S0172056,2015-08-25,2:08:55,0:24:18,1:54:41,0:38:38,0:14:22,0:21:01,6:09:39,1330,0,7
+Night Team,PGY1,S0181057,2015-11-21,1:04:50,0:34:18,0:06:29,0:37:37,0:06:08,0:22:30,2:58:33,994,0,36
+Night Team,PGY1,S0181057,2015-11-20,1:15:16,0:53:14,0:44:53,0:36:47,0:36:05,0:21:47,4:39:18,1731,0,31
+General Medicine,PGY2+,S0169411,2016-03-21,1:47:43,0:39:24,0:00:17,0:28:34,0:16:57,0:00:21,3:39:32,1286,0,14
+General Medicine,PGY2+,S0169411,2016-03-20,4:45:26,0:52:05,0:00:00,0:48:47,0:20:26,0:13:18,7:39:20,2788,0,32
+General Medicine,PGY2+,S0169411,2016-03-23,3:52:32,0:30:14,0:00:00,0:23:44,0:08:56,0:04:42,5:27:40,2116,0,17
+General Medicine,PGY2+,S0169411,2016-03-22,1:19:36,0:23:05,0:00:15,0:26:46,0:07:04,0:00:15,2:42:09,1202,0,21
+General Medicine,PGY2+,S0169411,2016-03-25,0:13:32,0:04:21,0:00:00,0:00:27,0:00:14,0:00:15,0:19:20,273,267,18
+General Medicine,PGY2+,S0169411,2016-03-24,1:38:15,0:27:56,0:00:07,0:08:02,0:09:39,0:00:12,2:38:25,1133,0,35
+General Medicine,PGY2+,S0169411,2016-03-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+General Medicine,PGY2+,S0169411,2016-03-26,2:22:07,1:08:13,0:00:07,0:30:40,0:11:48,0:01:22,4:49:33,1711,0,19
+General Medicine,PGY2+,S0169411,2016-03-29,1:42:53,0:46:01,0:03:47,0:46:29,0:09:14,0:01:01,3:54:14,1664,0,15
+General Medicine,PGY2+,S0169411,2016-03-28,3:54:15,1:07:25,0:00:11,0:46:14,0:17:28,0:04:52,6:54:37,2168,25,34
+General Medicine,PGY2+,S0181078,2016-09-22,2:43:23,1:23:31,0:05:33,0:37:10,0:16:38,0:12:59,6:05:40,1848,503,37
+General Medicine,PGY2+,S0181078,2016-09-23,4:41:55,1:37:09,0:00:04,0:56:34,0:42:38,0:09:54,8:40:09,2989,345,25
+Night Team,PGY2+,S0108287,2016-01-25,1:16:33,1:07:25,0:18:53,0:25:34,0:02:34,0:16:41,3:52:37,2147,22,20
+Night Team,PGY2+,S0108287,2016-01-24,0:40:14,1:14:31,1:10:03,0:14:32,0:08:51,0:12:03,3:48:51,1433,7,14
+Night Team,PGY2+,S0108287,2016-01-27,1:05:46,1:02:06,2:08:29,0:38:17,0:14:53,0:24:23,5:49:48,1650,20,22
+Night Team,PGY2+,S0108287,2016-01-26,1:53:52,2:11:48,2:34:14,0:22:24,0:16:29,0:21:36,8:20:00,2648,50,37
+Night Team,PGY2+,S0108287,2016-01-29,1:07:12,0:52:55,2:06:08,0:20:30,0:12:09,0:26:18,5:48:19,1621,14,27
+Intensive Care Unit,PGY2+,S0181073,2016-10-09,2:25:45,1:24:55,0:01:01,0:36:06,0:34:51,0:16:12,5:58:42,1776,0,11
+Night Team,PGY2+,S0108287,2016-01-28,1:40:45,1:35:26,1:29:17,0:31:24,0:21:49,0:34:07,6:39:05,3283,32,25
+General Medicine,PGY1,S0181075,2015-08-09,1:54:47,1:24:10,0:55:31,0:51:51,0:37:03,0:25:17,6:52:19,2317,125,7
+General Medicine,PGY1,S0181075,2015-08-08,0:11:04,0:03:07,0:06:20,0:04:16,0:05:44,0:01:37,0:37:28,424,418,6
+General Medicine,PGY1,S0181075,2015-08-01,1:28:30,1:16:01,0:43:03,0:38:00,0:10:14,0:13:55,4:55:04,1386,0,10
+Emergency Medicine,PGY1,S0169399,2015-05-11,1:29:16,0:00:08,1:54:47,0:00:12,0:03:48,0:00:00,3:34:41,270,0,3
+General Medicine,PGY1,S0181075,2015-08-03,0:16:25,0:19:44,1:29:55,0:06:24,0:00:00,0:01:44,2:18:21,491,485,11
+General Medicine,PGY1,S0181075,2015-08-02,2:02:36,1:20:25,2:29:03,0:46:00,0:15:55,0:13:35,7:26:34,2002,0,13
+General Medicine,PGY1,S0181075,2015-08-05,1:41:25,0:28:18,0:40:42,0:35:14,0:07:26,0:06:26,4:40:34,1934,519,18
+General Medicine,PGY1,S0181075,2015-08-04,2:26:13,1:19:19,2:29:39,0:45:21,0:12:37,0:23:17,8:28:03,2258,0,15
+General Medicine,PGY1,S0181075,2015-08-07,2:07:23,1:29:30,1:05:46,1:09:38,0:36:44,0:13:50,7:25:01,1998,141,14
+General Medicine,PGY1,S0181075,2015-08-06,1:44:23,0:25:33,1:22:17,0:42:46,0:02:48,0:08:06,4:53:57,1416,0,14
+Night Team,PGY1,S0169389,2014-07-05,2:45:53,0:03:23,0:06:00,0:18:55,0:17:23,0:54:53,4:38:08,1147,81,37
+Night Team,PGY1,S0169389,2014-07-04,2:07:02,0:00:51,0:00:11,0:09:58,0:14:36,0:18:11,3:18:10,947,577,75
+Night Team,PGY1,S0169389,2014-07-01,3:13:03,0:07:44,0:12:28,0:27:29,0:22:59,0:58:29,5:36:46,1216,0,42
+Night Team,PGY1,S0169389,2014-07-03,3:03:03,0:02:12,0:08:32,0:23:51,0:22:59,0:36:16,4:43:45,1184,72,32
+Night Team,PGY1,S0169389,2014-07-02,3:48:06,0:09:32,0:21:12,0:10:25,0:19:53,0:56:09,5:57:07,1326,27,43
+Night Team,PGY1,S0093383,2015-01-15,1:50:01,0:05:59,2:25:51,0:20:38,0:08:31,0:07:33,5:18:57,968,0,17
+Night Team,PGY1,S0093383,2015-01-14,2:29:42,0:06:21,3:41:16,0:36:59,0:16:56,0:35:22,8:07:00,1500,0,19
+Night Team,PGY1,S0093383,2015-01-17,1:52:58,0:08:31,2:33:23,0:29:37,0:11:08,0:26:49,5:52:53,1146,0,24
+Night Team,PGY1,S0093383,2015-01-16,0:36:24,0:00:59,0:49:39,0:06:50,0:04:40,0:12:57,1:57:57,484,68,20
+Night Team,PGY1,S0093383,2015-01-11,2:59:42,0:09:37,1:43:41,0:37:26,0:34:31,0:33:19,6:53:21,1744,0,50
+Night Team,PGY1,S0093383,2015-01-10,2:42:27,0:04:13,2:10:53,0:42:11,0:33:44,0:28:36,7:03:46,1474,0,45
+Night Team,PGY1,S0093383,2015-01-13,2:26:10,0:04:00,1:45:30,0:38:11,0:30:59,0:19:24,5:59:50,1389,0,34
+Night Team,PGY1,S0093383,2015-01-12,1:57:57,0:04:04,2:18:13,0:38:57,0:17:28,0:25:57,6:00:12,1163,0,32
+General Medicine,PGY1,S0195218,2016-09-09,0:11:04,0:16:52,0:00:09,0:02:15,0:00:15,0:00:04,0:39:08,1343,1325,25
+General Medicine,PGY1,S0195218,2016-09-08,1:46:44,1:56:38,0:00:31,0:32:38,0:11:33,0:00:51,4:56:54,1725,470,15
+Night Team,PGY2+,S0074196,2016-06-29,1:02:29,0:42:31,0:00:05,0:17:21,0:04:46,0:00:11,2:19:53,795,209,8
+Night Team,PGY2+,S0074196,2016-06-28,0:53:21,0:46:34,0:00:07,0:15:04,0:02:11,0:00:06,2:14:03,890,0,9
+General Medicine,PGY1,S0195218,2016-09-05,1:24:44,0:41:38,0:00:10,0:36:14,0:16:37,0:05:12,3:37:46,1444,12,28
+General Medicine,PGY1,S0195218,2016-09-04,1:21:20,1:14:52,0:00:12,0:35:02,0:15:38,0:00:23,3:42:06,1314,9,9
+General Medicine,PGY1,S0195218,2016-09-07,1:43:44,1:05:04,0:07:08,0:33:46,0:20:15,0:05:57,4:26:26,1770,17,9
+General Medicine,PGY1,S0195218,2016-09-06,1:55:06,1:05:29,0:00:17,0:51:56,0:16:02,0:07:49,5:09:43,2004,8,11
+General Medicine,PGY1,S0195218,2016-09-01,3:08:40,2:06:54,0:00:41,1:08:33,0:34:09,0:01:38,7:40:18,2677,80,10
+General Medicine,PGY1,S0195218,2016-09-03,2:26:19,1:19:48,0:01:02,1:03:59,0:27:51,0:02:45,5:56:19,2265,20,12
+General Medicine,PGY1,S0195218,2016-09-02,0:00:00,0:00:00,0:00:00,0:00:30,0:00:00,0:00:00,0:00:30,16,0,4
+General Medicine,PGY1,S0109179,2015-01-01,3:05:12,0:19:20,3:09:51,0:26:51,0:27:14,0:22:45,8:17:27,2195,22,10
+General Medicine,PGY1,S0109179,2015-01-03,0:00:00,0:00:00,0:00:00,0:02:56,0:00:00,0:00:00,0:02:56,30,0,5
+General Medicine,PGY1,S0109179,2015-01-02,2:05:14,0:07:08,2:46:24,0:19:28,0:20:21,0:14:00,6:07:55,1426,13,8
+Emergency Medicine,PGY1,S0109179,2015-01-05,5:59:52,0:08:18,1:04:57,0:27:40,0:18:19,0:43:07,9:04:51,2720,0,37
+Emergency Medicine,PGY1,S0109179,2015-01-04,1:54:56,0:09:17,0:57:38,0:15:53,0:13:57,0:26:03,4:12:47,1106,0,12
+Emergency Medicine,PGY1,S0109179,2015-01-07,1:20:47,0:02:56,0:48:17,0:26:26,0:04:01,0:02:18,3:17:31,1039,714,8
+Emergency Medicine,PGY1,S0109179,2015-01-06,2:33:15,0:04:12,0:42:00,0:21:19,0:06:26,0:16:13,4:25:11,1190,0,19
+Emergency Medicine,PGY1,S0109179,2015-01-09,5:32:27,0:06:46,2:16:39,0:11:48,0:16:59,0:13:33,9:00:18,2422,0,44
+Emergency Medicine,PGY1,S0109179,2015-01-08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,1
+General Medicine,PGY2+,S0181082,2016-08-19,2:53:59,0:57:48,0:00:03,0:34:27,0:39:01,0:13:16,6:01:59,1971,79,18
+General Medicine,PGY1,S0195213,2016-10-17,1:31:44,0:45:27,0:01:41,0:26:40,0:04:17,0:00:09,3:14:21,924,0,9
+General Medicine,PGY1,S0181075,2015-11-09,2:25:25,2:12:11,1:08:47,0:56:30,0:44:09,0:26:56,8:26:52,2891,0,13
+General Medicine,PGY1,S0181075,2015-11-08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0079337,2015-11-28,3:49:02,1:22:28,1:53:52,0:42:07,0:10:10,0:16:07,8:26:23,2410,0,23
+Night Team,PGY2+,S0079337,2015-11-29,3:16:28,1:04:11,0:58:18,0:30:40,0:19:10,0:17:11,6:43:57,2279,25,26
+Night Team,PGY2+,S0079337,2015-11-22,0:56:24,0:20:21,0:36:53,0:23:24,0:17:20,0:03:55,2:48:22,940,373,15
+Night Team,PGY2+,S0079337,2015-11-23,2:50:29,1:20:38,1:38:11,0:51:45,0:15:03,0:24:28,7:59:55,3533,0,35
+Night Team,PGY2+,S0079337,2015-11-20,3:48:40,1:47:09,1:30:51,0:59:27,0:14:43,0:13:24,9:17:28,3267,0,30
+Night Team,PGY2+,S0079337,2015-11-21,3:10:58,1:07:32,0:34:52,0:24:04,0:04:18,0:09:10,5:36:43,1922,0,30
+Night Team,PGY2+,S0079337,2015-11-26,0:53:16,1:02:08,1:16:39,0:34:31,0:09:01,0:08:43,4:16:30,1300,0,22
+Night Team,PGY2+,S0079337,2015-11-27,1:13:57,1:00:22,0:50:33,0:33:27,0:02:08,0:08:15,4:04:45,1886,0,19
+Night Team,PGY2+,S0079337,2015-11-24,2:46:49,1:17:13,0:59:16,0:38:42,0:10:36,0:12:35,6:35:39,2635,5,27
+Night Team,PGY2+,S0079337,2015-11-25,1:18:39,0:36:08,0:52:51,0:55:08,0:21:51,0:07:35,4:30:25,1696,326,27
+General Medicine,PGY1,S0169399,2014-12-08,2:17:56,0:04:58,2:12:35,0:38:30,0:08:24,0:06:35,5:50:55,906,0,8
+General Medicine,PGY1,S0169399,2014-12-09,1:11:21,0:00:13,1:49:29,0:08:52,0:08:09,0:00:12,3:29:36,667,0,13
+Night Team,PGY2+,S0094535,2016-06-24,1:36:15,0:58:51,0:00:02,0:21:24,0:07:11,0:00:31,3:20:18,807,0,12
+Night Team,PGY2+,S0094535,2016-06-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0094535,2016-06-22,3:02:40,1:25:34,0:00:06,0:28:27,0:06:58,0:07:09,5:57:38,1642,0,31
+General Medicine,PGY1,S0169399,2014-12-07,1:12:35,0:00:49,1:13:37,0:13:10,0:12:10,0:00:23,2:59:26,600,0,6
+Night Team,PGY2+,S0094535,2016-06-20,3:14:40,1:13:13,0:00:14,0:40:46,0:15:25,0:05:57,6:07:36,1573,91,27
+Night Team,PGY2+,S0094535,2016-06-21,3:06:05,1:32:30,0:00:14,0:28:25,0:14:33,0:01:59,5:54:12,1548,0,33
+Night Team,PGY2+,S0169390,2016-04-08,2:24:09,1:48:14,0:00:11,0:41:53,0:06:46,0:03:53,5:28:47,1617,12,60
+Night Team,PGY2+,S0169390,2016-04-09,3:33:50,0:49:25,0:00:02,0:26:54,0:13:14,0:02:21,5:33:43,1479,0,66
+Night Team,PGY2+,S0169390,2016-04-04,3:54:16,0:53:35,0:00:04,0:26:54,0:12:04,0:08:36,6:03:19,1495,11,72
+Night Team,PGY2+,S0169390,2016-04-05,1:30:38,0:33:44,0:00:04,0:13:09,0:25:43,0:02:26,3:10:26,1264,155,29
+Night Team,PGY2+,S0169390,2016-04-06,1:10:43,0:49:49,0:00:00,0:13:03,0:02:57,0:07:31,2:44:57,1398,48,31
+Night Team,PGY2+,S0169390,2016-04-07,4:05:15,1:46:47,0:00:13,0:39:20,0:28:04,0:02:36,7:49:49,2190,93,51
+Night Team,PGY2+,S0169390,2016-04-03,3:11:38,1:08:56,0:00:05,0:37:45,0:17:01,0:07:08,6:17:40,1828,24,41
+Emergency Medicine,PGY1,S0195094,2016-09-10,2:38:46,0:28:03,0:00:42,0:44:10,0:41:33,0:00:09,5:26:39,1371,0,32
+Emergency Medicine,PGY1,S0195094,2016-09-11,1:08:14,0:11:19,0:00:52,0:17:22,0:12:53,0:00:00,2:17:32,512,0,9
+Emergency Medicine,PGY1,S0195094,2016-09-12,2:30:39,0:53:33,0:01:51,0:49:28,0:39:19,0:02:33,5:31:25,1545,0,20
+Emergency Medicine,PGY1,S0195094,2016-09-13,0:21:08,0:00:41,0:00:00,0:00:00,0:00:00,0:02:23,0:24:54,49,0,1
+Emergency Medicine,PGY1,S0195094,2016-09-14,0:07:53,0:00:25,0:00:00,0:00:00,0:00:00,0:01:09,0:09:58,50,0,3
+Emergency Medicine,PGY1,S0195094,2016-09-15,2:42:17,0:53:27,0:00:23,0:29:34,0:53:29,0:01:39,5:52:42,1563,0,34
+Emergency Medicine,PGY1,S0195094,2016-09-16,3:03:58,0:42:24,0:02:55,0:48:03,1:01:26,0:01:01,6:24:56,1486,0,23
+Emergency Medicine,PGY1,S0195094,2016-09-17,0:18:20,0:02:11,0:00:00,0:00:00,0:00:00,0:00:00,0:21:53,65,0,13
+Intensive Care Unit,PGY2+,S0181059,2016-08-01,3:36:13,1:00:43,0:08:49,0:13:12,0:27:56,0:09:26,5:54:31,2665,0,25
+Intensive Care Unit,PGY2+,S0181059,2016-08-02,3:06:36,0:50:05,0:10:38,0:19:13,0:20:14,0:04:59,5:19:09,2193,0,29
+Intensive Care Unit,PGY2+,S0181059,2016-08-03,0:14:17,0:04:15,0:00:00,0:00:12,0:00:00,0:00:15,0:19:28,138,0,15
+Intensive Care Unit,PGY2+,S0181059,2016-08-04,4:14:25,1:41:03,0:01:00,0:21:49,0:28:48,0:04:07,7:29:16,4760,0,63
+Intensive Care Unit,PGY2+,S0181059,2016-08-05,3:33:11,0:43:02,0:07:28,0:23:48,0:20:37,0:03:24,5:55:45,3022,0,69
+Intensive Care Unit,PGY2+,S0181059,2016-08-06,0:22:12,0:01:13,0:00:00,0:01:00,0:00:00,0:01:39,0:26:39,188,0,15
+Intensive Care Unit,PGY2+,S0181059,2016-08-07,3:08:18,1:11:52,0:06:48,0:25:33,0:20:57,0:05:51,5:55:13,2191,401,43
+Night Team,PGY1,S0109179,2014-07-20,1:48:40,0:01:41,0:00:02,0:18:36,0:26:13,0:43:48,3:33:27,963,0,27
+Night Team,PGY1,S0109179,2014-07-21,2:28:05,0:12:21,0:09:05,0:37:18,0:18:06,0:21:01,4:13:50,1091,0,32
+Night Team,PGY1,S0109179,2014-07-22,2:24:09,0:02:08,0:11:50,0:40:22,0:40:38,0:36:28,4:48:26,1369,0,29
+Night Team,PGY1,S0109179,2014-07-23,2:53:12,0:10:15,0:21:21,1:03:08,0:32:28,0:08:59,5:19:38,1473,0,42
+Night Team,PGY1,S0109179,2014-07-24,1:58:34,0:08:03,0:14:51,0:24:56,0:24:40,0:19:07,3:45:06,965,0,28
+Night Team,PGY1,S0109179,2014-07-25,0:40:33,0:02:13,0:00:07,0:25:04,0:13:24,0:10:20,1:34:02,618,0,33
+Night Team,PGY1,S0109179,2014-07-26,1:49:10,0:01:32,0:12:54,0:30:22,0:31:11,0:08:39,3:25:28,930,0,44
+Night Team,PGY1,S0109179,2014-07-27,1:22:41,0:06:56,0:17:11,0:37:22,0:06:43,0:12:50,2:56:40,651,0,18
+Night Team,PGY1,S0109179,2014-07-28,0:45:07,0:01:43,0:00:12,0:13:07,0:13:11,0:03:28,1:22:36,572,0,22
+Night Team,PGY1,S0109179,2014-07-29,1:25:41,0:13:07,0:14:29,0:19:20,0:18:06,0:22:46,3:04:32,957,123,47
+General Medicine,PGY2+,S0181526,2016-07-19,0:58:38,0:11:51,0:00:00,0:04:49,0:01:45,0:08:17,1:40:17,628,458,13
+General Medicine,PGY2+,S0181526,2016-07-18,4:10:00,1:31:10,0:00:00,0:29:37,0:18:39,0:13:31,7:16:42,2462,256,28
+General Medicine,PGY1,S0181088,2016-06-01,0:53:41,0:11:41,0:00:00,0:03:17,0:02:05,0:01:41,1:18:41,375,19,9
+General Medicine,PGY1,S0181088,2016-06-02,1:40:40,0:45:05,0:00:20,0:32:34,0:08:35,0:01:09,3:37:13,1045,913,7
+General Medicine,PGY1,S0181088,2016-06-03,3:15:52,0:55:33,0:02:22,1:01:48,0:21:59,0:13:50,6:26:54,1932,17,15
+General Medicine,PGY1,S0181088,2016-06-04,0:07:33,0:06:35,0:00:04,0:00:27,0:00:24,0:00:16,0:26:13,277,261,16
+General Medicine,PGY1,S0181088,2016-06-05,1:21:45,0:25:28,0:00:08,0:30:57,0:12:11,0:05:16,2:54:04,1069,12,8
+General Medicine,PGY1,S0181088,2016-06-06,3:04:34,0:33:45,0:00:12,1:05:56,0:13:36,0:04:11,5:51:21,1994,120,10
+General Medicine,PGY1,S0172645,2015-09-28,0:24:59,1:33:46,0:43:09,0:20:58,0:16:00,0:16:49,3:56:24,1003,288,10
+General Medicine,PGY1,S0172645,2015-09-27,0:00:00,0:00:06,0:00:00,0:00:01,0:00:00,0:00:04,0:00:13,34,17,3
+General Medicine,PGY1,S0172645,2015-09-26,0:51:59,0:54:22,1:36:29,0:52:33,0:31:32,0:19:57,5:37:49,1589,0,11
+General Medicine,PGY1,S0172645,2015-09-25,0:45:37,1:02:06,1:00:40,0:22:47,0:32:01,0:19:05,4:27:53,1391,22,10
+General Medicine,PGY1,S0172645,2015-09-24,0:51:35,1:00:52,2:13:49,1:06:36,0:22:26,0:35:10,6:36:55,1687,0,9
+General Medicine,PGY1,S0172645,2015-09-23,0:32:52,0:48:55,2:44:00,0:15:46,0:12:52,0:14:37,5:14:40,1152,35,10
+General Medicine,PGY1,S0172645,2015-09-22,0:00:00,0:00:05,0:00:00,0:00:00,0:00:00,0:00:00,0:00:11,21,9,2
+General Medicine,PGY1,S0172645,2015-09-21,2:12:32,0:18:24,1:43:40,0:51:02,0:20:30,0:15:40,6:09:03,1841,0,12
+General Medicine,PGY1,S0172645,2015-09-20,0:47:02,0:00:05,0:28:03,0:33:00,0:06:07,0:02:04,2:06:16,662,0,3
+Night Team,PGY2+,S0169398,2016-10-16,3:06:23,0:58:52,0:00:02,0:23:59,0:14:02,0:02:08,5:01:37,976,12,21
+Night Team,PGY2+,S0169398,2016-10-17,3:20:13,1:31:44,0:00:10,0:30:45,0:08:12,0:05:13,6:05:12,1641,0,29
+Night Team,PGY2+,S0169398,2016-10-14,0:02:16,0:00:02,0:00:00,0:00:00,0:00:00,0:00:00,0:02:18,5,0,1
+Night Team,PGY2+,S0169398,2016-10-15,1:28:30,0:36:33,0:00:05,0:14:05,0:04:52,0:00:15,2:35:27,646,0,10
+Night Team,PGY2+,S0169398,2016-10-12,3:17:32,2:22:16,0:00:27,0:49:12,0:17:16,0:02:29,7:36:23,2253,0,28
+Night Team,PGY2+,S0169398,2016-10-13,2:05:38,0:40:32,0:00:29,0:13:51,0:03:13,0:00:58,3:30:27,1148,0,32
+General Medicine,PGY1,S0169411,2015-01-31,0:00:00,0:00:00,0:00:00,0:00:16,0:00:00,0:00:00,0:00:16,14,0,2
+General Medicine,PGY1,S0169411,2015-01-30,3:24:23,0:56:23,1:14:49,0:48:50,0:13:27,0:07:22,7:06:40,2325,0,12
+Night Team,PGY2+,S0169398,2016-10-18,1:58:10,1:38:00,0:00:12,0:11:41,0:08:01,0:06:12,4:25:29,1293,41,23
+Night Team,PGY2+,S0169398,2016-10-19,1:56:56,1:14:51,0:00:10,0:23:31,0:16:15,0:10:17,4:28:24,1233,37,32
+Night Team,PGY1,S0181060,2015-09-30,1:31:23,0:44:00,0:51:50,1:10:41,0:33:08,0:33:18,6:03:40,1370,224,27
+Emergency Medicine,PGY2+,S0172645,2016-10-19,0:25:16,0:00:00,0:00:00,0:00:19,0:00:14,0:00:00,0:25:49,52,0,12
+Emergency Medicine,PGY2+,S0172645,2016-10-18,1:37:08,0:28:53,0:00:28,0:12:41,0:22:02,0:05:49,3:03:39,759,0,23
+Emergency Medicine,PGY1,S0195086,2016-09-22,5:26:14,0:59:33,0:07:43,0:11:47,0:45:35,0:05:19,8:23:47,2163,0,50
+Night Team,PGY2+,S0169398,2015-09-04,2:56:10,1:08:33,1:58:52,0:40:12,0:26:19,1:03:25,8:45:58,2416,0,38
+Emergency Medicine,PGY1,S0195086,2016-09-23,4:04:46,0:46:10,0:01:02,0:15:21,0:31:41,0:02:57,6:22:52,1828,308,43
+Emergency Medicine,PGY2+,S0172645,2016-10-11,0:00:01,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:02:27,52,0,23
+Emergency Medicine,PGY2+,S0172645,2016-10-10,2:38:23,1:02:40,0:00:31,0:26:43,0:42:47,0:02:40,5:20:16,1603,0,19
+Emergency Medicine,PGY1,S0195086,2016-09-26,3:37:48,0:33:04,0:06:22,0:14:35,0:58:24,0:02:33,6:25:03,1935,133,46
+Emergency Medicine,PGY2+,S0172645,2016-10-16,2:41:34,0:36:58,0:00:48,0:28:37,0:26:01,0:01:43,4:44:25,1419,0,47
+Emergency Medicine,PGY1,S0195086,2016-09-24,1:54:26,0:36:07,0:01:15,0:13:50,0:34:22,0:03:53,4:10:28,1386,69,29
+General Medicine,PGY1,S0108268,2014-12-30,3:22:30,0:23:38,0:55:38,1:19:54,0:37:12,0:52:29,8:06:42,2625,257,11
+Night Team,PGY2+,S0176040,2016-10-01,1:34:33,1:39:51,0:00:07,0:33:36,0:12:15,0:02:55,4:22:04,1129,0,51
+Night Team,PGY2+,S0176040,2016-10-03,1:35:06,0:28:25,0:00:05,0:30:56,0:04:51,0:14:52,3:12:50,848,0,8
+Night Team,PGY2+,S0176040,2016-10-02,1:47:08,0:49:48,0:00:12,0:49:03,0:08:11,0:07:52,4:18:18,1093,0,16
+Night Team,PGY2+,S0176040,2016-10-05,2:14:57,0:51:30,0:00:02,0:38:20,0:14:35,0:06:28,4:31:31,1344,0,41
+Night Team,PGY2+,S0176040,2016-10-04,1:22:06,0:24:39,0:00:00,0:20:17,0:06:09,0:03:11,2:33:21,590,59,11
+Night Team,PGY2+,S0176040,2016-10-07,2:05:20,1:04:06,0:00:07,0:49:35,0:14:09,0:04:51,5:07:22,1432,0,28
+Night Team,PGY2+,S0176040,2016-10-06,2:20:47,1:29:44,0:00:06,0:53:03,0:16:56,0:12:42,5:35:54,1746,197,27
+Night Team,PGY2+,S0176040,2016-10-08,1:12:59,0:35:04,0:00:06,0:14:02,0:08:25,0:02:52,2:39:54,782,0,27
+Night Team,PGY2+,S0172085,2016-10-20,1:56:16,2:10:04,0:00:11,0:37:47,0:07:04,0:16:01,5:51:54,2246,0,32
+Night Team,PGY2+,S0172085,2016-10-21,1:53:33,2:34:18,0:00:12,0:18:34,0:13:53,0:04:16,5:26:36,2419,0,50
+Night Team,PGY2+,S0172085,2016-10-22,0:44:37,1:36:06,0:00:05,0:22:49,0:06:01,0:00:15,3:08:47,878,0,16
+General Medicine,PGY1,S0182038,2016-09-27,0:43:07,0:47:08,0:00:10,0:29:12,0:03:56,0:06:36,2:21:23,730,67,13
+General Medicine,PGY1,S0182038,2016-09-26,1:55:34,0:36:46,0:00:08,0:39:32,0:17:45,0:10:39,4:19:09,1260,123,10
+General Medicine,PGY1,S0182038,2016-09-25,0:31:04,0:14:46,0:00:19,0:07:50,0:03:17,0:00:05,1:17:53,454,118,10
+General Medicine,PGY1,S0182038,2016-09-24,1:30:37,1:12:09,0:00:12,1:03:11,0:19:52,0:01:17,4:45:20,1306,0,21
+General Medicine,PGY1,S0182038,2016-09-23,2:06:41,1:07:45,0:00:17,0:29:25,0:12:09,0:02:49,4:41:41,1581,50,20
+General Medicine,PGY1,S0182038,2016-09-22,1:28:09,0:45:56,0:04:07,0:34:00,0:10:40,0:13:22,3:47:24,1684,118,15
+General Medicine,PGY1,S0182038,2016-09-21,2:24:05,1:18:31,0:00:15,0:55:50,0:22:23,0:06:31,5:44:11,1624,0,21
+General Medicine,PGY1,S0182038,2016-09-20,1:11:02,1:00:24,0:00:08,0:40:25,0:13:48,0:00:38,3:31:27,1034,131,11
+General Medicine,PGY1,S0182038,2016-09-29,1:19:18,0:47:44,0:00:19,0:54:39,0:21:06,0:02:40,3:59:58,1286,0,11
+General Medicine,PGY1,S0182038,2016-09-28,2:22:53,1:28:17,0:00:19,0:45:56,0:35:09,0:03:17,6:08:01,1553,8,11
+Night Team,PGY2+,S0181057,2016-07-31,2:44:19,2:20:55,0:00:26,0:45:09,0:24:01,0:16:05,6:55:31,1714,0,21
+General Medicine,PGY1,S0169398,2015-01-16,3:58:29,0:11:20,1:27:17,1:03:42,0:26:00,0:26:09,7:53:27,2261,259,10
+Intensive Care Unit,PGY2+,S0093383,2016-07-14,0:43:40,0:21:14,0:04:15,0:08:25,0:19:03,0:02:05,1:52:49,649,0,12
+Intensive Care Unit,PGY2+,S0093383,2016-07-15,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:08,7,0,2
+Intensive Care Unit,PGY2+,S0093383,2016-07-16,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:10,9,0,2
+General Medicine,PGY1,S0169398,2015-01-10,4:19:14,0:15:49,0:56:44,0:49:51,0:42:11,0:24:53,7:56:00,2562,450,11
+Intensive Care Unit,PGY2+,S0093383,2016-07-10,1:19:17,0:31:29,0:07:13,0:24:43,0:13:03,0:01:58,2:52:15,1086,0,17
+Intensive Care Unit,PGY2+,S0093383,2016-07-11,1:20:48,0:54:17,0:05:57,0:21:11,0:20:27,0:07:11,3:35:54,1365,0,23
+Intensive Care Unit,PGY2+,S0093383,2016-07-12,1:04:43,0:32:46,0:00:55,0:23:10,0:11:35,0:05:03,2:28:56,949,0,17
+Intensive Care Unit,PGY2+,S0093383,2016-07-13,2:23:16,1:02:39,0:00:03,0:38:24,0:29:59,0:14:57,5:16:41,1323,0,23
+General Medicine,PGY1,S0169395,2015-06-03,1:00:35,0:00:27,1:13:18,0:13:12,0:09:14,0:05:28,3:00:29,729,0,7
+General Medicine,PGY1,S0169399,2014-07-19,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0169399,2014-07-18,0:00:00,0:00:00,0:00:00,0:01:07,0:00:00,0:00:00,0:01:07,11,0,2
+General Medicine,PGY1,S0169399,2014-07-17,2:27:46,0:09:15,0:14:12,0:36:11,0:15:21,0:14:34,4:05:54,978,0,13
+General Medicine,PGY1,S0169399,2014-07-16,1:56:37,0:04:14,0:08:13,0:15:27,0:05:27,0:07:52,3:03:51,693,0,8
+General Medicine,PGY1,S0169399,2014-07-15,2:24:08,0:00:00,0:09:50,0:15:09,0:16:33,0:02:49,3:25:48,703,61,9
+General Medicine,PGY1,S0169399,2014-07-14,1:29:29,0:01:31,0:11:33,0:23:09,0:09:13,0:00:07,2:29:21,636,0,4
+General Medicine,PGY1,S0169399,2014-07-13,0:30:12,0:02:57,0:09:40,0:07:42,0:07:27,0:07:21,1:14:19,477,0,10
+General Medicine,PGY1,S0169399,2014-07-12,1:02:35,0:07:56,0:05:24,0:16:57,0:15:26,0:03:54,2:00:43,681,0,13
+General Medicine,PGY1,S0169399,2014-07-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0169399,2014-07-10,2:25:21,0:03:17,0:17:23,0:24:13,0:12:36,0:04:36,3:43:57,962,0,13
+Night Team,PGY2+,S0094959,2016-10-30,4:58:48,1:32:11,0:00:11,0:27:24,0:21:37,0:18:24,8:16:41,3531,0,117
+Night Team,PGY2+,S0094959,2016-10-31,5:57:18,1:34:45,0:00:07,0:24:52,0:20:13,0:08:27,9:14:40,3397,322,94
+Emergency Medicine,PGY1,S0181526,2015-06-30,1:18:19,0:20:15,0:52:45,0:21:52,0:15:03,0:50:31,4:34:37,1305,19,14
+General Medicine,PGY2+,S0169399,2015-11-02,2:08:09,1:09:47,2:02:58,0:25:41,0:10:32,0:36:03,7:13:56,1570,182,25
+Emergency Medicine,PGY1,S0181088,2016-02-14,1:47:06,0:36:12,2:54:48,0:34:25,0:16:44,0:10:50,7:17:51,1939,0,13
+General Medicine,PGY2+,S0169399,2015-11-03,0:58:36,0:46:10,0:03:44,0:22:55,0:12:39,0:11:24,2:49:03,780,0,16
+General Medicine,PGY2+,S0169399,2016-05-29,1:52:48,1:10:02,0:00:14,0:39:55,0:16:31,0:05:10,4:46:19,1257,131,13
+General Medicine,PGY1,S0094959,2014-11-19,3:08:07,0:02:05,0:36:06,0:12:27,0:29:00,0:18:14,4:57:05,2353,32,32
+General Medicine,PGY1,S0094959,2014-11-18,5:32:09,0:05:33,1:22:54,0:26:14,0:38:52,0:30:34,9:02:04,3497,115,30
+General Medicine,PGY1,S0094959,2014-11-17,5:43:33,0:07:03,0:49:53,0:15:59,0:25:25,0:28:51,8:07:12,4413,282,34
+General Medicine,PGY1,S0094959,2014-11-16,0:00:00,0:04:53,0:00:00,0:00:04,0:00:00,0:00:51,0:06:02,68,60,7
+General Medicine,PGY1,S0094959,2014-11-15,5:09:07,0:05:07,0:38:43,0:37:53,0:31:00,0:21:26,7:41:33,3808,89,59
+General Medicine,PGY1,S0094959,2014-11-14,3:23:58,0:23:25,1:05:48,0:10:32,0:12:19,0:16:58,6:03:06,2415,224,15
+General Medicine,PGY1,S0094959,2014-11-13,5:22:38,0:06:16,0:26:27,0:14:39,0:22:36,0:44:24,7:51:46,3088,34,25
+General Medicine,PGY1,S0094959,2014-11-12,0:06:28,0:30:19,0:00:00,0:00:00,0:00:00,0:09:46,0:50:18,511,507,12
+General Medicine,PGY1,S0094959,2014-11-11,2:55:33,0:14:25,0:34:02,0:37:15,0:22:02,0:19:39,5:17:42,2293,176,14
+General Medicine,PGY1,S0094959,2014-11-10,4:13:01,0:11:30,2:10:43,0:38:03,0:27:54,0:18:30,8:28:33,3064,61,18
+Night Team,PGY1,S0195205,2016-09-18,1:41:11,0:46:02,0:00:00,0:13:52,0:25:47,0:02:14,3:24:42,1220,0,60
+Night Team,PGY1,S0195205,2016-09-19,1:25:24,0:34:25,0:00:08,0:15:00,0:16:15,0:01:49,2:41:33,905,120,37
+General Medicine,PGY1,S0195205,2016-09-10,0:52:54,1:10:24,0:00:38,0:22:49,0:09:18,0:07:44,3:08:11,1070,37,15
+General Medicine,PGY1,S0195205,2016-09-11,2:35:45,1:25:44,0:00:10,0:58:00,0:43:08,0:06:53,6:34:00,2136,50,11
+General Medicine,PGY1,S0195205,2016-09-12,2:20:50,0:53:18,0:00:06,0:49:30,0:32:41,0:05:03,5:12:26,2157,403,11
+General Medicine,PGY1,S0195205,2016-09-13,2:24:48,1:52:04,0:03:02,0:50:49,0:55:25,0:04:34,6:33:20,2210,160,16
+General Medicine,PGY1,S0195205,2016-09-14,1:43:46,1:43:41,0:00:18,0:28:19,0:24:49,0:00:43,4:40:41,1843,240,12
+General Medicine,PGY1,S0195205,2016-09-15,0:41:57,0:50:30,0:00:04,0:12:33,0:02:29,0:00:08,2:08:15,751,729,15
+General Medicine,PGY1,S0195205,2016-09-16,1:36:48,1:21:07,0:00:20,0:24:09,0:18:34,0:01:39,4:12:01,1964,87,11
+General Medicine,PGY1,S0195205,2016-09-17,0:52:26,0:09:26,0:00:00,0:16:31,0:10:32,0:03:38,1:39:40,398,0,14
+Night Team,PGY1,S0195207,2016-10-14,3:34:09,1:21:53,0:00:01,0:28:29,0:10:34,0:10:25,6:05:53,1957,98,66
+Night Team,PGY1,S0195207,2016-10-15,1:45:10,0:41:46,0:00:01,0:17:29,0:08:20,0:02:01,3:11:47,1111,0,33
+General Medicine,PGY2+,S0169401,2016-01-19,2:56:49,1:09:44,0:05:15,1:22:34,0:21:38,0:30:08,6:57:51,3999,148,59
+General Medicine,PGY2+,S0169401,2016-01-18,0:07:22,0:16:48,0:00:00,0:00:11,0:00:00,0:08:07,0:44:15,689,687,24
+Night Team,PGY1,S0195207,2016-10-10,1:54:23,1:20:12,0:00:06,0:22:59,0:12:33,0:06:49,4:10:42,1868,99,49
+Night Team,PGY1,S0195207,2016-10-11,0:54:57,0:19:07,0:00:01,0:05:02,0:01:44,0:00:07,1:25:26,559,0,26
+Night Team,PGY1,S0195207,2016-10-12,0:56:23,0:38:10,0:00:00,0:05:04,0:11:41,0:00:03,1:53:06,692,0,40
+Night Team,PGY1,S0195207,2016-10-13,2:49:01,1:04:28,0:00:02,0:33:56,0:05:56,0:00:19,5:05:44,1958,0,60
+General Medicine,PGY2+,S0169401,2016-01-13,1:55:23,1:09:14,1:09:57,0:39:07,0:20:28,0:20:09,6:06:32,2403,393,33
+General Medicine,PGY2+,S0169401,2016-01-12,2:36:13,1:02:43,2:02:39,1:07:05,0:26:52,0:31:05,8:47:36,3775,671,24
+General Medicine,PGY2+,S0169401,2016-01-11,2:40:35,1:56:42,0:19:19,0:36:18,0:12:52,0:48:20,7:03:13,3738,507,32
+General Medicine,PGY2+,S0169401,2016-01-10,2:56:54,0:59:05,1:29:43,1:11:40,0:31:01,0:40:22,8:21:34,3395,680,27
+General Medicine,PGY2+,S0169401,2016-01-17,2:29:49,0:39:12,0:47:23,0:34:42,0:15:38,0:25:58,5:51:26,2344,316,30
+General Medicine,PGY2+,S0169401,2016-01-16,1:05:51,1:05:20,0:07:54,0:43:34,0:05:01,0:42:04,4:42:19,3431,214,40
+General Medicine,PGY2+,S0169401,2016-01-15,1:02:27,0:56:39,0:00:02,0:21:54,0:03:12,0:12:25,2:55:55,1435,1406,27
+General Medicine,PGY2+,S0169401,2016-01-14,1:43:31,1:36:29,0:12:27,0:31:15,0:11:34,0:40:07,5:21:13,2773,73,43
+General Medicine,PGY1,S0181067,2016-01-31,1:00:22,0:28:00,0:55:04,0:32:25,0:20:39,0:11:45,3:52:53,1308,0,8
+Emergency Medicine,PGY1,S0188098,2016-08-23,3:59:56,0:27:12,0:07:08,0:26:31,0:26:01,0:00:28,6:32:06,1605,33,32
+Night Team,PGY1,S0174975,2016-03-28,2:24:00,1:03:20,0:00:32,0:37:51,0:15:44,0:00:10,5:05:25,2206,44,71
+Night Team,PGY1,S0174975,2016-03-29,3:28:46,1:16:04,0:01:53,1:40:33,0:21:18,0:04:03,7:10:00,2235,73,94
+General Medicine,PGY1,S0174975,2016-03-26,1:45:07,0:45:50,0:00:01,0:29:46,0:10:52,0:00:00,3:16:58,908,42,92
+Night Team,PGY1,S0174975,2016-03-27,2:33:02,0:41:10,0:00:22,0:47:09,0:24:31,0:01:59,4:44:18,1563,67,81
+General Medicine,PGY1,S0174975,2016-03-24,2:55:26,1:24:05,0:00:36,1:10:59,0:27:38,0:04:34,6:35:45,2527,85,21
+General Medicine,PGY1,S0174975,2016-03-25,0:24:22,0:09:33,0:00:00,0:00:38,0:00:26,0:00:00,0:35:31,176,158,7
+General Medicine,PGY1,S0174975,2016-03-22,2:55:23,1:40:40,0:02:52,1:05:00,0:21:10,0:05:13,6:50:32,2664,367,30
+General Medicine,PGY1,S0174975,2016-03-23,0:41:01,0:12:14,0:00:00,0:04:07,0:00:36,0:01:55,1:02:36,455,449,17
+General Medicine,PGY1,S0174975,2016-03-20,0:56:32,0:42:38,0:01:24,0:36:21,0:09:06,0:12:47,3:02:57,1019,181,8
+General Medicine,PGY1,S0174975,2016-03-21,1:47:43,0:40:38,0:00:27,1:13:08,0:19:50,0:05:22,4:34:36,1644,221,8
+Emergency Medicine,PGY1,S0188098,2016-08-24,1:30:39,0:26:58,0:02:43,0:04:12,0:02:23,0:00:10,2:25:53,564,270,33
+Night Team,PGY2+,S0169386,2016-02-29,3:51:08,1:16:22,0:00:02,0:20:47,0:14:51,0:07:00,6:28:53,3086,149,132
+General Medicine,PGY1,S0181083,2016-02-09,0:24:51,0:30:31,1:46:21,0:25:58,0:13:37,0:33:36,4:09:06,1151,0,8
+General Medicine,PGY1,S0181083,2016-02-08,0:00:24,0:08:14,0:05:09,0:00:07,0:00:14,0:00:42,0:15:15,145,117,6
+Night Team,PGY2+,S0169386,2016-02-28,4:47:31,1:13:39,0:06:30,0:31:31,0:15:44,0:01:25,7:53:30,2605,32,104
+General Medicine,PGY1,S0181083,2016-02-01,0:00:00,0:00:00,0:00:00,0:01:40,0:00:00,0:00:00,0:01:40,8,0,3
+Night Team,PGY1,S0195220,2016-07-01,2:17:30,0:45:11,0:00:02,0:15:55,0:26:24,0:03:45,4:02:01,1043,0,32
+Night Team,PGY1,S0195220,2016-07-02,0:56:20,0:36:10,0:00:15,0:08:24,0:12:05,0:01:12,2:01:37,376,0,10
+General Medicine,PGY1,S0181083,2016-02-02,1:33:01,1:01:51,1:41:20,0:47:07,0:28:44,1:08:21,7:27:15,2263,0,16
+Night Team,PGY1,S0195220,2016-07-04,2:19:06,0:39:42,0:00:04,0:40:10,0:27:19,0:11:15,4:32:17,930,0,21
+General Medicine,PGY1,S0181083,2016-02-04,0:52:06,0:57:13,1:14:53,0:32:24,0:07:36,0:29:03,4:44:32,2194,258,11
+General Medicine,PGY1,S0181083,2016-02-07,0:59:47,0:52:30,1:34:08,1:10:29,0:28:20,1:17:30,7:11:48,1900,101,14
+Night Team,PGY2+,S0169398,2015-12-09,3:01:52,1:43:52,2:56:02,0:35:38,0:08:42,0:30:41,9:26:35,2357,1126,52
+General Medicine,PGY1,S0176040,2015-12-01,1:07:20,0:47:14,1:07:46,0:36:48,0:29:19,0:21:55,5:04:22,1428,71,8
+General Medicine,PGY1,S0169381,2014-08-31,3:33:30,0:12:50,0:30:37,0:58:41,0:23:28,0:24:03,6:31:32,1434,241,11
+General Medicine,PGY1,S0169381,2014-08-30,2:01:16,0:03:28,0:12:16,0:15:59,0:19:32,0:19:13,3:26:56,837,70,13
+General Medicine,PGY1,S0181077,2015-12-17,1:18:18,1:05:03,0:51:29,0:27:02,0:10:53,0:15:58,4:34:43,1889,0,14
+General Medicine,PGY1,S0181077,2015-12-16,2:12:54,1:15:30,1:50:28,0:47:08,0:43:38,0:17:41,8:05:32,3927,13,36
+General Medicine,PGY1,S0181077,2015-12-15,2:39:07,0:52:59,1:18:09,1:24:36,0:30:51,0:19:05,7:38:22,2804,0,25
+General Medicine,PGY1,S0181077,2015-12-14,2:05:48,1:21:07,1:02:40,0:54:58,0:38:49,0:29:07,7:07:22,3094,670,16
+General Medicine,PGY1,S0181077,2015-12-13,3:08:57,2:17:16,1:52:10,0:41:29,0:30:53,0:41:07,9:34:50,3629,35,21
+General Medicine,PGY1,S0181077,2015-12-12,0:34:03,0:11:33,0:10:56,0:18:08,0:06:35,0:01:50,1:28:45,796,738,19
+General Medicine,PGY1,S0181077,2015-12-11,1:52:45,1:00:20,1:03:25,0:41:48,0:29:44,0:41:01,6:12:55,3379,306,14
+General Medicine,PGY1,S0181077,2015-12-10,2:31:58,1:37:57,1:16:43,1:16:07,0:41:52,0:31:53,8:51:09,3856,101,19
+General Medicine,PGY1,S0181064,2015-06-30,3:47:12,0:22:38,3:04:36,0:23:55,0:21:53,0:28:59,9:02:10,2650,187,15
+General Medicine,PGY1,S0181077,2015-12-19,0:05:00,0:15:22,0:01:44,0:01:37,0:01:23,0:01:57,0:34:28,453,436,12
+General Medicine,PGY1,S0181077,2015-12-18,2:18:22,1:50:45,1:23:51,0:44:31,0:30:16,0:16:59,7:33:01,2788,0,17
+Night Team,PGY2+,S0169403,2016-05-06,0:38:38,0:25:14,0:00:00,0:01:41,0:06:37,0:00:25,1:21:32,590,32,27
+Night Team,PGY2+,S0169403,2016-05-04,1:12:29,1:00:21,0:00:00,0:18:08,0:02:41,0:07:08,3:07:54,1160,39,19
+General Medicine,PGY1,S0181074,2015-07-25,2:28:27,0:52:32,1:03:47,2:06:17,0:35:42,0:10:00,8:05:35,3072,0,11
+General Medicine,PGY1,S0181074,2015-07-24,1:34:04,0:31:56,0:49:31,1:20:31,0:43:32,0:19:18,5:56:51,1903,255,10
+General Medicine,PGY1,S0181074,2015-07-27,1:54:37,1:07:08,1:23:49,1:34:45,0:22:51,0:14:59,7:34:40,2980,448,17
+General Medicine,PGY1,S0181074,2015-07-26,0:17:26,1:49:25,1:54:22,0:19:21,0:00:55,0:33:29,5:10:01,2261,2243,15
+General Medicine,PGY1,S0181074,2015-07-21,3:05:21,1:51:13,1:57:52,0:20:56,0:45:37,0:19:41,9:00:14,3400,65,21
+General Medicine,PGY1,S0181074,2015-07-20,3:00:41,2:26:52,1:37:57,1:14:56,1:03:05,0:25:39,10:56:40,3860,296,16
+General Medicine,PGY1,S0181074,2015-07-23,2:44:56,0:42:39,0:55:17,2:33:53,1:18:58,0:18:00,9:29:27,3720,453,12
+General Medicine,PGY1,S0181074,2015-07-22,2:37:55,0:58:59,1:23:40,2:08:07,1:13:24,0:26:44,9:38:42,3097,0,14
+Intensive Care Unit,PGY2+,S0163662,2016-05-18,1:18:24,0:15:31,0:06:53,0:16:06,0:20:12,0:04:20,2:55:09,634,24,15
+Intensive Care Unit,PGY2+,S0163662,2016-05-19,1:52:12,0:21:17,0:05:08,0:25:10,0:22:45,0:14:04,3:44:15,853,36,13
+General Medicine,PGY1,S0181074,2015-07-29,1:30:42,1:14:16,1:18:46,1:09:46,0:12:47,0:05:27,6:03:28,2024,409,18
+General Medicine,PGY1,S0181074,2015-07-28,2:00:54,1:02:47,1:12:01,1:31:27,0:26:42,0:16:00,7:03:21,2484,87,8
+General Medicine,PGY2+,S0169404,2016-04-28,4:09:23,1:38:20,0:00:14,1:03:16,0:15:35,0:14:24,8:06:43,3923,467,21
+General Medicine,PGY2+,S0169404,2016-04-29,1:30:38,0:59:29,0:00:01,0:23:56,0:06:06,0:04:31,3:20:25,1824,360,13
+General Medicine,PGY1,S0169389,2014-10-29,2:27:59,0:03:34,0:13:38,0:29:56,0:26:25,0:17:26,4:16:35,1340,4,16
+General Medicine,PGY1,S0169389,2014-10-28,1:25:55,0:06:34,0:03:50,0:28:04,0:16:14,0:11:29,2:45:14,894,74,8
+General Medicine,PGY1,S0169389,2014-10-23,0:53:19,0:14:09,0:00:00,0:00:00,0:00:27,0:06:42,1:28:17,352,72,13
+General Medicine,PGY1,S0169389,2014-10-22,3:35:17,0:12:56,0:04:32,0:24:14,0:30:18,0:43:44,5:47:33,1431,77,12
+General Medicine,PGY1,S0169389,2014-10-21,1:16:42,0:07:09,0:02:22,0:12:40,0:05:36,0:15:30,2:19:20,686,130,8
+General Medicine,PGY1,S0169389,2014-10-20,2:52:12,0:09:32,0:05:44,0:39:14,0:26:53,0:34:27,5:29:24,1653,304,11
+General Medicine,PGY1,S0169389,2014-10-27,3:04:14,0:21:06,0:07:07,0:24:34,0:25:20,0:30:54,5:10:09,1546,172,16
+General Medicine,PGY1,S0169389,2014-10-26,3:12:17,0:12:55,0:17:04,0:04:20,0:04:05,0:19:27,4:28:39,976,49,13
+General Medicine,PGY1,S0169389,2014-10-25,2:56:27,0:11:24,0:10:40,0:36:04,0:16:31,0:22:43,4:48:30,1479,211,12
+General Medicine,PGY1,S0169389,2014-10-24,4:03:17,0:16:59,0:06:03,0:33:37,0:41:49,0:36:22,6:40:05,1897,0,15
+General Medicine,PGY1,S0181078,2016-01-02,0:11:57,0:11:14,0:00:00,0:04:21,0:00:00,0:01:32,0:31:48,235,221,42
+Emergency Medicine,PGY1,S0195210,2016-07-09,1:38:07,0:51:31,0:18:54,0:34:44,0:09:23,0:00:07,3:58:18,927,0,6
+Emergency Medicine,PGY1,S0195210,2016-07-08,1:33:25,0:25:12,0:19:58,0:30:45,0:08:10,0:07:45,3:36:15,967,0,28
+Emergency Medicine,PGY1,S0195210,2016-07-01,2:50:03,1:21:10,0:23:15,0:25:07,0:12:28,0:06:37,5:45:18,1186,0,10
+Emergency Medicine,PGY1,S0195210,2016-07-06,1:36:25,0:30:03,0:18:13,0:13:28,0:19:13,0:00:39,3:22:27,770,0,6
+Emergency Medicine,PGY1,S0195210,2016-07-05,2:28:33,1:01:38,0:18:52,0:47:35,0:13:49,0:00:12,5:13:04,975,0,12
+Night Team,PGY1,S0169411,2014-11-30,3:31:22,0:06:02,1:16:54,0:28:51,0:32:41,0:03:00,6:13:51,1774,0,75
+General Medicine,PGY2+,S0169397,2016-11-09,3:08:38,0:36:54,0:03:10,1:10:10,0:32:48,0:01:27,6:25:26,2277,86,24
+General Medicine,PGY2+,S0169397,2016-11-08,0:04:47,0:00:29,0:00:00,0:00:00,0:00:00,0:00:51,0:06:23,60,0,6
+General Medicine,PGY2+,S0169397,2016-11-07,1:52:43,0:28:08,0:00:44,0:33:27,0:17:33,0:01:10,3:31:33,1221,0,14
+General Medicine,PGY2+,S0169397,2016-11-06,2:23:09,0:50:21,0:00:08,0:10:42,0:07:05,0:02:52,4:10:14,1448,0,19
+Intensive Care Unit,PGY2+,S0181070,2016-09-02,1:21:40,1:09:07,0:03:22,0:16:47,0:24:53,0:00:10,3:31:05,1325,95,13
+General Medicine,PGY1,S0163425,2015-06-14,0:05:03,0:01:10,0:00:54,0:00:57,0:00:02,0:00:00,0:08:19,153,147,5
+Intensive Care Unit,PGY2+,S0181070,2016-09-01,0:41:49,0:53:02,0:01:04,0:03:58,0:10:16,0:03:51,2:00:36,1066,0,15
+General Medicine,PGY1,S0169383,2015-01-08,5:00:37,0:08:37,1:27:14,1:22:22,0:42:47,0:27:22,10:06:45,2946,169,18
+General Medicine,PGY1,S0169383,2015-01-09,5:59:39,0:12:35,1:32:08,1:08:05,0:41:25,0:46:02,11:24:24,3401,434,17
+General Medicine,PGY1,S0169383,2015-01-04,3:49:17,0:11:19,1:46:38,0:43:59,0:43:12,0:31:06,8:29:45,1967,310,9
+Intensive Care Unit,PGY2+,S0181070,2016-09-06,2:45:17,1:00:54,0:17:45,0:17:26,0:10:48,0:03:10,5:03:10,1418,50,24
+General Medicine,PGY1,S0169383,2015-01-06,5:31:51,0:27:10,0:54:12,1:34:28,0:42:12,0:37:26,10:44:38,3293,168,14
+General Medicine,PGY1,S0169383,2015-01-07,5:52:01,0:13:35,1:59:26,1:23:51,1:00:01,0:42:05,12:01:49,3267,861,15
+General Medicine,PGY1,S0163425,2015-06-10,2:21:49,0:15:57,1:14:01,0:24:26,0:09:05,0:20:51,4:59:19,1587,0,15
+General Medicine,PGY2+,S0169398,2016-03-20,0:58:36,0:37:17,0:03:15,0:11:48,0:04:54,0:00:12,2:13:47,683,137,12
+Intensive Care Unit,PGY2+,S0181070,2016-09-04,1:30:54,0:49:05,0:03:47,0:17:59,0:13:46,0:02:19,3:15:32,966,155,10
+General Medicine,PGY1,S0163425,2015-06-12,1:43:13,0:16:32,1:18:39,0:11:07,0:14:17,0:09:05,4:13:23,1265,0,12
+General Medicine,PGY1,S0181079,2015-10-04,2:01:03,1:02:09,1:47:51,0:46:25,0:21:43,0:35:51,7:15:44,1917,109,17
+General Medicine,PGY1,S0181079,2015-10-05,1:23:22,0:56:52,0:50:32,0:35:44,0:23:17,0:17:19,4:48:45,1827,196,10
+General Medicine,PGY1,S0181079,2015-10-06,1:17:13,0:45:18,1:02:24,0:40:58,0:21:44,0:18:57,5:05:01,1912,18,8
+General Medicine,PGY1,S0181079,2015-10-07,1:21:10,1:23:06,2:51:09,0:41:05,0:22:53,0:14:37,7:25:00,2028,181,8
+General Medicine,PGY1,S0181079,2015-10-01,1:29:14,0:50:25,1:18:08,0:19:28,0:32:43,0:11:40,5:07:03,1876,267,8
+General Medicine,PGY1,S0181079,2015-10-02,2:09:41,1:13:10,1:09:34,0:25:20,0:24:40,0:32:24,6:28:00,2201,97,12
+General Medicine,PGY1,S0181079,2015-10-03,0:00:00,0:08:42,0:00:00,0:02:22,0:00:00,0:00:00,0:12:04,106,90,6
+General Medicine,PGY1,S0181079,2015-10-08,0:00:00,0:00:49,0:00:00,0:02:37,0:00:00,0:00:00,0:04:03,73,58,4
+General Medicine,PGY1,S0181079,2015-10-09,1:39:23,0:49:00,1:32:35,0:29:48,0:23:03,0:11:08,5:48:39,1551,210,10
+General Medicine,PGY1,S0106952,2015-01-30,4:01:44,0:42:31,2:24:42,0:23:00,0:23:03,0:22:56,8:55:42,2143,326,52
+General Medicine,PGY1,S0106952,2015-01-31,0:04:44,0:03:17,0:00:21,0:03:12,0:00:45,0:00:14,0:12:48,193,181,32
+Night Team,PGY1,S0195201,2016-08-22,2:17:49,0:21:43,0:00:00,0:12:02,0:07:54,0:03:29,3:30:45,1255,411,38
+Night Team,PGY1,S0195201,2016-08-23,4:50:48,1:20:24,0:00:44,0:33:24,0:19:32,0:09:29,8:38:11,3429,0,60
+Night Team,PGY1,S0195201,2016-08-21,2:16:54,0:27:47,0:02:41,0:17:33,0:06:17,0:03:59,3:50:41,1349,0,52
+Night Team,PGY1,S0195201,2016-08-26,4:37:45,1:31:18,0:00:05,0:25:42,0:21:42,0:10:27,8:33:29,3576,149,54
+Night Team,PGY1,S0195201,2016-08-27,3:42:19,1:33:59,0:00:08,0:21:06,0:30:44,0:02:29,7:11:32,3128,387,65
+Night Team,PGY1,S0195201,2016-08-24,5:09:43,0:57:49,0:01:44,0:23:54,0:11:42,0:07:32,8:15:16,3522,277,54
+Night Team,PGY1,S0195201,2016-08-25,3:40:05,1:48:26,0:00:07,0:20:48,0:18:58,0:07:35,7:48:18,3203,187,62
+Night Team,PGY1,S0195201,2016-08-28,2:44:53,0:46:03,0:00:00,0:30:44,0:15:56,0:04:51,5:08:32,2207,0,58
+Night Team,PGY1,S0195201,2016-08-29,2:54:09,1:01:32,0:00:05,0:22:44,0:09:47,0:00:37,5:38:31,2433,481,128
+Intensive Care Unit,PGY2+,S0169400,2015-07-20,1:26:17,0:15:20,0:05:22,0:22:35,0:16:41,0:17:25,3:05:26,927,236,29
+Intensive Care Unit,PGY2+,S0169400,2015-07-21,0:51:33,0:12:37,0:07:12,0:14:05,0:01:53,0:10:58,1:42:21,446,67,25
+Intensive Care Unit,PGY2+,S0169400,2015-07-22,0:00:00,0:34:23,0:00:00,0:04:43,0:00:00,0:04:28,0:44:50,307,263,20
+Intensive Care Unit,PGY2+,S0169400,2015-07-23,1:05:17,0:25:59,0:23:41,0:17:37,0:07:05,0:06:01,2:27:05,610,52,19
+Intensive Care Unit,PGY2+,S0169400,2015-07-24,1:03:47,0:11:03,0:06:00,0:16:35,0:16:17,0:04:55,2:18:22,565,55,16
+Intensive Care Unit,PGY2+,S0169400,2015-07-25,0:00:00,0:00:59,0:00:00,0:01:57,0:00:00,0:00:00,0:03:11,64,30,9
+Night Team,PGY2+,S0181086,2016-09-24,2:49:28,1:53:22,0:09:19,0:40:07,0:07:57,0:02:22,6:18:59,2102,387,21
+Night Team,PGY2+,S0181086,2016-09-21,4:12:58,1:21:44,0:00:15,0:50:57,0:32:48,0:17:36,7:56:24,2085,141,41
+Night Team,PGY2+,S0181086,2016-09-20,1:59:14,0:19:42,0:00:02,0:22:27,0:15:43,0:00:43,3:43:27,1081,493,103
+Night Team,PGY2+,S0181086,2016-09-23,2:51:29,1:01:34,0:03:23,0:58:42,0:16:03,0:00:40,5:46:39,1612,364,41
+Night Team,PGY2+,S0181086,2016-09-22,3:51:09,2:06:02,0:03:50,1:13:59,0:29:02,0:14:19,8:30:06,2502,718,30
+General Medicine,PGY1,S0181526,2015-09-27,1:24:12,1:02:48,0:55:47,0:25:30,0:11:28,0:24:52,5:09:35,1907,281,20
+General Medicine,PGY1,S0181526,2015-09-26,0:01:59,0:17:42,0:00:00,0:00:09,0:01:30,0:06:59,0:34:21,304,302,6
+General Medicine,PGY1,S0181526,2015-09-25,2:21:03,1:14:34,1:54:30,1:09:04,0:34:08,0:35:04,8:15:51,2210,60,17
+General Medicine,PGY1,S0181526,2015-09-24,1:20:21,0:37:25,1:40:35,0:46:37,0:18:26,0:17:15,5:42:47,1761,129,19
+General Medicine,PGY1,S0181526,2015-09-23,1:44:09,0:49:38,1:43:20,1:14:06,0:27:07,0:31:29,7:20:39,2001,58,11
+General Medicine,PGY1,S0181526,2015-09-22,1:31:45,0:04:58,0:47:32,0:23:10,0:10:32,0:30:48,3:54:52,1136,57,9
+General Medicine,PGY1,S0181526,2015-09-21,1:29:20,0:18:59,0:47:20,0:41:26,0:30:42,0:49:44,5:16:45,1651,102,12
+General Medicine,PGY1,S0181526,2015-09-20,2:47:21,0:28:35,2:06:56,0:42:53,0:23:56,0:43:11,8:27:27,2206,158,11
+General Medicine,PGY1,S0181526,2015-09-29,1:55:03,0:46:48,1:01:09,1:08:08,0:12:06,0:36:13,6:19:49,1822,90,16
+General Medicine,PGY1,S0181526,2015-09-28,2:21:35,1:12:17,1:18:18,1:08:21,0:21:36,1:12:28,8:31:45,2357,104,10
+General Medicine,PGY2+,S0181082,2016-08-31,4:34:49,1:38:57,0:00:02,0:45:53,0:28:22,0:03:03,8:19:10,2732,123,39
+General Medicine,PGY2+,S0181082,2016-08-30,2:40:51,0:58:28,0:00:21,1:04:33,0:24:07,0:04:23,5:54:34,2158,71,16
+General Medicine,PGY1,S0181075,2015-11-29,1:38:10,1:53:36,2:07:33,0:45:55,0:22:33,0:22:35,7:44:20,2875,0,22
+General Medicine,PGY1,S0181075,2015-11-28,0:19:28,0:11:10,0:05:38,0:07:01,0:01:42,0:06:13,1:20:50,573,555,13
+General Medicine,PGY1,S0181078,2015-12-30,1:51:57,0:28:11,2:13:41,1:26:25,0:14:05,0:18:22,6:54:20,1773,139,6
+General Medicine,PGY1,S0181078,2015-12-31,2:31:30,1:08:45,2:55:54,1:47:47,0:12:25,0:48:43,9:54:33,2203,122,10
+General Medicine,PGY1,S0181075,2015-11-21,1:28:11,0:54:14,2:36:24,1:01:42,0:13:12,0:21:57,7:20:19,2624,0,9
+General Medicine,PGY1,S0181075,2015-11-20,1:45:32,1:10:30,1:49:20,0:59:06,0:26:03,0:26:13,7:12:37,2488,317,17
+General Medicine,PGY1,S0181075,2015-11-23,0:00:00,0:00:00,0:00:00,0:04:38,0:00:00,0:00:00,0:04:38,18,0,5
+General Medicine,PGY1,S0181075,2015-11-22,1:53:22,1:22:28,1:22:33,1:46:00,0:46:53,0:33:21,8:30:24,3767,110,11
+General Medicine,PGY1,S0181075,2015-11-25,2:03:59,1:24:10,1:18:40,1:34:29,0:31:43,0:19:41,7:51:54,3180,0,17
+General Medicine,PGY1,S0181075,2015-11-24,2:43:13,1:12:51,2:10:17,1:50:34,0:40:46,0:28:16,9:53:13,3906,0,16
+General Medicine,PGY1,S0181075,2015-11-27,2:56:51,1:39:15,1:15:54,1:31:50,0:46:45,0:37:37,9:41:55,3836,60,13
+General Medicine,PGY1,S0181075,2015-11-26,0:56:55,0:52:06,0:53:18,0:21:27,0:16:10,0:17:30,3:58:07,1893,95,10
+General Medicine,PGY1,S0169411,2015-06-10,2:37:19,0:46:13,2:40:18,0:34:18,0:16:33,0:10:23,7:39:08,2547,67,19
+General Medicine,PGY1,S0169411,2015-06-11,1:13:19,0:15:43,0:32:35,0:16:51,0:12:29,0:06:11,2:59:16,1024,0,16
+General Medicine,PGY1,S0169411,2015-06-12,2:32:49,1:03:07,3:20:34,0:33:27,0:35:18,0:11:25,8:42:45,2738,0,18
+General Medicine,PGY1,S0169411,2015-06-13,0:00:00,0:00:00,0:00:00,0:04:32,0:00:00,0:00:00,0:04:32,10,0,2
+General Medicine,PGY1,S0169411,2015-06-14,1:11:29,0:23:44,0:32:37,0:26:24,0:15:39,0:15:42,3:15:25,1307,0,16
+General Medicine,PGY1,S0169411,2015-06-15,2:55:18,0:54:14,2:41:01,0:43:39,0:33:32,0:29:00,8:48:28,2812,0,15
+General Medicine,PGY1,S0169411,2015-06-16,1:56:59,0:48:16,1:43:50,0:22:40,0:39:50,0:11:22,6:09:54,1906,0,12
+General Medicine,PGY1,S0169411,2015-06-17,3:51:52,0:53:55,2:13:02,0:27:43,0:49:45,0:19:54,9:23:26,2944,0,25
+General Medicine,PGY1,S0169411,2015-06-18,3:15:57,0:45:48,1:37:18,0:41:58,0:35:25,0:10:44,7:37:38,2601,0,14
+General Medicine,PGY1,S0169411,2015-06-19,2:51:26,0:23:19,1:40:16,0:33:18,0:20:05,0:06:57,6:29:30,1908,17,10
+Intensive Care Unit,PGY2+,S0181077,2016-10-11,3:13:50,1:22:37,0:03:06,0:45:13,0:23:05,0:08:02,6:37:21,2254,46,23
+Emergency Medicine,PGY1,S0169384,2014-10-10,4:29:25,0:23:11,0:41:50,0:40:00,0:21:28,0:22:49,7:39:18,1705,11,16
+Emergency Medicine,PGY1,S0195216,2016-11-08,0:04:51,0:00:09,0:00:00,0:00:00,0:00:00,0:00:00,0:05:16,22,0,10
+Emergency Medicine,PGY1,S0195216,2016-11-02,0:00:23,0:00:02,0:00:00,0:00:00,0:00:01,0:00:00,0:00:30,15,15,1
+Emergency Medicine,PGY1,S0195216,2016-11-01,0:53:41,0:03:47,0:00:36,0:16:54,0:13:52,0:00:48,1:55:50,745,13,23
+Emergency Medicine,PGY1,S0195216,2016-11-06,5:56:27,0:45:56,0:09:53,0:21:28,0:43:06,0:03:13,8:55:57,2467,0,65
+Emergency Medicine,PGY1,S0195216,2016-11-07,5:26:53,0:49:28,0:10:15,0:39:59,1:07:34,0:02:37,9:42:10,3055,0,39
+Emergency Medicine,PGY1,S0195216,2016-11-04,3:59:58,0:43:50,0:10:01,0:38:03,0:34:23,0:02:27,6:48:30,1985,0,39
+Emergency Medicine,PGY1,S0195216,2016-11-05,3:06:50,0:35:19,0:03:23,0:27:01,0:32:45,0:00:55,5:28:57,1568,0,61
+General Medicine,PGY1,S0169385,2014-10-21,2:56:09,0:03:08,0:14:55,0:23:48,0:11:57,0:50:07,4:54:01,1273,15,5
+Intensive Care Unit,PGY2+,S0181077,2016-10-19,3:06:36,1:11:16,0:06:44,0:12:26,0:16:17,0:01:37,5:27:21,1918,0,31
+General Medicine,PGY2+,S0169406,2016-03-03,2:51:46,1:08:33,0:00:32,0:18:28,0:16:41,0:10:08,5:31:25,2205,2,25
+General Medicine,PGY1,S0163425,2015-05-30,2:47:35,0:20:28,0:46:52,0:27:20,0:11:26,0:13:20,5:00:25,1470,0,15
+General Medicine,PGY1,S0163425,2015-05-31,1:23:09,0:05:01,0:36:00,0:15:58,0:08:51,0:03:48,2:53:44,904,0,12
+Intensive Care Unit,PGY2+,S0169406,2016-03-07,3:01:51,0:26:52,0:00:07,0:22:57,0:18:44,0:09:57,5:03:43,1427,33,23
+Intensive Care Unit,PGY2+,S0169406,2016-03-06,2:13:07,0:35:49,0:08:06,0:14:13,0:15:49,0:03:42,3:57:42,1081,7,13
+General Medicine,PGY2+,S0169406,2016-03-05,0:02:43,0:00:58,0:00:00,0:00:00,0:00:00,0:00:05,0:04:18,31,29,9
+General Medicine,PGY2+,S0169406,2016-03-04,2:58:10,0:16:23,0:00:04,0:26:42,0:09:18,0:02:51,4:41:45,1899,12,20
+Intensive Care Unit,PGY2+,S0169406,2016-03-09,0:53:40,0:10:03,0:02:41,0:03:58,0:07:44,0:01:09,1:47:38,592,39,15
+Intensive Care Unit,PGY2+,S0169406,2016-03-08,2:51:06,0:22:09,0:06:09,0:26:41,0:21:22,0:07:09,4:41:03,1477,32,17
+General Medicine,PGY1,S0181061,2015-09-30,1:11:21,0:52:11,1:16:07,1:03:00,0:48:09,0:05:33,5:46:06,1866,0,9
+Night Team,PGY1,S0181059,2015-11-30,2:41:17,1:27:31,0:33:49,0:27:23,0:17:57,0:30:38,6:11:17,2847,1227,104
+General Medicine,PGY2+,S0181526,2016-07-31,1:28:43,0:34:45,0:00:46,0:17:39,0:17:35,0:04:46,3:29:28,1388,387,16
+General Medicine,PGY2+,S0181526,2016-07-30,4:06:16,1:34:02,0:00:17,0:43:55,0:24:03,0:07:34,7:59:26,2802,146,22
+Night Team,PGY2+,S0181059,2016-08-28,3:43:46,1:03:00,0:00:08,0:32:25,0:19:54,0:01:24,6:17:13,2509,0,87
+Night Team,PGY2+,S0181059,2016-08-29,4:42:18,1:23:49,0:00:03,0:20:47,0:14:58,0:07:30,7:35:50,3475,169,152
+General Medicine,PGY1,S0181088,2016-06-22,2:23:54,0:51:36,0:00:24,0:19:44,0:17:17,0:03:43,4:24:33,1452,32,9
+General Medicine,PGY1,S0181088,2016-06-23,3:36:43,1:00:21,0:00:03,0:43:46,0:12:52,0:07:11,5:56:13,2013,51,7
+General Medicine,PGY1,S0181088,2016-06-20,1:55:15,0:32:09,0:00:08,0:30:54,0:06:55,0:08:21,4:03:53,1688,49,20
+General Medicine,PGY1,S0181088,2016-06-21,4:36:54,1:03:11,0:00:03,0:31:21,0:21:05,0:07:26,7:06:24,2191,43,25
+Night Team,PGY1,S0181063,2015-07-19,1:03:51,0:08:46,0:04:08,0:24:47,0:34:43,0:31:59,2:56:40,654,0,34
+General Medicine,PGY1,S0181088,2016-06-24,0:06:59,0:06:06,0:00:00,0:00:35,0:00:00,0:01:37,0:15:37,81,73,4
+General Medicine,PGY1,S0181088,2016-06-25,0:00:30,0:00:03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:35,8,6,2
+General Medicine,PGY1,S0169411,2015-01-19,2:37:34,0:00:52,0:47:57,0:30:55,0:08:54,0:01:17,4:17:58,1515,78,7
+General Medicine,PGY1,S0169411,2015-01-18,2:40:41,0:06:43,0:45:57,0:25:33,0:09:46,0:00:45,4:16:34,1617,0,9
+General Medicine,PGY1,S0181060,2015-11-29,0:00:00,0:00:00,0:00:00,0:02:16,0:00:00,0:00:00,0:02:16,14,0,3
+General Medicine,PGY1,S0181060,2015-11-28,2:08:36,1:22:44,1:25:13,0:41:03,0:37:38,0:39:36,7:38:47,2579,21,18
+General Medicine,PGY1,S0169411,2015-01-11,4:30:24,0:02:40,1:29:38,0:24:46,0:19:07,0:03:41,7:09:50,1791,0,12
+General Medicine,PGY1,S0169411,2015-01-10,6:39:21,0:09:45,1:45:59,0:37:11,0:25:24,0:07:48,10:11:38,3127,0,29
+General Medicine,PGY1,S0169411,2015-01-13,4:36:43,0:02:51,3:01:44,0:38:13,0:14:50,0:03:17,8:53:17,2473,0,18
+General Medicine,PGY1,S0181060,2015-11-20,1:18:25,1:11:00,0:59:50,0:23:42,0:28:34,0:45:08,5:29:23,1827,9,16
+General Medicine,PGY1,S0181060,2015-11-27,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,10,0,3
+General Medicine,PGY1,S0169411,2015-01-14,3:08:34,0:05:19,2:34:24,0:46:20,0:30:18,0:09:16,7:46:18,2187,0,13
+General Medicine,PGY1,S0169411,2015-01-17,5:16:47,0:04:14,1:56:25,0:38:42,0:21:44,0:04:11,8:41:45,2791,0,23
+General Medicine,PGY1,S0169411,2015-01-16,0:03:43,0:00:00,0:00:00,0:03:44,0:00:00,0:00:00,0:07:27,27,17,7
+General Medicine,PGY1,S0169395,2015-06-06,3:30:31,0:23:39,4:08:09,0:36:35,0:32:29,0:25:52,10:05:15,2485,0,21
+General Medicine,PGY2+,S0181077,2016-09-09,4:07:36,1:30:22,0:00:23,0:51:50,0:24:34,0:08:56,8:01:33,3681,50,39
+General Medicine,PGY2+,S0181077,2016-09-08,0:18:10,0:06:30,0:00:02,0:00:38,0:01:08,0:00:54,0:30:12,320,279,30
+General Medicine,PGY2+,S0181077,2016-09-07,2:03:41,1:01:58,0:05:56,0:32:03,0:13:34,0:06:44,4:38:01,2243,235,24
+General Medicine,PGY2+,S0181077,2016-09-06,3:48:42,2:10:35,0:00:07,0:25:35,0:30:55,0:05:13,7:48:29,3679,74,44
+General Medicine,PGY2+,S0181077,2016-09-05,3:07:55,1:36:23,0:00:12,0:21:47,0:25:50,0:03:53,6:33:24,3001,472,16
+General Medicine,PGY2+,S0181077,2016-09-04,2:27:54,1:24:39,0:00:05,0:29:56,0:14:15,0:06:55,5:54:37,2560,302,30
+General Medicine,PGY2+,S0181077,2016-09-03,0:14:38,0:17:52,0:00:01,0:01:47,0:03:21,0:00:08,0:42:52,438,410,12
+General Medicine,PGY2+,S0181077,2016-09-02,2:57:41,1:08:53,0:00:14,0:58:08,0:21:36,0:05:57,6:14:24,2893,7,17
+General Medicine,PGY2+,S0181077,2016-09-01,3:42:50,2:18:15,0:05:24,0:38:36,0:20:07,0:07:57,8:07:12,3573,38,33
+Emergency Medicine,PGY2+,S0106952,2016-09-28,4:05:20,0:45:25,0:24:00,0:09:40,0:13:30,0:10:15,6:13:18,2192,299,68
+Emergency Medicine,PGY2+,S0106952,2016-09-29,5:31:01,1:06:17,0:22:06,0:14:05,0:18:50,0:13:29,8:22:36,2946,618,109
+Night Team,PGY1,S0195094,2016-07-11,1:04:44,0:39:29,0:00:14,0:25:25,0:27:36,0:00:15,2:58:23,643,511,22
+Night Team,PGY2+,S0169398,2015-12-19,1:27:37,0:54:35,1:24:57,0:30:45,0:16:34,0:33:06,5:22:35,1576,0,15
+Emergency Medicine,PGY2+,S0106952,2016-09-20,4:03:26,0:57:33,0:07:31,0:08:07,0:07:08,0:03:10,5:53:19,2025,542,82
+Emergency Medicine,PGY2+,S0106952,2016-09-21,0:26:19,0:30:16,0:00:00,0:00:00,0:00:26,0:00:06,1:00:12,427,427,41
+Emergency Medicine,PGY2+,S0106952,2016-09-22,0:20:18,0:25:57,0:00:00,0:00:00,0:00:06,0:00:02,0:47:18,418,416,46
+Emergency Medicine,PGY2+,S0106952,2016-09-23,5:05:58,1:16:22,0:08:27,0:22:53,0:16:57,0:07:00,7:39:20,2920,230,103
+Emergency Medicine,PGY2+,S0106952,2016-09-24,4:41:21,0:35:22,0:11:52,0:17:28,0:09:48,0:01:47,6:26:28,1956,32,62
+Emergency Medicine,PGY2+,S0106952,2016-09-25,4:41:33,0:55:14,0:14:32,0:22:08,0:20:47,0:05:30,7:12:44,2446,454,72
+Emergency Medicine,PGY2+,S0106952,2016-09-26,5:39:57,1:17:17,0:04:53,0:20:48,0:24:31,0:09:32,8:36:04,3541,631,87
+Emergency Medicine,PGY2+,S0106952,2016-09-27,0:38:11,0:43:59,0:06:31,0:00:00,0:02:44,0:04:11,1:44:57,945,945,55
+Night Team,PGY1,S0195094,2016-07-14,1:12:11,0:43:06,0:00:12,0:22:50,0:19:36,0:04:02,2:53:24,855,382,28
+General Medicine,PGY1,S0169395,2015-06-01,2:15:08,0:18:59,2:41:48,0:35:27,0:34:39,0:17:25,7:10:52,1711,14,12
+Intensive Care Unit,PGY2+,S0181081,2016-09-24,1:45:55,1:09:27,0:07:45,0:19:14,0:21:17,0:00:21,4:27:49,1211,0,16
+Intensive Care Unit,PGY2+,S0181081,2016-09-25,2:31:50,1:36:43,0:00:03,1:31:18,0:45:43,0:13:01,7:13:45,2437,0,12
+Intensive Care Unit,PGY2+,S0181081,2016-09-26,1:26:04,0:57:17,0:05:04,0:08:17,0:08:48,0:01:22,3:17:34,890,0,9
+Intensive Care Unit,PGY2+,S0181081,2016-09-27,0:18:24,0:00:17,0:00:00,0:00:00,0:00:00,0:01:31,0:23:05,69,0,7
+Intensive Care Unit,PGY2+,S0181081,2016-09-20,1:18:31,0:41:56,0:00:00,0:41:57,0:25:14,0:06:11,3:47:29,1342,0,23
+Intensive Care Unit,PGY2+,S0181081,2016-09-21,1:36:29,0:42:41,0:04:15,0:17:49,0:19:49,0:10:32,3:42:47,1529,0,23
+Intensive Care Unit,PGY2+,S0181081,2016-09-22,2:02:28,0:55:14,0:08:51,0:20:09,0:22:16,0:05:44,4:29:55,1179,0,18
+Intensive Care Unit,PGY2+,S0181081,2016-09-23,3:02:02,1:11:37,0:06:09,0:53:36,0:41:26,0:05:03,6:58:56,2290,0,18
+Intensive Care Unit,PGY2+,S0181081,2016-09-28,1:03:41,0:48:02,0:01:05,0:09:49,0:21:31,0:05:01,2:54:06,1121,0,24
+Intensive Care Unit,PGY2+,S0181081,2016-09-29,1:51:06,0:59:01,0:03:09,0:18:12,0:18:33,0:01:48,4:04:10,1416,0,13
+Emergency Medicine,PGY2+,S0169394,2016-07-30,4:33:51,1:11:49,0:04:11,0:36:06,1:03:37,0:16:47,8:51:22,3327,276,31
+Emergency Medicine,PGY2+,S0169394,2016-07-31,3:19:02,0:44:38,0:02:24,0:39:53,1:01:20,0:06:31,6:42:25,2429,0,21
+Night Team,PGY1,S0181069,2016-02-05,0:24:49,0:35:57,0:27:57,0:35:09,0:10:33,0:17:49,2:41:00,999,81,22
+Night Team,PGY1,S0181069,2016-02-04,1:00:21,0:28:27,0:03:33,0:35:42,0:27:45,0:22:11,3:17:19,1448,0,62
+Night Team,PGY1,S0181069,2016-02-07,0:49:09,0:26:52,0:10:23,0:28:31,0:22:48,0:21:47,2:57:09,1115,0,33
+Night Team,PGY1,S0181069,2016-02-06,2:07:26,0:47:31,1:27:11,1:09:35,0:19:45,0:31:45,6:50:07,1910,0,41
+Night Team,PGY1,S0181069,2016-02-01,1:12:23,0:36:05,0:51:12,0:48:37,0:34:24,0:15:33,4:35:05,1533,0,40
+Night Team,PGY1,S0181069,2016-02-03,1:49:24,0:51:11,1:24:43,1:09:10,0:30:34,0:39:21,6:54:10,2527,255,70
+Night Team,PGY1,S0181069,2016-02-02,1:41:29,0:51:49,2:04:41,0:51:02,0:23:33,0:48:25,7:29:14,2431,24,43
+Night Team,PGY1,S0181069,2016-02-09,1:15:17,0:56:43,1:12:59,0:48:28,0:30:13,0:35:04,5:54:04,2398,227,32
+Night Team,PGY1,S0181069,2016-02-08,0:23:42,0:18:06,0:04:18,0:29:41,0:17:25,0:17:49,2:04:25,958,0,72
+General Medicine,PGY1,S0169384,2015-02-14,1:19:10,0:04:35,1:43:05,0:07:04,0:02:50,0:08:27,3:43:29,1528,1520,21
+General Medicine,PGY2+,S0172085,2016-10-08,0:00:00,0:00:00,0:00:00,0:00:08,0:00:00,0:00:00,0:00:08,6,0,3
+Night Team,PGY2+,S0172085,2016-10-09,0:41:45,0:15:48,0:00:01,0:27:48,0:04:19,0:00:10,2:09:56,473,0,9
+General Medicine,PGY2+,S0172085,2016-10-06,1:23:52,0:50:25,0:00:06,0:08:32,0:11:43,0:08:42,3:03:39,1230,10,11
+General Medicine,PGY2+,S0172085,2016-10-07,0:06:26,0:18:19,0:00:00,0:00:29,0:01:46,0:00:20,0:30:51,154,108,12
+General Medicine,PGY2+,S0172085,2016-10-04,1:49:14,1:37:30,0:00:03,0:23:54,0:23:18,0:10:23,4:58:21,1912,0,18
+General Medicine,PGY2+,S0172085,2016-10-05,2:16:22,1:28:33,0:00:00,0:07:35,0:04:00,0:08:00,4:49:55,2392,0,25
+General Medicine,PGY2+,S0172085,2016-10-02,1:28:35,0:59:00,0:00:00,0:10:25,0:13:56,0:01:48,3:13:52,992,0,17
+General Medicine,PGY2+,S0172085,2016-10-03,3:00:16,1:41:01,0:00:19,0:24:20,0:08:10,0:11:47,5:50:43,3246,141,33
+General Medicine,PGY2+,S0172085,2016-10-01,0:17:28,0:01:18,0:00:00,0:00:09,0:00:00,0:00:00,0:20:03,87,0,14
+Night Team,PGY1,S0182038,2016-09-09,0:46:07,0:35:46,0:00:05,0:12:57,0:09:59,0:00:07,1:57:20,396,0,19
+Night Team,PGY1,S0182038,2016-09-08,1:12:31,0:49:12,0:00:00,0:24:34,0:09:44,0:05:03,3:10:28,651,0,20
+Night Team,PGY1,S0182038,2016-09-05,1:11:15,0:17:24,0:00:00,0:23:31,0:11:48,0:03:41,2:26:08,553,404,20
+Night Team,PGY1,S0182038,2016-09-04,1:01:30,0:06:39,0:00:00,0:20:51,0:13:33,0:00:03,2:11:57,605,257,25
+Night Team,PGY1,S0182038,2016-09-07,1:53:22,0:46:34,0:00:01,0:41:50,0:24:04,0:00:34,4:35:18,1059,0,42
+Night Team,PGY1,S0182038,2016-09-06,0:45:37,1:01:07,0:01:20,0:25:01,0:19:12,0:00:07,2:45:12,580,286,17
+General Medicine,PGY1,S0195229,2016-08-19,0:17:35,0:36:38,0:00:30,0:03:32,0:00:51,0:00:05,1:03:44,358,318,5
+General Medicine,PGY1,S0195229,2016-08-18,2:04:45,0:23:45,0:00:08,1:15:25,0:24:26,0:00:24,4:51:50,1704,9,13
+Night Team,PGY2+,S0172645,2016-07-13,0:39:53,0:37:21,0:00:04,0:14:18,0:07:53,0:00:22,1:45:10,777,0,21
+General Medicine,PGY1,S0195229,2016-08-10,2:42:21,0:39:05,0:00:01,0:50:02,0:34:12,0:02:00,5:05:26,1262,0,8
+General Medicine,PGY1,S0195229,2016-08-13,1:52:55,0:38:32,0:04:59,0:59:59,0:30:26,0:08:37,4:59:22,1277,0,9
+Night Team,PGY2+,S0172645,2016-07-10,1:56:03,1:28:47,0:00:01,0:24:27,0:07:42,0:08:06,4:36:20,1927,283,41
+General Medicine,PGY1,S0195229,2016-08-15,2:42:33,0:31:25,0:00:09,1:03:40,0:33:52,0:00:20,5:19:57,1391,0,10
+Night Team,PGY2+,S0172645,2016-07-16,0:31:06,0:41:21,0:00:02,0:20:04,0:07:59,0:03:25,1:49:33,714,23,9
+General Medicine,PGY1,S0195229,2016-08-17,1:17:50,0:35:43,0:00:15,0:54:26,0:26:15,0:00:36,3:41:00,1329,20,5
+Night Team,PGY2+,S0172645,2016-07-14,1:33:35,1:10:06,0:00:05,0:38:03,0:09:04,0:05:22,4:06:22,1278,0,16
+General Medicine,PGY1,S0195201,2016-10-01,3:17:56,2:29:29,0:01:47,0:42:03,0:23:47,0:01:56,7:49:27,3607,0,36
+General Medicine,PGY1,S0195201,2016-10-03,1:50:57,1:28:55,0:00:15,0:15:11,0:11:12,0:01:57,4:10:04,1653,0,10
+General Medicine,PGY1,S0195201,2016-10-02,2:14:35,1:15:47,0:00:15,0:46:59,0:03:59,0:09:22,5:11:54,2231,0,13
+General Medicine,PGY1,S0195201,2016-10-05,0:40:05,0:24:24,0:00:01,0:00:23,0:00:14,0:10:52,1:20:06,508,275,15
+General Medicine,PGY1,S0195201,2016-10-04,4:22:24,1:44:50,0:00:17,0:44:40,0:21:54,0:09:27,7:58:39,2603,33,27
+General Medicine,PGY1,S0195201,2016-10-07,2:17:24,1:33:59,0:00:58,0:34:50,0:09:31,0:01:06,5:07:46,2074,11,10
+General Medicine,PGY1,S0195201,2016-10-06,4:33:27,2:17:25,0:01:53,0:38:37,0:13:58,0:09:17,8:27:12,3387,0,14
+General Medicine,PGY1,S0195201,2016-10-09,4:12:41,2:33:21,0:02:10,1:07:50,0:07:15,0:05:01,9:04:28,3687,0,18
+General Medicine,PGY1,S0195201,2016-10-08,2:02:36,0:55:37,0:00:11,0:25:24,0:02:20,0:00:15,3:44:44,1139,0,5
+Intensive Care Unit,PGY2+,S0169394,2015-11-30,2:20:24,0:50:47,0:05:27,0:34:42,0:40:24,0:51:20,5:46:55,1594,96,15
+General Medicine,PGY2+,S0169406,2016-03-01,1:41:10,0:14:23,0:00:00,0:02:41,0:02:14,0:01:12,2:20:25,977,0,21
+Emergency Medicine,PGY1,S0181088,2016-02-04,1:07:41,0:44:41,3:01:41,0:41:46,0:25:27,0:53:02,8:32:23,2663,0,16
+Emergency Medicine,PGY1,S0181088,2016-02-05,0:31:02,0:12:11,0:56:26,0:09:02,0:15:44,0:16:21,2:53:43,1049,0,7
+Emergency Medicine,PGY1,S0181088,2016-02-06,1:22:22,0:31:39,2:53:26,0:17:13,0:40:44,0:35:26,7:25:42,2508,0,21
+Emergency Medicine,PGY1,S0181088,2016-02-07,1:13:32,0:21:33,4:09:32,0:28:24,0:34:00,0:29:26,7:58:12,1994,0,16
+General Medicine,PGY1,S0106952,2015-03-30,3:55:03,0:29:56,2:10:31,0:37:42,0:47:07,0:26:42,9:05:01,2464,652,13
+Emergency Medicine,PGY1,S0181088,2016-02-01,0:42:32,0:15:56,2:33:51,0:27:49,0:23:21,0:24:55,5:26:20,1562,0,9
+Emergency Medicine,PGY1,S0181088,2016-02-03,0:50:06,0:25:41,2:37:06,0:13:16,0:37:04,0:44:21,6:05:05,1528,0,10
+Emergency Medicine,PGY1,S0181088,2016-02-08,1:30:26,0:15:22,1:21:32,0:10:21,0:21:50,0:43:29,5:15:48,1822,0,9
+Night Team,PGY1,S0109179,2014-08-02,0:56:04,0:09:03,0:03:44,0:16:01,0:05:29,0:19:01,2:01:17,626,52,40
+Night Team,PGY1,S0109179,2014-08-01,1:53:04,0:04:35,0:08:55,0:33:54,0:21:35,0:14:43,3:36:23,1244,17,67
+General Medicine,PGY1,S0169400,2015-05-04,1:21:52,0:05:54,1:28:34,0:20:23,0:09:48,0:16:17,4:18:13,1068,31,12
+Night Team,PGY2+,S0074196,2016-10-06,2:13:13,0:46:00,0:00:21,0:29:08,0:05:40,0:00:09,3:57:14,985,9,20
+Night Team,PGY2+,S0074196,2016-10-07,2:00:53,0:59:16,0:00:03,0:22:36,0:07:12,0:06:46,4:04:18,1224,25,20
+Night Team,PGY2+,S0074196,2016-10-04,1:32:57,1:23:02,0:00:11,0:22:45,0:06:55,0:00:11,3:48:30,1452,0,17
+Night Team,PGY2+,S0074196,2016-10-05,0:41:33,0:16:52,0:00:06,0:14:19,0:13:28,0:04:06,1:37:17,530,0,13
+Night Team,PGY2+,S0074196,2016-10-02,1:01:34,0:24:40,0:00:11,0:36:49,0:08:45,0:05:02,2:41:21,973,0,20
+Night Team,PGY2+,S0074196,2016-10-03,1:52:58,0:57:07,0:02:22,0:38:33,0:06:36,0:02:11,4:30:53,1483,0,18
+Night Team,PGY2+,S0074196,2016-10-01,0:20:07,0:23:02,0:00:00,0:09:48,0:07:16,0:00:14,1:09:21,407,0,12
+General Medicine,PGY1,S0195206,2016-10-19,2:21:26,1:21:49,0:06:24,0:34:31,0:17:59,0:12:53,6:07:19,2071,277,18
+General Medicine,PGY1,S0195206,2016-10-18,2:27:20,1:43:33,0:00:06,0:17:48,0:24:28,0:12:23,5:49:02,2153,134,18
+Night Team,PGY2+,S0074196,2016-10-08,0:42:08,0:22:39,0:00:04,0:10:16,0:01:40,0:00:05,1:22:48,331,0,9
+General Medicine,PGY1,S0169400,2015-05-05,0:48:51,0:01:57,0:06:09,0:04:53,0:04:19,0:11:06,1:23:17,454,3,15
+General Medicine,PGY1,S0169385,2014-10-30,3:48:19,0:10:28,0:10:19,0:28:02,0:19:58,0:39:03,6:03:48,2157,0,11
+General Medicine,PGY1,S0169385,2014-10-31,0:51:45,0:00:18,0:13:57,0:00:08,0:01:40,0:01:25,1:13:51,551,544,13
+Intensive Care Unit,PGY2+,S0181081,2016-10-04,0:08:29,0:00:19,0:00:00,0:00:04,0:00:00,0:00:37,0:10:17,49,0,6
+Night Team,PGY1,S0195205,2016-09-30,2:25:04,0:51:53,0:00:05,0:56:38,0:27:55,0:03:30,5:23:18,1425,0,34
+Intensive Care Unit,PGY2+,S0181081,2016-10-05,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:05,13,0,4
+General Medicine,PGY2+,S0169401,2016-01-31,2:40:27,1:13:47,0:05:03,0:57:58,0:23:24,0:35:45,6:34:41,3323,284,39
+General Medicine,PGY2+,S0169401,2016-01-30,1:17:17,0:56:10,0:55:10,0:38:21,0:15:12,0:34:51,5:13:58,2530,486,22
+General Medicine,PGY1,S0094959,2014-11-30,6:24:16,0:07:39,0:29:58,0:33:57,0:29:22,0:28:45,8:57:04,4131,0,38
+Intensive Care Unit,PGY2+,S0079337,2016-06-06,2:18:54,0:30:25,0:08:03,0:21:55,0:19:24,0:04:23,4:03:24,1129,18,16
+Night Team,PGY1,S0169394,2014-10-27,3:19:08,0:07:49,0:17:50,0:36:35,0:30:32,0:27:36,5:35:09,1531,0,30
+Night Team,PGY1,S0169394,2014-10-26,2:57:56,0:02:29,0:29:50,0:21:30,0:29:43,0:46:13,5:19:39,1228,0,36
+Night Team,PGY1,S0169394,2014-10-29,3:03:54,0:04:35,0:03:09,0:16:19,0:30:17,0:16:21,4:27:44,1271,0,36
+Night Team,PGY1,S0169394,2014-10-28,3:20:56,0:09:51,0:10:15,0:26:56,0:32:19,0:43:15,5:48:33,2044,89,34
+General Medicine,PGY1,S0195203,2016-07-17,2:54:14,1:02:35,0:00:10,0:56:55,0:32:45,0:13:44,6:29:15,1866,160,11
+Intensive Care Unit,PGY2+,S0181088,2016-09-30,0:15:22,0:03:30,0:00:00,0:00:19,0:00:00,0:00:00,0:23:31,84,0,12
+Emergency Medicine,PGY1,S0169395,2014-11-18,1:03:43,0:00:36,1:24:19,0:11:24,0:24:19,0:20:15,3:59:24,1426,10,16
+Emergency Medicine,PGY1,S0169395,2014-11-19,3:39:55,0:07:14,1:42:02,0:33:05,0:20:24,0:32:17,7:17:36,2142,0,50
+Night Team,PGY2+,S0181069,2016-06-27,0:18:55,0:24:02,0:01:14,0:01:17,0:01:47,0:00:06,1:07:27,682,0,17
+Night Team,PGY2+,S0181069,2016-06-26,1:29:38,1:06:56,0:00:22,0:40:54,0:11:41,0:14:21,4:41:13,1365,6,10
+Night Team,PGY2+,S0181069,2016-06-29,2:41:20,1:47:24,0:00:05,0:51:39,0:18:12,0:05:12,6:30:49,2666,0,24
+Night Team,PGY2+,S0181069,2016-06-28,1:47:19,0:42:37,0:00:11,0:36:07,0:15:49,0:07:51,4:03:13,1466,56,17
+Emergency Medicine,PGY2+,S0106952,2016-10-01,0:34:24,0:35:06,0:01:07,0:00:00,0:03:36,0:00:27,1:16:01,565,565,119
+Emergency Medicine,PGY2+,S0106952,2016-10-02,4:46:15,0:41:22,0:10:07,0:26:01,0:13:57,0:03:11,7:05:12,2625,106,62
+Emergency Medicine,PGY2+,S0106952,2016-10-03,5:48:25,1:06:23,0:32:21,0:21:26,0:23:06,0:08:09,9:08:08,3095,251,85
+Emergency Medicine,PGY2+,S0106952,2016-10-04,1:01:07,0:36:57,0:02:29,0:00:00,0:00:29,0:00:06,1:51:05,983,868,99
+General Medicine,PGY1,S0195220,2016-07-27,1:50:24,0:40:50,0:00:30,0:32:24,0:15:14,0:00:04,3:43:27,1018,117,7
+Emergency Medicine,PGY2+,S0106952,2016-10-06,4:30:02,0:58:07,0:22:57,0:20:06,0:10:43,0:01:28,7:09:55,2510,138,78
+Emergency Medicine,PGY2+,S0106952,2016-10-07,3:20:08,0:25:31,0:04:04,0:05:32,0:05:38,0:06:40,4:42:04,1464,11,45
+Emergency Medicine,PGY2+,S0106952,2016-10-08,0:18:39,0:29:38,0:00:00,0:00:00,0:00:08,0:00:01,0:49:24,436,436,43
+General Medicine,PGY1,S0195220,2016-07-28,2:54:53,1:46:39,0:00:04,0:49:54,0:27:02,0:02:45,6:37:24,1875,362,10
+General Medicine,PGY1,S0195220,2016-07-29,0:13:11,0:02:45,0:00:01,0:01:09,0:00:27,0:00:03,0:18:51,128,117,9
+Emergency Medicine,PGY1,S0169395,2014-11-16,2:47:15,0:04:55,2:19:20,0:46:52,0:55:19,0:45:22,8:20:06,2535,0,20
+General Medicine,PGY2+,S0163662,2015-12-31,2:42:07,1:03:45,0:21:26,1:44:15,0:54:24,0:43:55,8:03:18,2878,209,15
+Emergency Medicine,PGY1,S0169395,2014-11-17,2:42:40,0:07:22,1:58:15,0:42:14,0:28:25,0:52:03,7:43:34,2571,0,25
+General Medicine,PGY2+,S0163662,2015-12-30,1:46:11,0:28:55,0:03:18,0:11:14,0:20:26,0:22:45,3:41:21,1450,0,19
+Emergency Medicine,PGY1,S0169395,2014-11-10,0:05:35,0:00:00,0:10:23,0:00:00,0:00:00,0:00:00,0:16:00,27,25,4
+General Medicine,PGY1,S0195203,2016-07-19,0:00:00,0:00:00,0:00:00,0:00:29,0:00:00,0:00:00,0:00:29,14,0,5
+General Medicine,PGY2+,S0169402,2016-06-30,2:05:31,0:50:52,0:00:01,0:35:06,0:14:13,0:13:33,4:47:34,2311,387,24
+Night Team,PGY1,S0169398,2015-05-24,2:57:45,0:33:06,1:47:24,0:23:57,0:19:32,0:55:16,7:16:05,1996,43,31
+General Medicine,PGY1,S0181077,2015-12-31,0:06:57,0:19:21,0:21:50,0:09:34,0:04:10,0:00:22,1:07:53,579,553,12
+General Medicine,PGY1,S0181077,2015-12-30,3:01:22,1:20:18,1:07:50,0:50:57,0:31:45,0:22:55,7:44:56,3242,14,32
+Emergency Medicine,PGY2+,S0172645,2016-11-04,0:00:41,0:00:06,0:00:00,0:00:00,0:00:00,0:00:00,0:01:07,20,0,5
+Emergency Medicine,PGY2+,S0172645,2016-11-03,3:23:45,0:38:32,0:00:35,0:37:52,0:45:31,0:01:09,6:00:56,1660,0,49
+Emergency Medicine,PGY2+,S0172645,2016-11-02,1:57:10,0:12:11,0:01:00,0:25:55,0:48:33,0:02:27,3:57:52,1224,0,20
+Emergency Medicine,PGY2+,S0172645,2016-11-01,2:57:15,0:32:51,0:01:29,0:22:20,0:21:59,0:06:46,4:44:27,1150,0,57
+General Medicine,PGY1,S0181070,2015-09-11,2:43:49,0:01:51,1:37:25,0:10:10,0:10:30,0:06:54,5:26:10,1419,0,13
+General Medicine,PGY1,S0169386,2014-12-24,0:00:00,0:02:10,0:00:00,0:00:00,0:00:00,0:05:36,0:08:04,64,58,6
+General Medicine,PGY1,S0169386,2014-12-25,2:27:15,0:16:05,2:21:40,0:33:55,0:25:35,1:08:09,8:01:33,2828,18,16
+General Medicine,PGY1,S0169386,2014-12-26,2:33:51,0:21:18,1:11:38,0:53:14,0:23:04,0:56:43,7:06:53,2205,78,16
+General Medicine,PGY1,S0169386,2014-12-27,0:00:00,0:00:00,0:00:00,0:03:36,0:00:00,0:00:01,0:05:09,17,9,5
+General Medicine,PGY1,S0169386,2014-12-20,2:29:48,0:06:11,2:50:38,0:56:50,0:34:29,0:30:57,7:59:56,2180,0,12
+General Medicine,PGY1,S0169386,2014-12-21,1:24:36,0:03:09,0:27:13,0:21:42,0:16:30,0:48:22,3:43:05,1581,171,11
+General Medicine,PGY1,S0169386,2014-12-22,1:50:14,0:06:06,0:51:50,0:19:58,0:12:13,0:25:31,4:21:13,1305,54,23
+General Medicine,PGY1,S0169386,2014-12-23,2:49:40,0:08:48,2:21:49,1:08:28,0:26:07,0:52:15,8:33:23,2653,32,13
+General Medicine,PGY1,S0169386,2014-12-28,3:00:41,0:21:09,1:28:02,0:43:43,0:32:39,0:31:10,7:05:14,2972,81,15
+General Medicine,PGY1,S0169386,2014-12-29,2:42:07,0:13:30,0:50:11,0:51:06,0:23:53,0:32:08,6:09:37,2490,80,8
+Night Team,PGY1,S0172056,2016-02-27,1:12:08,0:59:09,0:00:04,0:28:49,0:19:03,0:03:05,3:27:26,1271,0,22
+Night Team,PGY1,S0172056,2016-02-26,0:59:56,0:13:11,0:00:00,0:22:33,0:24:23,0:02:30,2:13:48,867,40,30
+Night Team,PGY1,S0172056,2016-02-25,1:40:48,0:35:27,0:00:00,0:19:26,0:37:56,0:02:09,3:33:55,1591,207,38
+Night Team,PGY1,S0172056,2016-02-24,1:08:39,0:10:24,0:00:00,0:24:58,0:24:32,0:05:46,2:40:17,1088,0,25
+Night Team,PGY1,S0172056,2016-02-23,1:41:43,1:01:32,0:00:04,0:47:42,0:16:25,0:05:06,4:09:20,1705,105,33
+Night Team,PGY1,S0172056,2016-02-22,1:52:02,0:45:30,0:00:04,0:33:29,0:19:48,0:01:03,3:49:56,1199,0,39
+Night Team,PGY1,S0172056,2016-02-21,0:42:11,0:13:34,0:00:00,0:20:54,0:10:39,0:00:27,1:38:07,501,0,14
+Night Team,PGY1,S0172056,2016-02-20,0:24:23,0:27:19,0:00:14,0:25:28,0:07:41,0:00:00,1:33:24,656,0,9
+Emergency Medicine,PGY1,S0169389,2014-10-09,5:45:52,0:24:34,1:26:54,0:41:22,0:48:22,0:37:58,10:38:16,3023,0,43
+Emergency Medicine,PGY1,S0169389,2014-10-08,4:37:56,0:08:07,0:42:11,0:16:03,0:22:36,0:23:38,6:47:53,1952,29,46
+Emergency Medicine,PGY1,S0169389,2014-10-05,0:16:34,0:00:02,0:00:36,0:00:00,0:00:02,0:00:00,0:19:42,140,140,11
+Emergency Medicine,PGY1,S0169389,2014-10-04,5:10:33,0:04:04,1:09:03,0:22:06,0:33:28,0:29:01,8:31:31,2718,0,37
+Emergency Medicine,PGY1,S0169389,2014-10-07,2:54:05,0:11:25,0:34:27,0:12:09,0:17:47,0:21:50,4:47:39,1434,0,33
+Emergency Medicine,PGY1,S0169389,2014-10-06,2:32:51,0:04:19,0:33:22,0:03:32,0:07:09,0:11:31,3:41:39,1127,0,38
+Emergency Medicine,PGY1,S0169389,2014-10-01,0:46:04,0:00:00,0:05:25,0:10:42,0:10:05,0:09:00,1:55:35,490,411,14
+Emergency Medicine,PGY1,S0169389,2014-10-03,5:24:09,0:14:43,0:48:26,0:29:13,0:29:53,0:33:38,8:54:09,2841,0,44
+Emergency Medicine,PGY1,S0169389,2014-10-02,7:10:13,0:19:23,0:49:24,0:22:59,0:27:18,0:29:29,10:22:04,2575,0,48
+Night Team,PGY2+,S0169400,2015-11-12,3:24:24,1:01:51,1:01:12,0:25:44,0:16:31,0:29:43,6:57:56,1656,22,46
+Night Team,PGY2+,S0169400,2015-11-13,3:11:03,1:02:11,2:01:56,0:11:39,0:06:34,0:34:24,7:31:59,1929,53,78
+Night Team,PGY2+,S0169400,2015-11-10,2:09:55,1:10:23,2:27:07,0:28:37,0:07:49,0:30:13,7:16:07,1780,93,44
+Night Team,PGY2+,S0169400,2015-11-11,3:44:07,0:55:08,1:28:01,0:19:51,0:12:43,0:22:18,7:18:29,1821,0,64
+Emergency Medicine,PGY1,S0195210,2016-07-21,1:16:22,0:02:42,0:05:02,0:14:05,0:05:17,0:00:03,2:09:55,503,0,5
+Emergency Medicine,PGY1,S0195210,2016-07-20,0:05:08,0:00:58,0:00:00,0:00:00,0:00:01,0:00:00,0:07:55,44,16,2
+Emergency Medicine,PGY1,S0195210,2016-07-23,0:00:00,0:00:42,0:00:00,0:00:00,0:00:00,0:00:00,0:00:51,15,15,2
+Emergency Medicine,PGY1,S0195210,2016-07-22,0:20:55,0:00:48,0:01:24,0:00:46,0:00:01,0:00:17,0:29:09,126,126,7
+General Medicine,PGY2+,S0169384,2016-07-10,2:33:35,1:24:55,0:00:16,1:05:43,0:31:54,0:05:25,6:21:57,1732,540,16
+Emergency Medicine,PGY1,S0181082,2016-03-14,1:22:00,0:26:53,0:02:06,0:24:18,0:31:29,0:01:10,3:08:30,1086,1,9
+Night Team,PGY1,S0106952,2014-12-15,2:45:30,0:10:43,4:07:06,0:26:38,0:32:20,0:41:35,8:57:38,1726,177,53
+General Medicine,PGY1,S0195220,2016-07-31,1:35:32,0:58:16,0:00:05,0:29:39,0:16:45,0:01:36,3:53:18,1008,162,23
+Emergency Medicine,PGY1,S0181057,2015-08-10,5:31:05,0:24:28,1:44:36,0:10:42,0:34:39,0:13:31,9:01:54,2771,0,36
+General Medicine,PGY1,S0195220,2016-07-30,2:44:51,1:45:46,0:00:01,1:20:39,0:16:56,0:00:43,6:47:45,1841,79,10
+Emergency Medicine,PGY1,S0181063,2016-06-15,1:44:31,1:51:56,0:00:36,0:30:03,0:39:26,0:39:15,6:04:50,2214,319,16
+General Medicine,PGY1,S0181088,2015-07-28,1:30:44,0:14:43,2:11:07,0:54:25,0:31:07,0:20:38,6:35:07,1990,10,11
+Emergency Medicine,PGY1,S0181063,2016-06-17,0:07:15,0:44:34,0:00:04,0:12:59,0:04:29,0:00:11,1:27:46,454,454,6
+Emergency Medicine,PGY1,S0181063,2016-06-10,1:10:36,1:25:07,0:00:45,0:25:01,0:54:24,0:42:09,5:16:33,2479,426,13
+Emergency Medicine,PGY1,S0181063,2016-06-11,0:47:22,1:11:24,0:03:12,0:21:17,0:27:05,0:18:58,3:33:58,1288,200,17
+Emergency Medicine,PGY1,S0181063,2016-06-12,1:16:25,1:06:32,0:04:13,0:27:23,0:48:01,0:34:14,4:49:27,1935,0,26
+Emergency Medicine,PGY1,S0181063,2016-06-13,1:01:58,0:49:28,0:03:25,0:38:42,0:35:33,0:13:48,3:35:42,2069,960,29
+General Medicine,PGY1,S0181088,2015-07-22,1:43:45,0:06:52,2:03:51,0:40:37,0:23:31,0:17:38,5:41:26,1152,56,6
+General Medicine,PGY1,S0181088,2015-07-23,1:22:09,0:05:08,2:43:26,0:30:36,0:13:58,0:16:39,5:41:27,1333,43,6
+General Medicine,PGY1,S0181088,2015-07-20,0:54:27,0:01:21,1:20:02,0:35:31,0:25:53,0:14:18,3:57:26,1151,162,8
+General Medicine,PGY1,S0181088,2015-07-21,2:26:20,0:12:59,1:46:04,0:38:58,0:27:12,0:15:12,6:20:54,1664,76,6
+Emergency Medicine,PGY1,S0181063,2016-06-18,1:18:19,1:30:17,0:07:35,0:28:18,0:34:46,0:33:05,5:33:45,2627,243,18
+Emergency Medicine,PGY1,S0181063,2016-06-19,0:05:04,0:49:34,0:00:00,0:02:58,0:00:00,0:00:07,1:01:13,916,916,16
+General Medicine,PGY1,S0181088,2015-07-24,0:08:28,0:00:00,0:02:02,0:00:00,0:00:04,0:00:00,0:10:58,69,67,4
+General Medicine,PGY1,S0181088,2015-07-25,0:37:40,0:00:34,1:00:14,0:25:26,0:21:38,0:06:32,2:58:20,632,38,3
+Night Team,PGY2+,S0169385,2016-08-17,2:03:36,1:03:35,0:00:11,0:20:31,0:10:35,0:23:14,4:36:51,1795,60,31
+General Medicine,PGY2+,S0169384,2016-07-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,2
+General Medicine,PGY1,S0181081,2016-06-10,0:05:05,0:00:44,0:00:00,0:00:43,0:00:00,0:00:00,0:06:40,38,24,7
+Intensive Care Unit,PGY2+,S0169386,2016-07-15,0:00:54,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:01:05,16,0,4
+Intensive Care Unit,PGY2+,S0169386,2016-07-14,1:15:55,0:17:17,0:06:54,0:14:22,0:06:21,0:01:37,2:15:48,624,9,34
+Intensive Care Unit,PGY2+,S0169386,2016-07-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,18,0,3
+Intensive Care Unit,PGY2+,S0169386,2016-07-11,3:06:33,0:26:46,0:06:01,0:22:14,0:08:08,0:11:04,4:52:54,1600,0,38
+Intensive Care Unit,PGY2+,S0169386,2016-07-10,4:39:58,0:46:24,0:04:03,1:17:39,0:25:22,0:30:43,8:29:59,2415,58,19
+Intensive Care Unit,PGY2+,S0169386,2016-07-13,1:58:43,0:43:02,0:12:45,0:20:13,0:18:51,0:13:05,4:09:08,1518,187,20
+Intensive Care Unit,PGY2+,S0169386,2016-07-12,3:41:33,0:39:34,0:14:15,0:28:10,0:27:04,0:11:33,7:04:54,2299,0,34
+General Medicine,PGY1,S0181063,2015-12-30,0:01:05,0:05:27,0:01:17,0:00:17,0:00:12,0:01:00,0:10:55,358,358,7
+General Medicine,PGY2+,S0169384,2016-07-15,2:28:06,1:53:53,0:00:39,0:40:52,0:20:34,0:12:26,6:03:54,2227,431,20
+General Medicine,PGY1,S0181060,2015-11-23,1:34:05,0:38:35,1:39:33,0:40:11,0:28:13,0:19:29,5:51:16,1477,0,21
+General Medicine,PGY1,S0181060,2015-11-22,1:03:08,0:52:26,1:15:15,0:34:49,0:13:34,0:15:39,4:50:48,1363,0,6
+General Medicine,PGY1,S0181060,2015-11-21,1:58:51,1:38:14,2:28:05,0:48:21,0:22:50,0:17:01,8:23:00,2392,0,9
+General Medicine,PGY1,S0169383,2015-01-26,4:25:51,0:14:12,3:30:19,1:01:49,0:42:16,0:29:45,10:58:00,2447,333,10
+General Medicine,PGY1,S0169383,2015-01-27,3:40:50,0:13:23,1:24:15,0:22:39,0:30:55,0:30:08,7:19:15,2105,117,12
+General Medicine,PGY1,S0169383,2015-01-24,0:00:00,0:00:00,0:00:00,0:02:16,0:00:00,0:00:00,0:02:16,18,0,4
+General Medicine,PGY1,S0169383,2015-01-25,2:38:12,0:07:24,1:29:36,0:13:47,0:16:49,0:13:07,5:30:29,1340,0,15
+General Medicine,PGY1,S0169383,2015-01-22,4:22:09,0:15:31,2:23:19,0:49:41,0:45:15,0:24:53,10:07:47,2711,1145,18
+General Medicine,PGY1,S0169383,2015-01-23,3:39:57,0:08:52,2:01:43,0:39:58,0:36:32,0:24:12,8:25:18,2414,0,12
+General Medicine,PGY1,S0169383,2015-01-20,3:46:37,0:06:51,0:44:23,0:25:44,0:24:09,0:37:08,6:47:45,2199,0,18
+General Medicine,PGY1,S0169383,2015-01-21,3:10:45,0:14:11,2:16:10,1:08:14,0:29:00,0:50:08,8:55:47,2151,0,11
+General Medicine,PGY1,S0169401,2014-10-01,3:47:52,0:27:17,0:13:06,0:52:20,0:16:57,0:16:53,6:36:13,2392,233,52
+General Medicine,PGY1,S0169401,2014-10-02,4:22:47,0:33:49,0:20:37,0:47:52,0:23:22,0:05:05,7:10:38,2829,173,17
+General Medicine,PGY1,S0169401,2014-10-03,4:45:33,0:20:55,0:03:02,0:52:18,0:49:27,0:03:13,7:55:43,2987,433,22
+General Medicine,PGY1,S0169401,2014-10-04,5:00:24,0:18:10,0:15:09,1:22:10,0:24:32,0:02:54,8:23:04,3023,426,34
+General Medicine,PGY1,S0169401,2014-10-05,2:23:46,0:26:16,0:14:19,0:00:34,0:01:41,0:10:50,3:32:08,1133,1127,28
+General Medicine,PGY1,S0169401,2014-10-06,4:48:30,0:32:09,0:09:23,0:51:07,0:36:51,0:03:01,7:12:01,2077,408,12
+General Medicine,PGY1,S0169401,2014-10-07,4:46:12,0:26:58,0:06:04,1:00:14,0:23:59,0:09:54,7:28:35,2868,151,32
+General Medicine,PGY1,S0106952,2015-01-16,4:50:25,0:10:52,2:04:40,0:38:36,0:58:07,0:23:32,9:39:18,3233,733,29
+General Medicine,PGY1,S0106952,2015-01-17,5:00:30,0:04:41,4:04:51,0:33:59,0:30:48,0:15:21,10:53:19,2504,597,33
+General Medicine,PGY1,S0106952,2015-01-14,3:41:42,0:05:43,3:07:26,0:05:27,0:09:39,0:08:05,7:44:47,2397,2383,106
+General Medicine,PGY1,S0106952,2015-01-15,5:16:52,0:14:52,2:48:46,0:34:22,0:48:34,0:44:18,10:54:24,3042,375,31
+General Medicine,PGY1,S0106952,2015-01-12,5:19:16,0:16:32,3:02:03,0:46:51,0:38:15,0:44:50,11:29:54,3033,869,28
+General Medicine,PGY1,S0106952,2015-01-13,5:09:19,0:05:51,3:10:56,0:50:23,0:42:22,0:37:32,11:16:00,2677,791,31
+General Medicine,PGY1,S0106952,2015-01-10,5:42:02,0:07:54,2:34:32,1:01:08,1:04:29,0:33:51,11:33:00,3022,710,25
+General Medicine,PGY1,S0106952,2015-01-11,4:13:49,0:06:39,3:03:52,0:32:49,0:15:36,0:24:46,8:57:15,1908,395,23
+General Medicine,PGY1,S0181060,2015-11-24,1:32:39,0:50:47,1:43:53,0:49:46,0:30:22,0:34:24,7:01:43,2342,0,16
+General Medicine,PGY1,S0106952,2015-01-18,4:37:30,0:01:41,1:33:48,0:53:13,0:40:22,0:27:09,8:36:34,2518,843,53
+General Medicine,PGY1,S0106952,2015-01-19,2:02:11,0:00:00,1:37:56,0:00:00,0:02:21,0:06:35,3:56:07,970,966,63
+Intensive Care Unit,PGY2+,S0169400,2015-07-02,2:13:18,0:05:10,0:16:07,0:17:11,0:12:54,0:29:36,3:52:17,1296,57,12
+Intensive Care Unit,PGY2+,S0169400,2015-07-06,2:03:26,0:10:16,0:20:30,0:11:04,0:13:02,0:27:31,3:37:21,928,44,26
+Intensive Care Unit,PGY2+,S0169400,2015-07-07,2:42:03,0:10:21,0:12:25,0:10:06,0:38:44,0:18:22,4:30:11,1141,0,15
+Intensive Care Unit,PGY2+,S0169400,2015-07-04,0:00:00,0:09:56,0:00:00,0:00:00,0:00:00,0:00:38,0:10:59,107,107,9
+Intensive Care Unit,PGY2+,S0169400,2015-07-05,4:13:50,0:05:00,0:39:43,0:40:17,0:31:16,0:32:03,7:10:29,1961,8,15
+Intensive Care Unit,PGY2+,S0169400,2015-07-08,0:00:00,0:03:24,0:00:00,0:00:17,0:00:00,0:00:00,0:04:00,60,38,7
+Intensive Care Unit,PGY2+,S0169400,2015-07-09,0:42:16,0:01:56,0:02:47,0:01:05,0:05:48,0:06:02,1:05:46,323,41,12
+Night Team,PGY2+,S0176040,2016-09-25,2:54:28,1:08:37,0:00:04,0:27:01,0:11:15,0:06:40,5:19:45,1605,0,25
+Night Team,PGY2+,S0176040,2016-09-27,0:31:48,0:24:59,0:00:00,0:15:04,0:03:17,0:02:15,1:36:00,544,0,11
+Night Team,PGY2+,S0176040,2016-09-26,1:42:29,1:13:08,0:00:08,0:17:50,0:07:45,0:02:49,3:39:44,1080,0,10
+Night Team,PGY2+,S0176040,2016-09-29,4:12:22,2:26:37,0:00:08,1:00:48,0:40:17,0:07:31,9:19:20,2538,408,42
+Night Team,PGY2+,S0176040,2016-09-28,2:36:01,1:03:56,0:00:04,0:42:13,0:17:07,0:08:33,5:11:36,1496,0,41
+Intensive Care Unit,PGY2+,S0169400,2016-07-12,4:16:17,0:46:03,0:03:13,0:45:59,0:25:54,0:11:27,6:58:39,2589,0,32
+Intensive Care Unit,PGY2+,S0169400,2016-07-13,3:37:37,0:33:07,0:15:52,0:26:02,0:28:59,0:12:11,6:12:39,2512,378,19
+Intensive Care Unit,PGY2+,S0169400,2016-07-10,0:10:00,0:00:27,0:00:00,0:00:17,0:00:00,0:00:29,0:11:28,88,0,11
+Intensive Care Unit,PGY2+,S0169400,2016-07-11,0:14:43,0:00:47,0:00:00,0:00:12,0:00:00,0:00:15,0:16:16,131,0,12
+Intensive Care Unit,PGY2+,S0169400,2016-07-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+Night Team,PGY1,S0094959,2014-07-19,0:10:20,0:00:05,0:00:00,0:00:38,0:00:00,0:00:00,0:11:08,187,173,70
+Intensive Care Unit,PGY2+,S0169400,2016-07-14,1:39:53,0:13:54,0:01:04,0:00:19,0:00:58,0:05:20,2:07:04,480,0,34
+Intensive Care Unit,PGY2+,S0169400,2016-07-15,1:23:17,0:27:07,0:11:29,0:07:34,0:10:00,0:02:35,2:47:49,832,0,18
+Night Team,PGY1,S0094959,2014-07-14,5:12:53,0:13:02,0:06:08,0:28:15,0:51:06,0:11:21,7:26:14,2223,0,46
+Night Team,PGY1,S0094959,2014-07-15,5:53:37,0:05:18,0:06:05,0:15:59,0:51:32,0:12:43,7:47:14,2461,208,75
+Night Team,PGY1,S0094959,2014-07-16,5:39:04,0:20:44,0:01:45,0:24:11,0:45:57,0:38:30,8:05:40,2723,217,59
+Night Team,PGY1,S0094959,2014-07-17,6:20:00,0:25:50,0:06:13,0:20:58,0:23:08,0:26:32,8:15:44,2623,83,71
+Night Team,PGY1,S0094959,2014-07-10,4:17:18,0:13:01,0:10:11,0:29:26,0:11:46,0:17:44,6:10:00,1919,0,68
+Night Team,PGY1,S0094959,2014-07-11,2:40:01,0:02:35,0:02:24,0:37:36,0:09:43,0:16:05,3:57:51,1256,48,75
+Night Team,PGY1,S0094959,2014-07-12,1:56:51,0:03:29,0:00:02,0:05:54,0:14:41,0:00:52,2:26:26,815,0,37
+Night Team,PGY1,S0094959,2014-07-13,7:03:20,0:05:04,0:00:07,0:16:50,0:21:23,0:13:06,8:20:11,2636,0,65
+General Medicine,PGY1,S0181077,2015-12-07,1:08:11,0:42:43,0:40:17,0:27:58,0:11:22,0:13:44,3:59:40,1793,119,15
+General Medicine,PGY1,S0181078,2015-12-18,2:03:41,0:46:55,1:12:37,1:08:52,0:19:30,0:18:03,6:28:05,1718,196,13
+General Medicine,PGY1,S0181078,2015-12-16,1:02:23,0:20:39,0:44:01,0:51:14,0:15:42,0:06:10,3:51:50,1277,94,9
+General Medicine,PGY1,S0181078,2015-12-17,2:29:52,0:44:04,2:16:24,0:49:01,0:15:51,0:18:47,8:00:33,2049,272,12
+General Medicine,PGY1,S0181078,2015-12-14,2:02:23,0:33:18,0:37:08,1:03:01,0:14:12,0:06:48,5:14:19,1667,133,7
+General Medicine,PGY1,S0181078,2015-12-15,1:08:04,0:51:03,1:50:53,1:15:38,0:10:22,0:10:43,5:45:45,1741,327,11
+General Medicine,PGY1,S0181078,2015-12-12,1:10:55,0:53:32,3:01:53,0:45:29,0:10:32,0:17:07,6:54:01,1608,565,6
+General Medicine,PGY1,S0181078,2015-12-13,2:06:22,1:11:00,1:15:24,1:02:30,0:26:33,0:17:56,6:55:30,1691,110,11
+General Medicine,PGY1,S0181078,2015-12-10,1:27:56,0:32:11,1:39:12,1:24:53,0:08:22,0:05:16,5:46:30,1573,301,5
+General Medicine,PGY1,S0181078,2015-12-11,1:57:12,0:55:38,2:25:41,1:39:11,0:25:50,0:16:34,8:12:27,2008,134,7
+Night Team,PGY2+,S0163662,2016-08-13,1:50:45,0:46:21,0:00:05,0:42:39,0:06:00,0:00:28,3:48:00,1680,0,16
+Night Team,PGY2+,S0163662,2016-08-12,2:23:53,1:15:39,0:00:07,0:49:45,0:11:38,0:10:55,5:31:17,3229,0,29
+Night Team,PGY2+,S0163662,2016-08-11,1:37:35,0:27:18,0:00:03,0:53:46,0:21:17,0:12:06,4:08:28,3410,0,24
+Night Team,PGY2+,S0163662,2016-08-10,1:41:51,1:15:15,0:01:41,0:27:15,0:17:05,0:14:46,4:29:01,3957,0,24
+Emergency Medicine,PGY1,S0074196,2015-10-30,2:39:33,0:38:27,2:31:42,0:22:00,0:35:53,0:35:04,7:58:33,1841,0,22
+Emergency Medicine,PGY1,S0074196,2015-10-31,1:57:58,0:35:48,2:54:07,0:19:14,0:28:44,0:27:10,7:49:48,1939,0,21
+General Medicine,PGY1,S0181077,2015-12-08,2:39:04,0:48:16,0:54:07,1:02:29,0:42:40,0:46:00,7:41:42,2872,74,12
+General Medicine,PGY1,S0181070,2015-09-04,2:42:09,0:08:37,1:20:42,0:26:01,0:28:14,0:28:23,5:55:59,1653,0,7
+General Medicine,PGY1,S0172085,2015-07-06,1:44:11,0:25:01,1:50:00,0:59:22,0:31:45,0:17:49,6:00:36,1378,17,9
+Intensive Care Unit,PGY2+,S0169406,2016-03-25,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,4,0,2
+Intensive Care Unit,PGY2+,S0169406,2016-03-24,0:35:38,0:06:27,0:02:23,0:05:03,0:01:08,0:00:00,1:03:30,321,6,12
+Intensive Care Unit,PGY2+,S0169394,2015-11-18,1:02:20,0:13:17,0:03:16,0:06:20,0:04:51,0:21:06,2:16:36,563,0,10
+Intensive Care Unit,PGY2+,S0169394,2015-11-19,2:56:10,1:22:50,0:27:10,0:53:03,0:34:22,0:39:25,7:46:09,2240,21,19
+Intensive Care Unit,PGY2+,S0169406,2016-03-21,1:36:56,0:21:54,0:11:23,0:14:16,0:15:10,0:00:13,2:55:04,881,0,9
+Intensive Care Unit,PGY2+,S0169406,2016-03-20,2:10:17,0:56:07,0:06:32,0:13:12,0:16:36,0:00:19,4:12:53,1049,0,10
+Intensive Care Unit,PGY2+,S0169406,2016-03-23,0:00:00,0:00:00,0:00:00,0:00:13,0:00:00,0:00:00,0:00:13,10,0,3
+Intensive Care Unit,PGY2+,S0169406,2016-03-22,0:19:33,0:01:00,0:04:36,0:01:42,0:03:15,0:00:00,0:38:58,248,25,12
+Night Team,PGY2+,S0169394,2015-11-12,1:42:05,1:07:07,1:46:01,0:26:25,0:15:17,0:38:05,6:19:23,1857,0,16
+Night Team,PGY2+,S0169394,2015-11-13,1:06:57,1:06:12,1:08:23,0:16:30,0:18:16,0:23:52,4:38:17,1348,201,38
+Night Team,PGY2+,S0169394,2015-11-10,1:22:53,0:55:05,1:21:43,0:45:22,0:21:21,0:19:49,5:26:38,1655,197,26
+Night Team,PGY2+,S0169394,2015-11-11,1:34:12,1:14:32,2:08:44,0:38:10,0:19:46,0:29:42,6:48:42,1634,603,20
+Intensive Care Unit,PGY2+,S0169394,2015-11-16,1:04:17,0:30:23,0:05:06,0:04:11,0:10:11,0:33:31,2:34:43,648,40,12
+Intensive Care Unit,PGY2+,S0169394,2015-11-17,2:11:06,0:23:52,0:09:23,0:27:09,0:38:04,0:35:58,5:07:27,1077,10,10
+Night Team,PGY2+,S0169394,2015-11-14,0:04:24,0:02:08,0:00:24,0:00:00,0:00:00,0:02:27,0:10:59,93,89,20
+Intensive Care Unit,PGY2+,S0169394,2015-11-15,2:19:14,0:44:20,0:14:09,0:18:41,0:29:22,0:21:42,4:42:30,1397,471,17
+Intensive Care Unit,PGY2+,S0172056,2016-10-28,0:00:00,0:00:00,0:00:00,0:00:19,0:00:00,0:00:00,0:00:19,12,0,5
+Intensive Care Unit,PGY2+,S0172056,2016-10-29,1:45:23,0:29:18,0:02:09,1:06:18,0:26:23,0:13:52,4:52:55,2104,0,26
+General Medicine,PGY1,S0108287,2015-02-18,2:54:19,0:12:15,3:41:20,0:43:35,0:17:44,0:18:36,8:32:00,1431,0,14
+General Medicine,PGY1,S0108287,2015-02-19,2:08:39,0:04:56,1:44:04,0:25:25,0:20:55,0:03:24,5:09:56,1185,0,15
+General Medicine,PGY1,S0108287,2015-02-16,3:08:01,0:27:18,2:53:18,0:36:49,0:24:39,0:11:25,8:08:12,1691,0,8
+General Medicine,PGY1,S0108287,2015-02-17,2:34:54,0:07:06,2:23:18,0:15:14,0:13:58,0:07:53,6:14:06,914,0,8
+General Medicine,PGY1,S0108287,2015-02-14,1:20:57,0:11:08,1:58:53,0:16:23,0:11:47,0:08:33,4:22:05,648,45,7
+General Medicine,PGY1,S0108287,2015-02-15,0:03:46,0:00:00,0:00:00,0:00:45,0:00:00,0:00:18,0:04:50,30,16,4
+General Medicine,PGY1,S0108287,2015-02-12,0:10:51,0:00:00,0:01:59,0:00:23,0:00:23,0:00:00,0:14:45,119,107,6
+General Medicine,PGY1,S0108287,2015-02-13,2:01:31,0:10:15,1:29:59,0:23:13,0:13:18,0:10:57,4:44:30,1075,0,11
+General Medicine,PGY1,S0108287,2015-02-10,2:06:51,0:15:36,2:07:59,0:30:13,0:24:00,0:03:48,5:42:26,912,0,5
+General Medicine,PGY1,S0108287,2015-02-11,2:16:24,0:19:02,2:00:50,0:33:51,0:26:40,0:24:19,6:30:08,1250,0,8
+General Medicine,PGY2+,S0169400,2015-08-30,1:07:53,0:01:34,0:48:27,0:22:00,0:09:58,0:11:23,3:01:31,1129,81,12
+General Medicine,PGY2+,S0169400,2015-08-31,2:47:43,0:43:29,0:11:02,0:33:42,0:23:32,0:40:53,5:46:54,1859,79,30
+Night Team,PGY1,S0181063,2015-07-30,0:56:27,0:08:47,0:01:03,0:08:31,0:20:21,0:21:53,2:05:55,574,10,15
+Night Team,PGY1,S0181063,2015-07-31,1:01:21,0:01:22,0:01:04,0:07:45,0:26:29,0:07:20,1:47:10,384,0,12
+Intensive Care Unit,PGY2+,S0106952,2015-10-18,0:05:23,0:08:09,0:00:19,0:01:39,0:00:00,0:00:00,0:15:37,183,173,26
+Intensive Care Unit,PGY2+,S0106952,2015-10-19,1:27:58,1:07:12,0:25:37,0:14:30,0:14:21,0:17:02,4:05:28,1689,412,26
+General Medicine,PGY1,S0181060,2015-11-09,0:45:44,0:35:48,0:46:04,0:26:35,0:14:58,0:11:34,3:31:14,1148,0,5
+General Medicine,PGY1,S0181060,2015-11-08,1:42:57,0:37:09,1:49:50,1:08:51,0:25:15,0:40:25,6:49:34,1741,0,7
+General Medicine,PGY2+,S0106952,2015-10-10,2:53:49,2:12:16,0:21:52,0:09:53,0:22:52,0:44:05,7:10:44,2451,493,54
+General Medicine,PGY2+,S0106952,2015-10-11,3:01:45,1:51:04,3:17:53,0:28:34,0:24:07,0:38:24,10:16:30,3064,867,35
+General Medicine,PGY2+,S0106952,2015-10-12,3:35:30,2:26:33,1:14:37,0:38:48,0:35:50,0:55:27,9:58:32,3847,991,72
+General Medicine,PGY2+,S0106952,2015-10-13,2:03:24,1:06:19,0:21:19,0:06:27,0:04:49,0:28:09,4:29:24,1951,1053,190
+General Medicine,PGY2+,S0106952,2015-10-14,2:53:43,1:30:29,0:23:35,0:49:52,0:41:08,0:30:19,7:29:42,2813,176,33
+General Medicine,PGY2+,S0106952,2015-10-15,3:09:16,1:51:06,0:11:33,0:16:31,0:16:42,0:54:17,7:10:22,2766,413,47
+General Medicine,PGY2+,S0106952,2015-10-16,0:09:14,0:05:36,0:00:27,0:00:38,0:00:00,0:00:06,0:16:12,250,220,58
+General Medicine,PGY2+,S0106952,2015-10-17,1:20:01,1:11:43,0:26:40,0:06:38,0:11:30,0:13:07,3:46:13,801,67,18
+Intensive Care Unit,PGY2+,S0169406,2016-03-11,1:50:47,0:18:21,0:08:01,0:24:00,0:26:58,0:17:56,4:01:02,1363,118,16
+General Medicine,PGY1,S0181064,2015-07-08,3:30:34,0:43:43,1:17:45,0:35:24,0:12:58,0:34:04,7:23:22,2068,216,16
+General Medicine,PGY1,S0195225,2016-11-05,3:00:45,1:41:11,0:00:55,0:34:10,0:30:13,0:03:05,6:33:01,2067,0,12
+Emergency Medicine,PGY2+,S0094535,2016-03-07,3:42:58,0:54:31,0:02:25,0:27:33,0:36:36,0:02:08,6:52:05,2042,4,20
+Emergency Medicine,PGY2+,S0094535,2016-03-06,2:51:58,0:37:00,0:03:11,0:32:04,0:33:55,0:02:45,5:55:45,1831,0,17
+Night Team,PGY2+,S0169386,2016-02-26,3:50:29,1:26:52,0:00:40,0:37:56,0:19:11,0:12:27,7:05:09,3048,169,100
+Emergency Medicine,PGY2+,S0094535,2016-03-08,4:59:24,0:42:40,0:03:35,0:17:58,0:51:32,0:02:15,8:36:23,2734,6,26
+General Medicine,PGY1,S0181064,2015-07-09,3:22:36,0:14:39,1:22:30,0:18:23,0:14:11,0:18:39,6:12:39,1412,86,15
+General Medicine,PGY2+,S0181057,2016-10-09,3:07:06,1:36:30,0:00:30,0:21:19,0:18:02,0:07:50,5:57:55,2126,0,28
+General Medicine,PGY1,S0181073,2016-06-19,3:11:44,1:13:06,0:00:06,1:46:04,0:30:13,0:04:36,8:02:58,2177,34,20
+General Medicine,PGY1,S0181073,2016-06-18,1:05:03,0:37:08,0:00:05,0:23:17,0:11:43,0:01:15,2:44:12,1078,3,6
+General Medicine,PGY1,S0181073,2016-06-11,2:21:59,1:11:16,0:00:10,0:56:04,0:28:45,0:05:46,5:47:19,2070,29,17
+General Medicine,PGY1,S0181073,2016-06-10,1:07:44,0:54:04,0:00:04,0:42:50,0:07:13,0:02:54,3:39:49,1296,18,10
+General Medicine,PGY1,S0181073,2016-06-13,1:24:11,0:54:39,0:00:07,0:37:13,0:08:53,0:01:21,3:37:37,1527,51,9
+General Medicine,PGY1,S0181073,2016-06-12,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:27,0:00:35,20,8,4
+General Medicine,PGY1,S0181073,2016-06-15,1:43:54,0:28:52,0:00:20,0:34:53,0:10:38,0:02:48,3:34:29,1464,128,9
+General Medicine,PGY1,S0181073,2016-06-14,3:37:09,1:31:32,0:00:08,0:59:24,0:24:14,0:08:16,7:29:54,2429,67,20
+General Medicine,PGY1,S0181073,2016-06-17,0:05:17,0:02:16,0:00:00,0:00:04,0:00:00,0:00:00,0:08:13,45,37,8
+General Medicine,PGY1,S0181073,2016-06-16,3:10:45,1:15:40,0:00:07,1:06:48,0:27:35,0:04:18,7:05:46,2497,19,20
+General Medicine,PGY1,S0195227,2016-09-14,1:52:33,1:30:02,0:00:22,0:41:01,0:09:50,0:00:57,4:39:14,1720,12,11
+General Medicine,PGY1,S0195227,2016-09-15,2:07:18,1:56:25,0:00:11,0:49:34,0:21:45,0:02:53,5:57:24,2301,0,9
+General Medicine,PGY1,S0195227,2016-09-16,0:04:14,0:00:11,0:00:00,0:00:13,0:00:00,0:00:00,0:04:41,17,0,3
+Intensive Care Unit,PGY2+,S0163662,2016-05-26,1:52:17,0:25:13,0:13:46,0:16:20,0:33:41,0:07:27,4:08:53,997,223,19
+General Medicine,PGY1,S0195227,2016-09-10,3:24:36,2:17:26,0:00:06,0:40:55,0:40:41,0:01:37,7:55:41,2701,0,15
+General Medicine,PGY1,S0195227,2016-09-11,1:56:30,1:02:22,0:00:06,0:38:13,0:14:03,0:02:17,4:18:14,1496,9,10
+General Medicine,PGY2+,S0084443,2013-06-28,3:25:52,0:11:42,1:02:25,0:57:26,0:17:41,0:32:09,6:50:02,2231,112,20
+General Medicine,PGY2+,S0084443,2013-06-29,1:52:19,0:02:40,0:27:53,0:06:25,0:08:09,0:15:16,3:10:15,1052,92,12
+General Medicine,PGY2+,S0084443,2013-06-26,2:36:17,0:06:33,0:33:29,0:19:25,0:10:21,0:18:03,4:11:12,1526,0,9
+General Medicine,PGY2+,S0084443,2013-06-27,0:51:51,0:02:26,0:11:17,0:28:18,0:05:14,0:07:49,1:52:56,853,33,9
+General Medicine,PGY2+,S0084443,2013-06-25,3:00:27,0:20:41,1:07:32,0:55:18,0:00:43,0:50:18,7:01:12,2876,433,25
+General Medicine,PGY2+,S0169389,2015-11-23,4:18:12,1:40:35,0:07:30,0:25:51,0:22:47,0:28:41,7:56:58,2863,220,68
+Emergency Medicine,PGY1,S0074196,2015-10-12,1:37:09,0:20:17,0:31:24,0:13:02,0:08:11,0:12:02,3:22:06,1080,0,20
+Emergency Medicine,PGY1,S0074196,2015-10-13,1:17:20,0:25:09,1:58:05,0:09:47,0:25:06,0:38:32,5:25:54,1509,0,24
+Night Team,PGY1,S0181061,2016-02-19,0:15:25,0:01:08,0:00:29,0:21:39,0:15:21,0:13:31,1:10:58,300,0,17
+Emergency Medicine,PGY1,S0074196,2015-10-16,1:07:08,0:32:36,0:36:07,0:07:26,0:11:25,0:09:14,3:07:14,977,0,13
+Emergency Medicine,PGY1,S0074196,2015-10-17,0:54:19,0:18:12,1:29:09,0:16:59,0:25:09,0:22:40,4:40:04,1689,0,12
+Emergency Medicine,PGY1,S0074196,2015-10-14,0:00:35,0:00:00,0:00:00,0:00:00,0:00:00,0:00:16,0:05:24,12,12,2
+Emergency Medicine,PGY1,S0074196,2015-10-15,2:41:41,0:25:02,2:29:07,0:21:57,0:41:02,0:32:58,8:28:37,2424,0,44
+Emergency Medicine,PGY1,S0074196,2015-10-18,3:08:37,0:25:06,2:39:57,0:29:32,0:28:09,0:38:37,8:58:51,2228,0,30
+Emergency Medicine,PGY2+,S0169394,2016-07-17,0:04:07,0:03:58,0:00:00,0:00:00,0:00:33,0:00:00,0:09:34,50,50,1
+Emergency Medicine,PGY2+,S0169394,2016-07-18,0:00:00,0:00:08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:44,7,0,2
+Emergency Medicine,PGY2+,S0169394,2016-07-19,0:00:47,0:02:47,0:00:00,0:00:00,0:00:00,0:00:00,0:03:35,35,0,1
+General Medicine,PGY1,S0094535,2015-04-29,1:31:04,0:04:36,1:17:00,0:17:44,0:11:58,0:17:57,3:47:04,844,57,3
+General Medicine,PGY1,S0094535,2015-04-28,2:16:02,0:59:21,2:09:51,0:38:51,0:16:12,0:36:12,7:22:48,1523,71,10
+General Medicine,PGY1,S0094535,2015-04-27,2:06:45,0:11:54,1:03:01,0:42:00,0:07:36,0:26:31,5:08:59,1898,0,23
+Intensive Care Unit,PGY2+,S0169389,2015-07-13,0:04:11,0:08:51,0:00:00,0:00:00,0:00:00,0:00:00,0:13:18,140,140,10
+Intensive Care Unit,PGY2+,S0169389,2015-07-12,0:03:11,0:05:51,0:00:00,0:00:00,0:00:00,0:00:00,0:09:18,112,110,11
+Intensive Care Unit,PGY2+,S0169389,2015-07-11,2:26:52,0:22:58,0:15:59,0:12:12,0:22:27,0:16:34,4:13:00,1245,56,11
+Intensive Care Unit,PGY2+,S0169389,2015-07-10,1:31:18,0:58:11,0:14:20,0:13:17,0:42:55,0:24:13,4:27:29,1418,0,15
+General Medicine,PGY1,S0079337,2014-12-08,1:12:03,0:04:22,0:34:04,0:16:17,0:04:51,0:00:38,2:17:45,665,0,5
+General Medicine,PGY1,S0079337,2014-12-09,2:02:11,0:03:11,1:06:28,0:27:43,0:13:19,0:00:27,4:15:11,1448,0,6
+Intensive Care Unit,PGY2+,S0169389,2015-07-15,2:46:10,0:38:29,0:03:15,0:55:15,0:28:14,0:19:55,5:25:00,1530,0,19
+Intensive Care Unit,PGY2+,S0169389,2015-07-14,1:23:34,0:31:49,0:05:54,0:04:40,0:08:53,0:10:58,2:36:54,783,0,19
+Intensive Care Unit,PGY2+,S0169389,2015-07-19,0:01:42,0:10:02,0:00:00,0:00:00,0:00:00,0:00:31,0:12:43,120,110,9
+General Medicine,PGY1,S0079337,2014-12-07,3:25:47,0:00:48,2:18:08,0:35:57,0:41:15,0:03:18,7:35:13,1740,85,7
+Emergency Medicine,PGY1,S0169401,2014-08-29,6:50:45,0:04:32,0:26:53,0:16:24,0:09:24,0:01:32,8:34:36,1761,125,84
+Emergency Medicine,PGY1,S0169401,2014-08-28,5:45:52,0:10:26,0:31:24,0:35:35,0:31:03,0:05:20,8:17:55,2949,340,73
+Emergency Medicine,PGY1,S0169401,2014-08-27,1:42:18,0:00:47,0:00:03,0:03:40,0:00:04,0:00:08,2:53:13,481,476,30
+Emergency Medicine,PGY1,S0169401,2014-08-26,2:06:50,0:02:04,0:05:39,0:07:24,0:21:46,0:01:48,3:20:06,1229,956,16
+Emergency Medicine,PGY1,S0169401,2014-08-25,3:58:58,0:07:35,0:33:07,0:29:00,0:14:23,0:02:38,6:02:18,1962,490,72
+Emergency Medicine,PGY1,S0169401,2014-08-24,5:22:52,0:40:13,0:21:24,0:33:27,0:39:15,0:09:34,8:39:31,2768,95,73
+Emergency Medicine,PGY1,S0169401,2014-08-23,6:36:46,0:09:34,0:18:06,0:47:35,0:34:14,0:03:27,9:24:23,2799,193,62
+Emergency Medicine,PGY1,S0169401,2014-08-22,2:34:50,0:07:09,0:11:53,0:24:23,0:17:38,0:00:19,3:53:35,1084,15,36
+Emergency Medicine,PGY1,S0169401,2014-08-21,6:53:38,0:08:05,0:23:30,0:32:29,0:38:28,0:06:46,9:23:32,3071,283,68
+Emergency Medicine,PGY1,S0169401,2014-08-20,0:22:49,0:00:00,0:00:03,0:00:00,0:00:28,0:00:00,0:23:38,136,136,22
+Intensive Care Unit,PGY2+,S0169395,2015-10-30,2:14:30,0:43:02,0:09:04,0:32:44,0:25:47,0:25:12,4:46:20,1575,0,25
+Intensive Care Unit,PGY2+,S0169395,2015-10-31,2:57:01,1:01:29,0:14:43,0:46:03,0:19:35,0:33:22,6:10:09,1979,0,23
+General Medicine,PGY1,S0181070,2016-01-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0169400,2015-11-05,3:21:31,0:53:14,0:51:58,0:35:28,0:14:56,0:37:01,6:54:42,2180,0,79
+Emergency Medicine,PGY1,S0195075,2016-08-12,2:07:50,0:22:44,0:00:35,0:28:21,0:34:58,0:05:11,4:09:35,999,589,21
+Emergency Medicine,PGY1,S0195075,2016-08-13,2:56:22,0:34:16,0:09:02,0:31:23,0:42:50,0:01:29,5:44:44,1636,0,25
+Emergency Medicine,PGY1,S0195075,2016-08-11,2:50:47,0:15:23,0:07:25,0:22:32,0:53:25,0:00:25,5:05:43,1299,0,35
+Emergency Medicine,PGY1,S0195075,2016-08-14,3:04:06,0:29:57,0:04:42,0:18:18,0:35:23,0:00:16,4:58:51,1083,0,28
+Emergency Medicine,PGY1,S0195075,2016-08-15,3:42:25,0:18:34,0:01:48,0:29:26,0:47:50,0:00:29,6:12:55,1706,0,32
+Emergency Medicine,PGY1,S0195075,2016-08-18,2:41:12,0:11:17,0:01:06,0:18:40,0:38:12,0:00:17,4:38:27,1163,0,29
+Emergency Medicine,PGY1,S0195075,2016-08-19,2:34:12,0:26:45,0:02:19,0:18:23,0:44:00,0:00:50,4:54:58,1407,0,24
+Emergency Medicine,PGY1,S0181088,2016-02-26,3:01:39,0:56:04,0:05:12,0:42:20,0:44:50,0:21:56,7:00:24,2166,0,29
+General Medicine,PGY1,S0174975,2016-02-29,0:30:06,0:11:34,0:00:00,0:00:02,0:01:10,0:01:00,0:51:19,328,320,15
+Emergency Medicine,PGY1,S0181088,2016-02-24,0:01:29,0:03:09,0:00:00,0:00:00,0:00:16,0:00:00,0:05:07,70,0,1
+Emergency Medicine,PGY1,S0181088,2016-02-25,0:01:42,0:02:41,0:00:00,0:00:00,0:00:00,0:00:00,0:04:47,94,94,1
+Emergency Medicine,PGY1,S0181088,2016-02-22,2:53:58,0:27:25,0:03:34,0:32:37,1:09:14,0:24:18,6:39:51,2471,0,49
+Emergency Medicine,PGY1,S0181088,2016-02-23,2:42:17,0:06:33,0:03:07,0:16:31,0:32:56,0:01:48,4:40:53,1887,0,23
+Emergency Medicine,PGY1,S0181088,2016-02-20,2:13:33,0:16:43,0:14:48,0:28:04,0:45:15,0:16:19,5:23:58,2088,0,34
+Emergency Medicine,PGY1,S0181088,2016-02-21,3:11:42,0:22:29,0:08:49,0:32:05,1:38:22,0:13:41,7:27:13,2553,0,35
+Night Team,PGY2+,S0169400,2015-11-09,3:53:49,0:11:40,0:20:04,0:20:51,0:09:48,0:33:00,5:40:30,1293,0,56
+Night Team,PGY2+,S0169400,2015-11-08,1:29:22,0:19:51,0:08:14,0:01:58,0:04:24,0:12:20,2:20:27,774,18,68
+General Medicine,PGY1,S0169398,2014-09-07,2:30:12,0:09:48,0:14:19,0:02:38,0:02:38,0:17:45,3:25:58,791,777,11
+General Medicine,PGY1,S0181087,2015-08-08,3:17:21,0:27:58,1:46:14,0:34:31,0:30:41,0:47:57,7:51:37,2261,0,6
+General Medicine,PGY1,S0181087,2015-08-09,0:06:46,0:00:00,0:00:41,0:00:00,0:00:07,0:00:07,0:09:48,138,128,5
+General Medicine,PGY1,S0181087,2015-08-04,0:00:00,0:06:07,0:00:00,0:00:00,0:00:00,0:00:00,0:06:52,30,26,4
+General Medicine,PGY1,S0181087,2015-08-05,3:25:59,0:17:25,2:30:20,1:03:38,0:36:39,0:52:42,9:12:42,2780,191,12
+General Medicine,PGY1,S0181087,2015-08-06,2:29:59,0:05:59,0:55:31,0:20:43,0:25:45,0:23:58,5:08:32,1716,204,9
+General Medicine,PGY1,S0181087,2015-08-07,1:54:26,0:04:02,0:42:31,0:23:11,0:17:02,0:24:40,4:08:02,1495,0,7
+General Medicine,PGY1,S0181087,2015-08-01,1:46:44,0:06:40,1:44:27,0:28:03,0:12:02,0:38:12,5:17:47,1763,19,11
+General Medicine,PGY1,S0181087,2015-08-02,0:47:49,0:01:31,0:37:00,0:07:22,0:06:11,0:12:11,2:09:23,832,0,7
+General Medicine,PGY1,S0181087,2015-08-03,2:15:41,0:22:03,2:28:07,0:34:13,0:20:00,0:21:53,6:58:35,1646,10,7
+General Medicine,PGY1,S0181057,2016-02-08,1:37:21,1:17:48,2:20:26,1:00:31,0:36:57,0:42:44,8:05:59,2928,133,13
+General Medicine,PGY1,S0181057,2016-02-09,1:09:39,1:05:31,1:33:30,1:23:12,0:38:50,0:34:13,7:47:26,2816,588,19
+General Medicine,PGY1,S0195206,2016-10-31,2:17:36,1:40:02,0:00:18,0:20:59,0:27:10,0:14:12,5:31:44,1613,0,13
+General Medicine,PGY1,S0195206,2016-10-30,0:22:05,0:08:25,0:00:02,0:00:00,0:00:59,0:00:00,0:34:08,137,0,12
+General Medicine,PGY1,S0181057,2016-02-01,1:02:16,1:04:56,0:56:29,0:59:15,0:20:28,0:23:58,5:10:15,1560,65,8
+General Medicine,PGY1,S0181057,2016-02-02,0:48:30,1:03:21,1:34:26,0:39:08,0:16:47,0:17:22,5:25:28,1821,396,13
+General Medicine,PGY1,S0181057,2016-02-03,1:33:22,1:41:14,2:09:21,0:46:21,0:26:10,0:23:35,7:50:33,2436,21,13
+General Medicine,PGY1,S0181057,2016-02-04,0:00:00,0:09:17,0:00:00,0:00:00,0:00:00,0:03:28,0:13:07,102,90,5
+General Medicine,PGY1,S0181057,2016-02-05,0:33:17,0:27:33,0:54:23,0:17:11,0:13:13,0:11:51,3:05:29,1160,11,10
+General Medicine,PGY1,S0181057,2016-02-06,2:02:49,1:17:07,0:34:23,0:59:58,0:26:40,0:34:30,6:31:47,2393,0,12
+General Medicine,PGY1,S0181057,2016-02-07,1:05:49,0:33:28,0:44:05,0:30:36,0:04:54,0:20:04,3:42:13,1332,522,10
+General Medicine,PGY1,S0187071,2016-08-26,1:48:13,0:36:05,0:00:03,0:48:08,0:20:11,0:02:50,4:18:39,1271,0,8
+General Medicine,PGY1,S0187071,2016-08-27,1:34:44,0:52:30,0:00:00,0:41:35,0:18:50,0:02:15,3:50:10,1443,0,15
+General Medicine,PGY1,S0187071,2016-08-24,0:11:22,0:00:18,0:00:00,0:00:00,0:00:00,0:00:00,0:12:10,70,0,7
+General Medicine,PGY1,S0187071,2016-08-25,1:59:56,1:15:55,0:00:12,0:23:04,0:21:28,0:03:40,4:31:50,1114,12,6
+General Medicine,PGY1,S0187071,2016-08-22,2:31:03,1:10:07,0:00:06,0:47:59,0:15:53,0:00:11,5:18:05,1816,0,17
+General Medicine,PGY1,S0187071,2016-08-23,0:44:06,0:26:29,0:00:03,0:00:02,0:00:46,0:02:37,1:23:41,588,403,18
+General Medicine,PGY1,S0187071,2016-08-21,1:21:20,1:01:38,0:00:19,0:25:43,0:07:21,0:00:11,3:35:58,1190,243,13
+General Medicine,PGY1,S0187071,2016-08-28,1:38:17,0:48:34,0:00:08,0:37:30,0:08:37,0:00:47,3:43:51,1488,0,18
+General Medicine,PGY1,S0187071,2016-08-29,1:53:51,1:26:11,0:00:13,0:20:36,0:09:49,0:04:39,4:27:36,1422,279,23
+General Medicine,PGY1,S0108268,2014-10-11,0:02:18,0:00:22,0:00:00,0:03:14,0:00:00,0:02:06,0:09:13,63,43,12
+General Medicine,PGY1,S0108268,2014-10-10,2:22:04,0:10:21,0:10:41,0:26:26,0:30:48,0:08:33,4:25:34,1807,50,29
+General Medicine,PGY1,S0109179,2014-08-21,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0109179,2014-08-20,2:03:04,0:02:22,0:05:12,0:23:15,0:15:13,0:18:44,3:29:42,1229,0,12
+General Medicine,PGY1,S0109179,2014-08-23,1:59:11,0:00:36,0:06:30,0:19:34,0:19:28,0:08:18,3:05:12,1107,0,5
+General Medicine,PGY1,S0109179,2014-08-22,2:48:35,0:08:56,0:20:53,0:11:14,0:13:13,0:24:21,4:27:39,1331,0,19
+General Medicine,PGY1,S0109179,2014-08-25,3:27:10,0:15:44,0:26:29,0:48:09,0:20:15,0:16:37,6:07:07,1701,0,14
+General Medicine,PGY1,S0109179,2014-08-24,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,8,0,2
+General Medicine,PGY1,S0109179,2014-08-27,2:56:33,0:00:28,0:27:15,0:36:41,0:30:58,0:20:52,5:10:53,1770,45,8
+General Medicine,PGY1,S0109179,2014-08-26,2:21:13,0:01:34,0:15:18,0:41:00,0:13:17,0:17:16,4:07:23,1387,0,5
+General Medicine,PGY1,S0109179,2014-08-29,2:01:56,0:03:13,0:21:57,0:18:35,0:12:21,0:19:15,3:24:11,896,0,6
+General Medicine,PGY1,S0109179,2014-08-28,2:40:02,0:05:23,0:24:08,0:38:32,0:18:47,0:06:36,4:27:47,1258,4,7
+Emergency Medicine,PGY1,S0172085,2015-07-29,3:17:41,0:36:45,1:29:36,0:21:18,0:26:09,0:34:39,7:07:18,1641,0,64
+Intensive Care Unit,PGY2+,S0169381,2016-01-30,2:18:00,0:38:05,0:19:21,0:53:12,0:49:54,0:46:44,6:08:04,2055,11,22
+Intensive Care Unit,PGY2+,S0169381,2016-01-31,1:24:02,0:20:46,0:07:43,0:06:56,0:12:06,0:31:25,2:54:22,860,21,18
+Emergency Medicine,PGY1,S0172085,2015-07-21,2:38:56,0:41:50,1:54:00,0:19:34,0:26:18,0:40:02,7:06:52,1778,0,57
+Emergency Medicine,PGY1,S0172085,2015-07-20,4:01:13,0:30:52,2:14:40,0:26:28,0:23:12,0:44:07,8:42:27,1939,0,56
+Emergency Medicine,PGY1,S0172085,2015-07-27,3:19:51,1:01:13,2:02:39,0:28:24,0:50:22,0:44:05,9:30:09,2989,0,43
+Emergency Medicine,PGY1,S0172085,2015-07-26,4:04:00,1:04:50,1:19:47,0:34:10,0:28:39,0:53:21,8:49:11,2429,0,33
+Emergency Medicine,PGY1,S0172085,2015-07-24,2:37:17,1:05:23,1:59:56,0:12:27,0:23:43,0:20:21,6:53:41,1575,0,34
+General Medicine,PGY1,S0169385,2014-10-16,2:43:38,0:13:10,0:21:09,0:48:04,0:40:23,0:35:46,5:45:04,1778,764,7
+General Medicine,PGY1,S0169385,2014-10-17,1:32:18,0:05:25,0:18:32,0:20:05,0:12:50,0:18:19,3:04:19,835,173,5
+General Medicine,PGY1,S0169385,2014-10-14,2:42:03,0:04:20,0:10:38,0:31:41,0:12:13,0:17:31,4:06:45,848,0,4
+General Medicine,PGY1,S0169385,2014-10-15,5:02:08,0:10:00,0:19:30,0:28:49,0:22:37,0:58:37,7:39:55,2043,228,12
+General Medicine,PGY1,S0169385,2014-10-12,0:43:15,0:00:00,0:02:53,0:00:12,0:04:12,0:04:49,1:03:16,292,292,7
+General Medicine,PGY1,S0169385,2014-10-13,3:23:49,0:12:40,0:22:37,0:20:17,0:26:34,0:30:46,5:49:09,1746,60,19
+Night Team,PGY1,S0056755,2014-12-18,3:48:38,0:14:47,0:52:32,0:19:36,0:32:09,0:58:23,6:54:38,1436,0,43
+Night Team,PGY1,S0056755,2014-12-19,2:01:55,0:04:08,2:25:39,0:19:53,0:19:54,0:27:51,5:51:15,1096,0,27
+Night Team,PGY1,S0056755,2014-12-16,1:17:00,0:09:41,0:04:14,0:02:04,0:12:59,0:15:10,2:08:37,540,0,17
+Night Team,PGY1,S0056755,2014-12-17,1:47:21,0:03:28,0:01:08,0:11:11,0:31:31,0:12:11,2:56:06,741,2,38
+Night Team,PGY1,S0056755,2014-12-14,1:08:34,0:03:41,0:15:14,0:10:07,0:15:38,0:17:04,2:16:06,562,0,14
+Night Team,PGY2+,S0172085,2016-07-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+Night Team,PGY1,S0056755,2014-12-12,2:01:53,0:08:12,0:18:37,0:14:03,0:22:35,0:21:29,3:54:26,899,0,30
+Night Team,PGY1,S0056755,2014-12-13,1:39:24,0:06:38,0:57:58,0:15:50,0:12:16,0:26:33,3:54:06,1066,0,21
+General Medicine,PGY1,S0169385,2014-10-18,2:42:52,0:09:15,0:30:37,0:50:09,0:29:03,0:41:31,5:59:17,1624,130,8
+General Medicine,PGY1,S0169385,2014-10-19,2:21:06,0:10:33,0:25:26,0:42:43,0:28:31,0:36:59,4:56:37,1004,10,4
+General Medicine,PGY1,S0181069,2015-09-13,2:59:23,0:48:56,2:01:22,0:52:38,0:24:55,0:09:43,7:38:08,2154,253,10
+General Medicine,PGY1,S0181069,2015-09-14,2:33:15,0:29:26,1:50:38,0:47:36,0:24:21,0:17:29,7:00:32,1454,156,10
+General Medicine,PGY1,S0181069,2015-09-15,2:22:24,0:26:28,0:50:00,1:48:08,0:49:17,0:20:39,7:13:15,2095,0,9
+General Medicine,PGY1,S0181069,2015-09-16,1:01:24,0:13:57,0:16:53,0:08:44,0:04:19,0:07:17,2:01:16,536,532,11
+General Medicine,PGY1,S0181069,2015-09-17,3:20:35,0:39:09,1:34:35,2:14:04,0:43:45,0:21:29,9:32:05,2751,0,11
+General Medicine,PGY1,S0181069,2015-09-18,3:50:15,0:17:02,1:41:43,1:32:23,0:44:35,0:22:47,8:56:23,2807,0,7
+General Medicine,PGY1,S0181069,2015-09-19,2:59:04,0:12:10,1:58:23,1:14:19,0:36:17,0:19:55,8:00:25,2202,0,8
+General Medicine,PGY1,S0181065,2016-03-31,3:43:46,1:04:52,0:01:11,0:28:01,0:19:33,0:05:30,6:21:28,3221,0,15
+General Medicine,PGY1,S0181065,2016-03-30,2:13:14,1:05:00,0:00:17,0:33:55,0:21:42,0:00:49,4:49:23,2266,81,10
+Intensive Care Unit,PGY2+,S0181088,2016-09-18,0:12:46,0:00:44,0:00:00,0:00:00,0:00:00,0:01:56,0:15:57,95,0,10
+Intensive Care Unit,PGY2+,S0181088,2016-09-19,2:05:12,0:47:38,0:05:07,0:21:30,0:33:45,0:17:08,4:26:08,1645,0,15
+Intensive Care Unit,PGY2+,S0181088,2016-09-16,3:22:23,0:31:00,0:05:55,0:35:02,0:26:42,0:15:23,5:38:05,1573,0,22
+Intensive Care Unit,PGY2+,S0181088,2016-09-17,1:35:18,0:31:40,0:00:52,0:08:43,0:33:18,0:00:13,3:04:14,890,0,11
+Intensive Care Unit,PGY2+,S0181088,2016-09-14,1:48:48,0:43:31,0:03:30,0:16:36,0:15:01,0:05:03,3:27:16,844,0,16
+Intensive Care Unit,PGY2+,S0181088,2016-09-15,2:04:46,0:59:21,0:05:03,0:42:10,0:43:45,0:11:43,5:24:19,1628,0,30
+Intensive Care Unit,PGY2+,S0181088,2016-09-12,2:26:40,0:43:49,0:16:15,0:12:17,0:25:18,0:01:42,4:36:13,1007,0,16
+Intensive Care Unit,PGY2+,S0181088,2016-09-13,1:33:02,0:26:25,0:01:13,0:11:26,0:20:05,0:03:17,2:52:07,890,0,12
+Intensive Care Unit,PGY2+,S0181088,2016-09-11,1:50:37,0:54:34,0:03:58,0:37:31,0:39:24,0:15:48,4:48:20,1413,0,12
+Emergency Medicine,PGY2+,S0169390,2015-08-09,3:54:59,0:34:40,2:36:26,0:16:02,0:24:30,0:49:05,9:33:50,2993,0,42
+Emergency Medicine,PGY2+,S0169390,2015-08-08,1:28:07,0:37:26,1:58:18,0:09:38,0:23:18,0:21:49,5:26:23,1572,0,24
+Emergency Medicine,PGY2+,S0169390,2015-08-03,2:07:43,0:40:28,3:20:25,0:32:15,0:41:18,0:31:11,9:18:58,2790,0,21
+Emergency Medicine,PGY2+,S0169390,2015-08-02,0:56:31,0:29:46,1:43:59,0:11:11,0:18:55,0:12:13,4:29:49,1584,0,15
+Emergency Medicine,PGY2+,S0169390,2015-08-01,1:33:35,0:39:25,3:36:33,0:34:48,0:40:18,0:39:49,8:38:46,2619,0,22
+Emergency Medicine,PGY2+,S0169390,2015-08-07,1:12:10,0:40:07,1:29:35,0:14:54,0:27:23,0:33:57,5:10:10,1525,0,17
+Emergency Medicine,PGY2+,S0169390,2015-08-04,1:32:00,0:52:34,3:05:37,0:22:27,0:24:04,0:37:49,7:27:49,2219,0,15
+Night Team,PGY2+,S0181073,2016-09-18,0:17:38,0:02:49,0:00:00,0:20:11,0:05:36,0:00:47,1:02:39,319,0,14
+Night Team,PGY2+,S0181073,2016-09-19,2:37:04,1:18:10,0:00:47,1:06:56,0:25:37,0:06:45,6:59:12,2283,208,32
+Night Team,PGY2+,S0181073,2016-09-11,1:36:57,0:53:47,0:00:03,0:35:35,0:11:07,0:05:17,3:54:54,1795,0,45
+Night Team,PGY2+,S0181073,2016-09-12,0:56:35,0:21:30,0:00:00,0:20:03,0:09:09,0:04:37,2:16:56,892,0,26
+Night Team,PGY2+,S0181073,2016-09-13,2:26:55,0:54:16,0:00:06,0:39:39,0:16:26,0:05:03,5:10:23,1442,382,34
+Night Team,PGY2+,S0181073,2016-09-14,3:02:27,1:47:32,0:00:03,0:47:26,0:15:13,0:08:05,6:51:17,1992,0,39
+Night Team,PGY2+,S0181073,2016-09-15,3:14:46,1:27:54,0:00:02,0:44:50,0:15:33,0:01:53,6:41:22,1798,0,29
+Night Team,PGY2+,S0181073,2016-09-16,3:15:25,1:01:22,0:00:06,0:23:11,0:11:16,0:03:31,5:47:35,1710,0,51
+Night Team,PGY2+,S0181073,2016-09-17,2:31:36,0:45:07,0:00:01,0:31:49,0:08:43,0:00:17,4:22:07,1324,0,31
+Emergency Medicine,PGY1,S0169398,2015-04-07,3:16:13,0:38:17,1:19:49,0:23:10,0:23:35,0:31:35,7:16:31,2202,0,21
+Emergency Medicine,PGY1,S0169398,2015-04-06,2:00:06,0:44:15,0:40:55,0:04:58,0:10:02,0:17:21,4:07:27,1184,197,32
+Emergency Medicine,PGY1,S0169398,2015-04-05,3:05:37,0:56:30,1:59:18,0:07:53,0:19:09,0:30:27,7:28:20,1932,88,17
+Emergency Medicine,PGY1,S0169398,2015-04-04,5:39:42,1:05:13,1:50:31,0:19:50,0:34:58,0:42:05,11:09:19,3008,0,28
+Emergency Medicine,PGY1,S0169398,2015-04-03,3:45:52,0:40:04,2:18:14,0:22:31,0:23:41,0:34:43,8:36:39,2173,77,18
+Emergency Medicine,PGY1,S0169398,2015-04-02,0:04:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:04:13,51,51,9
+Emergency Medicine,PGY1,S0169398,2015-04-01,1:07:51,0:20:29,1:40:04,0:26:50,0:22:07,0:12:13,5:08:36,1497,548,12
+Emergency Medicine,PGY1,S0169398,2015-04-09,1:00:13,0:34:52,1:02:22,0:27:17,0:11:58,0:09:47,4:01:20,1077,233,8
+Emergency Medicine,PGY1,S0169398,2015-04-08,1:39:32,0:38:28,0:59:29,0:06:13,0:08:48,0:13:07,4:07:45,1219,235,22
+General Medicine,PGY1,S0094959,2015-01-22,5:55:58,0:05:26,1:12:48,0:39:40,0:24:31,0:13:21,8:52:17,4179,14,33
+General Medicine,PGY1,S0094959,2015-01-23,3:09:04,0:03:33,0:55:02,0:47:28,0:15:05,0:06:20,5:46:56,2764,25,13
+General Medicine,PGY1,S0094959,2015-01-20,0:00:00,0:00:00,0:00:00,0:00:03,0:00:00,0:00:00,0:00:03,6,0,2
+General Medicine,PGY1,S0094959,2015-01-21,4:08:13,0:02:19,0:25:59,0:17:48,0:28:28,0:11:57,5:48:59,2884,30,19
+General Medicine,PGY1,S0094959,2015-01-24,0:00:00,0:09:03,0:00:00,0:00:00,0:00:00,0:00:55,0:12:52,142,134,10
+Intensive Care Unit,PGY2+,S0181077,2016-11-03,2:04:08,0:43:45,0:05:52,0:15:16,0:31:00,0:05:24,4:28:20,1708,174,16
+General Medicine,PGY1,S0169386,2014-12-07,1:48:39,0:03:47,0:36:58,0:24:35,0:16:27,0:24:59,3:53:16,1072,87,5
+General Medicine,PGY1,S0181073,2015-11-05,1:44:27,1:21:19,1:59:40,0:53:23,0:28:58,0:40:35,7:44:38,2379,62,8
+General Medicine,PGY1,S0108287,2014-07-25,2:00:36,0:02:18,0:28:55,0:36:42,0:16:09,0:06:28,4:08:35,1136,473,11
+Intensive Care Unit,PGY2+,S0181077,2016-11-01,2:37:56,0:46:19,0:07:57,0:09:47,0:20:44,0:11:17,4:34:08,1580,90,12
+General Medicine,PGY1,S0181074,2016-04-30,2:21:24,1:22:36,0:00:07,1:14:49,0:33:31,0:09:01,6:21:47,2200,56,20
+Night Team,PGY2+,S0181526,2016-11-07,2:41:46,1:01:48,0:00:07,0:24:43,0:11:49,0:10:49,5:16:15,1368,0,34
+Emergency Medicine,PGY2+,S0169403,2016-02-02,0:01:08,1:16:44,0:15:11,0:00:00,0:03:09,0:10:32,1:48:54,634,377,38
+Emergency Medicine,PGY2+,S0169403,2016-02-01,1:48:33,1:13:53,2:44:12,0:30:50,0:52:02,0:53:51,8:55:12,4020,17,31
+Night Team,PGY1,S0196595,2016-09-08,2:18:12,0:49:18,0:01:46,0:55:14,0:34:34,0:10:50,5:14:33,1449,0,46
+Night Team,PGY1,S0196595,2016-09-09,2:43:27,1:49:16,0:00:03,0:37:12,0:21:17,0:14:33,6:07:58,1627,24,43
+Emergency Medicine,PGY2+,S0169403,2016-02-05,0:54:50,1:11:57,2:08:54,0:18:37,0:36:58,0:40:40,6:24:08,2439,160,22
+Emergency Medicine,PGY2+,S0169403,2016-02-04,0:40:55,1:12:04,0:51:19,0:07:47,0:18:11,0:30:27,4:03:48,1836,342,36
+Night Team,PGY1,S0196595,2016-09-04,1:16:53,0:09:12,0:00:02,0:25:10,0:29:33,0:04:28,2:39:11,904,0,24
+Night Team,PGY1,S0196595,2016-09-05,3:23:25,0:32:52,0:00:01,0:55:12,0:27:29,0:10:25,5:54:21,1656,0,33
+Night Team,PGY1,S0196595,2016-09-06,2:14:45,0:51:46,0:00:00,0:33:56,0:23:36,0:02:14,4:49:13,1552,18,46
+Night Team,PGY1,S0196595,2016-09-07,3:09:18,0:34:53,0:00:07,0:34:58,0:32:41,0:05:13,5:23:53,1565,113,44
+Night Team,PGY2+,S0181526,2016-11-09,3:13:07,0:48:21,0:00:04,0:57:07,0:24:07,0:03:44,6:02:22,2149,10,35
+Night Team,PGY2+,S0181526,2016-11-08,2:47:35,1:00:25,0:02:11,0:31:33,0:17:02,0:06:12,5:59:43,1750,0,36
+Intensive Care Unit,PGY2+,S0108268,2016-01-13,1:30:50,0:52:18,0:13:42,0:10:12,0:17:23,0:36:13,3:53:50,1216,0,20
+Intensive Care Unit,PGY2+,S0108268,2016-01-12,1:56:48,0:56:13,0:19:32,0:10:35,0:17:19,0:21:15,4:17:47,1451,12,19
+Intensive Care Unit,PGY2+,S0108268,2016-01-11,0:46:55,0:52:00,0:09:59,0:16:44,0:38:57,0:16:05,3:24:00,1317,37,20
+Intensive Care Unit,PGY2+,S0108268,2016-01-10,1:34:58,1:00:02,0:42:15,0:10:49,0:20:49,0:23:38,4:59:22,1588,783,32
+Intensive Care Unit,PGY2+,S0108268,2016-01-17,2:23:42,1:05:33,0:10:19,0:19:31,0:42:23,0:50:14,5:55:25,2227,0,17
+Intensive Care Unit,PGY2+,S0108268,2016-01-16,1:32:21,0:31:28,0:04:35,0:10:49,0:22:42,0:24:31,3:31:44,1080,0,29
+Intensive Care Unit,PGY2+,S0108268,2016-01-15,1:48:25,0:30:00,0:17:47,0:20:39,0:30:02,0:31:32,4:07:41,1499,38,31
+Intensive Care Unit,PGY2+,S0108268,2016-01-14,2:57:13,1:40:53,0:12:31,0:31:56,0:43:00,1:04:48,7:32:27,2746,0,22
+Intensive Care Unit,PGY2+,S0108268,2016-01-19,0:01:52,0:10:48,0:00:22,0:00:52,0:00:00,0:00:00,0:14:43,178,164,14
+Intensive Care Unit,PGY2+,S0108268,2016-01-18,1:25:30,0:31:40,0:12:57,0:14:45,0:13:56,0:07:04,3:00:48,1273,11,17
+General Medicine,PGY1,S0181067,2015-08-30,1:05:10,0:01:45,0:37:09,0:35:50,0:16:10,0:13:09,3:15:16,1065,0,5
+General Medicine,PGY1,S0195236,2016-07-24,1:50:33,2:05:05,0:00:54,0:28:51,0:08:54,0:00:30,5:04:56,1203,146,12
+General Medicine,PGY1,S0181073,2015-11-01,0:00:00,0:14:35,0:00:00,0:00:35,0:00:00,0:04:54,0:21:19,172,152,7
+General Medicine,PGY1,S0169411,2014-09-18,4:27:40,0:14:15,0:07:46,0:17:31,0:20:08,0:07:39,5:52:28,1447,0,18
+General Medicine,PGY1,S0169411,2014-09-19,2:08:30,0:05:10,0:05:12,0:31:36,0:13:54,0:00:00,3:19:53,943,0,10
+General Medicine,PGY1,S0195224,2016-10-19,3:23:50,1:54:00,0:00:49,1:13:22,0:37:39,0:01:13,8:15:06,2248,88,33
+General Medicine,PGY1,S0195224,2016-10-18,2:12:26,1:01:17,0:08:14,0:37:43,0:10:35,0:10:20,5:07:58,1528,0,21
+General Medicine,PGY1,S0169411,2014-09-14,3:29:51,0:14:50,0:14:21,0:12:14,0:04:46,0:13:52,4:48:58,761,0,5
+General Medicine,PGY1,S0169411,2014-09-15,3:57:44,0:07:38,0:16:52,0:24:01,0:18:50,0:09:13,5:23:48,1130,0,6
+General Medicine,PGY1,S0169411,2014-09-16,3:17:06,0:03:21,0:15:27,0:37:15,0:19:03,0:13:54,5:18:22,1755,0,25
+General Medicine,PGY1,S0169411,2014-09-17,4:27:23,0:03:44,0:09:55,1:02:37,0:33:09,0:01:44,6:36:51,2048,0,14
+Night Team,PGY1,S0169401,2014-10-22,2:46:49,0:15:42,0:00:28,0:22:32,0:29:59,0:12:31,4:24:53,1588,0,52
+Night Team,PGY1,S0169401,2014-10-23,5:21:04,0:15:22,0:08:55,0:40:27,0:43:46,0:13:08,7:45:01,2725,422,55
+Night Team,PGY1,S0169401,2014-10-20,4:08:00,0:12:29,0:00:00,0:24:14,0:33:53,0:05:41,5:47:49,2065,93,64
+Night Team,PGY1,S0169401,2014-10-21,4:14:03,0:19:50,0:10:24,0:15:29,0:30:17,0:12:03,6:10:14,2052,0,53
+General Medicine,PGY1,S0181067,2016-02-18,0:59:32,0:33:29,0:39:15,0:15:16,0:08:38,0:06:28,3:10:37,1836,0,33
+General Medicine,PGY1,S0181067,2016-02-19,0:50:09,0:50:11,1:04:53,0:41:35,0:20:52,0:10:04,4:06:38,1491,0,10
+Night Team,PGY1,S0169401,2014-10-24,3:01:32,0:11:40,0:21:19,0:22:40,0:22:01,0:09:50,4:48:35,1807,293,60
+Night Team,PGY1,S0169401,2014-10-25,0:29:28,0:00:39,0:00:00,0:00:52,0:00:03,0:00:00,0:39:08,184,182,44
+General Medicine,PGY1,S0181067,2016-02-14,1:59:10,2:02:11,1:42:07,1:03:08,0:23:43,0:24:53,8:04:15,2349,0,11
+General Medicine,PGY1,S0181067,2016-02-15,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,16,0,3
+General Medicine,PGY1,S0181067,2016-02-16,1:13:40,1:35:22,1:36:55,0:48:21,0:40:12,0:12:38,6:40:07,2322,0,10
+General Medicine,PGY1,S0181067,2016-02-17,0:44:23,0:51:12,1:05:24,1:05:37,0:13:22,0:08:55,4:30:39,1490,0,8
+General Medicine,PGY1,S0181067,2016-02-10,1:12:36,0:50:46,0:23:20,0:41:04,0:24:12,0:17:30,4:16:19,1821,0,10
+General Medicine,PGY1,S0181067,2016-02-11,1:21:38,0:34:59,0:51:41,0:53:52,0:23:06,0:09:04,4:28:41,1848,0,11
+General Medicine,PGY1,S0181067,2016-02-12,0:49:34,0:38:51,0:51:58,0:26:13,0:21:53,0:19:05,3:53:24,1457,0,7
+General Medicine,PGY1,S0181067,2016-02-13,0:00:00,0:00:00,0:00:00,0:00:36,0:00:00,0:00:00,0:00:36,6,0,3
+Night Team,PGY2+,S0108287,2016-01-30,0:59:12,0:54:25,1:09:19,0:10:22,0:07:58,0:04:21,3:46:58,877,0,13
+Night Team,PGY2+,S0108287,2016-01-31,2:23:53,0:18:05,0:25:12,0:10:46,0:06:50,0:10:37,3:45:54,776,13,11
+General Medicine,PGY1,S0196597,2016-09-28,1:01:07,0:48:12,0:00:05,0:11:13,0:15:08,0:00:09,2:29:30,804,297,9
+General Medicine,PGY1,S0196597,2016-09-29,2:20:13,0:50:27,0:04:13,0:29:49,0:13:37,0:02:10,4:38:00,1722,393,19
+General Medicine,PGY1,S0174975,2016-02-28,3:02:54,1:29:39,0:00:27,1:08:59,0:44:18,0:00:18,7:34:43,3041,169,22
+General Medicine,PGY1,S0196597,2016-09-24,0:09:16,0:00:13,0:00:00,0:00:45,0:00:00,0:00:44,0:11:42,58,0,9
+General Medicine,PGY1,S0196597,2016-09-25,1:19:10,0:48:24,0:00:06,0:28:49,0:06:13,0:02:59,3:03:45,1022,326,18
+General Medicine,PGY1,S0196597,2016-09-26,1:28:32,0:56:35,0:00:50,0:23:14,0:11:29,0:03:45,3:28:37,1072,381,9
+General Medicine,PGY1,S0196597,2016-09-27,3:24:19,0:58:31,0:00:08,1:08:42,0:20:00,0:01:37,6:27:47,2405,66,11
+General Medicine,PGY1,S0196597,2016-09-20,1:47:55,1:18:21,0:00:09,0:45:23,0:11:30,0:04:04,4:46:20,2043,642,16
+General Medicine,PGY1,S0196597,2016-09-21,0:06:29,0:00:29,0:00:00,0:00:47,0:00:00,0:01:37,0:11:51,81,0,8
+General Medicine,PGY1,S0196597,2016-09-22,1:52:33,0:58:55,0:00:07,0:45:09,0:15:27,0:01:47,4:13:58,1542,0,14
+General Medicine,PGY1,S0181057,2016-06-03,0:58:42,0:55:29,0:00:08,0:18:41,0:18:08,0:06:36,3:05:25,1138,16,14
+General Medicine,PGY1,S0169400,2014-11-20,0:13:04,0:00:03,0:00:20,0:02:29,0:00:18,0:00:56,0:17:44,94,66,9
+General Medicine,PGY1,S0195086,2016-08-14,0:04:36,0:00:15,0:00:00,0:00:00,0:00:00,0:00:00,0:04:56,19,0,4
+General Medicine,PGY1,S0195086,2016-08-15,2:29:24,1:42:16,0:00:18,0:25:24,0:11:07,0:02:21,5:25:04,1536,1,15
+General Medicine,PGY1,S0195086,2016-08-16,0:47:09,0:38:59,0:00:05,0:07:53,0:10:32,0:01:49,2:12:35,676,0,5
+General Medicine,PGY1,S0195086,2016-08-17,2:33:43,1:49:56,0:00:11,0:17:27,0:27:46,0:00:48,5:41:35,1847,0,24
+General Medicine,PGY1,S0195086,2016-08-10,2:50:31,3:11:29,0:00:16,0:53:51,0:24:27,0:00:56,8:03:08,2119,53,19
+General Medicine,PGY1,S0195086,2016-08-11,0:12:47,0:18:10,0:00:04,0:03:19,0:00:16,0:00:06,0:37:06,255,0,13
+General Medicine,PGY1,S0195086,2016-08-12,3:14:25,2:18:55,0:00:17,0:34:59,0:22:27,0:01:16,7:26:33,1900,58,13
+General Medicine,PGY1,S0195086,2016-08-13,1:56:23,1:26:12,0:00:14,0:23:50,0:14:44,0:00:24,4:15:40,1387,0,7
+General Medicine,PGY1,S0195086,2016-08-18,0:00:22,0:00:09,0:00:00,0:00:49,0:00:00,0:00:00,0:01:23,12,0,3
+General Medicine,PGY1,S0195086,2016-08-19,1:57:00,1:58:02,0:00:11,0:21:42,0:27:21,0:00:21,5:05:20,1472,20,9
+General Medicine,PGY1,S0181077,2015-12-09,2:09:19,1:16:53,1:46:07,0:27:00,0:31:05,0:45:48,7:44:40,3240,426,11
+Night Team,PGY1,S0093399,2013-06-25,1:07:45,0:03:50,0:40:12,0:24:43,0:00:17,0:11:23,2:44:48,687,0,37
+Night Team,PGY2+,S0106952,2016-06-21,3:56:26,2:27:48,0:00:37,0:43:58,0:24:12,0:11:14,8:23:47,3177,299,103
+Night Team,PGY2+,S0106952,2016-06-20,3:40:34,2:51:08,0:00:18,0:25:32,0:22:48,0:18:42,8:23:35,3390,252,120
+Night Team,PGY2+,S0106952,2016-06-23,1:37:50,0:57:18,0:00:01,0:14:12,0:12:47,0:11:37,3:36:54,1569,0,41
+Night Team,PGY2+,S0106952,2016-06-22,1:57:32,1:38:33,0:00:03,0:19:40,0:21:39,0:15:27,4:52:49,1912,262,53
+Night Team,PGY2+,S0106952,2016-06-25,1:41:30,2:05:53,0:00:12,0:22:29,0:24:51,0:10:49,5:09:13,2037,236,84
+Night Team,PGY2+,S0106952,2016-06-24,2:45:16,2:43:09,0:00:17,0:49:13,0:20:25,0:13:34,7:30:30,3144,392,133
+Emergency Medicine,PGY2+,S0109179,2015-11-04,1:00:40,0:04:09,0:12:00,0:02:55,0:02:39,0:01:14,1:30:30,536,0,28
+Emergency Medicine,PGY2+,S0109179,2015-11-05,6:19:40,0:27:40,1:07:47,0:18:19,0:18:20,0:09:06,9:02:41,2369,0,74
+Emergency Medicine,PGY2+,S0109179,2015-11-06,7:55:59,1:13:15,0:49:31,0:12:12,0:15:49,0:17:54,11:19:15,3207,0,66
+General Medicine,PGY1,S0195229,2016-08-11,1:45:18,1:09:51,0:04:34,0:27:50,0:20:56,0:05:46,4:15:16,1053,0,9
+Emergency Medicine,PGY2+,S0109179,2015-11-07,6:37:51,0:31:09,1:49:11,0:29:27,0:28:23,0:11:54,10:31:50,3159,0,50
+Intensive Care Unit,PGY2+,S0169402,2015-07-08,1:23:29,0:14:05,0:11:31,0:49:22,1:06:10,0:30:45,4:40:44,1571,31,16
+Intensive Care Unit,PGY2+,S0169402,2015-07-09,2:07:49,0:07:01,0:26:46,0:28:16,0:21:32,0:41:20,4:37:28,1333,0,13
+Night Team,PGY2+,S0172645,2016-07-12,1:20:03,1:41:49,0:00:13,0:38:53,0:17:32,0:01:20,4:21:19,1133,0,21
+Intensive Care Unit,PGY2+,S0169402,2015-07-04,0:00:00,0:00:00,0:00:00,0:00:03,0:00:00,0:00:00,0:00:03,14,0,2
+Intensive Care Unit,PGY2+,S0169402,2015-07-05,0:52:20,0:00:00,0:07:59,0:01:04,0:00:00,0:00:06,1:02:24,191,157,12
+Intensive Care Unit,PGY2+,S0169402,2015-07-06,3:07:22,0:20:19,0:30:16,0:36:15,0:38:34,0:22:00,5:52:39,1252,185,16
+Intensive Care Unit,PGY2+,S0169402,2015-07-07,2:33:54,0:06:49,2:15:48,0:36:52,0:19:55,0:16:16,6:26:40,991,217,18
+Night Team,PGY2+,S0172645,2016-07-11,1:33:29,1:37:08,0:00:11,0:33:16,0:07:34,0:02:11,4:07:46,1373,0,19
+Intensive Care Unit,PGY2+,S0169402,2015-07-02,2:34:33,1:14:20,0:08:54,0:47:05,0:39:26,0:20:31,6:10:18,1666,63,15
+Intensive Care Unit,PGY2+,S0169402,2015-07-03,3:22:19,0:23:05,0:22:56,0:18:27,0:22:04,0:31:55,5:42:14,1195,0,15
+General Medicine,PGY1,S0195229,2016-08-12,1:36:23,0:37:21,0:00:10,0:35:51,0:24:47,0:02:15,3:43:10,1094,30,6
+Emergency Medicine,PGY2+,S0109179,2015-11-02,1:19:01,0:03:08,0:16:30,0:13:34,0:04:05,0:00:34,2:17:14,725,0,9
+Emergency Medicine,PGY2+,S0109179,2015-11-03,3:33:11,0:44:21,0:53:21,0:20:15,0:24:05,0:12:06,6:39:56,2419,0,43
+General Medicine,PGY1,S0195229,2016-08-14,0:04:58,0:00:21,0:00:00,0:00:00,0:00:00,0:01:49,0:07:36,40,0,3
+Night Team,PGY2+,S0172645,2016-07-15,0:59:46,2:16:44,0:00:14,0:44:09,0:19:12,0:00:44,4:42:27,1266,0,15
+General Medicine,PGY1,S0195229,2016-08-16,1:41:41,0:25:45,0:00:05,0:27:44,0:25:25,0:03:20,3:27:12,990,9,5
+Night Team,PGY1,S0169389,2014-06-30,3:06:49,0:10:42,0:05:47,0:35:12,0:27:53,0:58:54,5:53:07,1237,127,40
+Intensive Care Unit,PGY2+,S0172056,2016-10-09,2:18:39,0:41:33,0:00:01,1:11:04,0:38:46,0:10:34,5:35:50,1880,0,24
+Night Team,PGY2+,S0109179,2015-07-22,2:51:26,0:15:07,0:08:51,0:33:06,0:12:00,0:27:07,5:13:48,1818,0,44
+Night Team,PGY2+,S0169398,2015-08-30,1:26:50,0:44:42,2:06:30,0:24:22,0:20:48,0:28:54,5:57:15,1410,0,21
+Night Team,PGY2+,S0169398,2015-08-31,3:11:07,1:53:38,2:10:43,0:24:07,0:20:38,0:53:51,9:14:59,2702,0,46
+General Medicine,PGY2+,S0169384,2016-07-08,2:29:30,1:57:40,0:00:19,0:52:18,0:27:36,0:04:33,6:26:10,2097,432,17
+General Medicine,PGY2+,S0169384,2016-07-09,2:25:16,0:45:10,0:00:02,0:41:59,0:11:14,0:04:15,4:46:32,2482,0,23
+Night Team,PGY2+,S0079337,2015-09-21,3:04:12,0:44:10,1:52:00,0:36:42,0:15:14,0:28:20,7:22:15,2157,0,28
+Night Team,PGY2+,S0079337,2015-09-20,2:41:55,0:32:13,1:06:39,0:24:27,0:05:30,0:19:07,5:36:24,1996,39,10
+Night Team,PGY2+,S0079337,2015-09-27,1:30:23,0:33:25,0:59:26,0:27:11,0:11:21,0:13:14,4:06:12,1498,83,13
+Night Team,PGY2+,S0079337,2015-09-26,3:43:19,1:09:15,1:12:22,0:40:36,0:24:04,0:31:13,8:09:43,3011,79,34
+Night Team,PGY2+,S0079337,2015-09-25,3:07:25,1:52:24,1:46:40,0:31:17,0:09:18,0:38:55,8:25:53,3163,344,52
+Night Team,PGY2+,S0079337,2015-09-24,3:17:45,0:59:57,0:49:04,0:54:09,0:19:22,0:18:20,7:01:02,2512,0,45
+General Medicine,PGY2+,S0169384,2016-07-01,0:00:06,0:04:20,0:00:00,0:01:07,0:00:00,0:00:00,0:07:46,61,45,9
+Night Team,PGY2+,S0079337,2015-09-29,2:41:21,1:27:49,2:28:16,0:37:53,0:19:10,0:21:55,8:27:03,2564,0,33
+General Medicine,PGY2+,S0169384,2016-07-03,1:40:29,1:06:11,0:00:04,0:36:55,0:28:03,0:08:16,4:55:44,1740,194,16
+General Medicine,PGY2+,S0169384,2016-07-04,1:19:34,1:17:48,0:00:13,0:11:34,0:16:03,0:04:15,3:27:13,1308,0,19
+General Medicine,PGY2+,S0169384,2016-07-05,2:20:49,0:50:11,0:00:01,1:41:35,0:33:23,0:08:22,6:33:11,2201,65,16
+General Medicine,PGY2+,S0169384,2016-07-06,0:10:56,0:09:52,0:00:03,0:00:11,0:00:00,0:00:00,0:21:29,183,175,6
+General Medicine,PGY2+,S0169384,2016-07-07,3:09:44,1:48:02,0:00:04,0:39:53,0:17:35,0:04:08,6:46:29,2904,133,20
+Intensive Care Unit,PGY2+,S0106952,2015-10-30,1:16:36,1:06:27,0:18:07,0:12:39,0:22:43,0:25:35,4:01:49,1374,40,24
+Intensive Care Unit,PGY2+,S0106952,2015-10-31,2:20:43,1:30:14,0:17:33,0:35:19,0:19:31,0:47:30,6:26:35,2163,382,14
+Night Team,PGY2+,S0169406,2016-01-14,2:08:47,1:07:39,0:37:03,0:22:42,0:06:58,0:16:27,5:05:34,1721,0,44
+General Medicine,PGY2+,S0181057,2016-10-21,3:23:51,1:17:39,0:00:22,0:27:24,0:19:15,0:04:54,6:10:48,1833,43,33
+General Medicine,PGY2+,S0181057,2016-10-20,2:58:33,0:59:46,0:00:13,0:54:05,0:22:31,0:10:31,5:58:12,1884,0,24
+General Medicine,PGY2+,S0181057,2016-10-23,0:24:56,0:01:48,0:00:00,0:00:00,0:00:00,0:00:49,0:33:25,110,0,7
+General Medicine,PGY2+,S0181057,2016-10-22,1:17:23,0:40:47,0:00:19,0:20:14,0:06:52,0:04:42,2:55:37,1028,0,8
+General Medicine,PGY2+,S0181057,2016-10-25,2:23:12,1:11:51,0:00:07,0:17:31,0:31:14,0:04:22,5:06:26,1966,0,17
+General Medicine,PGY2+,S0181057,2016-10-24,2:29:39,1:10:13,0:00:00,0:30:32,0:18:16,0:04:59,5:08:17,1776,8,29
+General Medicine,PGY2+,S0181057,2016-10-27,1:49:23,0:32:24,0:00:02,0:10:09,0:14:44,0:07:27,3:30:22,1227,0,16
+General Medicine,PGY2+,S0181057,2016-10-26,2:52:53,1:29:55,0:00:01,0:30:01,0:25:46,0:06:59,5:53:28,2053,0,28
+General Medicine,PGY2+,S0181057,2016-10-29,3:15:19,1:45:05,0:00:12,0:18:33,0:13:53,0:11:38,6:05:11,2403,0,26
+General Medicine,PGY2+,S0181057,2016-10-28,0:25:22,0:06:41,0:00:00,0:00:00,0:00:00,0:01:00,0:37:47,188,0,31
+Emergency Medicine,PGY2+,S0094535,2016-03-29,2:33:39,0:24:50,0:02:14,0:22:32,0:42:01,0:06:29,5:10:10,1834,0,14
+Emergency Medicine,PGY2+,S0094535,2016-03-28,0:53:50,1:24:05,0:05:03,0:13:31,0:06:24,0:02:24,3:43:04,1206,0,14
+General Medicine,PGY1,S0106952,2015-01-08,5:14:55,0:11:13,0:46:07,0:57:51,0:40:09,0:32:10,9:05:41,2556,0,20
+Emergency Medicine,PGY2+,S0094535,2016-03-20,3:17:54,1:04:25,0:01:25,0:20:23,0:55:00,0:04:35,7:13:23,2488,0,33
+Emergency Medicine,PGY2+,S0094535,2016-03-23,1:49:41,0:29:40,0:05:07,0:26:49,0:36:51,0:00:35,4:13:29,1317,0,12
+Emergency Medicine,PGY2+,S0094535,2016-03-22,2:30:07,0:55:27,0:02:22,0:24:19,0:49:39,0:11:34,5:26:03,1949,0,14
+Emergency Medicine,PGY2+,S0094535,2016-03-24,1:02:22,0:55:25,0:11:12,0:18:35,0:10:41,0:04:26,4:23:11,1459,8,19
+Emergency Medicine,PGY2+,S0094535,2016-03-27,2:34:50,0:26:20,0:02:47,0:27:24,0:33:07,0:01:14,4:46:09,1465,25,31
+Emergency Medicine,PGY2+,S0094535,2016-03-26,2:52:25,0:38:53,0:08:01,0:27:36,0:57:02,0:03:19,5:58:41,1716,0,16
+General Medicine,PGY1,S0181074,2015-08-04,2:05:08,2:10:51,1:13:18,2:22:03,0:34:25,0:22:44,9:40:49,3418,84,13
+General Medicine,PGY1,S0169395,2014-10-22,3:17:32,0:08:56,0:24:08,1:03:12,0:27:06,0:11:07,5:52:17,1623,0,8
+General Medicine,PGY1,S0169395,2014-10-23,2:22:50,0:08:28,0:18:36,0:31:38,0:14:28,0:08:45,3:57:38,1209,0,7
+General Medicine,PGY1,S0169395,2014-10-20,1:36:34,0:13:52,0:19:48,0:23:29,0:12:37,0:09:56,3:11:20,957,0,7
+General Medicine,PGY1,S0169395,2014-10-21,3:18:34,0:10:33,0:14:28,0:30:06,0:26:37,0:17:54,5:36:51,1799,0,12
+General Medicine,PGY1,S0169395,2014-10-26,3:47:34,0:16:58,0:25:23,1:00:10,0:14:45,0:23:15,6:32:11,2036,0,23
+General Medicine,PGY1,S0169395,2014-10-27,1:03:29,0:00:09,0:15:56,0:19:36,0:08:45,0:09:13,2:20:21,964,0,11
+General Medicine,PGY1,S0169395,2014-10-24,2:59:12,0:04:28,0:17:40,0:34:29,0:33:51,0:34:42,5:14:19,1763,0,10
+General Medicine,PGY1,S0169395,2014-10-25,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,6,0,2
+General Medicine,PGY1,S0169395,2014-10-28,1:47:24,0:09:36,0:02:24,0:20:45,0:09:44,0:03:00,2:48:32,705,24,11
+General Medicine,PGY1,S0169395,2014-10-29,3:18:26,0:14:23,0:36:41,0:42:48,0:23:07,0:20:55,5:53:56,1736,45,21
+General Medicine,PGY1,S0108268,2014-12-29,4:14:15,0:18:20,1:18:30,0:47:59,1:04:25,0:45:04,9:02:05,3275,0,12
+General Medicine,PGY1,S0181086,2016-04-28,2:44:17,1:28:52,0:00:17,0:56:04,0:20:10,0:02:47,6:26:06,1635,122,8
+General Medicine,PGY1,S0181086,2016-04-29,2:02:44,1:10:04,0:01:28,0:28:39,0:26:54,0:00:43,5:00:48,1363,128,8
+General Medicine,PGY1,S0181086,2016-04-26,0:36:42,0:05:18,0:00:01,0:16:23,0:01:17,0:00:03,1:15:21,475,398,6
+General Medicine,PGY1,S0181086,2016-04-27,3:28:24,2:03:01,0:00:08,0:58:31,0:29:14,0:00:45,7:49:57,1980,0,10
+General Medicine,PGY1,S0181086,2016-04-24,1:50:30,1:10:21,0:00:23,0:32:20,0:16:27,0:00:10,4:29:25,1003,204,5
+General Medicine,PGY1,S0181086,2016-04-25,2:57:30,1:42:56,0:00:07,1:20:59,0:23:56,0:03:40,7:08:05,2002,7,8
+Intensive Care Unit,PGY2+,S0169411,2016-02-01,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,10,0,3
+Intensive Care Unit,PGY2+,S0169411,2016-02-02,4:06:40,0:51:34,0:07:37,0:35:05,0:23:16,0:29:22,7:01:05,2459,0,26
+Intensive Care Unit,PGY2+,S0169411,2016-02-03,2:07:10,0:26:33,0:09:41,0:21:22,0:12:11,0:23:20,3:58:17,1586,0,15
+Intensive Care Unit,PGY2+,S0169411,2016-02-04,1:27:28,0:37:17,0:07:32,0:18:29,0:16:47,0:11:48,3:23:24,1556,82,26
+Intensive Care Unit,PGY2+,S0169411,2016-02-05,4:02:42,1:22:33,0:03:35,0:41:07,0:39:48,0:31:18,7:49:04,2776,0,25
+Intensive Care Unit,PGY2+,S0169411,2016-02-06,2:02:12,0:25:56,0:02:34,0:37:53,0:05:52,0:44:12,4:21:40,1455,26,14
+General Medicine,PGY1,S0108268,2014-12-27,3:00:38,0:06:41,2:48:29,0:38:13,0:38:07,0:25:45,8:04:38,2762,25,21
+General Medicine,PGY1,S0195236,2016-08-15,1:19:06,0:33:39,0:00:05,0:14:25,0:07:47,0:06:12,2:33:26,751,0,9
+General Medicine,PGY1,S0195236,2016-08-14,2:19:39,1:17:18,0:04:05,0:59:03,0:25:06,0:00:39,5:44:12,1377,56,19
+General Medicine,PGY1,S0195236,2016-08-17,1:47:10,0:34:20,0:01:03,0:12:00,0:19:10,0:00:13,3:21:38,819,0,19
+General Medicine,PGY1,S0195236,2016-08-16,2:40:33,0:48:47,0:00:23,0:39:52,0:19:09,0:01:30,5:08:32,1420,93,6
+General Medicine,PGY1,S0195236,2016-08-11,2:13:43,1:42:03,0:00:11,0:40:38,0:08:59,0:02:06,5:22:10,1501,35,10
+General Medicine,PGY1,S0195236,2016-08-10,1:26:24,1:16:16,0:00:14,0:29:59,0:11:26,0:03:29,4:00:27,1439,101,23
+General Medicine,PGY1,S0195236,2016-08-13,0:01:04,0:00:19,0:00:00,0:00:00,0:00:00,0:01:59,0:03:41,39,0,2
+General Medicine,PGY1,S0195236,2016-08-12,2:37:52,1:13:15,0:10:43,1:03:26,0:13:35,0:02:34,6:34:15,1819,172,15
+General Medicine,PGY1,S0195236,2016-08-19,1:39:18,1:10:30,0:00:07,0:21:52,0:16:20,0:00:15,3:36:57,1035,0,5
+General Medicine,PGY1,S0195236,2016-08-18,1:23:29,0:48:06,0:00:26,0:32:14,0:15:10,0:15:20,3:43:51,1433,0,13
+General Medicine,PGY1,S0169384,2014-07-28,4:29:49,0:09:02,0:18:57,1:07:32,0:17:11,0:17:41,6:53:52,1678,0,10
+General Medicine,PGY1,S0169384,2014-07-29,4:11:19,0:05:30,0:26:00,0:47:48,0:37:15,0:24:27,6:48:52,1545,165,7
+General Medicine,PGY2+,S0181074,2016-10-16,2:54:05,1:10:40,0:00:09,1:07:50,0:15:08,0:11:18,6:09:35,3277,0,20
+General Medicine,PGY1,S0169384,2014-07-20,5:03:33,0:17:14,0:18:37,0:45:27,0:15:57,0:17:31,7:19:15,1296,267,5
+General Medicine,PGY1,S0169384,2014-07-21,3:32:09,0:23:40,0:10:14,0:35:51,0:39:56,0:13:21,5:56:02,1657,200,11
+General Medicine,PGY1,S0169384,2014-07-22,4:12:43,0:14:54,0:30:10,0:38:17,0:36:10,0:11:01,6:38:49,1505,397,6
+General Medicine,PGY1,S0169384,2014-07-23,3:24:37,0:09:56,0:28:52,0:24:46,0:27:07,0:11:14,5:29:25,1381,28,11
+General Medicine,PGY1,S0169384,2014-07-24,2:01:05,0:01:30,0:08:16,0:06:55,0:16:31,0:06:43,2:46:05,672,0,6
+General Medicine,PGY1,S0169384,2014-07-25,4:34:27,0:04:09,0:37:28,0:51:55,0:12:22,0:08:41,6:39:27,1579,138,6
+General Medicine,PGY1,S0169384,2014-07-26,3:52:16,0:07:58,0:15:59,0:38:27,0:27:58,0:10:54,5:48:59,1365,0,9
+General Medicine,PGY1,S0169384,2014-07-27,1:11:34,0:04:06,0:17:44,0:16:16,0:01:25,0:00:45,1:56:46,421,421,8
+Night Team,PGY1,S0181059,2015-12-04,3:49:11,1:22:06,0:31:08,0:26:56,0:16:08,0:34:28,7:18:55,4046,23,142
+Night Team,PGY1,S0181059,2015-12-05,0:58:01,0:38:18,0:55:46,0:11:59,0:04:34,0:10:59,3:14:03,1392,274,84
+General Medicine,PGY2+,S0181074,2016-10-14,2:55:07,1:06:47,0:10:37,0:48:54,0:16:06,0:10:28,6:26:21,3505,162,35
+Night Team,PGY1,S0181059,2015-12-01,2:23:01,1:06:53,0:01:45,0:15:08,0:12:16,0:31:21,4:41:14,2681,2511,88
+Night Team,PGY1,S0181059,2015-12-02,1:03:14,1:30:46,0:35:25,0:34:47,0:10:39,0:05:47,4:13:06,2239,65,83
+Night Team,PGY1,S0181059,2015-12-03,2:41:38,1:25:02,1:06:50,0:19:20,0:14:12,0:34:26,6:41:57,3640,12,120
+General Medicine,PGY2+,S0181074,2016-10-13,3:06:15,1:02:23,0:00:12,1:25:45,0:17:00,0:18:35,6:42:13,3205,0,24
+General Medicine,PGY2+,S0181074,2016-10-12,2:20:00,0:57:21,0:00:12,0:55:06,0:19:20,0:05:28,5:07:33,2184,0,27
+General Medicine,PGY2+,S0181074,2016-10-11,3:40:24,1:14:49,0:00:00,1:15:44,0:18:13,0:11:53,7:15:51,3268,600,35
+General Medicine,PGY2+,S0181074,2016-10-10,1:52:50,0:20:19,0:00:00,0:00:01,0:00:00,0:13:57,2:44:36,976,0,28
+General Medicine,PGY1,S0181066,2015-07-13,1:55:56,0:11:31,1:24:53,0:29:06,0:34:09,0:34:21,5:40:56,1151,24,7
+General Medicine,PGY1,S0181066,2015-07-12,0:00:00,0:00:00,0:00:00,0:00:35,0:00:00,0:00:00,0:00:35,8,0,3
+General Medicine,PGY1,S0181066,2015-07-11,1:50:43,0:28:00,1:55:21,0:32:48,0:22:34,0:44:45,6:35:22,1571,0,8
+General Medicine,PGY1,S0181066,2015-07-10,1:21:30,0:05:47,0:35:40,0:18:14,0:09:50,0:18:16,2:54:39,538,0,4
+General Medicine,PGY1,S0181066,2015-07-17,0:58:26,0:21:04,3:02:07,0:26:33,0:13:55,0:26:16,5:43:36,956,57,10
+General Medicine,PGY1,S0181066,2015-07-16,1:17:36,0:20:40,3:25:23,0:47:37,0:22:00,0:22:53,7:10:13,1300,302,8
+General Medicine,PGY1,S0181066,2015-07-15,1:17:44,0:13:17,2:06:25,0:24:40,0:17:04,0:17:21,5:02:31,1192,17,9
+General Medicine,PGY1,S0181066,2015-07-14,2:43:00,0:08:44,2:07:30,0:41:38,0:37:11,0:31:15,7:17:36,1747,0,10
+Intensive Care Unit,PGY2+,S0169404,2015-08-25,2:23:56,0:08:18,0:16:17,0:19:28,0:09:22,0:23:32,4:05:02,1622,440,17
+Intensive Care Unit,PGY2+,S0169404,2015-08-24,2:42:37,0:16:39,0:21:33,0:13:52,0:11:16,0:38:01,4:51:37,1966,348,17
+Intensive Care Unit,PGY2+,S0169404,2015-08-27,1:57:22,0:17:15,0:17:08,0:27:10,0:08:29,0:23:46,3:48:38,1873,742,15
+General Medicine,PGY1,S0181066,2015-07-18,0:00:00,0:00:00,0:00:00,0:02:28,0:00:00,0:00:00,0:02:28,4,0,1
+Intensive Care Unit,PGY2+,S0169404,2015-08-23,3:08:45,0:07:38,0:29:54,0:22:53,0:26:30,0:22:27,5:13:20,1876,155,12
+Night Team,PGY2+,S0169403,2015-12-30,0:40:32,1:38:52,0:57:14,0:20:07,0:15:00,0:43:23,5:12:12,2042,519,19
+Night Team,PGY2+,S0169403,2015-12-31,0:50:11,2:04:19,0:00:10,0:19:41,0:13:20,0:30:26,4:26:08,1876,381,32
+Intensive Care Unit,PGY2+,S0169400,2016-07-04,3:07:16,0:35:06,0:01:38,0:58:23,0:24:23,0:05:30,5:46:04,1914,0,11
+Intensive Care Unit,PGY2+,S0169400,2016-07-07,2:16:50,0:26:47,0:10:56,0:32:50,0:20:27,0:04:46,4:35:03,1402,0,16
+Intensive Care Unit,PGY2+,S0169400,2016-07-06,1:42:15,0:22:29,0:07:50,0:11:49,0:17:50,0:01:26,3:03:39,1026,0,16
+Intensive Care Unit,PGY2+,S0169400,2016-07-01,0:02:54,0:00:30,0:00:00,0:01:13,0:00:00,0:00:26,0:05:09,43,0,7
+General Medicine,PGY1,S0181070,2016-01-15,0:00:00,0:09:40,0:00:00,0:00:00,0:00:00,0:00:00,0:10:31,63,61,7
+General Medicine,PGY1,S0181070,2016-01-14,0:00:00,0:00:00,0:00:00,0:00:07,0:00:00,0:00:00,0:00:07,8,0,2
+General Medicine,PGY1,S0181070,2016-01-17,0:45:11,0:53:14,1:26:31,0:22:53,0:13:06,0:06:53,4:12:55,2298,0,18
+General Medicine,PGY1,S0181070,2016-01-16,2:02:54,2:02:26,1:52:30,1:21:28,0:17:21,0:25:11,8:40:13,2953,100,12
+General Medicine,PGY1,S0181070,2016-01-11,1:53:31,1:57:35,1:11:32,1:19:17,0:22:19,0:09:12,7:32:27,3586,4,12
+General Medicine,PGY1,S0181070,2016-01-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181070,2016-01-13,0:34:34,0:28:07,0:30:21,0:23:37,0:07:57,0:11:02,2:28:51,1062,3,7
+General Medicine,PGY1,S0181070,2016-01-12,1:47:59,1:00:22,1:13:07,0:28:28,0:30:38,0:13:18,5:54:54,2942,102,18
+General Medicine,PGY2+,S0181078,2016-10-07,2:12:20,0:20:56,0:00:01,0:27:57,0:23:20,0:04:58,3:54:14,1293,359,11
+General Medicine,PGY1,S0181070,2016-01-19,0:56:52,0:49:26,1:14:32,0:41:42,0:29:11,0:12:15,4:53:28,2528,19,12
+General Medicine,PGY1,S0181070,2016-01-18,1:10:47,1:27:12,2:21:06,0:45:51,0:17:01,0:21:25,7:09:39,2244,0,13
+General Medicine,PGY1,S0169399,2015-04-04,1:06:39,0:00:47,2:12:52,0:14:41,0:10:45,0:03:38,4:03:56,723,0,9
+General Medicine,PGY1,S0169399,2015-04-05,1:38:10,0:18:37,2:08:37,0:15:14,0:16:04,0:06:46,5:04:42,996,0,12
+General Medicine,PGY1,S0169399,2015-04-06,1:43:25,0:10:05,2:08:12,0:31:12,0:12:03,0:01:08,5:15:29,1104,237,14
+General Medicine,PGY1,S0169399,2015-04-07,1:26:31,0:21:55,3:36:36,0:38:41,0:20:39,0:02:55,6:54:36,1181,0,13
+General Medicine,PGY1,S0169399,2015-04-01,2:36:49,0:10:49,2:14:56,0:33:36,0:21:25,0:02:32,6:38:44,1444,170,9
+General Medicine,PGY1,S0169399,2015-04-02,2:25:44,0:22:49,3:12:30,0:46:42,0:33:44,0:05:28,7:56:26,1796,29,11
+General Medicine,PGY1,S0169399,2015-04-03,0:01:05,0:00:00,0:00:00,0:00:32,0:00:00,0:00:00,0:02:13,51,29,10
+General Medicine,PGY1,S0169399,2015-04-08,1:51:11,0:00:06,2:02:58,0:41:35,0:21:00,0:00:00,5:23:09,994,43,13
+General Medicine,PGY1,S0169399,2015-04-09,1:16:34,0:03:29,1:36:03,0:33:18,0:08:17,0:10:29,4:21:42,759,51,7
+General Medicine,PGY1,S0181088,2015-11-14,1:45:27,0:51:56,3:06:55,0:25:42,0:48:32,0:16:12,7:57:27,2269,0,14
+General Medicine,PGY1,S0181088,2015-11-15,1:05:30,0:41:25,1:17:28,0:24:08,0:12:14,0:14:52,4:44:55,1292,47,7
+General Medicine,PGY1,S0181088,2015-11-16,1:27:48,0:33:14,1:13:35,0:20:51,0:07:44,0:08:28,4:22:27,1039,67,4
+General Medicine,PGY1,S0181088,2015-11-17,1:39:26,1:15:48,2:04:24,0:40:53,0:33:13,0:24:09,7:15:39,2235,0,12
+General Medicine,PGY1,S0181088,2015-11-10,0:41:37,0:23:46,0:45:48,0:20:23,0:12:16,0:16:01,2:52:33,854,0,7
+General Medicine,PGY1,S0181088,2015-11-11,1:31:49,1:02:30,2:13:24,0:49:41,0:20:15,0:19:58,6:39:46,2035,0,8
+General Medicine,PGY1,S0181088,2015-11-12,1:19:52,1:15:04,3:11:38,0:34:01,0:25:55,0:28:19,7:48:24,2162,6,10
+General Medicine,PGY1,S0181088,2015-11-13,0:00:00,0:00:00,0:00:00,0:01:38,0:00:00,0:00:00,0:01:38,12,0,4
+General Medicine,PGY1,S0181088,2015-11-18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+General Medicine,PGY1,S0181088,2015-11-19,1:47:42,1:09:14,2:48:06,0:46:27,0:30:25,0:37:53,8:14:57,2757,0,14
+General Medicine,PGY1,S0079640,2016-08-22,0:07:59,0:05:16,0:00:00,0:00:01,0:00:00,0:00:09,0:13:56,75,73,2
+General Medicine,PGY1,S0079640,2016-08-23,2:42:14,1:16:52,0:00:12,1:34:57,0:23:20,0:01:03,6:37:57,1960,170,11
+General Medicine,PGY1,S0079337,2014-12-24,1:42:42,0:00:08,0:30:38,0:16:50,0:13:05,0:02:55,2:58:46,1014,0,8
+General Medicine,PGY1,S0181086,2015-12-18,1:33:09,0:53:41,1:26:02,0:49:36,0:08:05,0:20:15,5:29:14,1345,29,6
+General Medicine,PGY1,S0079640,2016-08-26,0:48:27,0:42:36,0:00:04,0:10:09,0:15:27,0:00:12,2:16:43,694,184,7
+General Medicine,PGY1,S0079640,2016-08-27,1:18:57,0:52:11,0:00:10,0:25:40,0:13:59,0:00:26,3:01:04,1053,153,6
+General Medicine,PGY1,S0079640,2016-08-24,1:39:32,0:34:56,0:00:11,0:28:19,0:10:29,0:01:14,3:18:56,1172,130,6
+General Medicine,PGY1,S0079640,2016-08-25,2:08:01,1:33:11,0:00:12,1:11:53,0:40:25,0:01:58,6:05:46,1697,275,10
+General Medicine,PGY1,S0181086,2015-12-13,2:13:10,0:42:33,1:31:55,1:12:54,0:30:58,0:25:06,7:16:17,2042,246,8
+General Medicine,PGY1,S0181086,2015-12-12,1:53:46,0:59:24,2:05:05,0:59:41,0:18:52,0:19:45,7:12:32,1826,300,6
+General Medicine,PGY1,S0079640,2016-08-28,1:57:21,0:50:56,0:00:19,0:51:38,0:36:45,0:00:33,5:07:10,1672,0,9
+General Medicine,PGY1,S0181086,2015-12-10,0:56:36,0:56:15,0:34:05,0:20:32,0:01:51,0:01:35,3:28:20,1171,1163,21
+General Medicine,PGY1,S0181086,2015-12-17,0:56:26,0:30:18,2:59:34,0:31:07,0:03:17,0:06:40,5:38:40,1388,1372,12
+General Medicine,PGY1,S0181086,2015-12-16,3:04:25,0:55:02,2:45:58,1:19:42,0:26:59,0:21:07,9:52:34,2977,411,12
+General Medicine,PGY1,S0079337,2014-12-28,2:51:10,0:00:54,0:39:01,0:24:31,0:18:11,0:08:46,4:57:01,1941,0,13
+General Medicine,PGY1,S0079337,2014-12-29,3:21:42,0:01:01,0:39:27,0:52:51,0:18:48,0:00:46,5:33:32,2321,1,13
+General Medicine,PGY1,S0195237,2016-11-10,2:05:58,1:54:48,0:01:05,0:36:22,0:37:49,0:06:51,5:48:33,2203,132,8
+General Medicine,PGY1,S0181057,2016-02-23,0:51:35,0:25:50,0:00:01,0:12:32,0:20:03,0:04:13,2:01:01,1105,0,8
+General Medicine,PGY1,S0181057,2016-02-20,0:47:49,1:33:45,0:00:25,0:15:00,0:08:51,0:02:49,3:14:20,1337,171,12
+General Medicine,PGY1,S0181057,2016-02-21,3:05:57,1:11:37,0:00:23,0:56:07,0:25:50,0:16:04,6:25:05,2159,0,12
+General Medicine,PGY1,S0181057,2016-02-26,0:03:46,0:13:08,0:00:00,0:00:01,0:00:00,0:00:02,0:19:13,163,151,10
+General Medicine,PGY1,S0181057,2016-02-27,0:02:58,0:00:13,0:00:00,0:00:00,0:00:00,0:00:00,0:03:13,20,10,5
+General Medicine,PGY1,S0181057,2016-02-24,0:06:37,0:01:48,0:00:00,0:00:02,0:00:02,0:00:00,0:09:47,90,78,10
+General Medicine,PGY1,S0181057,2016-02-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,3
+General Medicine,PGY2+,S0169385,2015-12-15,0:00:00,0:23:47,0:00:00,0:00:00,0:00:00,0:01:39,0:26:56,274,272,9
+General Medicine,PGY2+,S0169385,2015-12-14,0:49:12,1:34:59,0:38:57,0:15:48,0:29:59,0:46:26,5:13:51,3130,627,29
+General Medicine,PGY2+,S0169385,2015-12-17,0:50:39,0:42:46,0:15:17,0:46:17,0:21:59,0:42:39,4:06:34,2175,117,8
+General Medicine,PGY2+,S0169385,2015-12-16,0:56:13,2:19:38,0:32:16,0:08:54,0:15:01,1:02:06,5:35:15,3221,342,21
+General Medicine,PGY2+,S0169385,2015-12-11,1:59:32,2:06:26,0:29:51,0:14:43,0:20:34,1:24:39,7:20:05,4009,242,18
+General Medicine,PGY2+,S0169385,2015-12-10,0:06:26,0:17:46,0:00:08,0:00:19,0:01:32,0:02:35,0:29:47,351,349,9
+General Medicine,PGY2+,S0169385,2015-12-13,1:16:53,2:14:38,0:35:59,0:19:52,0:26:28,1:21:58,7:06:55,3181,586,20
+General Medicine,PGY2+,S0169385,2015-12-12,1:13:35,1:10:34,0:17:42,0:25:30,0:16:30,0:43:06,4:35:48,2717,184,8
+General Medicine,PGY2+,S0169385,2015-12-19,0:50:41,0:51:48,0:08:47,0:23:22,0:12:11,0:51:47,3:43:34,1884,92,16
+General Medicine,PGY2+,S0169385,2015-12-18,0:52:00,1:02:15,0:14:35,0:16:43,0:22:07,1:22:11,4:28:11,2045,319,13
+Emergency Medicine,PGY1,S0181087,2015-08-26,0:55:16,0:04:47,0:00:05,0:00:16,0:01:14,0:00:00,1:25:07,360,27,9
+Emergency Medicine,PGY1,S0181087,2015-08-27,1:39:06,0:26:24,1:00:55,0:22:13,0:24:37,0:02:24,4:21:04,1465,0,13
+Emergency Medicine,PGY1,S0181087,2015-08-24,0:55:13,0:35:32,1:13:15,0:15:43,0:05:51,0:02:00,3:21:13,991,0,19
+Emergency Medicine,PGY1,S0181087,2015-08-25,1:30:35,0:25:28,1:19:30,0:47:36,0:09:12,0:04:30,4:36:57,1406,0,13
+Emergency Medicine,PGY1,S0181087,2015-08-22,1:55:45,0:18:23,2:40:47,1:01:10,0:27:54,0:08:43,7:22:29,2589,0,18
+Emergency Medicine,PGY1,S0181087,2015-08-23,2:22:14,0:37:47,2:48:03,1:06:55,0:31:09,0:03:01,8:19:42,2756,0,20
+Emergency Medicine,PGY1,S0181087,2015-08-20,2:05:16,1:04:06,2:45:51,1:02:55,0:26:02,0:19:10,8:36:23,2734,1,27
+Emergency Medicine,PGY1,S0181087,2015-08-21,1:47:03,0:05:56,0:56:16,0:10:42,0:06:20,0:03:54,3:26:07,1079,200,10
+Emergency Medicine,PGY1,S0181087,2015-08-28,2:29:13,0:29:24,1:45:01,0:51:14,0:21:27,0:09:31,6:45:31,2245,0,20
+Emergency Medicine,PGY1,S0181087,2015-08-29,1:44:02,0:25:44,1:47:29,0:24:15,0:18:56,0:05:50,5:20:54,1744,60,21
+Night Team,PGY1,S0181525,2016-06-04,0:46:31,0:51:56,0:00:01,0:28:47,0:12:22,0:01:42,2:44:08,744,0,22
+Night Team,PGY1,S0181525,2016-06-02,1:42:26,0:38:20,0:02:07,0:15:28,0:07:48,0:05:34,3:06:37,915,3,35
+Night Team,PGY1,S0181525,2016-06-03,2:13:38,0:34:37,0:00:04,0:36:17,0:19:22,0:07:33,4:13:23,1084,242,41
+Night Team,PGY1,S0181525,2016-06-01,1:15:15,0:14:24,0:00:03,0:15:53,0:10:36,0:00:10,2:11:54,636,12,30
+Night Team,PGY2+,S0181056,2016-10-08,1:54:01,0:53:49,0:00:07,0:36:33,0:05:02,0:07:09,3:54:40,1480,100,31
+Night Team,PGY2+,S0181056,2016-10-01,3:40:07,2:15:26,0:00:05,0:31:56,0:19:33,0:17:15,8:04:11,2455,584,56
+Night Team,PGY2+,S0181056,2016-10-02,2:36:51,1:33:21,0:00:44,1:13:35,0:13:39,0:05:36,6:45:04,2359,715,22
+Night Team,PGY2+,S0181056,2016-10-03,4:11:35,1:47:53,0:01:18,1:42:58,0:18:27,0:14:02,9:17:54,2840,227,20
+Night Team,PGY2+,S0181056,2016-10-04,2:45:36,1:18:52,0:00:59,1:03:15,0:21:42,0:15:33,6:22:18,2609,12,55
+Night Team,PGY2+,S0181056,2016-10-05,2:09:08,1:07:21,0:00:02,0:24:46,0:05:31,0:12:41,4:46:31,1404,0,33
+Night Team,PGY2+,S0181056,2016-10-06,1:42:57,1:02:12,0:01:18,0:33:15,0:04:02,0:06:34,4:19:33,1370,0,11
+Night Team,PGY2+,S0181056,2016-10-07,1:24:37,0:42:48,0:00:04,0:41:58,0:02:40,0:02:54,3:09:43,1354,301,21
+Night Team,PGY2+,S0169404,2016-05-25,3:06:39,1:49:59,0:02:05,0:59:23,0:15:46,0:05:54,6:52:50,2764,61,27
+Night Team,PGY2+,S0169404,2016-05-24,1:23:31,0:56:15,0:00:01,0:22:06,0:04:40,0:04:43,3:20:18,1588,18,33
+Night Team,PGY2+,S0169404,2016-05-27,3:45:15,1:46:38,0:00:33,1:23:26,0:13:34,0:06:40,8:00:14,2760,93,40
+Night Team,PGY2+,S0169404,2016-05-26,3:29:23,2:08:38,0:00:13,0:22:07,0:10:04,0:05:38,6:45:38,2924,9,47
+General Medicine,PGY1,S0195201,2016-09-18,2:55:43,2:01:34,0:01:01,0:35:00,0:12:54,0:04:11,6:24:43,2418,651,38
+General Medicine,PGY1,S0195201,2016-09-19,4:29:42,2:06:50,0:02:16,0:51:49,0:28:31,0:11:33,9:31:04,3912,177,31
+Night Team,PGY2+,S0169404,2016-05-23,0:16:03,0:00:39,0:00:00,0:00:00,0:00:00,0:00:25,0:17:41,78,72,10
+Night Team,PGY2+,S0169404,2016-05-22,0:48:25,0:22:47,0:00:00,0:14:31,0:09:00,0:01:01,1:40:46,786,38,23
+Night Team,PGY1,S0181064,2016-03-18,1:14:35,0:30:13,0:00:01,0:17:37,0:15:59,0:03:36,2:45:20,1028,9,42
+Night Team,PGY2+,S0169404,2016-05-28,1:33:56,1:23:59,0:00:14,0:37:12,0:10:33,0:03:37,4:14:33,1518,68,18
+General Medicine,PGY1,S0169406,2015-04-30,1:30:17,0:02:29,0:06:59,0:23:46,0:13:57,0:05:45,2:37:16,1027,32,9
+General Medicine,PGY1,S0172085,2015-07-09,0:58:10,0:21:56,1:29:54,0:17:16,0:08:46,0:19:47,3:50:54,832,0,8
+General Medicine,PGY1,S0172085,2015-07-08,1:57:57,0:53:26,2:20:29,0:50:06,0:33:02,0:16:01,7:23:14,1516,13,8
+Intensive Care Unit,PGY2+,S0169381,2016-01-18,0:01:56,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:02:00,35,20,8
+Intensive Care Unit,PGY2+,S0169381,2016-01-19,2:08:57,0:56:33,0:16:50,0:31:44,0:21:01,0:39:40,5:16:40,1297,0,20
+General Medicine,PGY1,S0172085,2015-07-05,1:39:42,0:36:52,3:11:57,0:48:50,0:10:15,0:20:49,7:06:59,1309,0,5
+General Medicine,PGY1,S0172085,2015-07-04,0:00:00,0:02:47,0:00:00,0:00:00,0:00:00,0:00:00,0:03:32,22,20,2
+General Medicine,PGY1,S0172085,2015-07-07,1:04:39,0:26:32,1:16:45,0:10:40,0:09:11,0:13:47,3:38:56,778,54,7
+Intensive Care Unit,PGY2+,S0169385,2016-02-03,0:00:00,0:12:41,0:00:00,0:00:00,0:00:00,0:01:02,0:14:37,177,173,11
+General Medicine,PGY1,S0172085,2015-07-01,1:36:23,0:13:57,2:22:17,0:34:41,0:09:07,0:13:49,5:51:56,1194,0,4
+Intensive Care Unit,PGY2+,S0169385,2016-02-05,1:27:53,0:21:36,0:09:11,0:28:26,0:10:00,0:46:11,3:36:09,1589,15,12
+General Medicine,PGY1,S0172085,2015-07-03,2:05:43,0:44:33,2:02:24,0:41:19,0:14:55,0:30:52,6:41:07,1579,24,3
+Intensive Care Unit,PGY2+,S0169381,2016-01-11,2:00:20,1:06:23,0:07:01,0:11:44,0:33:28,0:26:08,4:54:24,1261,93,21
+Night Team,PGY2+,S0172085,2016-07-19,2:17:59,1:16:36,0:00:08,0:26:40,0:07:11,0:05:31,4:49:33,1508,106,20
+Night Team,PGY2+,S0172085,2016-07-18,3:55:11,2:13:33,0:00:09,1:03:00,0:12:25,0:10:06,8:15:27,2789,0,39
+Night Team,PGY2+,S0172085,2016-07-17,2:56:59,1:37:53,0:00:06,0:52:48,0:17:45,0:09:45,6:30:20,1507,1,39
+Emergency Medicine,PGY1,S0169398,2015-04-13,4:13:59,0:54:13,2:03:13,0:17:52,0:24:44,0:35:57,9:13:04,2465,0,20
+Night Team,PGY2+,S0169402,2016-08-28,1:35:12,0:52:52,0:02:41,0:16:54,0:01:44,0:06:06,3:06:59,839,0,11
+Night Team,PGY2+,S0169402,2016-08-29,0:32:25,0:37:08,0:00:02,0:22:32,0:05:04,0:00:30,1:59:17,783,3,12
+General Medicine,PGY1,S0181056,2015-12-01,2:51:27,0:51:03,1:05:48,0:42:31,0:16:30,0:24:27,7:11:22,2893,174,13
+General Medicine,PGY1,S0181069,2015-09-30,1:59:46,1:17:23,0:54:46,0:41:19,0:18:06,0:15:42,6:01:51,1890,0,10
+General Medicine,PGY1,S0181056,2015-12-02,2:58:44,2:01:49,1:06:32,0:56:11,0:42:47,0:31:10,9:41:26,3663,0,20
+General Medicine,PGY1,S0181056,2015-12-05,0:47:14,0:40:44,0:28:01,0:04:29,0:03:59,0:22:04,2:44:16,801,0,18
+General Medicine,PGY1,S0181056,2015-12-04,1:10:40,1:15:31,0:53:29,0:34:00,0:12:41,0:09:46,4:47:09,1924,53,14
+General Medicine,PGY2+,S0169384,2016-07-13,2:23:38,0:59:57,0:00:59,1:09:40,0:15:03,0:12:59,5:15:22,1858,234,18
+General Medicine,PGY1,S0108287,2014-08-11,0:13:11,0:00:41,0:00:42,0:00:00,0:00:25,0:02:05,0:18:53,106,94,5
+General Medicine,PGY1,S0108287,2014-08-10,3:33:05,0:06:05,0:17:11,0:36:05,0:15:38,0:47:10,5:57:11,1097,0,17
+General Medicine,PGY1,S0108287,2014-08-13,3:59:09,0:06:13,0:24:24,0:58:26,0:28:04,0:25:31,6:41:38,1350,21,6
+General Medicine,PGY1,S0108287,2014-08-12,3:21:24,0:11:02,0:18:20,0:34:48,0:29:23,0:28:47,5:43:22,1241,0,6
+General Medicine,PGY1,S0108287,2014-08-15,3:29:51,0:08:48,0:25:04,0:48:12,0:11:18,0:14:27,5:39:12,1325,0,7
+General Medicine,PGY1,S0108287,2014-08-14,2:40:56,0:00:06,0:14:03,0:44:29,0:34:36,0:20:36,4:57:11,1377,16,13
+General Medicine,PGY1,S0108287,2014-08-16,0:21:42,0:00:14,0:00:26,0:00:02,0:00:00,0:03:23,0:28:16,78,64,7
+General Medicine,PGY1,S0195207,2016-08-31,3:03:53,2:36:22,0:00:32,0:40:39,0:33:51,0:01:23,8:06:41,3142,0,29
+General Medicine,PGY1,S0195207,2016-08-30,0:02:35,0:00:13,0:00:00,0:00:26,0:00:00,0:00:00,0:05:01,24,0,3
+General Medicine,PGY1,S0108268,2014-09-28,0:49:13,0:03:55,0:07:33,0:24:10,0:19:32,0:16:45,2:25:22,786,23,6
+General Medicine,PGY1,S0108268,2014-09-29,1:54:07,0:08:51,0:04:57,0:24:13,0:44:45,0:23:42,4:08:52,1487,316,11
+Night Team,PGY1,S0181526,2016-01-22,2:39:16,1:29:24,1:35:35,0:35:40,0:25:52,1:18:31,8:44:43,2506,51,53
+Night Team,PGY1,S0181526,2016-01-23,2:01:26,0:40:05,1:10:22,0:41:52,0:23:37,0:34:21,6:12:55,1670,78,59
+Night Team,PGY1,S0181526,2016-01-24,2:17:51,0:47:21,1:04:03,0:39:50,0:45:01,1:16:38,7:36:05,2310,65,43
+General Medicine,PGY1,S0195230,2016-09-20,2:55:11,1:05:35,0:00:08,0:59:46,0:29:26,0:01:25,5:56:34,1398,42,13
+Night Team,PGY1,S0181526,2016-01-26,1:25:06,0:30:59,0:30:00,0:20:04,0:26:33,0:57:28,4:44:39,1329,388,40
+Night Team,PGY1,S0181526,2016-01-27,0:46:39,0:17:30,0:14:40,0:16:22,0:18:06,0:27:16,2:47:55,849,23,25
+General Medicine,PGY1,S0108268,2014-09-20,2:36:44,0:14:05,0:15:38,0:27:36,0:25:33,0:36:16,5:02:46,1706,360,9
+General Medicine,PGY1,S0181073,2015-11-04,0:43:11,1:24:28,1:25:56,0:23:22,0:17:22,0:16:27,4:44:58,1708,52,8
+General Medicine,PGY1,S0108268,2014-09-22,3:01:00,0:15:36,0:13:13,0:27:13,0:17:08,0:27:04,5:05:36,1659,93,12
+General Medicine,PGY1,S0108268,2014-09-23,2:37:33,0:12:43,0:04:57,0:22:31,0:30:18,0:39:29,5:10:01,1392,53,10
+General Medicine,PGY1,S0108268,2014-09-24,3:47:50,0:18:29,0:07:12,0:52:40,0:44:28,0:39:14,7:09:19,2197,0,18
+General Medicine,PGY1,S0108268,2014-09-25,2:24:01,0:15:29,0:08:55,1:04:00,0:21:20,0:30:55,5:20:52,1518,225,11
+General Medicine,PGY1,S0108268,2014-09-26,2:23:03,0:18:47,0:14:30,0:39:34,0:35:10,0:21:08,5:02:21,1442,115,10
+General Medicine,PGY1,S0181073,2015-11-02,2:41:19,1:34:21,1:14:27,0:35:03,0:30:24,0:35:17,7:38:57,2595,113,12
+Night Team,PGY2+,S0169390,2016-06-11,2:34:06,0:52:15,0:00:02,0:38:39,0:21:28,0:03:32,4:54:38,1117,4,40
+Night Team,PGY2+,S0169390,2016-06-10,2:26:22,1:16:52,0:00:23,0:32:51,0:13:41,0:05:54,5:19:47,1578,12,25
+Emergency Medicine,PGY1,S0169398,2015-04-21,2:48:37,0:21:01,1:13:24,0:12:20,0:13:01,0:38:23,5:53:34,1660,0,13
+Emergency Medicine,PGY1,S0169398,2015-04-20,4:15:43,0:41:40,2:16:09,0:33:49,0:36:53,0:35:23,9:29:24,2811,34,29
+Emergency Medicine,PGY1,S0169398,2015-04-23,1:25:30,0:28:51,1:17:40,0:22:24,0:08:59,0:10:23,4:30:57,1560,109,16
+Emergency Medicine,PGY1,S0169398,2015-04-22,3:24:26,0:44:42,1:43:18,0:15:43,0:16:37,0:24:17,7:31:27,2305,0,22
+General Medicine,PGY1,S0181083,2015-06-30,4:01:17,0:10:23,2:37:42,0:44:14,0:27:34,0:17:23,8:54:50,2259,175,11
+General Medicine,PGY1,S0169381,2014-08-28,3:09:47,0:07:31,0:18:54,0:49:28,0:12:34,0:11:59,5:06:24,1237,22,10
+General Medicine,PGY2+,S0181526,2016-08-11,0:13:43,0:01:06,0:00:02,0:01:21,0:00:13,0:01:46,0:29:36,264,195,54
+General Medicine,PGY1,S0181526,2016-05-20,0:04:29,0:01:06,0:00:00,0:00:01,0:00:00,0:01:17,0:07:23,81,69,7
+General Medicine,PGY1,S0181526,2016-05-21,0:00:40,0:00:06,0:00:00,0:00:00,0:00:00,0:00:04,0:02:59,21,13,4
+General Medicine,PGY1,S0169394,2014-08-08,2:34:21,0:05:42,0:03:03,0:20:29,0:14:42,0:17:43,4:01:34,1315,16,12
+General Medicine,PGY1,S0169394,2014-08-09,3:24:30,0:09:25,0:18:33,0:43:46,0:44:35,0:30:59,6:30:24,1716,0,16
+Intensive Care Unit,PGY2+,S0108268,2016-01-31,2:13:23,0:24:44,0:33:49,0:24:45,0:26:20,0:25:44,4:53:26,1785,0,15
+Intensive Care Unit,PGY2+,S0108268,2016-01-30,2:51:21,1:57:43,0:16:27,0:54:48,1:06:48,1:25:35,9:03:17,3033,0,17
+General Medicine,PGY1,S0169394,2014-08-02,3:07:10,0:12:05,0:12:02,0:26:52,0:41:45,0:37:39,5:36:24,1747,47,10
+General Medicine,PGY1,S0169394,2014-08-03,1:43:05,0:01:03,0:16:59,0:07:12,0:09:47,0:19:51,2:53:15,904,0,8
+General Medicine,PGY1,S0169394,2014-08-01,0:34:40,0:00:00,0:00:10,0:00:00,0:00:10,0:04:27,0:43:38,134,128,6
+General Medicine,PGY1,S0169394,2014-08-06,0:59:19,0:03:55,0:03:34,0:12:54,0:05:22,0:12:10,1:47:04,628,172,20
+General Medicine,PGY1,S0169394,2014-08-07,3:16:02,0:14:39,0:13:17,0:34:40,0:41:28,0:25:10,5:53:12,1698,0,16
+General Medicine,PGY1,S0169394,2014-08-04,3:34:07,0:20:21,0:08:21,0:34:50,0:21:45,0:31:34,6:06:39,1548,164,14
+General Medicine,PGY1,S0169394,2014-08-05,3:16:56,0:07:50,0:21:25,0:20:53,0:20:42,0:19:56,5:01:07,1565,0,16
+Night Team,PGY1,S0094959,2014-07-07,4:49:56,0:11:14,0:06:48,0:15:21,0:17:51,0:37:39,6:38:51,1871,0,46
+Night Team,PGY1,S0094959,2014-07-06,3:50:09,0:10:57,0:06:35,0:41:19,0:23:29,0:31:11,5:53:55,1514,42,53
+Night Team,PGY2+,S0181059,2016-09-10,2:42:42,1:30:01,0:00:08,0:25:20,0:14:25,0:05:25,5:24:57,2162,0,41
+Night Team,PGY1,S0169411,2014-11-28,3:51:21,0:01:28,0:45:02,0:22:30,0:19:08,0:08:28,5:42:51,1363,0,46
+General Medicine,PGY1,S0169400,2014-11-19,1:19:13,0:03:15,0:40:45,0:26:24,0:21:00,0:11:19,3:23:44,1069,18,9
+General Medicine,PGY1,S0169400,2014-11-18,3:20:01,0:05:11,2:31:56,0:42:40,0:26:32,0:31:32,7:55:51,1789,0,13
+General Medicine,PGY1,S0169400,2014-11-17,1:42:57,0:03:34,1:02:35,0:15:39,0:17:07,0:10:16,3:50:05,1081,0,6
+General Medicine,PGY1,S0169400,2014-11-16,3:35:51,0:05:45,2:37:20,0:34:37,0:16:03,0:20:45,7:49:55,1460,0,8
+General Medicine,PGY1,S0169400,2014-11-15,1:48:09,0:01:54,0:55:36,0:17:35,0:14:06,0:14:10,3:58:21,1109,0,6
+General Medicine,PGY1,S0169400,2014-11-14,0:00:00,0:00:00,0:00:00,0:00:44,0:00:00,0:00:00,0:00:44,18,0,2
+General Medicine,PGY1,S0169400,2014-11-13,3:10:49,0:11:55,1:51:34,0:40:19,0:20:18,0:19:29,7:07:30,1908,15,10
+General Medicine,PGY1,S0169400,2014-11-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0169400,2014-11-11,3:44:46,0:06:27,1:40:30,0:22:07,0:26:58,0:29:52,7:13:53,1660,0,9
+General Medicine,PGY1,S0169400,2014-11-10,2:18:28,0:13:34,1:21:17,0:27:56,0:16:25,0:29:02,5:18:27,1184,16,3
+Night Team,PGY2+,S0169404,2015-12-31,2:25:08,2:01:20,1:44:11,1:03:32,0:21:59,1:09:10,9:09:53,5070,668,40
+Night Team,PGY2+,S0169404,2015-12-30,1:32:02,1:42:39,1:36:11,1:13:10,0:19:13,1:10:34,8:18:09,4971,24,24
+General Medicine,PGY2+,S0169401,2015-12-11,3:47:55,1:15:37,0:30:50,0:55:04,0:46:12,1:13:52,9:07:03,4185,555,26
+General Medicine,PGY2+,S0093383,2016-02-08,2:23:14,1:44:50,1:41:25,0:37:47,0:14:53,1:15:57,8:34:55,3795,0,18
+General Medicine,PGY2+,S0093383,2016-02-09,1:45:36,0:55:05,0:02:52,0:36:25,0:31:26,0:26:58,4:51:25,2810,0,18
+General Medicine,PGY1,S0195206,2016-10-17,0:18:49,0:08:07,0:00:03,0:01:13,0:00:16,0:00:01,0:32:48,232,143,21
+General Medicine,PGY2+,S0093383,2016-02-07,1:26:39,0:23:36,0:13:17,0:20:48,0:15:05,0:20:58,3:17:35,1209,263,13
+General Medicine,PGY1,S0195206,2016-10-16,3:00:18,1:58:52,0:00:38,0:27:56,0:24:02,0:09:09,6:20:51,2166,0,19
+General Medicine,PGY1,S0181067,2015-07-10,0:43:17,0:01:48,1:14:11,0:26:13,0:09:54,0:08:24,2:53:27,757,174,6
+General Medicine,PGY1,S0181067,2015-07-11,2:04:16,0:21:39,1:12:55,0:29:43,0:39:11,0:29:49,5:41:07,1488,0,5
+General Medicine,PGY1,S0181067,2015-07-12,0:48:20,0:04:55,0:40:36,0:12:52,0:08:36,0:15:48,2:31:02,607,0,4
+General Medicine,PGY1,S0181067,2015-07-13,2:22:20,0:40:23,1:04:35,0:36:32,0:42:03,0:16:02,6:08:05,1533,0,5
+Night Team,PGY1,S0195079,2016-10-26,1:36:28,0:25:54,0:00:00,0:36:45,0:32:54,0:03:33,3:26:52,954,0,46
+General Medicine,PGY1,S0181067,2015-07-15,1:21:56,0:03:42,0:43:18,0:17:31,0:19:30,0:09:38,3:18:57,877,0,6
+General Medicine,PGY1,S0181067,2015-07-16,2:01:06,0:31:30,1:29:12,1:06:40,0:47:25,0:22:01,6:48:14,2083,87,6
+General Medicine,PGY1,S0181067,2015-07-17,1:51:01,0:06:35,1:29:11,0:29:21,0:36:20,0:22:02,5:16:16,1345,0,16
+General Medicine,PGY1,S0181067,2015-07-18,0:00:00,0:00:00,0:00:00,0:02:45,0:00:00,0:00:00,0:02:45,16,0,3
+Night Team,PGY1,S0195079,2016-10-28,0:58:30,0:15:11,0:00:00,0:14:46,0:15:21,0:00:25,1:49:16,563,0,25
+General Medicine,PGY1,S0169403,2015-03-29,0:00:14,0:06:58,0:00:00,0:00:07,0:00:00,0:00:06,0:07:51,50,33,5
+Night Team,PGY1,S0195218,2016-07-21,1:28:55,0:51:16,0:00:08,0:33:05,0:13:36,0:02:13,3:41:04,910,1,21
+Night Team,PGY1,S0195218,2016-07-20,2:00:08,0:38:39,0:00:04,0:17:52,0:11:52,0:09:00,3:48:33,1019,3,30
+Night Team,PGY1,S0195218,2016-07-23,1:38:17,0:27:24,0:00:00,0:36:35,0:12:51,0:18:03,3:22:04,969,3,31
+Night Team,PGY1,S0195218,2016-07-22,0:29:19,0:27:27,0:00:00,0:04:52,0:08:12,0:00:07,1:21:57,499,123,22
+General Medicine,PGY1,S0172645,2016-01-30,0:00:40,0:09:21,0:03:37,0:00:00,0:00:22,0:00:00,0:15:50,183,175,4
+General Medicine,PGY1,S0195224,2016-10-31,1:25:28,1:14:19,0:00:04,0:17:22,0:06:16,0:03:30,3:40:27,1046,92,12
+General Medicine,PGY1,S0195224,2016-10-30,1:35:47,1:13:40,0:00:06,0:16:29,0:02:47,0:00:28,3:20:55,705,0,9
+General Medicine,PGY1,S0169411,2014-09-30,5:31:41,0:09:38,0:21:48,1:13:45,0:31:35,0:03:36,8:18:44,2461,4,12
+General Medicine,PGY1,S0181057,2016-06-24,3:29:57,1:58:50,0:00:46,1:05:02,0:44:24,0:11:21,8:08:30,2766,15,14
+General Medicine,PGY1,S0181057,2016-06-25,0:00:00,0:00:00,0:00:00,0:00:54,0:00:00,0:00:00,0:00:54,12,0,4
+General Medicine,PGY1,S0172056,2015-09-10,1:50:20,0:20:32,1:42:05,0:39:29,0:16:12,0:43:43,5:55:51,1851,0,9
+General Medicine,PGY1,S0172056,2015-09-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0172056,2015-09-12,0:46:41,0:04:36,1:01:27,0:03:05,0:01:37,0:08:53,2:22:10,506,0,7
+General Medicine,PGY1,S0181057,2016-06-21,0:00:00,0:00:00,0:00:00,0:01:13,0:00:00,0:00:00,0:01:13,6,0,3
+Night Team,PGY1,S0181064,2016-03-13,2:37:52,0:43:19,0:00:03,0:41:49,0:16:45,0:02:13,4:44:48,1306,0,36
+General Medicine,PGY1,S0074196,2015-09-09,3:20:40,0:23:23,1:31:27,1:04:50,0:33:26,0:44:19,7:56:26,2208,0,20
+General Medicine,PGY1,S0074196,2015-09-08,1:13:02,0:26:10,1:31:16,0:39:31,0:11:38,0:25:53,4:41:50,1061,19,4
+Night Team,PGY1,S0181064,2016-03-16,1:59:30,0:31:07,0:00:04,0:27:20,0:18:30,0:06:31,3:38:41,1089,35,47
+Night Team,PGY1,S0181064,2016-03-17,2:13:19,0:40:40,0:00:01,0:36:51,0:26:16,0:02:14,4:26:17,1599,0,43
+Night Team,PGY1,S0181064,2016-03-14,1:07:35,0:11:49,0:00:00,0:15:56,0:10:35,0:02:42,2:10:38,640,0,41
+Night Team,PGY1,S0181064,2016-03-15,2:02:32,0:24:43,0:00:08,0:18:01,0:15:57,0:06:51,3:24:46,1018,4,40
+General Medicine,PGY1,S0106952,2015-04-24,1:10:02,0:34:25,1:57:53,0:03:18,0:02:31,0:02:54,3:55:36,1056,1032,91
+General Medicine,PGY1,S0106952,2015-04-25,0:00:00,0:00:00,0:00:00,0:00:38,0:00:00,0:00:00,0:00:38,6,0,2
+General Medicine,PGY1,S0074196,2015-09-01,0:00:00,0:00:00,0:00:00,0:00:48,0:00:00,0:00:00,0:00:48,8,0,3
+Night Team,PGY1,S0181064,2016-03-19,1:46:15,0:34:48,0:00:03,0:29:04,0:07:29,0:01:23,3:17:27,1027,11,24
+General Medicine,PGY1,S0106952,2015-04-20,0:45:45,0:04:31,1:02:55,0:02:45,0:00:00,0:00:36,2:01:47,632,614,25
+General Medicine,PGY1,S0106952,2015-04-21,4:48:51,0:43:10,1:53:32,0:39:33,0:25:56,0:42:40,9:58:32,2924,418,34
+General Medicine,PGY1,S0106952,2015-04-22,4:02:23,0:23:45,2:10:01,0:22:10,0:14:49,0:29:07,8:11:09,2022,319,20
+General Medicine,PGY1,S0106952,2015-04-23,4:46:59,0:28:21,0:59:17,0:42:55,0:32:53,0:47:39,8:53:44,3067,678,56
+General Medicine,PGY2+,S0169394,2016-02-15,1:37:04,0:46:54,0:36:42,0:38:13,0:28:20,0:27:51,5:18:03,2018,206,14
+General Medicine,PGY2+,S0169394,2016-02-14,0:57:12,0:38:40,0:02:00,0:14:29,0:20:21,0:36:44,3:12:00,1244,289,15
+General Medicine,PGY2+,S0169394,2016-02-17,1:47:33,0:29:34,0:01:48,0:07:48,0:13:20,0:17:34,3:36:56,1521,365,17
+General Medicine,PGY2+,S0169394,2016-02-16,0:51:19,0:09:12,0:24:53,0:04:23,0:16:14,0:15:40,2:16:26,910,218,21
+General Medicine,PGY2+,S0169394,2016-02-11,1:08:07,0:26:19,0:02:43,0:08:10,0:25:19,0:17:46,2:46:59,1197,45,19
+General Medicine,PGY2+,S0169394,2016-02-10,0:50:54,0:30:36,0:03:37,0:03:50,0:12:17,0:28:06,2:26:30,838,265,13
+General Medicine,PGY2+,S0169394,2016-02-13,1:20:34,0:42:15,0:32:50,0:50:29,0:26:50,0:22:03,4:51:28,1925,478,10
+General Medicine,PGY2+,S0169394,2016-02-12,0:55:29,0:33:35,0:00:18,0:06:05,0:11:45,0:33:53,2:33:27,926,133,11
+General Medicine,PGY1,S0195216,2016-08-31,3:46:57,2:53:30,0:00:37,0:40:25,0:37:33,0:06:14,8:57:04,3528,0,23
+General Medicine,PGY1,S0195216,2016-08-30,0:07:35,0:00:16,0:00:00,0:00:06,0:00:00,0:00:00,0:08:10,21,0,5
+General Medicine,PGY1,S0163662,2015-06-23,3:35:19,0:29:13,1:50:31,0:41:10,0:44:06,0:22:41,8:16:46,2761,212,16
+General Medicine,PGY1,S0163662,2015-06-22,3:01:31,0:31:04,2:12:47,0:37:14,0:29:59,0:13:50,7:39:35,2143,499,10
+General Medicine,PGY1,S0163662,2015-06-21,3:20:07,0:28:42,2:07:07,0:26:02,0:31:14,0:20:12,7:48:09,2372,0,10
+General Medicine,PGY1,S0163662,2015-06-20,2:59:46,0:06:10,1:11:23,0:14:42,0:13:15,0:22:58,5:24:49,1130,0,14
+General Medicine,PGY1,S0163662,2015-06-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0163662,2015-06-24,2:55:42,0:02:59,1:25:43,0:29:52,0:16:12,0:19:28,5:48:36,1654,0,8
+General Medicine,PGY1,S0181056,2015-11-28,0:00:00,0:04:07,0:00:00,0:00:00,0:00:00,0:01:56,0:07:53,169,161,8
+Night Team,PGY2+,S0108268,2016-05-14,0:56:57,0:47:17,0:00:02,0:08:35,0:04:49,0:00:43,2:06:00,644,0,17
+Night Team,PGY2+,S0108268,2016-05-13,2:02:04,1:11:39,0:00:12,0:30:56,0:08:39,0:01:30,4:17:59,1357,0,41
+Night Team,PGY2+,S0108268,2016-05-12,1:28:39,1:19:33,0:00:04,0:37:49,0:14:37,0:01:50,4:08:49,1460,0,33
+Night Team,PGY2+,S0108268,2016-05-11,2:15:59,1:32:06,0:00:18,0:40:35,0:20:25,0:01:07,5:42:03,1950,0,34
+Night Team,PGY2+,S0108268,2016-05-10,0:48:23,0:34:15,0:00:00,0:11:40,0:05:03,0:02:32,1:57:22,811,0,36
+General Medicine,PGY1,S0093383,2014-08-17,2:59:45,0:06:16,0:13:24,1:19:18,0:43:42,0:36:16,6:16:55,1347,0,8
+General Medicine,PGY2+,S0084443,2013-07-01,0:05:18,0:00:07,0:13:27,0:00:00,0:00:01,0:03:08,0:22:56,258,258,8
+Intensive Care Unit,PGY2+,S0176040,2016-07-30,0:04:03,0:00:18,0:00:00,0:00:24,0:00:00,0:00:00,0:04:46,23,0,5
+Intensive Care Unit,PGY2+,S0176040,2016-07-31,2:48:44,1:17:47,0:05:53,0:25:31,0:43:11,0:10:17,6:06:46,2756,0,15
+Intensive Care Unit,PGY2+,S0169403,2015-11-28,0:00:00,0:10:22,0:00:00,0:02:03,0:00:00,0:06:30,0:19:18,123,81,9
+General Medicine,PGY1,S0169384,2014-08-07,4:44:30,0:06:30,0:14:00,0:32:28,0:10:13,0:05:10,6:06:33,1759,528,21
+General Medicine,PGY1,S0169384,2014-08-06,0:58:23,0:02:24,0:09:14,0:00:00,0:00:07,0:03:04,1:13:31,177,171,4
+General Medicine,PGY1,S0169384,2014-08-05,4:47:05,0:24:31,0:23:51,0:43:12,0:20:08,0:09:24,7:08:24,1708,50,11
+General Medicine,PGY1,S0169384,2014-08-04,3:47:20,0:15:44,0:22:40,0:06:38,0:09:28,0:08:15,4:54:06,991,132,6
+General Medicine,PGY1,S0169384,2014-08-03,0:00:00,0:00:00,0:00:00,0:01:23,0:00:00,0:00:00,0:01:23,6,0,3
+General Medicine,PGY1,S0169384,2014-08-02,4:04:06,0:18:27,0:17:34,0:28:07,0:19:51,0:08:03,6:05:06,1592,74,12
+General Medicine,PGY1,S0169384,2014-08-01,3:50:26,0:07:49,0:28:31,0:26:43,0:16:27,0:14:42,5:38:33,1313,122,8
+Intensive Care Unit,PGY2+,S0169402,2015-07-21,2:12:10,0:06:17,0:19:04,0:32:03,0:19:41,0:20:13,4:12:19,1129,0,15
+General Medicine,PGY1,S0169384,2014-08-09,2:13:32,0:03:05,0:22:55,0:09:10,0:03:51,0:12:07,3:11:58,588,128,4
+General Medicine,PGY1,S0169384,2014-08-08,2:51:27,0:03:20,0:20:58,0:12:25,0:13:15,0:19:58,4:14:18,792,94,7
+Night Team,PGY2+,S0169406,2015-07-19,1:14:17,0:30:00,1:22:32,0:06:27,0:06:55,0:15:45,3:47:07,801,33,15
+Night Team,PGY2+,S0169406,2015-07-18,2:00:07,0:30:48,3:17:05,0:19:09,0:14:28,0:25:26,7:06:27,1498,88,29
+Night Team,PGY2+,S0169406,2015-07-13,0:21:06,0:15:35,0:45:20,0:02:37,0:01:48,0:06:01,1:36:40,387,0,7
+Night Team,PGY2+,S0169406,2015-07-12,1:16:35,0:15:17,0:46:30,0:14:01,0:06:22,0:04:05,2:58:10,857,0,19
+Night Team,PGY2+,S0169406,2015-07-17,1:54:44,1:00:02,2:44:30,0:36:00,0:15:30,0:32:36,7:24:40,2159,322,22
+Night Team,PGY2+,S0169406,2015-07-16,1:23:20,0:18:44,1:00:36,0:28:02,0:15:01,0:27:47,4:24:43,1230,271,29
+Night Team,PGY2+,S0169406,2015-07-15,1:34:44,0:21:11,0:07:57,0:17:31,0:10:38,0:10:42,2:56:38,1024,36,26
+Night Team,PGY2+,S0169406,2015-07-14,1:58:30,0:22:18,1:29:34,0:18:44,0:08:38,0:12:59,4:42:32,970,103,15
+Intensive Care Unit,PGY2+,S0169401,2016-03-24,2:29:35,0:46:25,0:05:09,0:20:59,0:23:17,0:08:25,4:35:41,1360,47,15
+Intensive Care Unit,PGY2+,S0169401,2016-03-25,0:01:57,0:00:10,0:00:00,0:00:04,0:00:00,0:00:00,0:02:29,18,8,5
+Intensive Care Unit,PGY2+,S0169401,2016-03-26,0:10:30,0:00:41,0:00:00,0:00:00,0:00:00,0:00:00,0:11:50,70,66,11
+Intensive Care Unit,PGY2+,S0169401,2016-03-27,2:52:08,0:45:29,0:08:13,0:49:05,0:31:20,0:02:56,6:15:36,2188,83,19
+Intensive Care Unit,PGY2+,S0169401,2016-03-20,0:05:18,0:00:40,0:00:00,0:01:11,0:00:00,0:01:06,0:09:31,111,95,10
+Intensive Care Unit,PGY2+,S0169401,2016-03-21,1:29:25,0:30:39,0:00:21,0:15:10,0:10:50,0:07:46,2:50:03,1055,12,17
+Intensive Care Unit,PGY2+,S0169401,2016-03-22,1:44:41,0:19:54,0:03:43,0:22:02,0:17:58,0:04:39,3:04:46,1032,34,17
+Intensive Care Unit,PGY2+,S0169401,2016-03-23,3:26:54,1:17:34,0:05:21,0:35:47,0:32:20,0:06:46,6:36:44,2622,44,25
+Intensive Care Unit,PGY2+,S0169401,2016-03-28,2:55:32,1:25:25,0:05:05,1:01:57,0:24:09,0:11:03,6:47:54,2731,0,23
+General Medicine,PGY1,S0196595,2016-07-21,3:06:57,1:46:11,0:00:12,0:49:26,0:31:36,0:05:14,7:15:12,1555,0,14
+General Medicine,PGY1,S0196595,2016-07-22,0:00:00,0:00:00,0:00:00,0:04:08,0:00:00,0:00:00,0:04:08,20,0,3
+General Medicine,PGY1,S0196595,2016-07-23,2:16:22,1:15:01,0:02:03,0:33:46,0:21:18,0:02:40,4:55:26,1267,118,15
+Emergency Medicine,PGY1,S0195086,2016-09-19,4:22:10,1:05:31,0:02:47,0:31:03,0:52:20,0:10:04,8:09:07,2210,0,17
+Emergency Medicine,PGY1,S0195086,2016-09-18,0:02:06,0:00:16,0:00:00,0:00:00,0:00:00,0:00:00,0:02:32,24,0,3
+Emergency Medicine,PGY1,S0169402,2014-07-08,4:05:37,0:06:26,0:19:28,0:15:01,0:22:27,0:15:15,5:29:21,1022,146,6
+Intensive Care Unit,PGY2+,S0169404,2016-09-10,1:39:17,0:21:35,0:06:14,0:19:42,0:17:29,0:02:36,2:58:49,1531,0,14
+General Medicine,PGY1,S0195223,2016-09-03,0:11:08,0:04:32,0:00:00,0:00:03,0:00:00,0:00:49,0:19:14,167,74,8
+General Medicine,PGY1,S0195223,2016-09-02,1:36:48,1:13:15,0:00:06,0:33:33,0:16:32,0:11:15,4:38:01,1446,0,8
+General Medicine,PGY1,S0195223,2016-09-01,1:25:19,0:58:12,0:00:18,0:13:54,0:10:05,0:04:43,3:08:15,1131,31,9
+General Medicine,PGY1,S0195223,2016-09-07,2:33:28,1:23:58,0:00:06,0:17:23,0:35:49,0:03:14,5:31:10,1834,54,7
+General Medicine,PGY1,S0195223,2016-09-06,1:09:10,0:44:16,0:00:08,0:24:21,0:09:14,0:00:17,2:41:08,974,152,5
+General Medicine,PGY1,S0195223,2016-09-05,1:16:05,1:29:34,0:03:57,0:20:38,0:13:10,0:04:36,4:00:28,1338,103,7
+General Medicine,PGY1,S0195223,2016-09-04,1:42:30,1:36:04,0:00:11,0:41:55,0:27:28,0:04:19,5:14:33,1765,0,14
+Night Team,PGY1,S0181070,2015-10-08,0:54:51,0:38:44,2:11:37,0:18:51,0:10:50,0:34:44,4:54:01,907,0,15
+General Medicine,PGY1,S0195223,2016-09-09,1:35:11,1:00:32,0:00:11,0:22:37,0:31:37,0:03:57,4:13:35,1371,18,14
+General Medicine,PGY1,S0195223,2016-09-08,0:56:26,0:46:25,0:01:02,0:18:54,0:14:53,0:06:01,2:50:06,1027,0,8
+General Medicine,PGY2+,S0169383,2016-06-30,3:01:12,2:05:26,0:00:06,0:37:30,0:17:55,0:06:19,6:58:42,2580,0,23
+Intensive Care Unit,PGY2+,S0056755,2016-06-24,2:00:44,1:11:41,0:14:37,0:52:45,0:28:13,0:05:00,5:12:03,1244,1,14
+Intensive Care Unit,PGY2+,S0056755,2016-06-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,5
+Intensive Care Unit,PGY2+,S0056755,2016-06-20,0:04:33,0:00:29,0:00:00,0:00:29,0:00:00,0:00:41,0:06:31,47,21,8
+Intensive Care Unit,PGY2+,S0056755,2016-06-21,2:11:34,1:02:44,0:16:08,0:28:21,0:38:58,0:02:36,4:58:37,1271,14,13
+Intensive Care Unit,PGY2+,S0056755,2016-06-22,3:35:10,1:10:01,0:01:13,1:12:41,0:52:05,0:09:12,7:39:45,1854,0,27
+Intensive Care Unit,PGY2+,S0056755,2016-06-23,1:41:49,0:46:38,0:05:13,0:08:51,0:16:11,0:03:18,3:21:01,862,11,13
+General Medicine,PGY1,S0169398,2014-08-17,4:26:44,0:10:12,0:16:43,0:43:20,0:13:45,0:42:50,6:59:15,1545,53,7
+Night Team,PGY1,S0094535,2014-11-19,2:08:20,0:04:39,0:33:23,0:44:41,0:36:30,0:25:49,4:51:46,1271,0,35
+General Medicine,PGY1,S0169398,2014-08-19,4:11:41,0:19:19,0:24:31,0:43:19,0:39:02,0:44:47,7:26:52,1694,76,14
+General Medicine,PGY1,S0169398,2014-08-18,2:45:08,0:06:28,0:17:09,0:14:03,0:25:31,0:28:42,4:36:38,1298,84,7
+Emergency Medicine,PGY1,S0195076,2016-08-08,0:55:30,0:07:42,0:00:26,0:15:01,0:09:05,0:00:05,1:49:27,567,0,17
+Emergency Medicine,PGY1,S0195076,2016-08-02,0:18:45,0:22:20,0:06:59,0:01:48,0:02:14,0:01:32,1:41:05,367,0,5
+Emergency Medicine,PGY1,S0195076,2016-08-03,1:51:08,0:42:39,0:05:46,0:13:32,0:34:07,0:00:30,3:47:33,1377,0,8
+Emergency Medicine,PGY1,S0195076,2016-08-04,1:36:26,0:21:52,0:01:08,0:17:58,0:18:01,0:01:50,3:06:01,975,0,14
+Emergency Medicine,PGY1,S0195076,2016-08-05,2:19:41,0:47:48,0:01:20,0:19:08,0:28:17,0:04:34,4:37:10,1544,0,22
+Emergency Medicine,PGY1,S0195076,2016-08-06,2:36:22,0:47:13,0:04:35,0:22:42,0:31:52,0:02:37,5:19:08,1946,0,33
+Emergency Medicine,PGY1,S0195076,2016-08-07,2:20:01,0:37:36,0:06:02,0:26:19,0:33:49,0:01:44,4:46:26,1511,0,24
+Night Team,PGY2+,S0169402,2016-04-28,3:09:46,1:46:39,0:04:50,0:41:45,0:09:44,0:14:34,6:30:27,1755,85,32
+Night Team,PGY2+,S0169402,2016-04-29,1:06:57,0:27:15,0:00:00,0:12:19,0:08:05,0:03:52,2:03:50,758,25,38
+Night Team,PGY2+,S0169402,2016-04-20,3:15:55,1:42:28,0:00:13,1:08:34,0:19:26,0:06:47,7:25:35,2255,237,52
+Night Team,PGY2+,S0169402,2016-04-21,1:35:00,1:20:57,0:00:07,0:43:24,0:06:07,0:02:49,4:15:17,918,43,12
+Night Team,PGY2+,S0169402,2016-04-22,0:50:49,0:20:35,0:00:00,0:10:59,0:05:37,0:00:09,1:44:34,459,30,17
+Night Team,PGY2+,S0169402,2016-04-23,2:19:07,2:04:57,0:03:23,0:34:19,0:06:13,0:14:57,6:03:42,1968,54,47
+Night Team,PGY2+,S0169402,2016-04-24,1:59:24,1:19:40,0:01:31,1:03:28,0:13:50,0:06:38,5:08:02,1323,20,18
+Night Team,PGY2+,S0169402,2016-04-25,2:54:22,1:39:59,0:03:33,0:23:54,0:16:13,0:03:05,6:06:10,1812,35,37
+Night Team,PGY2+,S0169402,2016-04-26,2:57:24,1:37:03,0:00:09,0:35:14,0:19:10,0:03:52,6:09:40,1576,15,37
+Night Team,PGY2+,S0169402,2016-04-27,2:10:49,1:14:10,0:04:17,0:35:28,0:09:14,0:03:18,4:30:35,1299,129,17
+General Medicine,PGY1,S0187071,2016-09-03,1:15:45,0:50:20,0:00:05,0:07:24,0:12:08,0:03:25,2:51:11,1156,0,13
+General Medicine,PGY1,S0187071,2016-09-02,1:46:50,0:36:23,0:04:03,0:38:23,0:11:31,0:03:36,4:19:35,1314,0,17
+General Medicine,PGY1,S0187071,2016-09-01,2:08:21,0:42:35,0:00:09,0:35:06,0:22:38,0:02:50,4:21:50,1235,0,12
+General Medicine,PGY1,S0187071,2016-09-07,2:20:16,1:10:31,0:00:04,0:21:58,0:40:27,0:10:47,5:08:18,1928,0,11
+General Medicine,PGY1,S0187071,2016-09-06,2:22:53,1:30:27,0:00:04,0:30:49,0:27:19,0:06:31,5:23:02,1937,18,18
+General Medicine,PGY1,S0187071,2016-09-05,0:12:41,0:02:55,0:00:05,0:00:20,0:00:08,0:00:00,0:19:51,157,95,12
+General Medicine,PGY1,S0187071,2016-09-04,1:07:44,1:25:57,0:00:12,0:15:22,0:08:56,0:04:37,3:23:16,1317,0,17
+General Medicine,PGY1,S0187071,2016-09-09,2:10:04,1:09:47,0:00:05,1:00:29,0:37:00,0:00:24,6:02:23,2216,0,22
+General Medicine,PGY1,S0187071,2016-09-08,2:33:25,1:00:48,0:00:03,0:58:47,0:32:25,0:00:24,5:31:12,2020,188,18
+General Medicine,PGY1,S0094959,2015-03-31,1:52:32,0:10:50,1:46:41,0:15:16,0:07:39,0:05:51,4:29:26,1525,134,14
+General Medicine,PGY1,S0094959,2015-03-30,5:04:23,1:46:25,1:55:43,0:29:23,0:32:25,0:20:16,10:28:56,3821,0,38
+General Medicine,PGY1,S0169381,2014-10-30,2:19:30,0:03:58,0:15:14,0:20:19,0:08:11,0:19:00,3:32:51,950,58,5
+General Medicine,PGY1,S0169381,2014-10-31,2:41:37,0:11:40,0:15:33,0:55:06,0:27:05,0:16:59,5:01:14,1377,76,6
+Night Team,PGY2+,S0094535,2016-02-07,0:33:46,0:14:12,0:15:48,0:15:59,0:05:39,0:16:25,1:51:41,593,0,16
+Night Team,PGY2+,S0094535,2016-02-08,1:19:18,1:09:39,2:33:39,0:45:02,0:13:09,0:39:18,7:12:38,1771,0,18
+Night Team,PGY2+,S0094535,2016-02-09,2:01:25,1:48:43,2:40:04,0:26:38,0:15:04,0:38:02,8:17:57,1899,0,39
+General Medicine,PGY1,S0186952,2016-09-30,2:24:45,1:13:04,0:01:04,1:05:34,0:25:08,0:21:22,6:21:53,2063,80,18
+General Medicine,PGY1,S0181079,2016-03-09,0:12:20,0:00:07,0:00:00,0:00:24,0:00:00,0:02:23,0:15:47,62,58,4
+General Medicine,PGY1,S0181079,2016-03-08,2:37:37,1:34:09,0:00:11,1:09:51,0:29:09,0:02:09,6:34:24,2180,42,18
+General Medicine,PGY1,S0181079,2016-03-07,1:55:59,0:52:31,0:00:47,0:26:37,0:10:51,0:00:32,3:46:13,1360,79,16
+General Medicine,PGY1,S0181079,2016-03-06,0:09:12,0:07:38,0:00:00,0:01:26,0:00:00,0:00:05,0:22:57,164,146,10
+General Medicine,PGY1,S0181079,2016-03-05,3:15:17,1:29:50,0:01:05,0:43:01,0:23:52,0:02:48,6:54:16,2280,0,20
+General Medicine,PGY1,S0181079,2016-03-04,2:15:07,1:03:19,0:03:44,0:39:00,0:06:27,0:09:31,5:28:21,1855,0,25
+General Medicine,PGY1,S0181079,2016-03-03,4:02:08,1:24:00,0:00:14,0:59:15,0:14:42,0:01:07,7:41:39,2263,77,12
+General Medicine,PGY1,S0181079,2016-03-02,1:40:23,0:40:43,0:00:11,0:25:10,0:11:56,0:00:57,3:11:55,1197,0,9
+General Medicine,PGY1,S0181079,2016-03-01,2:02:31,0:43:02,0:00:11,1:08:19,0:19:23,0:01:02,4:27:25,1355,0,11
+Emergency Medicine,PGY1,S0181086,2015-09-13,1:58:24,0:27:42,2:22:21,0:29:20,0:11:52,0:15:02,6:13:29,991,0,8
+Intensive Care Unit,PGY2+,S0169406,2016-03-10,1:23:15,0:11:52,0:15:41,0:07:51,0:07:19,0:07:54,2:47:37,737,128,18
+Night Team,PGY1,S0181525,2016-05-23,1:27:44,0:37:43,0:00:04,0:35:53,0:15:14,0:01:38,3:10:52,890,0,21
+Night Team,PGY1,S0181525,2016-05-22,0:58:18,0:23:18,0:00:00,0:15:43,0:13:24,0:04:45,2:06:36,526,0,22
+Emergency Medicine,PGY1,S0181086,2015-09-17,2:33:36,0:33:24,2:57:00,0:44:24,0:16:18,0:19:56,8:05:55,1617,43,11
+Emergency Medicine,PGY1,S0181086,2015-09-16,2:34:05,0:21:14,2:20:15,0:38:54,0:26:02,0:37:07,7:35:17,1536,81,19
+Emergency Medicine,PGY1,S0181086,2015-09-15,2:50:31,0:25:56,1:50:40,0:17:58,0:51:39,0:13:06,7:08:21,1562,0,15
+Emergency Medicine,PGY1,S0169399,2015-04-29,0:51:41,0:16:09,4:33:15,0:08:01,0:10:35,0:06:49,6:16:46,799,0,11
+Emergency Medicine,PGY1,S0169399,2015-04-26,2:12:37,0:31:37,4:13:19,0:09:08,0:17:38,0:28:35,8:18:55,1232,0,20
+Emergency Medicine,PGY1,S0169399,2015-04-27,3:14:04,0:30:20,4:59:25,0:11:32,0:18:56,0:22:16,9:54:41,1409,0,21
+Emergency Medicine,PGY1,S0181086,2015-09-19,0:12:20,0:01:19,0:00:00,0:00:18,0:00:00,0:01:46,0:18:10,105,105,9
+Emergency Medicine,PGY1,S0181086,2015-09-18,1:20:17,0:22:29,0:48:38,0:06:25,0:00:04,0:15:48,3:19:24,895,859,19
+General Medicine,PGY1,S0169399,2015-04-22,2:18:21,0:23:34,2:45:33,0:49:27,0:38:56,0:04:04,7:31:30,1537,0,13
+Intensive Care Unit,PGY2+,S0169406,2016-03-12,2:01:12,0:33:07,0:00:00,0:53:56,0:23:04,0:14:21,4:24:27,1329,2,16
+General Medicine,PGY1,S0169399,2015-04-20,1:37:08,0:26:20,2:36:01,0:25:27,0:24:39,0:06:45,6:09:03,1266,0,10
+General Medicine,PGY1,S0169399,2015-04-21,1:50:02,0:07:00,0:56:23,1:00:25,0:33:27,0:00:33,4:58:29,1228,0,10
+Intensive Care Unit,PGY2+,S0163425,2015-08-23,4:11:01,0:25:49,0:58:37,0:29:18,0:21:34,0:56:29,8:11:34,1916,157,16
+Intensive Care Unit,PGY2+,S0169406,2016-03-13,2:29:01,0:21:54,0:35:47,0:11:25,0:22:50,0:15:10,5:04:30,1325,581,14
+General Medicine,PGY1,S0195227,2016-08-31,3:36:17,1:49:54,0:00:31,1:01:45,0:35:50,0:01:57,8:02:27,2792,0,17
+General Medicine,PGY1,S0195227,2016-08-30,1:56:32,1:07:32,0:00:32,0:57:06,0:19:14,0:05:56,4:54:42,1648,0,6
+Intensive Care Unit,PGY2+,S0163425,2015-08-24,3:49:17,0:19:32,1:34:27,0:22:44,0:23:48,0:36:10,7:50:55,2055,0,24
+Intensive Care Unit,PGY2+,S0163425,2015-08-25,0:34:49,0:00:55,0:01:50,0:00:41,0:00:31,0:02:10,0:47:17,258,240,20
+Intensive Care Unit,PGY2+,S0169406,2016-03-14,0:58:20,0:05:39,0:04:38,0:02:05,0:08:50,0:02:04,1:36:10,544,43,13
+Intensive Care Unit,PGY2+,S0163425,2015-08-28,4:17:21,0:06:50,0:40:48,0:43:57,0:31:27,0:56:12,7:57:50,2226,0,18
+Intensive Care Unit,PGY2+,S0163425,2015-08-29,3:17:14,0:00:29,0:27:27,0:11:33,0:18:27,0:25:15,4:54:21,1115,0,12
+General Medicine,PGY1,S0181075,2015-07-20,3:49:37,1:55:05,0:40:13,0:24:17,0:31:17,0:21:39,8:14:34,2600,26,24
+General Medicine,PGY1,S0181088,2015-11-30,0:11:59,0:08:25,0:00:34,0:02:22,0:00:34,0:00:24,0:24:50,261,241,6
+Intensive Care Unit,PGY2+,S0169406,2016-03-16,0:36:06,0:07:55,0:08:46,0:09:03,0:11:02,0:00:32,1:33:32,475,30,10
+General Medicine,PGY1,S0181075,2015-07-26,0:11:54,0:12:00,0:00:00,0:00:48,0:02:04,0:00:55,0:28:30,197,179,9
+Intensive Care Unit,PGY2+,S0169406,2016-03-17,0:56:14,0:28:07,0:00:11,0:23:02,0:10:13,0:05:40,2:17:52,1043,17,24
+General Medicine,PGY1,S0181086,2015-12-31,2:51:53,1:24:14,2:50:52,1:16:43,0:49:09,0:41:37,10:36:59,2877,200,14
+General Medicine,PGY1,S0181086,2015-12-30,3:03:41,0:54:35,2:05:50,1:44:20,0:29:26,0:23:42,10:02:30,2796,250,12
+General Medicine,PGY1,S0181075,2015-07-27,1:23:44,1:02:35,1:05:31,0:18:22,0:19:34,0:08:59,4:50:58,1322,293,19
+General Medicine,PGY1,S0181075,2015-07-25,2:09:55,1:36:06,0:49:08,1:14:03,0:45:20,0:25:28,7:34:02,2416,11,21
+Emergency Medicine,PGY1,S0169383,2015-03-18,4:43:25,1:11:00,1:19:59,0:10:03,0:18:09,0:30:39,8:35:33,2313,48,42
+Emergency Medicine,PGY1,S0169383,2015-03-15,4:37:46,0:33:43,2:34:21,0:47:44,0:47:48,0:23:08,10:20:31,2639,0,24
+Emergency Medicine,PGY1,S0169383,2015-03-14,6:03:40,0:55:59,2:53:18,0:32:44,0:33:26,0:33:32,13:10:46,3253,182,30
+Emergency Medicine,PGY1,S0169383,2015-03-17,3:02:13,0:19:54,0:41:35,0:11:16,0:09:50,0:07:06,5:05:31,1371,378,27
+Emergency Medicine,PGY1,S0169383,2015-03-16,2:06:01,0:15:42,1:30:50,0:15:11,0:23:19,0:18:05,5:49:00,2073,147,14
+Emergency Medicine,PGY1,S0169383,2015-03-11,0:25:25,0:07:11,0:26:19,0:10:35,0:05:15,0:02:52,1:26:39,367,61,11
+Emergency Medicine,PGY1,S0195216,2016-10-31,3:01:27,0:50:20,0:02:06,0:55:18,0:33:37,0:01:31,6:55:35,2165,0,49
+Emergency Medicine,PGY1,S0169383,2015-03-13,4:34:38,0:31:21,0:57:09,0:44:42,0:35:06,0:21:09,8:27:05,2347,0,25
+Emergency Medicine,PGY1,S0169383,2015-03-12,5:00:07,0:42:35,2:33:17,0:25:17,0:22:43,0:28:46,10:23:32,2907,185,38
+Night Team,PGY1,S0181065,2015-07-30,2:10:39,0:29:38,0:00:03,0:34:01,0:15:44,0:47:30,4:26:42,1461,0,51
+Night Team,PGY1,S0181065,2015-07-31,1:42:32,0:46:59,0:48:44,0:16:06,0:25:51,0:26:54,4:48:55,1866,0,44
+Emergency Medicine,PGY1,S0169389,2014-09-18,2:34:33,0:04:09,0:26:46,0:11:05,0:24:41,0:21:09,4:25:25,1384,91,27
+Emergency Medicine,PGY1,S0169389,2014-09-19,5:04:52,0:10:50,0:41:59,0:03:39,0:15:40,0:22:55,7:02:52,1768,77,46
+Emergency Medicine,PGY1,S0169389,2014-09-14,5:04:12,0:05:09,1:01:32,0:27:39,0:34:50,0:44:13,8:37:01,2068,15,49
+Emergency Medicine,PGY1,S0169389,2014-09-15,2:49:05,0:05:17,0:23:13,0:08:45,0:07:58,0:26:17,4:18:59,1256,36,27
+Emergency Medicine,PGY1,S0169389,2014-09-16,5:20:53,0:22:39,1:23:18,0:41:43,0:32:23,0:43:13,9:40:59,2371,0,35
+Emergency Medicine,PGY1,S0169389,2014-09-17,0:09:07,0:00:00,0:00:14,0:00:01,0:00:03,0:00:00,0:09:38,67,67,3
+General Medicine,PGY1,S0108268,2014-12-18,2:21:05,0:07:20,0:33:34,0:34:57,0:30:20,0:46:19,5:31:18,1682,644,10
+General Medicine,PGY2+,S0169384,2016-07-14,2:25:28,0:58:02,0:00:05,0:13:13,0:10:18,0:01:26,4:18:37,2044,155,23
+Intensive Care Unit,PGY2+,S0172056,2016-10-31,1:31:06,0:46:57,0:08:26,0:17:28,0:36:10,0:04:50,3:48:34,1097,0,17
+Emergency Medicine,PGY1,S0169402,2014-07-01,2:43:36,0:03:44,0:13:49,0:07:31,0:10:20,0:05:43,3:46:25,776,203,19
+Emergency Medicine,PGY1,S0169402,2014-07-03,4:55:57,0:07:49,0:26:31,0:17:15,0:24:14,0:06:46,6:46:09,1459,0,18
+Emergency Medicine,PGY1,S0169402,2014-07-02,1:24:26,0:04:13,0:04:35,0:01:07,0:04:20,0:08:23,1:53:10,471,155,18
+Emergency Medicine,PGY1,S0169402,2014-07-05,0:00:07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:11,10,10,1
+Emergency Medicine,PGY1,S0169402,2014-07-04,3:46:20,0:06:51,0:09:36,0:20:05,0:04:32,0:14:41,4:59:27,1096,141,18
+Emergency Medicine,PGY1,S0169402,2014-07-07,0:54:05,0:00:00,0:02:01,0:00:00,0:01:37,0:00:28,0:58:28,170,170,16
+General Medicine,PGY1,S0181081,2015-08-09,3:00:32,0:15:26,1:13:50,0:42:02,0:21:08,0:35:03,7:05:54,2353,0,12
+General Medicine,PGY1,S0169406,2015-03-17,1:35:20,0:37:18,0:27:43,0:23:05,0:23:26,0:14:36,4:00:22,1700,0,19
+General Medicine,PGY1,S0169406,2015-03-16,0:17:51,0:07:48,1:11:55,0:01:54,0:00:04,0:00:12,1:42:01,314,310,10
+General Medicine,PGY1,S0181081,2015-08-04,2:22:23,0:03:05,1:29:55,1:09:02,0:22:42,0:11:47,6:15:54,1928,0,11
+General Medicine,PGY1,S0181081,2015-08-05,0:37:32,0:02:40,1:27:57,0:05:04,0:02:34,0:02:44,2:25:52,542,527,11
+General Medicine,PGY1,S0181081,2015-08-02,2:03:21,0:10:23,1:56:40,0:32:11,0:10:02,0:39:32,5:58:35,1258,0,5
+General Medicine,PGY1,S0181081,2015-08-03,2:34:31,0:14:40,1:18:48,0:15:41,0:10:03,0:38:26,5:47:15,1633,381,13
+General Medicine,PGY1,S0169406,2015-03-11,2:00:05,0:12:21,1:37:49,0:42:37,0:11:08,0:15:46,5:15:10,1442,207,7
+General Medicine,PGY1,S0181081,2015-08-01,3:10:13,0:13:30,2:59:57,1:00:19,0:25:15,0:38:54,8:57:12,2066,0,9
+Night Team,PGY1,S0169406,2015-04-18,1:19:09,0:05:35,0:07:24,0:08:24,0:09:45,0:10:50,2:04:26,855,152,53
+Night Team,PGY2+,S0169381,2016-07-22,2:06:27,1:12:53,0:01:16,0:20:00,0:23:44,0:02:34,4:29:19,1125,0,27
+General Medicine,PGY1,S0195201,2016-09-30,1:48:44,1:05:20,0:00:19,0:29:00,0:09:12,0:02:18,4:21:22,2101,0,14
+Night Team,PGY1,S0169406,2015-04-12,2:17:19,0:06:21,0:58:28,0:24:18,0:13:17,0:07:28,4:21:26,1007,0,33
+Night Team,PGY1,S0169406,2015-04-13,2:39:59,0:29:23,0:15:52,0:29:18,0:26:48,0:18:49,4:57:13,1656,0,48
+Night Team,PGY1,S0169406,2015-04-16,1:30:46,0:17:27,1:15:56,0:12:06,0:15:34,0:11:13,3:57:40,1002,217,37
+Night Team,PGY1,S0169406,2015-04-17,1:07:28,0:02:53,0:08:22,0:04:25,0:09:35,0:07:00,1:42:02,582,185,30
+Night Team,PGY1,S0169406,2015-04-14,1:26:47,0:10:50,0:00:58,0:09:25,0:06:47,0:16:02,2:26:49,843,66,41
+Night Team,PGY1,S0169406,2015-04-15,2:09:03,0:35:12,0:39:09,0:31:58,0:19:32,0:18:45,4:45:25,1547,49,39
+Night Team,PGY2+,S0169381,2016-07-27,2:35:20,0:39:29,0:00:09,0:17:30,0:19:49,0:00:31,4:35:20,949,0,26
+Night Team,PGY2+,S0169381,2016-07-26,3:05:24,1:56:44,0:00:10,0:40:55,0:14:31,0:04:18,6:41:43,1819,0,36
+Night Team,PGY2+,S0169381,2016-07-29,4:22:59,1:51:35,0:00:17,0:41:17,0:09:03,0:06:29,7:52:28,2132,0,28
+Night Team,PGY2+,S0169381,2016-07-28,2:12:27,1:11:24,0:00:06,0:19:07,0:10:55,0:12:21,4:30:11,973,33,16
+Night Team,PGY2+,S0109179,2015-07-14,1:30:01,0:16:14,1:21:31,0:21:01,0:06:31,0:21:27,4:08:14,1247,94,37
+Night Team,PGY2+,S0109179,2015-07-15,2:27:03,0:06:50,0:46:33,0:19:56,0:09:27,0:12:15,4:09:44,1046,3,51
+Night Team,PGY2+,S0109179,2015-07-16,2:37:37,0:57:36,2:43:48,0:16:51,0:14:12,0:44:28,7:58:45,2275,0,61
+Night Team,PGY2+,S0109179,2015-07-17,3:37:45,0:34:50,2:00:58,0:45:42,0:14:30,0:39:33,8:28:36,2112,0,44
+Emergency Medicine,PGY1,S0094959,2014-09-12,0:00:00,0:03:31,0:00:00,0:00:00,0:00:00,0:00:27,0:04:07,32,32,3
+Emergency Medicine,PGY1,S0094959,2014-09-10,5:03:40,0:11:02,0:39:22,0:24:01,0:40:20,0:19:11,7:46:03,3536,15,54
+Emergency Medicine,PGY1,S0094959,2014-09-11,4:36:55,0:23:10,1:27:57,0:33:37,1:08:00,0:43:03,9:43:58,4226,54,55
+General Medicine,PGY1,S0181085,2016-01-20,0:21:26,0:46:38,1:05:26,0:07:27,0:03:33,0:13:31,2:55:00,948,87,5
+General Medicine,PGY1,S0181085,2016-01-21,0:39:31,0:32:46,0:36:09,0:21:06,0:15:16,0:04:27,2:48:59,1120,35,5
+General Medicine,PGY1,S0181085,2016-01-22,1:12:56,1:11:26,1:53:20,0:19:51,0:05:36,0:29:00,5:32:48,1591,73,18
+General Medicine,PGY1,S0181085,2016-01-23,0:00:09,0:02:21,0:00:00,0:00:19,0:02:07,0:01:28,0:08:34,77,62,6
+Night Team,PGY2+,S0109179,2015-07-18,2:11:15,0:12:01,2:15:22,0:14:01,0:08:46,0:23:52,5:44:06,1539,0,24
+Night Team,PGY2+,S0109179,2015-07-19,0:40:35,0:17:57,0:31:00,0:02:00,0:05:24,0:09:15,1:54:06,743,0,24
+General Medicine,PGY1,S0181085,2016-01-26,0:25:20,0:29:39,1:07:10,0:10:51,0:12:55,0:10:46,2:53:46,863,133,8
+General Medicine,PGY1,S0181085,2016-01-27,0:55:48,1:00:13,2:00:01,0:07:07,0:06:21,0:12:33,4:41:11,1552,67,14
+Emergency Medicine,PGY1,S0181060,2016-03-27,4:06:01,0:47:09,0:05:08,0:37:14,0:52:45,0:07:06,7:31:53,2654,7,36
+Emergency Medicine,PGY1,S0181060,2016-03-29,1:59:17,0:44:01,0:05:03,0:16:26,0:34:14,0:00:47,4:05:44,2375,0,22
+Emergency Medicine,PGY1,S0181060,2016-03-28,4:10:28,0:53:02,0:02:05,0:39:19,0:59:05,0:07:27,7:31:00,2354,0,21
+Night Team,PGY2+,S0169385,2016-02-22,1:29:27,1:03:34,0:00:22,0:35:55,0:11:14,0:04:57,3:56:17,1450,45,11
+Night Team,PGY2+,S0169385,2016-02-23,1:06:50,0:40:36,0:00:01,0:07:09,0:07:14,0:01:29,2:22:06,1376,0,30
+Night Team,PGY2+,S0169385,2016-02-21,3:00:48,1:42:10,0:00:37,0:51:01,0:20:34,0:12:15,7:10:15,2774,0,36
+Night Team,PGY2+,S0169385,2016-02-26,2:47:57,2:34:56,0:00:14,0:47:25,0:17:22,0:05:07,7:14:33,2615,264,43
+Night Team,PGY2+,S0169385,2016-02-27,3:14:15,1:38:54,0:00:43,0:47:57,0:16:54,0:11:09,7:21:25,2756,24,35
+Night Team,PGY2+,S0169385,2016-02-24,2:16:27,2:00:54,0:00:33,0:55:37,0:27:41,0:19:23,6:43:07,2965,264,64
+Night Team,PGY2+,S0169385,2016-02-25,2:58:35,1:09:58,0:00:06,0:38:54,0:21:06,0:10:39,6:07:26,2318,109,35
+Night Team,PGY2+,S0169385,2016-02-28,1:52:08,1:19:16,0:03:24,0:31:57,0:14:08,0:10:10,4:44:23,2045,362,42
+Night Team,PGY2+,S0169385,2016-02-29,1:25:09,0:45:13,0:00:41,0:10:05,0:04:02,0:09:14,3:00:37,1315,60,36
+General Medicine,PGY1,S0195210,2016-10-29,0:00:03,0:00:06,0:00:00,0:00:00,0:00:00,0:00:23,0:00:48,10,0,1
+General Medicine,PGY1,S0195210,2016-10-28,1:23:27,2:01:32,0:00:05,0:31:31,0:07:35,0:00:20,4:28:36,1167,4,13
+General Medicine,PGY1,S0195210,2016-10-21,1:09:20,1:02:51,0:00:10,0:17:29,0:14:20,0:00:08,3:07:55,708,0,11
+General Medicine,PGY1,S0195210,2016-10-20,0:51:00,0:32:40,0:00:11,0:28:00,0:10:58,0:04:47,2:32:20,511,0,6
+General Medicine,PGY1,S0195210,2016-10-23,1:02:52,1:13:19,0:00:13,0:52:44,0:09:07,0:05:30,3:59:15,1122,15,11
+General Medicine,PGY1,S0195210,2016-10-22,0:01:30,0:00:28,0:00:00,0:01:11,0:00:00,0:00:00,0:09:26,118,0,89
+General Medicine,PGY1,S0195210,2016-10-25,0:00:02,0:00:28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:32,9,0,2
+General Medicine,PGY1,S0195210,2016-10-24,0:45:05,0:23:39,0:00:15,0:25:34,0:05:19,0:00:51,1:47:27,585,49,6
+General Medicine,PGY1,S0195210,2016-10-27,0:57:01,0:28:51,0:00:03,0:40:36,0:04:03,0:00:03,2:29:37,559,0,4
+General Medicine,PGY1,S0195210,2016-10-26,0:44:11,0:17:20,0:00:32,0:16:42,0:10:29,0:00:01,1:49:59,855,0,26
+Intensive Care Unit,PGY2+,S0181070,2016-08-29,0:48:23,0:57:46,0:04:50,0:04:11,0:15:36,0:00:32,2:26:05,1005,0,12
+Emergency Medicine,PGY1,S0181082,2016-02-28,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,14,0,4
+Emergency Medicine,PGY1,S0181082,2016-02-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,4
+Emergency Medicine,PGY1,S0195094,2016-08-26,3:30:14,0:38:44,0:01:00,0:42:30,0:49:12,0:01:45,6:25:17,1192,0,15
+Emergency Medicine,PGY1,S0195094,2016-08-27,0:01:30,0:00:21,0:00:00,0:00:00,0:00:00,0:00:00,0:02:11,18,0,2
+Emergency Medicine,PGY1,S0195094,2016-08-24,2:49:10,0:33:12,0:00:49,0:31:44,0:32:29,0:03:43,5:02:50,1088,0,18
+Emergency Medicine,PGY1,S0195094,2016-08-25,2:51:18,0:47:06,0:02:48,0:39:29,0:20:39,0:03:42,5:34:18,1219,0,16
+General Medicine,PGY1,S0196593,2016-11-10,0:08:35,0:29:47,0:00:00,0:00:00,0:00:00,0:00:02,0:38:35,148,146,7
+Emergency Medicine,PGY1,S0195094,2016-08-23,2:39:16,0:41:52,0:04:24,0:21:43,0:30:14,0:01:27,4:52:56,1184,1,27
+Emergency Medicine,PGY1,S0195094,2016-08-21,2:22:12,0:15:53,0:01:23,0:24:44,0:51:15,0:00:04,4:55:27,1263,0,16
+Emergency Medicine,PGY1,S0195094,2016-08-28,2:32:49,0:26:29,0:01:12,0:35:17,0:55:29,0:00:06,5:18:00,1343,72,26
+Emergency Medicine,PGY1,S0195094,2016-08-29,3:12:12,0:20:48,0:00:56,0:38:01,0:40:36,0:00:48,5:51:30,1337,1,22
+General Medicine,PGY1,S0056755,2014-10-10,1:15:02,0:09:33,0:02:36,0:00:47,0:01:50,0:06:27,1:44:57,989,979,40
+General Medicine,PGY1,S0056755,2014-10-11,3:36:24,0:18:15,0:19:43,0:21:55,0:14:11,0:21:09,5:43:35,1425,405,22
+General Medicine,PGY1,S0181085,2015-11-04,1:28:04,1:38:48,2:27:35,0:53:42,0:21:46,0:17:31,7:33:36,2051,0,10
+General Medicine,PGY1,S0181085,2015-11-05,0:02:49,0:13:52,0:25:17,0:00:00,0:00:08,0:00:45,0:45:34,243,239,10
+General Medicine,PGY1,S0181085,2015-11-06,1:05:05,1:55:21,2:11:47,0:42:04,0:24:39,0:13:25,7:07:22,2394,197,21
+General Medicine,PGY1,S0181085,2015-11-07,0:56:08,1:29:35,1:09:42,0:40:37,0:06:21,0:16:32,5:04:20,1453,11,11
+General Medicine,PGY1,S0181085,2015-11-01,1:10:50,1:13:23,1:08:14,0:24:05,0:18:06,0:20:11,5:02:24,1434,23,12
+General Medicine,PGY1,S0181085,2015-11-02,0:20:34,0:44:03,0:58:13,0:05:28,0:05:49,0:13:07,2:35:23,630,0,5
+General Medicine,PGY1,S0181085,2015-11-03,0:31:16,0:53:54,0:41:33,0:21:32,0:13:38,0:07:23,3:03:05,941,0,4
+General Medicine,PGY1,S0181082,2016-01-02,0:00:00,0:00:00,0:00:00,0:00:26,0:00:00,0:00:00,0:00:26,22,0,5
+General Medicine,PGY1,S0181082,2016-01-01,0:53:31,1:15:19,1:55:46,0:28:55,0:17:25,0:21:01,5:38:19,1966,0,10
+General Medicine,PGY1,S0181088,2015-08-11,0:31:37,0:00:14,0:42:22,0:08:32,0:05:45,0:11:29,2:05:07,457,11,4
+Night Team,PGY2+,S0181070,2016-09-28,2:14:05,2:01:56,0:00:17,0:46:25,0:22:19,0:06:41,6:02:34,2195,207,30
+Night Team,PGY2+,S0181070,2016-09-29,3:20:58,3:13:34,0:00:15,0:28:31,0:12:48,0:09:19,8:20:34,3522,0,41
+General Medicine,PGY1,S0181069,2016-04-01,2:48:12,1:13:51,0:00:10,1:03:00,0:17:18,0:04:51,6:00:06,1966,2,8
+General Medicine,PGY1,S0181069,2016-04-07,2:04:38,1:29:31,0:06:34,0:20:28,0:12:21,0:03:50,5:06:08,1720,0,18
+General Medicine,PGY1,S0181069,2016-04-06,2:37:38,2:20:34,0:00:14,0:46:32,0:22:59,0:03:31,6:49:20,3351,0,28
+General Medicine,PGY1,S0181069,2016-04-05,0:01:48,0:02:52,0:00:00,0:00:00,0:00:09,0:00:00,0:05:32,92,29,3
+General Medicine,PGY1,S0181069,2016-04-04,1:45:14,1:29:56,0:00:15,0:55:46,0:14:51,0:06:53,5:04:09,2163,246,19
+General Medicine,PGY1,S0181069,2016-04-09,0:04:24,0:07:49,0:00:00,0:00:00,0:00:55,0:00:03,0:13:45,150,146,5
+General Medicine,PGY1,S0181069,2016-04-08,2:19:40,1:00:05,0:00:02,1:06:31,0:27:12,0:06:43,5:50:07,2630,203,15
+Night Team,PGY2+,S0181070,2016-09-25,1:05:41,1:16:51,0:00:02,0:21:01,0:01:46,0:04:22,3:01:40,926,0,18
+Night Team,PGY2+,S0181070,2016-09-26,1:23:00,1:40:43,0:00:04,0:14:58,0:07:05,0:00:41,4:00:26,1315,19,30
+Night Team,PGY2+,S0181070,2016-09-27,2:09:10,1:47:02,0:00:36,0:26:17,0:08:12,0:04:06,4:59:00,1509,0,36
+General Medicine,PGY2+,S0093383,2016-02-26,1:32:10,2:01:28,0:00:45,0:50:48,0:23:48,0:08:50,5:20:11,2182,0,13
+General Medicine,PGY2+,S0093383,2016-02-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY2+,S0093383,2016-02-24,1:51:29,1:02:35,0:00:39,0:31:53,0:49:05,0:04:56,4:36:37,1365,0,13
+General Medicine,PGY2+,S0093383,2016-02-25,2:41:21,1:44:55,0:05:17,0:36:04,0:17:30,0:13:27,6:03:43,2747,79,23
+General Medicine,PGY2+,S0093383,2016-02-22,0:40:37,0:37:06,0:00:03,0:12:09,0:05:53,0:00:12,1:43:32,806,21,8
+General Medicine,PGY2+,S0093383,2016-02-23,2:36:08,1:12:06,0:00:01,0:39:39,0:17:59,0:09:29,5:25:04,2754,0,22
+General Medicine,PGY2+,S0093383,2016-02-20,1:49:19,1:16:23,0:00:01,0:05:41,0:15:43,0:08:18,3:59:20,1927,0,16
+General Medicine,PGY2+,S0093383,2016-02-21,1:17:06,0:33:05,0:00:07,0:26:41,0:18:14,0:00:28,2:50:07,1346,0,9
+General Medicine,PGY2+,S0093383,2016-02-28,2:30:57,1:11:50,0:00:04,0:32:09,0:16:33,0:13:57,5:26:53,2365,0,18
+General Medicine,PGY2+,S0093383,2016-02-29,1:00:32,0:48:38,0:01:37,0:04:41,0:07:20,0:03:12,2:22:50,1013,19,12
+General Medicine,PGY1,S0195213,2016-10-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,1
+General Medicine,PGY1,S0195213,2016-10-29,0:47:54,0:50:48,0:00:07,0:02:43,0:02:10,0:00:06,1:55:00,658,0,6
+General Medicine,PGY2+,S0169381,2016-04-30,0:14:52,0:16:17,0:00:00,0:00:34,0:00:00,0:00:06,0:46:06,428,422,16
+General Medicine,PGY1,S0195213,2016-10-24,1:21:00,0:29:35,0:00:03,0:44:14,0:15:07,0:00:19,3:07:01,716,0,4
+General Medicine,PGY1,S0195213,2016-10-25,2:20:56,1:31:39,0:00:12,1:04:03,0:11:27,0:02:58,5:44:23,1827,0,16
+General Medicine,PGY1,S0195213,2016-10-26,0:58:21,0:25:57,0:00:22,0:39:30,0:11:45,0:05:13,3:03:33,885,0,6
+General Medicine,PGY1,S0195213,2016-10-27,2:19:15,0:50:47,0:00:05,0:53:32,0:15:45,0:07:09,5:10:59,1373,0,14
+General Medicine,PGY1,S0195213,2016-10-20,1:55:15,1:11:48,0:00:02,0:40:58,0:20:53,0:00:24,4:22:17,1280,34,9
+General Medicine,PGY1,S0195213,2016-10-21,1:44:15,1:01:13,0:00:07,0:36:22,0:28:03,0:00:46,4:08:18,1119,0,7
+General Medicine,PGY1,S0195213,2016-10-22,2:16:19,0:44:25,0:00:08,0:43:59,0:11:51,0:04:38,4:27:49,1785,0,15
+General Medicine,PGY1,S0195213,2016-10-23,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181526,2016-05-06,1:34:08,1:03:17,0:03:56,0:08:56,0:15:46,0:00:41,3:52:10,1321,23,28
+General Medicine,PGY1,S0181526,2016-05-07,0:19:25,0:03:12,0:00:00,0:00:07,0:00:00,0:00:42,0:24:05,113,102,9
+General Medicine,PGY1,S0181526,2016-05-04,2:28:48,1:01:18,0:00:46,0:28:43,0:22:56,0:02:25,4:50:23,1654,26,16
+General Medicine,PGY1,S0181526,2016-05-05,1:10:13,0:39:37,0:01:15,0:21:18,0:11:04,0:01:03,3:02:57,1049,78,12
+General Medicine,PGY1,S0181526,2016-05-02,1:22:43,0:23:19,0:00:09,0:17:38,0:10:07,0:04:39,2:45:10,922,99,13
+General Medicine,PGY1,S0181526,2016-05-03,1:51:51,1:25:00,0:01:36,0:22:34,0:16:11,0:00:43,5:02:27,1756,33,23
+General Medicine,PGY1,S0181526,2016-05-01,2:42:57,0:59:44,0:00:11,0:45:48,0:15:07,0:04:36,5:44:03,1690,16,26
+General Medicine,PGY2+,S0106952,2015-10-09,1:39:23,0:52:33,0:01:29,0:27:12,0:05:01,0:05:55,4:13:04,2009,243,82
+General Medicine,PGY1,S0181526,2016-05-08,0:33:04,0:30:37,0:02:02,0:07:07,0:06:22,0:02:33,1:40:44,498,97,11
+General Medicine,PGY1,S0181526,2016-05-09,2:38:12,0:56:12,0:01:15,0:45:25,0:11:20,0:03:53,5:10:45,1515,136,38
+Night Team,PGY2+,S0094959,2016-03-01,4:50:57,1:53:31,0:00:13,0:29:01,0:26:48,0:11:09,8:28:22,3402,137,76
+Night Team,PGY2+,S0094959,2016-03-03,3:59:59,2:04:24,0:00:14,0:34:49,0:19:15,0:19:43,7:56:45,3366,221,67
+Night Team,PGY2+,S0094959,2016-03-02,3:43:13,1:54:50,0:00:15,0:44:42,0:24:47,0:29:45,8:19:32,4237,258,93
+Night Team,PGY2+,S0094959,2016-03-05,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,9,1,5
+Night Team,PGY2+,S0094959,2016-03-04,2:59:37,1:02:31,0:00:09,0:25:34,0:08:47,0:09:35,5:34:33,1972,149,55
+General Medicine,PGY1,S0195220,2016-07-26,2:18:59,0:52:52,0:00:16,1:02:25,0:20:03,0:01:04,5:07:40,1586,254,9
+General Medicine,PGY1,S0181075,2015-07-19,2:20:56,0:59:08,2:50:17,0:10:30,0:10:48,0:07:24,7:06:51,1679,412,13
+Emergency Medicine,PGY2+,S0106952,2016-10-05,1:11:04,0:07:35,0:00:22,0:00:23,0:00:31,0:03:05,1:31:00,822,484,54
+General Medicine,PGY1,S0195220,2016-07-24,2:06:39,0:47:33,0:00:21,0:18:20,0:17:05,0:02:53,3:56:05,1139,139,6
+General Medicine,PGY1,S0195220,2016-07-25,2:45:09,0:57:49,0:00:42,0:54:09,0:33:23,0:06:22,6:07:07,1560,69,11
+Intensive Care Unit,PGY2+,S0181086,2016-06-30,3:00:05,1:07:22,0:13:30,0:50:22,0:39:29,0:00:28,6:41:37,1633,338,19
+General Medicine,PGY2+,S0106952,2015-10-03,2:20:46,1:16:46,0:14:36,0:36:45,0:20:14,0:49:26,6:02:35,2219,203,17
+General Medicine,PGY2+,S0106952,2015-10-02,2:26:33,3:01:25,1:00:05,0:10:55,0:19:07,0:42:05,8:26:22,2673,610,108
+General Medicine,PGY2+,S0106952,2015-10-01,2:09:27,1:57:35,0:20:31,0:14:08,0:08:30,0:35:52,5:50:40,2099,611,61
+General Medicine,PGY1,S0169400,2014-11-30,1:47:56,0:01:25,1:55:50,0:15:30,0:13:32,0:06:18,4:38:35,1087,46,8
+Night Team,PGY2+,S0181065,2016-08-10,1:59:34,0:22:24,0:00:07,0:13:24,0:04:18,0:05:07,3:06:59,1500,0,90
+Night Team,PGY2+,S0181065,2016-08-11,3:46:16,1:11:25,0:00:08,0:16:00,0:17:42,0:03:07,6:07:49,3885,34,32
+Night Team,PGY2+,S0181065,2016-08-12,3:35:34,1:03:48,0:00:17,0:14:58,0:11:32,0:17:27,5:59:08,2878,136,37
+Night Team,PGY2+,S0181065,2016-08-13,1:40:55,0:22:36,0:00:13,0:11:34,0:12:22,0:00:39,2:39:14,1276,0,27
+General Medicine,PGY2+,S0106952,2015-10-04,0:06:31,0:24:45,1:12:25,0:01:15,0:04:00,0:00:30,1:49:44,337,327,6
+Night Team,PGY1,S0195079,2016-10-17,0:57:35,0:18:17,0:00:00,0:32:46,0:21:08,0:02:41,2:27:57,754,0,31
+General Medicine,PGY1,S0172645,2016-01-19,1:35:18,1:45:42,1:40:44,0:37:22,0:34:44,0:44:31,7:21:42,2335,20,12
+General Medicine,PGY1,S0172645,2016-01-18,0:35:00,0:54:45,2:28:44,0:15:49,0:03:00,0:03:38,4:31:41,1134,9,7
+General Medicine,PGY1,S0172645,2016-01-15,0:47:49,1:04:48,1:22:14,0:14:31,0:10:06,0:18:45,4:16:48,1227,295,9
+General Medicine,PGY1,S0172645,2016-01-14,1:00:56,1:38:21,3:40:18,0:31:25,0:19:49,0:14:48,7:40:52,1874,82,12
+General Medicine,PGY1,S0172645,2016-01-17,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,11,0,2
+General Medicine,PGY1,S0172645,2016-01-16,1:13:10,0:58:05,1:29:11,0:24:42,0:16:58,0:11:02,4:50:55,1619,27,13
+General Medicine,PGY1,S0172645,2016-01-11,1:19:59,1:30:59,1:51:01,0:40:38,0:29:08,0:30:27,6:37:37,1626,0,10
+General Medicine,PGY1,S0172645,2016-01-10,1:18:11,1:03:45,1:50:10,0:33:34,0:16:09,0:12:20,5:29:34,1609,0,9
+General Medicine,PGY1,S0172645,2016-01-13,0:00:00,0:13:28,0:00:00,0:00:00,0:00:00,0:01:19,0:15:28,83,75,4
+General Medicine,PGY1,S0172645,2016-01-12,1:30:18,1:01:14,1:36:49,0:36:41,0:25:53,0:32:06,6:01:30,1579,0,18
+General Medicine,PGY1,S0106952,2015-04-06,4:08:20,0:21:55,1:48:37,1:05:46,0:38:08,0:43:32,9:25:14,2857,24,25
+General Medicine,PGY1,S0106952,2015-04-07,0:00:00,0:00:00,0:00:00,0:00:34,0:00:00,0:00:00,0:00:34,8,0,2
+General Medicine,PGY1,S0106952,2015-04-04,3:35:10,0:17:08,1:05:30,0:46:29,0:39:14,0:30:23,7:14:15,1827,131,11
+General Medicine,PGY1,S0106952,2015-04-05,0:11:00,0:03:44,0:00:02,0:01:20,0:00:00,0:00:00,0:16:11,108,82,10
+General Medicine,PGY1,S0106952,2015-04-02,2:48:53,1:06:46,1:26:17,0:18:30,0:08:43,0:13:03,6:17:34,1755,1018,93
+General Medicine,PGY1,S0106952,2015-04-03,3:35:06,0:24:54,1:58:13,0:37:32,0:36:19,0:32:15,8:07:40,2336,195,16
+General Medicine,PGY1,S0195226,2016-07-31,2:46:11,1:09:36,0:00:18,0:54:42,0:22:14,0:04:04,5:52:54,1625,251,6
+General Medicine,PGY1,S0195226,2016-07-30,0:21:25,0:50:38,0:00:02,0:00:00,0:00:45,0:04:25,1:27:12,354,334,13
+General Medicine,PGY1,S0106952,2015-04-08,3:55:16,0:15:07,1:58:54,0:48:56,0:35:03,0:21:46,8:16:55,2061,0,30
+General Medicine,PGY1,S0106952,2015-04-09,4:11:30,0:18:47,3:08:02,0:22:25,0:19:57,0:22:14,9:11:18,2223,283,25
+Night Team,PGY2+,S0181083,2016-08-30,1:35:26,0:40:28,0:00:00,0:18:33,0:06:07,0:01:16,3:12:51,970,0,24
+Night Team,PGY2+,S0181083,2016-08-31,2:07:02,1:20:02,0:02:40,0:30:51,0:11:18,0:16:07,5:06:04,1510,505,21
+General Medicine,PGY1,S0163662,2015-06-05,1:27:03,0:27:25,0:46:10,0:14:55,0:07:23,0:10:12,3:43:42,810,0,7
+General Medicine,PGY1,S0163662,2015-06-04,1:42:09,0:26:52,0:45:38,0:38:42,0:14:57,0:12:54,4:27:42,1467,157,11
+General Medicine,PGY1,S0163662,2015-06-07,0:30:11,0:10:12,1:06:21,0:08:25,0:00:46,0:05:39,2:12:23,851,839,21
+General Medicine,PGY1,S0163662,2015-06-06,3:01:31,0:55:34,2:28:01,0:37:30,0:20:16,0:40:39,8:34:36,2213,0,7
+General Medicine,PGY1,S0163662,2015-06-01,2:58:55,0:47:02,1:36:49,0:31:45,0:12:42,0:22:12,6:45:00,1597,92,7
+General Medicine,PGY1,S0163662,2015-06-03,3:14:40,0:42:36,2:54:17,0:40:12,0:26:20,0:31:31,8:47:05,2437,67,11
+General Medicine,PGY1,S0195205,2016-08-29,1:46:32,1:26:43,0:00:18,0:56:51,0:39:43,0:05:18,5:46:27,2175,0,18
+General Medicine,PGY1,S0195205,2016-08-26,1:38:19,0:57:01,0:00:21,0:34:40,0:34:53,0:06:20,4:23:59,1786,372,7
+General Medicine,PGY1,S0195205,2016-08-27,3:23:46,1:31:34,0:00:09,0:49:12,0:53:20,0:03:46,7:23:46,2643,488,21
+General Medicine,PGY1,S0195205,2016-08-24,2:15:31,1:44:54,0:01:51,0:41:03,0:36:00,0:09:03,6:11:38,2250,53,14
+General Medicine,PGY1,S0195205,2016-08-25,3:20:14,1:29:16,0:00:49,0:54:36,0:47:04,0:05:40,7:08:40,2337,603,16
+General Medicine,PGY1,S0163662,2015-06-09,2:57:22,0:31:00,0:52:37,0:15:54,0:24:18,0:15:53,5:39:03,1546,159,17
+General Medicine,PGY1,S0163662,2015-06-08,2:55:47,0:58:05,2:31:09,0:56:47,0:19:54,0:22:41,8:28:53,2929,67,14
+General Medicine,PGY1,S0195205,2016-08-21,0:56:56,0:33:34,0:00:05,0:14:01,0:13:56,0:00:06,2:12:43,782,48,8
+Night Team,PGY1,S0163425,2015-02-06,2:10:27,0:19:38,1:02:34,0:20:49,0:32:33,0:41:10,5:31:56,1628,0,31
+Night Team,PGY1,S0181069,2015-11-30,0:39:32,0:25:42,0:51:09,0:32:53,0:08:27,0:11:45,3:01:39,881,839,19
+General Medicine,PGY1,S0181070,2015-09-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY2+,S0169403,2016-01-11,1:02:03,1:09:24,1:49:35,0:34:05,0:29:44,0:49:56,6:26:41,3024,88,15
+General Medicine,PGY1,S0195220,2016-08-02,2:04:38,0:47:53,0:00:09,0:39:52,0:22:07,0:04:28,4:29:34,1473,0,11
+General Medicine,PGY1,S0181070,2015-09-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0163425,2015-02-02,1:02:23,0:00:20,0:11:14,0:04:31,0:11:52,0:02:03,1:40:24,533,0,21
+Night Team,PGY1,S0163425,2015-02-03,0:55:26,0:09:00,0:14:51,0:24:32,0:07:50,0:11:30,2:12:56,942,91,26
+Night Team,PGY1,S0106952,2014-12-07,5:10:57,0:02:47,0:18:29,0:14:14,0:27:18,0:27:08,6:51:54,1781,0,49
+Night Team,PGY1,S0106952,2014-12-08,4:14:25,0:04:14,0:52:13,0:18:33,0:30:30,0:14:32,6:31:50,2240,151,146
+Night Team,PGY1,S0106952,2014-12-09,2:52:01,0:02:20,3:15:59,0:26:40,0:25:30,0:28:32,7:45:46,1523,147,41
+General Medicine,PGY1,S0195220,2016-08-06,2:26:13,0:50:24,0:00:07,0:45:10,0:20:55,0:07:49,5:03:25,1245,146,10
+Intensive Care Unit,PGY2+,S0176040,2016-07-17,2:03:01,0:23:55,0:09:47,0:14:45,0:26:30,0:00:51,3:47:05,1161,0,12
+General Medicine,PGY1,S0108287,2014-07-30,0:16:36,0:00:00,0:05:21,0:00:00,0:00:00,0:00:23,0:24:13,145,139,9
+General Medicine,PGY2+,S0181078,2016-09-30,0:09:31,0:01:08,0:00:00,0:00:09,0:00:00,0:00:01,0:10:58,63,49,13
+Intensive Care Unit,PGY2+,S0176040,2016-07-18,2:25:55,1:05:51,0:12:07,0:29:58,0:42:00,0:01:00,5:18:54,1276,24,15
+Intensive Care Unit,PGY2+,S0176040,2016-07-19,0:00:21,0:00:04,0:00:00,0:00:10,0:00:00,0:00:50,0:01:32,24,0,6
+General Medicine,PGY1,S0195076,2016-06-26,1:44:45,0:50:23,0:01:06,0:09:05,0:18:42,0:01:35,3:49:28,1086,0,6
+General Medicine,PGY1,S0195076,2016-06-27,0:05:23,0:05:49,0:00:00,0:00:00,0:00:19,0:00:04,0:15:03,88,88,6
+General Medicine,PGY1,S0195076,2016-06-28,2:51:56,2:00:57,0:00:20,0:40:28,0:31:12,0:01:05,6:40:38,1872,0,9
+General Medicine,PGY1,S0195076,2016-06-29,2:00:45,1:54:30,0:00:15,0:40:47,0:32:32,0:00:14,6:12:12,1590,0,7
+Night Team,PGY2+,S0169411,2015-10-09,4:52:15,2:02:53,0:47:50,0:48:04,0:08:47,0:25:32,9:35:53,3801,0,121
+Intensive Care Unit,PGY2+,S0169401,2016-03-08,3:27:22,0:35:44,0:02:29,0:46:33,0:13:34,0:13:51,5:44:17,2276,18,19
+Intensive Care Unit,PGY2+,S0169401,2016-03-09,2:55:41,1:26:41,0:05:45,0:26:06,0:13:01,0:08:50,5:43:35,2326,486,31
+Intensive Care Unit,PGY2+,S0169401,2016-03-06,0:18:27,0:01:03,0:00:00,0:00:00,0:00:00,0:00:27,0:20:42,154,150,11
+Intensive Care Unit,PGY2+,S0169401,2016-03-07,2:37:04,0:49:45,0:09:05,0:15:15,0:15:43,0:03:04,4:28:28,1406,38,20
+General Medicine,PGY1,S0181060,2015-07-31,2:40:50,0:23:18,2:10:44,0:58:36,0:26:58,0:25:08,7:39:05,2097,7,18
+General Medicine,PGY1,S0196595,2016-07-06,3:06:00,1:17:30,0:00:34,1:24:21,0:41:43,0:03:34,6:56:58,1573,0,7
+General Medicine,PGY1,S0196595,2016-07-07,2:02:59,0:47:06,0:02:12,0:29:32,0:16:52,0:00:32,3:55:22,851,141,7
+General Medicine,PGY1,S0196595,2016-07-04,2:14:46,1:13:17,0:02:00,0:50:31,0:24:19,0:08:27,5:23:09,1281,0,8
+General Medicine,PGY1,S0196595,2016-07-05,2:03:09,1:13:31,0:00:08,0:35:30,0:16:42,0:00:16,5:01:32,1071,0,7
+General Medicine,PGY1,S0196595,2016-07-02,1:48:58,0:34:45,0:00:03,0:31:15,0:25:02,0:00:26,3:56:43,892,0,5
+General Medicine,PGY1,S0196595,2016-07-03,1:30:52,1:18:43,0:00:37,0:14:33,0:08:06,0:00:11,3:38:42,920,0,7
+General Medicine,PGY1,S0196595,2016-07-01,2:58:28,1:15:56,0:00:43,0:46:10,0:28:41,0:06:19,6:11:37,1412,109,8
+General Medicine,PGY1,S0196595,2016-07-08,2:39:25,1:48:19,0:00:02,0:51:56,0:21:16,0:00:55,6:31:31,1290,0,5
+General Medicine,PGY1,S0196595,2016-07-09,3:54:53,0:56:17,0:02:14,1:05:50,0:38:47,0:07:50,7:37:56,1778,0,10
+Night Team,PGY2+,S0106952,2015-07-23,3:12:09,0:56:56,1:56:19,0:20:35,0:27:55,0:40:59,8:36:36,3008,376,80
+General Medicine,PGY1,S0094959,2015-04-10,3:01:36,0:37:07,0:25:04,0:25:38,0:14:19,0:19:33,5:17:44,2486,0,37
+Intensive Care Unit,PGY2+,S0056755,2016-06-07,2:38:32,1:45:21,0:02:50,0:30:20,0:26:07,0:00:09,5:47:18,1701,4,21
+General Medicine,PGY1,S0094959,2015-04-12,3:58:44,1:10:34,1:24:43,0:19:35,0:25:32,0:26:36,8:10:14,3766,0,71
+General Medicine,PGY1,S0094959,2015-04-13,3:23:29,0:20:21,0:55:12,0:22:51,0:20:57,0:09:26,5:45:48,2511,94,16
+General Medicine,PGY1,S0094959,2015-04-14,3:04:20,0:48:51,1:28:01,0:24:37,0:14:23,0:21:01,6:49:00,2572,0,30
+General Medicine,PGY1,S0094959,2015-04-15,0:00:00,0:03:03,0:00:00,0:05:33,0:00:00,0:00:00,0:09:24,42,24,4
+General Medicine,PGY1,S0094959,2015-04-16,2:17:30,0:36:19,0:30:32,0:33:18,0:10:23,0:16:39,4:49:11,2327,459,44
+General Medicine,PGY1,S0094959,2015-04-17,3:03:03,1:57:41,0:29:18,0:02:41,0:29:08,0:40:51,6:55:20,2701,0,95
+General Medicine,PGY1,S0094959,2015-04-18,0:00:00,0:05:25,0:00:00,0:00:35,0:00:00,0:05:33,0:11:58,109,103,8
+General Medicine,PGY1,S0094959,2015-04-19,3:23:35,1:44:28,0:03:33,0:13:35,0:31:10,0:59:13,7:33:57,4473,107,104
+Intensive Care Unit,PGY2+,S0056755,2016-06-08,1:11:27,0:52:18,0:01:46,0:15:38,0:18:20,0:02:50,2:59:40,888,2,13
+Intensive Care Unit,PGY2+,S0056755,2016-06-09,1:36:56,0:50:55,0:08:40,0:15:50,0:17:09,0:00:15,3:35:32,1073,0,14
+Intensive Care Unit,PGY2+,S0169398,2016-04-17,0:03:37,0:01:07,0:00:12,0:01:51,0:03:48,0:00:32,0:17:55,215,189,22
+Intensive Care Unit,PGY2+,S0169398,2016-04-16,3:00:26,0:30:21,0:13:30,0:16:37,0:18:47,0:10:04,4:48:57,1471,122,20
+Intensive Care Unit,PGY2+,S0169398,2016-04-15,3:17:26,0:45:31,0:00:00,0:32:58,0:45:48,0:16:53,6:30:11,2174,77,102
+Intensive Care Unit,PGY2+,S0169398,2016-04-14,3:57:21,1:24:16,0:14:32,0:30:08,0:34:00,0:08:05,7:50:19,2332,0,23
+Intensive Care Unit,PGY2+,S0169398,2016-04-13,2:56:39,0:31:26,0:10:06,0:32:09,0:32:06,0:09:04,5:26:51,1420,15,15
+Intensive Care Unit,PGY2+,S0169398,2016-04-12,1:42:56,0:50:16,0:05:04,0:32:38,0:25:27,0:05:59,4:19:10,1218,8,14
+Intensive Care Unit,PGY2+,S0169398,2016-04-11,2:04:12,1:12:15,0:07:58,0:17:56,0:31:47,0:02:32,4:45:16,1416,0,12
+Intensive Care Unit,PGY2+,S0169398,2016-04-10,3:15:56,1:27:06,0:03:28,0:31:11,0:34:22,0:03:18,6:42:15,2172,0,13
+Intensive Care Unit,PGY2+,S0169398,2016-04-19,1:29:54,0:35:01,0:02:54,0:10:25,0:18:43,0:01:16,3:05:12,1062,6,14
+Intensive Care Unit,PGY2+,S0169398,2016-04-18,1:26:05,0:08:33,0:00:00,0:07:16,0:12:45,0:04:04,2:15:55,504,0,9
+General Medicine,PGY1,S0181078,2015-08-31,1:25:51,0:16:55,0:59:12,0:27:14,0:08:56,0:02:45,3:53:41,1130,134,7
+General Medicine,PGY1,S0181078,2015-08-30,1:59:02,0:37:07,2:33:16,0:29:07,0:11:03,0:16:53,7:08:52,1585,19,9
+Intensive Care Unit,PGY2+,S0169386,2016-06-27,3:26:14,0:44:19,0:05:41,0:28:27,0:24:54,0:12:16,5:47:54,1906,78,19
+General Medicine,PGY1,S0176040,2016-03-13,3:24:57,2:31:45,0:00:10,0:27:14,0:18:48,0:05:00,7:39:13,2186,40,12
+General Medicine,PGY1,S0176040,2016-03-10,0:00:13,0:00:27,0:00:00,0:00:23,0:00:00,0:00:51,0:02:29,56,26,5
+General Medicine,PGY1,S0176040,2016-03-11,2:05:43,1:18:31,0:00:05,0:39:43,0:25:20,0:00:52,5:05:56,1946,0,14
+General Medicine,PGY1,S0176040,2016-03-16,1:52:52,1:02:13,0:00:12,0:24:04,0:22:25,0:04:34,4:07:04,1772,17,12
+General Medicine,PGY1,S0176040,2016-03-17,0:02:07,0:00:30,0:00:00,0:00:00,0:00:00,0:00:16,0:02:54,29,15,5
+General Medicine,PGY1,S0176040,2016-03-14,3:10:29,1:11:31,0:00:06,0:52:18,0:31:41,0:01:09,6:29:17,2339,92,11
+General Medicine,PGY1,S0176040,2016-03-15,1:58:12,0:56:20,0:00:05,0:26:11,0:21:40,0:01:36,3:58:01,1312,62,7
+Emergency Medicine,PGY1,S0195210,2016-06-30,0:56:01,0:17:47,0:11:45,0:23:37,0:05:40,0:00:07,2:11:31,570,0,6
+General Medicine,PGY1,S0176040,2016-03-18,0:59:12,0:32:32,0:00:20,0:11:38,0:16:04,0:03:40,2:23:14,1040,9,8
+General Medicine,PGY1,S0176040,2016-03-19,1:39:30,0:45:21,0:00:12,0:44:13,0:08:12,0:02:14,3:53:31,1505,31,10
+Intensive Care Unit,PGY2+,S0169386,2016-06-29,2:39:24,0:37:21,0:05:46,0:29:14,0:29:59,0:14:57,5:05:03,1695,175,23
+Intensive Care Unit,PGY2+,S0169386,2016-06-28,2:40:38,0:21:48,0:06:22,0:21:42,0:28:40,0:12:26,4:25:24,1685,47,32
+Night Team,PGY1,S0169400,2014-07-18,1:57:38,0:12:01,0:17:43,0:19:59,0:17:18,0:15:44,3:32:51,1028,0,26
+Night Team,PGY1,S0169400,2014-07-19,1:40:11,0:03:30,0:06:12,0:03:11,0:21:01,0:12:18,2:35:48,617,0,18
+Night Team,PGY1,S0169400,2014-07-14,2:11:15,0:08:39,0:02:17,0:38:06,1:08:22,0:46:18,5:11:04,1480,70,23
+Night Team,PGY1,S0169400,2014-07-15,1:17:45,0:01:47,0:15:25,0:19:54,0:42:58,0:28:43,3:19:33,656,11,16
+Night Team,PGY1,S0169400,2014-07-16,1:19:42,0:00:20,0:03:35,0:09:37,0:09:43,0:18:06,2:05:14,505,0,23
+Night Team,PGY1,S0169400,2014-07-17,2:20:12,0:07:13,0:07:55,0:21:58,0:16:23,0:20:21,3:47:56,1078,0,29
+Night Team,PGY1,S0169400,2014-07-10,0:38:03,0:05:37,0:07:52,0:08:10,0:06:44,0:10:41,1:19:15,424,0,17
+Night Team,PGY1,S0169400,2014-07-11,3:06:10,0:01:19,0:16:49,0:39:58,0:47:55,0:52:14,6:05:46,1449,0,29
+Night Team,PGY1,S0169400,2014-07-12,3:22:07,0:13:33,0:30:47,0:43:48,0:30:13,0:47:37,6:29:23,1601,0,30
+Night Team,PGY1,S0169400,2014-07-13,1:43:17,0:03:23,0:19:13,0:38:01,0:18:15,0:36:42,3:49:30,1079,0,29
+Emergency Medicine,PGY1,S0195076,2016-08-20,0:00:00,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:12,14,12,3
+Intensive Care Unit,PGY2+,S0169384,2015-08-31,1:57:58,0:02:19,0:19:59,0:15:02,0:20:09,0:16:19,3:18:09,644,148,9
+Intensive Care Unit,PGY2+,S0169384,2015-08-30,3:19:56,0:00:06,0:44:58,0:32:40,0:18:31,0:18:36,5:25:58,1234,99,13
+Night Team,PGY2+,S0181057,2016-08-06,2:23:50,1:33:29,0:00:04,1:13:01,0:30:35,0:19:20,6:45:58,2777,274,26
+Night Team,PGY2+,S0181057,2016-08-07,1:37:48,1:13:52,0:00:05,0:40:36,0:15:23,0:02:09,4:20:29,1875,541,24
+Night Team,PGY2+,S0181057,2016-08-04,2:52:07,1:57:14,0:00:15,1:25:50,0:21:20,0:15:49,7:32:49,2656,21,26
+Night Team,PGY2+,S0181057,2016-08-05,2:29:51,1:50:38,0:01:06,0:59:16,0:24:58,0:11:11,6:31:18,2389,0,30
+Intensive Care Unit,PGY2+,S0169404,2016-08-17,2:06:23,0:55:58,0:08:18,0:13:18,0:19:37,0:11:44,4:27:55,2128,0,17
+Intensive Care Unit,PGY2+,S0169404,2016-08-16,3:20:50,0:58:41,0:03:53,0:34:28,0:16:22,0:12:11,5:48:49,3115,0,17
+Intensive Care Unit,PGY2+,S0169404,2016-08-15,2:25:37,1:16:29,0:16:44,0:11:54,0:21:41,0:07:09,4:57:24,1870,0,12
+Night Team,PGY2+,S0181057,2016-08-01,1:26:25,1:10:45,0:00:04,0:38:35,0:13:05,0:05:31,3:52:25,1569,340,23
+Intensive Care Unit,PGY2+,S0169404,2016-08-19,2:25:10,1:12:24,0:10:46,0:23:57,0:22:07,0:04:46,5:00:08,2223,414,17
+Intensive Care Unit,PGY2+,S0169404,2016-08-18,0:10:57,0:01:53,0:00:00,0:00:05,0:00:00,0:00:00,0:13:18,72,0,14
+Night Team,PGY2+,S0181057,2016-08-08,1:56:51,0:49:17,0:00:07,0:42:15,0:14:32,0:01:54,4:07:39,1418,0,16
+Night Team,PGY2+,S0181057,2016-08-09,1:26:37,0:51:46,0:00:00,0:27:47,0:16:31,0:08:19,3:34:20,1121,106,29
+Night Team,PGY1,S0195220,2016-06-30,1:46:25,1:07:08,0:00:00,0:40:09,0:27:06,0:04:03,4:16:20,1075,0,30
+General Medicine,PGY1,S0181079,2016-03-21,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,6,0,2
+General Medicine,PGY1,S0181079,2016-03-20,3:06:35,1:27:51,0:00:35,0:56:35,0:28:04,0:07:20,6:53:13,2021,0,15
+General Medicine,PGY1,S0169381,2014-10-18,3:09:51,0:10:55,0:05:53,0:24:30,0:15:08,0:19:53,4:39:28,931,0,9
+General Medicine,PGY1,S0181079,2016-03-22,1:34:34,1:01:37,0:00:07,0:18:23,0:10:47,0:00:16,3:40:41,1053,0,5
+General Medicine,PGY1,S0181079,2016-03-24,1:57:12,0:56:34,0:00:40,0:18:38,0:25:11,0:01:51,4:08:23,1412,0,11
+General Medicine,PGY1,S0169381,2014-10-12,2:23:55,0:05:49,0:17:13,0:39:30,0:27:53,0:10:30,4:15:55,1020,0,5
+General Medicine,PGY1,S0169381,2014-10-13,3:37:49,0:10:42,0:16:49,0:35:04,0:30:50,0:22:46,5:44:07,1172,87,6
+General Medicine,PGY1,S0169381,2014-10-16,3:05:58,0:03:42,0:15:28,0:40:49,0:29:36,0:15:51,5:10:22,1465,172,8
+General Medicine,PGY1,S0169381,2014-10-17,0:19:04,0:00:22,0:01:18,0:00:00,0:02:00,0:00:28,0:26:27,214,210,7
+General Medicine,PGY1,S0169381,2014-10-14,3:29:38,0:08:24,0:39:56,0:30:14,0:23:30,0:31:21,5:57:19,1537,7,8
+General Medicine,PGY1,S0169381,2014-10-15,2:12:10,0:03:10,0:14:27,0:17:48,0:05:24,0:12:42,3:13:49,720,180,5
+Night Team,PGY2+,S0169406,2016-01-23,1:18:48,1:33:47,1:33:00,0:23:58,0:13:00,0:29:38,5:46:58,1878,197,39
+Night Team,PGY2+,S0169406,2016-01-22,2:26:51,1:18:52,2:22:46,0:22:52,0:13:56,0:42:24,7:43:44,2133,0,49
+Night Team,PGY2+,S0169406,2016-01-21,2:14:40,1:32:59,1:48:54,0:24:56,0:17:30,0:38:47,7:26:01,2409,0,35
+Night Team,PGY2+,S0169406,2016-01-20,1:25:11,1:04:52,1:39:33,0:18:10,0:08:32,0:30:34,5:34:11,1853,0,34
+Night Team,PGY2+,S0094535,2016-02-20,1:01:29,0:47:54,0:13:10,0:16:44,0:01:18,0:02:52,2:32:50,948,0,21
+Night Team,PGY2+,S0169402,2016-04-02,2:44:16,0:55:35,0:00:52,0:27:57,0:02:42,0:00:34,4:50:36,1107,38,14
+Night Team,PGY2+,S0169402,2016-04-01,3:17:31,1:50:36,0:00:43,0:49:01,0:14:09,0:02:21,7:00:49,2329,17,47
+General Medicine,PGY1,S0196597,2016-10-04,1:26:02,0:51:29,0:02:25,0:39:24,0:16:17,0:01:55,3:37:42,1523,0,9
+General Medicine,PGY1,S0196597,2016-10-05,1:05:57,1:01:26,0:01:00,0:13:53,0:08:59,0:00:16,2:48:27,1322,43,10
+General Medicine,PGY1,S0196597,2016-10-06,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:22,0:00:34,17,0,2
+General Medicine,PGY1,S0196597,2016-10-07,1:51:33,0:53:00,0:00:05,0:53:33,0:10:30,0:02:58,4:18:51,1502,0,13
+Night Team,PGY1,S0169381,2015-01-19,2:47:05,0:01:03,0:35:13,0:38:26,0:18:10,0:37:38,5:17:04,1048,14,42
+Night Team,PGY1,S0169381,2015-01-18,2:37:55,0:01:45,1:48:32,0:39:21,0:29:10,0:18:20,6:09:51,1077,0,22
+General Medicine,PGY1,S0196597,2016-10-02,1:48:13,0:52:48,0:00:14,0:41:31,0:14:29,0:05:31,4:16:06,1822,0,10
+General Medicine,PGY1,S0196597,2016-10-03,0:59:43,0:34:43,0:00:15,0:25:55,0:08:25,0:01:11,2:34:33,804,194,11
+General Medicine,PGY1,S0196597,2016-10-08,1:15:41,0:57:33,0:00:08,0:28:37,0:08:22,0:02:43,3:12:20,1171,446,8
+General Medicine,PGY1,S0186952,2016-09-18,3:00:11,0:30:57,0:00:25,0:42:56,0:09:39,0:04:42,5:37:41,1594,0,17
+Emergency Medicine,PGY1,S0181086,2015-09-30,2:09:52,0:15:28,1:45:19,0:44:46,0:27:47,0:18:59,6:17:53,1387,38,16
+General Medicine,PGY1,S0181063,2015-12-29,0:47:47,1:25:52,1:40:34,0:51:33,0:39:28,0:39:23,6:49:17,2661,321,19
+General Medicine,PGY1,S0181063,2015-12-28,0:35:52,1:57:22,0:37:06,0:53:06,0:13:58,0:13:38,5:22:14,2904,1535,28
+General Medicine,PGY1,S0181063,2015-12-27,0:53:25,1:37:28,2:12:12,0:40:34,0:21:25,0:23:29,6:39:48,2083,328,16
+General Medicine,PGY1,S0181063,2015-12-26,0:42:31,1:58:31,0:48:28,0:38:20,0:16:24,0:18:44,5:18:23,1949,937,12
+General Medicine,PGY1,S0181063,2015-12-25,0:47:36,2:28:14,0:32:17,0:16:15,0:13:59,0:21:42,5:04:31,2121,1093,13
+General Medicine,PGY1,S0181063,2015-12-24,0:44:55,1:11:45,1:19:53,0:38:03,0:24:38,0:32:38,5:26:11,1962,0,8
+Night Team,PGY2+,S0163425,2015-10-30,1:17:57,0:39:03,1:34:16,0:17:10,0:09:11,0:15:01,4:33:46,1248,54,40
+Night Team,PGY2+,S0163425,2015-10-31,0:00:00,0:01:29,0:00:00,0:00:00,0:00:00,0:00:00,0:01:33,6,6,1
+General Medicine,PGY1,S0181063,2015-12-21,0:25:38,0:45:39,0:42:46,0:16:56,0:05:31,0:17:38,3:02:54,1166,52,6
+General Medicine,PGY1,S0181063,2015-12-20,0:33:22,0:39:49,0:58:37,0:16:52,0:16:05,0:12:29,3:19:53,1237,0,8
+Night Team,PGY1,S0172085,2015-10-23,1:06:42,0:41:31,0:16:46,0:44:54,0:22:15,0:35:38,3:54:56,2425,0,41
+Night Team,PGY1,S0172085,2015-10-22,1:32:07,1:17:30,1:01:01,0:20:05,0:20:21,0:31:59,5:16:00,1752,0,48
+Night Team,PGY1,S0172085,2015-10-21,1:03:23,0:48:11,1:41:16,0:43:32,0:11:32,0:30:44,5:18:14,1753,0,32
+Night Team,PGY1,S0172085,2015-10-20,1:12:14,1:31:19,1:26:18,0:32:07,0:12:44,0:20:24,5:27:52,2230,18,48
+Night Team,PGY1,S0172085,2015-10-24,1:18:20,0:41:32,1:03:01,0:19:16,0:15:29,0:48:30,4:30:04,1538,0,24
+Night Team,PGY1,S0195220,2016-10-31,1:36:37,0:11:30,0:00:01,0:29:23,0:16:16,0:01:17,2:53:04,707,0,22
+Night Team,PGY1,S0195220,2016-10-30,1:38:40,0:25:48,0:00:00,0:25:28,0:11:22,0:13:26,3:02:58,869,0,21
+Emergency Medicine,PGY1,S0181057,2015-08-09,7:16:45,0:37:37,1:02:55,0:21:16,0:47:38,0:32:47,11:09:33,3775,0,40
+Emergency Medicine,PGY1,S0181057,2015-08-08,6:18:37,0:28:18,1:40:40,0:21:20,0:19:32,0:10:11,9:47:38,2722,0,32
+Emergency Medicine,PGY1,S0181057,2015-08-05,4:32:17,0:08:44,1:52:47,0:32:37,0:27:55,0:15:20,8:21:41,2244,142,16
+Emergency Medicine,PGY1,S0181057,2015-08-04,1:01:08,0:15:54,0:35:56,0:48:26,0:13:09,0:07:14,3:47:29,1231,0,11
+Emergency Medicine,PGY1,S0181057,2015-08-07,5:47:05,0:24:12,2:02:22,0:15:45,0:36:10,0:22:22,9:43:23,2951,0,39
+Night Team,PGY2+,S0169385,2016-08-27,2:26:31,1:39:48,0:00:06,0:19:49,0:05:10,0:05:36,4:59:14,1496,0,17
+Emergency Medicine,PGY1,S0181057,2015-08-01,3:05:18,0:17:47,2:23:52,0:04:56,0:17:18,0:14:10,6:44:52,1787,170,34
+Night Team,PGY2+,S0169385,2016-08-21,4:02:41,1:26:58,0:02:07,0:19:13,0:12:17,0:04:17,6:35:08,1884,0,25
+Emergency Medicine,PGY1,S0181057,2015-08-03,4:25:45,0:19:46,1:07:32,0:24:03,0:15:41,0:06:32,7:29:35,2112,289,25
+Night Team,PGY2+,S0169385,2016-08-23,2:48:02,1:14:08,0:00:21,0:19:15,0:08:33,0:15:07,5:05:54,1740,0,46
+Intensive Care Unit,PGY2+,S0169384,2015-09-10,0:00:00,0:00:00,0:00:00,0:03:36,0:00:00,0:00:00,0:03:36,16,0,6
+Night Team,PGY2+,S0169395,2016-03-08,3:04:22,1:31:27,0:00:16,0:52:04,0:15:43,0:01:05,6:09:01,1948,0,49
+Night Team,PGY2+,S0169395,2016-03-09,3:04:17,2:17:48,0:00:26,0:39:43,0:32:56,0:08:18,7:31:37,2540,0,39
+Night Team,PGY2+,S0169395,2016-03-06,0:40:43,0:47:05,0:00:04,0:08:55,0:13:45,0:01:25,1:53:11,586,0,33
+Night Team,PGY2+,S0169395,2016-03-07,1:14:18,0:28:55,0:00:01,0:12:53,0:04:12,0:00:40,2:12:18,586,0,42
+Intensive Care Unit,PGY2+,S0169384,2015-09-11,3:07:13,0:04:13,0:26:55,0:23:11,0:38:49,0:31:14,5:32:43,1392,0,13
+Night Team,PGY1,S0181065,2015-07-19,2:35:48,1:03:01,1:12:37,0:24:11,0:22:37,0:32:14,6:39:46,2061,10,34
+Night Team,PGY2+,S0169400,2015-11-14,2:17:35,0:44:00,0:46:49,0:21:20,0:07:50,0:13:31,4:47:19,1144,0,36
+Emergency Medicine,PGY1,S0195216,2016-10-16,1:22:59,0:38:39,0:01:03,0:13:48,0:28:33,0:01:33,3:13:30,957,50,14
+Emergency Medicine,PGY1,S0195216,2016-10-17,3:53:48,1:17:51,0:05:33,0:46:24,0:53:39,0:08:58,8:25:54,2953,0,45
+Emergency Medicine,PGY1,S0195216,2016-10-18,2:12:50,0:47:22,0:00:28,0:19:27,0:15:10,0:03:53,4:06:50,1076,0,32
+Emergency Medicine,PGY1,S0195216,2016-10-19,0:00:12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:17,57,57,1
+General Medicine,PGY2+,S0108287,2016-05-02,0:06:22,0:00:06,0:00:00,0:03:11,0:00:00,0:01:03,0:19:31,86,84,7
+Emergency Medicine,PGY1,S0169389,2014-09-30,5:18:07,0:10:52,0:21:18,0:18:36,0:28:40,0:22:25,7:32:25,2019,71,44
+General Medicine,PGY1,S0056755,2015-01-31,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0056755,2015-01-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0169390,2016-05-29,3:11:57,0:50:26,0:00:29,0:28:56,0:19:10,0:06:43,5:25:17,1409,6,34
+General Medicine,PGY1,S0181073,2015-10-22,0:27:22,0:45:47,0:32:55,0:19:06,0:06:01,0:17:05,2:45:36,846,290,3
+Night Team,PGY1,S0195206,2016-08-20,2:58:15,0:16:53,0:00:03,0:12:07,0:14:42,0:07:26,4:25:24,1341,0,42
+General Medicine,PGY1,S0181057,2015-07-08,2:19:53,0:12:14,0:56:12,0:27:33,0:28:06,0:11:12,4:53:27,1397,153,6
+General Medicine,PGY1,S0181057,2015-07-09,2:18:16,0:20:42,1:40:22,0:43:36,0:42:19,0:23:25,6:50:29,2156,55,12
+General Medicine,PGY1,S0181057,2015-07-04,2:46:12,0:25:16,2:34:55,0:24:09,0:25:36,0:19:34,7:11:48,1631,0,11
+General Medicine,PGY1,S0181057,2015-07-05,0:24:30,0:00:18,0:00:15,0:00:01,0:01:03,0:00:05,0:26:32,131,127,6
+General Medicine,PGY1,S0181057,2015-07-06,2:01:35,0:09:17,0:57:39,0:25:36,0:42:29,0:18:32,4:48:04,1353,0,5
+General Medicine,PGY1,S0181057,2015-07-07,2:40:26,0:10:59,1:10:37,0:23:45,0:38:34,0:21:49,5:49:02,1615,42,7
+General Medicine,PGY1,S0181057,2015-07-01,2:12:06,0:13:30,1:53:09,0:39:47,0:41:58,0:28:38,6:26:00,1861,288,11
+General Medicine,PGY1,S0181057,2015-07-02,3:00:32,0:24:09,1:26:08,0:40:10,1:01:37,0:37:56,7:39:45,1858,0,8
+General Medicine,PGY1,S0181057,2015-07-03,2:53:25,0:05:58,1:27:21,0:38:25,0:30:28,0:22:34,6:13:35,1866,350,9
+General Medicine,PGY1,S0181085,2016-01-03,0:51:54,1:05:00,1:15:58,0:15:10,0:11:03,0:13:13,4:23:51,1102,0,6
+General Medicine,PGY1,S0181085,2016-01-06,0:49:35,1:19:04,1:21:12,0:23:44,0:15:05,0:20:04,4:53:06,1675,52,8
+General Medicine,PGY1,S0181085,2016-01-07,1:14:06,1:46:58,1:56:18,0:28:34,0:15:03,0:15:37,6:22:43,1858,62,13
+General Medicine,PGY1,S0181085,2016-01-04,1:13:59,1:23:12,2:56:27,0:46:15,0:18:50,0:28:46,7:43:02,2162,160,28
+General Medicine,PGY1,S0181085,2016-01-05,0:36:35,0:48:29,0:48:09,0:15:19,0:09:16,0:21:09,3:15:34,952,16,8
+General Medicine,PGY1,S0181085,2016-01-08,0:34:46,0:41:49,1:03:20,0:12:15,0:13:07,0:21:45,3:32:47,1312,100,13
+General Medicine,PGY1,S0181085,2016-01-09,1:01:39,1:16:26,1:55:31,0:50:22,0:24:08,0:26:36,6:27:20,1658,66,17
+General Medicine,PGY1,S0195223,2016-08-24,1:00:39,0:50:11,0:00:16,0:31:32,0:11:44,0:03:21,3:05:07,808,0,4
+Night Team,PGY1,S0181088,2015-10-31,1:39:25,0:37:27,0:39:11,0:26:38,0:29:54,0:39:59,4:53:25,1577,105,37
+Night Team,PGY1,S0181088,2015-10-30,0:39:39,0:06:26,0:00:33,0:12:19,0:18:48,0:22:47,1:45:16,803,0,31
+Emergency Medicine,PGY1,S0169401,2014-09-09,4:51:42,0:18:07,0:19:10,0:29:04,0:15:58,0:06:34,6:47:21,2044,707,72
+General Medicine,PGY1,S0181074,2015-08-11,2:02:50,1:40:07,0:44:26,1:30:08,0:38:14,0:21:20,7:33:51,2444,42,16
+General Medicine,PGY1,S0181074,2015-08-10,1:11:31,1:49:51,1:10:39,1:26:09,0:07:50,0:06:48,6:18:04,1978,235,20
+General Medicine,PGY1,S0181074,2015-08-13,0:25:52,1:41:13,1:40:22,0:28:03,0:02:04,0:04:16,4:34:31,1443,1437,7
+General Medicine,PGY1,S0181074,2015-08-12,1:20:40,1:25:04,2:52:46,0:48:29,0:22:41,0:15:03,7:42:03,2563,427,10
+General Medicine,PGY1,S0181074,2015-08-15,0:18:43,0:41:53,0:19:04,0:10:07,0:06:33,0:04:06,1:47:08,489,485,9
+General Medicine,PGY1,S0181074,2015-08-14,2:45:49,1:20:43,0:53:20,2:13:46,0:30:08,0:10:09,8:42:43,3069,55,21
+General Medicine,PGY2+,S0169390,2016-08-13,0:16:14,0:03:47,0:00:01,0:05:35,0:00:20,0:00:02,0:33:49,151,0,40
+General Medicine,PGY2+,S0169390,2016-08-12,4:20:03,1:37:29,0:00:12,0:56:07,0:48:20,0:12:13,8:55:47,3292,0,22
+General Medicine,PGY2+,S0169390,2016-08-11,2:48:05,2:08:50,0:00:19,0:38:32,0:20:04,0:09:21,6:43:58,2978,78,23
+General Medicine,PGY2+,S0169390,2016-08-10,4:01:01,1:33:58,0:00:04,0:46:26,0:30:57,0:10:33,7:34:23,2998,21,27
+General Medicine,PGY1,S0181088,2015-07-29,1:09:14,0:00:36,1:16:02,0:23:58,0:14:10,0:12:06,3:36:31,1011,41,9
+General Medicine,PGY2+,S0169384,2016-05-31,1:56:32,1:25:04,0:00:06,0:47:05,0:16:17,0:07:47,5:01:47,1520,132,14
+General Medicine,PGY2+,S0169384,2016-05-30,3:11:49,1:25:43,0:01:46,0:29:47,0:20:53,0:17:46,6:25:30,2466,315,17
+General Medicine,PGY1,S0169383,2015-05-14,1:31:29,0:05:26,1:05:40,0:31:24,0:06:39,0:04:46,4:22:49,1041,0,5
+General Medicine,PGY1,S0169395,2015-05-30,2:34:59,0:33:23,1:55:44,0:29:09,0:23:56,0:32:49,7:01:45,2090,0,23
+General Medicine,PGY1,S0169395,2015-05-31,1:43:43,0:18:00,0:46:49,0:37:22,0:16:43,0:13:18,4:08:31,1325,0,6
+General Medicine,PGY1,S0181088,2015-07-26,1:43:22,0:10:21,1:49:39,0:48:54,0:37:10,0:28:15,6:40:42,1842,0,11
+General Medicine,PGY1,S0181088,2015-07-27,1:47:58,0:04:47,1:05:21,0:36:25,0:16:34,0:21:16,4:46:27,1659,0,8
+Night Team,PGY2+,S0056755,2016-05-07,1:23:36,1:12:32,0:00:05,0:11:43,0:05:11,0:02:05,3:19:38,887,0,30
+Night Team,PGY2+,S0056755,2016-05-06,1:20:43,1:15:50,0:01:19,0:17:12,0:06:11,0:03:55,3:27:10,1121,0,31
+Night Team,PGY2+,S0056755,2016-05-05,2:11:34,2:04:55,0:01:11,0:36:16,0:16:30,0:01:51,5:37:44,1714,48,21
+Night Team,PGY2+,S0056755,2016-05-04,2:30:38,1:51:14,0:03:36,0:57:14,0:27:33,0:04:25,6:35:32,1505,24,18
+Night Team,PGY2+,S0056755,2016-05-03,2:38:23,1:25:07,0:00:08,1:03:21,0:16:23,0:04:19,5:49:28,1321,62,14
+Night Team,PGY2+,S0056755,2016-05-02,1:30:54,1:17:44,0:00:06,0:29:40,0:09:57,0:00:07,3:32:43,719,0,12
+Night Team,PGY2+,S0056755,2016-05-01,0:33:06,0:21:26,0:00:00,0:11:04,0:18:51,0:00:05,1:28:56,399,0,23
+Night Team,PGY2+,S0056755,2016-05-09,2:49:21,2:16:03,0:00:08,0:46:30,0:09:54,0:00:45,6:48:13,2070,23,29
+Night Team,PGY2+,S0056755,2016-05-08,0:50:48,0:57:25,0:00:03,0:21:59,0:05:55,0:01:50,2:35:42,844,0,23
+General Medicine,PGY1,S0163425,2015-06-15,3:09:16,0:26:31,1:16:04,0:28:24,0:13:46,0:09:44,6:16:50,1863,0,14
+Intensive Care Unit,PGY2+,S0181070,2016-09-03,0:00:00,0:00:00,0:00:00,0:00:07,0:00:00,0:00:00,0:00:07,19,0,4
+General Medicine,PGY1,S0163425,2015-06-17,1:56:40,0:35:17,2:08:28,0:44:01,0:26:13,0:14:59,6:58:09,2161,0,13
+General Medicine,PGY1,S0163425,2015-06-16,2:57:24,1:03:40,1:23:24,0:43:04,0:19:44,0:32:52,7:31:55,2611,0,18
+General Medicine,PGY1,S0163425,2015-06-11,2:42:29,0:38:39,1:11:54,0:19:36,0:32:35,0:22:46,6:26:22,2145,0,20
+Intensive Care Unit,PGY2+,S0181070,2016-09-07,1:26:02,1:17:28,0:03:26,0:17:13,0:15:36,0:07:39,3:56:38,1268,0,29
+General Medicine,PGY1,S0163425,2015-06-13,3:01:07,0:24:18,2:14:06,0:44:48,0:36:11,0:11:04,7:45:25,2066,0,27
+Intensive Care Unit,PGY2+,S0181070,2016-09-05,0:13:28,0:16:41,0:06:24,0:02:04,0:04:17,0:05:20,0:51:46,404,0,6
+Intensive Care Unit,PGY2+,S0181070,2016-09-08,1:12:08,0:47:42,0:04:42,0:09:19,0:08:15,0:00:52,2:35:58,978,0,13
+Intensive Care Unit,PGY2+,S0181070,2016-09-09,0:57:27,0:58:39,0:11:00,0:08:34,0:22:51,0:02:46,3:00:34,1108,0,14
+General Medicine,PGY1,S0163425,2015-06-19,0:00:00,0:00:00,0:00:00,0:02:33,0:00:00,0:00:00,0:02:33,14,0,4
+General Medicine,PGY1,S0163425,2015-06-18,3:31:52,0:27:27,2:06:49,0:42:48,0:22:48,0:12:22,8:08:56,2110,0,18
+General Medicine,PGY1,S0195206,2016-11-01,0:01:57,0:00:34,0:00:00,0:00:00,0:00:00,0:00:00,0:02:40,35,0,7
+Night Team,PGY1,S0195076,2016-10-01,0:25:03,0:35:42,0:00:04,0:11:02,0:03:32,0:05:12,1:34:42,436,228,6
+General Medicine,PGY1,S0181069,2016-04-21,2:52:43,1:33:19,0:00:15,1:01:25,0:25:52,0:09:10,6:43:08,2955,32,13
+General Medicine,PGY1,S0181069,2016-04-20,1:58:52,1:25:29,0:00:29,0:57:46,0:14:00,0:10:54,5:21:20,1688,0,17
+General Medicine,PGY1,S0181069,2016-04-23,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0181069,2016-04-22,3:02:57,1:24:26,0:00:37,1:09:39,0:20:05,0:11:52,6:43:57,2573,322,11
+General Medicine,PGY1,S0169398,2014-08-28,3:34:10,0:12:04,0:20:19,0:18:43,0:17:49,0:19:14,5:33:18,1708,676,15
+General Medicine,PGY1,S0169398,2014-08-29,4:02:14,0:16:52,0:29:02,0:48:35,0:38:40,0:21:04,7:26:02,2194,181,23
+General Medicine,PGY1,S0169398,2014-08-24,4:34:43,0:07:13,0:19:27,0:14:43,0:40:36,0:52:04,7:05:53,1746,0,13
+General Medicine,PGY1,S0169398,2014-08-25,3:35:53,0:07:58,0:12:37,0:30:07,0:31:03,0:27:06,5:38:42,1709,145,10
+General Medicine,PGY1,S0169398,2014-08-26,3:33:19,0:09:43,0:13:18,0:49:45,0:40:26,0:25:52,6:10:29,1712,201,12
+General Medicine,PGY1,S0169398,2014-08-27,4:02:28,0:11:37,0:13:00,0:44:20,0:39:26,0:49:51,6:57:06,1946,0,11
+General Medicine,PGY1,S0169398,2014-08-20,3:37:47,0:16:35,0:23:42,0:32:07,0:36:16,0:19:56,6:05:19,1919,237,9
+General Medicine,PGY1,S0169398,2014-08-21,4:07:46,0:03:48,0:17:58,0:35:57,0:22:53,0:29:30,6:23:48,2377,429,12
+General Medicine,PGY1,S0169398,2014-08-22,3:11:57,0:08:11,0:25:05,0:29:46,0:21:15,0:29:03,5:22:11,1448,57,13
+General Medicine,PGY1,S0169398,2014-08-23,4:18:54,0:12:25,0:17:00,0:31:55,0:23:47,0:36:38,6:45:25,1933,275,12
+General Medicine,PGY2+,S0169381,2016-04-19,2:33:43,1:16:31,0:00:31,0:30:39,0:20:49,0:14:51,5:25:23,1893,70,25
+General Medicine,PGY2+,S0169381,2016-04-18,0:56:47,0:48:53,0:00:09,0:32:05,0:16:12,0:00:43,2:47:22,779,38,10
+Intensive Care Unit,PGY2+,S0169402,2015-07-23,3:06:35,0:27:38,0:16:29,0:27:16,0:21:26,0:41:37,5:29:08,1719,13,33
+General Medicine,PGY2+,S0169381,2016-04-11,1:47:26,1:05:02,0:00:02,0:21:30,0:14:35,0:07:56,4:06:23,1286,211,22
+General Medicine,PGY2+,S0169381,2016-04-10,0:51:03,0:24:47,0:00:41,0:21:35,0:16:42,0:04:47,2:20:01,734,128,8
+General Medicine,PGY2+,S0169381,2016-04-13,0:57:07,0:35:34,0:01:22,0:09:56,0:15:10,0:07:14,2:20:50,664,233,14
+General Medicine,PGY2+,S0169381,2016-04-12,0:14:10,0:00:50,0:00:00,0:01:40,0:00:00,0:01:04,0:18:10,69,57,9
+General Medicine,PGY2+,S0169381,2016-04-15,1:32:24,1:02:59,0:00:03,0:33:17,0:10:59,0:09:45,3:59:34,1232,10,13
+General Medicine,PGY2+,S0169381,2016-04-14,2:21:31,1:52:27,0:00:03,0:13:05,0:12:34,0:03:14,5:06:10,1715,272,28
+General Medicine,PGY2+,S0169381,2016-04-17,0:13:50,0:24:24,0:00:00,0:03:19,0:00:28,0:00:22,1:18:32,713,708,13
+General Medicine,PGY2+,S0169381,2016-04-16,2:04:21,1:00:54,0:00:06,0:09:42,0:07:29,0:12:03,3:56:17,1373,48,21
+Intensive Care Unit,PGY2+,S0163425,2015-09-09,3:10:43,0:01:57,0:29:40,0:21:03,0:27:13,0:15:34,5:12:15,1574,10,16
+Intensive Care Unit,PGY2+,S0163425,2015-09-08,3:44:09,0:12:45,0:43:36,0:49:34,0:28:43,1:15:23,7:49:51,2570,0,16
+General Medicine,PGY1,S0181075,2015-07-31,2:23:24,0:38:50,1:27:43,0:30:33,0:13:49,0:10:11,6:03:13,1786,0,25
+General Medicine,PGY1,S0181075,2015-07-30,2:28:39,0:59:44,1:35:18,0:41:39,0:31:58,0:19:46,7:18:07,2064,0,20
+Night Team,PGY1,S0181067,2016-04-27,1:33:43,0:45:02,0:00:06,0:24:49,0:22:55,0:12:25,3:42:48,1934,0,23
+Night Team,PGY1,S0181067,2016-04-26,2:00:02,1:23:21,0:00:12,0:35:12,0:30:32,0:26:50,5:17:57,2655,0,29
+Night Team,PGY1,S0181067,2016-04-25,0:34:27,0:20:02,0:00:11,0:14:20,0:07:31,0:17:14,1:50:47,1028,0,20
+Night Team,PGY1,S0181067,2016-04-24,0:47:23,0:13:39,0:00:00,0:13:47,0:06:34,0:04:02,1:32:33,820,0,20
+Night Team,PGY2+,S0169381,2016-07-18,2:38:11,1:17:06,0:00:41,0:23:30,0:13:10,0:05:27,4:55:18,1344,0,20
+Night Team,PGY2+,S0169381,2016-07-19,1:21:07,0:58:58,0:00:07,0:20:44,0:04:46,0:10:03,3:10:07,774,0,9
+General Medicine,PGY1,S0169398,2014-09-13,0:05:16,0:00:00,0:00:19,0:00:00,0:00:00,0:00:00,0:06:25,43,39,5
+Night Team,PGY2+,S0169381,2016-07-17,1:03:56,0:34:14,0:00:01,0:27:48,0:09:46,0:07:21,2:40:25,1313,0,11
+Night Team,PGY1,S0181067,2016-04-29,1:40:58,0:39:49,0:00:00,0:29:45,0:18:30,0:23:19,3:57:38,1969,0,24
+Night Team,PGY1,S0181067,2016-04-28,1:25:31,0:55:28,0:00:16,0:32:58,0:26:52,0:17:55,4:05:05,2302,0,22
+Night Team,PGY1,S0169395,2015-04-11,1:17:05,0:22:17,1:57:37,0:27:00,0:12:05,0:19:43,4:58:43,843,0,25
+Night Team,PGY1,S0169395,2015-04-10,3:02:06,0:11:55,2:31:27,0:51:26,0:21:57,0:26:57,7:49:23,1685,0,59
+General Medicine,PGY1,S0094959,2015-04-03,0:00:00,0:00:00,0:00:00,0:03:31,0:00:00,0:00:00,0:03:31,16,0,4
+General Medicine,PGY1,S0169402,2014-11-24,0:00:00,0:00:00,0:00:00,0:00:50,0:00:00,0:00:00,0:00:50,8,0,3
+General Medicine,PGY1,S0195203,2016-07-14,1:16:31,1:20:08,0:00:31,0:40:40,0:14:12,0:02:37,4:21:47,1104,171,11
+General Medicine,PGY1,S0195203,2016-07-15,2:50:47,1:03:34,0:00:26,0:54:29,0:57:23,0:07:10,6:49:16,1619,111,7
+General Medicine,PGY1,S0195203,2016-07-16,1:53:42,0:54:12,0:00:26,0:36:30,0:33:20,0:05:23,4:47:16,1494,307,7
+Night Team,PGY1,S0169398,2015-05-29,2:45:03,0:36:28,0:56:09,0:38:53,0:33:01,0:33:11,6:40:13,2010,104,35
+General Medicine,PGY1,S0196593,2016-10-17,2:07:58,2:08:54,0:00:20,0:37:07,0:34:18,0:04:37,6:42:52,1792,450,6
+General Medicine,PGY1,S0196593,2016-10-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0195203,2016-07-12,2:33:16,2:10:37,0:00:52,0:56:38,0:43:23,0:06:30,7:19:57,1840,239,12
+General Medicine,PGY1,S0195203,2016-07-13,0:19:53,0:00:37,0:00:00,0:00:33,0:00:00,0:03:32,0:25:55,89,0,8
+Emergency Medicine,PGY1,S0169395,2014-11-14,2:41:18,0:08:34,2:07:12,0:34:12,0:42:34,0:41:25,7:37:40,2463,0,18
+Emergency Medicine,PGY1,S0169395,2014-11-15,1:04:22,0:00:41,0:53:21,0:15:35,0:12:11,0:07:54,2:55:46,981,0,12
+General Medicine,PGY1,S0196593,2016-10-19,2:22:18,0:51:27,0:01:08,0:35:19,0:22:44,0:04:55,4:57:12,1418,140,8
+General Medicine,PGY1,S0196593,2016-10-18,2:22:26,1:29:55,0:00:21,1:12:17,0:49:30,0:05:54,6:50:33,2084,295,10
+Night Team,PGY1,S0169398,2015-05-26,1:51:46,0:12:46,0:23:31,0:37:39,0:21:14,0:42:19,4:24:04,1296,0,27
+Night Team,PGY1,S0169398,2015-05-27,1:50:04,0:28:36,1:45:28,0:24:11,0:14:49,0:37:26,5:39:03,1399,236,23
+Emergency Medicine,PGY1,S0169395,2014-11-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0169398,2015-05-25,1:38:09,0:38:12,0:51:29,0:18:11,0:12:31,0:33:57,4:29:46,1515,0,29
+General Medicine,PGY1,S0094959,2015-04-02,4:25:41,1:01:06,1:50:12,0:38:29,0:25:10,0:16:40,9:04:44,3952,53,32
+Intensive Care Unit,PGY2+,S0074196,2016-11-10,2:11:52,0:55:54,0:00:16,0:57:50,0:19:56,0:02:41,5:05:11,1601,0,17
+Intensive Care Unit,PGY2+,S0181059,2016-07-29,0:11:39,0:01:16,0:00:00,0:00:39,0:00:00,0:00:23,0:14:57,113,26,11
+Intensive Care Unit,PGY2+,S0181059,2016-07-28,3:16:15,1:19:26,0:20:51,0:17:50,0:35:03,0:07:12,6:39:11,2753,0,21
+Intensive Care Unit,PGY2+,S0181059,2016-07-27,3:17:54,0:34:05,0:07:05,0:13:28,0:17:23,0:06:05,4:50:59,2886,0,44
+Intensive Care Unit,PGY2+,S0181059,2016-07-26,2:27:10,1:31:31,0:04:36,0:32:30,0:34:45,0:09:13,5:52:41,3929,0,23
+Intensive Care Unit,PGY2+,S0181059,2016-07-25,3:13:14,1:03:35,0:11:54,0:15:21,0:14:27,0:05:26,5:26:25,2324,363,46
+Intensive Care Unit,PGY2+,S0181059,2016-07-24,2:06:35,0:55:07,0:08:13,0:05:29,0:11:00,0:04:43,3:51:36,1176,0,11
+Intensive Care Unit,PGY2+,S0181059,2016-07-23,0:40:57,0:01:53,0:00:00,0:00:00,0:00:00,0:01:55,0:49:57,263,0,11
+Intensive Care Unit,PGY2+,S0181059,2016-07-22,3:40:30,1:09:38,0:06:46,0:21:21,0:24:20,0:12:59,6:41:15,3257,266,22
+Intensive Care Unit,PGY2+,S0181059,2016-07-21,2:57:01,1:56:39,0:09:06,0:38:56,0:28:06,0:26:25,7:07:11,3554,0,25
+Intensive Care Unit,PGY2+,S0181059,2016-07-20,3:05:02,1:19:49,0:14:56,0:27:34,0:24:04,0:13:44,6:46:07,2562,177,23
+General Medicine,PGY1,S0109179,2014-09-08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,2
+General Medicine,PGY1,S0109179,2014-09-09,2:51:18,0:05:41,0:21:55,0:36:36,0:14:22,0:20:13,4:54:28,1578,22,8
+General Medicine,PGY1,S0109179,2014-09-01,3:51:08,0:05:04,0:10:25,1:07:26,0:23:29,0:11:31,6:41:35,2561,0,25
+General Medicine,PGY1,S0109179,2014-09-02,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,12,0,4
+General Medicine,PGY1,S0109179,2014-09-03,1:53:27,0:02:58,0:11:02,0:26:06,0:08:17,0:06:46,3:00:31,1321,112,22
+General Medicine,PGY1,S0109179,2014-09-04,3:07:35,0:10:32,0:30:58,1:02:28,0:18:39,0:35:58,6:24:55,2251,0,7
+General Medicine,PGY1,S0109179,2014-09-05,2:02:49,0:00:38,0:14:53,0:34:21,0:08:42,0:10:47,3:37:35,1143,85,5
+General Medicine,PGY1,S0109179,2014-09-06,3:12:51,0:09:57,0:29:08,1:00:43,0:25:03,0:14:06,5:54:18,2042,0,29
+General Medicine,PGY1,S0109179,2014-09-07,2:54:34,0:06:37,0:06:24,0:33:35,0:10:22,0:30:28,4:38:40,1450,28,7
+General Medicine,PGY1,S0163662,2014-08-31,2:36:03,0:24:01,0:18:46,0:34:48,0:26:26,0:28:09,4:57:30,1312,0,7
+General Medicine,PGY1,S0163662,2014-08-30,2:57:13,0:25:18,0:08:54,0:28:38,0:21:38,0:34:56,5:32:39,1356,201,5
+Night Team,PGY1,S0163425,2015-02-11,3:02:44,0:19:23,0:59:44,0:21:19,0:29:18,0:17:48,6:05:13,1689,0,51
+Night Team,PGY1,S0163425,2015-02-10,2:04:26,0:36:27,0:49:46,0:24:01,0:37:07,0:26:07,5:07:34,2163,2,48
+Night Team,PGY1,S0163425,2015-02-13,0:53:26,0:04:08,0:10:34,0:01:51,0:13:19,0:18:02,1:50:32,673,0,21
+Night Team,PGY1,S0163425,2015-02-12,2:40:53,0:26:09,1:03:15,0:21:01,0:21:25,0:32:07,6:00:16,2051,0,41
+Night Team,PGY1,S0182038,2016-09-14,1:06:36,0:21:26,0:00:01,0:31:01,0:21:09,0:04:34,2:30:50,663,0,30
+Night Team,PGY1,S0182038,2016-09-15,1:50:18,0:08:48,0:00:02,0:33:56,0:27:28,0:08:04,3:44:10,1098,71,47
+Night Team,PGY1,S0182038,2016-09-12,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,28,0,4
+Night Team,PGY1,S0182038,2016-09-13,0:36:30,0:15:43,0:00:00,0:12:31,0:13:37,0:00:01,1:23:01,302,66,14
+General Medicine,PGY2+,S0169389,2015-11-29,1:33:35,1:00:30,0:23:08,0:14:58,0:03:34,0:12:44,4:02:00,1475,194,23
+General Medicine,PGY2+,S0169389,2015-11-28,4:30:44,2:35:45,0:14:22,0:23:37,0:18:39,0:33:41,8:52:20,3053,64,74
+Night Team,PGY1,S0181061,2016-02-16,0:57:07,0:37:08,0:51:50,0:36:29,0:21:00,0:53:45,4:37:42,1217,0,33
+Night Team,PGY1,S0181061,2016-02-17,1:13:39,0:50:30,1:28:39,0:52:22,0:27:31,1:15:30,6:25:34,1545,0,27
+Night Team,PGY1,S0181061,2016-02-14,1:15:06,0:11:22,0:08:32,0:34:56,0:33:54,0:50:40,3:55:18,942,51,34
+Night Team,PGY1,S0181061,2016-02-15,1:34:38,0:41:26,0:41:32,0:21:43,0:23:07,0:42:25,4:39:55,1079,0,39
+General Medicine,PGY2+,S0169389,2015-11-21,0:03:43,0:05:47,0:00:00,0:00:00,0:00:06,0:01:57,0:13:27,109,107,11
+General Medicine,PGY2+,S0169389,2015-11-20,2:40:52,1:32:17,0:36:50,0:20:50,0:17:05,0:38:38,6:24:09,2019,37,65
+Night Team,PGY1,S0181061,2016-02-18,0:44:59,0:44:54,1:52:40,0:32:22,0:17:44,0:28:38,4:59:53,1506,0,19
+General Medicine,PGY2+,S0169389,2015-11-22,1:56:30,0:52:10,0:28:56,0:31:02,0:10:10,0:11:58,4:30:48,1323,55,29
+General Medicine,PGY2+,S0169389,2015-11-25,3:46:24,1:32:40,0:14:20,0:36:57,0:14:41,0:40:11,7:45:02,2467,72,60
+General Medicine,PGY2+,S0169389,2015-11-24,2:38:41,1:03:04,0:02:44,0:23:43,0:17:43,0:29:41,5:15:07,1829,305,25
+General Medicine,PGY2+,S0169389,2015-11-27,0:18:41,0:12:31,0:00:00,0:00:00,0:01:56,0:01:35,0:36:12,356,346,18
+General Medicine,PGY2+,S0169389,2015-11-26,1:43:44,0:29:24,0:31:43,0:39:14,0:13:14,0:20:05,4:19:36,1218,83,21
+General Medicine,PGY1,S0079337,2014-12-19,1:44:58,0:00:53,0:32:23,0:13:24,0:08:06,0:04:29,2:56:31,1079,34,11
+General Medicine,PGY1,S0079337,2014-12-18,3:48:54,0:00:42,1:19:34,0:35:49,0:28:05,0:00:33,6:28:45,2175,0,15
+General Medicine,PGY1,S0079337,2014-12-17,5:12:55,0:04:29,1:17:50,0:44:11,0:37:57,0:08:47,8:39:57,2901,0,26
+General Medicine,PGY1,S0195229,2016-08-07,2:33:35,1:00:44,0:00:15,0:26:50,0:14:14,0:05:03,4:57:07,1525,0,8
+General Medicine,PGY1,S0079337,2014-12-15,3:02:17,0:01:44,0:56:54,0:31:33,0:28:42,0:05:02,5:31:26,2130,0,13
+General Medicine,PGY1,S0079337,2014-12-14,1:14:09,0:00:25,0:14:52,0:04:01,0:01:28,0:00:03,1:38:10,468,0,9
+General Medicine,PGY1,S0079337,2014-12-13,2:05:56,0:01:15,0:55:15,0:18:35,0:08:00,0:04:11,3:56:06,1237,0,6
+General Medicine,PGY1,S0195229,2016-08-03,1:43:24,0:29:29,0:00:32,0:55:10,0:14:58,0:00:01,3:39:26,981,0,7
+General Medicine,PGY1,S0079337,2014-12-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0169399,2015-01-03,1:18:03,0:11:20,1:33:40,0:08:25,0:08:45,0:06:07,3:39:20,661,171,13
+Night Team,PGY2+,S0172645,2016-07-07,0:37:39,0:27:35,0:00:03,0:08:52,0:05:16,0:00:20,1:22:56,496,0,9
+General Medicine,PGY1,S0169399,2015-01-02,2:47:05,0:02:13,2:41:33,0:48:44,0:19:33,0:09:40,7:25:26,1724,0,13
+General Medicine,PGY1,S0169401,2014-10-08,5:08:14,0:19:00,0:18:17,0:58:16,0:24:50,0:15:13,8:13:41,3134,973,32
+General Medicine,PGY1,S0195236,2016-07-25,2:19:40,1:42:58,0:00:12,0:35:02,0:38:55,0:02:04,5:54:50,1440,227,13
+General Medicine,PGY1,S0169399,2015-01-01,2:39:27,0:04:44,2:29:56,0:23:15,0:26:05,0:01:27,6:31:50,1245,156,12
+General Medicine,PGY1,S0195236,2016-07-27,1:58:05,0:51:57,0:00:23,0:17:29,0:17:36,0:03:00,3:55:13,1132,0,9
+General Medicine,PGY1,S0195236,2016-07-26,1:18:38,0:40:38,0:01:16,0:25:45,0:12:18,0:03:28,3:27:24,1067,0,13
+General Medicine,PGY1,S0195236,2016-07-29,0:37:05,0:22:07,0:00:09,0:11:43,0:04:55,0:00:13,1:39:58,739,0,7
+General Medicine,PGY1,S0195236,2016-07-28,1:37:06,1:00:03,0:00:07,0:33:15,0:13:35,0:03:03,3:48:34,1241,0,17
+Night Team,PGY1,S0106952,2014-12-20,3:18:43,0:04:17,2:32:19,0:33:49,0:48:53,0:29:46,8:10:22,1707,0,41
+General Medicine,PGY2+,S0181078,2016-09-15,2:51:05,1:14:09,0:00:12,1:07:44,0:42:30,0:04:35,7:06:59,2653,238,15
+General Medicine,PGY2+,S0181078,2016-09-14,0:46:09,0:22:13,0:00:00,0:00:09,0:05:00,0:03:02,1:23:54,507,501,19
+General Medicine,PGY2+,S0181078,2016-09-17,3:32:34,1:03:19,0:00:12,0:54:30,0:51:42,0:02:41,7:18:51,2377,642,14
+General Medicine,PGY2+,S0181078,2016-09-16,3:43:04,1:09:26,0:00:03,0:46:47,0:26:20,0:13:08,7:08:04,2309,584,31
+General Medicine,PGY2+,S0181078,2016-09-11,4:06:52,1:09:29,0:00:01,0:18:57,0:20:43,0:09:01,6:37:44,2505,643,29
+Intensive Care Unit,PGY2+,S0181078,2016-09-10,2:52:35,1:04:34,0:00:11,0:51:13,0:09:07,0:02:33,5:43:11,1869,336,24
+General Medicine,PGY2+,S0181078,2016-09-13,3:55:36,0:53:49,0:01:35,0:16:26,0:12:09,0:03:20,6:14:29,1934,488,23
+General Medicine,PGY2+,S0181078,2016-09-12,3:29:51,1:17:28,0:00:12,1:00:16,0:24:52,0:12:51,7:22:46,2801,817,30
+Intensive Care Unit,PGY2+,S0181069,2016-11-10,2:34:28,1:28:49,0:01:33,0:22:17,0:25:33,0:05:04,5:25:34,1618,1155,12
+General Medicine,PGY2+,S0181078,2016-09-19,2:29:24,0:37:06,0:00:06,0:46:46,0:20:00,0:07:38,4:59:07,1628,285,15
+General Medicine,PGY2+,S0181078,2016-09-18,4:10:14,1:18:09,0:00:08,0:48:27,0:26:00,0:10:39,7:50:08,2562,1306,21
+General Medicine,PGY1,S0195224,2016-10-17,3:00:42,1:23:42,0:00:21,1:47:17,0:23:35,0:09:36,7:44:41,1897,39,16
+General Medicine,PGY1,S0169404,2015-02-27,3:04:53,0:09:48,1:14:10,0:23:54,0:12:03,0:29:09,5:49:04,2056,72,11
+General Medicine,PGY1,S0169404,2015-02-26,2:19:50,0:11:08,1:13:39,0:16:50,0:15:17,0:23:32,4:56:19,2003,23,7
+General Medicine,PGY1,S0169404,2015-02-25,1:11:59,0:21:40,0:42:48,0:14:39,0:07:32,0:33:23,3:25:11,1531,382,8
+General Medicine,PGY1,S0169404,2015-02-24,3:12:53,0:35:44,2:41:57,0:44:18,0:24:56,0:40:11,8:44:33,2958,27,17
+General Medicine,PGY1,S0169404,2015-02-23,0:03:51,0:17:03,0:01:28,0:00:34,0:00:57,0:05:51,0:33:08,359,298,7
+General Medicine,PGY1,S0169404,2015-02-22,3:20:07,0:28:41,1:07:05,0:28:55,0:24:17,0:23:23,6:34:51,2837,0,13
+General Medicine,PGY1,S0169404,2015-02-21,0:00:00,0:07:02,0:00:00,0:00:01,0:00:00,0:01:05,0:08:47,109,103,4
+General Medicine,PGY1,S0169404,2015-02-20,2:46:13,1:02:48,1:01:56,0:15:15,0:10:30,0:14:42,5:53:53,2058,65,15
+General Medicine,PGY1,S0181059,2016-03-23,3:15:08,1:01:10,0:03:22,0:35:12,0:14:12,0:01:17,5:48:13,2263,133,30
+General Medicine,PGY1,S0181059,2016-03-22,2:33:30,1:43:03,0:00:36,0:34:46,0:16:26,0:02:30,5:52:12,3134,109,28
+General Medicine,PGY1,S0181059,2016-03-21,0:12:20,0:00:48,0:00:00,0:00:49,0:00:00,0:01:06,0:15:44,169,137,9
+General Medicine,PGY1,S0181059,2016-03-20,2:53:55,2:14:29,0:00:25,0:46:03,0:17:52,0:08:05,6:55:01,2763,59,28
+General Medicine,PGY1,S0181059,2016-03-26,0:12:52,0:06:55,0:00:16,0:03:27,0:00:15,0:00:05,0:30:23,320,300,17
+General Medicine,PGY1,S0181059,2016-03-25,0:01:16,0:00:34,0:00:00,0:02:13,0:00:00,0:00:00,0:04:06,48,26,7
+General Medicine,PGY1,S0181059,2016-03-24,1:39:23,0:59:01,0:00:22,0:14:37,0:07:26,0:00:31,3:26:09,1666,177,11
+General Medicine,PGY1,S0195224,2016-10-16,1:54:13,0:51:52,0:00:03,0:20:51,0:06:54,0:00:46,3:45:53,912,385,10
+Emergency Medicine,PGY1,S0181073,2016-03-24,1:30:38,0:05:08,0:02:15,0:19:15,0:19:13,0:00:24,2:56:10,657,0,13
+Emergency Medicine,PGY1,S0181073,2016-03-23,1:22:50,0:03:20,0:00:03,0:11:48,0:04:14,0:00:19,1:54:17,362,0,9
+Night Team,PGY1,S0181083,2015-09-01,3:37:42,0:01:02,0:47:10,0:51:08,0:28:25,0:23:25,6:37:19,1443,0,49
+Emergency Medicine,PGY1,S0181073,2016-03-21,4:14:28,0:35:40,0:02:13,0:28:19,0:43:32,0:01:40,7:01:25,2424,0,14
+Emergency Medicine,PGY1,S0181073,2016-03-20,3:09:41,0:38:52,0:01:57,0:26:07,0:45:50,0:07:39,6:03:31,1588,0,16
+Emergency Medicine,PGY2+,S0172645,2016-10-09,2:03:08,0:41:37,0:00:51,0:29:44,0:29:00,0:02:54,4:21:34,1271,69,27
+General Medicine,PGY1,S0108268,2014-12-28,1:00:42,0:03:40,0:11:34,0:17:43,0:19:37,0:15:43,2:23:41,1160,115,6
+Night Team,PGY1,S0181060,2015-09-27,0:47:48,0:19:26,1:37:19,0:18:02,0:15:53,0:22:12,3:58:12,806,0,13
+General Medicine,PGY1,S0108268,2014-12-24,3:39:31,0:18:33,1:14:47,0:49:14,0:49:42,0:58:16,8:15:31,2737,0,13
+General Medicine,PGY1,S0108268,2014-12-25,1:49:41,0:07:32,0:49:14,0:47:17,0:18:12,0:17:46,4:25:54,1315,43,9
+Night Team,PGY1,S0181060,2015-09-28,1:03:13,0:23:48,1:53:42,1:15:17,0:45:24,0:26:17,6:10:55,1414,18,20
+Night Team,PGY1,S0181060,2015-09-29,1:09:01,0:45:18,0:52:16,0:38:05,0:32:02,0:45:11,5:03:49,1326,10,30
+General Medicine,PGY1,S0108268,2014-12-20,2:35:25,0:05:17,1:48:27,0:57:20,1:03:52,0:31:32,7:38:28,2336,21,10
+General Medicine,PGY1,S0108268,2014-12-21,2:29:41,0:02:44,0:52:57,0:23:53,0:33:43,0:25:33,5:24:48,1853,678,16
+General Medicine,PGY1,S0108268,2014-12-22,2:36:40,0:22:08,1:17:59,0:45:05,0:50:36,0:39:09,7:27:26,2374,0,16
+General Medicine,PGY1,S0108268,2014-12-23,1:04:00,0:02:16,0:02:10,0:07:28,0:03:50,0:07:34,1:32:18,516,484,15
+General Medicine,PGY2+,S0181079,2016-09-29,4:22:11,1:51:18,0:00:01,0:30:19,0:21:27,0:24:32,8:28:48,3411,51,31
+General Medicine,PGY2+,S0181079,2016-09-28,1:59:54,0:54:32,0:00:31,0:17:26,0:18:36,0:04:51,4:07:13,2093,76,14
+General Medicine,PGY2+,S0181079,2016-09-23,0:54:31,0:27:12,0:00:00,0:00:42,0:04:38,0:00:21,1:29:26,503,483,20
+General Medicine,PGY2+,S0181079,2016-09-22,2:36:17,1:04:27,0:01:22,0:31:32,0:16:49,0:01:38,5:08:29,2157,8,29
+General Medicine,PGY2+,S0181079,2016-09-21,4:11:31,1:19:29,0:00:07,0:15:42,0:14:49,0:02:18,6:59:30,2314,524,24
+General Medicine,PGY2+,S0181079,2016-09-20,3:29:45,1:18:02,0:00:30,0:50:11,0:36:20,0:03:34,7:29:45,2938,269,20
+General Medicine,PGY2+,S0181079,2016-09-27,2:56:25,1:17:27,0:00:19,0:34:31,0:27:28,0:01:49,6:03:02,2256,42,17
+General Medicine,PGY2+,S0181079,2016-09-26,2:53:09,1:37:16,0:00:23,0:21:53,0:18:44,0:14:57,6:31:51,2382,292,32
+General Medicine,PGY2+,S0181079,2016-09-25,2:53:26,0:46:08,0:00:25,0:28:13,0:32:10,0:01:08,5:12:21,1784,0,13
+General Medicine,PGY2+,S0181079,2016-09-24,3:06:08,1:35:14,0:00:18,0:23:00,0:14:40,0:12:26,6:00:06,2125,0,25
+General Medicine,PGY1,S0169398,2015-01-04,2:30:51,0:05:23,1:40:50,0:09:47,0:09:27,0:56:23,5:53:58,1335,202,6
+General Medicine,PGY1,S0169398,2015-01-05,3:49:31,0:23:49,2:16:40,0:44:18,0:23:13,0:17:03,8:21:14,2618,412,10
+General Medicine,PGY1,S0169398,2015-01-06,0:55:11,0:01:09,0:11:54,0:02:06,0:00:42,0:06:17,1:20:39,347,339,12
+General Medicine,PGY1,S0169398,2015-01-07,4:35:37,0:14:59,1:56:56,0:44:36,0:40:27,0:38:18,9:18:28,2659,623,15
+General Medicine,PGY1,S0195201,2016-10-12,0:40:45,0:07:30,0:00:00,0:00:26,0:00:11,0:03:06,0:54:50,376,146,23
+Night Team,PGY1,S0181083,2015-09-08,2:12:26,0:23:55,2:13:22,0:38:44,0:36:39,0:32:46,6:54:18,1399,12,31
+General Medicine,PGY1,S0169398,2015-01-08,4:23:57,0:09:35,1:38:24,0:43:36,0:46:06,0:21:04,8:44:03,2558,1364,16
+General Medicine,PGY1,S0169398,2015-01-09,2:38:29,0:15:23,1:12:44,0:17:26,0:14:58,0:16:24,5:05:51,1582,331,8
+General Medicine,PGY1,S0195201,2016-10-13,2:39:07,2:04:45,0:03:57,0:46:06,0:08:09,0:01:53,6:24:54,2959,565,17
+Intensive Care Unit,PGY2+,S0108268,2016-07-20,1:22:51,0:13:57,0:11:57,0:13:36,0:21:59,0:00:32,2:40:07,1170,0,13
+Intensive Care Unit,PGY2+,S0108268,2016-07-21,1:01:53,0:34:00,0:07:01,0:09:45,0:12:36,0:01:16,2:32:43,854,11,25
+Intensive Care Unit,PGY2+,S0108268,2016-07-22,0:42:45,0:19:08,0:03:53,0:12:59,0:07:34,0:06:45,2:11:05,750,0,24
+Intensive Care Unit,PGY2+,S0108268,2016-07-23,0:08:45,0:11:30,0:00:00,0:00:15,0:00:53,0:00:55,0:33:16,187,157,13
+Intensive Care Unit,PGY2+,S0108268,2016-07-24,1:56:56,1:07:32,0:00:00,0:33:30,0:28:18,0:06:31,4:43:22,1986,0,10
+Intensive Care Unit,PGY2+,S0108268,2016-07-25,2:17:15,1:28:55,0:03:32,0:15:27,0:32:11,0:11:43,5:13:10,1788,124,20
+Intensive Care Unit,PGY2+,S0108268,2016-07-26,1:47:00,1:02:46,0:03:23,0:11:20,0:17:12,0:06:47,3:46:00,1608,0,20
+Intensive Care Unit,PGY2+,S0108268,2016-07-27,1:43:33,1:10:22,0:05:14,0:07:09,0:14:22,0:00:48,3:43:50,1137,0,26
+Intensive Care Unit,PGY2+,S0108268,2016-07-28,1:44:23,1:04:54,0:05:28,0:29:53,0:22:43,0:00:43,4:13:39,1219,0,18
+Intensive Care Unit,PGY2+,S0108268,2016-07-29,2:33:44,0:52:11,0:00:00,0:29:32,0:33:19,0:07:58,5:32:40,1872,0,21
+General Medicine,PGY1,S0195201,2016-10-11,3:19:53,3:03:07,0:00:13,0:31:47,0:28:00,0:01:13,7:52:52,3231,10,14
+Intensive Care Unit,PGY2+,S0169398,2016-04-30,0:00:00,0:00:00,0:00:00,0:00:35,0:00:00,0:00:00,0:00:35,24,0,7
+Emergency Medicine,PGY1,S0181057,2015-08-11,0:39:04,0:40:23,0:27:58,0:33:00,0:09:35,0:09:11,3:01:49,1047,0,5
+General Medicine,PGY1,S0181078,2015-08-17,2:39:51,0:34:08,1:31:46,0:24:36,0:22:23,0:18:57,6:35:04,1604,178,6
+General Medicine,PGY1,S0181078,2015-08-16,0:00:44,0:11:21,0:00:00,0:00:00,0:00:00,0:00:00,0:16:37,84,80,2
+General Medicine,PGY1,S0181078,2015-08-19,1:48:04,0:37:52,2:08:09,0:27:13,0:22:36,0:14:20,6:19:06,1369,303,7
+General Medicine,PGY1,S0181078,2015-08-18,1:42:06,0:53:46,1:55:02,0:40:53,0:18:48,0:18:36,6:44:04,1892,968,16
+Intensive Care Unit,PGY2+,S0169404,2016-08-31,2:44:23,0:48:09,0:07:19,0:18:17,0:20:58,0:05:29,4:39:02,1864,0,19
+Intensive Care Unit,PGY2+,S0169404,2016-08-30,2:31:03,1:08:53,0:11:49,0:11:10,0:21:06,0:04:41,4:46:58,1976,0,14
+General Medicine,PGY2+,S0169399,2016-06-23,1:18:21,0:42:50,0:00:26,0:35:46,0:16:16,0:03:43,3:22:24,920,35,11
+General Medicine,PGY2+,S0169399,2016-06-22,1:43:54,1:00:03,0:00:00,0:22:51,0:11:55,0:00:31,3:45:08,759,4,19
+General Medicine,PGY1,S0195075,2016-06-30,0:50:14,0:52:28,0:00:09,0:08:11,0:20:15,0:00:16,2:40:35,728,23,5
+General Medicine,PGY2+,S0169399,2016-06-21,1:01:54,0:52:27,0:00:06,0:13:29,0:15:09,0:01:16,2:52:28,793,42,10
+General Medicine,PGY2+,S0169399,2016-06-20,1:24:46,0:33:03,0:00:01,0:30:55,0:07:51,0:03:54,3:09:50,929,37,24
+General Medicine,PGY2+,S0181074,2016-10-09,2:20:51,1:22:07,0:00:07,0:38:18,0:16:43,0:07:24,5:20:57,2406,234,27
+Intensive Care Unit,PGY2+,S0079337,2016-06-18,1:01:51,0:03:50,0:15:23,0:18:47,0:19:45,0:00:16,2:07:47,965,0,10
+Intensive Care Unit,PGY2+,S0079337,2016-06-19,0:04:43,0:00:40,0:00:00,0:00:03,0:00:00,0:00:00,0:05:35,58,50,13
+General Medicine,PGY2+,S0169399,2016-06-25,1:46:59,0:24:28,0:00:00,0:10:47,0:09:35,0:00:34,2:49:45,683,381,17
+Intensive Care Unit,PGY2+,S0079337,2016-06-10,1:44:03,0:09:30,0:15:52,0:07:17,0:14:03,0:00:11,2:40:14,868,134,15
+Intensive Care Unit,PGY2+,S0079337,2016-06-11,0:02:26,0:00:27,0:00:00,0:00:11,0:00:00,0:00:00,0:03:13,36,30,6
+Intensive Care Unit,PGY2+,S0079337,2016-06-12,1:30:03,0:19:00,0:15:35,0:09:18,0:22:33,0:00:30,2:56:49,865,60,18
+Intensive Care Unit,PGY2+,S0079337,2016-06-13,3:06:34,0:53:46,0:19:23,0:21:09,0:45:13,0:01:47,6:03:39,1982,3,19
+Intensive Care Unit,PGY2+,S0079337,2016-06-14,1:00:17,0:08:59,0:07:17,0:10:18,0:09:00,0:00:23,1:54:02,775,65,11
+Intensive Care Unit,PGY2+,S0079337,2016-06-15,1:51:04,0:25:13,0:11:32,0:21:47,0:32:46,0:04:53,3:48:31,1200,12,18
+Intensive Care Unit,PGY2+,S0079337,2016-06-16,2:44:23,0:33:39,0:14:43,0:19:20,0:27:12,0:05:12,4:43:37,1456,24,16
+Intensive Care Unit,PGY2+,S0079337,2016-06-17,1:59:35,0:42:12,0:03:02,0:30:40,0:38:47,0:11:55,4:28:52,1955,9,21
+Night Team,PGY1,S0174975,2016-04-09,2:00:13,1:03:30,0:01:57,1:02:18,0:16:10,0:03:48,4:51:15,1504,228,33
+Night Team,PGY1,S0174975,2016-04-08,2:38:50,1:08:36,0:00:16,2:11:05,0:24:03,0:08:54,7:21:22,2048,993,50
+Night Team,PGY1,S0174975,2016-04-05,3:23:31,0:37:31,0:00:00,1:54:50,0:34:43,0:04:38,6:57:56,2621,137,102
+Night Team,PGY1,S0174975,2016-04-04,1:18:45,0:12:59,0:00:00,0:33:29,0:25:44,0:00:05,2:54:33,847,98,47
+Night Team,PGY1,S0174975,2016-04-07,3:26:28,2:03:28,0:01:26,2:08:21,0:38:02,0:08:40,9:05:13,3074,1043,69
+Night Team,PGY1,S0174975,2016-04-06,3:34:39,1:17:10,0:00:32,0:50:02,0:35:09,0:05:29,6:49:56,1983,83,70
+Night Team,PGY1,S0174975,2016-04-01,0:45:00,0:08:49,0:00:00,0:27:23,0:05:11,0:00:00,1:26:56,432,45,40
+Night Team,PGY1,S0174975,2016-04-03,2:03:31,0:37:06,0:00:10,1:19:19,0:18:56,0:05:06,4:58:58,1487,81,41
+Night Team,PGY1,S0174975,2016-04-02,1:42:19,0:27:56,0:04:05,1:12:46,0:19:20,0:01:30,4:07:09,1504,31,61
+Night Team,PGY2+,S0108268,2015-11-30,1:00:26,0:22:28,0:14:54,0:05:13,0:03:35,0:08:23,2:07:22,537,74,22
+Night Team,PGY2+,S0169384,2016-01-20,0:20:42,0:27:15,0:35:17,0:34:44,0:09:16,0:20:49,2:29:29,802,0,12
+General Medicine,PGY1,S0181063,2015-12-07,0:30:14,0:45:30,1:02:27,0:28:50,0:09:40,0:09:07,3:21:26,1402,10,6
+General Medicine,PGY1,S0181063,2015-12-06,0:27:17,0:40:45,1:15:17,0:06:58,0:07:32,0:06:34,3:00:59,648,0,4
+General Medicine,PGY1,S0181063,2015-12-09,0:34:08,0:25:02,1:20:37,0:33:02,0:20:23,0:11:35,3:58:49,1091,0,10
+General Medicine,PGY1,S0181063,2015-12-08,0:31:01,0:23:43,0:29:12,0:13:42,0:04:44,0:06:30,2:14:57,891,143,6
+Night Team,PGY2+,S0163425,2015-10-18,1:29:07,1:23:05,0:45:37,0:11:30,0:23:06,0:45:43,5:12:55,1686,0,60
+Night Team,PGY2+,S0163425,2015-10-19,1:50:44,1:40:23,2:21:17,0:46:48,0:13:01,0:35:53,7:54:30,2565,0,26
+Intensive Care Unit,PGY2+,S0094959,2015-09-13,3:49:57,1:24:59,0:11:13,0:20:40,0:28:42,1:05:58,7:42:11,3423,404,25
+Intensive Care Unit,PGY2+,S0094959,2015-09-12,0:00:00,0:30:23,0:00:00,0:00:41,0:00:00,0:03:37,0:37:21,363,330,18
+Intensive Care Unit,PGY2+,S0094959,2015-09-11,3:38:41,1:11:05,0:05:08,0:07:34,0:16:51,0:50:32,6:28:15,2775,404,34
+Intensive Care Unit,PGY2+,S0094959,2015-09-10,3:29:49,1:11:39,0:01:21,0:26:15,0:33:53,0:43:53,7:03:19,3130,186,16
+Intensive Care Unit,PGY2+,S0094959,2015-09-17,3:30:40,1:16:11,0:06:23,0:14:04,0:14:33,0:44:27,6:25:47,2737,620,41
+Intensive Care Unit,PGY2+,S0094959,2015-09-16,2:56:53,1:11:52,0:02:07,0:04:40,0:15:13,0:35:28,5:24:43,2435,150,22
+Intensive Care Unit,PGY2+,S0094959,2015-09-15,1:58:28,0:56:10,0:08:57,0:04:46,0:16:20,0:41:38,4:18:33,1919,235,25
+Intensive Care Unit,PGY2+,S0094959,2015-09-14,0:16:24,0:45:56,0:00:00,0:00:58,0:00:22,0:11:48,1:17:29,858,811,21
+Night Team,PGY2+,S0106952,2015-07-21,2:33:34,0:32:22,3:09:46,0:10:48,0:12:41,0:30:11,7:25:53,2496,769,97
+Night Team,PGY2+,S0106952,2015-07-20,3:40:32,0:54:03,5:37:37,0:27:51,0:32:55,0:45:16,12:43:17,3621,1130,161
+Intensive Care Unit,PGY2+,S0094959,2015-09-19,0:00:00,0:55:35,0:00:00,0:00:35,0:00:00,0:12:43,1:10:53,562,518,38
+Intensive Care Unit,PGY2+,S0094959,2015-09-18,3:20:22,1:12:29,0:06:29,0:16:15,0:37:12,0:47:25,6:42:50,2942,314,40
+Night Team,PGY2+,S0106952,2015-07-25,2:16:57,0:44:29,2:05:01,0:19:12,0:22:51,0:26:39,6:37:23,2443,444,86
+Night Team,PGY2+,S0106952,2015-07-24,4:36:07,1:51:37,2:05:27,0:39:07,0:45:55,0:43:17,11:31:38,4721,1699,315
+Emergency Medicine,PGY2+,S0079337,2016-02-08,0:38:10,0:48:26,2:04:45,0:15:34,0:40:45,0:37:08,5:19:33,2358,0,20
+Intensive Care Unit,PGY2+,S0169399,2016-02-29,0:29:48,0:12:35,0:03:11,0:03:15,0:08:37,0:00:01,1:04:09,307,0,6
+Intensive Care Unit,PGY2+,S0169399,2016-02-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Intensive Care Unit,PGY2+,S0169399,2016-02-27,1:37:26,0:13:03,0:08:14,0:11:13,0:16:54,0:06:11,2:47:55,569,62,11
+Intensive Care Unit,PGY2+,S0169399,2016-02-26,1:53:45,0:52:22,0:05:56,0:43:34,0:25:58,0:07:02,4:52:50,1100,0,14
+Intensive Care Unit,PGY2+,S0169399,2016-02-25,0:48:14,0:22:56,0:03:19,0:09:33,0:12:10,0:01:07,1:59:23,538,0,25
+Intensive Care Unit,PGY2+,S0169399,2016-02-24,1:46:44,0:49:55,0:10:08,0:26:18,0:18:24,0:01:24,4:02:30,1039,391,19
+Intensive Care Unit,PGY2+,S0169399,2016-02-23,1:05:18,0:19:50,0:05:25,0:11:23,0:19:45,0:08:11,2:37:02,588,4,9
+Intensive Care Unit,PGY2+,S0169399,2016-02-22,0:02:37,0:00:45,0:00:00,0:00:09,0:00:00,0:01:10,0:05:22,40,25,4
+Intensive Care Unit,PGY2+,S0169399,2016-02-21,1:08:08,0:40:36,0:10:22,0:07:56,0:08:34,0:05:11,2:38:42,567,0,10
+Intensive Care Unit,PGY2+,S0169399,2016-02-20,2:05:46,0:26:00,0:05:44,0:36:22,0:27:30,0:05:36,4:28:36,1083,0,29
+Emergency Medicine,PGY1,S0181057,2015-07-26,3:54:01,0:30:48,1:36:08,0:25:06,0:38:10,0:28:03,8:29:04,2566,172,37
+Emergency Medicine,PGY1,S0181057,2015-07-27,1:44:57,0:20:08,0:47:24,0:03:11,0:09:01,0:09:12,3:24:18,979,88,17
+Emergency Medicine,PGY1,S0181057,2015-07-24,0:37:49,0:04:39,0:28:49,0:01:38,0:10:21,0:06:59,1:36:50,499,0,9
+Emergency Medicine,PGY1,S0181057,2015-07-25,4:03:28,0:31:28,1:21:07,0:21:31,0:26:53,0:32:02,7:41:35,1933,0,25
+Emergency Medicine,PGY1,S0181057,2015-07-22,1:23:34,0:16:13,0:15:56,0:12:12,0:04:22,0:12:02,2:25:55,473,34,13
+Emergency Medicine,PGY1,S0181057,2015-07-23,5:10:54,0:38:52,1:40:13,0:16:10,0:29:08,0:23:54,9:02:26,2222,19,42
+Emergency Medicine,PGY1,S0181057,2015-07-20,4:01:05,0:41:11,2:43:56,0:15:46,0:56:10,0:26:44,9:27:57,2571,0,32
+Emergency Medicine,PGY1,S0181057,2015-07-21,0:39:19,0:16:40,1:04:48,0:20:27,0:21:25,0:02:31,3:39:27,1226,36,38
+Emergency Medicine,PGY1,S0181057,2015-07-28,0:49:09,0:19:04,0:22:08,0:36:18,0:11:26,0:09:09,3:06:48,1209,2,8
+Emergency Medicine,PGY1,S0181057,2015-07-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0195206,2016-08-09,3:29:10,1:32:38,0:00:01,0:28:39,0:13:02,0:11:08,6:25:33,1984,0,53
+Night Team,PGY1,S0195206,2016-08-08,3:13:14,1:24:51,0:00:08,0:18:37,0:18:28,0:08:24,6:08:27,1900,617,44
+Emergency Medicine,PGY1,S0188098,2016-08-31,3:37:58,0:36:26,0:05:33,0:18:21,0:11:13,0:00:20,5:12:15,1083,0,21
+Emergency Medicine,PGY1,S0188098,2016-08-30,1:53:42,0:30:03,0:03:13,0:09:23,0:10:12,0:01:28,3:13:59,1068,97,18
+Night Team,PGY1,S0195206,2016-08-07,4:30:20,0:51:34,0:00:00,0:04:58,0:18:35,0:46:35,7:36:12,2994,782,52
+General Medicine,PGY1,S0195202,2016-06-27,3:36:58,1:39:19,0:00:32,1:22:25,0:40:54,0:05:32,8:10:08,2149,0,12
+General Medicine,PGY1,S0195202,2016-06-26,0:30:25,1:36:25,0:00:05,0:00:00,0:00:25,0:00:04,2:17:01,463,459,8
+General Medicine,PGY1,S0195202,2016-06-29,4:09:47,1:23:16,0:00:02,1:31:48,0:28:01,0:03:34,8:19:31,2596,119,13
+General Medicine,PGY1,S0195202,2016-06-28,2:38:43,2:26:10,0:00:10,0:24:04,0:34:49,0:02:44,6:35:20,2037,544,10
+Intensive Care Unit,PGY2+,S0169398,2016-04-29,2:58:30,0:31:57,0:05:45,0:22:53,0:31:07,0:11:47,5:28:01,1639,20,20
+General Medicine,PGY1,S0181067,2016-02-07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+Emergency Medicine,PGY1,S0181063,2016-05-28,1:34:08,1:07:34,0:03:54,0:24:13,0:57:28,0:43:40,5:16:23,1910,0,17
+Emergency Medicine,PGY1,S0181063,2016-05-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY1,S0181063,2016-05-24,2:11:17,0:54:43,0:01:26,0:17:17,0:50:28,0:26:35,5:25:20,1975,0,23
+Emergency Medicine,PGY1,S0181063,2016-05-25,0:03:03,0:04:36,0:00:00,0:01:32,0:00:00,0:00:00,0:13:18,143,135,12
+Emergency Medicine,PGY1,S0181063,2016-05-26,1:10:30,0:39:14,0:04:57,0:22:45,0:55:45,0:36:42,4:23:02,1882,0,28
+Emergency Medicine,PGY1,S0181063,2016-05-27,0:10:00,1:42:36,0:00:00,0:11:19,0:00:00,0:00:00,2:21:44,2569,2565,44
+Emergency Medicine,PGY1,S0181063,2016-05-22,3:17:32,1:08:31,0:00:59,0:31:37,1:09:41,0:04:02,6:58:41,2256,0,30
+Emergency Medicine,PGY1,S0181063,2016-05-23,2:35:55,1:19:37,0:05:42,0:28:53,1:04:45,0:14:46,6:22:00,2223,0,24
+General Medicine,PGY2+,S0093383,2016-03-03,0:09:42,0:18:22,0:00:01,0:00:08,0:00:00,0:00:27,0:39:50,686,671,51
+General Medicine,PGY2+,S0093383,2016-03-02,2:16:24,1:11:26,0:00:20,0:48:57,0:37:09,0:01:18,5:25:20,2500,241,20
+General Medicine,PGY2+,S0093383,2016-03-01,2:39:28,1:40:42,0:04:00,0:27:05,0:14:02,0:02:43,5:20:19,2769,0,26
+General Medicine,PGY2+,S0093383,2016-03-05,0:00:00,0:00:00,0:00:00,0:01:24,0:00:00,0:00:00,0:01:24,15,0,3
+General Medicine,PGY2+,S0093383,2016-03-04,3:48:46,1:27:20,0:00:00,0:37:45,0:18:44,0:11:27,7:06:38,3302,0,24
+Intensive Care Unit,PGY2+,S0169398,2016-04-23,2:28:03,0:32:27,0:05:37,0:24:56,0:24:52,0:00:46,4:24:05,1194,0,15
+General Medicine,PGY1,S0195206,2016-11-07,1:48:57,1:58:23,0:00:15,0:35:55,0:12:58,0:05:01,5:36:55,1630,42,20
+General Medicine,PGY1,S0195206,2016-11-06,0:03:41,0:00:49,0:00:00,0:00:01,0:00:00,0:00:22,0:05:19,72,0,9
+General Medicine,PGY1,S0195206,2016-11-05,1:41:48,1:02:37,0:00:03,0:19:25,0:11:17,0:11:46,4:23:56,1346,0,13
+General Medicine,PGY1,S0195206,2016-11-04,1:23:11,1:03:33,0:00:08,0:19:52,0:05:17,0:00:12,3:10:42,868,50,10
+General Medicine,PGY1,S0195206,2016-11-03,1:37:26,1:21:12,0:00:10,0:22:28,0:06:53,0:02:09,4:17:07,1769,0,20
+General Medicine,PGY1,S0195206,2016-11-02,1:44:14,1:21:11,0:00:11,0:14:42,0:15:41,0:03:20,4:01:51,1546,0,18
+General Medicine,PGY1,S0169394,2014-07-30,2:35:06,0:02:18,0:21:36,0:29:33,0:26:58,0:25:01,4:34:38,1519,51,15
+General Medicine,PGY1,S0169394,2014-07-31,3:09:55,0:05:48,0:12:40,1:02:00,0:23:04,0:18:04,5:41:59,2009,0,14
+General Medicine,PGY1,S0195206,2016-11-09,0:00:35,0:00:18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:54,18,0,5
+General Medicine,PGY1,S0195206,2016-11-08,1:35:45,1:31:37,0:00:15,0:37:04,0:13:15,0:01:54,4:49:36,1291,0,7
+General Medicine,PGY2+,S0169402,2016-07-05,1:58:02,1:07:18,0:00:04,0:27:40,0:18:34,0:05:49,4:39:55,1600,0,13
+General Medicine,PGY1,S0181064,2016-04-30,2:35:02,1:16:49,0:00:25,0:56:26,0:15:27,0:06:08,5:42:10,1728,219,28
+Night Team,PGY2+,S0169398,2015-12-24,1:12:14,0:33:02,1:01:46,0:30:09,0:05:17,0:09:06,3:45:51,765,1,22
+Night Team,PGY2+,S0169398,2015-12-25,2:19:53,0:40:35,1:32:44,0:29:12,0:10:47,0:44:47,6:33:29,1687,0,58
+General Medicine,PGY1,S0195216,2016-09-09,1:52:54,1:26:06,0:00:15,0:23:05,0:09:22,0:05:16,4:20:38,1483,0,12
+General Medicine,PGY1,S0195216,2016-09-08,2:39:40,2:04:47,0:00:10,0:25:38,0:16:06,0:04:07,6:05:36,2522,35,15
+General Medicine,PGY1,S0195216,2016-09-07,1:00:35,0:46:12,0:00:10,0:11:16,0:03:23,0:02:07,2:19:59,1246,0,16
+General Medicine,PGY1,S0195216,2016-09-06,0:04:55,0:00:39,0:00:00,0:00:00,0:00:00,0:00:00,0:05:50,25,0,5
+General Medicine,PGY1,S0195216,2016-09-05,2:10:40,1:23:38,0:00:06,0:29:04,0:25:49,0:11:04,5:05:53,1686,0,22
+General Medicine,PGY1,S0195216,2016-09-04,1:17:03,0:54:43,0:00:14,0:12:26,0:08:12,0:00:22,2:47:15,999,0,5
+General Medicine,PGY1,S0195216,2016-09-03,2:43:33,1:09:32,0:00:05,0:22:24,0:20:28,0:06:53,5:12:12,2048,0,19
+General Medicine,PGY1,S0195216,2016-09-02,1:55:17,1:51:20,0:00:54,0:43:19,0:18:03,0:02:00,5:26:14,2176,94,9
+General Medicine,PGY1,S0195216,2016-09-01,0:03:39,0:00:40,0:00:00,0:00:10,0:00:00,0:00:00,0:05:25,35,0,7
+General Medicine,PGY2+,S0169399,2016-06-24,0:21:30,0:01:52,0:00:00,0:00:00,0:00:00,0:03:29,0:29:16,98,86,8
+General Medicine,PGY1,S0108268,2014-12-19,2:34:35,0:10:09,0:53:45,0:56:39,1:02:35,0:43:37,7:07:13,2606,0,12
+Intensive Care Unit,PGY2+,S0169385,2016-01-20,0:57:15,0:42:25,0:09:07,0:28:29,0:32:12,0:52:35,3:59:09,2237,35,18
+Night Team,PGY1,S0181074,2015-11-01,1:23:42,1:11:25,0:14:41,1:20:49,0:21:03,0:19:00,5:08:16,2120,269,47
+Night Team,PGY2+,S0169398,2015-12-21,3:54:42,1:07:38,0:26:13,0:19:52,0:17:14,0:20:49,6:57:43,2721,15,93
+Night Team,PGY1,S0181074,2015-11-02,0:26:36,0:36:52,0:04:17,0:25:55,0:21:13,0:02:27,2:00:18,732,156,30
+Night Team,PGY1,S0181074,2015-11-03,1:27:56,1:36:58,0:58:45,1:55:08,0:47:26,0:21:27,7:33:35,2809,124,38
+Night Team,PGY1,S0181074,2015-11-04,1:33:58,1:26:47,0:45:02,1:19:17,0:28:38,0:22:33,6:18:32,2348,253,43
+Night Team,PGY1,S0181074,2015-11-05,2:12:42,1:12:12,0:10:45,2:12:00,0:39:23,0:23:27,7:28:02,3343,158,46
+Night Team,PGY1,S0181074,2015-11-06,1:55:56,1:43:12,1:53:28,2:04:26,0:38:42,0:11:32,9:02:00,3396,167,44
+Night Team,PGY1,S0181074,2015-11-07,0:44:01,0:41:16,0:24:47,0:42:30,0:10:35,0:00:47,2:51:33,1094,0,40
+General Medicine,PGY1,S0181075,2015-11-30,2:19:56,1:18:41,0:24:54,0:50:41,0:23:10,0:42:58,6:27:26,2556,0,17
+General Medicine,PGY1,S0181067,2016-02-02,0:42:22,0:20:43,0:30:58,0:09:25,0:15:58,0:07:48,2:12:23,795,75,7
+Night Team,PGY1,S0094959,2014-07-18,3:33:09,0:18:14,0:10:34,0:10:35,0:11:14,0:10:25,4:42:06,2025,76,49
+Intensive Care Unit,PGY2+,S0169390,2015-10-28,0:57:32,0:35:42,0:06:22,0:03:45,0:14:06,0:09:07,2:11:07,629,65,18
+General Medicine,PGY1,S0181061,2015-09-18,2:02:38,0:19:06,1:45:16,0:48:17,0:17:59,0:17:55,6:04:32,1478,0,7
+Intensive Care Unit,PGY2+,S0169390,2015-10-20,2:50:28,0:55:31,0:12:54,0:19:06,0:14:52,0:24:51,5:31:55,1542,0,12
+Intensive Care Unit,PGY2+,S0169390,2015-10-21,1:52:51,0:35:25,0:07:01,0:37:11,0:28:31,0:36:56,4:49:53,1193,70,12
+Intensive Care Unit,PGY2+,S0169390,2015-10-22,1:36:40,0:37:28,0:06:52,0:25:58,0:23:59,0:24:04,3:57:09,1296,79,21
+Intensive Care Unit,PGY2+,S0169390,2015-10-23,2:18:38,0:41:07,0:12:01,1:10:42,0:22:35,0:46:37,6:07:02,1799,43,18
+Intensive Care Unit,PGY2+,S0169390,2015-10-24,1:22:14,1:11:19,0:17:39,0:43:11,0:32:18,0:31:35,5:04:11,1795,61,17
+Intensive Care Unit,PGY2+,S0169390,2015-10-25,2:25:49,0:37:53,0:11:41,0:21:58,0:26:51,0:22:58,4:43:13,1213,146,24
+Intensive Care Unit,PGY2+,S0169390,2015-10-26,0:00:00,0:13:32,0:00:00,0:02:57,0:00:00,0:00:00,0:17:56,122,106,14
+Intensive Care Unit,PGY2+,S0169390,2015-10-27,1:10:24,0:48:49,0:12:41,0:22:20,0:26:02,0:08:42,3:31:26,1104,64,17
+Emergency Medicine,PGY1,S0172085,2015-08-08,4:19:57,1:00:21,2:09:05,0:21:52,0:23:03,0:40:17,9:31:42,2482,0,37
+Emergency Medicine,PGY1,S0172085,2015-08-09,2:56:53,0:31:59,1:12:28,0:20:16,0:19:19,0:37:04,6:12:36,1555,0,31
+Emergency Medicine,PGY1,S0181082,2016-03-22,2:38:52,0:50:34,0:04:18,0:38:03,0:45:08,0:03:40,5:35:02,1765,1,12
+Emergency Medicine,PGY1,S0181082,2016-03-21,3:42:39,0:55:55,0:03:36,0:47:36,0:43:47,0:00:34,7:04:54,2213,0,28
+Emergency Medicine,PGY1,S0181082,2016-03-20,2:28:36,0:38:08,0:03:03,0:28:25,0:25:54,0:00:19,4:41:14,1176,0,16
+General Medicine,PGY1,S0181082,2016-03-27,1:23:51,0:53:04,0:00:06,0:30:56,0:09:39,0:00:27,3:11:50,1150,0,6
+Emergency Medicine,PGY1,S0181082,2016-03-26,2:35:21,1:19:28,0:00:11,1:24:42,0:28:39,0:05:27,6:26:33,1961,0,13
+Emergency Medicine,PGY1,S0181082,2016-03-24,3:52:51,0:59:54,0:03:52,0:20:45,0:44:11,0:01:51,6:31:44,2245,0,20
+General Medicine,PGY1,S0181082,2016-03-29,1:22:44,0:59:31,0:00:04,0:13:50,0:13:29,0:06:34,3:17:19,918,19,6
+General Medicine,PGY1,S0169400,2015-05-18,2:37:57,0:26:45,1:01:41,0:31:40,0:28:34,0:39:05,6:03:28,1541,0,12
+General Medicine,PGY1,S0074196,2016-01-13,0:28:45,0:21:50,0:17:49,0:09:15,0:05:21,0:10:36,1:52:20,789,0,4
+General Medicine,PGY1,S0074196,2016-01-12,1:14:40,0:56:00,1:38:41,0:38:32,0:06:50,0:08:40,5:06:41,1433,0,11
+General Medicine,PGY1,S0074196,2016-01-11,0:47:02,0:40:23,0:45:38,0:24:11,0:06:39,0:15:19,3:11:45,1099,0,11
+General Medicine,PGY1,S0181061,2015-09-13,2:30:48,0:02:03,2:25:31,0:43:53,0:25:26,0:07:24,6:54:17,1363,12,7
+General Medicine,PGY1,S0074196,2016-01-17,1:09:56,1:44:02,2:03:13,0:41:43,0:15:10,0:37:40,7:13:12,2065,0,8
+General Medicine,PGY1,S0074196,2016-01-16,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,16,0,2
+General Medicine,PGY1,S0074196,2016-01-15,0:28:01,0:40:40,1:39:54,0:09:02,0:02:50,0:17:01,3:19:07,683,0,5
+General Medicine,PGY1,S0074196,2016-01-14,0:54:37,0:51:29,0:39:37,0:28:31,0:11:29,0:09:48,3:28:06,1188,0,12
+General Medicine,PGY1,S0181061,2015-09-14,2:15:58,0:03:51,2:03:05,0:32:32,0:21:18,0:10:48,5:41:29,1038,79,4
+General Medicine,PGY1,S0074196,2016-01-19,0:52:06,1:05:18,0:49:42,0:27:53,0:09:13,0:27:55,4:17:02,1480,45,13
+General Medicine,PGY1,S0074196,2016-01-18,0:17:18,0:23:29,0:06:21,0:13:19,0:05:51,0:09:51,1:28:20,875,0,7
+General Medicine,PGY1,S0181061,2015-09-15,2:15:20,0:28:50,1:48:29,0:53:09,0:28:58,0:11:33,6:42:32,1456,0,8
+General Medicine,PGY1,S0181061,2015-09-16,2:09:42,0:23:21,1:23:54,0:46:17,0:27:45,0:21:27,6:24:54,1810,0,13
+General Medicine,PGY1,S0181061,2015-09-17,1:49:30,0:02:07,0:26:56,0:26:21,0:16:34,0:10:29,3:27:40,922,70,9
+Intensive Care Unit,PGY2+,S0169381,2016-02-06,0:00:00,0:00:00,0:00:00,0:00:29,0:00:00,0:00:00,0:00:36,24,0,4
+General Medicine,PGY1,S0182038,2016-10-10,0:56:05,0:52:33,0:00:07,0:27:47,0:09:17,0:00:43,3:00:15,817,0,10
+General Medicine,PGY1,S0182038,2016-10-11,1:18:30,0:42:42,0:00:43,0:40:34,0:16:00,0:01:21,3:33:10,1023,0,11
+General Medicine,PGY1,S0182038,2016-10-12,1:14:30,1:15:38,0:00:19,0:40:46,0:09:27,0:00:36,3:51:06,1152,24,8
+General Medicine,PGY1,S0182038,2016-10-13,1:51:52,1:40:30,0:00:54,0:17:54,0:16:54,0:05:38,4:55:30,1309,0,10
+General Medicine,PGY1,S0182038,2016-10-14,1:42:47,1:07:25,0:00:11,0:24:29,0:16:36,0:09:24,4:37:15,1326,46,8
+General Medicine,PGY1,S0182038,2016-10-15,0:00:42,0:01:49,0:00:00,0:00:00,0:00:00,0:00:00,0:02:42,29,19,4
+Emergency Medicine,PGY1,S0181070,2015-10-18,1:24:28,0:23:08,2:39:30,0:23:28,0:25:07,0:25:02,6:05:51,1377,0,23
+Emergency Medicine,PGY1,S0181070,2015-10-19,1:15:31,0:42:21,3:29:48,0:22:38,0:26:50,0:32:37,7:33:21,2324,0,22
+Night Team,PGY2+,S0169381,2016-07-30,2:40:58,1:14:59,0:00:06,0:15:30,0:10:22,0:07:47,4:49:39,1126,36,14
+Night Team,PGY1,S0181070,2015-10-10,1:53:38,0:52:31,1:11:06,0:15:42,0:11:08,0:33:56,5:23:32,1374,0,27
+Emergency Medicine,PGY1,S0181070,2015-10-11,2:04:39,1:04:25,2:49:02,0:19:32,0:14:23,0:27:59,7:43:45,1478,0,20
+Emergency Medicine,PGY1,S0181070,2015-10-12,1:42:20,1:09:01,3:15:12,0:29:21,0:24:59,0:29:26,7:52:45,2006,0,31
+Emergency Medicine,PGY1,S0181070,2015-10-13,0:35:18,0:14:02,0:55:07,0:14:00,0:17:45,0:08:00,2:35:24,541,0,5
+Emergency Medicine,PGY1,S0181070,2015-10-14,1:03:13,0:35:28,1:41:11,0:15:00,0:09:45,0:16:48,4:33:18,1407,0,18
+Emergency Medicine,PGY1,S0181070,2015-10-15,0:34:12,0:23:37,1:07:53,0:08:59,0:15:59,0:00:55,2:56:37,1100,0,12
+Emergency Medicine,PGY1,S0181070,2015-10-16,3:19:36,0:26:51,2:00:01,0:16:59,0:14:17,0:19:34,7:03:10,1404,0,35
+Emergency Medicine,PGY1,S0181070,2015-10-17,2:06:52,0:51:34,2:29:50,0:26:41,0:29:09,0:24:09,7:41:46,2096,0,27
+General Medicine,PGY2+,S0169411,2016-04-02,2:38:36,0:38:22,0:00:00,0:29:28,0:12:35,0:07:16,4:38:15,1545,92,30
+General Medicine,PGY2+,S0169411,2016-04-01,0:23:19,0:00:00,0:00:00,0:00:00,0:00:08,0:00:00,0:28:55,55,55,12
+General Medicine,PGY1,S0196593,2016-10-31,1:52:14,0:59:29,0:04:57,0:13:46,0:09:16,0:10:43,4:08:40,1035,25,7
+General Medicine,PGY1,S0196593,2016-10-30,2:43:32,1:03:13,0:00:06,0:40:40,0:20:40,0:08:56,5:50:05,1639,80,13
+Emergency Medicine,PGY1,S0169395,2014-11-30,5:10:02,0:10:51,1:42:38,0:35:36,0:33:28,0:54:17,10:06:55,3212,0,54
+General Medicine,PGY2+,S0169401,2016-01-22,3:16:01,1:56:44,0:30:09,1:31:44,0:44:42,0:30:19,9:19:29,4639,1121,62
+Night Team,PGY2+,S0169404,2016-05-20,2:47:57,2:33:53,0:00:19,0:51:57,0:10:42,0:06:11,6:55:23,2944,82,42
+General Medicine,PGY2+,S0169401,2016-01-23,1:53:37,0:59:42,0:28:59,0:42:24,0:19:11,0:29:14,5:43:40,3051,764,47
+Intensive Care Unit,PGY2+,S0181087,2016-08-10,2:55:43,1:12:10,0:07:05,0:35:38,0:19:34,0:14:54,5:57:56,2472,182,23
+Intensive Care Unit,PGY2+,S0181087,2016-08-11,2:29:18,0:51:29,0:03:06,0:20:27,0:23:44,0:08:10,4:58:48,1584,0,20
+Intensive Care Unit,PGY2+,S0181087,2016-08-12,2:58:04,1:02:49,0:15:05,0:30:53,0:34:34,0:18:11,6:19:01,1902,0,17
+Intensive Care Unit,PGY2+,S0181087,2016-08-13,0:00:00,0:00:00,0:00:00,0:00:38,0:00:00,0:00:00,0:00:38,16,0,5
+General Medicine,PGY1,S0163662,2014-08-19,1:42:37,0:04:03,0:04:13,0:21:07,0:13:10,0:24:48,3:10:12,1200,70,10
+General Medicine,PGY1,S0163662,2014-08-18,3:39:14,0:08:39,0:15:39,0:51:43,0:14:40,0:15:50,6:04:51,2029,156,12
+General Medicine,PGY1,S0163662,2014-08-17,2:03:07,0:01:37,0:06:19,0:06:40,0:14:24,0:08:07,2:58:21,994,976,15
+General Medicine,PGY1,S0195220,2016-08-16,1:27:33,1:21:43,0:00:16,0:22:38,0:09:07,0:02:02,3:42:19,1031,45,13
+General Medicine,PGY1,S0195220,2016-08-17,2:09:18,2:02:31,0:00:39,0:56:33,0:22:56,0:00:29,6:23:40,2019,18,15
+General Medicine,PGY1,S0195220,2016-08-14,2:44:22,1:01:49,0:00:02,0:48:13,0:19:31,0:02:55,5:41:05,1527,0,12
+General Medicine,PGY1,S0195220,2016-08-15,1:47:08,0:28:10,0:00:13,0:44:28,0:07:35,0:00:17,3:47:27,1015,0,6
+General Medicine,PGY1,S0195220,2016-08-12,1:50:57,2:14:38,0:00:00,0:43:37,0:11:42,0:01:42,5:31:35,1442,0,8
+General Medicine,PGY1,S0195220,2016-08-13,1:34:43,1:30:41,0:00:09,0:35:07,0:10:58,0:00:21,4:14:38,1152,0,8
+General Medicine,PGY1,S0195220,2016-08-10,0:42:01,0:59:15,0:00:09,0:00:17,0:00:20,0:05:10,2:08:46,663,661,16
+General Medicine,PGY1,S0195220,2016-08-11,1:04:17,1:30:13,0:00:03,0:19:48,0:05:37,0:00:13,3:07:50,874,0,16
+General Medicine,PGY1,S0195220,2016-08-18,0:36:21,0:41:59,0:00:09,0:00:00,0:00:00,0:00:39,1:24:08,417,411,9
+General Medicine,PGY1,S0195220,2016-08-19,2:00:30,1:23:26,0:04:30,0:25:37,0:20:14,0:01:30,4:57:50,1051,55,7
+Intensive Care Unit,PGY2+,S0169390,2015-10-29,2:17:22,0:49:06,0:07:05,0:21:46,0:35:20,0:23:27,5:05:39,1433,14,24
+Emergency Medicine,PGY1,S0079640,2016-10-14,3:21:14,0:47:11,0:03:18,0:41:31,0:56:00,0:01:15,6:22:39,1817,0,43
+Intensive Care Unit,PGY2+,S0169381,2016-02-03,0:00:00,0:10:50,0:00:00,0:00:43,0:00:00,0:00:45,0:12:45,83,58,8
+Emergency Medicine,PGY1,S0079640,2016-10-15,0:02:07,0:00:16,0:00:00,0:00:00,0:00:00,0:00:00,0:02:53,27,0,12
+Emergency Medicine,PGY1,S0169399,2015-04-28,1:49:35,0:13:40,2:36:05,0:06:47,0:09:38,0:20:45,5:31:50,884,0,19
+Emergency Medicine,PGY1,S0169395,2014-11-13,2:03:51,0:04:42,2:53:43,0:26:34,0:47:49,0:54:08,8:16:49,2345,0,19
+Emergency Medicine,PGY1,S0079640,2016-10-12,2:41:36,0:33:14,0:00:36,0:21:59,0:41:44,0:05:43,5:02:40,1251,0,40
+General Medicine,PGY1,S0169383,2015-01-29,3:05:24,1:22:21,1:54:46,1:08:56,0:25:26,0:30:55,9:27:38,3319,902,18
+General Medicine,PGY1,S0169390,2015-05-31,1:45:19,0:15:11,1:00:38,0:25:15,0:12:46,0:11:55,4:13:35,1206,0,9
+General Medicine,PGY1,S0169390,2015-05-30,3:03:13,0:59:11,2:11:27,0:54:20,0:15:22,0:20:57,8:15:34,2385,245,12
+Emergency Medicine,PGY1,S0079640,2016-10-13,0:16:50,0:03:08,0:00:02,0:00:00,0:00:14,0:00:35,0:34:47,290,152,38
+Intensive Care Unit,PGY2+,S0163425,2015-09-18,2:15:07,0:04:22,0:19:46,0:22:46,0:20:13,0:20:22,4:03:42,1351,41,42
+Intensive Care Unit,PGY2+,S0163425,2015-09-19,0:00:00,0:01:38,0:00:00,0:00:14,0:00:00,0:00:39,0:02:39,63,19,7
+Intensive Care Unit,PGY2+,S0163425,2015-09-14,0:38:41,0:02:10,0:03:10,0:01:32,0:00:22,0:04:05,0:50:44,311,275,13
+Intensive Care Unit,PGY2+,S0163425,2015-09-15,3:33:45,0:11:20,1:00:24,0:35:57,0:39:31,0:42:39,7:10:59,2373,0,18
+Intensive Care Unit,PGY2+,S0163425,2015-09-16,2:59:29,0:08:24,0:22:00,0:17:40,0:22:32,0:34:21,5:04:31,1647,26,19
+Intensive Care Unit,PGY2+,S0163425,2015-09-17,2:39:07,0:01:24,0:18:42,0:24:58,0:28:10,0:25:39,4:50:23,1612,30,21
+Intensive Care Unit,PGY2+,S0163425,2015-09-10,2:07:35,0:01:04,0:17:26,0:17:12,0:27:00,0:24:30,3:52:39,1207,0,17
+Intensive Care Unit,PGY2+,S0163425,2015-09-11,2:05:01,0:02:11,0:17:04,0:29:47,0:49:08,0:33:44,4:41:34,1799,45,21
+Intensive Care Unit,PGY2+,S0163425,2015-09-12,3:11:39,0:04:52,0:29:22,0:17:17,0:14:54,0:20:37,4:56:21,1847,39,17
+Intensive Care Unit,PGY2+,S0163425,2015-09-13,2:28:25,0:02:04,0:33:54,0:15:42,0:21:59,0:25:14,4:22:38,1329,80,19
+Emergency Medicine,PGY1,S0079640,2016-10-10,3:05:36,0:37:14,0:05:56,0:20:11,0:31:52,0:02:39,5:22:43,1698,0,57
+General Medicine,PGY1,S0169404,2015-02-09,3:38:49,0:22:00,2:03:39,0:31:02,0:21:43,0:25:07,7:50:12,3169,103,11
+General Medicine,PGY1,S0169404,2015-02-08,2:33:59,0:15:29,0:40:50,0:19:43,0:15:32,0:22:44,4:43:47,1937,176,9
+General Medicine,PGY1,S0181059,2016-03-09,1:24:54,1:05:22,0:00:10,0:33:18,0:09:09,0:02:22,3:47:22,1763,275,11
+General Medicine,PGY1,S0181059,2016-03-08,0:31:12,0:02:02,0:00:00,0:01:50,0:00:00,0:01:45,0:39:15,207,196,9
+General Medicine,PGY2+,S0169401,2015-12-09,2:46:36,2:24:47,0:40:10,0:46:48,0:20:07,0:37:24,8:09:21,4636,1138,31
+General Medicine,PGY1,S0181059,2016-03-05,3:33:51,1:27:12,0:00:16,0:41:19,0:29:58,0:01:51,7:01:19,3899,41,65
+General Medicine,PGY1,S0181059,2016-03-04,2:06:04,1:02:04,0:00:27,0:33:04,0:15:45,0:03:07,4:26:07,2182,575,15
+General Medicine,PGY1,S0169404,2015-02-03,2:04:09,0:17:29,1:14:28,0:11:25,0:14:06,0:19:02,4:54:19,1752,18,10
+General Medicine,PGY1,S0169404,2015-02-02,2:44:05,0:59:13,0:49:27,0:48:16,0:14:26,0:30:29,6:27:56,2567,98,13
+General Medicine,PGY1,S0181059,2016-03-01,2:26:40,1:00:39,0:00:45,0:36:56,0:20:43,0:02:53,4:56:58,2719,108,16
+General Medicine,PGY1,S0169404,2015-02-04,3:53:43,0:51:26,1:42:31,0:20:10,0:25:30,0:47:29,8:54:44,3529,412,11
+General Medicine,PGY1,S0181059,2016-03-03,1:38:02,0:44:40,0:00:33,0:19:54,0:17:41,0:03:07,3:28:43,2130,237,12
+General Medicine,PGY1,S0181059,2016-03-02,3:48:11,1:47:23,0:00:18,0:50:43,0:39:13,0:07:56,7:56:30,4120,59,27
+Emergency Medicine,PGY1,S0181073,2016-03-09,0:43:18,0:07:25,0:00:14,0:04:56,0:09:14,0:00:01,1:12:23,243,0,9
+Night Team,PGY1,S0173803,2016-02-28,3:15:11,0:30:58,0:00:04,0:52:25,0:33:46,0:06:55,6:07:28,2103,242,41
+Night Team,PGY1,S0173803,2016-02-29,1:38:12,0:22:00,0:01:03,0:38:24,0:18:48,0:07:48,3:56:17,1758,789,39
+Emergency Medicine,PGY1,S0181073,2016-03-03,3:20:29,0:49:43,0:01:12,0:50:05,0:53:53,0:04:26,7:01:36,1979,7,12
+Emergency Medicine,PGY1,S0181073,2016-03-02,0:01:44,0:00:04,0:00:00,0:00:00,0:00:00,0:00:56,0:02:50,15,15,1
+Emergency Medicine,PGY1,S0181073,2016-03-05,3:18:28,0:26:49,0:01:02,0:21:51,0:49:29,0:02:44,5:40:43,1733,0,12
+Emergency Medicine,PGY1,S0181073,2016-03-04,4:01:31,0:27:15,0:07:13,0:25:29,1:17:15,0:04:02,7:33:59,2326,0,15
+Emergency Medicine,PGY1,S0181073,2016-03-07,4:04:54,0:31:15,0:08:03,0:18:18,0:51:06,0:05:16,7:23:23,2576,2,48
+Emergency Medicine,PGY1,S0181073,2016-03-06,3:06:54,0:37:40,0:00:44,0:27:03,0:51:37,0:02:33,6:02:16,1954,0,15
+General Medicine,PGY2+,S0169390,2016-07-27,1:17:51,0:28:44,0:00:15,0:08:56,0:21:16,0:00:54,2:38:16,1069,222,36
+General Medicine,PGY2+,S0169390,2016-07-26,4:30:09,1:17:04,0:00:00,0:35:02,0:31:09,0:06:23,7:51:06,2963,76,24
+General Medicine,PGY2+,S0169390,2016-07-25,1:52:54,0:23:00,0:00:01,0:10:59,0:09:31,0:04:06,2:51:06,964,0,18
+General Medicine,PGY2+,S0169390,2016-07-24,2:17:22,1:05:42,0:02:39,0:25:09,0:09:48,0:02:13,4:22:59,1199,0,10
+General Medicine,PGY2+,S0169390,2016-07-23,3:15:33,1:54:54,0:00:05,0:13:27,0:24:17,0:01:58,6:16:25,2105,0,15
+General Medicine,PGY2+,S0169390,2016-07-22,2:30:46,0:43:14,0:00:08,0:31:09,0:18:26,0:01:10,4:36:59,1593,0,14
+General Medicine,PGY2+,S0169390,2016-07-21,2:56:58,1:33:28,0:00:14,0:20:47,0:40:32,0:08:23,6:06:28,2225,78,23
+General Medicine,PGY2+,S0169390,2016-07-20,0:12:22,0:00:16,0:00:00,0:00:00,0:00:00,0:00:26,0:14:16,37,0,4
+General Medicine,PGY1,S0181526,2015-10-03,2:15:19,0:43:06,0:34:18,0:55:50,0:13:40,0:20:16,5:51:24,1465,46,11
+General Medicine,PGY1,S0181526,2015-10-02,0:33:07,0:58:03,0:49:49,0:10:28,0:00:32,0:20:54,3:08:44,731,124,13
+General Medicine,PGY1,S0181526,2015-10-01,0:12:45,0:33:35,0:04:29,0:00:00,0:01:55,0:08:33,1:13:18,363,359,15
+General Medicine,PGY1,S0181526,2015-10-07,3:17:28,0:32:39,1:11:27,1:19:49,0:15:47,0:33:42,8:20:48,2288,184,26
+General Medicine,PGY1,S0181526,2015-10-06,0:08:44,0:16:48,0:39:08,0:01:02,0:00:24,0:05:01,1:18:10,382,368,16
+General Medicine,PGY1,S0181526,2015-10-05,2:14:07,1:00:52,2:42:06,0:56:16,0:28:18,0:27:14,8:46:22,2121,43,20
+General Medicine,PGY1,S0181526,2015-10-04,1:00:38,0:21:13,0:16:35,0:23:42,0:13:45,0:19:24,2:54:14,1070,94,8
+Intensive Care Unit,PGY2+,S0108287,2016-01-08,2:24:38,1:05:02,1:06:01,0:42:25,0:45:43,0:24:16,7:06:26,1981,0,25
+Night Team,PGY2+,S0079337,2015-12-08,1:10:52,0:15:36,0:29:07,0:21:49,0:06:51,0:04:12,2:30:18,763,0,10
+Night Team,PGY2+,S0079337,2015-12-05,4:49:42,0:58:01,0:36:23,0:24:33,0:09:45,0:09:28,7:22:27,2055,0,49
+Night Team,PGY2+,S0079337,2015-12-04,1:47:43,0:56:56,0:44:53,0:23:00,0:08:14,0:11:24,4:22:36,1496,78,19
+Night Team,PGY2+,S0079337,2015-12-07,3:04:32,0:58:03,0:36:35,0:34:29,0:15:19,0:30:13,6:16:13,2064,0,37
+Night Team,PGY2+,S0079337,2015-12-06,1:55:41,0:38:03,1:11:01,0:33:56,0:09:03,0:12:15,5:00:43,1647,0,30
+Night Team,PGY2+,S0079337,2015-12-01,4:09:23,1:11:26,1:09:49,0:50:57,0:28:52,0:16:26,8:39:25,2632,0,38
+Night Team,PGY2+,S0079337,2015-12-03,3:35:05,0:54:17,0:34:37,0:12:04,0:09:27,0:08:36,5:55:35,2102,42,56
+Night Team,PGY2+,S0079337,2015-12-02,5:36:20,1:19:12,0:43:38,0:34:41,0:08:17,0:21:19,9:11:33,2669,102,48
+Night Team,PGY2+,S0169384,2016-01-19,1:03:13,2:05:46,2:54:02,1:26:13,0:29:29,0:40:28,9:09:25,3486,538,16
+Night Team,PGY2+,S0169384,2016-01-18,0:58:58,1:39:47,2:06:44,0:25:27,0:08:13,0:56:54,6:52:15,2859,0,28
+General Medicine,PGY1,S0169398,2015-01-28,2:36:00,0:55:01,1:54:42,0:38:58,0:22:26,0:25:50,7:36:30,2170,922,9
+General Medicine,PGY1,S0169398,2015-01-29,3:06:11,0:25:47,1:05:39,0:50:18,0:32:07,0:17:58,6:46:58,2112,824,9
+Night Team,PGY2+,S0169384,2016-01-15,1:46:59,2:32:17,2:31:59,1:14:33,0:29:42,1:03:46,10:32:24,4053,208,25
+Night Team,PGY2+,S0169384,2016-01-14,0:49:46,0:51:17,0:04:22,0:30:50,0:07:41,0:21:36,3:01:08,1821,0,16
+General Medicine,PGY1,S0169398,2015-01-24,2:13:24,0:03:05,1:00:34,0:13:22,0:11:59,0:14:30,4:12:26,1247,217,18
+General Medicine,PGY1,S0169398,2015-01-25,3:34:15,0:09:47,1:40:42,1:13:55,0:19:31,0:41:48,7:58:38,2247,567,10
+Night Team,PGY2+,S0169384,2016-01-11,0:50:20,1:13:15,2:01:31,0:34:29,0:12:07,0:24:13,5:22:45,1515,77,22
+Night Team,PGY2+,S0169384,2016-01-10,1:01:35,0:55:34,3:31:58,0:35:20,0:11:47,0:44:18,7:20:11,1650,292,15
+General Medicine,PGY1,S0169398,2015-01-20,3:11:20,0:13:59,1:40:16,0:21:08,0:23:04,0:26:38,6:30:18,2308,67,15
+Night Team,PGY2+,S0169384,2016-01-12,0:54:18,0:53:13,1:45:27,0:55:06,0:23:35,0:28:02,5:35:15,1748,0,18
+General Medicine,PGY1,S0181082,2016-03-28,2:19:28,1:20:15,0:00:15,0:37:23,0:20:53,0:01:14,5:27:02,1785,0,9
+Night Team,PGY2+,S0169401,2015-11-08,2:43:36,1:00:02,0:53:20,1:28:43,0:13:20,0:27:30,7:13:27,3412,0,52
+Night Team,PGY2+,S0169401,2015-11-09,2:11:41,1:02:29,2:55:17,1:09:57,0:15:17,0:29:15,8:28:09,2853,0,23
+Night Team,PGY2+,S0169401,2015-11-06,0:52:32,0:25:38,0:06:58,0:39:36,0:09:41,0:11:49,2:30:28,1241,351,19
+Night Team,PGY2+,S0169401,2015-11-07,2:13:10,0:41:49,2:03:36,1:11:00,0:15:14,0:19:25,7:25:24,2982,0,78
+Night Team,PGY2+,S0169401,2015-11-04,2:27:39,1:23:56,2:41:12,1:18:43,0:19:31,0:31:10,9:17:52,3814,1,53
+Night Team,PGY2+,S0169401,2015-11-05,0:38:18,0:35:48,2:02:54,0:46:02,0:07:12,0:24:10,5:01:20,1628,132,21
+Night Team,PGY2+,S0169401,2015-11-02,2:29:22,0:48:19,1:02:07,1:03:23,0:24:50,0:48:07,7:22:16,2290,62,32
+Night Team,PGY2+,S0169401,2015-11-03,1:59:17,1:09:36,0:15:06,0:46:11,0:12:01,0:21:52,5:09:20,2313,0,65
+Night Team,PGY2+,S0169401,2015-11-01,0:53:44,1:11:50,2:06:53,0:37:10,0:20:52,0:17:30,5:47:09,1988,0,41
+General Medicine,PGY1,S0108268,2014-09-21,1:56:04,0:04:56,0:04:06,0:01:48,0:07:27,0:04:50,2:36:33,783,763,27
+Intensive Care Unit,PGY2+,S0108287,2016-01-06,2:38:45,1:03:58,0:19:14,0:33:10,0:32:11,0:47:36,6:40:00,1678,28,23
+Emergency Medicine,PGY2+,S0172645,2016-10-23,2:34:43,0:46:35,0:02:16,0:20:33,0:40:48,0:02:00,5:07:47,1298,0,44
+Emergency Medicine,PGY2+,S0172645,2016-10-20,2:58:21,0:41:55,0:00:41,0:19:55,0:17:31,0:00:43,4:37:15,1140,0,36
+Emergency Medicine,PGY2+,S0172645,2016-10-21,0:02:06,0:00:11,0:00:00,0:00:00,0:00:00,0:00:00,0:03:28,40,0,24
+Emergency Medicine,PGY2+,S0172645,2016-10-26,0:11:55,0:00:51,0:00:00,0:00:00,0:00:05,0:00:05,0:13:39,95,0,12
+Emergency Medicine,PGY2+,S0172645,2016-10-27,0:58:56,0:18:20,0:00:12,0:13:50,0:05:28,0:02:56,1:52:53,576,0,13
+Emergency Medicine,PGY2+,S0172645,2016-10-24,0:05:42,0:00:29,0:00:00,0:00:00,0:00:00,0:00:00,0:07:11,51,0,25
+Emergency Medicine,PGY2+,S0172645,2016-10-25,2:37:03,0:40:16,0:05:35,0:41:14,0:29:46,0:03:09,5:01:14,1275,0,20
+General Medicine,PGY1,S0169411,2015-05-31,3:22:39,1:07:52,1:29:22,0:31:45,0:23:23,0:14:27,7:29:59,2521,0,16
+Emergency Medicine,PGY2+,S0172645,2016-10-29,2:48:35,0:32:22,0:02:02,0:18:00,0:36:42,0:01:39,5:16:01,1479,0,35
+General Medicine,PGY1,S0108268,2014-12-08,1:49:45,0:17:23,1:11:38,0:06:09,0:30:57,0:33:25,4:43:27,940,103,7
+General Medicine,PGY1,S0108268,2014-12-09,4:05:16,0:12:36,1:09:04,0:30:47,0:39:11,0:44:10,7:47:43,1848,0,6
+General Medicine,PGY1,S0181088,2016-05-29,3:01:32,1:20:22,0:00:22,1:06:03,0:20:38,0:04:15,6:26:14,2145,95,13
+General Medicine,PGY1,S0181088,2016-05-28,2:53:27,1:28:47,0:00:05,0:26:15,0:22:53,0:06:20,5:46:59,1586,21,11
+General Medicine,PGY1,S0181088,2016-05-23,1:06:22,0:45:40,0:00:10,0:17:08,0:03:06,0:00:10,2:37:17,821,0,3
+General Medicine,PGY1,S0181088,2016-05-22,1:54:25,0:52:38,0:02:20,0:19:46,0:08:19,0:01:04,3:39:57,1402,0,8
+General Medicine,PGY1,S0181088,2016-05-27,3:47:36,0:43:32,0:00:02,0:38:06,0:13:49,0:01:30,6:14:20,2732,15,11
+General Medicine,PGY1,S0181088,2016-05-26,1:34:05,0:41:41,0:00:07,0:40:04,0:06:31,0:04:22,3:43:01,1322,115,6
+General Medicine,PGY1,S0181088,2016-05-25,1:53:50,0:24:14,0:01:21,0:26:04,0:08:54,0:10:21,3:19:44,1121,127,8
+General Medicine,PGY1,S0181088,2016-05-24,2:31:24,1:09:52,0:00:06,0:30:00,0:17:50,0:02:48,5:01:12,1608,39,13
+Night Team,PGY2+,S0169385,2015-09-06,0:41:36,0:07:13,0:46:04,0:27:48,0:12:48,0:08:35,2:36:31,809,0,3
+Night Team,PGY2+,S0169385,2015-09-07,1:38:55,0:29:47,3:21:40,0:47:21,0:25:17,0:43:57,7:54:50,1715,35,16
+Intensive Care Unit,PGY2+,S0108287,2016-01-04,0:00:00,0:06:34,0:00:00,0:00:02,0:00:00,0:00:00,0:07:51,59,52,6
+Night Team,PGY2+,S0169385,2015-09-08,1:30:59,1:33:00,1:46:55,0:42:56,0:20:59,0:29:18,6:51:22,2285,142,22
+Night Team,PGY2+,S0169385,2015-09-09,2:23:06,0:57:49,1:53:59,0:46:46,0:23:51,0:51:20,7:53:19,2325,187,27
+Emergency Medicine,PGY1,S0169384,2014-09-28,3:46:05,0:32:30,0:20:00,0:25:55,0:18:53,0:21:30,6:14:23,1746,0,19
+Emergency Medicine,PGY1,S0169384,2014-09-29,3:19:37,0:29:52,0:24:14,0:32:44,0:07:30,0:08:15,5:35:56,1295,21,18
+Emergency Medicine,PGY1,S0169384,2014-09-23,2:30:56,0:07:13,0:15:48,0:09:24,0:05:15,0:44:30,4:19:12,1037,117,14
+Emergency Medicine,PGY1,S0169384,2014-09-20,3:15:10,0:17:06,0:38:43,0:29:44,0:24:13,0:24:53,6:03:58,1693,0,17
+Emergency Medicine,PGY1,S0169384,2014-09-21,2:03:17,0:04:07,0:28:58,0:09:02,0:12:52,0:06:04,3:33:07,782,0,10
+Emergency Medicine,PGY1,S0169384,2014-09-26,1:55:00,0:21:56,0:14:17,0:10:58,0:08:05,0:03:55,3:12:46,1411,697,18
+Emergency Medicine,PGY1,S0169384,2014-09-27,4:03:50,0:30:10,0:41:14,0:32:01,0:12:43,0:34:44,7:41:16,1891,48,15
+Emergency Medicine,PGY1,S0169384,2014-09-24,3:37:21,0:27:00,0:11:41,0:22:42,0:06:53,0:25:59,5:29:19,1357,0,11
+Emergency Medicine,PGY1,S0169384,2014-09-25,0:08:48,0:00:11,0:02:02,0:02:49,0:00:33,0:00:00,0:16:12,109,0,7
+Emergency Medicine,PGY1,S0169398,2015-03-31,1:48:55,0:44:42,1:10:23,0:15:50,0:13:43,0:22:08,5:10:04,1488,282,19
+Emergency Medicine,PGY1,S0169398,2015-03-30,3:30:03,0:17:44,2:32:55,0:24:50,0:21:58,0:33:46,8:44:08,2384,3,26
+General Medicine,PGY1,S0074196,2016-01-10,0:54:55,0:32:00,0:27:04,0:14:29,0:10:16,0:13:56,2:46:49,919,0,6
+General Medicine,PGY1,S0173803,2016-04-28,2:37:36,2:04:08,0:00:29,0:32:02,0:15:23,0:05:46,6:17:56,1759,145,20
+General Medicine,PGY1,S0173803,2016-04-29,2:02:10,1:07:02,0:07:47,0:26:19,0:07:45,0:10:28,4:51:56,1632,72,14
+General Medicine,PGY1,S0173803,2016-04-26,2:11:46,1:16:15,0:00:12,0:40:28,0:13:49,0:00:34,5:11:35,1720,23,15
+General Medicine,PGY1,S0173803,2016-04-27,1:55:53,0:44:50,0:00:07,0:27:58,0:07:24,0:01:41,4:07:58,1610,30,22
+General Medicine,PGY1,S0173803,2016-04-24,1:18:46,0:24:05,0:00:03,0:22:01,0:07:58,0:01:01,2:49:18,830,14,6
+General Medicine,PGY1,S0173803,2016-04-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181086,2016-05-14,1:25:34,1:28:40,0:00:23,0:22:07,0:09:14,0:00:45,3:54:33,1044,20,4
+General Medicine,PGY1,S0181086,2016-05-15,2:24:44,1:42:45,0:00:08,0:47:02,0:06:27,0:02:55,5:46:29,1679,6,9
+General Medicine,PGY1,S0181086,2016-05-16,1:59:53,1:45:00,0:00:23,1:05:07,0:20:11,0:03:19,5:47:28,1528,156,9
+General Medicine,PGY1,S0181086,2016-05-17,2:24:20,0:45:09,0:00:06,0:31:46,0:22:24,0:01:05,4:40:35,1367,120,10
+General Medicine,PGY1,S0181086,2016-05-10,3:04:20,1:24:31,0:00:22,1:12:24,0:12:01,0:12:49,6:51:29,2456,95,15
+General Medicine,PGY1,S0181086,2016-05-11,2:24:57,0:55:15,0:03:04,1:00:07,0:18:51,0:03:52,5:20:17,1690,216,14
+General Medicine,PGY1,S0181086,2016-05-12,2:30:17,1:08:55,0:01:56,1:54:06,0:19:51,0:00:11,6:33:39,1684,4,10
+General Medicine,PGY1,S0181086,2016-05-13,1:00:08,0:24:41,0:01:28,0:13:14,0:02:43,0:02:39,2:24:52,1198,1178,25
+General Medicine,PGY1,S0181086,2016-05-18,2:16:50,0:36:24,0:02:23,0:44:18,0:07:19,0:00:29,4:56:09,1588,91,22
+General Medicine,PGY1,S0181086,2016-05-19,0:17:49,0:35:42,0:00:21,0:10:52,0:01:28,0:00:47,1:28:42,507,500,9
+General Medicine,PGY1,S0181064,2016-05-18,0:21:11,0:20:54,0:02:23,0:02:51,0:01:26,0:00:04,0:56:17,389,381,9
+General Medicine,PGY1,S0181064,2016-05-19,1:07:54,0:38:25,0:00:04,0:10:03,0:04:39,0:00:02,2:15:40,714,116,16
+General Medicine,PGY1,S0181064,2016-05-10,2:26:26,1:21:22,0:00:02,0:47:22,0:13:32,0:06:18,5:31:44,1881,149,18
+General Medicine,PGY1,S0181064,2016-05-11,2:09:38,0:44:19,0:00:08,0:20:53,0:10:30,0:04:21,3:54:50,1095,139,9
+General Medicine,PGY1,S0181064,2016-05-12,2:25:25,1:16:07,0:00:04,0:46:48,0:18:55,0:06:12,5:19:42,1630,184,24
+General Medicine,PGY1,S0181064,2016-05-13,1:28:06,1:15:48,0:00:05,0:11:24,0:13:35,0:00:44,3:25:43,1328,175,14
+General Medicine,PGY1,S0181064,2016-05-14,1:44:16,1:30:27,0:00:11,0:10:44,0:10:43,0:03:10,3:57:27,1172,191,10
+General Medicine,PGY1,S0181064,2016-05-15,1:16:25,1:19:52,0:00:02,0:17:01,0:19:24,0:02:04,3:53:38,1307,8,28
+General Medicine,PGY1,S0181064,2016-05-16,1:51:10,1:08:34,0:03:03,0:40:19,0:10:49,0:05:23,4:57:29,1778,312,9
+General Medicine,PGY1,S0181064,2016-05-17,2:06:08,1:19:44,0:00:15,0:18:20,0:13:41,0:01:01,4:31:56,1385,100,36
+Intensive Care Unit,PGY2+,S0169381,2016-01-28,1:34:38,0:11:02,0:11:05,0:19:45,0:13:58,0:14:28,3:08:43,1124,78,20
+General Medicine,PGY1,S0094959,2014-11-26,4:08:52,0:09:59,0:25:10,0:31:06,0:19:32,0:24:17,6:24:00,3070,44,25
+General Medicine,PGY1,S0094959,2014-11-27,2:50:50,0:02:30,0:38:31,0:05:03,0:16:33,0:12:35,4:14:47,1904,28,12
+Intensive Care Unit,PGY2+,S0169406,2016-03-27,1:51:42,0:18:00,0:00:01,0:35:26,0:15:35,0:10:30,3:47:09,1096,0,14
+Intensive Care Unit,PGY2+,S0169406,2016-03-26,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0108268,2015-11-15,1:55:03,0:30:03,1:50:28,0:25:06,0:10:32,0:18:46,5:50:23,1419,41,21
+Night Team,PGY2+,S0108268,2015-11-17,0:30:52,0:37:07,0:56:55,0:11:53,0:10:44,0:13:16,2:57:27,874,0,16
+Night Team,PGY2+,S0108268,2015-11-16,1:42:43,0:40:49,0:34:05,0:10:32,0:10:18,0:13:43,3:38:04,1062,0,68
+Night Team,PGY2+,S0108268,2015-11-19,2:30:30,0:56:19,1:52:03,0:28:45,0:18:08,1:01:48,7:45:00,2262,3,38
+Night Team,PGY2+,S0108268,2015-11-18,3:39:26,1:02:18,1:34:55,0:21:07,0:22:28,0:35:15,8:07:59,1870,187,44
+General Medicine,PGY1,S0094959,2014-11-23,6:30:17,0:09:13,0:39:55,0:31:26,0:35:03,0:37:46,9:30:22,4667,201,81
+Night Team,PGY1,S0108287,2015-06-18,1:56:22,0:26:23,1:06:45,0:41:34,0:20:01,0:18:09,5:08:20,1846,0,31
+Night Team,PGY1,S0108287,2015-06-19,1:53:58,0:33:51,1:43:40,0:19:36,0:16:33,0:14:08,5:27:08,1743,0,42
+Night Team,PGY1,S0108287,2015-06-12,1:54:56,0:26:54,2:14:41,0:30:40,0:24:21,0:21:06,6:11:41,2774,0,30
+Night Team,PGY1,S0108287,2015-06-13,2:28:33,0:25:57,0:51:22,0:53:36,0:30:47,0:33:09,6:10:27,4402,0,25
+Night Team,PGY1,S0108287,2015-06-10,0:49:30,0:06:43,1:22:45,0:04:45,0:09:59,0:12:42,2:54:21,1224,955,12
+Night Team,PGY1,S0108287,2015-06-11,0:42:16,0:11:07,0:24:38,0:24:50,0:14:05,0:05:25,2:13:22,1464,0,12
+Night Team,PGY1,S0108287,2015-06-16,1:58:13,0:52:08,1:20:35,0:34:02,0:16:34,0:31:44,5:55:32,1788,66,34
+Night Team,PGY1,S0108287,2015-06-17,1:50:31,0:23:06,1:02:01,0:24:06,0:22:56,0:11:57,4:36:04,2084,0,21
+Night Team,PGY1,S0108287,2015-06-14,2:19:25,0:20:06,0:42:26,0:05:55,0:11:51,0:17:07,4:07:52,1903,361,24
+Night Team,PGY1,S0108287,2015-06-15,1:19:06,0:04:30,0:07:43,0:29:15,0:11:52,0:07:14,2:40:22,1343,0,21
+General Medicine,PGY1,S0181075,2015-11-15,0:05:31,0:06:29,0:59:44,0:13:12,0:00:45,0:01:43,1:28:54,230,214,5
+Night Team,PGY2+,S0181083,2016-09-04,1:59:11,0:48:38,0:00:09,0:23:41,0:09:37,0:06:40,4:21:32,1304,60,26
+Night Team,PGY2+,S0181083,2016-09-05,2:06:10,0:39:22,0:00:05,0:19:53,0:05:44,0:08:47,3:28:48,940,0,33
+Night Team,PGY2+,S0181083,2016-09-06,2:45:44,1:10:35,0:00:23,0:44:59,0:18:25,0:06:32,6:04:53,1696,32,32
+Night Team,PGY2+,S0181083,2016-09-07,2:51:05,1:30:06,0:03:41,0:48:05,0:10:49,0:15:39,6:16:33,2002,0,46
+General Medicine,PGY2+,S0169399,2016-02-05,1:24:15,0:37:16,2:14:51,0:53:14,0:24:21,0:21:58,6:21:43,1507,0,13
+Night Team,PGY2+,S0181083,2016-09-01,2:03:09,1:45:07,0:00:17,0:28:34,0:12:25,0:18:45,5:26:27,1426,517,36
+Night Team,PGY2+,S0181083,2016-09-02,1:47:59,1:13:31,0:00:08,0:57:36,0:15:13,0:12:44,5:16:06,1354,0,29
+Night Team,PGY2+,S0181083,2016-09-03,2:49:33,1:09:57,0:00:16,0:17:23,0:11:20,0:04:11,5:01:47,1327,57,28
+Intensive Care Unit,PGY2+,S0169399,2016-02-09,1:37:09,0:53:47,0:27:36,0:20:03,0:17:46,0:13:25,4:22:08,911,0,17
+Intensive Care Unit,PGY2+,S0169406,2016-03-29,0:55:03,0:24:16,0:00:01,0:09:45,0:05:44,0:03:16,1:52:55,820,262,22
+Night Team,PGY2+,S0181083,2016-09-08,4:25:28,1:13:34,0:00:19,0:42:55,0:15:34,0:18:12,7:39:45,1724,267,50
+Night Team,PGY2+,S0181083,2016-09-09,1:15:44,1:05:05,0:00:24,0:12:42,0:07:07,0:03:39,3:08:20,857,64,14
+Intensive Care Unit,PGY2+,S0169406,2016-03-28,2:10:10,0:25:13,0:09:52,0:21:28,0:18:58,0:04:57,4:06:59,1354,75,13
+General Medicine,PGY1,S0181086,2016-05-04,2:58:58,1:09:33,0:00:24,1:06:14,0:10:13,0:01:09,6:14:43,2183,467,12
+General Medicine,PGY1,S0195230,2016-10-10,2:15:52,1:59:19,0:00:05,0:24:40,0:16:52,0:00:34,5:29:04,1507,0,12
+General Medicine,PGY1,S0094959,2014-11-28,4:57:16,0:07:49,1:25:08,0:26:19,0:25:33,0:23:58,8:25:23,3955,33,46
+Emergency Medicine,PGY2+,S0079337,2016-02-29,2:50:09,0:27:46,0:01:35,0:27:51,1:02:13,0:06:49,5:38:29,2759,0,40
+Emergency Medicine,PGY2+,S0079337,2016-02-28,3:42:06,0:27:53,0:05:06,0:34:32,1:13:37,0:09:03,7:10:36,2965,0,44
+Emergency Medicine,PGY2+,S0079337,2016-02-21,4:01:14,0:51:47,0:11:37,1:02:04,1:14:12,0:09:00,8:42:39,4108,0,39
+Emergency Medicine,PGY2+,S0079337,2016-02-20,3:26:56,0:44:14,0:05:33,0:29:11,1:18:20,0:02:24,6:51:50,3154,0,47
+Emergency Medicine,PGY2+,S0079337,2016-02-22,3:06:12,0:36:49,0:07:49,0:40:12,0:59:47,0:12:14,6:35:18,3498,0,41
+Emergency Medicine,PGY2+,S0079337,2016-02-25,2:47:46,0:12:52,0:01:02,0:24:09,0:25:55,0:02:04,4:22:29,1572,0,20
+Emergency Medicine,PGY2+,S0079337,2016-02-24,0:46:27,0:11:36,0:00:33,0:15:19,0:09:50,0:01:27,1:46:40,952,0,18
+Emergency Medicine,PGY2+,S0079337,2016-02-26,3:57:54,0:47:24,0:07:57,0:24:19,1:13:02,0:14:45,7:44:06,3185,0,40
+General Medicine,PGY1,S0186952,2016-10-08,2:36:06,1:14:18,0:00:06,0:44:13,0:26:10,0:07:25,5:56:53,1650,0,14
+General Medicine,PGY1,S0186952,2016-10-09,0:03:13,0:00:14,0:00:00,0:00:00,0:00:00,0:01:11,0:05:02,69,0,7
+General Medicine,PGY1,S0195230,2016-10-11,1:48:12,1:10:07,0:00:11,0:21:32,0:10:40,0:00:26,3:54:45,1403,0,11
+General Medicine,PGY1,S0181063,2016-01-02,0:00:00,0:00:00,0:00:00,0:00:03,0:00:00,0:00:00,0:00:03,7,0,2
+General Medicine,PGY1,S0186952,2016-10-01,2:25:37,0:49:24,0:00:11,0:47:17,0:18:58,0:08:39,5:03:50,1659,0,14
+General Medicine,PGY1,S0186952,2016-10-02,1:27:12,0:36:08,0:01:02,0:31:09,0:11:29,0:02:56,3:21:52,1233,0,17
+General Medicine,PGY1,S0186952,2016-10-03,2:05:19,1:19:33,0:00:13,0:57:33,0:22:37,0:06:43,5:47:50,2067,66,10
+General Medicine,PGY1,S0186952,2016-10-04,2:18:21,0:46:17,0:00:53,0:34:44,0:16:55,0:10:47,4:52:18,2042,302,30
+General Medicine,PGY1,S0186952,2016-10-05,3:04:20,1:11:06,0:00:29,0:29:59,0:32:20,0:00:20,6:22:46,1785,19,17
+General Medicine,PGY1,S0186952,2016-10-06,0:12:23,0:01:27,0:00:00,0:00:08,0:00:01,0:04:23,0:20:27,156,85,12
+General Medicine,PGY1,S0186952,2016-10-07,1:09:16,0:34:21,0:00:09,0:19:25,0:11:18,0:05:10,2:47:09,1102,36,16
+Intensive Care Unit,PGY2+,S0108268,2016-08-09,0:36:12,0:23:00,0:00:04,0:00:00,0:00:06,0:00:03,4:34:34,1931,1921,149
+Intensive Care Unit,PGY2+,S0108268,2016-08-08,1:56:26,0:53:26,0:13:37,0:20:08,0:16:23,0:04:11,4:21:26,2154,0,31
+Intensive Care Unit,PGY2+,S0108268,2016-08-07,2:09:24,0:36:30,0:16:12,0:17:25,0:16:47,0:02:19,4:09:03,1499,0,11
+Intensive Care Unit,PGY2+,S0108268,2016-08-06,2:54:29,1:17:34,0:00:00,0:48:45,0:40:47,0:11:34,6:25:14,1929,336,15
+Intensive Care Unit,PGY2+,S0108268,2016-08-05,1:55:23,1:10:45,0:03:21,0:18:43,0:21:23,0:05:24,4:17:08,1459,218,23
+Intensive Care Unit,PGY2+,S0108268,2016-08-04,1:18:50,1:09:58,0:13:49,0:35:58,0:17:23,0:01:47,4:25:05,1839,54,40
+Intensive Care Unit,PGY2+,S0108268,2016-08-03,1:00:44,0:31:48,0:03:41,0:04:53,0:17:16,0:03:42,2:17:41,1261,0,15
+Intensive Care Unit,PGY2+,S0108268,2016-08-02,2:58:19,1:33:19,0:01:11,0:29:24,0:33:47,0:06:58,6:29:26,2584,0,21
+Intensive Care Unit,PGY2+,S0108268,2016-08-01,2:31:30,1:10:18,0:10:17,0:36:36,0:20:52,0:03:34,5:25:00,1723,0,16
+Intensive Care Unit,PGY2+,S0169395,2015-11-01,1:11:59,0:21:28,0:36:31,0:16:47,0:11:43,0:18:03,3:14:36,1014,0,31
+Intensive Care Unit,PGY2+,S0172056,2016-10-20,0:00:00,0:00:00,0:00:00,0:00:11,0:00:00,0:00:00,0:00:19,19,0,7
+Intensive Care Unit,PGY2+,S0172056,2016-10-21,0:38:30,0:10:22,0:00:00,0:50:59,0:33:29,0:01:03,2:36:34,1336,0,21
+Intensive Care Unit,PGY2+,S0172056,2016-10-22,1:33:56,0:42:38,0:01:21,0:25:40,0:24:37,0:10:15,4:01:51,1183,0,16
+General Medicine,PGY2+,S0181079,2016-10-06,3:22:17,1:12:37,0:00:01,0:24:09,0:15:33,0:01:21,6:05:18,2135,93,24
+General Medicine,PGY1,S0074196,2015-08-17,1:12:58,0:04:08,0:30:25,0:35:27,0:27:25,0:09:22,3:17:50,916,0,4
+General Medicine,PGY1,S0074196,2015-08-16,0:38:32,0:05:07,0:28:45,0:18:46,0:04:14,0:04:34,1:46:24,353,0,2
+General Medicine,PGY1,S0169383,2015-05-19,1:37:53,0:38:53,0:26:41,0:11:18,0:03:57,0:03:50,3:23:48,1059,0,16
+Intensive Care Unit,PGY2+,S0172056,2016-10-23,1:27:40,1:08:23,0:05:55,0:11:12,0:32:45,0:03:26,3:48:40,1235,0,18
+General Medicine,PGY1,S0188098,2016-08-19,2:00:51,1:46:40,0:00:32,0:26:18,0:24:06,0:03:40,5:13:34,1260,75,10
+General Medicine,PGY1,S0188098,2016-08-18,1:52:26,1:01:16,0:00:03,0:23:01,0:14:18,0:05:03,3:55:59,861,168,7
+General Medicine,PGY1,S0169383,2015-05-13,2:23:21,0:16:02,0:32:49,0:22:29,0:10:43,0:11:13,4:30:11,1631,0,13
+Emergency Medicine,PGY1,S0169404,2014-12-31,0:00:00,0:08:47,0:00:00,0:00:00,0:00:00,0:02:17,0:11:13,54,44,3
+General Medicine,PGY1,S0169383,2015-05-11,1:38:38,0:08:10,0:21:14,0:19:36,0:13:31,0:09:32,3:14:24,1323,84,20
+General Medicine,PGY1,S0169383,2015-05-10,2:11:19,0:44:39,2:43:05,0:41:33,0:12:29,0:16:54,7:24:35,1620,0,14
+General Medicine,PGY1,S0169383,2015-05-17,2:19:30,0:21:33,1:17:35,0:27:56,0:17:55,0:23:21,5:34:23,1679,11,19
+General Medicine,PGY1,S0169383,2015-05-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,14,0,2
+General Medicine,PGY1,S0169383,2015-05-15,2:36:57,0:42:11,1:39:59,0:17:26,0:18:55,0:08:18,6:19:16,1643,0,13
+General Medicine,PGY1,S0074196,2015-08-18,1:30:38,0:42:34,2:24:49,0:47:39,0:11:37,0:19:50,6:14:38,1134,54,7
+Intensive Care Unit,PGY2+,S0172056,2016-10-26,2:10:15,0:35:49,0:04:01,1:04:58,0:18:42,0:11:26,5:28:07,1492,267,25
+General Medicine,PGY1,S0163662,2015-03-02,2:28:19,0:37:30,1:48:31,0:55:04,0:32:01,0:20:28,7:02:27,2077,10,5
+Intensive Care Unit,PGY2+,S0172056,2016-10-27,1:49:53,1:07:16,0:07:06,0:11:42,0:33:28,0:06:28,4:49:42,1578,79,18
+General Medicine,PGY1,S0195230,2016-10-14,1:25:53,1:24:57,0:00:10,0:39:19,0:07:13,0:00:10,4:22:49,1366,0,7
+Night Team,PGY1,S0169384,2015-04-19,2:59:23,0:11:38,0:13:16,0:35:50,0:34:12,1:04:01,5:50:14,1743,0,17
+Night Team,PGY1,S0169384,2015-04-18,2:02:04,0:34:56,1:07:46,0:17:52,0:19:31,0:49:58,5:25:32,1378,0,25
+General Medicine,PGY1,S0181075,2015-11-12,2:01:33,1:26:37,0:50:01,1:42:54,0:35:53,0:25:02,7:30:59,2627,37,11
+General Medicine,PGY1,S0181064,2016-05-02,1:04:48,0:28:28,0:00:00,0:11:05,0:04:59,0:02:57,2:01:50,605,15,9
+Night Team,PGY1,S0169384,2015-04-13,3:48:11,0:39:37,1:51:54,0:25:17,0:23:56,0:39:05,8:13:27,1612,0,23
+Night Team,PGY1,S0169384,2015-04-12,2:55:52,0:13:03,1:10:27,0:31:42,0:17:02,0:57:56,6:29:05,1811,0,24
+Night Team,PGY1,S0169384,2015-04-15,1:27:57,0:21:19,1:37:03,0:23:32,0:19:11,0:11:34,4:27:15,1108,0,8
+Night Team,PGY1,S0169384,2015-04-14,2:18:01,0:23:24,1:07:56,0:28:42,0:30:08,0:19:25,5:23:25,1439,0,27
+Night Team,PGY1,S0169384,2015-04-17,2:36:02,0:11:08,0:09:59,0:27:23,0:16:14,0:32:18,4:25:09,992,0,22
+Night Team,PGY1,S0169384,2015-04-16,1:11:03,0:02:06,0:03:01,0:12:17,0:15:19,0:16:24,2:03:25,627,0,19
+Intensive Care Unit,PGY2+,S0108268,2016-07-18,2:05:52,1:13:03,0:11:49,0:15:36,0:38:38,0:02:21,4:52:14,1104,0,16
+General Medicine,PGY1,S0181064,2016-05-07,2:22:14,1:26:12,0:00:09,0:38:03,0:15:00,0:00:20,5:16:36,1534,0,23
+General Medicine,PGY1,S0181064,2016-05-06,1:19:42,1:05:49,0:00:05,0:26:40,0:09:56,0:02:54,3:21:19,1312,184,23
+General Medicine,PGY2+,S0169384,2016-06-18,0:00:53,0:27:50,0:00:00,0:00:04,0:00:02,0:00:00,0:29:06,205,195,5
+General Medicine,PGY2+,S0169384,2016-06-19,1:59:18,1:58:00,0:00:00,0:11:44,0:17:47,0:07:25,5:11:17,2114,124,23
+General Medicine,PGY2+,S0169384,2016-06-14,2:11:56,2:33:42,0:01:13,0:36:51,0:18:53,0:07:00,6:43:55,2522,429,17
+General Medicine,PGY2+,S0169384,2016-06-15,2:19:09,1:40:58,0:00:06,1:15:46,0:33:28,0:08:11,6:37:48,1838,291,13
+General Medicine,PGY2+,S0169384,2016-06-16,1:51:44,1:32:38,0:02:05,0:43:51,0:14:08,0:25:30,5:09:35,1420,176,12
+General Medicine,PGY2+,S0169384,2016-06-17,1:43:45,2:20:51,0:00:18,0:43:49,0:19:54,0:27:21,6:13:12,2714,410,20
+General Medicine,PGY2+,S0169384,2016-06-10,0:08:30,0:13:51,0:00:00,0:02:57,0:02:26,0:00:00,0:32:23,244,240,8
+General Medicine,PGY2+,S0169384,2016-06-11,1:39:28,2:04:56,0:05:55,0:39:23,0:14:19,0:05:05,5:36:46,1955,522,24
+General Medicine,PGY2+,S0169384,2016-06-12,2:15:22,1:40:31,0:03:50,0:41:35,0:22:05,0:14:33,6:04:47,1877,354,17
+General Medicine,PGY2+,S0169384,2016-06-13,0:42:28,0:19:28,0:00:04,0:10:05,0:07:33,0:00:51,1:33:57,1116,120,12
+General Medicine,PGY1,S0181081,2015-08-08,2:33:36,0:04:54,2:29:21,0:27:24,0:10:59,0:25:42,6:35:37,1702,7,8
+General Medicine,PGY1,S0169386,2015-01-03,0:00:00,0:08:41,0:00:00,0:01:27,0:00:00,0:20:35,0:33:24,389,367,10
+General Medicine,PGY1,S0169386,2015-01-02,3:15:40,0:15:52,1:49:27,0:35:36,0:32:58,1:04:13,8:05:52,2868,72,15
+General Medicine,PGY1,S0169386,2015-01-01,1:28:59,0:15:36,1:00:40,0:18:18,0:09:02,0:50:22,4:13:57,1212,22,13
+General Medicine,PGY2+,S0169399,2016-06-09,1:22:35,0:31:54,0:00:04,0:15:22,0:03:13,0:00:30,2:31:57,730,0,15
+General Medicine,PGY2+,S0169399,2016-06-08,1:13:52,0:41:06,0:02:49,0:29:03,0:19:56,0:02:25,3:23:26,838,0,13
+General Medicine,PGY2+,S0181074,2016-10-28,2:49:44,1:20:12,0:00:01,0:56:56,0:13:06,0:07:11,6:19:39,2872,107,31
+General Medicine,PGY2+,S0181074,2016-10-29,2:16:37,1:40:32,0:00:05,0:45:34,0:09:23,0:05:30,5:39:28,2228,238,15
+General Medicine,PGY2+,S0181074,2016-10-26,3:27:52,0:52:50,0:00:00,1:45:13,0:12:20,0:06:28,7:15:59,3397,817,30
+General Medicine,PGY2+,S0181074,2016-10-27,2:23:32,1:14:57,0:00:10,1:30:29,0:18:19,0:12:12,6:23:18,2577,102,21
+General Medicine,PGY2+,S0181074,2016-10-24,1:17:12,0:28:51,0:00:00,0:09:51,0:01:22,0:04:18,2:16:18,917,0,26
+General Medicine,PGY2+,S0181074,2016-10-25,0:40:28,0:09:51,0:00:00,0:00:00,0:00:26,0:06:47,1:03:47,449,88,29
+General Medicine,PGY2+,S0169399,2016-06-01,0:29:14,0:15:35,0:00:00,0:06:06,0:07:59,0:00:53,1:03:01,274,0,14
+General Medicine,PGY2+,S0181074,2016-10-23,3:06:14,1:04:43,0:00:00,1:40:47,0:15:12,0:15:11,7:31:41,3456,345,23
+General Medicine,PGY2+,S0181074,2016-10-20,1:50:30,0:25:40,0:00:00,0:10:06,0:14:58,0:23:41,3:41:16,1563,759,16
+General Medicine,PGY2+,S0181074,2016-10-21,2:47:33,0:55:14,0:00:00,0:58:13,0:11:43,0:02:48,6:06:57,2592,12,17
+General Medicine,PGY1,S0163425,2014-09-28,2:24:48,0:00:13,0:12:17,0:08:41,0:14:31,0:23:38,3:38:26,949,0,13
+General Medicine,PGY1,S0163425,2014-09-29,3:03:22,0:09:05,0:19:11,0:30:41,0:13:42,0:37:12,5:08:11,1326,0,16
+General Medicine,PGY1,S0163425,2014-09-24,3:49:43,0:06:16,0:24:58,1:11:24,0:29:22,0:42:40,7:18:00,2006,0,15
+General Medicine,PGY1,S0163425,2014-09-25,3:25:38,0:02:47,0:23:27,0:21:11,0:30:46,0:26:45,5:31:57,1660,0,19
+General Medicine,PGY1,S0163425,2014-09-26,3:38:51,0:07:10,0:16:39,0:31:34,0:22:22,0:52:06,6:19:47,1626,4,9
+General Medicine,PGY1,S0163425,2014-09-27,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,6,0,2
+General Medicine,PGY1,S0163425,2014-09-20,0:00:00,0:00:00,0:00:00,0:00:54,0:00:00,0:00:00,0:00:54,8,0,2
+General Medicine,PGY1,S0163425,2014-09-21,3:51:14,0:07:41,0:23:19,0:44:37,0:36:32,1:18:41,7:27:33,2016,0,27
+General Medicine,PGY1,S0163425,2014-09-22,3:52:26,0:04:11,0:15:33,0:33:34,0:23:00,0:56:49,6:24:34,1598,0,10
+General Medicine,PGY1,S0163425,2014-09-23,2:51:44,0:00:24,0:07:26,0:23:35,0:21:42,0:23:55,4:49:24,1494,17,13
+General Medicine,PGY1,S0169406,2015-05-20,0:47:08,0:04:44,1:12:16,0:08:59,0:02:55,0:03:01,2:30:26,517,0,6
+General Medicine,PGY1,S0169406,2015-05-22,2:22:40,0:27:38,0:03:26,0:40:52,0:18:55,0:23:29,4:54:40,1577,26,12
+General Medicine,PGY1,S0169406,2015-05-23,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Night Team,PGY2+,S0181056,2016-09-25,2:29:33,1:21:17,0:00:29,0:42:00,0:18:15,0:08:39,5:40:42,1912,0,28
+Night Team,PGY2+,S0181056,2016-09-26,4:05:24,1:09:28,0:02:07,0:49:14,0:15:51,0:07:36,7:22:08,2234,0,31
+Night Team,PGY2+,S0181056,2016-09-27,3:14:23,1:53:02,0:00:15,0:53:10,0:16:15,0:08:59,7:21:28,2477,256,20
+General Medicine,PGY1,S0195207,2016-09-12,3:04:49,1:46:17,0:00:35,0:45:37,0:24:14,0:01:01,6:44:06,2942,27,14
+General Medicine,PGY1,S0195207,2016-09-13,2:58:38,1:16:56,0:05:21,0:33:50,0:29:15,0:08:43,6:17:03,2546,89,15
+General Medicine,PGY1,S0195207,2016-09-10,3:33:33,2:25:54,0:00:27,0:47:36,0:36:33,0:01:26,8:20:57,3512,28,14
+General Medicine,PGY1,S0195207,2016-09-11,0:00:00,0:00:00,0:00:00,0:00:58,0:00:00,0:00:00,0:00:58,8,0,3
+General Medicine,PGY1,S0195207,2016-09-16,1:57:37,1:08:03,0:00:17,0:15:54,0:11:50,0:01:41,4:00:18,1596,36,14
+General Medicine,PGY1,S0195207,2016-09-17,0:08:33,0:00:22,0:00:00,0:00:59,0:00:00,0:00:19,0:11:06,75,0,7
+General Medicine,PGY1,S0195207,2016-09-14,2:40:03,1:35:26,0:00:18,0:36:15,0:11:40,0:02:10,5:46:12,2906,22,17
+General Medicine,PGY1,S0195207,2016-09-15,3:26:59,1:39:52,0:00:16,0:37:18,0:15:33,0:20:09,7:06:12,2831,12,21
+General Medicine,PGY1,S0169406,2014-06-30,1:43:39,0:02:31,0:13:05,0:08:38,0:14:07,0:19:20,2:49:30,738,0,4
+General Medicine,PGY1,S0188098,2016-07-29,1:05:29,1:37:08,0:00:10,0:10:37,0:05:10,0:00:48,3:30:25,724,60,5
+General Medicine,PGY1,S0188098,2016-07-28,1:53:51,1:41:22,0:00:07,0:25:36,0:27:07,0:04:43,5:05:19,1292,2,9
+General Medicine,PGY1,S0188098,2016-07-25,2:04:54,0:51:38,0:02:32,0:43:03,0:19:21,0:04:43,4:59:05,1240,36,8
+General Medicine,PGY1,S0188098,2016-07-24,1:15:40,0:24:51,0:00:47,0:14:13,0:24:20,0:01:04,2:55:01,684,222,5
+General Medicine,PGY1,S0188098,2016-07-27,0:45:21,0:27:56,0:00:01,0:35:23,0:19:35,0:00:04,2:25:28,566,8,4
+General Medicine,PGY1,S0188098,2016-07-26,0:57:08,0:50:31,0:00:23,0:26:41,0:03:09,0:05:33,2:49:31,852,60,6
+Night Team,PGY2+,S0181069,2016-06-30,2:24:40,1:31:21,0:00:03,0:48:04,0:08:57,0:07:17,5:43:06,2071,153,30
+Night Team,PGY2+,S0181060,2016-10-29,2:13:19,1:29:18,0:00:15,0:52:23,0:24:03,0:06:05,5:21:26,1754,0,26
+Emergency Medicine,PGY1,S0181082,2016-03-05,2:23:38,0:56:17,0:01:05,0:25:53,0:44:25,0:02:35,5:15:37,1783,0,12
+Emergency Medicine,PGY1,S0181082,2016-03-04,2:33:47,0:47:59,0:03:19,0:34:22,0:54:32,0:03:05,5:50:53,1883,0,13
+Emergency Medicine,PGY1,S0181082,2016-03-07,1:31:27,0:10:44,0:01:19,0:07:21,0:30:00,0:00:45,2:50:31,854,0,8
+Emergency Medicine,PGY1,S0181082,2016-03-06,1:04:53,0:25:28,0:00:36,0:08:15,0:11:43,0:03:40,2:11:01,762,0,8
+Emergency Medicine,PGY1,S0181082,2016-03-01,2:41:52,0:38:36,0:00:59,0:26:58,0:21:45,0:11:50,5:02:27,1652,0,23
+General Medicine,PGY1,S0181082,2015-12-19,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,19,0,3
+Emergency Medicine,PGY1,S0181082,2016-03-03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY1,S0181082,2016-03-02,1:58:13,0:50:10,0:03:02,0:29:12,0:49:12,0:04:27,4:47:28,1744,0,14
+General Medicine,PGY1,S0181082,2015-12-18,1:48:44,1:13:11,2:17:04,0:49:57,0:19:47,0:38:51,7:38:43,2275,0,17
+Emergency Medicine,PGY1,S0181082,2016-03-08,2:38:22,0:57:03,0:02:12,0:23:35,1:11:15,0:02:48,6:06:46,2066,0,15
+Emergency Medicine,PGY2+,S0181056,2016-08-27,3:57:34,0:48:20,0:08:42,0:40:53,0:38:22,0:00:50,6:57:01,2351,0,43
+General Medicine,PGY1,S0181082,2015-12-16,1:26:22,1:16:09,1:38:14,1:12:34,0:27:17,0:42:49,7:14:22,2673,55,19
+General Medicine,PGY1,S0181082,2015-12-15,1:58:34,1:22:55,2:05:04,1:07:49,0:39:11,0:41:45,8:35:33,2722,0,11
+General Medicine,PGY1,S0093383,2014-09-10,2:19:39,0:03:57,0:20:32,0:34:27,0:15:31,0:28:59,4:08:59,1277,0,8
+General Medicine,PGY1,S0093383,2014-09-11,0:24:13,0:02:37,0:00:13,0:00:47,0:00:40,0:01:01,0:29:39,228,204,7
+General Medicine,PGY1,S0093383,2014-09-12,3:48:31,0:05:40,0:23:58,0:29:17,0:18:35,0:35:52,6:05:29,1680,0,8
+General Medicine,PGY1,S0181082,2015-12-14,0:55:20,0:40:04,0:16:27,0:35:43,0:21:51,0:23:44,3:35:30,1527,248,10
+Night Team,PGY2+,S0169401,2015-08-09,1:36:40,1:18:44,1:23:03,0:48:42,0:18:11,0:21:48,6:15:37,2425,161,61
+Emergency Medicine,PGY2+,S0181056,2016-08-23,2:06:24,0:34:33,0:07:12,0:40:56,0:21:19,0:03:13,4:33:19,1606,0,26
+Night Team,PGY2+,S0181060,2016-10-26,1:27:45,0:48:39,0:00:02,0:14:13,0:05:57,0:01:23,3:10:56,1265,0,15
+Emergency Medicine,PGY2+,S0181056,2016-08-21,3:57:37,1:03:46,0:00:50,0:32:34,0:30:31,0:09:56,7:29:48,3089,0,51
+Emergency Medicine,PGY2+,S0181056,2016-08-20,3:37:23,1:14:14,0:02:59,1:02:24,0:30:53,0:08:11,7:29:07,2540,48,42
+General Medicine,PGY1,S0169403,2014-08-17,2:14:59,0:21:17,0:10:25,0:18:42,0:03:08,0:12:59,3:30:26,973,127,22
+Emergency Medicine,PGY2+,S0109179,2015-10-29,7:19:15,0:28:34,0:52:01,0:20:38,0:17:01,0:06:52,9:59:06,2874,0,59
+Emergency Medicine,PGY2+,S0109179,2015-10-28,1:09:14,0:16:35,0:12:30,0:01:26,0:03:47,0:03:30,1:57:44,748,0,40
+Emergency Medicine,PGY2+,S0109179,2015-10-25,5:02:58,0:52:06,1:15:50,0:24:53,0:22:29,0:22:58,8:58:50,3329,0,60
+Emergency Medicine,PGY2+,S0109179,2015-10-27,5:31:10,0:14:51,0:55:22,0:19:15,0:17:44,0:09:31,7:54:56,2028,0,45
+Emergency Medicine,PGY2+,S0109179,2015-10-26,1:11:13,0:09:18,0:27:32,0:05:09,0:09:13,0:00:39,2:28:43,853,0,14
+General Medicine,PGY1,S0169403,2014-08-18,3:12:47,0:09:29,0:27:30,0:53:13,0:25:44,0:17:42,5:51:47,1613,43,10
+General Medicine,PGY1,S0169403,2014-08-19,2:20:15,0:08:07,0:12:03,0:29:55,0:23:20,0:17:10,4:16:28,1218,52,8
+Emergency Medicine,PGY2+,S0109179,2015-10-23,3:42:20,0:30:14,0:39:29,0:08:43,0:09:52,0:15:58,5:45:22,1977,0,40
+Emergency Medicine,PGY2+,S0109179,2015-10-22,2:29:48,0:35:12,0:26:41,0:03:44,0:10:31,0:05:50,4:03:28,1525,0,49
+Night Team,PGY2+,S0169384,2016-10-31,2:31:56,1:34:30,0:00:32,1:01:48,0:21:29,0:04:50,5:59:59,1683,294,14
+Night Team,PGY2+,S0169384,2016-10-30,3:20:52,2:19:47,0:05:08,1:16:50,0:16:02,0:15:39,8:11:16,2374,236,18
+Night Team,PGY2+,S0169394,2016-04-03,1:12:07,0:22:07,0:00:00,0:06:25,0:11:25,0:00:16,1:57:10,475,0,14
+Night Team,PGY2+,S0169394,2016-04-04,2:22:21,1:32:59,0:00:44,0:35:38,0:13:50,0:06:21,5:50:10,1566,165,22
+Night Team,PGY2+,S0169394,2016-04-05,2:37:58,1:16:16,0:00:05,0:48:46,0:10:07,0:03:24,5:26:42,1708,461,16
+Night Team,PGY2+,S0169394,2016-04-06,4:02:32,1:39:55,0:00:11,0:45:46,0:15:52,0:10:24,7:42:01,1936,39,28
+Night Team,PGY2+,S0169394,2016-04-07,2:29:33,1:53:46,0:05:00,1:09:22,0:18:01,0:11:40,7:11:30,2182,15,27
+Night Team,PGY2+,S0169394,2016-04-08,4:16:28,0:57:39,0:00:10,0:34:47,0:15:58,0:09:40,6:56:06,2074,26,26
+Night Team,PGY2+,S0169394,2016-04-09,2:46:04,1:20:02,0:00:08,0:55:34,0:04:45,0:01:50,5:30:59,1229,94,20
+Night Team,PGY2+,S0108287,2016-10-18,2:18:41,1:15:53,0:02:18,0:23:32,0:07:56,0:03:04,4:23:53,1324,0,20
+Night Team,PGY2+,S0181087,2016-08-30,2:49:11,1:44:55,0:00:18,0:35:18,0:09:53,0:09:57,6:16:26,2432,711,31
+Night Team,PGY2+,S0181087,2016-08-31,3:55:52,2:18:35,0:00:17,0:53:52,0:14:15,0:04:02,8:05:26,2655,46,39
+Emergency Medicine,PGY1,S0181070,2015-10-31,2:09:53,0:29:06,3:14:18,0:22:54,0:23:49,0:10:57,7:57:47,3066,0,32
+Night Team,PGY1,S0195076,2016-09-18,1:04:00,0:06:03,0:00:00,0:30:21,0:29:32,0:05:26,2:22:02,743,35,26
+Night Team,PGY1,S0195076,2016-09-19,1:55:55,0:15:47,0:00:00,0:37:14,0:19:28,0:12:16,3:44:40,1046,44,24
+Night Team,PGY2+,S0181082,2016-11-10,3:21:44,1:55:59,0:00:45,0:37:20,0:21:29,0:15:01,6:50:51,2396,23,31
+General Medicine,PGY2+,S0169399,2016-01-17,0:00:00,0:00:00,0:00:00,0:00:30,0:00:00,0:00:00,0:00:30,10,0,5
+General Medicine,PGY2+,S0169399,2016-01-16,1:04:24,0:42:22,2:57:48,0:18:11,0:22:29,0:20:47,6:25:49,1332,0,14
+General Medicine,PGY2+,S0169399,2016-01-15,2:27:30,1:09:37,0:39:34,0:31:50,0:16:42,0:52:13,6:33:13,1517,28,35
+General Medicine,PGY2+,S0169399,2016-01-14,0:20:00,0:08:57,0:02:27,0:03:34,0:08:34,0:21:32,1:14:15,380,39,11
+General Medicine,PGY2+,S0169399,2016-01-13,1:45:42,0:39:07,0:26:54,0:21:30,0:16:39,0:41:42,4:35:04,970,0,28
+General Medicine,PGY2+,S0169399,2016-01-12,1:14:46,0:48:29,2:57:23,0:19:12,0:06:34,0:17:50,6:10:29,1091,0,15
+General Medicine,PGY2+,S0169399,2016-01-11,2:52:20,1:14:51,1:05:30,1:05:23,0:43:57,0:41:59,8:26:54,2170,0,20
+General Medicine,PGY2+,S0169399,2016-01-10,2:15:56,0:45:52,0:31:20,0:19:28,0:18:46,0:34:22,5:23:19,1237,0,34
+General Medicine,PGY2+,S0169399,2016-01-19,0:51:22,0:31:26,0:21:36,0:07:49,0:10:29,0:16:39,2:38:00,726,0,19
+General Medicine,PGY2+,S0169399,2016-01-18,2:27:58,0:53:59,0:26:24,0:11:31,0:10:59,0:40:41,5:25:17,1351,0,23
+General Medicine,PGY1,S0172056,2016-05-19,1:24:44,0:59:08,0:00:07,0:14:59,0:19:57,0:00:15,3:30:16,1235,8,14
+General Medicine,PGY1,S0172056,2016-05-18,0:04:35,0:00:00,0:00:00,0:04:29,0:00:11,0:00:00,0:09:46,66,62,2
+General Medicine,PGY1,S0172056,2016-05-13,0:08:08,0:04:05,0:00:09,0:00:22,0:00:08,0:00:00,0:15:33,202,202,5
+General Medicine,PGY1,S0172056,2016-05-12,0:00:05,0:04:36,0:00:00,0:00:00,0:00:00,0:00:00,0:04:43,25,25,1
+General Medicine,PGY1,S0172056,2016-05-11,1:44:14,0:50:16,0:00:40,0:23:03,0:11:45,0:06:31,4:12:45,1660,16,17
+General Medicine,PGY1,S0172056,2016-05-10,0:52:51,0:27:08,0:00:20,0:02:34,0:04:29,0:00:10,1:50:13,633,0,10
+General Medicine,PGY1,S0172056,2016-05-17,1:49:57,0:58:14,0:01:57,0:25:46,0:07:21,0:00:13,4:05:28,1250,63,10
+General Medicine,PGY1,S0172056,2016-05-16,1:34:28,0:56:32,0:00:03,0:22:29,0:09:13,0:06:12,3:33:27,1822,102,12
+General Medicine,PGY1,S0172056,2016-05-15,0:51:53,0:25:21,0:00:07,0:09:26,0:03:56,0:00:05,2:07:15,596,69,6
+General Medicine,PGY1,S0172056,2016-05-14,2:29:21,0:57:14,0:00:34,0:17:04,0:07:49,0:00:54,4:57:06,1895,0,23
+General Medicine,PGY1,S0181081,2016-06-11,3:13:09,1:41:13,0:00:07,1:25:40,0:18:07,0:17:03,7:56:12,2392,31,20
+Night Team,PGY1,S0108287,2015-06-09,2:05:21,0:26:51,1:00:35,0:37:22,0:36:06,0:18:25,5:48:31,3144,1125,17
+Night Team,PGY1,S0108287,2015-06-08,2:02:46,0:27:02,0:27:54,0:23:18,0:16:44,0:20:52,4:36:48,2884,2047,29
+Night Team,PGY1,S0172085,2015-10-15,1:48:21,1:25:32,1:50:35,0:31:08,0:19:44,1:04:54,7:19:27,1979,0,45
+General Medicine,PGY1,S0195237,2016-10-26,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+General Medicine,PGY1,S0195237,2016-10-27,1:35:58,0:58:47,0:00:12,0:18:00,0:06:40,0:06:08,3:35:42,1817,42,6
+General Medicine,PGY1,S0195237,2016-10-24,1:11:36,0:44:09,0:00:23,0:51:55,0:11:28,0:03:35,3:34:00,1155,0,5
+General Medicine,PGY1,S0195237,2016-10-25,3:01:59,2:00:54,0:00:29,0:54:18,0:30:36,0:03:11,7:34:07,2034,0,16
+General Medicine,PGY1,S0195237,2016-10-22,0:37:59,0:47:15,0:00:16,0:16:42,0:04:45,0:00:04,2:04:38,876,50,4
+General Medicine,PGY1,S0195203,2016-06-30,2:17:30,2:04:02,0:00:38,0:44:43,0:33:21,0:09:22,6:30:38,1984,194,12
+General Medicine,PGY1,S0195237,2016-10-20,3:09:24,1:52:44,0:00:21,1:21:50,0:22:24,0:04:01,7:48:37,2385,87,24
+General Medicine,PGY1,S0195237,2016-10-21,1:24:45,0:49:15,0:00:14,0:27:47,0:08:48,0:02:34,3:25:31,1317,421,7
+General Medicine,PGY1,S0169386,2014-07-13,2:05:59,0:10:49,0:33:38,0:04:04,0:13:06,0:25:12,3:57:36,1420,82,6
+General Medicine,PGY1,S0169386,2014-07-12,3:37:14,0:25:27,0:36:58,1:20:59,0:15:33,1:00:20,8:02:35,2423,53,7
+General Medicine,PGY1,S0169386,2014-07-11,1:45:26,0:04:32,0:01:42,0:10:57,0:03:18,0:14:52,2:46:42,797,196,10
+General Medicine,PGY1,S0169386,2014-07-10,0:00:00,0:00:47,0:00:00,0:00:00,0:00:00,0:00:00,0:01:27,43,43,4
+General Medicine,PGY1,S0169386,2014-07-17,3:32:47,0:10:28,0:25:18,0:50:24,0:24:42,1:01:57,6:44:18,1906,13,6
+General Medicine,PGY1,S0169386,2014-07-16,3:02:26,0:06:54,0:15:43,0:13:28,0:09:55,0:28:23,4:28:40,1458,32,8
+General Medicine,PGY1,S0195237,2016-10-28,2:31:48,1:36:57,0:00:11,1:09:11,0:11:56,0:13:24,6:32:01,1644,12,13
+General Medicine,PGY1,S0195237,2016-10-29,0:20:08,0:23:52,0:00:04,0:02:19,0:01:56,0:00:03,0:59:53,609,603,12
+Night Team,PGY1,S0195206,2016-08-14,2:58:33,0:49:05,0:00:00,0:17:44,0:18:36,0:08:32,4:52:21,1402,30,28
+General Medicine,PGY1,S0181077,2015-12-20,2:48:09,0:55:31,0:53:36,0:55:55,0:34:03,0:29:30,7:13:03,2782,36,20
+Emergency Medicine,PGY1,S0181086,2015-10-08,2:11:41,0:06:13,2:35:20,0:13:00,0:19:56,0:00:18,6:09:16,1111,0,14
+Emergency Medicine,PGY1,S0181086,2015-10-09,1:56:33,0:12:26,2:24:06,0:32:10,0:07:38,0:04:43,5:43:35,924,241,15
+Emergency Medicine,PGY1,S0181086,2015-10-06,1:54:31,0:34:08,2:37:34,0:35:32,0:23:24,0:23:15,7:04:07,1539,88,18
+General Medicine,PGY1,S0106952,2015-03-29,4:20:13,0:39:34,1:11:44,1:09:37,0:41:40,0:39:31,9:09:28,2633,102,26
+Emergency Medicine,PGY1,S0181086,2015-10-04,3:08:24,0:30:40,4:01:09,0:31:42,0:33:56,0:18:41,9:40:55,1812,175,17
+Emergency Medicine,PGY1,S0181086,2015-10-05,1:50:30,0:17:57,5:11:35,0:21:31,0:09:42,0:12:49,8:34:12,1349,444,16
+Emergency Medicine,PGY1,S0181086,2015-10-02,0:53:50,0:30:47,2:34:42,0:08:55,0:00:15,0:06:46,4:41:50,738,512,20
+Emergency Medicine,PGY1,S0181086,2015-10-03,2:56:38,0:18:22,2:56:11,0:28:55,0:17:05,0:07:04,7:43:16,1428,3,15
+Emergency Medicine,PGY1,S0181086,2015-10-01,2:21:03,0:18:21,5:19:21,0:23:40,0:22:23,0:30:15,10:06:24,1493,100,18
+General Medicine,PGY2+,S0169401,2015-12-21,2:32:29,2:05:11,0:39:01,0:43:34,0:28:19,0:54:04,7:50:28,3932,917,33
+General Medicine,PGY2+,S0169401,2015-12-20,3:03:46,1:34:04,0:49:34,0:41:08,0:27:51,0:56:36,8:18:53,3779,487,31
+General Medicine,PGY2+,S0169401,2015-12-23,3:00:18,1:01:40,0:35:47,0:49:54,0:25:33,0:29:37,7:24:08,3568,371,38
+General Medicine,PGY2+,S0169401,2015-12-22,2:12:03,1:36:46,1:04:03,0:33:10,0:21:23,0:46:32,6:56:55,2945,130,29
+Night Team,PGY1,S0108268,2014-08-30,2:18:08,0:04:08,0:02:31,0:05:16,0:15:45,0:24:41,3:47:35,1424,1165,41
+General Medicine,PGY2+,S0169401,2015-12-24,0:32:12,0:36:55,0:02:32,0:16:09,0:06:31,0:08:11,1:45:51,870,846,30
+General Medicine,PGY2+,S0169401,2015-12-27,3:48:16,2:38:23,0:46:55,0:43:56,0:27:23,0:33:47,9:41:16,4232,396,46
+General Medicine,PGY2+,S0169401,2015-12-26,4:01:09,1:58:06,0:25:15,1:24:08,0:41:11,0:51:27,10:17:04,5099,139,32
+General Medicine,PGY2+,S0169401,2015-12-28,0:35:12,0:04:09,0:00:04,0:01:13,0:00:43,0:00:24,0:42:30,326,314,22
+Night Team,PGY1,S0195229,2016-07-09,1:08:15,0:25:20,0:00:02,0:27:28,0:16:54,0:00:13,2:33:53,793,0,18
+Night Team,PGY1,S0195229,2016-07-08,2:03:36,0:21:20,0:00:00,0:44:41,0:44:39,0:04:58,4:23:52,1065,538,29
+Night Team,PGY1,S0195229,2016-07-03,1:57:51,0:44:48,0:00:01,0:13:36,0:15:35,0:04:12,3:38:41,1042,272,27
+Night Team,PGY1,S0195229,2016-07-02,2:12:56,0:13:32,0:00:01,0:41:48,0:23:45,0:00:12,3:51:55,1541,154,24
+Night Team,PGY1,S0195229,2016-07-01,1:31:40,0:17:57,0:00:00,0:28:02,0:29:04,0:00:51,2:57:44,780,0,17
+Night Team,PGY1,S0195229,2016-07-07,1:07:21,0:30:49,0:00:00,0:17:19,0:28:54,0:04:38,2:35:36,749,614,18
+Night Team,PGY1,S0195229,2016-07-06,2:00:20,0:17:17,0:00:01,0:33:00,0:31:33,0:09:25,3:53:51,916,582,23
+Night Team,PGY1,S0195229,2016-07-05,1:03:30,0:09:58,0:00:00,0:21:24,0:12:37,0:01:44,1:52:07,411,273,14
+Night Team,PGY1,S0195229,2016-07-04,0:32:26,0:26:35,0:00:49,0:24:16,0:07:37,0:01:46,1:52:12,849,445,10
+General Medicine,PGY1,S0169389,2014-08-04,3:50:52,0:10:10,0:19:35,0:43:52,0:15:14,0:16:35,6:11:01,1726,18,21
+General Medicine,PGY1,S0169389,2014-08-05,0:14:34,0:00:00,0:00:08,0:00:04,0:00:00,0:00:00,0:15:17,152,139,25
+General Medicine,PGY1,S0169389,2014-08-06,4:04:01,0:10:06,0:08:40,0:33:22,0:22:00,0:29:52,6:05:27,1225,0,14
+General Medicine,PGY1,S0169389,2014-08-07,2:38:01,0:06:04,0:04:26,0:24:11,0:10:33,0:29:30,4:28:26,1207,138,15
+General Medicine,PGY1,S0169389,2014-08-01,3:41:18,0:14:31,0:18:48,0:38:37,0:27:48,0:25:34,6:30:03,1695,0,25
+Night Team,PGY1,S0195220,2016-11-09,0:00:00,0:00:00,0:00:00,0:00:20,0:00:00,0:00:00,0:00:20,9,0,3
+Night Team,PGY1,S0195220,2016-11-08,0:42:11,0:13:22,0:00:00,0:19:00,0:13:54,0:00:08,1:33:09,464,164,14
+Night Team,PGY1,S0195220,2016-11-07,2:09:09,0:53:01,0:00:00,0:49:47,0:33:33,0:08:14,4:53:44,1211,50,27
+Night Team,PGY1,S0195220,2016-11-06,1:55:40,0:51:34,0:00:07,0:38:52,0:27:18,0:00:18,4:21:19,890,0,21
+Night Team,PGY1,S0195220,2016-11-05,0:37:04,0:27:05,0:00:00,0:14:05,0:03:29,0:00:09,1:34:39,299,0,11
+Night Team,PGY1,S0195220,2016-11-04,1:22:40,0:15:22,0:00:00,0:20:16,0:11:16,0:00:57,2:22:08,684,0,24
+Night Team,PGY1,S0195220,2016-11-03,2:12:55,1:04:22,0:00:04,0:55:26,0:29:14,0:01:02,5:02:14,1379,45,23
+Night Team,PGY1,S0195220,2016-11-02,2:10:05,0:38:10,0:01:06,0:45:03,0:19:45,0:02:23,4:17:06,1220,0,21
+Night Team,PGY1,S0195220,2016-11-01,2:03:07,0:19:24,0:00:02,0:52:22,0:46:35,0:04:59,4:22:04,1362,0,27
+Night Team,PGY1,S0169399,2015-06-23,1:21:13,0:05:40,0:29:54,0:11:25,0:20:22,0:08:17,2:54:29,650,180,36
+Night Team,PGY1,S0169399,2015-06-25,1:07:38,0:47:00,2:10:11,0:31:31,0:02:50,0:23:37,5:08:52,791,0,10
+Intensive Care Unit,PGY2+,S0094959,2015-09-01,3:05:40,1:02:34,0:05:49,0:19:29,0:37:30,1:07:59,6:39:34,2830,191,24
+Intensive Care Unit,PGY2+,S0094959,2015-09-02,3:16:27,1:11:04,0:00:10,0:22:26,0:34:06,1:12:48,6:49:13,3071,266,20
+Intensive Care Unit,PGY2+,S0094959,2015-09-03,3:56:00,1:08:10,0:12:20,0:14:11,0:32:40,0:43:12,7:00:39,2794,77,19
+Intensive Care Unit,PGY2+,S0094959,2015-09-04,3:14:04,1:38:03,0:08:31,0:15:17,0:34:52,0:54:27,7:09:51,3055,332,32
+Intensive Care Unit,PGY2+,S0094959,2015-09-05,3:29:34,1:04:31,0:16:33,0:19:39,0:22:10,0:47:12,6:42:18,3010,244,29
+Emergency Medicine,PGY1,S0195076,2016-07-29,2:02:33,0:47:21,0:01:25,0:09:42,0:12:37,0:00:28,4:20:22,1565,0,21
+Intensive Care Unit,PGY2+,S0094959,2015-09-06,0:00:13,0:54:40,0:00:00,0:05:36,0:00:00,0:19:16,1:22:06,738,708,26
+Emergency Medicine,PGY1,S0181073,2016-02-29,3:22:01,0:32:36,0:05:05,0:55:36,0:54:15,0:08:04,6:50:39,1667,1,13
+General Medicine,PGY1,S0181060,2015-07-27,2:17:29,0:13:28,0:48:32,0:31:37,0:34:52,0:36:19,5:58:10,2113,193,17
+Emergency Medicine,PGY1,S0169406,2015-01-28,2:28:16,0:22:51,1:46:30,0:20:04,0:19:44,0:08:31,5:52:50,1705,0,10
+Emergency Medicine,PGY1,S0169406,2015-01-29,3:51:00,0:27:14,1:34:36,0:21:09,0:13:07,0:15:46,7:32:04,1684,0,11
+General Medicine,PGY1,S0181082,2016-04-19,2:49:19,1:37:11,0:01:41,0:34:12,0:12:30,0:06:13,5:57:31,2058,55,14
+General Medicine,PGY1,S0181082,2016-04-18,0:00:00,0:00:00,0:00:00,0:00:09,0:00:00,0:00:00,0:00:09,8,0,2
+General Medicine,PGY1,S0181082,2016-04-17,2:43:23,1:24:36,0:06:00,0:47:49,0:23:08,0:01:28,6:07:28,1728,0,13
+Emergency Medicine,PGY1,S0169406,2015-01-21,3:30:07,0:09:07,1:28:23,0:19:06,0:11:40,0:12:44,6:07:49,1550,0,10
+Emergency Medicine,PGY1,S0169406,2015-01-22,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0181082,2016-04-14,3:52:13,2:03:22,0:01:13,1:18:26,0:19:00,0:08:03,8:24:02,2323,0,15
+Emergency Medicine,PGY1,S0169406,2015-01-24,1:58:07,0:02:57,0:34:20,0:00:50,0:05:24,0:02:36,2:50:52,627,0,11
+Emergency Medicine,PGY1,S0169406,2015-01-25,4:49:13,0:01:45,1:24:11,0:09:41,0:22:57,0:12:16,7:25:44,1701,0,43
+Emergency Medicine,PGY1,S0169406,2015-01-26,3:43:50,0:04:37,1:27:46,0:17:01,0:16:02,0:05:08,6:19:10,1985,0,41
+Emergency Medicine,PGY1,S0169406,2015-01-27,2:13:04,0:30:23,2:11:07,0:13:11,0:11:55,0:03:21,5:36:07,1416,69,20
+General Medicine,PGY1,S0195214,2016-08-20,0:13:28,0:19:04,0:00:00,0:04:20,0:05:38,0:00:17,0:49:21,176,0,3
+Intensive Care Unit,PGY2+,S0109179,2016-02-18,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,8,0,3
+Intensive Care Unit,PGY2+,S0109179,2016-02-19,1:58:15,1:15:30,0:18:27,0:34:28,0:15:24,0:28:31,5:20:26,2084,0,35
+Intensive Care Unit,PGY2+,S0109179,2016-02-10,1:58:28,1:01:00,0:19:38,0:19:07,0:18:46,0:28:44,4:35:16,1679,103,14
+Intensive Care Unit,PGY2+,S0109179,2016-02-11,1:37:07,0:49:20,0:11:06,0:28:44,0:13:01,0:21:39,4:01:23,1616,0,18
+Intensive Care Unit,PGY2+,S0109179,2016-02-12,1:45:32,0:44:10,0:45:31,0:17:54,0:21:53,0:44:50,5:03:40,1744,59,18
+Intensive Care Unit,PGY2+,S0109179,2016-02-13,1:37:24,0:31:46,0:05:30,0:13:04,0:18:00,0:08:58,3:04:26,1105,0,13
+Intensive Care Unit,PGY2+,S0109179,2016-02-14,0:00:00,0:00:00,0:00:00,0:00:05,0:00:00,0:00:00,0:00:05,28,0,4
+Intensive Care Unit,PGY2+,S0109179,2016-02-15,2:34:15,0:49:57,0:18:33,0:25:19,0:22:35,0:30:16,5:32:44,1828,0,20
+Intensive Care Unit,PGY2+,S0109179,2016-02-16,2:21:30,1:22:56,0:15:01,0:23:15,0:29:53,0:48:32,6:02:29,1972,0,26
+Intensive Care Unit,PGY2+,S0109179,2016-02-17,0:59:43,0:12:11,0:03:34,0:06:40,0:10:11,0:11:07,1:51:21,688,0,15
+General Medicine,PGY1,S0056755,2015-03-24,1:09:06,0:13:10,0:36:41,0:17:44,0:07:36,0:05:07,2:45:06,772,0,8
+General Medicine,PGY1,S0056755,2015-03-25,1:42:39,0:05:16,2:22:13,0:19:01,0:09:15,0:15:36,5:11:53,1332,0,23
+General Medicine,PGY1,S0056755,2015-03-26,2:55:14,0:36:52,0:59:33,0:39:31,0:32:17,0:26:55,6:26:48,2108,7,15
+General Medicine,PGY1,S0056755,2015-03-27,3:30:00,0:16:56,1:08:48,0:26:01,0:13:24,0:10:09,6:11:00,2028,0,13
+General Medicine,PGY1,S0056755,2015-03-20,1:12:08,0:02:14,0:44:06,0:18:21,0:05:18,0:02:32,2:37:57,1524,0,12
+General Medicine,PGY1,S0056755,2015-03-21,2:53:54,0:20:24,1:24:57,0:41:15,0:21:17,0:21:01,6:26:01,1991,0,14
+General Medicine,PGY1,S0056755,2015-03-22,0:00:00,0:00:00,0:00:00,0:00:31,0:00:00,0:00:00,0:00:31,8,0,3
+General Medicine,PGY1,S0056755,2015-03-23,3:29:24,0:40:23,0:54:14,0:51:09,0:31:55,0:13:37,7:19:08,2091,0,15
+General Medicine,PGY1,S0056755,2015-03-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0169394,2014-09-16,2:57:42,0:08:07,0:16:28,0:38:37,0:23:08,0:36:30,5:40:37,1333,0,29
+Night Team,PGY1,S0169394,2014-09-17,2:17:08,0:05:17,0:10:03,0:22:47,0:16:55,0:30:00,3:54:28,1058,0,31
+Night Team,PGY1,S0169394,2014-09-14,2:07:46,0:02:29,0:13:38,0:25:13,0:29:35,0:26:57,4:05:31,1138,15,17
+Night Team,PGY1,S0169394,2014-09-15,1:26:57,0:03:47,0:07:19,0:33:58,0:19:15,0:37:50,3:35:32,911,0,23
+Night Team,PGY1,S0169394,2014-09-18,2:16:07,0:09:06,0:19:42,0:49:14,0:47:18,0:38:08,5:27:14,1597,20,34
+Night Team,PGY1,S0169394,2014-09-19,2:06:55,0:10:10,0:01:01,0:23:07,0:31:42,0:32:13,3:53:20,1037,0,30
+General Medicine,PGY1,S0181063,2015-12-31,0:13:00,0:33:27,1:13:47,0:25:54,0:11:00,0:12:22,3:07:31,1159,0,7
+Intensive Care Unit,PGY2+,S0169383,2015-10-28,2:00:25,1:07:43,0:05:30,0:19:38,0:14:21,0:22:47,4:34:36,1679,48,27
+Intensive Care Unit,PGY2+,S0169383,2015-10-29,1:59:12,0:34:32,0:10:40,0:23:48,0:16:14,0:17:46,4:10:24,1172,30,19
+General Medicine,PGY1,S0181526,2016-04-30,2:00:42,1:34:09,0:02:03,0:13:22,0:19:00,0:00:45,5:08:40,1309,239,22
+Intensive Care Unit,PGY2+,S0169383,2015-10-21,1:30:51,0:35:33,0:11:25,0:22:01,0:15:13,0:07:21,3:35:49,1299,0,16
+Intensive Care Unit,PGY2+,S0169383,2015-10-22,1:32:27,1:22:17,0:03:17,0:33:16,0:21:50,0:18:23,4:41:05,1737,40,31
+Intensive Care Unit,PGY2+,S0169383,2015-10-23,2:06:54,0:55:21,0:16:49,0:21:46,0:14:43,0:17:10,4:40:10,1413,0,16
+Intensive Care Unit,PGY2+,S0169383,2015-10-24,0:00:00,0:00:00,0:00:00,0:00:17,0:00:00,0:00:00,0:00:17,4,0,2
+Intensive Care Unit,PGY2+,S0169383,2015-10-25,1:29:35,0:52:53,0:17:05,0:14:43,0:23:26,0:27:10,4:13:57,1398,0,13
+Intensive Care Unit,PGY2+,S0169383,2015-10-26,2:11:17,1:01:21,0:09:48,0:32:15,0:19:07,0:36:19,5:26:40,1638,142,20
+Intensive Care Unit,PGY2+,S0169383,2015-10-27,2:04:18,1:31:38,0:21:19,0:21:22,0:32:57,0:16:37,5:43:50,1944,4,18
+Intensive Care Unit,PGY2+,S0108287,2016-01-05,1:47:09,0:57:28,1:15:42,0:14:46,0:29:47,0:31:29,5:45:48,1316,0,27
+Night Team,PGY2+,S0169406,2016-11-04,1:42:52,0:20:44,0:00:02,0:10:22,0:06:19,0:13:44,2:54:56,1033,0,38
+Night Team,PGY2+,S0169406,2016-11-05,1:22:59,0:09:03,0:00:19,0:14:32,0:12:22,0:05:55,2:18:37,823,0,30
+Night Team,PGY2+,S0169406,2016-11-01,2:13:56,0:59:43,0:01:18,0:19:13,0:09:02,0:08:45,4:16:38,1730,0,30
+Night Team,PGY2+,S0169406,2016-11-02,1:36:06,0:26:31,0:00:06,0:11:34,0:02:00,0:00:25,2:41:31,731,69,15
+Night Team,PGY2+,S0169406,2016-11-03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0172645,2015-10-03,0:21:23,0:42:07,1:02:38,0:07:51,0:05:33,0:06:12,2:35:52,782,10,7
+General Medicine,PGY1,S0172645,2015-10-02,0:50:12,0:41:05,1:38:07,0:17:02,0:19:00,0:18:49,4:25:42,966,7,7
+General Medicine,PGY1,S0172645,2015-10-01,0:50:11,0:57:53,2:28:34,0:51:05,0:27:26,0:27:06,6:19:55,2059,0,13
+General Medicine,PGY1,S0172645,2015-10-07,0:00:00,0:02:49,0:00:00,0:03:05,0:00:00,0:00:00,0:06:14,51,43,5
+General Medicine,PGY1,S0172645,2015-10-06,1:06:35,1:00:20,1:53:18,0:45:18,0:25:11,0:32:33,6:12:48,1980,26,13
+General Medicine,PGY1,S0172645,2015-10-05,0:30:40,0:55:18,1:05:37,0:29:54,0:15:27,0:12:45,3:51:38,1033,113,7
+General Medicine,PGY1,S0172645,2015-10-04,0:44:53,0:49:50,0:48:47,0:17:48,0:18:04,0:10:06,3:22:06,1332,0,12
+General Medicine,PGY1,S0172645,2015-10-09,1:48:11,1:05:15,1:50:05,1:03:55,0:40:49,0:17:57,7:07:03,2388,51,10
+General Medicine,PGY1,S0172645,2015-10-08,0:58:46,1:06:14,1:05:11,0:27:23,0:41:50,0:17:40,5:06:56,1617,51,11
+General Medicine,PGY1,S0169401,2014-10-09,3:37:35,0:15:25,0:14:13,0:58:09,0:26:10,0:06:25,6:13:00,2157,206,28
+Intensive Care Unit,PGY2+,S0108287,2016-11-06,1:32:40,0:18:58,0:18:22,0:05:50,0:13:33,0:05:00,3:10:48,613,0,13
+Intensive Care Unit,PGY2+,S0108287,2016-11-07,1:54:06,1:03:15,0:16:48,0:23:01,0:30:47,0:06:15,4:50:29,1076,152,21
+Intensive Care Unit,PGY2+,S0108287,2016-11-08,2:18:43,1:42:11,0:04:15,0:42:07,0:34:26,0:02:19,5:58:16,1837,838,17
+Intensive Care Unit,PGY2+,S0108287,2016-11-09,1:39:33,0:38:22,0:00:22,0:15:30,0:12:01,0:00:49,3:06:17,1102,905,15
+Emergency Medicine,PGY1,S0056755,2014-06-26,0:25:27,0:00:00,0:00:04,0:01:10,0:05:00,0:00:05,0:31:47,106,0,2
+General Medicine,PGY1,S0181087,2015-07-30,2:57:34,0:02:05,1:12:30,0:54:16,0:37:55,0:40:05,7:04:31,2426,42,8
+General Medicine,PGY1,S0181087,2015-07-31,3:03:08,0:04:49,0:44:04,1:58:43,0:21:03,0:28:50,7:22:18,2438,0,14
+Intensive Care Unit,PGY2+,S0056755,2016-06-13,2:25:43,1:35:38,0:26:20,0:27:58,0:26:44,0:00:20,5:40:33,1558,15,12
+General Medicine,PGY1,S0195227,2016-09-12,3:09:07,1:40:46,0:00:05,0:56:27,0:24:25,0:01:09,6:52:53,2284,0,15
+General Medicine,PGY1,S0195227,2016-09-13,2:23:58,1:43:25,0:00:13,1:02:09,0:13:43,0:00:35,6:08:08,2395,0,19
+Night Team,PGY2+,S0169386,2016-04-03,3:57:03,1:26:10,0:00:43,0:46:29,0:16:34,0:09:17,7:19:42,2806,11,78
+Night Team,PGY2+,S0169386,2016-04-07,1:53:53,0:41:40,0:05:04,0:28:08,0:05:21,0:01:50,3:37:54,1678,49,43
+Night Team,PGY2+,S0169386,2016-04-06,2:32:35,1:00:13,0:00:17,0:28:50,0:06:54,0:06:25,4:41:21,1804,0,70
+Night Team,PGY2+,S0169386,2016-04-05,2:56:54,1:19:39,0:16:46,0:35:26,0:12:02,0:04:26,6:05:30,2389,120,94
+Night Team,PGY2+,S0169386,2016-04-04,3:58:30,1:39:04,0:02:27,0:27:06,0:18:24,0:04:19,7:19:18,3558,110,148
+Night Team,PGY2+,S0169386,2016-04-09,3:44:03,1:45:56,0:11:54,0:55:56,0:17:19,0:14:46,8:25:05,3504,62,111
+Night Team,PGY2+,S0169386,2016-04-08,3:37:01,1:35:50,0:04:53,1:24:11,0:27:12,0:02:44,8:07:48,2986,38,55
+General Medicine,PGY2+,S0169398,2016-03-31,2:34:20,1:19:40,0:00:03,0:22:05,0:18:56,0:01:31,5:10:26,1963,186,20
+General Medicine,PGY1,S0181087,2016-02-23,2:03:19,2:51:15,0:00:25,1:10:46,0:28:16,0:16:02,7:32:52,3378,26,14
+General Medicine,PGY1,S0181087,2016-02-22,0:01:57,0:04:51,0:00:00,0:00:43,0:00:00,0:00:02,0:08:37,221,211,8
+General Medicine,PGY1,S0181087,2016-02-21,3:02:59,1:42:43,0:00:38,0:43:13,0:23:38,0:01:28,6:20:44,2495,58,13
+General Medicine,PGY1,S0181087,2016-02-20,1:41:29,1:05:05,0:00:12,0:56:33,0:14:55,0:09:35,4:33:39,1528,31,9
+General Medicine,PGY1,S0181087,2016-02-27,0:03:08,0:05:05,0:00:00,0:00:17,0:02:32,0:00:01,0:11:27,119,109,7
+General Medicine,PGY1,S0181087,2016-02-26,2:29:58,1:38:19,0:00:09,1:14:16,0:34:31,0:10:32,6:57:20,2716,11,10
+General Medicine,PGY1,S0181087,2016-02-25,1:37:14,2:05:20,0:01:13,0:42:47,0:24:28,0:02:33,5:34:38,2256,113,16
+General Medicine,PGY1,S0181087,2016-02-24,2:11:33,1:43:28,0:02:58,0:55:24,0:23:22,0:04:19,6:22:06,2577,0,15
+Night Team,PGY1,S0108287,2015-06-24,3:20:56,0:52:05,2:01:43,0:21:24,0:16:21,0:16:23,7:23:03,1561,0,36
+Emergency Medicine,PGY2+,S0169395,2015-12-29,3:15:25,1:01:09,2:54:26,0:32:49,0:33:12,1:07:21,9:56:56,3286,0,24
+Emergency Medicine,PGY1,S0195076,2016-07-27,2:36:37,0:31:39,0:00:27,0:34:31,0:36:28,0:01:45,5:05:31,1417,0,10
+General Medicine,PGY2+,S0181065,2016-10-08,2:45:39,0:45:40,0:00:00,0:13:56,0:19:20,0:06:20,4:16:56,1089,813,18
+Emergency Medicine,PGY1,S0074196,2015-10-11,1:51:20,0:32:21,1:26:19,0:10:19,0:12:08,0:26:36,5:22:12,1295,0,28
+General Medicine,PGY2+,S0181065,2016-10-02,4:50:07,1:05:23,0:00:16,0:48:57,0:27:45,0:03:51,8:04:22,5049,143,20
+General Medicine,PGY2+,S0181065,2016-10-03,3:19:34,0:27:56,0:00:03,0:43:13,0:21:19,0:03:49,5:47:29,3476,0,15
+General Medicine,PGY2+,S0181065,2016-10-01,3:55:32,1:14:59,0:00:24,0:51:45,0:19:26,0:02:58,7:07:40,3841,0,19
+General Medicine,PGY2+,S0181065,2016-10-06,2:32:24,0:30:44,0:00:00,0:20:59,0:23:46,0:05:16,4:19:49,2687,27,14
+General Medicine,PGY2+,S0181065,2016-10-07,6:50:46,1:54:47,0:00:06,0:43:29,0:39:32,0:21:17,11:43:28,8387,24,234
+General Medicine,PGY2+,S0181065,2016-10-04,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,4,0,2
+General Medicine,PGY2+,S0181065,2016-10-05,4:50:58,1:23:07,0:00:00,0:27:01,0:24:11,0:11:29,8:02:32,5792,0,328
+General Medicine,PGY1,S0181067,2015-06-26,2:42:25,0:11:47,1:36:59,0:42:17,0:38:06,0:13:48,6:29:54,1360,0,8
+General Medicine,PGY1,S0181067,2015-06-27,0:00:00,0:00:00,0:00:00,0:00:21,0:00:00,0:00:00,0:00:21,4,0,2
+General Medicine,PGY1,S0169411,2014-10-10,4:42:58,0:10:51,0:16:27,0:44:10,0:17:38,0:05:29,6:26:47,2846,22,679
+General Medicine,PGY1,S0169411,2014-10-11,4:01:36,0:04:26,0:09:30,0:35:31,0:29:42,0:01:01,5:39:19,1552,0,12
+General Medicine,PGY1,S0181067,2015-06-28,2:21:11,0:10:17,1:38:00,0:16:45,0:40:44,0:21:15,5:59:05,1222,0,8
+General Medicine,PGY1,S0181067,2015-06-29,1:19:03,0:00:24,0:40:41,0:36:58,0:25:50,0:14:16,3:29:57,780,0,5
+General Medicine,PGY2+,S0169394,2016-03-01,2:22:15,0:27:17,0:00:01,0:11:57,0:13:40,0:01:15,3:35:16,1083,108,17
+General Medicine,PGY2+,S0169394,2016-03-03,2:09:59,0:15:41,0:00:01,0:09:41,0:10:46,0:03:38,3:25:26,1248,64,13
+General Medicine,PGY2+,S0169394,2016-03-02,2:16:09,0:48:24,0:00:01,0:28:54,0:11:39,0:07:50,4:28:49,1959,334,20
+General Medicine,PGY2+,S0169394,2016-03-05,0:14:53,0:10:46,0:00:00,0:00:00,0:00:00,0:00:03,0:29:49,204,202,22
+General Medicine,PGY2+,S0169394,2016-03-04,0:31:14,0:07:13,0:00:00,0:00:00,0:00:13,0:00:36,0:43:12,265,263,12
+Night Team,PGY1,S0181064,2016-01-29,0:50:22,0:48:20,2:21:48,0:22:05,0:23:21,0:11:44,5:19:09,1155,99,46
+Night Team,PGY1,S0181064,2016-01-28,1:28:12,1:05:11,1:05:23,0:21:46,0:20:33,0:17:44,5:04:03,1706,171,83
+General Medicine,PGY1,S0181085,2015-10-27,1:48:20,1:31:38,1:26:58,1:04:34,0:39:51,0:27:50,7:41:10,2735,234,11
+General Medicine,PGY1,S0181085,2015-10-26,0:24:28,0:46:21,0:41:11,0:24:04,0:08:32,0:08:24,2:42:03,944,0,6
+General Medicine,PGY1,S0181085,2015-10-21,0:00:00,0:04:45,0:00:00,0:01:38,0:00:00,0:00:00,0:06:38,65,57,5
+General Medicine,PGY1,S0181085,2015-10-20,0:54:21,1:50:11,3:25:04,0:45:55,0:32:40,0:25:31,8:19:37,2403,446,12
+General Medicine,PGY1,S0181085,2015-10-23,1:28:04,1:14:43,1:16:22,0:37:24,0:32:24,0:30:48,6:09:21,1913,195,11
+General Medicine,PGY1,S0181085,2015-10-22,1:11:29,2:15:41,3:08:43,0:40:58,0:18:55,0:26:43,8:25:26,2428,255,19
+Night Team,PGY1,S0181064,2016-01-21,0:51:05,0:41:50,1:41:06,0:28:06,0:19:35,0:36:22,4:58:59,1318,0,28
+Night Team,PGY1,S0181064,2016-01-20,1:28:08,1:15:30,1:40:18,0:26:44,0:17:25,0:27:40,5:49:43,1709,0,37
+Night Team,PGY1,S0181064,2016-01-23,2:00:05,0:38:56,0:47:29,0:38:18,0:35:33,0:36:02,5:44:41,1651,164,50
+Night Team,PGY1,S0181064,2016-01-22,0:43:07,0:36:14,0:13:23,0:08:12,0:10:44,0:16:59,2:18:41,882,152,49
+Night Team,PGY1,S0181064,2016-01-25,1:36:42,1:26:10,2:19:50,0:57:06,0:35:00,0:22:44,7:49:52,2067,1201,55
+Night Team,PGY1,S0181064,2016-01-24,1:33:12,0:55:21,1:53:15,0:28:32,0:21:35,0:24:41,5:57:27,1679,1555,49
+Night Team,PGY1,S0181064,2016-01-27,1:03:33,0:51:51,0:17:25,0:35:48,0:29:50,0:15:32,3:55:52,1697,0,78
+Night Team,PGY1,S0181064,2016-01-26,1:10:07,1:07:11,0:46:59,0:33:08,0:20:22,0:22:36,4:46:26,1535,139,69
+Intensive Care Unit,PGY2+,S0172056,2016-11-01,1:26:34,0:52:39,0:06:50,0:26:24,0:38:04,0:01:31,4:06:25,1107,0,19
+General Medicine,PGY2+,S0169390,2016-07-29,4:14:36,0:56:22,0:00:30,1:02:58,0:31:51,0:18:11,8:04:38,3029,0,18
+Intensive Care Unit,PGY2+,S0172056,2016-11-03,1:28:38,0:25:00,0:03:03,0:33:14,0:25:09,0:01:37,3:21:21,1310,0,15
+Intensive Care Unit,PGY2+,S0172056,2016-11-02,1:47:20,0:40:29,0:00:05,0:20:11,0:12:07,0:00:15,3:20:16,1254,0,28
+Intensive Care Unit,PGY2+,S0172056,2016-11-04,1:35:39,0:35:56,0:11:37,0:15:54,0:24:51,0:03:23,3:29:12,1046,46,18
+General Medicine,PGY2+,S0169390,2016-07-28,4:07:31,1:50:39,0:00:05,0:45:33,0:36:21,0:10:00,8:26:37,2927,0,29
+General Medicine,PGY2+,S0108287,2016-05-14,1:25:22,0:38:09,0:00:05,0:39:26,0:23:56,0:00:32,3:48:33,1340,4,19
+General Medicine,PGY2+,S0108287,2016-05-15,1:33:52,1:12:55,0:00:03,0:17:22,0:19:00,0:00:14,4:01:19,1628,0,23
+General Medicine,PGY1,S0169385,2015-05-19,2:17:09,0:29:27,2:03:50,0:54:07,0:21:00,0:56:07,7:38:13,1864,13,11
+General Medicine,PGY1,S0169385,2015-05-18,1:42:25,0:26:26,1:27:01,0:53:45,0:26:29,0:19:39,5:53:43,2403,0,14
+General Medicine,PGY1,S0169385,2015-05-15,0:12:30,0:06:41,1:25:27,0:00:00,0:00:03,0:00:00,1:45:20,175,171,5
+General Medicine,PGY1,S0169385,2015-05-14,2:55:18,0:31:22,1:25:29,0:40:20,0:24:08,0:11:11,6:24:00,1967,0,8
+General Medicine,PGY1,S0169385,2015-05-17,2:29:32,0:16:44,2:01:32,0:28:45,0:16:36,0:18:03,6:16:05,1770,0,21
+General Medicine,PGY1,S0169385,2015-05-16,1:53:39,0:06:34,1:42:14,0:33:43,0:16:31,0:15:01,4:59:50,1412,315,5
+General Medicine,PGY1,S0169385,2015-05-11,3:01:19,0:22:25,1:28:17,0:39:59,0:36:01,0:20:19,7:15:54,2674,0,18
+General Medicine,PGY1,S0169385,2015-05-10,0:03:47,0:09:47,0:19:15,0:00:00,0:00:32,0:02:15,0:39:59,239,233,5
+General Medicine,PGY1,S0169385,2015-05-13,1:44:09,0:01:28,1:38:11,0:38:24,0:13:24,0:20:37,4:52:55,1472,0,9
+General Medicine,PGY1,S0169385,2015-05-12,2:52:32,0:31:57,1:13:39,0:52:13,0:15:37,0:44:39,7:01:32,2188,0,11
+Emergency Medicine,PGY1,S0169404,2014-12-12,0:00:00,0:00:05,0:00:00,0:00:00,0:00:00,0:02:25,0:02:33,19,19,1
+General Medicine,PGY2+,S0108287,2016-05-18,3:25:22,1:31:24,0:00:03,0:35:18,0:26:48,0:12:52,6:51:57,2144,0,25
+Emergency Medicine,PGY1,S0169404,2014-12-10,1:53:13,0:30:27,1:23:54,0:08:18,0:20:07,0:40:49,5:22:34,1854,0,19
+Emergency Medicine,PGY1,S0169404,2014-12-11,2:37:48,0:39:05,1:54:30,0:16:40,0:20:10,0:27:24,6:34:05,2241,51,16
+Emergency Medicine,PGY1,S0169404,2014-12-16,2:10:04,0:22:24,1:22:18,0:14:57,0:14:56,0:17:26,4:51:17,1619,100,11
+Emergency Medicine,PGY1,S0169404,2014-12-17,0:00:00,0:02:10,0:00:00,0:00:00,0:00:00,0:01:14,0:03:45,40,40,3
+Emergency Medicine,PGY1,S0169404,2014-12-14,2:27:38,0:22:35,2:41:14,0:21:22,0:34:44,0:39:14,7:52:46,3022,0,21
+General Medicine,PGY2+,S0108287,2016-05-19,2:08:04,1:01:45,0:01:13,0:35:35,0:27:01,0:01:05,5:04:01,1780,453,25
+Emergency Medicine,PGY1,S0169404,2014-12-18,3:39:55,0:25:52,1:46:38,0:26:11,0:29:15,0:45:02,7:55:36,2778,30,22
+Emergency Medicine,PGY1,S0169404,2014-12-19,3:59:59,0:04:50,1:56:59,0:34:24,0:30:37,0:20:19,7:55:27,2602,0,21
+General Medicine,PGY2+,S0169384,2016-06-30,1:10:13,0:47:04,0:00:15,0:20:57,0:06:17,0:00:59,2:32:30,943,286,17
+Emergency Medicine,PGY1,S0056755,2014-07-09,4:50:42,0:17:40,0:14:22,0:06:59,0:13:42,0:00:21,6:02:46,1864,0,29
+Emergency Medicine,PGY1,S0056755,2014-07-08,3:46:32,0:10:12,0:10:30,0:13:30,0:03:28,0:00:10,4:47:52,1078,0,15
+General Medicine,PGY1,S0196595,2016-06-30,0:00:00,0:00:00,0:00:00,0:00:19,0:00:00,0:00:00,0:00:19,4,0,2
+Emergency Medicine,PGY1,S0056755,2014-07-06,5:55:18,0:25:40,0:09:12,0:06:02,0:05:39,0:01:05,7:00:22,1765,0,46
+Emergency Medicine,PGY1,S0056755,2014-07-05,5:37:57,0:54:43,0:08:05,0:08:38,0:01:34,0:16:17,7:37:37,1868,0,40
+Emergency Medicine,PGY1,S0056755,2014-07-04,2:30:33,0:24:58,0:14:18,0:05:50,0:13:27,0:03:41,3:45:09,1053,0,26
+Emergency Medicine,PGY1,S0056755,2014-07-02,3:56:03,0:05:48,0:10:40,0:17:35,0:06:08,0:17:17,5:03:14,882,0,23
+Emergency Medicine,PGY1,S0056755,2014-07-01,2:58:01,0:26:23,0:09:27,0:07:00,0:08:54,0:03:01,4:05:47,1347,0,29
+Night Team,PGY1,S0108287,2015-06-07,1:38:27,0:10:15,0:11:55,0:18:18,0:31:28,0:18:23,3:30:57,2687,1597,27
+Night Team,PGY1,S0169400,2015-03-11,2:15:05,0:05:48,1:19:16,0:31:00,0:13:41,0:18:19,4:53:51,1167,0,63
+Night Team,PGY1,S0169400,2015-03-10,1:34:05,0:13:03,0:16:23,0:25:09,0:12:17,0:34:34,3:24:54,1025,17,65
+Night Team,PGY1,S0169400,2015-03-13,1:48:38,0:15:28,0:54:28,0:30:44,0:31:30,0:36:09,4:46:35,1206,24,63
+Night Team,PGY1,S0169400,2015-03-12,2:55:25,0:21:04,2:04:40,0:30:28,0:23:06,0:26:08,6:55:44,1506,11,68
+Night Team,PGY1,S0169400,2015-03-14,2:25:43,0:21:50,0:24:40,0:27:24,0:34:21,0:17:44,4:47:23,1450,34,37
+General Medicine,PGY1,S0173803,2016-01-07,0:06:00,0:02:34,0:00:31,0:00:09,0:00:02,0:01:12,0:12:35,117,105,13
+General Medicine,PGY1,S0173803,2016-01-06,1:50:44,1:05:22,2:11:30,1:20:04,0:23:19,0:45:22,8:12:51,2574,14,17
+General Medicine,PGY1,S0173803,2016-01-05,0:57:32,0:46:41,0:47:47,0:46:21,0:15:15,0:19:16,4:22:38,1673,136,10
+General Medicine,PGY1,S0173803,2016-01-04,1:09:39,0:42:26,1:18:50,0:49:27,0:29:24,0:18:50,5:17:43,1597,0,13
+General Medicine,PGY1,S0173803,2016-01-03,2:11:52,0:49:42,1:55:54,0:44:11,0:56:46,0:58:01,8:26:25,2598,0,14
+General Medicine,PGY1,S0173803,2016-01-09,1:51:59,0:49:25,0:42:23,0:38:58,0:28:37,0:18:28,5:25:34,2427,55,11
+General Medicine,PGY1,S0173803,2016-01-08,2:07:17,0:51:38,1:20:58,0:47:39,0:24:58,0:37:58,6:39:30,2433,0,17
+General Medicine,PGY1,S0195223,2016-08-31,1:11:33,1:03:07,0:00:41,0:27:33,0:21:09,0:05:18,3:36:33,1242,0,6
+General Medicine,PGY1,S0195223,2016-08-30,1:51:55,1:50:24,0:00:18,0:29:19,0:14:03,0:04:59,5:20:20,1634,0,10
+Emergency Medicine,PGY2+,S0079337,2016-03-01,3:03:12,0:42:59,0:07:13,0:52:50,1:06:05,0:09:07,6:41:54,3229,0,28
+Emergency Medicine,PGY2+,S0079337,2016-03-02,0:55:28,0:14:29,0:01:00,0:13:12,0:24:30,0:00:40,2:01:52,836,0,10
+Emergency Medicine,PGY2+,S0079337,2016-03-03,2:55:02,0:37:32,0:15:13,0:34:15,1:17:20,0:13:01,6:36:07,2591,0,31
+Emergency Medicine,PGY2+,S0079337,2016-03-04,0:54:33,0:02:52,0:05:01,0:08:41,0:28:50,0:01:39,1:53:20,750,0,9
+Night Team,PGY1,S0195206,2016-08-16,3:50:36,0:50:56,0:00:07,0:36:07,0:36:47,0:13:34,7:01:34,2179,145,46
+Night Team,PGY1,S0195206,2016-08-17,2:40:34,0:46:01,0:00:04,0:19:11,0:26:01,0:05:20,4:39:15,1393,0,36
+Night Team,PGY1,S0181065,2016-05-20,2:21:20,0:06:00,0:00:00,0:06:29,0:12:19,0:04:07,2:53:50,1153,0,59
+Night Team,PGY1,S0181065,2016-05-21,1:11:52,0:35:55,0:00:19,0:11:24,0:11:57,0:11:27,2:42:56,993,40,16
+Emergency Medicine,PGY1,S0188098,2016-08-22,4:24:50,0:49:05,0:11:59,0:23:37,0:17:08,0:03:31,6:53:44,1480,0,57
+Emergency Medicine,PGY1,S0169404,2014-12-20,1:14:22,0:08:53,1:04:21,0:05:12,0:18:41,0:15:18,3:14:51,1175,10,21
+General Medicine,PGY1,S0169406,2015-05-08,1:01:48,0:05:43,0:16:11,0:18:55,0:04:58,0:03:48,2:06:25,799,0,5
+General Medicine,PGY1,S0169406,2015-05-09,2:21:11,0:03:58,0:38:28,0:29:52,0:26:22,0:16:13,4:38:32,1328,0,10
+General Medicine,PGY1,S0169406,2015-05-06,2:01:19,0:04:34,0:18:30,0:04:19,0:08:17,0:11:09,3:02:35,937,14,6
+General Medicine,PGY1,S0169406,2015-05-07,2:21:04,0:24:27,0:17:38,0:36:10,0:14:36,0:18:19,4:31:03,1543,117,10
+General Medicine,PGY1,S0169406,2015-05-04,2:09:40,0:04:01,0:54:20,0:12:04,0:13:28,0:30:37,4:28:35,1164,0,14
+General Medicine,PGY1,S0169406,2015-05-05,2:06:34,0:09:01,0:03:47,0:49:05,0:20:38,0:18:29,4:29:06,1894,37,13
+General Medicine,PGY1,S0169406,2015-05-02,2:10:06,0:21:21,0:05:56,0:25:16,0:17:27,0:14:38,4:03:34,1257,0,9
+General Medicine,PGY1,S0169406,2015-05-03,0:03:04,0:00:12,0:23:21,0:00:22,0:00:06,0:00:04,0:29:40,103,95,6
+General Medicine,PGY1,S0169406,2015-05-01,1:19:17,0:04:17,0:04:19,0:11:18,0:04:32,0:14:37,2:19:12,628,0,19
+General Medicine,PGY1,S0106952,2014-10-11,3:43:26,0:12:52,0:52:25,0:27:03,0:33:19,1:04:00,7:18:58,1823,121,18
+General Medicine,PGY1,S0106952,2014-10-10,5:03:57,0:21:35,0:17:54,0:28:17,0:29:18,0:12:04,7:32:37,2370,347,37
+Night Team,PGY2+,S0181060,2016-11-04,2:12:58,1:52:05,0:00:18,0:40:21,0:12:01,0:05:09,5:44:00,1800,0,21
+Night Team,PGY2+,S0181060,2016-11-05,1:18:55,1:40:17,0:00:49,0:23:05,0:10:29,0:02:08,4:13:45,1392,0,20
+Night Team,PGY2+,S0181060,2016-11-02,1:11:52,0:42:20,0:00:06,0:14:53,0:04:51,0:00:12,2:49:35,962,233,22
+Night Team,PGY2+,S0181060,2016-11-03,1:19:34,1:05:53,0:00:13,0:44:43,0:17:05,0:01:11,4:11:19,1472,0,30
+Night Team,PGY2+,S0181060,2016-11-01,0:35:04,0:31:47,0:00:04,0:22:03,0:06:50,0:00:19,1:45:49,566,0,15
+Emergency Medicine,PGY2+,S0181056,2016-09-02,3:14:16,1:13:31,0:01:21,0:30:59,0:21:21,0:18:10,6:25:29,2518,11,51
+Emergency Medicine,PGY2+,S0181056,2016-09-03,3:41:46,1:02:27,0:07:14,0:21:21,0:27:20,0:03:59,6:45:01,2339,0,37
+Emergency Medicine,PGY1,S0188098,2016-08-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY2+,S0181056,2016-09-06,0:37:37,0:50:45,0:12:24,0:04:16,0:00:48,0:05:42,2:20:53,825,6,8
+Emergency Medicine,PGY2+,S0181056,2016-09-07,4:08:39,1:01:29,0:04:25,0:51:52,0:46:29,0:01:14,7:28:30,2369,0,53
+Emergency Medicine,PGY2+,S0181056,2016-09-04,3:25:24,0:44:09,0:04:42,0:50:35,0:33:52,0:07:03,6:39:32,2335,0,28
+Emergency Medicine,PGY2+,S0181056,2016-09-05,1:31:16,0:18:39,0:00:20,0:06:06,0:16:54,0:01:47,2:54:56,828,0,13
+Emergency Medicine,PGY1,S0181063,2016-06-24,2:30:18,0:53:34,0:08:28,0:20:17,1:08:42,0:24:58,6:13:02,2643,0,35
+General Medicine,PGY1,S0181067,2015-08-28,1:29:13,0:02:38,0:57:21,0:26:39,0:15:25,0:11:04,3:51:06,1108,0,8
+General Medicine,PGY1,S0181067,2015-08-29,2:12:03,0:29:45,0:52:03,0:50:54,0:33:40,1:00:37,6:27:07,1637,0,9
+General Medicine,PGY1,S0181067,2015-08-24,2:12:08,0:24:56,1:26:29,0:56:59,0:41:22,0:24:04,6:52:25,2047,0,18
+General Medicine,PGY1,S0181067,2015-08-25,1:49:32,0:05:22,1:12:21,0:34:12,0:25:37,0:09:00,4:35:48,1461,35,9
+General Medicine,PGY1,S0181067,2015-08-26,2:11:50,0:12:31,1:47:35,0:58:58,0:41:33,0:14:51,6:31:08,1941,0,15
+General Medicine,PGY1,S0181067,2015-08-27,0:00:00,0:00:00,0:00:00,0:00:36,0:00:00,0:00:00,0:00:36,30,0,5
+General Medicine,PGY1,S0181067,2015-08-20,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,2
+General Medicine,PGY1,S0181067,2015-08-21,1:49:15,0:08:36,0:59:02,0:28:18,0:25:11,0:15:19,4:51:48,1536,0,11
+General Medicine,PGY1,S0181067,2015-08-22,1:14:11,0:14:02,0:41:21,0:20:58,0:26:55,0:14:10,3:30:45,1130,20,6
+General Medicine,PGY1,S0181067,2015-08-23,0:53:35,0:06:11,1:06:00,0:07:02,0:20:35,0:12:06,2:55:13,776,23,6
+General Medicine,PGY1,S0195075,2016-07-08,1:57:18,0:53:27,0:00:23,1:38:04,0:32:52,0:03:20,5:45:02,1686,0,8
+General Medicine,PGY1,S0195075,2016-07-09,1:18:40,0:42:44,0:00:05,0:25:09,0:13:59,0:01:43,3:05:10,962,56,7
+General Medicine,PGY1,S0181060,2015-08-14,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181060,2015-08-15,0:00:00,0:00:00,0:00:00,0:00:03,0:00:00,0:00:00,0:00:03,6,0,2
+General Medicine,PGY1,S0195075,2016-07-06,2:29:57,1:29:20,0:00:48,1:01:13,0:25:20,0:00:54,6:08:45,1489,0,10
+General Medicine,PGY1,S0195075,2016-07-07,1:49:12,1:20:05,0:00:23,0:33:36,0:21:15,0:01:11,4:35:38,1125,0,6
+General Medicine,PGY1,S0181060,2015-08-10,2:33:15,0:39:44,1:33:55,0:50:28,0:40:03,0:23:20,7:26:05,2041,46,13
+General Medicine,PGY1,S0181060,2015-08-11,1:59:44,0:23:57,1:26:05,0:33:22,0:15:59,0:16:46,5:26:42,1593,32,13
+General Medicine,PGY1,S0181060,2015-08-12,1:59:24,0:30:52,1:03:35,0:42:44,0:11:54,0:16:42,5:18:36,1676,15,18
+General Medicine,PGY1,S0181060,2015-08-13,2:15:45,0:34:18,1:25:53,0:47:18,0:22:36,0:13:42,6:10:11,1790,0,14
+Night Team,PGY2+,S0169401,2015-08-22,1:26:39,0:49:02,1:36:31,0:49:43,0:09:07,0:15:16,5:30:03,2283,288,77
+General Medicine,PGY1,S0169394,2015-05-11,0:13:05,0:03:24,0:06:31,0:00:01,0:00:16,0:01:35,0:29:34,213,209,5
+Night Team,PGY2+,S0169401,2015-08-20,1:03:42,0:34:08,0:21:03,0:14:00,0:06:59,0:14:07,2:40:55,1197,0,57
+Night Team,PGY2+,S0169401,2015-08-21,2:05:43,1:07:53,1:56:06,0:52:05,0:15:01,0:36:22,7:22:28,2452,0,54
+General Medicine,PGY1,S0169394,2015-05-10,3:02:46,0:23:29,1:53:32,0:49:15,0:43:19,0:28:00,7:51:25,1935,88,7
+Night Team,PGY2+,S0169411,2015-10-17,1:11:17,0:55:02,1:35:17,0:40:44,0:04:30,0:14:42,4:47:09,1552,0,42
+Night Team,PGY2+,S0169411,2015-10-16,5:48:55,1:37:54,1:59:13,0:32:29,0:11:54,0:30:32,10:54:05,2972,550,61
+Night Team,PGY2+,S0169411,2015-10-15,0:50:16,0:24:43,0:01:52,0:25:45,0:04:59,0:19:19,2:15:05,959,0,22
+Night Team,PGY2+,S0169411,2015-10-14,2:21:31,1:12:03,1:11:20,0:35:50,0:05:45,0:19:31,6:02:16,2178,0,20
+Night Team,PGY2+,S0169411,2015-10-13,4:01:33,1:54:54,2:07:48,0:53:14,0:13:30,0:19:28,10:04:39,3626,10,53
+Night Team,PGY2+,S0169411,2015-10-12,5:33:44,1:57:18,1:30:08,1:08:45,0:11:23,0:37:30,11:25:02,4251,0,56
+Night Team,PGY2+,S0169411,2015-10-11,1:52:38,0:33:44,0:43:55,0:23:51,0:02:33,0:12:25,4:04:30,1329,0,25
+Night Team,PGY2+,S0169411,2015-10-10,1:59:24,0:55:42,1:23:31,0:24:50,0:05:29,0:16:17,5:16:07,1844,0,44
+General Medicine,PGY1,S0181077,2016-05-10,0:42:06,0:16:21,0:00:03,0:05:57,0:03:31,0:00:34,1:17:42,500,51,7
+General Medicine,PGY1,S0181077,2016-05-11,2:29:38,1:14:55,0:00:04,0:38:09,0:16:46,0:15:04,5:09:25,1541,161,10
+General Medicine,PGY1,S0181077,2016-05-12,1:37:05,0:36:18,0:00:13,0:21:38,0:14:03,0:11:16,3:16:45,1279,160,10
+General Medicine,PGY1,S0181077,2016-05-13,3:08:28,1:40:15,0:02:25,0:49:37,0:24:59,0:01:07,6:42:28,2574,151,15
+General Medicine,PGY1,S0181077,2016-05-14,0:01:50,0:00:18,0:00:00,0:00:20,0:00:29,0:04:13,0:07:42,105,93,5
+General Medicine,PGY1,S0181077,2016-05-15,1:21:03,0:51:09,0:02:37,0:19:11,0:15:01,0:04:24,3:25:43,1704,46,12
+General Medicine,PGY1,S0181077,2016-05-16,4:09:30,1:39:25,0:00:07,0:49:37,0:29:29,0:12:00,7:57:37,2798,0,19
+General Medicine,PGY1,S0181077,2016-05-17,2:40:36,0:59:51,0:00:27,0:27:01,0:28:42,0:03:47,5:10:07,2175,5,15
+General Medicine,PGY1,S0169403,2014-08-30,2:44:47,0:18:01,0:31:40,0:51:47,0:17:51,0:50:19,6:06:53,2040,0,14
+General Medicine,PGY1,S0169403,2014-08-31,2:03:39,0:07:31,0:07:12,0:04:33,0:17:52,0:18:27,3:26:41,1134,102,6
+General Medicine,PGY1,S0108287,2014-07-24,2:30:45,0:04:56,0:12:51,0:37:38,0:28:51,0:14:26,4:17:07,1035,70,11
+Night Team,PGY2+,S0074196,2016-09-30,0:00:00,0:00:00,0:00:00,0:00:18,0:00:00,0:00:00,0:00:18,12,0,4
+General Medicine,PGY1,S0079337,2014-07-19,0:00:00,0:00:00,0:00:00,0:01:10,0:00:00,0:00:00,0:01:10,18,0,3
+General Medicine,PGY1,S0079337,2014-07-18,2:44:27,0:05:35,0:12:13,0:38:41,0:11:29,0:04:20,4:12:10,1127,0,5
+Night Team,PGY1,S0195076,2016-09-30,0:23:45,0:30:57,0:00:01,0:22:20,0:04:21,0:00:05,1:28:47,483,10,9
+General Medicine,PGY1,S0109179,2014-12-07,2:40:23,0:00:54,2:04:45,0:41:43,0:17:19,0:23:16,6:34:49,1748,0,12
+General Medicine,PGY1,S0079337,2014-07-11,3:33:05,0:04:43,0:16:15,0:50:13,0:23:11,0:22:40,6:02:07,1697,198,5
+General Medicine,PGY1,S0079337,2014-07-10,2:47:10,0:07:41,0:10:40,0:20:37,0:32:35,0:06:16,4:36:42,1548,95,5
+General Medicine,PGY1,S0079337,2014-07-13,1:16:55,0:01:55,0:08:49,0:04:48,0:08:02,0:02:44,2:01:42,618,0,10
+General Medicine,PGY1,S0079337,2014-07-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0079337,2014-07-15,2:06:51,0:02:02,0:08:33,0:21:40,0:18:59,0:00:59,3:29:23,1515,0,7
+General Medicine,PGY1,S0079337,2014-07-14,3:47:00,0:01:52,0:16:21,0:32:01,0:29:29,0:11:44,5:53:49,2075,0,9
+General Medicine,PGY1,S0079337,2014-07-17,0:00:00,0:00:00,0:00:00,0:01:51,0:00:00,0:00:00,0:01:51,12,0,4
+General Medicine,PGY1,S0079337,2014-07-16,3:38:23,0:05:31,0:44:01,0:32:34,0:26:45,0:05:44,6:02:31,2170,45,8
+General Medicine,PGY1,S0163662,2015-03-28,1:01:18,0:00:00,0:00:00,0:01:49,0:00:07,0:00:00,1:03:16,68,52,9
+General Medicine,PGY1,S0163662,2015-03-20,1:06:12,0:12:49,1:32:58,0:36:57,0:08:34,0:01:35,3:50:07,891,0,5
+General Medicine,PGY1,S0163662,2015-03-21,0:15:10,0:01:04,0:31:57,0:00:00,0:00:01,0:00:08,0:49:51,146,144,4
+General Medicine,PGY1,S0163662,2015-03-22,2:39:15,1:08:22,2:47:11,0:36:51,0:08:56,0:05:36,7:44:12,1753,0,10
+General Medicine,PGY1,S0163662,2015-03-23,3:03:54,0:46:39,1:07:33,0:38:12,0:23:36,0:04:22,6:20:56,1850,432,14
+General Medicine,PGY1,S0163662,2015-03-24,3:54:44,0:53:05,1:15:19,1:05:01,0:19:36,0:10:50,8:02:08,2265,64,13
+General Medicine,PGY1,S0163662,2015-03-25,3:04:59,0:28:31,0:53:50,0:51:05,0:23:33,0:20:36,6:26:15,1774,245,10
+General Medicine,PGY1,S0163662,2015-03-26,3:02:15,0:30:59,3:26:42,0:33:58,0:24:18,0:11:45,8:32:26,2475,0,19
+General Medicine,PGY1,S0163662,2015-03-27,3:28:44,0:28:04,1:35:23,0:56:44,0:24:34,0:13:59,7:31:42,1865,6,12
+Intensive Care Unit,PGY2+,S0169411,2016-01-30,2:28:39,0:48:06,0:22:20,0:22:07,0:12:33,0:21:54,5:08:57,2250,23,17
+Intensive Care Unit,PGY2+,S0169411,2016-01-31,2:05:37,0:26:33,0:05:30,0:27:31,0:11:56,0:21:42,3:59:31,1402,0,13
+General Medicine,PGY1,S0169402,2014-11-30,4:23:32,0:10:04,1:54:41,0:58:38,0:26:15,0:12:22,8:33:47,2020,38,10
+Intensive Care Unit,PGY2+,S0181061,2016-08-18,1:20:47,1:04:52,0:02:59,0:24:11,0:11:53,0:03:16,3:43:32,1032,0,17
+Intensive Care Unit,PGY2+,S0181061,2016-08-19,2:04:45,0:43:23,0:03:29,0:34:24,0:16:21,0:04:01,4:14:13,1212,0,9
+Intensive Care Unit,PGY2+,S0181061,2016-08-14,3:14:42,1:19:45,0:05:09,0:30:14,0:32:10,0:16:41,7:00:51,1851,0,19
+Intensive Care Unit,PGY2+,S0181061,2016-08-15,1:58:22,0:26:47,0:05:58,0:26:09,0:13:59,0:05:01,3:39:55,797,0,11
+Intensive Care Unit,PGY2+,S0181061,2016-08-16,2:43:46,1:02:33,0:11:18,0:15:12,0:22:31,0:04:37,5:22:40,1199,0,14
+Intensive Care Unit,PGY2+,S0181061,2016-08-17,1:49:38,0:38:10,0:16:25,0:31:51,0:35:13,0:13:51,4:54:45,1148,278,19
+General Medicine,PGY1,S0181077,2016-01-02,0:05:23,0:04:46,0:09:07,0:00:33,0:00:08,0:00:37,0:24:09,165,149,16
+General Medicine,PGY1,S0181077,2016-01-01,1:43:32,0:56:54,1:29:24,0:26:00,0:13:35,0:16:30,5:28:26,2378,304,29
+General Medicine,PGY2+,S0169399,2016-01-31,0:59:27,0:38:46,0:57:46,0:16:30,0:13:21,0:14:20,3:35:32,922,0,10
+General Medicine,PGY2+,S0169399,2016-01-30,1:14:20,1:10:17,4:02:44,0:12:26,0:14:27,0:25:04,7:55:00,1266,0,16
+General Medicine,PGY1,S0181074,2016-03-08,0:48:14,0:59:17,0:00:23,0:21:07,0:01:33,0:04:22,2:26:22,806,784,18
+General Medicine,PGY1,S0181074,2016-03-09,2:46:42,1:05:57,0:00:42,0:59:47,0:20:37,0:02:17,6:04:10,2583,91,15
+General Medicine,PGY1,S0181074,2016-03-01,1:25:32,0:56:44,0:00:21,0:55:24,0:23:04,0:06:34,4:26:31,1872,62,9
+General Medicine,PGY1,S0181074,2016-03-02,2:21:30,1:06:07,0:00:07,1:36:56,0:37:26,0:13:15,6:44:55,2475,49,20
+General Medicine,PGY1,S0181074,2016-03-03,1:12:55,1:15:34,0:00:01,0:07:01,0:05:55,0:03:54,3:00:58,830,826,17
+General Medicine,PGY1,S0181074,2016-03-04,3:15:49,2:04:53,0:00:13,1:45:49,0:41:56,0:06:55,9:01:43,3622,11,19
+General Medicine,PGY1,S0181074,2016-03-05,2:24:37,1:27:53,0:00:19,1:12:03,0:24:23,0:04:22,6:35:18,2757,305,16
+General Medicine,PGY1,S0181074,2016-03-06,2:12:59,0:36:14,0:00:04,1:08:40,0:23:09,0:05:50,5:25:30,2320,360,34
+General Medicine,PGY1,S0181074,2016-03-07,2:04:44,1:56:34,0:00:08,1:01:30,0:33:29,0:04:47,6:17:54,2592,122,16
+General Medicine,PGY1,S0195202,2016-07-19,0:36:02,0:39:50,0:00:20,0:02:15,0:03:10,0:00:15,1:32:27,514,75,6
+General Medicine,PGY1,S0195202,2016-07-18,0:37:28,1:06:23,0:00:04,0:15:23,0:06:21,0:00:17,2:14:13,620,614,4
+General Medicine,PGY1,S0195202,2016-07-17,2:36:12,1:43:08,0:01:04,1:01:27,0:15:41,0:15:19,6:28:12,1557,2,13
+General Medicine,PGY1,S0195202,2016-07-16,2:10:01,1:07:26,0:00:05,1:06:11,0:26:17,0:05:11,5:44:02,1889,451,6
+General Medicine,PGY1,S0195202,2016-07-15,1:54:34,1:13:33,0:01:39,0:49:16,0:15:51,0:00:49,4:49:56,1814,834,12
+General Medicine,PGY1,S0195202,2016-07-14,2:33:01,1:48:17,0:00:24,1:32:20,0:29:26,0:09:10,7:44:28,1848,0,13
+General Medicine,PGY1,S0195202,2016-07-13,2:09:17,1:39:41,0:01:40,0:49:06,0:31:41,0:07:08,6:05:50,1971,240,13
+General Medicine,PGY1,S0195202,2016-07-12,2:24:04,1:39:55,0:00:14,0:40:41,0:26:13,0:02:22,5:30:58,1566,36,17
+General Medicine,PGY1,S0195202,2016-07-11,3:03:45,1:31:49,0:00:14,0:51:21,0:39:11,0:05:03,6:35:24,1902,487,14
+General Medicine,PGY1,S0195202,2016-07-10,0:04:41,0:10:58,0:00:02,0:01:09,0:00:00,0:00:14,0:25:48,143,115,7
+Night Team,PGY2+,S0169402,2016-09-10,3:32:44,1:33:01,0:00:09,0:31:07,0:06:53,0:06:43,6:27:12,1703,74,29
+General Medicine,PGY1,S0169403,2014-09-13,2:31:43,0:26:20,0:01:57,0:17:05,0:12:07,0:22:08,4:04:03,1454,105,18
+General Medicine,PGY1,S0169403,2014-09-12,2:03:07,0:05:24,0:09:23,0:27:33,0:34:55,0:24:17,4:02:44,1258,31,12
+General Medicine,PGY1,S0169403,2014-09-11,0:00:00,0:00:00,0:00:21,0:00:00,0:00:00,0:00:00,0:02:02,15,9,2
+General Medicine,PGY1,S0169403,2014-09-10,0:25:13,0:17:28,0:00:09,0:00:00,0:02:28,0:02:15,0:54:14,400,390,7
+General Medicine,PGY1,S0195086,2016-07-31,1:52:39,1:34:33,0:00:14,0:25:07,0:24:03,0:20:20,5:22:13,1444,0,11
+General Medicine,PGY1,S0195086,2016-07-30,2:12:10,2:20:04,0:01:40,0:20:23,0:19:27,0:08:48,5:47:57,1556,417,7
+General Medicine,PGY2+,S0169403,2015-08-20,0:00:00,0:35:26,0:00:00,0:00:07,0:00:00,0:08:28,0:51:42,333,325,13
+General Medicine,PGY2+,S0169403,2015-08-21,2:57:02,2:18:27,0:33:20,0:46:28,0:32:43,1:03:22,8:58:34,3511,215,35
+General Medicine,PGY2+,S0169403,2015-08-22,1:27:53,1:31:37,0:40:23,0:10:35,0:22:12,0:23:09,5:09:00,1882,646,33
+Night Team,PGY1,S0108268,2014-08-18,2:35:13,0:09:57,0:00:41,0:20:07,1:11:56,1:15:18,6:00:53,2497,256,35
+Night Team,PGY1,S0108268,2014-08-19,3:24:37,0:23:58,0:11:45,0:29:03,1:02:29,0:58:53,7:07:36,2641,425,34
+Night Team,PGY1,S0108268,2014-08-17,1:35:49,0:07:35,0:00:38,0:18:26,1:17:41,1:16:15,5:02:55,2231,46,31
+Intensive Care Unit,PGY2+,S0174975,2016-09-30,2:06:17,0:39:20,0:05:46,0:57:13,0:28:49,0:01:10,4:53:16,1518,0,15
+General Medicine,PGY1,S0169404,2015-02-01,1:33:14,0:18:52,0:28:14,0:33:37,0:14:13,0:11:03,3:33:06,1563,96,14
+General Medicine,PGY1,S0195229,2016-07-29,1:43:47,0:33:57,0:01:18,0:47:13,0:22:25,0:14:53,4:09:46,1213,0,8
+General Medicine,PGY1,S0195229,2016-07-28,1:31:50,0:46:14,0:00:14,0:23:20,0:02:56,0:01:50,3:13:02,1282,0,7
+General Medicine,PGY2+,S0169384,2016-06-06,2:20:24,1:40:46,0:00:15,1:35:47,0:39:15,0:11:15,7:06:06,2094,528,17
+General Medicine,PGY1,S0195229,2016-07-25,1:12:07,0:51:56,0:00:12,0:31:47,0:09:58,0:02:04,3:07:49,1255,571,10
+General Medicine,PGY1,S0195229,2016-07-24,3:21:09,1:04:58,0:00:09,1:00:32,0:53:38,0:04:36,7:16:31,1763,14,11
+General Medicine,PGY1,S0195229,2016-07-27,2:04:05,0:37:45,0:00:05,0:49:34,0:06:48,0:02:55,4:24:27,1672,0,19
+General Medicine,PGY1,S0195229,2016-07-26,2:35:14,0:52:55,0:00:09,0:48:19,0:27:23,0:05:21,5:19:49,1627,25,16
+General Medicine,PGY1,S0181059,2016-03-07,2:37:59,1:18:54,0:01:12,0:24:07,0:26:40,0:05:26,5:28:14,3192,57,35
+General Medicine,PGY1,S0181059,2016-03-06,1:08:17,0:12:55,0:00:02,0:06:41,0:12:53,0:01:40,2:04:01,1053,71,7
+General Medicine,PGY1,S0169404,2015-02-05,2:57:49,0:18:12,1:32:00,0:29:47,0:18:20,0:19:54,6:19:16,2477,267,17
+General Medicine,PGY1,S0173803,2016-05-09,0:46:57,0:59:18,0:00:31,0:15:02,0:03:14,0:00:50,2:45:10,816,14,20
+General Medicine,PGY1,S0173803,2016-05-08,1:45:40,0:52:05,0:00:05,0:11:40,0:08:06,0:00:36,3:31:42,1161,131,27
+General Medicine,PGY1,S0169404,2015-02-07,3:48:36,0:40:58,2:58:24,0:35:17,0:34:51,0:33:30,9:42:04,3312,14,18
+General Medicine,PGY1,S0173803,2016-05-03,2:25:28,0:56:56,0:03:53,0:46:38,0:17:36,0:05:27,5:35:00,1501,76,15
+General Medicine,PGY1,S0173803,2016-05-02,0:00:00,0:00:00,0:00:00,0:02:46,0:00:00,0:00:00,0:02:46,11,0,3
+General Medicine,PGY1,S0173803,2016-05-01,2:19:42,1:40:41,0:00:09,1:02:55,0:19:42,0:00:19,6:27:52,2022,26,14
+General Medicine,PGY1,S0169404,2015-02-06,0:00:00,0:14:02,0:00:00,0:00:12,0:00:00,0:02:10,0:19:49,150,142,8
+General Medicine,PGY1,S0173803,2016-05-07,0:56:36,0:25:04,0:00:08,0:23:25,0:11:17,0:03:28,2:18:30,757,0,15
+General Medicine,PGY1,S0173803,2016-05-06,2:11:08,1:09:36,0:00:15,0:23:40,0:11:15,0:01:01,4:34:58,1376,15,17
+General Medicine,PGY1,S0173803,2016-05-05,0:58:55,0:36:25,0:00:10,0:27:44,0:08:03,0:02:42,2:55:21,1113,88,12
+General Medicine,PGY1,S0173803,2016-05-04,1:36:32,0:32:46,0:00:01,0:29:40,0:11:26,0:07:49,3:17:47,1105,31,11
+General Medicine,PGY1,S0181083,2015-07-09,2:11:25,0:12:12,1:28:32,0:30:14,0:12:52,0:14:21,5:07:41,1215,48,10
+General Medicine,PGY1,S0181083,2015-07-08,0:23:30,0:02:30,0:00:15,0:00:58,0:00:02,0:00:31,1:31:50,2762,2760,64
+Emergency Medicine,PGY1,S0169406,2015-01-06,2:27:58,0:03:26,1:33:11,0:15:11,0:13:06,0:08:25,5:02:28,1153,72,27
+Emergency Medicine,PGY1,S0169406,2015-01-07,2:02:00,0:07:28,1:29:06,0:09:36,0:18:59,0:17:54,4:35:12,1246,12,25
+Emergency Medicine,PGY1,S0169406,2015-01-04,2:36:13,0:10:52,1:30:23,0:19:03,0:22:50,0:26:09,5:56:15,1628,53,33
+Emergency Medicine,PGY1,S0169406,2015-01-05,2:42:08,0:05:04,2:21:31,0:20:53,0:31:31,0:08:19,6:41:45,2312,108,34
+General Medicine,PGY1,S0181083,2015-07-01,3:35:50,0:13:54,0:56:24,0:17:59,0:33:27,0:14:06,6:24:39,1806,105,7
+General Medicine,PGY1,S0181083,2015-07-03,3:47:40,0:05:03,1:29:25,0:49:14,0:35:07,0:17:38,7:40:52,2224,0,11
+General Medicine,PGY1,S0181083,2015-07-02,4:22:58,0:18:23,1:03:25,1:01:26,0:54:38,0:15:37,8:33:59,2277,191,15
+General Medicine,PGY1,S0181083,2015-07-05,2:49:22,0:27:24,1:39:47,1:04:30,0:28:48,0:37:10,7:52:33,2119,117,9
+General Medicine,PGY1,S0181083,2015-07-04,2:36:03,0:05:04,1:12:53,0:40:03,0:10:05,0:12:09,5:09:20,1099,0,11
+General Medicine,PGY1,S0181083,2015-07-07,2:38:53,0:08:02,1:40:28,0:38:36,0:20:14,0:16:34,6:13:22,1488,0,8
+General Medicine,PGY1,S0181083,2015-07-06,3:00:29,0:14:44,2:05:22,0:45:03,0:28:16,0:08:03,7:08:26,2278,141,11
+General Medicine,PGY1,S0195230,2016-10-05,1:24:57,0:51:13,0:00:15,0:30:56,0:15:33,0:00:39,3:18:23,884,0,9
+General Medicine,PGY1,S0195230,2016-10-04,0:43:23,0:13:26,0:00:11,0:05:20,0:02:58,0:00:58,1:14:35,551,44,11
+General Medicine,PGY1,S0195230,2016-10-07,1:54:54,1:08:33,0:00:09,0:42:38,0:21:54,0:05:04,4:39:54,1411,0,22
+General Medicine,PGY1,S0195230,2016-10-06,0:33:59,0:27:49,0:00:08,0:11:11,0:07:03,0:00:05,1:26:18,431,0,4
+General Medicine,PGY1,S0195230,2016-10-01,1:05:20,1:07:58,0:00:13,0:14:17,0:12:26,0:00:11,2:51:59,1063,386,11
+General Medicine,PGY1,S0195230,2016-10-03,1:52:02,0:41:17,0:00:02,0:46:24,0:09:10,0:00:20,3:52:24,942,0,10
+General Medicine,PGY1,S0195230,2016-10-02,2:05:41,1:21:44,0:00:23,0:35:16,0:25:15,0:00:42,5:06:26,2120,0,16
+Emergency Medicine,PGY2+,S0169394,2016-08-13,0:00:33,0:00:00,0:00:00,0:00:00,0:00:00,0:00:11,0:00:53,10,0,2
+Emergency Medicine,PGY2+,S0169394,2016-08-12,3:36:52,1:02:36,0:02:36,0:33:04,0:59:48,0:01:38,7:16:23,2714,0,37
+Emergency Medicine,PGY2+,S0169394,2016-08-11,3:30:16,0:54:18,0:00:59,0:31:53,0:51:28,0:02:49,6:40:04,2100,0,16
+Intensive Care Unit,PGY2+,S0174975,2016-10-03,3:02:46,0:51:04,0:09:20,0:42:09,0:35:07,0:02:41,6:06:16,1826,9,22
+General Medicine,PGY2+,S0169399,2015-10-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+General Medicine,PGY2+,S0169399,2015-10-31,1:50:00,1:10:27,3:06:53,0:31:55,0:14:50,0:28:25,7:50:08,1567,0,27
+General Medicine,PGY2+,S0169398,2016-03-13,0:06:05,0:18:07,0:00:01,0:00:00,0:00:10,0:00:31,0:26:38,345,333,19
+Intensive Care Unit,PGY2+,S0181070,2016-08-30,0:51:58,0:54:33,0:02:55,0:01:12,0:21:17,0:00:26,2:25:15,784,0,17
+Intensive Care Unit,PGY2+,S0181070,2016-08-31,2:39:23,2:36:41,0:02:24,0:37:31,0:31:50,0:06:25,7:05:56,2243,0,28
+General Medicine,PGY1,S0181059,2015-08-16,2:47:38,2:14:10,1:46:10,0:58:03,0:27:04,0:42:12,9:14:56,3440,233,22
+General Medicine,PGY1,S0181059,2015-08-17,1:47:51,0:56:35,1:02:51,0:51:51,0:18:02,0:30:14,5:51:42,2275,203,12
+General Medicine,PGY1,S0181059,2015-08-18,1:31:23,0:50:00,0:29:28,0:32:16,0:10:20,0:11:57,3:54:40,1644,76,15
+General Medicine,PGY1,S0181059,2015-08-19,1:44:22,0:43:12,1:35:42,0:46:32,0:20:02,0:39:59,6:17:13,2097,44,12
+General Medicine,PGY1,S0195214,2016-08-02,0:21:19,0:08:02,0:00:11,0:00:00,0:00:00,0:02:18,0:34:14,180,131,4
+General Medicine,PGY1,S0195214,2016-08-03,2:32:15,1:18:10,0:00:26,0:57:08,0:16:48,0:02:54,5:33:52,1440,0,10
+General Medicine,PGY1,S0195214,2016-08-01,1:35:12,1:05:52,0:03:01,0:51:20,0:09:27,0:02:35,4:20:48,1391,165,9
+General Medicine,PGY1,S0195214,2016-08-06,0:29:13,0:14:39,0:00:00,0:03:49,0:02:09,0:01:26,0:55:08,252,224,8
+General Medicine,PGY1,S0195214,2016-08-07,0:01:44,0:00:49,0:00:22,0:00:00,0:00:00,0:00:00,0:03:53,34,34,4
+General Medicine,PGY1,S0181056,2015-11-19,1:58:59,0:54:07,0:28:55,0:18:59,0:18:47,0:35:34,5:05:44,1278,15,10
+General Medicine,PGY1,S0195214,2016-08-05,2:55:26,0:56:05,0:01:36,0:35:09,0:12:55,0:03:19,5:39:51,1484,43,13
+General Medicine,PGY1,S0181056,2015-11-17,3:05:10,1:46:43,2:42:26,1:36:27,0:35:34,0:46:01,11:23:26,3523,145,18
+General Medicine,PGY1,S0181056,2015-11-16,1:58:24,1:02:38,1:41:59,0:29:44,0:23:27,1:01:33,7:04:23,2068,54,9
+General Medicine,PGY1,S0195214,2016-08-08,1:24:22,1:20:08,0:04:01,0:40:32,0:20:19,0:00:02,4:22:07,1184,0,8
+General Medicine,PGY1,S0195214,2016-08-09,1:29:11,1:15:48,0:00:07,0:36:12,0:15:52,0:01:08,3:59:28,956,0,6
+General Medicine,PGY1,S0181056,2015-11-13,2:23:56,1:32:10,1:17:15,0:30:22,0:19:25,0:21:21,7:05:09,2485,55,18
+General Medicine,PGY1,S0181056,2015-11-12,2:21:37,1:09:38,1:59:41,0:53:04,0:31:58,0:43:01,8:28:06,2332,36,10
+General Medicine,PGY1,S0181056,2015-11-11,0:00:00,0:07:23,0:00:00,0:00:02,0:00:00,0:00:59,0:09:13,61,46,5
+General Medicine,PGY1,S0181056,2015-11-10,1:42:23,1:14:34,0:43:06,0:38:11,0:21:58,0:36:12,5:47:44,1970,0,8
+General Medicine,PGY1,S0056755,2015-03-08,3:13:14,0:26:52,3:07:28,0:35:57,0:39:22,0:17:46,8:36:39,2058,0,12
+General Medicine,PGY1,S0056755,2015-03-09,2:31:45,0:19:19,2:16:47,0:37:06,0:23:57,0:21:11,7:07:11,2035,0,11
+General Medicine,PGY1,S0056755,2015-03-06,2:58:54,0:17:24,3:07:03,0:27:35,0:14:05,0:26:01,8:03:42,2111,0,10
+General Medicine,PGY1,S0056755,2015-03-07,1:41:01,0:18:45,0:45:04,0:11:32,0:17:44,0:05:25,3:33:58,1128,0,6
+General Medicine,PGY1,S0056755,2015-03-04,2:48:39,0:08:03,1:09:27,0:16:11,0:32:12,0:11:18,5:21:31,1387,27,6
+General Medicine,PGY1,S0056755,2015-03-05,0:00:00,0:04:18,0:00:00,0:00:00,0:00:00,0:00:19,0:04:41,34,32,4
+General Medicine,PGY1,S0056755,2015-03-02,1:20:12,0:13:14,0:29:00,0:10:58,0:11:15,0:06:18,2:40:49,861,0,8
+General Medicine,PGY1,S0056755,2015-03-03,3:04:56,0:11:57,1:23:26,0:31:58,0:21:37,0:15:39,6:20:38,1659,0,11
+General Medicine,PGY1,S0056755,2015-03-01,2:22:32,0:27:54,1:39:23,0:30:00,0:22:10,0:19:21,6:00:21,1539,0,6
+General Medicine,PGY1,S0181526,2015-10-08,1:43:40,1:17:31,3:00:12,0:46:27,0:27:34,1:08:31,9:22:09,2314,115,18
+General Medicine,PGY1,S0181073,2016-01-03,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,10,0,3
+General Medicine,PGY1,S0181073,2016-01-07,2:20:30,1:33:36,2:26:29,1:30:09,0:34:35,0:26:53,9:37:31,3093,0,11
+General Medicine,PGY1,S0181073,2016-01-06,1:11:22,1:08:48,1:01:13,0:31:13,0:29:36,0:29:15,5:20:21,1779,0,7
+General Medicine,PGY1,S0181073,2016-01-05,1:25:35,1:38:14,2:50:31,1:14:19,0:23:26,0:44:02,8:43:32,2698,0,9
+General Medicine,PGY1,S0181073,2016-01-04,0:40:50,0:55:00,1:00:49,0:27:39,0:17:10,0:20:45,3:59:09,1365,0,6
+General Medicine,PGY1,S0181073,2016-01-09,0:33:00,0:34:43,0:44:32,0:22:58,0:08:26,0:16:16,2:59:51,1170,0,5
+General Medicine,PGY1,S0181073,2016-01-08,1:32:56,1:01:44,2:07:24,0:46:13,0:20:59,0:28:57,6:56:02,2377,0,9
+General Medicine,PGY1,S0106952,2014-09-19,4:59:36,0:09:49,0:40:43,0:26:19,0:41:44,0:41:41,8:23:30,2628,423,24
+General Medicine,PGY1,S0106952,2014-09-18,4:20:23,0:10:08,0:05:47,0:46:04,0:39:47,1:08:12,7:46:42,2097,257,43
+General Medicine,PGY1,S0181088,2015-12-02,2:15:37,1:00:43,1:50:04,0:22:12,0:23:32,0:11:38,6:34:53,2296,0,9
+General Medicine,PGY1,S0181088,2015-12-03,0:31:59,0:13:13,1:11:50,0:13:36,0:07:10,0:22:09,2:56:30,880,0,6
+General Medicine,PGY1,S0181088,2015-12-01,0:55:47,0:32:24,1:19:39,0:26:48,0:15:20,0:02:21,3:58:46,1230,0,6
+General Medicine,PGY1,S0106952,2014-09-17,0:54:04,0:00:00,0:05:18,0:00:57,0:01:42,0:06:31,1:12:04,387,377,9
+General Medicine,PGY1,S0106952,2014-09-16,2:37:48,0:13:44,0:19:28,0:44:16,0:09:57,0:26:59,4:53:07,1072,1,12
+General Medicine,PGY1,S0106952,2014-09-15,3:43:26,0:18:01,0:11:22,0:15:57,0:14:34,0:27:37,5:40:21,1312,187,36
+General Medicine,PGY1,S0106952,2014-09-14,2:54:35,0:00:21,0:20:39,0:14:05,0:16:13,0:27:19,4:42:24,1261,1,19
+General Medicine,PGY1,S0181061,2015-10-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0195218,2016-08-28,1:28:56,0:48:52,0:00:14,0:28:50,0:08:27,0:02:16,3:31:22,1393,0,11
+General Medicine,PGY1,S0195218,2016-08-29,1:51:40,1:19:16,0:00:13,1:06:06,0:18:44,0:01:41,5:13:48,1888,39,9
+General Medicine,PGY1,S0195218,2016-08-24,2:04:11,1:48:10,0:00:21,0:54:10,0:24:22,0:00:39,6:04:09,1906,55,18
+General Medicine,PGY1,S0195218,2016-08-25,1:50:22,1:19:45,0:00:42,0:39:56,0:19:52,0:07:23,5:20:31,1881,60,11
+General Medicine,PGY1,S0195218,2016-08-26,1:02:51,0:57:18,0:00:12,0:43:08,0:21:36,0:00:47,3:40:10,1502,8,10
+General Medicine,PGY1,S0195218,2016-08-27,2:14:46,1:48:33,0:01:03,1:12:37,0:24:17,0:14:27,6:41:22,2572,52,20
+General Medicine,PGY1,S0195218,2016-08-21,1:12:18,0:54:19,0:00:06,0:10:17,0:10:23,0:02:38,3:11:46,883,31,7
+General Medicine,PGY1,S0195218,2016-08-22,2:04:28,2:01:20,0:00:14,0:50:35,0:22:15,0:04:15,6:17:56,1891,1,11
+Emergency Medicine,PGY1,S0108287,2015-03-07,0:21:38,0:03:15,0:18:15,0:00:47,0:00:01,0:00:05,0:44:57,82,0,7
+Intensive Care Unit,PGY2+,S0181064,2016-11-02,1:19:29,0:16:34,0:02:14,0:15:37,0:15:38,0:00:06,2:51:18,949,0,18
+Intensive Care Unit,PGY2+,S0181064,2016-11-03,2:53:06,1:29:12,0:01:18,0:28:42,0:16:41,0:01:49,6:00:39,2073,85,24
+Intensive Care Unit,PGY2+,S0181064,2016-11-01,1:54:29,0:50:16,0:02:30,0:17:10,0:12:30,0:04:57,4:04:46,1355,90,32
+Intensive Care Unit,PGY2+,S0181064,2016-11-04,1:10:34,0:15:47,0:02:37,0:08:41,0:16:18,0:01:04,2:01:21,835,0,21
+Intensive Care Unit,PGY2+,S0181064,2016-11-05,0:00:53,0:01:54,0:00:00,0:00:41,0:00:00,0:00:00,0:03:30,59,33,8
+Intensive Care Unit,PGY2+,S0169385,2016-01-30,1:52:14,0:28:13,0:25:12,0:19:32,0:33:02,0:58:39,4:49:20,1503,0,13
+Intensive Care Unit,PGY2+,S0169385,2016-01-31,2:51:52,1:04:37,0:20:55,1:10:36,1:06:30,1:39:15,8:53:48,3511,44,16
+General Medicine,PGY1,S0181069,2016-01-03,1:30:25,0:49:21,1:11:08,0:43:04,0:29:04,0:13:25,5:23:24,2067,145,7
+General Medicine,PGY1,S0181069,2016-01-04,1:30:17,1:09:42,1:13:34,1:10:46,0:20:54,0:19:58,6:11:45,2203,121,12
+General Medicine,PGY1,S0181069,2016-01-05,1:24:35,1:04:00,1:22:43,1:02:29,0:41:39,0:18:10,6:19:29,2005,253,10
+General Medicine,PGY1,S0181069,2016-01-06,1:24:07,0:50:26,1:03:24,0:34:26,0:44:01,0:18:32,5:34:27,1827,218,11
+General Medicine,PGY1,S0181069,2016-01-07,2:01:47,1:10:20,1:53:59,1:04:35,0:56:27,0:41:52,8:28:53,2632,569,16
+General Medicine,PGY1,S0181057,2015-06-27,2:14:44,0:18:57,3:07:38,0:16:44,0:56:33,0:20:41,7:34:33,1580,29,16
+Emergency Medicine,PGY1,S0108287,2015-03-09,4:42:56,0:33:52,2:32:43,0:21:23,0:20:49,0:02:41,8:52:04,1372,0,23
+General Medicine,PGY1,S0181057,2015-06-29,2:54:34,0:01:33,1:44:20,0:32:54,0:51:48,0:21:01,7:05:20,1991,0,12
+General Medicine,PGY1,S0181057,2015-06-28,2:43:22,0:12:45,1:12:49,0:26:05,0:37:10,0:19:59,5:51:41,1713,0,6
+General Medicine,PGY1,S0169404,2014-08-02,3:10:45,0:17:40,0:22:33,0:36:41,0:19:49,0:10:53,5:16:25,2349,65,7
+General Medicine,PGY2+,S0181074,2016-10-31,4:13:48,1:01:13,0:00:35,1:26:51,0:22:06,0:21:47,8:46:46,3826,0,56
+Night Team,PGY2+,S0169386,2016-02-27,3:21:32,1:57:37,0:05:35,0:38:33,0:17:30,0:14:13,7:26:37,2954,72,83
+General Medicine,PGY2+,S0181074,2016-10-30,1:06:29,0:05:13,0:00:00,0:03:32,0:00:00,0:11:35,1:34:36,418,0,35
+General Medicine,PGY1,S0181087,2015-07-19,3:53:18,0:39:31,1:55:02,0:37:50,0:26:53,0:36:16,8:43:03,2044,80,12
+Night Team,PGY1,S0169398,2015-02-07,2:28:10,0:16:08,2:25:28,0:25:41,0:20:07,0:18:13,6:28:28,1166,0,29
+General Medicine,PGY1,S0180927,2016-07-31,1:33:46,1:01:56,0:00:07,0:15:35,0:07:31,0:02:20,3:21:21,1119,62,6
+General Medicine,PGY2+,S0172085,2016-09-12,0:12:00,0:02:11,0:00:00,0:00:49,0:00:00,0:01:12,0:17:04,101,0,17
+General Medicine,PGY1,S0180927,2016-07-30,2:28:21,2:00:46,0:03:04,0:37:17,0:15:11,0:02:10,6:08:43,1977,0,13
+General Medicine,PGY2+,S0181065,2016-09-26,3:30:09,0:44:49,0:00:15,0:28:01,0:24:40,0:05:56,5:52:25,3563,172,20
+General Medicine,PGY2+,S0181065,2016-09-27,5:34:58,1:14:20,0:00:00,0:29:37,0:32:07,0:09:24,9:02:55,5476,0,27
+General Medicine,PGY2+,S0181065,2016-09-24,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0181065,2016-09-25,3:49:42,1:00:07,0:00:00,0:20:37,0:34:40,0:10:48,6:49:09,5235,0,24
+General Medicine,PGY2+,S0181065,2016-09-22,5:57:27,2:08:40,0:00:06,0:49:13,0:33:45,0:06:21,10:53:33,7497,984,86
+General Medicine,PGY2+,S0181065,2016-09-23,3:26:59,1:05:13,0:00:22,0:42:10,0:27:04,0:04:55,6:24:11,3434,0,17
+General Medicine,PGY2+,S0181065,2016-09-20,5:00:23,1:25:58,0:00:19,0:29:04,0:35:06,0:15:49,9:12:41,6353,65,41
+General Medicine,PGY2+,S0181065,2016-09-21,2:45:00,0:57:13,0:00:13,0:57:22,0:28:48,0:03:42,5:44:43,3619,83,16
+General Medicine,PGY2+,S0181065,2016-09-28,4:16:17,0:38:12,0:00:10,0:23:39,0:27:46,0:08:35,7:09:28,4899,178,16
+General Medicine,PGY2+,S0181065,2016-09-29,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,16,0,6
+General Medicine,PGY1,S0181087,2016-02-05,0:52:37,1:00:18,1:02:26,0:25:46,0:09:03,0:39:23,4:29:38,1515,160,8
+General Medicine,PGY1,S0181087,2016-02-04,0:00:00,0:01:20,0:00:00,0:00:20,0:00:00,0:00:00,0:01:51,43,33,7
+General Medicine,PGY1,S0181087,2016-02-07,1:03:11,0:51:01,0:40:57,0:38:49,0:09:30,0:20:50,4:06:06,1616,0,8
+General Medicine,PGY1,S0163425,2015-03-28,2:50:28,0:20:19,0:27:19,0:15:44,0:26:17,0:16:40,5:01:54,1494,0,18
+General Medicine,PGY1,S0181087,2016-02-01,1:23:17,1:25:43,0:45:16,0:57:49,0:21:15,0:32:42,6:11:59,3321,59,17
+General Medicine,PGY1,S0181087,2016-02-03,1:32:23,2:02:29,2:21:47,0:53:20,0:33:45,0:30:19,8:17:56,2775,7,12
+General Medicine,PGY1,S0181087,2016-02-02,0:52:46,0:42:04,0:21:06,0:30:28,0:09:53,0:28:00,3:16:30,1259,49,7
+General Medicine,PGY1,S0163425,2015-03-23,4:32:57,0:35:16,1:16:57,0:41:25,0:37:57,0:37:38,8:36:28,2632,0,13
+General Medicine,PGY1,S0163425,2015-03-22,0:22:26,0:03:07,0:00:09,0:00:00,0:00:22,0:04:58,0:32:44,192,188,6
+General Medicine,PGY1,S0163425,2015-03-21,3:42:29,0:24:57,1:15:19,1:08:56,0:24:04,0:18:52,7:41:04,2193,0,7
+General Medicine,PGY1,S0163425,2015-03-20,2:19:19,0:05:02,1:10:48,0:27:18,0:12:57,0:12:01,4:43:44,1022,0,13
+General Medicine,PGY1,S0163425,2015-03-27,4:25:30,0:21:15,2:09:36,0:37:10,0:17:25,0:14:18,8:42:18,3051,202,26
+General Medicine,PGY1,S0163425,2015-03-26,3:58:56,0:12:54,1:28:18,1:07:56,0:22:37,0:15:11,7:59:44,2383,0,15
+General Medicine,PGY1,S0163425,2015-03-25,2:47:26,0:36:45,1:34:05,0:46:14,0:28:33,0:21:19,7:06:34,2633,0,18
+General Medicine,PGY1,S0163425,2015-03-24,3:17:31,0:32:49,1:16:07,0:36:19,0:28:17,0:22:22,7:00:38,2290,0,19
+Night Team,PGY2+,S0094959,2016-05-15,3:51:46,1:26:31,0:00:36,0:13:35,0:22:09,0:03:36,6:37:08,3007,127,104
+Night Team,PGY2+,S0094959,2016-05-16,4:43:45,1:44:58,0:00:06,0:32:51,0:32:57,0:17:54,8:33:39,3782,204,112
+Night Team,PGY2+,S0094959,2016-05-17,5:43:51,1:28:08,0:00:10,0:21:59,0:17:54,0:24:01,9:00:23,4026,262,74
+General Medicine,PGY1,S0169404,2014-08-01,0:25:00,0:48:19,0:00:20,0:00:00,0:00:03,0:22:37,1:40:58,991,989,13
+Night Team,PGY2+,S0094959,2016-05-18,6:15:47,2:11:04,0:00:15,0:33:26,0:22:12,0:19:07,10:54:11,4210,611,90
+Night Team,PGY2+,S0094959,2016-05-19,2:29:18,0:56:58,0:00:01,0:06:08,0:13:22,0:24:13,4:29:51,2187,120,73
+General Medicine,PGY1,S0169398,2015-01-26,0:14:26,0:02:51,0:00:18,0:00:03,0:02:28,0:00:15,0:21:27,197,185,6
+General Medicine,PGY1,S0169398,2015-01-27,4:25:04,0:16:12,1:41:28,1:00:00,0:27:13,0:36:13,8:54:31,2742,553,13
+Night Team,PGY2+,S0169384,2016-01-17,0:25:51,0:54:14,2:23:15,0:40:27,0:06:41,0:11:30,5:00:09,1172,0,10
+General Medicine,PGY1,S0181526,2015-10-09,1:50:35,0:48:26,1:04:00,0:24:49,0:16:44,0:23:48,5:22:29,1640,358,19
+Night Team,PGY2+,S0169384,2016-01-16,0:58:09,1:41:57,3:27:19,0:44:23,0:16:37,0:28:39,8:02:56,2884,0,20
+General Medicine,PGY1,S0169398,2015-01-22,3:38:13,0:02:08,1:22:46,0:22:23,0:32:07,0:26:14,6:53:29,1978,119,12
+General Medicine,PGY1,S0169398,2015-01-23,2:59:32,0:06:06,1:23:04,0:34:02,0:34:21,0:23:20,6:16:39,1916,309,9
+General Medicine,PGY1,S0181066,2015-06-29,1:56:33,0:27:32,1:20:51,0:19:03,0:15:23,0:20:58,5:08:23,1215,0,6
+General Medicine,PGY1,S0181066,2015-06-28,1:32:12,0:31:01,1:53:33,0:06:52,0:10:48,0:26:30,5:12:29,1347,0,5
+Night Team,PGY2+,S0169384,2016-01-13,0:36:10,0:41:32,2:16:15,0:32:14,0:12:28,0:23:11,4:59:32,1356,0,12
+General Medicine,PGY1,S0169398,2015-01-21,3:37:10,0:07:33,1:53:22,0:52:23,0:24:18,0:11:35,7:29:15,2188,495,9
+General Medicine,PGY1,S0181066,2015-06-27,2:35:14,0:37:56,1:21:50,0:30:25,0:29:56,0:30:48,6:36:37,1339,0,8
+General Medicine,PGY1,S0181066,2015-06-26,2:19:51,0:22:04,1:04:54,0:36:17,0:42:09,0:20:43,6:33:23,1613,0,8
+General Medicine,PGY1,S0169400,2015-04-30,0:00:00,0:00:00,0:00:00,0:02:53,0:00:00,0:00:00,0:02:53,14,0,3
+General Medicine,PGY1,S0169395,2015-06-24,3:22:47,0:31:26,3:01:14,1:07:39,0:36:00,0:17:20,9:20:43,2911,0,14
+General Medicine,PGY1,S0169395,2015-06-25,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,12,0,5
+General Medicine,PGY1,S0196597,2016-09-30,1:37:48,0:39:43,0:00:07,0:45:02,0:05:55,0:04:18,3:45:33,1324,116,10
+General Medicine,PGY1,S0169395,2015-06-20,0:00:00,0:00:00,0:00:00,0:06:06,0:00:00,0:00:00,0:06:06,14,0,3
+General Medicine,PGY1,S0169395,2015-06-21,2:51:57,0:57:36,1:30:21,0:45:03,0:24:31,0:22:37,7:21:26,2614,0,23
+General Medicine,PGY1,S0169395,2015-06-22,1:26:21,0:13:21,0:54:05,0:06:30,0:12:35,0:09:24,3:13:38,1200,0,9
+General Medicine,PGY1,S0169395,2015-06-23,1:19:00,0:00:50,1:01:01,0:11:04,0:08:36,0:06:10,3:04:50,865,0,8
+General Medicine,PGY1,S0169381,2014-08-19,4:14:27,0:19:47,0:16:37,0:42:50,0:26:57,0:35:29,6:53:41,1495,115,9
+General Medicine,PGY1,S0169381,2014-08-18,3:35:53,0:02:38,0:38:01,0:38:37,0:31:10,0:15:42,5:53:56,1343,93,8
+General Medicine,PGY1,S0169381,2014-08-17,0:00:00,0:10:48,0:00:00,0:01:35,0:00:00,0:00:00,0:15:22,71,65,6
+Night Team,PGY1,S0169381,2014-08-16,3:19:17,0:09:19,0:25:56,0:25:24,0:31:08,0:48:32,5:59:52,1438,0,12
+Night Team,PGY1,S0169381,2014-08-15,1:33:26,0:10:46,0:10:27,0:11:30,0:15:21,0:17:48,2:46:37,774,69,19
+Night Team,PGY1,S0169381,2014-08-14,2:42:26,0:09:07,0:26:27,0:39:57,0:29:35,0:45:01,5:36:36,1260,0,31
+Night Team,PGY1,S0169381,2014-08-13,3:11:25,0:08:49,0:17:57,0:26:34,0:39:28,0:30:17,5:32:31,1146,0,30
+Night Team,PGY1,S0169381,2014-08-12,3:08:35,0:03:53,0:26:06,0:26:46,0:37:23,0:44:09,5:37:29,1211,0,30
+Night Team,PGY1,S0169381,2014-08-11,1:04:03,0:00:58,0:02:11,0:09:38,0:10:15,0:05:47,1:46:01,431,0,20
+Night Team,PGY1,S0169381,2014-08-10,1:32:52,0:00:08,0:05:56,0:14:33,0:10:46,0:11:23,2:23:28,411,0,12
+General Medicine,PGY1,S0169399,2015-03-30,2:22:49,0:05:45,3:41:05,0:45:41,0:12:57,0:06:15,7:49:47,1452,220,9
+General Medicine,PGY1,S0169399,2015-03-31,1:48:18,0:31:34,1:45:01,0:27:08,0:22:34,0:11:42,5:30:03,1180,131,10
+Intensive Care Unit,PGY2+,S0181078,2016-08-29,0:20:07,0:00:02,0:00:06,0:06:40,0:00:06,0:00:00,0:42:53,215,209,13
+Intensive Care Unit,PGY2+,S0181078,2016-08-28,1:13:35,0:20:18,0:11:25,0:16:45,0:13:21,0:00:16,2:29:08,642,0,10
+Intensive Care Unit,PGY2+,S0181078,2016-08-27,1:57:24,1:00:03,0:21:49,0:24:50,0:22:27,0:01:25,4:38:27,1285,258,14
+Intensive Care Unit,PGY2+,S0181078,2016-08-26,3:26:45,0:37:08,0:00:00,1:02:09,0:21:12,0:08:58,6:00:43,1913,0,25
+Intensive Care Unit,PGY2+,S0181078,2016-08-25,3:03:55,0:32:55,0:17:29,0:47:03,0:19:34,0:03:45,5:44:30,1460,0,25
+Intensive Care Unit,PGY2+,S0181078,2016-08-24,4:00:58,0:58:00,0:13:10,0:34:01,0:34:18,0:04:54,7:10:14,1971,407,36
+Intensive Care Unit,PGY2+,S0181078,2016-08-23,2:28:30,0:25:48,0:18:55,0:23:40,0:23:02,0:03:42,4:24:58,1101,0,17
+Intensive Care Unit,PGY2+,S0181078,2016-08-22,2:23:02,0:25:52,0:00:09,1:04:40,0:28:37,0:04:51,4:46:40,1468,0,13
+Intensive Care Unit,PGY2+,S0181078,2016-08-21,0:04:58,0:01:15,0:00:00,0:00:09,0:00:00,0:00:00,0:13:22,48,11,9
+Intensive Care Unit,PGY2+,S0181078,2016-08-20,2:36:12,0:09:04,0:17:37,0:32:37,0:15:08,0:05:57,4:19:47,975,2,12
+Emergency Medicine,PGY1,S0195076,2016-07-31,3:01:06,0:33:22,0:01:21,0:38:16,0:30:05,0:06:00,5:44:10,2146,0,11
+General Medicine,PGY1,S0195207,2016-09-06,1:54:08,1:55:53,0:00:27,0:25:03,0:14:03,0:00:23,5:18:39,2042,17,23
+Intensive Care Unit,PGY2+,S0169385,2016-02-01,2:24:33,0:46:28,0:17:57,0:46:04,0:31:04,1:16:08,6:37:23,2593,198,14
+Intensive Care Unit,PGY2+,S0169406,2016-04-02,1:14:47,0:05:52,0:01:39,0:09:46,0:17:44,0:03:35,2:15:56,981,91,12
+Intensive Care Unit,PGY2+,S0169406,2016-04-01,1:42:01,0:39:24,0:00:03,0:17:16,0:10:32,0:01:58,3:06:33,1080,146,16
+Night Team,PGY2+,S0169381,2015-11-09,0:37:05,1:22:43,0:04:39,0:03:06,0:03:29,0:12:51,2:29:14,931,0,62
+Night Team,PGY2+,S0169381,2015-11-08,1:16:00,2:48:39,1:36:23,0:23:03,0:17:17,0:19:38,7:00:30,1841,0,68
+Night Team,PGY2+,S0169381,2015-11-07,1:10:07,2:01:42,1:12:06,0:15:00,0:04:46,0:29:50,5:36:43,1553,0,62
+Night Team,PGY2+,S0169381,2015-11-06,0:59:14,1:38:37,3:46:13,0:51:55,0:23:18,0:57:03,9:04:58,1924,0,21
+Night Team,PGY2+,S0169381,2015-11-05,0:36:33,0:54:48,1:35:52,0:13:20,0:22:28,0:09:55,4:03:04,1067,352,47
+Night Team,PGY2+,S0169381,2015-11-04,0:57:53,0:34:21,1:11:57,0:17:21,0:23:50,0:17:05,3:49:29,943,0,12
+Night Team,PGY2+,S0169381,2015-11-03,1:03:13,1:11:54,1:30:54,0:32:31,0:17:13,0:39:29,5:26:21,1302,0,23
+Night Team,PGY2+,S0169381,2015-11-02,0:48:40,1:03:53,1:26:44,0:22:04,0:06:46,0:28:22,4:42:04,1264,0,32
+Night Team,PGY2+,S0169381,2015-11-01,1:15:27,1:20:48,1:30:12,0:39:37,0:22:50,0:46:21,6:21:53,1651,0,25
+General Medicine,PGY1,S0173803,2016-01-29,0:59:45,0:42:39,1:13:43,0:39:36,0:24:40,0:18:52,5:09:20,1926,47,24
+General Medicine,PGY1,S0173803,2016-01-28,1:49:30,1:33:58,2:32:48,0:51:04,0:24:13,0:35:27,8:24:53,3559,0,27
+General Medicine,PGY2+,S0169402,2016-07-16,0:13:03,0:02:29,0:00:00,0:00:02,0:04:48,0:01:51,0:22:56,163,0,12
+General Medicine,PGY1,S0169404,2014-08-08,4:19:34,0:29:04,0:25:56,0:38:20,0:19:39,0:15:53,7:35:04,3229,171,19
+General Medicine,PGY1,S0169404,2014-08-09,0:20:39,0:32:27,0:02:55,0:02:35,0:00:01,0:43:06,1:50:27,1092,1080,6
+General Medicine,PGY1,S0169404,2014-08-06,1:36:06,0:42:35,0:11:37,0:17:13,0:12:01,0:02:17,3:19:57,1597,622,10
+General Medicine,PGY1,S0169404,2014-08-07,3:50:06,0:44:25,0:04:55,0:34:04,0:24:34,0:05:14,6:00:35,2731,23,9
+General Medicine,PGY1,S0169404,2014-08-04,2:53:28,0:39:22,0:20:30,0:31:15,0:28:30,0:03:56,5:15:19,1938,299,13
+General Medicine,PGY1,S0169404,2014-08-05,3:57:54,0:47:13,0:25:11,1:32:47,0:33:40,0:00:36,7:45:57,2942,0,18
+General Medicine,PGY1,S0173803,2016-01-25,0:00:00,0:04:20,0:00:00,0:00:21,0:00:00,0:00:00,0:04:49,42,25,7
+General Medicine,PGY1,S0169404,2014-08-03,2:46:38,0:44:10,0:24:14,0:37:26,0:15:12,0:08:10,5:18:00,2423,111,14
+General Medicine,PGY1,S0173803,2016-01-27,1:20:59,0:47:58,1:03:59,0:28:15,0:14:33,0:10:50,4:34:34,1636,0,18
+General Medicine,PGY1,S0173803,2016-01-26,1:50:30,1:38:15,2:41:07,0:36:50,0:33:00,0:25:35,8:12:00,2630,47,20
+Night Team,PGY2+,S0163662,2015-08-18,1:23:56,0:49:55,3:57:53,0:23:05,0:19:05,0:33:47,7:46:56,3340,0,20
+Night Team,PGY2+,S0163662,2015-08-19,2:17:57,0:50:47,2:19:10,0:53:31,0:23:31,0:42:18,7:57:39,4212,0,18
+Night Team,PGY2+,S0163662,2015-08-10,1:01:43,0:37:22,3:43:26,0:26:21,0:09:00,0:27:32,6:44:40,2378,0,17
+Night Team,PGY2+,S0163662,2015-08-11,0:43:05,0:27:16,3:07:17,0:19:17,0:10:18,0:23:13,5:32:39,1477,40,12
+General Medicine,PGY2+,S0169383,2016-07-03,1:54:15,1:03:22,0:00:01,0:21:30,0:17:40,0:10:27,4:24:29,1461,0,17
+Night Team,PGY2+,S0163662,2015-08-13,0:46:09,0:35:20,2:19:21,0:39:04,0:24:23,0:25:46,5:25:04,2325,0,20
+Night Team,PGY2+,S0163662,2015-08-14,1:22:32,0:45:40,3:22:57,0:52:06,0:22:10,0:24:15,7:34:20,2648,0,21
+Night Team,PGY2+,S0163662,2015-08-15,7:20:50,0:38:58,2:55:08,0:09:25,0:07:38,0:15:25,11:37:52,1510,0,14
+Night Team,PGY2+,S0163662,2015-08-16,0:08:42,0:11:11,0:02:31,0:22:09,0:03:29,0:07:54,0:57:35,446,0,7
+Night Team,PGY2+,S0163662,2015-08-17,1:34:57,0:43:28,2:14:55,0:36:06,0:25:24,0:29:42,6:33:36,2538,0,24
+General Medicine,PGY2+,S0181074,2016-10-22,1:32:30,0:34:04,0:00:13,0:20:05,0:12:02,0:11:46,3:28:57,1602,0,18
+Emergency Medicine,PGY2+,S0169390,2015-07-26,0:22:41,0:15:05,1:14:45,0:14:44,0:14:32,0:14:28,2:56:34,938,0,9
+Emergency Medicine,PGY2+,S0169390,2015-07-27,1:02:53,0:19:43,1:37:30,0:23:57,0:17:55,0:13:18,4:43:20,1456,0,8
+Emergency Medicine,PGY2+,S0169390,2015-07-28,0:53:05,0:24:56,1:59:44,0:29:19,0:13:23,0:21:26,5:02:15,1356,0,18
+Emergency Medicine,PGY2+,S0172645,2016-10-28,0:03:44,0:00:29,0:00:00,0:00:00,0:00:00,0:01:00,0:05:30,29,0,4
+General Medicine,PGY1,S0169411,2015-05-30,0:05:06,0:00:23,0:00:03,0:01:22,0:00:19,0:00:00,0:07:16,95,63,6
+Night Team,PGY2+,S0176040,2016-09-30,2:30:03,1:18:24,0:00:04,0:36:23,0:27:08,0:01:42,5:25:49,1643,20,45
+Emergency Medicine,PGY1,S0181077,2015-10-08,1:22:59,0:45:57,2:04:12,0:26:13,0:33:40,1:04:37,6:56:41,2818,0,23
+Emergency Medicine,PGY1,S0181077,2015-10-09,1:08:52,0:34:08,2:40:05,0:35:33,0:44:12,0:42:47,7:19:26,2609,0,35
+Emergency Medicine,PGY1,S0181077,2015-10-02,1:38:25,1:02:11,2:46:06,0:20:26,0:33:52,0:41:36,7:43:39,2145,60,23
+Emergency Medicine,PGY1,S0181077,2015-10-03,1:28:07,0:23:52,2:29:28,0:17:40,0:41:37,0:39:02,7:25:28,2537,0,22
+Emergency Medicine,PGY1,S0181077,2015-10-01,1:01:50,0:41:46,2:17:33,0:33:38,0:33:13,0:36:45,6:23:37,2168,13,16
+Emergency Medicine,PGY1,S0181077,2015-10-06,1:14:37,0:50:55,2:09:16,0:29:00,0:29:51,0:39:40,6:49:58,2578,0,21
+Emergency Medicine,PGY1,S0181077,2015-10-07,0:43:27,0:44:44,0:50:53,0:18:17,0:11:30,0:07:43,3:56:52,1335,12,25
+Emergency Medicine,PGY1,S0181077,2015-10-04,0:00:09,0:00:52,0:00:02,0:00:00,0:00:00,0:00:14,0:01:20,27,27,1
+Emergency Medicine,PGY1,S0181077,2015-10-05,1:16:32,0:31:52,3:12:11,0:40:35,0:33:01,0:26:59,7:51:54,2761,54,23
+General Medicine,PGY2+,S0106952,2015-09-27,2:25:55,2:42:52,0:06:26,0:09:52,0:22:17,0:44:12,7:44:49,2830,832,30
+General Medicine,PGY2+,S0106952,2015-09-26,1:59:44,1:39:17,0:53:08,0:22:09,0:21:51,0:28:54,6:18:47,2304,362,30
+General Medicine,PGY2+,S0106952,2015-09-25,1:54:57,2:40:23,0:29:24,0:31:07,0:16:14,0:46:14,7:29:17,2833,449,31
+General Medicine,PGY2+,S0106952,2015-09-24,0:10:41,0:19:50,0:00:10,0:00:06,0:00:06,0:00:04,0:31:06,422,410,20
+General Medicine,PGY2+,S0106952,2015-09-23,2:44:55,2:14:53,1:27:40,0:40:19,0:27:45,0:30:48,8:43:40,2679,1628,89
+General Medicine,PGY2+,S0106952,2015-09-22,3:40:56,0:14:18,1:50:24,0:38:06,0:43:20,1:13:27,9:22:30,2438,365,25
+General Medicine,PGY2+,S0106952,2015-09-21,3:24:27,0:22:42,0:20:28,0:34:02,0:49:13,1:06:29,7:12:34,2157,547,38
+Night Team,PGY1,S0169386,2015-03-28,1:46:28,0:23:47,1:17:58,0:12:35,0:17:36,0:50:16,5:05:12,1465,76,47
+Night Team,PGY1,S0169386,2015-03-27,2:06:11,0:14:50,1:27:49,0:46:18,0:26:07,1:08:48,6:37:21,1778,82,46
+Night Team,PGY1,S0169386,2015-03-26,2:08:55,0:23:21,1:30:56,0:36:21,0:29:19,0:35:31,6:13:05,2239,126,65
+Night Team,PGY1,S0169386,2015-03-25,2:06:45,0:14:37,1:59:36,0:41:47,0:19:52,0:59:22,7:06:28,1722,0,36
+Night Team,PGY1,S0169386,2015-03-24,3:38:18,0:38:43,1:04:03,0:55:38,0:22:40,1:14:44,8:26:08,3194,27,65
+Night Team,PGY1,S0169386,2015-03-23,2:13:52,0:21:24,0:43:24,0:40:16,0:28:56,1:01:21,5:46:49,1857,125,42
+Night Team,PGY1,S0169386,2015-03-22,1:13:54,0:15:08,0:01:47,0:08:39,0:11:02,0:34:04,2:27:18,784,385,30
+Night Team,PGY1,S0169386,2015-03-21,1:07:15,0:26:22,1:03:45,0:24:03,0:13:57,0:28:07,4:05:54,1690,0,31
+General Medicine,PGY2+,S0106952,2015-09-28,2:42:46,1:09:38,1:41:37,0:17:54,0:22:08,0:42:43,7:37:23,2444,846,33
+General Medicine,PGY2+,S0169400,2015-09-13,1:47:23,0:11:33,0:26:35,0:27:31,0:10:35,0:20:16,3:53:52,1400,0,13
+General Medicine,PGY2+,S0169400,2015-09-12,4:08:09,0:20:10,0:44:18,0:31:12,0:20:37,0:33:56,7:03:01,2134,0,26
+General Medicine,PGY2+,S0169400,2015-09-11,1:28:02,0:02:37,0:37:41,0:15:01,0:11:13,0:26:55,3:24:00,1118,14,13
+General Medicine,PGY2+,S0169400,2015-09-10,3:19:21,1:01:53,0:18:07,0:22:19,0:28:48,1:17:28,7:39:01,2460,208,23
+General Medicine,PGY2+,S0169400,2015-09-17,2:47:40,0:46:57,0:15:49,0:49:23,0:13:45,1:32:45,6:58:48,2360,68,34
+General Medicine,PGY2+,S0169400,2015-09-16,2:13:22,0:26:05,0:44:12,0:57:35,0:34:29,1:07:00,6:51:49,2385,126,13
+General Medicine,PGY1,S0163425,2014-10-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,2
+General Medicine,PGY1,S0163425,2014-10-10,0:31:11,0:01:34,0:01:40,0:00:00,0:00:00,0:00:57,0:37:21,149,145,8
+General Medicine,PGY1,S0181074,2016-03-10,2:28:20,1:26:29,0:00:20,1:12:46,0:14:46,0:07:50,6:11:03,3366,145,24
+General Medicine,PGY2+,S0169400,2015-09-19,4:08:18,0:40:13,2:06:40,0:29:58,0:18:20,0:13:32,8:22:04,2442,0,40
+General Medicine,PGY2+,S0169400,2015-09-18,3:07:04,0:15:24,0:11:27,0:44:12,0:39:57,0:43:13,6:10:40,2322,107,15
+General Medicine,PGY1,S0169400,2015-05-08,2:02:44,0:41:06,1:45:07,0:26:06,0:16:16,0:34:18,6:18:03,1531,0,9
+General Medicine,PGY1,S0169400,2015-05-09,1:35:14,0:00:44,0:21:26,0:10:43,0:08:40,0:11:31,2:50:22,878,0,8
+General Medicine,PGY1,S0169394,2015-04-30,2:35:20,0:34:47,1:32:46,0:34:33,0:26:28,0:24:12,6:34:48,1903,342,15
+Emergency Medicine,PGY1,S0195075,2016-07-26,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY1,S0195075,2016-07-27,1:07:13,0:07:36,0:01:32,0:05:45,0:27:13,0:00:03,2:06:55,430,0,8
+Emergency Medicine,PGY1,S0195075,2016-07-24,3:06:06,0:29:28,0:00:37,0:28:30,0:38:44,0:00:32,5:13:05,1160,0,16
+Emergency Medicine,PGY1,S0195075,2016-07-25,3:27:42,0:37:38,0:02:19,0:32:00,0:51:36,0:05:10,6:08:39,1659,0,16
+General Medicine,PGY1,S0195075,2016-07-22,0:04:04,0:11:28,0:00:00,0:01:03,0:00:00,0:00:00,0:17:52,103,101,4
+General Medicine,PGY1,S0195075,2016-07-23,3:44:31,0:48:19,0:01:23,0:54:42,0:37:42,0:00:25,6:32:46,1336,0,20
+General Medicine,PGY1,S0195075,2016-07-20,0:52:03,0:31:46,0:00:05,0:14:16,0:05:43,0:00:02,2:01:00,540,20,5
+General Medicine,PGY1,S0195075,2016-07-21,2:23:42,0:53:32,0:00:03,0:59:21,0:22:49,0:09:48,5:14:00,1193,27,7
+General Medicine,PGY1,S0169400,2015-05-06,2:14:49,0:12:53,2:59:02,0:33:18,0:18:03,0:31:54,7:06:40,1480,0,13
+Emergency Medicine,PGY1,S0195075,2016-07-28,3:29:19,0:40:24,0:02:18,0:25:11,1:07:14,0:02:56,6:18:13,1850,0,27
+Emergency Medicine,PGY1,S0195075,2016-07-29,0:51:57,0:04:11,0:07:53,0:08:31,0:11:17,0:00:08,1:48:44,559,82,15
+General Medicine,PGY1,S0169400,2015-05-07,1:16:54,0:02:06,1:14:25,0:22:35,0:15:17,0:23:39,3:57:44,1020,37,15
+General Medicine,PGY1,S0074196,2015-09-07,2:34:21,0:50:18,1:39:35,0:48:00,0:28:33,0:23:57,7:21:17,2013,0,9
+General Medicine,PGY1,S0169400,2015-05-01,2:43:57,0:28:08,0:55:40,0:34:58,0:15:46,0:19:28,5:47:43,1672,28,15
+General Medicine,PGY1,S0169400,2015-05-02,1:32:27,0:02:00,1:12:18,0:34:55,0:07:56,0:08:40,3:59:54,1214,0,5
+General Medicine,PGY1,S0169400,2015-05-03,2:07:04,0:36:04,1:02:59,0:34:53,0:14:10,0:37:52,5:35:23,1504,0,14
+Night Team,PGY2+,S0181085,2016-11-10,1:36:38,2:10:39,0:00:08,0:25:48,0:04:40,0:00:50,4:40:14,1542,27,21
+General Medicine,PGY1,S0074196,2015-09-06,1:15:01,0:08:52,0:51:04,0:29:41,0:17:59,0:11:15,3:30:55,1047,32,10
+General Medicine,PGY1,S0074196,2015-09-05,0:00:00,0:00:00,0:00:00,0:00:29,0:00:00,0:00:00,0:00:29,18,0,3
+General Medicine,PGY1,S0181083,2016-01-31,0:42:32,0:55:30,2:28:11,1:03:50,0:32:00,0:42:48,6:59:37,1641,44,15
+Night Team,PGY2+,S0106952,2016-06-19,4:47:14,3:04:09,0:00:31,0:39:39,0:28:32,0:09:27,10:03:24,3867,536,121
+Night Team,PGY2+,S0169385,2016-08-19,1:20:57,1:22:40,0:00:04,0:23:54,0:03:47,0:06:58,3:43:49,1450,0,13
+General Medicine,PGY2+,S0169398,2016-04-01,3:52:36,1:23:11,0:00:06,0:24:56,0:34:37,0:03:01,6:52:21,1979,0,18
+General Medicine,PGY1,S0195225,2016-10-25,0:03:08,0:02:01,0:00:00,0:00:41,0:00:00,0:00:02,0:08:53,93,85,6
+General Medicine,PGY1,S0195225,2016-10-24,3:10:25,2:03:15,0:00:06,0:19:30,0:19:14,0:01:12,6:30:25,1989,0,10
+General Medicine,PGY1,S0195225,2016-10-27,2:17:56,1:01:21,0:00:17,0:42:51,0:18:12,0:00:42,5:15:43,1954,18,6
+General Medicine,PGY1,S0195225,2016-10-26,2:55:50,1:36:14,0:00:12,0:32:48,0:27:39,0:02:52,6:23:31,2189,0,12
+General Medicine,PGY1,S0195225,2016-10-21,2:29:49,1:41:20,0:00:12,0:43:15,0:36:01,0:00:35,5:57:16,1926,53,9
+General Medicine,PGY1,S0195225,2016-10-20,0:01:11,0:03:37,0:00:00,0:00:00,0:00:00,0:00:00,0:04:56,61,59,4
+General Medicine,PGY1,S0195225,2016-10-23,0:52:52,0:26:41,0:00:07,0:20:17,0:11:47,0:00:07,2:08:25,556,12,4
+General Medicine,PGY1,S0195225,2016-10-22,1:08:30,0:38:22,0:00:05,0:21:29,0:10:56,0:00:16,2:49:11,883,0,4
+General Medicine,PGY1,S0195225,2016-10-29,2:29:54,2:04:25,0:00:08,0:43:29,0:40:48,0:02:36,6:44:41,2247,16,10
+General Medicine,PGY1,S0195225,2016-10-28,1:17:01,0:51:40,0:00:07,0:32:47,0:14:25,0:00:19,3:36:39,1191,0,4
+General Medicine,PGY1,S0169385,2015-04-27,2:41:58,0:19:17,1:20:23,0:31:57,0:21:45,0:29:15,6:31:26,2045,50,10
+General Medicine,PGY1,S0169385,2015-04-26,1:45:12,0:02:22,0:30:35,0:24:23,0:14:34,0:12:24,3:25:20,1360,0,5
+Night Team,PGY2+,S0169398,2015-12-17,3:02:27,1:14:06,2:05:57,0:45:02,0:15:11,0:35:05,8:25:29,2055,0,16
+Night Team,PGY2+,S0169398,2015-12-16,1:37:49,1:41:24,1:52:08,0:32:01,0:14:55,0:41:12,7:00:09,1883,24,43
+Night Team,PGY2+,S0169398,2015-12-15,3:00:18,1:02:21,1:42:21,0:50:57,0:18:41,0:29:20,7:50:42,2066,0,57
+Night Team,PGY2+,S0169398,2015-12-14,3:00:30,0:38:52,0:21:46,0:10:01,0:09:29,0:20:52,5:00:05,1379,0,79
+Night Team,PGY2+,S0169398,2015-12-13,2:18:35,0:55:49,0:32:23,0:04:26,0:02:56,0:13:50,4:27:43,1170,294,71
+Night Team,PGY2+,S0169398,2015-12-12,1:57:18,0:41:32,1:34:00,0:19:07,0:14:41,0:17:56,5:21:43,1100,0,24
+Night Team,PGY2+,S0169398,2015-12-11,3:06:19,1:03:49,2:25:51,0:23:32,0:22:50,0:34:49,8:11:48,1676,0,31
+Night Team,PGY2+,S0169398,2015-12-10,1:50:11,1:28:58,1:43:15,0:33:50,0:16:44,0:29:52,6:45:22,1629,0,34
+Emergency Medicine,PGY2+,S0169403,2016-01-20,1:22:22,1:41:09,2:57:01,0:40:37,0:30:21,0:55:08,8:40:37,3179,516,32
+Emergency Medicine,PGY2+,S0169403,2016-01-21,0:06:10,0:42:19,0:12:46,0:00:14,0:00:58,0:23:00,1:32:40,505,505,11
+Emergency Medicine,PGY2+,S0169403,2016-01-22,0:49:52,1:24:01,2:31:33,0:31:05,0:45:08,1:06:58,7:47:42,2810,432,43
+Emergency Medicine,PGY2+,S0169403,2016-01-23,0:29:47,0:45:14,1:49:33,0:21:49,0:20:49,0:31:43,4:40:46,1730,104,30
+Emergency Medicine,PGY2+,S0169403,2016-01-24,0:56:50,1:32:07,2:45:39,0:27:54,0:30:30,0:53:25,7:53:59,3392,317,44
+Emergency Medicine,PGY2+,S0169403,2016-01-25,1:23:58,0:59:13,2:47:55,0:21:01,0:43:38,0:43:19,7:38:23,3293,150,32
+General Medicine,PGY1,S0109179,2014-12-24,2:50:40,0:06:47,3:45:51,0:39:18,0:16:43,0:19:35,8:23:02,2054,0,10
+Night Team,PGY2+,S0169398,2015-12-18,2:19:05,2:09:22,1:01:25,0:39:50,0:27:08,0:29:06,7:40:34,2345,0,31
+General Medicine,PGY1,S0181074,2016-03-22,2:25:07,1:42:15,0:00:11,2:16:36,0:36:07,0:09:40,7:58:56,3135,46,14
+General Medicine,PGY1,S0181057,2016-05-26,1:50:55,0:53:21,0:00:22,0:32:56,0:32:32,0:01:40,4:26:24,1674,0,14
+General Medicine,PGY1,S0181057,2016-05-25,2:46:33,1:45:23,0:00:06,1:00:10,0:30:00,0:06:15,7:02:41,2217,0,18
+General Medicine,PGY1,S0181074,2016-03-21,1:34:10,1:36:55,0:00:06,1:25:14,0:11:46,0:09:21,5:27:51,2098,95,15
+General Medicine,PGY1,S0181057,2016-05-23,4:08:20,1:47:37,0:00:03,0:55:21,0:41:56,0:09:16,8:32:40,2422,220,15
+General Medicine,PGY1,S0181057,2016-05-22,1:06:37,0:34:13,0:00:19,0:15:28,0:14:56,0:02:06,2:31:41,863,58,6
+General Medicine,PGY1,S0181074,2016-03-24,2:53:31,1:36:55,0:00:06,1:30:44,0:22:33,0:06:14,6:54:44,2393,36,17
+General Medicine,PGY1,S0181074,2016-03-25,0:02:21,0:00:24,0:00:00,0:00:00,0:00:00,0:01:10,0:04:27,52,48,5
+Night Team,PGY1,S0195227,2016-10-16,2:52:23,0:48:21,0:00:01,0:47:56,0:19:00,0:03:25,5:29:16,1503,0,38
+Night Team,PGY1,S0195227,2016-10-17,3:16:48,1:01:51,0:00:01,0:58:24,0:31:05,0:06:31,6:10:02,1928,1,41
+General Medicine,PGY1,S0181070,2015-08-31,2:04:22,0:00:07,1:22:45,0:20:03,0:16:44,0:00:52,4:23:17,1188,0,8
+General Medicine,PGY1,S0181070,2015-08-30,2:07:47,0:08:21,1:03:39,0:16:57,0:16:32,0:12:32,4:14:03,1192,0,10
+General Medicine,PGY1,S0181057,2016-05-29,0:21:41,0:24:53,0:00:00,0:00:59,0:03:43,0:00:02,0:57:25,213,209,5
+General Medicine,PGY1,S0181057,2016-05-28,1:54:06,2:02:52,0:00:05,0:25:24,0:12:33,0:02:36,5:02:22,1837,0,20
+Night Team,PGY2+,S0163425,2015-12-18,2:03:29,1:40:08,1:52:55,0:39:43,0:17:26,0:46:44,7:50:59,3158,7,85
+Night Team,PGY2+,S0163425,2015-12-19,2:48:36,1:14:10,1:24:28,0:53:04,0:32:46,0:49:02,8:06:57,2640,0,52
+General Medicine,PGY1,S0169402,2014-11-17,2:32:09,0:00:38,2:52:04,0:46:05,0:15:56,0:04:08,6:48:06,1237,0,7
+Intensive Care Unit,PGY2+,S0169411,2016-01-19,2:48:47,0:45:26,0:04:15,0:18:09,0:26:52,0:36:54,5:37:47,1830,20,22
+General Medicine,PGY1,S0163662,2015-03-06,1:30:51,0:17:41,1:52:12,0:22:47,0:09:44,0:10:45,4:46:24,1300,113,8
+General Medicine,PGY1,S0163662,2015-03-07,3:18:31,1:05:34,4:23:24,0:48:05,0:30:55,0:19:34,10:49:31,1922,0,9
+General Medicine,PGY1,S0163662,2015-03-04,5:15:23,0:41:46,1:55:51,1:13:39,0:49:49,0:24:11,10:50:16,2872,0,11
+General Medicine,PGY1,S0163662,2015-03-05,1:43:46,0:10:39,0:54:16,0:54:13,0:14:38,0:06:16,4:22:06,1561,381,8
+Night Team,PGY2+,S0163425,2015-12-10,1:00:00,1:24:49,1:40:01,0:30:37,0:22:35,0:58:53,6:17:39,2500,0,46
+Intensive Care Unit,PGY2+,S0169411,2016-01-13,4:33:00,0:39:55,0:07:37,1:00:19,0:56:22,0:49:37,8:37:58,2752,0,29
+Intensive Care Unit,PGY2+,S0169411,2016-01-10,2:05:34,0:27:59,0:05:24,0:08:07,0:13:13,0:16:17,3:23:03,917,13,16
+Night Team,PGY2+,S0163425,2015-12-13,1:44:09,1:28:20,1:49:09,0:52:50,0:29:26,0:30:32,7:13:04,2791,0,55
+Intensive Care Unit,PGY2+,S0169411,2016-01-16,0:00:00,0:00:00,0:00:00,0:01:36,0:00:00,0:00:00,0:01:36,25,0,6
+General Medicine,PGY1,S0169402,2014-11-18,0:16:08,0:00:00,0:00:56,0:00:00,0:00:00,0:00:00,0:17:21,50,48,5
+Night Team,PGY2+,S0163425,2015-12-16,2:10:26,1:43:49,2:11:51,0:50:54,0:20:54,0:46:31,8:37:25,2697,0,42
+Night Team,PGY2+,S0163425,2015-12-17,1:51:00,1:33:57,1:08:11,0:32:28,0:24:40,1:04:58,6:59:26,2668,0,70
+General Medicine,PGY1,S0195214,2016-07-29,3:09:38,1:12:25,0:01:04,1:07:25,0:25:08,0:00:40,6:21:12,1530,0,12
+General Medicine,PGY1,S0195214,2016-07-28,3:07:44,1:45:24,0:00:20,1:04:43,0:16:53,0:00:11,6:40:38,1473,416,11
+General Medicine,PGY1,S0195214,2016-07-25,0:36:00,0:12:01,0:00:01,0:01:44,0:00:39,0:00:00,0:59:53,278,270,9
+General Medicine,PGY1,S0195214,2016-07-24,2:39:20,1:19:38,0:01:03,0:44:49,0:48:00,0:00:00,5:53:36,1546,0,9
+General Medicine,PGY1,S0195214,2016-07-27,0:54:40,0:17:09,0:00:03,0:16:56,0:04:44,0:00:04,1:48:34,576,0,8
+General Medicine,PGY1,S0195214,2016-07-26,2:20:36,1:27:16,0:04:29,0:52:31,0:37:13,0:00:04,5:44:38,1459,0,15
+General Medicine,PGY1,S0181065,2015-11-20,1:37:10,0:46:38,0:32:09,0:44:35,0:13:22,0:27:26,4:53:05,1827,0,4
+General Medicine,PGY1,S0181065,2015-11-21,2:29:06,1:22:00,1:06:32,1:00:52,0:22:56,0:58:30,8:09:09,3768,0,7
+General Medicine,PGY1,S0181065,2015-11-22,0:00:00,0:00:00,0:00:00,0:02:11,0:00:00,0:00:00,0:02:11,30,0,4
+General Medicine,PGY1,S0181065,2015-11-23,1:31:23,1:00:34,0:35:44,0:28:22,0:26:50,0:30:08,5:03:58,2282,233,5
+General Medicine,PGY1,S0181065,2015-11-24,3:30:25,1:07:30,1:17:21,0:51:12,0:30:54,1:19:58,9:12:39,4166,0,13
+General Medicine,PGY1,S0181065,2015-11-25,2:30:13,0:39:11,1:30:17,0:40:17,0:29:37,0:38:11,7:10:21,2657,0,9
+General Medicine,PGY1,S0181065,2015-11-26,2:24:39,0:58:23,1:12:51,0:46:00,0:33:14,0:24:31,6:52:07,2823,0,13
+General Medicine,PGY1,S0181065,2015-11-27,0:00:00,0:00:00,0:00:00,0:00:27,0:00:00,0:00:00,0:00:27,28,0,4
+General Medicine,PGY1,S0181065,2015-11-28,2:06:01,0:59:53,0:21:39,0:16:21,0:31:52,0:16:17,5:12:46,2878,128,12
+General Medicine,PGY1,S0181065,2015-11-29,2:35:34,1:00:03,0:52:18,0:56:28,0:34:59,0:42:59,7:33:41,3673,77,14
+General Medicine,PGY1,S0169385,2015-04-28,2:49:42,0:10:50,1:12:30,0:41:36,0:17:41,0:20:05,5:47:47,1662,76,13
+General Medicine,PGY1,S0169384,2015-02-26,3:01:17,0:25:20,1:22:53,0:45:52,0:24:45,0:18:58,6:26:58,1693,62,11
+General Medicine,PGY1,S0169384,2015-02-27,3:41:13,0:13:38,1:15:38,0:52:10,0:18:58,0:23:14,6:51:19,1636,169,7
+Night Team,PGY2+,S0163425,2015-10-29,1:42:42,1:31:58,1:55:23,1:17:23,0:08:32,0:42:13,8:07:23,2945,125,39
+Intensive Care Unit,PGY2+,S0169385,2016-01-28,1:35:21,0:41:28,0:21:11,0:36:21,0:23:48,1:16:07,5:16:17,2750,118,23
+Night Team,PGY2+,S0169384,2015-10-10,1:17:45,2:02:49,4:13:16,0:47:47,0:27:36,0:48:10,9:52:59,3122,235,15
+Night Team,PGY2+,S0169384,2015-10-11,1:42:10,0:59:05,2:27:23,0:45:56,0:15:53,0:37:50,7:00:24,1662,1,17
+Night Team,PGY2+,S0169384,2015-10-12,1:40:24,1:13:38,4:01:44,1:10:06,0:22:02,0:39:17,9:34:34,2700,98,14
+Night Team,PGY2+,S0169384,2015-10-13,0:53:27,1:54:18,4:03:46,0:59:35,0:13:46,0:30:46,8:50:00,2464,17,15
+Night Team,PGY2+,S0169384,2015-10-14,1:19:07,2:21:25,2:55:29,1:06:19,0:15:20,0:27:07,8:44:26,2858,106,22
+Night Team,PGY2+,S0169384,2015-10-15,0:36:23,1:28:02,1:55:50,0:43:08,0:17:47,0:25:56,5:44:48,2587,118,15
+Night Team,PGY2+,S0169384,2015-10-16,0:34:40,0:54:28,0:53:29,0:12:25,0:06:06,0:17:10,3:09:19,1367,0,15
+Night Team,PGY2+,S0169384,2015-10-17,0:31:25,0:55:33,1:53:11,0:29:20,0:11:18,0:40:36,5:01:31,1741,0,14
+General Medicine,PGY2+,S0169403,2015-08-08,2:38:58,1:52:00,0:45:05,1:03:18,0:22:20,1:16:51,9:09:10,3122,168,25
+General Medicine,PGY2+,S0169403,2015-08-09,2:01:28,0:57:17,0:39:27,0:43:22,0:33:23,0:49:13,6:23:20,2783,116,18
+Intensive Care Unit,PGY2+,S0174975,2016-09-19,3:06:53,0:56:24,0:15:49,0:50:29,0:28:45,0:15:23,7:01:34,2146,9,27
+General Medicine,PGY1,S0169404,2014-07-28,1:59:59,0:19:38,0:12:56,0:26:32,0:14:28,0:09:13,3:33:13,1667,165,6
+Night Team,PGY2+,S0169404,2016-01-09,1:18:52,1:33:04,0:37:48,0:20:31,0:05:00,0:23:09,4:40:00,3558,147,57
+Night Team,PGY2+,S0169404,2016-01-08,1:29:11,2:36:34,1:09:05,0:48:19,0:11:18,0:34:59,7:24:15,4521,214,39
+Intensive Care Unit,PGY2+,S0174975,2016-09-15,2:14:52,1:11:51,0:07:38,0:49:51,0:49:59,0:00:30,5:44:01,2016,473,24
+Intensive Care Unit,PGY2+,S0174975,2016-09-14,2:07:47,0:38:51,0:07:08,0:22:43,0:08:04,0:00:17,3:53:53,1599,0,24
+General Medicine,PGY1,S0169404,2014-07-27,2:43:14,0:43:13,0:29:24,0:34:44,0:24:10,0:07:32,5:13:09,2275,408,16
+Intensive Care Unit,PGY2+,S0174975,2016-09-16,0:17:37,0:05:05,0:00:17,0:00:00,0:00:10,0:01:09,0:28:51,300,208,16
+Intensive Care Unit,PGY2+,S0174975,2016-09-11,2:42:32,1:14:32,0:08:12,0:54:30,0:47:32,0:07:40,6:41:05,1589,38,11
+General Medicine,PGY1,S0169404,2014-07-20,3:14:44,1:29:41,0:31:30,0:18:36,0:35:54,0:24:39,7:03:19,2605,1147,5
+Intensive Care Unit,PGY2+,S0174975,2016-09-13,2:32:29,1:13:39,0:04:21,0:55:14,0:18:27,0:04:27,6:06:34,1948,0,44
+Intensive Care Unit,PGY2+,S0174975,2016-09-12,0:04:19,0:00:58,0:00:00,0:00:00,0:00:00,0:00:00,0:05:45,33,0,5
+General Medicine,PGY1,S0169384,2015-02-28,1:30:46,0:13:03,2:38:06,0:02:06,0:01:36,0:10:01,4:48:10,1266,1248,16
+Emergency Medicine,PGY1,S0181077,2015-09-19,1:52:31,0:39:21,3:08:45,0:31:59,0:35:07,1:15:41,8:55:31,3153,93,31
+Emergency Medicine,PGY1,S0181077,2015-09-18,1:26:54,0:43:50,1:53:02,0:14:11,0:27:20,1:08:45,6:30:28,2372,61,22
+Emergency Medicine,PGY1,S0181077,2015-09-17,0:24:46,0:16:43,1:04:09,0:14:30,0:14:07,0:28:18,3:08:21,1056,0,10
+Emergency Medicine,PGY1,S0181077,2015-09-16,0:50:06,0:29:04,1:31:59,0:04:02,0:03:11,0:08:31,3:35:50,897,193,9
+Emergency Medicine,PGY1,S0181077,2015-09-15,0:53:39,0:50:03,1:45:01,0:30:02,0:25:46,0:35:55,5:27:59,1798,198,13
+Emergency Medicine,PGY1,S0181077,2015-09-14,0:38:33,0:13:19,0:51:44,0:18:11,0:12:07,0:30:05,3:07:59,1167,0,11
+Emergency Medicine,PGY1,S0181077,2015-09-13,1:40:47,0:55:03,2:22:40,0:19:44,0:26:18,0:23:42,6:48:42,2170,0,18
+Night Team,PGY1,S0181526,2016-06-08,3:13:44,0:53:06,0:00:01,0:21:25,0:15:05,0:05:04,5:09:47,2428,3,42
+General Medicine,PGY1,S0173803,2016-05-21,0:00:00,0:00:00,0:00:00,0:01:01,0:00:00,0:00:00,0:01:01,16,0,6
+General Medicine,PGY1,S0173803,2016-05-20,1:14:22,0:49:19,0:18:32,0:53:07,0:17:01,0:11:37,4:27:09,1469,12,12
+General Medicine,PGY1,S0180927,2016-08-09,3:13:40,1:37:19,0:00:46,0:41:36,0:24:41,0:10:50,6:52:14,2303,12,22
+General Medicine,PGY1,S0180927,2016-08-08,0:09:11,0:02:59,0:00:00,0:03:44,0:00:00,0:00:12,0:17:17,134,82,11
+General Medicine,PGY1,S0180927,2016-08-03,1:14:58,0:42:01,0:00:05,0:25:44,0:10:54,0:01:12,2:51:26,1022,70,14
+General Medicine,PGY1,S0180927,2016-08-02,1:42:54,1:26:52,0:00:08,0:25:29,0:13:19,0:01:03,4:04:38,1429,89,9
+General Medicine,PGY1,S0180927,2016-08-01,2:22:32,1:29:17,0:00:59,0:34:34,0:20:54,0:09:45,5:37:07,1834,0,15
+General Medicine,PGY1,S0180927,2016-08-07,0:05:19,0:00:42,0:00:00,0:04:08,0:00:00,0:01:02,0:12:24,111,0,9
+General Medicine,PGY1,S0180927,2016-08-06,2:46:02,1:57:27,0:00:13,0:33:43,0:38:03,0:11:00,6:40:50,2007,0,12
+General Medicine,PGY1,S0180927,2016-08-05,1:57:48,1:19:02,0:00:07,0:27:57,0:15:35,0:00:45,4:28:33,1424,21,9
+General Medicine,PGY1,S0180927,2016-08-04,2:49:11,1:07:38,0:00:16,0:53:18,0:26:01,0:13:59,6:07:08,2170,152,14
+Night Team,PGY2+,S0169406,2016-10-25,1:12:28,0:33:45,0:00:02,0:00:59,0:02:40,0:01:04,2:14:37,822,0,17
+Intensive Care Unit,PGY2+,S0181070,2016-08-18,1:22:48,1:06:25,0:12:54,0:05:59,0:10:07,0:09:43,3:26:59,867,26,20
+Intensive Care Unit,PGY2+,S0181070,2016-08-19,2:17:42,0:50:58,0:00:13,0:26:57,0:24:51,0:06:46,4:41:50,1282,0,16
+Intensive Care Unit,PGY2+,S0181070,2016-08-16,1:10:12,0:49:00,0:07:29,0:06:52,0:21:46,0:02:24,2:47:48,1133,0,18
+Intensive Care Unit,PGY2+,S0181070,2016-08-17,1:55:35,1:07:21,0:11:45,0:07:45,0:19:24,0:14:27,4:14:39,1089,66,27
+General Medicine,PGY1,S0181059,2015-08-30,0:00:00,0:11:30,0:00:00,0:01:11,0:00:00,0:03:05,0:18:55,161,142,6
+Intensive Care Unit,PGY2+,S0181070,2016-08-15,1:29:25,1:08:53,0:00:17,0:08:44,0:09:52,0:00:19,3:18:17,1027,50,13
+Night Team,PGY2+,S0169406,2016-10-27,0:35:11,0:23:11,0:00:05,0:23:23,0:03:36,0:02:21,1:39:23,769,44,15
+Intensive Care Unit,PGY2+,S0169400,2016-06-30,1:49:49,0:10:25,0:11:34,0:22:02,0:12:00,0:06:16,3:04:19,1192,0,15
+Intensive Care Unit,PGY2+,S0181073,2016-11-04,1:13:26,0:29:21,0:08:29,0:27:50,0:20:52,0:04:34,3:16:08,826,0,19
+Intensive Care Unit,PGY2+,S0181073,2016-11-05,0:00:34,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:38,25,0,4
+Intensive Care Unit,PGY2+,S0181073,2016-11-02,1:20:14,0:24:03,0:02:34,0:06:01,0:17:58,0:01:43,2:29:59,1010,0,14
+Intensive Care Unit,PGY2+,S0181073,2016-11-03,0:14:10,0:07:21,0:00:00,0:04:53,0:05:50,0:00:00,0:40:30,191,0,12
+Intensive Care Unit,PGY2+,S0181073,2016-11-01,1:56:51,0:49:28,0:05:10,0:17:14,0:24:44,0:07:41,4:12:19,1553,0,17
+General Medicine,PGY1,S0163425,2015-06-01,2:27:11,0:35:20,1:35:57,0:17:26,0:21:16,0:25:55,6:02:18,1810,0,21
+Night Team,PGY2+,S0169381,2016-03-07,1:43:25,0:52:46,0:00:19,0:32:38,0:11:38,0:08:08,3:48:30,1272,15,25
+General Medicine,PGY1,S0169389,2014-07-25,3:50:30,0:12:18,0:11:59,1:10:35,0:20:01,0:16:08,6:10:07,1736,0,23
+Night Team,PGY2+,S0169381,2016-03-06,1:44:08,1:06:05,0:00:14,0:33:35,0:15:22,0:03:50,4:29:31,1461,0,20
+Night Team,PGY2+,S0169406,2016-10-23,1:04:14,0:57:42,0:00:15,0:31:19,0:10:31,0:01:09,3:05:43,959,418,12
+Night Team,PGY1,S0195086,2016-11-10,0:33:28,0:08:00,0:00:00,0:04:51,0:11:06,0:00:21,1:00:34,337,0,18
+General Medicine,PGY1,S0181073,2016-01-25,1:40:18,1:25:43,0:31:32,0:51:59,0:52:16,0:56:39,6:54:01,2977,77,12
+General Medicine,PGY1,S0181073,2016-01-24,0:47:43,1:04:37,1:53:49,0:29:27,0:16:13,0:18:41,5:23:47,1624,9,6
+General Medicine,PGY1,S0181073,2016-01-27,1:34:37,1:49:45,1:47:28,0:55:35,0:23:02,0:48:47,7:50:16,2819,73,17
+General Medicine,PGY1,S0181073,2016-01-26,0:30:02,0:57:11,0:09:09,0:32:00,0:12:50,0:25:48,3:01:32,1394,401,18
+General Medicine,PGY1,S0181073,2016-01-21,0:32:30,0:33:37,0:29:17,0:59:15,0:13:47,0:13:36,3:23:48,1513,14,7
+General Medicine,PGY1,S0181073,2016-01-20,1:46:45,1:26:19,2:11:33,0:42:38,0:25:26,0:46:45,7:50:35,2496,0,8
+General Medicine,PGY1,S0181073,2016-01-23,0:00:00,0:11:13,0:00:00,0:00:18,0:00:00,0:00:00,0:12:10,96,92,7
+General Medicine,PGY1,S0181073,2016-01-22,2:17:45,0:48:26,0:54:03,0:51:35,0:39:34,0:25:01,6:32:28,2144,0,13
+General Medicine,PGY1,S0181073,2016-01-29,0:21:10,0:40:47,0:39:41,0:20:08,0:22:03,0:13:32,2:51:54,1184,15,11
+General Medicine,PGY1,S0181073,2016-01-28,0:00:00,0:20:20,0:00:00,0:01:33,0:00:00,0:09:59,0:36:00,471,459,11
+General Medicine,PGY1,S0181056,2015-11-30,2:47:38,1:50:35,1:24:12,1:09:29,0:34:49,0:28:00,9:08:01,3455,0,11
+General Medicine,PGY1,S0106952,2014-09-30,4:42:10,0:11:48,0:16:20,1:02:11,0:44:55,0:21:19,7:55:38,1915,52,24
+Night Team,PGY2+,S0181065,2016-07-31,4:27:10,1:30:59,0:00:13,0:19:28,0:13:06,0:20:35,7:36:42,3856,448,33
+Emergency Medicine,PGY1,S0169406,2015-01-09,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0169406,2016-02-18,1:33:57,0:15:17,0:14:56,0:10:10,0:13:34,0:14:58,2:54:19,1321,0,21
+Intensive Care Unit,PGY2+,S0169385,2016-01-18,0:02:14,0:12:53,0:01:25,0:01:42,0:00:21,0:05:27,0:25:07,501,49,15
+Intensive Care Unit,PGY2+,S0169385,2016-01-19,1:41:43,1:01:54,0:11:30,0:18:29,0:18:01,0:39:31,4:19:20,2296,0,38
+Intensive Care Unit,PGY2+,S0181086,2016-07-09,1:54:23,0:49:47,0:00:02,1:05:56,1:00:04,0:06:19,5:28:52,2043,0,16
+Intensive Care Unit,PGY2+,S0181086,2016-07-08,2:53:34,2:05:22,0:10:34,0:49:48,0:37:31,0:00:53,7:16:41,1937,104,19
+Intensive Care Unit,PGY2+,S0181086,2016-07-05,1:44:32,0:52:48,0:01:33,0:27:55,0:31:33,0:00:23,4:20:06,1623,0,21
+Intensive Care Unit,PGY2+,S0181086,2016-07-04,2:24:23,0:55:02,0:10:08,0:26:06,0:44:02,0:00:11,4:55:55,1047,101,9
+Intensive Care Unit,PGY2+,S0181086,2016-07-07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+Intensive Care Unit,PGY2+,S0181086,2016-07-06,4:00:00,0:46:19,0:08:16,1:11:33,0:55:36,0:01:45,7:36:47,2150,197,22
+Intensive Care Unit,PGY2+,S0181086,2016-07-01,2:16:28,0:38:39,0:00:01,0:44:52,0:34:37,0:08:05,4:59:06,1405,152,23
+Intensive Care Unit,PGY2+,S0169385,2016-01-17,2:27:27,1:22:12,0:26:14,0:11:54,0:20:22,0:49:28,5:55:44,1887,271,13
+Intensive Care Unit,PGY2+,S0181086,2016-07-03,0:22:44,0:42:40,0:00:00,0:06:20,0:02:58,0:00:22,1:16:54,602,592,12
+Intensive Care Unit,PGY2+,S0181086,2016-07-02,2:15:58,2:02:50,0:10:17,0:34:16,0:54:00,0:09:05,6:28:08,1511,27,12
+General Medicine,PGY1,S0074196,2016-01-06,0:34:38,1:03:55,0:29:52,0:35:20,0:09:55,0:10:03,3:25:23,1311,0,12
+General Medicine,PGY1,S0074196,2016-01-07,1:25:52,1:49:56,0:57:58,0:36:33,0:24:06,0:25:46,6:17:35,2356,6,17
+Night Team,PGY2+,S0169401,2015-08-17,2:39:24,0:44:47,0:57:23,0:57:59,0:17:03,0:17:18,6:21:24,2336,73,52
+General Medicine,PGY1,S0195213,2016-11-10,0:11:01,0:00:16,0:00:00,0:00:00,0:00:00,0:00:00,0:11:23,28,0,5
+Night Team,PGY2+,S0169401,2015-08-16,1:56:10,1:07:55,2:02:40,1:08:36,0:16:20,0:37:35,7:47:10,3110,281,68
+General Medicine,PGY1,S0181060,2015-11-26,1:41:14,0:48:19,0:57:03,0:26:15,0:10:45,0:52:52,5:31:41,1592,0,17
+Night Team,PGY2+,S0169401,2015-08-15,1:39:58,0:34:59,3:10:41,1:11:57,0:23:42,0:11:28,7:36:12,2176,0,30
+Night Team,PGY2+,S0169401,2015-08-14,1:49:10,0:38:40,0:44:42,1:13:10,0:12:12,0:15:34,5:28:49,2598,0,45
+Emergency Medicine,PGY2+,S0109179,2015-11-13,7:48:32,0:54:36,0:41:45,0:31:11,0:34:18,0:12:01,11:13:41,3874,0,42
+Emergency Medicine,PGY2+,S0109179,2015-11-12,5:41:22,0:39:16,1:16:42,0:18:41,0:20:14,0:17:00,8:57:34,2284,0,41
+Emergency Medicine,PGY2+,S0109179,2015-11-11,5:57:51,0:34:54,1:10:36,0:25:03,0:16:42,0:08:11,8:50:57,1865,0,39
+Emergency Medicine,PGY2+,S0109179,2015-11-10,6:35:10,0:47:11,0:57:41,0:29:40,0:20:12,0:09:16,9:58:26,3350,0,54
+General Medicine,PGY1,S0181082,2015-12-29,1:44:22,1:08:10,1:38:32,0:59:06,0:24:24,0:20:58,6:58:35,2704,0,23
+General Medicine,PGY1,S0169403,2015-04-24,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0169403,2015-04-25,1:33:09,0:57:50,0:30:18,0:04:51,0:11:00,0:24:04,3:56:13,1399,0,15
+General Medicine,PGY1,S0169403,2015-04-20,1:41:21,1:03:44,0:50:33,0:21:59,0:10:02,0:05:28,4:32:17,1684,0,111
+General Medicine,PGY1,S0169403,2015-04-21,0:31:33,0:37:59,0:10:45,0:10:09,0:03:13,0:06:49,1:48:13,681,34,12
+General Medicine,PGY1,S0169403,2015-04-22,2:39:40,1:23:02,1:19:41,0:38:47,0:35:14,0:36:35,7:47:04,2639,0,13
+General Medicine,PGY1,S0169403,2015-04-23,2:13:20,0:54:26,1:53:45,0:30:11,0:22:52,0:07:56,6:35:39,1890,2,16
+General Medicine,PGY1,S0181069,2016-01-22,2:13:21,0:46:03,1:10:20,1:02:56,0:36:45,0:33:22,7:14:03,2857,0,11
+General Medicine,PGY1,S0181069,2016-01-23,1:22:24,1:08:24,0:50:22,0:42:46,0:25:45,0:13:59,5:30:59,2422,408,19
+General Medicine,PGY1,S0181069,2016-01-20,1:49:39,0:55:38,1:28:24,1:03:37,0:39:58,0:26:04,7:07:48,2866,86,16
+General Medicine,PGY1,S0181069,2016-01-21,1:35:51,0:37:26,1:00:59,1:07:08,0:19:51,0:31:55,5:43:54,1971,10,11
+General Medicine,PGY1,S0181069,2016-01-26,1:34:43,0:55:42,1:13:14,1:04:28,0:22:49,0:17:50,6:22:07,2501,32,19
+General Medicine,PGY1,S0181069,2016-01-27,1:31:57,0:50:30,0:57:56,1:07:22,0:35:16,0:18:43,5:53:53,2359,0,15
+General Medicine,PGY1,S0181069,2016-01-24,0:46:27,0:28:59,0:54:25,0:37:40,0:17:44,0:16:13,3:40:55,1523,0,10
+General Medicine,PGY1,S0181069,2016-01-25,1:10:40,0:49:11,0:44:26,0:58:36,0:47:53,0:31:00,5:25:29,2224,108,15
+General Medicine,PGY1,S0181069,2016-01-28,1:18:16,0:43:46,1:10:26,0:52:48,0:22:47,0:15:47,5:35:50,2164,0,13
+General Medicine,PGY1,S0181069,2016-01-29,0:06:09,0:02:38,0:00:05,0:00:04,0:00:00,0:00:25,0:18:07,505,501,14
+General Medicine,PGY1,S0163425,2015-03-09,3:14:18,0:23:15,1:06:27,0:32:14,0:17:53,0:24:49,6:42:09,2477,0,11
+General Medicine,PGY1,S0163425,2015-03-08,4:01:26,0:56:19,1:25:21,0:26:13,0:22:08,0:31:44,7:51:49,1933,0,15
+General Medicine,PGY1,S0163425,2015-03-05,1:54:22,0:07:24,0:19:39,0:11:41,0:09:31,0:12:24,3:17:24,1153,0,17
+General Medicine,PGY1,S0163425,2015-03-04,3:13:19,0:25:39,1:18:13,0:45:42,0:15:41,0:26:54,6:53:16,2369,0,16
+General Medicine,PGY1,S0163425,2015-03-07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0163425,2015-03-06,2:43:32,0:40:11,0:40:30,0:25:24,0:20:21,0:24:45,5:43:48,1733,0,19
+General Medicine,PGY1,S0163425,2015-03-01,3:39:51,0:09:34,1:25:22,0:28:30,0:14:22,0:17:21,6:40:23,1603,0,14
+General Medicine,PGY1,S0163425,2015-03-03,3:20:46,1:24:59,1:25:06,1:05:52,0:21:06,0:31:25,8:51:00,2687,0,17
+General Medicine,PGY1,S0163425,2015-03-02,0:06:10,0:00:15,0:00:08,0:00:46,0:00:00,0:00:00,0:07:46,71,65,9
+Night Team,PGY2+,S0169398,2015-08-24,2:41:35,1:46:30,2:00:03,0:24:32,0:22:49,0:40:22,8:28:28,2313,91,38
+Night Team,PGY2+,S0169398,2015-08-27,1:37:01,0:43:23,1:31:05,0:22:25,0:09:20,0:30:34,5:16:37,1515,377,28
+Night Team,PGY2+,S0169398,2015-08-26,2:28:11,1:17:20,1:59:22,0:50:48,0:17:28,0:37:41,7:49:27,1987,57,22
+Night Team,PGY1,S0172645,2015-08-20,2:23:49,0:05:15,0:27:05,0:47:12,0:29:55,0:52:46,5:12:02,1214,2,30
+Night Team,PGY1,S0172645,2015-08-21,1:56:24,0:11:35,1:33:38,0:33:19,0:17:48,1:01:36,5:46:28,1175,0,24
+Night Team,PGY1,S0172645,2015-08-22,1:01:56,0:19:17,1:38:36,0:29:49,0:07:50,0:21:14,4:11:07,1000,0,22
+Night Team,PGY1,S0172645,2015-08-23,0:14:21,0:13:00,0:37:35,0:12:29,0:09:17,0:09:19,1:38:14,349,0,14
+Night Team,PGY1,S0172645,2015-08-24,1:56:00,0:27:35,0:25:48,0:47:03,0:30:14,0:40:36,5:04:45,1230,0,40
+Night Team,PGY1,S0172645,2015-08-25,1:14:45,0:15:37,1:57:34,0:28:03,0:25:07,0:26:35,5:06:33,1240,31,21
+Night Team,PGY1,S0172645,2015-08-26,1:17:59,0:06:34,0:26:04,0:26:54,0:28:07,0:27:45,3:36:12,1041,0,26
+Night Team,PGY1,S0172645,2015-08-27,0:54:17,0:06:42,0:36:57,0:32:07,0:50:49,0:28:26,3:48:47,961,7,26
+Night Team,PGY1,S0172645,2015-08-28,1:24:34,0:24:30,0:23:54,0:45:52,0:34:06,0:27:33,4:14:05,1349,0,27
+Night Team,PGY1,S0172645,2015-08-29,0:26:14,0:01:42,0:00:22,0:16:45,0:22:23,0:26:53,1:52:20,474,20,13
+Intensive Care Unit,PGY2+,S0174975,2016-10-07,2:18:51,0:27:52,0:07:23,0:42:21,0:38:10,0:04:09,4:44:40,1516,0,21
+Emergency Medicine,PGY1,S0181070,2015-11-04,1:14:21,0:06:24,1:40:01,0:23:05,0:25:51,0:29:27,4:34:22,1039,0,16
+Emergency Medicine,PGY1,S0181070,2015-11-05,1:30:09,0:10:04,0:46:09,0:16:18,0:11:26,0:07:37,3:34:10,1402,0,35
+Emergency Medicine,PGY1,S0181070,2015-11-06,0:04:45,0:16:10,0:00:53,0:00:00,0:00:00,0:00:56,0:22:57,110,110,7
+Emergency Medicine,PGY1,S0181070,2015-11-07,1:31:15,1:22:51,1:27:26,0:42:06,0:31:28,0:28:24,6:53:49,2405,0,21
+Emergency Medicine,PGY1,S0181070,2015-11-01,1:33:07,0:56:58,2:11:47,0:17:15,0:36:34,0:32:36,6:49:40,2282,0,20
+Emergency Medicine,PGY1,S0181070,2015-11-02,1:37:22,0:37:57,3:18:38,0:16:45,0:15:55,0:14:51,7:08:51,2047,0,23
+Emergency Medicine,PGY1,S0181070,2015-11-03,0:43:50,0:30:47,1:58:59,0:20:57,0:17:26,0:29:44,4:48:33,1460,0,7
+General Medicine,PGY2+,S0181526,2016-08-09,3:41:17,1:38:30,0:00:09,0:54:55,0:21:23,0:04:57,7:37:01,3137,118,37
+General Medicine,PGY2+,S0181526,2016-08-08,2:08:29,0:49:35,0:00:09,0:42:38,0:16:45,0:03:04,4:59:19,2147,0,32
+Night Team,PGY1,S0056755,2014-12-15,1:36:09,0:04:22,0:07:02,0:09:25,0:14:10,0:34:15,2:51:44,633,20,25
+Night Team,PGY2+,S0169403,2016-05-08,2:09:16,1:58:18,0:06:04,0:42:18,0:14:44,0:05:16,5:53:20,2171,78,34
+Night Team,PGY2+,S0169403,2016-05-09,2:39:21,1:50:07,0:00:11,0:35:49,0:15:40,0:11:02,6:11:56,2065,230,31
+General Medicine,PGY2+,S0181526,2016-08-01,1:29:16,0:45:02,0:00:02,0:03:29,0:11:11,0:02:00,2:59:38,1619,81,32
+Night Team,PGY2+,S0169403,2016-05-07,1:03:52,0:53:08,0:00:37,0:12:32,0:08:36,0:03:59,2:38:54,1030,26,34
+General Medicine,PGY2+,S0181526,2016-08-03,3:32:38,1:06:37,0:00:16,0:48:33,0:26:13,0:06:54,6:49:13,2618,546,16
+Night Team,PGY2+,S0169403,2016-05-05,2:10:22,1:58:02,0:00:18,0:34:12,0:09:37,0:08:18,5:44:50,1838,172,30
+Night Team,PGY2+,S0169403,2016-05-02,1:28:32,0:51:59,0:00:13,0:22:16,0:05:39,0:09:38,3:17:39,1048,116,22
+Night Team,PGY2+,S0169403,2016-05-03,1:26:51,0:26:17,0:00:18,0:22:53,0:04:37,0:01:18,2:41:34,753,135,16
+General Medicine,PGY2+,S0181526,2016-08-07,4:10:41,1:45:08,0:00:04,1:00:43,0:30:43,0:19:56,8:46:41,3176,292,32
+Night Team,PGY2+,S0169403,2016-05-01,2:15:04,1:29:04,0:00:12,0:18:22,0:06:53,0:08:24,4:38:19,1617,153,25
+General Medicine,PGY1,S0079337,2014-07-06,3:49:40,0:07:16,0:30:40,0:35:17,0:28:18,0:08:18,6:09:57,1857,0,10
+General Medicine,PGY2+,S0169398,2016-03-29,3:24:05,1:23:39,0:00:01,0:30:23,0:19:04,0:10:02,6:44:18,2103,247,21
+General Medicine,PGY2+,S0169398,2016-03-28,2:12:10,1:38:54,0:00:35,0:09:33,0:11:49,0:06:03,4:42:33,2069,1130,25
+Night Team,PGY1,S0056755,2014-12-10,1:47:47,0:08:23,1:40:11,0:28:30,0:21:08,0:17:26,5:00:55,1380,0,24
+General Medicine,PGY2+,S0169398,2016-03-23,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0169398,2016-03-22,2:22:07,1:12:02,0:01:54,0:34:52,0:27:51,0:00:56,5:08:32,2032,268,13
+General Medicine,PGY2+,S0169398,2016-03-21,3:13:22,1:34:57,0:00:07,0:08:39,0:14:28,0:12:50,5:53:41,2274,132,27
+Night Team,PGY1,S0056755,2014-12-11,2:01:11,0:10:17,0:43:24,0:23:56,0:26:32,0:35:03,4:38:00,1117,0,27
+General Medicine,PGY2+,S0169398,2016-03-27,1:49:36,1:34:17,0:00:03,0:16:47,0:12:50,0:02:34,4:19:49,1380,0,23
+General Medicine,PGY2+,S0169398,2016-03-26,3:30:12,2:37:42,0:00:05,0:36:30,0:17:41,0:04:45,7:49:05,2948,284,38
+General Medicine,PGY2+,S0169398,2016-03-25,0:08:47,0:00:51,0:00:00,0:00:00,0:00:00,0:00:36,0:10:28,63,61,11
+General Medicine,PGY2+,S0169398,2016-03-24,3:09:42,1:50:48,0:00:00,0:08:03,0:12:20,0:06:55,5:42:34,2508,220,26
+Intensive Care Unit,PGY2+,S0172056,2016-10-18,1:38:16,0:49:22,0:05:14,0:46:24,0:28:39,0:03:45,4:26:35,1398,0,20
+General Medicine,PGY2+,S0169404,2016-04-26,2:46:36,1:29:09,0:00:28,0:47:28,0:17:12,0:11:04,6:06:14,3173,333,13
+Night Team,PGY1,S0169403,2014-06-27,3:08:58,0:09:07,0:11:07,0:11:24,0:31:09,0:14:06,4:40:26,1011,0,19
+Night Team,PGY1,S0169403,2014-06-26,1:41:03,0:16:43,0:09:45,0:07:19,0:10:45,0:09:45,3:07:30,904,0,17
+Intensive Care Unit,PGY2+,S0172056,2016-10-14,2:21:34,1:01:39,0:07:08,0:32:04,0:28:34,0:13:43,5:12:12,1797,407,20
+General Medicine,PGY2+,S0169384,2016-07-11,0:00:15,0:12:27,0:00:00,0:03:01,0:00:00,0:00:00,0:15:56,167,135,9
+Night Team,PGY1,S0169403,2014-06-29,3:50:57,0:12:39,0:34:45,0:03:20,0:29:07,0:35:52,6:09:50,1341,0,23
+Night Team,PGY1,S0169403,2014-06-28,2:52:30,0:02:45,0:14:09,0:18:02,0:13:35,0:12:05,4:16:05,909,0,20
+Night Team,PGY1,S0195094,2016-07-12,1:08:16,0:20:32,0:02:12,0:28:45,0:29:17,0:00:07,2:36:17,533,287,28
+General Medicine,PGY1,S0169395,2015-06-07,0:00:00,0:00:00,0:00:00,0:00:31,0:00:00,0:00:00,0:00:31,4,0,2
+General Medicine,PGY1,S0169395,2015-06-04,2:24:45,0:26:46,1:43:57,0:22:51,0:27:41,0:34:11,6:21:00,1703,0,13
+General Medicine,PGY1,S0169395,2015-06-05,0:50:48,0:13:49,0:53:52,0:07:21,0:09:42,0:07:46,2:37:42,781,0,8
+General Medicine,PGY1,S0169395,2015-06-02,2:01:06,0:07:08,1:19:59,0:18:59,0:26:21,0:04:35,4:30:54,1187,0,6
+Night Team,PGY1,S0195094,2016-07-17,1:41:41,0:18:53,0:00:00,0:35:08,0:21:32,0:00:23,3:20:45,858,11,23
+General Medicine,PGY2+,S0169404,2016-04-27,1:18:15,0:52:19,0:00:02,0:07:29,0:05:32,0:08:50,2:39:41,999,978,28
+Night Team,PGY1,S0195094,2016-07-15,0:27:56,0:27:59,0:00:02,0:13:01,0:08:07,0:00:03,1:22:11,413,161,12
+Night Team,PGY1,S0195094,2016-07-18,1:50:35,0:29:27,0:00:02,0:18:56,0:39:33,0:00:28,3:30:55,805,0,40
+Night Team,PGY1,S0195094,2016-07-19,1:00:18,0:28:16,0:00:02,0:15:49,0:20:11,0:05:26,2:32:40,612,0,18
+General Medicine,PGY1,S0169395,2015-06-08,1:42:33,0:06:27,1:30:36,0:17:47,0:14:44,0:12:14,4:24:21,1456,0,16
+General Medicine,PGY1,S0169395,2015-06-09,2:07:49,0:08:35,2:18:17,0:26:57,0:13:15,0:08:02,5:38:12,1677,0,23
+General Medicine,PGY1,S0195076,2016-07-16,1:55:35,0:47:19,0:00:07,0:44:42,0:19:57,0:00:14,4:22:16,1392,0,8
+General Medicine,PGY1,S0195076,2016-07-17,0:01:22,0:06:53,0:00:04,0:00:54,0:00:09,0:00:01,0:11:36,175,159,8
+General Medicine,PGY1,S0195076,2016-07-14,1:22:28,0:53:58,0:01:33,0:20:55,0:14:12,0:00:24,3:17:19,1011,0,7
+General Medicine,PGY1,S0195076,2016-07-15,1:47:47,1:16:46,0:02:53,0:39:09,0:49:02,0:09:51,5:19:39,1835,0,12
+General Medicine,PGY1,S0195076,2016-07-12,1:50:36,1:03:46,0:00:08,0:29:48,0:14:28,0:00:25,4:14:35,1297,0,8
+General Medicine,PGY1,S0195076,2016-07-13,2:01:25,1:53:44,0:00:56,0:49:23,0:38:15,0:07:32,5:59:25,1548,16,7
+General Medicine,PGY1,S0195076,2016-07-10,2:01:06,1:13:57,0:01:10,0:31:42,0:14:25,0:00:47,4:40:21,1408,0,10
+General Medicine,PGY1,S0195076,2016-07-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Intensive Care Unit,PGY2+,S0172056,2016-10-12,0:42:21,0:28:36,0:00:35,0:12:27,0:08:25,0:03:48,1:55:02,931,0,21
+General Medicine,PGY1,S0195076,2016-07-18,2:11:25,1:55:46,0:02:29,0:34:46,0:31:49,0:01:04,5:58:45,2019,0,9
+General Medicine,PGY1,S0195076,2016-07-19,0:44:30,0:19:03,0:00:05,0:13:58,0:05:23,0:00:05,1:27:18,481,0,11
+General Medicine,PGY2+,S0169404,2016-04-24,2:02:33,1:17:11,0:00:04,0:32:15,0:17:19,0:04:48,4:38:32,2340,580,13
+General Medicine,PGY1,S0093383,2014-12-09,3:02:48,0:06:47,1:05:24,0:33:27,0:26:33,0:37:38,6:28:38,2024,0,7
+General Medicine,PGY1,S0093383,2014-12-08,3:12:41,0:09:48,3:28:15,0:35:57,0:20:55,0:21:53,8:29:59,2219,0,9
+General Medicine,PGY1,S0093383,2014-12-07,1:17:17,0:09:45,0:25:18,0:16:33,0:09:23,0:12:15,2:40:31,915,0,6
+General Medicine,PGY1,S0079337,2014-07-02,0:00:00,0:00:00,0:00:00,0:01:35,0:00:00,0:00:00,0:01:35,18,0,5
+General Medicine,PGY2+,S0169404,2016-04-25,3:54:06,2:11:06,0:00:07,1:05:04,0:24:13,0:08:32,8:24:55,4455,864,21
+Intensive Care Unit,PGY2+,S0181081,2016-10-08,2:27:51,1:08:39,0:00:00,0:07:27,0:14:27,0:01:08,4:13:44,1675,0,20
+General Medicine,PGY2+,S0181081,2016-10-09,2:09:37,1:14:43,0:00:04,0:42:08,0:16:36,0:01:33,5:00:58,1981,12,17
+Night Team,PGY1,S0181525,2015-12-06,0:27:22,0:15:22,1:14:48,0:12:25,0:04:58,0:21:13,2:45:31,505,0,12
+Night Team,PGY1,S0181525,2015-12-07,0:27:32,0:23:42,0:35:48,0:09:25,0:09:35,0:18:43,2:17:53,547,0,19
+Night Team,PGY1,S0181525,2015-12-08,1:46:07,0:34:51,1:02:25,0:30:10,0:33:19,0:39:16,5:30:20,1282,84,37
+Night Team,PGY1,S0181525,2015-12-09,0:53:26,0:36:10,0:25:44,0:32:12,0:20:30,0:37:54,3:46:18,843,0,38
+Intensive Care Unit,PGY2+,S0181081,2016-10-06,2:09:48,0:46:56,0:00:16,0:24:50,0:18:53,0:04:37,4:20:29,1645,0,27
+Intensive Care Unit,PGY2+,S0181081,2016-10-07,2:08:30,1:09:44,0:04:46,0:23:31,0:22:35,0:01:14,5:11:59,1824,0,14
+Intensive Care Unit,PGY2+,S0181081,2016-10-01,2:03:14,0:36:27,0:09:04,0:13:07,0:18:17,0:04:13,3:49:37,1182,0,17
+Intensive Care Unit,PGY2+,S0181081,2016-10-02,2:10:49,0:53:48,0:00:01,0:33:47,0:36:25,0:01:49,5:02:26,1885,0,14
+Intensive Care Unit,PGY2+,S0181081,2016-10-03,1:46:14,0:22:16,0:12:35,0:26:24,0:12:03,0:05:15,3:37:16,1261,0,19
+General Medicine,PGY2+,S0169404,2016-04-22,0:32:10,0:16:01,0:00:02,0:01:45,0:00:08,0:03:38,0:58:49,614,582,17
+General Medicine,PGY1,S0196593,2016-10-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0169404,2016-04-23,3:17:56,1:54:47,0:00:11,0:21:04,0:09:01,0:08:50,6:26:27,4494,49,25
+Night Team,PGY1,S0181056,2015-07-18,2:42:01,0:31:21,0:00:10,0:10:40,0:22:32,0:31:09,4:28:15,1166,172,29
+Night Team,PGY1,S0181056,2015-07-14,3:02:09,1:00:48,1:23:04,0:29:19,0:33:16,0:46:25,7:30:54,1924,91,54
+Night Team,PGY1,S0181056,2015-07-15,1:51:24,0:28:43,1:39:59,0:12:53,0:17:15,0:25:20,5:20:44,1181,190,23
+Night Team,PGY1,S0181056,2015-07-16,1:15:42,0:56:28,0:00:06,0:09:36,0:20:12,0:16:15,3:02:11,1071,373,62
+Night Team,PGY1,S0181056,2015-07-17,3:10:26,2:27:54,1:50:33,0:31:26,0:38:38,0:37:30,9:29:17,2602,360,40
+Night Team,PGY1,S0181056,2015-07-10,3:44:35,0:21:07,0:01:07,0:35:39,0:40:54,0:24:59,6:09:33,1486,0,40
+Night Team,PGY1,S0181056,2015-07-11,4:02:35,1:05:00,0:22:32,0:17:18,0:20:55,0:35:00,7:11:35,1787,19,57
+Night Team,PGY1,S0181056,2015-07-12,4:08:54,0:54:20,1:14:25,0:26:03,0:45:02,0:35:29,8:20:16,2003,297,45
+Night Team,PGY1,S0181056,2015-07-13,3:39:43,1:01:43,0:47:58,0:36:45,0:32:47,0:28:22,7:24:56,2026,361,41
+General Medicine,PGY1,S0181078,2015-09-01,2:39:40,0:44:50,2:19:55,0:48:03,0:15:38,0:24:01,8:18:47,2055,171,13
+General Medicine,PGY1,S0181078,2015-09-03,1:39:33,0:19:36,2:20:36,0:50:22,0:12:14,0:18:43,6:08:16,1399,453,16
+General Medicine,PGY1,S0181078,2015-09-02,1:59:08,0:39:52,1:41:36,0:49:56,0:27:10,0:32:37,6:46:36,1872,458,15
+General Medicine,PGY1,S0181078,2015-09-05,2:25:57,0:25:35,2:39:11,0:46:23,0:11:45,0:21:41,7:24:25,1644,649,9
+General Medicine,PGY1,S0181078,2015-09-04,2:01:07,0:26:02,1:05:26,1:04:38,0:14:22,0:17:51,5:54:06,1704,93,11
+General Medicine,PGY1,S0181078,2015-09-07,0:12:27,0:04:51,0:04:49,0:00:00,0:00:00,0:00:00,0:24:13,85,81,4
+General Medicine,PGY1,S0181078,2015-09-06,2:46:35,0:32:37,2:37:15,1:12:59,0:17:29,0:41:44,9:07:06,1917,276,11
+General Medicine,PGY1,S0181078,2015-09-09,2:51:37,0:50:36,1:21:22,1:27:04,0:41:38,0:15:41,8:32:55,2739,381,9
+General Medicine,PGY1,S0181078,2015-09-08,1:21:58,0:29:26,1:11:24,0:16:38,0:01:36,0:16:22,3:56:01,694,88,8
+General Medicine,PGY2+,S0169404,2016-04-20,5:06:12,2:29:31,0:00:16,0:40:40,0:37:35,0:23:39,10:11:21,5192,666,29
+General Medicine,PGY2+,S0169404,2016-04-21,3:20:19,1:27:32,0:00:06,0:24:34,0:12:27,0:03:02,6:23:41,3265,105,15
+General Medicine,PGY1,S0195203,2016-07-07,3:23:03,1:27:36,0:00:35,1:21:41,0:38:55,0:17:08,7:35:23,1697,114,9
+General Medicine,PGY1,S0181066,2015-12-31,1:31:58,0:51:20,1:37:33,1:12:06,0:27:45,0:16:41,6:46:10,2317,110,15
+General Medicine,PGY1,S0181066,2015-12-30,1:08:37,0:17:52,0:29:05,1:22:12,0:31:45,0:25:30,4:56:14,1529,176,12
+Emergency Medicine,PGY2+,S0169400,2015-09-30,1:31:11,0:07:26,0:31:06,0:02:28,0:04:31,0:02:29,2:19:17,509,0,13
+Intensive Care Unit,PGY2+,S0169384,2015-09-01,2:57:05,0:07:17,0:47:57,0:26:37,0:32:50,0:29:52,5:58:57,1694,0,16
+Intensive Care Unit,PGY2+,S0169384,2015-09-03,3:10:11,0:00:38,0:23:21,0:20:48,0:34:54,0:21:20,4:59:54,1164,0,16
+Intensive Care Unit,PGY2+,S0169384,2015-09-02,3:45:09,0:07:49,0:23:35,0:12:06,0:13:29,0:29:17,5:23:52,1350,0,14
+Intensive Care Unit,PGY2+,S0169384,2015-09-05,2:53:37,0:07:06,0:27:57,1:31:54,0:41:26,0:42:42,6:41:29,1674,0,13
+Intensive Care Unit,PGY2+,S0169384,2015-09-04,3:36:52,0:01:42,0:33:31,0:30:35,0:38:10,0:26:35,5:58:11,1235,0,13
+Intensive Care Unit,PGY2+,S0169384,2015-09-07,0:00:00,0:00:00,0:00:00,0:04:30,0:00:00,0:00:00,0:04:30,20,0,5
+Intensive Care Unit,PGY2+,S0169384,2015-09-06,4:22:53,0:20:33,0:22:10,0:35:15,0:17:07,0:53:30,7:24:08,1698,213,15
+Intensive Care Unit,PGY2+,S0169384,2015-09-09,3:07:22,0:06:08,0:32:22,0:18:10,0:16:13,0:42:18,5:19:25,1431,136,17
+Intensive Care Unit,PGY2+,S0169384,2015-09-08,1:30:38,0:01:24,0:18:02,0:38:03,0:46:54,0:39:49,4:04:30,1210,0,17
+General Medicine,PGY1,S0181078,2016-03-08,4:16:18,1:48:43,0:00:21,1:17:39,0:37:09,0:06:47,8:49:51,2848,190,15
+General Medicine,PGY1,S0181078,2016-03-09,2:48:43,0:58:49,0:00:12,1:45:18,0:38:36,0:03:14,7:07:35,1682,150,11
+Intensive Care Unit,PGY2+,S0181077,2016-11-02,3:15:24,1:13:27,0:06:07,0:23:13,0:32:33,0:10:38,6:25:13,2182,68,32
+General Medicine,PGY1,S0181078,2016-03-05,2:59:24,0:49:45,0:00:08,0:54:27,0:21:35,0:03:11,5:46:17,1828,472,19
+General Medicine,PGY1,S0181078,2016-03-06,2:55:36,0:51:28,0:00:15,1:10:58,0:30:25,0:04:38,6:31:17,2060,60,16
+General Medicine,PGY1,S0181078,2016-03-07,2:14:47,0:39:46,0:00:09,0:47:42,0:35:44,0:06:18,4:47:15,1678,191,13
+General Medicine,PGY1,S0181078,2016-03-01,3:48:15,1:04:12,0:00:16,1:42:45,0:42:02,0:08:34,8:15:29,2420,155,17
+Intensive Care Unit,PGY2+,S0181077,2016-11-04,1:39:23,0:32:27,0:00:22,0:16:13,0:15:58,0:01:45,3:05:03,1220,0,22
+Intensive Care Unit,PGY2+,S0181077,2016-11-05,1:25:16,0:28:08,0:05:15,0:02:05,0:06:49,0:00:23,2:29:50,821,33,12
+General Medicine,PGY1,S0106952,2015-03-31,2:39:17,0:39:09,1:51:17,0:25:04,0:09:44,0:17:26,6:16:40,1777,0,12
+Night Team,PGY1,S0181069,2015-12-05,1:04:04,0:36:36,0:57:36,0:21:05,0:14:07,0:20:06,3:52:08,1182,0,26
+Night Team,PGY1,S0181069,2015-12-04,1:29:12,1:30:32,1:08:27,1:20:46,0:33:05,0:54:45,7:36:54,2798,34,41
+Night Team,PGY1,S0181069,2015-12-03,1:29:17,1:19:39,1:41:36,1:37:23,0:43:12,0:42:26,7:57:13,2586,1531,37
+Night Team,PGY1,S0181069,2015-12-02,1:02:18,0:44:48,0:32:25,0:38:04,0:13:25,0:21:32,3:53:07,1480,692,30
+Night Team,PGY1,S0181069,2015-12-01,0:31:30,0:28:39,0:11:44,0:21:08,0:20:09,0:16:53,2:16:23,823,0,16
+Intensive Care Unit,PGY2+,S0106952,2015-10-28,1:18:25,0:45:39,0:14:37,0:15:02,0:12:46,0:11:25,3:13:46,1182,304,27
+General Medicine,PGY1,S0195203,2016-07-02,3:06:45,2:01:42,0:00:41,1:01:11,0:40:28,0:09:49,7:59:11,1999,254,13
+Intensive Care Unit,PGY2+,S0106952,2015-10-26,1:24:30,1:25:18,0:39:27,0:35:54,0:14:56,0:13:42,5:02:09,1780,648,39
+Night Team,PGY2+,S0181087,2016-08-28,1:27:13,0:23:04,0:00:08,0:13:09,0:17:48,0:06:27,2:47:36,1105,0,52
+Emergency Medicine,PGY1,S0181060,2016-04-19,4:34:18,0:49:34,0:04:01,0:45:37,0:41:18,0:00:31,8:13:03,2019,0,19
+Emergency Medicine,PGY1,S0181060,2016-04-18,2:08:32,0:26:10,0:01:29,0:27:08,0:34:02,0:03:06,4:14:58,1330,0,14
+Night Team,PGY1,S0181074,2015-10-25,0:53:43,1:57:33,0:55:39,0:51:34,0:18:47,0:15:37,5:28:43,1611,235,18
+Night Team,PGY1,S0181074,2015-10-27,1:17:01,2:12:25,1:24:57,1:43:46,0:50:22,0:12:09,8:05:23,2420,158,41
+Night Team,PGY1,S0181074,2015-10-26,0:47:08,1:24:44,0:00:17,0:35:35,0:19:05,0:07:12,3:21:38,987,131,26
+Night Team,PGY1,S0181074,2015-10-29,1:07:06,1:44:25,2:25:46,0:49:02,0:44:25,0:09:56,7:23:23,1980,201,65
+Night Team,PGY1,S0181074,2015-10-28,1:33:22,1:43:05,0:18:19,1:24:06,0:27:12,0:08:15,5:52:30,2275,227,50
+Emergency Medicine,PGY1,S0181060,2016-04-11,4:36:07,0:37:31,0:03:26,0:42:33,1:08:52,0:19:01,8:15:29,2283,0,21
+Emergency Medicine,PGY1,S0181060,2016-04-10,4:10:20,0:32:22,0:06:27,0:47:34,1:08:21,0:04:28,7:54:07,2395,0,21
+Emergency Medicine,PGY1,S0181060,2016-04-17,4:10:15,0:46:27,0:02:22,0:18:09,1:06:30,0:09:24,7:25:39,2210,0,31
+Emergency Medicine,PGY1,S0181060,2016-04-16,4:38:54,0:47:55,0:01:17,0:46:39,0:48:34,0:07:41,7:56:10,1982,4,25
+Emergency Medicine,PGY1,S0181060,2016-04-15,0:34:51,0:38:12,0:11:24,0:21:35,0:13:00,0:02:05,2:47:13,1063,0,18
+Night Team,PGY2+,S0169403,2016-01-02,1:07:20,2:11:08,1:44:44,0:41:52,0:26:07,0:42:28,8:01:29,3100,497,47
+Night Team,PGY2+,S0169403,2016-01-03,1:00:40,2:28:16,0:42:30,0:35:43,0:21:35,0:39:50,6:17:36,2940,561,29
+Night Team,PGY2+,S0169403,2016-01-01,1:30:44,1:06:50,1:35:44,0:30:05,0:21:19,0:41:57,6:04:54,2184,257,35
+Night Team,PGY2+,S0169403,2016-01-06,1:08:32,1:55:58,2:00:11,0:19:15,0:24:57,0:51:32,7:07:12,2647,313,27
+Night Team,PGY2+,S0169403,2016-01-07,1:11:51,2:19:16,1:30:08,0:23:28,0:22:16,0:45:18,7:13:01,3062,186,36
+Night Team,PGY2+,S0169403,2016-01-04,0:51:15,3:01:07,1:21:35,0:38:03,0:14:05,0:41:36,7:30:41,3142,554,25
+Night Team,PGY2+,S0169403,2016-01-05,0:18:19,1:05:37,0:09:26,0:17:37,0:07:06,0:26:11,2:36:01,1034,243,17
+Night Team,PGY2+,S0169403,2016-01-08,1:22:59,1:21:08,0:45:29,0:31:12,0:14:11,0:26:58,5:21:45,2483,198,27
+Night Team,PGY2+,S0169403,2016-01-09,1:01:33,1:09:55,1:31:20,0:25:35,0:21:06,0:42:36,5:37:44,2426,0,35
+General Medicine,PGY1,S0181070,2015-08-19,2:35:35,0:23:24,1:29:26,0:19:12,0:06:13,0:11:34,5:20:07,1309,0,4
+General Medicine,PGY1,S0181070,2015-08-18,2:21:53,0:27:05,1:51:16,0:20:55,0:11:55,0:29:46,5:52:31,1387,0,8
+General Medicine,PGY1,S0181070,2015-08-17,1:38:28,0:01:35,1:43:49,0:04:43,0:06:34,0:01:48,3:50:58,820,0,7
+General Medicine,PGY1,S0181070,2015-08-16,0:51:39,0:00:14,1:18:14,0:08:32,0:00:36,0:06:20,2:29:43,482,0,2
+General Medicine,PGY1,S0181064,2015-07-11,0:22:43,0:02:36,0:19:36,0:00:01,0:00:16,0:01:12,0:47:36,237,231,15
+General Medicine,PGY1,S0181064,2015-07-10,3:12:06,0:52:37,2:16:08,0:22:47,0:24:43,0:25:50,8:01:05,2011,197,17
+General Medicine,PGY1,S0181064,2015-07-13,0:51:00,0:09:09,0:25:18,0:08:40,0:08:01,0:06:20,2:03:01,1064,1052,20
+General Medicine,PGY1,S0181064,2015-07-12,3:14:58,0:22:40,1:16:10,1:02:09,0:46:19,0:51:34,8:18:18,2491,131,13
+General Medicine,PGY1,S0181064,2015-07-15,3:28:00,0:13:59,1:30:26,0:37:49,0:18:27,0:30:03,7:36:32,2439,383,41
+General Medicine,PGY1,S0181064,2015-07-14,2:59:12,0:18:02,0:56:30,0:38:16,0:32:09,0:35:12,6:58:27,2833,487,42
+General Medicine,PGY1,S0181064,2015-07-17,2:50:39,0:44:25,2:05:50,0:23:19,0:22:01,0:22:41,7:24:39,1933,24,14
+General Medicine,PGY1,S0181064,2015-07-16,2:10:01,0:19:30,2:14:52,0:12:52,0:20:56,0:23:48,6:13:29,1851,99,12
+General Medicine,PGY1,S0181064,2015-07-18,0:21:38,0:01:32,1:11:31,0:00:00,0:00:40,0:00:30,1:44:24,264,260,6
+General Medicine,PGY2+,S0169384,2016-06-28,1:33:46,1:04:26,0:01:08,1:00:40,0:29:26,0:01:18,4:39:40,1953,535,29
+General Medicine,PGY1,S0181065,2015-11-08,1:23:56,0:23:43,0:41:23,0:19:28,0:12:28,0:15:53,3:33:47,1419,156,8
+General Medicine,PGY1,S0181065,2015-11-09,2:44:49,0:57:59,0:58:45,0:58:16,0:34:46,0:29:45,7:29:49,2922,13,7
+Intensive Care Unit,PGY2+,S0169403,2015-11-26,1:37:08,0:58:07,0:10:41,0:09:32,0:09:05,0:31:18,4:14:43,1154,258,15
+Intensive Care Unit,PGY2+,S0169403,2015-11-27,1:46:44,0:39:52,0:04:57,0:26:48,0:30:26,0:46:39,4:36:29,1365,49,15
+Intensive Care Unit,PGY2+,S0169403,2015-11-24,1:50:31,0:54:42,0:13:25,0:37:15,0:30:46,0:54:32,5:36:38,1772,131,13
+Intensive Care Unit,PGY2+,S0169403,2015-11-25,1:47:40,1:10:09,0:07:48,0:27:29,0:41:48,1:10:38,5:58:57,1888,80,19
+Intensive Care Unit,PGY2+,S0169403,2015-11-22,2:41:34,1:42:48,0:29:48,0:33:29,0:42:20,0:48:06,7:41:22,3311,183,22
+Intensive Care Unit,PGY2+,S0169403,2015-11-23,1:06:05,0:26:19,0:05:58,0:18:23,0:23:58,0:24:24,3:13:36,1198,239,10
+Intensive Care Unit,PGY2+,S0169403,2015-11-20,1:31:01,0:46:46,0:20:16,0:19:47,0:26:08,0:42:18,4:35:12,1373,45,15
+Intensive Care Unit,PGY2+,S0169403,2015-11-21,0:00:00,0:09:26,0:00:00,0:00:09,0:00:00,0:00:05,0:13:30,72,60,9
+Night Team,PGY1,S0169389,2014-11-20,3:42:32,0:07:43,1:16:23,0:16:22,0:15:25,0:36:53,6:33:32,1494,0,47
+Night Team,PGY1,S0169389,2014-11-21,1:11:29,0:00:36,0:01:04,0:13:32,0:07:25,0:12:25,1:56:30,737,27,36
+Night Team,PGY1,S0169389,2014-11-22,0:05:39,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:05:40,18,18,1
+Intensive Care Unit,PGY2+,S0169403,2015-11-29,2:12:25,0:44:49,0:03:26,0:48:36,0:40:14,1:34:28,6:41:03,2779,10,25
+General Medicine,PGY1,S0079337,2014-06-30,1:52:57,0:00:00,0:15:03,0:14:36,0:33:08,0:12:28,3:16:26,806,0,4
+General Medicine,PGY1,S0169384,2015-02-08,3:34:06,0:20:47,1:57:03,1:05:53,0:21:07,0:14:37,8:09:41,2138,241,15
+General Medicine,PGY1,S0169384,2015-02-09,1:49:23,0:06:05,0:54:15,0:27:03,0:18:44,0:12:05,4:01:37,1175,318,9
+General Medicine,PGY1,S0169384,2015-02-04,0:56:27,0:19:24,2:25:25,0:04:16,0:05:44,0:03:26,3:54:52,617,599,6
+General Medicine,PGY1,S0169384,2015-02-05,3:18:14,0:32:48,2:16:07,0:51:45,0:08:19,0:22:40,8:03:13,1666,514,4
+General Medicine,PGY1,S0169384,2015-02-06,2:39:25,0:20:29,1:05:12,0:52:13,0:23:34,0:15:32,5:51:07,1931,72,20
+General Medicine,PGY1,S0169384,2015-02-07,2:34:55,0:12:13,2:37:15,0:25:56,0:14:14,0:17:53,6:41:44,1412,365,6
+General Medicine,PGY1,S0169384,2015-02-01,2:58:42,0:37:11,3:05:49,0:48:14,0:20:37,0:25:10,8:35:40,1978,150,10
+General Medicine,PGY1,S0169384,2015-02-02,1:59:44,0:20:13,0:37:10,0:11:08,0:15:00,0:10:13,3:37:29,860,217,8
+General Medicine,PGY1,S0169384,2015-02-03,3:07:00,0:42:42,0:51:50,1:08:36,0:35:08,0:10:51,6:54:38,2220,344,11
+Intensive Care Unit,PGY2+,S0079337,2016-05-31,1:23:14,0:22:57,0:05:58,0:12:55,0:19:15,0:03:59,2:48:01,1000,7,14
+Intensive Care Unit,PGY2+,S0079337,2016-05-30,1:29:51,0:21:27,0:00:03,0:27:26,0:16:42,0:00:10,2:45:14,942,98,18
+General Medicine,PGY1,S0195203,2016-07-08,1:55:16,1:20:32,0:00:19,0:38:25,0:21:33,0:08:14,5:02:34,1350,229,8
+Night Team,PGY1,S0169398,2015-05-28,0:51:22,0:18:12,0:01:35,0:15:13,0:13:36,0:14:06,2:05:44,842,0,20
+General Medicine,PGY2+,S0169399,2016-02-01,0:00:00,0:00:00,0:00:00,0:00:18,0:00:00,0:00:00,0:00:18,10,0,4
+General Medicine,PGY1,S0181088,2015-08-14,1:59:19,0:00:00,1:47:08,0:10:09,0:17:25,0:23:02,5:21:15,1597,0,10
+General Medicine,PGY1,S0181088,2015-08-15,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0181088,2015-08-12,2:01:21,0:14:05,2:59:52,0:53:28,0:16:26,0:24:49,7:35:33,1937,29,13
+General Medicine,PGY1,S0181088,2015-08-13,0:19:08,0:08:36,0:01:06,0:01:08,0:00:02,0:01:41,0:33:17,430,424,9
+General Medicine,PGY1,S0181088,2015-08-10,1:25:51,0:04:17,0:48:41,0:47:31,0:21:13,0:26:50,4:42:03,1783,13,6
+General Medicine,PGY2+,S0163662,2015-12-29,2:10:41,0:40:09,0:00:00,0:06:15,0:14:59,0:11:14,3:32:09,950,37,16
+General Medicine,PGY2+,S0169399,2016-02-02,2:05:08,0:39:08,0:04:04,0:46:58,0:21:19,0:36:31,5:20:30,1577,51,29
+General Medicine,PGY1,S0180927,2016-08-20,0:00:00,0:00:00,0:00:00,0:01:28,0:00:00,0:00:00,0:01:28,13,0,4
+General Medicine,PGY2+,S0169399,2016-02-04,1:43:52,1:04:26,0:02:21,0:32:35,0:21:55,0:44:48,5:07:03,1587,0,27
+Emergency Medicine,PGY1,S0181077,2015-09-30,0:19:00,0:42:36,0:53:20,0:16:17,0:06:29,0:09:58,2:51:03,869,0,10
+Intensive Care Unit,PGY2+,S0169399,2016-02-07,0:00:00,0:00:00,0:00:00,0:00:33,0:00:00,0:00:00,0:00:33,28,0,7
+General Medicine,PGY2+,S0169399,2016-02-06,1:52:21,1:07:22,0:15:46,0:24:12,0:25:21,0:23:04,5:02:09,951,0,17
+Intensive Care Unit,PGY2+,S0169399,2016-02-08,1:55:46,0:26:28,0:24:11,0:34:47,0:06:48,0:13:31,4:07:05,1105,1,23
+General Medicine,PGY2+,S0181057,2016-10-19,2:43:31,0:58:33,0:00:05,0:22:33,0:20:51,0:18:51,5:13:04,2019,0,28
+General Medicine,PGY1,S0169386,2014-12-08,3:29:28,0:16:33,2:26:11,1:12:14,0:52:24,0:48:58,9:49:43,3271,47,14
+General Medicine,PGY1,S0181060,2015-07-19,3:08:25,0:42:49,3:06:57,0:30:37,0:42:11,0:28:27,9:28:39,1941,0,15
+General Medicine,PGY1,S0169386,2014-12-09,3:14:51,0:06:17,2:01:58,0:27:42,0:33:26,0:46:13,8:11:56,2450,107,22
+General Medicine,PGY2+,S0181057,2016-10-17,2:11:02,0:33:39,0:00:00,0:25:21,0:21:44,0:07:41,4:02:07,1656,33,16
+General Medicine,PGY2+,S0169390,2015-11-30,3:48:03,1:09:12,0:12:27,0:33:28,0:25:44,1:17:40,8:09:50,2869,51,19
+Night Team,PGY2+,S0074196,2016-07-08,1:29:23,1:18:23,0:00:02,0:26:47,0:05:46,0:00:26,3:43:55,1593,10,10
+Night Team,PGY2+,S0074196,2016-07-09,1:01:49,0:30:12,0:03:46,0:23:26,0:03:15,0:01:45,2:12:30,649,201,11
+Night Team,PGY2+,S0074196,2016-07-02,2:25:55,1:17:21,0:00:05,0:37:29,0:14:13,0:00:49,5:29:41,1571,0,21
+Night Team,PGY2+,S0074196,2016-07-03,0:40:46,0:43:35,0:00:06,0:15:35,0:03:00,0:04:56,2:02:03,758,0,16
+Night Team,PGY2+,S0074196,2016-07-01,0:46:07,0:33:43,0:00:02,0:15:13,0:11:00,0:00:42,1:57:28,605,0,10
+Night Team,PGY2+,S0074196,2016-07-06,1:06:48,0:36:55,0:00:10,0:35:42,0:11:59,0:02:38,3:15:40,1033,31,17
+Night Team,PGY2+,S0074196,2016-07-07,1:45:45,0:55:22,0:00:03,0:11:49,0:10:23,0:06:32,3:38:20,1713,74,26
+Night Team,PGY2+,S0074196,2016-07-04,0:49:16,0:38:29,0:00:05,0:25:54,0:20:16,0:03:44,2:39:57,794,0,12
+Night Team,PGY2+,S0074196,2016-07-05,1:08:15,0:31:35,0:00:02,0:40:28,0:17:41,0:00:15,2:59:19,831,95,13
+General Medicine,PGY2+,S0108287,2016-05-03,2:11:16,1:23:19,0:00:07,0:23:31,0:14:09,0:04:10,4:45:38,1875,0,13
+General Medicine,PGY1,S0181081,2015-07-19,3:37:06,0:08:03,2:13:13,0:33:50,0:26:46,0:20:03,7:53:02,1750,11,7
+General Medicine,PGY2+,S0108287,2016-05-01,1:20:08,0:47:20,0:00:04,0:27:59,0:20:08,0:03:36,3:28:55,1112,23,11
+General Medicine,PGY2+,S0108287,2016-05-07,1:24:35,0:38:44,0:00:03,0:31:47,0:08:49,0:05:47,3:10:00,1035,76,11
+General Medicine,PGY2+,S0108287,2016-05-06,2:00:31,0:56:44,0:06:54,0:49:35,0:14:24,0:07:33,5:08:14,1432,0,24
+General Medicine,PGY2+,S0108287,2016-05-05,1:54:27,1:15:58,0:00:01,0:18:37,0:09:38,0:03:17,4:13:24,1449,5,18
+General Medicine,PGY2+,S0108287,2016-05-04,1:14:03,1:03:40,0:00:04,0:47:42,0:15:42,0:05:04,3:48:35,1216,33,17
+General Medicine,PGY2+,S0108287,2016-05-09,1:23:39,0:39:02,0:00:19,0:14:36,0:07:36,0:05:26,3:04:45,984,254,21
+General Medicine,PGY2+,S0108287,2016-05-08,2:11:00,1:21:02,0:00:04,0:26:34,0:11:49,0:03:27,4:59:46,1872,2,16
+Intensive Care Unit,PGY2+,S0093383,2015-09-30,1:34:29,0:27:02,0:06:47,0:21:21,0:23:29,0:33:42,3:53:04,1153,103,11
+General Medicine,PGY1,S0181073,2016-06-05,1:20:44,1:04:25,0:00:25,0:50:31,0:14:38,0:07:13,3:55:20,1261,13,12
+General Medicine,PGY1,S0169383,2015-04-27,2:29:41,0:40:41,1:57:35,0:33:12,0:24:53,0:08:49,6:52:18,1726,125,16
+General Medicine,PGY1,S0169383,2015-04-26,3:00:29,0:34:02,1:16:54,0:41:17,0:21:08,0:18:39,6:33:05,1876,0,14
+General Medicine,PGY1,S0169383,2015-04-29,2:20:48,0:11:07,1:13:29,0:17:06,0:10:54,0:07:43,4:37:58,1374,0,9
+General Medicine,PGY1,S0169383,2015-04-28,1:57:01,0:56:19,1:48:08,0:34:23,0:39:59,0:24:39,7:01:34,2166,0,15
+Intensive Care Unit,PGY2+,S0169411,2016-01-22,2:29:19,0:23:30,0:04:36,0:19:37,0:18:38,0:51:20,4:46:56,1384,40,19
+General Medicine,PGY1,S0169398,2014-09-12,3:26:36,0:09:21,0:18:04,0:36:58,0:24:22,0:18:16,5:35:12,1845,170,9
+Emergency Medicine,PGY2+,S0169403,2016-02-03,0:50:04,1:04:26,2:39:24,0:36:47,0:27:53,0:59:14,7:17:34,3042,6,20
+General Medicine,PGY1,S0169398,2014-09-10,0:14:56,0:03:01,0:00:06,0:00:16,0:00:00,0:00:48,0:23:18,215,201,9
+General Medicine,PGY1,S0169398,2014-09-11,3:40:50,0:15:00,0:20:22,0:32:09,0:20:24,0:21:40,5:57:12,1912,0,12
+Night Team,PGY2+,S0181526,2016-11-06,3:53:04,1:26:14,0:00:30,0:20:39,0:15:31,0:08:02,7:14:16,2134,0,63
+Night Team,PGY1,S0181075,2015-12-31,2:09:47,0:55:22,1:22:53,1:52:20,0:31:51,0:39:45,7:57:38,2624,0,43
+Night Team,PGY1,S0181075,2015-12-30,2:00:55,0:58:33,2:08:24,1:20:57,0:51:12,0:54:33,8:40:58,2556,0,49
+Emergency Medicine,PGY2+,S0169403,2016-02-06,0:00:00,0:33:47,0:00:00,0:00:00,0:00:00,0:00:50,0:34:57,127,127,8
+Night Team,PGY1,S0181083,2015-09-07,1:32:19,0:07:03,0:07:17,0:21:18,0:13:35,0:09:19,2:54:26,672,45,32
+Night Team,PGY1,S0181083,2015-09-06,1:03:18,0:08:18,0:35:12,0:27:20,0:26:21,0:35:37,3:31:53,781,0,14
+Night Team,PGY1,S0181083,2015-09-05,1:29:51,0:03:15,0:17:57,0:26:51,0:13:33,0:33:22,3:17:47,916,0,41
+Night Team,PGY1,S0181083,2015-09-04,2:11:27,0:08:35,1:36:51,0:40:09,0:23:19,0:25:36,5:48:33,1485,171,47
+Night Team,PGY1,S0181083,2015-09-03,1:40:21,0:08:51,2:52:36,0:38:53,0:26:36,0:31:38,7:12:44,1380,13,23
+Night Team,PGY1,S0181083,2015-09-02,3:03:23,0:18:14,0:49:12,0:24:24,0:22:19,0:50:19,6:16:12,1446,0,40
+General Medicine,PGY1,S0169403,2015-04-08,0:05:44,0:25:51,0:00:00,0:00:00,0:01:35,0:01:00,0:35:22,176,170,5
+General Medicine,PGY1,S0169403,2015-04-09,1:57:32,1:06:57,1:04:12,0:22:21,0:16:25,0:42:56,6:07:09,1997,0,16
+General Medicine,PGY1,S0169403,2015-04-06,1:07:54,0:24:07,0:36:29,0:23:32,0:15:03,0:18:48,3:19:33,1143,28,10
+General Medicine,PGY1,S0169403,2015-04-07,2:34:26,1:24:08,1:11:35,0:29:39,0:24:22,0:38:10,7:20:10,2125,0,14
+General Medicine,PGY1,S0169403,2015-04-04,1:58:32,0:51:49,2:50:41,0:46:53,0:19:37,0:30:32,7:58:23,2599,0,15
+General Medicine,PGY1,S0169403,2015-04-05,1:19:11,0:20:14,0:50:02,0:17:32,0:12:07,0:16:38,3:37:09,1271,0,8
+General Medicine,PGY1,S0169403,2015-04-02,1:58:57,1:11:18,1:23:13,1:02:41,0:23:06,0:28:37,7:02:44,2440,0,13
+General Medicine,PGY1,S0169403,2015-04-03,1:37:48,0:33:53,1:35:01,0:27:08,0:21:46,0:15:10,5:10:58,1548,0,18
+Night Team,PGY1,S0181083,2015-09-09,1:47:04,0:09:30,2:00:02,0:53:02,0:47:56,1:05:07,7:12:09,1541,65,48
+General Medicine,PGY1,S0169403,2015-04-01,1:23:38,0:18:09,1:19:09,0:38:22,0:15:05,0:11:56,4:31:58,1404,11,8
+General Medicine,PGY1,S0169385,2015-04-29,3:25:28,0:22:34,0:39:16,0:39:59,0:30:15,0:37:57,6:42:11,2507,0,20
+Emergency Medicine,PGY1,S0108287,2015-03-19,2:17:39,0:08:58,0:45:45,0:09:02,0:08:23,0:00:07,3:55:08,813,0,25
+Emergency Medicine,PGY1,S0108287,2015-03-18,2:27:53,0:12:42,0:50:04,0:13:04,0:31:37,0:01:12,4:38:37,1192,0,20
+Emergency Medicine,PGY1,S0108287,2015-03-15,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,4,2
+Emergency Medicine,PGY1,S0108287,2015-03-17,2:01:36,1:04:55,1:00:13,0:10:18,0:11:31,0:05:41,4:43:57,779,64,19
+Emergency Medicine,PGY1,S0108287,2015-03-16,2:33:04,0:26:49,0:41:50,0:06:14,0:16:27,0:05:19,4:27:37,900,0,15
+Emergency Medicine,PGY1,S0108287,2015-03-11,3:53:20,0:51:08,2:05:45,0:23:44,0:11:28,0:10:39,8:16:44,1405,185,24
+Emergency Medicine,PGY1,S0108287,2015-03-10,3:34:29,0:29:48,1:07:40,0:35:00,0:19:09,0:01:44,6:25:10,1073,0,16
+Emergency Medicine,PGY1,S0108287,2015-03-13,1:45:14,0:10:36,0:56:46,0:04:58,0:11:26,0:01:54,3:52:11,848,0,29
+Emergency Medicine,PGY1,S0108287,2015-03-12,4:16:40,0:28:36,1:08:46,0:26:30,0:16:41,0:05:23,7:07:13,1403,0,31
+Intensive Care Unit,PGY2+,S0169411,2016-01-27,1:30:16,0:28:03,0:05:53,0:05:21,0:04:48,0:06:34,2:28:34,1050,0,21
+Night Team,PGY2+,S0169384,2016-11-05,1:25:15,0:37:35,0:00:59,0:40:33,0:11:00,0:03:39,3:06:24,1286,6,8
+Night Team,PGY2+,S0169384,2016-11-04,4:12:39,1:32:15,0:03:00,0:47:32,0:07:18,0:12:34,7:10:25,2022,0,13
+Night Team,PGY2+,S0169384,2016-11-03,2:25:15,2:05:36,0:00:41,1:05:10,0:16:28,0:19:13,6:50:15,2439,152,16
+Night Team,PGY2+,S0169384,2016-11-02,1:40:41,1:18:59,0:00:55,0:43:47,0:10:31,0:06:55,4:23:03,1468,86,25
+Night Team,PGY2+,S0169384,2016-11-01,1:41:52,1:22:00,0:00:59,0:42:06,0:05:31,0:08:23,4:17:14,1860,474,15
+Intensive Care Unit,PGY2+,S0169411,2016-01-26,3:31:42,1:24:50,0:06:52,0:24:20,0:23:20,0:29:37,7:14:19,2793,0,31
+General Medicine,PGY2+,S0169403,2015-07-27,2:37:53,1:28:03,0:43:01,0:50:47,0:32:57,0:47:07,7:52:23,3091,42,70
+General Medicine,PGY2+,S0169403,2015-07-26,1:56:32,0:53:44,0:41:08,0:31:15,0:20:37,0:38:57,5:35:54,1752,0,17
+General Medicine,PGY2+,S0169403,2015-07-29,2:10:17,2:03:05,0:35:12,0:33:29,0:21:45,0:52:33,7:18:06,2536,28,26
+General Medicine,PGY2+,S0169403,2015-07-28,1:09:23,0:45:35,0:10:31,0:22:19,0:22:09,0:35:06,3:44:45,1553,99,17
+Intensive Care Unit,PGY2+,S0169389,2015-07-17,1:25:23,0:36:27,0:03:31,0:20:54,0:27:58,0:08:39,3:22:11,852,45,15
+General Medicine,PGY1,S0174975,2015-12-30,3:03:29,2:28:21,2:28:11,1:09:37,0:33:07,0:08:09,10:34:04,3960,621,52
+General Medicine,PGY1,S0174975,2015-12-31,0:10:50,0:31:21,0:32:34,0:06:42,0:00:56,0:03:08,1:32:32,706,690,9
+General Medicine,PGY1,S0181063,2016-01-01,0:23:37,0:25:23,1:34:03,0:18:56,0:23:14,0:10:55,3:47:06,1452,0,12
+General Medicine,PGY1,S0169402,2014-12-03,3:09:10,0:02:10,2:38:44,0:43:14,0:13:50,0:07:35,7:25:08,1680,0,9
+General Medicine,PGY1,S0169402,2014-12-02,3:26:43,0:14:19,2:15:16,0:53:43,0:40:59,0:12:13,8:02:53,2094,158,12
+General Medicine,PGY1,S0169402,2014-12-01,0:12:21,0:00:00,0:01:04,0:01:45,0:00:00,0:00:43,0:31:07,252,236,9
+General Medicine,PGY1,S0169402,2014-12-06,3:28:37,0:03:43,2:16:35,0:20:04,0:18:22,0:07:37,7:02:27,1414,61,14
+General Medicine,PGY1,S0169402,2014-12-05,3:49:04,0:15:09,2:14:31,1:07:59,0:33:16,0:25:33,9:05:17,2035,28,8
+General Medicine,PGY1,S0169402,2014-12-04,3:06:35,0:05:00,2:24:26,0:38:50,0:20:25,0:10:07,7:18:35,1645,51,6
+Intensive Care Unit,PGY2+,S0169389,2015-07-16,1:51:42,0:32:02,0:15:53,0:09:13,0:15:24,0:09:13,3:25:34,1260,0,16
+Intensive Care Unit,PGY2+,S0169404,2016-08-14,1:32:11,0:35:53,0:07:41,0:10:34,0:22:07,0:00:43,3:13:49,1124,2,8
+Intensive Care Unit,PGY2+,S0093383,2016-06-30,1:52:59,0:49:52,0:05:05,0:14:22,0:18:40,0:06:55,3:38:02,988,0,12
+General Medicine,PGY2+,S0169398,2016-03-09,4:13:07,1:31:42,0:00:24,0:32:04,0:16:18,0:12:19,7:51:08,4038,566,42
+General Medicine,PGY2+,S0169398,2016-03-08,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,10,0,2
+Emergency Medicine,PGY1,S0172085,2015-08-04,2:30:06,1:08:27,1:58:53,0:33:47,0:50:07,1:01:53,8:50:18,2562,0,43
+General Medicine,PGY2+,S0169398,2016-03-07,2:51:55,1:12:30,0:00:19,0:32:00,0:30:59,0:05:15,6:07:58,2534,134,23
+General Medicine,PGY2+,S0169398,2016-03-06,2:42:50,1:18:45,0:00:00,0:28:47,0:16:36,0:02:44,5:21:13,2033,93,23
+Emergency Medicine,PGY1,S0172085,2015-08-01,0:25:45,0:26:35,0:54:28,0:06:17,0:14:24,0:08:47,3:00:46,744,332,14
+Emergency Medicine,PGY1,S0172085,2015-08-02,4:15:18,0:59:15,2:24:19,0:19:38,0:32:05,0:41:08,9:47:57,2732,0,40
+Emergency Medicine,PGY1,S0172085,2015-08-03,4:22:33,0:47:13,2:54:28,0:21:04,0:40:04,0:39:18,10:40:22,2605,0,56
+General Medicine,PGY2+,S0109179,2016-07-28,1:01:29,0:28:05,0:00:02,0:08:44,0:02:16,0:00:22,1:55:18,747,0,24
+General Medicine,PGY2+,S0109179,2016-07-29,1:22:59,0:23:41,0:00:06,0:11:59,0:06:03,0:04:21,2:35:17,1004,208,7
+Intensive Care Unit,PGY2+,S0169395,2015-11-08,2:55:05,0:47:38,0:14:49,0:51:21,0:55:27,0:50:37,6:58:10,2685,0,37
+General Medicine,PGY2+,S0109179,2016-07-22,1:44:17,1:05:33,0:00:07,0:22:08,0:09:25,0:00:06,3:56:26,1545,146,16
+General Medicine,PGY2+,S0109179,2016-07-23,0:57:41,0:41:07,0:00:02,0:14:53,0:20:25,0:06:52,2:49:30,1040,0,19
+General Medicine,PGY2+,S0109179,2016-07-20,2:12:11,0:38:54,0:00:02,0:07:59,0:01:50,0:05:09,3:37:18,1204,0,27
+General Medicine,PGY2+,S0109179,2016-07-21,0:00:48,0:00:10,0:00:00,0:00:17,0:00:00,0:00:13,0:01:39,31,0,6
+General Medicine,PGY2+,S0109179,2016-07-26,1:08:05,0:32:16,0:00:00,0:17:26,0:12:51,0:00:14,2:24:34,866,123,14
+General Medicine,PGY2+,S0109179,2016-07-27,1:11:17,1:05:59,0:00:07,0:16:56,0:12:23,0:03:41,3:10:17,1774,0,30
+General Medicine,PGY2+,S0109179,2016-07-24,0:02:20,0:03:45,0:00:00,0:00:00,0:00:00,0:00:01,0:06:10,78,72,8
+General Medicine,PGY2+,S0109179,2016-07-25,1:48:49,1:01:51,0:00:03,0:06:48,0:03:08,0:06:12,3:24:03,1385,61,34
+Intensive Care Unit,PGY2+,S0169395,2015-11-06,1:52:21,0:49:59,0:12:29,0:14:36,0:22:16,0:33:00,4:23:17,1358,0,23
+Intensive Care Unit,PGY2+,S0169395,2015-11-07,1:21:06,0:41:31,0:09:36,0:10:03,0:15:49,0:17:01,3:03:20,1169,0,12
+Intensive Care Unit,PGY2+,S0169395,2015-11-04,2:01:49,0:42:39,0:13:52,0:11:04,0:27:56,0:09:55,4:26:20,2000,9,42
+Intensive Care Unit,PGY2+,S0169395,2015-11-05,1:17:38,0:26:27,0:04:21,0:10:21,0:13:15,0:14:20,2:54:21,1130,64,17
+Intensive Care Unit,PGY2+,S0169395,2015-11-02,1:40:18,0:27:47,0:09:13,0:09:03,0:15:37,0:16:23,3:03:17,1273,0,30
+Intensive Care Unit,PGY2+,S0169395,2015-11-03,0:01:40,0:00:00,0:00:00,0:00:57,0:00:00,0:00:00,0:02:37,43,25,14
+Night Team,PGY1,S0195076,2016-09-22,1:24:51,0:30:01,0:00:05,0:16:37,0:14:47,0:09:06,2:47:41,901,0,24
+General Medicine,PGY1,S0093383,2014-12-21,0:14:51,0:03:12,0:00:13,0:00:51,0:00:00,0:01:24,0:20:41,138,132,6
+General Medicine,PGY1,S0093383,2014-12-20,2:58:09,0:09:49,1:34:07,0:30:14,0:22:22,0:16:02,6:11:32,1726,0,19
+General Medicine,PGY1,S0093383,2014-12-23,3:06:20,0:08:20,3:08:22,0:43:02,0:18:26,0:19:20,8:01:31,1711,0,11
+General Medicine,PGY1,S0093383,2014-12-22,2:12:09,0:08:09,0:25:07,0:33:01,0:18:58,0:14:32,4:04:04,1467,0,8
+General Medicine,PGY1,S0093383,2014-12-25,3:30:25,0:12:42,3:31:12,0:47:30,0:09:59,0:29:46,9:09:05,2227,0,24
+General Medicine,PGY1,S0093383,2014-12-24,1:46:37,0:02:47,1:42:26,0:39:28,0:16:20,0:18:20,4:57:19,1394,0,13
+General Medicine,PGY1,S0093383,2014-12-27,1:20:15,0:09:47,1:12:52,0:17:32,0:14:23,0:10:30,3:41:13,1134,0,12
+General Medicine,PGY1,S0093383,2014-12-26,2:06:57,0:07:43,0:55:09,0:24:24,0:19:31,0:17:05,4:34:02,1492,0,17
+General Medicine,PGY1,S0093383,2014-12-29,2:12:17,0:05:48,0:32:30,0:20:10,0:17:45,0:22:34,4:07:48,1585,0,12
+General Medicine,PGY1,S0093383,2014-12-28,2:48:54,0:12:13,2:09:04,0:41:14,0:15:56,0:22:18,6:41:48,1635,66,11
+General Medicine,PGY1,S0181077,2015-12-21,1:20:17,1:16:15,1:14:11,0:14:19,0:13:22,0:17:47,4:50:55,2416,241,11
+Intensive Care Unit,PGY2+,S0169389,2015-07-18,0:00:00,0:00:00,0:00:00,0:02:13,0:00:00,0:00:00,0:02:13,22,0,4
+General Medicine,PGY2+,S0181081,2016-10-26,1:50:48,1:17:14,0:00:07,0:47:26,0:13:27,0:01:49,4:46:51,1831,0,12
+General Medicine,PGY2+,S0181081,2016-10-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY2+,S0181081,2016-10-24,1:28:45,0:23:07,0:00:00,0:11:27,0:05:55,0:02:04,2:37:49,1060,0,14
+General Medicine,PGY2+,S0181081,2016-10-25,2:05:23,0:56:42,0:00:04,0:05:04,0:02:48,0:04:25,3:48:28,1888,85,22
+General Medicine,PGY2+,S0181081,2016-10-22,0:00:37,0:00:07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:49,10,0,3
+General Medicine,PGY2+,S0181081,2016-10-23,2:04:14,0:54:19,0:00:00,0:20:47,0:06:59,0:02:41,4:11:16,1882,0,17
+General Medicine,PGY2+,S0181081,2016-10-20,1:55:22,1:13:22,0:00:27,0:04:44,0:10:03,0:02:07,4:25:39,2263,144,28
+General Medicine,PGY2+,S0181081,2016-10-21,0:56:36,0:48:44,0:00:00,0:37:39,0:10:16,0:00:39,2:48:51,1266,0,15
+General Medicine,PGY2+,S0181081,2016-10-28,1:48:42,1:01:16,0:01:54,0:05:00,0:05:43,0:00:39,3:39:09,2298,0,26
+General Medicine,PGY2+,S0181081,2016-10-29,2:36:37,1:02:26,0:00:12,0:39:30,0:13:32,0:06:51,5:25:56,1754,0,11
+General Medicine,PGY1,S0169383,2015-05-18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,14,0,2
+Emergency Medicine,PGY1,S0169404,2014-12-30,5:17:14,0:24:57,1:46:27,0:28:44,0:31:53,0:26:34,9:39:06,4262,77,19
+General Medicine,PGY1,S0169383,2015-05-12,2:17:43,0:30:12,1:55:03,1:12:04,0:26:57,0:17:37,7:21:53,2011,0,14
+General Medicine,PGY1,S0188098,2016-08-17,2:13:29,1:21:39,0:00:08,0:30:05,0:39:34,0:05:08,5:50:39,1803,0,11
+General Medicine,PGY1,S0181066,2015-12-11,1:21:52,1:10:57,1:23:45,0:45:07,0:34:21,0:27:21,6:16:11,1972,17,9
+General Medicine,PGY1,S0181066,2015-12-10,0:32:19,0:45:05,0:41:09,0:26:02,0:18:29,0:18:37,3:26:51,985,0,8
+General Medicine,PGY1,S0181066,2015-12-13,1:43:36,1:02:52,1:23:36,1:00:29,0:41:47,0:31:07,6:56:31,1981,195,18
+General Medicine,PGY1,S0181066,2015-12-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181066,2015-12-15,1:07:31,0:43:40,0:47:50,0:35:00,0:27:32,0:27:27,4:41:09,1598,233,15
+General Medicine,PGY1,S0181066,2015-12-14,0:53:04,0:17:09,1:36:12,0:26:55,0:23:16,0:12:07,4:23:13,1121,105,12
+General Medicine,PGY1,S0181066,2015-12-17,0:04:11,0:15:09,0:27:59,0:04:08,0:00:15,0:01:51,0:55:56,354,348,9
+General Medicine,PGY1,S0181066,2015-12-16,0:53:58,0:47:16,0:22:53,0:13:06,0:19:42,0:18:40,3:17:57,1042,126,8
+General Medicine,PGY1,S0181066,2015-12-19,0:32:05,0:07:26,0:25:19,0:16:06,0:14:27,0:05:21,1:58:01,668,0,4
+General Medicine,PGY1,S0181066,2015-12-18,0:48:08,0:39:42,1:37:11,1:02:06,0:33:20,0:19:10,5:46:19,1711,226,9
+General Medicine,PGY1,S0188098,2016-08-10,2:12:50,0:50:23,0:00:09,0:23:50,0:19:02,0:01:49,4:19:55,1330,27,7
+General Medicine,PGY1,S0074196,2015-08-19,1:01:27,0:04:42,0:33:42,0:08:44,0:06:50,0:08:57,2:15:41,464,0,4
+General Medicine,PGY1,S0188098,2016-08-12,2:00:53,1:45:19,0:00:21,0:29:08,0:18:55,0:05:12,5:13:36,1679,104,11
+General Medicine,PGY1,S0056755,2014-09-30,1:06:08,0:07:06,0:00:32,0:13:54,0:04:49,0:11:16,1:46:51,393,76,8
+General Medicine,PGY1,S0169390,2015-06-23,1:53:17,0:31:03,1:12:49,0:31:13,0:14:50,0:24:12,4:57:10,1093,125,6
+General Medicine,PGY1,S0169390,2015-06-22,1:56:38,0:28:36,4:03:50,0:29:45,0:16:24,0:10:26,7:44:05,1511,0,12
+General Medicine,PGY1,S0169390,2015-06-21,1:25:55,0:12:34,0:45:24,0:29:08,0:03:27,0:07:40,3:09:37,735,29,3
+General Medicine,PGY1,S0169390,2015-06-20,0:00:00,0:00:00,0:00:00,0:04:07,0:00:00,0:00:00,0:04:07,8,0,1
+General Medicine,PGY1,S0181059,2015-08-31,2:15:15,0:50:06,0:57:41,0:31:32,0:28:03,0:28:43,6:08:52,2445,166,20
+General Medicine,PGY1,S0169390,2015-06-24,3:30:10,0:26:00,1:59:36,1:00:26,0:39:21,0:28:16,8:36:43,2704,38,17
+Intensive Care Unit,PGY2+,S0109179,2016-03-04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Intensive Care Unit,PGY2+,S0109179,2016-03-05,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Intensive Care Unit,PGY2+,S0109179,2016-03-01,0:27:13,0:16:52,0:01:46,0:01:55,0:04:59,0:00:02,1:09:30,520,0,13
+Intensive Care Unit,PGY2+,S0109179,2016-03-02,2:03:32,1:10:47,0:00:59,0:18:56,0:32:08,0:07:55,4:35:52,1694,0,42
+Intensive Care Unit,PGY2+,S0109179,2016-03-03,0:54:56,0:35:10,0:03:01,0:18:18,0:17:05,0:00:04,2:28:27,935,0,13
+Night Team,PGY2+,S0163425,2015-12-15,0:41:52,0:42:48,0:36:56,0:04:48,0:17:13,0:12:28,2:40:53,1178,0,46
+Night Team,PGY1,S0108268,2014-08-27,3:01:07,0:18:13,0:13:07,0:43:11,0:57:31,0:58:36,6:35:46,2343,85,42
+Night Team,PGY1,S0108268,2014-08-26,2:00:12,0:10:13,0:07:30,0:09:58,0:19:09,0:19:56,3:21:36,1385,70,44
+Night Team,PGY1,S0108268,2014-08-25,1:38:44,0:14:35,0:11:35,0:14:09,0:30:03,0:15:17,3:21:51,1051,106,29
+General Medicine,PGY1,S0181078,2016-03-26,2:56:50,1:20:37,0:00:20,1:13:13,0:17:55,0:04:22,6:49:17,2330,242,17
+General Medicine,PGY1,S0181078,2016-03-24,2:11:22,0:32:37,0:02:25,1:19:49,0:13:25,0:13:03,5:16:24,1507,22,11
+General Medicine,PGY1,S0181078,2016-03-25,0:56:00,0:34:03,0:03:58,0:10:16,0:00:23,0:00:02,2:24:03,534,518,23
+General Medicine,PGY1,S0181078,2016-03-22,1:58:07,0:57:24,0:00:38,1:03:35,0:21:34,0:20:55,5:37:18,1889,72,25
+General Medicine,PGY1,S0181078,2016-03-23,3:10:37,0:42:28,0:00:17,1:00:16,0:25:51,0:05:04,5:59:05,1862,81,21
+General Medicine,PGY1,S0181078,2016-03-20,2:19:38,0:53:55,0:00:16,0:39:33,0:13:46,0:07:39,4:29:33,1420,221,9
+General Medicine,PGY1,S0181078,2016-03-21,3:29:20,1:11:50,0:00:59,1:15:35,0:17:33,0:09:39,7:18:06,2355,224,20
+Night Team,PGY1,S0108268,2014-08-23,1:52:08,0:06:31,0:13:12,0:35:50,0:42:58,0:34:53,4:28:37,1700,82,64
+Night Team,PGY1,S0108268,2014-08-22,0:41:43,0:02:17,0:00:00,0:07:57,0:13:27,0:22:55,1:37:38,740,0,17
+Night Team,PGY1,S0108268,2014-08-21,2:07:17,0:11:54,0:08:05,0:27:02,0:18:04,0:19:35,4:02:18,1748,264,42
+Night Team,PGY1,S0108268,2014-08-20,1:33:44,0:05:08,0:01:53,0:20:26,0:31:34,0:23:55,3:17:18,1669,218,34
+General Medicine,PGY1,S0108268,2014-06-30,4:08:04,0:10:09,0:30:44,0:17:22,0:35:24,0:27:32,6:27:56,1480,0,8
+General Medicine,PGY1,S0195201,2016-10-14,4:12:18,3:06:38,0:00:55,0:39:43,0:13:41,0:04:35,9:16:37,3439,0,21
+Night Team,PGY2+,S0093383,2016-01-09,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0093383,2016-01-08,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,6,0,2
+Night Team,PGY2+,S0093383,2016-01-01,1:05:03,1:22:17,4:04:42,0:45:23,0:09:34,0:20:36,8:00:43,2353,0,25
+Night Team,PGY2+,S0093383,2016-01-03,3:14:21,1:43:37,4:45:40,0:30:05,0:13:59,0:19:47,11:04:59,3497,0,21
+Night Team,PGY2+,S0093383,2016-01-02,1:54:45,1:28:40,2:33:39,0:30:47,0:11:28,0:24:03,7:36:08,3699,0,49
+Night Team,PGY2+,S0093383,2016-01-05,0:58:29,1:16:08,4:23:43,0:45:03,0:14:08,0:19:02,8:12:12,2845,0,29
+Night Team,PGY2+,S0093383,2016-01-04,0:59:41,0:55:45,3:48:56,0:30:56,0:16:29,0:13:29,7:19:53,2446,0,26
+Night Team,PGY2+,S0093383,2016-01-07,0:52:22,1:23:47,12:54:55,0:20:06,0:05:35,0:33:18,16:37:01,4593,2,17
+Night Team,PGY2+,S0093383,2016-01-06,1:36:43,1:12:58,4:24:32,0:38:17,0:29:46,0:46:55,9:41:38,3469,0,38
+General Medicine,PGY2+,S0169402,2016-07-10,2:25:50,1:14:08,0:00:03,0:39:12,0:12:30,0:06:29,5:21:28,1815,153,19
+General Medicine,PGY2+,S0169402,2016-07-11,2:03:41,2:19:43,0:00:12,1:09:08,0:42:11,0:04:33,6:57:26,2292,8,22
+General Medicine,PGY2+,S0169402,2016-07-12,2:59:53,1:02:44,0:00:03,0:47:57,0:22:50,0:06:24,6:12:44,2618,59,22
+General Medicine,PGY2+,S0169402,2016-07-13,1:40:35,0:57:14,0:00:08,1:09:37,0:24:18,0:00:55,5:01:15,1627,0,18
+General Medicine,PGY2+,S0169402,2016-07-14,0:59:27,0:19:56,0:00:02,0:15:35,0:14:10,0:01:36,2:03:50,633,0,19
+General Medicine,PGY2+,S0169402,2016-07-15,3:33:40,1:25:29,0:00:05,0:27:22,0:16:38,0:04:27,6:56:31,2543,0,19
+General Medicine,PGY1,S0181064,2016-04-28,2:19:55,1:12:50,0:02:45,0:37:35,0:13:25,0:00:21,5:06:42,2059,182,11
+General Medicine,PGY1,S0181064,2016-04-29,2:23:02,0:41:37,0:00:28,0:50:10,0:09:53,0:04:05,4:21:20,1267,228,17
+General Medicine,PGY1,S0181064,2016-04-26,1:40:04,0:41:50,0:01:51,0:31:15,0:06:56,0:03:57,3:29:37,1269,342,59
+General Medicine,PGY1,S0181064,2016-04-27,3:11:25,1:01:26,0:00:03,0:52:39,0:17:12,0:01:23,5:53:20,1719,6,12
+General Medicine,PGY1,S0181064,2016-04-24,1:52:27,1:08:29,0:00:05,0:23:00,0:07:05,0:00:53,3:49:15,1154,101,3
+General Medicine,PGY1,S0181064,2016-04-25,3:26:09,1:09:02,0:00:22,0:54:10,0:21:50,0:04:22,6:42:07,2095,30,18
+General Medicine,PGY1,S0181061,2015-09-29,1:22:41,0:40:42,1:18:13,0:48:43,0:27:36,0:21:02,5:47:54,1624,11,7
+General Medicine,PGY1,S0181061,2015-09-28,1:27:34,1:04:28,3:36:11,1:15:39,0:21:05,0:31:08,9:06:58,2009,17,8
+General Medicine,PGY1,S0181061,2015-09-25,2:40:35,0:50:27,1:35:08,1:05:32,0:53:13,0:39:19,8:40:17,2567,0,13
+General Medicine,PGY1,S0181061,2015-09-24,0:00:26,0:17:09,0:12:34,0:00:01,0:00:02,0:04:20,0:34:58,169,157,6
+General Medicine,PGY1,S0181061,2015-09-27,1:33:39,0:59:46,0:55:47,0:26:05,0:19:23,0:12:08,4:56:44,1884,1096,26
+General Medicine,PGY1,S0181061,2015-09-26,1:51:28,0:51:08,1:25:32,1:20:19,0:49:13,0:35:23,7:42:49,2193,149,8
+General Medicine,PGY1,S0181061,2015-09-21,2:23:18,0:27:46,2:49:53,0:50:31,0:22:44,0:21:54,8:06:13,2037,296,14
+General Medicine,PGY1,S0181061,2015-09-20,3:03:50,0:05:38,2:11:28,0:57:59,0:21:09,0:26:46,7:53:50,1778,0,21
+General Medicine,PGY1,S0181061,2015-09-23,2:08:45,1:56:33,2:34:20,1:15:16,0:42:35,0:34:25,10:01:28,2477,0,12
+General Medicine,PGY1,S0181061,2015-09-22,1:29:14,0:07:57,1:36:22,0:34:35,0:08:31,0:10:31,4:35:16,940,10,6
+General Medicine,PGY1,S0181069,2016-03-30,1:23:49,0:38:30,0:00:43,0:56:15,0:12:49,0:03:13,4:00:34,2198,520,12
+General Medicine,PGY1,S0169389,2014-08-10,0:09:40,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:10:04,93,78,10
+Emergency Medicine,PGY1,S0188098,2016-09-09,5:27:44,1:12:32,0:05:39,0:19:54,0:28:11,0:01:51,8:22:24,2759,0,30
+Emergency Medicine,PGY1,S0188098,2016-09-08,0:10:57,0:00:01,0:01:32,0:00:05,0:00:56,0:00:00,0:13:52,37,0,2
+Emergency Medicine,PGY1,S0188098,2016-09-01,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY1,S0188098,2016-09-03,1:37:50,0:34:01,0:01:05,0:00:39,0:03:14,0:01:16,2:30:43,848,0,18
+Emergency Medicine,PGY1,S0188098,2016-09-02,4:11:23,0:49:17,0:04:54,0:12:11,0:16:40,0:03:57,6:18:14,1460,0,36
+Emergency Medicine,PGY1,S0188098,2016-09-05,4:32:24,1:02:49,0:01:38,0:10:42,0:12:32,0:03:48,6:26:01,1389,0,30
+Emergency Medicine,PGY1,S0188098,2016-09-04,3:21:11,0:41:11,0:08:48,0:11:12,0:09:42,0:00:35,5:01:21,1286,0,35
+Emergency Medicine,PGY1,S0188098,2016-09-07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY1,S0188098,2016-09-06,2:03:23,0:33:48,0:06:01,0:11:14,0:14:53,0:01:12,3:32:49,1272,0,13
+Intensive Care Unit,PGY2+,S0176040,2016-08-08,2:01:49,0:54:51,0:00:01,0:56:22,0:28:19,0:17:19,5:05:52,1877,0,18
+Intensive Care Unit,PGY2+,S0176040,2016-08-09,3:25:05,1:28:00,0:19:14,0:25:44,0:25:42,0:14:17,7:02:04,2332,0,17
+General Medicine,PGY1,S0169384,2015-02-24,0:41:07,0:14:34,1:32:39,0:14:40,0:02:15,0:05:48,2:53:42,796,772,8
+General Medicine,PGY1,S0169384,2015-02-25,3:09:16,0:10:52,1:01:45,0:35:32,0:11:17,0:20:31,5:42:30,1574,116,8
+General Medicine,PGY1,S0169384,2015-02-22,3:18:58,0:06:31,2:26:50,0:29:41,0:26:43,0:16:56,7:17:31,1627,399,9
+General Medicine,PGY1,S0169384,2015-02-23,3:54:04,0:33:52,1:42:02,1:06:14,0:36:46,0:37:10,8:45:02,2870,25,8
+General Medicine,PGY1,S0169384,2015-02-20,3:53:30,0:05:59,3:07:38,1:32:00,0:39:58,0:29:32,10:20:49,2963,1131,12
+General Medicine,PGY1,S0169384,2015-02-21,2:57:12,0:33:04,3:38:56,0:46:32,0:17:52,0:18:23,8:48:14,2563,118,9
+Intensive Care Unit,PGY2+,S0176040,2016-08-01,2:34:33,1:09:42,0:10:09,0:36:33,0:19:03,0:19:10,5:36:05,1747,48,11
+Intensive Care Unit,PGY2+,S0176040,2016-08-02,1:37:32,0:41:54,0:09:00,0:20:39,0:21:48,0:04:41,3:28:17,1122,0,13
+Intensive Care Unit,PGY2+,S0176040,2016-08-03,0:05:59,0:00:34,0:00:00,0:00:00,0:00:00,0:00:00,0:06:43,30,0,7
+Intensive Care Unit,PGY2+,S0176040,2016-08-04,1:55:07,0:48:14,0:02:17,0:28:48,0:32:13,0:18:07,4:35:55,1869,0,18
+Intensive Care Unit,PGY2+,S0176040,2016-08-05,1:14:41,0:59:04,0:03:28,0:37:24,0:12:58,0:15:32,3:46:36,1421,0,15
+Intensive Care Unit,PGY2+,S0176040,2016-08-06,2:29:31,0:59:32,0:13:47,0:22:39,0:32:07,0:05:03,5:14:55,1634,0,13
+Intensive Care Unit,PGY2+,S0176040,2016-08-07,2:12:35,0:24:40,0:07:03,0:26:58,0:32:38,0:05:56,4:25:41,1491,0,15
+General Medicine,PGY1,S0169389,2014-11-06,3:20:59,0:19:53,0:14:57,0:25:08,0:30:56,0:26:36,5:40:37,1432,84,9
+General Medicine,PGY1,S0169389,2014-11-07,2:48:40,0:10:19,0:32:54,0:16:20,0:10:04,0:22:39,4:34:06,1206,67,12
+General Medicine,PGY1,S0169389,2014-11-04,0:06:20,0:00:00,0:00:00,0:02:28,0:00:10,0:00:00,0:09:05,42,36,5
+General Medicine,PGY1,S0169389,2014-11-05,2:56:05,0:01:34,0:01:16,0:12:17,0:13:06,0:15:25,3:49:02,1004,583,11
+General Medicine,PGY1,S0169389,2014-11-02,1:45:12,0:01:27,0:03:48,0:16:27,0:04:55,0:08:55,2:28:43,656,30,8
+General Medicine,PGY1,S0169389,2014-11-03,3:28:46,0:06:48,0:07:01,0:34:43,0:15:50,0:05:11,4:49:10,1015,0,21
+General Medicine,PGY1,S0169389,2014-11-01,4:24:42,0:07:55,0:19:59,0:27:11,0:22:39,0:24:55,6:26:29,1666,0,25
+General Medicine,PGY1,S0169389,2014-11-08,1:45:04,0:02:35,0:00:02,0:08:49,0:10:59,0:14:40,2:24:49,535,49,32
+Night Team,PGY1,S0169389,2014-11-09,3:24:59,0:15:39,0:34:23,0:24:16,0:27:12,0:12:28,5:29:10,1354,54,53
+Emergency Medicine,PGY1,S0181526,2015-07-07,3:15:04,0:41:44,1:27:42,0:31:31,0:39:14,1:18:16,8:38:21,2306,0,28
+Emergency Medicine,PGY1,S0181526,2015-07-06,1:35:42,0:28:20,0:24:04,0:09:24,0:07:25,0:13:51,3:40:30,1089,100,4
+Emergency Medicine,PGY1,S0181526,2015-07-05,0:00:00,0:01:37,0:00:00,0:00:00,0:00:00,0:01:44,0:06:44,40,40,2
+Emergency Medicine,PGY1,S0181526,2015-07-04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,9,0,2
+Emergency Medicine,PGY1,S0181526,2015-07-03,1:58:02,0:34:45,1:52:23,0:07:06,0:29:00,1:38:08,7:34:09,1521,0,23
+Emergency Medicine,PGY1,S0181526,2015-07-02,1:51:02,0:34:47,2:05:59,0:13:20,0:20:54,1:21:14,7:08:48,1656,0,14
+Emergency Medicine,PGY1,S0181526,2015-07-01,1:11:54,0:48:14,1:34:58,0:09:24,0:26:13,1:07:21,5:51:07,1450,0,23
+Emergency Medicine,PGY1,S0079640,2016-10-11,1:32:16,0:15:29,0:03:19,0:05:39,0:20:46,0:00:07,2:29:39,888,0,40
+Night Team,PGY2+,S0169381,2016-03-09,3:41:08,1:16:17,0:00:19,0:41:59,0:08:45,0:06:15,6:34:46,2331,0,32
+Night Team,PGY2+,S0169381,2016-03-08,1:34:58,0:48:33,0:00:10,0:20:29,0:13:33,0:07:12,3:40:14,1482,20,35
+Night Team,PGY2+,S0169406,2016-10-29,2:00:24,0:58:46,0:00:02,0:14:58,0:09:01,0:05:48,4:02:05,1643,334,26
+Night Team,PGY2+,S0169406,2016-10-28,0:36:33,0:48:41,0:03:22,0:11:28,0:04:16,0:02:08,2:08:24,665,0,11
+Emergency Medicine,PGY1,S0181526,2015-07-09,1:29:01,0:22:28,1:49:41,0:23:04,0:19:11,0:43:27,6:25:52,1684,25,13
+Emergency Medicine,PGY1,S0181526,2015-07-08,1:45:00,0:39:02,1:38:04,0:18:49,0:23:28,1:06:49,6:30:48,1454,0,21
+General Medicine,PGY2+,S0169406,2016-02-19,2:31:02,1:12:00,0:12:20,0:14:54,0:09:50,0:17:26,5:04:29,2131,0,21
+Emergency Medicine,PGY1,S0181065,2015-12-11,1:24:32,0:53:42,2:20:15,0:44:39,0:40:09,0:29:53,7:04:13,3711,9,15
+General Medicine,PGY2+,S0169406,2016-02-17,2:53:53,0:50:58,0:04:40,0:15:17,0:12:18,0:42:55,5:29:29,1983,95,26
+General Medicine,PGY2+,S0169406,2016-02-16,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,4,0,1
+General Medicine,PGY2+,S0169406,2016-02-15,0:47:38,0:08:23,0:03:31,0:09:17,0:04:42,0:02:50,1:27:21,726,0,8
+General Medicine,PGY2+,S0169406,2016-02-14,1:42:17,0:40:16,0:29:14,0:05:52,0:17:19,0:24:51,4:06:19,1454,1,36
+General Medicine,PGY2+,S0169406,2016-02-13,1:57:11,0:36:48,0:43:51,0:39:36,0:07:51,0:14:25,4:52:19,1742,0,21
+General Medicine,PGY2+,S0169406,2016-02-12,1:59:07,0:42:10,0:14:50,0:08:32,0:10:31,0:36:08,4:20:49,1829,0,27
+General Medicine,PGY2+,S0169406,2016-02-11,0:05:52,0:00:10,0:00:00,0:00:59,0:02:45,0:00:00,0:09:48,38,34,3
+General Medicine,PGY2+,S0169406,2016-02-10,1:24:21,0:20:33,0:03:49,0:20:16,0:14:15,0:12:26,2:50:41,1086,0,17
+General Medicine,PGY2+,S0169401,2015-12-14,2:24:55,1:11:03,0:02:29,0:43:41,0:19:51,0:25:07,5:38:49,2546,315,26
+General Medicine,PGY2+,S0169401,2015-12-15,2:28:06,1:21:53,0:06:46,1:04:16,0:22:33,0:58:12,6:49:30,3490,536,38
+General Medicine,PGY2+,S0169401,2015-12-16,2:17:02,1:17:23,0:28:50,0:56:33,0:23:20,0:26:30,6:29:46,2923,300,30
+General Medicine,PGY2+,S0169401,2015-12-17,2:06:43,1:51:34,1:07:40,1:05:30,0:31:04,1:00:05,8:39:04,3419,574,38
+General Medicine,PGY2+,S0169401,2015-12-10,3:07:21,2:00:44,0:24:25,0:31:39,0:16:17,0:54:22,7:53:36,3554,433,40
+General Medicine,PGY2+,S0181077,2016-08-22,3:40:53,0:58:13,0:00:05,0:54:52,0:34:39,0:07:52,7:18:53,2601,152,30
+General Medicine,PGY1,S0181079,2015-09-30,1:12:50,0:40:33,1:37:29,0:29:43,0:25:14,0:27:23,5:19:19,1799,221,23
+General Medicine,PGY1,S0181063,2016-04-12,0:35:44,0:31:41,0:00:04,0:12:59,0:04:59,0:00:08,1:42:27,567,0,6
+General Medicine,PGY1,S0181063,2016-04-13,0:21:15,0:26:55,0:00:23,0:10:19,0:01:23,0:03:26,1:16:31,556,31,4
+General Medicine,PGY1,S0181063,2016-04-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181063,2016-04-11,0:49:32,0:54:00,0:00:06,0:16:53,0:06:34,0:00:09,2:30:15,748,0,11
+General Medicine,PGY1,S0181063,2016-04-16,0:59:56,0:55:35,0:00:08,0:34:39,0:14:50,0:05:19,3:11:35,1258,0,13
+General Medicine,PGY1,S0181063,2016-04-17,0:31:55,0:24:15,0:00:05,0:16:56,0:09:15,0:04:08,1:40:13,537,0,5
+General Medicine,PGY1,S0181063,2016-04-14,1:03:28,1:15:17,0:00:05,0:32:59,0:17:26,0:05:33,3:48:10,1519,0,9
+General Medicine,PGY1,S0181063,2016-04-15,0:49:22,0:21:53,0:00:05,0:21:15,0:11:13,0:00:59,1:54:20,610,30,4
+General Medicine,PGY1,S0181063,2016-04-18,0:21:36,0:25:55,0:00:06,0:28:43,0:03:20,0:00:38,1:37:04,586,0,5
+General Medicine,PGY1,S0181063,2016-04-19,1:12:32,1:19:27,0:05:19,0:33:59,0:19:26,0:02:58,4:04:34,1334,0,8
+Intensive Care Unit,PGY2+,S0172056,2016-10-24,1:39:34,0:35:10,0:00:00,0:37:46,0:32:51,0:15:49,4:06:57,1609,44,23
+Night Team,PGY2+,S0079337,2016-10-09,1:26:55,0:32:15,0:00:13,0:30:12,0:10:12,0:01:43,3:15:21,1313,589,19
+General Medicine,PGY1,S0181082,2015-12-08,1:22:21,1:11:25,2:00:36,0:54:04,0:27:55,0:28:50,6:58:58,2093,0,13
+General Medicine,PGY1,S0181082,2015-12-09,1:29:45,0:53:48,0:50:41,0:45:47,0:23:55,0:16:05,5:21:28,1866,0,13
+Night Team,PGY2+,S0181060,2016-10-30,2:59:07,1:36:54,0:00:30,0:48:30,0:16:53,0:13:14,6:50:50,2147,0,26
+Night Team,PGY2+,S0181060,2016-10-31,1:00:43,1:06:56,0:00:05,0:37:03,0:06:30,0:03:34,3:53:05,1083,9,10
+General Medicine,PGY1,S0181082,2015-12-06,0:55:18,0:40:14,0:34:43,0:42:52,0:12:45,0:27:23,3:56:05,1526,38,12
+General Medicine,PGY1,S0181082,2015-12-07,0:38:20,0:29:00,1:12:30,0:26:55,0:15:03,0:20:12,3:38:50,1477,0,6
+Emergency Medicine,PGY2+,S0181056,2016-08-30,0:00:27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:02:30,15,0,4
+Emergency Medicine,PGY2+,S0181056,2016-08-31,0:09:06,0:04:50,0:00:00,0:01:43,0:00:51,0:00:05,0:19:55,164,0,11
+Intensive Care Unit,PGY2+,S0172056,2016-10-25,1:49:29,0:38:53,0:02:01,0:24:51,0:19:07,0:01:12,3:36:21,1243,0,20
+General Medicine,PGY2+,S0181077,2016-08-20,4:25:08,1:36:03,0:00:02,0:29:23,0:18:46,0:21:23,8:25:49,3742,456,35
+Night Team,PGY2+,S0169383,2015-07-08,0:16:32,0:23:53,0:18:50,0:30:47,0:06:51,0:11:36,1:59:32,675,0,30
+Night Team,PGY2+,S0169383,2015-07-09,2:37:37,0:52:27,2:06:08,0:41:01,0:40:48,0:22:58,7:49:54,2164,28,87
+Night Team,PGY2+,S0169383,2015-07-06,3:01:58,1:07:14,1:30:45,0:34:46,0:15:58,0:20:03,7:36:11,2246,34,56
+Night Team,PGY2+,S0169383,2015-07-07,0:50:57,0:31:50,2:18:38,0:23:11,0:05:46,0:25:40,5:01:08,1114,26,41
+Night Team,PGY2+,S0169383,2015-07-04,4:13:14,1:10:56,1:25:53,0:17:10,0:11:20,0:29:50,8:13:53,1639,12,46
+Night Team,PGY2+,S0169383,2015-07-05,3:33:18,0:40:30,2:47:56,0:41:18,0:14:14,0:26:53,8:49:15,1792,83,52
+Night Team,PGY2+,S0169383,2015-07-02,1:54:15,1:48:22,2:04:10,0:34:54,0:16:04,0:18:39,7:41:33,1929,0,49
+Night Team,PGY2+,S0169383,2015-07-03,3:48:43,1:39:02,2:54:52,0:29:10,0:09:08,0:23:52,10:00:16,2190,89,82
+Night Team,PGY1,S0169399,2015-06-15,1:20:00,0:33:28,0:34:04,0:24:21,0:19:09,0:03:04,3:45:06,819,181,31
+Night Team,PGY1,S0169399,2015-06-14,0:25:49,0:02:42,0:00:01,0:11:12,0:09:25,0:03:38,0:59:36,153,0,17
+Night Team,PGY1,S0169399,2015-06-17,1:41:26,0:31:51,2:16:51,0:34:19,0:17:11,0:11:42,5:57:04,961,0,25
+Night Team,PGY1,S0169399,2015-06-16,1:41:23,0:20:49,2:12:33,0:21:37,0:14:28,0:17:50,5:36:20,915,158,17
+Night Team,PGY1,S0169399,2015-06-11,1:04:09,0:24:53,2:50:39,0:41:53,0:13:45,0:13:55,5:48:19,985,762,26
+Night Team,PGY1,S0169399,2015-06-10,1:06:55,0:14:45,0:39:04,0:19:20,0:10:46,0:07:50,2:57:45,732,353,33
+Night Team,PGY1,S0169399,2015-06-13,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,1
+Night Team,PGY1,S0169399,2015-06-12,1:16:09,0:17:49,2:23:19,0:20:24,0:09:47,0:14:40,4:45:28,604,506,14
+Night Team,PGY1,S0169399,2015-06-19,1:25:45,0:19:19,1:49:07,0:31:57,0:34:48,0:11:52,5:14:54,921,157,30
+Night Team,PGY1,S0169399,2015-06-18,0:56:47,0:05:15,3:06:26,0:06:31,0:11:06,0:08:22,4:46:01,592,6,24
+General Medicine,PGY2+,S0169390,2015-11-18,1:14:17,0:47:06,1:12:18,0:25:38,0:19:18,0:15:29,4:39:11,1453,36,14
+General Medicine,PGY2+,S0169390,2015-11-19,0:00:00,0:00:00,0:00:00,0:00:36,0:00:00,0:00:00,0:00:36,14,0,4
+General Medicine,PGY2+,S0169390,2015-11-16,1:22:08,0:24:29,0:04:33,0:32:01,0:06:45,0:12:13,2:50:43,867,183,20
+General Medicine,PGY2+,S0169390,2015-11-17,2:49:20,1:16:39,0:00:52,0:33:00,0:35:41,0:46:42,6:55:52,2843,127,36
+Intensive Care Unit,PGY2+,S0169390,2015-11-14,1:06:30,0:51:01,0:56:11,0:13:06,0:13:55,0:11:40,3:59:11,1141,0,20
+General Medicine,PGY2+,S0169390,2015-11-15,3:19:34,1:07:21,0:11:11,0:15:48,0:33:02,0:40:32,6:31:27,2709,75,27
+Intensive Care Unit,PGY2+,S0169390,2015-11-12,2:29:45,0:53:57,0:09:57,0:25:58,0:28:06,0:37:35,5:26:43,1720,822,20
+Intensive Care Unit,PGY2+,S0169390,2015-11-13,2:47:11,1:22:44,0:45:32,0:32:14,0:32:31,0:47:29,7:32:07,2159,542,25
+Intensive Care Unit,PGY2+,S0169390,2015-11-10,2:33:10,0:40:46,0:02:45,0:27:14,0:29:14,0:13:29,4:54:05,1397,0,22
+Intensive Care Unit,PGY2+,S0169390,2015-11-11,1:48:33,0:35:16,0:01:23,0:17:24,0:23:41,0:23:22,3:50:26,1028,49,19
+General Medicine,PGY1,S0169399,2015-04-17,1:53:04,0:08:24,0:53:13,0:34:29,0:21:14,0:04:41,4:27:41,1050,30,12
+General Medicine,PGY1,S0169399,2015-04-16,2:48:46,0:15:21,1:30:50,0:38:22,0:13:33,0:02:49,5:55:49,1066,106,12
+General Medicine,PGY2+,S0181526,2016-07-17,2:33:57,1:18:00,0:00:04,0:25:52,0:20:39,0:15:57,5:55:49,2166,174,22
+General Medicine,PGY1,S0169399,2015-04-14,0:54:51,0:05:12,1:12:40,0:11:56,0:09:14,0:03:38,2:51:55,640,0,10
+General Medicine,PGY1,S0169399,2015-04-13,1:23:39,0:16:14,2:00:52,0:42:03,0:18:50,0:03:17,5:22:07,1260,14,14
+General Medicine,PGY1,S0169399,2015-04-12,1:28:40,0:21:50,2:29:58,0:26:03,0:18:45,0:11:09,5:44:09,988,0,11
+General Medicine,PGY2+,S0108287,2016-05-25,2:39:12,1:21:16,0:00:02,0:16:02,0:10:10,0:04:32,4:56:40,2096,12,28
+General Medicine,PGY2+,S0108287,2016-05-24,2:10:30,1:12:52,0:00:07,0:23:39,0:20:24,0:00:50,4:31:34,1619,49,22
+General Medicine,PGY2+,S0108287,2016-05-27,0:15:23,0:40:33,0:00:00,0:03:05,0:00:00,0:00:54,1:03:52,237,14,12
+General Medicine,PGY2+,S0108287,2016-05-26,1:59:04,0:47:05,0:00:13,0:44:19,0:12:54,0:05:38,4:18:50,1634,17,23
+General Medicine,PGY2+,S0108287,2016-05-21,2:09:00,0:44:54,0:00:03,0:24:22,0:25:35,0:09:14,4:25:57,1289,26,12
+General Medicine,PGY2+,S0108287,2016-05-20,2:36:33,1:09:48,0:00:10,0:29:03,0:18:38,0:03:16,5:08:02,2072,0,25
+General Medicine,PGY2+,S0108287,2016-05-23,3:19:48,1:33:14,0:00:02,0:31:25,0:14:42,0:02:30,6:16:03,2195,4,26
+General Medicine,PGY2+,S0108287,2016-05-22,1:16:55,0:29:50,0:00:04,0:25:08,0:08:15,0:01:02,2:51:07,930,75,12
+General Medicine,PGY1,S0169399,2015-04-10,2:13:56,0:02:44,4:06:46,0:26:43,0:07:51,0:06:26,7:26:27,1124,0,13
+General Medicine,PGY2+,S0108287,2016-05-28,0:00:00,0:00:00,0:00:00,0:00:10,0:00:00,0:00:00,0:00:10,6,0,3
+General Medicine,PGY1,S0181081,2015-07-30,2:29:44,0:15:18,2:36:48,0:59:40,0:25:53,0:40:57,7:50:40,1800,0,8
+General Medicine,PGY1,S0181081,2015-07-31,2:10:08,0:16:19,2:42:17,0:39:18,0:20:38,0:24:50,6:57:38,1558,143,8
+General Medicine,PGY2+,S0181077,2016-08-25,3:33:09,1:36:32,0:00:09,0:27:04,0:23:31,0:02:55,6:56:31,2815,194,26
+Night Team,PGY2+,S0169401,2015-08-19,1:02:14,0:34:26,0:53:21,0:39:39,0:09:10,0:02:58,3:43:15,1391,0,41
+General Medicine,PGY1,S0169394,2015-05-08,1:57:08,0:05:29,1:15:20,0:55:50,0:25:38,0:20:59,5:38:38,1692,317,11
+General Medicine,PGY1,S0169394,2015-05-09,0:39:22,0:04:23,1:09:19,0:09:51,0:05:48,0:09:47,2:31:45,672,188,5
+General Medicine,PGY1,S0169394,2015-05-04,0:06:09,0:00:57,0:00:17,0:00:01,0:00:00,0:00:45,0:08:46,102,96,4
+General Medicine,PGY1,S0169394,2015-05-05,2:32:06,0:34:40,1:52:45,1:05:36,0:22:44,0:18:56,7:13:29,2238,227,12
+General Medicine,PGY1,S0169394,2015-05-06,0:57:11,0:07:10,0:15:04,0:12:57,0:12:58,0:16:55,2:11:05,799,413,6
+General Medicine,PGY1,S0169394,2015-05-07,2:44:50,0:36:12,1:33:31,0:39:52,0:27:31,0:23:10,7:02:40,2174,537,9
+General Medicine,PGY1,S0169394,2015-05-01,0:00:00,0:03:55,0:00:00,0:00:09,0:00:00,0:00:25,0:08:32,95,91,5
+General Medicine,PGY1,S0169394,2015-05-02,2:44:47,0:32:26,1:16:35,0:48:24,0:28:24,0:33:35,6:47:29,1930,86,10
+General Medicine,PGY1,S0169394,2015-05-03,1:28:19,0:03:30,1:16:12,0:20:31,0:12:48,0:31:29,4:17:19,1090,183,4
+Night Team,PGY1,S0181088,2015-11-07,2:09:57,0:40:00,2:13:58,0:37:17,0:24:25,0:21:09,7:10:39,1880,0,14
+Night Team,PGY1,S0181088,2015-11-06,0:39:36,0:23:17,0:53:49,0:13:37,0:11:33,0:20:48,2:52:45,953,0,19
+Night Team,PGY1,S0181088,2015-11-05,1:15:30,0:38:18,0:58:17,0:11:14,0:20:08,0:33:55,4:06:55,1403,0,33
+General Medicine,PGY2+,S0169399,2016-06-05,1:34:40,1:00:44,0:00:10,0:35:43,0:19:19,0:08:05,4:24:55,1144,47,18
+Night Team,PGY1,S0181088,2015-11-04,1:18:55,0:37:12,1:11:04,0:24:18,0:14:06,0:36:16,4:42:49,1682,0,27
+General Medicine,PGY2+,S0169399,2016-06-04,0:48:55,0:41:24,0:00:15,0:16:39,0:08:40,0:04:51,2:29:16,732,61,17
+General Medicine,PGY2+,S0169399,2016-06-07,2:11:52,0:44:17,0:00:04,0:19:29,0:08:02,0:05:40,4:24:58,1059,25,27
+Emergency Medicine,PGY1,S0181065,2015-12-18,0:47:33,0:20:10,1:28:58,0:46:57,0:14:20,0:17:58,4:18:26,1697,158,4
+Emergency Medicine,PGY1,S0181065,2015-12-19,1:16:39,0:20:07,2:51:15,0:15:12,0:18:40,0:33:11,6:02:30,2451,0,44
+General Medicine,PGY2+,S0169399,2016-06-06,1:04:05,0:54:19,0:00:13,0:30:03,0:10:42,0:00:42,3:15:54,1017,14,10
+Emergency Medicine,PGY1,S0181065,2015-12-10,0:09:15,0:11:13,0:03:46,0:01:42,0:00:09,0:00:00,0:41:42,432,430,1
+Night Team,PGY1,S0172085,2016-01-31,1:47:58,1:13:00,1:29:44,0:34:29,0:26:36,0:54:40,6:57:17,2393,2133,39
+Emergency Medicine,PGY1,S0181065,2015-12-12,0:46:24,0:35:01,1:27:46,0:25:23,0:25:40,0:20:50,4:12:16,1744,89,13
+Emergency Medicine,PGY1,S0181065,2015-12-13,0:49:54,0:32:29,2:18:34,0:43:32,0:29:41,0:37:42,5:53:32,2950,0,13
+Emergency Medicine,PGY1,S0181065,2015-12-14,1:09:27,0:16:17,1:54:57,0:27:34,0:30:39,0:15:04,4:52:00,2088,0,8
+Emergency Medicine,PGY1,S0181065,2015-12-15,0:12:09,0:05:24,0:01:13,0:00:58,0:00:55,0:00:16,0:42:38,367,363,5
+Emergency Medicine,PGY1,S0181065,2015-12-16,0:11:22,0:26:21,0:13:33,0:06:40,0:03:38,0:05:08,1:07:17,718,0,6
+Emergency Medicine,PGY1,S0181065,2015-12-17,0:32:54,0:42:33,1:19:03,0:25:13,0:15:39,0:16:50,3:40:52,2051,84,9
+General Medicine,PGY2+,S0169399,2016-06-03,0:02:24,0:03:40,0:00:00,0:00:00,0:00:00,0:01:53,0:08:44,66,60,9
+General Medicine,PGY2+,S0169399,2016-06-02,2:23:12,1:23:17,0:00:07,0:29:06,0:25:19,0:09:53,5:48:39,1497,119,21
+Night Team,PGY1,S0181073,2016-04-19,1:37:42,0:37:53,0:00:01,0:40:00,0:33:11,0:03:49,4:06:49,922,675,20
+Night Team,PGY1,S0181073,2016-04-18,0:13:12,0:04:57,0:00:00,0:06:35,0:04:38,0:06:59,0:48:51,247,0,11
+Night Team,PGY1,S0181073,2016-04-13,2:32:34,1:03:34,0:00:06,0:37:53,0:27:23,0:08:15,5:04:39,1294,14,30
+Night Team,PGY1,S0181073,2016-04-12,3:11:05,0:58:00,0:00:08,1:22:21,1:01:40,0:07:41,7:08:55,1775,33,32
+Night Team,PGY1,S0181073,2016-04-11,1:20:03,0:12:05,0:00:00,0:15:00,0:17:02,0:00:27,2:09:23,586,0,13
+Night Team,PGY1,S0181073,2016-04-10,1:46:53,0:36:19,0:00:00,0:12:14,0:18:01,0:03:26,3:21:12,923,0,23
+Night Team,PGY1,S0181073,2016-04-17,2:22:48,0:15:27,0:00:00,0:19:51,0:22:42,0:01:15,3:33:04,934,383,25
+Night Team,PGY1,S0181073,2016-04-16,2:43:33,0:39:35,0:00:00,0:47:44,0:24:42,0:21:03,5:19:17,1331,57,32
+Night Team,PGY1,S0181073,2016-04-15,1:50:00,0:41:34,0:00:00,0:45:56,0:39:51,0:00:43,4:18:50,1183,0,29
+Night Team,PGY1,S0181073,2016-04-14,3:05:51,0:28:42,0:00:07,0:55:25,0:46:17,0:01:46,5:43:39,1378,0,28
+General Medicine,PGY1,S0181056,2016-03-19,2:02:57,0:40:02,0:00:10,0:45:57,0:11:37,0:01:05,4:46:33,1619,56,12
+General Medicine,PGY1,S0181056,2016-03-18,3:06:12,1:16:16,0:00:11,1:03:11,0:30:30,0:03:04,7:17:45,2513,39,15
+General Medicine,PGY1,S0181056,2016-03-17,2:15:53,1:05:34,0:00:09,0:49:52,0:12:26,0:01:30,5:17:28,1944,91,9
+General Medicine,PGY1,S0181056,2016-03-16,2:41:16,1:49:33,0:00:07,0:41:54,0:19:44,0:00:49,6:16:14,2223,31,20
+General Medicine,PGY1,S0181056,2016-03-15,2:03:39,0:47:55,0:00:08,0:15:18,0:05:41,0:02:11,3:40:10,1220,92,6
+General Medicine,PGY1,S0181056,2016-03-14,0:25:36,0:01:57,0:00:00,0:01:20,0:00:18,0:00:02,0:36:26,312,282,11
+General Medicine,PGY1,S0181056,2016-03-13,3:30:50,2:08:02,0:00:15,1:28:46,0:24:45,0:09:39,8:28:16,2946,21,18
+General Medicine,PGY1,S0181056,2016-03-12,1:34:53,0:48:57,0:00:08,0:29:21,0:10:17,0:02:33,3:30:36,1512,34,8
+General Medicine,PGY1,S0181056,2016-03-11,2:51:45,1:45:40,0:00:09,0:44:56,0:18:50,0:09:35,6:35:57,2440,11,21
+General Medicine,PGY1,S0181056,2016-03-10,1:46:33,1:25:06,0:00:07,1:02:23,0:11:28,0:00:19,5:11:57,1947,5,13
+Night Team,PGY1,S0181065,2016-05-08,2:23:28,0:33:44,0:00:00,0:08:01,0:07:20,0:06:54,3:32:14,1290,167,20
+Night Team,PGY1,S0181065,2016-05-09,3:17:21,0:41:36,0:02:00,0:21:19,0:15:28,0:08:16,5:24:35,1945,108,27
+General Medicine,PGY1,S0181057,2016-06-08,1:41:09,0:47:17,0:00:24,0:37:25,0:34:43,0:04:09,4:18:27,1608,0,10
+Intensive Care Unit,PGY2+,S0181061,2016-09-10,0:00:00,0:00:00,0:00:00,0:00:45,0:00:00,0:00:00,0:00:45,19,0,7
+General Medicine,PGY1,S0181057,2016-06-09,2:35:39,1:30:06,0:00:13,0:56:36,0:24:40,0:01:19,6:24:16,2298,0,17
+General Medicine,PGY2+,S0181079,2016-09-19,3:47:58,1:50:47,0:00:02,0:41:00,0:22:19,0:07:10,8:00:28,2881,68,27
+Night Team,PGY1,S0181060,2015-10-06,0:33:50,0:33:33,1:10:41,0:26:16,0:15:40,0:24:26,3:43:36,1073,0,13
+General Medicine,PGY1,S0181057,2016-06-04,2:21:53,1:32:26,0:00:23,1:14:16,0:34:54,0:01:10,6:43:40,2253,0,16
+Night Team,PGY1,S0181060,2015-10-04,1:20:49,1:00:32,0:13:19,0:33:20,0:43:09,0:59:53,5:08:56,1342,0,21
+Night Team,PGY1,S0181060,2015-10-05,0:45:42,0:49:33,1:15:02,0:45:30,0:34:52,0:24:07,5:01:28,1020,0,19
+Night Team,PGY1,S0181060,2015-10-02,0:25:04,0:07:16,0:00:07,0:10:27,0:21:26,0:20:13,1:29:38,338,0,16
+Night Team,PGY1,S0181060,2015-10-03,0:47:43,0:17:55,0:02:08,0:20:49,0:23:39,0:09:35,2:19:02,625,133,25
+General Medicine,PGY1,S0181057,2016-06-05,1:14:36,0:44:26,0:00:21,0:39:35,0:29:34,0:01:09,3:43:33,1528,217,8
+General Medicine,PGY2+,S0181057,2016-11-01,1:12:28,0:50:49,0:01:42,0:27:20,0:09:20,0:15:15,3:32:27,1353,0,23
+General Medicine,PGY2+,S0181057,2016-11-02,2:12:18,0:47:51,0:00:08,0:20:46,0:16:37,0:06:40,4:02:29,1355,0,17
+General Medicine,PGY2+,S0181057,2016-11-03,2:56:01,1:14:57,0:00:24,0:25:59,0:21:11,0:11:07,5:39:04,3253,0,36
+General Medicine,PGY2+,S0181057,2016-11-04,2:43:59,1:32:23,0:00:09,1:07:22,0:34:07,0:06:55,7:04:01,2557,0,18
+General Medicine,PGY1,S0181057,2016-06-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,2
+Night Team,PGY1,S0181060,2015-10-08,1:10:38,0:55:43,1:09:48,0:16:50,0:21:52,0:18:21,4:38:51,1148,0,32
+Night Team,PGY1,S0181060,2015-10-09,0:54:49,0:25:32,2:26:55,0:24:35,0:21:53,0:27:20,5:15:12,952,21,21
+General Medicine,PGY1,S0181057,2016-06-07,2:08:59,0:45:02,0:00:07,0:58:06,0:17:03,0:00:39,4:33:59,1859,0,11
+General Medicine,PGY1,S0181057,2016-06-01,0:16:48,0:08:54,0:00:00,0:06:02,0:02:55,0:00:07,0:39:24,306,0,7
+General Medicine,PGY1,S0108268,2014-07-13,0:12:15,0:00:06,0:00:02,0:00:00,0:00:00,0:00:45,0:14:26,111,109,8
+General Medicine,PGY1,S0108268,2014-07-12,3:09:55,0:18:28,0:18:51,0:31:06,0:23:24,0:46:51,5:46:43,1636,0,16
+General Medicine,PGY1,S0108268,2014-07-11,3:21:23,0:12:00,0:21:38,0:24:33,0:15:20,0:14:21,5:18:23,1659,0,11
+General Medicine,PGY1,S0108268,2014-07-10,3:05:15,0:22:48,0:20:47,0:28:42,0:16:12,0:24:34,5:20:01,1431,24,7
+General Medicine,PGY1,S0108268,2014-07-17,3:42:04,0:16:56,0:19:47,0:44:38,0:44:27,0:40:14,6:54:09,2353,23,9
+General Medicine,PGY1,S0181057,2016-06-02,1:59:43,1:17:49,0:01:10,0:40:57,0:34:01,0:00:41,5:14:41,2031,21,12
+General Medicine,PGY1,S0108268,2014-07-15,2:39:08,0:18:48,0:10:33,0:14:21,0:19:21,0:39:45,4:29:28,1221,0,5
+General Medicine,PGY1,S0108268,2014-07-14,3:52:25,0:07:38,0:13:12,0:42:20,0:37:27,0:33:57,6:50:46,1990,721,19
+General Medicine,PGY1,S0108268,2014-07-19,2:03:15,0:02:11,0:00:03,0:00:00,0:01:23,0:06:32,3:00:50,978,974,25
+General Medicine,PGY1,S0196597,2016-09-23,1:03:26,0:40:31,0:00:05,0:28:45,0:10:54,0:00:08,2:59:10,1083,0,9
+General Medicine,PGY1,S0174975,2015-12-16,2:07:12,1:11:51,0:59:11,1:42:17,0:33:44,0:10:37,7:29:11,2892,330,18
+General Medicine,PGY1,S0174975,2015-12-17,2:26:44,1:43:25,1:37:16,1:55:42,0:41:14,0:07:34,9:09:39,3429,79,27
+Night Team,PGY2+,S0173803,2016-08-19,2:57:13,1:03:56,0:00:11,0:35:12,0:29:01,0:05:01,5:29:31,1522,0,53
+General Medicine,PGY1,S0174975,2015-12-15,2:09:26,1:35:58,1:47:51,1:32:29,0:45:12,0:10:39,8:37:58,2835,50,28
+General Medicine,PGY1,S0174975,2015-12-12,1:54:40,1:35:18,0:57:25,1:05:33,0:22:48,0:20:54,6:49:48,2668,79,21
+General Medicine,PGY1,S0174975,2015-12-13,1:13:26,1:21:00,0:57:19,1:56:02,0:32:25,0:16:35,6:51:51,2674,255,14
+General Medicine,PGY1,S0174975,2015-12-10,2:23:58,1:37:13,1:34:44,1:49:57,0:37:34,0:08:23,8:56:42,3041,105,23
+General Medicine,PGY1,S0174975,2015-12-11,0:10:30,0:22:34,0:00:18,0:09:46,0:01:53,0:00:23,0:49:05,667,657,9
+Night Team,PGY2+,S0173803,2016-08-17,3:13:43,2:26:22,0:00:49,0:41:39,0:16:26,0:25:05,7:48:36,2609,83,44
+Night Team,PGY2+,S0173803,2016-08-16,0:51:25,0:37:24,0:00:08,0:30:30,0:06:28,0:02:42,2:26:48,793,0,28
+Night Team,PGY2+,S0173803,2016-08-15,0:48:02,0:23:51,0:00:02,0:07:57,0:03:25,0:00:21,1:33:51,673,503,44
+General Medicine,PGY1,S0195225,2016-11-10,3:19:11,2:15:17,0:00:13,1:02:29,0:42:55,0:04:08,8:03:32,2182,0,15
+Intensive Care Unit,PGY2+,S0093383,2016-06-26,1:26:41,0:39:48,0:03:26,0:19:58,0:20:26,0:03:12,3:16:22,1171,0,13
+Intensive Care Unit,PGY2+,S0093383,2016-06-27,1:46:12,0:43:03,0:16:21,0:20:55,0:13:23,0:04:25,3:52:26,951,0,11
+Intensive Care Unit,PGY2+,S0108287,2015-12-31,1:10:14,0:22:46,0:11:14,0:16:46,0:18:47,0:08:59,3:02:06,645,0,13
+Intensive Care Unit,PGY2+,S0108287,2015-12-30,1:49:26,0:28:28,0:20:32,0:09:24,0:14:37,0:12:49,3:40:49,979,0,18
+General Medicine,PGY2+,S0181082,2016-09-05,2:38:31,1:09:20,0:00:07,0:22:53,0:32:55,0:02:43,5:15:25,1853,156,24
+General Medicine,PGY2+,S0181082,2016-09-04,2:09:22,0:30:39,0:00:05,0:26:44,0:14:00,0:00:40,3:48:25,1092,115,11
+General Medicine,PGY1,S0163662,2014-09-12,2:59:46,0:28:56,0:25:12,0:18:28,0:34:43,0:17:38,5:24:19,1701,0,39
+General Medicine,PGY2+,S0181082,2016-09-02,0:09:54,0:08:26,0:00:00,0:00:31,0:02:44,0:00:10,0:25:05,221,201,9
+General Medicine,PGY2+,S0181082,2016-09-01,3:04:37,0:57:04,0:00:30,1:11:01,0:43:31,0:05:35,6:57:16,2308,0,14
+General Medicine,PGY2+,S0181082,2016-09-09,1:53:34,0:24:59,0:00:04,0:29:29,0:17:32,0:07:42,3:39:31,1475,0,12
+General Medicine,PGY2+,S0181082,2016-09-08,2:57:02,1:11:57,0:00:02,0:28:47,0:26:34,0:05:22,5:43:21,2964,177,22
+Night Team,PGY1,S0172056,2015-08-13,0:36:28,0:21:40,1:05:48,0:27:26,0:23:59,0:10:18,3:10:21,920,6,29
+Night Team,PGY1,S0172056,2015-08-12,1:13:14,0:30:57,2:00:30,0:39:41,0:36:50,0:28:40,5:43:38,1445,0,30
+Night Team,PGY1,S0172056,2015-08-11,1:18:03,0:38:44,3:12:44,0:26:45,0:19:11,0:34:04,6:46:26,1559,0,22
+Night Team,PGY1,S0172056,2015-08-10,1:49:20,0:03:26,1:28:21,0:37:08,0:40:46,0:29:50,5:31:50,1402,0,23
+General Medicine,PGY1,S0172056,2015-08-17,1:33:21,0:07:07,3:24:24,0:58:08,0:23:59,0:22:44,7:35:01,2123,52,8
+General Medicine,PGY1,S0172056,2015-08-16,2:00:43,0:48:55,2:01:19,1:03:10,0:46:15,0:44:08,8:51:37,2459,0,16
+Night Team,PGY1,S0172056,2015-08-15,1:02:25,0:02:50,1:35:16,0:08:17,0:02:12,0:18:43,3:26:30,522,0,6
+Night Team,PGY1,S0172056,2015-08-14,0:25:50,0:28:41,1:32:05,0:19:30,0:18:16,0:16:14,3:30:23,794,0,15
+General Medicine,PGY1,S0172056,2015-08-19,1:54:27,0:26:19,2:25:15,0:32:03,0:36:18,0:17:14,7:14:49,2150,0,13
+General Medicine,PGY1,S0172056,2015-08-18,2:43:00,0:29:52,3:40:37,0:40:36,0:33:26,0:23:12,9:10:33,2248,10,11
+Intensive Care Unit,PGY2+,S0094959,2015-08-29,2:48:52,1:45:15,0:07:39,0:06:38,0:16:30,0:43:16,6:08:51,2441,476,35
+Intensive Care Unit,PGY2+,S0094959,2015-08-28,4:00:56,1:02:34,0:03:07,0:15:38,0:25:08,0:55:23,7:17:43,3783,305,26
+General Medicine,PGY1,S0163662,2014-09-13,0:07:37,0:00:00,0:00:00,0:00:07,0:00:00,0:00:22,0:08:08,48,28,8
+General Medicine,PGY1,S0195237,2016-11-02,3:16:11,1:24:53,0:00:17,0:55:52,0:16:53,0:08:03,6:42:19,1649,36,13
+Night Team,PGY1,S0181074,2016-01-11,0:40:44,0:55:42,0:04:07,0:31:08,0:31:32,0:08:19,3:00:32,1021,55,32
+Night Team,PGY1,S0181074,2016-01-10,1:14:00,1:47:26,0:22:14,1:07:06,0:18:07,0:14:02,5:24:43,2067,219,50
+Night Team,PGY1,S0181074,2016-01-13,1:19:25,1:35:26,1:21:33,2:10:19,0:34:10,0:24:18,7:47:39,2929,111,51
+Night Team,PGY1,S0181074,2016-01-12,1:22:22,2:03:47,1:43:44,1:49:49,0:22:57,0:05:02,8:01:27,2607,33,43
+Intensive Care Unit,PGY2+,S0094959,2015-08-25,4:12:37,1:28:40,0:01:07,0:19:53,0:41:59,0:39:55,7:42:39,3734,173,29
+Night Team,PGY1,S0181074,2016-01-14,1:05:54,2:04:11,2:31:40,2:03:26,0:19:41,0:15:27,8:35:47,2713,93,34
+Intensive Care Unit,PGY2+,S0094959,2015-08-27,3:42:45,2:11:31,0:06:30,0:12:18,0:43:31,1:00:33,8:28:39,3704,387,43
+Intensive Care Unit,PGY2+,S0094959,2015-08-26,3:18:31,0:50:45,0:10:23,0:13:25,0:16:33,0:45:46,5:48:54,2278,0,22
+Night Team,PGY2+,S0181056,2016-09-28,1:13:50,0:35:59,0:00:08,0:17:57,0:05:42,0:06:10,2:41:06,921,34,20
+General Medicine,PGY1,S0176040,2015-11-21,1:14:55,1:14:13,2:57:57,1:18:10,0:21:25,0:34:39,8:38:46,2301,231,8
+Night Team,PGY2+,S0181056,2016-09-29,1:22:51,0:33:01,0:00:03,0:12:22,0:03:16,0:01:09,2:23:12,658,0,24
+General Medicine,PGY1,S0176040,2015-11-20,1:34:30,1:04:37,1:33:27,0:50:43,0:41:07,0:21:19,6:53:58,2156,0,9
+General Medicine,PGY1,S0163662,2014-09-10,2:58:34,0:12:43,0:06:39,0:27:19,0:26:25,0:24:24,4:52:29,1101,0,7
+Night Team,PGY1,S0181060,2015-10-07,0:39:49,0:19:20,0:08:29,0:29:30,0:27:07,0:38:22,2:59:42,885,0,16
+General Medicine,PGY1,S0195237,2016-11-04,3:19:50,1:38:36,0:00:32,1:28:37,0:36:48,0:06:33,8:09:00,2739,26,17
+Night Team,PGY2+,S0106952,2016-07-28,3:01:31,2:19:21,0:00:30,0:40:09,0:20:09,0:04:56,7:15:37,3039,413,72
+Night Team,PGY2+,S0106952,2016-07-29,0:59:52,1:14:13,0:00:00,0:00:56,0:13:19,0:06:58,2:45:09,1696,186,96
+Night Team,PGY2+,S0106952,2016-07-22,3:25:07,2:51:12,0:00:05,0:11:29,0:24:19,0:17:05,7:29:50,3400,88,131
+Night Team,PGY2+,S0106952,2016-07-23,2:02:55,2:34:52,0:00:02,0:12:36,0:28:47,0:12:59,5:49:24,2817,584,102
+Night Team,PGY2+,S0106952,2016-07-20,3:03:28,2:06:39,0:00:06,0:44:30,0:23:11,0:12:27,6:57:44,2706,274,67
+Night Team,PGY2+,S0106952,2016-07-21,3:23:33,2:52:29,0:00:54,0:30:12,0:25:43,0:14:10,7:59:56,3585,380,119
+Night Team,PGY2+,S0106952,2016-07-26,4:23:00,2:26:46,0:00:08,0:35:32,0:33:58,0:10:17,8:47:07,3732,205,110
+Night Team,PGY2+,S0106952,2016-07-27,2:53:08,2:57:46,0:01:06,0:29:18,0:25:20,0:10:48,7:39:25,3132,38,77
+General Medicine,PGY1,S0163662,2014-09-11,3:31:54,0:16:27,0:14:07,0:36:38,0:28:47,0:15:39,5:49:56,1376,260,6
+Night Team,PGY2+,S0106952,2016-07-25,4:03:18,2:42:28,0:00:20,0:45:09,0:36:38,0:24:41,9:21:55,4623,393,78
+General Medicine,PGY1,S0094535,2015-05-20,3:00:34,0:31:07,4:06:45,1:26:36,0:20:15,0:28:57,10:15:19,2483,113,22
+General Medicine,PGY1,S0094535,2015-05-21,2:05:43,0:30:29,2:35:57,0:43:45,0:23:33,0:26:45,7:40:01,2619,0,35
+General Medicine,PGY1,S0094535,2015-05-22,1:19:06,0:11:46,2:11:39,0:03:16,0:00:55,0:07:25,4:12:08,800,790,15
+General Medicine,PGY1,S0094535,2015-05-23,0:00:00,0:00:00,0:00:00,0:04:19,0:00:00,0:00:00,0:04:19,8,0,2
+Night Team,PGY1,S0181060,2015-10-01,1:10:46,0:37:02,1:20:23,0:38:16,0:20:40,0:17:12,4:48:07,964,0,11
+Emergency Medicine,PGY1,S0181087,2015-09-03,3:37:09,0:43:07,3:33:01,0:33:34,0:27:42,0:21:22,10:24:13,3082,333,28
+Emergency Medicine,PGY1,S0181087,2015-09-02,1:26:25,0:03:51,1:29:38,0:12:07,0:25:33,0:00:31,3:55:19,995,129,7
+Emergency Medicine,PGY1,S0181087,2015-09-01,1:54:22,0:44:03,2:20:07,1:02:47,0:29:15,0:06:54,7:25:08,2515,0,13
+Emergency Medicine,PGY1,S0181087,2015-09-07,2:03:25,0:43:44,3:00:14,0:32:21,0:21:13,0:13:55,7:35:33,1773,0,14
+Emergency Medicine,PGY1,S0181087,2015-09-06,2:47:22,0:30:00,2:42:07,0:48:33,0:23:47,0:08:38,8:02:33,2735,0,28
+Emergency Medicine,PGY1,S0181087,2015-09-04,2:24:50,0:40:24,3:04:05,0:49:43,0:40:55,0:20:29,8:49:33,2955,0,15
+General Medicine,PGY1,S0079640,2016-09-14,1:23:11,1:46:05,0:00:37,0:58:26,0:24:38,0:13:28,5:23:13,1749,0,15
+General Medicine,PGY1,S0079640,2016-09-15,0:10:25,0:01:01,0:00:00,0:00:19,0:00:00,0:00:14,0:12:18,101,37,7
+Emergency Medicine,PGY1,S0181087,2015-09-09,2:23:04,0:30:48,2:11:02,0:32:52,0:25:01,0:09:51,6:44:24,1967,0,20
+General Medicine,PGY1,S0079640,2016-09-17,0:00:28,0:00:09,0:00:00,0:00:07,0:00:00,0:00:00,0:00:47,28,0,6
+General Medicine,PGY1,S0079640,2016-09-10,1:36:38,1:47:42,0:00:13,0:22:33,0:18:34,0:04:12,4:38:10,1630,148,14
+General Medicine,PGY1,S0079640,2016-09-11,1:13:28,1:30:07,0:00:09,0:16:19,0:17:41,0:03:07,3:38:25,1206,59,10
+General Medicine,PGY1,S0079640,2016-09-12,1:26:24,1:27:30,0:00:17,1:00:37,0:45:41,0:08:44,5:46:10,2014,274,14
+General Medicine,PGY1,S0079640,2016-09-13,1:00:04,1:26:19,0:00:19,0:30:03,0:18:19,0:05:54,4:00:33,1500,0,11
+Night Team,PGY1,S0173803,2016-03-12,0:48:51,0:13:46,0:00:07,0:09:22,0:03:50,0:00:12,1:27:23,457,0,22
+General Medicine,PGY1,S0169401,2014-09-21,3:28:10,0:21:26,0:06:21,0:36:05,0:40:17,0:06:08,5:46:40,2136,498,12
+Night Team,PGY1,S0173803,2016-03-10,1:39:39,0:45:48,0:02:54,0:38:05,0:11:55,0:04:11,3:42:00,1253,877,53
+Night Team,PGY1,S0173803,2016-03-11,1:02:15,0:54:02,0:00:01,0:22:51,0:08:56,0:04:49,2:38:59,893,0,31
+General Medicine,PGY1,S0169401,2014-09-24,4:56:49,0:13:46,0:05:45,1:11:39,0:53:35,0:09:19,8:15:12,3161,219,18
+General Medicine,PGY1,S0169401,2014-09-25,6:06:48,0:18:21,0:24:40,0:39:43,0:24:33,0:22:13,9:03:03,2708,1493,32
+General Medicine,PGY1,S0169401,2014-09-26,4:41:39,0:29:20,0:07:36,0:48:31,0:16:47,0:13:46,7:22:27,2643,861,38
+General Medicine,PGY1,S0169401,2014-09-27,4:56:55,0:22:07,0:00:49,1:17:38,0:35:45,0:10:35,8:09:01,2558,237,14
+General Medicine,PGY1,S0169401,2014-09-28,2:47:11,0:12:59,0:12:11,0:08:34,0:04:43,0:01:55,3:55:41,1371,1341,33
+General Medicine,PGY1,S0169401,2014-09-29,5:26:10,0:18:57,0:00:55,0:43:48,0:43:42,0:02:39,7:50:24,2980,449,10
+General Medicine,PGY2+,S0181057,2016-11-05,0:06:34,0:01:21,0:00:00,0:00:04,0:00:00,0:00:31,0:11:26,93,0,10
+Night Team,PGY1,S0195229,2016-06-30,1:11:15,0:55:11,0:00:02,0:25:07,0:18:18,0:01:02,3:16:45,1252,493,11
+Intensive Care Unit,PGY2+,S0094535,2015-10-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,16,0,3
+Intensive Care Unit,PGY2+,S0094535,2015-10-16,0:47:40,0:33:13,0:27:26,0:18:08,0:09:38,0:38:12,3:06:03,865,142,15
+General Medicine,PGY1,S0169399,2014-06-30,3:30:38,0:01:16,0:21:02,1:09:54,0:28:55,0:02:16,6:17:57,1429,0,12
+Intensive Care Unit,PGY2+,S0094535,2015-10-14,2:25:50,0:43:12,0:17:10,0:25:06,0:20:56,1:01:31,5:44:08,1456,288,17
+Intensive Care Unit,PGY2+,S0094535,2015-10-13,1:54:32,1:43:26,0:03:42,0:37:50,0:24:46,1:36:40,6:48:12,1900,0,14
+Intensive Care Unit,PGY2+,S0094535,2015-10-12,1:27:58,0:48:46,0:05:35,0:36:51,0:22:28,0:34:54,4:10:36,1123,0,6
+Intensive Care Unit,PGY2+,S0094535,2015-10-11,1:10:42,0:29:40,0:06:23,0:48:13,0:24:56,0:49:59,4:09:10,1068,51,18
+Intensive Care Unit,PGY2+,S0094535,2015-10-10,0:00:00,0:00:09,0:00:00,0:01:25,0:00:00,0:00:00,0:01:44,33,13,6
+Night Team,PGY1,S0181526,2016-06-07,2:54:46,0:57:15,0:00:03,0:23:21,0:24:15,0:03:00,5:20:35,2090,57,42
+Night Team,PGY1,S0181526,2016-06-06,2:44:58,0:37:23,0:00:00,0:24:30,0:24:24,0:06:17,4:39:36,2235,434,43
+General Medicine,PGY1,S0195226,2016-08-09,4:24:00,0:55:42,0:00:03,1:10:03,0:18:12,0:14:43,8:42:11,2165,84,10
+General Medicine,PGY1,S0195226,2016-08-08,2:55:23,1:41:10,0:00:18,1:05:46,0:15:17,0:07:53,6:49:24,1685,0,15
+General Medicine,PGY1,S0181059,2015-09-08,2:43:21,0:52:56,0:48:08,0:38:18,0:16:58,0:23:51,6:19:28,3218,177,39
+General Medicine,PGY1,S0181059,2015-09-09,1:40:39,0:27:50,0:24:39,0:27:04,0:07:14,0:16:31,3:43:11,2152,275,18
+General Medicine,PGY1,S0181059,2015-09-04,0:12:39,0:21:17,0:00:08,0:04:57,0:00:34,0:04:05,0:49:10,489,468,16
+General Medicine,PGY1,S0195226,2016-08-02,2:29:36,1:18:06,0:00:07,0:53:40,0:13:37,0:05:31,5:58:43,1732,124,17
+General Medicine,PGY1,S0181059,2015-09-06,1:31:30,0:21:33,0:17:40,0:12:22,0:14:14,0:14:27,3:08:41,1885,200,36
+General Medicine,PGY1,S0181059,2015-09-07,1:27:57,0:07:23,0:24:26,0:18:54,0:08:18,0:15:30,3:08:40,1871,297,10
+General Medicine,PGY1,S0195226,2016-08-07,2:51:12,0:57:27,0:01:21,0:32:32,0:13:09,0:05:58,5:21:07,1504,274,10
+General Medicine,PGY1,S0195226,2016-08-06,3:19:17,1:57:26,0:00:43,0:48:45,0:21:53,0:11:29,7:15:31,1962,26,16
+Night Team,PGY1,S0181526,2016-06-09,1:18:55,0:12:53,0:00:41,0:11:10,0:04:24,0:01:08,2:08:01,1019,5,40
+General Medicine,PGY1,S0181059,2015-09-03,2:08:08,0:44:27,2:01:11,0:47:59,0:38:20,0:38:27,7:27:09,2916,72,21
+Night Team,PGY2+,S0172645,2016-06-27,1:15:48,1:11:10,0:00:01,0:21:41,0:15:18,0:01:31,3:26:09,1796,0,21
+Night Team,PGY2+,S0172645,2016-06-26,1:56:02,1:56:03,0:00:03,0:21:47,0:11:15,0:02:52,4:47:18,1496,14,25
+Night Team,PGY2+,S0172645,2016-06-29,0:18:28,0:11:09,0:00:03,0:02:39,0:02:13,0:00:29,0:38:57,643,207,10
+Night Team,PGY2+,S0172645,2016-06-28,0:22:47,0:41:24,0:00:01,0:19:07,0:05:37,0:02:58,1:45:15,875,351,13
+General Medicine,PGY1,S0169386,2014-06-27,2:11:48,0:05:44,0:12:37,1:02:41,0:27:11,0:30:32,4:44:16,1665,0,8
+General Medicine,PGY1,S0169386,2014-06-26,2:48:31,0:04:53,0:32:44,0:25:00,0:20:50,0:46:19,5:08:56,1281,95,7
+General Medicine,PGY1,S0169386,2014-06-29,0:10:15,0:00:00,0:00:02,0:00:01,0:00:20,0:00:00,0:21:06,248,244,26
+General Medicine,PGY1,S0181056,2016-02-28,1:39:54,0:58:13,0:00:07,0:50:37,0:13:01,0:02:09,4:26:49,1654,406,11
+General Medicine,PGY1,S0181088,2016-06-07,2:27:40,1:29:35,0:00:08,0:53:11,0:11:08,0:01:46,5:45:39,2291,616,10
+Intensive Care Unit,PGY2+,S0181064,2016-10-30,3:49:42,1:05:20,0:00:00,0:45:22,0:32:01,0:18:10,7:04:34,2184,0,20
+Intensive Care Unit,PGY2+,S0181064,2016-10-31,1:47:08,0:17:36,0:04:55,0:05:54,0:09:54,0:02:59,2:44:00,963,19,14
+Night Team,PGY1,S0169398,2015-02-01,1:31:57,0:06:05,0:08:11,0:15:57,0:10:54,0:40:27,3:07:29,677,24,14
+Night Team,PGY1,S0181085,2015-12-08,0:55:02,0:26:26,0:07:51,0:31:01,0:19:05,0:22:30,2:52:28,1069,22,39
+Night Team,PGY1,S0169398,2015-02-03,3:01:47,0:29:57,1:29:40,0:37:21,0:20:54,0:29:18,6:44:33,1708,207,28
+Night Team,PGY1,S0169398,2015-02-02,2:45:02,0:16:16,0:56:03,0:15:43,0:17:48,0:28:11,5:17:31,1221,0,34
+Night Team,PGY2+,S0079337,2015-11-17,2:43:14,1:12:45,1:13:25,0:47:36,0:18:56,0:14:42,7:03:22,2582,655,40
+Night Team,PGY2+,S0079337,2015-11-16,2:51:00,1:26:26,1:40:56,0:40:33,0:06:53,0:29:28,7:48:45,2969,0,25
+Night Team,PGY2+,S0079337,2015-11-15,3:43:46,1:39:51,1:01:19,0:26:03,0:13:51,0:19:26,7:55:41,2483,0,23
+Night Team,PGY2+,S0108287,2016-10-19,2:03:54,1:38:55,0:00:01,0:27:31,0:08:10,0:06:08,4:54:56,1804,0,25
+Night Team,PGY1,S0169398,2015-02-09,3:37:14,0:36:59,2:01:47,0:40:45,0:17:32,0:26:13,8:03:01,1732,0,31
+Night Team,PGY1,S0169398,2015-02-08,3:15:58,0:07:52,2:18:47,0:31:32,0:13:30,0:22:03,7:14:26,1341,0,37
+Night Team,PGY2+,S0108287,2016-10-14,2:52:00,1:27:30,0:03:54,0:22:18,0:15:51,0:08:47,5:31:27,1846,2,25
+Night Team,PGY2+,S0079337,2015-11-18,3:46:49,0:53:22,0:46:25,0:25:20,0:11:09,0:09:59,6:34:56,2224,0,30
+Night Team,PGY2+,S0108287,2016-10-12,2:20:37,1:42:43,0:00:27,0:18:53,0:09:21,0:01:53,5:04:22,1568,0,23
+Night Team,PGY2+,S0108287,2016-10-13,1:55:33,1:58:30,0:04:42,0:40:26,0:14:16,0:03:58,5:10:14,1472,0,16
+Night Team,PGY2+,S0108287,2016-10-10,2:36:41,1:21:28,0:00:32,0:15:56,0:12:25,0:01:11,5:02:11,1728,0,32
+Night Team,PGY2+,S0108287,2016-10-11,1:44:57,1:50:18,0:00:18,0:38:13,0:07:07,0:00:36,4:46:14,1637,0,18
+General Medicine,PGY1,S0056755,2014-09-16,3:25:13,0:05:05,0:03:58,0:41:04,0:40:30,0:26:19,5:38:31,1574,25,7
+General Medicine,PGY1,S0056755,2014-09-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+General Medicine,PGY1,S0056755,2014-09-14,3:54:37,0:16:15,0:04:07,0:53:47,0:44:36,0:45:52,7:05:14,1769,5,13
+General Medicine,PGY1,S0056755,2014-09-15,3:32:44,0:09:56,0:09:21,0:37:05,0:29:11,0:36:28,5:58:23,1390,89,6
+General Medicine,PGY1,S0169390,2015-06-01,1:45:23,0:09:05,1:00:54,0:32:22,0:17:29,0:03:26,4:10:03,1015,0,10
+General Medicine,PGY1,S0169390,2015-06-03,0:00:00,0:00:00,0:00:00,0:01:35,0:00:00,0:00:00,0:01:35,32,0,6
+General Medicine,PGY1,S0169390,2015-06-02,2:28:39,0:13:45,3:04:35,0:36:31,0:26:56,0:15:21,7:44:46,1922,21,14
+General Medicine,PGY1,S0169390,2015-06-09,2:13:25,0:23:08,2:02:22,0:51:08,0:25:40,0:07:48,6:27:38,1581,0,11
+General Medicine,PGY1,S0169390,2015-06-08,0:00:00,0:00:00,0:00:00,0:04:35,0:00:00,0:00:00,0:04:35,8,0,4
+General Medicine,PGY1,S0056755,2014-09-18,3:53:18,0:20:10,0:03:24,0:40:32,0:16:55,0:39:28,6:34:34,1877,11,10
+General Medicine,PGY1,S0056755,2014-09-19,4:17:36,0:10:35,0:04:53,0:53:10,0:41:21,0:39:09,7:07:55,1696,71,11
+General Medicine,PGY1,S0181065,2016-04-14,2:15:47,0:28:15,0:00:08,0:26:20,0:20:16,0:00:57,3:58:57,2007,9,11
+General Medicine,PGY1,S0181065,2016-04-15,3:31:32,0:48:52,0:00:08,0:26:53,0:26:00,0:06:20,6:02:50,2757,10,12
+General Medicine,PGY1,S0181065,2016-04-16,0:00:00,0:00:00,0:00:00,0:00:07,0:00:00,0:00:00,0:00:07,4,0,2
+General Medicine,PGY1,S0181065,2016-04-17,4:08:55,0:56:04,0:00:16,0:32:31,0:27:37,0:05:24,6:59:54,3516,231,27
+General Medicine,PGY1,S0181065,2016-04-10,3:11:14,0:38:44,0:00:13,0:31:39,0:18:24,0:20:22,5:49:36,2579,2,15
+General Medicine,PGY1,S0181065,2016-04-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+General Medicine,PGY1,S0181065,2016-04-12,3:33:59,1:06:21,0:00:31,0:31:27,0:37:21,0:08:04,6:44:03,3248,143,17
+General Medicine,PGY1,S0181065,2016-04-13,2:53:23,0:26:31,0:00:24,0:24:34,0:16:38,0:03:16,4:42:06,2073,0,13
+General Medicine,PGY1,S0181065,2016-04-18,2:05:55,0:24:47,0:00:06,0:22:33,0:20:43,0:03:30,3:41:39,1590,1,8
+General Medicine,PGY1,S0181065,2016-04-19,0:00:00,0:00:00,0:00:00,0:01:22,0:00:00,0:00:00,0:01:22,4,0,2
+General Medicine,PGY1,S0108268,2014-07-16,2:57:12,0:08:26,0:10:29,0:48:01,0:24:53,0:33:31,5:21:22,1810,83,14
+Night Team,PGY2+,S0093383,2016-08-20,2:55:44,1:35:04,0:00:08,0:56:15,0:11:17,0:13:51,6:18:44,2208,66,37
+Night Team,PGY2+,S0093383,2016-08-21,3:05:54,0:55:39,0:00:05,0:28:59,0:10:29,0:06:43,5:12:32,1715,0,32
+Night Team,PGY2+,S0093383,2016-08-22,1:57:47,0:16:05,0:00:00,0:13:42,0:05:23,0:01:44,2:44:07,622,0,21
+Night Team,PGY2+,S0093383,2016-08-23,1:24:03,0:21:22,0:00:00,0:07:25,0:03:15,0:02:03,2:14:10,671,0,13
+Night Team,PGY2+,S0093383,2016-08-24,2:17:19,1:30:23,0:00:44,0:17:36,0:11:06,0:06:27,4:44:20,1655,29,31
+Night Team,PGY2+,S0093383,2016-08-25,1:57:08,1:59:54,0:01:12,0:49:55,0:08:47,0:22:54,5:46:29,2082,70,26
+Night Team,PGY2+,S0093383,2016-08-26,1:38:12,1:03:47,0:05:21,0:43:37,0:18:15,0:19:19,4:27:49,1434,0,24
+Night Team,PGY2+,S0093383,2016-08-27,1:04:01,0:48:52,0:00:49,0:20:47,0:08:21,0:04:15,2:33:50,910,0,22
+General Medicine,PGY2+,S0169411,2016-03-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+Night Team,PGY2+,S0094535,2016-06-17,2:56:54,1:29:22,0:00:36,0:46:22,0:10:45,0:02:21,6:05:35,1918,0,41
+Night Team,PGY2+,S0094535,2016-06-16,3:47:44,1:21:27,0:00:07,0:13:31,0:07:24,0:04:54,6:25:56,1821,20,69
+General Medicine,PGY1,S0169399,2014-12-31,2:49:53,0:01:50,3:21:52,0:29:41,0:26:49,0:03:14,7:43:53,1375,0,16
+General Medicine,PGY1,S0108268,2014-07-18,2:28:33,0:08:36,0:09:46,0:21:47,0:19:17,0:24:42,4:08:43,1232,0,11
+General Medicine,PGY1,S0094535,2015-01-28,3:28:37,0:25:50,2:04:04,0:31:29,0:28:29,0:45:52,8:24:49,2102,0,13
+General Medicine,PGY1,S0094535,2015-01-29,3:21:21,0:06:03,3:06:05,0:42:28,0:20:06,0:25:22,8:31:51,2083,33,12
+General Medicine,PGY1,S0094535,2015-01-24,2:25:40,0:09:39,2:06:33,0:26:03,0:21:23,0:32:32,6:41:44,1991,0,20
+General Medicine,PGY1,S0094535,2015-01-25,2:26:20,0:11:43,1:44:25,0:15:30,0:08:29,0:18:51,5:31:50,1490,0,14
+General Medicine,PGY1,S0094535,2015-01-26,2:59:38,0:20:16,2:20:26,0:54:46,0:36:22,0:52:16,8:37:05,2202,0,14
+General Medicine,PGY1,S0094535,2015-01-27,2:29:36,0:10:54,2:07:25,0:39:39,0:30:44,0:31:05,6:52:35,1824,25,13
+General Medicine,PGY1,S0094535,2015-01-20,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:27,31,13,5
+General Medicine,PGY1,S0094535,2015-01-21,4:50:33,0:09:38,1:31:04,0:53:43,0:22:30,0:56:49,9:20:28,2518,13,10
+General Medicine,PGY1,S0094535,2015-01-22,2:40:02,0:13:17,1:22:28,0:37:19,0:27:18,0:40:15,6:30:57,1928,0,11
+General Medicine,PGY1,S0094535,2015-01-23,3:25:38,0:21:11,2:01:26,1:14:25,0:29:29,0:44:47,8:53:40,2659,0,11
+General Medicine,PGY1,S0169385,2015-05-22,2:24:47,0:22:37,1:11:20,0:37:57,0:18:58,1:00:08,6:17:56,2058,0,22
+General Medicine,PGY1,S0172085,2015-06-28,1:32:19,1:00:23,2:52:41,0:36:25,0:13:16,0:47:29,7:25:20,1236,0,5
+General Medicine,PGY1,S0172085,2015-06-29,1:06:38,0:35:11,2:08:42,0:06:59,0:06:05,0:40:09,4:55:46,1000,35,7
+General Medicine,PGY1,S0172085,2015-06-26,1:59:03,0:11:10,2:47:11,0:23:36,0:10:15,0:25:04,6:18:34,999,0,7
+General Medicine,PGY1,S0172085,2015-06-27,2:00:32,0:09:24,1:06:22,0:14:16,0:11:29,0:16:32,4:16:01,947,0,11
+General Medicine,PGY1,S0174975,2015-12-14,1:46:16,1:06:02,1:54:22,0:59:11,0:29:16,0:04:37,7:19:17,2949,662,13
+Night Team,PGY2+,S0173803,2016-08-18,2:35:18,2:04:26,0:00:11,0:40:26,0:26:42,0:11:24,6:44:30,2174,0,54
+General Medicine,PGY1,S0169385,2015-05-23,3:02:08,0:13:47,1:22:24,0:48:56,0:20:02,0:19:42,6:42:21,2225,0,13
+Emergency Medicine,PGY1,S0094959,2014-08-31,2:42:03,0:11:01,1:46:01,0:13:58,0:48:34,0:36:02,6:46:10,3330,112,37
+Emergency Medicine,PGY1,S0094959,2014-08-30,0:32:25,0:12:06,0:21:03,0:03:16,0:20:22,0:07:35,1:47:35,1155,64,17
+Night Team,PGY2+,S0169398,2016-10-22,2:18:05,0:35:05,0:00:10,0:32:51,0:04:01,0:10:01,4:07:52,1044,0,19
+Night Team,PGY2+,S0169398,2016-10-21,2:04:10,1:05:01,0:00:06,0:34:47,0:17:31,0:00:55,4:53:58,1477,18,46
+Night Team,PGY2+,S0169398,2016-10-20,3:37:47,0:53:29,0:00:04,0:35:57,0:27:16,0:04:41,6:08:56,1764,0,42
+General Medicine,PGY2+,S0109179,2016-01-31,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,1
+General Medicine,PGY2+,S0109179,2016-01-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,1
+General Medicine,PGY1,S0174975,2015-12-18,2:00:58,1:38:52,0:46:15,1:36:51,0:30:18,0:08:32,7:03:07,2762,219,10
+Night Team,PGY2+,S0173803,2016-08-14,3:16:23,1:23:09,0:00:54,0:44:37,0:09:02,0:04:49,6:09:56,1700,409,42
+Night Team,PGY2+,S0181069,2016-07-02,1:43:20,1:06:54,0:00:11,1:02:44,0:18:46,0:13:16,5:05:01,1655,50,12
+Night Team,PGY2+,S0181069,2016-07-03,1:55:23,1:03:45,0:00:04,0:48:34,0:10:27,0:01:47,4:51:06,1837,0,21
+Night Team,PGY2+,S0181069,2016-07-01,2:20:33,1:03:30,0:00:10,0:43:32,0:16:40,0:07:38,5:16:59,1845,0,34
+General Medicine,PGY1,S0181073,2015-10-28,2:05:53,1:00:48,1:24:05,1:09:12,0:39:58,0:43:52,7:54:18,2501,54,10
+General Medicine,PGY1,S0181073,2015-10-29,1:07:26,0:49:04,0:53:21,0:47:07,0:18:25,0:29:51,4:47:33,1591,51,6
+Night Team,PGY2+,S0181069,2016-07-04,1:30:41,0:39:56,0:00:05,0:35:40,0:21:34,0:06:28,4:05:50,1379,0,15
+Night Team,PGY2+,S0181069,2016-07-05,1:33:58,0:25:13,0:00:00,0:20:59,0:07:32,0:02:29,2:45:36,1012,0,15
+Night Team,PGY1,S0169411,2014-12-03,4:24:49,0:00:27,0:03:16,0:41:29,0:39:37,0:04:24,6:03:31,1748,0,51
+Night Team,PGY1,S0169411,2014-12-02,1:16:39,0:05:21,0:00:00,0:24:43,0:10:57,0:00:00,2:06:18,664,0,22
+General Medicine,PGY1,S0181073,2015-10-26,1:27:04,0:53:54,0:44:55,0:57:12,0:35:17,0:44:48,5:51:10,2257,45,9
+General Medicine,PGY1,S0181073,2015-10-27,0:00:00,0:16:32,0:00:00,0:00:06,0:00:00,0:08:19,0:27:47,295,281,6
+General Medicine,PGY1,S0181073,2015-10-20,0:53:17,0:45:02,0:39:07,0:29:08,0:17:27,0:36:20,4:05:19,1443,180,4
+General Medicine,PGY1,S0181073,2015-10-21,1:15:13,1:03:12,2:14:13,1:21:46,0:27:24,0:32:15,7:28:57,2147,45,6
+Night Team,PGY1,S0169411,2014-12-05,4:03:55,0:06:41,2:39:21,0:32:48,0:28:33,0:08:01,8:11:34,1853,0,44
+General Medicine,PGY2+,S0169399,2015-10-19,1:01:12,0:19:22,0:01:21,0:15:14,0:11:22,0:19:52,2:21:50,644,0,12
+Night Team,PGY1,S0169398,2015-06-05,1:16:26,0:11:24,1:03:54,0:24:09,0:13:16,0:12:35,3:33:30,837,544,12
+Night Team,PGY1,S0169398,2015-06-04,2:59:43,1:03:09,2:12:25,0:30:45,0:16:39,0:35:08,7:51:43,1360,0,30
+Night Team,PGY1,S0169398,2015-06-06,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,4,0,2
+Night Team,PGY1,S0169398,2015-06-01,3:10:11,0:14:36,0:58:24,0:29:09,0:11:26,0:32:24,6:03:05,1579,121,31
+Night Team,PGY1,S0169398,2015-06-03,2:13:50,0:07:45,0:44:44,0:19:17,0:14:32,0:18:50,4:10:22,958,156,39
+Night Team,PGY1,S0169398,2015-06-02,3:04:11,0:16:26,1:52:46,0:35:57,0:22:08,0:40:58,7:07:16,1866,640,35
+General Medicine,PGY2+,S0109179,2016-08-12,0:17:38,0:07:50,0:00:00,0:02:28,0:00:00,0:00:23,0:28:41,84,53,10
+General Medicine,PGY2+,S0109179,2016-08-10,1:07:31,0:34:45,0:00:04,0:16:04,0:07:48,0:00:08,2:18:56,1008,0,18
+General Medicine,PGY2+,S0109179,2016-08-11,2:15:59,1:46:55,0:00:04,0:14:04,0:10:15,0:02:54,4:58:15,2783,0,22
+General Medicine,PGY2+,S0181526,2016-08-02,3:43:54,1:28:26,0:00:34,0:11:45,0:26:17,0:13:44,6:53:27,2668,85,27
+Intensive Care Unit,PGY2+,S0181087,2016-07-31,0:14:22,0:22:53,0:00:00,0:00:14,0:00:23,0:00:10,0:40:29,384,365,15
+Intensive Care Unit,PGY2+,S0181087,2016-07-30,3:38:52,1:32:13,0:12:25,0:14:43,0:32:58,0:08:00,6:57:35,1938,0,12
+General Medicine,PGY1,S0108268,2014-10-05,3:36:21,0:12:08,0:14:24,0:14:35,0:30:04,0:25:37,5:51:13,1727,556,22
+Intensive Care Unit,PGY2+,S0169394,2016-05-14,3:51:17,0:49:04,0:00:21,1:23:50,0:58:13,0:28:23,8:47:21,2425,0,20
+Intensive Care Unit,PGY2+,S0169394,2016-05-15,2:45:56,0:46:14,0:09:44,0:43:35,0:35:49,0:11:46,6:04:58,1728,397,13
+Intensive Care Unit,PGY2+,S0169394,2016-05-16,2:11:29,0:24:50,0:07:08,0:37:16,0:35:05,0:09:24,4:35:14,1461,29,15
+Intensive Care Unit,PGY2+,S0169394,2016-05-17,2:11:30,1:07:10,0:01:53,0:30:00,0:44:25,0:06:34,5:15:57,1763,0,22
+Intensive Care Unit,PGY2+,S0169394,2016-05-10,0:19:51,0:21:18,0:00:10,0:08:19,0:00:38,0:00:04,0:53:02,368,0,21
+Intensive Care Unit,PGY2+,S0169394,2016-05-11,0:29:16,0:29:41,0:01:00,0:16:44,0:05:49,0:03:19,1:31:21,478,7,11
+Intensive Care Unit,PGY2+,S0169394,2016-05-12,0:45:50,0:21:11,0:01:18,0:07:18,0:03:35,0:05:43,1:34:08,580,204,28
+Intensive Care Unit,PGY2+,S0169394,2016-05-13,1:16:34,0:54:13,0:06:11,0:43:52,0:12:06,0:00:56,3:57:07,1179,142,19
+Intensive Care Unit,PGY2+,S0169394,2016-05-18,2:19:21,0:26:46,0:05:33,0:21:53,0:31:29,0:02:46,4:20:50,1097,203,17
+Intensive Care Unit,PGY2+,S0169394,2016-05-19,2:34:50,0:21:35,0:03:11,0:32:18,0:33:59,0:04:11,4:51:12,1292,16,21
+General Medicine,PGY2+,S0181526,2016-08-05,3:45:39,1:25:56,0:00:09,0:38:14,0:38:47,0:02:12,7:31:46,2670,365,19
+General Medicine,PGY1,S0169400,2015-05-22,0:55:11,0:03:37,0:47:51,0:12:08,0:04:46,0:14:36,2:24:53,636,0,6
+General Medicine,PGY1,S0169400,2015-05-23,0:00:00,0:01:46,0:00:00,0:04:30,0:00:00,0:00:00,0:06:30,45,37,6
+General Medicine,PGY1,S0169400,2015-05-20,2:10:07,0:04:07,1:16:00,0:18:11,0:07:39,0:26:33,4:44:33,860,22,9
+General Medicine,PGY1,S0169400,2015-05-21,2:21:29,0:14:39,1:47:47,0:24:46,0:24:32,0:48:19,6:17:24,1386,12,12
+General Medicine,PGY2+,S0181526,2016-08-04,4:15:39,1:50:49,0:00:05,0:41:23,0:21:59,0:07:21,8:31:10,3356,279,25
+General Medicine,PGY1,S0181074,2016-05-19,1:45:09,1:36:14,0:01:06,1:05:49,0:16:11,0:16:28,5:48:52,1953,215,19
+General Medicine,PGY1,S0181074,2016-05-18,2:34:07,1:54:49,0:00:26,1:26:46,0:16:52,0:21:19,7:35:01,2955,146,19
+General Medicine,PGY1,S0181074,2016-05-15,3:20:48,1:09:24,0:01:28,1:55:36,0:17:10,0:15:23,7:45:22,2720,78,20
+Night Team,PGY2+,S0169398,2015-09-05,0:50:57,0:33:26,1:28:20,0:24:57,0:04:04,0:28:12,4:00:59,893,0,16
+General Medicine,PGY1,S0181074,2016-05-17,1:38:08,1:24:48,0:00:08,1:06:05,0:32:20,0:14:48,6:00:06,2207,252,19
+General Medicine,PGY1,S0181074,2016-05-16,0:41:33,0:23:43,0:02:28,0:11:40,0:01:50,0:06:23,1:38:35,768,750,18
+General Medicine,PGY1,S0181074,2016-05-11,1:14:22,1:22:33,0:17:30,0:58:57,0:25:47,0:23:00,5:52:52,2432,290,12
+Night Team,PGY2+,S0169398,2015-09-01,3:48:31,1:05:05,1:32:06,0:56:18,0:30:17,0:51:50,9:32:57,2706,187,45
+General Medicine,PGY1,S0181074,2016-05-13,1:50:50,1:18:25,0:00:59,1:41:28,0:23:17,0:21:33,6:37:34,2473,113,11
+Night Team,PGY2+,S0169398,2015-09-03,1:43:19,1:01:58,0:17:57,0:20:32,0:08:46,0:26:29,4:22:33,1674,728,41
+Intensive Care Unit,PGY2+,S0169404,2015-09-14,3:23:14,0:11:47,0:17:28,0:31:28,0:17:28,0:34:44,5:37:42,2578,45,15
+Intensive Care Unit,PGY2+,S0106952,2015-11-04,1:23:04,1:08:13,0:59:07,0:15:31,0:22:23,0:32:52,5:00:23,1967,88,33
+Intensive Care Unit,PGY2+,S0106952,2015-11-05,2:24:38,1:30:17,0:22:55,0:26:05,0:21:35,0:31:48,5:54:00,2184,527,38
+Intensive Care Unit,PGY2+,S0106952,2015-11-06,1:58:13,1:44:48,0:57:18,0:24:02,0:36:17,0:22:07,6:17:06,1849,536,47
+Intensive Care Unit,PGY2+,S0106952,2015-11-07,3:19:29,1:36:11,0:27:16,1:02:15,0:51:35,0:44:38,8:16:14,2192,145,14
+Night Team,PGY1,S0163425,2014-08-16,0:56:24,0:06:11,0:06:04,0:11:19,0:14:58,1:15:08,3:08:19,823,0,17
+Intensive Care Unit,PGY2+,S0106952,2015-11-01,1:37:14,0:54:31,0:21:48,0:30:25,0:21:30,0:13:54,4:15:43,1434,404,25
+Intensive Care Unit,PGY2+,S0106952,2015-11-02,1:35:24,0:40:07,0:14:23,0:24:50,0:24:05,0:25:16,3:59:23,1121,48,10
+Night Team,PGY1,S0163425,2014-08-15,1:19:43,0:02:21,0:08:19,0:37:59,0:43:31,1:29:01,4:31:05,1247,0,29
+Intensive Care Unit,PGY2+,S0106952,2015-11-08,0:11:26,0:14:51,0:02:10,0:00:07,0:00:00,0:00:00,0:29:00,290,274,33
+Intensive Care Unit,PGY2+,S0106952,2015-11-09,1:37:01,1:27:21,0:27:24,0:33:12,0:23:14,0:33:08,5:41:06,2300,238,22
+General Medicine,PGY1,S0109179,2014-08-30,4:47:12,0:06:14,0:34:15,1:05:19,0:27:38,0:19:49,8:02:13,2945,0,19
+Night Team,PGY2+,S0169402,2016-03-30,2:49:40,1:46:24,0:00:16,1:18:39,0:10:38,0:01:04,6:39:44,2113,166,39
+Night Team,PGY2+,S0169402,2016-03-31,2:50:32,1:46:19,0:04:52,0:49:14,0:18:10,0:11:33,6:44:26,1848,28,33
+General Medicine,PGY2+,S0181526,2016-08-06,0:02:44,0:00:30,0:00:00,0:00:43,0:00:00,0:00:39,0:05:01,69,0,10
+General Medicine,PGY1,S0109179,2014-08-31,1:44:32,0:00:29,0:07:38,0:22:46,0:23:57,0:05:19,3:04:36,1220,0,7
+General Medicine,PGY1,S0181079,2015-09-13,2:26:04,0:03:23,0:41:08,0:45:26,0:20:56,0:09:17,5:17:55,1731,106,16
+General Medicine,PGY1,S0181079,2015-09-15,0:00:06,0:00:00,0:00:12,0:01:30,0:00:01,0:00:00,0:01:50,33,17,5
+General Medicine,PGY1,S0181079,2015-09-14,3:13:07,0:17:08,1:14:32,0:51:54,0:49:30,0:28:14,7:33:40,1994,250,10
+General Medicine,PGY1,S0181079,2015-09-17,3:16:55,0:08:51,3:18:29,0:33:48,0:23:53,0:27:41,8:33:14,2088,138,13
+General Medicine,PGY1,S0181079,2015-09-16,2:14:09,0:11:16,1:29:47,1:04:48,0:27:17,0:34:29,6:28:00,2018,45,20
+General Medicine,PGY1,S0181079,2015-09-19,2:50:42,0:16:43,1:40:11,0:46:25,0:28:02,0:40:47,7:19:38,2108,192,17
+General Medicine,PGY1,S0181079,2015-09-18,2:46:07,0:08:45,1:21:32,0:31:03,0:25:59,0:25:49,6:19:46,1998,71,10
+Night Team,PGY2+,S0094959,2016-11-04,2:53:23,1:01:43,0:01:28,0:15:32,0:16:22,0:04:45,5:14:01,1688,0,68
+Night Team,PGY2+,S0094959,2016-11-05,0:13:53,0:00:46,0:00:00,0:00:00,0:00:00,0:00:25,0:17:59,75,0,15
+Night Team,PGY2+,S0094959,2016-11-02,3:46:11,1:31:48,0:00:09,0:40:24,0:22:20,0:12:45,7:10:58,2550,0,69
+Night Team,PGY2+,S0094959,2016-11-03,4:06:37,1:12:13,0:00:10,0:16:35,0:19:12,0:18:07,6:53:49,3240,74,77
+Night Team,PGY2+,S0094959,2016-11-01,5:18:23,2:06:44,0:00:23,0:22:21,0:18:50,0:07:26,8:51:46,3403,229,75
+Night Team,PGY2+,S0079337,2016-10-22,2:16:52,0:33:31,0:00:20,0:14:11,0:04:05,0:04:31,3:33:52,996,0,15
+Night Team,PGY2+,S0079337,2016-10-20,1:32:48,0:23:24,0:00:07,0:24:02,0:04:53,0:02:07,2:46:17,1035,0,20
+Night Team,PGY2+,S0079337,2016-10-21,2:08:08,0:35:19,0:00:13,0:08:11,0:04:03,0:04:29,3:14:12,934,0,23
+Intensive Care Unit,PGY2+,S0169404,2015-09-11,0:16:55,0:21:06,0:00:40,0:00:00,0:00:16,0:05:53,1:12:03,596,590,14
+General Medicine,PGY1,S0181078,2016-03-04,2:09:39,0:58:14,0:00:20,0:31:06,0:17:12,0:10:14,4:57:46,1577,40,14
+General Medicine,PGY1,S0181081,2016-05-23,1:01:23,0:23:21,0:00:05,0:17:43,0:03:39,0:00:21,2:20:19,867,15,7
+General Medicine,PGY1,S0181081,2016-05-24,0:51:46,0:39:16,0:00:04,0:21:39,0:09:58,0:00:22,2:14:49,695,9,3
+General Medicine,PGY1,S0181081,2016-05-25,2:36:25,1:40:42,0:00:12,0:35:46,0:19:17,0:00:55,6:03:48,2045,25,11
+General Medicine,PGY1,S0181081,2016-05-26,3:56:16,1:18:18,0:05:47,0:43:23,0:21:35,0:00:41,7:45:49,3052,0,20
+General Medicine,PGY1,S0181081,2016-05-27,2:10:50,1:10:40,0:00:06,0:39:09,0:14:44,0:01:33,4:42:30,2201,0,13
+General Medicine,PGY1,S0181081,2016-05-28,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,4,0,1
+General Medicine,PGY1,S0181081,2016-05-29,0:58:12,0:30:10,0:00:13,0:27:12,0:08:38,0:00:07,2:32:40,724,14,6
+General Medicine,PGY1,S0169406,2014-07-09,2:51:10,0:05:24,0:18:50,0:31:31,0:32:48,0:41:32,5:34:48,1833,0,14
+General Medicine,PGY1,S0169406,2014-07-08,4:18:33,0:01:56,0:15:06,0:31:19,0:49:37,0:54:28,7:05:44,1983,32,14
+Emergency Medicine,PGY1,S0169398,2015-03-29,3:02:45,0:43:46,2:29:56,0:09:10,0:09:48,0:35:21,7:31:08,1613,45,17
+General Medicine,PGY1,S0169406,2014-07-05,3:03:45,0:01:53,0:07:21,0:21:40,0:06:15,0:17:28,4:11:12,1028,185,10
+General Medicine,PGY1,S0169406,2014-07-04,2:39:58,0:02:21,0:16:18,0:13:29,0:07:49,0:09:57,3:41:41,970,150,11
+General Medicine,PGY1,S0169406,2014-07-07,1:19:56,0:00:38,0:05:44,0:06:24,0:22:00,0:20:59,2:33:27,943,0,6
+General Medicine,PGY1,S0169406,2014-07-06,4:59:00,0:02:10,0:19:14,0:18:32,0:25:27,0:48:24,7:11:57,1591,0,16
+General Medicine,PGY1,S0169406,2014-07-01,3:07:27,0:02:40,0:22:02,0:32:49,0:21:58,0:17:27,5:01:30,1503,15,7
+General Medicine,PGY1,S0169406,2014-07-03,3:37:22,0:06:50,0:32:56,0:25:01,0:41:14,0:36:15,6:23:16,1879,0,14
+General Medicine,PGY1,S0169406,2014-07-02,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0093383,2015-05-24,2:19:02,0:28:28,3:54:21,0:29:49,0:09:31,0:28:20,7:57:56,1279,1,32
+General Medicine,PGY2+,S0181082,2016-09-07,0:06:55,0:09:17,0:00:03,0:00:00,0:01:10,0:00:07,0:20:47,343,337,11
+Night Team,PGY1,S0093383,2015-05-26,2:06:01,0:12:00,0:57:17,0:48:14,0:21:00,0:32:07,5:11:26,1468,0,38
+Night Team,PGY1,S0093383,2015-05-27,2:00:09,0:11:42,1:19:41,0:30:37,0:24:32,0:25:54,4:58:20,1224,0,32
+General Medicine,PGY2+,S0181082,2016-09-06,1:31:23,0:29:54,0:00:07,0:16:10,0:12:28,0:01:12,2:56:25,1121,0,18
+Night Team,PGY1,S0093383,2015-05-28,1:21:54,0:12:35,0:43:43,0:31:14,0:15:07,0:15:15,3:25:20,837,10,23
+Emergency Medicine,PGY1,S0181526,2015-07-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:01:19,0:04:53,20,20,2
+Emergency Medicine,PGY1,S0181526,2015-07-17,4:54:54,0:36:23,1:18:23,0:21:47,0:37:04,0:59:11,9:34:46,2323,0,27
+Emergency Medicine,PGY1,S0181526,2015-07-10,4:22:58,0:45:41,1:16:31,0:17:07,0:16:26,1:22:23,9:32:54,2398,0,41
+Emergency Medicine,PGY1,S0181526,2015-07-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:20,0:00:20,10,10,1
+General Medicine,PGY1,S0181082,2015-12-26,1:22:31,0:48:50,0:52:01,0:38:08,0:23:09,0:17:00,4:49:26,1860,0,9
+General Medicine,PGY1,S0181082,2015-12-27,0:58:15,0:35:36,1:49:39,0:56:17,0:35:13,0:26:15,5:52:18,1803,0,12
+Emergency Medicine,PGY2+,S0181056,2016-08-14,2:29:11,0:49:23,0:07:35,0:31:40,0:40:52,0:09:02,5:21:03,2150,46,69
+Emergency Medicine,PGY2+,S0181056,2016-08-15,4:02:54,1:18:55,0:06:26,0:53:14,0:31:26,0:18:54,7:59:46,3289,0,60
+General Medicine,PGY1,S0181082,2015-12-22,1:14:42,1:12:04,1:14:11,1:00:07,0:28:33,0:26:05,6:19:53,1774,0,7
+General Medicine,PGY1,S0181082,2015-12-23,1:20:08,1:50:33,2:39:41,1:01:53,0:27:46,0:42:47,8:44:17,3001,0,20
+General Medicine,PGY1,S0181082,2015-12-20,1:42:13,1:14:06,2:23:10,0:41:41,0:37:53,0:47:20,8:05:50,2309,0,13
+General Medicine,PGY1,S0181082,2015-12-21,0:42:07,0:34:29,0:14:40,0:18:31,0:13:26,0:23:53,2:41:12,1083,0,9
+Emergency Medicine,PGY1,S0181526,2015-07-13,2:28:34,0:06:06,0:21:35,0:17:00,0:23:23,0:03:52,4:15:57,1504,2,9
+General Medicine,PGY1,S0181082,2015-12-28,1:27:05,1:18:59,2:44:11,0:58:14,0:34:47,0:28:01,8:25:34,2454,0,18
+Emergency Medicine,PGY2+,S0181056,2016-08-19,4:55:39,1:43:00,0:03:23,0:43:32,0:26:59,0:21:25,9:24:47,3497,220,78
+Night Team,PGY2+,S0169399,2016-05-13,0:36:40,0:29:18,0:00:01,0:10:45,0:02:09,0:00:07,1:30:14,392,16,10
+Intensive Care Unit,PGY2+,S0169404,2015-09-16,0:33:30,0:39:39,0:00:07,0:00:37,0:00:00,0:09:02,1:38:35,1110,1104,24
+Night Team,PGY2+,S0169399,2016-05-11,1:30:46,1:01:37,0:03:23,0:34:32,0:07:12,0:02:16,4:15:34,1080,0,15
+Night Team,PGY2+,S0169399,2016-05-10,0:58:14,1:13:37,0:00:03,0:10:14,0:07:44,0:04:10,3:13:47,1131,37,24
+General Medicine,PGY2+,S0181077,2016-08-26,2:21:30,2:37:07,0:00:17,0:49:04,0:27:33,0:04:18,7:20:03,3503,659,31
+General Medicine,PGY2+,S0181077,2016-08-27,3:18:14,1:33:47,0:03:33,0:32:55,0:13:11,0:01:22,6:30:51,3188,81,27
+General Medicine,PGY2+,S0181077,2016-08-24,1:45:40,1:38:10,0:00:11,0:29:47,0:16:11,0:00:48,5:02:04,2042,423,19
+Intensive Care Unit,PGY2+,S0169404,2015-09-10,2:42:19,0:10:30,0:13:11,0:36:23,0:26:37,0:29:23,5:00:33,2098,336,17
+General Medicine,PGY2+,S0181077,2016-08-28,1:54:52,1:03:26,0:00:05,0:44:41,0:24:17,0:07:49,4:53:40,2474,110,17
+General Medicine,PGY2+,S0181077,2016-08-29,0:12:46,0:18:13,0:00:09,0:00:52,0:03:11,0:00:25,0:42:09,544,490,17
+Intensive Care Unit,PGY2+,S0169404,2015-09-19,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,22,0,4
+Intensive Care Unit,PGY2+,S0169404,2015-09-18,3:25:36,0:18:04,0:17:34,0:37:48,0:24:36,0:40:08,6:02:59,3055,48,22
+General Medicine,PGY1,S0169381,2014-09-10,3:06:07,0:19:36,0:28:18,0:29:52,0:27:43,0:21:53,5:31:23,1342,93,8
+General Medicine,PGY1,S0169381,2014-09-11,2:30:20,0:04:44,0:24:30,0:27:57,0:14:32,0:33:51,4:39:42,1209,47,12
+General Medicine,PGY1,S0169381,2014-09-12,1:38:54,0:05:44,0:15:55,0:29:19,0:09:02,0:18:20,3:14:52,980,266,13
+General Medicine,PGY1,S0169381,2014-09-13,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0195210,2016-11-01,0:12:44,0:04:44,0:00:00,0:00:06,0:00:00,0:01:05,0:19:19,108,0,6
+General Medicine,PGY1,S0195210,2016-11-02,1:36:39,1:21:06,0:00:27,1:04:01,0:21:38,0:02:30,4:59:31,1397,3,19
+General Medicine,PGY1,S0195210,2016-11-03,1:12:14,0:39:54,0:05:50,0:29:32,0:08:28,0:07:33,3:05:06,710,0,9
+General Medicine,PGY1,S0195210,2016-11-04,0:29:24,0:21:01,0:00:04,0:06:13,0:01:27,0:00:03,1:19:34,367,0,7
+General Medicine,PGY1,S0195210,2016-11-05,2:03:17,0:51:59,0:00:04,1:17:37,0:18:19,0:04:23,5:28:38,1426,0,13
+General Medicine,PGY1,S0195210,2016-11-06,1:20:38,1:00:01,0:00:08,0:43:54,0:09:46,0:05:21,3:37:29,852,175,4
+General Medicine,PGY1,S0195210,2016-11-07,1:44:37,0:55:32,0:00:24,0:46:26,0:14:48,0:07:07,4:13:58,1317,0,8
+General Medicine,PGY1,S0195210,2016-11-08,0:05:56,0:00:27,0:00:00,0:00:00,0:00:00,0:00:12,0:07:16,25,0,4
+General Medicine,PGY1,S0195210,2016-11-09,0:00:03,0:00:18,0:00:00,0:00:01,0:00:00,0:00:59,0:03:13,57,0,23
+General Medicine,PGY1,S0079337,2014-12-25,3:53:38,0:09:02,2:07:33,1:01:39,0:19:53,0:07:04,8:27:45,2831,0,9
+Night Team,PGY2+,S0169404,2016-11-10,2:09:53,0:50:44,0:00:04,0:30:53,0:09:13,0:07:20,4:08:01,1520,4,35
+General Medicine,PGY1,S0093383,2014-09-13,0:23:00,0:00:00,0:01:05,0:00:00,0:00:14,0:00:03,0:26:39,282,278,18
+Night Team,PGY2+,S0181069,2016-07-07,2:22:32,1:13:40,0:00:26,0:37:52,0:11:04,0:16:36,5:22:36,2354,0,36
+General Medicine,PGY1,S0181078,2016-03-02,2:04:01,1:12:44,0:00:14,0:42:13,0:10:33,0:10:21,5:03:25,1616,202,13
+General Medicine,PGY1,S0074196,2016-01-30,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,14,0,3
+General Medicine,PGY1,S0181073,2016-06-08,1:34:07,1:29:08,0:01:01,0:42:14,0:17:43,0:01:56,4:35:40,1589,13,9
+General Medicine,PGY1,S0174975,2016-03-17,2:24:43,1:26:28,0:00:11,0:37:32,0:28:53,0:00:10,5:31:50,1845,121,17
+General Medicine,PGY1,S0174975,2016-03-16,0:03:29,0:03:23,0:00:00,0:01:33,0:00:09,0:00:00,0:13:58,174,174,11
+General Medicine,PGY1,S0174975,2016-03-15,2:12:48,1:25:56,0:00:10,1:16:28,0:12:55,0:03:49,5:53:02,2000,171,9
+General Medicine,PGY1,S0174975,2016-03-14,2:31:24,1:22:04,0:00:21,1:33:17,0:29:19,0:04:09,6:33:41,2284,144,18
+General Medicine,PGY1,S0181078,2016-03-03,3:59:38,0:50:15,0:00:10,1:41:59,0:26:52,0:05:37,8:03:51,2345,53,15
+General Medicine,PGY1,S0174975,2016-03-12,2:36:12,1:29:52,0:04:33,1:54:24,0:29:48,0:00:26,7:18:14,2640,7,14
+General Medicine,PGY1,S0174975,2016-03-11,1:59:22,1:21:40,0:00:17,0:41:53,0:12:38,0:00:12,4:51:10,1603,57,18
+General Medicine,PGY1,S0174975,2016-03-10,2:29:19,2:25:59,0:17:04,0:53:09,0:29:07,0:03:08,8:03:12,2889,387,42
+General Medicine,PGY1,S0174975,2016-03-19,2:15:56,1:17:49,0:02:26,1:49:10,0:24:12,0:05:24,6:27:56,2254,76,17
+General Medicine,PGY1,S0174975,2016-03-18,0:59:17,0:57:27,0:01:27,0:27:09,0:10:09,0:00:00,2:49:31,1179,170,12
+Emergency Medicine,PGY1,S0181065,2015-12-30,1:04:24,0:11:21,2:00:36,0:23:54,0:27:44,0:12:45,4:49:17,3053,0,32
+Emergency Medicine,PGY1,S0181065,2015-12-31,0:13:17,0:13:18,0:49:59,0:01:42,0:15:38,0:22:13,2:02:59,1392,0,11
+Night Team,PGY1,S0172085,2016-02-05,1:51:59,0:37:34,0:12:37,0:53:58,0:21:27,0:56:58,5:20:24,1612,0,34
+Night Team,PGY1,S0172085,2016-02-04,0:23:14,0:11:12,0:00:13,0:12:43,0:04:31,0:02:29,1:01:36,585,0,36
+Night Team,PGY1,S0172085,2016-02-07,0:50:03,0:23:25,0:18:09,0:08:20,0:30:20,0:19:17,2:51:52,1163,55,72
+Night Team,PGY1,S0172085,2016-02-06,1:15:56,1:07:45,1:12:18,0:32:45,0:21:42,0:38:46,5:31:47,1728,0,68
+Night Team,PGY1,S0172085,2016-02-01,1:18:47,0:18:40,0:02:01,0:38:12,0:29:56,0:22:41,3:23:09,1185,820,37
+General Medicine,PGY1,S0169394,2015-05-23,0:09:25,0:03:18,0:26:56,0:05:08,0:03:26,0:03:54,0:53:48,278,260,7
+Night Team,PGY1,S0172085,2016-02-03,1:22:37,0:53:30,1:34:50,0:29:33,0:18:09,0:37:33,5:32:39,1946,1728,30
+General Medicine,PGY1,S0169394,2015-05-21,2:01:09,0:10:04,0:59:47,0:25:51,0:16:18,0:20:19,4:27:58,1569,180,8
+Night Team,PGY1,S0172085,2016-02-09,0:56:25,0:58:47,1:40:24,0:22:54,0:25:26,0:30:49,5:15:54,2039,854,63
+Night Team,PGY1,S0172085,2016-02-08,1:38:29,0:49:19,0:13:14,0:14:40,0:20:35,0:24:17,3:57:59,2621,1311,40
+Emergency Medicine,PGY2+,S0169400,2015-10-08,2:19:43,0:27:29,0:46:22,0:12:31,0:11:04,0:14:40,4:32:07,1250,14,23
+Emergency Medicine,PGY2+,S0169400,2015-10-09,5:21:50,0:56:29,2:08:03,0:24:37,0:25:40,0:39:01,10:26:35,2917,0,49
+Emergency Medicine,PGY2+,S0169400,2015-10-07,4:21:23,0:37:51,0:52:30,0:27:17,0:16:38,0:23:18,7:16:49,2358,13,49
+Emergency Medicine,PGY2+,S0169400,2015-10-04,2:53:32,0:28:54,1:58:31,0:21:30,0:06:43,0:23:01,6:32:51,1565,0,17
+Emergency Medicine,PGY2+,S0169400,2015-10-05,3:02:50,0:27:08,1:53:54,0:30:09,0:32:39,0:35:53,7:35:31,2474,0,28
+Emergency Medicine,PGY2+,S0169400,2015-10-02,5:22:42,0:22:24,1:43:35,0:17:00,0:16:50,0:31:03,9:16:27,2778,0,36
+Intensive Care Unit,PGY2+,S0094959,2015-08-23,0:00:00,0:59:00,0:00:00,0:00:00,0:00:00,0:02:32,1:02:43,293,293,26
+Emergency Medicine,PGY2+,S0169400,2015-10-01,4:02:51,0:22:22,2:04:18,0:17:46,0:20:45,0:24:50,7:57:33,2345,0,32
+General Medicine,PGY2+,S0172085,2016-09-28,3:33:25,1:37:00,0:00:00,0:17:11,0:10:28,0:06:57,6:34:51,2642,0,30
+General Medicine,PGY2+,S0172085,2016-09-29,1:07:36,0:10:23,0:00:00,0:06:58,0:03:52,0:00:47,1:36:46,459,0,23
+General Medicine,PGY2+,S0172085,2016-09-22,0:18:55,0:01:29,0:00:00,0:00:11,0:00:00,0:05:18,0:26:43,120,0,15
+Night Team,PGY1,S0181074,2016-01-15,1:02:09,2:05:18,1:35:56,2:19:38,0:25:11,0:15:20,8:33:02,3532,94,38
+General Medicine,PGY2+,S0172085,2016-09-20,3:18:31,2:03:57,0:00:00,0:26:37,0:11:44,0:01:34,6:32:43,2756,257,26
+General Medicine,PGY2+,S0172085,2016-09-21,1:51:37,0:58:55,0:00:11,0:30:04,0:30:20,0:03:54,4:19:05,1637,0,19
+General Medicine,PGY2+,S0172085,2016-09-26,1:12:42,0:50:17,0:00:26,0:25:33,0:09:39,0:02:27,3:10:38,1036,58,20
+General Medicine,PGY2+,S0172085,2016-09-27,1:22:25,0:30:40,0:00:04,0:29:36,0:06:37,0:00:32,2:49:51,1317,8,21
+General Medicine,PGY2+,S0172085,2016-09-24,0:54:18,0:35:04,0:00:03,0:02:02,0:03:22,0:05:58,1:55:59,825,36,22
+Intensive Care Unit,PGY2+,S0094959,2015-08-24,2:32:19,1:45:54,0:11:10,0:10:22,0:27:20,0:32:04,5:54:15,2435,227,23
+Night Team,PGY1,S0181074,2016-01-16,0:23:21,1:17:11,1:52:01,1:14:38,0:11:07,0:14:20,5:36:34,2035,163,22
+General Medicine,PGY1,S0169402,2015-02-09,2:35:16,0:08:03,1:42:31,0:30:48,0:37:43,0:08:58,6:06:40,1499,357,9
+General Medicine,PGY1,S0169402,2015-02-08,3:01:50,0:07:38,2:08:05,0:24:40,0:21:50,0:00:54,6:40:22,1598,0,11
+General Medicine,PGY1,S0169402,2015-02-07,3:06:01,0:40:07,2:30:12,0:46:25,0:29:53,0:13:24,8:10:35,1999,0,11
+General Medicine,PGY1,S0169402,2015-02-06,2:31:29,0:00:01,1:13:20,0:24:06,0:20:05,0:03:27,4:51:34,1394,449,9
+General Medicine,PGY1,S0169402,2015-02-05,3:57:15,0:31:49,2:43:39,1:06:08,0:26:16,0:10:18,9:39:04,2224,30,10
+General Medicine,PGY1,S0169402,2015-02-04,0:00:00,0:00:00,0:00:00,0:00:50,0:00:00,0:00:00,0:10:52,93,75,5
+General Medicine,PGY1,S0169402,2015-02-03,3:34:01,0:01:28,2:18:31,1:02:45,0:29:57,0:08:44,8:25:43,2229,0,8
+General Medicine,PGY1,S0169402,2015-02-02,3:10:50,0:14:46,2:59:45,0:43:10,0:26:34,0:10:01,8:34:20,1974,30,10
+General Medicine,PGY1,S0169402,2015-02-01,1:51:59,0:00:00,1:01:21,0:30:10,0:21:34,0:00:07,3:58:48,1030,58,7
+Night Team,PGY2+,S0169384,2016-01-21,1:16:11,1:46:30,3:10:35,1:08:57,0:29:34,0:57:22,9:09:23,2741,0,24
+General Medicine,PGY1,S0181077,2016-04-28,3:13:32,1:33:04,0:00:16,0:46:43,0:14:11,0:06:15,6:48:00,2385,60,21
+General Medicine,PGY1,S0181077,2016-04-29,0:12:33,0:08:37,0:00:02,0:04:07,0:01:01,0:00:00,0:28:46,181,159,8
+General Medicine,PGY1,S0181077,2016-04-26,3:39:32,1:31:41,0:00:13,0:36:12,0:25:35,0:07:48,7:07:28,3220,111,23
+General Medicine,PGY1,S0181077,2016-04-27,2:24:59,1:08:12,0:11:25,0:16:34,0:12:09,0:19:09,5:17:45,2361,53,13
+General Medicine,PGY1,S0181077,2016-04-24,1:06:28,0:50:06,0:01:54,0:23:14,0:05:34,0:00:25,2:47:46,1382,242,12
+General Medicine,PGY1,S0181077,2016-04-25,0:08:24,0:08:53,0:00:15,0:01:42,0:00:51,0:00:20,0:28:56,339,328,9
+General Medicine,PGY1,S0181073,2016-06-09,3:13:57,1:45:00,0:00:11,1:17:42,0:37:03,0:05:43,7:42:27,2256,54,19
+General Medicine,PGY1,S0172056,2016-04-25,1:33:59,0:48:29,0:00:03,0:31:06,0:23:22,0:03:40,4:07:13,1355,47,12
+General Medicine,PGY1,S0172056,2016-04-24,1:44:02,1:10:06,0:00:06,0:25:04,0:15:53,0:02:49,4:06:41,1278,124,6
+General Medicine,PGY1,S0172056,2016-04-27,2:30:49,1:12:34,0:02:02,0:54:23,0:19:07,0:10:52,6:38:31,1818,325,20
+General Medicine,PGY1,S0172056,2016-04-26,2:25:26,0:37:49,0:00:04,1:13:15,0:21:30,0:03:40,5:28:37,2020,96,13
+Night Team,PGY1,S0094535,2014-11-15,2:06:27,0:06:25,1:14:21,0:27:21,0:36:43,0:40:47,5:37:24,1348,0,30
+Night Team,PGY1,S0094535,2014-11-14,1:57:41,0:11:09,0:04:48,0:21:00,0:40:20,0:32:08,4:17:19,1304,17,34
+Night Team,PGY1,S0094535,2014-11-17,0:56:45,0:06:02,1:03:12,0:12:03,0:14:05,0:20:37,3:00:05,557,0,12
+Night Team,PGY1,S0094535,2014-11-16,0:59:54,0:02:14,2:00:33,0:36:56,0:16:23,0:17:55,4:39:21,1008,0,23
+Night Team,PGY1,S0094535,2014-11-11,2:11:35,0:05:21,1:10:41,0:31:02,0:35:41,0:16:49,5:11:48,1075,0,21
+Night Team,PGY1,S0094535,2014-11-10,1:55:34,0:05:06,0:10:01,0:33:06,0:37:14,0:57:31,4:43:24,1046,8,30
+Night Team,PGY1,S0094535,2014-11-13,0:46:28,0:01:44,0:02:55,0:24:32,0:29:39,0:22:20,2:16:18,594,0,23
+Night Team,PGY1,S0094535,2014-11-12,0:00:00,0:00:00,0:00:00,0:00:15,0:00:00,0:00:00,0:00:15,10,0,2
+Night Team,PGY2+,S0079337,2015-09-23,0:32:22,0:24:22,0:22:03,0:10:06,0:10:01,0:08:35,1:52:58,815,0,33
+Night Team,PGY2+,S0079337,2015-09-22,2:04:03,0:42:52,1:15:19,0:34:21,0:15:04,0:14:49,5:28:37,1948,0,41
+General Medicine,PGY2+,S0169404,2016-04-19,3:02:44,1:41:28,0:02:35,0:46:46,0:25:20,0:09:25,6:48:13,3764,589,20
+General Medicine,PGY2+,S0169404,2016-04-18,1:16:00,0:46:54,0:00:00,0:23:46,0:08:04,0:08:42,3:03:28,1524,195,15
+General Medicine,PGY2+,S0169404,2016-04-17,2:01:21,1:36:29,0:00:09,0:33:25,0:20:01,0:02:22,5:10:25,2460,687,15
+General Medicine,PGY2+,S0169404,2016-04-16,1:39:02,1:03:06,0:00:34,0:19:58,0:15:48,0:00:45,3:44:56,2207,636,18
+General Medicine,PGY2+,S0169404,2016-04-15,3:32:45,2:31:39,0:00:23,1:02:52,0:20:16,0:05:21,8:26:36,4673,455,21
+General Medicine,PGY2+,S0169404,2016-04-14,2:49:07,1:43:33,0:00:08,0:52:21,0:18:28,0:09:03,6:27:03,3642,705,30
+General Medicine,PGY2+,S0169404,2016-04-13,3:51:59,2:27:09,0:00:15,1:28:35,0:28:47,0:06:10,9:06:45,5030,589,27
+General Medicine,PGY2+,S0169404,2016-04-12,0:45:41,0:42:28,0:00:03,0:04:34,0:00:35,0:00:53,1:45:53,852,846,20
+General Medicine,PGY2+,S0169404,2016-04-11,2:00:06,1:09:13,0:00:12,0:19:45,0:20:40,0:00:38,4:20:19,2400,315,16
+General Medicine,PGY2+,S0169404,2016-04-10,3:09:22,2:21:02,0:01:26,0:30:31,0:20:29,0:17:35,7:23:39,3632,576,22
+Emergency Medicine,PGY2+,S0169385,2016-11-08,2:07:29,0:25:43,0:00:24,0:07:09,0:06:15,0:01:00,3:11:09,1119,0,23
+Emergency Medicine,PGY2+,S0169385,2016-11-09,0:43:30,0:08:38,0:00:00,0:00:35,0:03:24,0:00:10,0:59:16,265,0,17
+General Medicine,PGY2+,S0169411,2016-03-18,1:27:24,0:26:14,0:00:00,0:05:18,0:04:30,0:02:31,2:17:20,982,170,18
+General Medicine,PGY2+,S0169411,2016-03-19,2:31:53,1:09:10,0:00:07,0:53:34,0:25:45,0:04:43,5:33:20,1995,0,14
+General Medicine,PGY2+,S0169411,2016-03-14,3:19:59,0:36:35,0:06:13,0:17:49,0:11:33,0:03:18,5:12:54,1695,0,20
+Night Team,PGY2+,S0169402,2016-03-25,1:11:35,0:44:52,0:02:38,0:22:22,0:05:54,0:00:58,2:46:15,1230,27,40
+General Medicine,PGY2+,S0169411,2016-03-16,2:07:37,0:13:09,0:00:00,0:15:43,0:09:45,0:00:46,3:12:43,1328,0,14
+General Medicine,PGY2+,S0169411,2016-03-17,0:05:47,0:08:11,0:00:00,0:00:00,0:00:00,0:00:00,0:15:54,182,166,11
+General Medicine,PGY2+,S0169411,2016-03-10,4:14:46,0:59:11,0:00:02,0:26:10,0:11:04,0:11:48,6:30:42,2026,0,32
+General Medicine,PGY2+,S0169411,2016-03-11,2:44:33,0:26:39,0:00:47,0:23:21,0:11:50,0:02:15,4:23:12,1588,0,21
+Emergency Medicine,PGY2+,S0169385,2016-11-06,1:42:37,0:38:33,0:03:01,0:34:44,0:32:16,0:01:51,4:15:30,1745,0,31
+General Medicine,PGY2+,S0169411,2016-03-13,3:24:07,0:58:14,0:00:01,0:16:16,0:07:32,0:04:14,5:28:25,1985,0,41
+Night Team,PGY2+,S0079337,2015-09-28,1:17:19,0:38:56,0:15:05,0:25:06,0:07:12,0:03:43,2:56:04,1182,0,33
+Night Team,PGY2+,S0169402,2016-03-29,2:27:43,1:22:18,0:00:06,0:27:42,0:07:11,0:07:51,4:54:57,1737,27,47
+General Medicine,PGY1,S0172056,2015-08-31,2:32:19,0:35:04,2:51:32,0:48:33,0:30:06,0:24:02,8:18:24,1975,107,15
+General Medicine,PGY1,S0172056,2015-08-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY2+,S0169402,2016-03-28,3:23:52,1:13:38,0:00:08,0:47:07,0:11:04,0:08:40,6:25:41,1939,169,56
+Night Team,PGY1,S0181057,2015-11-18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0181057,2015-11-19,0:47:54,0:13:24,0:00:28,0:10:06,0:19:50,0:23:41,2:00:54,539,0,16
+Night Team,PGY1,S0181057,2015-11-10,1:15:20,0:56:44,1:01:55,1:04:46,0:20:08,0:24:51,5:21:23,1557,0,20
+Night Team,PGY1,S0181057,2015-11-11,1:58:35,0:44:38,0:46:13,1:06:26,0:45:54,1:19:36,6:54:37,1841,0,36
+Night Team,PGY1,S0181057,2015-11-12,1:41:17,1:02:12,1:40:58,0:46:58,0:22:35,1:02:51,6:46:23,1552,0,24
+Night Team,PGY1,S0181057,2015-11-13,1:23:31,0:59:31,0:52:18,0:30:22,0:20:20,0:20:00,4:41:36,1176,0,28
+Night Team,PGY1,S0181057,2015-11-14,1:21:56,0:28:11,0:54:04,0:49:43,0:33:35,0:36:50,4:49:51,1202,0,28
+Night Team,PGY1,S0181057,2015-11-15,0:50:42,0:18:13,0:00:22,0:18:44,0:07:59,0:34:01,2:15:48,726,0,19
+Night Team,PGY1,S0181057,2015-11-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0181057,2015-11-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0169411,2016-03-30,5:17:00,1:46:06,0:00:02,0:39:55,0:31:28,0:09:50,9:15:37,3337,0,29
+General Medicine,PGY1,S0094535,2015-05-08,1:44:12,0:34:00,1:21:48,0:41:10,0:13:28,0:40:36,5:31:51,1225,0,10
+General Medicine,PGY1,S0094535,2015-05-09,1:26:02,0:07:49,2:40:51,0:13:10,0:09:46,0:11:48,4:53:45,860,0,6
+General Medicine,PGY1,S0094535,2015-05-02,1:48:33,0:02:51,0:48:13,0:33:02,0:15:35,0:17:59,4:07:42,1158,0,5
+General Medicine,PGY1,S0094535,2015-05-03,3:00:57,0:38:57,2:40:04,0:51:16,0:30:24,0:40:31,9:00:05,2176,17,15
+General Medicine,PGY1,S0094535,2015-05-01,2:19:55,0:16:56,1:59:15,0:32:53,0:19:02,0:23:54,6:13:22,1403,0,6
+General Medicine,PGY1,S0094535,2015-05-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY1,S0094535,2015-05-07,1:25:43,0:07:14,0:59:12,0:29:57,0:12:20,0:13:03,3:54:54,1122,0,16
+General Medicine,PGY1,S0094535,2015-05-04,2:23:22,0:28:47,2:55:31,0:55:50,0:25:31,1:01:17,8:59:06,2546,0,16
+General Medicine,PGY1,S0094535,2015-05-05,3:46:48,0:31:55,2:06:35,0:56:11,0:39:46,0:40:05,9:05:00,2383,0,32
+Intensive Care Unit,PGY2+,S0181073,2016-10-30,2:48:56,0:26:10,0:10:32,0:41:57,0:26:21,0:03:18,5:04:24,1693,0,21
+Intensive Care Unit,PGY2+,S0181073,2016-10-31,0:13:52,0:02:18,0:00:00,0:02:47,0:00:00,0:00:12,0:19:41,103,0,11
+Emergency Medicine,PGY1,S0109179,2015-01-30,6:06:48,0:40:39,1:07:36,0:23:17,0:30:11,0:27:03,9:29:21,3149,0,60
+Emergency Medicine,PGY1,S0169399,2015-05-09,2:23:31,0:17:40,5:28:02,0:23:33,0:22:04,0:09:35,9:33:55,1297,0,19
+Emergency Medicine,PGY1,S0169399,2015-05-08,1:37:38,0:18:16,1:57:42,0:21:58,0:11:49,0:09:31,5:16:09,1013,0,16
+Emergency Medicine,PGY1,S0169399,2015-05-03,3:09:51,0:15:05,5:24:59,0:19:13,0:25:12,0:05:48,10:02:59,1257,0,21
+Emergency Medicine,PGY1,S0169399,2015-05-02,3:57:39,0:26:56,3:50:00,0:29:06,0:14:34,0:24:27,9:40:22,1292,0,25
+Emergency Medicine,PGY1,S0169399,2015-05-01,2:50:36,0:37:24,5:20:29,0:16:59,0:13:17,0:14:04,10:01:06,1527,0,13
+Emergency Medicine,PGY1,S0169401,2014-09-01,7:38:54,0:12:20,0:30:02,0:35:17,0:33:16,0:05:48,10:55:34,3152,165,58
+Emergency Medicine,PGY1,S0169401,2014-09-06,5:13:22,0:03:03,0:03:32,0:06:19,0:05:28,0:04:05,5:56:56,1559,23,28
+Emergency Medicine,PGY1,S0169401,2014-09-07,6:40:26,0:26:23,0:24:15,0:30:51,0:33:24,0:03:16,9:42:44,2800,246,66
+Emergency Medicine,PGY1,S0169399,2015-05-05,0:13:36,0:06:33,0:03:36,0:02:48,0:01:17,0:00:00,0:34:26,230,44,4
+Emergency Medicine,PGY1,S0169401,2014-09-05,0:28:17,0:00:57,0:00:02,0:00:21,0:00:25,0:00:27,0:32:25,352,348,21
+General Medicine,PGY1,S0169389,2014-07-30,4:36:30,0:12:12,0:11:09,0:46:15,0:37:38,0:16:34,7:04:27,2177,0,34
+General Medicine,PGY1,S0169389,2014-07-31,0:21:41,0:00:00,0:00:00,0:00:00,0:00:39,0:00:00,0:22:30,118,110,21
+Night Team,PGY2+,S0106952,2016-07-24,1:37:47,1:23:44,0:00:06,0:05:57,0:16:39,0:06:03,3:53:09,1806,73,83
+General Medicine,PGY1,S0181082,2015-12-10,1:15:22,1:18:23,1:48:14,1:09:40,0:35:22,0:32:11,7:09:10,2414,0,23
+Emergency Medicine,PGY2+,S0109179,2015-10-21,5:00:08,0:36:35,0:54:24,0:20:09,0:20:39,0:25:34,7:59:18,2598,0,66
+Emergency Medicine,PGY2+,S0109179,2015-10-20,4:23:50,1:07:58,0:55:43,0:25:21,0:26:44,0:51:21,8:37:14,2428,0,58
+Night Team,PGY1,S0181526,2016-06-21,2:12:16,0:32:44,0:00:00,0:11:46,0:15:07,0:01:30,3:39:51,1143,12,42
+Night Team,PGY1,S0181526,2016-06-20,3:20:41,0:46:41,0:00:01,0:45:17,0:32:06,0:16:56,6:16:45,1879,20,52
+Night Team,PGY1,S0181526,2016-06-23,2:46:09,1:22:24,0:00:01,0:10:09,0:22:24,0:03:35,5:12:06,1858,9,54
+Night Team,PGY1,S0181526,2016-06-22,1:42:35,0:24:39,0:01:23,0:14:13,0:06:48,0:01:59,2:45:11,907,34,28
+Night Team,PGY1,S0181526,2016-06-25,3:15:50,0:34:32,0:00:00,0:17:32,0:10:53,0:06:10,5:02:48,1268,251,37
+General Medicine,PGY1,S0195226,2016-08-20,0:00:37,0:03:08,0:00:00,0:00:00,0:00:00,0:00:00,0:04:00,39,39,3
+Emergency Medicine,PGY1,S0079640,2016-09-30,0:11:32,0:00:21,0:00:00,0:00:00,0:00:00,0:00:00,0:11:58,35,0,4
+Intensive Care Unit,PGY2+,S0181061,2016-08-31,2:29:18,0:22:32,0:13:16,0:41:21,0:24:04,0:01:42,4:39:41,1257,0,12
+Intensive Care Unit,PGY2+,S0169383,2015-11-14,0:00:00,0:00:00,0:00:00,0:01:11,0:00:00,0:00:00,0:01:11,34,0,7
+Intensive Care Unit,PGY2+,S0169383,2015-11-12,2:04:51,1:32:06,0:12:10,0:38:58,0:38:07,0:33:05,6:07:23,1787,0,17
+Intensive Care Unit,PGY2+,S0169383,2015-11-13,1:46:04,0:35:23,0:04:34,0:14:23,0:34:09,0:30:04,4:08:23,1245,24,17
+Intensive Care Unit,PGY2+,S0169383,2015-11-10,1:29:48,0:30:12,0:11:46,0:35:50,0:38:05,0:21:38,4:06:00,1240,15,18
+Intensive Care Unit,PGY2+,S0169383,2015-11-11,1:17:06,1:09:21,0:02:04,0:19:12,0:33:37,0:30:13,4:06:41,1439,33,17
+Intensive Care Unit,PGY2+,S0181064,2016-10-16,2:07:30,0:34:30,0:04:55,0:18:58,0:15:49,0:06:37,3:52:36,1011,264,20
+Intensive Care Unit,PGY2+,S0181064,2016-10-17,0:04:34,0:04:34,0:00:00,0:00:16,0:00:00,0:00:01,0:09:43,137,115,10
+Intensive Care Unit,PGY2+,S0181064,2016-10-14,2:38:56,1:10:34,0:00:49,0:47:45,0:26:48,0:09:19,6:06:51,1913,237,29
+Intensive Care Unit,PGY2+,S0181064,2016-10-15,1:17:30,0:22:29,0:05:16,0:05:18,0:09:02,0:06:34,2:25:30,1147,301,26
+Intensive Care Unit,PGY2+,S0181064,2016-10-12,2:06:55,0:38:57,0:05:25,0:15:19,0:26:13,0:03:42,4:00:36,1124,0,13
+Night Team,PGY2+,S0079337,2015-11-30,2:54:39,0:38:53,0:39:26,0:21:17,0:06:07,0:11:54,4:57:13,1449,13,23
+Emergency Medicine,PGY1,S0169402,2014-06-26,2:13:16,0:08:10,0:28:25,0:01:54,0:13:13,0:13:39,3:34:53,735,0,10
+Emergency Medicine,PGY1,S0169402,2014-06-27,0:46:46,0:02:27,0:13:10,0:01:56,0:07:09,0:12:12,1:28:48,340,116,6
+Emergency Medicine,PGY1,S0169402,2014-06-28,0:16:32,0:00:00,0:06:59,0:00:00,0:00:03,0:00:00,0:24:13,148,148,30
+Emergency Medicine,PGY1,S0169402,2014-06-29,4:04:33,0:15:30,0:48:18,0:11:52,0:28:28,0:30:19,6:44:07,1702,0,13
+Intensive Care Unit,PGY2+,S0181064,2016-10-18,2:57:36,1:37:38,0:00:09,0:30:55,0:35:42,0:11:24,6:24:33,2257,0,24
+Intensive Care Unit,PGY2+,S0181064,2016-10-19,2:15:15,0:30:40,0:03:27,0:31:03,0:11:47,0:26:07,4:21:43,2057,369,29
+Intensive Care Unit,PGY2+,S0169395,2015-11-09,1:35:36,0:29:44,0:19:00,0:25:05,0:16:59,0:48:45,4:05:54,1211,187,12
+General Medicine,PGY2+,S0093383,2016-11-09,0:37:20,0:32:40,0:00:10,0:16:09,0:09:11,0:00:21,1:53:41,937,75,6
+Emergency Medicine,PGY1,S0079640,2016-09-18,2:16:54,0:27:32,0:05:16,0:26:51,0:48:12,0:01:21,5:05:24,1227,0,24
+General Medicine,PGY2+,S0093383,2016-11-06,0:57:35,0:53:18,0:00:10,0:07:32,0:06:00,0:00:38,2:14:54,836,0,8
+General Medicine,PGY2+,S0093383,2016-11-07,2:23:38,1:08:39,0:00:01,0:12:52,0:11:10,0:05:31,4:42:18,2038,0,23
+Emergency Medicine,PGY1,S0079640,2016-09-19,0:04:29,0:00:11,0:00:00,0:00:00,0:00:00,0:00:00,0:04:43,28,0,7
+Night Team,PGY1,S0195075,2016-10-04,0:35:58,0:14:32,0:00:01,0:23:12,0:18:00,0:02:33,1:44:56,551,158,17
+Night Team,PGY1,S0195075,2016-10-05,0:34:48,0:12:33,0:00:00,0:30:39,0:26:10,0:02:11,1:51:06,406,31,18
+Night Team,PGY1,S0195075,2016-10-06,0:52:04,0:03:44,0:00:00,0:18:39,0:17:53,0:15:27,1:52:23,481,322,24
+Night Team,PGY1,S0195075,2016-10-07,0:14:50,0:16:11,0:00:00,0:13:45,0:08:00,0:01:11,1:02:45,361,0,12
+Night Team,PGY1,S0195075,2016-10-02,0:33:39,0:37:00,0:00:05,0:15:43,0:15:19,0:05:11,1:57:07,474,185,22
+Night Team,PGY1,S0195075,2016-10-03,0:36:28,0:49:27,0:00:03,0:20:12,0:11:00,0:01:14,2:07:16,507,392,22
+Night Team,PGY1,S0195075,2016-10-08,0:49:02,0:11:09,0:00:01,0:31:46,0:23:53,0:04:28,2:10:47,721,0,31
+Night Team,PGY1,S0195075,2016-10-09,1:14:23,0:26:33,0:00:00,1:05:23,0:38:15,0:15:48,3:50:58,920,0,29
+General Medicine,PGY1,S0079640,2016-09-16,1:06:31,1:48:21,0:00:14,0:31:40,0:36:44,0:11:05,4:39:32,1311,36,9
+General Medicine,PGY1,S0169399,2014-12-15,2:00:05,0:03:03,3:04:18,0:39:29,0:15:14,0:00:00,6:28:59,1268,129,11
+General Medicine,PGY1,S0169399,2014-12-14,0:00:00,0:00:00,0:00:00,0:02:18,0:00:00,0:00:00,0:02:18,12,0,5
+General Medicine,PGY1,S0169399,2014-12-17,1:30:33,0:02:13,1:35:08,0:13:10,0:20:42,0:00:29,3:58:50,805,20,9
+Emergency Medicine,PGY1,S0181087,2015-09-08,2:41:36,0:45:22,2:54:37,0:35:48,0:38:30,0:09:14,8:29:31,2592,0,13
+General Medicine,PGY1,S0169399,2014-12-11,2:27:20,0:09:26,2:27:34,0:18:41,0:22:57,0:00:00,6:00:48,1226,83,14
+General Medicine,PGY1,S0169399,2014-12-10,1:15:28,0:00:05,0:56:57,0:34:26,0:03:57,0:00:00,3:13:02,813,0,9
+General Medicine,PGY1,S0169399,2014-12-13,2:59:49,0:08:02,3:43:15,0:48:23,0:32:44,0:01:16,8:31:24,1473,13,13
+General Medicine,PGY1,S0169399,2014-12-12,1:59:31,0:02:45,1:52:24,0:23:00,0:20:42,0:00:12,4:43:28,950,70,8
+General Medicine,PGY1,S0169399,2014-12-19,1:39:11,0:02:10,1:31:41,0:34:40,0:23:42,0:04:53,4:36:46,915,196,8
+General Medicine,PGY1,S0169399,2014-12-18,1:45:30,0:13:24,2:14:06,0:53:38,0:35:58,0:02:32,6:14:38,1429,0,14
+General Medicine,PGY1,S0094535,2015-01-06,4:10:15,0:22:23,2:48:37,0:38:22,0:22:37,1:00:21,10:05:02,2022,0,11
+General Medicine,PGY1,S0094535,2015-01-07,2:53:08,0:06:17,1:42:22,0:24:31,0:26:47,0:24:52,6:24:20,1593,39,10
+General Medicine,PGY1,S0094535,2015-01-04,2:14:50,0:04:59,1:38:34,0:14:45,0:09:37,0:33:35,5:24:24,1373,0,12
+General Medicine,PGY1,S0094535,2015-01-05,2:32:05,0:00:53,1:17:41,0:40:03,0:22:14,0:23:00,6:19:49,1778,1,15
+Night Team,PGY2+,S0169390,2016-04-16,2:05:28,0:57:03,0:00:04,0:21:58,0:08:00,0:01:07,3:49:57,1159,0,25
+Night Team,PGY2+,S0169390,2016-04-15,2:45:17,1:03:20,0:00:14,0:42:53,0:15:57,0:07:12,5:35:42,1829,8,52
+Night Team,PGY2+,S0169390,2016-04-14,3:27:48,0:47:24,0:00:14,0:55:10,0:31:52,0:06:49,6:29:23,1627,5,38
+Night Team,PGY2+,S0169390,2016-04-13,1:37:53,0:29:04,0:00:00,0:21:35,0:13:44,0:03:28,2:54:39,948,0,43
+Night Team,PGY2+,S0169390,2016-04-12,2:54:19,1:07:14,0:00:12,0:23:10,0:18:45,0:04:36,5:13:26,1466,128,30
+Night Team,PGY2+,S0169390,2016-04-11,3:05:00,0:50:03,0:00:06,0:49:08,0:26:11,0:14:14,5:55:22,2021,223,69
+Night Team,PGY2+,S0169390,2016-04-10,3:24:16,0:56:21,0:00:28,0:20:51,0:21:55,0:05:11,5:50:04,1818,0,49
+Night Team,PGY1,S0181525,2016-05-26,2:09:31,0:52:32,0:00:02,0:33:59,0:16:52,0:10:43,4:23:57,1033,0,24
+General Medicine,PGY1,S0169401,2014-09-20,4:33:21,0:24:01,0:15:51,0:55:03,0:34:36,0:08:39,7:20:06,2614,556,19
+Emergency Medicine,PGY1,S0195094,2016-09-01,2:28:18,1:06:05,0:03:00,0:45:24,0:44:55,0:00:23,5:40:24,1403,0,33
+Emergency Medicine,PGY1,S0195094,2016-09-07,0:25:17,0:03:05,0:00:01,0:00:53,0:00:52,0:00:00,0:30:40,116,0,11
+Emergency Medicine,PGY1,S0195094,2016-09-05,0:02:36,0:00:29,0:00:00,0:00:00,0:00:00,0:00:00,0:03:08,15,0,4
+Emergency Medicine,PGY1,S0195094,2016-09-04,0:00:27,0:00:18,0:00:00,0:00:00,0:00:00,0:00:22,0:01:19,19,0,1
+Intensive Care Unit,PGY2+,S0181059,2016-08-13,2:40:22,0:49:46,0:01:01,0:14:37,0:12:23,0:02:39,4:27:39,2203,0,67
+Intensive Care Unit,PGY2+,S0181059,2016-08-12,3:59:35,1:25:34,0:00:46,0:55:09,0:30:52,0:19:41,7:51:40,4393,0,27
+Emergency Medicine,PGY1,S0195094,2016-09-09,2:30:41,0:23:40,0:00:33,0:31:01,0:16:27,0:00:48,4:02:29,848,0,27
+Intensive Care Unit,PGY2+,S0181059,2016-08-10,2:21:09,0:59:20,0:06:18,0:12:29,0:08:22,0:05:28,4:28:36,2437,0,26
+General Medicine,PGY1,S0169401,2014-09-22,5:26:34,0:40:57,0:06:57,0:56:04,0:31:15,0:05:38,8:09:51,3006,319,19
+Emergency Medicine,PGY2+,S0169390,2015-08-10,4:00:08,0:24:03,1:55:14,0:08:30,0:09:46,0:12:26,7:19:28,1706,2,22
+Night Team,PGY1,S0109179,2014-07-31,2:07:31,0:00:28,0:05:25,0:26:41,0:16:26,0:16:27,3:27:01,907,0,56
+General Medicine,PGY1,S0169401,2014-09-23,1:57:57,0:06:32,0:04:49,0:40:39,0:08:35,0:04:53,3:19:40,1341,1283,15
+Emergency Medicine,PGY2+,S0169390,2015-08-11,4:14:01,0:53:57,2:45:52,0:32:03,0:27:18,0:29:27,10:04:56,2451,0,27
+General Medicine,PGY1,S0181088,2016-06-13,3:09:22,0:59:44,0:00:09,1:09:24,0:22:32,0:03:37,6:29:32,2249,18,13
+General Medicine,PGY1,S0181088,2016-06-12,0:10:19,0:02:39,0:00:01,0:00:00,0:00:04,0:02:57,0:16:52,307,305,8
+Emergency Medicine,PGY1,S0094959,2014-08-17,2:31:05,0:05:34,2:03:23,0:25:43,0:57:03,1:09:40,8:03:08,3534,195,29
+General Medicine,PGY1,S0181088,2016-06-10,2:44:02,0:44:08,0:00:24,0:56:24,0:12:57,0:05:52,5:10:35,1437,260,8
+General Medicine,PGY1,S0181088,2016-06-17,2:14:46,0:44:39,0:00:17,0:54:42,0:13:24,0:04:42,4:59:29,1801,0,9
+General Medicine,PGY1,S0181088,2016-06-16,4:39:20,1:50:27,0:00:10,0:57:07,0:18:03,0:07:23,8:45:16,2886,132,21
+General Medicine,PGY1,S0181088,2016-06-15,2:06:09,0:40:52,0:00:03,0:19:02,0:07:56,0:00:50,3:39:24,1244,206,16
+General Medicine,PGY1,S0181088,2016-06-14,2:41:23,0:25:37,0:00:03,0:52:03,0:12:23,0:06:58,4:46:30,1228,46,7
+General Medicine,PGY1,S0181088,2016-06-19,0:05:08,0:00:20,0:00:00,0:00:25,0:00:00,0:00:37,0:06:57,83,43,7
+General Medicine,PGY1,S0181088,2016-06-18,3:04:42,0:48:11,0:00:08,0:31:32,0:19:19,0:11:33,5:28:34,1735,39,24
+Night Team,PGY2+,S0169398,2016-10-09,1:56:56,2:41:36,0:00:55,0:34:36,0:25:41,0:03:18,6:15:47,1893,0,16
+General Medicine,PGY2+,S0109179,2016-01-15,0:00:00,0:00:00,0:00:00,0:00:30,0:00:00,0:00:00,0:00:30,14,0,5
+General Medicine,PGY2+,S0109179,2016-01-14,1:58:19,1:09:14,0:09:41,0:14:42,0:08:20,0:51:39,4:50:49,2074,226,37
+General Medicine,PGY2+,S0109179,2016-01-17,1:56:13,1:04:18,0:19:55,0:10:12,0:06:06,0:43:57,4:46:05,2166,0,32
+General Medicine,PGY2+,S0109179,2016-01-16,1:08:19,0:39:18,1:08:21,0:16:31,0:10:52,0:21:59,4:01:45,1724,0,14
+General Medicine,PGY2+,S0109179,2016-01-11,1:01:15,0:28:43,0:04:58,0:22:45,0:05:23,0:10:18,2:24:53,1266,125,17
+General Medicine,PGY2+,S0109179,2016-01-10,0:40:46,0:27:46,0:16:41,0:13:05,0:08:52,0:05:46,2:03:14,914,0,15
+General Medicine,PGY2+,S0109179,2016-01-13,0:34:50,0:14:14,0:04:34,0:11:35,0:03:17,0:14:47,1:29:45,866,76,13
+General Medicine,PGY2+,S0109179,2016-01-12,2:28:24,0:53:46,0:30:53,0:17:03,0:10:04,0:28:19,5:15:46,2572,0,36
+General Medicine,PGY2+,S0109179,2016-01-19,1:11:42,0:55:33,0:15:26,0:13:32,0:17:19,0:19:06,3:37:19,1838,143,31
+General Medicine,PGY2+,S0109179,2016-01-18,0:53:14,0:08:30,0:06:58,0:07:02,0:05:16,0:01:58,1:28:48,636,0,14
+Intensive Care Unit,PGY2+,S0094535,2015-09-24,2:28:29,0:54:02,0:16:38,0:48:40,0:44:22,1:00:19,6:42:31,1682,3,21
+Intensive Care Unit,PGY2+,S0094535,2015-09-25,1:53:42,0:50:24,0:37:44,1:04:15,0:55:59,1:05:56,7:12:12,2274,18,25
+Intensive Care Unit,PGY2+,S0094535,2015-09-26,1:59:50,0:46:21,0:11:40,0:40:50,0:25:23,1:15:43,5:55:22,1821,0,18
+Intensive Care Unit,PGY2+,S0094535,2015-09-27,2:05:17,1:04:25,0:41:34,0:28:34,0:21:41,0:50:37,6:03:55,1545,71,19
+Intensive Care Unit,PGY2+,S0094535,2015-09-20,3:23:40,0:14:35,0:19:50,0:11:47,0:21:28,0:45:10,5:41:50,1203,196,16
+Intensive Care Unit,PGY2+,S0094535,2015-09-21,2:18:57,0:20:15,0:19:16,0:59:55,0:34:56,0:39:57,5:39:59,1541,0,16
+Intensive Care Unit,PGY2+,S0094535,2015-09-22,2:24:36,0:11:00,0:34:12,0:29:22,0:14:30,0:33:27,4:48:58,1125,49,15
+Intensive Care Unit,PGY2+,S0094535,2015-09-23,2:29:03,0:45:01,0:20:32,0:33:39,0:32:10,1:03:30,6:11:41,1688,70,23
+Intensive Care Unit,PGY2+,S0094535,2015-09-28,0:00:00,0:07:43,0:00:00,0:02:21,0:00:00,0:01:49,0:12:53,110,64,14
+Intensive Care Unit,PGY2+,S0094535,2015-09-29,1:49:33,1:20:13,0:26:22,0:53:05,0:39:34,1:06:03,6:44:48,2176,5,24
+General Medicine,PGY1,S0176040,2015-11-16,1:19:35,0:51:12,0:42:03,0:21:06,0:17:37,0:40:02,4:31:53,1505,0,6
+General Medicine,PGY1,S0176040,2015-11-17,0:01:52,0:15:15,0:44:53,0:01:03,0:00:09,0:01:56,1:08:11,300,282,9
+General Medicine,PGY1,S0176040,2015-11-14,0:48:30,0:48:02,1:11:04,0:38:42,0:20:41,0:43:27,5:06:04,1728,0,7
+General Medicine,PGY1,S0176040,2015-11-15,1:35:17,1:13:35,1:07:12,0:38:02,0:38:38,0:50:56,6:47:33,2333,8,14
+General Medicine,PGY1,S0176040,2015-11-12,0:38:08,0:48:02,2:20:50,0:15:35,0:04:11,0:28:51,4:59:46,1309,57,6
+General Medicine,PGY1,S0176040,2015-11-13,1:18:11,1:03:43,1:39:56,0:53:24,0:38:07,0:55:57,7:34:48,2434,0,9
+General Medicine,PGY1,S0176040,2015-11-10,2:08:29,1:22:54,1:18:32,1:00:08,0:24:54,0:39:55,7:44:23,2675,485,10
+General Medicine,PGY1,S0176040,2015-11-11,0:00:41,0:05:16,0:00:02,0:00:45,0:00:08,0:02:02,0:09:36,163,147,8
+General Medicine,PGY1,S0176040,2015-11-18,0:00:00,0:07:42,0:00:00,0:00:32,0:00:00,0:00:00,0:09:26,66,52,7
+General Medicine,PGY1,S0176040,2015-11-19,1:00:04,1:12:10,2:20:23,0:20:40,0:11:33,0:31:10,6:16:08,1833,233,8
+Emergency Medicine,PGY1,S0181082,2016-03-15,3:23:10,0:47:43,0:04:10,0:53:08,0:58:20,0:02:06,6:51:25,2069,0,23
+Intensive Care Unit,PGY2+,S0181064,2016-10-11,2:01:23,0:44:28,0:04:42,0:05:20,0:13:29,0:05:32,3:30:52,1123,257,21
+Intensive Care Unit,PGY2+,S0181087,2016-07-19,3:32:23,1:12:35,0:09:46,0:11:11,0:23:18,0:10:34,6:04:00,1578,0,12
+Intensive Care Unit,PGY2+,S0181087,2016-07-18,3:22:20,1:32:20,0:00:07,0:38:17,0:27:01,0:32:36,7:10:35,2711,0,21
+Night Team,PGY2+,S0169386,2015-10-12,1:53:22,1:08:39,2:05:47,0:37:14,0:16:07,1:16:31,8:03:14,3041,757,78
+Night Team,PGY2+,S0169386,2015-10-13,1:25:07,1:11:54,1:08:26,0:30:21,0:10:48,1:01:58,5:57:27,2101,579,82
+Intensive Care Unit,PGY2+,S0181087,2016-07-17,0:15:24,0:20:18,0:00:00,0:00:04,0:00:00,0:00:05,0:39:45,296,277,13
+Night Team,PGY2+,S0169386,2015-10-11,2:23:41,1:58:04,0:58:05,0:54:23,0:21:38,1:23:34,8:38:36,3034,370,71
+Night Team,PGY2+,S0169386,2015-10-16,0:56:46,1:02:17,1:15:37,0:41:41,0:10:34,1:01:40,5:29:01,1740,182,32
+Night Team,PGY2+,S0169386,2015-10-17,0:02:07,0:03:17,0:00:00,0:00:00,0:00:00,0:04:07,0:15:45,311,309,8
+Night Team,PGY2+,S0169386,2015-10-14,1:50:12,1:30:17,1:59:01,0:54:13,0:16:44,1:28:59,9:07:34,3259,183,95
+Night Team,PGY2+,S0169386,2015-10-15,2:02:30,1:41:32,1:43:00,0:55:35,0:12:19,1:23:24,8:43:33,3209,36,82
+General Medicine,PGY1,S0163662,2015-05-26,0:53:40,0:19:38,0:42:04,0:27:11,0:02:50,0:05:18,2:37:47,616,0,1
+General Medicine,PGY1,S0163662,2015-05-27,2:26:50,0:31:10,1:14:58,0:37:40,0:15:16,0:09:36,5:42:12,2298,12,13
+General Medicine,PGY1,S0163662,2015-05-24,3:01:29,0:45:08,1:49:03,0:49:32,0:15:24,0:21:47,7:18:22,2089,20,12
+General Medicine,PGY1,S0163662,2015-05-25,1:49:21,0:19:07,0:54:37,0:39:24,0:13:02,0:10:17,4:33:23,1672,17,7
+General Medicine,PGY1,S0163662,2015-05-29,2:13:24,0:54:31,1:33:55,0:50:15,0:23:09,0:17:06,6:58:33,2286,34,6
+Intensive Care Unit,PGY2+,S0094535,2015-10-15,1:50:11,1:04:27,0:33:01,0:25:13,0:25:56,0:36:13,5:11:09,1544,0,25
+General Medicine,PGY1,S0195224,2016-11-10,0:10:38,0:34:31,0:00:02,0:01:41,0:00:27,0:00:00,0:49:53,169,145,5
+Emergency Medicine,PGY1,S0169398,2015-04-15,0:47:40,0:52:38,1:15:21,0:25:52,0:21:09,0:21:55,4:53:43,1808,1252,22
+General Medicine,PGY1,S0181070,2015-09-08,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,4,0,1
+Night Team,PGY2+,S0094959,2016-02-21,4:49:35,1:54:34,0:01:31,0:21:18,0:19:30,0:16:17,8:34:59,3484,295,72
+Night Team,PGY2+,S0094959,2016-02-22,2:32:58,1:19:37,0:01:01,0:40:37,0:19:08,0:12:42,5:53:36,2628,178,62
+Night Team,PGY2+,S0094959,2016-02-23,4:10:19,2:00:43,0:01:08,0:28:24,0:14:33,0:07:18,7:56:41,3338,204,94
+Night Team,PGY2+,S0094959,2016-02-24,3:44:56,1:21:09,0:00:08,0:24:57,0:15:27,0:17:39,7:12:54,2938,488,98
+Night Team,PGY2+,S0094959,2016-02-25,4:15:43,1:36:59,0:02:08,0:30:04,0:11:48,0:13:52,7:42:34,3300,172,105
+Night Team,PGY2+,S0094959,2016-02-26,3:37:36,1:35:21,0:00:16,0:18:43,0:16:44,0:33:53,7:20:10,3333,319,103
+Night Team,PGY2+,S0094959,2016-02-27,2:19:28,1:28:33,0:00:14,0:29:36,0:17:16,0:05:35,5:13:37,2620,50,52
+Night Team,PGY2+,S0094959,2016-02-28,2:07:37,0:48:30,0:00:00,0:07:51,0:13:09,0:18:09,3:58:17,1812,86,62
+Night Team,PGY2+,S0094959,2016-02-29,4:13:57,1:51:31,0:00:09,0:37:50,0:17:55,0:30:53,8:13:26,3684,341,83
+General Medicine,PGY1,S0169404,2015-02-28,0:00:00,0:01:01,0:00:00,0:00:04,0:00:00,0:02:02,0:03:18,74,60,3
+Night Team,PGY1,S0181526,2016-06-05,2:47:55,0:41:02,0:00:02,0:43:31,0:18:34,0:05:37,5:02:49,1691,207,34
+Night Team,PGY1,S0169399,2015-06-09,1:46:09,0:17:52,1:15:37,0:31:45,0:21:59,0:25:35,5:02:11,1072,0,37
+Night Team,PGY2+,S0109179,2016-05-15,2:06:12,0:47:42,0:00:02,0:30:05,0:09:54,0:08:06,3:55:16,1834,142,42
+Night Team,PGY2+,S0109179,2016-05-17,2:10:16,1:08:52,0:00:07,0:08:47,0:06:19,0:05:53,3:51:23,1394,0,25
+Night Team,PGY2+,S0109179,2016-05-16,2:50:47,1:03:03,0:00:02,0:16:35,0:08:07,0:08:30,4:43:12,1511,0,39
+Night Team,PGY2+,S0109179,2016-05-19,1:44:06,0:35:43,0:00:02,0:12:29,0:06:04,0:05:51,3:17:34,1993,40,33
+Night Team,PGY2+,S0109179,2016-05-18,0:57:03,0:37:27,0:00:37,0:17:22,0:02:15,0:00:08,2:13:59,827,0,23
+Night Team,PGY1,S0093383,2015-06-05,1:56:10,0:21:02,3:13:50,0:21:41,0:22:20,0:35:26,7:02:53,1590,475,51
+Night Team,PGY1,S0093383,2015-06-04,2:04:34,0:47:28,2:21:14,0:39:00,0:32:00,0:40:59,7:18:31,1857,977,50
+Night Team,PGY1,S0093383,2015-06-03,0:53:20,0:04:45,0:25:07,0:04:59,0:09:20,0:13:18,2:01:45,361,190,14
+Night Team,PGY1,S0093383,2015-06-02,1:32:36,0:21:52,1:53:30,0:14:30,0:08:13,0:13:20,4:34:27,1115,0,37
+Night Team,PGY1,S0093383,2015-06-01,2:34:07,0:08:29,1:39:43,0:40:49,0:13:59,0:40:14,6:14:21,1225,677,33
+Intensive Care Unit,PGY2+,S0093383,2016-07-09,3:24:16,1:45:28,0:02:14,1:03:12,0:47:58,0:20:10,8:14:57,2474,53,30
+Intensive Care Unit,PGY2+,S0093383,2016-07-08,3:24:12,1:21:10,0:12:46,1:00:25,0:41:11,0:03:52,7:23:11,1832,0,15
+Intensive Care Unit,PGY2+,S0093383,2016-07-07,0:00:00,0:00:00,0:00:00,0:00:27,0:00:00,0:00:00,0:00:27,14,0,2
+Intensive Care Unit,PGY2+,S0093383,2016-07-06,0:00:00,0:00:00,0:00:00,0:00:30,0:00:00,0:00:00,0:00:30,18,0,4
+Intensive Care Unit,PGY2+,S0093383,2016-07-05,1:48:06,0:38:23,0:10:53,0:41:11,0:19:05,0:10:15,4:21:45,1202,101,11
+Intensive Care Unit,PGY2+,S0093383,2016-07-04,3:29:31,1:17:37,0:02:13,1:07:26,0:40:03,0:11:07,7:43:15,1956,0,12
+Intensive Care Unit,PGY2+,S0093383,2016-07-03,1:54:53,0:31:02,0:10:42,0:30:22,0:18:03,0:00:28,3:40:58,734,0,13
+Intensive Care Unit,PGY2+,S0093383,2016-07-02,0:00:00,0:00:00,0:00:00,0:01:16,0:00:00,0:00:00,0:01:16,8,0,3
+Intensive Care Unit,PGY2+,S0093383,2016-07-01,1:27:57,0:36:19,0:04:23,0:22:37,0:11:33,0:06:21,3:14:13,803,0,11
+Emergency Medicine,PGY1,S0169398,2015-04-19,1:05:13,0:35:56,0:41:55,0:06:11,0:05:40,0:15:45,2:55:13,673,0,8
+Night Team,PGY2+,S0169389,2016-03-18,4:35:12,1:59:46,0:00:12,0:51:16,0:15:06,0:05:48,8:16:02,2579,0,75
+General Medicine,PGY1,S0195226,2016-08-03,2:39:10,1:11:21,0:02:31,0:45:14,0:27:17,0:03:53,5:58:54,1732,0,21
+Night Team,PGY2+,S0163662,2015-08-12,0:25:57,0:21:22,0:53:21,0:01:15,0:16:00,0:02:54,2:13:06,1093,0,15
+General Medicine,PGY1,S0181059,2015-09-05,2:49:23,1:08:42,0:32:55,0:34:02,0:26:30,0:33:29,6:44:03,2914,47,24
+Night Team,PGY2+,S0169389,2016-03-10,5:08:42,1:55:08,0:00:06,0:31:13,0:14:06,0:04:45,8:30:11,2497,84,71
+Night Team,PGY2+,S0169389,2016-03-11,1:26:29,0:56:22,0:01:06,0:35:28,0:05:27,0:02:07,3:15:37,778,0,17
+Night Team,PGY2+,S0169389,2016-03-12,0:03:56,0:08:13,0:00:00,0:00:02,0:00:00,0:00:00,0:16:38,80,74,9
+Night Team,PGY2+,S0169389,2016-03-13,1:22:06,0:29:16,0:00:01,0:28:19,0:03:42,0:00:04,2:31:39,833,8,38
+Night Team,PGY2+,S0169389,2016-03-14,5:55:56,1:27:22,0:00:10,0:23:07,0:12:18,0:05:06,8:31:27,1956,119,66
+General Medicine,PGY1,S0195226,2016-08-01,2:38:44,1:34:03,0:00:21,0:24:51,0:14:46,0:03:31,6:03:20,1865,14,27
+Night Team,PGY2+,S0169389,2016-03-16,2:24:55,1:42:56,0:04:38,0:32:50,0:11:10,0:05:06,5:25:48,1830,0,62
+Night Team,PGY2+,S0169389,2016-03-17,4:57:59,1:36:02,0:00:42,0:33:31,0:12:52,0:07:15,7:57:40,2627,0,75
+General Medicine,PGY1,S0181059,2015-09-01,0:10:19,0:30:43,0:00:00,0:03:11,0:00:18,0:03:29,0:56:53,407,391,12
+Night Team,PGY1,S0181077,2015-11-16,1:33:41,0:36:21,0:11:53,0:07:00,0:13:16,0:20:55,3:10:29,1007,494,36
+Night Team,PGY1,S0181077,2015-11-17,1:26:18,0:30:39,0:42:13,0:24:18,0:16:52,0:21:19,3:50:35,1140,13,24
+Night Team,PGY1,S0181077,2015-11-14,2:17:11,0:34:07,0:02:51,0:41:28,0:43:34,0:55:29,5:23:46,1756,143,56
+Night Team,PGY1,S0181077,2015-11-15,1:44:48,0:23:14,0:28:42,0:20:23,0:15:31,0:11:34,3:41:03,1072,961,51
+Night Team,PGY1,S0181077,2015-11-12,1:13:53,0:05:53,0:01:17,0:09:20,0:21:48,0:08:29,2:08:21,775,52,33
+General Medicine,PGY1,S0181059,2015-09-02,2:15:21,1:08:21,0:29:10,0:31:36,0:29:31,0:21:45,5:55:22,3066,390,28
+Night Team,PGY1,S0181077,2015-11-10,2:43:57,1:02:29,1:06:21,0:30:21,0:27:47,0:19:03,6:47:22,2359,0,30
+Night Team,PGY1,S0181077,2015-11-11,1:10:00,0:32:51,0:41:24,0:16:29,0:22:58,0:14:07,3:31:14,1311,0,25
+Intensive Care Unit,PGY2+,S0181070,2016-08-20,0:52:41,0:51:41,0:01:54,0:02:55,0:09:49,0:03:41,2:17:35,1062,200,9
+General Medicine,PGY2+,S0181087,2016-11-10,2:41:06,1:50:56,0:00:35,0:38:37,0:23:54,0:01:11,6:08:21,2314,12,24
+General Medicine,PGY1,S0195226,2016-08-04,2:03:58,0:56:29,0:00:11,0:31:58,0:11:39,0:02:16,4:49:11,1363,280,9
+Night Team,PGY1,S0181077,2015-11-18,0:00:00,0:00:00,0:00:00,0:00:50,0:00:00,0:00:00,0:00:50,8,0,1
+Night Team,PGY1,S0181077,2015-11-19,1:00:35,0:09:46,0:06:07,0:07:02,0:07:25,0:10:45,1:49:29,742,686,41
+Intensive Care Unit,PGY2+,S0181073,2016-10-11,2:19:33,0:49:08,0:21:46,0:22:26,0:31:27,0:04:38,5:03:03,1282,0,14
+Night Team,PGY2+,S0094959,2016-10-23,2:23:00,0:43:38,0:00:01,0:06:57,0:07:14,0:19:00,3:58:11,1803,121,53
+Night Team,PGY2+,S0094959,2016-10-27,3:42:26,2:04:38,0:00:06,0:12:32,0:19:46,0:09:53,7:18:31,3399,0,98
+Night Team,PGY2+,S0094959,2016-10-26,3:52:08,1:03:30,0:00:11,0:21:36,0:06:12,0:03:56,5:55:08,2546,16,93
+Night Team,PGY2+,S0094959,2016-10-25,4:08:45,1:22:38,0:00:06,0:19:12,0:18:17,0:24:39,7:07:42,3518,0,96
+Night Team,PGY2+,S0094959,2016-10-24,1:57:59,0:58:35,0:00:05,0:21:34,0:13:26,0:03:36,4:02:22,1908,90,25
+Night Team,PGY2+,S0094959,2016-10-29,5:31:54,2:08:30,0:00:08,0:22:55,0:12:29,0:27:04,9:18:58,4393,5,110
+Night Team,PGY2+,S0094959,2016-10-28,5:22:12,1:42:13,0:00:05,0:19:27,0:19:22,0:07:24,8:42:19,3624,197,73
+General Medicine,PGY1,S0188098,2016-08-15,0:01:43,0:00:16,0:00:00,0:00:00,0:00:00,0:00:18,0:02:25,25,0,4
+General Medicine,PGY1,S0181525,2015-10-13,1:23:44,1:13:43,2:25:19,0:41:57,0:32:44,0:25:42,7:19:22,1863,0,12
+General Medicine,PGY1,S0181525,2015-10-12,0:58:51,1:17:31,1:09:08,0:23:05,0:32:05,0:33:37,5:12:02,1451,11,6
+General Medicine,PGY1,S0181525,2015-10-15,1:36:16,0:55:57,1:21:46,1:04:58,0:26:23,0:28:27,6:25:20,2062,22,12
+General Medicine,PGY1,S0181525,2015-10-14,0:45:30,0:48:18,0:32:00,0:06:24,0:14:55,0:26:09,3:12:16,873,0,8
+General Medicine,PGY1,S0181525,2015-10-17,0:58:09,0:59:45,1:34:11,0:38:33,0:19:01,0:09:59,5:05:12,1470,0,10
+General Medicine,PGY1,S0181525,2015-10-16,1:04:21,0:38:24,1:32:31,0:20:21,0:18:45,0:16:01,4:40:17,1335,0,8
+General Medicine,PGY1,S0181525,2015-10-19,0:17:50,0:00:00,0:00:00,0:00:00,0:00:00,0:03:26,0:21:18,46,42,2
+General Medicine,PGY1,S0181525,2015-10-18,0:55:22,1:13:52,2:18:18,0:29:24,0:13:39,0:13:42,5:52:42,1330,0,7
+General Medicine,PGY1,S0188098,2016-08-14,1:55:23,0:44:58,0:00:11,0:42:52,0:14:14,0:00:32,4:19:44,1209,0,12
+Intensive Care Unit,PGY2+,S0181077,2016-10-30,0:09:54,0:11:21,0:00:00,0:03:29,0:00:12,0:00:02,0:26:15,223,178,15
+Intensive Care Unit,PGY2+,S0181077,2016-10-31,3:12:50,0:54:58,0:05:38,0:35:11,0:34:50,0:06:58,6:06:43,1965,144,16
+General Medicine,PGY2+,S0169399,2016-05-31,2:07:46,0:48:49,0:00:13,0:46:20,0:15:24,0:00:57,5:08:55,1525,127,22
+General Medicine,PGY2+,S0169399,2016-05-30,0:21:13,0:04:17,0:00:00,0:00:00,0:00:00,0:02:17,0:29:16,116,114,14
+General Medicine,PGY1,S0181067,2015-09-12,0:00:00,0:00:00,0:00:00,0:02:37,0:00:00,0:00:00,0:02:37,22,0,5
+General Medicine,PGY1,S0181067,2015-09-10,1:36:03,0:38:57,0:57:24,1:02:38,0:38:40,0:38:35,6:04:15,2039,77,17
+General Medicine,PGY1,S0195205,2016-09-08,2:50:14,1:04:41,0:03:11,0:56:47,0:41:19,0:06:11,6:18:15,2102,202,10
+General Medicine,PGY1,S0195205,2016-09-03,2:27:38,2:26:44,0:00:13,0:41:08,0:31:26,0:07:47,6:58:36,2288,111,15
+General Medicine,PGY1,S0195205,2016-09-02,0:15:49,0:25:43,0:00:00,0:00:22,0:03:17,0:00:09,0:47:33,1433,1406,14
+General Medicine,PGY1,S0195205,2016-09-01,2:11:41,2:21:35,0:00:19,0:35:21,0:34:56,0:01:07,6:05:47,2298,8,15
+General Medicine,PGY1,S0195205,2016-09-07,2:01:09,0:51:11,0:02:45,0:29:43,0:26:33,0:03:15,4:21:28,1813,606,11
+General Medicine,PGY1,S0195205,2016-09-06,2:43:25,2:17:23,0:00:17,0:46:08,0:37:19,0:06:18,7:18:26,2572,404,13
+General Medicine,PGY1,S0195205,2016-09-05,1:43:43,0:42:04,0:00:12,0:19:08,0:29:27,0:05:49,3:50:01,1419,161,11
+General Medicine,PGY1,S0195205,2016-09-04,0:22:29,0:21:59,0:00:00,0:01:02,0:01:37,0:00:05,0:48:19,448,422,11
+Night Team,PGY1,S0195207,2016-10-07,3:12:20,1:10:28,0:00:41,0:43:48,0:20:07,0:01:58,6:04:08,2451,596,51
+Night Team,PGY1,S0195207,2016-10-06,1:08:07,0:26:19,0:00:00,0:06:11,0:12:47,0:04:31,2:07:50,895,171,37
+Night Team,PGY1,S0195207,2016-10-05,1:55:47,0:54:42,0:00:00,0:03:16,0:11:58,0:01:09,3:33:01,1526,62,43
+Night Team,PGY1,S0195207,2016-10-04,3:13:46,1:32:32,0:00:16,0:25:00,0:22:26,0:13:42,6:17:06,2384,0,52
+Night Team,PGY1,S0195207,2016-10-03,3:05:33,1:20:36,0:00:00,0:33:41,0:12:09,0:05:48,5:56:41,2695,0,58
+Night Team,PGY1,S0195207,2016-10-02,3:50:48,1:16:47,0:00:02,0:14:43,0:13:51,0:05:02,6:36:30,2991,87,48
+Night Team,PGY1,S0195207,2016-10-09,2:41:17,1:50:07,0:00:17,0:28:52,0:11:38,0:00:58,5:55:37,2193,25,91
+Night Team,PGY1,S0195207,2016-10-08,1:55:17,1:36:56,0:00:02,0:19:01,0:15:24,0:05:20,4:46:30,2033,742,69
+Night Team,PGY1,S0172056,2016-02-15,1:02:37,0:35:02,0:01:52,0:14:19,0:31:19,0:16:27,2:56:02,1081,0,38
+General Medicine,PGY1,S0188098,2016-08-16,0:44:02,0:50:56,0:00:07,0:04:39,0:04:01,0:03:11,1:59:33,522,0,9
+Night Team,PGY2+,S0093383,2015-12-29,1:11:50,1:10:37,4:36:53,0:34:32,0:21:55,0:46:51,9:06:06,4134,0,20
+Night Team,PGY1,S0181064,2016-01-17,1:25:26,0:42:31,0:21:29,0:09:33,0:11:34,0:28:30,3:34:09,1349,68,42
+General Medicine,PGY1,S0169386,2014-06-28,0:21:59,0:00:34,0:01:48,0:00:42,0:00:37,0:01:30,0:27:36,232,216,6
+General Medicine,PGY1,S0188098,2016-08-11,1:21:42,0:47:32,0:00:32,0:51:09,0:23:48,0:04:20,4:14:34,1410,152,22
+Intensive Care Unit,PGY2+,S0181064,2016-10-13,2:39:16,1:12:45,0:05:10,0:08:16,0:13:45,0:13:25,5:00:17,1745,102,25
+General Medicine,PGY2+,S0181079,2016-10-08,0:00:00,0:00:00,0:00:00,0:00:15,0:00:00,0:00:00,0:00:15,12,0,4
+General Medicine,PGY2+,S0181079,2016-10-07,1:47:23,0:46:45,0:00:21,0:23:11,0:15:49,0:03:43,3:52:26,1376,0,31
+Night Team,PGY1,S0174975,2016-03-30,2:16:15,2:04:53,0:00:17,1:21:09,0:14:17,0:05:07,6:28:41,2188,145,56
+General Medicine,PGY2+,S0181079,2016-10-05,2:37:27,1:25:11,0:00:25,0:29:51,0:25:31,0:07:23,5:55:56,2246,345,24
+General Medicine,PGY2+,S0181079,2016-10-04,3:45:34,1:21:32,0:00:11,0:24:26,0:29:20,0:02:22,7:11:17,2844,369,22
+General Medicine,PGY2+,S0181079,2016-10-03,0:16:34,0:06:11,0:00:00,0:01:53,0:01:35,0:00:05,0:29:01,360,338,12
+General Medicine,PGY2+,S0181079,2016-10-02,2:06:15,1:23:51,0:01:52,0:43:38,0:19:21,0:05:37,5:17:57,1999,0,17
+General Medicine,PGY2+,S0181079,2016-10-01,3:46:54,2:08:57,0:00:03,0:36:51,0:15:33,0:05:59,7:53:23,2538,0,25
+Emergency Medicine,PGY1,S0195086,2016-10-06,3:43:08,0:31:27,0:05:34,0:16:30,0:46:03,0:06:12,6:21:42,2264,24,37
+Emergency Medicine,PGY1,S0195086,2016-10-07,4:21:26,0:37:22,0:03:09,0:13:45,0:57:32,0:03:13,7:08:03,2457,143,50
+Emergency Medicine,PGY1,S0195086,2016-10-04,2:19:48,0:24:54,0:00:59,0:17:23,0:34:27,0:03:21,4:25:40,1451,52,26
+Emergency Medicine,PGY1,S0195086,2016-10-05,0:05:41,0:00:12,0:00:00,0:00:00,0:00:00,0:00:00,0:07:01,31,0,5
+Emergency Medicine,PGY1,S0195086,2016-10-02,3:58:36,0:45:15,0:01:20,0:16:09,0:31:52,0:00:54,6:07:52,1885,0,47
+Emergency Medicine,PGY1,S0195086,2016-10-03,3:06:35,0:52:45,0:02:10,0:20:39,0:39:28,0:02:22,5:45:48,1968,0,31
+Emergency Medicine,PGY1,S0195086,2016-10-01,2:43:02,0:30:37,0:00:13,0:22:50,0:47:27,0:09:55,5:17:45,1722,0,27
+General Medicine,PGY1,S0188098,2016-08-13,1:15:53,0:51:26,0:00:15,0:22:43,0:30:24,0:00:22,3:27:22,780,5,9
+Intensive Care Unit,PGY2+,S0181070,2016-08-28,0:49:28,0:47:22,0:07:19,0:07:07,0:30:06,0:00:08,2:31:20,1069,0,13
+Intensive Care Unit,PGY2+,S0093383,2015-10-08,1:45:00,0:42:06,1:09:09,0:39:54,0:39:06,0:30:34,5:41:05,1644,0,23
+Intensive Care Unit,PGY2+,S0093383,2015-10-09,1:46:14,0:33:26,0:17:21,0:21:59,0:18:24,0:21:11,3:55:31,1381,0,15
+Intensive Care Unit,PGY2+,S0093383,2015-10-01,1:33:42,0:42:18,0:19:14,0:26:00,0:21:36,0:28:35,4:07:42,1058,0,13
+Intensive Care Unit,PGY2+,S0093383,2015-10-02,1:32:49,0:50:00,0:13:10,0:09:47,0:16:38,0:23:25,3:49:48,1318,0,29
+Intensive Care Unit,PGY2+,S0093383,2015-10-03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,4
+Intensive Care Unit,PGY2+,S0093383,2015-10-04,1:58:02,1:03:37,0:22:46,0:39:09,0:29:44,0:26:09,5:16:15,1543,0,12
+Intensive Care Unit,PGY2+,S0093383,2015-10-05,1:38:38,0:40:36,0:36:14,0:19:30,0:32:37,0:30:02,4:46:51,1413,120,21
+Intensive Care Unit,PGY2+,S0093383,2015-10-06,1:31:20,0:35:42,0:14:33,0:41:16,0:23:44,0:34:04,4:11:40,1205,0,12
+Intensive Care Unit,PGY2+,S0093383,2015-10-07,1:45:50,0:37:00,0:11:14,0:17:47,0:36:02,0:38:21,4:19:17,1344,0,16
+General Medicine,PGY1,S0181060,2015-11-25,1:01:10,0:41:09,0:48:30,0:18:06,0:08:10,0:27:32,3:43:42,1138,14,12
+Night Team,PGY1,S0181070,2015-09-27,1:19:02,0:47:45,0:17:50,0:13:46,0:13:49,0:22:23,3:34:54,1297,5,48
+General Medicine,PGY1,S0181064,2015-06-29,0:46:17,0:09:05,0:37:32,0:00:00,0:00:25,0:04:09,1:44:56,382,378,8
+General Medicine,PGY1,S0181064,2015-06-28,2:36:11,0:11:03,1:41:59,0:19:13,0:37:31,0:38:28,6:39:45,2068,281,7
+Emergency Medicine,PGY1,S0163662,2015-02-18,2:13:42,0:49:00,3:21:32,0:26:16,0:34:08,0:31:12,8:10:55,2126,223,24
+Emergency Medicine,PGY1,S0163662,2015-02-19,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,3,0,1
+Emergency Medicine,PGY1,S0163662,2015-02-16,0:04:59,0:02:52,0:59:46,0:00:01,0:02:04,0:00:40,1:10:38,185,0,9
+Emergency Medicine,PGY1,S0163662,2015-02-17,4:49:04,0:54:32,1:09:03,0:23:55,0:32:21,0:30:29,9:09:35,3551,101,21
+Emergency Medicine,PGY1,S0163662,2015-02-14,3:30:14,0:25:59,4:21:14,0:21:35,0:25:13,0:26:42,10:22:42,2755,0,30
+Emergency Medicine,PGY1,S0163662,2015-02-15,4:07:09,1:04:55,1:08:36,0:40:35,0:32:31,0:23:41,8:17:27,2455,0,18
+General Medicine,PGY1,S0181064,2015-06-27,3:29:28,0:26:18,3:01:40,0:23:04,0:45:44,0:29:57,8:54:46,2189,151,9
+General Medicine,PGY1,S0181064,2015-06-26,2:48:38,0:12:35,1:28:37,0:36:16,0:18:28,0:23:27,6:11:37,1760,177,6
+Emergency Medicine,PGY1,S0163662,2015-02-10,1:22:04,0:44:20,1:47:47,0:19:43,0:18:18,0:28:33,5:27:20,1551,0,13
+Emergency Medicine,PGY1,S0163662,2015-02-11,3:12:54,0:39:32,1:56:55,0:27:31,0:31:39,0:16:33,7:26:11,1709,0,17
+Night Team,PGY1,S0181085,2015-12-09,1:19:12,0:26:00,0:03:35,0:32:35,0:33:39,0:37:01,3:50:58,1448,0,41
+General Medicine,PGY1,S0169402,2015-02-21,3:44:17,0:13:09,2:21:57,1:23:37,0:30:50,0:12:52,9:05:57,2274,68,10
+General Medicine,PGY1,S0169402,2015-02-20,3:00:37,0:33:40,2:59:07,1:11:35,0:26:44,0:08:34,8:54:25,2063,0,12
+General Medicine,PGY1,S0169402,2015-02-23,0:58:57,0:00:00,1:00:35,0:20:00,0:13:50,0:01:46,2:44:26,894,0,8
+General Medicine,PGY1,S0169402,2015-02-22,3:48:04,0:35:22,3:52:02,0:45:38,0:22:30,0:12:41,10:12:14,2481,11,14
+General Medicine,PGY1,S0169402,2015-02-25,3:08:06,0:37:53,3:17:55,0:53:26,0:17:23,0:07:43,9:06:14,1886,60,9
+General Medicine,PGY1,S0169402,2015-02-24,0:12:34,0:00:00,0:10:33,0:01:25,0:00:01,0:00:09,0:25:50,91,77,6
+General Medicine,PGY1,S0169402,2015-02-27,2:32:12,0:34:58,2:13:05,0:50:21,0:19:26,0:20:26,7:20:58,1960,23,10
+General Medicine,PGY1,S0169402,2015-02-26,1:47:43,0:05:00,0:53:23,0:14:04,0:09:39,0:03:33,3:29:44,1093,57,24
+General Medicine,PGY1,S0169402,2015-02-28,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,8,0,3
+General Medicine,PGY1,S0195226,2016-08-05,0:06:11,0:11:45,0:00:00,0:00:00,0:00:00,0:00:15,0:20:38,169,136,5
+Night Team,PGY1,S0169398,2015-02-04,2:56:21,0:19:17,1:26:11,0:29:24,0:23:38,0:20:35,6:11:52,1617,47,36
+Intensive Care Unit,PGY2+,S0163662,2016-05-03,1:28:06,0:36:08,0:08:35,0:03:04,0:08:22,0:01:53,2:40:02,829,0,12
+Intensive Care Unit,PGY2+,S0163662,2016-05-02,0:03:57,0:24:37,0:00:07,0:00:00,0:00:01,0:00:00,0:30:56,149,0,5
+Intensive Care Unit,PGY2+,S0163662,2016-05-01,0:00:00,0:00:00,0:00:00,0:00:32,0:00:00,0:00:00,0:00:32,6,0,3
+General Medicine,PGY1,S0169401,2015-03-15,2:30:34,1:27:54,1:57:10,0:58:57,0:28:55,0:18:33,8:03:54,2921,146,15
+Intensive Care Unit,PGY2+,S0163662,2016-05-07,1:52:31,0:25:58,0:01:31,1:06:39,0:21:34,0:07:17,4:26:32,1311,0,13
+Intensive Care Unit,PGY2+,S0163662,2016-05-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+Intensive Care Unit,PGY2+,S0163662,2016-05-05,1:03:10,0:19:41,0:05:51,0:10:50,0:10:27,0:00:06,2:04:43,507,86,7
+Intensive Care Unit,PGY2+,S0163662,2016-05-04,0:05:27,0:00:36,0:04:13,0:00:00,0:00:21,0:00:00,0:16:52,71,0,4
+General Medicine,PGY1,S0181074,2015-07-19,3:29:28,1:55:34,0:41:40,1:05:24,0:58:45,0:22:56,9:53:19,3548,84,13
+Intensive Care Unit,PGY2+,S0163662,2016-05-09,1:06:20,0:33:55,0:07:31,0:19:02,0:21:44,0:03:19,3:00:22,904,146,6
+Intensive Care Unit,PGY2+,S0163662,2016-05-08,1:20:12,0:07:59,0:06:46,0:13:22,0:19:06,0:02:46,2:27:42,528,0,9
+General Medicine,PGY1,S0169401,2015-03-18,4:36:26,1:42:29,1:51:41,1:04:59,0:42:43,0:15:24,10:34:08,3568,660,19
+General Medicine,PGY1,S0169401,2015-03-19,3:47:24,0:54:23,1:30:39,0:39:02,0:24:45,0:11:07,7:54:55,2673,1480,19
+Night Team,PGY2+,S0108287,2016-10-16,0:15:27,0:03:41,0:00:00,0:00:00,0:00:00,0:00:15,0:25:12,181,0,15
+Night Team,PGY2+,S0108287,2016-10-17,1:45:19,1:25:24,0:00:47,0:25:41,0:17:21,0:01:39,4:20:25,1134,0,13
+Night Team,PGY2+,S0079337,2015-11-19,2:14:20,0:50:56,0:45:34,0:24:33,0:28:20,0:04:55,4:58:44,1653,0,34
+Night Team,PGY2+,S0108287,2016-10-15,1:48:25,0:21:40,0:00:04,0:10:25,0:03:51,0:00:06,2:31:08,602,51,18
+Night Team,PGY1,S0181085,2015-12-07,0:48:43,0:29:54,0:06:34,0:44:12,0:30:13,0:27:21,3:19:43,1018,0,40
+Night Team,PGY1,S0181085,2015-12-06,0:58:45,0:35:22,0:15:02,0:54:00,0:14:21,0:53:11,4:16:20,1227,0,36
+General Medicine,PGY1,S0169390,2015-06-05,2:14:47,0:11:10,1:22:01,1:05:58,0:14:28,0:06:42,5:30:10,1287,0,9
+General Medicine,PGY1,S0169390,2015-06-04,2:54:52,0:48:05,2:42:44,0:57:27,0:33:37,0:26:46,8:54:13,2266,57,15
+General Medicine,PGY1,S0169390,2015-06-07,3:19:26,0:34:36,2:58:42,0:39:34,0:21:09,0:21:08,8:35:05,1771,0,12
+General Medicine,PGY1,S0169390,2015-06-06,1:05:53,0:16:22,0:40:24,0:10:46,0:09:35,0:02:44,2:37:22,803,0,15
+General Medicine,PGY1,S0169383,2015-01-19,3:51:55,0:11:01,1:16:24,0:45:23,0:13:47,0:19:16,7:27:35,2019,0,13
+General Medicine,PGY1,S0169383,2015-01-18,4:29:19,0:11:01,1:06:51,0:37:28,0:44:19,0:20:47,8:03:33,2531,29,18
+General Medicine,PGY1,S0169383,2015-01-17,3:17:44,0:08:01,0:41:52,0:50:12,0:24:47,0:15:57,6:10:10,1822,0,7
+General Medicine,PGY1,S0169383,2015-01-16,4:25:38,0:12:26,1:07:41,0:54:38,0:18:02,0:57:58,8:23:28,2099,0,11
+General Medicine,PGY1,S0169383,2015-01-15,0:51:49,0:03:56,0:40:51,0:01:00,0:00:39,0:01:34,2:10:38,548,540,8
+General Medicine,PGY1,S0169383,2015-01-14,3:04:28,0:03:10,0:50:16,0:40:48,0:18:16,0:30:21,6:04:27,1926,0,12
+General Medicine,PGY1,S0169383,2015-01-13,4:12:05,0:11:01,2:17:57,0:49:03,0:34:02,0:22:09,9:40:05,2553,0,22
+General Medicine,PGY1,S0169383,2015-01-12,3:10:42,0:04:06,0:46:14,0:45:20,0:30:47,0:22:01,6:20:06,2248,0,16
+General Medicine,PGY1,S0169383,2015-01-11,3:13:31,0:25:39,1:30:34,1:01:31,0:36:38,0:44:28,8:32:12,2363,0,10
+General Medicine,PGY1,S0169383,2015-01-10,0:47:22,0:01:42,0:26:43,0:07:22,0:04:12,0:02:53,1:45:44,421,409,7
+General Medicine,PGY1,S0181079,2015-10-10,0:00:00,0:00:00,0:00:00,0:02:20,0:00:00,0:00:00,0:02:20,8,0,3
+Night Team,PGY2+,S0169399,2015-09-30,0:44:28,0:44:56,6:28:23,0:17:23,0:07:32,0:13:10,8:45:16,1177,91,18
+General Medicine,PGY2+,S0169411,2016-03-31,2:24:38,0:39:50,0:00:30,0:26:54,0:19:39,0:01:26,4:26:15,2118,45,19
+General Medicine,PGY1,S0106952,2015-01-27,4:30:36,0:07:00,1:14:36,0:21:14,0:19:01,0:26:57,7:32:43,2540,526,35
+General Medicine,PGY1,S0106952,2015-01-26,6:05:10,0:07:39,2:08:24,0:32:26,0:44:25,0:31:26,11:16:36,3840,1279,49
+General Medicine,PGY1,S0106952,2015-01-25,4:37:21,0:05:55,2:00:29,0:54:42,0:32:13,0:25:16,9:11:59,2320,484,35
+General Medicine,PGY1,S0106952,2015-01-24,0:00:00,0:00:00,0:00:00,0:02:03,0:00:00,0:00:00,0:02:03,26,0,3
+Night Team,PGY1,S0195201,2016-08-31,3:52:27,0:57:23,0:00:11,0:14:29,0:11:49,0:02:21,6:43:13,2742,0,113
+Night Team,PGY1,S0195201,2016-08-30,3:05:23,0:59:09,0:00:01,0:20:13,0:14:28,0:00:44,6:00:14,2583,788,67
+General Medicine,PGY1,S0106952,2015-01-21,6:00:32,0:11:14,1:57:10,0:38:05,0:44:35,0:32:07,10:49:54,3670,1257,42
+General Medicine,PGY1,S0106952,2015-01-20,6:57:39,0:07:07,2:34:34,0:55:47,0:48:15,0:19:48,12:13:09,3194,934,63
+General Medicine,PGY1,S0093383,2015-01-02,3:31:12,0:08:31,1:38:58,0:31:57,0:20:58,0:39:38,7:13:09,2208,31,19
+General Medicine,PGY1,S0106952,2015-01-29,3:30:08,0:16:30,2:28:56,0:09:56,0:12:36,0:15:43,7:16:16,1555,937,37
+General Medicine,PGY1,S0093383,2015-01-01,1:39:49,0:07:59,1:39:34,0:04:13,0:05:48,0:08:57,3:54:09,966,0,24
+Night Team,PGY1,S0093383,2015-01-06,2:46:03,0:22:21,2:04:13,0:53:30,0:24:27,0:29:14,7:19:29,1534,109,35
+Night Team,PGY1,S0093383,2015-01-07,1:45:22,0:04:46,0:41:13,0:34:42,0:23:55,0:22:56,4:07:40,1060,122,35
+Night Team,PGY1,S0093383,2015-01-04,2:15:30,0:02:59,1:57:54,0:32:52,0:29:08,0:20:07,5:58:18,1409,76,49
+Night Team,PGY1,S0093383,2015-01-05,2:28:21,0:11:21,2:26:46,0:27:40,0:31:59,0:32:26,6:45:35,1391,60,41
+Night Team,PGY1,S0093383,2015-01-08,1:18:49,0:03:28,1:47:41,0:09:31,0:11:48,0:07:07,3:43:57,696,0,32
+Night Team,PGY1,S0093383,2015-01-09,0:49:31,0:04:48,0:08:32,0:20:05,0:12:13,0:18:54,2:02:51,463,0,20
+General Medicine,PGY1,S0169386,2014-07-19,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,16,0,3
+General Medicine,PGY1,S0169386,2014-07-18,2:19:43,0:05:23,0:24:21,0:27:21,0:18:33,0:45:48,4:44:58,1749,224,8
+General Medicine,PGY1,S0195237,2016-10-23,2:06:14,1:56:55,0:00:12,1:21:04,0:20:03,0:14:03,6:50:56,1694,0,15
+General Medicine,PGY2+,S0163662,2016-01-09,0:00:00,0:00:00,0:00:00,0:01:13,0:00:00,0:00:00,0:01:13,20,0,5
+General Medicine,PGY2+,S0163662,2016-01-08,2:48:52,0:56:37,0:41:43,0:32:55,0:22:08,0:23:08,6:10:49,1751,536,17
+General Medicine,PGY2+,S0163662,2016-01-07,2:39:44,0:53:06,0:16:51,0:26:03,0:25:49,0:32:46,5:31:07,3209,0,19
+General Medicine,PGY2+,S0163662,2016-01-06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY2+,S0163662,2016-01-05,1:19:27,0:15:01,0:08:03,0:12:04,0:17:32,0:07:05,2:28:27,889,0,19
+General Medicine,PGY2+,S0163662,2016-01-04,2:07:35,0:52:31,0:21:01,0:31:40,0:32:24,0:35:53,5:31:35,2832,0,23
+Emergency Medicine,PGY1,S0169399,2015-05-21,2:30:36,0:20:56,6:45:39,0:31:27,0:22:21,0:09:43,11:07:37,1408,0,24
+Emergency Medicine,PGY1,S0169399,2015-05-20,2:46:38,0:14:16,3:20:12,0:37:21,0:18:34,0:13:19,7:58:10,1520,0,16
+Emergency Medicine,PGY1,S0169399,2015-05-23,0:11:22,0:00:01,0:30:07,0:00:00,0:00:01,0:00:22,0:45:11,72,0,2
+Emergency Medicine,PGY1,S0169399,2015-05-22,2:51:30,0:23:49,4:18:03,0:28:34,0:16:18,0:14:39,9:12:26,1408,0,16
+General Medicine,PGY1,S0169385,2015-05-21,0:56:43,0:10:54,0:27:59,0:11:52,0:02:09,0:21:10,2:14:28,691,0,4
+General Medicine,PGY1,S0169383,2015-05-02,3:23:20,0:10:26,1:39:03,0:48:31,0:17:36,0:10:34,6:54:53,2208,0,17
+Night Team,PGY2+,S0181086,2016-09-11,0:26:29,0:14:13,0:00:00,0:07:35,0:18:32,0:04:57,1:15:28,312,46,15
+Night Team,PGY2+,S0181086,2016-09-12,3:07:26,1:27:04,0:00:15,2:02:43,0:38:54,0:06:56,8:18:09,2430,317,29
+Night Team,PGY2+,S0181086,2016-09-13,2:45:13,1:42:25,0:00:15,0:55:31,0:09:46,0:03:00,6:11:16,1717,587,18
+Night Team,PGY2+,S0181086,2016-09-14,3:18:23,1:22:11,0:00:08,1:15:27,0:18:09,0:05:33,6:43:53,1761,866,31
+Night Team,PGY2+,S0181086,2016-09-15,2:55:36,1:44:14,0:00:25,1:12:43,0:22:00,0:09:12,6:56:37,1815,70,34
+Night Team,PGY2+,S0181086,2016-09-16,2:52:51,1:00:37,0:00:05,1:14:35,0:38:21,0:04:26,6:16:05,1962,639,30
+Night Team,PGY2+,S0181086,2016-09-17,2:53:57,1:17:37,0:01:04,0:57:32,0:28:06,0:03:47,6:49:08,1989,535,29
+Night Team,PGY2+,S0181086,2016-09-18,3:38:00,0:40:45,0:00:04,0:37:48,0:16:11,0:15:23,6:11:42,1509,624,67
+Night Team,PGY2+,S0181086,2016-09-19,2:52:01,1:49:06,0:00:21,0:59:23,0:20:25,0:09:47,6:38:51,2304,1554,22
+Night Team,PGY2+,S0074196,2016-06-30,1:00:51,1:09:39,0:00:03,0:52:55,0:09:55,0:00:56,3:29:37,1725,67,15
+General Medicine,PGY1,S0195218,2016-09-12,0:08:40,0:07:38,0:00:01,0:02:47,0:00:25,0:00:00,0:22:53,268,0,16
+General Medicine,PGY1,S0195218,2016-09-13,1:27:05,1:01:00,0:00:13,0:59:22,0:44:55,0:07:31,5:01:46,1854,44,15
+General Medicine,PGY1,S0195218,2016-09-10,0:04:41,0:04:26,0:00:00,0:00:01,0:00:00,0:00:00,0:09:17,103,85,6
+General Medicine,PGY1,S0195218,2016-09-11,2:21:57,1:55:38,0:00:34,0:58:38,0:26:32,0:02:07,6:35:32,2402,2,17
+General Medicine,PGY1,S0195218,2016-09-16,2:21:02,1:25:58,0:00:12,0:30:17,0:24:22,0:03:17,5:14:45,1849,24,14
+General Medicine,PGY1,S0169386,2014-07-15,3:50:18,0:11:08,0:11:03,0:30:19,0:26:18,0:22:54,6:18:17,2558,153,18
+General Medicine,PGY1,S0195218,2016-09-14,2:27:41,1:42:05,0:01:46,1:21:43,0:25:25,0:15:15,7:31:12,2882,43,10
+General Medicine,PGY1,S0195218,2016-09-15,0:00:00,0:00:00,0:00:00,0:00:20,0:00:00,0:00:00,0:00:20,22,0,6
+General Medicine,PGY1,S0169386,2014-07-14,4:05:03,0:24:32,0:21:51,0:58:00,0:27:54,0:23:04,7:22:08,2720,147,12
+Emergency Medicine,PGY1,S0109179,2015-01-16,4:35:05,0:01:17,1:30:09,0:38:04,0:19:08,0:32:38,7:54:18,2503,0,42
+Emergency Medicine,PGY1,S0109179,2015-01-17,7:44:56,0:04:27,1:11:45,0:17:35,0:20:39,0:34:09,10:45:12,3252,0,48
+Emergency Medicine,PGY1,S0109179,2015-01-14,1:17:26,0:00:00,0:01:35,0:02:03,0:02:35,0:03:59,1:32:33,530,5,12
+Emergency Medicine,PGY1,S0109179,2015-01-15,5:48:05,0:03:06,1:28:20,0:15:42,0:16:37,0:20:46,8:35:38,2460,0,57
+Intensive Care Unit,PGY2+,S0181073,2016-10-16,0:00:00,0:00:00,0:00:00,0:00:03,0:00:00,0:00:00,0:00:07,26,0,7
+Intensive Care Unit,PGY2+,S0181073,2016-10-17,2:15:17,1:04:13,0:01:40,0:54:33,1:05:53,0:12:24,6:08:59,2016,0,17
+Intensive Care Unit,PGY2+,S0181073,2016-10-14,1:40:14,0:59:10,0:07:25,0:29:35,0:31:36,0:02:40,4:25:03,1299,124,19
+Emergency Medicine,PGY1,S0109179,2015-01-11,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,14,0,1
+Intensive Care Unit,PGY2+,S0181073,2016-10-18,2:44:10,0:30:34,0:04:06,0:23:39,0:20:11,0:02:16,4:46:32,1454,0,23
+Intensive Care Unit,PGY2+,S0181073,2016-10-19,2:25:07,0:41:23,0:12:16,0:48:59,0:52:21,0:04:40,5:45:59,1574,0,17
+Emergency Medicine,PGY1,S0109179,2015-01-18,3:09:29,0:00:18,1:19:29,0:14:48,0:19:21,0:10:32,5:37:59,1181,0,16
+Emergency Medicine,PGY1,S0109179,2015-01-19,4:46:22,0:01:01,2:32:44,0:15:54,0:13:46,0:28:35,8:48:15,2287,0,45
+General Medicine,PGY1,S0181078,2015-12-27,1:47:13,0:35:13,2:35:47,1:06:37,0:32:19,0:24:47,7:27:00,1812,230,11
+General Medicine,PGY1,S0181078,2015-12-26,2:10:52,0:58:23,2:31:40,0:58:27,0:29:27,0:41:01,8:33:41,2238,91,13
+General Medicine,PGY1,S0181078,2015-12-25,0:00:00,0:00:00,0:00:00,0:01:03,0:00:00,0:00:00,0:01:03,18,0,3
+General Medicine,PGY1,S0181078,2015-12-24,1:24:00,0:31:26,0:44:20,0:40:18,0:29:08,0:27:45,4:54:46,1455,0,6
+General Medicine,PGY1,S0181078,2015-12-23,2:53:43,1:10:37,1:59:34,1:37:26,0:30:37,0:29:10,9:19:12,2309,0,6
+General Medicine,PGY1,S0181078,2015-12-22,1:02:08,0:32:21,1:39:43,0:36:03,0:02:29,0:11:41,4:30:46,775,63,6
+General Medicine,PGY1,S0181078,2015-12-21,1:05:45,0:46:39,2:16:50,0:42:43,0:10:53,0:18:43,5:41:17,1043,0,7
+General Medicine,PGY1,S0181078,2015-12-20,0:27:52,0:24:51,1:41:00,0:01:39,0:00:08,0:02:23,2:49:17,536,534,13
+General Medicine,PGY1,S0181075,2015-11-18,0:05:53,0:04:32,0:00:16,0:06:48,0:01:25,0:00:15,0:21:13,303,277,9
+General Medicine,PGY1,S0181075,2015-11-19,2:51:50,1:27:25,1:07:55,1:17:15,0:46:16,0:28:53,8:36:10,3420,47,15
+General Medicine,PGY1,S0181078,2015-12-29,1:42:16,0:37:41,3:13:32,0:59:03,0:21:57,0:30:29,8:00:56,2165,195,18
+General Medicine,PGY1,S0181078,2015-12-28,2:23:10,0:42:25,2:05:11,1:13:41,0:23:17,0:48:28,8:26:53,2102,187,17
+General Medicine,PGY1,S0169411,2015-06-03,1:56:49,0:22:46,1:12:46,0:18:24,0:16:44,0:13:56,5:09:42,2058,0,18
+General Medicine,PGY1,S0169411,2015-06-02,2:49:44,0:57:44,2:32:31,1:02:40,0:38:03,0:16:02,8:48:47,2916,98,21
+General Medicine,PGY1,S0169411,2015-06-01,2:27:44,0:48:58,1:49:17,0:23:52,0:32:30,0:10:33,6:56:57,2541,0,24
+General Medicine,PGY1,S0169411,2015-06-07,3:22:47,1:14:51,3:50:04,0:33:26,0:45:59,0:26:36,10:48:29,3315,0,28
+General Medicine,PGY1,S0169411,2015-06-06,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,8,0,3
+General Medicine,PGY1,S0169411,2015-06-05,3:01:31,1:05:55,1:40:50,0:46:26,0:39:56,0:17:13,8:10:51,2578,0,16
+General Medicine,PGY1,S0169411,2015-06-04,1:02:11,0:16:20,0:07:40,0:25:15,0:14:16,0:03:53,2:26:57,992,0,9
+General Medicine,PGY1,S0169411,2015-06-09,1:53:36,0:36:23,1:46:42,0:23:22,0:21:21,0:13:47,5:39:05,1867,0,13
+General Medicine,PGY1,S0169411,2015-06-08,1:46:21,0:21:21,1:26:04,0:27:42,0:36:43,0:10:47,5:19:21,1963,163,15
+General Medicine,PGY1,S0163425,2015-05-29,2:51:52,0:54:14,1:08:03,0:32:01,0:26:00,0:19:42,6:34:29,2008,49,19
+General Medicine,PGY1,S0163425,2015-05-28,1:54:46,0:05:16,0:29:42,0:08:11,0:10:40,0:06:44,2:59:41,972,0,9
+Emergency Medicine,PGY1,S0181086,2015-10-07,0:43:15,0:10:07,2:22:34,0:07:24,0:09:47,0:03:00,3:48:39,512,248,15
+General Medicine,PGY1,S0163425,2015-05-25,0:07:48,0:00:10,0:00:34,0:00:23,0:00:12,0:01:23,0:10:35,70,66,5
+General Medicine,PGY1,S0163425,2015-05-24,3:01:29,0:38:43,2:22:57,0:37:47,0:18:21,0:50:24,8:10:45,2124,0,12
+General Medicine,PGY1,S0163425,2015-05-27,3:44:40,0:43:08,0:29:13,0:34:50,0:39:18,0:22:53,7:00:24,1976,0,22
+General Medicine,PGY1,S0163425,2015-05-26,1:14:22,0:03:34,0:29:37,0:02:48,0:05:49,0:05:19,2:05:53,570,28,8
+Emergency Medicine,PGY1,S0195216,2016-11-10,6:49:29,0:44:28,0:05:30,0:39:06,0:49:54,0:01:52,10:04:41,2549,0,53
+Night Team,PGY1,S0181059,2015-11-29,2:23:47,0:55:25,0:44:04,0:27:13,0:15:11,0:32:47,5:38:26,2453,46,115
+Night Team,PGY1,S0181059,2015-11-28,2:45:39,1:26:53,0:47:48,0:23:36,0:13:28,0:33:49,6:27:55,2830,0,110
+Night Team,PGY1,S0181059,2015-11-27,3:18:26,1:17:16,0:41:17,0:25:50,0:15:21,0:55:52,7:07:06,3849,134,125
+Night Team,PGY1,S0181059,2015-11-26,2:11:36,0:50:44,0:25:22,1:00:18,0:26:24,0:47:31,6:09:11,3308,129,68
+Night Team,PGY1,S0181059,2015-11-25,1:04:58,1:09:16,0:10:03,0:10:16,0:12:27,0:41:05,3:45:37,1668,80,55
+Night Team,PGY1,S0181059,2015-11-24,1:34:30,0:21:23,0:07:59,0:09:03,0:11:46,0:25:22,2:56:35,1668,208,63
+Night Team,PGY1,S0181059,2015-11-23,3:06:21,0:37:20,0:43:50,0:28:48,0:36:14,0:56:19,6:55:48,3655,30,109
+Night Team,PGY1,S0181059,2015-11-22,2:59:04,0:56:54,0:21:16,0:39:02,0:29:08,0:30:38,6:16:33,2412,44,63
+General Medicine,PGY2+,S0169384,2016-06-22,2:02:59,1:20:04,0:00:00,0:39:30,0:28:19,0:06:24,5:11:58,2260,153,22
+General Medicine,PGY1,S0108268,2014-12-26,1:42:12,0:00:52,0:42:04,0:33:52,0:11:54,0:06:30,3:27:58,1070,0,8
+General Medicine,PGY2+,S0169401,2015-12-25,3:58:23,1:35:07,0:32:32,0:46:04,0:47:03,0:51:02,9:34:02,4319,142,34
+Intensive Care Unit,PGY2+,S0169394,2015-11-23,1:45:25,0:15:42,0:06:04,0:12:16,0:29:19,0:48:17,3:56:12,1154,0,11
+Intensive Care Unit,PGY2+,S0169394,2015-11-22,0:04:07,0:05:35,0:00:21,0:00:12,0:00:43,0:03:27,0:15:21,164,153,7
+Intensive Care Unit,PGY2+,S0169394,2015-11-21,2:08:04,0:28:36,0:12:11,0:12:11,0:16:18,0:47:24,4:28:48,1132,324,10
+Intensive Care Unit,PGY2+,S0169394,2015-11-20,1:47:51,0:56:04,0:17:36,0:36:55,0:16:51,0:41:38,4:53:41,1482,65,12
+Intensive Care Unit,PGY2+,S0169394,2015-11-27,2:10:43,0:42:22,0:16:16,0:31:35,0:35:26,0:41:20,5:29:27,1619,0,17
+Intensive Care Unit,PGY2+,S0169394,2015-11-26,0:05:38,0:04:01,0:00:04,0:00:27,0:00:37,0:05:34,0:18:51,161,148,11
+Intensive Care Unit,PGY2+,S0169394,2015-11-25,2:03:26,0:45:52,0:10:42,0:13:29,0:17:28,0:44:05,4:33:24,1234,0,14
+Intensive Care Unit,PGY2+,S0169394,2015-11-24,1:56:32,0:31:18,0:23:30,0:40:56,0:44:12,0:28:35,5:09:28,1472,9,14
+Intensive Care Unit,PGY2+,S0169394,2015-11-29,0:00:33,0:00:46,0:00:01,0:00:12,0:00:27,0:00:15,0:02:17,50,30,6
+Intensive Care Unit,PGY2+,S0169394,2015-11-28,2:39:28,1:02:15,0:31:16,0:17:09,0:16:40,1:00:30,6:13:49,1526,245,15
+General Medicine,PGY1,S0108287,2015-02-27,1:37:33,0:22:42,1:38:49,0:35:17,0:23:42,0:06:49,5:19:57,1498,0,11
+General Medicine,PGY1,S0108287,2015-02-26,2:27:46,0:39:47,2:06:59,0:21:11,0:13:32,0:28:17,6:39:13,1556,0,11
+General Medicine,PGY1,S0108287,2015-02-25,1:18:28,0:07:33,1:08:22,0:25:21,0:11:32,0:04:50,3:39:00,835,0,7
+General Medicine,PGY1,S0108287,2015-02-24,2:08:42,0:00:34,2:10:33,1:04:19,0:23:55,0:08:25,6:17:57,1260,0,14
+General Medicine,PGY1,S0108287,2015-02-23,2:09:54,0:13:37,1:56:40,1:02:17,0:23:39,0:11:28,6:25:08,1536,0,9
+General Medicine,PGY1,S0108287,2015-02-22,0:00:00,0:00:00,0:00:00,0:00:52,0:00:00,0:00:00,0:00:52,4,0,2
+General Medicine,PGY1,S0108287,2015-02-21,2:48:26,0:37:19,2:51:28,0:57:07,0:15:50,0:18:26,8:21:39,1751,0,11
+General Medicine,PGY1,S0108287,2015-02-20,1:20:58,0:07:57,0:52:23,0:17:39,0:07:19,0:02:28,3:02:58,624,0,10
+Intensive Care Unit,PGY2+,S0169404,2015-08-29,3:20:12,0:12:11,0:23:15,0:21:08,0:28:09,0:22:19,5:33:58,3157,796,15
+General Medicine,PGY1,S0108287,2015-02-28,3:24:51,0:55:32,1:46:41,0:23:48,0:22:13,0:33:18,7:51:38,1378,0,19
+Night Team,PGY2+,S0181059,2016-08-31,3:12:02,1:52:34,0:00:02,0:34:49,0:15:07,0:07:41,6:31:30,3517,0,107
+Night Team,PGY2+,S0094535,2016-06-15,2:04:30,0:52:40,0:00:11,0:32:07,0:09:06,0:04:27,4:25:00,998,0,23
+General Medicine,PGY1,S0169399,2014-12-30,1:47:15,0:04:59,1:53:05,0:04:47,0:11:44,0:04:12,4:22:56,695,0,7
+Night Team,PGY2+,S0094535,2016-06-13,2:50:23,1:07:06,0:00:08,0:20:48,0:21:29,0:03:40,5:32:29,1661,107,33
+Night Team,PGY2+,S0094535,2016-06-12,3:18:37,1:12:22,0:00:12,0:23:24,0:09:16,0:07:37,5:54:59,1406,0,50
+Intensive Care Unit,PGY2+,S0169404,2015-08-26,4:27:17,0:16:27,0:27:54,0:42:10,0:21:15,0:33:58,7:09:07,3167,28,23
+General Medicine,PGY1,S0172645,2015-09-30,0:15:10,0:36:04,0:41:12,0:06:05,0:04:38,0:17:12,2:06:06,585,35,8
+General Medicine,PGY1,S0169411,2015-01-24,1:51:45,0:09:10,1:01:39,0:11:49,0:03:06,0:05:02,3:36:43,1034,0,7
+General Medicine,PGY1,S0169411,2015-01-25,4:47:40,0:09:09,1:15:13,0:34:12,0:20:12,0:11:49,7:44:07,2026,0,12
+General Medicine,PGY1,S0169411,2015-01-26,3:53:16,0:02:16,0:57:54,1:23:27,0:20:04,0:04:31,7:04:50,2296,26,15
+General Medicine,PGY1,S0169411,2015-01-27,4:27:10,0:06:15,1:50:30,0:57:10,0:14:04,0:04:14,7:57:48,2277,19,16
+General Medicine,PGY1,S0169411,2015-01-20,4:39:12,0:11:25,1:08:37,0:55:37,0:19:27,0:11:35,7:40:32,2813,0,20
+General Medicine,PGY1,S0169411,2015-01-21,3:02:58,0:04:31,1:12:35,0:54:03,0:27:53,0:06:21,6:09:45,1996,48,10
+General Medicine,PGY1,S0169411,2015-01-22,3:47:03,0:09:10,1:18:04,0:33:45,0:20:55,0:06:47,6:34:48,1797,51,13
+General Medicine,PGY1,S0169411,2015-01-23,0:09:33,0:00:06,0:13:04,0:04:02,0:00:26,0:00:00,0:28:35,199,189,7
+General Medicine,PGY1,S0169411,2015-01-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+General Medicine,PGY1,S0169411,2015-01-29,2:14:12,0:51:45,1:42:36,0:25:39,0:06:55,0:02:21,5:56:52,2417,0,29
+General Medicine,PGY1,S0181067,2015-07-03,1:52:05,0:22:00,1:37:01,0:43:11,0:20:42,0:25:55,5:46:15,1786,0,8
+General Medicine,PGY1,S0181067,2015-07-02,1:31:38,0:12:09,1:11:16,0:25:30,0:32:16,0:41:10,4:55:27,985,0,4
+General Medicine,PGY1,S0181067,2015-07-01,1:46:48,0:12:18,1:29:56,0:24:08,0:26:36,0:19:19,5:01:58,1320,0,7
+Emergency Medicine,PGY1,S0074196,2015-10-29,5:36:01,1:11:47,1:36:00,0:22:37,0:33:24,0:44:17,10:45:26,2166,0,23
+Intensive Care Unit,PGY2+,S0181525,2016-10-08,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,20,0,5
+Intensive Care Unit,PGY2+,S0181525,2016-10-05,2:37:34,0:51:40,0:00:31,0:28:52,0:41:09,0:08:06,5:27:58,1760,0,21
+Intensive Care Unit,PGY2+,S0181525,2016-10-04,1:40:27,0:27:15,0:09:33,0:12:28,0:29:13,0:08:38,3:33:23,802,0,14
+Intensive Care Unit,PGY2+,S0181525,2016-10-07,1:36:47,0:35:17,0:10:57,0:21:24,0:24:36,0:00:22,3:36:59,861,0,12
+Intensive Care Unit,PGY2+,S0181525,2016-10-06,1:28:40,1:04:13,0:03:15,0:11:27,0:32:42,0:07:28,3:56:46,1128,0,11
+Intensive Care Unit,PGY2+,S0181525,2016-10-01,2:01:05,0:26:50,0:00:00,0:35:30,0:34:22,0:15:38,4:50:00,1444,0,18
+Emergency Medicine,PGY1,S0074196,2015-10-26,3:11:47,1:25:39,1:27:25,0:31:32,0:26:05,0:26:01,8:16:37,1869,0,24
+Intensive Care Unit,PGY2+,S0181525,2016-10-03,0:01:50,0:01:59,0:00:00,0:00:07,0:00:00,0:00:00,0:04:30,47,19,8
+Emergency Medicine,PGY1,S0074196,2015-10-24,4:39:26,0:27:23,1:34:15,0:19:45,0:29:13,0:27:51,9:10:03,2257,0,32
+General Medicine,PGY1,S0181067,2015-07-05,1:07:19,0:11:03,0:23:07,0:08:22,0:07:39,0:17:17,2:30:21,660,0,4
+General Medicine,PGY1,S0196595,2016-06-27,1:24:17,1:01:34,0:00:27,0:41:15,0:20:55,0:04:03,4:06:28,1087,0,5
+General Medicine,PGY1,S0181067,2015-07-04,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,1
+Emergency Medicine,PGY1,S0181088,2016-02-27,1:06:32,0:38:18,0:00:27,0:07:48,0:10:43,0:00:05,2:41:47,717,0,18
+Intensive Care Unit,PGY2+,S0181081,2016-09-30,1:58:44,1:28:25,0:06:33,0:40:10,0:28:14,0:01:14,5:28:41,1729,0,20
+Emergency Medicine,PGY2+,S0169394,2016-07-29,1:44:23,0:38:34,0:00:16,0:17:24,0:14:29,0:04:34,3:48:05,1380,202,28
+Emergency Medicine,PGY2+,S0169394,2016-07-28,0:02:22,0:00:36,0:00:00,0:00:00,0:00:00,0:00:00,0:12:06,42,34,2
+Emergency Medicine,PGY2+,S0169394,2016-07-23,0:06:02,0:00:21,0:00:00,0:00:00,0:00:00,0:01:05,0:07:42,41,0,5
+Emergency Medicine,PGY2+,S0169394,2016-07-22,3:18:59,0:52:11,0:03:15,0:39:47,0:37:29,0:00:48,6:47:38,2202,0,18
+Emergency Medicine,PGY2+,S0169394,2016-07-21,0:26:52,0:31:10,0:00:00,0:00:00,0:00:00,0:00:26,1:06:45,263,263,25
+Emergency Medicine,PGY2+,S0169394,2016-07-20,0:09:11,0:03:47,0:00:00,0:00:00,0:00:33,0:00:13,0:16:18,137,111,5
+Emergency Medicine,PGY2+,S0169394,2016-07-27,0:02:41,0:00:09,0:00:00,0:00:16,0:00:01,0:00:01,0:04:10,34,26,7
+Emergency Medicine,PGY2+,S0169394,2016-07-26,2:58:13,0:43:56,0:02:09,0:26:29,0:33:52,0:02:43,5:31:02,2093,0,41
+Emergency Medicine,PGY2+,S0169394,2016-07-25,2:35:05,0:34:10,0:03:02,0:23:53,0:36:07,0:12:13,5:04:07,2082,0,10
+Emergency Medicine,PGY2+,S0169394,2016-07-24,2:47:43,0:27:11,0:03:27,0:35:16,0:56:55,0:06:27,6:09:06,1730,0,19
+Intensive Care Unit,PGY2+,S0181064,2016-10-10,1:48:22,0:47:39,0:00:02,0:29:45,0:29:27,0:12:28,4:19:22,1443,50,35
+General Medicine,PGY1,S0169389,2014-08-02,2:57:33,0:06:31,0:17:47,0:15:43,0:16:29,0:15:26,4:33:45,1167,46,39
+General Medicine,PGY1,S0169389,2014-08-03,2:53:48,0:04:02,0:15:05,0:28:13,0:17:00,0:22:50,4:58:28,1293,0,10
+General Medicine,PGY1,S0169389,2014-08-08,3:40:16,0:01:33,0:08:40,0:21:36,0:12:37,0:16:57,5:13:36,1324,706,13
+General Medicine,PGY1,S0169389,2014-08-09,3:40:11,0:17:32,0:18:41,0:09:44,0:34:00,0:40:18,6:05:16,1551,0,32
+Night Team,PGY2+,S0172085,2016-10-19,1:21:58,0:28:45,0:00:04,0:18:53,0:07:23,0:20:30,2:48:27,1237,0,43
+Night Team,PGY2+,S0172085,2016-10-18,0:12:05,0:21:55,0:00:06,0:19:48,0:07:10,0:01:45,1:23:54,547,0,14
+General Medicine,PGY2+,S0169383,2016-07-02,0:00:00,0:00:00,0:00:00,0:00:28,0:00:00,0:00:00,0:00:28,16,0,6
+Night Team,PGY2+,S0172085,2016-10-11,2:44:04,2:17:09,0:00:06,0:42:17,0:06:24,0:10:55,6:33:11,2088,1,26
+Night Team,PGY2+,S0172085,2016-10-10,2:43:32,2:08:45,0:03:30,0:24:39,0:06:45,0:16:44,6:36:06,3004,0,41
+Night Team,PGY2+,S0172085,2016-10-13,1:57:09,1:20:13,0:01:44,0:35:39,0:24:09,0:02:14,4:54:54,1731,316,47
+Night Team,PGY2+,S0172085,2016-10-12,2:33:17,1:40:47,0:00:07,0:50:48,0:21:28,0:06:10,6:13:43,2115,0,57
+Night Team,PGY2+,S0172085,2016-10-15,2:03:06,2:22:06,0:00:07,0:42:19,0:17:17,0:14:39,6:11:13,2070,0,39
+Night Team,PGY2+,S0172085,2016-10-14,1:36:50,1:41:32,0:00:36,0:27:21,0:16:37,0:16:03,4:37:47,1867,0,34
+Night Team,PGY2+,S0172085,2016-10-17,0:43:54,0:18:39,0:00:42,0:09:18,0:01:30,0:04:00,1:21:42,662,0,18
+Night Team,PGY2+,S0172085,2016-10-16,1:49:47,1:42:17,0:02:48,0:31:11,0:10:52,0:09:03,5:15:09,1984,0,28
+General Medicine,PGY1,S0182038,2016-09-30,1:15:42,0:23:59,0:00:07,0:41:12,0:05:11,0:01:17,2:57:31,922,0,10
+General Medicine,PGY2+,S0169383,2016-07-05,3:35:57,1:46:57,0:00:09,0:27:37,0:17:10,0:12:01,6:51:18,2445,76,33
+Night Team,PGY1,S0181063,2015-08-01,1:07:55,0:22:29,1:05:20,0:25:16,0:33:38,0:12:53,4:04:13,972,0,15
+General Medicine,PGY1,S0195229,2016-08-20,0:00:17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:25,12,0,3
+General Medicine,PGY2+,S0169383,2016-07-07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+General Medicine,PGY2+,S0169383,2016-07-06,1:17:59,0:37:40,0:00:27,0:19:14,0:07:18,0:00:18,2:36:21,1189,17,13
+Emergency Medicine,PGY1,S0181088,2016-02-16,1:02:15,0:07:48,0:55:08,0:07:29,0:05:38,0:20:11,2:54:36,1088,0,7
+Emergency Medicine,PGY1,S0181088,2016-02-15,1:21:04,0:11:46,1:05:24,0:11:43,0:25:21,0:16:02,4:03:29,1359,0,9
+Intensive Care Unit,PGY2+,S0169395,2015-10-19,2:11:48,1:26:53,0:21:35,0:26:04,0:28:09,0:11:45,5:27:30,2048,0,37
+Emergency Medicine,PGY1,S0181088,2016-02-13,1:55:29,0:35:32,3:26:39,0:35:07,0:21:07,0:31:10,8:20:40,1870,0,15
+Emergency Medicine,PGY1,S0181088,2016-02-12,1:38:10,0:37:55,2:56:24,0:25:46,0:18:20,0:34:16,7:41:29,2160,0,13
+Emergency Medicine,PGY1,S0181088,2016-02-19,0:56:47,0:05:28,1:00:47,0:20:41,0:23:11,0:14:58,3:19:24,967,0,8
+General Medicine,PGY1,S0176040,2015-12-04,1:13:03,1:15:11,1:45:12,0:52:29,0:12:49,0:16:54,6:09:42,1750,7,13
+General Medicine,PGY1,S0176040,2015-12-05,0:00:00,0:00:00,0:00:00,0:01:21,0:00:00,0:00:00,0:01:21,6,0,2
+General Medicine,PGY1,S0169399,2014-07-08,1:33:12,0:03:33,0:17:50,0:24:02,0:14:14,0:09:49,3:02:18,769,0,6
+General Medicine,PGY1,S0169399,2014-07-09,1:46:03,0:01:25,0:21:22,0:38:09,0:24:12,0:03:11,3:50:25,964,115,12
+General Medicine,PGY1,S0176040,2015-12-02,0:22:57,0:13:38,0:21:20,0:05:55,0:02:11,0:11:57,1:25:46,421,0,4
+General Medicine,PGY1,S0176040,2015-12-03,1:39:34,1:51:28,1:02:08,0:56:51,0:25:45,0:46:42,7:24:16,2638,0,8
+General Medicine,PGY1,S0169399,2014-07-05,1:17:03,0:00:19,0:07:43,0:29:55,0:20:59,0:00:59,2:43:16,679,0,7
+General Medicine,PGY1,S0169399,2014-07-06,1:44:49,0:00:00,0:08:23,0:17:26,0:10:15,0:05:46,2:39:31,583,40,7
+General Medicine,PGY1,S0169399,2014-07-07,2:39:24,0:13:46,0:13:22,0:39:11,0:23:39,0:05:11,4:25:04,994,20,20
+General Medicine,PGY1,S0169399,2014-07-01,2:08:12,0:00:00,0:13:08,0:20:33,0:21:21,0:05:06,3:29:10,797,0,6
+General Medicine,PGY1,S0169399,2014-07-02,2:22:27,0:07:00,0:22:58,0:45:09,0:14:31,0:02:18,4:19:39,1166,0,8
+General Medicine,PGY1,S0169399,2014-07-03,0:00:00,0:00:00,0:00:00,0:00:36,0:00:00,0:00:00,0:00:36,12,0,3
+Emergency Medicine,PGY1,S0181526,2015-06-28,0:55:22,0:04:05,0:56:02,0:03:36,0:09:03,0:12:21,3:25:49,1077,800,70
+Emergency Medicine,PGY1,S0181526,2015-06-29,0:56:44,0:12:33,0:41:26,0:02:11,0:11:50,0:28:14,2:45:13,730,0,12
+Emergency Medicine,PGY1,S0181526,2015-06-26,2:14:29,0:39:14,2:24:12,0:08:17,0:20:11,1:15:15,7:50:27,1504,0,17
+Emergency Medicine,PGY1,S0181526,2015-06-27,1:52:59,0:20:16,1:04:47,0:04:02,0:19:22,1:00:37,4:55:26,1003,0,8
+Night Team,PGY2+,S0181087,2016-09-10,2:10:55,1:11:12,0:00:22,0:39:46,0:23:34,0:09:12,5:14:26,1834,0,30
+Intensive Care Unit,PGY2+,S0181077,2016-10-16,1:36:40,0:31:45,0:05:39,0:04:01,0:12:44,0:00:25,2:48:22,960,0,10
+Intensive Care Unit,PGY2+,S0181077,2016-10-17,2:54:03,1:02:11,0:14:36,0:18:38,0:27:25,0:16:40,6:11:30,1546,0,19
+Intensive Care Unit,PGY2+,S0181077,2016-10-14,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,7,0,1
+Intensive Care Unit,PGY2+,S0181077,2016-10-15,2:55:29,1:00:47,0:00:07,0:21:26,0:31:59,0:01:48,5:36:29,1635,0,35
+Intensive Care Unit,PGY2+,S0181077,2016-10-12,2:57:47,0:29:31,0:02:50,0:17:31,0:21:24,0:02:09,4:34:05,1829,0,30
+General Medicine,PGY1,S0094959,2014-11-09,0:11:08,0:25:23,0:00:00,0:00:00,0:00:04,0:04:53,0:45:23,450,448,29
+General Medicine,PGY1,S0169385,2014-10-29,4:01:35,0:19:03,0:29:40,1:01:00,0:20:39,0:19:13,6:53:22,2309,365,9
+General Medicine,PGY1,S0169385,2014-10-28,3:36:58,0:19:25,0:23:23,0:37:02,0:28:12,0:34:00,6:19:07,2034,113,9
+General Medicine,PGY1,S0169385,2014-10-27,3:05:39,0:06:29,0:25:15,0:43:27,0:09:37,0:31:56,5:24:53,1277,93,4
+General Medicine,PGY1,S0169385,2014-10-26,0:06:30,0:00:11,0:04:58,0:00:04,0:00:01,0:04:22,0:17:04,153,149,14
+General Medicine,PGY1,S0169385,2014-10-25,3:10:31,0:09:06,0:25:59,0:55:59,0:15:53,0:43:47,6:09:25,1912,171,10
+General Medicine,PGY1,S0169385,2014-10-24,3:33:43,0:15:30,0:24:52,0:33:55,0:25:11,0:33:04,6:04:46,1762,0,8
+General Medicine,PGY1,S0169385,2014-10-23,3:19:36,0:11:12,0:20:08,1:20:19,0:48:39,0:30:35,6:51:57,2253,0,8
+General Medicine,PGY1,S0169385,2014-10-22,0:14:49,0:05:58,0:10:18,0:03:13,0:04:27,0:05:18,0:46:55,355,341,7
+Intensive Care Unit,PGY2+,S0181077,2016-10-18,2:33:35,1:08:44,0:08:16,0:45:01,0:32:53,0:05:39,6:13:52,2229,143,28
+General Medicine,PGY1,S0169385,2014-10-20,2:54:36,0:09:13,0:26:49,0:32:46,0:12:35,0:36:16,5:04:21,1209,0,4
+Night Team,PGY1,S0195205,2016-09-29,1:09:02,0:12:09,0:00:00,0:19:54,0:13:49,0:02:05,2:00:36,570,101,35
+Night Team,PGY1,S0195205,2016-09-28,1:50:00,0:32:08,0:00:05,0:35:01,0:16:48,0:15:27,3:37:05,1129,167,33
+Night Team,PGY1,S0195205,2016-09-25,2:57:32,1:05:31,0:00:03,0:35:47,0:27:04,0:24:28,6:00:51,2119,300,55
+Night Team,PGY1,S0195205,2016-09-24,2:57:11,1:15:55,0:01:33,0:31:35,0:23:59,0:12:12,6:12:27,2214,426,39
+Night Team,PGY1,S0195205,2016-09-27,3:07:54,1:13:38,0:00:05,0:41:08,0:34:02,0:04:45,6:04:06,1654,182,43
+Night Team,PGY1,S0195205,2016-09-26,2:57:47,1:01:29,0:00:00,0:30:06,0:23:17,0:08:30,5:24:56,1996,458,54
+Night Team,PGY1,S0195205,2016-09-21,2:12:30,1:12:00,0:00:01,0:31:09,0:25:15,0:11:44,4:49:45,1697,117,65
+Night Team,PGY1,S0195205,2016-09-20,0:57:00,0:03:20,0:00:00,0:13:53,0:13:02,0:15:33,1:49:24,530,1,22
+Night Team,PGY1,S0195205,2016-09-23,3:43:53,1:44:45,0:00:04,0:42:06,0:45:24,0:10:26,7:53:07,2363,201,64
+Night Team,PGY1,S0195205,2016-09-22,1:49:29,1:00:51,0:00:01,0:17:12,0:45:10,0:15:50,4:27:11,1346,430,53
+General Medicine,PGY1,S0181060,2015-08-09,1:37:27,0:21:51,0:48:06,0:54:20,0:26:11,0:14:22,4:57:46,1580,80,12
+Night Team,PGY1,S0169411,2014-12-06,1:31:14,0:03:39,1:33:32,0:14:28,0:10:51,0:01:56,3:51:39,953,0,37
+Night Team,PGY1,S0056755,2014-12-20,1:51:45,0:11:28,1:33:00,0:19:22,0:14:39,0:19:13,4:47:52,1054,0,24
+Night Team,PGY1,S0169411,2014-12-04,3:07:12,0:03:30,1:06:12,0:33:52,0:19:50,0:04:48,5:29:30,1512,2,45
+Intensive Care Unit,PGY2+,S0181088,2016-09-27,2:15:18,1:39:55,0:00:02,0:21:38,0:26:45,0:09:07,5:25:51,2149,0,19
+Intensive Care Unit,PGY2+,S0181088,2016-09-26,0:13:36,0:00:21,0:00:00,0:00:03,0:00:00,0:00:06,0:14:32,89,0,8
+Intensive Care Unit,PGY2+,S0181088,2016-09-25,1:22:40,0:21:09,0:03:53,0:23:52,0:17:27,0:00:08,2:38:56,755,0,10
+Intensive Care Unit,PGY2+,S0181088,2016-09-24,2:45:18,0:32:34,0:21:08,0:09:28,0:25:12,0:06:55,4:47:52,1420,0,15
+Intensive Care Unit,PGY2+,S0181088,2016-09-23,2:22:22,0:27:31,0:00:00,0:55:46,0:31:15,0:15:30,4:57:28,1894,0,17
+Intensive Care Unit,PGY2+,S0181088,2016-09-22,1:53:37,0:33:37,0:07:36,0:11:51,0:17:09,0:12:54,3:37:31,1015,135,21
+Intensive Care Unit,PGY2+,S0181088,2016-09-21,1:51:32,0:28:47,0:16:25,0:21:13,0:30:40,0:02:11,3:42:51,1149,0,22
+Intensive Care Unit,PGY2+,S0181088,2016-09-20,2:12:12,0:27:46,0:09:50,0:29:04,0:22:37,0:14:00,4:06:56,1176,0,17
+Intensive Care Unit,PGY2+,S0181088,2016-09-29,1:24:28,0:12:34,0:10:17,0:05:59,0:07:00,0:01:15,2:19:12,659,0,13
+Intensive Care Unit,PGY2+,S0181088,2016-09-28,2:19:13,0:28:04,0:08:37,0:12:55,0:26:02,0:12:04,4:13:12,1463,0,14
+General Medicine,PGY1,S0181083,2016-02-18,2:39:16,1:08:09,1:38:06,1:04:55,0:19:26,0:45:14,8:32:19,2786,271,17
+General Medicine,PGY1,S0181083,2016-02-19,0:26:57,0:30:03,0:38:52,0:23:17,0:04:53,0:27:08,2:57:52,1136,168,16
+General Medicine,PGY1,S0181083,2016-02-16,0:04:15,0:00:00,0:00:00,0:00:00,0:00:00,0:00:33,0:04:54,79,0,1
+General Medicine,PGY1,S0181083,2016-02-17,2:00:56,1:39:09,1:39:13,1:01:03,0:46:41,1:05:46,8:59:35,2771,84,16
+General Medicine,PGY1,S0181083,2016-02-14,1:02:50,0:29:40,0:15:13,0:30:08,0:13:29,0:26:50,3:29:52,1217,0,12
+General Medicine,PGY1,S0181083,2016-02-15,2:08:23,1:35:05,2:15:40,0:35:34,0:20:40,0:40:35,8:03:21,2659,0,22
+General Medicine,PGY1,S0181083,2016-02-12,1:32:29,1:14:24,1:19:44,1:11:06,0:27:05,1:04:41,7:55:53,2370,0,16
+General Medicine,PGY1,S0181083,2016-02-13,0:59:10,1:02:02,1:43:10,1:10:24,0:32:13,0:24:30,6:25:07,1778,15,8
+General Medicine,PGY1,S0181083,2016-02-10,1:58:13,1:08:22,2:06:17,1:06:01,0:26:13,1:05:47,8:53:47,2371,61,24
+General Medicine,PGY1,S0181083,2016-02-11,2:08:57,0:57:33,1:20:20,0:21:40,0:35:09,0:48:06,6:58:47,1999,670,13
+General Medicine,PGY1,S0079337,2014-12-26,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,2
+General Medicine,PGY1,S0079337,2014-12-27,4:03:37,0:04:16,1:34:27,0:43:52,0:41:56,0:05:52,8:02:02,3453,0,14
+General Medicine,PGY1,S0181086,2015-12-19,2:52:39,1:26:19,3:11:52,1:35:46,0:44:40,0:23:33,11:11:56,2809,274,9
+General Medicine,PGY1,S0181070,2015-09-09,3:24:55,0:09:36,2:47:30,0:46:45,0:18:17,0:17:04,7:59:53,2212,0,13
+Emergency Medicine,PGY1,S0169406,2015-01-20,2:50:38,0:10:25,0:45:34,0:10:10,0:10:26,0:09:46,4:40:41,1038,0,24
+General Medicine,PGY1,S0181077,2015-12-06,1:04:03,0:45:55,0:59:37,0:08:46,0:18:02,0:34:33,4:06:29,1597,145,9
+General Medicine,PGY1,S0079640,2016-08-21,1:20:57,0:45:04,0:00:07,0:10:09,0:09:42,0:00:11,2:39:06,781,0,6
+General Medicine,PGY1,S0181082,2016-04-16,2:10:52,1:08:08,0:06:34,0:38:44,0:31:51,0:02:12,5:13:54,1578,0,11
+General Medicine,PGY1,S0181070,2015-09-01,1:38:25,0:02:35,1:02:19,0:38:02,0:05:09,0:04:01,3:48:10,1318,0,6
+General Medicine,PGY1,S0079337,2014-12-22,4:08:08,0:01:27,1:43:24,0:51:13,0:31:58,0:01:50,7:46:00,2525,0,12
+General Medicine,PGY1,S0181070,2015-09-02,1:49:03,0:10:57,0:49:04,0:18:26,0:08:32,0:12:35,3:45:06,1044,0,10
+General Medicine,PGY1,S0181070,2015-09-05,1:20:46,0:09:10,0:55:51,0:19:37,0:07:02,0:12:20,3:20:43,1073,0,6
+General Medicine,PGY1,S0181082,2016-04-15,0:17:10,0:03:51,0:00:05,0:00:05,0:00:57,0:00:00,0:33:02,710,704,85
+General Medicine,PGY1,S0181070,2015-09-07,3:34:56,0:08:11,0:56:43,0:35:04,0:19:06,0:06:51,6:12:13,2223,0,12
+General Medicine,PGY1,S0181070,2015-09-06,1:50:09,0:07:29,0:48:47,0:28:15,0:16:09,0:06:38,3:56:05,1230,0,6
+Intensive Care Unit,PGY2+,S0181061,2016-09-08,2:42:59,0:42:45,0:00:18,1:00:15,0:20:42,0:01:20,5:27:55,1306,0,15
+General Medicine,PGY1,S0094959,2015-01-19,5:12:20,0:04:51,1:15:29,0:21:15,0:25:16,0:20:19,8:01:57,3694,0,42
+General Medicine,PGY1,S0094959,2015-01-18,2:55:23,0:06:41,1:16:55,0:12:54,0:21:16,0:22:06,5:27:34,2270,0,19
+General Medicine,PGY1,S0079337,2014-12-20,3:44:19,0:08:47,1:18:57,0:56:55,0:33:37,0:18:44,7:45:31,2550,0,10
+General Medicine,PGY1,S0181082,2016-04-13,0:55:18,0:35:19,0:00:07,0:28:25,0:13:46,0:01:03,2:29:48,834,218,6
+General Medicine,PGY1,S0079337,2014-12-21,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181082,2016-04-12,3:05:01,1:29:03,0:00:22,0:50:11,0:29:57,0:07:47,6:28:24,2000,0,10
+General Medicine,PGY1,S0181082,2016-04-11,1:18:07,0:23:01,0:00:08,0:32:11,0:02:29,0:00:07,2:32:54,688,3,3
+General Medicine,PGY1,S0169386,2014-12-31,0:02:44,0:06:19,0:00:01,0:03:34,0:00:00,0:12:42,0:29:19,258,234,10
+General Medicine,PGY1,S0169386,2014-12-30,3:31:34,0:18:21,0:45:33,0:58:01,1:00:37,1:41:10,9:05:44,3278,46,14
+General Medicine,PGY1,S0181082,2016-04-10,1:22:34,0:46:42,0:00:45,0:36:47,0:08:27,0:03:20,3:20:46,1023,0,17
+General Medicine,PGY1,S0181086,2015-12-11,3:23:13,1:05:53,1:44:19,1:50:47,0:47:01,0:30:32,10:09:07,3378,134,19
+Intensive Care Unit,PGY2+,S0163662,2016-05-25,1:38:08,0:21:23,0:22:15,0:07:46,0:29:10,0:06:54,3:45:32,1051,18,21
+Intensive Care Unit,PGY2+,S0163662,2016-05-24,1:22:08,0:33:44,0:04:53,0:14:45,0:30:02,0:07:08,3:20:05,972,129,18
+Intensive Care Unit,PGY2+,S0163662,2016-05-27,2:22:51,0:27:18,0:00:00,1:14:40,0:56:36,0:12:40,6:07:19,1870,16,22
+General Medicine,PGY1,S0079640,2016-08-29,1:06:42,1:05:41,0:00:16,0:37:15,0:28:12,0:06:06,3:56:14,1344,0,12
+Intensive Care Unit,PGY2+,S0163662,2016-05-21,1:11:53,0:12:46,0:06:51,0:15:10,0:25:39,0:08:54,2:35:59,648,0,10
+Intensive Care Unit,PGY2+,S0163662,2016-05-20,0:00:00,0:00:00,0:00:00,0:00:10,0:00:00,0:00:00,0:00:10,7,0,2
+Intensive Care Unit,PGY2+,S0163662,2016-05-23,1:25:55,0:38:01,0:04:58,0:29:20,0:28:41,0:06:48,3:51:44,1126,0,16
+Intensive Care Unit,PGY2+,S0163662,2016-05-22,1:13:52,0:21:15,0:00:00,0:53:44,0:39:47,0:08:33,3:51:59,1503,0,16
+General Medicine,PGY1,S0181074,2015-07-30,2:05:35,1:17:24,0:42:20,2:12:19,0:27:00,0:19:15,7:44:21,2586,239,15
+General Medicine,PGY1,S0181074,2015-07-31,1:34:32,1:12:47,0:54:20,1:15:50,0:19:50,0:10:33,5:56:51,2365,109,24
+Intensive Care Unit,PGY2+,S0163662,2016-05-28,2:18:00,0:22:13,0:06:25,0:34:44,0:19:24,0:09:04,4:17:15,1198,48,17
+Night Team,PGY2+,S0169381,2016-07-20,1:21:10,1:07:51,0:00:08,0:39:22,0:16:00,0:02:37,3:44:57,1377,0,21
+General Medicine,PGY1,S0181086,2015-12-15,2:42:33,0:42:48,0:46:51,0:52:28,0:31:21,0:54:00,7:08:54,2095,224,13
+General Medicine,PGY1,S0169389,2014-10-30,2:25:42,0:06:13,0:07:44,0:18:06,0:30:34,0:07:42,3:51:08,991,0,9
+General Medicine,PGY1,S0169389,2014-10-31,0:08:21,0:00:00,0:00:00,0:02:03,0:00:00,0:00:00,0:10:48,66,58,5
+General Medicine,PGY1,S0181086,2015-12-14,2:07:53,1:12:32,2:39:48,1:11:35,0:20:28,0:34:00,8:54:40,2473,302,9
+Emergency Medicine,PGY1,S0195210,2016-07-18,0:18:10,0:21:30,0:00:00,0:02:39,0:00:01,0:00:00,0:53:03,210,210,3
+Night Team,PGY2+,S0169400,2015-11-04,3:12:20,0:45:12,0:53:42,0:24:26,0:22:02,0:31:20,6:25:16,1892,74,65
+Night Team,PGY2+,S0169400,2015-11-07,2:45:15,0:29:00,0:40:47,0:12:25,0:07:29,0:07:55,4:27:52,1034,60,38
+Night Team,PGY2+,S0169400,2015-11-06,4:29:10,0:57:01,0:54:27,0:30:55,0:07:01,0:41:37,8:01:52,2367,42,83
+Night Team,PGY2+,S0169400,2015-11-01,2:03:52,0:27:43,2:07:26,0:40:01,0:13:59,0:49:01,7:02:29,1522,0,35
+Night Team,PGY2+,S0169400,2015-11-03,1:24:56,0:23:23,0:21:27,0:22:53,0:09:05,0:13:37,3:02:51,1031,0,50
+Night Team,PGY2+,S0169400,2015-11-02,2:06:36,0:22:20,0:44:49,0:08:03,0:04:38,0:11:30,3:52:33,967,43,35
+Emergency Medicine,PGY1,S0195210,2016-07-10,2:41:06,0:49:09,0:10:46,1:03:47,0:20:32,0:01:05,5:39:54,1444,45,12
+Emergency Medicine,PGY1,S0195210,2016-07-11,3:01:11,0:20:58,0:07:22,0:09:05,0:08:46,0:00:30,4:25:11,863,284,16
+Emergency Medicine,PGY1,S0195210,2016-07-12,0:16:13,0:01:41,0:00:00,0:00:17,0:00:06,0:03:25,0:48:01,250,241,5
+Emergency Medicine,PGY1,S0195210,2016-07-13,0:23:26,0:01:53,0:01:19,0:03:10,0:00:56,0:00:11,1:06:44,332,332,5
+Emergency Medicine,PGY1,S0195210,2016-07-14,3:01:14,1:02:23,0:22:01,0:25:51,0:23:13,0:00:21,6:08:20,1472,6,18
+Emergency Medicine,PGY1,S0195210,2016-07-15,3:08:50,0:59:57,0:31:57,0:37:44,0:21:37,0:02:25,6:30:14,1426,0,11
+Emergency Medicine,PGY1,S0195210,2016-07-16,1:44:45,0:26:14,0:28:40,0:30:48,0:10:08,0:02:55,3:31:53,731,0,10
+Emergency Medicine,PGY1,S0195210,2016-07-17,2:16:22,0:15:55,0:25:58,0:44:38,0:06:13,0:03:29,4:26:25,847,0,9
+Intensive Care Unit,PGY2+,S0181061,2016-09-07,1:37:12,0:21:43,0:07:43,0:09:35,0:18:44,0:01:08,3:02:12,1009,12,35
+General Medicine,PGY2+,S0169397,2016-11-10,2:40:17,0:36:33,0:00:02,0:34:47,0:16:17,0:00:31,4:39:23,1568,0,17
+General Medicine,PGY1,S0172056,2015-09-02,2:08:39,0:31:43,3:32:13,0:46:01,0:20:29,0:23:50,8:23:00,1985,0,9
+Emergency Medicine,PGY1,S0181063,2016-06-07,1:17:23,0:30:00,0:00:37,0:28:01,0:46:32,0:52:07,5:03:22,2039,0,10
+Emergency Medicine,PGY1,S0181063,2016-06-06,1:51:52,0:39:09,0:05:00,0:34:31,1:01:41,0:41:51,5:50:10,2245,180,26
+General Medicine,PGY1,S0181088,2015-07-31,2:07:45,0:11:38,1:29:10,1:13:50,0:33:37,0:27:23,6:51:51,2063,0,9
+General Medicine,PGY1,S0181088,2015-07-30,0:03:44,0:05:02,0:00:26,0:04:03,0:00:00,0:00:45,0:18:18,239,235,7
+Night Team,PGY2+,S0181069,2016-07-06,2:14:29,1:28:45,0:03:09,0:54:40,0:25:17,0:00:43,6:08:16,2483,0,18
+Emergency Medicine,PGY1,S0181063,2016-06-09,0:03:51,0:50:06,0:00:00,0:13:52,0:00:00,0:00:00,1:15:10,424,424,5
+Emergency Medicine,PGY1,S0181063,2016-06-08,0:05:00,1:05:44,0:00:00,0:21:18,0:00:00,0:00:00,1:49:09,727,727,9
+Night Team,PGY2+,S0169381,2016-07-25,1:49:57,0:29:27,0:00:00,0:12:27,0:06:22,0:05:14,3:01:34,936,0,20
+General Medicine,PGY1,S0181073,2015-10-24,0:39:33,1:06:34,0:58:48,0:18:17,0:16:06,0:13:17,3:54:43,1354,87,7
+General Medicine,PGY1,S0181073,2015-10-25,0:38:11,0:29:27,0:58:04,0:21:24,0:12:21,0:18:48,3:04:14,825,25,3
+Night Team,PGY1,S0169411,2014-12-01,1:39:11,0:02:12,1:14:49,0:13:47,0:23:53,0:00:31,3:50:13,985,0,38
+General Medicine,PGY1,S0181057,2016-02-22,1:58:14,1:22:54,0:00:23,0:38:59,0:25:47,0:02:15,4:59:32,2078,4,30
+Night Team,PGY2+,S0181069,2016-07-09,3:12:20,2:03:51,0:00:19,0:50:17,0:22:16,0:12:29,7:42:48,3340,69,35
+General Medicine,PGY1,S0176040,2015-11-30,1:32:06,1:04:26,2:59:11,0:34:58,0:19:53,0:25:26,7:34:57,2073,16,13
+General Medicine,PGY1,S0181079,2016-02-28,2:19:53,0:57:59,0:00:51,0:30:11,0:15:23,0:01:20,4:36:36,1418,0,6
+General Medicine,PGY1,S0181079,2016-02-29,4:13:32,1:05:26,0:00:10,1:04:27,0:21:44,0:06:13,7:56:12,2201,39,11
+General Medicine,PGY1,S0108268,2014-12-07,3:07:08,0:14:03,1:42:43,0:39:20,0:40:24,0:27:59,7:27:09,1786,0,23
+General Medicine,PGY2+,S0169399,2015-10-18,2:25:17,1:15:59,0:09:25,0:53:01,0:32:10,0:37:53,6:25:21,1671,262,19
+General Medicine,PGY1,S0181073,2015-10-23,2:02:34,1:26:12,0:58:48,0:50:26,0:31:46,0:37:47,7:07:22,2572,61,9
+General Medicine,PGY1,S0169383,2015-01-31,0:03:18,0:03:54,0:00:00,0:00:06,0:00:00,0:00:00,0:07:30,83,72,7
+General Medicine,PGY1,S0169383,2015-01-30,0:25:05,0:34:35,1:36:43,0:13:49,0:03:05,0:03:12,3:16:25,712,685,12
+Night Team,PGY1,S0169401,2014-10-19,4:36:18,0:16:10,0:00:00,0:23:14,0:30:35,0:10:44,6:30:25,2279,0,44
+Night Team,PGY1,S0169401,2014-10-18,3:34:19,0:17:29,0:21:13,0:51:45,0:35:08,0:07:31,7:03:13,2220,113,53
+Night Team,PGY1,S0169401,2014-10-13,2:44:02,0:13:58,0:00:06,0:14:44,0:15:51,0:06:06,3:44:38,1149,0,37
+Night Team,PGY1,S0169401,2014-10-12,3:16:12,0:20:02,0:10:37,0:08:04,0:09:32,0:21:00,4:38:24,1463,55,44
+General Medicine,PGY1,S0169401,2014-10-11,4:19:02,0:27:07,0:23:07,0:13:55,0:15:01,0:18:45,6:31:47,2049,933,51
+General Medicine,PGY1,S0169401,2014-10-10,4:04:41,0:14:06,0:07:49,0:53:18,0:16:30,0:07:26,5:56:49,1998,625,20
+Night Team,PGY1,S0169401,2014-10-17,3:21:47,0:06:17,0:01:18,0:17:19,0:14:40,0:08:57,4:25:44,1388,141,63
+Night Team,PGY1,S0169401,2014-10-16,5:10:45,0:24:56,0:15:13,0:33:43,0:31:39,0:07:38,7:38:12,2624,192,42
+Night Team,PGY1,S0169401,2014-10-15,5:53:52,0:12:44,0:09:01,0:34:24,0:29:50,0:12:16,8:02:56,2946,67,82
+Night Team,PGY1,S0169401,2014-10-14,5:06:45,0:17:39,0:09:01,0:29:32,0:17:52,0:12:40,7:16:29,2233,0,66
+Emergency Medicine,PGY2+,S0169395,2016-01-08,0:55:39,0:36:15,1:26:44,0:11:17,0:14:18,0:21:05,4:06:46,1663,0,67
+General Medicine,PGY1,S0106952,2015-01-05,4:59:07,0:14:29,1:39:15,0:29:25,0:48:08,0:25:45,9:08:44,2452,523,20
+General Medicine,PGY1,S0106952,2015-01-04,4:07:58,0:03:03,1:08:54,0:35:44,0:21:08,0:14:15,6:45:15,1937,472,79
+General Medicine,PGY1,S0106952,2015-01-07,0:58:00,0:03:35,1:40:05,0:00:00,0:02:40,0:03:00,2:53:46,627,625,22
+General Medicine,PGY1,S0106952,2015-01-06,4:26:29,0:10:55,4:15:35,0:29:48,0:15:48,0:25:33,10:23:41,1809,1022,42
+General Medicine,PGY1,S0106952,2015-01-09,5:26:37,0:03:34,2:08:59,0:40:04,0:37:48,0:22:50,9:46:47,2774,796,21
+Emergency Medicine,PGY2+,S0169395,2016-01-01,2:42:12,0:47:56,3:17:32,0:33:12,0:25:48,0:37:29,9:01:30,2542,0,39
+Emergency Medicine,PGY2+,S0169395,2016-01-02,3:45:29,1:19:03,2:34:29,0:37:35,0:29:42,0:42:38,10:10:40,3684,0,48
+Emergency Medicine,PGY2+,S0169395,2016-01-03,1:29:12,0:48:02,1:23:08,0:18:36,0:14:45,0:21:36,4:51:12,1973,0,29
+Emergency Medicine,PGY2+,S0169395,2016-01-04,1:35:47,0:15:12,1:01:21,0:17:15,0:12:05,0:18:48,3:54:10,1209,0,20
+Emergency Medicine,PGY2+,S0169395,2016-01-05,4:39:54,0:39:13,2:10:20,0:41:47,0:39:53,0:46:24,10:16:59,3621,0,37
+Emergency Medicine,PGY2+,S0169395,2016-01-06,0:50:43,0:06:34,0:29:33,0:12:29,0:06:52,0:04:32,1:53:10,538,0,7
+Emergency Medicine,PGY2+,S0169395,2016-01-07,2:39:17,0:56:53,2:34:38,0:47:02,0:30:47,0:52:45,9:05:01,3140,0,30
+Intensive Care Unit,PGY2+,S0181525,2016-09-15,2:29:32,0:38:54,0:00:01,0:32:20,0:46:03,0:09:02,5:20:43,1802,0,20
+General Medicine,PGY2+,S0181078,2016-10-08,0:12:10,0:07:30,0:00:48,0:00:35,0:00:01,0:00:03,0:25:30,133,128,7
+Intensive Care Unit,PGY2+,S0169400,2016-07-09,2:25:52,0:17:10,0:11:58,0:32:05,0:20:20,0:05:16,4:13:28,1417,0,15
+Intensive Care Unit,PGY2+,S0169400,2016-07-08,2:58:31,0:36:23,0:00:01,0:50:40,0:35:15,0:24:01,5:52:33,2014,32,18
+Intensive Care Unit,PGY2+,S0169400,2016-07-05,1:09:38,0:08:05,0:00:14,0:08:02,0:07:25,0:01:24,1:38:22,715,95,14
+General Medicine,PGY2+,S0181078,2016-10-01,4:46:21,1:33:50,0:00:02,0:25:33,0:23:40,0:10:55,7:58:50,2921,756,31
+General Medicine,PGY2+,S0181078,2016-10-02,2:57:47,0:32:17,0:00:06,0:40:05,0:22:20,0:00:39,5:15:03,1674,421,14
+General Medicine,PGY2+,S0181078,2016-10-03,4:05:26,1:12:37,0:00:01,0:23:30,0:19:09,0:08:55,6:54:32,2754,356,26
+General Medicine,PGY2+,S0181078,2016-10-04,3:46:15,1:06:56,0:00:05,0:38:34,0:18:07,0:03:18,6:50:02,2036,502,17
+General Medicine,PGY2+,S0181078,2016-10-05,1:25:22,0:39:13,0:00:01,0:07:45,0:06:45,0:05:32,2:44:30,757,226,15
+Intensive Care Unit,PGY2+,S0169400,2016-07-03,1:22:53,0:23:32,0:08:38,0:02:30,0:08:36,0:03:17,2:36:45,630,0,13
+Intensive Care Unit,PGY2+,S0169400,2016-07-02,0:11:12,0:00:25,0:00:00,0:00:02,0:00:00,0:00:28,0:12:31,61,0,8
+Intensive Care Unit,PGY2+,S0169400,2015-07-19,3:03:10,0:14:40,0:33:50,0:23:27,0:24:06,0:43:45,5:44:10,1677,50,20
+Intensive Care Unit,PGY2+,S0169400,2015-07-18,1:39:59,0:24:41,0:52:52,0:10:02,0:16:23,0:25:55,4:07:19,1199,160,19
+Intensive Care Unit,PGY2+,S0169400,2015-07-15,3:27:41,0:34:26,0:54:42,0:33:08,0:24:30,0:31:58,6:48:16,1843,28,29
+Intensive Care Unit,PGY2+,S0169400,2015-07-14,1:30:44,0:15:08,0:27:07,0:11:58,0:14:09,0:23:11,3:13:14,792,57,15
+Intensive Care Unit,PGY2+,S0169400,2015-07-17,1:22:44,0:06:29,0:05:22,0:02:19,0:09:57,0:12:11,2:13:20,695,63,18
+Intensive Care Unit,PGY2+,S0169400,2015-07-16,1:22:46,0:13:24,0:20:54,0:07:45,0:14:38,0:16:56,2:44:28,889,198,14
+Intensive Care Unit,PGY2+,S0169400,2015-07-11,1:37:56,0:06:19,0:39:58,0:16:36,0:12:43,0:48:22,3:54:03,1081,0,10
+Intensive Care Unit,PGY2+,S0169400,2015-07-10,2:01:02,0:16:55,2:11:40,0:37:21,0:11:55,0:41:59,6:26:12,1386,88,13
+Intensive Care Unit,PGY2+,S0169400,2015-07-13,1:39:31,0:16:52,0:25:46,0:11:58,0:22:41,0:19:56,3:40:21,947,15,17
+Intensive Care Unit,PGY2+,S0169400,2015-07-12,0:00:00,0:06:50,0:00:00,0:00:02,0:00:00,0:00:00,0:06:58,44,32,7
+General Medicine,PGY1,S0181057,2016-01-31,0:43:25,0:54:17,0:57:23,0:16:19,0:15:16,0:10:52,3:48:04,1306,0,6
+General Medicine,PGY1,S0181526,2015-09-30,1:51:25,0:57:05,0:45:44,0:36:03,0:17:14,0:36:01,5:48:32,1571,58,16
+General Medicine,PGY2+,S0181082,2016-08-28,0:07:09,0:07:23,0:00:00,0:00:02,0:01:31,0:00:08,0:16:45,184,176,8
+General Medicine,PGY2+,S0181082,2016-08-29,3:54:14,1:19:14,0:00:05,0:33:17,0:31:43,0:08:37,7:14:23,2383,136,27
+Night Team,PGY1,S0094959,2014-07-09,5:23:48,0:04:43,0:03:49,0:23:47,0:17:57,0:24:10,6:47:23,2114,90,98
+Night Team,PGY1,S0094959,2014-07-08,5:34:50,0:13:37,0:08:06,0:21:23,0:18:20,0:32:05,7:24:30,1941,117,100
+General Medicine,PGY2+,S0181082,2016-08-22,1:05:31,0:41:12,0:00:02,0:10:25,0:10:41,0:00:26,2:22:40,863,0,12
+General Medicine,PGY2+,S0181082,2016-08-23,0:16:58,0:12:09,0:00:00,0:00:19,0:00:55,0:00:46,0:32:34,246,234,9
+General Medicine,PGY2+,S0181082,2016-08-20,2:28:06,1:05:14,0:00:05,0:17:17,0:23:19,0:05:05,4:53:43,1654,94,28
+General Medicine,PGY2+,S0181082,2016-08-21,2:43:33,1:07:21,0:00:01,0:30:41,0:28:01,0:08:33,5:34:53,1483,0,15
+General Medicine,PGY2+,S0181082,2016-08-26,3:43:34,1:35:38,0:00:05,0:20:51,0:27:21,0:10:52,6:59:01,2607,225,38
+General Medicine,PGY2+,S0181082,2016-08-27,0:20:29,0:05:14,0:00:00,0:00:06,0:01:40,0:00:07,0:32:19,313,301,11
+General Medicine,PGY2+,S0181082,2016-08-24,3:56:00,1:35:36,0:00:09,0:23:49,0:27:44,0:12:40,7:12:23,2460,130,29
+General Medicine,PGY2+,S0181082,2016-08-25,2:00:10,0:44:46,0:02:01,0:49:43,0:26:34,0:01:51,4:50:29,1868,9,13
+General Medicine,PGY1,S0181078,2015-12-09,0:09:31,0:24:20,0:24:58,0:02:21,0:01:12,0:00:22,1:06:13,365,345,10
+General Medicine,PGY1,S0181078,2015-12-08,2:33:08,0:34:30,1:08:42,1:20:12,0:48:03,0:23:09,7:56:12,2387,199,10
+General Medicine,PGY1,S0181078,2015-12-07,1:18:24,0:58:55,1:07:37,0:59:48,0:04:31,0:28:36,5:27:51,1141,11,4
+General Medicine,PGY1,S0181078,2015-12-06,1:03:50,0:58:38,2:13:31,0:37:57,0:08:26,0:18:47,5:50:21,1265,0,8
+General Medicine,PGY1,S0169411,2015-06-25,0:00:00,0:00:00,0:00:00,0:01:16,0:00:00,0:00:00,0:01:16,4,0,2
+General Medicine,PGY1,S0169411,2015-06-24,1:34:44,0:25:59,2:03:13,0:29:25,0:08:01,0:07:43,5:08:49,1587,0,7
+General Medicine,PGY1,S0169411,2015-06-21,0:00:00,0:00:00,0:00:00,0:01:59,0:00:00,0:00:00,0:01:59,10,0,4
+General Medicine,PGY1,S0169411,2015-06-20,3:19:37,1:21:42,1:41:21,0:46:10,0:29:30,0:17:25,8:34:48,2843,0,12
+General Medicine,PGY1,S0169411,2015-06-23,2:35:39,0:27:41,1:02:49,0:20:44,0:14:47,0:13:51,5:35:44,2267,52,12
+General Medicine,PGY1,S0169411,2015-06-22,3:33:27,1:04:38,1:42:02,0:27:56,0:26:58,0:28:16,8:17:51,3042,0,18
+Emergency Medicine,PGY1,S0169384,2014-10-09,5:05:22,0:27:10,0:53:27,0:54:28,0:33:36,0:22:59,8:46:06,2083,83,17
+Emergency Medicine,PGY1,S0169384,2014-10-06,1:54:20,0:23:14,0:23:08,0:10:36,0:04:49,0:15:13,3:41:50,895,0,8
+Emergency Medicine,PGY1,S0169384,2014-10-04,4:12:14,0:40:44,0:47:27,0:23:32,0:20:29,0:25:10,7:43:26,1996,40,16
+Emergency Medicine,PGY1,S0169384,2014-10-05,4:25:50,0:21:10,0:20:52,0:35:12,0:12:24,0:15:57,6:58:05,1886,218,15
+Emergency Medicine,PGY1,S0169384,2014-10-02,0:20:27,0:04:35,0:03:58,0:03:20,0:03:18,0:00:11,0:36:14,203,203,6
+Emergency Medicine,PGY1,S0169384,2014-10-03,1:25:38,0:09:28,0:07:46,0:16:40,0:06:58,0:25:50,2:51:23,786,0,7
+Emergency Medicine,PGY1,S0169384,2014-10-01,2:37:28,0:16:29,0:14:44,0:19:22,0:04:58,0:32:09,4:44:57,1103,7,8
+General Medicine,PGY2+,S0169390,2015-12-05,3:02:45,1:27:53,0:10:58,0:24:34,0:15:00,0:34:14,6:41:43,2873,54,24
+Night Team,PGY2+,S0169394,2015-11-05,1:36:35,1:07:20,1:20:56,0:53:02,0:21:21,0:25:34,6:28:16,2201,99,24
+Night Team,PGY2+,S0169394,2015-11-04,1:32:45,1:40:34,1:38:39,0:24:36,0:23:58,0:42:16,6:53:53,1910,301,23
+Night Team,PGY2+,S0169394,2015-11-07,0:44:08,0:37:06,0:16:15,0:24:14,0:15:37,0:16:35,3:24:48,1538,86,30
+Night Team,PGY2+,S0169394,2015-11-06,1:10:29,0:56:04,2:56:14,0:35:54,0:16:25,0:53:53,7:20:17,2165,24,32
+Night Team,PGY2+,S0169394,2015-11-01,0:24:16,0:25:45,0:52:38,0:33:54,0:09:12,0:18:02,2:56:57,764,14,8
+Intensive Care Unit,PGY2+,S0169406,2016-03-15,0:38:54,0:04:53,0:07:05,0:03:10,0:03:24,0:00:06,1:12:32,329,11,13
+Night Team,PGY2+,S0169394,2015-11-03,2:00:22,1:59:42,1:47:49,0:56:45,0:12:39,0:47:58,8:09:03,2322,0,32
+Night Team,PGY2+,S0169394,2015-11-02,1:17:16,0:42:19,1:10:01,0:06:00,0:22:55,0:14:37,4:11:52,1298,36,26
+Intensive Care Unit,PGY2+,S0169406,2016-03-18,0:38:56,0:13:10,0:00:00,0:03:50,0:06:54,0:02:01,1:08:44,354,35,9
+Intensive Care Unit,PGY2+,S0169406,2016-03-19,0:01:40,0:00:17,0:00:00,0:00:00,0:00:00,0:00:00,0:01:58,10,10,2
+Night Team,PGY2+,S0169394,2015-11-09,1:58:35,0:46:54,0:39:53,0:28:07,0:21:26,0:15:48,4:45:11,1434,36,36
+Night Team,PGY2+,S0169394,2015-11-08,1:16:14,0:58:44,2:40:41,0:34:08,0:16:10,0:28:48,6:52:35,1725,0,16
+General Medicine,PGY1,S0108287,2015-02-09,2:36:49,0:05:13,2:29:19,0:25:15,0:22:57,0:03:03,6:51:11,1465,325,12
+General Medicine,PGY1,S0108287,2015-02-08,2:01:48,0:05:15,2:34:08,0:26:16,0:24:45,0:12:21,6:03:10,1139,0,9
+General Medicine,PGY1,S0108287,2015-02-01,1:36:15,0:08:51,1:05:59,0:35:00,0:15:21,0:27:10,4:29:40,1320,13,11
+General Medicine,PGY1,S0108287,2015-02-03,2:38:37,0:19:17,4:31:54,0:41:14,0:20:01,0:11:18,9:02:23,1731,0,10
+General Medicine,PGY1,S0108287,2015-02-02,0:12:04,0:03:11,0:02:37,0:00:00,0:00:08,0:00:23,0:19:58,146,140,9
+General Medicine,PGY1,S0108287,2015-02-05,2:07:14,0:00:06,1:47:00,0:15:36,0:20:26,0:06:03,5:12:23,966,0,10
+General Medicine,PGY1,S0108287,2015-02-04,1:54:21,0:07:06,3:13:12,0:25:38,0:39:38,0:03:53,7:05:07,1620,248,18
+General Medicine,PGY1,S0108287,2015-02-07,1:36:44,0:03:43,0:42:13,0:20:56,0:14:13,0:06:07,3:29:38,888,13,11
+General Medicine,PGY1,S0108287,2015-02-06,3:07:21,0:06:45,1:38:13,1:09:28,0:12:29,0:05:40,6:41:06,1399,0,11
+General Medicine,PGY2+,S0169400,2015-08-29,1:28:28,0:13:57,0:50:28,0:28:05,0:22:19,0:18:55,4:10:41,1600,276,14
+General Medicine,PGY2+,S0169400,2015-08-28,3:20:33,0:28:19,0:21:38,0:35:14,0:33:26,1:13:05,7:10:33,2179,133,36
+General Medicine,PGY2+,S0169400,2015-08-23,3:07:40,0:38:25,0:22:48,0:26:26,0:11:48,1:07:56,6:38:43,2063,74,22
+General Medicine,PGY2+,S0169400,2015-08-25,0:12:38,0:14:46,0:03:27,0:00:44,0:01:32,0:06:24,0:41:24,391,383,11
+General Medicine,PGY2+,S0169400,2015-08-24,1:28:18,0:19:53,0:46:59,0:14:05,0:13:42,0:32:04,4:00:05,1316,154,17
+General Medicine,PGY2+,S0169400,2015-08-27,1:55:27,0:21:44,0:25:09,0:07:54,0:16:09,0:35:34,4:00:45,1406,170,22
+General Medicine,PGY2+,S0169400,2015-08-26,2:53:50,1:21:37,0:48:43,0:16:04,0:22:11,1:07:00,7:36:33,1979,144,25
+General Medicine,PGY2+,S0181526,2016-07-28,3:30:49,1:49:01,0:00:10,0:29:35,0:25:35,0:06:01,7:18:03,2297,183,17
+General Medicine,PGY2+,S0181526,2016-07-29,1:48:40,1:08:28,0:00:05,0:36:24,0:16:12,0:01:57,5:01:32,2312,143,32
+General Medicine,PGY1,S0181067,2015-08-31,2:00:47,0:17:23,1:02:34,0:20:12,0:20:14,0:15:45,4:52:58,1360,211,9
+General Medicine,PGY2+,S0181526,2016-07-22,0:07:24,0:00:14,0:00:00,0:00:00,0:00:00,0:00:35,0:08:59,50,0,5
+General Medicine,PGY2+,S0181526,2016-07-23,4:13:42,1:23:14,0:00:00,0:34:43,0:21:00,0:02:13,7:32:45,2705,143,20
+General Medicine,PGY2+,S0181526,2016-07-20,3:28:48,0:58:23,0:00:00,0:05:45,0:08:16,0:08:36,5:28:17,2081,20,27
+General Medicine,PGY2+,S0181526,2016-07-21,1:15:57,0:46:03,0:00:05,0:25:54,0:23:58,0:08:49,3:31:10,1493,394,13
+General Medicine,PGY2+,S0181526,2016-07-26,1:18:21,0:30:12,0:00:14,0:15:08,0:11:50,0:01:57,2:57:25,1446,0,13
+General Medicine,PGY2+,S0181526,2016-07-27,0:12:23,0:01:22,0:00:00,0:02:23,0:00:00,0:08:08,0:31:03,238,0,12
+General Medicine,PGY2+,S0181526,2016-07-24,1:30:45,0:27:16,0:00:02,0:06:55,0:08:44,0:06:02,2:54:16,1289,0,16
+General Medicine,PGY2+,S0181526,2016-07-25,4:25:18,1:08:38,0:00:39,0:48:09,0:24:05,0:11:21,8:30:30,2992,123,21
+Night Team,PGY1,S0181063,2015-07-29,1:00:57,0:04:48,0:38:50,0:45:45,0:38:56,0:18:42,3:40:12,1123,0,26
+Night Team,PGY1,S0181063,2015-07-28,1:13:47,0:03:30,0:01:39,0:28:21,0:44:52,0:31:08,3:15:42,903,0,29
+Night Team,PGY1,S0181063,2015-07-21,1:47:54,0:00:59,0:06:44,0:19:13,0:22:56,0:17:34,3:04:13,756,0,22
+Night Team,PGY1,S0181063,2015-07-20,2:13:04,0:04:12,0:29:29,0:33:22,0:43:43,0:40:20,4:57:48,1116,0,27
+Night Team,PGY1,S0181063,2015-07-23,1:02:02,0:02:14,0:35:28,0:26:43,0:17:02,0:18:34,2:59:44,534,0,12
+Night Team,PGY1,S0181063,2015-07-22,1:37:37,0:05:33,0:31:49,0:23:52,0:24:11,0:24:26,3:41:33,819,0,26
+Night Team,PGY1,S0181063,2015-07-25,1:02:49,0:08:59,1:15:02,0:23:11,0:29:40,0:26:51,4:00:18,839,0,23
+Night Team,PGY1,S0181063,2015-07-24,0:22:51,0:00:02,0:07:59,0:10:30,0:28:38,0:07:22,1:22:47,326,0,12
+Night Team,PGY1,S0181063,2015-07-27,0:58:55,0:06:19,0:01:32,0:26:59,0:24:46,0:30:54,3:00:45,720,0,15
+Night Team,PGY1,S0181063,2015-07-26,1:14:05,0:11:15,0:06:09,0:32:38,0:27:42,0:17:59,3:09:27,782,0,25
+General Medicine,PGY1,S0172645,2015-09-16,1:53:03,0:02:45,2:20:41,0:40:44,0:23:07,0:27:12,6:25:14,1613,0,10
+General Medicine,PGY1,S0172645,2015-09-17,0:58:06,0:00:00,1:08:08,0:41:28,0:09:39,0:07:35,3:20:15,732,0,6
+General Medicine,PGY1,S0172645,2015-09-14,1:04:39,0:07:20,1:15:17,0:55:11,0:41:14,0:28:18,4:58:17,1370,0,7
+General Medicine,PGY1,S0172645,2015-09-15,0:42:47,0:02:06,1:00:54,0:07:27,0:09:15,0:23:38,2:40:35,605,0,10
+General Medicine,PGY1,S0172645,2015-09-13,1:07:54,0:10:17,1:14:40,0:09:35,0:03:33,0:06:44,3:09:39,555,128,3
+General Medicine,PGY1,S0169411,2015-01-08,3:09:09,0:02:49,2:14:41,0:32:17,0:14:13,0:03:42,6:27:27,1860,0,10
+General Medicine,PGY1,S0169411,2015-01-09,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,14,0,3
+General Medicine,PGY1,S0169411,2015-01-06,3:34:45,0:05:35,0:44:06,0:32:50,0:18:41,0:04:49,5:40:23,2010,22,12
+General Medicine,PGY1,S0169411,2015-01-07,5:20:08,0:02:13,1:16:46,1:16:39,0:23:56,0:03:42,8:55:45,2445,0,14
+General Medicine,PGY1,S0169411,2015-01-04,2:51:00,0:01:43,0:51:32,0:09:46,0:14:03,0:06:45,4:28:58,1444,0,18
+General Medicine,PGY1,S0169411,2015-01-05,4:28:35,0:13:09,2:26:55,0:38:37,0:25:12,0:10:35,8:45:54,2494,0,11
+General Medicine,PGY2+,S0106952,2015-10-07,2:43:28,1:55:01,0:23:59,0:04:57,0:12:31,0:55:55,6:35:20,2340,781,49
+General Medicine,PGY2+,S0106952,2015-10-06,2:33:17,1:24:54,1:04:01,0:29:17,0:14:22,0:28:01,6:31:26,2573,740,73
+General Medicine,PGY2+,S0106952,2015-10-05,3:04:52,1:31:57,0:12:49,0:15:05,0:28:32,0:52:44,7:03:38,2460,745,72
+General Medicine,PGY1,S0172645,2015-09-19,1:51:11,0:22:27,2:18:06,0:57:25,0:22:51,0:25:29,6:47:20,1646,0,6
+General Medicine,PGY2+,S0181077,2016-09-10,0:14:07,0:12:23,0:00:00,0:00:47,0:03:37,0:00:07,0:37:12,507,276,34
+Emergency Medicine,PGY2+,S0106952,2016-09-19,5:04:21,1:10:55,0:16:05,0:25:57,0:15:46,0:07:02,8:00:19,3133,462,80
+Emergency Medicine,PGY2+,S0106952,2016-09-18,5:08:55,2:05:44,0:19:00,0:09:52,0:25:04,0:17:28,9:04:26,3909,1265,79
+Emergency Medicine,PGY1,S0169399,2015-05-12,3:16:01,0:33:40,3:41:28,0:41:49,0:50:53,0:13:31,9:50:56,1984,0,13
+Emergency Medicine,PGY2+,S0106952,2016-09-14,0:27:51,0:27:54,0:00:00,0:00:00,0:00:01,0:00:05,1:02:49,816,816,105
+Emergency Medicine,PGY2+,S0106952,2016-09-17,5:17:42,0:40:10,0:04:10,0:14:07,0:07:16,0:09:27,7:09:30,2304,67,60
+Emergency Medicine,PGY2+,S0106952,2016-09-11,4:25:05,1:24:50,0:15:59,0:20:56,0:20:15,0:12:11,7:56:29,3129,192,71
+Emergency Medicine,PGY2+,S0106952,2016-09-13,0:20:17,0:53:30,0:00:00,0:00:00,0:00:07,0:00:04,1:15:35,1003,1003,72
+Emergency Medicine,PGY2+,S0106952,2016-09-12,4:39:06,1:47:32,0:11:10,0:21:46,0:21:55,0:08:37,8:19:43,3574,829,41
+General Medicine,PGY1,S0169395,2014-10-19,3:19:06,0:16:55,0:16:12,0:33:30,0:13:25,0:24:56,5:44:50,1481,0,9
+General Medicine,PGY1,S0169395,2014-10-18,3:13:48,0:13:25,0:20:53,1:00:04,0:20:09,0:21:27,5:44:43,1573,0,6
+General Medicine,PGY1,S0169395,2014-10-13,2:35:36,0:16:28,0:24:10,0:24:46,0:19:13,0:18:06,4:33:26,1319,0,8
+General Medicine,PGY1,S0169395,2014-10-12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY1,S0169395,2014-10-17,1:19:14,0:11:43,0:10:24,0:07:07,0:05:27,0:16:32,2:21:08,670,0,6
+General Medicine,PGY1,S0169395,2014-10-16,4:07:56,0:17:15,0:26:14,0:37:00,0:22:48,0:29:21,6:50:52,1749,0,7
+General Medicine,PGY1,S0169395,2014-10-15,2:04:28,0:12:38,0:08:46,0:15:15,0:14:51,0:22:22,3:39:33,959,2,5
+General Medicine,PGY1,S0169395,2014-10-14,4:04:14,0:22:48,0:17:34,0:31:24,0:21:32,0:36:58,6:46:01,1747,6,11
+Intensive Care Unit,PGY2+,S0181081,2016-09-19,1:52:04,1:03:27,0:08:54,0:19:41,0:26:29,0:05:59,4:32:35,1339,0,17
+Intensive Care Unit,PGY2+,S0181081,2016-09-18,0:00:00,0:00:00,0:00:00,0:00:12,0:00:00,0:00:00,0:00:12,19,0,5
+Intensive Care Unit,PGY2+,S0181081,2016-09-11,2:45:58,1:54:49,0:10:45,0:22:15,0:38:08,0:02:16,6:24:11,1965,0,17
+Intensive Care Unit,PGY2+,S0181081,2016-09-13,1:40:59,1:12:49,0:00:01,0:37:38,0:16:20,0:01:47,4:11:27,1485,0,15
+Intensive Care Unit,PGY2+,S0181081,2016-09-12,1:45:40,0:46:35,0:16:58,0:23:40,0:14:32,0:10:24,4:01:38,931,98,9
+Intensive Care Unit,PGY2+,S0181081,2016-09-15,2:55:45,1:40:28,0:11:55,0:54:19,0:23:31,0:06:41,8:01:03,1965,0,21
+Intensive Care Unit,PGY2+,S0181081,2016-09-14,2:42:25,1:24:59,0:10:34,0:27:25,0:16:27,0:01:05,5:48:17,1871,116,18
+Intensive Care Unit,PGY2+,S0181081,2016-09-17,2:44:40,0:31:02,0:06:00,0:30:22,0:18:16,0:01:21,4:38:18,1755,0,12
+Intensive Care Unit,PGY2+,S0181081,2016-09-16,2:54:51,1:41:57,0:00:03,1:18:20,0:41:45,0:03:31,7:21:13,2201,0,23
+General Medicine,PGY1,S0181073,2016-06-24,2:54:40,2:01:14,0:00:08,0:56:15,0:31:01,0:02:06,7:32:29,2486,18,8
+General Medicine,PGY1,S0181073,2016-06-25,0:00:00,0:00:00,0:00:00,0:00:41,0:00:00,0:00:00,0:00:41,12,0,3
+General Medicine,PGY1,S0181073,2016-06-20,2:08:23,0:51:13,0:00:25,1:17:47,0:23:28,0:01:51,5:39:49,2341,0,23
+General Medicine,PGY1,S0181073,2016-06-21,2:26:12,1:05:17,0:00:04,1:24:58,0:22:17,0:09:41,6:14:25,1922,58,14
+General Medicine,PGY1,S0181073,2016-06-22,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:01,7,3,3
+General Medicine,PGY1,S0181073,2016-06-23,1:09:48,0:45:05,0:00:10,0:07:32,0:10:11,0:00:22,2:22:43,958,12,8
+General Medicine,PGY1,S0195227,2016-09-07,3:06:48,1:09:42,0:00:08,1:01:27,0:26:02,0:00:29,6:33:02,2005,8,15
+General Medicine,PGY1,S0195227,2016-09-06,0:08:52,0:00:55,0:00:00,0:00:00,0:00:00,0:00:30,0:13:01,138,0,4
+General Medicine,PGY1,S0195227,2016-09-05,2:14:13,1:09:28,0:00:13,0:53:30,0:21:36,0:07:25,5:37:40,1769,0,13
+General Medicine,PGY1,S0195227,2016-09-04,1:20:27,0:56:43,0:00:11,0:22:42,0:08:41,0:02:58,3:18:27,1615,0,13
+General Medicine,PGY1,S0195227,2016-09-03,2:54:22,2:12:53,0:00:33,0:58:46,0:19:01,0:01:52,7:31:34,2513,0,10
+General Medicine,PGY1,S0195227,2016-09-02,3:30:45,1:38:23,0:00:07,0:53:55,0:22:30,0:06:04,7:32:55,2740,0,24
+General Medicine,PGY1,S0195227,2016-09-01,0:03:34,0:04:53,0:00:01,0:02:38,0:00:01,0:00:01,0:11:28,113,89,5
+General Medicine,PGY2+,S0084443,2013-06-30,2:54:57,0:11:24,0:39:02,0:15:01,0:06:31,0:34:57,4:52:38,1794,0,31
+General Medicine,PGY1,S0195227,2016-09-09,1:55:53,1:07:36,0:01:29,1:08:20,0:20:48,0:04:16,6:02:09,1816,1,10
+General Medicine,PGY1,S0195227,2016-09-08,1:12:52,0:29:22,0:00:04,0:27:51,0:07:06,0:00:34,2:32:06,823,0,7
+General Medicine,PGY1,S0169411,2014-10-01,2:54:42,0:00:36,0:11:06,0:31:20,0:24:04,0:02:51,4:27:40,1233,0,6
+General Medicine,PGY1,S0169411,2014-10-03,0:32:12,0:00:30,0:00:16,0:00:00,0:00:25,0:03:56,0:55:12,442,442,21
+General Medicine,PGY1,S0169411,2014-10-02,5:04:20,0:05:58,0:10:59,0:50:01,0:30:16,0:05:02,7:16:12,2077,0,18
+General Medicine,PGY1,S0169411,2014-10-05,4:34:15,0:00:58,0:06:38,0:27:45,0:18:17,0:02:17,5:40:47,1339,127,11
+General Medicine,PGY1,S0169411,2014-10-04,2:35:26,0:01:16,0:06:27,0:07:04,0:03:44,0:04:34,3:12:17,922,0,10
+General Medicine,PGY1,S0169411,2014-10-07,4:41:41,0:16:00,0:07:21,0:47:12,0:43:32,0:04:37,7:09:44,2226,54,16
+General Medicine,PGY1,S0169411,2014-10-06,0:00:00,0:00:00,0:00:00,0:00:08,0:00:00,0:00:00,0:00:08,6,0,3
+General Medicine,PGY1,S0169411,2014-10-09,3:30:33,0:06:34,0:14:39,0:14:45,0:13:53,0:02:03,4:40:47,1103,0,10
+General Medicine,PGY1,S0169411,2014-10-08,4:12:05,0:02:50,0:25:41,0:53:56,0:24:12,0:05:25,7:07:06,2378,277,19
+Intensive Care Unit,PGY2+,S0169383,2015-10-20,1:26:51,0:34:16,1:34:37,0:29:57,0:20:02,0:26:38,5:35:42,1521,0,15
+General Medicine,PGY1,S0056755,2015-03-12,1:06:02,0:05:06,0:11:41,0:12:48,0:09:38,0:04:37,2:02:36,712,0,6
+Night Team,PGY1,S0181069,2016-02-12,1:42:52,0:38:00,2:30:29,0:40:19,0:24:08,0:14:56,6:53:19,1942,0,36
+Night Team,PGY1,S0181069,2016-02-13,0:55:56,0:15:52,0:04:37,0:17:50,0:08:33,0:15:17,2:21:59,1131,0,38
+Night Team,PGY1,S0181069,2016-02-10,1:15:23,1:21:15,1:11:10,1:01:32,0:27:27,0:17:24,6:20:29,2989,0,45
+Night Team,PGY1,S0181069,2016-02-11,1:41:37,0:58:39,1:00:49,0:36:33,0:43:38,0:30:16,6:02:55,2552,110,38
+General Medicine,PGY1,S0182038,2016-09-18,2:20:08,1:07:18,0:00:20,0:29:08,0:22:16,0:09:33,5:35:18,1476,0,19
+General Medicine,PGY1,S0182038,2016-09-19,1:18:39,1:02:18,0:00:35,0:30:51,0:20:51,0:10:07,3:55:13,1126,50,13
+Night Team,PGY1,S0182038,2016-09-16,2:29:31,0:14:44,0:00:00,0:42:26,0:24:49,0:05:15,4:17:58,1000,669,38
+Night Team,PGY1,S0182038,2016-09-17,0:59:57,0:18:48,0:00:00,0:19:38,0:09:22,0:01:04,2:05:37,552,0,29
+Night Team,PGY2+,S0169386,2016-03-04,3:34:36,1:52:07,0:00:16,0:30:27,0:24:10,0:07:24,7:17:35,2855,51,78
+Night Team,PGY2+,S0169386,2016-03-05,2:38:13,0:58:55,0:00:09,0:23:44,0:09:43,0:00:53,4:48:14,1708,18,60
+Night Team,PGY2+,S0169386,2016-03-02,2:27:53,0:54:13,0:06:57,0:31:16,0:12:13,0:08:14,4:44:46,1731,303,60
+Night Team,PGY2+,S0169386,2016-03-03,1:56:14,0:49:55,0:00:02,0:19:39,0:05:27,0:00:55,3:38:52,1455,0,33
+Night Team,PGY1,S0182038,2016-09-10,1:54:19,1:00:07,0:00:10,0:57:18,0:24:46,0:00:32,4:58:57,1394,0,26
+Night Team,PGY2+,S0169386,2016-03-01,4:22:39,1:58:16,0:06:30,0:46:25,0:25:48,0:07:54,8:59:08,3248,79,58
+Night Team,PGY2+,S0172645,2016-07-08,0:08:49,0:06:09,0:00:00,0:09:44,0:03:41,0:00:05,0:31:56,279,8,13
+Night Team,PGY2+,S0172645,2016-07-09,1:19:02,1:42:15,0:00:11,0:48:41,0:22:39,0:05:29,4:35:37,1255,0,24
+General Medicine,PGY1,S0195229,2016-08-08,1:52:53,0:38:38,0:00:08,0:48:31,0:25:08,0:05:52,4:29:18,1123,0,7
+General Medicine,PGY1,S0195229,2016-08-09,1:31:34,0:32:46,0:00:08,0:56:11,0:16:07,0:10:54,3:55:36,1134,206,7
+General Medicine,PGY1,S0195229,2016-08-06,2:14:50,0:37:28,0:00:03,0:52:50,0:11:44,0:02:08,4:31:01,1213,0,5
+Night Team,PGY2+,S0169404,2016-05-21,2:34:18,1:13:36,0:00:13,0:35:13,0:16:30,0:08:10,5:09:41,2453,32,44
+General Medicine,PGY1,S0195229,2016-08-04,0:06:09,0:01:56,0:00:01,0:00:13,0:00:01,0:00:00,0:16:43,118,106,4
+General Medicine,PGY1,S0195229,2016-08-05,2:15:21,0:32:09,0:00:01,0:56:16,0:34:11,0:10:35,4:49:39,1619,0,7
+General Medicine,PGY1,S0195229,2016-08-02,1:52:18,0:17:35,0:00:03,0:32:51,0:09:27,0:01:12,3:15:28,1154,270,6
+General Medicine,PGY1,S0079337,2014-12-12,4:06:19,0:08:49,2:00:40,0:38:52,0:24:01,0:06:36,8:03:18,2638,11,13
+Night Team,PGY2+,S0172645,2016-07-06,2:50:40,1:24:55,0:00:08,0:59:55,0:17:09,0:01:48,6:04:21,1587,0,19
+General Medicine,PGY1,S0195229,2016-08-01,0:31:32,0:27:22,0:00:07,0:21:47,0:02:07,0:00:04,1:34:28,525,24,5
+Intensive Care Unit,PGY2+,S0169395,2015-10-23,3:04:08,1:20:48,0:14:38,0:31:10,0:50:39,0:30:03,6:58:46,2924,0,27
+Intensive Care Unit,PGY2+,S0169395,2015-10-22,1:36:14,0:50:03,0:09:20,0:14:16,0:25:16,0:25:55,3:55:53,1438,0,17
+Intensive Care Unit,PGY2+,S0169395,2015-10-21,1:24:55,0:35:33,0:22:42,0:17:57,0:12:33,0:19:44,3:32:37,1583,0,16
+General Medicine,PGY1,S0195201,2016-10-15,2:22:25,0:59:36,0:00:13,0:12:50,0:01:29,0:10:10,4:16:11,1375,839,24
+Intensive Care Unit,PGY2+,S0169395,2015-10-27,1:35:55,1:22:45,0:19:18,0:39:34,0:33:40,0:18:12,5:23:18,1991,0,32
+Intensive Care Unit,PGY2+,S0169395,2015-10-26,0:00:00,0:00:00,0:00:00,0:00:09,0:00:00,0:00:00,0:00:09,12,0,5
+General Medicine,PGY1,S0195201,2016-10-10,1:28:11,1:30:50,0:00:20,0:05:20,0:05:46,0:02:20,3:39:15,1657,340,13
+Intensive Care Unit,PGY2+,S0169395,2015-10-24,2:24:49,0:33:23,0:28:00,0:26:57,0:20:22,0:25:21,4:52:39,1790,0,19
+Intensive Care Unit,PGY2+,S0169395,2015-10-29,2:35:49,1:21:05,0:21:16,0:17:07,0:28:26,0:21:38,5:46:02,1774,0,18
+Intensive Care Unit,PGY2+,S0169395,2015-10-28,1:41:37,0:25:53,0:14:10,0:21:33,0:18:39,0:20:13,3:34:59,1279,45,17
+Emergency Medicine,PGY1,S0195075,2016-08-09,1:02:29,0:10:46,0:03:16,0:09:49,0:16:30,0:00:11,2:05:55,565,0,15
+Emergency Medicine,PGY1,S0195075,2016-08-08,2:34:12,0:13:33,0:11:21,0:24:38,0:39:30,0:01:01,4:40:54,1103,0,28
+Night Team,PGY1,S0195094,2016-07-13,1:14:20,0:16:28,0:00:00,0:19:52,0:15:54,0:00:11,2:24:24,637,453,23
+Emergency Medicine,PGY1,S0195075,2016-08-04,0:01:08,0:00:04,0:00:00,0:00:00,0:00:00,0:00:00,0:01:21,36,30,4
+Emergency Medicine,PGY1,S0195075,2016-08-07,1:47:04,0:15:37,0:02:53,0:23:41,0:31:55,0:00:19,3:21:56,961,0,21
+Emergency Medicine,PGY1,S0195075,2016-08-06,3:06:41,0:22:15,0:01:56,0:44:00,0:59:53,0:00:29,6:15:31,1876,0,25
+Emergency Medicine,PGY1,S0195075,2016-08-02,3:17:24,0:25:51,0:01:14,0:09:56,0:36:30,0:00:29,5:03:29,1126,0,16
+Emergency Medicine,PGY2+,S0169385,2016-11-10,4:08:23,1:07:19,0:05:07,0:39:05,0:55:45,0:06:40,7:56:20,2952,0,63
+Night Team,PGY1,S0195094,2016-07-10,2:28:36,0:16:03,0:00:00,0:28:27,0:30:08,0:00:13,3:53:11,738,516,28
+General Medicine,PGY1,S0181060,2015-08-08,2:07:32,0:47:20,1:38:48,0:44:57,0:37:11,0:10:54,6:54:59,1827,7,13
+Emergency Medicine,PGY1,S0181082,2016-03-12,1:29:57,0:19:56,0:00:13,0:11:20,0:14:48,0:01:31,2:29:40,783,0,9
+Emergency Medicine,PGY1,S0181082,2016-03-13,3:24:38,0:59:16,0:04:42,0:37:49,0:46:04,0:03:06,6:28:59,2167,0,34
+Emergency Medicine,PGY1,S0181082,2016-03-10,2:55:03,1:06:49,0:04:17,0:32:41,0:53:10,0:10:17,6:20:36,2003,0,22
+Emergency Medicine,PGY1,S0181082,2016-03-11,2:45:06,0:42:38,0:01:49,0:32:02,0:37:16,0:03:11,5:20:29,1789,0,17
+General Medicine,PGY1,S0181060,2015-08-03,2:14:21,0:29:06,1:30:39,0:42:44,0:12:19,0:25:25,6:22:44,1513,20,5
+Emergency Medicine,PGY1,S0181082,2016-03-17,2:37:27,0:28:44,0:09:24,0:36:19,0:39:46,0:04:53,5:23:48,2262,0,35
+General Medicine,PGY1,S0109179,2014-08-18,2:09:55,0:04:10,0:10:29,0:29:08,0:17:42,0:19:42,4:06:26,1561,737,13
+General Medicine,PGY1,S0109179,2014-08-19,1:29:58,0:01:46,0:13:17,0:07:51,0:03:36,0:08:37,2:25:40,880,62,11
+General Medicine,PGY1,S0109179,2014-08-17,2:48:05,0:05:28,0:23:11,0:21:08,0:17:44,0:20:42,4:34:12,1308,0,54
+Night Team,PGY1,S0195094,2016-07-16,1:11:48,0:36:37,0:00:00,0:08:20,0:09:07,0:00:24,2:12:21,479,0,23
+Emergency Medicine,PGY1,S0181087,2015-08-19,0:02:00,0:00:00,0:00:27,0:00:00,0:00:00,0:00:06,0:02:46,74,72,4
+Emergency Medicine,PGY1,S0181087,2015-08-18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Emergency Medicine,PGY1,S0181087,2015-08-17,2:31:03,0:26:09,3:10:09,1:07:20,0:24:05,0:07:04,8:27:01,2559,0,17
+Emergency Medicine,PGY1,S0181087,2015-08-16,2:32:32,0:24:32,3:16:09,0:51:55,0:29:16,0:02:33,8:19:24,2075,0,18
+General Medicine,PGY1,S0181087,2015-08-15,1:45:10,0:45:48,2:58:53,0:43:38,0:17:05,0:23:08,7:31:37,1997,0,13
+General Medicine,PGY1,S0181087,2015-08-14,2:19:18,0:13:27,2:12:57,1:10:56,0:17:46,0:18:21,7:09:14,2007,14,8
+General Medicine,PGY1,S0181087,2015-08-13,2:01:13,0:21:17,1:49:46,0:46:35,0:18:12,0:34:16,6:29:07,1761,6,11
+General Medicine,PGY1,S0181087,2015-08-12,2:13:29,0:08:34,1:10:40,0:11:00,0:09:34,0:21:00,4:37:30,1423,3,16
+General Medicine,PGY1,S0181087,2015-08-11,1:49:12,0:05:51,0:33:33,0:17:29,0:20:37,0:23:20,3:55:10,1197,8,5
+General Medicine,PGY1,S0181087,2015-08-10,2:55:06,0:11:11,1:04:30,0:31:10,0:18:36,0:43:20,6:14:10,2270,0,10
+General Medicine,PGY1,S0181057,2016-02-19,0:57:58,0:44:45,1:04:31,0:25:49,0:24:06,0:17:13,4:13:39,1251,0,8
+General Medicine,PGY1,S0181057,2016-02-18,2:04:15,2:19:28,1:44:17,1:00:51,0:19:50,0:28:55,8:24:00,2819,222,16
+General Medicine,PGY1,S0181057,2016-02-13,2:01:09,1:20:35,0:58:25,1:03:49,0:56:35,0:26:54,7:30:52,2510,93,9
+General Medicine,PGY1,S0181057,2016-02-12,0:12:54,0:46:29,0:31:58,0:13:12,0:01:50,0:06:42,2:04:08,568,556,12
+General Medicine,PGY1,S0181057,2016-02-11,1:48:50,0:56:37,1:13:35,1:24:55,0:32:00,0:45:47,7:15:15,2586,0,17
+General Medicine,PGY1,S0181057,2016-02-10,1:00:12,1:19:06,0:31:15,1:01:06,0:25:06,0:23:23,5:15:11,1818,199,16
+General Medicine,PGY1,S0181057,2016-02-17,0:09:24,0:16:58,0:27:27,0:03:47,0:02:07,0:03:01,1:14:01,675,646,16
+General Medicine,PGY1,S0181057,2016-02-16,1:36:03,1:13:22,2:06:17,0:53:12,0:24:13,0:25:20,7:20:52,2261,118,14
+General Medicine,PGY1,S0181057,2016-02-15,0:58:01,1:22:42,1:33:20,0:38:47,0:27:46,0:18:41,6:05:26,1985,414,12
+General Medicine,PGY1,S0181057,2016-02-14,1:03:27,1:05:15,1:17:23,0:28:45,0:22:00,0:15:31,5:12:01,1462,0,8
+General Medicine,PGY1,S0187071,2016-08-31,1:04:58,0:46:19,0:00:55,0:09:09,0:12:42,0:03:49,2:27:00,1107,0,16
+General Medicine,PGY1,S0187071,2016-08-30,1:47:49,0:44:51,0:04:23,0:44:51,0:17:22,0:01:52,4:06:17,1134,70,9
+General Medicine,PGY2+,S0169401,2016-01-20,2:53:38,1:05:20,0:06:50,0:29:15,0:11:31,0:49:25,6:14:01,3373,839,45
+General Medicine,PGY2+,S0169401,2016-01-21,3:06:11,1:30:06,0:12:06,1:12:10,0:23:26,0:47:54,7:46:04,4253,597,64
+General Medicine,PGY2+,S0169401,2016-01-26,3:18:27,1:20:35,0:18:56,0:55:32,0:29:30,0:57:04,7:56:15,3447,399,47
+General Medicine,PGY2+,S0169401,2016-01-27,2:15:34,1:28:03,0:48:30,0:49:40,0:34:13,0:53:30,7:16:03,3406,659,73
+General Medicine,PGY2+,S0169401,2016-01-24,3:04:41,1:35:15,0:43:16,1:18:49,0:22:02,0:39:47,8:30:16,4430,362,42
+General Medicine,PGY2+,S0169401,2016-01-25,2:19:55,1:17:07,0:20:38,0:58:56,0:14:12,0:34:25,6:10:49,3311,973,45
+General Medicine,PGY2+,S0169401,2016-01-28,0:52:12,0:31:19,0:06:15,0:02:11,0:01:03,0:07:12,1:46:33,849,832,31
+General Medicine,PGY2+,S0169401,2016-01-29,2:44:34,1:30:45,0:37:43,0:33:13,0:20:47,0:36:45,7:05:48,4159,222,48
+General Medicine,PGY2+,S0169384,2016-07-02,2:02:02,1:55:13,0:00:01,0:26:12,0:24:42,0:07:46,5:54:55,2283,151,20
+Night Team,PGY1,S0182038,2016-09-11,1:40:32,0:19:23,0:00:00,0:41:36,0:18:49,0:05:24,3:21:35,793,0,22
+Night Team,PGY1,S0169389,2014-06-28,4:09:12,0:05:27,0:06:35,0:54:18,0:42:39,1:01:26,7:19:52,1889,0,50
+General Medicine,PGY1,S0074196,2016-01-28,0:00:00,0:00:00,0:00:00,0:01:10,0:00:00,0:00:00,0:01:10,20,0,7
+Night Team,PGY1,S0169389,2014-06-29,2:40:58,0:06:00,0:04:47,0:37:03,0:24:19,0:47:50,5:02:02,1115,0,30
+Intensive Care Unit,PGY2+,S0169381,2016-01-27,1:09:59,0:31:14,0:27:34,0:22:32,0:13:40,0:05:58,3:04:08,1008,14,23
+Intensive Care Unit,PGY2+,S0169381,2016-01-26,2:02:50,0:52:00,0:09:49,0:20:16,0:49:31,0:35:00,5:10:27,1597,0,21
+Intensive Care Unit,PGY2+,S0169381,2016-01-25,0:02:35,0:05:55,0:00:19,0:01:50,0:00:00,0:00:26,0:11:42,153,128,16
+Intensive Care Unit,PGY2+,S0169381,2016-01-24,2:18:16,1:15:32,0:19:49,0:34:00,0:32:27,0:33:36,6:10:19,1639,0,16
+Intensive Care Unit,PGY2+,S0169381,2016-01-23,1:12:05,0:30:41,0:02:13,0:41:17,0:19:26,0:38:58,3:56:07,1446,23,18
+Intensive Care Unit,PGY2+,S0169381,2016-01-22,1:01:04,0:08:52,0:09:03,0:07:10,0:03:35,0:08:19,1:53:32,642,0,12
+Intensive Care Unit,PGY2+,S0169381,2016-01-21,1:31:12,0:21:59,0:13:39,0:21:05,0:22:36,0:17:27,3:26:14,979,7,15
+Intensive Care Unit,PGY2+,S0169381,2016-01-20,1:29:35,0:10:09,0:03:18,0:15:57,0:15:16,0:23:41,2:53:10,781,32,12
+Intensive Care Unit,PGY2+,S0169381,2016-01-29,1:47:45,0:30:46,0:06:55,0:15:39,0:22:52,0:50:40,4:15:58,1145,29,19
+General Medicine,PGY2+,S0169401,2016-02-05,2:41:54,1:27:23,0:31:15,1:02:55,0:22:57,1:09:17,7:46:52,3635,301,35
+Night Team,PGY1,S0056755,2014-12-09,3:01:03,0:26:18,1:57:20,0:35:03,0:28:45,0:24:43,7:15:09,1547,0,34
+Night Team,PGY1,S0056755,2014-12-08,3:10:12,0:03:55,0:10:52,0:32:52,0:29:41,0:33:11,5:13:09,1357,0,33
+General Medicine,PGY1,S0094959,2014-11-24,4:18:40,0:10:04,0:53:26,0:10:22,0:29:08,0:18:55,6:43:11,3016,53,28
+General Medicine,PGY1,S0094959,2014-11-25,5:44:33,0:14:06,1:27:37,0:30:41,0:30:37,0:27:11,9:15:51,3760,0,28
+General Medicine,PGY1,S0094959,2014-11-22,4:16:03,0:09:35,0:33:25,0:06:28,0:24:30,0:22:28,6:06:37,2564,99,10
+General Medicine,PGY1,S0181075,2015-08-12,1:48:55,1:10:25,2:43:05,0:55:53,0:20:01,0:12:10,7:42:58,2182,0,7
+General Medicine,PGY1,S0094959,2014-11-20,4:46:56,0:07:14,2:06:33,0:21:30,0:25:59,0:21:41,8:21:58,2931,37,19
+General Medicine,PGY1,S0094959,2014-11-21,0:07:42,0:16:32,0:00:00,0:00:17,0:02:05,0:04:37,0:34:17,403,392,12
+Intensive Care Unit,PGY2+,S0169395,2015-10-20,2:06:57,1:02:06,0:47:09,0:16:45,0:12:57,0:28:23,5:18:43,1912,53,23
+Night Team,PGY1,S0056755,2014-12-07,2:06:59,0:14:34,0:13:31,0:06:51,0:33:27,0:33:42,4:05:04,1093,60,32
+General Medicine,PGY1,S0094959,2014-11-29,0:00:00,0:13:57,0:00:00,0:08:29,0:00:00,0:14:02,0:39:04,419,413,7
+Night Team,PGY2+,S0169398,2015-09-02,0:48:58,0:28:44,2:24:17,0:14:55,0:11:48,0:18:06,4:38:07,956,0,14
+General Medicine,PGY1,S0074196,2016-01-23,0:00:00,0:00:00,0:00:00,0:00:16,0:00:00,0:00:00,0:00:16,12,0,4
+General Medicine,PGY1,S0181074,2016-05-12,1:45:59,0:39:03,0:00:13,0:27:18,0:30:59,0:11:46,4:15:26,1691,413,16
+General Medicine,PGY2+,S0169401,2016-02-04,2:44:43,1:18:02,0:46:01,1:05:02,0:30:13,0:44:18,7:41:53,3770,617,25
+General Medicine,PGY1,S0181075,2015-08-13,1:43:33,0:39:54,2:21:42,0:39:48,0:20:03,0:20:36,6:43:09,1919,102,7
+Night Team,PGY1,S0169394,2014-10-30,2:25:06,0:08:35,0:16:22,0:18:42,0:22:52,0:32:58,4:11:50,1103,27,31
+Night Team,PGY1,S0169394,2014-10-31,1:17:53,0:00:00,0:13:46,0:13:29,0:04:34,0:10:01,2:14:50,637,0,15
+Intensive Care Unit,PGY2+,S0169381,2016-01-16,1:55:36,0:45:57,0:12:15,0:55:15,0:22:46,0:52:21,5:15:49,1553,53,15
+General Medicine,PGY1,S0196597,2016-10-09,1:42:19,0:41:08,0:00:07,0:42:36,0:14:50,0:05:31,4:14:12,1530,0,15
+Intensive Care Unit,PGY2+,S0169381,2016-01-17,2:53:45,0:18:38,0:17:27,0:16:46,0:16:34,0:40:15,4:57:01,1009,0,14
+General Medicine,PGY1,S0074196,2016-01-25,0:39:19,0:20:13,0:09:58,0:16:32,0:08:30,0:08:58,1:53:43,954,32,7
+Intensive Care Unit,PGY2+,S0169381,2016-01-14,1:34:15,0:36:00,0:11:07,0:20:46,0:21:29,0:23:33,3:53:12,1084,15,18
+General Medicine,PGY1,S0181075,2015-08-10,1:48:45,0:43:53,1:24:16,1:44:51,0:17:09,0:36:34,7:13:55,2424,308,7
+Intensive Care Unit,PGY2+,S0169381,2016-01-15,1:37:32,0:31:47,0:17:49,0:24:47,0:30:49,0:26:52,4:16:05,1438,0,15
+General Medicine,PGY1,S0181059,2016-02-28,0:07:48,0:01:05,0:00:07,0:00:25,0:00:16,0:03:09,0:14:50,106,106,7
+General Medicine,PGY1,S0181059,2016-02-29,2:33:50,1:55:26,0:00:42,1:00:16,0:26:32,0:04:20,6:58:47,3535,52,11
+Intensive Care Unit,PGY2+,S0169381,2016-01-12,1:29:57,0:45:50,0:13:21,0:29:57,0:41:39,0:22:06,4:14:44,1576,0,20
+Intensive Care Unit,PGY2+,S0169381,2016-01-13,1:55:09,0:46:18,0:12:05,0:17:03,0:29:22,0:29:17,4:27:28,1324,17,21
+Intensive Care Unit,PGY2+,S0169381,2016-01-10,0:10:52,0:37:28,0:03:51,0:00:00,0:00:19,0:01:37,0:54:32,240,240,13
+Intensive Care Unit,PGY2+,S0169395,2015-10-25,1:35:02,0:43:58,0:24:43,0:20:58,0:29:36,0:29:28,4:13:49,1531,0,16
+General Medicine,PGY2+,S0169402,2016-06-26,1:50:59,1:13:04,0:00:06,0:21:27,0:15:03,0:01:41,4:15:53,1812,374,24
+General Medicine,PGY2+,S0169402,2016-06-27,3:33:53,1:40:50,0:00:02,1:09:37,0:27:14,0:07:28,7:57:11,2967,249,42
+General Medicine,PGY1,S0181077,2015-12-28,2:27:17,2:00:27,1:31:32,0:51:24,0:33:56,0:24:24,8:32:09,3778,102,23
+General Medicine,PGY1,S0181077,2015-12-29,2:19:37,1:00:21,0:59:01,1:01:01,0:40:45,0:32:07,7:05:01,3087,17,17
+General Medicine,PGY1,S0181077,2015-12-26,2:53:34,1:29:55,0:47:31,0:14:04,0:30:33,0:30:10,7:02:18,2709,62,20
+General Medicine,PGY1,S0181077,2015-12-27,1:22:47,0:42:57,0:57:56,0:28:31,0:12:34,0:14:28,4:19:10,1854,191,9
+General Medicine,PGY1,S0181077,2015-12-24,0:05:32,0:10:24,0:00:33,0:04:56,0:01:17,0:04:07,0:28:35,461,436,11
+General Medicine,PGY1,S0181077,2015-12-25,2:50:43,1:17:01,1:14:53,0:49:46,0:32:52,0:40:51,7:52:04,3314,144,49
+General Medicine,PGY1,S0181077,2015-12-22,1:05:23,0:31:07,0:48:41,0:12:14,0:09:10,0:17:41,3:16:52,1386,165,11
+General Medicine,PGY1,S0181077,2015-12-23,2:49:18,1:26:57,2:42:59,0:25:15,0:26:38,0:28:21,8:50:41,3340,256,38
+General Medicine,PGY2+,S0169402,2016-06-28,1:44:48,1:00:08,0:00:07,1:11:34,0:30:54,0:16:00,5:12:59,1500,58,15
+General Medicine,PGY2+,S0169402,2016-06-29,0:36:12,0:25:43,0:00:00,0:01:55,0:00:22,0:04:11,1:16:47,391,360,18
+Night Team,PGY1,S0163425,2014-08-12,1:44:59,0:13:06,0:10:09,0:18:53,0:40:36,1:19:52,4:42:42,1399,0,29
+Night Team,PGY1,S0163425,2014-08-13,1:32:47,0:08:24,0:07:06,0:14:50,0:15:37,0:40:26,3:16:17,862,19,25
+Night Team,PGY1,S0163425,2014-08-10,2:39:31,0:04:59,0:12:17,0:39:09,0:53:41,1:29:04,6:18:36,1805,45,30
+General Medicine,PGY1,S0169386,2014-12-19,2:16:19,0:02:04,2:28:58,0:27:54,0:36:05,0:51:45,7:15:35,2111,506,13
+General Medicine,PGY1,S0169386,2014-12-18,3:05:07,0:24:16,1:43:47,0:51:19,0:32:41,0:55:27,8:00:21,2850,30,20
+Night Team,PGY1,S0163425,2014-08-11,2:26:21,0:04:39,0:17:06,0:34:51,0:20:32,0:52:23,5:00:08,1428,10,30
+General Medicine,PGY1,S0181064,2016-05-03,0:12:09,0:05:50,0:00:00,0:02:05,0:00:30,0:00:01,0:20:48,117,72,11
+General Medicine,PGY1,S0169386,2014-12-11,2:46:13,0:08:56,1:05:19,0:42:22,0:39:53,1:10:04,6:58:40,2228,98,12
+General Medicine,PGY1,S0169386,2014-12-10,3:33:18,0:11:04,1:57:52,1:21:27,1:00:11,1:18:48,9:55:28,3077,0,14
+General Medicine,PGY1,S0169386,2014-12-13,0:24:24,0:08:06,0:22:16,0:04:48,0:00:29,0:13:22,1:28:17,631,611,20
+General Medicine,PGY1,S0169386,2014-12-12,3:40:09,0:11:06,1:25:04,0:47:09,0:30:00,0:41:55,8:08:57,2675,19,12
+General Medicine,PGY1,S0169386,2014-12-15,0:00:00,0:10:18,0:00:00,0:00:01,0:00:00,0:00:29,0:12:26,90,80,9
+General Medicine,PGY1,S0169386,2014-12-14,0:00:00,0:02:30,0:00:00,0:00:02,0:00:00,0:00:00,0:02:37,19,7,6
+General Medicine,PGY1,S0169386,2014-12-17,0:00:00,0:00:18,0:00:00,0:00:03,0:00:00,0:06:49,0:07:36,51,37,6
+General Medicine,PGY1,S0169386,2014-12-16,3:37:37,0:17:54,1:12:06,0:26:49,0:25:14,0:38:23,6:50:07,1944,11,10
+General Medicine,PGY1,S0181074,2016-04-27,1:18:21,1:35:12,0:00:11,1:17:25,0:24:39,0:08:11,5:30:23,2087,150,8
+General Medicine,PGY1,S0181074,2016-04-26,0:28:36,0:02:54,0:00:00,0:00:00,0:00:00,0:05:31,0:40:19,233,231,8
+General Medicine,PGY1,S0181074,2016-04-25,1:42:09,1:43:50,0:00:36,1:26:15,0:16:42,0:18:53,6:22:22,2518,48,15
+General Medicine,PGY1,S0181074,2016-04-24,0:51:14,1:29:57,0:00:14,0:30:12,0:12:06,0:07:26,3:25:21,1173,20,7
+Night Team,PGY1,S0163425,2014-08-14,0:31:11,0:02:39,0:09:01,0:16:19,0:27:16,0:53:47,2:27:51,536,0,24
+General Medicine,PGY1,S0174975,2016-01-02,0:02:58,0:05:12,0:00:00,0:00:34,0:00:00,0:01:59,0:14:25,206,202,9
+General Medicine,PGY1,S0174975,2016-01-01,2:27:53,1:33:18,1:15:26,1:43:55,0:27:48,0:01:46,8:14:13,3200,166,62
+General Medicine,PGY1,S0181074,2016-04-29,2:26:38,1:38:08,0:00:22,1:18:11,0:23:09,0:11:05,6:21:34,2104,181,10
+General Medicine,PGY1,S0181074,2016-04-28,2:26:23,1:40:22,0:00:31,1:18:29,0:33:32,0:21:40,7:17:21,2799,153,11
+Night Team,PGY1,S0196595,2016-09-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Night Team,PGY1,S0196595,2016-09-16,1:59:26,0:34:58,0:02:46,0:25:16,0:11:17,0:03:27,3:28:32,758,0,33
+Night Team,PGY1,S0196595,2016-09-15,1:29:54,0:44:27,0:00:00,0:29:31,0:30:07,0:04:04,3:25:16,1103,67,28
+Night Team,PGY1,S0196595,2016-09-14,1:23:32,0:48:48,0:00:00,0:22:44,0:13:57,0:05:10,3:05:54,745,159,27
+Night Team,PGY1,S0196595,2016-09-13,1:48:04,1:27:30,0:00:20,0:32:27,0:17:57,0:10:14,4:46:42,1483,677,30
+Night Team,PGY1,S0196595,2016-09-12,2:46:59,0:54:43,0:00:03,0:31:00,0:20:02,0:01:05,5:03:47,1538,0,36
+Night Team,PGY1,S0196595,2016-09-11,0:59:41,0:28:00,0:00:05,0:11:19,0:13:50,0:00:03,2:07:56,461,126,13
+Night Team,PGY1,S0196595,2016-09-10,1:50:50,1:02:33,0:04:38,0:38:30,0:12:24,0:03:46,4:01:00,1005,0,16
+General Medicine,PGY2+,S0169401,2016-02-02,0:53:33,0:15:48,0:01:36,0:04:39,0:01:22,0:08:59,1:40:28,788,776,38
+Intensive Care Unit,PGY2+,S0169386,2016-07-02,3:09:46,0:56:35,0:18:55,0:31:10,0:18:50,0:09:28,5:58:18,1903,0,15
+Intensive Care Unit,PGY2+,S0169386,2016-07-03,0:00:50,0:00:06,0:00:00,0:00:00,0:00:00,0:00:00,0:03:56,38,0,9
+Intensive Care Unit,PGY2+,S0169386,2016-07-01,2:08:49,0:40:13,0:01:19,0:42:24,0:18:39,0:14:55,4:35:15,1372,0,27
+Intensive Care Unit,PGY2+,S0169386,2016-07-06,3:34:27,1:11:39,0:05:39,0:38:03,0:30:27,0:11:05,7:01:54,2194,0,31
+Intensive Care Unit,PGY2+,S0169386,2016-07-07,3:27:26,0:37:41,0:06:15,0:26:16,0:23:42,0:07:13,5:51:09,1982,104,29
+Intensive Care Unit,PGY2+,S0169386,2016-07-04,0:00:25,0:00:04,0:00:00,0:00:00,0:00:00,0:00:36,0:02:15,43,0,7
+Intensive Care Unit,PGY2+,S0169386,2016-07-05,2:41:53,0:30:33,0:07:46,0:44:15,0:24:26,0:19:20,5:28:10,1757,0,18
+Intensive Care Unit,PGY2+,S0169386,2016-07-08,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,6,0,3
+Intensive Care Unit,PGY2+,S0169386,2016-07-09,1:41:14,0:35:31,0:05:24,0:16:23,0:13:52,0:02:42,3:18:17,891,0,15
+Emergency Medicine,PGY1,S0181063,2016-06-20,0:01:01,0:03:08,0:00:00,0:00:00,0:00:00,0:00:00,0:04:31,206,206,5
+Emergency Medicine,PGY1,S0181063,2016-06-23,1:44:46,0:32:53,0:00:37,0:07:54,0:36:41,0:14:55,3:37:22,1713,0,16
+Emergency Medicine,PGY1,S0181063,2016-06-22,1:08:12,0:46:44,0:06:20,0:17:00,0:51:43,0:23:06,4:04:50,2140,0,15
+Emergency Medicine,PGY1,S0181063,2016-06-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,1
+General Medicine,PGY1,S0181056,2015-12-03,0:00:00,0:06:13,0:00:00,0:00:00,0:00:00,0:00:42,0:12:05,180,168,7
+General Medicine,PGY1,S0169389,2014-10-12,4:56:54,0:15:29,0:19:11,0:23:44,0:31:28,0:23:31,7:13:16,2027,0,12
+General Medicine,PGY1,S0169389,2014-10-13,2:58:59,0:15:12,0:06:56,0:18:44,0:19:57,0:35:41,5:10:06,1412,0,7
+Emergency Medicine,PGY1,S0169389,2014-10-10,1:23:42,0:04:00,0:10:24,0:05:18,0:10:49,0:03:46,2:18:55,886,719,16
+Emergency Medicine,PGY1,S0169389,2014-10-11,1:47:06,0:04:41,0:11:48,0:15:53,0:08:17,0:07:17,2:55:46,954,35,9
+General Medicine,PGY1,S0169389,2014-10-16,1:03:30,0:03:45,0:04:27,0:02:00,0:06:43,0:00:08,1:59:38,533,527,14
+General Medicine,PGY1,S0169389,2014-10-17,4:41:47,0:08:39,0:13:59,0:38:01,0:22:05,0:16:43,6:53:50,1501,0,8
+General Medicine,PGY1,S0169389,2014-10-14,3:59:41,0:22:20,0:03:21,0:27:54,0:49:46,0:52:25,7:03:03,2043,0,18
+General Medicine,PGY1,S0169389,2014-10-15,3:55:31,0:10:02,0:14:21,0:27:10,0:32:42,0:23:01,6:50:04,2102,688,23
+General Medicine,PGY1,S0169389,2014-10-18,1:37:55,0:02:17,0:06:13,0:08:21,0:06:26,0:11:31,2:23:30,525,0,7
+General Medicine,PGY1,S0169389,2014-10-19,3:43:17,0:16:19,0:17:29,0:24:26,0:14:01,0:24:32,5:40:19,1296,0,12
+General Medicine,PGY1,S0181073,2016-05-27,2:19:51,1:19:18,0:00:06,0:49:12,0:19:32,0:08:31,5:35:52,1864,68,15
+General Medicine,PGY1,S0181064,2016-05-05,3:01:02,1:23:41,0:03:37,0:29:21,0:23:08,0:04:23,5:59:56,1985,435,39
+Intensive Care Unit,PGY2+,S0169401,2016-03-30,2:00:55,0:27:05,0:04:04,0:09:17,0:06:52,0:04:12,3:21:19,1155,60,31
+Night Team,PGY1,S0195079,2016-10-19,1:02:10,0:51:40,0:00:00,0:22:28,0:13:12,0:04:40,2:48:32,800,85,31
+Night Team,PGY1,S0195079,2016-10-18,2:04:04,0:44:25,0:00:00,0:22:14,0:19:15,0:02:03,3:48:33,1148,0,30
+General Medicine,PGY1,S0181067,2016-02-09,1:05:33,0:44:11,0:59:38,0:29:49,0:20:10,0:16:49,4:19:33,1289,0,8
+General Medicine,PGY1,S0181067,2016-02-08,0:24:45,0:20:48,0:06:32,0:15:15,0:17:49,0:07:12,1:41:24,510,9,7
+Intensive Care Unit,PGY2+,S0169384,2015-09-13,3:07:59,0:09:13,0:34:01,0:22:39,0:25:16,0:23:07,5:24:05,1472,0,13
+General Medicine,PGY1,S0181067,2016-02-06,1:37:50,1:06:43,1:11:07,0:37:29,0:16:00,0:29:06,5:56:54,1836,0,10
+General Medicine,PGY1,S0181067,2016-02-05,0:27:07,0:16:36,0:19:01,0:13:09,0:11:13,0:18:43,2:02:03,759,21,7
+General Medicine,PGY1,S0181067,2016-02-04,1:23:02,1:10:12,0:41:44,0:31:59,0:30:47,0:12:04,4:52:50,1687,0,19
+General Medicine,PGY1,S0181067,2016-02-03,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,14,0,5
+Night Team,PGY1,S0195079,2016-10-16,0:58:11,0:31:07,0:00:00,0:19:19,0:20:59,0:06:53,2:39:24,721,0,39
+General Medicine,PGY1,S0181067,2016-02-01,0:56:31,0:24:24,0:19:27,0:32:05,0:15:33,0:20:19,3:02:49,1112,0,8
+General Medicine,PGY1,S0181064,2016-05-04,2:21:24,1:12:53,0:00:04,0:48:51,0:12:29,0:04:20,5:13:35,1463,185,24
+General Medicine,PGY1,S0181057,2016-06-19,2:28:08,1:44:11,0:01:36,0:53:54,0:40:05,0:03:02,6:45:23,2582,1,20
+General Medicine,PGY1,S0181057,2016-06-18,1:42:04,1:21:31,0:00:14,0:29:09,0:16:30,0:00:19,4:12:23,1179,0,10
+General Medicine,PGY1,S0181057,2016-06-17,2:24:37,1:10:51,0:00:11,1:03:06,0:17:46,0:01:42,5:31:09,1778,0,10
+General Medicine,PGY1,S0181057,2016-06-16,1:13:50,0:38:56,0:00:10,0:11:06,0:11:12,0:00:10,2:31:27,731,15,9
+General Medicine,PGY1,S0181057,2016-06-15,0:10:00,0:10:03,0:00:00,0:02:14,0:00:39,0:01:31,0:33:03,180,180,5
+General Medicine,PGY1,S0181057,2016-06-14,2:21:24,1:14:08,0:00:09,1:02:31,0:22:31,0:00:52,5:29:31,1583,0,12
+General Medicine,PGY1,S0181057,2016-06-13,1:04:01,0:37:41,0:00:25,0:12:36,0:14:50,0:01:53,2:48:51,1178,10,9
+General Medicine,PGY1,S0181057,2016-06-12,2:11:31,1:16:50,0:00:57,0:49:20,0:26:22,0:01:02,5:32:53,1956,18,27
+General Medicine,PGY1,S0181057,2016-06-11,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181057,2016-06-10,2:08:33,0:46:49,0:00:19,0:28:57,0:14:02,0:00:29,4:11:53,1552,8,9
+General Medicine,PGY1,S0195086,2016-08-07,2:40:22,2:34:52,0:00:26,0:32:43,0:18:18,0:08:46,6:53:31,1914,0,13
+General Medicine,PGY1,S0195086,2016-08-06,0:07:25,0:00:19,0:00:00,0:00:00,0:00:00,0:00:00,0:10:00,37,9,5
+General Medicine,PGY1,S0195086,2016-08-05,3:00:22,3:04:54,0:01:08,0:30:41,0:28:23,0:06:59,7:56:39,1697,0,9
+General Medicine,PGY1,S0195086,2016-08-04,1:33:15,1:15:12,0:00:10,0:09:51,0:16:18,0:03:45,3:44:52,932,0,4
+General Medicine,PGY1,S0195086,2016-08-03,1:37:07,2:11:23,0:01:06,0:26:37,0:08:06,0:16:42,5:22:30,1733,630,21
+General Medicine,PGY1,S0195086,2016-08-02,2:05:29,1:29:30,0:01:31,0:31:07,0:27:23,0:15:10,5:19:03,1489,0,17
+General Medicine,PGY1,S0181526,2015-09-18,2:33:48,0:26:22,2:06:51,1:03:09,0:13:36,0:48:58,8:07:24,1906,134,13
+General Medicine,PGY1,S0181526,2015-09-19,1:32:08,0:07:50,1:35:24,0:30:36,0:20:42,0:27:03,4:52:46,1252,46,5
+General Medicine,PGY1,S0181526,2015-09-16,0:00:00,0:12:23,0:00:00,0:00:00,0:00:00,0:04:46,0:22:19,168,164,7
+General Medicine,PGY1,S0181526,2015-09-17,1:12:57,0:31:00,0:28:43,0:16:54,0:13:54,0:22:54,3:38:57,990,38,13
+General Medicine,PGY1,S0181526,2015-09-14,2:34:59,0:31:04,0:42:49,0:38:00,0:08:45,0:22:19,5:51:08,1693,855,11
+General Medicine,PGY1,S0181526,2015-09-15,2:00:43,0:38:00,1:13:55,0:45:39,0:35:47,0:59:16,6:55:18,1827,30,14
+General Medicine,PGY1,S0181526,2015-09-13,3:00:07,0:53:22,2:22:40,0:40:59,0:12:52,0:35:40,8:30:11,1926,0,12
+General Medicine,PGY1,S0195086,2016-08-09,3:34:20,2:00:13,0:00:32,1:24:57,0:35:56,0:02:51,8:18:40,2584,0,15
+General Medicine,PGY1,S0195086,2016-08-08,2:07:07,2:50:00,0:01:08,0:18:44,0:23:57,0:09:02,6:18:50,1518,0,8
+General Medicine,PGY1,S0196597,2016-10-10,1:33:38,0:27:56,0:00:21,0:34:49,0:05:32,0:00:31,2:59:56,1131,0,9
+Night Team,PGY2+,S0106952,2016-06-14,3:22:38,2:51:40,0:04:42,0:55:54,0:25:36,0:14:06,8:40:09,3498,356,84
+Night Team,PGY2+,S0106952,2016-06-15,3:36:31,2:39:42,0:01:22,0:33:47,0:27:49,0:21:45,8:15:28,3246,168,88
+Night Team,PGY2+,S0106952,2016-06-16,3:04:20,2:28:48,0:04:17,0:29:26,0:10:51,0:03:18,6:48:14,2455,771,103
+Night Team,PGY2+,S0106952,2016-06-17,1:06:34,0:37:34,0:00:00,0:13:20,0:14:11,0:11:44,2:28:51,1279,96,56
+Night Team,PGY2+,S0106952,2016-06-12,3:34:23,2:51:39,0:00:06,0:16:47,0:18:06,0:04:40,7:34:10,2738,132,70
+Night Team,PGY2+,S0106952,2016-06-13,4:05:52,3:55:05,0:01:01,1:03:48,0:20:25,0:10:27,10:09:27,3610,629,153
+Night Team,PGY1,S0181064,2016-03-23,1:22:35,1:18:49,0:00:02,0:12:49,0:06:10,0:02:10,3:17:03,1045,37,41
+Night Team,PGY1,S0181064,2016-03-22,1:37:37,1:17:47,0:00:02,0:35:27,0:21:13,0:03:33,4:19:13,1358,13,62
+Night Team,PGY1,S0181064,2016-03-21,2:15:07,0:47:28,0:00:02,0:18:51,0:16:47,0:02:43,4:04:00,1219,0,52
+Night Team,PGY1,S0181064,2016-03-20,0:49:23,0:23:18,0:00:00,0:07:26,0:03:41,0:00:07,1:28:03,505,0,32
+Night Team,PGY2+,S0106952,2016-06-18,5:48:14,2:05:50,0:00:23,1:09:53,0:34:16,0:12:26,10:40:41,3912,125,53
+Night Team,PGY2+,S0169406,2016-10-24,0:57:37,1:02:39,0:00:08,0:18:29,0:05:16,0:00:33,2:36:15,823,149,7
+Night Team,PGY1,S0181064,2016-03-25,0:03:07,0:03:39,0:00:00,0:00:03,0:00:00,0:00:00,0:09:38,94,86,6
+Night Team,PGY1,S0181064,2016-03-24,0:52:54,0:22:50,0:00:02,0:26:23,0:09:31,0:02:40,2:11:49,649,40,24
+Night Team,PGY2+,S0163662,2016-08-01,1:05:50,1:02:51,0:00:24,0:07:05,0:11:14,0:05:33,3:08:40,1546,0,4
+Night Team,PGY2+,S0163662,2016-08-02,0:18:45,0:07:14,0:00:00,0:00:31,0:00:50,0:07:44,0:37:49,685,247,13
+Night Team,PGY2+,S0163662,2016-08-03,1:44:49,1:02:23,0:00:09,0:22:09,0:15:41,0:03:59,4:07:52,2908,0,28
+Night Team,PGY2+,S0163662,2016-08-04,2:16:53,1:47:23,0:02:01,0:59:55,0:22:47,0:20:07,6:31:11,4038,309,20
+Night Team,PGY2+,S0163662,2016-08-05,1:28:16,0:33:23,0:00:07,0:27:45,0:14:39,0:04:32,3:08:56,2922,675,14
+Night Team,PGY2+,S0163662,2016-08-06,1:45:25,1:12:28,0:00:10,0:47:53,0:19:11,0:22:38,5:24:40,3349,100,22
+Night Team,PGY2+,S0163662,2016-08-07,1:16:04,0:42:50,0:00:04,0:40:34,0:11:09,0:05:12,3:39:28,3015,132,19
+Night Team,PGY2+,S0163662,2016-08-08,0:52:26,0:26:15,0:00:01,0:15:13,0:13:46,0:03:13,2:09:40,1362,0,18
+Night Team,PGY2+,S0163662,2016-08-09,1:42:36,1:10:36,0:02:45,1:13:40,0:12:42,0:11:56,5:08:06,2822,832,18
+Intensive Care Unit,PGY2+,S0169402,2015-07-19,1:46:52,0:32:50,0:48:49,0:59:36,0:56:48,0:26:51,6:09:21,1705,87,14
+Intensive Care Unit,PGY2+,S0169402,2015-07-18,2:40:24,0:09:56,0:39:20,1:15:01,0:38:05,0:30:08,6:23:23,1888,135,18
+Intensive Care Unit,PGY2+,S0169402,2015-07-17,2:02:47,0:04:40,0:29:03,0:50:54,0:22:02,0:15:38,4:10:38,1053,58,13
+Intensive Care Unit,PGY2+,S0169402,2015-07-16,0:42:47,0:00:08,0:12:33,0:10:25,0:11:40,0:01:32,1:19:36,349,0,16
+Intensive Care Unit,PGY2+,S0169402,2015-07-15,2:30:24,0:35:50,1:08:01,0:28:44,0:20:49,0:28:26,5:47:46,1505,193,14
+Intensive Care Unit,PGY2+,S0169402,2015-07-14,2:13:41,0:19:33,0:23:33,0:42:52,0:31:24,0:21:55,4:47:03,1207,136,19
+Intensive Care Unit,PGY2+,S0169402,2015-07-13,2:14:31,0:11:29,0:08:38,0:32:15,0:13:13,0:43:13,4:24:45,1133,121,22
+Intensive Care Unit,PGY2+,S0169402,2015-07-12,2:20:59,0:14:24,0:23:00,0:34:38,0:56:35,0:59:17,5:45:07,1767,0,11
+Intensive Care Unit,PGY2+,S0169402,2015-07-11,1:08:42,0:31:06,0:17:17,0:00:00,0:15:08,0:04:13,2:53:23,1146,1144,15
+Intensive Care Unit,PGY2+,S0169402,2015-07-10,1:52:18,2:11:45,0:04:11,0:04:22,0:32:12,0:17:46,5:25:14,1398,1384,14
+General Medicine,PGY1,S0186952,2016-09-28,3:07:38,0:56:17,0:00:44,1:24:44,0:40:39,0:07:16,7:07:03,2302,50,21
+Night Team,PGY2+,S0169406,2016-10-26,1:34:56,0:52:55,0:00:12,0:38:27,0:13:31,0:00:10,3:45:19,1129,3,18
+General Medicine,PGY2+,S0181074,2016-11-03,1:58:47,0:40:27,0:00:00,0:26:28,0:15:57,0:11:53,4:05:38,1798,0,21
+General Medicine,PGY2+,S0181074,2016-11-02,4:37:07,1:54:52,0:00:00,1:07:24,0:17:51,0:13:37,8:57:45,3630,195,24
+General Medicine,PGY2+,S0181074,2016-11-01,3:12:46,1:13:57,0:00:19,1:24:30,0:21:13,0:16:24,7:34:26,3050,574,26
+General Medicine,PGY1,S0186952,2016-09-29,2:03:00,0:39:57,0:00:08,0:43:08,0:24:26,0:02:49,4:34:16,1581,177,12
+General Medicine,PGY2+,S0181074,2016-11-05,2:53:22,0:44:54,0:00:00,0:46:52,0:12:39,0:07:47,5:37:38,2118,0,30
+General Medicine,PGY2+,S0181074,2016-11-04,1:08:21,0:12:01,0:00:00,0:00:02,0:00:47,0:16:50,1:55:48,768,113,23
+Emergency Medicine,PGY1,S0109179,2015-01-27,6:28:02,0:03:37,1:52:52,0:28:54,0:21:40,0:19:43,10:07:44,3493,0,61
+Emergency Medicine,PGY1,S0109179,2015-01-26,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,1
+Night Team,PGY2+,S0108268,2015-12-03,1:51:08,1:19:44,1:51:29,0:44:31,0:20:30,0:38:43,7:16:07,2648,66,37
+Intensive Care Unit,PGY2+,S0181073,2016-10-21,3:22:13,0:34:49,0:10:51,1:08:24,0:54:06,0:24:42,7:20:11,2588,0,21
+Night Team,PGY2+,S0169398,2015-08-25,2:21:56,1:07:11,1:24:33,0:19:14,0:16:54,0:31:50,6:31:03,2022,0,27
+Night Team,PGY1,S0181526,2016-01-20,1:17:13,0:28:58,0:51:22,0:39:00,0:13:21,0:39:54,4:25:05,982,0,26
+Intensive Care Unit,PGY2+,S0174975,2016-10-02,1:41:14,0:38:02,0:08:23,0:28:09,0:26:46,0:01:20,4:02:56,1111,0,24
+Emergency Medicine,PGY1,S0109179,2015-01-24,4:08:32,0:06:54,1:38:18,0:22:05,0:25:53,0:46:47,7:52:42,2161,0,51
+Intensive Care Unit,PGY2+,S0174975,2016-10-04,3:40:54,0:46:38,0:08:26,0:52:09,0:15:49,0:07:58,6:40:43,1847,0,24
+Intensive Care Unit,PGY2+,S0174975,2016-10-05,2:21:35,0:45:47,0:06:45,0:17:06,0:24:50,0:03:52,4:35:42,1357,35,29
+Intensive Care Unit,PGY2+,S0174975,2016-10-06,1:05:42,0:20:55,0:00:21,0:40:43,0:26:57,0:00:56,2:59:02,1120,41,17
+Night Team,PGY1,S0181526,2016-01-21,0:58:31,0:51:41,0:01:52,0:15:09,0:15:10,0:28:44,3:06:47,1360,59,32
+Intensive Care Unit,PGY2+,S0174975,2016-10-08,0:03:27,0:08:26,0:00:03,0:00:03,0:00:19,0:00:00,0:16:36,144,132,14
+Emergency Medicine,PGY1,S0109179,2015-01-23,4:30:34,0:05:04,1:20:01,0:33:55,0:34:03,0:27:49,8:02:42,2138,0,18
+Intensive Care Unit,PGY2+,S0169406,2016-03-30,1:22:08,0:34:17,0:01:01,0:20:29,0:08:38,0:02:21,2:52:56,1066,16,16
+Intensive Care Unit,PGY2+,S0169406,2016-03-31,0:34:11,0:12:00,0:05:29,0:03:46,0:09:40,0:09:08,1:38:09,614,44,17
+Night Team,PGY2+,S0169398,2015-08-29,2:30:06,0:45:41,2:29:52,0:41:55,0:20:42,0:49:24,8:16:51,2016,17,20
+Night Team,PGY2+,S0169398,2015-08-28,1:48:55,0:44:25,0:18:28,0:16:30,0:06:34,0:22:12,3:49:59,1246,0,20
+Intensive Care Unit,PGY2+,S0181073,2016-10-26,2:12:31,0:46:19,0:09:18,0:29:49,0:20:30,0:05:15,4:41:31,1632,0,16
+General Medicine,PGY1,S0195230,2016-09-26,0:04:54,0:00:09,0:00:00,0:00:00,0:00:00,0:00:56,0:06:10,25,0,3
+Intensive Care Unit,PGY2+,S0172056,2016-10-19,1:49:05,0:46:55,0:02:00,0:28:31,0:44:33,0:04:21,4:31:55,1481,136,20
+Intensive Care Unit,PGY2+,S0181073,2016-10-25,2:03:25,0:37:27,0:00:04,0:50:39,0:44:07,0:10:50,5:07:37,1881,0,34
+Intensive Care Unit,PGY2+,S0172056,2016-10-15,0:00:00,0:00:00,0:00:00,0:00:05,0:00:00,0:00:00,0:00:33,15,0,6
+General Medicine,PGY2+,S0169384,2016-07-12,3:16:46,1:25:52,0:00:03,0:59:37,0:32:29,0:13:31,6:46:33,2928,202,27
+Intensive Care Unit,PGY2+,S0172056,2016-10-17,0:58:44,0:28:12,0:01:15,0:35:18,0:31:19,0:02:21,3:17:47,1416,0,22
+Intensive Care Unit,PGY2+,S0181073,2016-10-24,2:12:51,0:15:19,0:06:24,0:33:37,0:29:15,0:00:25,4:04:09,1045,0,14
+Intensive Care Unit,PGY2+,S0172056,2016-10-11,1:25:57,1:10:18,0:06:03,0:16:35,0:14:21,0:00:15,3:38:49,1368,0,23
+Intensive Care Unit,PGY2+,S0172056,2016-10-10,1:42:07,1:20:14,0:15:31,0:27:15,0:20:30,0:03:21,4:36:17,1462,277,29
+Intensive Care Unit,PGY2+,S0172056,2016-10-13,1:38:13,0:20:32,0:01:39,0:31:20,0:17:36,0:05:04,3:30:30,1249,214,28
+Night Team,PGY1,S0181526,2016-01-25,1:55:33,0:40:55,1:01:57,0:36:05,0:35:35,0:46:32,6:29:00,2235,95,44
+General Medicine,PGY1,S0181060,2015-11-12,0:00:00,0:00:00,0:00:00,0:02:01,0:00:00,0:00:00,0:02:01,8,0,3
+General Medicine,PGY1,S0181060,2015-11-13,1:41:44,1:13:53,1:19:13,0:47:03,0:19:44,0:47:38,6:40:19,2385,14,18
+General Medicine,PGY1,S0181060,2015-11-10,0:34:04,0:25:06,0:31:51,0:26:06,0:13:09,0:37:36,3:00:26,841,2,5
+General Medicine,PGY1,S0181060,2015-11-11,1:30:21,1:12:08,1:38:17,0:57:12,0:38:28,0:50:30,7:19:29,1918,0,10
+General Medicine,PGY1,S0181060,2015-11-16,1:46:01,1:03:55,1:16:36,1:20:36,0:48:58,0:27:15,7:16:02,2373,25,10
+General Medicine,PGY1,S0181060,2015-11-17,1:05:58,0:53:59,1:02:13,0:45:11,0:28:53,0:27:36,5:12:58,1543,10,9
+General Medicine,PGY1,S0181060,2015-11-14,1:12:00,1:03:02,0:59:18,0:25:53,0:21:24,0:54:22,5:25:30,1791,197,9
+General Medicine,PGY1,S0181060,2015-11-15,1:12:55,0:48:47,1:11:38,0:58:21,0:19:05,0:19:24,5:30:24,1865,17,5
+General Medicine,PGY1,S0181060,2015-11-18,0:00:00,0:00:00,0:00:00,0:00:36,0:00:00,0:00:00,0:00:36,14,0,4
+General Medicine,PGY1,S0181060,2015-11-19,0:00:00,0:00:00,0:00:00,0:02:06,0:00:00,0:00:00,0:02:06,8,0,2
+General Medicine,PGY1,S0181081,2016-05-22,1:50:16,1:19:04,0:00:04,0:32:25,0:19:44,0:05:14,4:52:51,1534,6,13
+Night Team,PGY1,S0181526,2016-01-28,2:23:11,0:38:43,1:05:03,0:36:46,0:46:47,1:09:15,7:32:47,2880,96,53
+Night Team,PGY1,S0181526,2016-01-29,2:54:22,0:26:07,0:29:25,0:32:56,0:31:52,1:10:29,6:45:03,1869,122,41
+General Medicine,PGY1,S0181073,2015-11-07,0:00:00,0:00:24,0:00:00,0:00:00,0:00:00,0:00:23,0:01:03,23,13,3
+General Medicine,PGY1,S0181073,2015-11-06,0:00:00,0:02:52,0:00:00,0:00:01,0:00:00,0:00:22,0:03:52,52,38,4
+General Medicine,PGY1,S0195230,2016-09-29,1:28:05,0:20:43,0:00:06,0:35:09,0:09:56,0:00:41,2:55:38,884,141,10
+General Medicine,PGY2+,S0169399,2015-11-08,0:47:14,0:36:22,1:14:27,0:19:14,0:10:09,0:25:14,3:54:05,788,0,10
+Emergency Medicine,PGY2+,S0169390,2015-08-20,2:47:49,0:46:47,1:59:45,0:23:06,0:34:33,0:18:35,7:20:06,2200,0,34
+General Medicine,PGY2+,S0169399,2015-11-09,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Intensive Care Unit,PGY2+,S0106952,2015-10-29,0:09:08,0:37:34,0:02:48,0:00:28,0:00:25,0:00:00,0:52:09,463,441,38
+General Medicine,PGY1,S0181073,2015-11-03,0:49:39,0:42:15,0:44:36,0:39:06,0:16:33,0:29:57,4:04:50,1807,167,10
+Intensive Care Unit,PGY2+,S0106952,2015-10-25,1:29:58,1:24:54,0:58:43,0:45:45,0:30:19,0:25:31,5:57:30,2033,0,21
+Intensive Care Unit,PGY2+,S0106952,2015-10-24,2:01:00,0:32:50,0:18:31,0:23:04,0:30:15,0:17:25,4:10:57,1008,255,19
+Intensive Care Unit,PGY2+,S0106952,2015-10-27,1:17:41,0:48:57,0:08:13,0:11:50,0:22:17,0:01:38,3:02:28,1005,251,38
+General Medicine,PGY1,S0108268,2014-09-27,3:01:36,0:12:55,0:21:33,0:38:43,0:44:12,0:38:03,6:08:17,2104,0,10
+Intensive Care Unit,PGY2+,S0106952,2015-10-21,2:15:56,1:17:54,0:42:21,0:22:57,0:16:18,0:33:32,5:51:50,1660,316,26
+Intensive Care Unit,PGY2+,S0106952,2015-10-20,2:01:57,1:04:38,0:19:30,0:51:43,0:32:28,1:11:52,6:27:34,2096,36,18
+Intensive Care Unit,PGY2+,S0106952,2015-10-23,1:37:25,1:45:15,0:28:02,0:16:36,0:23:58,0:27:51,5:40:36,2100,613,89
+Intensive Care Unit,PGY2+,S0106952,2015-10-22,1:29:55,0:28:05,0:10:10,0:28:33,0:20:22,0:21:54,3:38:30,1098,14,24
+Night Team,PGY1,S0195076,2016-09-28,1:30:37,1:04:10,0:00:04,0:43:24,0:22:18,0:05:17,3:56:06,1189,605,26
+Emergency Medicine,PGY2+,S0094535,2016-03-14,1:51:33,0:14:47,0:00:11,0:09:13,0:18:10,0:08:37,3:04:03,1183,0,9
+Emergency Medicine,PGY2+,S0094535,2016-03-15,1:19:32,0:18:55,0:00:34,0:16:20,0:29:00,0:00:39,2:46:27,1089,0,19
+Emergency Medicine,PGY2+,S0094535,2016-03-16,3:09:27,0:42:10,0:02:17,0:28:30,0:36:30,0:04:12,5:53:05,1546,0,33
+Emergency Medicine,PGY2+,S0094535,2016-03-17,1:02:21,0:36:22,0:06:03,0:32:38,0:09:41,0:07:07,3:29:04,1138,0,10
+Emergency Medicine,PGY2+,S0094535,2016-03-10,1:14:22,0:56:45,0:07:45,0:12:46,0:22:43,0:05:37,4:15:34,1236,768,15
+Emergency Medicine,PGY2+,S0094535,2016-03-11,3:47:38,0:35:16,0:11:06,0:41:37,0:35:45,0:02:33,7:11:47,1853,0,16
+Emergency Medicine,PGY2+,S0094535,2016-03-12,2:56:51,0:30:03,0:04:57,0:22:14,0:51:59,0:01:35,6:06:30,1880,7,26
+Emergency Medicine,PGY2+,S0094535,2016-03-13,4:41:15,0:41:55,0:03:55,0:31:17,0:47:30,0:01:23,8:28:49,2602,0,41
+Emergency Medicine,PGY2+,S0094535,2016-03-18,1:36:01,0:12:29,0:00:30,0:08:22,0:18:39,0:01:22,2:35:17,1170,0,7
+Emergency Medicine,PGY2+,S0094535,2016-03-19,1:59:07,0:08:36,0:04:32,0:09:21,0:22:26,0:00:14,3:17:44,902,5,15
+General Medicine,PGY2+,S0169399,2015-11-01,1:04:27,0:48:22,1:12:16,0:19:59,0:29:09,0:27:21,4:46:21,1222,57,16
+General Medicine,PGY2+,S0169399,2015-11-06,0:52:32,0:30:41,1:19:55,0:22:56,0:17:22,0:20:03,4:04:31,1001,123,16
+General Medicine,PGY1,S0169395,2014-10-31,3:13:47,0:19:41,0:17:09,0:43:03,0:22:27,0:13:12,5:38:28,1909,0,23
+General Medicine,PGY1,S0169395,2014-10-30,0:00:00,0:00:00,0:00:00,0:00:51,0:00:00,0:00:00,0:00:51,4,0,2
+General Medicine,PGY2+,S0169399,2015-11-07,1:32:12,0:53:51,2:21:31,0:16:22,0:17:27,0:29:05,6:20:18,1200,0,25
+General Medicine,PGY2+,S0181057,2016-10-18,0:14:19,0:01:43,0:00:00,0:00:56,0:00:00,0:01:02,0:26:16,163,0,12
+General Medicine,PGY2+,S0169399,2015-11-04,0:36:23,0:32:40,2:03:04,0:23:11,0:09:07,0:21:12,4:19:36,899,22,15
+General Medicine,PGY2+,S0181057,2016-10-14,2:28:01,1:18:51,0:00:36,0:46:23,0:20:51,0:03:05,5:34:21,2058,86,25
+General Medicine,PGY2+,S0181057,2016-10-15,3:02:13,0:54:46,0:00:11,0:43:29,0:31:15,0:05:45,5:59:18,1929,0,17
+General Medicine,PGY2+,S0181057,2016-10-16,3:21:12,1:11:37,0:00:03,0:20:44,0:22:18,0:09:28,5:44:59,2136,59,39
+General Medicine,PGY2+,S0169399,2015-11-05,1:14:53,1:27:07,0:50:26,0:16:53,0:20:25,0:38:13,5:14:50,1335,0,26
+General Medicine,PGY2+,S0181057,2016-10-10,1:51:30,1:21:06,0:00:32,0:39:29,0:29:08,0:00:53,5:08:56,1898,191,22
+General Medicine,PGY2+,S0181057,2016-10-11,3:01:50,1:31:26,0:00:09,0:50:31,0:22:10,0:12:58,6:38:28,2180,69,31
+General Medicine,PGY2+,S0181057,2016-10-12,1:29:18,1:01:21,0:00:10,0:29:24,0:21:47,0:05:43,4:10:10,1672,0,21
+General Medicine,PGY2+,S0181057,2016-10-13,0:18:04,0:01:55,0:00:00,0:04:33,0:00:00,0:01:12,0:30:32,174,0,12
+General Medicine,PGY1,S0181086,2016-04-30,2:25:54,1:17:39,0:00:07,0:44:34,0:22:56,0:07:11,5:26:14,1592,21,10
+Night Team,PGY2+,S0093383,2015-12-31,1:16:20,1:19:15,4:08:47,0:37:27,0:21:07,0:35:13,8:42:24,3086,0,30
+Night Team,PGY2+,S0093383,2015-12-30,0:49:27,1:11:41,4:13:38,0:35:59,0:18:49,0:25:33,7:55:51,3299,0,21
+General Medicine,PGY1,S0181073,2016-06-06,3:30:46,2:08:09,0:00:34,1:19:38,0:25:10,0:14:23,8:41:46,3184,0,14
+General Medicine,PGY1,S0181073,2016-06-07,1:17:41,0:39:31,0:00:09,0:27:28,0:08:15,0:00:37,3:05:03,1103,23,9
+General Medicine,PGY1,S0181073,2016-06-04,3:25:01,2:19:37,0:00:12,1:13:36,0:39:19,0:02:10,8:38:27,2554,48,14
+General Medicine,PGY1,S0169389,2014-07-24,2:22:16,0:01:29,0:09:44,0:13:32,0:09:15,0:25:51,3:35:12,1079,51,11
+General Medicine,PGY1,S0181073,2016-06-02,0:07:43,0:00:59,0:00:00,0:00:41,0:00:00,0:03:08,0:13:02,84,72,8
+General Medicine,PGY1,S0181073,2016-06-03,1:47:05,0:52:19,0:00:07,0:40:05,0:18:48,0:04:06,4:37:24,1480,40,12
+General Medicine,PGY1,S0181073,2016-06-01,0:17:48,0:01:00,0:00:00,0:03:51,0:00:13,0:00:24,0:29:05,145,34,11
+General Medicine,PGY1,S0169381,2014-11-08,1:18:32,0:05:10,2:26:55,0:28:05,0:14:19,0:13:38,5:03:42,909,15,7
+General Medicine,PGY1,S0169381,2014-11-06,2:51:05,0:15:36,0:40:23,0:42:18,0:23:25,0:35:33,5:52:51,1221,19,7
+General Medicine,PGY1,S0169381,2014-11-07,1:29:24,0:01:18,0:29:48,0:23:15,0:11:41,0:18:01,3:02:31,903,93,12
+General Medicine,PGY1,S0169381,2014-11-04,0:08:22,0:00:00,0:00:04,0:00:01,0:00:09,0:00:00,0:08:48,48,44,5
+General Medicine,PGY1,S0169381,2014-11-05,2:11:13,0:14:26,0:12:56,0:31:28,0:26:14,0:51:45,4:52:29,1193,74,7
+General Medicine,PGY1,S0169381,2014-11-02,2:39:11,0:04:32,0:17:42,0:39:49,0:13:01,0:11:20,4:13:23,913,0,5
+General Medicine,PGY1,S0169381,2014-11-03,2:43:56,0:01:40,0:14:11,0:36:00,0:23:28,0:13:44,4:30:59,1323,11,10
+General Medicine,PGY1,S0169381,2014-11-01,0:00:00,0:00:00,0:00:00,0:00:20,0:00:00,0:00:00,0:00:20,12,0,4
+Intensive Care Unit,PGY2+,S0181525,2016-09-22,1:00:35,0:24:20,0:05:53,0:11:47,0:16:55,0:00:50,2:23:07,729,0,12
+Night Team,PGY1,S0093383,2015-05-25,1:05:06,0:05:33,1:21:11,0:27:16,0:17:25,0:29:12,3:51:44,767,0,22
+General Medicine,PGY1,S0169384,2014-07-31,4:28:46,0:16:49,0:34:31,0:45:30,0:30:07,0:03:24,6:52:51,1547,25,12
+General Medicine,PGY1,S0169384,2014-07-30,5:23:03,0:03:12,0:26:34,0:49:37,0:30:51,0:09:45,7:33:49,1458,193,9
+General Medicine,PGY1,S0109179,2014-12-13,0:00:00,0:00:00,0:00:00,0:00:14,0:00:00,0:00:00,0:00:14,8,0,2
+Night Team,PGY1,S0195076,2016-09-20,0:56:12,0:02:06,0:00:00,0:38:40,0:16:12,0:09:05,2:08:44,627,0,20
+General Medicine,PGY1,S0109179,2014-12-11,1:10:33,0:02:01,1:17:26,0:06:06,0:04:21,0:14:49,3:03:49,547,13,3
+General Medicine,PGY1,S0181081,2016-06-08,1:36:02,1:17:06,0:01:36,0:33:07,0:10:44,0:01:04,4:34:15,2054,272,17
+Night Team,PGY1,S0195076,2016-09-25,0:57:32,0:19:33,0:01:06,0:33:09,0:22:14,0:01:39,2:30:41,680,238,20
+Night Team,PGY1,S0195076,2016-09-24,1:23:34,1:04:23,0:00:07,0:43:42,0:21:10,0:07:30,4:00:16,1229,362,29
+Night Team,PGY1,S0195076,2016-09-27,0:55:52,0:42:58,0:00:00,0:43:04,0:17:05,0:00:24,2:50:52,856,138,25
+Night Team,PGY1,S0195076,2016-09-26,1:11:23,0:48:02,0:00:02,0:27:34,0:08:38,0:06:14,2:53:59,893,145,23
+General Medicine,PGY1,S0094535,2015-04-30,2:31:59,0:29:42,1:57:34,0:37:53,0:13:54,0:41:06,7:25:35,1792,44,17
+Night Team,PGY1,S0093383,2015-05-29,0:48:59,0:06:38,0:03:30,0:18:07,0:09:45,0:15:31,1:45:09,568,0,21
+Night Team,PGY1,S0195076,2016-09-29,1:28:26,1:19:12,0:00:03,0:47:13,0:17:51,0:07:38,4:27:17,1255,15,28
+Intensive Care Unit,PGY2+,S0169389,2015-07-02,1:47:27,0:16:52,0:03:36,0:24:44,0:14:25,0:12:02,3:04:08,699,0,14
+Intensive Care Unit,PGY2+,S0169389,2015-07-03,0:04:03,0:15:03,0:00:00,0:00:01,0:00:00,0:00:11,0:20:34,118,111,8
+Intensive Care Unit,PGY2+,S0169389,2015-07-04,2:01:02,0:42:34,0:08:57,0:56:44,0:31:49,0:28:10,5:09:12,1175,0,11
+Intensive Care Unit,PGY2+,S0169389,2015-07-05,1:37:07,1:00:03,0:01:50,0:27:59,0:47:10,0:43:23,5:16:42,1327,0,12
+Intensive Care Unit,PGY2+,S0169389,2015-07-06,1:14:14,0:39:35,0:09:14,0:22:15,0:15:04,0:23:56,3:28:32,1088,0,11
+Intensive Care Unit,PGY2+,S0169389,2015-07-07,1:44:49,1:19:33,0:14:52,0:02:33,0:20:06,0:22:40,4:30:33,1119,69,28
+Intensive Care Unit,PGY2+,S0169389,2015-07-08,1:50:30,0:37:28,0:11:04,0:15:35,0:15:49,0:20:13,4:03:23,1427,36,43
+Intensive Care Unit,PGY2+,S0169389,2015-07-09,1:29:04,0:43:11,0:03:43,0:24:03,0:21:42,0:12:00,3:37:58,1290,0,64
+General Medicine,PGY1,S0079640,2016-08-31,0:04:19,0:03:10,0:00:01,0:00:24,0:00:00,0:00:00,0:14:56,89,69,5
+General Medicine,PGY1,S0079640,2016-08-30,2:01:17,1:30:45,0:00:09,0:52:48,0:35:29,0:03:19,5:19:02,1700,0,15
+Night Team,PGY2+,S0169403,2015-12-29,1:51:28,3:54:27,1:18:07,0:52:36,0:25:29,1:13:48,10:44:43,4454,1478,28
+Night Team,PGY1,S0181526,2016-06-14,1:50:37,0:28:03,0:00:10,0:28:01,0:15:44,0:04:17,3:24:41,926,0,23
+Night Team,PGY1,S0181526,2016-06-15,1:19:21,0:37:15,0:00:01,0:06:10,0:12:53,0:02:21,2:38:51,1164,5,36
+Emergency Medicine,PGY1,S0169401,2014-08-30,6:07:36,0:15:53,0:29:26,0:49:52,0:30:55,0:02:01,9:04:44,2334,206,75
+Emergency Medicine,PGY1,S0169401,2014-08-31,1:29:35,0:01:03,0:00:06,0:00:00,0:00:41,0:00:25,1:39:17,478,478,58
+General Medicine,PGY1,S0181070,2016-01-20,0:41:43,0:47:15,0:56:18,0:33:57,0:15:53,0:17:24,3:49:15,1447,0,5
+General Medicine,PGY1,S0181070,2016-01-21,2:03:37,2:11:19,2:08:35,1:06:58,0:42:27,0:29:49,9:20:35,3980,152,19
+General Medicine,PGY1,S0181070,2016-01-22,0:00:00,0:00:00,0:00:00,0:00:56,0:00:00,0:00:00,0:00:56,22,4,6
+General Medicine,PGY1,S0181070,2016-01-23,1:41:27,2:19:21,2:13:47,0:52:06,0:24:17,0:20:53,8:18:58,3071,0,14
+General Medicine,PGY1,S0181070,2016-01-24,0:36:57,1:07:13,1:08:23,0:28:15,0:14:38,0:07:32,4:02:51,1753,38,5
+General Medicine,PGY1,S0181070,2016-01-25,0:43:50,0:41:33,0:33:58,0:18:13,0:08:34,0:10:12,2:52:45,1694,0,12
+General Medicine,PGY1,S0181070,2016-01-26,1:13:57,2:06:37,2:15:38,0:45:06,0:45:03,0:30:47,8:04:13,2829,0,14
+General Medicine,PGY1,S0181070,2016-01-27,1:39:01,1:13:32,1:13:51,0:45:39,0:36:04,0:16:17,6:28:15,2667,0,10
+General Medicine,PGY1,S0181070,2016-01-28,2:04:14,2:09:02,1:52:17,0:39:02,0:48:45,0:19:48,8:28:41,3872,56,28
+General Medicine,PGY1,S0181070,2016-01-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+Night Team,PGY1,S0181067,2016-05-02,0:20:09,0:23:06,0:02:23,0:20:25,0:11:05,0:00:34,1:26:04,582,0,11
+Night Team,PGY1,S0181067,2016-05-03,0:57:20,0:21:33,0:00:06,0:24:28,0:08:54,0:04:03,2:15:24,773,32,18
+General Medicine,PGY1,S0169399,2015-04-15,1:45:43,0:36:37,4:03:56,0:32:24,0:21:48,0:08:33,8:12:33,1388,0,12
+Night Team,PGY1,S0181067,2016-05-01,0:38:10,0:42:01,0:01:02,0:21:43,0:06:57,0:01:23,2:02:41,810,0,13
+Night Team,PGY1,S0181067,2016-05-06,0:40:09,0:33:13,0:00:00,0:21:10,0:15:48,0:00:13,1:59:16,966,0,23
+Night Team,PGY1,S0181067,2016-05-07,1:36:45,0:30:50,0:00:00,0:23:00,0:15:22,0:09:14,3:17:26,1408,0,26
+Night Team,PGY1,S0181067,2016-05-04,1:01:04,1:54:29,0:02:44,0:40:28,0:22:58,0:04:19,4:23:21,1717,0,22
+Night Team,PGY1,S0181067,2016-05-05,1:03:32,1:23:18,0:03:26,0:19:22,0:12:36,0:10:53,3:25:59,1251,0,18
+General Medicine,PGY1,S0169399,2015-04-19,0:00:00,0:00:00,0:00:00,0:00:45,0:00:00,0:00:00,0:00:45,12,0,4
+General Medicine,PGY1,S0169399,2015-04-18,0:00:49,0:00:00,0:00:00,0:14:46,0:00:03,0:00:00,0:17:05,125,96,6
+General Medicine,PGY1,S0180927,2016-07-24,2:09:37,1:25:37,0:00:20,0:31:41,0:13:19,0:04:23,4:55:30,1608,350,6
+General Medicine,PGY1,S0180927,2016-07-25,1:55:29,1:51:33,0:01:18,0:23:20,0:22:33,0:07:15,5:03:30,1401,165,15
+General Medicine,PGY1,S0180927,2016-07-26,1:13:11,1:13:39,0:00:04,0:27:29,0:24:27,0:05:46,3:50:55,1359,303,9
+General Medicine,PGY1,S0180927,2016-07-27,2:43:41,2:02:49,0:00:12,0:57:17,0:27:35,0:05:42,6:52:51,1907,42,18
+Night Team,PGY1,S0181088,2015-11-03,0:57:47,0:15:51,0:10:54,0:33:36,0:27:02,0:45:16,3:28:20,1281,0,31
+Night Team,PGY1,S0181088,2015-11-02,1:04:06,0:32:03,1:18:51,0:32:39,0:15:11,0:20:45,4:15:31,1175,0,32
+Night Team,PGY1,S0181088,2015-11-01,1:35:41,0:54:21,1:11:32,0:53:03,0:29:45,0:33:24,5:48:18,1828,0,28
+General Medicine,PGY1,S0180927,2016-07-28,1:12:09,1:09:57,0:00:23,0:34:36,0:19:05,0:00:54,3:33:35,1084,0,13
+General Medicine,PGY1,S0180927,2016-07-29,0:00:00,0:00:00,0:00:00,0:03:21,0:00:00,0:00:00,0:03:21,10,0,4
+General Medicine,PGY1,S0181088,2015-11-09,1:51:41,1:10:57,2:25:29,0:52:58,0:23:17,0:23:52,7:48:13,2173,0,11
+General Medicine,PGY1,S0181088,2015-11-08,1:46:55,0:58:32,1:48:45,0:27:03,0:22:25,0:22:17,6:16:23,1663,0,8
+General Medicine,PGY1,S0079337,2014-12-31,1:15:11,0:00:12,0:21:52,0:08:36,0:17:37,0:04:04,2:14:28,764,0,8
+General Medicine,PGY1,S0079337,2014-12-30,3:40:19,0:04:11,1:26:48,0:28:28,0:22:52,0:08:52,6:46:32,2008,0,17
+Night Team,PGY1,S0181526,2016-06-18,2:58:12,0:53:26,0:00:06,0:26:53,0:20:07,0:03:42,5:28:10,1485,15,35
+Emergency Medicine,PGY1,S0169404,2015-01-01,0:00:00,0:03:49,0:00:00,0:00:00,0:00:00,0:01:55,0:05:54,74,64,5
+Emergency Medicine,PGY1,S0169404,2015-01-02,0:00:00,0:08:07,0:00:00,0:04:28,0:00:00,0:11:14,0:37:38,185,179,7
+Emergency Medicine,PGY1,S0169404,2015-01-03,3:03:24,0:16:36,2:19:25,0:18:24,0:15:30,0:15:51,6:42:20,2196,48,12
+General Medicine,PGY1,S0195237,2016-11-03,1:32:57,1:12:44,0:00:22,0:39:50,0:13:36,0:02:00,4:10:25,1483,203,7
+Emergency Medicine,PGY2+,S0181056,2016-08-16,1:11:34,0:40:57,0:24:17,0:08:48,0:10:45,0:10:38,3:48:06,1253,0,24
+General Medicine,PGY1,S0195237,2016-11-01,1:37:43,1:10:19,0:00:19,0:25:50,0:16:35,0:04:52,4:15:56,1110,127,6
+General Medicine,PGY1,S0195237,2016-11-07,3:19:04,1:53:23,0:00:07,0:54:44,0:12:58,0:07:31,7:03:02,1389,32,7
+General Medicine,PGY1,S0195237,2016-11-06,0:27:15,0:35:03,0:03:43,0:04:38,0:04:02,0:00:07,1:19:34,634,624,10
+General Medicine,PGY1,S0195237,2016-11-05,1:38:34,0:31:28,0:00:28,0:36:48,0:25:39,0:05:18,3:47:50,1417,443,14
+Emergency Medicine,PGY2+,S0181056,2016-08-17,0:04:11,0:01:17,0:00:10,0:00:00,0:00:01,0:00:12,0:08:07,69,47,10
+General Medicine,PGY1,S0195237,2016-11-09,0:16:36,0:27:20,0:00:03,0:00:56,0:00:16,0:00:00,0:46:07,225,217,8
+General Medicine,PGY1,S0195237,2016-11-08,1:08:05,1:33:58,0:00:09,0:21:22,0:09:09,0:02:58,3:32:20,956,30,5
+General Medicine,PGY1,S0181082,2015-12-24,0:06:30,0:24:35,0:00:00,0:00:01,0:01:08,0:09:26,0:42:05,276,264,8
+General Medicine,PGY1,S0181082,2015-12-25,1:20:48,1:42:03,2:21:51,0:48:43,0:26:12,0:45:49,7:57:04,2666,0,11
+General Medicine,PGY2+,S0169385,2015-12-20,0:00:00,0:20:27,0:00:00,0:00:01,0:00:00,0:03:41,0:27:52,416,406,22
+General Medicine,PGY2+,S0169385,2015-12-21,1:04:31,0:43:44,0:20:49,0:28:54,0:20:56,1:07:45,5:11:42,2092,23,11
+General Medicine,PGY2+,S0169385,2015-12-22,0:23:43,0:30:19,0:10:50,0:05:07,0:06:09,0:45:55,2:22:06,1277,154,13
+General Medicine,PGY2+,S0169385,2015-12-23,1:10:56,1:48:41,0:22:49,0:41:21,0:55:42,1:29:19,7:34:33,3535,151,17
+General Medicine,PGY2+,S0169385,2015-12-24,1:15:45,1:09:54,0:34:46,0:50:55,0:28:46,1:00:16,5:46:24,2656,0,14
+Night Team,PGY2+,S0172645,2016-06-30,1:10:36,0:53:19,0:00:03,0:13:32,0:04:57,0:04:27,2:40:26,972,0,15
+General Medicine,PGY2+,S0169385,2015-12-26,1:23:17,1:47:19,0:32:43,0:19:38,0:27:09,1:35:42,6:43:29,2884,164,25
+General Medicine,PGY2+,S0169385,2015-12-27,1:04:36,1:17:35,0:53:10,0:55:10,0:39:39,1:23:19,6:51:16,3049,837,19
+General Medicine,PGY2+,S0169385,2015-12-28,0:00:00,0:03:15,0:00:00,0:00:23,0:00:00,0:07:22,0:12:14,324,302,12
+Emergency Medicine,PGY1,S0181087,2015-08-31,3:09:15,0:33:22,3:15:07,0:42:33,0:32:08,0:16:12,9:20:26,2792,0,18
+Emergency Medicine,PGY1,S0181087,2015-08-30,1:35:32,0:16:00,2:04:42,0:27:58,0:22:27,0:01:23,5:38:58,1891,0,16
+General Medicine,PGY1,S0195206,2016-10-28,1:57:16,1:02:43,0:00:15,0:26:22,0:21:26,0:01:57,4:31:32,1681,0,25
+General Medicine,PGY1,S0195206,2016-10-29,1:11:58,0:50:16,0:00:05,0:25:57,0:07:35,0:04:57,3:29:13,1055,52,20
+General Medicine,PGY1,S0195206,2016-10-22,1:20:44,1:01:52,0:00:30,0:12:38,0:05:00,0:04:27,3:23:54,1254,0,11
+General Medicine,PGY1,S0195206,2016-10-23,2:19:14,1:53:55,0:00:49,0:37:47,0:17:44,0:06:11,5:43:45,1551,0,12
+General Medicine,PGY1,S0195206,2016-10-20,1:56:09,1:09:27,0:00:14,0:38:12,0:18:39,0:01:14,4:32:23,1781,0,17
+General Medicine,PGY1,S0195206,2016-10-21,2:01:09,1:29:14,0:00:08,0:25:18,0:11:05,0:05:02,4:50:32,1801,61,16
+General Medicine,PGY1,S0195206,2016-10-26,2:38:15,0:56:05,0:00:02,0:09:30,0:16:07,0:04:13,4:21:18,1451,33,19
+General Medicine,PGY1,S0195206,2016-10-27,0:04:27,0:00:29,0:00:00,0:00:00,0:00:00,0:00:00,0:05:04,45,0,8
+General Medicine,PGY1,S0195206,2016-10-24,0:50:40,0:31:40,0:00:08,0:06:46,0:02:36,0:07:43,1:49:07,930,8,9
+General Medicine,PGY1,S0195206,2016-10-25,0:01:08,0:00:08,0:00:00,0:00:00,0:00:00,0:00:00,0:01:20,19,0,4
+Intensive Care Unit,PGY2+,S0169411,2016-01-24,0:00:00,0:00:00,0:00:00,0:01:30,0:00:00,0:00:00,0:01:30,16,0,4
+General Medicine,PGY1,S0108268,2014-10-01,2:43:31,0:09:17,0:16:11,0:15:26,0:25:30,0:22:36,4:31:15,1135,50,7
+General Medicine,PGY1,S0108268,2014-10-02,3:47:41,0:12:22,0:20:39,1:01:51,0:38:10,1:12:32,7:39:28,1888,73,11
+General Medicine,PGY1,S0108268,2014-10-03,3:29:07,0:12:01,0:09:12,0:35:48,0:33:32,0:29:17,6:00:15,1705,421,25
+General Medicine,PGY1,S0108268,2014-10-04,2:56:43,0:16:04,0:24:30,0:23:18,0:22:47,0:32:47,5:21:10,1641,35,15
+Emergency Medicine,PGY2+,S0181056,2016-08-18,2:17:22,0:32:52,0:01:06,0:20:46,0:10:54,0:07:29,4:11:49,1441,0,66
+General Medicine,PGY1,S0108268,2014-10-06,2:09:42,0:16:15,0:20:41,0:43:50,0:53:45,0:35:21,5:38:13,2155,461,12
+General Medicine,PGY1,S0108268,2014-10-07,3:38:01,0:14:24,0:11:12,1:07:56,1:16:48,1:18:10,8:41:36,2757,5,11
+General Medicine,PGY1,S0108268,2014-10-08,2:47:41,0:06:01,0:11:03,0:54:54,0:58:05,0:19:08,5:41:44,1991,173,10
+General Medicine,PGY1,S0108268,2014-10-09,3:23:24,0:14:29,0:22:22,0:52:45,0:36:48,0:39:00,6:41:42,2257,23,11
+General Medicine,PGY1,S0181525,2015-10-30,2:01:05,0:58:14,2:01:17,1:09:37,0:32:35,0:25:48,7:42:54,2043,0,13
+Intensive Care Unit,PGY2+,S0169411,2016-01-29,3:04:54,0:39:36,0:08:33,0:23:45,0:22:37,0:29:36,5:52:50,2708,96,22
+Intensive Care Unit,PGY2+,S0174975,2016-10-01,0:20:08,0:01:05,0:00:00,0:00:00,0:00:00,0:00:00,0:21:46,90,0,12
+Intensive Care Unit,PGY2+,S0169411,2016-01-28,1:50:05,0:33:06,0:03:55,0:15:08,0:23:02,0:24:54,3:43:08,1516,0,22
+Night Team,PGY2+,S0169404,2016-05-15,3:02:59,2:20:32,0:00:34,0:54:28,0:10:49,0:16:40,7:20:07,3878,75,22
+Night Team,PGY2+,S0169404,2016-05-16,1:57:44,1:31:50,0:00:16,0:49:04,0:13:25,0:07:38,5:40:41,2193,291,31
+Night Team,PGY2+,S0169404,2016-05-17,2:57:11,2:41:18,0:00:20,0:58:28,0:07:18,0:01:02,7:34:42,3682,25,53
+Night Team,PGY2+,S0169404,2016-05-18,4:01:12,2:32:29,0:00:17,0:41:15,0:14:30,0:16:00,8:31:18,4379,85,36
+Night Team,PGY2+,S0169404,2016-05-19,3:25:54,2:17:46,0:00:14,0:33:11,0:13:53,0:04:50,7:23:04,2853,67,29
+Night Team,PGY1,S0195201,2016-09-03,0:16:00,0:00:50,0:00:00,0:02:17,0:00:00,0:00:52,0:20:25,100,0,16
+Night Team,PGY1,S0195201,2016-09-02,2:06:22,0:52:09,0:00:25,0:09:01,0:06:31,0:11:38,3:51:50,1830,0,48
+Night Team,PGY1,S0195201,2016-09-01,3:20:18,1:28:39,0:00:24,0:22:41,0:07:46,0:07:12,6:29:03,2354,0,92
+Night Team,PGY1,S0169406,2015-04-23,1:48:29,0:14:49,0:04:27,0:11:24,0:11:07,0:05:33,2:46:40,765,26,31
+Night Team,PGY1,S0169406,2015-04-22,3:25:36,0:07:50,0:31:34,0:24:26,0:16:08,0:13:06,5:26:13,1361,0,48
+Night Team,PGY1,S0169406,2015-04-21,2:04:49,0:29:15,0:56:17,0:44:03,0:15:45,0:14:52,5:09:33,1686,0,58
+Night Team,PGY1,S0169406,2015-04-20,1:23:49,0:06:53,0:41:47,0:03:29,0:11:52,0:21:16,2:55:18,1033,99,49
+General Medicine,PGY1,S0169406,2015-04-27,1:56:03,0:19:21,0:35:51,0:29:03,0:17:37,0:15:11,4:23:49,1810,55,20
+General Medicine,PGY1,S0169406,2015-04-26,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,4,0,2
+Night Team,PGY1,S0169406,2015-04-25,2:43:44,0:08:28,0:36:42,0:52:03,0:18:13,0:13:17,5:37:04,2133,32,17
+Night Team,PGY1,S0169406,2015-04-24,0:00:00,0:02:52,0:00:00,0:00:35,0:00:00,0:00:00,0:03:45,29,21,6
+General Medicine,PGY1,S0169406,2015-04-29,2:27:55,0:32:36,0:24:38,0:41:28,0:16:42,0:17:08,5:13:39,1737,19,9
+General Medicine,PGY1,S0169406,2015-04-28,1:05:16,0:10:32,0:43:46,0:33:34,0:11:12,0:05:05,3:08:03,1188,41,13
+General Medicine,PGY1,S0181085,2015-10-25,1:16:45,1:02:24,1:23:13,0:41:35,0:23:41,0:29:22,5:49:50,1887,0,8
+General Medicine,PGY1,S0181085,2015-10-24,0:43:47,1:13:49,1:41:34,0:13:35,0:10:09,0:07:39,4:33:00,1637,13,9
+Emergency Medicine,PGY1,S0172085,2015-07-31,2:05:33,0:43:57,0:59:55,0:17:11,0:17:09,0:39:50,6:24:39,2061,78,41
+Night Team,PGY2+,S0172085,2016-07-28,1:41:40,1:44:55,0:00:11,0:31:34,0:13:28,0:08:11,5:10:36,2095,178,43
+Night Team,PGY2+,S0172085,2016-07-29,1:51:53,1:05:44,0:00:06,0:37:55,0:05:50,0:04:29,4:30:15,1008,25,17
+Night Team,PGY2+,S0172085,2016-07-20,1:01:07,0:28:34,0:03:10,0:21:21,0:12:40,0:00:09,2:19:15,737,0,16
+Night Team,PGY2+,S0172085,2016-07-21,2:49:39,2:23:59,0:00:11,0:27:01,0:09:12,0:07:00,6:21:44,1872,0,45
+Night Team,PGY2+,S0172085,2016-07-22,2:15:35,3:06:54,0:00:01,0:30:43,0:09:38,0:20:55,7:00:20,2191,0,40
+Night Team,PGY2+,S0172085,2016-07-23,2:06:54,2:08:12,0:00:11,0:35:52,0:09:50,0:03:55,5:41:17,1859,0,26
+Night Team,PGY2+,S0172085,2016-07-24,2:20:55,0:49:05,0:01:03,0:43:18,0:17:05,0:05:22,5:12:56,1472,0,36
+Night Team,PGY2+,S0172085,2016-07-25,1:26:24,1:06:00,0:00:14,0:27:49,0:06:01,0:12:01,3:53:05,1380,492,30
+Night Team,PGY2+,S0172085,2016-07-26,3:24:22,2:16:22,0:00:05,0:51:16,0:13:35,0:08:18,8:16:26,2700,0,33
+Night Team,PGY2+,S0172085,2016-07-27,2:14:09,1:41:48,0:00:11,0:16:22,0:15:53,0:00:31,5:07:11,1658,0,30
+General Medicine,PGY1,S0181069,2015-09-25,1:29:32,1:23:00,1:16:48,1:21:23,0:25:20,0:11:12,6:48:01,2016,211,11
+General Medicine,PGY1,S0181069,2015-09-24,1:25:39,1:31:21,1:18:22,0:47:41,0:06:11,0:04:57,5:44:18,1792,287,20
+General Medicine,PGY1,S0181069,2015-09-27,2:25:26,0:55:21,1:46:04,1:06:09,0:32:51,0:14:26,7:52:29,2215,0,14
+General Medicine,PGY1,S0181069,2015-09-26,1:04:01,1:06:01,1:25:48,0:47:38,0:15:10,0:03:26,5:40:53,2117,0,6
+General Medicine,PGY1,S0181069,2015-09-21,0:55:13,0:03:05,0:19:29,0:06:20,0:03:17,0:08:10,1:41:50,499,491,10
+General Medicine,PGY1,S0181069,2015-09-20,3:18:14,0:31:58,1:35:46,1:12:56,0:22:09,0:22:31,7:55:28,1796,0,9
+General Medicine,PGY1,S0181069,2015-09-23,1:30:13,1:19:43,3:07:52,0:32:32,0:21:31,0:25:53,7:47:38,2003,211,10
+General Medicine,PGY1,S0181069,2015-09-22,3:58:20,0:10:44,0:58:39,1:25:07,0:50:40,0:41:15,9:03:40,2567,599,7
+General Medicine,PGY1,S0181069,2015-09-29,1:28:32,0:55:00,2:15:14,0:43:06,0:10:41,0:12:23,6:09:46,1745,45,9
+General Medicine,PGY1,S0181069,2015-09-28,0:13:33,0:19:24,0:18:41,0:06:57,0:02:34,0:02:54,1:08:28,510,500,9
+Night Team,PGY2+,S0169402,2016-08-31,3:01:26,1:50:14,0:00:24,0:45:09,0:13:28,0:02:49,6:27:15,2721,0,33
+Night Team,PGY2+,S0169402,2016-08-30,2:25:22,1:12:52,0:00:04,0:46:18,0:08:30,0:07:08,5:21:28,1814,0,15
+General Medicine,PGY1,S0181085,2015-10-29,1:06:27,1:11:18,2:17:18,0:27:32,0:21:19,0:22:43,6:08:25,1926,245,13
+General Medicine,PGY1,S0181085,2015-10-28,0:50:15,1:29:14,0:56:01,0:24:38,0:19:06,0:24:59,4:48:40,1729,0,8
+Intensive Care Unit,PGY2+,S0079337,2016-06-24,2:50:50,0:21:59,0:22:43,0:42:38,0:24:46,0:09:34,5:14:04,1616,29,15
+General Medicine,PGY1,S0181065,2016-03-27,2:42:01,0:56:14,0:00:05,0:48:24,0:12:51,0:01:47,5:09:45,1780,0,7
+General Medicine,PGY1,S0181065,2016-03-28,3:01:21,0:35:13,0:00:26,0:29:30,0:33:12,0:01:56,5:11:52,2473,171,12
+General Medicine,PGY1,S0181065,2016-03-29,0:12:18,0:09:26,0:02:44,0:00:11,0:01:23,0:00:09,0:31:16,202,186,10
+Emergency Medicine,PGY2+,S0169390,2015-08-18,3:55:06,0:29:32,3:54:18,0:29:03,0:37:25,0:29:48,10:56:53,3065,0,23
+Emergency Medicine,PGY2+,S0169390,2015-08-19,0:04:44,0:02:36,0:18:35,0:00:00,0:00:14,0:01:30,0:27:57,243,243,4
+Night Team,PGY1,S0181526,2016-01-30,0:38:35,0:21:12,0:12:51,0:32:58,0:35:09,0:41:57,3:39:06,1113,178,28
+General Medicine,PGY1,S0195230,2016-09-18,1:16:19,0:26:03,0:00:08,0:12:09,0:07:54,0:00:21,2:19:26,489,0,8
+General Medicine,PGY1,S0195230,2016-09-19,1:27:17,1:01:35,0:00:09,0:45:54,0:21:35,0:01:27,4:13:43,1161,8,10
+Emergency Medicine,PGY2+,S0169390,2015-08-13,5:34:15,0:36:37,2:39:00,0:36:39,0:33:23,0:34:14,11:37:29,3491,0,38
+Emergency Medicine,PGY2+,S0169390,2015-08-14,3:25:52,0:33:27,3:30:12,0:24:24,0:14:31,0:20:58,9:17:52,2216,0,28
+Emergency Medicine,PGY2+,S0169390,2015-08-16,4:26:11,0:41:40,2:44:59,0:30:47,0:31:34,0:30:53,10:17:43,3064,0,21
+Night Team,PGY2+,S0181059,2016-08-30,4:43:16,1:22:53,0:00:04,0:26:51,0:17:17,0:05:33,7:38:12,2886,122,95
+Night Team,PGY2+,S0181073,2016-09-24,1:59:24,1:11:24,0:00:35,0:14:47,0:03:06,0:00:32,3:47:35,1105,0,25
+Night Team,PGY2+,S0181073,2016-09-21,3:23:32,1:23:35,0:00:08,1:00:02,0:10:46,0:15:41,7:06:47,1822,0,27
+Night Team,PGY2+,S0181073,2016-09-20,3:57:39,1:20:39,0:00:05,0:40:12,0:16:16,0:07:36,7:12:28,2385,0,47
+Night Team,PGY2+,S0181073,2016-09-23,2:33:43,1:25:40,0:00:35,1:11:04,0:14:53,0:01:50,6:18:30,1726,0,31
+Night Team,PGY2+,S0181073,2016-09-22,3:09:06,1:29:12,0:00:02,0:36:43,0:07:36,0:07:06,6:33:17,1921,0,47
+General Medicine,PGY1,S0181074,2016-03-12,2:42:37,1:24:07,0:00:12,1:21:29,0:25:43,0:03:14,6:34:06,2393,51,51
+General Medicine,PGY1,S0181074,2016-03-11,2:07:32,0:42:08,0:06:50,1:07:30,0:16:55,0:05:11,5:12:52,2261,82,23
+Emergency Medicine,PGY1,S0169398,2015-04-14,1:48:52,0:35:58,1:48:51,0:08:41,0:12:24,0:34:25,5:30:39,1385,130,20
+Intensive Care Unit,PGY2+,S0169404,2015-09-17,4:13:57,0:08:18,0:15:12,0:27:15,0:25:43,0:47:48,6:46:57,3411,406,32
+Emergency Medicine,PGY1,S0169398,2015-04-16,4:04:39,0:51:54,2:14:51,0:33:10,0:42:26,0:58:59,9:58:45,2657,0,24
+Emergency Medicine,PGY1,S0169398,2015-04-17,3:09:51,0:31:37,1:26:18,0:33:58,0:24:58,0:41:24,7:23:44,2201,55,16
+Emergency Medicine,PGY1,S0169398,2015-04-10,3:49:28,0:40:08,2:10:13,0:19:44,0:32:23,0:43:56,9:10:59,2311,0,19
+Emergency Medicine,PGY1,S0169398,2015-04-12,3:56:19,0:41:35,2:43:37,0:30:59,0:23:16,0:28:29,9:33:49,2805,0,23
+General Medicine,PGY2+,S0181077,2016-08-23,1:48:56,0:48:46,0:00:07,0:35:10,0:19:26,0:00:27,4:12:42,2339,226,27
+General Medicine,PGY1,S0181074,2016-03-17,2:27:59,2:04:48,0:00:13,1:40:59,0:21:46,0:01:06,7:10:03,3236,40,16
+Emergency Medicine,PGY1,S0169398,2015-04-18,3:03:39,0:26:52,1:50:06,0:21:32,0:14:56,0:45:46,7:28:05,1863,0,18
+Intensive Care Unit,PGY2+,S0169404,2015-09-15,1:44:28,0:02:17,0:06:01,0:12:08,0:04:50,0:07:14,2:25:17,1038,46,18
+Night Team,PGY1,S0181526,2016-06-19,3:06:39,0:45:08,0:00:10,0:28:17,0:20:54,0:07:10,5:17:41,1885,8,55
+General Medicine,PGY1,S0181074,2016-03-16,1:01:59,1:05:30,0:00:15,0:24:36,0:08:11,0:00:19,3:09:48,966,51,21
+General Medicine,PGY2+,S0181077,2016-08-21,1:49:03,0:31:40,0:00:12,0:29:55,0:08:57,0:12:26,3:35:12,1745,353,15
+General Medicine,PGY1,S0181074,2016-03-15,0:14:38,0:03:46,0:00:00,0:00:00,0:00:00,0:07:13,0:29:18,161,157,7
+Intensive Care Unit,PGY2+,S0169404,2015-09-13,4:53:57,0:12:40,0:42:51,0:58:22,0:36:52,0:55:09,8:59:39,4981,780,17
+General Medicine,PGY1,S0181074,2016-03-14,2:03:03,1:15:58,0:00:09,1:18:39,0:23:05,0:06:10,5:46:31,1997,59,27
+Intensive Care Unit,PGY2+,S0169404,2015-09-12,2:46:14,0:42:37,0:23:01,0:18:23,0:06:04,0:25:06,5:15:28,2148,614,18
+Night Team,PGY1,S0169398,2015-02-10,3:14:38,0:14:19,0:05:07,0:40:21,0:29:49,0:22:15,5:39:26,1498,0,38
+Night Team,PGY1,S0172056,2016-02-18,0:34:13,0:31:23,0:00:46,0:05:30,0:07:21,0:06:52,1:36:09,725,146,29
+Night Team,PGY1,S0172056,2016-02-19,1:08:59,1:00:14,0:25:22,0:30:04,0:15:46,0:05:19,3:46:30,1218,0,22
+Night Team,PGY1,S0172056,2016-02-16,0:57:13,1:01:58,1:30:25,0:25:01,0:19:00,0:10:55,4:53:41,1305,0,35
+Night Team,PGY1,S0172056,2016-02-17,0:23:58,0:57:39,2:07:51,0:09:51,0:03:24,0:01:17,3:48:55,753,0,9
+Night Team,PGY1,S0172056,2016-02-14,0:44:45,0:52:26,2:08:39,0:30:25,0:18:24,0:18:07,5:35:09,1827,0,37
+Night Team,PGY1,S0181085,2015-12-19,0:44:25,0:45:57,0:56:08,0:17:09,0:18:50,0:16:48,3:30:46,1010,0,34
+Night Team,PGY1,S0181085,2015-12-16,0:47:44,0:17:30,0:02:21,0:10:47,0:19:57,0:19:17,2:04:57,902,8,39
+Night Team,PGY2+,S0181526,2016-11-10,5:42:57,0:48:49,0:00:01,0:19:07,0:05:50,0:01:49,7:14:52,1472,0,60
+General Medicine,PGY1,S0169394,2014-08-15,2:24:40,0:11:32,0:11:22,0:16:58,0:19:26,0:19:29,3:52:12,1099,54,9
+General Medicine,PGY1,S0169394,2014-08-14,3:06:22,0:08:35,0:13:47,0:35:00,0:41:30,0:22:31,5:30:27,1346,27,16
+General Medicine,PGY1,S0169394,2014-08-16,0:00:00,0:00:00,0:00:00,0:00:58,0:00:00,0:00:00,0:00:58,8,0,2
+General Medicine,PGY1,S0169394,2014-08-11,2:19:48,0:08:05,0:12:17,0:08:19,0:11:41,0:24:18,3:45:25,961,69,10
+General Medicine,PGY1,S0169394,2014-08-10,0:00:04,0:00:00,0:00:00,0:00:33,0:00:00,0:00:00,0:00:37,15,3,6
+General Medicine,PGY1,S0169394,2014-08-13,2:15:55,0:10:39,0:15:38,0:57:04,0:30:05,0:19:25,4:48:38,1306,208,9
+General Medicine,PGY1,S0169394,2014-08-12,2:47:20,0:07:58,0:13:37,0:50:32,0:30:05,0:23:30,5:14:43,1337,101,11
+Intensive Care Unit,PGY2+,S0079337,2016-06-23,1:47:58,0:37:18,0:04:55,0:22:08,0:16:24,0:11:01,3:34:12,1230,120,16
+Night Team,PGY2+,S0181059,2016-09-09,3:19:28,2:12:10,0:00:14,0:19:55,0:14:54,0:10:17,7:16:38,3346,0,65
+Night Team,PGY2+,S0181059,2016-09-08,4:27:23,2:57:08,0:03:24,0:09:24,0:14:08,0:09:03,8:53:11,3890,0,143
+Night Team,PGY2+,S0181059,2016-09-07,3:25:01,2:39:52,0:00:23,0:42:38,0:20:02,0:12:37,8:09:09,4380,53,119
+Night Team,PGY2+,S0181059,2016-09-06,3:41:02,2:18:00,0:01:53,0:40:11,0:23:38,0:14:15,8:17:37,4019,0,112
+Night Team,PGY2+,S0181059,2016-09-05,3:08:05,2:25:15,0:00:06,0:20:57,0:19:07,0:23:15,7:22:23,3553,0,68
+Night Team,PGY2+,S0181059,2016-09-04,3:36:28,1:48:13,0:00:28,0:20:56,0:15:13,0:11:05,7:24:05,3260,406,123
+Night Team,PGY2+,S0181059,2016-09-03,1:01:36,0:49:26,0:00:01,0:00:00,0:02:18,0:00:02,2:05:23,613,347,41
+Night Team,PGY2+,S0181059,2016-09-02,0:01:01,0:00:02,0:00:00,0:00:00,0:00:00,0:00:00,0:01:09,13,0,3
+Night Team,PGY2+,S0181059,2016-09-01,1:34:52,1:20:57,0:00:09,0:08:10,0:11:05,0:03:45,3:37:10,2096,0,70
+General Medicine,PGY1,S0176040,2016-03-23,0:46:36,0:20:29,0:02:18,0:06:40,0:06:20,0:00:39,1:37:16,618,14,5
+General Medicine,PGY1,S0176040,2016-03-22,0:00:00,0:00:06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:07,17,5,3
+General Medicine,PGY1,S0176040,2016-03-20,1:01:47,0:57:40,0:00:20,0:26:17,0:11:10,0:00:53,3:08:58,1021,15,7
+General Medicine,PGY1,S0176040,2016-03-26,0:03:07,0:00:17,0:00:00,0:00:06,0:00:00,0:00:00,0:03:33,24,20,5
+General Medicine,PGY1,S0176040,2016-03-25,0:01:19,0:00:10,0:00:00,0:00:06,0:00:00,0:01:01,0:03:02,33,25,7
+General Medicine,PGY2+,S0093383,2016-02-19,0:17:12,0:12:53,0:23:42,0:00:53,0:02:27,0:03:45,1:06:32,434,0,7
+General Medicine,PGY2+,S0093383,2016-02-18,0:59:08,0:31:19,0:15:09,0:21:11,0:17:38,0:18:00,3:06:02,1495,214,14
+General Medicine,PGY2+,S0093383,2016-02-17,0:02:06,0:07:55,0:00:17,0:00:08,0:00:50,0:00:20,0:12:04,263,255,5
+General Medicine,PGY2+,S0093383,2016-02-16,1:18:56,0:42:49,0:36:56,0:31:37,0:15:06,0:21:23,4:11:33,2049,458,16
+General Medicine,PGY2+,S0093383,2016-02-15,1:35:21,1:21:50,2:57:14,0:21:02,0:23:02,0:26:15,7:33:34,2017,0,20
+General Medicine,PGY2+,S0093383,2016-02-14,1:14:29,1:33:02,1:02:56,0:41:24,0:31:52,0:21:10,5:44:15,2018,0,12
+General Medicine,PGY2+,S0093383,2016-02-13,1:34:16,0:48:35,0:05:58,0:47:40,0:13:08,0:51:16,4:57:37,2712,0,22
+General Medicine,PGY2+,S0093383,2016-02-12,1:43:27,1:17:55,1:26:08,0:35:11,0:24:03,0:28:58,6:15:52,2266,0,16
+General Medicine,PGY2+,S0093383,2016-02-11,1:04:03,0:51:23,0:03:30,0:02:43,0:05:23,0:28:38,2:37:40,1446,1420,15
+General Medicine,PGY2+,S0093383,2016-02-10,0:05:20,0:01:05,0:00:35,0:00:14,0:00:00,0:01:06,0:09:09,119,0,15
+General Medicine,PGY1,S0181067,2016-02-21,2:33:22,1:01:17,0:00:18,0:51:55,0:34:38,0:02:11,5:53:41,2926,0,14
+General Medicine,PGY1,S0181067,2016-02-20,0:44:07,0:21:18,0:00:32,0:36:47,0:09:35,0:00:09,2:11:08,922,0,5
+General Medicine,PGY1,S0181067,2016-02-23,0:47:45,0:58:38,0:00:36,0:25:48,0:14:13,0:02:06,2:51:38,1396,0,13
+General Medicine,PGY1,S0181067,2016-02-22,0:58:01,0:27:02,0:00:31,0:08:28,0:15:18,0:00:12,2:10:28,803,0,8
+General Medicine,PGY1,S0181067,2016-02-25,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,21,0,3
+General Medicine,PGY1,S0181067,2016-02-24,0:00:00,0:00:00,0:00:00,0:00:55,0:00:00,0:00:00,0:00:55,25,0,4
+General Medicine,PGY1,S0181067,2016-02-27,1:44:47,0:48:50,0:00:02,0:25:42,0:35:33,0:00:14,3:49:19,1160,49,9
+General Medicine,PGY1,S0181067,2016-02-26,0:03:40,0:13:35,0:00:00,0:00:01,0:00:33,0:00:00,0:19:22,183,166,17
+General Medicine,PGY1,S0181067,2015-07-09,0:43:24,0:04:17,0:37:00,0:22:49,0:10:36,0:03:39,2:14:57,585,0,4
+General Medicine,PGY1,S0181067,2015-07-08,1:43:37,0:24:26,0:15:45,0:07:46,0:41:50,0:13:08,4:08:29,1083,0,10
+General Medicine,PGY1,S0172085,2015-07-13,1:29:50,1:11:14,1:39:38,1:29:23,0:22:23,0:34:44,7:21:49,1920,20,8
+Night Team,PGY1,S0195218,2016-07-10,2:26:23,0:53:54,0:00:08,0:38:32,0:39:08,0:12:26,5:10:15,1546,61,39
+Night Team,PGY1,S0195218,2016-07-11,1:41:14,0:39:36,0:00:02,0:35:52,0:23:22,0:15:28,3:54:46,1271,0,27
+Night Team,PGY1,S0195218,2016-07-12,1:36:03,0:19:19,0:00:00,0:25:04,0:20:50,0:07:47,3:03:40,993,0,30
+Night Team,PGY1,S0195218,2016-07-13,2:08:17,1:16:05,0:00:02,0:40:13,0:42:29,0:07:02,5:13:27,1296,0,27
+Night Team,PGY1,S0195218,2016-07-14,1:22:52,0:59:55,0:00:09,0:26:25,0:29:20,0:11:40,3:44:17,1249,0,30
+Night Team,PGY1,S0195218,2016-07-15,1:57:55,1:01:04,0:00:25,0:38:56,0:30:18,0:10:49,4:36:27,1305,1,37
+Night Team,PGY1,S0195218,2016-07-16,2:34:51,1:20:08,0:00:00,1:04:36,0:27:34,0:10:47,6:02:34,1548,1,55
+Night Team,PGY1,S0195218,2016-07-17,2:12:02,0:51:44,0:00:09,0:17:17,0:13:02,0:03:58,3:56:33,1032,2,39
+Night Team,PGY1,S0195218,2016-07-18,1:03:00,0:29:33,0:00:00,0:05:16,0:13:57,0:04:20,1:58:14,533,0,28
+Night Team,PGY1,S0195218,2016-07-19,1:41:17,0:25:48,0:00:04,0:27:01,0:10:44,0:16:56,3:36:21,735,0,18
+Night Team,PGY1,S0169398,2015-02-05,3:22:48,0:26:11,1:33:19,0:54:33,0:23:26,0:22:37,7:18:54,1652,0,29
+General Medicine,PGY2+,S0181065,2016-09-16,3:55:30,1:00:13,0:00:09,0:30:38,0:29:16,0:02:59,6:49:22,3851,0,21
+General Medicine,PGY1,S0169411,2014-09-21,0:00:00,0:00:00,0:00:00,0:00:41,0:00:00,0:00:00,0:00:41,18,0,2
+General Medicine,PGY1,S0169411,2014-09-20,4:28:39,0:10:00,0:09:48,0:42:11,0:19:43,0:02:02,6:08:39,1585,0,13
+General Medicine,PGY1,S0169411,2014-09-23,3:33:17,0:00:27,0:09:24,0:31:29,0:22:37,0:05:50,5:08:40,1721,0,15
+General Medicine,PGY1,S0169411,2014-09-22,4:05:54,0:10:54,0:13:55,1:20:31,0:17:00,0:01:28,6:45:40,1509,0,10
+General Medicine,PGY1,S0169411,2014-09-25,4:46:36,0:11:00,0:12:27,0:38:05,0:26:19,0:00:58,6:32:54,1557,0,9
+General Medicine,PGY1,S0169411,2014-09-24,3:31:59,0:03:59,0:13:18,0:48:30,0:24:42,0:00:00,5:27:27,1578,0,7
+General Medicine,PGY1,S0169411,2014-09-27,5:55:24,0:00:59,0:11:21,0:54:19,0:28:28,0:00:46,7:54:52,2056,0,13
+General Medicine,PGY1,S0169411,2014-09-26,0:20:09,0:00:00,0:00:28,0:00:04,0:00:27,0:00:00,0:33:37,395,383,27
+General Medicine,PGY1,S0169411,2014-09-29,3:08:23,0:00:00,0:13:18,0:12:49,0:11:35,0:00:00,4:05:46,1085,0,16
+General Medicine,PGY1,S0169411,2014-09-28,3:31:39,0:01:07,0:07:06,0:21:26,0:10:05,0:06:08,4:31:29,1210,0,12
+General Medicine,PGY1,S0172056,2015-09-07,1:33:08,0:17:09,0:49:19,0:26:51,0:26:14,0:41:01,4:54:38,1574,0,11
+General Medicine,PGY1,S0074196,2015-08-31,1:40:35,0:22:22,0:54:02,0:19:02,0:11:11,0:17:09,4:13:13,1061,0,10
+General Medicine,PGY1,S0172056,2015-09-05,1:42:52,0:15:08,3:48:44,0:42:09,0:23:24,0:14:51,7:36:10,1737,0,11
+General Medicine,PGY1,S0172056,2015-09-04,1:02:24,0:08:05,1:01:52,0:24:43,0:09:22,0:11:00,3:21:51,1080,0,7
+General Medicine,PGY1,S0172056,2015-09-03,0:07:31,0:07:16,0:02:33,0:10:19,0:00:01,0:04:18,0:48:17,245,235,5
+Intensive Care Unit,PGY2+,S0181086,2016-07-12,1:27:31,1:31:21,0:05:03,0:35:49,0:29:36,0:04:12,4:26:30,914,0,12
+General Medicine,PGY1,S0172056,2015-09-01,1:33:40,0:22:14,2:21:19,0:44:36,0:28:30,0:20:48,6:21:55,1846,0,9
+General Medicine,PGY1,S0074196,2015-08-30,1:19:48,0:24:25,1:35:58,0:08:05,0:11:42,0:10:11,4:14:26,832,0,5
+General Medicine,PGY1,S0196597,2016-09-19,2:35:29,0:47:39,0:01:07,0:40:48,0:20:01,0:01:03,4:57:59,1774,73,18
+General Medicine,PGY1,S0196597,2016-09-18,1:01:52,0:34:26,0:00:42,0:17:03,0:03:30,0:00:18,2:15:22,777,199,13
+General Medicine,PGY1,S0172056,2015-09-09,0:55:12,0:05:11,0:55:26,0:12:19,0:13:41,0:12:27,2:49:55,647,0,5
+General Medicine,PGY1,S0172056,2015-09-08,1:27:48,0:19:02,1:05:16,0:12:37,0:10:52,0:11:06,3:48:55,1032,27,6
+Intensive Care Unit,PGY2+,S0181525,2016-09-18,0:57:49,0:20:59,0:13:49,0:04:00,0:15:36,0:03:25,2:12:50,480,0,11
+Intensive Care Unit,PGY2+,S0181525,2016-09-19,2:14:22,0:26:53,0:00:11,0:30:40,0:37:07,0:02:39,4:39:28,1474,0,18
+Intensive Care Unit,PGY2+,S0181525,2016-09-11,2:28:30,0:31:26,0:00:08,1:09:50,0:46:34,0:10:34,5:44:50,1593,0,15
+Intensive Care Unit,PGY2+,S0181525,2016-09-12,1:02:32,0:17:37,0:10:56,0:17:10,0:07:42,0:00:08,2:11:19,471,0,9
+Intensive Care Unit,PGY2+,S0181525,2016-09-13,1:09:29,0:20:35,0:04:32,0:10:54,0:12:29,0:00:11,2:27:29,512,0,13
+Intensive Care Unit,PGY2+,S0181525,2016-09-14,1:51:25,0:43:20,0:07:42,0:13:17,0:23:23,0:02:32,3:43:51,855,0,13
+Night Team,PGY1,S0195075,2016-10-15,0:00:00,0:00:00,0:00:00,0:00:05,0:00:00,0:00:00,0:00:05,4,0,2
+Intensive Care Unit,PGY2+,S0181525,2016-09-16,2:16:01,0:39:38,0:11:21,0:41:54,0:32:51,0:07:04,5:08:51,1473,0,20
+Intensive Care Unit,PGY2+,S0181525,2016-09-17,0:00:00,0:00:00,0:00:00,0:00:14,0:00:00,0:00:00,0:00:30,27,0,8
+Night Team,PGY1,S0195075,2016-10-14,0:53:07,0:32:56,0:00:02,0:33:40,0:04:39,0:08:01,2:21:04,776,146,22
+General Medicine,PGY1,S0195086,2016-08-20,0:03:58,0:00:16,0:00:00,0:00:00,0:00:00,0:00:00,0:04:50,20,0,3
+Emergency Medicine,PGY1,S0169402,2014-07-12,5:14:54,0:05:34,0:29:49,0:35:43,0:26:04,0:11:34,7:53:44,1569,101,20
+General Medicine,PGY2+,S0169394,2016-02-20,1:28:06,0:10:08,0:00:00,0:10:30,0:02:35,0:17:44,2:35:26,954,206,18
+General Medicine,PGY2+,S0169394,2016-02-21,3:06:32,1:40:08,0:00:04,0:10:38,0:12:06,0:09:44,5:54:25,1801,222,30
+General Medicine,PGY2+,S0169394,2016-02-22,0:36:42,0:03:36,0:00:03,0:03:05,0:06:18,0:03:29,0:55:44,419,39,16
+General Medicine,PGY2+,S0169394,2016-02-23,0:13:12,0:01:29,0:00:00,0:00:00,0:00:00,0:01:59,0:18:43,156,154,11
+General Medicine,PGY2+,S0169394,2016-02-24,2:10:06,0:51:15,0:00:04,0:14:13,0:13:50,0:10:41,4:25:11,1452,87,21
+General Medicine,PGY2+,S0169394,2016-02-25,2:27:12,0:42:39,0:00:03,0:40:18,0:34:23,0:05:09,5:24:26,2166,498,26
+General Medicine,PGY2+,S0169394,2016-02-26,3:33:39,0:47:59,0:00:13,0:27:22,0:15:58,0:05:30,6:08:25,2074,0,21
+General Medicine,PGY2+,S0169394,2016-02-27,1:21:04,0:19:48,0:00:00,0:23:32,0:18:13,0:04:14,3:02:31,1235,161,15
+General Medicine,PGY2+,S0169394,2016-02-28,1:47:09,0:34:37,0:00:05,0:47:08,0:22:09,0:00:57,4:06:10,1902,436,13
+General Medicine,PGY2+,S0169394,2016-02-29,2:00:13,0:44:23,0:00:00,0:32:02,0:20:41,0:02:23,4:11:16,1536,169,19
+Night Team,PGY1,S0169398,2015-02-06,4:05:59,0:12:16,1:51:28,0:17:46,0:17:19,0:25:17,7:27:31,1611,0,49
+Emergency Medicine,PGY1,S0169402,2014-07-13,5:23:36,0:14:38,0:19:28,0:25:35,0:25:16,0:01:49,7:24:11,1612,56,17
+Night Team,PGY2+,S0109179,2015-07-13,2:37:45,0:41:13,1:55:38,0:11:48,0:07:09,0:23:21,6:17:52,2038,187,49
+Emergency Medicine,PGY1,S0169404,2014-12-15,3:02:39,0:19:39,2:24:55,0:19:41,0:32:21,0:43:53,7:37:42,2627,0,20
+General Medicine,PGY2+,S0169399,2016-02-03,1:36:21,0:36:03,1:00:50,1:06:55,0:19:59,0:20:22,5:40:17,1807,0,20
+Intensive Care Unit,PGY2+,S0169399,2016-03-02,0:58:37,0:20:54,0:03:47,0:05:43,0:16:18,0:06:04,2:15:36,557,42,9
+Intensive Care Unit,PGY2+,S0169399,2016-03-03,2:11:48,0:47:43,0:23:13,0:37:36,0:19:42,0:08:25,5:06:41,1674,83,13
+Intensive Care Unit,PGY2+,S0169399,2016-03-01,1:12:18,0:24:57,0:03:53,0:00:03,0:09:36,0:01:03,2:14:48,534,23,12
+Intensive Care Unit,PGY2+,S0169399,2016-03-04,1:42:56,0:23:12,0:02:00,0:08:47,0:11:03,0:02:02,2:48:30,615,4,13
+Intensive Care Unit,PGY2+,S0169399,2016-03-05,0:46:07,0:17:01,0:09:54,0:14:08,0:19:01,0:00:44,1:57:20,395,0,9
+Night Team,PGY1,S0169389,2014-06-26,1:53:17,0:18:43,0:02:59,0:30:08,0:22:20,1:14:07,4:32:42,937,0,33
+Night Team,PGY1,S0169389,2014-06-27,2:33:48,0:03:48,0:11:09,0:20:39,0:30:55,0:43:40,4:38:14,1080,0,36
+Intensive Care Unit,PGY2+,S0176040,2016-07-29,2:27:40,0:43:46,0:06:14,0:10:37,0:24:09,0:04:50,4:36:56,1427,0,13
+Intensive Care Unit,PGY2+,S0176040,2016-07-28,1:39:22,0:37:05,0:06:16,0:04:13,0:15:21,0:06:03,2:56:30,790,0,13
+Intensive Care Unit,PGY2+,S0176040,2016-07-27,2:21:35,0:29:55,0:11:26,0:19:36,0:16:01,0:09:49,4:27:35,1143,0,12
+Intensive Care Unit,PGY2+,S0176040,2016-07-26,2:08:38,0:34:10,0:00:01,0:40:21,0:25:06,0:12:12,4:29:30,1691,0,16
+Intensive Care Unit,PGY2+,S0176040,2016-07-25,2:24:42,0:55:39,0:23:24,0:21:29,0:39:00,0:04:01,5:26:28,1371,0,11
+Intensive Care Unit,PGY2+,S0176040,2016-07-24,2:50:41,1:11:42,0:21:40,0:27:24,1:08:31,0:03:50,6:31:14,1688,0,8
+Intensive Care Unit,PGY2+,S0176040,2016-07-23,0:00:01,0:00:10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:12,10,0,3
+Intensive Care Unit,PGY2+,S0176040,2016-07-22,2:13:39,0:37:44,0:12:45,0:09:05,0:18:25,0:01:17,3:56:39,1254,0,12
+Intensive Care Unit,PGY2+,S0176040,2016-07-21,1:14:48,1:00:02,0:02:59,0:28:47,0:18:01,0:03:51,3:31:51,1411,0,16
+Intensive Care Unit,PGY2+,S0176040,2016-07-20,2:01:29,0:45:53,0:14:12,0:19:37,0:13:04,0:02:29,3:57:36,1134,0,13
+General Medicine,PGY2+,S0169390,2015-12-08,0:47:58,0:58:58,2:07:11,0:03:21,0:06:54,0:16:42,4:40:19,1173,1163,17
+General Medicine,PGY2+,S0169390,2015-12-04,0:00:00,0:15:53,0:00:00,0:05:57,0:00:00,0:01:04,0:23:49,120,100,8
+General Medicine,PGY1,S0195230,2016-10-12,2:08:38,0:42:17,0:00:05,0:36:38,0:12:59,0:00:27,4:19:49,947,0,12
+General Medicine,PGY2+,S0169390,2015-12-06,2:13:41,1:03:44,1:10:44,0:30:25,0:22:58,0:28:41,6:05:01,1981,11,11
+General Medicine,PGY2+,S0169390,2015-12-07,2:24:53,1:26:17,0:26:29,0:16:40,0:21:12,0:33:24,5:58:50,2505,203,24
+General Medicine,PGY2+,S0169390,2015-12-01,3:24:51,0:49:19,0:46:45,1:02:42,0:40:54,0:30:22,7:58:56,2934,49,19
+General Medicine,PGY2+,S0169390,2015-12-02,3:31:47,1:36:24,0:11:54,0:46:18,0:31:41,0:38:19,8:19:00,3445,200,26
+General Medicine,PGY2+,S0169390,2015-12-03,2:21:49,0:38:07,0:41:53,1:08:13,0:30:36,0:25:32,6:24:22,2366,103,16
+Night Team,PGY2+,S0079337,2015-09-30,3:05:00,1:02:18,0:46:32,0:42:17,0:07:22,0:23:14,6:23:28,2271,0,31
+General Medicine,PGY1,S0181073,2016-05-23,0:47:10,0:25:07,0:00:01,0:16:09,0:03:51,0:01:29,1:41:14,518,25,8
+General Medicine,PGY1,S0181073,2016-05-22,2:24:16,1:40:05,0:00:04,0:42:38,0:23:21,0:03:49,5:49:53,1672,0,17
+General Medicine,PGY1,S0181073,2016-05-25,2:32:44,1:12:16,0:00:11,1:27:57,0:28:31,0:09:01,6:41:38,2304,3,17
+General Medicine,PGY1,S0181073,2016-05-24,0:30:22,0:21:29,0:00:30,0:07:34,0:05:04,0:00:37,1:10:28,407,25,4
+Intensive Care Unit,PGY2+,S0169401,2016-03-31,2:54:48,0:44:00,0:10:22,0:35:38,0:12:15,0:06:31,5:29:39,2182,34,37
+General Medicine,PGY1,S0181073,2016-05-26,1:39:13,0:30:28,0:00:07,0:28:53,0:08:03,0:02:49,3:25:47,997,16,10
+General Medicine,PGY1,S0181073,2016-05-29,0:42:13,0:30:05,0:00:05,0:19:52,0:04:44,0:00:11,2:05:22,674,0,3
+General Medicine,PGY1,S0181073,2016-05-28,0:56:45,0:31:13,0:00:07,0:32:11,0:06:21,0:05:52,2:31:04,848,31,3
+General Medicine,PGY1,S0169400,2014-11-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0169399,2014-12-23,2:12:05,0:08:02,3:41:23,0:39:19,0:20:54,0:07:47,7:22:39,1193,0,11
+Intensive Care Unit,PGY2+,S0169404,2016-09-07,2:16:08,0:40:08,0:16:35,0:17:59,0:19:52,0:00:37,4:04:06,1738,0,21
+Intensive Care Unit,PGY2+,S0169404,2016-09-06,2:22:38,0:44:11,0:07:33,0:23:38,0:12:46,0:03:17,4:10:21,2306,0,24
+Intensive Care Unit,PGY2+,S0169404,2016-09-05,4:18:19,1:14:09,0:00:20,0:53:33,0:25:42,0:20:51,7:37:57,4386,0,24
+Intensive Care Unit,PGY2+,S0169404,2016-09-04,0:11:00,0:00:25,0:00:00,0:01:50,0:00:00,0:00:33,0:15:59,91,0,9
+Intensive Care Unit,PGY2+,S0169404,2016-09-03,1:55:58,0:43:05,0:10:27,0:13:19,0:22:43,0:08:48,3:54:24,1429,0,18
+Intensive Care Unit,PGY2+,S0169404,2016-09-02,2:18:55,0:54:29,0:07:02,0:20:31,0:17:59,0:13:42,4:33:20,2108,0,19
+Intensive Care Unit,PGY2+,S0169404,2016-09-01,3:44:25,1:49:41,0:07:22,0:47:18,0:24:11,0:14:46,7:43:03,3795,4,27
+Night Team,PGY2+,S0181087,2016-09-01,2:22:00,1:46:25,0:00:12,0:34:37,0:11:03,0:13:48,5:28:32,2320,378,35
+Night Team,PGY2+,S0181087,2016-09-02,2:58:31,2:08:51,0:00:12,0:59:33,0:12:59,0:01:25,7:16:30,2342,67,45
+Intensive Care Unit,PGY2+,S0169404,2016-09-09,4:36:24,1:37:21,0:03:15,0:37:14,0:20:51,0:10:53,7:41:28,3455,0,24
+Intensive Care Unit,PGY2+,S0169404,2016-09-08,0:25:37,0:00:39,0:00:00,0:00:31,0:00:00,0:02:14,0:29:51,143,0,15
+Night Team,PGY2+,S0181087,2016-09-03,3:15:59,1:28:13,0:00:25,0:24:12,0:09:56,0:07:42,6:27:02,2415,0,48
+General Medicine,PGY1,S0169399,2014-12-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Night Team,PGY2+,S0181087,2016-09-04,1:33:14,0:55:26,0:00:04,0:24:15,0:05:52,0:09:48,3:35:52,1413,102,25
+Night Team,PGY1,S0195079,2016-10-22,0:48:48,0:12:34,0:00:00,0:06:16,0:15:38,0:00:13,1:29:02,326,0,44
+Night Team,PGY2+,S0181087,2016-09-06,3:15:15,1:44:39,0:05:08,1:20:46,0:15:19,0:07:46,7:27:38,2741,576,42
+General Medicine,PGY2+,S0181057,2016-10-30,2:11:34,0:36:23,0:00:00,0:04:25,0:11:17,0:08:11,3:28:25,1170,0,16
+General Medicine,PGY2+,S0181057,2016-10-31,3:30:22,1:27:19,0:00:00,0:17:28,0:21:38,0:02:23,6:10:30,2025,0,28
+Night Team,PGY2+,S0181087,2016-09-07,2:41:50,2:55:22,0:00:13,0:59:15,0:09:35,0:08:18,7:55:15,2888,26,58
+Night Team,PGY1,S0195079,2016-10-20,2:10:12,1:23:46,0:00:04,0:33:20,0:19:21,0:00:36,4:51:02,1198,0,31
+Emergency Medicine,PGY1,S0056755,2014-07-07,4:58:31,0:10:17,0:12:59,0:08:14,0:07:07,0:05:55,6:01:30,1205,0,34
+Night Team,PGY1,S0195079,2016-10-21,1:12:16,0:42:51,0:00:02,0:16:42,0:06:44,0:00:11,2:47:57,664,0,12
+Emergency Medicine,PGY2+,S0094535,2016-03-30,4:16:20,0:46:24,0:07:16,0:13:25,0:39:11,0:04:37,6:57:56,2172,7,12
+Emergency Medicine,PGY2+,S0094535,2016-03-31,2:15:35,0:22:19,0:13:45,0:14:22,0:11:12,0:05:02,4:18:28,1459,632,41
+Night Team,PGY1,S0195079,2016-10-24,3:20:25,0:31:25,0:00:00,1:00:41,0:29:31,0:03:58,6:00:41,1454,0,47
+Night Team,PGY1,S0195079,2016-10-25,2:36:59,0:22:49,0:00:00,0:48:06,0:31:01,0:01:14,4:45:20,1188,0,44
+General Medicine,PGY1,S0174975,2016-03-13,0:16:16,0:35:09,0:00:01,0:06:22,0:00:12,0:00:46,1:05:33,420,420,15
+General Medicine,PGY1,S0094535,2015-01-14,3:32:03,0:06:40,0:52:02,0:55:03,0:24:28,0:32:27,6:52:59,1856,0,11
+General Medicine,PGY1,S0094535,2015-01-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Emergency Medicine,PGY1,S0195076,2016-08-19,3:16:50,0:40:03,0:01:30,0:16:49,0:41:49,0:02:33,5:51:43,1758,0,17
+Emergency Medicine,PGY1,S0195076,2016-08-18,1:58:35,0:19:23,0:02:58,0:09:29,0:30:35,0:06:25,3:42:23,1329,0,15
+Intensive Care Unit,PGY2+,S0169403,2015-12-08,0:40:41,0:51:29,0:52:42,0:05:53,0:03:03,0:12:09,3:02:49,955,107,21
+Emergency Medicine,PGY1,S0195076,2016-08-13,3:36:26,0:47:04,0:04:42,0:18:14,0:38:10,0:02:22,6:11:08,1722,0,20
+Emergency Medicine,PGY1,S0195076,2016-08-12,3:41:38,0:58:11,0:05:02,0:25:45,0:38:09,0:01:45,6:41:41,2197,0,23
+Emergency Medicine,PGY1,S0195076,2016-08-11,1:26:01,0:26:55,0:06:44,0:14:12,0:12:36,0:00:22,2:50:23,988,0,12
+Emergency Medicine,PGY1,S0195076,2016-08-10,2:56:40,0:40:24,0:01:24,0:17:26,0:39:17,0:00:48,5:23:32,1429,0,13
+Emergency Medicine,PGY1,S0195076,2016-08-17,1:42:30,0:24:02,0:10:08,0:18:56,0:42:31,0:07:49,4:20:17,1468,0,12
+Emergency Medicine,PGY1,S0195076,2016-08-16,0:00:01,0:01:43,0:00:00,0:00:00,0:00:02,0:00:00,0:01:53,28,28,2
+Emergency Medicine,PGY1,S0195076,2016-08-15,2:48:00,0:51:28,0:03:20,0:26:21,0:45:48,0:04:23,6:04:32,2080,182,17
+Emergency Medicine,PGY1,S0195076,2016-08-14,0:29:03,0:04:56,0:00:16,0:09:18,0:23:23,0:00:06,1:16:44,534,0,6
+General Medicine,PGY1,S0195236,2016-08-08,1:30:07,0:44:46,0:00:11,0:44:31,0:15:53,0:01:05,3:36:57,1176,63,6
+General Medicine,PGY1,S0195236,2016-08-09,2:27:49,1:10:13,0:00:07,0:32:19,0:13:55,0:00:18,5:02:20,1366,35,8
+General Medicine,PGY1,S0195236,2016-08-02,1:53:05,1:15:04,0:00:12,0:22:03,0:21:29,0:00:35,4:09:36,1531,182,13
+General Medicine,PGY1,S0195236,2016-08-03,0:54:07,0:53:35,0:00:16,0:13:09,0:07:48,0:00:09,2:28:52,869,0,12
+General Medicine,PGY1,S0176040,2016-03-21,1:42:22,0:45:53,0:00:09,0:31:16,0:19:13,0:01:05,3:39:08,1609,7,11
+General Medicine,PGY1,S0195236,2016-08-01,2:10:02,1:48:04,0:00:16,0:28:09,0:17:22,0:01:30,5:25:07,1423,189,13
+General Medicine,PGY1,S0195236,2016-08-06,2:48:02,1:43:37,0:00:18,0:58:42,0:19:37,0:03:57,6:39:18,1840,0,22
+General Medicine,PGY1,S0195236,2016-08-07,0:03:03,0:00:05,0:00:00,0:00:00,0:00:00,0:00:13,0:03:29,20,0,3
+General Medicine,PGY1,S0195236,2016-08-04,2:53:07,2:02:54,0:00:12,0:43:22,0:16:55,0:03:28,6:30:12,1783,118,11
+General Medicine,PGY1,S0195236,2016-08-05,0:03:32,0:03:33,0:00:01,0:00:02,0:00:02,0:02:21,0:10:33,139,97,7
+General Medicine,PGY1,S0169400,2014-11-21,2:55:25,0:12:27,3:00:58,0:55:09,0:13:08,0:24:58,8:10:48,1684,0,8
+Intensive Care Unit,PGY2+,S0169404,2015-08-30,1:36:01,0:10:14,0:11:12,0:09:25,0:09:44,0:14:46,2:51:31,1221,123,11
+Intensive Care Unit,PGY2+,S0169404,2015-08-31,2:02:11,0:07:17,0:34:31,0:29:21,0:17:23,0:18:19,4:02:44,1462,35,11
+Intensive Care Unit,PGY2+,S0169403,2015-12-01,1:37:07,1:16:00,0:15:30,0:07:58,0:17:31,0:34:02,4:34:44,1913,307,22
+Night Team,PGY2+,S0169402,2016-04-30,0:00:04,0:00:12,0:00:00,0:00:00,0:00:00,0:00:00,0:00:27,19,15,6
+Intensive Care Unit,PGY2+,S0169389,2015-07-20,1:50:54,1:07:13,0:08:00,0:15:21,0:14:23,0:07:50,3:59:48,1087,59,13
+Intensive Care Unit,PGY2+,S0169389,2015-07-21,1:36:44,0:56:34,0:06:28,0:26:48,0:27:17,0:12:13,4:06:56,1129,26,19
+General Medicine,PGY1,S0181066,2015-07-01,1:50:09,0:16:07,2:00:29,0:57:13,0:28:35,0:32:07,6:46:56,1850,3,9
+General Medicine,PGY1,S0181066,2015-07-02,1:20:35,0:17:25,2:08:02,0:34:42,0:06:50,0:10:41,4:54:32,1042,0,5
+General Medicine,PGY1,S0181066,2015-07-03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181066,2015-07-04,1:20:00,0:17:43,2:35:02,0:06:25,0:09:50,0:08:25,4:49:52,769,31,4
+Intensive Care Unit,PGY2+,S0169403,2015-12-07,2:26:20,1:22:44,0:06:41,0:22:41,0:28:17,0:39:59,6:04:38,1858,171,15
+General Medicine,PGY1,S0181066,2015-07-06,1:16:09,0:21:48,1:22:56,0:32:34,0:14:52,0:13:11,4:37:02,1110,6,8
+General Medicine,PGY1,S0181066,2015-07-07,1:13:10,0:14:30,1:52:25,0:22:23,0:26:54,0:18:45,4:44:58,1191,23,10
+General Medicine,PGY1,S0181066,2015-07-08,1:57:18,0:20:08,2:20:57,0:08:46,0:19:58,0:09:34,5:38:54,1223,0,10
+General Medicine,PGY1,S0181066,2015-07-09,2:00:55,0:13:30,0:33:18,0:36:15,0:20:36,0:46:07,5:11:47,1763,0,12
+Intensive Care Unit,PGY2+,S0169403,2015-12-06,1:14:50,0:31:22,0:18:04,0:19:44,0:16:53,0:28:35,3:38:41,1267,321,13
+General Medicine,PGY1,S0169381,2014-10-23,3:41:36,0:19:34,0:20:28,0:35:31,0:27:25,0:22:16,6:01:29,1408,15,5
+General Medicine,PGY1,S0169381,2014-10-22,0:00:00,0:00:07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:20,15,13,2
+General Medicine,PGY1,S0169381,2014-10-21,2:57:06,0:16:21,0:28:32,0:36:28,0:28:41,0:34:45,5:41:47,1254,49,10
+General Medicine,PGY1,S0169381,2014-10-20,1:44:56,0:00:38,0:05:17,0:27:17,0:08:08,0:15:28,2:55:51,741,61,6
+General Medicine,PGY1,S0169381,2014-10-27,2:25:47,0:08:08,0:20:23,0:27:02,0:10:37,0:17:43,4:02:13,1130,90,8
+General Medicine,PGY1,S0169381,2014-10-26,3:08:02,0:12:33,0:16:50,0:54:16,0:27:21,0:26:09,5:36:57,1305,0,9
+General Medicine,PGY1,S0169381,2014-10-25,2:28:43,0:06:35,0:17:37,0:21:43,0:17:22,0:17:49,4:04:17,847,125,12
+General Medicine,PGY1,S0169381,2014-10-24,2:44:46,0:19:34,0:13:50,0:53:40,0:24:56,0:37:09,5:23:51,1524,20,10
+General Medicine,PGY1,S0169381,2014-10-29,2:33:32,0:08:28,0:19:20,0:53:09,0:25:37,0:16:02,4:44:20,1325,60,9
+General Medicine,PGY1,S0169381,2014-10-28,2:29:51,0:08:26,0:15:23,0:32:29,0:15:59,0:22:35,4:10:23,1025,47,5
+Night Team,PGY2+,S0094535,2016-02-13,1:24:16,1:27:21,2:41:43,0:32:03,0:07:39,0:33:38,7:37:01,2054,0,38
+Night Team,PGY2+,S0094535,2016-02-12,0:27:22,0:49:52,0:50:43,0:11:03,0:04:48,0:20:55,3:01:35,1396,0,6
+Night Team,PGY2+,S0094535,2016-02-11,0:39:12,0:20:09,2:02:18,0:29:07,0:15:56,0:54:22,4:59:57,1301,0,24
+Night Team,PGY2+,S0094535,2016-02-10,1:37:21,1:27:19,1:33:54,0:16:55,0:05:47,1:05:08,6:35:06,2113,0,38
+Night Team,PGY2+,S0094535,2016-02-17,1:05:09,1:12:19,2:25:43,0:44:16,0:19:11,0:58:31,7:18:13,2334,0,50
+Night Team,PGY2+,S0094535,2016-02-16,1:07:33,1:11:01,2:53:30,0:35:22,0:17:07,0:56:45,7:41:51,2208,0,34
+Night Team,PGY2+,S0094535,2016-02-15,1:09:31,0:48:44,1:13:39,0:07:19,0:08:03,1:01:05,5:00:18,1578,0,33
+Night Team,PGY2+,S0094535,2016-02-14,1:42:39,1:38:10,1:05:40,0:43:41,0:30:15,1:22:56,7:39:43,2267,0,46
+Intensive Care Unit,PGY2+,S0169389,2015-07-22,1:28:55,0:21:24,0:04:57,0:35:13,0:17:59,0:52:55,3:58:03,1110,0,19
+Intensive Care Unit,PGY2+,S0169389,2015-07-23,3:05:54,0:32:01,0:28:57,0:40:34,0:28:32,1:16:12,6:50:32,1974,63,19
+Night Team,PGY2+,S0094535,2016-02-19,1:39:43,1:08:42,1:49:26,0:57:11,0:26:31,0:45:12,7:09:28,2545,0,51
+Night Team,PGY2+,S0094535,2016-02-18,2:16:27,0:54:38,2:37:16,0:37:15,0:22:51,0:49:36,8:01:28,1992,0,67
+Intensive Care Unit,PGY2+,S0169403,2015-12-05,3:03:05,1:41:27,0:17:09,0:40:09,1:01:44,1:28:58,9:04:41,3186,0,20
+Intensive Care Unit,PGY2+,S0169403,2015-12-04,0:00:00,0:03:17,0:00:00,0:07:02,0:00:00,0:00:15,0:10:43,92,51,8
+Intensive Care Unit,PGY2+,S0169389,2015-07-24,0:02:22,0:02:25,0:00:00,0:00:00,0:00:16,0:00:29,0:05:41,60,40,5
+Intensive Care Unit,PGY2+,S0169389,2015-07-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,3
+Emergency Medicine,PGY1,S0169401,2014-08-17,3:14:31,0:05:53,0:03:07,0:25:19,0:12:14,0:13:57,4:36:21,1582,255,60
+General Medicine,PGY1,S0181070,2016-01-08,1:11:56,1:40:15,1:39:23,0:25:25,0:31:13,0:14:57,6:23:46,2572,0,22
+General Medicine,PGY1,S0181070,2016-01-09,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0181070,2016-01-03,2:32:08,2:30:39,2:48:40,0:45:53,0:37:27,0:50:42,10:43:30,3882,0,34
+General Medicine,PGY1,S0181070,2016-01-06,1:10:41,1:47:57,1:37:37,0:39:47,0:46:45,0:41:13,7:24:08,2964,57,12
+General Medicine,PGY1,S0181070,2016-01-07,0:58:27,1:30:07,1:19:59,1:05:22,0:27:54,0:15:13,6:11:34,2624,17,8
+Emergency Medicine,PGY1,S0169401,2014-08-18,6:24:38,0:17:16,0:10:35,0:31:27,0:23:32,0:12:08,8:57:44,2689,218,77
+Emergency Medicine,PGY1,S0169401,2014-08-19,4:05:57,0:10:28,0:27:27,0:16:42,0:14:14,0:04:08,5:45:52,2006,328,65
+General Medicine,PGY1,S0169404,2014-07-25,0:13:25,0:06:21,0:01:24,0:00:00,0:00:03,0:02:55,0:30:02,458,454,8
+Emergency Medicine,PGY1,S0169399,2015-04-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Intensive Care Unit,PGY2+,S0163425,2015-08-31,2:34:47,0:15:13,0:42:17,0:25:04,0:29:37,0:36:29,5:15:45,1748,0,19
+Intensive Care Unit,PGY2+,S0163425,2015-08-30,2:32:29,0:17:53,0:40:15,0:54:39,0:50:32,0:42:05,6:37:33,2148,328,22
+General Medicine,PGY1,S0181088,2015-11-29,2:25:49,1:04:50,1:52:13,0:41:29,0:17:07,0:19:42,7:14:32,2487,0,17
+General Medicine,PGY1,S0181088,2015-11-28,1:50:31,0:32:34,1:25:29,0:36:57,0:23:27,0:26:16,5:52:14,2037,0,9
+General Medicine,PGY1,S0181088,2015-11-21,1:08:57,0:58:26,1:48:37,0:49:49,0:22:42,0:33:11,6:10:51,1998,143,14
+General Medicine,PGY1,S0181088,2015-11-20,0:00:00,0:00:00,0:00:00,0:03:22,0:00:00,0:00:00,0:03:22,12,0,5
+General Medicine,PGY1,S0181088,2015-11-23,1:05:46,0:38:39,1:11:54,0:48:31,0:23:40,0:28:31,5:06:19,1649,104,14
+General Medicine,PGY1,S0181088,2015-11-22,2:18:39,1:12:57,2:23:44,0:56:10,0:21:26,0:23:31,8:25:23,2560,0,24
+General Medicine,PGY1,S0181088,2015-11-25,0:05:19,0:12:06,0:00:14,0:03:01,0:00:00,0:00:49,0:27:08,215,204,5
+General Medicine,PGY1,S0181088,2015-11-24,2:51:25,1:03:40,2:17:23,0:46:38,0:37:36,0:30:55,8:57:06,3808,0,19
+General Medicine,PGY1,S0181088,2015-11-27,2:12:01,1:04:07,2:55:15,0:49:14,0:31:35,0:42:21,8:48:03,2730,0,20
+General Medicine,PGY1,S0181088,2015-11-26,1:05:43,0:58:44,2:08:37,0:21:45,0:09:37,0:16:54,5:23:43,1575,0,10
+General Medicine,PGY1,S0181086,2015-12-08,2:02:03,0:33:43,1:54:29,0:55:34,0:20:05,0:11:30,6:34:04,1604,355,4
+General Medicine,PGY1,S0181086,2015-12-09,2:17:57,1:23:39,3:02:04,1:03:56,0:41:14,0:34:00,9:35:37,2420,263,10
+General Medicine,PGY1,S0169404,2014-07-24,2:57:03,0:35:35,0:28:07,0:46:55,0:31:24,0:28:10,6:21:41,2195,376,7
+General Medicine,PGY1,S0181086,2015-12-06,1:27:19,1:02:41,2:53:44,0:55:25,0:24:10,0:30:52,7:42:20,1712,237,9
+General Medicine,PGY1,S0181086,2015-12-07,3:06:56,0:35:48,2:28:41,0:33:12,0:16:05,0:12:39,7:58:01,1776,293,13
+General Medicine,PGY1,S0169400,2014-11-28,2:59:49,0:05:34,1:45:10,0:45:39,0:32:12,0:30:40,7:14:15,1689,20,20
+General Medicine,PGY1,S0169394,2015-05-22,2:17:57,0:36:20,2:28:33,0:57:18,0:28:56,0:32:24,7:44:33,2148,277,16
+General Medicine,PGY1,S0169404,2014-07-26,4:23:32,1:02:26,0:14:11,0:20:08,0:23:21,0:32:24,7:19:25,3150,470,12
+General Medicine,PGY2+,S0169385,2015-12-09,1:49:45,1:33:28,0:47:14,1:00:33,0:23:43,0:29:58,6:30:48,3369,62,13
+General Medicine,PGY1,S0169394,2015-05-20,2:19:22,0:24:41,1:09:18,0:52:38,0:36:39,0:39:31,6:24:40,2191,318,15
+Emergency Medicine,PGY1,S0195216,2016-10-29,3:50:59,0:37:51,0:09:35,0:46:43,1:00:48,0:01:13,7:07:08,2207,0,65
+Emergency Medicine,PGY1,S0195216,2016-10-28,3:52:49,1:03:41,0:07:26,0:30:12,0:58:59,0:03:55,7:29:45,2480,0,58
+Emergency Medicine,PGY1,S0195216,2016-10-27,1:28:45,0:30:33,0:03:00,0:24:40,0:31:35,0:00:41,3:19:15,1174,0,23
+Emergency Medicine,PGY1,S0169383,2015-03-21,3:50:26,0:16:13,2:47:32,0:31:29,0:33:23,0:23:54,9:33:26,2828,0,29
+Emergency Medicine,PGY1,S0195216,2016-10-25,1:36:24,0:08:18,0:00:11,0:13:50,0:11:49,0:00:12,2:22:04,621,0,32
+Emergency Medicine,PGY1,S0195216,2016-10-24,3:54:41,0:41:57,0:03:42,0:31:49,0:32:38,0:03:07,6:46:52,1911,0,74
+Emergency Medicine,PGY1,S0195216,2016-10-23,3:31:52,0:54:53,0:01:37,0:34:57,0:38:31,0:02:18,6:40:29,2220,0,83
+Emergency Medicine,PGY1,S0169383,2015-03-25,3:03:06,0:32:21,1:53:17,0:26:41,0:23:51,0:28:03,7:46:11,2374,42,23
+Emergency Medicine,PGY1,S0169383,2015-03-26,4:38:59,0:45:41,1:58:42,0:24:25,0:32:53,0:27:43,9:35:48,2773,2,45
+Emergency Medicine,PGY1,S0169383,2015-03-27,3:50:21,0:37:40,2:05:14,0:27:10,0:40:32,0:28:54,8:38:47,2428,0,20
+General Medicine,PGY1,S0181070,2016-01-04,1:07:16,1:09:26,0:43:29,0:29:47,0:17:43,0:12:59,4:29:24,2247,406,10
+Night Team,PGY1,S0181065,2015-07-29,2:35:21,0:28:51,0:41:20,0:33:47,0:44:35,0:17:29,5:33:48,1710,38,50
+Night Team,PGY1,S0181065,2015-07-28,2:38:44,1:19:22,0:53:21,0:16:08,0:23:43,0:27:09,6:17:06,1794,0,53
+Night Team,PGY1,S0181065,2015-07-27,2:05:08,0:54:01,0:00:53,0:23:54,0:21:07,0:22:10,4:18:12,1584,0,47
+Night Team,PGY1,S0181065,2015-07-26,2:55:29,0:27:09,0:00:07,0:27:51,0:22:34,0:43:34,5:14:47,1787,0,50
+Night Team,PGY1,S0181065,2015-07-25,2:25:15,0:41:31,0:00:00,0:14:20,0:20:30,0:35:45,4:36:20,1659,0,65
+General Medicine,PGY1,S0181070,2016-01-05,1:04:20,1:05:27,1:20:09,0:34:24,0:11:54,0:05:21,4:56:40,2414,3,17
+Night Team,PGY1,S0181065,2015-07-23,0:55:15,0:05:42,0:00:00,0:04:41,0:04:50,0:16:12,1:30:50,488,0,30
+Night Team,PGY1,S0181065,2015-07-22,1:01:40,0:04:22,0:00:00,0:13:35,0:09:39,0:12:43,1:55:31,900,0,41
+Night Team,PGY1,S0181065,2015-07-21,2:38:42,0:31:16,0:20:54,0:34:04,0:36:39,0:57:49,5:58:16,1973,0,43
+Night Team,PGY1,S0181065,2015-07-20,2:42:40,0:34:25,0:00:07,0:29:33,0:26:05,0:34:39,5:03:24,1654,0,34
+General Medicine,PGY1,S0169404,2014-07-21,2:59:19,0:41:47,0:14:54,0:44:31,0:49:59,0:12:13,6:01:06,2154,32,8
+General Medicine,PGY1,S0181081,2015-08-11,1:54:57,0:12:55,3:14:36,0:25:02,0:20:44,0:17:52,6:58:36,1615,0,10
+General Medicine,PGY1,S0181081,2015-08-10,2:24:07,0:24:24,2:51:54,0:50:05,0:20:42,0:26:39,8:30:06,2625,489,18
+General Medicine,PGY1,S0181081,2015-08-13,1:13:59,0:06:02,2:09:02,0:29:13,0:02:02,0:15:06,4:52:32,969,0,4
+General Medicine,PGY1,S0181081,2015-08-12,0:46:37,0:00:53,1:15:17,0:05:20,0:00:29,0:04:46,2:32:10,471,461,9
+General Medicine,PGY1,S0181081,2015-08-15,2:26:17,0:03:49,1:42:50,0:22:15,0:11:28,0:34:47,5:56:37,1460,168,13
+General Medicine,PGY1,S0181081,2015-08-14,2:17:23,0:14:07,4:01:50,0:44:29,0:11:54,0:26:09,8:19:33,1693,0,5
+General Medicine,PGY1,S0195201,2016-09-21,2:57:02,2:21:21,0:00:18,1:06:28,0:28:19,0:04:16,7:53:23,3461,27,18
+General Medicine,PGY1,S0195201,2016-09-20,0:30:28,0:31:12,0:00:36,0:00:13,0:01:29,0:00:32,1:06:56,684,625,16
+General Medicine,PGY1,S0195201,2016-09-23,4:14:30,3:14:02,0:01:40,0:34:19,0:15:15,0:09:04,9:17:26,3790,422,12
+General Medicine,PGY1,S0195201,2016-09-22,1:58:39,0:51:34,0:00:08,0:41:44,0:10:18,0:02:34,4:07:42,1859,0,8
+General Medicine,PGY1,S0195201,2016-09-25,2:18:18,1:53:21,0:03:28,0:33:43,0:12:09,0:05:29,5:34:14,2050,155,9
+General Medicine,PGY1,S0195201,2016-09-24,3:32:05,2:41:18,0:01:59,1:15:27,0:13:19,0:04:17,8:31:06,3599,0,25
+General Medicine,PGY1,S0195201,2016-09-27,1:18:31,1:13:52,0:00:33,0:36:39,0:06:25,0:02:41,3:48:51,1776,384,15
+General Medicine,PGY1,S0195201,2016-09-26,2:43:17,1:27:30,0:00:18,0:28:50,0:23:42,0:00:57,5:54:29,2187,54,21
+General Medicine,PGY1,S0195201,2016-09-29,3:20:12,2:31:14,0:00:58,1:12:31,0:14:16,0:08:09,8:15:41,3110,0,27
+General Medicine,PGY1,S0195201,2016-09-28,0:39:16,0:17:50,0:00:06,0:05:44,0:00:05,0:00:33,1:07:07,548,452,13
+General Medicine,PGY1,S0169404,2014-07-23,2:42:18,0:17:36,0:07:02,0:36:12,0:31:04,0:24:13,4:53:30,2238,543,9
+Night Team,PGY2+,S0169385,2016-08-18,2:20:46,1:11:50,0:00:04,0:24:06,0:18:47,0:19:38,5:13:45,1950,98,23
+General Medicine,PGY1,S0169404,2014-07-22,3:07:17,0:33:35,0:14:18,0:36:53,0:29:01,0:07:52,5:21:23,1840,191,9
+General Medicine,PGY2+,S0169401,2016-02-01,2:18:09,0:22:16,0:00:52,0:44:51,0:25:57,0:24:47,4:28:24,2176,344,27
+Emergency Medicine,PGY2+,S0169400,2015-10-03,4:53:54,0:37:32,1:34:05,0:56:22,0:23:03,0:19:25,9:33:46,2845,0,38
+Emergency Medicine,PGY1,S0094959,2014-09-01,3:23:17,0:12:13,1:01:48,0:50:00,0:57:07,1:16:28,8:17:37,4013,2,27
+Emergency Medicine,PGY1,S0094959,2014-09-03,0:41:04,0:08:05,0:06:50,0:01:48,0:06:00,0:12:35,1:19:52,529,98,17
+Emergency Medicine,PGY1,S0094959,2014-09-02,0:50:55,0:17:13,0:13:37,0:03:25,0:07:17,0:21:07,2:02:54,1068,205,17
+Emergency Medicine,PGY1,S0094959,2014-09-05,5:46:48,0:12:54,0:55:14,0:23:19,0:36:35,0:48:24,9:21:06,3891,61,49
+Emergency Medicine,PGY1,S0094959,2014-09-04,1:56:09,0:17:01,0:27:41,0:14:47,0:28:15,0:29:04,4:30:07,2255,188,26
+Emergency Medicine,PGY1,S0094959,2014-09-07,4:46:58,0:19:57,1:18:22,0:19:41,0:46:10,0:51:03,9:09:49,4623,34,65
+Emergency Medicine,PGY1,S0094959,2014-09-09,0:38:38,0:05:38,0:19:18,0:06:10,0:05:00,0:06:14,1:28:57,666,0,7
+Emergency Medicine,PGY1,S0094959,2014-09-08,3:15:55,0:25:48,0:53:06,0:15:38,0:27:15,0:58:02,6:53:06,2694,116,44
+General Medicine,PGY1,S0181085,2016-01-30,0:00:27,0:02:29,0:00:01,0:00:00,0:00:00,0:00:00,0:03:20,65,49,6
+General Medicine,PGY1,S0108287,2014-08-08,2:25:10,0:01:33,0:41:05,1:00:13,0:17:08,0:19:16,4:58:23,1131,0,8
+General Medicine,PGY1,S0108287,2014-08-09,0:06:47,0:00:00,0:01:35,0:00:13,0:00:42,0:00:49,0:10:18,57,47,6
+General Medicine,PGY1,S0108287,2014-08-06,2:44:51,0:00:22,0:23:45,0:38:52,0:22:42,0:12:59,5:14:05,1393,485,20
+General Medicine,PGY1,S0108287,2014-08-07,3:10:42,0:04:04,0:14:35,0:36:26,0:35:59,0:30:43,5:45:01,1452,208,23
+General Medicine,PGY1,S0108287,2014-08-04,2:49:29,0:08:24,0:04:22,0:45:18,0:14:42,0:11:10,4:31:27,972,0,16
+General Medicine,PGY1,S0108287,2014-08-05,2:44:06,0:13:13,0:27:42,1:25:59,0:16:38,0:35:07,6:02:25,1330,0,8
+General Medicine,PGY1,S0108287,2014-08-02,2:38:20,0:02:06,0:13:31,0:16:19,0:24:21,0:18:38,4:13:44,937,75,12
+General Medicine,PGY1,S0108287,2014-08-03,2:55:28,0:15:53,0:11:18,0:31:57,0:19:10,0:29:15,5:00:44,1165,0,7
+General Medicine,PGY1,S0108287,2014-08-01,2:36:30,0:01:24,0:16:11,0:25:08,0:20:42,0:17:55,4:07:34,1109,45,10
+Night Team,PGY2+,S0181070,2016-10-01,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,4
+Night Team,PGY2+,S0181070,2016-10-02,1:42:36,0:43:10,0:00:02,0:00:40,0:02:26,0:00:11,2:43:28,633,0,33
+Night Team,PGY2+,S0181070,2016-10-03,2:07:19,1:17:30,0:00:04,0:44:54,0:03:30,0:01:25,4:33:11,1609,0,37
+Night Team,PGY2+,S0181070,2016-10-04,2:14:38,1:33:49,0:00:06,0:44:34,0:04:20,0:00:40,5:02:06,1380,0,31
+Night Team,PGY2+,S0181070,2016-10-05,1:25:32,1:12:50,0:00:04,0:15:17,0:01:43,0:04:00,3:09:31,1010,0,37
+Night Team,PGY2+,S0181070,2016-10-06,2:04:40,1:21:30,0:00:10,0:17:27,0:07:28,0:01:37,4:25:04,1774,0,31
+Night Team,PGY2+,S0181070,2016-10-07,1:40:31,1:16:30,0:00:06,0:29:00,0:10:54,0:00:10,4:03:31,1274,0,25
+Night Team,PGY2+,S0181070,2016-10-08,0:52:39,0:50:54,0:00:02,0:08:34,0:04:21,0:02:30,2:11:36,741,0,22
+General Medicine,PGY1,S0172085,2015-07-12,0:47:33,0:18:33,2:30:21,0:43:29,0:13:22,0:11:56,5:20:42,917,0,4
+General Medicine,PGY1,S0195207,2016-08-25,0:07:01,0:06:09,0:00:09,0:00:01,0:00:00,0:00:30,0:17:26,354,300,6
+General Medicine,PGY1,S0172085,2015-07-10,2:03:27,0:23:24,2:44:13,0:48:25,0:28:06,0:19:32,7:03:49,1463,21,7
+General Medicine,PGY1,S0195207,2016-08-27,1:10:38,1:17:09,0:00:24,0:14:14,0:08:07,0:02:25,3:09:36,1400,302,14
+General Medicine,PGY1,S0172085,2015-07-16,1:48:00,0:38:13,1:07:30,0:39:43,0:16:22,0:28:35,5:30:39,1561,0,9
+General Medicine,PGY1,S0172085,2015-07-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0172085,2015-07-14,0:00:00,0:00:00,0:00:00,0:02:31,0:00:00,0:00:00,0:02:31,6,0,3
+General Medicine,PGY1,S0172085,2015-07-15,2:26:21,0:28:56,1:30:31,0:55:38,0:22:49,0:27:45,6:48:22,2071,13,18
+General Medicine,PGY1,S0172085,2015-07-18,3:47:30,1:11:31,2:12:31,0:46:11,0:33:30,0:43:40,9:45:50,2757,58,71
+Emergency Medicine,PGY1,S0172085,2015-07-19,3:49:14,0:36:14,1:56:34,0:28:08,0:28:41,0:56:53,8:43:39,2294,0,72
+General Medicine,PGY1,S0195207,2016-08-28,1:11:47,0:47:14,0:01:04,0:06:29,0:07:24,0:00:37,2:30:22,771,0,11
+General Medicine,PGY1,S0195207,2016-08-29,2:25:56,2:01:41,0:00:16,0:46:29,0:21:09,0:01:44,6:12:06,2243,8,16
+General Medicine,PGY1,S0195210,2016-10-16,1:36:00,0:58:31,0:00:58,0:27:54,0:27:33,0:01:15,4:07:22,1084,0,14
+General Medicine,PGY1,S0195210,2016-10-17,1:16:22,1:42:21,0:06:36,1:02:58,0:04:22,0:04:54,5:44:15,1355,165,8
+General Medicine,PGY1,S0108268,2014-09-19,3:39:38,0:25:02,0:20:32,0:52:33,1:05:18,0:41:00,7:24:18,2443,29,11
+General Medicine,PGY1,S0108268,2014-09-18,3:24:40,0:25:19,0:12:14,0:40:56,0:51:07,0:40:40,6:50:25,2220,402,13
+General Medicine,PGY2+,S0172085,2016-09-23,2:20:00,1:29:23,0:00:00,0:03:27,0:07:08,0:04:29,4:28:17,2052,0,29
+General Medicine,PGY1,S0108268,2014-09-15,0:57:53,0:00:00,0:00:00,0:00:02,0:00:00,0:02:20,1:04:56,331,321,18
+General Medicine,PGY1,S0108268,2014-09-14,4:16:57,0:12:15,0:28:25,0:38:23,1:01:00,1:21:17,8:24:18,2657,0,13
+General Medicine,PGY1,S0195230,2016-09-30,1:41:16,1:19:58,0:00:13,0:36:33,0:24:46,0:04:35,4:26:32,1413,0,10
+General Medicine,PGY1,S0108268,2014-09-16,1:40:55,0:10:04,0:04:57,0:00:29,0:06:57,0:05:06,2:46:02,1017,1007,20
+General Medicine,PGY1,S0195210,2016-10-18,1:17:53,1:03:51,0:01:18,0:22:49,0:25:17,0:04:07,3:46:15,1117,0,9
+General Medicine,PGY1,S0195210,2016-10-19,1:09:24,0:46:29,0:00:58,0:25:21,0:12:54,0:05:18,3:26:24,1031,13,12
+Emergency Medicine,PGY1,S0169406,2015-01-18,1:30:44,0:03:36,0:15:09,0:06:52,0:08:31,0:00:37,2:16:58,738,0,17
+Night Team,PGY2+,S0169390,2016-06-02,0:49:45,0:18:46,0:00:03,0:10:12,0:04:36,0:04:08,1:51:54,399,14,7
+Night Team,PGY2+,S0169390,2016-06-03,1:32:50,0:52:19,0:00:00,0:17:33,0:11:31,0:01:35,3:11:02,716,41,41
+Night Team,PGY2+,S0169390,2016-06-01,1:38:27,1:01:15,0:00:04,0:20:29,0:06:12,0:00:19,3:17:54,865,0,35
+Night Team,PGY2+,S0169390,2016-06-06,2:39:11,0:38:01,0:00:01,0:27:38,0:26:02,0:05:01,4:44:08,1334,46,63
+Night Team,PGY2+,S0169390,2016-06-07,2:07:10,0:45:46,0:00:06,0:26:09,0:17:53,0:07:56,3:59:56,835,26,16
+Night Team,PGY2+,S0169390,2016-06-04,2:58:33,2:06:58,0:00:12,0:43:05,0:16:07,0:02:55,6:41:28,1874,6,32
+Night Team,PGY2+,S0169390,2016-06-05,2:49:45,1:17:43,0:00:33,0:29:18,0:20:20,0:07:07,5:29:46,1487,17,47
+Night Team,PGY2+,S0169390,2016-06-08,1:23:40,0:34:24,0:00:00,0:11:46,0:05:31,0:02:56,2:34:21,814,25,21
+General Medicine,PGY2+,S0172085,2016-09-25,3:01:26,1:54:24,0:00:10,0:07:34,0:07:35,0:10:12,5:54:36,2811,0,28
+Emergency Medicine,PGY1,S0169406,2015-01-17,4:08:55,0:08:33,1:19:24,0:20:54,0:25:58,0:18:20,7:16:18,1790,0,26
+General Medicine,PGY1,S0196593,2016-11-07,2:07:57,1:02:47,0:00:11,1:02:51,0:27:40,0:11:54,5:28:25,1586,0,9
+General Medicine,PGY1,S0196593,2016-11-06,1:15:21,0:39:48,0:00:04,0:43:11,0:20:14,0:16:53,3:58:15,1210,58,7
+General Medicine,PGY1,S0196593,2016-11-05,1:32:34,1:14:00,0:00:14,0:47:40,0:11:07,0:12:44,4:40:14,1415,109,10
+General Medicine,PGY1,S0196593,2016-11-04,2:27:25,1:02:36,0:00:06,1:17:47,0:21:38,0:05:31,5:59:59,1745,0,11
+General Medicine,PGY1,S0196593,2016-11-03,1:07:39,1:02:44,0:00:09,0:41:49,0:22:13,0:00:32,3:45:25,1186,38,7
+General Medicine,PGY1,S0196593,2016-11-02,2:09:48,1:19:05,0:00:09,1:00:37,0:22:44,0:01:57,5:43:40,1482,28,7
+General Medicine,PGY1,S0196593,2016-11-01,1:09:20,0:37:28,0:00:05,0:27:53,0:07:21,0:00:15,2:49:03,963,60,8
+General Medicine,PGY1,S0196593,2016-11-09,0:02:57,0:08:05,0:00:00,0:00:15,0:00:21,0:00:01,0:12:32,114,110,5
+General Medicine,PGY1,S0196593,2016-11-08,0:57:34,0:23:24,0:00:10,0:18:19,0:04:25,0:02:13,2:21:57,816,47,6
+General Medicine,PGY1,S0181083,2015-06-28,2:37:41,0:06:55,1:18:38,0:29:48,0:51:13,0:26:21,6:27:32,1706,110,9
+General Medicine,PGY1,S0056755,2014-10-02,0:04:09,0:00:00,0:00:20,0:00:00,0:00:00,0:00:00,0:07:35,73,69,7
+General Medicine,PGY1,S0056755,2014-10-01,3:38:09,0:20:48,0:16:51,0:27:44,0:15:23,0:20:33,5:32:22,1306,0,7
+General Medicine,PGY1,S0056755,2014-10-07,1:34:27,0:00:41,0:05:18,0:03:36,0:06:27,0:06:47,2:00:25,420,0,10
+General Medicine,PGY1,S0056755,2014-10-06,5:00:03,0:17:37,0:20:21,0:56:51,0:25:39,0:06:54,7:41:59,2107,44,8
+General Medicine,PGY1,S0056755,2014-10-05,4:58:40,0:17:30,0:24:21,0:45:28,0:21:04,0:16:24,8:01:03,2238,610,13
+General Medicine,PGY1,S0056755,2014-10-04,4:48:25,0:15:07,0:43:38,0:47:44,0:39:57,0:17:13,7:57:04,2131,166,27
+General Medicine,PGY1,S0056755,2014-10-09,3:07:02,0:03:35,0:26:11,0:31:13,0:28:14,0:09:41,5:07:00,1267,0,9
+General Medicine,PGY1,S0056755,2014-10-08,3:34:05,0:11:41,0:08:23,0:48:32,0:16:39,0:02:36,5:22:06,1451,0,9
+General Medicine,PGY1,S0181083,2015-06-26,2:19:19,0:18:22,1:03:38,0:50:52,0:29:09,0:21:49,6:02:07,1503,0,6
+General Medicine,PGY1,S0181083,2015-06-27,3:29:10,0:05:53,1:25:45,0:59:15,0:54:56,0:28:47,8:12:46,1654,0,11
+Intensive Care Unit,PGY2+,S0181070,2016-08-26,0:00:00,0:00:00,0:00:00,0:00:22,0:00:00,0:00:00,0:00:22,9,0,3
+Intensive Care Unit,PGY2+,S0181070,2016-08-24,1:07:38,0:54:28,0:13:06,0:05:40,0:18:58,0:01:18,2:53:07,1129,0,13
+Intensive Care Unit,PGY2+,S0181070,2016-08-23,2:22:08,1:38:07,0:02:56,0:38:09,0:25:59,0:06:27,5:27:14,1666,27,17
+Intensive Care Unit,PGY2+,S0181070,2016-08-22,1:04:03,1:01:34,0:12:59,0:04:37,0:22:13,0:00:13,3:09:30,1030,0,11
+Intensive Care Unit,PGY2+,S0108268,2016-01-28,1:42:27,0:44:26,0:13:19,0:16:54,0:33:04,0:22:19,4:23:50,1636,0,25
+Intensive Care Unit,PGY2+,S0108268,2016-01-29,1:56:30,0:50:53,0:24:48,0:14:10,0:25:01,0:33:01,4:40:19,1766,0,17
+Intensive Care Unit,PGY2+,S0108268,2016-01-22,1:47:03,1:01:03,0:34:05,0:18:09,0:23:01,0:31:49,5:17:19,1843,0,29
+Intensive Care Unit,PGY2+,S0108268,2016-01-23,0:01:11,0:01:34,0:00:40,0:00:01,0:00:00,0:00:00,0:03:46,60,50,9
+Intensive Care Unit,PGY2+,S0108268,2016-01-20,1:56:51,0:57:31,0:16:08,0:03:35,0:24:51,0:19:48,4:12:13,1268,41,26
+Intensive Care Unit,PGY2+,S0108268,2016-01-21,1:14:57,1:06:24,0:53:58,0:28:31,0:23:49,0:25:23,5:12:53,1885,41,28
+Intensive Care Unit,PGY2+,S0108268,2016-01-26,2:09:52,1:03:16,0:16:38,0:15:42,0:26:35,0:30:27,5:05:04,1559,0,19
+Intensive Care Unit,PGY2+,S0108268,2016-01-27,0:01:22,0:04:26,0:00:08,0:01:37,0:00:00,0:00:30,0:08:09,69,51,8
+Intensive Care Unit,PGY2+,S0108268,2016-01-24,1:33:35,0:59:42,0:07:23,0:30:26,1:06:14,1:14:46,6:03:41,2548,20,20
+Intensive Care Unit,PGY2+,S0108268,2016-01-25,1:41:43,0:50:17,0:11:40,0:16:48,0:21:23,0:29:37,4:06:15,1465,0,17
+General Medicine,PGY1,S0195214,2016-08-14,2:20:43,0:58:07,0:00:31,0:39:27,0:22:15,0:00:18,5:05:53,1352,127,9
+General Medicine,PGY1,S0181057,2016-06-22,0:05:23,0:20:03,0:00:00,0:02:39,0:01:01,0:00:03,0:31:24,382,380,20
+Night Team,PGY2+,S0181070,2016-09-30,1:45:54,0:47:13,0:00:04,0:43:20,0:01:46,0:03:18,3:36:27,1042,48,17
+General Medicine,PGY1,S0181057,2016-06-23,2:27:32,2:13:56,0:00:11,0:52:22,0:29:07,0:01:28,6:53:11,2503,0,23
+General Medicine,PGY1,S0181057,2016-06-20,1:00:32,0:50:41,0:00:06,0:48:04,0:13:08,0:00:27,3:16:55,1285,101,11
+General Medicine,PGY1,S0169400,2014-11-09,2:06:18,0:01:48,1:29:41,0:25:40,0:11:24,0:15:23,4:46:41,1083,0,5
+General Medicine,PGY1,S0195214,2016-08-12,2:12:36,1:59:39,0:01:04,0:29:37,0:25:39,0:00:01,5:25:02,1364,0,6
+General Medicine,PGY1,S0195213,2016-10-31,0:59:11,0:37:52,0:04:04,0:14:39,0:06:30,0:03:05,2:45:37,960,0,10
+General Medicine,PGY1,S0195213,2016-10-30,2:44:40,1:25:52,0:00:04,0:42:32,0:19:44,0:04:28,5:49:27,1401,64,13
+General Medicine,PGY1,S0181526,2016-05-11,2:41:51,1:04:52,0:00:08,0:17:48,0:11:53,0:05:43,5:03:30,1303,186,23
+General Medicine,PGY1,S0181526,2016-05-10,0:52:43,0:25:40,0:00:24,0:03:59,0:02:12,0:00:02,1:48:54,519,109,16
+General Medicine,PGY1,S0181526,2016-05-13,1:37:34,0:58:11,0:00:08,0:20:10,0:05:32,0:03:43,3:27:31,993,193,11
+General Medicine,PGY1,S0181526,2016-05-12,0:11:49,0:01:03,0:00:00,0:00:00,0:00:00,0:07:59,0:24:23,107,101,6
+General Medicine,PGY1,S0181526,2016-05-15,0:24:17,0:05:44,0:00:00,0:00:00,0:00:00,0:07:01,0:43:02,161,155,7
+General Medicine,PGY1,S0181063,2016-03-28,0:41:53,0:44:09,0:01:13,0:46:02,0:12:54,0:01:06,3:00:57,1050,46,6
+General Medicine,PGY1,S0181526,2016-05-17,3:03:42,0:48:03,0:00:12,0:19:26,0:13:23,0:05:24,5:24:27,1531,89,19
+General Medicine,PGY1,S0181526,2016-05-16,2:22:53,1:25:51,0:00:08,0:23:57,0:17:12,0:07:26,5:18:40,1367,31,29
+General Medicine,PGY1,S0181526,2016-05-19,1:41:41,0:58:35,0:00:18,0:19:16,0:08:19,0:11:45,4:04:16,1023,53,19
+General Medicine,PGY1,S0181526,2016-05-18,3:04:50,0:31:36,0:02:54,0:45:58,0:03:35,0:10:51,6:06:30,1515,95,20
+General Medicine,PGY1,S0181063,2016-03-29,0:01:08,0:05:06,0:00:00,0:00:00,0:00:00,0:00:01,0:06:22,46,44,3
+Emergency Medicine,PGY2+,S0181056,2016-09-08,4:36:59,1:30:19,0:07:12,0:35:48,0:41:21,0:03:01,8:52:30,3182,0,30
+Night Team,PGY2+,S0169404,2015-12-29,1:38:20,1:45:26,1:28:25,1:20:17,0:12:47,0:47:29,7:51:11,4569,1660,31
+Emergency Medicine,PGY2+,S0181056,2016-09-09,3:32:18,1:15:37,0:04:35,0:40:03,0:29:25,0:04:05,6:46:51,2217,0,59
+General Medicine,PGY1,S0181063,2016-03-27,0:58:37,1:05:46,0:00:34,0:32:14,0:25:13,0:02:55,3:33:28,1339,27,8
+Intensive Care Unit,PGY2+,S0109179,2016-02-24,2:40:47,1:09:25,0:01:50,0:25:08,0:16:42,0:05:21,4:50:11,1815,136,29
+Intensive Care Unit,PGY2+,S0181525,2016-09-29,0:55:39,0:16:06,0:06:31,0:03:22,0:10:33,0:04:47,2:04:53,476,20,11
+Intensive Care Unit,PGY2+,S0181525,2016-09-28,1:50:50,0:13:03,0:00:41,0:11:37,0:13:34,0:01:28,2:43:11,802,0,11
+Emergency Medicine,PGY2+,S0181056,2016-09-01,3:25:07,0:38:03,0:15:33,0:42:30,0:34:58,0:02:18,6:50:52,2598,206,35
+General Medicine,PGY1,S0169403,2015-03-30,2:54:07,1:06:44,1:52:35,0:43:07,0:41:45,0:39:55,8:34:10,2344,0,15
+General Medicine,PGY1,S0169403,2015-03-31,1:57:09,1:08:12,1:02:01,0:37:17,0:33:06,0:46:38,6:31:32,1932,10,17
+Night Team,PGY2+,S0181065,2016-08-03,2:42:23,0:29:35,0:00:03,0:24:22,0:07:26,0:13:41,4:26:51,2635,1044,28
+General Medicine,PGY1,S0074196,2015-09-03,1:21:13,0:23:02,0:52:11,0:33:56,0:05:24,0:19:39,3:45:40,957,0,16
+Night Team,PGY2+,S0181065,2016-08-01,4:57:18,0:35:43,0:00:04,0:26:33,0:10:48,0:06:12,6:41:07,3002,0,39
+Night Team,PGY2+,S0181065,2016-08-07,5:27:30,0:54:40,0:00:07,0:14:58,0:25:58,0:05:54,7:30:35,2176,156,26
+Night Team,PGY2+,S0181065,2016-08-06,0:56:52,0:25:44,0:00:06,0:06:26,0:04:00,0:07:19,2:10:36,1188,0,16
+Night Team,PGY2+,S0181065,2016-08-05,2:43:11,0:28:12,0:00:44,0:17:20,0:09:34,0:02:07,3:57:23,1345,0,23
+General Medicine,PGY1,S0074196,2015-09-02,2:10:49,0:16:16,1:21:29,1:00:03,0:17:26,0:14:16,5:55:52,1465,28,15
+Night Team,PGY2+,S0181065,2016-08-09,3:20:32,0:52:35,0:00:57,0:21:29,0:09:39,0:16:38,5:18:06,1744,173,27
+Night Team,PGY2+,S0181065,2016-08-08,3:25:02,0:52:53,0:00:07,0:21:38,0:26:28,0:15:17,5:56:23,3074,0,155
+Intensive Care Unit,PGY2+,S0181525,2016-09-27,1:26:56,0:37:39,0:01:23,0:34:38,0:26:12,0:00:56,3:26:20,1056,0,16
+Intensive Care Unit,PGY2+,S0181525,2016-09-26,1:31:26,0:37:54,0:13:09,0:09:13,0:16:03,0:00:02,3:29:29,649,0,11
+Intensive Care Unit,PGY2+,S0181525,2016-09-21,1:43:08,0:27:56,0:09:38,0:28:43,0:29:14,0:01:03,3:50:32,1084,0,17
+Intensive Care Unit,PGY2+,S0181525,2016-09-20,1:46:46,1:00:04,0:05:25,0:20:25,0:38:13,0:01:42,4:13:22,1115,0,18
+General Medicine,PGY1,S0172645,2016-01-28,0:50:49,1:10:02,1:26:24,0:19:47,0:12:53,0:10:04,4:20:06,1740,10,9
+General Medicine,PGY1,S0181073,2015-10-19,0:59:32,1:09:47,1:22:34,0:27:47,0:27:18,0:42:49,5:35:26,1815,126,5
+General Medicine,PGY1,S0108268,2014-12-31,0:04:12,0:00:00,0:00:26,0:00:06,0:00:01,0:00:00,0:09:21,142,120,12
+Intensive Care Unit,PGY2+,S0181525,2016-09-23,1:59:23,0:23:39,0:00:49,0:52:59,0:44:12,0:18:47,5:07:15,1124,0,18
+General Medicine,PGY1,S0176040,2015-11-08,1:34:02,1:22:42,1:57:14,0:16:32,0:24:55,0:41:32,7:02:47,1960,28,10
+General Medicine,PGY1,S0172645,2016-01-20,0:38:57,0:59:26,2:10:40,0:19:59,0:13:06,0:14:38,4:55:07,1496,15,9
+General Medicine,PGY1,S0172645,2016-01-21,1:46:12,1:46:57,2:34:04,0:44:47,0:17:21,0:19:43,7:48:45,2113,0,13
+General Medicine,PGY1,S0172645,2016-01-22,1:01:56,1:37:40,1:43:13,0:34:53,0:16:07,0:17:23,5:56:44,1793,0,11
+General Medicine,PGY1,S0074196,2015-09-04,1:51:49,0:28:40,2:20:54,0:31:28,0:16:01,0:21:11,6:15:20,1579,0,13
+General Medicine,PGY1,S0172645,2016-01-24,1:26:14,1:20:18,3:07:10,0:39:54,0:33:37,0:22:15,7:52:02,1972,7,11
+General Medicine,PGY1,S0172645,2016-01-25,0:59:05,1:11:08,1:24:07,0:36:15,0:13:58,0:19:24,5:03:01,1574,54,8
+General Medicine,PGY1,S0172645,2016-01-26,1:31:46,1:30:31,1:59:33,1:02:46,0:26:00,0:47:58,7:57:42,2269,67,13
+General Medicine,PGY1,S0172645,2016-01-27,1:25:13,1:15:26,1:31:20,0:43:27,0:20:33,0:26:22,6:00:43,1897,0,14
+General Medicine,PGY1,S0195224,2016-10-26,1:40:28,1:27:54,0:00:08,0:20:04,0:10:08,0:00:12,3:56:35,1092,86,7
+General Medicine,PGY1,S0195224,2016-10-27,2:19:15,1:48:55,0:00:18,0:52:53,0:27:49,0:05:32,6:37:40,1915,143,21
+General Medicine,PGY1,S0195224,2016-10-24,5:11:39,1:29:34,0:00:10,1:05:24,0:29:29,0:17:52,9:33:36,3703,41,23
+General Medicine,PGY1,S0195224,2016-10-25,1:46:47,1:34:20,0:12:52,1:04:30,0:25:10,0:19:21,6:20:14,1999,20,16
+General Medicine,PGY1,S0195224,2016-10-22,3:53:35,1:21:33,0:01:34,1:00:37,0:32:19,0:07:30,7:46:16,2390,0,17
+General Medicine,PGY1,S0195224,2016-10-23,2:31:46,1:05:49,0:00:11,0:23:55,0:22:57,0:02:04,5:00:29,1359,0,10
+General Medicine,PGY1,S0195224,2016-10-20,0:16:20,0:25:04,0:00:02,0:07:00,0:01:35,0:04:47,1:02:44,272,211,14
+General Medicine,PGY1,S0195224,2016-10-21,2:12:44,1:13:55,0:00:06,0:45:16,0:31:10,0:09:18,5:11:16,1546,258,5
+General Medicine,PGY1,S0195224,2016-10-28,0:16:30,0:01:19,0:00:00,0:00:00,0:00:00,0:02:46,0:22:49,116,0,8
+General Medicine,PGY1,S0195224,2016-10-29,2:19:21,1:25:18,0:00:08,1:13:51,0:24:58,0:01:56,5:47:22,1381,15,12
+General Medicine,PGY1,S0181525,2015-11-05,1:48:08,0:50:46,1:19:31,1:23:46,0:37:37,0:46:04,7:05:58,1619,0,9
+General Medicine,PGY1,S0181525,2015-11-04,2:28:07,1:27:29,1:27:30,0:52:46,0:43:15,1:01:02,8:34:24,2221,76,14
+General Medicine,PGY1,S0181525,2015-11-07,0:00:00,0:00:00,0:00:00,0:00:13,0:00:00,0:00:00,0:00:13,26,0,4
+General Medicine,PGY1,S0181525,2015-11-06,0:55:35,0:48:16,1:20:19,0:36:00,0:36:38,0:41:15,5:23:26,1638,10,14
+General Medicine,PGY1,S0181525,2015-11-01,1:08:55,1:01:20,2:44:01,0:28:36,0:12:38,0:31:19,6:27:07,1196,69,5
+General Medicine,PGY1,S0181525,2015-11-03,0:07:39,0:10:24,0:01:06,0:00:53,0:00:11,0:02:27,0:22:49,197,171,9
+General Medicine,PGY1,S0181525,2015-11-02,1:27:39,0:51:21,2:45:08,0:59:17,0:48:10,0:36:25,7:55:33,2259,105,12
+General Medicine,PGY2+,S0169401,2015-12-12,1:54:52,1:08:14,0:16:08,0:22:02,0:08:53,0:44:57,4:51:50,1861,315,40
+General Medicine,PGY1,S0074196,2015-09-10,0:00:00,0:00:00,0:00:00,0:00:03,0:00:00,0:00:00,0:00:03,14,0,4
+General Medicine,PGY1,S0074196,2015-09-11,1:41:10,0:05:56,1:03:58,0:42:03,0:13:34,0:18:36,4:20:35,1339,100,11
+General Medicine,PGY1,S0074196,2015-09-12,0:18:11,0:01:19,0:04:16,0:00:50,0:02:59,0:01:49,0:32:30,207,52,10
+Night Team,PGY1,S0181061,2016-02-20,1:50:15,0:28:48,0:00:00,0:50:43,0:23:23,0:12:31,4:32:04,1278,0,37
+Night Team,PGY1,S0181061,2016-02-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Night Team,PGY1,S0181061,2016-02-26,1:04:58,0:18:26,0:00:08,0:15:42,0:07:43,0:07:16,2:10:59,477,3,11
+Night Team,PGY1,S0181061,2016-02-25,2:02:59,0:26:22,0:00:03,0:25:47,0:23:09,0:02:23,3:38:45,1072,616,34
+Night Team,PGY1,S0181061,2016-02-24,1:37:49,0:46:27,0:00:08,0:34:50,0:16:07,0:10:43,3:49:22,1254,326,34
+General Medicine,PGY1,S0106952,2015-04-11,3:22:05,0:48:34,2:09:01,0:43:06,0:22:38,0:37:26,8:26:26,2172,502,26
+General Medicine,PGY1,S0106952,2015-04-10,2:49:07,0:18:19,1:16:06,0:17:51,0:18:54,0:06:54,5:41:12,1437,113,19
+General Medicine,PGY1,S0106952,2015-04-13,3:56:00,0:53:40,1:03:29,0:37:29,0:33:48,0:28:55,8:15:30,2268,58,14
+General Medicine,PGY1,S0106952,2015-04-12,0:57:30,0:10:15,1:21:37,0:04:39,0:00:57,0:00:07,2:35:29,416,399,7
+General Medicine,PGY1,S0106952,2015-04-15,3:54:39,0:20:53,3:03:00,0:22:01,0:18:23,0:28:57,8:56:09,1861,441,14
+General Medicine,PGY1,S0106952,2015-04-14,3:33:17,1:00:55,2:54:50,0:22:21,0:28:51,0:27:42,9:13:26,2616,1557,41
+General Medicine,PGY1,S0106952,2015-04-17,4:05:18,1:15:46,2:02:27,0:35:10,0:27:52,0:22:10,9:29:01,2890,1100,23
+General Medicine,PGY1,S0106952,2015-04-16,4:15:35,0:28:47,1:31:32,0:52:49,0:44:08,0:27:15,8:52:55,2329,0,20
+General Medicine,PGY1,S0106952,2015-04-19,3:39:55,0:08:20,1:43:07,0:47:46,0:12:22,0:26:26,7:38:22,1761,480,20
+General Medicine,PGY1,S0106952,2015-04-18,4:50:52,0:13:43,1:16:15,1:07:30,0:54:27,0:10:28,8:57:50,2292,321,36
+Night Team,PGY1,S0094535,2014-11-18,1:02:32,0:04:46,0:01:35,0:13:02,0:11:10,0:35:05,2:15:26,596,0,24
+General Medicine,PGY2+,S0169401,2015-12-13,0:16:43,0:17:19,0:15:03,0:01:14,0:00:54,0:02:30,0:57:51,521,511,18
+General Medicine,PGY2+,S0169394,2016-02-08,0:11:18,0:11:21,0:00:46,0:00:10,0:00:00,0:04:06,0:28:32,194,188,8
+General Medicine,PGY2+,S0169394,2016-02-09,0:46:07,0:44:39,0:45:37,0:11:50,0:09:43,0:23:46,3:38:35,1509,140,22
+Night Team,PGY2+,S0181083,2016-08-29,1:46:19,0:41:39,0:00:13,0:01:46,0:03:50,0:04:34,2:59:33,878,0,24
+Night Team,PGY2+,S0181083,2016-08-28,2:10:32,1:00:03,0:02:52,0:32:56,0:04:48,0:00:43,4:32:03,1330,386,23
+General Medicine,PGY2+,S0169394,2016-02-07,1:16:23,0:30:28,0:43:05,0:28:38,0:24:44,0:37:38,4:39:55,1449,486,20
+General Medicine,PGY1,S0195216,2016-08-22,1:25:45,0:49:47,0:00:05,0:25:16,0:13:09,0:01:57,3:03:10,954,17,8
+General Medicine,PGY1,S0172056,2016-04-29,1:25:53,1:44:45,0:00:02,0:15:19,0:25:11,0:00:13,4:37:27,1623,0,17
+General Medicine,PGY1,S0195216,2016-08-21,2:16:34,1:16:48,0:00:07,0:41:57,0:29:46,0:10:52,5:44:43,2365,20,18
+General Medicine,PGY1,S0195216,2016-08-26,2:58:21,1:57:51,0:00:17,0:36:18,0:19:31,0:05:16,6:36:25,2642,0,20
+General Medicine,PGY1,S0195216,2016-08-27,1:22:18,1:15:00,0:00:27,0:22:14,0:11:43,0:04:47,3:44:20,1773,111,9
+General Medicine,PGY1,S0195216,2016-08-24,2:14:39,1:49:40,0:00:28,0:37:06,0:27:36,0:05:29,6:06:47,2454,160,8
+General Medicine,PGY1,S0172056,2016-04-28,0:13:39,0:08:19,0:00:59,0:00:32,0:00:26,0:00:07,0:40:10,518,506,16
+General Medicine,PGY1,S0195216,2016-08-28,1:01:28,0:25:43,0:00:04,0:09:57,0:06:32,0:00:10,1:52:48,770,0,8
+General Medicine,PGY1,S0195216,2016-08-29,2:57:28,2:42:56,0:00:09,0:50:31,0:21:59,0:00:18,7:27:41,2476,0,17
+General Medicine,PGY1,S0195205,2016-08-31,1:22:52,1:13:35,0:00:07,0:32:39,0:20:28,0:00:18,3:55:08,1514,77,10
+General Medicine,PGY1,S0195205,2016-08-30,0:10:45,0:18:37,0:00:00,0:00:42,0:05:40,0:00:11,0:36:47,216,208,8
+Night Team,PGY2+,S0108268,2016-05-04,1:45:51,2:13:37,0:00:34,0:44:10,0:12:03,0:07:01,5:49:52,2081,0,38
+Emergency Medicine,PGY1,S0169383,2015-03-20,4:42:05,0:38:33,1:31:31,0:35:04,0:28:37,0:26:02,9:31:00,2602,0,40
+Night Team,PGY2+,S0108268,2016-05-06,1:31:09,0:48:03,0:00:25,0:18:51,0:08:20,0:01:18,3:10:18,1064,17,33
+Night Team,PGY2+,S0108268,2016-05-07,1:09:28,0:53:37,0:00:57,0:04:15,0:08:50,0:04:32,2:42:40,992,34,25
+Night Team,PGY2+,S0108268,2016-05-01,1:34:48,1:06:33,0:00:38,0:19:19,0:06:25,0:02:29,3:38:05,1273,77,21
+Night Team,PGY2+,S0108268,2016-05-02,0:00:00,0:00:00,0:00:00,0:03:15,0:00:00,0:00:00,0:03:15,10,0,4
+Night Team,PGY2+,S0108268,2016-05-03,3:44:31,2:02:32,0:00:11,0:12:03,0:08:57,0:04:01,6:58:40,2449,0,44
+Night Team,PGY2+,S0108268,2016-05-08,1:11:34,0:54:39,0:00:04,0:23:43,0:12:33,0:00:48,2:53:28,954,76,16
+Emergency Medicine,PGY1,S0169383,2015-03-22,4:16:20,1:08:56,2:14:49,0:31:20,0:21:43,0:16:25,9:35:55,2680,0,27
+Night Team,PGY1,S0106952,2014-12-13,2:58:52,0:07:59,2:14:31,0:44:52,0:30:49,0:32:38,7:30:51,1756,93,46
+Night Team,PGY1,S0106952,2014-12-12,2:03:20,0:05:52,0:05:57,0:15:07,0:35:35,0:20:22,3:32:59,968,81,38
+Night Team,PGY1,S0106952,2014-12-11,2:14:37,0:01:39,2:25:14,0:29:43,0:12:07,0:26:18,6:09:22,1170,57,20
+Emergency Medicine,PGY1,S0169383,2015-03-23,4:02:43,0:28:36,2:51:19,0:35:41,0:29:13,0:38:08,9:33:31,2331,0,31
+General Medicine,PGY1,S0108287,2014-07-29,3:32:11,0:11:15,0:23:57,0:41:30,0:27:03,0:18:51,6:01:41,1406,0,13
+General Medicine,PGY1,S0108287,2014-07-28,2:22:17,0:02:14,0:20:39,0:05:11,0:10:27,0:04:45,3:39:54,739,0,11
+Intensive Care Unit,PGY2+,S0108287,2016-01-09,0:00:00,0:00:00,0:00:00,0:00:04,0:00:00,0:00:00,0:00:04,7,0,2
+Night Team,PGY1,S0106952,2014-12-14,3:39:31,0:05:40,2:38:28,0:54:04,0:30:39,0:33:56,8:48:22,1952,0,50
+Intensive Care Unit,PGY2+,S0108287,2016-01-07,2:11:16,0:39:27,0:17:11,0:09:36,0:10:07,0:20:25,4:01:24,947,120,18
+General Medicine,PGY2+,S0169394,2016-02-19,1:47:39,0:45:34,0:37:25,0:04:48,0:10:49,0:34:13,4:33:27,1701,237,19
+Night Team,PGY1,S0106952,2014-12-19,4:09:58,0:02:18,1:35:34,0:56:04,0:55:32,0:36:24,8:30:24,2292,159,53
+Night Team,PGY1,S0106952,2014-12-18,1:49:49,0:02:26,0:02:54,0:13:52,0:17:01,0:16:17,2:47:56,897,193,51
+Intensive Care Unit,PGY2+,S0108287,2016-01-03,1:36:40,1:02:44,0:21:08,0:09:44,0:13:00,0:17:32,4:17:11,1223,22,14
+Intensive Care Unit,PGY2+,S0108287,2016-01-02,2:05:22,1:42:55,0:33:20,0:30:24,0:39:30,0:38:25,6:36:06,1727,0,17
+General Medicine,PGY1,S0108287,2014-07-23,3:17:01,0:17:42,0:16:59,0:34:33,0:26:30,0:21:09,5:42:53,1184,4,9
+General Medicine,PGY2+,S0169394,2016-02-18,0:07:49,0:12:07,0:00:04,0:00:16,0:00:00,0:04:43,0:33:24,311,302,13
+General Medicine,PGY1,S0106952,2015-04-01,3:15:08,0:45:32,1:11:55,0:58:34,0:40:27,0:35:58,8:00:28,2281,239,16
+Emergency Medicine,PGY1,S0195216,2016-10-21,3:25:06,1:03:03,0:03:57,0:29:19,0:38:24,0:00:51,6:13:59,2079,0,72
+Emergency Medicine,PGY1,S0195216,2016-10-20,1:41:23,0:26:51,0:00:49,0:26:53,0:19:44,0:04:41,3:11:48,851,0,33
+General Medicine,PGY1,S0169384,2014-08-14,3:07:50,0:06:44,0:13:25,0:19:35,0:12:50,0:22:18,4:27:17,1013,205,9
+General Medicine,PGY1,S0169384,2014-08-15,3:58:36,0:05:52,0:22:22,0:34:29,0:11:10,0:12:44,5:33:04,1453,84,11
+General Medicine,PGY1,S0169384,2014-08-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,1
+General Medicine,PGY1,S0195076,2016-06-30,3:07:08,1:35:43,0:00:50,1:10:38,0:45:47,0:00:39,7:26:16,1975,16,11
+General Medicine,PGY1,S0169384,2014-08-10,3:17:25,0:00:55,0:28:49,0:39:27,0:15:48,0:14:14,5:07:28,1205,0,12
+General Medicine,PGY1,S0169384,2014-08-11,1:33:23,0:01:34,0:06:43,0:07:54,0:07:49,0:09:01,2:08:30,439,0,4
+General Medicine,PGY1,S0169384,2014-08-12,3:36:27,0:11:21,0:32:49,0:48:54,0:17:18,0:07:50,5:52:54,1087,14,14
+General Medicine,PGY1,S0169384,2014-08-13,0:28:38,0:00:00,0:00:19,0:02:00,0:00:00,0:00:00,0:31:15,101,95,3
+Intensive Care Unit,PGY2+,S0169401,2016-03-19,3:07:50,0:50:53,0:06:42,0:25:16,0:14:48,0:05:58,5:42:09,2021,20,18
+Intensive Care Unit,PGY2+,S0169401,2016-03-18,3:09:27,1:29:04,0:01:27,0:38:18,0:18:47,0:05:09,6:20:45,2736,15,26
+Intensive Care Unit,PGY2+,S0169401,2016-03-11,3:28:11,0:48:26,0:04:16,0:29:47,0:27:50,0:02:47,5:58:21,1768,12,24
+Intensive Care Unit,PGY2+,S0169401,2016-03-10,1:34:56,0:11:59,0:02:04,0:10:39,0:13:30,0:00:36,2:25:05,911,109,17
+Intensive Care Unit,PGY2+,S0169401,2016-03-13,4:14:04,0:48:21,0:00:02,0:32:53,0:18:12,0:15:01,6:44:08,2393,62,21
+Intensive Care Unit,PGY2+,S0169401,2016-03-12,0:03:40,0:00:17,0:00:00,0:00:07,0:00:00,0:00:17,0:04:39,54,37,6
+Intensive Care Unit,PGY2+,S0169401,2016-03-15,1:05:21,0:35:08,0:00:35,0:12:47,0:12:21,0:00:08,2:22:23,1077,6,25
+Intensive Care Unit,PGY2+,S0169401,2016-03-14,2:37:33,0:10:27,0:09:28,0:28:14,0:16:17,0:11:24,4:22:34,1393,165,12
+Intensive Care Unit,PGY2+,S0169401,2016-03-17,1:28:02,0:24:30,0:02:51,0:15:45,0:14:21,0:05:19,2:50:32,984,49,18
+Intensive Care Unit,PGY2+,S0169401,2016-03-16,2:00:04,0:31:16,0:00:38,0:38:58,0:14:30,0:02:25,3:56:37,1371,24,19
+General Medicine,PGY1,S0196595,2016-07-11,2:46:25,1:06:27,0:05:37,1:08:43,1:00:56,0:00:42,6:29:09,1875,128,17
+General Medicine,PGY1,S0196595,2016-07-10,2:17:21,1:40:42,0:02:10,1:07:42,0:39:22,0:01:57,6:27:42,1457,0,8
+General Medicine,PGY1,S0196595,2016-07-13,2:38:36,1:35:39,0:01:06,0:49:47,0:33:31,0:04:21,6:10:35,1536,182,13
+General Medicine,PGY1,S0196595,2016-07-12,0:19:45,0:12:36,0:01:28,0:00:44,0:00:00,0:00:01,0:44:02,419,349,9
+Night Team,PGY1,S0169394,2014-11-08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0196595,2016-07-14,2:27:46,1:05:38,0:00:02,0:55:14,0:29:10,0:00:29,5:40:06,1505,0,19
+General Medicine,PGY1,S0196595,2016-07-17,0:00:00,0:00:00,0:00:00,0:00:17,0:00:00,0:00:00,0:00:17,6,0,3
+General Medicine,PGY1,S0196595,2016-07-16,2:30:24,1:44:42,0:02:36,0:34:51,0:40:24,0:02:12,5:59:43,1801,13,16
+Night Team,PGY1,S0169394,2014-11-04,3:02:07,0:10:29,0:18:52,0:25:46,0:15:17,0:40:15,5:13:00,1111,14,28
+Night Team,PGY1,S0169394,2014-11-05,2:00:19,0:08:06,0:13:58,0:28:29,0:13:44,0:31:38,3:56:07,1142,0,27
+Night Team,PGY1,S0169394,2014-11-06,4:11:15,0:06:36,0:19:11,0:34:07,0:21:16,0:48:54,6:50:59,1771,14,33
+Night Team,PGY1,S0169394,2014-11-07,1:10:51,0:00:39,0:00:17,0:04:50,0:06:35,0:13:20,1:38:44,433,13,25
+Night Team,PGY1,S0169394,2014-11-01,3:15:21,0:01:50,0:04:04,0:15:33,0:30:34,0:42:52,5:09:44,1213,0,42
+Night Team,PGY1,S0169394,2014-11-02,2:34:08,0:07:37,0:06:34,0:44:49,0:22:29,0:24:02,4:33:36,1090,0,32
+Night Team,PGY1,S0169394,2014-11-03,2:37:19,0:06:14,0:14:06,0:20:39,0:21:35,0:46:37,4:34:19,1126,32,30
+Night Team,PGY1,S0181065,2015-07-24,2:24:10,0:22:11,1:08:21,0:38:29,0:18:22,0:44:06,5:58:50,1676,10,43
+General Medicine,PGY1,S0195223,2016-09-10,0:06:50,0:01:51,0:00:00,0:00:00,0:00:00,0:00:44,0:11:45,110,0,10
+General Medicine,PGY1,S0174975,2015-12-19,1:49:12,1:36:16,1:32:27,1:35:25,0:27:12,0:29:52,8:17:14,3324,212,21
+General Medicine,PGY1,S0195223,2016-09-12,1:25:39,1:19:05,0:00:18,0:32:39,0:24:05,0:04:18,4:28:24,1417,0,17
+General Medicine,PGY1,S0195223,2016-09-13,0:25:37,0:04:56,0:00:00,0:00:00,0:00:00,0:01:13,0:34:29,118,0,8
+General Medicine,PGY1,S0195223,2016-09-14,2:07:47,1:34:13,0:00:56,0:45:00,0:30:33,0:10:04,6:10:19,1834,0,14
+General Medicine,PGY1,S0195223,2016-09-15,1:46:41,1:29:09,0:00:11,0:45:40,0:23:06,0:10:02,5:34:31,1977,22,14
+General Medicine,PGY1,S0195223,2016-09-16,1:20:44,1:06:41,0:00:11,0:29:09,0:13:12,0:01:49,3:37:03,1419,0,11
+General Medicine,PGY1,S0195223,2016-09-17,0:00:30,0:00:03,0:00:00,0:00:00,0:00:00,0:00:00,0:00:34,6,0,2
+Intensive Care Unit,PGY2+,S0056755,2016-06-11,1:55:55,0:24:48,0:05:11,0:25:19,0:25:35,0:06:03,3:48:04,917,0,11
+Intensive Care Unit,PGY2+,S0056755,2016-06-10,3:22:11,1:03:14,0:01:46,0:44:40,0:43:18,0:01:04,6:29:11,1549,0,16
+General Medicine,PGY1,S0094959,2015-04-01,3:27:26,0:50:15,1:08:56,0:15:04,0:20:04,0:22:15,6:43:50,2506,0,25
+Intensive Care Unit,PGY2+,S0056755,2016-06-12,0:00:09,0:00:13,0:00:00,0:00:01,0:00:00,0:00:00,0:00:26,16,5,5
+Intensive Care Unit,PGY2+,S0056755,2016-06-15,1:44:17,0:46:06,0:00:41,0:32:17,0:21:36,0:01:38,3:46:18,1106,0,17
+General Medicine,PGY1,S0094959,2015-04-06,2:27:05,0:33:49,0:39:18,0:18:42,0:14:39,0:13:27,4:52:05,2173,40,17
+General Medicine,PGY1,S0094959,2015-04-05,1:54:38,0:26:21,0:39:10,0:46:07,0:12:58,0:07:44,4:23:29,1763,7,30
+Intensive Care Unit,PGY2+,S0056755,2016-06-16,0:44:55,0:23:17,0:03:19,0:13:35,0:17:13,0:00:04,1:43:58,455,13,17
+Intensive Care Unit,PGY2+,S0056755,2016-06-19,2:25:24,0:38:20,0:10:24,0:23:38,0:46:50,0:04:28,4:50:10,1236,0,15
+Intensive Care Unit,PGY2+,S0056755,2016-06-18,1:50:08,0:35:13,0:06:28,0:31:40,0:31:59,0:00:25,3:56:02,1338,0,15
+General Medicine,PGY1,S0094959,2015-04-09,3:51:26,1:26:38,0:34:15,0:13:06,0:34:55,0:41:11,7:45:33,3383,0,60
+General Medicine,PGY1,S0094959,2015-04-08,2:35:21,0:35:54,0:16:54,0:22:49,0:21:21,0:07:18,4:37:54,2196,8,24
+General Medicine,PGY2+,S0169383,2016-06-26,2:01:38,0:46:21,0:00:10,0:18:57,0:12:10,0:08:14,3:46:08,1092,0,16
+General Medicine,PGY2+,S0169383,2016-06-27,1:45:40,1:01:03,0:01:07,0:18:34,0:16:02,0:03:11,3:50:06,1380,1,20
+Night Team,PGY2+,S0169386,2015-10-05,1:32:18,1:14:49,1:20:34,0:26:48,0:15:34,0:53:35,6:28:00,2631,771,70
+Night Team,PGY2+,S0169386,2015-10-04,2:07:22,1:03:14,1:13:43,0:36:15,0:09:54,1:00:11,6:57:55,2329,768,59
+Intensive Care Unit,PGY2+,S0169383,2015-10-19,1:47:34,1:08:02,0:08:48,0:18:22,0:22:05,0:36:35,4:52:30,1335,130,14
+Night Team,PGY2+,S0169399,2016-05-12,0:52:40,0:28:26,0:00:01,0:09:22,0:05:22,0:05:23,1:50:19,451,27,15
+Intensive Care Unit,PGY2+,S0169383,2015-10-18,1:54:46,0:35:56,0:04:52,0:10:49,0:09:56,0:18:44,3:44:49,1099,36,13
+Night Team,PGY1,S0169400,2014-07-09,2:10:56,0:04:45,0:13:18,0:26:34,0:17:15,0:31:44,4:14:34,1125,110,18
+Night Team,PGY1,S0169400,2014-07-08,2:14:10,0:04:08,0:12:49,0:48:24,0:46:57,0:39:59,5:24:01,1554,0,38
+Night Team,PGY1,S0169400,2014-07-07,1:39:02,0:06:02,0:05:06,0:22:24,0:29:14,0:32:17,3:34:12,958,0,24
+Night Team,PGY2+,S0169386,2015-10-09,1:22:49,0:44:24,1:38:19,0:24:35,0:10:43,0:32:58,5:27:25,1513,343,46
+Night Team,PGY2+,S0169386,2015-10-08,2:28:41,1:22:27,1:58:16,0:36:24,0:28:25,1:18:00,8:50:25,2865,461,63
+General Medicine,PGY1,S0176040,2016-03-09,2:19:51,1:09:45,0:00:24,0:41:08,0:35:34,0:00:46,5:28:16,1972,0,12
+General Medicine,PGY1,S0176040,2016-03-08,1:12:25,1:03:28,0:00:10,0:16:24,0:10:54,0:01:13,3:18:35,1263,0,19
+General Medicine,PGY1,S0176040,2016-03-05,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,22,0,3
+General Medicine,PGY1,S0176040,2016-03-04,2:16:15,0:56:40,0:00:58,0:32:54,0:22:53,0:04:48,5:06:12,2195,0,12
+General Medicine,PGY1,S0176040,2016-03-07,1:46:40,1:11:32,0:00:34,0:36:37,0:18:03,0:03:35,4:38:33,1976,0,21
+General Medicine,PGY1,S0176040,2016-03-06,2:37:16,0:52:56,0:00:56,0:50:26,0:25:24,0:03:40,5:39:20,2074,0,15
+General Medicine,PGY1,S0176040,2016-03-01,2:52:03,1:22:17,0:00:46,1:25:41,0:29:06,0:01:56,7:10:41,2260,0,16
+General Medicine,PGY1,S0176040,2016-03-03,2:15:14,1:12:37,0:01:28,0:59:14,0:26:15,0:09:32,5:50:13,1940,94,11
+General Medicine,PGY1,S0176040,2016-03-02,0:54:35,0:25:36,0:00:03,0:20:24,0:20:26,0:00:37,2:19:00,994,0,14
+General Medicine,PGY2+,S0169406,2016-02-26,0:07:01,0:00:09,0:00:13,0:00:49,0:01:58,0:00:03,0:21:00,70,60,6
+General Medicine,PGY2+,S0169406,2016-02-27,2:13:32,0:46:26,0:00:00,0:02:16,0:18:15,0:04:19,3:55:13,1553,0,35
+General Medicine,PGY2+,S0169406,2016-02-25,1:07:01,0:41:37,0:00:03,0:14:56,0:11:10,0:00:59,2:38:42,1221,280,15
+Night Team,PGY2+,S0169402,2016-04-19,3:34:32,1:12:18,0:00:20,1:08:52,0:09:49,0:01:48,6:59:13,2309,329,45
+Night Team,PGY2+,S0169402,2016-04-18,1:53:39,0:53:16,0:00:38,0:33:06,0:06:53,0:05:50,3:47:57,1289,86,28
+General Medicine,PGY2+,S0169406,2016-02-22,2:36:26,1:00:30,0:00:01,0:12:49,0:18:15,0:04:40,4:46:26,1794,147,20
+Night Team,PGY2+,S0169402,2016-04-17,2:16:37,1:53:43,0:00:12,0:33:26,0:24:36,0:02:48,5:53:25,2070,48,38
+General Medicine,PGY2+,S0169406,2016-02-23,1:50:37,0:11:20,0:00:00,0:14:59,0:10:27,0:02:17,2:44:38,1133,28,11
+Night Team,PGY2+,S0181057,2016-08-11,1:24:28,1:40:27,0:01:23,0:51:16,0:20:32,0:08:25,4:48:54,1578,0,24
+Night Team,PGY2+,S0181057,2016-08-10,1:48:13,1:05:16,0:00:04,0:43:32,0:38:55,0:11:45,4:54:06,1493,2,31
+General Medicine,PGY1,S0187071,2016-09-12,0:28:08,0:47:57,0:00:02,0:01:56,0:01:23,0:00:01,1:28:10,621,611,33
+Night Team,PGY2+,S0181057,2016-08-12,1:18:12,1:23:30,0:00:11,0:27:09,0:17:51,0:04:47,3:45:59,1749,0,39
+General Medicine,PGY1,S0187071,2016-09-14,2:19:08,1:45:22,0:00:08,0:57:29,0:28:17,0:03:44,6:16:25,2441,0,14
+General Medicine,PGY2+,S0169406,2016-02-20,1:48:35,0:40:33,0:00:05,0:17:51,0:15:01,0:00:25,3:11:37,1030,0,10
+General Medicine,PGY1,S0187071,2016-09-16,2:41:01,1:15:18,0:01:21,0:41:44,0:27:25,0:00:57,5:56:32,1929,52,12
+General Medicine,PGY1,S0187071,2016-09-17,2:14:40,1:49:28,0:00:12,0:50:32,0:16:50,0:00:19,5:46:55,2233,0,22
+General Medicine,PGY2+,S0169406,2016-02-21,1:29:58,0:11:20,0:00:03,0:14:49,0:07:58,0:00:18,2:20:12,956,0,6
+Night Team,PGY1,S0195220,2016-06-27,1:55:34,0:19:05,0:00:00,0:26:44,0:13:16,0:06:02,3:08:05,670,0,25
+Night Team,PGY1,S0195220,2016-06-26,2:14:24,0:30:07,0:00:00,0:18:50,0:29:05,0:04:06,3:43:11,783,0,18
+Night Team,PGY1,S0195220,2016-06-29,1:27:20,0:35:14,0:00:15,0:12:47,0:10:00,0:03:02,2:46:22,597,101,20
+Night Team,PGY1,S0195220,2016-06-28,1:37:33,0:43:28,0:00:01,0:16:19,0:09:29,0:04:48,3:03:01,873,90,21
+Intensive Care Unit,PGY2+,S0169394,2016-05-23,1:51:18,0:13:52,0:10:37,0:20:01,0:29:15,0:02:48,3:38:18,946,0,17
+Intensive Care Unit,PGY2+,S0169394,2016-05-22,0:00:59,0:00:08,0:00:00,0:02:02,0:00:00,0:00:13,0:03:37,60,11,13
+Intensive Care Unit,PGY2+,S0169394,2016-05-25,3:46:38,0:51:15,0:01:05,1:19:56,0:49:52,0:12:14,7:35:10,2174,46,29
+Intensive Care Unit,PGY2+,S0169394,2016-05-24,2:01:36,0:28:16,0:14:12,0:21:36,0:28:03,0:01:39,4:00:10,1316,436,27
+Intensive Care Unit,PGY2+,S0169394,2016-05-27,0:00:00,0:00:00,0:00:00,0:01:20,0:00:00,0:00:00,0:01:20,22,0,9
+Intensive Care Unit,PGY2+,S0169394,2016-05-26,2:13:36,0:53:00,0:03:27,0:23:28,0:18:51,0:05:59,4:30:54,1418,128,26
+Night Team,PGY1,S0169381,2015-01-28,1:01:59,0:05:20,0:00:35,0:06:53,0:12:27,0:07:50,1:39:01,489,0,33
+Night Team,PGY1,S0169381,2015-01-29,2:35:32,0:11:53,0:37:27,0:53:24,0:41:52,0:38:34,5:54:27,1402,7,34
+General Medicine,PGY1,S0196597,2016-10-15,0:10:28,0:14:20,0:00:08,0:00:01,0:00:07,0:00:00,0:27:43,113,103,8
+General Medicine,PGY1,S0196597,2016-10-14,2:37:20,0:52:57,0:00:13,0:35:55,0:17:37,0:05:27,5:23:43,1871,0,12
+General Medicine,PGY1,S0186952,2016-09-24,1:05:17,0:42:23,0:00:04,0:17:28,0:07:47,0:06:12,2:39:58,896,23,14
+General Medicine,PGY1,S0196597,2016-10-12,1:58:10,0:47:04,0:00:11,0:55:30,0:23:57,0:00:26,4:30:50,1664,0,16
+General Medicine,PGY1,S0196597,2016-10-11,0:01:04,0:00:07,0:00:00,0:00:01,0:00:00,0:02:12,0:03:53,48,0,4
+General Medicine,PGY1,S0186952,2016-09-27,0:02:35,0:00:17,0:00:00,0:00:04,0:01:32,0:01:17,0:07:04,111,48,7
+General Medicine,PGY1,S0181079,2016-03-14,2:10:14,0:56:39,0:00:05,0:35:29,0:16:32,0:02:44,4:30:05,1615,6,7
+General Medicine,PGY1,S0181079,2016-03-15,2:43:43,1:31:56,0:00:04,1:03:53,0:27:39,0:02:21,6:40:52,2042,0,21
+Night Team,PGY1,S0169381,2015-01-22,0:45:23,0:00:18,0:04:29,0:11:24,0:06:46,0:07:31,1:18:59,413,0,19
+Night Team,PGY1,S0169381,2015-01-23,0:57:31,0:01:08,0:03:24,0:07:55,0:19:44,0:17:31,1:49:26,509,43,16
+Night Team,PGY1,S0169381,2015-01-24,2:46:31,0:07:09,1:57:22,0:31:15,0:33:07,0:29:12,6:39:29,1303,0,50
+Night Team,PGY1,S0169381,2015-01-25,2:47:10,0:06:38,0:27:41,0:31:08,0:30:20,0:22:13,4:59:09,1141,0,30
+Night Team,PGY1,S0169381,2015-01-26,2:48:58,0:01:05,0:05:26,0:42:12,0:41:39,0:39:57,5:14:30,1240,19,44
+Night Team,PGY1,S0169381,2015-01-27,1:52:51,0:07:14,0:01:46,0:26:08,0:22:06,0:32:10,3:30:52,833,2,32
+Night Team,PGY1,S0181525,2016-05-30,0:54:56,0:21:40,0:00:01,0:12:38,0:12:58,0:00:48,1:44:32,534,96,26
+Night Team,PGY1,S0181525,2016-05-31,2:25:16,0:36:33,0:00:02,0:26:45,0:18:56,0:03:23,4:12:10,1307,479,43
+Night Team,PGY2+,S0163425,2015-10-27,1:31:43,1:44:37,1:28:20,0:44:04,0:19:01,1:06:46,7:20:34,2999,0,48
+Night Team,PGY2+,S0163425,2015-10-26,2:12:54,1:26:50,1:40:36,1:23:13,0:26:48,0:34:41,8:24:22,3488,0,54
+Night Team,PGY2+,S0163425,2015-10-25,0:53:36,0:54:28,0:43:40,0:24:19,0:02:27,0:21:09,3:30:33,1338,33,17
+Night Team,PGY2+,S0163425,2015-10-24,1:19:25,0:39:14,1:02:24,0:20:37,0:11:15,0:07:06,4:03:42,1766,14,63
+Night Team,PGY2+,S0163425,2015-10-23,1:48:03,0:53:09,0:40:50,0:36:22,0:10:57,0:35:24,5:12:47,2467,0,42
+Night Team,PGY2+,S0163425,2015-10-22,1:49:13,2:11:52,3:00:51,0:38:54,0:09:45,0:44:13,9:06:15,3345,0,39
+Night Team,PGY2+,S0163425,2015-10-21,1:57:28,1:27:43,1:28:13,0:44:20,0:17:15,0:28:21,7:01:20,2859,0,37
+Night Team,PGY2+,S0163425,2015-10-20,1:27:40,2:04:58,1:55:46,0:43:55,0:12:17,0:22:42,7:16:21,3002,11,25
+General Medicine,PGY1,S0195227,2016-08-28,2:54:46,1:41:11,0:00:23,0:44:43,0:20:37,0:00:21,6:42:49,2229,0,21
+General Medicine,PGY1,S0195227,2016-08-29,0:04:46,0:01:00,0:00:00,0:02:02,0:00:00,0:00:41,0:13:40,99,0,6
+General Medicine,PGY1,S0181056,2016-03-09,0:00:18,0:00:02,0:00:00,0:01:37,0:00:00,0:00:00,0:01:59,27,5,6
+General Medicine,PGY2+,S0169411,2016-03-15,4:43:05,1:17:12,0:00:00,0:19:09,0:14:09,0:03:07,7:11:43,3307,0,31
+General Medicine,PGY1,S0195227,2016-08-22,1:48:28,0:54:58,0:00:17,0:40:44,0:21:19,0:03:46,4:20:21,1272,0,6
+General Medicine,PGY1,S0195227,2016-08-23,1:58:28,2:08:18,0:00:23,0:48:59,0:23:11,0:02:30,6:03:49,1750,0,7
+General Medicine,PGY1,S0195227,2016-08-21,2:13:44,1:05:12,0:00:15,0:27:20,0:19:08,0:05:07,4:43:02,1486,0,8
+General Medicine,PGY1,S0195227,2016-08-26,3:07:20,2:04:16,0:00:14,0:47:18,0:20:11,0:12:52,7:24:16,2263,22,18
+General Medicine,PGY1,S0195227,2016-08-27,2:00:11,1:02:55,0:00:10,0:28:50,0:15:50,0:01:57,4:12:01,1517,35,7
+General Medicine,PGY1,S0195227,2016-08-24,2:55:10,1:14:54,0:04:55,0:50:30,0:26:34,0:03:52,6:32:25,1817,0,10
+General Medicine,PGY1,S0195227,2016-08-25,0:00:09,0:00:18,0:00:00,0:00:00,0:00:00,0:00:00,0:00:30,21,0,2
+General Medicine,PGY1,S0181086,2015-12-22,1:41:34,0:53:54,1:24:02,0:26:32,0:19:35,0:22:25,5:37:28,1648,164,8
+General Medicine,PGY1,S0195075,2016-07-04,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:07,9,0,2
+General Medicine,PGY1,S0181086,2015-12-20,1:54:53,1:22:36,1:51:40,0:33:23,0:30:44,0:18:07,6:59:28,1791,187,6
+General Medicine,PGY1,S0181086,2015-12-21,2:51:32,1:08:08,2:07:34,1:19:05,0:30:28,0:38:48,9:45:37,2596,93,9
+General Medicine,PGY1,S0181086,2015-12-26,3:30:34,1:46:43,2:17:30,1:28:44,0:36:16,0:28:25,11:01:30,2937,0,9
+General Medicine,PGY1,S0181086,2015-12-27,2:28:07,1:15:23,2:34:43,0:56:34,0:23:14,0:16:05,8:34:20,2090,134,9
+General Medicine,PGY1,S0181086,2015-12-24,2:00:00,1:30:44,2:40:51,0:39:38,0:12:38,0:36:07,8:35:25,2074,17,14
+General Medicine,PGY1,S0181086,2015-12-25,0:12:29,0:43:37,2:58:06,0:00:58,0:00:20,0:04:22,4:05:19,586,569,7
+Night Team,PGY1,S0195079,2016-10-23,3:05:36,0:29:50,0:00:02,0:56:36,0:35:16,0:00:37,5:29:01,1302,0,45
+General Medicine,PGY1,S0181086,2015-12-28,1:43:56,1:18:08,1:23:44,1:05:39,0:23:48,0:17:50,7:01:10,2414,275,15
+General Medicine,PGY1,S0181086,2015-12-29,3:26:55,1:00:52,1:27:46,1:23:10,0:44:01,0:38:50,9:57:45,3219,147,11
+Night Team,PGY2+,S0169395,2016-03-19,1:48:38,1:12:45,0:00:13,0:20:59,0:11:45,0:02:50,3:52:27,1089,0,22
+Night Team,PGY2+,S0169395,2016-03-18,3:54:43,1:58:16,0:02:13,0:41:18,0:12:06,0:11:22,7:32:33,2283,0,50
+General Medicine,PGY1,S0093383,2014-08-19,2:13:32,0:15:12,0:15:24,0:15:29,0:11:57,0:11:47,3:46:43,1091,267,9
+Night Team,PGY2+,S0169395,2016-03-11,4:05:22,1:22:53,0:00:17,0:34:18,0:19:09,0:07:38,6:51:58,2041,6,57
+Night Team,PGY2+,S0169395,2016-03-10,3:21:09,1:46:05,0:00:17,0:35:03,0:11:04,0:06:10,6:25:16,2126,0,66
+Night Team,PGY2+,S0169395,2016-03-13,1:26:20,0:59:02,0:00:10,0:31:03,0:06:26,0:02:30,3:25:27,1124,0,18
+General Medicine,PGY1,S0093383,2014-08-18,3:20:57,0:06:03,0:28:50,0:22:55,0:10:55,0:17:47,4:58:04,1178,172,7
+Night Team,PGY2+,S0169395,2016-03-15,2:22:41,1:32:41,0:00:19,0:42:14,0:20:50,0:09:35,5:40:41,2063,4,34
+Night Team,PGY2+,S0169395,2016-03-14,1:18:57,0:33:58,0:00:06,0:18:00,0:11:07,0:02:42,2:39:58,1123,0,28
+Night Team,PGY2+,S0169395,2016-03-17,2:42:41,2:17:35,0:00:14,0:38:22,0:10:04,0:06:31,6:41:20,2314,0,41
+Night Team,PGY2+,S0169395,2016-03-16,2:13:36,1:09:12,0:00:17,0:19:41,0:07:56,0:01:51,4:26:16,1458,0,44
+General Medicine,PGY1,S0195075,2016-07-02,1:01:16,0:42:55,0:00:16,0:23:56,0:13:45,0:00:24,2:45:47,923,0,6
+General Medicine,PGY1,S0195075,2016-07-03,1:58:46,1:09:31,0:04:39,0:48:20,0:20:23,0:00:36,4:55:40,1268,0,6
+Intensive Care Unit,PGY2+,S0169385,2016-01-21,1:49:28,0:23:51,0:12:18,0:31:44,0:09:34,0:51:45,4:25:20,1590,0,15
+Emergency Medicine,PGY1,S0169383,2015-03-02,4:43:11,0:38:08,2:21:47,0:20:51,0:14:53,0:36:41,9:36:04,2451,0,20
+Emergency Medicine,PGY1,S0169383,2015-03-03,1:59:28,0:24:07,1:04:12,0:26:08,0:36:09,0:44:40,5:40:33,1847,200,15
+Emergency Medicine,PGY1,S0169383,2015-03-01,3:04:08,0:45:56,1:12:49,0:23:58,0:21:05,0:57:51,7:27:22,2296,0,19
+Emergency Medicine,PGY1,S0169383,2015-03-06,5:19:47,0:41:27,1:55:11,0:34:24,0:14:59,0:32:36,9:54:34,2279,0,21
+Emergency Medicine,PGY1,S0169383,2015-03-07,1:03:08,0:28:27,0:46:06,0:09:00,0:06:14,0:10:14,2:55:38,888,89,14
+Emergency Medicine,PGY1,S0169383,2015-03-04,2:57:17,0:19:21,0:58:23,0:22:45,0:17:37,0:35:34,6:03:35,1790,0,13
+Emergency Medicine,PGY1,S0169383,2015-03-05,4:07:40,0:53:01,2:09:32,0:16:47,0:19:42,0:18:12,8:43:19,1940,114,29
+Emergency Medicine,PGY1,S0169383,2015-03-09,4:31:42,0:18:48,1:30:13,0:29:47,0:21:35,0:20:20,8:12:27,1950,4,16
+Emergency Medicine,PGY1,S0169389,2014-09-29,5:58:45,0:27:19,1:06:45,0:28:11,0:30:50,0:33:47,9:41:41,2720,0,49
+Emergency Medicine,PGY1,S0169389,2014-09-28,6:49:01,0:17:28,1:08:46,0:20:16,0:25:31,0:17:35,9:53:44,2466,0,45
+Emergency Medicine,PGY1,S0169389,2014-09-25,0:06:38,0:00:00,0:00:02,0:00:00,0:00:03,0:00:00,0:07:10,54,54,4
+Emergency Medicine,PGY1,S0169389,2014-09-24,1:56:54,0:00:00,0:06:41,0:19:05,0:02:30,0:00:00,2:52:53,875,778,11
+Emergency Medicine,PGY1,S0169389,2014-09-27,6:51:21,0:04:29,1:29:03,0:18:56,0:45:31,0:22:04,10:35:42,2887,0,51
+Emergency Medicine,PGY1,S0169389,2014-09-26,5:39:01,0:29:39,0:54:34,0:17:07,0:28:18,1:30:42,9:55:11,2432,0,45
+Emergency Medicine,PGY1,S0169389,2014-09-21,5:13:09,0:28:09,0:52:40,0:37:24,0:28:49,0:42:11,9:00:10,2517,0,75
+Emergency Medicine,PGY1,S0169389,2014-09-20,3:50:43,0:15:20,0:52:23,0:12:22,0:32:16,0:26:12,6:57:47,2352,52,52
+Emergency Medicine,PGY1,S0169389,2014-09-23,2:50:52,0:06:08,0:47:03,0:16:01,0:34:27,0:25:50,5:36:47,1595,116,36
+Emergency Medicine,PGY1,S0169389,2014-09-22,5:00:38,0:15:37,1:11:59,0:29:03,0:24:37,0:47:32,9:06:16,2630,19,52
+Intensive Care Unit,PGY2+,S0169385,2016-01-29,1:42:12,0:23:06,0:10:43,0:17:27,0:26:36,0:31:22,3:41:08,1306,81,12
+Emergency Medicine,PGY1,S0169402,2014-07-16,0:43:11,0:05:24,0:04:22,0:00:23,0:01:57,0:04:38,1:02:23,285,0,10
+Emergency Medicine,PGY1,S0169402,2014-07-17,3:43:50,0:00:08,0:26:15,0:16:17,0:23:25,0:02:17,5:01:42,1331,82,14
+Emergency Medicine,PGY1,S0169402,2014-07-14,3:02:13,0:09:53,0:13:58,0:12:31,0:17:44,0:06:39,4:12:38,758,47,14
+Emergency Medicine,PGY1,S0169402,2014-07-15,3:42:05,0:05:15,0:18:16,0:01:49,0:12:13,0:03:35,4:46:01,1007,38,23
+General Medicine,PGY1,S0169406,2015-03-08,1:46:01,0:10:17,1:31:26,0:23:13,0:13:56,0:03:18,4:19:10,1051,153,8
+General Medicine,PGY1,S0169406,2015-03-09,0:13:26,0:02:26,0:09:01,0:00:08,0:00:34,0:02:18,0:27:59,124,116,4
+Emergency Medicine,PGY1,S0169402,2014-07-10,4:00:04,0:06:24,0:19:30,0:05:54,0:13:38,0:07:34,4:58:49,851,97,12
+Emergency Medicine,PGY1,S0169402,2014-07-11,5:41:30,0:13:05,0:24:46,0:12:21,0:55:22,0:13:21,8:14:17,2020,83,26
+General Medicine,PGY1,S0169406,2015-03-04,1:18:12,0:32:09,0:48:59,0:20:10,0:03:42,0:03:12,3:21:19,974,0,10
+General Medicine,PGY1,S0169406,2015-03-05,1:56:31,0:30:58,0:58:17,0:33:23,0:14:20,0:18:23,4:59:34,1442,82,13
+General Medicine,PGY1,S0169406,2015-03-06,0:38:44,0:15:52,0:54:29,0:03:52,0:04:28,0:08:30,2:17:03,638,69,6
+General Medicine,PGY1,S0169406,2015-03-07,1:17:52,0:30:20,1:16:40,0:12:04,0:15:39,0:06:54,4:08:32,1386,131,12
+General Medicine,PGY1,S0169406,2015-03-01,0:01:36,0:04:35,0:00:00,0:00:00,0:00:00,0:01:44,0:09:06,83,81,3
+General Medicine,PGY1,S0169406,2015-03-02,1:59:34,0:43:33,2:14:55,0:19:08,0:34:30,0:13:01,6:36:38,1491,125,9
+General Medicine,PGY1,S0169406,2015-03-03,1:22:42,0:52:09,1:11:53,0:30:51,0:08:48,0:07:07,4:20:02,1439,84,12
+Emergency Medicine,PGY1,S0181057,2015-07-19,2:31:35,0:45:27,3:22:37,0:35:11,0:39:48,0:28:45,8:50:52,1916,0,17
+General Medicine,PGY1,S0181057,2015-07-18,0:00:00,0:00:09,0:00:00,0:00:00,0:00:00,0:01:58,0:02:09,31,29,2
+General Medicine,PGY1,S0181057,2015-07-17,2:00:23,0:18:23,0:41:24,0:45:20,0:21:00,0:33:56,4:54:43,1473,18,7
+General Medicine,PGY1,S0181057,2015-07-16,2:07:47,0:24:28,1:25:09,0:37:47,0:36:05,0:15:08,6:00:47,1513,74,6
+General Medicine,PGY1,S0181057,2015-07-15,1:50:39,0:08:00,1:14:16,0:17:48,0:36:02,0:22:29,4:47:57,1321,194,9
+General Medicine,PGY1,S0181057,2015-07-14,2:11:17,0:17:02,1:48:49,0:40:18,0:20:21,0:23:19,6:44:50,1921,357,38
+General Medicine,PGY1,S0181057,2015-07-13,0:00:00,0:03:25,0:00:00,0:00:00,0:00:00,0:00:55,0:04:31,45,39,5
+General Medicine,PGY1,S0181057,2015-07-12,2:24:36,0:04:21,1:51:42,0:24:05,0:30:47,0:31:46,6:09:56,1719,100,8
+General Medicine,PGY1,S0181057,2015-07-11,2:06:54,0:06:00,1:11:59,0:18:45,0:28:36,0:16:08,4:47:33,1224,283,8
+General Medicine,PGY1,S0181057,2015-07-10,1:38:18,0:05:20,0:52:50,0:19:15,0:49:57,0:20:20,4:25:33,1247,84,9
+Night Team,PGY1,S0169395,2015-04-02,1:03:24,0:02:21,0:39:06,0:07:49,0:13:30,0:03:00,2:23:23,601,0,18
+General Medicine,PGY1,S0181085,2016-01-19,0:51:14,1:01:57,2:37:12,0:53:28,0:28:24,0:19:35,6:50:07,1633,44,17
+General Medicine,PGY1,S0181085,2016-01-18,0:28:21,0:25:27,1:22:39,0:21:22,0:05:30,0:19:43,3:18:36,645,118,3
+General Medicine,PGY1,S0181085,2016-01-15,0:43:28,1:03:20,2:12:24,0:16:16,0:03:50,0:28:21,4:59:16,1182,27,9
+General Medicine,PGY1,S0181085,2016-01-14,0:41:53,1:55:37,3:22:25,0:29:08,0:11:27,0:17:46,7:10:39,1621,148,8
+General Medicine,PGY1,S0181085,2016-01-17,0:48:36,1:17:27,3:14:46,0:36:43,0:09:36,0:25:09,6:52:35,1406,0,9
+General Medicine,PGY1,S0181085,2016-01-16,0:00:32,0:09:00,0:00:02,0:00:00,0:00:00,0:00:11,0:09:57,79,73,4
+General Medicine,PGY1,S0181085,2016-01-11,0:13:44,0:50:23,0:46:33,0:04:43,0:07:48,0:16:03,2:28:15,653,43,4
+General Medicine,PGY1,S0181085,2016-01-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181085,2016-01-13,0:21:50,0:59:41,1:42:11,0:17:57,0:13:31,0:16:08,4:05:24,944,18,6
+General Medicine,PGY1,S0181085,2016-01-12,0:55:29,2:08:54,4:22:11,0:21:01,0:11:52,0:10:10,8:21:16,1630,19,9
+Emergency Medicine,PGY1,S0181060,2016-03-30,2:53:13,0:59:59,0:03:22,0:29:05,1:11:47,0:12:18,6:31:21,2644,0,18
+Emergency Medicine,PGY1,S0181060,2016-03-31,3:49:58,0:47:04,0:01:39,0:50:12,1:18:13,0:06:31,7:59:26,2957,0,20
+Night Team,PGY2+,S0169399,2015-09-20,0:26:09,0:00:00,0:04:54,0:05:54,0:07:11,0:06:09,0:55:49,175,0,12
+Night Team,PGY2+,S0169399,2015-09-23,1:01:34,0:55:40,3:06:38,0:28:13,0:08:13,0:05:19,6:15:02,1260,21,19
+General Medicine,PGY1,S0181081,2016-06-09,3:00:00,1:22:50,0:00:07,0:58:47,0:34:13,0:06:08,6:46:47,2567,27,14
+General Medicine,PGY1,S0195210,2016-10-30,0:14:17,0:13:04,0:00:01,0:02:19,0:00:25,0:00:00,0:41:52,134,0,6
+General Medicine,PGY1,S0195210,2016-10-31,1:54:44,0:51:07,0:00:08,0:19:20,0:17:39,0:02:51,3:49:23,808,0,15
+Intensive Care Unit,PGY2+,S0169402,2015-07-24,3:06:50,0:28:55,0:38:27,0:22:58,0:16:54,0:44:36,6:00:03,1327,2,14
+General Medicine,PGY1,S0169395,2015-05-28,0:43:07,0:15:42,0:55:25,0:11:14,0:11:24,0:04:35,2:27:53,715,0,5
+Intensive Care Unit,PGY2+,S0169402,2015-07-25,1:39:24,0:15:35,0:00:17,0:02:34,0:00:01,0:01:00,2:15:32,1676,1650,15
+Intensive Care Unit,PGY2+,S0169402,2015-07-22,0:40:08,2:08:54,0:01:00,0:02:14,0:00:11,0:08:09,3:02:32,1018,992,18
+General Medicine,PGY1,S0169395,2015-05-25,2:49:35,1:07:36,2:24:15,0:38:48,0:29:49,0:24:42,8:20:10,2513,186,11
+General Medicine,PGY1,S0169395,2015-05-24,1:19:53,0:08:15,1:13:12,0:30:30,0:10:46,0:09:29,3:48:02,1174,0,7
+General Medicine,PGY1,S0169395,2015-05-27,3:04:38,0:23:03,1:11:30,1:06:21,0:22:56,0:12:12,6:46:33,2334,0,16
+General Medicine,PGY1,S0169395,2015-05-26,1:57:38,0:11:19,1:04:57,0:43:34,0:20:53,0:15:07,4:50:28,1426,0,10
+General Medicine,PGY2+,S0106952,2015-10-08,1:27:17,0:37:39,0:02:14,0:04:24,0:06:41,0:10:34,2:40:26,1107,689,57
+Intensive Care Unit,PGY2+,S0169402,2015-07-20,3:02:01,0:05:41,0:10:21,0:44:04,0:27:05,0:27:08,5:25:09,1214,0,10
+General Medicine,PGY1,S0163425,2015-06-20,2:18:35,0:13:15,0:25:57,0:04:46,0:11:10,0:10:10,3:49:27,1352,0,20
+General Medicine,PGY1,S0163425,2015-06-21,3:36:30,0:45:07,1:31:47,0:23:12,0:22:13,0:26:42,7:53:36,2568,0,19
+General Medicine,PGY1,S0163425,2015-06-22,2:34:10,0:10:23,0:39:21,0:28:04,0:29:10,0:03:55,4:39:06,1573,0,18
+General Medicine,PGY1,S0163425,2015-06-23,2:43:16,0:15:51,1:33:01,0:29:30,0:32:49,0:08:40,6:13:10,1888,56,20
+General Medicine,PGY1,S0163425,2015-06-24,2:25:10,0:10:01,1:40:38,0:42:42,0:26:36,0:12:03,6:12:15,1885,0,19
+General Medicine,PGY1,S0163425,2015-06-25,0:00:00,0:03:20,0:00:00,0:00:13,0:00:00,0:00:00,0:04:47,59,49,5
+General Medicine,PGY1,S0169402,2014-11-14,1:07:21,0:03:16,1:54:30,0:15:48,0:01:54,0:10:34,3:41:01,486,0,5
+General Medicine,PGY1,S0181069,2016-04-10,1:41:38,1:18:06,0:01:16,0:44:42,0:09:03,0:03:18,4:52:31,2091,0,16
+General Medicine,PGY1,S0181069,2016-04-11,2:54:01,1:37:52,0:00:12,1:23:12,0:28:05,0:04:51,7:29:31,2939,57,17
+General Medicine,PGY1,S0181069,2016-04-12,1:37:16,1:14:12,0:00:07,0:39:15,0:14:15,0:02:53,4:25:28,1894,28,13
+General Medicine,PGY1,S0181069,2016-04-13,2:43:38,2:11:14,0:02:07,0:58:50,0:27:01,0:07:03,7:14:55,2595,2,12
+General Medicine,PGY1,S0181069,2016-04-14,2:36:08,1:05:52,0:07:07,0:42:46,0:27:02,0:10:27,5:44:30,2124,70,15
+General Medicine,PGY1,S0181069,2016-04-15,1:38:00,0:51:18,0:00:33,1:12:05,0:26:19,0:05:12,5:03:03,1500,5,18
+General Medicine,PGY1,S0181069,2016-04-16,3:08:52,1:56:12,0:00:23,1:03:09,0:25:26,0:05:57,7:54:57,2907,5,16
+General Medicine,PGY1,S0181069,2016-04-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0181069,2016-04-18,1:29:26,0:35:48,0:00:04,0:33:12,0:11:14,0:06:28,3:22:46,1132,11,7
+General Medicine,PGY1,S0181069,2016-04-19,0:03:12,0:09:27,0:00:00,0:00:57,0:00:48,0:00:02,0:15:29,145,135,7
+Intensive Care Unit,PGY2+,S0181070,2016-09-10,0:00:55,0:00:05,0:00:00,0:00:05,0:00:00,0:00:00,0:01:16,33,0,5
+General Medicine,PGY1,S0169398,2014-08-31,0:22:27,0:00:00,0:07:04,0:00:10,0:00:54,0:01:56,0:36:49,274,258,10
+General Medicine,PGY1,S0169398,2014-08-30,2:03:09,0:00:16,0:13:48,0:06:41,0:10:23,0:12:56,3:03:42,842,59,7
+General Medicine,PGY2+,S0169381,2016-04-24,1:53:06,1:30:02,0:00:00,0:16:12,0:13:31,0:06:39,4:36:17,1666,120,20
+General Medicine,PGY2+,S0169381,2016-04-25,0:23:42,1:03:49,0:00:00,0:01:34,0:00:54,0:05:11,2:00:48,852,848,21
+General Medicine,PGY2+,S0169381,2016-04-26,1:53:10,0:59:28,0:02:55,0:32:51,0:18:23,0:19:08,4:38:07,1618,53,27
+General Medicine,PGY2+,S0169381,2016-04-27,1:03:49,0:37:44,0:00:00,0:40:13,0:19:40,0:05:57,3:11:26,1227,92,17
+General Medicine,PGY2+,S0169381,2016-04-20,2:18:50,1:26:05,0:00:09,1:08:28,0:35:22,0:13:55,6:22:58,2377,187,17
+General Medicine,PGY2+,S0169381,2016-04-21,2:41:23,1:28:24,0:00:53,0:19:17,0:16:39,0:12:22,5:35:20,2032,35,31
+General Medicine,PGY2+,S0169381,2016-04-22,0:55:52,0:28:13,0:00:02,0:13:09,0:04:52,0:03:45,2:07:13,871,60,16
+General Medicine,PGY2+,S0169381,2016-04-23,0:40:15,0:35:58,0:00:43,0:14:11,0:02:31,0:00:44,1:41:15,404,6,6
+General Medicine,PGY2+,S0169381,2016-04-28,1:45:13,1:13:13,0:00:38,0:48:01,0:29:07,0:04:23,4:55:53,1515,16,11
+General Medicine,PGY2+,S0169381,2016-04-29,1:46:24,1:33:57,0:00:01,0:25:06,0:19:04,0:10:15,4:48:16,1771,317,19
+General Medicine,PGY1,S0181077,2016-05-18,3:05:34,1:58:03,0:00:16,1:01:46,0:25:13,0:04:27,7:16:39,3188,268,16
+General Medicine,PGY2+,S0181065,2016-09-30,6:12:44,1:52:02,0:00:00,0:41:44,0:30:59,0:09:34,10:33:47,7052,459,326
+Night Team,PGY2+,S0109179,2016-05-27,3:17:34,1:44:50,0:00:14,0:21:02,0:14:21,0:08:27,6:14:05,2404,0,45
+Intensive Care Unit,PGY2+,S0181086,2016-06-28,3:14:58,2:11:42,0:20:22,0:36:38,0:37:02,0:00:29,7:36:40,2016,417,15
+Intensive Care Unit,PGY2+,S0181086,2016-06-29,2:46:59,1:10:42,0:15:38,0:53:23,0:59:09,0:12:10,7:00:53,1819,220,15
+Intensive Care Unit,PGY2+,S0181086,2016-06-26,0:00:33,0:00:00,0:00:00,0:00:10,0:00:00,0:00:00,0:04:07,20,14,4
+Intensive Care Unit,PGY2+,S0181086,2016-06-27,2:08:25,1:16:45,0:00:12,0:51:45,0:43:18,0:05:52,5:49:49,1506,190,16
+General Medicine,PGY1,S0169400,2014-11-26,3:44:35,0:06:29,1:47:05,0:26:33,0:18:53,0:12:34,6:51:08,1417,0,17
+General Medicine,PGY1,S0195203,2016-07-06,0:12:26,0:00:31,0:00:00,0:00:09,0:00:00,0:03:07,0:17:00,66,0,5
+General Medicine,PGY1,S0169400,2014-11-24,1:52:08,0:04:48,0:59:00,0:13:53,0:05:59,0:13:31,3:51:48,1034,0,10
+General Medicine,PGY1,S0195203,2016-07-04,1:20:18,1:19:34,0:00:12,0:07:19,0:21:49,0:05:59,3:55:55,1267,228,5
+General Medicine,PGY1,S0195203,2016-07-03,0:58:38,0:33:45,0:02:18,0:12:57,0:09:33,0:00:29,2:20:58,601,127,6
+Night Team,PGY1,S0169398,2015-05-30,3:01:05,0:27:49,1:42:22,0:30:31,0:15:23,0:29:14,6:52:35,1539,0,36
+Emergency Medicine,PGY1,S0169395,2014-11-09,2:13:44,0:12:19,2:45:07,0:39:00,0:48:46,0:24:08,7:41:04,2174,29,18
+General Medicine,PGY1,S0169395,2014-11-08,2:04:27,0:09:52,2:28:58,0:41:39,0:41:45,1:01:23,8:09:46,2152,0,18
+General Medicine,PGY1,S0195213,2016-10-19,0:40:33,0:15:15,0:00:02,0:09:51,0:06:59,0:00:04,1:41:33,695,184,14
+General Medicine,PGY1,S0195213,2016-10-18,0:41:09,0:25:15,0:00:02,0:13:50,0:03:13,0:00:03,1:36:48,441,0,2
+General Medicine,PGY1,S0169395,2014-11-05,3:05:05,0:07:14,0:18:09,0:39:30,0:32:17,0:38:37,5:48:39,1999,0,13
+General Medicine,PGY1,S0169395,2014-11-04,1:53:44,0:09:04,0:12:51,0:37:24,0:25:20,0:21:26,4:04:22,1399,10,10
+General Medicine,PGY1,S0169395,2014-11-03,2:34:47,0:14:08,0:11:57,0:31:35,0:23:19,0:23:05,4:39:59,1562,0,16
+General Medicine,PGY1,S0169395,2014-11-02,1:12:17,0:10:57,0:08:18,0:26:59,0:09:11,0:21:25,2:45:14,805,0,7
+General Medicine,PGY1,S0195203,2016-07-09,1:01:31,0:51:43,0:00:15,0:26:31,0:26:11,0:04:37,3:30:57,1060,196,7
+General Medicine,PGY1,S0169400,2014-11-29,1:30:10,0:03:57,1:30:34,0:22:33,0:10:16,0:23:44,4:14:56,978,0,7
+General Medicine,PGY1,S0196595,2016-07-20,1:16:17,1:02:12,0:00:58,0:51:00,0:18:01,0:00:14,3:53:11,847,0,6
+Intensive Care Unit,PGY2+,S0169401,2016-03-29,4:04:34,0:46:31,0:11:24,0:59:30,0:29:37,0:05:46,7:39:16,2278,123,22
+General Medicine,PGY1,S0109179,2014-09-13,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY1,S0109179,2014-09-12,1:50:54,0:00:24,0:04:41,0:16:48,0:10:26,0:18:54,2:51:02,842,0,3
+General Medicine,PGY1,S0109179,2014-09-11,2:43:45,0:01:12,0:13:54,0:36:33,0:23:32,0:06:14,4:20:45,1547,12,18
+General Medicine,PGY1,S0109179,2014-09-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0195220,2016-08-20,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Intensive Care Unit,PGY2+,S0074196,2016-11-09,1:38:49,0:55:25,0:02:50,0:09:55,0:18:29,0:01:22,3:26:55,977,379,11
+Intensive Care Unit,PGY2+,S0074196,2016-11-08,2:14:56,1:08:33,0:07:32,0:38:23,0:37:07,0:07:33,5:18:47,1100,613,12
+Intensive Care Unit,PGY2+,S0074196,2016-11-07,2:25:26,0:44:15,0:07:16,0:24:51,0:10:53,0:02:30,4:09:55,1161,0,10
+Intensive Care Unit,PGY2+,S0074196,2016-11-06,3:03:18,2:07:41,0:04:39,1:00:24,0:30:25,0:12:47,7:30:11,1901,31,11
+General Medicine,PGY1,S0195226,2016-07-24,2:31:09,1:07:21,0:00:05,0:32:48,0:44:54,0:12:58,6:14:12,1483,0,8
+General Medicine,PGY2+,S0169389,2015-11-15,3:31:15,1:47:24,0:13:49,0:10:19,0:16:36,0:30:44,6:48:39,2496,26,54
+General Medicine,PGY2+,S0169389,2015-11-16,1:35:52,0:42:41,0:00:03,0:39:12,0:25:37,0:53:24,4:34:24,1594,0,20
+General Medicine,PGY1,S0195226,2016-07-27,3:17:58,1:07:31,0:00:07,0:38:31,0:27:47,0:11:21,6:54:49,1596,164,13
+General Medicine,PGY1,S0195086,2016-08-01,2:13:44,2:23:28,0:04:12,0:31:45,0:26:44,0:09:45,6:29:08,1628,294,10
+General Medicine,PGY2+,S0169389,2015-11-18,2:25:34,1:41:42,0:57:56,1:01:46,0:26:21,0:36:28,8:02:28,2798,130,50
+General Medicine,PGY2+,S0169389,2015-11-19,1:55:02,1:04:06,1:04:29,0:36:04,0:27:07,0:25:42,6:09:58,1897,0,17
+General Medicine,PGY1,S0172645,2016-01-03,0:51:59,0:44:08,0:56:49,0:38:05,0:05:07,0:04:29,3:33:14,952,20,6
+General Medicine,PGY1,S0172645,2016-01-06,1:35:37,1:12:47,1:52:10,0:36:53,0:40:40,0:23:09,6:36:59,1847,0,10
+General Medicine,PGY1,S0172645,2016-01-07,0:57:08,0:53:44,1:53:01,0:36:02,0:26:06,0:14:46,5:13:58,1344,170,10
+General Medicine,PGY1,S0172645,2016-01-04,0:57:51,0:43:19,1:37:25,0:25:58,0:13:31,0:16:49,4:30:41,1443,0,14
+General Medicine,PGY1,S0172645,2016-01-05,0:00:00,0:00:00,0:00:00,0:00:06,0:00:00,0:00:00,0:00:06,12,0,2
+General Medicine,PGY1,S0172645,2016-01-08,1:03:45,0:59:03,1:34:29,0:32:27,0:13:40,0:08:06,4:43:50,1633,53,17
+General Medicine,PGY1,S0172645,2016-01-09,1:13:06,1:47:25,3:41:56,0:38:32,0:26:52,0:09:15,8:23:43,2041,0,13
+Emergency Medicine,PGY1,S0074196,2015-10-22,1:42:49,0:11:14,0:47:00,0:08:01,0:22:06,0:08:12,3:47:53,1201,0,11
+General Medicine,PGY1,S0093383,2014-08-26,2:38:02,0:06:19,0:13:24,0:25:22,0:11:15,0:22:38,4:12:44,1224,0,8
+General Medicine,PGY1,S0093383,2014-08-27,1:19:11,0:06:37,0:09:16,0:04:28,0:14:04,0:09:43,2:06:09,710,129,7
+General Medicine,PGY1,S0093383,2014-08-24,2:10:57,0:05:14,0:11:29,0:19:43,0:11:27,0:23:06,3:31:44,874,15,8
+General Medicine,PGY1,S0093383,2014-08-25,4:30:25,0:30:33,0:33:20,0:27:31,0:17:43,0:26:19,6:55:55,1844,57,10
+General Medicine,PGY1,S0093383,2014-08-22,1:50:04,0:02:53,0:17:11,0:04:45,0:07:56,0:07:50,2:44:08,608,103,10
+General Medicine,PGY1,S0093383,2014-08-23,2:47:33,0:10:33,0:27:53,0:29:16,0:31:01,0:23:38,5:04:47,1294,41,9
+General Medicine,PGY1,S0093383,2014-08-20,3:28:00,0:10:45,0:22:54,0:54:33,0:19:14,0:30:01,6:06:32,1835,56,15
+General Medicine,PGY1,S0093383,2014-08-21,0:00:00,0:00:00,0:00:00,0:04:07,0:00:00,0:00:00,0:04:07,8,0,3
+General Medicine,PGY1,S0163662,2015-06-12,0:54:29,0:07:27,1:53:33,0:01:55,0:00:16,0:04:48,3:10:39,739,727,15
+General Medicine,PGY1,S0163662,2015-06-13,4:07:56,1:02:22,1:57:17,0:55:45,0:28:10,0:19:08,9:45:03,2766,0,14
+General Medicine,PGY1,S0163662,2015-06-10,2:49:08,0:41:15,0:34:47,0:54:43,0:20:28,0:09:01,6:11:19,1999,311,13
+General Medicine,PGY1,S0163662,2015-06-11,2:56:13,0:43:58,1:31:37,0:42:30,0:25:33,0:31:32,7:29:45,2305,149,8
+General Medicine,PGY1,S0163662,2015-06-16,3:36:43,0:21:29,1:15:47,1:28:55,0:33:52,0:17:53,8:13:59,3169,0,10
+General Medicine,PGY1,S0163662,2015-06-17,0:09:41,0:04:02,0:20:14,0:10:52,0:02:19,0:02:05,0:54:20,202,192,4
+General Medicine,PGY1,S0093383,2014-08-28,5:22:21,0:23:48,0:38:53,0:54:56,0:38:18,0:29:19,8:52:59,2156,0,10
+General Medicine,PGY1,S0093383,2014-08-29,0:00:00,0:00:00,0:00:00,0:03:03,0:00:00,0:00:00,0:03:03,6,0,2
+Emergency Medicine,PGY1,S0074196,2015-10-21,0:17:47,0:01:36,0:16:06,0:02:04,0:02:00,0:08:04,0:58:39,353,0,10
+General Medicine,PGY1,S0172085,2015-07-11,1:32:29,0:29:39,1:02:50,0:25:39,0:22:39,0:04:33,4:24:38,1012,68,7
+Night Team,PGY2+,S0108287,2016-02-06,0:16:10,0:40:26,0:37:56,0:07:42,0:04:15,0:09:03,1:59:19,570,537,13
+Night Team,PGY2+,S0108287,2016-02-04,0:58:25,1:20:45,2:03:52,0:24:37,0:09:07,0:08:56,5:25:40,1482,1209,35
+Night Team,PGY2+,S0108287,2016-02-05,1:55:12,1:04:01,0:52:46,0:27:47,0:31:49,0:30:19,5:42:17,1960,1657,29
+General Medicine,PGY1,S0169402,2014-11-19,0:09:49,0:00:00,0:01:56,0:00:00,0:00:03,0:00:00,0:12:21,90,88,5
+Night Team,PGY2+,S0108287,2016-02-03,1:39:12,1:17:40,2:19:25,0:30:49,0:16:04,0:34:37,7:05:32,2099,772,25
+Night Team,PGY2+,S0108287,2016-02-01,2:26:13,0:45:34,0:42:28,0:32:00,0:15:01,0:16:56,5:11:46,1467,0,23
+Night Team,PGY1,S0181069,2015-11-24,1:33:37,1:14:45,1:40:07,1:18:44,0:32:27,0:23:14,7:15:32,1966,1257,34
+Night Team,PGY1,S0181069,2015-11-25,1:07:46,0:33:08,0:32:32,0:18:53,0:11:49,0:07:04,2:59:55,940,11,24
+Night Team,PGY1,S0181069,2015-11-26,0:19:43,0:21:17,0:05:38,0:27:48,0:14:44,0:19:19,1:57:27,697,25,16
+Night Team,PGY1,S0181069,2015-11-27,1:31:41,0:58:28,2:11:21,1:03:43,0:23:09,0:20:40,7:09:46,2401,0,33
+General Medicine,PGY1,S0195207,2016-08-22,0:53:40,0:49:47,0:00:09,0:03:46,0:09:37,0:00:10,2:25:03,1044,22,7
+Night Team,PGY1,S0181069,2015-11-22,1:47:47,0:52:41,1:17:42,1:11:05,0:11:34,0:21:54,6:01:16,1844,533,37
+Night Team,PGY1,S0181069,2015-11-23,1:34:23,0:51:40,2:37:12,0:58:27,0:20:39,0:24:45,7:10:48,1877,1794,26
+General Medicine,PGY1,S0195207,2016-08-23,1:32:03,1:08:17,0:00:14,0:18:34,0:12:35,0:00:16,3:40:45,1303,75,7
+Night Team,PGY1,S0181069,2015-11-28,1:26:56,0:33:04,0:54:31,0:40:24,0:26:46,0:13:31,4:33:41,1442,557,34
+Night Team,PGY1,S0181069,2015-11-29,1:32:52,0:28:53,0:13:21,0:50:59,0:26:26,0:33:17,4:16:59,1517,1226,32
+Night Team,PGY2+,S0169389,2016-03-08,6:26:08,0:36:20,0:00:03,0:12:50,0:14:37,0:00:54,7:49:08,2078,23,73
+Night Team,PGY2+,S0169406,2015-07-22,1:58:34,0:32:06,2:08:12,0:11:06,0:10:11,0:18:54,5:37:30,1321,1,63
+Night Team,PGY2+,S0169406,2015-07-23,1:47:27,0:18:25,1:53:50,0:21:24,0:09:40,0:07:32,4:58:29,1183,0,50
+Night Team,PGY2+,S0169406,2015-07-20,0:59:08,0:06:15,0:03:08,0:10:36,0:06:34,0:06:20,1:38:51,747,19,22
+Night Team,PGY2+,S0169406,2015-07-21,1:59:14,0:24:45,0:43:56,0:19:06,0:13:18,0:23:09,4:18:12,1249,208,35
+Night Team,PGY2+,S0169406,2015-07-24,2:41:49,0:53:43,1:09:08,0:06:50,0:09:29,0:26:11,6:01:23,1485,0,29
+Night Team,PGY2+,S0169406,2015-07-25,1:18:42,0:26:23,1:29:05,0:15:16,0:11:08,0:18:30,4:18:00,1086,79,13
+Emergency Medicine,PGY1,S0074196,2015-10-27,2:00:26,0:13:17,1:19:15,0:15:36,0:18:41,0:16:34,4:59:07,1283,0,22
+General Medicine,PGY2+,S0181078,2016-09-20,0:21:08,0:23:27,0:00:03,0:00:02,0:00:07,0:05:08,1:03:09,502,437,33
+General Medicine,PGY2+,S0181078,2016-09-21,4:45:32,1:35:16,0:00:09,0:59:52,0:35:51,0:04:51,8:49:30,3133,689,23
+Intensive Care Unit,PGY2+,S0181069,2016-11-07,2:35:29,1:21:19,0:14:32,0:13:32,0:33:13,0:05:08,5:34:14,1427,520,11
+Intensive Care Unit,PGY2+,S0181069,2016-11-06,2:10:36,0:49:10,0:12:19,0:26:59,0:36:50,0:05:23,4:54:44,1548,0,12
+General Medicine,PGY2+,S0181078,2016-09-24,2:20:38,1:09:37,0:00:09,0:14:51,0:20:36,0:03:56,4:55:12,1538,266,16
+General Medicine,PGY2+,S0181078,2016-09-25,0:34:11,0:12:12,0:00:00,0:08:25,0:00:01,0:00:07,1:07:59,331,327,19
+General Medicine,PGY2+,S0181078,2016-09-26,4:28:21,1:05:36,0:00:00,0:48:33,0:24:20,0:09:11,7:41:15,2306,960,20
+General Medicine,PGY2+,S0181078,2016-09-27,3:05:29,0:31:28,0:00:37,0:19:12,0:12:10,0:03:16,4:43:51,1622,172,22
+General Medicine,PGY2+,S0181078,2016-09-28,4:32:46,1:29:09,0:00:01,0:27:21,0:36:35,0:10:40,8:09:12,3277,624,42
+General Medicine,PGY2+,S0181078,2016-09-29,2:35:14,0:31:31,0:00:06,0:37:10,0:20:21,0:11:00,4:49:28,1688,365,22
+Intensive Care Unit,PGY2+,S0181069,2016-11-09,2:15:52,1:27:42,0:02:34,0:40:31,0:25:21,0:07:35,5:36:06,1868,787,27
+Intensive Care Unit,PGY2+,S0181069,2016-11-08,2:06:36,1:09:33,0:03:29,0:29:55,0:39:55,0:07:51,5:10:42,1568,369,22
+Night Team,PGY1,S0181526,2016-01-17,2:37:42,0:47:34,0:10:05,0:25:18,0:27:42,1:14:55,6:08:22,2252,72,69
+General Medicine,PGY1,S0195230,2016-09-23,1:12:56,0:30:15,0:00:08,0:49:34,0:20:49,0:00:16,3:19:19,975,0,7
+Emergency Medicine,PGY1,S0169401,2014-09-08,5:53:26,0:10:15,0:28:27,0:38:28,0:32:43,0:02:04,8:43:57,2542,254,73
+Emergency Medicine,PGY1,S0195086,2016-09-28,0:08:26,0:00:24,0:00:00,0:00:00,0:00:00,0:00:00,0:09:53,27,0,6
+Emergency Medicine,PGY1,S0195086,2016-09-29,5:30:44,0:53:49,0:04:13,0:26:21,0:48:51,0:01:11,8:25:14,2217,0,71
+Emergency Medicine,PGY2+,S0169403,2016-01-26,0:25:50,0:39:49,1:30:35,0:19:36,0:17:15,0:13:07,3:40:04,1380,154,18
+Emergency Medicine,PGY2+,S0172645,2016-10-13,2:55:36,1:07:20,0:09:12,0:23:14,0:39:22,0:06:38,5:49:45,1244,0,47
+Emergency Medicine,PGY2+,S0172645,2016-10-12,0:06:47,0:00:00,0:00:07,0:00:00,0:04:09,0:00:00,0:11:03,37,0,9
+Emergency Medicine,PGY1,S0195086,2016-09-20,0:02:48,0:15:34,0:00:00,0:00:00,0:00:03,0:00:00,0:19:27,206,206,10
+Emergency Medicine,PGY1,S0195086,2016-09-21,1:06:36,0:11:20,0:00:00,0:01:54,0:00:31,0:00:15,1:22:12,252,0,10
+Emergency Medicine,PGY2+,S0172645,2016-10-17,2:05:18,0:58:44,0:01:15,0:16:37,0:37:01,0:02:29,4:24:06,1396,0,42
+Emergency Medicine,PGY1,S0195086,2016-09-27,1:29:12,0:22:04,0:00:19,0:08:00,0:21:30,0:07:42,2:52:57,865,0,13
+Emergency Medicine,PGY2+,S0172645,2016-10-15,1:24:41,0:32:37,0:00:07,0:07:30,0:16:30,0:01:11,2:42:00,643,0,19
+Emergency Medicine,PGY1,S0195086,2016-09-25,2:16:12,0:48:51,0:07:30,0:15:03,0:39:05,0:04:56,4:38:56,1527,0,26
+Night Team,PGY1,S0181526,2016-01-19,2:50:50,0:44:49,1:39:24,0:20:47,0:29:20,1:02:38,7:55:39,2291,0,56
+Night Team,PGY1,S0181526,2016-01-18,2:17:03,0:39:03,0:41:39,0:51:44,0:26:39,0:54:28,6:34:18,2157,89,43
+General Medicine,PGY1,S0169398,2015-01-17,3:36:29,0:08:06,2:10:13,0:54:29,0:32:12,0:23:07,8:20:37,2479,398,13
+Emergency Medicine,PGY1,S0169401,2014-09-02,4:36:08,0:16:42,0:19:31,0:09:18,0:11:58,0:00:26,6:09:53,1823,0,42
+General Medicine,PGY1,S0169398,2015-01-15,4:47:38,0:06:23,1:28:29,1:09:02,0:26:01,0:23:15,8:35:51,3192,42,26
+General Medicine,PGY1,S0169398,2015-01-14,3:30:46,0:05:25,1:11:04,0:47:52,0:19:24,0:21:08,6:31:27,2220,711,10
+General Medicine,PGY1,S0169398,2015-01-13,1:03:39,0:01:51,0:01:07,0:05:01,0:00:59,0:00:33,1:17:52,817,773,22
+General Medicine,PGY1,S0169398,2015-01-12,3:56:59,0:12:30,2:00:35,0:58:46,0:23:56,0:26:41,8:14:24,2330,408,12
+General Medicine,PGY1,S0169398,2015-01-11,3:50:41,0:20:42,1:41:07,0:30:50,0:24:31,0:37:16,7:49:56,2148,414,9
+Emergency Medicine,PGY1,S0169401,2014-09-03,4:56:29,0:09:38,0:15:52,0:38:28,0:29:18,0:00:55,7:08:00,2152,0,35
+General Medicine,PGY1,S0169398,2015-01-19,0:06:11,0:00:00,0:00:07,0:00:00,0:00:15,0:00:23,0:07:00,62,60,2
+General Medicine,PGY1,S0169398,2015-01-18,3:40:10,0:16:10,2:31:24,0:59:34,0:15:50,0:23:58,8:35:56,2677,1164,12
+General Medicine,PGY1,S0094959,2015-04-25,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,8,0,3
+General Medicine,PGY1,S0094959,2015-04-24,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+Intensive Care Unit,PGY2+,S0108268,2016-07-31,0:00:03,0:00:00,0:00:00,0:03:48,0:00:00,0:00:00,0:04:02,46,0,8
+Intensive Care Unit,PGY2+,S0108268,2016-07-30,2:32:23,0:49:58,0:08:34,0:40:56,0:39:29,0:05:39,5:30:16,2203,0,17
+General Medicine,PGY1,S0094959,2015-04-21,1:56:53,0:34:41,0:16:50,0:17:29,0:17:17,0:12:48,3:46:54,1615,0,28
+General Medicine,PGY1,S0094959,2015-04-20,2:50:47,0:40:53,0:23:56,0:09:41,0:20:55,0:05:28,4:40:54,2267,0,46
+General Medicine,PGY1,S0094959,2015-04-23,2:49:09,0:41:07,0:58:20,0:12:54,0:07:08,0:18:41,5:16:48,2332,585,19
+General Medicine,PGY1,S0094959,2015-04-22,4:59:37,1:22:05,1:39:39,0:48:38,0:30:15,0:25:39,10:33:34,5058,253,41
+Emergency Medicine,PGY1,S0169399,2015-05-07,3:45:22,0:22:11,4:35:46,0:17:14,0:19:31,0:10:56,9:56:43,1740,0,37
+Emergency Medicine,PGY1,S0169399,2015-05-06,0:18:22,0:04:12,0:17:38,0:05:01,0:01:01,0:01:01,0:48:38,138,0,4
+Intensive Care Unit,PGY2+,S0169398,2016-04-04,2:10:20,1:09:47,0:07:35,0:10:45,0:27:20,0:00:51,4:35:50,1202,20,11
+Intensive Care Unit,PGY2+,S0169398,2016-04-05,2:20:12,0:50:53,0:02:31,0:13:23,0:38:18,0:00:24,4:33:11,1209,39,16
+Intensive Care Unit,PGY2+,S0169398,2016-04-06,2:37:34,1:15:29,0:02:24,0:28:54,0:16:51,0:13:40,5:28:57,1909,10,22
+Intensive Care Unit,PGY2+,S0169398,2016-04-07,2:11:16,0:45:17,0:10:05,0:16:01,0:29:44,0:01:30,4:18:40,1269,0,16
+Emergency Medicine,PGY1,S0169401,2014-09-04,4:03:11,0:09:18,0:08:13,0:05:40,0:06:31,0:03:42,5:03:29,1721,1412,71
+General Medicine,PGY2+,S0169398,2016-04-02,3:19:08,1:18:20,0:06:37,0:51:22,0:35:26,0:05:26,6:36:49,1843,0,14
+Intensive Care Unit,PGY2+,S0169398,2016-04-03,1:57:50,0:36:07,0:07:13,0:08:13,0:22:34,0:05:47,3:40:26,1089,0,13
+Emergency Medicine,PGY1,S0169399,2015-05-04,1:50:42,0:24:48,5:54:33,0:38:47,0:33:34,0:29:27,10:11:58,1457,0,15
+Intensive Care Unit,PGY2+,S0169398,2016-04-08,2:14:30,0:33:20,0:04:47,0:15:09,0:37:59,0:03:57,4:26:27,1306,0,16
+Intensive Care Unit,PGY2+,S0169398,2016-04-09,0:16:36,0:28:11,0:00:01,0:05:26,0:00:25,0:00:46,1:07:37,724,702,24
+General Medicine,PGY1,S0181078,2015-08-20,2:48:29,0:39:20,1:34:13,1:21:22,0:46:55,0:54:30,9:09:07,2211,322,12
+General Medicine,PGY1,S0181078,2015-08-21,2:13:48,0:12:30,0:43:49,0:34:45,0:28:27,0:44:02,6:02:27,1800,313,11
+General Medicine,PGY1,S0181078,2015-08-22,3:04:08,0:41:32,1:11:14,1:12:09,0:48:42,0:31:10,8:27:24,2606,169,11
+General Medicine,PGY1,S0181078,2015-08-23,2:29:13,0:29:49,2:04:42,0:29:28,0:18:32,0:30:31,7:10:36,1581,629,12
+General Medicine,PGY1,S0181078,2015-08-24,2:16:01,0:18:44,0:52:02,0:30:35,0:15:56,0:25:52,5:11:15,1189,394,8
+General Medicine,PGY1,S0181078,2015-08-25,2:25:40,0:40:04,2:04:52,0:37:10,0:24:38,0:37:41,7:28:09,2006,188,13
+General Medicine,PGY1,S0181078,2015-08-26,0:48:36,0:10:53,0:45:21,0:04:28,0:01:44,0:01:54,1:57:56,365,363,6
+General Medicine,PGY1,S0181078,2015-08-27,2:21:32,0:50:32,3:12:47,1:15:31,0:36:40,0:28:45,9:44:42,2161,322,13
+General Medicine,PGY1,S0181078,2015-08-28,2:10:29,0:23:09,2:24:38,0:20:59,0:06:51,0:05:28,6:07:46,1363,313,17
+General Medicine,PGY1,S0181078,2015-08-29,0:03:36,0:15:15,0:00:01,0:00:00,0:01:19,0:00:39,0:21:01,59,57,5
+Emergency Medicine,PGY1,S0169395,2014-11-11,1:36:31,0:02:00,2:11:27,0:27:16,0:38:20,0:32:52,5:54:44,2433,0,15
+Intensive Care Unit,PGY2+,S0169386,2016-06-30,2:51:57,0:50:21,0:05:25,0:19:59,0:24:45,0:08:47,5:02:17,1759,15,32
+Emergency Medicine,PGY1,S0195210,2016-06-28,2:02:53,0:25:32,0:19:11,0:24:22,0:10:53,0:00:22,3:30:55,673,0,10
+Emergency Medicine,PGY1,S0195210,2016-06-29,0:58:16,0:08:12,0:10:04,0:03:22,0:05:29,0:01:14,1:29:50,311,0,6
+Emergency Medicine,PGY1,S0195210,2016-06-26,1:23:52,0:11:31,0:24:24,0:22:29,0:20:56,0:00:05,3:04:28,555,0,6
+Emergency Medicine,PGY1,S0195210,2016-06-27,0:45:20,0:15:35,0:10:44,0:16:38,0:05:12,0:00:01,1:45:05,341,0,4
+Emergency Medicine,PGY1,S0169384,2014-09-17,0:15:32,0:00:51,0:01:37,0:00:15,0:03:10,0:00:34,0:24:44,102,0,3
+Emergency Medicine,PGY1,S0169384,2014-09-16,2:35:08,0:09:32,0:12:20,0:19:20,0:14:54,0:05:39,4:08:28,1070,0,7
+Emergency Medicine,PGY1,S0169384,2014-09-15,4:23:56,0:04:01,0:35:07,0:23:32,0:22:10,0:29:38,6:50:39,1515,0,13
+Emergency Medicine,PGY1,S0169384,2014-09-14,3:36:06,0:16:26,0:34:32,0:16:48,0:27:40,0:27:14,5:51:18,1327,0,17
+Emergency Medicine,PGY1,S0169384,2014-09-19,1:39:35,0:16:22,0:16:38,0:08:25,0:07:16,0:34:17,3:13:39,759,0,9
+Emergency Medicine,PGY1,S0169384,2014-09-18,1:23:49,0:06:50,0:16:34,0:13:31,0:03:47,0:10:12,2:46:45,772,20,7
+General Medicine,PGY1,S0181073,2016-05-30,2:42:19,1:29:52,0:00:27,0:56:00,0:33:40,0:02:12,6:46:32,2149,16,14
+General Medicine,PGY2+,S0169403,2015-08-06,2:02:26,1:49:20,1:19:23,0:36:53,0:29:32,0:46:08,7:49:41,3113,43,21
+Intensive Care Unit,PGY2+,S0169384,2015-08-23,3:07:26,0:30:39,0:51:33,0:11:05,0:10:52,0:15:15,5:15:38,1100,53,10
+Intensive Care Unit,PGY2+,S0169384,2015-08-24,1:43:02,0:02:25,0:05:16,1:14:43,0:49:25,0:46:26,4:53:45,1180,0,12
+Intensive Care Unit,PGY2+,S0169384,2015-08-25,3:55:58,0:16:00,0:48:56,0:43:39,0:27:45,0:30:47,6:56:20,1427,265,13
+Intensive Care Unit,PGY2+,S0169384,2015-08-26,3:15:50,0:09:34,0:24:37,0:19:39,0:29:10,0:32:32,5:30:22,1310,388,14
+Intensive Care Unit,PGY2+,S0169384,2015-08-27,3:17:58,0:12:02,0:12:34,0:35:39,0:31:54,0:16:51,5:25:12,1629,0,17
+Intensive Care Unit,PGY2+,S0169384,2015-08-28,3:55:56,0:09:48,0:50:14,0:28:29,0:11:01,0:37:16,6:36:50,1695,49,11
+Intensive Care Unit,PGY2+,S0169384,2015-08-29,0:00:00,0:00:00,0:00:00,0:00:15,0:00:00,0:00:00,0:00:15,4,0,2
+General Medicine,PGY1,S0181073,2016-05-31,0:35:35,0:31:15,0:00:01,0:18:38,0:10:26,0:00:11,1:50:53,491,0,5
+General Medicine,PGY1,S0181081,2016-06-18,1:02:59,0:35:32,0:00:04,0:17:09,0:03:43,0:03:14,2:12:10,803,8,5
+Night Team,PGY2+,S0169390,2016-06-09,3:56:43,1:19:13,0:00:06,0:48:56,0:35:45,0:04:58,7:24:21,2011,0,58
+General Medicine,PGY1,S0181081,2016-06-19,2:43:41,1:46:52,0:00:04,0:53:54,0:15:08,0:07:56,6:29:18,2074,7,18
+General Medicine,PGY1,S0195075,2016-06-27,1:51:04,0:47:57,0:00:04,0:26:24,0:14:13,0:00:44,3:39:11,883,9,4
+General Medicine,PGY1,S0195075,2016-06-26,1:39:47,0:45:40,0:00:04,0:16:40,0:17:42,0:06:03,3:32:32,1096,0,5
+General Medicine,PGY1,S0195075,2016-06-29,0:00:43,0:06:18,0:00:00,0:00:00,0:00:00,0:00:00,0:07:13,66,66,3
+General Medicine,PGY1,S0195075,2016-06-28,2:23:54,1:33:03,0:00:28,0:31:34,0:35:18,0:02:30,5:49:26,1712,175,7
+Emergency Medicine,PGY1,S0195094,2016-08-31,0:08:15,0:01:17,0:00:00,0:00:00,0:00:00,0:00:49,0:10:35,51,0,7
+Emergency Medicine,PGY1,S0195094,2016-08-30,2:46:01,0:34:08,0:01:08,0:42:34,0:53:52,0:00:33,5:42:04,1477,0,18
+General Medicine,PGY1,S0109179,2014-12-08,2:02:15,0:03:08,1:25:27,0:31:36,0:13:01,0:11:25,4:55:01,1927,680,15
+Intensive Care Unit,PGY2+,S0079337,2016-06-09,2:13:44,0:35:50,0:03:53,0:19:29,0:25:22,0:10:16,4:09:09,1368,21,17
+Intensive Care Unit,PGY2+,S0079337,2016-06-08,2:42:43,0:41:08,0:14:17,0:17:01,0:26:09,0:04:54,4:46:17,1395,34,18
+General Medicine,PGY1,S0109179,2014-12-09,3:01:57,0:14:18,2:30:39,0:16:14,0:13:00,0:31:47,7:01:35,1538,48,6
+Intensive Care Unit,PGY2+,S0079337,2016-06-03,2:08:24,0:14:47,0:02:29,0:54:38,0:34:56,0:11:39,4:45:36,1783,0,19
+Intensive Care Unit,PGY2+,S0079337,2016-06-02,1:06:38,0:29:43,0:09:53,0:06:46,0:18:05,0:00:13,2:33:14,937,0,14
+Intensive Care Unit,PGY2+,S0079337,2016-06-01,0:30:52,0:19:27,0:06:15,0:01:15,0:04:29,0:00:00,1:12:32,400,0,9
+Intensive Care Unit,PGY2+,S0079337,2016-06-07,2:04:08,0:45:25,0:14:20,0:14:46,0:13:50,0:06:12,3:56:24,1242,34,15
+General Medicine,PGY1,S0181081,2016-06-12,0:50:59,0:54:20,0:00:10,0:25:58,0:03:55,0:00:13,2:33:55,853,0,7
+Intensive Care Unit,PGY2+,S0079337,2016-06-05,0:02:25,0:00:27,0:00:00,0:00:11,0:00:00,0:00:00,0:03:06,36,28,10
+Intensive Care Unit,PGY2+,S0079337,2016-06-04,1:56:09,0:31:25,0:13:08,0:26:19,0:16:37,0:00:16,3:48:23,1589,36,14
+General Medicine,PGY1,S0181081,2016-06-13,1:04:23,1:15:08,0:00:12,0:33:49,0:12:10,0:02:49,3:43:41,1464,0,16
+General Medicine,PGY1,S0181081,2016-06-14,2:13:53,1:27:18,0:00:06,0:30:04,0:12:51,0:05:57,5:24:29,1817,38,14
+General Medicine,PGY1,S0181525,2015-10-31,1:04:26,0:58:35,1:25:40,0:23:47,0:29:00,0:34:09,5:22:08,1324,53,6
+General Medicine,PGY1,S0181081,2016-06-15,0:00:00,0:00:00,0:00:00,0:00:05,0:00:00,0:00:00,0:00:05,6,0,2
+General Medicine,PGY1,S0181081,2016-06-16,2:40:25,1:49:52,0:00:11,1:01:59,0:23:49,0:05:02,6:41:04,2797,34,16
+General Medicine,PGY1,S0181081,2016-06-17,1:27:22,1:02:10,0:00:09,0:52:34,0:11:52,0:08:59,4:14:10,1493,26,15
+Emergency Medicine,PGY1,S0181057,2015-08-12,1:14:27,0:18:15,0:25:18,0:02:29,0:03:41,0:09:51,2:19:30,613,0,6
+Emergency Medicine,PGY1,S0181057,2015-08-13,5:06:30,0:26:21,1:41:51,0:19:54,0:31:02,0:12:37,8:39:42,2568,0,42
+Emergency Medicine,PGY1,S0181086,2015-09-28,2:27:08,0:15:41,2:53:29,0:45:03,0:22:08,0:06:26,7:35:49,1562,264,20
+Emergency Medicine,PGY1,S0181086,2015-09-29,2:38:51,1:09:06,4:36:07,0:38:11,0:08:55,0:10:41,9:55:46,1674,573,14
+Night Team,PGY2+,S0169385,2016-08-15,2:54:36,1:00:53,0:00:58,0:26:06,0:14:30,0:05:00,5:11:23,2007,0,37
+Night Team,PGY2+,S0169385,2016-08-14,2:07:02,0:46:18,0:00:41,0:11:57,0:05:13,0:08:04,3:38:25,1354,164,35
+Emergency Medicine,PGY1,S0181057,2015-08-14,4:09:34,0:16:57,1:00:18,0:12:57,0:13:48,0:06:52,6:30:00,1983,84,31
+Night Team,PGY2+,S0169385,2016-08-16,3:00:00,2:06:31,0:00:11,0:37:04,0:13:12,0:08:42,6:43:51,2555,1,24
+Emergency Medicine,PGY1,S0181086,2015-09-22,3:14:47,0:44:19,2:49:00,0:26:09,0:11:27,0:17:18,8:44:12,2106,868,20
+Emergency Medicine,PGY1,S0181086,2015-09-23,0:16:45,0:02:36,0:00:11,0:03:42,0:04:10,0:02:03,0:59:18,417,415,14
+Emergency Medicine,PGY1,S0181086,2015-09-20,0:39:03,0:08:28,0:30:14,0:12:19,0:21:34,0:06:17,2:12:43,714,74,23
+Emergency Medicine,PGY1,S0181086,2015-09-21,3:26:47,0:22:29,5:00:50,0:52:24,0:16:44,0:29:44,11:43:19,2150,435,21
+Emergency Medicine,PGY1,S0181086,2015-09-26,1:25:34,0:02:57,1:04:45,0:03:40,0:01:09,0:03:48,3:00:10,671,238,10
+Emergency Medicine,PGY1,S0181086,2015-09-27,3:24:58,0:15:13,3:31:19,0:49:52,0:24:28,0:25:52,9:30:41,1925,125,14
+Emergency Medicine,PGY1,S0181086,2015-09-24,2:39:20,0:38:45,2:36:55,0:22:30,0:12:03,0:25:24,7:14:02,1347,24,12
+Emergency Medicine,PGY1,S0181086,2015-09-25,1:38:02,0:20:45,1:33:53,0:13:18,0:08:10,0:14:09,4:22:48,944,283,19
+Night Team,PGY1,S0108287,2015-06-23,3:05:34,1:13:14,1:59:23,0:40:39,0:07:43,0:19:45,7:56:23,1798,0,19
+Night Team,PGY1,S0108287,2015-06-22,0:17:49,0:05:29,0:14:23,0:09:08,0:11:43,0:05:51,1:08:53,307,0,12
+Night Team,PGY1,S0108287,2015-06-21,1:05:53,0:16:27,0:49:01,0:12:38,0:06:58,0:19:46,3:02:27,1070,148,27
+Night Team,PGY1,S0108287,2015-06-20,1:43:26,0:28:32,1:53:15,0:22:45,0:07:08,0:15:01,5:10:31,1407,337,28
+Night Team,PGY1,S0108287,2015-06-25,2:05:31,0:10:06,0:27:16,0:04:53,0:06:32,0:12:07,3:13:18,685,0,24
+General Medicine,PGY1,S0181083,2015-06-29,0:00:56,0:01:34,0:00:00,0:00:00,0:00:15,0:03:31,0:07:15,88,88,4
+Night Team,PGY1,S0181526,2016-06-24,3:01:32,1:16:52,0:00:30,0:26:38,0:12:10,0:01:37,5:34:59,1853,0,68
+Night Team,PGY2+,S0106952,2015-07-14,3:30:02,1:30:54,2:53:20,0:37:31,0:38:20,0:54:30,10:47:00,3487,494,40
+Night Team,PGY2+,S0106952,2015-07-15,2:27:45,1:03:27,1:47:43,0:23:03,0:35:48,0:34:58,7:24:35,2119,312,62
+Night Team,PGY2+,S0106952,2015-07-16,3:23:54,1:14:24,3:09:56,0:40:02,0:22:16,0:42:39,10:09:56,3422,1405,105
+Night Team,PGY2+,S0106952,2015-07-17,2:43:35,2:08:24,0:59:13,0:11:26,0:15:51,0:37:08,7:59:47,3785,2377,121
+Night Team,PGY2+,S0106952,2015-07-12,2:25:37,1:08:54,3:25:25,0:40:09,0:42:15,0:38:41,9:32:51,2622,153,35
+Night Team,PGY2+,S0106952,2015-07-13,3:42:19,0:43:06,4:04:56,0:35:48,0:26:58,1:01:18,11:06:45,2336,410,42
+Night Team,PGY2+,S0106952,2015-07-18,3:20:41,0:56:20,2:50:07,0:49:44,0:46:37,0:38:53,10:15:23,2995,785,79
+Night Team,PGY2+,S0106952,2015-07-19,2:43:19,0:37:42,3:50:42,0:48:55,0:31:09,0:26:49,9:37:14,2439,616,92
+General Medicine,PGY1,S0181079,2016-03-18,3:33:07,1:27:38,0:00:06,0:41:25,0:29:48,0:10:35,7:18:39,2255,12,12
+Emergency Medicine,PGY1,S0181057,2015-07-31,5:52:45,0:26:43,2:15:54,0:23:33,0:32:09,0:31:25,10:27:22,2924,0,27
+Emergency Medicine,PGY1,S0181057,2015-07-30,4:53:31,0:16:00,1:51:23,0:16:05,0:30:47,0:30:40,8:45:34,2482,0,26
+General Medicine,PGY1,S0169394,2015-04-28,2:35:06,0:00:36,1:28:47,0:43:42,0:18:52,0:18:54,5:59:45,1719,117,8
+Night Team,PGY1,S0181525,2015-12-15,1:30:40,0:26:19,0:02:42,1:05:27,0:21:03,0:58:52,4:38:17,1445,12,41
+Night Team,PGY1,S0181525,2015-12-14,1:31:03,0:16:04,0:04:19,0:34:30,0:29:58,0:54:12,3:58:16,1289,0,41
+General Medicine,PGY1,S0169406,2015-03-26,0:54:15,0:20:28,0:28:57,0:20:37,0:10:05,0:08:51,2:33:59,977,19,7
+General Medicine,PGY1,S0169406,2015-03-27,1:31:04,0:21:54,0:24:03,0:19:51,0:18:37,0:19:06,3:31:01,1430,48,12
+General Medicine,PGY1,S0169406,2015-03-24,0:18:53,0:08:32,0:36:30,0:00:21,0:00:09,0:01:57,1:09:07,300,292,10
+General Medicine,PGY1,S0169406,2015-03-25,2:05:06,0:57:18,1:29:41,0:26:50,0:27:33,0:05:58,5:59:18,1989,0,16
+General Medicine,PGY1,S0169406,2015-03-22,1:29:59,0:29:56,1:50:28,0:41:23,0:13:14,0:19:54,5:29:33,1433,0,9
+General Medicine,PGY1,S0169406,2015-03-23,0:33:51,0:17:41,1:14:17,0:08:37,0:07:33,0:12:51,2:43:42,683,49,7
+General Medicine,PGY1,S0169406,2015-03-20,1:24:28,0:34:49,1:08:41,0:29:43,0:08:07,0:08:24,4:09:23,1326,0,8
+General Medicine,PGY1,S0169406,2015-03-21,2:01:01,0:45:27,1:52:41,0:08:54,0:18:34,0:29:54,5:56:07,1184,34,5
+General Medicine,PGY1,S0169406,2015-03-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0195230,2016-09-25,1:48:02,0:43:15,0:00:05,0:23:14,0:11:09,0:00:59,3:38:15,1134,0,10
+Night Team,PGY1,S0181088,2015-10-28,1:13:02,0:30:56,0:56:31,0:43:43,0:26:41,0:30:19,4:48:07,1791,0,38
+Night Team,PGY1,S0181088,2015-10-29,0:50:22,0:13:44,0:02:33,0:22:45,0:19:32,0:23:21,2:27:55,1085,0,23
+Night Team,PGY1,S0181088,2015-10-26,1:09:50,0:36:53,1:29:50,0:58:48,0:31:22,0:25:21,5:30:23,1829,0,19
+Night Team,PGY1,S0181088,2015-10-27,0:40:23,0:19:37,1:06:13,0:18:40,0:18:21,0:25:37,3:23:30,1322,25,25
+Emergency Medicine,PGY1,S0181063,2016-05-31,1:51:15,1:08:12,0:03:13,0:26:00,1:13:38,0:55:02,6:16:19,2745,152,22
+Night Team,PGY1,S0181088,2015-10-25,1:12:25,0:29:40,0:13:29,0:33:52,0:40:28,0:24:18,3:49:23,1246,0,34
+General Medicine,PGY1,S0056755,2015-01-26,2:41:42,0:04:27,1:07:12,0:21:57,0:13:40,0:12:44,5:10:36,1471,0,17
+General Medicine,PGY1,S0056755,2015-01-27,3:07:05,0:03:33,1:12:05,0:52:18,0:28:38,0:15:38,6:41:01,2222,0,10
+General Medicine,PGY1,S0056755,2015-01-25,2:47:55,0:12:58,1:21:14,0:28:34,0:21:35,0:22:00,6:02:13,1756,171,8
+General Medicine,PGY1,S0056755,2015-01-28,2:06:44,0:12:20,1:25:06,0:40:17,0:09:41,0:11:21,5:28:04,1698,0,9
+General Medicine,PGY1,S0056755,2015-01-29,2:45:30,0:34:40,1:59:40,0:25:14,0:13:07,0:15:43,6:30:03,1817,11,20
+General Medicine,PGY1,S0195230,2016-09-24,1:38:24,1:13:59,0:00:47,0:18:35,0:08:53,0:00:34,3:39:27,1459,0,13
+General Medicine,PGY1,S0169394,2014-07-21,1:10:14,0:01:12,0:20:12,0:02:34,0:18:01,0:23:02,2:30:55,1012,0,7
+General Medicine,PGY1,S0169394,2014-07-20,2:59:34,0:08:08,0:23:28,0:09:03,0:21:11,0:48:45,5:21:01,1465,0,14
+General Medicine,PGY1,S0169394,2014-07-23,2:23:29,0:07:26,0:15:28,0:48:51,0:19:45,0:31:01,4:51:24,1628,96,9
+General Medicine,PGY1,S0169394,2014-07-22,1:22:12,0:05:27,0:12:44,0:15:24,0:09:29,0:27:47,2:56:47,892,11,9
+General Medicine,PGY1,S0169394,2014-07-25,3:24:28,0:09:09,0:17:44,0:50:05,0:33:35,0:36:53,6:13:27,1700,22,14
+General Medicine,PGY1,S0169394,2014-07-24,2:44:37,0:03:38,0:31:52,0:34:20,0:13:23,0:43:11,5:27:05,1649,92,8
+General Medicine,PGY1,S0169394,2014-07-27,0:55:05,0:20:43,0:00:17,0:02:13,0:00:00,0:03:21,1:24:37,320,302,10
+General Medicine,PGY1,S0169394,2014-07-26,1:34:55,0:10:05,0:13:16,0:27:48,0:26:23,0:34:57,3:44:12,957,14,10
+General Medicine,PGY1,S0169394,2014-07-29,2:10:48,0:05:18,0:15:27,0:31:06,0:14:28,0:17:13,4:03:09,1397,0,13
+General Medicine,PGY1,S0169394,2014-07-28,2:43:51,0:12:55,0:13:05,0:44:16,0:26:22,0:25:08,5:08:55,1562,51,13
+General Medicine,PGY1,S0195230,2016-09-27,1:29:35,0:51:39,0:01:08,0:54:44,0:19:26,0:02:30,4:26:32,1745,0,12
+General Medicine,PGY1,S0181074,2015-08-06,2:27:45,1:57:38,1:03:03,2:10:38,0:37:13,0:16:18,9:24:44,3209,102,17
+General Medicine,PGY1,S0181074,2015-08-07,0:00:00,0:17:47,0:00:00,0:01:58,0:00:00,0:03:24,0:23:45,171,155,6
+General Medicine,PGY1,S0163662,2015-03-03,2:40:53,0:32:33,1:11:49,0:33:21,0:28:47,0:23:20,6:04:56,1590,148,6
+General Medicine,PGY1,S0181074,2015-08-05,1:27:42,1:38:41,1:00:23,1:05:05,0:20:16,0:18:05,6:13:13,2209,184,12
+General Medicine,PGY1,S0181074,2015-08-02,0:11:07,0:46:24,0:31:18,0:18:52,0:01:56,0:02:05,1:53:51,643,621,13
+General Medicine,PGY1,S0181074,2015-08-03,0:47:47,1:21:02,1:42:02,1:28:54,0:11:02,0:08:23,6:13:00,1843,0,9
+General Medicine,PGY1,S0181074,2015-08-01,1:45:18,1:28:15,1:32:00,2:12:47,0:55:05,0:17:02,8:59:35,2920,40,9
+General Medicine,PGY1,S0181074,2015-08-08,1:14:41,1:29:26,1:30:41,1:01:15,0:36:16,0:08:54,6:42:44,2068,98,14
+General Medicine,PGY1,S0181074,2015-08-09,2:27:49,1:19:12,0:15:18,1:48:53,0:35:37,0:21:15,8:03:17,3123,101,17
+General Medicine,PGY2+,S0181074,2016-10-17,2:22:30,1:02:28,0:00:17,0:58:45,0:14:29,0:27:12,5:48:46,2651,51,30
+General Medicine,PGY2+,S0169390,2016-08-01,3:53:06,1:00:39,0:00:49,0:44:31,0:31:20,0:04:24,7:07:38,2732,64,23
+General Medicine,PGY2+,S0181074,2016-10-15,2:35:36,0:39:59,0:00:00,0:27:14,0:10:27,0:04:40,4:41:27,2547,239,20
+General Medicine,PGY2+,S0169390,2016-08-03,4:23:51,1:07:49,0:00:05,1:10:24,0:33:54,0:15:52,8:15:37,3019,0,20
+General Medicine,PGY2+,S0169390,2016-08-04,0:09:02,0:00:42,0:00:00,0:00:07,0:00:00,0:00:22,0:11:28,48,0,9
+General Medicine,PGY2+,S0169390,2016-08-05,3:57:18,1:28:06,0:00:06,0:33:22,0:38:34,0:15:10,7:55:55,3418,58,20
+General Medicine,PGY2+,S0169390,2016-08-06,1:57:23,0:36:18,0:00:03,0:28:07,0:19:34,0:06:39,3:59:00,1476,35,16
+General Medicine,PGY2+,S0169390,2016-08-07,3:50:15,1:32:29,0:00:07,0:31:58,0:23:51,0:07:17,7:28:26,3294,0,23
+General Medicine,PGY2+,S0169390,2016-08-08,3:05:40,1:30:19,0:00:16,0:44:55,0:38:58,0:06:06,6:27:28,2905,357,16
+General Medicine,PGY2+,S0169390,2016-08-09,0:19:53,0:01:37,0:00:00,0:01:10,0:00:00,0:01:22,0:27:36,121,0,14
+General Medicine,PGY2+,S0181074,2016-10-19,2:52:11,0:36:48,0:00:22,0:32:33,0:12:52,0:10:13,5:26:54,2839,456,25
+General Medicine,PGY2+,S0181074,2016-10-18,3:55:27,1:05:02,0:00:00,0:59:25,0:13:38,0:12:56,7:24:18,3293,0,25
+General Medicine,PGY1,S0195230,2016-09-21,0:00:00,0:00:00,0:00:00,0:00:28,0:00:00,0:00:00,0:00:28,4,0,2
+General Medicine,PGY2+,S0169384,2016-05-29,2:24:40,0:59:24,0:03:44,0:41:16,0:20:53,0:02:18,4:50:04,1284,127,8
+General Medicine,PGY1,S0163662,2015-03-01,0:57:50,0:22:29,1:18:55,0:06:27,0:03:20,0:04:46,2:59:51,549,0,8
+Night Team,PGY2+,S0169399,2016-05-01,1:19:54,0:36:38,0:00:03,0:20:10,0:01:36,0:00:29,2:42:39,1129,23,18
+Night Team,PGY2+,S0169399,2016-05-02,0:38:05,0:41:21,0:00:03,0:17:43,0:09:39,0:01:37,2:02:38,640,0,9
+Night Team,PGY2+,S0056755,2016-05-14,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,2
+Night Team,PGY2+,S0169399,2016-05-03,1:12:11,0:46:40,0:00:04,0:25:32,0:09:16,0:04:01,3:06:37,1588,0,24
+Night Team,PGY2+,S0056755,2016-05-10,2:43:06,2:23:29,0:00:08,0:36:58,0:14:45,0:05:46,6:25:24,2447,97,45
+Night Team,PGY2+,S0056755,2016-05-11,1:58:25,1:39:56,0:00:09,0:38:53,0:18:10,0:03:50,5:01:36,1438,73,27
+Night Team,PGY2+,S0056755,2016-05-12,2:03:35,1:28:07,0:03:23,0:44:17,0:17:08,0:09:16,5:06:33,1301,16,31
+Night Team,PGY2+,S0056755,2016-05-13,1:54:14,1:14:32,0:00:19,0:36:58,0:14:47,0:02:56,4:30:51,1350,0,42
+Night Team,PGY2+,S0169399,2016-05-05,1:25:35,0:47:43,0:00:02,0:20:46,0:05:09,0:01:02,2:56:06,1390,2,42
+General Medicine,PGY1,S0163425,2015-06-08,3:43:37,0:33:53,1:39:41,0:33:57,0:22:43,0:33:33,7:57:04,2571,0,24
+General Medicine,PGY1,S0163425,2015-06-09,0:11:08,0:00:00,0:00:40,0:00:00,0:00:11,0:00:20,0:13:07,105,103,5
+Night Team,PGY2+,S0169399,2016-05-06,0:36:46,0:31:20,0:00:03,0:00:00,0:00:08,0:00:19,1:21:07,360,41,17
+General Medicine,PGY1,S0163425,2015-06-02,1:14:04,0:23:22,0:24:31,0:10:35,0:07:59,0:04:01,2:45:53,878,0,11
+General Medicine,PGY1,S0163425,2015-06-03,2:36:04,0:30:22,1:50:27,0:33:51,0:18:53,0:14:34,6:23:15,1832,0,20
+Night Team,PGY2+,S0169399,2016-05-07,1:16:00,1:18:48,0:00:04,0:20:05,0:06:43,0:00:48,3:26:33,708,0,16
+General Medicine,PGY1,S0163425,2015-06-06,3:07:44,0:32:50,2:29:15,0:19:54,0:20:24,0:33:46,8:06:57,1987,0,26
+General Medicine,PGY1,S0163425,2015-06-07,3:14:03,0:05:33,1:10:47,0:11:05,0:19:27,0:22:40,5:49:07,1597,0,19
+General Medicine,PGY1,S0163425,2015-06-04,0:03:02,0:00:00,0:00:09,0:01:30,0:00:15,0:00:00,0:04:58,42,36,4
+General Medicine,PGY1,S0163425,2015-06-05,1:47:01,0:09:56,0:48:54,0:15:53,0:09:47,0:07:21,3:35:17,1410,0,14
+General Medicine,PGY1,S0181079,2016-03-12,2:07:32,0:52:10,0:00:26,0:35:07,0:07:02,0:01:40,4:20:40,1440,0,14
+General Medicine,PGY1,S0195230,2016-09-22,2:30:39,1:40:01,0:00:12,0:58:12,0:27:05,0:00:10,6:16:59,1712,0,8
+Intensive Care Unit,PGY2+,S0169390,2015-10-31,2:03:58,0:14:10,0:06:24,0:14:40,0:15:04,0:16:18,3:29:23,755,17,10
+Intensive Care Unit,PGY2+,S0169390,2015-10-30,1:18:32,0:17:42,0:05:00,0:18:28,0:12:09,0:06:56,2:37:48,783,35,12
+General Medicine,PGY1,S0181079,2016-03-13,3:10:09,1:11:00,0:00:08,0:42:18,0:13:04,0:03:23,5:58:48,1546,17,17
+Intensive Care Unit,PGY2+,S0079337,2016-06-25,0:09:22,0:01:01,0:00:00,0:00:00,0:00:00,0:00:00,0:10:48,87,83,9
+General Medicine,PGY2+,S0169381,2016-04-08,1:40:29,0:38:07,0:00:04,0:52:22,0:17:50,0:04:21,4:03:14,1091,72,10
+General Medicine,PGY2+,S0169381,2016-04-09,2:16:56,1:20:35,0:00:05,0:13:23,0:16:39,0:04:03,4:34:09,1846,15,33
+General Medicine,PGY2+,S0169381,2016-04-06,1:30:16,0:37:04,0:00:01,0:12:33,0:11:25,0:05:12,3:02:30,1464,14,19
+General Medicine,PGY2+,S0169381,2016-04-07,1:09:43,0:35:33,0:00:02,0:29:36,0:14:07,0:05:30,2:50:15,946,196,14
+General Medicine,PGY2+,S0169381,2016-04-04,2:16:26,1:15:23,0:00:05,0:45:41,0:11:31,0:05:17,5:13:08,1871,316,19
+General Medicine,PGY2+,S0169381,2016-04-05,1:08:16,0:35:07,0:00:24,0:10:09,0:08:57,0:03:05,2:26:25,849,175,14
+General Medicine,PGY2+,S0169381,2016-04-03,2:17:10,1:09:17,0:00:29,0:33:38,0:27:33,0:07:21,5:02:56,1377,449,11
+General Medicine,PGY1,S0056755,2014-10-03,2:58:21,0:18:16,0:14:25,0:24:42,0:16:37,0:13:39,4:37:09,1024,0,7
+General Medicine,PGY1,S0181075,2015-07-28,2:42:26,1:57:53,1:33:13,1:29:19,0:25:30,0:16:44,9:06:58,2687,229,13
+General Medicine,PGY1,S0181075,2015-07-29,2:08:05,1:45:19,1:45:57,0:56:07,0:27:26,0:12:51,7:33:33,2299,0,14
+General Medicine,PGY1,S0181075,2015-07-22,1:44:06,1:29:50,1:27:54,0:37:28,0:23:43,0:19:15,6:24:55,2099,334,14
+General Medicine,PGY1,S0181075,2015-07-23,2:16:11,0:49:30,1:45:04,1:07:45,0:40:01,0:32:23,7:53:47,1842,139,16
+Emergency Medicine,PGY1,S0169395,2014-12-04,5:28:35,0:11:51,2:16:06,0:32:10,0:28:32,0:17:36,10:06:09,2793,0,56
+General Medicine,PGY1,S0181075,2015-07-21,0:25:19,0:20:06,0:46:10,0:00:00,0:04:19,0:06:54,1:47:46,547,547,15
+Emergency Medicine,PGY1,S0169395,2014-12-02,2:46:06,0:01:03,1:24:51,0:14:16,0:17:11,0:16:26,5:40:48,1944,0,29
+Emergency Medicine,PGY1,S0169395,2014-12-03,4:22:39,0:07:27,1:38:00,0:23:38,0:17:29,0:29:35,8:03:40,2010,0,49
+General Medicine,PGY1,S0181075,2015-07-24,2:07:19,1:01:13,2:35:18,1:03:07,0:30:27,0:15:17,8:25:46,2185,588,17
+Emergency Medicine,PGY1,S0169395,2014-12-01,2:51:12,0:04:47,0:37:04,0:17:06,0:14:48,0:17:14,4:43:04,1558,0,23
+General Medicine,PGY1,S0182038,2016-10-08,1:39:06,1:06:25,0:00:09,0:36:42,0:12:34,0:02:00,4:15:01,1200,0,15
+Night Team,PGY1,S0181067,2016-04-30,0:47:42,0:39:26,0:00:00,0:10:27,0:09:07,0:03:25,1:53:50,824,0,22
+General Medicine,PGY1,S0182038,2016-10-03,1:05:39,0:57:46,0:00:05,0:16:58,0:15:56,0:08:30,3:14:52,737,1,10
+General Medicine,PGY1,S0182038,2016-10-02,0:05:03,0:00:41,0:00:00,0:00:00,0:00:00,0:00:58,0:07:29,39,0,6
+General Medicine,PGY1,S0182038,2016-10-01,0:00:28,0:00:06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:36,11,0,4
+General Medicine,PGY1,S0182038,2016-10-07,1:05:03,0:26:38,0:00:08,0:12:49,0:11:44,0:02:16,2:12:52,758,0,15
+General Medicine,PGY1,S0182038,2016-10-06,2:04:10,1:27:43,0:00:11,0:44:51,0:13:50,0:00:19,5:12:12,1166,16,11
+General Medicine,PGY1,S0182038,2016-10-05,0:26:34,0:16:51,0:00:08,0:00:18,0:02:01,0:02:02,1:07:12,337,21,7
+General Medicine,PGY1,S0182038,2016-10-04,0:52:12,0:37:11,0:00:07,0:47:56,0:07:50,0:08:48,3:07:20,871,53,8
+Night Team,PGY2+,S0169381,2016-07-21,3:09:09,1:01:35,0:00:13,0:29:54,0:28:30,0:01:41,5:45:19,1395,0,23
+Night Team,PGY1,S0169395,2015-04-01,1:49:20,0:29:51,1:21:43,0:27:52,0:11:38,0:15:48,4:49:20,942,0,23
+Night Team,PGY2+,S0169381,2016-07-23,2:51:46,0:43:09,0:01:33,0:43:23,0:04:18,0:05:03,4:56:30,1182,0,38
+Night Team,PGY1,S0169395,2015-04-03,3:00:57,0:13:56,1:15:06,0:33:08,0:19:49,0:18:54,5:58:50,1444,0,48
+Night Team,PGY1,S0169395,2015-04-04,3:29:47,0:28:40,2:06:57,1:04:46,0:34:55,0:26:41,8:32:49,1920,0,50
+Night Team,PGY2+,S0169381,2016-07-24,1:18:13,0:41:11,0:00:19,0:23:18,0:07:16,0:01:36,3:00:18,760,0,12
+Night Team,PGY1,S0169395,2015-04-06,2:40:21,0:25:38,0:25:50,0:40:35,0:36:02,0:26:38,5:34:33,1460,0,41
+Night Team,PGY1,S0169395,2015-04-07,1:54:55,0:30:29,1:43:04,0:15:51,0:21:43,0:15:23,5:07:18,1196,0,36
+Night Team,PGY1,S0169395,2015-04-08,1:35:19,0:07:42,0:21:28,0:18:03,0:22:15,0:11:38,3:04:22,910,0,47
+Night Team,PGY1,S0169395,2015-04-09,3:21:23,0:28:10,1:09:08,0:54:11,0:20:58,0:39:49,7:21:38,1770,0,52
+Night Team,PGY1,S0181070,2015-10-01,1:24:08,1:40:16,1:48:43,0:27:50,0:18:11,0:34:55,6:34:12,1670,0,52
+Night Team,PGY1,S0181070,2015-10-07,2:05:27,0:34:55,0:06:25,0:13:11,0:08:54,0:31:26,3:42:33,1212,0,26
+Night Team,PGY1,S0181070,2015-10-06,1:11:26,1:17:54,1:10:05,0:34:23,0:14:33,0:21:56,5:04:21,1517,0,31
+Night Team,PGY1,S0181070,2015-10-05,1:21:04,0:18:50,0:01:12,0:32:50,0:07:39,1:05:49,3:32:35,799,0,37
+Night Team,PGY1,S0181070,2015-10-04,1:24:23,0:43:27,1:58:41,0:25:14,0:18:46,0:43:24,5:59:55,1476,0,24
+General Medicine,PGY1,S0163662,2015-03-08,2:07:03,0:26:25,0:13:31,1:08:11,0:24:50,0:13:20,4:46:04,1353,22,5
+General Medicine,PGY1,S0195203,2016-07-21,0:49:37,0:48:29,0:00:36,0:10:23,0:15:14,0:05:40,2:41:43,1068,301,12
+General Medicine,PGY1,S0195203,2016-07-20,2:08:09,1:18:29,0:00:11,0:39:17,0:36:46,0:04:09,5:31:20,1566,292,10
+General Medicine,PGY1,S0195203,2016-07-23,2:19:55,1:08:55,0:00:08,1:03:07,0:32:30,0:09:38,5:52:09,1417,160,11
+General Medicine,PGY1,S0195203,2016-07-22,1:15:46,1:03:22,0:00:18,0:33:25,0:20:33,0:02:04,3:59:54,1210,180,13
+Emergency Medicine,PGY1,S0169395,2014-11-20,4:59:35,0:02:41,2:12:11,0:15:38,0:18:07,0:23:34,9:32:30,2537,0,47
+Emergency Medicine,PGY1,S0169395,2014-11-23,5:20:13,0:18:39,1:54:27,0:14:10,0:24:49,0:41:45,9:35:37,2723,0,49
+Emergency Medicine,PGY1,S0169395,2014-11-22,4:38:20,0:01:16,2:14:32,0:22:27,0:30:56,0:32:58,9:15:25,3106,0,33
+Emergency Medicine,PGY1,S0169395,2014-11-27,3:43:10,0:05:00,1:57:14,0:34:10,0:22:43,0:27:14,7:39:48,2045,0,35
+Emergency Medicine,PGY1,S0169395,2014-11-26,3:38:13,0:03:25,1:41:22,0:20:56,0:33:00,0:26:06,7:26:55,2867,0,34
+Emergency Medicine,PGY1,S0169395,2014-11-29,1:40:21,0:03:28,0:52:18,0:16:47,0:05:26,0:06:41,3:25:03,1140,0,34
+General Medicine,PGY2+,S0093383,2016-11-08,0:35:16,0:31:41,0:00:13,0:04:38,0:05:43,0:00:18,1:25:45,725,247,7
+General Medicine,PGY1,S0181074,2016-03-18,1:22:45,1:03:43,0:12:36,0:50:11,0:13:41,0:08:20,4:53:07,1610,91,13
+General Medicine,PGY1,S0163662,2015-03-09,3:09:43,1:09:03,2:03:29,1:18:00,0:27:19,0:45:29,9:19:00,2429,0,10
+Night Team,PGY2+,S0169402,2016-03-24,1:49:37,1:02:06,0:00:10,0:20:47,0:14:42,0:02:34,3:51:44,1282,242,18
+Intensive Care Unit,PGY2+,S0181059,2016-07-30,0:26:59,0:01:14,0:00:00,0:00:00,0:00:00,0:00:27,0:28:58,124,0,10
+Intensive Care Unit,PGY2+,S0181059,2016-07-31,4:01:00,2:15:19,0:06:08,0:24:23,0:38:17,0:15:30,8:16:24,4836,0,15
+Intensive Care Unit,PGY2+,S0181087,2016-08-07,0:08:15,0:15:42,0:00:00,0:01:29,0:00:00,0:00:02,0:25:55,183,154,16
+Intensive Care Unit,PGY2+,S0181087,2016-08-06,2:57:55,1:01:12,0:16:26,0:32:06,0:15:47,0:11:43,5:58:44,1790,9,19
+Intensive Care Unit,PGY2+,S0181087,2016-08-05,2:55:54,1:52:09,0:02:39,1:13:19,0:31:51,0:26:30,7:41:37,3360,7,36
+Intensive Care Unit,PGY2+,S0181087,2016-08-04,2:46:24,0:34:09,0:09:34,0:38:37,0:28:33,0:09:56,5:24:17,1685,0,22
+Intensive Care Unit,PGY2+,S0181087,2016-08-03,3:31:12,2:02:56,0:11:27,0:35:25,0:35:47,0:13:08,7:48:10,2872,0,23
+Intensive Care Unit,PGY2+,S0181087,2016-08-02,2:57:27,1:16:23,0:05:39,0:16:51,0:21:45,0:13:25,5:37:38,1747,239,23
+Intensive Care Unit,PGY2+,S0181087,2016-08-01,4:11:21,1:47:18,0:00:27,0:53:04,0:40:25,0:29:37,9:03:11,3024,0,21
+General Medicine,PGY1,S0195230,2016-09-28,1:46:16,0:38:32,0:03:16,0:37:19,0:10:39,0:00:18,3:25:57,995,102,9
+Intensive Care Unit,PGY2+,S0181087,2016-08-09,3:25:34,1:33:40,0:03:01,0:55:15,0:27:35,0:33:07,7:58:06,3071,2,38
+Intensive Care Unit,PGY2+,S0181087,2016-08-08,2:45:34,1:13:23,0:08:24,0:21:40,0:28:04,0:03:00,5:51:27,1897,191,23
+General Medicine,PGY1,S0163662,2014-08-24,2:09:48,0:10:03,0:10:22,0:28:36,0:23:57,0:35:07,4:27:35,1328,231,6
+General Medicine,PGY1,S0163662,2014-08-25,2:27:08,0:07:08,0:05:28,0:38:37,0:22:03,0:21:30,4:24:08,1195,0,4
+General Medicine,PGY1,S0163662,2014-08-26,3:13:00,0:16:25,0:11:33,0:56:39,0:17:55,0:33:48,5:57:28,2376,0,11
+General Medicine,PGY1,S0163662,2014-08-27,1:08:42,0:06:59,0:09:32,0:00:00,0:02:30,0:04:02,1:33:59,474,464,8
+General Medicine,PGY1,S0163662,2014-08-20,0:58:04,0:05:37,0:08:39,0:07:34,0:01:23,0:06:12,1:32:49,391,385,6
+General Medicine,PGY1,S0163662,2014-08-21,2:48:54,0:07:13,0:03:39,0:22:01,0:21:58,0:31:03,4:30:49,1430,149,7
+General Medicine,PGY1,S0163662,2014-08-22,2:16:25,0:07:20,0:16:16,0:30:15,0:16:51,0:25:08,4:10:15,1258,182,5
+General Medicine,PGY1,S0163662,2014-08-23,2:16:09,0:12:11,0:05:38,0:28:02,0:25:28,0:49:50,4:35:52,1498,37,10
+Emergency Medicine,PGY1,S0195076,2016-07-28,4:05:42,0:51:04,0:04:17,0:29:48,0:35:02,0:02:30,7:06:35,2623,0,23
+General Medicine,PGY1,S0163662,2014-08-28,4:07:22,0:19:59,0:10:22,0:48:23,0:55:22,0:48:36,7:41:21,1856,70,12
+General Medicine,PGY1,S0163662,2014-08-29,0:47:20,0:13:20,0:00:18,0:13:27,0:05:22,0:19:11,1:43:46,616,94,6
+General Medicine,PGY1,S0195220,2016-08-01,1:14:46,0:50:05,0:00:07,0:35:46,0:11:10,0:05:21,3:36:37,1166,57,10
+Night Team,PGY1,S0163425,2015-02-07,1:53:36,0:17:31,1:10:05,0:09:19,0:21:32,0:29:07,4:44:48,1370,0,46
+Night Team,PGY1,S0163425,2015-02-04,2:32:39,0:30:11,1:39:53,0:20:53,0:41:12,0:35:39,6:44:07,2038,0,48
+Night Team,PGY1,S0163425,2015-02-05,1:59:40,0:19:18,0:32:05,0:16:21,0:22:26,0:49:32,4:34:26,1665,12,35
+General Medicine,PGY1,S0195220,2016-08-05,0:07:47,0:22:23,0:00:01,0:00:41,0:00:41,0:00:03,0:35:56,193,175,11
+General Medicine,PGY1,S0195220,2016-08-04,3:58:57,1:48:03,0:00:12,1:37:53,0:41:02,0:03:54,8:44:44,2179,0,12
+General Medicine,PGY1,S0195220,2016-08-07,2:04:21,1:17:52,0:00:08,1:22:47,0:23:47,0:02:49,5:58:03,1650,14,19
+Night Team,PGY1,S0163425,2015-02-01,2:17:08,0:10:47,0:11:55,0:09:16,0:16:18,0:17:21,3:39:43,1105,0,40
+General Medicine,PGY1,S0195220,2016-08-09,2:04:20,0:55:13,0:00:17,0:37:03,0:12:54,0:02:10,4:42:44,1412,0,12
+General Medicine,PGY1,S0195220,2016-08-08,1:43:07,0:54:34,0:00:07,0:41:02,0:09:12,0:08:44,4:07:13,1373,15,20
+Emergency Medicine,PGY1,S0195076,2016-07-25,1:03:57,0:36:00,0:00:44,0:25:18,0:16:38,0:01:56,3:05:25,1224,83,16
+Night Team,PGY1,S0163425,2015-02-08,1:46:15,0:14:38,2:49:49,0:28:08,0:17:57,0:26:16,6:29:13,1250,0,41
+Night Team,PGY1,S0163425,2015-02-09,3:10:34,0:15:54,0:57:51,0:37:16,0:34:16,0:23:37,6:19:47,1718,0,51
+Emergency Medicine,PGY1,S0195076,2016-07-24,0:32:16,0:07:45,0:00:15,0:07:57,0:03:23,0:00:52,0:57:28,265,0,4
+General Medicine,PGY1,S0195076,2016-07-23,1:59:17,0:16:33,0:01:08,0:18:42,0:22:26,0:01:34,3:20:57,754,0,4
+General Medicine,PGY1,S0195076,2016-07-22,1:21:42,0:41:09,0:00:08,0:16:21,0:26:29,0:10:10,3:14:24,893,0,5
+General Medicine,PGY1,S0195076,2016-07-21,1:40:38,0:32:13,0:00:04,0:28:31,0:16:40,0:01:53,3:30:52,1094,9,5
+General Medicine,PGY1,S0195237,2016-10-19,1:40:28,1:47:13,0:02:53,0:52:49,0:17:11,0:01:44,5:23:34,1651,72,11
+General Medicine,PGY1,S0195237,2016-10-18,2:58:08,1:41:41,0:00:13,1:25:08,0:28:52,0:02:54,7:54:34,1942,23,21
+General Medicine,PGY1,S0195237,2016-10-17,1:38:16,1:27:50,0:00:26,0:31:14,0:15:22,0:01:35,4:27:29,1144,291,16
+General Medicine,PGY1,S0195237,2016-10-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY1,S0195076,2016-07-20,2:35:50,1:25:36,0:00:08,1:18:19,0:34:15,0:06:06,7:07:58,2214,126,20
+General Medicine,PGY2+,S0169389,2015-11-30,4:42:19,2:21:49,0:25:11,0:46:37,0:31:58,1:14:04,10:31:20,2944,65,75
+General Medicine,PGY1,S0169385,2015-05-02,2:53:50,0:49:30,2:03:47,0:19:14,0:10:43,0:20:37,7:03:03,2525,0,10
+General Medicine,PGY1,S0169385,2015-05-03,1:25:48,0:02:18,0:46:50,0:07:08,0:13:17,0:11:14,2:57:41,864,0,5
+General Medicine,PGY1,S0169385,2015-05-01,1:40:13,0:08:25,0:33:35,0:27:10,0:10:26,0:13:17,3:26:57,1011,0,5
+General Medicine,PGY1,S0169385,2015-05-04,2:21:25,0:25:29,1:22:42,0:44:17,0:23:23,0:28:17,5:55:51,1922,0,11
+General Medicine,PGY2+,S0181081,2016-11-03,1:15:49,0:26:35,0:03:57,0:06:55,0:06:24,0:04:26,2:56:08,1063,0,17
+General Medicine,PGY2+,S0181081,2016-11-02,2:15:45,1:41:02,0:00:00,0:00:45,0:04:59,0:03:10,4:32:29,1788,145,18
+General Medicine,PGY2+,S0181081,2016-11-01,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:59,6,0,2
+General Medicine,PGY2+,S0181081,2016-11-04,2:46:25,1:26:06,0:00:37,0:01:50,0:18:35,0:03:43,5:17:04,2674,0,21
+General Medicine,PGY1,S0181526,2016-05-14,2:36:19,0:43:12,0:00:04,0:15:48,0:08:08,0:10:02,4:26:27,938,21,25
+General Medicine,PGY1,S0195236,2016-07-30,2:26:11,1:31:52,0:00:12,1:07:38,0:16:56,0:05:02,6:00:40,1552,0,10
+General Medicine,PGY1,S0195236,2016-07-31,1:23:34,0:43:43,0:00:10,0:23:32,0:15:27,0:00:16,3:17:05,916,0,5
+Intensive Care Unit,PGY2+,S0163425,2015-09-07,3:49:44,0:00:24,1:00:07,0:32:35,0:34:26,0:51:54,7:22:15,2221,0,15
+Intensive Care Unit,PGY2+,S0163425,2015-09-06,0:28:01,0:03:49,0:04:28,0:01:17,0:00:11,0:09:05,0:53:02,335,331,18
+Intensive Care Unit,PGY2+,S0163425,2015-09-05,4:01:55,0:03:25,1:06:34,0:35:20,0:24:04,0:42:54,7:15:11,2057,0,16
+Intensive Care Unit,PGY2+,S0163425,2015-09-04,3:44:06,0:14:12,1:16:51,0:44:06,0:31:39,0:35:12,7:33:09,2280,0,16
+Intensive Care Unit,PGY2+,S0163425,2015-09-03,3:18:38,0:03:54,0:34:17,0:22:15,0:30:08,0:27:59,5:46:55,1841,12,32
+Intensive Care Unit,PGY2+,S0163425,2015-09-02,0:40:27,0:01:12,0:01:54,0:00:32,0:00:07,0:01:12,0:52:15,346,342,21
+Intensive Care Unit,PGY2+,S0163425,2015-09-01,0:00:00,0:05:57,0:00:00,0:00:00,0:00:00,0:01:18,0:08:58,34,30,5
+Intensive Care Unit,PGY2+,S0181078,2016-09-08,2:51:42,0:42:08,0:09:38,0:40:36,0:24:36,0:07:10,5:26:15,1813,203,30
+Intensive Care Unit,PGY2+,S0181078,2016-09-09,2:35:40,1:14:06,0:02:42,0:40:28,0:31:46,0:00:27,5:55:55,1663,341,25
+Intensive Care Unit,PGY2+,S0181078,2016-09-02,2:21:41,0:35:27,0:07:32,0:23:09,0:27:54,0:02:53,4:13:50,1305,0,17
+Intensive Care Unit,PGY2+,S0181078,2016-09-03,4:30:02,0:36:37,0:00:00,1:14:01,0:29:19,0:11:13,7:24:18,2026,0,13
+Intensive Care Unit,PGY2+,S0181078,2016-09-01,2:54:41,0:45:46,0:13:45,0:50:30,0:24:52,0:08:15,5:49:13,1661,156,31
+Intensive Care Unit,PGY2+,S0181078,2016-09-06,1:57:30,0:26:25,0:14:56,0:32:12,0:24:13,0:04:11,4:12:05,1253,0,11
+Intensive Care Unit,PGY2+,S0181078,2016-09-07,3:35:39,0:51:59,0:00:19,0:40:50,0:36:12,0:10:06,6:29:43,1926,0,17
+Intensive Care Unit,PGY2+,S0181078,2016-09-04,1:52:09,0:24:07,0:06:44,0:13:04,0:19:01,0:00:15,3:12:46,1048,0,11
+Intensive Care Unit,PGY2+,S0181078,2016-09-05,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,4
+General Medicine,PGY1,S0181074,2016-03-23,2:26:07,1:21:50,0:00:29,1:30:54,0:19:24,0:08:24,6:34:20,3155,300,23
+General Medicine,PGY1,S0169399,2014-12-16,3:16:02,0:08:39,3:26:52,0:30:09,0:24:58,0:00:36,8:19:49,1575,238,9
+Emergency Medicine,PGY2+,S0172645,2016-10-31,3:37:51,0:17:42,0:01:01,0:14:48,0:43:45,0:05:19,5:27:44,1759,0,54
+Emergency Medicine,PGY2+,S0172645,2016-10-30,3:12:39,0:41:01,0:11:34,0:34:09,0:47:28,0:01:47,6:12:14,1618,0,51
+General Medicine,PGY1,S0094959,2014-12-01,4:18:59,0:20:43,1:08:01,0:25:48,0:20:31,0:33:17,7:20:30,2836,129,26
+General Medicine,PGY2+,S0181079,2016-09-30,2:31:59,0:30:43,0:02:57,0:05:59,0:03:47,0:03:43,4:05:16,1775,953,41
+General Medicine,PGY1,S0169398,2015-01-31,0:01:28,0:03:20,0:00:00,0:00:00,0:00:00,0:01:25,0:06:30,235,219,6
+General Medicine,PGY1,S0169398,2015-01-30,3:53:49,0:21:49,2:41:29,1:07:30,0:19:09,0:17:14,9:17:22,2741,628,9
+Night Team,PGY2+,S0169384,2016-01-22,1:29:59,2:00:39,3:15:18,1:04:59,0:22:12,1:03:01,9:53:15,3942,0,41
+Night Team,PGY2+,S0169384,2016-01-23,0:57:16,1:48:47,2:33:54,1:29:33,0:18:28,0:45:09,8:19:53,3108,152,19
+Night Team,PGY2+,S0169401,2015-11-11,2:25:36,1:07:38,1:07:08,0:28:06,0:22:28,0:18:10,6:02:58,2425,0,54
+Night Team,PGY2+,S0169401,2015-11-10,3:02:57,0:53:40,0:40:57,1:02:38,0:13:02,0:28:57,6:56:43,2689,0,55
+Night Team,PGY2+,S0169401,2015-11-13,1:07:53,0:52:09,1:05:43,0:41:52,0:09:53,0:09:23,4:32:52,2319,535,26
+Night Team,PGY2+,S0169401,2015-11-12,0:42:48,0:46:33,1:14:30,0:51:01,0:10:24,0:07:46,4:16:41,1717,264,30
+Intensive Care Unit,PGY2+,S0108268,2016-07-19,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:05,13,0,9
+Night Team,PGY2+,S0169401,2015-11-14,1:04:07,0:14:41,1:15:05,0:36:37,0:05:24,0:17:44,3:52:51,1278,0,14
+General Medicine,PGY1,S0108268,2014-09-17,2:23:46,0:08:03,0:07:15,0:11:52,0:31:02,0:52:55,4:35:40,1979,249,13
+Intensive Care Unit,PGY2+,S0169398,2016-04-28,2:06:33,0:30:34,0:08:34,0:32:55,0:27:32,0:00:41,4:33:47,1421,0,16
+General Medicine,PGY1,S0176040,2016-03-24,2:14:21,1:06:02,0:00:07,0:31:44,0:25:00,0:02:12,4:48:51,1800,5,12
+Intensive Care Unit,PGY2+,S0169398,2016-04-26,4:01:35,1:10:11,0:13:17,0:35:03,0:39:44,0:19:48,7:58:14,2096,2,19
+Intensive Care Unit,PGY2+,S0169398,2016-04-27,3:48:34,0:42:30,0:01:49,0:48:42,0:32:42,0:09:38,7:12:49,2238,30,24
+Intensive Care Unit,PGY2+,S0169398,2016-04-24,2:36:55,0:29:26,0:06:04,0:48:08,0:31:37,0:07:16,5:21:15,1580,27,17
+Intensive Care Unit,PGY2+,S0169398,2016-04-25,0:05:34,0:11:13,0:00:27,0:01:56,0:00:02,0:00:01,0:35:10,212,192,18
+Intensive Care Unit,PGY2+,S0169398,2016-04-22,2:46:07,0:31:18,0:00:09,0:28:54,0:34:28,0:08:17,4:55:19,1837,16,23
+General Medicine,PGY1,S0181069,2015-10-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,1
+Intensive Care Unit,PGY2+,S0169398,2016-04-20,2:35:38,0:57:58,0:04:34,0:53:26,0:13:55,0:00:33,6:05:10,1553,0,27
+Intensive Care Unit,PGY2+,S0169398,2016-04-21,0:36:08,0:15:05,0:03:24,0:00:26,0:00:09,0:00:04,1:31:23,577,555,21
+General Medicine,PGY1,S0108268,2014-12-11,2:39:12,0:06:36,1:20:33,0:30:01,0:25:55,0:22:18,5:51:21,1599,871,12
+General Medicine,PGY1,S0108268,2014-12-10,3:39:10,0:14:52,0:27:09,0:21:59,0:07:36,0:34:05,5:45:24,1348,1309,20
+General Medicine,PGY1,S0108268,2014-12-13,2:05:26,0:10:22,1:01:10,0:19:16,0:27:17,0:18:29,4:42:38,1396,0,8
+General Medicine,PGY1,S0108268,2014-12-12,2:36:53,0:21:06,0:36:35,0:54:03,0:42:35,0:41:39,6:12:59,1682,300,12
+General Medicine,PGY1,S0108268,2014-12-15,3:00:11,0:04:12,0:52:20,0:53:59,1:04:16,0:52:38,7:30:55,2219,192,7
+General Medicine,PGY1,S0108268,2014-12-14,2:05:21,0:16:50,1:25:29,0:41:50,0:58:41,0:52:21,6:56:27,2192,0,8
+General Medicine,PGY1,S0108268,2014-12-17,2:47:40,0:28:05,1:17:29,1:24:02,1:08:23,0:36:44,8:09:22,2690,56,9
+General Medicine,PGY1,S0108268,2014-12-16,1:14:51,0:02:00,0:10:08,0:21:00,0:03:09,0:02:14,2:01:39,543,502,17
+Night Team,PGY1,S0169394,2014-09-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,2
+Night Team,PGY1,S0169394,2014-09-26,2:15:32,0:05:24,0:13:18,0:15:24,0:07:21,0:13:55,3:29:13,947,52,25
+Night Team,PGY1,S0169394,2014-09-25,2:08:44,0:08:32,0:14:00,0:40:42,0:21:20,0:27:12,4:11:34,907,0,27
+Night Team,PGY1,S0169394,2014-09-24,2:52:41,0:00:41,0:08:07,0:10:57,0:15:31,0:23:00,4:04:42,1134,0,58
+Night Team,PGY1,S0169394,2014-09-23,1:23:41,0:00:00,0:09:54,0:03:44,0:04:25,0:11:27,1:54:41,498,20,17
+Night Team,PGY1,S0169394,2014-09-22,2:03:05,0:00:42,0:13:21,0:20:12,0:18:16,0:17:43,3:21:05,828,11,20
+Night Team,PGY1,S0169394,2014-09-21,2:55:58,0:06:44,0:05:59,0:31:09,0:31:15,0:44:35,5:21:05,1604,15,47
+Night Team,PGY1,S0169394,2014-09-20,3:34:57,0:01:41,0:02:59,0:34:06,0:28:28,0:44:40,5:49:02,1775,0,48
+Night Team,PGY2+,S0169385,2015-09-11,1:00:52,0:15:24,0:31:12,0:44:51,0:16:33,0:15:48,3:31:32,1008,38,15
+Night Team,PGY2+,S0169385,2015-09-10,1:14:11,0:10:58,1:33:08,0:40:21,0:33:26,0:25:09,4:57:24,1446,98,19
+Night Team,PGY2+,S0169385,2015-09-13,1:24:43,0:56:33,1:53:26,0:46:25,0:18:21,1:04:40,6:47:34,2504,109,23
+Night Team,PGY2+,S0169385,2015-09-12,1:59:08,0:47:07,2:03:20,0:22:59,0:10:42,0:47:06,6:36:39,2463,95,85
+Night Team,PGY2+,S0169385,2015-09-15,2:00:08,0:40:56,1:16:44,0:51:07,0:29:36,0:47:39,6:46:35,2517,242,37
+Night Team,PGY2+,S0169385,2015-09-14,1:41:23,0:39:42,2:34:37,0:31:29,0:22:12,0:38:34,6:48:22,1498,36,30
+Night Team,PGY2+,S0169385,2015-09-17,1:44:23,1:00:56,1:03:26,0:29:08,0:39:39,0:49:38,6:30:23,2856,175,51
+Night Team,PGY2+,S0169385,2015-09-16,1:47:12,0:47:01,1:42:28,0:41:03,0:17:53,1:03:10,6:44:44,2074,0,26
+Night Team,PGY2+,S0169385,2015-09-19,0:59:22,0:51:29,1:04:31,0:18:25,0:17:13,0:44:06,4:29:04,1383,20,15
+Night Team,PGY2+,S0169385,2015-09-18,2:23:01,0:46:04,1:48:39,0:50:39,0:36:55,1:24:49,8:27:23,2695,216,25
+Emergency Medicine,PGY1,S0169384,2014-09-30,2:01:39,0:09:47,0:10:07,0:10:49,0:13:40,0:07:17,3:21:44,991,110,8
+Night Team,PGY1,S0169395,2015-03-30,1:50:03,0:23:40,1:44:58,0:34:46,0:14:33,0:15:28,5:16:47,1164,0,44
+Night Team,PGY1,S0169395,2015-03-31,2:18:36,0:18:47,1:18:50,0:44:49,0:30:21,0:25:52,6:02:39,1402,0,33
+General Medicine,PGY1,S0094535,2015-01-08,2:39:23,0:13:44,2:20:08,0:55:47,0:22:10,0:44:38,8:05:37,1950,0,20
+General Medicine,PGY1,S0094535,2015-01-09,2:38:07,0:11:12,2:18:04,0:30:14,0:13:52,0:35:43,6:51:42,1695,0,15
+Emergency Medicine,PGY2+,S0172645,2016-10-14,1:34:58,0:26:38,0:03:59,0:11:14,0:30:16,0:00:25,3:11:04,779,0,21
+Emergency Medicine,PGY1,S0195094,2016-09-03,2:58:14,0:30:41,0:05:26,0:36:10,0:58:57,0:00:24,6:01:36,1482,0,35
+Emergency Medicine,PGY1,S0195094,2016-09-02,2:46:19,0:32:34,0:02:26,0:37:23,0:53:39,0:00:58,5:39:52,1436,0,20
+General Medicine,PGY1,S0173803,2016-04-30,1:08:21,0:56:40,0:00:03,0:12:31,0:16:26,0:00:49,2:50:44,945,5,7
+Intensive Care Unit,PGY2+,S0169404,2016-08-28,1:51:00,0:51:13,0:00:41,0:19:49,0:20:41,0:01:47,3:44:55,1307,0,22
+Intensive Care Unit,PGY2+,S0169404,2016-08-29,1:35:00,1:09:15,0:02:12,0:24:03,0:06:00,0:08:46,3:42:12,1788,0,23
+Intensive Care Unit,PGY2+,S0169404,2016-08-22,3:13:31,0:54:23,0:08:50,0:27:07,0:24:50,0:24:21,6:03:46,2310,0,14
+Intensive Care Unit,PGY2+,S0169404,2016-08-23,1:45:31,0:37:55,0:12:57,0:20:29,0:19:12,0:08:51,3:40:38,1962,0,24
+Intensive Care Unit,PGY2+,S0169404,2016-08-20,2:10:33,0:49:03,0:02:01,0:22:13,0:16:49,0:11:40,4:17:02,2466,0,16
+Intensive Care Unit,PGY2+,S0169404,2016-08-21,1:12:36,0:17:09,0:05:24,0:07:49,0:11:00,0:04:28,2:09:50,1175,1,12
+Intensive Care Unit,PGY2+,S0169404,2016-08-26,1:31:04,0:17:12,0:10:31,0:28:04,0:10:55,0:00:31,2:47:36,1043,0,13
+Intensive Care Unit,PGY2+,S0169404,2016-08-27,0:01:46,0:00:03,0:00:00,0:00:01,0:00:00,0:00:00,0:02:08,27,0,6
+Intensive Care Unit,PGY2+,S0169404,2016-08-24,3:59:08,1:05:52,0:01:10,0:51:10,0:27:56,0:06:31,6:58:51,3116,1,26
+Intensive Care Unit,PGY2+,S0169404,2016-08-25,1:40:10,0:16:46,0:03:22,0:10:03,0:13:09,0:01:38,2:37:31,1343,0,16
+Intensive Care Unit,PGY2+,S0181059,2016-08-11,2:34:27,0:32:10,0:08:13,0:16:25,0:13:43,0:04:11,4:18:58,1815,0,21
+General Medicine,PGY1,S0181086,2016-05-07,3:12:14,1:26:48,0:00:17,1:00:40,0:29:20,0:12:41,6:56:33,1975,0,13
+General Medicine,PGY1,S0181086,2016-05-06,0:35:36,0:14:13,0:02:36,0:06:03,0:02:00,0:00:10,1:14:22,395,387,6
+General Medicine,PGY1,S0181086,2016-05-05,4:03:53,1:34:11,0:00:08,1:12:38,0:20:42,0:05:03,7:55:45,2446,67,14
+Emergency Medicine,PGY1,S0195094,2016-09-08,2:07:48,0:31:32,0:00:15,0:51:30,0:38:54,0:00:20,4:58:12,1312,0,15
+General Medicine,PGY1,S0181086,2016-05-03,3:22:30,1:36:41,0:00:21,1:12:22,0:21:25,0:04:32,7:11:47,2336,469,10
+General Medicine,PGY1,S0181086,2016-05-02,1:37:20,0:15:14,0:00:00,0:14:01,0:06:39,0:03:03,2:28:42,801,85,9
+General Medicine,PGY1,S0181086,2016-05-01,1:42:41,0:57:17,0:00:15,0:41:50,0:05:53,0:05:32,4:08:28,1285,280,12
+General Medicine,PGY1,S0181086,2016-05-09,0:38:22,0:39:10,0:00:03,0:12:49,0:05:33,0:00:02,1:55:14,931,164,11
+General Medicine,PGY1,S0181086,2016-05-08,1:23:57,1:24:11,0:00:09,0:20:06,0:16:11,0:00:43,4:03:41,1296,234,8
+General Medicine,PGY1,S0181064,2016-05-09,0:01:29,0:00:05,0:00:00,0:00:00,0:00:00,0:00:00,0:01:35,5,5,1
+General Medicine,PGY1,S0181064,2016-05-08,0:57:40,0:43:04,0:00:08,0:16:10,0:06:52,0:00:14,2:26:47,927,35,10
+General Medicine,PGY1,S0169383,2015-01-28,2:48:31,1:31:49,2:53:05,1:00:53,0:36:59,0:46:07,10:26:03,2951,0,14
+Night Team,PGY2+,S0169406,2016-01-18,0:53:07,0:08:02,1:20:41,0:04:41,0:03:56,0:05:06,2:44:58,497,30,20
+Night Team,PGY2+,S0169406,2016-01-19,1:22:29,1:20:40,1:49:46,0:31:44,0:22:01,0:41:04,6:44:31,2228,54,33
+Night Team,PGY2+,S0169406,2016-01-16,1:32:16,0:46:01,1:27:11,0:33:35,0:07:02,0:24:29,5:10:44,1681,0,61
+Night Team,PGY2+,S0169406,2016-01-17,0:37:43,0:25:28,1:40:28,0:02:16,0:08:27,0:12:00,3:15:02,648,106,19
+General Medicine,PGY1,S0181064,2016-05-01,1:27:03,0:53:07,0:00:05,0:37:30,0:15:27,0:05:34,3:46:51,1285,58,4
+Night Team,PGY2+,S0169406,2016-01-15,2:36:24,1:27:57,1:22:53,0:48:55,0:27:48,0:45:50,8:10:12,3001,0,59
+Night Team,PGY2+,S0169406,2016-01-12,0:55:44,0:10:45,0:00:51,0:01:10,0:14:23,0:08:27,1:41:38,673,0,20
+Night Team,PGY2+,S0169406,2016-01-13,1:16:24,0:53:24,0:24:24,0:14:40,0:11:10,0:25:40,3:48:27,1350,0,24
+Night Team,PGY2+,S0169406,2016-01-10,2:22:39,1:00:26,1:41:59,0:25:03,0:16:03,0:29:26,6:45:37,1627,0,36
+Night Team,PGY2+,S0169406,2016-01-11,1:20:03,0:31:23,0:26:37,0:21:09,0:22:27,0:22:40,3:48:04,1238,224,40
+Night Team,PGY1,S0109179,2014-07-30,1:31:00,0:01:43,0:12:44,0:31:57,0:31:40,0:26:42,3:40:38,1112,0,59
+Night Team,PGY2+,S0169390,2016-05-30,3:23:03,2:09:01,0:00:09,0:27:32,0:19:25,0:12:49,6:52:25,2004,6,42
+Night Team,PGY2+,S0169390,2016-05-31,3:26:59,0:34:41,0:00:04,0:09:02,0:12:05,0:05:10,4:55:35,1130,22,66
+Night Team,PGY2+,S0108268,2015-11-28,1:46:24,1:01:20,2:02:19,0:23:50,0:19:02,0:49:00,6:47:27,2079,93,40
+Night Team,PGY2+,S0108268,2015-11-29,1:09:13,0:14:47,0:30:44,0:05:36,0:06:35,0:13:15,2:38:02,717,79,21
+Night Team,PGY2+,S0108268,2015-11-24,1:39:11,1:27:53,2:47:35,0:38:02,0:15:04,0:25:08,7:32:50,2078,238,31
+Night Team,PGY2+,S0108268,2015-11-25,1:50:00,1:24:02,0:48:01,0:13:13,0:05:27,0:26:51,5:48:57,1669,47,33
+Night Team,PGY2+,S0108268,2015-11-26,1:57:04,0:50:26,2:32:21,0:44:14,0:19:55,0:46:04,7:52:25,2107,12,34
+Night Team,PGY2+,S0108268,2015-11-27,2:07:56,1:17:02,1:59:11,0:34:17,0:30:00,0:42:23,7:33:48,2631,0,34
+Night Team,PGY2+,S0108268,2015-11-20,1:40:54,1:20:44,2:41:12,0:37:34,0:19:57,0:32:39,7:51:41,2344,195,34
+Night Team,PGY2+,S0108268,2015-11-21,2:25:24,1:17:33,0:32:50,0:34:19,0:27:44,0:41:23,6:11:20,1777,0,45
+Night Team,PGY2+,S0108268,2015-11-22,1:52:32,1:28:34,2:44:07,0:40:57,0:21:07,1:08:14,8:40:39,2586,0,35
+Night Team,PGY2+,S0108268,2015-11-23,2:25:46,1:21:00,1:25:27,0:20:15,0:12:06,0:30:12,6:32:42,2059,0,51
+General Medicine,PGY1,S0181063,2015-12-16,0:39:14,0:39:40,0:33:59,0:20:09,0:05:58,0:02:29,2:43:36,1035,0,5
+General Medicine,PGY1,S0181063,2015-12-17,0:45:21,1:00:14,1:43:26,0:49:12,0:27:10,0:20:19,5:40:24,1791,0,9
+General Medicine,PGY1,S0181063,2015-12-14,0:26:23,0:43:08,1:48:03,0:18:15,0:12:41,0:14:49,4:13:09,1175,0,10
+General Medicine,PGY1,S0181063,2015-12-15,0:36:28,0:53:17,0:51:26,0:29:04,0:10:22,0:13:36,3:44:53,1394,0,11
+General Medicine,PGY1,S0181063,2015-12-12,0:24:09,0:25:46,0:41:07,0:40:53,0:10:11,0:22:00,3:04:22,977,0,6
+General Medicine,PGY1,S0181063,2015-12-13,0:10:36,0:12:26,0:30:49,0:19:58,0:05:58,0:05:03,1:46:55,634,0,4
+General Medicine,PGY1,S0181063,2015-12-10,0:28:26,0:27:15,0:46:42,0:46:50,0:14:41,0:06:01,3:14:09,1210,0,7
+General Medicine,PGY1,S0181063,2015-12-11,0:24:43,0:18:57,0:45:58,0:13:31,0:14:02,0:02:30,2:24:17,742,0,3
+General Medicine,PGY1,S0181063,2015-12-18,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,8,0,3
+General Medicine,PGY1,S0181063,2015-12-19,0:47:01,0:58:12,2:13:52,0:31:55,0:20:20,0:13:48,6:00:30,1988,0,14
+Night Team,PGY1,S0172085,2015-10-18,0:21:09,0:12:33,0:54:37,0:14:14,0:05:30,0:08:17,2:10:27,619,0,57
+Night Team,PGY1,S0172085,2015-10-19,1:05:35,0:48:54,0:46:33,0:38:52,0:12:20,0:18:49,4:01:52,1984,32,60
+Night Team,PGY2+,S0181065,2016-08-02,3:01:59,0:52:40,0:00:33,0:33:08,0:13:33,0:11:22,5:19:32,2745,188,49
+Night Team,PGY1,S0172085,2015-10-12,1:34:10,0:59:47,2:07:02,0:18:39,0:21:20,0:25:25,6:24:02,2161,0,35
+Night Team,PGY1,S0172085,2015-10-13,0:46:49,1:20:03,1:26:05,0:24:09,0:20:49,0:36:27,5:09:36,2152,466,26
+Night Team,PGY1,S0172085,2015-10-11,1:01:18,1:06:03,1:42:56,0:16:28,0:12:04,0:30:15,5:00:19,1244,0,29
+Night Team,PGY1,S0172085,2015-10-16,1:29:50,0:32:44,1:34:11,0:26:11,0:21:11,0:54:13,5:29:16,1773,0,39
+Night Team,PGY1,S0172085,2015-10-17,0:42:22,0:17:30,1:08:42,0:10:46,0:07:48,0:23:45,3:10:31,837,0,30
+Night Team,PGY1,S0172085,2015-10-14,0:37:17,0:17:37,0:04:58,0:06:46,0:07:34,0:07:18,1:28:25,466,143,33
+Intensive Care Unit,PGY2+,S0079337,2016-06-20,2:02:01,1:00:31,0:11:58,0:36:44,0:29:17,0:03:33,4:50:34,1447,21,17
+Emergency Medicine,PGY1,S0169406,2015-01-08,0:01:38,0:00:08,0:08:28,0:00:56,0:00:15,0:00:00,0:11:44,52,46,4
+General Medicine,PGY1,S0181060,2015-07-20,2:22:23,0:12:52,1:41:03,0:25:29,0:31:25,0:04:34,5:44:35,1162,0,6
+General Medicine,PGY1,S0181060,2015-07-21,3:27:34,0:31:50,1:51:58,1:16:50,0:40:39,0:26:21,8:53:09,2166,10,14
+General Medicine,PGY1,S0181060,2015-07-22,1:35:56,0:07:05,0:28:09,0:49:32,0:23:39,0:20:12,4:28:49,1259,98,11
+General Medicine,PGY1,S0181060,2015-07-23,2:35:26,0:05:52,1:16:49,1:20:03,0:35:49,0:19:39,6:32:35,2011,0,7
+General Medicine,PGY1,S0181060,2015-07-24,2:59:18,0:27:33,1:58:58,0:40:51,0:15:20,0:32:23,7:34:22,1911,0,9
+General Medicine,PGY1,S0181060,2015-07-25,1:54:36,0:07:32,0:59:44,1:13:53,0:35:57,0:29:20,5:45:57,1697,0,7
+General Medicine,PGY1,S0181060,2015-07-26,2:59:42,0:33:46,1:44:54,0:51:14,0:40:42,0:36:52,8:00:17,2544,102,14
+Night Team,PGY2+,S0181083,2016-09-10,0:01:12,0:00:12,0:00:00,0:00:00,0:00:00,0:00:22,0:01:53,19,0,3
+General Medicine,PGY1,S0181060,2015-07-28,2:40:52,0:22:04,2:39:03,1:02:57,0:28:25,0:16:26,8:37:08,2432,102,22
+General Medicine,PGY1,S0181060,2015-07-29,2:34:40,0:16:32,1:03:23,0:43:48,0:45:17,0:21:20,6:40:38,1950,23,12
+Night Team,PGY2+,S0181065,2016-08-04,4:24:11,0:53:53,0:01:19,0:46:18,0:12:42,0:15:37,7:11:41,2882,563,51
+General Medicine,PGY1,S0181088,2016-06-11,3:02:07,1:12:32,0:00:09,1:23:07,0:06:52,0:01:25,6:34:20,1955,12,14
+General Medicine,PGY1,S0173803,2016-01-19,1:33:06,0:37:49,0:53:49,0:21:13,0:18:13,0:15:05,4:23:00,1550,14,16
+General Medicine,PGY1,S0108268,2015-01-03,0:00:00,0:00:00,0:00:00,0:00:39,0:00:00,0:00:00,0:00:39,10,0,4
+General Medicine,PGY1,S0108268,2015-01-02,2:05:37,0:09:53,0:54:43,0:25:38,0:21:53,0:29:43,4:48:12,2040,74,24
+General Medicine,PGY1,S0108268,2015-01-01,3:09:22,0:17:08,1:22:59,0:41:45,0:29:48,1:00:23,7:40:37,2661,0,15
+Night Team,PGY1,S0169381,2015-01-31,1:51:51,0:05:17,0:59:41,0:27:26,0:19:49,0:22:33,4:16:04,703,91,13
+Emergency Medicine,PGY2+,S0079337,2016-02-14,0:37:41,0:28:44,2:01:21,0:18:11,0:27:19,0:36:53,4:55:25,1954,33,15
+Emergency Medicine,PGY2+,S0079337,2016-02-15,1:29:36,0:42:36,2:47:45,1:01:48,1:23:50,0:53:49,8:57:47,4366,0,28
+Night Team,PGY1,S0169381,2015-01-30,1:54:16,0:03:58,0:02:03,0:30:11,0:25:43,0:36:54,3:46:35,900,0,33
+Emergency Medicine,PGY2+,S0079337,2016-02-10,0:21:06,0:06:44,0:38:10,0:09:00,0:09:30,0:09:50,1:36:13,657,0,9
+Emergency Medicine,PGY2+,S0079337,2016-02-11,0:51:48,0:33:37,2:26:54,0:26:22,0:49:46,0:55:42,6:23:17,2704,0,28
+Emergency Medicine,PGY2+,S0079337,2016-02-12,1:13:11,0:25:46,3:29:59,0:33:54,0:50:43,0:38:27,7:40:54,2717,21,24
+Emergency Medicine,PGY2+,S0079337,2016-02-13,1:23:52,0:46:07,3:02:41,0:41:47,0:33:03,0:48:35,7:53:56,3152,14,30
+Emergency Medicine,PGY2+,S0079337,2016-02-18,1:15:04,0:29:40,3:31:27,0:39:30,0:58:34,0:48:46,8:12:23,3583,0,25
+Emergency Medicine,PGY2+,S0079337,2016-02-19,1:14:38,1:03:05,1:58:51,0:26:00,0:29:39,0:50:41,6:23:14,2926,0,29
+General Medicine,PGY1,S0186952,2016-10-13,3:32:38,0:55:23,0:00:15,1:22:25,0:36:27,0:06:24,7:41:00,2113,0,17
+General Medicine,PGY1,S0186952,2016-10-12,0:24:26,0:16:11,0:00:01,0:00:20,0:00:21,0:00:35,0:52:40,266,175,27
+General Medicine,PGY1,S0186952,2016-10-11,1:17:34,0:30:18,0:03:53,0:54:26,0:14:13,0:03:47,3:43:36,1128,0,13
+General Medicine,PGY1,S0186952,2016-10-10,3:03:47,1:18:59,0:00:06,0:41:56,0:12:06,0:04:31,6:13:49,1612,117,14
+General Medicine,PGY1,S0186952,2016-10-15,0:00:36,0:00:13,0:00:00,0:00:00,0:00:00,0:00:00,0:01:00,24,0,7
+General Medicine,PGY1,S0186952,2016-10-14,1:56:37,1:24:08,0:07:30,0:37:42,0:17:39,0:02:09,5:09:54,1963,526,17
+Intensive Care Unit,PGY2+,S0169395,2015-11-11,2:02:11,1:32:58,0:21:01,0:27:12,0:20:00,0:18:22,5:30:35,1798,0,20
+Intensive Care Unit,PGY2+,S0169395,2015-11-10,0:02:00,0:06:16,0:00:34,0:00:00,0:00:00,0:00:00,0:08:55,94,78,14
+Intensive Care Unit,PGY2+,S0169395,2015-11-13,1:41:52,0:57:42,0:17:22,0:17:49,0:21:50,0:29:48,4:22:01,1895,80,45
+Intensive Care Unit,PGY2+,S0169395,2015-11-12,0:41:47,0:25:59,0:09:38,0:06:13,0:08:15,0:11:06,1:55:18,633,0,14
+Intensive Care Unit,PGY2+,S0172056,2016-11-05,0:00:00,0:00:00,0:00:00,0:00:25,0:00:00,0:00:00,0:00:25,6,0,3
+Intensive Care Unit,PGY2+,S0169395,2015-11-14,2:24:13,0:54:00,0:12:21,0:43:29,0:40:51,0:47:12,6:10:58,2111,27,29
+Night Team,PGY1,S0181525,2016-05-25,1:27:23,0:50:57,0:00:00,0:33:32,0:24:00,0:05:26,3:34:32,994,0,26
+Night Team,PGY1,S0181525,2016-05-24,1:51:40,0:20:11,0:00:02,0:49:15,0:18:10,0:00:16,3:33:19,997,0,24
+Emergency Medicine,PGY1,S0188098,2016-08-28,4:24:55,1:05:58,0:03:25,0:37:19,0:37:24,0:03:18,7:55:31,2336,0,46
+Emergency Medicine,PGY1,S0188098,2016-08-29,4:26:59,1:02:42,0:02:51,0:13:22,0:29:43,0:05:09,7:19:20,2721,0,32
+Emergency Medicine,PGY1,S0169404,2014-12-29,0:00:00,0:14:19,0:00:00,0:00:00,0:00:00,0:01:51,0:16:44,86,76,9
+Emergency Medicine,PGY1,S0169404,2014-12-28,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,10,0,1
+Night Team,PGY1,S0181525,2016-05-27,2:40:02,0:57:09,0:04:24,0:34:00,0:18:58,0:01:25,4:52:06,1389,0,38
+Night Team,PGY1,S0195206,2016-08-18,1:41:08,0:32:39,0:00:00,0:07:34,0:13:26,0:08:07,2:55:06,1081,0,30
+Night Team,PGY1,S0195206,2016-08-19,3:58:58,1:25:10,0:00:02,0:34:36,0:35:31,0:01:16,7:12:33,2401,99,42
+Emergency Medicine,PGY1,S0169404,2014-12-23,4:04:11,0:15:37,2:45:11,0:30:54,0:37:04,0:21:44,8:55:26,3308,17,15
+Emergency Medicine,PGY1,S0169404,2014-12-22,1:04:59,0:11:27,1:54:25,0:07:30,0:11:45,0:08:54,3:51:59,1237,64,9
+Emergency Medicine,PGY1,S0169404,2014-12-21,2:37:39,0:13:23,2:35:58,0:31:15,0:30:38,0:25:23,7:18:01,2899,0,23
+Emergency Medicine,PGY1,S0181086,2015-09-14,1:59:44,0:05:03,4:06:57,0:18:46,0:04:21,0:12:43,7:14:15,1025,410,13
+Emergency Medicine,PGY1,S0169404,2014-12-27,3:30:43,0:35:33,3:34:43,0:28:27,0:24:03,0:29:09,9:30:38,3797,79,25
+Emergency Medicine,PGY1,S0188098,2016-08-25,4:09:43,0:45:00,0:09:53,0:23:30,0:19:55,0:02:08,6:36:05,2015,107,22
+Emergency Medicine,PGY1,S0188098,2016-08-26,2:09:30,0:25:46,0:01:39,0:08:56,0:13:58,0:01:33,3:28:23,1037,0,16
+Emergency Medicine,PGY1,S0169404,2014-12-24,0:00:00,0:05:41,0:00:00,0:00:00,0:00:00,0:00:00,0:06:57,52,42,4
+Night Team,PGY1,S0181525,2016-05-29,1:15:29,0:43:18,0:00:03,0:38:55,0:09:38,0:00:16,3:15:24,736,0,26
+Night Team,PGY1,S0181525,2016-05-28,2:31:25,1:02:12,0:01:59,0:33:54,0:16:27,0:07:58,5:08:08,1363,0,45
+General Medicine,PGY1,S0195202,2016-06-30,2:50:53,1:41:11,0:00:26,0:48:58,0:24:07,0:07:23,6:32:24,1987,466,12
+General Medicine,PGY1,S0169399,2015-04-24,0:10:23,0:00:00,0:00:26,0:00:24,0:00:02,0:00:08,0:11:26,66,44,6
+General Medicine,PGY1,S0169399,2015-04-25,2:08:50,0:47:54,4:44:21,0:21:52,0:21:12,0:41:28,9:48:47,1482,0,36
+General Medicine,PGY1,S0169399,2015-04-23,2:38:19,0:04:20,2:08:50,0:38:04,0:33:34,0:03:35,6:36:19,1510,166,16
+Night Team,PGY1,S0169384,2015-04-24,0:51:04,0:00:00,0:01:32,0:00:00,0:00:00,0:00:02,0:53:16,190,186,7
+Night Team,PGY1,S0169384,2015-04-25,3:15:10,0:08:41,1:31:41,0:29:58,0:30:07,0:29:03,6:42:14,1618,198,7
+Night Team,PGY1,S0169384,2015-04-20,1:19:58,0:03:19,0:08:12,0:22:34,0:20:58,0:29:37,3:01:04,850,58,23
+Night Team,PGY1,S0169384,2015-04-21,2:10:02,0:28:17,0:50:46,0:45:27,0:38:02,0:33:47,5:53:30,1789,0,28
+Night Team,PGY1,S0169384,2015-04-22,2:10:05,0:30:29,1:25:56,0:26:47,0:29:58,0:52:26,6:14:27,1663,0,28
+Night Team,PGY1,S0169384,2015-04-23,1:05:34,0:08:15,0:09:35,0:09:38,0:10:56,0:11:33,1:57:18,556,0,18
+General Medicine,PGY1,S0172645,2016-01-29,1:06:02,1:22:14,2:52:25,0:50:02,0:26:08,0:12:51,7:13:48,2213,0,15
+General Medicine,PGY1,S0195206,2016-11-10,1:59:24,0:57:50,0:00:39,0:46:21,0:16:36,0:00:26,4:40:49,1553,71,15
+General Medicine,PGY2+,S0169384,2016-06-09,2:59:20,2:12:22,0:05:26,0:34:28,0:14:15,0:35:39,7:05:27,3124,571,27
+General Medicine,PGY2+,S0169384,2016-06-08,2:03:13,1:28:58,0:00:31,0:35:51,0:28:40,0:08:12,5:11:16,1575,355,20
+General Medicine,PGY2+,S0169384,2016-06-07,1:32:01,1:46:27,0:00:38,0:17:47,0:07:39,0:09:57,4:25:02,1723,406,20
+Intensive Care Unit,PGY2+,S0163425,2015-08-26,2:57:42,0:06:44,0:33:56,0:38:06,0:34:20,0:48:49,6:00:46,1898,18,21
+General Medicine,PGY2+,S0169384,2016-06-05,2:07:32,1:43:18,0:00:36,1:12:34,0:39:01,0:03:01,6:23:51,1968,535,16
+General Medicine,PGY2+,S0169384,2016-06-04,2:54:58,1:33:19,0:01:28,0:51:34,0:32:19,0:11:38,6:50:03,2691,25,27
+General Medicine,PGY2+,S0169384,2016-06-03,2:10:38,2:05:18,0:01:58,1:05:58,0:26:12,0:09:00,6:38:54,2378,380,17
+General Medicine,PGY2+,S0169384,2016-06-02,2:49:08,1:59:23,0:03:37,0:46:33,0:23:21,0:02:58,6:38:05,2435,284,18
+General Medicine,PGY2+,S0169384,2016-06-01,0:00:07,0:04:54,0:00:00,0:00:03,0:00:00,0:00:00,0:05:51,29,27,2
+Intensive Care Unit,PGY2+,S0163425,2015-08-27,1:58:32,0:04:14,0:34:35,0:06:29,0:10:29,0:03:27,3:21:11,1093,35,17
+Emergency Medicine,PGY2+,S0169390,2015-08-21,4:49:33,0:27:11,2:40:16,0:13:04,0:23:29,0:30:00,9:53:07,2614,0,30
+General Medicine,PGY1,S0172645,2016-01-23,0:00:00,0:00:06,0:00:00,0:00:00,0:00:00,0:01:20,0:01:31,31,27,2
+General Medicine,PGY1,S0195216,2016-09-14,1:19:19,1:35:35,0:00:14,0:17:41,0:05:18,0:02:25,3:41:22,1401,490,9
+General Medicine,PGY1,S0195216,2016-09-15,2:35:36,1:41:47,0:00:55,1:02:03,0:28:00,0:02:17,6:28:56,2389,228,14
+General Medicine,PGY1,S0195216,2016-09-16,0:00:14,0:00:00,0:00:00,0:00:26,0:00:00,0:00:08,0:01:59,27,0,6
+General Medicine,PGY1,S0195216,2016-09-17,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0195216,2016-09-10,2:47:29,1:44:31,0:00:07,0:37:52,0:37:45,0:01:42,6:21:48,2723,0,21
+General Medicine,PGY1,S0195216,2016-09-11,1:40:38,1:10:23,0:00:06,0:32:33,0:13:49,0:00:21,4:10:24,1759,0,15
+General Medicine,PGY1,S0195216,2016-09-12,1:02:32,1:04:38,0:00:12,0:20:51,0:05:53,0:00:21,2:55:02,1145,0,11
+General Medicine,PGY1,S0195216,2016-09-13,2:26:07,2:26:38,0:00:12,0:42:41,0:21:18,0:03:11,6:37:12,2488,0,22
+Night Team,PGY1,S0181065,2016-05-13,4:07:41,0:57:49,0:01:11,0:15:39,0:26:09,0:11:34,6:47:50,2394,96,38
+Night Team,PGY1,S0181065,2016-05-12,4:02:34,0:33:00,0:00:00,0:26:38,0:23:52,0:10:04,6:10:07,2263,10,43
+Night Team,PGY1,S0181065,2016-05-11,4:13:16,0:33:43,0:00:30,0:21:41,0:29:56,0:24:10,6:55:42,2492,49,27
+Night Team,PGY1,S0181065,2016-05-10,4:07:51,0:49:26,0:00:14,0:39:33,0:36:32,0:34:00,7:21:11,2603,21,23
+Night Team,PGY1,S0181065,2016-05-17,3:41:34,0:28:26,0:00:00,0:21:03,0:22:46,0:02:31,5:18:06,2390,0,142
+Night Team,PGY1,S0181065,2016-05-16,4:16:35,0:44:49,0:00:04,0:25:29,0:25:36,0:01:43,6:16:46,2411,220,41
+Night Team,PGY1,S0181065,2016-05-15,1:28:01,0:19:03,0:00:29,0:08:37,0:13:02,0:01:49,2:19:03,953,61,25
+Night Team,PGY1,S0181065,2016-05-14,0:37:43,0:12:21,0:00:00,0:04:58,0:03:12,0:02:54,1:07:37,675,0,110
+Night Team,PGY1,S0181065,2016-05-19,4:11:05,0:41:50,0:00:28,0:31:24,0:25:31,0:02:56,6:31:11,2598,0,115
+Night Team,PGY1,S0181065,2016-05-18,2:59:23,0:40:35,0:00:33,0:27:35,0:20:19,0:13:20,5:21:15,2452,296,68
+General Medicine,PGY1,S0163425,2014-09-30,3:24:47,0:14:34,0:15:43,0:35:48,0:18:42,0:27:26,5:42:55,1547,0,8
+General Medicine,PGY1,S0195207,2016-09-05,2:00:40,1:32:40,0:00:21,0:43:47,0:19:51,0:03:13,5:11:50,1710,8,16
+General Medicine,PGY1,S0195207,2016-09-04,0:00:19,0:00:05,0:00:00,0:00:21,0:00:00,0:00:00,0:00:46,16,0,5
+General Medicine,PGY1,S0195207,2016-09-07,1:07:19,0:41:17,0:00:18,0:23:05,0:11:59,0:01:09,2:50:30,985,13,8
+Night Team,PGY2+,S0181056,2016-09-30,3:10:02,2:00:14,0:00:09,1:10:05,0:20:36,0:10:45,7:39:19,2272,104,32
+General Medicine,PGY1,S0195207,2016-09-01,0:00:59,0:00:07,0:00:00,0:00:55,0:00:00,0:00:27,0:02:42,23,0,3
+General Medicine,PGY1,S0195207,2016-09-03,2:30:33,2:11:35,0:00:09,0:26:40,0:09:44,0:00:12,5:55:25,1761,0,15
+General Medicine,PGY1,S0195207,2016-09-02,2:49:36,1:31:43,0:00:25,0:27:29,0:20:22,0:08:31,6:04:34,2060,0,33
+Intensive Care Unit,PGY2+,S0079337,2016-06-22,2:13:16,0:40:17,0:07:22,0:18:43,0:22:20,0:05:18,4:07:04,1390,48,18
+General Medicine,PGY1,S0195207,2016-09-09,0:50:32,0:47:53,0:00:20,0:14:25,0:07:54,0:01:28,2:37:19,957,0,10
+General Medicine,PGY1,S0195207,2016-09-08,2:26:38,2:09:53,0:00:23,0:29:05,0:16:09,0:07:42,6:01:33,2060,32,17
+General Medicine,PGY1,S0181056,2016-03-26,1:46:47,0:58:34,0:00:20,0:54:20,0:10:02,0:00:15,4:26:29,1446,33,14
+Intensive Care Unit,PGY2+,S0056755,2016-05-30,1:00:03,0:32:36,0:00:05,0:58:21,0:27:12,0:03:12,3:23:18,928,6,14
+Intensive Care Unit,PGY2+,S0056755,2016-05-31,1:16:51,0:46:28,0:11:56,0:15:07,0:23:27,0:03:56,3:14:02,1032,0,14
+General Medicine,PGY1,S0181056,2016-03-21,2:55:13,0:41:46,0:00:17,1:20:29,0:24:41,0:00:30,6:46:30,2389,48,24
+Night Team,PGY2+,S0094535,2016-06-14,4:02:17,1:19:58,0:00:08,0:29:37,0:09:23,0:04:22,6:29:23,1840,0,46
+Intensive Care Unit,PGY2+,S0169390,2015-10-19,1:43:26,1:11:52,0:05:36,0:35:20,0:27:03,0:19:44,4:53:36,1559,0,13
+Intensive Care Unit,PGY2+,S0169390,2015-10-18,2:19:10,0:35:39,0:09:55,0:31:31,0:31:55,0:28:37,5:06:55,1242,149,10
+Emergency Medicine,PGY1,S0181526,2015-07-14,3:42:44,0:34:28,2:35:27,0:15:57,0:45:06,1:11:40,9:58:09,2446,5,28
+Night Team,PGY1,S0195079,2016-10-27,2:33:54,0:44:44,0:00:10,0:35:43,0:23:31,0:00:29,4:38:37,1178,0,45
+General Medicine,PGY1,S0181082,2016-03-30,2:32:11,1:53:35,0:00:19,0:42:42,0:23:38,0:15:36,6:34:42,2191,0,10
+General Medicine,PGY1,S0181082,2016-03-31,0:00:00,0:00:00,0:00:00,0:01:44,0:00:00,0:00:00,0:03:17,15,0,5
+Intensive Care Unit,PGY2+,S0169403,2015-12-02,2:01:26,1:04:11,0:05:00,0:42:35,0:31:13,0:47:55,5:49:18,1912,160,17
+Emergency Medicine,PGY1,S0181526,2015-07-15,4:49:45,0:41:34,1:15:58,0:16:55,0:22:08,0:52:07,9:01:13,2326,9,14
+Intensive Care Unit,PGY2+,S0181088,2016-10-08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,3
+Intensive Care Unit,PGY2+,S0181088,2016-10-03,1:38:48,0:15:43,0:10:14,0:11:54,0:17:51,0:02:01,2:51:23,769,0,14
+Intensive Care Unit,PGY2+,S0181088,2016-10-02,2:11:59,0:34:40,0:04:51,0:08:28,0:15:59,0:03:35,3:48:31,1103,0,13
+Intensive Care Unit,PGY2+,S0181088,2016-10-01,0:59:12,0:36:27,0:00:00,0:19:16,0:17:33,0:02:11,2:37:39,1242,0,14
+Intensive Care Unit,PGY2+,S0181088,2016-10-07,2:26:18,0:44:21,0:03:36,0:31:13,0:26:08,0:10:10,4:58:46,1269,0,15
+Intensive Care Unit,PGY2+,S0181088,2016-10-06,2:29:54,0:28:53,0:11:36,0:18:15,0:14:42,0:15:34,4:18:45,1547,311,16
+Intensive Care Unit,PGY2+,S0181088,2016-10-05,2:14:11,0:34:11,0:00:01,0:32:55,0:18:15,0:06:22,4:09:46,1714,0,19
+Intensive Care Unit,PGY2+,S0181088,2016-10-04,3:30:09,0:46:49,0:19:28,0:29:36,0:28:59,0:15:03,6:37:05,1875,0,15
+General Medicine,PGY1,S0093383,2014-09-03,1:05:12,0:03:13,0:06:58,0:04:39,0:01:33,0:12:20,1:35:24,445,435,9
+General Medicine,PGY1,S0093383,2014-09-02,4:47:46,0:12:50,0:40:55,1:03:37,0:33:13,0:30:18,8:21:48,2455,38,11
+General Medicine,PGY1,S0093383,2014-09-01,3:22:33,0:04:53,0:23:55,0:38:40,0:20:38,0:29:30,5:39:53,1583,371,11
+General Medicine,PGY1,S0074196,2016-01-03,0:58:35,0:33:48,0:52:43,0:36:37,0:17:15,0:26:55,4:03:38,1055,0,4
+General Medicine,PGY1,S0093383,2014-09-07,3:00:20,0:18:17,0:38:18,0:20:38,0:33:38,0:27:32,5:33:51,1529,166,18
+General Medicine,PGY1,S0093383,2014-09-06,0:11:04,0:00:25,0:00:14,0:00:00,0:00:00,0:01:55,0:13:50,73,67,3
+General Medicine,PGY1,S0093383,2014-09-05,3:55:11,0:31:03,0:17:17,0:30:46,0:12:12,0:23:26,6:06:46,1557,361,15
+General Medicine,PGY1,S0093383,2014-09-04,3:30:11,0:07:30,0:32:11,0:40:49,0:27:33,0:32:39,6:03:45,1731,29,12
+General Medicine,PGY1,S0074196,2016-01-08,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0074196,2016-01-09,1:15:33,1:33:56,2:07:56,0:32:29,0:20:45,0:16:30,6:41:52,2174,19,13
+General Medicine,PGY1,S0093383,2014-09-09,4:29:31,0:14:40,0:20:36,0:18:32,0:40:52,0:25:46,6:48:08,2065,254,20
+General Medicine,PGY1,S0093383,2014-09-08,2:07:53,0:06:44,0:19:56,0:20:27,0:15:41,0:17:19,3:46:36,1187,122,8
+Night Team,PGY2+,S0169401,2015-08-13,1:56:15,0:54:17,0:39:12,0:55:37,0:19:13,0:29:00,5:36:24,2138,0,51
+Night Team,PGY2+,S0169401,2015-08-12,2:19:59,0:42:30,1:23:10,1:15:45,0:17:44,0:27:34,7:05:17,2683,113,49
+Night Team,PGY2+,S0169401,2015-08-11,0:45:07,0:25:58,0:33:30,0:42:30,0:05:36,0:13:42,3:06:22,1963,0,28
+Night Team,PGY2+,S0169401,2015-08-10,0:53:55,0:21:33,1:32:59,0:42:52,0:05:20,0:10:07,4:13:44,1336,0,17
+General Medicine,PGY1,S0181060,2015-07-30,1:42:16,0:03:58,1:00:46,0:27:46,0:17:45,0:16:20,4:08:13,1102,17,6
+General Medicine,PGY1,S0195075,2016-07-05,0:59:53,0:56:52,0:01:25,0:13:38,0:17:46,0:04:11,2:52:57,677,0,3
+General Medicine,PGY1,S0181086,2016-01-02,0:04:12,0:06:03,0:00:00,0:00:47,0:00:00,0:02:17,0:13:39,106,100,5
+General Medicine,PGY1,S0181086,2016-01-01,1:52:19,1:52:57,3:29:50,0:40:17,0:10:15,0:17:03,9:20:09,2549,2535,21
+General Medicine,PGY1,S0195214,2016-08-04,2:04:12,0:42:04,0:00:18,1:12:07,0:14:13,0:00:51,4:50:20,1328,0,9
+Intensive Care Unit,PGY2+,S0181525,2016-09-25,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,18,0,4
+General Medicine,PGY1,S0181056,2015-11-18,0:18:29,0:02:26,0:00:39,0:00:56,0:00:47,0:04:23,0:31:28,279,273,6
+Emergency Medicine,PGY1,S0181070,2015-10-29,1:19:19,0:23:14,3:40:41,0:27:01,0:18:13,0:49:25,7:36:34,1698,0,14
+Emergency Medicine,PGY1,S0181070,2015-10-28,1:00:08,0:26:14,2:41:33,0:19:16,0:13:13,0:21:49,5:31:51,1361,0,15
+Emergency Medicine,PGY1,S0181070,2015-10-25,2:30:16,0:21:10,2:07:59,0:15:28,0:26:26,0:12:21,6:56:47,2566,0,26
+Emergency Medicine,PGY1,S0181070,2015-10-24,0:56:50,0:09:43,0:48:54,0:08:14,0:05:24,0:13:34,2:34:27,811,0,27
+Emergency Medicine,PGY1,S0181070,2015-10-27,0:16:49,0:07:31,1:41:16,0:34:41,0:12:32,0:14:10,3:28:21,1258,0,4
+Emergency Medicine,PGY1,S0181070,2015-10-26,1:33:26,0:25:22,0:47:28,0:07:38,0:08:08,0:35:06,3:51:48,919,0,15
+Emergency Medicine,PGY1,S0181070,2015-10-21,1:43:34,0:22:24,2:18:43,0:24:43,0:19:56,0:21:46,6:05:59,1714,2,15
+General Medicine,PGY1,S0181056,2015-11-15,2:13:40,1:31:29,3:18:42,0:39:59,0:19:50,0:37:12,9:30:40,2397,13,10
+Emergency Medicine,PGY1,S0181070,2015-10-23,1:20:32,0:27:23,2:46:43,0:10:59,0:19:18,0:19:22,6:03:38,1719,0,21
+Emergency Medicine,PGY1,S0181070,2015-10-22,0:00:07,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:07,2,0,1
+Emergency Medicine,PGY2+,S0109179,2015-10-18,5:18:14,0:45:00,1:09:15,0:16:50,0:16:48,0:34:45,8:57:20,2516,0,56
+Emergency Medicine,PGY2+,S0109179,2015-10-19,5:18:31,0:47:40,1:48:07,0:30:15,0:22:19,0:16:47,9:56:11,3521,0,36
+General Medicine,PGY1,S0196593,2016-10-28,2:38:31,0:59:05,0:00:02,0:59:41,0:21:43,0:00:36,5:48:13,1723,0,11
+General Medicine,PGY1,S0181056,2015-11-14,1:01:03,0:41:10,1:35:19,0:43:48,0:21:28,0:31:19,5:20:45,1420,9,5
+General Medicine,PGY1,S0196593,2016-10-22,0:28:23,0:26:15,0:04:39,0:17:49,0:07:49,0:01:43,1:57:09,541,0,7
+General Medicine,PGY1,S0196593,2016-10-23,1:46:34,0:38:20,0:00:07,0:47:21,0:27:59,0:06:20,4:31:43,1388,0,12
+General Medicine,PGY1,S0196593,2016-10-20,2:38:10,1:31:56,0:00:09,1:35:44,0:35:28,0:04:05,7:29:40,2134,224,10
+General Medicine,PGY1,S0196593,2016-10-21,1:12:48,0:54:53,0:00:26,0:38:00,0:21:01,0:01:36,3:46:24,1268,41,9
+General Medicine,PGY1,S0196593,2016-10-26,0:02:23,0:05:49,0:00:01,0:00:01,0:00:03,0:00:01,0:11:32,267,247,8
+General Medicine,PGY1,S0196593,2016-10-27,1:07:11,1:45:17,0:00:17,0:10:06,0:12:26,0:03:11,3:43:16,1152,160,4
+General Medicine,PGY1,S0196593,2016-10-24,2:09:48,0:48:59,0:10:38,0:38:00,0:10:47,0:08:59,4:55:30,1770,233,12
+General Medicine,PGY1,S0196593,2016-10-25,2:44:53,0:39:42,0:00:05,0:46:22,0:33:23,0:06:15,5:49:53,1704,0,7
+General Medicine,PGY1,S0181083,2016-02-03,0:46:04,0:36:52,0:29:49,0:52:05,0:28:24,0:29:05,4:14:32,1670,422,12
+Night Team,PGY1,S0181061,2016-02-22,1:47:40,0:35:40,0:00:03,0:32:57,0:13:18,0:06:20,3:32:34,1024,0,23
+Night Team,PGY1,S0181061,2016-02-21,2:25:48,0:40:45,0:00:04,0:38:13,0:16:20,0:05:09,5:01:50,1587,0,33
+Night Team,PGY2+,S0169394,2016-04-13,3:31:38,1:14:04,0:00:20,0:48:37,0:20:29,0:08:20,6:35:14,2190,21,37
+Night Team,PGY2+,S0169394,2016-04-12,3:06:14,1:47:06,0:00:16,1:01:07,0:28:23,0:10:40,7:10:01,2374,178,32
+Night Team,PGY2+,S0169394,2016-04-11,3:44:07,1:13:57,0:00:10,0:37:13,0:24:13,0:14:22,7:24:26,2163,109,27
+Night Team,PGY2+,S0169394,2016-04-10,0:44:15,0:08:52,0:00:02,0:20:26,0:15:41,0:00:07,1:48:34,536,4,13
+Night Team,PGY2+,S0169394,2016-04-16,1:13:45,0:46:00,0:00:32,0:12:44,0:03:31,0:04:18,2:44:32,744,0,27
+Night Team,PGY2+,S0169394,2016-04-15,3:11:12,1:36:09,0:00:11,0:18:58,0:15:37,0:10:37,6:21:37,1997,134,37
+Night Team,PGY2+,S0169394,2016-04-14,3:39:23,1:35:12,0:00:12,0:54:46,0:20:34,0:02:32,7:18:53,2195,7,24
+Intensive Care Unit,PGY2+,S0181077,2016-10-13,2:27:24,0:37:57,0:02:29,0:17:03,0:18:19,0:02:29,4:02:33,1460,32,30
+Night Team,PGY2+,S0181087,2016-08-29,2:44:37,1:48:14,0:00:25,0:36:04,0:14:42,0:03:41,6:17:49,2150,524,44
+Intensive Care Unit,PGY2+,S0181059,2016-07-17,2:32:31,0:42:27,0:04:09,0:08:45,0:20:28,0:01:31,4:24:19,2186,0,30
+Intensive Care Unit,PGY2+,S0181059,2016-07-18,2:22:46,0:44:37,0:12:02,0:17:22,0:16:21,0:01:21,4:20:15,1751,0,22
+Intensive Care Unit,PGY2+,S0181059,2016-07-19,1:41:35,0:15:51,0:04:19,0:06:38,0:23:14,0:04:10,2:48:40,995,0,13
+Emergency Medicine,PGY1,S0181526,2015-07-12,1:56:48,0:20:27,1:39:50,0:33:31,0:39:40,1:19:45,7:32:00,1981,42,21
+Intensive Care Unit,PGY2+,S0181077,2016-10-10,2:40:20,1:00:44,0:12:18,0:10:45,0:25:42,0:03:06,5:14:53,1828,0,15
+Intensive Care Unit,PGY2+,S0181525,2016-09-30,0:00:41,0:09:15,0:00:00,0:00:05,0:00:00,0:00:00,0:10:19,74,65,4
+General Medicine,PGY1,S0195075,2016-07-01,1:36:55,1:02:48,0:00:11,0:36:47,0:23:49,0:00:58,4:19:22,1328,0,7
+General Medicine,PGY1,S0181065,2016-04-22,4:41:18,1:01:55,0:00:04,0:42:28,0:39:01,0:08:50,7:47:19,4115,40,33
+Intensive Care Unit,PGY2+,S0181525,2016-09-24,1:25:58,0:29:07,0:05:44,0:29:20,0:18:35,0:03:16,3:21:26,1018,0,18
+General Medicine,PGY1,S0172056,2016-04-30,1:38:51,0:35:40,0:00:09,0:36:33,0:10:29,0:00:19,3:29:03,1169,45,8
+General Medicine,PGY1,S0169401,2015-03-09,0:26:32,0:07:50,0:05:17,0:01:16,0:06:55,0:12:51,1:06:24,803,779,14
+General Medicine,PGY1,S0169401,2015-03-08,3:58:42,0:42:31,0:44:54,0:55:02,0:22:22,0:11:18,7:38:59,2712,189,14
+General Medicine,PGY1,S0181083,2016-02-06,0:46:10,0:25:37,1:33:21,0:38:14,0:17:37,0:42:44,4:47:08,1448,217,11
+Emergency Medicine,PGY1,S0181088,2016-01-31,0:51:42,0:31:05,2:32:55,0:23:31,0:45:34,0:59:28,7:05:23,2136,0,12
+General Medicine,PGY1,S0169395,2014-11-01,2:37:02,0:10:22,0:18:04,0:23:18,0:12:46,0:06:57,3:57:09,1378,94,12
+Night Team,PGY2+,S0094535,2016-06-19,1:23:04,0:39:54,0:00:01,0:16:02,0:03:21,0:00:40,2:43:00,690,0,25
+General Medicine,PGY1,S0195237,2016-10-31,2:33:39,0:59:38,0:00:24,0:45:53,0:22:10,0:02:50,5:17:18,1321,213,6
+General Medicine,PGY1,S0195237,2016-10-30,3:43:34,1:48:20,0:00:14,1:24:31,0:29:46,0:06:01,8:25:56,2375,228,14
+Emergency Medicine,PGY1,S0169383,2015-03-24,1:43:18,0:39:58,1:38:26,0:10:50,0:18:08,0:05:50,4:57:18,1557,172,20
+General Medicine,PGY1,S0169390,2015-05-26,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,14,0,4
+General Medicine,PGY1,S0169390,2015-05-27,2:21:16,0:11:51,1:31:22,0:05:34,0:06:49,0:11:45,4:38:12,1215,199,8
+General Medicine,PGY1,S0169390,2015-05-24,2:14:44,0:29:42,0:45:11,0:23:14,0:14:37,0:18:36,4:35:11,1056,0,6
+General Medicine,PGY1,S0169390,2015-05-25,2:57:52,0:46:35,1:48:56,0:44:25,0:33:24,0:28:30,7:39:22,2290,0,11
+Emergency Medicine,PGY1,S0181086,2015-10-10,0:05:17,0:05:32,0:00:00,0:00:00,0:00:00,0:00:00,0:13:59,92,92,9
+General Medicine,PGY1,S0169390,2015-05-28,2:59:07,0:38:31,1:50:13,0:46:15,0:30:35,0:28:55,7:36:46,2039,0,9
+General Medicine,PGY1,S0169390,2015-05-29,1:46:29,0:04:18,0:26:37,0:23:26,0:05:13,0:06:33,3:03:37,978,295,8
+Night Team,PGY2+,S0094535,2016-06-18,2:18:14,0:40:57,0:00:10,0:25:52,0:04:14,0:01:26,3:59:22,1357,122,18
+General Medicine,PGY1,S0181059,2016-03-12,2:42:15,1:26:53,0:00:16,0:45:38,0:20:01,0:02:27,6:08:12,2848,24,46
+General Medicine,PGY1,S0181059,2016-03-13,1:15:30,0:38:49,0:00:05,0:14:51,0:07:11,0:01:03,2:31:19,1160,99,4
+General Medicine,PGY1,S0181059,2016-03-10,3:27:31,2:02:39,0:00:17,0:41:26,0:30:09,0:03:24,7:26:16,4115,33,40
+General Medicine,PGY1,S0181059,2016-03-11,1:33:20,1:12:43,0:00:15,0:34:08,0:36:35,0:05:15,4:20:50,2584,259,14
+General Medicine,PGY1,S0181059,2016-03-16,2:34:28,1:28:48,0:05:29,0:35:40,0:11:14,0:07:18,5:56:06,2445,410,17
+General Medicine,PGY1,S0181059,2016-03-17,2:27:21,1:37:21,0:00:43,0:30:41,0:28:39,0:02:19,5:37:09,2568,47,30
+General Medicine,PGY1,S0181059,2016-03-14,2:22:22,0:42:13,0:00:18,0:27:15,0:21:08,0:01:55,4:18:32,2050,318,15
+General Medicine,PGY1,S0181059,2016-03-15,2:41:19,1:44:50,0:00:24,0:38:54,0:23:47,0:05:59,6:21:57,3071,128,34
+Emergency Medicine,PGY1,S0195216,2016-10-30,3:21:38,0:31:55,0:04:35,0:38:38,0:42:06,0:02:58,6:15:53,1818,0,51
+General Medicine,PGY1,S0181059,2016-03-18,0:01:53,0:00:17,0:00:00,0:00:42,0:00:00,0:01:43,0:06:53,106,96,6
+General Medicine,PGY1,S0181059,2016-03-19,1:24:57,0:50:36,0:00:22,0:03:59,0:10:38,0:01:28,2:52:48,1466,170,23
+Night Team,PGY1,S0108268,2014-08-29,4:42:57,0:11:55,0:12:41,0:23:58,0:50:16,0:43:24,7:24:41,2257,1061,46
+Emergency Medicine,PGY1,S0169383,2015-03-10,2:31:33,0:34:32,0:48:50,0:09:07,0:11:19,0:08:11,4:52:50,1332,107,19
+Night Team,PGY1,S0169403,2014-07-02,1:12:43,0:08:10,0:11:37,0:05:38,0:11:28,0:11:12,2:05:13,534,0,23
+Night Team,PGY1,S0169403,2014-07-03,1:25:51,0:06:14,0:00:00,0:06:34,0:21:25,0:23:22,2:36:52,682,0,20
+Night Team,PGY1,S0169403,2014-07-01,2:33:55,0:17:53,0:07:37,0:28:00,0:34:41,0:40:16,4:56:44,1175,0,43
+Night Team,PGY1,S0169403,2014-07-04,2:45:11,0:09:42,0:15:07,0:27:01,0:48:02,0:33:55,5:24:05,1362,61,36
+Night Team,PGY1,S0169403,2014-07-05,1:32:43,0:02:37,0:08:06,0:17:16,0:12:21,0:09:00,2:35:10,662,0,15
+General Medicine,PGY1,S0169389,2014-08-16,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,3
+General Medicine,PGY1,S0169389,2014-08-15,3:55:34,0:14:00,0:22:39,0:24:32,0:17:44,0:26:03,6:36:40,1941,927,20
+General Medicine,PGY1,S0169389,2014-08-14,4:37:51,0:17:59,0:20:53,0:17:39,0:29:23,0:22:10,6:51:55,1603,0,21
+General Medicine,PGY1,S0169389,2014-08-13,3:59:52,0:13:03,0:09:00,0:41:20,0:22:30,0:13:34,6:10:50,1863,118,16
+General Medicine,PGY1,S0169389,2014-08-12,4:05:32,0:18:09,0:06:38,0:39:24,0:16:10,0:17:11,6:07:01,1682,186,25
+General Medicine,PGY1,S0169389,2014-08-11,4:04:51,0:23:36,0:10:38,0:37:44,0:20:49,0:34:22,6:45:08,2014,0,19
+General Medicine,PGY1,S0195216,2016-08-23,1:25:03,0:27:20,0:00:06,0:06:23,0:08:20,0:00:38,2:18:43,738,66,3
+General Medicine,PGY2+,S0169401,2015-12-18,1:43:57,1:09:35,0:16:39,0:58:07,0:27:15,0:30:51,5:49:52,2713,304,21
+General Medicine,PGY2+,S0169401,2015-12-19,0:29:12,0:19:21,0:14:55,0:01:12,0:01:05,0:02:53,1:10:56,573,556,16
+General Medicine,PGY1,S0169404,2015-02-18,2:20:55,0:26:06,1:32:59,0:38:49,0:13:16,0:27:17,6:02:42,2417,217,20
+General Medicine,PGY1,S0169404,2015-02-19,2:46:21,0:13:34,1:40:26,0:27:05,0:11:58,0:15:26,5:59:58,2346,21,14
+General Medicine,PGY1,S0169404,2015-02-16,2:28:29,0:05:13,1:08:07,0:40:20,0:19:55,0:14:01,5:25:26,2619,14,9
+General Medicine,PGY1,S0169404,2015-02-17,3:42:31,0:25:59,1:36:19,0:41:35,0:20:32,0:41:25,8:15:36,3868,153,14
+General Medicine,PGY1,S0169404,2015-02-14,2:26:15,0:20:35,1:34:47,0:34:44,0:14:25,0:36:42,6:08:40,2103,21,14
+General Medicine,PGY1,S0169404,2015-02-15,0:00:00,0:15:15,0:00:00,0:01:35,0:00:00,0:00:10,0:18:23,156,128,10
+General Medicine,PGY1,S0169404,2015-02-12,3:53:52,0:40:03,1:27:33,0:38:24,0:30:03,0:45:47,8:29:12,3123,96,11
+General Medicine,PGY1,S0169404,2015-02-13,1:23:59,0:14:05,0:31:42,0:18:02,0:07:57,0:18:23,3:05:54,1431,360,10
+General Medicine,PGY1,S0169404,2015-02-10,2:22:24,0:19:36,0:48:38,0:22:20,0:21:44,0:16:34,4:44:58,2204,255,13
+General Medicine,PGY1,S0169404,2015-02-11,2:12:49,0:11:23,0:53:23,0:21:03,0:09:55,0:26:40,4:30:49,1938,132,15
+Emergency Medicine,PGY1,S0181073,2016-03-18,3:04:43,0:59:49,0:06:54,0:27:44,0:48:29,0:08:25,6:28:35,1785,0,16
+Emergency Medicine,PGY1,S0181073,2016-03-19,3:50:35,0:47:05,0:01:09,0:51:00,1:16:07,0:03:38,7:39:01,2243,0,19
+Emergency Medicine,PGY1,S0181073,2016-03-16,0:16:48,0:07:01,0:00:03,0:12:02,0:10:13,0:00:26,1:02:28,290,0,12
+Emergency Medicine,PGY1,S0181073,2016-03-17,1:25:41,0:08:03,0:03:31,0:28:56,0:33:27,0:02:01,3:19:04,849,0,7
+Emergency Medicine,PGY1,S0181073,2016-03-14,2:39:48,0:39:08,0:00:54,0:29:26,0:44:35,0:03:08,5:09:14,1546,0,15
+General Medicine,PGY1,S0195216,2016-08-25,0:06:19,0:23:22,0:00:07,0:00:00,0:00:54,0:00:00,0:31:07,139,133,4
+Emergency Medicine,PGY1,S0181073,2016-03-12,1:55:10,0:12:20,0:01:03,0:35:18,0:32:17,0:00:19,3:36:57,948,0,7
+Emergency Medicine,PGY1,S0181073,2016-03-13,3:11:22,0:52:28,0:01:44,0:32:02,0:51:00,0:02:54,6:26:57,2156,0,23
+Emergency Medicine,PGY1,S0181073,2016-03-10,3:47:38,0:33:19,0:09:41,0:28:57,1:03:17,0:05:40,7:07:13,2107,0,20
+Emergency Medicine,PGY1,S0181073,2016-03-11,3:08:59,0:59:35,0:02:17,0:23:19,1:06:40,0:02:36,6:46:33,2160,0,18
+General Medicine,PGY2+,S0169390,2016-07-30,0:21:30,0:00:46,0:00:00,0:00:03,0:00:00,0:00:48,0:24:29,87,0,14
+General Medicine,PGY2+,S0169390,2016-07-31,3:50:20,2:01:33,0:01:35,0:32:51,0:28:56,0:09:30,7:37:37,3273,0,21
+General Medicine,PGY1,S0181526,2015-10-10,0:14:47,0:11:13,0:07:15,0:04:00,0:00:03,0:02:56,0:46:55,282,276,14
+Night Team,PGY1,S0172085,2016-02-02,1:44:22,1:11:19,1:41:20,0:46:24,0:35:29,0:34:34,6:55:23,2417,2176,44
+General Medicine,PGY2+,S0181079,2016-09-12,2:43:24,1:31:55,0:00:12,0:33:35,0:22:51,0:02:00,5:46:49,2214,0,20
+General Medicine,PGY2+,S0181079,2016-09-13,0:59:15,0:29:41,0:00:00,0:00:27,0:04:36,0:00:50,1:41:31,847,829,22
+General Medicine,PGY2+,S0181079,2016-09-11,4:10:36,1:37:45,0:00:04,0:26:42,0:30:31,0:06:20,7:57:58,2751,13,29
+General Medicine,PGY2+,S0181079,2016-09-16,4:06:07,1:26:03,0:01:31,0:52:53,0:30:15,0:12:23,8:04:08,3239,0,25
+General Medicine,PGY2+,S0181079,2016-09-17,2:11:36,0:40:03,0:00:14,0:16:52,0:17:26,0:06:03,4:20:58,1745,34,15
+General Medicine,PGY2+,S0181079,2016-09-14,4:30:06,1:34:13,0:00:25,0:27:33,0:40:31,0:17:15,8:40:08,3753,90,24
+General Medicine,PGY2+,S0181079,2016-09-15,3:18:23,1:08:45,0:00:09,0:28:30,0:25:05,0:03:18,6:12:12,2316,470,20
+General Medicine,PGY2+,S0181079,2016-09-18,0:43:16,0:25:47,0:00:00,0:01:51,0:00:17,0:00:14,1:20:26,567,531,21
+Emergency Medicine,PGY1,S0169406,2015-01-30,0:00:02,0:00:00,0:07:30,0:00:00,0:00:10,0:00:00,0:07:50,32,32,2
+Night Team,PGY2+,S0169399,2016-05-14,0:46:28,0:25:05,0:00:01,0:12:01,0:04:02,0:01:30,1:37:43,470,51,12
+Intensive Care Unit,PGY2+,S0109179,2016-02-09,1:37:00,1:20:01,0:16:18,0:18:02,0:25:24,0:26:09,4:56:56,1756,0,28
+Intensive Care Unit,PGY2+,S0109179,2016-02-08,1:21:14,1:09:10,0:16:08,0:02:43,0:18:28,0:19:18,3:38:38,1057,0,17
+General Medicine,PGY2+,S0109179,2016-02-03,2:55:27,0:46:49,0:23:53,0:19:56,0:19:14,0:40:54,6:00:32,2986,293,35
+General Medicine,PGY2+,S0109179,2016-02-02,0:54:54,0:24:37,0:11:03,0:04:06,0:01:28,0:09:29,1:55:24,716,0,20
+General Medicine,PGY2+,S0109179,2016-02-01,0:36:18,0:15:17,0:05:37,0:00:09,0:00:17,0:10:53,1:10:53,379,377,12
+Intensive Care Unit,PGY2+,S0109179,2016-02-07,1:28:01,0:45:54,0:40:32,0:21:49,0:18:01,0:21:54,4:04:42,1159,0,28
+General Medicine,PGY2+,S0109179,2016-02-06,0:04:09,0:00:20,0:00:53,0:00:00,0:00:00,0:00:16,0:06:10,74,74,10
+General Medicine,PGY2+,S0109179,2016-02-05,0:11:54,0:06:00,0:03:42,0:00:00,0:00:00,0:00:29,0:22:20,147,139,12
+General Medicine,PGY2+,S0109179,2016-02-04,1:45:32,0:30:46,0:54:04,0:22:19,0:15:43,0:20:42,4:30:17,2361,259,12
+General Medicine,PGY1,S0169411,2015-05-28,3:41:35,1:13:06,1:25:34,0:49:29,0:18:21,0:22:51,8:17:27,2947,0,20
+General Medicine,PGY1,S0169411,2015-05-29,3:30:18,0:28:08,1:35:32,0:59:48,0:28:42,0:17:42,7:35:31,2871,30,13
+General Medicine,PGY1,S0169411,2015-05-24,2:00:48,0:36:03,1:18:08,0:29:17,0:20:48,0:12:05,5:18:43,1678,0,9
+General Medicine,PGY1,S0169411,2015-05-25,1:36:28,0:40:44,0:18:06,0:23:17,0:08:51,0:07:48,3:31:45,1494,0,11
+General Medicine,PGY1,S0169411,2015-05-26,2:55:59,1:01:16,0:59:12,0:37:12,0:23:37,0:27:13,7:02:08,2311,0,15
+General Medicine,PGY1,S0169411,2015-05-27,2:43:49,0:59:48,1:24:30,0:31:45,0:11:27,0:30:10,6:48:45,1914,0,11
+General Medicine,PGY1,S0181088,2016-05-30,0:07:56,0:00:12,0:00:00,0:01:35,0:00:00,0:04:41,0:15:36,111,97,9
+General Medicine,PGY2+,S0169404,2016-04-08,3:58:01,2:23:02,0:00:07,0:58:04,0:28:26,0:23:13,8:56:09,5066,378,26
+General Medicine,PGY2+,S0169404,2016-04-09,2:29:25,1:43:23,0:01:48,0:44:17,0:20:55,0:09:16,6:06:00,3201,740,19
+General Medicine,PGY1,S0181088,2015-12-04,2:32:31,0:58:29,2:10:07,0:38:45,0:20:55,0:18:11,7:46:38,2403,44,11
+General Medicine,PGY1,S0181088,2015-12-05,0:31:22,0:29:08,0:35:44,0:05:09,0:07:56,0:11:44,2:21:03,742,53,15
+Intensive Care Unit,PGY2+,S0169404,2015-08-28,2:05:32,0:26:23,0:17:29,0:23:01,0:14:08,0:26:52,4:07:42,1657,308,20
+Night Team,PGY2+,S0169386,2015-10-10,0:40:15,0:37:05,0:35:35,0:22:13,0:06:16,0:37:18,3:15:04,1275,11,28
+Night Team,PGY2+,S0169404,2016-01-01,1:17:29,1:31:00,1:48:07,1:13:26,0:14:13,0:53:18,7:33:15,3732,132,36
+General Medicine,PGY1,S0181526,2016-04-29,2:32:59,1:17:52,0:01:50,0:29:05,0:20:03,0:02:34,5:41:44,1624,6,17
+General Medicine,PGY1,S0181526,2016-04-28,0:06:01,0:00:17,0:00:00,0:00:00,0:00:00,0:02:22,0:09:13,59,53,5
+Intensive Care Unit,PGY2+,S0169383,2015-10-31,1:51:53,1:09:23,0:03:00,0:38:55,0:28:19,0:21:47,4:58:17,1846,290,34
+Intensive Care Unit,PGY2+,S0169383,2015-10-30,0:12:30,0:16:36,0:01:35,0:05:58,0:00:09,0:01:06,0:46:00,365,0,16
+General Medicine,PGY1,S0181526,2016-04-27,2:48:05,0:53:20,0:00:40,0:29:59,0:20:45,0:02:46,5:48:08,1868,230,17
+General Medicine,PGY1,S0181526,2016-04-26,2:37:40,1:00:09,0:00:18,0:38:21,0:37:45,0:03:46,6:00:48,1862,91,19
+General Medicine,PGY1,S0181526,2016-04-25,2:25:30,0:34:21,0:05:04,0:37:50,0:23:13,0:04:49,5:19:46,1881,128,22
+General Medicine,PGY1,S0181526,2016-04-24,3:04:21,1:42:17,0:00:07,0:51:05,0:23:14,0:02:05,6:43:12,1841,21,19
+General Medicine,PGY1,S0169401,2015-03-13,2:55:51,1:06:55,1:19:59,0:46:04,0:25:41,0:08:25,7:06:17,2344,243,15
+Intensive Care Unit,PGY2+,S0169394,2016-05-28,0:00:00,0:00:00,0:00:00,0:00:26,0:00:00,0:00:00,0:00:26,20,0,8
+General Medicine,PGY1,S0195207,2016-08-24,1:53:17,1:49:40,0:00:21,0:39:50,0:23:39,0:04:38,5:48:13,2519,13,11
+General Medicine,PGY1,S0108268,2014-09-30,1:21:34,0:02:33,0:05:45,0:01:28,0:03:26,0:06:31,1:46:49,488,475,12
+Intensive Care Unit,PGY2+,S0108287,2016-11-10,2:04:22,0:44:14,0:00:29,0:10:23,0:25:06,0:00:43,4:01:30,763,205,19
+General Medicine,PGY1,S0181087,2015-07-29,3:24:55,0:20:25,2:01:25,1:11:04,0:33:20,0:53:40,8:59:09,2740,0,18
+General Medicine,PGY1,S0181087,2015-07-28,2:31:04,0:04:01,1:07:49,0:33:11,0:24:46,0:16:08,5:17:53,1942,0,10
+General Medicine,PGY1,S0188098,2016-08-20,3:51:22,0:36:02,0:05:36,0:23:17,0:08:02,0:05:02,5:41:35,1053,46,29
+General Medicine,PGY1,S0181087,2015-07-23,3:02:29,0:06:15,1:08:00,0:41:29,0:28:21,0:20:17,6:33:52,1900,314,10
+General Medicine,PGY1,S0181087,2015-07-22,1:59:40,0:00:18,1:05:17,0:30:22,0:21:11,0:17:40,5:00:25,1479,309,7
+General Medicine,PGY1,S0181087,2015-07-21,3:08:54,0:08:12,1:57:36,0:33:03,0:29:20,0:28:31,7:08:12,1475,0,12
+General Medicine,PGY1,S0181087,2015-07-20,1:43:56,0:07:50,0:22:37,0:00:38,0:18:02,0:27:35,3:06:49,657,647,9
+General Medicine,PGY1,S0181087,2015-07-27,0:11:02,0:00:00,0:01:26,0:01:22,0:00:00,0:00:00,0:15:10,146,132,8
+General Medicine,PGY1,S0181087,2015-07-26,3:06:25,0:06:23,2:03:13,0:23:44,0:37:41,0:29:19,7:05:47,2296,0,12
+General Medicine,PGY1,S0181087,2015-07-25,2:32:09,0:12:31,1:46:05,0:40:46,0:25:57,0:30:57,6:26:47,1825,191,10
+General Medicine,PGY1,S0181087,2015-07-24,3:09:24,0:25:38,1:43:46,1:07:10,0:27:19,0:30:02,7:49:32,2123,73,10
+General Medicine,PGY1,S0181066,2016-01-02,0:05:10,0:21:03,0:29:32,0:01:57,0:01:46,0:00:15,1:01:07,290,290,6
+General Medicine,PGY1,S0181060,2015-11-30,1:27:09,1:37:25,0:54:54,0:28:15,0:18:52,0:38:13,5:52:20,2048,0,6
+General Medicine,PGY1,S0181078,2016-02-29,1:54:23,0:37:13,0:00:06,0:42:34,0:19:15,0:01:17,4:05:30,1062,70,7
+General Medicine,PGY1,S0181078,2016-02-28,0:45:44,0:47:22,0:00:01,0:12:59,0:00:02,0:00:40,1:53:17,366,354,16
+General Medicine,PGY1,S0195207,2016-08-26,3:00:53,2:03:36,0:00:25,0:38:51,0:21:03,0:05:57,6:56:03,2428,28,11
+General Medicine,PGY2+,S0181065,2016-09-19,0:04:29,0:04:10,0:00:00,0:00:01,0:00:00,0:00:00,0:10:13,117,111,5
+General Medicine,PGY2+,S0181065,2016-09-18,2:22:53,0:30:27,0:00:52,0:10:03,0:17:57,0:10:02,4:03:21,2107,0,9
+General Medicine,PGY2+,S0181065,2016-09-17,5:04:15,1:04:26,0:00:48,0:24:44,0:28:33,0:04:22,8:06:35,4688,188,44
+General Medicine,PGY1,S0181086,2016-05-20,1:58:43,1:18:05,0:00:12,1:10:13,0:25:50,0:02:30,5:13:20,1567,0,6
+General Medicine,PGY2+,S0181065,2016-09-15,4:18:19,1:10:44,0:00:03,0:51:12,0:38:13,0:09:32,7:57:57,5201,215,195
+General Medicine,PGY2+,S0181065,2016-09-14,2:19:01,0:23:23,0:00:09,0:22:34,0:26:46,0:02:46,4:09:36,2285,26,139
+General Medicine,PGY2+,S0181065,2016-09-13,4:06:12,1:16:00,0:09:12,0:53:55,0:21:03,0:12:51,8:10:50,4175,1,30
+General Medicine,PGY2+,S0181065,2016-09-12,5:07:57,1:23:34,0:00:02,0:19:53,0:25:28,0:16:17,8:38:22,5891,67,40
+General Medicine,PGY2+,S0181065,2016-09-11,3:43:36,0:38:28,0:00:13,0:34:45,0:25:16,0:08:44,6:03:55,2999,256,15
+Intensive Care Unit,PGY2+,S0169381,2016-02-04,1:32:01,0:46:12,0:08:47,0:24:15,0:18:13,0:21:08,3:52:04,1193,0,19
+Intensive Care Unit,PGY2+,S0169381,2016-02-05,1:16:05,0:23:31,0:09:48,0:05:45,0:10:28,0:26:01,2:43:46,829,0,14
+General Medicine,PGY1,S0181064,2016-05-21,0:02:29,0:00:17,0:00:00,0:00:00,0:00:00,0:04:01,0:07:32,42,34,4
+General Medicine,PGY1,S0181064,2016-05-20,2:26:23,1:18:46,0:00:24,0:41:23,0:16:44,0:07:26,5:31:32,1788,173,12
+General Medicine,PGY1,S0169406,2015-03-19,2:02:40,0:36:08,1:45:50,0:17:58,0:27:04,0:24:19,6:08:53,1798,167,9
+General Medicine,PGY1,S0169406,2015-03-18,1:37:22,0:17:51,1:20:17,0:39:03,0:27:47,0:25:18,5:05:47,1579,205,13
+Intensive Care Unit,PGY2+,S0169381,2016-02-01,2:26:01,1:17:26,0:19:19,0:31:12,0:22:20,1:06:09,6:47:18,1825,26,19
+General Medicine,PGY1,S0181081,2015-08-06,3:44:43,0:16:23,2:48:26,1:01:42,0:24:15,0:42:38,9:35:25,2783,0,14
+Emergency Medicine,PGY1,S0169404,2014-12-26,0:00:00,0:07:34,0:00:00,0:00:00,0:00:00,0:01:23,0:09:10,86,76,6
+Intensive Care Unit,PGY2+,S0169381,2016-02-02,2:48:10,0:41:35,0:20:34,0:32:24,0:23:28,0:54:28,6:26:23,1644,0,18
+General Medicine,PGY1,S0181081,2015-08-07,2:00:43,0:11:09,2:11:36,0:53:23,0:25:54,0:38:57,7:16:35,1871,57,8
+General Medicine,PGY1,S0195218,2016-08-23,1:21:06,1:42:34,0:00:21,0:29:53,0:22:06,0:01:17,4:19:41,1424,0,10
+Night Team,PGY1,S0195205,2016-10-01,1:36:09,0:47:03,0:00:00,0:22:37,0:17:27,0:03:57,3:18:12,1173,24,50
+General Medicine,PGY1,S0169406,2015-03-15,1:42:10,0:16:58,0:49:43,0:46:04,0:27:33,0:15:02,4:48:48,1892,200,11
+General Medicine,PGY1,S0169406,2015-03-14,1:08:21,0:10:30,0:43:44,0:26:13,0:18:59,0:05:20,3:06:48,1033,0,8
+General Medicine,PGY1,S0169406,2015-03-13,1:32:42,0:26:58,1:52:12,0:28:18,0:21:17,0:18:06,5:36:39,1452,103,12
+General Medicine,PGY1,S0169406,2015-03-12,2:01:55,0:30:54,1:14:58,0:24:54,0:33:22,0:23:25,5:32:29,1950,87,18
+General Medicine,PGY2+,S0169399,2015-11-14,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,8,0,4
+General Medicine,PGY2+,S0169399,2015-11-11,1:00:05,0:54:56,1:32:08,0:38:36,0:19:29,0:23:32,5:13:51,1194,0,10
+General Medicine,PGY2+,S0169399,2015-11-10,1:03:14,0:57:50,0:07:39,0:27:36,0:22:23,0:40:06,4:08:10,1229,0,16
+General Medicine,PGY2+,S0169399,2015-11-13,1:13:04,0:46:17,0:09:22,0:22:47,0:32:44,0:22:09,3:48:32,1092,0,12
+General Medicine,PGY2+,S0169399,2015-11-12,1:54:55,0:52:04,0:08:11,0:17:21,0:14:54,0:29:55,4:16:03,1251,37,16
+General Medicine,PGY1,S0195207,2016-08-21,2:46:58,2:02:46,0:00:21,0:22:49,0:23:36,0:02:32,6:27:31,2170,0,25
+General Medicine,PGY1,S0169406,2015-03-10,2:17:33,0:37:51,0:37:49,1:10:37,0:38:05,0:24:20,6:11:43,2204,11,10
+General Medicine,PGY1,S0181067,2015-06-30,1:24:28,0:00:12,0:45:27,0:18:41,0:20:48,0:21:18,3:33:03,882,0,4
+Night Team,PGY2+,S0108268,2016-05-05,2:36:05,1:18:45,0:00:06,0:19:22,0:09:30,0:03:45,4:54:26,1558,0,46
+Night Team,PGY1,S0169406,2015-04-19,1:31:42,0:12:56,0:00:47,0:08:09,0:09:15,0:06:34,2:18:29,832,27,69
+Emergency Medicine,PGY1,S0188098,2016-08-21,4:47:06,0:36:11,0:09:08,0:23:27,0:10:56,0:00:04,6:49:04,1183,0,51
+Intensive Care Unit,PGY2+,S0169399,2016-02-16,1:59:46,0:32:12,0:18:07,0:09:43,0:21:50,0:19:42,4:04:15,731,0,15
+Intensive Care Unit,PGY2+,S0169399,2016-02-17,1:29:41,0:21:46,0:00:12,0:28:39,0:33:31,0:38:49,3:55:01,904,0,18
+Intensive Care Unit,PGY2+,S0169399,2016-02-14,2:13:21,0:33:57,0:16:08,0:26:08,0:14:32,0:37:36,4:38:23,1148,0,21
+Intensive Care Unit,PGY2+,S0169399,2016-02-15,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,20,0,4
+Intensive Care Unit,PGY2+,S0169399,2016-02-12,1:28:18,0:23:38,0:21:37,0:27:46,0:23:09,0:18:37,3:57:39,810,0,16
+Intensive Care Unit,PGY2+,S0169399,2016-02-13,2:28:25,0:22:44,0:04:29,0:53:46,0:45:35,0:42:56,6:04:36,1421,0,24
+Intensive Care Unit,PGY2+,S0169399,2016-02-10,0:55:34,0:28:43,0:04:16,0:11:28,0:21:15,0:24:37,2:36:01,588,0,14
+Intensive Care Unit,PGY2+,S0169399,2016-02-11,2:12:15,1:04:58,0:14:03,0:52:09,0:31:05,0:40:12,6:19:04,1638,187,23
+Intensive Care Unit,PGY2+,S0169399,2016-02-18,2:23:46,0:35:46,0:15:55,0:33:30,0:15:26,0:28:51,5:03:09,1131,0,16
+Intensive Care Unit,PGY2+,S0169399,2016-02-19,2:08:07,0:47:35,1:10:21,0:35:18,0:19:06,0:17:58,5:42:43,1106,20,17
+General Medicine,PGY1,S0181085,2015-10-11,0:49:52,1:05:45,2:05:50,0:20:12,0:18:02,0:43:52,5:44:30,1301,9,5
+General Medicine,PGY1,S0181085,2015-10-12,1:12:15,1:49:58,1:56:13,0:31:13,0:27:30,0:18:38,6:29:37,1858,0,9
+General Medicine,PGY1,S0181085,2015-10-13,0:56:49,1:57:22,2:18:47,0:14:48,0:16:05,0:17:21,6:37:51,2019,92,11
+General Medicine,PGY1,S0181085,2015-10-14,0:03:19,0:12:06,0:00:24,0:00:00,0:00:02,0:00:47,0:18:28,236,230,8
+General Medicine,PGY1,S0181085,2015-10-15,1:27:04,2:44:28,2:22:20,0:37:01,0:21:05,0:28:30,8:31:16,2358,0,9
+General Medicine,PGY1,S0181085,2015-10-16,0:44:42,1:37:22,1:28:36,0:18:18,0:28:23,0:18:30,5:14:13,1610,14,10
+General Medicine,PGY1,S0181085,2015-10-17,1:11:23,1:25:15,2:36:32,0:41:15,0:25:23,0:25:09,7:04:48,2088,81,12
+General Medicine,PGY1,S0181085,2015-10-18,0:53:54,1:10:53,1:38:50,0:16:10,0:08:17,0:04:52,4:31:16,1140,0,7
+General Medicine,PGY1,S0181085,2015-10-19,1:05:39,1:24:37,1:57:19,0:40:21,0:12:04,0:09:58,5:45:04,1908,16,13
+Night Team,PGY1,S0181064,2016-01-18,1:01:39,0:41:21,0:14:44,0:20:11,0:16:14,0:25:15,3:15:00,1419,0,45
+Night Team,PGY1,S0181064,2016-01-19,1:48:44,0:38:05,0:48:01,0:38:59,0:17:44,0:35:54,5:19:39,1389,0,31
+Night Team,PGY2+,S0108268,2016-05-09,1:48:07,0:33:22,0:00:04,0:15:22,0:06:47,0:02:42,3:09:52,893,0,15
+Emergency Medicine,PGY1,S0169404,2014-12-25,3:50:38,0:10:58,2:21:37,0:20:07,0:34:16,0:26:55,8:07:43,3228,112,30
+Intensive Care Unit,PGY2+,S0108268,2016-08-10,3:53:46,1:55:09,0:00:03,0:39:25,1:07:35,0:04:07,8:18:51,2830,0,18
+Intensive Care Unit,PGY2+,S0108268,2016-08-11,2:07:21,0:49:34,0:07:24,0:09:23,0:23:33,0:00:56,4:06:07,1388,0,12
+Intensive Care Unit,PGY2+,S0108268,2016-08-12,0:36:25,0:33:16,0:02:13,0:01:53,0:17:23,0:00:07,1:55:47,729,0,21
+Intensive Care Unit,PGY2+,S0108268,2016-08-13,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Night Team,PGY1,S0106952,2014-12-10,3:36:21,0:09:02,2:32:05,1:04:38,0:43:49,0:47:04,9:23:19,2290,242,69
+Night Team,PGY1,S0106952,2014-12-17,2:46:39,0:03:38,0:26:24,0:18:48,0:16:34,0:33:29,4:45:25,1435,119,112
+Night Team,PGY1,S0106952,2014-12-16,3:49:25,0:08:03,1:18:22,0:45:32,0:57:33,0:40:17,8:05:47,2207,214,77
+General Medicine,PGY1,S0169383,2015-05-08,1:31:55,0:51:56,1:39:36,0:14:40,0:07:30,0:08:57,5:05:35,1477,0,16
+General Medicine,PGY1,S0169383,2015-05-09,1:16:08,0:06:07,0:15:18,0:15:20,0:03:01,0:15:05,2:19:38,633,0,6
+Emergency Medicine,PGY1,S0169404,2014-12-09,0:00:00,0:08:21,0:00:00,0:00:00,0:00:00,0:01:16,0:10:16,64,64,5
+Emergency Medicine,PGY1,S0169404,2014-12-08,4:04:24,0:27:47,2:21:21,0:33:30,0:28:47,0:36:47,9:09:16,2648,53,49
+General Medicine,PGY1,S0169385,2015-05-20,0:12:48,0:03:56,0:00:08,0:00:00,0:00:58,0:02:22,0:20:36,120,112,4
+General Medicine,PGY1,S0169383,2015-05-01,0:00:00,0:00:00,0:00:00,0:04:08,0:00:00,0:00:00,0:04:08,10,0,3
+Emergency Medicine,PGY1,S0169404,2014-12-07,2:06:25,0:17:35,2:00:59,0:18:57,0:30:58,0:42:19,6:18:56,2059,0,19
+General Medicine,PGY1,S0169383,2015-05-03,1:43:08,0:02:06,0:18:15,0:15:12,0:04:11,0:05:41,2:52:44,890,0,15
+General Medicine,PGY1,S0169383,2015-05-04,0:19:21,0:01:52,0:01:42,0:00:08,0:00:02,0:01:48,0:32:07,196,186,11
+General Medicine,PGY1,S0169383,2015-05-05,2:01:18,0:04:44,2:04:41,0:41:34,0:09:58,0:06:00,5:34:59,1447,58,10
+General Medicine,PGY1,S0169383,2015-05-06,1:51:01,0:10:07,0:31:44,0:09:33,0:07:33,0:10:16,3:30:26,1289,69,10
+General Medicine,PGY1,S0169383,2015-05-07,2:52:35,0:25:43,1:17:24,0:49:27,0:17:38,0:23:15,6:55:51,1999,145,19
+General Medicine,PGY1,S0188098,2016-08-02,2:06:37,0:59:29,0:00:05,0:37:53,0:26:43,0:04:21,4:39:21,1191,5,14
+General Medicine,PGY1,S0188098,2016-08-03,0:13:28,0:11:30,0:00:13,0:14:38,0:00:59,0:00:02,0:45:49,123,91,7
+General Medicine,PGY1,S0188098,2016-08-01,1:55:17,1:08:09,0:00:41,0:18:43,0:19:43,0:01:17,4:22:28,1331,71,5
+General Medicine,PGY1,S0188098,2016-08-06,1:31:28,1:11:36,0:00:12,0:33:23,0:29:25,0:02:35,4:39:00,1412,30,11
+General Medicine,PGY1,S0188098,2016-08-07,2:43:11,0:43:37,0:00:10,0:43:17,0:32:30,0:00:45,5:07:32,1344,52,12
+General Medicine,PGY1,S0188098,2016-08-04,2:48:05,1:45:27,0:00:06,0:37:46,0:23:06,0:01:07,6:28:23,1772,5,16
+General Medicine,PGY1,S0188098,2016-08-05,1:29:41,0:45:41,0:05:57,0:10:36,0:18:52,0:05:58,3:25:22,982,269,10
+General Medicine,PGY1,S0188098,2016-08-08,0:01:00,0:00:00,0:00:00,0:00:11,0:00:00,0:01:28,0:02:55,38,0,6
+General Medicine,PGY1,S0188098,2016-08-09,2:18:20,1:52:05,0:00:08,0:45:47,0:21:22,0:08:56,6:13:32,1655,8,9
+General Medicine,PGY1,S0108287,2014-07-27,1:49:08,0:05:36,0:15:16,0:05:12,0:21:48,0:10:37,3:07:28,715,0,7
+General Medicine,PGY1,S0195230,2016-10-09,0:08:57,0:21:53,0:00:02,0:00:05,0:00:25,0:00:01,0:33:49,240,236,6
+General Medicine,PGY2+,S0169384,2016-06-21,0:06:54,0:09:43,0:00:01,0:14:18,0:01:20,0:00:00,0:32:17,98,80,9
+General Medicine,PGY2+,S0169384,2016-06-20,1:46:50,1:31:15,0:00:23,0:52:59,0:27:55,0:04:25,5:37:52,1857,364,18
+General Medicine,PGY2+,S0169384,2016-06-23,1:30:28,1:11:46,0:00:06,0:18:27,0:22:39,0:07:34,4:16:59,2500,41,17
+General Medicine,PGY1,S0108287,2014-07-26,2:08:35,0:05:44,0:14:12,0:31:50,0:23:42,0:23:10,4:20:54,1044,0,12
+General Medicine,PGY2+,S0169384,2016-06-25,2:09:15,0:55:19,0:01:29,0:44:00,0:27:37,0:22:52,5:15:07,1825,22,17
+General Medicine,PGY2+,S0169384,2016-06-24,2:58:11,1:54:33,0:00:00,0:40:08,0:24:00,0:04:48,7:09:58,2770,57,23
+General Medicine,PGY2+,S0169384,2016-06-27,2:25:40,1:43:06,0:00:01,0:38:54,0:20:40,0:09:36,5:58:32,2525,419,20
+General Medicine,PGY2+,S0169384,2016-06-26,1:38:56,1:12:39,0:05:10,0:33:35,0:14:27,0:12:20,4:26:59,1469,260,14
+General Medicine,PGY2+,S0169384,2016-06-29,2:27:59,1:33:16,0:01:09,0:15:21,0:12:23,0:06:06,5:25:55,2352,47,23
+General Medicine,PGY1,S0108287,2014-07-21,3:28:05,0:20:03,0:12:59,0:43:59,0:22:33,0:17:36,5:47:13,1210,28,6
+General Medicine,PGY1,S0108287,2014-07-20,2:01:48,0:02:41,0:09:33,0:08:12,0:15:34,0:14:38,3:00:33,458,36,5
+General Medicine,PGY1,S0196595,2016-06-29,2:52:25,1:11:13,0:01:59,1:00:13,0:36:08,0:02:47,6:32:44,1316,19,9
+General Medicine,PGY1,S0181057,2015-06-26,1:48:47,0:04:14,1:12:55,0:35:07,0:12:07,0:17:45,4:25:09,1135,0,6
+General Medicine,PGY1,S0108287,2014-07-22,0:21:40,0:00:12,0:00:14,0:00:00,0:01:48,0:03:16,0:28:37,116,116,4
+General Medicine,PGY1,S0195230,2016-10-08,0:03:53,0:00:05,0:00:00,0:00:00,0:00:00,0:00:14,0:04:18,17,0,3
+General Medicine,PGY1,S0196595,2016-06-26,2:43:31,0:58:41,0:00:44,0:58:13,0:39:00,0:10:46,6:08:49,1246,0,7
+General Medicine,PGY1,S0163425,2014-09-15,3:40:07,0:03:22,0:16:08,0:37:01,0:26:14,0:46:29,6:21:57,1730,0,9
+General Medicine,PGY1,S0163425,2014-09-14,4:06:46,0:19:45,0:09:44,0:40:07,0:43:30,1:02:39,7:29:03,1913,0,8
+General Medicine,PGY1,S0163425,2014-09-17,3:00:28,0:05:37,0:32:05,0:38:57,0:33:29,0:33:10,6:10:23,1684,399,10
+General Medicine,PGY1,S0163425,2014-09-16,4:23:32,0:05:53,0:15:55,0:50:54,0:37:30,0:31:18,7:21:54,1864,0,11
+General Medicine,PGY1,S0163425,2014-09-19,4:58:39,0:06:31,0:15:37,0:40:05,0:24:04,0:33:56,7:40:49,1753,19,11
+General Medicine,PGY1,S0163425,2014-09-18,4:11:45,0:13:05,0:16:40,0:17:44,0:30:57,0:27:46,6:37:07,1458,49,10
+General Medicine,PGY2+,S0169399,2016-06-12,1:45:34,1:00:05,0:00:05,0:03:40,0:07:28,0:01:58,3:32:36,1181,9,53
+General Medicine,PGY2+,S0169399,2016-06-13,1:07:04,0:46:15,0:00:03,0:15:22,0:10:57,0:00:05,2:55:28,769,141,20
+General Medicine,PGY2+,S0169399,2016-06-10,2:08:37,0:46:21,0:00:02,0:37:57,0:13:17,0:10:38,4:36:41,1374,73,19
+General Medicine,PGY2+,S0169399,2016-06-11,1:11:42,0:51:26,0:00:12,0:22:26,0:16:09,0:02:32,3:12:50,885,11,9
+General Medicine,PGY2+,S0169399,2016-06-16,0:54:00,0:10:15,0:00:02,0:13:30,0:04:56,0:02:20,1:28:18,347,0,11
+General Medicine,PGY2+,S0169399,2016-06-17,1:48:24,1:18:22,0:00:18,0:31:57,0:08:09,0:01:15,4:41:34,1379,8,17
+General Medicine,PGY2+,S0169399,2016-06-14,0:51:29,0:21:23,0:00:02,0:25:28,0:05:55,0:00:03,2:06:28,616,119,6
+General Medicine,PGY2+,S0169399,2016-06-15,1:31:28,0:47:00,0:01:34,0:24:08,0:06:59,0:03:09,3:24:19,921,22,17
+General Medicine,PGY2+,S0169399,2016-06-18,0:01:17,0:01:23,0:00:00,0:00:00,0:00:00,0:04:15,0:08:01,45,39,4
+General Medicine,PGY2+,S0169399,2016-06-19,0:08:01,0:01:46,0:00:00,0:00:00,0:00:00,0:01:01,0:12:45,47,41,5
+General Medicine,PGY1,S0169406,2015-05-19,2:20:31,0:08:24,1:28:48,0:21:01,0:08:27,0:13:49,4:53:29,1208,0,25
+General Medicine,PGY1,S0169406,2015-05-18,0:00:00,0:03:49,0:00:00,0:00:42,0:00:00,0:06:22,0:14:14,102,94,4
+General Medicine,PGY1,S0169406,2015-05-11,0:37:10,0:06:07,0:05:31,0:16:14,0:02:47,0:03:47,1:26:33,695,94,6
+General Medicine,PGY1,S0169406,2015-05-10,0:00:00,0:09:46,0:00:00,0:00:00,0:00:00,0:00:47,0:10:50,58,56,2
+General Medicine,PGY1,S0169406,2015-05-13,0:02:07,0:00:00,0:00:04,0:00:06,0:00:17,0:00:19,0:03:07,33,29,2
+General Medicine,PGY1,S0169406,2015-05-12,2:54:28,0:36:23,0:07:44,0:28:04,0:17:58,0:19:48,5:16:53,1212,32,11
+General Medicine,PGY1,S0169406,2015-05-15,1:40:04,0:05:45,0:04:32,0:32:25,0:17:59,0:08:06,3:02:57,1137,29,11
+General Medicine,PGY1,S0169406,2015-05-14,2:42:34,0:27:13,0:12:50,0:42:22,0:14:12,0:10:02,5:02:11,1808,88,12
+General Medicine,PGY1,S0169406,2015-05-17,3:22:42,0:17:53,0:27:28,0:29:17,0:07:49,0:23:43,5:29:44,1387,0,8
+General Medicine,PGY1,S0169406,2015-05-16,1:16:33,0:02:30,0:42:20,0:14:28,0:04:48,0:04:46,2:36:05,810,53,15
+Emergency Medicine,PGY2+,S0181056,2016-09-10,0:21:21,0:08:25,0:00:00,0:00:24,0:00:00,0:01:19,0:38:02,178,82,14
+General Medicine,PGY1,S0169406,2014-06-28,3:41:33,0:11:08,0:44:18,0:15:54,0:34:42,0:55:35,6:32:08,1401,0,8
+General Medicine,PGY1,S0169406,2014-06-29,3:06:57,0:04:55,0:12:31,0:24:53,0:14:40,0:22:22,4:38:42,1319,0,6
+General Medicine,PGY1,S0169406,2014-06-26,3:20:01,0:03:59,0:15:53,0:35:59,0:32:24,0:10:03,5:05:14,1290,0,6
+General Medicine,PGY1,S0169406,2014-06-27,3:17:05,0:02:40,0:13:50,0:14:18,0:17:15,0:09:33,4:28:13,1281,11,5
+General Medicine,PGY1,S0188098,2016-07-30,2:05:45,1:35:12,0:00:06,0:43:22,0:33:45,0:03:39,5:50:12,1447,90,14
+General Medicine,PGY1,S0188098,2016-07-31,0:07:42,0:01:59,0:00:00,0:00:28,0:00:00,0:01:12,0:13:41,88,0,5
+General Medicine,PGY1,S0181085,2016-01-28,0:24:14,0:43:14,0:50:18,0:16:12,0:06:07,0:06:50,2:35:03,723,0,6
+General Medicine,PGY1,S0181085,2016-01-29,0:42:56,1:09:08,2:36:14,0:19:25,0:22:35,0:15:29,5:51:42,1373,159,19
+General Medicine,PGY1,S0196595,2016-07-15,1:49:38,1:08:04,0:01:02,0:57:52,0:38:34,0:01:05,4:58:34,1420,203,9
+Night Team,PGY2+,S0169399,2015-09-21,1:54:41,0:33:15,3:06:30,0:20:51,0:24:28,0:23:10,7:06:40,981,0,37
+Emergency Medicine,PGY1,S0181082,2016-03-19,0:42:30,0:18:05,0:01:34,0:06:10,0:13:55,0:00:27,1:39:43,655,0,12
+Night Team,PGY2+,S0109179,2015-07-12,1:09:50,0:24:20,0:21:37,0:12:06,0:02:33,0:26:12,2:51:30,917,0,24
+General Medicine,PGY1,S0195075,2016-07-19,0:48:44,0:09:30,0:00:03,0:13:49,0:03:57,0:00:02,1:16:54,319,0,3
+General Medicine,PGY1,S0195075,2016-07-18,1:58:50,0:54:05,0:00:10,0:41:50,0:22:08,0:00:28,4:29:36,1145,30,9
+General Medicine,PGY1,S0195075,2016-07-17,0:40:20,0:42:47,0:00:06,0:08:56,0:12:57,0:05:25,2:22:19,741,30,5
+General Medicine,PGY1,S0195075,2016-07-16,2:45:50,1:07:27,0:00:07,0:39:07,0:28:04,0:09:30,5:31:22,1372,0,10
+General Medicine,PGY1,S0195075,2016-07-15,0:37:12,0:18:02,0:00:03,0:03:37,0:04:25,0:01:26,1:22:59,377,0,3
+General Medicine,PGY1,S0195075,2016-07-14,0:00:09,0:11:56,0:00:00,0:00:00,0:00:00,0:00:00,0:12:11,55,53,2
+General Medicine,PGY1,S0195075,2016-07-13,2:04:33,0:54:58,0:00:17,0:59:31,0:17:13,0:01:12,4:45:42,1368,11,7
+General Medicine,PGY1,S0195075,2016-07-12,1:41:24,0:34:18,0:00:10,1:11:23,0:29:11,0:04:33,4:47:54,1509,233,7
+General Medicine,PGY1,S0195075,2016-07-11,1:53:44,1:25:56,0:00:07,0:47:11,0:16:07,0:04:25,4:58:52,1370,0,10
+General Medicine,PGY1,S0195075,2016-07-10,0:46:59,0:35:22,0:00:06,0:26:49,0:04:34,0:00:51,2:10:22,507,0,3
+Night Team,PGY2+,S0169399,2015-09-22,1:27:59,1:13:28,2:27:29,0:15:50,0:12:34,0:22:00,6:17:12,1321,87,25
+Night Team,PGY2+,S0169399,2015-09-25,0:40:01,0:18:42,2:47:38,0:07:59,0:09:01,0:09:44,4:31:36,604,0,11
+General Medicine,PGY1,S0181069,2016-01-08,0:06:06,0:07:55,0:08:05,0:04:14,0:00:05,0:03:14,0:31:48,191,159,10
+Night Team,PGY2+,S0169399,2015-09-24,1:49:53,1:16:36,3:06:26,0:39:00,0:11:51,0:16:23,7:45:01,1352,195,50
+General Medicine,PGY1,S0181060,2015-12-01,2:02:07,1:03:30,0:12:25,0:14:29,0:16:18,0:33:55,4:54:53,1357,9,7
+General Medicine,PGY1,S0181060,2015-12-02,1:10:26,0:45:35,0:50:34,0:17:22,0:06:32,0:23:52,4:04:22,1326,8,19
+General Medicine,PGY1,S0181060,2015-12-03,1:43:47,0:54:02,0:57:51,1:14:07,0:37:05,1:10:52,7:08:15,2468,0,10
+General Medicine,PGY1,S0181060,2015-12-04,2:12:08,1:34:02,2:49:43,0:51:01,0:34:15,0:44:13,9:40:16,3623,28,15
+Night Team,PGY2+,S0169399,2015-09-27,0:57:18,0:53:04,5:33:11,0:36:23,0:04:52,0:10:30,8:38:45,1239,0,16
+General Medicine,PGY1,S0074196,2016-01-22,0:58:28,1:31:04,1:21:51,0:25:00,0:14:52,0:17:59,5:21:14,1942,0,21
+General Medicine,PGY1,S0181085,2016-01-24,0:53:05,1:14:57,1:35:10,0:28:55,0:16:59,0:39:12,5:42:31,1453,61,14
+General Medicine,PGY1,S0074196,2016-01-20,0:50:12,0:27:50,0:15:57,0:18:20,0:10:51,0:10:00,2:26:07,1100,0,9
+Night Team,PGY2+,S0169399,2015-09-26,0:22:18,0:06:56,0:57:01,0:13:23,0:06:21,0:02:02,1:53:59,299,0,8
+General Medicine,PGY1,S0074196,2016-01-26,0:28:37,0:45:31,0:26:01,0:10:57,0:11:09,0:05:51,2:17:01,987,72,7
+General Medicine,PGY1,S0074196,2016-01-27,1:19:42,1:32:25,1:57:59,0:49:21,0:23:36,0:16:29,6:54:12,2655,0,15
+General Medicine,PGY1,S0074196,2016-01-24,1:15:04,1:11:21,1:46:53,0:43:13,0:19:47,0:18:16,6:00:01,2218,0,16
+General Medicine,PGY1,S0181085,2016-01-25,0:00:00,0:00:06,0:00:00,0:00:00,0:00:00,0:00:00,0:00:15,31,27,4
+Night Team,PGY2+,S0169399,2015-09-29,0:22:34,0:29:15,0:44:22,0:11:29,0:06:10,0:09:14,2:15:41,628,0,27
+Night Team,PGY2+,S0169399,2015-09-28,0:28:13,0:33:02,2:12:15,0:19:14,0:02:01,0:06:58,3:49:40,758,0,8
+General Medicine,PGY1,S0181069,2016-01-09,1:04:03,0:55:37,1:11:58,0:29:30,0:08:49,0:19:26,4:26:42,1777,0,19
+Night Team,PGY2+,S0169398,2015-08-23,3:43:13,0:36:00,1:21:00,0:29:56,0:25:16,0:26:06,7:27:56,1530,20,30
+Night Team,PGY2+,S0169384,2016-10-23,3:53:42,2:04:08,0:00:08,1:15:49,0:22:21,0:03:41,8:08:44,3964,206,27
+General Medicine,PGY1,S0079337,2014-12-10,3:23:35,0:00:14,1:24:45,0:44:24,0:20:11,0:05:42,6:27:16,1811,16,8
+Emergency Medicine,PGY1,S0056755,2014-06-29,4:36:37,0:03:55,0:16:19,0:13:08,0:11:05,0:01:19,5:48:03,1392,0,33
+Night Team,PGY2+,S0169384,2016-10-26,0:58:41,0:32:35,0:00:11,0:30:29,0:03:36,0:03:19,2:22:07,710,0,9
+Night Team,PGY2+,S0169384,2016-10-27,1:49:40,0:40:18,0:00:03,0:25:00,0:05:13,0:12:37,3:32:35,1203,0,16
+Night Team,PGY2+,S0169384,2016-10-24,1:34:51,1:05:31,0:02:24,0:44:00,0:23:22,0:08:08,4:18:24,1787,813,16
+Night Team,PGY2+,S0169384,2016-10-25,2:30:19,1:39:55,0:05:26,0:43:12,0:12:40,0:08:36,5:42:21,1545,553,17
+Night Team,PGY2+,S0169404,2016-01-07,1:34:53,2:01:07,1:46:10,0:55:31,0:10:26,0:39:01,7:34:43,3256,119,32
+Night Team,PGY2+,S0169384,2016-10-28,1:46:13,1:55:41,0:00:24,0:39:12,0:15:22,0:04:27,5:01:00,1910,119,13
+Night Team,PGY2+,S0169384,2016-10-29,2:16:31,1:20:02,0:00:11,1:15:10,0:31:29,0:15:41,6:08:41,2030,132,17
+General Medicine,PGY1,S0181077,2016-05-09,2:07:44,0:47:08,0:04:29,0:17:16,0:04:10,0:01:01,4:05:31,1516,132,14
+General Medicine,PGY1,S0181077,2016-05-08,2:18:04,1:05:00,0:00:17,0:17:18,0:14:07,0:01:28,4:15:10,1489,6,19
+General Medicine,PGY1,S0181077,2016-05-03,4:08:41,1:36:18,0:00:11,0:27:55,0:18:11,0:05:36,7:04:30,2577,60,20
+General Medicine,PGY1,S0181077,2016-05-02,1:46:46,0:44:29,0:00:04,0:15:02,0:12:10,0:01:19,3:07:07,1132,81,4
+General Medicine,PGY1,S0181077,2016-05-01,3:15:11,1:16:54,0:00:38,0:31:11,0:17:59,0:04:49,5:59:36,2118,930,34
+General Medicine,PGY1,S0181077,2016-05-07,0:01:29,0:00:18,0:00:00,0:00:00,0:00:00,0:01:24,0:04:04,52,50,4
+General Medicine,PGY1,S0181077,2016-05-06,1:53:13,1:34:39,0:01:32,0:40:59,0:23:54,0:05:56,5:12:14,1946,98,17
+General Medicine,PGY1,S0181077,2016-05-05,1:37:16,0:56:27,0:00:12,0:14:35,0:11:39,0:02:29,3:32:31,1793,127,9
+General Medicine,PGY1,S0181077,2016-05-04,2:18:03,0:40:52,0:00:11,0:39:45,0:13:00,0:00:22,4:10:01,1625,85,11
+General Medicine,PGY1,S0169403,2014-08-29,0:00:00,0:36:01,0:00:00,0:00:00,0:00:00,0:00:04,0:43:11,375,369,18
+General Medicine,PGY1,S0169403,2014-08-28,3:42:23,0:19:24,0:14:02,0:34:18,0:29:40,0:48:11,6:30:02,1769,0,13
+Night Team,PGY2+,S0169404,2016-01-06,1:48:21,1:45:56,2:19:47,0:36:25,0:12:08,0:33:27,7:50:15,3437,149,39
+General Medicine,PGY1,S0169403,2014-08-21,1:39:40,0:00:08,0:01:34,0:29:07,0:07:15,0:04:22,2:36:54,692,0,6
+General Medicine,PGY1,S0169403,2014-08-20,3:02:36,0:12:31,0:13:19,0:28:59,0:28:04,0:09:13,4:48:43,1228,70,9
+General Medicine,PGY1,S0169403,2014-08-23,3:07:44,0:07:15,0:20:05,0:27:48,0:18:10,0:21:43,5:09:11,1647,0,8
+General Medicine,PGY1,S0169403,2014-08-22,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,2,0,1
+General Medicine,PGY1,S0169403,2014-08-25,3:10:04,0:34:20,0:20:39,0:10:03,0:19:03,0:40:56,5:27:38,1447,17,15
+General Medicine,PGY1,S0169403,2014-08-24,2:07:44,0:16:51,0:04:01,0:28:04,0:24:32,0:41:16,4:17:27,1116,88,9
+General Medicine,PGY1,S0169403,2014-08-27,2:05:21,0:02:39,0:11:22,0:26:27,0:24:31,0:29:05,3:57:38,1468,20,12
+General Medicine,PGY1,S0169403,2014-08-26,2:13:09,0:02:59,0:11:01,0:14:28,0:07:53,0:16:15,3:26:02,1437,192,8
+Emergency Medicine,PGY2+,S0109179,2015-10-30,4:41:31,0:13:47,0:48:56,0:19:23,0:12:50,0:07:22,6:42:44,1764,0,46
+Emergency Medicine,PGY2+,S0094535,2016-04-01,1:21:40,0:35:16,0:04:16,0:18:54,0:21:56,0:09:50,3:34:33,801,187,16
+Night Team,PGY2+,S0108268,2015-12-07,1:00:25,0:24:53,0:21:00,0:18:01,0:06:24,0:20:38,2:48:33,949,0,27
+Night Team,PGY2+,S0108268,2015-12-06,0:13:21,0:17:49,0:02:21,0:00:36,0:02:33,0:00:04,0:49:17,528,518,20
+Night Team,PGY2+,S0108268,2015-12-05,0:00:00,0:00:00,0:00:00,0:02:15,0:00:00,0:00:00,0:02:15,12,0,5
+Night Team,PGY2+,S0108268,2015-12-04,1:00:07,0:37:18,0:44:56,0:22:42,0:07:28,0:19:17,3:21:25,1231,0,14
+Night Team,PGY2+,S0169404,2016-01-05,1:35:07,2:12:16,1:29:14,1:00:46,0:13:40,0:32:17,7:45:00,4244,364,46
+Night Team,PGY2+,S0108268,2015-12-02,2:25:40,1:19:41,1:54:39,1:03:15,0:17:00,0:33:42,8:19:31,2832,329,43
+Night Team,PGY2+,S0108268,2015-12-01,4:31:05,1:08:37,1:55:10,0:38:22,0:29:59,0:45:12,9:52:15,2310,0,31
+Night Team,PGY2+,S0108268,2015-12-08,1:49:35,0:47:43,1:05:00,0:27:51,0:08:24,0:18:27,5:03:30,1762,0,27
+Night Team,PGY1,S0093383,2015-06-06,1:10:37,0:36:14,0:50:24,0:07:48,0:21:20,0:31:14,3:41:39,957,878,33
+Night Team,PGY2+,S0169404,2016-01-04,0:53:16,0:56:52,0:19:10,0:11:13,0:01:26,0:20:05,2:51:39,2514,83,30
+Night Team,PGY1,S0195076,2016-09-21,0:38:07,0:05:47,0:00:00,0:13:11,0:09:30,0:03:47,1:15:02,428,0,17
+General Medicine,PGY1,S0109179,2014-12-12,2:36:34,0:05:18,1:50:22,0:28:00,0:16:11,0:40:21,6:12:51,1600,0,14
+Night Team,PGY1,S0195076,2016-09-23,1:22:16,0:17:32,0:00:10,0:19:18,0:20:52,0:04:59,2:42:40,921,38,26
+General Medicine,PGY1,S0109179,2014-12-10,1:19:38,0:03:08,1:09:02,0:38:44,0:04:46,0:08:00,3:41:24,1109,0,5
+General Medicine,PGY1,S0109179,2014-12-17,3:05:15,0:06:01,3:27:07,0:43:58,0:22:07,0:35:15,8:52:05,2300,30,26
+General Medicine,PGY1,S0109179,2014-12-16,1:27:16,0:02:40,1:13:15,0:18:50,0:11:48,0:18:20,3:42:22,996,0,6
+General Medicine,PGY1,S0109179,2014-12-15,2:00:05,0:00:07,1:08:31,0:31:26,0:27:42,0:10:57,4:48:32,1946,0,23
+General Medicine,PGY1,S0109179,2014-12-14,3:10:19,0:12:34,2:24:46,0:27:26,0:22:00,0:48:01,7:46:57,1846,0,19
+General Medicine,PGY1,S0181081,2016-06-03,0:51:20,1:18:20,0:00:17,0:18:51,0:08:32,0:00:54,3:06:58,1016,12,12
+General Medicine,PGY1,S0181081,2016-06-02,2:41:24,1:50:35,0:03:50,0:21:11,0:13:48,0:01:24,6:35:56,2103,12,19
+General Medicine,PGY1,S0109179,2014-12-19,2:38:33,0:03:40,5:15:38,0:56:47,0:18:06,0:26:09,10:13:54,2701,0,24
+General Medicine,PGY1,S0109179,2014-12-18,1:16:13,0:06:27,1:16:55,0:21:18,0:10:37,0:18:28,3:51:27,1217,0,8
+General Medicine,PGY1,S0181081,2016-06-07,2:32:09,1:26:59,0:00:10,0:38:11,0:28:39,0:05:07,5:47:09,1991,9,14
+General Medicine,PGY1,S0181081,2016-06-06,2:38:32,1:57:32,0:00:14,1:24:23,0:35:27,0:00:58,7:16:40,2560,0,15
+General Medicine,PGY1,S0181081,2016-06-05,0:11:40,0:06:32,0:00:03,0:00:59,0:00:01,0:00:05,0:19:46,132,35,7
+General Medicine,PGY1,S0181081,2016-06-04,3:25:04,1:38:51,0:01:35,1:20:34,0:25:35,0:08:53,7:42:30,2759,34,15
+Night Team,PGY1,S0195227,2016-10-27,1:13:10,0:46:23,0:00:02,0:11:27,0:12:23,0:00:57,2:27:52,904,58,34
+Night Team,PGY1,S0195227,2016-10-26,0:59:07,1:08:46,0:00:00,0:10:48,0:06:48,0:00:08,2:33:50,787,106,23
+Night Team,PGY1,S0195227,2016-10-25,0:25:02,0:04:32,0:00:01,0:00:17,0:01:01,0:00:50,0:33:33,125,0,6
+Night Team,PGY1,S0195227,2016-10-24,1:41:36,1:09:08,0:00:45,0:27:34,0:08:44,0:02:55,3:42:31,1198,123,25
+Night Team,PGY1,S0195227,2016-10-23,2:29:49,1:24:52,0:00:09,0:38:57,0:12:23,0:00:24,5:04:23,1417,54,29
+Night Team,PGY1,S0195227,2016-10-22,2:43:53,1:46:01,0:00:01,0:16:37,0:10:49,0:02:03,5:24:17,1662,0,46
+Night Team,PGY1,S0195227,2016-10-21,2:44:10,1:41:14,0:00:04,0:52:24,0:22:31,0:04:34,6:10:28,1868,0,35
+Night Team,PGY1,S0195227,2016-10-20,0:46:41,0:18:06,0:00:00,0:18:26,0:10:10,0:00:14,1:41:27,501,0,22
+Night Team,PGY1,S0195227,2016-10-29,1:14:22,0:31:33,0:00:02,0:21:38,0:13:15,0:04:17,2:34:30,827,0,21
+Night Team,PGY1,S0195227,2016-10-28,1:35:57,0:39:28,0:00:01,0:11:48,0:07:52,0:08:15,2:49:23,897,185,31
+Intensive Care Unit,PGY2+,S0169411,2016-01-23,1:43:31,0:18:13,0:07:27,0:06:56,0:18:22,0:26:20,3:23:53,1087,0,15
+General Medicine,PGY1,S0196595,2016-07-19,0:37:38,0:08:29,0:00:02,0:01:22,0:02:22,0:00:08,0:52:51,235,0,6
+Intensive Care Unit,PGY2+,S0169411,2016-01-21,1:46:38,1:00:38,0:03:00,0:26:38,0:15:55,0:28:05,4:17:33,1766,0,20
+Intensive Care Unit,PGY2+,S0169411,2016-01-20,1:41:52,0:38:19,1:13:46,0:15:30,0:24:03,0:22:23,5:21:59,1953,0,24
+Night Team,PGY2+,S0181082,2016-11-06,1:00:16,0:31:48,0:00:03,0:05:21,0:04:22,0:00:03,1:53:29,479,441,17
+Night Team,PGY2+,S0181082,2016-11-07,1:22:38,0:52:35,0:00:02,0:04:59,0:04:47,0:03:28,2:41:36,1387,732,71
+Intensive Care Unit,PGY2+,S0169411,2016-01-25,2:59:52,1:10:12,0:05:11,0:12:02,0:17:43,0:22:28,5:36:25,2188,0,24
+General Medicine,PGY1,S0196595,2016-07-18,0:37:14,0:50:36,0:00:03,0:04:24,0:11:20,0:00:05,1:52:53,348,0,3
+Night Team,PGY2+,S0181082,2016-11-08,2:37:10,1:13:44,0:00:26,0:15:32,0:06:51,0:18:49,4:50:47,2769,69,48
+Night Team,PGY2+,S0181082,2016-11-09,3:51:57,2:28:12,0:00:23,0:30:22,0:22:19,0:12:14,7:56:46,2901,72,53
+Night Team,PGY2+,S0169404,2016-01-02,1:07:06,1:13:32,1:15:08,0:33:35,0:12:31,0:35:05,5:24:26,2301,378,32
+General Medicine,PGY1,S0172056,2016-05-08,0:51:38,0:21:46,0:00:05,0:16:17,0:07:42,0:04:05,2:03:06,738,124,6
+General Medicine,PGY1,S0172056,2016-05-09,1:18:25,0:59:18,0:00:42,0:22:52,0:10:50,0:12:12,4:11:25,1378,238,9
+General Medicine,PGY1,S0181074,2016-03-19,2:13:17,0:57:20,0:00:04,1:16:29,0:14:06,0:05:39,5:39:50,2086,43,20
+General Medicine,PGY1,S0181087,2016-02-06,1:57:13,1:33:20,1:36:58,0:31:48,0:18:57,0:41:40,7:17:30,3162,0,13
+General Medicine,PGY1,S0181074,2016-03-13,1:42:41,1:39:57,0:00:10,0:50:02,0:10:43,0:06:50,4:58:34,1590,115,43
+General Medicine,PGY1,S0172056,2016-05-01,1:39:04,1:15:25,0:05:32,0:45:55,0:29:01,0:03:29,4:59:45,1939,246,13
+General Medicine,PGY1,S0172056,2016-05-02,0:50:27,0:08:01,0:00:00,0:10:21,0:11:20,0:00:16,1:27:35,732,0,7
+General Medicine,PGY1,S0172056,2016-05-03,1:28:25,1:04:47,0:00:10,0:14:14,0:13:54,0:00:24,3:42:45,1268,240,5
+General Medicine,PGY1,S0172056,2016-05-04,1:49:54,0:42:09,0:00:00,0:35:28,0:11:13,0:00:55,3:46:10,1184,66,11
+General Medicine,PGY1,S0172056,2016-05-05,0:37:02,0:33:41,0:02:44,0:21:45,0:10:15,0:03:54,2:24:35,952,147,11
+General Medicine,PGY1,S0172056,2016-05-06,0:58:35,0:31:20,0:00:12,0:40:45,0:06:42,0:00:15,2:51:00,1023,144,8
+General Medicine,PGY1,S0172056,2016-05-07,0:00:47,0:06:58,0:00:00,0:00:00,0:00:00,0:00:01,0:07:54,136,134,5
+Intensive Care Unit,PGY2+,S0169385,2016-01-15,1:49:06,0:29:59,0:08:26,0:28:52,0:28:31,0:55:51,4:39:37,1853,47,11
+Night Team,PGY2+,S0169402,2016-09-09,3:39:44,2:19:39,0:00:09,1:01:38,0:12:37,0:12:41,7:58:02,2605,0,34
+Night Team,PGY2+,S0169402,2016-09-08,3:13:13,1:09:37,0:00:12,0:54:53,0:28:34,0:13:55,6:32:29,1772,941,41
+Night Team,PGY2+,S0169402,2016-09-05,2:32:40,1:39:31,0:00:06,0:44:31,0:15:38,0:06:42,6:20:03,1980,0,24
+Night Team,PGY2+,S0169402,2016-09-04,2:19:26,1:46:49,0:00:03,0:43:55,0:11:57,0:04:34,5:32:11,1587,40,17
+Night Team,PGY2+,S0169402,2016-09-07,0:30:42,0:21:07,0:00:00,0:23:51,0:11:09,0:01:23,1:39:37,593,236,18
+Night Team,PGY2+,S0169402,2016-09-06,2:23:48,1:13:42,0:00:08,0:18:35,0:06:47,0:04:52,4:27:40,1406,0,25
+Night Team,PGY2+,S0169402,2016-09-01,2:46:14,1:43:17,0:00:11,0:36:26,0:09:41,0:06:06,5:55:01,2269,367,25
+Night Team,PGY2+,S0169402,2016-09-03,1:49:50,1:34:32,0:00:03,0:21:37,0:12:43,0:10:46,4:36:51,1414,112,30
+Night Team,PGY2+,S0169402,2016-09-02,1:59:02,1:21:02,0:00:12,0:46:30,0:13:25,0:09:22,5:10:00,1702,0,31
+General Medicine,PGY1,S0181060,2015-08-02,0:54:34,0:21:35,1:12:26,0:03:36,0:08:28,0:06:14,3:02:26,646,100,5
+General Medicine,PGY1,S0169386,2014-07-08,0:44:55,0:00:47,0:01:47,0:00:00,0:00:14,0:04:28,0:56:59,308,308,11
+General Medicine,PGY1,S0169386,2014-07-09,0:00:00,0:04:31,0:00:00,0:00:00,0:00:00,0:03:08,0:12:49,136,136,6
+General Medicine,PGY1,S0195203,2016-06-28,2:44:58,1:29:20,0:00:12,0:51:52,0:46:48,0:09:41,6:40:16,1739,192,10
+General Medicine,PGY1,S0195203,2016-06-29,1:47:08,1:18:49,0:01:52,0:28:15,0:34:07,0:02:39,4:49:44,1228,227,10
+Night Team,PGY2+,S0169389,2016-03-19,2:11:58,0:58:07,0:00:17,0:29:42,0:13:52,0:04:48,4:13:49,1538,0,34
+General Medicine,PGY1,S0181087,2016-02-09,1:44:07,1:05:48,0:33:30,0:45:17,0:09:42,0:32:51,5:16:07,2282,130,16
+General Medicine,PGY1,S0195203,2016-06-26,3:33:01,1:28:37,0:00:08,0:35:31,0:35:51,0:08:40,7:23:38,1551,647,7
+General Medicine,PGY1,S0195203,2016-06-27,3:34:19,1:28:44,0:00:08,0:39:56,0:42:54,0:06:40,7:19:13,1662,224,10
+General Medicine,PGY1,S0181087,2016-02-08,1:44:34,1:27:50,2:09:13,1:21:28,0:42:08,0:37:30,8:46:20,3303,24,11
+General Medicine,PGY1,S0181069,2015-10-05,2:22:11,1:04:22,1:12:22,1:41:06,0:50:38,0:25:47,8:32:58,2870,0,9
+General Medicine,PGY1,S0181069,2015-10-04,1:17:54,1:32:10,1:05:52,1:19:08,0:18:40,0:28:59,6:38:44,2337,458,13
+General Medicine,PGY1,S0181069,2015-10-07,1:49:29,1:00:13,2:13:22,1:01:45,0:22:28,0:19:20,7:23:39,2149,0,8
+General Medicine,PGY1,S0181069,2015-10-06,1:51:39,1:04:40,1:28:37,0:56:16,0:40:27,0:23:48,7:04:56,2552,0,18
+General Medicine,PGY1,S0181069,2015-10-01,1:58:44,1:14:33,1:26:59,1:20:18,0:28:43,0:31:53,7:38:10,2603,0,8
+Night Team,PGY2+,S0181087,2016-09-05,1:13:38,0:54:47,0:00:02,0:16:34,0:03:13,0:00:21,2:49:07,1066,0,33
+General Medicine,PGY1,S0181069,2015-10-03,1:32:29,1:21:23,1:26:12,0:48:40,0:20:00,0:16:53,6:14:25,2323,0,9
+General Medicine,PGY1,S0181069,2015-10-02,1:52:13,1:32:44,0:58:56,1:37:41,0:17:14,0:22:17,7:22:41,2561,0,6
+Night Team,PGY2+,S0181087,2016-09-08,2:43:21,2:02:30,0:03:04,0:54:54,0:07:21,0:02:08,6:42:16,3224,95,58
+Night Team,PGY2+,S0181087,2016-09-09,2:47:21,1:48:27,0:00:16,0:53:21,0:15:45,0:07:19,6:44:34,3102,0,49
+Night Team,PGY1,S0169395,2015-04-05,2:36:35,0:37:45,2:03:13,0:27:24,0:28:49,0:38:20,7:23:37,1878,0,50
+General Medicine,PGY1,S0181069,2015-10-09,0:14:18,0:15:04,0:19:48,0:01:08,0:02:56,0:01:45,1:08:32,517,511,15
+General Medicine,PGY1,S0181069,2015-10-08,1:16:12,1:07:58,1:59:29,0:46:14,0:14:25,0:24:41,6:19:09,1718,169,15
+General Medicine,PGY1,S0195223,2016-09-11,1:02:58,0:38:22,0:00:09,0:13:47,0:09:08,0:02:01,2:28:15,1084,0,7
+Night Team,PGY1,S0195220,2016-11-10,0:36:48,0:15:03,0:00:00,0:07:08,0:12:45,0:03:23,1:17:43,505,318,19
+Night Team,PGY2+,S0169389,2016-03-15,3:49:43,1:50:08,0:00:04,0:46:14,0:13:45,0:05:11,7:22:06,2403,189,79
+Emergency Medicine,PGY1,S0169399,2015-05-18,0:25:02,0:08:20,2:38:11,0:14:08,0:10:36,0:03:05,3:46:28,497,0,10
+Emergency Medicine,PGY1,S0169399,2015-05-19,0:25:45,0:09:51,3:19:11,0:07:08,0:07:35,0:09:56,4:43:53,579,0,7
+General Medicine,PGY1,S0169399,2015-04-11,0:00:00,0:00:00,0:00:00,0:00:52,0:00:00,0:00:00,0:00:52,38,0,7
+General Medicine,PGY2+,S0169390,2016-07-18,3:43:43,1:07:43,0:00:15,0:28:55,0:27:00,0:13:28,6:39:09,2196,0,26
+General Medicine,PGY2+,S0169390,2016-07-19,0:44:59,0:06:33,0:00:00,0:12:20,0:01:52,0:00:21,1:14:42,337,0,13
+General Medicine,PGY2+,S0169390,2016-07-17,2:31:38,0:42:09,0:00:06,0:25:54,0:17:56,0:13:03,4:22:30,1186,0,18
+General Medicine,PGY2+,S0181078,2016-10-06,4:22:22,1:27:42,0:00:01,0:23:25,0:19:58,0:17:55,7:31:21,2675,500,21
+Emergency Medicine,PGY1,S0169399,2015-05-10,1:34:01,0:11:01,2:31:57,0:03:06,0:01:18,0:07:10,4:33:09,507,0,14
+General Medicine,PGY1,S0181082,2016-04-08,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,8,0,3
+General Medicine,PGY1,S0181082,2016-04-09,2:28:44,1:10:32,0:01:38,0:45:15,0:17:02,0:05:01,5:11:45,1349,0,10
+General Medicine,PGY1,S0181082,2016-04-04,2:24:54,1:34:37,0:01:39,0:54:07,0:18:44,0:02:11,5:38:39,1955,116,9
+General Medicine,PGY1,S0181082,2016-04-05,1:43:28,1:01:11,0:02:07,0:38:08,0:08:15,0:02:14,3:58:56,1322,0,12
+General Medicine,PGY1,S0181082,2016-04-06,1:19:07,1:14:23,0:00:11,0:25:05,0:07:32,0:00:33,3:21:24,1054,75,7
+General Medicine,PGY1,S0181082,2016-04-07,1:57:30,1:20:58,0:03:02,0:49:47,0:13:03,0:01:01,4:50:41,1411,0,8
+Emergency Medicine,PGY1,S0169406,2015-01-11,3:03:05,0:03:31,2:34:39,0:16:42,0:27:05,0:36:12,7:49:33,2684,195,26
+General Medicine,PGY1,S0181082,2016-04-01,1:49:53,1:19:51,0:00:08,1:01:44,0:29:48,0:08:58,5:13:30,1655,0,8
+General Medicine,PGY1,S0181082,2016-04-02,2:26:37,1:32:40,0:00:13,0:44:38,0:15:26,0:06:30,5:40:20,1631,0,9
+General Medicine,PGY1,S0181082,2016-04-03,1:37:20,0:37:02,0:00:11,0:25:20,0:12:35,0:03:43,3:30:18,1169,0,9
+Intensive Care Unit,PGY2+,S0181070,2016-08-27,2:36:22,1:25:45,0:00:17,0:34:54,0:34:53,0:13:09,5:40:27,1763,0,17
+General Medicine,PGY2+,S0169383,2016-06-28,2:34:45,1:03:10,0:00:06,0:28:26,0:12:01,0:05:44,5:02:52,1888,148,36
+Intensive Care Unit,PGY2+,S0181070,2016-08-25,1:26:50,1:38:58,0:04:06,0:19:45,0:23:44,0:04:36,4:16:34,1283,0,18
+Emergency Medicine,PGY1,S0169399,2015-05-13,0:22:50,0:05:07,0:27:06,0:03:00,0:07:47,0:05:33,1:11:26,136,0,6
+General Medicine,PGY1,S0195214,2016-08-19,0:51:08,0:55:12,0:00:21,0:18:27,0:03:01,0:00:06,2:22:40,689,157,5
+General Medicine,PGY1,S0195214,2016-08-18,2:29:02,2:04:43,0:05:12,1:00:39,0:22:33,0:07:11,6:29:47,1616,0,10
+Intensive Care Unit,PGY2+,S0181070,2016-08-21,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,7,0,1
+General Medicine,PGY2+,S0169383,2016-06-29,1:40:49,1:23:28,0:00:14,0:29:20,0:18:26,0:02:58,4:37:14,1896,99,19
+General Medicine,PGY1,S0195214,2016-08-15,3:02:50,1:00:06,0:00:33,0:54:38,0:21:34,0:00:15,5:56:54,1373,0,12
+Emergency Medicine,PGY1,S0169399,2015-05-14,2:10:18,0:25:37,2:08:59,0:10:58,0:12:09,0:11:41,5:42:34,874,0,9
+General Medicine,PGY1,S0195214,2016-08-17,2:08:59,1:45:28,0:01:20,0:42:30,0:20:40,0:00:11,5:30:15,1712,243,13
+General Medicine,PGY1,S0195214,2016-08-16,0:17:49,0:14:58,0:00:18,0:00:39,0:00:00,0:00:05,0:40:00,179,144,6
+General Medicine,PGY1,S0195214,2016-08-11,0:19:55,0:20:25,0:00:03,0:01:23,0:00:56,0:01:03,0:49:52,361,295,10
+General Medicine,PGY1,S0195214,2016-08-10,2:12:38,1:31:28,0:00:10,1:06:21,0:18:28,0:00:00,5:47:03,1666,133,16
+General Medicine,PGY1,S0195214,2016-08-13,2:37:31,1:01:21,0:00:09,1:05:30,0:13:57,0:00:00,5:18:38,1362,0,10
+Emergency Medicine,PGY1,S0169399,2015-05-15,2:36:30,0:31:56,6:05:33,0:33:12,0:12:31,0:20:16,10:55:59,1597,0,19
+Intensive Care Unit,PGY2+,S0109179,2016-02-29,0:59:18,0:43:59,0:03:20,0:10:14,0:21:58,0:00:17,2:37:34,847,0,22
+Intensive Care Unit,PGY2+,S0109179,2016-02-28,1:13:17,0:42:51,0:04:36,0:37:19,0:09:46,0:03:52,3:06:57,1393,0,18
+General Medicine,PGY1,S0195225,2016-10-19,2:18:54,1:47:48,0:00:12,0:58:26,0:33:55,0:02:20,6:25:48,2367,0,10
+Intensive Care Unit,PGY2+,S0109179,2016-02-25,1:26:27,0:15:07,0:02:25,0:11:21,0:08:58,0:17:15,2:29:03,1424,0,11
+General Medicine,PGY1,S0094959,2015-04-07,3:31:17,1:42:12,1:12:54,0:30:16,0:12:29,0:24:28,7:55:27,3272,0,16
+Intensive Care Unit,PGY2+,S0109179,2016-02-27,0:46:56,0:18:53,0:05:44,0:14:45,0:09:27,0:04:11,2:00:07,746,0,10
+Intensive Care Unit,PGY2+,S0109179,2016-02-26,1:28:23,0:58:29,0:07:49,0:14:12,0:16:28,0:00:49,3:28:05,1446,0,18
+Intensive Care Unit,PGY2+,S0109179,2016-02-21,1:27:39,0:46:58,0:07:52,0:05:19,0:19:51,0:00:19,3:13:41,1141,0,17
+Intensive Care Unit,PGY2+,S0109179,2016-02-20,0:20:02,0:07:45,0:01:36,0:07:09,0:03:39,0:00:02,0:52:27,299,0,9
+Intensive Care Unit,PGY2+,S0109179,2016-02-23,2:01:23,0:59:02,0:15:29,0:26:20,0:26:48,0:01:07,4:38:02,1434,1,15
+Intensive Care Unit,PGY2+,S0109179,2016-02-22,3:13:57,0:35:39,0:12:38,0:17:20,0:16:01,0:01:42,5:43:23,1434,0,21
+General Medicine,PGY1,S0056755,2015-03-19,2:34:19,0:21:27,1:19:49,0:27:31,0:08:06,0:10:02,5:22:11,1708,0,12
+General Medicine,PGY1,S0056755,2015-03-18,1:48:17,0:11:46,0:53:38,0:34:57,0:22:55,0:04:02,4:22:05,1362,0,14
+Intensive Care Unit,PGY2+,S0056755,2016-06-17,3:21:50,0:58:38,0:00:47,0:51:17,0:34:55,0:04:05,6:09:00,1459,0,30
+General Medicine,PGY1,S0056755,2015-03-11,3:11:10,0:30:46,2:00:56,0:42:57,0:36:08,0:11:28,7:45:14,2198,39,11
+General Medicine,PGY1,S0056755,2015-03-10,2:30:11,0:13:41,0:34:13,0:46:50,0:28:59,0:22:13,5:24:27,1716,0,11
+General Medicine,PGY1,S0056755,2015-03-13,3:23:13,0:12:58,1:16:37,0:44:17,0:28:33,0:27:21,6:59:18,2100,0,14
+General Medicine,PGY1,S0094959,2015-04-04,4:07:13,1:32:56,0:27:18,0:30:29,0:24:49,0:47:59,8:19:22,3670,0,47
+General Medicine,PGY1,S0056755,2015-03-15,0:00:00,0:00:00,0:00:00,0:01:13,0:00:00,0:00:00,0:01:13,10,0,3
+General Medicine,PGY1,S0056755,2015-03-14,1:40:36,0:11:08,0:36:14,0:13:40,0:20:42,0:15:42,3:26:02,1091,0,9
+General Medicine,PGY1,S0056755,2015-03-17,0:00:00,0:00:00,0:00:00,0:00:54,0:00:00,0:00:00,0:00:54,10,0,4
+General Medicine,PGY1,S0056755,2015-03-16,3:01:46,0:23:59,1:38:31,0:26:43,0:21:24,0:19:16,6:31:44,1850,0,11
+Night Team,PGY1,S0181077,2015-11-13,2:18:06,0:28:36,0:24:30,0:33:02,0:24:31,0:31:33,5:04:27,2115,0,40
+General Medicine,PGY1,S0181063,2015-12-23,0:11:02,1:11:27,0:03:24,0:07:58,0:01:10,0:08:51,1:46:24,512,502,7
+General Medicine,PGY1,S0181061,2015-10-09,0:11:59,0:08:37,0:00:11,0:00:00,0:00:00,0:00:00,0:20:53,56,52,5
+General Medicine,PGY1,S0181061,2015-10-08,2:15:01,1:10:08,1:54:46,1:05:24,0:34:42,0:25:59,8:26:18,1981,0,8
+General Medicine,PGY1,S0181061,2015-10-05,1:35:09,1:11:25,3:31:04,0:30:27,0:20:46,0:23:41,8:17:22,1794,0,9
+General Medicine,PGY1,S0181061,2015-10-04,0:00:02,0:03:05,0:01:31,0:00:00,0:00:01,0:00:00,0:05:54,27,23,2
+General Medicine,PGY1,S0181061,2015-10-07,1:00:31,0:46:47,1:43:19,0:38:52,0:15:28,0:16:19,5:11:15,1195,10,7
+General Medicine,PGY1,S0181061,2015-10-06,1:08:18,0:37:21,1:04:11,0:32:33,0:21:25,0:16:25,4:21:55,1237,104,9
+General Medicine,PGY1,S0181061,2015-10-01,1:15:07,1:07:07,1:35:25,0:34:18,0:14:22,0:06:57,5:40:43,1673,0,29
+General Medicine,PGY1,S0181061,2015-10-03,1:24:36,0:41:04,2:43:55,0:54:14,0:24:57,0:09:34,6:59:10,1529,0,6
+General Medicine,PGY1,S0181061,2015-10-02,0:28:43,0:59:36,2:07:54,0:05:54,0:10:31,0:09:52,4:24:46,1345,90,8
+Emergency Medicine,PGY1,S0172085,2015-08-07,2:54:39,0:57:27,2:30:16,0:20:28,0:32:44,0:36:42,8:33:02,2404,0,45
+General Medicine,PGY1,S0181063,2015-12-22,1:06:56,1:23:39,1:31:31,0:40:36,0:40:52,0:29:16,6:36:47,2104,0,13
+General Medicine,PGY1,S0195218,2016-08-31,1:21:39,1:06:59,0:01:36,0:40:00,0:11:56,0:03:13,4:10:16,1246,0,7
+General Medicine,PGY1,S0195218,2016-08-30,1:27:54,1:01:18,0:02:57,0:13:34,0:17:52,0:00:30,3:42:05,1715,54,26
+General Medicine,PGY1,S0172645,2015-10-10,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,2
+Intensive Care Unit,PGY2+,S0169385,2016-01-23,0:03:18,0:12:48,0:00:56,0:01:31,0:00:09,0:01:38,0:20:49,274,0,12
+Intensive Care Unit,PGY2+,S0169385,2016-01-22,1:38:12,0:45:18,0:14:11,0:23:21,0:25:23,0:42:53,4:19:26,1544,22,14
+General Medicine,PGY1,S0181069,2016-01-19,0:50:34,0:50:53,1:14:05,0:37:02,0:14:21,0:10:42,4:20:13,2207,60,12
+General Medicine,PGY1,S0181069,2016-01-18,0:00:00,0:00:00,0:00:00,0:01:45,0:00:00,0:00:00,0:01:45,26,0,5
+Intensive Care Unit,PGY2+,S0169385,2016-01-27,1:20:56,0:33:43,0:56:47,0:19:25,0:30:20,0:49:00,4:42:22,2204,319,20
+Intensive Care Unit,PGY2+,S0169385,2016-01-26,1:21:38,0:30:46,0:27:56,0:20:27,0:38:09,0:30:20,4:06:46,1469,35,17
+Intensive Care Unit,PGY2+,S0169385,2016-01-25,1:27:47,0:51:49,0:41:34,1:00:36,0:17:22,0:50:14,6:00:17,2278,0,19
+Intensive Care Unit,PGY2+,S0169385,2016-01-24,2:42:48,0:55:13,0:24:49,0:34:18,0:34:10,0:57:12,6:45:02,2311,0,16
+General Medicine,PGY1,S0181069,2016-01-13,1:29:56,0:25:47,0:40:58,1:06:22,0:25:48,0:12:19,5:02:35,2607,195,14
+General Medicine,PGY1,S0181069,2016-01-12,1:50:58,1:05:00,1:42:39,1:08:58,0:30:32,0:34:45,7:42:08,2469,0,16
+General Medicine,PGY1,S0181069,2016-01-11,1:58:40,0:48:24,0:40:44,1:02:31,0:43:07,0:17:34,6:07:21,2626,0,18
+General Medicine,PGY1,S0181069,2016-01-10,2:34:11,1:11:18,1:35:40,1:04:11,0:32:22,0:36:25,8:18:05,2781,0,11
+General Medicine,PGY1,S0181069,2016-01-17,1:57:05,1:21:43,1:24:48,1:08:56,0:22:45,0:24:02,7:18:41,3047,0,22
+General Medicine,PGY1,S0181069,2016-01-16,0:03:04,0:11:33,0:00:06,0:00:29,0:02:39,0:00:35,0:20:56,337,315,9
+General Medicine,PGY1,S0181069,2016-01-15,1:52:33,1:29:44,0:59:08,1:29:54,0:55:52,0:16:23,7:47:40,3089,0,13
+General Medicine,PGY1,S0181069,2016-01-14,1:04:54,0:35:20,0:40:24,0:33:32,0:14:20,0:10:26,4:00:57,1607,16,11
+Night Team,PGY2+,S0169384,2015-10-04,1:22:15,1:15:38,2:08:40,0:55:31,0:12:28,0:23:34,6:44:39,2404,83,16
+Night Team,PGY2+,S0074196,2016-06-27,0:18:30,0:34:42,0:00:05,0:17:37,0:00:38,0:00:01,1:24:24,463,0,2
+Night Team,PGY2+,S0074196,2016-06-26,0:29:26,0:30:10,0:00:02,0:19:38,0:04:54,0:00:12,1:35:38,616,258,12
+Night Team,PGY2+,S0169386,2016-04-10,3:48:19,1:42:54,0:15:39,0:51:17,0:15:15,0:03:56,8:30:48,3269,109,68
+Night Team,PGY2+,S0169386,2016-04-11,3:43:35,1:49:18,0:08:26,0:45:27,0:14:09,0:11:26,7:46:28,3213,54,97
+Night Team,PGY2+,S0169386,2016-04-12,3:43:14,1:54:37,0:14:28,0:58:56,0:28:18,0:15:24,8:29:37,3428,94,74
+Night Team,PGY2+,S0169386,2016-04-13,4:11:10,1:48:20,0:11:53,0:35:24,0:18:51,0:04:04,7:43:01,3251,195,88
+Night Team,PGY2+,S0169386,2016-04-14,2:59:22,1:29:09,0:02:31,0:27:27,0:12:54,0:07:13,6:01:02,2177,184,54
+Night Team,PGY2+,S0169386,2016-04-15,1:33:26,0:21:53,0:10:32,0:28:00,0:12:07,0:09:40,3:22:42,1016,33,58
+Night Team,PGY2+,S0169386,2016-04-16,2:01:40,1:02:05,0:04:48,0:27:36,0:05:47,0:05:23,4:06:37,1421,78,33
+General Medicine,PGY1,S0079337,2014-07-07,1:40:05,0:02:16,0:03:00,0:12:55,0:12:10,0:03:37,2:32:39,904,148,6
+Emergency Medicine,PGY1,S0195094,2016-08-22,0:02:55,0:00:40,0:00:00,0:00:00,0:00:00,0:00:22,0:04:07,25,0,4
+General Medicine,PGY1,S0079337,2014-07-05,2:42:02,0:01:22,0:13:44,0:15:19,0:25:36,0:06:22,4:17:37,1078,0,4
+General Medicine,PGY1,S0181081,2016-06-21,3:51:35,2:16:58,0:00:15,1:06:16,0:29:11,0:01:53,8:57:35,2931,19,19
+General Medicine,PGY1,S0079337,2014-07-03,2:42:02,0:05:35,0:28:51,0:31:31,0:22:04,0:04:19,4:48:08,1609,215,6
+General Medicine,PGY1,S0079337,2014-07-01,4:17:51,0:15:22,0:18:11,0:55:34,0:45:07,0:16:16,7:18:56,1921,281,8
+General Medicine,PGY1,S0181077,2016-05-19,2:11:15,1:11:04,0:00:10,0:21:26,0:13:29,0:08:27,4:41:23,2005,471,12
+Emergency Medicine,PGY2+,S0169395,2015-12-30,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+Emergency Medicine,PGY1,S0195216,2016-10-22,2:58:38,0:54:29,0:03:33,0:14:10,0:22:37,0:01:40,5:18:05,2107,0,69
+Emergency Medicine,PGY1,S0169404,2014-12-13,1:29:22,0:09:18,0:44:50,0:13:12,0:20:23,0:19:40,3:24:05,1179,47,15
+General Medicine,PGY1,S0169400,2015-04-27,2:15:06,0:10:47,1:21:02,0:42:55,0:14:35,0:13:46,5:27:55,1646,96,7
+General Medicine,PGY1,S0169400,2015-04-26,2:34:36,0:19:52,1:13:54,0:42:33,0:25:11,0:36:00,6:07:22,1536,60,21
+General Medicine,PGY1,S0181088,2015-07-19,0:56:13,0:05:00,1:41:07,0:05:30,0:15:08,0:25:52,4:05:46,807,0,5
+General Medicine,PGY1,S0169400,2015-04-29,1:13:37,0:08:11,0:30:44,0:11:16,0:15:11,0:20:12,2:47:40,719,0,6
+General Medicine,PGY1,S0169400,2015-04-28,1:55:12,0:25:51,1:12:42,0:46:54,0:24:07,0:25:38,5:32:24,1665,0,11
+Night Team,PGY1,S0195094,2016-07-23,1:25:40,1:07:17,0:03:38,0:05:11,0:12:22,0:00:22,3:13:45,823,0,10
+Night Team,PGY1,S0195094,2016-07-22,0:39:38,0:17:00,0:00:00,0:03:56,0:08:31,0:01:31,1:28:45,380,0,16
+Night Team,PGY1,S0195094,2016-07-21,2:03:40,0:25:19,0:00:03,0:27:25,0:35:05,0:01:42,4:01:28,886,94,21
+Night Team,PGY1,S0195094,2016-07-20,1:33:04,0:50:53,0:00:22,0:13:07,0:22:10,0:00:23,3:30:50,911,0,22
+General Medicine,PGY1,S0173803,2016-01-18,2:29:19,1:12:39,1:46:25,0:45:48,0:29:28,0:41:23,8:01:06,2790,52,22
+General Medicine,PGY2+,S0181082,2016-08-16,3:51:26,1:24:38,0:00:02,0:53:24,0:38:11,0:02:54,7:27:27,2310,97,36
+General Medicine,PGY1,S0169402,2014-11-20,2:52:58,0:04:24,2:48:52,0:51:49,0:40:14,0:09:53,7:57:56,1579,113,9
+General Medicine,PGY2+,S0181082,2016-08-15,1:36:31,0:26:43,0:00:06,0:19:55,0:19:06,0:04:37,3:14:09,1312,81,10
+General Medicine,PGY1,S0169402,2014-11-21,1:58:19,0:04:10,1:26:33,0:34:00,0:12:03,0:07:11,4:37:12,932,114,7
+General Medicine,PGY1,S0181085,2015-10-30,1:01:39,0:55:34,1:48:13,1:05:16,0:13:31,0:10:58,5:41:47,2026,30,11
+General Medicine,PGY2+,S0181082,2016-08-14,3:22:59,1:07:24,0:00:03,0:33:08,0:24:20,0:05:02,6:28:32,2277,119,26
+General Medicine,PGY1,S0169402,2014-11-22,3:29:01,0:04:55,1:50:48,1:19:51,0:29:24,0:08:24,7:57:40,2074,0,10
+Night Team,PGY1,S0181064,2016-01-30,0:01:05,0:01:07,0:00:02,0:00:09,0:00:00,0:00:00,0:02:35,59,45,8
+General Medicine,PGY1,S0169402,2014-11-23,2:26:22,0:00:52,2:50:58,0:14:02,0:13:25,0:01:15,6:07:33,984,145,10
+General Medicine,PGY2+,S0169389,2015-11-17,2:47:02,0:59:24,0:11:18,0:51:26,0:27:12,0:51:47,6:30:43,2042,249,19
+Night Team,PGY1,S0195206,2016-08-13,4:01:35,1:32:07,0:00:07,0:16:33,0:20:23,0:03:39,6:55:12,1956,407,49
+General Medicine,PGY1,S0169399,2015-03-29,1:48:09,0:03:13,1:57:30,0:15:25,0:17:05,0:00:00,4:38:36,783,0,6
+General Medicine,PGY2+,S0181082,2016-08-18,1:34:55,0:39:03,0:00:48,0:18:08,0:24:13,0:03:21,3:27:42,1237,237,12
+General Medicine,PGY1,S0169402,2014-11-26,4:03:30,0:14:50,2:34:55,0:41:37,0:23:05,0:07:21,8:55:43,1942,207,7
+General Medicine,PGY1,S0169402,2014-11-27,3:34:50,0:02:52,2:55:12,1:13:10,0:29:38,0:09:51,8:45:50,1712,82,15
+General Medicine,PGY1,S0074196,2015-08-28,1:58:15,0:26:03,0:44:04,0:50:00,0:20:34,0:26:13,5:22:12,1399,15,14
+General Medicine,PGY1,S0074196,2015-08-29,0:05:20,0:02:44,0:18:29,0:00:00,0:00:12,0:00:57,0:27:48,62,60,2
+General Medicine,PGY1,S0074196,2015-08-26,1:32:37,0:20:03,1:59:37,0:26:33,0:08:16,0:09:30,5:11:10,1228,11,9
+General Medicine,PGY1,S0074196,2015-08-27,0:01:12,0:00:00,0:00:00,0:00:10,0:00:01,0:00:00,0:01:36,31,29,1
+General Medicine,PGY1,S0074196,2015-08-24,1:30:34,0:07:13,0:19:03,0:40:50,0:08:22,0:26:34,3:30:33,1287,36,7
+General Medicine,PGY1,S0074196,2015-08-25,1:35:44,1:01:40,1:39:15,1:11:05,0:13:06,0:29:26,6:36:24,1816,0,16
+General Medicine,PGY1,S0074196,2015-08-22,1:09:51,0:21:08,0:21:53,0:10:22,0:14:32,0:19:22,3:00:55,786,0,8
+General Medicine,PGY1,S0074196,2015-08-23,1:34:14,0:34:14,5:00:23,0:36:26,0:13:00,0:30:54,8:50:12,1501,0,19
+General Medicine,PGY1,S0074196,2015-08-20,2:04:36,0:25:56,2:14:32,0:49:22,0:25:36,0:13:08,6:48:23,1570,0,8
+General Medicine,PGY1,S0074196,2015-08-21,0:13:52,0:00:00,0:18:14,0:01:33,0:00:03,0:00:23,0:37:49,125,115,3
+General Medicine,PGY1,S0169383,2015-05-22,2:18:11,0:49:18,2:03:57,0:33:06,0:14:18,0:21:07,6:45:43,1711,0,18
+General Medicine,PGY1,S0169383,2015-05-23,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,16,0,3
+General Medicine,PGY1,S0169383,2015-05-20,2:26:12,0:47:28,1:40:13,0:35:39,0:19:42,0:22:59,6:38:29,1787,10,14
+General Medicine,PGY1,S0169383,2015-05-21,3:05:42,0:16:56,1:25:04,0:33:26,0:21:46,0:14:43,6:31:48,2126,0,21
+General Medicine,PGY1,S0169385,2015-05-06,2:19:55,0:15:05,1:04:10,0:31:56,0:17:10,0:31:12,5:26:44,2266,0,14
+General Medicine,PGY1,S0169385,2015-05-07,3:04:01,0:39:11,2:01:04,0:34:36,0:24:26,0:20:40,7:38:38,2437,0,13
+Intensive Care Unit,PGY2+,S0106952,2015-11-03,0:01:14,0:17:47,0:04:39,0:00:47,0:00:00,0:00:30,0:26:08,280,270,14
+General Medicine,PGY1,S0169385,2015-05-05,0:04:10,0:00:00,0:00:45,0:00:00,0:00:15,0:00:28,0:09:18,92,90,8
+General Medicine,PGY1,S0169385,2015-05-08,2:23:17,0:17:30,1:45:27,0:42:35,0:13:50,0:24:58,6:07:44,1742,146,6
+General Medicine,PGY1,S0169385,2015-05-09,3:24:20,1:24:23,1:53:29,0:57:13,0:16:58,0:44:09,9:07:34,3169,0,23
+Emergency Medicine,PGY1,S0056755,2014-07-18,5:43:37,0:20:33,0:35:06,0:14:31,0:09:12,0:02:07,7:40:36,1616,0,20
+Emergency Medicine,PGY1,S0056755,2014-07-14,2:22:10,0:00:33,0:04:00,0:01:10,0:07:28,0:00:37,2:43:05,688,0,11
+Emergency Medicine,PGY1,S0056755,2014-07-15,2:48:04,0:35:32,0:14:14,0:04:37,0:11:45,0:01:45,4:14:28,1279,0,22
+Emergency Medicine,PGY1,S0056755,2014-07-11,3:36:32,0:14:43,0:19:43,0:13:52,0:14:56,0:01:17,4:58:06,1602,1,26
+Emergency Medicine,PGY1,S0056755,2014-07-12,5:47:47,0:23:03,0:25:49,0:08:21,0:11:50,0:02:22,7:19:06,1671,0,18
+Emergency Medicine,PGY1,S0056755,2014-07-13,5:45:31,0:26:05,0:35:00,0:06:51,0:09:00,0:01:02,7:27:55,1627,0,18
+Night Team,PGY1,S0169400,2015-03-08,0:39:13,0:00:05,0:09:22,0:01:42,0:02:02,0:11:31,1:09:03,342,0,35
+Night Team,PGY1,S0169400,2015-03-09,1:23:48,0:02:12,0:07:13,0:13:51,0:21:01,0:10:44,2:24:15,667,0,59
+Night Team,PGY1,S0169400,2015-03-06,1:30:31,0:43:59,2:05:05,0:30:15,0:20:01,0:22:05,5:46:40,1086,17,32
+Night Team,PGY1,S0169400,2015-03-07,1:59:57,0:33:48,1:40:15,0:43:55,0:24:56,0:38:39,6:24:59,1550,84,60
+Night Team,PGY1,S0169400,2015-03-04,1:52:07,0:19:32,1:33:36,0:48:29,0:23:03,0:41:39,5:55:19,1571,0,37
+Night Team,PGY1,S0169400,2015-03-05,2:13:32,0:11:16,0:23:35,0:38:38,0:33:42,0:20:46,4:38:19,1274,13,45
+Night Team,PGY1,S0169400,2015-03-02,2:10:45,0:11:50,0:48:27,0:19:54,0:31:04,0:18:26,4:38:01,1337,27,57
+Night Team,PGY1,S0169400,2015-03-03,2:01:18,0:12:16,0:52:15,0:32:14,0:32:12,0:39:30,5:10:48,1293,70,49
+Night Team,PGY1,S0169400,2015-03-01,0:38:09,0:00:03,0:39:01,0:11:38,0:10:47,0:10:12,2:06:33,436,0,21
+General Medicine,PGY1,S0173803,2016-01-14,1:39:54,0:48:21,0:47:47,0:19:22,0:24:08,0:16:07,4:47:07,1997,11,13
+General Medicine,PGY1,S0173803,2016-01-15,0:00:00,0:00:00,0:00:00,0:00:01,0:00:00,0:00:00,0:00:01,10,0,3
+General Medicine,PGY1,S0173803,2016-01-16,2:09:40,1:10:35,1:28:02,0:49:35,0:35:12,0:35:02,7:22:18,2342,0,16
+General Medicine,PGY1,S0173803,2016-01-17,1:46:20,0:59:35,1:07:43,1:06:50,0:26:17,0:28:15,6:26:01,2392,500,16
+General Medicine,PGY1,S0173803,2016-01-10,0:00:00,0:00:00,0:00:00,0:00:20,0:00:00,0:00:00,0:00:21,15,0,5
+General Medicine,PGY1,S0173803,2016-01-11,1:50:05,1:59:49,2:02:13,1:16:38,0:27:14,0:35:50,8:45:22,3132,46,18
+General Medicine,PGY1,S0173803,2016-01-12,1:40:21,1:09:48,1:12:28,0:58:22,0:21:08,0:40:27,6:28:27,2585,0,19
+General Medicine,PGY1,S0173803,2016-01-13,1:53:40,0:37:55,1:46:30,0:58:55,0:26:55,0:27:22,6:43:22,2489,0,19
+General Medicine,PGY1,S0169404,2014-08-11,1:05:47,0:21:57,0:01:54,0:05:10,0:02:35,0:11:08,1:53:54,950,940,17
+General Medicine,PGY1,S0169404,2014-08-10,4:13:48,0:44:15,0:18:02,0:51:32,0:27:29,0:22:49,7:25:21,2840,172,12
+General Medicine,PGY1,S0169404,2014-08-13,3:57:23,0:57:10,0:25:34,0:24:37,0:39:25,0:19:23,7:01:01,3154,534,18
+General Medicine,PGY1,S0169404,2014-08-12,3:48:33,0:51:50,0:25:29,0:29:25,0:22:07,0:13:00,6:26:37,2658,280,15
+General Medicine,PGY1,S0169404,2014-08-15,3:58:21,0:43:37,0:18:57,0:32:14,0:31:28,0:33:32,7:09:50,2848,27,17
+General Medicine,PGY1,S0169404,2014-08-14,3:12:22,0:40:22,0:15:07,0:48:27,0:30:24,0:16:29,6:01:06,2425,209,10
+General Medicine,PGY1,S0169404,2014-08-16,0:00:00,0:05:23,0:00:00,0:02:11,0:00:00,0:00:37,0:08:21,93,85,6
+General Medicine,PGY1,S0195223,2016-08-26,0:06:36,0:00:08,0:00:00,0:00:00,0:00:00,0:00:37,0:08:19,36,0,2
+General Medicine,PGY1,S0195223,2016-08-27,1:17:05,0:44:31,0:00:16,0:24:53,0:13:41,0:03:53,3:31:06,1230,0,8
+Night Team,PGY2+,S0163662,2015-08-09,1:03:26,0:33:42,3:50:41,0:19:58,0:26:04,0:26:52,7:00:45,2884,0,18
+General Medicine,PGY1,S0195223,2016-08-25,2:37:23,0:33:56,0:02:15,1:22:37,0:18:38,0:08:50,5:50:16,1557,0,10
+General Medicine,PGY1,S0195223,2016-08-22,1:28:05,1:08:50,0:00:13,0:43:10,0:19:41,0:13:13,4:31:43,1216,0,11
+General Medicine,PGY1,S0195223,2016-08-23,2:05:09,1:14:07,0:00:07,0:29:23,0:26:17,0:02:23,4:43:08,984,0,9
+General Medicine,PGY1,S0195223,2016-08-21,1:20:32,1:30:44,0:01:39,0:21:07,0:25:38,0:11:06,4:10:59,1142,0,8
+General Medicine,PGY1,S0195223,2016-08-28,2:19:27,1:23:29,0:00:07,0:51:42,0:18:48,0:05:20,6:02:36,1561,0,9
+General Medicine,PGY1,S0195223,2016-08-29,0:05:19,0:02:54,0:00:00,0:00:00,0:00:00,0:00:08,0:10:38,77,0,7
+Emergency Medicine,PGY2+,S0169390,2015-07-31,2:00:23,0:52:17,3:29:41,0:32:32,0:32:25,0:29:33,9:04:00,2397,0,24
+Emergency Medicine,PGY2+,S0169390,2015-07-30,0:48:43,0:08:55,0:53:37,0:13:46,0:10:21,0:15:27,3:03:50,1050,0,13
+General Medicine,PGY1,S0186952,2016-09-25,2:43:49,1:08:44,0:00:08,0:49:44,0:31:56,0:05:25,6:12:14,1903,0,20
+General Medicine,PGY1,S0094959,2014-12-03,5:28:25,0:14:00,1:06:56,0:41:55,0:21:14,0:12:51,8:20:22,3358,40,40
+General Medicine,PGY1,S0094959,2014-12-02,3:29:06,0:02:20,0:26:24,0:13:27,0:13:35,0:11:41,4:53:23,2200,0,30
+General Medicine,PGY1,S0094959,2014-12-05,4:05:21,0:14:00,1:04:40,0:21:08,0:17:21,0:20:21,6:39:09,3118,238,50
+General Medicine,PGY1,S0094959,2014-12-04,2:00:47,0:04:49,0:15:21,0:06:14,0:09:55,0:16:44,2:58:16,1426,103,34
+General Medicine,PGY1,S0094959,2014-12-06,0:00:00,0:08:49,0:00:00,0:00:00,0:00:00,0:00:39,0:10:45,98,96,10
+General Medicine,PGY1,S0106952,2014-10-06,0:58:28,0:00:55,0:06:05,0:00:02,0:01:13,0:06:55,1:21:59,630,622,31
+General Medicine,PGY1,S0106952,2014-10-07,0:26:13,0:00:14,0:00:40,0:00:01,0:01:23,0:00:32,0:32:44,207,199,15
+General Medicine,PGY1,S0106952,2014-10-04,3:18:43,0:12:20,0:22:04,0:34:27,0:29:11,0:24:53,5:53:07,1741,334,31
+General Medicine,PGY1,S0106952,2014-10-05,4:44:45,0:09:45,0:16:07,1:00:15,0:35:31,1:08:01,8:38:10,2178,23,13
+General Medicine,PGY1,S0106952,2014-10-02,2:49:55,0:03:51,0:20:56,0:12:16,0:12:12,0:09:29,4:12:48,1195,409,22
+General Medicine,PGY1,S0106952,2014-10-03,4:48:11,0:22:49,0:10:08,0:43:04,0:36:51,0:27:50,7:46:31,2383,307,28
+General Medicine,PGY1,S0106952,2014-10-01,0:54:59,0:00:26,0:11:25,0:00:02,0:01:41,0:03:46,1:31:38,548,540,32
+General Medicine,PGY1,S0106952,2014-10-08,6:05:14,0:07:57,0:10:58,1:33:47,0:35:58,0:43:22,10:01:01,2654,470,30
+General Medicine,PGY1,S0106952,2014-10-09,4:45:34,0:16:55,0:14:51,0:23:55,0:28:25,0:21:44,7:01:27,1903,417,21
+Emergency Medicine,PGY1,S0181077,2015-10-10,0:03:25,0:00:41,0:01:33,0:00:00,0:00:00,0:00:07,0:05:51,37,37,4
+Night Team,PGY1,S0169400,2014-07-06,2:44:27,0:09:55,0:12:40,0:34:48,0:35:28,0:41:52,5:26:55,1298,66,27
+General Medicine,PGY2+,S0169390,2016-08-02,4:22:37,1:09:10,0:00:03,0:36:38,0:40:53,0:03:32,7:37:27,2751,0,22
+General Medicine,PGY1,S0181067,2015-08-17,1:13:08,0:15:51,0:35:35,0:12:50,0:11:38,0:11:06,2:48:17,697,57,4
+General Medicine,PGY1,S0181067,2015-08-16,1:46:44,0:20:06,0:24:46,0:45:02,0:38:16,0:27:05,4:50:11,1712,48,11
+General Medicine,PGY1,S0181067,2015-08-19,2:24:34,0:54:30,1:48:12,0:41:41,0:22:44,0:33:55,7:10:23,1710,0,11
+General Medicine,PGY1,S0181067,2015-08-18,1:33:14,0:18:02,0:53:37,0:25:33,0:21:18,0:10:58,4:10:27,1216,166,4
+General Medicine,PGY1,S0181069,2016-04-03,2:53:53,2:01:24,0:00:19,1:00:45,0:22:16,0:09:11,7:30:21,3161,0,13
+General Medicine,PGY1,S0181069,2016-04-02,1:03:39,0:40:44,0:00:12,0:31:48,0:05:49,0:00:11,2:46:17,1224,1,15
+General Medicine,PGY1,S0169394,2015-04-27,1:49:00,0:12:06,1:18:45,0:42:31,0:20:16,0:18:22,5:01:25,1472,54,8
+General Medicine,PGY1,S0169394,2015-04-26,2:26:46,0:18:23,0:53:59,0:24:30,0:17:04,0:23:50,5:05:35,1306,253,7
+General Medicine,PGY1,S0169394,2015-04-29,2:37:08,0:06:54,1:20:37,0:35:03,0:12:21,0:12:43,5:33:27,1662,343,9
+General Medicine,PGY1,S0195205,2016-09-09,1:30:49,1:20:44,0:00:11,0:57:58,0:25:32,0:02:04,4:50:50,1836,962,15
+General Medicine,PGY1,S0195202,2016-07-01,2:46:47,1:57:36,0:00:44,0:36:59,0:32:34,0:01:11,6:32:56,1957,557,8
+Emergency Medicine,PGY1,S0195075,2016-07-31,2:18:19,0:20:01,0:06:33,0:26:51,0:37:48,0:00:52,4:23:31,1279,0,28
+General Medicine,PGY1,S0181067,2015-09-11,0:00:00,0:00:00,0:00:00,0:02:38,0:00:00,0:00:00,0:02:38,20,0,6
+Night Team,PGY1,S0181075,2016-01-01,1:22:57,0:28:47,1:19:33,0:56:23,0:24:21,0:36:34,5:26:24,1673,0,29
+Night Team,PGY1,S0181075,2016-01-02,0:57:40,0:59:08,0:42:46,0:33:56,0:09:59,0:19:22,4:00:16,1444,0,18
+Night Team,PGY2+,S0181085,2016-11-09,1:49:18,2:16:03,0:03:54,0:28:34,0:14:53,0:09:16,5:16:57,1341,96,29
+Night Team,PGY2+,S0181085,2016-11-08,0:33:39,0:44:23,0:00:00,0:19:35,0:18:58,0:03:53,2:15:49,619,0,21
+Night Team,PGY2+,S0181085,2016-11-07,1:08:05,0:19:55,0:00:02,0:22:13,0:04:24,0:01:00,2:08:38,664,0,21
+Night Team,PGY2+,S0181085,2016-11-06,1:25:20,1:47:06,0:00:13,0:40:27,0:09:04,0:02:53,4:37:38,1426,0,29
+Night Team,PGY2+,S0169411,2015-10-04,2:04:34,1:22:59,2:12:43,0:49:30,0:04:18,0:15:13,7:15:40,2390,0,61
+Night Team,PGY2+,S0169411,2015-10-05,2:51:12,1:06:52,1:43:52,0:25:40,0:05:16,0:18:27,6:55:54,2253,0,71
+Night Team,PGY2+,S0169411,2015-10-06,3:48:26,1:09:30,2:20:33,1:22:10,0:15:07,0:26:36,9:42:34,3037,2,47
+Night Team,PGY2+,S0169411,2015-10-07,3:27:22,1:16:16,2:28:18,0:50:18,0:18:00,0:21:12,9:07:21,2981,0,63
+Night Team,PGY2+,S0169411,2015-10-08,3:10:55,1:29:24,1:31:23,0:55:06,0:07:10,0:31:45,8:01:51,2933,0,46
+General Medicine,PGY1,S0181077,2016-05-20,0:10:53,0:30:34,0:00:17,0:09:33,0:04:18,0:03:12,1:08:38,507,486,10
+General Medicine,PGY2+,S0181082,2016-08-17,1:08:30,0:25:54,0:00:18,0:16:45,0:20:38,0:05:13,2:44:37,818,87,12
+General Medicine,PGY1,S0169403,2014-09-01,1:21:52,0:07:44,0:11:14,0:24:08,0:20:27,0:21:02,3:05:03,1032,239,6
+General Medicine,PGY1,S0169390,2015-06-25,0:00:00,0:00:39,0:00:00,0:01:45,0:00:00,0:01:13,0:04:19,53,34,5
+General Medicine,PGY1,S0169403,2014-09-02,3:11:14,0:13:42,0:16:11,0:57:02,0:22:59,0:39:33,6:10:43,1848,0,7
+General Medicine,PGY1,S0169403,2014-09-03,1:59:35,0:08:44,0:30:49,0:12:58,0:21:06,0:20:47,4:02:20,1096,48,8
+Night Team,PGY2+,S0074196,2016-09-26,1:34:45,0:34:13,0:00:01,0:10:40,0:06:45,0:00:36,2:44:28,1180,345,15
+Night Team,PGY2+,S0074196,2016-09-27,1:44:54,1:16:09,0:00:15,0:42:12,0:12:01,0:08:18,4:46:56,1758,0,17
+General Medicine,PGY1,S0195225,2016-10-16,2:38:46,1:21:07,0:00:07,1:21:56,0:35:07,0:03:09,6:49:10,2247,0,7
+Night Team,PGY2+,S0074196,2016-09-25,0:27:52,0:27:16,0:00:01,0:09:22,0:01:05,0:00:14,1:10:14,486,0,2
+General Medicine,PGY1,S0195225,2016-10-18,1:59:37,1:31:48,0:00:13,0:30:03,0:15:06,0:00:16,4:48:11,1663,0,6
+Intensive Care Unit,PGY2+,S0181061,2016-08-25,1:33:07,0:17:53,0:06:06,0:15:12,0:16:48,0:00:31,2:58:18,677,0,10
+Night Team,PGY2+,S0074196,2016-09-28,2:21:37,1:02:13,0:00:10,0:38:48,0:09:38,0:00:14,4:35:16,1741,0,18
+Night Team,PGY2+,S0074196,2016-09-29,1:34:28,1:13:30,0:00:07,0:15:22,0:07:42,0:08:01,3:35:55,1135,0,11
+Night Team,PGY2+,S0172645,2016-07-01,1:24:08,1:24:38,0:03:26,0:16:22,0:13:27,0:03:08,3:38:47,1357,0,10
+Night Team,PGY2+,S0169384,2015-10-06,1:24:22,1:26:34,2:32:53,0:40:37,0:30:37,0:56:29,7:54:47,2773,319,14
+Night Team,PGY2+,S0169384,2015-10-05,0:45:45,0:43:25,1:08:25,0:25:41,0:15:14,0:10:57,3:39:18,1104,0,16
+Night Team,PGY2+,S0181069,2016-07-08,2:17:16,1:05:57,0:00:14,0:53:01,0:28:34,0:04:12,5:20:34,1881,0,33
+General Medicine,PGY1,S0169403,2014-09-08,1:52:27,0:29:22,0:13:41,0:23:56,0:09:19,0:33:39,3:58:56,1493,157,11
+General Medicine,PGY1,S0109179,2014-12-31,2:38:16,0:06:46,1:16:39,0:28:13,0:23:29,0:16:45,5:35:56,1893,0,26
+General Medicine,PGY1,S0109179,2014-12-30,0:00:00,0:00:00,0:00:00,0:01:25,0:00:00,0:00:00,0:01:25,26,0,5
+General Medicine,PGY1,S0079337,2014-07-08,2:44:02,0:02:13,0:19:07,0:07:27,0:22:30,0:08:51,3:50:53,1249,35,9
+General Medicine,PGY1,S0079337,2014-07-09,3:52:07,0:03:40,0:28:34,0:35:42,0:25:10,0:06:25,6:02:19,1883,1,11
+General Medicine,PGY1,S0181081,2016-06-25,0:04:20,0:04:21,0:00:00,0:01:15,0:00:00,0:00:12,0:11:06,89,69,7
+General Medicine,PGY1,S0181081,2016-06-24,2:56:15,1:23:25,0:00:11,0:55:55,0:28:32,0:09:10,6:49:07,2602,0,16
+General Medicine,PGY1,S0079337,2014-07-04,4:17:44,0:08:28,0:19:50,0:33:21,0:13:11,0:25:22,6:16:53,1559,0,5
+Emergency Medicine,PGY2+,S0169403,2016-01-30,3:32:20,1:39:18,2:43:48,0:30:44,0:44:13,0:39:30,10:21:50,4410,232,50
+Night Team,PGY2+,S0172645,2016-07-02,1:00:04,1:52:13,0:00:08,0:22:14,0:13:41,0:03:46,3:47:34,1986,0,17
+General Medicine,PGY1,S0181081,2016-06-20,0:12:44,0:04:44,0:00:22,0:01:11,0:00:04,0:02:01,0:28:34,276,264,16
+General Medicine,PGY1,S0181081,2016-06-23,2:08:57,2:25:04,0:00:40,0:31:54,0:17:04,0:02:53,6:32:07,2399,516,27
+General Medicine,PGY1,S0181081,2016-06-22,1:30:45,0:53:03,0:00:09,0:59:14,0:11:59,0:02:25,4:14:51,1609,8,8
+General Medicine,PGY1,S0172056,2016-05-20,1:08:35,1:03:27,0:01:44,0:13:09,0:08:07,0:12:33,3:34:08,1379,0,16
+General Medicine,PGY1,S0172056,2016-05-21,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,4,0,2
+General Medicine,PGY1,S0163662,2015-03-19,3:05:55,0:17:12,2:31:10,0:57:39,0:22:26,0:10:48,7:52:15,1697,0,11
+General Medicine,PGY1,S0163662,2015-03-18,3:13:09,0:48:04,1:41:56,0:36:45,0:36:42,0:08:31,7:27:08,2076,554,14
+General Medicine,PGY1,S0163662,2015-03-15,3:26:41,0:40:08,1:16:31,1:14:37,0:36:29,0:22:07,7:52:41,1878,288,9
+General Medicine,PGY1,S0163662,2015-03-14,3:32:15,0:54:35,2:06:18,0:42:02,0:49:21,0:32:30,8:51:29,2505,0,16
+General Medicine,PGY1,S0163662,2015-03-17,3:15:37,1:17:30,2:14:35,0:54:19,0:53:42,0:20:31,9:18:31,2359,0,9
+General Medicine,PGY1,S0163662,2015-03-16,0:48:27,0:13:31,2:37:01,0:09:01,0:00:15,0:00:29,3:51:28,950,930,12
+General Medicine,PGY1,S0163662,2015-03-11,0:23:11,0:14:18,0:35:33,0:00:14,0:00:50,0:02:30,1:20:08,419,413,9
+General Medicine,PGY1,S0163662,2015-03-10,2:40:18,0:36:51,0:53:30,0:32:44,0:27:01,0:29:37,5:55:43,1426,135,6
+General Medicine,PGY1,S0163662,2015-03-13,4:01:58,0:46:29,1:08:52,0:41:17,0:43:02,0:13:23,8:01:16,2160,296,10
+General Medicine,PGY1,S0163662,2015-03-12,4:05:10,1:44:01,1:06:01,1:18:46,0:44:41,0:45:54,10:15:43,3166,22,12
+General Medicine,PGY1,S0163662,2014-09-05,2:37:14,0:20:18,0:12:38,0:21:55,0:16:29,0:21:27,4:31:25,1321,16,14
+General Medicine,PGY1,S0163662,2014-09-04,2:30:43,0:07:01,0:05:31,0:28:00,0:31:17,0:14:20,4:13:13,1725,537,9
+General Medicine,PGY1,S0163662,2014-09-07,2:27:59,0:15:47,0:28:38,0:34:01,0:10:36,0:38:53,5:15:02,1658,0,10
+General Medicine,PGY1,S0163662,2014-09-06,0:25:39,0:00:07,0:01:20,0:03:19,0:00:06,0:01:05,0:32:39,339,337,6
+General Medicine,PGY1,S0163662,2014-09-01,0:14:24,0:00:08,0:00:00,0:00:00,0:00:00,0:00:31,0:15:30,105,97,6
+General Medicine,PGY1,S0169402,2014-11-25,2:57:39,0:05:26,3:03:02,1:28:50,0:25:35,0:18:38,8:40:00,1972,59,10
+General Medicine,PGY1,S0163662,2014-09-03,2:16:53,0:06:31,0:25:23,0:31:19,0:22:11,0:37:19,4:39:04,1568,0,9
+General Medicine,PGY1,S0163662,2014-09-02,4:06:21,0:08:37,0:12:02,1:10:11,0:26:38,0:30:22,7:01:34,2148,0,11
+General Medicine,PGY1,S0169402,2014-11-28,3:02:06,0:04:22,1:24:34,0:42:48,0:39:27,0:07:45,6:18:45,1682,13,9
+General Medicine,PGY1,S0169402,2014-11-29,1:50:14,0:05:49,2:00:27,0:26:35,0:14:35,0:05:20,4:54:38,975,49,6
+Night Team,PGY2+,S0172645,2016-07-03,0:46:18,0:19:54,0:00:02,0:49:46,0:08:05,0:00:21,2:25:32,1105,95,10
+General Medicine,PGY1,S0163662,2014-09-09,0:35:38,0:02:20,0:09:47,0:06:10,0:00:14,0:00:46,0:58:26,234,224,4
+General Medicine,PGY1,S0163662,2014-09-08,3:24:51,0:06:55,0:08:29,0:37:24,0:18:29,0:16:11,5:09:55,1459,90,6
+General Medicine,PGY2+,S0169399,2016-01-28,2:52:33,1:19:53,1:00:54,0:30:12,0:24:45,0:38:05,7:19:43,1699,0,38
+General Medicine,PGY2+,S0169399,2016-01-29,1:50:04,1:14:09,1:27:17,0:27:00,0:19:14,0:12:59,6:19:49,2047,96,17
+General Medicine,PGY2+,S0169399,2016-01-26,0:00:00,0:00:00,0:00:00,0:00:02,0:00:00,0:00:00,0:00:02,8,0,3
+General Medicine,PGY2+,S0169399,2016-01-27,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,6,0,3
+General Medicine,PGY2+,S0169399,2016-01-24,0:38:39,0:39:22,1:05:38,0:14:43,0:12:39,0:15:04,3:32:30,893,0,13
+General Medicine,PGY2+,S0169399,2016-01-25,2:03:57,1:03:28,0:37:52,0:24:22,0:17:38,0:27:45,5:16:15,1254,0,30
+General Medicine,PGY2+,S0169399,2016-01-22,1:12:34,0:23:09,0:02:00,0:20:08,0:07:02,0:12:43,2:24:51,720,0,9
+General Medicine,PGY2+,S0169399,2016-01-23,1:37:14,1:12:31,0:54:45,0:39:19,0:17:25,0:17:22,5:26:08,1681,0,22
+General Medicine,PGY2+,S0169399,2016-01-20,2:26:58,0:55:27,0:08:10,1:02:29,0:19:55,0:40:52,6:18:32,2068,0,27
+General Medicine,PGY2+,S0169399,2016-01-21,1:42:54,1:42:46,1:47:56,1:08:46,0:25:56,0:21:50,7:45:15,2116,153,17
+General Medicine,PGY1,S0181065,2015-11-30,2:06:15,0:58:23,1:12:43,0:34:31,0:26:57,0:27:20,6:40:45,2798,0,14
+Night Team,PGY2+,S0172645,2016-07-04,0:20:01,0:20:35,0:00:00,0:06:30,0:03:11,0:00:06,0:51:28,515,0,13
+General Medicine,PGY1,S0187071,2016-09-10,0:22:42,0:35:00,0:00:03,0:01:17,0:00:17,0:00:52,1:07:13,593,432,18
+General Medicine,PGY1,S0195202,2016-07-08,2:41:12,1:38:10,0:01:37,0:51:33,0:14:59,0:01:58,6:07:14,1888,299,9
+General Medicine,PGY1,S0195202,2016-07-09,2:49:17,1:27:04,0:00:11,0:58:31,0:34:50,0:04:47,7:02:59,2180,0,11
+General Medicine,PGY1,S0187071,2016-09-11,2:47:31,1:40:13,0:00:14,0:56:34,0:17:23,0:02:49,6:00:22,2108,0,17
+General Medicine,PGY1,S0195202,2016-07-04,3:28:03,1:54:01,0:01:09,0:47:38,0:36:15,0:08:26,7:38:08,2113,134,13
+General Medicine,PGY1,S0195202,2016-07-05,2:15:39,1:13:57,0:04:16,0:40:57,0:15:16,0:02:01,5:09:17,1652,639,7
+General Medicine,PGY1,S0195202,2016-07-06,2:22:15,1:28:20,0:00:13,0:30:30,0:17:14,0:01:33,5:19:04,1711,10,7
+General Medicine,PGY1,S0195202,2016-07-07,2:58:10,1:58:50,0:00:58,1:07:23,0:36:56,0:04:56,7:32:38,2004,45,9
+Night Team,PGY2+,S0181057,2016-08-13,1:13:01,0:44:09,0:00:03,0:25:24,0:07:16,0:01:31,2:45:21,1070,258,30
+General Medicine,PGY1,S0195202,2016-07-02,3:21:54,2:13:35,0:00:24,0:45:32,0:25:46,0:06:09,7:38:02,2081,0,8
+General Medicine,PGY1,S0195202,2016-07-03,0:17:27,0:20:06,0:00:01,0:00:51,0:00:24,0:00:09,0:41:43,330,320,7
+General Medicine,PGY1,S0187071,2016-09-13,1:24:42,0:43:29,0:00:24,0:27:24,0:10:51,0:00:17,3:08:22,1186,42,9
+General Medicine,PGY1,S0163662,2015-06-14,3:09:54,0:24:10,1:03:25,0:48:03,0:24:16,0:34:16,7:05:13,2225,50,10
+Night Team,PGY2+,S0172645,2016-07-05,2:25:02,1:50:38,0:00:00,0:16:31,0:08:20,0:12:36,5:10:18,1890,0,32
+Intensive Care Unit,PGY2+,S0169403,2015-11-15,0:00:00,0:05:12,0:00:00,0:00:18,0:00:00,0:00:00,0:05:40,60,46,8
+General Medicine,PGY1,S0187071,2016-09-15,2:20:27,1:12:28,0:00:31,0:42:33,0:22:23,0:00:23,5:22:18,1852,21,10
+Intensive Care Unit,PGY2+,S0181061,2016-08-21,1:44:21,0:30:33,0:14:32,0:19:02,0:15:05,0:00:39,3:32:14,726,0,14
+Intensive Care Unit,PGY2+,S0181061,2016-08-20,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,5,0,3
+Intensive Care Unit,PGY2+,S0181061,2016-08-23,1:32:48,0:44:32,0:13:38,0:17:03,0:23:37,0:05:52,3:44:58,866,0,12
+Intensive Care Unit,PGY2+,S0181061,2016-08-22,1:39:26,0:51:50,0:00:28,0:37:53,0:26:56,0:05:26,4:06:37,1311,0,18
+Night Team,PGY2+,S0169384,2015-10-07,0:23:34,1:01:45,3:23:29,0:20:38,0:07:22,0:14:54,5:42:43,1409,146,7
+Intensive Care Unit,PGY2+,S0181061,2016-08-24,2:46:25,0:25:07,0:09:16,0:35:58,0:21:57,0:08:10,5:11:41,1238,0,13
+Intensive Care Unit,PGY2+,S0181061,2016-08-27,3:02:57,0:39:34,0:12:43,0:34:22,0:18:32,0:06:08,5:27:04,1455,196,12
+Intensive Care Unit,PGY2+,S0181061,2016-08-26,2:26:24,1:13:06,0:00:48,0:58:38,0:24:39,0:03:59,5:37:51,1462,0,17
+Intensive Care Unit,PGY2+,S0181061,2016-08-29,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,0:00:00,12,0,3
+Intensive Care Unit,PGY2+,S0181061,2016-08-28,0:00:21,0:00:08,0:00:00,0:00:00,0:00:00,0:00:00,0:03:39,22,0,5
+Night Team,PGY2+,S0169384,2015-10-09,1:12:18,1:42:03,2:50:11,0:46:43,0:17:49,0:34:11,7:53:17,2388,149,18
+Night Team,PGY2+,S0169384,2015-10-08,0:18:09,0:46:10,0:02:00,0:16:54,0:06:55,0:08:34,1:42:10,1020,217,9
+Intensive Care Unit,PGY2+,S0056755,2016-06-14,2:35:01,1:32:46,0:00:38,1:02:49,0:34:36,0:09:25,6:28:10,1669,0,21
+General Medicine,PGY1,S0195086,2016-07-28,2:04:22,2:36:52,0:01:15,0:54:52,0:29:05,0:04:13,7:23:43,1906,0,17
+General Medicine,PGY1,S0195086,2016-07-29,0:01:25,0:00:57,0:00:00,0:05:47,0:01:22,0:03:28,0:13:36,79,0,5
+Intensive Care Unit,PGY2+,S0169403,2015-11-19,1:25:50,0:42:54,0:10:29,0:15:10,0:21:14,0:35:06,3:57:01,1219,40,15
+Intensive Care Unit,PGY2+,S0169403,2015-11-18,1:03:58,0:37:35,0:04:06,0:08:34,0:17:39,0:27:57,2:56:04,1252,285,15
+Intensive Care Unit,PGY2+,S0169403,2015-11-17,1:59:57,1:33:43,0:15:50,0:22:38,0:34:28,1:19:10,6:49:55,2570,149,22
+Intensive Care Unit,PGY2+,S0169403,2015-11-16,1:45:13,0:53:26,0:10:08,0:20:53,0:25:44,0:35:46,4:33:02,1315,85,15
+Intensive Care Unit,PGY2+,S0094959,2015-09-07,4:04:39,1:45:48,0:07:23,0:33:21,0:46:04,0:51:40,8:30:44,4537,227,21
+General Medicine,PGY1,S0195086,2016-07-24,1:29:04,1:42:35,0:00:19,0:15:39,0:14:29,0:05:37,4:04:44,1030,0,4
+General Medicine,PGY1,S0195086,2016-07-25,1:28:37,1:31:12,0:02:14,0:09:40,0:08:30,0:04:06,3:51:56,743,0,4
+General Medicine,PGY1,S0195086,2016-07-26,2:41:26,1:51:36,0:00:58,0:26:26,0:18:50,0:07:24,6:01:56,1551,0,11
+General Medicine,PGY1,S0195086,2016-07-27,1:17:02,1:14:59,0:00:15,0:25:57,0:19:39,0:03:20,3:46:58,1089,0,7
diff --git a/scripts/Archive/ResidentRotationEHRUsage/README b/scripts/Archive/ResidentRotationEHRUsage/README
new file mode 100644
index 00000000..df656bd7
--- /dev/null
+++ b/scripts/Archive/ResidentRotationEHRUsage/README
@@ -0,0 +1,11 @@
+DATA
+1) ehr_action_categories.csv: maps each EHR action ID to a broad category (e.g. chart review, results review, etc.)
+2) ehr_action_descriptions.tsv: maps each EHR action ID to its corresponding description
+3) raw_resident_access_logs.csv: dump of all resident access log data; each row/entry refers to some action by some resident user at some timestamp
+4) rotations.csv: maps each resident provider to all their rotation start and end dates; also includes the resident's during the given rotation (e.g. R1, R2, R3)
+5) usage_spreadsheet.csv: master spreadsheet containing time spent per EHR section, # EHR actions, # patient records accessed per user-day (e.g. each entry shows stats for one day that a resident was active)
+
+CODE
+1) generate_usage_spreadsheet.py: using the raw_resident_access_logs.csv, compute daily statistics for each user-day; output usage_spreadsheet.csv
+2) compute_summary_stats.py: compute summary statistics using the usage_spreadsheet.csv; includes mean (stdev), median (IQR) of EHR section usage, # patient records accessed, etc. based on year (PGY1 vs PGY2+) and rotation (night time, ICU, etc.)
+3) compute_number_ehr_actions_per_24hour_cycle.py: computes average number of EHR actions per half hour bin in a 24 hour cycle; generates data for Figure 1
diff --git a/scripts/SepsisICU/exampleDates.py b/scripts/Archive/SepsisICU/exampleDates.py
similarity index 100%
rename from scripts/SepsisICU/exampleDates.py
rename to scripts/Archive/SepsisICU/exampleDates.py
diff --git a/scripts/SepsisICU/extractData.py b/scripts/Archive/SepsisICU/extractData.py
similarity index 88%
rename from scripts/SepsisICU/extractData.py
rename to scripts/Archive/SepsisICU/extractData.py
index 6859c0ba..bf4ae21b 100644
--- a/scripts/SepsisICU/extractData.py
+++ b/scripts/Archive/SepsisICU/extractData.py
@@ -7,7 +7,7 @@
 
 import sys, os;
 import time;
-from cStringIO import StringIO;
+from io import StringIO;
 from datetime import datetime, timedelta;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.db import DBUtil;
@@ -33,6 +33,7 @@
     "Resp",
     "Temp",
     "Urine",
+    "Weight_kg"
 );
 
 LAB_BASE_NAMES = \
@@ -64,9 +65,9 @@ def main(argv):
     #patientEpisodes = extractor.parsePatientEpisodeFile(stdOpen("patientEpisodes.tab"), list()); # Read from prior file if main query already done to avoid expensive query
     patientIds = set(columnFromModelList(patientEpisodes, "patient_id"));
 
-    extractor.queryFlowsheet(stdOpen("Flowsheet.tab.gz","w"), FLOWSHEET_NAMES, patientIds);
-    extractor.queryLabResults(stdOpen("LabResults.tab.gz","w"), LAB_BASE_NAMES, patientIds);
-    
+    extractor.queryFlowsheet(FLOWSHEET_NAMES, patientIds, stdOpen("Flowsheet.tab.gz","w"));
+    extractor.queryLabResults(LAB_BASE_NAMES, patientIds, stdOpen("LabResults.tab.gz","w"));
+
     # Look for specific IV fluid medication subset
     ivfMedIds = set();
     for row in extractor.loadMapData("Medication.IVFluids"):
@@ -101,16 +102,23 @@ def main(argv):
     extractor.queryClinicalItemsByName(("RaceNativeAmerican",), patientIds, stdOpen("RaceNativeAmerican.tab","w"));
 
     # Extract out lists of ICD9 prefixes per disease category
-    icd9prefixesByDisease = dict();
+    icd_prefixesByDisease = dict();
     for row in extractor.loadMapData("CharlsonComorbidity-ICD9CM"):
         (disease, icd9prefix) = (row["charlson"],row["icd9cm"]);
-        if disease not in icd9prefixesByDisease:
-            icd9prefixesByDisease[disease] = list();
-        icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
-    for disease, icd9prefixes in icd9prefixesByDisease.iteritems():
+        if disease not in icd_prefixesByDisease:
+            icd_prefixesByDisease[disease] = list();
+        icd_prefixesByDisease[disease].append("^ICD9."+icd9prefix);
+
+    for row in extractor.loadMapData("CharlsonComorbidity-ICD10"):
+        (disease, icd10prefix) = (row["Category"], row["Code"]);
+        if disease not in icd_prefixesByDisease:
+            icd_prefixesByDisease[disease] = list();
+        icd_prefixesByDisease[disease].append("^ICD10." + icd10prefix + '.*'); # For ICD10, match the prefix
+
+    for disease, icd_prefixes in icd_prefixesByDisease.items():
         disease = disease.translate(None," ()-/");   # Strip off punctuation
-        extractor.queryClinicalItemsByName(icd9prefixes, patientIds, stdOpen("Charlson."+disease+".tab","w"), operator="~*");
-    
+        extractor.queryClinicalItemsByName(icd_prefixes, patientIds, stdOpen("Charlson."+disease+".tab","w"), operator="~*");
+
     # Extract out lists of treatment team names per care category
     teamNameByCategory = dict();
     for row in extractor.loadMapData("TreatmentTeamGroups"):
@@ -118,11 +126,11 @@ def main(argv):
         if category not in teamNameByCategory:
             teamNameByCategory[category] = list();
         teamNameByCategory[category].append(teamName);
-    for category, teamNames in teamNameByCategory.iteritems():
+    for category, teamNames in teamNameByCategory.items():
         extractor.queryClinicalItemsByName(teamNames, patientIds, stdOpen("TT."+category+".tab","w"), col="description");
 
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 def loadIVAntibioticItemIds(extractor):
     # ivMedCategoryId = 72;
@@ -221,15 +229,15 @@ def queryPatientEpisodes(outputFile, extractor):
             adt1.event_in = 'Admission' and
             adt1.pat_anon_id in
             (    -- Select any patient with any suspected sepsis related order (i.e., IV antibiotics or blood cultures)
-                -- select patient_id
-                -- from patient_item as pi
-                -- where pi.clinical_item_id in (%s)
-                -- except
-                -- -- Exclude any patient who has been on a primary surgery team
-                -- select patient_id
-                -- from patient_item
-                -- where clinical_item_id in (%s)
-                -12434586418575,-12432455207729,-12428492282572,-12428492282572,-12424048595257,-12414081679705
+                select patient_id
+                from patient_item as pi
+                where pi.clinical_item_id in (%s)
+                except
+                -- Exclude any patient who has been on a primary surgery team
+                select patient_id
+                from patient_item
+                where clinical_item_id in (%s)
+                -- -12434586418575,-12432455207729,-12428492282572,-12428492282572,-12424048595257,-12414081679705
             ) and
             
             adt1.pat_enc_csn_anon_id = adt2.pat_enc_csn_anon_id and
@@ -239,7 +247,7 @@ def queryPatientEpisodes(outputFile, extractor):
             
         order by adt1.shifted_transf_in_dt_tm
         """ % (suspectSepsisItemIdsStr, excludeTeamItemIdsStr);
-        print >> sys.stderr, cohortQuery;
+        print(cohortQuery, file=sys.stderr);
         cursor.execute(cohortQuery);
 
         patientEpisodes = list();
diff --git a/scripts/SepsisICU/formatData.py b/scripts/Archive/SepsisICU/formatData.py
similarity index 94%
rename from scripts/SepsisICU/formatData.py
rename to scripts/Archive/SepsisICU/formatData.py
index 09502fdb..0f3ff8ae 100644
--- a/scripts/SepsisICU/formatData.py
+++ b/scripts/Archive/SepsisICU/formatData.py
@@ -2,7 +2,7 @@
 import time;
 import numpy as np;
 from datetime import datetime, timedelta;
-from cStringIO import StringIO;
+from io import StringIO;
 from medinfo.common.Util import stdOpen, log, ProgressDots;
 from medinfo.common.Const import NULL_STRING;
 from medinfo.db import DBUtil;
@@ -24,6 +24,7 @@
     "Resp",
     "Temp",
     "Urine",
+    "Weight_kg"
 );
 # How far to look back and forward in time for flowsheet data
 FLOWSHEET_PRE_TIME_DELTA = timedelta(-1); # Use up to the past day of flowsheet data
@@ -48,7 +49,7 @@
 
 # Threshold volumes and Checkpoint Times to note accumulated isotonic IV Fluids
 IVF_THRESHOLD_VOLUMES = [500,1000,2000,3000,4000,5000,6000,7000,8000,9000,10000];  # Volumes (mL) of fluid interested in time until encountering
-IVF_CHECKPOINT_TIMES = [1*60*60, 2*60*60, 4*60*60, 12*60*60, 24*60*60, 48*60*60, 72*60*60];  # Time checkpoints (seconds) interested in accumulated fluid volume by that time
+IVF_CHECKPOINT_TIMES = [1*60*60, 2*60*60, 3*60*60, 4*60*60, 12*60*60, 24*60*60, 48*60*60, 72*60*60];  # Time checkpoints (seconds) interested in accumulated fluid volume by that time
 
 def main(argv=None):
     timer = time.time();
@@ -123,12 +124,12 @@ def main(argv=None):
     log.info("Output feature matrix file with row per patient day");
     formatter = TextResultsFormatter(featureMatrixFile);
     formatter.formatTuple(colNames);
-    for patientId, patientByIndexTime in patientByIndexTimeById.iteritems():
-        patientResults = patientByIndexTime.values();
+    for patientId, patientByIndexTime in patientByIndexTimeById.items():
+        patientResults = list(patientByIndexTime.values());
         formatter.formatResultDicts(patientResults, colNames);
 
     timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
+    print("%.3f seconds to complete" % timer, file=sys.stderr);
 
 if __name__ == "__main__":
     main(sys.argv);
\ No newline at end of file
diff --git a/scripts/Archive/SimulationRecommender/Clinical Recommender Pipeline .ipynb b/scripts/Archive/SimulationRecommender/Clinical Recommender Pipeline .ipynb
new file mode 100644
index 00000000..3f43a228
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/Clinical Recommender Pipeline .ipynb	
@@ -0,0 +1,8604 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recommender_path = \"/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Design and Implementation of Clinical Recommender: \n",
+    "\n",
+    "Aims: \n",
+    "\n",
+    "- Create a Framework to Evaluate the Impact of a Recommender System on Physician Behavior\n",
+    "- Create a Data Pipeline that Preprocess Raw Clinical Behavioral Data into Actionable Clinical Insights\n",
+    "- Unit Testing\n",
+    "- Terminal Run Commands \n",
+    "- Git Pull and Git Run: \n",
+    "- Configuration: \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HCI PostDoc Feedback: \n",
+    "- Survey questioning is useless. \n",
+    "     you couldn’t simply obtain from observing them interact with the system\n",
+    "- Keep them short\n",
+    "- Anecdotal Inverse relationship between # questions asked afterward and quality/truthfulness of response. They’ll humor you for a bit, but after that just click click click\n",
+    "- Most useful is having people think outloud during the protocol. \n",
+    "- They independently said this is the most useful thing. Or have each person observing for one specific different task\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluation Metrics: \n",
+    "- Number of mouse clicks\n",
+    "- Resolution down to individual buttons and items\n",
+    "- Elapsed time\n",
+    "- From start of simulation to end\n",
+    "- Number of signed orders\n",
+    "- Number of (unique) recommendations\n",
+    "- Signed orders from recommender\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Methods: \n",
+    "\n",
+    "Our goal was to design a grading pipeline that incorporates an in-house clinical recommender system (CRS) designed by Jonathan Chen. We had to design a data pipeline that actively stores data into a postgres database when physicians select clinical orders from CRS.\n",
+    "\n",
+    "The clinical orders correspond to different simulation states. A simulation state represents a patient's current diagnostic and procedural mock-up as designed by an expert physician. We developed 2 cases in-house and reached out to three different Stanford physicians to design three different cases.\n",
+    "\n",
+    "For each clinical order that is capture within a simulation state there is a corresponding score. A specific case has anywhere between 3 to 6 states that are triggered due to specific clinical decisions. We are abstracting a clinical grading platform that has never been done before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Responses were recording using Google Forms, which tabulates the results into a Google Sheet, which is made accessible to R and Python. The forms were joined on physician id from the CRS and manually reviewed for validation. The purpose of the google forms, captures HCI survey information about clinical recommender utility for clinical workflow. The survey also captures physician background information, such as years since receiving medical degree and board certifications.\n",
+    "\n",
+    "The majority of the initial participants were Stanford University Medical residents. After the initial trials ended, we made some subsequent quality of life improvements to the recommender system, after the first ~25 physician feedback."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A grading module was prototyped in R stats language and subsequently a more robust test driven python module was used to automatically score from inputs from three clinical experts. The grading module treats each physician id and clinical case as a key which the subsequent orders inside the key have an associated grade and confidence. From the key groups, we can sum the values to derive a score for each doctor's case.\n",
+    "\n",
+    "This provides insight on physician decision-making and a quantitative score associated with each decision.\n",
+    "\n",
+    "This process was an iterated delphi method where a panel of three clinical experts grade separately on each unique clinical order, and reconvened to discuss guidelines, grading and clinical confidence in treatments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We developed a methodology group interactions: sim state interactions and orders dependent on the state matter differently \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Random Trial Setup: How Were Cases Randomized: \n",
+    "\n",
+    "<pre>\n",
+    "Purpose:  \n",
+    "    Join sim_state_id and clinical_item_id to Grading Sheet\n",
+    "        Then: \n",
+    "    Generate Deterministic Random Numbers: \n",
+    "        Reproducible: \n",
+    "            pseudorandom (deterministic) based on an internal state \n",
+    "        Set.Seed\n",
+    "            \n",
+    "\n",
+    "</pre>  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# how to get pandas data from postgree sql using python\n",
+    "# psycopg2 is a module designed to read dataframes from databases \n",
+    "# pandas is a module that is R-like Magic for data manipulation \n",
+    "\n",
+    "import psycopg2 as pg\n",
+    "import pandas.io.sql as psql\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(['Headache', 'Fever B', 'Shortness of Breath', 'Hematemesis', 'Palpitations'], [False, True, False, True])\n",
+      "(['Fever B', 'Hematemesis', 'Shortness of Breath', 'Headache', 'Palpitations'], [True, False, True, False])\n",
+      "(['Headache', 'Fever B', 'Shortness of Breath', 'Hematemesis', 'Palpitations'], [True, False, False, True])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "'''\n",
+    "--------------------------------------------------\n",
+    "sample() is an inbuilt function of random module \n",
+    "in Python that returns a particular length list \n",
+    "of items chosen from the sequence i.e. list, tuple, \n",
+    "string or set\n",
+    "--------------------------------------------------\n",
+    "Used for random sampling without replacement\n",
+    "--------------------------------------------------\n",
+    "x denotes: \n",
+    "    expects: \n",
+    "        - list \n",
+    "        - cases that you want to randomize \n",
+    "y denotes:\n",
+    "    - list of boolean values that indicate whether or not \n",
+    "          the recommender is turned on \n",
+    "n denotes:\n",
+    "    - the number of times you want to sample without replacement\n",
+    "    - should equal the length of x and y (should I make this explicit?) \n",
+    "Purpose of Script:\n",
+    "    - writing a function that accepts a list of physician cases and randomly orders them\n",
+    "    - making it reproducible (can run again) (may need to review documentation on seed) \n",
+    "    \n",
+    "Learning Points to Incorporate: \n",
+    "    - more test driven development\n",
+    "    - functional programming versus Object Oriented Programming \n",
+    "    - Less Script-Like   \n",
+    "----------------------------------------------------        \n",
+    "'''\n",
+    "\n",
+    "\n",
+    "def testRandomizeCase(x, y):\n",
+    "    assert type(x) == list\n",
+    "    assert len(x) == len(y)\n",
+    "\n",
+    "\n",
+    "def randomizeCase(x,y):\n",
+    "    # set the seed \n",
+    "    random.seed(a=1)\n",
+    "    # initialize an empty list \n",
+    "    output = []\n",
+    "    # construct for loop for number of physicians in your study\n",
+    "    for _ in range(50):\n",
+    "        a = random.sample(x, 5)\n",
+    "        b = random.sample(y, 4)\n",
+    "        #c = [] \n",
+    "        #c.append(\"True\")\n",
+    "        output.append((a,b))\n",
+    "    return(output)\n",
+    "\n",
+    "# p1 denotes the cases represented by letters in an alphabet     \n",
+    "cases = ['Fever B','Headache','Palpitations', 'Hematemesis', 'Shortness of Breath']\n",
+    "\n",
+    "# TRUE or FALSE (True means recommender is turned on) \n",
+    "booleanList = [True,True, False, False]\n",
+    "\n",
+    "# running script: \n",
+    "t = randomizeCase(cases, booleanList)\n",
+    "# assumes first case recommender is on\n",
+    "print(t[0])\n",
+    "print(t[1])\n",
+    "print(t[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Assign Paths for Pipeline: \n",
+    "\n",
+    "<pre>\n",
+    "Purpose:  \n",
+    "    Treat Recommender Path as Configuration Folder File\n",
+    "        Then: \n",
+    "            Assign Appropriate Paths for Clinical Recommender Pipeline     \n",
+    "\n",
+    "</pre>  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "physician_grading = recommender_path + \"physician_grading/\"\n",
+    "physician_response = recommender_path + \"physician_response/\"\n",
+    "tracker_data = recommender_path + \"tracker_data/\"\n",
+    "unit_test = recommender_path + \"unit_test/\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parse Github API for Open Issues with Associated Hash Tag Dates: \n",
+    "\n",
+    "<pre>\n",
+    "Purpose: List Issues from Github with Deadlines: \n",
+    "    Parse Github API: \n",
+    "            Then:\n",
+    "                Convert Json file Format to Pandas DataFrame\n",
+    "            Then: \n",
+    "                Parse for Due Date Hashtag \n",
+    "            Then: \n",
+    "                Drop NA values (no due date)\n",
+    "            Then: \n",
+    "                Create Column Names:\n",
+    "            Then: \n",
+    "                Sort By Date \n",
+    "\n",
+    "</pre>  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 459,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100  108k  100  108k    0     0   164k      0 --:--:-- --:--:-- --:--:--  164k\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>issue</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Expand Recruitment Process  - Send out recruit...</td>\n",
+       "      <td>2019-06-24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Automated UI Test Grader that accounts for gro...</td>\n",
+       "      <td>2019-06-26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Manual validation of a couple data rows from m...</td>\n",
+       "      <td>2019-06-26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Automated grading of cases, given expert panel...</td>\n",
+       "      <td>2019-06-27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Get individual expert panel to deliver their f...</td>\n",
+       "      <td>2019-07-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Additional Visualizations of Time Streams #Est...</td>\n",
+       "      <td>2019-07-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Data Analysis - Define analysis plan of which ...</td>\n",
+       "      <td>2019-07-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Convene expert panel to reconcile grading for ...</td>\n",
+       "      <td>2019-07-20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Paper - Methods Description of Grading Process...</td>\n",
+       "      <td>2019-07-25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Completing remaining UI tests with physicians ...</td>\n",
+       "      <td>2019-07-30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Second round expert panel review once collecte...</td>\n",
+       "      <td>2019-08-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>AMIA Informatics Summits Submission: Aspire fo...</td>\n",
+       "      <td>2019-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Paper Results: Data analysis - Associations / ...</td>\n",
+       "      <td>2019-08-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Paper - Intro to motivate (based on prior) + D...</td>\n",
+       "      <td>2019-08-30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Paper internal reviews and revisions - #Estima...</td>\n",
+       "      <td>2019-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Journal Paper submission on all results:</td>\n",
+       "      <td>2019-09-20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                issue       date\n",
+       "28  Expand Recruitment Process  - Send out recruit... 2019-06-24\n",
+       "11  Automated UI Test Grader that accounts for gro... 2019-06-26\n",
+       "18  Manual validation of a couple data rows from m... 2019-06-26\n",
+       "2   Automated grading of cases, given expert panel... 2019-06-27\n",
+       "17  Get individual expert panel to deliver their f... 2019-07-01\n",
+       "4   Additional Visualizations of Time Streams #Est... 2019-07-02\n",
+       "15  Data Analysis - Define analysis plan of which ... 2019-07-12\n",
+       "16  Convene expert panel to reconcile grading for ... 2019-07-20\n",
+       "14  Paper - Methods Description of Grading Process... 2019-07-25\n",
+       "19  Completing remaining UI tests with physicians ... 2019-07-30\n",
+       "10  Second round expert panel review once collecte... 2019-08-10\n",
+       "9   AMIA Informatics Summits Submission: Aspire fo... 2019-08-15\n",
+       "20  Paper Results: Data analysis - Associations / ... 2019-08-15\n",
+       "13  Paper - Intro to motivate (based on prior) + D... 2019-08-30\n",
+       "12  Paper internal reviews and revisions - #Estima... 2019-09-15\n",
+       "8           Journal Paper submission on all results:  2019-09-20"
+      ]
+     },
+     "execution_count": 459,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# parses due dates on github \n",
+    "!curl -i \"https://api.github.com/repos/HealthRex/CDSS/issues?state=open\" | tail -n +25  > 'issues3.json'\n",
+    "github_issues = pd.read_json('/Users/jonc101/Documents/Biomedical_Data_Science/issues3.json')['title']\n",
+    "due_date = github_issues.str.split(\"#Due:\", n = 1, expand = True) \n",
+    "c = pd.DataFrame(due_date.dropna())\n",
+    "c.columns = ['issue', 'date']\n",
+    "c['date'] = pd.to_datetime(c['date'])\n",
+    "c.sort_values(by='date')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read Data from Database into Memory: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection = pg.connect(\"host='localhost' dbname=stride_inpatient_2014 user=postgres password='MANUAL PASSWORD'\")\n",
+    "\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# to do :\n",
+    "#         Feature: Generate Grading Scheme\n",
+    "#             1) help visualize processes\n",
+    "#             2) introduce best grading schemes for each case\n",
+    "#             3) create a list of common errors seen\n",
+    "#\n",
+    "#             4) clean up exploratory analysis\n",
+    "#             5) convert to python module\n",
+    "#\n",
+    "#\n",
+    "#\n",
+    "# ---------------------------------------------------------------------------------\n",
+    "\n",
+    "\n",
+    "# reading in data from different tables \n",
+    "\n",
+    "clinical_item = pd.read_sql_query('select * from clinical_item', con=connection)\n",
+    "sim_patient_order = pd.read_sql_query('select * from sim_patient_order',con=connection)\n",
+    "sim_state = pd.read_sql_query('select * from sim_state',con=connection)\n",
+    "sim_user = pd.read_sql_query('select * from sim_user',con=connection)\n",
+    "sim_state_transition = pd.read_sql_query('select * from sim_state_transition',con=connection)\n",
+    "sim_state['sim_state_name'] = sim_state['description']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Read Sim State Data into Memory: \n",
+    "    Then: \n",
+    "        join the sim_state with the sim_patient orders \n",
+    "    Then:\n",
+    "        find all the unique clinical item orders \n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 460,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "325"
+      ]
+     },
+     "execution_count": 460,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_order = sim_patient_order.merge(sim_state, left_on='sim_state_id', right_on='sim_state_id')\n",
+    "clinical_items_list = merged_order['clinical_item_id'].unique()\n",
+    "len(clinical_items_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Create vector of unique sim states (sim_state_id):\n",
+    "    Then: \n",
+    "        filter vector of unique orders from clinical item table \n",
+    "    Then: \n",
+    "        create a description based table for orders and clinical items \n",
+    "    Then: \n",
+    "        split by sim_states into group by object \n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 461,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim_state_list = merged_order['sim_state_id'].unique()\n",
+    "ordered_clinical_item_table = clinical_item[clinical_item['clinical_item_id'].isin(clinical_items_list)]\n",
+    "remerged_order = merged_order.merge(ordered_clinical_item_table, left_on='clinical_item_id', right_on='clinical_item_id')\n",
+    "split_state = remerged_order.groupby('sim_state_id')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "explicitly write the lists of objects: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 462,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#--------------------------------------------------------------------------------\n",
+    "# afib\n",
+    "#--------------------------------------------------------------------------------\n",
+    "# \"Afib-RVR Initial\"\n",
+    "# \"Afib-RVR Stabilized\"\n",
+    "# \"Afib-RVR Worse\"\n",
+    "#--------------------------------------------------------------------------------\n",
+    "afib_states = [\"Afib-RVR Initial\",\n",
+    "                \"Afib-RVR Stabilized\" ,\n",
+    "                \"Afib-RVR Worse\" ]\n",
+    "#--------------------------------------------------------------------------------\n",
+    "# meningitis\n",
+    "#--------------------------------------------------------------------------------\n",
+    "# \"Mening Active\"\n",
+    "# \"Meningitis Adequately Treated\"\n",
+    "# \"Meningits Worsens\"\n",
+    "#--------------------------------------------------------------------------------\n",
+    "mening_states =  [\"Mening Active\",\n",
+    "                   \"Meningitis Adequately Treated\",\n",
+    "                   \"Meningits Worsens\"]\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# pulmonary embolism\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# \"PE-COPD-LungCA\"\n",
+    "# \"PE-COPD-LungCA + Anticoagulation\"\n",
+    "# \"PE-COPD-LungCA + O2\"\n",
+    "# \"PE-COPD-LungCA + O2 + Anticoagulation\"\n",
+    "# -------------------------------------------------------------------------------\n",
+    "pulmonary_emolism_states = [\"PE-COPD-LungCA\",\n",
+    "                              \"PE-COPD-LungCA + Anticoagulation\",\n",
+    "                              \"PE-COPD-LungCA + O2\",\n",
+    "                              \"PE-COPD-LungCA + O2 + Anticoagulation\"]\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# neutropenic fever\n",
+    "# -------------------------------------------------------------------------------\n",
+    "#  \"Neutropenic Fever Treated with IVF\"\n",
+    "#  \"Neutropenic Fever Treated with IVF + ABX\"\n",
+    "#  \"Neutropenic Fever v2\"\n",
+    "#  \"NFever\"\n",
+    "# -------------------------------------------------------------------------------\n",
+    "\n",
+    "neutropenic_fever_states = [\"Neutropenic Fever Treated with IVF\",\n",
+    "                              \"Neutropenic Fever Treated with IVF + ABX\",\n",
+    "                              \"Neutropenic Fever v2\"]\n",
+    "\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# GIBLEED\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# \"EtOH-GIBleed Active\"\n",
+    "# \"EtOH-GIBleed Bleeding Out\"\n",
+    "# \"EtOH-GIBleed Coag Stabilized\"\n",
+    "# \"EtOH-GIBleed Post-EGD\"\n",
+    "# -------------------------------------------------------------------------------\n",
+    "\n",
+    "gi_bleed_states = [\"EtOH-GIBleed Active\",\n",
+    "                      \"EtOH-GIBleed Bleeding Out\",\n",
+    "                      \"EtOH-GIBleed Coag Stabilized\",\n",
+    "                      \"EtOH-GIBleed Post-EGD\" ]\n",
+    "\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# DKA\n",
+    "# -------------------------------------------------------------------------------\n",
+    "# \"DKA Euglycemic\"\n",
+    "# \"DKA Hyperglycemic\"\n",
+    "# \"DKA Onset\"\n",
+    "# -------------------------------------------------------------------------------\n",
+    "\n",
+    "dka_states = [\"DKA Euglycemic\" ,\n",
+    "                \"DKA Hyperglycemic\" ,\n",
+    "                \"DKA Onset\"]\n",
+    "\n",
+    "list_of_states = [gi_bleed_states,\n",
+    "                       mening_states,\n",
+    "                       pulmonary_emolism_states,\n",
+    "                       afib_states,\n",
+    "                       neutropenic_fever_states]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "    Split the states into separate dataframes:\n",
+    "        Then: \n",
+    "            explicitly add a label for the case name \n",
+    "        Then: \n",
+    "            select features for grading \n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      sim_state_id  clinical_item_id  sim_user_id  sim_patient_id  \\\n",
+      "55              14             45763           26             134   \n",
+      "67              15             45763           26             123   \n",
+      "68              15             45763            0             126   \n",
+      "69              15             45763            0             153   \n",
+      "70               2             45763           31             141   \n",
+      "71               2             45763           13              39   \n",
+      "72               2             45763           53             293   \n",
+      "99              14             45801           48             248   \n",
+      "177             14             45866           10              23   \n",
+      "178             14             45866           11              31   \n",
+      "179             14             45866           13              39   \n",
+      "180             14             45866           15              75   \n",
+      "181             14             45866           17              79   \n",
+      "182             14             45866           21               5   \n",
+      "183             14             45866           26             108   \n",
+      "184             14             45866           27             113   \n",
+      "185             14             45866           29             120   \n",
+      "186             14             45866           33             148   \n",
+      "187             14             45866            0             153   \n",
+      "188             14             45866           39             172   \n",
+      "189             14             45866           40             180   \n",
+      "190             14             45866           43             205   \n",
+      "191             14             45866           46             230   \n",
+      "192             14             45866           47             238   \n",
+      "193             14             45866           48             248   \n",
+      "194             14             45866           51             277   \n",
+      "195             14             45866           53             293   \n",
+      "198             15             45866           29             120   \n",
+      "199             15             45866            0             126   \n",
+      "290             15             45838           29             120   \n",
+      "...            ...               ...          ...             ...   \n",
+      "3180          5000             41759            0             256   \n",
+      "3181          5000             41759            0             257   \n",
+      "3182          5000             41759            0             267   \n",
+      "3183          5000             41759            0             275   \n",
+      "3184          5000             41759            0             282   \n",
+      "3185          5000             41759            0             290   \n",
+      "3186          5000             41759            0             291   \n",
+      "3187          5000             41759            0             299   \n",
+      "3188          5000             62023           17              77   \n",
+      "3189          5000             62023           39             170   \n",
+      "3190          5000             62023           42             192   \n",
+      "3191          5000             62023           44             210   \n",
+      "3192          5000             62023           49             257   \n",
+      "3194          5000             46000           29             118   \n",
+      "3196          5000             48775           31             139   \n",
+      "3197          5000             44237           31             139   \n",
+      "3198          5000             48686           40             177   \n",
+      "3199          5003             48686           30             129   \n",
+      "3200          5003             48686           46             227   \n",
+      "3201          5003             48686           52             282   \n",
+      "3202          5003             48686           53             291   \n",
+      "3203          5000             45903           47             236   \n",
+      "3204          5000             45995           47             236   \n",
+      "3205          5002             46291           39             170   \n",
+      "3206          5002             63735           46             227   \n",
+      "3207          5003             63735           46             227   \n",
+      "3208          5003             48980           41             184   \n",
+      "3212          5003             44395           44             210   \n",
+      "3213          5003             52757           53             291   \n",
+      "3214          5003             48711           53             291   \n",
+      "\n",
+      "                                        name_x  \\\n",
+      "55                         EtOH-GIBleed Active   \n",
+      "67                   EtOH-GIBleed Bleeding Out   \n",
+      "68                   EtOH-GIBleed Bleeding Out   \n",
+      "69                   EtOH-GIBleed Bleeding Out   \n",
+      "70                       EtOH-GIBleed Post-EGD   \n",
+      "71                       EtOH-GIBleed Post-EGD   \n",
+      "72                       EtOH-GIBleed Post-EGD   \n",
+      "99                         EtOH-GIBleed Active   \n",
+      "177                        EtOH-GIBleed Active   \n",
+      "178                        EtOH-GIBleed Active   \n",
+      "179                        EtOH-GIBleed Active   \n",
+      "180                        EtOH-GIBleed Active   \n",
+      "181                        EtOH-GIBleed Active   \n",
+      "182                        EtOH-GIBleed Active   \n",
+      "183                        EtOH-GIBleed Active   \n",
+      "184                        EtOH-GIBleed Active   \n",
+      "185                        EtOH-GIBleed Active   \n",
+      "186                        EtOH-GIBleed Active   \n",
+      "187                        EtOH-GIBleed Active   \n",
+      "188                        EtOH-GIBleed Active   \n",
+      "189                        EtOH-GIBleed Active   \n",
+      "190                        EtOH-GIBleed Active   \n",
+      "191                        EtOH-GIBleed Active   \n",
+      "192                        EtOH-GIBleed Active   \n",
+      "193                        EtOH-GIBleed Active   \n",
+      "194                        EtOH-GIBleed Active   \n",
+      "195                        EtOH-GIBleed Active   \n",
+      "198                  EtOH-GIBleed Bleeding Out   \n",
+      "199                  EtOH-GIBleed Bleeding Out   \n",
+      "290                  EtOH-GIBleed Bleeding Out   \n",
+      "...                                        ...   \n",
+      "3180                      Neutropenic Fever v2   \n",
+      "3181                      Neutropenic Fever v2   \n",
+      "3182                      Neutropenic Fever v2   \n",
+      "3183                      Neutropenic Fever v2   \n",
+      "3184                      Neutropenic Fever v2   \n",
+      "3185                      Neutropenic Fever v2   \n",
+      "3186                      Neutropenic Fever v2   \n",
+      "3187                      Neutropenic Fever v2   \n",
+      "3188                      Neutropenic Fever v2   \n",
+      "3189                      Neutropenic Fever v2   \n",
+      "3190                      Neutropenic Fever v2   \n",
+      "3191                      Neutropenic Fever v2   \n",
+      "3192                      Neutropenic Fever v2   \n",
+      "3194                      Neutropenic Fever v2   \n",
+      "3196                      Neutropenic Fever v2   \n",
+      "3197                      Neutropenic Fever v2   \n",
+      "3198                      Neutropenic Fever v2   \n",
+      "3199  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3200  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3201  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3202  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3203                      Neutropenic Fever v2   \n",
+      "3204                      Neutropenic Fever v2   \n",
+      "3205        Neutropenic Fever Treated with IVF   \n",
+      "3206        Neutropenic Fever Treated with IVF   \n",
+      "3207  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3208  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3212  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3213  Neutropenic Fever Treated with IVF + ABX   \n",
+      "3214  Neutropenic Fever Treated with IVF + ABX   \n",
+      "\n",
+      "                                     description_x  \\\n",
+      "55           Alcoholic Hepatitis - GI Bleed Active   \n",
+      "67     Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+      "68     Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+      "69     Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+      "70         Alcoholic Hepatitis - GI Bleed EGD Done   \n",
+      "71         Alcoholic Hepatitis - GI Bleed EGD Done   \n",
+      "72         Alcoholic Hepatitis - GI Bleed EGD Done   \n",
+      "99           Alcoholic Hepatitis - GI Bleed Active   \n",
+      "177          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "178          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "179          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "180          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "181          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "182          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "183          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "184          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "185          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "186          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "187          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "188          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "189          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "190          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "191          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "192          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "193          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "194          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "195          Alcoholic Hepatitis - GI Bleed Active   \n",
+      "198    Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+      "199    Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+      "290    Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+      "...                                            ...   \n",
+      "3180  Neutropenic Fever (v2 example) Initial State   \n",
+      "3181  Neutropenic Fever (v2 example) Initial State   \n",
+      "3182  Neutropenic Fever (v2 example) Initial State   \n",
+      "3183  Neutropenic Fever (v2 example) Initial State   \n",
+      "3184  Neutropenic Fever (v2 example) Initial State   \n",
+      "3185  Neutropenic Fever (v2 example) Initial State   \n",
+      "3186  Neutropenic Fever (v2 example) Initial State   \n",
+      "3187  Neutropenic Fever (v2 example) Initial State   \n",
+      "3188  Neutropenic Fever (v2 example) Initial State   \n",
+      "3189  Neutropenic Fever (v2 example) Initial State   \n",
+      "3190  Neutropenic Fever (v2 example) Initial State   \n",
+      "3191  Neutropenic Fever (v2 example) Initial State   \n",
+      "3192  Neutropenic Fever (v2 example) Initial State   \n",
+      "3194  Neutropenic Fever (v2 example) Initial State   \n",
+      "3196  Neutropenic Fever (v2 example) Initial State   \n",
+      "3197  Neutropenic Fever (v2 example) Initial State   \n",
+      "3198  Neutropenic Fever (v2 example) Initial State   \n",
+      "3199  Appropriate Treatments for patient condition   \n",
+      "3200  Appropriate Treatments for patient condition   \n",
+      "3201  Appropriate Treatments for patient condition   \n",
+      "3202  Appropriate Treatments for patient condition   \n",
+      "3203  Neutropenic Fever (v2 example) Initial State   \n",
+      "3204  Neutropenic Fever (v2 example) Initial State   \n",
+      "3205     Appropriate IVF improve patient condition   \n",
+      "3206     Appropriate IVF improve patient condition   \n",
+      "3207  Appropriate Treatments for patient condition   \n",
+      "3208  Appropriate Treatments for patient condition   \n",
+      "3212  Appropriate Treatments for patient condition   \n",
+      "3213  Appropriate Treatments for patient condition   \n",
+      "3214  Appropriate Treatments for patient condition   \n",
+      "\n",
+      "                                          description_y         case  \n",
+      "55                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "67                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "68                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "69                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "70                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "71                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "72                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+      "99                                    XR CHEST 2V [CXR]     gi_bleed  \n",
+      "177                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "178                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "179                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "180                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "181                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "182                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "183                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "184                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "185                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "186                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "187                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "188                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "189                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "190                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "191                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "192                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "193                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "194                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "195                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "198                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "199                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+      "290                                    ISTAT TROPONIN I     gi_bleed  \n",
+      "...                                                 ...          ...  \n",
+      "3180  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3181  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3182  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3183  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3184  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3185  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3186  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3187  Fever and other physiologic disturbances of te...  neutropenic  \n",
+      "3188                              RESPIRATORY DFA PANEL  neutropenic  \n",
+      "3189                              RESPIRATORY DFA PANEL  neutropenic  \n",
+      "3190                              RESPIRATORY DFA PANEL  neutropenic  \n",
+      "3191                              RESPIRATORY DFA PANEL  neutropenic  \n",
+      "3192                              RESPIRATORY DFA PANEL  neutropenic  \n",
+      "3194                                 POC URINE DIPSTICK  neutropenic  \n",
+      "3196                                       BLADDER SCAN  neutropenic  \n",
+      "3197      Glucose-Piperacillin-Tazobactam (Intravenous)  neutropenic  \n",
+      "3198                                 DIET LOW MICROBIAL  neutropenic  \n",
+      "3199                                 DIET LOW MICROBIAL  neutropenic  \n",
+      "3200                                 DIET LOW MICROBIAL  neutropenic  \n",
+      "3201                                 DIET LOW MICROBIAL  neutropenic  \n",
+      "3202                                 DIET LOW MICROBIAL  neutropenic  \n",
+      "3203                          LDH TOTAL, SERUM / PLASMA  neutropenic  \n",
+      "3204                          URIC ACID, SERUM / PLASMA  neutropenic  \n",
+      "3205             BLOOD CULT - FIRST SET, VIA PHLEBOTOMY  neutropenic  \n",
+      "3206                         VIRAL CULTURE, RESPIRATORY  neutropenic  \n",
+      "3207                         VIRAL CULTURE, RESPIRATORY  neutropenic  \n",
+      "3208                      CONSULT TO INFECTIOUS DISEASE  neutropenic  \n",
+      "3212  Calcium Chloride-Glucose-Lactate-Potassium Chl...  neutropenic  \n",
+      "3213                       REFERRAL TO PHYSICAL THERAPY  neutropenic  \n",
+      "3214                                  CONTACT ISOLATION  neutropenic  \n",
+      "\n",
+      "[2680 rows x 8 columns]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jonc101/Documents/Biomedical_Data_Science/venv/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  # This is added back by InteractiveShellApp.init_path()\n",
+      "/Users/jonc101/Documents/Biomedical_Data_Science/venv/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  if sys.path[0] == '':\n",
+      "/Users/jonc101/Documents/Biomedical_Data_Science/venv/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  del sys.path[0]\n",
+      "/Users/jonc101/Documents/Biomedical_Data_Science/venv/lib/python3.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  \n",
+      "/Users/jonc101/Documents/Biomedical_Data_Science/venv/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  from ipykernel import kernelapp as app\n"
+     ]
+    }
+   ],
+   "source": [
+    "def state_split(state_names, df):\n",
+    "    df2 = df[df['name_x'].isin(state_names)]\n",
+    "    return(df2)\n",
+    "\n",
+    "gi_test = state_split(gi_bleed_states, remerged_order)\n",
+    "mening_test = state_split(mening_states, remerged_order)\n",
+    "pulmonary_embolism_test = state_split(pulmonary_emolism_states, remerged_order)\n",
+    "afib_test = state_split(afib_states, remerged_order)\n",
+    "neutropenic_test = state_split(neutropenic_fever_states, remerged_order)\n",
+    "\n",
+    "gi_test['case'] = \"gi_bleed\"\n",
+    "mening_test['case'] = \"meningitis\"\n",
+    "pulmonary_embolism_test['case'] = \"pulmonary_embolism\"\n",
+    "afib_test['case'] = \"atrial_fibrillation\"\n",
+    "neutropenic_test['case'] = \"neutropenic\"\n",
+    "\n",
+    "df_grading_pre = pd.concat([gi_test,\n",
+    "                        mening_test,\n",
+    "                        pulmonary_embolism_test,\n",
+    "                        afib_test,\n",
+    "                        neutropenic_test])\n",
+    "\n",
+    "\n",
+    "df_grading = pd.DataFrame(df_grading_pre[['sim_state_id',\n",
+    "                                        'clinical_item_id',\n",
+    "                                        'sim_user_id',\n",
+    "                                        'sim_patient_id',\n",
+    "                                        'name_x',\n",
+    "                                        'description_x',\n",
+    "                                        'description_y',\n",
+    "                                        'case']])\n",
+    "\n",
+    "\n",
+    "print(df_grading)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Purpose:  \n",
+    "    Split: sim states and get unique orders \n",
+    "        Then: \n",
+    "            each list/dictionary \n",
+    "            key = sim_state_id \n",
+    "            value = all orders \n",
+    "        Then: \n",
+    "            for each key: \n",
+    "                get unique orders: \n",
+    "        Then: \n",
+    "            convert list/groupby object into dataframe\n",
+    "</pre>                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sim_state_id</th>\n",
+       "      <th>clinical_item_id</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "      <th>sim_patient_id</th>\n",
+       "      <th>name_x</th>\n",
+       "      <th>description_x</th>\n",
+       "      <th>description_y</th>\n",
+       "      <th>case</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>26</td>\n",
+       "      <td>134</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>15</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>26</td>\n",
+       "      <td>123</td>\n",
+       "      <td>EtOH-GIBleed Bleeding Out</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Bleeding Out</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>15</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>0</td>\n",
+       "      <td>126</td>\n",
+       "      <td>EtOH-GIBleed Bleeding Out</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Bleeding Out</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>15</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>0</td>\n",
+       "      <td>153</td>\n",
+       "      <td>EtOH-GIBleed Bleeding Out</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Bleeding Out</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>2</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>31</td>\n",
+       "      <td>141</td>\n",
+       "      <td>EtOH-GIBleed Post-EGD</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed EGD Done</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>2</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>13</td>\n",
+       "      <td>39</td>\n",
+       "      <td>EtOH-GIBleed Post-EGD</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed EGD Done</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>2</td>\n",
+       "      <td>45763</td>\n",
+       "      <td>53</td>\n",
+       "      <td>293</td>\n",
+       "      <td>EtOH-GIBleed Post-EGD</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed EGD Done</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45801</td>\n",
+       "      <td>48</td>\n",
+       "      <td>248</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>XR CHEST 2V [CXR]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>10</td>\n",
+       "      <td>23</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>11</td>\n",
+       "      <td>31</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>13</td>\n",
+       "      <td>39</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>15</td>\n",
+       "      <td>75</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>17</td>\n",
+       "      <td>79</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>21</td>\n",
+       "      <td>5</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>26</td>\n",
+       "      <td>108</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>27</td>\n",
+       "      <td>113</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>29</td>\n",
+       "      <td>120</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>33</td>\n",
+       "      <td>148</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>0</td>\n",
+       "      <td>153</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>39</td>\n",
+       "      <td>172</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>40</td>\n",
+       "      <td>180</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>43</td>\n",
+       "      <td>205</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>46</td>\n",
+       "      <td>230</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>47</td>\n",
+       "      <td>238</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>48</td>\n",
+       "      <td>248</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>51</td>\n",
+       "      <td>277</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>53</td>\n",
+       "      <td>293</td>\n",
+       "      <td>EtOH-GIBleed Active</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>15</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>29</td>\n",
+       "      <td>120</td>\n",
+       "      <td>EtOH-GIBleed Bleeding Out</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Bleeding Out</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>15</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>0</td>\n",
+       "      <td>126</td>\n",
+       "      <td>EtOH-GIBleed Bleeding Out</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Bleeding Out</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>15</td>\n",
+       "      <td>45838</td>\n",
+       "      <td>29</td>\n",
+       "      <td>120</td>\n",
+       "      <td>EtOH-GIBleed Bleeding Out</td>\n",
+       "      <td>Alcoholic Hepatitis - GI Bleed Bleeding Out</td>\n",
+       "      <td>ISTAT TROPONIN I</td>\n",
+       "      <td>gi_bleed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3180</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3181</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>257</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3182</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>267</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3183</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>275</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3184</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>282</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3185</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>290</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3186</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>291</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3187</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>41759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Fever and other physiologic disturbances of te...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3188</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>62023</td>\n",
+       "      <td>17</td>\n",
+       "      <td>77</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>RESPIRATORY DFA PANEL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3189</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>62023</td>\n",
+       "      <td>39</td>\n",
+       "      <td>170</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>RESPIRATORY DFA PANEL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3190</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>62023</td>\n",
+       "      <td>42</td>\n",
+       "      <td>192</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>RESPIRATORY DFA PANEL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3191</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>62023</td>\n",
+       "      <td>44</td>\n",
+       "      <td>210</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>RESPIRATORY DFA PANEL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3192</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>62023</td>\n",
+       "      <td>49</td>\n",
+       "      <td>257</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>RESPIRATORY DFA PANEL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3194</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>46000</td>\n",
+       "      <td>29</td>\n",
+       "      <td>118</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>POC URINE DIPSTICK</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3196</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>48775</td>\n",
+       "      <td>31</td>\n",
+       "      <td>139</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>BLADDER SCAN</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3197</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>44237</td>\n",
+       "      <td>31</td>\n",
+       "      <td>139</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>Glucose-Piperacillin-Tazobactam (Intravenous)</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3198</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>48686</td>\n",
+       "      <td>40</td>\n",
+       "      <td>177</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>DIET LOW MICROBIAL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3199</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>48686</td>\n",
+       "      <td>30</td>\n",
+       "      <td>129</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>DIET LOW MICROBIAL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3200</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>48686</td>\n",
+       "      <td>46</td>\n",
+       "      <td>227</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>DIET LOW MICROBIAL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3201</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>48686</td>\n",
+       "      <td>52</td>\n",
+       "      <td>282</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>DIET LOW MICROBIAL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3202</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>48686</td>\n",
+       "      <td>53</td>\n",
+       "      <td>291</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>DIET LOW MICROBIAL</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3203</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>45903</td>\n",
+       "      <td>47</td>\n",
+       "      <td>236</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>LDH TOTAL, SERUM / PLASMA</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3204</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>45995</td>\n",
+       "      <td>47</td>\n",
+       "      <td>236</td>\n",
+       "      <td>Neutropenic Fever v2</td>\n",
+       "      <td>Neutropenic Fever (v2 example) Initial State</td>\n",
+       "      <td>URIC ACID, SERUM / PLASMA</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3205</th>\n",
+       "      <td>5002</td>\n",
+       "      <td>46291</td>\n",
+       "      <td>39</td>\n",
+       "      <td>170</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF</td>\n",
+       "      <td>Appropriate IVF improve patient condition</td>\n",
+       "      <td>BLOOD CULT - FIRST SET, VIA PHLEBOTOMY</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3206</th>\n",
+       "      <td>5002</td>\n",
+       "      <td>63735</td>\n",
+       "      <td>46</td>\n",
+       "      <td>227</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF</td>\n",
+       "      <td>Appropriate IVF improve patient condition</td>\n",
+       "      <td>VIRAL CULTURE, RESPIRATORY</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3207</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>63735</td>\n",
+       "      <td>46</td>\n",
+       "      <td>227</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>VIRAL CULTURE, RESPIRATORY</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3208</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>48980</td>\n",
+       "      <td>41</td>\n",
+       "      <td>184</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3212</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>44395</td>\n",
+       "      <td>44</td>\n",
+       "      <td>210</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>Calcium Chloride-Glucose-Lactate-Potassium Chl...</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3213</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>52757</td>\n",
+       "      <td>53</td>\n",
+       "      <td>291</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>REFERRAL TO PHYSICAL THERAPY</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3214</th>\n",
+       "      <td>5003</td>\n",
+       "      <td>48711</td>\n",
+       "      <td>53</td>\n",
+       "      <td>291</td>\n",
+       "      <td>Neutropenic Fever Treated with IVF + ABX</td>\n",
+       "      <td>Appropriate Treatments for patient condition</td>\n",
+       "      <td>CONTACT ISOLATION</td>\n",
+       "      <td>neutropenic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2680 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      sim_state_id  clinical_item_id  sim_user_id  sim_patient_id  \\\n",
+       "55              14             45763           26             134   \n",
+       "67              15             45763           26             123   \n",
+       "68              15             45763            0             126   \n",
+       "69              15             45763            0             153   \n",
+       "70               2             45763           31             141   \n",
+       "71               2             45763           13              39   \n",
+       "72               2             45763           53             293   \n",
+       "99              14             45801           48             248   \n",
+       "177             14             45866           10              23   \n",
+       "178             14             45866           11              31   \n",
+       "179             14             45866           13              39   \n",
+       "180             14             45866           15              75   \n",
+       "181             14             45866           17              79   \n",
+       "182             14             45866           21               5   \n",
+       "183             14             45866           26             108   \n",
+       "184             14             45866           27             113   \n",
+       "185             14             45866           29             120   \n",
+       "186             14             45866           33             148   \n",
+       "187             14             45866            0             153   \n",
+       "188             14             45866           39             172   \n",
+       "189             14             45866           40             180   \n",
+       "190             14             45866           43             205   \n",
+       "191             14             45866           46             230   \n",
+       "192             14             45866           47             238   \n",
+       "193             14             45866           48             248   \n",
+       "194             14             45866           51             277   \n",
+       "195             14             45866           53             293   \n",
+       "198             15             45866           29             120   \n",
+       "199             15             45866            0             126   \n",
+       "290             15             45838           29             120   \n",
+       "...            ...               ...          ...             ...   \n",
+       "3180          5000             41759            0             256   \n",
+       "3181          5000             41759            0             257   \n",
+       "3182          5000             41759            0             267   \n",
+       "3183          5000             41759            0             275   \n",
+       "3184          5000             41759            0             282   \n",
+       "3185          5000             41759            0             290   \n",
+       "3186          5000             41759            0             291   \n",
+       "3187          5000             41759            0             299   \n",
+       "3188          5000             62023           17              77   \n",
+       "3189          5000             62023           39             170   \n",
+       "3190          5000             62023           42             192   \n",
+       "3191          5000             62023           44             210   \n",
+       "3192          5000             62023           49             257   \n",
+       "3194          5000             46000           29             118   \n",
+       "3196          5000             48775           31             139   \n",
+       "3197          5000             44237           31             139   \n",
+       "3198          5000             48686           40             177   \n",
+       "3199          5003             48686           30             129   \n",
+       "3200          5003             48686           46             227   \n",
+       "3201          5003             48686           52             282   \n",
+       "3202          5003             48686           53             291   \n",
+       "3203          5000             45903           47             236   \n",
+       "3204          5000             45995           47             236   \n",
+       "3205          5002             46291           39             170   \n",
+       "3206          5002             63735           46             227   \n",
+       "3207          5003             63735           46             227   \n",
+       "3208          5003             48980           41             184   \n",
+       "3212          5003             44395           44             210   \n",
+       "3213          5003             52757           53             291   \n",
+       "3214          5003             48711           53             291   \n",
+       "\n",
+       "                                        name_x  \\\n",
+       "55                         EtOH-GIBleed Active   \n",
+       "67                   EtOH-GIBleed Bleeding Out   \n",
+       "68                   EtOH-GIBleed Bleeding Out   \n",
+       "69                   EtOH-GIBleed Bleeding Out   \n",
+       "70                       EtOH-GIBleed Post-EGD   \n",
+       "71                       EtOH-GIBleed Post-EGD   \n",
+       "72                       EtOH-GIBleed Post-EGD   \n",
+       "99                         EtOH-GIBleed Active   \n",
+       "177                        EtOH-GIBleed Active   \n",
+       "178                        EtOH-GIBleed Active   \n",
+       "179                        EtOH-GIBleed Active   \n",
+       "180                        EtOH-GIBleed Active   \n",
+       "181                        EtOH-GIBleed Active   \n",
+       "182                        EtOH-GIBleed Active   \n",
+       "183                        EtOH-GIBleed Active   \n",
+       "184                        EtOH-GIBleed Active   \n",
+       "185                        EtOH-GIBleed Active   \n",
+       "186                        EtOH-GIBleed Active   \n",
+       "187                        EtOH-GIBleed Active   \n",
+       "188                        EtOH-GIBleed Active   \n",
+       "189                        EtOH-GIBleed Active   \n",
+       "190                        EtOH-GIBleed Active   \n",
+       "191                        EtOH-GIBleed Active   \n",
+       "192                        EtOH-GIBleed Active   \n",
+       "193                        EtOH-GIBleed Active   \n",
+       "194                        EtOH-GIBleed Active   \n",
+       "195                        EtOH-GIBleed Active   \n",
+       "198                  EtOH-GIBleed Bleeding Out   \n",
+       "199                  EtOH-GIBleed Bleeding Out   \n",
+       "290                  EtOH-GIBleed Bleeding Out   \n",
+       "...                                        ...   \n",
+       "3180                      Neutropenic Fever v2   \n",
+       "3181                      Neutropenic Fever v2   \n",
+       "3182                      Neutropenic Fever v2   \n",
+       "3183                      Neutropenic Fever v2   \n",
+       "3184                      Neutropenic Fever v2   \n",
+       "3185                      Neutropenic Fever v2   \n",
+       "3186                      Neutropenic Fever v2   \n",
+       "3187                      Neutropenic Fever v2   \n",
+       "3188                      Neutropenic Fever v2   \n",
+       "3189                      Neutropenic Fever v2   \n",
+       "3190                      Neutropenic Fever v2   \n",
+       "3191                      Neutropenic Fever v2   \n",
+       "3192                      Neutropenic Fever v2   \n",
+       "3194                      Neutropenic Fever v2   \n",
+       "3196                      Neutropenic Fever v2   \n",
+       "3197                      Neutropenic Fever v2   \n",
+       "3198                      Neutropenic Fever v2   \n",
+       "3199  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3200  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3201  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3202  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3203                      Neutropenic Fever v2   \n",
+       "3204                      Neutropenic Fever v2   \n",
+       "3205        Neutropenic Fever Treated with IVF   \n",
+       "3206        Neutropenic Fever Treated with IVF   \n",
+       "3207  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3208  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3212  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3213  Neutropenic Fever Treated with IVF + ABX   \n",
+       "3214  Neutropenic Fever Treated with IVF + ABX   \n",
+       "\n",
+       "                                     description_x  \\\n",
+       "55           Alcoholic Hepatitis - GI Bleed Active   \n",
+       "67     Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+       "68     Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+       "69     Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+       "70         Alcoholic Hepatitis - GI Bleed EGD Done   \n",
+       "71         Alcoholic Hepatitis - GI Bleed EGD Done   \n",
+       "72         Alcoholic Hepatitis - GI Bleed EGD Done   \n",
+       "99           Alcoholic Hepatitis - GI Bleed Active   \n",
+       "177          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "178          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "179          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "180          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "181          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "182          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "183          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "184          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "185          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "186          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "187          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "188          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "189          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "190          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "191          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "192          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "193          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "194          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "195          Alcoholic Hepatitis - GI Bleed Active   \n",
+       "198    Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+       "199    Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+       "290    Alcoholic Hepatitis - GI Bleed Bleeding Out   \n",
+       "...                                            ...   \n",
+       "3180  Neutropenic Fever (v2 example) Initial State   \n",
+       "3181  Neutropenic Fever (v2 example) Initial State   \n",
+       "3182  Neutropenic Fever (v2 example) Initial State   \n",
+       "3183  Neutropenic Fever (v2 example) Initial State   \n",
+       "3184  Neutropenic Fever (v2 example) Initial State   \n",
+       "3185  Neutropenic Fever (v2 example) Initial State   \n",
+       "3186  Neutropenic Fever (v2 example) Initial State   \n",
+       "3187  Neutropenic Fever (v2 example) Initial State   \n",
+       "3188  Neutropenic Fever (v2 example) Initial State   \n",
+       "3189  Neutropenic Fever (v2 example) Initial State   \n",
+       "3190  Neutropenic Fever (v2 example) Initial State   \n",
+       "3191  Neutropenic Fever (v2 example) Initial State   \n",
+       "3192  Neutropenic Fever (v2 example) Initial State   \n",
+       "3194  Neutropenic Fever (v2 example) Initial State   \n",
+       "3196  Neutropenic Fever (v2 example) Initial State   \n",
+       "3197  Neutropenic Fever (v2 example) Initial State   \n",
+       "3198  Neutropenic Fever (v2 example) Initial State   \n",
+       "3199  Appropriate Treatments for patient condition   \n",
+       "3200  Appropriate Treatments for patient condition   \n",
+       "3201  Appropriate Treatments for patient condition   \n",
+       "3202  Appropriate Treatments for patient condition   \n",
+       "3203  Neutropenic Fever (v2 example) Initial State   \n",
+       "3204  Neutropenic Fever (v2 example) Initial State   \n",
+       "3205     Appropriate IVF improve patient condition   \n",
+       "3206     Appropriate IVF improve patient condition   \n",
+       "3207  Appropriate Treatments for patient condition   \n",
+       "3208  Appropriate Treatments for patient condition   \n",
+       "3212  Appropriate Treatments for patient condition   \n",
+       "3213  Appropriate Treatments for patient condition   \n",
+       "3214  Appropriate Treatments for patient condition   \n",
+       "\n",
+       "                                          description_y         case  \n",
+       "55                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "67                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "68                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "69                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "70                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "71                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "72                         METABOLIC PANEL, BASIC [BMP]     gi_bleed  \n",
+       "99                                    XR CHEST 2V [CXR]     gi_bleed  \n",
+       "177                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "178                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "179                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "180                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "181                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "182                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "183                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "184                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "185                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "186                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "187                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "188                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "189                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "190                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "191                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "192                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "193                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "194                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "195                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "198                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "199                                   ECG 12-LEAD [EKG]     gi_bleed  \n",
+       "290                                    ISTAT TROPONIN I     gi_bleed  \n",
+       "...                                                 ...          ...  \n",
+       "3180  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3181  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3182  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3183  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3184  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3185  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3186  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3187  Fever and other physiologic disturbances of te...  neutropenic  \n",
+       "3188                              RESPIRATORY DFA PANEL  neutropenic  \n",
+       "3189                              RESPIRATORY DFA PANEL  neutropenic  \n",
+       "3190                              RESPIRATORY DFA PANEL  neutropenic  \n",
+       "3191                              RESPIRATORY DFA PANEL  neutropenic  \n",
+       "3192                              RESPIRATORY DFA PANEL  neutropenic  \n",
+       "3194                                 POC URINE DIPSTICK  neutropenic  \n",
+       "3196                                       BLADDER SCAN  neutropenic  \n",
+       "3197      Glucose-Piperacillin-Tazobactam (Intravenous)  neutropenic  \n",
+       "3198                                 DIET LOW MICROBIAL  neutropenic  \n",
+       "3199                                 DIET LOW MICROBIAL  neutropenic  \n",
+       "3200                                 DIET LOW MICROBIAL  neutropenic  \n",
+       "3201                                 DIET LOW MICROBIAL  neutropenic  \n",
+       "3202                                 DIET LOW MICROBIAL  neutropenic  \n",
+       "3203                          LDH TOTAL, SERUM / PLASMA  neutropenic  \n",
+       "3204                          URIC ACID, SERUM / PLASMA  neutropenic  \n",
+       "3205             BLOOD CULT - FIRST SET, VIA PHLEBOTOMY  neutropenic  \n",
+       "3206                         VIRAL CULTURE, RESPIRATORY  neutropenic  \n",
+       "3207                         VIRAL CULTURE, RESPIRATORY  neutropenic  \n",
+       "3208                      CONSULT TO INFECTIOUS DISEASE  neutropenic  \n",
+       "3212  Calcium Chloride-Glucose-Lactate-Potassium Chl...  neutropenic  \n",
+       "3213                       REFERRAL TO PHYSICAL THERAPY  neutropenic  \n",
+       "3214                                  CONTACT ISOLATION  neutropenic  \n",
+       "\n",
+       "[2680 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_grading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df_grading.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "convert groupby object to dictionary of dataframes:\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grading_folder = '/Users/jonc101/Documents/Biomedical_Data_Science/physician_grading/'\n",
+    "grading_folder = recommender_path + \"physician_grading/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Purpose:  \n",
+    "    Read Grade Data from Grading Folder \n",
+    "        Then: \n",
+    "            Append each score as column labeled from Physician \n",
+    "        Then: \n",
+    "            Concat to a Dataframe of All Grades \n",
+    "</pre>                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak = pd.read_excel(physician_grading + 'andre_kumar_v4.xlsx', index_col=0)\n",
+    "ls = pd.read_excel(physician_grading + 'lisa_shieh_v4.xlsx', index_col=0)\n",
+    "jh = pd.read_excel(physician_grading + 'jason_hom_v4.xlsx', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.drop of                                             clinical_order  \\\n",
+       "Column1                                                      \n",
+       "395                    DCCV - Direct Current Cardioversion   \n",
+       "396                                Diltiazem (Intravenous)   \n",
+       "397                               Amiodarone (Intravenous)   \n",
+       "398                                   Cardiac dysrhythmias   \n",
+       "399                                Midazolam (Intravenous)   \n",
+       "400                               Furosemide (Intravenous)   \n",
+       "401      Sodium Chloride (Intravenous) [NS IVF][Normal ...   \n",
+       "402                                       Donepezil (Oral)   \n",
+       "403                                         Aspirin (Oral)   \n",
+       "404                                 Fentanyl (Intravenous)   \n",
+       "405                                    Rosuvastatin (Oral)   \n",
+       "406                               Metoprolol (Intravenous)   \n",
+       "407                                 Diazepam (Intravenous)   \n",
+       "408                                  Heparin (Intravenous)   \n",
+       "409                              PROTHROMBIN TIME [PT/INR]   \n",
+       "410                           METABOLIC PANEL, BASIC [BMP]   \n",
+       "411                        PTT PARTIAL THROMBOPLASTIN TIME   \n",
+       "412                   METABOLIC PANEL, COMPREHENSIVE [CMP]   \n",
+       "413                                  PT EVALUATE AND TREAT   \n",
+       "414                               PHOSPHORUS, SERUM/PLASMA   \n",
+       "415                                  OT EVALUATE AND TREAT   \n",
+       "416                                          CBC WITH DIFF   \n",
+       "417                                                    CBC   \n",
+       "418                                      XR CHEST 2V [CXR]   \n",
+       "419                                MAGNESIUM, SERUM/PLASMA   \n",
+       "420                                               DIET NPO   \n",
+       "421                                      XR CHEST 1V [CXR]   \n",
+       "422                                        CARDIAC MONITOR   \n",
+       "423                                       ISTAT TROPONIN I   \n",
+       "424                                            NT - PROBNP   \n",
+       "...                                                    ...   \n",
+       "121                                         CT ANGIO CHEST   \n",
+       "122                                      D - DIMER (ELISA)   \n",
+       "123                     RESPIRATORY CULTURE AND GRAM STAIN   \n",
+       "124                                   NO ASPIRIN OR NSAIDS   \n",
+       "125                       CT PULMONARY EMBOLISM CHEST ONLY   \n",
+       "126                                              D - DIMER   \n",
+       "127                               CT CHEST HIGH RESOLUTION   \n",
+       "128                                    CONSULT TO ONCOLOGY   \n",
+       "129                                  CONSULT TO CARDIOLOGY   \n",
+       "130                           OXYGEN ADMIN (BY OTHER MODE)   \n",
+       "131               ECHO - TRANSTHORACIC ECHO +DOPPLER (TTE)   \n",
+       "132                            LIPID PANEL WITH DIRECT LDL   \n",
+       "133                               FOLEY RETENTION CATHETER   \n",
+       "134                                  RESPIRATORY DFA PANEL   \n",
+       "135                                          PROCALCITONIN   \n",
+       "136                            RESPIRATORY VIRUS PCR PANEL   \n",
+       "137                                     ADMIT TO INPATIENT   \n",
+       "138                           CONSULT TO HEART FAILURE CNS   \n",
+       "22                               PROTHROMBIN TIME [PT/INR]   \n",
+       "23                         PTT PARTIAL THROMBOPLASTIN TIME   \n",
+       "24                                              TROPONIN I   \n",
+       "25                                 DIET NPO AFTER MIDNIGHT   \n",
+       "26                     WEAN WITH O2SAT/PETCO2 PER PROTOCOL   \n",
+       "27                                     CONSULT TO ONCOLOGY   \n",
+       "28                                    PTT INHIBITOR SCREEN   \n",
+       "29                                     ISTAT CG4, ARTERIAL   \n",
+       "30          POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED)   \n",
+       "31                ECHO - TRANSTHORACIC ECHO +DOPPLER (TTE)   \n",
+       "32                    REFERRAL TO INTERVENTIONAL RADIOLOGY   \n",
+       "33       Ultrasound - US RADIOLOGY VEIN LOWER EXTREMITY...   \n",
+       "\n",
+       "                        case                                 name.x  grade  \\\n",
+       "Column1                                                                      \n",
+       "395      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "396      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "397      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "398      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "399      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "400      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "401      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "402      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "403      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "404      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "405      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "406      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "407      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "408      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "409      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "410      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "411      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "412      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "413      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "414      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "415      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "416      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "417      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "418      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "419      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "420      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "421      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "422      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "423      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "424      atrial_fibrillation                       Afib-RVR Initial    NaN   \n",
+       "...                      ...                                    ...    ...   \n",
+       "121       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "122       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "123       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "124       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "125       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "126       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "127       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "128       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "129       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "130       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "131       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "132       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "133       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "134       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "135       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "136       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "137       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "138       pulmonary_embolism                    PE-COPD-LungCA + O2    NaN   \n",
+       "22        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "23        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "24        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "25        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "26        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "27        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "28        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "29        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "30        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "31        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "32        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "33        pulmonary_embolism  PE-COPD-LungCA + O2 + Anticoagulation    NaN   \n",
+       "\n",
+       "         confidence group_name Commentary:   \n",
+       "Column1                                      \n",
+       "395             NaN        NaN          NaN  \n",
+       "396             NaN        NaN          NaN  \n",
+       "397             NaN        NaN          NaN  \n",
+       "398             NaN        NaN          NaN  \n",
+       "399             NaN        NaN          NaN  \n",
+       "400             NaN        NaN          NaN  \n",
+       "401             NaN        NaN          NaN  \n",
+       "402             NaN        NaN          NaN  \n",
+       "403             NaN        NaN          NaN  \n",
+       "404             NaN        NaN          NaN  \n",
+       "405             NaN        NaN          NaN  \n",
+       "406             NaN        NaN          NaN  \n",
+       "407             NaN        NaN          NaN  \n",
+       "408             NaN        NaN          NaN  \n",
+       "409             NaN        NaN          NaN  \n",
+       "410             NaN        NaN          NaN  \n",
+       "411             NaN        NaN          NaN  \n",
+       "412             NaN        NaN          NaN  \n",
+       "413             NaN        NaN          NaN  \n",
+       "414             NaN        NaN          NaN  \n",
+       "415             NaN        NaN          NaN  \n",
+       "416             NaN        NaN          NaN  \n",
+       "417             NaN        NaN          NaN  \n",
+       "418             NaN        NaN          NaN  \n",
+       "419             NaN        NaN          NaN  \n",
+       "420             NaN        NaN          NaN  \n",
+       "421             NaN        NaN          NaN  \n",
+       "422             NaN        NaN          NaN  \n",
+       "423             NaN        NaN          NaN  \n",
+       "424             NaN        NaN          NaN  \n",
+       "...             ...        ...          ...  \n",
+       "121             NaN        NaN          NaN  \n",
+       "122             NaN        NaN          NaN  \n",
+       "123             NaN        NaN          NaN  \n",
+       "124             NaN        NaN          NaN  \n",
+       "125             NaN        NaN          NaN  \n",
+       "126             NaN        NaN          NaN  \n",
+       "127             NaN        NaN          NaN  \n",
+       "128             NaN        NaN          NaN  \n",
+       "129             NaN        NaN          NaN  \n",
+       "130             NaN        NaN          NaN  \n",
+       "131             NaN        NaN          NaN  \n",
+       "132             NaN        NaN          NaN  \n",
+       "133             NaN        NaN          NaN  \n",
+       "134             NaN        NaN          NaN  \n",
+       "135             NaN        NaN          NaN  \n",
+       "136             NaN        NaN          NaN  \n",
+       "137             NaN        NaN          NaN  \n",
+       "138             NaN        NaN          NaN  \n",
+       "22              NaN        NaN          NaN  \n",
+       "23              NaN        NaN          NaN  \n",
+       "24              NaN        NaN          NaN  \n",
+       "25              NaN        NaN          NaN  \n",
+       "26              NaN        NaN          NaN  \n",
+       "27              NaN        NaN          NaN  \n",
+       "28              NaN        NaN          NaN  \n",
+       "29              NaN        NaN          NaN  \n",
+       "30              NaN        NaN          NaN  \n",
+       "31              NaN        NaN          NaN  \n",
+       "32              NaN        NaN          NaN  \n",
+       "33              NaN        NaN          NaN  \n",
+       "\n",
+       "[598 rows x 7 columns]>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls.drop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak['grade'] = 3\n",
+    "#ls['grade'] = 30\n",
+    "jh['grade'] = 300\n",
+    "# reassign grade terms \n",
+    "ak['grade_ak'] = ak['grade']\n",
+    "ls['grade_ls'] = ls['grade']\n",
+    "jh['grade_jh'] = jh['grade']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concat the column grades \n",
+    "# write tests for grading columns TO DO \n",
+    "# grading_delphi\n",
+    "\n",
+    "gd = pd.DataFrame(pd.concat([ak,  ls['grade_ls'], jh['grade_jh']], axis =1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gd['grade_mean'] = (gd['grade_ak'].values + gd['grade_ls'].values + gd['grade_jh'].values )/ 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_grading['sim_state_name'] = df_grading['description_x']\n",
+    "gd['sim_state_name'] = gd['name.x']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Purpose:  \n",
+    "    Join sim_state_id and clinical_item_id to Grading Sheet\n",
+    "        Then: \n",
+    "             \n",
+    "            key = sim_state_id \n",
+    "            value = all orders \n",
+    "        Then: \n",
+    "            for each key: \n",
+    "                get unique orders: \n",
+    "        Then: \n",
+    "            convert list/groupby object into dataframe\n",
+    "</pre>    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#gd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clinical_item_key = pd.DataFrame(ordered_clinical_item_table[['clinical_item_id', 'description']])\n",
+    "clinical_item_key['clinical_order'] = clinical_item_key['description']\n",
+    "df_grading2 = pd.merge(df_grading_pre, clinical_item_key, how='left', on=['clinical_item_id'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gd2 = pd.merge(gd, clinical_item_key, how='left', on=['clinical_order'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gd2['sim_state_name'] = gd2['name.x']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order_grade = pd.merge(gd2, df_grading2, how='outer', on=['clinical_item_id', 'sim_state_name'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_grading2['sim_state_clinical_order_id'] = df_grading2['sim_state_id'].apply(str) + '_' + df_grading2['clinical_item_id'].apply(str)\n",
+    "sim_clinical_orders = df_grading2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#gd2['sim_state_clinical_order_id'] = gd2['sim_state_id'].apply(str) + '_' + gd2['clinical_item_id'].apply(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim_state_link = sim_state[['sim_state_id', 'name']]\n",
+    "gd2['name'] = gd2['sim_state_name']\n",
+    "gd3 = pd.merge(gd2, sim_state_link, how='left', on=['name'])\n",
+    "gd3['sim_state_clinical_order_id'] = gd3['sim_state_id'].apply(str) + '_' + gd3['clinical_item_id'].apply(str)\n",
+    "physician_grading_key = gd3 \n",
+    "sim_orders_grade = pd.merge(sim_clinical_orders, physician_grading_key, how='left', on=['sim_state_clinical_order_id'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Def: \n",
+    "    Sum Values of Each Score\n",
+    "    \n",
+    "Generate Scores for Each Case: \n",
+    "    Then: \n",
+    "        Split by each case \n",
+    "    Then: \n",
+    "        Group By Each \n",
+    "    Then: \n",
+    "        split by sim_states into group by object \n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# WRITE TEST FUNCTION \n",
+    "\n",
+    "def grade_sum(case):\n",
+    "    return case['grade_mean'].sum(axis = 0, skipna = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "gk = sim_orders_grade.groupby('sim_patient_id') \n",
+    "ctd = gk.apply(grade_sum)\n",
+    "sim_grade_groups = sim_orders_grade.groupby('sim_patient_id').groups\n",
+    "sim_grade_group_list = list(sim_grade_groups.keys())\n",
+    "sim_grades_list = list(ctd)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>\n",
+    "Def: \n",
+    "    Sum Values of Each Score\n",
+    "        Use Merged Total Score have a list of total scores for each case \n",
+    "    Then: \n",
+    "        split for each case \n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_total_score = pd.DataFrame(zip(sim_grade_group_list, sim_grades_list))\n",
+    "merged_total_score.columns = ['sim_patient_id', 'case_grade']\n",
+    "#merged_total_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sim_orders_grade.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GET STAR: TO RUN v4_data Script \n",
+    "# RUN SCRIPT HERE TO GENERATE OUTPUT IN TRACKER OUTPUT\n",
+    "tracker_data_out = pd.read_csv(tracker_data + 'tracker_output/output.csv')\n",
+    "\n",
+    "# preprocessing data columns \n",
+    "\n",
+    "tracker_data_out['sim_patient_id'] = tracker_data_out['patient']\n",
+    "tracker_data_out['sim_user_id'] = tracker_data_out['user']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# merge on grade orders \n",
+    "sim_user['sim_name'] = sim_user['name']\n",
+    "tracker_user_join = pd.merge(tracker_data_out, sim_user, how='left', on=['sim_user_id'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read out physician response \n",
+    "\n",
+    "physician_response_out = pd.read_csv(physician_response + 'physician_responses2.csv')\n",
+    "physician_response_join = pd.merge(physician_response_out, sim_user, how='left', on=['sim_name'])\n",
+    "tracker_response_join = pd.merge(tracker_user_join, physician_response_join,  how='left', on=['sim_user_id'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sim_user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sim_user_id', 'sim_name']"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#tracker_response_merge = pd.merge(tracker_user_join, physician_response_out, how='left', on=['name'])\n",
+    "def intersection(lst1, lst2): \n",
+    "    return list(set(lst1) & set(lst2)) \n",
+    "intersection(tracker_user_join.columns, physician_response_join.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tracker_response_score_join = pd.merge(tracker_response_join, merged_total_score,  how='left', on=['sim_patient_id'])\n",
+    "#tracker_response_score_join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write to folder \n",
+    "tracker_response_score_join.to_csv(recommender_path + 'recommender_generated_outputs/tracker_response_grade.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### UNIT TESTING \n",
+    "\n",
+    "<pre>\n",
+    "\n",
+    "In Addition we Developed Test Suites to facilitate most cases of physician ordering. We had to design a grading methodology that incorporated many types of grading interactions that could potentially happen. \n",
+    "\n",
+    "For example we designed many \"sim states\" \n",
+    "\n",
+    "Points of interest in the development of physician grading feedback is the ability to resolve the difference of opinion in expert panels. this combines qualitative and quantitative assessement of clinical decisions in a simulated setting. \n",
+    "\n",
+    "Discussion: \n",
+    "\n",
+    " In the design of a simulated environment, we could expand the work further by providing more clinical feedback by annotating the types of clinical decisions for continuous feedback of physicians. The nature of feedback of a software system is the ability to distribtue the software indefnitely. We could abstract the databases to collate all results in a distributed system. \n",
+    "\n",
+    "This provides an EHR open source platform that can provide feedback given case design, while also utilizing an EHR that can benefit in resource constrained clinics. \n",
+    "\n",
+    "\n",
+    "</pre> \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "len(os.listdir(recommender_path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'tracker_output' in list(os.listdir(recommender_path + '/tracker_data' ))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['.DS_Store', 'v4.csv', 'old_data', 'v4_data', 'tracker_output']"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(os.listdir(recommender_path + '/tracker_data' ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tracker Data Analysis:\n",
+    "\n",
+    "- Data analysis \n",
+    "- Associations \n",
+    "- Treatment Effect of recommender : (clinical grade, time to completion)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reliability (statistics)\n",
+    "\n",
+    "\n",
+    "\n",
+    ">Reliability in statistics and psychometrics is the overall consistency of a measure.[1] A measure is said to have a high reliability if it produces similar results under consistent conditions. \"It is the characteristic of a set of test scores that relates to the amount of random error from the measurement process that might be embedded in the scores. Scores that are highly reliable are accurate, reproducible, and consistent from one testing occasion to another. That is, if the testing process were repeated with a group of test takers, essentially the same results would be obtained. Various kinds of reliability coefficients, with values ranging between 0.00 (much error) and 1.00 (no error), are usually used to indicate the amount of error in the scores.\" [2] For example, measurements of people's height and weight are often extremely reliable.[3][4\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_test = pd.read_excel(physician_grading + 'lisa_shieh_v4.xlsx', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_non_na= list(ls_test['grade'].dropna().index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ls_test[:index_non_na]\n",
+    "sample_grading = ls_test.iloc[index_non_na]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Grouping and Grading Methodology: \n",
+    "\n",
+    "> filtering cases based on conditions on states and group interactions: \n",
+    "    our group is making clinical decisions on evaluating simulated patients \n",
+    "    in a simulated environment \n",
+    "\n",
+    "> grading doctors decision making in a game: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 264,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def rFilter(df,col, condition):\n",
+    "    return df[df[col]==condition]\n",
+    "\n",
+    "\n",
+    "sample_grading['case'] == 'meningitis'\n",
+    "meng_grading = sample_grading[sample_grading['case']=='meningitis']\n",
+    "meng_gradingR = rFilter(sample_grading, 'case', 'meningitis')\n",
+    "meng_grading.equals(meng_gradingR)\n",
+    "\n",
+    "mgr = meng_grading\n",
+    "uni_mgr_order = mgr['clinical_order'].values\n",
+    "\n",
+    "mgr_order = mgr['clinical_order'].values\n",
+    "\n",
+    "list([type(uni_mgr_order), type(mgr_order)])\n",
+    "\n",
+    "len(mgr_order) == len(uni_mgr_order)\n",
+    "\n",
+    "mgr_set = list(set(mgr_order))\n",
+    "\n",
+    "len(mgr_set)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://seaborn.pydata.org/tutorial/categorical.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      Negative\n",
+       "1      Negative\n",
+       "2      Negative\n",
+       "3      Negative\n",
+       "4      Negative\n",
+       "5      Negative\n",
+       "6      Negative\n",
+       "7      Negative\n",
+       "8      Negative\n",
+       "9      Negative\n",
+       "10     Negative\n",
+       "11     Negative\n",
+       "12     Negative\n",
+       "13     Negative\n",
+       "14     Negative\n",
+       "15     Negative\n",
+       "16     Negative\n",
+       "17     Negative\n",
+       "18     Negative\n",
+       "19          Sat\n",
+       "20          Sat\n",
+       "21          Sat\n",
+       "22          Sat\n",
+       "23          Sat\n",
+       "24          Sat\n",
+       "25          Sat\n",
+       "26          Sat\n",
+       "27          Sat\n",
+       "28          Sat\n",
+       "29          Sat\n",
+       "         ...   \n",
+       "214         Sat\n",
+       "215         Sat\n",
+       "216         Sat\n",
+       "217         Sat\n",
+       "218         Sat\n",
+       "219         Sat\n",
+       "220         Fri\n",
+       "221         Fri\n",
+       "222         Fri\n",
+       "223         Fri\n",
+       "224         Fri\n",
+       "225         Fri\n",
+       "226         Fri\n",
+       "227         Sat\n",
+       "228         Sat\n",
+       "229         Sat\n",
+       "230         Sat\n",
+       "231         Sat\n",
+       "232         Sat\n",
+       "233         Sat\n",
+       "234         Sat\n",
+       "235         Sat\n",
+       "236         Sat\n",
+       "237         Sat\n",
+       "238         Sat\n",
+       "239         Sat\n",
+       "240         Sat\n",
+       "241         Sat\n",
+       "242         Sat\n",
+       "243        Thur\n",
+       "Name: group_interaction, Length: 244, dtype: object"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set(style=\"ticks\", color_codes=True)\n",
+    "\n",
+    "tips = sns.load_dataset(\"tips\")\n",
+    "# sns.catplot(x=\"day\", y=\"total_bill\", data=tips)\n",
+    "tips\n",
+    "tips['time'] = tips['time'].replace('Dinner', 'group_dependent')\n",
+    "tips['time'] = tips['time'].replace('Lunch', 'state_dependent')\n",
+    "tips['clinical_order_entry'] = tips['total_bill']\n",
+    "tips['group_interaction'] = tips['day']\n",
+    "tips['group_interaction'].replace(\"Thur\", \"Lab Order\")\n",
+    "tips['group_interaction'].replace(\"Fri\", \"ABX\")\n",
+    "tips['group_interaction'].replace(\"Sat\", \"Treatment\")\n",
+    "tips['group_interaction'].replace(\"Sun\", \"Negative\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x11f8c2320>"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 486.1x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "sns.catplot(x = \"clinical_order_entry\", \n",
+    "            y = \"group_interaction\", \n",
+    "            hue = \"time\", \n",
+    "            kind = \"swarm\", \n",
+    "            data = tips)\n",
+    "\n",
+    "'''\n",
+    "\n",
+    "\n",
+    "def timelinePlot(df, x, y, color_label):\n",
+    "    c = sns.catplot(x = \"clinical_order_entry\", \n",
+    "            y = \"group_interaction\", \n",
+    "            hue = \"time\", \n",
+    "            kind = \"swarm\", \n",
+    "            data = tips)\n",
+    "    return c \n",
+    "\n",
+    "\n",
+    "\n",
+    "timelinePlot(tips, \"clinical_order_entry\", \"group_interaction\", \"swarm\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 445,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "      <th>clinical_order_entry</th>\n",
+       "      <th>group_interaction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>group_dependent</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16.99</td>\n",
+       "      <td>Sun</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>group_dependent</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10.34</td>\n",
+       "      <td>Sun</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>group_dependent</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21.01</td>\n",
+       "      <td>Sun</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>group_dependent</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23.68</td>\n",
+       "      <td>Sun</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>group_dependent</td>\n",
+       "      <td>4</td>\n",
+       "      <td>24.59</td>\n",
+       "      <td>Sun</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_bill   tip     sex smoker  day             time  size  \\\n",
+       "0       16.99  1.01  Female     No  Sun  group_dependent     2   \n",
+       "1       10.34  1.66    Male     No  Sun  group_dependent     3   \n",
+       "2       21.01  3.50    Male     No  Sun  group_dependent     3   \n",
+       "3       23.68  3.31    Male     No  Sun  group_dependent     2   \n",
+       "4       24.59  3.61  Female     No  Sun  group_dependent     4   \n",
+       "\n",
+       "   clinical_order_entry group_interaction  \n",
+       "0                 16.99               Sun  \n",
+       "1                 10.34               Sun  \n",
+       "2                 21.01               Sun  \n",
+       "3                 23.68               Sun  \n",
+       "4                 24.59               Sun  "
+      ]
+     },
+     "execution_count": 445,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set(tips['group_interaction'])\n",
+    "tips.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 446,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://developers.google.com/sheets/api/quickstart/python \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 447,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)',\n",
+    "                           'Acetaminophen (Oral)',\n",
+    "                           'Vancomycin (Intravenous)',\n",
+    "                           'Dexamethasone (Intravenous)',\n",
+    "                           'DIET NPO']))\n",
+    "\n",
+    "case = pd.DataFrame(np.array(['meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis']))\n",
+    "\n",
+    "name_x =  pd.DataFrame(np.array(['Mening Active',\n",
+    "                    'Meningits Worsens',\n",
+    "                    'Meningits Worsens',\n",
+    "                    'Meningits Worsens',\n",
+    "                    'Meningits Worsens']))\n",
+    "\n",
+    "grade = pd.DataFrame(np.array([10,\n",
+    "                  4,\n",
+    "                  10,\n",
+    "                  10,\n",
+    "                  3]))\n",
+    "\n",
+    "\n",
+    "confidence = pd.DataFrame(np.array([3,\n",
+    "                  2,\n",
+    "                  1,\n",
+    "                  2,\n",
+    "                  3]))\n",
+    "\n",
+    "state_dependent = pd.DataFrame(np.array([False,\n",
+    "                            False,\n",
+    "                            True,\n",
+    "                            False,\n",
+    "                            True]))\n",
+    "\n",
+    "group_dependent = pd.DataFrame(np.array([False,\n",
+    "                            False,\n",
+    "                            True,\n",
+    "                            False,\n",
+    "                            True]))\n",
+    "group_x =  pd.DataFrame(np.array(['abx',\n",
+    "                    'null',\n",
+    "                    'abx',\n",
+    "                    'null',\n",
+    "                    'null']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 448,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_worsens = pd.concat([clinical_order,\n",
+    "          case,\n",
+    "          name_x,\n",
+    "         grade,\n",
+    "         confidence,\n",
+    "         group_x,\n",
+    "         state_dependent,\n",
+    "        group_dependent], axis=1, ignore_index = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 449,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)',\n",
+    "                           'Acetaminophen (Oral)',\n",
+    "                           'Vancomycin (Intravenous)',\n",
+    "                           'Dexamethasone (Intravenous)',\n",
+    "                           'DIET NPO']))\n",
+    "\n",
+    "case = pd.DataFrame(np.array(['meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis']))\n",
+    "\n",
+    "name_x =  pd.DataFrame(np.array(['Mening Active',\n",
+    "                    'Mening Active',\n",
+    "                    'Mening Active',\n",
+    "                    'Mening Active',\n",
+    "                    'Mening Active']))\n",
+    "\n",
+    "grade = pd.DataFrame(np.array([10,\n",
+    "                  4,\n",
+    "                  10,\n",
+    "                  10,\n",
+    "                  3]))\n",
+    "\n",
+    "\n",
+    "confidence = pd.DataFrame(np.array([3,\n",
+    "                  2,\n",
+    "                  1,\n",
+    "                  2,\n",
+    "                  3]))\n",
+    "\n",
+    "state_dependent = pd.DataFrame(np.array([False,\n",
+    "                            False,\n",
+    "                            True,\n",
+    "                            False,\n",
+    "                            True]))\n",
+    "\n",
+    "group_dependent = pd.DataFrame(np.array([False,\n",
+    "                            False,\n",
+    "                            True,\n",
+    "                            False,\n",
+    "                            True]))\n",
+    "\n",
+    "group_x =  pd.DataFrame(np.array(['abx',\n",
+    "                    'null',\n",
+    "                    'abx',\n",
+    "                    'null',\n",
+    "                    'null']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 450,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_active = pd.concat([clinical_order,\n",
+    "          case,\n",
+    "          name_x,\n",
+    "          grade,\n",
+    "          confidence,\n",
+    "          group_x,\n",
+    "          state_dependent,\n",
+    "          group_dependent], axis=1, ignore_index = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 451,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)',\n",
+    "                           'Acetaminophen (Oral)',\n",
+    "                           'Vancomycin (Intravenous)',\n",
+    "                           'Dexamethasone (Intravenous)',\n",
+    "                           'DIET NPO']))\n",
+    "\n",
+    "case = pd.DataFrame(np.array(['meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis',\n",
+    "                 'meningitis']))\n",
+    "\n",
+    "name_x =  pd.DataFrame(np.array(['Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated']))\n",
+    "\n",
+    "grade = pd.DataFrame(np.array([10,\n",
+    "                  4,\n",
+    "                  10,\n",
+    "                  10,\n",
+    "                  3]))\n",
+    "\n",
+    "\n",
+    "confidence = pd.DataFrame(np.array([3,\n",
+    "                  2,\n",
+    "                  1,\n",
+    "                  2,\n",
+    "                  3]))\n",
+    "\n",
+    "state_dependent = pd.DataFrame(np.array([False,\n",
+    "                            False,\n",
+    "                            True,\n",
+    "                            False,\n",
+    "                            True]))\n",
+    "\n",
+    "group_dependent = pd.DataFrame(np.array([False,\n",
+    "                            False,\n",
+    "                            True,\n",
+    "                            False,\n",
+    "                            True]))\n",
+    "\n",
+    "group_x =  pd.DataFrame(np.array(['abx',\n",
+    "                    'null',\n",
+    "                    'abx',\n",
+    "                    'null',\n",
+    "                    'null']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 452,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_adequately_treated = pd.concat([clinical_order,\n",
+    "          case,\n",
+    "          name_x,\n",
+    "          grade,\n",
+    "          confidence,\n",
+    "          group_x,\n",
+    "          state_dependent,\n",
+    "          group_dependent], axis=1, ignore_index = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 453,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             0           1                              2   3  \\\n",
+       "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   4   \n",
+       "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "4                     DIET NPO  meningitis  Meningitis Adequately Treated   3   \n",
+       "\n",
+       "   4     5      6      7  \n",
+       "0  3   abx  False  False  \n",
+       "1  2  null  False  False  \n",
+       "2  1   abx   True   True  \n",
+       "3  2  null  False  False  \n",
+       "4  3  null   True   True  "
+      ]
+     },
+     "execution_count": 453,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m_adequately_treated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 454,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
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+       "      <th>2</th>\n",
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+       "      <th>5</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             0           1                              2   3  \\\n",
+       "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   4   \n",
+       "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "4                     DIET NPO  meningitis  Meningitis Adequately Treated   3   \n",
+       "0    Ceftriaxone (Intravenous)  meningitis                  Mening Active  10   \n",
+       "1         Acetaminophen (Oral)  meningitis              Meningits Worsens   4   \n",
+       "2     Vancomycin (Intravenous)  meningitis              Meningits Worsens  10   \n",
+       "3  Dexamethasone (Intravenous)  meningitis              Meningits Worsens  10   \n",
+       "4                     DIET NPO  meningitis              Meningits Worsens   3   \n",
+       "0    Ceftriaxone (Intravenous)  meningitis                  Mening Active  10   \n",
+       "1         Acetaminophen (Oral)  meningitis                  Mening Active   4   \n",
+       "2     Vancomycin (Intravenous)  meningitis                  Mening Active  10   \n",
+       "3  Dexamethasone (Intravenous)  meningitis                  Mening Active  10   \n",
+       "4                     DIET NPO  meningitis                  Mening Active   3   \n",
+       "\n",
+       "   4     5      6      7  \n",
+       "0  3   abx  False  False  \n",
+       "1  2  null  False  False  \n",
+       "2  1   abx   True   True  \n",
+       "3  2  null  False  False  \n",
+       "4  3  null   True   True  \n",
+       "0  3   abx  False  False  \n",
+       "1  2  null  False  False  \n",
+       "2  1   abx   True   True  \n",
+       "3  2  null  False  False  \n",
+       "4  3  null   True   True  \n",
+       "0  3   abx  False  False  \n",
+       "1  2  null  False  False  \n",
+       "2  1   abx   True   True  \n",
+       "3  2  null  False  False  \n",
+       "4  3  null   True   True  "
+      ]
+     },
+     "execution_count": 454,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mf2 = m_adequately_treated.append(m_worsens)\n",
+    "mf3 = mf2.append(m_active)\n",
+    "mf3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 455,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
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+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             0           1                  2   3  4     5  \\\n",
+       "0    Ceftriaxone (Intravenous)  meningitis      Mening Active  10  3   abx   \n",
+       "1         Acetaminophen (Oral)  meningitis  Meningits Worsens   4  2  null   \n",
+       "2     Vancomycin (Intravenous)  meningitis  Meningits Worsens  10  1   abx   \n",
+       "3  Dexamethasone (Intravenous)  meningitis  Meningits Worsens  10  2  null   \n",
+       "4                     DIET NPO  meningitis  Meningits Worsens   3  3  null   \n",
+       "\n",
+       "       6      7  \n",
+       "0  False  False  \n",
+       "1  False  False  \n",
+       "2   True   True  \n",
+       "3  False  False  \n",
+       "4   True   True  "
+      ]
+     },
+     "execution_count": 455,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m_worsens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 456,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             0           1                              2   3  \\\n",
+       "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   4   \n",
+       "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated  10   \n",
+       "4                     DIET NPO  meningitis  Meningitis Adequately Treated   3   \n",
+       "\n",
+       "   4     5      6      7  \n",
+       "0  3   abx  False  False  \n",
+       "1  2  null  False  False  \n",
+       "2  1   abx   True   True  \n",
+       "3  2  null  False  False  \n",
+       "4  3  null   True   True  "
+      ]
+     },
+     "execution_count": 456,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m_adequately_treated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 457,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mf3.columns = ['clinical_order', 'case', 'sim_state_name', 'score', 'confidence', 'group','state_dependent', 'group_dependent']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 458,
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <td>Ceftriaxone (Intravenous)</td>\n",
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+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>True</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
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+       "      <td>False</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                clinical_order        case                 sim_state_name  \\\n",
+       "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+       "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "4                     DIET NPO  meningitis  Meningitis Adequately Treated   \n",
+       "0    Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+       "1         Acetaminophen (Oral)  meningitis              Meningits Worsens   \n",
+       "2     Vancomycin (Intravenous)  meningitis              Meningits Worsens   \n",
+       "3  Dexamethasone (Intravenous)  meningitis              Meningits Worsens   \n",
+       "4                     DIET NPO  meningitis              Meningits Worsens   \n",
+       "0    Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+       "1         Acetaminophen (Oral)  meningitis                  Mening Active   \n",
+       "2     Vancomycin (Intravenous)  meningitis                  Mening Active   \n",
+       "3  Dexamethasone (Intravenous)  meningitis                  Mening Active   \n",
+       "4                     DIET NPO  meningitis                  Mening Active   \n",
+       "\n",
+       "   score  confidence group  state_dependent  group_dependent  \n",
+       "0     10           3   abx            False            False  \n",
+       "1      4           2  null            False            False  \n",
+       "2     10           1   abx             True             True  \n",
+       "3     10           2  null            False            False  \n",
+       "4      3           3  null             True             True  \n",
+       "0     10           3   abx            False            False  \n",
+       "1      4           2  null            False            False  \n",
+       "2     10           1   abx             True             True  \n",
+       "3     10           2  null            False            False  \n",
+       "4      3           3  null             True             True  \n",
+       "0     10           3   abx            False            False  \n",
+       "1      4           2  null            False            False  \n",
+       "2     10           1   abx             True             True  \n",
+       "3     10           2  null            False            False  \n",
+       "4      3           3  null             True             True  "
+      ]
+     },
+     "execution_count": 458,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mf3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th>sim_patient_id</th>\n",
+       "      <th>name_x</th>\n",
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+       "      <th>description_y</th>\n",
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+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>METABOLIC PANEL, BASIC [BMP]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45801</td>\n",
+       "      <td>17</td>\n",
+       "      <td>78</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>XR CHEST 2V [CXR]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>XR CHEST 2V [CXR]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45801</td>\n",
+       "      <td>44</td>\n",
+       "      <td>211</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>XR CHEST 2V [CXR]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>XR CHEST 2V [CXR]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>15</td>\n",
+       "      <td>74</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>40</td>\n",
+       "      <td>179</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>44</td>\n",
+       "      <td>211</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>52</td>\n",
+       "      <td>283</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>31</td>\n",
+       "      <td>45866</td>\n",
+       "      <td>30</td>\n",
+       "      <td>130</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>ECG 12-LEAD [EKG]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>15</td>\n",
+       "      <td>74</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>17</td>\n",
+       "      <td>78</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>21</td>\n",
+       "      <td>88</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>25</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>27</td>\n",
+       "      <td>112</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>29</td>\n",
+       "      <td>119</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>30</td>\n",
+       "      <td>130</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>31</td>\n",
+       "      <td>140</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>33</td>\n",
+       "      <td>147</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>34</td>\n",
+       "      <td>156</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>35</td>\n",
+       "      <td>163</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>39</td>\n",
+       "      <td>171</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>40</td>\n",
+       "      <td>179</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>41</td>\n",
+       "      <td>185</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>42</td>\n",
+       "      <td>193</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>43</td>\n",
+       "      <td>204</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>44</td>\n",
+       "      <td>211</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>48</td>\n",
+       "      <td>247</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>30</td>\n",
+       "      <td>45788</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CBC WITH DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3133</th>\n",
+       "      <td>31</td>\n",
+       "      <td>44595</td>\n",
+       "      <td>33</td>\n",
+       "      <td>147</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>Ampicillin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>Ampicillin (Intravenous)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3134</th>\n",
+       "      <td>33</td>\n",
+       "      <td>44595</td>\n",
+       "      <td>30</td>\n",
+       "      <td>130</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>Ampicillin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>Ampicillin (Intravenous)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3135</th>\n",
+       "      <td>30</td>\n",
+       "      <td>61852</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>NON-GYN CYTOLOGY</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>NON-GYN CYTOLOGY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3136</th>\n",
+       "      <td>30</td>\n",
+       "      <td>61852</td>\n",
+       "      <td>48</td>\n",
+       "      <td>247</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>NON-GYN CYTOLOGY</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>NON-GYN CYTOLOGY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3137</th>\n",
+       "      <td>30</td>\n",
+       "      <td>48586</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>REVIEW BY PATHOLOGIST</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>REVIEW BY PATHOLOGIST</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3138</th>\n",
+       "      <td>30</td>\n",
+       "      <td>46103</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>FUNGAL CULTURE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>FUNGAL CULTURE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3139</th>\n",
+       "      <td>31</td>\n",
+       "      <td>46103</td>\n",
+       "      <td>51</td>\n",
+       "      <td>276</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>FUNGAL CULTURE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>FUNGAL CULTURE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3140</th>\n",
+       "      <td>30</td>\n",
+       "      <td>46131</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>SYPHILIS TREPONEMAL SCREEN</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>SYPHILIS TREPONEMAL SCREEN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3141</th>\n",
+       "      <td>30</td>\n",
+       "      <td>46131</td>\n",
+       "      <td>54</td>\n",
+       "      <td>300</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>SYPHILIS TREPONEMAL SCREEN</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>SYPHILIS TREPONEMAL SCREEN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3142</th>\n",
+       "      <td>30</td>\n",
+       "      <td>50241</td>\n",
+       "      <td>49</td>\n",
+       "      <td>258</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CT HEAD WWO CONTRAST</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CT HEAD WWO CONTRAST</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3143</th>\n",
+       "      <td>30</td>\n",
+       "      <td>48519</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>VARICELLA ZOSTER VIR. DNA,QUAL</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>VARICELLA ZOSTER VIR. DNA,QUAL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3144</th>\n",
+       "      <td>30</td>\n",
+       "      <td>61843</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>EBV PCR QUANTITATIVE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>EBV PCR QUANTITATIVE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3145</th>\n",
+       "      <td>30</td>\n",
+       "      <td>61853</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CMV DNA PCR QUANT, PLASMA</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CMV DNA PCR QUANT, PLASMA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3146</th>\n",
+       "      <td>30</td>\n",
+       "      <td>48561</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>POC URINE PREGNANCY</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>POC URINE PREGNANCY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3147</th>\n",
+       "      <td>30</td>\n",
+       "      <td>44230</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>Hydrocortisone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>Hydrocortisone (Intravenous)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3148</th>\n",
+       "      <td>30</td>\n",
+       "      <td>48603</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CMV DNA PCR QUAL, NON-BLOOD</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>CMV DNA PCR QUAL, NON-BLOOD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3149</th>\n",
+       "      <td>30</td>\n",
+       "      <td>49142</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ENTEROVIRUS PCR</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>ENTEROVIRUS PCR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3150</th>\n",
+       "      <td>30</td>\n",
+       "      <td>50425</td>\n",
+       "      <td>50</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>HERPES VIRAL 6 DNA QUALITATIVE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>HERPES VIRAL 6 DNA QUALITATIVE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3151</th>\n",
+       "      <td>30</td>\n",
+       "      <td>44626</td>\n",
+       "      <td>54</td>\n",
+       "      <td>300</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>Doxycycline (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Active</td>\n",
+       "      <td>Doxycycline (Intravenous)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3209</th>\n",
+       "      <td>31</td>\n",
+       "      <td>48980</td>\n",
+       "      <td>15</td>\n",
+       "      <td>74</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3210</th>\n",
+       "      <td>31</td>\n",
+       "      <td>48980</td>\n",
+       "      <td>46</td>\n",
+       "      <td>229</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3211</th>\n",
+       "      <td>33</td>\n",
+       "      <td>48980</td>\n",
+       "      <td>49</td>\n",
+       "      <td>258</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>CONSULT TO INFECTIOUS DISEASE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3246</th>\n",
+       "      <td>31</td>\n",
+       "      <td>61864</td>\n",
+       "      <td>47</td>\n",
+       "      <td>237</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>ANTI-HIV</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>ANTI-HIV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3247</th>\n",
+       "      <td>31</td>\n",
+       "      <td>44641</td>\n",
+       "      <td>53</td>\n",
+       "      <td>292</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>Morphine (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>Morphine (Oral)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3248</th>\n",
+       "      <td>31</td>\n",
+       "      <td>50841</td>\n",
+       "      <td>54</td>\n",
+       "      <td>300</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>CONTACT AND DROPLET ISOLATION</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>CONTACT AND DROPLET ISOLATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3249</th>\n",
+       "      <td>31</td>\n",
+       "      <td>46065</td>\n",
+       "      <td>54</td>\n",
+       "      <td>300</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>MR BRAIN WITH AND WITHOUT CONTRAST</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Appropriate antibiotic improve patient condition</td>\n",
+       "      <td>MR BRAIN WITH AND WITHOUT CONTRAST</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3252</th>\n",
+       "      <td>33</td>\n",
+       "      <td>35969</td>\n",
+       "      <td>21</td>\n",
+       "      <td>88</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>Dexmedetomidine (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>Dexmedetomidine (Intravenous)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3253</th>\n",
+       "      <td>33</td>\n",
+       "      <td>61826</td>\n",
+       "      <td>49</td>\n",
+       "      <td>258</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>COCCI CF AB, CSF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>COCCI CF AB, CSF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3254</th>\n",
+       "      <td>33</td>\n",
+       "      <td>50510</td>\n",
+       "      <td>49</td>\n",
+       "      <td>258</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>LACTIC ACID, CSF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>LACTIC ACID, CSF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3255</th>\n",
+       "      <td>33</td>\n",
+       "      <td>49594</td>\n",
+       "      <td>49</td>\n",
+       "      <td>258</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>CRYPTOCOCCAL AG LATEX, CSF</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Patient condition worsens after 1 hr</td>\n",
+       "      <td>CRYPTOCOCCAL AG LATEX, CSF</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>495 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      sim_state_id  clinical_item_id  sim_user_id  sim_patient_id  \\\n",
+       "87              30             45763           17              78   \n",
+       "88              30             45763           49             258   \n",
+       "118             30             45801           17              78   \n",
+       "119             30             45801           44             211   \n",
+       "249             30             45866           15              74   \n",
+       "250             30             45866           40             179   \n",
+       "251             30             45866           44             211   \n",
+       "252             30             45866           47             237   \n",
+       "253             30             45866           52             283   \n",
+       "273             31             45866           30             130   \n",
+       "444             30             45788           15              74   \n",
+       "445             30             45788           17              78   \n",
+       "446             30             45788           21              88   \n",
+       "447             30             45788           25             100   \n",
+       "448             30             45788           27             112   \n",
+       "449             30             45788           29             119   \n",
+       "450             30             45788           30             130   \n",
+       "451             30             45788           31             140   \n",
+       "452             30             45788           33             147   \n",
+       "453             30             45788           34             156   \n",
+       "454             30             45788           35             163   \n",
+       "455             30             45788           39             171   \n",
+       "456             30             45788           40             179   \n",
+       "457             30             45788           41             185   \n",
+       "458             30             45788           42             193   \n",
+       "459             30             45788           43             204   \n",
+       "460             30             45788           44             211   \n",
+       "461             30             45788           47             237   \n",
+       "462             30             45788           48             247   \n",
+       "463             30             45788           50             268   \n",
+       "...            ...               ...          ...             ...   \n",
+       "3133            31             44595           33             147   \n",
+       "3134            33             44595           30             130   \n",
+       "3135            30             61852           47             237   \n",
+       "3136            30             61852           48             247   \n",
+       "3137            30             48586           47             237   \n",
+       "3138            30             46103           47             237   \n",
+       "3139            31             46103           51             276   \n",
+       "3140            30             46131           47             237   \n",
+       "3141            30             46131           54             300   \n",
+       "3142            30             50241           49             258   \n",
+       "3143            30             48519           50             268   \n",
+       "3144            30             61843           50             268   \n",
+       "3145            30             61853           50             268   \n",
+       "3146            30             48561           50             268   \n",
+       "3147            30             44230           50             268   \n",
+       "3148            30             48603           50             268   \n",
+       "3149            30             49142           50             268   \n",
+       "3150            30             50425           50             268   \n",
+       "3151            30             44626           54             300   \n",
+       "3209            31             48980           15              74   \n",
+       "3210            31             48980           46             229   \n",
+       "3211            33             48980           49             258   \n",
+       "3246            31             61864           47             237   \n",
+       "3247            31             44641           53             292   \n",
+       "3248            31             50841           54             300   \n",
+       "3249            31             46065           54             300   \n",
+       "3252            33             35969           21              88   \n",
+       "3253            33             61826           49             258   \n",
+       "3254            33             50510           49             258   \n",
+       "3255            33             49594           49             258   \n",
+       "\n",
+       "                             name_x  \\\n",
+       "87                    Mening Active   \n",
+       "88                    Mening Active   \n",
+       "118                   Mening Active   \n",
+       "119                   Mening Active   \n",
+       "249                   Mening Active   \n",
+       "250                   Mening Active   \n",
+       "251                   Mening Active   \n",
+       "252                   Mening Active   \n",
+       "253                   Mening Active   \n",
+       "273   Meningitis Adequately Treated   \n",
+       "444                   Mening Active   \n",
+       "445                   Mening Active   \n",
+       "446                   Mening Active   \n",
+       "447                   Mening Active   \n",
+       "448                   Mening Active   \n",
+       "449                   Mening Active   \n",
+       "450                   Mening Active   \n",
+       "451                   Mening Active   \n",
+       "452                   Mening Active   \n",
+       "453                   Mening Active   \n",
+       "454                   Mening Active   \n",
+       "455                   Mening Active   \n",
+       "456                   Mening Active   \n",
+       "457                   Mening Active   \n",
+       "458                   Mening Active   \n",
+       "459                   Mening Active   \n",
+       "460                   Mening Active   \n",
+       "461                   Mening Active   \n",
+       "462                   Mening Active   \n",
+       "463                   Mening Active   \n",
+       "...                             ...   \n",
+       "3133  Meningitis Adequately Treated   \n",
+       "3134              Meningits Worsens   \n",
+       "3135                  Mening Active   \n",
+       "3136                  Mening Active   \n",
+       "3137                  Mening Active   \n",
+       "3138                  Mening Active   \n",
+       "3139  Meningitis Adequately Treated   \n",
+       "3140                  Mening Active   \n",
+       "3141                  Mening Active   \n",
+       "3142                  Mening Active   \n",
+       "3143                  Mening Active   \n",
+       "3144                  Mening Active   \n",
+       "3145                  Mening Active   \n",
+       "3146                  Mening Active   \n",
+       "3147                  Mening Active   \n",
+       "3148                  Mening Active   \n",
+       "3149                  Mening Active   \n",
+       "3150                  Mening Active   \n",
+       "3151                  Mening Active   \n",
+       "3209  Meningitis Adequately Treated   \n",
+       "3210  Meningitis Adequately Treated   \n",
+       "3211              Meningits Worsens   \n",
+       "3246  Meningitis Adequately Treated   \n",
+       "3247  Meningitis Adequately Treated   \n",
+       "3248  Meningitis Adequately Treated   \n",
+       "3249  Meningitis Adequately Treated   \n",
+       "3252              Meningits Worsens   \n",
+       "3253              Meningits Worsens   \n",
+       "3254              Meningits Worsens   \n",
+       "3255              Meningits Worsens   \n",
+       "\n",
+       "                                         description_x  \\\n",
+       "87                                   Meningitis Active   \n",
+       "88                                   Meningitis Active   \n",
+       "118                                  Meningitis Active   \n",
+       "119                                  Meningitis Active   \n",
+       "249                                  Meningitis Active   \n",
+       "250                                  Meningitis Active   \n",
+       "251                                  Meningitis Active   \n",
+       "252                                  Meningitis Active   \n",
+       "253                                  Meningitis Active   \n",
+       "273   Appropriate antibiotic improve patient condition   \n",
+       "444                                  Meningitis Active   \n",
+       "445                                  Meningitis Active   \n",
+       "446                                  Meningitis Active   \n",
+       "447                                  Meningitis Active   \n",
+       "448                                  Meningitis Active   \n",
+       "449                                  Meningitis Active   \n",
+       "450                                  Meningitis Active   \n",
+       "451                                  Meningitis Active   \n",
+       "452                                  Meningitis Active   \n",
+       "453                                  Meningitis Active   \n",
+       "454                                  Meningitis Active   \n",
+       "455                                  Meningitis Active   \n",
+       "456                                  Meningitis Active   \n",
+       "457                                  Meningitis Active   \n",
+       "458                                  Meningitis Active   \n",
+       "459                                  Meningitis Active   \n",
+       "460                                  Meningitis Active   \n",
+       "461                                  Meningitis Active   \n",
+       "462                                  Meningitis Active   \n",
+       "463                                  Meningitis Active   \n",
+       "...                                                ...   \n",
+       "3133  Appropriate antibiotic improve patient condition   \n",
+       "3134              Patient condition worsens after 1 hr   \n",
+       "3135                                 Meningitis Active   \n",
+       "3136                                 Meningitis Active   \n",
+       "3137                                 Meningitis Active   \n",
+       "3138                                 Meningitis Active   \n",
+       "3139  Appropriate antibiotic improve patient condition   \n",
+       "3140                                 Meningitis Active   \n",
+       "3141                                 Meningitis Active   \n",
+       "3142                                 Meningitis Active   \n",
+       "3143                                 Meningitis Active   \n",
+       "3144                                 Meningitis Active   \n",
+       "3145                                 Meningitis Active   \n",
+       "3146                                 Meningitis Active   \n",
+       "3147                                 Meningitis Active   \n",
+       "3148                                 Meningitis Active   \n",
+       "3149                                 Meningitis Active   \n",
+       "3150                                 Meningitis Active   \n",
+       "3151                                 Meningitis Active   \n",
+       "3209  Appropriate antibiotic improve patient condition   \n",
+       "3210  Appropriate antibiotic improve patient condition   \n",
+       "3211              Patient condition worsens after 1 hr   \n",
+       "3246  Appropriate antibiotic improve patient condition   \n",
+       "3247  Appropriate antibiotic improve patient condition   \n",
+       "3248  Appropriate antibiotic improve patient condition   \n",
+       "3249  Appropriate antibiotic improve patient condition   \n",
+       "3252              Patient condition worsens after 1 hr   \n",
+       "3253              Patient condition worsens after 1 hr   \n",
+       "3254              Patient condition worsens after 1 hr   \n",
+       "3255              Patient condition worsens after 1 hr   \n",
+       "\n",
+       "                           description_y        case  \\\n",
+       "87          METABOLIC PANEL, BASIC [BMP]  meningitis   \n",
+       "88          METABOLIC PANEL, BASIC [BMP]  meningitis   \n",
+       "118                    XR CHEST 2V [CXR]  meningitis   \n",
+       "119                    XR CHEST 2V [CXR]  meningitis   \n",
+       "249                    ECG 12-LEAD [EKG]  meningitis   \n",
+       "250                    ECG 12-LEAD [EKG]  meningitis   \n",
+       "251                    ECG 12-LEAD [EKG]  meningitis   \n",
+       "252                    ECG 12-LEAD [EKG]  meningitis   \n",
+       "253                    ECG 12-LEAD [EKG]  meningitis   \n",
+       "273                    ECG 12-LEAD [EKG]  meningitis   \n",
+       "444                        CBC WITH DIFF  meningitis   \n",
+       "445                        CBC WITH DIFF  meningitis   \n",
+       "446                        CBC WITH DIFF  meningitis   \n",
+       "447                        CBC WITH DIFF  meningitis   \n",
+       "448                        CBC WITH DIFF  meningitis   \n",
+       "449                        CBC WITH DIFF  meningitis   \n",
+       "450                        CBC WITH DIFF  meningitis   \n",
+       "451                        CBC WITH DIFF  meningitis   \n",
+       "452                        CBC WITH DIFF  meningitis   \n",
+       "453                        CBC WITH DIFF  meningitis   \n",
+       "454                        CBC WITH DIFF  meningitis   \n",
+       "455                        CBC WITH DIFF  meningitis   \n",
+       "456                        CBC WITH DIFF  meningitis   \n",
+       "457                        CBC WITH DIFF  meningitis   \n",
+       "458                        CBC WITH DIFF  meningitis   \n",
+       "459                        CBC WITH DIFF  meningitis   \n",
+       "460                        CBC WITH DIFF  meningitis   \n",
+       "461                        CBC WITH DIFF  meningitis   \n",
+       "462                        CBC WITH DIFF  meningitis   \n",
+       "463                        CBC WITH DIFF  meningitis   \n",
+       "...                                  ...         ...   \n",
+       "3133            Ampicillin (Intravenous)  meningitis   \n",
+       "3134            Ampicillin (Intravenous)  meningitis   \n",
+       "3135                    NON-GYN CYTOLOGY  meningitis   \n",
+       "3136                    NON-GYN CYTOLOGY  meningitis   \n",
+       "3137               REVIEW BY PATHOLOGIST  meningitis   \n",
+       "3138                      FUNGAL CULTURE  meningitis   \n",
+       "3139                      FUNGAL CULTURE  meningitis   \n",
+       "3140          SYPHILIS TREPONEMAL SCREEN  meningitis   \n",
+       "3141          SYPHILIS TREPONEMAL SCREEN  meningitis   \n",
+       "3142                CT HEAD WWO CONTRAST  meningitis   \n",
+       "3143      VARICELLA ZOSTER VIR. DNA,QUAL  meningitis   \n",
+       "3144                EBV PCR QUANTITATIVE  meningitis   \n",
+       "3145           CMV DNA PCR QUANT, PLASMA  meningitis   \n",
+       "3146                 POC URINE PREGNANCY  meningitis   \n",
+       "3147        Hydrocortisone (Intravenous)  meningitis   \n",
+       "3148         CMV DNA PCR QUAL, NON-BLOOD  meningitis   \n",
+       "3149                     ENTEROVIRUS PCR  meningitis   \n",
+       "3150      HERPES VIRAL 6 DNA QUALITATIVE  meningitis   \n",
+       "3151           Doxycycline (Intravenous)  meningitis   \n",
+       "3209       CONSULT TO INFECTIOUS DISEASE  meningitis   \n",
+       "3210       CONSULT TO INFECTIOUS DISEASE  meningitis   \n",
+       "3211       CONSULT TO INFECTIOUS DISEASE  meningitis   \n",
+       "3246                            ANTI-HIV  meningitis   \n",
+       "3247                     Morphine (Oral)  meningitis   \n",
+       "3248       CONTACT AND DROPLET ISOLATION  meningitis   \n",
+       "3249  MR BRAIN WITH AND WITHOUT CONTRAST  meningitis   \n",
+       "3252       Dexmedetomidine (Intravenous)  meningitis   \n",
+       "3253                    COCCI CF AB, CSF  meningitis   \n",
+       "3254                    LACTIC ACID, CSF  meningitis   \n",
+       "3255          CRYPTOCOCCAL AG LATEX, CSF  meningitis   \n",
+       "\n",
+       "                                        sim_state_name  \\\n",
+       "87                                   Meningitis Active   \n",
+       "88                                   Meningitis Active   \n",
+       "118                                  Meningitis Active   \n",
+       "119                                  Meningitis Active   \n",
+       "249                                  Meningitis Active   \n",
+       "250                                  Meningitis Active   \n",
+       "251                                  Meningitis Active   \n",
+       "252                                  Meningitis Active   \n",
+       "253                                  Meningitis Active   \n",
+       "273   Appropriate antibiotic improve patient condition   \n",
+       "444                                  Meningitis Active   \n",
+       "445                                  Meningitis Active   \n",
+       "446                                  Meningitis Active   \n",
+       "447                                  Meningitis Active   \n",
+       "448                                  Meningitis Active   \n",
+       "449                                  Meningitis Active   \n",
+       "450                                  Meningitis Active   \n",
+       "451                                  Meningitis Active   \n",
+       "452                                  Meningitis Active   \n",
+       "453                                  Meningitis Active   \n",
+       "454                                  Meningitis Active   \n",
+       "455                                  Meningitis Active   \n",
+       "456                                  Meningitis Active   \n",
+       "457                                  Meningitis Active   \n",
+       "458                                  Meningitis Active   \n",
+       "459                                  Meningitis Active   \n",
+       "460                                  Meningitis Active   \n",
+       "461                                  Meningitis Active   \n",
+       "462                                  Meningitis Active   \n",
+       "463                                  Meningitis Active   \n",
+       "...                                                ...   \n",
+       "3133  Appropriate antibiotic improve patient condition   \n",
+       "3134              Patient condition worsens after 1 hr   \n",
+       "3135                                 Meningitis Active   \n",
+       "3136                                 Meningitis Active   \n",
+       "3137                                 Meningitis Active   \n",
+       "3138                                 Meningitis Active   \n",
+       "3139  Appropriate antibiotic improve patient condition   \n",
+       "3140                                 Meningitis Active   \n",
+       "3141                                 Meningitis Active   \n",
+       "3142                                 Meningitis Active   \n",
+       "3143                                 Meningitis Active   \n",
+       "3144                                 Meningitis Active   \n",
+       "3145                                 Meningitis Active   \n",
+       "3146                                 Meningitis Active   \n",
+       "3147                                 Meningitis Active   \n",
+       "3148                                 Meningitis Active   \n",
+       "3149                                 Meningitis Active   \n",
+       "3150                                 Meningitis Active   \n",
+       "3151                                 Meningitis Active   \n",
+       "3209  Appropriate antibiotic improve patient condition   \n",
+       "3210  Appropriate antibiotic improve patient condition   \n",
+       "3211              Patient condition worsens after 1 hr   \n",
+       "3246  Appropriate antibiotic improve patient condition   \n",
+       "3247  Appropriate antibiotic improve patient condition   \n",
+       "3248  Appropriate antibiotic improve patient condition   \n",
+       "3249  Appropriate antibiotic improve patient condition   \n",
+       "3252              Patient condition worsens after 1 hr   \n",
+       "3253              Patient condition worsens after 1 hr   \n",
+       "3254              Patient condition worsens after 1 hr   \n",
+       "3255              Patient condition worsens after 1 hr   \n",
+       "\n",
+       "                          clinical_order  \n",
+       "87          METABOLIC PANEL, BASIC [BMP]  \n",
+       "88          METABOLIC PANEL, BASIC [BMP]  \n",
+       "118                    XR CHEST 2V [CXR]  \n",
+       "119                    XR CHEST 2V [CXR]  \n",
+       "249                    ECG 12-LEAD [EKG]  \n",
+       "250                    ECG 12-LEAD [EKG]  \n",
+       "251                    ECG 12-LEAD [EKG]  \n",
+       "252                    ECG 12-LEAD [EKG]  \n",
+       "253                    ECG 12-LEAD [EKG]  \n",
+       "273                    ECG 12-LEAD [EKG]  \n",
+       "444                        CBC WITH DIFF  \n",
+       "445                        CBC WITH DIFF  \n",
+       "446                        CBC WITH DIFF  \n",
+       "447                        CBC WITH DIFF  \n",
+       "448                        CBC WITH DIFF  \n",
+       "449                        CBC WITH DIFF  \n",
+       "450                        CBC WITH DIFF  \n",
+       "451                        CBC WITH DIFF  \n",
+       "452                        CBC WITH DIFF  \n",
+       "453                        CBC WITH DIFF  \n",
+       "454                        CBC WITH DIFF  \n",
+       "455                        CBC WITH DIFF  \n",
+       "456                        CBC WITH DIFF  \n",
+       "457                        CBC WITH DIFF  \n",
+       "458                        CBC WITH DIFF  \n",
+       "459                        CBC WITH DIFF  \n",
+       "460                        CBC WITH DIFF  \n",
+       "461                        CBC WITH DIFF  \n",
+       "462                        CBC WITH DIFF  \n",
+       "463                        CBC WITH DIFF  \n",
+       "...                                  ...  \n",
+       "3133            Ampicillin (Intravenous)  \n",
+       "3134            Ampicillin (Intravenous)  \n",
+       "3135                    NON-GYN CYTOLOGY  \n",
+       "3136                    NON-GYN CYTOLOGY  \n",
+       "3137               REVIEW BY PATHOLOGIST  \n",
+       "3138                      FUNGAL CULTURE  \n",
+       "3139                      FUNGAL CULTURE  \n",
+       "3140          SYPHILIS TREPONEMAL SCREEN  \n",
+       "3141          SYPHILIS TREPONEMAL SCREEN  \n",
+       "3142                CT HEAD WWO CONTRAST  \n",
+       "3143      VARICELLA ZOSTER VIR. DNA,QUAL  \n",
+       "3144                EBV PCR QUANTITATIVE  \n",
+       "3145           CMV DNA PCR QUANT, PLASMA  \n",
+       "3146                 POC URINE PREGNANCY  \n",
+       "3147        Hydrocortisone (Intravenous)  \n",
+       "3148         CMV DNA PCR QUAL, NON-BLOOD  \n",
+       "3149                     ENTEROVIRUS PCR  \n",
+       "3150      HERPES VIRAL 6 DNA QUALITATIVE  \n",
+       "3151           Doxycycline (Intravenous)  \n",
+       "3209       CONSULT TO INFECTIOUS DISEASE  \n",
+       "3210       CONSULT TO INFECTIOUS DISEASE  \n",
+       "3211       CONSULT TO INFECTIOUS DISEASE  \n",
+       "3246                            ANTI-HIV  \n",
+       "3247                     Morphine (Oral)  \n",
+       "3248       CONTACT AND DROPLET ISOLATION  \n",
+       "3249  MR BRAIN WITH AND WITHOUT CONTRAST  \n",
+       "3252       Dexmedetomidine (Intravenous)  \n",
+       "3253                    COCCI CF AB, CSF  \n",
+       "3254                    LACTIC ACID, CSF  \n",
+       "3255          CRYPTOCOCCAL AG LATEX, CSF  \n",
+       "\n",
+       "[495 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#rFilter(df,col, condition):\n",
+    "    \n",
+    "case = rFilter(df_grading, 'case', 'meningitis')\n",
+    "case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pandas Unit Testing: \n",
+    "\n",
+    "> column for clinical order\n",
+    "\n",
+    "> column for case \n",
+    "\n",
+    "> column for sim_state_name \n",
+    "\n",
+    "> column for sim_user_id \n",
+    "\n",
+    "> develop basic test cases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clinical_order1 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)']))\n",
+    "clinical_order2 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))\n",
+    "clinical_order3 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)',\n",
+    "                           'Acetaminophen (Oral)',\n",
+    "                           'Vancomycin (Intravenous)',\n",
+    "                           'Dexamethoasonen (Intravenous)',\n",
+    "                           'DIET NPO']))\n",
+    "\n",
+    "# sample cases with len states\n",
+    "case = pd.DataFrame(np.array(['meningitis']))\n",
+    "case2 = pd.DataFrame(np.array(['meningitis', 'meningitis']))\n",
+    "case3 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis']))\n",
+    "\n",
+    "# states  \n",
+    "name_x1 =  pd.DataFrame(np.array(['Meningitis Adequately Treated']))\n",
+    "\n",
+    "name_x2 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "\n",
+    "name_x3 =  pd.DataFrame(np.array(['Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated']))\n",
+    "\n",
+    "# sim user id \n",
+    "sim_user_id1 = pd.DataFrame(np.array(['1']))\n",
+    "sim_user_id2 = pd.DataFrame(np.array(['2','2']))\n",
+    "sim_user_id3 = pd.DataFrame(np.array(['3','3','3','3','3']))\n",
+    "\n",
+    "\n",
+    "# \n",
+    "caseX1 = pd.concat([clinical_order1,\n",
+    "          case,\n",
+    "          name_x1,\n",
+    "          sim_user_id1], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "caseX2 = pd.concat([clinical_order2,\n",
+    "          case2,\n",
+    "          name_x2,\n",
+    "          sim_user_id2], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "caseX3 = pd.concat([clinical_order3,\n",
+    "          case3,\n",
+    "          name_x3,\n",
+    "          sim_user_id3], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "\n",
+    "caseX1.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX2.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX3.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Made Three Cases: \n",
+    "\n",
+    "> TEST STATE DEPENDENT SCORES Across States\n",
+    "\n",
+    "- True means sum \n",
+    "- False means take unique and sum \n",
+    "\n",
+    "\n",
+    "> Test Group Dependent Scores\n",
+    "- ABX example (only take one score) \n",
+    "- lab test\n",
+    "\n",
+    "#### Create Test Set \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_grading_key = mf3 \n",
+    "\n",
+    "test_grading_key ['clinical_item_name'] = test_grading_key['clinical_order']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id  \n",
+       "0           1  "
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Case 1 2 3: test single order score \n",
+    "\n",
+    "caseX1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "      <th>clinical_order</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id             clinical_order  score  confidence  state_dependent  \\\n",
+       "0           1  Ceftriaxone (Intravenous)     10           3            False   \n",
+       "\n",
+       "   group_dependent  \n",
+       "0            False  "
+      ]
+     },
+     "execution_count": 308,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# state_dependent \n",
+    "# group_dependent \n",
+    "test_grading_key['clinical_item_name']  = test_grading_key['clinical_order']\n",
+    "\n",
+    "new_df = pd.DataFrame(pd.merge(caseX1, test_grading_key,  how='left', on=['clinical_item_name', 'case', 'sim_state_name']))\n",
+    "\n",
+    "new_df "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 467,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 467,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def grade_case(casex):\n",
+    "    case = pd.DataFrame(pd.merge(casex, test_grading_key,  how='left', on=['clinical_item_name', 'case', 'sim_state_name']))\n",
+    "    return case['score'].sum(axis = 0, skipna = True)\n",
+    "\n",
+    "graded_case1 = grade_case(caseX1)\n",
+    "graded_case1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 463,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "empty_grades = []\n",
+    "\n",
+    "list_grades = list([grade_case(caseX1), grade_case(caseX2), grade_case(caseX3)])\n",
+    "list_grades_test = list([10, 14, 37])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 472,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "      <th>clinical_order</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethoasonen (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              clinical_item_name        case                 sim_state_name  \\\n",
+       "0      Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1           Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+       "2       Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "3  Dexamethoasonen (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "4                       DIET NPO  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id             clinical_order  score  confidence state_dependent  \\\n",
+       "0           3  Ceftriaxone (Intravenous)   10.0         3.0           False   \n",
+       "1           3       Acetaminophen (Oral)    4.0         2.0           False   \n",
+       "2           3   Vancomycin (Intravenous)   10.0         1.0            True   \n",
+       "3           3                        NaN    NaN         NaN             NaN   \n",
+       "4           3                   DIET NPO    3.0         3.0            True   \n",
+       "\n",
+       "  group_dependent  \n",
+       "0           False  \n",
+       "1           False  \n",
+       "2            True  \n",
+       "3             NaN  \n",
+       "4            True  "
+      ]
+     },
+     "execution_count": 472,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#grade_case(caseX3)\n",
+    "case3214123 = pd.DataFrame(pd.merge(caseX3, test_grading_key,  how='left', on=['clinical_item_name', 'case', 'sim_state_name']))\n",
+    "case3214123\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 464,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 464,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_grades == list_grades_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test group dependency \n",
+    "# case 4 (two ceftriaxone)\n",
+    "# case 5 (two ceftriaxone and 1 vancomyocin)\n",
+    "# case 6 (two ceftriaxone and 2 vancomyocin)\n",
+    "\n",
+    "\n",
+    "clinical_order4 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)']))\n",
+    "clinical_order5 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))\n",
+    "clinical_order6 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)',\n",
+    "                           'Acetaminophen (Oral)',\n",
+    "                           'Vancomycin (Intravenous)',\n",
+    "                           'Dexamethoasonen (Intravenous)',\n",
+    "                           'DIET NPO']))\n",
+    "\n",
+    "# sample cases with len states\n",
+    "case4 = pd.DataFrame(np.array(['meningitis']))\n",
+    "case5 = pd.DataFrame(np.array(['meningitis', 'meningitis']))\n",
+    "case6 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis']))\n",
+    "\n",
+    "# states  \n",
+    "name_x4 =  pd.DataFrame(np.array(['Meningitis Adequately Treated']))\n",
+    "\n",
+    "name_x5 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "\n",
+    "name_x6 =  pd.DataFrame(np.array(['Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated']))\n",
+    "\n",
+    "# sim user id \n",
+    "sim_user_id4 = pd.DataFrame(np.array(['4']))\n",
+    "sim_user_id5 = pd.DataFrame(np.array(['5', '5']))\n",
+    "sim_user_id6 = pd.DataFrame(np.array(['6','6','6','6','6']))\n",
+    "\n",
+    "\n",
+    "# \n",
+    "caseX4 = pd.concat([clinical_order1,\n",
+    "          case,\n",
+    "          name_x4,\n",
+    "          sim_user_id1], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "caseX5 = pd.concat([clinical_order5,\n",
+    "          case5,\n",
+    "          name_x5,\n",
+    "          sim_user_id5], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "caseX6 = pd.concat([clinical_order6,\n",
+    "          case6,\n",
+    "          name_x6,\n",
+    "          sim_user_id6], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "\n",
+    "caseX4.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX5.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX6.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 473,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test group dependency \n",
+    "# case 7 (three ceftriaxone)\n",
+    "# case 8 (three ceftriaxone and 1 vancomyocin)\n",
+    "# case 9 (three ceftriaxone and 3 vancomyocin)\n",
+    "\n",
+    "\n",
+    "clinical_order7 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)']))\n",
+    "clinical_order8 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))\n",
+    "clinical_order9  = pd.DataFrame(np.array(['Ceftriaxone',\n",
+    "                           'Acetaminophen (Oral)',\n",
+    "                           'Vancomycin (Intravenous)',\n",
+    "                           'Dexamethoasonen (Intravenous)',\n",
+    "                           'DIET NPO']))\n",
+    "\n",
+    "# sample cases with len states\n",
+    "case7 = pd.DataFrame(np.array(['meningitis']))\n",
+    "case8 = pd.DataFrame(np.array(['meningitis', 'meningitis']))\n",
+    "case9 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis']))\n",
+    "\n",
+    "# states  \n",
+    "name_x7 =  pd.DataFrame(np.array(['Meningitis Adequately Treated']))\n",
+    "\n",
+    "name_x8 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "\n",
+    "name_x9 =  pd.DataFrame(np.array(['Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated',\n",
+    "                    'Meningitis Adequately Treated']))\n",
+    "\n",
+    "# sim user id \n",
+    "sim_user_id7 = pd.DataFrame(np.array(['7']))\n",
+    "sim_user_id8= pd.DataFrame(np.array(['8', '8']))\n",
+    "sim_user_id9 = pd.DataFrame(np.array(['9','9','9','9','9']))\n",
+    "\n",
+    "\n",
+    "# \n",
+    "caseX7 = pd.concat([clinical_order1,\n",
+    "          case,\n",
+    "          name_x7,\n",
+    "          sim_user_id1], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "caseX8 = pd.concat([clinical_order5,\n",
+    "          case8,\n",
+    "          name_x8,\n",
+    "          sim_user_id8], \n",
+    "          axis=1, \n",
+    "          ignore_index = True)\n",
+    "\n",
+    "caseX9 = pd.concat([clinical_order9, case9, name_x9,sim_user_id9], axis=1, ignore_index = True)\n",
+    "\n",
+    "\n",
+    "caseX7.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX8.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX9.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [DONE] Create another table in the SQL database, called sim_grading_key\n",
+    "\n",
+    "medinfo/cpoe/cpoeSim/simData/cpoeSim.sql\n",
+    "\n",
+    "- [DONE] Dump out an empty copy of the new table\n",
+    "\n",
+    "- [DONE] Modify the dump, restore, revert scripts in the same directory so they will start capturing this table as well.\n",
+    "\n",
+    "- [DONE] Add a function to SimManager.py, \"gradeSimulatedCase\", start by just writing the stub. Which is just the function name and what parameters (ID of the case/user, and which grading key to use)… leave content blank or copy prior implementation...\n",
+    "\n",
+    "- [DONE] Add test (can extent TestSimManager or copy it and make next version)…\n",
+    "\n",
+    "- [DONE] Copy or modify or make up fake simulated user data and grading key to test typical inputs but also predictable edge cases (E.g., same order CBC gets ordered multiple times in a row across multiple sim states.\n",
+    "\n",
+    "- [DONE] Or multiple orders from the same interaction group are ordered (e.g., multiple antibiotics) either within or across multiple states. Probably simplest way is to find these groups and only give points for the first one that occurs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id  \n",
+       "0           1  "
+      ]
+     },
+     "execution_count": 316,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " caseX1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_id</th>\n",
+       "      <th>case_x</th>\n",
+       "      <th>clinical_order</th>\n",
+       "      <th>sim_state_name_x</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>426 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     clinical_item_id      case_x             clinical_order  \\\n",
+       "0               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "1               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "2               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "3               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "4               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "5               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "6               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "7               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "8               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "9               35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "10              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "11              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "12              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "13              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "14              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "15              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "16              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "17              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "18              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "19              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "20              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "21              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "22              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "23              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "24              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "25              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "26              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "27              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "28              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "29              35733  meningitis  Ceftriaxone (Intravenous)   \n",
+       "..                ...         ...                        ...   \n",
+       "396             45811  meningitis                   DIET NPO   \n",
+       "397             45811  meningitis                   DIET NPO   \n",
+       "398             45811  meningitis                   DIET NPO   \n",
+       "399             45811  meningitis                   DIET NPO   \n",
+       "400             45811  meningitis                   DIET NPO   \n",
+       "401             45811  meningitis                   DIET NPO   \n",
+       "402             45811  meningitis                   DIET NPO   \n",
+       "403             45811  meningitis                   DIET NPO   \n",
+       "404             45811  meningitis                   DIET NPO   \n",
+       "405             45811  meningitis                   DIET NPO   \n",
+       "406             45811  meningitis                   DIET NPO   \n",
+       "407             45811  meningitis                   DIET NPO   \n",
+       "408             45811  meningitis                   DIET NPO   \n",
+       "409             45811  meningitis                   DIET NPO   \n",
+       "410             45811  meningitis                   DIET NPO   \n",
+       "411             45811  meningitis                   DIET NPO   \n",
+       "412             45811  meningitis                   DIET NPO   \n",
+       "413             45811  meningitis                   DIET NPO   \n",
+       "414             45811  meningitis                   DIET NPO   \n",
+       "415             45811  meningitis                   DIET NPO   \n",
+       "416             45811  meningitis                   DIET NPO   \n",
+       "417             45811  meningitis                   DIET NPO   \n",
+       "418             45811  meningitis                   DIET NPO   \n",
+       "419             45811  meningitis                   DIET NPO   \n",
+       "420             45811  meningitis                   DIET NPO   \n",
+       "421             45811  meningitis                   DIET NPO   \n",
+       "422             45811  meningitis                   DIET NPO   \n",
+       "423             45811  meningitis                   DIET NPO   \n",
+       "424             45811  meningitis                   DIET NPO   \n",
+       "425             45811  meningitis                   DIET NPO   \n",
+       "\n",
+       "                  sim_state_name_x  score  confidence  state_dependent  \\\n",
+       "0    Meningitis Adequately Treated     10           3            False   \n",
+       "1    Meningitis Adequately Treated     10           3            False   \n",
+       "2    Meningitis Adequately Treated     10           3            False   \n",
+       "3    Meningitis Adequately Treated     10           3            False   \n",
+       "4    Meningitis Adequately Treated     10           3            False   \n",
+       "5    Meningitis Adequately Treated     10           3            False   \n",
+       "6    Meningitis Adequately Treated     10           3            False   \n",
+       "7    Meningitis Adequately Treated     10           3            False   \n",
+       "8    Meningitis Adequately Treated     10           3            False   \n",
+       "9    Meningitis Adequately Treated     10           3            False   \n",
+       "10   Meningitis Adequately Treated     10           3            False   \n",
+       "11   Meningitis Adequately Treated     10           3            False   \n",
+       "12   Meningitis Adequately Treated     10           3            False   \n",
+       "13   Meningitis Adequately Treated     10           3            False   \n",
+       "14   Meningitis Adequately Treated     10           3            False   \n",
+       "15   Meningitis Adequately Treated     10           3            False   \n",
+       "16   Meningitis Adequately Treated     10           3            False   \n",
+       "17   Meningitis Adequately Treated     10           3            False   \n",
+       "18   Meningitis Adequately Treated     10           3            False   \n",
+       "19   Meningitis Adequately Treated     10           3            False   \n",
+       "20   Meningitis Adequately Treated     10           3            False   \n",
+       "21   Meningitis Adequately Treated     10           3            False   \n",
+       "22   Meningitis Adequately Treated     10           3            False   \n",
+       "23   Meningitis Adequately Treated     10           3            False   \n",
+       "24   Meningitis Adequately Treated     10           3            False   \n",
+       "25   Meningitis Adequately Treated     10           3            False   \n",
+       "26   Meningitis Adequately Treated     10           3            False   \n",
+       "27   Meningitis Adequately Treated     10           3            False   \n",
+       "28   Meningitis Adequately Treated     10           3            False   \n",
+       "29   Meningitis Adequately Treated     10           3            False   \n",
+       "..                             ...    ...         ...              ...   \n",
+       "396              Meningits Worsens      3           3             True   \n",
+       "397              Meningits Worsens      3           3             True   \n",
+       "398              Meningits Worsens      3           3             True   \n",
+       "399              Meningits Worsens      3           3             True   \n",
+       "400              Meningits Worsens      3           3             True   \n",
+       "401              Meningits Worsens      3           3             True   \n",
+       "402              Meningits Worsens      3           3             True   \n",
+       "403              Meningits Worsens      3           3             True   \n",
+       "404              Meningits Worsens      3           3             True   \n",
+       "405              Meningits Worsens      3           3             True   \n",
+       "406              Meningits Worsens      3           3             True   \n",
+       "407              Meningits Worsens      3           3             True   \n",
+       "408              Meningits Worsens      3           3             True   \n",
+       "409              Meningits Worsens      3           3             True   \n",
+       "410              Meningits Worsens      3           3             True   \n",
+       "411              Meningits Worsens      3           3             True   \n",
+       "412              Meningits Worsens      3           3             True   \n",
+       "413              Meningits Worsens      3           3             True   \n",
+       "414              Meningits Worsens      3           3             True   \n",
+       "415              Meningits Worsens      3           3             True   \n",
+       "416              Meningits Worsens      3           3             True   \n",
+       "417              Meningits Worsens      3           3             True   \n",
+       "418              Meningits Worsens      3           3             True   \n",
+       "419              Meningits Worsens      3           3             True   \n",
+       "420              Meningits Worsens      3           3             True   \n",
+       "421              Meningits Worsens      3           3             True   \n",
+       "422              Meningits Worsens      3           3             True   \n",
+       "423              Meningits Worsens      3           3             True   \n",
+       "424              Meningits Worsens      3           3             True   \n",
+       "425              Meningits Worsens      3           3             True   \n",
+       "\n",
+       "     group_dependent  \n",
+       "0              False  \n",
+       "1              False  \n",
+       "2              False  \n",
+       "3              False  \n",
+       "4              False  \n",
+       "5              False  \n",
+       "6              False  \n",
+       "7              False  \n",
+       "8              False  \n",
+       "9              False  \n",
+       "10             False  \n",
+       "11             False  \n",
+       "12             False  \n",
+       "13             False  \n",
+       "14             False  \n",
+       "15             False  \n",
+       "16             False  \n",
+       "17             False  \n",
+       "18             False  \n",
+       "19             False  \n",
+       "20             False  \n",
+       "21             False  \n",
+       "22             False  \n",
+       "23             False  \n",
+       "24             False  \n",
+       "25             False  \n",
+       "26             False  \n",
+       "27             False  \n",
+       "28             False  \n",
+       "29             False  \n",
+       "..               ...  \n",
+       "396             True  \n",
+       "397             True  \n",
+       "398             True  \n",
+       "399             True  \n",
+       "400             True  \n",
+       "401             True  \n",
+       "402             True  \n",
+       "403             True  \n",
+       "404             True  \n",
+       "405             True  \n",
+       "406             True  \n",
+       "407             True  \n",
+       "408             True  \n",
+       "409             True  \n",
+       "410             True  \n",
+       "411             True  \n",
+       "412             True  \n",
+       "413             True  \n",
+       "414             True  \n",
+       "415             True  \n",
+       "416             True  \n",
+       "417             True  \n",
+       "418             True  \n",
+       "419             True  \n",
+       "420             True  \n",
+       "421             True  \n",
+       "422             True  \n",
+       "423             True  \n",
+       "424             True  \n",
+       "425             True  \n",
+       "\n",
+       "[426 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 379,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# clinical_order, case, sim_state_name, score, confidence, state_dependent, group_dependent\n",
+    "# join on sim_state_id, clinical_item_id, \n",
+    "\n",
+    "df_grading['clinical_order'] = df_grading['description_y']\n",
+    "\n",
+    "testx = pd.DataFrame(pd.merge(test_grading_key, df_grading,  how='left', on=['clinical_order']))\n",
+    "df2 = testx[[\"clinical_item_id\", 'case_x', 'clinical_order', 'sim_state_name_x', 'score', 'confidence', 'state_dependent', 'group_dependent']]\n",
+    "df2\n",
+    "                     \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 380,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# look up how insert postgres database \n",
+    "\n",
+    "#df.to_sql('users', con=connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 402,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3 = pd.DataFrame(df2.drop_duplicates())\n",
+    "df3.columns = ['clinical_item_id', 'case', 'clinical_item_name', \n",
+    "               'name', \n",
+    "               'score', \n",
+    "               'confidence', \n",
+    "               'state_dependent', \n",
+    "               'group_dependent']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 403,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_id</th>\n",
+       "      <th>case</th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>44281</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>43997</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>44017</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>35733</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>44281</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>43997</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>44017</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>44281</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>43997</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>44017</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>45811</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     clinical_item_id        case           clinical_item_name  \\\n",
+       "0               35733  meningitis    Ceftriaxone (Intravenous)   \n",
+       "42              44281  meningitis         Acetaminophen (Oral)   \n",
+       "59              43997  meningitis     Vancomycin (Intravenous)   \n",
+       "88              44017  meningitis  Dexamethasone (Intravenous)   \n",
+       "95              45811  meningitis                     DIET NPO   \n",
+       "142             35733  meningitis    Ceftriaxone (Intravenous)   \n",
+       "184             44281  meningitis         Acetaminophen (Oral)   \n",
+       "201             43997  meningitis     Vancomycin (Intravenous)   \n",
+       "230             44017  meningitis  Dexamethasone (Intravenous)   \n",
+       "237             45811  meningitis                     DIET NPO   \n",
+       "326             44281  meningitis         Acetaminophen (Oral)   \n",
+       "343             43997  meningitis     Vancomycin (Intravenous)   \n",
+       "372             44017  meningitis  Dexamethasone (Intravenous)   \n",
+       "379             45811  meningitis                     DIET NPO   \n",
+       "\n",
+       "                              name  score  confidence  state_dependent  \\\n",
+       "0    Meningitis Adequately Treated     10           3            False   \n",
+       "42   Meningitis Adequately Treated      4           2            False   \n",
+       "59   Meningitis Adequately Treated     10           1             True   \n",
+       "88   Meningitis Adequately Treated     10           2            False   \n",
+       "95   Meningitis Adequately Treated      3           3             True   \n",
+       "142                  Mening Active     10           3            False   \n",
+       "184                  Mening Active      4           2            False   \n",
+       "201                  Mening Active     10           1             True   \n",
+       "230                  Mening Active     10           2            False   \n",
+       "237                  Mening Active      3           3             True   \n",
+       "326              Meningits Worsens      4           2            False   \n",
+       "343              Meningits Worsens     10           1             True   \n",
+       "372              Meningits Worsens     10           2            False   \n",
+       "379              Meningits Worsens      3           3             True   \n",
+       "\n",
+       "     group_dependent  \n",
+       "0              False  \n",
+       "42             False  \n",
+       "59              True  \n",
+       "88             False  \n",
+       "95              True  \n",
+       "142            False  \n",
+       "184            False  \n",
+       "201             True  \n",
+       "230            False  \n",
+       "237             True  \n",
+       "326            False  \n",
+       "343             True  \n",
+       "372            False  \n",
+       "379             True  "
+      ]
+     },
+     "execution_count": 403,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 404,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss_join = sim_state.filter(items=['sim_state_id', 'name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 411,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pd.merge(df3, ss_join, '')\n",
+    "\n",
+    "insert_grading_key = pd.DataFrame(pd.merge(df3, ss_join, how = 'left',  on='name'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection = pg.connect(\"host='localhost' dbname=stride_inpatient_2014 user=postgres password='MANUAL PASSWORD'\")\n",
+    "from sqlalchemy import create_engine\n",
+    "import psycopg2 \n",
+    "import io\n",
+    "#insert_grading_key.to_sql('sim_grading_key', con=connection)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create an engine to insert postgres data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "engine = create_engine('postgresql+psycopg2://postgres:postgres@localhost:5432/stride_inpatient_2014')\n",
+    "insert_grading_key.to_sql('sim_grading_key', engine)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 623,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "PRINTING MENINGITIS_GRADING_KEY_UNIT_1 \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "            clinical_item_name        case                 sim_state_name  \\\n",
+      "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+      "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "4                     DIET NPO  meningitis  Meningitis Adequately Treated   \n",
+      "0    Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+      "1         Acetaminophen (Oral)  meningitis              Meningits Worsens   \n",
+      "2     Vancomycin (Intravenous)  meningitis              Meningits Worsens   \n",
+      "3  Dexamethasone (Intravenous)  meningitis              Meningits Worsens   \n",
+      "4                     DIET NPO  meningitis              Meningits Worsens   \n",
+      "0    Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+      "1         Acetaminophen (Oral)  meningitis                  Mening Active   \n",
+      "2     Vancomycin (Intravenous)  meningitis                  Mening Active   \n",
+      "3  Dexamethasone (Intravenous)  meningitis                  Mening Active   \n",
+      "4                     DIET NPO  meningitis                  Mening Active   \n",
+      "\n",
+      "   score  confidence group  state_dependent  group_dependent  \n",
+      "0      5           3   abx            False             True  \n",
+      "1      4           2  null            False            False  \n",
+      "2      5           1   abx            False             True  \n",
+      "3      5           2  null            False            False  \n",
+      "4      3           3  null            False            False  \n",
+      "0      3           3   abx            False             True  \n",
+      "1      4           2  null            False            False  \n",
+      "2      3           1   abx            False             True  \n",
+      "3      3           2  null            False            False  \n",
+      "4      3           3  null            False            False  \n",
+      "0     10           3   abx            False             True  \n",
+      "1      4           2  null            False            False  \n",
+      "2     10           1   abx            False             True  \n",
+      "3     10           2  null            False            False  \n",
+      "4      3           3  null            False            False  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      " RUNNING UNIT TESTS: caseX1, caseX2, caseX3\n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX1\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           1  \n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX2\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1       Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           2  \n",
+      "1           2  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX3\n",
+      "            clinical_item_name        case                 sim_state_name  \\\n",
+      "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+      "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "4                     DIET NPO  meningitis  Meningitis Adequately Treated   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           3  \n",
+      "1           3  \n",
+      "2           3  \n",
+      "3           3  \n",
+      "4           3  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "RUNNING UNIT TESTS: caseX4, caseX5, caseX6\n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX4\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1  Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           4  \n",
+      "1           4  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX5\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1  Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+      "2   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           5  \n",
+      "1           5  \n",
+      "2           5  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX6\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "2   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "3   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           6  \n",
+      "1           6  \n",
+      "2           6  \n",
+      "3           6  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      " RUNNING UNIT TESTS: caseX7, caseX8, caseX9\n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX7\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1  Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+      "2  Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           7  \n",
+      "1           7  \n",
+      "2           7  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX8\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1       Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           8  \n",
+      "1           8  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n",
+      "---------\n",
+      "caseX9\n",
+      "          clinical_item_name        case                 sim_state_name  \\\n",
+      "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "1  Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+      "2  Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+      "3   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+      "4   Vancomycin (Intravenous)  meningitis              Meningits Worsens   \n",
+      "5   Vancomycin (Intravenous)  meningitis                  Mening Active   \n",
+      "\n",
+      "  sim_user_id  \n",
+      "0           9  \n",
+      "1           9  \n",
+      "2           9  \n",
+      "3           9  \n",
+      "4           9  \n",
+      "5           9  \n",
+      "---------\n",
+      "--------------------------------------------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create Groups List\n",
+    "grading_groups = ['abx']\n",
+    "\n",
+    "# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "# Meningitis Worsens\n",
+    "clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)', 'Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))\n",
+    "case = pd.DataFrame(np.array(['meningitis', 'meningitis','meningitis','meningitis','meningitis']))\n",
+    "name_x =  pd.DataFrame(np.array(['Meningits Worsens','Meningits Worsens','Meningits Worsens','Meningits Worsens','Meningits Worsens']))\n",
+    "grade = pd.DataFrame(np.array([3, 4, 3, 3, 3]))\n",
+    "confidence = pd.DataFrame(np.array([3, 2, 1, 2,3]))\n",
+    "state_dependent = pd.DataFrame(np.array([False, False, False, False, False]))\n",
+    "group_dependent = pd.DataFrame(np.array([True, False, True, False, False ]))\n",
+    "group_x =  pd.DataFrame(np.array(['abx', 'null', 'abx', 'null', 'null']))\n",
+    "m_worsens = pd.concat([clinical_order, case, name_x, grade, confidence, group_x, state_dependent, group_dependent], axis=1, ignore_index = True)\n",
+    "\n",
+    "# Meningitis Active\n",
+    "clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Acetaminophen (Oral)', 'Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))\n",
+    "case = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis', 'meningitis', 'meningitis']))\n",
+    "name_x =  pd.DataFrame(np.array(['Mening Active', 'Mening Active', 'Mening Active', 'Mening Active', 'Mening Active']))\n",
+    "grade = pd.DataFrame(np.array([10, 4, 10, 10, 3]))\n",
+    "confidence = pd.DataFrame(np.array([3, 2, 1, 2, 3]))\n",
+    "state_dependent = pd.DataFrame(np.array([False, False, False, False, False]))\n",
+    "group_dependent = pd.DataFrame(np.array([True, False, True, False, False ]))\n",
+    "group_x =  pd.DataFrame(np.array(['abx', 'null', 'abx', 'null', 'null']))\n",
+    "m_active = pd.concat([clinical_order, case, name_x, grade, confidence, group_x, state_dependent, group_dependent], axis=1, ignore_index = True)\n",
+    "\n",
+    "# Meningitis Adequately Treated\n",
+    "clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Acetaminophen (Oral)','Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))\n",
+    "case = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis', 'meningitis', 'meningitis']))\n",
+    "name_x =  pd.DataFrame(np.array(['Meningitis Adequately Treated','Meningitis Adequately Treated','Meningitis Adequately Treated','Meningitis Adequately Treated','Meningitis Adequately Treated']))\n",
+    "grade = pd.DataFrame(np.array([5, 4, 5, 5, 3]))\n",
+    "confidence = pd.DataFrame(np.array([3, 2, 1, 2, 3]))\n",
+    "state_dependent = pd.DataFrame(np.array([False, False, False, False, False]))\n",
+    "group_dependent = pd.DataFrame(np.array([True, False, True, False, False ]))\n",
+    "group_x =  pd.DataFrame(np.array(['abx', 'null', 'abx', 'null', 'null']))\n",
+    "m_adequately_treated = pd.concat([clinical_order, case, name_x, grade, confidence, group_x, state_dependent, group_dependent], axis=1, ignore_index = True)\n",
+    "\n",
+    "# APPENDING THE DATAFRAME TOGETHER\n",
+    "mf2 = m_adequately_treated.append(m_worsens)\n",
+    "mf3 = mf2.append(m_active)\n",
+    "mf3.columns = ['clinical_item_name', 'case', 'sim_state_name', 'score', 'confidence', 'group','state_dependent', 'group_dependent']\n",
+    "meningitis_grading_key_unit1 = mf3\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('PRINTING MENINGITIS_GRADING_KEY_UNIT_1 ')\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print(meningitis_grading_key_unit1)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "\n",
+    "# Initialize the first test case of one order\n",
+    "#\n",
+    "\n",
+    "# Initializing Case Scores:\n",
+    "\n",
+    "unit_grade_casex1 = 5\n",
+    "unit_grade_casex2 = 9\n",
+    "unit_grade_casex3 = 5\n",
+    "unit_grade_casex4 = 5\n",
+    "unit_grade_casex5 = 5\n",
+    "unit_grade_casex6 = 5\n",
+    "unit_grade_casex7 = 5\n",
+    "unit_grade_casex8 = 5\n",
+    "unit_grade_casex9 = 5\n",
+    "\n",
+    "\n",
+    "# TODO\n",
+    "\n",
+    "# test basic addition (one order)\n",
+    "# test basic addition (two order)\n",
+    "# test basic addition (five order)\n",
+    "\n",
+    "\n",
+    "# TODO\n",
+    "clinical_order1 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)']))\n",
+    "clinical_order2 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))\n",
+    "clinical_order3 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Acetaminophen (Oral)', 'Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))\n",
+    "\n",
+    "# sample cases with len states\n",
+    "case = pd.DataFrame(np.array(['meningitis']))\n",
+    "case2 = pd.DataFrame(np.array(['meningitis', 'meningitis']))\n",
+    "case3 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis']))\n",
+    "\n",
+    "# states\n",
+    "name_x1 =  pd.DataFrame(np.array(['Meningitis Adequately Treated']))\n",
+    "name_x2 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "name_x3 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "\n",
+    "# sim user id\n",
+    "sim_user_id1 = pd.DataFrame(np.array(['1']))\n",
+    "sim_user_id2 = pd.DataFrame(np.array(['2','2']))\n",
+    "sim_user_id3 = pd.DataFrame(np.array(['3','3','3','3','3']))\n",
+    "\n",
+    "\n",
+    "#\n",
+    "caseX1 = pd.concat([clinical_order1, case, name_x1, sim_user_id1], axis=1, ignore_index = True)\n",
+    "caseX2 = pd.concat([clinical_order2, case2, name_x2, sim_user_id2], axis=1, ignore_index = True)\n",
+    "caseX3 = pd.concat([clinical_order3, case3, name_x3, sim_user_id3], axis=1, ignore_index = True)\n",
+    "\n",
+    "\n",
+    "caseX1.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX2.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX3.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "\n",
+    "\n",
+    "# TODO\n",
+    "\n",
+    "# test group_dependent_action\n",
+    "\n",
+    "# TODO\n",
+    "\n",
+    "# test state_dependent_action\n",
+    "# test group dependency\n",
+    "# case 4 (two ceftriaxone) (two sim_states)\n",
+    "# case 5 (two ceftriaxone and 1 vancomyocin) (two sim_states)\n",
+    "# case 6 (two ceftriaxone and 2 vancomyocin) (two sim_states)\n",
+    "\n",
+    "clinical_order4 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)']))\n",
+    "clinical_order5 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)', 'Vancomycin (Intravenous)']))\n",
+    "clinical_order6 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)', 'Vancomycin (Intravenous)', 'Vancomycin (Intravenous)']))\n",
+    "\n",
+    "# sample cases with len states\n",
+    "case4 = pd.DataFrame(np.array(['meningitis', 'meningitis']))\n",
+    "case5 = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis']))\n",
+    "case6 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis']))\n",
+    "\n",
+    "# states\n",
+    "name_x4 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens']))\n",
+    "name_x5 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens', 'Meningitis Adequately Treated']))\n",
+    "name_x6 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "\n",
+    "# sim user id\n",
+    "sim_user_id4 = pd.DataFrame(np.array(['4', '4']))\n",
+    "sim_user_id5 = pd.DataFrame(np.array(['5', '5', '5']))\n",
+    "sim_user_id6 = pd.DataFrame(np.array(['6','6','6','6']))\n",
+    "\n",
+    "caseX4 = pd.concat([clinical_order4, case4, name_x4, sim_user_id4], axis=1, ignore_index = True)\n",
+    "caseX5 = pd.concat([clinical_order5, case5, name_x5, sim_user_id5], axis=1, ignore_index = True)\n",
+    "caseX6 = pd.concat([clinical_order6, case6, name_x6, sim_user_id6], axis=1, ignore_index = True)\n",
+    "\n",
+    "caseX4.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX5.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX6.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "\n",
+    "\n",
+    "# test group dependency\n",
+    "# case 7 (three ceftriaxone)\n",
+    "# case 8 (three ceftriaxone and 1 vancomyocin)\n",
+    "# case 9 (three ceftriaxone and 3 vancomyocin)\n",
+    "\n",
+    "clinical_order7 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)']))\n",
+    "clinical_order8 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))\n",
+    "clinical_order9  = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)','Ceftriaxone (Intravenous)', 'Vancomycin (Intravenous)', 'Vancomycin (Intravenous)', 'Vancomycin (Intravenous)']))\n",
+    "\n",
+    "# sample cases with len states\n",
+    "case7 = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis']))\n",
+    "case8 = pd.DataFrame(np.array(['meningitis', 'meningitis']))\n",
+    "case9 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis','meningitis']))\n",
+    "\n",
+    "# states\n",
+    "name_x7 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens', 'Mening Active']))\n",
+    "name_x8 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))\n",
+    "name_x9 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens', 'Mening Active','Meningitis Adequately Treated', 'Meningits Worsens', 'Mening Active']))\n",
+    "\n",
+    "# sim user id\n",
+    "sim_user_id7 = pd.DataFrame(np.array(['7', '7', '7']))\n",
+    "sim_user_id8= pd.DataFrame(np.array(['8', '8']))\n",
+    "sim_user_id9 = pd.DataFrame(np.array(['9','9','9','9','9', '9']))\n",
+    "\n",
+    "\n",
+    "#\n",
+    "caseX7 = pd.concat([clinical_order7, case7, name_x7, sim_user_id7], axis=1, ignore_index = True)\n",
+    "caseX8 = pd.concat([clinical_order8, case8, name_x8, sim_user_id8], axis=1, ignore_index = True)\n",
+    "caseX9 = pd.concat([clinical_order9, case9, name_x9, sim_user_id9], axis=1, ignore_index = True)\n",
+    "\n",
+    "\n",
+    "caseX7.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX8.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "caseX9.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print(' RUNNING UNIT TESTS: caseX1, caseX2, caseX3')\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX1')\n",
+    "print(caseX1)\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX2')\n",
+    "print(caseX2)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX3')\n",
+    "print(caseX3)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "class TestCaseSet123(unittest.TestCase):\n",
+    "\n",
+    "    def test_upper(self):\n",
+    "        self.assertEqual('foo'.upper(), 'FOO')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('RUNNING UNIT TESTS: caseX4, caseX5, caseX6')\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "class TestCaseSet456(unittest.TestCase):\n",
+    "\n",
+    "    def test_upper(self):\n",
+    "        self.assertEqual('foo'.upper(), 'FOO')\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX4')\n",
+    "print(caseX4)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX5')\n",
+    "print(caseX5)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX6')\n",
+    "print(caseX6)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print(' RUNNING UNIT TESTS: caseX7, caseX8, caseX9')\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "class TestCaseSet789(unittest.TestCase):\n",
+    "\n",
+    "    def test_upper(self):\n",
+    "        self.assertEqual('foo'.upper(), 'FOO')\n",
+    "\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX7')\n",
+    "print(caseX7)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX8')\n",
+    "print(caseX8)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n",
+    "print('---------')\n",
+    "print('caseX9')\n",
+    "print(caseX9)\n",
+    "print('---------')\n",
+    "print('--------------------------------------------------------------------------------------------------------------------------------------')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 663,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "2   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "3   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id  \n",
+       "0           6  \n",
+       "1           6  \n",
+       "2           6  \n",
+       "3           6  "
+      ]
+     },
+     "execution_count": 663,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caseX6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 616,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>group</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dexamethasone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DIET NPO</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>null</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            clinical_item_name        case                 sim_state_name  \\\n",
+       "0    Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1         Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+       "2     Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "3  Dexamethasone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "4                     DIET NPO  meningitis  Meningitis Adequately Treated   \n",
+       "0    Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+       "1         Acetaminophen (Oral)  meningitis              Meningits Worsens   \n",
+       "2     Vancomycin (Intravenous)  meningitis              Meningits Worsens   \n",
+       "3  Dexamethasone (Intravenous)  meningitis              Meningits Worsens   \n",
+       "4                     DIET NPO  meningitis              Meningits Worsens   \n",
+       "0    Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+       "1         Acetaminophen (Oral)  meningitis                  Mening Active   \n",
+       "2     Vancomycin (Intravenous)  meningitis                  Mening Active   \n",
+       "3  Dexamethasone (Intravenous)  meningitis                  Mening Active   \n",
+       "4                     DIET NPO  meningitis                  Mening Active   \n",
+       "\n",
+       "   score  confidence group  state_dependent  group_dependent  \n",
+       "0      5           3   abx            False             True  \n",
+       "1      4           2  null            False            False  \n",
+       "2      5           1   abx            False             True  \n",
+       "3      5           2  null            False            False  \n",
+       "4      3           3  null            False            False  \n",
+       "0      3           3   abx            False             True  \n",
+       "1      4           2  null            False            False  \n",
+       "2      3           1   abx            False             True  \n",
+       "3      3           2  null            False            False  \n",
+       "4      3           3  null            False            False  \n",
+       "0     10           3   abx            False             True  \n",
+       "1      4           2  null            False            False  \n",
+       "2     10           1   abx            False             True  \n",
+       "3     10           2  null            False            False  \n",
+       "4      3           3  null            False            False  "
+      ]
+     },
+     "execution_count": 616,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meningitis_grading_key_unit1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Made Three Cases: \n",
+    "\n",
+    "> TEST STATE DEPENDENT SCORES Across States\n",
+    "- True means sum \n",
+    "- False means take unique and sum \n",
+    "\n",
+    "\n",
+    "> Test Group Dependent Scores\n",
+    "- ABX example (only take one score) \n",
+    "- lab test\n",
+    "\n",
+    "#### Create Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 617,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip3 install networkx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    " \n",
+    "#### Build a dataframe with your connections\n",
+    "df = pd.DataFrame({ 'from':['A', 'B', 'C','A'], 'to':['D', 'A', 'E','C'], 'value':[1, 10, 5, 5]})\n",
+    "df\n",
+    " \n",
+    "#### Build your graph\n",
+    "G=nx.from_pandas_edgelist(df, 'from', 'to', create_using=nx.Graph() )\n",
+    " \n",
+    "#### Custom the nodes:\n",
+    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=1500, edge_color=df['value'], width=10.0, edge_cmap=plt.cm.Blues)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 624,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cx1 =  pd.merge(caseX1, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx2 =  pd.merge(caseX2, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx3 =  pd.merge(caseX3, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx4 =  pd.merge(caseX4, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx5 =  pd.merge(caseX5, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx6 =  pd.merge(caseX6, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx7 =  pd.merge(caseX7, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx8 =  pd.merge(caseX8, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "cx9 =  pd.merge(caseX9, meningitis_grading_key_unit1, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name']) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 664,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>group</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "2   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "3   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id  score  confidence group  state_dependent  group_dependent  \n",
+       "0           6      5           3   abx            False             True  \n",
+       "1           6      5           3   abx            False             True  \n",
+       "2           6      5           1   abx            False             True  \n",
+       "3           6      5           1   abx            False             True  "
+      ]
+     },
+     "execution_count": 664,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write a function to filter a set based on group dependnet \n",
+    "\n",
+    "#gd0 = pd.DataFrame(rFilter(cx2, 'group_dependent', False))\n",
+    "#gd1 = rFilter(cx2, 'group_dependent', True)\n",
+    "#gd2 = rFilter(gd1, 'group', 'abx')\n",
+    "# check to see scores are all the same here (if not default to higher one)\n",
+    "#gd3 = pd.DataFrame(gd2.iloc[[0]])\n",
+    "\n",
+    "#gd4 = pd.concat([gd0, gd3])\n",
+    "#gd4\n",
+    "cx6\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 660,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rSort(df, col, boolean):\n",
+    "    return df.sort_values(by=[col], ascending = boolean)\n",
+    "    \n",
+    "rSort(cx9, 'score', False)\n",
+    "\n",
+    "def rRows(df, single_row_index):\n",
+    "    return pd.DataFrame(df.iloc[[single_row_index]])\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 669,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "      <th>score</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>group</th>\n",
+       "      <th>state_dependent</th>\n",
+       "      <th>group_dependent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningits Worsens</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Vancomycin (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Mening Active</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>abx</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1  Ceftriaxone (Intravenous)  meningitis              Meningits Worsens   \n",
+       "2  Ceftriaxone (Intravenous)  meningitis                  Mening Active   \n",
+       "3   Vancomycin (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "4   Vancomycin (Intravenous)  meningitis              Meningits Worsens   \n",
+       "5   Vancomycin (Intravenous)  meningitis                  Mening Active   \n",
+       "\n",
+       "  sim_user_id  score  confidence group  state_dependent  group_dependent  \n",
+       "0           9      5           3   abx            False             True  \n",
+       "1           9      3           3   abx            False             True  \n",
+       "2           9     10           3   abx            False             True  \n",
+       "3           9      5           1   abx            False             True  \n",
+       "4           9      3           1   abx            False             True  \n",
+       "5           9     10           1   abx            False             True  "
+      ]
+     },
+     "execution_count": 669,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cx9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 672,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 672,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def sim_grader(case, grading_key):\n",
+    "    dt = pd.merge(case, grading_key, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])\n",
+    "    gd0 = pd.DataFrame(rFilter(dt, 'group_dependent', False))\n",
+    "    gd1 = rFilter(dt, 'group_dependent', True)\n",
+    "    gd2 = rFilter(gd1, 'group', 'abx')\n",
+    "    gd3 = rSort(gd2, 'score', False)\n",
+    "    gd4 = pd.DataFrame(gd3.iloc[[0]])\n",
+    "    gd5 = pd.concat([gd0, gd4])\n",
+    "    return sum(gd5['score'])\n",
+    "\n",
+    "\n",
+    "sim_grader(caseX9, meningitis_grading_key_unit1)\n",
+    "\n",
+    "# if state dependent you can choose to sum that score \n",
+    "# if group dependent should only sum the single score in the state "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 621,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unit_grade_casex1 = 5\n",
+    "unit_grade_casex2 = 5\n",
+    "unit_grade_casex3 = 5\n",
+    "unit_grade_casex4 = 5\n",
+    "unit_grade_casex5 = 5\n",
+    "unit_grade_casex6 = 5\n",
+    "unit_grade_casex7 = 5\n",
+    "unit_grade_casex8 = 5\n",
+    "unit_grade_casex9 = 5\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 607,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clinical_item_name</th>\n",
+       "      <th>case</th>\n",
+       "      <th>sim_state_name</th>\n",
+       "      <th>sim_user_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ceftriaxone (Intravenous)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acetaminophen (Oral)</td>\n",
+       "      <td>meningitis</td>\n",
+       "      <td>Meningitis Adequately Treated</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          clinical_item_name        case                 sim_state_name  \\\n",
+       "0  Ceftriaxone (Intravenous)  meningitis  Meningitis Adequately Treated   \n",
+       "1       Acetaminophen (Oral)  meningitis  Meningitis Adequately Treated   \n",
+       "\n",
+       "  sim_user_id  \n",
+       "0           2  \n",
+       "1           2  "
+      ]
+     },
+     "execution_count": 607,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caseX2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/readme.md b/scripts/Archive/SimulationRecommender/RecommenderPipeline/readme.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/readme.md
@@ -0,0 +1 @@
+
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/CPOETrackerAnalysis.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/CPOETrackerAnalysis.py
new file mode 100644
index 00000000..24119335
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/CPOETrackerAnalysis.py
@@ -0,0 +1,646 @@
+import sys, os
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import datetime
+from optparse import OptionParser
+import csv
+
+class SimulationAnalyzer:
+	def __init__(self, data_file):
+		self.data_file = data_file
+		self.version = data_file.split('.')[0].split('_')[-1]  # Get tracker version from filename
+		# assert(self.version in ['v4', 'v5'])
+		self.load_tracker_data()
+		self.results_collection = self.normalize_results()
+		# If using older version of tracker data, will need to parse signed collection
+		# differently
+		if self.version in ['v4', 'v5']:
+			self.signed_orders_collection = self.normalize_signed_orders()
+		else:
+			self.signed_orders_collection = self.normalize_signed_orders_older()
+
+	def load_tracker_data(self):
+		with open(self.data_file, 'r') as fp:
+			json_data = json.load(fp)
+			self.event_tracker_data = json.loads(json_data['eventTracker'])
+			self.results_tracker_data = json.loads(json_data['resultsTracker'])
+			self.signed_item_tracker_data = json.loads(json_data['signedItemsTracker'])
+			self.user = json_data['user']
+			self.patient = json_data['patient']
+			self.start_time = json_data['startTime']
+			self.end_time = json_data['endTime']
+
+	def parse_result_item_into_dict(self, base_item_dict, item_str):
+		""" Extract item's clinical_item_id, name, and description from item_str
+		and insert into item dict based on base_item_dict.
+		Args:
+			base_item_dict (dict): dict containing base attributes for item
+			item_str (str): string representation of item. Expected to be of
+				form: 'clinical_item_id|name|description'
+		Returns:
+			result_item_dict (dict): dict representing item_str passed in and
+				all other global attributes of the item
+		"""
+		# print(item_str)
+		result_item_dict = dict(base_item_dict)
+		attributes = list(item_str.split('|'))
+		result_item_dict['clinicalItemId'] = attributes[0]
+		result_item_dict['name'] = ""
+		result_item_dict['description'] = ""
+		if len(attributes) > 1:
+			result_item_dict['name'] = attributes[1]
+			result_item_dict['description'] = attributes[2]
+		return result_item_dict
+
+	def parse_signed_item_into_dict(self, item_dict, item_str):
+		"""Extract item's attributes and insert into item_dict.
+		Args:
+			item_dict (dict): dict to which item attributes will be added
+			item_str (str): string containing item's attributes. Expected to be
+				of form: 'clinical_item_id|source|searchQuery|mode|list_idx'
+		"""
+		clinical_item_id, source, searchQuery, mode, list_idx = list(item_str.split('|'))
+		item_dict['clinicalItemId'] = clinical_item_id
+		# Rename source to match names used for result items
+		if source == 'resultSpace1':
+			source = 'commonOrders'
+		elif source == 'resultSpace2':
+			source = 'specificOrders'
+		elif source == 'non-recommender':
+			source = 'data'
+		item_dict['source'] = source if source != 'undefined' else 'data'  # Quick fix for issue where order set items are not recorded correctly
+		item_dict['searchQuery'] = searchQuery  # May be undefined due to issue where order set items are not correctly recorded
+		item_dict['mode'] = mode if mode != 'undefined' else 'OrderSets'  # Quick fix for issue where order set items are not recorded correctly
+		item_dict['listIndex'] = 1000 if list_idx == 'undefined' else int(list_idx)  # Quick fix for issue where order set items are not recorded correctly - set listIndex to something arbitarily high
+
+	def normalize_results(self):
+		"""Join all results into a single, 'flat' collection of result item dicts
+		Returns:
+			results (list): list of result item dicts
+		"""
+		results = []
+		for mode in list(self.results_tracker_data.keys()):
+			for mode_object in self.results_tracker_data[mode]:
+				base_result_item_dict = dict()
+				items_collection = None
+				base_result_item_dict['mode'] = mode
+				for instance_collection_name in list(mode_object.keys()):  # ['items', 'state']
+					for attr_name in list(mode_object[instance_collection_name].keys()):
+						# If attribute is a list, then items have been found.
+						if isinstance(mode_object[instance_collection_name][attr_name], list):
+							# Store type of result [commonOrders, specificOrders, data]
+							base_result_item_dict['source'] = attr_name
+							# Store items for processing after all other attributes have been stored
+							items_collection = mode_object[instance_collection_name][attr_name]
+						else:
+							# Store non-list value into item dict
+							base_result_item_dict[attr_name] = mode_object[instance_collection_name][attr_name]
+				# Process items
+				for item_str in items_collection:
+					result_item_dict = self.parse_result_item_into_dict(base_result_item_dict, item_str)
+					# Store item dict into results
+					results.append(result_item_dict)
+		return results
+
+
+	def normalize_signed_orders_older(self):
+		"""
+		Map signed items from older version of tracker data to their corresponding
+		result items.
+		Returns:
+			signed_orders (list): list of signed order item dicts.
+				Note that these item dicts will not contain some of the attributes
+				found in the item dicts returned by `normalize_signed_orders`
+		"""
+		signed_orders = []
+		# Construct signed order clinical ids set and signed order tuples
+		signed_orders_id_set = set()
+		signed_orders_tuples = []
+		for timestamp in self.signed_item_tracker_data:
+			for item_str in self.signed_item_tracker_data[timestamp]:
+				signed_orders_id_set.add(item_str)
+				signed_orders_tuples.append((int(timestamp), item_str))
+
+		# Filter out result items that do not correspond to items signed
+		filtered_results = list([r for r in self.results_collection if r['clinicalItemId'] in signed_orders_id_set])
+		# For each signed item, find most likely result item
+		for signed_order in signed_orders_tuples:
+			# For each result item that has the same clinical id as the current
+			# signed order AND a store_time < the signed_time of the current item,
+			# compute timestamp delta (signed_time - store_time).
+			min_delta = float('inf')
+			best_result = None
+			for potential_result in list([r for r in filtered_results if r['clinicalItemId'] == signed_order[1] and r['storeTime'] < signed_order[0]]):
+				delta = signed_order[0] - potential_result['storeTime']
+				if delta < min_delta:
+					min_delta = delta
+					best_result = potential_result
+
+			# Map current signed item to result item with smallest delta
+			signed_item_dict = dict(potential_result)
+			signed_item_dict['signedTime'] = signed_order[0]
+			signed_orders.append(signed_item_dict)
+
+		return signed_orders
+
+
+	def normalize_signed_orders(self):
+		"""Join all signed items into a single, 'flat' colleciton of signed item dicts
+		Returns:
+			signed_orders (list): list of of signed order item dicts
+		"""
+		signed_orders = []
+		for timestamp in self.signed_item_tracker_data:
+			for item_str in self.signed_item_tracker_data[timestamp]:
+				signed_item_dict = dict()
+				signed_item_dict['signedTime'] = timestamp
+				self.parse_signed_item_into_dict(signed_item_dict, item_str)
+				signed_orders.append(signed_item_dict)
+
+		return signed_orders
+
+	def number_mouse_clicks_all(self):
+		"""Aggregate the number of mouse clicks on buttons and inputs on page"""
+		event_tracker = self.event_tracker_data
+		clicks = self.number_mouse_clicks(filters=list(event_tracker.keys()), perc=False)
+
+		return clicks
+
+	def number_mouse_clicks(self, filters=[], perc=False):
+		"""Aggregate number of mouse clicks on particular buttons and inputs on page
+		Args:
+			filters (list): names of events to count
+			perc (bool): whether to return frequency of clicks as percentage (of total clicks)
+		"""
+		event_tracker = self.event_tracker_data
+		clicks = 0.
+		for event in filters:
+			try:
+				event_ocurrences = len(event_tracker[event])
+				clicks += event_ocurrences
+			except Exception as e:
+				continue
+		if perc:
+			total_clicks = self.number_mouse_clicks_all()
+			percentage = clicks / total_clicks
+			return percentage
+
+		return clicks
+
+	def click_summary(self, perc=False):
+		"""Compute click summary for each event
+		Args:
+			perc (bool): whether to return event click summaries as percentages (of total clicks)
+		Return:
+			summary (dict): dict mapping event to frequency or percentage (based on perc)
+		"""
+		summary = dict()
+		event_tracker = self.event_tracker_data
+		for event in list(event_tracker.keys()):
+			summary[event] = self.number_mouse_clicks(filters=[event], perc=perc)
+
+		return summary
+
+	def convert_timestamp(self, ms_timestamp):
+		"""Convert millisecond timestamp to (hours, minutes, seconds)"""
+		seconds=(ms_timestamp/1000)%60
+		seconds = int(seconds)
+		minutes=(ms_timestamp/(1000*60))%60
+		minutes = int(minutes)
+		hours=(ms_timestamp/(1000*60*60))%24
+
+		return (hours, minutes, seconds)
+
+	def elapsed_time(self):
+		"""Compute total time spent on case"""
+		elapsed_time_millis = int(self.end_time) - int(self.start_time)
+		hours, minutes, seconds = self.convert_timestamp(elapsed_time_millis)
+
+		return ("%d:%d:%d" % (hours, minutes, seconds))
+
+	def total_orders(self, unique=False):
+		"""Aggregate total number of signed orders
+		Args:
+			unique (bool): if set to True, duplicate orders will be ignored
+		"""
+		signed_items = self.retrieve_signed_orders(unique=unique)
+		count = len(signed_items)
+
+		return count
+
+	def retrieve_signed_orders(self, batch=False, unique=False):
+		"""Return all signed order items
+		Args:
+			batch (bool): whether to preserve the groups in which orders appear
+			unique (bool): if set to True, duplicate orders will be ignored, but
+						   if batch is set to True, only duplicates within the batch will
+						   be ignored. Note: Using unique=True will unpreserve
+						   the order in which signed orders appear.
+		"""
+		signed_items_tracker = self.signed_item_tracker_data
+		signed_items = []
+		for timestamp in list(signed_items_tracker.keys()):
+			signed_items_batch = signed_items_tracker[timestamp]
+			if batch:
+				if unique:
+					signed_items_batch = list(set(signed_items_batch))
+				signed_items.append(signed_items_batch)
+			else:
+				signed_items += signed_items_batch
+
+		if unique and not batch:
+			signed_items = list(set(signed_items))
+
+		return signed_items
+
+	def total_recommendations(self, unique=True):
+		"""Aggregate total number of recommendations
+		Args:
+			unique (bool): if set to True, duplicate recommendations will be ignored
+		"""
+		# Recommender specifically triggered when search is '' and by Find Orders when search query is ''
+		count = self.total_search_results(search_modes=["", "FindOrders"], search_query="", unique=unique)
+
+		return count
+
+
+	def total_search_results(self, search_modes=[], search_query=None, unique=True):
+		"""Aggregate total number of results for particular search modes
+		Args:
+			search_modes (iterable): search actions of {"", "FindOrders", "OrderSets",
+									 "Diagnoses"} to filter by.
+			search_query (str): search query used for result (substring matching is used)
+			unique (bool): if set to True, duplicate results are ignored
+		"""
+		if not search_modes:
+			search_modes = list(self.results_tracker_data.keys())
+		results = self.retrieve_results(search_modes=search_modes, search_query=search_query, batch=False, unique=unique)
+		count = len(results)
+
+		return count
+
+
+	def retrieve_results(self, search_modes=[], search_query=None, batch=False, unique=True):
+		"""Return results for particular search modes
+		Args:
+			search_modes (iterable): search actions of {"", "FindOrders", "OrderSets",
+									 "Diagnoses"} to filter by.
+			search_query (str): search query used for result (substring matching is used)
+			batch (bool): whether to preserve the groups in which recommendations appear
+			unique (bool): if set to True, duplicate recommendations will be ignored,
+						   if batch is set to True, only duplicates within each batch will
+						   be ignored. Note: Using unique=True will unpreserve
+						   the order in which recommendations appear.
+		"""
+		results_tracker = self.results_tracker_data
+		if not search_modes:
+			search_modes = list(results_tracker.keys())
+		results = []
+		for mode in search_modes:
+			try:
+				mode_results = results_tracker[mode]
+				for obj in mode_results:
+					state = obj['state']
+					#	  Wild card search 					Recommendations search											Other results search
+					if (search_query is None) or (search_query == "" and state['searchQuery'] == "") or (search_query != "" and search_query in state['searchQuery']):
+						items = obj['items']
+						for data_type in list(items.keys()):
+							items_list = items[data_type]
+							if not (type(items_list) is list): continue
+							# Extract item ids
+							items_list = list([item.split('|')[0] for item in items_list])
+							if batch:
+								if unique:
+									# Remove duplicates within batch
+									items_list = list(set(items_list))
+								results.append(items_list)
+							else:
+								results += items_list
+			except Exception as e:
+				print(e)
+				continue
+
+		if unique and not batch:
+			results = list(set(results))
+
+		return results
+
+	def number_signed_in_recommended(self, perc=False):
+		"""Number of signed orders that appear in recommender
+		Args:
+			perc (bool): whether to return frequency as percentage (over all signed orders)
+		"""
+		signed_orders = self.retrieve_signed_orders(unique=True, batch=False)
+		recommendations = self.retrieve_results(search_modes=[""], batch=False, unique=True)
+		# Map signed_orders to their ids (to be intersected with recommendations)
+		signed_orders_ids = list([orders.split('|')[0] for orders in signed_orders])
+		intersection = set(signed_orders_ids) & set(recommendations)
+		count = len(intersection)
+		if perc:
+			count = float(count) / len(signed_orders)
+
+		return count
+
+	def get_manually_searched_options(self):
+		"""Get list of result items that were presented from a manual search
+		Returns:
+			results (list): list of item dicts corresponding to items that were
+				shown after a manual search (not from recommender)
+		"""
+		results = list([item for item in self.results_collection if item['mode'] != '' and item['searchQuery'] != ''])
+		return results
+
+	def get_recommended_options(self, include_related=True):
+		"""Get list of result items that were presented by recommender
+		Args:
+			include_related (bool): whether to include items that were shown
+				after clicking 'related' button for a particular clinical item
+		Returns:
+			results (list): list of item dicts corresponding to items that were
+				shown by recommender
+		"""
+		filter_fn = (lambda item: item['mode'] == '' or item['searchQuery'] == '')
+		if include_related:
+			filter_fn = (lambda item: (item['mode'] == '' or item['searchQuery'] == '') or (item['mode'] == 'related'))
+		results = list(filter(filter_fn, self.results_collection))
+		return results
+
+	def _merge_intersection_items(self, merged_dict, result_item):
+		"""Merge the two dicts corresponding to intersection items
+		Args:
+			merged_dict (dict): base dict where attributes of result item will
+				be merged into
+			result_dict (dict): dict corresponding to result item
+		"""
+		merged_dict.update(result_item)  # Note that result listIndex will overwrite that in merged_dict!
+
+	def _result_signed_intersection(self, result_items, signed_items, filter_fn):
+		"""Get the intersection between result items and signed items
+		Args:
+			result_items (list): list of result item dicts
+			signed_items (list): list of signed item dicts
+			filter_fn (fn): filter function called on result, signed item pair,
+				where evaluation to True results in items being considered
+				compatible and 'merged'. This funciton should take two items,
+				one result item and one signed item, as parameters
+		Returns:
+			intersection (list): list of merged item dicts of items that are
+				in both item lists, according to provided filter function
+		"""
+		intersection = []
+		for signed_item in signed_items:
+			merged_dict = dict(signed_item)
+			found = False
+			for result_item in result_items:  # Use some form of binary search?
+				if filter_fn(result_item, signed_item):
+					self._merge_intersection_items(merged_dict, result_item)
+					# Corresponding result item found, stop searching
+					found = True
+					break;
+			if found:
+				intersection.append(merged_dict)
+		return intersection
+
+	def get_signed_from_manual_search(self):
+		"""Get item dicts for items that were signed from manual-search results
+		Returns:
+			signed_from_manual (list): list of item dicts that were signed from
+			 	manually-searching
+		"""
+		# Define filter function for determining intersection
+		# This function checks that result item and signed item have same
+		# clinicalItemId, listIndex (though s_item index will be ahead), and location source        r_item listIndex starts from 1, but s_item listIndex starts from 0
+		filter_fn = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['listIndex'] == s_item['listIndex'] - 1 and r_item['source'] == s_item['source']
+
+		if self.version == 'v5':  # version 5 of tracker correctly tracks order set items in results
+			# Get manually-searched signed items
+			manual_search_results = self.get_manually_searched_options()
+			signed_from_manual = self._result_signed_intersection(manual_search_results, self.signed_orders_collection, filter_fn=filter_fn)
+		# elif self.version == 'v4':  # versions 4 of the tracker only tracks item sources in the signed orders collection
+		else:
+			signed_from_manual = list([s_item for s_item in self.signed_orders_collection if s_item['source'] == 'data'])
+		return signed_from_manual
+
+	def get_signed_from_recommended(self, include_related=True):
+		"""Get item dicts for items that were signed from recommender results
+		Args:
+			include_related (bool): whether to include items that were shown
+				after clicking 'related' button for a particular clinical item
+		Returns:
+			signed_from_manual (list): list of item dicts that were signed from
+			 	recommender options
+		"""
+		# Define filter function for determining intersection
+		# This function checks that result item and signed item have same
+		# clinicalItemId, listIndex (though s_item index will be ahead), and location source
+		filter_fn = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and (r_item['listIndex'] == s_item['listIndex'] - 1 or r_item['listIndex']+1 == s_item['listIndex'] - 1) and r_item['source'] == s_item['source']
+
+		if self.version == 'v5':  # version 5 of tracker correctly tracks order set items in results
+			# Get recommended signed items
+			recommended_results = self.get_recommended_options(include_related=include_related)
+			signed_from_recommended = self._result_signed_intersection(recommended_results, self.signed_orders_collection, filter_fn)
+		# elif self.version == 'v4':  # versions 4 of the tracker only tracks item sources in the signed orders collection
+		else:
+			signed_from_recommended = list([s_item for s_item in self.signed_orders_collection if s_item['source'] != 'data'])
+		return signed_from_recommended
+
+	def get_signed_missed_recommended(self, include_related=True):
+		"""Get item dicts for items that were signed from manual-search results,
+		but had appeared previously from recommended results
+		Args:
+			include_related (bool): whether to include items that were shown
+				after clicking 'related' button for a particular clinical item
+		Returns:
+			missed_items (list): list of item dicts that appeared from the
+				recommender, but were ultimately chosen from a manual search
+		"""
+		# Define filter function for determining intersection between signed
+		# items and manually-searched items
+		# This function checks that result item and signed item have same
+		# clinicalItemId, listIndex (though s_item index will be ahead), and location source
+		filter_fn_manual = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['listIndex'] == s_item['listIndex'] - 1 and r_item['source'] == s_item['source']
+		# For older versions of tracker data:
+		filter_fn_manual_older = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['storeTime'] == s_item['storeTime'] and r_item['source'] == s_item['source']
+		# Define filter function for determining intersection between signed
+		# items from manual search and recommomended
+		# This function checks that result item and signed item have same
+		# clinicalItemId and that the listIndex of recommended item is less
+		# than or equal to that of the signed item. This ignores
+		# results of the item that appeared after the item was already signed.
+		filter_fn_recommended = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['listIndex'] <= s_item['listIndex'] - 1
+		# For older versions of tracker data:
+		filter_fn_recommended_older = lambda r_item, s_item: r_item['clinicalItemId'] == s_item['clinicalItemId'] and r_item['storeTime'] <= s_item['storeTime']
+
+		# First get the manually searched signed items
+		manual_search_results = self.get_manually_searched_options()
+		if self.version in ['v4', 'v5']:
+			signed_from_manual = self._result_signed_intersection(manual_search_results, self.signed_orders_collection, filter_fn=filter_fn_manual)
+		else:
+			signed_from_manual = self._result_signed_intersection(manual_search_results, self.signed_orders_collection, filter_fn=filter_fn_manual_older)
+		# Next get the manually searched items that also appeared previously
+		# in recommended results
+		recommended_results = self.get_recommended_options(include_related=include_related)
+		if self.version in ['v4', 'v5']:
+			missed_items = self._result_signed_intersection(recommended_results, signed_from_manual, filter_fn_recommended)
+		else:
+			missed_items = self._result_signed_intersection(recommended_results, signed_from_manual, filter_fn_recommended_older)
+		return missed_items
+
+	def get_unique(self, collection, key_fn):
+		""""Return list of unique values, where the value of the objects in the
+		collection is determined by the key function. Note that any order
+		in the collection will not be preserved
+		Args:
+			collection (list): a list of objects to be filtered
+			key_fn (fn): function that takes an object from collection and
+				returns a value that will be used to consider uniqueness
+		Returns:
+			unique_vals (list): list of unique values extracted from collection
+		"""
+		values = list(map(key_fn, collection))
+		unique_vals = list(set(values))
+		return unique_vals
+
+	def _clinical_item_id_key_fn(self, item):
+		"""Returns clinical item value from item object"""
+		return item['clinicalItemId']
+
+	def construct_timeline(self):
+		"""Flatten trackers and order all events by timestamp"""
+		# Flatten eventTracker
+		event_tracker = self.event_tracker_data
+		events = []
+		for event_name in list(event_tracker.keys()):
+			for event_ocurrence in event_tracker[event_name]:
+				event_time = event_ocurrence['eventTime']
+				events.append((event_time, event_name, event_ocurrence))
+		# Sort events by timestamp
+		sorted_events = sorted(events, key=lambda x: x[0])
+		return sorted_events
+
+	def visualize_timeline(self, timeline):
+		"""Draw timeline
+		Args:
+			timeline (iterable): iterable of tuples (position, name, info_dict)
+		"""
+		times = list([datetime.datetime.fromtimestamp((x[0] - timeline[0][0])/1000.0) for x in timeline])
+		names = list([x[1] for x in timeline])
+		names_unique = sorted(list(set(list([x for x in names]))))
+
+
+		norm = plt.Normalize(1,4)
+		c = np.random.randint(1,5,size=len(names_unique))
+		cmap = plt.cm.inferno
+		fig, ax = plt.subplots(figsize=(15,4))
+
+		ax.yaxis.set_visible(False)
+		ax.spines['right'].set_visible(False)
+		ax.spines['left'].set_visible(False)
+		ax.spines['top'].set_visible(False)
+		ax.xaxis.set_ticks_position('bottom')
+
+		ax.get_yaxis().set_ticklabels([])
+		ms = pd.to_timedelta("1", unit='ms')
+		plt.xlim(times[0] - 5*ms, times[-1] + 5*ms)
+
+		plots = []
+
+		for i, name in enumerate(names_unique):
+			named_events = list([x for x in timeline if x[1] == name])
+			named_times = list([datetime.datetime.fromtimestamp((x[0] - timeline[0][0])/1000.0) for x in named_events])
+			sc = plt.scatter(named_times, [i*5+1]*len(named_times), marker='o', s=100, alpha=0.8)
+			plots.append(sc)
+
+		ax.legend(plots, names_unique)
+		plt.show()
+
+
+def aggregate_simulation_data(data_home, output_path, append_to_existing=False, source_path=None):
+	"""Generate 'flat' csv file or append to existing csv file from directory of simulation (json) files
+	Args:
+		data_home (str): path to directory containing simulation json files
+		output_path (str): path to where csv file should be saved to
+		append_to_existing (bool): whether to append aggregated data from data_home to existing csv file
+		source_path (str): if append_to_existing is True, source_path is path to used csv file
+	"""
+	# Filter out files that do not end with .json
+	filenames = [f for f in os.listdir(data_home) if f.endswith('.json')]
+	# Append directory path to filenames
+	filenames = list([os.path.join(data_home, f) for f in filenames])
+	headers = ["user", "patient", "start_time", "elapsed_time", "total_num_clicks", "num_note_clicks", "num_results_review_clicks", "recommended_options", "unqique_recommended_options", "manual_search_options", "total_orders", "orders_from_recommender", "orders_from_manual_search", "orders_from_recommender_missed"]
+	if not append_to_existing:
+		# Create initial csv file
+		with open(output_path,'w') as out_csv:
+			file_writer = csv.writer(out_csv)
+			file_writer.writerow(headers)
+		source_path = output_path
+	# Use same logic as appending to existing (now that there is an initial csv to append to)
+	with open(source_path, 'a') as out_csv:
+		file_writer = csv.writer(out_csv)
+		for filename in filenames:
+			print("Processing {}".format(filename))
+			fields = []
+			# Instantiate SimulationAnalyzer for current data file
+			siman = SimulationAnalyzer(filename)
+			# Add current base metrics
+			fields.append(siman.user)  # "user"
+			fields.append(siman.patient)  # "patient"
+			fields.append(siman.start_time)  # "start_time"
+			fields.append(siman.elapsed_time())  # "elapsed_time"
+			fields.append(siman.number_mouse_clicks_all())  # "total_num_clicks"
+			# fields.append(siman.number_mouse_clicks(filters=['FindOrders']))  # "num_manual_searches"
+			fields.append(siman.number_mouse_clicks(filters=['Notes']))  # "num_note_clicks"
+			fields.append(siman.number_mouse_clicks(filters=['ResultsReview']))  # "num_results_review_clicks"
+			fields.append(len(siman.get_recommended_options()))  # "recommended_options"
+			fields.append(len(siman.get_unique(siman.get_recommended_options(), key_fn=siman._clinical_item_id_key_fn)))  # "unqique_recommended_options"
+			fields.append(len(siman.get_manually_searched_options()))  # "manual_search_options"
+			fields.append(len(siman.signed_orders_collection))  # "total_orders"
+			fields.append(len(siman.get_signed_from_recommended()))  # "orders_from_recommender"
+			fields.append(len(siman.get_signed_from_manual_search()))  # "orders_from_manual_search"
+			fields.append(len(siman.get_signed_missed_recommended()))  # "orders_from_recommender_missed"
+			file_writer.writerow(fields)
+
+			# Delete SimulationAnalyzer to free any useless memory
+			del siman
+
+
+def main(argv):
+	parser = OptionParser()
+	parser.add_option("-f", "--file",  dest="data_file", help="JSON file containing collected data.")
+	parser.add_option("-a", "--agg", dest="aggregate", help="Create aggregate csv file?")
+	parser.add_option("-d", "--agg_home", dest="data_home", help="path to directory containing simulation json files")
+	parser.add_option("-o", "--agg_output", dest="output_path", help="path to where csv file should be saved to")
+	parser.add_option("-s", "--agg_source", dest="source_path", help="source_path is path to existing csv file to append to")
+	options, args = parser.parse_args(argv[1:])
+
+	if not options.aggregate:
+		siman = SimulationAnalyzer(options.data_file)
+		print("Total mouse clicks: ", siman.number_mouse_clicks_all())
+		print("Click summary: ", siman.click_summary(perc=False))
+		print("Elapsed time: ", siman.elapsed_time())
+		print("Total signed orders: ", siman.total_orders())
+		print("Signed order ids: ", siman.retrieve_signed_orders())
+		print("Signed order batches:", siman.retrieve_signed_orders(batch=True))
+		print("Total recommendations: ", siman.total_recommendations())
+		print("Recommendation ids: ", siman.retrieve_results(search_modes=["", "FindOrders"], batch=False, search_query=""))
+		print("Recommendation batches: ", siman.retrieve_results(search_modes=["", "FindOrders"], batch=True, search_query=""))
+		print("Signed/Recommended overlap: ", siman.number_signed_in_recommended())
+		print("Total results:", siman.total_search_results())
+		print("Timeline data: ", siman.construct_timeline())
+		print("Timeline view: ", siman.visualize_timeline(siman.construct_timeline()))
+	else:
+		data_home = options.data_home
+		output_path = options.output_path
+		append_to_existing = False
+		source_path = None
+		if options.source_path:
+			append_to_existing = True
+			source_path = options.source_path
+		aggregate_simulation_data(data_home, output_path, append_to_existing=append_to_existing, source_path=source_path)
+
+
+
+if __name__ == '__main__':
+	main(sys.argv)
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/clinicalRecommenderGradingTests.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/clinicalRecommenderGradingTests.py
new file mode 100644
index 00000000..065221a2
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/clinicalRecommenderGradingTests.py
@@ -0,0 +1,323 @@
+# gradingRecommenderTests.py
+import unittest
+import pandas as pd
+import numpy as np
+import os
+from helper import *
+
+# Create Three Groups
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('Initializing Data for Test Case 1 ')
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+# Create Groups List
+grading_groups = ['abx']
+
+# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+# Meningitis Worsens
+clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)', 'Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))
+case = pd.DataFrame(np.array(['meningitis', 'meningitis','meningitis','meningitis','meningitis']))
+name_x =  pd.DataFrame(np.array(['Meningits Worsens','Meningits Worsens','Meningits Worsens','Meningits Worsens','Meningits Worsens']))
+grade = pd.DataFrame(np.array([3, 4, 3, 3, 3]))
+confidence = pd.DataFrame(np.array([3, 2, 1, 2,3]))
+state_dependent = pd.DataFrame(np.array([False, False, False, False, False]))
+group_dependent = pd.DataFrame(np.array([True, False, True, False, False ]))
+group_x =  pd.DataFrame(np.array(['abx', 'null', 'abx', 'null', 'null']))
+m_worsens = pd.concat([clinical_order, case, name_x, grade, confidence, group_x, state_dependent, group_dependent], axis=1, ignore_index = True)
+
+# Meningitis Active
+clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Acetaminophen (Oral)', 'Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))
+case = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis', 'meningitis', 'meningitis']))
+name_x =  pd.DataFrame(np.array(['Mening Active', 'Mening Active', 'Mening Active', 'Mening Active', 'Mening Active']))
+grade = pd.DataFrame(np.array([10, 4, 10, 10, 3]))
+confidence = pd.DataFrame(np.array([3, 2, 1, 2, 3]))
+state_dependent = pd.DataFrame(np.array([False, False, False, False, False]))
+group_dependent = pd.DataFrame(np.array([True, False, True, False, False ]))
+group_x =  pd.DataFrame(np.array(['abx', 'null', 'abx', 'null', 'null']))
+m_active = pd.concat([clinical_order, case, name_x, grade, confidence, group_x, state_dependent, group_dependent], axis=1, ignore_index = True)
+
+# Meningitis Adequately Treated
+clinical_order = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Acetaminophen (Oral)','Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))
+case = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis', 'meningitis', 'meningitis']))
+name_x =  pd.DataFrame(np.array(['Meningitis Adequately Treated','Meningitis Adequately Treated','Meningitis Adequately Treated','Meningitis Adequately Treated','Meningitis Adequately Treated']))
+grade = pd.DataFrame(np.array([5, 4, 5, 5, 3]))
+confidence = pd.DataFrame(np.array([3, 2, 1, 2, 3]))
+state_dependent = pd.DataFrame(np.array([False, False, False, False, False]))
+group_dependent = pd.DataFrame(np.array([True, False, True, False, False ]))
+group_x =  pd.DataFrame(np.array(['abx', 'null', 'abx', 'null', 'null']))
+m_adequately_treated = pd.concat([clinical_order, case, name_x, grade, confidence, group_x, state_dependent, group_dependent], axis=1, ignore_index = True)
+
+# APPENDING THE DATAFRAME TOGETHER
+mf2 = m_adequately_treated.append(m_worsens)
+mf3 = mf2.append(m_active)
+mf3.columns = ['clinical_item_name', 'case', 'sim_state_name', 'score', 'confidence', 'group','state_dependent', 'group_dependent']
+meningitis_grading_key_unit1 = mf3
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('PRINTING MENINGITIS_GRADING_KEY_UNIT_1 ')
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print(meningitis_grading_key_unit1)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+# Initialize the first test case of one order
+#
+
+# Initializing Case Scores:
+
+unit_grade_casex1 = 5
+unit_grade_casex2 = 9
+unit_grade_casex3 = 17
+unit_grade_casex4 = 5
+unit_grade_casex5 = 5
+unit_grade_casex6 = 5
+unit_grade_casex7 = 10
+unit_grade_casex8 = 9
+unit_grade_casex9 = 10
+
+
+# TODO
+
+# test basic addition (one order)
+# test basic addition (two order)
+# test basic addition (five order)
+
+
+# TODO
+clinical_order1 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)']))
+clinical_order2 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))
+clinical_order3 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Acetaminophen (Oral)', 'Vancomycin (Intravenous)', 'Dexamethasone (Intravenous)', 'DIET NPO']))
+
+# sample cases with len states
+case = pd.DataFrame(np.array(['meningitis']))
+case2 = pd.DataFrame(np.array(['meningitis', 'meningitis']))
+case3 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis']))
+
+# states
+name_x1 =  pd.DataFrame(np.array(['Meningitis Adequately Treated']))
+name_x2 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))
+name_x3 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated']))
+
+# sim user id
+sim_user_id1 = pd.DataFrame(np.array(['1']))
+sim_user_id2 = pd.DataFrame(np.array(['2','2']))
+sim_user_id3 = pd.DataFrame(np.array(['3','3','3','3','3']))
+
+
+#
+caseX1 = pd.concat([clinical_order1, case, name_x1, sim_user_id1], axis=1, ignore_index = True)
+caseX2 = pd.concat([clinical_order2, case2, name_x2, sim_user_id2], axis=1, ignore_index = True)
+caseX3 = pd.concat([clinical_order3, case3, name_x3, sim_user_id3], axis=1, ignore_index = True)
+
+
+caseX1.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+caseX2.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+caseX3.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+
+
+# TODO
+
+# test group_dependent_action
+
+# TODO
+
+# test state_dependent_action
+# test group dependency
+# case 4 (two ceftriaxone) (two sim_states)
+# case 5 (two ceftriaxone and 1 vancomyocin) (two sim_states)
+# case 6 (two ceftriaxone and 2 vancomyocin) (two sim_states)
+
+clinical_order4 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)']))
+clinical_order5 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)', 'Vancomycin (Intravenous)']))
+clinical_order6 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)', 'Vancomycin (Intravenous)', 'Vancomycin (Intravenous)']))
+
+# sample cases with len states
+case4 = pd.DataFrame(np.array(['meningitis', 'meningitis']))
+case5 = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis']))
+case6 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis']))
+
+# states
+name_x4 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens']))
+name_x5 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens', 'Meningitis Adequately Treated']))
+name_x6 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated', 'Meningitis Adequately Treated']))
+
+# sim user id
+sim_user_id4 = pd.DataFrame(np.array(['4', '4']))
+sim_user_id5 = pd.DataFrame(np.array(['5', '5', '5']))
+sim_user_id6 = pd.DataFrame(np.array(['6','6','6','6']))
+
+caseX4 = pd.concat([clinical_order4, case4, name_x4, sim_user_id4], axis=1, ignore_index = True)
+caseX5 = pd.concat([clinical_order5, case5, name_x5, sim_user_id5], axis=1, ignore_index = True)
+caseX6 = pd.concat([clinical_order6, case6, name_x6, sim_user_id6], axis=1, ignore_index = True)
+
+caseX4.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+caseX5.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+caseX6.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+
+
+# test group dependency
+# case 7 (three ceftriaxone)
+# case 8 (three ceftriaxone and 1 vancomyocin)
+# case 9 (three ceftriaxone and 3 vancomyocin)
+
+clinical_order7 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)']))
+clinical_order8 = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)','Acetaminophen (Oral)']))
+clinical_order9  = pd.DataFrame(np.array(['Ceftriaxone (Intravenous)', 'Ceftriaxone (Intravenous)','Ceftriaxone (Intravenous)', 'Vancomycin (Intravenous)', 'Vancomycin (Intravenous)', 'Vancomycin (Intravenous)']))
+
+# sample cases with len states
+case7 = pd.DataFrame(np.array(['meningitis', 'meningitis', 'meningitis']))
+case8 = pd.DataFrame(np.array(['meningitis', 'meningitis']))
+case9 = pd.DataFrame(np.array(['meningitis','meningitis','meningitis','meningitis','meningitis','meningitis']))
+
+# states
+name_x7 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens', 'Mening Active']))
+name_x8 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningitis Adequately Treated']))
+name_x9 =  pd.DataFrame(np.array(['Meningitis Adequately Treated', 'Meningits Worsens', 'Mening Active','Meningitis Adequately Treated', 'Meningits Worsens', 'Mening Active']))
+
+# sim user id
+sim_user_id7 = pd.DataFrame(np.array(['7', '7', '7']))
+sim_user_id8= pd.DataFrame(np.array(['8', '8']))
+sim_user_id9 = pd.DataFrame(np.array(['9','9','9','9','9', '9']))
+
+
+#
+caseX7 = pd.concat([clinical_order7, case7, name_x7, sim_user_id7], axis=1, ignore_index = True)
+caseX8 = pd.concat([clinical_order8, case8, name_x8, sim_user_id8], axis=1, ignore_index = True)
+caseX9 = pd.concat([clinical_order9, case9, name_x9, sim_user_id9], axis=1, ignore_index = True)
+
+
+caseX7.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+caseX8.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+caseX9.columns = ['clinical_item_name', 'case', 'sim_state_name', 'sim_user_id']
+
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print(' RUNNING UNIT TESTS: caseX1, caseX2, caseX3')
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX1')
+print(caseX1)
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX2')
+print(caseX2)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX3')
+print(caseX3)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('RUNNING UNIT TESTS: caseX4, caseX5, caseX6')
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX4')
+print(caseX4)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX5')
+print(caseX5)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX6')
+print(caseX6)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print(' RUNNING UNIT TESTS: caseX7, caseX8, caseX9')
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+class TestCaseSet789(unittest.TestCase):
+
+    def test_upper(self):
+        self.assertEqual('foo'.upper(), 'FOO')
+
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX7')
+print(caseX7)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX8')
+print(caseX8)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+print('---------')
+print('caseX9')
+print(caseX9)
+print('---------')
+print('--------------------------------------------------------------------------------------------------------------------------------------')
+
+# TODO
+
+class TestCaseSet123(unittest.TestCase):
+
+    def test_123(self):
+        self.assertEqual(sim_grader(caseX1, meningitis_grading_key_unit1), unit_grade_casex1)
+        self.assertEqual(sim_grader(caseX2, meningitis_grading_key_unit1), unit_grade_casex2)
+        self.assertEqual(sim_grader(caseX3, meningitis_grading_key_unit1), unit_grade_casex3)
+
+
+class TestCaseSet456(unittest.TestCase):
+
+    def test_456(self):
+        self.assertEqual(sim_grader(caseX4, meningitis_grading_key_unit1), unit_grade_casex4)
+        self.assertEqual(sim_grader(caseX5, meningitis_grading_key_unit1), unit_grade_casex5)
+        self.assertEqual(sim_grader(caseX6, meningitis_grading_key_unit1), unit_grade_casex6)
+
+
+class TestCaseSet789(unittest.TestCase):
+
+    def test_789(self):
+        self.assertEqual(sim_grader(caseX7, meningitis_grading_key_unit1), unit_grade_casex7)
+        self.assertEqual(sim_grader(caseX8, meningitis_grading_key_unit1), unit_grade_casex8)
+        self.assertEqual(sim_grader(caseX9, meningitis_grading_key_unit1), unit_grade_casex9)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/clinicalRecommenderTests.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/clinicalRecommenderTests.py
new file mode 100644
index 00000000..17f4470f
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/clinicalRecommenderTests.py
@@ -0,0 +1,45 @@
+import unittest
+import psycopg2 as pg
+import pandas.io.sql as psql
+import pandas as pd
+import numpy as np
+import os
+from helper import *
+from configuration import *
+
+# checks to see if paths have folders
+class TestBoxConnection(unittest.TestCase):
+    def test_recommender_path(self):
+        self.assertTrue(len(os.listdir(recommender_path))> 0)
+    def test_physician_grading(self):
+        self.assertTrue(len(os.listdir(physician_grading))> 0)
+    def test_physician_response(self):
+        self.assertTrue(len(os.listdir(physician_response))> 0)
+    def test_tracker_path(self):
+        self.assertTrue(len(os.listdir(tracker_data)) > 0)
+
+class TestDataConnection(unittest.TestCase):
+    def test_sim_user(self):
+        self.assertTrue(len(sim_user) > 0)
+    def test_sim_patient_order(self):
+        self.assertTrue(len(sim_patient_order) > 0)
+    def test_sim_state(self):
+        self.assertTrue(len(sim_state) > 0)
+    def test_sim_user(self):
+        self.assertTrue(len(sim_user) > 0)
+    def test_clinical_item(self):
+        self.assertTrue(len(clinical_item) == 45979)
+
+# TODO
+class TestTracker(unittest.TestCase):
+    # only checks folders are available
+    def check_tracker_path(self):
+        self.assertTrue(len(os.listdir(tracker_data))> 0)
+        # checks to see if proper folders are in directory
+    def check_tracker_data_path(self):
+        self.assertTrue('v4_data' in os.listdir(tracker_data))
+    def check_tracker_output_path(self):
+        self.assertTrue('tracker_output' in os.listdir(tracker_data))
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/configuration.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/configuration.py
new file mode 100644
index 00000000..a7dde178
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/configuration.py
@@ -0,0 +1,29 @@
+import unittest
+import psycopg2 as pg
+import pandas.io.sql as psql
+import pandas as pd
+import numpy as np
+import os
+
+# SET PATH TO CLINICAL RECOMMENDER FOLDER
+recommender_path = "/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/"
+# SET DB CONNECTION
+connection = pg.connect("host='localhost' dbname=stride_inpatient_2014 user=postgres password='MANUAL PASSWORD'")
+
+
+physician_grading = recommender_path + "physician_grading/"
+physician_response = recommender_path + "physician_response/"
+tracker_data = recommender_path + "tracker_data/"
+unit_test = recommender_path + "unit_test/"
+
+
+clinical_item = pd.read_sql_query('select * from clinical_item', con=connection)
+sim_patient_order = pd.read_sql_query('select * from sim_patient_order',con=connection)
+sim_patient = pd.read_sql_query('select * from sim_patient',con=connection)
+sim_state = pd.read_sql_query('select * from sim_state',con=connection)
+sim_user = pd.read_sql_query('select * from sim_user',con=connection)
+sim_state_transition = pd.read_sql_query('select * from sim_state_transition',con=connection)
+
+# merge orders from sim patient order and sim_state
+merged_order = sim_patient_order.merge(sim_state, left_on='sim_state_id', right_on='sim_state_id')
+clinical_items_list = merged_order['clinical_item_id'].unique()
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/helper.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/helper.py
new file mode 100644
index 00000000..2623335d
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/helper.py
@@ -0,0 +1,29 @@
+# recommender pipeline helper
+import pandas as pd
+
+
+def state_split(state_names, df):
+    df2 = df[df['name_x'].isin(state_names)]
+    return(df2)
+
+def grade_sum(case):
+    return case['grade'].sum(axis = 0, skipna = True)
+
+def sim_grader(case, grading_key):
+    dt = pd.merge(case, grading_key, how = 'left', on = ['clinical_item_name', 'case', 'sim_state_name'])
+    gd0 = pd.DataFrame(rFilter(dt, 'group_dependent', False))
+    gd1 = rFilter(dt, 'group_dependent', True)
+    gd2 = rFilter(gd1, 'group', 'abx')
+    gd3 = rSort(gd2, 'score', False)
+    gd4 = pd.DataFrame(gd3.iloc[[0]])
+    gd5 = pd.concat([gd0, gd4])
+    return sum(gd5['score'])
+
+def rFilter(df,col, condition):
+    return df[df[col]==condition]
+
+def rSort(df, col, boolean):
+    return df.sort_values(by=[col], ascending = boolean)
+
+def rRows(df, single_row_index):
+    return pd.DataFrame(df.iloc[[single_row_index]])
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/oneClick.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/oneClick.py
new file mode 100644
index 00000000..880cada0
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/oneClick.py
@@ -0,0 +1,65 @@
+from helper import *
+from configuration import *
+import pandas as pd
+import datetime
+import re
+
+
+
+# read pandas data from tracker into environment
+df_4 = pd.read_csv(tracker_data + 'tracker_output/final_4.csv')
+df_5 = pd.read_csv(tracker_data + 'tracker_output/final_5.csv')
+
+# checks columns is same
+set1 = set(df_5.columns)
+set2 = set(df_4.columns)
+
+# confirms columns are similar
+diff1 = set1 - set2
+diff2 = set2 - set1
+print(diff1 == set())
+print(diff2 == set())
+
+df_tracker = pd.DataFrame(df_4.append(df_5))
+
+now = datetime.datetime.now()
+date_string = str(now.year) + '_' + str(now.month) + '_' + str(now.day) + '_' + str(now.hour) + '_' + str(now.second) + '_'
+out_file = tracker_data + 'tracker_output/' + date_string + 'tracker_data_join.csv'  # Fill in with path to which csv output will be saved
+
+#df_tracker.to_csv(out_file)
+
+# join df_tracker with database joins (sim_patient joins on patient and sim_patient_id):
+
+'''
+$sim_patient_state
+> base_table['sim_patient']
+$sim_patient
+   sim_patient_id                         name age_years gender
+1              23 (User3) Hematemesis, Alcohol        59   Male
+
+'''
+
+df_tracker_merge = df_tracker.merge(sim_patient, left_on='patient', right_on='sim_patient_id')
+
+
+# join df_tracker to user
+'''
+$sim_user
+   sim_user_id            name
+1            0    Default User
+2            1   Jonathan Chen
+3
+
+'''
+
+df_tracker_merge2 = df_tracker_merge.merge(sim_user, left_on='user', right_on='sim_user_id')
+
+dx = df_tracker_merge2.drop_duplicates()
+
+#print(df_tracker_merge2)
+
+dx.to_csv(out_file)
+
+
+#print(re.split('_ , _', dx.name_x))
+#
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/readme.md b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/readme.md
new file mode 100644
index 00000000..6d517a45
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/readme.md
@@ -0,0 +1,11 @@
+# To Run Configuration File:
+
+1) go to 'configuration.py'
+  - a) set recommender_path to your box sync folder: clinical_recommender_pipeline
+  - b) set database connection for recommender
+2) run script 'python clinicalRecommenderTests.py' (unit testing for folders)
+3) run script 'python tracker_script_v4.py'
+4) run script 'python tracker_script_v5.py'
+5) go to 'oneClick.py'
+  - a) set path to tracker_script_results for v4 and v5 (subsequent testing would all be v5)
+6) run script 'python oneClick.py'
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tables_figures.R b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tables_figures.R
new file mode 100644
index 00000000..d24f9a14
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tables_figures.R
@@ -0,0 +1,418 @@
+library(data.table)
+library(RPostgreSQL)
+library(DBI)
+library(tidyverse)
+library(dplyr)
+library(xlsx)
+
+# FUNCTIONS: AGGREGATE 
+
+# database driver connection
+drv <- dbDriver("PostgreSQL")
+
+# connection to database
+con <- dbConnect(drv, dbname = "stride_inpatient_2014",
+                 host = "localhost", port = 5432,
+                 user = "postgres", password = "postgres")
+
+# lists all table names 
+# assumes connection is called con 
+table.names <- dbListTables(con)
+
+# function to apply the function of all tables 
+list_table_head <- function(table_name){
+  sql <- paste("select * from ", table_name, "limit 10")
+  return(dbGetQuery(con, sql))
+}
+
+
+# creates the first 10 rows of data for each datatable in DB 
+base_table <- lapply(table.names, list_table_head)
+names(base_table) <- table.names
+library(dplyr)
+base_table_colnames <- lapply(base_table, colnames)
+names(base_table_colnames) <- table.names
+
+
+# -------------------------------------------------------------------------------
+# to do : 
+#         Feature: Generate Grading Scheme 
+#             1) help visualize processes 
+#             2) introduce best grading schemes for each case 
+#             3) create a list of common errors seen 
+#            
+#             4) clean up exploratory analysis 
+#             5) convert to python module 
+#
+#
+# 
+# ---------------------------------------------------------------------------------
+
+# makes connections to tables 
+
+adf <- function(df){
+  return(as.data.frame(df))
+}
+
+clinical_item <- tbl(con, "clinical_item")
+sim_patient_order <- tbl(con, "sim_patient_order")
+sim_state_transition <- tbl(con, "sim_state_transition")
+sim_state <- adf(tbl(con,"sim_state"))
+sim_user <- tbl(con,"sim_user")
+sim_patient_state <- adf(tbl(con,"sim_patient_state"))
+sim_result <- adf(tbl(con,"sim_result"))
+
+sim_tracker_result_join <- list(sim_result, 
+                                sim_state,
+                                sim_patient_state)
+
+sim_tracker_result <- merge(sim_state, sim_patient_state, by = 'sim_state_id')
+
+# TRACKER DF 
+tdx <- read.csv('/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/tracker_data/tracker_output/2019_7_31_18_47_tracker_data_join.csv')
+path <- setwd()
+setwd('/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/tracker_data/tracker_output/')
+
+tracker_df <- merge(sim_tracker_result, tdx, by.x = 'sim_patient_id', by.y = 'patient' )
+
+afib_states <- c("Afib-RVR Initial", 
+                 "Afib-RVR Stabilized" ,
+                 "Afib-RVR Worse" )
+
+#--------------------------------------------------------------------------------
+# meningitis 
+#--------------------------------------------------------------------------------
+# "Mening Active"                           
+# "Meningitis Adequately Treated"           
+# "Meningits Worsens"
+#--------------------------------------------------------------------------------
+
+mening_states <- c( "Mening Active",                            
+                    "Meningitis Adequately Treated",          
+                    "Meningits Worsens")
+
+# -------------------------------------------------------------------------------
+# pulmonary embolism 
+# -------------------------------------------------------------------------------
+# "PE-COPD-LungCA"                          
+# "PE-COPD-LungCA + Anticoagulation"        
+# "PE-COPD-LungCA + O2"                     
+# "PE-COPD-LungCA + O2 + Anticoagulation" 
+# -------------------------------------------------------------------------------
+
+pulmonary_emolism_states <- c( "PE-COPD-LungCA",                         
+                               "PE-COPD-LungCA + Anticoagulation",      
+                               "PE-COPD-LungCA + O2",                 
+                               "PE-COPD-LungCA + O2 + Anticoagulation")
+
+# -------------------------------------------------------------------------------
+# neutropenic fever 
+# -------------------------------------------------------------------------------
+#  "Neutropenic Fever Treated with IVF"      
+#  "Neutropenic Fever Treated with IVF + ABX"
+#  "Neutropenic Fever v2"                    
+#  "NFever"  
+# -------------------------------------------------------------------------------
+
+neutropenic_fever_states <- c("Neutropenic Fever Treated with IVF",
+                              "Neutropenic Fever Treated with IVF + ABX",
+                              "Neutropenic Fever v2")
+
+# -------------------------------------------------------------------------------
+# GIBLEED 
+# -------------------------------------------------------------------------------
+# "EtOH-GIBleed Active"                     
+# "EtOH-GIBleed Bleeding Out"               
+# "EtOH-GIBleed Coag Stabilized"            
+# "EtOH-GIBleed Post-EGD"   
+# -------------------------------------------------------------------------------
+gi_bleed_states <- c( "EtOH-GIBleed Active",                     
+                      "EtOH-GIBleed Bleeding Out",               
+                      "EtOH-GIBleed Coag Stabilized",            
+                      "EtOH-GIBleed Post-EGD" )
+
+# -------------------------------------------------------------------------------
+# DKA 
+# -------------------------------------------------------------------------------
+# "DKA Euglycemic"                          
+# "DKA Hyperglycemic"                       
+# "DKA Onset"
+# -------------------------------------------------------------------------------
+dka_states <- c("DKA Euglycemic" ,                          
+                "DKA Hyperglycemic" ,                       
+                "DKA Onset")
+
+# write a function to modularize the above 
+# expects remerged order
+state_split <- function(state_names, df){
+  return(df %>% filter(name %in% state_names))
+} 
+
+list_of_states <- list(gi_bleed_states, 
+                       mening_states, 
+                       pulmonary_emolism_states, 
+                       afib_states,
+                       neutropenic_fever_states)
+
+state_dataframe_split <- lapply(list_of_states, state_split, df = tracker_df)
+remerged_order <- tracker_df
+gi_test                  <- state_split(gi_bleed_states, remerged_order)
+mening_test              <- state_split(mening_states, remerged_order)
+pulmonary_embolism_test  <- state_split(pulmonary_emolism_states, remerged_order)
+afib_test                <- state_split(afib_states, remerged_order)
+neutropenic_test         <- state_split(neutropenic_fever_states, remerged_order)
+
+gi_test$case                 <- "gi_bleed"
+mening_test$case             <- "meningitis"
+pulmonary_embolism_test$case <- "pulmonary_embolism"
+afib_test$case               <- "atrial_fibrillation"
+neutropenic_test$case        <- "neutropenic"
+
+df_grading_pre <- rbind(gi_test, 
+                        mening_test,
+                        pulmonary_embolism_test, 
+                        afib_test, 
+                        neutropenic_test)
+df_grading  <- df_grading_pre %>% select(sim_state_id, 
+                                         clinical_item_id, 
+                                         sim_user_id, 
+                                         sim_patient_id, 
+                                         description.x, 
+                                         name.x, 
+                                         description.x, 
+                                         description.y, 
+                                         case)
+
+
+#View(df_grading_pre)
+
+#grading_data3$description <- grading_data3$`unique(df$description.y)`
+#test <-  merge(grading_data3, clinical_item, by = 'description')
+setwd("/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/tracker_data/tracker_output/")
+
+df_tracker <- read.csv('tracker_output_v10.csv')
+
+tg <- df_tracker %>% filter(!name_y %in% c('LisaShieh', 'Default User'))
+
+write.csv(tg, 'tracker_output_v11.csv')
+
+tg$ratioOrderFromRecommender <- (tg$orders_from_recommender/tg$unqique_recommended_options)
+tg$ratioOrderFromManualSearchVsManualSearchOptions <- (tg$orders_from_manual_search/tg$manual_search_options)
+
+tg_case_split <- split(tg, tg$sim_patient_id)
+
+take_first_row <- function(x){
+  return(x[1,])
+}
+
+first_case <- bind_rows(lapply(tg_case_split, take_first_row))
+#View(first_case)
+case_split <- split(first_case, first_case$case)
+
+
+#unique(case_split$atrial_fibrillation$name_y)
+#afib <- case_split$atrial_fibrillation %>% filter(!name_y %in% 'LisaShieh')
+#gi_bleed <- case_split$gi_bleed %>% filter(!name_y %in% 'LisaShieh')
+#meningitis <- case_split$meningitis %>% filter(!name_y %in% 'LisaShieh')
+#neutropenic <- case_split$neutropenic %>% filter(!name_y %in% 'LisaShieh')
+#pulmonary_embolism <- case_split$pulmonary_embolism %>% filter(!name_y %in% 'LisaShieh')
+#afib$elapsed_time_secs <- as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,2])) * 60 +  as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,3]))
+
+clean_elapsed_time <- function(df){
+  afib <- df
+  afib$elapsed_time_secs <- as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,2])) * 60 +  as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,3]))
+  return(afib)
+}
+
+case_split_elapsed_time <- lapply(case_split, clean_elapsed_time)
+case_split_elapsed_time$atrial_fibrillation
+
+#summary(case_split_elapsed_time[[1]]$elapsed_time_secs)
+
+
+elapsed_summary_recommender_on <- function(x){
+  x2 <- x %>% filter(recommended_options > 0 )
+  return((x2$elapsed_time_secs))
+}
+
+elapsed_summary_recommender_off <- function(x){
+  x2 <- x %>% filter(recommended_options == 0 )
+  return(summary(x2$elapsed_time_secs))
+}
+
+
+total_orders_recommender_on <- function(x){
+  x2 <- x %>% filter(recommended_options > 0 )
+  iqr(x2$total_orders)
+}  
+
+
+total_orders_recommender_off <- function(x){
+  x2 <- x %>% filter(recommended_options == 0 )
+  return(summary(x2$total_orders))
+} 
+
+
+# TODO 
+total_orders_recommender_on_off_iqr <- function(x){
+  #  t(summary(mtcars$mpg))[[1]] 
+  x2 <- x %>% filter(recommended_options == 0 )
+  iqr_25 <- t(summary(x2$e))[[2]]
+  iqr_median <- t(summary(x2$e))[[3]]
+  iqr_75 <- t(summary(x2$e))[[4]]
+  cbind(iqr)
+} 
+
+lapply(case_split_elapsed_time, total_orders_recommender_off_iqr)
+
+mean_elapsed_time_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$elapsed_time_secs), mean(x.off$elapsed_time_secs))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}
+
+mean_total_orders_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$total_orders), mean(x.off$total_orders))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+summary_total_orders_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t = summary(x.on$total_orders)
+  return(t)
+}
+
+lapply()
+
+mean_total_num_clicks_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$total_num_clicks), mean(x.off$total_num_clicks))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+
+
+mean_total_manual_search_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$orders_from_manual_search), mean(x.off$orders_from_manual_search))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+ttest_total_manual_search_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- t.test(x.on$orders_from_manual_search, x.off$orders_from_manual_search)
+  return(t)
+}  
+
+mean_total_recommender_missed_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$orders_from_recommender_missed), mean(x.off$orders_from_recommender_missed))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+ttest_total_recommender_missed_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- t.test(x.on$orders_from_recommender_missed, x.off$orders_from_recommender_missed)
+  #colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+
+
+
+ttest_total_recommender_missed_list  <- lapply(case_split_elapsed_time, ttest_total_recommender_missed_recommender_on_off )
+ttest_total_manual_search_list  <- lapply(case_split_elapsed_time, ttest_total_manual_search_recommender_on_off) 
+
+
+mean_elapsed_time_list  <- lapply(case_split_elapsed_time, mean_elapsed_time_recommender_on_off )
+mean_total_orders_list <- lapply(case_split_elapsed_time, mean_total_orders_recommender_on_off )
+mean_total_recommender_missed_list  <- lapply(case_split_elapsed_time, mean_total_recommender_missed_recommender_on_off )
+mean_total_manual_search_recommender_list <- lapply(case_split_elapsed_time, mean_total_manual_search_recommender_on_off)
+mean_total_num_clicks_list <- lapply(case_split_elapsed_time, mean_total_num_clicks_recommender_on_off)
+
+
+View(case_split_elapsed_time)
+
+cases_elapsed <- names(mean_elapsed_time_list)
+cases_total_orders <- names(mean_elapsed_time_list)
+cases_total_recommender_missed <- names(mean_total_manual_search_recommender_list)
+cases_total_manual_search_recommener_list <- names(mean_total_manual_search_recommender_list)
+cases_total_num_clicks_list <- names(mean_total_num_clicks_list)
+
+elapsed_time <- as.data.frame(cbind(t(bind_rows(mean_elapsed_time_list))))
+colnames(elapsed_time) <- c('on', 'off')
+elapsed_time$metric <- 'elapsed_time'
+elapsed_time$case <- rownames(elapsed_time)
+
+total_orders <- as.data.frame(cbind(t(bind_rows(mean_total_orders_list))))
+colnames(total_orders) <- c('on', 'off')
+total_orders$metric <- 'total_number_of_orders'
+total_orders$case <- rownames(total_orders)
+
+total_recommender_missed <- as.data.frame(cbind(t(bind_rows(mean_total_recommender_missed_list))))
+colnames(total_recommender_missed) <- c('on', 'off')
+total_recommender_missed$metric <- 'total_number_recommender_missed'
+total_recommender_missed$case <- rownames(total_recommender_missed)
+
+total_manual_search <- as.data.frame(cbind(t(bind_rows(mean_total_manual_search_recommender_list))))
+colnames(total_manual_search) <- c('on', 'off')
+total_manual_search$metric <- 'orders_from_manual_search'
+total_manual_search$case <- rownames(total_manual_search)
+
+total_num_clicks<- as.data.frame(cbind(t(bind_rows(mean_total_num_clicks_list))))
+colnames(total_num_clicks) <- c('on', 'off')
+total_num_clicks$metric <- 'total_number_clicks'
+total_num_clicks$case <- rownames(total_num_clicks)
+
+
+
+
+setwd("/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/")
+
+table3v2<- rbind(elapsed_time, 
+          total_orders, 
+          total_recommender_missed, 
+          total_manual_search, 
+          total_num_clicks)
+table3v3 <- table3v2
+table3v3$on  <- round(table3v2$on,2)
+table3v3$off <- round(table3v2$off,2)
+
+#View(table3v3)
+
+#write.csv(table3v3, 'table3v3.csv')
+
+#lapply(case_split_elapsed_time, elapsed_summary_recommender_off)
+#lapply(case_split_elapsed_time, elapsed_summary_recommender_on)
+#lapply(case_split_elapsed_time, total_orders_recommender_on)
+#lapply(case_split_elapsed_time, total_orders_recommender_off)
+
+library(testthat)
+#assertthat::are_equal()
+
+setwd("/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/")
+
+load('case_split_elapsed_time.rda')
+
+View(case_split_elapsed_time)
+case_split_elapsed_time$atrial_fibrillation
+
+
+
+spo <- adf(sim_patient_order)
+table_of_orders <- table(spo$sim_patient_order_id)
+View(table_of_orders)
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tables_figures_part2.R b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tables_figures_part2.R
new file mode 100644
index 00000000..3b451829
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tables_figures_part2.R
@@ -0,0 +1,303 @@
+library(data.table)
+library(RPostgreSQL)
+library(DBI)
+library(tidyverse)
+library(dplyr)
+library(xlsx)
+
+setwd("/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/tracker_data/tracker_output/")
+tg <-  read.csv('tracker_output_v11.csv')
+#df_tracker <- read.csv('tracker_output_v10.csv')
+#tg <- df_tracker %>% filter(!name_y %in% c('LisaShieh', 'Default User'))
+#write.csv(tg, 'tracker_output_v11.csv')
+
+tg$ratioOrderFromRecommender <- (tg$orders_from_recommender/tg$unqique_recommended_options)
+tg$ratioOrderFromManualSearchVsManualSearchOptions <- (tg$orders_from_manual_search/tg$manual_search_options)
+
+tg_case_split <- split(tg, tg$sim_patient_id)
+
+take_first_row <- function(x){
+  return(x[1,])
+}
+
+first_case <- bind_rows(lapply(tg_case_split, take_first_row))
+#View(first_case)
+case_split <- split(first_case, first_case$case)
+
+
+#unique(case_split$atrial_fibrillation$name_y)
+#afib <- case_split$atrial_fibrillation %>% filter(!name_y %in% 'LisaShieh')
+#gi_bleed <- case_split$gi_bleed %>% filter(!name_y %in% 'LisaShieh')
+#meningitis <- case_split$meningitis %>% filter(!name_y %in% 'LisaShieh')
+#neutropenic <- case_split$neutropenic %>% filter(!name_y %in% 'LisaShieh')
+#pulmonary_embolism <- case_split$pulmonary_embolism %>% filter(!name_y %in% 'LisaShieh')
+#afib$elapsed_time_secs <- as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,2])) * 60 +  as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,3]))
+
+clean_elapsed_time <- function(df){
+  afib <- df
+  afib$elapsed_time_secs <- as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,2])) * 60 +  as.numeric(as.character(stringr::str_split_fixed(afib$elapsed_time, ':',3)[,3]))
+  return(afib)
+}
+
+case_split_elapsed_time <- lapply(case_split, clean_elapsed_time)
+case_split_elapsed_time$atrial_fibrillation
+
+# summary(case_split_elapsed_time[[1]]$elapsed_time_secs)
+# IQR all columns: 
+#apply(counts, 2, max)
+# return  IQR  for all columns:  
+
+
+
+# -------------------------------------------------------------------------------------------------- #
+#
+#
+#                                 TODO 
+#
+#
+#
+# --------------------------------------------------------------------------------------------------
+
+case_table_figures <- function(tg){
+
+
+    x.off <- tg %>% filter(recommended_options == 0 )
+    x.on  <- tg %>% filter(recommended_options > 0 )
+    
+    iqr_column <- function(column_df, colname, on_off){
+      
+      # RECOMMENDER OFF
+      iqr_25 <- t(summary(column_df))[[2]]
+      iqr_median <- t(summary(column_df))[[3]]
+      iqr_75 <- t(summary(column_df))[[4]]
+      
+      x_iqr  <- cbind(on_off, colname, round(iqr_25, 2) , round(iqr_median,2), round(iqr_75,2) )
+      
+      # RECOMMENDER ON
+      #x.on_iqr_25 <- t(summary(x.on$column_df))[[2]]
+      #x.on_iqr_median <- t(summary(x.on$column_df))[[3]]
+      #x.on_iqr_75 <- t(summary(x.on$column_df))[[4]]
+      return(x_iqr)
+    } 
+    
+    #  off  
+    #  on 
+    
+    
+    
+    # num_note_clicks 
+    off_num_note_clicks <- iqr_column(x.off$num_note_clicks, 'num_note_clicks', 'off')
+    on_num_note_clicks <- iqr_column(x.on$num_note_clicks, 'num_note_clicks', 'on')
+    
+    # "num_results_review_clicks"    
+    off_num_results_review_clicks <- iqr_column(x.off$num_results_review_clicks, 'num_results_review_clicks', 'off')
+    on_num_results_review_clicks <- iqr_column(x.on$num_results_review_clicks, 'num_results_review_clicks', 'on')
+    
+    
+    # "recommended_options" 
+    off_recommended_options <- iqr_column(x.off$recommended_options, 'recommended_options', 'off')
+    on_recommended_options <- iqr_column(x.on$recommended_options, 'recommended_options', 'on')
+    
+    # unqique_recommended_options" 
+    off_unqique_recommended_options <- iqr_column(x.off$unqique_recommended_options, 'unique_recommended_options', 'off')
+    on_unqique_recommended_options <- iqr_column(x.on$unqique_recommended_options, 'unique_recommended_options', 'on')
+    
+    
+    # "manual_search_options"        
+    off_manual_search_options <- iqr_column(x.off$manual_search_options, 'manual_search_options', 'off')
+    on_manual_search_options <- iqr_column(x.on$manual_search_options, 'manual_search_options', 'on')
+    
+    # "total_orders"    
+    off_total_orders <- iqr_column(x.off$total_orders, 'total_orders', 'off')
+    on_total_orders <- iqr_column(x.on$total_orders, 'total_orders', 'on')
+    
+    # "orders_from_recommender"       
+    off_orders_from_recommender <- iqr_column(x.off$orders_from_recommender, 'orders_from_recommender', 'off')
+    on_orders_from_recommender <- iqr_column(x.on$orders_from_recommender, 'orders_from_recommender', 'on')
+    
+    # "orders_from_manual_search"
+    off_orders_from_manual_search <- iqr_column(x.off$orders_from_manual_search, 'orders_from_manual_search', 'off')
+    on_orders_from_manual_search <- iqr_column(x.on$orders_from_manual_search, 'orders_from_manual_search', 'on')
+    
+    # "orders_from_recommender_missed" 
+    off_orders_from_recommender_missed <- iqr_column(x.off$orders_from_recommender_missed, 'orders_from_recommender_missed', 'off')
+    on_orders_from_recommender_missed <- iqr_column(x.on$orders_from_recommender_missed, 'orders_from_recommender_missed', 'on')
+    
+    case  <-  as.data.frame(rbind(off_manual_search_options , on_manual_search_options, off_num_note_clicks, 
+            on_num_note_clicks, 
+            off_num_results_review_clicks, 
+            on_num_results_review_clicks, 
+            off_orders_from_manual_search, 
+            on_orders_from_manual_search, 
+            off_orders_from_recommender,  
+            on_orders_from_recommender, 
+            off_orders_from_recommender_missed, 
+            on_orders_from_recommender_missed, 
+            off_recommended_options,
+            on_recommended_options, 
+            off_total_orders, 
+            on_total_orders, 
+            off_unqique_recommended_options, 
+            on_unqique_recommended_options))
+    colnames(case) <- c("recommender status", 'feature', 'iqr_25', 'median', 'iqr_75')                          
+    return(case)               
+                    
+}                  
+       
+case_split <- split(tg, tg$case)               
+lapply(case_split, case_table_figures)    
+                         
+                               
+
+
+
+mean_elapsed_time_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$elapsed_time_secs), mean(x.off$elapsed_time_secs))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}
+
+mean_total_orders_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$total_orders), mean(x.off$total_orders))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+summary_total_orders_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t = summary(x.on$total_orders)
+  return(t)
+}
+
+
+mean_total_num_clicks_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$total_num_clicks), mean(x.off$total_num_clicks))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+
+
+mean_total_manual_search_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$orders_from_manual_search), mean(x.off$orders_from_manual_search))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+ttest_total_manual_search_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- t.test(x.on$orders_from_manual_search, x.off$orders_from_manual_search)
+  return(t)
+}  
+
+mean_total_recommender_missed_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- cbind(mean(x.on$orders_from_recommender_missed), mean(x.off$orders_from_recommender_missed))
+  colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+ttest_total_recommender_missed_recommender_on_off <- function(x){
+  x.on <- x %>% filter(recommended_options > 0 )
+  x.off <- x %>% filter(recommended_options == 0 )
+  t <- t.test(x.on$orders_from_recommender_missed, x.off$orders_from_recommender_missed)
+  #colnames(t) <- c('on', 'off')
+  return(t)
+}  
+
+
+
+
+ttest_total_recommender_missed_list  <- lapply(case_split_elapsed_time, ttest_total_recommender_missed_recommender_on_off )
+ttest_total_manual_search_list  <- lapply(case_split_elapsed_time, ttest_total_manual_search_recommender_on_off) 
+
+
+mean_elapsed_time_list  <- lapply(case_split_elapsed_time, mean_elapsed_time_recommender_on_off )
+mean_total_orders_list <- lapply(case_split_elapsed_time, mean_total_orders_recommender_on_off )
+mean_total_recommender_missed_list  <- lapply(case_split_elapsed_time, mean_total_recommender_missed_recommender_on_off )
+mean_total_manual_search_recommender_list <- lapply(case_split_elapsed_time, mean_total_manual_search_recommender_on_off)
+mean_total_num_clicks_list <- lapply(case_split_elapsed_time, mean_total_num_clicks_recommender_on_off)
+
+
+View(case_split_elapsed_time)
+
+cases_elapsed <- names(mean_elapsed_time_list)
+cases_total_orders <- names(mean_elapsed_time_list)
+cases_total_recommender_missed <- names(mean_total_manual_search_recommender_list)
+cases_total_manual_search_recommener_list <- names(mean_total_manual_search_recommender_list)
+cases_total_num_clicks_list <- names(mean_total_num_clicks_list)
+
+elapsed_time <- as.data.frame(cbind(t(bind_rows(mean_elapsed_time_list))))
+colnames(elapsed_time) <- c('on', 'off')
+elapsed_time$metric <- 'elapsed_time'
+elapsed_time$case <- rownames(elapsed_time)
+
+total_orders <- as.data.frame(cbind(t(bind_rows(mean_total_orders_list))))
+colnames(total_orders) <- c('on', 'off')
+total_orders$metric <- 'total_number_of_orders'
+total_orders$case <- rownames(total_orders)
+
+total_recommender_missed <- as.data.frame(cbind(t(bind_rows(mean_total_recommender_missed_list))))
+colnames(total_recommender_missed) <- c('on', 'off')
+total_recommender_missed$metric <- 'total_number_recommender_missed'
+total_recommender_missed$case <- rownames(total_recommender_missed)
+
+total_manual_search <- as.data.frame(cbind(t(bind_rows(mean_total_manual_search_recommender_list))))
+colnames(total_manual_search) <- c('on', 'off')
+total_manual_search$metric <- 'orders_from_manual_search'
+total_manual_search$case <- rownames(total_manual_search)
+
+total_num_clicks<- as.data.frame(cbind(t(bind_rows(mean_total_num_clicks_list))))
+colnames(total_num_clicks) <- c('on', 'off')
+total_num_clicks$metric <- 'total_number_clicks'
+total_num_clicks$case <- rownames(total_num_clicks)
+
+
+
+
+setwd("/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/")
+
+table3v2<- rbind(elapsed_time, 
+          total_orders, 
+          total_recommender_missed, 
+          total_manual_search, 
+          total_num_clicks)
+table3v3 <- table3v2
+table3v3$on  <- round(table3v2$on,2)
+table3v3$off <- round(table3v2$off,2)
+
+#View(table3v3)
+
+#write.csv(table3v3, 'table3v3.csv')
+
+#lapply(case_split_elapsed_time, elapsed_summary_recommender_off)
+#lapply(case_split_elapsed_time, elapsed_summary_recommender_on)
+#lapply(case_split_elapsed_time, total_orders_recommender_on)
+#lapply(case_split_elapsed_time, total_orders_recommender_off)
+
+library(testthat)
+#assertthat::are_equal()
+
+setwd("/Users/jonc101/Box Sync/jichiang_folders/clinical_recommender_pipeline/")
+
+load('case_split_elapsed_time.rda')
+
+View(case_split_elapsed_time)
+case_split_elapsed_time$atrial_fibrillation
+
+
+
+spo <- adf(sim_patient_order)
+table_of_orders <- table(spo$sim_patient_order_id)
+#View(table_of_orders)
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script.py
new file mode 100644
index 00000000..8d17b3b8
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script.py
@@ -0,0 +1,15 @@
+import pandas as pd
+import datetime
+from CPOETrackerAnalysis import aggregate_simulation_data
+from configuration import *
+
+# setting date when saving
+now = datetime.datetime.now()
+date_string = str(now.year) + '_' + str(now.month) + '_' + str(now.day) + '_'
+
+out_file = tracker_data + 'tracker_output/' + date_string + 'tracker_data.csv'  # Fill in with path to which csv output will be saved
+data_home = tracker_data + 'v4_data'  # Fill in with path to directory containing v4 json files
+
+# Run aggregator
+aggregate_simulation_data(data_home, output_path=out_file)
+print(date_string)
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script_v4.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script_v4.py
new file mode 100644
index 00000000..707f277d
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script_v4.py
@@ -0,0 +1,15 @@
+import pandas as pd
+import datetime
+from CPOETrackerAnalysis import aggregate_simulation_data
+from configuration import *
+
+# setting date when saving
+now = datetime.datetime.now()
+date_string = str(now.year) + '_' + str(now.month) + '_' + str(now.day) + '_' + str(now.hour) + '_' + str(now.second) + '_'
+
+out_file = tracker_data + 'tracker_output/' + date_string + 'tracker_data_4.csv'  # Fill in with path to which csv output will be saved
+data_home = tracker_data + 'v4_data'  # Fill in with path to directory containing v4 json files
+
+# Run aggregator
+aggregate_simulation_data(data_home, output_path=out_file)
+print(date_string)
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script_v5.py b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script_v5.py
new file mode 100644
index 00000000..b3e99a42
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/tracker_script_v5.py
@@ -0,0 +1,15 @@
+import pandas as pd
+import datetime
+from CPOETrackerAnalysis import aggregate_simulation_data
+from configuration import *
+
+# setting date when saving
+now = datetime.datetime.now()
+date_string = str(now.year) + '_' + str(now.month) + '_' + str(now.day) + '_' + str(now.hour) + '_' + str(now.second) + '_'  
+
+out_file = tracker_data + 'tracker_output/' + date_string + 'tracker_data_5.csv'  # Fill in with path to which csv output will be saved
+data_home = tracker_data + 'v5_data'  # Fill in with path to directory containing v4 json files
+
+# Run aggregator
+aggregate_simulation_data(data_home, output_path=out_file)
+print(date_string)
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/unique_grading.R b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/unique_grading.R
new file mode 100644
index 00000000..7da09d5d
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/UnitTests/unique_grading.R
@@ -0,0 +1,141 @@
+library(data.table)
+library(RPostgreSQL)
+library(DBI)
+library(tidyverse)
+library(dplyr)
+library(xlsx)
+
+# FUNCTIONS: AGGREGATE 
+
+# database driver connection
+drv <- dbDriver("PostgreSQL")
+
+# connection to database
+con <- dbConnect(drv, dbname = "stride_inpatient_2014",
+                 host = "localhost", port = 5432,
+                 user = "postgres", password = "")
+
+adf <- function(df){
+  return(as.data.frame(df))
+}
+
+sim_patient_order <- tbl(con, "sim_patient_order")
+spo <-  adf(sim_patient_order)
+
+clinical_item <- tbl(con, "clinical_item")
+ci <-  adf(clinical_item)
+
+sim_state <- tbl(con, "sim_state")
+ss <-  adf(sim_state)
+
+
+# split on sim_state_id  
+
+
+# generate unique sim_state_order 
+
+split_unique_orders <- function(x){
+  clinical_item_id <-  as.data.frame(unique(x$clinical_item_id))
+  colnames(clinical_item_id) <- 'clinical_item_id'
+  return(clinical_item_id)
+}
+
+
+
+join_unique_orders_name <- function(x){
+# ASSUMES ci is clinical_item_table 
+  y <- ci %>% select(clinical_item_id, description)
+  return(merge(x, y, by = 'clinical_item_id' ))
+}
+
+return_unique_orders <- function(x){
+  sim_state_split <- split(x, x$sim_state_id)
+  list_of_unique_orders <- lapply(sim_state_split, split_unique_orders)
+  order_description <- lapply(list_of_unique_orders, join_unique_orders_name)
+  name_vector_order  <- names(order_description)
+  ndf <- adf(name_vector_order)
+  colnames(ndf)  <-  'sim_state_id'
+  state_df <- merge(ndf,sim_state, by =  'sim_state_id')
+  names(name_vector_order)  <-  state_df$description
+  return(ndf)
+  }
+
+return_unique_orders <- function(x){
+  sim_state_split <- split(x, x$sim_state_id)
+  list_of_unique_orders <- lapply(sim_state_split, split_unique_orders)
+  order_description <- lapply(list_of_unique_orders, join_unique_orders_name)
+  clinical_state_orders <-  bind_rows(order_description)
+  #name_vector_order  <- names(order_description)
+  #ndf <- adf(name_vector_order)
+  #colnames(ndf)  <-  'sim_state_id'
+  #state_df <- merge(ndf,sim_state, by =  'sim_state_id')
+  #names(name_vector_order)  <-  state_df$description
+  return(clinical_state_orders)
+}
+
+
+mening_state_id <- c(30,31,32)
+pulmonary_emolism_state_id <- c( 10, 11,12,8)
+neutropenic_fever_state_id <- c(5000,5001,5002,5003)
+gi_bleed_state_id <- c(14,15,16,17)
+afib_state_id <- c(40,41,43,46,42,45,44)
+
+
+unique_meningitis_30 <- return_unique_orders(spo %>% filter(sim_state_id  == 30))
+unique_meningitis_31 <- return_unique_orders(spo %>% filter(sim_state_id  ==  31))
+unique_meningitis_32 <- return_unique_orders(spo %>% filter(sim_state_id  == 32))
+
+unique_meningitis_30$sim_state_id <- 30
+unique_meningitis_31$sim_state_id <- 31
+unique_meningitis_32$sim_state_id <- 32
+
+
+unique_pe_10 <- return_unique_orders(spo %>% filter(sim_state_id  == 10))
+unique_pe_11 <- return_unique_orders(spo %>% filter(sim_state_id  == 11))
+unique_pe_12 <- return_unique_orders(spo %>% filter(sim_state_id  == 12))
+unique_pe_8 <- return_unique_orders(spo %>% filter(sim_state_id  == 8))
+
+unique_pe_8$sim_state_id  <- 8
+unique_pe_10$sim_state_id  <- 10
+unique_pe_11$sim_state_id  <- 11
+unique_pe_12$sim_state_id  <- 12
+
+unique_neutropenic_5000 <- return_unique_orders(spo %>% filter(sim_state_id  == 5000))
+unique_neutropenic_5001 <- return_unique_orders(spo %>% filter(sim_state_id  == 5001))
+unique_neutropenic_5002 <- return_unique_orders(spo %>% filter(sim_state_id  == 5002))
+unique_neutropenic_5003 <- return_unique_orders(spo %>% filter(sim_state_id  == 5003))
+
+unique_neutropenic_5000$sim_state_id  <- 5000
+unique_neutropenic_5001$sim_state_id  <- 5001
+unique_neutropenic_5002$sim_state_id  <- 5002
+unique_neutropenic_5003$sim_state_id  <- 5003
+
+unique_gi_14 <- return_unique_orders(spo %>% filter(sim_state_id  == 14))
+unique_gi_15 <- return_unique_orders(spo %>% filter(sim_state_id  == 15))
+unique_gi_16 <- return_unique_orders(spo %>% filter(sim_state_id  == 16))
+unique_gi_17 <- return_unique_orders(spo %>% filter(sim_state_id  == 17))
+
+unique_gi_14$sim_state_id  <- 14
+unique_gi_15$sim_state_id  <- 15
+unique_gi_16$sim_state_id  <- 16
+unique_gi_17$sim_state_id  <- 17
+
+
+
+unique_afib_40 <- return_unique_orders(spo %>% filter(sim_state_id  == 40))
+unique_afib_41 <- return_unique_orders(spo %>% filter(sim_state_id  == 41))
+unique_afib_42 <- return_unique_orders(spo %>% filter(sim_state_id  == 42))
+unique_afib_43 <- return_unique_orders(spo %>% filter(sim_state_id  == 43))
+unique_afib_44 <- return_unique_orders(spo %>% filter(sim_state_id  == 44))
+unique_afib_45 <- return_unique_orders(spo %>% filter(sim_state_id  == 45))
+unique_afib_46 <- return_unique_orders(spo %>% filter(sim_state_id  == 46))
+
+unique_afib_40$sim_state_id  <- 40
+unique_afib_41$sim_state_id  <- 41
+unique_afib_42$sim_state_id  <- 42
+unique_afib_43$sim_state_id  <- 43
+unique_afib_44$sim_state_id  <- 44
+unique_afib_45$sim_state_id  <- 45
+unique_afib_46$sim_state_id  <- 46
+
+
diff --git a/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/readme.md b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/readme.md
new file mode 100644
index 00000000..8d35876f
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/RecommenderPipeline/syncPipeline/readme.md
@@ -0,0 +1,9 @@
+ - [X] Test Connection path to box clinical recommender folder 
+ - [X] Test Database Connection 
+ - [X] Test Clinical Orders from Database 
+ - [ ] Test Physician Grading 
+ - [ ] Test Tracker 
+ - [ ] Test Data Merges (report out missing values on physicians) 
+
+
+
diff --git a/scripts/Archive/SimulationRecommender/UserTestingNotes.txt b/scripts/Archive/SimulationRecommender/UserTestingNotes.txt
new file mode 100644
index 00000000..67696927
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/UserTestingNotes.txt
@@ -0,0 +1,25 @@
+== Prep ==
+[ ] Load consent form for viewing
+[ ] Create test UserID and Templated Patients
+    [ ] Multi-tabs for Survey, then 4 cases
+[ ] Prep screen capture
+[ ] Open signature page on S Note
+
+== Consent / Signature ==
+
+[ ] Allow user to review consent form copies on computer
+[ ] User sign S Note indicating consent to participate in named research study receipt of respective payment
+[ ] Phone e-mail S Note PDF + consent forms under My Documents
+
+== Survey Response Collection ==
+[ ] Google Forms entry, add manual UserX ID
+
+== Testing Protocol ==
+[ ] Startup screen recording
+[ ] Do one sample case with no recommender prompts (DKA)
+  COPD Example Order Set
+[ ] Run through remaining sample cases
+
+== Wrap Up ==
+[ ] Complete Survey
+[ ] Gift card / compensation
diff --git a/scripts/Archive/SimulationRecommender/interview_script.md b/scripts/Archive/SimulationRecommender/interview_script.md
new file mode 100644
index 00000000..667baecd
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/interview_script.md
@@ -0,0 +1,73 @@
+Advertisement Email message approved by IRB:
+"I am looking to pay $195 for one hour of time from MD participants (anyone who has admitted patients to an inpatient general medical service in the past year).  If you are interested, you will participate in a study on the utility of clinical decision support tools integrated into the electronic medical record interface.  Specifically you will be asked to work on a computer terminal simulating an electronic medical record interface and enter orders for a series of virtual patients while being observed by researchers, concluding with a survey.
+
+Please contact Jonathan H. Chen (jonc101@stanford.edu) to arrange a session time if you are interested in participating."
+
+
+
+
+From Sim Ui Testing :
+
+== Prep ==
+- [ ] Load consent form for viewing (**docusign?**)
+- [ ] Create test UserID and Templated Patients (multitab for cases)
+- [ ] Multi-tabs for Survey, then 4 cases (Clean Up Final Survey?)
+- [ ] Prep screen capture (Zoom Meeting Recording)
+- [ ] Open signature page on S Note (**Need Direction**)
+
+== Consent / Signature ==
+
+- [ ] Allow user to review consent form copies on computer (**Find Link to [Consent Form](https://stanfordmedicine.app.box.com/folder/5712396741) **)
+- [ ] User sign S Note indicating consent to participate in named research study receipt of respective payment (**need direction**)
+- [ ] Phone e-mail S Note PDF + consent forms under My Documents ()
+
+== Survey Response Collection ==
+- [ ] Google Forms entry, add manual UserX ID
+
+== Testing Protocol ==
+- [ ] Startup screen recording (zoom)
+- [ ] Do one sample case with no recommender prompts (DKA)
+COPD Example Order Set (Diabetes)
+- [ ] Run through remaining sample cases (**Random Sample 3 cases with 2 cases without**)
+- [ ] **Update Protocol for new cases **
+
+== Wrap Up ==
+- [ ] Complete Survey (**Clean Up Survey**)
+- [ ] Gift card / compensation
+
+**NOTES from Watching Video**
+
+- [ ] This is low stakes: writing the code (nothing broke) you can order whatever you want
+- [ ] Oddities names won't always match
+- [ ] Sodium chloride intravenous (not sodium)
+- [ ] This is all basic functionality not what we are testing on
+- [ ] Get you familiar with the case
+- [ ] Look for things out loud
+- [ ] Now the difference is the point of the study the new functionality that doesn't exist in the EHR
+- [ ] Understand basic structure but this kind of functionality
+- [ ] You can completely ignore or you can
+- [ ] **Are these Stat orders??**
+- [ ] **Do I need to order this?** Like Real Life
+- [ ] Now this is a slightly different thing (remove statistics)
+- [ ] Keep Searching and sign
+- [ ] Probably want to see lab results now
+- [ ] Results review on the left
+- [ ] Meant to be a shell for basic function not the point of the study
+- [ ] you might want to order (dka orderset)
+- [ ] diet NPO
+- [ ] Sodium Chloride
+- [ ] Sense of Basic Functionaliy
+- [ ] Real test and see what happens
+- [ ] Person Orders (everything you did before)
+- [ ] You can talk outloud if you'd like (not realistic behavior)
+- [ ] List is adapting to your orders
+- [ ] Admitting Decision versus ER treatment decision ()
+
+  
+Feel free to ask me any questions about the interface.
+
+-- Begin Recommender Setup: o
+
+-- Wrap Up
+
+Thanks for coming in today, we have a survey for you to follow up with. Once this is completed you will receive your $195 gift card.
diff --git a/scripts/Archive/SimulationRecommender/parser.R b/scripts/Archive/SimulationRecommender/parser.R
new file mode 100644
index 00000000..2952f844
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/parser.R
@@ -0,0 +1,271 @@
+library(data.table)
+library(RPostgreSQL)
+library(DBI)
+library(tidyverse)
+library(dplyr)
+library(xlsx)
+# database driver connection
+drv <- dbDriver("PostgreSQL")
+
+# connection to database
+con <- dbConnect(drv, dbname = "stride_inpatient_2014",
+                 host = "localhost", port = 5432,
+                 user = "postgres", password = "postgres")
+
+
+# lists all table names 
+# assumes connection is called con 
+table.names <- dbListTables(con)
+
+# function to apply the function of all tables 
+list_table_head <- function(table_name){
+  sql <- paste("select * from ", table_name, "limit 10")
+  return(dbGetQuery(con, sql))
+}
+
+# creates the first 10 rows of data for each datatable in DB 
+base_table <- lapply(table.names, list_table_head)
+
+# -------------------------------------------------------------------------------
+# to do : 
+#         Feature: Generate Grading Scheme 
+#             1) help visualize processes 
+#             2) introduce best grading schemes for each case 
+#             3) create a list of common errors seen 
+#            
+#             4) clean up exploratory analysis 
+#             5) convert to python module 
+#
+#
+# 
+# ---------------------------------------------------------------------------------
+
+# makes connections to tables 
+
+clinical_item <- tbl(con, "clinical_item")
+sim_patient_order <- tbl(con, "sim_patient_order")
+sim_state_transition <- tbl(con, "sim_state_transition")
+sim_state <- tbl(con,"sim_state")
+sim_user <- tbl(con,"sim_user")
+
+
+# merges two tables (similar to join in sql)
+# purpose is to capture the description 
+merged_order <- merge(sim_patient_order, 
+                      sim_state, 
+                      by.x="sim_state_id", 
+                      by.y = "sim_state_id")
+
+
+# finds unique clinical item list 
+clinical_items_list <- unique(merged_order$clinical_item_id)
+
+# finds unique sim_state_id's 
+sim_state_list <- unique(merged_order$sim_state_id)
+
+# creates clinical_item order key to reduce merge space: 
+ordered_clinical_item_table <- clinical_item %>% filter(clinical_item_id %in% clinical_items_list)
+
+# joins tables by clinical item id (creates a dataframe that includes clinical descriptions)
+remerged_order <- merge(merged_order, ordered_clinical_item_table, 
+                        by.x="clinical_item_id", 
+                        by.y="clinical_item_id")
+
+# 
+split_state <- split(remerged_order, remerged_order$sim_state_id)
+
+#--------------------------------------------------------------------------------
+# afib 
+#--------------------------------------------------------------------------------
+# "Afib-RVR Initial"                        
+# "Afib-RVR Stabilized"                     
+# "Afib-RVR Worse"
+#--------------------------------------------------------------------------------
+
+afib_states <- c("Afib-RVR Initial", 
+                 "Afib-RVR Stabilized" ,
+                 "Afib-RVR Worse" )
+
+#--------------------------------------------------------------------------------
+# meningitis 
+#--------------------------------------------------------------------------------
+# "Mening Active"                           
+# "Meningitis Adequately Treated"           
+# "Meningits Worsens"
+#--------------------------------------------------------------------------------
+
+mening_states <- c( "Mening Active",                            
+                    "Meningitis Adequately Treated",          
+                    "Meningits Worsens")
+
+# -------------------------------------------------------------------------------
+# pulmonary embolism 
+# -------------------------------------------------------------------------------
+# "PE-COPD-LungCA"                          
+# "PE-COPD-LungCA + Anticoagulation"        
+# "PE-COPD-LungCA + O2"                     
+# "PE-COPD-LungCA + O2 + Anticoagulation" 
+# -------------------------------------------------------------------------------
+
+pulmonary_emolism_states <- c( "PE-COPD-LungCA",                         
+                               "PE-COPD-LungCA + Anticoagulation",      
+                               "PE-COPD-LungCA + O2",                 
+                               "PE-COPD-LungCA + O2 + Anticoagulation")
+
+
+# -------------------------------------------------------------------------------
+# neutropenic fever 
+# -------------------------------------------------------------------------------
+#  "Neutropenic Fever Treated with IVF"      
+#  "Neutropenic Fever Treated with IVF + ABX"
+#  "Neutropenic Fever v2"                    
+#  "NFever"  
+# -------------------------------------------------------------------------------
+
+neutropenic_fever_states <- c("Neutropenic Fever Treated with IVF",
+                              "Neutropenic Fever Treated with IVF + ABX",
+                              "Neutropenic Fever v2")
+
+# -------------------------------------------------------------------------------
+# GIBLEED 
+# -------------------------------------------------------------------------------
+# "EtOH-GIBleed Active"                     
+# "EtOH-GIBleed Bleeding Out"               
+# "EtOH-GIBleed Coag Stabilized"            
+# "EtOH-GIBleed Post-EGD"   
+# -------------------------------------------------------------------------------
+gi_bleed_states <- c( "EtOH-GIBleed Active",                     
+                      "EtOH-GIBleed Bleeding Out",               
+                      "EtOH-GIBleed Coag Stabilized",            
+                      "EtOH-GIBleed Post-EGD" )
+
+
+# -------------------------------------------------------------------------------
+# DKA 
+# -------------------------------------------------------------------------------
+# "DKA Euglycemic"                          
+# "DKA Hyperglycemic"                       
+# "DKA Onset"
+# -------------------------------------------------------------------------------
+dka_states <- c("DKA Euglycemic" ,                          
+                "DKA Hyperglycemic" ,                       
+                "DKA Onset")
+
+# write a function to modularize the above 
+# expects remerged order
+state_split <- function(state_names, df){
+  return(df %>% filter(name.x %in% state_names))
+} 
+
+list_of_states <- list(gi_bleed_states, 
+                       mening_states, 
+                       pulmonary_emolism_states, 
+                       afib_states,
+                       neutropenic_fever_states)
+
+
+state_dataframe_split <- lapply(list_of_states, state_split, df = remerged_order)
+
+gi_test <- state_split(gi_bleed_states, remerged_order)
+mening_test <- state_split(mening_states, remerged_order)
+pulmonary_embolism_test <- state_split(pulmonary_emolism_states, remerged_order)
+afib_test <- state_split(afib_states, remerged_order)
+neutropenic_test <- state_split(neutropenic_fever_states, remerged_order)
+
+gi_test$case <- "gi_bleed"
+mening_test$case <- "meningitis"
+pulmonary_embolism_test$case <- "pulmonary_embolism"
+afib_test$case <- "atrial_fibrillation"
+neutropenic_test$case <- "neutropenic"
+
+df_grading_pre <- rbind(gi_test, 
+                        mening_test,
+                        pulmonary_embolism_test, 
+                        afib_test, 
+                        neutropenic_test)
+
+df_grading <- df_grading_pre %>% select(sim_state_id, 
+                                        clinical_item_id, 
+                                        sim_user_id, 
+                                        sim_patient_id, 
+                                        description.x, 
+                                        name.x, description.x, 
+                                        description.y, 
+                                        case)
+
+sim_state_list <- split(df_grading, df_grading$sim_state_id)
+
+unique_orders <- function(df){
+  df2 <- as.data.frame(unique(df$description.y))
+  df2$case <- as.character(unique(df$case))
+  df2$name.x <- as.character(unique(df$name.x))
+  return(df2)
+}  
+
+unique_sim_state <- lapply(sim_state_list, unique_orders)
+
+grading_data <- bind_rows(unique_sim_state)
+
+grading_data$grade <- 1.5
+grading_data$confidence <- NA
+grading_data$group_interaction <- NA
+grading_data$commentary <- NA
+
+grading_data2 <- grading_data[order(grading_data$name.x),]
+grading_data2$description.y <- grading_data2$`unique(df$description.y)`
+
+grading_data3 <- merge(grading_data2, sim_state, 
+                            by.x="name.x", 
+                            by.y="name") 
+grading_data4 <- grading_data3 %>% select(name.x, description.y, description, case, grade, confidence, group_interaction, commentary)
+
+
+write.xlsx(grading_data4, "grading_doctors_v5.xlsx", sheetName = "unique_orders_case", 
+           col.names = TRUE, row.names = TRUE, append = FALSE)
+
+## Actual Grading: 
+## Methods: 
+# We are creating a methodology to systematically grade physician's decision-making. We created an expert panel of physicians as consultants and 
+# "physician case developers" 
+# 
+# Code Wise: 
+# I am taking the remerged_order dataframe and splitting the results into physician_caseid_sim_state to create groupings that I will apply the grading with 
+# I believe this is an elegant design of dataframe manipulation, because it allows you to join the list of case dataframes with clinical orders 
+# 
+
+# can split on sim_patient_id: represents a patient that is treated 
+#sim_case_split <- split(remerged_order, remerged_order$sim_patient_id)
+
+# TODO MERGE on clinical item id not name.x
+# merged_order 
+#clinical_key <- df_grading %>% select(clinical_item_id,name.x) %>% unique
+
+grading_data2$state_key <- paste0(grading_data2$description.y,"_",grading_data2$name.x)
+remerged_order$state_key <- paste0(remerged_order$description.y,"_",remerged_order$name.x)
+remerged_grade_key <- merge(grading_data2, remerged_order, 
+                   by.x="state_key", 
+                   by.y="state_key")
+
+# can split on sim_patient_id: represents a patient that is treated 
+sim_case_split <- split(remerged_grade_key, remerged_grade_key$sim_patient_id)
+# 
+case_grader <- function(x){
+  # accepts list of sim_patient_id 
+  # purpose is to sum the results 
+  return(sum(x$grade))
+}
+
+graded_cases <- lapply(sim_case_split, case_grader)
+
+## HUGE WIO 
+# TODO make into test suite 
+# - [ ] manually label the google responses: so it is up to date 
+# - [ ]  
+# - [ ]
+
+
+#library(googlesheets)
+#gs_auth(new_user = TRUE)
+#gs_ls()$sheet_title
+#for_gs <- gs_read(gs_title("Clinical Decision Support Interface Survey (Responses)"))
+
diff --git a/scripts/Archive/SimulationRecommender/readme.md b/scripts/Archive/SimulationRecommender/readme.md
new file mode 100644
index 00000000..47aa9b03
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/readme.md
@@ -0,0 +1,89 @@
+
+#### \dt List of Tables from psql (David M.)  
+
+- backup_link_patient_item
+- clinical_item
+- clinical_item_association
+- clinical_item_category
+- clinical_item_link
+- collection_type
+- data_cache
+- item_collection
+- item_collection_item
+- order_result_stat
+- patient_item
+- patient_item_collection_link
+- sim_note
+- sim_note_type
+- sim_order_result_map
+- sim_patient
+- sim_patient_order
+- sim_patient_state
+- sim_result
+- sim_state_result
+- sim_state_transition
+- sim_user 
+
+### Shell Script to Dump DataTables (Jonathan Chen) 
+### Med Box directory 
+[STRIDE-Inpatient-2008-2014]("https://stanfordmedicine.app.box.com")
+
+
+#### Configuring your Database and LocalEnv.py 
+
+ LocalEnv.py is created dynamically by setup.sh, but if you don't need <br />
+ to do any of the other steps in setup.sh (e.g. installing libraries, <br />
+ initializing DB) it may be easier to just make a copy of this file <br />
+ and name it LocalEnv.py before editing the actual values. <br />
+
+ medinfo/db/Env.py imports these DB variables, so all DB connections will <br />
+ fail if these two environment variables are not defined. <br />
+
+ Other variables which are likely to vary between dev environments should <br />
+ be added to this template file, then be given default values via setup.sh. <br />
+
+BOX_CLIENT_ID = "BOX_API_CLIENT_ID" <br />
+BOX_CLIENT_SECRET = "BOX_API_CLIENT_SECRET" <br />
+BOX_ACCESS_TOKEN = "BOX_API_ACCESS_TOKEN" <br />
+BOX_STRIDE_FOLDER_ID = "BOX_STRIDE_FOLDER_ID" <br />
+
+LOCAL_PROD_DB_PARAM = {}  <br />
+LOCAL_PROD_DB_PARAM["HOST"] = 'localhost'  <br />   
+LOCAL_PROD_DB_PARAM["DSN"] = 'medinfo'     <br />
+LOCAL_PROD_DB_PARAM["UID"] = 'postgres'  <br />
+LOCAL_PROD_DB_PARAM["PWD"] = "DB_PASSWORD"   <br />
+
+
+LOCAL_TEST_DB_PARAM = {}  <br />
+LOCAL_TEST_DB_PARAM["HOST"] = 'localhost'  <br />
+LOCAL_TEST_DB_PARAM["DSN"] = "DATABASE_NAME"  <br />
+LOCAL_TEST_DB_PARAM["UID"] = "DB_UID"  <br />
+LOCAL_TEST_DB_PARAM["PWD"] = "DB_PASSWORD"  <br />
+
+PATH_TO_CDSS = "/Users/jonc101/Documents/Biomedical_Data_Science/ui_server/CDSS/"; # Directory where this file is contained in
+
+TEST_RUNNER_VERBOSITY = 2
+
+DATASET_SOURCE_NAME = 'STRIDE'  <br />
+DATABASE_CONNECTOR_NAME = "psycopg2"  <br />
+SQL_PLACEHOLDER = "%s"  <br />
+
+
+#### Define constants for connecting to database from which to dump tables. For example, if dumping from local database named test_database and accessing as user test_user, you would use the following lines:
+export DB_HOST=localhost <br />
+export DB_DSN=database <br />
+export DB_USER=user <br />
+
+#### Pipe the uncompressed data from gzip to psql to load to database.
+gzip -d -c clinical_item_category.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c clinical_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c clinical_item_link.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c patient_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c backup_link_patient_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c clinical_item_association.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c item_collection.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c collection_type.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c item_collection_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c patient_item_collection_link.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c order_result_stat.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
+gzip -d -c data_cache.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN <br />
diff --git a/scripts/Archive/SimulationRecommender/simDataReader.py b/scripts/Archive/SimulationRecommender/simDataReader.py
new file mode 100644
index 00000000..994b1147
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/simDataReader.py
@@ -0,0 +1,214 @@
+
+# how to get pandas data from postgree sql using python
+import psycopg2 as pg
+import pandas.io.sql as psql
+import pandas as pd
+connection = pg.connect("host='localhost' dbname=stride_inpatient_2014 user=postgres password='MANUAL PASSWORD'")
+
+# -------------------------------------------------------------------------------
+# to do :
+#         Feature: Generate Grading Scheme
+#             1) help visualize processes
+#             2) introduce best grading schemes for each case
+#             3) create a list of common errors seen
+#
+#             4) clean up exploratory analysis
+#             5) convert to python module
+#
+#
+#
+# ---------------------------------------------------------------------------------
+
+clinical_item = pd.read_sql_query('select * from clinical_item',con=connection)
+print("|_____________________|")
+print("clinical_item")
+print("|_____________________|")
+#print(sim_patient.head)
+
+sim_patient_order = pd.read_sql_query('select * from sim_patient_order',con=connection)
+print("|_____________________|")
+print("sim_patient_order")
+print("|_____________________|")
+#print(sim_patient.head)
+
+sim_state = pd.read_sql_query('select * from sim_state',con=connection)
+print("|_____________________|")
+print("sim_state_head")
+print("|_____________________|")
+#print(sim_state.head)
+#
+
+
+sim_user = pd.read_sql_query('select * from sim_user',con=connection)
+print("|_____________________|")
+print("sim_user")
+print("|_____________________|")
+#print(sim_patient.head)
+
+
+sim_state_transition = pd.read_sql_query('select * from sim_state_transition',con=connection)
+print("|_____________________|")
+print("sim_state_transition")
+print("|_____________________|")
+#print(sim_state_result.head)
+
+'''
+
+merge sim_patient_order and sim_state by sim_state_id
+
+'''
+print("merging sim state and transition")
+merged_order = sim_patient_order.merge(sim_state, left_on='sim_state_id', right_on='sim_state_id')
+#print(merged_order)
+
+'''
+
+find unique clinical items from merged_order
+
+'''
+
+print("running clinical items unique")
+clinical_items_list = merged_order['clinical_item_id'].unique()
+#print(clinical_items_list)
+
+# finds unique sim_state_id's
+# RCODE:
+#   sim_state_list <- unique(merged_order$sim_state_id)
+# Python:
+sim_state_list = merged_order['sim_state_id'].unique()
+
+# creates clinical_item order key to reduce merge space:
+# RCode: ordered_clinical_item_table <- clinical_item %>% filter(clinical_item_id %in% clinical_items_list)
+ordered_clinical_item_table = clinical_item[clinical_item['clinical_item_id'].isin(clinical_items_list)]
+#print(ordered_clinical_item_table)
+
+remerged_order = merged_order.merge(ordered_clinical_item_table, left_on='clinical_item_id', right_on='clinical_item_id')
+
+# https://medium.com/analytics-vidhya/split-apply-combine-strategy-for-data-mining-4fd6e2a0cc99
+split_state = remerged_order.groupby('sim_state_id')
+# print(list(split_state))
+
+#--------------------------------------------------------------------------------
+# afib
+#--------------------------------------------------------------------------------
+# "Afib-RVR Initial"
+# "Afib-RVR Stabilized"
+# "Afib-RVR Worse"
+#--------------------------------------------------------------------------------
+afib_states = ["Afib-RVR Initial",
+                "Afib-RVR Stabilized" ,
+                "Afib-RVR Worse" ]
+#--------------------------------------------------------------------------------
+# meningitis
+#--------------------------------------------------------------------------------
+# "Mening Active"
+# "Meningitis Adequately Treated"
+# "Meningits Worsens"
+#--------------------------------------------------------------------------------
+mening_states =  ["Mening Active",
+                   "Meningitis Adequately Treated",
+                   "Meningits Worsens"]
+# -------------------------------------------------------------------------------
+# pulmonary embolism
+# -------------------------------------------------------------------------------
+# "PE-COPD-LungCA"
+# "PE-COPD-LungCA + Anticoagulation"
+# "PE-COPD-LungCA + O2"
+# "PE-COPD-LungCA + O2 + Anticoagulation"
+# -------------------------------------------------------------------------------
+pulmonary_emolism_states = ["PE-COPD-LungCA",
+                              "PE-COPD-LungCA + Anticoagulation",
+                              "PE-COPD-LungCA + O2",
+                              "PE-COPD-LungCA + O2 + Anticoagulation"]
+# -------------------------------------------------------------------------------
+# neutropenic fever
+# -------------------------------------------------------------------------------
+#  "Neutropenic Fever Treated with IVF"
+#  "Neutropenic Fever Treated with IVF + ABX"
+#  "Neutropenic Fever v2"
+#  "NFever"
+# -------------------------------------------------------------------------------
+
+neutropenic_fever_states = ["Neutropenic Fever Treated with IVF",
+                              "Neutropenic Fever Treated with IVF + ABX",
+                              "Neutropenic Fever v2"]
+
+# -------------------------------------------------------------------------------
+# GIBLEED
+# -------------------------------------------------------------------------------
+# "EtOH-GIBleed Active"
+# "EtOH-GIBleed Bleeding Out"
+# "EtOH-GIBleed Coag Stabilized"
+# "EtOH-GIBleed Post-EGD"
+# -------------------------------------------------------------------------------
+
+gi_bleed_states = ["EtOH-GIBleed Active",
+                      "EtOH-GIBleed Bleeding Out",
+                      "EtOH-GIBleed Coag Stabilized",
+                      "EtOH-GIBleed Post-EGD" ]
+
+# -------------------------------------------------------------------------------
+# DKA
+# -------------------------------------------------------------------------------
+# "DKA Euglycemic"
+# "DKA Hyperglycemic"
+# "DKA Onset"
+# -------------------------------------------------------------------------------
+
+dka_states = ["DKA Euglycemic" ,
+                "DKA Hyperglycemic" ,
+                "DKA Onset"]
+
+list_of_states = [gi_bleed_states,
+                       mening_states,
+                       pulmonary_emolism_states,
+                       afib_states,
+                       neutropenic_fever_states]
+print("LIST OF STATES")
+#print(list_of_states[1])
+
+
+def state_split(state_names, df):
+    df2 = df[df['name_x'].isin(state_names)]
+    return(df2)
+
+
+print(list(remerged_order.columns))
+
+
+gi_test = state_split(gi_bleed_states, remerged_order)
+mening_test = state_split(mening_states, remerged_order)
+pulmonary_embolism_test = state_split(pulmonary_emolism_states, remerged_order)
+afib_test = state_split(afib_states, remerged_order)
+neutropenic_test = state_split(neutropenic_fever_states, remerged_order)
+
+gi_test['case'] = "gi_bleed"
+mening_test['case'] = "meningitis"
+pulmonary_embolism_test['case'] = "pulmonary_embolism"
+afib_test['case'] = "atrial_fibrillation"
+neutropenic_test['case'] = "neutropenic"
+print(neutropenic_test['case'])
+
+df_grading_pre = pd.concat([gi_test,
+                        mening_test,
+                        pulmonary_embolism_test,
+                        afib_test,
+                        neutropenic_test])
+
+
+df_grading = pd.DataFrame(df_grading_pre[['sim_state_id',
+                                        'clinical_item_id',
+                                        'sim_user_id',
+                                        'sim_patient_id',
+                                        'name_x',
+                                        'description_x',
+                                        'description_y',
+                                        'case']])
+
+
+print(df_grading)
+
+#sim_state_list = list(df_grading.groupby('sim_state_name'))
+
+#print('sim_state_list')
+#print(sim_state_list)
diff --git a/scripts/Archive/SimulationRecommender/simTest.md b/scripts/Archive/SimulationRecommender/simTest.md
new file mode 100644
index 00000000..16174568
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/simTest.md
@@ -0,0 +1,47 @@
+#### Steps to Reproduce Database
+
+- insert stride tables 2014
+
+STEP 1
+
+- make sure you update your Postgres connection at the top of the system for restoreSimTables and restoreSimTables
+
+STEP 2
+- run revertSimTables.sh "./revertSimTables.sh"
+
+
+##### Testing Chemo Fever 2:
+Things to Test
+
+
+|Action   | Result   |
+|---   |---       |
+|amoxicillin      | state doesn't change      |
+|doxycline        | state doesn't change      |
+|cephalexin       | state doesn't change      |
+|ciprofloxacin    | state doesn't change      |
+|Glucose-Piperacillin-Tazobactam    |state doesn't change   |
+|cefepime         | state -> 5001             |
+|lactated ringers | 5000 -> 5002              |
+|Sodium-Chloride  | 5000 -> 5001              |
+|Zosyn            |   |
+|Meropenem        |5000 -> 5001   |
+|Aztreonam        | 5000 -> 5001   |
+|Ceftazidime      | 5000 -> 5001    |
+|Ceftazidime -> IVF | 5000 -> 5001 -> 5003    |
+|Zosyn2           |    |
+|Aztreonam + NS IVF      | 5000 -> 5001 + 5003       |
+|NS IVF -> Moropenum   | 5000 -> 5002 -> 5003        |
+|cefepime + IVF   | 5000 -> (5001,5003)              |
+|Aztreonam + IVF   | 5000 -> (5001,5003)             |
+|Lactated Ringers  + Cefepime  | 5000 -> (5002,5003) |
+|Lactated Ringers + Meropenum   |5000 -> (5002,5003) |
+
+#### Meningitis (Neck Stiffness) Testing
+
+|Action   | Result   |
+|---   |---       |
+|Ceftriaxone  (intravenous)   | 30 -> 31      |
+|cefepime         | 30 -> 31             |
+|Meropenem        | 30 -> 31 |
+|Do nothing for two hours        | 30 -> 32 -> 33|
diff --git a/scripts/Archive/SimulationRecommender/simulateCases.py b/scripts/Archive/SimulationRecommender/simulateCases.py
new file mode 100644
index 00000000..df8c3be4
--- /dev/null
+++ b/scripts/Archive/SimulationRecommender/simulateCases.py
@@ -0,0 +1,66 @@
+import random
+'''
+--------------------------------------------------
+sample() is an inbuilt function of random module 
+in Python that returns a particular length list 
+of items chosen from the sequence i.e. list, tuple, 
+string or set
+--------------------------------------------------
+Used for random sampling without replacement
+--------------------------------------------------
+x denotes: 
+    expects: 
+        - list 
+        - cases that you want to randomize 
+
+y denotes:
+    - list of boolean values that indicate whether or not 
+          the recommender is turned on 
+
+n denotes:
+    - the number of times you want to sample without replacement
+    - should equal the length of x and y (should I make this explicit?) 
+
+Purpose of Script:
+    - writing a function that accepts a list of physician cases and randomly orders them
+    - making it reproducible (can run again) (may need to review documentation on seed) 
+    
+Learning Points to Incorporate: 
+    - more test driven development
+    - functional programming versus Object Oriented Programming 
+    - Less Script-Like   
+----------------------------------------------------        
+'''
+
+
+def testRandomizeCase(x, y):
+    assert type(x) == list
+    assert len(x) == len(y)
+
+
+def randomizeCase(x,y):
+    # set the seed 
+    random.seed(a=1)
+    # initialize an empty list 
+    output = []
+    # construct for loop for number of physicians in your study
+    for _ in range(50):
+        a = random.sample(x, 5)
+        b = random.sample(y, 4)
+        #c = [] 
+        #c.append("True")
+        output.append((a,b))
+    return(output)
+
+# p1 denotes the cases represented by letters in an alphabet     
+cases = ['Fever B','Headache','Palpitations', 'Hematemesis', 'Shortness of Breath']
+
+# TRUE or FALSE (True means recommender is turned on) 
+booleanList = [True,True, False, False]
+
+# running script: 
+t = randomizeCase(cases, booleanList)
+# assumes first case recommender is on
+print(t[0])
+print(t[1])
+print(t[2])
diff --git a/scripts/Archive/TSI_prediction/TSI_Prediction_scripts.sql b/scripts/Archive/TSI_prediction/TSI_Prediction_scripts.sql
new file mode 100644
index 00000000..5378d045
--- /dev/null
+++ b/scripts/Archive/TSI_prediction/TSI_Prediction_scripts.sql
@@ -0,0 +1,19 @@
+TSI Prediction
+
+
+--
+-- Count of labs with TSI/TRAB
+SELECT lab_name, count(lab_name) FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` 
+      WHERE lab_name IN ('Thyroid-Stimulating Immunoglob, Serum', 'Thyroid Stim Immunoglobulin', 'Thyroid Stimulating Immunoglobulins', 'Thyroid Stim Immuno', 'Thyrotropin Receptor Ab, Serum', 'TSH Receptor Ab', 'Thyrotropin Receptor Ab', 'TSH Receptor Antibody', 'TBII' )
+     -- AND lab_name NOT IN ('Thyroid Peroxidase Abs', 'Thyroid Peroxidase (TPO) Antibody', 'Anti-Thyroid Peroxidase IgG', 'Thyroid Ab') -- antibodies related to hypothyroidism
+GROUP BY lab_name 
+
+-- 
+-- Patients with TSI/TRAB
+-- TRAB max is 1.75, TSI cutoff is 1.3/139. "High" means high but "Null" doesn't mean in range necessarily
+-- TimeToResult is the difference between when the test was ordered and when it resulted
+-- Average time to result is 4.33 days
+SELECT rit_uid, pat_enc_csn_id_coded, lab_name, ord_num_value, reference_low, reference_high, result_in_range_yn, result_flag, ordering_mode, taken_time_jittered, result_time_jittered, DATE_DIFF(DATE(result_time_jittered), DATE(taken_time_jittered), DAY) as TimeToResult  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` 
+      WHERE lab_name IN ('Thyroid-Stimulating Immunoglob, Serum', 'Thyroid Stim Immunoglobulin', 'Thyroid Stimulating Immunoglobulins', 'Thyroid Stim Immuno', 'Thyrotropin Receptor Ab, Serum', 'TSH Receptor Ab', 'Thyrotropin Receptor Ab', 'TSH Receptor Antibody', 'TBII' )
+      AND ord_num_value < 9999999
+
diff --git a/scripts/Archive/TopicModelsVsOrderSets/orderSetsUsed.py b/scripts/Archive/TopicModelsVsOrderSets/orderSetsUsed.py
new file mode 100644
index 00000000..015c58d3
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/orderSetsUsed.py
@@ -0,0 +1,4863 @@
+import sys, os;
+from medinfo.common.Util import stdOpen, ProgressDots;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import modelListFromTable, RowItemFieldComparator;
+from medinfo.db.ResultsFormatter import TextResultsFormatter;
+from medinfo.cpoe.Const import AD_HOC_SECTION;
+
+
+orderSetLookupTable = DBUtil.execute("""
+    select ic.external_id, ic.name, count(distinct ici.clinical_item_id) as item_count
+    from item_collection as ic, item_collection_item as ici
+    where ic.item_collection_id = ici.item_collection_id
+    and ic.section <> %s
+    group by ic.external_id, ic.name
+    """, (AD_HOC_SECTION,), includeColumnNames=True);
+orderSetModels = modelListFromTable(orderSetLookupTable);
+
+orderSetIdSets = \
+[
+    set([522, 1451, 698, 1455]),
+    set([625, 618, 3035, 1748]),
+    set([3572, 806]),
+    set([698]),
+    set([625, 3509, 813]),
+    set([698, 1453]),
+    set([1841, 805, 1759]),
+    set([904]),
+    set([698]),
+    set([698, 1470]),
+    set([698, 1595, 1470, 1455]),
+    set([624, 3509, 1598]),
+    set([609, 547]),
+    set([625, 1841, 3509, 805]),
+    set([3977, 698, 1049]),
+    set([624, 625, 3509, 1470, 721]),
+    set([698, 1451, 1759, 927]),
+    set([1248, 698, 547, 1759]),
+    set([698, 1451]),
+    set([625, 3509, 637]),
+    set([3574, 806, 946, 3573, 3734, 1049]),
+    set([904, 698, 734]),
+    set([1841, 698, 1453]),
+    set([2961, 1841, 698, 1807, 3015]),
+    set([616, 976, 3509, 625]),
+    set([625, 1458, 805, 3574]),
+    set([625, 3509, 1470, 1693]),
+    set([625, 1748, 1764]),
+    set([625, 1645]),
+    set([616, 625, 976]),
+    set([904, 2457, 2956, 1705, 1807]),
+    set([755, 1759]),
+    set([1841, 3572, 805]),
+    set([698, 2972]),
+    set([698, 547]),
+    set([698, 547, 3015]),
+    set([625, 618, 886]),
+    set([904, 3572, 1759]),
+    set([625, 618, 891, 3509]),
+    set([616, 625, 1748]),
+    set([625, 1645, 1470]),
+    set([698, 946, 821]),
+    set([625, 811]),
+    set([1807, 753, 1803, 1841, 1049]),
+    set([625, 3509, 1693]),
+    set([625, 3509, 814]),
+    set([625, 891, 1518]),
+    set([625, 1594, 3509]),
+    set([698, 692, 1470]),
+    set([625, 3509, 806, 737]),
+    set([698, 1842, 1445]),
+    set([625, 1645, 1470]),
+    set([1638, 904, 713, 1841, 946, 698, 1791]),
+    set([625, 1677]),
+    set([904, 1841, 698]),
+    set([609]),
+    set([1841, 698]),
+    set([1049, 698, 547]),
+    set([1049, 727, 625]),
+    set([625, 978, 3509]),
+    set([672, 1049, 625]),
+    set([3572, 805, 3574, 1759]),
+    set([625, 618, 1841, 946, 3509, 891]),
+    set([848, 625, 3509]),
+    set([824, 1800, 1307, 821, 1841]),
+    set([698, 1759]),
+    set([805]),
+    set([1049, 3509, 814, 625]),
+    set([1049, 698, 1470, 1759]),
+    set([1049, 1645, 625]),
+    set([1841, 698, 1470, 1807]),
+    set([1049, 978, 755, 625]),
+    set([1049, 698, 547]),
+    set([625, 886, 3572, 1470, 3574]),
+    set([1049, 946, 3572, 3574, 806]),
+    set([698]),
+    set([698]),
+    set([698, 1759]),
+    set([625, 891, 3509, 3015, 1841]),
+    set([609, 542]),
+    set([976, 625, 1289, 3509, 1841]),
+    set([2457, 698, 2972, 2973, 591]),
+    set([616, 1049, 1747, 1750, 625]),
+    set([1289, 698, 2973]),
+    set([698]),
+    set([1272, 698]),
+    set([625, 1842, 801, 3509, 1841]),
+    set([1049, 698, 1807]),
+    set([962, 708, 805, 625, 1049, 1759]),
+    set([2956, 3509, 973]),
+    set([624, 801, 3509]),
+    set([625, 946, 1645, 1677, 1759]),
+    set([698, 1470]),
+    set([1049, 978]),
+    set([625, 618, 1841, 3509, 1049, 891]),
+    set([625, 886, 3509, 1470]),
+    set([1049, 698, 547, 1759]),
+    set([698, 547]),
+    set([625, 1842, 801]),
+    set([1777]),
+    set([698]),
+    set([962, 1694, 1759]),
+    set([891, 1841, 547, 1049]),
+    set([1289, 755, 1841]),
+    set([625, 684, 3509, 679]),
+    set([625, 972, 3509, 1841]),
+    set([3976, 973, 4045, 982, 1653]),
+    set([976, 616, 973]),
+    set([1289, 755]),
+    set([625, 3571, 958, 1759]),
+    set([801, 806, 3593, 625, 3509, 1049]),
+    set([624, 1643, 3509]),
+    set([1136, 1049, 727, 3509, 625]),
+    set([1595, 618, 625, 3509, 891, 1470]),
+    set([864, 865, 1764, 3015, 625, 2457]),
+    set([698]),
+    set([625, 3035, 1748]),
+    set([625, 886]),
+    set([625, 891, 3509]),
+    set([679]),
+    set([1841, 698, 1759]),
+    set([962, 698]),
+    set([625, 3572, 886]),
+    set([816, 625, 978, 766, 1470]),
+    set([812]),
+    set([625, 1594, 3509]),
+    set([1049, 3509, 806, 625]),
+    set([1273, 698, 547]),
+    set([1049, 1645, 1470, 625]),
+    set([864, 625, 1841, 865]),
+    set([805, 525, 1759]),
+    set([698]),
+    set([698]),
+    set([2956, 1724, 3509, 1049, 973]),
+    set([672, 625, 1470]),
+    set([625, 3509, 1677]),
+    set([904, 625, 978, 3199]),
+    set([2457, 863, 867, 625]),
+    set([625, 978, 1841, 3509, 1677]),
+    set([1665, 801, 625]),
+    set([1841, 891, 1518, 625]),
+    set([1248, 698]),
+    set([625, 3509, 814]),
+    set([904, 1049, 698, 1455]),
+    set([1807, 1623, 1049, 698, 1437, 734]),
+    set([944, 805]),
+    set([625, 1049, 806, 1841]),
+    set([]),
+    set([1841, 805]),
+    set([547, 805, 821]),
+    set([1553, 2956, 1841]),
+    set([625, 3509, 1645]),
+    set([944, 1842, 805]),
+    set([625, 946, 1748, 1841]),
+    set([805]),
+    set([625, 708, 1470, 886]),
+    set([625, 972, 3509]),
+    set([616, 625, 3509, 742, 741]),
+    set([962]),
+    set([625, 805, 1455]),
+    set([698, 1470]),
+    set([698]),
+    set([1841, 978]),
+    set([616, 625, 805]),
+    set([904, 1049, 1451, 2956, 1841]),
+    set([624, 886]),
+    set([547, 1667, 1759]),
+    set([625, 1628, 1470, 812]),
+    set([1049, 1748, 625]),
+    set([737, 3509, 1470, 625]),
+    set([697, 698, 525, 1841]),
+    set([805]),
+    set([618, 886]),
+    set([1049, 801, 3509, 625]),
+    set([624, 812, 1470]),
+    set([1049, 1667, 625, 715, 1759]),
+    set([1049, 891, 3509, 625]),
+    set([625, 972, 3509]),
+    set([625, 3509, 637]),
+    set([625, 618, 893]),
+    set([891, 625, 3035, 3509]),
+    set([2440, 704, 524]),
+    set([805, 625, 904, 1841, 3509, 1049]),
+    set([3976, 973, 1651, 1653, 982, 1049]),
+    set([1273, 698]),
+    set([624, 1628]),
+    set([1049, 625, 3509, 801]),
+    set([2956, 3509, 973]),
+    set([618, 625, 946, 3509, 1049, 891]),
+    set([698]),
+    set([1352, 1841, 1842, 524]),
+    set([1553]),
+    set([1841, 618, 891, 3509, 625]),
+    set([1136, 625, 3509, 886, 1470]),
+    set([1677]),
+    set([1049, 1554, 1470, 625]),
+    set([3591]),
+    set([624, 698, 1470]),
+    set([1049, 1842, 1693]),
+    set([698, 1470]),
+    set([698, 1451]),
+    set([624, 1841, 618, 891, 3509]),
+    set([1049, 891, 625, 886, 1841]),
+    set([904, 2955]),
+    set([625, 1841, 3509, 741]),
+    set([805]),
+    set([3449, 3450, 619]),
+    set([904, 1841, 3015]),
+    set([625, 1841, 3509, 1693]),
+    set([904, 698, 1437, 1455]),
+    set([1841, 859, 1724]),
+    set([944, 1049, 698]),
+    set([1841, 814, 1049]),
+    set([625, 618, 891, 3509, 3015]),
+    set([609, 542]),
+    set([698, 547]),
+    set([2956, 3509, 973]),
+    set([624, 848]),
+    set([822]),
+    set([946, 1807, 698, 3015, 1455]),
+    set([625, 805, 1470]),
+    set([698, 1455]),
+    set([625, 679]),
+    set([904, 625]),
+    set([698, 2972, 2973]),
+    set([1694, 625, 3509, 1470]),
+    set([1553, 1582]),
+    set([698, 1470]),
+    set([1841, 1651]),
+    set([904, 944, 1049]),
+    set([698, 1470]),
+    set([698, 708]),
+    set([625, 1842, 727, 3509, 3015]),
+    set([698, 2972, 2973]),
+    set([625, 3370, 1455, 1841, 2389, 698, 1437]),
+    set([624, 616, 832]),
+    set([625, 891]),
+    set([698, 1660, 1455]),
+    set([624, 737, 3509]),
+    set([1553, 1049]),
+    set([944, 697, 698, 547]),
+    set([1660, 805]),
+    set([624, 618, 1470, 886]),
+    set([698]),
+    set([547, 814, 1455]),
+    set([625, 1554, 3509]),
+    set([698]),
+    set([625, 618, 891, 3509]),
+    set([1841, 698]),
+    set([698]),
+    set([1841, 698, 1807, 1049]),
+    set([625, 618, 891, 3509]),
+    set([1777]),
+    set([698, 1724]),
+    set([625, 946, 805]),
+    set([616, 625, 1748]),
+    set([698, 1470, 1759]),
+    set([891, 1049, 3035, 3509, 625]),
+    set([904, 1759]),
+    set([705, 698]),
+    set([698]),
+    set([609, 698]),
+    set([1049, 698, 1759]),
+    set([2961, 1841, 698, 1049]),
+    set([698, 1759]),
+    set([698]),
+    set([1470, 727]),
+    set([859, 3580]),
+    set([904, 547, 1582, 1807]),
+    set([1049, 891, 3035, 625]),
+    set([978, 766]),
+    set([904, 2457, 3976, 1437]),
+    set([698]),
+    set([1841, 698, 1759]),
+    set([625, 886]),
+    set([625, 618, 3509, 886]),
+    set([1625, 698, 1455]),
+    set([978]),
+    set([625, 618, 891]),
+    set([805]),
+    set([978, 1842, 3573, 3574, 3437]),
+    set([625, 618, 547, 886, 1759]),
+    set([625, 3509, 805]),
+    set([805]),
+    set([904]),
+    set([625, 3509, 886]),
+    set([625, 698]),
+    set([1841, 698, 524]),
+    set([805]),
+    set([978, 798]),
+    set([625, 891, 3509]),
+    set([624, 1841, 3509, 801]),
+    set([2275, 904, 3593, 3595, 1841, 821, 1705, 1049, 3386, 1759]),
+    set([944, 1049, 698, 1759]),
+    set([625, 618, 891, 3509]),
+    set([1841, 698, 1759]),
+    set([805]),
+    set([625, 1713, 3509, 1841]),
+    set([1049, 698]),
+    set([1841, 1842, 663]),
+    set([1841, 859, 2457]),
+    set([625, 3509, 1645]),
+    set([698]),
+    set([1352, 1841]),
+    set([625, 1841, 805, 801]),
+    set([547, 1451, 1841, 698, 1724, 1470]),
+    set([904, 1470]),
+    set([1841, 698, 547, 734, 1759]),
+    set([1309]),
+    set([698]),
+    set([1049, 1651, 973, 625]),
+    set([801, 1842]),
+    set([609]),
+    set([625, 618, 893, 3509, 946]),
+    set([3370, 1437, 3591]),
+    set([1842, 698]),
+    set([625, 3509, 1693]),
+    set([1554]),
+    set([625, 1667, 716, 3509, 598]),
+    set([528, 698]),
+    set([3568, 1049, 715, 1470, 625]),
+    set([1841, 698, 1759]),
+    set([618, 625, 946, 3509, 886, 1049]),
+    set([962, 805, 1759]),
+    set([3509, 902, 637]),
+    set([609, 1841]),
+    set([805]),
+    set([1800, 1841, 978, 2473]),
+    set([2955, 2956, 3509, 973]),
+    set([625, 3509, 886]),
+    set([]),
+    set([625, 1628, 3509, 1470]),
+    set([1049, 2956, 3509, 973]),
+    set([1289, 698, 886, 1807]),
+    set([1049, 886]),
+    set([698]),
+    set([1841, 698, 1612]),
+    set([624, 618, 3509, 886]),
+    set([624, 618, 3509, 886, 1470]),
+    set([625, 891, 3509]),
+    set([1693, 1049, 3509, 625]),
+    set([1049, 886, 625]),
+    set([824, 698]),
+    set([625, 618, 891, 3509]),
+    set([625, 1693]),
+    set([1049, 698, 1841, 1759]),
+    set([672, 625, 3509]),
+    set([1841, 3452, 1759]),
+    set([1841, 698]),
+    set([698]),
+    set([625, 716, 3509]),
+    set([1280, 547, 3389, 755]),
+    set([1842, 1841, 946, 821, 824, 669, 3509]),
+    set([625, 946, 884, 3509]),
+    set([720, 625, 3509]),
+    set([625, 891, 3509]),
+    set([625, 618, 891, 3509]),
+    set([625, 618, 891, 1470, 1595]),
+    set([625, 3509, 801]),
+    set([1667]),
+    set([713, 698]),
+    set([944, 1049, 698, 526]),
+    set([]),
+    set([625, 805, 525, 1759]),
+    set([904]),
+    set([]),
+    set([624, 805]),
+    set([978]),
+    set([624, 891, 1595, 3509, 1470]),
+    set([1049, 618, 891, 3509, 625]),
+    set([547, 708, 542]),
+    set([1049, 1595, 625, 3509, 669]),
+    set([3552, 904, 1238]),
+    set([625, 884, 3509, 1470]),
+    set([1049, 946, 547, 1437, 806]),
+    set([1841, 698, 547]),
+    set([1595, 618, 625, 3509, 891, 1470]),
+    set([698, 708, 543]),
+    set([3509, 1458, 1049, 2973, 4045]),
+    set([625, 1594, 3509]),
+    set([1841, 3452, 3015]),
+    set([946, 547, 805]),
+    set([698]),
+    set([1841, 698, 1289]),
+    set([698]),
+    set([625, 1594, 3509]),
+    set([962, 525, 698]),
+    set([1625, 698, 547, 1759]),
+    set([944, 1049, 698, 526]),
+    set([698, 1759]),
+    set([698]),
+    set([625, 669]),
+    set([755]),
+    set([698, 525]),
+    set([805, 904, 1551, 1841, 920, 1049]),
+    set([859, 2972, 1470]),
+    set([]),
+    set([]),
+    set([1841, 698, 1759, 1470, 1807]),
+    set([609]),
+    set([1136, 1049, 886, 625]),
+    set([1049, 946, 637, 625]),
+    set([609, 542]),
+    set([624, 1841, 3509, 741]),
+    set([698, 734]),
+    set([1289, 698, 1437, 1841]),
+    set([976, 624, 946, 1518, 616]),
+    set([1777]),
+    set([625, 1724, 884, 3509]),
+    set([698, 734]),
+    set([904, 1841, 1842]),
+    set([904, 708]),
+    set([625, 3509, 805]),
+    set([1307, 1437]),
+    set([1448, 856, 698, 3534, 1455]),
+    set([1841, 1842, 1693, 1049]),
+    set([625, 946, 805, 598]),
+    set([1049, 698, 1470, 526]),
+    set([1595, 698, 3370, 1437, 1470]),
+    set([609, 1470]),
+    set([1289, 698, 523, 734, 1807]),
+    set([891, 3509]),
+    set([1841, 698]),
+    set([1049, 886, 3509, 1470, 625]),
+    set([698]),
+    set([2457, 755, 1677, 3015]),
+    set([3449, 3450]),
+    set([3437, 1807, 1841, 1049, 2457, 698]),
+    set([1049, 698]),
+    set([616, 625, 805, 1841]),
+    set([755]),
+    set([698, 2955, 2956, 1759]),
+    set([904, 1841]),
+    set([1049, 886, 1759]),
+    set([1049, 1748, 1841, 625]),
+    set([698]),
+    set([904, 1841]),
+    set([1049, 891, 625]),
+    set([1841, 698]),
+    set([625, 3509, 669]),
+    set([609, 1470]),
+    set([609, 1595]),
+    set([625, 812, 3509, 1470]),
+    set([698, 547, 1470]),
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+    set([625, 725, 1470]),
+    set([698, 1455]),
+    set([755, 1759]),
+    set([891, 625, 3035, 3509]),
+    set([1273, 698, 1049, 1759]),
+    set([1841, 698]),
+    set([2457, 625, 821, 1049]),
+    set([962, 755]),
+    set([2961, 698]),
+    set([625, 946, 3509, 637]),
+    set([625, 1628]),
+    set([625, 2956, 2958, 1841, 978, 1049]),
+    set([616, 625, 1748, 1049, 1595, 1470]),
+    set([904, 625, 3509, 886]),
+    set([698]),
+    set([2457, 698]),
+    set([616, 625, 3509, 886]),
+    set([1049, 698, 713]),
+    set([625, 1841, 3509, 805]),
+    set([1049, 978, 766]),
+    set([1841, 978, 766]),
+    set([978, 3509, 1693]),
+    set([1049, 2956, 3509, 973]),
+    set([1049, 3572, 625, 806, 1759]),
+    set([2956, 973]),
+    set([1049, 1841, 886, 625]),
+    set([1841, 698]),
+    set([1049, 1645, 625]),
+    set([625, 3509, 814]),
+    set([698, 1759, 1437, 1455]),
+    set([1449, 698]),
+    set([1628]),
+    set([625, 891, 3509]),
+    set([624, 1841, 3509, 805]),
+    set([625, 618, 886]),
+    set([740, 3593, 624, 1841, 1049, 3580, 1470]),
+    set([609]),
+    set([1049, 886]),
+    set([625, 3509, 1694]),
+    set([2457, 859, 1470]),
+    set([904, 759, 1451, 1759]),
+    set([1049, 698, 1841]),
+    set([904, 1841, 755, 1453]),
+    set([625, 1841, 3509, 1677]),
+    set([625, 3509]),
+    set([2457, 859, 2972, 2973]),
+    set([619]),
+    set([]),
+    set([1841, 698, 3015]),
+    set([698]),
+    set([1352]),
+    set([904, 1458, 1842]),
+    set([625, 1628]),
+    set([698, 591]),
+    set([625, 1841, 3509, 669]),
+    set([1841, 698, 1796, 1470, 609]),
+    set([616, 625, 946, 3509, 805]),
+    set([619]),
+    set([976, 625]),
+    set([625, 891, 3509, 1470, 1595]),
+    set([625, 946, 884, 3509]),
+    set([805]),
+    set([1632, 978, 3509]),
+    set([755, 1455]),
+    set([698, 927, 3015]),
+    set([625, 3509, 814, 1841]),
+    set([625, 1628]),
+    set([1777]),
+    set([936, 698, 1470, 1526]),
+    set([1280, 1289, 962, 755, 1759]),
+    set([1049, 698, 1582]),
+    set([708, 543]),
+    set([698]),
+    set([864, 865, 698]),
+    set([1841, 698, 2958, 598]),
+    set([698, 1470, 3015]),
+    set([625, 3509, 805]),
+    set([805, 1759]),
+    set([616, 625, 1747, 1750, 3035]),
+    set([891, 625, 3035, 3509]),
+    set([2972, 698, 3452, 2973]),
+    set([625, 1645, 1470]),
+    set([3976, 2956, 1653, 982]),
+    set([1841, 698, 708]),
+    set([698, 539, 1470, 1455]),
+    set([625, 805, 1759]),
+    set([812]),
+    set([625, 1645]),
+    set([904, 732, 1238]),
+    set([]),
+    set([805]),
+    set([698, 708, 543]),
+    set([1651]),
+    set([1841, 698, 932, 524, 2957]),
+    set([1841, 1651, 2457]),
+    set([625, 3509, 741]),
+    set([1049, 946, 740, 2457]),
+    set([698, 2973]),
+    set([1841, 698, 547]),
+    set([848, 625, 3509]),
+    set([625, 1554]),
+    set([886]),
+    set([864, 865, 624, 625, 1049, 2973, 863]),
+    set([624, 886, 3509, 1470]),
+    set([891, 1049, 618, 3035, 625]),
+    set([1446, 904, 1455, 625, 3509, 1049]),
+    set([824, 821]),
+    set([1841, 698]),
+    set([698, 1470, 3015]),
+    set([1841, 805]),
+    set([978]),
+    set([625, 946, 1841, 3509, 1677]),
+    set([698, 1437]),
+    set([2457, 859, 2973, 734, 1759]),
+    set([1841, 698, 3567, 1437, 1049]),
+    set([978]),
+    set([625, 1594, 3509]),
+    set([755]),
+    set([904, 625, 1628]),
+    set([625, 3509, 637]),
+    set([805]),
+    set([625, 886]),
+    set([1777]),
+    set([1049, 2956, 525, 806, 2957]),
+    set([698]),
+    set([625, 3509, 805]),
+    set([1049, 698, 1842, 1841]),
+    set([976, 625, 3509, 1841]),
+    set([1841, 1049, 741, 2973, 625]),
+    set([1136, 624, 625, 3509, 886, 1470]),
+    set([1049, 1307]),
+    set([976, 625, 3509]),
+    set([904]),
+    set([2457, 698]),
+    set([944, 698]),
+    set([1713]),
+    set([698]),
+    set([1777]),
+    set([904, 1049, 2956, 1841, 1455]),
+    set([698, 3595]),
+    set([525, 805]),
+    set([904, 2955, 2957]),
+    set([625, 3509, 805]),
+    set([698, 525]),
+    set([904, 2457, 1437]),
+    set([624, 737, 3509, 1470]),
+    set([625, 1645, 1470]),
+    set([625, 1554, 3509]),
+    set([625, 812]),
+    set([755]),
+    set([2457, 741]),
+    set([698]),
+    set([698, 691]),
+    set([1759, 698, 547, 1582, 927]),
+    set([698, 708]),
+    set([625, 1677]),
+    set([616, 3509, 806]),
+    set([625, 946, 775]),
+    set([1049, 886, 1455]),
+    set([1651]),
+    set([1632, 1841, 978, 3509]),
+    set([755]),
+    set([625, 3509, 805]),
+    set([978]),
+    set([609, 1759]),
+    set([1352]),
+    set([962, 543]),
+    set([1595, 1841, 618, 625, 3509, 891, 1470]),
+    set([]),
+    set([]),
+    set([2956, 3509, 973]),
+    set([625, 3509, 805]),
+    set([1049, 698, 1437, 3566]),
+    set([2972, 2973]),
+    set([1841, 1651]),
+    set([978, 3509]),
+    set([813]),
+    set([755]),
+    set([698, 525]),
+    set([625, 3509, 1677]),
+    set([3552, 904, 1841, 1842, 1238, 732]),
+    set([1049, 978]),
+    set([698, 1446]),
+    set([2956, 973]),
+    set([978, 766]),
+    set([816, 978, 766]),
+    set([625, 946, 3509, 637]),
+    set([776, 625, 778, 525]),
+    set([1841, 3452]),
+    set([1841, 3370, 805]),
+    set([625, 1693]),
+    set([616, 625, 3509, 805]),
+    set([547, 814]),
+    set([891, 3509, 1470]),
+    set([1694, 625, 3509, 1470]),
+    set([698]),
+    set([625, 1748]),
+    set([625, 891, 3509]),
+    set([698]),
+    set([522, 859, 1724, 3015]),
+    set([962, 886, 3572, 618, 3574]),
+    set([1841, 3452, 3015]),
+    set([625, 3509]),
+    set([904, 1638]),
+    set([609, 1453]),
+    set([1632, 625, 978, 1841]),
+    set([962, 3572, 1759]),
+    set([609, 1841]),
+    set([1049]),
+    set([1049, 698, 1841]),
+    set([616, 1049, 715, 625, 727]),
+    set([609]),
+    set([698]),
+    set([1632, 1841, 978]),
+    set([1553, 1582, 1049]),
+    set([1049, 740, 741, 625]),
+    set([805, 1437]),
+    set([616, 625, 3509, 741]),
+    set([755]),
+    set([1841, 698]),
+    set([805, 625, 1841, 821, 824, 3509]),
+    set([625, 755, 3509]),
+    set([3572, 805, 1470]),
+    set([946, 3509, 806]),
+    set([1136, 1049, 716, 3509, 625]),
+    set([1049, 978, 3509]),
+    set([720, 625, 1470, 1759]),
+    set([1049, 946, 625, 805, 801]),
+    set([944, 624, 637]),
+    set([755, 1470]),
+    set([609, 1759]),
+    set([698]),
+    set([904, 3509, 637]),
+    set([698, 3015]),
+    set([625, 3509, 1677]),
+    set([625, 1764, 3509, 637]),
+    set([904, 1759]),
+    set([2457, 859, 1455, 3015]),
+    set([1841, 698]),
+    set([944, 625, 946, 805]),
+    set([1136, 625, 1645, 1470]),
+    set([1841, 805]),
+    set([625, 3509, 669]),
+    set([1049, 1651, 973]),
+    set([625, 1748]),
+    set([755]),
+    set([625, 1841, 3509, 637]),
+    set([1841, 525, 805]),
+    set([1665, 1666, 946, 625]),
+    set([805]),
+    set([672, 1049, 625]),
+    set([1049, 1645, 625]),
+    set([904, 547, 1759]),
+    set([962, 591, 596, 1759]),
+    set([616, 625, 976, 3509, 1841]),
+    set([1049, 1651, 2956, 973]),
+    set([698, 547]),
+    set([625, 805, 525, 3509]),
+    set([816, 625, 978, 766]),
+    set([2973, 1470, 973]),
+    set([962, 698]),
+    set([624, 1595, 891, 3509, 1470]),
+    set([616, 625, 3509, 669]),
+    set([522, 524, 698]),
+    set([1841, 698, 3595, 1049]),
+    set([625, 3509, 805]),
+    set([625, 1594, 3509, 1841]),
+    set([904, 1841, 1842, 1470, 1238]),
+    set([2457, 698, 547]),
+    set([1049, 618, 891, 3509, 625]),
+    set([625, 3509, 1470, 741]),
+    set([1841, 1595, 3572, 806, 3574]),
+    set([1049, 698, 1841]),
+    set([537, 698, 1470]),
+    set([625, 715, 1470, 721]),
+    set([]),
+    set([625, 805, 1470]),
+    set([3593, 3595, 2956, 2957, 1841, 792, 1049, 698]),
+    set([547, 698, 859, 1841]),
+    set([698, 1724]),
+    set([698, 1470, 734]),
+    set([698, 1470]),
+    set([904, 944, 1841, 792, 1049, 698]),
+    set([698]),
+    set([616, 625, 1748]),
+    set([625, 3509]),
+    set([698, 547]),
+    set([1713, 1458]),
+    set([625, 3509, 814]),
+    set([609, 547]),
+];
+
+countByOrderSetId = {None:0};
+for orderSetIds in orderSetIdSets:
+    for orderSetId in orderSetIds:
+        if orderSetId not in countByOrderSetId:
+            countByOrderSetId[orderSetId] = 0;
+        countByOrderSetId[orderSetId] += 1;
+    countByOrderSetId[None] += 1;
+totalCases = float(countByOrderSetId[None]);
+    
+for orderSetModel in orderSetModels:
+    orderSetId = orderSetModel["external_id"];
+    orderSetModel["count"] = 0;
+    if orderSetId in countByOrderSetId:
+        orderSetModel["count"] = countByOrderSetId[orderSetId];
+    orderSetModel["rate"] = orderSetModel["count"] / totalCases;
+
+orderSetModels.sort( RowItemFieldComparator("count"), reverse=True);
+
+colNames = ["count","external_id","name","item_count", "rate"];
+formatter = TextResultsFormatter(sys.stdout);
+formatter.formatTuple(colNames);    # Header Row
+formatter.formatResultDicts(orderSetModels, colNames);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/patchAdHocOrderSet.py b/scripts/Archive/TopicModelsVsOrderSets/patchAdHocOrderSet.py
new file mode 100644
index 00000000..06535856
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/patchAdHocOrderSet.py
@@ -0,0 +1,20 @@
+import sys,os;
+import shutil;
+from medinfo.common.Util import *;
+
+SUFFIX = "\t3106\n"
+
+shutil.copy(sys.argv[1], sys.argv[2]);  # Temp copy
+
+ifs = stdOpen(sys.argv[2]);
+ofs = stdOpen(sys.argv[1],"w");
+
+prog = ProgressDots();
+
+for i, line in enumerate(ifs):
+    prog.update();
+    if not line.endswith(SUFFIX):
+        ofs.write(line);
+    else:
+        print("Skipping Line %d" % i, file=sys.stderr);
+prog.printStatus();
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step10.preparePatientItems.py b/scripts/Archive/TopicModelsVsOrderSets/step10.preparePatientItems.py
new file mode 100644
index 00000000..ca47a08f
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step10.preparePatientItems.py
@@ -0,0 +1,73 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+
+EXCLUDE_CATEGORY_DESCRIPTIONS = ['Nursing','Discharge','Transfer','BB Call Slip','Transport','Admission','Pharmacy Supplies','Diet Communication'];
+EXCLUDE_CATEGORY_IDS = [4,5,11,13,18,23,46,66];    # From 5 year data set
+EXCLUDE_CATEGORY_IDS_STR = str.join(',', [str(catId) for catId in EXCLUDE_CATEGORY_IDS] );   # Concatenate into string form
+
+ADMIT_DX_CATEGORY_ID = 2;
+DEATH_ITEM_ID = 72416;
+DEMOGRAPHICS_CATEGORY_ID = 159;
+
+START_DATE_STR = "2013-01-01"
+END_DATE_STR = "2014-01-01"
+BASE_YEAR = 2013;
+
+#QUERY_TIME = 14400;
+#VERIFY_TIME = 86400;
+
+QUERY_TIME = 14400;
+#QUERY_TIME = 28800;
+#QUERY_TIME = 57600;
+#QUERY_TIME = 86400;
+VERIFY_TIME = QUERY_TIME;
+
+SOURCE_DATA_DIR = "sourceData";
+
+def main_prepPatientItems(argv):
+    prep = PreparePatientItems();
+    for i in range(-9,10):
+        prep.main(["PreparePatientItems","-S",START_DATE_STR, "-E",END_DATE_STR, "-p",str(DEMOGRAPHICS_CATEGORY_ID),"-c", str(ADMIT_DX_CATEGORY_ID), "-Q", str(QUERY_TIME), "-V", str(VERIFY_TIME), "-o", str(DEATH_ITEM_ID), "-t", "2592000", "%s/patientIds.5year.%s.tab.gz" % (SOURCE_DATA_DIR, i), "%s/firstItems.q%s.v%s.%s.%s.tab.gz" % (SOURCE_DATA_DIR, QUERY_TIME, VERIFY_TIME, BASE_YEAR, i)]);
+    
+    # Convert to (filtered) Bag of Words
+    for i in range(-9,10):
+        prep.main(["PreparePatientItems","-B","qvo","-X", EXCLUDE_CATEGORY_IDS_STR,"%s/firstItems.q%s.v%s.%s.%d.tab.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,i),"%s/firstItems.q%s.v%s.%s.%d.filter.bow.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,i)]);
+
+    # Concatenate batch of files
+    ofs = stdOpen("%s/firstItems.q%s.v%s.%s.1234567890.filter.bow.gz" % (SOURCE_DATA_DIR, QUERY_TIME, VERIFY_TIME, BASE_YEAR),"w");
+    for i in [1,2,3,4,5,6,7,8,9,0]:
+        ifs = stdOpen("%s/firstItems.q%s.v%s.%s.%d.filter.bow.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,i) );
+        ofs.write(ifs.read());
+        ifs.close();
+    ofs.close();
+    
+    # For comment and header row of csv files, drop repeats
+    baseIds = [-1,-2,-3,-4,-5];
+    ofs = stdOpen("%s/firstItems.q%s.v%s.%s.-12345.tab.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR),"w");
+    isHeaderRowWritten = False;
+    for baseId in baseIds:
+        ifs = stdOpen("%s/firstItems.q%s.v%s.%s.%d.tab.gz" % (SOURCE_DATA_DIR,QUERY_TIME,VERIFY_TIME,BASE_YEAR,baseId) );
+        for iLine, line in enumerate(ifs):
+            if not line.startswith(COMMENT_TAG):    # Skip comment lines
+                if line[0].isalpha():   # Starts with a letter/label, must be header row, not data
+                    if isHeaderRowWritten:
+                        continue;   # Skip text/header rows, except for the first one encountered
+                    else:
+                        isHeaderRowWritten = True;
+                ofs.write(line);
+        ifs.close();
+    ofs.close();
+ 
+if __name__ == "__main__":
+    main_prepPatientItems(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step110.prepareOrderSetItems.py b/scripts/Archive/TopicModelsVsOrderSets/step110.prepareOrderSetItems.py
new file mode 100644
index 00000000..d5d9f66e
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step110.prepareOrderSetItems.py
@@ -0,0 +1,68 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+
+EXCLUDE_CATEGORY_DESCRIPTIONS = ['Nursing','Discharge','Transfer','BB Call Slip','Transport','Admission','Pharmacy Supplies','Diet Communication'];
+EXCLUDE_CATEGORY_IDS = [4,5,11,13,18,23,46,66];    # From 5 year data set
+EXCLUDE_CATEGORY_IDS_STR = str.join(',', [str(catId) for catId in EXCLUDE_CATEGORY_IDS] );   # Concatenate into string form
+
+ADMIT_DX_CATEGORY_ID = 2;
+DEATH_ITEM_ID = 72416;
+DEMOGRAPHICS_CATEGORY_ID = 159;
+
+START_DATE_STR = "2013-01-01"
+END_DATE_STR = "2014-01-01"
+
+SOURCE_DATA_DIR = "sourceData";
+
+EVAL_TIMESPAN = 86400;
+VERIFY_TIMESPAN = 3600;
+
+BASE_OUTPUT_NAME = "first24hourOrderSets.2013.q%d.v%d" % (EVAL_TIMESPAN, VERIFY_TIMESPAN);
+
+def main_prepPatientItems(argv):
+    prep = PreparePatientItems();
+    for i in range(-9,10):
+        prep.main(["PreparePatientItems","-S",START_DATE_STR, "-E",END_DATE_STR, "-O", "-p",str(DEMOGRAPHICS_CATEGORY_ID),"-c", str(ADMIT_DX_CATEGORY_ID), "-Q", str(EVAL_TIMESPAN), "-V", str(VERIFY_TIMESPAN), "%s/patientIds.5year.%s.tab.gz" % (SOURCE_DATA_DIR, i), "%s/%s.%s.tab.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME, i)]);
+    
+    # Convert to (filtered) Bag of Words
+    for i in range(-9,10):
+        prep.main(["PreparePatientItems","-B","qvo","-X", EXCLUDE_CATEGORY_IDS_STR,"%s/%s.%s.tab.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME,i),"%s/%s.%s.filter.bow.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME,i)]);
+
+    # Concatenate batch of files
+    ofs = stdOpen("%s/%s.1234567890.filter.bow.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME),"w");
+    for i in [1,2,3,4,5,6,7,8,9,0]:
+        ifs = stdOpen("%s/%s.%d.filter.bow.gz" % (SOURCE_DATA_DIR, BASE_OUTPUT_NAME, i) );
+        ofs.write(ifs.read());
+        ifs.close();
+    ofs.close();
+    
+    # For comment and header row of csv files, drop repeats
+    baseIds = [-1,-2,-3,-4,-5];
+    ofs = stdOpen("%s/%s.-12345.tab.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME),"w");
+    isHeaderRowWritten = False;
+    for baseId in baseIds:
+        ifs = stdOpen("%s/%s.%d.tab.gz" % (SOURCE_DATA_DIR,BASE_OUTPUT_NAME,baseId) );
+        for iLine, line in enumerate(ifs):
+            if not line.startswith(COMMENT_TAG):    # Skip comment lines
+                if line[0].isalpha():   # Starts with a letter/label, must be header row, not data
+                    if isHeaderRowWritten:
+                        continue;   # Skip text/header rows, except for the first one encountered
+                    else:
+                        isHeaderRowWritten = True;
+                ofs.write(line);
+        ifs.close();
+    ofs.close();
+ 
+if __name__ == "__main__":
+    main_prepPatientItems(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step130.orderSetTopicModelAnalysis.py b/scripts/Archive/TopicModelsVsOrderSets/step130.orderSetTopicModelAnalysis.py
new file mode 100644
index 00000000..1e8bda8c
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step130.orderSetTopicModelAnalysis.py
@@ -0,0 +1,60 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.TopicModel import TopicModel;
+from medinfo.cpoe.analysis.TopicModelAnalysis import TopicModelAnalysis;
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+numTopicsOptions = [0,2,4,8,16,32,64,128,256,512,1024,2048];
+#numTopicsOptions = [16,32,64];
+
+RESULT_DIR = "results";
+
+BASE_MODEL_FILENAMES = ["models/topicModel.first24hourItems.2013.1234567890.filter.bow.gz.%sTopic.model"];
+VALIDATE_FILENAMES = ["sourceData/first24hourOrderSets.2013.-12345.tab.gz"];
+
+""" # Time matched model alternative
+BASE_MODEL_FILENAMES = \
+    [   "models/topicModel.firstItems.q14400.v14400.2013.1234567890.filter.bow.gz.%sTopic.model",
+        "models/topicModel.firstItems.q28800.v28800.2013.1234567890.filter.bow.gz.%sTopic.model",
+        "models/topicModel.firstItems.q57600.v57600.2013.1234567890.filter.bow.gz.%sTopic.model",
+        "models/topicModel.firstItems.q86400.v86400.2013.1234567890.filter.bow.gz.%sTopic.model",
+    ];
+VALIDATE_FILENAMES = \
+    [   "sourceData/first24hourOrderSets.2013.q86400.v14400.-12345.tab.gz",
+        "sourceData/first24hourOrderSets.2013.q86400.v28800.-12345.tab.gz",
+        "sourceData/first24hourOrderSets.2013.q86400.v57600.-12345.tab.gz",
+        "sourceData/first24hourOrderSets.2013.q86400.v86400.-12345.tab.gz",
+    ];
+"""
+
+#BASE_MODEL_FILENAME = "D:\Box Sync\SecureFiles\CDSS\models/topicModel.first24hourItems.2013.1234567890.filter.bow.gz.%sTopic.model";
+#VALIDATE_FILENAME = "sourceData/first24hourOrderSets.2013.sample.tab.gz";
+
+OUTPUT_BASENAME = "recClassification.byOrderSets";
+
+def main_topicModelAnalysis(argv):
+    instance = TopicModelAnalysis();
+
+    for baseModelFilename, validateFilename in zip(BASE_MODEL_FILENAMES, VALIDATE_FILENAMES):
+
+        for numTopics in numTopicsOptions:
+            modelFilename = baseModelFilename % numTopics;
+            argv = ["TopicModelAnalysis.py", "-M",modelFilename, "--numRecsByOrderSet", "-s","tf", validateFilename, RESULT_DIR+"/%s.tf.%s.%s" % (OUTPUT_BASENAME, os.path.basename(modelFilename),os.path.basename(validateFilename))];
+            instance.main(argv);
+
+            # Again with TF*IDF score weights
+            #argv = ["TopicModelAnalysis.py", "-M",modelFilename, "--numRecsByOrderSet", "-s","tfidf", validateFilename, RESULT_DIR+"/%s.tfidf.%s.%s" % (OUTPUT_BASENAME, os.path.basename(modelFilename),os.path.basename(validateFilename))];
+            #instance.main(argv);
+
+if __name__ == "__main__":
+    main_topicModelAnalysis(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step140.orderSetAssociationAnalysis.py b/scripts/Archive/TopicModelsVsOrderSets/step140.orderSetAssociationAnalysis.py
new file mode 100644
index 00000000..26bd6fcf
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step140.orderSetAssociationAnalysis.py
@@ -0,0 +1,32 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+RESULT_DIR = "results";
+
+VALIDATE_FILENAME = "sourceData/first24hourOrderSets.2013.-12345.tab.gz";
+#VALIDATE_FILENAME = "sourceData/first24hourOrderSets.2013.sample.tab.gz";
+
+OUTPUT_BASENAME = "recClassification.byOrderSets";
+
+def main_associationRec(argv):
+    validateFilename = VALIDATE_FILENAME; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    mod = RecommendationClassificationAnalysis();
+    sortOptions = ["prevalence","PPV","OR","RR","P-YatesChi2-NegLog"];
+    for sortOption in sortOptions:
+        subargv = ["RecommendationClassificationAnalysis.py", "-P", validateFilename, "--numRecsByOrderSet", "-R", "ItemAssociationRecommender", "-a", "weighted", "-t", "86400", "-p", "patient_", 
+                    "-s", sortOption, RESULT_DIR+"/%s.ItemAssociationRecommender.%s.tab.gz" % (OUTPUT_BASENAME, sortOption) ]
+        mod.main(subargv);
+
+if __name__ == "__main__":
+    main_associationRec(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step150.orderSetUsage.py b/scripts/Archive/TopicModelsVsOrderSets/step150.orderSetUsage.py
new file mode 100644
index 00000000..888c30d6
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step150.orderSetUsage.py
@@ -0,0 +1,29 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.OrderSetRecommenderClassificationAnalysis import OrderSetRecommenderClassificationAnalysis;
+from medinfo.cpoe.analysis.OrderSetUsageAnalysis import OrderSetUsageAnalysis;
+
+RESULT_DIR = "results";
+VALIDATE_FILENAME = "sourceData/first24hourOrderSets.2013.-12345.tab.gz";
+#VALIDATE_FILENAME = "sourceData/first24hourOrderSets.2013.sample.tab.gz";
+OUTPUT_BASENAME = "recClassification.byOrderSets";
+
+def main_associationRec(argv):
+    validateFilename = VALIDATE_FILENAME; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    mod = OrderSetUsageAnalysis();
+    subargv = ["OrderSetUsageAnalysis.py", "--numRecsByOrderSet"];
+    subargv.extend([validateFilename, RESULT_DIR+"/%s.OrderSetUsage.tab.gz" % (OUTPUT_BASENAME)]);
+    mod.main(subargv);
+
+if __name__ == "__main__":
+    main_associationRec(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step170.collateResults.py b/scripts/Archive/TopicModelsVsOrderSets/step170.collateResults.py
new file mode 100644
index 00000000..98d0322d
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step170.collateResults.py
@@ -0,0 +1,86 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.analysis.ConcatenateDataFiles import ConcatenateDataFiles;
+from medinfo.analysis.SQLQueryDataFile import SQLQueryDataFile;
+from medinfo.analysis.BatchTTests import BatchTTests;
+
+BASE_RESULT_DIR = "results/byOrderSets/";
+RESULT_DIRS = \
+    [   BASE_RESULT_DIR+"01minutes/",
+        BASE_RESULT_DIR+"30minutes/",
+        BASE_RESULT_DIR+"60minutes/",
+        BASE_RESULT_DIR+"120minutes/",
+        BASE_RESULT_DIR+"240minutes/",
+        BASE_RESULT_DIR+"480minutes/",
+        BASE_RESULT_DIR+"960minutes/",
+        BASE_RESULT_DIR+"1440minutes/",
+        BASE_RESULT_DIR+"timeMatched/",
+    ];
+RESULT_BASENAME = "recClassification.byOrderSets";
+FILELIST_FILENAME = "resultFileList.txt";
+CONCATENATE_FILENAME = "concatenatedResults.tab.gz";
+FILTERED_FILENAME = "filteredResults.tab.gz";   # Screen out rows with 0 verify / recommended items
+TTEST_FILENAME = "ttests.tab.gz";
+
+#DATA_QUERY = "select * from data where numQueryItems <> 0 and numVerifyItems <> 0 and numRecommendedItems <> 0";
+# Add normalized precision calculations. Obtuse functions based on absolute values to simulate min / max functions for two input values
+DATA_QUERY = """select *, 
+    (tp+0.0) / ( ((numVerifyItems+numRecommendedItems) - abs(numVerifyItems-numRecommendedItems) ) / 2 ) as normalprecision
+    from data 
+    where numQueryItems <> 0 and numVerifyItems <> 0 and numRecommendedItems <> 0""";
+
+LABEL_COLS = "_s,_m";  # Sort field separates methods. _m for topic model variant. Underscore instead of dash for SQL query filter accomodation
+VALUE_COLS = "numqueryitems,numverifyitems,numrecommendeditems,tp,precision,recall,normalprecision,weightrecall,roc_auc";
+MATCH_COLS = "patient_id,order_set_id";
+BASE_LABELS = "None,None";
+
+def main_concatenate(argv):
+    mod = ConcatenateDataFiles();
+    for resultDir in RESULT_DIRS:
+        fileListFile = stdOpen(resultDir+FILELIST_FILENAME, "w");
+        for filename in os.listdir(resultDir):
+            if filename.startswith(RESULT_BASENAME):  
+                print(resultDir+filename, file=fileListFile);
+        fileListFile.close();
+        subargv = ["ConcatenateDataFiles.py","-o",resultDir+CONCATENATE_FILENAME];
+        subargv.append(resultDir+FILELIST_FILENAME);
+        mod.main(subargv);
+
+def main_filter(argv):
+    mod = SQLQueryDataFile();
+    for resultDir in RESULT_DIRS:
+        subargv = ["SQLQueryDataFile.py", "-q", DATA_QUERY];
+        subargv.extend([resultDir+CONCATENATE_FILENAME, resultDir+FILTERED_FILENAME]);
+        mod.main(subargv);
+
+def main_ttests(argv):
+    mod = BatchTTests();
+    for resultDir in RESULT_DIRS:
+        subargv = ["BatchTTests.py", "-l",LABEL_COLS, "-v",VALUE_COLS, "-m",MATCH_COLS, "-b", BASE_LABELS];
+        subargv.extend([resultDir+FILTERED_FILENAME, resultDir+TTEST_FILENAME]);
+        mod.main(subargv);
+
+def main_mergeTTestResults(argv):
+    mod = ConcatenateDataFiles();
+    fileListFile = stdOpen(BASE_RESULT_DIR+FILELIST_FILENAME, "w");
+    for resultDir in RESULT_DIRS:
+        print(resultDir+TTEST_FILENAME, file=fileListFile);
+    fileListFile.close();
+    subargv = ["ConcatenateDataFiles.py","-o", BASE_RESULT_DIR+CONCATENATE_FILENAME];
+    subargv.append(BASE_RESULT_DIR+FILELIST_FILENAME);
+    mod.main(subargv);
+
+if __name__ == "__main__":
+    main_concatenate(sys.argv);
+    main_filter(sys.argv);
+    main_ttests(sys.argv);
+    main_mergeTTestResults(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step180.formatResults.py b/scripts/Archive/TopicModelsVsOrderSets/step180.formatResults.py
new file mode 100644
index 00000000..4fb51012
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step180.formatResults.py
@@ -0,0 +1,69 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+import json;
+from io import StringIO;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.analysis.ConcatenateDataFiles import ConcatenateDataFiles;
+from medinfo.analysis.SQLQueryDataFile import SQLQueryDataFile;
+from medinfo.analysis.BatchTTests import BatchTTests;
+
+BASE_RESULT_DIR = "results/byOrderSets/";
+RESULT_BASENAME = "recClassification.byOrderSets";
+FILELIST_FILENAME = "resultFileList.txt";
+CONCATENATE_FILENAME = "concatenatedResults.tab.gz";
+FILTERED_FILENAME = "filteredResults.tab.gz";   # Simplified results format for easier interpretation / visualization
+SORTED_FILENAME = "sortedResults.tab.gz";   # Should be able to copy these results into Excel to make PivotTables / PivotCharts of results
+
+DATA_QUERY = "select * from data order by SortType, TopicCount, VerifyTime";
+
+def main_formatMergedTTests(argv):
+    ifs = stdOpen(BASE_RESULT_DIR+CONCATENATE_FILENAME);
+    ofs = stdOpen(BASE_RESULT_DIR+FILTERED_FILENAME, "w");
+
+    summaryData = {"argv": argv};
+    print(COMMENT_TAG, json.dumps(summaryData), file=ofs);
+
+    outputCols = ["SortType","TopicCount","VerifyTime","Group1.precision.mean","Group1.recall.mean","Group1.normalprecision.mean","Group1.weightrecall.mean","Group1.roc_auc.mean","ttest_rel.precision","ttest_rel.recall","ttest_rel.weightrecall","ttest_rel.roc_auc","Group1.numqueryitems.mean","Group1.numverifyitems.mean","Group1.numrecommendeditems.mean","Group1.tp.mean"];
+    formatter = TextResultsFormatter(ofs);
+    formatter.formatTuple(outputCols);  # Output header row
+
+    reader = TabDictReader(ifs);
+    for row in reader:
+        row["SortType"] = row["Group1._s"];
+
+        # Extract out numerical data from filename text parameters
+        row["TopicCount"] = None;
+        if row["Group1._m"] != 'None':
+            # Expecting model name strings of the form: "models/topicModel.first24hourItems.2013.1234567890.filter.bow.gz.64Topic.model"
+            topicChunk = row["Group1._m"].split(".")[-2];   # Expect second to last period-delimited chunk to contain topic count
+            topicChunk = topicChunk[:topicChunk.find("Topic")]; # Remove trailing Topic text
+            row["TopicCount"] = int(topicChunk);
+
+        # Expecting result file name argument of the form: "results/byOrderSets/01minutes/filteredResults.tab.gz"
+        timeChunk = row["args[0]"].split("/")[-2];
+        timeChunk = timeChunk[:timeChunk.find("minutes")];
+        row["VerifyTime"] = int(timeChunk);
+
+        formatter.formatResultDict(row, outputCols);
+
+    ifs.close();
+    ofs.close();
+
+def main_sortResults(argv):
+    mod = SQLQueryDataFile();
+    subargv = ["SQLQueryDataFile.py", "-q", DATA_QUERY];
+    subargv.extend([BASE_RESULT_DIR+FILTERED_FILENAME, BASE_RESULT_DIR+SORTED_FILENAME]);
+    mod.main(subargv);
+
+if __name__ == "__main__":
+    main_formatMergedTTests(sys.argv);
+    main_sortResults(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step185.formatResults.TimeMatched.py b/scripts/Archive/TopicModelsVsOrderSets/step185.formatResults.TimeMatched.py
new file mode 100644
index 00000000..1c188679
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step185.formatResults.TimeMatched.py
@@ -0,0 +1,85 @@
+"""
+Shell script to run several processes.
+Different layout for Time Matched experiment series. Won't have an order set reference to compare against?
+"""
+import sys,os;
+import time;
+import json;
+from io import StringIO;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.analysis.ConcatenateDataFiles import ConcatenateDataFiles;
+from medinfo.analysis.SQLQueryDataFile import SQLQueryDataFile;
+from medinfo.analysis.BatchTTests import BatchTTests;
+
+BASE_RESULT_DIR = "results/byOrderSets/timeMatched/";
+FILTERED_FILENAME = "filteredResults.tab.gz";   # Previously filtered already, now format and sort results
+FORMATTED_FILENAME = "formattedResults.tab.gz";
+SORTED_FILENAME = "sortedResults.tab.gz";
+
+DATA_QUERY = \
+"""select SortType, TopicCount, TrainTime, VerifyTime, 
+   avg(precision) as precision_mean, avg(recall) as recall_mean, avg(normalprecision) as normalprecision_mean, avg(weightrecall) as weightrecall_mean, avg(roc_auc) as roc_auc_mean 
+from data
+group by SortType, TopicCount, TrainTime, VerifyTime
+order by SortType, TopicCount, TrainTime, VerifyTime
+""";
+
+def main_formatResults(argv):
+    ifs = stdOpen(BASE_RESULT_DIR+FILTERED_FILENAME);
+    ofs = stdOpen(BASE_RESULT_DIR+FORMATTED_FILENAME, "w");
+
+    summaryData = {"argv": argv};
+    print(COMMENT_TAG, json.dumps(summaryData), file=ofs);
+
+    outputCols = ["SortType","TopicCount","TrainTime","VerifyTime","precision","recall","normalprecision","weightrecall","roc_auc"];
+    formatter = TextResultsFormatter(ofs);
+    formatter.formatTuple(outputCols);  # Output header row
+
+    reader = TabDictReader(ifs);
+    for row in reader:
+        row["SortType"] = row["_s"];
+
+        # Extract out numerical data from filename text parameters
+        row["TopicCount"] = None;
+        row["TrainTime"] = None;
+        if row["_m"] != 'None':
+            # Expecting model name strings of the form: "models/topicModel.firstItems.q14400.v14400.2013.1234567890.filter.bow.gz.16Topic.model"
+            chunks = row["_m"].split(".");
+            topicChunk = chunks[-2];   # Expect second to last period-delimited chunk to contain topic count
+            topicChunk = topicChunk[:topicChunk.find("Topic")]; # Remove trailing Topic text
+            row["TopicCount"] = int(topicChunk);
+
+            for chunk in chunks:
+                if chunk[0] == "q" and chunk[-1].isdigit(): # This should be the query time in seconds
+                    queryTimeSeconds = int(chunk[1:]);
+                    queryTimeMinutes = queryTimeSeconds / 60;
+                    row["TrainTime"] = queryTimeMinutes;
+
+        # Expecting training file name argument of the form: "sourceData/first24hourOrderSets.2013.q86400.v14400.-12345.tab.gz"
+        row["VerifyTime"] = None;
+        for chunk in row["args_0_"].split("."):
+            if chunk[0] == "v" and chunk[-1].isdigit(): # This should be the verify time in seconds
+                verifyTimeSeconds = int(chunk[1:]);
+                verifyTimeMinutes = verifyTimeSeconds / 60;
+                row["VerifyTime"] = verifyTimeMinutes;
+
+        formatter.formatResultDict(row, outputCols);
+
+    ifs.close();
+    ofs.close();
+
+def main_sortResults(argv):
+    mod = SQLQueryDataFile();
+    subargv = ["SQLQueryDataFile.py", "-q", DATA_QUERY];
+    subargv.extend([BASE_RESULT_DIR+FORMATTED_FILENAME, BASE_RESULT_DIR+SORTED_FILENAME]);
+    mod.main(subargv);
+
+if __name__ == "__main__":
+    main_formatResults(sys.argv);
+    main_sortResults(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step20.buildTopicModel.py b/scripts/Archive/TopicModelsVsOrderSets/step20.buildTopicModel.py
new file mode 100644
index 00000000..f7c9a763
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step20.buildTopicModel.py
@@ -0,0 +1,44 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from scipy.stats import ttest_rel, ttest_ind;
+from numpy import mean;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.TopicModel import TopicModel;
+from medinfo.cpoe.analysis.TopicModelAnalysis import TopicModelAnalysis;
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+SOURCE_DATA_DIR = "sourceData/";
+#MODEL_DIR = "D:\Box Sync\SecureFiles\CDSS\models";
+MODEL_DIR = "D:/Documents/tempModels";
+
+#INPUT_FILENAME = "first24HourItems.2013.1.filter.bow.gz";
+INPUT_FILENAME = "first24HourItems.2013.1234567890.filter.bow.gz";
+#INPUT_FILENAME = "firstItems.q14400.v14400.2013.1234567890.filter.bow.gz";
+#INPUT_FILENAME = "firstItems.q28800.v28800.2013.1234567890.filter.bow.gz";
+#INPUT_FILENAME = "firstItems.q57600.v57600.2013.1234567890.filter.bow.gz";
+#INPUT_FILENAME = "firstItems.q86400.v86400.2013.1234567890.filter.bow.gz";
+
+#numTopicsOptions = [0,2,4,8,16,32,64,128,256,512,1024,2048];
+numTopicsOptions = [2,4,8,16,32,64,128,256,512,1024,2048];
+
+def main_buildTopicModel(argv):
+    bowInputFilename = SOURCE_DATA_DIR+INPUT_FILENAME;
+
+    mod = TopicModel();
+    for numTopics in numTopicsOptions:
+        subargv = ["TopicModel", "-n", str(numTopics)];
+        subargv.extend([bowInputFilename, MODEL_DIR+"/topicModel."+os.path.basename(bowInputFilename)+".%dTopic.model" % (numTopics), ]);
+        mod.main(subargv);
+    return mod.model;
+
+if __name__ == "__main__":
+    model = main_buildTopicModel(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step30.topicModelAnalysis.py b/scripts/Archive/TopicModelsVsOrderSets/step30.topicModelAnalysis.py
new file mode 100644
index 00000000..d0caee97
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step30.topicModelAnalysis.py
@@ -0,0 +1,42 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from scipy.stats import ttest_rel, ttest_ind;
+from numpy import mean;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.TopicModel import TopicModel;
+from medinfo.cpoe.analysis.TopicModelAnalysis import TopicModelAnalysis;
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+NUM_RECOMMENDATIONS = 10;
+
+#numTopicsOptions = [0,2,4,8,16,32,64,128,256,512];
+numTopicsOptions = [512,1024,2048,4096];
+
+RESULT_DIR = "results";
+
+def main_topicModelAnalysis(argv):
+    baseModelFilename = argv[1];    # e.g., models/topicModel.first24hourItems.2013.1234567890.filter.bow.gz.%sTopic.model
+    validateFilename = argv[2]; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    instance = TopicModelAnalysis();
+
+    for numTopics in numTopicsOptions:
+        modelFilename = baseModelFilename % numTopics;
+        argv = ["TopicModelAnalysis.py", "-M",modelFilename, "-r",str(NUM_RECOMMENDATIONS), "-s","tf", validateFilename, RESULT_DIR+"/recClassification.tf.%s.%s" % (os.path.basename(modelFilename),os.path.basename(validateFilename))];
+        instance.main(argv);
+
+        # Again with TF*IDF score weights
+        argv = ["TopicModelAnalysis.py", "-M",modelFilename, "-r",str(NUM_RECOMMENDATIONS), "-s","tfidf", validateFilename, RESULT_DIR+"/recClassification.tfidf.%s.%s" % (os.path.basename(modelFilename),os.path.basename(validateFilename))];
+        instance.main(argv);
+
+if __name__ == "__main__":
+    main_topicModelAnalysis(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step40.associationAnalysis.py b/scripts/Archive/TopicModelsVsOrderSets/step40.associationAnalysis.py
new file mode 100644
index 00000000..83a59a29
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step40.associationAnalysis.py
@@ -0,0 +1,30 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from scipy.stats import ttest_rel, ttest_ind;
+from numpy import mean;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+NUM_RECOMMENDATIONS = 10;
+RESULT_DIR = "results";
+
+def main_associationRec(argv):
+    validateFilename = argv[1]; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    mod = RecommendationClassificationAnalysis();
+    sortOptions = ["prevalence","PPV","OR","RR","P-YatesChi2-NegLog"];
+    for sortOption in sortOptions:
+        subargv = ["RecommendationClassificationAnalysis.py", "-P", validateFilename, "-r", str(NUM_RECOMMENDATIONS), "-R", "ItemAssociationRecommender", "-a", "weighted", "-t", "86400", "-p", "patient_", 
+                    "-s", sortOption, RESULT_DIR+"/recClassification.ItemAssociationRecommender.%s.tab.gz" % sortOption]
+        mod.main(subargv);
+
+if __name__ == "__main__":
+    main_associationRec(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/step50.orderSetUsage.py b/scripts/Archive/TopicModelsVsOrderSets/step50.orderSetUsage.py
new file mode 100644
index 00000000..55b10713
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/step50.orderSetUsage.py
@@ -0,0 +1,42 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from scipy.stats import ttest_rel, ttest_ind;
+from numpy import mean;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.analysis.OrderSetRecommenderClassificationAnalysis import OrderSetRecommenderClassificationAnalysis;
+from medinfo.cpoe.analysis.OrderSetUsageAnalysis import OrderSetUsageAnalysis;
+
+NUM_RECOMMENDATIONS = 10;
+RESULT_DIR = "results";
+
+def main_associationRec(argv):
+    validateFilename = argv[1]; # e.g., sourceData/first24hourItems.2013.-12345.tab.gz
+
+    mod = OrderSetRecommenderClassificationAnalysis();
+    sortOptions = ["tf","tfidf"];
+    for sortOption in sortOptions:
+        subargv = ["OrderSetRecommenderClassificationAnalysis.py", "-r", str(NUM_RECOMMENDATIONS), "-s", sortOption, 
+                    validateFilename, RESULT_DIR+"/recClassification.OrderSetRecommender.%s.tab.gz" % sortOption]
+        mod.main(subargv);
+
+    mod = OrderSetUsageAnalysis();
+    numRecsOptions = [0,NUM_RECOMMENDATIONS];
+    sortFieldOptions = ["patient_count","name"];
+    #numRecsOptions = [0];
+    #sortFieldOptions = ["name"];
+    #for numRecsOption in numRecsOptions:
+    #    for sortField in sortFieldOptions:
+    #        subargv = ["OrderSetUsageAnalysis.py", "-r", str(numRecsOption), "-s", sortField];
+    #        subargv.extend([validateFilename, RESULT_DIR+"/recClassification.OrderSetUsage.%sRecs.%s.tab.gz" % (numRecsOption,sortField)]);
+    #        mod.main(subargv);
+
+if __name__ == "__main__":
+    main_associationRec(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/stepXX.quickTest.py b/scripts/Archive/TopicModelsVsOrderSets/stepXX.quickTest.py
new file mode 100644
index 00000000..b0b443fe
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/stepXX.quickTest.py
@@ -0,0 +1,50 @@
+"""
+Shell script to run several processes
+"""
+import sys,os;
+import time;
+from scipy.stats import ttest_rel, ttest_ind;
+from numpy import mean;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
+from medinfo.db.Model import RowItemModel;
+from medinfo.db.Model import RowItemFieldComparator, columnFromModelList;
+from medinfo.db import DBUtil;
+
+from medinfo.cpoe.TopicModel import TopicModel;
+from medinfo.cpoe.analysis.TopicModelAnalysis import TopicModelAnalysis;
+from medinfo.cpoe.analysis.PreparePatientItems import PreparePatientItems;
+from medinfo.cpoe.analysis.RecommendationClassificationAnalysis import RecommendationClassificationAnalysis;
+
+def main_quickTest(argv):
+    modelFilename = argv[1];
+    modeler = TopicModel();
+
+    timer = time.time();
+    (model, docCountByWordId) = modeler.loadModelAndDocCounts(modelFilename);
+    timer = time.time() - timer;
+    log.info("%.2f seconds to load",timer);
+    
+    timer = time.time();
+    weightByItemIdByTopicId = modeler.generateWeightByItemIdByTopicId(model, 100);
+    timer = time.time() - timer;
+    log.info("%.2f seconds to generate weights",timer);
+
+    for i in range(3):
+        prog = ProgressDots();
+        for (topicId, weightByItemId) in weightByItemIdByTopicId.items():
+            for (itemId, itemWeight) in weightByItemId.items():
+                prog.update();
+        prog.printStatus();
+    
+    """
+    for i in xrange(3):
+        prog = ProgressDots();
+        for (topicId, topicItems) in modeler.enumerateTopics(model, 100):
+            for (itemId, itemScore) in topicItems:
+                prog.update();
+        prog.printStatus();
+    """
+   
+if __name__ == "__main__":
+    main_quickTest(sys.argv);
diff --git a/scripts/Archive/TopicModelsVsOrderSets/testGenSim.py b/scripts/Archive/TopicModelsVsOrderSets/testGenSim.py
new file mode 100644
index 00000000..2147df95
--- /dev/null
+++ b/scripts/Archive/TopicModelsVsOrderSets/testGenSim.py
@@ -0,0 +1,115 @@
+import sys, os;
+from pprint import pprint   # pretty-printer
+
+import gensim;
+from gensim import corpora, models, similarities
+from medinfo.common.Util import *;
+
+def main(argv):
+    pass;
+    
+    
+documents = \
+    [   "Human machine interface for lab abc computer applications",
+        "A survey of user opinion of computer system response time",
+        "The EPS user interface management system",
+        "System and human system engineering testing of EPS",
+        "Relation of user perceived response time to error measurement",
+        "The generation of random binary unordered trees",
+        "The intersection graph of paths in trees",
+        "Graph minors IV Widths of trees and well quasi ordering",
+        "Graph minors A survey",
+    ];
+
+# Texts: Lists of words
+stoplist = set('for a of the and to in'.split())
+texts = \
+    [   [word for word in document.lower().split() if word not in stoplist] 
+        for document in documents];
+
+pprint(texts);
+
+# Word/Token/Term frequency counts
+from collections import defaultdict
+frequency = defaultdict(int)
+for text in texts:
+    for token in text:
+        frequency[token] += 1
+
+
+# Remove words that appear only once
+texts = \
+    [   [token for token in text if frequency[token] > 1]
+        for text in texts
+    ];
+
+pprint(texts);
+
+
+
+# GenSim Dictionary format to convert item lists into count vectors/dicts
+dictionary = corpora.Dictionary(texts); # Instead of full list of texts, could feed in an iterator/generator
+# dictionary.save("temp.dict");
+print(dictionary);
+print(dictionary.token2id); # Mapping of text tokens to generated indexes
+print(dictionary.id2token); # Mapping of text tokens to generated indexes
+
+# Convert a document into vector form
+newDoc = "Human computer interaction";
+newVec = dictionary.doc2bow(newDoc.lower().split());
+print(newVec);
+
+# Corpus: Bag of Words (BoW) sparse vector/dictionary formats (list of 2-ples: (wordId, frequencyCount))
+corpus = [dictionary.doc2bow(text) for text in texts];
+corpora.MmCorpus.serialize("temp.mm",corpus)
+pprint(corpus); # Table of (wordId, count) pairs.  One row per document.
+"""
+print "---------"
+
+reloadCorpus = corpora.MmCorpus("temp.mm");
+for bag in reloadCorpus:
+    print bag
+"""
+# NumPy and SciPy conversions   (Matrixes seem to be transposed compared to what would expect)
+"""
+numpyMatrix = gensim.matutils.corpus2dense(corpus, len(dictionary));
+print numpyMatrix;
+
+scipyCSC = gensim.matutils.corpus2csc(corpus);
+print scipyCSC;
+"""
+
+# Model data.  TFIDF simple example.  Later Latent Dirichlet Allocation will be much more costly
+tfidf = models.TfidfModel(corpus);  # Computes document frequencies of each item
+# Use model to convert (bag-of-word count) vectors into new (TFIDF floating point weights).  Normalize each document vector so Eucledean magnitude of 1
+corpus_tfidf = tfidf[corpus]
+
+"""
+# Latent Semantic Indexing
+print "LSI?????"
+lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2);
+corpus_lsi = lsi[corpus_tfidf]; # Chaining transformation wrappers
+pprint(lsi.print_topics(2));
+"""
+print("LDA...")
+nTopics = 4;
+#lda = models.LdaMulticore(corpus_tfidf,id2word=dictionary, num_topics=nTopics, workers=2);
+lda = models.LdaModel(corpus_tfidf,id2word=dictionary, num_topics=nTopics);
+hdp = models.hdpmodel.HdpModel(corpus_tfidf,id2word=dictionary);
+
+#for iTopic in xrange(nTopics):
+#    print lda.show_topic(iTopic, topn=20);
+
+#topics = lda.show_topics(num_topics=-1, num_words=20, formatted=False)
+#topics = lda.show_topics(topics=-1, topn=20, formatted=False)
+#for topicId, topicItems in enumerate(topics):
+#    for (itemDescr, itemScore) in topicItems:
+#        print topicId, itemDescr, itemScore;
+
+#corpus_lda = lda[corpus_tfidf];
+#for doc in corpus_lda:
+#    print(doc);
+
+if __name__ == "__main__":
+    main(sys.argv);
+    
\ No newline at end of file
diff --git a/scripts/AuditLog/MooreMeeting/July10.md b/scripts/AuditLog/MooreMeeting/July10.md
new file mode 100644
index 00000000..f34a8352
--- /dev/null
+++ b/scripts/AuditLog/MooreMeeting/July10.md
@@ -0,0 +1,39 @@
+select * from starr_datalake2018.order_med where 
+pat_enc_csn_id_coded in (
+select distinct(pat_enc_csn_id_coded) 
+from
+(
+select 
+  op.jc_uid, op.pat_enc_csn_id_coded, 
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  datalake_47618.order_proc as op, 
+  datalake_47618.adt as admit, 
+  datalake_47618.order_proc as opCT,
+  datalake_47618.order_med as om,
+  datalake_47618.mar as mar,
+  datalake_47618.adt as inpatient
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, 
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime
+) 
+)
+
diff --git a/scripts/AuditLog/ct_head.R b/scripts/AuditLog/ct_head.R
new file mode 100644
index 00000000..5315b363
--- /dev/null
+++ b/scripts/AuditLog/ct_head.R
@@ -0,0 +1,397 @@
+library(bigrquery)
+library(DBI)
+library(dplyr)
+library(lubridate)
+library(stringr)
+library(data.table)
+library(ggplot2)
+
+path <- "/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read"
+setwd(path)
+
+# Assigning GCP Project Name
+project <- "mining-clinical-decisions" # put your project ID here
+
+#https://console.cloud.google.com/bigquery?project=mining-clinical-decisions&page=savedqueries&sq=103208666750:607c18a7b9254ec4b1543533d242d46d
+
+# Creating DBI connection to Google BigQuery 1
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+# libraries to read in
+library(bigrquery)
+library(data.table)
+library(feather)
+library(reticulate)
+library(dplyr)
+library(psych)
+library(timevis)
+library(shiny)
+
+# python libraries 
+pandas <- import("pandas")
+pa <- import("pyarrow")
+pq <- pa$parquet
+
+# Google Project Name 
+project <- "mining-clinical-decisions" # put your project ID here
+
+# writes csv to Sync Folder
+push_quiver <- function(df, name, env){
+  write.csv(df, paste0(env ,name,".csv"))
+}
+
+# writes df to feather format to Sync Folder
+push_quiver_feather <- function(df, name, env){
+  write_feather(df, paste0(env ,name,".feather"))
+}
+
+# lists files from Sync Folder
+list_quiver_csv <- function(env){
+  files <- list.files(env, pattern='*\\.csv', recursive=TRUE)
+  return(unlist(as.vector(strsplit(files, ".csv"))))
+}
+
+# Lists feather files from Sync Folder
+list_quiver_feather <- function(env){
+  files <- list.files(env, pattern='*\\.feather', recursive=TRUE)
+  return(unlist(as.vector(strsplit(files, ".feather"))))
+}
+
+# reads CSV from Sync Folder
+read_quiver <- function(file,env){
+  return(read.csv(paste0(env,file,".csv")))
+}
+
+# reads CSV using fread 
+fread_quiver <- function(file,env){
+  return(fread(paste0(env,file,".csv")))
+}
+
+# reads feather from sync folder
+fread_quiver_feather <- function(file,env){
+  return(read_feather(paste0(env,file,".feather")))
+}
+
+
+# generic reticulate function to read Parquet file in R
+read_parquet <- function(path, columns = NULL) {
+  
+  path <- path.expand(path)
+  path <- normalizePath(path)
+  
+  if (!is.null(columns)) columns = as.list(columns)
+  
+  xdf <- pandas$read_parquet(path, columns = columns)
+  
+  xdf <- as.data.frame(xdf, stringsAsFactors = FALSE)
+  
+  dplyr::tbl_df(xdf)
+  
+}
+
+
+# File Read System 
+stroke_cohort       <- fread_quiver("stroke_cohort", sync_environment)
+stroke_cohort_demo  <- fread_quiver("stroke_cohort_demo", sync_environment)
+
+# splits patients into individual lists 
+patient_list <- split(stroke_cohort,stroke_cohort$jc_uid)
+
+# patient 1: need to convert to timevis format 
+p1 <- patient_list$JCcb69cc
+
+# DESIRED DATA FORMAT RESULT 
+timevisData <- data.frame(
+  id = 1:11,
+  content = c("Open", "Open",
+              "Open", "Open", "Half price entry",
+              "Staff meeting", "Open", "Adults only", "Open", "Hot tub closes",
+              "Siesta"),
+  start = c("2016-05-01 07:30:00", "2016-05-01 14:00:00",
+            "2016-05-01 06:00:00", "2016-05-01 14:00:00", "2016-05-01 08:00:00",
+            "2016-05-01 08:00:00", "2016-05-01 08:30:00", "2016-05-01 14:00:00",
+            "2016-05-01 16:00:00", "2016-05-01 19:30:00",
+            "2016-05-01 12:00:00"),
+  end   = c("2016-05-01 12:00:00", "2016-05-01 20:00:00",
+            "2016-05-01 12:00:00", "2016-05-01 22:00:00", "2016-05-01 10:00:00",
+            "2016-05-01 08:30:00", "2016-05-01 12:00:00", "2016-05-01 16:00:00",
+            "2016-05-01 20:00:00", NA,
+            "2016-05-01 14:00:00"),
+  group = c(rep("lib", 2), rep("gym", 3), rep("pool", 5), NA),
+  type = c(rep("range", 9), "point", "background")
+)
+
+timevisDataGroups <- data.frame(
+  id = c("lib", "gym", "pool"),
+  content = c("Lab", "Emergency Room", "CT Head")
+)
+
+# write a function to convert single patient to timeline vis data frame format 
+
+# time start is inpatient admit time 
+
+# group1 = time to CT head order 
+# group2 = time to TPA order 
+# group3 = time from order to admin 
+# group4 = time from emergency admit time to inpatient admit time 
+
+start    <- p1 %>% select(emergencyAdmitTime) #admit_time
+end  <-  p1 %>% select(ctHeadOrderTime) #ct_order_time
+content <- "CT Head Order Time"
+group <- "EHR"
+type <- "RANGE"
+id <- 1
+t <- cbind(start,end,content,group,type,id)
+colnames(t) <- c("start","end","content","group","type","id")
+
+
+# function to clean data 
+# 
+library(lubridate)
+#with_tz(ymd_hms(stroke_cohort$emergencyAdmitTime),"America/Los_Angeles")
+
+convert_datetime <- function(timeEHR){
+  #convert to America_Pacific in lubridate
+  # requires format like this
+  # "2014-06-18 17:21:00" "2014-08-05 16:14:00" "2014-08-31 19:10:00" "2014-09-02 19:23:00" "2014-09-24 15:27:00" "2014-09-27 16:21:00" "2014-09-27 18:16:00"
+  # "2014-09-28 18:46:00" "2014-10-18 15:55:00" "2014-10-29 19:21:00" "2014-10-29 22:53:00" "2014-10-30 18:52:00" "2014-11-10 13:30:00" "2014-11-23 18:21:00"
+  # "2014-11-25 11:43:00" "2014-11-29 10:17:00"
+  
+  #into this format
+  
+  #[31] "2011-10-17 06:19:00 PDT" "2011-11-06 07:03:00 PST" "2011-11-07 12:36:00 PST" "2011-11-12 03:29:00 PST" "2011-11-16 23:37:00 PST"
+  #[36] "2011-11-18 09:11:00 PST" "2011-11-29 05:21:00 PST" "2011-12-11 09:54:00 PST" "2011-12-19 01:49:00 PST" "2011-12-21 04:14:00 PST"
+  #[41] "2012-01-04 10:31:00 PST" "2012-01-22 09:31:00 PST" 
+  return(with_tz(ymd_hms(timeEHR),"America/Los_Angeles"))
+}
+
+
+find_time_difference <- function(time_start,time_end, jc_uid){
+  time_to_ct <- as.numeric(convert_datetime(time_end) - convert_datetime(time_start)) /60
+  #return(time_to_ct)
+  ct.df <- as_tibble(cbind(jc_uid, time_to_ct))
+  colnames(ct.df)[1] <- "id"
+  colnames(ct.df)[2] <- "time_diff"
+  ct.df$time_diff <- as.numeric(ct.df$time_diff)
+  return(ct.df)
+}
+
+
+data_tables <- dbListTables(con)
+encounter <- tbl(con, "encounter")
+order_med <- tbl(con, "order_med")
+
+#order_med.dt <- 
+
+# function to clean data 
+# 
+library(lubridate)
+#with_tz(ymd_hms(stroke_cohort$emergencyAdmitTime),"America/Los_Angeles")
+
+convert_datetime <- function(timeEHR){
+  #convert to America_Pacific in lubridate
+  # requires format like this
+  # "2014-06-18 17:21:00" "2014-08-05 16:14:00" "2014-08-31 19:10:00" "2014-09-02 19:23:00" "2014-09-24 15:27:00" "2014-09-27 16:21:00" "2014-09-27 18:16:00"
+  # "2014-09-28 18:46:00" "2014-10-18 15:55:00" "2014-10-29 19:21:00" "2014-10-29 22:53:00" "2014-10-30 18:52:00" "2014-11-10 13:30:00" "2014-11-23 18:21:00"
+  # "2014-11-25 11:43:00" "2014-11-29 10:17:00"
+  
+  #into this format
+  
+  #[31] "2011-10-17 06:19:00 PDT" "2011-11-06 07:03:00 PST" "2011-11-07 12:36:00 PST" "2011-11-12 03:29:00 PST" "2011-11-16 23:37:00 PST"
+  #[36] "2011-11-18 09:11:00 PST" "2011-11-29 05:21:00 PST" "2011-12-11 09:54:00 PST" "2011-12-19 01:49:00 PST" "2011-12-21 04:14:00 PST"
+  #[41] "2012-01-04 10:31:00 PST" "2012-01-22 09:31:00 PST" 
+  return(with_tz(ymd_hms(timeEHR),"America/Los_Angeles"))
+}
+
+
+find_time_difference <- function(time_start,time_end, jc_uid){
+  time_to_ct <- as.numeric(convert_datetime(time_end) - convert_datetime(time_start)) /60
+  #return(time_to_ct)
+  ct.df <- as_tibble(cbind(jc_uid, time_to_ct))
+  colnames(ct.df)[1] <- "id"
+  colnames(ct.df)[2] <- "time_diff"
+  ct.df$time_diff <- as.numeric(ct.df$time_diff)
+  return(ct.df)
+}
+# converting a date to numeric from bq string date
+
+date_to_numeric_val <- function(date_column){
+  day <- str_split_fixed(date_column, " ",2)[,1]
+  time <- str_split_fixed(date_column, " ",2)[,2]
+  year <- str_split_fixed(day, "-", 3)[,1]
+  month <- str_split_fixed(day, "-", 3)[,2]
+  day_time <- str_split_fixed(day, "-", 3)[,3]
+  numeric_date <- paste0(year,month,day_time)
+  return(numeric_date)  
+}
+
+df_convert_date_to_numeric <- function(df){
+  df$date_num <- date_to_numeric_val(df$date)
+  return(df)
+}
+# ------------------------------------------------------
+# get number of rows through dim()
+# accepts dataframe as object
+# ------------------------------------------------------
+get_row <- function(df){
+  rows <- dim(df)[1]
+  return(rows)
+}
+
+# write a function to remove duplicates in lapply 
+remove_duplicate <- function(dt){
+  dtx <- dt[!duplicated(dt), ]
+  return(dtx)
+}
+
+
+
+
+aggregate_event <- function(df){
+  df$sub_date<- as.POSIXct(df$order_time_jittered,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  testing <- aggregate( df , by = list(df$Day) , length )
+  return(testing)
+}
+
+aggregate_name <- function(dfx){
+  require(dplyr)
+  dt <- dfx %>% select(Group.1, order_type) 
+  return(dt)
+}
+
+list_aggregate_rename <- function(listx){
+  # accepts list 
+  t <- names(listx)
+  
+}
+
+bargg <- function(df){
+  p4 <- ggplot() + geom_bar(aes(y = order_type, x = as.Date(Group.1), fill = order_event), data = df,
+                            stat="identity")
+  p4
+}
+
+bargg.02 <- function(df){
+  p4 <- ggplot() + geom_bar(aes(y = order_type, 
+                                x = as.Date(Group.1), 
+                                fill = order_event), 
+                            data = df,
+                            stat="identity") + 
+    xlab("Date") +
+    ylab("Count") +
+    ggtitle( paste0("Count of Stroke Orders Type: ")
+    ) +
+    theme(plot.title = element_text(hjust = 0.5))
+  p4
+}
+
+convert_aggregate<- function(df){
+  df$sub_date<- as.POSIXct(df$order_time_jittered,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  day_split <- split(df, df$order_type)
+  df_aggregate_order <- lapply(day_split, aggregate_event)
+  df_aggregate <- lapply(df_aggregate_order, aggregate_name)
+  event <- bind_rows(df_aggregate, .id = "order_event")
+  return(event)
+}
+
+convert_aggregate_date<- function(df){
+  df$sub_date<- as.POSIXct(df$min_time,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  day_split <- split(df, df$order_type)
+  df_aggregate_order <- lapply(day_split, aggregate_event)
+  df_aggregate <- lapply(df_aggregate_order, aggregate_name)
+  event <- bind_rows(df_aggregate, .id = "order_event")
+  return(event)
+}
+
+plot_density_difference <- function(dataframe){
+  #ct.df2 <- dataframe %>% filter(time_diff < 100)
+  pg2  <- ggplot(dataframe, aes(x=time_diff)) + 
+    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
+                   binwidth=1,
+                   colour="black", fill="white") +
+    geom_density(alpha=.2, fill="#FF6666")   # Overlay with transparent density plot 
+  return(pg2)
+}
+
+# TO DO function to download data by sql query into R 
+
+download_bq <- function(project, sql){
+  tb <- bq_project_query(project, sql)
+  tb.dtx <- bq_table_download(tb)
+  return(tb.dtx)
+  
+}
+
+#--------------------------------------------------------
+
+# -------------------------------------------------------
+
+# Big Computation: 
+# Goal Pull all Referral and Save 
+
+# Sql Call that Pulls the All the Patients that had a description of referral
+#sql <- "select * from `clinical_inpatient.stroke_ministudy`"
+sql <- "
+select 
+  opCT.jc_uid, 
+cast(opCT.pat_enc_csn_id_coded as string) as string_id, 
+admit.event_type,  
+admit.pat_class, 
+admit.effective_time_jittered as emergencyAdmitTime, 
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+dc.dx_name,
+dc.icd10
+from 
+datalake_47618.adt as admit, 
+datalake_47618.order_proc as opCT,
+datalake_47618.diagnosis_code as dc
+
+where admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL -60 MINUTE) < TIMESTAMP(opCT.order_inst_jittered)
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL 60 MINUTE) >= TIMESTAMP(opCT.order_inst_jittered)
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and admit.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and dc.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded 
+and dc.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+group by 
+opCT.jc_uid,
+opCT.pat_enc_csn_id_coded,
+admit.event_type, 
+admit.pat_class, 
+admit.effective_time_jittered,
+dc.dx_name,
+dc.icd10
+"
+
+
+data <- download_bq(project, sql)
+
+data_split <- split(data, data$string_id)
+data <- find_time_difference(data$emergencyAdmitTime, data$ctHeadOrderTime, data$jc_uid)
+
+table_dx_name <- as.data.frame(table(data$dx_name))
+colnames(table_dx_name)
+newdata <- table_dx_name[order(-table_dx_name$Freq),] 
+
+# TO DO 
+# GET ASSOCIATIONS FOR TPA ORDER 
+# GET ASSOCIATIONS FOR CT HEAD UNDER 10 and Then Greater than 50 
+# Construct Cox Regression for TPA order as Diagnostic Procedure 
+
+
+
+
+
+
+
+
+
diff --git a/scripts/AuditLog/general_inpatient_population_diagnosis.sql b/scripts/AuditLog/general_inpatient_population_diagnosis.sql
new file mode 100644
index 00000000..2ee58687
--- /dev/null
+++ b/scripts/AuditLog/general_inpatient_population_diagnosis.sql
@@ -0,0 +1,44 @@
+-- REQUIRES REVIEW: RUNS WAY TOO LONG 
+-- general population: people 
+-- stroke cohort table 
+-- take stroke cohort and 
+Select jc_uid, dx_id, dx_name, icd9, icd10, start_date_jittered, pat_enc_csn_id_coded
+from `datalake_47618.diagnosis_code` 
+where jc_uid in( 
+  select jc_uid as rit_uid from(
+    select 
+      op.jc_uid, op.pat_enc_csn_id_coded, 
+      admit.event_type, admit.pat_class, 
+      admit.effective_time_jittered as emergencyAdmitTime, 
+      --min(opCT.order_inst_jittered) as ctHeadOrderTime,
+      om.med_description as tpaDescription, 
+      --min(om.order_time_jittered) as tpaOrderTime,
+      --min(mar.taken_time_jittered) as tpaAdminTime,
+      inpatient.pat_class as inptClass, 
+      min(inpatient.effective_time_jittered) as inpatientAdmitTime
+    from 
+      datalake_47618.order_proc as op, 
+      datalake_47618.adt as admit, 
+      datalake_47618.order_proc as opCT,
+      datalake_47618.order_med as om,
+      datalake_47618.mar as mar,
+      datalake_47618.adt as inpatient
+    --where op.display_name like 'Patient on TPA%'
+    where op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+    and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+    and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+    and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+    and om.order_med_id_coded = mar.order_med_id_coded
+    and admit.event_type_c = 1 -- Admission
+    and admit.pat_class_c = '112' -- Emergency Services
+    --and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+    --and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+    and inpatient.pat_class_c = '126' -- Inpatient
+    group by 
+      op.jc_uid, op.pat_enc_csn_id_coded, 
+      admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+      om.med_description,
+      inpatient.pat_class
+    order by emergencyAdmitTime
+    ))
+
diff --git a/scripts/AuditLog/mlSplitScript.py b/scripts/AuditLog/mlSplitScript.py
new file mode 100644
index 00000000..264bd939
--- /dev/null
+++ b/scripts/AuditLog/mlSplitScript.py
@@ -0,0 +1,28 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+sync_environment = "/Users/jonc101/Box Sync/Jonathan Chiang's Files/mining-clinical-decisions/stroke/"
+# using venv in biomedical data science folder for version control
+df = pd.read_csv(sync_environment + "stroke_cohort.csv")
+#print(df.tail())
+
+for col in df.columns:
+    print(col)
+
+y_val = df["tpaOrderTime"]
+x_val = df.drop(columns=['jc_uid',
+                        'tpaOrderTime',
+                        'event_type',
+                        'pat_enc_csn_id_coded',
+                        'pat_class',
+                        'jc_uid'])
+
+print(x_val.tail())
+
+x_train, x_test, y_train, y_test = train_test_split(x_val,
+                                                    y_val,
+                                                    test_size = 0.25,
+                                                    random_state = 0)
+
+print(x_train)
+print(x_test)
diff --git a/scripts/AuditLog/pipeline.R b/scripts/AuditLog/pipeline.R
new file mode 100644
index 00000000..2fc00dfd
--- /dev/null
+++ b/scripts/AuditLog/pipeline.R
@@ -0,0 +1,329 @@
+# three lists of data
+
+setwd("/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read/stroke_module/")
+source("source.R")
+source("queries.R")
+library(ggplot2)
+library(dplyr)
+
+
+stroke_cohort <- read.csv("stroke_cohort.csv")
+sc_lab_result <- read.csv("sc_lab_result.csv")
+sc_order_proc <- read.csv("sc_order_proc.csv")
+sc_order_med  <- read.csv("sc_order_med.csv")
+
+# three lists of data
+sc_lab_result$encounter_id <- paste0(sc_lab_result$string_id, "_", sc_lab_result$jc_uid)
+sc_order_proc$encounter_id <- paste0(sc_order_proc$string_id, "_", sc_order_proc$jc_uid)
+sc_order_med$encounter_id  <- paste0(sc_order_med$string_id,  "_", sc_order_med$jc_uid)
+
+
+stroke_cohort$encounter_id <- paste0(stroke_cohort$pat_enc_csn_id_coded,"_", stroke_cohort$jc_uid)
+
+stroke_lab  <- merge(stroke_cohort, sc_lab_result, by = "encounter_id")
+stroke_proc <- merge(stroke_cohort, sc_order_proc, by = "encounter_id")
+stroke_med  <- merge(stroke_cohort, sc_order_med, by = "encounter_id")
+
+
+stroke_lab$lab_time_difference_tpaOrderTime   <- as.numeric(convert_datetime(stroke_lab$tpaOrderTime) - convert_datetime(stroke_lab$order_time_jittered)) / 60
+stroke_proc$proc_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_proc$tpaOrderTime) - convert_datetime(stroke_proc$order_inst_jittered)) / 60
+stroke_med$med_time_difference_tpaOrderTime   <- as.numeric(convert_datetime(stroke_med$tpaOrderTime) - convert_datetime(stroke_med$order_time_jittered)) / 60
+
+stroke_lab$ed_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_lab$tpaOrderTime) - convert_datetime(stroke_lab$emergencyAdmitTime)) /60
+stroke_proc$ed_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_proc$tpaOrderTime) - convert_datetime(stroke_proc$emergencyAdmitTime)) /60
+stroke_med$ed_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_med$tpaOrderTime) - convert_datetime(stroke_med$emergencyAdmitTime)) /60
+
+stroke_lab$ed_time_difference  <- as.numeric(convert_datetime(stroke_lab$tpaOrderTime) - convert_datetime(stroke_lab$emergencyAdmitTime)) /60
+stroke_proc$ed_time_difference <- as.numeric(convert_datetime(stroke_proc$tpaOrderTime) - convert_datetime(stroke_proc$emergencyAdmitTime)) /60
+stroke_med$ed_time_difference  <- as.numeric(convert_datetime(stroke_med$tpaOrderTime) - convert_datetime(stroke_med$emergencyAdmitTime)) /60
+
+#check to see 12:00 time point orders 
+#sort(table(unique(stroke_lab$order_time_jittered)))
+
+stroke_lab_pre  <- stroke_lab  %>% filter(lab_time_difference_tpaOrderTime > 0) 
+stroke_proc_pre <- stroke_proc %>% filter(proc_time_difference_tpaOrderTime > 0) 
+stroke_med_pre  <- stroke_med  %>% filter(med_time_difference_tpaOrderTime > 0) 
+
+stroke_lab_pre2  <- stroke_lab_pre  %>% filter(ed_time_difference < 0) 
+stroke_proc_pre2 <- stroke_proc_pre %>% filter(ed_time_difference < 0) 
+stroke_med_pre2  <- stroke_med_pre  %>% filter(ed_time_difference < 0) 
+
+# Mini Test:  test length
+dim(stroke_lab)[1]  == dim(sc_lab_result)[1]
+dim(stroke_proc)[1] == dim(sc_order_proc)[1]
+dim(stroke_med)[1]  == dim(sc_order_med)[1]
+
+# Create List Where Each Item is a Patient
+stroke_labs_list <- split(stroke_lab_pre, stroke_lab_pre$encounter_id)
+stroke_proc_list <- split(stroke_proc_pre, stroke_proc_pre$encounter_id)
+stroke_med_list  <- split(stroke_med_pre, stroke_med_pre$encounter_id)
+
+get_labs_before_tpa_order <- function(x){
+  num_labs_before_tpa_order = get_row(x)
+  x$num_labs_before_tpa_order <- num_labs_before_tpa_order
+  return(x)
+}
+
+get_procs_before_tpa_order <- function(x){
+  num_procs_before_tpa_order = get_row(x)
+  x$num_procs_before_tpa_order <- num_procs_before_tpa_order
+  return(x)
+}
+
+get_meds_before_tpa_order <- function(x){
+  num_meds_before_tpa_order = get_row(x)
+  x$num_meds_before_tpa_order <- num_meds_before_tpa_order
+  return(x)
+}
+
+stroke_labs_list2 <- lapply(stroke_labs_list, get_labs_before_tpa_order)
+stroke_proc_list2 <- lapply(stroke_proc_list, get_procs_before_tpa_order)
+stroke_meds_list2 <- lapply(stroke_med_list, get_meds_before_tpa_order)
+
+get_first_row <- function(df){
+  return(df[1,])
+}
+
+stroke_lab_one  <- lapply(stroke_labs_list2, get_first_row)
+stroke_proc_one <- lapply(stroke_proc_list2, get_first_row)
+stroke_meds_one <- lapply(stroke_meds_list2, get_first_row)
+
+stroke_lab_df  <- bind_rows(stroke_lab_one)
+stroke_proc_df <- bind_rows(stroke_proc_one)
+stroke_meds_df <- bind_rows(stroke_meds_one)
+
+stroke_proc_features <- stroke_proc_df %>% 
+  select(encounter_id, 
+         num_procs_before_tpa_order)
+
+outersect <- function(x, y) {
+  sort(c(setdiff(x, y),
+         setdiff(y, x)))
+}
+
+# must account for people who recieved 0 procedures and 0 meds 
+
+encounter_id_proc0 <- as.data.frame(outersect(stroke_lab_df$encounter_id, stroke_proc_features$encounter_id))
+colnames(encounter_id_proc0)[1] <- "encounter_id"
+encounter_id_proc0$num_procs_before_tpa_order <- 0 
+
+stroke_proc_complete <- rbind(stroke_proc_features, encounter_id_proc0)
+
+stroke_meds_features <- stroke_meds_df %>% select(encounter_id, 
+                                                  num_meds_before_tpa_order)
+
+encounter_id_meds0 <- as.data.frame(outersect(stroke_lab_df$encounter_id, stroke_meds_features$encounter_id))
+colnames(encounter_id_meds0)[1] <- "encounter_id"
+encounter_id_meds0$num_meds_before_tpa_order <- 0 
+
+stroke_meds_complete <- rbind(stroke_meds_features, encounter_id_meds0)
+
+stroke_df_pre1 <- merge(stroke_lab_df, stroke_proc_complete, by="encounter_id")
+stroke_df_ml <- merge(stroke_df_pre1, stroke_meds_complete, by="encounter_id")
+
+
+
+#stroke_labs_features <- stroke_lab_df %>% select(encounter_id, 
+#                                                  num_labs_before_tpa_order)
+#encounter_id_labs0 <- as.data.frame(outersect(stroke_lab_df$encounter_id, stroke_labs_features$encounter_id))
+#colnames(encounter_id_labs0)[1] <- "encounter_id"
+#encounter_id_labs0$num_labs_before_tpa_order <- 0 
+#stroke_labs_complete <- rbind(stroke_proc_features, encounter_id_labs0)
+
+#stroke_df_ml <- merge(stroke_df_pre2, stroke_labs_complete, by="encounter_id")
+
+# converting data long to wide 
+
+test = stroke_labs_list2[[1]]
+
+# x1 x2 x3 x4 (association) 
+
+# THINGS HAPPEN BEFORE: TEST: OCCURED AT MIDNIGHT: (TIMING CAVEAT)
+# Y VARIABLE (TIME BETWEEN) EXPLICIT FUNCTION TIME DIFFERENE BETWEEN
+# counts: 
+
+# BEFORE ED ADMIT (TIME BEFORE THEY REACHED)
+# Outcomes: linear regression: 
+
+# [1] lm(y1, x1,x2,x3,x4) counts with age gender demographics 
+# [2] Plots: 
+# regression trees: 
+
+# data_wide <- spread(olddata_long, condition, measurement)
+
+colnames(stroke_df_ml)
+
+ggplotRegression <- function (fit) {
+  
+  require(ggplot2)
+  
+  ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) + 
+    geom_point() +
+    stat_smooth(method = "lm", col = "red") +
+    labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
+                       "Intercept =",signif(fit$coef[[1]],5 ),
+                       " Slope =",signif(fit$coef[[2]], 5),
+                       " P =",signif(summary(fit)$coef[2,4], 5)))
+}
+
+fit1 <- lm(ed_time_difference_tpaOrderTime ~ num_labs_before_tpa_order, data = stroke_df_ml)
+fit2 <- lm(ed_time_difference_tpaOrderTime ~ num_procs_before_tpa_order, data = stroke_df_ml)
+fit3 <- lm(ed_time_difference_tpaOrderTime ~ num_meds_before_tpa_order, data = stroke_df_ml)
+
+ggplotRegression(fit1)
+ggplotRegression(fit2)
+ggplotRegression(fit3)
+
+
+stroke_df_ml <- stroke_df_ml %>% 
+  filter(num_procs_before_tpa_order < 300) %>%
+  filter(num_meds_before_tpa_order < 300) %>%
+  filter(num_meds_before_tpa_order < 300)  
+
+#summary(fit1)
+
+#stroke_lab_df$age <- convert_datetime(stroke_lab_df$order_time_jittered) - convert_datetime(stroke_lab$birth_date_jittered) 
+stroke_df_ml$age <- year(ymd_hms(stroke_df_ml$emergencyAdmitTime)) - year(ymd_hms(stroke_df_ml$birth_date_jittered))
+
+# cyclical treatment of time 
+stroke_df_ml$ed_hour_sin_admit <- sin(2*pi*hour(stroke_df_ml$emergencyAdmitTime)/24)
+stroke_df_ml$ed_hour_cos_admit <- cos(2*pi*hour(stroke_df_ml$emergencyAdmitTime)/24)
+
+stroke_df_ml$ed_month_sin_admit <- sin(2*pi*month(stroke_df_ml$emergencyAdmitTime)/12)
+stroke_df_ml$ed_month_cos_admit <- cos(2*pi*month(stroke_df_ml$emergencyAdmitTime)/12)
+
+ml.fit <- lm(ed_time_difference_tpaOrderTime ~ num_labs_before_tpa_order + 
+                                               num_meds_before_tpa_order + 
+                                               num_procs_before_tpa_order +
+                                               gender + 
+                                               canonical_race + 
+                                              age + 
+                                              ed_hour_sin_admit + 
+                                              ed_hour_cos_admit + 
+                                              ed_month_sin_admit + 
+                                              ed_month_cos_admit, 
+                                               data=stroke_df_ml)
+
+
+
+plot(stroke_df_ml$ed_time_difference_tpaOrderTime, stroke_df_ml$num_labs_before_tpa_order)
+plot(stroke_df_ml$ed_time_difference_tpaOrderTime, stroke_df_ml$num_meds_before_tpa_order)
+plot(stroke_df_ml$ed_time_difference_tpaOrderTime, stroke_df_ml$num_procs_before_tpa_order)
+
+#View(stroke_df_ml)
+ggplot(stroke_df_ml, aes(x = num_meds_before_tpa_order, y = ed_time_difference_tpaOrderTime)) + 
+  geom_point() +
+  stat_smooth(method = "lm", col = "red")
+
+ggplot(stroke_df_ml, aes(x = num_procs_before_tpa_order, y = ed_time_difference_tpaOrderTime)) + 
+  geom_point() +
+  stat_smooth(method = "lm", col = "red")
+
+ggplot(stroke_df_ml, aes(x = num_labs_before_tpa_order, y = ed_time_difference_tpaOrderTime)) + 
+  geom_point() +
+  stat_smooth(method = "lm", col = "red")
+
+stroke_df_ml_col <- stroke_df_ml %>% select(ed_time_difference_tpaOrderTime, 
+                                            num_labs_before_tpa_order , 
+                                              num_meds_before_tpa_order , 
+                                              num_procs_before_tpa_order ,
+                                              age , 
+                                              ed_hour_sin_admit , 
+                                              ed_hour_cos_admit ,
+                                              ed_month_sin_admit , 
+                                              ed_month_cos_admit)
+
+library(rsample)      # data splitting 
+library(randomForest) # basic implementation
+library(ranger)       # a faster implementation of randomForest
+library(caret)        # an aggregator package for performing many machine learning models
+library(h2o)          # an extremely fast java-based platformset.seed(123)
+ames_split <- initial_split(stroke_df_ml_col, prop = .7)
+ames_train <- training(ames_split)
+ames_test  <- testing(ames_split)
+
+# for reproduciblity
+set.seed(123)
+
+# default RF model
+m1 <- randomForest(
+  formula = ed_time_difference_tpaOrderTime ~ .,
+  data    = ames_train
+)
+
+plot(m1)
+
+which.min(m1$mse)
+
+sqrt(m1$mse[which.min(m1$mse)])
+
+
+# create training and validation data 
+set.seed(123)
+valid_split <- initial_split(ames_train, .8)
+
+# training data
+ames_train_v2 <- analysis(valid_split)
+
+# validation data
+ames_valid <- assessment(valid_split)
+x_test <- ames_valid[setdiff(names(ames_valid), "ed_time_difference_tpaOrderTime")]
+y_test <- ames_valid$ed_time_difference_tpaOrderTime
+
+rf_oob_comp <- randomForest(
+  formula =  ed_time_difference_tpaOrderTime ~ .,
+  data    = ames_train_v2,
+  xtest   = x_test,
+  ytest   = y_test
+)
+
+# extract OOB & validation errors
+oob <- sqrt(rf_oob_comp$mse)
+validation <- sqrt(rf_oob_comp$test$mse)
+
+# compare error rates
+tibble::tibble(
+  `Out of Bag Error` = oob,
+  `Test error` = validation,
+  ntrees = 1:rf_oob_comp$ntree
+) %>%
+  gather(Metric, RMSE, -ntrees) %>%
+  ggplot(aes(ntrees, RMSE, color = Metric)) +
+  geom_line() +
+  xlab("Number of trees")
+
+
+
+lab_rows <- stroke_lab_pre %>% 
+  group_by(group_lab_name) %>%
+  summarise(no_rows = length(group_lab_name)) %>% 
+  arrange(desc(no_rows))
+
+proc_rows <- stroke_proc_pre %>% 
+  group_by(description) %>%
+  summarise(no_rows = length(description))%>% 
+  arrange(desc(no_rows))
+
+med_rows <- stroke_med_pre %>% 
+  group_by(med_description)%>%
+  summarise(no_rows = length(med_description))%>% 
+  arrange(desc(no_rows))
+
+labAll_rows <- stroke_lab %>% 
+  group_by(group_lab_name) %>%
+  summarise(no_rows = length(group_lab_name)) %>% 
+  arrange(desc(no_rows))
+
+procAll_rows <- stroke_proc %>% 
+  group_by(description) %>%
+  summarise(no_rows = length(description))%>% 
+  arrange(desc(no_rows))
+
+medAll_rows <- stroke_med %>% 
+  group_by(med_description)%>%
+  summarise(no_rows = length(med_description))%>% 
+  arrange(desc(no_rows))
+
+# function to decode a history of information: 
+
+
diff --git a/scripts/AuditLog/readme.md b/scripts/AuditLog/readme.md
new file mode 100644
index 00000000..3689d48d
--- /dev/null
+++ b/scripts/AuditLog/readme.md
@@ -0,0 +1 @@
+# Place to Store Audit Log Information 
diff --git a/scripts/AuditLog/sql_scripts/ct_head.sql b/scripts/AuditLog/sql_scripts/ct_head.sql
new file mode 100644
index 00000000..c40a764e
--- /dev/null
+++ b/scripts/AuditLog/sql_scripts/ct_head.sql
@@ -0,0 +1,23 @@
+select 
+  opCT.jc_uid, 
+  opCT.pat_enc_csn_id_coded, 
+  admit.event_type, 
+  admit.pat_class, 
+  admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime
+from 
+  datalake_47618.adt as admit, 
+  datalake_47618.order_proc as opCT
+
+where admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL -60 MINUTE) < TIMESTAMP(opCT.order_inst_jittered)
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL 60 MINUTE) >= TIMESTAMP(opCT.order_inst_jittered)
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and admit.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+group by 
+  opCT.jc_uid,
+  opCT.pat_enc_csn_id_coded,
+  admit.event_type, 
+  admit.pat_class, 
+  admit.effective_time_jittered
diff --git a/scripts/AuditLog/strokeOrder.R b/scripts/AuditLog/strokeOrder.R
new file mode 100644
index 00000000..4a7d33cb
--- /dev/null
+++ b/scripts/AuditLog/strokeOrder.R
@@ -0,0 +1,694 @@
+## TODO: 
+# write tests for handling date:times: 
+# write modules for usage in SQL
+# write modules for Python 
+
+library(bigrquery)
+library(DBI)
+library(dplyr)
+library(lubridate)
+library(stringr)
+library(data.table)
+library(ggplot2)
+#library(bigrquery)
+#library(data.table)
+library(feather)
+library(reticulate)
+#library(dplyr)
+library(psych)
+library(timevis)
+#library(shiny)
+
+path <- "/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read"
+setwd(path)
+#dir.create("/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read/stroke_module")
+
+# Assigning GCP Project Name
+project <- "mining-clinical-decisions" # put your project ID here
+
+#https://console.cloud.google.com/bigquery?project=mining-clinical-decisions&page=savedqueries&sq=103208666750:607c18a7b9254ec4b1543533d242d46d
+
+# Creating DBI connection to Google BigQuery 1
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+# python libraries 
+pandas <- import("pandas")
+pa <- import("pyarrow")
+pq <- pa$parquet
+
+# Google Project Name 
+project <- "mining-clinical-decisions" # put your project ID here
+
+# writes csv to Sync Folder
+push_quiver <- function(df, name, env){
+  write.csv(df, paste0(env ,name,".csv"))
+}
+
+# writes df to feather format to Sync Folder
+push_quiver_feather <- function(df, name, env){
+  write_feather(df, paste0(env ,name,".feather"))
+}
+
+# lists files from Sync Folder
+list_quiver_csv <- function(env){
+  files <- list.files(env, pattern='*\\.csv', recursive=TRUE)
+  return(unlist(as.vector(strsplit(files, ".csv"))))
+}
+
+# Lists feather files from Sync Folder
+list_quiver_feather <- function(env){
+  files <- list.files(env, pattern='*\\.feather', recursive=TRUE)
+  return(unlist(as.vector(strsplit(files, ".feather"))))
+}
+
+# reads CSV from Sync Folder
+read_quiver <- function(file,env){
+  return(read.csv(paste0(env,file,".csv")))
+}
+
+# reads CSV using fread 
+fread_quiver <- function(file,env){
+  return(fread(paste0(env,file,".csv")))
+}
+
+# reads feather from sync folder
+fread_quiver_feather <- function(file,env){
+  return(read_feather(paste0(env,file,".feather")))
+}
+
+
+# generic reticulate function to read Parquet file in R
+read_parquet <- function(path, columns = NULL) {
+  
+  path <- path.expand(path)
+  path <- normalizePath(path)
+  
+  if (!is.null(columns)) columns = as.list(columns)
+  
+  xdf <- pandas$read_parquet(path, columns = columns)
+  
+  xdf <- as.data.frame(xdf, stringsAsFactors = FALSE)
+  
+  dplyr::tbl_df(xdf)
+  
+}
+
+sync_environment <- "/Users/jonc101/Box Sync/Jonathan Chiang's Files/Mining-Clinical-Decisions/stroke/"
+
+# File Read System 
+stroke_cohort       <- fread_quiver("stroke_cohort", sync_environment)
+stroke_cohort_demo  <- fread_quiver("stroke_cohort_demo", sync_environment)
+stroke_cohort_order  <- fread_quiver("stroke_cohort_order", sync_environment)
+
+# splits patients into individual lists 
+patient_list <- split(stroke_cohort, stroke_cohort$jc_uid)
+
+# patient 1: need to convert to timevis format 
+p1 <- patient_list$JCcb69cc
+
+# DESIRED DATA FORMAT RESULT 
+timevisData <- data.frame(
+  id = 1:11,
+  content = c("Open", "Open",
+              "Open", "Open", "Half price entry",
+              "Staff meeting", "Open", "Adults only", "Open", "Hot tub closes",
+              "Siesta"),
+  start = c("2016-05-01 07:30:00", "2016-05-01 14:00:00",
+            "2016-05-01 06:00:00", "2016-05-01 14:00:00", "2016-05-01 08:00:00",
+            "2016-05-01 08:00:00", "2016-05-01 08:30:00", "2016-05-01 14:00:00",
+            "2016-05-01 16:00:00", "2016-05-01 19:30:00",
+            "2016-05-01 12:00:00"),
+  end   = c("2016-05-01 12:00:00", "2016-05-01 20:00:00",
+            "2016-05-01 12:00:00", "2016-05-01 22:00:00", "2016-05-01 10:00:00",
+            "2016-05-01 08:30:00", "2016-05-01 12:00:00", "2016-05-01 16:00:00",
+            "2016-05-01 20:00:00", NA,
+            "2016-05-01 14:00:00"),
+  group = c(rep("lib", 2), rep("gym", 3), rep("pool", 5), NA),
+  type = c(rep("range", 9), "point", "background")
+)
+
+timevisDataGroups <- data.frame(
+  id = c("lib", "gym", "pool"),
+  content = c("Lab", "Emergency Room", "CT Head")
+)
+
+# write a function to convert single patient to timeline vis data frame format 
+
+# time start is inpatient admit time 
+
+# group1 = time to CT head order 
+# group2 = time to TPA order 
+# group3 = time from order to admin 
+# group4 = time from emergency admit time to inpatient admit time 
+
+start    <- p1 %>% select(emergencyAdmitTime) #admit_time
+end  <-  p1 %>% select(ctHeadOrderTime) #ct_order_time
+content <- "CT Head Order Time"
+group <- "EHR"
+type <- "RANGE"
+id <- 1
+t <- cbind(start,end,content,group,type,id)
+colnames(t) <- c("start","end","content","group","type","id")
+
+
+# function to clean data 
+# 
+library(lubridate)
+#with_tz(ymd_hms(stroke_cohort$emergencyAdmitTime),"America/Los_Angeles")
+
+convert_datetime <- function(timeEHR){
+  #convert to America_Pacific in lubridate
+  # requires format like this
+  # "2014-06-18 17:21:00" "2014-08-05 16:14:00" "2014-08-31 19:10:00" "2014-09-02 19:23:00" "2014-09-24 15:27:00" "2014-09-27 16:21:00" "2014-09-27 18:16:00"
+  # "2014-09-28 18:46:00" "2014-10-18 15:55:00" "2014-10-29 19:21:00" "2014-10-29 22:53:00" "2014-10-30 18:52:00" "2014-11-10 13:30:00" "2014-11-23 18:21:00"
+  # "2014-11-25 11:43:00" "2014-11-29 10:17:00"
+  
+  #into this format
+  
+  #[31] "2011-10-17 06:19:00 PDT" "2011-11-06 07:03:00 PST" "2011-11-07 12:36:00 PST" "2011-11-12 03:29:00 PST" "2011-11-16 23:37:00 PST"
+  #[36] "2011-11-18 09:11:00 PST" "2011-11-29 05:21:00 PST" "2011-12-11 09:54:00 PST" "2011-12-19 01:49:00 PST" "2011-12-21 04:14:00 PST"
+  #[41] "2012-01-04 10:31:00 PST" "2012-01-22 09:31:00 PST" 
+  return(with_tz(ymd_hms(timeEHR),"America/Los_Angeles"))
+}
+
+
+find_time_difference <- function(time_start,time_end, jc_uid){
+  time_to_ct <- as.numeric(convert_datetime(time_end) - convert_datetime(time_start)) /60
+  #return(time_to_ct)
+  ct.df <- as_tibble(cbind(jc_uid, time_to_ct))
+  colnames(ct.df)[1] <- "id"
+  colnames(ct.df)[2] <- "time_diff"
+  ct.df$time_diff <- as.numeric(ct.df$time_diff)
+  return(ct.df)
+}
+
+
+
+
+data_tables <- dbListTables(con)
+encounter <- tbl(con, "encounter")
+order_med <- tbl(con, "order_med")
+
+#order_med.dt <- 
+
+# function to clean data 
+# 
+library(lubridate)
+#with_tz(ymd_hms(stroke_cohort$emergencyAdmitTime),"America/Los_Angeles")
+
+convert_datetime <- function(timeEHR){
+  #convert to America_Pacific in lubridate
+  # requires format like this
+  # "2014-06-18 17:21:00" "2014-08-05 16:14:00" "2014-08-31 19:10:00" "2014-09-02 19:23:00" "2014-09-24 15:27:00" "2014-09-27 16:21:00" "2014-09-27 18:16:00"
+  # "2014-09-28 18:46:00" "2014-10-18 15:55:00" "2014-10-29 19:21:00" "2014-10-29 22:53:00" "2014-10-30 18:52:00" "2014-11-10 13:30:00" "2014-11-23 18:21:00"
+  # "2014-11-25 11:43:00" "2014-11-29 10:17:00"
+  
+  #into this format
+  
+  #[31] "2011-10-17 06:19:00 PDT" "2011-11-06 07:03:00 PST" "2011-11-07 12:36:00 PST" "2011-11-12 03:29:00 PST" "2011-11-16 23:37:00 PST"
+  #[36] "2011-11-18 09:11:00 PST" "2011-11-29 05:21:00 PST" "2011-12-11 09:54:00 PST" "2011-12-19 01:49:00 PST" "2011-12-21 04:14:00 PST"
+  #[41] "2012-01-04 10:31:00 PST" "2012-01-22 09:31:00 PST" 
+  return(with_tz(ymd_hms(timeEHR),"America/Los_Angeles"))
+}
+
+
+find_time_difference <- function(time_start,time_end, jc_uid){
+  time_to_ct <- as.numeric(convert_datetime(time_end) - convert_datetime(time_start)) /60
+  #return(time_to_ct)
+  ct.df <- as_tibble(cbind(jc_uid, time_to_ct))
+  colnames(ct.df)[1] <- "id"
+  colnames(ct.df)[2] <- "time_diff"
+  ct.df$time_diff <- as.numeric(ct.df$time_diff)
+  return(ct.df)
+}
+# converting a date to numeric from bq string date
+
+date_to_numeric_val <- function(date_column){
+  day <- str_split_fixed(date_column, " ",2)[,1]
+  time <- str_split_fixed(date_column, " ",2)[,2]
+  year <- str_split_fixed(day, "-", 3)[,1]
+  month <- str_split_fixed(day, "-", 3)[,2]
+  day_time <- str_split_fixed(day, "-", 3)[,3]
+  numeric_date <- paste0(year,month,day_time)
+  return(numeric_date)  
+}
+
+df_convert_date_to_numeric <- function(df){
+  df$date_num <- date_to_numeric_val(df$date)
+  return(df)
+}
+# ------------------------------------------------------
+# get number of rows through dim()
+# accepts dataframe as object
+# ------------------------------------------------------
+get_row <- function(df){
+  rows <- dim(df)[1]
+  return(rows)
+}
+
+# write a function to remove duplicates in lapply 
+remove_duplicate <- function(dt){
+  dtx <- dt[!duplicated(dt), ]
+  return(dtx)
+}
+
+
+aggregate_event <- function(df){
+  df$sub_date<- as.POSIXct(df$order_time_jittered,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  testing <- aggregate( df , by = list(df$Day) , length )
+  return(testing)
+}
+
+aggregate_name <- function(dfx){
+  require(dplyr)
+  dt <- dfx %>% select(Group.1, order_type) 
+  return(dt)
+}
+
+list_aggregate_rename <- function(listx){
+  # accepts list 
+  t <- names(listx)
+  
+}
+
+bargg <- function(df){
+  p4 <- ggplot() + geom_bar(aes(y = order_type, x = as.Date(Group.1), fill = order_event), data = df,
+                            stat="identity")
+  p4
+}
+
+bargg.02 <- function(df){
+  p4 <- ggplot() + geom_bar(aes(y = order_type, 
+                                x = as.Date(Group.1), 
+                                fill = order_event), 
+                            data = df,
+                            stat="identity") + 
+    xlab("Date") +
+    ylab("Count") +
+    ggtitle( paste0("Count of Stroke Orders Type: ")
+    ) +
+    theme(plot.title = element_text(hjust = 0.5))
+  p4
+}
+
+convert_aggregate<- function(df){
+  df$sub_date<- as.POSIXct(df$order_time_jittered,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  day_split <- split(df, df$order_type)
+  df_aggregate_order <- lapply(day_split, aggregate_event)
+  df_aggregate <- lapply(df_aggregate_order, aggregate_name)
+  event <- bind_rows(df_aggregate, .id = "order_event")
+  return(event)
+}
+
+convert_aggregate_date<- function(df){
+  df$sub_date<- as.POSIXct(df$min_time,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  day_split <- split(df, df$order_type)
+  df_aggregate_order <- lapply(day_split, aggregate_event)
+  df_aggregate <- lapply(df_aggregate_order, aggregate_name)
+  event <- bind_rows(df_aggregate, .id = "order_event")
+  return(event)
+}
+
+plot_density_difference <- function(dataframe){
+  #ct.df2 <- dataframe %>% filter(time_diff < 100)
+  pg2  <- ggplot(dataframe, aes(x=time_diff)) + 
+    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
+                   binwidth=1,
+                   colour="black", fill="white") +
+    geom_density(alpha=.2, fill="#FF6666")   # Overlay with transparent density plot 
+  return(pg2)
+}
+
+# TO DO function to download data by sql query into R 
+
+download_bq <- function(project, sql){
+  tb <- bq_project_query(project, sql)
+  tb.dtx <- bq_table_download(tb)
+  return(tb.dtx)
+  
+}
+
+#--------------------------------------------------------
+
+# -------------------------------------------------------
+
+# Big Computation: 
+# Goal Pull all Referral and Save 
+
+# Sql Call that Pulls the All the Patients that had a description of referral
+#sql <- "select * from `clinical_inpatient.stroke_ministudy`"
+sql <- "
+select 
+opCT.jc_uid, 
+cast(opCT.pat_enc_csn_id_coded as string) as string_id, 
+admit.event_type,  
+admit.pat_class, 
+admit.effective_time_jittered as emergencyAdmitTime, 
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+dc.dx_name,
+dc.icd10
+from 
+datalake_47618.adt as admit, 
+datalake_47618.order_proc as opCT,
+datalake_47618.diagnosis_code as dc
+where admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL -60 MINUTE) < TIMESTAMP(opCT.order_inst_jittered)
+and TIMESTAMP_ADD(TIMESTAMP(admit.effective_time_jittered), INTERVAL 60 MINUTE) >= TIMESTAMP(opCT.order_inst_jittered)
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and admit.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and dc.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded 
+and dc.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+group by 
+opCT.jc_uid,
+opCT.pat_enc_csn_id_coded,
+admit.event_type, 
+admit.pat_class, 
+admit.effective_time_jittered,
+dc.dx_name,
+dc.icd10
+"
+
+
+data <- download_bq(project, sql)
+
+data_split <- split(data, data$string_id)
+data <- find_time_difference(data$emergencyAdmitTime, data$ctHeadOrderTime, data$jc_uid)
+
+table_dx_name <- as.data.frame(table(data$dx_name))
+colnames(table_dx_name)
+newdata <- table_dx_name[order(-table_dx_name$Freq),] 
+
+# STROKE ORDERS 
+
+order_med = "
+SELECT jc_uid, cast(pat_enc_csn_id_coded as string) as string_id, pat_enc_csn_id_coded, order_time_jittered, order_med_id_coded, medication_id, med_description, order_class, amb_med_disp_name, quantity, authr_prov_map_id, med_route  
+FROM `starr_datalake2018.order_med` 
+WHERE pat_enc_csn_id_coded in  (
+select pat_enc_csn_id_coded from (
+select 
+op.jc_uid, op.pat_enc_csn_id_coded, 
+admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+min(mar.taken_time_jittered) as tpaAdminTime,
+inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+datalake_47618.order_proc as op, 
+datalake_47618.adt as admit, 
+datalake_47618.order_proc as opCT,
+datalake_47618.order_med as om,
+datalake_47618.mar as mar,
+datalake_47618.adt as inpatient
+where op.display_name like 'Patient on TPA%'
+and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+and om.order_med_id_coded = mar.order_med_id_coded
+and admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+op.jc_uid, op.pat_enc_csn_id_coded, 
+admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+om.med_description,
+inpatient.pat_class
+order by emergencyAdmitTime)
+)
+"
+
+data2 <- download_bq(project, order_med)
+
+order_proc = "
+SELECT jc_uid, cast(pat_enc_csn_id_coded as string) as string_id, pat_enc_csn_id_coded, order_proc_id_coded, order_type, proc_code, description, display_name, authrzing_prov_map_id, billing_prov_map_id, order_inst_jittered  
+FROM `starr_datalake2018.order_proc` 
+WHERE pat_enc_csn_id_coded in  (
+select pat_enc_csn_id_coded from (
+select 
+op.jc_uid, op.pat_enc_csn_id_coded, 
+admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+min(mar.taken_time_jittered) as tpaAdminTime,
+inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+datalake_47618.order_proc as op, 
+datalake_47618.adt as admit, 
+datalake_47618.order_proc as opCT,
+datalake_47618.order_med as om,
+datalake_47618.mar as mar,
+datalake_47618.adt as inpatient
+where op.display_name like 'Patient on TPA%'
+and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+and om.order_med_id_coded = mar.order_med_id_coded
+and admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+op.jc_uid, op.pat_enc_csn_id_coded, 
+admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+om.med_description,
+inpatient.pat_class
+order by emergencyAdmitTime)
+)
+"
+
+data3 <- download_bq(project, order_proc)
+
+lab_result_qry <- "
+SELECT rit_uid as jc_uid, cast(pat_enc_csn_id_coded as string) as string_id, pat_enc_csn_id_coded, order_time_jittered, proc_code, order_type, group_lab_name, lab_name, base_name, ord_value, result_flag, auth_prov_map_id, ordering_mode
+FROM `starr_datalake2018.lab_result` 
+WHERE  pat_enc_csn_id_coded in  (
+select pat_enc_csn_id_coded from (
+select 
+op.jc_uid, op.pat_enc_csn_id_coded, 
+admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+min(opCT.order_inst_jittered) as ctHeadOrderTime,
+om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+min(mar.taken_time_jittered) as tpaAdminTime,
+inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+datalake_47618.order_proc as op, 
+datalake_47618.adt as admit, 
+datalake_47618.order_proc as opCT,
+datalake_47618.order_med as om,
+datalake_47618.mar as mar,
+datalake_47618.adt as inpatient
+where op.display_name like 'Patient on TPA%'
+and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+and om.order_med_id_coded = mar.order_med_id_coded
+and admit.event_type_c = 1 -- Admission
+and admit.pat_class_c = '112' -- Emergency Services
+and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+op.jc_uid, op.pat_enc_csn_id_coded, 
+admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+om.med_description,
+inpatient.pat_class
+order by emergencyAdmitTime)
+)
+"
+
+data4 <- download_bq(project, lab_result_qry)
+
+sc_lab_result <- data4
+sc_order_proc <- data3
+sc_order_med <- data2
+
+# three lists of data
+sc_lab_result$encounter_id <- paste0(sc_lab_result$string_id, "_", sc_lab_result$jc_uid)
+sc_order_proc$encounter_id <- paste0(sc_order_proc$string_id, "_", sc_order_proc$jc_uid)
+sc_order_med$encounter_id  <- paste0(sc_order_med$string_id,  "_", sc_order_med$jc_uid)
+
+
+stroke_cohort$encounter_id <- paste0(stroke_cohort$pat_enc_csn_id_coded,"_", stroke_cohort$jc_uid)
+
+stroke_lab  <- merge(stroke_cohort, sc_lab_result, by = "encounter_id")
+stroke_proc <- merge(stroke_cohort, sc_order_proc, by = "encounter_id")
+stroke_med  <- merge(stroke_cohort, sc_order_med, by = "encounter_id")
+
+### TODO TEST 
+
+stroke_lab$lab_time_difference_tpaOrderTime   <- as.numeric(convert_datetime(stroke_lab$tpaOrderTime) - convert_datetime(stroke_lab$order_time_jittered)) /60
+stroke_proc$proc_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_proc$tpaOrderTime) - convert_datetime(stroke_proc$order_time_jittered)) /60
+stroke_med$med_time_difference_tpaOrderTime   <- as.numeric(convert_datetime(stroke_med$tpaOrderTime) - convert_datetime(stroke_med$order_time_jittered)) /60
+
+#stroke_lab$ed_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_lab$tpaOrderTime) - convert_datetime(stroke_lab$emergencyAdmitTime)) /60
+#stroke_proc$ed_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_proc$tpaOrderTime) - convert_datetime(stroke_proc$emergencyAdmitTime)) /60
+#stroke_med$ed_time_difference_tpaOrderTime <- as.numeric(convert_datetime(stroke_med$tpaOrderTime) - convert_datetime(stroke_med$emergencyAdmitTime)) /60
+
+stroke_lab$ed_time_difference  <- as.numeric(convert_datetime(stroke_lab$tpaOrderTime) - convert_datetime(stroke_lab$emergencyAdmitTime)) /60
+stroke_proc$ed_time_difference <- as.numeric(convert_datetime(stroke_proc$tpaOrderTime) - convert_datetime(stroke_proc$emergencyAdmitTime)) /60
+stroke_med$ed_time_difference  <- as.numeric(convert_datetime(stroke_med$tpaOrderTime) - convert_datetime(stroke_med$emergencyAdmitTime)) /60
+
+#check to see 12:00 time point orders 
+#sort(table(unique(stroke_lab$order_time_jittered)))
+
+stroke_lab_pre  <- stroke_lab  %>% filter(lab_time_difference_tpaOrderTime > 0) 
+stroke_proc_pre <- stroke_proc %>% filter(proc_time_difference_tpaOrderTime > 0) 
+stroke_med_pre  <- stroke_med  %>% filter(med_time_difference_tpaOrderTime > 0) 
+
+stroke_lab_pre2  <- stroke_lab_pre  %>% filter(ed_time_difference < 0) 
+stroke_proc_pre2 <- stroke_proc_pre %>% filter(ed_time_difference < 0) 
+stroke_med_pre2  <- stroke_med_pre  %>% filter(ed_time_difference < 0) 
+
+# Mini Test:  test length
+dim(stroke_lab)[1]  == dim(sc_lab_result)[1]
+dim(stroke_proc)[1] == dim(sc_order_proc)[1]
+dim(stroke_med)[1]  == dim(sc_order_med)[1]
+
+# Create List Where Each Item is a Patient
+stroke_labs_list <- split(stroke_lab_pre, stroke_lab_pre$encounter_id)
+stroke_proc_list <- split(stroke_proc_pre, stroke_proc_pre$encounter_id)
+stroke_med_list  <- split(stroke_med_pre, stroke_med_pre$encounter_id)
+
+get_labs_before_tpa_order <- function(x){
+  num_labs_before_tpa_order = get_row(x)
+  x$num_labs_before_tpa_order <- num_labs_before_tpa_order
+  return(x)
+}
+
+get_procs_before_tpa_order <- function(x){
+  num_procs_before_tpa_order = get_row(x)
+  x$num_procs_before_tpa_order <- num_procs_before_tpa_order
+  return(x)
+}
+
+get_meds_before_tpa_order <- function(x){
+  num_meds_before_tpa_order = get_row(x)
+  x$num_meds_before_tpa_order <- num_meds_before_tpa_order
+  return(x)
+}
+
+stroke_labs_list2 <- lapply(stroke_labs_list, get_labs_before_tpa_order)
+stroke_proc_list2 <- lapply(stroke_proc_list, get_procs_before_tpa_order)
+stroke_meds_list2 <- lapply(stroke_med_list, get_meds_before_tpa_order)
+
+get_first_row <- function(df){
+  return(df[1,])
+}
+
+stroke_lab_one  <- lapply(stroke_labs_list2, get_first_row)
+stroke_proc_one <- lapply(stroke_proc_list2, get_first_row)
+stroke_meds_one <- lapply(stroke_meds_list2, get_first_row)
+
+stroke_lab_df  <- bind_rows(stroke_lab_one)
+stroke_proc_df <- bind_rows(stroke_proc_one)
+stroke_meds_df <- bind_rows(stroke_meds_one)
+
+stroke_proc_features <- stroke_proc_df %>% 
+  select(encounter_id, 
+         num_procs_before_tpa_order)
+
+outersect <- function(x, y) {
+  sort(c(setdiff(x, y),
+         setdiff(y, x)))
+}
+
+# must account for people who recieved 0 procedures and 0 meds 
+
+encounter_id_proc0 <- as.data.frame(outersect(stroke_lab_df$encounter_id, stroke_proc_features$encounter_id))
+colnames(encounter_id_proc0)[1] <- "encounter_id"
+encounter_id_proc0$num_procs_before_tpa_order <- 0 
+
+stroke_proc_complete <- rbind(stroke_proc_features, encounter_id_proc0)
+
+stroke_meds_features <- stroke_meds_df %>% select(encounter_id, 
+                                                  num_meds_before_tpa_order)
+
+encounter_id_meds0 <- as.data.frame(outersect(stroke_lab_df$encounter_id, stroke_meds_features$encounter_id))
+colnames(encounter_id_meds0)[1] <- "encounter_id"
+encounter_id_meds0$num_meds_before_tpa_order <- 0 
+
+stroke_meds_complete <- rbind(stroke_meds_features, encounter_id_meds0)
+
+stroke_df_pre1 <- merge(stroke_lab_df, stroke_proc_complete, by="encounter_id")
+stroke_df_ml <- merge(stroke_df_pre1, stroke_meds_complete, by="encounter_id")
+
+
+
+#stroke_labs_features <- stroke_lab_df %>% select(encounter_id, 
+#                                                  num_labs_before_tpa_order)
+#encounter_id_labs0 <- as.data.frame(outersect(stroke_lab_df$encounter_id, stroke_labs_features$encounter_id))
+#colnames(encounter_id_labs0)[1] <- "encounter_id"
+#encounter_id_labs0$num_labs_before_tpa_order <- 0 
+#stroke_labs_complete <- rbind(stroke_proc_features, encounter_id_labs0)
+
+#stroke_df_ml <- merge(stroke_df_pre2, stroke_labs_complete, by="encounter_id")
+
+# converting data long to wide 
+
+test = stroke_labs_list2[[1]]
+
+# x1 x2 x3 x4 (association) 
+
+# THINGS HAPPEN BEFORE: TEST: OCCURED AT MIDNIGHT: (TIMING CAVEAT)
+# Y VARIABLE (TIME BETWEEN) EXPLICIT FUNCTION TIME DIFFERENE BETWEEN
+# counts: 
+
+# BEFORE ED ADMIT (TIME BEFORE THEY REACHED)
+# Outcomes: linear regression: 
+
+# [1] lm(y1, x1,x2,x3,x4) counts with age gender demographics 
+# [2] Plots: 
+# regression trees: 
+
+# data_wide <- spread(olddata_long, condition, measurement)
+
+colnames(stroke_df_ml)
+
+
+stroke_df_ml <- stroke_df_ml %>% 
+  filter(num_procs_before_tpa_order < 300) %>%
+  filter(num_meds_before_tpa_order < 300) %>%
+  filter(num_meds_before_tpa_order < 300) %>% 
+  filter(ed_time_difference_tpaOrderTime < 100) 
+  
+  
+
+fit1 <- lm(ed_time_difference_tpaOrderTime ~ num_labs_before_tpa_order, data = stroke_df_ml)
+fit2 <- lm(ed_time_difference_tpaOrderTime ~ num_procs_before_tpa_order, data = stroke_df_ml)
+fit3 <- lm(ed_time_difference_tpaOrderTime ~ num_meds_before_tpa_order, data = stroke_df_ml)
+
+  
+#summary(fit1)
+plot(stroke_df_ml$ed_time_difference_tpaOrderTime, stroke_df_ml$num_labs_before_tpa_order)
+plot(stroke_df_ml$ed_time_difference_tpaOrderTime, stroke_df_ml$num_meds_before_tpa_order)
+plot(stroke_df_ml$ed_time_difference_tpaOrderTime, stroke_df_ml$num_procs_before_tpa_order)
+
+#View(stroke_df_ml)
+ggplot(stroke_df_ml, aes(x = num_meds_before_tpa_order, y = ed_time_difference_tpaOrderTime)) + 
+  geom_point() +
+  stat_smooth(method = "lm", col = "red")
+
+ggplot(stroke_df_ml, aes(x = num_procs_before_tpa_order, y = ed_time_difference_tpaOrderTime)) + 
+  geom_point() +
+  stat_smooth(method = "lm", col = "red")
+
+ggplot(stroke_df_ml, aes(x = num_labs_before_tpa_order, y = ed_time_difference_tpaOrderTime)) + 
+  geom_point() +
+  stat_smooth(method = "lm", col = "red")
+
+
+ml.fit <- lm(ed_time_difference_tpaOrderTime ~ num_labs_before_tpa_order + num_meds_before_tpa_order + num_procs_before_tpa_order, data=stroke_df_ml)
diff --git a/scripts/AuditLog/stroke_cohort.sql b/scripts/AuditLog/stroke_cohort.sql
new file mode 100644
index 00000000..39935352
--- /dev/null
+++ b/scripts/AuditLog/stroke_cohort.sql
@@ -0,0 +1,31 @@
+select 
+  op.jc_uid, op.pat_enc_csn_id_coded, 
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  datalake_47618.order_proc as op, 
+  datalake_47618.adt as admit, 
+  datalake_47618.order_proc as opCT,
+  datalake_47618.order_med as om,
+  datalake_47618.mar as mar,
+  datalake_47618.adt as inpatient
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, 
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime
diff --git a/scripts/AuditLog/stroke_cohort_summary.R b/scripts/AuditLog/stroke_cohort_summary.R
new file mode 100644
index 00000000..0c830136
--- /dev/null
+++ b/scripts/AuditLog/stroke_cohort_summary.R
@@ -0,0 +1,272 @@
+sync_environment = "/Users/jonc101/Box Sync/Jonathan Chiang's Files/mining-clinical-decisions/"
+#sync_environment = "/Users/jonc101/Box Sync/Jonathan Chiang's Files/audit_log/"
+#install.packages("data.table")
+
+# libraries to read in
+library(bigrquery)
+library(data.table)
+library(feather)
+library(reticulate)
+library(dplyr)
+library(psych)
+library(timevis)
+library(shiny)
+
+# python libraries 
+pandas <- import("pandas")
+pa <- import("pyarrow")
+pq <- pa$parquet
+
+# Google Project Name 
+project <- "mining-clinical-decisions" # put your project ID here
+
+# writes csv to Sync Folder
+push_quiver <- function(df, name, env){
+  write.csv(df, paste0(env ,name,".csv"))
+}
+
+# writes df to feather format to Sync Folder
+push_quiver_feather <- function(df, name, env){
+  write_feather(df, paste0(env ,name,".feather"))
+}
+
+# lists files from Sync Folder
+list_quiver_csv <- function(env){
+  files <- list.files(env, pattern='*\\.csv', recursive=TRUE)
+  return(unlist(as.vector(strsplit(files, ".csv"))))
+}
+
+# Lists feather files from Sync Folder
+list_quiver_feather <- function(env){
+  files <- list.files(env, pattern='*\\.feather', recursive=TRUE)
+  return(unlist(as.vector(strsplit(files, ".feather"))))
+}
+
+# reads CSV from Sync Folder
+read_quiver <- function(file,env){
+  return(read.csv(paste0(env,file,".csv")))
+}
+
+# reads CSV using fread 
+fread_quiver <- function(file,env){
+  return(fread(paste0(env,file,".csv")))
+}
+
+# reads feather from sync folder
+fread_quiver_feather <- function(file,env){
+  return(read_feather(paste0(env,file,".feather")))
+}
+
+
+# generic reticulate function to read Parquet file in R
+read_parquet <- function(path, columns = NULL) {
+  
+  path <- path.expand(path)
+  path <- normalizePath(path)
+  
+  if (!is.null(columns)) columns = as.list(columns)
+  
+  xdf <- pandas$read_parquet(path, columns = columns)
+  
+  xdf <- as.data.frame(xdf, stringsAsFactors = FALSE)
+  
+  dplyr::tbl_df(xdf)
+  
+}
+
+
+# File Read System 
+stroke_cohort       <- fread_quiver("stroke_cohort", sync_environment)
+stroke_cohort_demo  <- fread_quiver("stroke_cohort_demo", sync_environment)
+
+# splits patients into individual lists 
+patient_list <- split(stroke_cohort,stroke_cohort$jc_uid)
+
+# patient 1: need to convert to timevis format 
+p1 <- patient_list$JCcb69cc
+
+# DESIRED DATA FORMAT RESULT 
+timevisData <- data.frame(
+  id = 1:11,
+  content = c("Open", "Open",
+              "Open", "Open", "Half price entry",
+              "Staff meeting", "Open", "Adults only", "Open", "Hot tub closes",
+              "Siesta"),
+  start = c("2016-05-01 07:30:00", "2016-05-01 14:00:00",
+            "2016-05-01 06:00:00", "2016-05-01 14:00:00", "2016-05-01 08:00:00",
+            "2016-05-01 08:00:00", "2016-05-01 08:30:00", "2016-05-01 14:00:00",
+            "2016-05-01 16:00:00", "2016-05-01 19:30:00",
+            "2016-05-01 12:00:00"),
+  end   = c("2016-05-01 12:00:00", "2016-05-01 20:00:00",
+            "2016-05-01 12:00:00", "2016-05-01 22:00:00", "2016-05-01 10:00:00",
+            "2016-05-01 08:30:00", "2016-05-01 12:00:00", "2016-05-01 16:00:00",
+            "2016-05-01 20:00:00", NA,
+            "2016-05-01 14:00:00"),
+  group = c(rep("lib", 2), rep("gym", 3), rep("pool", 5), NA),
+  type = c(rep("range", 9), "point", "background")
+)
+
+timevisDataGroups <- data.frame(
+  id = c("lib", "gym", "pool"),
+  content = c("Lab", "Emergency Room", "CT Head")
+)
+
+# write a function to convert single patient to timeline vis data frame format 
+
+# time start is inpatient admit time 
+
+# group1 = time to CT head order 
+# group2 = time to TPA order 
+# group3 = time from order to admin 
+# group4 = time from emergency admit time to inpatient admit time 
+
+start    <- p1 %>% select(emergencyAdmitTime) #admit_time
+end  <-  p1 %>% select(ctHeadOrderTime) #ct_order_time
+content <- "CT Head Order Time"
+group <- "EHR"
+type <- "RANGE"
+id <- 1
+t <- cbind(start,end,content,group,type,id)
+colnames(t) <- c("start","end","content","group","type","id")
+
+
+# function to clean data 
+# 
+library(lubridate)
+#with_tz(ymd_hms(stroke_cohort$emergencyAdmitTime),"America/Los_Angeles")
+
+convert_datetime <- function(timeEHR){
+  #convert to America_Pacific in lubridate
+  # requires format like this
+  # "2014-06-18 17:21:00" "2014-08-05 16:14:00" "2014-08-31 19:10:00" "2014-09-02 19:23:00" "2014-09-24 15:27:00" "2014-09-27 16:21:00" "2014-09-27 18:16:00"
+  # "2014-09-28 18:46:00" "2014-10-18 15:55:00" "2014-10-29 19:21:00" "2014-10-29 22:53:00" "2014-10-30 18:52:00" "2014-11-10 13:30:00" "2014-11-23 18:21:00"
+  # "2014-11-25 11:43:00" "2014-11-29 10:17:00"
+  
+  #into this format
+  
+  #[31] "2011-10-17 06:19:00 PDT" "2011-11-06 07:03:00 PST" "2011-11-07 12:36:00 PST" "2011-11-12 03:29:00 PST" "2011-11-16 23:37:00 PST"
+  #[36] "2011-11-18 09:11:00 PST" "2011-11-29 05:21:00 PST" "2011-12-11 09:54:00 PST" "2011-12-19 01:49:00 PST" "2011-12-21 04:14:00 PST"
+  #[41] "2012-01-04 10:31:00 PST" "2012-01-22 09:31:00 PST" 
+  return(with_tz(ymd_hms(timeEHR),"America/Los_Angeles"))
+}
+
+
+find_time_difference <- function(time_start,time_end, jc_uid){
+    time_to_ct <- as.numeric(convert_datetime(time_end) - convert_datetime(time_start)) /60
+    #return(time_to_ct)
+    ct.df <- as_tibble(cbind(jc_uid, time_to_ct))
+    colnames(ct.df)[1] <- "id"
+    colnames(ct.df)[2] <- "time_diff"
+    ct.df$time_diff <- as.numeric(ct.df$time_diff)
+    return(ct.df)
+}
+
+
+plot_density_difference_plotly <- function(dataframe){
+  #ct.df2 <- dataframe %>% filter(time_diff < 100)
+  pg2  <- ggplot(dataframe, aes(x=time_diff)) + 
+    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
+                   binwidth=1,
+                   colour="black", fill="white") +
+    geom_density(alpha=.2, fill="#FF6666")   # Overlay with transparent density plot 
+  return(plotly::ggplotly(pg2))
+}
+
+plot_density_difference <- function(dataframe){
+  #ct.df2 <- dataframe %>% filter(time_diff < 100)
+  pg2  <- ggplot(dataframe, aes(x=time_diff)) + 
+    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
+                   binwidth=1,
+                   colour="black", fill="white") +
+    geom_density(alpha=.2, fill="#FF6666")   # Overlay with transparent density plot 
+  return(pg2)
+}
+
+plot_density_difference_long <- function(dataframe){
+  #ct.df2 <- dataframe %>% filter(time_diff < 100)
+  pg2  <- ggplot(dataframe, aes(x=time_diff,color = event)) + 
+    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
+                   binwidth=1) +
+    geom_density(alpha=.2) + ggtitle("Stroke Density Plot Histograms") +
+    xlab("Time Differences in Minutes") + ylab("Density")  # Overlay with transparent density plot 
+  return(pg2)
+}
+
+plot_density_difference_long_facet <- function(dataframe){
+  #ct.df2 <- dataframe %>% filter(time_diff < 100)
+  pg2  <- ggplot(dataframe, aes(x=time_diff,color = event)) + 
+    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
+                   binwidth=1) +
+    geom_density(alpha=.2) +  # Overlay with transparent density plot 
+    facet_grid(rows = vars(event)) + ggtitle("Stroke Density Histograms") +
+    xlab("Time Differences in Minutes") + ylab("Density") + 
+    theme(
+    plot.title = element_text(color="red", size=14, face="bold.italic",hjust = 0.5),
+    axis.title.x = element_text(color="blue", size=14, face="bold"),
+    axis.title.y = element_text(color="#993333", size=14, face="bold"))
+  return(pg2)
+}
+
+
+
+test1 <- find_time_difference(stroke_cohort$emergencyAdmitTime, stroke_cohort$ctHeadOrderTime, stroke_cohort$jc_uid)
+test1.1 <- test1 %>% filter(time_diff < 100) %>% filter(time_diff > -16 )
+
+test2 <- find_time_difference(stroke_cohort$emergencyAdmitTime, stroke_cohort$inpatientAdmitTime, stroke_cohort$jc_uid)
+test2.1 <- test2 %>% filter(time_diff < 100) %>% filter(time_diff > -16 )
+
+test3 <- find_time_difference(stroke_cohort$emergencyAdmitTime, stroke_cohort$tpaOrderTime, stroke_cohort$jc_uid)
+test3.1 <- test3 %>% filter(time_diff < 100) %>% filter(time_diff > -16 )
+
+test4 <- find_time_difference(stroke_cohort$emergencyAdmitTime, stroke_cohort$tpaAdminTime, stroke_cohort$jc_uid)
+test4.1 <- test4 %>% filter(time_diff < 100) %>% filter(time_diff > -16 )
+
+test5 <- find_time_difference(stroke_cohort$tpaOrderTime, stroke_cohort$tpaAdminTime, stroke_cohort$jc_uid)
+test5.1 <- test5 %>% filter(time_diff < 100) %>% filter(time_diff > -16 )
+
+test1.1$event <- 'ER to CT Order' # Try: Red: Time diff between ERAdmitTime to CT Head Order
+test2.1$event <- 'ER to Inpatient' # Time diff between ERAdmitTime to Inpatient Transfer
+test3.1$event <- 'ER to tPA Order' # Green: Time diff between ERAdmitTime to tPA Order
+test4.1$event <- 'ER to tPAAdmin' # Blue: Time diff between ERAdmitTime to tPA administration (unless there's a way to tie directly to previous?)
+
+test5.1$event <- 'tPA Order to Admin'
+
+
+test_time_long <- rbind(test1.1, 
+                        test3.1,
+                        test2.1,
+                        test4.1)
+                        #test5.1)
+
+
+
+plot_density_difference(test1.1)
+plot_density_difference(test2.1)
+plot_density_difference(test3.1)
+plot_density_difference(test4.1)
+plot_density_difference(test5.1)
+
+
+
+# 
+colnames(stroke_cohort)
+colnames(stroke_cohort_demo)
+stroke_cohort_demo$jc_uid <- stroke_cohort_demo$rit_uid
+
+stroke_cohort_merge <- merge(stroke_cohort, stroke_cohort_demo, by= "jc_uid")
+sdf <- stroke_cohort_merge
+
+# push_quiver <- function(df, name, env){
+# push_quiver(stroke_cohort_merge, "stroke_cohort_merge", sync_environment)
+# list_quiver_csv(sync_environment)
+
+
+test6 <- find_time_difference(sdf$emergencyAdmitTime, sdf$ctHeadOrderTime, sdf$jc_uid)
+# Try: Red: Time diff between ERAdmitTime to CT Head Order
+# Green: Time diff between ERAdmitTime to tPA Order
+# Blue: Time diff between ERAdmitTime to tPA administration (unless there's a way to tie directly to previous?)
+# Time diff between ERAdmitTime to Inpatient Transfer
+
+# changnig colors 
+#http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
+
diff --git a/scripts/BloodCulturePrediction/LabCultureMatrix.py b/scripts/BloodCulturePrediction/LabCultureMatrix.py
new file mode 100644
index 00000000..71e8ae1a
--- /dev/null
+++ b/scripts/BloodCulturePrediction/LabCultureMatrix.py
@@ -0,0 +1,346 @@
+#!/usr/bin/python
+"""
+Class for generating LabCultureMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+class LabCultureMatrix(FeatureMatrix):
+    def __init__(self, lab_panel, num_episodes):
+        FeatureMatrix.__init__(self, lab_panel, num_episodes)
+
+        # Parse arguments.
+        self._lab_panel = lab_panel
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+    def _get_components_in_lab_panel(self):
+        # Initialize DB connection.
+        cursor = self._connection.cursor()
+
+        # Doing a single query results in a sequential scan through
+        # stride_order_results. To avoid this, break up the query in two.
+
+        # First, get all the order_proc_ids for proc_code.
+
+        query = SQLQuery()
+        query.addSelect('order_proc_id')
+        query.addFrom('stride_order_proc')
+        query.addWhereIn('proc_code', [self._lab_panel])
+        query.addGroupBy('order_proc_id')
+        log.debug('Querying order_proc_ids for %s...' % self._lab_panel)
+        results = DBUtil.execute(query)
+        lab_order_ids = [row[0] for row in results]
+
+        # Second, get all base_names from those orders.
+        query = SQLQuery()
+        query.addSelect('base_name')
+        query.addFrom('stride_order_results')
+        query.addWhereIn('order_proc_id', lab_order_ids)
+        query.addGroupBy('base_name')
+        log.debug('Querying base_names for order_proc_ids...')
+        results = DBUtil.execute(query)
+        components = [row[0] for row in results]
+
+        return components
+
+    def _get_average_orders_per_patient(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        query = SQLQuery()
+        query.addSelect('pat_id')
+        query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
+        query.addFrom('stride_order_proc AS sop')
+        query.addFrom('stride_order_results AS sor')
+        query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhereIn("proc_code", [self._lab_panel])
+        components = self._get_components_in_lab_panel()
+        query.addWhereIn("base_name", components)
+        query.addGroupBy('pat_id')
+        log.debug('Querying median orders per patient...')
+        results = DBUtil.execute(query)
+        order_counts = [ row[1] for row in results ]
+        if len(order_counts) == 0:
+            error_msg = '0 orders for lab panel "%s."' % self._lab_panel
+            log.critical(error_msg)
+            sys.exit('[ERROR] %s' % error_msg)
+        else:
+            return numpy.median(order_counts)
+
+    def _get_random_patient_list(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        avg_orders_per_patient = self._get_average_orders_per_patient()
+        log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
+        # Based on average # of results, figure out how many patients we'd
+        # need to get for a feature matrix of requested size.
+        self._num_patients = int(numpy.max([self._num_requested_episodes / \
+            avg_orders_per_patient, 1]))
+
+        # Get numPatientsToQuery random patients who have gotten test.
+        # TODO(sbala): Have option to feed in a seed for the randomness.
+        query = SQLQuery()
+        query.addSelect('pat_id')
+        query.addFrom('stride_order_proc AS sop')
+        query.addWhereIn('proc_code', [self._lab_panel])
+        query.addOrderBy('RANDOM()')
+        query.setLimit(self._num_patients)
+        log.debug('Querying random patient list...')
+        results = DBUtil.execute(query)
+
+        # Get patient list.
+        random_patient_list = [ row[0] for row in results ]
+
+        return random_patient_list
+
+    def _query_patient_episodes(self, pat_id_col="pat_deid"):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        self._lab_components = self._get_components_in_lab_panel()
+        random_patient_list = self._get_random_patient_list()
+
+        print("RANDOM PATIENT LIST", random_patient_list)
+
+        # Build SQL query for list of patient episodes.
+        # Note that for 2008-2014 data, result_flag can take on any of the
+        # following values:  High, Low, High Panic, Low Panic,
+        # Low Off-Scale, Negative, Positive, Resistant, Susceptible, Abnormal, *
+        # (NONE): Only 27 lab components can have this flag. None has this
+        #           value for more than 5 results, so ignore it.
+        # *: Only 10 lab components can have this flag. Only 6 have it for
+        #           >10 tests, and each of them is a microbiology test for which
+        #           a flag is less meaningful, e.g. Gram Stain, AFB culture.
+        # Susceptible: Only 15 lab components can have this flag. All of those
+        #           only have this value for 2 results, so ignore it.
+        # Resistant: Only 1 lab component can have this flag. Only two results
+        #           have this value, so ignore it.
+        # Abnormal: 1462 lab components can have this flag. Many (e.g. UBLOOD)
+        #           have this value for thousands of results, so include it.
+        # Negative: Only 10 lab components can have this flag, and all for
+        #           less than 5 results, so ignore it.
+        # Positive: Only 3 lab components can have this flag, and all for
+        #           only 1 result, so ignore it.
+        # Low Off-Scale: Only 1 lab component can have this flag, and only for
+        #           3 results, so ignore it.
+        # Low Panic: 1401 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        # High Panic: 8084 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        query = SQLQuery()
+        #query.addSelect('CAST(micro.pat_deid AS BIGINT)')
+        query.addSelect('CAST(micro.order_time AS TIMESTAMP)')
+        #query.addSelect('sop.order_proc_id AS order_proc_id')
+        query.addSelect('proc_code')
+        query.addSelect('order_time')
+        query.addSelect('organism_name') #one for the result
+        # query.addSelect("CASE WHEN abnormal_yn = 'Y' THEN 1 ELSE 0 END AS abnormal_panel")
+        # query.addSelect("SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL THEN 1 ELSE 0 END) AS num_components")
+        # query.addSelect("SUM(CASE WHEN result_flag IS NULL THEN 1 ELSE 0 END) AS num_normal_components")
+        # query.addSelect("CAST(SUM(CASE WHEN result_flag IN ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') THEN 1 ELSE 0 END) = 0 AS INT) AS all_components_normal")
+        # query.addFrom('stride_order_proc AS sop')
+        # query.addFrom('stride_order_results AS sor')
+        query.addFrom('micro')
+        #query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        #query.addWhere("(result_flag in ('High', 'Low', 'High Panic', 'Low Panic', '*', 'Abnormal') OR result_flag IS NULL)")
+        #query.addWhereIn("micro.proc_code", [self._lab_panel])
+        query.addWhereIn("micro.pat_deid", random_patient_list)
+        query.addGroupBy('micro.pat_deid')
+        #query.addGroupBy('sop.order_proc_id')
+        query.addGroupBy('proc_code')
+        query.addGroupBy('order_time')
+        query.addGroupBy('organism_name')
+        #query.addGroupBy('abnormal_yn')
+        query.addOrderBy('micro.pat_deid')
+        #query.addOrderBy('sop.order_proc_id')
+        query.addOrderBy('proc_code')
+        query.addOrderBy('micro.order_time')
+        query.setLimit(self._num_requested_episodes)
+
+        self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query, index_time_col='order_time')
+
+    def _add_features(self):
+        # Add lab panel order features.
+        self._factory.addClinicalItemFeatures([self._lab_panel], features="pre")
+
+        # Add lab component result features, for a variety of time deltas.
+        LAB_PRE_TIME_DELTAS = [datetime.timedelta(-14)]
+        LAB_POST_TIME_DELTA = datetime.timedelta(0)
+        log.info('Adding lab component features...')
+        for pre_time_delta in LAB_PRE_TIME_DELTAS:
+            log.info('\t%s' % pre_time_delta)
+            self._factory.addLabResultFeatures(self._lab_components, False, pre_time_delta, LAB_POST_TIME_DELTA)
+
+        FeatureMatrix._add_features(self, index_time_col='order_time')
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab panel across %s inpatients from Stanford hospital.' % (self._lab_panel, self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab panel orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % self._lab_panel
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        line = 'Also included %s panel components: %s' % (self._lab_panel, self._lab_components)
+        summary.append(line)
+
+        return summary
+
+if __name__ == "__main__":
+    log.level = logging.DEBUG
+    start_time = time.time()
+    # Initialize lab test matrix.
+    ltm = LabCultureMatrix("LABBLC", 2)
+    # Output lab test matrix.
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    ltm.write_matrix("LABBLC-panel-5-episodes-%s-sec.tab" % str(elapsed_time))
diff --git a/scripts/CDSS/dataConversion.py b/scripts/CDSS/dataConversion.py
deleted file mode 100644
index 3307ab27..00000000
--- a/scripts/CDSS/dataConversion.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""
-Example shell script background call to run script to start up several data conversion processes in series
-nohup python scripts/CDSS/dataConversion.py &> log/log.driverDataConversion &
-"""
-
-import subprocess;
-from medinfo.common.Util import stdOpen, log, ProgressDots;
-
-LOG_FILE_TEMPLATE = "log/dataConversion.%s";
-
-baseArgv = ["python","-m"];
-
-DATE_LIMITS = \
-    [   ["2008-01-01",  "2009-01-01"],
-        ["2009-01-01",  "2010-01-01"],
-        ["2010-01-01",  "2011-01-01"],
-        ["2011-01-01",  "2012-01-01"],
-        ["2012-01-01",  "2013-01-01"],
-        ["2013-01-01",  "2014-01-01"],
-        ["2014-01-01",  "2015-01-01"],
-    ];
-MODULE_PARAMS = \
-    [   #("medinfo.dataconversion.STRIDEOrderMedConversion",),
-        #("medinfo.dataconversion.STRIDEDxListConversion",),
-        #("medinfo.dataconversion.STRIDEOrderProcConversion",),
-        #("medinfo.dataconversion.STRIDEOrderResultsConversion",),
-        #("medinfo.dataconversion.STRIDEPreAdmitMedConversion",),
-        #("medinfo.dataconversion.STRIDETreatmentTeamConversion","-a"),
-    ];
-
-specificArgvList = \
-    [   
-        #["medinfo.dataconversion.STRIDEDemographicsConversion"],
-    ];
-
-for moduleParam in MODULE_PARAMS:
-    for (startDate, endDate) in DATE_LIMITS:
-            specificArgv = list(moduleParam);
-            specificArgv.extend(["-s", startDate,"-e", endDate]);
-            specificArgvList.append(specificArgv);
-
-prog = ProgressDots(10,1,"Processes",total=len(specificArgvList));
-for specificArgv in specificArgvList:
-    key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
-    argv = list(baseArgv)
-    argv.extend(specificArgv);
-    log.info( LOG_FILE_TEMPLATE % key );
-    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
-    subprocess.call(argv, stderr=logFile);
-    prog.update();
-prog.printStatus();
diff --git a/scripts/CDSS/setupDataPreprocessingNotes.txt b/scripts/CDSS/setupDataPreprocessingNotes.txt
deleted file mode 100644
index f4f625b5..00000000
--- a/scripts/CDSS/setupDataPreprocessingNotes.txt
+++ /dev/null
@@ -1,570 +0,0 @@
-####### See CDSS/scripts/insertData.sh ########
-#   Populate with STRIDE data.  
-#
-#		For huge files, may want to "split" into pieces.  Then "cat back" together after edits, or just insert in pieces to facilitate debugging
-#			split -l10000 -a4 -d ../order_proc.txt split.order_proc.
-#			cat split.order_proc* > patch.order_proc.txt
-#
-#	Can batch together in a script, and consider running as background process for long import
-#		nohup ./batch.sh &> output.log &
-
-# Unify flowsheet labels. Can still recover original distinctions based on flo_meas_id
-	update stride_flowsheet
-	set flowsheet_name = 'Pulse' 
-	where flowsheet_name = 'Heart Rate';
-
-	update stride_flowsheet
-	set flowsheet_name = 'Urine' 
-	where flowsheet_name = 'Urine Output';
-
-# Merge Encounter info by inserting newly merged records with negative IDs, then discarding and relabeling old records
-	insert into stride_patient_encounter(pat_enc_csn_id, pat_id, payor_name, title, bp_systolic, bp_diastolic, temperature, pulse, respirations)
-	select 
-	  -spe1.pat_enc_csn_id, pat_id, payor_name, title,
-	  maxBPS, maxBPD, maxTemp, maxPulse, maxResp
-	from
-	(
-	select 
-	   pat_enc_csn_id, pat_id, payor_name, title
-	from
-	   stride_patient_encounter
-	where bp_systolic is null
-	) as spe1,
-	(
-	select 
-	   pat_enc_csn_id, max(bp_systolic) as maxBPS, max(bp_diastolic) as maxBPD, max(temperature) as maxTemp, max(pulse) as maxPulse, max(respirations) as maxResp
-	from stride_patient_encounter
-	group by pat_enc_csn_id
-	) spe2
-	where spe1.pat_enc_csn_id = spe2.pat_enc_csn_id
-
-	delete from stride_patient_encounter where pat_enc_csn_id > 0;
-	update stride_patient_encounter set pat_enc_csn_id = -pat_enc_csn_id;
-
-
-##### CDSS/scripts/CDSS/dataConversion.py ####
-#   Convert STRIDE data into structured analysis data tables.  
-# May want to drop table contraints and indexes until all inserts complete (still need unique constraint).
-# 	Create indexes afterwards to help speed up batch process
-# Consider different Med normalization options, whether want to completely separate active ingredients, 
-#	or consider each active ingredient combination as a unique item.  The former is more normalized,
-#	but get junk like TPN separated into 12 components, Zosyn listed as two items piperacillin and tazobactam,
-#	and essentially all IV fluid variations using a common Sodium Chloride IV item.  
-#	Likewise consider more administration variations for IV fluids where ingredients are less relevant than
-#	dose amount and formulation (IV bolus, IV maintenance, 3% saline vs. 1/2 NS, etc.)
-# Treatment Team aggregation option
-
-# Helper driver script
-nohup python scripts/CDSS/dataConversiony.py &> log/driver.conversion &
-
-python medinfo/dataconversion/STRIDEOrderMedConversion.py
-python medinfo/dataconversion/STRIDEOrderProcConversion.py
-python medinfo/dataconversion/STRIDEOrderResultsConversion.py
-...
-
-# Manually deactivate low-information item analysis as below
-update clinical_item
-set analysis_status = 0
-where name in ('RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','146535','LABSPR','NUR1940','NUR1025','NUR194000','NUR350','NURSE 1500','146541','146543','COD2')
-
-update clinical_item
-set analysis_status = 0
-where name in ('DIET990','TRN2','POC14','LABPOCGLU','146537')
-
-# Excessive variants of TPN that are distracting and has a bizarrely long name when unravel amino acid ingredients
-update clinical_item
-set analysis_status = 0
-where description like 'Alanine-Arginine%';
-
--- delete
--- from clinical_item_association
--- where clinical_item_id in
--- (44618,44621,44624,44667,44668,44669,44768,44800,45037,45163,52105);
--- 
--- delete
--- from clinical_item_association
--- where subsequent_item_id in
--- (44618,44621,44624,44667,44668,44669,44768,44800,45037,45163,52105)
--- 
--- CREATE INDEX clinical_item_association_subsequent_item_id ON clinical_item_association(subsequent_item_id, clinical_item_id);
-
-
-TRANSFUSION BLOOD PRODUCT X
-# Not even clear what these are orders for, since there are separate orders for RBCs, platelets, and nursing to transfuse them
-#	By coincidence, total count appears to be ~2x sum of all other blood bank orders
-update clinical_item
-set analysis_status = 0
-where name in ('LABTSLIP','LABTBP','LABTBP9','LABTBP10','LABTBP11','LABTBP12','LABTBP13','LABTBP14','LABTBP15','LABTBP16','LABTBP17','LABTBP18','LABTBP1','LABTBP19','LABTBP20','LABTBP21','LABTBP22','LABTBP23','LABTBP24','LABTBP25','LABTBP26','LABTBP27','LABTBP28','LABTBP2','LABTBP29','LABTBP30','LABTBP31','LABTBP32','LABTBP33','LABTBP34','LABTBP35','LABTBP36','LABTBP37','LABTBP38','LABTBP3','LABTBP39','LABTBP40','LABTBP41','LABTBP42','LABTBP43','LABTBP44','LABTBP45','LABTBP46','LABTBP47','LABTBP48','LABTBP4','LABTBP49','LABTBP50','LABTBP51','LABTBP52','LABTBP53','LABTBP54','LABTBP55','LABTBP56','LABTBP57','LABTBP58','LABTBP5','LABTBP59','LABTBP60','LABTBP61','LABTBP62','LABTBP63','LABTBP64','LABTBP65','LABTBP66','LABTBP67','LABTBP68','LABTBP6','LABTBP69','LABTBP70','LABTBP71','LABTBP72','LABTBP73','LABTBP74','LABTBP75','LABTBP76','LABTBP77','LABTBP78','LABTBP7','LABTBP79','LABTBP80','LABTBP81','LABTBP82','LABTBP83','LABTBP84','LABTBP85','LABTBP86','LABTBP87','LABTBP88','LABTBP8','LABTBP89','LABTBP90','LABTBP91','LABTBP92','LABTBP93');
-
-# Result comments
-update clinical_item
-set analysis_status = 0
-where name in ('BM15CO(Result)','UCMT(Result)','DOACOM(Result)','PCCOM(Result)','SREQ(Result)','SREQ(Result)','REVUE(Result)','HIACOM(Result)','PTCMT(Result)','LEICMT(Result)','PF4COM(Result)','DIMCOM(Result)','CEPCM(Result)','FCMT(Result)','SCMT(Result)','CCMT(Result)','COM(Result)','CTRXNW(Result)','11200R(Result)','PLTFCM(Result)','QBCRCM(Result)','B2GPCM(Result)','DHTLV(Result)','ATHCT(Result)','RFCOM(Result)','BMBRCT(Result)','QBMBRC(Result)','RSVCOM(Result)','KITCO(Result)','T15CO(Result)','BCRCMT(Result)','BCRKCM(Result)','BMTCMT(Result)','MTHCMT(Result)','MGMCO(Result)','SREQ(InRange)','TEGCOM(Result)','BMBCMT(Result)','BSQCO(Result)','BM11CT(Result)','SREQ(Result)','VWFPIC(Result)','CEBCM(Result)','YIFB2(Result)','YPLAB2(Result)','BM14CT(Result)','YUOPCO(Result)','YFCHY6(Result)','YUBZCO(Result)','FBCMT(Result)','PPPCOM(Result)','CF32CM(Result)','YUMECO(Result)','BCMT(Result)','YSTNCO(Result)','TCMT(Result)','11152R(Result)','TMSICO(Result)','UCMT(Result)','JAKCO(Result)','YHISS5(Result)','YHISU5(Result)','T11CT(Result)','11996R(Result)','KITCO(Result)','YVB1W2(Result)','CTSMIS(Result)','NJAKCO(Result)','T14CMT(Result)','YLCFA9(Result)','QFBCRC(Result)','TT15CO(Result)','YHISC5(Result)','TTCCMT(Result)','TBCCMT(Result)','NBTCOM(Result)','NBTCBC(Result)','FT15CO(Result)','FTCMT(Result)','SREQ(Abnormal)','9078R(Result)','9260R(Result)','MGMCOB(Result)','9584R(Result)','9574R(Result)','9464R(Result)','9450R(Result)','12291R(Result)','9433R(Result)','9420R(Result)','9181R(Result)','12486R(Result)','12473R(Result)','9280R(Result)','12255R(Result)','12468R(Result)','12461R(Result)','TBCRCT(Result)','9233R(Result)','HITI2(Result)','9120R(Result)','BGSPR(Result)','BGSPRC(Result)','HCHCO(Result)','9404R(Result)','9391R(Result)','9207R(Result)','ABID2E(Result)','PEDCOM(Result)','9344R(Result)','9586R(Result)','9582R(Result)','9374R(Result)','9361R(Result)','9099R(Result)','PMCOMMENT(Result)');
-
-# Normal / Non-Specific Results
-update clinical_item
-set analysis_status = 0
-where name like '%%(InRange)' 
-or name like '%%(Result)'
-
-
-# Customize list of items to be included in default recommendations.  
-# Or maybe better, exclude them from association analysis to minimize excess computation and storage
-
-# Exclude categories including:
-source_table	description	
-order_proc	Nursing	
-order_proc	Transport	
-order_proc	BB Call Slip	
-order_proc	None	
-order_proc	Pharmacy Supplies	
-order_proc	Diet Communication	
-order_proc	Transfer	
-order_proc	Admission	
-order_proc	Discharge	
-
-stride_order_results	Lab Result	
-stride_order_results	Point of Care Testing Result	
-stride_order_results	HIV Lab Restricted Result	
-stride_order_results	HIV Lab Non-Restricted Result	
-
-stride_preadmit_med	Preadmit Med
-stride_treatment_team	Treatment Team
-
-stride_patient	Demographics
-
-# "Recommending" diagnoses may actually be useful as diagnostic tool
-#	stride_dx_list Diagnosis (PROBLEM_LIST)
-#	stride_dx_list Diagnosis (ADMIT_DX) 
-
-update clinical_item_category
-set default_recommend = 0
-where description in ('Nursing','Transfer','Admission','Transport','BB Call Slip','None','Pharmacy Supplies','Diet Communication','Discharge')
-
-update clinical_item_category
-set default_recommend = 0
-where source_table in ('stride_order_results','stride_preadmit_med','stride_treatment_team','stride_dx_list','stride_patient')
-
-
-
-
-
-# Skip analysis for very rare orders that excessively increase association item list without adding much value
-# Decide XXX on the fly, can do summary stats and try to exclude such that still 99% of orders are represented
-# Better to break down by category, since many are nursing orders or POC glucose that are not actually interesting?
-
-	select pi.clinical_item_id, cic.source_table, cic.description, ci.name, ci.description, count(distinct patient_id), count(distinct encounter_id), count(patient_item_id)
-	from patient_item as pi, clinical_item as ci, clinical_item_category as cic
-	where pi.clinical_item_id = ci.clinical_item_id
-	and ci.clinical_item_category_id = cic.clinical_item_category_id
-	and ci.analysis_status = 1
-	group by pi.clinical_item_id, cic.source_table, cic.description, ci.name, ci.description 
-
-# Use above to identify items to deactivate
-	
-	update clinical_item set analysis_status = 0 
-	where clinical_item_id in
-	(
-		select clinical_item_id
-		from patient_item 
-		group by clinical_item_id
-		having count(patient_item_id) < 256
-	);
-
-
-##### See addSynonyms.sql for adding item description synonyms to facilitate user testing searches
-
-
-################## Composite Clinical Items #######################
-Need specific ID numbers, but may be different in different database versions
-Try working of descriptions as below to find the relevant ID numbers
-
-
-
-
-# Composite certain outcome measures, for example, ICU interventions (sub-categorization based on vasopressors)
-36086	72	7512	RXCUI7512	Norepinephrine
-43993	72	8163	RXCUI8163	Phenylephrine
-44020	72	3992	RXCUI3992	Epinephrine
-44251	72	3616	RXCUI3616	Dobutamine
-44323	72	3628	RXCUI3628	Dopamine
-65411	72	224819	MED224819	VASOPRESSIN...
-python medinfo/cpoe/DataManager.py --compositeRelated "36086,43993,44020,44251,44323,65411|AnyVasoactive|Any Vasoactive Infusion|72"
-
-# CaCl infusion and IV Citrate are very closely correlated to CRRT use, but are rarely used in other scenarios
-45328	82	205894	MED205894	BICARB
-51019	82	36676	RXCUI36676	Sodium
-51150	82	-5.55816E+18	RXCUI205894,36676	BICARB
-54189	82	500028	MED500028	CRRT
-58948	82	9863	RXCUI9863	Sodium
-59107	82	1908	RXCUI1908	Calcium
-64279	82	1.55731E+18	RXCUI205894,11295	BICARB
-49140	4	633105	NUR4084	CONT RENAL REPLACEMENT THERAPY
-python medinfo/cpoe/DataManager.py --compositeRelated "45328,51019,51150,54189,58948,59107,64279,49140|AnyCRRT|Continuous Renal Replacement Therapy Components|82"
-
-# Ventilator orders
-45768	7	472340	RT75	RESP - EXTUBATION
-45832	7	35000476272	RT104	RESP - VENTILATOR SETTINGS
-45990	7	473786	RT97	RESP - WEAN VENTILATOR
-46106	7	473357	RT90	RESP - SPONTANEOUS BREATHING TRIAL
-48769	7	660692	RT130	RESP - REPOSITION ET TUBE
-61530	7	472542	RT88	RESP - HIGH FREQUENCY OSCILLATORY VENTILATION
-62003	7	871225	RT997	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL
-63826	7	897630	RT202	RESP - MONITOR, PERFORM VENTILATOR AND PATIENT ASSESSMENT
-66038	7	473327	RT91	RESP - SIMV/PRESSURE SUPPORT
-python medinfo/cpoe/DataManager.py --compositeRelated "45768,45832,45990,46106,48769,61530,62003,63826,66038|AnyVentilator|Ventilator Orders|7"
- 
-# Can populate leaf linked items directly, or link to the above intermediate nodes and allow inheritance to pickup the rest
-Category: 12	stride_order_proc	Procedures
-71045 Vasoactives
-71046 CRRT
-71047 Ventilator
-python medinfo/cpoe/DataManager.py --compositeRelated "71045,71046,71047|AnyICULifeSupport|ICU Life Support (Vasoactives,Ventilator,CRRT)|12"
-
-# Or incrementally update an existing compomsite item
-#python medinfo/cpoe/DataManager.py -g "23701,23702|23700"
-
-# Any ICU specific (or highly correlated) orders to capture events occuring in the ICU, including other drips, nursing orders
-??????????
-	530008	MED530008	NITROPRUSSIDE IV INFUSION
-	8782	RXCUI8782	Propofol (Intravenous)
-	530025	MED530025	DEXMEDETOMIDINE IV INFUSION
-	530004	MED530004	MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION
-	8163	RXCUI8163	Phenylephrine (Intravenous)
-	114200	RXCUI114200	Citrate (Intravenous)
-	530120	MED530120	HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)
-	36676	RXCUI36676	Sodium Bicarbonate (CRRT)
-	530007	MED530007	VECURONIUM IV INFUSION
-	4177	RXCUI4177	Etomidate (Intravenous)
-	10154	RXCUI10154	Succinylcholine (Intravenous)
-	520130	MED520130	CALCIUM CHLORIDE 8 G/1000 ML IV INFUSION
-	530009	MED530009	CISATRACURIUM IV INFUSION
-	530003	MED530003	FENTANYL  10 MCG/ML IV INFUSION
-	530111	MED530111	MIDAZOLAM 100 MG IN D5W 100 ML IV INFUSION
-	530114	MED530114	MILRINONE CUSTOM IV INFUSION
-	530050	MED530050	DOBUTAMINE IV INFUSION CUSTOM MIXTURE
-	530046	MED530046	HYDROMORPHONE IV INFUSION
-	530112	MED530112	MIDAZOLAM IV INFUSION (CUSTOM)
-	37217	NUR2254	MONITOR PULMONARY ARTERY CATHETER
-	78347	RT106	RESP - NITRIC OXIDE VENT
-	37120	NUR2247	MONITOR CENTRAL LINE
-	26704	NUR1942	EXTERNAL VENTRICULAR DRAIN CARE
-	17833	NUR1528	MONITOR ARTERIAL LINE
-	399682	NUR30020	RASS SEDATION ASSESSMENT
-	396282	NUR1599	MONITOR CARDIAC ASSIST DEVICE
-	17896	NUR1591	OROGASTRIC TUBE
-	17834	NUR1529	MONITOR CO/CI
-	46677	NUR2999	MONITOR SCV02
-		AnyICULifeSupport	ICU Life Support (Vasopressors,Ventilator,CRRT)
-?????
-python medinfo/cpoe/DataManager.py --compositeRelated "23700,4455,3858,634,710,466,192,113,4303,4216,4507,629,3866,4452,180,3950,4078,3951,43,117,153,201,529,793,1096,1612,1752,2205,2337,2349|ICUOrders|ICU Specific/Correlated Orders|100"
-
-
-# DNR / non-Full Code Orders
-#48737	COD3	PARTIAL CODE
-45905	COD1	DNR
-62758	COD7	DNR/C
-62911	COD5	DNR/DNE
-63717	COD1	DNR/DNI
-python medinfo/cpoe/DataManager.py --compositeRelated "45905,62758,62911,63717|AnyDNR|Any DNR Code Status|22"
-
-
-
-# Readmission by looking for triples containing Discharge then Admit
-Category: 23	stride_order_proc	Admission
-# Composite admission orders
-45814	23	471546	ADT1	ADMIT TO INPATIENT
-61845	23	35000476278	ADT16	ADMIT POST SURGERY
-61982	23	471546	ADT1	ADMIT/PLACE PATIENT
-65641	23	776046	ADT100	ADMIT TO INPATIENT
-python medinfo/cpoe/DataManager.py --compositeRelated "45814,61845,61982,65641|AnyAdmit|Any Admit (not observation nor outpatient surgery)|23"
-
-45805	13	472204	ADT12	DISCHARGE PATIENT
-
-insert into clinical_item
-(clinical_item_category_id, name, description, analysis_status, outcome_interest)
-values
-(23,'READT','Readmission',1,1);
-
-# Identify triples, but effectively requires runs full association analysis work.  Very time intensive
-python medinfo/cpoe/TripleAssociationAnalysis.py -s 45805,73932 -v 73926 patientIds.XYZ.tab
-
-
-# Run Pre-Computation Association Analysis
-# 	Memory intensive, but if can use large amount on 64bit Python saves a lot of DB hits / persistence.
-#   Use -a option to specify how many associations to keep in memory before commit to DB
-#	1,000,000 seems to just fit within 7.5 GB of RAM
-#	Still a DB intensive I/O process that can be helped by moving more database processing to memory than disk
-python medinfo/cpoe/AssociationAnalysis.py -i <patientIDFile> -u 10000 -a 900000
-(Find Patient IDs to work on, by query like 
-	select distinct patient_id 
-	from patient_item 
-	where analyze_date is null 
-	and patient_id % 10 = -1
-Use arbitrary mod parameter on patient ID and item dates to select out data subsets
-)
-
-# Helper driver script to break up into multiple processes (beware total memory overhead)
-nohup python scripts/CDSS/assocAnalysis.py &> log/assocAnalysis &
-
-#  Or just restore from another database
-medinfo/db/support/restoreCPOETables.bat
-
-
-
-
-====================================
-
-
-# Pre-Compute Analyzed Clinical Item Counts to facilitate subsequent interface queries
-python medinfo/cpoe/DataManager.py --updateClinicalItemCounts
-or
-Item Recommender Web Interface, Cache or Query = PreCompute
-or
-# Transfer baseline counts to clinical_item summary counts
-update clinical_item as ci, clinical_item_association as cia
-set ci.item_count = cia.count_0, 
-    ci.patient_count = cia.patient_count_0, 
-    ci.encounter_count = cia.encounter_count_0
-where ci.clinical_item_id = 5 
-and cia.clinical_item_id = ci.clinical_item_id and cia.subsequent_item_id = ci.clinical_item_id;
-
-
-===================================================================
-Setup simultation data
-python scripts\CDSS\populateOrderResultStats.py
-
-
-
-
-===================================================================
-Data Cleanup Options
-
-# Check for duplicate patient items (possibly from repeated data conversion runs)
-# (May have to patch conversion scripts to check for and ignore duplicate errors)
-ALTER TABLE stride_order_proc ENGINE MyISAM;
-ALTER IGNORE TABLE stride_order_proc ADD UNIQUE INDEX stride_order_proc_id (order_proc_id);
-ALTER TABLE stride_order_proc ENGINE InnoDB;
-
-	# Need temporary table option if foreign key references need to remain intact
-   CREATE TABLE tmp_patient_item LIKE patient_item;
-   ALTER IGNORE TABLE tmp_patient_item ADD UNIQUE INDEX patient_item_composite (patient_id, clinical_item_id, item_date);
-   INSERT IGNORE INTO tmp_patient_item SELECT * FROM patient_item;
-   DROP TABLE patient_item;
-   RENAME TABLE tmp_patient_item TO patient_item;
-
-	# Another in place option, but requires a unique ID column to exist
-	delete
-	from patient_item as test
-	 where exists ( select 'x' 
-					  from patient_item as i
-					 where i.patient_id = test.patient_id
-					   and i.clinical_item_id = test.clinical_item_id
-					   and i.item_date = test.item_date
-					   and i.patient_item_id < test.patient_item_id
-				 )
-	limit 10;
-
-
-
-# Use data/analysis/queryItemCount.py to help guide threshold decision
-
-select clinical_item_id, count(*)
-from patient_item as pi
-group by clinical_item_id
-having count(*) < XXX;
-
-python medinfo/cpoe/DataManager.py --deactivateAnalysisByCount <thresholdInstanceCount>
-
-
-
-# Exclude Individual Items including, should not even be analyzed:
-external_id	name	description	
-854932	RXCUI854932	Pneumococcal Capsular Polysaccharide Type 10a Vaccine (SUBCUTANEOUS)	
-854934	RXCUI854934	Pneumococcal Capsular Polysaccharide Type 11a Vaccine (SUBCUTANEOUS)	
-854936	RXCUI854936	Pneumococcal Capsular Polysaccharide Type 12f Vaccine (SUBCUTANEOUS)	
-854938	RXCUI854938	Pneumococcal Capsular Polysaccharide Type 14 Vaccine (SUBCUTANEOUS)	
-854940	RXCUI854940	Pneumococcal Capsular Polysaccharide Type 15b Vaccine (SUBCUTANEOUS)	
-854942	RXCUI854942	Pneumococcal Capsular Polysaccharide Type 17f Vaccine (SUBCUTANEOUS)	
-854944	RXCUI854944	Pneumococcal Capsular Polysaccharide Type 18c Vaccine (SUBCUTANEOUS)	
-854946	RXCUI854946	Pneumococcal Capsular Polysaccharide Type 19a Vaccine (SUBCUTANEOUS)	
-854948	RXCUI854948	Pneumococcal Capsular Polysaccharide Type 19f Vaccine (SUBCUTANEOUS)	
-854950	RXCUI854950	Pneumococcal Capsular Polysaccharide Type 2 Vaccine (SUBCUTANEOUS)	
-854952	RXCUI854952	Pneumococcal Capsular Polysaccharide Type 20 Vaccine (SUBCUTANEOUS)	
-854954	RXCUI854954	Pneumococcal Capsular Polysaccharide Type 22f Vaccine (SUBCUTANEOUS)	
-854956	RXCUI854956	Pneumococcal Capsular Polysaccharide Type 23f Vaccine (SUBCUTANEOUS)	
-854958	RXCUI854958	Pneumococcal Capsular Polysaccharide Type 3 Vaccine (SUBCUTANEOUS)	
-854960	RXCUI854960	Pneumococcal Capsular Polysaccharide Type 33f Vaccine (SUBCUTANEOUS)	
-854962	RXCUI854962	Pneumococcal Capsular Polysaccharide Type 4 Vaccine (SUBCUTANEOUS)	
-854964	RXCUI854964	Pneumococcal Capsular Polysaccharide Type 5 Vaccine (SUBCUTANEOUS)	
-854966	RXCUI854966	Pneumococcal Capsular Polysaccharide Type 6b Vaccine (SUBCUTANEOUS)	
-854968	RXCUI854968	Pneumococcal Capsular Polysaccharide Type 7f Vaccine (SUBCUTANEOUS)	
-854970	RXCUI854970	Pneumococcal Capsular Polysaccharide Type 8 Vaccine (SUBCUTANEOUS)	
-854972	RXCUI854972	Pneumococcal Capsular Polysaccharide Type 9n Vaccine (SUBCUTANEOUS)	
-854974	RXCUI854974	Pneumococcal Capsular Polysaccharide Type 9v Vaccine (SUBCUTANEOUS)	
-854932	RXCUI854932	Pneumococcal Capsular Polysaccharide Type 10a Vaccine (Intramuscular)	
-854934	RXCUI854934	Pneumococcal Capsular Polysaccharide Type 11a Vaccine (Intramuscular)	
-854936	RXCUI854936	Pneumococcal Capsular Polysaccharide Type 12f Vaccine (Intramuscular)	
-854938	RXCUI854938	Pneumococcal Capsular Polysaccharide Type 14 Vaccine (Intramuscular)	
-854940	RXCUI854940	Pneumococcal Capsular Polysaccharide Type 15b Vaccine (Intramuscular)	
-854942	RXCUI854942	Pneumococcal Capsular Polysaccharide Type 17f Vaccine (Intramuscular)	
-854944	RXCUI854944	Pneumococcal Capsular Polysaccharide Type 18c Vaccine (Intramuscular)	
-854946	RXCUI854946	Pneumococcal Capsular Polysaccharide Type 19a Vaccine (Intramuscular)	
-854948	RXCUI854948	Pneumococcal Capsular Polysaccharide Type 19f Vaccine (Intramuscular)	
-854950	RXCUI854950	Pneumococcal Capsular Polysaccharide Type 2 Vaccine (Intramuscular)	
-854952	RXCUI854952	Pneumococcal Capsular Polysaccharide Type 20 Vaccine (Intramuscular)	
-854954	RXCUI854954	Pneumococcal Capsular Polysaccharide Type 22f Vaccine (Intramuscular)	
-854956	RXCUI854956	Pneumococcal Capsular Polysaccharide Type 23f Vaccine (Intramuscular)	
-854958	RXCUI854958	Pneumococcal Capsular Polysaccharide Type 3 Vaccine (Intramuscular)	
-854960	RXCUI854960	Pneumococcal Capsular Polysaccharide Type 33f Vaccine (Intramuscular)	
-854962	RXCUI854962	Pneumococcal Capsular Polysaccharide Type 4 Vaccine (Intramuscular)	
-854964	RXCUI854964	Pneumococcal Capsular Polysaccharide Type 5 Vaccine (Intramuscular)	
-854966	RXCUI854966	Pneumococcal Capsular Polysaccharide Type 6b Vaccine (Intramuscular)	
-854968	RXCUI854968	Pneumococcal Capsular Polysaccharide Type 7f Vaccine (Intramuscular)	
-854970	RXCUI854970	Pneumococcal Capsular Polysaccharide Type 8 Vaccine (Intramuscular)	
-854972	RXCUI854972	Pneumococcal Capsular Polysaccharide Type 9n Vaccine (Intramuscular)	
-854974	RXCUI854974	Pneumococcal Capsular Polysaccharide Type 9v Vaccine (Intramuscular)	
-147420	146535	ECG PROCEDURE SCANNED REPORT	
-1883	LABSPR	SPECIMEN REMARK	
-26702	NUR1940	NURSING COMMUNICATION(FOR ORDERSET ONLY)	
-1435	NUR1025	VITAL SIGNS	
-412485	NUR194000	NURSING COMMUNICATION	
-46821	NUR350	NOTIFY MD:	
-17581	NURSE 1500	NOTIFY MD:VITAL SIGNS	
-147415	146541	OTHER PROCEDURE SCANNED REPORT	
-147416	146543	OTHER ORDER SCANNED REPORT	
-958	COD2	FULL CODE	
-
-706	DIET990	DIET REGULAR	
-1604	TRN2	PATIENT TRANSPORT	
-119815	POC14	POC GLUCOSE BY METER	
-1001	LABPOCGLU	GLUCOSE BY METER	
-
-# If setting analysis status to 0, don't need to set default_recommend to 0 since will inherently be excluded
-update clinical_item
-set analysis_status = 0
-where name in ('RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','RXCUI854932','RXCUI854934','RXCUI854936','RXCUI854938','RXCUI854940','RXCUI854942','RXCUI854944','RXCUI854946','RXCUI854948','RXCUI854950','RXCUI854952','RXCUI854954','RXCUI854956','RXCUI854958','RXCUI854960','RXCUI854962','RXCUI854964','RXCUI854966','RXCUI854968','RXCUI854970','RXCUI854972','RXCUI854974','146535','LABSPR','NUR1940','NUR1025','NUR194000','NUR350','NURSE 1500','146541','146543','COD2')
-
-update clinical_item
-set analysis_status = 0
-where name in ('DIET990','TRN2','POC14','LABPOCGLU','146537')
-
-
-TRANSFUSION BLOOD PRODUCT X
-# Not even clear what these are orders for, since there are separate orders for RBCs, platelets, and nursing to transfuse them
-#	By coincidence, total count appears to be ~2x sum of all other blood bank orders
-update clinical_item
-set analysis_status = 0
-where name in ('LABTSLIP','LABTBP','LABTBP9','LABTBP10','LABTBP11','LABTBP12','LABTBP13','LABTBP14','LABTBP15','LABTBP16','LABTBP17','LABTBP18','LABTBP1','LABTBP19','LABTBP20','LABTBP21','LABTBP22','LABTBP23','LABTBP24','LABTBP25','LABTBP26','LABTBP27','LABTBP28','LABTBP2','LABTBP29','LABTBP30','LABTBP31','LABTBP32','LABTBP33','LABTBP34','LABTBP35','LABTBP36','LABTBP37','LABTBP38','LABTBP3','LABTBP39','LABTBP40','LABTBP41','LABTBP42','LABTBP43','LABTBP44','LABTBP45','LABTBP46','LABTBP47','LABTBP48','LABTBP4','LABTBP49','LABTBP50','LABTBP51','LABTBP52','LABTBP53','LABTBP54','LABTBP55','LABTBP56','LABTBP57','LABTBP58','LABTBP5','LABTBP59','LABTBP60','LABTBP61','LABTBP62','LABTBP63','LABTBP64','LABTBP65','LABTBP66','LABTBP67','LABTBP68','LABTBP6','LABTBP69','LABTBP70','LABTBP71','LABTBP72','LABTBP73','LABTBP74','LABTBP75','LABTBP76','LABTBP77','LABTBP78','LABTBP7','LABTBP79','LABTBP80','LABTBP81','LABTBP82','LABTBP83','LABTBP84','LABTBP85','LABTBP86','LABTBP87','LABTBP88','LABTBP8','LABTBP89','LABTBP90','LABTBP91','LABTBP92','LABTBP93');
-
-
-
-
-
-# For some of above like PNA vaccine components, may actually be more appropriate to merge/composite items 
-python medinfo/cpoe/DataManager.py --compositeRelated <clinicalItemIds>
-python medinfo/cpoe/DataManager.py --mergeRelated <clinicalItemIds>
-python medinfo/cpoe/DataManager.py --unifyRedundant <clinicalItemIds>
-
-
-# ???Highlight items that may be of interest for outcome measures
-clinical_item_id	description	count	
-20	DISCHARGE PATIENT	1532	
-31	ADMIT TO PHASE	769	
-34	TRANSFER PATIENT	581	
-55	INSERT AIRWAY	360	
-58	RESP - SPONTANEOUS BREATHING TRIAL	76	
-59	ADMIT/PLACE PATIENT	1294	
-66	PATIENT TRANSPORT	2384	
-147	DISCHARGE PATIENT WHEN CRITERIA MET	770	
-207	DIALYZE PATIENT	85	
-332	NON-VIOLENT RESTRAINTS	316	
-374	DNR	237	
-461	RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA	72	
-465	OK TO USE CENTRAL LINE	30	
-522	HEMODIALYSIS	120	
-523	INTERAGENCY REFERRAL TO HOME HOSPICE	18	
-563	CAR CATH CORS POSSIBLE	37	
-612	COMFORT CARE MEASURES	42	
-616	CCPD  PERITONEAL DIALYSIS	15	
-723	INTUBATION	6	
-957	PLACE IN OBSERVATION-CDU	23	
-1086	HEMODIALYSIS WITH PUF	6	
-1087	CAPD PERITONEAL DIALYSIS	3	
-1312	CENTRAL LINE	10	
-1521	CENTRAL LINE CARE	4	
-1636	NON INVASIVE POSTIVE PRESS VENT BIPAP/CPAP	3	
-1643	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL	6	
-1909	TRACHEOSTOMY  CARE	2	
-6551	ARGATROBAN 1000 MCG/ML (250 ML) IV INFUSION	18	
-6700	Fentanyl (Intravenous)	77	
-6724	Dopamine (Intravenous)	72	
-6738	DEXMEDETOMIDINE IV INFUSION	146	
-6743	VASOPRESSIN 1 UNIT/ML IV INFUSION	39	
-6745	Bicarb Dialys Soln #16 W/O Ca K (4 Meq/L) -Mg (1.5 Meq/L) Hemodial Soln (CRRT)	16	
-6797	AMIODARONE IVPB	80	
-6801	AMIODARONE INFUSION CENTRAL LINE ADMIN	44	
-6823	PHENYLEPHRINE IV INFUSION	87	
-6872	Eptifibatide (Intravenous)	11	
-6936	AMIODARONE INFUSION PERIPH ADMIN	29	
-6950	MIDAZOLAM IV INFUSION (CUSTOM)	2	
-7014	Dobutamine (Intravenous)	22	
-7066	Bicarb Dialys Soln No.8 W-O Ca K (2 Meq/L) -Mg (1 Meq/L) Hemodial Soln (CRRT)	22	
-7111	Amiodarone (Intravenous)	1	
-7148	D5W WITH SODIUM BICARBONATE IV INFUSION	7	
-7167	Epinephrine (Intravenous)	12	
-7192	Milrinone (Intravenous)	10	
-20	DISCHARGE PATIENT	1532	
-31	ADMIT TO PHASE	769	
-1660	CALL DIALYSIS RN CAP/DRAIN CAPD	2	
-1087	CAPD PERITONEAL DIALYSIS	3	
-616	CCPD  PERITONEAL DIALYSIS	15	
-1521	CENTRAL LINE CARE	4	
-612	COMFORT CARE MEASURES	42	
-674	CONT RENAL REPLACEMENT THERAPY	18	
-1097	DIALYSIS ACCESS	6	
-1995	DIALYSIS CATHETER CARE	1	
-207	DIALYZE PATIENT	85	
-522	HEMODIALYSIS	120	
-1086	HEMODIALYSIS WITH PUF	6	
-465	OK TO USE CENTRAL LINE	30	
-403	TRANSFUSION EMERGENCY ISSUE	20	
-34	TRANSFER PATIENT	581	
-158	RESP - EXTUBATION	98	
-1643	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL	6	
-139	RESP - NON INVASIVE POS. PRESS VENT (BIPAP/CPAP)	136	
-58	RESP - SPONTANEOUS BREATHING TRIAL	76	
-164	RESP - VENTILATOR SETTINGS	467	
-137	RESP - WEAN VENTILATOR	110	
-461	RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA	72	
-130	ADMIT POST SURGERY	656	
-59	ADMIT/PLACE PATIENT	1294	
-957	PLACE IN OBSERVATION-CDU	23	
-66	PATIENT TRANSPORT	2384	
-374	DNR	237	
-99	PARTIAL CODE	17	
-902	ARTERIAL LINE	11	
-1312	CENTRAL LINE	10	
-723	INTUBATION	6	
-6801	AMIODARONE INFUSION CENTRAL LINE ADMIN	44	
-6936	AMIODARONE INFUSION PERIPH ADMIN	29	
-6797	AMIODARONE IVPB	80	
-7014	Dobutamine (Intravenous)	22	
-6724	Dopamine (Intravenous)	72	
-6659	EPINEPHRINE IV INFUSION	61	
-6696	Midazolam (Intravenous)	70	
-7192	Milrinone (Intravenous)	10	
-6681	NITROPRUSSIDE IV INFUSION	99	
-6788	NOREPINEPHRINE IV INFUSION	46	
-6900	Nitroglycerin (Intravenous)	59	
-
-xxxx	Death Date	???
-xxxx	Length of Stay	???
-
-
-update clinical_item
-set outcome_interest = 1
-where description in
-('DISCHARGE PATIENT','ADMIT TO PHASE','TRANSFER PATIENT','INSERT AIRWAY','RESP - SPONTANEOUS BREATHING TRIAL','ADMIT/PLACE PATIENT','PATIENT TRANSPORT','DISCHARGE PATIENT WHEN CRITERIA MET','DIALYZE PATIENT','NON-VIOLENT RESTRAINTS','DNR','RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA','OK TO USE CENTRAL LINE','HEMODIALYSIS','INTERAGENCY REFERRAL TO HOME HOSPICE','CAR CATH CORS POSSIBLE','COMFORT CARE MEASURES','CCPD  PERITONEAL DIALYSIS','INTUBATION','PLACE IN OBSERVATION-CDU','HEMODIALYSIS WITH PUF','CAPD PERITONEAL DIALYSIS','CENTRAL LINE','CENTRAL LINE CARE','NON INVASIVE POSTIVE PRESS VENT BIPAP/CPAP','RESP - LUNG PROTECTIVE VENTILATION PROTOCOL','TRACHEOSTOMY  CARE','ARGATROBAN 1000 MCG/ML (250 ML) IV INFUSION','Fentanyl (Intravenous)','Dopamine (Intravenous)','DEXMEDETOMIDINE IV INFUSION','VASOPRESSIN 1 UNIT/ML IV INFUSION','Bicarb Dialys Soln #16 W/O Ca K (4 Meq/L) -Mg (1.5 Meq/L) Hemodial Soln (CRRT)','AMIODARONE IVPB','AMIODARONE INFUSION CENTRAL LINE ADMIN','PHENYLEPHRINE IV INFUSION','Eptifibatide (Intravenous)','AMIODARONE INFUSION PERIPH ADMIN','MIDAZOLAM IV INFUSION (CUSTOM)','Dobutamine (Intravenous)','Bicarb Dialys Soln No.8 W-O Ca K (2 Meq/L) -Mg (1 Meq/L) Hemodial Soln (CRRT)','Amiodarone (Intravenous)','D5W WITH SODIUM BICARBONATE IV INFUSION','Epinephrine (Intravenous)','Milrinone (Intravenous)','DISCHARGE PATIENT','ADMIT TO PHASE','CALL DIALYSIS RN CAP/DRAIN CAPD','CAPD PERITONEAL DIALYSIS','CCPD  PERITONEAL DIALYSIS','CENTRAL LINE CARE','COMFORT CARE MEASURES','CONT RENAL REPLACEMENT THERAPY','DIALYSIS ACCESS','DIALYSIS CATHETER CARE','DIALYZE PATIENT','HEMODIALYSIS','HEMODIALYSIS WITH PUF','OK TO USE CENTRAL LINE','TRANSFUSION EMERGENCY ISSUE','TRANSFER PATIENT','RESP - EXTUBATION','RESP - LUNG PROTECTIVE VENTILATION PROTOCOL','RESP - NON INVASIVE POS. PRESS VENT (BIPAP/CPAP)','RESP - SPONTANEOUS BREATHING TRIAL','RESP - VENTILATOR SETTINGS','RESP - WEAN VENTILATOR','RESP-CPAP/BIPAP FOR OBSTRUCTIVE SLEEP APNEA','ADMIT POST SURGERY','ADMIT/PLACE PATIENT','PLACE IN OBSERVATION-CDU','PATIENT TRANSPORT','DNR','PARTIAL CODE','ARTERIAL LINE','CENTRAL LINE','INTUBATION','AMIODARONE INFUSION CENTRAL LINE ADMIN','AMIODARONE INFUSION PERIPH ADMIN','AMIODARONE IVPB','Dobutamine (Intravenous)','Dopamine (Intravenous)','EPINEPHRINE IV INFUSION','Midazolam (Intravenous)','Milrinone (Intravenous)','NITROPRUSSIDE IV INFUSION','NOREPINEPHRINE IV INFUSION','Nitroglycerin (Intravenous)','Death Date');
-
-
diff --git a/scripts/CDSS/setupNotes.txt b/scripts/CDSS/setupNotes.txt
deleted file mode 100644
index 9571d8be..00000000
--- a/scripts/CDSS/setupNotes.txt
+++ /dev/null
@@ -1,188 +0,0 @@
-======== AWS Server Setup Notes ==============
-- Sign-up for AWS account
-- Free Tier eligible EC2 Micro Instance create
-- Change Security Group for instance to allow TCP ports 22 for SSH connection any others
-- Connect as ec2-user (or ubuntu) first off using public-private key default method (requires local download)
-  ssh -i privateKey.pem ec2-user@xxx.xxx.xxx.xxx
-- Need to prefix "sudo " to most commands to act as root
-- Create user to login as
-sudo adduser jonc101
-sudo passwd jonc101
-- Grant user sudo privileges
-sudo visudo
-	jonc101 ALL=(ALL) ALL
-- Change setting to allow SSH password login. Beware that this is less secure!
-sudo vi /etc/ssh/sshd_config
-PasswordAuthentication yes
-sudo service sshd restart
-
-Should now be able to SSH to instance using the created username and password
-
-- Install Amazon packages
-sudo yum install svn
-sudo yum install postgresql93
-sudo yum install python27-psycopg2
-sudo yum install httpd
-sudo yum install mod_wsgi-python27
-sudo yum install python27-scipy
-
-sudo easy_install gensim
-
-sudo yum install mysql
-sudo yum install MySQL-python
-
-(Consider general SciPy dev environment setup)
-(http://blog.yhathq.com/posts/setting-up-scientific-python.html)
-  Avoid mixing installation modalities (PIP, downloadable executables, Enthought Canopy distribution) as tend to be incompatible
-
-- Use AWS Console interface (RDService) to create a PostgreSQL or MySQL relational database if want separate DB server (vs. installed on local EC2 instance)
-  Consider Provisioned IOPS on a "large' instance for computation periods to better handle high I/O cost
-  Additional MySQL configurations that helped with local installation to move more processing to memory than disk:
-	query_cache_size=128M
-	innodb_buffer_pool_size=6000M
-	innodb_log_file_size=2000M
-
-
-==============================================
-Local Installation Dependencies
-- Python 2.7
-- PostgreSQL
-- Apache 2.2
-- Scipy
-- Numpy
-- Psycopg2
-- Mod_WSGI
-
-
-==============================================
-Download code from source control
-
-svn checkout https://xp-dev.com/svn/jonc101-Projects/CDSS
-
-=============================================
-Update ~/.bash_profile with paths and references
-
-vi ~/.bash_profile
-
-	BASE_DIR=$HOME/CDSS
-	export BASE_DIR
-
-	PYTHONPATH=$PYTHONPATH:$BASE_DIR
-	export PYTHONPATH
-
-then
-
-source ~/.bash_profile
-
-==============================================
-
-
-#   Create blank database to work with
-PostgreSQL: 
-	createdb -U postgres -O jonc101 medinfo
-
-# Create DB users and access privileges from remote connections
-PostgreSQL:
-    CREATE USER jonc101 PASSWORD '<yourPassword>';
-
-
-# If restoring from a previously pre-processed database, than just restore each data table's contents in turn
-restoreCPOETables.sh (should be stored with data dump source)
-# See setupDataPreprocessingNotes.txt for details on reconstructing the pre-processing steps from scratch
-
-#   Create data definitions (If did direct restore from previous data dump, OK to expect many errors regarding "Relation already exists")
-PostgreSQL: # Use timestamp data type
-	psql -f medinfo/db/definition/strideData.sql -U jonc101 medinfo		# This may be in the source data directory as schema.strideData.sql
-	psql -f medinfo/db/definition/cpoeStats.sql -U jonc101 medinfo
-	psql -f medinfo/db/definition/cpoeSimulation.sql -U jonc101 medinfo
-
-# Point Python scripts to database
-vi medinfo/db/Env.py
-
-#	Database connection test
-python medinfo/db/test/TestDBUtil.py
-
-#   Basic stat table unit tests
-python medinfo/cpoe/test/TestAssociationAnalysis.py
-python medinfo/cpoe/test/TestItemRecommender.py
-
-#   Data Conversion unit test
-python medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py
-python medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py
-python medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py
-
-# Multiple Unit tests, auto discover
-python -m unittest discover -v -p "Test*.py" -s medinfo/analysis
-
-# Remote copying / synchronizing data files
-pscp * jonc101@corn.stanford.edu:private/data
-# Patching / resuming incompletely transmitted files.  Do for one file at a time to allow for proper interrupt / resume function?
-rsync --partial --progress order_results.txt jonc101@corn.stanford.edu:private/data/order_results.txt
-
-
-===================================================================
-
-#  Setup Web Server (Apache HTTP, mod_wsgi, psycopg2 or MySQL DB access).  
-# Install mod_wsgi using an install or Copy the version appropriate mod_wsgi.so file to the 
-#	apache modules directory and rename to simply mod_wsgi.so
-# Sample Apache Config:
-sudo vi /etc/httpd/conf/httpd.conf
-
-	LoadModule wsgi_module modules/mod_wsgi.so
-	DocumentRoot "/home/jonc101/CDSS/medinfo/web"
-
-	# Medical Informatics web application configuration
-	WSGIPythonPath /home/jonc101/CDSS
-	Alias /medinfo/web/ "/home/jonc101/CDSS/medinfo/web/"
-	<Directory "/home/jonc101/CDSS/medinfo/web">
-		DirectoryIndex index.html index.htm
-
-		AllowOverride None
-		Allow from all
-
-		Order allow,deny
-	</Directory>
-	<Directory "/home/jonc101/CDSS/medinfo/web/cgibin">
-		# Handler setup for Python CGI scripts (works on files ending in .py)
-		Options +ExecCGI
-
-		# Basic CGI support
-		#AddHandler cgi-script py
-		#SetEnv PYTHONPATH /home/jonc101/CDSS
-
-		# mod_wsgi support.  # Need to define PythonPAth and other environment variables at level of user starting Apache process, or PythonPath in above WSGIPythonPath
-		AddHandler wsgi-script py
-		WSGIApplicationGroup %{GLOBAL}
-
-		AllowOverride None
-		Allow from all
-
-		Order allow,deny
-	</Directory>
-
-
-# Update medinfo/web/cgibin/Env.py, CGI_TEXT_RESPONSE depending on if using CGI or WSGI setup
-# Windows: May need to add Apache directive, so CGI knows how to run .py files
-	ScriptInterpreterResoruce Registry
-
-# Link with tips on setting up apache environment variable configuration if needed
-# Basically adding values to a configuration file like below, though CGI and WSGI should accept PYTHONPATH specification as above
-sudo vi /etc/sysconfig/httpd 
-	TEST_ENV_VAR=TestValue
-	export TEXT_ENV_VAR
-# If use "apachectl" rather than "service httpd" for control, can use starting user's environment as well
-# For Windows environment, add via My Computer > Advanced Settings > Environment Variables
-http://www.centos.org/docs/5/html/Deployment_Guide-en-US/s1-apache-startstop.html
-
-# Need read/execute permission on directory tree up to web files, so may need to
-chmod 755 /home/jonc101
-
-# Web server start / control
-sudo service httpd restart
-
-
-#  Test Pages:
-http://localhost/medinfo/web/cgibin/admin/DebugWeb.py
-http://localhost/medinfo/web/cgibin/admin/DBUtilWeb.py
-http://localhost/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py
-
diff --git a/scripts/DevWorkshop/AWSDevEnvironment/ExampleQueryApp.py b/scripts/DevWorkshop/AWSDevEnvironment/ExampleQueryApp.py
new file mode 100644
index 00000000..5c2e903f
--- /dev/null
+++ b/scripts/DevWorkshop/AWSDevEnvironment/ExampleQueryApp.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+"""
+Example app module to run queries against the processed clincal database, 
+more as fodder to test management of driver scripts to manage this.
+"""
+import sys, os
+import time;
+from optparse import OptionParser;
+import json;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, ProgressDots, log;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import SQLQuery;
+from medinfo.db.ResultsFormatter import TextResultsFormatter;
+
+class ExampleQueryApp:
+    def __init__(self):
+        self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
+
+    def queryItems(self, options, outputFile):
+        """Query for all clinical item records that fulfill the options criteria
+        and then send the results as tab-delimited output to the outputFile.
+        """
+        pauseSeconds = float(options.pauseSeconds);
+
+        query = SQLQuery();
+        query.addSelect("cic.description, ci.clinical_item_id, ci.name, ci.description");
+        query.addFrom("clinical_item_category as cic");
+        query.addFrom("clinical_item as ci");
+        query.addWhere("cic.clinical_item_category_id = ci.clinical_item_category_id");
+        if options.itemPrefix:
+            query.addWhereOp("ci.description","like", options.itemPrefix+"%%");    # Add wildcard to enabe prefix search
+        if options.categoryNames:
+            query.addWhereIn("cic.description", options.categoryNames.split(",") );
+        query.addOrderBy("cic.description, ci.name, ci.description, ci.clinical_item_id");
+
+        formatter = TextResultsFormatter(outputFile);
+        
+        prog = ProgressDots();
+        for row in DBUtil.execute(query, includeColumnNames=True, connFactory=self.connFactory):
+            formatter.formatTuple(row);
+            time.sleep(pauseSeconds);
+            prog.update();
+        prog.printStatus();
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usageStr =  "Query for the clinical_item records that exist with the specified criteria\n"+\
+                    "usage: %prog [options] [<outputFile>]\n"+\
+                    "   <outputFile>    Results file. Leave blank or specify \"-\" to send to stdout.\n"
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-i", "--itemPrefix",  dest="itemPrefix", help="Look for clinical_items whose description starts with this prefix.");
+        parser.add_option("-c", "--categoryNames",  dest="categoryNames", help="Comma separated list of clinical_item_category.descriptions to look for.");
+        parser.add_option("-p", "--pauseSeconds",  dest="pauseSeconds", default="0", help="Number of seconds to pause between processing each record.");
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: "+str.join(" ", argv))
+        timer = time.time();
+        if len(args) > 0:
+            outputFile = stdOpen(args[0],"w");
+
+            # Print comment line with arguments to allow for deconstruction later as well as extra results
+            summaryData = {"argv": argv};
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
+
+            self.queryItems(options, outputFile);
+
+        else:
+            parser.print_help()
+            sys.exit(-1)
+
+        timer = time.time() - timer;
+        log.info("%.3f seconds to complete",timer);
+
+if __name__ == "__main__":
+    instance = ExampleQueryApp();
+    instance.main(sys.argv);
diff --git a/scripts/DevWorkshop/AWSDevEnvironment/batchDriver.py b/scripts/DevWorkshop/AWSDevEnvironment/batchDriver.py
new file mode 100644
index 00000000..47a097b0
--- /dev/null
+++ b/scripts/DevWorkshop/AWSDevEnvironment/batchDriver.py
@@ -0,0 +1,55 @@
+"""
+Example shell script background call to run script to start up several sub processes
+People often just make bash shell scripts, but I find Python a more flexible language that can do all of the above and better integrate.
+    nohup python batchDriver.py &> driver.log &
+
+"""
+
+import subprocess;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+
+LOG_FILE_TEMPLATE = "log/exampleQuery.%s.log";
+
+baseArgv = \
+    [   "python","ExampleQueryApp.py",
+        "-p","1",
+    ];
+
+CATEGORY_LIST = \
+    [   "Med (Oral)", "Med (Intravenous)", "Diagnosis (ADMIT_DX)", "Lab","Lab Result",
+    ];
+ITEM_PREFIXES = \
+    [   "A","B","C","D","E","F","G","H","I",        
+    ];
+specificArgvList = \
+    [   
+    ];
+
+for categoryName in CATEGORY_LIST:
+    for itemPrefix in ITEM_PREFIXES:
+        specificArgv = ["-c", categoryName, "-i", itemPrefix, "results/queryResults.%s.%s.tab.gz" % (itemPrefix, categoryName) ];
+        specificArgvList.append(specificArgv);
+
+prog = ProgressDots(1,1,"Processes",total=len(specificArgvList));
+for specificArgv in specificArgvList:
+    key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
+    key = key.replace("/","..");    # Don't want to use directory separator in temp log file name
+    argv = list(baseArgv)
+    argv.extend(specificArgv);
+    log.info( "Starting: "+str.join(" ", argv) );
+    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
+    
+    # Blocking sub-process call if want serial processes.
+    #subprocess.call(argv, stderr=logFile);
+
+    # Non-blocking subprocess.Popen to spawn parallel processes
+    process = subprocess.Popen(argv, stderr=logFile);
+    log.info("Process ID: %s" % process.pid);
+
+    # Print command lines to effectively generate a .sh script
+    #print "nohup",
+    #print str.join(" ", argv),
+    #print "&>", LOG_FILE_TEMPLATE % key,"&"
+ 
+    prog.update();
+prog.printStatus();
diff --git a/scripts/DevWorkshop/AWSDevEnvironment/batchDriver.sh b/scripts/DevWorkshop/AWSDevEnvironment/batchDriver.sh
new file mode 100644
index 00000000..08d773c0
--- /dev/null
+++ b/scripts/DevWorkshop/AWSDevEnvironment/batchDriver.sh
@@ -0,0 +1,45 @@
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i A "results/queryResults.A.Med (Oral).tab.gz" &> log/exampleQuery.000.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i B "results/queryResults.B.Med (Oral).tab.gz" &> log/exampleQuery.001.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i C "results/queryResults.C.Med (Oral).tab.gz" &> log/exampleQuery.002.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i D "results/queryResults.D.Med (Oral).tab.gz" &> log/exampleQuery.003.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i E "results/queryResults.E.Med (Oral).tab.gz" &> log/exampleQuery.004.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i F "results/queryResults.F.Med (Oral).tab.gz" &> log/exampleQuery.005.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i G "results/queryResults.G.Med (Oral).tab.gz" &> log/exampleQuery.006.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i H "results/queryResults.H.Med (Oral).tab.gz" &> log/exampleQuery.007.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Oral)" -i I "results/queryResults.I.Med (Oral).tab.gz" &> log/exampleQuery.008.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i A "results/queryResults.A.Med (Intravenous).tab.gz" &> log/exampleQuery.009.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i B "results/queryResults.B.Med (Intravenous).tab.gz" &> log/exampleQuery.010.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i C "results/queryResults.C.Med (Intravenous).tab.gz" &> log/exampleQuery.011.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i D "results/queryResults.D.Med (Intravenous).tab.gz" &> log/exampleQuery.012.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i E "results/queryResults.E.Med (Intravenous).tab.gz" &> log/exampleQuery.013.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i F "results/queryResults.F.Med (Intravenous).tab.gz" &> log/exampleQuery.014.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i G "results/queryResults.G.Med (Intravenous).tab.gz" &> log/exampleQuery.015.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i H "results/queryResults.H.Med (Intravenous).tab.gz" &> log/exampleQuery.016.log &
+nohup python ExampleQueryApp.py -p 1 -c "Med (Intravenous)" -i I "results/queryResults.I.Med (Intravenous).tab.gz" &> log/exampleQuery.017.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i A "results/queryResults.A.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.018.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i B "results/queryResults.B.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.019.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i C "results/queryResults.C.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.020.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i D "results/queryResults.D.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.021.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i E "results/queryResults.E.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.022.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i F "results/queryResults.F.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.023.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i G "results/queryResults.G.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.024.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i H "results/queryResults.H.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.025.log &
+nohup python ExampleQueryApp.py -p 1 -c "Diagnosis (ADMIT_DX)" -i I "results/queryResults.I.Diagnosis (ADMIT_DX).tab.gz" &> log/exampleQuery.026.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i A "results/queryResults.A.Lab.tab.gz" &> log/exampleQuery.027.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i B "results/queryResults.B.Lab.tab.gz" &> log/exampleQuery.028.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i C "results/queryResults.C.Lab.tab.gz" &> log/exampleQuery.029.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i D "results/queryResults.D.Lab.tab.gz" &> log/exampleQuery.030.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i E "results/queryResults.E.Lab.tab.gz" &> log/exampleQuery.031.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i F "results/queryResults.F.Lab.tab.gz" &> log/exampleQuery.032.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i G "results/queryResults.G.Lab.tab.gz" &> log/exampleQuery.033.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i H "results/queryResults.H.Lab.tab.gz" &> log/exampleQuery.034.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab" -i I "results/queryResults.I.Lab.tab.gz" &> log/exampleQuery.035.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i A "results/queryResults.A.Lab Result.tab.gz" &> log/exampleQuery.036.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i B "results/queryResults.B.Lab Result.tab.gz" &> log/exampleQuery.037.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i C "results/queryResults.C.Lab Result.tab.gz" &> log/exampleQuery.038.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i D "results/queryResults.D.Lab Result.tab.gz" &> log/exampleQuery.039.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i E "results/queryResults.E.Lab Result.tab.gz" &> log/exampleQuery.040.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i F "results/queryResults.F.Lab Result.tab.gz" &> log/exampleQuery.041.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i G "results/queryResults.G.Lab Result.tab.gz" &> log/exampleQuery.042.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i H "results/queryResults.H.Lab Result.tab.gz" &> log/exampleQuery.043.log &
+nohup python ExampleQueryApp.py -p 1 -c "Lab Result" -i I "results/queryResults.I.Lab Result.tab.gz" &> log/exampleQuery.044.log &
diff --git a/scripts/DevWorkshop/DevWorkshopR/02_unit_testing.R b/scripts/DevWorkshop/DevWorkshopR/02_unit_testing.R
new file mode 100644
index 00000000..8349edb1
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/02_unit_testing.R
@@ -0,0 +1,22 @@
+library(testthat)
+library(lubridate)
+
+setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/DevWorkshopR/")
+
+test_that ( 
+  "June and November", 
+  {
+    expect_equal(first_of_month("2004-06-15"), as.character("2004-06-01"))
+    expect_equal(first_of_month("2009-11-06"), as.character("2009-11-01"))
+  }
+)
+
+test_that ( 
+  "December and January", 
+  {
+    expect_equal(first_of_month("2018-12-15"), as.character("2018-12-01"))
+    expect_equal(first_of_month("2012-01-07"), as.character("2012-01-01"))
+    
+  }
+)
+
diff --git a/scripts/DevWorkshop/DevWorkshopR/command_line01.R b/scripts/DevWorkshop/DevWorkshopR/command_line01.R
new file mode 100644
index 00000000..6258fa8f
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/command_line01.R
@@ -0,0 +1,25 @@
+#!/usr/bin/env Rscript
+
+# Jonathan Chiang 
+# adapted from: https://linuxconfig.org/how-to-access-a-command-line-arguments-using-rscript-gnu-r
+# how to write a arg in R script
+
+# ------------------------------- # 
+#  make sure R is in your path!   #
+# ------------------------------- #
+
+args <- commandArgs(TRUE)
+
+# -----------------------------------------------
+# MODULE  
+# -----------------------------------------------
+
+# 1) Hello World 
+
+#print("Hello R")
+
+# 2) Simple Addition 
+
+#print(as.double(args[1]) + as.double(args[2]))
+
+#-----------------------------------------------
\ No newline at end of file
diff --git a/scripts/DevWorkshop/DevWorkshopR/command_line02.R b/scripts/DevWorkshop/DevWorkshopR/command_line02.R
new file mode 100644
index 00000000..50bd2cf8
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/command_line02.R
@@ -0,0 +1,22 @@
+#!/usr/bin/env Rscript
+
+# Jonathan Chiang 
+# adapted from: https://linuxconfig.org/how-to-access-a-command-line-arguments-using-rscript-gnu-r
+# how to write a arg in R script
+
+# ------------------------------- # 
+#  make sure R is in your path!   #
+# ------------------------------- #
+
+#args = commandArgs(trailingOnly=TRUE)
+
+setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/DevWorkshopR/")
+source("unit_testing_c.R")
+
+args <- commandArgs(TRUE)
+
+#commandArgs()
+
+print(first_of_month(args[1]))
+
+# "2003-03-15"
\ No newline at end of file
diff --git a/scripts/DevWorkshop/DevWorkshopR/markup_python_R.Rmd b/scripts/DevWorkshop/DevWorkshopR/markup_python_R.Rmd
new file mode 100644
index 00000000..ee1844b7
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/markup_python_R.Rmd
@@ -0,0 +1,124 @@
+---
+title: 'TidyVerse: Python and R'
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+```{r}
+mtcars2 <- mtcars 
+colnames(mtcars2)[1] <- "car"
+write.csv(mtcars2, "mtcars2.csv")
+```
+
+
+## Reading CSV in R versus Python 
+
+#### Chunks are useful for interactively writing code and publishing documents to share 
+#### 
+
+```{python}
+import pandas as pd
+df = pd.read_csv('mtcars2.csv')
+print df
+```
+
+
+```{r, echo=FALSE}
+library(tidyverse)
+df <- read.csv('mtcars2.csv')
+df
+```
+
+# Filtering Data in R
+
+
+```{r}
+#filter(df, mpg > 15 & mpg < 30) 
+#filter(df, car == 'Toyota Corolla') 
+#df %>% filter(car != 'Toyota Corolla')
+```
+
+# EXERCISE 
+
+1) Filter for column wt greater than 2 
+
+```{r}
+# INSERT CODE HERE 
+```
+
+
+# Filtering Data in Python 
+
+```{python}
+import pandas as pd
+df = pd.read_csv('mtcars2.csv')
+#print df
+#print df[(df['mpg'] > 15) & (df['mpg'] < 30)]
+#print df[df['car'] == 'Toyota Corolla']
+#print df[df['car'] != 'Toyota Corolla']
+
+```
+
+# Piping Data in R 
+
+```{r}
+iris %>%
+  subset(Sepal.Length > 5) %>%
+  aggregate(. ~ Species, ., mean)
+```
+
+# BEFORE PIPES 
+
+```{r}
+a <- filter(mtcars, carb > 1)
+
+b <- group_by(a, cyl)
+
+c <- summarise(b, Avg_mpg = mean(mpg))
+
+d <- arrange(c, Avg_mpg)
+
+e <- arrange(c, desc(Avg_mpg))
+
+a 
+b
+c
+d
+e
+```
+
+```{r}
+mtcars %>%
+        filter(carb > 1) %>%
+        group_by(cyl) %>%
+        summarise(Avg_mpg = mean(mpg)) %>%
+        arrange(desc(Avg_mpg))
+```
+
+## EXERCISE 2 
+
+2) Write your own piped where you filter hp greater than 100, Group by carb, summarize the average mpg in 
+  ascending order 
+  
+```{r}
+# INSERT CODE HERE 
+```
+  
+
+## Bonus Interactive Visualization in 1 line! 
+
+```{r}
+library(plotly)
+p <- ggplot(mtcars, aes(wt, mpg))
+p1 <- p + geom_point(size=4)
+p2 <- p1 + geom_point(aes(colour=factor(cyl)))
+p2
+```
+
+```{r}
+ggplotly(p2)
+```
+
diff --git a/scripts/DevWorkshop/DevWorkshopR/mtcars2.csv b/scripts/DevWorkshop/DevWorkshopR/mtcars2.csv
new file mode 100644
index 00000000..ebe0787b
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/mtcars2.csv
@@ -0,0 +1,33 @@
+"","car","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
+"1","Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4
+"2","Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4
+"3","Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
+"4","Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
+"5","Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
+"6","Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
+"7","Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
+"8","Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
+"9","Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
+"10","Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
+"11","Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
+"12","Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
+"13","Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
+"14","Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
+"15","Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
+"16","Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4
+"17","Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
+"18","Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
+"19","Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
+"20","Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
+"21","Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
+"22","Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
+"23","AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
+"24","Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
+"25","Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
+"26","Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
+"27","Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
+"28","Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
+"29","Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
+"30","Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
+"31","Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8
+"32","Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2
diff --git a/scripts/DevWorkshop/DevWorkshopR/readme.md b/scripts/DevWorkshop/DevWorkshopR/readme.md
new file mode 100644
index 00000000..e75c6f97
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/readme.md
@@ -0,0 +1,46 @@
+## Lab Meeting January 29th 2019 
+
+#### Covers:
+#####  1) Basic Unit Testing 
+
+1. unit_testing.R 
+> overview of scripting in R and introduce "testthat" 
+> introduce test driven development 
+2. 02_unit_testing.R
+> writing a basic unit test
+3. unit_testing_a.R , unit_testing_b.R, unit_testing_c.R 
+> three different functions that can the same unit test 
+> actively debug 
+
+#####  2) Introduction to Tidyverse 
+
+1. Introduction to Rmarkdown (chunks) 
+2. Introduction to Piping in dplyr 
+3. Introduction to ggplot2 and plotly 
+
+#####  3) Running Basic Scripts with Arguments from Terminal 
+
+1. Hello World 
+2. Addition 
+3. Running first_of_month 
+
+
+
+#### Packages Required 
+1. tidyverse (lots of packages) 
+2. rmarkdown 
+3. lubridate 
+4. testthat 
+5. stringr
+6. reticulate
+7. devtools 
+8. ggplot2
+9. plotly
+
+#### Notes 
+1. may need to setup R in path (export R bin to the path)
+
+can check if it works by typing R into command prompt/terminal and R will run in console 
+
+2. you need to setwd() to where DevWorkshopR is located on your local path 
+
diff --git a/scripts/DevWorkshop/DevWorkshopR/unit_testing.R b/scripts/DevWorkshop/DevWorkshopR/unit_testing.R
new file mode 100644
index 00000000..61e0e19d
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/unit_testing.R
@@ -0,0 +1,115 @@
+# Unit Testing in R 
+
+# Jonathan Chiang 
+
+# ------------------- #
+# PACKAGES TO INSTALL # 
+# ------------------- # 
+
+
+#install.packages("testthat")
+#install.packages("devtools")
+#install.packages("lubridate")
+
+# adapted from 
+# http://r-pkgs.had.co.nz/tests.html
+
+library(testthat)
+library(devtools)
+library(lubridate)
+library(stringr)
+
+tmp <- tempfile()
+
+#setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/DevWorkshopR")
+
+# A context defines a set of tests that test related functionality. 
+# Usually you will have one context per file, 
+# but you may have multiple contexts in a single file if you so choose.
+
+context("String length")
+
+test_that("str_length is number of characters", {
+  expect_equal(str_length("a"), 1)
+  expect_equal(str_length("ab"), 2)
+  expect_equal(str_length("abc"), 3)
+})
+
+test_that("str_length of factor is length of level", {
+  expect_equal(str_length(factor("a")), 1)
+  expect_equal(str_length(factor("ab")), 2)
+  expect_equal(str_length(factor("abc")), 3)
+})
+
+
+# Example of FAILING A TEST 
+
+test_that("str_length of factor is length of level failure ", {
+  expect_equal(str_length(factor("a")), 1)
+  expect_equal(str_length(factor("ab")), 2)
+  expect_equal(str_length(factor("abcd")), 3)
+})
+
+
+# Tests are organised hierarchically: 
+# expectations are grouped into tests which are organised in files:
+  
+# An expectation is the atom of testing. 
+# It describes the expected result of a computation: 
+# Does it have the right value and right class? 
+# Does it produce error messages when it should? 
+# An expectation automates visual checking of results in the console. 
+# Expectations are functions that start with expect_.
+
+# called unit as they test one unit of functionality. 
+
+# -----------------------------
+# General Tips for Writing Test 
+# -----------------------------
+
+# Each test should have an informative name and cover a single unit of functionality. 
+# The idea is that when a test fails, you’ll know what’s wrong and where in your code to look for the problem. 
+# You create a new test using test_that(), with test name and code block as arguments. 
+# The test name should complete the sentence “Test that …”. The code block should be a collection of expectations.
+
+
+
+
+# -----------------------------
+# What to test 
+# -----------------------------
+
+# Whenever you are tempted to type something into a print statement or a debugger expression, 
+# write it as a test instead. — Martin Fowler
+
+# Focus on testing the external interface to your functions
+# Strive to test each behaviour in one and only one test
+# Avoid testing simple code that you’re confident will work
+# Always write a test when you discover a bug
+
+
+
+
+first_of_month <- function(mystring){
+  yr = substr(mystring, 1, 4)
+  mon <- as.numeric(substr(mystring,6,7))
+  lastmon <- stringr::str_pad(mon , width = 2, pad = 0)
+  result <- paste(yr, lastmon, "01", sep = "-")
+  return(result)  
+}
+
+
+#first_of_month("2018-05-31")
+#first_of_month("2018-06-15")
+
+
+
+first_of_month <- function(x) {
+  x <- ymd(x)
+  return(rollback(x, roll_to_first = TRUE))
+}
+
+
+#first_of_month("2018-07-01")
+#first_of_month("2018-01-07")
+
diff --git a/scripts/DevWorkshop/DevWorkshopR/unit_testing_a.R b/scripts/DevWorkshop/DevWorkshopR/unit_testing_a.R
new file mode 100644
index 00000000..ac6b6682
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/unit_testing_a.R
@@ -0,0 +1,23 @@
+#unit_testing_b.R
+library(lubridate)
+library(testthat)
+
+# correct the error one 
+
+first_of_month <- function(mystring){
+  yr = substr(mystring, 1, 4)
+  mon <- as.numeric(substr(mystring,6,7))
+  thismon <- stringr::str_pad(mon , width = 2, pad = 0)
+  # hint 
+  result <- as.character(paste(yr, thismon, "01", sep = "-"))
+  return(result)  
+}
+
+first_of_month("2014-12-03")
+first_of_month("2014-02-13")
+first_of_month("2003-03-15")
+
+
+
+setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/DevWorkshopR/")
+source("02_unit_testing.R")
diff --git a/scripts/DevWorkshop/DevWorkshopR/unit_testing_b.R b/scripts/DevWorkshop/DevWorkshopR/unit_testing_b.R
new file mode 100644
index 00000000..1c5f5b5c
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/unit_testing_b.R
@@ -0,0 +1,14 @@
+#unit_testing_b.R
+library(lubridate)
+library(testthat)
+
+
+first_of_month <- function(mystring){
+  mydate <- as.Date(mystring)
+  mydate_first_of_month <- floor_date(mydate, "month")
+  result <- as.character(mydate_first_of_month )
+  return(result)
+}
+
+setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/DevWorkshopR/")
+source("02_unit_testing.R")
diff --git a/scripts/DevWorkshop/DevWorkshopR/unit_testing_c.R b/scripts/DevWorkshop/DevWorkshopR/unit_testing_c.R
new file mode 100644
index 00000000..97d920d9
--- /dev/null
+++ b/scripts/DevWorkshop/DevWorkshopR/unit_testing_c.R
@@ -0,0 +1,18 @@
+#unit_testing_b.R
+library(lubridate)
+library(testthat)
+
+first_of_month <- function(x) {
+  x <- ymd(x)
+  #return(rollback(x, roll_to_first = TRUE))
+  
+  x2 <- as.character(rollback(x, roll_to_first = TRUE))
+  return(x2)
+}
+
+
+setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/DevWorkshopR/")
+source("02_unit_testing.R")
+
+
+# SHOULD THROW ATTRIBUTE ERROR 
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GithubWorkshop/github_workshop.txt b/scripts/DevWorkshop/GithubWorkshop/github_workshop.txt
new file mode 100644
index 00000000..70692d14
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/github_workshop.txt
@@ -0,0 +1,67 @@
+Git is an open source version control system that's useful for 
+1. Saving your code to a place that's not your local computer or a university IT server that
+could spontaenously combust at any moment. 
+2. Keep track of changes made to your code by yourself and other users working off the same
+code base so that you can revert to prior versions of your code should new versions break things. 
+
+By the end of this workshop, you should feel comfortable using basic git commands. 
+This will include cloning a git repository to your local
+workstation, making changes to files locally, adding and comitting 
+these changes to your local repository, and finally pushing these changes to the remote repository. 
+
+On top of this, you will practice pulling changes that have been pushed to the remote repository
+by other users and merging them into your local branch.  This will include handling any
+merge conflicts that should arise. 
+
+Step 1: Everyone should pull the most recent changes from the master branch of CDSS. 
+If you haven't cloned this repository to your local workstation previously, do so now. In a
+terminal, navigate to a directory of choice and execute the command below.  
+
+git clone https://github.com/HealthRex/CDSS.git
+
+If you've already cloned this repository, navigate to where you have it cloned and execute
+
+git pull 
+
+The master branch on your local repository is now up to date. 
+
+Step 2 [Main Task]: Break into groups and head to breakout rooms. Your group will be assigned a 
+number. Remember your number (it's your breakout room number).
+Each member of your group will sign their name (just type your name) 
+into the corresponding group signatures file in /CDSS/scripts/DevWorkshop/GithubWorkshop/. 
+The group signature file you want to edit will be called group_signature_[group_number].txt
+where group_number is your assigned group number.  Your task will be completed when
+the remote master branch of CDSS has a file called group_signature_[group_number].txt
+with all your group member's names (one name per line) AND no other names.  
+
+Some useful commands to guide you through the above. 
+
+To view the status of files you've made changes to, and whether they have been staged, execute
+
+git status
+
+To stage to_add.txt (a text file in the /file/you/want/ directory), execute
+
+git add /file/you/want/to_add.txt 
+
+Once this file is staged, you can commit changes to this file to your local
+repository's master branch. To do this, execute
+
+git commit -m "insert a useful message pertaining to the changes you've made"
+
+You now want to push these changes to the remote repository's master branch, but you know that
+multiple people are also trying to do the same thing.  Before you ever push changes to the remote
+master, it's best practice to pull changes made by other users into your local repository. Do 
+this by executing the following:
+
+git pull 
+
+Uh oh... you have a merge conflict. It looks like you have an imposter trying to pass themselves
+off as a member of your group. In your editor of choice, handle the merge conflict by deleting the
+imposter's name and anything else in the file you do not want pushed to the remote master branch. 
+Stage the changes again, commit them, and then finally push to the remote master branch by executing:
+
+git push origin master
+
+Have each member of your group step through the above process until your group text file has all
+of your names and no imposters. 
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_1.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_1.txt
new file mode 100644
index 00000000..7e7421e0
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_1.txt
@@ -0,0 +1 @@
+Birju and Christian
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_2.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_2.txt
new file mode 100644
index 00000000..2743c62c
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_2.txt
@@ -0,0 +1 @@
+Conor Corbin
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_3.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_3.txt
new file mode 100644
index 00000000..2743c62c
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_3.txt
@@ -0,0 +1 @@
+Conor Corbin
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_4.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_4.txt
new file mode 100644
index 00000000..eb9b4651
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_4.txt
@@ -0,0 +1,4 @@
+Wui Ip
+Rocky Aikens
+Rocky Rocks
+Hooray!
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_5.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_5.txt
new file mode 100644
index 00000000..2743c62c
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_5.txt
@@ -0,0 +1 @@
+Conor Corbin
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_6.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_6.txt
new file mode 100644
index 00000000..2743c62c
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_6.txt
@@ -0,0 +1 @@
+Conor Corbin
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_7.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_7.txt
new file mode 100644
index 00000000..2743c62c
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_7.txt
@@ -0,0 +1 @@
+Conor Corbin
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GithubWorkshop/group_signature_8.txt b/scripts/DevWorkshop/GithubWorkshop/group_signature_8.txt
new file mode 100644
index 00000000..2743c62c
--- /dev/null
+++ b/scripts/DevWorkshop/GithubWorkshop/group_signature_8.txt
@@ -0,0 +1 @@
+Conor Corbin
\ No newline at end of file
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/ExampleQueryApp.py b/scripts/DevWorkshop/GoogleCloudPlatform/ExampleQueryApp.py
new file mode 100644
index 00000000..095b6b12
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/ExampleQueryApp.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+"""
+Example app module to run queries against a GCP BigQuery database, 
+more as fodder to test management of driver scripts to manage this.
+"""
+import sys, os
+import time;
+from optparse import OptionParser;
+import json;
+from medinfo.common.Const import COMMENT_TAG;
+from medinfo.common.Util import stdOpen, ProgressDots, log;
+from medinfo.db import DBUtil;
+from medinfo.db.Model import SQLQuery;
+from medinfo.db.ResultsFormatter import TextResultsFormatter;
+
+class ExampleQueryApp:
+    def __init__(self):
+        self.connFactory = DBUtil.ConnectionFactory();  # Default connection source
+
+    def queryItems(self, options, outputFile):
+        """Query for all clinical item records that fulfill the options criteria
+        and then send the results as tab-delimited output to the outputFile.
+        """
+        pauseSeconds = float(options.pauseSeconds);
+
+        query = SQLQuery();
+        query.addSelect("count(order_med_id_coded) as nOrders");
+        query.addSelect("om.med_route, om.medication_id, om.med_description");
+        query.addFrom("starr_datalake2018.order_med as om");
+        if options.descriptionPrefix:
+            query.addWhereOp("om.med_description","like", options.descriptionPrefix+"%%");    # Add wildcard to enabe prefix search
+        if options.medRoutes:
+            query.addWhereIn("om.med_route", options.medRoutes.split(",") );
+        query.addGroupBy("om.medication_id, om.med_description, om.med_route");
+        query.addOrderBy("nOrders desc, om.med_description");
+
+        formatter = TextResultsFormatter(outputFile);
+        
+        prog = ProgressDots();
+        for row in DBUtil.execute(query, includeColumnNames=True, connFactory=self.connFactory):
+            formatter.formatTuple(row);
+            time.sleep(pauseSeconds);
+            prog.update();
+        prog.printStatus();
+
+    def main(self, argv):
+        """Main method, callable from command line"""
+        usageStr =  "Query for order_med record counts that exist with the specified criteria\n"+\
+                    "usage: %prog [options] [<outputFile>]\n"+\
+                    "   <outputFile>    Results file. Specify \"-\" to send to stdout.\n"
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-d", "--descriptionPrefix",  dest="descriptionPrefix", help="Look for medication orders whose description starts with this prefix.");
+        parser.add_option("-r", "--medRoutes",  dest="medRoutes", help="Comma separated list of medication routes to consider (e.g., Intravenous, Oral).");
+        parser.add_option("-p", "--pauseSeconds",  dest="pauseSeconds", default="0", help="Number of seconds to pause between processing each record.");
+        (options, args) = parser.parse_args(argv[1:])
+
+        log.info("Starting: "+str.join(" ", argv))
+        timer = time.time();
+        if len(args) > 0:
+            outputFile = stdOpen(args[0],"w");
+
+            # Print comment line with arguments to allow for deconstruction later as well as extra results
+            summaryData = {"argv": argv};
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
+
+            self.queryItems(options, outputFile);
+
+        else:
+            parser.print_help()
+            sys.exit(-1)
+
+        timer = time.time() - timer;
+        log.info("%.3f seconds to complete",timer);
+
+if __name__ == "__main__":
+    instance = ExampleQueryApp();
+    instance.main(sys.argv);
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/batchDriver.py b/scripts/DevWorkshop/GoogleCloudPlatform/batchDriver.py
new file mode 100644
index 00000000..0950f48a
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/batchDriver.py
@@ -0,0 +1,53 @@
+"""
+Example shell script background call to run script to start up several sub processes
+People often just make bash shell scripts, but I find Python a more flexible language that can do all of the above and better integrate.
+    nohup python batchDriver.py &> driver.log &
+
+"""
+
+import subprocess;
+from medinfo.common.Util import stdOpen, log, ProgressDots;
+
+LOG_FILE_TEMPLATE = "log/exampleQuery.%s.log";
+
+baseArgv = \
+    [   "python3","ExampleQueryApp.py",
+        "-p","1",
+    ];
+
+ROUTE_LIST = \
+    [   "Oral", "Intravenous", "Subcutaneous", "Intramuscular", "Topical",
+    ];
+DESCRIPTION_PREFIXES = \
+    [   "A","B","C","D","E","F","G","H","I",        
+    ];
+specificArgvList = \
+    [   
+    ];
+
+for medRoute in ROUTE_LIST:
+    for descriptionPrefix in DESCRIPTION_PREFIXES:
+        specificArgv = ["-r", medRoute, "-d", descriptionPrefix, "results/queryResults.%s.%s.tab" % (descriptionPrefix, medRoute) ];
+        specificArgvList.append(specificArgv);
+
+prog = ProgressDots(1,1,"Processes",total=len(specificArgvList));
+for specificArgv in specificArgvList:
+    key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
+    key = key.replace("/","..");    # Don't want to use directory separator in temp log file name
+    argv = list(baseArgv)
+    argv.extend(specificArgv);
+    log.info( "Starting: "+str.join(" ", argv) );
+    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
+    
+    # Blocking sub-process call if want serial processes.
+    #subprocess.call(argv, stderr=logFile);
+
+    # Non-blocking subprocess.Popen to spawn parallel processes
+    process = subprocess.Popen(argv, stderr=logFile);
+    log.info("Process ID: %s" % process.pid);
+
+    # Print command lines to effectively generate a .sh script
+    #print("nohup", str.join(" ", argv), "&>", LOG_FILE_TEMPLATE % key, "&");
+ 
+    prog.update();
+prog.printStatus();
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/batchDriver.sh b/scripts/DevWorkshop/GoogleCloudPlatform/batchDriver.sh
new file mode 100644
index 00000000..746c7dd9
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/batchDriver.sh
@@ -0,0 +1,45 @@
+nohup python3 ExampleQueryApp.py -p 1 -r Oral -d A results/queryResults.A.Oral.tab &> log/exampleQuery.000.-r_Oral_-d_A_results..queryResults.A.Oral.tab.log &
+nohup python3 ExampleQueryApp.py -p 1 -r Oral -d B results/queryResults.B.Oral.tab &> log/exampleQuery.001.-r_Oral_-d_B_results..queryResults.B.Oral.tab.log &
+nohup python3 ExampleQueryApp.py -p 1 -r Oral -d C results/queryResults.C.Oral.tab &> log/exampleQuery.002.-r_Oral_-d_C_results..queryResults.C.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Oral -d D results/queryResults.D.Oral.tab &> log/exampleQuery.003.-r_Oral_-d_D_results..queryResults.D.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Oral -d E results/queryResults.E.Oral.tab &> log/exampleQuery.004.-r_Oral_-d_E_results..queryResults.E.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Oral -d F results/queryResults.F.Oral.tab &> log/exampleQuery.005.-r_Oral_-d_F_results..queryResults.F.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Oral -d G results/queryResults.G.Oral.tab &> log/exampleQuery.006.-r_Oral_-d_G_results..queryResults.G.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Oral -d H results/queryResults.H.Oral.tab &> log/exampleQuery.007.-r_Oral_-d_H_results..queryResults.H.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Oral -d I results/queryResults.I.Oral.tab &> log/exampleQuery.008.-r_Oral_-d_I_results..queryResults.I.Oral.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d A results/queryResults.A.Intravenous.tab &> log/exampleQuery.009.-r_Intravenous_-d_A_results..queryResults.A.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d B results/queryResults.B.Intravenous.tab &> log/exampleQuery.010.-r_Intravenous_-d_B_results..queryResults.B.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d C results/queryResults.C.Intravenous.tab &> log/exampleQuery.011.-r_Intravenous_-d_C_results..queryResults.C.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d D results/queryResults.D.Intravenous.tab &> log/exampleQuery.012.-r_Intravenous_-d_D_results..queryResults.D.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d E results/queryResults.E.Intravenous.tab &> log/exampleQuery.013.-r_Intravenous_-d_E_results..queryResults.E.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d F results/queryResults.F.Intravenous.tab &> log/exampleQuery.014.-r_Intravenous_-d_F_results..queryResults.F.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d G results/queryResults.G.Intravenous.tab &> log/exampleQuery.015.-r_Intravenous_-d_G_results..queryResults.G.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d H results/queryResults.H.Intravenous.tab &> log/exampleQuery.016.-r_Intravenous_-d_H_results..queryResults.H.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intravenous -d I results/queryResults.I.Intravenous.tab &> log/exampleQuery.017.-r_Intravenous_-d_I_results..queryResults.I.Intravenous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d A results/queryResults.A.Subcutaneous.tab &> log/exampleQuery.018.-r_Subcutaneous_-d_A_results..queryResults.A.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d B results/queryResults.B.Subcutaneous.tab &> log/exampleQuery.019.-r_Subcutaneous_-d_B_results..queryResults.B.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d C results/queryResults.C.Subcutaneous.tab &> log/exampleQuery.020.-r_Subcutaneous_-d_C_results..queryResults.C.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d D results/queryResults.D.Subcutaneous.tab &> log/exampleQuery.021.-r_Subcutaneous_-d_D_results..queryResults.D.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d E results/queryResults.E.Subcutaneous.tab &> log/exampleQuery.022.-r_Subcutaneous_-d_E_results..queryResults.E.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d F results/queryResults.F.Subcutaneous.tab &> log/exampleQuery.023.-r_Subcutaneous_-d_F_results..queryResults.F.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d G results/queryResults.G.Subcutaneous.tab &> log/exampleQuery.024.-r_Subcutaneous_-d_G_results..queryResults.G.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d H results/queryResults.H.Subcutaneous.tab &> log/exampleQuery.025.-r_Subcutaneous_-d_H_results..queryResults.H.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Subcutaneous -d I results/queryResults.I.Subcutaneous.tab &> log/exampleQuery.026.-r_Subcutaneous_-d_I_results..queryResults.I.Subcutaneous.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d A results/queryResults.A.Intramuscular.tab &> log/exampleQuery.027.-r_Intramuscular_-d_A_results..queryResults.A.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d B results/queryResults.B.Intramuscular.tab &> log/exampleQuery.028.-r_Intramuscular_-d_B_results..queryResults.B.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d C results/queryResults.C.Intramuscular.tab &> log/exampleQuery.029.-r_Intramuscular_-d_C_results..queryResults.C.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d D results/queryResults.D.Intramuscular.tab &> log/exampleQuery.030.-r_Intramuscular_-d_D_results..queryResults.D.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d E results/queryResults.E.Intramuscular.tab &> log/exampleQuery.031.-r_Intramuscular_-d_E_results..queryResults.E.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d F results/queryResults.F.Intramuscular.tab &> log/exampleQuery.032.-r_Intramuscular_-d_F_results..queryResults.F.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d G results/queryResults.G.Intramuscular.tab &> log/exampleQuery.033.-r_Intramuscular_-d_G_results..queryResults.G.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d H results/queryResults.H.Intramuscular.tab &> log/exampleQuery.034.-r_Intramuscular_-d_H_results..queryResults.H.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Intramuscular -d I results/queryResults.I.Intramuscular.tab &> log/exampleQuery.035.-r_Intramuscular_-d_I_results..queryResults.I.Intramuscular.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d A results/queryResults.A.Topical.tab &> log/exampleQuery.036.-r_Topical_-d_A_results..queryResults.A.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d B results/queryResults.B.Topical.tab &> log/exampleQuery.037.-r_Topical_-d_B_results..queryResults.B.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d C results/queryResults.C.Topical.tab &> log/exampleQuery.038.-r_Topical_-d_C_results..queryResults.C.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d D results/queryResults.D.Topical.tab &> log/exampleQuery.039.-r_Topical_-d_D_results..queryResults.D.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d E results/queryResults.E.Topical.tab &> log/exampleQuery.040.-r_Topical_-d_E_results..queryResults.E.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d F results/queryResults.F.Topical.tab &> log/exampleQuery.041.-r_Topical_-d_F_results..queryResults.F.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d G results/queryResults.G.Topical.tab &> log/exampleQuery.042.-r_Topical_-d_G_results..queryResults.G.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d H results/queryResults.H.Topical.tab &> log/exampleQuery.043.-r_Topical_-d_H_results..queryResults.H.Topical.tab.log &
+#nohup python3 ExampleQueryApp.py -p 1 -r Topical -d I results/queryResults.I.Topical.tab &> log/exampleQuery.044.-r_Topical_-d_I_results..queryResults.I.Topical.tab.log &
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/log/placeholder.txt b/scripts/DevWorkshop/GoogleCloudPlatform/log/placeholder.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/auth_cloud.py b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/auth_cloud.py
new file mode 100644
index 00000000..b6cd412a
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/auth_cloud.py
@@ -0,0 +1,56 @@
+
+'''
+request .json api key
+get project id
+write sql
+profit
+
+Setting Up Virtual Environment
+
+https://www.google.com/search?client=firefox-b-1-d&q=create+a+new+virtual+environment
+
+#! [mac]        python -m virtualenv env_gcp
+#! [windows]    py -m virtualenv env_gcp
+
+#! [mac]        source env_gcp/bin/activate
+#! [windows]    .\env_gcp\Scripts\activate
+
+# [installing pip requirements]
+
+#!              pip install -r  requirements.txt
+
+Authentication for Big Query
+https://cloud.google.com/docs/authentication/getting-started
+https://cloud.google.com/bigquery/docs/authentication/
+https://cloud.google.com/python/setup
+'''
+
+from google.cloud import bigquery
+
+
+# CONFIGURATION
+client = bigquery.Client.from_service_account_json('gcp_key.json')
+project_id = 'mining-clinical-decisions'
+
+sql = '''
+
+
+      SELECT
+        jc_uid, order_type,  description
+      FROM
+        `datalake_47618_sample.order_proc`
+      Order By
+         ordering_date_jittered DESC
+
+    '''
+
+
+"""
+select patient_item_id, external_id, clinical_item_id, item_date, encounter_id, text_value, num_value, source_id from `clinical_inpatient.patient_item` where item_date >= timestamp('2014-01-01 00:00:00')
+"""
+# Run a Standard SQL query using the environment's default project
+df = client.query(sql).to_dataframe()
+
+
+df = client.query(sql, project=project_id).to_dataframe()
+print(df)
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/auth_quiver.py b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/auth_quiver.py
new file mode 100644
index 00000000..6b87f638
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/auth_quiver.py
@@ -0,0 +1,7 @@
+import pandas as pd
+#import pyarrow.parquet as pq
+#import pyarrow as pa
+sync_environment  = "/Users/jonc101/Box Sync/Jonathan Chiang's Files/mining-clinical-decisions/devworkshop"
+gcp = pd.read_csv("/Users/jonc101/Box Sync/Jonathan Chiang's Files/mining-clinical-decisions/devworkshop/dev_chiang.csv")
+print(gcp.head)
+
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_driver.sh b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_driver.sh
new file mode 100644
index 00000000..7a71f15d
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_driver.sh
@@ -0,0 +1,27 @@
+nohup python2 -u cloud_read.py 1 a 100 &> results0.log &
+nohup python2 -u cloud_read.py 1 b 100 &> results1.log &
+nohup python2 -u cloud_read.py 1 c 100 &> results2.log &
+nohup python2 -u cloud_read.py 1 d 100 &> results3.log &
+nohup python2 -u cloud_read.py 1 e 100 &> results4.log &
+nohup python2 -u cloud_read.py 1 f 100 &> results5.log &
+nohup python2 -u cloud_read.py 1 g 100 &> results6.log &
+nohup python2 -u cloud_read.py 1 h 100 &> results7.log &
+nohup python2 -u cloud_read.py 1 i 100 &> results8.log &
+nohup python2 -u cloud_read.py 1 j 100 &> results9.log &
+nohup python2 -u cloud_read.py 1 k 100 &> results10.log &
+nohup python2 -u cloud_read.py 1 l 100 &> results11.log &
+nohup python2 -u cloud_read.py 1 i 100 &> results12.log &
+nohup python2 -u cloud_read.py 1 m 100 &> results13.log &
+nohup python2 -u cloud_read.py 1 n 100 &> results14.log &
+nohup python2 -u cloud_read.py 1 o 100 &> results15.log &
+nohup python2 -u cloud_read.py 1 p 100 &> results16.log &
+nohup python2 -u cloud_read.py 1 q 100 &> results17.log &
+nohup python2 -u cloud_read.py 1 r 100 &> results18.log &
+nohup python2 -u cloud_read.py 1 s 100 &> results19.log &
+nohup python2 -u cloud_read.py 1 t 100 &> results20.log &
+nohup python2 -u cloud_read.py 1 u 100 &> results21.log &
+nohup python2 -u cloud_read.py 1 v 100 &> results22.log &
+nohup python2 -u cloud_read.py 1 w 100 &> results23.log &
+nohup python2 -u cloud_read.py 1 x 100 &> results24.log &
+nohup python2 -u cloud_read.py 1 y 100 &> results25.log &
+nohup python2 -u cloud_read.py 1 z 100 &> results26.log &
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_log.sh b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_log.sh
new file mode 100644
index 00000000..2f4d6493
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_log.sh
@@ -0,0 +1,26 @@
+nohup python2 -u cloud_read.py 1 a 100 &> results1.log  &
+nohup python2 -u cloud_read.py 1 b 100 &> results2.log  &
+nohup python2 -u cloud_read.py 1 c 100 &> results3.log  &
+nohup python2 -u cloud_read.py 1 d 100 &> results4.log  &
+nohup python2 -u cloud_read.py 1 e 100 &> results5.log  &
+nohup python2 -u cloud_read.py 1 f 100 &> results6.log  &
+nohup python2 -u cloud_read.py 1 g 100 &> results7.log  &
+nohup python2 -u cloud_read.py 1 h 100 &> results8.log  &
+nohup python2 -u cloud_read.py 1 i 100 &> results9.log  &
+nohup python2 -u cloud_read.py 1 j 100 &> results10.log  &
+nohup python2 -u cloud_read.py 1 k 100 &> results11.log  &
+nohup python2 -u cloud_read.py 1 l 100 &> results12.log  &
+nohup python2 -u cloud_read.py 1 i 100 &> results13.log  &
+nohup python2 -u cloud_read.py 1 m 100 &> results14.log  &
+nohup python2 -u cloud_read.py 1 n 100 &> results15.log  &
+nohup python2 -u cloud_read.py 1 o 100 &> results16.log  &
+nohup python2 -u cloud_read.py 1 p 100 &> results17.log  &
+nohup python2 -u cloud_read.py 1 q 100 &> results18.log  &
+nohup python2 -u cloud_read.py 1 r 100 &> results19.log  &
+nohup python2 -u cloud_read.py 1 s 100 &> results20.log  &
+nohup python2 -u cloud_read.py 1 t 100 &> results21.log  &
+nohup python2 -u cloud_read.py 1 u 100 &> results22.log  &
+nohup python2 -u cloud_read.py 1 v 100 &> results23.log  &
+nohup python2 -u cloud_read.py 1 w 100 &> results24.log  &
+nohup python2 -u cloud_read.py 1 x 100 &> results25.log  &
+nohup python2 -u cloud_read.py 1 y 100 &> results26.log  &
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_read.py b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_read.py
new file mode 100644
index 00000000..51c198c3
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_read.py
@@ -0,0 +1,28 @@
+from google.cloud import bigquery
+from medinfo.db.bigquery import bigQueryUtil
+import datetime
+import pandas
+import sys 
+import time
+
+bq_client = bigQueryUtil.BigQueryClient()
+
+# sleep  time 
+a1 = sys.argv[1] 
+
+# letter that you searching for  first 
+a2 = sys.argv[2]
+
+# number of rows
+a3 = sys.argv[3]
+
+
+sql = ["select count(med_description) as med_count, med_description from datalake_47618.order_med where lower(med_description) like \'"  , a2 , "%' group by med_description order by med_count  desc limit ", a3 ]
+sql1 = ''.join(sql)
+query1 = bq_client.queryBQ(sql1)
+df = query1.to_dataframe()
+
+for row_index,row in df.iterrows():
+   print('\nrow number:',row_index, '\n-------------') 
+   time.sleep(float(a1))
+   print(row)
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_write.py b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_write.py
new file mode 100644
index 00000000..0e13c5d3
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/cloud_write.py
@@ -0,0 +1,18 @@
+def main():
+    t = []
+    f = []
+    letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j' , 'k' , 'l', 'i', 'm', 'n', 'o', 'p', 'q', 'r',  's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
+    for i  in letters:
+        t.append((str('nohup python2 -u cloud_read.py ' + str(1) + " " + i)))
+    for j in range(1,27):
+        f.append(str(' 100') + ' &> results' + str(j) + '.log  &')
+    z = [x + y for x,y in zip(t,f)]
+    outF = open("cloud_log.sh", "w")
+    for line in z:
+      # write line to output file
+      outF.write(line)
+      outF.write("\n")
+    outF.close()
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/generateDriverScript.py b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/generateDriverScript.py
new file mode 100644
index 00000000..22d209c6
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/batch/generateDriverScript.py
@@ -0,0 +1,13 @@
+# Example script to generate a file to batch run a bunch of processes
+
+def main():
+    outF = open("cloud_driver.sh", "w");
+
+    letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j' , 'k' , 'l', 'i', 'm', 'n', 'o', 'p', 'q', 'r',  's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
+    for i, letter in enumerate(letters):
+        commandLine = 'nohup python2 -u cloud_read.py 1 %s 100 &> results%s.log &\n' % (letter, i);
+        outF.write(commandLine)
+    outF.close()
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/readme.md b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/readme.md
new file mode 100644
index 00000000..6cd5ed97
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/readme.md
@@ -0,0 +1,35 @@
+## Google Cloud Platform 
+### Purpose: 
+
+ 1. **Store terabytes of health data that you can query with SQL Syntax**  
+ 2. **Increase Reproducibility by Sharing and Manipulating DataFrames and Code on Collaborative Projects** 
+
+
+### Goals to get through 
+ 
+1. Making Queries in BQ  
+2. Saving a Table in BQ 
+3. Loading Data into Google Storage 
+
+
+### SQL Introduced 
+1. SQL commands 
+2. Sub Queries
+3. Joins 
+
+
+### PART ONE: 
+1. [Navigate to Google Cloud](https://console.cloud.google.com/) make sure you are logged in Stanford (google)
+2. Go to Mining Clinical Decisions 
+3. Go to Saved Queries and click on Project Queries for DevWorkshop 
+
+### PART TWO 
+1. [Navigate to Google Cloud Storage](https://console.cloud.google.com/storage/browser?project=mining-clinical-decisions&folder&organizationId=302681460499)
+2. save your table 
+
+### PART THREE
+1. Going through auth_cloud.py steps 
+2. Install virtual environment
+3. Install GCP credentials 
+4. Query from GCP from python scripts locally 
+
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/requirements.txt b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/requirements.txt
new file mode 100644
index 00000000..aa2c89f8
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/requirements.txt
@@ -0,0 +1,28 @@
+cachetools==3.1.0
+certifi==2018.11.29
+chardet==3.0.4
+futures==3.2.0
+google-api-core==1.7.0
+google-auth==1.6.2
+google-auth-oauthlib==0.2.0
+google-cloud-bigquery==1.8.1
+google-cloud-core==0.29.1
+google-resumable-media==0.3.2
+googleapis-common-protos==1.5.6
+idna==2.8
+numpy==1.16.1
+oauthlib==3.0.1
+pandas==0.24.0
+pandas-gbq==0.9.0
+protobuf==3.18.3
+pyarrow==0.12.1
+pyasn1==0.4.5
+pyasn1-modules==0.2.4
+pydata-google-auth==0.1.1
+python-dateutil==2.7.5
+pytz==2018.9
+requests==2.21.0
+requests-oauthlib==1.2.0
+rsa==4.7
+six==1.12.0
+urllib3==1.26.5
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/sleep_loop.py b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/sleep_loop.py
new file mode 100644
index 00000000..dd869d71
--- /dev/null
+++ b/scripts/DevWorkshop/GoogleCloudPlatform/moreExamples/sleep_loop.py
@@ -0,0 +1,8 @@
+import time
+import sys
+t = int(sys.argv[1]) 
+for i in range(0,t):
+	while i < t:
+		time.sleep(1)
+		print('step', i + 1)
+		break
diff --git a/scripts/DevWorkshop/GoogleCloudPlatform/results/placeholder.txt b/scripts/DevWorkshop/GoogleCloudPlatform/results/placeholder.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/DevWorkshop/PredictiveModelling/predictive-modelling-workshop-solution.ipynb b/scripts/DevWorkshop/PredictiveModelling/predictive-modelling-workshop-solution.ipynb
new file mode 100644
index 00000000..f46ae12c
--- /dev/null
+++ b/scripts/DevWorkshop/PredictiveModelling/predictive-modelling-workshop-solution.ipynb
@@ -0,0 +1,744 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fcd061db",
+   "metadata": {},
+   "source": [
+    "## [SOLUTION] Supervised Learning with EHR Data\n",
+    "\n",
+    "This is a mostly filled in version of the workshop presented on 2/25/2022.  You'll still need to fill in our sunet_id in various locations to write to proper tables, and you'll need to point to your local application_default_credentials.json.  Have not filled in a solution for any of the optional extra material. \n",
+    "\n",
+    "**Objective**: The goal of this workship is to enable you to quickly develop and evaluate a baseline model for an arbitrary clinical prediction task. In this workshop you'll train and evaluate models that predict whether various components of a CBC with Differential lab will result outside of their normal range. This walkthrough should empower you to replicate this kind of analysis on any arbitrary supervised learning task you may care about. \n",
+    "\n",
+    "### Modules\n",
+    "Developing and evaluating a clinical supervised learning model can be broken into three distinct tasks.  \n",
+    "1. **Cohort Construction**: You need some mechanism to define a cohort of interest and assign outcomes of interest (labels) to each observation (example). \n",
+    "2. **Featurization**: You need a way to take your cohort and construct a feature matrix for each observation. \n",
+    "3. **Modelling**: You need a mechanism to train and evaluate a predictive model given features and labels. \n",
+    "\n",
+    "Each of these tasks have been broken down into distinct modules in code you will be using throughout this workshop. In the following notebook we break each of these three tasks down in considerable detail, and provide starter code for you construct, train, and evaluate a supervised learning task from scratch.   \n",
+    "\n",
+    "Before we start however, you'll need to run a few setup steps.\n",
+    "\n",
+    "**Disclaimer** : Current version of the code only supports constructing feature matrices from the `shc_core` dataset. See bottom of this noteook for future TODOs in developing this out further.  Would be great to get this supporting `lpch_core` and `OMOP` versions of our data soon.\n",
+    "\n",
+    "### 0.1 Install requried packages\n",
+    "Before beginning I'd recommend creating a new environemnt to avoid any conflicting package dependencies in your current environemnt.  If you don't know how to do this it's likely not a huge deal - you'll just want to know that the cell below will update a set of your python packages to specific versions (the most recent version as of 2/25/22).  See [here](https://docs.python.org/3/tutorial/venv.html) for info about python virtual environemnts, or [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) for info about conda environements.\n",
+    "\n",
+    "Below is the list of  packages you'll need installed to execute code in this workshop. Running the cell will install these packages into your current python environment.  This shouldn't take more than 2 minutes. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e44f2dec",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: google-cloud-bigquery==2.34.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (2.34.0)\n",
+      "Requirement already satisfied: grpcio<2.0dev,>=1.38.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (1.44.0)\n",
+      "Requirement already satisfied: google-cloud-core<3.0.0dev,>=1.4.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (2.2.2)\n",
+      "Requirement already satisfied: proto-plus>=1.10.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (1.20.3)\n",
+      "Requirement already satisfied: google-resumable-media<3.0dev,>=0.6.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (2.3.0)\n",
+      "Requirement already satisfied: google-api-core[grpc]<3.0.0dev,>=1.29.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (2.5.0)\n",
+      "Requirement already satisfied: protobuf>=3.12.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (3.19.4)\n",
+      "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (2.27.1)\n",
+      "Requirement already satisfied: python-dateutil<3.0dev,>=2.7.2 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (2.8.2)\n",
+      "Requirement already satisfied: packaging>=14.3 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-cloud-bigquery==2.34.0) (21.3)\n",
+      "Requirement already satisfied: google-auth<3.0dev,>=1.25.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (2.6.0)\n",
+      "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.52.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (1.55.0)\n",
+      "Requirement already satisfied: grpcio-status<2.0dev,>=1.33.2 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (1.44.0)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-auth<3.0dev,>=1.25.0->google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (0.2.8)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-auth<3.0dev,>=1.25.0->google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (5.0.0)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-auth<3.0dev,>=1.25.0->google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (4.8)\n",
+      "Requirement already satisfied: six>=1.9.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-auth<3.0dev,>=1.25.0->google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (1.16.0)\n",
+      "Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from google-resumable-media<3.0dev,>=0.6.0->google-cloud-bigquery==2.34.0) (1.3.0)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from packaging>=14.3->google-cloud-bigquery==2.34.0) (3.0.4)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3.0dev,>=1.25.0->google-api-core[grpc]<3.0.0dev,>=1.29.0->google-cloud-bigquery==2.34.0) (0.4.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-bigquery==2.34.0) (3.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-bigquery==2.34.0) (2021.10.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-bigquery==2.34.0) (2.0.12)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-bigquery==2.34.0) (1.26.8)\n",
+      "Requirement already satisfied: numpy==1.22.2 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (1.22.2)\n",
+      "Requirement already satisfied: pandas==1.4.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (1.4.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from pandas==1.4.1) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from pandas==1.4.1) (2021.3)\n",
+      "Requirement already satisfied: numpy>=1.18.5 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from pandas==1.4.1) (1.22.2)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas==1.4.1) (1.16.0)\n",
+      "Requirement already satisfied: tqdm==4.62.3 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (4.62.3)\n",
+      "Requirement already satisfied: scipy==1.8.0 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (1.8.0)\n",
+      "Requirement already satisfied: numpy<1.25.0,>=1.17.3 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (from scipy==1.8.0) (1.22.2)\n",
+      "Requirement already satisfied: scikit-learn==1.0.2 in /Users/conorcorbin/opt/anaconda3/envs/devfinal/lib/python3.9/site-packages (1.0.2)\n",
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+     ]
+    },
+    {
+     "name": "stdout",
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+     ]
+    }
+   ],
+   "source": [
+    "!pip install google-cloud-bigquery==2.34.0\n",
+    "!pip install numpy==1.22.2\n",
+    "!pip install pandas==1.4.1\n",
+    "!pip install tqdm==4.62.3\n",
+    "!pip install scipy==1.8.0\n",
+    "!pip install scikit-learn==1.0.2\n",
+    "!pip install pandas-gbq==0.17.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61a0b5ab",
+   "metadata": {},
+   "source": [
+    "### 0.2 Add medinfo.dataconversion to your system path\n",
+    "Assuming you haven't installed CDSS as a package in your python environement, you'll need to add the `medinfo.dataconversions` directory to your system path so that you can import python modules from it. To do this, execute the code below.  We'll be importanting code from three python files in this directory\n",
+    "1. `cohorts.py`\n",
+    "2. `featurizers.py`\n",
+    "3. `trainers.py`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9abcd6f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(1, '../../../medinfo/dataconversion/') # I'm assuming you haven't moved the location of this notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ee7c13",
+   "metadata": {},
+   "source": [
+    "### 0.3 Point  GOOGLE_APPLICATION_CREDENTIALS to your application_default_credentials.json\n",
+    "Assuming you've already followed steps [here](https://github.com/HealthRex/CDSS/wiki/Dev-Environment-and-Google-BigQuery-Database-Access-Setup#2-set-an-environment-variable-so-your-code-knows-where-to-find-this-key-file) to create an application_default_credentials.json, you'll need to set your GOOGLE_APPLICATION_CREDENTIALS environment variable so that is points to it. You can do this using the code snippet below, which may produce a warning that you can ignore. \n",
+    "\n",
+    "It's worth setting this env variable in your .bashrc / .bash_profile so that you don't have to do this each time you want to programmatically access our bigquery projects. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9a4011ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (\n",
+    "    '/Your/Path/To/application_default_credentials.json'\n",
+    ")\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff45494",
+   "metadata": {},
+   "source": [
+    "## 1. Cohort Construction\n",
+    "Now onto the fun stuff. To develop and evaluate any clinical prediction model, you'll first need to define a patient cohort of interest. This is one of the more nuanced aspects of any predictive modelling task as it often requires a deep understanding of the underlying data (ex: our clarity extracts) to get right, and is task specific. Every time you embark on a new predictive modelling endeavor you need to design the cohort construction process essentially from scratch.  Some aspects of constructing a cohort will remain constant from task to task, and we'll leverage this fact to design a `CohortBuilder` class that can be used by a user (you) to construct any arbitrary cohort that can be fed into downstream predictive modelling pipelines. \n",
+    "\n",
+    "Concretely, at the end of a cohort construction you will have a table where each row represents an observation (or example) and has **at least** the following four colummns. \n",
+    "1. `anon_id` : the de-identified id for a patient associated with this observation.  \n",
+    "2. `observation_id` : an id unique to the observation (note you may have multiple observations for the same patient)\n",
+    "3. `index_time` : a timestamp that marks the time at which your prediction would have been made for the patient for this observation.\n",
+    "4. `label` : if binary then 1 indicates positive label 0 indicates negative label.  If multiclass this takes a categorical value. \n",
+    "\n",
+    "If you have a multi-label task, that is you have multiple outcomes that are not mutually exclusive (which we will demonstrate in this workshop), then your cohort table will have multiple \"label\" columns. \n",
+    "\n",
+    "Additionally you may find it useful to append additional information to a cohort table (example demographic variables or additional metadata about your labels/outcomes).  Adding these columns shouldn't break my code...  \n",
+    "\n",
+    "### Constructing the Cohort Table\n",
+    "As a user you have flexibility in how you choose to construct your cohort table.  If the logic to construct your cohort is simple enough, you may be able to generate the table with a small/medium sized SQL query. When you can, I recommend doing this.  As is not infrequently the case, the logic needed to construct your cohort can get quite complex.  Other times, you may require merging datasets (some maybe not on bigquery) together to create your cohort table.  In both of these scenarios, it becomes favorable to apply additional python logic to construct your final cohort table. The `CohortBuilder` class defined in `medinfo.dataconversion.cohorts` allows this flexibility. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4459d93e",
+   "metadata": {},
+   "source": [
+    "#### 1.1 Import the cohorts module and instantiate a CohortBuilder Object \n",
+    "The `CohortBuilder` class provides skeleton logic for you to construct any arbitrary cohort table.  Given a SQL query and an optional python function, it creates a cohort and saves it to a user specified table in our bigquery project. Specifically, it's constructor takes in: \n",
+    "1. a dataset name\n",
+    "2. a table name\n",
+    "3. a list of label columns (names of the columns that correspond to outcomes in your cohort)\n",
+    "\n",
+    "Execute the code below to import the cohorts module and create an instance of `CohortBuilder` that will save a cohort table titled `{your_sunetid}_devworkshop_cohort` in the `devworkshop` dataset within the `mining-clinical-decisions` project. We'll pass in a list of strings to populate the `label_columns` attribute.  Our SQL query to generate this table will then be tailored such that the resulting table contains columns listed in `label_columns`.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4a748332",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cohorts\n",
+    "sunet_id='[TODO]' ### TODO Fill in \n",
+    "cb = cohorts.CohortBuilder(dataset_name='devworkshop',\n",
+    "                           table_name=f'{sunet_id}_devworkshop_cohort',\n",
+    "                           label_columns=['label_WBC', 'label_HCT', 'label_PLT'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ba38d3d",
+   "metadata": {},
+   "source": [
+    "#### 1.2 Develop a SQL query to generate your cohort table\n",
+    "This step is often one of the most nuanced.  In this workshop we'll be generating a cohort of patients who have had CBC w/ Differential labs ordered for them. The prediction task will be to, at the moment in time the order is placed, predict whether various components (White Blood Cell, Hematocrit, and Platelets) will return outside of the normal range. This is a **multi-label** prediction task, as outcomes for each of these three components are not mutually exclusive. We'll thus generate a cohort table that has the following columns. \n",
+    "1. `anon_id`\n",
+    "2. `observation_id`: we'll use the order_id associated with the lab order\n",
+    "3. `index_time`: this will be the timestamp associated with the lab order\n",
+    "4. `label_WBC`: 1 if the resulting white blood cell count component falls outside the normal range, 0 otherwise.\n",
+    "5. `label_HCT`: 1 if the resulting hematocrit component falls outside the normal range, 0 otherwise.\n",
+    "6. `label_PLT`: 1 if the resulting platelets component falls outside the normal range, 0 otherwise.\n",
+    "\n",
+    "In this workshop we won't restrict observations based on `anon_id` — which means the same patient may have multiple observations (examples, or rows) in the cohort table. We'll also only look at labs ordered between 2015 and 2020, and we'll randomly sample 2000 observations per year to keep the dataset lightweight (12k rows total).  Finally, we'll only make predictions for lab orders that a) result and b) result with all three components we care about.  At prediction time we may not know if the ordered lab will result (ex will it be cancelled, will only a subset of components result?), which can have implications on your estimates of model performance (think censoring and how that biases things). Limitations aside, below I've provided a skeleton SQL query with **TODOs** for ya'll to fill in so that this logic can be executed.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "947535d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"\"\"\n",
+    "WITH cbcd_lab_results as (\n",
+    "    SELECT DISTINCT\n",
+    "        anon_id,\n",
+    "        order_id_coded,\n",
+    "        order_time_utc as index_time,\n",
+    "        base_name,\n",
+    "        CASE WHEN result_flag is NULL OR result_flag = \"Normal\" Then 0\n",
+    "        ELSE 1\n",
+    "        END label\n",
+    "    FROM \n",
+    "        shc_core.lab_result # what table in shc_core has lab results?\n",
+    "    WHERE \n",
+    "        proc_code = 'LABCBCD' # Can we get a proc_code associated with CBC w/ Differential?\n",
+    "    AND\n",
+    "        base_name in ('WBC', 'PLT', 'HCT')\n",
+    "    AND \n",
+    "        EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2020\n",
+    "),\n",
+    "\n",
+    "# Pivot lab result to wide\n",
+    "cohort_wide as (\n",
+    "    SELECT \n",
+    "        * \n",
+    "    FROM \n",
+    "        cbcd_lab_results\n",
+    "    PIVOT (\n",
+    "        MAX(label) as label -- should be max of one value or no value (hence check not null)\n",
+    "        FOR base_name in ('WBC', 'PLT', 'HCT')\n",
+    "    )\n",
+    "    WHERE \n",
+    "        -- only keep labs where all three components we care about result\n",
+    "        label_WBC is not NULL and label_PLT is not NULL and label_HCT is not NULL\n",
+    ")\n",
+    "\n",
+    "### 2000 observations randomly sampled per year\n",
+    "SELECT \n",
+    "    anon_id, order_id_coded as observation_id, index_time, label_WBC, label_PLT, label_HCT\n",
+    "FROM \n",
+    "     (SELECT *,\n",
+    "             ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time) ORDER BY RAND()) \n",
+    "             AS seqnum\n",
+    "      FROM cohort_wide \n",
+    "     ) \n",
+    "WHERE \n",
+    "    seqnum <= 2000\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fdd517a",
+   "metadata": {},
+   "source": [
+    "#### 1.3 Generate your cohort and save to bigquery. \n",
+    "You can optionally pass in a table schema to control the datatypes of each column.  Here we force `anon_id` to be a string and `observation_id` to be an integer as an example. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0f705407",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████| 12000/12000 [00:01<00:00, 10690.61rows/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5817.34it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "cb.build_cohort(query) # Executes query, and stores cohort in a pandas dataframe in the `df` attribute\n",
+    "schema=[{'name' : 'anon_id', 'type' : 'STRING'},\n",
+    "        {'name' : 'observation_id', 'type' : 'INTEGER'}]\n",
+    "cb.write_cohort_table(overwrite='True', schema=schema) # Writes the cohort to bigquery"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "117e3df0",
+   "metadata": {},
+   "source": [
+    "####  1.4 [Optional] Additional notes\n",
+    "If you'd like to further process your table using python logic you may implement an additional function that takes in a pandas dataframe and outputs a pandas dataframe. You can then pass this function into the `build_cohort` method as an argument: `transform`. By default the `transform` argument takes in an identity function, that is it takes in a dataframe and returns the same dataframe. Example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "26ff48ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████| 12000/12000 [00:01<00:00, 11979.98rows/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3048.19it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def other_cohort_logic(df):\n",
+    "    \"\"\"\n",
+    "    Here we'll just return the same dataframe, but you can imagine writing any other custom code to transform\n",
+    "    the result of your SQL query in any way shape or form. You don't even need to use the result\n",
+    "    of the query at all if you don't want to you.  You could for example read in a CSV external\n",
+    "    from bigquery that has all of your cohort information and return that as a dataframe.  The\n",
+    "    result of this function is what gets written to bigquery and becomes your final cohort. \n",
+    "    \"\"\"\n",
+    "    return df\n",
+    "\n",
+    "cb.build_cohort(query, transform=other_cohort_logic) # stores cohort in `df` attribute\n",
+    "schema=[{'name' : 'anon_id', 'type' : 'STRING'},\n",
+    "        {'name' : 'observation_id', 'type' : 'INTEGER'}]\n",
+    "cb.write_cohort_table(overwrite='True', schema=schema) # Writes the cohort to bigquery"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2f88e97",
+   "metadata": {},
+   "source": [
+    "### 2. Featurization\n",
+    "Once you have a cohort table saved with appropriate columns on bigquery, the next step becomes extracting features for each of your observations.  You can dive deep into crazy rabbit holes generating custom features for any given prediction task.  That's not the point of this workshop. Here we'll use a standard cookie cutter featurization approach with various feature types found in EHR data that people commonly use for a wide array of prediction tasks. \n",
+    "\n",
+    "#### Feature Represention\n",
+    "As a primer, we'll first categorize feature types seen in structured EHR data into two buckets. These are 1) categorical features [these are features that do not have corresponding values - ex an ICD code] and 2) numerical features [these features have numerical information tied to them - ex a lab result, a vital sign, age]. \n",
+    "\n",
+    "We'll use feature types from both of these categories in our lab prediction models. We'll represent features from both categories as counts in \"bag of words\" fashion.  For categorical features, this is trivial.  Each feature (think ICD code, lab order, medication order) will be represented with its own column in the resulting feature matrix.  The value for a particular observation will be the number of times that feature is present in the patient record starting at `index_time` and looking back some user defined amount of time.\n",
+    "\n",
+    "For numerical features, representing as counts (bag of words) requires an additional set of transformations.  We need some mechanism of tokenizing numerical features before we can treat them as counts.  To do this, we will bin each feature into discrete buckets with thresholds defined as percentile cutoffs from the distribution of the feature we see in the training set. The number of buckets for a given feature is user defined. As an example, let's say we want to tokenize a temperature measurement.  In a pre-processing step, we'll build a distribution of temperature measurements using all measurements found in the training set. We'll create thresholds for each feature based on the percentile values in the training set distribution.  If we specify to create 5 buckets, then values that land in the 0-20th percentile get placed in bucket 1 ... 80-100th percentile becomes bucket 5. Each bucket for each numerical feature then becomes a column in the final feature matrix, and the value is, like before, the number of times a value in that bucket is present in the patient timeline starting at `index_time` and looking back some user defined amount of time.\n",
+    "\n",
+    "#### Splitting\n",
+    "The `featurizer` module also defines a splitting mechanism — the mechanism at which you will split your data into training and test sets. By default we do a time split where the last year of data is held out as the test set. This mimics the deployment scenario where you train on prior data and deploy on future data. Model decay due to dataset drift will be accounted for in your model performance estimates — though clearly dataset drift can be more or less of a factor depending on the time window of your study / deployment. \n",
+    "\n",
+    "#### Storing a feature matrix\n",
+    "Our bag of words feature representation will result in a wide and sparse feature matrix. Saving this in wide format is innefficient. By default your feature matrix will be saved to bigquery in long form, and will be saved to your working directory as [csr matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html).  The featurizer module never actually stores your feature matrix as a table in wide format. This is all abstracted away for you, but what's nice about this representation is you can fit matrices that would otherwise use ungodly amounts of RAM into memory on a standard computer. Sklearn models support csr matrices, which is also quite nice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2c38f77",
+   "metadata": {},
+   "source": [
+    "### 2.1 Import the featurizer module and instantiate a BagOfWordsFeaturizer\n",
+    "The constructor takes in the following arguments \n",
+    "1. cohort_table : name of the cohort table you saved to bigquery in prior step\n",
+    "2. label_columns : names of label columns in cohort table (same as before)\n",
+    "3. dataset_name : name of dataset on bigquery with your cohort table\n",
+    "4. table_name : name of feature matrix table you will save back to bigquery (in long form)\n",
+    "5. outpath : a local (your computer) path you want to save feature matrices and labels to\n",
+    "6. feature_config : optionally define a custom featurization schema - more explained in 2.2\n",
+    "\n",
+    "Edit the code below to instantiate a BagOfWordsFeaturizer object that points to your cohort table.  Save your long form feature matrix in a bigquery table called `{your_sunet}_feature_matrix` in the `devworkshop` dataset. Save in a local directory of your choosing. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4c72b9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import featurizers\n",
+    "\n",
+    "your_sunetid='[TODO]' # TODO\n",
+    "featurizer = featurizers.BagOfWordsFeaturizer(\n",
+    "  cohort_table=f'{your_sunetid}_devworkshop_cohort', # TODO Your cohort table\n",
+    "  label_columns=['label_WBC', 'label_HCT', 'label_PLT'],\n",
+    "  dataset_name='devworkshop',\n",
+    "  table_name=f'{your_sunetid}_feature_matrix',\n",
+    "  outpath='./model_info/' # edit however you want - this path will be created if it doesn't already exist. \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e16cd44f",
+   "metadata": {},
+   "source": [
+    "### 2.2 Call your featurizer object to generate a feature matrix. \n",
+    "Feature types, number of bins, and look back windows are all customizable. Here however you'll use the default. The default featurization schema is below. \n",
+    "\n",
+    "```json\n",
+    "DEFAULT_DEPLOY_CONFIG = {\n",
+    "    'Categorical': {\n",
+    "        'Sex': [{'look_back' : None}],\n",
+    "        'Race': [{'look_back' : None}],\n",
+    "        'Diagnoses': [{'look_back' : None}],\n",
+    "        'Medications': [{'look_back' : 28}]\n",
+    "    },\n",
+    "    'Numerical': {\n",
+    "        'Age': [{'look_back': None, 'num_bins': 5}],\n",
+    "        'LabResults': [{'look_back': 14, 'num_bins': 5}],\n",
+    "        'Vitals': [{'look_back': 3, 'num_bins': 5}]\n",
+    "    }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "You'll notice the first level classifies feature types into categorical and numerical features. Categorical features include sex, race, diagnoses, and medications. Numerical features include, age, lab results, and vital signs.\n",
+    "\n",
+    "Categorical features have specified one or more look back windows. When a look back window is not appropriate (ie sex, race) it's defined as None. We specificy a look back window of None for diagnoses to indicate that by default we look back over the entire patient timeline for prior diagnoses. For medications, we look back 28 days. \n",
+    "\n",
+    "Numerical features have one or more look back windows and number of bins assigned to them. In the default config, we will featurize lab results available 14 days prior to index time, and we will bin each lab result into 5 buckets. For vital signs, we look back three days from index time. \n",
+    "\n",
+    "**Observation**\n",
+    "You'll notice the name of this particular config has the word DEPLOY.  Models trained using this specification can easily be silently deployed into an epic production environemnt (perhaps a future group meeting).  \n",
+    "\n",
+    "Execute the code below to generate and save your feature matrix. Check your `outpath` afterwords to see what gets dumped in there.\n",
+    "\n",
+    "**Disclaimer** \n",
+    "Messing around too much with this config may break the code in it's current state. You can subtract feature types, change look back windows, and number of bins — but having multiple look back windows and number of bins for a single feature type is currently not supported. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bda2d9cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Featurizing sex\n",
+      "Featurizing race\n",
+      "Featurizing diagnosis codes\n",
+      "Featurizing medication orders\n",
+      "Featurizing age\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 8905.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Featurizing lab results\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████| 69322/69322 [00:05<00:00, 13570.77rows/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 8612.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Featurizing Vitals\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████| 67183/67183 [00:04<00:00, 14098.07rows/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 14074.85it/s]\n",
+      "Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████| 557325/557325 [00:24<00:00, 23148.03rows/s]\n",
+      "Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████| 12000/12000 [00:00<00:00, 12711.61rows/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature matrix generated with 16670 features\n"
+     ]
+    }
+   ],
+   "source": [
+    "featurizer() # this just calls the featurizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80c6e4f2",
+   "metadata": {},
+   "source": [
+    "### 3. Model Training\n",
+    "The final part of this workshop entails actually training and evaluating models for the lab prediction task. There are many nuances that come up with model training and model selection. For our purposes here we will follow a minimally viable procedure. Our task is again **multi-label**, meaning our outcomes are not mutually exclusive.  To model this, we will train three separate binary classifiers. One classifier will predict the hematocrit component, one white blood cell, one platelets. We'll train and evaluate a random forest using default hyperparamters in sklearn.  Again — clearly way more you could do with model selection, that's not the point of this workshop.  \n",
+    "\n",
+    "#### 3.1 Import the trainers module and instantiate a BaselineModelTrainer\n",
+    "`BaselineModelTrainer` defines logic to train a minimally viable classifier that can get you some *lower* bound on the predictive performance associated with your task of interest. It implements one of the simplest supervised learning pipelines imaginable — it trains and evalutes a random forest with default hyperparameters. The constructor takes in one argument — the path to the directory you saved your feature matrices. In the cell below, write code to import the trainers submodule and instantiate a `BaselineModelTrainer`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d430757a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import trainers\n",
+    "path='./model_info/' # TODO where are your feature matrices stored?\n",
+    "trainer = trainers.BaselineModelTrainer(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67d06b6e",
+   "metadata": {},
+   "source": [
+    "#### 3.2 Call your BaselineModelTrainer to train a random forest\n",
+    "By calling your newly created object (just like you called your featurizer), you will execute logic to train a random forest for a given `task`.  Each task is defined by it's corresponding `label_column`: ie (label_WBC, label_HCT, label_PLT).  \n",
+    "\n",
+    "Upon being called a `BaselineModelTrainer` will print out an AUC (area under the ROC curve) using predictions and labels from your test set (which by default is your last year of data). A dataframe containing the columns `predictions` and `labels` is written to a csv in the same directory containing your feature matrix.  You can load in this information later to further evaluate your model however you see fit (generate ROC curves, precision recall curves, calibration plots, etc). More on this at a later time, but on top of this all necessary information needed to **deploy** the model in an epic environment is stored in a `deployment_config` file. This includes\n",
+    "1. The model itself,\n",
+    "2. The order of features in an observations feature vector\n",
+    "3. The binning thresholds for all numerical features\n",
+    "4. The feature config file indicating which feature types to pull in and transform using EPIC/FHIR apis. \n",
+    "\n",
+    "Execute the code below to train and evaluate three random forests (one for each lab component). \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f3a04435",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC: 0.74\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Component 1 - Predict white blood cell count\n",
+    "trainer(task='label_WBC')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ef6f6bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC: 0.81\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Component 2 - Predict hematrocrit\n",
+    "trainer(task='label_HCT')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "26a0feaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC: 0.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Component 3 - Predict platelets\n",
+    "trainer(task='label_PLT')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b4b3f8",
+   "metadata": {},
+   "source": [
+    "### [Optional] 3.3 Further analyze performance of your models\n",
+    "A CSV containing predictions and labels associated with each of these three tasks has been written to your specified local path. Read in these CSV's and try generating some ROC and Precision recall curves.  Documentation about how to do that is [here](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html) and [here](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc19281f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### TODO: generate three ROC and Precision recall curves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d5c4e0f",
+   "metadata": {},
+   "source": [
+    "### [Optional] 4. Train a baseline model for a task of your choosing\n",
+    "Done already?  Try following the same three step procedure to train and evaluate a baseline model for a task of your choosing.  Is there a particular cohort of patients you have in mind and a potentially intersting/meaningful prediction task that could benefit patients like them?  Are you in the midst of on ongoing ML project?  Try repeating this workflow for your task of interest. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e39d5461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### TODO, repeat workflow for your task of interest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b175749b",
+   "metadata": {},
+   "source": [
+    "### [Optional] 5: Contributing to this workflow\n",
+    "If you found this workflow helpful and want to make it even better - there are many ways we can think about improving.  As of now the code you all executed is fairly barebones. Only models built against the `shc_core` extract are supported, the only feature representation supported is bag of words, and the only modelling infrastucture is defined in `BaselineModelTrainer`.  Below I list some additional features (overloaded term I know) that if supported would make this infrastucture better. If ya'll have time, feel free to claim one of these feature requests for yourself.  We can talk offline about how to go about integrating into existing infrastructure. \n",
+    "\n",
+    "**CohortBuidler**\n",
+    "* Would be good to support functionality that does not require you to write your cohort table back to bigquery.\n",
+    "\n",
+    "**Featurizers**\n",
+    "* Support inclusion of different feature types found in the EHR ex orders in order_proc (including lab orders, procedure orders, microbiology orders, imaging orders etc) as well as other features from various other tables we have available\n",
+    "* Support different feature representations\n",
+    "    * TF-IDF representation (instead of bag of words)\n",
+    "    * SVD reprsentation (reduce dimensionality of TF-IDF feature vector with SVD for final representation)\n",
+    "    * Summary stats representation (still treat categorical variables as counts, but take summary stats ex mean, std, first, last values over continuious features instead of binning) -- this is what was supported in the original FeatureMatrixFactory module built for the HealthRex group.\n",
+    "    * Timeline representation: feature vectors don't have to be fixed length to be fed into RNN style model classes. When using these kinds of models we could represent each observation as a timeline of tokens. \n",
+    "* Support feature selection (ie remove low and high IC features or features with low variance prior to modelling). \n",
+    "\n",
+    "**Modelling**\n",
+    "* Support a model selection pipeline that includes hyperparamter sweeps. I have code for this, just have yet to plug in...\n",
+    "* Support pytorch models and model trainers. \n",
+    "    * Functionality to easily train FFNNs, GRUs, transformers would be beneficial and would be nice to plug into model selection workflow. \n",
+    "  \n",
+    "**Supporting other data sources**\n",
+    "* lpch_core is an easy first target as structure to shc_core is nearly identical.  \n",
+    "* Supporting OMOP would be more challenging but likely worth it, especially for people who want to include features from clinical notes.  \n",
+    "\n",
+    "**Any other ideas welcomed! -- Thanks for taking part of this workshop!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be3b4278",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/DevWorkshop/PredictiveModelling/predictive-modelling-workshop.ipynb b/scripts/DevWorkshop/PredictiveModelling/predictive-modelling-workshop.ipynb
new file mode 100644
index 00000000..57d55136
--- /dev/null
+++ b/scripts/DevWorkshop/PredictiveModelling/predictive-modelling-workshop.ipynb
@@ -0,0 +1,555 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fcd061db",
+   "metadata": {},
+   "source": [
+    "## Supervised Learning with EHR Data\n",
+    "\n",
+    "**Objective**: The goal of this workship is to enable you to quickly develop and evaluate a baseline model for an arbitrary clinical prediction task. In this workshop you'll train and evaluate models that predict whether various components of a CBC with Differential lab will result outside of their normal range. This walkthrough should empower you to replicate this kind of analysis on any arbitrary supervised learning task you may care about. \n",
+    "\n",
+    "### Modules\n",
+    "Developing and evaluating a clinical supervised learning model can be broken into three distinct tasks.  \n",
+    "1. **Cohort Construction**: You need some mechanism to define a cohort of interest and assign outcomes of interest (labels) to each observation (example). \n",
+    "2. **Featurization**: You need a way to take your cohort and construct a feature matrix for each observation. \n",
+    "3. **Modelling**: You need a mechanism to train and evaluate a predictive model given features and labels. \n",
+    "\n",
+    "Each of these tasks have been broken down into distinct modules in code you will be using throughout this workshop. In the following notebook we break each of these three tasks down in considerable detail, and provide starter code for you construct, train, and evaluate a supervised learning task from scratch.   \n",
+    "\n",
+    "Before we start however, you'll need to run a few setup steps.\n",
+    "\n",
+    "**Disclaimer** : Current version of the code only supports constructing feature matrices from the `shc_core` dataset. See bottom of this noteook for future TODOs in developing this out further.  Would be great to get this supporting `lpch_core` and `OMOP` versions of our data soon.\n",
+    "\n",
+    "### 0.1 Install requried packages\n",
+    "Before beginning I'd recommend creating a new environemnt to avoid any conflicting package dependencies in your current environemnt.  If you don't know how to do this it's likely not a huge deal - you'll just want to know that the cell below will update a set of your python packages to specific versions (the most recent version as of 2/25/22).  See [here](https://docs.python.org/3/tutorial/venv.html) for info about python virtual environemnts, or [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) for info about conda environements.\n",
+    "\n",
+    "Below is the list of  packages you'll need installed to execute code in this workshop. Running the cell will install these packages into your current python environment.  This shouldn't take more than 2 minutes. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e44f2dec",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install google-cloud-bigquery==2.34.0\n",
+    "!pip install numpy==1.22.2\n",
+    "!pip install pandas==1.4.1\n",
+    "!pip install tqdm==4.62.3\n",
+    "!pip install scipy==1.8.0\n",
+    "!pip install scikit-learn==1.0.2\n",
+    "!pip install pandas-gbq==0.17.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61a0b5ab",
+   "metadata": {},
+   "source": [
+    "### 0.2 Add medinfo.dataconversion to your system path\n",
+    "Assuming you haven't installed CDSS as a package in your python environement, you'll need to add the `medinfo.dataconversions` directory to your system path so that you can import python modules from it. To do this, execute the code below.  We'll be importanting code from three python files in this directory\n",
+    "1. `cohorts.py`\n",
+    "2. `featurizers.py`\n",
+    "3. `trainers.py`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9abcd6f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(1, '../../../medinfo/dataconversion/') # I'm assuming you haven't moved the location of this notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ee7c13",
+   "metadata": {},
+   "source": [
+    "### 0.3 Point  GOOGLE_APPLICATION_CREDENTIALS to your application_default_credentials.json\n",
+    "Assuming you've already followed steps [here](https://github.com/HealthRex/CDSS/wiki/Dev-Environment-and-Google-BigQuery-Database-Access-Setup#2-set-an-environment-variable-so-your-code-knows-where-to-find-this-key-file) to create an application_default_credentials.json, you'll need to set your GOOGLE_APPLICATION_CREDENTIALS environment variable so that is points to it. You can do this using the code snippet below, which may produce a warning that you can ignore. \n",
+    "\n",
+    "It's worth setting this env variable in your .bashrc / .bash_profile so that you don't have to do this each time you want to programmatically access our bigquery projects. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a4011ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (\n",
+    "    '[TODO] PATH_TO_YOUR_application_default_credentials.json'\n",
+    ")\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff45494",
+   "metadata": {},
+   "source": [
+    "## 1. Cohort Construction\n",
+    "Now onto the fun stuff. To develop and evaluate any clinical prediction model, you'll first need to define a patient cohort of interest. This is one of the more nuanced aspects of any predictive modelling task as it often requires a deep understanding of the underlying data (ex: our clarity extracts) to get right, and is task specific. Every time you embark on a new predictive modelling endeavor you need to design the cohort construction process essentially from scratch.  Some aspects of constructing a cohort will remain constant from task to task, and we'll leverage this fact to design a `CohortBuilder` class that can be used by a user (you) to construct any arbitrary cohort that can be fed into downstream predictive modelling pipelines. \n",
+    "\n",
+    "Concretely, at the end of a cohort construction you will have a table where each row represents an observation (or example) and has **at least** the following four colummns. \n",
+    "1. `anon_id` : the de-identified id for a patient associated with this observation.  \n",
+    "2. `observation_id` : an id unique to the observation (note you may have multiple observations for the same patient)\n",
+    "3. `index_time` : a timestamp that marks the time at which your prediction would have been made for the patient for this observation.\n",
+    "4. `label` : if binary then 1 indicates positive label 0 indicates negative label.  If multiclass this takes a categorical value. \n",
+    "\n",
+    "If you have a multi-label task, that is you have multiple outcomes that are not mutually exclusive (which we will demonstrate in this workshop), then your cohort table will have multiple \"label\" columns. \n",
+    "\n",
+    "Additionally you may find it useful to append additional information to a cohort table (example demographic variables or additional metadata about your labels/outcomes).  Adding these columns shouldn't break my code...  \n",
+    "\n",
+    "### Constructing the Cohort Table\n",
+    "As a user you have flexibility in how you choose to construct your cohort table.  If the logic to construct your cohort is simple enough, you may be able to generate the table with a small/medium sized SQL query. When you can, I recommend doing this.  As is not infrequently the case, the logic needed to construct your cohort can get quite complex.  Other times, you may require merging datasets (some maybe not on bigquery) together to create your cohort table.  In both of these scenarios, it becomes favorable to apply additional python logic to construct your final cohort table. The `CohortBuilder` class defined in `medinfo.dataconversion.cohorts` allows this flexibility. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4459d93e",
+   "metadata": {},
+   "source": [
+    "#### 1.1 Import the cohorts module and instantiate a CohortBuilder Object \n",
+    "The `CohortBuilder` class provides skeleton logic for you to construct any arbitrary cohort table.  Given a SQL query and an optional python function, it creates a cohort and saves it to a user specified table in our bigquery project. Specifically, it's constructor takes in: \n",
+    "1. a dataset name\n",
+    "2. a table name\n",
+    "3. a list of label columns (names of the columns that correspond to outcomes in your cohort)\n",
+    "\n",
+    "Execute the code below to import the cohorts module and create an instance of `CohortBuilder` that will save a cohort table titled `{your_sunetid}_devworkshop_cohort` in the `devworkshop` dataset within the `mining-clinical-decisions` project. We'll pass in a list of strings to populate the `label_columns` attribute.  Our SQL query to generate this table will then be tailored such that the resulting table contains columns listed in `label_columns`.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a748332",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cohorts\n",
+    "sunet_id='' ### TODO Fill in \n",
+    "cb = cohorts.CohortBuilder(dataset_name='devworkshop',\n",
+    "                           table_name=f'{sunet_id}_devworkshop_cohort',\n",
+    "                           label_columns=['label_WBC', 'label_HCT', 'label_PLT'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ba38d3d",
+   "metadata": {},
+   "source": [
+    "#### 1.2 Develop a SQL query to generate your cohort table\n",
+    "This step is often one of the most nuanced.  In this workshop we'll be generating a cohort of patients who have had CBC w/ Differential labs ordered for them. The prediction task will be to, at the moment in time the order is placed, predict whether various components (White Blood Cell, Hematocrit, and Platelets) will return outside of the normal range. This is a **multi-label** prediction task, as outcomes for each of these three components are not mutually exclusive. We'll thus generate a cohort table that has the following columns. \n",
+    "1. `anon_id`\n",
+    "2. `observation_id`: we'll use the order_id associated with the lab order\n",
+    "3. `index_time`: this will be the timestamp associated with the lab order\n",
+    "4. `label_WBC`: 1 if the resulting white blood cell count component falls outside the normal range, 0 otherwise.\n",
+    "5. `label_HCT`: 1 if the resulting hematocrit component falls outside the normal range, 0 otherwise.\n",
+    "6. `label_PLT`: 1 if the resulting platelets component falls outside the normal range, 0 otherwise.\n",
+    "\n",
+    "In this workshop we won't restrict observations based on `anon_id` — which means the same patient may have multiple observations (examples, or rows) in the cohort table. We'll also only look at labs ordered between 2015 and 2020, and we'll randomly sample 2000 observations per year to keep the dataset lightweight (12k rows total).  Finally, we'll only make predictions for lab orders that a) result and b) result with all three components we care about.  At prediction time we may not know if the ordered lab will result (ex will it be cancelled, will only a subset of components result?), which can have implications on your estimates of model performance (think censoring and how that biases things). Limitations aside, below I've provided a skeleton SQL query with **TODOs** for ya'll to fill in so that this logic can be executed.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "947535d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"\"\"\n",
+    "WITH cbcd_lab_results as (\n",
+    "    SELECT DISTINCT\n",
+    "        anon_id,\n",
+    "        order_id_coded,\n",
+    "        order_time_utc as index_time,\n",
+    "        base_name,\n",
+    "        CASE WHEN result_flag is NULL OR result_flag = \"Normal\" Then 0\n",
+    "        ELSE 1\n",
+    "        END label\n",
+    "    FROM \n",
+    "        [TODO] # what table in shc_core has lab results?\n",
+    "    WHERE \n",
+    "        proc_code = [TODO] # Can we get a proc_code associated with CBC w/ Differential?\n",
+    "    AND\n",
+    "        base_name in ('WBC', 'PLT', 'HCT')\n",
+    "    AND \n",
+    "        EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2020\n",
+    "),\n",
+    "\n",
+    "# Pivot lab result to wide\n",
+    "cohort_wide as (\n",
+    "    SELECT \n",
+    "        * \n",
+    "    FROM \n",
+    "        cbcd_lab_results\n",
+    "    PIVOT (\n",
+    "        MAX(label) as label -- should be max of one value or no value (hence check not null)\n",
+    "        FOR base_name in ('WBC', 'PLT', 'HCT')\n",
+    "    )\n",
+    "    WHERE \n",
+    "        -- only keep labs where all three components we care about result\n",
+    "        label_WBC is not NULL and label_PLT is not NULL and label_HCT is not NULL\n",
+    ")\n",
+    "\n",
+    "### 2000 observations randomly sampled per year\n",
+    "SELECT \n",
+    "    anon_id, order_id_coded as observation_id, index_time, label_WBC, label_PLT, label_HCT\n",
+    "FROM \n",
+    "     (SELECT *,\n",
+    "             ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time) ORDER BY RAND()) \n",
+    "             AS seqnum\n",
+    "      FROM cohort_wide \n",
+    "     ) \n",
+    "WHERE \n",
+    "    seqnum <= 2000\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fdd517a",
+   "metadata": {},
+   "source": [
+    "#### 1.3 Generate your cohort and save to bigquery. \n",
+    "You can optionally pass in a table schema to control the datatypes of each column.  Here we force `anon_id` to be a string and `observation_id` to be an integer as an example. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f705407",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cb.build_cohort(query) # Executes query, and stores cohort in a pandas dataframe in the `df` attribute\n",
+    "schema=[{'name' : 'anon_id', 'type' : 'STRING'},\n",
+    "        {'name' : 'observation_id', 'type' : 'INTEGER'}]\n",
+    "cb.write_cohort_table(overwrite='True', schema=schema) # Writes the cohort to bigquery"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "117e3df0",
+   "metadata": {},
+   "source": [
+    "####  1.4 [Optional] Additional notes\n",
+    "If you'd like to further process your table using python logic you may implement an additional function that takes in a pandas dataframe and outputs a pandas dataframe. You can then pass this function into the `build_cohort` method as an argument: `transform`. By default the `transform` argument takes in an identity function, that is it takes in a dataframe and returns the same dataframe. Example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26ff48ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def other_cohort_logic(df):\n",
+    "    \"\"\"\n",
+    "    Here we'll just return the same dataframe, but you can imagine writing any other custom code to transform\n",
+    "    the result of your SQL query in any way shape or form. You don't even need to use the result\n",
+    "    of the query at all if you don't want to you.  You could for example read in a CSV external\n",
+    "    from bigquery that has all of your cohort information and return that as a dataframe.  The\n",
+    "    result of this function is what gets written to bigquery and becomes your final cohort. \n",
+    "    \"\"\"\n",
+    "    return df\n",
+    "\n",
+    "cb.build_cohort(query, transform=other_cohort_logic) # stores cohort in `df` attribute\n",
+    "schema=[{'name' : 'anon_id', 'type' : 'STRING'},\n",
+    "        {'name' : 'observation_id', 'type' : 'INTEGER'}]\n",
+    "cb.write_cohort_table(overwrite='True', schema=schema) # Writes the cohort to bigquery"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2f88e97",
+   "metadata": {},
+   "source": [
+    "### 2. Featurization\n",
+    "Once you have a cohort table saved with appropriate columns on bigquery, the next step becomes extracting features for each of your observations.  You can dive deep into crazy rabbit holes generating custom features for any given prediction task.  That's not the point of this workshop. Here we'll use a standard cookie cutter featurization approach with various feature types found in EHR data that people commonly use for a wide array of prediction tasks. \n",
+    "\n",
+    "#### Feature Represention\n",
+    "As a primer, we'll first categorize feature types seen in structured EHR data into two buckets. These are 1) categorical features [these are features that do not have corresponding values - ex an ICD code] and 2) numerical features [these features have numerical information tied to them - ex a lab result, a vital sign, age]. \n",
+    "\n",
+    "We'll use feature types from both of these categories in our lab prediction models. We'll represent features from both categories as counts in \"bag of words\" fashion.  For categorical features, this is trivial.  Each feature (think ICD code, lab order, medication order) will be represented with its own column in the resulting feature matrix.  The value for a particular observation will be the number of times that feature is present in the patient record starting at `index_time` and looking back some user defined amount of time.\n",
+    "\n",
+    "For numerical features, representing as counts (bag of words) requires an additional set of transformations.  We need some mechanism of tokenizing numerical features before we can treat them as counts.  To do this, we will bin each feature into discrete buckets with thresholds defined as percentile cutoffs from the distribution of the feature we see in the training set. The number of buckets for a given feature is user defined. As an example, let's say we want to tokenize a temperature measurement.  In a pre-processing step, we'll build a distribution of temperature measurements using all measurements found in the training set. We'll create thresholds for each feature based on the percentile values in the training set distribution.  If we specify to create 5 buckets, then values that land in the 0-20th percentile get placed in bucket 1 ... 80-100th percentile becomes bucket 5. Each bucket for each numerical feature then becomes a column in the final feature matrix, and the value is, like before, the number of times a value in that bucket is present in the patient timeline starting at `index_time` and looking back some user defined amount of time.\n",
+    "\n",
+    "#### Splitting\n",
+    "The `featurizer` module also defines a splitting mechanism — the mechanism at which you will split your data into training and test sets. By default we do a time split where the last year of data is held out as the test set. This mimics the deployment scenario where you train on prior data and deploy on future data. Model decay due to dataset drift will be accounted for in your model performance estimates — though clearly dataset drift can be more or less of a factor depending on the time window of your study / deployment. \n",
+    "\n",
+    "#### Storing a feature matrix\n",
+    "Our bag of words feature representation will result in a wide and sparse feature matrix. Saving this in wide format is innefficient. By default your feature matrix will be saved to bigquery in long form, and will be saved to your working directory as [csr matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html).  The featurizer module never actually stores your feature matrix as a table in wide format. This is all abstracted away for you, but what's nice about this representation is you can fit matrices that would otherwise use ungodly amounts of RAM into memory on a standard computer. Sklearn models support csr matrices, which is also quite nice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2c38f77",
+   "metadata": {},
+   "source": [
+    "### 2.1 Import the featurizer module and instantiate a BagOfWordsFeaturizer\n",
+    "The constructor takes in the following arguments \n",
+    "1. cohort_table : name of the cohort table you saved to bigquery in prior step\n",
+    "2. label_columns : names of label columns in cohort table (same as before)\n",
+    "3. dataset_name : name of dataset on bigquery with your cohort table\n",
+    "4. table_name : name of feature matrix table you will save back to bigquery (in long form)\n",
+    "5. outpath : a local (your computer) path you want to save feature matrices and labels to\n",
+    "6. feature_config : optionally define a custom featurization schema - more explained in 2.2\n",
+    "\n",
+    "Edit the code below to instantiate a BagOfWordsFeaturizer object that points to your cohort table.  Save your long form feature matrix in a bigquery table called `{your_sunet}_feature_matrix` in the `devworkshop` dataset. Save in a local directory of your choosing. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4c72b9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import featurizers\n",
+    "\n",
+    "your_sunetid='' # TODO\n",
+    "featurizer = featurizers.BagOfWordsFeaturizer(\n",
+    "  cohort_table='', # TODO Your cohort table\n",
+    "  label_columns=['label_WBC', 'label_HCT', 'label_PLT'],\n",
+    "  dataset_name='devworkshop',\n",
+    "  table_name=f'{your_sunetid}_feature_matrix',\n",
+    "  outpath='./model_info/' # edit however you want - this path will be created if it doesn't already exist. \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e16cd44f",
+   "metadata": {},
+   "source": [
+    "### 2.2 Call your featurizer object to generate a feature matrix. \n",
+    "Feature types, number of bins, and look back windows are all customizable. Here however you'll use the default. The default featurization schema is below. \n",
+    "\n",
+    "```json\n",
+    "DEFAULT_DEPLOY_CONFIG = {\n",
+    "    'Categorical': {\n",
+    "        'Sex': [{'look_back' : None}],\n",
+    "        'Race': [{'look_back' : None}],\n",
+    "        'Diagnoses': [{'look_back' : None}],\n",
+    "        'Medications': [{'look_back' : 28}]\n",
+    "    },\n",
+    "    'Numerical': {\n",
+    "        'Age': [{'look_back': None, 'num_bins': 5}],\n",
+    "        'LabResults': [{'look_back': 14, 'num_bins': 5}],\n",
+    "        'Vitals': [{'look_back': 3, 'num_bins': 5}]\n",
+    "    }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "You'll notice the first level classifies feature types into categorical and numerical features. Categorical features include sex, race, diagnoses, and medications. Numerical features include, age, lab results, and vital signs.\n",
+    "\n",
+    "Categorical features have specified one or more look back windows. When a look back window is not appropriate (ie sex, race) it's defined as None. We specificy a look back window of None for diagnoses to indicate that by default we look back over the entire patient timeline for prior diagnoses. For medications, we look back 28 days. \n",
+    "\n",
+    "Numerical features have one or more look back windows and number of bins assigned to them. In the default config, we will featurize lab results available 14 days prior to index time, and we will bin each lab result into 5 buckets. For vital signs, we look back three days from index time. \n",
+    "\n",
+    "**Observation**\n",
+    "You'll notice the name of this particular config has the word DEPLOY.  Models trained using this specification can easily be silently deployed into an epic production environemnt (perhaps a future group meeting).  \n",
+    "\n",
+    "Execute the code below to generate and save your feature matrix. Check your `outpath` afterwords to see what gets dumped in there.\n",
+    "\n",
+    "**Disclaimer** \n",
+    "Messing around too much with this config may break the code in it's current state. You can subtract feature types, change look back windows, and number of bins — but having multiple look back windows and number of bins for a single feature type is currently not supported. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bda2d9cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "featurizer() # this just calls the featurizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80c6e4f2",
+   "metadata": {},
+   "source": [
+    "### 3. Model Training\n",
+    "The final part of this workshop entails actually training and evaluating models for the lab prediction task. There are many nuances that come up with model training and model selection. For our purposes here we will follow a minimally viable procedure. Our task is again **multi-label**, meaning our outcomes are not mutually exclusive.  To model this, we will train three separate binary classifiers. One classifier will predict the hematocrit component, one white blood cell, one platelets. We'll train and evaluate a random forest using default hyperparamters in sklearn.  Again — clearly way more you could do with model selection, that's not the point of this workshop.  \n",
+    "\n",
+    "#### 3.1 Import the trainers module and instantiate a BaselineModelTrainer\n",
+    "`BaselineModelTrainer` defines logic to train a minimally viable classifier that can get you some *lower* bound on the predictive performance associated with your task of interest. It implements one of the simplest supervised learning pipelines imaginable — it trains and evalutes a random forest with default hyperparameters. The constructor takes in one argument — the path to the directory you saved your feature matrices. In the cell below, write code to import the trainers submodule and instantiate a `BaselineModelTrainer`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d430757a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import trainers\n",
+    "path=\"\" # TODO where are your feature matrices stored?\n",
+    "trainer = trainers.BaselineModelTrainer(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67d06b6e",
+   "metadata": {},
+   "source": [
+    "#### 3.2 Call your BaselineModelTrainer to train a random forest\n",
+    "By calling your newly created object (just like you called your featurizer), you will execute logic to train a random forest for a given `task`.  Each task is defined by it's corresponding `label_column`: ie (label_WBC, label_HCT, label_PLT).  \n",
+    "\n",
+    "Upon being called a `BaselineModelTrainer` will print out an AUC (area under the ROC curve) using predictions and labels from your test set (which by default is your last year of data). A dataframe containing the columns `predictions` and `labels` is written to a csv in the same directory containing your feature matrix.  You can load in this information later to further evaluate your model however you see fit (generate ROC curves, precision recall curves, calibration plots, etc). More on this at a later time, but on top of this all necessary information needed to **deploy** the model in an epic environment is stored in a `deployment_config` file. This includes\n",
+    "1. The model itself,\n",
+    "2. The order of features in an observations feature vector\n",
+    "3. The binning thresholds for all numerical features\n",
+    "4. The feature config file indicating which feature types to pull in and transform using EPIC/FHIR apis. \n",
+    "\n",
+    "Execute the code below to train and evaluate three random forests (one for each lab component). \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3a04435",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Component 1 - Predict white blood cell count\n",
+    "trainer(task='label_WBC')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef6f6bab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Component 2 - Predict hematrocrit\n",
+    "trainer(task='label_HCT')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26a0feaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Component 3 - Predict platelets\n",
+    "trainer(task='label_PLT')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b4b3f8",
+   "metadata": {},
+   "source": [
+    "### [Optional] 3.3 Further analyze performance of your models\n",
+    "A CSV containing predictions and labels associated with each of these three tasks has been written to your specified local path. Read in these CSV's and try generating some ROC and Precision recall curves.  Documentation about how to do that is [here](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html) and [here](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc19281f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### TODO: generate three ROC and Precision recall curves. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d5c4e0f",
+   "metadata": {},
+   "source": [
+    "### [Optional] 4. Train a baseline model for a task of your choosing\n",
+    "Done already?  Try following the same three step procedure to train and evaluate a baseline model for a task of your choosing.  Is there a particular cohort of patients you have in mind and a potentially intersting/meaningful prediction task that could benefit patients like them?  Are you in the midst of on ongoing ML project?  Try repeating this workflow for your task of interest. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e39d5461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### TODO, repeat workflow for your task of interest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b175749b",
+   "metadata": {},
+   "source": [
+    "### [Optional] 5: Contributing to this workflow\n",
+    "If you found this workflow helpful and want to make it even better - there are many ways we can think about improving.  As of now the code you all executed is fairly barebones. Only models built against the `shc_core` extract are supported, the only feature representation supported is bag of words, and the only modelling infrastucture is defined in `BaselineModelTrainer`.  Below I list some additional features (overloaded term I know) that if supported would make this infrastucture better. If ya'll have time, feel free to claim one of these feature requests for yourself.  We can talk offline about how to go about integrating into existing infrastructure. \n",
+    "\n",
+    "**CohortBuidler**\n",
+    "* Would be good to support functionality that does not require you to write your cohort table back to bigquery.\n",
+    "\n",
+    "**Featurizers**\n",
+    "* Support inclusion of different feature types found in the EHR ex orders in order_proc (including lab orders, procedure orders, microbiology orders, imaging orders etc) as well as other features from various other tables we have available\n",
+    "* Support different feature representations\n",
+    "    * TF-IDF representation (instead of bag of words)\n",
+    "    * SVD reprsentation (reduce dimensionality of TF-IDF feature vector with SVD for final representation)\n",
+    "    * Summary stats representation (still treat categorical variables as counts, but take summary stats ex mean, std, first, last values over continuious features instead of binning) -- this is what was supported in the original FeatureMatrixFactory module built for the HealthRex group.\n",
+    "    * Timeline representation: feature vectors don't have to be fixed length to be fed into RNN style model classes. When using these kinds of models we could represent each observation as a timeline of tokens. \n",
+    "* Support feature selection (ie remove low and high IC features or features with low variance prior to modelling). \n",
+    "\n",
+    "**Modelling**\n",
+    "* Support a model selection pipeline that includes hyperparamter sweeps. I have code for this, just have yet to plug in...\n",
+    "* Support pytorch models and model trainers. \n",
+    "    * Functionality to easily train FFNNs, GRUs, transformers would be beneficial and would be nice to plug into model selection workflow. \n",
+    "  \n",
+    "**Supporting other data sources**\n",
+    "* lpch_core is an easy first target as structure to shc_core is nearly identical.  \n",
+    "* Supporting OMOP would be more challenging but likely worth it, especially for people who want to include features from clinical notes.  \n",
+    "\n",
+    "**Any other ideas welcomed! -- Thanks for taking part of this workshop!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be3b4278",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/DevWorkshop/ReadMe.AWSDevEnvironment.txt b/scripts/DevWorkshop/ReadMe.AWSDevEnvironment.txt
new file mode 100644
index 00000000..9090ed68
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.AWSDevEnvironment.txt
@@ -0,0 +1,239 @@
+
+= Dev Workshop =
+Amazon Web Services and Development Environment Setup
+
+== Learning Goals ==
+- Setting up Amazon Web Services EC2 and RDS Instances
+    - SSH remote connections + public-private key security
+    - Snapshot management
+    - Elastic compute scales + hourly billing
+    - Setting up Linux server dev environment from scratch / base image
+- Running batch queries on Linux server
+    - Process management to allow for multiple parallel queries or allow process to keep running in background while logged off
+    - (nohup command &> logFile &)
+
+== Preconditions ==
+- Account on Amazon Web Services (use shared lab account via LastPass or you can setup a free one)
+
+== Workshop Steps ==
+- Login to the AWS Management Console and then find the respective service section (e.g., EC2 or RDS)
+
+=== Version A - Starting from blank EC2 Linux Server and Connecting to a separate RDS (Database) Server ===
+(This is worth going through to learn how the different parts connect, but many will find it more convenient to just
+load up an EC2 Linux instance that already has a database locally installed. Skip to Version B in that case.)
+
+- Startup / Restore an RDS (Relational Database Server) Instance
+	Under Snapshots section, find a copy of the database server you want to clone
+		(e.g., healthrex-db-snapshot or stride-inpatient-2008-2017)
+		Note you will need to know the database username and password to subsequently connect
+		(Ask someone or check shared LastPass account)
+	- Actions > Restore Snapshot
+		Most default options are fine
+		- DB Instance Class
+			Specify how "big" a server you want. For testing, micro instance is good enough
+		- DB Instance Identifier
+			Specify a unique name so you will be able to track the instance you created
+		- Availability Zone
+			Specific choice is not as important as being consistent
+			(Keep all your spawned servers in the same area to minimize network latency and security risks).
+			I've usually been using "us-east-1c."
+		- Auto Minor Version Upgrade
+			No, don't bother if just doing some quick testing.
+			Yes, if planning to keep a server on a for a while
+			(Which you probably shouldn't be, since you're paying by the hour).
+
+- Startup an EC2 (Elastic Compute) Instance
+	These are general purpose (Linux) servers that can basically do whatever you want them to
+	(Including running relational databases, if you don't need the convenience of the RDS setup).
+
+	- Launch Instance
+		- Pick the Amazon Machine Image to start from
+			Server (AMI) Image Snapshots and Restore is also possible here if you did a lot of custom configuration,
+			but here we want to illustrate how to get going from a "blank" starter image
+			(i.e., first option: Amazon Linux AMI)
+		- Choose an Instance Type
+			For testing purposes, a micro instance should again be good enough.
+			If subsequently going to be doing some heavy computing, then pick a server with more CPUs and more RAM.
+		- Configure Instance Details
+			Most defaults are fine.
+			- Subnet / Availability Zone
+				As above, pick a consistent zone so all of your servers spawn in the same place
+		- Add Storage
+			Defaults to 8GB local SSD storage space.
+			Should be plenty for most development, but may need more if transferring large data files through.
+		- Add Tags
+			Primarily just to add a "Name" tag so you can keep track of your server(s) and what you wanted to do with them
+		- Configure Security Group
+			Allows for more fine tuned control of which ports each server will accept communication from
+			(e.g., SSH terminal + PostgreSQL databases + Apache web servers, etc.).
+			For basic testing purposes, should be good enough to use the existing Default security group.
+		- Public-Private Key Authentication
+			Rather than passwords, the default connection authentication is based on public-private key pairs.
+			You can just create your own private key file here if you have not done so before. Remember where you put it.
+		- SSH Connection
+			Under the EC2 View Instances section, find your server and can click on "Connect" for tips.
+			- Your private key file must NOT be readable by "everyone" or any "group" (indicates an unsecure file).
+			  This can be modifed under file properties "Get Info" or running chmod (e.g., chmod 400 privateKeyFile.pem).
+			- Use an SSH client to connect to the server (likely built in as a command-line executable)
+				`ssh -i "/path/to/privateKeyFile.pem" ec2-user@ec2-XX-XX-XX-XX.compute-1.amazonaws.com`
+
+				Note ec2-user is the default superuser name, but the address of the server will be different for you.
+				You should get a warning about the authenticity of the server/host not being established.
+				This makes sense since you've never connected to this server before, and you could be the
+				subject of a "man-in-the-middle" attack. Just agree to the connection for test/dev purposes,
+				but for real applications with security risks, you should authenticate the server fingerprint
+				by other channels.
+		- Install Libraries and Dependencies / Package Managers
+			(The `CDSS/setup/setup.sh` script is intended to facilitate many of the subsequent steps,
+			but makes some assumptions about the local setup that may not work as intended.)
+
+			Default server image has Python and some other utilities already installed,
+			but otherwise you'll be in an empty /home/ec2-user directory.
+			Highly advisable to use a package manager to take care of installing libraries and dependencies,
+			otherwise expect to end up in "dependency hell." For example, Linux servers such as here use "yum"
+			while Ubuntu servers have apt-get, Mac OS has Homebrew.
+			Windows doesn't have such a clean system at this point, so you may end up downloading
+			custom binary installers off the internet (https://www.lfd.uci.edu/~gohlke/pythonlibs/).
+
+			For simple Python packages, Python's PIP package manager works very well.
+			Beware of using the Anaconda Python distribution. If everything you need is included in Anaconda,
+			you'll be in good shape, but it may make things much more difficult if a subsequent dependency
+			(e.g., mod_wsgi web server connector) is not directly supported in the Anaconda distribution.
+
+			On the Amazon Linux servers, we will "sudo" (tell the superuser to do) an installation of several packages.
+
+			`sudo yum install git`			# So we can get our application source code
+			`sudo yum install python27-psycopg2`	# Allows Python to talk to a PostgreSQL database
+			`sudo yum install postgresql96`		# Optional: PostgreSQL binaries, including psql for connection testing
+
+		- Download Copy of Application Code Repository
+		    `git clone https://github.com/HealthRex/CDSS.git`
+
+	    - Setup PYTHONPATH so Python knows where to find Application Code
+	    	`export PYTHONPATH=/home/ec2-user/CDSS:$PYTHONPATH`
+
+	    	Or wherever you put the CDSS directory. Followed by $PYTHONPATH so if you already had anything set,
+		you'll still copy it here.
+		Better yet, add to the .bash_profile startup script so that it will run every time you connect to this server:
+	    		`echo "export PYTHONPATH=/home/ec2-user/CDSS:$PYTHONPATH" >> .bash_profile`
+
+	    	Or use a Unix text editor to add the export PYTHONPATH line
+	    		(`vi /home/ec2-user/.bash_profile`)
+
+	    - Setup LocalEnv.py file so the application code knows where to find the RDS database
+	    	Copy the CDSS/LocalEnv.py.template into a CDSS/LocalEnv.py configuration file.
+
+	    	Edit the contents of the LocalEnv.py file's LOCAL_PROD_DB_PARAM to refer to the RDS database you setup.
+	    	Look back to the configuration details of the RDS instance.
+				LOCAL_PROD_DB_PARAM["HOST"] = 'YourDBID.cwyfvxgvic6c.us-east-1.rds.amazonaws.com'	# "Endpoint"
+				LOCAL_PROD_DB_PARAM["DSN"] = "medinfo"		# "DB Name"
+				LOCAL_PROD_DB_PARAM["UID"] = 'postgres'		# "Username" default master account name at this time.
+				LOCAL_PROD_DB_PARAM["PWD"] = '<password>'	# Get this from someone else or LastPass. Or see "Version B's" LocalEnv.py file.
+
+
+=== Version B - Restore an EC2 Linux Server with Local Database ===
+If you just want to run some code against a local copy of a database, this will likely be a simpler, efficient approach.
+
+- Restore EC2 (Elastic Compute) Instance
+	- Pick the Amazon Machine Image to [Launch]
+		Find and select the image in EC2 > Images > AMIs section you want to restore. For example:
+		- stride-inpatient-2008-2014-postgresql - Has local PostgreSQL database installed with inpatient data from 2008-2014
+		- stride-inpatient-2008-2017-postgresql - Should be a superset of the above, updating dataset to 2017
+	- Additional Configuration and Connection Steps
+		See Version A above for the basic configuration steps, which should all basically be the same,
+		except for "Add Storage" there should be a default Root volume (100GB or larger) with a locally installed database.
+
+	- Pre-Installed Server Setup
+		Once the EC2 server is started / restored and you're able to connect to it, you should find several things there
+		~/CDSS directory with code repo. But run `git pull` to make sure it's up-to-date.
+		~/CDSS/LocalEnv.py should already have the connection parameters to the local database.
+		~/.bash_profile should already have some auto-startup configurations (runs everytime you connect to server).
+		    This should include command to start the local PostgreSQL server (`sudo service postgresql start`)
+
+		Depending upon your needs, you may still need to install or update some additional library dependencies
+		(e.g., `sudo yum install XXX` or `sudo python -m pip install XXX`).
+
+== Testing and Running (Batch) Processes ==
+On EC2 Linux Server:
+
+- Run a unit test to verify functional setup
+	`python -m medinfo.db.test.TestDBUtil`
+	(For more thorough testing, CDSS/TestCDSS.py will run all unit test modules in the medinfo directory tree.)
+
+- Test a simple database query from the application code
+	`python -m medinfo.db.DBUtil "select count(*) from clinical_item"`
+
+- Go to CDSS/scripts/DevWorkshop and Try running an example script to process data from database
+	`python ExampleQueryApp.py -c Lab -i A -p 0 -`
+
+	Should output a bunch of data rows to the console.
+	If you don't want to see it all, you can send the output to a text file
+
+	`python ExampleQueryApp.py -c Lab -i A -p 0 results/dataRows.tab`
+
+- Artificially simulate a slow/long process by adding in a 0.1 second pause between each result row
+	`python ExampleQueryApp.py -c Lab -i A -p 0.1 results/dataRows.tab`
+
+	When you get bored waiting for above to finish, Ctrl+C to abort the process
+
+- Run the process again, but do so in the background
+	`nohup python ExampleQueryApp.py -c Lab -i A -p 0.1 results/dataRows.tab &> log/progress.log &`
+
+	Above will run the process in the background (ending &) and continue even if you logoff (nohup = "no hangup").
+	So you can start a long process and just let the server continue to work on it,
+	without requiring you to keep your (laptop) client computer logged in.
+	Any error messages, progress indicators, or other text that you normally see in the console window will be
+	redirected (&>) to the specified log file (log/progress.log)
+
+- Check on the progress of the process you have running in the background
+	`ps -u ec2-user -f`
+		Checks which processes are running under the ec2-user, with full details. Note the Process ID (PID)
+	`kill <PID>`
+		If you need to kill a process that you don't want to continue anymore
+	`top`
+		Running monitor of all the most intensive processes running on the server
+		Overall reporting can track how much total free memory (RAM) the server still has available,
+		and how much processor (CPU) is being used. Helpful when trying to gauge the bottleneck for intensive processes
+		(need more processors or need more RAM?). Note that total CPU load can be >100% for servers with multiple CPUs.
+		The whole point of using a multi-processor server is that you should run multiple simultaneous (parallel) processes
+		to take advantage of extra CPUs working for you. You can't make a single process run at 200% speed with two CPUs,
+		but you can break up the work into two separate tasks, and have each running at 100% on separate CPUs.
+		Beware that the multiple processors are both using the same shared memory (RAM), so if you have a process
+		that uses a lot of RAM, parallelizing the process will also multiply the amount of total RAM needed.
+
+		"M" to sort the results by which processes are using the most memory
+		"q" to quit/exit when done.
+	`cat log/process.log`
+		Show the output of the redirected console output from your application process
+	`tail -f log/process.log`
+		Show just the last few lines of the redirected console output, and continue watching it until Ctrl+C to abort.
+		(Ctrl+C will abort the "tail" monitoring process, not the original application process.)
+
+- Use a batch driver script to run multiple (parallel) processes
+	`bash batchDriver.sh`
+
+	Though bash (.sh) scripts are more common, I often prefer Python when it can do all of the above, is more flexible,
+	platform independent, and unifies the programming/scripting language used.
+	For example, rather than copy-pasting a dozen similar but different command line calls in the batchDriver.sh script,
+	I use a Python loop to effectively generate those commands in the script below and spawn them via the subprocess module.
+
+	`python batchDriver.py`
+
+	If you prefer serial, rather than parallel, processes for more control.
+	Remove the "nohup &" background commands from the .sh script, or change the Python subprocess.Popen to subprocess.call.
+	You may then want to run the batchDriver itself as a background process with a redirected log file:
+
+	`nohup python batchDriver.py &> log/driver.log &`
+
+	Additional support functionality:
+		medinfo/common/support/awaitProcess.py - Wait until an existing process completes before starting another one
+		medinfo/common/ProcessManager.py - Not implemented yet (5/14/2018). Intended to consolidate above support functionality.
+
+	Large compute clusters often have their own job submission and parallelization schemes (e.g., qsub, bsub grid engines).
+	AWS for example has Elastic Map Reduce (EMR) and AWS Batch services. Depending on the scale of your needs,
+	you may want to look into such services. Otherwise, you can get a lot done cheaply by just taking advantage of
+	multiple CPU servers as above. For example, once you've got your EC2 compute setup, create a SnapShot AMImage,
+	then restore that image onto a new, bigger server with dozens more CPUs and then just run your processes on that server.
+	Don't forget we're paying for these servers by the hour, but the pricing is proportional to capacity.
+	Given that proportionality, you can pay twice as much for twice as many CPUs that will get your job done in half the time.
+	This is perfectly worth it since the amount of dollars spent is the same, but you save half your human time waiting for results.
diff --git a/scripts/DevWorkshop/ReadMe.Git.BasicVersionControl.txt b/scripts/DevWorkshop/ReadMe.Git.BasicVersionControl.txt
new file mode 100644
index 00000000..086127bf
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.Git.BasicVersionControl.txt
@@ -0,0 +1,63 @@
+Visualization of Git version control commands.
+https://twitter.com/MJAlwajeeh/status/984177298076262401?s=20
+
+See CDSS/scripts/DevWorkshop/GithubWorkshop for additional examples.
+Otherwise, quick test commands here:
+
+
+# Get a copy/clone of the code repository to work with
+	git clone https://github.com/HealthRex/CDSS.git
+
+# Pull any latest updates or changes from the master online repository to your local copy
+	git pull
+
+# Identify yourself to the GitHub repository
+	git config --global user.email "yourGitHubEmail"
+	git config --global user.name "yourGitHubUserName"
+
+# Try creating a file in the sandbox directory
+	cd CDSS/scripts/DevWorkshop/sandbox
+	vi yourTestFile.txt
+
+	    Quick vi commands
+	    - x: Delete a character
+	    - i: Change to "insert" (i.e., edit mode)
+	    - Escape: Exit out of edit mode
+	    - ":wq": Change to "colon-command" mode, then (w)rite your changes to the file, then (q)uit the editor
+	    - ":q!": Change to "colon-command" mode, (q)uit the editor, WITHOUT saving changes (!)
+
+# Tell Git that you want this file to be tracked
+	git add yourTextFile.txt
+
+# Save the version of the file to your LOCAL repository
+	git commit yourTextFile.txt
+		# It will ask for a commit message. Add a 1 or 2 liner comment describing the major changes you made with this commit
+
+# Save your local repository changes to the master online repository
+	git push
+		# It may ask for your GitHub login info. You can later configure ways to automatically do this
+		# It may complain about your local repository not being up to date if others have already pushed their own changes.
+		# If so, then first update your own local copy...
+
+# Update your local working code with whatever has been pushed to the master online repository
+	git pull
+
+
+# Try making further edits to one of the files (including ones created by others), and manually correcting any merged file conflicts
+	vi yourTestFile.txt
+
+# Review what changes have been made to the file since it was last commit/pushed
+	git diff yourTestFile.txt
+
+# Commit changes to local repository with comment message at the same time
+	git commit -m "Test revision message" yourTestFile.txt
+
+# Push your changes up to the server again
+	git push
+
+# Check a log of all the changes made to the file
+	git log yourTestFile.txt
+	
+
+# Git contains much more advanced functionality for code branches and remerges, but likely excessive for small-medium sized teams
+# Essentially all code content, logs, as well as a Wiki + basic Issue and Project Tracker are accessible on the GitHub website.
diff --git a/scripts/DevWorkshop/ReadMe.GoogleCloud-BigQuery-VPC.txt b/scripts/DevWorkshop/ReadMe.GoogleCloud-BigQuery-VPC.txt
new file mode 100644
index 00000000..193573c6
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.GoogleCloud-BigQuery-VPC.txt
@@ -0,0 +1,202 @@
+= Dev Workshop =
+Accessing GoogleCloud BigQuery within Virtual Private Cloud
+(Safe for Protected Health Information)
+
+== Learning Goals ==
+- Review tiers of access restriction for moderate to high-risk patient data
+  https://uit.stanford.edu/guide/riskclassifications
+- Access Control requests
+- Web GUI access to BigQuery databases
+- Virtual Private Network connection into PHI secure Virtual Private Cloud
+- Programmatic (Python) API access to BigQuery databases
+
+== Preconditions ==
+- Know how to run command line programs and set environment variables
+- Python Installed
+
+Recommended to save time during workshop:
+- Access granted to Virtual Private Cloud containing BigQuery database
+- Google Cloud SDK Installed
+  https://cloud.google.com/sdk/install
+- Google Cloud - Python API Libraries Installed
+  python -m pip install --upgrade google-cloud-bigquery
+
+== Workshop Steps ==
+- Review tiers of access restriction for moderate to high-risk patient data
+  * Name (non-obvious) data elements that qualify as Protected Health Information 
+      (and thus High-Risk Data that require stronger security)
+    - Real dates (relative times or date shifted / jittered okay)
+    - Location more precise than 3 digit ZIP code (including hospital bed numbers)
+    - Age when >90 years old
+    - Any freely entered text fields (risk of incidentally embedded PHI written in a text field)
+
+  * Distinguish between public Google Cloud Platform, Stanford-only GCP, Stanford-VPC GCP
+    https://github.com/HealthRex/CDSS/wiki/Google-BigQuery-Access
+
+=== Stanford-only GCP project "mining-clinical-decisions" (still not safe for PHI) ===
+- Access Control requests
+  If you want in this club, complete the data usage requirements first and have a group sys admin grant 
+  BigQuery User and Viewer privileges through the GCP interface linked below.  
+  https://github.com/HealthRex/CDSS/wiki/Data-usage-training-and-agreements
+  https://console.cloud.google.com/iam-admin/iam?authuser=1&project=mining-clinical-decisions
+  
+- Web GUI access to BigQuery databases
+  Connect to the project through a web browser  
+  https://console.cloud.google.com/bigquery?authuser=1&project=mining-clinical-decisions
+  - Note that you have to select your stanford.edu account login in the top-right
+  - Note that you may have to select the project name from the drop list on the top-middle nav bar
+
+
+  It should fail with an error like: "VPC Service Controls: Request is prohibited by organization's policy..."
+  To get through, you will first need to Virtual Private Network (VPN) connection into a Stanford secured network:
+
+- Virtual Private Network connection into PHI secure Virtual Private Cloud
+  Download and run the respective VPN client from the link below
+  https://uit.stanford.edu/service/vpn
+  
+  Login (SUNetID and password with two-factor authentication) through the VPN client and be sure to select
+  "Full Traffic non-split-tunnel" for the Group option.
+  
+  Try refreshing your web browser to reconnect through the web GUI to the BigQuery project database again.
+  You can continue to do any regular work in the meantime, though technically any internet traffic 
+  you're conducting is not being sent direct. It is being encrypted and sent through the Stanford 
+  secure network first and then relayed on to the actual websites or email servers you're trying to reach. 
+  This allows your computer now to mimic being within the secured virtual private network.
+
+- IPv4 vs. IPv6
+  If you're still getting VPC access control errors despite the above (particularly when logging in from home),
+  be sure your network adapter is set to use IPv4 rather than IPv6. 
+  This reflects an updated version of the way internet addresses are allocated, 
+  but looks like GCP VPC protocols don't accomodate for the newer system yet.
+  
+  Apple Systems:
+  - Go to Apple - > System Preferences -> Network
+  - Select the current network connection listed on the left-hand side, then click the Advanced button.
+  - Go to the TCP/IP tab at the top
+  - Beside "Configure IPv6", set it to "Link-local Only" and "Apply"
+  
+  Windows Systems:
+  - Settings > Network & Internet
+  - Change Adapter Options
+  - Select your WiFi or Ethernet or other network connection device
+  - Properies
+  - Uncheck "Internet Protocol Version 6 (TCP/IPv6)"
+
+- VPN Group option:
+  If you're still getting VPC access control errors despite the above,
+  try to go back and change the group option from "Full Traffic non-split-tunnel" to "Stanford Default ..." and reconnect the vpn. 
+
+- Test Queries
+  - See bottom left for lists of projects, which contain lists of datasets/databases, 
+    which contain lists of tables that you can query.
+    (You may need to manually search for the project "mining-clinical-decisions" and "Pin" it for later use.)
+    For example:
+
+          SELECT *
+          FROM starr_datalake2018.order_med
+          LIMIT 100;
+
+=== Stanford-VPC GCP project "som-nero-phi-jonc101" (safe for PHI) ===
+- Access Control requests
+  If you want in this club, you will further need to be added to a respective IRB that allows for 
+  PHI access and have a group sys admin grant membership to the 
+  nero:jonc101-bq-users workgroup (or subgroups for specific datasets) via the link below. 
+  If additional privileges are needed, they need to be requested through 
+  Stanford Research Computing Center support <srcc-support@stanford.edu>
+  https://workgroup.stanford.edu/main/WGApp
+
+- Web GUI access to BigQuery databases
+  Try connecting to the secure project through your browser similar to above...
+  https://console.cloud.google.com/bigquery?authuser=1&project=som-nero-phi-jonc101
+
+- Programmatic (Python) API access to BigQuery databases (Google Cloud SDK)
+  You will need to create a local "Application Default Credentials" JSON key file 
+  to identify yourself in your (Python) programs as 
+  if you want them to access these secured databases.
+  https://cloud.google.com/docs/authentication/getting-started
+  https://cloud.google.com/docs/authentication/best-practices-applications
+
+  - Find, download and run the respective Google Cloud SDK installer for your system from the link below. 
+    (If you already have Python installed on your system, you can skip that dependency)
+    https://cloud.google.com/sdk/install
+  - Run the Google Cloud SDK initializer from command-line 
+
+    gcloud init
+
+  - Run the Google Cloud application authentication
+
+    gcloud auth application-default login
+    
+    This should spawn a web browser (or create a web link you can use) to login as a specific user 
+    After completing the above, go back to your command terminal and it should report a message 
+    that it created a JSON key file in a local directory 
+    E.g., "Credentials saved to file: [C:\Users\jonc1\AppData\Roaming\gcloud\application_default_credentials.json]"
+    (recommend you rename it to something that includes your user name, and store it in a place you will remember).
+
+  - Install Python-Google Cloud connection libraries with the PIP installer
+
+    python -m pip install --upgrade google-cloud-bigquery
+
+  - Create a local environment variable to tell the Google Cloud libraries where to find the key file
+    Python code in medinfo tree can just import LocalEnv.py that can set the environment variable at runtime
+    Mac/Linux: export GOOGLE_APPLICATION_CREDENTIALS=[PathToKeyFile]
+    Windows(cmd): set GOOGLE_APPLICATION_CREDENTIALS=[PathToKeyFile] 
+       (Replace [PathToKeyFile] with the location of the JSON key file created in the prior step)
+
+  - Try connecting to a secured project BigQuery database and query for some data
+    
+    Python
+    >>> from google.cloud import bigquery;
+    >>> from google.cloud.bigquery import dbapi;
+    >>> client = bigquery.Client("som-nero-phi-jonc101"); # Alternatively, set GOOGLE_CLOUD_PROJECT environment variable
+    >>> conn = dbapi.connect(client);
+    >>> cursor = conn.cursor();
+    >>> query = "select rit_uid, gender, birth_date_jittered from `starr_datalake2018.demographic` limit 10"; # Example dataset table
+    >>> cursor.execute(query);
+    >>> results = cursor.fetchall();
+    >>> for row in results:
+    ...   print( row );
+
+    Or if you prefer Pandas dataframes
+    >>> import pandas as pd;
+    >>> resultsDF = pd.read_sql_query(query, conn);
+    >>> print( resultsDF );
+    
+    If getting errors about access issues (e.g., "VPC Service Controls: Request is prohibited by organization's policy"),
+    make sure you're able to query through the BigQuery web interface and review above steps.
+    - Did you fix your internet connection to use IPv4 instead of IPv6 protocol?
+    - Did you connect to the VPN?
+    - Did you login through your stanford.edu address, not gmail.com?
+    Consider saving the above python code snippets as a small script file so you don't have to keep retyping it, 
+    you can just quickly rerun them to test variations.
+
+
+== See Also ==
+Notes on connecting to BigQuery databases through R API
+https://cloud.google.com/blog/products/gcp/google-cloud-platform-for-data-scientists-using-r-with-google-bigquery
+
+
+Support Notebooks in Jupyter for examples of tips and tricks for reviewing and manipulating datasets.
+https://code.stanford.edu/starr/nero-starr-notebooks
+
+
+If you set the CDSS code tree LocalEnv.DATABASE_CONNECTOR_NAME parameters, 
+then medinfo.db.DBUtil.connection should take care of setting up a Python DB-API compliant connection object 
+to the BigQuery database (not all functionality supported at this time).
+
+If you need to do direct manipulations against the database 
+(e.g., creating and renaming tables, importing data, etc.), 
+then you may want to directly use the BigQueryClient interface. 
+Otherwise I recommended sticking to the generic DB-API interface, 
+so you remain cross-platform compatible with other databases.
+
+
+Alternative option to create service account authentication keys instead of user authentication keys
+https://cloud.google.com/bigquery/docs/authentication/
+          
+===== Troubleshooting =====
+Here, common issues that we faced while using the google cloud are presented and appropriate solutions and troubleshootings are described.
+
+Issue: I should have access to database X (for example som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021), but cannot find the database when I search for it.
+Solutions: Try typing only the database name (Jon_Chen_data_Oct_2021 in this example) in the search box on the top middle search bar (in the middle of the top blue ribbon). 
+          
diff --git a/scripts/DevWorkshop/ReadMe.GoogleCloudDevEnvironment.txt b/scripts/DevWorkshop/ReadMe.GoogleCloudDevEnvironment.txt
new file mode 100644
index 00000000..2f3f7b1a
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.GoogleCloudDevEnvironment.txt
@@ -0,0 +1,287 @@
+= Dev Workshop =
+Google Cloud and Compute Instance Setup
+
+== Learning Goals ==
+- Setting up Google Cloud Compute Instances
+    - SSH remote connections + public-private key security
+    - Setting up a Linux Instance
+- Running queries on Linux server
+    - Process management to allow (parallel) processes to keep running in background while logged off
+    - (nohup command &> logFile &)
+
+== Preconditions ==
+- Google Stanford Account with VPN setup
+- Project (mining-clinical-decisions) Permissions, including *Compute Engine Editor*
+  (See ReadMe.GoogleCloud-BigQuery-VPC.txt DevWorkshop)
+
+== Workshop Steps ==
+=== Setting up a Compute Engine Instance from Scratch ===
+- Login to the Google Cloud Platform
+    https://console.cloud.google.com/home/dashboard?project=mining-clinical-decisions&authuser=1
+  - Make sure connected as your stanford.edu account
+  - Pick the correct GCP Project on the top nav bar ([Mining Clinical Decisions] depending on access needed)
+
+- Top Left Menu > [Compute Engine] > [VM instances]
+
+- Startup a Compute VM Instance
+  - [Create Instance] under the top nav bar header
+    - Name
+    - Region: A region close by can reduce network latency, but more important is consistency (e.g., us-central1-a)
+    - Zone: As above, pick a consistent zone so all of your servers spawn in the same place
+    - Machine Configuration: What type of hardware do you want to use
+      - Series + Machine Type
+        Different machines types cost different amounts based on how many CPUs and RAM you need
+        For testing purposes, a small g1-small should be sufficient (the smallest f1-micro looks like it doesn't have enough RAM for subsequents steps)
+    - Boot Disk: Allow specification of different default Operating Systems (default Debian GNU/Linux for now)
+    - Identity and API access
+      Select 'mining-clinical-dev' for simplicity for now, 
+      as this gives read/view and create job access the BigQuery databases through a Service Account 
+      without requiring individual login key files.
+      See accompanying workshop on Google-VPC access on setting youe
+      GOOGLE_APPLICATION_CREDENTIALS environment variable to a generated JSON key file
+      for individual end user based login.
+    - Create
+
+- Accessing your Compute Instance:
+  SSH secure shell terminals allow for secure encrypted command-line access to computers
+  This requires SSH public-private key pairs or a login/password (though the latter is not as secure)
+
+  - Option 1 - Using the browser based SSH client
+    - Find the compute instance you created under Compute Engine > VM Instances
+    - Click the [SSH] Connect option to the right of your instance name
+      This should spawn a terminal console window that logs you into the 
+      home directory of a Linux server where you can interactively enter shell commands (e.g., ls, mkdir, pwd)
+
+  - Option 2 - Using gcloud/SSH command-line tools
+    Requires gcloud installation (see ReadMe.GoogleCloudDevEnvironment.txt DevWorkshop)
+  	From your local command-line terminal/console:
+			
+      gcloud init
+			- Pick configuration (typically 1)
+			- Choose account (stanford.edu)
+			- Pick a cloud project (mining-clinical-decisions)
+			- Select the region associated with instance (optional)
+			
+      From the browser VM list, look under the other menu options after the [SSH] default Connect option, 
+      including the gcloud command line option. This should an SSH terminal console to the compute instance.
+      You should get a warning about the authenticity of the server/host not being established.
+      This makes sense since you've never connected to this server before, and you could be the
+      subject of a "man-in-the-middle" attack. Just agree to the connection for test/dev purposes,
+      but for real applications with security risks, you should authenticate the server by other channels.
+
+        gcloud compute ssh <instanceName>
+
+- Install Libraries and Dependencies / Package Managers
+  You'll need software packages and dependencies installed on the compute instance for development work
+  Default system comes with Python 3 installed ("python3") (as of 1/29/2021),
+  but is largely barebones in terms of other software packages. Using package management software tools,
+  such as apt and pip in this case, are essentially for easing the pain of "dependency hell."
+
+  Run the following commands to install several dependencies:
+
+    sudo apt update                               # Tell the "superuser" admin to "do" an update of the apt tool
+    sudo apt install git                          # Git client to communicate with source code version control repository
+    sudo apt install python3-pip                  # Install PIP tool for Python package management
+    python3 -m pip install google-cloud-bigquery  # Python - Google Cloud interface to connect to BigQuery databases 
+                                                    ***This takes over 10 minutes. Consider opening a second terminal connection to the compute instance so you can continue the subsequent steps in parallel
+    python3 -m pip install google-cloud-bigquery-stroage
+    python3 -m pip install --prefer-binary pyarrow		# Dependency for reading bigquery results
+
+    python3 -m pip install pandas                 # Popular Python package for manipulating tabular dataframes
+
+- Download a Copy of the Application Code Repository
+	
+  git clone https://github.com/HealthRex/CDSS.git
+
+- Setup PYTHONPATH so Python knows where to find Application Code
+  Enter the following shell command to create/update a PYTHONPATH shell environment variable
+  to tell Python where to look for code package and module imports beyond the current working directory.
+
+    export PYTHONPATH=/home/yourUserName/CDSS:$PYTHONPATH
+
+  Or wherever you put the CDSS directory. Followed by a $ reference to any existing $PYTHONPATH 
+  so if you already had anything set, you'll copy the prior value and just preprend your additions here.
+  Better yet, append the command to the .bash_profile script:
+
+    echo "export PYTHONPATH=/home/yourUserName/CDSS:$PYTHONPATH" >> .bash_profile
+
+  Or use a Unix text editor to edit the profile script (`vi /home/yourUserName/.bash_profile`)
+  The .bash_profile script (the . prefix indicates a hidden system file you can find with `ls -la`)
+  will run every time you connect to the server, or you can direct invoke it with:
+
+    source .bash_profile
+
+- Setup LocalEnv.py file so the application DBUtil knows how to find the BigQuery database
+  
+  Copy the CDSS/LocalEnv.py.template into a CDSS/LocalEnv.py configuration file.
+    cp CDSS/LocalEnv.py.template CDSS/LocalEnv.py
+
+  Edit the contents of the LocalEnv.py file to refer to the database of interest.
+  You can use the not great, but ubiquitous "vi" editor on Unix systems
+    vi CDSS/LocalEnv.py
+
+    Quick vi commands
+    - x: Delete a character
+    - i: Change to "insert" (i.e., edit mode)
+    - Escape: Exit out of edit mode
+    - ":wq": Change to "colon-command" mode, then (w)rite your changes to the file, then (q)uit the editor
+    - ":q!": Change to "colon-command" mode, (q)uit the editor, WITHOUT saving changes (!)
+    
+  Most of the other settings can be ignored, as they are for other environments (e.g., PostgreSQL databases)
+  
+    DATABASE_CONNECTOR_NAME = "bigquery"
+
+    LOCAL_PROD_DB_PARAM["HOST"] = 'mining-clinical-decisions"
+
+== Testing and Running (Batch) Processes ==
+On GCP Linux Server:
+
+- Test Run an Application Module that Connects to Database
+  Should report the number of patient records in the example dataset
+
+    python3 -m medinfo.db.DBUtil "select count(*) from starr_datalake2018.demographic"
+
+- Running an example script to process data from database and manage intermediate results files
+  - Go to CDSS/scripts/DevWorkshop/GoogleCloudPlatform 
+  - Run the following commands
+
+    python3 ExampleQueryApp.py -r Intravenous -d CEF -p 0 -
+
+  Should output a bunch of data rows to the console.
+  If you don't want to see it all, you can send the output to a text file
+
+    python3 ExampleQueryApp.py -r Intravenous -d CEF -p 0 results/dataRows.tab
+
+  For potentially very large (intermediate) data files, consider storing them in gzipped format
+  by piping the output through the gzip program and redirecting to a respectively named file.
+
+    python3 ExampleQueryApp.py -r Intravenous -d CEF -p 0 - | gzip > results/dataRows.tab.gz
+
+  Note that this functionality is already embedded in medinfo.common.Util.stdOpen to transparently
+  treat any text files with the .gz suffix as presumptive gzipped content and treat "-" as stdin or stdout.
+
+    python3 ExampleQueryApp.py -r Intravenous -d CEF -p 0 results/dataRows.tab.gz
+
+  You can similarly then decompress and read the contents of gzipped files on-demand
+
+    gzip -dc results/dataRows.tab.gz
+
+
+
+- Artificially simulate a slow/long process by adding in a 0.5 second pause between each result row
+
+    python3 ExampleQueryApp.py -r Intravenous -d CEF -p 0.5 results/dataRows.tab
+
+  When you get bored waiting for above to finish, Ctrl+C to abort the process
+
+- Run the process again, but do so in the background
+
+    nohup python3 ExampleQueryApp.py -r Intravenous -d CEF -p 0.5 results/dataRows.tab &> log/progress.log &
+
+  Above will run the process in the background (ending &) and continue even if you logoff (nohup = "no hangup").
+  So you can start a long process and just let the server continue to work on it,
+  without requiring you to keep your (laptop) client computer logged in.
+  Any error messages, progress indicators, or other text that you normally see in the console window will be
+  redirected (&>) to the specified log file (log/progress.log)
+
+- Check on the progress of the process you have running in the background
+  `ps -u yourUserName -f`
+    Checks which processes are running under your username, with full details. Note the Process ID (PID)
+  `kill <PID>`
+    If you need to kill a process that you don't want to continue anymore
+  `top`
+    Running monitor of all the most intensive processes running on the server
+    Overall reporting can track how much total free memory (RAM) the server still has available,
+    and how much processor (CPU) is being used. Helpful when trying to gauge the bottleneck 
+    for intensive processes (need more processors or need more RAM?).
+    Note that total CPU load can be >100% for servers with multiple CPUs.
+    The whole point of using a multi-processor server is that you should run 
+    multiple simultaneous (parallel) processes to take advantage of extra CPUs working for you.
+    You can't make a single process run at 200% speed with two CPUs,
+    but you can break up the work into two separate tasks, and have each running at 100% on separate CPUs.
+    Beware that the multiple processors are both using the same shared memory (RAM), so if you have a process
+    that uses a lot of RAM, parallelizing the process will also multiply the amount of total RAM needed.
+
+    "M" to sort the results by which processes are using the most memory
+    "q" to quit/exit when done.
+  `cat log/progress.log`
+    Show the output of the redirected console output from your application process
+  `tail -f log/progress.log`
+    Show just the last few lines of the redirected console output, 
+    and continue watching it until Ctrl+C to abort.
+    (Ctrl+C will abort the "tail" monitoring process, not the original application process.)
+
+- Use a batch driver script to run multiple (parallel) processes
+  (Beware that this is likely to overwhelm the RAM (memory) available on your tiny test compute instance)
+
+  `bash batchDriver.sh`
+
+  Though bash (.sh) scripts are more common, I often prefer Python when it can do all of the above, 
+  is more flexible, platform independent, and unifies the programming/scripting language used.
+  For example, rather than copy-pasting a dozen similar but different command line calls in batchDriver.sh,
+  use a Python loop to dynamically generate those commands and spawn them via the subprocess module:
+
+  `python3 batchDriver.py`
+
+  If you prefer serial, rather than parallel, processes for more control.
+  Remove the "nohup &" background commands from the .sh script, or change the Python subprocess.Popen to subprocess.call.
+  You may then want to run the batchDriver itself as a background process with a redirected log file:
+
+  `nohup python3 batchDriver.py &> log/driver.log &`
+
+  Additional support functionality:
+    medinfo/common/support/awaitProcess.py - Wait until an existing process completes before starting another one
+    medinfo/common/ProcessManager.py - Not implemented yet (5/14/2018). Intended to consolidate above support functionality.
+
+  Large compute clusters often have their own job submission and parallelization schemes (e.g., qsub, bsub grid engines).
+  Depending on the scale of your needs, you may want to look into such services. 
+  Otherwise, you can get a lot done cheaply by just taking advantage of multiple CPU servers as above.
+  For example, once you've got your compute instance setup, create a SnapShot image of the hard disk,
+  then restore that image onto a server with dozens more CPUs and then just run your processes on that server.
+  We're paying for these servers by the hour, but the pricing is proportional to capacity.
+  Given that proportionality, you can pay twice as much for twice as many CPUs that will get your
+  job done in half the time. This is perfectly worth it since the amount of dollars spent is the same,
+  but you save half your human time waiting for results.
+
+
+
+
+OPTIONAL LEARNING 
+- SnapShots and Machine Images
+  	Useful when you need more/less compute or want to backup your virtual machine setup at a particular time:
+
+    Option 1 - SnapShots (only saves changes compared to last copy, reducing storage costs)
+    - Store / Save a copy of a currently running compute engine instance hard disk as a SnapShot
+      - Compute Engine > Disks > (Find your running Instance) > [Create Snapshot]
+    - Restore / Spawn a copy of a compute engine instance that was previously saved as a SnapShot
+      - Compute Engine > Snapshots > (Find the snapshot of interest) > [Create Instance]
+        Most settings will be similar to creating a new compute instance, 
+        but note how you can choose a "bigger" computer with more CPU, RAM but still have the same
+        Boot hard disk / code setup ready to go.
+
+    Option 2 - Machine Images (includes copy of entire hard disk and operating system)
+    - Store / Save a copy of a currently running compute engine instance
+      - [New Machine Image] from the ... menu for your running instance
+    - Restore / Spawn a copy of a compute engine instance that was previously saved as a Machine Image
+      - Machine Image section > Create an Instance
+
+
+
+
+
+- Notes on How to Upload/Download any File
+  - Option 1 - Using SSH/Web Browser client
+    - Top right corner of window has a "Gear" icon with a Upload and Download file option
+    - To download specify your path and file you wish to download: for example:
+      - ("/home/yourUserName/CDSS/scripts/DevWorkshop/ReadMe.GoogleCloudDevEnvironment.txt")
+    - To upload:
+      - select 'Upload file' and select the file you wish to upload to the current working directory
+
+  - Option 2 - Using gcloud from you local command-line console to simulate an SCP (secure copy) command
+    
+    gcloud compute scp yourLocalFile.txt <instance-name>:/home/yourUserName/yourRemoteFileCopy.txt
+
+
+
+- Nero Compute GCP Cheat Sheet for PHI Secure Compute Version?
+https://docs.google.com/drawings/d/14Ixgar6XTSWiXwlBG16AFNxpZzPl2gNrF7P3U4s7JHc/edit?usp=sharing
diff --git a/scripts/DevWorkshop/ReadMe.PHSDataAccess.txt b/scripts/DevWorkshop/ReadMe.PHSDataAccess.txt
new file mode 100644
index 00000000..94be7ac9
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.PHSDataAccess.txt
@@ -0,0 +1,108 @@
+
+
+
+1) PHS Access to Restricted Datasets:
+2) (Optional) Install Cardinal Key:
+	- a digital certificate that is installed on a device and provides a user’s
+		identity to a remote server in place of a SUNet ID and password
+	- https://phsdocs.developerhub.io/software-installation/cardinal-key
+3) Sign up for PHS Data Portal Account
+	 	- Go to https://redivis.com/StanfordPHS/ and click on create account
+			- additional information:
+			- https://phsdocs.developerhub.io/computing-environment/quick-tour-phs-data-portal
+		- Strongly recommend signing up for the PHS data slack channel
+		- https://docs.google.com/document/d/1n304k9zMM3dji_ngJ9SQBovllK_s7zIg-du2XoRpQPc/edit
+5) Getting Access to Dataset:
+	- a) apply for permissions
+	- b) Levels of Access:
+		- overview access
+		- metadata access
+		- data access
+	- c) Go to https://redivis.com/StanfordPHS/datasets
+	- d) Select Data Access on Restricted Datasets you want access to
+
+		E.g., Optum databases, note they are separated into versions that include Socioeconomic Status (SES), ZIP code, Date of Death, specifically because they do not want to allow you to reconstruct records with all of that information at one time (too identifying).
+
+		IBM (previously Truven) MarketScan databases, including some supporting lookup databases like Redbook for pharamacy category data. Not as up to date as Optum and has some bad Diagnosis coding data structure based around encounters.
+
+	- e) Complete steps for metadata access and data access
+		- typically training
+		- encryption verification
+		- study approval: "HealthRex and Diagnostics" has already been approved
+
+		Once access granted, can "Add Dataset" to "Projects" which will bring to Redivis graphical tree view to query 1% or 100% data samples
+		Not allowed to download data or results from here, but enough to query and review high level reports.
+
+		Can hack together queries through graphical interface, or click on code symbol button ("</>") to enter direct SQL queries.
+		Add a "Transform" to a table of interest, which is basically a SQL query that can be custom edited and run, to generate a new Table of results.
+		For large databases, note the different between full database and 1% sample (suffix ":sample" to the table name)
+	- Note that there is a channel in the PHS slack called #access-admin if you have difficulty / substantial delays with any step
+
+
+
+Creating API credentials:
+	- Credentials enable you to access PHS projects: The token will be  used in scripts (R and Python) for authorization
+			- https://redivis.com/workspace/settings
+	- Scroll down to API tokens:
+	- Generate new token
+	- Select all access measures(all boxes): Public, Data, Organization: including edit/read
+	- Save token to a file somewhere secure for reference later:
+
+Accessing Nero Computing Environment:
+
+Prereq:
+	Stanford Sunet:
+	PI emails srcc-support@stanford.edu for Nero Account
+		- https://nero-docs.stanford.edu/user-prerequisites.html#submit-request-for-nero-account
+		(Specify Jonathan Chen as PI / research group / supervisor and CC jonc101@stanford.edu)
+
+
+Accessing PHS Data through Nero Compute:
+
+1) GO TO: nero.compute.stanford.edu
+	- make sure your stanford vpn is on (See notes from GoogleCloud VPC DevWorkshop)
+	- https://nero-docs.stanford.edu/jupyter.html
+2) Open a terminal under 'Other'   (Or if happy with base R, Stata, Jupyter notebook, etc. can use those interfaces via a "New Launcher...")
+
+# https://apidocs.redivis.com/client-libraries/redipy.bigquery
+3) git clone https://github.com/redivis/redipy.git		# Works for other GitHub repositories like general lab repo
+4) export PYTHONPATH=/home/[SUNETID]/redipy/bigquery;$PYTHONPATH		# Better yet, add this to .bash_profile (or .bashrc if not autorun, or just do `source .bash_profile` when startup)
+
+
+6) enter python console: type python
+7) in python:
+	import os
+	from redivis import bigquery 	# or from google.cloud import bigquery
+	import pandas
+	os.environ['REDIVIS_API_TOKEN'] = "[TOKEN_FROM_REDIVIS]"	# Better yet, add to .bash_profile. Note the token is not a file, it's a string of characters that should be copied directly.
+	client=bigquery.Client()
+	# Perform a query.
+	# Table at https://redivis.com/StanfordPHS/datasets/1411/tables
+	QUERY = 'SELECT * FROM `jonc101.test_project_multi_user:1.query_counts_for_diabetic_med_class_claims_by_year_output:7` LIMIT 10'
+	#QUERY = ('SELECT * FROM `stanfordphs.optum_zip5:139:v3_0.rx_pharmacy:9` LIMIT 10')	# Of can query from your own named sample/tables from your Redivis UI project or some general table name you have access to? Permissions note allowed to directly query off the source tables?
+	df = client.query(QUERY).to_dataframe()  # API request
+	print(df)
+
+Accessing through Jupyter Notebook:
+	- select home directory on left toolbar:
+	- select your sunetid folder: ex: (jichiang)
+	- select redipy
+	- select bigquery
+		- this will bring you into home/SUNETID/redivis/bigquery folder:
+	- select jupyer notebook on the middle panel:
+	- Repeat steps in 7 to copy code into jupyter noteboook
+	
+	
+Install new packages:
+	-R packages: http://nero-docs.stanford.edu/jupyter-installing-r-packages.html
+	-Python packages: http://nero-docs.stanford.edu/anaconda_usage.html
+	
+Tips on working with PHS data:
+-The export limit is 1 GB (though the size may change) - querying PHS data using the API from Nero counts as an export! This means that you cannot query most of the original tables in Optum (or other datasets) as they are usually >1 GB
+-Two possible solutions are: 
+i) you can request an exemption to the export size (there's a button in the upper right corner of the Redivis GUI) - I had no issues getting these quickly approved so long as I clarified that I was requesting the exemption to query the tables from Nero (and NOT to export data >1 GB onto my local machine)
+ii) you can create smaller tables in the Redivis GUI and query those
+-If you're using the BigQuery API, it is much faster to do data processing/cleaning via SQL commands since BigQuery is optimized for speed. Doing larger queries, converting the result to a dataframe, and then doing data processing is much slower
+
+
+
diff --git a/scripts/DevWorkshop/ReadMe.PivotCharts.txt b/scripts/DevWorkshop/ReadMe.PivotCharts.txt
new file mode 100644
index 00000000..b6053330
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.PivotCharts.txt
@@ -0,0 +1,42 @@
+= Dev Workshop =
+Pivot Charts - Pivot Tables - Quick Data Manipulation and Visualization in Excel
+
+== Learning Goals ==
+- Long vs. Wide Data Formats
+- Excel Table Controls (Freeze Panes, Auto-Filter)
+- Excel VLOOKUP (code table reference)
+- Excel Pivot Tables and Pivot Charts
+- Raster vs. Vector Graphics
+
+== Preconditions ==
+- Microsoft Excel installed (or your preferred data manipulation environment)
+- Review the Difference between "Wide" vs. "Long" Data Formats
+  https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/
+
+== Workshop Steps ==
+- Download and review Figure 1a and 2a from the linked paper to see an example of what we're trying to reproduce
+https://informaticssummit2018.zerista.com/event/member/470474
+
+- Create a new blank Excel Workbook
+- Copy
+  [sampleData/labConsecutiveNormalStats.tsv](https://github.com/HealthRex/CDSS/blob/master/scripts/DevWorkshop/sampleData/labConsecutiveNormalStats.tsv) and
+  [sampleData/labBaseDisplayNameLookup.tsv](https://github.com/HealthRex/CDSS/blob/master/scripts/DevWorkshop/sampleData/labBaseDisplayNameLookup.tsv)
+  (Tab-separated files, like CSV, but with tabs) to Excel worksheet(s). Be sure to use the first row.
+
+- Apply "Freeze Panes" to the Top Row, so that the data will still be visible under the header row, even if scroll down
+
+- Apply "Auto-Filter" to the data, to make it easy to quickly sort and filter the data
+
+- Add columns to the primary data with the "Display Name" for each lab (rather than the coded name), using VLOOKUP on the lookup table.
+
+- Add a column for a "Normal Rate" to the primary data (normal count / total count)
+
+- Select all of the columns of potential interest in the primary data table, and create a new Pivot Chart 
+  (Which necessarily creates a respective Pivot Table)
+
+- Drag-and-Drop the columns of interest into different filters, x-axis, y-axis, and value fields in the PivotTable/Chart
+- Try different chart types to visualize (will almost always be column/bar and line)
+
+
+- See final file used in paper for a completed example
+https://drive.google.com/file/d/0B01s8toiGe4NTnRTTFBCRzBUTVE/view?usp=sharing
diff --git a/scripts/DevWorkshop/ReadMe.PowerCalculationSimulation.txt b/scripts/DevWorkshop/ReadMe.PowerCalculationSimulation.txt
new file mode 100644
index 00000000..61ff9e59
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.PowerCalculationSimulation.txt
@@ -0,0 +1,52 @@
+= Dev Workshop =
+Power Calculations - Empirically Estimating by Simulation
+
+== Learning Goals ==
+- Basic R DataFrame manipulation, including dplyr
+- Basic Categorical Hypothesis Testing with Chi-square and Fisher exact test
+- Meaning and relationships between Type I Error, Type II Error, alpha, beta, confidence intervals, p-values
+- Relationship of Statistical Power with sample size, sample imbalance, treatment effect
+
+
+== Preconditions ==
+- R installed
+  - install.packages("dplyr","reshape2")
+  - Convenient to also install RStudio and bonus points for getting R to work in Jupyter notebooks (https://www.datacamp.com/community/blog/jupyter-notebook-r)
+- Git installed so able to download / clone code repository
+- Suggest online pre-reading to review Power calculations, Type I error, Type II error, alpha, beta, etc.
+
+== Workshop Steps ==
+- Download copy of the code repository
+    git clone https://github.com/HealthRex/CDSS.git
+       or if you just need to update existing copy...
+    git pull
+
+- Open the following scripts in CDSS/scripts/DevWorkshop/statsSimCalc directory in RStudio
+  powerCalculation.R
+  powerCalculation.examples.R 
+
+- "source" each of the above two files to load/execute them (the latter will take a minute as it runs through a battery of simulations)
+
+- Step through the code in the above example files 
+  
+  If you don't understand what's happening in any lines of code, then ask for input
+
+  When you see comment lines that start with "# >" like
+  # > head(dfManual)
+  This means you can try to copy-paste the command after the > and execute it in the interactive console.
+
+  When you see an extended comment header ############ like
+  ########## For the manually constructed treatment vs. outcome data above, what is p-value for for whether the treatment affects the outcome?
+  This means to pause and review the conceptual question
+
+- Beware that the above largely applies to prospective experiments / data collection / study design planning.
+  Once data is already collected, retrospective analysis / building predictive models, etc., a "post-hoc power analysis" generally doesn't make sense.
+  Similar but alternative approaches are necessary to estimate whether a given database sample size is likely sufficient to conduct such a study.
+
+
+== Further Reading ==
+The Use of Predicted Confidence Intervals When Planning Experiments and the Misuse of Power When Interpreting Results
+Steven N. Goodman, MD, PhD, Jesse A. Berlin, ScD
+https://www.acpjournals.org/doi/10.7326/0003-4819-121-3-199408010-00008
+
+
diff --git a/scripts/DevWorkshop/ReadMe.SQLQuery.txt b/scripts/DevWorkshop/ReadMe.SQLQuery.txt
new file mode 100644
index 00000000..f79373bc
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.SQLQuery.txt
@@ -0,0 +1,398 @@
+= Dev Workshop =
+SQL Queries and Databases
+
+== Learning Goals ==
+- Data Tables in Databases in Database Servers
+- Querying from Relational Databases with SQL
+  - FROM
+      - Table Alias
+  - SELECT
+  - WHERE
+      - =, <, >, <>, IN
+      - LIKE
+      - Date/Time functions
+  - ORDER BY
+  - LIMIT OFFSET
+  - Aggregate Functions (count, sum, min, max, distinct)
+  - GROUP BY, HAVING
+  - CASE WHEN
+
+  - Subqueries
+  - Inner Join, Outer Join
+
+- Bonus Topics to lookup but not covered here:
+  - Indexes
+  - Explain Queries
+  - Insert/Delete/Update
+  - ACID transactional properties (atomicity, consistency, isolation, durability)
+  - Constraints (unique/primary keys, foreign keys)
+  - 3rd Normal Form vs. Denormalized data
+
+- See also 
+  https://github.com/HealthRex/CDSS/wiki/STRIDE-Inpatient-SQL-Tutorial for more examples on the STRIDE derived clinical_item tables.   
+  medinfo.db.Model.SQLQuery for a convenient Python object to dynamically construct SQL queries in application code.
+
+
+== Preconditions ==
+- Access to run queries on a SQL database, for example, Google BigQuery hosted copy of STARR datalake
+- Review basic SQL syntax: https://www.w3schools.com/sql/default.asp
+
+== Workshop Steps ==
+- Data Tables in Databases in Database Servers
+  A database server can host one or more databases/datasets. 
+  Usually you will be working with one primary database that contains many different 2D tables of data.
+  Each table is of fixed width (number of columns/features/covariates), but arbitrary length (number of rows/instances/observations).
+  Database Schema and Data Dictionaries ("metadata") define what tables exist, what columns they have, 
+    and what data types each column holds (e.g., integer, floating point, string, datetime) .
+  Entity-Relationship diagrams depict the relationship of different tables, usually through ID integer keys that reference rows in other tables.
+  http://www.sqlitetutorial.net/sqlite-sample-database/
+  https://med.stanford.edu/content/dam/sm/researchit/Content/STARR-Data-Synopsis-Jun-2018.pdf
+
+    * Under BigQuery resources, find the database of interest (e.g., starr_datalake2018)
+    * Browse through the data tables in the database (e.g., order_med) to see the types of data columns contained 
+    (e.g.,  jc_uid = Deidentified unique patient identifier, 
+            order_med_id_coded = Primary key for this table, uniquely identifying each individual medication order
+            medication_id = Foreign key ID for what type of medication is being ordered, 
+            med_description = Text description of what that medication is)
+
+- Querying from Relational Databases with SQL
+    Main interaction method with SQL databases is pulling out data with SELECT queries that effectively generate temporary result data tables.
+
+  - FROM - Specify which table(s) of data you want to query from
+      - Table Alias - Create an alternative (usually short abbreviation) name for a table to make it easier to reference later
+  - SELECT - Specify which columns of the data you want to retrieve. '*' to retrieve all available columns, useful for browsing
+  - LIMIT OFFSET - Restrict the number of result rows returned. Useful when browsing through database and don't want millions of results
+
+    * Querying for a sample of medication order data
+
+          SELECT *
+          FROM starr_datalake2018.order_med
+          LIMIT 100;
+
+    * Query for just some of the most relevant columns information
+
+        SELECT
+          jc_uid,   -- Unique patient identifier (Deidentified. Not the "real" MRN, but can be mapped back to real data for specific IRB approved projects)
+          pat_enc_csn_id_coded,   -- Unique encounter identifier (e.g., patient in hospital or patient in clinic or patient in laboratory)
+          order_med_id_coded,   -- Primary key for this table, uniquely identifying an individual medication order. "Coded" for deidentification (not the original value in EMR).
+          order_time_jittered,  -- When the order was place. Deidentification "jitterred" the date by +/- X days. Time of day still correct, and date shift consistent for each patient, allowing for valid relative time comparisons.
+          medication_id,
+          med_description,
+          med_route,  -- Description of how medication administered (by mouth, by injection, etc.)
+          hv_discrete_dose, -- Medication dose per administration
+          hv_dose_unit, -- Units of dosing information
+          freq_name,  -- How often medication to be given
+          pharm_class_name -- Description of the broad class of pharmaceutical the medication belongs to. Beware that meds don't have clean relationships with such categories. Usually a many-to-many relationship. (
+        FROM
+          starr_datalake2018.order_med
+        LIMIT 100
+
+  - WHERE - Specify / filter which rows of data you want to retrieve using different logical operators.
+      - =, <, >, <=, >=    -- Usual logical operators    
+      - IS, IS NOT         -- For checking whether a value is or is not NULL ("NA" / "None"), as technically invalid to say something = null.
+      - <>                -- SQL's version of "not equals"
+      - IN                 -- Allow for multiple equals options. Handy when trying to match multiple ID keys
+      - LIKE                 -- String operator that can match wildcard '%'. Handy for prefix and suffix searches.
+      - Date/Time functions
+
+      * Query for all of the medication orders where the description starts with "INSULIN"
+
+        SELECT
+          *
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          UPPER(med_description) like 'INSULIN%'
+        LIMIT 100
+	
+	Note the "UPPER" string function applied to the text field to effectively make this a case-insensitive search.
+
+      * And occurs in the inpatient setting (ordering_mode_c = 2)
+
+        SELECT
+          *
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          med_description like 'INSULIN%' AND 
+          ordering_mode_c = 2 -- Inpatient 
+        LIMIT 100
+
+    * And is injected by subcutaneous or intravenous route (med_route_c = 18 or 11)
+
+        SELECT
+          *
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          med_description like 'INSULIN%' AND 
+          ordering_mode_c = 2 AND -- Inpatient 
+          med_route_c IN (18,11) -- Subcutaneous or Intravenous
+        LIMIT 100
+
+      * And occurred in the year 2016
+        There are many datetime specific functions (e.g., DATEDIFF), but these are often not standardized across SQL implementations
+	E.g., Alternative implementation to below could use EXTRACT(YEAR FROM order_time_jittered) = 2016.
+	https://cloud.google.com/bigquery/docs/reference/standard-sql/date_functions
+
+        SELECT
+          *
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          med_description like 'INSULIN%' AND 
+          ordering_mode_c = 2 AND -- Inpatient 
+          med_route_c IN (18,11) AND -- Subcutaneous or Intravenous
+          order_time_jittered >= '2016-01-01' AND order_time_jittered < '2017-01-01'
+        LIMIT 100
+
+  - ORDER BY - Sort query results by column values
+
+      * Query for inpatient insulin orders like above for just the following two patients ('JCde49f8','JCd08052'), 
+          showing results in chronological order for each patient.
+
+        SELECT
+          *
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          med_description like 'INSULIN%' AND 
+          ordering_mode_c = 2 AND -- Inpatient 
+          med_route_c IN (18,11) AND -- Subcutaneous or Intravenous
+          order_time_jittered >= '2016-01-01' and order_time_jittered < '2017-01-01' AND
+          jc_uid IN ('JCde49f8','JCd08052')
+        ORDER BY 
+          jc_uid, order_time_jittered
+        LIMIT 100
+
+  - Aggregate Functions (count, sum, min, max, distinct)
+
+      * For the inpatient insulin orders in 2016, query for the total number of orders, number of unique patients, unique encounters (hospitalizations), and the sum of all maximum dose values (max_discrete_dose).
+
+        SELECT
+          count(*) as totalOrders, -- Result column renaming/aliasing for convenience
+          count(distinct jc_uid) as totalPatients,
+          count(distinct pat_enc_csn_id_coded) as totalEncounters,
+          sum(max_discrete_dose) sumMaxDoses
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          med_description like 'INSULIN%' AND 
+          ordering_mode_c = 2 AND -- Inpatient 
+          med_route_c in (18,11) AND -- Subcutaneous or Intravenous
+          order_time_jittered >= '2016-01-01' and order_time_jittered < '2017-01-01'
+        LIMIT 100
+
+  - GROUP BY, HAVING -- Convenient way to aggregate rows of data into common categories, then easily report summary (aggregate) stats for each
+
+      * Report similar stats for inpatient insulin orders (total orders, patients, encounters) for each month from 2015-2017,
+      	but only include months where have at least 700 patients
+
+        SELECT
+          EXTRACT(YEAR FROM order_time_jittered) as orderYear,    -- Any column NOT an aggregate function (e.g., count, sum),  
+          EXTRACT(MONTH FROM order_time_jittered) as orderMonth,    --    MUST be in GROUP BY expression to make sense
+          count(*) as totalOrders,
+          count(distinct jc_uid) as totalPatients,
+          count(distinct pat_enc_csn_id_coded) as totalEncounters
+        FROM
+          starr_datalake2018.order_med
+        WHERE
+          med_description like 'INSULIN%' AND 
+          ordering_mode_c = 2 AND -- Inpatient 
+          med_route_c in (18,11) AND -- Subcutaneous or Intravenous
+          EXTRACT(YEAR FROM order_time_jittered) >= 2015 AND 
+          EXTRACT(YEAR FROM order_time_jittered) < 2018
+        GROUP BY
+          orderYear,
+          orderMonth
+        HAVING
+          totalPatients >= 700
+        ORDER BY
+          orderYear,
+          orderMonth
+        LIMIT 100
+
+  - CASE WHEN -- Embed an if/then statement into a query. Can be a convenient way to extract out a "one hot" encoding of categorical data
+
+  	* Count up the total number Male vs. Female patients as well as the most common categories of canonical_race
+
+		SELECT 
+		    SUM(CASE WHEN gender = 'Male' THEN 1 ELSE 0 END) AS males,
+		    SUM(CASE WHEN gender = 'Female' THEN 1 ELSE 0 END) AS females,
+		    SUM(CASE WHEN canonical_race LIKE '%Asian%' THEN 1 ELSE 0 END) AS asians,
+		    SUM(CASE WHEN canonical_race LIKE '%Black%' THEN 1 ELSE 0 END) AS blacks,    
+		    SUM(CASE WHEN canonical_race LIKE '%Pacific%' THEN 1 ELSE 0 END) AS pacific_islanders,
+		    SUM(CASE WHEN canonical_race LIKE '%White%' THEN 1 ELSE 0 END) AS whites,
+		    SUM(CASE WHEN canonical_race LIKE '%Other%' THEN 1 ELSE 0 END) AS race_other,
+		    SUM(CASE WHEN canonical_race LIKE '%Unknown%' THEN 1 ELSE 0 END) AS race_unknown,
+		    SUM(CASE WHEN canonical_ethnicity LIKE 'Hispanic%' THEN 1 ELSE 0 END) AS hispanics
+		FROM
+		    starr_datalake2018.demographic
+
+  - Subqueries -- Can treat the results of a query like a temporary table itself, by wrapping in parantheses, and then querying off that again
+
+  	* Find all of patients who had an encounter of appt_type = 'Anesthesia' on a contact_date of '2016-01-01'
+
+        SELECT DISTINCT jc_uid
+        FROM starr_datalake2018.encounter
+        WHERE appt_type = 'Anesthesia'
+        AND contact_date_jittered = '2016-01-01'
+
+  	* Report the age, in years of the patients who had the above encounters
+
+        SELECT DISTINCT   -- Note need to use distinct, because some patients had multiple encounters. Don't want to report twice
+          rit_uid, 
+          DATE_DIFF(DATE('2016-01-01'), DATE(birth_date_jittered), YEAR) as ageYears
+        FROM starr_datalake2018.demographic
+        WHERE rit_uid IN
+        (   SELECT DISTINCT jc_uid
+            FROM starr_datalake2018.encounter
+            WHERE appt_type = 'Anesthesia'
+            AND contact_date_jittered = '2016-01-01'
+        )
+
+  - Inner Join - Subqueries like above where matching by an ID column are almost always incorrect (or at least inefficient).
+  		Generally should be joining tables together by some matching criteria (usually a foreign key ID field)
+
+  	* Repeat above, but directly joining on demographic and encounter table
+        SELECT DISTINCT
+          dem.rit_uid, 
+          DATE_DIFF(DATE('2016-01-01'), DATE(dem.birth_date_jittered), YEAR) as ageYears
+        FROM
+          starr_datalake2018.demographic AS dem JOIN  -- Create a shorthand table alias for convenience when referencing
+          starr_datalake2018.encounter AS enc ON dem.rit_uid = jc_uid
+        WHERE
+          enc.appt_type = 'Anesthesia' AND 
+          enc.contact_date_jittered = '2016-01-01'
+
+  - Outer Join - When joining tables, sometimes there is no matching record in the other table. 
+  		Default (inner) join behavior is to skip those records then, but then you might lose track of data on the side
+  		of the query that does have data. Sometimes want to keep those and just match to "null" on the other side.
+
+    * Find all of the patient demographic records for patients with these IDs
+      ('JCd9a485','JCdbb0dd','JCcf7fef','JCe48eb5','JCe01809','JCdc9a27','JCd7e138','JCcf3c57','JCe38e82','JCd31c2f')
+
+        SELECT *
+        FROM starr_datalake2018.demographic
+        WHERE rit_uid IN ('JCd9a485','JCdbb0dd','JCcf7fef','JCe48eb5','JCe01809','JCdc9a27','JCd7e138','JCcf3c57','JCe38e82','JCd31c2f')
+
+    * Find all of the encounters that occurred on 2016-01-01
+
+        SELECT *
+        FROM starr_datalake2018.encounter
+        WHERE contact_date_jittered = '2016-01-01'
+
+    * For each of the patients in the list above, report how many encounters each had on 2016-01-01.
+      To start, just make a query result with one row per patient encounter that matches the above criteria    
+
+        SELECT
+          dem.rit_uid, 
+          enc.pat_enc_csn_id_coded 
+        FROM 
+          starr_datalake2018.demographic AS dem JOIN
+          starr_datalake2018.encounter AS enc ON dem.rit_uid = jc_uid
+        WHERE
+           dem.rit_uid IN ('JCd9a485','JCdbb0dd','JCcf7fef','JCe48eb5','JCe01809','JCdc9a27','JCd7e138','JCcf3c57','JCe38e82','JCd31c2f') AND
+           contact_date_jittered = '2016-01-01'
+        ORDER BY
+          dem.rit_uid;
+
+    * Then apply GROUP BY and row counts to get the summary stats
+
+        SELECT
+          dem.rit_uid,
+          COUNT(DISTINCT enc.pat_enc_csn_id_coded)
+        FROM 
+          starr_datalake2018.demographic AS dem JOIN
+          starr_datalake2018.encounter AS enc ON dem.rit_uid = jc_uid
+        WHERE
+           dem.rit_uid IN ('JCd9a485','JCdbb0dd','JCcf7fef','JCe48eb5','JCe01809','JCdc9a27','JCd7e138','JCcf3c57','JCe38e82','JCd31c2f') AND
+           contact_date_jittered = '2016-01-01'
+        GROUP BY
+          dem.rit_uid
+        ORDER BY
+          COUNT(DISTINCT enc.pat_enc_csn_id_coded) DESC;
+
+    * Problem with above is that it only shows results for 7 patients (those that had some encounters on 2016-01-01). 
+      No results are shown at all for the patients who had no encounters on that date. (Would like to report "0" encounters.)
+      Go back to the query for one row per patient encounter and do an outer join to at least fill in "null" value entries
+      for the patients who didn't have any specific encounters.
+
+        SELECT
+          dem.rit_uid, 
+          enc.pat_enc_csn_id_coded 
+        FROM 
+          starr_datalake2018.demographic AS dem LEFT OUTER JOIN
+          starr_datalake2018.encounter AS enc ON dem.rit_uid = jc_uid
+        WHERE
+           dem.rit_uid IN ('JCd9a485','JCdbb0dd','JCcf7fef','JCe48eb5','JCe01809','JCdc9a27','JCd7e138','JCcf3c57','JCe38e82','JCd31c2f') AND
+           (contact_date_jittered IS NULL OR contact_date_jittered = '2016-01-01')
+        ORDER BY
+          dem.rit_uid;
+
+    * Now redo aggregate query with outer join so will appropriately count up at least zero records for each patient
+
+        SELECT
+          dem.rit_uid,
+          COUNT(DISTINCT enc.pat_enc_csn_id_coded)
+        FROM 
+          starr_datalake2018.demographic AS dem LEFT JOIN
+          starr_datalake2018.encounter AS enc ON dem.rit_uid = jc_uid
+        WHERE
+           dem.rit_uid IN ('JCd9a485','JCdbb0dd','JCcf7fef','JCe48eb5','JCe01809','JCdc9a27','JCd7e138','JCcf3c57','JCe38e82','JCd31c2f') AND
+           (contact_date_jittered IS NULL OR contact_date_jittered = '2016-01-01')
+        GROUP BY
+          dem.rit_uid
+        ORDER BY
+          COUNT(DISTINCT enc.pat_enc_csn_id_coded) DESC;
+
+
+  - WITH Common Table Expressions, Temporary Table Views - Very large, nested sub-queries can quickly grow out of control. Organize the results of subqueries as tables/views themselves that you can query from in the same space. Similar to abstracting out code blocks into modular functions.
+
+	WITH
+	-- High Creatine lab result values >2.0
+	highCreatineLabResults AS
+	(
+	  SELECT rit_uid, result_time_jittered, EXTRACT(DATE FROM result_time_jittered) as result_date, base_name, ord_num_value
+	  FROM `starr_datalake2018.lab_result` 
+	  WHERE base_name = 'CR'
+	  AND ord_num_value > 2.0
+
+	),
+	-- High white blood cell count values (flagged by lab) from complete blood counts
+	highWBCLabResults AS
+	(
+	  SELECT rit_uid, result_time_jittered, EXTRACT(DATE FROM result_time_jittered) as result_date, base_name, ord_num_value
+	  FROM `starr_datalake2018.lab_result` 
+	  WHERE proc_code LIKE 'LABCBC%'
+	  AND base_name = 'WBC'
+	  AND result_flag LIKE 'High%'
+	)
+
+	-- Draw from the above sub-queries to identify dates where patients had both a high Creatine and high WBC count and report the max value for each patient-date
+	SELECT rit_uid, result_date, MAX(highCr.ord_num_value) as maxCr, MAX(highWBC.ord_num_value) as maxWBC
+	FROM highCreatineLabResults as highCr 
+	   JOIN highWBCLabResults as highWBC
+	      USING (rit_uid, result_date)  -- Join on multiple columns, and when they have exact same names, USING function even better to eliminate ambiguity afterwards
+	GROUP BY rit_uid, result_date
+
+	LIMIT 100
+
+
+
+- Challenge Queries
+  - Top 10 Diagnoses recorded in 2017? In 2018?
+  - Top 5 medication routes of administration in Inpatient setting in 2017?
+  - What are other medications/drugs are in the same pharmaceutical class as Metoprolol?
+  - For Blood Culture (2 Aerobic) results, what were Top 10 bugs grown? How often were they Susceptible to Ceftriaxone? To Ciprofloxacin?
+  - What percent of patients had a Glucose By Meter lab_result value >250 at some point in 2017?
+  - For each of the patients above, what was the average number of units of Insulin administered to them on any day they received insulin?
+    (Look in mar = Medication Administration Record table. For some reason, the dose given is recorded in the "sig" field.)
+  - Find all patients who received a tPA infusion (medication_id = 86145) and report the 
+  	time the patient presented to the emergency department (adt.effective_time_jittered
+  	    (where admit.event_type_c = 1 -- Admission 
+         and admit.pat_class_c = '112' -- Emergency Services)
+  	time the medication was ordered (order_med.order_time_jittered),
+  	time the medication was administered (mar.taken_time_jittered)
+
+  	Use above to estimate the average time between showing up in the emergency department and receiving tPA for those who received it.
diff --git a/scripts/DevWorkshop/ReadMe.TestFirstDev.txt b/scripts/DevWorkshop/ReadMe.TestFirstDev.txt
new file mode 100644
index 00000000..3cb08529
--- /dev/null
+++ b/scripts/DevWorkshop/ReadMe.TestFirstDev.txt
@@ -0,0 +1,86 @@
+= Dev Workshop =
+Test First Development
+
+== Learning Goals ==
+- Unit test execution and debugging
+- Test First Development example
+- Python command-line processing tips
+- Further reading:
+  http://agiledata.org/essays/tdd.html
+
+== Preconditions ==
+- Python 3.6+ installed and executable from command-line / terminal
+- Git installed so able to download / clone code repository
+
+== Workshop Steps ==
+- Download copy of the code repository
+    git clone https://github.com/HealthRex/CDSS.git
+       or if you just need to update existing copy...
+    git pull
+
+- Go to the CDSS/scripts/DevWorkshop/UnitTesting directory
+
+- Verify you can run the example ApplicationModule and respective test
+    python ApplicationModule.py -w 3 ../sampleData/abstractLines.txt abstractWords.3.txt
+		The abstractLines.txt input file contains a bunch of research abstract text (one per line).
+		Running the above should create an output file with a sample word for each input line.
+    
+    python TestApplicationModule.py
+		Unit test to automatically verify the correct function of the respective application code.
+		Running the above should yield a "failed" test at this point,
+			but just make sure it doesn't yield a compiler error, or python dependency issue, etc.
+
+- Review the ApplicationModule
+	- Example usage of setting up a main function and OptionParser to enable command-line interaction
+	- Example of outputting the argv and other metadata as a comment line in the output file, 
+		to make it easier to retrace/reproduce your data pipelines. 
+		This is the meta-data that allows you to reconstruct where your (intermediate) data files, 
+		without having to come up with excessively detailed and unreliable filenames.
+
+- Review the TestApplicationModule
+	- Example suite function called from main driver code to tell the test runner where to find tests to run
+		(default is any method that starts with "test").
+	- Standard setUp and tearDown to run before and after each test within the test class.
+		For example, creating temporary input files (and deleting them when done) or database contents, etc.
+	- Example test_extractWordsByIndex test, following basic repeated structure: 
+		(1) Setup test input, 
+		(2) Run application code on input, 
+		(3) Verify (assert) output matches expected results.
+
+- Test and Debug
+	- Figure out why the test_extractWordsByIndex fails and modify the code until it passes.
+	- When there is an error/failure, beware the urge to change the test to match the application's behavior!
+		Unless you're sure the application is the one that is correct... 
+		which you can't be sure of unless you've (manually) run some tests on it.
+
+- Test First Development Example
+	- Try adding another application function and test case, such as the simple Fibonacci example.
+	- Test First Dev: 
+		(1) Write the application function stub + documentation of what you want it to do in English, 
+			but NOT the actual implementation. 
+		(2) Write the unit test function which asserts manually verified expected output given
+			specific test input data if your application function does what it's supposed to do.
+			Again, do this BEFORE you've implemented the actual application code.
+			Forces you to effectively design your code API
+			(application programming interface, i.e., function names, parameters, return values).
+		(3) Run the unit test above, expecting it to fail (since application code not written),
+			but verifies you stitched the API calls together correctly.
+			Mimics how easy/hard it will be for a user of your code to follow.
+		(4) Implement the application code, run the unit test, and iterate until your unit tests pass.
+
+- Automated Regression Testing Empowers Bold Changes in Implementation
+	- If you got the Fibonacci example working, including a unit test to verify,
+		try running it from the command line for say, the 5th value:
+
+		python ApplicationModule.py -f 5
+
+	- Now try against an example input value of 50 or more
+		(i.e., you'll be calculating the 50th Fibonacci value, or higher).
+
+		python ApplicationModule.py -f 50
+
+	- What happened? Depending on how you implemented the Fibonacci application code,
+		it is highly likely that the above will never finish (you'll have to Ctrl-C to cancel the process).
+	- Try implementing the application code a different way,
+		while confidently being able to re-run your existing unit test 
+		to verify that whatever changes you make will still produce valid results (but hopefully much faster).
diff --git a/scripts/DevWorkshop/STARR-OMOP/StarterNotes.txt b/scripts/DevWorkshop/STARR-OMOP/StarterNotes.txt
new file mode 100644
index 00000000..635cbd86
--- /dev/null
+++ b/scripts/DevWorkshop/STARR-OMOP/StarterNotes.txt
@@ -0,0 +1,92 @@
+
+***** In progress. Collating different notes, but not a purposeful tutorial yet ********
+
+
+
+
+
+Here are the steps to get access to STARR omop data:
+•	Request a Nero account for STARR-OMOP deID data access. A Stanford PI affiliation is required for a Nero account. More information on getting Nero account is available here. 
+•	You will need a Nero GCP account and a Nero on-premise account.
+•	In parallel, confirm that you have a valid DPA that allows you to view deidentified structured data and clinical text. I would need you and all the people you want to access this de-id data to complete a Data Privacy Attestation.  Just select “STARR Nav deID data” from the pull-down menu under Project Type – and complete the form. Let me know if you run into any issues.
+•	Once a Nero project has been created, you will get an email from Stanford Research Computing Center (SRCC) informing you that access has been granted with some introductory information. Please continue to email srcc-support for any Nero related questions.
+•	Email Priya Desai, Biomedical Informatics Product Manager, Research IT, at prd@stanford.edu with the following additional information:
+•	Your Nero GCP project name
+•	Your PI’s ORCID number  
+•	Your own ORCID number
+
+Here is a link to the STARR-OMOP User Docs to give you a better picture: (for access, log in using your Stanford email):
+
+•	User Docs: https://docs.google.com/document/d/1dTjCEvvU8sMd8CuaVx94YFoPTsW_xjOMoF9-xl5zHik/view#heading=h.b7frls1oh2pm
+
+
+
+
+
+
+
+
+
+
+Priya Desai (4/11/2020)
+
+
+Welcome to STARR Data Users group
+Your access to STARR-OMOP-deid data has been enabled. The STARR-OMOP-deid data resides in the GCP project som-rit-phi-starr-prod. You can access the data using jupyter notebooks from your nero on-prem project. You have also been granted access to:
+1.	Slack Data users channel
+2.	Nero-STARR-notebooks repository in the Stanford gitlab
+Accessing STARR-Nav de-id data:
+1)	Connect to the Stanford Full Tunnel VPN; information about setting up the VPN is available at https://vpn.stanford.edu
+2)	Confirm access using the BigQuery console:
+a)	Log into you nero GCP project from https://console.cloud.google.com using your Stanford SUNet ID.
+3)	Click on the following link:   https://console.cloud.google.com/bigquery?authuser=0&folder=&organizationId=&project= your nero gcp projectname &p=som-rit-phi-starr-prod&page=project , after replacing the highlighted portion with your Nero GCP project name.
+4)	In the BigQuery console, under the Resources tab; type in som-rit-phi-starr-prod and click on it. You should be able to view the following 5 datasets:
+i)	starr_omop_cdm5_deid_1pcent_latest
+ii)	starr_omop_cdm5_deid_latest
+iii)	starr_omop_cdm5_deid_1pcent_lite
+iv)	starr_omop_cdm5_deid_1pcent_lite_latest
+v)	Synpuf_2m_cdm_53
+5)	If you see VPC control errors, you may need to reset your IPv6  settings. See Resolving VPC Service Control Errors. If you continue to experience problems please email prd@stanford.edu.
+
+	We highly discourage the use of the BQ console for data exploration due to security risks.
+	You can now access all these datasets via Jupyter Notebooks from the nero on prem server.
+	More information about STARR-Nav Deid data can be found in:
+1.	User Docs:   https://docs.google.com/document/d/1dTjCEvvU8sMd8CuaVx94YFoPTsW_xjOMoF9-xl5zHik/view# 
+2.	OMOP 5.3 specs: https://drive.google.com/open?id=1WCj8zp0ZbMG1yHdYNFpG3xWVfsV_tnPJ
+3.	Technical Specs: https://drive.google.com/a/stanford.edu/file/d/16OfTSUEdxMe5fddGB4I_qtc5O2SrOPf0/view?usp=sharing
+4.	To see more documentation and  introductory material on using Jupyter notebooks on
+Nero to explore BQ datasets, see the project Nero-STARR-notebooks in the Stanford  gitlab.
+5.	You have also been added to the STARR-data users Slack channel at https://starrdatausers.slack.com/ where notifications will be posted. Slack is our primary method of communication. You should have links to the user guide and technical docs in the list on pinned items under the starr-general.
+
+# Added April 7, 2022
+#===========
+More information about STARR-OMOP data including Office hours can be found in:
+
+Website: https://med.stanford.edu/starr-omop.html.
+User Docs:   https://docs.google.com/document/d/1dTjCEvvU8sMd8CuaVx94YFoPTsW_xjOMoF9-xl5zHik/view# 
+OMOP 5.3 specs: https://drive.google.com/open?id=1WCj8zp0ZbMG1yHdYNFpG3xWVfsV_tnPJ
+Technical Specs: https://drive.google.com/a/stanford.edu/file/d/16OfTSUEdxMe5fddGB4I_qtc5O2SrOPf0/view?usp=sharing
+      Office Hours schedule: https://tiny.cc/STARR_Office_Hours
+      Questions other users have asked: https://docs.google.com/document/d/1_9x2KNKLez5o0-vV4UQU2dp9pn6ezsAEsJXDh7n--3o/edit
+      Stanford STARR Youtube channel: https://www.youtube.com/channel/UC6iGiAO1dKwuC2wOrxnKiNw
+      Tools for DataScience in Healthcare You tube Tutorials:
+		Tutorial 1 playlist: https://www.youtube.com/playlist?list=PLSKQ1ikU3kiHD1f87uoszXbo9FT9Eutag             
+		Tutorial 2 playlist: https://www.youtube.com/playlist?list=PLSKQ1ikU3kiEVfQ3iXioZGMrTJ2lU7YJ3
+		Tutorial 3 playlist: https://www.youtube.com/playlist?list=PLSKQ1ikU3kiGkpYmVmudyE6DvN4KHqsgT
+		Tutorial 4 playlist: https://www.youtube.com/playlist?list=PLSKQ1ikU3kiFFwt0wSy3DFjWK0z_m0TCj
+To see more documentation and  introductory material on using Jupyter notebooks on Nero to explore BQ datasets, see the project Nero-STARR-notebooks in the 		    Stanford  gitlab: https://code.stanford.edu/starr/nero-starr-notebooks
+You have also been added to the STARR-data users Slack channel at https://starrdatausers.slack.com/ where notifications will be posted. Slack is our primary method of communication.
+Book of OHDSI: https://ohdsi.github.io/TheBookOfOhdsi/
+Ehden Academy: https://academy.ehden.eu: This has multiple, very useful courses
+Coursera Courses:
+	AI in HealthCare: 
+	Clinical Data Science Specialization
+
+
+Thanks !
+--
+Priya Desai, 
+Biomedical Informatics Product Manager
+Research IT, Stanford School of Medicine,
+408-688-7464 | prd@stanford.edu
+
diff --git a/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.01.txt b/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.01.txt
new file mode 100644
index 00000000..652514b0
--- /dev/null
+++ b/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.01.txt
@@ -0,0 +1,148 @@
+11/5/2019
+
+Here are some useful resources for you to prepare for Tutorial 1.
+	1. Unix: 
+			a. Unix for Beginners: http://www.ee.surrey.ac.uk/Teaching/Unix/
+			b. Unix Tutorial for Beginners: https://www.cs.sfu.ca/~ggbaker/reference/unix/
+			c. Unix Commands: https://www.unixtutorial.org/commands
+	2. Python, the preferred language to accelerate your data science career:
+			a. Introduction to Python Programming at Udacity: https://www.udacity.com/course/introduction-to-python--ud1110
+	3. Jupyter Notebooks, modern collaborative data science platform:
+			a. Installing Jupyter Notebooks:  https://jupyter.readthedocs.io/en/latest/install.html
+	4. Git (version control, the foundation for reproducible collaborative science):
+			a. Basics of Git: https://www.freecodecamp.org/news/learn-the-basics-of-git-in-under-10-minutes-da548267cc91/
+			b. git - the simple guide: https://rogerdudler.github.io/git-guide/
+			c. Gitlab ssh key setup:  https://www.tutorialspoint.com/gitlab/gitlab_ssh_key_setup.htm
+			d. Gitlab: https://docs.gitlab.com/ee/gitlab-basics/create-your-ssh-keys.html 
+	5. Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM):
+			a. OMOP CDM: https://www.ohdsi.org/data-standardization/the-common-data-model/
+			b. The Book of OHDSI: https://ohdsi.github.io/TheBookOfOhdsi/
+			c. Notable Publications: 
+					i. Identifying Appropriate Reference Data Models for Comparative Effectiveness Research (CER) Studies Based on Data from Clinical Information Systems
+					ii. Transforming the Premier Perspective Hospital Database into the Observational Medical Outcomes Partnership (OMOP) Common Data Model.
+We recommend you install Jupyter Notebooks , git and set up your ssh keys before the tutorial. 
+
+
+https://docs.google.com/presentation/d/13SDhC7A8o_rR3pbiO8Gnz2gEP5-awzlfJyu-jrUpyCg/edit?usp=sharing
+
+
+Priya Desai - Product Manager
+Jose Posada - Clinical Research Scientist (also in Shah lab)
+
+PhD + MD students from Khatri, Boussard lab, Immunology
+Staff in BMIR, Pop Health Sciences
+  Interest in data access
+Stefanie Leonard - Instructor - Perinatal Epidemiology - Interest in OMOP
+
+
+
+Quick Hands on
+- https://console.cloud.google.com
+- Project: 
+  https://console.cloud.google.com/home/dashboard?project=som-rit-starr-training
+  Under resources: 
+     som-rit-starr-training (Project)
+     synpuf_2m_20191023 (Sample, De-ID database with 2M person sample)
+
+
+Stanford centrally managed workgroups, otherwise individual GCP IAM access control
+   May take 10 minutes to propagate access controls through
+
+
+
+Will need own GCP project once want to start accessing STARR-OMOP data, tracking own billing.
+GCP Overview
+- Resources: Physical data centers, VMs, etc.
+- Services: Compute Engines, Cloud Storage (buckets), BigQuery
+- Projects: Organizing entity for resources
+   Services need to be part of a project to be able to use them
+   Manages sets of users, permissions, APIs, billing, authentication information
+   https://cloud.google.com/bigquery/pricing
+   Overall very cheap for storage. Charge per query processing, but largely cheap too.
+
+
+Why BigQuery?
+- Scalable (petabytes) with more data and users. Don't need to buy more hardware, etc.
+  (On prem STRIDE/Clarity requires army to keep maintaining)
+- Fully managed, highly available data warehoues solution. No IT personnel
+  (Don't worry about database admin/optimization)
+- Standard SQL
+- Near real time analytics
+- Programmatic interaction - REST APIs support Java, Python, Ruby, PHP, etc.
+- Big Data ecosystem of tools
+
+Why Cloud Computing?
+- Scalable, provision as needed instead of capital investment
+- Multiple managed services can use
+- Infrastructure as code (reproducible systems), easier to share
+- HIPAA compliant services for high risk data (specific services already signed off by Stanford)
+- Faster, cheaper, reliable, more secure (if architected correctly)
+
+
+Can add more Dataset Resources, including publicly available ones already loaded, like synthetic data
+Example: SynPUF is synthetic patient dataset based on CMS claims
+Stanford Research IT adapted this into OMOP common data model (v5.3) to explore that structure
+Patient IDs continuity across time / insurance plan, unlike Optum/Truven that will change ID if patient switches insurance.
+
+Why CDM - Common Data Model?
+- No single observational databases captures all clinical events equally well (e.g., EMR and claims databases serve different purposes, so capture different data emphasis)
+- Research results from disparate data sources need to be compared and contrasted
+- CDM as standard and extensible collection of schemas (entities, attributes, relationships)
+  Well-defined semantics, to facilitate interoperability
+- FAIR principles
+- Open science community: OHDSI
+- OMOP: Observational Medical Outcomes Partnership
+- Becoming de facto international standard
+  Tries to use standards: 
+     SNOMED-CT for diagnosis codes. 
+     RXnorm for drugs.
+     LOINC for labs
+     Procedures are still a hodgepodge
+  Don't lose provenance. Keeps original data copy as well
+- Everything is a "concept" in OMOP (similar to clinical_item model)
+  concept_relationship and concept_ancestor as well
+    Sometimes loops in ancestors as well? But mostly a tree.
+- Patient centric data model, so only tries to capture things that happen to patient, not workflow/process. Essentially everything needs a patient_id column. Cancelled orders will be dropped if wasn't executed on patient.
+- Lots of "Concept IDs" in OMOP data. Concept table is like "one true code table" to lookup all data types (gender, ethnicity, meds, diagnoses, essentially anything). Persistent concept ID index across versions so don't clobber prior versions.
+  - Major data/table types
+  	- Condition: Basically diagnoses from problem lists, etc.
+  	- Procedure: 
+  	- Measurement: Mostly LOINC codes. Values with units, including vitals? Many concept operators, values as concepts, etc.
+  	- Drug exposure: Distinguish prescription vs. dispense vs. administration. Latter usually only available for inpatient MAR.
+
+
+
+Google Cloud SDK (requires Python)
+https://cloud.google.com/sdk/install
+- Install then run "gcloud init"
+
+Need to create authentication key JSON file
+- gcloud auth application-default login
+  Should require a web login, then creates local JSON file that can use to simulate login via stanford.edu account?
+
+
+Python - Google Cloud connection libraries 
+
+  pip install --upgrade google-cloud-bigquery
+
+
+
+  https://cloud.google.com/bigquery/docs/visualize-jupyter
+
+
+
+
+Jupytr Notebooks
+- 
+
+See support Notebooks in Jupyter for examples of tips and tricks for reviewing and manipulating datasets.
+https://code.stanford.edu/starr/nero-starr-notebooks
+
+
+Nero projects are much more restricted in terms of access between users and APIs
+- STARR OMOP will live in central project, but others will have read access to it
+  Have to be in your own project, then do a view connection to STARR-OMOP production project
+  User level permissions
+
+
+
diff --git a/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.02.txt b/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.02.txt
new file mode 100644
index 00000000..35e6ee83
--- /dev/null
+++ b/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.02.txt
@@ -0,0 +1,74 @@
+11/12/2019
+
+https://docs.google.com/presentation/d/1XIj1zYbFtr1dhAicxLa2ywCMhJcfdJ_DE4d90iBQMPU/edit#slide=id.g63d9e8e100_2_36
+
+- Epic is only one vendor for hospital, of 400+
+  Databases: Chronicles -> Clarity -> STRIDE
+  (Epic Hyperspace is GUI interfacing with Chronicles)
+  10,000+ tables
+- STRIDE homegrown model that extracts out samples of Clarity tables more likely to be relevant for researchers. Developed (2003) before OMOP and other common data models. Back then, very siloed incidental hospital contacts.
+- STRIDE homegrown system taking data streams from Clarity as well as HL7 feeds from other systems
+
+- Storage and compute is cheap now, so able to ask questions now that were previously impractical.
+- STRIDE was housed on-premises in an Oracle database
+- Now want to expand to not just EMR. Radiology images, pathology images. On-premises compute not scalable, so migrating to cloud compute environment.
+
+- Current permissions to get data into cloud
+  Clarity LPCH
+  Clairty SHC
+  PACS Dicom
+  CGS
+  Philips Waveform
+  Power Path pathology
+- Other data sources in 400+ that are still siloed, but trying to consolidate
+
+- Aim to put all queryable metadata into BigQuery, adapted into OMOP data model where possible
+
+
+- RIT - Backend infrastructure to manage data. Building STARR-OMOP data lake. Have deidentified STARR-OMOP database, which allows preliminary to research interaction even more than cohort tool.
+- RIC - Used to be part of RIT, but separated out to be the "data vendor." Not involved with how data is setup, they just run SQL queries to pull out samples for users
+- SRCC - Worked with SOM to make Nero, PHI compliant platform beyond just Box folders
+
+
+Resource Naming
+- STARR-OMOP: Identified clarity data from two hospitals in OMOP CDM. Can only access subsets for specific IRB approved projects?
+- STARR-OMOP-deid: De-identified version + clinical text and annotations, so risk of incidental PHI. Still High-Risk
+- STARR-OMOP-deid-Lite: Above without clinical text and annotations
+  (Very similar to STARR-Tahoe datalake that PHS was hosting, except with OMOP formatting)
+- STARR-OMOP de-id Lite 1%
+
+Risk Classifications (can be institution specific)
+- https://uit.stanford.edu/guide/riskclassifications
+- Low Risk - Publicly available, doesn't matter if disclose
+- Medium Risk - Not if if disclosed, not generally available
+- High Risk - Stanford has legal requirements to protect and would have to report/investigate if disclose.
+
+  HIPAA Identifiers
+  - Name
+  - Dates
+  - Telephone
+  - Geopgrahic
+  - SSN
+  - Email
+  - MRN other account numbers
+  - ...
+
+Minimum Security Requirements
+- https://uit.stanford.edu/guide/securitystandards
+
+
+
+= OMOP Common Data Model =
+Concept Table - One True Code Table
+- Fields named "_code" are the original source strings (e.g., ICD9, ICD10, SNOMED codes, MeSH, etc.), whereas  "_ID"
+fields reference OMOP unique concept_id sequence numbers, meant to be a universal global ID.
+
+
+ATHENA tool for public browsing of concept relationships
+- http://athena.ohdsi.org
+
+RX Nav
+- Browse through option of RxNorm drug relationships
+- Or directly query traverse OMOP concept table and concept_ancestor tables to find items with component ingredients
+
+
diff --git a/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.03.txt b/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.03.txt
new file mode 100644
index 00000000..d4fc096b
--- /dev/null
+++ b/scripts/DevWorkshop/STARR-OMOP/WorkshopNotes.03.txt
@@ -0,0 +1,104 @@
+12/17/2019
+
+= PreConditions =
+- Contact SRCC to be granted access to STARR-OMOP database?
+- Get link from Priya Desai so have GCP project access
+
+
+
+
+
+Tutorial 3 Slide Deck
+https://docs.google.com/presentation/d/14p831SdHJHmCL8dF-nLU3_8Gj8cgYbGSy7Gc1BKoNeo/edit#slide=id.g63d9e8e100_2_36
+
+
+
+Notebook on using Nero on-prem compute instance to connect to GCP BigQuery Database
+
+https://code.stanford.edu/starr/nero-starr-notebooks/blob/master/markdown/Accessing_BigQuery_Datasets_using_Jupyter_notebooks_running_on_Nero-On-Prem.md
+
+
+Have to manually type in (copy-paste doesn't work through terminal???) gcloud auth URL and code. Or maybe create JSON key file locally and then just upload that file to Nero on-prem compute instance? 
+
+Then saves authentication key file to
+
+~/.config/gcloud/application_default_credentials.json
+
+
+
+
+
+STARR-OMOP-deid with notes, even with mixed in fake ID info, still high-risk with possible embedded PHI
+
+Not allowed to direct connect to that project (som-rit-phi-starr-prod). Have to authenticate/connect through own som-nero-phi-jonc101 project, which can then query to the starr-prod project (incurring logs, query costs, results storage on your own project).
+
+Could in theory copy all of the contents into your own project, but then have to responsible for whatever happens there.
+
+
+Nero Compute environment to maintain PHI compliant compute infrastructure.
+
+"On-prem" have on-premises computers can use, including GPUs
+Vs. Nero GCP have fully scalable cloud infrastructure can use
+
+Prefer to use Jupyter notebooks, but can get terminal access. SLURM for job control.
+No internet access however, so can't use CURL or WGET to access more modules. Have to contact SRCC for installation.
+
+
+
+1pcent sample for convenience of developing and testing queries and ideas out, but will also have access to full dataset that sample is from.
+
+
+Note table
+- Note_id
+- Person_id
+- Note_datetime
+- Note_type_concept_id: What type of note (discharge summary, H&P, etc.)
+- Note_class_concept_id: E.g., telephone vs. student?
+- Note_source_Value - ID of origin record (e.g., Clarity note_id or order_proc_id for procedure notes/reports)
+
+Note_NLP
+- NLP algorithm pre-processing of raw note text to pull out clinical concept vocabulary instances
+- Term modifiers for negation, prior history, experience of other (than patient), etc.
+- Uses NegEx and ConText (LePendu + Chapman). 
+  - A bit different than Shah lab pipeline, that adds more ontology reconciliation.
+  - Don't try to deal with abbreviations or words <4 letters, so avoid misassignment. 
+    Could add more in future.
+- Records presence of concepts in note, including offset in note
+- Lexical variant column is (close to) the snippet from note that generated NLP concept? Verbal representation of the concept being extracted?
+- Helpful to trackdown cohort and other info occurrence that was missed in structured data
+- Concept vocabulary is same as in other fields (e.g., condition occurrences)
+- Complete string matching now, not trying to do stemming or lemmatization
+
+Approaches to De-identifying Data
+- Safe Harbor: Remvoe 18 PHI elements
+- Other options
+  - Delete
+  - Substitute with random identifier
+  - Add random numbers
+  - Jitter dates (consistent for each patient, but different per patient) within +/- 30 days (used to be 180 days)
+  - Truncation - Date years only
+  - Masking: Replace identifying elements with placeholder (e.g., "[NAME]")
+  - Generic: Replace with generic string (e.g., 999-99-9999)
+  - Surrogate: Replace identifiers with credible / realistic element. E.g., Jon Smith can replace with Dan Brown (for names and locations) "Hiding in Plain Sight," Carrell D, 2013
+
+Use multiple de-identifying approaches, while trying to retain sensitivity over specificity.
+
+STARR "TiDE" Approach
+- RegEx matches for known PHIs and NLP Entity Recognition
+- Replace surrogate (Name/Location) or generic. Pull surrogates from Bay Area census database, FDA, etc.
+- Dates: Generic replacement or jitter
+- Considered DeID data, but compliance/privacy still considers it High-Risk. Would need IRB if want the original note content, but above can already reach as pre-IRB, preparatory to research resource.
+- Java implementation, very fast to run RegEx, so not compute time limits. Conditional Random Fields running as well.
+
+Quality Control with sampling from common note types to count up "leak rate." Not a clear target goal though.
+
+Flowsheets
+- Massive size, 4B rows, bottleneck for processing. Generic catch all that should go through more breakdown, but right now just gets dropped into Observation JSONs.
+- Trying to run de-id pipeline on flowsheet hits bottleneck, as BigQuery default limits processing to 10TB per day
+
+
+
+
+For more additions or reidentification customization, probably need custom cuts of the datalake in future. But would still benefit of being in OMOP and notes extract form, etc.
+
+
diff --git a/scripts/DevWorkshop/UnitTesting/ApplicationModule.py b/scripts/DevWorkshop/UnitTesting/ApplicationModule.py
new file mode 100644
index 00000000..389eed25
--- /dev/null
+++ b/scripts/DevWorkshop/UnitTesting/ApplicationModule.py
@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+"""
+Application module to perform example calculation and data manipulation functions.
+
+!!!! This is test demonstration code that goes with the TestFirstDev workshop.
+!!!! There are supposed to be bugs in the code. 
+!!!! Do NOT commit the fixes back into the code repository.
+!!!! The point is to allow new people to encounter, identify, and debug them.
+"""
+
+import sys, os;
+import time;
+import json;
+from optparse import OptionParser
+import re;
+import string;  # FYI: See string.punctuation, and str.strip functions 
+
+COMMENT_TAG = "#";
+
+class ApplicationClass:
+    def extractWordsByIndex(self, wordIndex, inputFile, outputFile):
+        """Look through each line of the input file
+        and output a respective line in the output file 
+        containing the k-th word of each input line (k=wordIndex).
+        If there is no k-th word, then output an empty string.
+        For example, if k=2 and a line of text is "This is an example line", 
+        the function should identify the SECOND word of the line: "is".
+        """
+        k = wordIndex;
+        for line in inputFile:
+            words = line.split(); # Breakup up the line of text into words separated by whitespace
+            selectedWord = "";  # Default to empty string if cannot find a word
+            if k < len(words):
+                selectedWord = words[k];
+            print(selectedWord, file=outputFile);
+
+    def fibonacci(self, n):
+        """Simple function to output the n-th Fibonacci number.
+
+        # Fibonacci example
+        # Fib(n) = Fib(n-1) + Fib(n-2)
+        # Fib(1) = Fib(2) = 1
+        #
+        #   n       1   2   3   4   5   6   7   8   9   10
+        #   Fib(n)  1   1   2   3   5   8   13  21  34  55
+        """
+        raise NotImplementedError();    # Function not completed yet, stub needs to be implemented
+
+    def main(self, argv):
+        """Main method, deigned to parse command-line arguments"""
+        usageStr =  "usage: %prog [options] <inputFile> <outputFile>\n"+\
+                    "   <inputFile>    Input file to process.\n"+\
+                    "   <ouputFile>    Result file to generate.\n"
+        parser = OptionParser(usage=usageStr)
+        parser.add_option("-w", "--wordIndex",  dest="wordIndex", help="If set wordIndex=k, then look for and output the k-th word of each input line. If there is no k-th word, then output an empty string.");
+        parser.add_option("-f", "--fibonacci",  dest="fibonacci", help="If a value is provided, interpret as n, and print out the n-th Fibonacci number to stdout. Fib(n) = Fib(n-1) + Fib(n-2), Fib(<=2) = 1.");
+        (options, args) = parser.parse_args(argv[1:])
+
+        print("Starting: "+str.join(" ", argv), file=sys.stderr);
+        timer = time.time();
+        if options.wordIndex and len(args) > 1:
+            # Parse out command-line arguments
+            wordIndex = int(options.wordIndex);
+            inputFile = open(args[0]);
+            outputFile = open(args[1],"w");
+            
+            # Print comment line with arguments to allow for deconstruction later as well as extra results
+            summaryData = {"argv": argv};
+            print(COMMENT_TAG, json.dumps(summaryData), file=outputFile);
+
+            # Run application code to process input and send results to output
+            self.extractWordsByIndex(wordIndex, inputFile, outputFile);
+        
+        elif options.fibonacci:
+            n = int(options.fibonacci);
+            fibonacciResult = self.fibonacci(n);
+            print(fibonacciResult);
+        
+        else:
+            parser.print_help()
+            sys.exit(-1)
+
+        timer = time.time() - timer;
+        print("%.3f seconds to complete" % timer, file=sys.stderr);
+
+if __name__ == "__main__":
+    # Python boilerplate to run the code below if run directly from the commandline
+    # In this case, just create an instance of the ApplicationClass and run it's "main" method
+    instance = ApplicationClass();
+    instance.main(sys.argv);
diff --git a/scripts/DevWorkshop/UnitTesting/TestApplicationModule.py b/scripts/DevWorkshop/UnitTesting/TestApplicationModule.py
new file mode 100644
index 00000000..903a5ef0
--- /dev/null
+++ b/scripts/DevWorkshop/UnitTesting/TestApplicationModule.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package
+
+!!!! This is test demonstration code that goes with the TestFirstDev workshop.
+!!!! There are supposed to be bugs in the code. 
+!!!! Do NOT commit the fixes back into the code repository.
+!!!! The point is to allow new people to encounter, identify, and debug them.
+"""
+
+import sys, os
+from io import StringIO
+import unittest
+
+from ApplicationModule import ApplicationClass;
+
+RUNNER_VERBOSITY=2; # How much log text you want the test runner to express
+
+TEMP_FILENAME = "tempData.TestApplicationModule.txt";
+SAMPLE_INPUT_FILE_STR = \
+"""To be, or not to be, Ay there's the point,
+To Die, to sleep, is that all? Aye all:
+No to sleep, to dream, aye marry there it goes,
+For in that dream of death, when we awake,
+"""
+
+class TestApplicationModule(unittest.TestCase):
+    def setUp(self):
+        """Prepare state for test cases.
+        For example, preparing test input files, database connections and sample data.
+        """
+        unittest.TestCase.setUp(self);  # Superclass setUp
+        
+        # Instance to test on
+        self.app = ApplicationClass();
+
+        # Sample input lines.
+        # Can convert into a "file" by wrapping a StringIO object around it, 
+        #   but will illustrate creating temp file here
+        self.tempFile = open(TEMP_FILENAME,"w");
+        self.tempFile.write(SAMPLE_INPUT_FILE_STR);
+        self.tempFile.close();
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps.
+        For example, deleting any temp files or temp data records in the database.
+        """
+        if os.path.exists(TEMP_FILENAME):
+            os.remove(TEMP_FILENAME);
+        unittest.TestCase.tearDown(self);   # Superclass tearDown
+
+    def test_extractWordsByIndex(self):
+        # Prepare sample input into application code and respective expected output
+        wordIndex = 1;
+        inputFile = open(TEMP_FILENAME);
+        expectedOutputList = ["To","To","No","For"];
+        
+        # Run application code against sample input, and collect output
+        outputFile = StringIO();    # Empty IO object can write to like a file, but will just save as an in memory string
+        self.app.extractWordsByIndex(wordIndex, inputFile, outputFile);
+        inputFile.close();
+        # Extract the sample results and verify they match the expected results
+        actualOutputFileStr = outputFile.getvalue();
+        actualOutputList = actualOutputFileStr[:-1].split("\n"); # Get one value per line, ignoring the ending newline
+        self.assertEqual(expectedOutputList, actualOutputList);
+
+
+        ######## More test iterations ########
+        # Prepare sample input into application code and respective expected output
+        wordIndex = 3;
+        inputFile = open(TEMP_FILENAME);
+        expectedOutputList = ["or","to","sleep","that"];
+        
+        # Run application code against sample input, and collect output
+        outputFile = StringIO();
+        self.app.extractWordsByIndex(wordIndex, inputFile, outputFile);
+        inputFile.close();
+        # Extract the sample results and verify they match the expected results
+        actualOutputFileStr = outputFile.getvalue();
+        actualOutputList = actualOutputFileStr[:-1].split("\n"); # Get one value per line, ignoring the ending newline
+        self.assertEqual(expectedOutputList, actualOutputList);
+        # HINT: str.strip(string.punctuation)
+        # https://stackoverflow.com/questions/265960/best-way-to-strip-punctuation-from-a-string
+
+
+        ######## More test iterations ########
+        # Prepare sample input into application code and respective expected output
+        wordIndex = 10;
+        inputFile = open(TEMP_FILENAME);
+        expectedOutputList = ["point","","goes",""];
+
+        # Run application code against sample input, and collect output
+        outputFile = StringIO();
+        self.app.extractWordsByIndex(wordIndex, inputFile, outputFile);
+        inputFile.close();
+        # Extract the sample results and verify they match the expected results
+        actualOutputFileStr = outputFile.getvalue();
+        actualOutputList = actualOutputFileStr[:-1].split("\n"); # Get one value per line, ignoring the ending newline
+        self.assertEqual(expectedOutputList, actualOutputList);
+
+
+        ######## More test iterations ########
+        # Prepare sample input into application code, but this time expect an Error
+        wordIndex = 1.2;    # Invalid index. Must be an integer
+        inputFile = open(TEMP_FILENAME);
+        
+        # Run application code against sample input, and collect output
+        expectError = True;
+        actualError = False;
+        try:
+            outputFile = StringIO();
+            self.app.extractWordsByIndex(wordIndex, inputFile, outputFile);
+        except TypeError:   # Look for a specific "TypeError" because provided a floating point type instead of an integer type value
+            actualError = True;
+        inputFile.close();
+        # In this case, expect an error to have occurred (invalid wordIndex)
+        self.assertEqual(expectError, actualError);
+
+    #def test_fibonacci(self):
+    #    output = self.app.fibonacci(input)
+    #    raise NotImplementedError("Needs to be added");
+
+def suite():
+    suite = unittest.TestSuite();
+    # Automatically looks for any method whose name starts with "test" and assumes it is a test case
+    suite.addTest(unittest.makeSuite(TestApplicationModule));   
+
+    # Alternatively, you can itemize individual functions to test
+    #suite.addTest(TestApplicationModule('test_extractWordsByIndex'));
+    #suite.addTest(TestApplicationModule('test_fibonacci'));
+    
+    return suite;
+    
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/scripts/DevWorkshop/UnitTesting/UnitTesting.R b/scripts/DevWorkshop/UnitTesting/UnitTesting.R
new file mode 100644
index 00000000..9fc38fdb
--- /dev/null
+++ b/scripts/DevWorkshop/UnitTesting/UnitTesting.R
@@ -0,0 +1,90 @@
+# Unit Testing in R 
+
+# Jonathan Chiang 
+
+#install.packages("testthat")
+#install.packages("devtools")
+
+# adapted from 
+# http://r-pkgs.had.co.nz/tests.html
+
+library(testthat)
+library(devtools)
+
+tmp <- tempfile()
+
+#setwd("/Users/jonc101/Documents/Biomedical_Data_Science/data_engineer/")
+
+# A context defines a set of tests that test related functionality. 
+# Usually you will have one context per file, 
+# but you may have multiple contexts in a single file if you so choose.
+
+context("String length")
+library(stringr)
+
+test_that("str_length is number of characters", {
+  expect_equal(str_length("a"), 1)
+  expect_equal(str_length("ab"), 2)
+  expect_equal(str_length("abc"), 3)
+})
+
+test_that("str_length of factor is length of level", {
+  expect_equal(str_length(factor("a")), 1)
+  expect_equal(str_length(factor("ab")), 2)
+  expect_equal(str_length(factor("abc")), 3)
+})
+
+test_that("str_length of missing is missing", {
+  expect_equal(str_length(NA), NA_integer_)
+  expect_equal(str_length(c(NA, 1)), c(NA, 1))
+  expect_equal(str_length("NA"), 2)
+})
+
+# Example of FAILING A TEST 
+
+test_that("str_length of factor is length of level failure ", {
+  expect_equal(str_length(factor("a")), 2)
+  expect_equal(str_length(factor("ab")), 3)
+  expect_equal(str_length(factor("abc")), 4)
+})
+
+
+# Tests are organised hierarchically: 
+# expectations are grouped into tests which are organised in files:
+  
+# An expectation is the atom of testing. 
+# It describes the expected result of a computation: 
+# Does it have the right value and right class? 
+# Does it produce error messages when it should? 
+# An expectation automates visual checking of results in the console. 
+# Expectations are functions that start with expect_.
+
+# A test groups together multiple expectations to test the output from a simple function, 
+# a range of possibilities for a single parameter from a more complicated function, 
+# or tightly related functionality from across multiple functions. 
+# This is why they are sometimes called unit as they test one unit of functionality. 
+# A test is created with test_that() .
+
+# -----------------------------
+# General Tips for Writing Test 
+# -----------------------------
+
+# Each test should have an informative name and cover a single unit of functionality. 
+# The idea is that when a test fails, you’ll know what’s wrong and where in your code to look for the problem. 
+# You create a new test using test_that(), with test name and code block as arguments. 
+# The test name should complete the sentence “Test that …”. The code block should be a collection of expectations.
+
+
+# -----------------------------
+# What to test 
+# -----------------------------
+
+# Whenever you are tempted to type something into a print statement or a debugger expression, 
+# write it as a test instead. — Martin Fowler
+
+# Focus on testing the external interface to your functions
+# Strive to test each behaviour in one and only one test
+# Avoid testing simple code that you’re confident will work
+# Always write a test when you discover a bug
+
+
diff --git a/scripts/DevWorkshop/UnitTesting/__init__.py b/scripts/DevWorkshop/UnitTesting/__init__.py
new file mode 100644
index 00000000..4e16c564
--- /dev/null
+++ b/scripts/DevWorkshop/UnitTesting/__init__.py
@@ -0,0 +1 @@
+# Nothing much for now, just enable importing of various modules as a common package
diff --git a/scripts/DevWorkshop/sampleData/abstractLines.txt b/scripts/DevWorkshop/sampleData/abstractLines.txt
new file mode 100644
index 00000000..cb9e13c2
--- /dev/null
+++ b/scripts/DevWorkshop/sampleData/abstractLines.txt
@@ -0,0 +1,50 @@
+Over the past two years, our understanding of anti-hyperglycemic medications used to treat patients with type 2 diabetes (T2D) has fundamentally changed. Before the EMPA-REG OUTCOME trial, agents used to lower blood glucose were felt to prevent or delay the development of microvascular complications, but were not known to definitively reduce cardiovascular risk or mortality. Previous studies with then novel sodium-glucose cotransport-2 (SGLT2) inhibitors demonstrated improvements in several cardiovascular and renal risk factors, including HbA1c, blood pressure, weight, renal hyperfiltration, and albuminuria. However, as with other antihyperglycemic drugs, it could not be known if these salutary effects would translate into improved cardiorenal outcomes. In the EMPA-REG OUTCOME trial, SGLT2 inhibition with empagliflozin reduced the primary outcome of major adverse cardiovascular events (MACE), while also reducing mortality, hospitalization for heart failure, and progression of diabetic kidney disease. In the CANVAS Program trials using canagliflozin, the rates of the 3-point MACE endpoint, the risk of heart failure and the renal composite endpoint were also reduced, albeit with an increased risk of lower extremity amputation and fracture. As a result, clinical practice guidelines recommend the consideration of SGLT2 inhibition in high-risk patient subgroups for cardiovascular risk reduction. Ongoing primary renal endpoint trials will inform the cardio-metabolic-renal community about how to optimally treat patients with chronic kidney disease - including those with and without diabetes. Our aim is to review the rationale for renal protection with SGLT2 inhibitors, and their current place in the clinical management of patients with kidney disease.
+Compared to enalapril, use of angiotensin-receptor blocker and neprilysin inhibitor sacubitril/valsartan to treat patients with heart failure and reduced ejection fraction (HFrEF) is associated with substantial reductions in both cardiovascular mortality and heart failure progression. The purpose of this review is to discuss the real-world experience of sacubitril/valsartan.    In the years following the publication of the landmark PARADIGM-HF trial in 2014 and its subsequent FDA approval, a growing evidence base supports the safety and efficacy of sacubitril/valsartan in a broad spectrum of patients with HFrEF. Updated clinical practice guidelines have embraced the use of sacubitril/valsartan in preference to ACE inhibitors or ARBs in selected patients. In this review, we highlight the clinical trials that led to these key updates to clinical guidelines, offer practical strategies for patient selection and utilization in clinical practice, and identify important areas of uncertainty that require future research.
+The goal of this paper is to provide a summary of the new recommendations in the most recent 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. The intent is to provide the background and the supporting evidence for the recommendations and to provide practical guidance for management strategies in treatment of heart failure patients.    In the 2017 ACC/AHA/HFSA Focused Update of HF guidelines, important additions include new information on biomarkers, specifically on the topics of the diagnostic, prognostic role of natriuretic peptides in heart failure, and the role of natriuretic peptides in screening in patients high risk for HF and prevention of HF. There are important recommendations for treatment of patients with HF with reduced EF (HFrEF), including the beneficial role of angiotensin receptor blocker and neprilysin inhibition (ARNI) treatment in reducing outcomes including mortality, ivabradine in reducing heart failure hospitalizations in stable HFrEF patients with sinus rhythm and heart rate\u2009\u2265\u200970 bpm despite \u03b2-blockers. In patients with HF with preserved EF (HFpEF), though there are no studies demonstrating survival benefit, potential benefit with aldosterone antagonism in reducing HF hospitalizations is noted. In treatment of comorbidities, optimization of blood pressure control to less than 130 mmHg is recommended in hypertensive patients to prevent HF or in patients with hypertension and HFrEF or HFpEF. In addition to recognition on the potential role of treatment of iron deficiency anemia to improve symptoms and functional capacity, caution against use of adaptive servo-ventilation in patients with HFrEF and central sleep apnea and against use of erythropoietin stimulating agents in patients with HFrEF is provided. There are new treatment strategies that are associated with significant improvements in mortality and other outcomes in patients with HF. Successful management of HF requires recognition of indications, contraindications, benefits, safety, and risk of these new therapies. In addition to incorporation of these new treatment strategies, it is critical to focus also on patient education, care coordination, identification of goals of care, monitoring, management of comorbidities, and individualization of therapies. New treatment modalities increase the choices for treatment and provide the opportunity to implement individualized treatment strategies for our patients.
+The diverse causes of right-sided heart failure (RHF) include, among others, primary cardiomyopathies with right ventricular (RV) involvement, RV ischemia and infarction, volume loading caused by cardiac lesions associated with congenital heart disease and valvular pathologies, and pressure loading resulting from pulmonic stenosis or pulmonary hypertension from a variety of causes, including left-sided heart disease. Progressive RV dysfunction in these disease states is associated with increased morbidity and mortality. The purpose of this scientific statement is to provide guidance on the assessment and management of RHF.    The writing group used systematic literature reviews, published translational and clinical studies, clinical practice guidelines, and expert opinion/statements to summarize existing evidence and to identify areas of inadequacy requiring future research. The panel reviewed the most relevant adult medical literature excluding routine laboratory tests using MEDLINE, EMBASE, and Web of Science through September 2017. The document is organized and classified according to the American Heart Association to provide specific suggestions, considerations, or reference to contemporary clinical practice recommendations.    Chronic RHF is associated with decreased exercise tolerance, poor functional capacity, decreased cardiac output and progressive end-organ damage (caused by a combination of end-organ venous congestion and underperfusion), and cachexia resulting from poor absorption of nutrients, as well as a systemic proinflammatory state. It is the principal cause of death in patients with pulmonary arterial hypertension. Similarly, acute RHF is associated with hemodynamic instability and is the primary cause of death in patients presenting with massive pulmonary embolism, RV myocardial infarction, and postcardiotomy shock associated with cardiac surgery. Functional assessment of the right side of the heart can be hindered by its complex geometry. Multiple hemodynamic and biochemical markers are associated with worsening RHF and can serve to guide clinical assessment and therapeutic decision making. Pharmacological and mechanical interventions targeting isolated acute and chronic RHF have not been well investigated. Specific therapies promoting stabilization and recovery of RV function are lacking.    RHF is a complex syndrome including diverse causes, pathways, and pathological processes. In this scientific statement, we review the causes and epidemiology of RV dysfunction and the pathophysiology of acute and chronic RHF and provide guidance for the management of the associated conditions leading to and caused by RHF.
+Exercise rehabilitation in heart failure patients has been shown to improve quality of life (QoL) and survival. It is also recommended in clinical practice guidelines for ventricular assist device (VAD) recipients. However, there have only been two meta-analyses on the effects of exercise rehabilitation in VAD patients, on only two outcomes. The objective of the review was to quantitatively evaluate the effect of exercise rehabilitation in VAD recipients on functional capacity, exercise physiology parameters, chronotropic responses, inflammatory biomarkers and neurohormones, heart structure and function, and clinical outcomes. The following databases were systematically searched: CCTR, CDSR, CINAHL, EMBASE, PsycInfo, and Medline through to November 2015, for studies reporting on VAD recipients receiving \u2265\u20092 sessions of aerobic training. Citations were considered for inclusion, and data were extracted in included studies as well as quality assessed, each by two investigators independently. Random-effects meta-analyses were performed where possible. The meta-analysis showed that compared to usual care, exercise rehabilitation significantly improved peak VO
+Differential diagnosis of lower limb oedema is a common exercise in clinical practice. Taking the example of a patient presenting with such clinical picture in the presence of type 2 diabetes and arterial hypertension with coronary heart disease and chronic kidney disease as comorbidities, we discuss here the respective contributions of congestive heart failure, renal impairment (and possibly nephrotic syndrome) and liver disease in the development of lower limb oedema. The focus is made on a careful patient history and a meticulous clinical examination, two crucial steps that should allow prescribing well selected simple complementary procedures and rapidly make the final diagnosis.
+The study sought to characterize patient- and hospital-level variation in early angiography use among non-ST-segment elevation myocardial infarction (NSTEMI) patients.    Contemporary implementation of guideline recommendations for early angiography use in NSTEMI patients in the United States have not been described.    The study analyzed NSTEMI patients included in ACTION (Acute Coronary Treatment and Intervention Outcomes Network) registry (2012 to 2014) who underwent in-hospital angiography. Timing of angiography was categorized as early (\u226424 h) vs. delayed (>24 h). The study evaluated factors associated with early angiography, hospital-level variation in early angiography use, and the relationship with quality-of-care measures.    A total of 79,760 of 138,688 (57.5%) patients underwent early angiography. Factors most strongly associated with delayed angiography included weekend or holiday presentation, lower initial troponin ratio values, higher initial creatinine values, heart failure on presentation, and older age. Median hospital-level use of early angiography was 58.5% with wide variation across hospitals (21.7% to 100.0%). Patient characteristics did not differ substantially across hospitals grouped by tertiles of early angiography use (low, middle, and high). Hospitals in the highest tertile tended to more commonly use guideline-recommended medications and had higher defect-free care quality scores.    In contemporary U.S. practice, high-risk clinical characteristics were associated with lower use of early angiography in NSTEMI patients; hospital-level use of early angiography varied widely despite few differences in case mix. Hospitals that most commonly utilized early angiography also had higher quality-of-care metrics, highlighting the need for improved NSTEMI guideline adherence.
+Two classes of antidiabetic agents have shown a cardiovascular and renal protection in patients with type 2 diabetes and high cardiovascular risk. Empagliflozin, a sodium-glucose cotransporter type 2 (SGLT2) inhibitor, and liraglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, were granted a reduction of major cardiovascular events and mortality after the positive results of EMPA-REG OUTCOME and LEADER outcome trials, respectively. Protection mechanisms most probably differ between the two pharmacological classes and are perhaps complementary. The question that may arise in clinical practice is to know which drug should be selected between these two options in a patient with type 2 and established cardiovascular disease, before considering a potential combination. The aim of this article is to discuss about the best therapeutic choice according to the patient individual profile and the specificities of each molecule.
+The Association of American Medical Colleges' (AAMC) initiative for Core Entrustable Professional Activities for Entering Residency includes as an element of Entrustable Professional Activity 13 to "identify system failures and contribute to a culture of safety and improvement." We set out to determine the feasibility of using medical students' action learning projects (ALPs) to expedite implementation of evidence-based pathways for three common patient diagnoses in the emergency department (ED) setting (Atrial fibrillation, congestive heart failure, and pulmonary embolism).    These prospective quality improvement (QI) initiatives were performed over six months in three Northeastern PA hospitals. Emergency physician mentors were recruited to facilitate a QI experience for third-year medical students for each project. Six students were assigned to each mentor and given class time and network infrastructure support (information technology, consultant experts in lean management) to work on their projects. Students had access to background network data that revealed potential for improvement in disposition (home) for patients.    Under the leadership of their mentors, students accomplished standard QI processes such as performing the background literature search and assessing key stakeholders' positions that were involved in the respective patient's care. Students effectively developed flow diagrams, computer aids for clinicians and educational programs, and participated in recruiting champions for the new practice standard. They met with other departmental clinicians to determine barriers to implementation and used this feedback to help set specific parameters to make clinicians more comfortable with the changes in practice that were recommended. All three clinical practice guidelines were initiated at consummation of the students' projects. After implementation, 86% (38/44) of queried ED providers felt comfortable with medical students being a part of future ED QI initiatives, and 84% (26/31) of the providers who recalled communicating with students on these projects felt they were effective.    Using this novel technique of aligning small groups of medical students with seasoned mentors, it is feasible for medical students to learn important aspects of QI implementation and allows for their engagement to more efficiently move evidence-based medicine from the literature to the bedside.
+Sacubitril/valsartan (LCZ696) is a first-in-class, novel-acting, angiotensin receptor neprilysin inhibitor (ARNI) that provides inhibition of neprilysin and the angiotensin (AT    A recent mechanistic study PARAMETER demonstrated that sacubitril/valsartan (LCZ696) is superior to angiotensin receptor blocker (ARB) monotherapy for reducing central aortic systolic pressure (primary endpoint) as well as for central aortic pulse pressure (secondary endpoint) and nocturnal BP preferentially. Considering these results, sacubitril/valsartan may be an attractive therapeutic agent to treat the elderly with age-related hypertension phenotypes, such as drug-uncontrolled (resistant) hypertension characterized as systolic (central) hypertension (structural hypertension) and/or nocturnal hypertension (salt-sensitive hypertension). These are the high-risk hypertension phenotypes which are prone to develop heart failure with preserved EF and chronic kidney disease. Sacubitril/valsartan may be effective to suppress the age-related continuum from hypertension to heart failure, and it could be clinically useful not only for secondary prevention, but also as primary prevention of heart failure in uncontrolled elderly hypertensive patients.
+The ACC/AHA/HRS (American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society) guidelines recommend implantable cardioverter-defibrillator (ICD) therapy primary prevention in all patients with severely reduced left ventricular ejection fraction (\u226430%) regardless of New York Heart Association (NYHA) functional class, whereas recent European guidelines limit the indication to those with symptomatic heart failure (NYHA\u2009\u2265\u2009II). We therefore aimed to evaluate the long-term survival benefit of primary ICD therapy among postmyocardial infarction patients with and without heart failure (HF) symptoms who were enrolled in MADIT-II (Multicenter Automatic Defibrillator Implantation Trial II). We classified 1,164 MADIT-II patient groups according to the baseline NYHA class (NYHA I [n\u2009=\u2009442], NYHA II [n\u2009=\u2009425], and NYHA III [n\u2009=\u2009297]); patients with NYHA IV were excluded. Multivariate Cox proportional hazards regression modeling was performed to compare the mortality reduction with ICD versus non-ICD therapy during 8 years of follow-up between the 3 NYHA groups. The median (interquartile range) follow-up time was 7.6 (3.5 to 9) years. At 8 years of follow-up, the cumulative probability of mortality in the non-ICD treatment arm was 57% for NYHA I, 57% for NYHA II, and 76% for NYHA III (p\u2009<0.001). Multivariate models demonstrated similar long-term mortality risk reduction with ICD compared with the non-ICD treatment arm regardless of HF symptoms: NYHA I (HR\u2009=\u20090.63, 0.46 to 0.85, p\u2009=\u20090.003), NYHA II (HR\u2009=\u20090.68, 0.50 to 0.93, p\u2009=\u20090.017), and NYHA III (HR\u2009=\u20090.68, 0.50 to 0.94, p\u2009=\u20090.018); p for NYHA class by treatment arm interaction >0.10. In conclusion, primary ICD therapy provides consistent long-term survival benefit among patients with previous myocardial infarction and severe left ventricular dysfunction, regardless of HF symptoms.
+We aimed to assess the effectiveness of recommended drug prescriptions at hospital discharge on 1-year mortality in patients with heart failure (HF) and reduced ejection fraction (HFREF).    We used data from the EPICAL2 cohort study. HF patients \u226518years old with left ventricular ejection fraction (LVEF) <40% and alive at discharge were included and followed up for mortality. Socio-demographic, clinical and therapeutic data were collected at admission. Therapeutic data were collected at discharge and at 6month. Prescription of an angiotensin-converting enzyme (ACE) inhibitor (or an angiotensin II receptor blocker [ARB] in case of ACE inhibitor intolerance) and a \u03b2-blocker at discharge were considered "guideline-consistent discharge prescription" (GCDP). A frailty Cox model after propensity score (PS) matching was used to assess the association of GCDP with survival.    Among 624 patients included, the mean (SD) age was 73.6 (12.8) years; 65% were male. A total of 412 (65.6%) patients received GCDP, and 82.8% still had guideline consistent prescription at 6months. A total of 166 patients died during the follow-up, 78 in the GCDP group and 88 in the other group. Before PS matching, patients with GCDP were younger (|StDiff|=48.32%) and had higher body mass index (BMI) (|StDiff|=11.71%), lower LVEF (|StDiff|=23.13%) and lower Charlson index (|StDiff|=55.27%) than patients without GCDP. After PS matching, all characteristics were balanced between the two treatment groups, and GCDP was associated with reduced mortality (pooled HR=0.51, 95% CI [0.35-0.73]).    Prescription of ACE (or ARB) inhibitors and \u03b2-blockers for patients with HFREF may be low despite the evidence for morbidity and mortality improvement with these medications but remains associated with reduced 1-year mortality in unselected HFREF patients.
+Clinical practice guidelines currently endorse a reliance on clinical symptoms of overt left ventricular (LV) failure to time aortic valve replacement for severe aortic stenosis; however, delayed aortic valve replacement can result in irreversible LV injury and adverse outcomes. Blood metabolomic signatures possess prognostic value in heart failure; this study assesses whether they are informative in aortic stenosis.    To evaluate the value of metabolomic signatures in reflecting the extent of maladaptive LV remodeling in patients with end-stage aortic stenosis undergoing transcatheter aortic valve replacement, and to assess whether this procedure reverses metabolomic aberrations.    This study of 44 patients with symptomatic severe aortic stenosis who underwent transfemoral transcatheter aortic valve replacement at a single-center tertiary care hospital. Liquid chromatography-mass spectrometry-based metabolomic profiling was performed on blood samples collected before and 24 hours after the procedure, and analyses were conducted to identify metabolites related to the measures of LV remodeling.    We evaluated LV ejection fraction, LV mass index, and relative wall thickness, as well as levels of the acylcarnitines C16, C18:1, C18:2, C18, C26, choline, and kynurenine.    We enrolled 44 patients with severe aortic stenosis with a mean (SD) age of 81.9 (8.5) years, of whom 23 (52%) were women. The mean (SD) LV ejection fraction was 56.7% (18.2%), mean (SD) LV mass index was 117.3 (41.4) g/m2, and relative wall thickness was 0.53 (0.14). The mean \u03b2 values of acylcarnitines C16, C18:1, C18:2, C18, and C26 were independently associated with LV mass index (C16: mean, 19.24; 95% CI, 5.48-33.01; P\u2009=\u2009.008; C18:1: mean, 26.18; 95% CI, 14.04-38.32; P\u2009<\u20091.0\u2009\xd7\u200910-4; C18:2: mean, 17.42; 95% CI, 3.40-31.43; P\u2009=\u2009.02; C18: mean, 25.25; 95% CI, 10.91-39.58; P\u2009=\u2009.001; C26: mean, 19.93; 95% CI, 4.41-35.45; P\u2009=\u2009.01), even after adjustments for age, sex, diabetes status, renal function, and B-type natriuretic peptide (BNP). Circulating levels of C18:2 acylcarnitine were associated with LV ejection fraction before and after multivariable adjustment (mean, -6.11; 95% CI, -10.88 to 1.34; P\u2009=\u2009.01). Blood metabolite levels did not independently relate to relative wall thickness. Within 24 hours of transcatheter aortic valve replacement, circulating levels of C16 decreased by 30.2% (P\u2009=\u20097.3\u2009\xd7\u200910-6), C18:1 by 42.7% (P\u2009=\u20093.7\u2009\xd7\u200910-8), C18:2 by 37.3% (P\u2009=\u20095.1\u2009\xd7\u200910-6), and C18 by 38.3% (P\u2009=\u20093.4\u2009\xd7\u200910-5).    In symptomatic patients with severe aortic stenosis undergoing transcatheter aortic valve replacement, circulating levels of long-chain acylcarnitines were independently associated with measures of maladaptive LV remodeling, and metabolic perturbations lessened after procedure completion. Further efforts are needed to determine the clinical applicability of these novel biomarkers.
+Pulmonary hypertension (PH) is a serious complication to left heart disease (LHD), affecting a majority of the patients during the course of the disease. Initially, PH-LHD is caused by passive congestion of the pulmonary vessels due to increased left atrial pressures, a condition that is currently denoted as isolated post-capillary PH (Icp-PH). In the majority of patients the increased atrial pressure is a result of elevated left ventricular filling pressures. Furthermore, chronically elevated filling pressures may yield endothelial damage, resulting in structural and functional alterations in the pre-capillary bed with further elevation in pulmonary pressures as well as elevated vascular resistance, defined as combined precapillary and postcapillary PH (Cpc-PH). With previous definitions of PH-LHD it has been difficult to differentiate between the subgroups, so a new classification was presented in the 2015 PH guidelines. Despite PH-LHD being common and serious, specific therapies are lacking for the pulmonary component. Instead, treatments focus on optimizing the underlying cause of PH-LHD and involve medical as well as surgical therapies. In the present review we presents, based on the new guidelines, current knowledge on pathophysiological and pathobiological mechanisms, epidemiology, investigation and treatment of PH-LHD.
+Treatment of heart failure depends on etiology, left ventricular function and severity of symptoms. Appropriate diagnosis and treatment requires close collaboration between health care providers. In heart failure with reduced ejection fraction, pharmacological treatment and device therapies are based on solid scientific evidence. Less data exist on treatment of heart failure with preserved ejection fraction, where management of underlying diseases contributing to the syndrome is the primary goal. Patient education and self-care may reduce the need for hospitalizations and improve quality of life. Physical exercise reduces symptoms and improves exercise tolerance.
+Major bleeding is a frequent complication for patients with acute myocardial infarction (AMI) and is associated with significant morbidity and mortality.    To develop a contemporary model for inhospital major bleeding that can both support clinical decision-making and serve as a foundation for assessing hospital quality.    An inhospital major bleeding model was developed using the Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines (ACTION Registry-GWTG) database. Patients hospitalized with AMI between January 1, 2012 and December 31, 2013 across 657 hospitals were used to create a derivation cohort (n=144,800) and a validation cohort (n=96,684). Multivariable hierarchal logistic regression was used to identify significant predictors of major bleeding. A simplified risk score was created to enable prospective risk stratification for clinical care.    The rate of major bleeding in the overall population was 7.53%. There were 8 significant, independent factors associated with major bleeding: presentation after cardiac arrest (OR 2.99 [2.77-3.22]); presentation in cardiogenic shock (OR 2.22 [2.05-2.40]); STEMI (OR 1.72 [1.65-1.80]); presentation in heart failure (OR 1.55 [1.47-1.63]); baseline hemoglobin less than 12 g/dL (1.55 [1.48-1.63]); heart rate (per 10 beat per minute increase) (OR 1.13 [1.12-1.14]); weight (per 10 kilogram decrease) (OR 1.12 [1.11-1.14]); creatinine clearance (per 5-mL decrease) (OR 1.07 [1.07-1.08]). The model discriminated well in the derivation (C-statistic = 0.74) and validation (C-statistic = 0.74) cohorts. In the validation cohort, a risk score for major bleeding corresponded well with observed bleeding: very low risk (2.2%), low risk (5.1%), moderate risk (10.1%), high risk (16.3%), and very high risk (25.2%).    The new ACTION Registry-GWTG inhospital major bleeding risk model and risk score offer a robust, parsimonious, and contemporary risk-adjustment method to support clinical decision-making and enable hospital quality assessment. Strategies to mitigate risk should be developed and tested as a means to lower costs and improve outcomes in an era of alternative payment models.
+Clinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians.    Clinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6\u2009months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm.    The Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters.    NCT02742545.
+The purpose of this study was to describe subjective and objective physical activity (PA) levels of two groups of cardiovascular patients who were either post-coronary artery bypass graft (CABG) surgery or diagnosed with heart failure (HF).    A descriptive comparative design was used for this secondary analysis of data from two prior studies.    A convenience sample of 62 outpatients was used to examine PA objectively (Actiheart accelerometer) and subjectively (PA interview).    Objectively, 33% of CABG patients and no HF patients met PA recommendations of \u2265 150 min/week. Subjectively, 56% of CABG and 38% of HF patients reported meeting PA recommendations.    Few patients in the current study met PA recommendations.    Innovative rehabilitation nursing practice strategies are needed (e.g., use of activity trackers, making PA a vital sign) to assist patients in gaining the knowledge and skills to be more active and adhere to PA recommendations.
+Heart failure clinical practice guidelines recommend applying validated clinical predictive models (CPMs) to support decision making. While CPMs are now widely available, the generalizability of heart failure CPMs is largely unknown.    We identified CPMs derived in North America that predict mortality for patients with acute heart failure and validated these models in different world regions to assess performance in a contemporary international clinical trial (N=4133) of patients with acute heart failure treated with guideline-directed medical therapy. We performed independent external validations of 3 CPMs predicting in-hospital mortality, 60-day mortality, and 1-year mortality, respectively. CPM discrimination decreased in all regional validation cohorts. The median change in area under the receiver operating curve was -0.09 (range -0.05 to -0.23). Regional calibration was highly variable (90th percentile of absolute difference between smoothed observed and predicted values range <1% to >50%). Calibration remained poor after global recalibrations; however, region-specific recalibration procedures significantly improved regional performance (recalibrated 90th percentile of absolute difference range <1% to 5% across all regions and all models).    Acute heart failure CPM discrimination and calibration vary substantially across different world regions; region-specific (as opposed to global) recalibration techniques are needed to improve CPM calibration.
+To review the literature systematically and perform meta-analyses to address these questions: 1) Is there evidence that self-measured blood pressure (BP) without other augmentation is superior to office-based measurement of BP for achieving better BP control or for preventing adverse clinical outcomes that are related to elevated BP? 2) What is the optimal target for BP lowering during antihypertensive therapy in adults? 3) In adults with hypertension, how do various antihypertensive drug classes differ in their benefits and harms compared with each other as first-line therapy?    Electronic literature searches were performed by Doctor Evidence, a global medical evidence software and services company, across PubMed and EMBASE from 1966 to 2015 using key words and relevant subject headings for randomized controlled trials that met eligibility criteria defined for each question. We performed analyses using traditional frequentist statistical and Bayesian approaches, including random-effects Bayesian network meta-analyses.    Our results suggest that: 1) There is a modest but significant improvement in systolic BP in randomized controlled trials of self-measured BP versus usual care at 6 but not 12 months, and for selected patients and their providers self-measured BP may be a helpful adjunct to routine office care. 2) systolic BP lowering to a target of <130 mm Hg may reduce the risk of several important outcomes including risk of myocardial infarction, stroke, heart failure, and major cardiovascular events. No class of medications (i.e., angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, calcium channel blockers, or beta blockers) was significantly better than thiazides and thiazide-like diuretics as a first-line therapy for any outcome.
+To review the literature systematically and perform meta-analyses to address these questions: 1) Is there evidence that self-measured blood pressure (BP) without other augmentation is superior to office-based measurement of BP for achieving better BP control or for preventing adverse clinical outcomes that are related to elevated BP? 2) What is the optimal target for BP lowering during antihypertensive therapy in adults? 3) In adults with hypertension, how do various antihypertensive drug classes differ in their benefits and harms compared with each other as first-line therapy?    Electronic literature searches were performed by Doctor Evidence, a global medical evidence software and services company, across PubMed and EMBASE from 1966 to 2015 using key words and relevant subject headings for randomized controlled trials that met eligibility criteria defined for each question. We performed analyses using traditional frequentist statistical and Bayesian approaches, including random-effects Bayesian network meta-analyses.    Our results suggest that: 1) There is a modest but significant improvement in systolic BP in randomized controlled trials of self-measured BP versus usual care at 6 but not 12 months, and for selected patients and their providers self-measured BP may be a helpful adjunct to routine office care. 2) systolic BP lowering to a target of <130 mm Hg may reduce the risk of several important outcomes including risk of myocardial infarction, stroke, heart failure, and major cardiovascular events. No class of medications (i.e., angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, calcium channel blockers, or beta blockers) was significantly better than thiazides and thiazide-like diuretics as a first-line therapy for any outcome.
+Since the inception of the Canadian Cardiovascular Society heart failure (HF) guidelines in 2006, much has changed in the care for patients with HF. Over the past decade, the HF Guidelines Committee has published regular updates. However, because of the major changes that have occurred, the Guidelines Committee believes that a comprehensive reassessment of the HF management recommendations is presently needed, with a view to producing a full and complete set of updated guidelines. The primary and secondary Canadian Cardiovascular Society HF panel members as well as external experts have reviewed clinically relevant literature to provide guidance for the practicing clinician. The 2017 HF guidelines provide updated guidance on the diagnosis and management (self-care, pharmacologic, nonpharmacologic, device, and referral) that should aid in day-to-day decisions for caring for patients with HF. Among specific issues covered are risk scores, the differences in management for HF with preserved vs reduced ejection fraction, exercise and rehabilitation, implantable devices, revascularization, right ventricular dysfunction, anemia, and iron deficiency, cardiorenal syndrome, sleep apnea, cardiomyopathies, HF in pregnancy, cardio-oncology, and myocarditis. We devoted attention to strategies and treatments to prevent HF, to the organization of HF care, comorbidity management, as well as practical issues around the timing of referral and follow-up care. Recognition and treatment of advanced HF is another important aspect of this update, including how to select advanced therapies as well as end of life considerations. Finally, we acknowledge the remaining gaps in evidence that need to be filled by future research.
+Orthotopic heart transplantation (OHT) is limited by a chronic shortage of donors. With the evolution of technology, more patients have been bridged to transplant (BTT) through various pathways using various types of mechanical circulatory support. We compared short- and long-term outcomes among these various strategies of BTT.    We retrospectively reviewed 410 patients who had OHT between January 2009 and April 2015. Patients were divided into 4 groups according to BTT status: primary OHT without bridging (Group A, n\u2009=\u2009246); bridge with implantable continuous-flow left ventricular assist device (CF-LVAD) (Group B, n\u2009=\u2009130); bridge with short-term mechanical circulatory support (Group C, n\u2009=\u200916) and bridge with multiple mechanical circulatory supports, including short-term mechanical circulatory support and CF-LVAD (Group D, n\u2009=\u200918). Early and late outcomes after OHT were compared among the groups.    The total duration of device support was 110.4 patient-years, 1.8 patient-years and 21.0 patient-years in Groups B, C and D, respectively. Patients who were bridged with CF-LVAD (Groups B and D) were more likely to have larger body size, blood type O, idiopathic dilated cardiomyopathy and ischaemic cardiomyopathy as an aetiology, lower total bilirubin level and longer waiting time on the United Network for Organ Sharing Status 1A. There was no statistical difference between the 4 groups in serum panel reactive antigen levels before OHT. Hospital mortality was 4% in Group A, 8% in Group B, 13% in Group C and 6% in Group D (P\u2009=\u20090.307). Post-transplant survival at 3\u2009years was 80% in Group A, 82% in Group B, 75% in Group C and 88% in Group D (P\u2009=\u20090.752).    BTT strategies using various mechanical circulatory support devices can provide comparable clinical outcomes to primary OHT. Flexibility in the use of both short-term mechanical circulatory support and CF-LVAD is necessary depending on the patient's background.
+Acute heart failure (HF) is a prevalent disease with important socio-economic repercussions. Due to the aging of population, these values will increase in the coming years, so it may be useful to the implementation of intervention programs in these patients to decrease morbidity and mortality. A quasi-experimental prospective study (n = 262) of patients admitted at the Internal Medicine Department of the Hospital Cl\xednico Universitario Lozano Blesa, in Zaragoza, Spain, diagnosed of HF between November 2013 and October 2014 (both dates inclusive) (n = 108) followed up for 1 year was performed. Within this group, a subgroup with an intensive intervention (n = 30) was performed. The data were compared with a historical cohort of patients admitted to the same department during the same time in the previous year (from November 2012 to October 2013) (n = 154). Statistically significant differences between groups attending to the therapeutical adherence to clinical guidelines (p < 0.011) were observed. Considering the intensive intervention subgroup, statistically significant differences were observed in the rate of exitus (p < 0.032) and survival (log rank <0.030) compared to the control group. The close monitoring of patients with HF improves adherence, reduces mortality and improves survival. This May result in a decline in the use of health resources, which entails significant socio-economic benefits.
+Although large randomized clinical trials have found that primary prevention use of an implantable cardioverter-defibrillator (ICD) improves survival in patients with cardiomyopathy and heart failure symptoms, patients who receive ICDs in practice are often older and have more comorbidities than patients who were enrolled in the clinical trials. In addition, there is a debate among clinicians on the usefulness of electrophysiological study for risk stratification of asymptomatic patients with Brugada syndrome.    Our analysis has 2 objectives. First, to evaluate whether ventricular arrhythmias (VAs) induced with programmed electrostimulation in asymptomatic patients with Brugada syndrome identify a higher risk group that may require additional testing or therapies. Second, to evaluate whether implantation of an ICD is associated with a clinical benefit in older patients and patients with comorbidities who would otherwise benefit on the basis of left ventricular ejection fraction and heart failure symptoms.    Traditional statistical approaches were used to address 1) whether programmed ventricular stimulation identifies a higher-risk group in asymptomatic patients with Brugada syndrome and 2) whether ICD implantation for primary prevention is associated with improved outcomes in older patients (>75 years of age) and patients with significant comorbidities who would otherwise meet criteria for ICD implantation on the basis of symptoms or left ventricular function RESULTS: Evidence from 6 studies of 1138 asymptomatic patients were identified. Brugada syndrome with inducible VA on electrophysiological study was identified in 390 (34.3%) patients. To minimize patient overlap, the primary analysis used 5 of the 6 studies and found an odds ratio of 2.3 (95% CI: 0.63-8.66; p=0.2) for major arrhythmic events (sustained VAs, sudden cardiac death, or appropriate ICD therapy) in asymptomatic patients with Brugada syndrome and inducible VA on electrophysiological study versus those without inducible VA. Ten studies were reviewed that evaluated ICD use in older patients and 4 studies that evaluated unique patient populations were identified. In our analysis, ICD implantation was associated with improved survival (overall hazard ratio: 0.75; 95% confidence interval: 0.67-0.83; p<0.001). Ten studies were identified that evaluated ICD use in patients with various comorbidities including renal disease, chronic obstructive pulmonary disease, atrial fibrillation, heart disease, and others. A random effects model demonstrated that ICD use was associated with reduced all-cause mortality (overall hazard ratio: 0.72; 95% confidence interval: 0.65-0.79; p<0.0001), and a second "minimal overlap" analysis also found that ICD use was associated with reduced all-cause mortality (overall hazard ratio: 0.71; 95% confidence interval: 0.61-0.82; p<0.0001). In 5 studies that included data on renal dysfunction, ICD implantation was associated with reduced all-cause mortality (overall hazard ratio: 0.71; 95% confidence interval: 0.60-0.85; p<0.001).
+Although large randomized clinical trials have found that primary prevention use of an implantable cardioverter-defibrillator (ICD) improves survival in patients with cardiomyopathy and heart failure symptoms, patients who receive ICDs in practice are often older and have more comorbidities than patients who were enrolled in the clinical trials. In addition, there is a debate among clinicians on the usefulness of electrophysiological study for risk stratification of asymptomatic patients with Brugada syndrome.    Our analysis has 2 objectives. First, to evaluate whether ventricular arrhythmias (VAs) induced with programmed electrostimulation in asymptomatic patients with Brugada syndrome identify a higher risk group that may require additional testing or therapies. Second, to evaluate whether implantation of an ICD is associated with a clinical benefit in older patients and patients with comorbidities who would otherwise benefit on the basis of left ventricular ejection fraction and heart failure symptoms.    Traditional statistical approaches were used to address 1) whether programmed ventricular stimulation identifies a higher-risk group in asymptomatic patients with Brugada syndrome and 2) whether ICD implantation for primary prevention is associated with improved outcomes in older patients (>75 years of age) and patients with significant comorbidities who would otherwise meet criteria for ICD implantation on the basis of symptoms or left ventricular function RESULTS: Evidence from 6 studies of 1138 asymptomatic patients were identified. Brugada syndrome with inducible VA on electrophysiological study was identified in 390 (34.3%) patients. To minimize patient overlap, the primary analysis used 5 of the 6 studies and found an odds ratio of 2.3 (95% CI: 0.63-8.66; p=0.2) for major arrhythmic events (sustained VAs, sudden cardiac death, or appropriate ICD therapy) in asymptomatic patients with Brugada syndrome and inducible VA on electrophysiological study versus those without inducible VA. Ten studies were reviewed that evaluated ICD use in older patients and 4 studies that evaluated unique patient populations were identified. In our analysis, ICD implantation was associated with improved survival (overall hazard ratio: 0.75; 95% confidence interval: 0.67-0.83; p<0.001). Ten studies were identified that evaluated ICD use in patients with various comorbidities including renal disease, chronic obstructive pulmonary disease, atrial fibrillation, heart disease, and others. A random effects model demonstrated that ICD use was associated with reduced all-cause mortality (overall hazard ratio: 0.72; 95% confidence interval: 0.65-0.79; p<0.0001), and a second "minimal overlap" analysis also found that ICD use was associated with reduced all-cause mortality (overall hazard ratio: 0.71; 95% confidence interval: 0.61-0.82; p<0.0001). In 5 studies that included data on renal dysfunction, ICD implantation was associated with reduced all-cause mortality (overall hazard ratio: 0.71; 95% confidence interval: 0.60-0.85; p<0.001).
+Despite the improvements in the management of hypertension during the last three decades, it continues to be one of the leading causes of cardiovascular morbidity and mortality worldwide. Effective and sustained reductions in blood pressure (BP) reduce the incidence of myocardial infarction, stroke, congestive heart failure and cardiovascular death. However, the proportion of patients who achieve the recommended BP goal (< 140/90 mmHg) is persistently low, worldwide. Poor adherence to therapy, complex therapeutic regimens, clinical inertia, drug-related adverse events and multiple risk factors or comorbidities contribute to the disparity between the potential and actual BP control rate. Previously we published a practical therapeutic platform for the treatment of hypertension based on clinical evidence, guidelines, best practice and clinical experience. This platform provides a personalised treatment approach and can be used to improve BP control and simplify treatment. It uses long-acting, effective and well-tolerated angiotensin receptor blocker (ARB) olmesartan, in combination with a calcium channel blocker amlodipine, and/or a thiazide diuretic hydrochlorothiazide. These drugs were selected based on the availability in most European Countries of single-pill, fixed formulations in a wide range of doses for both dual- and triple-drug combinations. The platform approach could be applied to other ARBs or angiotensin-converting enzyme inhibitors available in single-pill, fixed-dose combinations. Here, we present an update, which takes into account the results of the recently published studies and extends the applicability of the platform to common conditions that are often neglected or poorly considered in clinical practice guidelines.
+Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework-termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS.
+In the past years, cardiovascular mortality has decreased but despite these cardiovascular diseases are responsible for millions of deaths every year in the world and approximately 25% of which are sudden cardiac death (SCD). Implantable defibrillators (ICD) is proven therapy used in primary and secondary SCD prevention. Currently majority of devices use transvenous leads inserted predominantly into the right heart for both pacing and defibrillation. On the other hand, ICD may cause complica-tions, including inappropriate shocks, device-related infection and lead failure. Problems with transvenous leads prompted the development of a subcutaneous defibrillator (S-ICD) with an electrode system that is placed entirely subcutaneously, outside the thoracic cavity. The device may be useful when venous access is difficult, in young patients and in patients at particular risk of infection. S-ICD is not suitable for patients with indications for bradycardia pacing, cardiac resynchronisation therapy and in cases with tachyarrhythmia easily terminated by antitachycardia pacing.
+Oxygenator failure during cardiopulmonary bypass constitutes a life-threatening event, especially when perfusion is conducted under normothermia. An alternative solution to emergency oxygenator changeover is described.    A supplementary oxygenator is added in the venous line without interrupting perfusion. De-airing is achieved through the cardiotomy reservoir. Oxygen supply is adapted to ensure physiologic partial oxygen pressure.    On 5 occasions in the past 4\u2009years, Capiox Baby FX 05 oxygenator (n\u2009=\u20094) and Capiox FX15 (n\u2009=\u20091) failed to exchange blood gases after bypass run ranging from 290\u2009min to 563\u2009min. Hypoxia ensued with partial oxygen pressure values of 49-79\u2009mmHg with a fraction of inspired oxygen of 1. An additional veno-venous Terumo Capiox FX 05 oxygenator immediately improved oxygenation with resulting partial oxygen pressure increasing to at least 291\u2009mmHg.    An additional veno-venous oxygenator effectively corrects failing oxygenator during cardiopulmonary bypass. The method does not require circulation arrest. It does not carry the risk of air embolism. It can be carried out without any help from a second perfusionist or member of operation team.
+With an estimated prevalence of 5.8 million in the USA and over 23 million people worldwide, heart failure (HF) is growing in epidemic proportions. Despite the use of guideline-directed medical therapies such as angiotensin-converting enzyme inhibitors, beta-adrenergic blockers, angiotensin receptor blockers, and mineralocorticoid receptor antagonists for chronic systolic HF for almost two decades, HF remains a leading cause of morbidity, mortality, and health care expenditures. The Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Converting Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure (PARADIGM-HF) trial provided compelling evidence for the cardiovascular and mortality benefit of sacubitril/valsartan when compared to enalapril in patients with heart failure and reduced ejection fraction (HFrEF). Sacubitril/valsartan performed better than enalapril across various HFrEF patient characteristics and showed substantial benefit in patients with other common comorbidities. Following the trial, the US Food and Drug Administration approved this drug for the treatment of HF. Various international HF consensus guidelines endorse sacubitril/valsartan as a class I recommendation for the management of symptomatic HFrEF. Although this high-quality clinical study is the largest and the most globally represented trial in HFrEF patients, concerns have been raised regarding the generalizability of the trial results in real-world HF population. The gaps in US Food and Drug Administration labeling and guideline recommendations might lead to this medication being used in a larger population than it was studied in. In this review, we will discuss the current role of sacubitril/valsartan in the management of HF, concerns related to PARADIGM-HF and answers, shortcomings of this novel drug, effects on patient characteristics, real-world eligibility, and the role of ongoing and further investigations to clarify the profile of sacubitril/valsartan in the management of HF.
+To determine the optimum chest compression site during cardiopulmonary resuscitation (CPR) with regard to heart failure (HF) by applying three-dimensional (3D) coordinates on computed tomography (CT).    This retrospective, cross-sectional study involved adults who underwent echocardiography and CT on the same day from 2007 to 2017. Incomplete CT images or information on HF, cardiac medication between echocardiography and CT, or thoracic abnormalities were excluded. Cases were checked whether they had HF through symptom/sign assessment, N-terminal pro-B type natriuretic peptide, and echocardiography. We set the xiphisternal joint's midpoint as the reference (0, 0, 0) to draw a 3D coordinate system, designating leftward, upward, and into-the-thorax directions as positive. The coordinate of the maximum LV diameter's midpoint (P_max.LV) was identified.    Enrolled were 148 patients (63.0\xb115.1 years) with 87 females and 76 HF cases. P_max.LV of HF cases was located more leftwards, lower, and deeper than non-HF cases (5.69\xb10.98, -1.51\xb11.67, 5.76\xb11.09 cm vs. 5.00\xb10.83, -0.99\xb11.36, 5.25\xb10.71 cm, all p<0.05). Fewer HF cases had their LV compressed than non-HF cases (59.2% vs. 77.8%, p=0.025) when being compressed according to the current guidelines. The aorta (vs. LV) was compressed in 85.5% and 81.9% of HF and non-HF cases, respectively, at 3 cm above the xiphisternal joint. At 6cm above the joint, the highest allowable position according to the current guidelines, all victims would have their aorta compressed directly during CPR rather than the LV.    The lowest possible sternum just above the xiphisternal joint should be compressed especially for HF patients during CPR.
+Globally, there are 18-million individuals living with heart failure, a disease that is responsible for 12-15 million office visits and 6.5 million inpatient hospitalizations each year. As HF becomes advanced or end-stage, patients often live in a cycle of frequent transitions between care settings, and with unmet needs, including distress from inadequately managed symptoms. Prognostication in patients with heart failure can be challenging due to the unpredictable exacerbating-remitting illness trajectory that is associated with this progressive disease. Recurrent hospitalizations, worsening functional status and refractory symptoms, despite optimal therapies, are among the most salient predictors indicating that patients with advanced heart failure are nearing the end of life. Palliative care is a specialized form of medical care that has been shown to help improve severity of symptoms, facilitate discussions regarding medical decision making/advance care planning, and provide support for patients and their families. Palliative care can be used alongside curative treatments and has been shown to improve patient satisfaction and quality of life. Anorexia-cachexia syndrome, dyspnea, fatigue, pain and depression are among the most common symptoms experienced by patients suffering from advanced heart failure. Palliative care can help alleviate these symptoms and also facilitate conversations about decision making surrounding resuscitation status and use or deactivation of medical devices, such as an implantable-cardioverter-defibrillator (ICD). Clinical practice guidelines from the American College of Cardiology and American Heart Association report that aggressive life-sustaining treatments and therapies should not be utilized in patients with advanced heart failure who have refractory symptoms that are not responding to medical therapy. The focus of care should switch to controlling symptoms, reducing hospital admissions and improving health-related quality of life, which can be supported by the incorporation of palliative care into the treatment plan.
+Standards for bereavement care propose that support should be matched to risk and need. However, studies in many countries demonstrate that palliative care services continue to adopt a generic approach in offering support to bereaved families.    To identify patterns of bereavement support in palliative care services based upon the experience of bereaved people from a population based survey and in relation to clinical practice guidelines.    An anonymous postal survey collected information from clients of six funeral providers in four Australian states (2014-15), 6 to 24 months after the death of their family member or friend, with 1,139 responding. Responses from 506 bereaved relatives of people who had terminal illnesses were analysed. Of these, 298 had used palliative care services and 208 had not.    More people with cancer (64%) had received palliative care in comparison to other illnesses such as heart disease, dementia and organ failure (4-10%). The support for family caregivers before and after their relative's death was not considered optimal. Only 39.4% of the bereaved reported being specifically asked about their emotional/ psychological distress pre-bereavement, and just half of the bereaved perceived they had enough support from palliative care services. Half of the bereaved had a follow up contact from the service at 3-6 weeks, and a quarter had a follow-up at 6 months. Their qualitative feedback underlined the limited helpfulness of the blanket approach to bereavement support, which was often described as "not personal" or "generic", or "just standard practice".    Timeliness and consistency of relationship is crucial to building rapport and trust in the service's ability to help at post-bereavement as well as a focus on the specific rather than the generic needs of the bereaved. In light of these limitations, palliative care services might do better investing their efforts principally in assessing and supporting family caregivers during the pre-bereavement period and developing community capacity and referral pathways for bereavement care. Our findings suggest that bereavement support in Australian palliative care services has only a tenuous relationship with guidelines and assessment tools, a conclusion also drawn in studies from other countries, emphasizing the international implications of our study.
+To identify managerial and organizational characteristics of multi-specialty medicine wards and individual characteristics of health professionals that are most strongly associated with clinical practice guidelines (CPG) adherence.    Cross-sectional stratified cluster sample design.    Data were gathered from 36 randomly selected multi-specialty medicine wards.    The study population included all health professionals involved in patient care working in the participating wards.    The degree of CPG adherence was measured using clinical vignettes on three topics: pain management, managing heart failure and managing diabetes. Responses from each professional to each clinical case were quantified using a 10-point scale. Managerial and organizational characteristics of medical department and individual characteristics of health professionals were obtained using three questionnaires.    The study sample consisted of 859 professionals (362 orderlies, 361 nurses and 136 physicians). Factors independently and positively associated with CPG adherence were (i) individual factors: low age of professionals, expertise in diabetology and activity in cardiology; (ii) organizational and managerial factors: good understanding between physicians and other personnel; and (iii) structural factors: computer-based test results and prescriptions, presence of medical specialists, inter-department mobility of orderlies, medium-length stay (between 7 and 10 days) and large bed capacity.    Good CPG adherence in general medicine needs institutional dynamism, availability of clinical competence and team culture based on cooperation.
+Pleural effusions occur in 1.5 million patients yearly and are a common cause of dyspnea. For nonmalignant effusions, initial treatment is directed at the underlying cause, but when effusions become refractory to medical therapy, palliative options are limited. Tunneled pleural catheters (TPCs) are commonly used for palliation of malignant effusions, but many clinicians are reluctant to recommend these devices for palliation of nonmalignant effusions, citing concerns of infection, renal failure, electrolyte disturbances and protein-loss malnutrition. Based on the published experience to date, TPCs relieve dyspnea and can result in spontaneous pleurodesis in patients with nonmalignant effusions. The infection rate compares favorably to that for malignant effusions with possible increased risk in patients with hepatic hydrothorax and posttransplant patients. Renal failure, electrolyte disturbance and protein-loss malnutrition have not been observed. TPCs are a reasonable option in select patients to palliate nonmalignant effusions refractory to maximal medical therapy.
+The benefit or harm of a single medication recommended for one specific condition can be difficult to determine in individuals with multiple chronic conditions and polypharmacy. There is limited information on the associations between guideline-recommended medications and physical function in older adults with multiple chronic conditions. The objective of this study was to estimate the beneficial or harmful associations between guideline-recommended medications and decline in physical function in older adults with multiple chronic conditions.    Prospective observational cohort.    National.    Community-dwelling adults aged 65 and older from the Medicare Current Beneficiary Survey study (N = 3,273). Participants with atrial fibrillation, coronary artery disease, depression, diabetes mellitus, or heart failure were included.    Self-reported decline in physical function; guideline-recommended medications; polypharmacy (taking <7 vs \u22657 concomitant medications); chronic conditions; and sociodemographic, behavioral, and health risk factors.    The risk of decline in function in the overall sample was highest in participants with heart failure (35.4%, 95% confidence interval (CI) = 26.3-44.5) and lowest for those with atrial fibrillation (20.6%, 95% CI = 14.9-26.2). In the overall sample, none of the six guideline-recommended medications was associated with decline in physical function across the five study conditions, although in the group with low polypharmacy exposure, there was lower risk of decline in those with heart failure taking renin angiotensin system blockers (hazard ratio (HR) = 0.40, 95% CI = 0.16-0.99) and greater risk of decline in physical function for participants with diabetes mellitus taking statins (HR = 2.27, 95% CI = 1.39-3.69).    In older adults with multiple chronic conditions, guideline-recommended medications for atrial fibrillation, coronary artery disease, depression, diabetes mellitus, and heart failure were largely not associated with self-reported decline in physical function, although there were associations for some medications in those with less polypharmacy.
+Treatment of acute emergencies in patients with pulmonary arterial hypertension (PAH) can be challenging. In the UK and Ireland, management of adult patients with PAH is centred in eight nationally designated pulmonary hypertension (PH) centres. However, many patients live far from these centres and physicians in local hospitals are often required to manage PAH emergencies. A committee of physicians from nationally designated PH centres identified the 'most common' emergency clinical scenarios encountered in patients with PAH. Thereafter, a review of the literature was performed centred on these specified topics and a management approach was developed based on best available evidence and expert consensus. Management protocols were developed on the following PAH emergencies: chest pain (including myocardial ischaemia), right ventricular failure, arrhythmias, sepsis, haemoptysis ('CRASH'), as well as considerations relevant to surgery, anaesthesia and pregnancy. Emergencies are not uncommon in PAH. While expertise in PAH management is essential, all physicians involved in acute care should be aware of the principles of acute management of PAH emergencies. A multidisciplinary approach is necessary, with physicians from tertiary PH centres supporting care locally and planning safe transfer of patients to PH centres when appropriate.
+Heart failure is raising an increasing interest in patients with type 2 diabetes. Indeed, they combine different risk factors for this complication and they have time to develop it because they survive longer due to a better prevention of atherothrombotic cardiovascular events. Beyond the classical therapy of heart failure, management should select the most suited glucose-lowering agents. Indeed, all do not have the same impact as some of them have proven their ability to reduce the risk of hospitalisation for heart failure whereas others are associated with an increased risk, either well proven or at least suspected. The aim of this clinical case is to discuss the use of glucose-lowering drugs in a patient with type 2 diabetes with or at risk to develop heart failure.
+Despite the great progress made in the management of heart failure (HF) with reduced ejection fraction (HFrEF), its prevalence continues to rise owing to an aging population and an epidemic of hypertension, obesity and coronary artery disease. For decades, angiotensin converting enzyme inhibitors and beta blockers have been the mainstay of HFrEF therapy. The recent addition of sacubitril/valsartan and ivabradine to the HF armamentarium has the potential to transform our therapeutic approach to HFrEF, while simultaneously raising some questions and uncertainties on their applicability. In this paper, we review the pathophysiology of HFrEF, discuss already established and novel evidenced-based pharmacologic therapies available for these patients. We also share some therapeutic strategies aimed to optimize HF therapy in specific undertreated patient populations including the elderly and patients with chronic kidney disease, while offering insight on how to tailor therapy in the "real-world."
+For many neuromuscular diseases (NMDs), cardiac disease represents a major cause of morbidity and mortality. The management of cardiac disease in NMDs is made challenging by the broad clinical heterogeneity that exists among many NMDs and by limited knowledge about disease-specific cardiovascular pathogenesis and course-modifying interventions. The overlay of compromise in peripheral muscle function and other organ systems, such as the lungs, also makes the simple application of endorsed adult or pediatric heart failure guidelines to the NMD population problematic. In this statement, we provide background on several NMDs in which there is cardiac involvement, highlighting unique features of NMD-associated myocardial disease that require clinicians to tailor their approach to prevention and treatment of heart failure. Undoubtedly, further investigations are required to best inform future guidelines on NMD-specific cardiovascular health risks, treatments, and outcomes.
+The immense symptom burden and healthcare expenditure associated with heart failure (HF) has resulted in hospital systems, insurance companies, and federal agencies playing close attention to systems of care delivery. In particular, there has been a large extent of focus on decreasing the frequency of HF readmissions through the development of hospital quality measures and the expansion of post discharge services to improve transitions of care from the inpatient to the outpatient setting. The post discharge clinic visit (PDV) serves an important role in this process as it acts as a fulcrum for the multi-disciplinary services available to HF patients, as well as an opportunity to fill any gaps that might have occurred in evidence based care of the patient. The objective of this review is to provide a blueprint for the PDV that will allow clinicians to construct the key elements of the PDV in a patient-centered fashion that is firmly rooted in the guidelines.
+Background Digoxin is a cardiac glycoside that is recommended by clinical practice guidelines to be used in patients with heart failure with reduced ejection fraction (HFrEF) who still have persistent symptoms despite optimal medical therapy. However, this recommendation is based on limited and old trial data. Moreover, pharmacoepidemiologic studies are important in determining the prevalence of digoxin use and factors influencing this. Objective This study aimed to determine the prevalence and the predictors of digoxin utilization among patients with HFrEF with or without atrial fibrillation (AF) in Qatar. Setting Heart Hospital, a specialized tertiary care center in Qatar. Methods A retrospective observational study was conducted using Cerner electronic medical records. Subjects included 736 patients admitted between January 1, 2013 and December 31, 2014 with the diagnosis of HFrEF with or without AF. Two groups of patients were studied: digoxin users and digoxin non-users at index hospitalization until discharge. Univariate and multivariate binary logistic regression analyses were conducted to determine the predictors of digoxin prescription. Data analyses were performed using IBM SPSS
+Clinical practice guidelines recommend referral to nephrology when estimated glomerular filtration rate (eGFR) decreases to <30mL/min/1.73m    Observational cohort using landmark analysis.    A national cohort of veterans with advanced chronic kidney disease, defined as an outpatient eGFR\u226430mL/min/1.73m    Receipt and frequency of outpatient nephrology care over 12 months.    Survival and progression to end-stage renal disease (ESRD; receipt of dialysis or kidney transplantation) were the primary outcomes. In addition, control of associated clinical parameters over 12 months were intermediate outcomes.    Of 39,669 patients included in the cohort, 14,983 (37.8%) received nephrology care. Older age, heart failure, dementia, depression, and rapidly declining kidney function were independently associated with the absence of nephrology care. During a mean follow-up of 2.9 years, 14,719 (37.1%) patients died and 4,310 (10.9%) progressed to ESRD. In models adjusting for demographics, comorbid conditions, and trajectory of kidney function, nephrology care was associated with lower risk for death (HR, 0.88; 95% CI, 0.85-0.91), but higher risk for ESRD (HR, 1.48; 95% CI, 1.38-1.58). Among patients with clinical parameters outside guideline recommendations at cohort entry, a significantly higher adjusted proportion of patients who received nephrology care had improvement in control of hemoglobin, potassium, albumin, calcium, and phosphorus concentrations compared with those who did not receive nephrology care.    May not be generalizable to nonveterans.    Among patients with advanced chronic kidney disease, nephrology care was associated with lower mortality, but was not associated with lower risk for progression to ESRD.
+Hyperkalemia is a potentially life-threatening electrolyte disorder appreciated with greater frequency in patients with renal disease, heart failure, and with use of certain medications such as renin angiotensin aldosterone inhibitors. The traditional views that hyperkalemia can be reliably diagnosed by electrocardiogram and that particular levels of hyperkalemia confer cardiotoxic risk have been challenged by several reports of patients with atypic presentations. Epidemiologic data demonstrate strong associations of morbidity and mortality in patients with hyperkalemia but these associations appear disconnected in certain patient populations and in differing clinical presentations. Physiologic adaptation, structural cardiac disease, medication use, and degree of concurrent illness might predispose certain patients presenting with hyperkalemia to a lower or higher threshold for toxicity. These factors are often overlooked; yet data suggest that the clinical context in which hyperkalemia develops is at least as important as the degree of hyperkalemia is in determining patient outcome. This review summarizes the clinical data linking hyperkalemia with poor outcomes and discusses how the efficacy of certain treatments might depend on the clinical presentation.
+We present in this article the review of 6 scientific Hot Line sessions of the 2016 European Congress of Cardiology devoted to novel approaches to treatment of heart failure, strategies of prevention of complications of cardiovascular diseases, dyslipidemia, imaging methods of investigation, and stenting in coronary heart disease.
+Despite the known cardiotoxic effects of doxorubicin and other anthracyclines, no evidence-based guidelines exist for the surveillance and prevention of chemotherapy-induced cardiotoxicity in adult survivors of breast cancer who have had limited previous doses of anthracyclines (ie, total cumulative dose 240 mg/m
+Heart failure is the main chronic disease in cardiology. Its prognosis remains poor despite improvements in its management that allow patients to live increasingly longer with this disease, alternating periods of stability and episodes of decompensation. Treatment guidelines are regularly updated to integrate new results of recent trials that are likely to influence routine care. These guidelines are proposed with different classes of recommendations and difference levels of evidence. It is of paramount importance to summarize the guidelines to make them accessible to the vast majority of cardiologists and easier to read to promote their application. Among the main novelties of the last set of European guidelines for the management of heart failure, we note the proposal for a new classification based on the level of left ventricular ejection fraction (LVEF) with a new class, called heart failure with mid-range ejection fraction (LVEF 40-50 %), new algorithms for diagnosis and treatment, including the diagnosis of heart failure with preserved ejection fraction, a special focus on preventive strategies, the management of comorbidities including iron deficiency, simplification of the indications for cardiac resynchronization therapy, and finally a growing attention to patient pathways and to the management of hospital discharge. According to these guidelines, it is important that the physician choose the appropriate medications; but it is equally fundamental that the patient understands the disease and acquires self-care skills needed to become a real player in its management. This requires patient education, which is underdeveloped in France.
+The GERS (Exercise Rehabilitation and Sports Group of the French Society of Cardiology) has decided to update current guidelines regarding the practice of EKG stress tests. Since the last update dates from 1997, the GERS judged it necessary to integrate data from new works and advancements made in the last 20 years. Good clinical practices and safety conditions are better defined regarding the structure, location, material, staff competency, as well as convention with hospital structures. The diagnosis of coronary artery disease remains the principal indication for a stress test. Interpretation of the results is crucial-it must be multivariate and provide either a low, intermediate or strong probability of the existence of coronary lesions, taking into account the studied population (risk factors, age, sex and symptoms). Several new indications for a stress test have been defined for the assessment of cardiac pathologies. With such indications, the use of gas expiration measurements is highly recommended in order to provide a precise prognosis for all the various cardiac pathologies : congenital, ischemic, valvular, cardiomyopathies, congestive heart failure, rhythm and conduction disorders, pacemaker fine-tuning, or pulmonary hypertension. Indications for stress tests and contraindications are defined according to different population subgroups, for instance : athletes, women, children, the elderly, asymptomatic patients, diabetics, hypertensive patients, PAD patients, or in the context of a non-cardiac surgery pre-op visit. The new guidelines (due for publication soon) are considerably different from those dating from 1997 and further pinpoint the relevance and importance of an EKG stress test within the arsenal of complementary cardiologic exams. With the improvements made in providing diagnostic value in CAD, as well as better prognostic value for any underlying pathology, the indication for an EKG stress test has extended to all cardiovascular disease.
+Long-term aspirin prevents vascular events but is only modestly effective. Rivaroxaban alone or in combination with aspirin might be more effective than aspirin alone for vascular prevention in patients with stable coronary artery disease (CAD) or peripheral artery disease (PAD). Rivaroxaban as well as aspirin increase upper gastrointestinal (GI) bleeding and this might be prevented by proton pump inhibitor therapy.    Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) is a double-blind superiority trial comparing rivaroxaban 2.5 mg twice daily combined with aspirin 100 mg once daily or rivaroxaban 5 mg twice daily vs aspirin 100 mg once daily for prevention of myocardial infarction, stroke, or cardiovascular death in patients with stable CAD or PAD. Patients not taking a proton pump inhibitor were also randomized, using a partial factorial design, to pantoprazole 40 mg once daily or placebo. The trial was designed to have at least 90% power to detect a 20% reduction in each of the rivaroxaban treatment arms compared with aspirin and to detect a 50% reduction in upper GI complications with pantoprazole compared with placebo.    Between February 2013 and May 2016, we recruited 27,395 participants from 602 centres in 33 countries; 17,598 participants were included in the pantoprazole vs placebo comparison. At baseline, the mean age was 68.2 years, 22.0% were female, 90.6% had CAD, and 27.3% had PAD.    COMPASS will provide information on the efficacy and safety of rivaroxaban, alone or in combination with aspirin, in the long-term management of patients with stable CAD or PAD, and on the efficacy and safety of pantoprazole in preventing upper GI complications in patients receiving antithrombotic therapy.
diff --git a/scripts/DevWorkshop/sampleData/abstracts.parser.py b/scripts/DevWorkshop/sampleData/abstracts.parser.py
new file mode 100644
index 00000000..a10a319e
--- /dev/null
+++ b/scripts/DevWorkshop/sampleData/abstracts.parser.py
@@ -0,0 +1,13 @@
+import sys,os;
+import urllib.request, urllib.parse, urllib.error;
+from xml.etree import ElementTree;
+
+tree = ElementTree.parse(sys.argv[1]);
+
+for abstract in tree.iter("Abstract"):
+    comboTextList = list();
+    for abstractText in abstract.iter("AbstractText"):
+        if abstractText.text is not None:
+            comboTextList.append(abstractText.text);
+    comboText = str.join("    ", comboTextList );
+    print(comboText.encode("unicode-escape"));
diff --git a/scripts/DevWorkshop/sampleData/abstracts.xml b/scripts/DevWorkshop/sampleData/abstracts.xml
new file mode 100644
index 00000000..f9feab67
--- /dev/null
+++ b/scripts/DevWorkshop/sampleData/abstracts.xml
@@ -0,0 +1,1416 @@
+<AllDocs>
+<PubmedArticle>
+    <MedlineCitation Status="Publisher" Owner="NLM">
+        <PMID Version="1">29735306</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>05</Month>
+            <Day>08</Day>
+        </DateRevised>
+        <Article PubModel="Print-Electronic">
+            <Journal>
+                <ISSN IssnType="Electronic">1523-1755</ISSN>
+                <JournalIssue CitedMedium="Internet">
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>May</Month>
+                        <Day>05</Day>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Kidney international</Title>
+                <ISOAbbreviation>Kidney Int.</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>Renoprotective effects of sodium-glucose cotransporter-2 inhibitors.</ArticleTitle>
+            <ELocationID EIdType="pii" ValidYN="Y">S0085-2538(18)30125-X</ELocationID>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1016/j.kint.2017.12.027</ELocationID>
+            <Abstract>
+                <AbstractText>Over the past two years, our understanding of anti-hyperglycemic medications used to treat patients with type 2 diabetes (T2D) has fundamentally changed. Before the EMPA-REG OUTCOME trial, agents used to lower blood glucose were felt to prevent or delay the development of microvascular complications, but were not known to definitively reduce cardiovascular risk or mortality. Previous studies with then novel sodium-glucose cotransport-2 (SGLT2) inhibitors demonstrated improvements in several cardiovascular and renal risk factors, including HbA1c, blood pressure, weight, renal hyperfiltration, and albuminuria. However, as with other antihyperglycemic drugs, it could not be known if these salutary effects would translate into improved cardiorenal outcomes. In the EMPA-REG OUTCOME trial, SGLT2 inhibition with empagliflozin reduced the primary outcome of major adverse cardiovascular events (MACE), while also reducing mortality, hospitalization for heart failure, and progression of diabetic kidney disease. In the CANVAS Program trials using canagliflozin, the rates of the 3-point MACE endpoint, the risk of heart failure and the renal composite endpoint were also reduced, albeit with an increased risk of lower extremity amputation and fracture. As a result, clinical practice guidelines recommend the consideration of SGLT2 inhibition in high-risk patient subgroups for cardiovascular risk reduction. Ongoing primary renal endpoint trials will inform the cardio-metabolic-renal community about how to optimally treat patients with chronic kidney disease - including those with and without diabetes. Our aim is to review the rationale for renal protection with SGLT2 inhibitors, and their current place in the clinical management of patients with kidney disease.</AbstractText>
+                <CopyrightInformation>Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.</CopyrightInformation>
+            </Abstract>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Heerspink</LastName>
+                    <ForeName>Hiddo J L</ForeName>
+                    <Initials>HJL</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Kosiborod</LastName>
+                    <ForeName>Mikhail</ForeName>
+                    <Initials>M</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Saint Luke's Mid America Heart Institute and University of Missouri, Kansas City, Missouri, USA.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Inzucchi</LastName>
+                    <ForeName>Silvio E</ForeName>
+                    <Initials>SE</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Section of Endocrinology, Yale School of Medicine, New Haven, Connecticut, USA.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Cherney</LastName>
+                    <ForeName>David Z I</ForeName>
+                    <Initials>DZI</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Department of Medicine, Division of Nephrology, Toronto General Hospital, University of Toronto, Toronto, Ontario, Canada. Electronic address: david.cherney@uhn.ca.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+                <PublicationType UI="D016454">Review</PublicationType>
+            </PublicationTypeList>
+            <ArticleDate DateType="Electronic">
+                <Year>2018</Year>
+                <Month>05</Month>
+                <Day>05</Day>
+            </ArticleDate>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>United States</Country>
+            <MedlineTA>Kidney Int</MedlineTA>
+            <NlmUniqueID>0323470</NlmUniqueID>
+            <ISSNLinking>0085-2538</ISSNLinking>
+        </MedlineJournalInfo>
+        <KeywordList Owner="NOTNLM">
+            <Keyword MajorTopicYN="N">SGLT2 inhibition</Keyword>
+            <Keyword MajorTopicYN="N">albuminuria</Keyword>
+            <Keyword MajorTopicYN="N">blood pressure</Keyword>
+            <Keyword MajorTopicYN="N">cardiovascular</Keyword>
+            <Keyword MajorTopicYN="N">diabetic kidney disease</Keyword>
+            <Keyword MajorTopicYN="N">heart failure</Keyword>
+            <Keyword MajorTopicYN="N">inflammation</Keyword>
+        </KeywordList>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="received">
+                <Year>2017</Year>
+                <Month>09</Month>
+                <Day>28</Day>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="revised">
+                <Year>2017</Year>
+                <Month>11</Month>
+                <Day>16</Day>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="accepted">
+                <Year>2017</Year>
+                <Month>12</Month>
+                <Day>13</Day>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>5</Month>
+                <Day>9</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>5</Month>
+                <Day>8</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>5</Month>
+                <Day>8</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>aheadofprint</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29735306</ArticleId>
+            <ArticleId IdType="pii">S0085-2538(18)30125-X</ArticleId>
+            <ArticleId IdType="doi">10.1016/j.kint.2017.12.027</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM">
+        <PMID Version="1">29687191</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>26</Day>
+        </DateRevised>
+        <Article PubModel="Electronic">
+            <Journal>
+                <ISSN IssnType="Print">1092-8464</ISSN>
+                <JournalIssue CitedMedium="Print">
+                    <Volume>20</Volume>
+                    <Issue>6</Issue>
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Apr</Month>
+                        <Day>23</Day>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Current treatment options in cardiovascular medicine</Title>
+                <ISOAbbreviation>Curr Treat Options Cardiovasc Med</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>Sacubitril/Valsartan: From Clinical Trials to Real-world Experience.</ArticleTitle>
+            <Pagination>
+                <MedlinePgn>45</MedlinePgn>
+            </Pagination>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1007/s11936-018-0638-7</ELocationID>
+            <Abstract>
+                <AbstractText Label="PURPOSE OF REVIEW" NlmCategory="OBJECTIVE">Compared to enalapril, use of angiotensin-receptor blocker and neprilysin inhibitor sacubitril/valsartan to treat patients with heart failure and reduced ejection fraction (HFrEF) is associated with substantial reductions in both cardiovascular mortality and heart failure progression. The purpose of this review is to discuss the real-world experience of sacubitril/valsartan.</AbstractText>
+                <AbstractText Label="RECENT FINDINGS" NlmCategory="RESULTS">In the years following the publication of the landmark PARADIGM-HF trial in 2014 and its subsequent FDA approval, a growing evidence base supports the safety and efficacy of sacubitril/valsartan in a broad spectrum of patients with HFrEF. Updated clinical practice guidelines have embraced the use of sacubitril/valsartan in preference to ACE inhibitors or ARBs in selected patients. In this review, we highlight the clinical trials that led to these key updates to clinical guidelines, offer practical strategies for patient selection and utilization in clinical practice, and identify important areas of uncertainty that require future research.</AbstractText>
+            </Abstract>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Joly</LastName>
+                    <ForeName>Joanna M</ForeName>
+                    <Initials>JM</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Cardiovascular Division, Brigham &amp; Women's Hospital, 75 Francis St., Boston, MA, 02115, USA.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Desai</LastName>
+                    <ForeName>Akshay S</ForeName>
+                    <Initials>AS</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Cardiovascular Division, Brigham &amp; Women's Hospital, 75 Francis St., Boston, MA, 02115, USA. adesai@bwh.harvard.edu.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+                <PublicationType UI="D016454">Review</PublicationType>
+            </PublicationTypeList>
+            <ArticleDate DateType="Electronic">
+                <Year>2018</Year>
+                <Month>04</Month>
+                <Day>23</Day>
+            </ArticleDate>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>United States</Country>
+            <MedlineTA>Curr Treat Options Cardiovasc Med</MedlineTA>
+            <NlmUniqueID>9815942</NlmUniqueID>
+            <ISSNLinking>1092-8464</ISSNLinking>
+        </MedlineJournalInfo>
+        <CommentsCorrectionsList>
+            <CommentsCorrections RefType="Cites">
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+                <PMID Version="1">27438344</PMID>
+            </CommentsCorrections>
+        </CommentsCorrectionsList>
+        <KeywordList Owner="NOTNLM">
+            <Keyword MajorTopicYN="N">ARNI</Keyword>
+            <Keyword MajorTopicYN="N">Enalapril</Keyword>
+            <Keyword MajorTopicYN="N">HFrEF</Keyword>
+            <Keyword MajorTopicYN="N">PARADIGM-HF</Keyword>
+            <Keyword MajorTopicYN="N">Sacubitril/valsartan</Keyword>
+        </KeywordList>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>25</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>25</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>25</Day>
+                <Hour>6</Hour>
+                <Minute>1</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>epublish</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29687191</ArticleId>
+            <ArticleId IdType="doi">10.1007/s11936-018-0638-7</ArticleId>
+            <ArticleId IdType="pii">10.1007/s11936-018-0638-7</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="In-Data-Review" Owner="NLM">
+        <PMID Version="1">29667019</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>20</Day>
+        </DateRevised>
+        <Article PubModel="Electronic">
+            <Journal>
+                <ISSN IssnType="Electronic">1534-3170</ISSN>
+                <JournalIssue CitedMedium="Internet">
+                    <Volume>20</Volume>
+                    <Issue>6</Issue>
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Apr</Month>
+                        <Day>17</Day>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Current cardiology reports</Title>
+                <ISOAbbreviation>Curr Cardiol Rep</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>What Is New in Heart Failure Management in 2017? Update on ACC/AHA Heart Failure Guidelines.</ArticleTitle>
+            <Pagination>
+                <MedlinePgn>39</MedlinePgn>
+            </Pagination>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1007/s11886-018-0978-7</ELocationID>
+            <Abstract>
+                <AbstractText Label="PURPOSE OF REVIEW" NlmCategory="OBJECTIVE">The goal of this paper is to provide a summary of the new recommendations in the most recent 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. The intent is to provide the background and the supporting evidence for the recommendations and to provide practical guidance for management strategies in treatment of heart failure patients.</AbstractText>
+                <AbstractText Label="RECENT FINDINGS" NlmCategory="RESULTS">In the 2017 ACC/AHA/HFSA Focused Update of HF guidelines, important additions include new information on biomarkers, specifically on the topics of the diagnostic, prognostic role of natriuretic peptides in heart failure, and the role of natriuretic peptides in screening in patients high risk for HF and prevention of HF. There are important recommendations for treatment of patients with HF with reduced EF (HFrEF), including the beneficial role of angiotensin receptor blocker and neprilysin inhibition (ARNI) treatment in reducing outcomes including mortality, ivabradine in reducing heart failure hospitalizations in stable HFrEF patients with sinus rhythm and heart rate ≥ 70 bpm despite β-blockers. In patients with HF with preserved EF (HFpEF), though there are no studies demonstrating survival benefit, potential benefit with aldosterone antagonism in reducing HF hospitalizations is noted. In treatment of comorbidities, optimization of blood pressure control to less than 130 mmHg is recommended in hypertensive patients to prevent HF or in patients with hypertension and HFrEF or HFpEF. In addition to recognition on the potential role of treatment of iron deficiency anemia to improve symptoms and functional capacity, caution against use of adaptive servo-ventilation in patients with HFrEF and central sleep apnea and against use of erythropoietin stimulating agents in patients with HFrEF is provided. There are new treatment strategies that are associated with significant improvements in mortality and other outcomes in patients with HF. Successful management of HF requires recognition of indications, contraindications, benefits, safety, and risk of these new therapies. In addition to incorporation of these new treatment strategies, it is critical to focus also on patient education, care coordination, identification of goals of care, monitoring, management of comorbidities, and individualization of therapies. New treatment modalities increase the choices for treatment and provide the opportunity to implement individualized treatment strategies for our patients.</AbstractText>
+            </Abstract>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Bozkurt</LastName>
+                    <ForeName>Biykem</ForeName>
+                    <Initials>B</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Medical Care Line Executive, DeBakey VA Medical Center, Houston, TX, USA. bbozkurt@bcm.edu.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>Department of Medicine at Baylor College of Medicine, Houston, TX, USA. bbozkurt@bcm.edu.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>Cardiovascular Research Institute, Houston, TX, USA. bbozkurt@bcm.edu.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>Winters Center for Heart Failure Research, Baylor College of Medicine, Houston, TX, USA. bbozkurt@bcm.edu.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>MEDVAMC, Medical Care Line, 2002 Holcombe Blvd, Houston, TX, 77030, USA. bbozkurt@bcm.edu.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+                <PublicationType UI="D016454">Review</PublicationType>
+            </PublicationTypeList>
+            <ArticleDate DateType="Electronic">
+                <Year>2018</Year>
+                <Month>04</Month>
+                <Day>17</Day>
+            </ArticleDate>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>United States</Country>
+            <MedlineTA>Curr Cardiol Rep</MedlineTA>
+            <NlmUniqueID>100888969</NlmUniqueID>
+            <ISSNLinking>1523-3782</ISSNLinking>
+        </MedlineJournalInfo>
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+            <CommentsCorrections RefType="Cites">
+                <RefSource>Eur J Heart Fail. 2015 Dec;17(12):1252-61</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>JAMA. 2017 Aug 22;318(8):713-720</RefSource>
+                <PMID Version="1">28829876</PMID>
+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>J Am Coll Cardiol. 2013 Oct 8;62(15):1365-72</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>N Engl J Med. 2002 Jul 18;347(3):161-7</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Circulation. 2013 Oct 15;128(16):e240-327</RefSource>
+                <PMID Version="1">23741058</PMID>
+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>J Am Coll Cardiol. 2007 Apr 24;49(16):1733-9</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Am Heart J. 2011 May;161(5):822-831.e2</RefSource>
+                <PMID Version="1">21570510</PMID>
+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>J Am Coll Cardiol. 2004 Feb 18;43(4):635-41</RefSource>
+                <PMID Version="1">14975475</PMID>
+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>N Engl J Med. 2015 Nov 26;373(22):2103-16</RefSource>
+                <PMID Version="1">26551272</PMID>
+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Circulation. 2001 Oct 9;104(15):1856-62</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Heart Fail Rev. 2015 Jan;20(1):69-80</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Circulation. 2002 May 21;105(20):2392-7</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>N Engl J Med. 2009 Dec 17;361(25):2436-48</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>N Engl J Med. 2015 Dec 10;373(24):2314-24</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Circulation. 2003 Mar 11;107(9):1278-83</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Lancet. 2000 Apr 1;355(9210):1126-30</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Circulation. 2003 Dec 16;108(24):2964-6</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Circulation. 2003 Aug 19;108(7):833-8</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Eur Heart J. 2015 Mar 14;36(11):657-68</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>Eur Heart J. 2014 Jun 14;35(23):1559-67</RefSource>
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+            </CommentsCorrections>
+            <CommentsCorrections RefType="Cites">
+                <RefSource>N Engl J Med. 2016 Sep 8;375(10 ):919-31</RefSource>
+                <PMID Version="1">27571048</PMID>
+            </CommentsCorrections>
+        </CommentsCorrectionsList>
+        <KeywordList Owner="NOTNLM">
+            <Keyword MajorTopicYN="N">ARNI</Keyword>
+            <Keyword MajorTopicYN="N">Biomarkers</Keyword>
+            <Keyword MajorTopicYN="N">Comorbidities</Keyword>
+            <Keyword MajorTopicYN="N">Heart failure guidelines</Keyword>
+            <Keyword MajorTopicYN="N">Ivabradine</Keyword>
+        </KeywordList>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>19</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>19</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>19</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>epublish</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29667019</ArticleId>
+            <ArticleId IdType="doi">10.1007/s11886-018-0978-7</ArticleId>
+            <ArticleId IdType="pii">10.1007/s11886-018-0978-7</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="Publisher" Owner="NLM">
+        <PMID Version="1">29650544</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>13</Day>
+        </DateRevised>
+        <Article PubModel="Print-Electronic">
+            <Journal>
+                <ISSN IssnType="Electronic">1524-4539</ISSN>
+                <JournalIssue CitedMedium="Internet">
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Apr</Month>
+                        <Day>12</Day>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Circulation</Title>
+                <ISOAbbreviation>Circulation</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>Evaluation and Management of Right-Sided Heart Failure: A Scientific Statement From the American Heart Association.</ArticleTitle>
+            <ELocationID EIdType="pii" ValidYN="Y">CIR.0000000000000560</ELocationID>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1161/CIR.0000000000000560</ELocationID>
+            <Abstract>
+                <AbstractText Label="BACKGROUND AND PURPOSE" NlmCategory="OBJECTIVE">The diverse causes of right-sided heart failure (RHF) include, among others, primary cardiomyopathies with right ventricular (RV) involvement, RV ischemia and infarction, volume loading caused by cardiac lesions associated with congenital heart disease and valvular pathologies, and pressure loading resulting from pulmonic stenosis or pulmonary hypertension from a variety of causes, including left-sided heart disease. Progressive RV dysfunction in these disease states is associated with increased morbidity and mortality. The purpose of this scientific statement is to provide guidance on the assessment and management of RHF.</AbstractText>
+                <AbstractText Label="METHODS" NlmCategory="METHODS">The writing group used systematic literature reviews, published translational and clinical studies, clinical practice guidelines, and expert opinion/statements to summarize existing evidence and to identify areas of inadequacy requiring future research. The panel reviewed the most relevant adult medical literature excluding routine laboratory tests using MEDLINE, EMBASE, and Web of Science through September 2017. The document is organized and classified according to the American Heart Association to provide specific suggestions, considerations, or reference to contemporary clinical practice recommendations.</AbstractText>
+                <AbstractText Label="RESULTS" NlmCategory="RESULTS">Chronic RHF is associated with decreased exercise tolerance, poor functional capacity, decreased cardiac output and progressive end-organ damage (caused by a combination of end-organ venous congestion and underperfusion), and cachexia resulting from poor absorption of nutrients, as well as a systemic proinflammatory state. It is the principal cause of death in patients with pulmonary arterial hypertension. Similarly, acute RHF is associated with hemodynamic instability and is the primary cause of death in patients presenting with massive pulmonary embolism, RV myocardial infarction, and postcardiotomy shock associated with cardiac surgery. Functional assessment of the right side of the heart can be hindered by its complex geometry. Multiple hemodynamic and biochemical markers are associated with worsening RHF and can serve to guide clinical assessment and therapeutic decision making. Pharmacological and mechanical interventions targeting isolated acute and chronic RHF have not been well investigated. Specific therapies promoting stabilization and recovery of RV function are lacking.</AbstractText>
+                <AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">RHF is a complex syndrome including diverse causes, pathways, and pathological processes. In this scientific statement, we review the causes and epidemiology of RV dysfunction and the pathophysiology of acute and chronic RHF and provide guidance for the management of the associated conditions leading to and caused by RHF.</AbstractText>
+                <CopyrightInformation>© 2018 American Heart Association, Inc.</CopyrightInformation>
+            </Abstract>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Konstam</LastName>
+                    <ForeName>Marvin A</ForeName>
+                    <Initials>MA</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Kiernan</LastName>
+                    <ForeName>Michael S</ForeName>
+                    <Initials>MS</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Bernstein</LastName>
+                    <ForeName>Daniel</ForeName>
+                    <Initials>D</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Bozkurt</LastName>
+                    <ForeName>Biykem</ForeName>
+                    <Initials>B</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Jacob</LastName>
+                    <ForeName>Miriam</ForeName>
+                    <Initials>M</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Kapur</LastName>
+                    <ForeName>Navin K</ForeName>
+                    <Initials>NK</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Kociol</LastName>
+                    <ForeName>Robb D</ForeName>
+                    <Initials>RD</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Lewis</LastName>
+                    <ForeName>Eldrin F</ForeName>
+                    <Initials>EF</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Mehra</LastName>
+                    <ForeName>Mandeep R</ForeName>
+                    <Initials>MR</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Pagani</LastName>
+                    <ForeName>Francis D</ForeName>
+                    <Initials>FD</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Raval</LastName>
+                    <ForeName>Amish N</ForeName>
+                    <Initials>AN</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Ward</LastName>
+                    <ForeName>Carey</ForeName>
+                    <Initials>C</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <CollectiveName>American Heart Association Council on Clinical Cardiology; Council on Cardiovascular Disease in the Young; and Council on Cardiovascular Surgery and Anesthesia</CollectiveName>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+                <PublicationType UI="D016454">Review</PublicationType>
+            </PublicationTypeList>
+            <ArticleDate DateType="Electronic">
+                <Year>2018</Year>
+                <Month>04</Month>
+                <Day>12</Day>
+            </ArticleDate>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>United States</Country>
+            <MedlineTA>Circulation</MedlineTA>
+            <NlmUniqueID>0147763</NlmUniqueID>
+            <ISSNLinking>0009-7322</ISSNLinking>
+        </MedlineJournalInfo>
+        <KeywordList Owner="NOTNLM">
+            <Keyword MajorTopicYN="N">AHA Scientific Statements</Keyword>
+            <Keyword MajorTopicYN="N">causality</Keyword>
+            <Keyword MajorTopicYN="N">disease management</Keyword>
+            <Keyword MajorTopicYN="N">heart failure</Keyword>
+            <Keyword MajorTopicYN="N">ventricular dysfunction, right</Keyword>
+        </KeywordList>
+        <CoiStatement>The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.</CoiStatement>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>14</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>14</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>14</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>aheadofprint</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29650544</ArticleId>
+            <ArticleId IdType="pii">CIR.0000000000000560</ArticleId>
+            <ArticleId IdType="doi">10.1161/CIR.0000000000000560</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="In-Data-Review" Owner="NLM">
+        <PMID Version="1">29650096</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>13</Day>
+        </DateRevised>
+        <Article PubModel="Print">
+            <Journal>
+                <ISSN IssnType="Electronic">2352-3840</ISSN>
+                <JournalIssue CitedMedium="Internet">
+                    <Volume>42 Suppl 1</Volume>
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Apr</Month>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Canadian journal of diabetes</Title>
+                <ISOAbbreviation>Can J Diabetes</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>Treatment of Diabetes in People With Heart Failure.</ArticleTitle>
+            <Pagination>
+                <MedlinePgn>S196-S200</MedlinePgn>
+            </Pagination>
+            <ELocationID EIdType="pii" ValidYN="Y">S1499-2671(17)30836-5</ELocationID>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jcjd.2017.10.026</ELocationID>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <CollectiveName>Diabetes Canada Clinical Practice Guidelines Expert Committee</CollectiveName>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Connelly</LastName>
+                    <ForeName>Kim A</ForeName>
+                    <Initials>KA</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Gilbert</LastName>
+                    <ForeName>Richard E</ForeName>
+                    <Initials>RE</Initials>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Liu</LastName>
+                    <ForeName>Peter</ForeName>
+                    <Initials>P</Initials>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+            </PublicationTypeList>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>Canada</Country>
+            <MedlineTA>Can J Diabetes</MedlineTA>
+            <NlmUniqueID>101148810</NlmUniqueID>
+            <ISSNLinking>1499-2671</ISSNLinking>
+        </MedlineJournalInfo>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="received">
+                <Year>2017</Year>
+                <Month>09</Month>
+                <Day>29</Day>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>14</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>14</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>14</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>ppublish</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29650096</ArticleId>
+            <ArticleId IdType="pii">S1499-2671(17)30836-5</ArticleId>
+            <ArticleId IdType="doi">10.1016/j.jcjd.2017.10.026</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="Publisher" Owner="NLM">
+        <PMID Version="1">29623546</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>06</Day>
+        </DateRevised>
+        <Article PubModel="Print-Electronic">
+            <Journal>
+                <ISSN IssnType="Electronic">1573-7322</ISSN>
+                <JournalIssue CitedMedium="Internet">
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Apr</Month>
+                        <Day>06</Day>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Heart failure reviews</Title>
+                <ISOAbbreviation>Heart Fail Rev</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>Exercise rehabilitation in ventricular assist device recipients: a meta-analysis of effects on physiological and clinical outcomes.</ArticleTitle>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1007/s10741-018-9695-y</ELocationID>
+            <Abstract>
+                <AbstractText>Exercise rehabilitation in heart failure patients has been shown to improve quality of life (QoL) and survival. It is also recommended in clinical practice guidelines for ventricular assist device (VAD) recipients. However, there have only been two meta-analyses on the effects of exercise rehabilitation in VAD patients, on only two outcomes. The objective of the review was to quantitatively evaluate the effect of exercise rehabilitation in VAD recipients on functional capacity, exercise physiology parameters, chronotropic responses, inflammatory biomarkers and neurohormones, heart structure and function, and clinical outcomes. The following databases were systematically searched: CCTR, CDSR, CINAHL, EMBASE, PsycInfo, and Medline through to November 2015, for studies reporting on VAD recipients receiving ≥ 2 sessions of aerobic training. Citations were considered for inclusion, and data were extracted in included studies as well as quality assessed, each by two investigators independently. Random-effects meta-analyses were performed where possible. The meta-analysis showed that compared to usual care, exercise rehabilitation significantly improved peak VO<sub>2</sub> (n = 74, mean difference = 1.94 mL kg<sup>-1</sup> min<sup>-1</sup>, 95% CI 0.63-3.26, p = 0.004) and 6-min walk test distance (n = 52, mean difference = 42.46 m, 95% CI 8.45-76.46, p = 0.01). No significant differences were found for the ventilatory equivalent slope (VE/VCO<sub>2</sub>) or ventilatory anaerobic threshold (VAT). In the six studies which reported QoL, exercise rehabilitation was beneficial in four, with no difference observed in two studies. Exercise rehabilitation is associated with improved outcomes in VAD recipients, and therefore should be more systematically delivered in this population.</AbstractText>
+            </Abstract>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Grosman-Rimon</LastName>
+                    <ForeName>Liza</ForeName>
+                    <Initials>L</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Lalonde</LastName>
+                    <ForeName>Spencer D</ForeName>
+                    <Initials>SD</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Western University, London, ON, Canada.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Sieh</LastName>
+                    <ForeName>Nina</ForeName>
+                    <Initials>N</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>School of Kinesiology and Health Science, Faculty of Health, York University, Bethune 368, 4700 Keele St, Toronto, ON, M3J 1P3, Canada.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Pakosh</LastName>
+                    <ForeName>Maureen</ForeName>
+                    <Initials>M</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Rao</LastName>
+                    <ForeName>Vivek</ForeName>
+                    <Initials>V</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>University of Toronto, Toronto, ON, Canada.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>Cardiovascular Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON, Canada.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Oh</LastName>
+                    <ForeName>Paul</ForeName>
+                    <Initials>P</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Grace</LastName>
+                    <ForeName>Sherry L</ForeName>
+                    <Initials>SL</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada. sgrace@yorku.ca.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>School of Kinesiology and Health Science, Faculty of Health, York University, Bethune 368, 4700 Keele St, Toronto, ON, M3J 1P3, Canada. sgrace@yorku.ca.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>University of Toronto, Toronto, ON, Canada. sgrace@yorku.ca.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+                <PublicationType UI="D016454">Review</PublicationType>
+            </PublicationTypeList>
+            <ArticleDate DateType="Electronic">
+                <Year>2018</Year>
+                <Month>04</Month>
+                <Day>06</Day>
+            </ArticleDate>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>United States</Country>
+            <MedlineTA>Heart Fail Rev</MedlineTA>
+            <NlmUniqueID>9612481</NlmUniqueID>
+            <ISSNLinking>1382-4147</ISSNLinking>
+        </MedlineJournalInfo>
+        <KeywordList Owner="NOTNLM">
+            <Keyword MajorTopicYN="N">Cardiac rehabilitation</Keyword>
+            <Keyword MajorTopicYN="N">Exercise therapy</Keyword>
+            <Keyword MajorTopicYN="N">Heart-assist devices</Keyword>
+            <Keyword MajorTopicYN="N">Oxygen consumption</Keyword>
+        </KeywordList>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>7</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>7</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>7</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>aheadofprint</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29623546</ArticleId>
+            <ArticleId IdType="doi">10.1007/s10741-018-9695-y</ArticleId>
+            <ArticleId IdType="pii">10.1007/s10741-018-9695-y</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="MEDLINE" Owner="NLM">
+        <PMID Version="1">29584845</PMID>
+        <DateCompleted>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>12</Day>
+        </DateCompleted>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>04</Month>
+            <Day>12</Day>
+        </DateRevised>
+        <Article PubModel="Print">
+            <Journal>
+                <ISSN IssnType="Electronic">1538-3598</ISSN>
+                <JournalIssue CitedMedium="Internet">
+                    <Volume>319</Volume>
+                    <Issue>12</Issue>
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Mar</Month>
+                        <Day>27</Day>
+                    </PubDate>
+                </JournalIssue>
+                <Title>JAMA</Title>
+                <ISOAbbreviation>JAMA</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>β-Blockers in Myocardial Infarction: Issues With Standard Admission Order Sets.</ArticleTitle>
+            <Pagination>
+                <MedlinePgn>1269-1270</MedlinePgn>
+            </Pagination>
+            <ELocationID EIdType="doi" ValidYN="Y">10.1001/jama.2018.0845</ELocationID>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Gupta</LastName>
+                    <ForeName>Arjun</ForeName>
+                    <Initials>A</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Das</LastName>
+                    <ForeName>Sandeep R</ForeName>
+                    <Initials>SR</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>Division of Cardiology, University of Texas Southwestern Medical Center, Dallas.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Pandey</LastName>
+                    <ForeName>Ambarish</ForeName>
+                    <Initials>A</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas.</Affiliation>
+                    </AffiliationInfo>
+                    <AffiliationInfo>
+                        <Affiliation>Division of Cardiology, University of Texas Southwestern Medical Center, Dallas.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+            </AuthorList>
+            <Language>eng</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D002363">Case Reports</PublicationType>
+                <PublicationType UI="D016428">Journal Article</PublicationType>
+            </PublicationTypeList>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>United States</Country>
+            <MedlineTA>JAMA</MedlineTA>
+            <NlmUniqueID>7501160</NlmUniqueID>
+            <ISSNLinking>0098-7484</ISSNLinking>
+        </MedlineJournalInfo>
+        <ChemicalList>
+            <Chemical>
+                <RegistryNumber>0</RegistryNumber>
+                <NameOfSubstance UI="D000319">Adrenergic beta-Antagonists</NameOfSubstance>
+            </Chemical>
+            <Chemical>
+                <RegistryNumber>0</RegistryNumber>
+                <NameOfSubstance UI="D002227">Carbazoles</NameOfSubstance>
+            </Chemical>
+            <Chemical>
+                <RegistryNumber>0</RegistryNumber>
+                <NameOfSubstance UI="D011412">Propanolamines</NameOfSubstance>
+            </Chemical>
+            <Chemical>
+                <RegistryNumber>0K47UL67F2</RegistryNumber>
+                <NameOfSubstance UI="C043211">carvedilol</NameOfSubstance>
+            </Chemical>
+        </ChemicalList>
+        <CitationSubset>AIM</CitationSubset>
+        <CitationSubset>IM</CitationSubset>
+        <CommentsCorrectionsList>
+            <CommentsCorrections RefType="CommentIn">
+                <RefSource>JAMA. 2018 Mar 27;319(12 ):1207-1208</RefSource>
+                <PMID Version="1">29584825</PMID>
+            </CommentsCorrections>
+        </CommentsCorrectionsList>
+        <MeshHeadingList>
+            <MeshHeading>
+                <DescriptorName UI="D000319" MajorTopicYN="N">Adrenergic beta-Antagonists</DescriptorName>
+                <QualifierName UI="Q000009" MajorTopicYN="Y">adverse effects</QualifierName>
+                <QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D002227" MajorTopicYN="N">Carbazoles</DescriptorName>
+                <QualifierName UI="Q000009" MajorTopicYN="Y">adverse effects</QualifierName>
+                <QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D000075203" MajorTopicYN="Y">Contraindications, Drug</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D004562" MajorTopicYN="N">Electrocardiography</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D006327" MajorTopicYN="N">Heart Block</DescriptorName>
+                <QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName>
+                <QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D006333" MajorTopicYN="N">Heart Failure</DescriptorName>
+                <QualifierName UI="Q000209" MajorTopicYN="Y">etiology</QualifierName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D008508" MajorTopicYN="N">Medication Errors</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D010343" MajorTopicYN="N">Patient Admission</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D062645" MajorTopicYN="Y">Percutaneous Coronary Intervention</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D017410" MajorTopicYN="Y">Practice Guidelines as Topic</DescriptorName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D011412" MajorTopicYN="N">Propanolamines</DescriptorName>
+                <QualifierName UI="Q000009" MajorTopicYN="Y">adverse effects</QualifierName>
+                <QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D000072657" MajorTopicYN="N">ST Elevation Myocardial Infarction</DescriptorName>
+                <QualifierName UI="Q000150" MajorTopicYN="N">complications</QualifierName>
+                <QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName>
+                <QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
+            </MeshHeading>
+            <MeshHeading>
+                <DescriptorName UI="D012770" MajorTopicYN="N">Shock, Cardiogenic</DescriptorName>
+                <QualifierName UI="Q000139" MajorTopicYN="Y">chemically induced</QualifierName>
+            </MeshHeading>
+        </MeshHeadingList>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>3</Month>
+                <Day>28</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>3</Month>
+                <Day>28</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>4</Month>
+                <Day>13</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>ppublish</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29584845</ArticleId>
+            <ArticleId IdType="pii">2676113</ArticleId>
+            <ArticleId IdType="doi">10.1001/jama.2018.0845</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+<PubmedArticle>
+    <MedlineCitation Status="In-Data-Review" Owner="NLM">
+        <PMID Version="1">29517874</PMID>
+        <DateRevised>
+            <Year>2018</Year>
+            <Month>03</Month>
+            <Day>08</Day>
+        </DateRevised>
+        <Article PubModel="Print">
+            <Journal>
+                <ISSN IssnType="Print">0370-629X</ISSN>
+                <JournalIssue CitedMedium="Print">
+                    <Volume>73</Volume>
+                    <Issue>2</Issue>
+                    <PubDate>
+                        <Year>2018</Year>
+                        <Month>Feb</Month>
+                    </PubDate>
+                </JournalIssue>
+                <Title>Revue medicale de Liege</Title>
+                <ISOAbbreviation>Rev Med Liege</ISOAbbreviation>
+            </Journal>
+            <ArticleTitle>[Lower limb oedema in a patient with type 2 diabetes].</ArticleTitle>
+            <Pagination>
+                <MedlinePgn>101-106</MedlinePgn>
+            </Pagination>
+            <Abstract>
+                <AbstractText>Differential diagnosis of lower limb oedema is a common exercise in clinical practice. Taking the example of a patient presenting with such clinical picture in the presence of type 2 diabetes and arterial hypertension with coronary heart disease and chronic kidney disease as comorbidities, we discuss here the respective contributions of congestive heart failure, renal impairment (and possibly nephrotic syndrome) and liver disease in the development of lower limb oedema. The focus is made on a careful patient history and a meticulous clinical examination, two crucial steps that should allow prescribing well selected simple complementary procedures and rapidly make the final diagnosis.</AbstractText>
+            </Abstract>
+            <AuthorList CompleteYN="Y">
+                <Author ValidYN="Y">
+                    <LastName>Scheen</LastName>
+                    <ForeName>A J</ForeName>
+                    <Initials>AJ</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Service de Diabétologie, Nutrition et Maladies métaboliques et Unité de Pharmacologie clinique, CHU Sart Tilman, Liège, Belgique.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Paquot</LastName>
+                    <ForeName>N</ForeName>
+                    <Initials>N</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Service de Diabétologie, Nutrition et Maladies métaboliques, CHU Sart Tilman, Liège, Belgique.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Lancellotti</LastName>
+                    <ForeName>P</ForeName>
+                    <Initials>P</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Service de Cardiologie, CHU Sart Tilman, Liège, Belgique.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+                <Author ValidYN="Y">
+                    <LastName>Krzesinski</LastName>
+                    <ForeName>J M</ForeName>
+                    <Initials>JM</Initials>
+                    <AffiliationInfo>
+                        <Affiliation>Service de Néphrologie, Transplantation, Hypertension, CHU Sart Tilman, Liège, Belgique.</Affiliation>
+                    </AffiliationInfo>
+                </Author>
+            </AuthorList>
+            <Language>fre</Language>
+            <PublicationTypeList>
+                <PublicationType UI="D004740">English Abstract</PublicationType>
+                <PublicationType UI="D017065">Practice Guideline</PublicationType>
+            </PublicationTypeList>
+            <VernacularTitle>Vignette diagnostique de l’étudiant. Oedème des membres inférieurs chez un patient diabétique de type 2.</VernacularTitle>
+        </Article>
+        <MedlineJournalInfo>
+            <Country>Belgium</Country>
+            <MedlineTA>Rev Med Liege</MedlineTA>
+            <NlmUniqueID>0404317</NlmUniqueID>
+            <ISSNLinking>0370-629X</ISSNLinking>
+        </MedlineJournalInfo>
+        <OtherAbstract Type="Publisher" Language="fre">
+            <AbstractText>Le diagnostic différentiel des oedèmes des membres inférieurs est un exercice fréquemment rencontré en pratique clinique. A la lumière de l’histoire d’un patient obèse avec un diabète de type 2 et une hypertension artérielle, compliqués d’une insuffisance coronarienne et d’une néphropathie, présentant des oedèmes des membres inférieurs, les contributions respectives d’une insuffisance cardiaque, d’une altération de la fonction rénale (avec possible syndrome néphrotique) et d’une éventuelle atteinte hépatique sont discutées sur la base d’éléments anamnestiques et cliniques évocateurs. Le diagnostic final sera conforté par les résultats d’examens paracliniques simples, judicieusement sélectionnés.</AbstractText>
+        </OtherAbstract>
+        <KeywordList Owner="NOTNLM">
+            <Keyword MajorTopicYN="N">Heart failure – Chronic kidney disease – Cirrhosis – Oedema – Nephrotic syndrome – Type 2 diabetes </Keyword>
+        </KeywordList>
+    </MedlineCitation>
+    <PubmedData>
+        <History>
+            <PubMedPubDate PubStatus="entrez">
+                <Year>2018</Year>
+                <Month>3</Month>
+                <Day>9</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="pubmed">
+                <Year>2018</Year>
+                <Month>3</Month>
+                <Day>9</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+            <PubMedPubDate PubStatus="medline">
+                <Year>2018</Year>
+                <Month>3</Month>
+                <Day>9</Day>
+                <Hour>6</Hour>
+                <Minute>0</Minute>
+            </PubMedPubDate>
+        </History>
+        <PublicationStatus>ppublish</PublicationStatus>
+        <ArticleIdList>
+            <ArticleId IdType="pubmed">29517874</ArticleId>
+        </ArticleIdList>
+    </PubmedData>
+</PubmedArticle>
+
+
+
+</AllDocs>
\ No newline at end of file
diff --git a/scripts/DevWorkshop/sampleData/labBaseDisplayNameLookup.tsv b/scripts/DevWorkshop/sampleData/labBaseDisplayNameLookup.tsv
new file mode 100644
index 00000000..cfad060e
--- /dev/null
+++ b/scripts/DevWorkshop/sampleData/labBaseDisplayNameLookup.tsv
@@ -0,0 +1,4476 @@
+base_name	description	display_name	count
+LAB10545R	HLA - CD31 TESTING SUMMARY	HLA - CD31 Testing Summary	6
+LAB11000R	CEA, PANCREATIC CYST	CEA, Pancreatic Cyst	44
+LAB11000R	CEA, PANCREATIC CYST		20
+LAB11004R	HYPERSENS. PNEUMONITIS PNL		4
+LAB11013R	PLATELET ANTIBODY ID PANEL	Platelet Antibody ID Panel	327
+LAB11013R	PLATELET ANTIBODY ID PANEL		30
+LAB11017R	BRUCELLA AB SCREEN		4
+LAB11019R	MYELIN BASIC PROTEIN, CSF	Myelin Basic Protein, CSF	111
+LAB11019R	MYELIN BASIC PROTEIN, CSF		62
+LAB11019R	MYELIN BASIC PROTEIN, CSF	Myelin basic protein, CSF	42
+LAB11020R	EB VIRUS EARLY AG (R+D) IGG		474
+LAB11020R	EB VIRUS EARLY AG (R+D) IGG	EB Virus Early Ag (R+D) IgG	6
+LAB11020R	EB VIRUS EARLY AG (R+D) IGG	EB VIRUS EARLY AG (R+D) IGG	1
+LAB11021R	ADRENAL AB	ADRENAL AB	31
+LAB11021R	ADRENAL AB		2
+LAB11022R	ISLET CELL AB	ISLET CELL AB	4
+LAB11023R	EB NUCLEAR AG AB		478
+LAB11023R	EB NUCLEAR AG AB	EB Nuclear Ag Ab	11
+LAB11024R	EB VIRAL CAPSID AG IGM AB		477
+LAB11024R	EB VIRAL CAPSID AG IGM AB	EB Viral Capsid Ag IgM Ab	4
+LAB11025R	C1 INHIBITOR, QUANT	C1 INHIBITOR, QUANT	73
+LAB11026R	C1 INHIBITOR, FUNCT	C1 INHIBITOR, FUNCT	34
+LAB11027R	IMMUNE COMPLEXES	IMMUNE COMPLEXES	94
+LAB11027R	IMMUNE COMPLEXES		4
+LAB11028R	ACE, CSF	ACE, CSF	219
+LAB11028R	ACE, CSF		50
+LAB11028R	ANGIOTENSIN CONVERTING ENZYME, CSF		1
+LAB11029R	CADMIUM	CADMIUM	2
+LAB11030R	EB VIRAL CAPSID AG IGG AB		475
+LAB11030R	EB VIRAL CAPSID AG IGG AB	EB Viral Capsid Ag IgG Ab	5
+LAB11031R	HER 2	HER 2	1
+LAB11032R	PSEUDOCHOLINESTERASE	PSEUDOCHOLINESTERASE	10
+LAB11034R	ETHOSUXIMIDE LEVEL	Ethosuximide, Serum	2
+LAB11034R	ETHOSUXIMIDE LEVEL	ETHOSUXIMIDE LEVEL	1
+LAB11036R	FIBRINOGEN, QUANT	FIBRINOGEN, QUANT	259
+LAB11036R	FIBRINOGEN, QUANT		76
+LAB11036R	FIBRINOGEN, QUANT	Fibrinogen, Quant	49
+LAB11037R	FLECAINIDE LEVEL	FLECAINIDE LEVEL	32
+LAB11037R	FLECAINIDE LEVEL		2
+LAB11039R	GBM AB	GBM AB	200
+LAB11039R	GBM AB		8
+LAB11040R	HEPARIN COFACTOR	HEPARIN COFACTOR	20
+LAB11041R	FREE HEMOGLOBIN	FREE HEMOGLOBIN	1436
+LAB11041R	FREE HEMOGLOBIN		118
+LAB11041R	FREE HEMOGLOBIN	Free Hemoglobin	60
+LAB11041R	FREE HEMOGLOBIN	Hemoglobin Serum	16
+LAB11041R	HEMOGLOBIN, SERUM	Hemoglobin, Serum	2
+LAB11042R	HISTAMINE LEVEL	HISTAMINE LEVEL	40
+LAB11042R	HISTAMINE LEVEL		8
+LAB11043R	IGD	IGD	30
+LAB11044R	IL6	IL6	38
+LAB11044R	IL6		2
+LAB11045R	LKM AB	LKM AB	169
+LAB11045R	LKM AB		4
+LAB11048R	NEURON AB	NEURON AB	6
+LAB11048R	NEURON AB	Neuronal Cell Ab, Serum	1
+LAB11049R	NSE	NSE	719
+LAB11049R	NSE	Neuron Spec Enolase	439
+LAB11049R	NSE		72
+LAB11049R	NEURON SPEC ENOLASE	Neuron Spec Enolase	9
+LAB11049R	NEURON SPEC ENOLASE		3
+LAB11051R	OLANZAPINE LEVEL	OLANZAPINE LEVEL	14
+LAB11051R	OLANZAPINE LEVEL		2
+LAB11053R	PBG DEAMINASE, RBC	PBG DEAMINASE, RBC	16
+LAB11053R	PBG DEAMINASE, RBC		2
+LAB11054R	PM1 AB	PM1 AB	24
+LAB11054R	PM1 AB		8
+LAB11055R	PROPAFENONE LEVEL	PROPAFENONE LEVEL	7
+LAB11056R	FREE CORTISOL	FREE CORTISOL	240
+LAB11056R	FREE CORTISOL	Cortisol Free, Serum	114
+LAB11056R	FREE CORTISOL		22
+LAB11056R	CORTISOL FREE, SERUM	Cortisol Free, Serum	7
+LAB11057R	TNF	TNF	10
+LAB11058R	TRAZODONE LEVEL	TRAZODONE LEVEL	12
+LAB11058R	TRAZODONE LEVEL		2
+LAB11060R	THIAMINE	THIAMINE	1220
+LAB11060R	THIAMINE		80
+LAB11060R	VITAMIN B1, PLASMA	Vitamin B1, Plasma	3
+LAB11060R	THIAMINE	Vitamin B1, Plasma	2
+LAB11060R	THIAMINE	Vitamin B1, Plasma (LABVITB1P)	1
+LAB11060R	VITAMIN B1, PLASMA	Vitamin B1, Plasma (LABVITB1P)	1
+LAB11067R	17OH PROG	17OH PROG	28
+LAB11068R	ACE	ACE	685
+LAB11068R	ACE	Angiotensin converting enzyme	64
+LAB11068R	ACE		34
+LAB11068R	ACE	Angiotensin Converting Enzyme	8
+LAB11068R	ANGIOTENSIN CONVERTING ENZYME, SERUM	Angiotensin Converting Enzyme, Serum	3
+LAB11068R	ACE	Angiotensin Converting Enzyme, Serum	2
+LAB11069R	ALDOLASE	ALDOLASE	594
+LAB11069R	ALDOLASE		26
+LAB11070R	ALDOSTERONE	Aldosterone, Serum	222
+LAB11070R	ALDOSTERONE	ALDOSTERONE	198
+LAB11070R	ALDOSTERONE		34
+LAB11071R	MITOCHONDRIAL AB	MITOCHONDRIAL AB	907
+LAB11071R	MITOCHONDRIAL AB	Mitochondrial AB M2, Serum	38
+LAB11071R	MITOCHONDRIAL AB		34
+LAB11071R	MITOCHONDRIAL AB M2, SERUM	Mitochondrial Ab M2, Serum	2
+LAB11071R	MITOCHONDRIAL AB M2, SERUM	Mitochondrial AB M2, Serum	1
+LAB11072R	APO A1	APO A1	10
+LAB11073R	APO B	APO B	8
+LAB11074R	ACH RECEPTOR AB	ACH RECEPTOR AB	6
+LAB11074R	ACH RECEPTOR AB		2
+LAB11075R	SMOOTH MUSCLE AB	SMOOTH MUSCLE AB	1115
+LAB11075R	SMOOTH MUSCLE AB	Anti-Smooth Muscle AB	60
+LAB11075R	SMOOTH MUSCLE AB		42
+LAB11075R	ANTI - SMOOTH MUSCLE AB	Anti-Smooth Muscle Ab	1
+LAB11075R	ANTI - SMOOTH MUSCLE AB	Anti-Smooth Muscle AB	1
+LAB11075R	SMOOTH MUSCLE AB	Anti-Smooth Muscle Ab	1
+LAB11077R	CALCITONIN	Calcitonin, Plasma	109
+LAB11077R	CALCITONIN	CALCITONIN	56
+LAB11077R	CALCITONIN		6
+LAB11078R	ACETYLCHOLINESTERASE	ACETYLCHOLINESTERASE	22
+LAB11078R	ACETYLCHOLINESTERASE		2
+LAB11079R	GABAPENTIN	Gabapentin, Serum/Plasma	68
+LAB11079R	GABAPENTIN	GABAPENTIN	58
+LAB11079R	GABAPENTIN		6
+LAB11080R	GAD AB	GAD AB	42
+LAB11080R	GAD AB		10
+LAB11080R	GLUTAMIC ACID DECARBOXY AB	Glutamic Acid Decarboxy Ab	1
+LAB11080R	GLUTAMIC ACID DECARBOXY AB		1
+LAB11081R	GASTRIN	GASTRIN	385
+LAB11081R	GASTRIN		26
+LAB11084R	LORAZEPAM LEVEL	LORAZEPAM LEVEL	10
+LAB11084R	LORAZEPAM LEVEL	Lorazepam, Serum	2
+LAB11085R	MYELOPEROXIDASE ANTIBODY	Myeloperoxidase Antibody	156
+LAB11085R	MPO AB	MPO AB	110
+LAB11085R	MYELOPEROXIDASE ANTIBODY	MYELOPEROXIDASE ANTIBODY	108
+LAB11085R	MYELOPEROXIDASE ANTIBODY	MPO AB	64
+LAB11085R	MPO AB		18
+LAB11086R	NORTRIPTYLINE LEVEL	NORTRIPTYLINE LEVEL	180
+LAB11086R	NORTRIPTYLINE LEVEL		10
+LAB11088R	PP	PP	42
+LAB11089R	ACID PHOSPHATASE	ACID PHOSPHATASE	4
+LAB11089R	ACID PHOSPHATASE		2
+LAB11090R	PARIETAL CELL AB	PARIETAL CELL AB	36
+LAB11091R	QUINIDINE LEVEL	QUINIDINE LEVEL	27
+LAB11092R	RENIN	RENIN	364
+LAB11092R	RENIN		26
+LAB11092R	RENIN ACTIVITY, PLASMA	Renin Activity, Plasma	2
+LAB11092R	RENIN	Renin Activity, Plasma	1
+LAB11093R	SCL 70 AB	SCL 70 AB	202
+LAB11093R	SCL 70 AB		7
+LAB11093R	AUTOANTIBODIES TO SCL 70	AutoAb To Scl 70	1
+LAB11093R	SCL 70 AB	AutoAb To Scl 70	1
+LAB11094R	RO AB	RO AB	285
+LAB11094R	RO AB		12
+LAB11094R	RO AB	AutoAb To SS-A/RO	2
+LAB11095R	LA AB	LA AB	272
+LAB11095R	LA AB		14
+LAB11095R	LA AB	AutoAb To SS-B/LA	2
+LAB11096R	ALPHA 1 ANTITRYPSIN, STOOL	ALPHA 1 ANTITRYPSIN, STOOL	298
+LAB11096R	ALPHA 1 ANTITRYPSIN, STOOL		88
+LAB11096R	ALPHA 1 ANTITRYPSIN, STOOL	Alpha-1-Antitrypsin,Stool	28
+LAB11096R	ALPHA-1-ANTITRYPSIN, FECAL	Alpha-1-Antitrypsin, Fecal	2
+LAB11097R	TSH RECEPTOR AB, STIM	TSI, Serum	223
+LAB11097R	TSH RECEPTOR AB, STIM		14
+LAB11097R	TSH RECEPTOR AB, STIM	TSH RECEPTOR AB, STIM	2
+LAB11097R	TSI, SERUM	TSI, Serum	1
+LAB11098R	1 25 OH VITAMIN D	1 25 OH Vitamin D	515
+LAB11098R	1 25 OH VITAMIN D	1 25 OH VITAMIN D	80
+LAB11098R	1 25 OH VITAMIN D		70
+LAB11098R	1 25 OH VITAMIN D	Vit D 1,25-Dihydroxy Serum	10
+LAB11098R	PEDIATRIC 1 25 OH VITAMIN D	Pediatric 1 25 OH Vitamin D	3
+LAB11098R	1 25 OH VITAMIN D	Pediatric 1 25 OH Vitamin D	1
+LAB11098R	1 25 OH VITAMIN D	Vitamin D 1,25-Dihydroxy, Serum	1
+LAB11098R	1,25-DIHYDROXYVITAMIN D, SERUM	Vitamin D 1,25-Dihydroxy, Serum	1
+LAB11099R	VIP	VIP	114
+LAB11099R	VIP		8
+LAB11100R	RIBOFLAVIN	RIBOFLAVIN	30
+LAB11100R	RIBOFLAVIN		2
+LAB11101R	VITAMIN C	VITAMIN C	141
+LAB11101R	VITAMIN C		2
+LAB11102R	VITAMIN E	VITAMIN E	277
+LAB11102R	VITAMIN E		8
+LAB11105R	CANDIDA AB	CANDIDA AB	4
+LAB11106R	HBE AB	HEPATITIS B E AB	171
+LAB11106R	HBE AB	HBE AB	34
+LAB11106R	HBE AB	Hepatitis B E AB	22
+LAB11106R	HBE AB	Hepatitis B E Ab	21
+LAB11106R	HBE AB		10
+LAB11106R	HEPATITIS B E AB	Hepatitis B E Ab	1
+LAB11106R	HEPATITIS B E AB	Hepatitis B E AB	1
+LAB11107R	HBE ANTIGEN	HEPATITIS B E AG	203
+LAB11107R	HBE ANTIGEN	HBE ANTIGEN	43
+LAB11107R	HBE ANTIGEN	Hepatitis B E Antigen	20
+LAB11107R	HBE ANTIGEN		10
+LAB11107R	HBE ANTIGEN	Hepatitis B E Ag	2
+LAB11107R	HEPATITIS B E AG	Hepatitis B E Antigen	1
+LAB11112R	DONOR TOXOPLASMA AB	DONOR TOXOPLASMA AB	12
+LAB11112R	DONOR TOXOPLASMA AB		4
+LAB11113R	TOXOPLASMA AB, IGA	TOXOPLASMA AB, IGA	10
+LAB11113R	TOXOPLASMA AB, IGA		2
+LAB11116R	FANCONI MUTATION ANALYSIS	FANCONI MUTATION ANALYSIS	6
+LAB11118R	ELASTASE, STOOL	ELASTASE, STOOL	470
+LAB11118R	ELASTASE, STOOL		88
+LAB11118R	ELASTASE, STOOL	Elastase-1,Stool	23
+LAB11118R	ELASTASE - 1,STOOL		2
+LAB11120R	TOXOPLASMOSIS, CSF	TOXOPLASMOSIS, CSF	117
+LAB11120R	TOXOPLASMOSIS, CSF		42
+LAB11120R	TOXOPLASMOSIS, CSF	Toxoplasmosis, CSF	22
+LAB11121R	TOXOPLASMA IGG DYE TEST	Toxoplasma IgG Dye Test	4
+LAB11122R	TOXOPLASMA AB, IGM	TOXOPLASMA AB, IGM	60
+LAB11122R	TOXOPLASMA AB, IGM		16
+LAB11122R	TOXOPLASMA IGM ELISA	Toxoplasma IgM ELISA	6
+LAB11122R	TOXOPLASMA IGM ELISA		2
+LAB11122R	TOXOPLASMOSIS IGM		1
+LAB11123R	COCCI CF AB, CSF	COCCI CF AB, CSF	956
+LAB11123R	COCCI CF AB, CSF		221
+LAB11123R	COCCI CF AB, CSF	Coccidioides Complement Fixation, CSF	162
+LAB11123R	COCCI CF AB, CSF	Coccidioides Comp Fix, CSF	64
+LAB11123R	COCCIDIOIDES COMPLEMENT FIXATION, CSF	Coccidioides Complement Fixation, CSF	2
+LAB11123R	COCCIDIOIDES COMPLEMENT FIXATION, CSF		1
+LAB11124R	COCCI CF	COCCI CF	2090
+LAB11124R	COCCI CF		134
+LAB11124R	COCCI CF	Coccidioides Comp Fix, Serum	17
+LAB11124R	COCCI CF	Coccidioides Complement Fixation, Serum	11
+LAB11124R	COCCIDIOIDES COMPLEMENT FIXATION, SERUM	Coccidioides Complement Fixation, Serum	10
+LAB11125R	COCCI ID	Cocci ID (Serum)	3180
+LAB11125R	COCCI ID		210
+LAB11125R	COCCI ID	COCCI ID	80
+LAB11125R	COCCI ID	Cocci ID (Serum) (Phase II labs to be drawn on the second day)	3
+LAB11125R	COCCI ID (SERUM)	Cocci ID (Serum)	1
+LAB11125R	COCCI ID (SERUM)		1
+LAB11126R	ASCARIASIS	ASCARIASIS	22
+LAB11126R	ASCARIASIS		2
+LAB11127R	CASPOFUNGIN LEVEL	CASPOFUNGIN LEVEL	74
+LAB11127R	CASPOFUNGIN LEVEL		10
+LAB11128R	FUNGAL MIC		86
+LAB11128R	FUNGAL MIC	FUNGAL MIC	4
+LAB11129R	KETOCONAZOLE LEVEL	KETOCONAZOLE LEVEL	2
+LAB11131R	VORICONAZOLE LEVEL	VORICONAZOLE LEVEL	1343
+LAB11131R	VORICONAZOLE LEVEL		115
+LAB11131R	VORICONAZOLE LEVEL	Voriconazole Level	5
+LAB11132R	DONOR HEP B SURF. AG		50
+LAB11132R	DONOR HEP B SURF. AG	DONOR HEP B SURF. AG	12
+LAB11133R	DONOR HBC AB		50
+LAB11133R	DONOR HBC AB	DONOR HBC AB	16
+LAB11134R	DONOR HCV AB		54
+LAB11134R	DONOR HCV AB	DONOR HCV AB	18
+LAB11136R	HTLV AB	HTLV-1 AB SCREEN	419
+LAB11136R	HTLV AB	HTLV-1 Ab Screen	164
+LAB11136R	HTLV AB		88
+LAB11136R	HTLV AB	HTLV AB	58
+LAB11136R	HTLV-1 AB SCREEN	HTLV-1 Ab Screen	3
+LAB11137R	HSV AB, IMMUNOBLOT	HSV AB, IMMUNOBLOT	4
+LAB11138R	HIV PHENO EXPR ANTIVIR RESIST	PHENOSENSE HIV SELECT	2
+LAB11139R	CARDIAC REJECTION RISK	CARDIAC REJECTION RISK	16
+LAB11139R	CARDIAC REJECTION RISK		6
+LAB11140R	MATERNAL SCREEN	MATERNAL SCREEN	6
+LAB11140R	MATERNAL SCREEN		2
+LAB11145R	ADULTERANTS SURVEY, URINE		376
+LAB11145R	ADULTERANTS SURVEY, URINE	Adulterants Survey, Urine	108
+LAB11153R	HERPES VIRUS 6 DNA, QUANT		101
+LAB11153R	HERPES VIRUS 6 DNA, QUANT	Herpes Virus 6 DNA, Quant	55
+LAB11170R	PREGABALIN	PREGABALIN	12
+LAB11170R	PREGABALIN	Pregablin	4
+LAB11170R	PREGABALIN		2
+LAB11173R	IODINE	IODINE	5
+LAB11175R	LIPASE,BF	LIPASE,BF	140
+LAB11175R	LIPASE,BF		36
+LAB11179R	NICOTINE & METABOLITES, U	NICOTINE & METABOLITES, U	52
+LAB11179R	NICOTINE & METABOLITES, U		18
+LAB11182R	NICOTINE & METABOLITES	NICOTINE & METABOLITES	57
+LAB11182R	NICOTINE & METABOLITES		4
+LAB11188R	M. PNEUMONIAE DNA	M. PNEUMONIAE DNA	16
+LAB11190R	BORRELIA HERMSII AB PANL	BORRELIA HERMSII AB PANL	10
+LAB11190R	BORRELIA HERMSII AB PANL		2
+LAB11195R	N-TELOPEPTIDE, CROSS-LINKED	N-TELOPEPTIDE, CROSS-LINKED	4
+LAB11198R	VIT A & VIT E	VIT A & VIT E	35
+LAB11198R	VIT A & VIT E		4
+LAB11199R	PARANEOPLASTIC AUTOANTIBODY EVAL, SERUM	Paraneoplastic Autoantibody Eval, Serum	185
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL	PARANEOPLASTIC AUTOAB EVAL	179
+LAB11199R	PARANEOPLASTIC AUTOAB EVAL		30
+LAB11214R	BETA-2 TRANSFERRIN, BF	BETA-2 TRANSFERRIN, BF	207
+LAB11214R	BETA-2 TRANSFERRIN, BF		74
+LAB11217R	IRON,LIVER TISSUE	IRON,LIVER TISSUE	6
+LAB11219R	IODINE,U	IODINE,U	6
+LAB11225R	LEAD,U	LEAD,U	6
+LAB11229R	METANEPHRINE,FRACT,RANM U	METANEPHRINE,FRACT,RANM U	76
+LAB11229R	METANEPHRINE,FRACT,RANM U		18
+LAB11234R	VZV TOTAL AND IGM AB PANEL	VZV TOTAL AND IGM AB PANEL	533
+LAB11234R	VZV TOTAL AND IGM AB PANEL		16
+LAB11238R	HERPESVIRUS-8 IGG AB	HERPESVIRUS-8 IGG AB	16
+LAB11240R	MEASLES IGG	MEASLES IGG	30
+LAB11242R	VZV QUANT	VZV QUANT	595
+LAB11242R	VZV QUANT		96
+LAB11250R	HHV-8 DNA, QUANT	HHV-8 DNA, QUANT	180
+LAB11250R	HHV-8 DNA, QUANT		6
+LAB11253R	HELICOBACTER PYLORI AGA AB	HELICOBACTER PYLORI AGA AB	81
+LAB11253R	HELICOBACTER PYLORI AGA AB		8
+LAB11254R	M AVIUM COMPLEX MIC PANL	M AVIUM COMPLEX MIC PANL	2
+LAB11265R	ENTAMOEBA HISTOLYTICA AG	ENTAMOEBA HISTOLYTICA AG	204
+LAB11265R	ENTAMOEBA HISTOLYTICA AG		48
+LAB11269R	ACETYL RECEPTOR AB PANL	ACETYL RECEPTOR AB PANL	2
+LAB11269R	ACETYL RECEPTOR AB PANL		2
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS	Sulfonylurea Hypoglycemics	3
+LAB11299R	SULFONYLUREA HYPOGLYCEMICS		2
+LAB11312R	SOLUBLE LIVER AG AB, IGG	SOLUBLE LIVER AG AB, IGG	43
+LAB11322R	MOTOR NEUROPATHY AB SCREEN	CO-GM1 TRIAD AB TEST	10
+LAB11322R	MOTOR NEUROPATHY AB SCREEN	MOTOR NEUROPATHY AB SCREEN	2
+LAB11324R	CADASIL EVALUATION, COMPLETE	CADASIL EVALUATION, COMPLETE	12
+LAB11324R	CADASIL EVALUATION, COMPLETE		2
+LAB11340R	ADAMTS 13 PROFILE	ADAMTS 13 PROFILE	24
+LAB11340R	ADAMTS 13 PROFILE		10
+LAB11343R	SRA UNFRACTIONATED HEPARIN	SRA UNFRACTIONATED HEPARIN	223
+LAB11343R	SRA UNFRACTIONATED HEPARIN		8
+LAB11347R	C-TELOPEPTIDE	C-TELOPEPTIDE	2
+LAB11357R	FAMILIAL MEDITERRANEAN FEVER	FAMILIAL MEDITERRANEAN FEVER	18
+LAB11357R	FAMILIAL MEDITERRANEAN FEVER		4
+LAB11365R	FAMILIAL HIBERNIAN FEVER/TRAPS	FAMILIAL HIBERNIAN FEVER/TRAPS	6
+LAB11369R	LEUKEMIA/LYMPHOMA IMMUPHENTYP	LEUKEMIA/LYMPHOMA IMMUPHENTYP	8
+LAB11369R	LEUKEMIA/LYMPHOMA IMMUPHENTYP		8
+LAB11373R	MARFAN(FBN1) SEQUENCING	MARFAN(FBN1) SEQUENCING	6
+LAB11373R	MARFAN(FBN1) SEQUENCING		4
+LAB11375R	OSTEOGENESIS IMPERFECTA SEQUEN	OSTEOGENESIS IMPERFECTA SEQUEN	4
+LAB11401R	HALIBUT IGE	HALIBUT IGE	2
+LAB11401R	HALIBUT IGE		2
+LAB11429R	PTERINS, URINE/BLOOD	TETRAHYDROBIOPTERIN&NEOPTERIN	1
+LAB11435R	CELIAC GENETICS	CELIAC GENETICS	10
+LAB11436R	SERM INFLIXIMAB/HACA MEASUREMT	SERM INFLIXIMAB/HACA MEASUREMT	1
+LAB11437R	IBD DIAGNOSTIC	IBD DIAGNOSTIC	10
+LAB11438R	CHLAMYDIA TRACHOMATIS CULTURE	Chlamydia Trachomatis Culture	25
+LAB11440R	H. PYLORI UREA BREATH TEST	H. PYLORI UREA BREATH TEST	37
+LAB11443R	NF-1 DNA TESTING COMP TEST 1	NEUROFIBROMATOSIS TYPE	2
+LAB11444R	RESPIRATORY VIRAL PANL PCR	RESPIRATORY VIRAL PANL PCR	2
+LAB11512R	CYSTIC FIBROSIS, SEQUENCING	CYSTIC FIBROSIS, SEQUENCING	16
+LAB11512R	CYSTIC FIBROSIS, SEQUENCING		2
+LAB11516R	PANCREATITIS PANEL	PANCREATITIS PANEL	19
+LAB11518R	BCR-ABL1 KINASE DOMAIN MUT ANL	BCR-ABL1 KINASE DOMAIN MUT ANL	6
+LAB11522R	HU AB	HU AB	27
+LAB11522R	HU AB		4
+LAB11523R	GQ1B AB	GQ1B AB	118
+LAB11523R	GQ1B AB		12
+LAB11525R	MUSCLE TYROSINE KINASE AB	MUSCLE TYROSINE KINASE AB	62
+LAB11525R	MUSCLE TYROSINE KINASE AB		2
+LAB11526R	K CHANNEL AB	K CHANNEL AB	30
+LAB11526R	K CHANNEL AB		2
+LAB11527R	SPINAL MUSCULAR ATROPHY DIAGNO	SPINAL MUSCULAR ATROPHY DIAGNO	4
+LAB11528R	PERIPHERAL NEUROPATHY AB SCN	PERIPHERAL NEUROPATHY AB SCN	2
+LAB11529R	ENCEPHALITIS AB SCREEN	ENCEPHALITIS AB SCREEN	9
+LAB11529R	ENCEPHALITIS AB SCREEN		3
+LAB11530R	CEREBELLAR PARANEOPLASTIC AB	CEREBELLAR PARANEOPLASTIC AB	16
+LAB11530R	CEREBELLAR PARANEOPLASTIC AB	Cerebellar Paraneoplastic AB	4
+LAB11530R	CEREBELLAR PARANEOPLASTIC AB		2
+LAB11531R	PARANEOPLASTIC AB SCREEN	PARANEOPLASTIC AB SCREEN	200
+LAB11531R	PARANEOPLASTIC AB SCREEN		12
+LAB11533R	SENSORIMOTOR SCREEN AB	SENSORIMOTOR SCREEN AB	2
+LAB11534R	MTDNA SCREENING PANEL	MTDNA SCREENING PANEL	2
+LAB11534R	MTDNA SCREENING PANEL		2
+LAB11540R	OTC SEQUENCING	OTC SEQUENCING	4
+LAB11547R	HEPATITIS C CONFIRMATORY TEST	HEPATITIS C CONFIRMATORY TEST	62
+LAB11547R	HEPATITIS C CONFIRMATORY TEST		2
+LAB11549R	CALIFORNIA ENCEPHALITIS PANEL	CALIFORNIA ENCEPHALITIS PANEL	131
+LAB11549R	CALIFORNIA ENCEPHALITIS PANEL		70
+LAB11551R	NK FUNCTION	NK FUNCTION	117
+LAB11551R	NK FUNCTION		18
+LAB11554R	S-SULFOCYSTEINE, URINE	S-SULFOCYSTEINE, URINE	4
+LAB11556R	BLASTOMYCES SEROLOGIES	BLASTOMYCES SEROLOGIES	255
+LAB11556R	BLASTOMYCES SEROLOGIES		12
+LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB	LYMPHOCYTIC CHORIOMENINGITI AB	60
+LAB11562R	LYMPHOCYTIC CHORIOMENINGITI AB		2
+LAB11565R	PARVOVIRUS B19 DNA, QUANT	PARVOVIRUS B19 DNA, QUANT	2
+LAB11566R	ENTEROVIRUS RNA, QUAL	ENTEROVIRUS RNA, QUAL	70
+LAB11566R	ENTEROVIRUS RNA, QUAL		20
+LAB11567R	HTLV-I/II AB, CSF	HTLV-I/II AB, CSF	20
+LAB11567R	HTLV-I/II AB, CSF		12
+LAB11568R	BRUCELLA SEROLOGIES, CSF	BRUCELLA SEROLOGIES, CSF	4
+LAB11570R	SPINAL MUSC ATROPHY CARRIER	SPINAL MUSC ATROPHY CARRIER	2
+LAB11571R	CYSTIC FIBROSIS, 97 MUTATIONS	CYSTIC FIBROSIS, 97 MUTATIONS	6
+LAB11571R	CYSTIC FIBROSIS, 97 MUTATIONS		4
+LAB11572R	TAY-SACHS ENZYME (LEUKOCYTES)	TAY-SACHS ENZYME (LEUKOCYTES)	4
+LAB11573R	CONGENITAL CYCLIC NEUTROPENIA	Congenital Cyclic Neutropenia	2
+LAB11576R	COBALAMIN C DEFICIENCY(MMACHC)	COBALAMIN C DEFICIENCY(MMACHC)	44
+LAB11576R	COBALAMIN C DEFICIENCY(MMACHC)		12
+LAB11577R	ALAGILLE (JAG1) MUTATION	ALAGILLE (JAG1) MUTATION	8
+LAB11590R	FREE FATTY ACIDS	FREE FATTY ACIDS	4
+LAB11591R	ARSENIC, HAIR	Arsenic, Hair	6
+LAB11592R	COPPER, TISSUE	COPPER, TISSUE	4
+LAB11593R	ETHYLENE GLYCOL	ETHYLENE GLYCOL	102
+LAB11593R	ETHYLENE GLYCOL		4
+LAB11596R	LYMPHO PROLIFERATION, AG	LYMPHO PROLIFERATION, AG	2
+LAB11596R	LYMPHO PROLIFERATION, AG		2
+LAB11599R	GANICICLOVIR	GANICICLOVIR	2
+LAB11604R	IL 5	IL 5	6
+LAB11606R	CYTOCHROME P450 2D6 GENOTYPE	CYTOCHROME P450 2D6 GENOTYPE	10
+LAB11607R	COENZYME Q10, REDUCED & TOTAL	COENZYME Q10, REDUCED & TOTAL	24
+LAB11607R	COENZYME Q10, REDUCED & TOTAL		4
+LAB11608R	MARFAN (FBN1) SEQUENCING	MARFAN (FBN1) SEQUENCING	16
+LAB11610R	DONOR, HTLVS(QNS/LIPEMIC ONLY)		16
+LAB11610R	DONOR, HTLVS(QNS/LIPEMIC ONLY)	DONOR, HTLVS(QNS/LIPEMIC ONLY)	6
+LAB11615R	SODIUM BENZOATE LEVEL	SODIUM BENZOATE LEVEL	11
+LAB11617R	CMV PCR, AQUEOUS FLUID	CMV PCR, AQUEOUS FLUID	152
+LAB11617R	CMV PCR, AQUEOUS FLUID		8
+LAB11618R	HSV 1/2 PCR, AQUEOUS FLUID	HSV 1/2 PCR, AQUEOUS FLUID	154
+LAB11618R	HSV 1/2 PCR, AQUEOUS FLUID		2
+LAB11619R	VZV PCR, AQUEOUS FLUID	VZV PCR, AQUEOUS FLUID	315
+LAB11619R	VZV PCR, AQUEOUS FLUID		10
+LAB11620R	TOXOPLASMA GONDII, AQUEOUS FLUID	TOXOPLASMA GONDII, AQUEOUS FLUID	16
+LAB11620R	TOXOPLASMA GONDII, AQUEOUS FLUID		2
+LAB11824R	BUSULFAN	Busulfan	1376
+LAB11824R	BUSULFAN		82
+LAB11851R	FECAL LIPIDS, TOTAL	Fecal Lipids, Total	26
+LAB11851R	FECAL LIPIDS, TOTAL		6
+LAB11855R	HEPATITIS C VIRAL RNA, QUANT		16
+LAB11855R	HEPATITIS C VIRAL RNA, QUANT	Hepatitis C Viral RNA, Quant	8
+LAB11858R	HEPTIMAX(R) HCV RNA	HEPTIMAX(R) HCV RNA	35
+LAB11858R	HEPTIMAX(R) HCV RNA		4
+LAB11860R	VASCULAR ENDOTHELIAL GROW.FACT	Vascular Endothelial Grow.Fact	23
+LAB11860R	VASCULAR ENDOTHELIAL GROW.FACT		4
+LAB11863R	ASPIRIN RESISTANCE	Aspirin Resistance	6
+LAB11863R	ASPIRIN RESISTANCE		4
+LAB11867R	C3A DESARG FRAGMENT	C3a desArg Fragment	4
+LAB11869R	HEPATITIS C VIRUS RNA, QUAL	Hepatitis C Virus RNA, Qual	2
+LAB11871R	HIV-2 AB, IMMUNOBLOT	HIV-2 AB, Immunoblot	23
+LAB11871R	HIV-2 ANTIBODY, IMMUNOBLOT	HIV-2 Antibody, Immunoblot	6
+LAB11871R	HIV-2 AB, IMMUNOBLOT		2
+LAB11878R	CORTISOL BINDING GLOBULIN	Cortisol Binding Globulin	2
+LAB11882R	MAGNESIUM, RBC	Magnesium, RBC	18
+LAB11885R	C. TRACHOMATIS RNA, RECTAL	C. trachomatis RNA, Rectal	10
+LAB11885R	C. TRACHOMATIS RNA, RECTAL		2
+LAB11887R	C. TRACHOMATIS RNA, THROAT	C. trachomatis RNA, Throat	10
+LAB11887R	C. TRACHOMATIS RNA, THROAT		2
+LAB11899R	N. GONORRHOEAE RNA, RECTAL	N. gonorrhoeae RNA, Rectal	18
+LAB11899R	N. GONORRHOEAE RNA, RECTAL		6
+LAB11901R	N. GONORRHOEAE RNA, THROAT	N. gonorrhoeae RNA, Throat	14
+LAB11901R	N. GONORRHOEAE RNA, THROAT		8
+LAB11982R	POSACONAZOLE	Posaconazole	342
+LAB11982R	POSACONAZOLE		16
+LAB11984R	NIACIN	Niacin	86
+LAB11984R	NIACIN		4
+LAB11989R	LONG QT SYNDROME		4
+LAB11991R	DNA EXTRACTION, NPL		6
+LAB11991R	DNA EXTRACTION, NPL	DNA Extraction, NPL	4
+LAB12003R	HERPESVIRUS 6 AB, IGG	Herpesvirus 6 AB, IgG	2
+LAB12016R	CYTOMEGALOVIRUS IGG AB, ELISA	Cytomegalovirus IgG AB, ELISA	2
+LAB12017R	ECHOVIRUS AB, SERUM	Echovirus Ab, Serum	14
+LAB12017R	ECHOVIRUS AB, SERUM		1
+LAB12023R	MYCOPLASMA PNEUMONIA IGG AB	Mycoplasma pneumonia IgG AB	2
+LAB12043R	IL 5	IL 5	2
+LAB12043R	IL 5		2
+LAB12050R	RICKETTSIA AB PNL W/RFLX TITER	Rickettsia Ab Pnl w/Rflx Titer	38
+LAB12055R	RMSF IGG TITER	RMSF IgG Titer	2
+LAB12059R	R. TYPHI IGG TITER	R. Typhi IgG Titer	2
+LAB12066R	CRMP-5-IGG WESTERN BLOT	CRMP-5-IgG Western Blot	20
+LAB12066R	CRMP-5-IGG WESTERN BLOT		10
+LAB12090R	HE4, OVARIAN CANCER MONITORING	HE4, Ovarian Cancer Monitoring	24
+LAB12090R	HE4, OVARIAN CANCER MONITORING		4
+LAB12092R	PAIN MNGMNT, FENTANYL, QUANT,U	Pain Mngmnt, Fentanyl, Quant,U	4
+LAB12095R	DARUNAVIR,ANTIRETROVIRAL LEVEL	Darunavir,Antiretroviral Level	2
+LAB12099R	BRUCELLA TOTAL AB,AGGLU (RFLX)	Brucella Total Ab,Agglu (RFLX)	8
+LAB12099R	BRUCELLA TOTAL AB,AGGLU (RFLX)		8
+LAB12206R	CYSTATIN C	Cystatin C, Serum/Plasma	12
+LAB12210R	LIPOPROTN ASSOCTD PHOSPHLPSEA2	Lipoprotn Assoctd PhosphlpseA2	10
+LAB12212R	WA1 IGG AB	WA1 IgG Ab	30
+LAB12212R	WA1 IGG AB		2
+LAB12214R	B. BURGDORFERI C6 PEP AB,RFLX	B. burgdorferi C6 Pep Ab,Rflx	254
+LAB12214R	LYME SCREEN, SERUM	Lyme Screen, Serum	11
+LAB12214R	B. BURGDORFERI C6 PEP AB,RFLX		2
+LAB12214R	LYME SCREEN, SERUM		2
+LAB12220R	MSUD MUTATION ANALYSIS	MSUD Mutation Analysis	4
+LAB12228R	LATEX SPECIFIC IGE (RIA) PANEL	Latex Specific IgE (RIA) Panel	6
+LAB12230R	SOLUBLE IL 2R	Soluble IL 2R	104
+LAB12230R	SOLUBLE IL 2R		8
+LAB12232R	THYROGLOBULIN & THYROID AB	Thyroglobulin & Thyroid Ab	157
+LAB12232R	THYROGLOBULIN & THYROID AB		2
+LAB12234R	VOLATILES	Volatiles	26
+LAB12234R	VOLATILES		10
+LAB12236R	IBUPROFEN	Ibuprofen	32
+LAB12238R	LACOSAMIDE	Lacosamide	233
+LAB12241R	B CELL PHENOTYPING PROFILE	B Cell Phenotyping Profile	25
+LAB12279R	CVID CONFIRMATION FLOW PANEL		2
+LAB12284R	LYMPHO PROLIFERATION, AG	LYMPHO PROLIFERATION, AG	8
+LAB12292R	T CELL PHENOTYPING, ADVANCED	T Cell Phenotyping, Advanced	49
+LAB12320R	THIOCYANATE	Thiocyanate	18
+LAB12322R	VON WILLEBRAND MULTIMERS	Von Willebrand Multimers	65
+LAB12328R	AMYLOID PROTEIN IDENTIFICATION	Amyloid Protein Identification	2
+LAB12330R	EVEROLIMUS, BLOOD	EVEROLIMUS, BLOOD	75
+LAB12330R	EVEROLIMUS, BLOOD	Everolimus, Blood	18
+LAB12331R	MLH1 HYPERMETHYLATION ANALYSIS	MLH1 Hypermethylation Analysis	6
+LAB12332R	KIT MUTATION ANALYSIS EXON 11	KIT Mutation Analysis Exon 11	16
+LAB12333R	KIT MUTATION ANALYSIS EXON 9	KIT Mutation Analysis Exon 9	6
+LAB12352R	NMO EVALUATION WITH REFLEX	NMO EVALUATION WITH REFLEX	42
+LAB12352R	NMO EVALUATION WITH REFLEX	NMO Evaluation with Reflex	11
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY EVAL, CSF	Paraneoplastic Autoantibody Eval, CSF	172
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	Paraneoplastic Autoantibody	16
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	PARANEOPLASTIC AUTOANTIBODY	14
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY	Paraneoplastic Autoantibody Eval, CSF	2
+LAB12362R	PARANEOPLASTIC AUTOANTIBODY		2
+LAB12371R	COBALT	COBALT	10
+LAB12371R	COBALT	Cobalt	4
+LAB12373R	CARBAMAZEPINE-10,11-EPOXIDE	Carbamazepine-10,11-Epoxide	4
+LAB12395R	5\'NUCLEOTIDASE	5\'NUCLEOTIDASE	9
+LAB12395R	5\'NUCLEOTIDASE	5\'Nucleotidase	2
+LAB12397R	ACETYLCHOLINE BLOCKING AB	ACETYLCHOLINE BLOCKING AB	31
+LAB12397R	ACETYLCHOLINE BLOCKING AB	Acetylcholine Blocking Ab	8
+LAB12399R	ACETYLCHOLINE MODULATING AB	ACETYLCHOLINE MODULATING AB	17
+LAB12399R	ACETYLCHOLINE MODULATING AB	Acetylcholine Modulating Ab	6
+LAB12401R	ALKP. PHOSPHATASE ISOENZYMES	Alkp. Phosphatase Isoenzymes	32
+LAB12415R	ACVRL1 AND ENG (HHT)	ACVRL1 and ENG (HHT)	6
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	SULFONYLUREA HYPOGLYCEMICS PNL	49
+LAB12418R	SULFONYLUREA HYPOGLYCEMICS PNL	Sulfonylurea Hypoglycemics Pnl	6
+LAB12443R	NEOCOMPLETE PARANEOPLASTIC PRFL, SERUM	NeoComplete Paraneoplastic Prfl, Serum	26
+LAB12443R	NEO PARANEOPLASTIC PRFL	NEO PARANEOPLASTIC PRFL	2
+LAB12447R	AP SPECIAL STUDIES REVIEW	AP Special Studies Review	10
+LAB12470R	STAIN, CONGO RED	Stain, Congo Red	2
+LAB12471R	SURGICAL PATHOLOGY CONSULT	Surgical Pathology Consult	2
+LAB12494R	PATH CONSULT, OUTSIDE SLIDE	Path Consult, Outside Slide	2
+LAB12503R	POTASSIUM, 24-HR STOOL	POTASSIUM, 24-HR STOOL	29
+LAB12503R	POTASSIUM, 24-HR STOOL	Potassium, 24-Hr Stool	6
+LAB12505R	SODIUM, 24-HR STOOL	SODIUM, 24-HR STOOL	29
+LAB12505R	SODIUM, 24-HR STOOL	Sodium, 24-Hr Stool	8
+LAB12507R	IBD SGI DIAGNOSTIC	IBD SGI DIAGNOSTIC	8
+LAB12507R	IBD SGI DIAGNOSTIC	IBD sgi Diagnostic	2
+LAB12512R	VON HIPPEL-LINDAU SYNDROME	VON HIPPEL-LINDAU SYNDROME	2
+LAB12548R	SIROLIMUS, BLOOD (HPLC-MS MS)	SIROLIMUS, BLOOD (HPLC-MS MS)	14
+LAB12571R	FLT3-ITD, FLT3-D835 MUTATIONS	FLT3-ITD, FLT3-D835 Mutations	286
+LAB12571R	FLT3-ITD, FLT3-D835 MUTATIONS	FLT3-ITD, FLT3-D835 MUTATIONS	152
+LAB12573R	ANCA	ANCA	510
+LAB12578R	CHLORIDE, 24 HR STOOL	Chloride, 24 Hr Stool	11
+LAB12580R	CU INDEX PANEL	CU Index Panel	3
+LAB12581R	S PNEUMONIAE AB IGG (PREVNAR 13)	S pneumoniae Ab IgG (Prevnar 13)	4
+LAB12582R	LYMPH MITOGEN AND ANTIGEN SCRN	Lymph Mitogen and Antigen Scrn	4
+LAB12584R	TIAGABINE CONCENTRATION	Tiagabine Concentration	2
+LAB12585R	HEPATITIS D RNA, QUAL PCR	HEPATITIS D RNA, QUAL PCR	12
+LAB12586R	THIAMIN (VITAMIN B1)	Thiamin (Vitamin B1)	44
+LAB12586R	VITAMIN B1	Vitamin B1	2
+LAB12599R	DILATED CARDIOMYOPATHY PANEL	Dilated Cardiomyopathy Panel	6
+LAB12605R	N-METHYL-D-ASPARTATE RECEPTOR AB, IGG	N-Methyl-D-Aspartate Receptor Ab, IgG	65
+LAB12607R	N-METHYL-D-ASPARTATE RECEPTOR AB, CSF	N-Methyl-D-Aspartate Receptor Ab, CSF	59
+LAB12631R	VORICONAZOLE LEVEL	VORICONAZOLE LEVEL	1048
+LAB12631R	VORICONAZOLE LEVEL		1
+LAB12637R	MENINGOCOCCAL AB VACCINE	Meningococcal Ab Vaccine	25
+LAB12643R	BRUTON\'S TYROSINE KINASE (BTK)	Bruton\'s Tyrosine Kinase (BTK)	4
+LAB12645R	COMPLEMENT, ALTERNATE PATHWAY	Complement, Alternate Pathway	15
+LAB12651R	HYPERTROPHIC CARDIOMYOPATHY PAN	Hypertrophic Cardiomyopathy Pan	10
+LAB12657R	T CELL ACTIVATION	T Cell Activation	24
+LAB12664R	NTX TELOPEPTIDE, URINE	NTX Telopeptide, Urine	22
+LAB12689R	INFLIXIMAB, SERUM LEVEL AND AB	Infliximab, Serum Level and Ab	18
+LAB12689R	SERM INFLIXIMAB/HACA MEASUREMT	SERM INFLIXIMAB/HACA MEASUREMT	1
+LAB12711R	S PNEUMONIAE AB IGG (23 SEROTYPES)	S pneumoniae Ab IgG (23 Serotypes)	27
+LAB12721R	ADENOSINE DEAMINASE, PLEURAL	Adenosine Deaminase, Pleural	72
+LAB12723R	ADENOSINE DEAMINASE, PERITONEAL	Adenosine Deaminase, Peritoneal	14
+LAB12733R	ALPHA-2 PLASMIN INHIBITOR	Alpha-2 Plasmin Inhibitor	5
+LAB12733R	ALPHA-2-ANTIPLASMIN	Alpha-2-Antiplasmin	1
+LAB12761R	HEPATITIS B VIRUS GENOTYPE	Hepatitis B Virus Genotype	2
+LAB12762R	TACROLIMUS	TACROLIMUS	384
+LAB12772R	ENCEPHALITIS AB SCREEN	Encephalitis Ab Screen	3
+LAB12772R	ENCEPHALITIS AB SCREEN		3
+LAB12772R	ENCEPHALITIS AB SCREEN	ENCEPHALITIS AB SCREEN	1
+LAB12774R	MOTOR NEUROPATHY AB SCREEN	Motor Neuropathy Ab Screen	11
+LAB12774R	MOTOR NEUROPATHY AB SCREEN	CO-GM1 TRIAD AB TEST	7
+LAB12781R	BRUCELLA ANTIBODIES	Brucella Antibodies	63
+LAB12790R	CYSTATIN C WITH ESTIMATED GFR	Cystatin C with Estimated GFR	10
+LAB12796R	FLUCONAZOLE	Fluconazole	36
+LAB12796R	FLUCONAZOLE LEVEL	Fluconazole Level	8
+LAB12798R	CALPROTECTIN	Calprotectin	4
+LAB12802R	HEPARIN COFACTOR	HEPARIN COFACTOR	2
+LAB12808R	CK ISOENZYME WITH REFLEX	CK Isoenzyme With Reflex	262
+LAB12809R	CK ISOENZYME ELECTROPHORESIS	CK Isoenzyme Electrophoresis	82
+LAB12810R	ELECTROLYTE & OSMO PNL, FECES	Electrolyte & Osmo Pnl, Feces	123
+LAB12811R	MYOGLOBIN, URINE	Myoglobin, Urine	63
+LAB12811R	MYOGLOBIN, URINE		3
+LAB12813R	PARVOVIRUS B19 AB, IGG	Parvovirus B19 AB, IgG	3
+LAB12814R	MYCOPLASMA IGG/IGM AB	Mycoplasma IgG/IgM AB	69
+LAB12815R	LYME DISEASE AB IGG IGM W/RFLX	Lyme Disease Ab IgG IgM w/Rflx	46
+LAB12817R	DONOR, HCV AB SCREEN W/RFLX	Donor, HCV Ab Screen w/Rflx	2
+LAB12836R	ETHYLENE GLYCOL	Ethylene Glycol	4
+LAB12838R	INSULIN-LIKE GROWTH FACTOR 1	Insulin-like Growth Factor 1	25
+LAB12845R	VMA AND HVA, PEDIATRIC, URINE	VMA and HVA, Pediatric, Urine	4
+LAB12853R	EVEROLIMUS, BLOOD	Everolimus, Blood	10
+LAB12853R	EVEROLIMUS, BLOOD	EVEROLIMUS, BLOOD	6
+LAB12866R	B HENSELAE IGG TITER	B henselae IgG Titer	2
+LAB13000R	AMINOLEVULINIC ACID 24HR UR	Aminolevulinic Acid 24Hr Ur	20
+LAB13002R	COCCIDIOIDES AB IMMUNODIFF CSF	Coccidioides Ab Immunodiff CSF	70
+LAB13006R	REDUCING SUBSTANCE, FECES	Reducing Substance, Feces	22
+LAB13006R	REDUCING SUBSTANCE, STOOL	Reducing Substance, Stool	1
+LAB13006R	REDUCING SUBSTANCE, STOOL		1
+LAB13007R	Q FEVER IGG PHASE I TITER	Q Fever IgG Phase I Titer	2
+LAB13008R	Q FEVER IGG PHASE II TITER	Q Fever IgG Phase II Titer	4
+LAB13009R	Q FEVER IGM PHASE I TITER	Q Fever IgM Phase I Titer	2
+LAB13010R	Q FEVER IGM PHASE II TITER	Q Fever IgM Phase II Titer	2
+LABA1C	HEMOGLOBIN A1C	Hemoglobin A1c	52345
+LABA1C	HEMOGLOBIN A1C		5498
+LABA1C	HEMOGLOBIN A1C	Hemoglobon A1c	2007
+LABA1C	HEMOGLOBIN A1C	HEMOGLOBIN A1C	2
+LABAACSF	AMINO ACIDS, CSF	Amino Acids, CSF	8
+LABAACSF	AMINO ACIDS, CSF		6
+LABAAP	AMINO ACIDS, PLASMA	Amino Acids, Plasma	53
+LABAAP	AMINO ACIDS, PLASMA		4
+LABAAUR	AMINO ACIDS, URINE	Amino Acids, Urine	32
+LABAAUR	AMINO ACIDS, URINE		6
+LABABC	AUTOMATED BLOOD COUNT		22
+LABABC	AUTOMATED BLOOD COUNT	Automated Blood Count	2
+LABABG	BLOOD GASES, ARTERIAL	Blood Gases, Arterial	115012
+LABABG	BLOOD GASES, ARTERIAL		64096
+LABABG	BLOOD GASES, ARTERIAL	Blood gases arterial	106
+LABABG	BLOOD GASES, ARTERIAL	BLOOD GASES ARTERIAL	92
+LABABG	BLOOD GASES, ARTERIAL	Blood Gases Arterial	39
+LABABIDI	AB ID		3720
+LABABIDI	AB ID	Ab ID	2954
+LABABIDI	AB ID	Antibody ID	41
+LABABODIS	ABO DISCREPANCY		70
+LABABODIS	ABO DISCREPANCY	ABO Discrepancy	42
+LABABTITI	AB TITER	Ab Titer	189
+LABABTITI	AB TITER		12
+LABABTITI	AB TITER	Antibody Titer	2
+LABACA	ANTI CARDIOLIPIN AB	Anti Cardiolipin Ab	1086
+LABACA	ANTI CARDIOLIPIN AB	Anti Cardiolipin AB	524
+LABACA	ANTI CARDIOLIPIN AB		106
+LABACA	ANTI CARDIOLIPIN AB	Anti-cardiolipin Ab	4
+LABACETA	ACETAMINOPHEN, SERUM	Acetaminophen, Serum	6008
+LABACETA	ACETAMINOPHEN, SERUM	Acetaminophen	998
+LABACETA	ACETAMINOPHEN, SERUM		854
+LABACETA	ACETAMINOPHEN, SERUM	Acetaminophen Level	106
+LABACHER	ACETYLCHOLINESTERASE	Acetylcholinesterase, AF	2
+LABACHOO	AIRBORNE ALLERGY SCREEN	Airborne Allergy Screen	12
+LABACHOO	AIRBORNE ALLERGY SCREEN		2
+LABACRBLO	ACETYLCHOLINE BLOCKING AB	Acetylcholine Blocking Ab	26
+LABACRMOD	ACETYLCHOLINE MOD AB	Acetylcholine Mod Ab	16
+LABACTHO	ADRENOCORTICOTROPIC HORMONE (ACTH)	Adrenocorticotropic Hormone (ACTH)	960
+LABACTHO	ADRENOCORTICOTROPIC HORMONE (ACTH)	ACTH	280
+LABACTHO	ADRENOCORTICOTROPIC HORMONE (ACTH)		104
+LABACYLP	ACYLCARNITINE PROFILE QUANTITATIVE	Acylcarnitine Profile Quantitative	20
+LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADAMTS-13 Activity and Antigen Testing	54
+LABADAM13	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING		13
+LABADAMTS	ADAMTS-13 PROFILE	ADAMTS-13 PROFILE	154
+LABADAMTS	ADAMTS-13 PROFILE		8
+LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING	ADAMTS-13 Activity and Antigen Testing	4
+LABADAMTS	ADAMTS-13 ACTIVITY AND ANTIGEN TESTING		1
+LABADAMTS	ADAMTS-13 PROFILE	ADAMTS-13 Activity and Antigen Testing	1
+LABADAQ	ADENOSINE DEAMINASE, RBC	Adenosine Deaminase, RBC	26
+LABADGM	ADENOVIRUS IGG AND IGM AB	Adenovirus IgG And IgM Ab	125
+LABADGM	ADENOVIRUS IGG AND IGM AB		14
+LABADIFF	DIFFERENTIAL		42
+LABADIFF	DIFFERENTIAL	Differential	16
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW		8
+LABADIFFS	DIFFERENTIAL AND SLIDE REVIEW	Differential And Slide Review	2
+LABADMARK	ADMARK TAU/AB42, CSF	Admark TAU/Ab42, CSF	78
+LABADMARK	ADMARK TAU/AB42, CSF		14
+LABADPCRQ	ADENOVIRUS REAL TIME PCR,QUANTITATIVE	Adenovirus Real Time PCR,Quantitative	480
+LABADPCRQ	ADENOVIRUS REAL TIME PCR,QUANTITATIVE		132
+LABADPCRQ	ADENOVIRUS PCR QUANT		1
+LABADVDNA	ADENOVIRUS DNA, QUALITATIVE PCR	Adenovirus DNA, Qualitative PCR	418
+LABADVDNA	ADENOVIRUS DNA, QUALITATIVE PCR		92
+LABADVQL	ADENOVIRUS DNA PCR QUAL	Adenovirus DNA PCR Qual	120
+LABADVQL	ADENOVIRUS PCR QUAL		8
+LABADVQL	ADENOVIRUS DNA, QUALITATIVE PCR	Adenovirus DNA, Qualitative PCR	5
+LABADVQL	ADENOVIRUS PCR QUAL	Adenovirus PCR Qual	4
+LABADVQL	ADENOVIRUS DNA, QUALITATIVE PCR		2
+LABADVQT	ADENOVIRUS DNA PCR QUANT	Adenovirus DNA PCR Quant	580
+LABADVQT	ADENOVIRUS PCR QUANT		53
+LABADVQT	ADENOVIRUS DNA PCR QUANT	ADENOVIRUS DNA PCR QUANT	25
+LABADVQT	ADENOVIRUS REAL TIME PCR,QUANTITATIVE	Adenovirus Real Time PCR,Quantitative	22
+LABADVQT	ADENOVIRUS PCR QUANT	Adenovirus PCR Quant	2
+LABADVRC	ADENOVIRUS RAPID CULTURE	Adenovirus Rapid Culture	36
+LABAFBC	AFB CULTURE, RESPIRATORY	AFB Culture, Respiratory	9384
+LABAFBC	AFB CULTURE, RESPIRATORY		5772
+LABAFBC	AFB CULTURE, RESPIRATORY	AFB Culture + Smear, Induced Sputum	1810
+LABAFBC	AFB CULTURE, RESPIRATORY	AFB Culture + Smear, Expectorated Sputum	466
+LABAFBC	AFB CULTURE, RESPIRATORY	AFB Culture + Smear, Bronchoalveolar lavage	172
+LABAFBC	AFB CULTURE, RESPIRATORY	AFB Culture + Smear, Tracheal Aspirate	170
+LABAFBC	AFB CULTURE, RESPIRATORY	AFB culture (respiratory)	3
+LABAFBCF	AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS	AFB Culture, Respiratory-Cystic Fibrosis	983
+LABAFBCF	AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS	AFB Culture, Respiratory For Cystic Fibrosis	560
+LABAFBCF	AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS		466
+LABAFBCF	AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS	AFB Culture, Respiratory (Cystic fibrosis patient)	10
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture, Non-respiratory	6159
+LABAFBD	AFB CULTURE, NON - RESPIRATORY		5172
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB culture (non-respiratory)	632
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture + Smear, CSF (>5ml required)	622
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture + Smear, Pleural Fluid	286
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture + Smear, Peritoneal Fluid	82
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture + Smear, Pericardial Fluid	60
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture + Smear, Synovial Fluid	26
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture +Smear CSF (>5 mL required)	22
+LABAFBD	AFB CULTURE, NON - RESPIRATORY	AFB Culture, CSF Shunt Fluid	16
+LABAFBSO	AFB SMEAR ONLY	AFB Smear Only	598
+LABAFBSO	AFB SMEAR ONLY	STAT AFB Smear Only	60
+LABAFBSO	AFB SMEAR ONLY		20
+LABAFCT	CELL CT & DIFF, APHERESIS FLD	Cell Ct & Diff, Apheresis fld	2
+LABAFP	ALPHA FETOPROTEIN, SERUM	Alpha Fetoprotein, Serum	2167
+LABAFP	ALPHA FETOPROTEIN, SERUM	AFP, SERUM	405
+LABAFP	ALPHA FETOPROTEIN, SERUM		272
+LABAFP	ALPHA FETOPROTEIN, SERUM	Alpha Feto-Protein, Serum	134
+LABAFP	ALPHA FETOPROTEIN, SERUM	Int Alpha Fetoprotein, Serum Every 6 Months	2
+LABAG	ALBUMIN / GLOBULIN RATIO	Albumin/Globulin Ratio	6
+LABAG	ALBUMIN / GLOBULIN RATIO		6
+LABAGALA	ASPERGILLUS GALACTOMANNAN	Aspergillus Galactomannan	1867
+LABAGALA	ASPERGILLUS GALACTOMANNAN		66
+LABAGAP	ANION GAP		50
+LABAHP2	ACUTE HEPATITIS PANEL	Acute Hepatitis Panel	2710
+LABAHP2	ACUTE HEPATITIS PANEL		164
+LABAID	AEROBIC ORGANISM ID		10
+LABAID	AEROBIC ORGANISM ID	Aerobic Organism ID	2
+LABALB	ALBUMIN, SERUM/PLASMA	Albumin, Serum/Plasma	12021
+LABALB	ALBUMIN, SERUM/PLASMA		1544
+LABALB	ALBUMIN, SERUM/PLASMA	Albumin, Serum/Plasma (LABALB)	5
+LABALB	ALBUMIN, SERUM/PLASMA	Albumin, Serum/Plasma  	2
+LABALB	ALBUMIN, SERUM/PLASMA	Serum Albumin	2
+LABALKP	ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA		864
+LABALKP	ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA	Alkaline Phosphatase Total Serum/Plasma	623
+LABALKP	ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA	Alkaline phosphatase total serum/plasma	4
+LABALLMC	INHALANT ALLERGY PANEL, SHC, MENLO	Inhalant Allergy Panel, SHC, Menlo	2
+LABALPHA2	ALPHA-2-ANTIPLASMIN	Alpha-2-Antiplasmin	9
+LABALPHA2	ALPHA-2-ANTIPLASMIN	Alpha2 Antiplasmin	6
+LABALT	ALT, SERUM/PLASMA	ALT, Serum/Plasma	1020
+LABALT	ALT, SERUM/PLASMA		876
+LABALT	ALT, SERUM/PLASMA	ALT, Serum/Plasma 	2
+LABAMAONC	ANTI - MALIGNIN AB	Anti-Malignin Ab	2
+LABAMIPOL	AMIKACIN PEAK LEVEL	Amikacin Peak Level	143
+LABAMIPOL	AMIKACIN PEAK LEVEL		6
+LABAMIPRL	AMIKACIN TROUGH LEVEL	Amikacin Trough Level	272
+LABAMIPRL	AMIKACIN TROUGH LEVEL		24
+LABAMLP	AML PROGNOSIS ASSAY	AML Prognosis Assay	425
+LABAMLP	AML PROGNOSIS ASSAY		42
+LABAMLP	AML PROGNOSIS ASSAY	AML Prognosis Assay  (molecular diagnostic)	5
+LABAMY	AMYLASE, SERUM	Amylase, Serum	10120
+LABAMY	AMYLASE, SERUM		1828
+LABAMY	AMYLASE, SERUM	Amylase, serum	1541
+LABAMY	AMYLASE, SERUM	Amylase, Serum : POD # 1	243
+LABANAS	ANTI - NUCLEAR AB	Anti-Nuclear Ab	5577
+LABANAS	ANTI - NUCLEAR AB		352
+LABANAS	ANTI - NUCLEAR AB	Anti-nuclear Ab	6
+LABANAS	ANTI - NUCLEAR AB	FANA, (Anti-Nuclear Ab)	5
+LABANAS	ANTI - NUCLEAR AB	Anti-Nuclear Antibody	4
+LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL	Neutrophil Cytoplasma Reflex Ab Panel	612
+LABANCA	NEUTROPHIL CYTOPLASMA REFLEX AB PANEL		98
+LABANDRO	ANDROSTENEDIONE, SERUM	Androstenedione, Serum	6
+LABANER	ANAEROBIC CULTURE		14350
+LABANER	ANAEROBIC CULTURE	Anaerobic Culture	10931
+LABANER	ANAEROBIC CULTURE	Anaerobic Culture, Pleural Fluid	304
+LABANER	ANAEROBIC CULTURE	Anaerobic Culture, Peritoneal  Fluid	182
+LABANER	ANAEROBIC CULTURE	Anaerobic Culture, CSF Shunt Fluid	128
+LABANER	ANAEROBIC CULTURE	Anaerobic Culture, Synovial Fluid	114
+LABANER	ANAEROBIC CULTURE	Anaerobic Culture, Pericardial Fluid	50
+LABANER2	ANAEROBIC CULTURE, SHORT BATTERY		4290
+LABANER2	ANAEROBIC CULTURE, SHORT BATTERY	Anaerobic Culture, Short Battery	2160
+LABANERB	ANAEROBIC CULTURE BB		188
+LABANTIM	ANTI - TPO AB	Anti-TPO Ab	730
+LABANTIM	ANTI - TPO AB		44
+LABANTITG	ANTI - THYROGLOBULIN AB	Anti-Thyroglobulin Ab	386
+LABANTITG	ANTI - THYROGLOBULIN AB		24
+LABANTIXA	LMWH ACTIVITY BY ANTIXA	LMWH ACTIVITY BY ANTIXA	892
+LABANTIXA	LMW HEPARIN, ANTIXA U/ML	LMW Heparin, AntiXA u/mL	700
+LABANTIXA	ANTI XA / LMWH	Anti XA/LMWH	504
+LABANTIXA	ANTI XA / LMWH		88
+LABANTIXA	LMW HEPARIN, ANTIXA U/ML		76
+LABANTIXA	ANTI XA / LMWH	Anti XA / LMWH	18
+LABANTIXA	LMWH ACTIVITY BY ANTIXA	Anti XA / LMWH	14
+LABANTIXA	LMW HEPARIN, ANTIXA U/ML	Anti XA / LMWH	10
+LABANTIXA	LMW HEPARIN, ANTIXA U/ML	Anti XA/LMWH	1
+LABANTIXA	LMW HEPARIN, ANTIXA U/ML	Anti XA/LMWH (LABANTIXA)	1
+LABANTNEU	ANTI - NEUTROPHIL AB	Anti-Neutrophil Ab	159
+LABANTNEU	ANTI - NEUTROPHIL AB		12
+LABANTNEU	ANTI - NEUTROPHIL AB	Anti-neutrophil antibody	4
+LABAPC	ACTIVATED PROTEIN C RESISTANCE	Activated Protein C Resistance	152
+LABAPC	ACTIVATED PROTEIN C RESISTANCE	APC Protein C Resistance	42
+LABAPC	ACTIVATED PROTEIN C RESISTANCE		10
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	Anti Phospholipid Ab Panel	525
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL	Anti Phospholipid AB Panel	28
+LABAPHSA2	ANTI PHOSPHOLIPID AB PANEL		3
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	Anti Phospholipid Ab Panel	464
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	Anti Phospholipid AB Panel	36
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL		23
+LABAPHSAB	ANTI PHOSPHOLIPID AB PANEL	Anti-phospholipid Ab panel	2
+LABAPT	APT TEST FOR FETAL HGB	APT Test For Fetal Hgb	4
+LABARBO	ARBOVIRUS AB PANEL, IFA (TITER)	Arbovirus Ab Panel, IFA (Titer)	2
+LABARGI	ABO/RH	ABO/Rh	394
+LABARGI	ABO/RH		22
+LABARGI	ABO/RH	ABO/Rh  (Stanford laboratory)	1
+LABARGI	ABO/RH	ABO/Rh (Draw once a day for 2 days - count 2)	1
+LABARI	ABO/RH (ARI)		12334
+LABARI	ABO/RH (ARI)	ABO/Rh (ARI)	1145
+LABARI	ABO/RH (ARI)	ABO/RH (ARI)	304
+LABARI	ABO/RH (ARI)	ABO/Rh (ARI) Type and Screen	26
+LABARI	ABO/RH (ARI)	ABO/Rh (ARI) 	17
+LABARIX	ARIXTRA	Arixtra	8
+LABARIX	ARIXTRA		2
+LABARS	ARSENIC, WHOLE BLOOD	Arsenic, Whole Blood	27
+LABARS	ARSENIC, WHOLE BLOOD		2
+LABARSAL	ARYLSULFATASE A, LEUKOCYTES	Arylsulfatase A, Leukocytes	8
+LABARSAL	ARYLSULFATASE A, LEUKOCYTES		2
+LABARVI	BLOOD TYPE VERIFICATION	Blood Type Verification	38783
+LABARVI	BLOOD TYPE VERIFICATION		30944
+LABAS4I	AB SCREEN	Antibody Screen	37
+LABAS4I	AB SCREEN	Ab Screen	16
+LABAS4I	AB SCREEN		10
+LABASCA	ASCARIASIS AB	Ascariasis Ab	2
+LABASI	AB SCREEN (ASI)		11952
+LABASI	AB SCREEN (ASI)	Ab Screen (ASI)	95
+LABASI	AB SCREEN (ASI)	AB SCREEN (ASI)	84
+LABASO	ANTISTREPTOLYSIN O TITER	Antistreptolysin O Titer	4
+LABASPERP	ASPERGILLUS AB ID	Aspergillus Ab ID	22
+LABASPID	ASPERGILLUS AB, ID, S	Aspergillus Precipitins ID	87
+LABASPID	ASPERGILLUS AB, ID, S		12
+LABASPPCP	ASPERGILLUS AB PRECIPITINS	Aspergillus Ab Precipitins	62
+LABASPPCP	ASPERGILLUS AB PRECIPITINS		2
+LABASPR	ASPERGILLUS AB, CF, S	Aspergillus Comp Fix	4
+LABASQ	ACETONE SEMI QUALITATIVE	Acetone Semi Qualitative	1450
+LABASQ	ACETONE SEMI QUALITATIVE		244
+LABAST	AST, SERUM/PLASMA	AST, Serum/Plasma	989
+LABAST	AST, SERUM/PLASMA		896
+LABAST	AST, SERUM/PLASMA	Ast, Serum/Plasma	40
+LABAST	AST, SERUM/PLASMA	Ast, serum/plasma	4
+LABAST	AST, SERUM/PLASMA	AST, Serum/Plasma 	2
+LABASTRO	ANTI STREPTOLYSIN O	Antistreptolysin O Titer	198
+LABASTRO	ANTI STREPTOLYSIN O		20
+LABAT3	ANTITHROMBIN III	Antithrombin III	1086
+LABAT3	ANTITHROMBIN III		68
+LABATHAL	ALPHA THALASSEMIA PCR	Alpha Thalassemia PCR	32
+LABATHAL	ALPHA THALASSEMIA PCR		8
+LABATTG	IGA ANTI  TTG	IgA Anti  TTG	341
+LABATTG	IGA ANTI  TTG		4
+LABAVIN	HIV-1 INTEGRASE RESISTANCE	HIV-1 Integrase Resistance	9
+LABAVIN	HIV-1 INTEGRASE RESISTANCE		8
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	HIV-1 Genotype & Antivir Resist	251
+LABAVRT	HIV - 1 GENOTYPING	HIV-1 Genotyping	12
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	HIV GENO EXPR ANTIVIR RESIST	10
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	HIV-1 Genotyping	6
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST	HIV GENOTYPING	6
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	HIV-1 Genotyping	2
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST		2
+LABAVRT	HIV GENO EXPR ANTIVIR RESIST		2
+LABAVRT	HIV-1 GENOTYPE & ANTIVIR RESIST	HIV GENOTYPING	1
+LABAVRT1	HIV-1 RT-PROTEASE RESISTANCE	HIV-1 RT-Protease Resistance	26
+LABAVRT1	HIV-1 RT-PROTEASE RESISTANCE		4
+LABB12	VITAMIN B12	Vitamin B12	15573
+LABB12	VITAMIN B12		1514
+LABB12	VITAMIN B12	Vitamin B12 (LABB12)	2
+LABB2GP1	BETA - 2 - GLYCOPROTEIN 1	BETA - 2 - GLYCOPROTEIN 1	362
+LABB2GP1	BETA - 2 - GLYCOPROTEIN	Beta-2-Glycoprotein	95
+LABB2GP1	BETA - 2 - GLYCOPROTEIN 1	Beta - 2 - Glycoprotein 1	18
+LABB2GP1	BETA - 2 - GLYCOPROTEIN 1		8
+LABB2GP1	BETA - 2 - GLYCOPROTEIN		6
+LABB2GPCO	BETA - 2 - GLYCOPROTEIN	Beta-2-Glycoprotein	231
+LABB2GPCO	BETA - 2 - GLYCOPROTEIN		20
+LABB2M	BETA - 2 - MICROGLOBULIN, SERUM	Beta-2-Microglobulin, Serum	444
+LABB2M	BETA - 2 - MICROGLOBULIN, SERUM		36
+LABB2M	BETA - 2 - MICROGLOBULIN, SERUM	Beta-2-Microglobulin, Ser	7
+LABB2M	BETA - 2 - MICROGLOBULIN, SERUM	BETA - 2 - MICROGLOBULIN, SERUM	2
+LABBACIDI	BACTERIA ID BY SEQUENCING, ISOLATE	Bacteria ID by Sequencing, Isolate	2
+LABBACIDI	BACTERIA ID BY SEQUENCING, ISOLATE		2
+LABBACIDS	BACTERIA ID BY SEQUENCING, SPECIMEN	Bacteria ID by Sequencing, Specimen	184
+LABBACIDS	BACTERIAL ID BY SEQUENCING, SPECIMEN	BACTERIAL ID BY SEQUENCING, SPECIMEN	128
+LABBACIDS	BACTERIAL ID BY SEQUENCING, SPECIMEN	Bacterial ID By Sequencing, Specimen	108
+LABBACIDS	BACTERIA ID BY SEQUENCING, SPECIMEN		34
+LABBARSAB	BARTONELLA SPECIES AB	Bartonella Species Ab	276
+LABBARSAB	BARTONELLA SPECIES AB		6
+LABBARTPC	BARTONELLA DNA, QUALI, PCR	Bartonella DNA PCR	122
+LABBARTPC	BARTONELLA DNA, QUALI, PCR		8
+LABBBPCRY	CRYOPRECIPITATE	Cryoprecipitate	2960
+LABBBPCRY	CRYOPRECIPITATE	Cryoprecipitate (Not an Order to Transfuse)	2639
+LABBBPCRY	CRYOPRECIPITATE		482
+LABBBPCRY	CRYOPRECIPITATE	Prepare Cryoprecipitate	2
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	Frozen Plasma	17253
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	Frozen Plasma (Not an Order to Transfuse)	17149
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	Fresh Frozen Plasma Product	5242
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT		3948
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	Frozen Plasma Product	68
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	Prepare Fresh Frozen Plasma	12
+LABBBPFFP	FRESH FROZEN PLASMA PRODUCT	Fresh Frozen Plasma Product (Not an Order to Transfuse)	8
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets (plateletpheresis) (Not an Order to Transfuse)	23675
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets (plateletpheresis)	11796
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelet Apheresis Product	5127
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Hematologic Disorders 	4455
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Hematologic Disorders (Not an Order to Transfuse)	4325
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Bone Marrow Transplant - all ages (Not an Order to Transfuse)	3940
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Bone Marrow Transplant - all ages	2751
+LABBBPPLT	PLATELET APHERESIS PRODUCT		2004
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Kidney, Liver-Kidney, Pancreas, Heart, Lung, Heart-Lung Transplant (Not an Order to Transfuse)	659
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Kidney, Liver-Kidney, Pancreas, Heart, Lung, Heart-Lung Transplant	412
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Cancer Pt > 18 yrs old (non-hematologic)	188
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Other	143
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets	138
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Pt with HLA Antibodies (Not an Order to Transfuse)	132
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, All Transplant Donors (including BMT and solid Organ Donors) (Not an Order to Transfuse)	115
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, All Transplant Donors (including BMT and solid Organ Donors)	102
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Other (Not an Order to Transfuse)	92
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Cancer Pt > 18 yrs old (non-hematologic) (Not an Order to Transfuse)	86
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets (Plateletpheresis)	55
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Pt with HLA Antibodies	53
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, HIV/AIDS	11
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, (SCID, Di George Syndrome, Immunodeficiency)	10
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, HIV/AIDS (Not an Order to Transfuse)	6
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Cancer Pt > 18 yrs old (non-hematological)	4
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Prepare Platelets	4
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelet	3
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, Sickle Cell Anemia (Not an Order to Transfuse)	2
+LABBBPPLT	PLATELET APHERESIS PRODUCT	Platelets, All Transplant Donors (including BMT and Solid Organs)	1
+LABBBPRBC	RED BLOOD CELLS	Red blood cells (crossmatch) (Not an Order to Transfuse)	58894
+LABBBPRBC	RED BLOOD CELLS	Red blood cells (crossmatch)	42082
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells	15365
+LABBBPRBC	RED BLOOD CELLS		10636
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Hematologic Disorders (Not an Order to Transfuse)	3192
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Hematologic Disorders 	3077
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Bone Marrow Transplant - all ages (Not an Order to Transfuse)	2602
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Bone Marrow Transplant - all ages 	2410
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Kidney, Liver-Kidney, Pancreas, Heart, Lung, Heart-Lung Transplant 	1406
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells (Crossmatch)	970
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Kidney, Liver-Kidney, Pancreas, Heart, Lung, Heart-Lung Transplant (Not an Order to Transfuse)	937
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Cancer Pt > 18 yrs old (non-hematologic) ONE TIME, Routine 	565
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Other Special Needs	383
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Cancer Pt > 18 yrs old (non-hematologic) ONE TIME, Routine (Not an Order to Transfuse)	298
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Other Special Needs (Not an Order to Transfuse)	133
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells (crossmatch) (Not an Order to Transfuse)	105
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, All Transplant Donors (including BMT and Solid Organ Donors) (Not an Order to Transfuse)	88
+LABBBPRBC	RED BLOOD CELLS	Prepare Red Blood Cells	70
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Kidney, Liver-Kidney, Pancreas, Heart, Lung, Heart-Lung Transplant	70
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Hematologic Disorders	58
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, All Transplant Donors (including BMT and Solid Organ Donors)	55
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Pt with HLA Antibodies 	34
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells (in units only)	31
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Pt with HLA Antibodies (Not an Order to Transfuse)	22
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Sickle Cell Anemia 	22
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Sickle Cell Anemia (Not an Order to Transfuse)	21
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells AUTOLOGOUS / DIRECTED ONLY	10
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, (SCID, Di George Syndrome, Immunodeficiency) (Not an Order to Transfuse)	8
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Bone Marrow Transplant - all ages	8
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, HIV/AIDS	7
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, HIV/AIDS (Not an Order to Transfuse)	6
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Cancer Pt > 18 yrs old (non-hematologic) 	5
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, (SCID, Di George Syndrome, Immunodeficiency) 	4
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Cancer Pt > 18 yrs old (non-hematological) 	4
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Pregnancy 	4
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Thalassemia (Not an Order to Transfuse)	4
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells (Cross Match)	2
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, All Transplant Donors (including BMT and Solid Organs)	2
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Hemoglobinopathies (Hemoglobin C, SC, E)	2
+LABBBPRBC	RED BLOOD CELLS	Red Blood Cells, Thalassemia 	2
+LABBBPRH	RHO(D) IMMUNE GLOB (RHOGAM)	Rho(D) Immune Glob (RhoGam)	8
+LABBBPRH	RHO(D) IMMUNE GLOB (RHOGAM)		4
+LABBCAA	BRANCHED CHAIN AMINO ACIDS	Branched Chain Amino Acids	4
+LABBCGMIS	BIOCHEM GENETIC MISCELLANEOUS TEST	Biochem Genetic Miscellaneous Test	47
+LABBCLON	B - CELL CLONALITY PCR	B-Cell Clonality PCR	10
+LABBCLON	B - CELL CLONALITY PCR		6
+LABBCRABL	BCR - ABL, BLOOD	BCR-ABL, Blood	424
+LABBCRABL	BCR - ABL, BLOOD		152
+LABBCRABL	BCR - ABL, BLOOD	BCR-ABL, Qualitative Blood (molecular diagnostic)	12
+LABBCRABL	BCR - ABL, BLOOD	BCR-ABL Qualitative, Blood	2
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCR-ABL Kinase Domain Mutation Analysis	63
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS		22
+LABBCRKDM	BCR - ABL KINASE DOMAIN MUTATION ANALYSIS	BCR-ABL Kinase Domain Mutation Analysis  (molecular diagnostic)	8
+LABBCRQT	BCR - ABL QUANTITATIVE, BLOOD	BCR-ABL Quantitative, Blood	326
+LABBCRQT	BCR - ABL QUANTITATIVE, BLOOD		54
+LABBCRQT	BCR - ABL QUANTITATIVE, BLOOD	BCR-ABL Quantitative, Blood (molecular diagnostic)	16
+LABBCRQT	BCR - ABL QUANTITATIVE, BLOOD	BCR - ABL QUANTITATIVE, BLOOD	1
+LABBFCT	CELL COUNT AND DIFF, FLUID	Cell Count and Diff, Fluid	9890
+LABBFCT	CELL COUNT AND DIFF, FLUID		4354
+LABBFCT	CELL COUNT AND DIFF, FLUID	Cell Count and Diff, Pleural Fluid	722
+LABBFCT	CELL COUNT AND DIFF, FLUID	Cell Count and Diff, Peritoneal/Ascites Fluid	596
+LABBFCT	CELL COUNT AND DIFF, FLUID	Cell Count and Diff, Synovial Fluid	272
+LABBFCT	CELL COUNT AND DIFF, FLUID	Cell Count and Diff, Pericardial Fluid	72
+LABBFCTS	CELL COUNT, BODY FLUID		40
+LABBFCTS	CELL COUNT, BODY FLUID	Cell Count, Body Fluid	18
+LABBHB	BETA-HYDROXYBUTYRATE	Beta-Hydroxybutyrate	799
+LABBHCG	BETA HCG	Beta HCG	1827
+LABBHCG	BETA HCG		150
+LABBHCG	BETA HCG	Pregnancy Test, serum, Quantitative	40
+LABBHCG	BETA HCG	Beta HCG, Quantitative	3
+LABBHCG	BETA HCG	Pregnancy Test, serum, quantitive	2
+LABBKDNA	BK VIRUS DNA PCR		2
+LABBKVPC	BK VIRUS PCR, QUANT, PLASMA	BK Virus PCR, Quant, Plasma	424
+LABBKVPC	BK VIRUS PCR, QUANT, PLASMA		103
+LABBKVPC	BK VIRUS DNA QUANTITATION	BK Virus DNA Quantitation	8
+LABBKVPC	BK VIRUS DNA QUANTITATION		2
+LABBKVPCU	BK VIRUS PCR, QUANT, URINE	BK Virus PCR, Quant, Urine	535
+LABBKVPCU	BK VIRUS PCR, QUANT, URINE		96
+LABBKVQU	BK VIRUS DNA QUANTITATION	BK Virus DNA Quantitation	156
+LABBKVQU	BK VIRUS DNA QUANTITATION		57
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	Blood Culture (Aerobic & Anaerobic Bottles)	105610
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)		7586
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	Blood Culture - 2 Aerobic Bottles	2516
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	Blood Culture (Aero & Anaerobic bottles)	412
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	"Blood Culture (Aerobic &amp	 Anaerobic Bottles)"	147
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	Blood culture (aero & anaerobic bottles)	76
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	Blood Culture	21
+LABBLC	BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)	Blood culture	13
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	Blood Culture (2 Aerobic Bottles)	100791
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)		4794
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	Blood Cult - Aerobic & Anaerobic Bottles	2632
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	Blood Culture (2 Aerobic bottles)	75
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	Blood culture (2 aerobic bottles)	48
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	Blood culture	2
+LABBLC2	BLOOD CULTURE (2 AEROBIC BOTTLES)	Blood Culture x 2	2
+LABBLCA	AFB CULTURE, BLOOD	AFB Culture, Blood	756
+LABBLCA	AFB CULTURE, BLOOD		54
+LABBLCA	AFB CULTURE, BLOOD	AFB, Bone Marrow	24
+LABBLCSTK	BLOOD CULT - FIRST SET, VIA PHLEBOTOMY	Blood Cult - first set, via phlebotomy	11584
+LABBLCSTK	BLOOD CULT - FIRST SET, VIA PHLEBOTOMY	Blood Culture - Peripheral via phlebotomy	1275
+LABBLCSTK	BLOOD CULT - FIRST SET, VIA PHLEBOTOMY		98
+LABBLCSTK	BLOOD CULT - FIRST SET, VIA PHLEBOTOMY	Blood Cult - Peripheral via phlebotomy	20
+LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER	Blood Cult - 2nd set via catheter	12096
+LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	Blood Culture - Central line by NURSE	1326
+LABBLCTIP	BLOOD CULT - 2ND SET VIA CATHETER		148
+LABBLCTIP	BLOOD CULT CENTRAL LINE CATHETER BY NURSE	Blood Cult central line catheter by NURS	18
+LABBLDCR	BLOOD FOR CREATININE CLEARANCE	Bld for Creatinine Clearance	2
+LABBLDCR	BLOOD FOR CREATININE CLEARANCE	Blood for Creatinine Clearance	2
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"		26
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	"T(11	14) By PCR,Bone Marrow (molecular diagnostic)"	10
+LABBM1114	"T(11	14) BY PCR,BONE MARROW"	"T(11	14) By PCR,Bone Marrow"	4
+LABBM1418	"T(14	18), BONE MARROW"		24
+LABBM1418	"T(14	18), BONE MARROW"	"T(14	18), Bone Marrow"	16
+LABBM1418	"T(14	18), BONE MARROW"	"T(14	18), Bone Marrow (molecular diagnostic)"	12
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	"T(15	17) PML/RAR, Bone Marrow (molecular diagnostic)"	46
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"	"T(15	17) PML/RAR, Bone Marrow"	40
+LABBM1517	"T(15	17) PML/RAR, BONE MARROW"		34
+LABBMAML	AML PROGNOSIS ASSAY, BONE MARROW	AML Prognosis Assay, Bone Marrow	252
+LABBMAML	AML PROGNOSIS ASSAY, BONE MARROW		154
+LABBMAML	AML PROGNOSIS ASSAY, BONE MARROW	AML Prognosis Assay, Bone Marrow  (molecular diagnostic)	61
+LABBMBCLO	B - CELL CLONALITY PCR, BONE MARROW	B-Cell Clonality PCR, Bone Marrow  (molecular diagnostic)	25
+LABBMBCLO	B - CELL CLONALITY PCR, BONE MARROW		16
+LABBMBCLO	B - CELL CLONALITY PCR, BONE MARROW	B-Cell Clonality PCR, Bone Marrow	8
+LABBMBCLO	B - CELL CLONALITY PCR, BONE MARROW	B-Cell Clonality, PCR, Bone Marrow	1
+LABBMBCR	BCR - ABL, BONE MARROW		182
+LABBMBCR	BCR - ABL, BONE MARROW	BCR-ABL, Qualitative Bone Marrow (molecular diagnostic)	86
+LABBMBCR	BCR - ABL, BONE MARROW	BCR-ABL, Bone Marrow	50
+LABBMBCRQ	BCR - ABL QUANTITATIVE, BONE MARROW		110
+LABBMBCRQ	BCR - ABL QUANTITATIVE, BONE MARROW	BCR-ABL Quantitative, Bone Marrow (molecular diagnostic)	78
+LABBMBCRQ	BCR - ABL QUANTITATIVE, BONE MARROW	BCR-ABL Quantitative, Bone Marrow	44
+LABBMBCRQ	BCR - ABL QUANTITATIVE, BONE MARROW	BCR-ABL Quant, Bone Marrow	2
+LABBMC	BONE MARROW CULTURE	Bone Marrow Culture	52
+LABBMC	BONE MARROW CULTURE		40
+LABBMC	BONE MARROW CULTURE	Bone Marrow Culture + Gram Stain	27
+LABBMCD34	CD34 PANEL, BONE MARROW		18
+LABBMCD34	CD34 PANEL, BONE MARROW	CD34 Panel, Bone Marrow	8
+LABBMCEBP	DETECTION OF CEBPA, BONE MARROW	Detection of CEBPA, Bone Marrow	304
+LABBMCEBP	DETECTION OF CEBPA, BONE MARROW	Detection of CEBPA, Bone Marrow  (molecular diagnostic)	219
+LABBMCEBP	DETECTION OF CEBPA, BONE MARROW		84
+LABBMCEBP	DETECTION OF CEBPA, BONE MARROW	Detection of CEBPA, Bone Marrow (molecular diagnostic)	14
+LABBMNPM1	NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	NPM1, Nucleophosmin 1 Mutation, BM	282
+LABBMNPM1	NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	NPM1, NUCLEOPHOSMIN 1 MUTATION, BM	18
+LABBMPTP	BMT PRETRANSPLANT PANEL	BMT Pretransplant Panel	41
+LABBMPTP	BMT PRETRANSPLANT PANEL		4
+LABBMS	BMT STERILITY SAMPLE		6
+LABBMS	BMT STERILITY SAMPLE	BMT Sterility Sample	4
+LABBMT1	BMT PANEL 1	BMT Panel 1	17569
+LABBMT1	BMT PANEL 1		694
+LABBMT1	BMT PANEL 1	Bmt Panel I	602
+LABBMT1	BMT PANEL 1	BMT PANEL 1	13
+LABBMT2	BMT PANEL 2	BMT Panel 2	16864
+LABBMT2	BMT PANEL 2		408
+LABBMT2	BMT PANEL 2	BMT PANEL 2	12
+LABBMTCLO	T-CELL CLONALITY PCR, BONE MARROW	T-Cell Clonality PCR, Bone Marrow (molecular diagnostic)	41
+LABBMTCLO	T-CELL CLONALITY PCR, BONE MARROW		34
+LABBMTCLO	T-CELL CLONALITY PCR, BONE MARROW	T-Cell Clonality PCR, Bone Marrow	28
+LABBMTP	BMT PRETRANSPLANT PANEL	BMT Pretransplant Panel	267
+LABBMTP	BMT PRETRANSPLANT PANEL		32
+LABBMTP2	BMT REPEAT TRANSPLANT PANEL	BMT Repeat Transplant Panel	2
+LABBONC	BONE CULTURE AND GRAM STAIN		52
+LABBONC	BONE CULTURE AND GRAM STAIN	Bone Culture And Gram Stain	42
+LABBPERP	BORDETELLA PERTUSSIS PCR	Bordetella Pertussis PCR	197
+LABBPERP	BORDETELLA PERTUSSIS PCR		34
+LABBRAF	BRAF		58
+LABBRAF	BRAF	BRAF	44
+LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN	Bronchopulmonary Aspergillus Screen	183
+LABBRASP	BRONCHOPULMONARY ASPERGILLUS SCREEN		8
+LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)	Blood Parasite Exam (Not Malaria)	239
+LABBSME	BLOOD PARASITE EXAM (NOT MALARIA)		14
+LABBTDASE	BIOTINIDASE	Biotinidase	1
+LABBTHSQ	BETA THALASSEMIA SEQUENCING	Beta Thalassemia Sequencing	10
+LABBTHSQ	BETA THALASSEMIA SEQUENCING		2
+LABBUN	UREA NITROGEN, SERUM/PLASMA	Urea Nitrogen, Serum/Plasma	8341
+LABBUN	UREA NITROGEN, SERUM/PLASMA		722
+LABBUNCR	BUN / CREATININE		220
+LABBUNCR	BUN / CREATININE	BUN/Creatinine	12
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN		6736
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	Biopsy/Tissue Culture And Gram Stain	2513
+LABBXTG	BIOPSY/TISSUE WITH GRAM STAIN	Biopsy/Tissue with Gram Stain	1554
+LABBXTG	BIOPSY/TISSUE(GEN)GS	Biopsy/Tissue(Gen)GS	844
+LABBXTG	BIOPSY / TISSUE CULTURE AND GRAM STAIN	Biopsy/Tissue(Gen)GS	52
+LABC125	CANCER AG 125	Cancer Ag 125	888
+LABC125	CANCER AG 125		80
+LABC125	CANCER AG 125	Cancer Antigen 125	4
+LABC153	CANCER AG 15-3	Cancer Ag 15-3	40
+LABC1INHF	C1 ESTERASE INHIBITOR FN	C1 Esterase Inhibitor FN	4
+LABC1Q	C1Q BINDING ASSAY	C1Q Binding Assay	2
+LABC3	COMPLEMENT C3, SERUM	Complement C3, Serum	1726
+LABC3	COMPLEMENT C3, SERUM		98
+LABC3	COMPLEMENT C3, SERUM	Complement C3, serum	6
+LABC4	COMPLEMENT C4, SERUM	Complement C4, Serum	1739
+LABC4	COMPLEMENT C4, SERUM		92
+LABC4	COMPLEMENT C4, SERUM	Complement C4, serum	6
+LABC4C8	CD4 / CD8 PANEL, BLOOD	CD4/CD8 Panel, Blood	241
+LABC4C8	CD4 / CD8 PANEL, BLOOD	CD4/CD8 panel, blood	20
+LABC4C8	CD4 / CD8 PANEL, BLOOD		16
+LABC4C8	CD4 / CD8 PANEL, BLOOD	CD4/CD8 Panel, Blood  [LABC4C8]	2
+LABCA	CALCIUM, SERUM/PLASMA	Calcium, Serum/Plasma	25520
+LABCA	CALCIUM, SERUM/PLASMA		700
+LABCA	CALCIUM, SERUM/PLASMA	Calcium, serum/plasma	351
+LABCA	CALCIUM, SERUM/PLASMA	Calcium, Serum/Plasma (LABCA)	11
+LABCA199	CANCER AG 19-9	Cancer Ag 19-9	1899
+LABCA199	CANCER AG 19-9		142
+LABCA199	CANCER AG 19-9	Cancer Antigen 19-9	28
+LABCACE	ANGIOTENSIN CONVERTING ENZYME, CSF		3
+LABCAD	CADMIUM, WHOLE BLOOD	Cadmium, Whole Blood	9
+LABCAD	CADMIUM, WHOLE BLOOD		4
+LABCAI	CALCIUM IONIZED, SERUM/PLASMA	Calcium Ionized, Serum/Plasma	234235
+LABCAI	CALCIUM IONIZED, SERUM/PLASMA		14012
+LABCAI	CALCIUM IONIZED, SERUM/PLASMA	Calcium Ionized, Serum/Plasma in AM	7592
+LABCAI	CALCIUM IONIZED, SERUM/PLASMA	Calcium Ionized, serum/plasma	493
+LABCAI	CALCIUM IONIZED, SERUM/PLASMA	Calcium ionized, serum/plasma	16
+LABCANNAB	NEURONAL NUCLEAR AB, CSF	Anti-Neuronal Nuclear Ab, CSF	42
+LABCANNAB	NEURONAL NUCLEAR AB, CSF		14
+LABCANNAB	NEURONAL NUCLEAR AB, CSF	Anti-neuronal nuclear Ab, CSF	2
+LABCAPBG	CAPILLARY BLOOD GAS	Capillary Blood Gas	2
+LABCARN	CARNITINE FREE AND TOTAL	Carnitine Free And Total	83
+LABCARN	CARNITINE FREE AND TOTAL		2
+LABCBCD	CBC WITH DIFF	CBC With Diff	524331
+LABCBCD	CBC WITH DIFF	CBC with diff	154492
+LABCBCD	CBC WITH DIFF		30776
+LABCBCD	CBC WITH DIFF	CBC With Diff in AM	25030
+LABCBCD	CBC WITH DIFF	CBC with Diff	2276
+LABCBCD	CBC WITH DIFF	CBC With Diff (LABCBCD)	167
+LABCBCD	CBC WITH DIFF	CBC With Diff (in purple top tube for special hematology)	100
+LABCBCD	CBC WITH DIFF	CBC With Diff  [LABCBCD]  	2
+LABCBCD	CBC WITH DIFF	CBC With Diff [LABCBCD]	2
+LABCBCD	CBC WITH DIFF	Complete Blood Count (CBC With Diff)	2
+LABCBCD	CBC WITH DIFF	Int CBC With Diff Every 3 Months	2
+LABCBCD	CBC WITH DIFF	Int CBC With Diff Every 4 Weeks	2
+LABCBCD	CBC WITH DIFF	CBC With Diff  [LABCBCD]	1
+LABCBCD	CBC WITH DIFF	CBC WITH DIFF IN 1 MONTH	1
+LABCBCD	CBC WITH DIFF	Ext CBC With Diff - 2 Times A Week	1
+LABCBCO	CBC	CBC	412894
+LABCBCO	CBC	CBC in AM	20472
+LABCBCO	CBC		18224
+LABCBCO	CBC	CBC: POD # 1	3918
+LABCBCO	CBC	CBC: Evening of surgery	2469
+LABCBCO	CBC	CBC (LABCBCO)	106
+LABCBCO	CBC	CBC POD 1	58
+LABCBCO	CBC	CBC [LABCBCO]	6
+LABCBCO	CBC	CBC IN 1 MONTH	5
+LABCBCO	CBC	CBC IN 3 MONTHS	2
+LABCBCO	CBC	Int CBC - 2 Times A Week	2
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	CBC With Diff And Slide Review	18479
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW		1630
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	CBC with Diff and Slide Review	10
+LABCBCS	CBC WITH DIFF AND SLIDE REVIEW	CBC With Diff And Slide Review [LABCDCS]	1
+LABCCP	ANTI CCP	Anti CCP	5
+LABCCT	CELL COUNT, CSF		30
+LABCCT	CELL COUNT, CSF	Cell Count, CSF	14
+LABCD34	CD34 PANEL, BLOOD	CD34 Panel, Blood	165
+LABCD34	CD34 PANEL, BLOOD		14
+LABCD3B	CD3, BLOOD	CD3, Blood	6
+LABCD3B	CD3, BLOOD	CD3, BLOOD	2
+LABCD3B	CD3, BLOOD		2
+LABCD3F	CD3, FLUID	CD3, FLUID	10
+LABCD4CT	CD4 COUNT	CD4 Count	493
+LABCD4CT	CD4 COUNT		34
+LABCDAS	CELIAC DISEASE SCREEN	Celiac Disease Screen	201
+LABCDAS	CELIAC DISEASE SCREEN		10
+LABCDH1	FAMILIAL GASTRIC CANCER I	Familial Gastric Cancer I	8
+LABCDHPET	FAMILIAL GASTRIC CANCER, PET	Familial Gastric Cancer, PET	4
+LABCDIFF	DIFFERENTIAL, CSF		30
+LABCDIFF	DIFFERENTIAL, CSF	Differential, CSF	10
+LABCDMLPA	CDH1 MLPA	CDH1 MLPA	4
+LABCDPLI	COLD AGGLUTININ SCREEN		312
+LABCDPLI	COLD AGGLUTININ SCREEN	Cold Agglutinin Screen	263
+LABCDPLTI	COLD AGGLUTININ TITER	Cold Agglutinin Titer	123
+LABCDPLTI	COLD AGGLUTININ TITER		6
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR	C.diff Toxin B Gene, Qual. Real-Time PCR	40822
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR		9700
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR	C Difficile Toxin B, Stool	516
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR	Stool C Diff Toxin B	112
+LABCDTPCR	C DIFFICILE TOXIN B, STOOL	C Difficile Toxin B, Stool	84
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR	Stool C diff toxin B	44
+LABCDTPCR	C DIFFICILE TOXIN B, STOOL		35
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR	C.diff Toxin B Gene, Stool	14
+LABCDTPCR	C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR	C. dificile toxin B, Stool	9
+LABCDTPCR	C DIFFICILE TOXIN B, STOOL	Stool C diff toxin B	2
+LABCDTPCR	C DIFFICILE TOXIN B, STOOL	Stool C Diff Toxin B	1
+LABCEA	CEA	CEA	2056
+LABCEA	CEA		176
+LABCEA	CEA	CEA (LABCEA)	4
+LABCEBPA	DETECTION OF CEBPA, BLOOD	Detection of CEBPA, Blood	407
+LABCEBPA	DETECTION OF CEBPA, BLOOD		40
+LABCEBPA	DETECTION OF CEBPA, BLOOD	Detection of CEBPA, Blood  (molecular diagnostic)	29
+LABCELDIS	CELIAC DISEASE SYSTEM	Celiac Disease System	8
+LABCELDIS	CELIAC DISEASE SYSTEM		2
+LABCENABY	ANTI CENTROMERE ANTIBODY	Anti Centromere Antibody	243
+LABCENABY	ANTI CENTROMERE ANTIBODY	Centromere Antibodies	12
+LABCENABY	CENTROMERE ANTIBODIES	Centromere Antibodies	4
+LABCENABY	ANTI CENTROMERE ANTIBODY		2
+LABCENABY	CENTROMERE ANTIBODIES		1
+LABCEPLUS	CELIAC PLUS	Celiac Plus	4
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY	Cystic Fibrosis Mutation Study	98
+LABCF32	CYSTIC FIBROSIS MUTATION STUDY		6
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING	CFTR Diagnostic Sequencing	34
+LABCFDS	CFTR DIAGNOSTIC SEQUENCING		8
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFTR Deletion/Duplication Analysis by MLPA	2
+LABCFMLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	CFTR DELETION/DUPLICATION ANALYSIS BY MLPA	2
+LABCFPT	CF POLY T ANALYSIS	CF Poly T Analysis	2
+LABCFPT	CF POLY T ANALYSIS		2
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	Cystic Fibrosis Respiratory Culture	1345
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE		398
+LABCFRESP	CYSTIC FIBROSIS RESPIRATORY CULTURE	Respiratory Culture (Cystic fibrosis patient)	10
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFTR SCREEN BY SEQUENCING	14
+LABCFSS	CFTR SCREEN BY SEQUENCING	CFTR Screen by Sequencing	2
+LABCGD	NEUTROPHIL FUNC. OXI. BURST	Chronic Granulomatosis Disease (CGD)	39
+LABCGMISC	MISCELLANEOUS TEST COAGULATION		6
+LABCGMISC	MISCELLANEOUS TEST COAGULATION	Miscellaneous Test Coagulation	2
+LABCHLAM	CHLAMYDIA TRACHOMATIS / PSITTACI CULTURE	Chlamydia Trachomatis/Psittaci Culture	46
+LABCHMISC	MISCELLANEOUS CHEMISTRY		126
+LABCHMISC	MISCELLANEOUS CHEMISTRY	Miscellaneous Chemistry	58
+LABCHOHDL	CHOLESTEROL HDL / TOTAL	Cholesterol HDL/Total	114
+LABCHOHDL	CHOLESTEROL HDL / TOTAL	Cholesterol, HDL/Tot	20
+LABCHOHDL	CHOLESTEROL HDL / TOTAL		10
+LABCHOL	CHOLESTEROL TOTAL, SERUM/PLASMA	Cholesterol Total, Serum/Plasma	431
+LABCHOL	CHOLESTEROL TOTAL, SERUM/PLASMA		74
+LABCHOL	CHOLESTEROL TOTAL, SERUM/PLASMA	Cholesterol, Total, Serum/Plasma	20
+LABCHON	A/HYPOCHONDROPLASIA, SERUM	A/Hypochondroplasia, Peritoneal  Fluid	2
+LABCHON	A/HYPOCHONDROPLASIA, SERUM	A/Hypochondroplasia, Pleural Fluid	2
+LABCIGA	IGA, CSF	IgA, CSF	38
+LABCIGA	IGA, CSF		8
+LABCIGMT	IGM, CSF	IgM, CSF	44
+LABCIGMT	IGM, CSF		10
+LABCIMM	COCCI AB, ID, S	Coccidioides Immunodiffusion	60
+LABCIMM	COCCI AB, ID, S		6
+LABCIRCAI	CIRCUIT IONIZED CALCIUM	Circuit Ionized Calcium	4
+LABCK	CREATINE KINASE, TOTAL	Creatine Kinase, Total	27765
+LABCK	CREATINE KINASE, TOTAL	Creatin Kinase, Total	12706
+LABCK	CREATINE KINASE, TOTAL	Creatin kinase, total	6046
+LABCK	CREATINE KINASE, TOTAL		3408
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	Detection of C-KIT Mutations	380
+LABCKITMU	DETECTION OF C-KIT MUTATIONS	Detection of C-KIT Mutations  (molecular diagnostic)	152
+LABCKITMU	DETECTION OF C-KIT MUTATIONS		38
+LABCL	CHLORIDE, SERUM/PLASMA		386
+LABCL	CHLORIDE, SERUM/PLASMA	Chloride, Serum/Plasma	181
+LABCLACT	LACTIC ACID, CSF	Lactic Acid, CSF	2412
+LABCLACT	LACTIC ACID, CSF		672
+LABCLAMP	CHLAMYDIA AB PANEL	CHLAMYDIA AB PANEL	2
+LABCLCR2	CHLAMYDIA MOLECULAR DETECTION	Chlamydia Molecular Detection	84
+LABCLCR2	CHLAMYDIA MOLECULAR DETECTION		4
+LABCLGC2	CHLAMYDIA GC, MOLECULAR DETECTION	Chlamydia GC Molecular Detection	1169
+LABCLGC2	CHLAMYDIA GC, MOLECULAR DETECTION		292
+LABCLGC2	CHLAMYDIA GC, MOLECULAR DETECTION	CHLAMYDIA GC MOLECULAR DETECTION	177
+LABCLGC2	CHLAMYDIA GC, MOLECULAR DETECTION	Chlamydia GC, Molecular Detection	14
+LABCLGCT2	CHLAMYDIA GC, PAP FLUID	Chlamydia GC, Pap Fluid	8
+LABCLGCT2	CHLAMYDIA GC, PAP FLUID		2
+LABCLOMIP	CLOMIPRAMINE AND METABOLITE	Clomipramine And Metabolite	2
+LABCLOMIP	CLOMIPRAMINE AND METABOLITE		2
+LABCMV	CMV AB TOTAL	CMV Ab Total	557
+LABCMV	CMV AB IGG	CMV AB IGG	453
+LABCMV	CMV AB TOTAL	CMV Ab, Total	400
+LABCMV	CMV AB IGG	CMV Ab, Total	343
+LABCMV	CMV AB IGG		261
+LABCMV	CMV AB TOTAL		128
+LABCMV	CMV AB TOTAL	CMV Ab, total	58
+LABCMV	CMV AB IGG	CMV Ab, total	43
+LABCMV	CMV AB IGG	CMV Ab Total	2
+LABCMV	CMV AB TOTAL	CMV AB Total	2
+LABCMVAV	CMV IGG AVIDITY	CMV IgG Avidity	4
+LABCMVG	CMV GENOTYPE	CMV Genotype	178
+LABCMVG	CMV GENOTYPE		12
+LABCMVIGG	CMV IGG	CMV IgG	1214
+LABCMVIGG	CMV IGG	CMV Ab, Total	476
+LABCMVIGG	CMV IGG		86
+LABCMVIGG	CMV AB IGG	CMV Ab, Total	32
+LABCMVIGG	CMV IGG	CMV Ab Total	32
+LABCMVIGG	CMV AB IGG	CMV AB IGG	29
+LABCMVIGG	CMV AB TOTAL	CMV Ab, Total	29
+LABCMVIGG	CMV IGG	CMV Ab, total	22
+LABCMVIGG	CMV AB IGG		17
+LABCMVIGG	CMV AB TOTAL	CMV Ab Total	13
+LABCMVIGG	CMV IGG	CMV Ab, IgG	6
+LABCMVIGG	CMV AB TOTAL		4
+LABCMVIGG	CMV IGG	CMV AB Total	2
+LABCMVIGG	CMV AB IGG	CMV Ab Total	1
+LABCMVIGM	CMV IGM	CMV IgM	1185
+LABCMVIGM	CMV IGM		29
+LABCMVIGM	CMV AB IGM	CMV AB IGM	25
+LABCMVIGM	CMV AB IGM		16
+LABCMVIGM	CMV AB IGM	CMV IgM	6
+LABCMVIGT	CMV IGG (TRANSFUSION)	CMV IgG (Transfusion)	810
+LABCMVM	CMV IGM	CMV IgM	574
+LABCMVM	CMV AB IGM	CMV AB IGM	441
+LABCMVM	CMV IGM		261
+LABCMVM	CMV AB IGM		198
+LABCMVM	CMV AB IGM	CMV IgM	114
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA		1485
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD		1188
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qualitative, CSF	252
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qualitative, CSF	211
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR QUALITATIVE	201
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR QUALITATIVE	196
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV DNA PCR Qual, Non-Plasma	155
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR	81
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR	58
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qualitative, Bronchoalveolar Lavage	57
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR Qualitative, Non-Blood	48
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qual, Pleural Fluid	22
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qual, Pleural Fluid	20
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qualitative, Bronchoalveolar Lavage	16
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qual, Pericardial  Fluid	12
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qual,Peritoneal Fluid	5
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qualitative, Bone Marrow (ACD)	4
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qual,Peritoneal Fluid	4
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qual, Pericardial  Fluid	2
+LABCMVP	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qualitative	2
+LABCMVP	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qualitative, Bone Marrow (ACD)	2
+LABCMVPQ	CMV DNA PCR QUANT, PLASMA	CMV DNA PCR Quant, Plasma	4876
+LABCMVPQ	CMV PCR QUANTITATIVE	CMV PCR Quantitative	3012
+LABCMVPQ	CMV DNA PCR QUANT, PLASMA	CMV PCR Quantitative	1732
+LABCMVPQ	CMV PCR QUANTITATIVE		222
+LABCMVPQ	CMV DNA PCR QUANT, PLASMA		212
+LABCMVPQ	CMV PCR QUANTITATIVE	CMV PCR Quant	14
+LABCMVPQ	CMV DNA PCR QUANT, PLASMA	CMV PCR Quant	9
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV DNA PCR Qual, Non-Plasma	529
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qualitative, CSF	315
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR QUALITATIVE	125
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR	74
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qualitative, Bronchoalveolar Lavage	41
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA		30
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR Qualitative CSF	25
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qual, Pericardial  Fluid	16
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qual, Pleural Fluid	16
+LABCMVQL	CMV DNA PCR QUAL, NON-BLOOD		11
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qualitative, Bone Marrow (ACD)	4
+LABCMVQL	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR QUALITATIVE	2
+LABCMVQL	CMV DNA PCR QUAL, NON-PLASMA	CMV PCR, Qual,Peritoneal Fluid	2
+LABCMVQL	CMV DNA PCR QUAL, NON-BLOOD	CMV PCR, Qual,Peritoneal Fluid	1
+LABCMVQT	CMV DNA PCR QUANT, PLASMA	CMV DNA PCR Quant, Plasma	2746
+LABCMVQT	CMV DNA PCR QUANT, PLASMA	CMV PCR Quantitative	1098
+LABCMVQT	CMV PCR QUANTITATIVE	CMV PCR Quantitative	8
+LABCMVQT	CMV DNA PCR QUANT, PLASMA	CMV PCR Quant	3
+LABCMVQT	CMV PCR QUANTITATIVE		2
+LABCMVRC	CMV RAPID CULTURE	CMV RAPID CULTURE	44
+LABCMVRO	CMV RULE OUT	CMV RULE OUT	1059
+LABCMVTSI	CMV AB TOTAL SOLID PHASE (GALILEO)		834
+LABCO2	CO2, SERUM/PLASMA	CO2, Serum/Plasma	852
+LABCO2	CO2, SERUM/PLASMA		378
+LABCOCC	COCCIDIOIDES COMPLEMENT FIXATION, CSF	Coccidioides Complement Fixation, CSF	40
+LABCOCC	COCCIDIOIDES COMPLEMENT FIXATION, CSF		28
+LABCOCFS	COCCIDIOIDES COMPLEMENT FIXATION, SERUM	Coccidioides Complement Fixation, Serum	97
+LABCOCFS	COCCIDIOIDES COMPLEMENT FIXATION, SERUM		8
+LABCOCI	COCCI ID (SERUM)	Cocci ID (Serum)	45
+LABCOCI	COCCI ID (SERUM)		9
+LABCOHB	CARBOXYHEMOGLOBIN	Carboxyhemoglobin	134
+LABCOHB	CARBOXYHEMOGLOBIN		12
+LABCOPYPT	COPY REQUEST	Copy Request	4
+LABCOPYPT	COPY REQUEST		2
+LABCORT	CORTISOL, SERUM	Cortisol, Serum	11928
+LABCORT	CORTISOL, SERUM		1140
+LABCORT	CORTISOL, SERUM	Cortisol, Serum - post	3
+LABCORT	CORTISOL, SERUM	Cortisol, Serum - pre	3
+LABCORT	CORTISOL, SERUM	Cortisol, Serum - AM / or Baseline level	1
+LABCORTS	CORTISOL, POST OP	Cortisol, Post Op	1803
+LABCORTS	CORTISOL, POST OP		52
+LABCOXBAB	COXSACKIE B AB, SERUM	COXSACKIE B AB, SERUM	2
+LABCOXPNL	COXSACKIE A AB PANEL	Coxsackie A Ab Panel	151
+LABCOXPNL	COXSACKIE A AB PANEL		10
+LABCOXPNL	COXSACKIE A AB PANEL	Coxsackie A AB Panel	2
+LABCOXPNL	COXSACKIE A AB PANEL	COXSACKIE A AB, SERUM	2
+LABCR	CREATININE, SERUM/PLASMA	Creatinine, Serum/Plasma	3684
+LABCR	CREATININE, SERUM/PLASMA		710
+LABCR	CREATININE, SERUM/PLASMA	Creatinine, Serum/Plasma (LABCR)	4
+LABCR	CREATININE, SERUM/PLASMA	Creatinine, Serum/Plasma 	2
+LABCRCL	CREATININE CLEARANCE, URINE	Creatinine Clearance, Urine	1196
+LABCRCL	CREATININE CLEARANCE, URINE		556
+LABCRCL	CREATININE CLEARANCE	Creatinine Clearance	340
+LABCRCL	CREATININE CLEARANCE	Creatinine Clearance, Urine	103
+LABCRCL	CREATININE CLEARANCE, URINE	Creatinine Clearance, Urine  	3
+LABCRDABG	ARTERIAL BLOOD GASES, CORD BLOOD	Arterial Blood Gases, Cord Blood	18
+LABCRDABG	ARTERIAL BLOOD GASES, CORD BLOOD		18
+LABCRDVBG	VENOUS BLOOD GASES, CORD BLOOD	Venous Blood Gases, Cord Blood	18
+LABCRDVBG	VENOUS BLOOD GASES, CORD BLOOD		18
+LABCRFA	CRYPTOSPORIDIUM DFA	Cryptosporidium DFA	341
+LABCRFA	CRYPTOSPORIDIUM DFA		126
+LABCRP	C - REACTIVE PROTEIN	C-Reactive Protein	29138
+LABCRP	C - REACTIVE PROTEIN		2770
+LABCRP	C - REACTIVE PROTEIN	CRP	24
+LABCRP	C - REACTIVE PROTEIN	C-Reactive protein	10
+LABCRPLB	CRYPTOCOCCAL AG LATEX, BLOOD	Cryptococcal Ag Latex, Blood	1697
+LABCRPLB	CRYPTOCOCCAL AG LATEX, BLOOD		74
+LABCRPLB	CRYPTOCOCCAL AG LATEX, BLOOD	Cryptococcal Ag (latex), blood	6
+LABCRPLB	CRYPTOCOCCAL AG LATEX, BLOOD	Cryptococcal Antigen Latex, Blood	6
+LABCRPLC	CRYPTOCOCCAL AG LATEX, CSF	Cryptococcal Ag Latex, CSF	866
+LABCRPLC	CRYPTOCOCCAL AG LATEX, CSF	Cryptococcal Ag (Latex), CSF	820
+LABCRPLC	CRYPTOCOCCAL AG LATEX, CSF		578
+LABCRPLC	CRYPTOCOCCAL AG LATEX, CSF	Cryptococcal Ag (Latex), CSF Shunt Fluid	18
+LABCRPLFB	CRYPTOCOCCAL ANTIGEN LATERAL FLOW, BLOOD	Cryptococcal Antigen Lateral Flow, Blood	280
+LABCRPLFC	CRYPTOCOCCAL ANTIGEN LATERAL FLOW, CSF	Cryptococcal Antigen Lateral Flow, CSF	326
+LABCRTINE	CREATINE, SER/PLAS	Creatine, Serum	101
+LABCRTINE	CREATINE, SERUM	Creatine, Serum	40
+LABCRTINE	CREATINE, SER/PLAS	Creatine, Ser/Plas	2
+LABCRTINE	CREATINE, SER/PLAS		2
+LABCRTINE	CREATINE, SERUM		2
+LABCRYEIA	CRYPTOSPORIDIUM EIA	CRYPTOSPORIDIUM EIA	174
+LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE	Cryoprotein Semi-Quantitative	660
+LABCRYO	CRYOPROTEIN SEMI - QUANTITATIVE		30
+LABCRYST	CRYSTAL ANALYSIS, SYNOVIAL FLUID	Crystal Analysis, Synovial Fluid	1082
+LABCRYST	CRYSTAL ANALYSIS, SYNOVIAL FLUID		346
+LABCSFALB	ALBUMIN, CSF	Albumin, CSF	16
+LABCSFALB	ALBUMIN, CSF		4
+LABCSFC	CSF CULTURE AND GRAM STAIN	CSF Culture + Gram Stain, CSF	4560
+LABCSFC	CSF CULTURE AND GRAM STAIN	CSF Culture And Gram Stain	4113
+LABCSFC	CSF CULTURE AND GRAM STAIN		2544
+LABCSFC	CSF CULTURE AND GRAM STAIN	CSF culture	383
+LABCSFC	CSF CULTURE AND GRAM STAIN	CSF Culture + Gram Stain, CSF Shunt Fluid - Always order Anaerobic Culture with this test	122
+LABCSFC	CSF CULTURE AND GRAM STAIN	CSF Culture + Gram Stain	10
+LABCSFC	CSF CULTURE AND GRAM STAIN	CSFCulture	6
+LABCSFCT	CELL COUNT AND DIFF, CSF	Cell Count and Diff, CSF	12380
+LABCSFCT	CELL COUNT AND DIFF, CSF		3834
+LABCSFCT	CELL COUNT AND DIFF, CSF	Cell count and diff, CSF	813
+LABCSFCT	CELL COUNT AND DIFF, CSF	CSF Cell Count and Diff	2
+LABCSFGL	GLUCOSE, CSF	Glucose, CSF	10122
+LABCSFGL	GLUCOSE, CSF		2840
+LABCSFGL	GLUCOSE, CSF	CSF Glucose	2
+LABCSFLD	LDH, CSF	LDH, CSF	442
+LABCSFLD	LDH, CSF		124
+LABCSFTP	PROTEIN TOTAL, CSF	Protein, Total, CSF	4944
+LABCSFTP	PROTEIN TOTAL, CSF	Protein Total, CSF	4485
+LABCSFTP	PROTEIN TOTAL, CSF		2852
+LABCSFTP	PROTEIN TOTAL, CSF	Protein, total, CSF	462
+LABCSFTP	PROTEIN TOTAL, CSF	CSF Total Protein	2
+LABCSMP	CANCER SOMATIC MUTATION PANEL	CANCER SOMATIC MUTATION PANEL	135
+LABCSMP	CANCER SOMATIC MUTATION PANEL	Cancer Somatic Mutation Panel	110
+LABCSMP	CANCER SOMATIC MUTATION PANEL	Cancer Somatic Mutation Panel (molecular diagnostic)	26
+LABCSMP	CANCER SOMATIC MUTATION PANEL		6
+LABCSMPB	CANCER SOMATIC MUTATION PNL, BLOOD	CANCER SOMATIC MUTATION PNL, BLOOD	251
+LABCSMPB	CANCER SOMATIC MUTATION PNL, BLOOD	Cancer Somatic Mutation Pnl, Blood	25
+LABCSMPBM	CANCER SOMATIC MUTATION PNL, BM	Cancer Somatic Mutation Panel, Bone Marrow (molecular diagnostic)	236
+LABCSMPBM	CANCER SOMATIC MUTATION PNL, BM	Cancer Somatic Mutation Pnl, BM	228
+LABCSMPBM	CANCER SOMATIC MUTATION PNL, BM	CANCER SOMATIC MUTATION PNL, BM	52
+LABCSTOTA	PROTEIN C&S, TOTAL	Protein C&S, Total	426
+LABCSTOTA	PROTEIN C&S, TOTAL		14
+LABCTC	CATHETER TIP CULTURE	Catheter Tip Culture	1286
+LABCTC	CATHETER TIP CULTURE		1222
+LABCYAB	CYSTICERCUS AB	Cysticercus Ab	27
+LABCYAB	CYSTICERCUS AB		6
+LABCYCWBL	CYCLOSPORINE, WHOLE BLOOD	Cyclosporine, Whole Blood	9247
+LABCYCWBL	CYCLOSPORINE, WHOLE BLOOD		722
+LABCYCWBL	CYCLOSPORINE, WHOLE BLOOD	CYCLOSPORINE, WHOLE BLOOD	36
+LABCYCWBL	CYCLOSPORINE, WHOLE BLOOD	Int Cyclosporine, Whole Blood Every 4 Weeks	2
+LABCYLEX	IMMUNE CELL FUNCTION (CYLEX)	Immune Cell Function (CYLEX)	65
+LABCYLX	IMMUNE FUNCTION TEST	Immune Function Test	139
+LABCYLX	IMMUNE FUNCTION TEST		10
+LABCYLX	IMMUNE FUNCTION TEST	Immune Function Test (Cylex)	1
+LABCYSBLD	CYSTICERCUS AB REFLEX PANEL, SERUM	Cysticercus Ab Reflex Panel, Serum	59
+LABCYSBLD	CYSTICERCUS AB REFLEX PANEL, SERUM		2
+LABCYSCSF	CYSTICERCUS AB REFLEX PANEL, CSF	Cysticercus Ab Reflex Panel, CSF	47
+LABCYSCSF	CYSTICERCUS AB REFLEX PANEL, CSF		8
+LABCYSI	CYSTICERCOSIS, CSF AND SERUM	Cysticercosis, CSF and Serum	79
+LABCYSI	CYSTICERCOSIS, CSF AND SERUM		18
+LABCYTGEN	CYTOGENETICS FISH ORDER		1006
+LABCYTOG	CYTOGENETICS REPORT		1300
+LABCYTOG	CYTOGENETICS REPORT	Cytogenetics Report	1220
+LABCZPL	CARBAMAZEPINE, SERUM	Carbamazepine, Serum	694
+LABCZPL	CARBAMAZEPINE, SERUM		54
+LABCZPL	CARBAMAZEPINE, SERUM	Carbamazepine	15
+LABD816V	KIT D816V MUTATION, AS-PCR, BLOOD	KIT D816V MUTATION, AS-PCR, BLOOD	12
+LABD816V	KIT D816V MUTATION, AS-PCR, BLOOD	KIT D816V Mutation, AS-PCR, Blood	6
+LABDATBSI	DIRECT COOMBS BROAD SPECTRUM	Direct Coombs Broad Spectrum	3488
+LABDATBSI	DIRECT COOMBS BROAD SPECTRUM		534
+LABDATLA	DIRECT ANTIGLOBULIN TEST - LARC		4
+LABDBGT	DABIGATRAN	Dabigatran	8
+LABDBIL	BILIRUBIN DIRECT, SERUM/PLASMA	Bilirubin Direct, Serum/Plasma	5528
+LABDBIL	BILIRUBIN DIRECT, SERUM/PLASMA		3338
+LABDBIL	BILIRUBIN DIRECT, SERUM/PLASMA	Bilirubin, Direct, Serum/Plasma	12
+LABDBIL	BILIRUBIN DIRECT, SERUM/PLASMA	Bilirubin Direct, Serum/Plasma  [LABDBIL]	2
+LABDBQ	DIRECT LIPID FRACTIONATION	Direct Lipid Fractionation	2
+LABDCGSN	DONOR, CHAGAS SCN W/REFL		56
+LABDCGSN	DONOR, CHAGAS SCN W/REFL	Donor, Chagas Scn w/Refl	12
+LABDCMVAB	DONOR, CMV TOTAL ANTIBODIES	Donor, CMV Total Antibodies	2
+LABDCMVL	DONOR CMV AB (LIVING)		54
+LABDCMVL	DONOR CMV- TOTAL AB	Donor CMV- Total Ab	28
+LABDCMVL	DONOR CMV AB (LIVING)	Donor CMV AB (Living)	12
+LABDCMVL	DONOR CMV AB (LIVING)	Donor CMV Ab (Living)	6
+LABDCMVL	DONOR CMV- TOTAL AB	Donor CMV AB (Living)	2
+LABDDIM	D - DIMER	D-Dimer	3494
+LABDDIM	D - DIMER		1366
+LABDDIM	D - DIMER	D-DIMER	1254
+LABDDIM	D - DIMER	D-Dimer (Elisa)	18
+LABDDIM	D - DIMER	PTT Partial Thromboplastin Time	16
+LABDDIML	D - DIMER (ELISA)	D-Dimer (ELISA)	2642
+LABDDIML	D - DIMER (ELISA)		530
+LABDDIML	D - DIMER (ELISA)	D-DIMER (ELISA)	112
+LABDFA	DENGUE FEVER AB	Dengue Fever Ab	168
+LABDFA	DENGUE FEVER AB		10
+LABDGHBP	DIAG. HEPATITIS B PANEL	DIAG. HEPATITIS B PANEL	734
+LABDGHBP	DIAG. HEPATITIS B PANEL	Hepatitis B Diagnostic Panel	231
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	Hepatitis B Diagnostic Panel	13
+LABDGHBP	HEPATITIS B DIAGNOSTIC PANEL	DIAG. HEPATITIS B PANEL	2
+LABDGHBP	DIAG. HEPATITIS B PANEL	*  Hepatitis B Diagnostic Panel	1
+LABDHB	DONOR HEPATITIS B SURFACE AG		4
+LABDHBAG	DONOR, HEP B SURFACE AG	Donor, Hep B Surface AG	2
+LABDHBCAB	DONOR, HEP B CORE TOTAL AB	Donor, Hep B Core Total AB	2
+LABDHBCC	DONOR HEPATITIS B CORE AB (LIVING)		4
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL	Hepatitis B Diagnostic Panel	2749
+LABDHBP	HEPATITIS B DIAGNOSTIC PANEL		100
+LABDHEPC	DONOR HEPATITIS C AB		4
+LABDHIVAB	DONOR,HIV-1/HIV-2 AB SCN W/RFL	Donor,HIV-1/HIV-2 AB Scn w/Rfl	2
+LABDHIVB	DONOR HIV AB		54
+LABDHIVB	DONOR HIV AB	Donor HIV Ab	14
+LABDHTLVQ	DONOR, HTLV-I/II AB SCN	Donor, HTLV-I/II AB Scn	2
+LABDHTLVQ	DONOR, HTLV-I/II AB SCN		2
+LABDIC	DIC SCREEN	DIC Screen	39924
+LABDIC	DIC SCREEN		1472
+LABDIC	DIC SCREEN	*  DIC Screen (PT / PTT/ Fib /TT / D-Dimer)	11
+LABDIC	DIC SCREEN	DIC screen	2
+LABDIFF	SLIDE DIFFERENTIAL		6
+LABDIFF	SLIDE DIFFERENTIAL	Slide Differential	2
+LABDIGL	DIGOXIN, SERUM	Digoxin, Serum	9389
+LABDIGL	DIGOXIN, SERUM		1306
+LABDIGL	DIGOXIN, SERUM	Digoxin	339
+LABDIGL	DIGOXIN, SERUM	Digoxin Level	8
+LABDNAISO	NUCLEIC ACID ISOLATION, DNA		2
+LABDOA	DRUGS OF ABUSE SCREEN, URINE	Drugs Of Abuse Screen, Urine	24704
+LABDOA	DRUGS OF ABUSE SCREEN, URINE		4800
+LABDOA	DRUGS OF ABUSE SCREEN, URINE	Drugs of abuse screen, urine	1220
+LABDOA	DRUGS OF ABUSE SCREEN, URINE	DRUGS OF ABUSE SCREEN, URINE	455
+LABDONLAI	DONATH LANDSTEINER	Donath Landsteiner	36
+LABDOXEPI	DOXEPIN AND METABOLITE	Doxepin And Metabolite	2
+LABDPARE	DIRECT PARASITE EXAM		20
+LABDPARE	DIRECT PARASITE EXAM	Direct Parasite Exam	12
+LABDRPR	DONOR, RPR W/RFL TO SYPH IGG	Donor, RPR W/Rfl to Syph IgG	2
+LABDRPRS	DONOR, RPR		54
+LABDRPRS	DONOR SYPHILIS SCREEN	Donor Syphilis Screen	6
+LABDRPRS	DONOR, RPR	Donor, RPR	6
+LABDRPRS	DONOR, SYPHILIS SCREEN	Donor, syphilis screen	4
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL	Dilute Russell Viper Venom Panel	320
+LABDRVVTP	DILUTE RUSSELL VIPER VENOM PANEL		34
+LABDSDNA	ANTI - DSDNA AB	Anti-DSDNA Ab	1442
+LABDSDNA	ANTI - DSDNA AB		74
+LABDT	DIPHTHERIA AND TETANUS AB IGG	Diphtheria And Tetanus Ab IgG	23
+LABDT	DIPHTHERIA AND TETANUS AB IGG		4
+LABDUTYPE	WEAK D TYPING		6
+LABDVEAB	RAPID FLU A/B EIA	Rapid Flu A/B EIA	74
+LABDVEAB	RAPID FLU A/B EIA		52
+LABDVEADV	DIRECT VIRAL EXAM ADENO	Direct Viral Exam Adeno	16
+LABDVEADV	DIRECT VIRAL EXAM ADENO		2
+LABDVEAV3	ADENO DFA	Adeno DFA	28
+LABDVEAV3	ADENO DFA		2
+LABDVEFLA	DIRECT VIRAL EXAM FLU A	Direct Viral Exam Flu A	50
+LABDVEFLA	DIRECT VIRAL EXAM FLU A		22
+LABDVEFLB	DIRECT VIRAL EXAM FLU B	Direct Viral Exam Flu B	92
+LABDVEFLB	DIRECT VIRAL EXAM FLU B		36
+LABDVEHV	HSV/VZV DFA	HSV/VZV DFA	1845
+LABDVEHV	HSV/VZV DFA		492
+LABDVEHVR	HSV/VZV DFA REFLEX PCR	HSV/VZV DFA REFLEX PCR	154
+LABDVEHVR	HSV/VZV DFA REFLEX PCR	HSV/VZV DFA reflex PCR	88
+LABDVEIAR	FLU A DFA-REFLEX PCR	Flu A DFA-Reflex PCR	38
+LABDVEIAR	FLU A DFA-REFLEX PCR		16
+LABDVEIAV	FLU A DFA	Flu A DFA	134
+LABDVEIAV	FLU A DFA		16
+LABDVEIBV	FLU B DFA	Flu B DFA	100
+LABDVEIBV	FLU B DFA		14
+LABDVEMP	MPV DFA	MPV DFA	13
+LABDVEMP	MPV DFA		2
+LABDVEMPV	DIRECT VIRAL EXAM MPV	Direct Viral Exam MPV	16
+LABDVEMPV	DIRECT VIRAL EXAM MPV		4
+LABDVEP1	DIRECT VIRAL EXAM PARA 1	Direct Viral Exam Para 1	12
+LABDVEP1	DIRECT VIRAL EXAM PARA 1		4
+LABDVEP2	DIRECT VIRAL EXAM PARA 2	Direct Viral Exam Para 2	6
+LABDVEP3	DIRECT VIRAL EXAM PARA 3	Direct Viral Exam Para 3	6
+LABDVEPI1	PARA 1 DFA	Para 1 DFA	6
+LABDVEPI2	PARA 2 DFA	Para 2 DFA	4
+LABDVEPI3	PARA 3 DFA	Para 3 DFA	4
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	Rotavirus Direct Viral Exam	164
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)		153
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	STOOL FOR ROTAVIRUS EIA	117
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	Stool For Rotavirus EIA	106
+LABDVERO	ROTAVIRUS EIA (DIRECT VIRAL EXAM)	Direct vir exm: rotavirus	25
+LABDVERS	RSV RAPID ANTIGEN, DIRECT EIA	RSV Rapid Antigen, Direct EIA	18
+LABDVERS	RSV RAPID ANTIGEN, DIRECT EIA		2
+LABDVERSV	DIRECT VIRAL EXAM RSV	Direct Viral Exam RSV	158
+LABDVERSV	DIRECT VIRAL EXAM RSV		22
+LABDVERTV	ROTAVIRUS ANTIGEN, STOOL	ROTAVIRUS ANTIGEN, STOOL	239
+LABDVERTV	ROTAVIRUS ANTIGEN, STOOL	Stool For Rotavirus EIA	32
+LABDVERTV	ROTAVIRUS ANTIGEN, STOOL	Direct vir exm: rotavirus	4
+LABDVERTV	ROTAVIRUS ANTIGEN, STOOL	Direct Viral Exam: Rotavirus	3
+LABDVERTV	ROTAVIRUS ANTIGEN, STOOL	Rotavirus Direct Viral Exam	2
+LABDVERTV	ROTAVIRUS EIA (DIRECT VIRAL EXAM)		1
+LABDVERV	RESPIRATORY DFA PANEL		1926
+LABDVERV	RESPIRATORY DFA PANEL	DIRECT VIRAL EXAM, RESPIRATORY	1716
+LABDVERV	RESPIRATORY DFA PANEL	Direct Viral Exam, Respiratory	1272
+LABDVERV	RESPIRATORY DFA PANEL	RESPIRATORY DFA PANEL	26
+LABDVERV	RESPIRATORY DFA PANEL	Direct Viral Exam, Resp	10
+LABDVERV	RESPIRATORY DFA PANEL	Direct Viral Exam: Resp	6
+LABDVERV3	RSV DFA	RSV DFA	337
+LABDVERV3	RSV DFA		14
+LABDVERVP	RESPIRATORY DFA PANEL	Respiratory DFA Panel	8764
+LABDVERVP	RESPIRATORY DFA PANEL		2292
+LABDVERVP	RESPIRATORY DFA PANEL	RESPIRATORY DFA PANEL	345
+LABDVERVR	RESP DFA PANEL-REFLEX PCR	Resp DFA Panel-Reflex PCR	1006
+LABDVERVR	RESP DFA PANEL-REFLEX PCR		300
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)		540
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	DIRECT VIR EXAM: HSV/VZV	508
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	HSV/VZV Direct Viral Exam	255
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	DIRECT VIRAL EXAM: HSV/ADENO	14
+LABDVEVH	HSV/VZ DFA (DIRECT VIRAL EXAM)	VZ/HSV, Direct Slide Exam	2
+LABDWNIL	DONOR, WEST NILE VIRUS NAT		56
+LABDWNIL	DONOR, WEST NILE VIRUS NAT	Donor, West Nile Virus NAT	12
+LABE2O	ESTRADIOL	Estradiol	102
+LABE2O	ESTRADIOL		8
+LABEAIGG	EBV EARLY ANTIGEN IGG	EBV EARLY ANTIGEN IgG	41
+LABEAIGG	EB VIRUS EARLY AG (R+D) IGG		32
+LABEAIGG	EBV EARLY ANTIGEN IGG		4
+LABEAIGG	EB VIRUS EARLY AG (R+D) IGG	EB VIRUS EARLY AG (R+D) IGG	1
+LABEB	EBV AB PANEL	Epstein Barr Ab Panel	574
+LABEB	EBV AB PANEL	EBV Ab Panel	371
+LABEB	EBV AB PANEL	EPSTEIN BARR AB PANEL	241
+LABEB	EBV AB PANEL		78
+LABEBNABY	EBV NUCLEAR ANTIGEN (EBNA) IGG	EBV Nuclear Antigen (EBNA) IGG	43
+LABEBNABY	EB NUCLEAR AG AB		32
+LABEBNABY	EBV NUCLEAR ANTIGEN IGG	EB Nuclear Ag Ab	4
+LABEBNABY	EB NUCLEAR AG AB	EB Nuclear Ag Ab	1
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBV By PCR Quantitative	1418
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBV PCR Quantitative	1379
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBV By PCR Quantitative	230
+LABEBPCRQ	EBV PCR QUANTITATIVE		97
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBV By PCR, Quantitative	53
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBV By PCR, Quantitative	46
+LABEBPCRQ	EBV BY PCR QUANTITATIVE		33
+LABEBPCRQ	EBV PCR QUANTITATIVE	EBV PCR, Quantitative	13
+LABEBPCRQ	EBV BY PCR QUANTITATIVE	EBV PCR, Quantitative	6
+LABEBVC	EBV CAPSID AG	EBV Capsid Ag	20
+LABEBVC	EBV CAPSID AG	EPSTEIN BARR VIRAL CAPSID AG	10
+LABEBVC	EBV VIRAL CAPSID ANTIGEN IGA	Ebv Viral Capsid Antigen IGA	8
+LABEBVC	EBV CAPSID AG		2
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EPSTEIN-BARR VIRUS AB PANEL	1009
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	Epstein Barr Ab Panel	542
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL		70
+LABEBVPAN	EBV AB PANEL	Epstein Barr Ab Panel	45
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBV Ab Panel	34
+LABEBVPAN	EBV AB PANEL	EBV Ab Panel	22
+LABEBVPAN	EBV AB PANEL	EPSTEIN BARR AB PANEL	6
+LABEBVPAN	EBV AB PANEL		2
+LABEBVPAN	EPSTEIN-BARR VIRUS AB PANEL	EBV AB Panel	2
+LABEBVQL	EBV DNA PCR QUAL	EBV DNA PCR Qual	85
+LABEBVQP	EBV PCR QUANT, PLASMA	EBV PCR QUANT, PLASMA	2036
+LABEBVQP	EBV PCR QUANT, PLASMA	EBV PCR Quant, Plasma	362
+LABEBVQP	EBV PCR QUANT, PLASMA		50
+LABEBVQP	EBV PCR QUANT, PLASMA	EBV Quant PCR 	28
+LABEBVQPB	EBV NPC PCR PANEL	EBV NPC PCR Panel	52
+LABECHIG	ECHINOCOCCUS AB, IGG	Echinococcus Ab IgG ELISA	92
+LABECHIG	ECHINOCOCCUS AB, IGG W/REFLEX	Echinococcus Ab, IgG w/Reflex	22
+LABECHIG	ECHINOCOCCUS AB, IGG		12
+LABECL	EUGLOBULIN CLOT LYSIS	Euglobulin Clot Lysis	51
+LABECL	EUGLOBULIN CLOT LYSIS		2
+LABEGFRP	EGFR GENE MUTATION PANEL		94
+LABEGFRP	EGFR GENE MUTATION PANEL	EGFR Gene Mutation Panel	31
+LABEITC	TRANSFUSION EMERGENCY ISSUE		936
+LABEITC	TRANSFUSION EMERGENCY ISSUE	Transfusion Emergency Issue	596
+LABEOSSP	SPUTUM FOR EOSINOPHILS	Sputum For Eosinophils	32
+LABEOSSP	SPUTUM FOR EOSINOPHILS		8
+LABEP1	ELECTROLYTE PANEL	Electrolyte Panel	2773
+LABEP1	ELECTROLYTE PANEL		188
+LABEP1	ELECTROLYTE PANEL	Electrolyte panel	6
+LABEP2	LYTES II		10
+LABEP2	LYTES II	Lytes II	4
+LABEPO	ERYTHROPOIETIN	ERYTHROPOIETIN	287
+LABEPO	ERYTHROPOIETIN	Erythropoietin	34
+LABEPO	ERYTHROPOIETIN	Erythropoietin,	6
+LABEPO	ERYTHROPOIETIN		5
+LABERUCRY	EMERGENCY RELEASE CRYO	Emergency Release Cryoprecipitate	40
+LABERUFFP	EMERGENCY RELEASE PLASMA	Emergency Release Plasma Product	1108
+LABERUFFP	EMERGENCY RELEASE PLASMA	Massive Plasma Product	82
+LABERUPLT	EMERGENCY RELEASE PLT	Emergency Release Platelet Apheresis Product	593
+LABERUPLT	EMERGENCY RELEASE PLT	Massive Platelet Apheresis Product	82
+LABERURBC	EMERGENCY RELEASE RBC	Emergency Release Red Blood Cells	1326
+LABERURBC	EMERGENCY RELEASE RBC	Massive Red Blood Cells	82
+LABESRP	SEDIMENTATION RATE (ESR)	Sedimentation Rate (ESR)	19048
+LABESRP	SEDIMENTATION RATE (ESR)		2056
+LABESRP	SEDIMENTATION RATE (ESR)	Sedimentation rate (ESR)	121
+LABESRP	SEDIMENTATION RATE (ESR)	Sedimentation Rate (ESR) [LABESRP]	1
+LABEVPCR	ENTEROVIRUS BY PCR	Enterovirus By PCR	417
+LABEVPCR	ENTEROVIRUS BY PCR	Enterovirus, PCR, CSF	318
+LABEVPCR	ENTEROVIRUS BY PCR	Enterovirus, PCR	94
+LABEVPCR	ENTEROVIRUS BY PCR		68
+LABEVPCR	ENTEROVIRUS BY PCR	Enterovirus PCR CSF	35
+LABEVPCR	ENTEROVIRUS PCR	Enterovirus, PCR, CSF	5
+LABEVPCR	ENTEROVIRUS PCR		1
+LABEXCHTX	EXCHANGE TRANSFUSION		2
+LABF8INH	FACTOR VIII INHIBITOR HUMAN	Factor VIII Inhibitor Human	135
+LABF8INH	FACTOR VIII INHIBITOR HUMAN		20
+LABF8INH	FACTOR VIII INHIBITOR HUMAN	Factor VIII Inhibitor, Human	2
+LABF8POR	FACTOR VIII INHIBITOR PORCINE	Factor Viii Inhibitor Porcine	7
+LABF8POR	FACTOR VIII INHIBITOR PORCINE	Factor VIII Inhibitor, Porcine	2
+LABFABRO	ARBOVIRUS AB PANEL, IFA (TITER)	Arbovirus Ab Panel, IFA (Titer)	3
+LABFACT10	FACTOR X ASSAY	Factor X Assay	212
+LABFACT10	FACTOR X ASSAY		28
+LABFACT11	FACTOR XI ASSAY	Factor XI Assay	144
+LABFACT11	FACTOR XI ASSAY		10
+LABFACT12	FACTOR XII ASSAY	Factor XII Assay	70
+LABFACT12	FACTOR XII ASSAY		10
+LABFACT13	FACTOR XIII SCREEN	Factor XIII Screen	27
+LABFACT2	FACTOR II ASSAY	Factor II Assay	136
+LABFACT2	FACTOR II ASSAY		24
+LABFACT5	FACTOR V ASSAY	Factor V Assay	178
+LABFACT5	FACTOR V ASSAY		34
+LABFACT7	FACTOR VII ASSAY	Factor VII Assay	213
+LABFACT7	FACTOR VII ASSAY		32
+LABFACT8	FACTOR VIII ASSAY	Factor VIII Assay	1200
+LABFACT8	FACTOR VIII ASSAY		104
+LABFACT9	FACTOR IX ASSAY	Factor IX Assay	340
+LABFACT9	FACTOR IX ASSAY		24
+LABFADA	ADENOSINE DEAMINASE, FLUID	Adenosine Deaminase, Fluid	236
+LABFADA	ADENOSINE DEAMINASE, FLUID		230
+LABFADA	ADENOSINE DEAMINASE, FLUID	Adenosine Deamin, Fluid	210
+LABFADA	ADENOSINE DEAMINASE, FLUID	Adenosine Deamin, Pleural Fluid	80
+LABFADA	ADENOSINE DEAMINASE, FLUID	Adenosine Deamin, Pericardial Fluid	22
+LABFADA	ADENOSINE DEAMINASE, FLUID	Adenosine Deamin, Peritoneal  Fluid	14
+LABFADA	ADENOSINE DEAMINASE, FLUID	Adenosine Deamin, Synovial Fluid	2
+LABFADAG	ADENOVIRUS AG, STOOL	ADENOVIRUS AG	624
+LABFADAG	ADENOVIRUS AG, STOOL	Adenovirus AG, Stool	148
+LABFADAG	ADENOVIRUS AG, STOOL		82
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Peritoneal FL	2155
+LABFAL	ALBUMIN, FLUID		1272
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid	899
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Ascites Fluid	521
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Peritoneal Fluid	490
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Pleural Fluid	327
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Pericardial Fluid	24
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Other	16
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Jackson Pratt	6
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Synovial Fluid	4
+LABFAL	ALBUMIN, FLUID	Albumin, Fluid Bile	2
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Jackson Pratt	2253
+LABFAMY	AMYLASE, FLUID		1724
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid	1259
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Ascites Fluid	532
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Pleural Fluid	480
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Peritoneal FL	293
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Other	292
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Peritoneal Fluid	191
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Bile	12
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Pericardial Fluid	12
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Synovial Fluid	6
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Amniotic Fluid	4
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Dialysate	2
+LABFAMY	AMYLASE, FLUID	Amylase, Fluid Peritoneal Lavage	2
+LABFAMYA	AMYLASE, ASCITES	Amylase, Ascites	34
+LABFAMYA	AMYLASE, ASCITES		14
+LABFASAB	FASCIOLA HEPATICA AB	Fasciola Hepatica Ab	8
+LABFBBURG	LYME WESTERN BLOT, CSF	Lyme Western Blot, CSF	231
+LABFBBURG	LYME WESTERN BLOT, CSF	Lyme western blot, CSF	61
+LABFBBURG	LYME WESTERN BLOT, CSF		10
+LABFBCLO	B - CELL CLONALITY PCR, FLUID	B-Cell Clonality PCR, Fluid	8
+LABFBCLO	B - CELL CLONALITY PCR, FLUID	B-Cell Clonality, PCR, Pleural Fluid	6
+LABFBCLO	B - CELL CLONALITY PCR, FLUID		6
+LABFBCRQT	BCR ABL QUANTITATIVE, FLUID	BCR ABL Quantitative, Fluid	8
+LABFBCRQT	BCR ABL QUANTITATIVE, FLUID	Fluid BCR ABL Quant., Pleural Fluid	2
+LABFBCRQT	BCR ABL QUANTITATIVE, FLUID		2
+LABFBILA	BILIRUBIN TOTAL, FLUID		210
+LABFBILA	BILIRUBIN, TOTAL BODY FLUID	Bilirubin, Total Body Fluid	192
+LABFBILA	BILIRUBIN TOTAL, FLUID	Bilirubin Total, Fluid	152
+LABFBILA	BILIRUBIN, TOTAL BODY FLUID	BILIRUBIN, TOTAL BODY FLUID	89
+LABFBILA	BILIRUBIN TOTAL, FLUID	Bilirubin, Total, Peritoneal  Fluid	16
+LABFBILA	BILIRUBIN, TOTAL BODY FLUID	Bilirubin, Total, Pleural Fluid	16
+LABFBILA	BILIRUBIN, TOTAL BODY FLUID	Bilirubin, Total, Peritoneal  Fluid	12
+LABFBILA	BILIRUBIN TOTAL, FLUID	Bilirubin, Total, Pleural Fluid	10
+LABFBILA	BILIRUBIN, TOTAL BODY FLUID	Bilirubin, Total, Pericardial Fluid	2
+LABFCD34	CD34 PANEL, FLUID	CD34 Panel, Apheresis Fluid	102
+LABFCD34	CD34 PANEL, FLUID	CD34 Panel, Fluid	93
+LABFCD34	CD34 PANEL, FLUID		46
+LABFCD34	CD34 PANEL, FLUID	CD34 Panel, Pleural Fluid	2
+LABFCHGA	A. PHAGOCYTOPHILUM AB	A. Phagocytophilum Ab	4
+LABFCHGA	A. PHAGOCYTOPHILUM AB		2
+LABFCHOLA	CHOLESTEROL, FLUID	Cholesterol, Fluid	240
+LABFCHOLA	CHOLESTEROL, FLUID		90
+LABFCLAB	CHLAMYDIA TRACHOMATIS SEROLOGY FOR OCCULT S	Chlamydia Trachomatis Serology for Occult STD	28
+LABFCLPN	CHLAMYDIA PNEUMONIAE CULTURE	Chlamydia Pneumoniae Culture	42
+LABFCLPN	CHLAMYDIA PNEUMONIAE CULTURE		8
+LABFCO2	CO2 (BICARB), FLUID	CO2 (BICARB), FLUID	6
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Jackson Pratt	2404
+LABFCR	CREATININE, FLUID		1184
+LABFCR	CREATININE, FLUID	Creatinine, Fluid	1062
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Other	194
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Peritoneal Fluid	80
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Ascites Fluid	68
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Pleural Fluid	12
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Dialysate	2
+LABFCR	CREATININE, FLUID	Creatinine, Fluid Pericardial Fluid	2
+LABFCUL	FUNGAL CULTURE	Fungal Culture	13256
+LABFCUL	FUNGAL CULTURE		10620
+LABFCUL	FUNGAL CULTURE	Fungus Culture	1785
+LABFCUL	FUNGAL CULTURE	Fungus Culture (Cryptococcus), CSF        Always order Crypto Ag with this test	981
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Induced Sputum	837
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Sputum, Expectorated	552
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Tracheal Aspirate	384
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Pleural Fluid	253
+LABFCUL	FUNGAL CULTURE	Fungus culture	188
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Bronchoalveolar Lavage	148
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Peritoneal Fluid	82
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Pericardial Fluid	42
+LABFCUL	FUNGAL CULTURE	Fungus Culture, CSF Shunt Fluid - Always order Cryptococcal AG with this test	38
+LABFCUL	FUNGAL CULTURE	Fungus culture CSF	34
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Synovial Fluid	32
+LABFCUL	FUNGAL CULTURE	Fungus Culture, Bone Marrow	20
+LABFCULS	FUNGAL CULTURE, SKIN/NAIL	Fungal Culture, Skin/Nail	80
+LABFCULS	FUNGAL CULTURE, SKIN/NAIL		8
+LABFCYST	CYSTICERCUS IGG AB	Cysticercus IgG Ab	17
+LABFCYST	CYSTICERCUS IGG AB		12
+LABFDIFF	DIFFERENTIAL, BODY FLUID		34
+LABFDIFF	DIFFERENTIAL, BODY FLUID	Differential, Body Fluid	14
+LABFDP	FIBRIN DEGRADATION PRODUCTS	Fibrin Degradation Products	406
+LABFDP	FIBRIN DEGRADATION PRODUCTS		88
+LABFE	IRON, TOTAL	Iron, Total	11746
+LABFE	IRON, TOTAL	Iron, total	454
+LABFE	IRON, TOTAL		216
+LABFECFIB	MEAT FIBERS, FECAL	Meat Fibers, Stool	4
+LABFECFIB	MEAT FIBERS, FECAL	Meat Fibers, Fecal	2
+LABFECHO	ECHOVIRUS AB, SERUM	Echovirus Ab, Serum	30
+LABFECHO	ECHOVIRUS AB, SERUM	Echovirus AB Panel CF	2
+LABFECHO	ECHOVIRUS AB, SERUM	ECHOVIRUS AB, SERUM	2
+LABFECHO	ECHOVIRUS AB, SERUM		1
+LABFEHRC	EHRLICHIA CHAFFEENSIS AB	Ehrlichia Chaffeensis Ab	42
+LABFEHRC	EHRLICHIA CHAFFEENSIS AB		6
+LABFENTA	ENTAMOEBA HISTOLYTICA AB	Entamoeba Histolytica Ab	183
+LABFENTA	ENTAMOEBA HISTOLYTICA AB		8
+LABFENTP	ENTEROVIRUS PANEL II	Enterovirus Panel II	2
+LABFER	FERRITIN	Ferritin	15483
+LABFER	FERRITIN		1586
+LABFFIBN	FETAL FIBRONECTIN	Fetal Fibronectin	32
+LABFFIBN	FETAL FIBRONECTIN		4
+LABFHAN	HANTAVIRUS AB ELISA	Hantavirus Ab ELISA	47
+LABFHCT	HEMATOCRIT, FLUID	Hematocrit, Fluid	347
+LABFHCT	HEMATOCRIT, FLUID		164
+LABFHCT	HEMATOCRIT, FLUID	Hematocrit, Pleural Fluid	62
+LABFHCT	HEMATOCRIT, FLUID	Hematocrit, Peritoneal  Fluid	40
+LABFHCT	HEMATOCRIT, FLUID	Hematocrit, Pericardial Fluid	18
+LABFHCT	HEMATOCRIT, FLUID	Hematocrit, Synovial Fluid	4
+LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB	Herpes Virus 6 IgG And IgM Ab	46
+LABFHHV6	HERPES VIRUS 6 IGG AND IGM AB		8
+LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB	Herpes Virus 7 IgG And IgM Ab	8
+LABFHHV7	HERPES VIRUS 7 IGG AND IGM AB		4
+LABFHIVQA	HIV-1 PROVIRALL DNA PCR QUAL	HIV-1 ProviralL DNA PCR Qual	26
+LABFHIVQA	HIV-1 DNA, QUALITATIVE PCR	HIV-1 Proviral Qualitative	4
+LABFHTLVQ	HTLV-1/2  DNA QUALITATIVE	HTLV-1/2  DNA Qualitative	132
+LABFHTLVQ	HTLV-1/2  DNA QUALITATIVE		14
+LABFHV12	HSV 1 AND 2 PCR, NON CSF	HSV 1 And 2 PCR, Non CSF	1017
+LABFHV12	HSV 1 AND 2 PCR, NON CSF		320
+LABFHV12	HSV 1/2 PCR, NON CSF	HSV 1/2 PCR, Non CSF	6
+LABFHVIG	HIV-1 AB, WESTERN BLOT	HIV-1 AB, Western Blot	151
+LABFHVIG	HIV-1 AB, WESTERN BLOT	HIV 1 IgG AB, Whole Blood	81
+LABFHVIG	HIV 1 IGG-AB, WESTERN BLOT CONFIRM	HIV 1 IgG-Ab, Western Blot Confirmation	78
+LABFHVIG	HIV 1 IGG-AB, WESTERN BLOT CONFIRM	HIV 1 IgG AB, Whole Blood	58
+LABFHVIG	HIV 1 IGG-AB, WESTERN BLOT CONFIRM		36
+LABFHVIG	HIV-1 AB, WESTERN BLOT		32
+LABFIB	FIBRINOGEN	Fibrinogen	24896
+LABFIB	FIBRINOGEN		4244
+LABFIBANT	FIBRINOGEN ANTIGEN	Fibrinogen Antigen	80
+LABFIBRO	FIBROSPECT FOR LIVER FIBROSIS	Fibrospect For Liver Fibrosis	6
+LABFILAB	FILARIA IGG4 AB ELISA	Filaria IgG4 Ab ELISA	72
+LABFK506L	TACROLIMUS (FK506)	Tacrolimus (FK506)	57108
+LABFK506L	TACROLIMUS (FK506)		5048
+LABFK506L	TACROLIMUS (FK506)	TACROLIMUS (FK506)	302
+LABFK506L	TACROLIMUS (FK506)	Tacrolimus (FK506) (trough)	7
+LABFK506L	TACROLIMUS (FK506)	Tacrolimus (Prograf)	1
+LABFKOH	FUNGAL CULTURE AND KOH	Fungal Culture And KOH	3194
+LABFKOH	FUNGAL CULTURE AND KOH		2954
+LABFKOH	FUNGAL CULTURE AND KOH	Fungus Culture + KOH/Calcofluor, Induced Sputum	701
+LABFKOH	FUNGAL CULTURE AND KOH	Fungus Culture + KOH/Calcofluor,  Expectorated Sputum	418
+LABFKOH	FUNGAL CULTURE AND KOH	Fungus Culture + KOH/Calcofluor, Tracheal Aspirate	329
+LABFKOH	FUNGAL CULTURE AND KOH	Fungal Culture + KOH/Calcofluor, Pleural Fluid	201
+LABFKOH	FUNGAL CULTURE AND KOH	Fungus Culture + KOH/Calcofluor, Bronchoalveolar Lavage	172
+LABFKOH	FUNGAL CULTURE AND KOH	Fungal Culture + KOH/Calcofluor, Peritoneal Fluid	68
+LABFKOH	FUNGAL CULTURE AND KOH	Fungus Culture + KOH/Calcofluor, Synovial Fluid	14
+LABFLCL	CHLORIDE, FLUID	Chloride, Fluid	20
+LABFLCL	CHLORIDE, FLUID		14
+LABFLCL	CHLORIDE, FLUID	Chloride, Peritoneal Fluid	2
+LABFLCL	CHLORIDE, FLUID	Chloride, Pleural Fluid	2
+LABFLCR	FREE LIGHT CHAIN RATIO	Free Light Chain Ratio	988
+LABFLCR	FREE LIGHT CHAIN RATIO		52
+LABFLDB	FLUID CULTURE / BB GRAM STAIN		1382
+LABFLDC	FLUID CULTURE AND GRAM STAIN	Fluid Culture and Gram Stain	12568
+LABFLDC	FLUID CULTURE AND GRAM STAIN		7790
+LABFLDC	FLUID CULTURE AND GRAM STAIN	Fluid Culture and Gram stain	3758
+LABFLDC	FLUID CULTURE AND GRAM STAIN	Fluid Culture and Gram Stain, Pleural Fluid	810
+LABFLDC	FLUID CULTURE AND GRAM STAIN	Fluid Culture and Gram Stain, Peritoneal Fluid	627
+LABFLDC	FLUID CULTURE AND GRAM STAIN	Fluid Culture and Gram Stain, Pericardial Fluid	82
+LABFLDC	FLUID CULTURE AND GRAM STAIN	Fluid Culture	6
+LABFLDH	LDH TOTAL, FLUID		1820
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Pleural Fluid	1301
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid	1279
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Pleural FL	1247
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Peritoneal Fluid	839
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Ascites Fluid	511
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Pericardial Fluid	80
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Other	36
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Synovial Fluid	36
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Jackson Pratt	6
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Amniotic Fluid	4
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Peritoneal Lavage	4
+LABFLDH	LDH TOTAL, FLUID	LDH Total, Fluid Bronchaolveolar Lavage (BAL)	2
+LABFLDS	FLUID STERILITY TEST		6
+LABFLDS	FLUID STERILITY TEST	Fluid Sterility Test	2
+LABFLGL	GLUCOSE, FLUID	Glucose, Fluid	3203
+LABFLGL	GLUCOSE, FLUID		1048
+LABFLGL	GLUCOSE, FLUID	Glucose, Pleural Fluid	172
+LABFLGL	GLUCOSE, FLUID	Glucose, Peritoneal  Fluid	70
+LABFLGL	GLUCOSE, FLUID	Glucose, Synovial Fluid	12
+LABFLGL	GLUCOSE, FLUID	Glucose, Pericardial  Fluid	10
+LABFLK	POTASSIUM, FLUID	Potassium, Fluid	16
+LABFLK	POTASSIUM, FLUID		12
+LABFLK	POTASSIUM, FLUID	Potassium, Pleural Fluid	4
+LABFLK	POTASSIUM, FLUID	Potassium, Synovial Fluid	2
+LABFLL	FSH	FSH	395
+LABFLL	FSH		28
+LABFLNA	SODIUM, FLUID		16
+LABFLNA	SODIUM, FLUID	Sodium, Fluid	15
+LABFLNA	SODIUM, FLUID	Sodium, Pleural Fluid	4
+LABFLNA	SODIUM, FLUID	Sodium, Synovial Fluid	2
+LABFLUAPC	FLU A PCR		81
+LABFLUAPC	FLU A PCR	FLU A PCR	57
+LABFLUAPC 	FLU A PCR		543
+LABFLUAPC 	FLU A PCR	FLU A PCR	224
+LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1		1062
+LABFLUAPX	FLU A PCR REFLEX TO 2009 H1N1	FLU A PCR Reflex to 2009 H1N1	433
+LABFLUOX	FLUOXETINE AND NORFLUOXETINE	Fluoxetine And Norfluoxetine	4
+LABFMYAB	MYCOPLASMA IGG/IGM AB	Mycoplasma IgG/IgM AB	534
+LABFMYAB	MYCOPLASMA IGG/IGM AB		26
+LABFNKSEN	NATURAL KILLER CELLS ENUMERATION	Natural Killer Cells Enumeration	2
+LABFOL	FOLIC ACID	Folic Acid	8179
+LABFOL	FOLIC ACID		800
+LABFOL	FOLIC ACID	Folic Acid (Folate) (LABFOL)	2
+LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME	PARVOVIRUS (B19) DNA	448
+LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME	Parvovirus B19 PCR Qualitative Realtime	120
+LABFPARVD	PARVOVIRUS B19 PCR QUALITATIVE REALTIME		60
+LABFPARVO	PARVOVIRUS IGG / IGM AB	Parvovirus IgG/IgM Ab	95
+LABFPARVO	PARVOVIRUS IGG / IGM AB	Parvovirus AB, IgG / IgM	2
+LABFPLTS	PLATELET COUNT, FLUID		4
+LABFPLTS	PLATELET COUNT, FLUID	Platelet Count, Fluid	2
+LABFPOLIO	POLIOVIRUS 1-3 AB, SERUM	Poliovirus 1-3 Ab, Serum	10
+LABFQFVR	COXIELLA BURNETII AB	Coxiella Burnetii Ab	418
+LABFQFVR	COXIELLA BURNETII AB		14
+LABFRAB	FRANCISELLA TULARENSIS AB	Francisella Tularensis Ab	61
+LABFRAB	FRANCISELLA TULARENSIS AB		2
+LABFRAGX	FRAGILE X	Fragile X	6
+LABFRPSA	FREE PSA	FREE PSA	6
+LABFSFTA	TREPONEMA PALLIDUM AB		4
+LABFSFTA	TREPONEMA PALLIDUM AB	Treponema Pallidum Ab	2
+LABFSPG	SPECIFIC GRAVITY, FLUID	Specific Gravity, Fluid	20
+LABFSPG	SPECIFIC GRAVITY, FLUID		2
+LABFSYN	FUNGAL SYNERGY		18
+LABFSYN	FUNGAL SYNERGY	Fungal Synergy	6
+LABFT1114	"T(11	14) BY PCR, FLUID"	T(11:14) by PCR, Pleural Fluid	2
+LABFT1517	"T(15	17) PML/RAR, FLUID"	"T(15	17) PML/RAR, Fluid"	4
+LABFT1517	"T(15	17) PML/RAR, FLUID"		2
+LABFT3DIA	T3, FREE, TRACER DIALYSIS	T3, FREE, TRACER DIALYSIS	6
+LABFT4	T4, FREE	T4, Free	19344
+LABFT4	T4, FREE		2636
+LABFT4	T4, FREE	T4, Free (LABT4)	9
+LABFT4ED	FREE T4 BY EQUILIBRIUM DIALYSIS	Free T4 by Equilibrium Dialysis	44
+LABFTBLYM	T SUBSETS AND B LYMPHOCYTES, FLUID		70
+LABFTBLYM	T SUBSETS AND B LYMPHOCYTES, FLUID	T Subsets and B Lymphocytes, Fluid	56
+LABFTBLYM	T SUBSETS AND B LYMPHOCYTES, FLUID	T Subsets and B Lymphocytes, Apheresis Fluid	18
+LABFTBLYM	T SUBSETS AND B LYMPHOCYTES, FLUID	T Subsets and B Lymphs, Pericardial Fluid	4
+LABFTBLYM	T SUBSETS AND B LYMPHOCYTES, FLUID	T and B lymphocyte subset analysis - Other (specify)	2
+LABFTBLYM	T SUBSETS AND B LYMPHOCYTES, FLUID	T Subsets and B Lymphs, Pleural Fluid	2
+LABFTCLO	T-CELL CLONALITY PCR, FLUID	T-Cell Clonality PCR, Fluid	6
+LABFTCLO	T-CELL CLONALITY PCR, FLUID		6
+LABFTCLO	T-CELL CLONALITY PCR, FLUID	T-Cell Clonality, PCR, Pleural Fluid	4
+LABFTP	PROTEIN TOTAL, FLUID		2062
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid	1465
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Pleural FL	1253
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Pleural Fluid	1234
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Peritoneal FL	1079
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Peritoneal Fluid	650
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Ascites Fluid	348
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Other	90
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Pericardial Fluid	80
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Synovial Fluid	78
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Jackson Pratt	6
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Bronchaolveolar Lavage (BAL)	2
+LABFTP	PROTEIN TOTAL, FLUID	Protein Total, Fluid Peritoneal Lavage	2
+LABFTRIGA	TRIGLYCERIDES, NON-PLEURAL	Triglycerides, Non-Pleural	478
+LABFTRIGA	TRIGLYCERIDES, NON-PLEURAL		176
+LABFTRIGA	TRIGLYCERIDES, NON-PLEURAL	Triglycerides, (non-pleural)	1
+LABFTRPL	TRIGLYCERIDES, PLEURAL	Triglycerides, Pleural	1438
+LABFTRPL	TRIGLYCERIDES, PLEURAL		428
+LABFTRPL	TRIGLYCERIDES, PLEURAL	Triglycerides, Pleural Fluid	198
+LABFTRPL	TRIGLYCERIDES, PLEURAL	Triglycerides, Peritoneal Fluid	34
+LABFTRPL	TRIGLYCERIDES, PLEURAL	Triglycerides, Pericardial Fluid	6
+LABFUNAB	FUNGAL DISEASE AB PANEL	Fungal Disease Ab Panel	2
+LABFUNDAP	FUNGAL DISEASE AB PANEL	Fungal Disease Ab Panel	14
+LABFUNIDI	FUNGI ID BY SEQUENCING, ISOLATE		16
+LABFUNIDI	FUNGAL ID BY SEQUENCING FROM ISOLATE	FUNGAL ID BY SEQUENCING FROM ISOLATE	8
+LABFUNIDI	FUNGI ID BY SEQUENCING, ISOLATE	Fungi ID by Sequencing, Isolate	2
+LABFUNIDS	FUNGAL ID BY SEQUENCING FROM SPECIMEN	FUNGAL ID BY SEQUENCING FROM SPECIMEN	66
+LABFUNIDS	FUNGAL ID BY SEQUENCING	FUNGAL ID BY SEQUENCING	16
+LABFUNIDS	FUNGAL ID BY SEQUENCING	Fungal ID by Sequencing	8
+LABFURICA	URIC ACID, FLUID	Uric Acid, Synovial  Fluid	44
+LABFURICA	URIC ACID, FLUID	Uric Acid, Fluid	24
+LABFURICA	URIC ACID, FLUID		22
+LABFURICA	URIC ACID, FLUID	Uric Acid, Pleural Fluid	10
+LABFURICA	URIC ACID, FLUID	Uric Acid, Peritoneal Fluid	6
+LABFURICA	URIC ACID, FLUID	Uric Acid, Pericardial Fluid	2
+LABFVARZ	VARICELLA ZOSTER VIR. DNA,QUAL	Varicella Zoster Virus DNA	847
+LABFVARZ	VARICELLA ZOSTER VIR. DNA,QUAL		482
+LABFVARZ	VARICELLA ZOSTER VIR. DNA,QUAL	VARICELLA ZOSTER VIRUS DNA	108
+LABG6PD	G6PD SCREEN	G6PD Screen	209
+LABG6PD	G6PD SCREEN		8
+LABG6PDQT	G6PD QUANTITATIVE	G6PD Quantitative	57
+LABG6PDQT	G6PD QUANTITATIVE		4
+LABGASBLD	OCCULT BLOOD GASTRIC	Occult Blood Gastric	739
+LABGASBLD	OCCULT BLOOD GASTRIC		186
+LABGASBLD	OCCULT BLOOD GASTRIC	Occult Blood, Gastric	2
+LABGASPH	PH, GASTRIC	pH, Gastric	32
+LABGASPH	PH, GASTRIC	Ph, Gastric	15
+LABGASPH	PH, GASTRIC		12
+LABGBMIFA	GBM IGA AND IGG AB	GBM IgA And IgG Ab	107
+LABGBMIFA	GBM IGA AND IGG AB		6
+LABGBMIGA	GBM AB, IGA	GBM Ab, IgA	2
+LABGBMIGG	GBM IGM, ELISA	GBM IgM, ELISA	1
+LABGCCR2	GC MOLECULAR DETECTION	GC Molecular Detection	508
+LABGCCR2	GC MOLECULAR DETECTION		56
+LABGDGP	IGG ANTI DGP	IgG Anti DGP	86
+LABGDGP	IGG ANTI DGP		8
+LABGDMC	GLUCOSE GDM CONFIRMATION	Glucose GDM Confirmation	2
+LABGDMSCR	GLUCOSE GDM SCREEN	Glucose GDM Screen	8
+LABGDMSCR	GLUCOSE GDM SCREEN		2
+LABGDMTOL	GDM GLUCOSE TOLERANCE	GDM Glucose Tolerance	2
+LABGENC	GENITAL CULTURE	Genital Culture	120
+LABGENC	GENITAL CULTURE		70
+LABGGT	GAMMA - GLUTAMYL TRANSFERASE	Gamma-Glutamyl Transferase	5371
+LABGGT	GAMMA - GLUTAMYL TRANSFERASE	Gamma-Glutamyl Trans	1539
+LABGGT	GAMMA - GLUTAMYL TRANSFERASE		810
+LABGGT	GAMMA - GLUTAMYL TRANSFERASE	Int Gamma-Glutamyl Transferase Every 4 Weeks	2
+LABGH	GROWTH HORMONE	Growth Hormone	178
+LABGH	GROWTH HORMONE		8
+LABGHBUR	GAMMA - HYDROXYBUTYRIC ACID, URINE	Gamma-Hydroxybutyric Acid, Urine	2
+LABGHBUR	GAMMA - HYDROXYBUTYRIC ACID, URINE		2
+LABGIARS	GIARDIA SCREEN	Giardia Screen	786
+LABGIARS	GIARDIA SCREEN	Giardia Screen, Stool	420
+LABGIARS	GIARDIA SCREEN		304
+LABGIARS	GIARDIA SCREEN	Giardia screen	26
+LABGLF	GLUCOSE FASTING	Glucose Fasting	875
+LABGLF	GLUCOSE FASTING		274
+LABGLF	GLUCOSE FASTING	Glucose, Fasting	2
+LABGLNF	GLUCOSE NON-FASTING, SERUM/PLASMA	Glucose Non-fasting, Serum/Plasma	1042
+LABGLNF	GLUCOSE NON-FASTING, SERUM/PLASMA		558
+LABGLNF	GLUCOSE NON-FASTING, SERUM/PLASMA	Glucose, Nonfasting, Serum/Plasma	99
+LABGLNF	GLUCOSE NON-FASTING, SERUM/PLASMA	Glucose, nonfasting, serum/plasma	42
+LABGLOB	GLOBULIN		704
+LABGLOB	GLOBULIN	Globulin	264
+LABGLWB	GLUCOSE, WHOLE BLOOD	Glucose, Whole Blood	124
+LABGLWB	GLUCOSE, WHOLE BLOOD		30
+LABGNTPOL	GENTAMICIN PEAK LEVEL	Gentamicin Peak Level	734
+LABGNTPOL	GENTAMICIN PEAK LEVEL		64
+LABGNTPRL	GENTAMICIN TROUGH LEVEL	Gentamicin Trough Level	1741
+LABGNTPRL	GENTAMICIN TROUGH LEVEL		180
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	Gentamicin Single Daily Dose, Serum	17
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL	Gentamicin Single Daily Dose Level	9
+LABGNTUNL	GENTAMICIN SINGLE DAILY DOSE LEVEL		6
+LABGRAM	GRAM STAIN	Gram Stain	14018
+LABGRAM	GRAM STAIN		11856
+LABGRAM	GRAM STAIN	Gram Stain, Expectorated Sputum	3316
+LABGRAM	GRAM STAIN	Gram Stain, Induced Sputum	2338
+LABGRAM	GRAM STAIN	Gram Stain, Tracheal Aspirate	2040
+LABGRAM	GRAM STAIN	STAT Gram Stain, CSF	524
+LABGRAM	GRAM STAIN	STAT Gram Stain, Pleural Fluid	499
+LABGRAM	GRAM STAIN	STAT Gram Stain, Peritoneal Fluid	444
+LABGRAM	GRAM STAIN	STAT Gram Stain, Synovial Fluid - Always order Joint Fluid Culture with this test	270
+LABGRAM	GRAM STAIN	STAT Gram Stain, Pericardial  Fluid	48
+LABGRAM	GRAM STAIN	STAT Gram Stain, CSF Shunt Fluid	26
+LABGRAM	GRAM STAIN	Gram stain	9
+LABGTOL	GLUCOSE TOLERANCE	Glucose Tolerance	10
+LABGTOL	GLUCOSE TOLERANCE		2
+LABGTT	TRADITIONAL GLUCOSE TOLERANCE	Traditional Glucose Tolerance	82
+LABGYN	PAP SMEAR	PAP Smear	64
+LABGYN	PAP SMEAR		44
+LABH1N1PC	2009 H1N1 PCR, H275Y MUTATION	2009 H1N1 PCR, H275Y Mutation	88
+LABH1N1PC	2009 H1N1 PCR, H275Y MUTATION		68
+LABH1N1PC	2009 H1N1 PCR, H274Y MUTATION		32
+LABH1N1PC	2009 H1N1 PCR, H274Y MUTATION	2009 H1N1 PCR, H274Y Mutation	6
+LABHA1C	HGB A1C W/ EST MEAN GLUCOSE	Hgb A1c w/ Est Mean Glucose	359
+LABHA1C	HGB A1C W/ EST MEAN GLUCOSE		4
+LABHAB	HEPATITIS B SURFACE AB	Hepatitis B Surface Ab	51
+LABHAB	HEPATITIS B SURFACE AB	Hepatitis B Surface AB	8
+LABHAB	HEPATITIS B SURFACE AB		7
+LABHAP	HAPTOGLOBIN	Haptoglobin	11428
+LABHAP	HAPTOGLOBIN		1308
+LABHATA	HEPATITIS A AB TOTAL	Hepatitis A Ab Total	49
+LABHATA	HEPATITIS A AB TOTAL		11
+LABHAV	HEPATITIS A IGM AB	Hepatitis A IgM Ab	44
+LABHAV	HEPATITIS A IGM AB	Hepatitis A AB IgM	4
+LABHAV	HEPATITIS A IGM AB		2
+LABHAVM	IGM ANTI-HAV	HEPATITIS A AB, IGM	827
+LABHAVM	IGM ANTI-HAV	IgM Anti-HAV	380
+LABHAVM	IGM ANTI-HAV		134
+LABHAVM	IGM ANTI-HAV	Hepatitis A AB IgM	54
+LABHAVT	ANTI-HAV	Hepatitis A AB, TOTAL	847
+LABHAVT	ANTI-HAV	Anti-HAV	523
+LABHAVT	ANTI-HAV		140
+LABHAVT	ANTI-HAV	Hepatitis A Ab, total	115
+LABHAVT	ANTI-HAV	Hepatitis A (Anti-HAV)	33
+LABHAVT	ANTI-HAV	Hepatitis A Ab Total	4
+LABHAVT	ANTI-HAV	Hepatitis A AB Total	4
+LABHAVT	HEPATITIS A AB TOTAL	Hepatitis A Ab Total	1
+LABHAVT	HEPATITIS A AB TOTAL		1
+LABHB	HEPATITIS B SURFACE AG	Hepatitis B Surface Ag	89
+LABHB	HEPATITIS B SURFACE AG		10
+LABHB	HEPATITIS B SURFACE AG	Hepatitis B Surface Antigen	8
+LABHBC	HEPATITIS B CORE AB IGM	Hepatitis B Core Ab IgM	6
+LABHBC	HEPATITIS B CORE AB IGM	Hepatitis B Core AB IgM	6
+LABHBC	HEPATITIS B CORE AB IGM		2
+LABHBCA	IGM ANTI-HBC	IgM Anti-HBC	370
+LABHBCA	IGM ANTI-HBC		106
+LABHBCA	IGM ANTI-HBC	HEPATITIS B CORE AB IGM	60
+LABHBCA	IGM ANTI-HBC	Hepatitis B Core AB IgM	56
+LABHBCA	IGM ANTI-HBC	Hepatitis B Core Ab IgM (IgM Anti-HBC)  [LABHBCA]	3
+LABHBCC	HEPATITIS B CORE TOTAL AB	Hepatitis B Core Total Ab	17
+LABHBCC	HEPATITIS B CORE TOTAL AB	Hepatitis B Core Total AB	3
+LABHBCC	HEPATITIS B CORE TOTAL AB		3
+LABHBCM	HEPATITIS B CORE / PRECORE MUTATION	Hepatitis B Core/Precore Mutation	22
+LABHBCT	ANTI-HBC	Anti-HBC	926
+LABHBCT	ANTI-HBC	Hepatitis B Core Total Ab	150
+LABHBCT	ANTI-HBC		140
+LABHBCT	ANTI-HBC	HEPATITIS B CORE TOTAL AB	133
+LABHBCT	ANTI-HBC	Hepatitis B Core Total AB	35
+LABHBCT	ANTI-HBC	Hepatitis B Core Total Antibody	6
+LABHBCT	ANTI-HBC	Hepatitis B core total Ab	4
+LABHBCT	ANTI-HBC	Hepatitis B Core Total Ab (Anti-HBC)  [LABHBCC]	3
+LABHBCT	HEPATITIS B CORE TOTAL AB	Hepatitis B Core Total AB	1
+LABHBCT	HEPATITIS B CORE TOTAL AB		1
+LABHBODY	HEINZ BODY PREP	Heinz Body Prep	40
+LABHBODY	HEINZ BODY PREP		4
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	Hepatitis B DNA PCR Quantitative	948
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE		58
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	Hep B PCR Quantitative	18
+LABHBPQ	HEPATITIS B PCR QUANTITATIVE	Hepatitis B PCR Quantitative	5
+LABHBPQD	HEPATITIS B PCR QUANTITATIVE DILUTED		16
+LABHBSAG	HBSAG	Hepatitis B Surface Ag	6384
+LABHBSAG	HBSAG	HBSAG	1755
+LABHBSAG	HBSAG		448
+LABHBSAG	HBSAG	HEPATITIS B SURFACE AG	280
+LABHBSAG	HBSAG	Hepatitis B Surface Antigen	121
+LABHBSAG	HBSAG	Hepatitis B Surface Antigen (HBSAG)	35
+LABHBSAG	HEPATITIS B SURFACE AG	Hepatitis B Surface Ag	4
+LABHBSAG	HBSAG	Hepatitis B Surface Ag   [LABHBSAG]	3
+LABHBSQL	ANTI-HBS QUAL	Hepatitis B Surface Ab	568
+LABHBSQL	ANTI-HBS QUAL	Anti-HBS Qual	416
+LABHBSQL	ANTI-HBS QUAL		164
+LABHBSQL	ANTI-HBS QUAL	HEPATITIS B SURFACE AB	139
+LABHBSQL	ANTI-HBS QUAL	Hepatitis B Surface AB	111
+LABHBSQL	HEPATITIS B SURFACE AB	Hepatitis B Surface Ab	4
+LABHBSQL	ANTI-HBS QUAL	Hepatitis B Surface Ab (Anti-HBS Qual)  [LABHBSQL]	3
+LABHBSQL	HEPATITIS B SURFACE AB		3
+LABHBSQL	ANTI-HBS QUAL	Hepatitis B surface Ab	2
+LABHBSQT	HEP B SURF AB QUANT	HEP B SURF AB QUANT	517
+LABHBSQT	HEP B SURF AB QUANT	Hepatitis B Surface Ab Quantitative	43
+LABHBSQT	HEP B SURF AB QUANT	Hep B Surf AB Quant	24
+LABHBSQT	ANTI HBS QUANT	HEPATITIS B SURFACE AB (M)	4
+LABHBSQT	HEP B SURF AB QUANT		4
+LABHBSQT	ANTI HBS QUANT	ANTI HBS QUANT	2
+LABHBSQT	HEPATITIS B SURFACE AB QUANTITATIVE	Hepatitis B Surface Ab Quantitative	1
+LABHBV	HBV GENOTYPE: S AND POL GENE	HBV Genotype: S And Pol Gene	2
+LABHCHGA	HEMOCHROMATOSIS GENOTYPING ANALYSIS	Hemochromatosis Genotyping Analysis	36
+LABHCLOTI	HOLD CLOT		1228
+LABHCLOTI	HOLD CLOT	Hold Clot	576
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HEPATITIS C PCR QUANTITATIVE	1052
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	Hep C RNA PCR Quant	665
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE		58
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	Hepatitis C PCR Quantitative	35
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	Hep C PCR Quantitative	4
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	Hepatitis C RNA PCR Quantitative	2
+LABHCPQ	HEPATITIS C RNA PCR QUANTITATIVE	HEPATITIS C RNA PCR QUANTITATIVE	2
+LABHCTX	HEMATOCRIT	Hematocrit	70865
+LABHCTX	HEMATOCRIT	Hematocrit 8 hours after end of surgery	2879
+LABHCTX	HEMATOCRIT		2858
+LABHCTX	HEMATOCRIT	Hematocrit one time in PACU	916
+LABHCTX	HEMATOCRIT	Hematocrit 4 hours after arrival to ICU	529
+LABHCTX	HEMATOCRIT	Hematocrit in AM	406
+LABHCTX	HEMATOCRIT	NM RBC Volume Study	308
+LABHCTX	HEMATOCRIT	Hematocrit one time	57
+LABHCVA	HEPATITIS C AB IGG	Hepatitis C AB IGG	2453
+LABHCVA	HEPATITIS C AB IGG	Hepatitis C Ab EIA	763
+LABHCVA	ANTI-HCV	Anti-HCV	638
+LABHCVA	ANTI-HCV	HEPATITIS C AB EIA	615
+LABHCVA	ANTI-HCV	Hepatitis C Ab EIA	403
+LABHCVA	ANTI-HCV		208
+LABHCVA	HEPATITIS C AB IGG		126
+LABHCVA	HEPATITIS C AB IGG	Hepatitis C AB EIA	55
+LABHCVA	ANTI-HCV	Hepatitis C AB EIA	50
+LABHCVA	HEPATITIS C AB IGG	Hepatitis C AB (Anti-HCV)	34
+LABHCVA	ANTI-HCV	Hepatitis C Ab EIA  (Anti-HCV)  [LABHEPC]	3
+LABHCVA	ANTI-HCV	Hepatitis C AB (Anti-HCV)	1
+LABHCVA	HEPATITIS C AB EIA	Hepatitis C Ab EIA	1
+LABHCVA	HEPATITIS C AB EIA	Hepatitis C AB EIA	1
+LABHCVA	HEPATITIS C AB EIA		1
+LABHCVA	HEPATITIS C AB IGG	HEPATITIS C AB EIA	1
+LABHCVG	HEPATITIS C VIRUS GENOTYPING	Hepatitis C Virus Genotyping	23
+LABHCVGE	HEPATITIS C VIRUS GENOTYPING	Hepatitis C Virus Genotyping	30
+LABHCVGE	HEPATITIS C VIRUS GENOTYPING		2
+LABHCVGEN	HEPATITIS C (HCV) GENOTYPE	HEPATITIS C (HCV) GENOTYPE	210
+LABHCVGEN	HEPATITIS C (HCV) GENOTYPE		10
+LABHCVGTP	HEPATITIS C (HCV) GENOTYPE	HEPATITIS C (HCV) GENOTYPE	4
+LABHCVPCX	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	HEPATITIS C (HCV) PCR WITH REFLEX TO GENOTYPING	4
+LABHDL	HDL, SERUM	HDL, Serum	102
+LABHDL	HDL, SERUM		16
+LABHDLGGE	HDL GRADIENT GEL ELECTROPHORESIS	HDL Gradient Gel Electrophoresis	4
+LABHDQ	HDL CHOLESTEROL FRACTIONATION	HDL Cholesterol Fractionation	12
+LABHDQ	HDL CHOLESTEROL FRACTIONATION		4
+LABHEAB	HEPATITIS B E AB	Hepatitis B E Ab	3
+LABHEAB	HEPATITIS B E AB	Hepatitis B E AB	3
+LABHEAG	HEPATITIS B E AG	Hepatitis B E Ag	6
+LABHEAG	HEPATITIS B E AG	Hepatitis B E Antigen	3
+LABHELAG	HELICOBACTER PYLORI AG, STOOL	Helicobacter Pylori Ag, Stool	2232
+LABHELAG	HELICOBACTER PYLORI AG, STOOL		376
+LABHELI	HELICOBACTER PYLORI AB	Helicobacter Pylori Ab	2172
+LABHELI	HELICOBACTER PYLORI AB		80
+LABHELPM	HELICOBACTER PYLORI IGM AB	Helicobacter Pylori IgM Ab	4
+LABHEPAR	HEPARIN ACTIVITY LEVEL	Heparin Activity Level	35597
+LABHEPAR	HEPARIN, UNFRACTIONATED, ANTI XA	HEPARIN, UNFRACTIONATED, ANTI XA	18955
+LABHEPAR	HEPARIN ACTIVITY LEVEL		16114
+LABHEPAR	HEPARIN, UNFRACTIONATED, ANTI XA	Heparin Activity Level	10090
+LABHEPAR	HEPARIN, UNFRACTIONATED, ANTI XA	Unfractionated Heparin	5901
+LABHEPAR	HEPARIN ACTIVITY LEVEL	HEPARIN, UNFRACTIONATED, ANTI XA	56
+LABHEPAR	HEPARIN, UNFRACTIONATED, ANTI XA	UNFRACTIONATED HEPARIN	56
+LABHEPAR	UNFRACTIONATED HEPARIN	HEPARIN, UNFRACTIONATED, ANTI XA	52
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML	Heparin Activity Level	32
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML		32
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY	Heparin Activity Level	23
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML	Heparin Conc, AntiXA u/mL	22
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY		20
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY	Heparin Conc, AntiXA u/mL	3
+LABHEPAR	HEPARIN ACTIVITY LEVEL	Heparin Conc, AntiXA u/mL	2
+LABHEPAR	HEPARIN CONC, ANTIXA U/ML, HEPARIN ACTIVITY	Heparin Conc, AntiXA u/mL, Heparin Activity Level	2
+LABHEPAT	HEPATIC PROFILE		2
+LABHEPBG	HEPATITIS B GENOTYPE	Hepatitis B Genotype	21
+LABHEPC	HEPATITIS C AB EIA	Hepatitis C Ab EIA	131
+LABHEPC	HEPATITIS C AB EIA		9
+LABHEPC	HEPATITIS C AB EIA	Hepatitis C AB EIA	7
+LABHEPDAB	HEPATITIS D VIRUS AB TOTAL	Hepatitis D Virus Ab Total	101
+LABHEPDAB	HEPATITIS D VIRUS AB TOTAL		20
+LABHEVG	HEPATITIS E AB IGG PANEL	Hepatitis E Ab IgG Panel	16
+LABHEVG	HEPATITIS E AB IGG PANEL		16
+LABHEVM	HEPATITIS E AB IGM PANEL	Hepatitis E Ab IgM Panel	22
+LABHEVM	HEPATITIS E AB IGM PANEL		22
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic Function Panel A	92829
+LABHFP	HEPATIC FUNCTION PANEL A		9304
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic Function Panel A in AM	6126
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic function panel A	403
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic function Panel A	34
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic Function Panel A (LABHFP)	30
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic Function Panel	9
+LABHFP	HEPATIC FUNCTION PANEL A	HEPATIC FUNCTION PANEL A IN 1 MONTH	9
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic Function Panel 	1
+LABHFP	HEPATIC FUNCTION PANEL A	Hepatic Function Panel A (alb, bilit T & D, alk phos, TP, ALT, AST)	1
+LABHGBFQ	HGB F QUANTITATIVE	Hgb F Quantitative	12
+LABHGBFQ	HGB F QUANTITATIVE		2
+LABHGBP	HEMOGLOBIN, PLASMA	Hemoglobin, Plasma	794
+LABHGBP	HEMOGLOBIN, PLASMA		207
+LABHGBS	HEMOGLOBIN, SERUM	Hemoglobin, Serum	53
+LABHGBS	HEMOGLOBIN, SERUM		8
+LABHGBS	HEMOGLOBIN, SERUM	Hemoglobin Serum	2
+LABHGBSQ	HGB S QUANTITATIVE	Hgb S Quantitative	137
+LABHGBSQ	HGB S QUANTITATIVE		18
+LABHGBX	HEMOGLOBIN	Hemoglobin	5465
+LABHGBX	HEMOGLOBIN		952
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	Hgb Quantitative/Fractionated HPLC	278
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC		34
+LABHGHPLC	HGB QUANTITATIVE / FRACTIONATED HPLC	Hgb quant/fract, HPLC	8
+LABHHV6QL	HHV-6 DNA PCR QUAL	HHV-6 DNA PCR QUAL	350
+LABHHV6QL	HHV-6 DNA PCR QUAL	Herpes Viral 6 DNA Qualitative	20
+LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE	HERPES VIRAL 6 DNA Qualitative	8
+LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE	HERPES VIRAL 6 DNA QUANTITATIVE	2
+LABHHV6QL	HERPES VIRAL 6 DNA QUALITATIVE		1
+LABHHV6QT	HHV-6 PCR QUANT	HHV-6 PCR Quant	308
+LABHHV6QT	HHV-6 PCR QUANT	HHV-6 PCR QUANT	300
+LABHHV6QT	HHV-6 PCR QUANT	Herpes Virus 6 DNA, Quant	256
+LABHHV6QT	HERPES VIRUS 6 DNA, QUANT	Herpes Virus 6 DNA, Quant	61
+LABHHV6QT	HERPES VIRUS 6 DNA, QUANT		3
+LABHIAB	HEPARIN INDUCED AB	Heparin Induced Ab	922
+LABHIAB	HEPARIN INDUCED AB		98
+LABHIAB	HEPARIN INDUCED AB	Heparin-Induced Antibody	3
+LABHIBCV	DONOR HIV1 / HBV / HCV NAT		58
+LABHIBCV	DONOR, HIV-1/HCV/HBV NAT TESTING	DONOR, HIV-1/HCV/HBV NAT testing	28
+LABHIBCV	DONOR HIV1 / HBV / HCV NAT	Donor HIV1/HBV/HCV NAT	6
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION	Heparin Induced Platelet Aggregation	3037
+LABHIPA	HEPARIN INDUCED PLATELET AGGREGATION		172
+LABHITAB	HEPARIN (HIT) AB	HEPARIN PLATELET FACTOR 4 AB	3395
+LABHITAB	HEPARIN PLATELET FACTOR 4 AB	HEPARIN PLATELET FACTOR 4 AB	1135
+LABHITAB	HEPARIN (HIT) AB	HEPARIN (HIT) AB	218
+LABHITAB	HEPARIN PLATELET FACTOR 4 AB	Heparin Platelet Factor 4 AB	206
+LABHITAB	HEPARIN PLATELET FACTOR 4 AB		130
+LABHITAB	HEPARIN (HIT) AB		120
+LABHITIA	HIT FUNCTIONAL ASSAY	HIT Functional Assay	286
+LABHITPNL	HEPARIN INDUCED THROMBOCYTOPENIA PANEL	Heparin Induced Thrombocytopenia Panel	115
+LABHIVA2	HIV - 2 AB		2
+LABHIVAB	HIV (TYPE 1 AND TYPE 2) ABY	HIV-1 And 2 Ab Screening	120
+LABHIVAB	HIV (TYPE 1 AND TYPE 2) ABY	HIV (Type 1 and Type 2) ABY	66
+LABHIVAB	HIV (TYPE 1 AND TYPE 2) ABY		23
+LABHIVAB	HIV (TYPE 1 AND TYPE 2) ABY	HIV 1 And 2 Ab Screening	18
+LABHIVDAG	HIV - 1 DIRECT AG (ICD)	HIV-1 Direct Ag (ICD)	50
+LABHIVDAG	HIV - 1 DIRECT AG (ICD)		6
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE	HIV-1 Ag Donor Screen Qualitative	109
+LABHIVDS	HIV - 1 AG DONOR SCREEN QUALITATIVE		14
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIV Quantitative PCR	490
+LABHIVQ2	HIV QUANTITATIVE PCR	HIV Quantitative PCR	439
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIV-1 RNA PCR Quantitative	437
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE		24
+LABHIVQ2	HIV QUANTITATIVE PCR		22
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIV quantitative PCR	17
+LABHIVQ2	HIV-1 RNA PCR QUANTITATIVE	HIV Quantitative PCR  [LABHIVQ2]	2
+LABHIVQ2	HIV QUANTITATIVE PCR	HIV quantitative PCR	2
+LABHIVWBL	ANTI-HIV	HIV-1 And 2 Ab Screening	3920
+LABHIVWBL	ANTI-HIV	HIV 1 AND 2 AB SCREENING	3554
+LABHIVWBL	ANTI-HIV	Anti-HIV	1495
+LABHIVWBL	ANTI-HIV	HIV 1 And 2 Ab Screening	859
+LABHIVWBL	ANTI-HIV		528
+LABHIVWBL	ANTI-HIV	HIV 1 and 2 Ab screening	330
+LABHIVWBL	ANTI-HIV	HIV 1 and 2 Ab Screening	129
+LABHIVWBL	ANTI-HIV	HIV (Anti-HIV)	26
+LABHIVWBL	ANTI-HIV	HIV (Type 1 and Type 2) ABY	8
+LABHIVWBL	ANTI-HIV	Anti-HIV  [HIVAB]	3
+LABHIVWBL	ANTI-HIV	HIV (TYPE 1 AND TYPE 2) ABY	2
+LABHIVWBL	ANTI-HIV	HIV (Anti-HIV) 	1
+LABHIVWBL	HIV (TYPE 1 AND TYPE 2) ABY	HIV-1 And 2 Ab Screening	1
+LABHIVWBL	HIV (TYPE 1 AND TYPE 2) ABY		1
+LABHMCY	HOMOCYSTEINE TOTAL, PLASMA	Homocysteine Total, Plasma	839
+LABHMCY	HOMOCYSTEINE TOTAL, PLASMA	Homocysteine, Plac Total	317
+LABHMCY	HOMOCYSTEINE TOTAL, PLASMA		76
+LABHMCY	HOMOCYSTEINE TOTAL, PLASMA	Homocysteine, plasma total	29
+LABHMET	HEAVY METALS SCREEN, BLOOD	Heavy Metals Screen, Blood	87
+LABHPVHR2	HPV DNA PROBE HIGH RISK	HPV DNA Probe High Risk	38
+LABHPVHR2	HPV DNA PROBE HIGH RISK		2
+LABHPVHRT	HPV HIGH RISK-16/18 REFLEX	HPV High Risk-16/18 reflex	4
+LABHPVPCR	HPV 16/18 TYPING		4
+LABHPVPCR	HPV 16/18 TYPING	HPV 16/18 TYPING	2
+LABHSAB	HSV VIRAL AB	HERPES SIMPLEX AB IGG 1 AND 2	179
+LABHSAB	HSV VIRAL AB	Herpes Simplex Ab IgG 1 and 2	73
+LABHSAB	HSV VIRAL AB		56
+LABHSAB	HSV VIRAL AB	HSV Viral Ab	49
+LABHSAB	HSV VIRAL AB	Herpes Simplex Virus (HSV Viral Ab)	23
+LABHSAB	HSV VIRAL AB	HSV 1 & 2 IgG	2
+LABHSCRP	HIGH SENSITIVITY C-REACTIVE PROTEIN	High Sensitivity C-Reactive Protein	350
+LABHSCRP	HIGH SENSITIVITY C-REACTIVE PROTEIN		38
+LABHSCRP	HIGH SENSITIVITY C-REACTIVE PROTEIN	High Sensitivity C-Reactive Protein (LABHSCRP)	27
+LABHSCRP	HIGH SENSITIVITY C-REACTIVE PROTEIN	High Sensitivity CRP	4
+LABHSCUL	HERPES SIMPLEX CULTURE ONLY	Herpes Simplex Culture Only	208
+LABHSCUL	HERPES SIMPLEX CULTURE ONLY		60
+LABHSP70	ANTI - 68 KD (HSP - 70) ANTIBODIES	Anti-68 KD (HSP-70) Antibodies	2
+LABHSVCSF	HSV DNA PCR QUAL, CSF	HSV DNA PCR QUAL, CSF	762
+LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG	HERPES ELECT HSV 1/2, IGG	53
+LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG	Herpeselect HSV 1 And 2 Ab IgG	29
+LABHSVG	HSV 1/2 TYPE-SPECIFIC IGG		8
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSV 1 AND 2, IGM AND IGG	597
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSV-1 And 2 IgM And IgG Ab	54
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG		22
+LABHSVGM	HSV 1/2 IGM&TYPE-SPECIFIC IGG	HSV 1/2 IGM&TYPE-SPECIFIC IGG	2
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	HERPES SIMPLEX VIRUS IGG PANEL	277
+LABHSVIGG	HERPES SIMPLEX VIRUS IGG PANEL	Herpes Simplex Virus IGG Panel	6
+LABHSVM	HSV 1 AND 2 IGM	HSV 1 And 2 IgM	77
+LABHSVM	HSV 1 AND 2 IGM	HSV 1 and 2, IgM	50
+LABHSVM	HSV 1 AND 2 IGM		12
+LABHSVM	HSV 1 AND 2 IGM	HSV 1 And 2, IgM	2
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	HERPES SIMPLEX VIRUS AB PANEL	313
+LABHSVPAN	HERPES SIMPLEX VIRUS AB PANEL	Herpes Simplex Virus AB Panel	6
+LABHSVPCR	HERPES SIMPLEX PCR, CSF	Herpes Simplex Virus PCR, CSF	620
+LABHSVPCR	HERPES SIMPLEX PCR, CSF		484
+LABHSVPCR	HERPES SIMPLEX PCR, CSF	Herpes Simplex Virus PCR	388
+LABHSVPCR	HERPES SIMPLEX PCR, CSF	HERPES SIMPLEX VIRUS PCR	351
+LABHSVQL	HSV DNA PCR QUAL, NON LESION	HSV DNA PCR Qual, Non Lesion	394
+LABHSVQLP	HSV DNA PCR QUAL, PLASMA	HSV DNA PCR QUAL, PLASMA	371
+LABHSVZL	HSV/VZV PCR QUAL, LESION	HSV/VZV PCR Qual, Lesion	1264
+LABHSVZL	HSV/VZV PCR QUAL, LESION		10
+LABHTLVB	 HTLV-1/2 AB WESTERN BLOT	HTLV-I/II ANTIBODY	16
+LABHTLVB	 HTLV-1/2 AB WESTERN BLOT	HTLV-1/2 AB Westerm Blot	14
+LABHTLVB	 HTLV-1/2 AB WESTERN BLOT	HTLV-I/II Antibody	10
+LABHTLVS	HTLV-1 AB SCREEN	HTLV-1 Ab Screen	22
+LABHTLVS	HTLV-1 AB SCREEN		10
+LABHUNDNA	HUNTINGTON\'S DNA	Huntington\'s DNA	6
+LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	HERPES VIRAL 6 DNA QUANTITATIVE	900
+LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE		431
+LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	HERPES VIRAL 6 DNA Qualitative	359
+LABHV6DNA	HERPES VIRAL 6 DNA QUALITATIVE	Herpes Viral 6 DNA Qualitative	34
+LABHYPP	HYPERSENSITIVITY PNEUMONITIS SCREEN	Hypersensitivity Pneumonitis Screen	8
+LABIBD	ANCA	ANCA	226
+LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX	ANCA Inflammatory Bowel Disease With Reflex	192
+LABIBD	ANCA		12
+LABIBD	ANCA IBD WITH REFLEX	ANCA IBD with reflex	12
+LABIBD	ANCA INFLAMMATORY BOWEL DISEASE WITH REFLEX		12
+LABIBD	ANCA IBD WITH REFLEX	ANCA IBD WITH REFLEX	8
+LABIBD	ANCA	ANCA inflammatory bowel disease with reflex	2
+LABIBD	ANCA IBD WITH REFLEX	ANCA inflammatory bowel disease with reflex	2
+LABIBDDI	IBD DIAGNOSTIC SYSTEM		2
+LABIBIL	BILIRUBIN INDIRECT, SERUM		2448
+LABIBIL	BILIRUBIN INDIRECT, SERUM	Bilirubin Indirect, Serum	966
+LABICTO	ICTOTEST		4
+LABICTO	ICTOTEST	Ictotest	2
+LABIDWEE	WORM ID	ID Worm / Ectoparasite	3
+LABIFEDE	IMMUNOFIXATION IGD AND IGE, SERUM	Immunofixation IgD and IgE, Serum	32
+LABIFEDE	IMMUNOFIXATION IGD AND IGE, SERUM		2
+LABIGA	IGA, SERUM	IgA, Serum	488
+LABIGA	IGA, SERUM		28
+LABIGET	TOTAL IGE	Total IgE	1772
+LABIGET	TOTAL IGE		70
+LABIGF	INSULIN-LIKE GROWTH FACTOR 1	Insulin-like Growth Factor 1	278
+LABIGF	INSULIN-LIKE GROWTH FACTOR 1		20
+LABIGG	IGG, SERUM	IgG, Serum	3700
+LABIGG	IGG, SERUM		210
+LABIGG	IGG, SERUM	Serum IgG	2
+LABIGGS1	IGG SUBCLASS 1	IgG Subclass 1	14
+LABIGGS1	IGG SUBCLASS 1		2
+LABIGGS2	IGG  SUBCLASS 2	IgG  Subclass 2	14
+LABIGGS3	IGG  SUBCLASS 3	IgG  Subclass 3	14
+LABIGGS4	IGG  SUBCLASS 4	IgG  Subclass 4	282
+LABIGGS4	IGG  SUBCLASS 4		8
+LABIGGSUB	IGG  SUBCLASSES	IgG  Subclasses	180
+LABIGGSUB	IGG  SUBCLASSES		8
+LABIGGSUB	IGG  SUBCLASSES	CSF IgG Subclasses	2
+LABIGM	IGM, SERUM	IgM, Serum	377
+LABIGM	IGM, SERUM		28
+LABIGP	IG PANEL, SERUM	Ig Panel, Serum	1117
+LABIGP	IG PANEL, SERUM		52
+LABIGP	IG PANEL, SERUM	IG PANEL, SERUM	1
+LABIMM1	IMMUNOLOGY PANEL 1	Immunology Panel 1	4
+LABIMM2	IMMUNOLOGY PANEL 2	Immunology Panel 2	4
+LABINFO	MICROBIOLOGY INFORMATION ONLY		6
+LABINHA	INHIBIN A DIMER	Inhibin A Dimer	30
+LABINHA	INHIBIN A DIMER		4
+LABINSL	INSULIN UNSPECIFIED	Insulin Unspecified	184
+LABINSL	INSULIN UNSPECIFIED		98
+LABINSLF	INSULIN FASTING	Insulin Fasting	587
+LABINSLF	INSULIN FASTING		168
+LABIOPTH	INTRA OPERATIVE PTH		268
+LABIOPTH	INTRA OPERATIVE PTH	Intra Operative PTH	178
+LABISOBR	ISOLATOR BRUCELLA		4
+LABISOF	ISOLATOR FUNGUS	Isolator Fungus	708
+LABISOF	ISOLATOR FUNGUS	Fungus Culture - Isolator tube, Blood	609
+LABISOF	ISOLATOR FUNGUS		244
+LABISOHTI	ISOHEMAGGLUTININ TITER	Isohemagglutinin Titer	197
+LABISOHTI	ISOHEMAGGLUTININ TITER		2
+LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST	Isolator Culture Special Request	66
+LABISOSR	ISOLATOR CULTURE SPECIAL REQUEST		6
+LABITCH	FOOD ALLERGY SCREEN		2
+LABJAK2	JANUS KINASE 2 V617F MUTATION, BLOOD	Janus Kinase 2 V617F Mutation, Blood	257
+LABJAK2	JANUS KINASE 2 V617F MUTATION, BLOOD		32
+LABJO1ABY	ANTI-JO1 ANTIBODY	AutoAb To JO1	182
+LABJO1ABY	AUTOANTIBODIES TO JO1	AutoAb To JO1	7
+LABJO1ABY	ANTI-JO1 ANTIBODY		4
+LABJO1ABY	AUTOANTIBODIES TO JO1		1
+LABK	POTASSIUM, SERUM/PLASMA	Potassium, Serum/Plasma	127928
+LABK	POTASSIUM, SERUM/PLASMA		4726
+LABKBST	KLEIHAUER - BETKE STAIN		6
+LABKOHP	KOH / CALCOFLUOR	KOH Prep	504
+LABKOHP	KOH / CALCOFLUOR		212
+LABKOHP	KOH / CALCOFLUOR	KOH/Calcofluor	162
+LABKOHP	KOH / CALCOFLUOR	KOH/Calcoflour, Pericardial Fluid	28
+LABKOHP	KOH / CALCOFLUOR	KOH/Calcoflour, Bone Marrow	12
+LABKOHP	KOH / CALCOFLUOR	KOH/Calcoflour	2
+LABKRAS	KRAS MUTATION DETECTION		126
+LABKRAS	KRAS MUTATION DETECTION	KRAS Mutation Detection	71
+LABKWB	POTASSIUM, WHOLE BLOOD	Potassium, Whole Blood	446
+LABKWB	POTASSIUM, WHOLE BLOOD		16
+LABLAC	LACTIC ACID	Lactic Acid	128983
+LABLAC	LACTIC ACID		15446
+LABLAC	LACTIC ACID	Lactic acid	360
+LABLACWB	LACTATE, WHOLE BLOOD	LACTATE, WHOLE BLOOD	30833
+LABLACWB	LACTATE, WHOLE BLOOD	Lactate, Whole Blood	4017
+LABLACWB	LACTATE, WHOLE BLOOD	Lactate,Whole Blood	220
+LABLACWB	LACTATE, WHOLE BLOOD		34
+LABLAMOL	LAMOTRIGINE LEVEL	Lamotrigine Level	373
+LABLAMOL	LAMOTRIGINE LEVEL	Lamotrigine	191
+LABLAMOL	LAMOTRIGINE	Lamotrigine	8
+LABLAMOL	LAMOTRIGINE LEVEL		6
+LABLAMOL	LAMOTRIGINE		2
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN	Lymphocyte Ag Stimulation Screen	12
+LABLAS	LYMPHOCYTE AG STIMULATION SCREEN		2
+LABLCESRP	LABCORP - SEDIMENTATION RATE-WESTERGREN		1
+LABLCMETB7	LABCORP - BASIC METABOLIC PANEL (7)		3
+LABLCUR24	URIC ACID, 24 HOUR URINE (LABCORP)		1
+LABLDH	LDH TOTAL, SERUM / PLASMA	LDHTotal, Serum/Plasma	24400
+LABLDH	LDH TOTAL, SERUM / PLASMA	LDH Total,Serum/Plasma	18019
+LABLDH	LDH TOTAL, SERUM / PLASMA	LDH, total, serum/plasma	7177
+LABLDH	LDH TOTAL, SERUM / PLASMA		4488
+LABLDH	LDH TOTAL, SERUM / PLASMA	LDH Total, Serum/Plasma	2605
+LABLDH	LDH TOTAL, SERUM / PLASMA	LDH, Total, Serum/Plasma	2068
+LABLDH	LDH TOTAL, SERUM / PLASMA	LDH TOTAL, SERUM/PLASMA	240
+LABLDL	LDL CALCULATED		18
+LABLDL	LDL CALCULATED	LDL Calculated	8
+LABLDLD	DIRECT LDL CHOLESTEROL	Direct LDL Cholesterol	117
+LABLDLD	DIRECT LDL CHOLESTEROL		10
+LABLDLGGE	LDL GRADIENT GEL ELECTROPHORESIS	LDL Gradient Gel Electrophoresis	2
+LABLEGA	LEGIONELLA CULTURE, ACID		36
+LABLEGA	LEGIONELLA CULTURE, ACID	Legionella Culture, Acid	28
+LABLEGD	LEGIONELLA CULTURE, DIRECT	Legionella Culture, Dir, Pleural Fluid (>1ml required)	91
+LABLEGD	LEGIONELLA CULTURE, DIRECT		58
+LABLEGD	LEGIONELLA CULTURE, DIRECT	Legionella Culture, Direct	34
+LABLEGDA	LEGIONELLA CULTURE		1206
+LABLEGDA	LEGIONELLA CULTURE	Legionella Culture	952
+LABLEGDA	LEGIONELLA CULTURE	Legionella Culture, Induced Sputum	369
+LABLEGDA	LEGIONELLA CULTURE	Legionella Culture, Bronchoalveolar Lavage	149
+LABLEGDD	LEGIONELLA CULTURE, D AND D	Legionella Culture, D And D	12
+LABLEGS	LEGIONELLA SEROLOGY	LEGIONELLA SEROLOGY	2
+LABLEGU	LEGIONELLA AG, URINE	Legionella AG, Urine	2488
+LABLEGU	LEGIONELLA AG, URINE		650
+LABLEGU	LEGIONELLA AG, URINE	Legionella Ag, Urine	54
+LABLEID	FACTOR V LEIDEN	Factor V Leiden	1290
+LABLEID	FACTOR V LEIDEN		62
+LABLEISAB	LEISHMANIA AB, IFA	Leishmania Ab, IFA	26
+LABLEMSAB	LEMS AUTOAB	LEMS AutoAb	6
+LABLEMSAB	LEMS AUTOAB		4
+LABLEVETL	LEVETIRACETAM LEVEL	Levetiracetam Level	607
+LABLEVETL	LEVETIRACETAM LEVEL	Levetiracetam	228
+LABLEVETL	LEVETIRACETAM LEVEL		10
+LABLEVETL	LEVETIRACETAM	Levetiracetam	6
+LABLEVETL	LEVETIRACETAM		1
+LABLIDOL	LIDOCAINE	Lidocaine	16676
+LABLIDOL	LIDOCAINE		678
+LABLIL	LITHIUM	Lithium	3032
+LABLIL	LITHIUM		154
+LABLIL	LITHIUM	Lithium Level	14
+LABLIPS	LIPASE	Lipase	45697
+LABLIPS	LIPASE		4832
+LABLIVER	PRE LIVER TRANSPLANT PANEL	Pre Liver Transplant Panel	10
+LABLKM1AB	LIVER, KID MICROSOME-1 AB, IGG	Liver, Kid Microsome-1 Ab, IgG	18
+LABLKMAB	LIVER KID MICROSOME AB	Liver Kid Microsome Ab	12
+LABLKMAB	LIVER KID MICROSOME AB		2
+LABLMS	LYMPHOCYTE MITOGEN SCREEN	Lymphocyte Mitogen Screen	8
+LABLMS	LYMPHOCYTE MITOGEN SCREEN		2
+LABLPA	LIPOPROTEIN	Lipoprotein	86
+LABLPA	LIPOPROTEIN		18
+LABLPD	LIPID PANEL WITH DIRECT LDL	Lipid Panel With Direct LDL	12178
+LABLPD	LIPID PANEL WITH DIRECT LDL	Lipid Panel	6087
+LABLPD	LIPID PANEL WITH DIRECT LDL		1558
+LABLPD	LIPID PANEL	Lipid Panel	1149
+LABLPD	LIPID PANEL		204
+LABLPD	LIPID PANEL WITH DIRECT LDL	Lipid panel	13
+LABLPD	LIPID PANEL, NON-FASTING PATIENT	Lipid Panel, Non-Fasting Patient	2
+LABLPD	LIPID PANEL. NON-FASTING PATIENT	Lipid Panel. Non-Fasting Patient	2
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	Lipid Panel With Calculated LDL	3489
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	Lipid Panel with Calculated LDL	1473
+LABLPDC	LIPID PANEL WITH CALCULATED LDL		592
+LABLPDC	LIPID PANEL WITH CALCULATED LDL	Fasting Lipid Panel with Calculated LDL	456
+LABLPDC	LIPID PANEL, FASTING PATIENT	Lipid Panel, Fasting Patient	6
+LABLPDC	LIPID PANEL, FASTING PATIENT	Lipid Panel With Calculated LDL	2
+LABLPDP	LIPID PANEL PLUS RISK	Lipid Panel Plus Risk	93
+LABLPDP	LIPID PANEL PLUS RISK		12
+LABLPDP	LIPID PANEL PLUS RISK	LIPID PANEL PLUS RISK IN 1 MONTH	1
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	Lipid Plus Risk Panel With Calculated LDL	115
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL		46
+LABLPDPC	LIPID PLUS RISK PANEL WITH CALCULATED LDL	Lipid Plus Risk Panel With Calculated LDL (fasting required)	1
+LABLUPUS	LUPUS ANTICOAGULANT	Lupus Anticoagulant	1774
+LABLUPUS	LUPUS ANTICOAGULANT		152
+LABLUT	LEUTEINIZING HORMONE	Leuteinizing Hormone	149
+LABLUT	LUTEINIZING HORMONE	Luteinizing Hormone	126
+LABLUT	LUTEINIZING HORMONE	Leuteinizing Hormone	48
+LABLUT	LEUTEINIZING HORMONE		20
+LABLWBCC	LYME WESTERN BLOT, CSF	Lyme Western Blot, CSF	132
+LABLWBCC	LYME WESTERN BLOT, CSF		80
+LABLWBCC	LYME WESTERN BLOT, CSF	Lyme western blot, CSF	35
+LABLWBCS2	LYME WESTERN BLOT, SERUM	Lyme Western Blot, Serum	156
+LABLWBCS2	LYME WESTERN BLOT, SERUM		8
+LABLYMES2	LYME SCREEN, SERUM	Lyme Screen, Serum	322
+LABLYMES2	LYME SCREEN, SERUM		26
+LABMAGATH	MAG AUTOAB	MAG AutoAb	20
+LABMAGATH	MAG AUTOAB		2
+LABMALAB	MALARIA AB SCREEN, IFA, SERUM	Malaria Ab Screen, IFA, Serum	6
+LABMALAR	MALARIA PREP	Malaria Prep	771
+LABMALAR	MALARIA PREP		52
+LABMALL1	MISCELLANEOUS ALLERGEN 1	Miscellaneous Allergen 1	8
+LABMALL1	MISCELLANEOUS ALLERGEN 1		2
+LABMALL2	MISCELLANEOUS ALLERGEN 2	Miscellaneous Allergen 2	2
+LABMALL3	MISCELLANEOUS ALLERGEN 3	Miscellaneous Allergen 3	2
+LABMALSC	MALARIA PREP AND ANTIGEN SCREEN	MALARIA PREP and ANTIGEN SCREEN	150
+LABMASSTA	MASSIVE TRANSFUSION ADULT (>50KG)	Massive Transfusion Adult (>50KG)	616
+LABMASSTP	MASSIVE TRANSFUSION PEDIATRIC (<50 KG)	Massive Transfusion Pediatric (<50 Kg)	14
+LABMB	CK, MB (MASS)	CK, MB (Mass)	52062
+LABMB	CK, MB (MASS)	CK-MB	25689
+LABMB	CK, MB (MASS)		12986
+LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST	Molecular Pathology Miscellaneous Test	111
+LABMDXMIS	MOLECULAR PATHOLOGY MISCELLANEOUS TEST		28
+LABME	RUBEOLA VIRAL AB	Rubeola Viral Ab	1
+LABMEAIGG	RUBEOLA VIRAL AB	Rubeola Viral Ab	1
+LABMEARC	MEASLES RAPID CULTURE	Measles Rapid Culture	10
+LABMECAPC	MECA BY PCR	mecA by PCR	10
+LABMELAS	MELAS MTDNA PROFILE	Melas MtDNA Profile	2
+LABMELAS	MELAS MTDNA PROFILE		2
+LABMEP	MEDICAL EMERGENCY PANEL		30
+LABMEP	MEDICAL EMERGENCY PANEL	Medical Emergency Panel	28
+LABMERC	MERCURY, WHOLE BLOOD	Mercury, Whole Blood	33
+LABMERC	MERCURY, WHOLE BLOOD		4
+LABMETB	METABOLIC PANEL, BASIC	Metabolic Panel, Basic	707555
+LABMETB	METABOLIC PANEL, BASIC	Metabolic Panel, Basic in AM	35927
+LABMETB	METABOLIC PANEL, BASIC		35116
+LABMETB	METABOLIC PANEL, BASIC	Metabolic panel, basic	8667
+LABMETB	METABOLIC PANEL, BASIC	Metabolic Panel, Basic: POD # 1	3926
+LABMETB	METABOLIC PANEL, BASIC	Metabolic Panel, Basic: Evening of surgery	2460
+LABMETB	METABOLIC PANEL, BASIC	Metabolic Panel, Basic (LABMETB)	124
+LABMETB	METABOLIC PANEL, BASIC	Metabolic Panel, Basic [LABMETB]	12
+LABMETB	METABOLIC PANEL, BASIC	Basic Metabolic Panel, Comprehensive	2
+LABMETB	METABOLIC PANEL, BASIC	METABOLIC PANEL, BASIC IN 1 MONTH	2
+LABMETB	METABOLIC PANEL, BASIC	* Metabolic Panel, Basic	1
+LABMETB	METABOLIC PANEL, BASIC	METABOLIC PANEL, BASIC IN 3 MONTHS	1
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic Panel, Comprehensive	301082
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic panel, comprehensive	90547
+LABMETC	METABOLIC PANEL, COMPREHENSIVE		19806
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic Panel, Comprehensive in AM	16610
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic Panel, Comprehensive :POD # 1	1656
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic Panel, Comprehensive (LABMETC)	206
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Comprehensive Metabolic Panel	10
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic Panel, Comprehensive [LABMETC]	4
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	METABOLIC PANEL, COMPREHENSIVE IN 1 MONTH	4
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Metabolic Panel, Comprehensive  [LABMETC]	3
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Int Metabolic Panel, Comprehensive - 2 Times A Week	2
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Int Metabolic Panel, Comprehensive Every 3 Months	2
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Int Metabolic Panel, Comprehensive Every 4 Weeks	2
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	METABOLIC PANEL, COMPREHENSIVE IN 6 MONTHS	2
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	* Metabolic Panel, Comprehensive	1
+LABMETC	METABOLIC PANEL, COMPREHENSIVE	Ext Metabolic Panel, Comprehensive - 2 Times A Week	1
+LABMETHB	METHEMOGLOBIN	Methemoglobin	676
+LABMETHB	METHEMOGLOBIN		94
+LABMGMT	MGMT, METHYLATION SPECIFIC PCR		146
+LABMGMT	MGMT, METHYLATION SPECIFIC PCR	MGMT, Methylation Specific PCR	65
+LABMGMT	MGMT, METHYLATION SPECIFIC PCR	MGMT, Methylation Specific PCR  (molecular diagnostic)	30
+LABMGMTB	MGMT, METHYLATION SPECIFIC PCR, BLOOD	MGMT, METHYLATION SPECIFIC PCR, BLOOD	10
+LABMGMTB	MGMT, METHYLATION SPECIFIC PCR, BLOOD	MGMT, Methylation Specific PCR, Blood	4
+LABMGN	MAGNESIUM, SERUM/PLASMA	Magnesium, Serum/Plasma	554763
+LABMGN	MAGNESIUM, SERUM/PLASMA		51056
+LABMGN	MAGNESIUM, SERUM/PLASMA	Magnesium, Serum/Plasma in AM	25392
+LABMGN	MAGNESIUM, SERUM/PLASMA	Magnesium, serum/plasma	5324
+LABMGN	MAGNESIUM, SERUM/PLASMA	Magnesium, Serum/Plasma (for autologous patients only)	36
+LABMGN	MAGNESIUM, SERUM/PLASMA	Int Magnesium, Serum/Plasma Every 4 Weeks	2
+LABMISCH	MISCELLANEOUS HEMATOLOGY	Miscellaneous Hematology	2
+LABMISCH	MISCELLANEOUS HEMATOLOGY		2
+LABMISPRO	MISCELLANEOUS PROCESSING	Miscellaneous Processing	46
+LABMISPRO	MISCELLANEOUS PROCESSING		40
+LABMMAS	METHYLMALONIC ACID, SERUM	Methylmalonic Acid	777
+LABMMAS	METHYLMALONIC ACID	Methylmalonic Acid	184
+LABMMAS	METHYLMALONIC ACID		26
+LABMMAS	METHYLMALONIC ACID, SERUM		20
+LABMMIC	UNLISTED MICROBIOLOGY ORDER		4
+LABMMIC	UNLISTED MICROBIOLOGY ORDER	Unlisted Microbiology Order	2
+LABMOLDS	MOLDS ALLERGY SCREEN	Molds Allergy Screen	8
+LABMONEBV	MONO SCREEN WITH REFLEX EBV AB	Mono Screen With Reflex EBV Ab	455
+LABMONEBV	MONO SCREEN WITH REFLEX EBV AB		30
+LABMONOSC	INFECTIOUS MONO SCREEN	Infectious Mono Screen	144
+LABMONOSC	INFECTIOUS MONO SCREEN		24
+LABMPOPR3	ANCA MPO / PR - 3M IGG		66
+LABMPOPR3	ANCA MPO / PR - 3M IGG	ANCA MPO/PR-3M IgG	2
+LABMPSQNT	MUCOPOLYSACCHARIDES QUANTITATIVE, URINE	Mucopolysaccharides Quantitative, Urine	4
+LABMPSQNT	MUCOPOLYSACCHARIDES QUANTITATIVE, URINE		2
+LABMPSTLC	MUCOPOLYSACCHARIDES TLC, URINE	Mucopolysaccharides TLC, Urine	2
+LABMPVPCR	MPV PCR	MPV PCR	4
+LABMPVPCR	MPV PCR		2
+LABMTHFR	METHYLENE- THF REDUCTASE	Methylene-THF Reductase	38
+LABMTHFR	METHYLENE- THF REDUCTASE		6
+LABMTHFR	METHYLENE- THF REDUCTASE	Methylene- THF Reductase	2
+LABMTXL	METHOTREXATE	Methotrexate	4742
+LABMTXL	METHOTREXATE		356
+LABMTXL	METHOTREXATE	Methotrexate Level	1
+LABMTXL	METHOTREXATE	Methotrexate Level Start of infusion	1
+LABMUMPSG	MUMPS IGG	MUMPS IGG	26
+LABMUMPSG	MUMPS IGG	Mumps IgG	2
+LABMUMRC	MUMPS RAPID CULTURE	Mumps Rapid Culture	12
+LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST		1324
+LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	Unlisted Microbiology/Virology Test	949
+LABMVSOB	UNLISTED MICROBIOLOGY/VIROLOGY TEST	UNLISTED VIROLOGY SENDOUT SEROLOGY	309
+LABMYCOC	MYCOPLASMA CULTURE, FASTIDIOUS	Mycoplasma Culture, Fastidious	26
+LABMYCOC	MYCOPLASMA CULTURE, FASTIDIOUS		10
+LABMYCPA	MYCOPHENOLIC ACID	Mycophenolic Acid	3338
+LABMYCPA	MYCOPHENOLIC ACID		227
+LABMYCPA	MYCOPHENOLIC ACID	Cellcept/Mycophenolic Acid (Target Level:  2-5 mcg/ml during the first year)	6
+LABMYD88	MYD88 MUTATION ANALYSIS	MyD88 Mutation Analysis	2
+LABMYO	MYOGLOBIN, SERUM / PLASMA	Myoglobin, Serum/Plasma	1709
+LABMYO	MYOGLOBIN, SERUM / PLASMA		328
+LABMYO	MYOGLOBIN, SERUM / PLASMA	Myoglobin, serum/plasma	34
+LABNA	SODIUM, SERUM / PLASMA	Sodium, Serum/Plasma	64620
+LABNA	SODIUM, SERUM / PLASMA		2472
+LABNA	SODIUM, SERUM / PLASMA	SODIUM, SERUM / PLASMA	1
+LABNASC	NP / NASAL CULTURE		42
+LABNASC	NP / NASAL CULTURE	NP/Nasal Culture	30
+LABNBTS	NITROBLUE TETRAZOLIUM RED	Nitroblue Tetrazolium Red	12
+LABNBTS	NITROBLUE TETRAZOLIUM RED		2
+LABND816V	KIT D816V MUT., AS-PCR, NON-BLOOD	KIT D816V Mut., AS-PCR, Non-blood	2
+LABND816V	KIT D816V MUT., AS-PCR, NON-BLOOD	KIT D816V MUT., AS-PCR, NON-BLOOD	2
+LABNEURON	NEURONAL CELL AB, SERUM	Neuronal Cell Ab, Serum	1
+LABNEURSP	NEURON SPEC ENOLASE	Neuron Spec Enolase	21
+LABNEURSP	NEURON SPEC ENOLASE		1
+LABNH3	AMMONIA, PLASMA	Ammonia, Plasma	14094
+LABNH3	AMMONIA, PLASMA		1350
+LABNH3	AMMONIA, PLASMA	Ammonia, plasma	4
+LABNJAK2	JANUS KINASE 2 V617F MUTATION, NON-BLOOD	Janus Kinase 2 V617F Mutation, Non-Blood	84
+LABNJAK2	JANUS KINASE 2 V617F MUTATION, NON-BLOOD	Janus Kinase 2 V617F Mutation, Non-Blood  (molecular diagnostic)	24
+LABNJAK2	JANUS KINASE 2 V617F MUTATION, NON-BLOOD		8
+LABNONGYN	NON-GYN CYTOLOGY EXAM		7192
+LABNONGYN	NON-GYN CYTOLOGY EXAM	Non-Gyn Cytology Exam	5194
+LABNORW	NOROVIRUS ANTIGEN, STOOL EIA	Norovirus Antigen, Stool EIA	840
+LABNORW	NOROVIRUS ANTIGEN, STOOL EIA	NOROVIRUS ANTIGEN	372
+LABNORW	NOROVIRUS ANTIGEN, STOOL EIA		120
+LABNPM1	NPM1, NUCLEOPHOSMIN 1 MUTATION	NPM1, NUCLEOPHOSMIN 1 MUTATION	112
+LABNPM1	NPM1, NUCLEOPHOSMIN 1 MUTATION	NPM1, Nucleophosmin 1 Mutation	36
+LABNSF3B1	SF3B1 MUTATION ANALYSIS, NON-BLOOD	SF3B1 Mutation Analysis, Non-blood	2
+LABNTBNP	NT - PROBNP	NT-proBNP	23128
+LABNTBNP	NT - PROBNP	NT-ProBNP	4983
+LABNTBNP	NT - PROBNP		4000
+LABNTBNP	NT - PROBNP	BNP (NT-proBNP)	29
+LABNVHHA	VH HYPERMUTATION ASSAY, NON-BLOOD	VH Hypermutation Assay, Non-Blood	16
+LABNVHHA	VH HYPERMUTATION ASSAY, NON-BLOOD	VH Hypermutation Assay, Non-Blood  (molecular diagnostic)	6
+LABNVHHA	VH HYPERMUTATION ASSAY, NON-BLOOD		6
+LABOAPE	OVA AND PARASITES	Ova and Parasites	5286
+LABOAPE	OVA AND PARASITES		1648
+LABOAPE	OVA AND PARASITES	Ova and Parasites, Stool	892
+LABOF	OSMOTIC FRAGILITY	Osmotic Fragility	35
+LABOF	OSMOTIC FRAGILITY		2
+LABOGTT	GLUCOSE TOLERANCE FOR CYSTIC FIBROSIS	Glucose Tolerance for Cystic Fibrosis	7
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	Oligoclonal Banding, CSF	458
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CSF Protein & Immunofix Elect.	402
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.		214
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	Oligoclonal Banding CSF	29
+LABOLIG	CSF PROTEIN & IMMUNOFIX ELECT.	CSF Protein And Oligoclonal Bands	2
+LABORAERO	ORTHOPEDIC AEROBIC CULT W/GS	Orthopedic Aerobic Cult w/GS	1050
+LABORANER	ORTHOPEDIC ANAEROBIC CULTURE	Orthopedic Anaerobic Culture	1044
+LABORSHOR	ORTHOPEDIC CULT MULT SPECIMENS	Orthopedic Cult Mult Specimens	1028
+LABOSM	OSMOLALITY, SERUM	Osmolality, Serum	20189
+LABOSM	OSMOLALITY, SERUM		1862
+LABOSM	OSMOLALITY, SERUM	Osmolality, serum	35
+LABOSR	OUTSIDE SLIDE REVIEW		530
+LABOSR	OUTSIDE SLIDE REVIEW	Outside Slide Review	514
+LABOSTLC	OLIGOSACCHARIDES TLC, URINE	Oligosaccharides TLC, Urine	6
+LABOSTLC	OLIGOSACCHARIDES TLC, URINE		2
+LABOXCMET	OXCARBAZEPINE METABOLITE LEVEL	Oxcarbazepine Metabolite Level	263
+LABOXCMET	OXCARBAZEPINE METABOLITE LEVEL		12
+LABP20210	PROTHROMBIN 20210A	Prothrombin 20210A	751
+LABP20210	PROTHROMBIN 20210A	Prothrombin-20210A	420
+LABP20210	PROTHROMBIN 20210A		70
+LABP20210	PROTHROMBIN 20210A	Prothrombin - 20210a	10
+LABP20210	PROTHROMBIN 20210A	PROTHROMBIN-20210A	2
+LABPABN	POLIO AB, NEUTRALIZATION	Polio Ab, Neutralization	5
+LABPABN	POLIO AB, NEUTRALIZATION		2
+LABPAI1AG	PAI-1 AG	PAI-1 Ag	2
+LABPAI1AT	PAI-1 ACTIVITY	PAI-1 Activity	11
+LABPALB	PREALBUMIN	Prealbumin	6217
+LABPALB	PREALBUMIN		5
+LABPALL	PROCAINAMIDE	Procainamide	8
+LABPANANC	NEUTROPHIL COUNT, ABSOLUTE	Neutrophil Count, Absolute	4
+LABPANAPL	PROCAINAMIDE AND NAPA	Procainamide and Napa	56
+LABPANHCT	HEMATOCRIT	Hematocrit	118
+LABPANHGB	HEMOGLOBIN	Hemoglobin	100
+LABPANPLT	PLATELET COUNT	Platelet Count	2
+LABPANWBC	WBC COUNT	WBC Count	12
+LABPARAG	PARAGONIMIASIS ANTIBODY, ELISA	Paragonimiasis Antibody, ELISA	2
+LABPATHR	REVIEW BY PATHOLOGIST		7292
+LABPATHR	REVIEW BY PATHOLOGIST	Review By Pathologist	5072
+LABPB	PHENOBARBITAL LEVEL	Phenobarbital Level	922
+LABPB	PHENOBARBITAL LEVEL		56
+LABPB	PHENOBARBITAL LEVEL	Phenobarbital	6
+LABPBGD	PBG DEAMINASE, RBC	PBG Deaminase, RBC	2
+LABPBXTG	AUTOPSY BX/TISSUE/GENERAL		2
+LABPCACT	ACTIVATED CLOTTING TIME (POCT)		876
+LABPCACT	ACTIVATED CLOTTING TIME (POCT)	Activated Clotting Time (POCT)	1
+LABPCACTK	ISTAT ACTIVATED CLOTTING TIME	iSTAT Activated Clotting Time	2726
+LABPCACTK	ISTAT ACTIVATED CLOTTING TIME		2384
+LABPCCG4	ISTAT CG4, ARTERIAL		1486
+LABPCCG4	ISTAT CG4, ARTERIAL	iSTAT Arterial Blood Gases And Lactate	1092
+LABPCCG4O	ISTAT CG4, OTHER		4510
+LABPCCG4O	ISTAT CG4, OTHER	iSTAT Others Blood Gases And Lactate	3750
+LABPCCG4V	ISTAT CG4,VENOUS		15788
+LABPCCG4V	ISTAT CG4,VENOUS	iSTAT Venous Blood Gases And Lactate	14688
+LABPCCH8	ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2	iSTAT Cr, Hct/Hb, Cl , BUN, Na, K, Glucose, iCa, TCO2	1690
+LABPCCH8	ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2	POC ISTAT, HCT/Hb,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2	234
+LABPCCH8	POC ISTAT, HCT/HB,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2	POC ISTAT, HCT/Hb,NA,K,CHL,BUN,CR,GLUCOSE,ICA,TCO2	8
+LABPCCR	ISTAT CREATININE		12966
+LABPCCR	ISTAT CREATININE	iSTAT Creatinine	7082
+LABPCEC4	ISTAT EC4, ARTERIAL		100
+LABPCEC4	ISTAT EC4, ARTERIAL	iSTAT Arterial Na, K, Glucose, HCT, Hemoglobin	54
+LABPCEC4O	ISTAT EC4, OTHER		552
+LABPCEC4O	ISTAT EC4, OTHER	iSTAT Other Na, K, Glucose, HCT, Hemoglobin	424
+LABPCEC4V	ISTAT EC4, VENOUS		4408
+LABPCEC4V	ISTAT EC4, VENOUS	iSTAT Venous Na, K, Glucose, HCT, Hemoglobin	2496
+LABPCEG7	ISTAT EG7, ARTERIAL		15900
+LABPCEG7	ISTAT EG7, ARTERIAL	iSTAT Arterial Na, K, Blood Gases, Ionized Calcium, HCT, Hemoglobin	11236
+LABPCEG7O	ISTAT EG7, OTHER		456
+LABPCEG7O	ISTAT EG7, OTHER	iSTAT Other Na, K, Blood Gases, Ionized Calcium, HCT, Hemoglobin	446
+LABPCEG7V	ISTAT EG7, VENOUS		14092
+LABPCEG7V	ISTAT EG7, VENOUS	iSTAT Venous Na, K, Blood Gases, Ionized Calcium, HCT, Hemoglobin	10628
+LABPCG3	ISTAT G3+, ARTERIAL		223676
+LABPCG3	ISTAT G3+, ARTERIAL	iSTAT Arterial Blood Gases	159628
+LABPCG3O	ISTAT G3+, OTHER		3948
+LABPCG3O	ISTAT G3+, OTHER	iSTAT Other Blood Gases	3368
+LABPCG3V	ISTAT G3+, VENOUS		10050
+LABPCG3V	ISTAT G3+, VENOUS	iSTAT Venous Blood Gases	7032
+LABPCINR	ISTAT INR AND PROTIME		3294
+LABPCINR	ISTAT INR AND PROTIME	iSTAT PT And INR	2763
+LABPCTNI	ISTAT TROPONIN I		28864
+LABPCTNI	ISTAT TROPONIN I	iSTAT Troponin I	18682
+LABPCYS	PNEUMOCYSTIS STAIN	Pneumocystis Stain	1983
+LABPCYS	PNEUMOCYSTIS STAIN		1684
+LABPCYS	PNEUMOCYSTIS STAIN	Pneumocystis Stain, Induced Sputum	551
+LABPCYS	PNEUMOCYSTIS STAIN	Pneumocystis Stain, Bronchoalveolar Lavage	155
+LABPENALL	PENICILLIN ALLERGY SCREEN	Penicillin Allergy Screen	76
+LABPENTO	PENTOBARBITAL LEVEL	Pentobarbital Level	21
+LABPENTO	PENTOBARBITAL LEVEL		6
+LABPETSIN	PETROSAL SINUS TEST		2
+LABPFCATE	CATECHOLAMINES FRACTIONATED	CATECHOLAMINES,PLASMA	73
+LABPFCATE	CATECHOLAMINES FRACTIONATED		14
+LABPFCATE	CATECHOLAMINES FRACTIONATED	CATECHOLAMINES FRACTIONATED	4
+LABPGN	PROGESTERONE	Progesterone	29
+LABPGN	PROGESTERONE		2
+LABPHATYR	PHENYLALANINE AND TYROSINE	Phenylalanine and Tyrosine	2
+LABPHEN	PHENYTOIN LEVEL	Phenytoin Level	9440
+LABPHEN	PHENYTOIN LEVEL		870
+LABPHEN	PHENYTOIN LEVEL	Phenytoin	196
+LABPHEN	PHENYTOIN LEVEL	Phenytoin : QAM	23
+LABPHEN	PHENYTOIN LEVEL	Phenytoin : After Load	12
+LABPHEN	PHENYTOIN LEVEL	Phenytoin: QAM	7
+LABPHME	PH BY METER	pH By Meter Fluid	1156
+LABPHME	PH BY METER		906
+LABPHME	PH BY METER	pH By Meter	530
+LABPHME	PH BY METER	pH By Meter Fluid (specify source in quesiont #2)	502
+LABPHME	PH BY METER	pH By Meter Urine	392
+LABPHME	PH BY METER	pH By Meter Whole Blood	2
+LABPHOS	PHOSPHORUS, SERUM/PLASMA	Phosphorus, Serum/Plasma	276882
+LABPHOS	PHOSPHORUS, SERUM/PLASMA		23742
+LABPHOS	PHOSPHORUS, SERUM/PLASMA	Phosphorus, Serum/Plasma in AM	16748
+LABPHOS	PHOSPHORUS, SERUM/PLASMA	Phosphorus, serum/plasma	4335
+LABPHOS	PHOSPHORUS, SERUM/PLASMA	Phosphorus, serum/Plasma	49
+LABPHOS	PHOSPHORUS, SERUM/PLASMA	PHOSPHORUS, SERUM/PLASMA	1
+LABPLAG	PLAGUE AB	Plague Ab	4
+LABPLASMN	PLASMINOGEN	Plasminogen	95
+LABPLASMN	PLASMINOGEN		2
+LABPLTAGG	PLATELET AGGREGATION	Platelet Aggregation	88
+LABPLTAGG	PLATELET AGGREGATION		6
+LABPLTFUN	PLATELET FUNCTION SCREEN	Platelet Function Screen	362
+LABPLTFUN	PLATELET FUNCTION SCREEN		46
+LABPLTFUN	PLATELET FUNCTION SCREEN	Platlet Function Screen	8
+LABPLTMAP	PLATELET MAPPING	Platelet Mapping	435
+LABPLTMAP	PLATELET MAPPING		38
+LABPLTS	PLATELET COUNT	Platelet Count	61386
+LABPLTS	PLATELET COUNT		2282
+LABPLTS	PLATELET COUNT	PLATELET COUNT	2
+LABPNAB	PNEUMOCOCCAL AB (PNEUMOVAX RESPONSE)	Pneumococcal AB (Pneumovax Response)	31
+LABPNET2	PNEUMOCOCCAL AB (PREVNAR RESPONSE)	Pneumococcal Ab (Prevnar Response)	8
+LABPOCGLU	GLUCOSE BY METER		1295992
+LABPOCGLU	GLUCOSE BY METER	Glucose By Meter	984493
+LABPOLYNS	NASAL SMEAR FOR PMN	Nasal Smear for PMN	2
+LABPOLYSP	SPUTUM FOR PMNS	Sputum For PMNs	26
+LABPORFEC	PORPHYRINS FRACTIONATED, FECAL	Porphyrins Fractionated, Fecal	26
+LABPORFEC	PORPHYRINS FRACTIONATED, FECAL		10
+LABPORFEC	PORPHYRINS FRACTIONATED, FECAL	Porphyrins, Fecal Fract	6
+LABPORS	PORPHYRINS, SERUM	Porphyrins, Serum	60
+LABPORS	PORPHYRINS, SERUM		2
+LABPREG	PREGNANCY TEST, SERUM	Pregnancy Test, Serum	3341
+LABPREG	PREGNANCY TEST, SERUM		438
+LABPREG	PREGNANCY TEST, SERUM	Pregnancy Test, Sr	354
+LABPREG	PREGNANCY TEST, SERUM	Pregnancy Test, serum, Qualitative	40
+LABPREG	PREGNANCY TEST, SERUM	Pregnancy Test, Serum (LABPREG)	4
+LABPREG	PREGNANCY TEST, SERUM	Pregnancy Test - serum	2
+LABPRO6MP	PRO-PREDICT ENZACT	Pro-Predict Enzact	29
+LABPRO6MP	PRO-PREDICT ENZACT		6
+LABPROC	SPECIMEN PROCESSING	Specimen Processing	1572
+LABPROC	SPECIMEN PROCESSING		672
+LABPROCT	PROCALCITONIN	Procalcitonin	7022
+LABPROCT	PROCALCITONIN		462
+LABPROINS	PROINSULIN, PLASMA		137
+LABPROL	PROLACTIN	Prolactin	1040
+LABPROL	PROLACTIN		128
+LABPROPAF	PROPAFENONE	Propafenone	4
+LABPROTC	PROTEIN C ACTIVITY	Protein C Activity	822
+LABPROTC	PROTEIN C ACTIVITY		62
+LABPROTPM	PRO-PREDICT TPMT	Pro-Predict TPMT	194
+LABPROTPM	PRO-PREDICT TPMT		10
+LABPROTS	PROTEIN S ACTIVITY	Protein S Activity	803
+LABPROTS	PROTEIN S ACTIVITY		58
+LABPROTSF	PROTEIN S, FREE		58
+LABPROTSF	PROTEIN S, FREE	Protein S, Free	28
+LABPSA	PSA	PSA	1650
+LABPSA	PSA		194
+LABPSA	PSA	PSA Ultrasensitive	64
+LABPSA	PSA	PSA (men over 50)	8
+LABPSA	PSA	PSA (LABPSA)	1
+LABPSA	PSA	PSA IN 3 MONTHS	1
+LABPSACX	PSA WITH REFLEX COMPLEXED PSA	PSA W/ Reflex Complexed PSA	10
+LABPSACX	PSA WITH REFLEX COMPLEXED PSA		2
+LABPSARX	PSA WITH REFLEX FREE PSA	PSA With Reflex Free PSA	17
+LABPSAU	POST-THERAPY PSA	Post-Therapy PSA	9
+LABPSAU	POST-THERAPY PSA		4
+LABPT	PROTHROMBIN TIME	Prothrombin Time	433312
+LABPT	PROTHROMBIN TIME	Prothrombin time	42844
+LABPT	PROTHROMBIN TIME		36824
+LABPT	PROTHROMBIN TIME	Prothrombin Time in AM	14402
+LABPT	PROTHROMBIN TIME	PT Prothrombin Time	1227
+LABPT	PROTHROMBIN TIME	Prothrombin Time - Q6H	1116
+LABPT	PROTHROMBIN TIME	Prothrombin Time - Recheck INR immediately after completion of FFP infusion	214
+LABPT	PROTHROMBIN TIME	Prothrombin Time, No Therapy	205
+LABPT	PROTHROMBIN TIME	Prothrombin Time (LABPT)	76
+LABPT	PROTHROMBIN TIME	Prothrombin Time (PT)	67
+LABPT	PROTHROMBIN TIME	Prothrombin Time - 15 minutes after completion of FFP infusion	32
+LABPT	PROTHROMBIN TIME	Prothrombin Time, Heparin Therapy	20
+LABPT	PROTHROMBIN TIME	Prothrombin Time, Warfarin Therapy	19
+LABPT	PROTHROMBIN TIME	PT/INR	15
+LABPT	PROTHROMBIN TIME	Prothrombin Time, Unknown Therapy	13
+LABPT	PROTHROMBIN TIME	Prothrombin Time, LMW Heparin Therapy	8
+LABPT	PROTHROMBIN TIME	Prothrombin Time, Other Therapy	4
+LABPT	PROTHROMBIN TIME	Prothrombin Time [LABPT]	3
+LABPT	PROTHROMBIN TIME	Int Prothrombin Time - 2 Times A Week	2
+LABPT	PROTHROMBIN TIME	Int Prothrombin Time Every 3 Months	2
+LABPT	PROTHROMBIN TIME	Lab PT/INR	2
+LABPT	PROTHROMBIN TIME	PROTHROMBIN TIME	2
+LABPT	PROTHROMBIN TIME	Prothrombin Time (INR)	1
+LABPT12	PROTHROMBIN FRAGMENT 1 AND 2	Prothrombin Fragment 1 And 2	11
+LABPTEG	TEG, PERIOPERATIVE	TEG, Perioperative	2450
+LABPTH	PARATHYROID HORMONE	Parathyroid Hormone	3245
+LABPTH	PARATHYROID HORMONE		242
+LABPTH	PARATHYROID HORMONE	Parathyroid Hormone (LABPTH)	4
+LABPTINH	PT INHIBITOR SCREEN	PT Inhibitor Screen	427
+LABPTINH	PT INHIBITOR SCREEN		46
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time	345540
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME		36174
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time in AM	3912
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial thromboplastin time	1908
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time, No Therapy	198
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Tme	34
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time, Heparin Therapy	24
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	Prothrombin Time	20
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time, Warfarin Therapy	15
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time (LABPTT)	13
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time, Unknown Therapy	11
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT partial thromboplastin time	10
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time, Other Therapy	10
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT Partial Thromboplastin Time, LMW Heparin Therapy	8
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	Partial Thromboplastin Time	4
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	Partial Thromboplastin Time (PTT )	2
+LABPTT	PTT PARTIAL THROMBOPLASTIN TIME	PTT PARTIAL THROMBOPLASTIN TIME	2
+LABPTTINH	PTT INHIBITOR SCREEN	PTT Inhibitor Screen	900
+LABPTTINH	PTT INHIBITOR SCREEN		78
+LABQFTB	QUANTIFERON TEST FOR LATENT TB	Quantiferon Test For Latent TB	3755
+LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	QUANTIFERON FOR LATENT TB INFECTION	542
+LABQFTB	QUANTIFERON TEST FOR LATENT TB		389
+LABQFTB	QUANTIFERON TEST FOR LATENT TB	Quantiferon test for latent TB	120
+LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	Quantiferon for Latent TB Infection	39
+LABQFTB	QUANTIFERON FOR LATENT TB INFECTION	Quantiferon Test For Latent TB	4
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QuantiFERON TEST FOR LATENT TB	2018
+LABQFTBG	TB BY QUANTIFERON	TB by QuantiFERON	119
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QUANTIFERON FOR LATENT TB INFECTION	115
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	Quantiferon Test For Latent TB	43
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	Quantiferon for Latent TB Infection	38
+LABQFTBG	TB BY QUANTIFERON	QUANTIFERON FOR LATENT TB INFECTION	23
+LABQFTBG	QUANTIFERON FOR LATENT TB INFECTION	QUANTIFERON FOR LATENT TB INFECTION	20
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	QuantiFERON for Latent TB Infection	8
+LABQFTBG	TB BY QUANTIFERON	Quantiferon Test For Latent TB	6
+LABQFTBG	QUANTIFERON FOR LATENT TB INFECTION	Quantiferon for Latent TB Infection	5
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB		3
+LABQFTBG	QUANTIFERON TEST FOR LATENT TB	Quantiferon Test for Latent TB	2
+LABQFTBG	TB BY QUANTIFERON	Quantiferon for Latent TB Infection	1
+LABQUECORSL	QUEST - CORTISOL, SERUM LC/MS/MS		1
+LABQUECU24H	CREATININE, URINE/24 HR (QUEST)		1
+LABQUEPT	QUEST - PROTHROMBIN TIME (PT)		2
+LABRAAS	RHEUMATOID ARTHRITIS AB SCREEN	Rheumatoid Arthritis Ab Screen	55
+LABRBCENZ	RED CELL ENZYMES	Red Cell Enzymes	4
+LABRBCENZ	RED CELL ENZYMES		2
+LABRBCFOL	FOLATE, RBC	Folate, RBC	4287
+LABRBCFOL	FOLATE, RBC		490
+LABRBCMOR	RBC MORPHLOGY, COMPLETE	RBC Morphlogy, Complete	34
+LABRBCMOR	RBC MORPHLOGY, COMPLETE		2
+LABRBCPH	RBC PHENOTYPE		98
+LABRBCPH	RBC PHENOTYPE	RBC Phenotype	68
+LABRBP	RETINOL BINDING PROTEIN	Retinol Binding Protein	4
+LABRDATAC	REVERSE TO COOMBS A CELLS	Reverse To Coombs A Cells	64
+LABRDATAC	REVERSE TO COOMBS A CELLS		44
+LABRDATBC	REVERSE TO COOMBS B CELLS	Reverse To Coombs B Cells	18
+LABRDATBC	REVERSE TO COOMBS B CELLS		14
+LABREF1	LAB UNLISTED 1	Lab Unlisted 1	3963
+LABREF1	LAB UNLISTED 1		1360
+LABREF1	LAB UNLISTED 1	HLA and DR typing (Lab Unlisted 1) HLA Antibody Screen send with HLA requisition provided by transplant team	26
+LABREF1	LAB UNLISTED 1	HLA and DR typing (Lab Unlisted 1) Fill out and send Req to patient	4
+LABREF1	LAB UNLISTED 1	HLA B57	2
+LABREF1	LAB UNLISTED 1	Athena Labs - Lab Unlisted 1	1
+LABREF1	LAB UNLISTED 1	Cytology - Lab Unlisted 1	1
+LABREF2	LAB UNLISTED 2	Lab Unlisted 2	386
+LABREF2	LAB UNLISTED 2		100
+LABREF3	LAB UNLISTED 3	Lab Unlisted 3	153
+LABREF3	LAB UNLISTED 3		30
+LABREF4	LAB UNLISTED 4	Lab Unlisted 4	44
+LABREF4	LAB UNLISTED 4		10
+LABREF5	LAB UNLISTED 5	Lab Unlisted 5	16
+LABREF5	LAB UNLISTED 5		6
+LABREF6	LAB UNLISTED 6	Lab Unlisted 6	18
+LABREF6	LAB UNLISTED 6		2
+LABREPT	REPTILASE TIME		1872
+LABREPT	REPTILASE TIME	Reptilase Time	1560
+LABRESP	RESPIRATORY CULTURE	Respiratory Culture	25310
+LABRESP	RESPIRATORY CULTURE		8774
+LABRESP	RESPIRATORY CULTURE	Respiratory Culture, Expectorated Sputum	3320
+LABRESP	RESPIRATORY CULTURE	Respiratory Culture, Induced Sputum	2357
+LABRESP	RESPIRATORY CULTURE	Respiratory Culture, Tracheal Aspirate	2058
+LABRESP	RESPIRATORY CULTURE	RESPIRATORY CULTURE	806
+LABRESP	RESPIRATORY CULTURE	Respiratory Culture with Gram Stain	427
+LABRESP	RESPIRATORY CULTURE	Respiratory Culture with Gram stain	412
+LABRESP	RESPIRATORY CULTURE	Respiratory culture with gram stain	67
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	RESPIRATORY VIRUS PCR PANEL	1416
+LABRESPCR	RESPIRATORY VIRUS PCR PANEL	Respiratory Virus PCR Panel	1275
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	Respiratory Culture And Gram Stain	5889
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN		2654
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	Respiratory Culture with Gram stain, Bronchoalveolar Lavage	265
+LABRESPG	RESPIRATORY CULTURE AND GRAM STAIN	Respiratory Culture with Gram stain	2
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	Reticulocyte Count Automated	11451
+LABRETIC	RETICULOCYTE COUNT AUTOMATED		1572
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	Reticulopyte count, automated	387
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	Reticulopyte Count, Automated	16
+LABRETIC	RETICULOCYTE COUNT AUTOMATED	Reticulocyte Count, Automated	7
+LABRF	RHUEMATOID FACTOR	Rheumatoid Factor, Serum	888
+LABRF	RHUEMATOID FACTOR	Rheumatoid Factor	695
+LABRF	RHUEMATOID FACTOR		168
+LABRF	RHEUMATOID FACTOR	Rheumatoid Factor, Serum	137
+LABRF	RHEUMATOID FACTOR	Rheumatoid Factor	117
+LABRF	RHUEMATOID FACTOR	Rheumatoid factor, serum	18
+LABRFRPT	REFERENCE REPORT		2140
+LABRFRPT	REFERENCE REPORT	Reference Report	2021
+LABRHTYP	RH BLOOD TYPE		2
+LABRIPA	RISTOCETIN INDUCED PLATELET AGGREGATION	Ristocetin Induced Platelet Aggregation	41
+LABRIPA	RISTOCETIN INDUCED PLATELET AGGREGATION		2
+LABRIST	RISTOCETIN COFACTOR	Ristocetin Cofactor	237
+LABRIST	RISTOCETIN COFACTOR		32
+LABRITUX	RITUXAN PANEL	Rituxan Panel	977
+LABRITUX	RITUXAN PANEL		42
+LABRITUX	RITUXAN PANEL	RITUXAN PANEL	2
+LABRMPCR	RSV/MPV PCR	RSV/MPV PCR	14
+LABRMPCR	RSV/MPV PCR		6
+LABRNPABY	ANTI-RNP ANTIBODY	Anti-RNP Antibody	365
+LABRNPABY	RNP AB, IGG	RNP Ab, IgG	9
+LABRNPABY	ANTI-RNP ANTIBODY		8
+LABROACAN	ACANTHAMOEBA CULTURE	Acanthamoeba culture	4
+LABROACAN	ACANTHAMOEBA CULTURE		2
+LABROGC	GC CULTURE SCREEN	GC Culture Screen	211
+LABROGC	GC CULTURE SCREEN		34
+LABROMRS	MRSA SCREEN	MRSA Screen	111582
+LABROMRS	MRSA SCREEN		31576
+LABROMRSP	R/O MRSA BY PCR	R/O MRSA by PCR	2
+LABROMSP	MICROSPORIDIUM SCREEN	Microsporidium Screen	350
+LABROMSP	MICROSPORIDIUM SCREEN		114
+LABROMSP	MICROSPORIDIUM SCREEN	Microsporidium Screen, Stool	92
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN	R/O Parasite Kinyoun	451
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN		114
+LABROPK	ISOSPORA/CYCLOSPORA SCREEN	Isospora/Cyclospora Screen, stool	94
+LABROSBBP	R/O STREP BETA GP B BY PCR	R/O Strep Beta Gp B by PCR	30
+LABROSBBP	R/O STREP BETA GP B BY PCR		4
+LABROSTRO	STRONGYLOIDES CULTURE	Strongyloides culture	78
+LABROSTRO	STRONGYLOIDES CULTURE		12
+LABROVRE	VRE SCREEN	VRE Screen	37
+LABROVRE	VRE SCREEN		18
+LABRP	RENAL FUNCTION PANEL	Renal Function Panel	352
+LABRP	RENAL FUNCTION PANEL		16
+LABRPRQL	RPR QUALITATIVE	RPR Qualitative	1597
+LABRPRQL	RPR QUALITATIVE		840
+LABRPRQL	RPR QUALITATIVE	RPR Qualitative  (lab reflux VDRL if positive)  [LABRPRQL]	3
+LABRPRQT	RPR QUANTITATIVE	RPR Quantitative	1435
+LABRPRQT	RPR QUANTITATIVE		44
+LABRSVPCR	RSV PCR	RSV PCR	19
+LABRSVPCR	RSV PCR		8
+LABRUB	RUBELLA AB IGG	Rubella Ab IgG	52
+LABRUB	RUBELLA AB IGG		6
+LABRUBG	RUBELLA IGG ANTIBODY	Rubella IgG Antibody	6
+LABRUBM	RUBELLA AB IGM	Rubella Ab IgM	2
+LABSALCOR	CORTISOL, SALIVA	CORTISOL, SALIVA	29
+LABSALCOR	CORTISOL, SALIVA	Cortisol, Saliva	14
+LABSALICY	SALICYLATE LEVEL	Salicylate Level	4745
+LABSALICY	SALICYLATE LEVEL		554
+LABSALICY	SALICYLATE LEVEL	Salicylate	64
+LABSCHIG	SCHISTOSOMA IGG AB ELISA	Schistosoma IgG Ab Elisa	123
+LABSCHIG	SCHISTOSOMA IGG AB ELISA		2
+LABSCLABY	ANTI-SCL70 ANTIBODY	Anti-Scl70 Antibody	350
+LABSCLABY	ANTI-SCL70 ANTIBODY	AutoAb To Scl 70	12
+LABSCLABY	ANTI-SCL70 ANTIBODY		10
+LABSCLABY	SCL 70 AB	SCL 70 AB	9
+LABSCLABY	SCL 70 AB		1
+LABSCMV	CMV SHELL VIAL CULTURE		2752
+LABSCMV	CMV SHELL VIAL CULTURE	CMV Shell Vial Culture	541
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV, CSF	428
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV, Bronchoalveolar Lavage	76
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV, Pleural Fluid	40
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV	10
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV, Pericardial Fluid	10
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV, Peritoneal  Fluid	10
+LABSCMV	CMV SHELL VIAL CULTURE	Shell Vial Culture: CMV, Bone Marrow	4
+LABSEZ	SEZARY CELL SCREEN	Sezary Cell Screen	4
+LABSEZ	SEZARY CELL SCREEN		2
+LABSEZPNL	SEZARY PANEL	Sezary Panel	14
+LABSEZPNL	SEZARY PANEL		2
+LABSF3B1	SF3B1 MUTATION ANALYSIS	SF3B1 Mutation Analysis	2
+LABSFCORT	CORTISOL FREE, SERUM	Cortisol Free, Serum	90
+LABSFCORT	CORTISOL FREE, SERUM		10
+LABSHIV12	RAPID HIV-1/2 ANTIBODY	RAPID HIV-1/2 ANTIBODY	427
+LABSHIV12	RAPID HIV-1/2 ANTIBODY		100
+LABSHIVAB	RAPID HIV-1 AB	Rapid HIV-1 Ab	78
+LABSHIVAB	RAPID HIV-1 AB		28
+LABSHSTGS	SHC STAT GRAM STAIN		6
+LABSHSTGS	SHC STAT GRAM STAIN	SHC STAT Gram Stain	2
+LABSHSV	HSV SHELL VIAL CULTURE		2
+LABSICKL	SICKLE CELL PREP	Sickle Cell Prep	28
+LABSIROL	SIROLIMUS LEVEL	Sirolimus Level	3429
+LABSIROL	SIROLIMUS LEVEL		472
+LABSIROL	SIROLIMUS LEVEL	Sirolimus	78
+LABSIROL	SIROLIMUS LEVEL	Sirolimus 24 hour Trough	2
+LABSIROL	SIROLIMUS LEVEL	Sirolimus Level/Rapamune (Target Level:  5-7 ng/ml)	2
+LABSLHELD	SLIDE HELD FOR REVIEW	Slide Held for Review	5742
+LABSLHELD	SLIDE HELD FOR REVIEW		4126
+LABSLIDE	SLIDE SAVE FOR CLINICIAN	Slide Save for Clinician	42
+LABSLIDE	SLIDE SAVE FOR CLINICIAN		4
+LABSLINTP	SLIDE INTERPRETATION BY PATHOLOGIST	Slide Interpretation by Pathologist	89
+LABSLINTP	SLIDE INTERPRETATION BY PATHOLOGIST		12
+LABSMABY	ANTI-SM ANTIBODY	Anti-Sm Antibody	326
+LABSMABY	ANTI-SM ANTIBODY		6
+LABSMABY	AUTOANTIBODIES TO SM	AutoAb To SM	2
+LABSNA	SPECIFIC ANA PANEL	Specific ANA Panel	43
+LABSNP	SENSORIMOTOR PROFILE	Sensorimotor Profile	2
+LABSPAGD	S. PNEUMONIAE AG DETECTION LA	S. Pneumoniae Ag Detection LA	244
+LABSPAGD	S. PNEUMONIAE AG DETECTION LA		50
+LABSPE	PROTEIN ELECTROPHORESIS	Protein Electrophoresis	300
+LABSPE	PROTEIN ELECTROPHORESIS		22
+LABSPIE	PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM	Protein Immunofix Electrophoresis, Serum	3296
+LABSPIE	PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM		206
+LABSPIE	PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM	Serum Protein Immunofix Electrophoresis	17
+LABSPIE	PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM	Serum protein immunofix electrophoresis	6
+LABSPLAC	SEPSIS PROTOCOL LACTATE	Sepsis Protocol Lactate	14089
+LABSPR	SPECIMEN REMARK		323942
+LABSPR	SPECIMEN REMARK	Specimen Remark	178398
+LABSSAABY	ANTI-RO ANTIBODY	Anti-Ro Antibody	454
+LABSSAABY	RO AB	RO AB	14
+LABSSAABY	ANTI-RO ANTIBODY	Autoantibodies to Ss-A/Ro	10
+LABSSBABY	ANTI-LA ANTIBODY	Anti-La Antibody	446
+LABSSBABY	LA AB	LA AB	15
+LABSSBABY	ANTI-LA ANTIBODY	Autoantibodies to Ss-B/La	10
+LABSTAND	STANDARD PRE TRANSPLANT PANEL	Standard Pre Transplant Panel	17
+LABSTAND	STANDARD PRE TRANSPLANT PANEL		2
+LABSTDY1	STUDY TEST	Study Test	336
+LABSTDY1	STUDY TEST		86
+LABSTDY2	STUDY TEST	Study Test	4
+LABSTELAS	ELASTASE - 1,STOOL	Elastase-1,Stool	2
+LABSTFAT	FAT STAIN, STOOL	Fat Stain, Stool	455
+LABSTFAT	FAT STAIN, STOOL		158
+LABSTLC	STOOL CULTURE	Stool Culture	2197
+LABSTLC	STOOL CULTURE		910
+LABSTLCP	STOOL CULTURE	Stool Culture	312
+LABSTLCP	STOOL CULTURE		9
+LABSTLCX	STOOL CULTURE	Stool Culture	3613
+LABSTLCX	STOOL CULTURE		62
+LABSTLCX	STOOL CULTURE	Culture Stool	2
+LABSTOBGC	OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.	Occult Bld, Guaiac, Stool, Colorectal Sc	1575
+LABSTOBGC	OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.		334
+LABSTOBGC	OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.	Occult Blood, Guaiac, Stool, Colorectal Screen	4
+LABSTOBGD	OCCULT BLD, GUAIAC, STOOL, DIAG TEST	Occult Bld, Guaiac, Stool, Diag Test	9850
+LABSTOBGD	OCCULT BLD, GUAIAC, STOOL, DIAG TEST		4194
+LABSTOBIC	OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.	Occult Bld, Immuno, Stool, Colorectal Sc	196
+LABSTOBIC	OCCULT BLD, IMMUNO, STOOL, COLORECTAL SC.		10
+LABSTOBID	OCCULT BLD, IMMUNO, STOOL, DIAG TEST	Occult Bld, Immuno, Stool, Diag Test	120
+LABSTOBID	OCCULT BLD, IMMUNO, STOOL, DIAG TEST		18
+LABSTOBLD	OCCULT BLD, GUAIAC DIAG, SINGLE		124
+LABSTOBLD	OCCULT BLD, GUAIAC DIAG, SINGLE	Occult bld, Guaiac Diag, Single	78
+LABSTOOLC	STOOL CULTURE	Stool Culture	2890
+LABSTOOLC	STOOL CULTURE		1191
+LABSTORE	SPECIMEN STORAGE ONLY		246
+LABSTORE	SPECIMEN STORAGE ONLY	Specimen Storage Only	78
+LABSTPCR	THROAT STREP SCREEN BY PCR	Throat Strep Screen by PCR	413
+LABSTPCR	THROAT STREP SCREEN BY PCR		74
+LABSTPH	PH, STOOL	pH, Stool	115
+LABSTPH	PH, STOOL		60
+LABSTPOLY	WBC FOR IBD, STOOL	WBC for IBD, Stool	1220
+LABSTPOLY	WBC FOR IBD, STOOL		296
+LABSTPOLY	PMNS / EOSINOPHILS, STOOL		2
+LABSTRED	REDUCING SUBSTANCE, STOOL	Reducing Substance, Stool	351
+LABSTRED	REDUCING SUBSTANCE, STOOL		201
+LABSTREP	BETA STREP THROAT SCREEN RAPID EIA	Beta Strep Throat Screen (2 Step)	324
+LABSTREP	BETA STREP THROAT SCREEN RAPID EIA	Beta Strep Throat Screen Rapid EIA	238
+LABSTREP	BETA STREP THROAT SCREEN RAPID EIA		130
+LABSTREP	BETA STREP THROAT SCREEN RAPID EIA	Beta Strep Throat Screen	10
+LABSTRONG	STRONGYLOIDES IGG AB, ELISA	Strongyloides IgG Ab, Elisa	488
+LABSTRONG	STRONGYLOIDES IGG AB, ELISA		18
+LABSTSS	SYPHILIS TREPONEMAL SCREEN	Syphilis Treponemal Screen	4431
+LABSTSS	SYPHILIS TREPONEMAL SCREEN		206
+LABSTUDY	RESEARCH LAB KIT	Research Lab Kit	13
+LABSULFAT	SULFATIDE AUTOANTIBODY	Sulfatide Autoantibody	4
+LABSURG	SURGICAL PROCEDURE		39660
+LABSURG	SURGICAL PROCEDURE	Surgical Procedure	31300
+LABSWTCL	CHLORIDE, SWEAT	Chloride, Sweat	88
+LABSWTCL	CHLORIDE, SWEAT		26
+LABSYCT	CELL COUNT, SYNOVIAL FLUID	Cell Count, Synovial Fluid	128
+LABSYCT	CELL COUNT, SYNOVIAL FLUID		52
+LABSYDIFF	DIFFERENTIAL, SYNOVIAL FLUID	Differential, Synovial Fluid	78
+LABSYDIFF	DIFFERENTIAL, SYNOVIAL FLUID		44
+LABSYNCT	CELL COUNT AND DIFF, SYNOVIAL FLUID	Cell Count and Diff, Synovial Fluid	1225
+LABSYNCT	CELL COUNT AND DIFF, SYNOVIAL FLUID		636
+LABT1114	"T(11	14) BY PCR"	"T(11	14) By PCR"	6
+LABT1114	"T(11	14) BY PCR"		6
+LABT1418	"T(14	18), BLOOD"	"T(14	18), Blood"	10
+LABT1418	"T(14	18), BLOOD"		6
+LABT1418	"T(14	18), BLOOD"	"T(14	18), Blood (molecular diagnostic)"	2
+LABT1517	"T(15	17) PML/RAR, BLOOD"	"T(15	17) PML-RARA , Blood"	123
+LABT1517	"T(15	17) PML/RAR, BLOOD"		24
+LABT1517	"T(15	17) PML/RAR, BLOOD"	"T(15	17) PML-RARA , Blood (molecular diagnostic)"	6
+LABT1517	"T(15	17) PML/RAR, BLOOD"	"t(15	17) PML/RAR, Blood"	6
+LABT3	T3, TOTAL	T3, Total	845
+LABT3	T3, TOTAL		126
+LABTBCLO	B - CELL CLONALITY PCR, TISSUE		6
+LABTBCLO	B - CELL CLONALITY PCR, TISSUE	B-Cell Clonality PCR, Tissue	2
+LABTBCR	BCR - ABL, TISSUE	BCR-ABL, Tissue	4
+LABTBIL	BILIRUBIN TOTAL, SERUM/PLASMA	Bilirubin Total, Serum/Plasma	3546
+LABTBIL	BILIRUBIN TOTAL, SERUM/PLASMA		1272
+LABTBIL	BILIRUBIN TOTAL, SERUM/PLASMA	Bilirubin, Total, Serum/Plasma	351
+LABTBIL	BILIRUBIN TOTAL, SERUM/PLASMA	Bilirubin, total, serum/plasma	4
+LABTBILFR	BILIRUBIN TOTAL AND FRACTIONATED, SERUM	Bilirubin Total and Fractionated, Serum	9257
+LABTBILFR	BILIRUBIN TOTAL AND FRACTIONATED, SERUM		574
+LABTBLYM	T SUBSETS AND B LYMPHOCYTES, BLOOD	T Subsets And B Lymphocytes, Blood	876
+LABTBLYM	T SUBSETS AND B LYMPHOCYTES, BLOOD		32
+LABTBP	TRANSFUSION BLOOD PRODUCT 1		129012
+LABTBP	TRANSFUSION BLOOD PRODUCT 1	Transfusion Blood Product 1	90728
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2		79246
+LABTBP1	TRANSFUSION BLOOD PRODUCT 2	Transfusion Blood Product 2	49997
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11		2492
+LABTBP10	TRANSFUSION BLOOD PRODUCT 11	Transfusion Blood Product 11	1728
+LABTBP100	TRANSFUSION BLOOD PRODUCT 101		2
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12		2260
+LABTBP11	TRANSFUSION BLOOD PRODUCT 12	Transfusion Blood Product 12	1568
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13		1822
+LABTBP12	TRANSFUSION BLOOD PRODUCT 13	Transfusion Blood Product 13	1262
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14		1622
+LABTBP13	TRANSFUSION BLOOD PRODUCT 14	Transfusion Blood Product 14	1156
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15		1366
+LABTBP14	TRANSFUSION BLOOD PRODUCT 15	Transfusion Blood Product 15	1034
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16		1216
+LABTBP15	TRANSFUSION BLOOD PRODUCT 16	Transfusion Blood Product 16	918
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17		1016
+LABTBP16	TRANSFUSION BLOOD PRODUCT 17	Transfusion Blood Product 17	768
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18		926
+LABTBP17	TRANSFUSION BLOOD PRODUCT 18	Transfusion Blood Product 18	698
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19		790
+LABTBP18	TRANSFUSION BLOOD PRODUCT 19	Transfusion Blood Product 19	606
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20		714
+LABTBP19	TRANSFUSION BLOOD PRODUCT 20	Transfusion Blood Product 20	558
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3		23404
+LABTBP2	TRANSFUSION BLOOD PRODUCT 3	Transfusion Blood Product 3	15371
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21		608
+LABTBP20	TRANSFUSION BLOOD PRODUCT 21	Transfusion Blood Product 21	506
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22		580
+LABTBP21	TRANSFUSION BLOOD PRODUCT 22	Transfusion Blood Product 22	450
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23		508
+LABTBP22	TRANSFUSION BLOOD PRODUCT 23	Transfusion Blood Product 23	388
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24		476
+LABTBP23	TRANSFUSION BLOOD PRODUCT 24	Transfusion Blood Product 24	344
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25		412
+LABTBP24	TRANSFUSION BLOOD PRODUCT 25	Transfusion Blood Product 25	308
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26		366
+LABTBP25	TRANSFUSION BLOOD PRODUCT 26	Transfusion Blood Product 26	286
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27		334
+LABTBP26	TRANSFUSION BLOOD PRODUCT 27	Transfusion Blood Product 27	272
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28		300
+LABTBP27	TRANSFUSION BLOOD PRODUCT 28	Transfusion Blood Product 28	254
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29		278
+LABTBP28	TRANSFUSION BLOOD PRODUCT 29	Transfusion Blood Product 29	218
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30		250
+LABTBP29	TRANSFUSION BLOOD PRODUCT 30	Transfusion Blood Product 30	208
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4		19918
+LABTBP3	TRANSFUSION BLOOD PRODUCT 4	Transfusion Blood Product 4	13279
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31		228
+LABTBP30	TRANSFUSION BLOOD PRODUCT 31	Transfusion Blood Product 31	186
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32		204
+LABTBP31	TRANSFUSION BLOOD PRODUCT 32	Transfusion Blood Product 32	176
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33		184
+LABTBP32	TRANSFUSION BLOOD PRODUCT 33	Transfusion Blood Product 33	162
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34		174
+LABTBP33	TRANSFUSION BLOOD PRODUCT 34	Transfusion Blood Product 34	156
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35		162
+LABTBP34	TRANSFUSION BLOOD PRODUCT 35	Transfusion Blood Product 35	148
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36		156
+LABTBP35	TRANSFUSION BLOOD PRODUCT 36	Transfusion Blood Product 36	138
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37		138
+LABTBP36	TRANSFUSION BLOOD PRODUCT 37	Transfusion Blood Product 37	128
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38		124
+LABTBP37	TRANSFUSION BLOOD PRODUCT 38	Transfusion Blood Product 38	120
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39		110
+LABTBP38	TRANSFUSION BLOOD PRODUCT 39	Transfusion Blood Product 39	108
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40	Transfusion Blood Product 40	108
+LABTBP39	TRANSFUSION BLOOD PRODUCT 40		104
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5		9096
+LABTBP4	TRANSFUSION BLOOD PRODUCT 5	Transfusion Blood Product 5	6166
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41		98
+LABTBP40	TRANSFUSION BLOOD PRODUCT 41	Transfusion Blood Product 41	94
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42	Transfusion Blood Product 42	88
+LABTBP41	TRANSFUSION BLOOD PRODUCT 42		86
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43		82
+LABTBP42	TRANSFUSION BLOOD PRODUCT 43	Transfusion Blood Product 43	78
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44		80
+LABTBP43	TRANSFUSION BLOOD PRODUCT 44	Transfusion Blood Product 44	72
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45		78
+LABTBP44	TRANSFUSION BLOOD PRODUCT 45	Transfusion Blood Product 45	70
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46		72
+LABTBP45	TRANSFUSION BLOOD PRODUCT 46	Transfusion Blood Product 46	66
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47		68
+LABTBP46	TRANSFUSION BLOOD PRODUCT 47	Transfusion Blood Product 47	56
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48		68
+LABTBP47	TRANSFUSION BLOOD PRODUCT 48	Transfusion Blood Product 48	54
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49		60
+LABTBP48	TRANSFUSION BLOOD PRODUCT 49	Transfusion Blood Product 49	54
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50		58
+LABTBP49	TRANSFUSION BLOOD PRODUCT 50	Transfusion Blood Product 50	48
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6		8216
+LABTBP5	TRANSFUSION BLOOD PRODUCT 6	Transfusion Blood Product 6	5626
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51		58
+LABTBP50	TRANSFUSION BLOOD PRODUCT 51	Transfusion Blood Product 51	48
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52		52
+LABTBP51	TRANSFUSION BLOOD PRODUCT 52	Transfusion Blood Product 52	46
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53		50
+LABTBP52	TRANSFUSION BLOOD PRODUCT 53	Transfusion Blood Product 53	42
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54		44
+LABTBP53	TRANSFUSION BLOOD PRODUCT 54	Transfusion Blood Product 54	40
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55		42
+LABTBP54	TRANSFUSION BLOOD PRODUCT 55	Transfusion Blood Product 55	36
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56		40
+LABTBP55	TRANSFUSION BLOOD PRODUCT 56	Transfusion Blood Product 56	32
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57		36
+LABTBP56	TRANSFUSION BLOOD PRODUCT 57	Transfusion Blood Product 57	30
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58		34
+LABTBP57	TRANSFUSION BLOOD PRODUCT 58	Transfusion Blood Product 58	30
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59		28
+LABTBP58	TRANSFUSION BLOOD PRODUCT 59	Transfusion Blood Product 59	26
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60		28
+LABTBP59	TRANSFUSION BLOOD PRODUCT 60	Transfusion Blood Product 60	26
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7		5662
+LABTBP6	TRANSFUSION BLOOD PRODUCT 7	Transfusion Blood Product 7	3870
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61		26
+LABTBP60	TRANSFUSION BLOOD PRODUCT 61	Transfusion Blood Product 61	24
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62		26
+LABTBP61	TRANSFUSION BLOOD PRODUCT 62	Transfusion Blood Product 62	24
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63		26
+LABTBP62	TRANSFUSION BLOOD PRODUCT 63	Transfusion Blood Product 63	20
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64		26
+LABTBP63	TRANSFUSION BLOOD PRODUCT 64	Transfusion Blood Product 64	16
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65		22
+LABTBP64	TRANSFUSION BLOOD PRODUCT 65	Transfusion Blood Product 65	14
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66		22
+LABTBP65	TRANSFUSION BLOOD PRODUCT 66	Transfusion Blood Product 66	14
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67		22
+LABTBP66	TRANSFUSION BLOOD PRODUCT 67	Transfusion Blood Product 67	14
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68		22
+LABTBP67	TRANSFUSION BLOOD PRODUCT 68	Transfusion Blood Product 68	12
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69		22
+LABTBP68	TRANSFUSION BLOOD PRODUCT 69	Transfusion Blood Product 69	10
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70		18
+LABTBP69	TRANSFUSION BLOOD PRODUCT 70	Transfusion Blood Product 70	10
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8		5218
+LABTBP7	TRANSFUSION BLOOD PRODUCT 8	Transfusion Blood Product 8	3588
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71		18
+LABTBP70	TRANSFUSION BLOOD PRODUCT 71	Transfusion Blood Product 71	10
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72		18
+LABTBP71	TRANSFUSION BLOOD PRODUCT 72	Transfusion Blood Product 72	10
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73		16
+LABTBP72	TRANSFUSION BLOOD PRODUCT 73	Transfusion Blood Product 73	10
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74		16
+LABTBP73	TRANSFUSION BLOOD PRODUCT 74	Transfusion Blood Product 74	10
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75		16
+LABTBP74	TRANSFUSION BLOOD PRODUCT 75	Transfusion Blood Product 75	8
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76		14
+LABTBP75	TRANSFUSION BLOOD PRODUCT 76	Transfusion Blood Product 76	8
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77		14
+LABTBP76	TRANSFUSION BLOOD PRODUCT 77	Transfusion Blood Product 77	8
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78		10
+LABTBP77	TRANSFUSION BLOOD PRODUCT 78	Transfusion Blood Product 78	8
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79		10
+LABTBP78	TRANSFUSION BLOOD PRODUCT 79	Transfusion Blood Product 79	6
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80		10
+LABTBP79	TRANSFUSION BLOOD PRODUCT 80	Transfusion Blood Product 80	6
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9		3550
+LABTBP8	TRANSFUSION BLOOD PRODUCT 9	Transfusion Blood Product 9	2332
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81		10
+LABTBP80	TRANSFUSION BLOOD PRODUCT 81	Transfusion Blood Product 81	4
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82		10
+LABTBP81	TRANSFUSION BLOOD PRODUCT 82	Transfusion Blood Product 82	4
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83		10
+LABTBP82	TRANSFUSION BLOOD PRODUCT 83	Transfusion Blood Product 83	4
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84		10
+LABTBP83	TRANSFUSION BLOOD PRODUCT 84	Transfusion Blood Product 84	4
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85		8
+LABTBP84	TRANSFUSION BLOOD PRODUCT 85	Transfusion Blood Product 85	4
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86		8
+LABTBP85	TRANSFUSION BLOOD PRODUCT 86	Transfusion Blood Product 86	4
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87		6
+LABTBP86	TRANSFUSION BLOOD PRODUCT 87	Transfusion Blood Product 87	4
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88		6
+LABTBP87	TRANSFUSION BLOOD PRODUCT 88	Transfusion Blood Product 88	4
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89		6
+LABTBP88	TRANSFUSION BLOOD PRODUCT 89	Transfusion Blood Product 89	4
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90		6
+LABTBP89	TRANSFUSION BLOOD PRODUCT 90	Transfusion Blood Product 90	4
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10		3202
+LABTBP9	TRANSFUSION BLOOD PRODUCT 10	Transfusion Blood Product 10	2132
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91		6
+LABTBP90	TRANSFUSION BLOOD PRODUCT 91	Transfusion Blood Product 91	2
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92		6
+LABTBP91	TRANSFUSION BLOOD PRODUCT 92	Transfusion Blood Product 92	2
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93		6
+LABTBP92	TRANSFUSION BLOOD PRODUCT 93	Transfusion Blood Product 93	2
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94		4
+LABTBP93	TRANSFUSION BLOOD PRODUCT 94	Transfusion Blood Product 94	2
+LABTBP94	TRANSFUSION BLOOD PRODUCT 95	Transfusion Blood Product 95	2
+LABTBP94	TRANSFUSION BLOOD PRODUCT 95		2
+LABTBP95	TRANSFUSION BLOOD PRODUCT 96		2
+LABTBP96	TRANSFUSION BLOOD PRODUCT 97		2
+LABTBP97	TRANSFUSION BLOOD PRODUCT 98		2
+LABTBP98	TRANSFUSION BLOOD PRODUCT 99		2
+LABTBP99	TRANSFUSION BLOOD PRODUCT 100		2
+LABTBPCRS	M. TUBERCULOSIS PCR, SPECIMEN	M. Tuberculosis PCR, Specimen	1203
+LABTBPCRS	M. TUBERCULOSIS PCR, SPECIMEN		262
+LABTCDIF	C DIFFICILE TOXIN B, STOOL	C Difficile Toxin B, Stool	2019
+LABTCDIF	C DIFFICILE TOXIN B, STOOL		995
+LABTCDIF	C DIFFICILE TOXIN B, STOOL	Stool C Diff Toxin B	21
+LABTCLON	T-CELL CLONALITY PCR, BLOOD	T-Cell Clonality PCR, Blood	173
+LABTCLON	T-CELL CLONALITY PCR, BLOOD	T-Cell Clonality PCR, Blood (molecular diagnostic)	4
+LABTCLON	T-CELL CLONALITY PCR, BLOOD	"T-Cell Clonality, PCR	 TCRG/TCRB"	2
+LABTCLON	T-CELL CLONALITY PCR, BLOOD		2
+LABTCPSA	TOTAL AND COMPLEXED PSA	Total And Complexed PSA	10
+LABTCPSA	TOTAL AND COMPLEXED PSA	Total & Complexed PSA	8
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE	Thrombelastograph Clotting Profile	2095
+LABTEGCP	THROMBELASTOGRAPH CLOTTING PROFILE		238
+LABTES	TESTOSTERONE	Testosterone	874
+LABTES	TESTOSTERONE		30
+LABTESFBT	TESTOSTERONE, TOTAL, BIO, FREE	Testosterone, Total, Bio, Free	21
+LABTFPSA	TOTAL AND FREE PSA	Total And Free PSA	23
+LABTHAMP	THERMAL AMPLITUDE	Thermal Amplitude	64
+LABTHAMP	THERMAL AMPLITUDE SCREEN	THERMAL AMPLITUDE SCREEN	20
+LABTHAMP	THERMAL AMPLITUDE		8
+LABTHEOL	THEOPHYLLINE LEVEL	Theophylline Level	551
+LABTHEOL	THEOPHYLLINE LEVEL		64
+LABTHEOL	THEOPHYLLINE LEVEL	Theophylline	2
+LABTHG	THYROGLOBULIN	Thyroglobulin	142
+LABTHG	THYROGLOBULIN		6
+LABTHG	THYROGLOBULIN	SHC Thyroglobulin, serum	2
+LABTHIOMT	THIOPURINE METABOLITES	Thiopurine Metabolites	58
+LABTHIOMT	THIOPURINE METABOLITES		8
+LABTHS	GROUP A STREP CULTURE		238
+LABTHS	GROUP A STREP CULTURE	Group A Strep Culture	134
+LABTMONA	TRICHOMONAS EXAM	Trichomonas Exam	28
+LABTMONA	TRICHOMONAS EXAM		4
+LABTMSI	MICROSATELLITE INSTABILITY		60
+LABTMSI	MICROSATELLITE INSTABILITY	Microsatellite Instability	28
+LABTNI	TROPONIN I	Troponin I	149073
+LABTNI	TROPONIN I		20534
+LABTNI	TROPONIN I	TROPONIN I	2
+LABTOBPOL	TOBRAMYCIN PEAK LEVEL	Tobramycin Peak Level	196
+LABTOBPOL	TOBRAMYCIN PEAK LEVEL		2
+LABTOBPRL	TOBRAMYCIN TROUGH LEVEL	Tobramycin Trough Level	1524
+LABTOBPRL	TOBRAMYCIN TROUGH LEVEL		50
+LABTOBUNL	TOBRAMYCIN SINGLE DAILY DOSE LEVEL	Tobramycin Single Daily Dose Level	2
+LABTOTALC	PROTEIN C AG, TOTAL		82
+LABTOTALC	PROTEIN C AG, TOTAL	Protein C Ag, Total	58
+LABTOTALS	PROTEIN S AG, TOTAL	Protein S Ag, Total	46
+LABTOTALS	PROTEIN S AG, TOTAL		28
+LABTOXATP	TOXO AB TRANSPLANT PNL, SERUM	Toxo Ab Transplant Pnl, Serum	12
+LABTOXGM	TOXOPLASMA IGG IGM	Toxoplasma IgG IgM	339
+LABTOXGM	TOXOPLASMA IGG IGM	TOXOPLASMA IGG IGM	19
+LABTOXGM	TOXOPLASMA IGM & IGG	Toxoplasma IgM & IgG	2
+LABTOXGMR	TOXO PREGNANCY PANEL	Toxo Pregnancy Panel	4
+LABTOXIG	TOXOPLASMA IGG	Toxoplasma IgG	231
+LABTOXIG	TOXOPLASMA IGG	TOXOPLASMA IGG	7
+LABTOXIG	TOXOPLASMA IGG	Toxoplasma Serology  Details	4
+LABTOXIG	TOXOPLASMA IGG	Toxoplasma Serology (Phase II labs to be drawn on the second day)	2
+LABTOXIG	TOXOPLASMA SEROLOGY	Toxoplasma Serology	1
+LABTOXIM	TOXOPLASMA IGM	Toxoplasma IgM	50
+LABTOXO	TOXOPLASMA SEROLOGY	Toxoplasma Serology	256
+LABTOXO	TOXOPLASMA IGG	TOXOPLASMA IGG	180
+LABTOXO	TOXOPLASMA IGG		14
+LABTOXO	TOXOPLASMA SEROLOGY		10
+LABTOXO	TOXOPLASMA IGG	Toxoplasmosis Serology	2
+LABTOXO	TOXOPLASMA SEROLOGY	Toxoplasma Serology (Phase II labs to be drawn on the second day)	1
+LABTOXOAB	TOXOPLASMA IGM & IGG	TOXOPLASMA IGM & IGG	12
+LABTOXOAB	TOXOPLASMA IGM & IGG		4
+LABTOXOC	TOXOPLASMOSIS, CSF	Toxoplasmosis, CSF	6
+LABTOXOC	TOXOPLASMOSIS, CSF		2
+LABTOXOIM	TOXOPLASMA IGM		6
+LABTOXOM	TOXOPLASMOSIS IGM		5
+LABTOXOP2	TOXOPLASMOSIS PCR	Toxoplasmosis PCR	123
+LABTOXOP2	TOXOPLASMOSIS PCR	Toxoplasmosis, PCR	90
+LABTOXOP2	TOXOPLASMOSIS PCR		83
+LABTOXOPR	TOXO PREGNANCY PANEL	Toxo Pregnancy Panel	8
+LABTOXOPR	TOXO PREGNANCY PANEL		2
+LABTOXPCR	TOXOPLASMOSIS PCR	Toxoplasmosis PCR	159
+LABTOXPCR	TOXOPLASMA PCR	Toxoplasma PCR	104
+LABTOXPCR	TOXOPLASMOSIS PCR	Toxoplasmosis, PCR	25
+LABTOXPCR	TOXOPLASMOSIS PCR		11
+LABTOXPCR	TOXOPLASMOSIS PCR	Toxoplasma PCR	2
+LABTP	PROTEIN TOTAL, SERUM/PLASMA	Protein, Total, Serum/Plasma	2702
+LABTP	PROTEIN TOTAL, SERUM/PLASMA		1202
+LABTRAZO	TRAZODONE	Trazodone	2
+LABTRFN	TRANSFERRIN	Transferrin	4922
+LABTRFN	TRANSFERRIN		72
+LABTRFS	TRANSFERRIN SATURATION	Transferrin Saturation	13462
+LABTRFS	TRANSFERRIN SATURATION		1454
+LABTRFS	TRANSFERRIN SATURATION	TRANSFERRIN SATURATION	2
+LABTRICHG	TRICHINELLA IGG AB ELISA	Trichinella IgG Ab ELISA	16
+LABTRIG	TRIGLYCERIDES, SERUM / PLASMA	Triglycerides, Serum/Plasma	8547
+LABTRIG	TRIGLYCERIDES, SERUM / PLASMA	Triglyceride, Serum/Plasma	6010
+LABTRIG	TRIGLYCERIDES, SERUM / PLASMA		1016
+LABTRIG	TRIGLYCERIDES, SERUM / PLASMA	Triglyceride, serum/plasma	30
+LABTRXNWI	TRANSFUSION REACTION WORKUP		858
+LABTRXNWI	TRANSFUSION REACTION WORKUP	Transfusion Reaction Workup	608
+LABTSH	TSH	TSH	43306
+LABTSH	TSH		4908
+LABTSH	TSH	TSH (LABTSH)	12
+LABTSHFT4	TSH W/ REFLEX FT4	TSH W/ Reflex FT4	3590
+LABTSHFT4	TSH W/ REFLEX FT4	TSH With Reflex FT4	134
+LABTSHFT4	TSH W/ REFLEX FT4		52
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP	Red Blood Cells - Transfusion Service Call Slip	100681
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP	Platelets - Transfusion Service Call Slip	38016
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP	Plasma - Transfusion Service Call Slip	29473
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP (RELEASE BLOOD PRODUCT ONLY)	Red Blood Cells - Transfusion Service Call Slip (Release Blood Product Only)	21927
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP (RELEASE BLOOD PRODUCT ONLY)	Platelets - Transfusion Service Call Slip (Release Blood Product Only)	9787
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP (RELEASE BLOOD PRODUCT ONLY)	Plasma - Transfusion Service Call Slip (Release Blood Product Only)	5568
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP	Cryoprecipitate - Transfusion Service Call Slip	3089
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP (RELEASE BLOOD PRODUCT ONLY)	Cryoprecipitate - Transfusion Service Call Slip (Release Blood Product Only)	749
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP	#NAME?	318
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP (RELEASE BLOOD PRODUCT ONLY)	#NAME?	68
+LABTSLIP	TRANSFUSION SERVICE CALL SLIP	Rho(D) Immune Globulin (RhoGam) - Transfusion Service Call Slip	23
+LABTSMISI	UNLISTED TRANSFUSION TEST	Unlisted Transfusion Test	51
+LABTT	THROMBIN TIME		716
+LABTT	THROMBIN TIME	Thrombin Time	714
+LABTT1418	"T(14	18), TISSUE"	"T(14	18), Tissue"	2
+LABTT1517	"T(15	17) PML/RAR, TISSUE"	"T(15	17) PML/RAR, Tissue"	32
+LABTT1517	"T(15	17) PML/RAR, TISSUE"		2
+LABTT4	T4, TOTAL	T4, Total	641
+LABTT4	T4, TOTAL	T4, total	140
+LABTT4	T4, TOTAL		112
+LABTTCLO	T-CELL CLONALITY PCR, TISSUE	T-Cell Clonality PCR, Tissue	11
+LABTTCLO	T-CELL CLONALITY PCR, TISSUE		10
+LABTTM	TRANSPLANT THERAPY MONITORING	Transplant Therapy Monitoring	132
+LABTTM	TRANSPLANT THERAPY MONITORING		10
+LABTTRMET	AMYLOIDOSIS DNA TITER	Amyloidosis DNA Titer	14
+LABTTRMET	AMYLOIDOSIS DNA TITER		4
+LABTXCAB	TOXOCARA AB ELISA	Toxocara Ab Elisa	32
+LABTXCAB	TOXOCARA AB ELISA		2
+LABTYPSNI	TYPE AND SCREEN	Type and Screen	108898
+LABTYPSNI	TYPE AND SCREEN	TYPE AND SCREEN	26358
+LABTYPSNI	TYPE AND SCREEN		10270
+LABTYPSNI	TYPE AND SCREEN	Type and screen	6
+LABTYPSNI	TYPE AND SCREEN	Type And Screen	6
+LABTYPSNI	TYPE AND SCREEN	Type and Screen #1	6
+LABTYPSNI	TYPE AND SCREEN	Type and Screen #2	2
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic	106910
+LABUA	URINALYSIS WITH MICROSCOPIC		27418
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis with microscopic	7597
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis with Microscopic, Clean Catch	4064
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic: Clean Catch	3233
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis with Microscopic, Cath	1308
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic:Catheter	684
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic, Clean Catch	408
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic, Cath	226
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic, Catheter	114
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic - Clean Catch	14
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis with Microscopic	8
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic  	3
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic - Catheter	2
+LABUA	URINALYSIS WITH MICROSCOPIC	Urinalysis With Microscopic, Not Specifiec	2
+LABUALB	ALBUMIN WITH CREATININE, URINE (RANDOM)	Albumin with Creatinine, Urine (Random)	652
+LABUALB	ALBUMIN WITH CREATININE, URINE (RANDOM)		126
+LABUALB	ALBUMIN WITH CREATININE, URINE (RANDOM)	Albumin with Creat, Urine (Random)	18
+LABUALB	ALBUMIN WITH CREATININE, URINE (RANDOM)	Albumin with Creatinine, Urine (Random) (microalbumin)	1
+LABUALBT	ALBUMIN, URINE (TIMED)	Albumin, Urine (Timed)	25
+LABUALBT	ALBUMIN, URINE (TIMED)		4
+LABUAMICR	URINALYSIS MICROSCOPIC ONLY	Urinalysis Microscopic Only	2
+LABUAMY	AMYLASE WITH CREATININE, URINE (RANDOM)	Amylase with Creatinine, Urine (Random)	313
+LABUAMY	AMYLASE WITH CREATININE, URINE (RANDOM)		70
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture	77241
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE		17718
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture (Clean Catch)	675
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture ( Clean Catch)	396
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture, Catheter	240
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture (Catheter)	162
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture, Clean Catch	90
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture, Not Specified	16
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture LAB COLLECT	4
+LABUAPRN	URINALYSIS, SCREEN FOR CULTURE	Urinalysis, Screen for Culture, Supra Pubic	2
+LABUAS	URINALYSIS MACROSCOPIC ONLY	Urinalysis Macroscopic Only	9237
+LABUAS	URINALYSIS MACROSCOPIC ONLY		2644
+LABUAS	URINALYSIS MACROSCOPIC ONLY	Urinalysis macroscopic only	745
+LABUAS	URINALYSIS MACROSCOPIC ONLY	Urinalysis Macroscopic Only, Catheter	60
+LABUAS	URINALYSIS MACROSCOPIC ONLY	Urinalysis Macroscopic Only, Clean Catch	24
+LABUAS	URINALYSIS MACROSCOPIC ONLY	Urinalysis Macroscopic Only, Not Specified	8
+LABUAS	URINALYSIS MACROSCOPIC ONLY	Urinalysis Macroscopic Only (LABUAS)	2
+LABUB2MG	BETA - 2 - MICROGLOBULIN, URINE	Beta-2-Microglobulin, Urine	30
+LABUB2MG	BETA - 2 - MICROGLOBULIN, URINE		10
+LABUBICAR	BICARBONATE, URINE	Bicarbonate, Urine	68
+LABUBICAR	BICARBONATE, URINE		26
+LABUCA	CALCIUM WITH CREATININE, URINE (RANDOM)	Calcium With Creatinine, Urine (Random)	80
+LABUCA	CALCIUM WITH CREATININE, URINE (RANDOM)		14
+LABUCARN	CARNITINE, URINE	Carnitine, Urine	10
+LABUCARN	CARNITINE, URINE		4
+LABUCAT	CALCIUM, URINE (TIMED)	Calcium, Urine (Timed)	92
+LABUCAT	CALCIUM, URINE (TIMED)		30
+LABUCL	CHLORIDE WITH CREATININE, URINE (RANDOM)	Chloride with Creatinine, Urine (Random)	135
+LABUCL	CHLORIDE WITH CREATININE, URINE (RANDOM)		78
+LABUCL	CHLORIDE WITH CREATININE, URINE (RANDOM)	Chloride with Creat, Urine (Random)	4
+LABUCLT	CHLORIDE, URINE (TIMED)	Chloride, Urine (Timed)	28
+LABUCLT	CHLORIDE, URINE (TIMED)		8
+LABUCORT	FREE CORTISOL, URINE (TIMED)	FREE CORTISOL, URINE (TIMED)	42
+LABUCORT	FREE CORTISOL, URINE (TIMED)	Free Cortisol, Urine (Timed)	22
+LABUCORT	CORTISOL FREE, URINE	Cortisol Free, Urine	2
+LABUCORT	CORTISOL FREE, URINE		2
+LABUCR	CREATININE, URINE (RANDOM)	Creatinine, Urine (Random)	11858
+LABUCR	CREATININE, URINE (RANDOM)		3770
+LABUCRT	CREATININE, URINE (TIMED)	Creatinine, Urine (Timed)	2709
+LABUCRT	CREATININE, URINE (TIMED)		488
+LABUCRT	CREATININE, URINE (TIMED)	Creatinine Clearance, Urine (Timed)	8
+LABUCRT	CREATININE, URINE (TIMED)	Creatinine, Urine (Timed)(24 hour)	8
+LABUELP	ELECTROLYTE PANEL, URINE W/O CREATININE		8
+LABUELP	ELECTROLYTE PANEL, URINE W/O CREATININE	Electrolyte Panel, Urine W/o Creatinine	2
+LABUEOS	EOSINOPHILS, URINE	Eosinophils, Urine	3842
+LABUEOS	EOSINOPHILS, URINE		1220
+LABUEOS	EOSINOPHILS, URINE	Eosinophils, urine	14
+LABUEP	ELECTROLYTE PANEL, URINE WITH CREATININE	Electrolyte Panel, Urine With Creatinine	9745
+LABUEP	ELECTROLYTE PANEL, URINE WITH CREATININE		2896
+LABUEP	ELECTROLYTE PANEL, URINE WITH CREATININE	Electrolyte Panel,Ur w/ Creat	74
+LABUEP	ELECTROLYTE PANEL, URINE WITH CREATININE	Electrolyte panel, urine	19
+LABUFCATE	CATECHOLAMINES, URN FREE	Catecholamines, Urn Free	2
+LABUFCATE	CATECHOLAMINES, URN FREE		2
+LABUGL	GLUCOSE WITH CREATININE, URINE (RANDOM)		8
+LABUGL	GLUCOSE WITH CREATININE, URINE (RANDOM)	Glucose with Creatinine, Urine (Random)	2
+LABUGLT	GLUCOSE, URINE (TIMED)	Glucose, Urine (Timed)	8
+LABUHEMO	HEMOSIDERIN, URINE	Hemosiderin, Urine	224
+LABUHEMO	HEMOSIDERIN, URINE		34
+LABUHGB	HEMOGLOBIN, URINE	Hemoglobin, Urine	116
+LABUHGB	HEMOGLOBIN, URINE		26
+LABUHIST	HISTAMINE, URINE	Histamine, Urine	8
+LABUHIST	HISTAMINE, URINE		2
+LABUHVA	HOMOVANILLIC ACID, URINE	Homovanillic Acid, Urine	6
+LABUHVA	HOMOVANILLIC ACID, URINE		2
+LABUK	POTASSIUM WITH CREATININE, URINE (RANDOM)	Potassium With Creatinine, Urine (Random)	320
+LABUK	POTASSIUM WITH CREATININE, URINE (RANDOM)		116
+LABUKT	POTASSIUM, URINE (TIMED)	Potassium, Urine (Timed)	88
+LABUKT	POTASSIUM, URINE (TIMED)		26
+LABUMAG	MAGNESIUM WITH CREATININE, URINE (RANDOM)	Magnesium with Creatinine, Urine (Random)	26
+LABUMAG	MAGNESIUM WITH CREATININE, URINE (RANDOM)		2
+LABUMAGT	MAGNESIUM, URINE (TIMED)	Magnesium, Urine (Timed)	22
+LABUMAGT	MAGNESIUM, URINE (TIMED)		6
+LABUMYOSC	MYOGLOBIN SCREEN, URINE	Myoglobin Screen, Urine	152
+LABUMYOSC	MYOGLOBIN SCREEN, URINE		96
+LABUNA	SODIUM WITH CREATININE, URINE (RANDOM)	Sodium with Creatinine, Urine (Random)	7033
+LABUNA	SODIUM WITH CREATININE, URINE (RANDOM)		2096
+LABUNA	SODIUM WITH CREATININE, URINE (RANDOM)	Sodium with Creat, Urine (Random)	89
+LABUNA	SODIUM WITH CREATININE, URINE (RANDOM)	Sodium	1
+LABUNAT	SODIUM, URINE (TIMED)	Sodium, Urine (Timed)	493
+LABUNAT	SODIUM, URINE (TIMED)		102
+LABUORG	ORGANIC ACIDS, URINE	Organic Acids, Urine	55
+LABUORG	ORGANIC ACIDS, URINE		14
+LABUOROT	OROTIC ACID, URINE	Orotic Acid, Urine	24
+LABUOROT	OROTIC ACID, URINE		8
+LABUOSM	OSMOLALITY, URINE	Osmolality, Urine	14531
+LABUOSM	OSMOLALITY, URINE		4308
+LABUOSM	OSMOLALITY, URINE	Osmolality, urine	8
+LABUPBGQT	PORPHOBILINOGEN QUANTITATIVE, URINE	Porphobilinogen Quantitative, Urine	168
+LABUPBGQT	PORPHOBILINOGEN QUANTITATIVE, URINE		42
+LABUPHOS	PHOSPHATE WITH CREATININE, URINE (RANDOM)	Phosphate With Creatinine, Urine (Random)	73
+LABUPHOS	PHOSPHATE WITH CREATININE, URINE (RANDOM)		30
+LABUPHOS	PHOSPHATE WITH CREATININE, URINE (RANDOM)	Phosphate With Creat, Urine (Random)	2
+LABUPHOST	PHOSPHORUS, URINE (TIMED)	Phosphorus, Urine (Timed)	33
+LABUPHOST	PHOSPHORUS, URINE (TIMED)		8
+LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	Urine Protein Immunofixation Electrophoresis	2446
+LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS		664
+LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	Urine protein immunofix electrophoresis	6
+LABUPIE	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS	Urine Protein Immunofix Electrophoresis	4
+LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)	Urine Protein Immunofixation Electrophoresis (Timed)	98
+LABUPIET	URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS (TIMED)		66
+LABUPOR	PORPHYRINS FRACTIONATED, URINE	Porphyrins Fractionated, Urine	127
+LABUPOR	PORPHYRINS FRACTIONATED, URINE		52
+LABUPREG	PREGNANCY TEST, URINE	Pregnancy Test, Urine	6939
+LABUPREG	PREGNANCY TEST, URINE		1592
+LABUPREG	PREGNANCY TEST, URINE	Pregnancy test, urine	405
+LABUPREG	PREGNANCY TEST, URINE	Pregnancy Test, urine, Qualitative	48
+LABURAMY	AMYLASE, URINE (RANDOM)	Amylase, Urine (Random)	26
+LABURAMY	AMYLASE, URINE (RANDOM)		4
+LABURCA	CALCIUM, URINE (RANDOM)	Calcium, Urine (Random)	142
+LABURCA	CALCIUM, URINE (RANDOM)		48
+LABURCL	CHLORIDE, URINE (RANDOM)	Chloride, Urine (Random)	879
+LABURCL	CHLORIDE, URINE (RANDOM)		240
+LABURCL	CHLORIDE, URINE (RANDOM)	Chloride Urine (Random)	36
+LABURED	REDUCING SUBSTANCE, URINE	Reducing Substance, Urine	2
+LABURGL	GLUCOSE, URINE (RANDOM)	Glucose, Urine (Random)	116
+LABURGL	GLUCOSE, URINE (RANDOM)		30
+LABURIC	URIC ACID, SERUM / PLASMA	Uric Acid, Serum/Plasma	32813
+LABURIC	URIC ACID, SERUM / PLASMA		2120
+LABURK	POTASSIUM, URINE (RANDOM)	Potassium, Urine (Random)	1618
+LABURK	POTASSIUM, URINE (RANDOM)		432
+LABURMAG	MAGNESIUM, URINE (RANDOM)	Magnesium, Urine (Random)	38
+LABURMAG	MAGNESIUM, URINE (RANDOM)		14
+LABURNA	SODIUM, URINE (RANDOM)	Sodium, Urine (Random)	16768
+LABURNA	SODIUM, URINE (RANDOM)		4790
+LABURNC	URINE CULTURE	Urine Culture	72694
+LABURNC	URINE CULTURE		25252
+LABURNC	URINE CULTURE	Urine culture	5660
+LABURPHOS	PHOSPHATE, URINE (RANDOM)	Phosphate, Urine (Random)	425
+LABURPHOS	PHOSPHATE, URINE (RANDOM)		38
+LABURTP	PROTEIN, URINE (RANDOM)	Protein, Urine (Random)	1269
+LABURTP	PROTEIN, URINE (RANDOM)		364
+LABURUN	UREA, URINE (RANDOM)	Urea, Urine (Random)	5783
+LABURUN	UREA, URINE (RANDOM)		1544
+LABURURIC	URIC ACID, URINE (RANDOM)	Uric Acid, Urine (Random)	110
+LABURURIC	URIC ACID, URINE (RANDOM)		18
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Use one of the following orders for Lab Transfusion Services:	477
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -*** REMOVE this order, it is for your information only. ***	58
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	40
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -	29
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -*** REMOVE this order, it is for your information only. ***    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	22
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -*** REMOVE this order, it is for your information only. *** Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	16
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	13
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only.	8
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen	4
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -  	3
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only.     Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	3
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only. Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	3
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units RBC	3
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen prior to surgery please	3
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	3
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -*** REMOVE this order, it is for your information only. ***    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 units PRBC	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -a. [1049]  IP LAB TRANSFUSION SERVICE, STANDARD	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross 2 units of PRBC	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and Cross 6 units of PRBC, 2 units FFP, 2 units platelets, 2 units of cryoprecipitate. Patient will be going to OR at 11 AM. Hold for OR USE. Do not give in the floor.	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen please	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen, pending surgery	2
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -  Carotid prec.  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -  Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -*2 units PRBC	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -1 unit FFP	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -1 unit Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -1Use one of the following orders for Lab Transfusion Services:	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 EDTA tubes.Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 U PRBCs	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 units for OR Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 units of PRBC, on call in the OR Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 units of PRBC\'s	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 units PRBCs	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -2 units rbcs Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -4 units FFP 4 units PRBC For surgery 11/25/2013 Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -A	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -ARVI, lavender top	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Autologous unit only REMOVE this order, it is for your information only.     Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -baUse one of the following orders for Lab Transfusion Services:	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Blood type verification	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Blood Verification	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	"Use Order Set for Lab Transfusion -Check HCT 8 hours after end of surgery.  If HCT 26-30, transfuse 1 U PRBC	  if HCT 22-26 transfuse 2 U PRBC	 if HCT < 22 transfuse 2 U PRBC and call PAMF orthopedist on call."	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Cross-match 2U PRBC	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Drop in Hb after the procedure (PPM implant).	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -FFP	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -FFP 2 units	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -For OR tomorrow    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -For PRBC 2 units during dialysis	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -For surgery scheduled 7/27/12	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -HOLD prbc 2 units	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Keep ahead 2 units of pRBC and 2 units of platelets	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Keep ahead 2 units PRBC	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Low h/h    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Low hct, 2 unit of prbc Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -On call to OR Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Platelet and PRBC for every Monday and Friday, possible transfusion	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please draw 2 EDTA tubes. Thanks.  Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please have 2 units of FFP available for transfusion for OR. Thank you.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please have 2 units of PRBC ready and on hold in preparation for surgery tomorrow. Thanks.  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please hold 2 10-packs of platelets for possible operation in the OR	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please hold 4-units of blood for the OR	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	"Use Order Set for Lab Transfusion -Please hold 4 UNITS PRBC for pt	 actively bleeding    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  "	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please hold FFP STAT	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please keep 2 units of platelets available typed and cross ahead	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please keep ahead 4 UNITS pRBC.    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please send 1 prbc	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please stat type and cross upon arrival to PACU	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please transfuse 1unit aulologous blood this am	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Please transfuse autologous Blood  Thank You Matt Bernard MD 13515	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -PLEASE TYPE AND CROSS, 2 UNITS READY FOR OPERATING ROOM, STAT ORDER IN PREOP AREA Use one of the following orders for Lab Transfusion Services:	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Possible platelet transfusion pre and post-op	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -PRBC x1 Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Prepare 1 unit of PRBC for transfusion ASAP. Thank you.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Pt going to OR now	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only.    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -REMOVE this order, it is for your information only.  Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -STAT PLEASE      	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -To do on Sunday (7/14/2013): type and cross for TWO UNITS please. Thanks.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -tpe and cross 4 units of blood please	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -transfuse  1 unit of FFP now and  1unit on hold for procedure	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1  Unit packed red blood cells	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 pRBC    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 unit	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 unit cryoprecipitate	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 unit of PRBC  . Give  Blood  Over  2 hours.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 unit packed red blood cells    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 unit PRBC now for postop Hct of 28.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 1 unit PRBC\'s	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 Units FFP	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units Fresh Frozen plasma	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units of FFP	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units of PRBC over 2 hours each bag.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units of PRBC.      	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units PRBC over 2 hrs each	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse 2 units RBC  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse one unit platelet, irradiated leukoreduced.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse platelet 1 unit	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse platelet concentrate 1 unit Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse platelets	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse RBC  1 unit	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse second unit of packed red cells in PACU    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Transfuse stat RBC\'s and FFP	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Two units please	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type &  Cross. For 2 units RBC.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type & Cross for 3UPRBC    Use this Lab Transfusion Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARD  	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross 2 untis PRBC	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross for 2 units  Packed cells and plasma      	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross for 2 units RBC\'s	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and Cross for 4.	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross in anticipation of pelvic surgery with orthopaedics tomorrow.   	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and Cross match  2 units PRBC	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and Cross one unit PRBCs	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross only	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross please	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and cross: Hold 2 units PRBCs. Going to OR for exploratory laparotomy with colorectal surgery 2/8/11	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen pending surgery	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen pending surgery please	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and screen please prior to surgery	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Type and scrren prior to surgery please	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Use one of the following orders for Lab Transfusion Services: a	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Use one of the following orders for Lab Transfusion Services: b Autologous blood, Type and Cross 1 unit	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Use one of the following orders for Lab Transfusion Services: Process as STAT pt to go to OR today	1
+LABUSEOSTS	USE ORDER SET FOR LAB TRANSFUSION	Use Order Set for Lab Transfusion -Usn Services order set:  (1049) IP LAB TRANSFUSION SERVICE, STANDARDe this Lab Transfusio	1
+LABUSPG	SPECIFIC GRAVITY, URINE	Specific Gravity, Urine	11422
+LABUSPG	SPECIFIC GRAVITY, URINE		8162
+LABUSPG	SPECIFIC GRAVITY, URINE	SPECIFIC GRAVITY, URINE	4465
+LABUTHI	THIOCYANATE, URINE (RANDOM)	Thiocyanate, Urine (Random)	6
+LABUTHI	THIOCYANATE, URINE (RANDOM)		2
+LABUTP	PROTEIN, URINE (TIMED)	Protein, Urine (Timed)	1116
+LABUTP	PROTEIN, URINE (TIMED)		320
+LABUTP	PROTEIN, URINE (TIMED)	Protein, Urine (Timed)(24 hour)	6
+LABUTPRAN	PROTEIN WITH CREATININE, URINE (RANDOM)	Protein with Creatinine, Urine (Random)	2957
+LABUTPRAN	PROTEIN WITH CREATININE, URINE (RANDOM)		738
+LABUTPRAN	PROTEIN WITH CREATININE, URINE (RANDOM)	Protein with Creat, Urine (Random)	28
+LABUUN	UREA WITH CREATININE, URINE (RANDOM)	Urea with Creatinine, Urine (Random)	2728
+LABUUN	UREA WITH CREATININE, URINE (RANDOM)		1058
+LABUUN	UREA WITH CREATININE, URINE (RANDOM)	Urea with Creat, Urine (Random)	92
+LABUUNT	UREA NITROGEN, URINE (TIMED)	Urea Nitrogen, Urine (Timed)	1715
+LABUUNT	UREA NITROGEN, URINE (TIMED)		316
+LABUURIC	URIC ACID WITH CREATININE, URINE (RANDOM)	Uric Acid With Creatinine, Urine (Random)	27
+LABUURIC	URIC ACID WITH CREATININE, URINE (RANDOM)		12
+LABUURIC	URIC ACID WITH CREATININE, URINE (RANDOM)	Uric Acid With Creat, Urine (Random)	6
+LABUURICT	URIC ACID, URINE (TIMED)	Uric Acid, Urine (Timed)	22
+LABUURICT	URIC ACID, URINE (TIMED)		8
+LABUVH	VMA AND HVA	VMA And HVA	54
+LABUVH	VMA AND HVA		24
+LABUVMA	VANILLYMANDELIC ACID, URINE	Vanillymandelic Acid, Urine	66
+LABVANPOL	VANCOMYCIN PEAK LEVEL	Vancomycin Peak Level	400
+LABVANPOL	VANCOMYCIN PEAK LEVEL		52
+LABVANPOL	VANCOMYCIN PEAK LEVEL	Vancomycin, Peak	10
+LABVANPOL	VANCOMYCIN PEAK LEVEL	Vancomycin, peak	1
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	Vancomycin Trough Level	84928
+LABVANPRL	VANCOMYCIN TROUGH LEVEL		8482
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	Vancomycin, Trough	591
+LABVANPRL	VANCOMYCIN TROUGH LEVEL	Vancomycin, trough	5
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	Vancomycin Single Daily Dose Level	5165
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL		1292
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	191
+LABVANUNL	VANCOMYCIN SINGLE DAILY DOSE LEVEL	Vancomycin, Single Daily Dose	63
+LABVANUNL	VANCOMYCIN SINGLE DAILY LEVEL	Vancomycin Single Daily Level	8
+LABVBG	BLOOD GASES, VENOUS	Blood Gases, Venous	85094
+LABVBG	BLOOD GASES, VENOUS		12038
+LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG	EBV VIRAL CAPSID ANTIGEN IgG	124
+LABVCAIGG	EB VIRAL CAPSID AG IGG AB		33
+LABVCAIGG	EBV VIRAL CAPSID ANTIGEN IGG		4
+LABVCAIGG	EB VIRAL CAPSID AG IGG AB	EB Viral Capsid Ag IgG Ab	1
+LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM	EBV VIRAL CAPSID ANTIGEN IgM	138
+LABVCAIGM	EB VIRAL CAPSID AG IGM AB		33
+LABVCAIGM	EBV VIRAL CAPSID ANTIGEN IGM		4
+LABVCUL	VIRAL CULTURE		3426
+LABVCUL	VIRAL CULTURE	Viral Culture	1555
+LABVCUL	VIRAL CULTURE	Viral Culture, CSF	578
+LABVCUL	VIRAL CULTURE	Viral culture	128
+LABVCUL	VIRAL CULTURE	Viral Culture, Bronchoalveolar lavage	96
+LABVCUL	VIRAL CULTURE	Viral Culture, Pleural Fluid	68
+LABVCUL	VIRAL CULTURE	Viral Culture, Pericardial Fluid	16
+LABVCUL	VIRAL CULTURE	Viral Culture, Peritoneal Fluid	10
+LABVCUL	VIRAL CULTURE	Viral Culture, Bone Marrow (ACD, Citrate or EDTA)	8
+LABVCULF	VIRAL CULTURE, FLUID	VIRAL CULTURE, FLUID	216
+LABVCULF	VIRAL CULTURE, FLUID	Viral Culture CSF	31
+LABVCULO	VIRAL CULTURE, OCULAR	VIRAL CULTURE, OCULAR	36
+LABVCULR	VIRAL CULTURE, RESPIRATORY	VIRAL CULTURE, RESPIRATORY	638
+LABVCULS	VIRAL CULTURE, STOOL/RECTAL	VIRAL CULTURE, STOOL/RECTAL	187
+LABVCULT	VIRAL CULTURE, TISSUE	VIRAL CULTURE, TISSUE	608
+LABVCULU	VIRAL CULTURE, URINE	VIRAL CULTURE, URINE	4
+LABVD25H	VITAMIN D, 25-HYDROXYVITAMIN	Vitamin D, 25-Hydroxyvitamin	2646
+LABVD25H	VITAMIN D, 25-HYDROXYVITAMIN		90
+LABVHHA	VH HYPERMUTATION ASSAY	VH Hypermutation Assay	26
+LABVHHA	VH HYPERMUTATION ASSAY		4
+LABVIRSO1	MISC. VIROLOGY TEST	Misc. Virology Test	70
+LABVIRSO1	MISC. VIROLOGY TEST		40
+LABVISC	VISCOSITY, SERUM	Viscosity, Serum	229
+LABVISC	VISCOSITY, SERUM		14
+LABVITB1P	VITAMIN B1, PLASMA	Vitamin B1, Plasma	20
+LABVITB1P	VITAMIN B1, PLASMA		6
+LABVITK	VITAMIN K1, SERUM	Vitamin K1, Serum	71
+LABVITK	VITAMIN K1, SERUM		4
+LABVLDL	VLDL (CALCULATED)		6
+LABVLDL	VLDL (CALCULATED)	VLDL (Calculated)	4
+LABVOLTL	VOLATILE SCREEN	Volatile Screen	9448
+LABVOLTL	VOLATILE SCREEN		1594
+LABVOLTL	VOLATILE SCREEN	Volatile screen, serum alcohol	1014
+LABVOLTL	VOLATILE SCREEN	ETOH/Drugs: Contract, toxicology screen (Volatile Screen)	20
+LABVOLTL	VOLATILE SCREEN	Volatile screen	6
+LABVOLTL	VOLATILE SCREEN	ETOH/Drugs:  Contract, toxicology screen (Volatile Screen)	2
+LABVORI	VORICONAZOLE LEVEL	Voriconazole Level	44
+LABVORI	VORICONAZOLE LEVEL		4
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	Comprehensive Hepatitis Panel	2272
+LABVP3	COMPREHENSIVE HEPATITIS PANEL		102
+LABVP3	COMPREHENSIVE HEPATITIS PANEL	Comprehensive hepatitis panel	47
+LABVP4	COMP HEPATITIS PANEL	COMP HEPATITIS PANEL	1038
+LABVP4	COMPREHENSIVE HEPATITIS PANEL	Comprehensive Hepatitis Panel	20
+LABVP4	COMP HEPATITIS PANEL	Comprehensive Hepatitis Panel	16
+LABVP4	COMP HEPATITIS PANEL	Comprehensive hepatitis panel	8
+LABVP5	COMP HEPATITIS PANEL	COMP HEPATITIS PANEL	7
+LABVP5	COMPREHENSIVE HEPATITIS PANEL	Comprehensive Hepatitis Panel	1
+LABVPAL	VALPROIC ACID	Valproic Acid	4354
+LABVPAL	VALPROIC ACID		298
+LABVPAL	VALPROIC ACID	Valproic acid	123
+LABVSTLC	VIRAL STOOL CULTURE	Viral Stool Culture	345
+LABVSTLC	VIRAL STOOL CULTURE		252
+LABVSTLC	VIRAL STOOL CULTURE	Viral Stool Culture, Stool	178
+LABVWAG	VON WILLEBRAND AG	Von Willebrand Ag	208
+LABVWAG	VON WILLEBRAND AG		32
+LABVWAG	VON WILLEBRAND AG	Von Willebrand Antigen	4
+LABVWFPI	VON WILLEBRAND PROTEASE INHIBITOR	Von Willebrand Protease Inhibitor	20
+LABVWFPI	VON WILLEBRAND PROTEASE INHIBITOR		2
+LABVWNTRP	INTERPRETATION, VW SCREEN		6
+LABVWSCN	VON WILLEBRAND SCREEN	Von Willebrand Screen	170
+LABVWSCN	VON WILLEBRAND SCREEN		10
+LABVZAB	VARICELLA ZOSTER VIRUS AB IGG	VZ Virus Ab IgG	619
+LABVZAB	VARICELLA ZOSTER VIRUS AB IGG	VZ VIRUS AB IGG	160
+LABVZAB	VARICELLA ZOSTER VIRUS AB IGG		62
+LABVZAB	VARICELLA ZOSTER VIRUS AB IGG	Varicella Zoster Virus Ab IgG	45
+LABVZPAN	VZV IGG IGM PANEL	VZV IGG IGM Panel	74
+LABVZVCSF	VZV DNA PCR QUAL, CSF	VZV DNA PCR QUAL, CSF	475
+LABVZVIGG	VZ IGG	VZ Virus Ab IgG	353
+LABVZVIGG	VZ IGG	VZ IgG	200
+LABVZVIGG	VARICELLA ZOSTER VIRUS AB IGG	VZ Virus Ab IgG	56
+LABVZVIGG	VZ IGG	Varicella Zoster Virus Ab IgG	36
+LABVZVIGG	VZ IGG		8
+LABVZVIGG	VARICELLA ZOSTER VIRUS AB IGG	VZ VIRUS AB IGG	2
+LABVZVIGG	VZ IGG	VZ VIRUS AB IGG	2
+LABVZVQL	VZV DNA PCR QUAL, NON LESION	VZV DNA PCR Qual, Non Lesion	322
+LABVZVQLP	VZV DNA PCR QUAL, PLASMA	VZV DNA PCR QUAL, PLASMA	318
+LABWDC	WOUND CULTURE	Wound Culture	6211
+LABWDC	WOUND CULTURE		1714
+LABWDC	WOUND CULTURE	Wound Culture (LABWDC)	2
+LABWDCG	WOUND CULTURE AND GRAM STAIN		1156
+LABWDCG	WOUND CULTURE AND GRAM STAIN	Wound Culture And Gram Stain	872
+LABWDCS	WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP		1902
+LABWDCS	WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP	Wound Culture And Gram Stain, Deep/Surgical/Asp	1112
+LABWNVAB	WEST NILE VIRUS AB PANEL, CSF	West Nile Virus Ab Panel, CSF	926
+LABWNVAB	WEST NILE VIRUS AB PANEL, CSF		204
+LABWNVAB	WEST NILE VIRUS AB PANEL, CSF	West nile virus Ab panel, CSF	2
+LABWNVRNA	WEST NILE VIRUS RNA, RT  -PCR	West Nile Virus RNA, RT-PCR	104
+LABWNVRNA	WEST NILE VIRUS RNA, RT  -PCR		18
+LABXAMH	ANTI - MULLERIAN HORMONE	Anti-Mullerian Hormone	2
+LABXGAD65	ANTI GAD 65 ANTIBODIES	Anti GAD 65 Antibodies	20
+LABXGAD65	ANTI GAD 65 ANTIBODIES		2
+LABXHIA	HUMAN INSULIN ANTIBODY	Human Insulin Ab	45
+LABXHIA	HUMAN INSULIN ANTIBODY		2
+LABXICA51	ICA 512 AB	ICA 512 Ab	102
+LABXICA51	ICA 512 AB		4
+LABXIGF1	PEDIATRIC IGF-1	PEDIATRIC IGF-1	32
+LABY17OHP	17 - HYDROXYPROGESTERONE, SERUM	17-Hydroxyprogesterone, Serum	2
+LABY21HYX	21 - HYDROXYLASE AB, SERUM	21-Hydroxylase Ab, Serum	4
+LABY25VD	VITAMIN D, 25-HYDROXY	Vitamin D, 25-Hydroxy	184
+LABY25VD	VITAMIN D, 25-HYDROXY		18
+LABY25VD	PEDIATRIC VITAMIN D, 25-HYDROXY	Pediatric Vitamin D, 25-Hydroxy	10
+LABY5NUC	5 - NUCLEOTIDASE	5-Nucleotidase	5
+LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	Alpha-1-Antitrypsin Phenotype	131
+LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE	Alpha 1 Antitrypsin Phen	24
+LABYA1APH	ALPHA - 1 - ANTITRYPSIN PHENOTYPE		8
+LABYA1AS	ALPHA - 1 - ANTITRYPSIN	Alpha-1-Antitrypsin	857
+LABYA1AS	ALPHA - 1 - ANTITRYPSIN	Alpha 1-Antitrypsin	72
+LABYA1AS	ALPHA - 1 - ANTITRYPSIN		36
+LABYACE	ANGIOTENSIN CONVERTING ENZYME, SERUM	Angiotensin Converting Enzyme, Serum	38
+LABYACE	ANGIOTENSIN CONVERTING ENZYME, SERUM		6
+LABYACIGA	CARDIOLIPIN AB, IGA	Cardiolipin Ab, IgA	50
+LABYACIGA	CARDIOLIPIN AB, IGA	Cardiolipin AB, IgA	6
+LABYACIGA	CARDIOLIPIN AB, IGA		4
+LABYAFCSF	ALPHA FETOPROTEIN, CSF	AFP, CSF	42
+LABYAFCSF	ALPHA FETOPROTEIN, CSF	Alpha Fetoprotein, CSF	22
+LABYAFCSF	ALPHA FETOPROTEIN, CSF		16
+LABYAHA	HISTONE ANTIBODIES	Histone Antibodies	111
+LABYAHA	HISTONE ANTIBODIES		4
+LABYALDO	ALDOLASE, SERUM	Aldolase, Serum	28
+LABYALDO	ALDOLASE, SERUM		10
+LABYALDOS	ALDOSTERONE, SERUM	Aldosterone, Serum	25
+LABYALDOS	ALDOSTERONE, SERUM		6
+LABYALUM	ALUMINUM	Aluminum	4
+LABYAMA	MITOCHONDRIAL AB M2, SERUM	Mitochondrial Ab M2, Serum	48
+LABYAMA	MITOCHONDRIAL AB M2, SERUM	Mitochondrial AB M2, Serum	5
+LABYAMEB	E. HISTOLYTICA AB	E. Histolytica Ab	44
+LABYAMEB	E. HISTOLYTICA AB		6
+LABYAMIO	AMIODARONE	Amiodarone, Serum	31
+LABYAMIO	AMIODARONE, SERUM	Amiodarone, Serum	11
+LABYAMISO	AMYLASE, PANCREATIC	Amylase, Pancreatic	244
+LABYAMISO	AMYLASE, PANCREATIC		12
+LABYAMNOR	AMITRIPTYLINE AND NORTRIPTYLINE	Amitriptyline and Nortriptyline	50
+LABYAMPHB	AMPHOTERICIN B	Amphotericin B	2
+LABYAMPHB	AMPHOTERICIN B		2
+LABYANDRO	ANDROSTENEDIONE, SERUM	Androstenedione, Serum	6
+LABYANRNP	RNP AB, IGG	RNP Ab, IgG	117
+LABYANRNP	AUTOANTIBODIES TO U1RNP	AutoAb To U1RNP	46
+LABYANRNP	AUTOANTIBODIES TO U1RNP		14
+LABYANRNP	RNP AB, IGG		8
+LABYANTIN	INSULIN ANTIBODIES	Insulin Antibodies	18
+LABYANTIN	INSULIN ANTIBODIES		2
+LABYANTJO	AUTOANTIBODIES TO JO1	AutoAb To JO1	55
+LABYANTJO	AUTOANTIBODIES TO JO1		7
+LABYANTSM	AUTOANTIBODIES TO SM	AutoAb To SM	57
+LABYANTSM	AUTOANTIBODIES TO SM		22
+LABYAPOE	APOLIPOPROTEIN E GENOTYPING	Apolipoprotein E Genotyping	2
+LABYARBIN	ACH RECEPTOR BINDING AB	ACH Receptor Binding Ab	2
+LABYARSNC	ARSENIC, BLOOD	Arsenic, Blood	4
+LABYASMA	ANTI - SMOOTH MUSCLE AB	Anti-Smooth Muscle Ab	50
+LABYASMA	ANTI - SMOOTH MUSCLE AB		8
+LABYASMA	ANTI - SMOOTH MUSCLE AB	Anti-Smooth Muscle AB	6
+LABYASU	ALPHASUBUNIT PITUITARY MARKER	Alpha Sub Unit of PGH	60
+LABYASU	ALPHASUBUNIT PITUITARY MARKER		10
+LABYAT3AG	ANTITHROMBIN AG, PLASMA	Antithrombin Antigen, Plasma	30
+LABYAT3AG	ANTITHROMBIN AG, PLASMA	Antithrombin Ag, Plasma	26
+LABYAT3AG	ANTITHROMBIN AG, PLASMA		8
+LABYAVH	ARGININE VASOPRESSIN (ADH)	Arginine Vasopressin (ADH)	56
+LABYAVH	ARGININE VASOPRESSIN (ADH)		12
+LABYBABE	BABESIA MICROTI IGG AND IGM AB		4
+LABYBILEA	BILE ACIDS TOTAL	Bile Acids Total	8
+LABYBILEA	BILE ACIDS TOTAL		2
+LABYBLAS	BLASTOMYCES AB	Blastomyces Ab	141
+LABYBLAS	BLASTOMYCES AB		12
+LABYBLAS	BLASTOMYCES AB	Blastomyces AB	6
+LABYBOH	BETA-HYDROXYBUTYRATE	Beta-Hydroxybutyrate	652
+LABYBOH	BETA-HYDROXYBUTYRATE		156
+LABYBRUC	BRUCELLA IGG AND IGM AB	Brucella IgG And IgM Ab	332
+LABYBRUC	BRUCELLA IGG AND IGM AB		14
+LABYBSAP	BONE ALKALINE PHOSPHATASE, SERUM	Bone Alkaline Phosphatase, Serum	240
+LABYBSAP	BONE ALKALINE PHOSPHATASE, SERUM		28
+LABYBUPRO	BUPROPION, SERUM	Bupropion, Serum	2
+LABYC1IN	C1 ESTERASE INHIBITOR AG	C1 Esterase Inhibitor Ag	51
+LABYC2729	BCA AB  (CA27.29)	BCA Ab  (CA27.29)	21
+LABYC2REF	C2 COMPLEMENT AG	C2 Complement Ag	8
+LABYC4F	C4 COMPLEMENT FUNCTIONAL	C4 Complement Functional	27
+LABYC4F	C4 COMPLEMENT FUNCTIONAL		4
+LABYC5REF	C5 COMPLEMENT AG	C5 Complement Ag	12
+LABYCALCI	CALCITONIN, PLASMA	Calcitonin, Plasma	2
+LABYCARO	CAROTENE	Carotene	20
+LABYCARO	CAROTENE		2
+LABYCCPIG	ANTI CCP	Anti CCP	497
+LABYCCPIG	ANTI CCP		30
+LABYCCPIG	ANTI CCP	Cyclic Citrullinated Peptide Ab	10
+LABYCDTN	CARBOHYDRATE DEFICIENT TRANSFERRIN	Carbohydrate DeficientTransferrin	8
+LABYCEAC	CEA, CSF	CEA, Spinal Fluid	18
+LABYCEAC	CEA, CSF	CEA, CSF	10
+LABYCEAC	CEA, CSF		6
+LABYCENT	CENTROMERE ANTIBODIES	Centromere Antibodies	187
+LABYCENT	CENTROMERE ANTIBODIES		11
+LABYCERU	CERULOPLASMIN	Ceruloplasmin	1124
+LABYCERU	CERULOPLASMIN	Ceruloplasmin, Total	136
+LABYCERU	CERULOPLASMIN		74
+LABYCH50	COMPLEMENT TOTAL	Complement Total	341
+LABYCH50	COMPLEMENT TOTAL		14
+LABYCHAGA	TRYPANOSOMA CRUZI AB, IGG	Trypanosoma Cruzi Ab, IgG	116
+LABYCHAGA	TRYPANOSOMA CRUZI AB, IGG		10
+LABYCHRA	CHROMOGRANIN A	Chromogranin A	281
+LABYCHRA	CHROMOGRANIN A		8
+LABYCHRBC	ACETYLCHOLINESTERASE, RBC	Acetylcholinesterase, RBC	2
+LABYCHRBC	ACETYLCHOLINESTERASE, RBC		2
+LABYCHROM	CHROMIUM	Chromium	67
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY	Chlamydia Species Serology for Respiratory	100
+LABYCLAMP	CHLAMYDIA SPECIES SEROLOGY FOR RESPIRATORY		10
+LABYCLON	CLONAZEPAM, SERUM	Clonazepam, Serum	81
+LABYCLOZ	CLOZAPINE, SERUM	Clozapine, Serum	118
+LABYCLOZ	CLOZAPINE, SERUM		6
+LABYCMBP	MYELIN BASIC PROTEIN, CSF	Myelin Basic Protein, CSF	2
+LABYCMBP	MYELIN BASIC PROTEIN, CSF		2
+LABYCOPPR	COPPER, SERUM	Copper, Serum	591
+LABYCOPPR	COPPER, SERUM		16
+LABYCP	C - PEPTIDE, SERUM	C-Peptide, Serum	639
+LABYCP	C - PEPTIDE, SERUM		60
+LABYCPYRU	PYRUVATE, CSF	Pyruvate, CSF	41
+LABYCPYRU	PYRUVATE, CSF		8
+LABYCYANI	CYANIDE, BLOOD	Cyanide, Blood	103
+LABYCYANI	CYANIDE, BLOOD		12
+LABYCYSTC	CYSTATIN C, SERUM / PLASMA	Cystatin C, Serum/Plasma	4
+LABYDESIP	DESIPRAMINE, SERUM	Desipramine, Serum	70
+LABYDESIP	DESIPRAMINE, SERUM		4
+LABYDGIGA	GLIADIN (DEAMIDATED) IGA,SERUM	Gliadin Deamidated IgA, Serum	59
+LABYDGIGA	GLIADIN (DEAMIDATED) IGA,SERUM		10
+LABYDHEA	DEHYDROEPIANDROSTERONE, SERUM	Dehydroepiandrosterone, Serum	20
+LABYDHEA	DEHYDROEPIANDROSTERONE, SERUM		6
+LABYDHEAS	DEHYDROEPIANDROSTERONE, SULFATE	Dehydroepiandrosterone Sulfate	28
+LABYDHEAS	DEHYDROEPIANDROSTERONE, SULFATE		2
+LABYDIAZE	DIAZEPAM AND NORDIAZEPAM	Diazepam and Nordiazepam	4
+LABYDIAZE	DIAZEPAM AND NORDIAZEPAM		2
+LABYDIGT	DIGITOXIN, SERUM	Digitoxin, Serum	150
+LABYDIGT	DIGITOXIN, SERUM	Digitoxin	9
+LABYDIGT	DIGITOXIN, SERUM		6
+LABYDIPT	DIPHTHERIA TOXOID IGA AB	Diphtheria Toxoid IgA Ab	18
+LABYDISO	DISOPYRAMIDE, SERUM	Disopyramide, Serum	2
+LABYDNBT	ANTI - DNASE B TITER	Anti-DNASE B Titer	24
+LABYDOC	11-DEOXYCORTISOL, SERUM	Deoxycorticosteroids, Plasma	2
+LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC	Drug Screen Prescription/OTC	52
+LABYDROTC	DRUG SCREEN PRESCRIPTION / OTC		8
+LABYEB	EBV AB PROFILE, SERUM		28
+LABYEB	EBV AB PROFILE, SERUM	EBV Ab Panel	18
+LABYEBPCR	EPSTEIN-BARR VIRUS PCR, NON-BLOOD	Epstein-Barr Virus PCR, Non-Blood	248
+LABYEBPCR	EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)		96
+LABYEBPCR	EPSTEIN-BARR VIRUS PCR, CSF	EBV BY RAPID PCR, CSF	81
+LABYEBPCR	EBV BY RAPID PCR, CSF		60
+LABYEBPCR	EPSTEIN-BARR VIRUS PCR, CSF		52
+LABYEBPCR	EBV PCR, CSF	EBV PCR, CSF	38
+LABYEBPCR	EPSTEIN BARR VIRUS PCR, FLUIDS		31
+LABYEBPCR	EBV PCR, CSF		29
+LABYEBPCR	EBV BY RAPID PCR, CSF	EBV BY RAPID PCR, CSF	26
+LABYEBPCR	EBV PCR, FLUIDS (EPSTEIN-BARR VIRUS PCR, FLUIDS)	EBV BY RAPID PCR, CSF	24
+LABYEBPCR	EPSTEIN BARR VIRUS PCR, FLUIDS	Epstein Barr Virus PCR, Fluids	18
+LABYEBPCR	EBV PCR, CSF	EBV BY RAPID PCR, CSF	12
+LABYEBPCR	EPSTEIN BARR VIRUS PCR, FLUIDS	EBV BY RAPID PCR, CSF	10
+LABYEBPCR	EPSTEIN-BARR VIRUS PCR, NON-BLOOD	EBV BY RAPID PCR, CSF	2
+LABYEMA	ENDOMYSIAL ANTIBODIES, SERUM	Endomysial Antibodies, Serum	4
+LABYENABE	EXTRACTABLE NUCLEAR AG EVALUATION, SERUM		6
+LABYENABE	EXTRACTABLE NUCLEAR AG EVALUATION, SERUM	Extractable Nuclear Ag Evaluation, Serum	1
+LABYENPCR	ENTEROVIRUS PCR	Enterovirus, PCR, CSF	203
+LABYENPCR	ENTEROVIRUS PCR		149
+LABYENPCR	ENTEROVIRUS PCR	Enterovirus PCR	78
+LABYENPCR	ENTEROVIRUS PCR	Enterovirus, PCR	64
+LABYENTE	YERSINIA ENTEROCOLITICA AB	Yersinia Enterocolitica Ab	85
+LABYENTE	YERSINIA ENTEROCOLITICA AB		2
+LABYEPIAB	CUTANEOUS IMMUNOFLUOR AB	Cutaneous Immunofluor Ab	2
+LABYERYTH	ERYTHROPOIETIN	Erythropoietin,	555
+LABYERYTH	ERYTHROPOIETIN		65
+LABYERYTH	ERYTHROPOIETIN, SERUM	ERYTHROPOIETIN	12
+LABYERYTH	ERYTHROPOIETIN	ERYTHROPOIETIN	4
+LABYESTRI	ESTRIOL FREE	Estriol Free	2
+LABYESTRN	ESTRONE, SERUM	Estrone, Serum	2
+LABYESTRO	ESTROGENS E1 AND E2 FRACTIONATED	Estrogens E1 And E2 Fractionated	34
+LABYFACE	FATTY ACID PROFILE ESSENTIAL	Fatty Acid Profile Essential	20
+LABYFACE	FATTY ACID PROFILE ESSENTIAL		2
+LABYFCAR	CARBAMAZEPINE FREE AND TOTAL, SERUM	Carbamazepine Free And Total, Serum	93
+LABYFCAR	CARBAMAZEPINE FREE AND TOTAL, SERUM		6
+LABYFCHYL	LIPID ANALYSIS, BODY FLUID	Lipid Analysis, Body Fluid	12
+LABYFCHYL	LIPID ANALYSIS, BODY FLUID		10
+LABYFCL	CHLORIDE, FLUID	Chloride, Fluid	10
+LABYFCL	CHLORIDE, FLUID		10
+LABYFCL	CHLORIDE, FLUID	Chloride, Pleural Fluid	4
+LABYFCL	CHLORIDE, FLUID	Chloride, Peritoneal Fluid	2
+LABYFELBA	FELBAMATE	Felbamate	14
+LABYFFATQ	FAT, FECAL QUANTITATIVE	Fat, Fecal Quantitative	543
+LABYFFATQ	FAT, FECAL QUANTITATIVE		160
+LABYFGL	GLUCOSE, FLUID	Glucose, Fluid	578
+LABYFGL	GLUCOSE, FLUID		528
+LABYFGL	GLUCOSE, FLUID	Glucose, Pleural Fluid	72
+LABYFGL	GLUCOSE, FLUID	Glucose, Peritoneal  Fluid	18
+LABYFGL	GLUCOSE, FLUID	Glucose, Synovial Fluid	6
+LABYFGL	GLUCOSE, FLUID	Glucose, Pericardial  Fluid	2
+LABYFK	POTASSIUM, FLUID	Potassium, Fluid	8
+LABYFK	POTASSIUM, FLUID		4
+LABYFLUO	5 - FLUCYTOSINE	5-Flucytosine	86
+LABYFLUO	5 - FLUCYTOSINE		2
+LABYFLUZ	FLUCONAZOLE LEVEL	Fluconazole Level	225
+LABYFLUZ	FLUCONAZOLE LEVEL		14
+LABYFMG	MAGNESIUM, FLUID	Magnesium, Fluid	4
+LABYFMG	MAGNESIUM, FLUID	Magnesium, Pleural Fluid	2
+LABYFNA	SODIUM, FLUID	Sodium, Fluid	10
+LABYFNA	SODIUM, FLUID		6
+LABYFNA	SODIUM, FLUID	Sodium, Peritoneal Fluid	2
+LABYFPHEN	PHENYTOIN TOTAL AND FREE, PLASMA	Phenytoin Total And Free, Plasma	317
+LABYFPHEN	PHENYTOIN TOTAL AND FREE, PLASMA	Phenytoin Total and Free, Plasma	68
+LABYFPHEN	PHENYTOIN TOTAL AND FREE, PLASMA		16
+LABYFPHEN	PHENYTOIN TOTAL AND FREE, PLASMA	Phenytoin total, plasma	14
+LABYFRUCM	FRUCTOSAMINE	Fructosamine	163
+LABYFRUCM	FRUCTOSAMINE		2
+LABYFT3	T3, FREE	T3, Free	1777
+LABYFT3	T3, FREE		170
+LABYFUN	UREA NITROGEN, FLUID	Urea Nitrogen, Fluid	84
+LABYFUN	UREA NITROGEN, FLUID		36
+LABYFUN	UREA NITROGEN, FLUID	Urea Nitrogen, Pleural Fluid	16
+LABYFUN	UREA NITROGEN, FLUID	Urea Nitrogen, Peritoneal Fluid	4
+LABYFUN	UREA NITROGEN, FLUID	Urea Nitrogen, Pericardial  Fluid	2
+LABYFUN	UREA NITROGEN, FLUID	Urea Nitrogen, Synovial  Fluid	2
+LABYFVPA	VALPROIC ACID FREE AND TOTAL	Valproic Acid Free And Total	463
+LABYFVPA	VALPROIC ACID FREE AND TOTAL		20
+LABYFVPA	VALPROIC ACID FREE AND TOTAL	Valproic acid, total	14
+LABYGADAB	GLUTAMIC ACID DECARBOXY AB	Glutamic Acid Decarboxy Ab	15
+LABYGADAB	GLUTAMIC ACID DECARBOXY AB		5
+LABYGAS	GASTRIN, SERUM	Gastrin, Serum	6
+LABYGBMGE	GBM AB, IGG	GBM Ab, IgG	22
+LABYGBMGE	GBM AB, IGG	Gbm antibody, IgG	4
+LABYGDA	DRUG SCREEN, FLUIDS	Drug screen, fluids	110
+LABYGDA	DRUG SCREEN, FLUIDS		8
+LABYGDA	DRUG SCREEN, FLUIDS	Drug Screen, Fluids	2
+LABYGLUC	GLUCAGON	Glucagon	46
+LABYGLUC	GLUCAGON		8
+LABYHABQ	HEP B SURF AB QUANT	Hep B Surf AB Quant	325
+LABYHABQ	HEP B SURF AB QUANT	Hepatitis B Surface Ab Quantitative	119
+LABYHABQ	ANTI HBS QUANT	HEPATITIS B SURFACE AB (M)	107
+LABYHABQ	HEPATITIS B SURFACE AB QUANTITATIVE	Hepatitis B Surface Ab Quantitative	82
+LABYHABQ	ANTI HBS QUANT	Hepatitis B Surface Ab Quantitative	28
+LABYHABQ	HEP B SURF AB QUANT		22
+LABYHABQ	ANTI HBS QUANT	ANTI HBS QUANT	19
+LABYHABQ	ANTI HBS QUANT		16
+LABYHABQ	HEPATITIS B SURFACE AB QUANTITATIVE		4
+LABYHABQ	HEP B SURF AB QUANT	HEPATITIS B SURFACE AB (M)	1
+LABYHAET	HEMOPHILUS INFLUENZA IGG	Hemophilus Influenza IgG	44
+LABYHALO	HALOPERIDOL, SERUM	Haloperidol, Serum	4
+LABYHALO	HALOPERIDOL, SERUM		2
+LABYHCCSF	HCG BETA-SUBUNIT QUANTITATIVE, CSF	HCG Beta-Subunit Quantitative, CSF	42
+LABYHCCSF	HCG BETA-SUBUNIT QUANTITATIVE, CSF	HCG Beta-Subunit Qn, CSF	18
+LABYHCCSF	HCG BETA-SUBUNIT QUANTITATIVE, CSF		8
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE	Hemochromatosis HFE Gene	192
+LABYHEMOC	HEMOCHROMATOSIS HFE GENE		8
+LABYHGBP	HEMOGLOBIN, PLASMA	Hemoglobin, Plasma	402
+LABYHGBP	HEMOGLOBIN, PLASMA		17
+LABYHGH	GROWTH HORMONE, SERUM	Growth Hormone, Serum	8
+LABYHGH	GROWTH HORMONE, SERUM		2
+LABYHIST	HISTOPLASMA AB SCREEN	Histoplasma Ab Screen	38
+LABYHIST	HISTOPLASMA AB SCREEN		6
+LABYHIST	HISTOPLASMA AB SCREEN	Histoplasma Ab Screen (Phase II labs: To be drawn on second day)	3
+LABYHISTA	HISTAMINE, SERUM	Histamine, Serum	10
+LABYHISTA	HISTAMINE, PLASMA	HISTAMINE, PLASMA	6
+LABYHISTC	HISTOPLASMA AG, CSF	Histoplasma Ag, CSF	95
+LABYHISTC	HISTOPLASMA AG, CSF		24
+LABYHISTS	HISTOPLASMA AG, SERUM	Histoplasma Ag, Serum	1081
+LABYHISTS	HISTOPLASMA AG, SERUM		68
+LABYHISTU	HISTOPLASMA AG, URINE	Histoplasma Ag, Urine	2104
+LABYHISTU	HISTOPLASMA AG, URINE		612
+LABYHISTU	HISTOPLASMA AG, URINE	Histoplasma Antigen, Urine	6
+LABYHMPVP	HUMAN METAPNEUMOVIRUS PCR		24
+LABYHMPVP	HUMAN METAPNEUMOVIRUS PCR	Human Metapneumovirus PCR	12
+LABYHMS	HEAVY METALS SCREEN, BLOOD	Heavy Metals Screen, Blood	131
+LABYHMS	HEAVY METALS SCREEN, BLOOD		22
+LABYHPL	HUMAN PLACENTAL LACTOGEN	Human Placental Lactogen	6
+LABYICP	IMMUNE COMPLEX PANEL	Immune Complex Panel	8
+LABYIFBA	INTRINSIC FACTOR BLOCKING AB	Intrinsic Factor Blocking Ab	88
+LABYIFBA	INTRINSIC FACTOR BLOCKING AB		6
+LABYIGF1	INSULIN LIKE GROWTH FACTOR I	Insulin Like Growth Factor I	17
+LABYIGF1	INSULIN LIKE GROWTH FACTOR I		4
+LABYIGF2	INSULIN LIKE GROWTH FACTOR II	Insulin Like Growth Factor II	39
+LABYIGF2	INSULIN LIKE GROWTH FACTOR II		4
+LABYIMIPR	IMIPRAMINE AND DESIPRAMINE	Imipramine And Desipramine	13
+LABYINFAB	INFLUENZA AB PANEL	Influenza Ab Panel	131
+LABYINFAB	INFLUENZA AB PANEL		18
+LABYINH	INHIBIN B	Inhibin B	32
+LABYINH	INHIBIN B		4
+LABYINSFT	INSULIN FREE AND TOTAL	Insulin Free And Total	20
+LABYINSFT	INSULIN FREE AND TOTAL		2
+LABYITRA	ITRACONAZOLE LEVEL	Itraconazole	195
+LABYITRA	ITRACONAZOLE LEVEL		12
+LABYJCPCR	JC VIRUS DNA, PCR	JC VIRUS DNA, PCR	231
+LABYJCPCR	JC VIRUS DNA, PCR	JC Virus DNA, PCR	110
+LABYJCPCR	JC VIRUS DNA, PCR		60
+LABYKLFRE	IMMUNO FREE LIGHT CHAINS	Immuno Free Light Chains	20
+LABYLACTS	LACTOFERRIN DETECTION, STOOL	Lactoferrin Detection, Stool	209
+LABYLACTS	LACTOFERRIN DETECTION, STOOL		40
+LABYLAMO	LAMOTRIGINE	Lamotrigine	251
+LABYLAMO	LAMOTRIGINE		10
+LABYLAP	LEUKOCYTE ALKALINE PHOSPATASE SCORE	Leukocyte Alkaline Phospatase Score	16
+LABYLAP	LEUKOCYTE ALKALINE PHOSPATASE SCORE		2
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)	Fatty Acid Prof Peroxidase (C22-C26)	6
+LABYLCFA	FATTY ACID PROF PEROXIDASE (C22 - C26)		4
+LABYLDISO	LD ISOENZYMES	LD Isoenzymes	10
+LABYLDISO	LD ISOENZYMES		2
+LABYLEADB	LEAD, BLOOD	Lead, Blood	150
+LABYLEADB	LEAD, BLOOD		10
+LABYLEGS	LEGIONELLA PNEUMOPHILA, AB	Legionella Pneumophila, Ab	87
+LABYLEGS	LEGIONELLA PNEUMOPHILA, AB		4
+LABYLEPT	LEPTOSPIROSIS AB	Leptospirosis AB	135
+LABYLEPT	LEPTOSPIROSIS AB		10
+LABYLEPTN	LEPTIN	Leptin	2
+LABYLEVT	LEVETIRACETAM	Levetiracetam	214
+LABYLEVT	LEVETIRACETAM		27
+LABYLYSO	LYSOZYME, PLASMA	Lysozyme, Plasma	36
+LABYLYSO	LYSOZYME, PLASMA		2
+LABYMANG	MANGANESE	Manganese	67
+LABYMEM	MEASLES AB, IGM	Measles Ab, IgM	4
+LABYMERC	MERCURY, BLOOD	Mercury, Blood	4
+LABYMETFF	METANEPHRINES FRACTIONATED FREE, PLASMA	Metanephrines Fractionated Free, Plasma	491
+LABYMETFF	METANEPHRINES FRACTIONATED FREE, PLASMA		40
+LABYMETHS	METHSUXIMIDE	Methsuximide	4
+LABYMEXIL	MEXILETINE	Mexiletine	50
+LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT	Myasthenia Gravis Evaluation Adult	95
+LABYMGEA	MYASTHENIA GRAVIS EVALUATION ADULT		2
+LABYMPA	MYCOPHENOLIC ACID	Mycophenolic Acid	129
+LABYMPA	MYCOPHENOLIC ACID		19
+LABYMPO	MYELOPERIOXIDASE AB		4
+LABYMYCRB	MYCOPLASMA CULTURE, RESPIRATORY	Mycoplasma Culture, Respiratory	56
+LABYMYCRB	MYCOPLASMA CULTURE, RESPIRATORY		16
+LABYNABF	INTERFERON NEUTRALIZING  AB	Interferon Neutralizing Ab	2
+LABYNICK	NICKEL, SERUM	Nickel, Serum	4
+LABYNMOG	NMO IGG	NMO IgG	82
+LABYNMOG	NMO IGG	NMO-IgG	20
+LABYNMOG	NMO IGG		12
+LABYNOB	NEUTROPHIL OXIDATIVE BURST	Neutrophil Oxidative Burst	2
+LABYOSTEO	OSTEOCALCIN HUMAN	Osteocalcin Human	2
+LABYP4AB	PARAINFLUENZA AB	Parainfluenza Ab	4
+LABYPA	PROCAINAMIDE	Procainamide	3
+LABYPANPP	PANCREATIC POLYPEPTIDE	Pancreatic Polypeptide	4
+LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB	Parvovirus B19 IgG And IgM Ab	440
+LABYPARVO	PARVOVIRUS B19 IGG AND IGM AB		38
+LABYPCA	PARIETAL CELL AB, SERUM	Parietal Cell Ab, Serum	2
+LABYPENTO	PENTOBARBITAL LEVEL	Pentobarbital Level	56
+LABYPERAB	BORDETELLA PERTUSSIS IGG ABS	Bordetella pertussis IgG Abs	22
+LABYPERAB	BORDETELLA PERTUSSIS IGG AND IGM AB	Bordetella Pertussis IgG And IgM Ab	6
+LABYPERAB	BORDETELLA PERTUSSIS IGG ABS		2
+LABYPERAB	BORDETELLA PERTUSSIS IGG AND IGM AB		2
+LABYPHOLP	PHOSPHOLIPIDS	Phospholipids	4
+LABYPLAB	PLATELET AB	Platelet Ab	90
+LABYPLAB	PLATELET AB		10
+LABYPR3	PROTEINASE 3 ANTIBODY	PROTEINASE 3 ANTIBODY	288
+LABYPR3	PROTEINASE 3 AUTOANTIBODIES	Proteinase 3 Autoantibodies	70
+LABYPR3	PROTEINASE 3 ANTIBODY	Proteinase 3 Antibody	12
+LABYPR3	PROTEINASE 3 AUTOANTIBODIES		10
+LABYPREAL	PREALBUMIN	Prealbumin	15483
+LABYPREAL	PREALBUMIN		1353
+LABYPRIMI	PRIMIDONE AND PHENOBARBITAL	Primidone And Phenobarbital	6
+LABYPROIN	PROINSULIN, PLASMA	Proinsulin, Plasma	100
+LABYPROIN	PROINSULIN, PLASMA		39
+LABYPTHRP	PTH-RELATED PEPTIDE, PLASMA	PTH-Related Peptide, Plasma	609
+LABYPTHRP	PTH-RELATED PEPTIDE, PLASMA		34
+LABYPTSAB	PHOSPHATIDYLSERINE AB PANEL	Phosphatidylserine Ab Panel	4
+LABYPYRU	PYRUVIC ACID	Pyruvic Acid	75
+LABYPYRU	PYRUVIC ACID		8
+LABYQUIN	QUINIDINE	Quinidine	2
+LABYRAB	RABIES AB TITER CHK ENDPOINT	RABIES AB TITER CHK ENDPOINT	10
+LABYRENIN	RENIN ACTIVITY, PLASMA	Renin Activity, Plasma	18
+LABYRENIN	RENIN ACTIVITY, PLASMA		8
+LABYRETAB	RETICULIN AB	Reticulin Ab	10
+LABYRIBAB	RIBOSOMAL P PROTEIN AB	Ribosomal P Protein Ab	28
+LABYRIBAB	RIBOSOMAL P PROTEIN AB		4
+LABYRICK	RICKETTSIA AB PANEL	Rickettsia Ab Panel	74
+LABYRICK	RICKETTSIA AB PANEL		10
+LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE	Risperidone And 9-Hydroxyrisperdone	12
+LABYRISPM	RISPERIDONE AND 9 - HYDROXYRISPERDONE		6
+LABYRS	RSV IGG AND IGM AB	RSV IgG and IgM Ab	12
+LABYRUBM	RUBELLA AB IGM		2
+LABYSCERE	INFLAMM. BOWEL DISEASE PANEL,S	IDB Panel	6
+LABYSCERE	INFLAMM. BOWEL DISEASE PANEL,S		2
+LABYSCL70	AUTOANTIBODIES TO SCL 70	AutoAb To Scl 70	34
+LABYSCL70	AUTOANTIBODIES TO SCL 70		6
+LABYSDA	DRUG SCREEN, PLASMA	Drug Screen, Plasma	181
+LABYSDA	DRUG SCREEN, PLASMA	Drug screen, plasma	45
+LABYSDA	DRUG SCREEN, PLASMA		18
+LABYSEROT	SEROTONIN, SERUM	Serotonin, Serum	103
+LABYSEROT	SEROTONIN, SERUM		6
+LABYSERTR	SERTRALINE LEVEL	Sertraline Level	9
+LABYSES	SELENIUM, SERUM	Selenium, Serum	137
+LABYSES	SELENIUM, SERUM		2
+LABYSHBG	SEX HORMONE BINDING GLOBULIN	Sex Hormone Binding Globulin	6
+LABYSHBG	SEX HORMONE BINDING GLOBULIN		2
+LABYSMG	MAGNESIUM QUANTITATIVE, FECES	Magnesium Quantitative, Feces	24
+LABYSMG	MAGNESIUM QUANTITATIVE, FECES		4
+LABYSOMAT	SOMATOSTATIN	Somatostatin	20
+LABYSOMAT	SOMATOSTATIN		4
+LABYSSARO	AUTOANTIBODIES TO SS-A/RO	AutoAb To SS-A/RO	10
+LABYSSBLA	AUTOANTIBODIES TO SS-B/LA	AutoAb To SS-B/LA	8
+LABYSTA1A	ALPHA-1-ANTITRYPSIN, FECAL	Alpha-1-Antitrypsin, Fecal	6
+LABYSTA1A	ALPHA-1-ANTITRYPSIN, FECAL		2
+LABYSTCL	CHLORIDE, STOOL	Chloride, Stool	128
+LABYSTCL	CHLORIDE, STOOL		46
+LABYSTFR	SOLUBLE TRANSFERRIN RECEPTOR	Soluble Transferrin Receptor	215
+LABYSTFR	SOLUBLE TRANSFERRIN RECEPTOR		18
+LABYSTK	POTASSIUM, STOOL	Potassium, Stool	313
+LABYSTK	POTASSIUM, STOOL		190
+LABYSTK	POTASSIUM, RANDOM STOOL	Potassium, Random Stool	112
+LABYSTK	POTASSIUM, RANDOM STOOL	POTASSIUM, RANDOM STOOL	70
+LABYSTNA	SODIUM, STOOL	Sodium, Stool	309
+LABYSTNA	SODIUM, STOOL		194
+LABYSTNA	SODIUM, RANDOM STOOL	Sodium, Random Stool	116
+LABYSTNA	SODIUM, RANDOM STOOL	SODIUM, RANDOM STOOL	66
+LABYSTONE	KIDNEY STONE ANALYSIS		162
+LABYSTONE	KIDNEY STONE ANALYSIS	KIDNEY STONE ANALYSIS	88
+LABYSTONE	KIDNEY STONE ANALYSIS	KIDNEY STONE ANALYSIS Renal	25
+LABYSTONE	STONE ANALYSIS		18
+LABYSTONE	STONE ANALYSIS	Stone Analysis Renal	4
+LABYSTOSM	OSMOLALITY, STOOL	Osmolality, Stool	435
+LABYSTOSM	OSMOLALITY, STOOL		196
+LABYSTRAB	STRIATIONAL (STRIATED MUSCLE) AB	Striational (Striated Muscle) Ab	8
+LABYSTRYP	TRYPSIN, FECAL	Trypsin, Fecal	30
+LABYSTRYP	TRYPSIN, FECAL		16
+LABYSTRYP	TRYPSIN, FECAL	Trypsin, Stool	14
+LABYT3UP	T3 UPTAKE	T3 Uptake	2
+LABYT7P	FREE THYROXINE INDEX	Free Thyroxine Index	10
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN	Tricyclic Antidepressant Screen	47
+LABYTADID	TRICYCLIC ANTIDEPRESSANT SCREEN		2
+LABYTAT	THROMBIN - ANTITHROMBIN COMPLEX	Thrombin-Antithrombin Complex	54
+LABYTAT	THROMBIN - ANTITHROMBIN COMPLEX		4
+LABYTBG	THYROXINE BINDING GLOBULIN	Thyroxine Binding Globulin	2
+LABYTESFB	TESTOSTERONE, TOTAL, BIO, FREE	Testosterone, Total, Bio, Free	54
+LABYTESFB	TESTOSTERONE, TOTAL, BIO, FREE		2
+LABYTESFT	TESTOSTERONE, TOTAL AND FREE	Testosterone, Total And Free	262
+LABYTESFT	TESTOSTERONE, TOTAL AND FREE		18
+LABYTETT	TETANUS TOXOID IGG AB	Tetanus Toxoid IgG Ab	36
+LABYTHI	THIOCYANATE	Thiocyanate	56
+LABYTHI	THIOCYANATE		6
+LABYTICK	COLORADO TICK FEVER AB	Colorado Tick Fever Ab	2
+LABYTICK	COLORADO TICK FEVER AB		2
+LABYTOPIR	TOPIRAMATE	Topiramate	190
+LABYTOPIR	TOPIRAMATE		6
+LABYTRAB	TSH RECEPTOR AB	TSH Receptor Ab	36
+LABYTRYPS	TRYPSIN-LIKE IMMUNOREACTIVITY	Trypsin-like Immunoreactivity	12
+LABYTRYPT	TRYPTASE, SERUM	Tryptase, Serum	251
+LABYTRYPT	TRYPTASE, SERUM		36
+LABYTSIG	TSH IMMUNOGLOBULINS, SERUM	TSH Immunoglobulins, Serum	10
+LABYTSIG	TSH IMMUNOGLOBULINS, SERUM		2
+LABYTSIG	TSI, SERUM	TSI, Serum	2
+LABYTTGG	TRANSGLUTAMINASE IGA AND IGG AB, TISSUE	Transglutaminase IgA and IgG Ab, Tissue	4
+LABYU5HIA	5 - OH-INDOLEACETIC AC, URINE	5-OH-Indoleacetic AC, Urine	258
+LABYU5HIA	5 - OH-INDOLEACETIC AC, URINE		68
+LABYUALA	AMINOLEVULINIC ACID, URINE	Aminolevulinic Acid, Urine	34
+LABYUALA	AMINOLEVULINIC ACID, URINE		16
+LABYUALDO	ALDOSTERONE, URINE	Aldosterone, Urine	16
+LABYUALDO	ALDOSTERONE, URINE		12
+LABYUAMC	AMPHETAMINES CONFIRMATION, URINE		20
+LABYUAMC	AMPHETAMINES CONFIRMATION, URINE	Amphetamines Confirmation, Urine	8
+LABYUARS	ARSENIC FRACTIONATION, URINE	Arsenic Fractionation, Urine	16
+LABYUARS	ARSENIC FRACTIONATION, URINE		6
+LABYUARSR	ARSENIC, URINE (RANDOM)	Arsenic, Urine (Random)	24
+LABYUARSR	ARSENIC, URINE (RANDOM)		12
+LABYUBBC	BARBITURATE CONFIRMATION, URINE		18
+LABYUBBC	BARBITURATE CONFIRMATION, URINE	Barbiturate Confirmation, Urine	4
+LABYUBZC	BENZODIAZEPINE CONFIRMATION, URINE		94
+LABYUBZC	BENZODIAZEPINE CONFIRMATION, URINE	Benzodiazepine Confirmation, Urine	18
+LABYUCADM	CADMIUM, URINE	Cadmium, Urine	2
+LABYUCC	COCAINE CONFIRMATION		26
+LABYUCC	COCAINE CONFIRMATION	Cocaine Confirmation	4
+LABYUCD12	CONFIRMED DRUG ABUSE SURVEY 12		2
+LABYUCDA	CONFIRMED DRUG ABUSE SURVEY, URINE	Confirmed Drug Abuse Survey, Urine	1956
+LABYUCDA	CONFIRMED DRUG ABUSE SURVEY, URINE		238
+LABYUCITR	CITRATE EXCRETION, URINE	Citrate Excretion, Urine	17
+LABYUCITR	CITRATE EXCRETION, URINE		4
+LABYUCLAX	CATHARTIC LAXATIVE PANEL, URINE	Cathartic Laxative Panel, Urine	2
+LABYUCOP	COPPER, URINE	Copper, Urine	175
+LABYUCOP	COPPER, URINE		66
+LABYUCORT	CORTISOL FREE, URINE	Cortisol Free, Urine	134
+LABYUCORT	CORTISOL FREE, URINE		70
+LABYUETHC	ETHANOL CONFIRMATION		16
+LABYUETHC	ETHANOL CONFIRMATION	Ethanol Confirmation	6
+LABYUFCAT	CATECHOLAMINES FRACTIONATED FREE, URINE	Catecholamines Fractionated Free, Urine	217
+LABYUFCAT	CATECHOLAMINES FRACTIONATED FREE, URINE		50
+LABYUHOMO	HOMOCYSTEINE TOTAL, URINE	Homocysteine Total, Urine	8
+LABYUHOMO	HOMOCYSTEINE TOTAL, URINE		2
+LABYUHYME	HEAVY METALS, URINE	Heavy Metals, Urine	283
+LABYUHYME	HEAVY METALS, URINE		76
+LABYUMERC	MERCURY, URINE (TIMED)	Mercury, Urine (Timed)	2
+LABYUMERC	MERCURY, URINE (TIMED)		2
+LABYUMETF	METANEPHRINES FRACTIONATED, 24H URINE	Metanephrines Fractionated, 24H Urine	402
+LABYUMETF	METANEPHRINES FRACTIONATED, 24H URINE		142
+LABYUMYO	MYOGLOBIN, URINE	Myoglobin, Urine	345
+LABYUMYO	MYOGLOBIN, URINE		137
+LABYUNTX	TELOPEPTIDE, URINE	Telopeptide, Urine	4
+LABYUNTX	TELOPEPTIDE, URINE		2
+LABYUOPC	OPIATE CONFIRMATION, URINE		162
+LABYUOPC	OPIATE CONFIRMATION, URINE	Opiate Confirmation, Urine	34
+LABYUOXAL	OXALATE URINE (TIMED)	Oxalate Urine (Timed)	19
+LABYUOXAL	OXALATE, 24-HOUR/TIMED URINE	Oxalate, 24-Hour/Timed Urine	10
+LABYUOXAL	OXALATE URINE (TIMED)		6
+LABYUPCPC	PHENCYCLIDINE CONFIRMATION, URINE		2
+LABYUPPP	PURINE AND PYRINIDINE PANEL, URINE	Purine And Pyrinidine Panel, Urine	2
+LABYUTHC	TETRAHYDROCANNABINOL, URINE		76
+LABYUTHC	TETRAHYDROCANNABINOL, URINE	Tetrahydrocannabinol, Urine	38
+LABYUVMA	VANILLYMANDELIC ACID, URINE	Vanillymandelic Acid, Urine	85
+LABYUVMA	VANILLYMANDELIC ACID, URINE		20
+LABYVB1W	VITAMIN B1	Vitamin B1	98
+LABYVB1W	VITAMIN B1		42
+LABYVD125	1,25-DIHYDROXYVITAMIN D, SERUM	Vitamin D 1,25-Dihydroxy, Serum	46
+LABYVD125	1,25-DIHYDROXYVITAMIN D, SERUM	Vit D 1,25-Dihydroxy Serum	2
+LABYVDRLC	VDRL, CSF	VDRL, CSF	290
+LABYVDRLC	VDRL, CSF		126
+LABYVIP	VASOACTIVE INTESTINAL POLYPEPTIDE	Vasoactive Intestinal Polypeptide	2
+LABYVITA	VITAMIN A	Vitamin A	389
+LABYVITA	VITAMIN A		16
+LABYVITB6	VITAMIN B6	Vitamin B6	134
+LABYVITB6	VITAMIN B6		8
+LABYVITE	VITAMIN E, SERUM	Vitamin E, Serum	6
+LABYVWM	VON WILLEBRAND MULTIMERS	Von Willebrand Multimers	53
+LABYVWM	VON WILLEBRAND MULTIMERS		8
+LABYWNILE	WEST NILE VIRUS AB, IGG AND IGM	West Nile Virus Ab, IgG and IgM	583
+LABYWNILE	WEST NILE VIRUS AB, IGG AND IGM		96
+LABYZINCS	ZINC	Zinc	405
+LABYZINCS	ZINC		14
+LABYZONIS	ZONISAMIDE	Zonisamide	122
diff --git a/scripts/DevWorkshop/sampleData/labConsecutiveNormalStats.tsv b/scripts/DevWorkshop/sampleData/labConsecutiveNormalStats.tsv
new file mode 100644
index 00000000..2ec17e9a
--- /dev/null
+++ b/scripts/DevWorkshop/sampleData/labConsecutiveNormalStats.tsv
@@ -0,0 +1,19450 @@
+base_name	Window Size	Prior Consecutive Normal	Total Count	Normal Count
+LABPOCGLU	1	NULL	1139434	104707
+LABPOCGLU	2	NULL	1139434	104707
+LABPOCGLU	4	NULL	1139434	104707
+LABPOCGLU	30	NULL	1139434	104707
+LABPOCGLU	90	NULL	1139434	104707
+LABPOCGLU	7	NULL	1139434	104707
+LABMETB	1	NULL	454479	18881
+LABMETB	2	NULL	454479	18881
+LABMETB	4	NULL	454479	18881
+LABMETB	30	NULL	454479	18881
+LABMETB	90	NULL	454479	18881
+LABMETB	7	NULL	454479	18881
+LABCBCD	1	NULL	446825	9685
+LABCBCD	2	NULL	446825	9685
+LABCBCD	4	NULL	446825	9685
+LABCBCD	30	NULL	446825	9685
+LABCBCD	90	NULL	446825	9685
+LABCBCD	7	NULL	446825	9685
+LABMGN	1	NULL	389911	282201
+LABMGN	2	NULL	389911	282201
+LABMGN	4	NULL	389911	282201
+LABMGN	30	NULL	389911	282201
+LABMGN	90	NULL	389911	282201
+LABMGN	7	NULL	389911	282201
+LABPT	1	NULL	285199	51065
+LABPT	2	NULL	285199	51065
+LABPT	4	NULL	285199	51065
+LABPT	30	NULL	285199	51065
+LABPT	90	NULL	285199	51065
+LABPT	7	NULL	285199	51065
+LABMETC	1	NULL	257247	3793
+LABMETC	2	NULL	257247	3793
+LABMETC	4	NULL	257247	3793
+LABMETC	30	NULL	257247	3793
+LABMETC	90	NULL	257247	3793
+LABMETC	7	NULL	257247	3793
+LABCBCO	1	NULL	238570	7172
+LABCBCO	2	NULL	238570	7172
+LABCBCO	4	NULL	238570	7172
+LABCBCO	30	NULL	238570	7172
+LABCBCO	90	NULL	238570	7172
+LABCBCO	7	NULL	238570	7172
+LABCBCD	1	0	232953	6901
+LABPHOS	1	NULL	199539	130494
+LABPHOS	2	NULL	199539	130494
+LABPHOS	4	NULL	199539	130494
+LABPHOS	30	NULL	199539	130494
+LABPHOS	90	NULL	199539	130494
+LABPHOS	7	NULL	199539	130494
+LABMETB	1	0	199532	11167
+LABPTT	1	NULL	196971	83579
+LABPTT	2	NULL	196971	83579
+LABPTT	4	NULL	196971	83579
+LABPTT	30	NULL	196971	83579
+LABPTT	90	NULL	196971	83579
+LABPTT	7	NULL	196971	83579
+LABPCG3	1	NULL	191184	9290
+LABPCG3	2	NULL	191184	9290
+LABPCG3	4	NULL	191184	9290
+LABPCG3	30	NULL	191184	9290
+LABPCG3	90	NULL	191184	9290
+LABPCG3	7	NULL	191184	9290
+LABCAI	1	NULL	163328	75559
+LABCAI	2	NULL	163328	75559
+LABCAI	4	NULL	163328	75559
+LABCAI	30	NULL	163328	75559
+LABCAI	90	NULL	163328	75559
+LABCAI	7	NULL	163328	75559
+LABMGN	1	0	162015	109779
+LABMETC	1	0	143738	3155
+LABPT	1	0	140543	34873
+LABCBCO	1	0	117119	5155
+LABMGN	1	1	113371	95385
+LABCBCD	2	0	109337	5169
+LABMGN	2	1	104146	84365
+LABMETB	2	0	97342	5312
+LABCBCD	4	0	93797	4912
+LABPHOS	1	0	91565	61849
+LABPTT	1	0	89100	48678
+LABCBCD	7	0	88896	4778
+LABMETB	4	0	87227	4588
+LABMETB	7	0	83192	4329
+LABCBCD	30	0	77749	4486
+LABPT	2	0	77132	26133
+LABMETB	30	0	75484	3931
+LABMETC	2	0	74545	2799
+LABTNI	1	NULL	73778	52197
+LABTNI	2	NULL	73778	52197
+LABTNI	4	NULL	73778	52197
+LABTNI	30	NULL	73778	52197
+LABTNI	90	NULL	73778	52197
+LABTNI	7	NULL	73778	52197
+LABMGN	2	0	71052	43133
+LABCBCD	90	0	70324	4264
+LABCBCO	2	0	69848	3954
+LABPT	4	0	69842	24415
+LABMETB	90	0	69652	3637
+LABPOCGLU	90	1	69599	18226
+LABPOCGLU	30	1	69384	18188
+LABMETC	4	0	69207	2723
+LABPOCGLU	7	1	69132	18146
+LABPOCGLU	4	1	68994	18131
+LABPOCGLU	2	1	68840	18109
+LABLAC	1	NULL	68691	50614
+LABLAC	2	NULL	68691	50614
+LABLAC	4	NULL	68691	50614
+LABLAC	30	NULL	68691	50614
+LABLAC	90	NULL	68691	50614
+LABLAC	7	NULL	68691	50614
+LABPOCGLU	1	1	68691	18213
+LABMETC	7	0	66155	2677
+LABMGN	4	1	66147	50601
+LABPT	7	0	65670	23267
+LABMGN	7	1	64369	48957
+LABMGN	2	2	64359	55788
+LABMGN	30	1	62817	47353
+LABABG	1	NULL	62465	800
+LABABG	2	NULL	62465	800
+LABABG	4	NULL	62465	800
+LABABG	30	NULL	62465	800
+LABABG	90	NULL	62465	800
+LABABG	7	NULL	62465	800
+LABMGN	90	1	62289	46645
+LABMGN	4	0	61648	37282
+LABCAI	1	0	60526	29817
+LABCBCO	4	0	59786	3788
+LABPTT	2	0	59035	36195
+LABMGN	7	0	58718	35470
+LABPT	30	0	58141	21372
+LABMETC	30	0	57669	2536
+LABCBCO	7	0	56768	3725
+LABPOCGLU	1	0	56432	7121
+LABPHOS	2	1	56238	43460
+LABHFP	1	NULL	55643	2099
+LABHFP	2	NULL	55643	2099
+LABHFP	4	NULL	55643	2099
+LABHFP	30	NULL	55643	2099
+LABHFP	90	NULL	55643	2099
+LABHFP	7	NULL	55643	2099
+LABPTT	4	0	54380	33977
+LABPT	90	0	53101	19965
+LABPHOS	1	1	53092	41852
+LABMGN	30	0	52766	31861
+LABMETC	90	0	51893	2378
+LABPTT	7	0	51646	32582
+LABCBCO	30	0	51388	3630
+LABBLC	1	NULL	49360	44673
+LABBLC	2	NULL	49360	44673
+LABBLC	4	NULL	49360	44673
+LABBLC	30	NULL	49360	44673
+LABBLC	90	NULL	49360	44673
+LABBLC	7	NULL	49360	44673
+LABMGN	90	0	48706	29542
+LABK	1	NULL	48118	41660
+LABK	2	NULL	48118	41660
+LABK	4	NULL	48118	41660
+LABK	30	NULL	48118	41660
+LABK	90	NULL	48118	41660
+LABK	7	NULL	48118	41660
+LABCBCO	90	0	47869	3541
+LABPOCGLU	2	0	46675	5973
+LABPTT	30	0	46434	30027
+LABUA	1	NULL	45959	10210
+LABUA	2	NULL	45959	10210
+LABUA	4	NULL	45959	10210
+LABUA	30	NULL	45959	10210
+LABUA	90	NULL	45959	10210
+LABUA	7	NULL	45959	10210
+LABMGN	4	3	45826	40437
+LABBLC	1	0	45134	40808
+LABBLC2	1	NULL	43897	40010
+LABBLC2	2	NULL	43897	40010
+LABBLC2	4	NULL	43897	40010
+LABBLC2	30	NULL	43897	40010
+LABBLC2	90	NULL	43897	40010
+LABBLC2	7	NULL	43897	40010
+LABPOCGLU	4	0	43599	5581
+LABUA	1	0	42972	9681
+LABPTT	90	0	42965	28185
+LABBLC	2	0	42414	38275
+LABPOCGLU	7	0	41869	5367
+LABBLC2	1	0	41795	38162
+LABUA	2	0	41343	9393
+LABMGN	4	2	41094	34367
+LABMGN	7	2	40278	33496
+LABMB	1	NULL	40041	21793
+LABMB	2	NULL	40041	21793
+LABMB	4	NULL	40041	21793
+LABMB	30	NULL	40041	21793
+LABMB	90	NULL	40041	21793
+LABMB	7	NULL	40041	21793
+LABROMRS	1	NULL	39655	37804
+LABROMRS	2	NULL	39655	37804
+LABROMRS	4	NULL	39655	37804
+LABROMRS	30	NULL	39655	37804
+LABROMRS	90	NULL	39655	37804
+LABROMRS	7	NULL	39655	37804
+LABUA	4	0	39573	9098
+LABBLC2	2	0	39559	36107
+LABHEPAR	1	NULL	39437	17342
+LABHEPAR	2	NULL	39437	17342
+LABHEPAR	4	NULL	39437	17342
+LABHEPAR	30	NULL	39437	17342
+LABHEPAR	90	NULL	39437	17342
+LABHEPAR	7	NULL	39437	17342
+LABMGN	30	2	39285	32428
+LABFK506L	1	NULL	39273	33906
+LABFK506L	2	NULL	39273	33906
+LABFK506L	4	NULL	39273	33906
+LABFK506L	30	NULL	39273	33906
+LABFK506L	90	NULL	39273	33906
+LABFK506L	7	NULL	39273	33906
+LABMGN	90	2	39127	32165
+LABROMRS	1	0	38935	37106
+LABPHOS	2	0	38681	26521
+LABROMRS	2	0	38495	36674
+LABPOCGLU	30	0	38068	4893
+LABROMRS	4	0	38068	36271
+LABUA	7	0	37978	8816
+LABBLC	4	0	37518	33680
+LABVBG	1	NULL	37486	641
+LABVBG	2	NULL	37486	641
+LABVBG	4	NULL	37486	641
+LABVBG	30	NULL	37486	641
+LABVBG	90	NULL	37486	641
+LABVBG	7	NULL	37486	641
+LABROMRS	7	0	37446	35674
+LABHFP	1	0	36423	1770
+LABPHOS	4	1	35254	25349
+LABPOCGLU	90	0	35239	4535
+LABBLC2	4	0	35231	32015
+LABURNC	1	NULL	34444	20668
+LABURNC	2	NULL	34444	20668
+LABURNC	4	NULL	34444	20668
+LABURNC	30	NULL	34444	20668
+LABURNC	90	NULL	34444	20668
+LABURNC	7	NULL	34444	20668
+LABPHOS	7	1	34278	24445
+LABMGN	1	2	34060	30563
+LABCAI	2	1	34047	22299
+LABPHOS	4	0	33825	23275
+LABBLC	7	0	33672	30035
+LABUA	30	0	33626	7880
+LABPHOS	30	1	33460	23689
+LABLDH	1	NULL	33432	17382
+LABLDH	2	NULL	33432	17382
+LABLDH	4	NULL	33432	17382
+LABLDH	30	NULL	33432	17382
+LABLDH	90	NULL	33432	17382
+LABLDH	7	NULL	33432	17382
+LABROMRS	30	0	33405	31851
+LABPHOS	90	1	33264	23502
+LABMGN	4	4	33073	29543
+LABHCTX	1	NULL	32757	2355
+LABHCTX	2	NULL	32757	2355
+LABHCTX	4	NULL	32757	2355
+LABHCTX	30	NULL	32757	2355
+LABHCTX	90	NULL	32757	2355
+LABHCTX	7	NULL	32757	2355
+LABVANPRL	1	NULL	32524	16620
+LABVANPRL	2	NULL	32524	16620
+LABVANPRL	4	NULL	32524	16620
+LABVANPRL	30	NULL	32524	16620
+LABVANPRL	90	NULL	32524	16620
+LABVANPRL	7	NULL	32524	16620
+LABURNC	1	0	32366	19524
+LABPHOS	7	0	32169	22205
+LABBLC2	7	0	31674	28648
+LABCAI	1	1	31499	20783
+LABPHOS	2	2	31149	25662
+LABURNC	2	0	31053	18699
+LABROMRS	90	0	30756	29443
+LABUA	90	0	30709	7245
+LABNA	1	NULL	30680	14384
+LABNA	2	NULL	30680	14384
+LABNA	4	NULL	30680	14384
+LABNA	30	NULL	30680	14384
+LABNA	90	NULL	30680	14384
+LABNA	7	NULL	30680	14384
+LABCAI	2	0	30257	12890
+LABPTT	2	1	29745	20040
+LABURNC	4	0	29011	17326
+LABTNI	1	0	28870	24268
+LABPHOS	30	0	28708	19776
+LABPTT	1	1	28551	19689
+LABBLC	30	0	28418	25308
+LABMGN	7	3	28063	24297
+LABCAI	4	0	28022	11769
+LABMGN	90	3	27393	23384
+LABMGN	30	3	27307	23437
+LABURNC	7	0	27180	16149
+LABUAPRN	1	NULL	27157	5354
+LABUAPRN	2	NULL	27157	5354
+LABUAPRN	4	NULL	27157	5354
+LABUAPRN	30	NULL	27157	5354
+LABUAPRN	90	NULL	27157	5354
+LABUAPRN	7	NULL	27157	5354
+LABCAI	7	0	27072	11327
+LABPHOS	90	0	26933	18663
+LABBLC2	30	0	26845	24241
+LABTNI	2	0	26832	22917
+LABMGN	2	3	26695	23934
+LABUAPRN	1	0	26158	5201
+LABCAI	4	1	25904	15596
+LABCAI	90	1	25781	15322
+LABCAI	30	1	25768	15360
+LABPTT	90	1	25762	16165
+LABCAI	7	1	25720	15383
+LABDIC	1	NULL	25716	520
+LABDIC	2	NULL	25716	520
+LABDIC	4	NULL	25716	520
+LABDIC	30	NULL	25716	520
+LABDIC	90	NULL	25716	520
+LABDIC	7	NULL	25716	520
+LABVANPRL	1	0	25698	12962
+LABCAI	30	0	25589	10681
+LABBLC	90	0	25427	22764
+LABTNI	4	0	25391	21800
+LABPTT	30	1	25352	15905
+LABUAPRN	2	0	25284	5048
+LABCAI	90	0	24753	10308
+LABPTT	7	1	24694	15578
+LABTNI	7	0	24480	21068
+LABPTT	4	1	24446	15482
+LABPHOS	4	3	24383	20787
+LABABG	1	0	24150	397
+LABBLC2	90	0	24135	21879
+LABPCTNI	1	NULL	23727	20747
+LABPCTNI	2	NULL	23727	20747
+LABPCTNI	4	NULL	23727	20747
+LABPCTNI	30	NULL	23727	20747
+LABPCTNI	90	NULL	23727	20747
+LABPCTNI	7	NULL	23727	20747
+LABUAPRN	4	0	23650	4759
+LABURNC	30	0	23379	14090
+LABABG	2	0	23358	375
+LABFK506L	1	0	23137	19830
+LABTNI	30	0	22599	19442
+LABA1C	1	NULL	22550	10594
+LABA1C	2	NULL	22550	10594
+LABA1C	4	NULL	22550	10594
+LABA1C	30	NULL	22550	10594
+LABA1C	90	NULL	22550	10594
+LABA1C	7	NULL	22550	10594
+LABABG	4	0	22495	349
+LABURIC	1	NULL	22347	21222
+LABURIC	2	NULL	22347	21222
+LABURIC	4	NULL	22347	21222
+LABURIC	30	NULL	22347	21222
+LABURIC	90	NULL	22347	21222
+LABURIC	7	NULL	22347	21222
+LABUAPRN	7	0	22144	4461
+LABLIPS	1	NULL	22004	16537
+LABLIPS	2	NULL	22004	16537
+LABLIPS	4	NULL	22004	16537
+LABLIPS	30	NULL	22004	16537
+LABLIPS	90	NULL	22004	16537
+LABLIPS	7	NULL	22004	16537
+LABABG	7	0	21737	338
+LABURNC	90	0	21368	13039
+LABTNI	90	0	21344	18351
+LABHFP	2	0	21224	1555
+LABA1C	1	0	21025	10027
+LABPT	2	1	20796	10603
+LABPHOS	4	2	20775	16234
+LABABG	30	0	20561	305
+LABPHOS	7	2	20491	15910
+LABA1C	2	0	20455	9768
+LABMGN	30	4	20410	17822
+LABMGN	90	4	20365	17704
+LABPCTNI	1	0	20347	18127
+LABPCTNI	2	0	20323	18108
+LABPCTNI	4	0	20234	18039
+LABMGN	7	4	20177	17752
+LABPCTNI	7	0	20039	17866
+LABABG	90	0	20016	293
+LABPT	90	1	19993	9101
+LABPHOS	90	2	19983	15328
+LABPHOS	30	2	19962	15374
+LABA1C	4	0	19954	9564
+LABUAPRN	30	0	19667	4019
+LABA1C	7	0	19613	9413
+LABPT	30	1	19525	8900
+LABTSH	1	NULL	19385	14306
+LABTSH	2	NULL	19385	14306
+LABTSH	4	NULL	19385	14306
+LABTSH	30	NULL	19385	14306
+LABTSH	90	NULL	19385	14306
+LABTSH	7	NULL	19385	14306
+LABLAC	1	0	18922	14740
+LABPT	7	1	18845	8630
+LABPCTNI	30	0	18831	16788
+LABA1C	30	0	18583	8979
+LABUAPRN	90	0	18564	3804
+LABPCG3	1	0	18531	721
+LABPT	4	1	18473	8478
+LABLIPS	1	0	18407	14566
+LABLDH	1	0	18399	10655
+LABHFP	4	0	18335	1478
+LABMGN	2	4	18214	16666
+LABVANPRL	2	0	18103	8708
+LABHCTX	1	0	18101	1764
+LABPT	1	1	17993	9222
+LABTSH	1	0	17893	13243
+LABLACWB	1	NULL	17767	14577
+LABLACWB	2	NULL	17767	14577
+LABLACWB	4	NULL	17767	14577
+LABLACWB	30	NULL	17767	14577
+LABLACWB	90	NULL	17767	14577
+LABLACWB	7	NULL	17767	14577
+LABTSH	2	0	17552	13011
+LABA1C	90	0	17484	8525
+LABPCTNI	90	0	17481	15593
+LABPOCGLU	90	2	17430	6531
+LABPOCGLU	30	2	17347	6506
+LABTSH	4	0	17292	12868
+LABPOCGLU	1	2	17289	6620
+LABPOCGLU	7	2	17215	6478
+LABHFP	7	0	17198	1443
+LABPOCGLU	4	2	17179	6470
+LABPOCGLU	2	2	17116	6467
+LABTSH	7	0	17064	12734
+LABMB	1	0	17025	10421
+LABLIPS	2	0	16349	13473
+LABTSH	30	0	16241	12259
+LABCK	1	NULL	16113	8515
+LABCK	2	NULL	16113	8515
+LABCK	4	NULL	16113	8515
+LABCK	30	NULL	16113	8515
+LABCK	90	NULL	16113	8515
+LABCK	7	NULL	16113	8515
+LABFK506L	2	1	15982	15013
+LABMGN	30	5	15841	13996
+LABMGN	90	5	15824	13893
+LABMGN	7	5	15803	14030
+LABMB	2	0	15752	9761
+LABHFP	30	0	15742	1404
+LABLIPS	4	0	15733	13082
+LABTNI	1	1	15650	14703
+LABCAI	2	2	15601	11463
+LABLAC	2	0	15579	11788
+LABTSH	90	0	15572	11844
+LABCRP	1	NULL	15439	2468
+LABCRP	2	NULL	15439	2468
+LABCRP	4	NULL	15439	2468
+LABCRP	30	NULL	15439	2468
+LABCRP	90	NULL	15439	2468
+LABCRP	7	NULL	15439	2468
+LABTNI	2	1	15371	14337
+LABLIPS	7	0	15322	12782
+LABK	1	0	15321	12416
+LABPCCG4V	1	NULL	15202	295
+LABPCCG4V	2	NULL	15202	295
+LABPCCG4V	4	NULL	15202	295
+LABPCCG4V	30	NULL	15202	295
+LABPCCG4V	90	NULL	15202	295
+LABPCCG4V	7	NULL	15202	295
+LABTNI	4	1	15181	14099
+LABMB	4	0	15138	9401
+LABPHOS	4	4	15107	13052
+LABTNI	7	1	14958	13866
+LABHFP	90	0	14884	1360
+LABMB	7	0	14745	9171
+LABLAC	4	0	14515	10909
+LABPCG3	2	0	14438	541
+LABTNI	30	1	14371	13263
+LABHCTX	2	0	14290	1607
+LABLIPS	30	0	14199	11899
+LABCRP	1	0	14080	2265
+LABTNI	90	1	13946	12848
+LABMB	30	0	13916	8669
+LABPHOS	7	3	13874	11435
+LABHCTX	4	0	13829	1598
+LABFIB	1	NULL	13824	5063
+LABFIB	2	NULL	13824	5063
+LABFIB	4	NULL	13824	5063
+LABFIB	30	NULL	13824	5063
+LABFIB	90	NULL	13824	5063
+LABFIB	7	NULL	13824	5063
+LABVANPRL	4	0	13820	6368
+LABLAC	7	0	13790	10324
+LABHCTX	7	0	13619	1596
+LABPCEG7	1	NULL	13512	49
+LABPCEG7	2	NULL	13512	49
+LABPCEG7	4	NULL	13512	49
+LABPCEG7	30	NULL	13512	49
+LABPCEG7	90	NULL	13512	49
+LABPCEG7	7	NULL	13512	49
+LABPHOS	90	3	13414	10858
+LABPTT	2	2	13386	10232
+LABPHOS	30	3	13378	10871
+LABPCCG4V	1	0	13363	257
+LABPCCG4V	2	0	13346	257
+LABMB	90	0	13315	8313
+LABPCCG4V	4	0	13314	256
+LABPCG3	4	0	13252	502
+LABLIPS	90	0	13213	11066
+LABPCCG4V	7	0	13213	255
+LABOSM	1	NULL	13160	6388
+LABOSM	2	NULL	13160	6388
+LABOSM	4	NULL	13160	6388
+LABOSM	30	NULL	13160	6388
+LABOSM	90	NULL	13160	6388
+LABOSM	7	NULL	13160	6388
+LABFK506L	1	1	13158	12441
+LABHCTX	30	0	13136	1589
+LABCRP	2	0	13048	2134
+LABLAC	1	1	12958	11733
+LABHCTX	90	0	12903	1576
+LABPCG3	7	0	12829	484
+LABCAI	90	2	12745	8724
+LABCAI	4	2	12704	8830
+LABNTBNP	1	NULL	12690	2125
+LABNTBNP	2	NULL	12690	2125
+LABNTBNP	4	NULL	12690	2125
+LABNTBNP	30	NULL	12690	2125
+LABNTBNP	90	NULL	12690	2125
+LABNTBNP	7	NULL	12690	2125
+LABCAI	30	2	12682	8708
+LABCAI	7	2	12678	8774
+LABCDTPCR	1	NULL	12659	10239
+LABCDTPCR	2	NULL	12659	10239
+LABCDTPCR	4	NULL	12659	10239
+LABCDTPCR	30	NULL	12659	10239
+LABCDTPCR	90	NULL	12659	10239
+LABCDTPCR	7	NULL	12659	10239
+LABVANPRL	7	0	12609	5753
+LABPCCG4V	30	0	12558	245
+LABMGN	4	5	12488	11176
+LABLAC	30	0	12465	9288
+LABPCEG7V	1	NULL	12330	37
+LABPCEG7V	2	NULL	12330	37
+LABPCEG7V	4	NULL	12330	37
+LABPCEG7V	30	NULL	12330	37
+LABPCEG7V	90	NULL	12330	37
+LABPCEG7V	7	NULL	12330	37
+LABCDTPCR	1	0	12237	10027
+LABPCG3	30	0	12190	468
+LABVBG	1	0	12132	251
+LABANER	1	NULL	12118	10667
+LABANER	2	NULL	12118	10667
+LABANER	4	NULL	12118	10667
+LABANER	30	NULL	12118	10667
+LABANER	90	NULL	12118	10667
+LABANER	7	NULL	12118	10667
+LABCDTPCR	2	0	11983	9886
+LABPCG3	90	0	11914	458
+LABK	1	1	11883	10879
+LABLAC	90	0	11749	8742
+LABPCCG4V	90	0	11716	226
+LABNTBNP	1	0	11701	1970
+LABLDH	2	0	11649	6700
+LABCDTPCR	4	0	11631	9720
+LABPTT	90	2	11620	8266
+LABBMT2	1	NULL	11550	514
+LABBMT2	2	NULL	11550	514
+LABBMT2	4	NULL	11550	514
+LABBMT2	30	NULL	11550	514
+LABBMT2	90	NULL	11550	514
+LABBMT2	7	NULL	11550	514
+LABGRAM	1	NULL	11526	10254
+LABGRAM	2	NULL	11526	10254
+LABGRAM	4	NULL	11526	10254
+LABGRAM	30	NULL	11526	10254
+LABGRAM	90	NULL	11526	10254
+LABGRAM	7	NULL	11526	10254
+LABBMT1	1	NULL	11461	215
+LABBMT1	2	NULL	11461	215
+LABBMT1	4	NULL	11461	215
+LABBMT1	30	NULL	11461	215
+LABBMT1	90	NULL	11461	215
+LABBMT1	7	NULL	11461	215
+LABNTBNP	2	0	11301	1915
+LABK	2	0	11245	8861
+LABVANPRL	30	0	11214	5030
+LABPTT	30	2	11209	8027
+LABDIC	1	0	11180	235
+LABCDTPCR	7	0	11078	9439
+LABNTBNP	4	0	10892	1888
+LABPTT	7	2	10729	7734
+LABMGN	1	3	10684	9750
+LABRESP	1	NULL	10620	7315
+LABRESP	2	NULL	10620	7315
+LABRESP	4	NULL	10620	7315
+LABRESP	30	NULL	10620	7315
+LABRESP	90	NULL	10620	7315
+LABRESP	7	NULL	10620	7315
+LABLAC	2	1	10579	9411
+LABCAI	4	3	10547	8262
+LABBMT1	1	0	10537	203
+LABFCUL	1	NULL	10532	8509
+LABFCUL	2	NULL	10532	8509
+LABFCUL	4	NULL	10532	8509
+LABFCUL	30	NULL	10532	8509
+LABFCUL	90	NULL	10532	8509
+LABFCUL	7	NULL	10532	8509
+LABNTBNP	7	0	10478	1856
+LABVANPRL	90	0	10443	4645
+LABPTT	4	2	10395	7503
+LABBLC	90	1	10344	9509
+LABFK506L	2	2	10317	9904
+LABLDH	4	0	10316	6115
+LABGRAM	1	0	10249	9103
+LABMETB	90	1	10210	2861
+LABCBCS	1	NULL	10177	76
+LABCBCS	2	NULL	10177	76
+LABCBCS	4	NULL	10177	76
+LABCBCS	30	NULL	10177	76
+LABCBCS	90	NULL	10177	76
+LABCBCS	7	NULL	10177	76
+LABVBG	2	0	10122	215
+LABBMT2	1	0	10102	446
+LABCRP	4	0	10062	1869
+LABPHOS	1	2	10043	8253
+LABBLC	30	1	10013	9320
+LABPCCR	1	NULL	9993	6177
+LABPCCR	2	NULL	9993	6177
+LABPCCR	4	NULL	9993	6177
+LABPCCR	30	NULL	9993	6177
+LABPCCR	90	NULL	9993	6177
+LABMETB	2	1	9993	3189
+LABPCCR	7	NULL	9993	6177
+LABMETB	30	1	9938	2838
+LABDOA	1	NULL	9906	4195
+LABDOA	2	NULL	9906	4195
+LABDOA	4	NULL	9906	4195
+LABDOA	30	NULL	9906	4195
+LABDOA	90	NULL	9906	4195
+LABDOA	7	NULL	9906	4195
+LABPLTS	1	NULL	9837	4646
+LABPLTS	2	NULL	9837	4646
+LABPLTS	4	NULL	9837	4646
+LABPLTS	30	NULL	9837	4646
+LABPLTS	90	NULL	9837	4646
+LABPLTS	7	NULL	9837	4646
+LABPCCR	1	0	9798	6084
+LABPCCR	2	0	9795	6083
+LABPCCR	4	0	9793	6081
+LABPCCR	7	0	9774	6068
+LABLDH	7	0	9726	5850
+LABLIDOL	1	NULL	9707	7128
+LABLIDOL	2	NULL	9707	7128
+LABLIDOL	4	NULL	9707	7128
+LABLIDOL	30	NULL	9707	7128
+LABLIDOL	90	NULL	9707	7128
+LABLIDOL	7	NULL	9707	7128
+LABRESP	1	0	9665	6563
+LABPCCR	30	0	9656	5995
+LABMETB	7	1	9652	2807
+LABK	4	0	9624	7468
+LABPHOS	30	4	9624	8083
+LABNTBNP	30	0	9609	1801
+LABDOA	1	0	9596	4095
+LABPHOS	90	4	9585	8005
+LABBLC	7	1	9575	9084
+LABMETB	4	1	9550	2801
+LABGRAM	2	0	9533	8457
+LABPCCR	90	0	9506	5921
+LABDOA	2	0	9476	4070
+LABDOA	4	0	9341	4044
+LABVBG	4	0	9323	199
+LABFK506L	4	3	9316	8890
+LABPHOS	7	4	9313	7867
+LABDOA	7	0	9238	4012
+LABLAC	4	1	9133	7961
+LABLAC	7	1	9057	7855
+LABTNI	1	2	9052	8794
+LABCDTPCR	30	0	9043	7665
+LABRESP	2	0	9012	6078
+LABANER	1	0	8990	7759
+LABK	7	0	8990	6930
+LABBLC2	90	1	8973	8315
+LABLAC	1	2	8957	8403
+LABK	2	1	8944	8037
+LABNTBNP	90	0	8939	1736
+LABLAC	30	1	8911	7679
+LABVBG	7	0	8900	190
+LABDOA	30	0	8888	3882
+LABFT4	1	NULL	8884	8022
+LABFT4	2	NULL	8884	8022
+LABFT4	4	NULL	8884	8022
+LABFT4	30	NULL	8884	8022
+LABFT4	90	NULL	8884	8022
+LABFT4	7	NULL	8884	8022
+LABESRP	1	NULL	8864	2968
+LABESRP	2	NULL	8864	2968
+LABESRP	4	NULL	8864	2968
+LABESRP	30	NULL	8864	2968
+LABESRP	90	NULL	8864	2968
+LABESRP	7	NULL	8864	2968
+LABPLTS	1	0	8824	4474
+LABLAC	90	1	8817	7559
+LABK	1	2	8804	8273
+LABANER	2	0	8774	7609
+LABCRP	7	0	8720	1713
+LABGRAM	4	0	8663	7695
+LABLDH	30	0	8613	5213
+LABYPREAL	1	NULL	8606	2148
+LABYPREAL	2	NULL	8606	2148
+LABYPREAL	4	NULL	8606	2148
+LABYPREAL	30	NULL	8606	2148
+LABYPREAL	90	NULL	8606	2148
+LABYPREAL	7	NULL	8606	2148
+LABANER	4	0	8548	7451
+LABTRIG	1	NULL	8510	5142
+LABTRIG	2	NULL	8510	5142
+LABTRIG	4	NULL	8510	5142
+LABTRIG	30	NULL	8510	5142
+LABTRIG	90	NULL	8510	5142
+LABTRIG	7	NULL	8510	5142
+LABPCG3V	1	NULL	8507	492
+LABPCG3V	2	NULL	8507	492
+LABPCG3V	4	NULL	8507	492
+LABPCG3V	30	NULL	8507	492
+LABPCG3V	90	NULL	8507	492
+LABPCG3V	7	NULL	8507	492
+LABBLC2	30	1	8499	7943
+LABTNI	2	2	8488	8214
+LABTNI	7	2	8483	8180
+LABLPD	1	NULL	8466	3053
+LABLPD	2	NULL	8466	3053
+LABLPD	4	NULL	8466	3053
+LABLPD	30	NULL	8466	3053
+LABLPD	90	NULL	8466	3053
+LABLPD	7	NULL	8466	3053
+LABDOA	90	0	8455	3724
+LABTNI	4	2	8453	8165
+LABBLC	4	1	8429	8011
+LABFT4	1	0	8421	7621
+LABHEPAR	1	0	8404	2515
+LABFCUL	1	0	8390	6599
+LABTNI	30	2	8389	8058
+LABCDTPCR	90	0	8356	7133
+LABTNI	90	2	8352	8002
+LABK	30	0	8330	6426
+LABFT4	2	0	8298	7526
+LABVBG	30	0	8254	177
+LABPTT	4	3	8242	6684
+LABFCUL	2	0	8206	6469
+LABANER	7	0	8203	7161
+LABFT4	4	0	8197	7444
+LABHEPAR	1	1	8181	5452
+LABFLDC	1	NULL	8166	6113
+LABFLDC	2	NULL	8166	6113
+LABFLDC	4	NULL	8166	6113
+LABFLDC	30	NULL	8166	6113
+LABFLDC	90	NULL	8166	6113
+LABFLDC	7	NULL	8166	6113
+LABPLTS	2	0	8151	4363
+LABYPREAL	1	0	8080	2039
+LABK	90	0	8078	6251
+LABFT4	7	0	8064	7344
+LABLPD	1	0	8047	2909
+LABVBG	90	0	8038	171
+LABURIC	1	0	8007	7348
+LABRESP	4	0	7995	5423
+LABESRP	1	0	7964	2718
+LABFCUL	4	0	7935	6263
+LABLDH	90	0	7924	4808
+LABLPD	2	0	7908	2876
+LABGRAM	7	0	7894	7016
+LABURIC	1	1	7892	7853
+LABFK506L	2	0	7859	6270
+LABLPD	4	0	7849	2863
+LABLPD	7	0	7788	2846
+LABCRP	30	0	7751	1580
+LABBLC2	7	1	7740	7328
+LABLPD	30	0	7685	2818
+LABFT4	30	0	7676	7033
+LABPHOS	2	3	7642	6237
+LABYPREAL	2	0	7635	1942
+LABCK	1	0	7623	5143
+LABPLTS	4	0	7611	4294
+LABFCUL	7	0	7587	5995
+LABLPD	90	0	7566	2774
+LABANER	30	0	7544	6597
+LABFK506L	4	1	7489	6903
+LABESRP	2	0	7477	2617
+LABBMT1	2	0	7474	195
+LABFLDC	1	0	7449	5611
+LABLAC	2	2	7446	6928
+LABFT4	90	0	7428	6818
+LABCBCS	1	0	7411	66
+LABPLTS	7	0	7371	4272
+LABK	4	1	7351	6521
+LABCRP	90	0	7339	1522
+LABCAI	90	3	7256	5331
+LABCAI	7	3	7252	5390
+LABNH3	1	NULL	7224	2680
+LABNH3	2	NULL	7224	2680
+LABNH3	4	NULL	7224	2680
+LABNH3	30	NULL	7224	2680
+LABNH3	90	NULL	7224	2680
+LABNH3	7	NULL	7224	2680
+LABRESP	7	0	7219	4960
+LABCAI	30	3	7215	5322
+LABFLDC	2	0	7199	5442
+LABTRIG	1	0	7198	4452
+LABMETB	1	1	7188	2142
+LABPHOS	30	5	7176	6133
+LABANER	90	0	7161	6256
+LABESRP	4	0	7151	2566
+LABPHOS	90	5	7097	6037
+LABPLTS	30	0	7070	4216
+LABK	2	2	7067	6549
+LABLAC	1	3	7061	6726
+LABHEPAR	2	1	6988	4479
+LABPHOS	7	5	6970	5990
+LABSPLAC	1	NULL	6952	6315
+LABSPLAC	2	NULL	6952	6315
+LABSPLAC	4	NULL	6952	6315
+LABSPLAC	30	NULL	6952	6315
+LABSPLAC	90	NULL	6952	6315
+LABSPLAC	7	NULL	6952	6315
+LABFER	1	NULL	6942	2666
+LABFER	2	NULL	6942	2666
+LABFER	4	NULL	6942	2666
+LABFER	30	NULL	6942	2666
+LABFER	90	NULL	6942	2666
+LABFER	7	NULL	6942	2666
+LABB12	1	NULL	6917	4428
+LABB12	2	NULL	6917	4428
+LABB12	4	NULL	6917	4428
+LABB12	30	NULL	6917	4428
+LABB12	90	NULL	6917	4428
+LABB12	7	NULL	6917	4428
+LABMGN	2	5	6912	6334
+LABESRP	7	0	6905	2526
+LABUSPG	1	NULL	6903	6853
+LABUSPG	2	NULL	6903	6853
+LABUSPG	4	NULL	6903	6853
+LABUSPG	30	NULL	6903	6853
+LABUSPG	90	NULL	6903	6853
+LABUSPG	7	NULL	6903	6853
+LABK	7	1	6899	6093
+LABPLTS	90	0	6881	4161
+LABGRAM	30	0	6839	6079
+LABFLDC	4	0	6838	5174
+LABCAI	1	2	6815	4671
+LABFCUL	30	0	6798	5436
+LABFK506L	4	4	6780	6539
+LABK	30	1	6685	5872
+LABK	90	1	6676	5847
+LABMB	1	1	6648	5681
+LABPOCGLU	1	3	6613	3225
+LABPCG3	90	1	6598	1408
+LABPCG3	30	1	6584	1407
+LABFER	1	0	6580	2546
+LABCK	2	0	6558	4554
+LABPCG3	7	1	6553	1407
+LABB12	1	0	6546	4199
+LABPCG3	4	1	6535	1404
+LABGRAM	90	0	6531	5801
+LABMB	2	1	6508	5523
+LABPCG3	2	1	6497	1401
+LABBLC2	4	1	6486	6154
+LABFLDC	7	0	6475	4912
+LABB12	2	0	6469	4158
+LABFER	2	0	6453	2519
+LABMB	7	1	6448	5421
+LABMB	30	1	6441	5384
+LABPTT	90	3	6436	4920
+LABESRP	30	0	6434	2430
+LABMB	4	1	6429	5427
+LABMB	90	1	6424	5331
+LABB12	4	0	6410	4120
+LABPCG3	1	1	6405	1420
+LABHEPAR	90	1	6389	4009
+LABHEPAR	30	1	6379	4014
+LABB12	7	0	6375	4103
+LABFIB	1	0	6371	2338
+LABHEPAR	4	1	6370	4028
+LABHEPAR	7	1	6365	4016
+LABFER	4	0	6350	2498
+LABFCUL	90	0	6346	5146
+LABPOCGLU	90	3	6301	2929
+LABB12	30	0	6278	4048
+LABFER	7	0	6275	2485
+LABPOCGLU	30	3	6274	2923
+LABVANPRL	90	1	6265	3631
+LABPT	90	2	6250	3889
+LABCK	4	0	6247	4341
+LABAMY	1	NULL	6241	5102
+LABAMY	2	NULL	6241	5102
+LABAMY	4	NULL	6241	5102
+LABAMY	30	NULL	6241	5102
+LABAMY	90	NULL	6241	5102
+LABAMY	7	NULL	6241	5102
+LABPOCGLU	7	3	6238	2910
+LABPOCGLU	4	3	6218	2900
+LABRESP	30	0	6211	4401
+LABPTT	30	3	6171	4738
+LABPOCGLU	2	3	6169	2891
+LABESRP	90	0	6149	2354
+LABVANPRL	30	1	6142	3575
+LABB12	90	0	6134	3966
+LABTRIG	2	0	6117	3871
+LABLAC	4	2	6101	5613
+LABVANPRL	7	1	6098	3576
+LABVANPRL	4	1	6083	3622
+LABROMRS	90	1	6081	5916
+LABFER	30	0	6065	2440
+LABDIC	2	0	6047	168
+LABTRFS	1	NULL	6036	687
+LABTRFS	2	NULL	6036	687
+LABTRFS	4	NULL	6036	687
+LABTRFS	30	NULL	6036	687
+LABTRFS	90	NULL	6036	687
+LABTRFS	7	NULL	6036	687
+LABCAI	4	4	6034	4905
+LABLAC	7	2	6030	5537
+LABHEPAR	2	0	6005	1373
+LABPT	2	2	6005	4143
+LABCK	7	0	5988	4106
+LABLAC	30	2	5974	5453
+LABLAC	90	2	5953	5408
+LABRESP	90	0	5911	4213
+LABPT	30	2	5887	3712
+LABFER	90	0	5820	2360
+LABBMT2	2	0	5778	236
+LABTRFS	1	0	5761	648
+LABPTT	7	3	5753	4456
+LABHAP	1	NULL	5750	3152
+LABHAP	2	NULL	5750	3152
+LABHAP	4	NULL	5750	3152
+LABHAP	30	NULL	5750	3152
+LABHAP	90	NULL	5750	3152
+LABHAP	7	NULL	5750	3152
+LABFLDC	30	0	5748	4391
+LABK	4	2	5723	5262
+LABTRFS	2	0	5701	635
+LABYPREAL	4	0	5693	1410
+LABTRFS	4	0	5654	634
+LABHEPAR	4	0	5639	1303
+LABCK	30	0	5629	3793
+LABRETIC	1	NULL	5628	2109
+LABRETIC	2	NULL	5628	2109
+LABRETIC	4	NULL	5628	2109
+LABRETIC	30	NULL	5628	2109
+LABRETIC	90	NULL	5628	2109
+LABRETIC	7	NULL	5628	2109
+LABCBCS	2	0	5618	52
+LABTRFS	7	0	5614	628
+LABLAC	2	3	5579	5267
+LABFK506L	4	2	5559	5273
+LABAFBC	1	NULL	5539	5174
+LABAFBC	2	NULL	5539	5174
+LABAFBC	4	NULL	5539	5174
+LABAFBC	30	NULL	5539	5174
+LABAFBC	90	NULL	5539	5174
+LABAFBC	7	NULL	5539	5174
+LABK	2	3	5537	5240
+LABBXTG	1	NULL	5522	3479
+LABBXTG	2	NULL	5522	3479
+LABBXTG	4	NULL	5522	3479
+LABBXTG	30	NULL	5522	3479
+LABBXTG	90	NULL	5522	3479
+LABBXTG	7	NULL	5522	3479
+LABPT	7	2	5500	3517
+LABCK	90	0	5479	3659
+LABNH3	1	0	5472	2193
+LABHEPAR	7	0	5470	1267
+LABTRFS	30	0	5454	616
+LABBLC	2	1	5440	5171
+LABALB	1	NULL	5411	759
+LABALB	2	NULL	5411	759
+LABALB	4	NULL	5411	759
+LABALB	30	NULL	5411	759
+LABALB	90	NULL	5411	759
+LABALB	7	NULL	5411	759
+LABFLDC	90	0	5370	4100
+LABPTT	1	2	5364	3883
+LABPHEN	1	NULL	5324	2215
+LABPHEN	2	NULL	5324	2215
+LABPHEN	4	NULL	5324	2215
+LABPHEN	30	NULL	5324	2215
+LABPHEN	90	NULL	5324	2215
+LABPHEN	7	NULL	5324	2215
+LABK	7	2	5299	4837
+LABURIC	2	1	5288	5242
+LABCBCS	4	0	5286	50
+LABPT	4	2	5283	3381
+LABROMRS	30	1	5274	5183
+LABLAC	4	3	5272	4949
+LABTRFS	90	0	5248	599
+LABLDH	1	1	5242	4886
+LABVANPRL	2	1	5231	3250
+LABLDH	2	1	5208	4756
+LABCBCS	7	0	5183	50
+LABHEPAR	30	0	5168	1216
+LABDIC	4	0	5166	155
+LABCORT	1	NULL	5150	3553
+LABCORT	2	NULL	5150	3553
+LABCORT	4	NULL	5150	3553
+LABCORT	30	NULL	5150	3553
+LABCORT	90	NULL	5150	3553
+LABCORT	7	NULL	5150	3553
+LABHAP	1	0	5113	2857
+LABURNC	90	1	5111	3566
+LABDIGL	1	NULL	5109	3923
+LABDIGL	2	NULL	5109	3923
+LABDIGL	4	NULL	5109	3923
+LABDIGL	30	NULL	5109	3923
+LABDIGL	90	NULL	5109	3923
+LABDIGL	7	NULL	5109	3923
+LABFK506L	7	1	5106	4591
+LABURIC	2	2	5093	5070
+LABPTT	4	4	5075	4263
+LABK	30	2	5061	4609
+LABLACWB	1	0	5043	4316
+LABK	90	2	5042	4584
+LABHEPAR	90	0	5013	1190
+LABFK506L	7	2	5003	4730
+LABCBCD	90	1	4964	1237
+LABCBCS	30	0	4964	50
+LABK	1	3	4964	4712
+LABAMY	1	0	4946	4116
+LABRETIC	1	0	4939	1973
+LABVOLTL	1	NULL	4886	3562
+LABVOLTL	2	NULL	4886	3562
+LABVOLTL	4	NULL	4886	3562
+LABVOLTL	30	NULL	4886	3562
+LABVOLTL	90	NULL	4886	3562
+LABVOLTL	7	NULL	4886	3562
+LABDIC	7	0	4869	147
+LABAFBD	1	NULL	4858	4757
+LABAFBD	2	NULL	4858	4757
+LABAFBD	4	NULL	4858	4757
+LABAFBD	30	NULL	4858	4757
+LABAFBD	90	NULL	4858	4757
+LABAFBD	7	NULL	4858	4757
+LABCBCD	30	1	4843	1215
+LABCBCS	90	0	4816	50
+LABFK506L	4	0	4800	3470
+LABHAP	2	0	4789	2704
+LABBLC	90	2	4728	4404
+LABBXTG	1	0	4723	2978
+LABFK506L	7	3	4697	4501
+LABCBCD	7	1	4677	1174
+LABFIB	2	0	4656	1824
+LABK	4	3	4644	4339
+LABCBCD	2	1	4641	1328
+LABBXTG	2	0	4625	2946
+LABURNC	30	1	4605	3280
+LABVOLTL	1	0	4595	3422
+LABVOLTL	2	0	4578	3410
+LABVOLTL	4	0	4570	3407
+LABBLCTIP	1	NULL	4569	4131
+LABBLCTIP	2	NULL	4569	4131
+LABBLCTIP	4	NULL	4569	4131
+LABBLCTIP	30	NULL	4569	4131
+LABBLCTIP	90	NULL	4569	4131
+LABBLCTIP	7	NULL	4569	4131
+LABCBCD	4	1	4565	1160
+LABHIVWBL	1	NULL	4562	4511
+LABHIVWBL	2	NULL	4562	4511
+LABHIVWBL	4	NULL	4562	4511
+LABHIVWBL	30	NULL	4562	4511
+LABHIVWBL	90	NULL	4562	4511
+LABHIVWBL	7	NULL	4562	4511
+LABVOLTL	7	0	4546	3395
+LABNA	1	0	4536	2150
+LABFK506L	30	1	4531	4049
+LABBLCSTK	1	NULL	4529	4126
+LABBLCSTK	2	NULL	4529	4126
+LABBLCSTK	4	NULL	4529	4126
+LABBLCSTK	30	NULL	4529	4126
+LABBLCSTK	90	NULL	4529	4126
+LABBLCSTK	7	NULL	4529	4126
+LABCAI	90	4	4521	3496
+LABBXTG	4	0	4515	2910
+LABNH3	2	0	4502	1891
+LABFK506L	7	0	4498	3224
+LABLAC	7	3	4490	4173
+LABLAC	90	3	4480	4133
+LABCAI	30	4	4477	3469
+LABCMVPQ	1	NULL	4456	3656
+LABCMVPQ	2	NULL	4456	3656
+LABCMVPQ	4	NULL	4456	3656
+LABCMVPQ	30	NULL	4456	3656
+LABCMVPQ	90	NULL	4456	3656
+LABCMVPQ	7	NULL	4456	3656
+LABLAC	30	3	4454	4126
+LABRETIC	2	0	4439	1920
+LABBXTG	7	0	4428	2874
+LABVOLTL	30	0	4416	3314
+LABCAI	2	3	4409	3214
+LABCAI	7	4	4403	3428
+LABBLCSTK	1	0	4400	4019
+LABHIVWBL	1	0	4382	4333
+LABDIC	30	0	4371	131
+LABFIB	4	0	4363	1726
+LABYPREAL	7	0	4350	985
+LABNA	1	1	4346	3273
+LABHIVWBL	2	0	4325	4277
+LABRETIC	4	0	4320	1891
+LABPHOS	2	4	4282	3636
+LABBLCTIP	1	0	4281	3883
+LABHIVWBL	4	0	4280	4233
+LABLACWB	2	0	4275	3596
+LABHIVWBL	7	0	4257	4210
+LABHEPAR	2	2	4255	3055
+LABFIB	7	0	4245	1686
+LABLAC	2	4	4239	4052
+LABVOLTL	90	0	4239	3201
+LABBXTG	30	0	4226	2785
+LABRETIC	7	0	4225	1862
+LABHIVWBL	30	0	4222	4175
+LABTNI	90	3	4216	3999
+LABNA	1	2	4212	3618
+LABCSFCT	1	NULL	4204	925
+LABCSFCT	2	NULL	4204	925
+LABCSFCT	4	NULL	4204	925
+LABCSFCT	30	NULL	4204	925
+LABCSFCT	90	NULL	4204	925
+LABCSFCT	7	NULL	4204	925
+LABK	7	3	4193	3909
+LABHIVWBL	90	0	4189	4142
+LABBLCSTK	2	0	4185	3820
+LABNH3	4	0	4165	1788
+LABPCG3V	1	0	4164	228
+LABHAP	4	0	4145	2390
+LABMGN	1	4	4143	3810
+LABFK506L	90	1	4130	3670
+LABBLC	30	2	4121	3892
+LABPCCG4O	1	NULL	4112	3455
+LABPCCG4O	2	NULL	4112	3455
+LABPCCG4O	4	NULL	4112	3455
+LABPCCG4O	30	NULL	4112	3455
+LABPCCG4O	90	NULL	4112	3455
+LABPCCG4O	7	NULL	4112	3455
+LABDBIL	1	NULL	4103	1414
+LABDBIL	2	NULL	4103	1414
+LABDBIL	4	NULL	4103	1414
+LABDBIL	30	NULL	4103	1414
+LABDBIL	90	NULL	4103	1414
+LABDBIL	7	NULL	4103	1414
+LABDIC	90	0	4081	120
+LABBLCTIP	2	0	4065	3689
+LABK	2	4	4061	3836
+LABAMY	2	0	4059	3432
+LABCMVPQ	1	0	4052	3351
+LABURIC	1	2	4040	4027
+LABBXTG	90	0	4022	2651
+LABFIB	30	0	4011	1609
+LABRETIC	30	0	4004	1801
+LABLAC	4	4	3978	3787
+LABPTT	90	4	3975	3176
+LABK	30	3	3973	3692
+LABLACWB	4	0	3971	3324
+LABTNI	30	3	3966	3788
+LABTRIG	4	0	3966	2519
+LABURIC	4	3	3963	3949
+LABSTOBGD	1	NULL	3959	695
+LABSTOBGD	2	NULL	3959	695
+LABSTOBGD	4	NULL	3959	695
+LABSTOBGD	30	NULL	3959	695
+LABSTOBGD	90	NULL	3959	695
+LABSTOBGD	7	NULL	3959	695
+LABNH3	7	0	3955	1710
+LABK	90	3	3953	3669
+LABFIB	90	0	3904	1569
+LABAMY	4	0	3898	3301
+LABDIGL	1	0	3895	2902
+LABURIC	4	4	3881	3867
+LABGGT	1	NULL	3876	688
+LABGGT	2	NULL	3876	688
+LABGGT	4	NULL	3876	688
+LABGGT	30	NULL	3876	688
+LABGGT	90	NULL	3876	688
+LABGGT	7	NULL	3876	688
+LABBLC2	90	2	3873	3595
+LABAFBD	1	0	3866	3779
+LABPCCG4O	1	0	3858	3277
+LABPCCG4O	2	0	3858	3277
+LABPCCG4O	4	0	3856	3275
+LABHAP	7	0	3844	2261
+LABRETIC	90	0	3844	1752
+LABPCCG4O	7	0	3843	3266
+LABCBCD	1	1	3838	1026
+LABCMVPQ	2	0	3838	3174
+LABAFBD	2	0	3819	3733
+LABAMY	7	0	3787	3206
+LABHBSAG	1	NULL	3786	3704
+LABHBSAG	2	NULL	3786	3704
+LABHBSAG	4	NULL	3786	3704
+LABHBSAG	30	NULL	3786	3704
+LABHBSAG	90	NULL	3786	3704
+LABHBSAG	7	NULL	3786	3704
+LABPTT	30	4	3779	3041
+LABPCCG4O	30	0	3778	3212
+LABPCTNI	90	1	3772	3420
+LABCORT	1	0	3770	2712
+LABCAI	1	3	3758	2828
+LABLACWB	7	0	3740	3121
+LABBLCSTK	4	0	3730	3391
+LABPALB	1	NULL	3721	832
+LABPALB	2	NULL	3721	832
+LABPALB	4	NULL	3721	832
+LABPALB	30	NULL	3721	832
+LABPALB	90	NULL	3721	832
+LABPALB	7	NULL	3721	832
+LABFK506L	30	0	3708	2570
+LABALB	1	0	3697	525
+LABPCCG4O	90	0	3696	3138
+LABAFBC	1	0	3690	3461
+LABAFBD	4	0	3686	3601
+LABSTOBGD	1	0	3685	659
+LABFK506L	30	2	3669	3441
+LABLAC	1	4	3652	3525
+LABFOL	1	NULL	3642	2540
+LABFOL	2	NULL	3642	2540
+LABFOL	4	NULL	3642	2540
+LABFOL	30	NULL	3642	2540
+LABFOL	90	NULL	3642	2540
+LABFOL	7	NULL	3642	2540
+LABURNC	7	1	3605	2792
+LABBLCTIP	4	0	3600	3251
+LABCSFCT	1	0	3600	781
+LABK	4	4	3598	3377
+LABTNI	7	3	3583	3446
+LABAFBD	7	0	3580	3502
+LABAMY	30	0	3573	3018
+LABHBSAG	1	0	3568	3489
+LABNA	2	0	3563	1657
+LABOSM	1	0	3557	1454
+LABPTT	7	4	3553	2893
+LABCORT	2	0	3548	2561
+LABHAP	30	0	3546	2126
+LABSTOBGD	2	0	3544	632
+LABHBSAG	2	0	3537	3460
+LABURIC	2	0	3534	2971
+LABHBSAG	4	0	3518	3442
+LABLDH	2	2	3514	3379
+LABPALB	1	0	3512	806
+LABNA	2	1	3509	2513
+LABHBSAG	7	0	3500	3426
+LABASI	1	NULL	3496	3491
+LABASI	2	NULL	3496	3491
+LABASI	4	NULL	3496	3491
+LABASI	30	NULL	3496	3491
+LABASI	90	NULL	3496	3491
+LABASI	7	NULL	3496	3491
+LABCMVPQ	4	0	3491	2942
+LABFOL	1	0	3490	2432
+LABASI	1	0	3488	3483
+LABBLC	1	1	3487	3326
+LABHEPAR	90	2	3485	2434
+LABHEPAR	4	2	3484	2449
+LABBLC2	2	1	3479	3266
+LABASI	2	0	3476	3471
+LABHEPAR	30	2	3471	2430
+LABAMY	90	0	3470	2927
+LABNH3	30	0	3470	1549
+LABPCG3V	2	0	3468	198
+LABHEPAR	1	2	3461	2518
+LABCSFGL	1	NULL	3458	3123
+LABCSFGL	2	NULL	3458	3123
+LABCSFGL	4	NULL	3458	3123
+LABCSFGL	30	NULL	3458	3123
+LABCSFGL	90	NULL	3458	3123
+LABCSFGL	7	NULL	3458	3123
+LABFOL	2	0	3458	2412
+LABAFBC	2	0	3454	3238
+LABHEPAR	7	2	3452	2428
+LABCORT	4	0	3447	2492
+LABCSFTP	1	NULL	3442	1304
+LABCSFTP	2	NULL	3442	1304
+LABCSFTP	4	NULL	3442	1304
+LABCSFTP	30	NULL	3442	1304
+LABCSFTP	90	NULL	3442	1304
+LABCSFTP	7	NULL	3442	1304
+LABPCEC4V	1	NULL	3438	485
+LABPCEC4V	2	NULL	3438	485
+LABPCEC4V	4	NULL	3438	485
+LABPCEC4V	30	NULL	3438	485
+LABPCEC4V	90	NULL	3438	485
+LABPCEC4V	7	NULL	3438	485
+LABBUN	1	NULL	3436	1827
+LABBUN	2	NULL	3436	1827
+LABBUN	4	NULL	3436	1827
+LABBUN	30	NULL	3436	1827
+LABBUN	90	NULL	3436	1827
+LABBUN	7	NULL	3436	1827
+LABFOL	4	0	3432	2393
+LABPT	4	3	3431	2741
+LABFOL	7	0	3420	2383
+LABUEP	1	NULL	3419	1755
+LABUEP	2	NULL	3419	1755
+LABUEP	4	NULL	3419	1755
+LABUEP	30	NULL	3419	1755
+LABUEP	90	NULL	3419	1755
+LABUEP	7	NULL	3419	1755
+LABSTOBGD	4	0	3411	613
+LABCORT	7	0	3392	2450
+LABHAP	90	0	3389	2054
+LABFOL	30	0	3388	2358
+LABTNI	4	3	3388	3272
+LABCSFCT	2	0	3386	762
+LABLACWB	30	0	3382	2821
+LABAFBD	30	0	3381	3311
+LABNA	4	0	3375	1577
+LABCA	1	NULL	3374	1077
+LABCA	2	NULL	3374	1077
+LABCA	4	NULL	3374	1077
+LABCA	30	NULL	3374	1077
+LABCA	90	NULL	3374	1077
+LABCA	7	NULL	3374	1077
+LABFK506L	90	2	3374	3150
+LABPTT	2	3	3374	2610
+LABBLCSTK	7	0	3372	3058
+LABPHOS	4	5	3369	2801
+LABHBSAG	30	0	3363	3293
+LABDBIL	1	0	3358	1240
+LABLAC	90	4	3352	3111
+LABDVERVP	1	NULL	3346	2731
+LABDVERVP	2	NULL	3346	2731
+LABDVERVP	4	NULL	3346	2731
+LABDVERVP	30	NULL	3346	2731
+LABDVERVP	90	NULL	3346	2731
+LABDVERVP	7	NULL	3346	2731
+LABPALB	2	0	3345	780
+LABFOL	90	0	3344	2333
+LABSTOBGD	7	0	3336	594
+LABNA	4	1	3313	2348
+LABLAC	30	4	3307	3085
+LABASI	4	0	3306	3301
+LABNA	90	1	3302	2328
+LABNA	30	1	3301	2329
+LABNA	7	1	3300	2334
+LABOSM	2	0	3296	1326
+LABK	7	4	3292	3082
+LABCSFGL	1	0	3274	2975
+LABLACWB	1	1	3274	3037
+LABPHEN	1	0	3274	1265
+LABCSFTP	1	0	3262	1260
+LABNA	7	0	3262	1538
+LABMB	1	2	3255	3019
+LABCORT	30	0	3248	2338
+LABBLCTIP	7	0	3246	2920
+LABBLC2	30	2	3245	3035
+LABLAC	7	4	3237	3051
+LABYPREAL	30	0	3233	653
+LABPROCT	1	NULL	3229	1531
+LABPROCT	2	NULL	3229	1531
+LABPROCT	4	NULL	3229	1531
+LABPROCT	30	NULL	3229	1531
+LABPROCT	90	NULL	3229	1531
+LABPROCT	7	NULL	3229	1531
+LABANER2	1	NULL	3217	2523
+LABANER2	2	NULL	3217	2523
+LABANER2	4	NULL	3217	2523
+LABANER2	30	NULL	3217	2523
+LABANER2	90	NULL	3217	2523
+LABANER2	7	NULL	3217	2523
+LABOSM	4	0	3215	1299
+LABUEP	1	0	3210	1668
+LABAFBD	90	0	3196	3129
+LABLACWB	90	0	3196	2672
+LABCBCO	2	1	3186	1174
+LABCBCO	90	1	3184	1024
+LABSTOBGD	30	0	3179	565
+LABPCEG7	1	0	3176	20
+LABPCTNI	30	1	3172	2888
+LABNH3	90	0	3168	1469
+LABPCEC4V	1	0	3168	475
+LABPCEC4V	2	0	3167	475
+LABCORT	90	0	3166	2279
+LABPCEC4V	4	0	3165	475
+LABOSM	7	0	3160	1284
+LABPCEC4V	7	0	3160	474
+LABNA	30	0	3155	1498
+LABURIC	4	0	3154	2604
+LABCSFC	1	NULL	3140	3017
+LABCSFC	2	NULL	3140	3017
+LABCSFC	4	NULL	3140	3017
+LABCSFC	30	NULL	3140	3017
+LABCSFC	90	NULL	3140	3017
+LABCSFC	7	NULL	3140	3017
+LABMB	90	2	3138	2803
+LABCBCO	30	1	3133	1019
+LABPCEC4V	30	0	3122	461
+LABNA	90	0	3116	1487
+LABUEP	2	0	3115	1627
+LABK	30	4	3099	2887
+LABALB	2	0	3094	497
+LABK	90	4	3092	2876
+LABAFBC	4	0	3091	2894
+LABDVERVP	1	0	3082	2526
+LABPCEC4V	90	0	3081	454
+LABOSM	30	0	3072	1257
+LABMB	30	2	3070	2760
+LABSTOBGD	90	0	3070	550
+LABFK506L	7	4	3066	2931
+LABFK506L	90	0	3062	2045
+LABTRIG	7	0	3058	1968
+LABUAS	1	NULL	3056	1198
+LABUAS	2	NULL	3056	1198
+LABUAS	4	NULL	3056	1198
+LABUAS	30	NULL	3056	1198
+LABUAS	90	NULL	3056	1198
+LABUAS	7	NULL	3056	1198
+LABASI	7	0	3053	3048
+LABCSFGL	2	0	3050	2795
+LABCBCO	7	1	3048	1010
+LABYPREAL	90	0	3047	606
+LABCSFTP	2	0	3044	1203
+LABFK506L	30	3	3044	2888
+LABUEP	4	0	3039	1594
+LABPCG3V	4	0	3034	175
+LABCAI	90	5	3027	2412
+LABCBCO	4	1	3020	1015
+LABOSM	90	0	3020	1244
+LABPCINR	1	NULL	3017	1606
+LABPCINR	2	NULL	3017	1606
+LABPCINR	4	NULL	3017	1606
+LABPCINR	30	NULL	3017	1606
+LABPCINR	90	NULL	3017	1606
+LABPCINR	7	NULL	3017	1606
+LABURIC	7	0	3011	2472
+LABMB	7	2	3000	2732
+LABCSFCT	4	0	2993	734
+LABCSFC	1	0	2990	2878
+LABUEP	7	0	2990	1572
+LABMB	2	2	2977	2735
+LABCAI	30	5	2970	2382
+LABMB	4	2	2969	2719
+LABTNI	2	3	2967	2870
+LABDVERVP	2	0	2962	2422
+LABPCINR	1	0	2954	1593
+LABPCINR	2	0	2953	1592
+LABPCINR	4	0	2953	1592
+LABAFBC	7	0	2948	2769
+LABPCINR	7	0	2948	1589
+LABHBSAG	90	0	2940	2874
+LABALB	4	0	2933	486
+LABCAI	7	5	2927	2364
+LABPCINR	30	0	2923	1575
+LABLDH	4	3	2922	2807
+LABLAC	2	5	2918	2805
+LABCMVPQ	7	0	2917	2533
+LABLIPS	90	1	2916	2559
+LABPHOS	1	3	2900	2425
+LABUAS	1	0	2896	1149
+LABPCINR	90	0	2895	1560
+LABUEP	30	0	2883	1520
+LABPCEG7	2	0	2876	19
+LABDVERVP	4	0	2867	2356
+LABALB	7	0	2856	478
+LABFK506L	90	3	2852	2706
+LABGGT	1	0	2851	532
+LABANER	30	1	2843	2667
+LABUAS	2	0	2840	1133
+LABDIGL	2	0	2836	1989
+LABK	2	5	2833	2686
+LABPT	90	3	2832	2054
+LABANER	90	1	2830	2651
+LABPROCT	1	0	2828	1393
+LABUEP	90	0	2828	1490
+LABVANPRL	1	1	2826	1845
+LABPOCGLU	90	4	2811	1524
+LABPCG3V	7	0	2803	161
+LABURNC	4	1	2800	2213
+LABPOCGLU	30	4	2797	1521
+LABUAS	4	0	2797	1117
+LABCSFC	2	0	2793	2689
+LABURIC	2	3	2787	2784
+LABTNI	1	3	2784	2709
+LABDVERVP	7	0	2779	2294
+LABPOCGLU	7	4	2776	1512
+LABUAS	7	0	2765	1104
+LABPCEG7	4	0	2761	19
+LABPOCGLU	4	4	2761	1506
+LABPOCGLU	2	4	2757	1496
+LABK	4	5	2755	2593
+LABBLCSTK	30	0	2753	2495
+LABALB	30	0	2747	464
+LABFKOH	1	NULL	2745	1810
+LABFKOH	2	NULL	2745	1810
+LABFKOH	4	NULL	2745	1810
+LABFKOH	30	NULL	2745	1810
+LABFKOH	90	NULL	2745	1810
+LABDBIL	2	0	2745	1110
+LABFKOH	7	NULL	2745	1810
+LABLACWB	2	1	2742	2527
+LABLDH	90	1	2734	2200
+LABCSFCT	7	0	2733	704
+LABDDIM	1	NULL	2726	396
+LABDDIM	2	NULL	2726	396
+LABDDIM	4	NULL	2726	396
+LABDDIM	30	NULL	2726	396
+LABDDIM	90	NULL	2726	396
+LABDDIM	7	NULL	2726	396
+LABURIC	30	0	2712	2197
+LABASI	30	0	2705	2700
+LABUAS	30	0	2699	1072
+LABLDH	4	1	2692	2280
+LABBLC	7	2	2687	2580
+LABUNA	1	NULL	2683	1704
+LABUNA	2	NULL	2683	1704
+LABUNA	4	NULL	2683	1704
+LABUNA	30	NULL	2683	1704
+LABUNA	90	NULL	2683	1704
+LABUNA	7	NULL	2683	1704
+LABALB	90	0	2680	454
+LABPOCGLU	1	4	2679	1464
+LABPCEG7	7	0	2674	17
+LABHGBX	1	NULL	2671	116
+LABHGBX	2	NULL	2671	116
+LABHGBX	4	NULL	2671	116
+LABHGBX	30	NULL	2671	116
+LABHGBX	90	NULL	2671	116
+LABHGBX	7	NULL	2671	116
+LABAFBC	30	0	2669	2557
+LABCSFTP	4	0	2667	1114
+LABPTT	90	5	2663	2208
+LABCSFGL	4	0	2660	2462
+LABLDH	30	1	2659	2157
+LABUAS	90	0	2659	1054
+LABPT	30	3	2655	1952
+LABK	7	5	2645	2481
+LABANER	7	1	2642	2499
+LABBLCTIP	30	0	2631	2370
+LABNA	1	3	2624	2316
+LABLAC	90	5	2605	2453
+LABRESPG	1	NULL	2595	1656
+LABRESPG	2	NULL	2595	1656
+LABRESPG	4	NULL	2595	1656
+LABRESPG	30	NULL	2595	1656
+LABRESPG	90	NULL	2595	1656
+LABRESPG	7	NULL	2595	1656
+LABLAC	4	5	2594	2476
+LABTRIG	30	0	2581	1686
+LABASI	90	0	2574	2569
+LABLDH	7	1	2570	2133
+LABPCEG7	30	0	2562	15
+LABDBIL	4	0	2551	1046
+LABPCACTK	1	NULL	2549	644
+LABPCACTK	2	NULL	2549	644
+LABPCACTK	4	NULL	2549	644
+LABPCACTK	30	NULL	2549	644
+LABPCACTK	90	NULL	2549	644
+LABPCACTK	7	NULL	2549	644
+LABLAC	30	5	2548	2410
+LABDVERVP	30	0	2547	2107
+LABPROCT	2	0	2547	1286
+LABUNA	1	0	2544	1611
+LABBMT1	4	0	2542	186
+LABLAC	7	5	2536	2421
+LABDIGL	4	0	2535	1733
+LABUA	90	1	2528	929
+LABPCG3V	30	0	2527	144
+LABPTT	30	5	2525	2117
+LABURIC	90	0	2522	2032
+LABANAS	1	NULL	2518	2064
+LABANAS	2	NULL	2518	2064
+LABANAS	4	NULL	2518	2064
+LABANAS	30	NULL	2518	2064
+LABANAS	90	NULL	2518	2064
+LABANAS	7	NULL	2518	2064
+LABPCEG7	90	0	2507	15
+LABBLCSTK	90	0	2503	2278
+LABANER	4	1	2502	2381
+LABFK506L	30	4	2501	2382
+LABLIPS	30	1	2500	2196
+LABUNA	2	0	2499	1586
+LABTBILFR	1	NULL	2498	469
+LABTBILFR	2	NULL	2498	469
+LABTBILFR	4	NULL	2498	469
+LABTBILFR	30	NULL	2498	469
+LABTBILFR	90	NULL	2498	469
+LABTBILFR	7	NULL	2498	469
+LABFK506L	7	5	2491	2367
+LABDBIL	7	0	2481	1028
+LABAFBC	90	0	2466	2365
+LABTRIG	90	0	2466	1616
+LABCSFC	4	0	2459	2374
+LABPCG3V	90	0	2458	142
+LABUNA	4	0	2445	1549
+LABUPREG	1	NULL	2441	2393
+LABUPREG	2	NULL	2441	2393
+LABUPREG	4	NULL	2441	2393
+LABUPREG	30	NULL	2441	2393
+LABUPREG	90	NULL	2441	2393
+LABUPREG	7	NULL	2441	2393
+LABCSFGL	7	0	2439	2269
+LABCSFTP	7	0	2439	1046
+LABCBCO	1	1	2438	873
+LABK	30	5	2435	2275
+LABPT	7	3	2433	1827
+LABPCTNI	7	1	2432	2202
+LABBUN	1	0	2431	1209
+LABPTT	7	5	2431	2067
+LABURIC	2	4	2428	2425
+LABVANPRL	90	2	2427	1448
+LABK	90	5	2426	2263
+LABBMT1	7	0	2422	186
+LABANAS	1	0	2417	1987
+LABLACWB	4	1	2417	2201
+LABFKOH	1	0	2413	1596
+LABUNA	7	0	2410	1527
+LABANER	2	1	2408	2316
+LABGRAM	7	1	2406	2156
+LABGRAM	30	1	2406	2166
+LABTNI	90	4	2402	2311
+LABFK506L	90	4	2398	2278
+LABPROCT	4	0	2394	1241
+LABUPREG	1	0	2392	2353
+LABLACWB	1	2	2391	2281
+LABANAS	2	0	2386	1962
+LABBLCTIP	90	0	2385	2151
+LABUPREG	2	0	2384	2346
+LABGRAM	90	1	2379	2135
+LABUPREG	4	0	2372	2334
+LABCAI	2	4	2370	1821
+LABLACWB	7	1	2369	2152
+LABDVERVP	90	0	2368	1956
+LABNA	2	2	2364	1960
+LABUPREG	7	0	2362	2324
+LABDBIL	30	0	2361	1009
+LABFKOH	2	0	2358	1564
+LABDIGL	7	0	2354	1589
+LABLPDC	1	NULL	2348	859
+LABLPDC	2	NULL	2348	859
+LABLPDC	4	NULL	2348	859
+LABLPDC	30	NULL	2348	859
+LABLPDC	90	NULL	2348	859
+LABLPDC	7	NULL	2348	859
+LABLIDOL	1	2	2345	2149
+LABUNA	30	0	2345	1482
+LABBLC	90	3	2342	2180
+LABBMT2	4	0	2339	80
+LABANAS	4	0	2335	1922
+LABLACWB	1	3	2325	2252
+LABDBIL	90	0	2317	998
+LABUNA	90	0	2317	1461
+LABUPREG	30	0	2311	2275
+LABANAS	7	0	2306	1903
+LABCSFCT	30	0	2300	602
+LABPCTNI	4	1	2300	2080
+LABFKOH	4	0	2298	1525
+LABLDH	4	4	2295	2232
+LABPROCT	7	0	2295	1198
+LABRESPG	1	0	2292	1483
+LABVANPRL	30	2	2289	1372
+LABCDTPCR	90	1	2287	1639
+LABANAS	30	0	2285	1889
+LABLPDC	1	0	2285	837
+LABANER	1	1	2284	2224
+LABLACWB	30	1	2284	2057
+LABTBILFR	1	0	2279	441
+LABBUN	2	0	2273	1180
+LABHCVA	1	NULL	2271	2011
+LABHCVA	2	NULL	2271	2011
+LABHCVA	4	NULL	2271	2011
+LABHCVA	30	NULL	2271	2011
+LABHCVA	90	NULL	2271	2011
+LABHITAB	1	NULL	2271	1922
+LABHITAB	2	NULL	2271	1922
+LABHITAB	4	NULL	2271	1922
+LABHITAB	30	NULL	2271	1922
+LABHITAB	90	NULL	2271	1922
+LABHCVA	7	NULL	2271	2011
+LABHITAB	7	NULL	2271	1922
+LABLPDC	2	0	2269	831
+LABANAS	90	0	2266	1880
+LABPCTNI	2	1	2248	2034
+LABLPDC	4	0	2247	830
+LABBMT2	7	0	2246	74
+LABCSFC	7	0	2246	2167
+LABLACWB	90	1	2242	2004
+LABFKOH	7	0	2237	1485
+LABPCTNI	1	1	2236	2024
+LABLPDC	7	0	2235	830
+LABWDC	1	NULL	2230	650
+LABWDC	2	NULL	2230	650
+LABWDC	4	NULL	2230	650
+LABWDC	30	NULL	2230	650
+LABWDC	90	NULL	2230	650
+LABWDC	7	NULL	2230	650
+LABRESPG	2	0	2229	1451
+LABSIROL	1	NULL	2224	835
+LABSIROL	2	NULL	2224	835
+LABSIROL	4	NULL	2224	835
+LABSIROL	30	NULL	2224	835
+LABSIROL	90	NULL	2224	835
+LABSIROL	7	NULL	2224	835
+LABHCVA	1	0	2220	1969
+LABLPDC	30	0	2219	828
+LABSALICY	1	NULL	2212	27
+LABSALICY	2	NULL	2212	27
+LABSALICY	4	NULL	2212	27
+LABSALICY	30	NULL	2212	27
+LABSALICY	90	NULL	2212	27
+LABSALICY	7	NULL	2212	27
+LABLPDC	90	0	2207	823
+LABHCVA	2	0	2201	1954
+LABPTT	1	3	2196	1837
+LABUPREG	90	0	2196	2163
+LABCSFTP	30	0	2193	945
+LABCSFGL	30	0	2192	2057
+LABHCVA	4	0	2185	1947
+LABCDTPCR	30	1	2183	1543
+LABCMVPQ	30	0	2183	1947
+LABDDIM	1	0	2182	340
+LABHCVA	7	0	2180	1944
+LABVCUL	1	NULL	2171	1993
+LABVCUL	2	NULL	2171	1993
+LABVCUL	4	NULL	2171	1993
+LABVCUL	30	NULL	2171	1993
+LABVCUL	90	NULL	2171	1993
+LABHCVA	30	0	2171	1936
+LABVCUL	7	NULL	2171	1993
+LABBUN	4	0	2166	1157
+LABHCVA	90	0	2160	1925
+LABHEPAR	4	3	2157	1545
+LABRESPG	4	0	2157	1404
+LABCSFCT	90	0	2144	551
+LABNA	4	2	2141	1756
+LABTNI	30	4	2141	2065
+LABNA	7	2	2134	1740
+LABPROCT	30	0	2128	1118
+LABFK506L	30	5	2125	2014
+LABTBILFR	2	0	2125	422
+LABNA	90	2	2123	1731
+LABHITAB	1	0	2122	1793
+LABNA	30	2	2122	1731
+LABGRAM	4	1	2121	1916
+LABSALICY	1	0	2118	13
+LABSALICY	2	0	2113	13
+LABSALICY	4	0	2111	13
+LABDIGL	30	0	2102	1390
+LABSALICY	7	0	2101	13
+LABVPAL	1	NULL	2099	1043
+LABVPAL	2	NULL	2099	1043
+LABVPAL	4	NULL	2099	1043
+LABVPAL	30	NULL	2099	1043
+LABVPAL	90	NULL	2099	1043
+LABVPAL	7	NULL	2099	1043
+LABCSFGL	90	0	2093	1962
+LABCSFTP	90	0	2091	894
+LABUSPG	1	3	2084	2079
+LABTSH	90	1	2079	1740
+LABRESPG	7	0	2077	1358
+LABBUN	7	0	2074	1132
+LABPROCT	90	0	2073	1086
+LABCK	1	1	2057	1893
+LABVANPRL	7	2	2057	1265
+LABWDC	1	0	2057	595
+LABHEPAR	90	3	2055	1448
+LABHEPAR	2	3	2053	1521
+LABROMRS	7	1	2053	2032
+LABSALICY	30	0	2051	13
+LABGGT	2	0	2048	447
+LABMETB	90	2	2047	894
+LABDDIM	2	0	2041	337
+LABHEPAR	30	3	2041	1446
+LABFK506L	90	5	2037	1926
+LABHEPAR	7	3	2032	1443
+LABFCUL	90	1	2031	1786
+LABUA	30	1	2027	751
+LABCSFC	30	0	2015	1946
+LABDDIM	4	0	2014	336
+LABRESP	90	1	2014	1502
+LABHITAB	2	0	2005	1694
+LABWDC	2	0	2005	581
+LABFKOH	30	0	1998	1335
+LABRESP	30	1	1997	1499
+LABDDIM	7	0	1993	336
+LABSALICY	90	0	1991	13
+LABTRIG	4	1	1982	1660
+LABOAPE	1	NULL	1972	1931
+LABOAPE	2	NULL	1972	1931
+LABOAPE	4	NULL	1972	1931
+LABOAPE	30	NULL	1972	1931
+LABOAPE	90	NULL	1972	1931
+LABGGT	4	0	1972	436
+LABOAPE	7	NULL	1972	1931
+LABLACWB	2	2	1971	1869
+LABDIGL	90	0	1969	1300
+LABPALB	4	0	1965	447
+LABMETB	30	2	1961	882
+LABWDC	4	0	1961	570
+LABCMVPQ	90	0	1956	1760
+LABFCUL	30	1	1956	1751
+LABVCUL	1	0	1955	1789
+LABTBILFR	4	0	1953	376
+LABDDIM	30	0	1948	332
+LABLIPS	7	1	1941	1719
+LABVCUL	2	0	1939	1775
+LABSTSS	1	NULL	1934	1859
+LABSTSS	2	NULL	1934	1859
+LABSTSS	4	NULL	1934	1859
+LABSTSS	30	NULL	1934	1859
+LABSTSS	90	NULL	1934	1859
+LABSTSS	7	NULL	1934	1859
+LABCSFC	90	0	1930	1862
+LABWDC	7	0	1930	564
+LABHITAB	4	0	1928	1634
+LABDDIM	90	0	1925	328
+LABK	1	4	1925	1840
+LABGGT	7	0	1923	431
+LABLIPS	2	1	1909	1742
+LABPCEG7V	1	0	1904	7
+LABVCUL	4	0	1896	1737
+LABMYCPA	1	NULL	1895	400
+LABMYCPA	2	NULL	1895	400
+LABMYCPA	4	NULL	1895	400
+LABMYCPA	30	NULL	1895	400
+LABMYCPA	90	NULL	1895	400
+LABMYCPA	7	NULL	1895	400
+LABSPLAC	1	0	1893	1734
+LABRBCFOL	1	NULL	1890	602
+LABRBCFOL	2	NULL	1890	602
+LABRBCFOL	4	NULL	1890	602
+LABRBCFOL	30	NULL	1890	602
+LABRBCFOL	90	NULL	1890	602
+LABFKOH	90	0	1890	1266
+LABRBCFOL	7	NULL	1890	602
+LABNA	2	3	1884	1599
+LABSTSS	1	0	1881	1809
+LABRESP	7	1	1878	1463
+LABBLC2	90	3	1877	1744
+LABBUN	30	0	1871	1073
+LABBMT1	30	0	1867	156
+LABMETB	7	2	1866	869
+LABWDC	30	0	1866	546
+LABHITAB	7	0	1864	1600
+LABTBILFR	7	0	1862	349
+LABSTSS	2	0	1860	1788
+LABBMT2	30	0	1854	67
+LABBLC	30	3	1848	1747
+LABVCUL	7	0	1846	1691
+LABBLC2	7	2	1845	1772
+LABRESPG	30	0	1845	1207
+LABOAPE	1	0	1835	1797
+LABSTSS	4	0	1834	1765
+LABHGBX	1	0	1830	91
+LABGGT	30	0	1828	414
+LABSTSS	7	0	1828	1759
+LABRBCFOL	1	0	1821	578
+LABURIC	4	1	1816	1785
+LABSTSS	30	0	1814	1749
+LABPT	4	4	1808	1465
+LABCMVQT	1	NULL	1805	1292
+LABCMVQT	2	NULL	1805	1292
+LABCMVQT	4	NULL	1805	1292
+LABCMVQT	30	NULL	1805	1292
+LABCMVQT	90	NULL	1805	1292
+LABCMVQT	7	NULL	1805	1292
+LABMETB	4	2	1804	856
+LABWDC	90	0	1801	534
+LABLIDOL	1	1	1799	1498
+LABSTSS	90	0	1798	1733
+LABTSH	30	1	1795	1529
+LABRBCFOL	2	0	1792	569
+LABOAPE	2	0	1780	1742
+LABRBCFOL	4	0	1775	565
+LABSPLAC	2	0	1771	1620
+LABTRIG	7	1	1771	1445
+LABBUN	90	0	1768	1044
+LABRBCFOL	7	0	1766	557
+LABCAI	1	4	1763	1380
+LABTNI	7	4	1759	1702
+LABHITAB	30	0	1756	1524
+LABCAI	4	5	1754	1364
+LABRBCFOL	30	0	1753	552
+LABPHEN	2	0	1747	646
+LABLIPS	4	1	1746	1557
+LABLIDOL	1	0	1745	926
+LABGGT	90	0	1742	406
+LABRBCFOL	90	0	1736	550
+LABURNC	2	1	1736	1346
+LABBLC2	1	1	1726	1606
+LABHITAB	90	0	1726	1498
+LABOAPE	4	0	1725	1689
+LABRESPG	90	0	1721	1136
+LABTBILFR	30	0	1720	321
+LABFCUL	7	1	1715	1588
+LABREPT	1	NULL	1709	602
+LABREPT	2	NULL	1709	602
+LABREPT	4	NULL	1709	602
+LABREPT	30	NULL	1709	602
+LABREPT	90	NULL	1709	602
+LABREPT	7	NULL	1709	602
+LABIBIL	1	NULL	1699	1134
+LABIBIL	2	NULL	1699	1134
+LABIBIL	4	NULL	1699	1134
+LABIBIL	30	NULL	1699	1134
+LABIBIL	90	NULL	1699	1134
+LABIBIL	7	NULL	1699	1134
+LABOAPE	7	0	1693	1657
+LABQFTB	1	NULL	1689	1313
+LABQFTB	2	NULL	1689	1313
+LABQFTB	4	NULL	1689	1313
+LABQFTB	30	NULL	1689	1313
+LABQFTB	90	NULL	1689	1313
+LABQFTB	7	NULL	1689	1313
+LABVPAL	1	0	1685	813
+LABSPLAC	4	0	1683	1536
+LABLACWB	4	2	1681	1584
+LABCMVQT	1	0	1674	1193
+LABHGBX	2	0	1673	86
+LABCA	1	0	1667	655
+LABTBILFR	90	0	1667	316
+LABQFTB	1	0	1660	1291
+LABLACWB	7	2	1652	1554
+LABHGBX	4	0	1648	84
+LABNA	4	3	1642	1367
+LABURIC	7	1	1640	1606
+LABOAPE	30	0	1637	1602
+LABCR	1	NULL	1634	777
+LABCR	2	NULL	1634	777
+LABCR	4	NULL	1634	777
+LABCR	30	NULL	1634	777
+LABCR	90	NULL	1634	777
+LABCR	7	NULL	1634	777
+LABHGBX	7	0	1632	84
+LABLIPS	1	1	1628	1493
+LABVANPRL	4	2	1627	1025
+LABVCUL	30	0	1623	1478
+LABLDH	7	2	1621	1506
+LABQFTB	2	0	1619	1263
+LABSCMV	1	NULL	1618	1570
+LABSCMV	2	NULL	1618	1570
+LABSCMV	4	NULL	1618	1570
+LABSCMV	30	NULL	1618	1570
+LABSCMV	90	NULL	1618	1570
+LABSCMV	7	NULL	1618	1570
+LABGRAM	2	1	1616	1479
+LABA1C	90	1	1614	1364
+LABLDH	4	2	1613	1512
+LABSPLAC	7	0	1609	1467
+LABROMRS	90	2	1607	1567
+LABOAPE	90	0	1602	1567
+LABOSM	1	3	1601	1495
+LABRESP	4	1	1601	1298
+LABANER2	1	0	1595	1199
+LABFCUL	4	1	1592	1494
+LABNA	7	3	1583	1317
+LABLDH	90	2	1582	1423
+LABCMVQT	2	0	1580	1131
+LABIBIL	1	0	1580	1058
+LABLACWB	30	2	1579	1481
+LABPREG	1	NULL	1578	1522
+LABPREG	2	NULL	1578	1522
+LABPREG	4	NULL	1578	1522
+LABPREG	30	NULL	1578	1522
+LABPREG	90	NULL	1578	1522
+LABPREG	7	NULL	1578	1522
+LABURIC	4	2	1577	1565
+LABNA	90	3	1576	1309
+LABPTT	4	5	1576	1316
+LABQFTB	4	0	1576	1235
+LABLIDOL	1	3	1575	1466
+LABNA	30	3	1575	1311
+LABPHOS	2	5	1573	1346
+LABHGBX	30	0	1570	83
+LABURNC	90	2	1570	1166
+LABPT	90	4	1569	1205
+LABCK	2	1	1568	1412
+LABMETC	90	1	1568	258
+LABFIB	2	1	1565	1014
+LABNA	2	5	1564	1393
+LABQFTB	7	0	1564	1227
+LABLACWB	90	2	1555	1455
+LABPREG	1	0	1553	1504
+LABLIDOL	2	1	1552	1289
+LABNA	2	4	1552	1365
+LABPREG	2	0	1549	1502
+LABTNI	90	5	1547	1500
+LABQFTB	30	0	1543	1214
+LABTNI	4	4	1543	1494
+LABPREG	4	0	1542	1495
+LABHGBX	90	0	1541	82
+LABLDH	30	2	1539	1402
+LABPREG	7	0	1534	1487
+LABTSHFT4	1	NULL	1530	1155
+LABTSHFT4	2	NULL	1530	1155
+LABTSHFT4	4	NULL	1530	1155
+LABTSHFT4	30	NULL	1530	1155
+LABTSHFT4	90	NULL	1530	1155
+LABTSHFT4	7	NULL	1530	1155
+LABTBIL	1	NULL	1527	1255
+LABTBIL	2	NULL	1527	1255
+LABTBIL	4	NULL	1527	1255
+LABTBIL	30	NULL	1527	1255
+LABTBIL	90	NULL	1527	1255
+LABTBIL	7	NULL	1527	1255
+LABQFTB	90	0	1525	1203
+LABPTT	2	4	1519	1253
+LABANER2	2	0	1518	1163
+LABCK	7	1	1517	1345
+LABCK	90	1	1517	1334
+LABPREG	30	0	1516	1470
+LABVCUL	90	0	1516	1379
+LABURIC	30	1	1513	1477
+LABLACWB	2	3	1512	1446
+LABUSPG	1	2	1511	1499
+LABTSHFT4	1	0	1502	1134
+LABDVERV	1	NULL	1501	1389
+LABDVERV	2	NULL	1501	1389
+LABDVERV	4	NULL	1501	1389
+LABDVERV	30	NULL	1501	1389
+LABDVERV	90	NULL	1501	1389
+LABDVERV	7	NULL	1501	1389
+LABWDCS	1	NULL	1500	551
+LABWDCS	2	NULL	1500	551
+LABWDCS	4	NULL	1500	551
+LABWDCS	30	NULL	1500	551
+LABWDCS	90	NULL	1500	551
+LABCK	30	1	1500	1322
+LABWDCS	7	NULL	1500	551
+LABCA	2	0	1494	601
+LABPREG	90	0	1492	1446
+LABROMRS	4	1	1492	1477
+LABTSHFT4	2	0	1492	1126
+LABTSHFT4	4	0	1485	1120
+LABPT	1	2	1484	913
+LABTP	1	NULL	1480	1008
+LABTP	2	NULL	1480	1008
+LABTP	4	NULL	1480	1008
+LABTP	30	NULL	1480	1008
+LABTP	90	NULL	1480	1008
+LABTP	7	NULL	1480	1008
+LABTSHFT4	7	0	1479	1116
+LABMETC	30	1	1478	243
+LABSPLAC	30	0	1475	1345
+LABANER2	4	0	1470	1150
+LABMYCPA	1	0	1468	305
+LABPHEN	4	0	1467	548
+LABPOCGLU	90	5	1466	821
+LABFIB	1	1	1465	1001
+LABCA	4	0	1463	585
+LABUEOS	1	NULL	1462	1405
+LABUEOS	2	NULL	1462	1405
+LABUEOS	4	NULL	1462	1405
+LABUEOS	30	NULL	1462	1405
+LABUEOS	90	NULL	1462	1405
+LABPOCGLU	30	5	1462	818
+LABUEOS	7	NULL	1462	1405
+LABTSHFT4	30	0	1460	1106
+LABPT	30	4	1458	1130
+LABTBIL	1	0	1457	1202
+LABANER2	7	0	1455	1144
+LABFCUL	2	1	1455	1387
+LABBLC2	30	3	1452	1377
+LABFIB	90	1	1449	886
+LABURIC	7	2	1449	1437
+LABCA	7	0	1448	577
+LABPOCGLU	7	5	1447	813
+LABCMVQT	4	0	1444	1046
+LABMB	90	3	1444	1272
+LABPOCGLU	4	5	1443	811
+LABSIROL	1	0	1441	550
+LABTSHFT4	90	0	1441	1097
+LABSCMV	1	0	1439	1398
+LABURIC	1	3	1438	1437
+LABURIC	90	1	1437	1399
+LABFIB	30	1	1436	879
+LABTP	1	0	1436	987
+LABPTH	1	NULL	1435	516
+LABPTH	2	NULL	1435	516
+LABPTH	4	NULL	1435	516
+LABPTH	30	NULL	1435	516
+LABPTH	90	NULL	1435	516
+LABMETB	2	2	1435	671
+LABPOCGLU	2	5	1435	814
+LABPTH	7	NULL	1435	516
+LABCA	30	0	1434	572
+LABIBIL	2	0	1431	959
+LABHIPA	1	NULL	1428	1197
+LABHIPA	2	NULL	1428	1197
+LABHIPA	4	NULL	1428	1197
+LABHIPA	30	NULL	1428	1197
+LABHIPA	90	NULL	1428	1197
+LABHIPA	7	NULL	1428	1197
+LABLIDOL	4	1	1428	1198
+LABCA	90	0	1424	568
+LABLIDOL	2	0	1421	767
+LABTSH	7	1	1421	1241
+LABLIDOL	7	1	1417	1188
+LABSCMV	2	0	1415	1375
+LABCK	4	1	1414	1246
+LABCMVP	1	NULL	1413	1306
+LABCMVP	2	NULL	1413	1306
+LABCMVP	4	NULL	1413	1306
+LABCMVP	30	NULL	1413	1306
+LABCMVP	90	NULL	1413	1306
+LABCMVP	7	NULL	1413	1306
+LABTP	2	0	1413	974
+LABBLC	4	2	1409	1357
+LABLIDOL	30	1	1404	1178
+LABANER2	30	0	1403	1123
+LABFIB	4	1	1403	884
+LABLIDOL	90	1	1398	1172
+LABSPLAC	90	0	1396	1274
+LABFIB	7	1	1395	869
+LABTP	4	0	1395	965
+LABLACWB	4	3	1394	1337
+LABDVERV	1	0	1393	1288
+LABBMT1	90	0	1391	118
+LABCR	1	0	1386	686
+LABSCMV	4	0	1386	1346
+LABURIC	7	3	1383	1375
+LABTP	7	0	1382	957
+LABLACWB	1	4	1380	1343
+LABPHEN	7	0	1379	522
+LABTBIL	2	0	1377	1138
+LABLIDOL	4	0	1376	738
+LABIGG	1	NULL	1366	582
+LABIGG	2	NULL	1366	582
+LABIGG	4	NULL	1366	582
+LABIGG	30	NULL	1366	582
+LABIGG	90	NULL	1366	582
+LABIGG	7	NULL	1366	582
+LABLIL	1	NULL	1363	625
+LABLIL	2	NULL	1363	625
+LABLIL	4	NULL	1363	625
+LABLIL	30	NULL	1363	625
+LABLIL	90	NULL	1363	625
+LABLIL	7	NULL	1363	625
+LABLIDOL	7	0	1362	730
+LABMB	30	3	1360	1210
+LABPALB	7	0	1358	292
+LABPCEG7V	2	0	1358	5
+LABVPAL	2	0	1357	617
+LABREPT	1	0	1356	475
+LABFCUL	1	1	1354	1319
+LABA1C	30	1	1353	1184
+LABIBIL	4	0	1352	901
+LABTP	30	0	1351	941
+LABSCMV	7	0	1350	1310
+LABMETC	7	1	1345	223
+LABBMT2	90	0	1341	45
+LABDVERV	2	0	1338	1240
+LABWDCS	1	0	1335	470
+LABMYCPA	2	0	1334	279
+LABTP	90	0	1333	931
+LABTSH	4	1	1330	1166
+LABANER2	90	0	1322	1060
+LABHEPAR	4	4	1322	962
+LABLDH	7	3	1321	1261
+LABPCG3	90	2	1320	389
+LABPCG3	30	2	1318	389
+LABTNI	30	5	1318	1278
+LABLIDOL	30	0	1317	701
+LABIBIL	7	0	1316	879
+LABPTH	1	0	1316	469
+LABHIPA	1	0	1315	1120
+LABTRIG	30	1	1313	1046
+LABPCG3	7	2	1311	389
+LABMETC	4	1	1306	217
+LABPCG3	4	2	1304	389
+LABPT	7	4	1304	1029
+LABSPLAC	1	1	1303	1232
+LABWDCS	2	0	1301	463
+LABTRIG	90	1	1298	1029
+LABCR	2	0	1294	634
+LABBLC	90	4	1290	1214
+LABLIDOL	90	0	1289	685
+LABTBIL	4	0	1289	1062
+LABIGG	1	0	1286	550
+LABMETC	2	1	1286	225
+LABDVERV	4	0	1284	1196
+LABPHEN	30	0	1284	502
+LABPCG3	2	2	1283	389
+LABEBPCRQ	1	NULL	1282	971
+LABEBPCRQ	2	NULL	1282	971
+LABEBPCRQ	4	NULL	1282	971
+LABEBPCRQ	30	NULL	1282	971
+LABEBPCRQ	90	NULL	1282	971
+LABEBPCRQ	7	NULL	1282	971
+LABPCCG4	1	NULL	1281	30
+LABPCCG4	2	NULL	1281	30
+LABPCCG4	4	NULL	1281	30
+LABPCCG4	30	NULL	1281	30
+LABPCCG4	90	NULL	1281	30
+LABPCCG4	7	NULL	1281	30
+LABPTH	2	0	1280	459
+LABURIC	30	2	1280	1267
+LABURIC	2	5	1275	1270
+LABWDCS	4	0	1274	459
+LABHIPA	2	0	1270	1086
+LABIBIL	30	0	1269	853
+LABIGG	2	0	1263	542
+LABCMVP	1	0	1261	1167
+LABCR	4	0	1260	618
+LABURNC	30	2	1260	984
+LABSCMV	30	0	1258	1220
+LABAFBC	90	1	1256	1219
+LABTNI	2	4	1256	1223
+LABIBIL	90	0	1254	843
+LABPTH	4	0	1252	449
+LABCMVP	2	0	1249	1156
+LABDVERV	7	0	1249	1165
+LABUAPRN	90	1	1248	445
+LABWDCS	7	0	1248	454
+LABCR	7	0	1247	608
+LABPHEN	90	0	1243	489
+LABAFBC	30	1	1242	1207
+LABNA	4	4	1242	1086
+LABEBPCRQ	1	0	1238	943
+LABIGG	4	0	1238	529
+LABTSH	2	1	1234	1094
+LABLACWB	7	3	1232	1179
+LABMB	7	3	1232	1118
+LABPTH	7	0	1231	444
+LABFLDC	90	1	1230	1025
+LABTBIL	7	0	1227	1012
+LABAFP	7	NULL	1226	895
+LABAFP	1	NULL	1226	895
+LABAFP	2	NULL	1226	895
+LABAFP	4	NULL	1226	895
+LABAFP	30	NULL	1226	895
+LABAFP	90	NULL	1226	895
+LABPCG3	1	2	1225	374
+LABEBPCRQ	2	0	1224	931
+LABSPLAC	2	1	1224	1156
+LABCMVP	4	0	1223	1134
+LABURIC	90	2	1222	1209
+LABPTEG	1	NULL	1221	606
+LABPTEG	2	NULL	1221	606
+LABPTEG	4	NULL	1221	606
+LABPTEG	30	NULL	1221	606
+LABPTEG	90	NULL	1221	606
+LABPTEG	7	NULL	1221	606
+LABCR	30	0	1220	594
+LABMGN	1	5	1219	1135
+LABMB	4	3	1214	1107
+LABURIC	7	4	1213	1210
+LABDHBP	1	NULL	1212	564
+LABDHBP	2	NULL	1212	564
+LABDHBP	4	NULL	1212	564
+LABDHBP	30	NULL	1212	564
+LABDHBP	90	NULL	1212	564
+LABDHBP	7	NULL	1212	564
+LABUA	7	1	1212	458
+LABCR	90	0	1210	590
+LABUEOS	1	0	1210	1167
+LABIGG	7	0	1204	513
+LABPCEG7V	4	0	1202	4
+LABHIPA	4	0	1201	1030
+LABCMVP	7	0	1199	1113
+LABHEPAR	90	4	1199	847
+LABAFBC	4	1	1198	1175
+LABAFBC	7	1	1198	1176
+LABNA	7	4	1198	1044
+LABLACWB	30	3	1195	1137
+LABNA	90	4	1194	1039
+LABHEPAR	30	4	1193	845
+LABNA	30	4	1193	1038
+LABDHBP	1	0	1192	554
+LABWDCS	30	0	1191	440
+LABDDIML	1	NULL	1190	365
+LABDDIML	2	NULL	1190	365
+LABDDIML	4	NULL	1190	365
+LABDDIML	30	NULL	1190	365
+LABDDIML	90	NULL	1190	365
+LABDDIML	7	NULL	1190	365
+LABDIGL	2	1	1188	1100
+LABPTH	30	0	1188	431
+LABDHBP	2	0	1187	551
+LABDHBP	4	0	1185	551
+LABUEOS	2	0	1185	1145
+LABDHBP	7	0	1183	550
+LABSCMV	90	0	1182	1148
+LABHEPAR	1	3	1181	894
+LABDHBP	30	0	1180	550
+LABDHBP	90	0	1180	550
+LABLDH	90	3	1180	1102
+LABHEPAR	7	4	1179	843
+LABHIPA	7	0	1177	1011
+LABEBPCRQ	4	0	1175	898
+LABLACWB	2	4	1174	1131
+LABURIC	30	3	1174	1167
+LABLACWB	90	3	1173	1112
+LABCMVQT	7	0	1171	898
+LABWDCS	90	0	1167	434
+LABLDH	30	3	1166	1100
+LABAFP	1	0	1165	852
+LABURIC	7	5	1165	1163
+LABVPAL	4	0	1163	494
+LABDVERV	30	0	1162	1083
+LABAHP2	7	NULL	1161	1016
+LABAHP2	1	NULL	1161	1016
+LABAHP2	2	NULL	1161	1016
+LABAHP2	4	NULL	1161	1016
+LABAHP2	30	NULL	1161	1016
+LABAHP2	90	NULL	1161	1016
+LABSPLAC	4	1	1161	1092
+LABAFBC	2	1	1160	1139
+LABUEOS	4	0	1159	1119
+LABTBIL	30	0	1158	949
+LABFLDC	30	1	1154	966
+LABPTH	90	0	1151	413
+LABAFP	2	0	1149	844
+LABUEOS	7	0	1148	1110
+LABDDIML	1	0	1138	348
+LABAFP	4	0	1137	836
+LABSPLAC	7	1	1137	1069
+LABDDIML	2	0	1136	348
+LABDDIML	4	0	1135	348
+LABDDIML	7	0	1135	348
+LABOSM	1	1	1133	911
+LABDDIML	30	0	1132	347
+LABHIPA	30	0	1132	976
+LABVD25H	1	NULL	1131	404
+LABVD25H	2	NULL	1131	404
+LABVD25H	4	NULL	1131	404
+LABVD25H	30	NULL	1131	404
+LABVD25H	90	NULL	1131	404
+LABVD25H	7	NULL	1131	404
+LABUSPG	1	0	1129	1117
+LABAFP	7	0	1128	830
+LABPCEG7V	7	0	1127	4
+LABDDIML	90	0	1126	346
+LABLIL	1	0	1126	498
+LABAHP2	1	0	1124	981
+LABUUN	1	NULL	1123	828
+LABUUN	2	NULL	1123	828
+LABUUN	4	NULL	1123	828
+LABUUN	30	NULL	1123	828
+LABUUN	90	NULL	1123	828
+LABUUN	7	NULL	1123	828
+LABCMVP	30	0	1121	1045
+LABLIDOL	2	2	1120	969
+LABAFBC	1	1	1119	1101
+LABAHP2	2	0	1116	975
+LABGRAM	1	1	1114	1017
+LABTBIL	90	0	1113	908
+LABURIC	90	3	1112	1104
+LABHIPA	90	0	1111	957
+LABUEOS	30	0	1111	1076
+LABAHP2	4	0	1108	969
+LABLACWB	4	4	1108	1067
+LABAHP2	7	0	1107	968
+LABPALB	30	0	1107	224
+LABAHP2	30	0	1104	965
+LABRPRQL	1	NULL	1104	1086
+LABRPRQL	2	NULL	1104	1086
+LABRPRQL	4	NULL	1104	1086
+LABRPRQL	30	NULL	1104	1086
+LABRPRQL	90	NULL	1104	1086
+LABRPRQL	7	NULL	1104	1086
+LABCAI	2	5	1103	846
+LABAHP2	90	0	1102	963
+LABMB	1	3	1102	1055
+LABURIC	30	4	1102	1099
+LABROMRS	2	1	1100	1091
+LABUEOS	90	0	1100	1067
+LABEBVQP	1	NULL	1098	830
+LABEBVQP	2	NULL	1098	830
+LABEBVQP	4	NULL	1098	830
+LABEBVQP	30	NULL	1098	830
+LABEBVQP	90	NULL	1098	830
+LABEBVQP	7	NULL	1098	830
+LABAFP	30	0	1096	812
+LABMB	2	3	1096	1015
+LABEBPCRQ	7	0	1091	847
+LABDVERV	90	0	1086	1011
+LABLEGDA	1	NULL	1084	1079
+LABLEGDA	2	NULL	1084	1079
+LABLEGDA	4	NULL	1084	1079
+LABLEGDA	30	NULL	1084	1079
+LABLEGDA	90	NULL	1084	1079
+LABLEGDA	7	NULL	1084	1079
+LABVD25H	1	0	1081	390
+LABA1C	7	1	1076	949
+LABRESP	2	1	1076	945
+LABSPLAC	30	1	1074	1008
+LABOSM	1	4	1073	1011
+LABRPRQL	1	0	1073	1059
+LABUAPRN	30	1	1073	394
+LABUUN	1	0	1073	798
+LABSPLAC	1	2	1071	1045
+LABVD25H	2	0	1071	385
+LABPCCG4	1	0	1069	22
+LABPCCG4	2	0	1069	22
+LABAFP	90	0	1068	796
+LABPCCG4	4	0	1068	22
+LABREPT	2	0	1068	375
+LABRPRQL	2	0	1068	1054
+LABPCCG4	7	0	1067	22
+LABPCTNI	90	2	1067	992
+LABURIC	4	5	1066	1064
+LABRPRQL	4	0	1065	1051
+LABURNC	1	1	1065	826
+LABIGG	30	0	1063	467
+LABPCCG4	30	0	1061	22
+LABRPRQL	7	0	1061	1047
+LABRPRQL	30	0	1059	1045
+LABPCEG7V	30	0	1056	4
+LABRPRQL	90	0	1056	1042
+LABCMVP	90	0	1052	984
+LABUUN	2	0	1052	782
+LABPALB	90	0	1050	210
+LABVD25H	4	0	1050	377
+LABEBVQP	1	0	1048	798
+LABPCCG4	90	0	1047	22
+LABSPLAC	90	1	1045	979
+LABTSH	1	1	1044	923
+LABVP3	1	NULL	1043	262
+LABVP3	2	NULL	1043	262
+LABVP3	4	NULL	1043	262
+LABVP3	30	NULL	1043	262
+LABVP3	90	NULL	1043	262
+LABVP3	7	NULL	1043	262
+LABURIC	90	4	1039	1035
+LABLIDOL	4	2	1038	894
+LABUUN	4	0	1035	772
+LABUSPG	1	1	1033	1024
+LABVD25H	7	0	1033	373
+LABLIDOL	7	2	1032	886
+LABVPAL	7	0	1031	408
+LABEBVQP	2	0	1030	788
+LABLIDOL	30	2	1027	879
+LABOSM	2	1	1024	800
+LABPCEG7V	90	0	1024	4
+LABLIDOL	90	2	1023	876
+LABTEGCP	1	NULL	1021	437
+LABTEGCP	2	NULL	1021	437
+LABTEGCP	4	NULL	1021	437
+LABTEGCP	30	NULL	1021	437
+LABTEGCP	90	NULL	1021	437
+LABTEGCP	7	NULL	1021	437
+LABURIC	30	5	1021	1018
+LABUUN	7	0	1021	759
+LABUTPRAN	1	NULL	1017	146
+LABUTPRAN	2	NULL	1017	146
+LABUTPRAN	4	NULL	1017	146
+LABUTPRAN	30	NULL	1017	146
+LABUTPRAN	90	NULL	1017	146
+LABCDTPCR	7	1	1017	423
+LABGRAM	90	2	1017	915
+LABUTPRAN	7	NULL	1017	146
+LABDATBSI	1	NULL	1016	1005
+LABDATBSI	2	NULL	1016	1005
+LABDATBSI	4	NULL	1016	1005
+LABDATBSI	30	NULL	1016	1005
+LABDATBSI	90	NULL	1016	1005
+LABDATBSI	7	NULL	1016	1005
+LABPTT	1	4	1011	911
+LABLDH	1	2	1010	961
+LABVANPRL	90	3	1010	636
+LABWDCG	1	NULL	1009	382
+LABWDCG	2	NULL	1009	382
+LABWDCG	4	NULL	1009	382
+LABWDCG	30	NULL	1009	382
+LABWDCG	90	NULL	1009	382
+LABLEGDA	1	0	1009	1004
+LABWDCG	7	NULL	1009	382
+LABVP3	1	0	1008	254
+LABVD25H	30	0	1004	363
+LABBLC2	90	4	1002	935
+LABLEGDA	2	0	1002	997
+LABMYCPA	4	0	999	212
+LABOSM	90	1	999	756
+LABMETC	1	1	997	157
+LABAMY	2	1	995	953
+LABLIDOL	2	5	995	930
+LABDATBSI	1	0	994	983
+LABPHEN	2	1	994	729
+LABEBVQP	4	0	993	766
+LABVP3	2	0	993	253
+LABUUN	30	0	990	735
+LABAFBD	90	1	989	980
+LABHCTX	2	1	988	329
+LABLIL	2	0	988	423
+LABOSM	30	1	987	752
+LABLEGDA	4	0	986	981
+LABVP3	4	0	986	252
+LABHCTX	90	1	985	310
+LABVP3	7	0	985	252
+LABUSPG	2	0	983	972
+LABUUN	90	0	981	728
+LABVP3	30	0	981	250
+LABSTLCX	1	NULL	980	881
+LABSTLCX	2	NULL	980	881
+LABSTLCX	4	NULL	980	881
+LABSTLCX	30	NULL	980	881
+LABSTLCX	90	NULL	980	881
+LABSTLCX	7	NULL	980	881
+LABVP3	90	0	980	250
+LABFLDC	7	1	978	846
+LABDATBSI	2	0	977	966
+LABIGG	90	0	977	444
+LABURIC	90	5	976	971
+LABGRAM	30	2	975	874
+LABPT	90	5	973	759
+LABOSM	7	1	971	743
+LABVANPRL	2	2	970	657
+LABHCTX	30	1	969	305
+LABOSM	4	1	966	742
+LABBLCTIP	30	1	965	885
+LABHCTX	7	1	964	305
+LABLEGDA	7	0	963	958
+LABVD25H	90	0	963	351
+LABSTOOLC	1	NULL	961	868
+LABSTOOLC	2	NULL	961	868
+LABSTOOLC	4	NULL	961	868
+LABSTOOLC	30	NULL	961	868
+LABSTOOLC	90	NULL	961	868
+LABSTOOLC	7	NULL	961	868
+LABHCTX	4	1	959	305
+LABPOCGLU	1	5	958	555
+LABNA	4	5	957	846
+LABUTPRAN	1	0	957	141
+LABDATBSI	4	0	956	945
+LABLDH	7	4	956	917
+LABLACWB	7	4	955	912
+LABLDH	30	4	955	909
+LABHCTX	1	1	954	325
+LABNA	7	5	953	837
+LABSPLAC	2	2	952	927
+LABTNI	7	5	952	926
+LABA1C	4	1	951	838
+LABOSM	1	2	951	857
+LABTRIG	2	1	951	832
+LABCMV	1	NULL	947	240
+LABCMV	2	NULL	947	240
+LABCMV	4	NULL	947	240
+LABCMV	30	NULL	947	240
+LABCMV	90	NULL	947	240
+LABCMV	7	NULL	947	240
+LABSTLCX	1	0	946	853
+LABBLC	30	4	945	900
+LABLIPS	90	2	945	851
+LABNA	90	5	945	829
+LABBLCTIP	90	1	944	860
+LABNA	30	5	944	829
+LABDIGL	1	1	943	884
+LABTRIG	7	2	943	821
+LABDATBSI	7	0	942	931
+LABREPT	4	0	939	334
+LABUA	4	1	938	363
+LABLDH	90	4	936	887
+LABCMV	1	0	935	238
+LABSTOOLC	1	0	933	843
+LABCMVIGG	1	NULL	929	833
+LABCMVIGG	2	NULL	929	833
+LABCMVIGG	4	NULL	929	833
+LABCMVIGG	30	NULL	929	833
+LABCMVIGG	90	NULL	929	833
+LABCMV	2	0	929	237
+LABCMVIGG	7	NULL	929	833
+LABLACWB	30	4	929	880
+LABLACWB	90	4	929	877
+LABMYCPA	7	0	929	202
+LABUSPG	4	0	927	917
+LABHELI	1	NULL	926	669
+LABHELI	2	NULL	926	669
+LABHELI	4	NULL	926	669
+LABHELI	30	NULL	926	669
+LABHELI	90	NULL	926	669
+LABHELI	7	NULL	926	669
+LABUTPRAN	2	0	926	139
+LABCMV	4	0	925	237
+LABSPLAC	1	3	924	897
+LABSTLCX	2	0	924	837
+LABCEA	1	NULL	923	623
+LABCEA	2	NULL	923	623
+LABCEA	4	NULL	923	623
+LABCEA	30	NULL	923	623
+LABCEA	90	NULL	923	623
+LABBLCSTK	30	1	923	856
+LABCEA	7	NULL	923	623
+LABVANPRL	30	3	922	589
+LABCMV	7	0	921	236
+LABBLCSTK	90	1	919	840
+LABAFBD	30	1	918	909
+LABEBVQP	7	0	918	713
+LABPHOS	1	4	916	786
+LABSTOOLC	2	0	916	832
+LABANER	90	2	914	862
+LABCMV	30	0	911	233
+LABFT4	90	1	910	831
+LABDATBSI	30	0	909	899
+LABREPT	7	0	908	325
+LABCMV	90	0	906	233
+LABCORT	90	1	906	641
+LABLAC	1	5	904	869
+LABANTIXA	7	NULL	903	56
+LABANTIXA	1	NULL	903	56
+LABANTIXA	2	NULL	903	56
+LABANTIXA	4	NULL	903	56
+LABANTIXA	30	NULL	903	56
+LABANTIXA	90	NULL	903	56
+LABCMVIGG	1	0	903	807
+LABUSPG	7	0	903	893
+LAB11041R	7	NULL	902	701
+LAB11041R	1	NULL	902	701
+LAB11041R	2	NULL	902	701
+LAB11041R	4	NULL	902	701
+LAB11041R	30	NULL	902	701
+LAB11041R	90	NULL	902	701
+LABQFTBG	1	NULL	902	680
+LABQFTBG	2	NULL	902	680
+LABQFTBG	4	NULL	902	680
+LABQFTBG	30	NULL	902	680
+LABQFTBG	90	NULL	902	680
+LABPT	30	5	902	719
+LABQFTBG	7	NULL	902	680
+LABLEGDA	30	0	901	897
+LABUTPRAN	4	0	901	139
+LABCMVIGG	2	0	900	804
+LABSTOOLC	4	0	900	819
+LABSTLCX	4	0	897	811
+LABSPLAC	4	2	896	873
+LABWDCG	1	0	896	324
+LABMYO	1	NULL	894	274
+LABMYO	2	NULL	894	274
+LABMYO	4	NULL	894	274
+LABMYO	30	NULL	894	274
+LABMYO	90	NULL	894	274
+LABMYO	7	NULL	894	274
+LABAMY	1	1	889	852
+LABVPAL	30	0	888	327
+LABCMVIGG	4	0	887	791
+LABSTLCX	7	0	887	803
+LABUTPRAN	7	0	887	138
+LABQFTBG	1	0	886	666
+LABCMVIGG	7	0	885	789
+LABEBPCRQ	30	0	885	703
+LABEP1	1	NULL	884	314
+LABEP1	2	NULL	884	314
+LABEP1	4	NULL	884	314
+LABEP1	30	NULL	884	314
+LABEP1	90	NULL	884	314
+LABCORT	30	1	884	618
+LABDATBSI	90	0	884	875
+LABEP1	7	NULL	884	314
+LABCEA	1	0	883	600
+LABCK	1	2	883	836
+LABSTOOLC	7	0	882	804
+LABWDCG	2	0	882	323
+LABBLCTIP	7	1	879	823
+LABHELI	1	0	879	635
+LABCEA	2	0	878	597
+LABHELI	2	0	878	634
+LABLACWB	2	5	878	854
+LABHELI	4	0	877	633
+LABCA199	1	NULL	874	384
+LABCA199	2	NULL	874	384
+LABCA199	4	NULL	874	384
+LABCA199	30	NULL	874	384
+LABCA199	90	NULL	874	384
+LABCA199	7	NULL	874	384
+LABQFTBG	2	0	872	657
+LABCEA	4	0	871	591
+LABCMVIGG	30	0	871	776
+LABHELI	7	0	871	631
+LABSPLAC	7	2	871	848
+LABUSPG	30	0	870	860
+LABUSPG	90	0	869	859
+LABCEA	7	0	866	588
+LABAGALA	7	NULL	864	823
+LABAGALA	1	NULL	864	823
+LABAGALA	2	NULL	864	823
+LABAGALA	4	NULL	864	823
+LABAGALA	30	NULL	864	823
+LABAGALA	90	NULL	864	823
+LABHELI	30	0	863	626
+LABQFTBG	4	0	862	651
+LABCMVIGG	90	0	860	766
+LABUTPRAN	30	0	860	137
+LABWDCG	4	0	860	316
+LABRF	1	NULL	859	840
+LABRF	2	NULL	859	840
+LABRF	4	NULL	859	840
+LABRF	30	NULL	859	840
+LABRF	90	NULL	859	840
+LABRF	7	NULL	859	840
+LABCEA	30	0	857	585
+LABHELI	90	0	857	621
+LABSTLCX	30	0	857	779
+LABAFBC	90	2	856	833
+LABREPT	30	0	856	308
+LABTEGCP	1	0	856	373
+LABAFBD	7	1	855	849
+LABVPAL	90	0	855	315
+LABCORT	7	1	854	590
+LABLIDOL	2	3	854	759
+LABPTT	2	5	853	752
+LABCEA	90	0	851	581
+LABLEGDA	90	0	850	847
+LABCMVQT	30	0	847	689
+LABWDCG	7	0	847	312
+LABCORT	4	1	846	588
+LABUTPRAN	90	0	845	135
+LABSTOOLC	30	0	844	769
+LABUSPG	2	5	844	839
+LABQFTBG	7	0	843	637
+LABUSPG	1	4	842	839
+LABCORT	2	1	841	580
+LABREPT	90	0	841	303
+LABCORTS	1	NULL	840	508
+LABCORTS	2	NULL	840	508
+LABCORTS	4	NULL	840	508
+LABCORTS	30	NULL	840	508
+LABCORTS	90	NULL	840	508
+LABCORTS	7	NULL	840	508
+LABPHEN	1	1	840	662
+LABLIDOL	4	3	839	741
+LABCA199	1	0	837	370
+LABPT	7	5	834	682
+LABWDCG	30	0	834	310
+LABMYCPA	30	0	833	181
+LABHEPAR	2	4	832	591
+LABSTLCX	90	0	832	755
+LABCA199	2	0	830	370
+LABSPLAC	30	2	829	806
+LABANER2	30	1	828	781
+LABQFTBG	30	0	828	628
+LABWDCG	90	0	826	309
+LABAFBC	30	2	825	804
+LABANER2	7	1	822	783
+LABRF	1	0	822	803
+LABCA199	4	0	820	368
+LABANER2	4	1	819	782
+LABBLCSTK	7	1	819	768
+LABRF	2	0	819	800
+LABQFTBG	90	0	818	623
+LAB11041R	1	0	817	632
+LABRF	4	0	816	797
+LABSPLAC	90	2	816	790
+LABANER2	90	1	815	767
+LABAFBD	4	1	814	810
+LABSTOOLC	90	0	811	738
+LABIGET	1	NULL	810	463
+LABIGET	2	NULL	810	463
+LABIGET	4	NULL	810	463
+LABIGET	30	NULL	810	463
+LABIGET	90	NULL	810	463
+LABIGET	7	NULL	810	463
+LABTNI	4	5	810	790
+LABCA199	7	0	809	366
+LABRF	7	0	808	789
+LABANER2	2	1	807	779
+LABPT	2	3	807	577
+LABTNI	2	5	807	792
+LABLIL	4	0	806	303
+LABEBPCRQ	90	0	805	647
+LABRF	30	0	803	784
+LABTEGCP	2	0	803	347
+LABAMY	90	1	801	739
+LABFT4	30	1	801	734
+LABRF	90	0	801	782
+LABFLDC	4	1	799	701
+LABBHCG	1	NULL	798	526
+LABBHCG	2	NULL	798	526
+LABBHCG	4	NULL	798	526
+LABBHCG	30	NULL	798	526
+LABBHCG	90	NULL	798	526
+LABBHCG	7	NULL	798	526
+LABLIDOL	7	3	797	707
+LABAGALA	1	0	795	756
+LABC3	1	NULL	795	543
+LABC3	2	NULL	795	543
+LABC3	4	NULL	795	543
+LABC3	30	NULL	795	543
+LABC3	90	NULL	795	543
+LABC4	1	NULL	795	468
+LABC4	2	NULL	795	468
+LABC4	4	NULL	795	468
+LABC4	30	NULL	795	468
+LABC4	90	NULL	795	468
+LABYFT3	1	NULL	795	295
+LABYFT3	2	NULL	795	295
+LABYFT3	4	NULL	795	295
+LABYFT3	30	NULL	795	295
+LABYFT3	90	NULL	795	295
+LABC3	7	NULL	795	543
+LABC4	7	NULL	795	468
+LABLDH	2	3	795	763
+LABYFT3	7	NULL	795	295
+LABAGALA	2	0	794	755
+LABANER2	1	1	794	774
+LABEBVPAN	1	NULL	794	759
+LABEBVPAN	2	NULL	794	759
+LABEBVPAN	4	NULL	794	759
+LABEBVPAN	30	NULL	794	759
+LABEBVPAN	90	NULL	794	759
+LABCA199	30	0	794	364
+LABEBVPAN	7	NULL	794	759
+LABCRPLB	1	NULL	793	736
+LABCRPLB	2	NULL	793	736
+LABCRPLB	4	NULL	793	736
+LABCRPLB	30	NULL	793	736
+LABCRPLB	90	NULL	793	736
+LABCRPLB	7	NULL	793	736
+LABAGALA	4	0	791	752
+LABLIDOL	90	3	791	701
+LABAST	7	NULL	790	465
+LABAST	1	NULL	790	465
+LABAST	2	NULL	790	465
+LABAST	4	NULL	790	465
+LABAST	30	NULL	790	465
+LABAST	90	NULL	790	465
+LABEBVQP	30	0	790	621
+LABLIDOL	30	3	790	700
+LABIGET	1	0	789	451
+LABIGET	2	0	789	451
+LABANER	30	2	788	744
+LABA1C	2	1	787	694
+LABBLC2	4	2	783	737
+LABIGET	4	0	782	450
+LABAFBC	7	2	780	765
+LABCTC	1	NULL	780	687
+LABCTC	2	NULL	780	687
+LABCTC	4	NULL	780	687
+LABCTC	30	NULL	780	687
+LABCTC	90	NULL	780	687
+LABHCPQ	1	NULL	780	533
+LABHCPQ	2	NULL	780	533
+LABHCPQ	4	NULL	780	533
+LABHCPQ	30	NULL	780	533
+LABHCPQ	90	NULL	780	533
+LABCTC	7	NULL	780	687
+LABHCPQ	7	NULL	780	533
+LABIGET	7	0	780	450
+LABCK	2	2	779	726
+LABEBVPAN	1	0	779	744
+LABALT	7	NULL	777	540
+LABALT	1	NULL	777	540
+LABALT	2	NULL	777	540
+LABALT	4	NULL	777	540
+LABALT	30	NULL	777	540
+LABALT	90	NULL	777	540
+LABCA199	90	0	777	359
+LABEBVPAN	2	0	777	742
+LABPSA	1	NULL	776	605
+LABPSA	2	NULL	776	605
+LABPSA	4	NULL	776	605
+LABPSA	30	NULL	776	605
+LABPSA	90	NULL	776	605
+LABPSA	7	NULL	776	605
+LABCORT	1	1	774	519
+LABLDH	30	5	774	732
+LABBLC	90	5	773	716
+LABEBVPAN	4	0	768	733
+LABYFT3	1	0	768	284
+LABMYCPA	90	0	767	170
+LABC3	1	0	765	526
+LABLACWB	7	5	765	738
+LABAGALA	7	0	764	734
+LABEBVPAN	7	0	764	729
+LABMB	90	4	764	697
+LABBHCG	1	0	763	520
+LABCLACT	1	NULL	763	323
+LABCLACT	2	NULL	763	323
+LABCLACT	4	NULL	763	323
+LABCLACT	30	NULL	763	323
+LABCLACT	90	NULL	763	323
+LABCLACT	7	NULL	763	323
+LABAST	1	0	762	453
+LABC4	1	0	762	448
+LABEBVPAN	30	0	762	727
+LABSPLAC	2	3	762	739
+LABAMY	30	1	761	706
+LABCBCD	90	2	761	386
+LABLACWB	4	5	761	731
+LAB11041R	2	0	757	590
+LABGLNF	1	NULL	757	563
+LABGLNF	2	NULL	757	563
+LABGLNF	4	NULL	757	563
+LABGLNF	30	NULL	757	563
+LABGLNF	90	NULL	757	563
+LABEBVPAN	90	0	757	722
+LABGLNF	7	NULL	757	563
+LABYFT3	2	0	757	280
+LABLDH	90	5	755	714
+LABALT	1	0	753	528
+LABCMVQT	90	0	753	626
+LABCRPLB	1	0	753	706
+LABC3	2	0	752	519
+LABHCPQ	1	0	752	519
+LABRESPCR	1	NULL	751	508
+LABRESPCR	2	NULL	751	508
+LABRESPCR	4	NULL	751	508
+LABRESPCR	30	NULL	751	508
+LABRESPCR	90	NULL	751	508
+LABC4	2	0	751	442
+LABRESPCR	7	NULL	751	508
+LABAST	2	0	749	446
+LABBLCTIP	4	1	749	712
+LABTRIG	90	2	748	630
+LABYFT3	4	0	748	278
+LABHCPQ	2	0	746	518
+LABC3	4	0	745	518
+LABLACWB	90	5	745	716
+LABC4	4	0	744	441
+LABC3	7	0	742	516
+LABLDH	7	5	742	716
+LABLIDOL	2	4	742	674
+LABC4	7	0	741	438
+LABCMVPQ	7	1	741	674
+LABPCACTK	1	0	741	207
+LABTRIG	30	2	741	631
+LABTCDIF	1	NULL	740	728
+LABTCDIF	2	NULL	740	728
+LABTCDIF	4	NULL	740	728
+LABTCDIF	30	NULL	740	728
+LABTCDIF	90	NULL	740	728
+LABPCACTK	2	0	740	206
+LABPCACTK	4	0	740	206
+LABTCDIF	7	NULL	740	728
+LABALT	2	0	739	519
+LABPCACTK	7	0	739	206
+LABPSA	1	0	739	575
+LABAFBC	4	2	738	727
+LABCRPLB	2	0	738	692
+LABLACWB	30	5	738	712
+LABBHCG	2	0	737	519
+LABRESP	90	2	737	570
+LABDIGL	90	1	736	643
+LABFCUL	90	2	736	661
+LABAFBD	2	1	735	731
+LABPSA	2	0	735	573
+LABUAPRN	7	1	735	296
+LABPCTNI	30	2	734	683
+LABAST	4	0	733	436
+LABPCACTK	30	0	733	206
+LABPSA	4	0	733	573
+LABYFT3	7	0	733	272
+LABHCPQ	4	0	732	511
+LABCK	90	2	731	671
+LABCRPLB	4	0	731	688
+LABPSA	7	0	731	572
+LABSIROL	2	0	731	271
+LABUSPG	2	4	731	729
+LABYPREAL	7	1	731	524
+LABSTLC	1	NULL	728	690
+LABSTLC	2	NULL	728	690
+LABSTLC	4	NULL	728	690
+LABSTLC	30	NULL	728	690
+LABSTLC	90	NULL	728	690
+LABSTLC	7	NULL	728	690
+LABCLACT	1	0	727	312
+LABNA	1	4	727	646
+LABPSA	30	0	727	570
+LABIGET	30	0	726	427
+LABLIPS	30	2	726	649
+LABHCPQ	7	0	725	508
+LABBHCG	4	0	724	518
+LABCTC	1	0	724	635
+LABCRPLB	7	0	723	685
+LABEBVQP	90	0	721	567
+LABAST	7	0	720	429
+LABCTC	2	0	720	632
+LABPCACTK	90	0	720	201
+LABPSA	90	0	720	566
+LABHAVT	1	NULL	718	235
+LABHAVT	2	NULL	718	235
+LABHAVT	4	NULL	718	235
+LABHAVT	30	NULL	718	235
+LABHAVT	90	NULL	718	235
+LABCK	30	2	718	661
+LABHAVT	7	NULL	718	235
+LABAGALA	30	0	715	694
+LABALT	4	0	715	502
+LABC3	30	0	714	505
+LABC4	30	0	714	425
+LABDIGL	30	1	714	625
+LABSPLAC	1	4	713	703
+LABTT	1	NULL	712	355
+LABTT	2	NULL	712	355
+LABTT	4	NULL	712	355
+LABTT	30	NULL	712	355
+LABTT	90	NULL	712	355
+LABTT	7	NULL	712	355
+LABUSPG	2	1	712	705
+LABCBCD	30	2	711	371
+LABHEPAR	4	5	711	521
+LABRESPCR	1	0	711	482
+LABYFT3	30	0	711	266
+LABSYNCT	1	NULL	710	2
+LABSYNCT	2	NULL	710	2
+LABSYNCT	4	NULL	710	2
+LABSYNCT	30	NULL	710	2
+LABSYNCT	90	NULL	710	2
+LABDIGL	4	1	710	636
+LABSYNCT	7	NULL	710	2
+LABCRPLB	30	0	709	679
+LABAMY	7	1	707	659
+LABCK	2	3	707	687
+LABHAVT	1	0	707	231
+LABBHCG	7	0	706	514
+LABCMVPQ	90	1	706	604
+LABHCPQ	30	0	706	498
+LABSPLAC	4	3	705	683
+LABAFBD	1	1	704	700
+LABAST	30	0	704	419
+LABBLC2	30	4	704	670
+LABHEPAR	90	5	704	492
+LABMB	30	4	704	644
+LABALT	7	0	702	492
+LABHAVT	2	0	702	231
+LABAST	90	0	699	415
+LABCMVPQ	30	1	699	608
+LABHCPQ	90	0	699	496
+LABCTC	4	0	698	613
+LABHAVT	4	0	698	229
+LABHAVT	7	0	698	229
+LABYFT3	90	0	698	266
+LABVANPRL	7	3	697	444
+LABDIGL	7	1	696	610
+LABHAVT	30	0	696	227
+LABNH3	90	1	696	376
+LABANTIXA	1	0	695	48
+LABHAVT	90	0	694	227
+LABHEPAR	30	5	694	489
+LABC3	90	0	693	500
+LABC4	90	0	693	420
+LABRESP	1	1	693	620
+LABSTLC	1	0	693	658
+LABSPLAC	7	3	690	668
+LABTCDIF	1	0	689	677
+LABAGALA	90	0	687	668
+LABFLDB	1	NULL	687	555
+LABFLDB	2	NULL	687	555
+LABFLDB	4	NULL	687	555
+LABFLDB	30	NULL	687	555
+LABFLDB	90	NULL	687	555
+LABFLDB	7	NULL	687	555
+LABHEPAR	7	5	687	490
+LABALT	30	0	686	481
+LABRESPCR	2	0	686	465
+LABCRPLB	90	0	685	660
+LABRESP	30	2	685	530
+LABCK	7	2	683	627
+LABROMRS	1	1	683	678
+LABPHEN	90	1	682	483
+LABCTC	7	0	681	598
+LABALT	90	0	680	477
+LABOSM	2	2	680	601
+LABALKP	7	NULL	679	434
+LABALKP	1	NULL	679	434
+LABALKP	2	NULL	679	434
+LABALKP	4	NULL	679	434
+LABALKP	30	NULL	679	434
+LABALKP	90	NULL	679	434
+LABSTLC	2	0	679	648
+LABBHCG	30	0	676	496
+LABBLCSTK	4	1	675	642
+LABGNTPRL	1	NULL	675	536
+LABGNTPRL	2	NULL	675	536
+LABGNTPRL	4	NULL	675	536
+LABGNTPRL	30	NULL	675	536
+LABGNTPRL	90	NULL	675	536
+LABFIB	2	2	675	501
+LABGNTPRL	7	NULL	675	536
+LABHFP	90	1	674	240
+LABSYNCT	1	0	674	2
+LABAMY	4	1	673	627
+LABUSPG	2	3	673	669
+LABPHEN	30	1	672	477
+LABCMVM	1	NULL	670	631
+LABCMVM	2	NULL	670	631
+LABCMVM	4	NULL	670	631
+LABCMVM	30	NULL	670	631
+LABCMVM	90	NULL	670	631
+LABCLACT	2	0	670	288
+LABCMVM	7	NULL	670	631
+LABRESPCR	4	0	669	455
+LABBHCG	90	0	668	491
+LABMETB	90	3	667	325
+LABALKP	1	0	666	427
+LABPCCH8	1	NULL	666	3
+LABPCCH8	2	NULL	666	3
+LABPCCH8	4	NULL	666	3
+LABPCCH8	30	NULL	666	3
+LABPCCH8	90	NULL	666	3
+LABLIL	7	0	666	210
+LABPCCH8	7	NULL	666	3
+LABGRAM	7	2	665	612
+LABPHEN	4	1	665	483
+LABCBCD	7	2	664	359
+LABSYNCT	2	0	664	2
+LABCRPLC	1	NULL	663	622
+LABCRPLC	2	NULL	663	622
+LABCRPLC	4	NULL	663	622
+LABCRPLC	30	NULL	663	622
+LABCRPLC	90	NULL	663	622
+LABCRPLC	7	NULL	663	622
+LABPHEN	7	1	663	480
+LABMETB	4	3	662	351
+LABSTLC	4	0	662	632
+LABUA	2	1	661	271
+LABSYNCT	4	0	660	2
+LABALKP	2	0	659	424
+LABSPLAC	30	3	659	633
+LABCMVM	1	0	658	622
+LABFCUL	30	2	658	604
+LABUSPG	2	2	658	652
+LABFLUAPX	1	NULL	656	593
+LABFLUAPX	2	NULL	656	593
+LABFLUAPX	4	NULL	656	593
+LABFLUAPX	30	NULL	656	593
+LABFLUAPX	90	NULL	656	593
+LABFLUAPX	7	NULL	656	593
+LABFIB	90	2	655	472
+LABNH3	30	1	655	356
+LABCMVM	2	0	654	619
+LABSTLC	7	0	653	624
+LABSYNCT	7	0	651	2
+LABUSPG	4	1	651	643
+LABALKP	4	0	650	421
+LABCMVM	4	0	650	617
+LABRESPCR	7	0	650	445
+LABCDTPCR	90	2	648	516
+LABACTHO	7	NULL	647	349
+LABANER	7	2	647	612
+LABACTHO	1	NULL	647	349
+LABACTHO	2	NULL	647	349
+LABACTHO	4	NULL	647	349
+LABACTHO	30	NULL	647	349
+LABACTHO	90	NULL	647	349
+LABCK	4	2	647	595
+LABHFP	30	1	647	234
+LABDSDNA	1	NULL	646	572
+LABDSDNA	2	NULL	646	572
+LABDSDNA	4	NULL	646	572
+LABDSDNA	30	NULL	646	572
+LABDSDNA	90	NULL	646	572
+LABDSDNA	7	NULL	646	572
+LABSPLAC	90	3	646	619
+LABCMVM	7	0	645	613
+LABTCDIF	2	0	645	633
+LABFLDB	1	0	644	520
+LABLIPS	2	2	644	591
+LABTRIG	1	1	644	578
+LABFE	1	NULL	643	373
+LABFE	2	NULL	643	373
+LABFE	4	NULL	643	373
+LABFE	30	NULL	643	373
+LABFE	90	NULL	643	373
+LABFE	7	NULL	643	373
+LABSYNCT	30	0	643	2
+LABFIB	30	2	642	461
+LABOSM	90	2	642	563
+LABALKP	7	0	641	418
+LABCMVM	30	0	641	609
+LABOSM	30	2	640	562
+LABLIDOL	4	4	639	581
+LABOSM	4	2	639	560
+LABOSM	7	2	638	560
+LABCMVM	90	0	637	606
+LABACA	7	NULL	636	569
+LABACA	1	NULL	636	569
+LABACA	2	NULL	636	569
+LABACA	4	NULL	636	569
+LABACA	30	NULL	636	569
+LABACA	90	NULL	636	569
+LABMETB	30	3	636	316
+LABCBCD	4	2	635	347
+LABDIGL	2	2	635	612
+LABSTLC	30	0	635	606
+LABTT	1	0	635	314
+LABUSPG	7	1	634	627
+LABVANPRL	4	3	634	436
+LABALKP	30	0	633	413
+LABCRPLC	1	0	633	601
+LABCBCO	90	2	632	371
+LABROMRS	30	2	632	622
+LABALKP	90	0	630	412
+LABFIB	7	2	629	459
+LABASQ	7	NULL	627	355
+LABASQ	1	NULL	627	355
+LABASQ	2	NULL	627	355
+LABASQ	4	NULL	627	355
+LABASQ	30	NULL	627	355
+LABASQ	90	NULL	627	355
+LABDSDNA	1	0	627	560
+LABFLUAPX	1	0	627	567
+LABFE	1	0	626	362
+LABCTC	30	0	624	543
+LABSYNCT	90	0	623	2
+LABFE	2	0	622	360
+LABSTLC	90	0	622	594
+LABYPREAL	90	1	622	405
+LABFE	4	0	621	359
+LABFE	7	0	621	359
+LABPTEG	1	0	621	327
+LABBLC	7	3	620	598
+LABDSDNA	2	0	620	556
+LABFE	30	0	620	359
+LABFIB	4	2	619	451
+LABYPREAL	4	1	618	458
+LABFE	90	0	615	358
+LABDSDNA	4	0	614	551
+LABACA	1	0	613	549
+LABUSPG	30	1	613	606
+LABUSPG	90	1	613	606
+LABYPREAL	30	1	613	407
+LABDSDNA	7	0	612	549
+LABFLDB	2	0	612	490
+LABCBCO	30	2	611	366
+LABHFP	7	1	611	228
+LABACA	2	0	610	546
+LABPCCH8	1	0	610	3
+LABPCCH8	2	0	610	3
+LABPCCH8	4	0	610	3
+LABPCCH8	7	0	610	3
+LABBXTG	90	1	609	484
+LABCBCD	2	2	609	355
+LABFLUAPX	2	0	609	549
+LABCRPLC	2	0	608	577
+LABMB	7	4	608	565
+LABPTEG	2	0	608	323
+LABMETB	7	3	607	311
+LABACA	4	0	605	542
+LABK	1	5	605	576
+LABDSDNA	30	0	603	541
+LABCTC	90	0	602	525
+LABMYO	1	0	602	208
+LABPCCH8	30	0	600	3
+LABTCDIF	4	0	600	588
+LABLIDOL	7	4	597	544
+LABPTEG	4	0	597	316
+LABDSDNA	90	0	596	536
+LABACA	7	0	594	532
+LABCBCO	7	2	594	363
+LABCDTPCR	4	1	594	240
+LABPCCH8	90	0	594	3
+LABANTIXA	2	0	593	44
+LABLIDOL	90	4	593	537
+LABTT	2	0	593	286
+LABHIVQ2	1	NULL	592	465
+LABHIVQ2	2	NULL	592	465
+LABHIVQ2	4	NULL	592	465
+LABHIVQ2	30	NULL	592	465
+LABHIVQ2	90	NULL	592	465
+LABHBSAG	90	1	592	590
+LABHIVQ2	7	NULL	592	465
+LABLIDOL	30	4	592	537
+LABFLUAPX	4	0	591	531
+LABHFP	4	1	591	224
+LABCMVIGM	1	NULL	590	551
+LABCMVIGM	2	NULL	590	551
+LABCMVIGM	4	NULL	590	551
+LABCMVIGM	30	NULL	590	551
+LABCMVIGM	90	NULL	590	551
+LABCMVIGM	7	NULL	590	551
+LABACA	30	0	589	528
+LABFLDC	2	1	587	522
+LABMB	4	4	587	544
+LABACA	90	0	586	526
+LABRESPCR	30	0	586	397
+LABTT	4	0	586	285
+LABDIGL	4	3	585	558
+LABCBCO	4	2	583	357
+LABTOBPRL	1	NULL	583	523
+LABTOBPRL	2	NULL	583	523
+LABTOBPRL	4	NULL	583	523
+LABTOBPRL	30	NULL	583	523
+LABTOBPRL	90	NULL	583	523
+LABTOBPRL	7	NULL	583	523
+LABHAVM	1	NULL	582	578
+LABHAVM	2	NULL	582	578
+LABHAVM	4	NULL	582	578
+LABHAVM	30	NULL	582	578
+LABHAVM	90	NULL	582	578
+LABFT4	7	1	582	550
+LABHAVM	7	NULL	582	578
+LABEP1	1	0	580	234
+LABHIVQ2	1	0	580	457
+LABPTEG	7	0	580	312
+LABTT	7	0	580	284
+LABAFBC	2	2	579	574
+LABFLUAPX	7	0	579	522
+LABHBSQL	1	NULL	577	323
+LABHBSQL	2	NULL	577	323
+LABHBSQL	4	NULL	577	323
+LABHBSQL	30	NULL	577	323
+LABHBSQL	90	NULL	577	323
+LABHBSQL	7	NULL	577	323
+LABHIVQ2	2	0	577	454
+LABFLDB	4	0	576	456
+LABURNC	90	3	576	463
+LABUSPG	4	2	576	572
+LABIGET	90	0	575	349
+LABCMVIGM	1	0	573	536
+LABSPLAC	2	4	573	560
+LABTT	30	0	573	284
+LABBLC2	90	5	572	539
+LABOSM	2	3	572	521
+LABCMVIGM	2	0	570	533
+LABHIVQ2	4	0	570	452
+LABLDH	2	4	570	546
+LABSPLAC	4	4	570	558
+LABTT	90	0	570	282
+LABCRPLC	4	0	569	543
+LABHAVM	1	0	568	564
+LABHFP	2	1	567	233
+LABHIVQ2	7	0	567	450
+LABYCERU	1	NULL	566	421
+LABYCERU	2	NULL	566	421
+LABYCERU	4	NULL	566	421
+LABYCERU	30	NULL	566	421
+LABYCERU	90	NULL	566	421
+LABYCERU	7	NULL	566	421
+LABA1C	1	1	565	489
+LABANER	4	2	565	538
+LABCMVIGM	4	0	565	528
+LABGLNF	1	0	564	422
+LABCMVIGM	7	0	563	526
+LABPTEG	30	0	563	309
+LABOSM	4	3	562	513
+LABAFBCF	7	NULL	559	531
+LABAFBCF	1	NULL	559	531
+LABAFBCF	2	NULL	559	531
+LABAFBCF	4	NULL	559	531
+LABAFBCF	30	NULL	559	531
+LABAFBCF	90	NULL	559	531
+LABESRP	90	1	559	291
+LABHAVM	2	0	559	556
+LABLIL	30	0	559	145
+LABNH3	7	1	558	304
+LABPTEG	90	0	558	309
+LABRESPCR	90	0	558	380
+LABUSPG	7	2	558	554
+LABFLUAPX	30	0	557	501
+LABHAVM	4	0	557	554
+LABHBCT	1	NULL	556	472
+LABHBCT	2	NULL	556	472
+LABHBCT	4	NULL	556	472
+LABHBCT	30	NULL	556	472
+LABHBCT	90	NULL	556	472
+LABHAVM	7	0	556	553
+LABHBCT	7	NULL	556	472
+LABMYO	2	0	556	190
+LABTCDIF	7	0	556	544
+LABCMVIGM	30	0	555	519
+LABHAVM	30	0	555	552
+LABCLACT	4	0	553	259
+LABGLNF	2	0	553	415
+LABHBSQL	1	0	553	313
+LABHAVM	90	0	552	549
+LABHIVQ2	30	0	552	442
+LABHBSQL	2	0	549	311
+LABMYO	4	0	549	188
+LABNH3	2	1	549	333
+LABBLC	2	2	548	525
+LABCMVIGM	90	0	547	512
+LABFLDB	7	0	547	432
+LABCRPLC	7	0	546	522
+LABFLUAPX	90	0	546	490
+LABHBSQL	4	0	546	309
+LABMYO	7	0	546	187
+LABYCERU	1	0	546	407
+LABGLNF	4	0	544	408
+LABHBCT	1	0	544	463
+LABANTIXA	4	0	543	39
+LABHBSQL	7	0	543	308
+LABMYO	30	0	542	187
+LABUSPG	30	2	542	538
+LABUSPG	90	2	542	538
+LABGLNF	7	0	541	406
+LABMYO	90	0	541	186
+LABDVEHV	1	NULL	540	337
+LABDVEHV	2	NULL	540	337
+LABDVEHV	4	NULL	540	337
+LABDVEHV	30	NULL	540	337
+LABDVEHV	90	NULL	540	337
+LABHMCY	1	NULL	540	453
+LABHMCY	2	NULL	540	453
+LABHMCY	4	NULL	540	453
+LABHMCY	30	NULL	540	453
+LABHMCY	90	NULL	540	453
+LABDVEHV	7	NULL	540	337
+LABHBSQL	30	0	540	306
+LABHBSQL	90	0	540	306
+LABHMCY	7	NULL	540	453
+LABYCERU	2	0	540	404
+LABYCERU	4	0	540	404
+LABYCERU	7	0	540	404
+LABHAP	4	1	539	459
+LABLIL	90	0	538	135
+LABFCUL	7	2	537	499
+LABHBCT	2	0	537	457
+LABYCERU	30	0	537	404
+LABBXTG	30	1	536	434
+LABSPLAC	7	4	536	522
+LABHIVQ2	90	0	535	434
+LABYCERU	90	0	535	402
+LABHBCT	4	0	534	454
+LABHBCT	7	0	532	452
+LABUSPG	4	3	532	530
+LABCORTS	1	0	531	313
+LABGLNF	30	0	531	398
+LABTNI	1	4	531	515
+LABEP1	2	0	530	209
+LABLIPS	7	2	530	484
+LABCBCO	2	2	529	326
+LABABG	90	1	527	33
+LABGLNF	90	0	527	395
+LABUAPRN	4	1	527	224
+LABCSFGL	90	1	526	495
+LABOSM	7	3	526	480
+LABOSM	30	3	526	480
+LABOSM	90	3	526	480
+LABVBG	90	1	526	21
+LABHELAG	1	NULL	525	467
+LABHELAG	2	NULL	525	467
+LABHELAG	4	NULL	525	467
+LABHELAG	30	NULL	525	467
+LABHELAG	90	NULL	525	467
+LABESRP	30	1	525	267
+LABHELAG	7	NULL	525	467
+LABCK	4	3	524	508
+LABEP1	4	0	524	207
+LABSPLAC	30	4	524	507
+LABVBG	30	1	524	21
+LABANTIXA	7	0	522	38
+LABHBCT	30	0	522	443
+LAB11060R	7	NULL	521	240
+LABAT3	7	NULL	521	294
+LAB11060R	1	NULL	521	240
+LAB11060R	2	NULL	521	240
+LAB11060R	4	NULL	521	240
+LAB11060R	30	NULL	521	240
+LAB11060R	90	NULL	521	240
+LABAT3	1	NULL	521	294
+LABAT3	2	NULL	521	294
+LABAT3	4	NULL	521	294
+LABAT3	30	NULL	521	294
+LABAT3	90	NULL	521	294
+LABVBG	7	1	521	21
+LABHAP	7	1	520	430
+LABHBCT	90	0	520	441
+LABAFBCF	1	0	519	493
+LABEP1	7	0	519	206
+LABABG	30	1	518	33
+LABANER	2	2	518	502
+LABORANER	1	NULL	518	493
+LABORANER	2	NULL	518	493
+LABORANER	4	NULL	518	493
+LABORANER	30	NULL	518	493
+LABORANER	90	NULL	518	493
+LABORANER	7	NULL	518	493
+LABAFBCF	2	0	517	491
+LABHMCY	1	0	517	434
+LABVBG	4	1	517	21
+LABORAERO	1	NULL	516	406
+LABORAERO	2	NULL	516	406
+LABORAERO	4	NULL	516	406
+LABORAERO	30	NULL	516	406
+LABORAERO	90	NULL	516	406
+LABHMCY	2	0	516	434
+LABORAERO	7	NULL	516	406
+LABMB	2	4	515	482
+LABASI	90	1	514	514
+LABCRP	90	1	514	303
+LABHMCY	4	0	514	432
+LABTOBPRL	1	0	514	465
+LABPB	1	NULL	513	370
+LABPB	2	NULL	513	370
+LABPB	4	NULL	513	370
+LABPB	30	NULL	513	370
+LABPB	90	NULL	513	370
+LABEP1	30	0	513	204
+LABHMCY	7	0	513	431
+LABHMCY	30	0	513	431
+LABPB	7	NULL	513	370
+LABAMY	2	2	512	497
+LABEP1	90	0	512	204
+LABHMCY	90	0	512	430
+LABIGP	1	NULL	511	71
+LABIGP	2	NULL	511	71
+LABIGP	4	NULL	511	71
+LABIGP	30	NULL	511	71
+LABIGP	90	NULL	511	71
+LABORSHOR	1	NULL	511	379
+LABORSHOR	2	NULL	511	379
+LABORSHOR	4	NULL	511	379
+LABORSHOR	30	NULL	511	379
+LABORSHOR	90	NULL	511	379
+LABGNTPRL	1	0	511	408
+LABIGP	7	NULL	511	71
+LABORSHOR	7	NULL	511	379
+LABSPLAC	90	4	511	493
+LABAFBCF	4	0	510	484
+LABFIB	1	2	509	408
+LABURNC	7	2	509	437
+LABVBG	2	1	509	21
+LAB11060R	1	0	507	238
+LABFACT8	1	NULL	507	202
+LABFACT8	2	NULL	507	202
+LABFACT8	4	NULL	507	202
+LABFACT8	30	NULL	507	202
+LABFACT8	90	NULL	507	202
+LABFACT8	7	NULL	507	202
+LABCSFC	7	1	506	492
+LABNH3	4	1	505	278
+LABOSM	2	4	505	468
+LABAFBCF	7	0	504	478
+LABPCTNI	7	2	504	470
+LABFT4	4	1	503	478
+LABHELAG	1	0	503	447
+LABHELAG	2	0	503	447
+LABHELAG	4	0	503	447
+LABROMRS	90	3	503	493
+LABUSPG	7	3	503	501
+LABABG	7	1	502	33
+LABHAP	90	1	502	392
+LABHELAG	7	0	502	446
+LAB11049R	7	NULL	501	127
+LAB11049R	1	NULL	501	127
+LAB11049R	2	NULL	501	127
+LAB11049R	4	NULL	501	127
+LAB11049R	30	NULL	501	127
+LAB11049R	90	NULL	501	127
+LABCSFGL	7	1	500	469
+LABFK506L	2	3	500	468
+LABTCDIF	30	0	499	487
+LAB11060R	2	0	498	233
+LABGRAM	90	3	498	458
+LABPHEN	2	2	495	392
+LABANTIXA	30	0	494	36
+LABHELAG	30	0	494	439
+LABHELAG	90	0	494	439
+LABABG	4	1	493	33
+LABASQ	1	0	493	289
+LABCSFGL	30	1	493	461
+LABIGP	1	0	493	70
+LABOSM	2	5	493	457
+LABVBG	1	1	493	21
+LABDVEHV	1	0	492	318
+LABUSPG	30	3	492	490
+LABBLC	30	5	491	465
+LABTCDIF	90	0	491	479
+LABCRPLC	30	0	490	472
+LABCSFC	90	1	490	474
+LABDVERVP	90	1	490	411
+LABUSPG	90	3	490	488
+LAB11060R	4	0	487	229
+LABAT3	1	0	486	283
+LABCSFGL	4	1	486	464
+LABIGP	2	0	486	70
+LABABG	2	1	485	33
+LABPCEC4O	1	NULL	485	17
+LABPCEC4O	2	NULL	485	17
+LABPCEC4O	4	NULL	485	17
+LABPCEC4O	30	NULL	485	17
+LABPCEC4O	90	NULL	485	17
+LABPCEC4O	7	NULL	485	17
+LAB11060R	7	0	483	227
+LABANER	1	2	483	472
+LABIGP	4	0	483	70
+LABLIPS	4	2	483	442
+LABASI	30	1	482	482
+LABGLOB	1	NULL	481	442
+LABGLOB	2	NULL	481	442
+LABGLOB	4	NULL	481	442
+LABGLOB	30	NULL	481	442
+LABGLOB	90	NULL	481	442
+LABGLOB	7	NULL	481	442
+LABANTIXA	90	0	480	36
+LABCRPLC	90	0	480	462
+LABASQ	2	0	479	281
+LABCRP	30	1	479	297
+LABIGP	7	0	479	70
+LABUSPG	4	4	479	477
+LABPROL	1	NULL	478	252
+LABPROL	2	NULL	478	252
+LABPROL	4	NULL	478	252
+LABPROL	30	NULL	478	252
+LABPROL	90	NULL	478	252
+LABGLOB	1	0	478	439
+LABPROL	7	NULL	478	252
+LABGLOB	2	0	477	438
+LABGLOB	4	0	477	438
+LABCSFC	30	1	476	460
+LABFLDB	30	0	476	371
+LABGLOB	7	0	476	437
+LAB11060R	30	0	475	223
+LABCRP	7	1	475	313
+LABBXTG	7	1	474	401
+LABDVEHV	2	0	474	307
+LABGLOB	30	0	474	435
+LABPCTNI	4	2	473	441
+LABASQ	4	0	472	276
+LABCFRESP	7	NULL	472	27
+LABCK	2	4	472	463
+LABCFRESP	1	NULL	472	27
+LABCFRESP	2	NULL	472	27
+LABCFRESP	4	NULL	472	27
+LABCFRESP	30	NULL	472	27
+LABCFRESP	90	NULL	472	27
+LABABG	1	1	470	33
+LABCLACT	7	0	470	229
+LABMB	90	5	470	430
+LABVANPRL	90	4	470	314
+LABGLOB	90	0	469	430
+LABLIDOL	4	5	469	431
+LABCDTPCR	30	2	468	355
+LABCSFC	4	1	468	451
+LABTOBPRL	2	0	468	430
+LABASQ	7	0	467	273
+LAB11060R	90	0	466	217
+LABFK506L	4	5	466	448
+LABOSM	4	4	466	431
+LABUA	1	1	466	204
+LABHGBP	1	NULL	465	395
+LABHGBP	2	NULL	465	395
+LABHGBP	4	NULL	465	395
+LABHGBP	30	NULL	465	395
+LABHGBP	90	NULL	465	395
+LABHGBP	7	NULL	465	395
+LABAT3	2	0	464	278
+LABVP4	1	NULL	464	151
+LABVP4	2	NULL	464	151
+LABVP4	4	NULL	464	151
+LABVP4	30	NULL	464	151
+LABVP4	90	NULL	464	151
+LABHAP	30	1	464	371
+LABIGP	30	0	464	69
+LABVP4	7	NULL	464	151
+LABCK	90	3	463	442
+LABESRP	7	1	463	237
+LABAFBCF	30	0	462	438
+LABFCUL	4	2	462	433
+LABAT3	4	0	461	276
+LABPCTNI	2	2	461	432
+LAB11041R	4	0	460	341
+LABANER2	30	2	459	418
+LABANER2	90	2	459	414
+LABASQ	30	0	458	268
+LABAT3	7	0	458	276
+LABDVEHV	4	0	458	296
+LABPCTNI	1	2	458	430
+LABTRIG	90	3	458	392
+LABAT3	30	0	457	276
+LABLIDOL	7	5	457	420
+LABPCEC4O	1	0	457	17
+LABPCEC4O	2	0	457	17
+LABPCEC4O	4	0	457	17
+LABPCEC4O	7	0	457	17
+LABUSPG	7	4	457	456
+LABASQ	90	0	456	267
+LABCMVPQ	4	1	456	421
+LABFT4	2	1	456	434
+LABIGP	90	0	456	69
+LABAT3	90	0	455	274
+LABCRCL	1	NULL	455	114
+LABCRCL	2	NULL	455	114
+LABCRCL	4	NULL	455	114
+LABCRCL	30	NULL	455	114
+LABCRCL	90	NULL	455	114
+LABCRCL	7	NULL	455	114
+LABPCEC4O	30	0	455	17
+LABVP4	1	0	455	149
+LABAFBC	1	2	454	451
+LABCFRESP	1	0	453	24
+LABTRIG	30	3	453	384
+LABVP4	2	0	453	149
+LABVP4	4	0	452	149
+LABCFRESP	2	0	451	24
+LABHBPQ	1	NULL	451	327
+LABHBPQ	2	NULL	451	327
+LABHBPQ	4	NULL	451	327
+LABHBPQ	30	NULL	451	327
+LABHBPQ	90	NULL	451	327
+LABISOF	1	NULL	451	434
+LABISOF	2	NULL	451	434
+LABISOF	4	NULL	451	434
+LABISOF	30	NULL	451	434
+LABISOF	90	NULL	451	434
+LABDVEHV	7	0	451	292
+LABHBPQ	7	NULL	451	327
+LABISOF	7	NULL	451	434
+LABPCEC4O	90	0	451	17
+LABRESP	7	2	451	358
+LABVP4	7	0	451	149
+LABTBPCRS	1	NULL	450	251
+LABTBPCRS	2	NULL	450	251
+LABTBPCRS	4	NULL	450	251
+LABTBPCRS	30	NULL	450	251
+LABTBPCRS	90	NULL	450	251
+LABTBPCRS	7	NULL	450	251
+LABANER2	7	2	449	412
+LABVP4	30	0	449	149
+LABGLF	1	NULL	448	249
+LABGLF	2	NULL	448	249
+LABGLF	4	NULL	448	249
+LABGLF	30	NULL	448	249
+LABGLF	90	NULL	448	249
+LABFLDB	90	0	448	347
+LABGLF	7	NULL	448	249
+LABGRAM	30	3	448	411
+LABVP4	90	0	448	149
+LABCRCL	1	0	447	111
+LABLIDOL	30	5	447	411
+LABLIDOL	90	5	447	411
+LABBXTG	4	1	446	383
+LABCK	30	3	445	428
+LABPCEG7O	1	NULL	445	52
+LABPCEG7O	2	NULL	445	52
+LABPCEG7O	4	NULL	445	52
+LABPCEG7O	30	NULL	445	52
+LABPCEG7O	90	NULL	445	52
+LABOSM	7	4	445	412
+LABOSM	30	4	445	412
+LABOSM	90	4	445	412
+LABPCEG7O	7	NULL	445	52
+LABANER2	4	2	444	410
+LABUSPG	30	4	444	443
+LABUSPG	90	4	443	442
+LABCFRESP	4	0	442	22
+LABCRCL	2	0	441	111
+LABHFP	1	1	440	180
+LABCK	7	3	439	425
+LABPCACT	1	NULL	439	93
+LABPCACT	2	NULL	439	93
+LABPCACT	4	NULL	439	93
+LABPCACT	30	NULL	439	93
+LABPCACT	90	NULL	439	93
+LABPCACT	7	NULL	439	93
+LABCFRESP	7	0	438	22
+LABCRP	4	1	437	295
+LABLIPS	4	3	437	405
+LABSIROL	4	0	437	151
+LABFLCR	1	NULL	435	73
+LABFLCR	2	NULL	435	73
+LABFLCR	4	NULL	435	73
+LABFLCR	30	NULL	435	73
+LABFLCR	90	NULL	435	73
+LABFLCR	7	NULL	435	73
+LABNH3	1	1	435	267
+LABUSPG	4	5	435	431
+LABBLCTIP	90	2	432	411
+LABESRP	4	1	432	219
+LABTBLYM	1	NULL	431	22
+LABTBLYM	2	NULL	431	22
+LABTBLYM	4	NULL	431	22
+LABTBLYM	30	NULL	431	22
+LABTBLYM	90	NULL	431	22
+LABCRCL	4	0	431	110
+LABDVEHV	30	0	431	280
+LABFLDC	1	1	431	395
+LABHBPQ	1	0	431	318
+LABTBLYM	7	NULL	431	22
+LABHBPQ	2	0	428	316
+LAB11071R	7	NULL	427	388
+LABANER2	2	2	427	405
+LAB11071R	1	NULL	427	388
+LAB11071R	2	NULL	427	388
+LAB11071R	4	NULL	427	388
+LAB11071R	30	NULL	427	388
+LAB11071R	90	NULL	427	388
+LABMETHB	1	NULL	427	390
+LABMETHB	2	NULL	427	390
+LABMETHB	4	NULL	427	390
+LABMETHB	30	NULL	427	390
+LABMETHB	90	NULL	427	390
+LABMETHB	7	NULL	427	390
+LABVANPRL	30	4	426	290
+LABCLACT	30	0	424	211
+LABFLDC	90	2	424	369
+LABISOF	1	0	424	409
+LABB12	90	1	423	364
+LABPROL	1	0	423	226
+LABDGHBP	1	NULL	422	324
+LABDGHBP	2	NULL	422	324
+LABDGHBP	4	NULL	422	324
+LABDGHBP	30	NULL	422	324
+LABDGHBP	90	NULL	422	324
+LABDGHBP	7	NULL	422	324
+LABURNC	30	3	422	343
+LABHIAB	1	NULL	421	358
+LABHIAB	2	NULL	421	358
+LABHIAB	4	NULL	421	358
+LABHIAB	30	NULL	421	358
+LABHIAB	90	NULL	421	358
+LABCRCL	7	0	421	108
+LABHIAB	7	NULL	421	358
+LABTEGCP	4	0	421	210
+LABFLCR	1	0	420	72
+LABHBPQ	4	0	420	312
+LABDGHBP	1	0	419	322
+LABDVERVP	30	1	419	349
+LABFLCR	2	0	419	72
+LABGRAM	4	2	419	388
+LABISOF	2	0	419	404
+LABTOBPRL	4	0	419	385
+LABFLCR	4	0	418	72
+LABMB	30	5	418	389
+LABUSPG	7	5	418	414
+LABDGHBP	2	0	417	320
+LABDGHBP	4	0	417	320
+LABFIB	4	3	417	328
+LABDGHBP	7	0	416	319
+LABDIC	90	1	416	35
+LABESRP	2	1	416	216
+LABSPLAC	4	5	416	410
+LAB11071R	1	0	415	377
+LABMMAS	1	NULL	415	308
+LABMMAS	2	NULL	415	308
+LABMMAS	4	NULL	415	308
+LABMMAS	30	NULL	415	308
+LABMMAS	90	NULL	415	308
+LABDVEHV	90	0	415	268
+LABMMAS	7	NULL	415	308
+LABANER2	1	2	414	400
+LABASI	7	1	414	414
+LABDGHBP	30	0	414	318
+LABTES	1	NULL	413	236
+LABTES	2	NULL	413	236
+LABTES	4	NULL	413	236
+LABTES	30	NULL	413	236
+LABTES	90	NULL	413	236
+LABDGHBP	90	0	413	317
+LABSIROL	7	0	413	144
+LABTES	7	NULL	413	236
+LABSPLAC	1	5	412	406
+LABAMY	4	3	410	404
+LABCLACT	90	0	410	203
+LABFLCR	7	0	410	72
+LABISOF	4	0	410	396
+LABSPLAC	2	5	410	405
+LABDIC	30	1	409	35
+LABHBPQ	7	0	409	307
+LABISOF	7	0	408	395
+LABCRCL	30	0	407	106
+LABDIGL	90	2	407	365
+LABTES	1	0	407	236
+LABPHEN	4	2	406	302
+LABTES	2	0	406	235
+LAB11071R	2	0	405	371
+LABBLCTIP	2	1	405	381
+LABDIGL	4	4	405	387
+LABFK506L	1	2	405	388
+LABFKOH	90	1	405	301
+LABBXTG	2	1	404	366
+LABCRCL	90	0	404	106
+LAB11071R	4	0	403	370
+LABLIPS	90	3	403	362
+LABPROL	2	0	403	217
+LABTES	4	0	403	233
+LABYA1AS	1	NULL	402	228
+LABYA1AS	2	NULL	402	228
+LABYA1AS	4	NULL	402	228
+LABYA1AS	30	NULL	402	228
+LABYA1AS	90	NULL	402	228
+LABDIC	7	1	402	33
+LABTES	7	0	402	232
+LABYA1AS	7	NULL	402	228
+LAB11071R	7	0	401	368
+LABANER	90	3	401	377
+LABCFRESP	30	0	401	22
+LAB11071R	30	0	400	367
+LAB11071R	90	0	400	367
+LABDOA	90	1	400	281
+LABMMAS	1	0	400	298
+LABTBLYM	1	0	400	20
+LABFCUL	2	2	399	383
+LABSPLAC	7	5	399	392
+LABUSPG	30	5	399	395
+LABBLCTIP	30	2	398	372
+LABC125	7	NULL	398	153
+LABC125	1	NULL	398	153
+LABC125	2	NULL	398	153
+LABC125	4	NULL	398	153
+LABC125	30	NULL	398	153
+LABC125	90	NULL	398	153
+LABT3	1	NULL	398	164
+LABT3	2	NULL	398	164
+LABT3	4	NULL	398	164
+LABT3	30	NULL	398	164
+LABT3	90	NULL	398	164
+LABDIC	2	1	398	36
+LABDIC	4	1	398	33
+LABT3	7	NULL	398	164
+LABUSPG	90	5	398	394
+LABCORTS	2	0	397	213
+LABMMAS	2	0	397	295
+LABTES	30	0	397	230
+LABLACWB	1	5	396	391
+LABMB	2	5	396	379
+LABMMAS	4	0	396	294
+LABPHEN	90	2	396	282
+LABBLCSTK	90	2	394	370
+LABCRYST	1	NULL	394	201
+LABCRYST	2	NULL	394	201
+LABCRYST	4	NULL	394	201
+LABCRYST	30	NULL	394	201
+LABCRYST	90	NULL	394	201
+LABCRYST	7	NULL	394	201
+LABSIROL	2	1	394	272
+LABBUN	90	1	393	150
+LABPROL	4	0	393	214
+LABFLCR	30	0	392	70
+LABTES	90	0	392	228
+LABAFBC	90	3	391	381
+LABPHEN	30	2	391	280
+LABCORTS	4	0	389	207
+LABMMAS	7	0	388	290
+LABMMAS	30	0	388	290
+LABSPLAC	30	5	388	382
+LABYA1AS	1	0	388	219
+LABCORTS	7	0	387	205
+LABHBPQ	30	0	387	294
+LABHGBP	1	0	387	328
+LABISOF	30	0	387	375
+LABPROL	7	0	387	210
+LABRESPG	90	1	387	284
+LABGIARS	1	NULL	386	384
+LABGIARS	2	NULL	386	384
+LABGIARS	4	NULL	386	384
+LABGIARS	30	NULL	386	384
+LABGIARS	90	NULL	386	384
+LABVZAB	1	NULL	386	11
+LABVZAB	2	NULL	386	11
+LABVZAB	4	NULL	386	11
+LABVZAB	30	NULL	386	11
+LABVZAB	90	NULL	386	11
+LABGIARS	7	NULL	386	384
+LABMMAS	90	0	386	288
+LABVZAB	7	NULL	386	11
+LABCORTS	30	0	385	203
+LABCORTS	90	0	385	203
+LABFLCR	90	0	385	70
+LABPROL	30	0	385	209
+LABSPLAC	90	5	385	379
+LABT3	1	0	385	162
+LABHIAB	1	0	384	327
+LABPROL	90	0	384	208
+LABVZAB	1	0	384	11
+LABVZAB	2	0	384	11
+LABVZAB	4	0	384	11
+LABVZAB	7	0	384	11
+LABVZAB	30	0	383	10
+LABVZAB	90	0	383	10
+LABFT4	1	1	382	364
+LABC125	1	0	381	147
+LABPROTC	1	NULL	381	298
+LABPROTC	2	NULL	381	298
+LABPROTC	4	NULL	381	298
+LABPROTC	30	NULL	381	298
+LABPROTC	90	NULL	381	298
+LABPCACTK	90	1	381	75
+LABPHEN	7	2	381	280
+LABPROTC	7	NULL	381	298
+LABAFBCF	90	0	380	357
+LABC125	2	0	380	146
+LABFKOH	30	1	380	290
+LABHBPQ	90	0	380	290
+LABPCACTK	1	1	380	74
+LABPCACTK	2	1	380	75
+LABPCACTK	4	1	380	75
+LABPCACTK	7	1	380	75
+LABPCACTK	30	1	380	75
+LABT3	2	0	379	160
+LABVCUL	90	1	379	364
+LABISOF	90	0	378	366
+LABYA1AS	2	0	378	212
+LABBXTG	1	1	377	355
+LABC125	4	0	377	143
+LABTBLYM	2	0	377	17
+LABTBPCRS	1	0	377	206
+LABT3	4	0	376	159
+LABC125	7	0	375	143
+LABSTOBGC	1	NULL	375	61
+LABSTOBGC	2	NULL	375	61
+LABSTOBGC	4	NULL	375	61
+LABSTOBGC	30	NULL	375	61
+LABSTOBGC	90	NULL	375	61
+LABDIC	1	1	375	41
+LABSTOBGC	7	NULL	375	61
+LABGNTPRL	2	0	373	303
+LABYA1AS	4	0	373	210
+LABYA1AS	7	0	373	210
+LABPROTS	1	NULL	372	316
+LABPROTS	2	NULL	372	316
+LABPROTS	4	NULL	372	316
+LABPROTS	30	NULL	372	316
+LABPROTS	90	NULL	372	316
+LABNTBNP	90	1	372	219
+LABPCEG7O	1	0	372	43
+LABPROTS	7	NULL	372	316
+LABLEVETL	1	NULL	371	220
+LABLEVETL	2	NULL	371	220
+LABLEVETL	4	NULL	371	220
+LABLEVETL	30	NULL	371	220
+LABLEVETL	90	NULL	371	220
+LABLEVETL	7	NULL	371	220
+LABMB	7	5	371	348
+LABOSM	30	5	371	341
+LABOSM	90	5	371	341
+LABYA1AS	30	0	371	209
+LABDIGL	30	2	370	337
+LABOSM	7	5	370	340
+LABGIARS	1	0	369	367
+LABPROTC	1	0	369	288
+LABOSM	4	5	368	339
+LABPROTC	2	0	368	287
+LABT3	7	0	368	155
+LABYA1AS	90	0	368	207
+LABHSVZL	1	NULL	367	208
+LABHSVZL	2	NULL	367	208
+LABHSVZL	4	NULL	367	208
+LABHSVZL	30	NULL	367	208
+LABHSVZL	90	NULL	367	208
+LABTT4	1	NULL	367	236
+LABTT4	2	NULL	367	236
+LABTT4	4	NULL	367	236
+LABTT4	30	NULL	367	236
+LABTT4	90	NULL	367	236
+LABGIARS	2	0	367	365
+LABHSVZL	7	NULL	367	208
+LABTT4	7	NULL	367	236
+LABESRP	1	1	366	192
+LABFIB	90	3	366	273
+LABPROTC	4	0	365	286
+LABTBPCRS	2	0	365	200
+LABHSVPCR	1	NULL	364	342
+LABHSVPCR	2	NULL	364	342
+LABHSVPCR	4	NULL	364	342
+LABHSVPCR	30	NULL	364	342
+LABHSVPCR	90	NULL	364	342
+LABHBSAG	30	1	364	362
+LABHSVPCR	7	NULL	364	342
+LABPB	1	0	364	260
+LABC125	30	0	363	139
+LABHIAB	2	0	363	310
+LABPCEG7O	2	0	363	43
+LABPROTC	7	0	363	285
+LABBLCSTK	30	2	362	334
+LABTBLYM	4	0	362	17
+LABFCUL	1	2	361	353
+LABPROTC	30	0	361	284
+LABTT4	1	0	361	232
+LABTT4	2	0	361	232
+LABTT4	4	0	361	232
+LABUA	90	2	361	160
+LABDVEVH	1	NULL	360	285
+LABDVEVH	2	NULL	360	285
+LABDVEVH	4	NULL	360	285
+LABDVEVH	30	NULL	360	285
+LABDVEVH	90	NULL	360	285
+LABDVEVH	7	NULL	360	285
+LABHSVPCR	1	0	360	341
+LABLDH	4	5	360	341
+LABPLTS	90	1	360	266
+LABPROTC	90	0	360	283
+LABPROTS	1	0	360	305
+LABT3	30	0	360	154
+LABTT4	7	0	360	232
+LABSTPOLY	1	NULL	359	268
+LABSTPOLY	2	NULL	359	268
+LABSTPOLY	4	NULL	359	268
+LABSTPOLY	30	NULL	359	268
+LABSTPOLY	90	NULL	359	268
+LABFLDC	30	2	359	317
+LABPROTS	2	0	359	304
+LABSTPOLY	7	NULL	359	268
+LABT3	90	0	359	154
+LABTT4	30	0	359	232
+LABTT4	90	0	359	232
+LABAMY	90	2	358	344
+LABCK	4	4	358	346
+LABPCG3	30	3	358	105
+LABPCG3	90	3	358	105
+LABC125	90	0	357	138
+LABDIGL	4	2	357	344
+LABPCG3	7	3	357	105
+LABPROTS	4	0	356	303
+LABBLC2	7	3	355	337
+LABFIB	30	3	355	271
+LABPCG3	4	3	355	105
+LABINSLF	1	NULL	354	303
+LABINSLF	2	NULL	354	303
+LABINSLF	4	NULL	354	303
+LABINSLF	30	NULL	354	303
+LABINSLF	90	NULL	354	303
+LABGIARS	4	0	354	352
+LABHSVPCR	2	0	354	336
+LABINSLF	7	NULL	354	303
+LABPCEG7O	4	0	354	42
+LABPROTS	7	0	354	302
+LABTBLYM	7	0	354	17
+LABCLGC2	7	NULL	353	344
+LABCLGC2	1	NULL	353	344
+LABCLGC2	2	NULL	353	344
+LABCLGC2	4	NULL	353	344
+LABCLGC2	30	NULL	353	344
+LABCLGC2	90	NULL	353	344
+LABPROTS	30	0	353	301
+LABPALB	7	1	352	246
+LABPROTS	90	0	352	300
+LABSIROL	30	0	352	129
+LABSTPOLY	1	0	352	263
+LABCMVPQ	2	1	351	332
+LABPCG3	2	3	351	108
+LABRESPG	30	1	351	263
+LABCMVPQ	90	2	350	324
+LABVCUL	30	1	350	336
+LABCDTPCR	2	1	349	146
+LABGIARS	7	0	349	347
+LABHGBP	2	0	349	291
+LABPHEN	4	3	349	248
+LABTOBPRL	7	0	349	316
+LABMALAR	1	NULL	348	308
+LABMALAR	2	NULL	348	308
+LABMALAR	4	NULL	348	308
+LABMALAR	30	NULL	348	308
+LABMALAR	90	NULL	348	308
+LABMALAR	7	NULL	348	308
+LABHSVPCR	4	0	347	331
+LABLEVETL	1	0	347	204
+LABNORW	1	NULL	346	314
+LABNORW	2	NULL	346	314
+LABNORW	4	NULL	346	314
+LABNORW	30	NULL	346	314
+LABNORW	90	NULL	346	314
+LABDIGL	7	2	346	324
+LABFIB	7	3	346	267
+LABNORW	7	NULL	346	314
+LABPCEG7O	7	0	346	42
+LABTBPCRS	4	0	346	188
+LABGIARS	30	0	345	343
+LABTBLYM	30	0	345	17
+LABACTHO	1	0	343	182
+LABBUN	30	1	343	138
+LABSIROL	4	1	343	217
+LABB12	30	1	342	301
+LABFVARZ	1	NULL	342	334
+LABFVARZ	2	NULL	342	334
+LABFVARZ	4	NULL	342	334
+LABFVARZ	30	NULL	342	334
+LABFVARZ	90	NULL	342	334
+LABFVARZ	7	NULL	342	334
+LABSTPOLY	2	0	342	256
+LABAFBC	30	3	340	331
+LABCLGC2	1	0	340	331
+LABCLGC2	2	0	340	331
+LABGIARS	90	0	340	338
+LABHITAB	90	1	340	298
+LABHSVPCR	7	0	340	326
+LABHSVZL	1	0	340	191
+LABLEVETL	2	0	340	199
+LABSTPOLY	4	0	340	254
+LABBLC2	30	5	339	325
+LABTHEOL	1	NULL	339	144
+LABTHEOL	2	NULL	339	144
+LABTHEOL	4	NULL	339	144
+LABTHEOL	30	NULL	339	144
+LABTHEOL	90	NULL	339	144
+LABPCEG7O	30	0	339	42
+LABSTOBGC	1	0	339	56
+LABTHEOL	7	NULL	339	144
+LABCFRESP	90	0	338	21
+LABPCEG7O	90	0	338	42
+LABSTPOLY	7	0	338	253
+LABAFBD	90	2	337	335
+LABCLGC2	4	0	337	328
+LABHIAB	4	0	337	290
+LABHSVZL	2	0	337	189
+LABSTPOLY	30	0	337	252
+LABSTPOLY	90	0	337	252
+LABCK	30	4	336	325
+LABCMVPQ	30	2	336	313
+LABORANER	1	0	336	312
+LABTBLYM	90	0	336	17
+LABANTIM	7	NULL	335	276
+LABANTIM	1	NULL	335	276
+LABANTIM	2	NULL	335	276
+LABANTIM	4	NULL	335	276
+LABANTIM	30	NULL	335	276
+LABANTIM	90	NULL	335	276
+LABTBPCRS	7	0	335	182
+LABAMY	30	2	334	320
+LABCLGC2	7	0	334	326
+LABCLGC2	30	0	334	326
+LABPALB	4	1	334	228
+LAB11068R	7	NULL	333	265
+LAB11068R	1	NULL	333	265
+LAB11068R	2	NULL	333	265
+LAB11068R	4	NULL	333	265
+LAB11068R	30	NULL	333	265
+LAB11068R	90	NULL	333	265
+LABCK	90	4	332	321
+LABHSVPCR	30	0	332	318
+LABMB	4	5	332	311
+LABNORW	1	0	332	300
+LABCLGC2	90	0	331	323
+LABVZVIGG	1	NULL	331	275
+LABVZVIGG	2	NULL	331	275
+LABVZVIGG	4	NULL	331	275
+LABVZVIGG	30	NULL	331	275
+LABVZVIGG	90	NULL	331	275
+LABORAERO	1	0	331	250
+LABVZVIGG	7	NULL	331	275
+LABHIAB	7	0	330	286
+LABHIVWBL	90	1	330	327
+LABVZVIGG	1	0	330	274
+LABVZVIGG	2	0	330	274
+LABDVEVH	1	0	329	261
+LABLEVETL	4	0	329	193
+LABVZVIGG	4	0	329	273
+LABVZVIGG	7	0	329	273
+LABCMVQL	7	NULL	328	276
+LABCMVQL	1	NULL	328	276
+LABCMVQL	2	NULL	328	276
+LABCMVQL	4	NULL	328	276
+LABCMVQL	30	NULL	328	276
+LABCMVQL	90	NULL	328	276
+LABVZVIGG	30	0	328	272
+LABVZVIGG	90	0	327	271
+LABCZPL	7	NULL	326	99
+LABCZPL	1	NULL	326	99
+LABCZPL	2	NULL	326	99
+LABCZPL	4	NULL	326	99
+LABCZPL	30	NULL	326	99
+LABCZPL	90	NULL	326	99
+LABSTOBGC	2	0	326	56
+LABHITAB	30	1	325	283
+LABHSVPCR	90	0	325	312
+LABHSVZL	4	0	325	182
+LABEB	1	NULL	324	196
+LABEB	2	NULL	324	196
+LABEB	4	NULL	324	196
+LABEB	30	NULL	324	196
+LABEB	90	NULL	324	196
+LABEB	7	NULL	324	196
+LABLEVETL	7	0	324	189
+LABNORW	2	0	324	294
+LABFVARZ	1	0	323	316
+LABORANER	2	0	323	299
+LABDVERVR	1	NULL	322	245
+LABDVERVR	2	NULL	322	245
+LABDVERVR	4	NULL	322	245
+LABDVERVR	30	NULL	322	245
+LABDVERVR	90	NULL	322	245
+LABDVERVR	7	NULL	322	245
+LABORAERO	2	0	322	246
+LABANTIM	1	0	321	264
+LABHAP	2	1	321	280
+LABSTOBGC	4	0	321	55
+LABLIPS	30	3	320	289
+LABANTIM	2	0	319	263
+LABCMVRO	7	NULL	319	242
+LABCMVRO	1	NULL	319	242
+LABCMVRO	2	NULL	319	242
+LABCMVRO	4	NULL	319	242
+LABCMVRO	30	NULL	319	242
+LABCMVRO	90	NULL	319	242
+LABEB	1	0	319	192
+LABFVARZ	2	0	319	312
+LABHSVZL	7	0	319	177
+LABTBPCRS	30	0	319	171
+LABANTIM	4	0	318	262
+LABBMT2	4	1	318	78
+LAB11068R	1	0	317	251
+LABANTIM	7	0	317	261
+LABDVEVH	2	0	317	251
+LABFCUL	90	3	317	280
+LABGLF	1	0	317	182
+LABSTOBGC	7	0	317	55
+LABTBPCRS	90	0	317	170
+LABCRYST	1	0	316	170
+LABORANER	4	0	316	293
+LABRESP	90	3	316	246
+LABANER	30	3	315	296
+LABANTIM	30	0	315	259
+LABTRFN	1	NULL	315	121
+LABTRFN	2	NULL	315	121
+LABTRFN	4	NULL	315	121
+LABTRFN	30	NULL	315	121
+LABTRFN	90	NULL	315	121
+LABEB	2	0	315	190
+LABHIAB	30	0	315	277
+LABPCTNI	90	3	315	298
+LABPT	4	5	315	263
+LABSTOBGC	30	0	315	55
+LABTRFN	7	NULL	315	121
+LABANTIM	90	0	314	258
+LABBMT2	90	1	314	77
+LABYBOH	1	NULL	314	155
+LABYBOH	2	NULL	314	155
+LABYBOH	4	NULL	314	155
+LABYBOH	30	NULL	314	155
+LABYBOH	90	NULL	314	155
+LABEB	4	0	314	189
+LABEB	7	0	314	189
+LABFIB	1	3	314	276
+LABYBOH	7	NULL	314	155
+LABFVARZ	4	0	313	306
+LABLEVETL	30	0	313	181
+LABORAERO	4	0	313	245
+LAB11068R	2	0	312	247
+LABACTHO	2	0	312	165
+LABBMT2	30	1	312	77
+LABCRYST	2	0	312	169
+LABEB	30	0	312	189
+LABHIAB	90	0	312	274
+LABNTBNP	30	1	312	183
+LABPLTS	30	1	312	233
+LABSTOBGC	90	0	312	55
+LABTRIG	30	4	312	275
+LAB11068R	4	0	311	246
+LABDVEVH	4	0	311	245
+LABEB	90	0	311	189
+LABFVARZ	7	0	311	304
+LABNORW	4	0	311	282
+LABCA	1	1	310	208
+LABCA	2	1	310	201
+LABCRYST	4	0	310	168
+LABHSVZL	30	0	310	172
+LAB11068R	7	0	309	245
+LABRESP	4	2	309	252
+LABSIROL	90	0	309	118
+LABANCA	7	NULL	308	277
+LABCORT	90	2	308	212
+LABANCA	1	NULL	308	277
+LABANCA	2	NULL	308	277
+LABANCA	4	NULL	308	277
+LABANCA	30	NULL	308	277
+LABANCA	90	NULL	308	277
+LABCRYST	7	0	307	166
+LABLEVETL	90	0	307	178
+LABCK	7	4	306	297
+LABORANER	7	0	306	284
+LABPTT	1	5	306	280
+LABVOLTL	90	1	306	246
+LAB11068R	30	0	305	245
+LAB11068R	90	0	305	245
+LABBMT2	7	1	305	77
+LAB11041R	7	1	304	246
+LABAMY	7	2	303	294
+LABBHB	7	NULL	303	165
+LABCA	90	1	303	196
+LABBHB	1	NULL	303	165
+LABBHB	2	NULL	303	165
+LABBHB	4	NULL	303	165
+LABBHB	30	NULL	303	165
+LABBHB	90	NULL	303	165
+LABDVERVR	1	0	303	228
+LAB11049R	1	0	302	75
+LABHSVGM	1	NULL	302	301
+LABHSVGM	2	NULL	302	301
+LABHSVGM	4	NULL	302	301
+LABHSVGM	30	NULL	302	301
+LABHSVGM	90	NULL	302	301
+LABHIVWBL	30	1	302	299
+LABHSVGM	7	NULL	302	301
+LABNORW	7	0	302	276
+LABCRYST	30	0	301	164
+LABFVARZ	30	0	301	294
+LABHEPAR	2	5	301	219
+LAB11041R	4	1	300	251
+LABCA	30	1	300	193
+LABDVERVR	2	0	300	225
+LABHSVZL	90	0	300	165
+LABCA	7	1	299	193
+LABCZPL	1	0	299	89
+LABDVERVR	4	0	299	224
+LABDVEVH	7	0	299	234
+LABORAERO	7	0	299	234
+LABB12	7	1	298	265
+LABCRYST	90	0	298	163
+LABGLF	2	0	298	178
+LABTRFN	1	0	298	109
+LABANCA	1	0	297	268
+LABCMVQL	1	0	297	253
+LABCMVQL	2	0	297	253
+LABVCULR	1	NULL	297	277
+LABVCULR	2	NULL	297	277
+LABVCULR	4	NULL	297	277
+LABVCULR	30	NULL	297	277
+LABVCULR	90	NULL	297	277
+LABFVARZ	90	0	297	290
+LABVCULR	7	NULL	297	277
+LABAMY	4	2	296	288
+LABBLCSTK	2	1	296	277
+LABAFBD	30	2	295	295
+LABDIGL	7	3	295	280
+LABPCG3	1	3	295	94
+LABCA	4	1	294	189
+LABCMVQL	4	0	294	250
+LABCMVQT	90	1	294	238
+LABDVERVR	7	0	294	223
+LABHSVGM	1	0	294	293
+LABGNTPRL	4	0	293	235
+LABHSVGM	2	0	293	292
+LABTOBPRL	30	0	293	264
+LABYPREAL	90	2	293	206
+LABGLF	4	0	292	176
+LABCMVQL	7	0	291	247
+LABYCP	1	NULL	291	135
+LABYCP	2	NULL	291	135
+LABYCP	4	NULL	291	135
+LABYCP	30	NULL	291	135
+LABYCP	90	NULL	291	135
+LABYCP	7	NULL	291	135
+LABGLF	7	0	290	175
+LABHITAB	7	1	290	261
+LABTRFN	2	0	290	103
+LABANCA	2	0	289	265
+LABORAERO	30	0	289	231
+LABPCG3V	30	1	289	61
+LABPCG3V	90	1	288	60
+LABTRFN	4	0	288	103
+LABTRIG	4	2	288	250
+LABB12	4	1	287	256
+LABCORT	30	2	287	199
+LABWNVAB	1	NULL	287	270
+LABWNVAB	2	NULL	287	270
+LABWNVAB	4	NULL	287	270
+LABWNVAB	30	NULL	287	270
+LABWNVAB	90	NULL	287	270
+LABGLF	30	0	287	172
+LABGLF	90	0	287	172
+LABHSVGM	4	0	287	286
+LABHSVGM	7	0	287	286
+LABORANER	30	0	287	270
+LABSIROL	7	1	287	175
+LABSIROL	30	1	287	177
+LABSIROL	90	1	287	174
+LABWNVAB	7	NULL	287	270
+LAB11041R	7	0	286	203
+LABDVERVR	30	0	286	218
+LABHSVGM	30	0	286	285
+LABCSFC	2	1	285	272
+LABHSVGM	90	0	285	284
+LABPT	1	3	285	220
+LABUEOS	90	1	285	270
+LABFACT8	1	0	284	84
+LABRETIC	90	1	284	166
+LABCMVRO	1	0	283	216
+LABDOA	30	1	283	198
+LABFER	90	1	283	202
+LABTRIG	2	2	283	257
+LABVCULR	1	0	283	263
+LABWNVAB	1	0	283	266
+LABACTHO	4	0	282	152
+LABCMVQL	30	0	282	238
+LABCMVRO	2	0	282	215
+LABDVERVP	7	1	282	228
+LABFKOH	7	1	282	227
+LABSCMV	90	1	282	276
+LABSIROL	1	1	282	197
+LABTRFN	7	0	282	103
+LABTRIG	90	4	282	245
+LABTSH	90	2	282	219
+LABVCULR	2	0	282	262
+LABLIPS	4	4	281	267
+LABWNVAB	2	0	281	264
+LABANCA	4	0	280	258
+LABDVERVR	90	0	280	213
+LABUEOS	30	1	280	264
+LABAFBC	7	3	279	272
+LABCMVRO	4	0	279	214
+LABFIB	2	3	279	226
+LABANCA	7	0	278	256
+LABCO2	7	NULL	278	235
+LABCO2	1	NULL	278	235
+LABCO2	2	NULL	278	235
+LABCO2	4	NULL	278	235
+LABCO2	30	NULL	278	235
+LABCO2	90	NULL	278	235
+LABLPD	90	1	278	189
+LABVCULR	4	0	278	258
+LABACTHO	7	0	277	148
+LABACTHO	30	0	277	148
+LABCSFGL	2	1	277	262
+LABDVEVH	30	0	277	218
+LAB11098R	7	NULL	276	148
+LABACTHO	90	0	276	148
+LAB11098R	1	NULL	276	148
+LAB11098R	2	NULL	276	148
+LAB11098R	4	NULL	276	148
+LAB11098R	30	NULL	276	148
+LAB11098R	90	NULL	276	148
+LABNORW	30	0	276	255
+LABANCA	30	0	275	253
+LABANCA	90	0	275	253
+LABCZPL	2	0	275	84
+LABTRFN	30	0	275	103
+LABWNVAB	4	0	275	261
+LABCMVQL	90	0	274	232
+LABCMVRO	7	0	274	211
+LABCSFCT	90	1	274	138
+LABPTEG	90	1	274	131
+LABTRFN	90	0	274	103
+LABWNVAB	7	0	274	260
+LABYPREAL	30	2	274	201
+LABHIVWBL	7	1	273	270
+LABORAERO	90	0	273	219
+LABPALB	90	1	273	178
+LABPROCT	90	1	273	197
+LABPTEG	30	1	273	131
+LABCMVQT	30	1	272	225
+LABORANER	90	0	272	256
+LABPCG3V	7	1	272	59
+LABPTEG	7	1	271	130
+LABFCUL	30	3	270	238
+LABPALB	30	1	270	177
+LABUAPRN	2	1	270	113
+LABVCULR	7	0	270	250
+LABWNVAB	30	0	270	256
+LAB11098R	1	0	269	144
+LABCL	7	NULL	269	189
+LABCO2	1	0	269	226
+LABCL	1	NULL	269	189
+LABCL	2	NULL	269	189
+LABCL	4	NULL	269	189
+LABCL	30	NULL	269	189
+LABCL	90	NULL	269	189
+LABYPTHRP	1	NULL	269	209
+LABYPTHRP	2	NULL	269	209
+LABYPTHRP	4	NULL	269	209
+LABYPTHRP	30	NULL	269	209
+LABYPTHRP	90	NULL	269	209
+LABVANPRL	7	4	269	199
+LABVPAL	30	1	269	210
+LABWNVAB	90	0	269	255
+LABYPTHRP	7	NULL	269	209
+LABADVQT	7	NULL	268	213
+LABCRP	2	1	268	195
+LABCSFCT	30	1	268	137
+LABADVQT	1	NULL	268	213
+LABADVQT	2	NULL	268	213
+LABADVQT	4	NULL	268	213
+LABADVQT	30	NULL	268	213
+LABADVQT	90	NULL	268	213
+LABPTEG	4	1	268	129
+LABVPAL	90	1	268	205
+LAB11049R	2	0	267	68
+LABCL	1	0	267	189
+LABNORW	90	0	267	246
+LABCO2	2	0	266	223
+LABRESPG	7	1	266	201
+LABFKOH	4	1	265	216
+LABPTEG	1	1	265	127
+LABPTEG	2	1	265	127
+LAB11098R	2	0	264	143
+LABYCOPPR	1	NULL	264	181
+LABYCOPPR	2	NULL	264	181
+LABYCOPPR	4	NULL	264	181
+LABYCOPPR	30	NULL	264	181
+LABYCOPPR	90	NULL	264	181
+LABYCOPPR	7	NULL	264	181
+LABCO2	4	0	263	221
+LABAMY	4	4	262	255
+LABCL	2	0	262	184
+LABCO2	7	0	262	220
+LABDVEVH	90	0	262	207
+LABRESP	30	3	262	203
+LABYPTHRP	1	0	262	203
+LAB11069R	7	NULL	260	91
+LABCMVQT	7	1	260	219
+LAB11069R	1	NULL	260	91
+LAB11069R	2	NULL	260	91
+LAB11069R	4	NULL	260	91
+LAB11069R	30	NULL	260	91
+LAB11069R	90	NULL	260	91
+LABGNTPRL	7	0	260	204
+LABUEOS	7	1	260	245
+LABCL	4	0	259	182
+LABCMVPQ	1	1	259	246
+LABDIGL	90	3	259	237
+LABHAP	1	1	259	232
+LAB11098R	4	0	258	138
+LABBHB	1	0	258	138
+LABCBCO	4	3	258	178
+LABCL	7	0	258	181
+LABCO2	30	0	258	216
+LABCORT	7	2	258	172
+LABPCCG4O	90	1	258	236
+LABPLTS	4	1	258	200
+LABPLTS	7	1	258	197
+LABCBCD	4	3	257	185
+LABGRAM	90	4	257	231
+LABHIVWBL	4	1	257	254
+LABPCG3V	4	1	257	60
+LABTOBPRL	90	0	257	230
+LABYPTHRP	2	0	257	198
+LAB11098R	7	0	256	137
+LABA1C	90	2	256	225
+LABCO2	90	0	256	214
+LABLIPS	7	3	256	230
+LABNTBNP	7	1	256	156
+LABVPAL	2	1	256	207
+LABYCOPPR	1	0	256	176
+LABCK	30	5	255	249
+LABHITAB	4	1	255	240
+LABVPAL	7	1	255	210
+LABBHB	2	0	254	138
+LABCK	90	5	254	248
+LABCL	30	0	254	177
+LABCMVRO	30	0	254	197
+LABOAPE	90	1	254	249
+LABPROCT	30	1	254	184
+LABB2GP1	7	NULL	253	224
+LABB2GP1	1	NULL	253	224
+LABB2GP1	2	NULL	253	224
+LABB2GP1	4	NULL	253	224
+LABB2GP1	30	NULL	253	224
+LABB2GP1	90	NULL	253	224
+LABLAMOL	1	NULL	253	171
+LABLAMOL	2	NULL	253	171
+LABLAMOL	4	NULL	253	171
+LABLAMOL	30	NULL	253	171
+LABLAMOL	90	NULL	253	171
+LABDVERV	90	1	253	239
+LABLAMOL	7	NULL	253	171
+LABUEOS	4	1	253	240
+LABB12	2	1	252	226
+LABCL	90	0	252	175
+LABYPTHRP	4	0	252	193
+LABADVQT	1	0	251	202
+LABHBCA	1	NULL	251	244
+LABHBCA	2	NULL	251	244
+LABHBCA	4	NULL	251	244
+LABHBCA	30	NULL	251	244
+LABHBCA	90	NULL	251	244
+LABHV6DNA	1	NULL	251	240
+LABHV6DNA	2	NULL	251	240
+LABHV6DNA	4	NULL	251	240
+LABHV6DNA	30	NULL	251	240
+LABHV6DNA	90	NULL	251	240
+LABHBCA	7	NULL	251	244
+LABHV6DNA	7	NULL	251	240
+LABAPHSA2	7	NULL	250	139
+LABCZPL	4	0	250	75
+LABAPHSA2	1	NULL	250	139
+LABAPHSA2	2	NULL	250	139
+LABAPHSA2	4	NULL	250	139
+LABAPHSA2	30	NULL	250	139
+LABAPHSA2	90	NULL	250	139
+LABHGBP	4	0	250	200
+LABYERYTH	1	NULL	249	98
+LABYERYTH	2	NULL	249	98
+LABYERYTH	4	NULL	249	98
+LABYERYTH	30	NULL	249	98
+LABYERYTH	90	NULL	249	98
+LABORSHOR	1	0	249	174
+LABPHEN	7	3	249	179
+LABPT	2	4	249	197
+LABSCMV	30	1	249	247
+LABYCOPPR	2	0	249	173
+LABYERYTH	7	NULL	249	98
+LABADVQT	2	0	248	200
+LABAFBD	7	2	248	248
+LABHBCA	1	0	248	241
+LABMETB	90	4	248	128
+LABYPREAL	7	2	248	200
+LABBHB	4	0	247	132
+LABCBCO	90	3	247	167
+LABFIB	4	4	247	193
+LABGNTPRL	30	0	247	193
+LABPCG3V	2	1	247	60
+LABRETIC	30	1	247	149
+LABTEGCP	7	0	247	148
+LABUEP	90	1	247	161
+LAB11098R	30	0	246	130
+LABDIGL	30	3	246	229
+LABFMYAB	1	NULL	246	144
+LABFMYAB	2	NULL	246	144
+LABFMYAB	4	NULL	246	144
+LABFMYAB	30	NULL	246	144
+LABFMYAB	90	NULL	246	144
+LABFMYAB	7	NULL	246	144
+LABVANPRL	90	5	246	160
+LAB11234R	7	NULL	245	48
+LABBLC	1	2	245	236
+LABCORT	4	2	245	161
+LAB11234R	1	NULL	245	48
+LAB11234R	2	NULL	245	48
+LAB11234R	4	NULL	245	48
+LAB11234R	30	NULL	245	48
+LAB11234R	90	NULL	245	48
+LABB2GP1	1	0	244	215
+LABBHB	7	0	244	132
+LABYPTHRP	7	0	244	187
+LAB11069R	1	0	243	88
+LAB11234R	1	0	243	48
+LABAFBC	4	3	243	237
+LABALB	90	1	243	109
+LABB2GP1	2	0	243	214
+LABCMVRO	90	0	243	187
+LABHBCA	2	0	243	236
+LABHBCA	4	0	243	236
+LABHBSAG	7	1	243	241
+LABURNC	90	4	243	182
+LABYCOPPR	4	0	243	169
+LAB11098R	90	0	242	129
+LAB11234R	2	0	242	48
+LAB11041R	30	0	241	165
+LABBLCTIP	7	2	241	231
+LABCSFGL	90	2	241	229
+LABDVERVP	4	1	241	196
+LABOLIG	1	NULL	241	68
+LABOLIG	2	NULL	241	68
+LABOLIG	4	NULL	241	68
+LABOLIG	30	NULL	241	68
+LABOLIG	90	NULL	241	68
+LABGNTPRL	90	0	241	188
+LABLIDOL	1	4	241	230
+LABLPD	30	1	241	168
+LABOLIG	7	NULL	241	68
+LAB11234R	4	0	240	47
+LABAPHSA2	1	0	240	135
+LABAPHSA2	2	0	240	135
+LABB2GP1	4	0	240	212
+LABBHB	30	0	240	131
+LABHBCA	7	0	240	234
+LABHV6DNA	1	0	240	230
+LABORSHOR	2	0	240	170
+LABPHEN	30	3	240	174
+LABPHEN	90	3	240	174
+LABUEOS	2	1	240	227
+LAB11234R	7	0	239	46
+LABBLCA	7	NULL	239	235
+LABCSFC	90	2	239	232
+LABBLCA	1	NULL	239	235
+LABBLCA	2	NULL	239	235
+LABBLCA	4	NULL	239	235
+LABBLCA	30	NULL	239	235
+LABBLCA	90	NULL	239	235
+LABHBCA	30	0	239	233
+LABVCUL	7	1	239	229
+LABYCOPPR	7	0	239	168
+LABYPTHRP	30	0	239	186
+LABBLCTIP	90	3	238	219
+LABCSFTP	90	1	238	138
+LABGNTPOL	1	NULL	238	37
+LABGNTPOL	2	NULL	238	37
+LABGNTPOL	4	NULL	238	37
+LABGNTPOL	30	NULL	238	37
+LABGNTPOL	90	NULL	238	37
+LABGNTPOL	7	NULL	238	37
+LABHBCA	90	0	238	232
+LABPLTS	2	1	238	190
+LABYERYTH	1	0	238	94
+LABAPHSA2	4	0	237	132
+LABGASBLD	1	NULL	237	42
+LABGASBLD	2	NULL	237	42
+LABGASBLD	4	NULL	237	42
+LABGASBLD	30	NULL	237	42
+LABGASBLD	90	NULL	237	42
+LABGASBLD	7	NULL	237	42
+LABLAMOL	1	0	237	157
+LABOLIG	1	0	237	65
+LABB2GP1	7	0	236	210
+LABB2GP1	30	0	236	210
+LABBHB	90	0	236	127
+LABBMT2	2	1	236	68
+LABCBCO	30	3	236	163
+LABOLIG	2	0	236	65
+LABOLIG	4	0	236	65
+LABPCCR	90	1	236	197
+LABYBOH	1	0	236	130
+LABYPTHRP	90	0	236	183
+LAB11234R	30	0	235	46
+LABALB	30	1	235	106
+LABCMVPQ	90	3	235	222
+LABEVPCR	1	NULL	235	152
+LABEVPCR	2	NULL	235	152
+LABEVPCR	4	NULL	235	152
+LABEVPCR	30	NULL	235	152
+LABEVPCR	90	NULL	235	152
+LABEVPCR	7	NULL	235	152
+LABVCULR	30	0	235	217
+LAB11234R	90	0	234	46
+LABAPHSA2	7	0	234	130
+LABBUN	7	1	234	98
+LABCBCD	90	3	234	145
+LABTEGCP	7	1	234	112
+LABYERYTH	2	0	234	91
+LABYERYTH	4	0	234	91
+LAB11041R	90	0	233	158
+LABAPHSA2	30	0	233	129
+LABCMVP	90	1	233	217
+LABCZPL	7	0	233	68
+LABYCCPIG	1	NULL	233	230
+LABYCCPIG	2	NULL	233	230
+LABYCCPIG	4	NULL	233	230
+LABYCCPIG	30	NULL	233	230
+LABYCCPIG	90	NULL	233	230
+LABFIB	90	4	233	177
+LABRESPG	4	1	233	180
+LABYCCPIG	7	NULL	233	230
+LABYERYTH	7	0	233	91
+LABB2GP1	90	0	232	207
+LABBLCA	1	0	232	228
+LABHV6DNA	2	0	232	223
+LABOAPE	30	1	232	227
+LABYCOPPR	30	0	232	165
+LABADVQT	4	0	231	191
+LABAPHSA2	90	0	231	127
+LABBLCA	2	0	231	227
+LABFIB	30	4	231	176
+LABFMYAB	1	0	231	134
+LABMETB	30	4	231	122
+LABALB	2	1	230	108
+LABVCULT	1	NULL	230	224
+LABVCULT	2	NULL	230	224
+LABVCULT	4	NULL	230	224
+LABVCULT	30	NULL	230	224
+LABVCULT	90	NULL	230	224
+LABYMETFF	1	NULL	230	149
+LABYMETFF	2	NULL	230	149
+LABYMETFF	4	NULL	230	149
+LABYMETFF	30	NULL	230	149
+LABYMETFF	90	NULL	230	149
+LABFACT8	2	0	230	58
+LABFKOH	2	1	230	194
+LABNTBNP	4	1	230	143
+LABOLIG	7	0	230	62
+LABORSHOR	4	0	230	165
+LABTEGCP	4	1	230	106
+LABTRIG	4	3	230	209
+LABUEOS	1	1	230	218
+LABVCULT	7	NULL	230	224
+LABYCOPPR	90	0	230	163
+LABYMETFF	7	NULL	230	149
+LABALB	7	1	229	103
+LABGASBLD	1	0	229	42
+LABHBSAG	4	1	229	228
+LABYERYTH	30	0	229	88
+LAB11069R	2	0	228	84
+LABLAMOL	2	0	228	150
+LABVPAL	4	1	228	188
+LABBLCTIP	1	1	227	213
+LABGASBLD	2	0	227	42
+LABMB	1	4	227	220
+LABYCCPIG	1	0	227	224
+LABYCCPIG	2	0	227	224
+LABYCCPIG	4	0	227	224
+LABYCCPIG	7	0	227	224
+LABYERYTH	90	0	227	87
+LAB11023R	7	NULL	226	224
+LABBLCA	4	0	226	223
+LABBLCA	7	0	226	223
+LABBLCSTK	90	3	226	211
+LABCBCO	7	3	226	155
+LAB11023R	1	NULL	226	224
+LAB11023R	2	NULL	226	224
+LAB11023R	4	NULL	226	224
+LAB11023R	30	NULL	226	224
+LAB11023R	90	NULL	226	224
+LABFIB	7	4	226	172
+LABUEP	30	1	226	147
+LABALB	4	1	225	101
+LABCD4CT	7	NULL	225	25
+LABDVERV	30	1	225	211
+LABCD4CT	1	NULL	225	25
+LABCD4CT	2	NULL	225	25
+LABCD4CT	4	NULL	225	25
+LABCD4CT	30	NULL	225	25
+LABCD4CT	90	NULL	225	25
+LABOLIG	30	0	225	60
+LABORSHOR	7	0	225	163
+LABBLC2	2	2	224	209
+LABCK	7	5	224	218
+LABSSAABY	1	NULL	224	192
+LABSSAABY	2	NULL	224	192
+LABSSAABY	4	NULL	224	192
+LABSSAABY	30	NULL	224	192
+LABSSAABY	90	NULL	224	192
+LABEVPCR	1	0	224	145
+LABGASBLD	4	0	224	41
+LABGASBLD	7	0	224	41
+LABSSAABY	7	NULL	224	192
+LABVCULR	90	0	224	206
+LABYBOH	2	0	224	122
+LABCDTPCR	90	3	223	168
+LABIOPTH	1	NULL	223	77
+LABIOPTH	2	NULL	223	77
+LABIOPTH	4	NULL	223	77
+LABIOPTH	30	NULL	223	77
+LABIOPTH	90	NULL	223	77
+LABEVPCR	2	0	223	145
+LABIOPTH	7	NULL	223	77
+LABYCCPIG	30	0	223	220
+LAB11069R	4	0	222	84
+LABBLCA	30	0	222	220
+LABCSFC	30	2	222	215
+LABFER	30	1	222	165
+LABHIVWBL	2	1	222	220
+LABHV6DNA	4	0	222	214
+LABYCCPIG	90	0	222	219
+LAB11023R	1	0	221	220
+LABB12	1	1	221	199
+LABSSBABY	1	NULL	221	212
+LABSSBABY	2	NULL	221	212
+LABSSBABY	4	NULL	221	212
+LABSSBABY	30	NULL	221	212
+LABSSBABY	90	NULL	221	212
+LABEVPCR	4	0	221	145
+LABNTBNP	2	1	221	141
+LABOLIG	90	0	221	60
+LABSSBABY	7	NULL	221	212
+LABVANPRL	30	5	221	145
+LAB11023R	2	0	220	219
+LABCSFGL	30	2	220	209
+LABGASBLD	30	0	220	38
+LABGASBLD	90	0	220	38
+LAB11023R	4	0	219	218
+LABCBCD	30	3	219	142
+LABFMYAB	2	0	219	125
+LABHBSAG	2	1	219	218
+LABHEPAR	1	4	219	177
+LABLAMOL	4	0	219	143
+LABPHEN	4	4	219	175
+LABTHEOL	1	0	219	90
+LABURNC	4	2	219	180
+LABVOLTL	30	1	219	181
+LAB11023R	7	0	218	217
+LABAMY	90	3	218	212
+LABHHV6QT	1	NULL	218	157
+LABHHV6QT	2	NULL	218	157
+LABHHV6QT	4	NULL	218	157
+LABHHV6QT	30	NULL	218	157
+LABHHV6QT	90	NULL	218	157
+LABEVPCR	7	0	218	142
+LABHHV6QT	7	NULL	218	157
+LABPCG3V	1	1	218	57
+LAB11023R	30	0	217	216
+LAB11069R	7	0	217	83
+LABADVQT	7	0	217	186
+LABANER	7	3	217	209
+LABCMVPQ	30	4	217	207
+LABSTRONG	1	NULL	217	208
+LABSTRONG	2	NULL	217	208
+LABSTRONG	4	NULL	217	208
+LABSTRONG	30	NULL	217	208
+LABSTRONG	90	NULL	217	208
+LABEVPCR	30	0	217	142
+LABSTRONG	7	NULL	217	208
+LABAFBD	4	2	216	216
+LABFDP	1	NULL	216	44
+LABFDP	2	NULL	216	44
+LABFDP	4	NULL	216	44
+LABFDP	30	NULL	216	44
+LABFDP	90	NULL	216	44
+LABFDP	7	NULL	216	44
+LABSSAABY	1	0	216	185
+LABSSAABY	2	0	216	185
+LABSSAABY	4	0	216	185
+LABSSAABY	7	0	216	185
+LAB11023R	90	0	215	214
+LABGRAM	2	2	215	198
+LABMETHB	1	0	215	193
+LABPROCT	7	1	215	165
+LABSSAABY	30	0	215	184
+LABSSAABY	90	0	215	184
+LABYBOH	4	0	215	119
+LABYMETFF	1	0	215	139
+LABCMVPQ	30	3	214	204
+LABCORT	2	2	214	140
+LABEVPCR	90	0	214	141
+LABRITUX	1	NULL	214	12
+LABRITUX	2	NULL	214	12
+LABRITUX	4	NULL	214	12
+LABRITUX	30	NULL	214	12
+LABRITUX	90	NULL	214	12
+LABFMYAB	4	0	214	120
+LABHITAB	2	1	214	204
+LABHV6DNA	7	0	214	207
+LABLAMOL	7	0	214	142
+LABLPD	7	1	214	151
+LABRITUX	7	NULL	214	12
+LABTEGCP	30	1	214	100
+LABFHV12	1	NULL	213	195
+LABFHV12	2	NULL	213	195
+LABFHV12	4	NULL	213	195
+LABFHV12	30	NULL	213	195
+LABFHV12	90	NULL	213	195
+LABFHV12	7	NULL	213	195
+LABFMYAB	7	0	213	119
+LABLIPS	90	4	213	192
+LABSSBABY	1	0	213	204
+LABSSBABY	2	0	213	204
+LABSSBABY	4	0	213	204
+LABSSBABY	7	0	213	204
+LABTEGCP	90	1	213	99
+LABUA	30	2	213	98
+LABYBOH	7	0	213	118
+LABCSFTP	30	1	212	124
+LABCZPL	30	0	212	62
+LABKOHP	1	NULL	212	195
+LABKOHP	2	NULL	212	195
+LABKOHP	4	NULL	212	195
+LABKOHP	30	NULL	212	195
+LABKOHP	90	NULL	212	195
+LABFMYAB	30	0	212	119
+LABKOHP	7	NULL	212	195
+LABLAMOL	30	0	212	141
+LABSSBABY	30	0	212	203
+LABSSBABY	90	0	212	203
+LABVANPRL	4	4	212	166
+LABUALB	1	NULL	211	68
+LABUALB	2	NULL	211	68
+LABUALB	4	NULL	211	68
+LABUALB	30	NULL	211	68
+LABUALB	90	NULL	211	68
+LABFMYAB	90	0	211	119
+LABHAP	7	2	211	187
+LABUALB	7	NULL	211	68
+LABYMETFF	2	0	211	135
+LABALB	1	1	210	97
+LABAMY	30	3	210	206
+LABAPHSAB	7	NULL	210	185
+LABAPHSAB	1	NULL	210	185
+LABAPHSAB	2	NULL	210	185
+LABAPHSAB	4	NULL	210	185
+LABAPHSAB	30	NULL	210	185
+LABAPHSAB	90	NULL	210	185
+LABTOXO	1	NULL	210	172
+LABTOXO	2	NULL	210	172
+LABTOXO	4	NULL	210	172
+LABTOXO	30	NULL	210	172
+LABTOXO	90	NULL	210	172
+LABORSHOR	30	0	210	157
+LABTOXO	7	NULL	210	172
+LABURIC	1	4	210	210
+LABCZPL	90	0	209	62
+LABHHV6QT	1	0	209	154
+LABUNA	90	1	209	170
+LABYBOH	30	0	209	114
+LAB11069R	30	0	208	80
+LABIGA	1	NULL	208	142
+LABIGA	2	NULL	208	142
+LABIGA	4	NULL	208	142
+LABIGA	30	NULL	208	142
+LABIGA	90	NULL	208	142
+LABSHIV12	1	NULL	208	189
+LABSHIV12	2	NULL	208	189
+LABSHIV12	4	NULL	208	189
+LABSHIV12	30	NULL	208	189
+LABSHIV12	90	NULL	208	189
+LABIGA	7	NULL	208	142
+LABINSLF	1	0	208	167
+LABSHIV12	7	NULL	208	189
+LABYBOH	90	0	208	113
+LABRITUX	1	0	207	8
+LABASI	90	2	206	206
+LABBLCA	90	0	206	204
+LABSTREP	1	NULL	206	194
+LABSTREP	2	NULL	206	194
+LABSTREP	4	NULL	206	194
+LABSTREP	30	NULL	206	194
+LABSTREP	90	NULL	206	194
+LABNH3	90	2	206	129
+LABSTREP	7	NULL	206	194
+LABTOXO	1	0	206	171
+LABUPREG	90	1	206	195
+LABYMETFF	4	0	206	133
+LABCRYO	7	NULL	205	192
+LABCRYO	1	NULL	205	192
+LABCRYO	2	NULL	205	192
+LABCRYO	4	NULL	205	192
+LABCRYO	30	NULL	205	192
+LABCRYO	90	NULL	205	192
+LABGRAM	30	4	205	184
+LABPB	2	0	205	143
+LABRITUX	2	0	205	8
+LABCD4CT	1	0	204	22
+LABFACT8	4	0	204	52
+LABFHV12	1	0	204	189
+LABHGBP	7	0	204	159
+LABLAMOL	90	0	204	138
+LABRITUX	4	0	204	7
+LABSCMV	7	1	204	201
+LABSHIV12	1	0	204	187
+LABSHIV12	2	0	204	187
+LABTOXO	2	0	204	169
+LABHSVCSF	1	NULL	203	187
+LABHSVCSF	2	NULL	203	187
+LABHSVCSF	4	NULL	203	187
+LABHSVCSF	30	NULL	203	187
+LABHSVCSF	90	NULL	203	187
+LABGNTPOL	1	0	203	33
+LABHHV6QT	2	0	203	149
+LABHSVCSF	7	NULL	203	187
+LABKOHP	1	0	203	187
+LABKOHP	2	0	203	187
+LABMETB	7	4	203	114
+LABSHIV12	4	0	203	187
+LABSHIV12	7	0	203	187
+LABSHIV12	30	0	203	187
+LABSHIV12	90	0	203	187
+LABVCUL	4	1	203	194
+LAB11049R	4	0	202	55
+LABCRYO	1	0	202	190
+LABDBIL	2	1	202	183
+LABKOHP	4	0	202	186
+LABKOHP	7	0	202	186
+LABRITUX	7	0	202	7
+LAB11069R	90	0	201	78
+LABAPHSAB	1	0	201	177
+LABAPHSAB	2	0	201	177
+LABB2M	7	NULL	201	58
+LABBLC	4	3	201	194
+LABCBCD	7	3	201	136
+LABCRYO	2	0	201	189
+LABB2M	1	NULL	201	58
+LABB2M	2	NULL	201	58
+LABB2M	4	NULL	201	58
+LABB2M	30	NULL	201	58
+LABB2M	90	NULL	201	58
+LABVSTLC	1	NULL	201	199
+LABVSTLC	2	NULL	201	199
+LABVSTLC	4	NULL	201	199
+LABVSTLC	30	NULL	201	199
+LABVSTLC	90	NULL	201	199
+LABYFVPA	1	NULL	201	84
+LABYFVPA	2	NULL	201	84
+LABYFVPA	4	NULL	201	84
+LABYFVPA	30	NULL	201	84
+LABYFVPA	90	NULL	201	84
+LABHSVCSF	1	0	201	185
+LABKOHP	30	0	201	185
+LABVSTLC	7	NULL	201	199
+LABYFVPA	7	NULL	201	84
+LABAPHSAB	4	0	200	176
+LABBLCTIP	30	3	200	191
+LABHGBP	30	0	200	157
+LABHSVCSF	2	0	200	185
+LABMALAR	1	0	200	179
+LABORSHOR	90	0	200	150
+LABSTREP	1	0	200	190
+LABSTREP	2	0	200	190
+LABSTRONG	1	0	200	191
+LABSTRONG	2	0	200	191
+LABBLCSTK	7	2	199	190
+LABCSFCT	7	1	199	111
+LABIGA	1	0	199	134
+LABIGA	2	0	199	134
+LABIGA	4	0	199	134
+LABIGA	7	0	199	134
+LABSTREP	4	0	199	189
+LABSTREP	7	0	199	189
+LABSTREP	30	0	199	189
+LABSTREP	90	0	199	189
+LABTOXO	4	0	199	168
+LABTOXO	7	0	199	168
+LABYMETFF	7	0	199	130
+LABCSFGL	7	2	198	193
+LABYFPHEN	1	NULL	198	37
+LABYFPHEN	2	NULL	198	37
+LABYFPHEN	4	NULL	198	37
+LABYFPHEN	30	NULL	198	37
+LABYFPHEN	90	NULL	198	37
+LABYPARVO	1	NULL	198	188
+LABYPARVO	2	NULL	198	188
+LABYPARVO	4	NULL	198	188
+LABYPARVO	30	NULL	198	188
+LABYPARVO	90	NULL	198	188
+LABHBSAG	1	1	198	198
+LABHGBP	90	0	198	155
+LABIGA	30	0	198	133
+LABIGA	90	0	198	133
+LABKOHP	90	0	198	182
+LABLPD	4	1	198	140
+LABMETB	1	2	198	72
+LABYFPHEN	7	NULL	198	37
+LABYPARVO	7	NULL	198	188
+LABDBIL	90	1	197	165
+LABFHV12	2	0	197	184
+LABHSVCSF	4	0	197	182
+LABHV6DNA	30	0	197	191
+LABOAPE	7	1	197	192
+LABUNA	30	1	197	162
+LABVSTLC	1	0	197	195
+LABAPHSAB	7	0	196	172
+LABCRYO	4	0	196	184
+LABFACT8	7	0	196	50
+LABFKOH	1	1	196	175
+LABINSLF	2	0	196	155
+LABNTBNP	1	1	196	122
+LABTOXO	30	0	196	166
+LABTOXO	90	0	196	166
+LABYMETFF	30	0	196	130
+LABAMY	7	3	195	191
+LABAPHSAB	30	0	195	171
+LABAPHSAB	90	0	195	171
+LABCDTPCR	1	1	195	71
+LABCMVP	30	1	195	186
+LABCSFC	7	2	195	187
+LABDBIL	4	1	195	172
+LABHCTX	2	2	195	103
+LABHSVCSF	7	0	195	180
+LABINSLF	4	0	195	154
+LABPHOS	1	5	195	167
+LAB11242R	7	NULL	194	185
+LABBUN	4	1	194	85
+LABCRYO	7	0	194	183
+LAB11242R	1	NULL	194	185
+LAB11242R	2	NULL	194	185
+LAB11242R	4	NULL	194	185
+LAB11242R	30	NULL	194	185
+LAB11242R	90	NULL	194	185
+LABHAP	90	2	194	162
+LABYPARVO	1	0	194	184
+LAB11049R	7	0	193	55
+LABB2M	1	0	193	57
+LABB2M	2	0	193	57
+LABCSTOTA	7	NULL	193	91
+LABCSTOTA	1	NULL	193	91
+LABCSTOTA	2	NULL	193	91
+LABCSTOTA	4	NULL	193	91
+LABCSTOTA	30	NULL	193	91
+LABCSTOTA	90	NULL	193	91
+LABLDH	1	3	193	182
+LABLIL	30	1	193	156
+LABPCCG4O	30	1	193	175
+LABRESPG	2	1	193	151
+LABSTRONG	4	0	193	185
+LABVSTLC	2	0	193	191
+LABYMETFF	90	0	193	129
+LABFCUL	7	3	192	176
+LABIBD	1	NULL	192	152
+LABIBD	2	NULL	192	152
+LABIBD	4	NULL	192	152
+LABIBD	30	NULL	192	152
+LABIBD	90	NULL	192	152
+LABGRAM	7	3	192	182
+LABHCTX	90	2	192	95
+LABHSVCSF	30	0	192	177
+LABIBD	7	NULL	192	152
+LABINSLF	7	0	192	153
+LABTRIG	90	5	192	168
+LABUALB	1	0	192	66
+LABYPREAL	2	1	192	147
+LABCRYO	30	0	191	180
+LABCSTOTA	1	0	191	89
+LABDVERV	7	1	191	180
+LABHCTX	30	2	191	95
+LABIBD	1	0	191	152
+LABPROCT	4	1	191	145
+LABRETIC	7	1	191	123
+LABSTRONG	7	0	191	183
+LABSTRONG	30	0	191	183
+LABTRIG	7	3	191	169
+LABYPARVO	2	0	191	181
+LAB11049R	30	0	190	54
+LAB11049R	90	0	190	54
+LABB2M	4	0	190	56
+LABB2M	7	0	190	56
+LABCSTOTA	2	0	190	89
+LABDBIL	30	1	190	160
+LABHCTX	4	2	190	95
+LABHHV6QT	4	0	190	140
+LABINSLF	30	0	190	151
+LABTRIG	30	5	190	165
+LABVSTLC	4	0	190	188
+LABYPARVO	4	0	190	180
+LABYPARVO	7	0	190	180
+LABYPARVO	30	0	190	180
+LABYPARVO	90	0	190	180
+LAB11242R	1	0	189	181
+LABASI	4	1	189	189
+LABB2M	30	0	189	56
+LABB2M	90	0	189	56
+LABBKVPCU	7	NULL	189	67
+LABCAI	1	5	189	149
+LABDBIL	7	1	189	164
+LABBKVPCU	1	NULL	189	67
+LABBKVPCU	2	NULL	189	67
+LABBKVPCU	4	NULL	189	67
+LABBKVPCU	30	NULL	189	67
+LABBKVPCU	90	NULL	189	67
+LABFHVIG	1	NULL	189	160
+LABFHVIG	2	NULL	189	160
+LABFHVIG	4	NULL	189	160
+LABFHVIG	30	NULL	189	160
+LABFHVIG	90	NULL	189	160
+LABFHV12	4	0	189	177
+LABFHVIG	7	NULL	189	160
+LABHCTX	7	2	189	94
+LABIBD	2	0	189	150
+LABINSLF	90	0	189	150
+LABLIL	7	1	189	159
+LABLIL	90	1	189	151
+LABPROCT	2	1	189	150
+LABSTRONG	90	0	189	181
+LAB11085R	7	NULL	188	171
+LABCSTOTA	4	0	188	88
+LAB11085R	1	NULL	188	171
+LAB11085R	2	NULL	188	171
+LAB11085R	4	NULL	188	171
+LAB11085R	30	NULL	188	171
+LAB11085R	90	NULL	188	171
+LABHV6DNA	90	0	188	183
+LABRITUX	30	0	188	7
+LABCMVPQ	7	2	187	173
+LABCSTOTA	7	0	187	87
+LABMONEBV	1	NULL	187	156
+LABMONEBV	2	NULL	187	156
+LABMONEBV	4	NULL	187	156
+LABMONEBV	30	NULL	187	156
+LABMONEBV	90	NULL	187	156
+LABMONEBV	7	NULL	187	156
+LABVSTLC	7	0	187	185
+LABVSTLC	30	0	187	185
+LABVSTLC	90	0	187	185
+LABCD4CT	2	0	186	20
+LABDIGL	90	4	186	177
+LABFACT8	30	0	186	47
+LABHSVCSF	90	0	186	172
+LABAFBD	2	2	185	185
+LABASI	30	2	185	185
+LABCRP	1	1	185	132
+LABCRYO	90	0	185	175
+LABIBD	4	0	185	147
+LABADVQT	30	0	184	166
+LABBKVPCU	1	0	184	67
+LABFHV12	7	0	184	174
+LABIBD	7	0	184	146
+LABIBD	30	0	184	146
+LABIBD	90	0	184	146
+LAB11085R	1	0	183	166
+LAB11242R	2	0	183	175
+LABCSTOTA	30	0	183	85
+LABEBPCRQ	90	1	183	149
+LABFACT8	90	0	183	47
+LABFDP	1	0	183	42
+LABFQFVR	1	NULL	183	148
+LABFQFVR	2	NULL	183	148
+LABFQFVR	4	NULL	183	148
+LABFQFVR	30	NULL	183	148
+LABFQFVR	90	NULL	183	148
+LABSTPCR	1	NULL	183	174
+LABSTPCR	2	NULL	183	174
+LABSTPCR	4	NULL	183	174
+LABSTPCR	30	NULL	183	174
+LABSTPCR	90	NULL	183	174
+LABYHGBP	1	NULL	183	140
+LABYHGBP	2	NULL	183	140
+LABYHGBP	4	NULL	183	140
+LABYHGBP	30	NULL	183	140
+LABYHGBP	90	NULL	183	140
+LABFQFVR	7	NULL	183	148
+LABOAPE	4	1	183	178
+LABSTPCR	7	NULL	183	174
+LABVCULT	1	0	183	178
+LABVCULT	2	0	183	178
+LABYHGBP	7	NULL	183	140
+LAB11070R	7	NULL	182	165
+LABBKVPCU	2	0	182	66
+LABBMT1	90	1	182	21
+LABCSTOTA	90	0	182	84
+LABFDP	2	0	182	41
+LAB11070R	1	NULL	182	165
+LAB11070R	2	NULL	182	165
+LAB11070R	4	NULL	182	165
+LAB11070R	30	NULL	182	165
+LAB11070R	90	NULL	182	165
+LABFT4	90	2	182	164
+LABLPD	2	1	182	131
+LABMALAR	2	0	182	163
+LABPB	4	0	182	127
+LABSTPCR	1	0	182	174
+LABCD4CT	4	0	181	20
+LABDIGL	30	4	181	173
+LABDVERVP	2	1	181	150
+LABUEP	7	1	181	118
+LAB11085R	2	0	180	163
+LAB11242R	4	0	180	172
+LABFDP	4	0	180	41
+LABFOL	90	1	180	153
+LABMETB	4	4	180	104
+LABMONEBV	1	0	180	154
+LABMONEBV	2	0	180	154
+LABMONEBV	4	0	180	154
+LABMONEBV	7	0	180	154
+LABMONEBV	30	0	180	154
+LABMONEBV	90	0	180	154
+LABYFVPA	1	0	180	83
+LAB11242R	7	0	179	171
+LABFDP	7	0	179	41
+LABFHVIG	1	0	179	152
+LABTSH	30	2	179	151
+LABVCULT	4	0	179	174
+LABVPAL	1	1	179	142
+LAB11085R	4	0	178	162
+LAB11085R	7	0	178	162
+LAB11085R	30	0	178	162
+LABANTITG	7	NULL	178	160
+LABCD4CT	7	0	178	20
+LABFIB	2	4	178	149
+LABANTITG	1	NULL	178	160
+LABANTITG	2	NULL	178	160
+LABANTITG	4	NULL	178	160
+LABANTITG	30	NULL	178	160
+LABANTITG	90	NULL	178	160
+LABFLL	1	NULL	178	94
+LABFLL	2	NULL	178	94
+LABFLL	4	NULL	178	94
+LABFLL	30	NULL	178	94
+LABFLL	90	NULL	178	94
+LABFLL	7	NULL	178	94
+LABLDH	2	5	178	169
+LABNH3	30	2	178	114
+LABSTPCR	2	0	178	171
+LAB11070R	1	0	177	161
+LAB11085R	90	0	177	161
+LABDVERV	4	1	177	167
+LABYVITA	1	NULL	177	76
+LABYVITA	2	NULL	177	76
+LABYVITA	4	NULL	177	76
+LABYVITA	30	NULL	177	76
+LABYVITA	90	NULL	177	76
+LABFLDC	90	3	177	160
+LABFQFVR	1	0	177	144
+LABFQFVR	2	0	177	144
+LABHHV6QT	7	0	177	132
+LABMALAR	4	0	177	159
+LABMALAR	7	0	177	159
+LABROMRS	90	4	177	170
+LABSTPCR	4	0	177	170
+LABSTPCR	7	0	177	170
+LABSTPCR	30	0	177	170
+LABTEGCP	30	0	177	118
+LABUALB	2	0	177	64
+LABYVITA	1	0	177	76
+LABYVITA	2	0	177	76
+LABYVITA	7	NULL	177	76
+LABADVQT	90	0	176	159
+LABANTITG	1	0	176	159
+LABBLCSTK	30	3	176	170
+LABFHVIG	2	0	176	150
+LABFHVIG	4	0	176	150
+LABMALAR	30	0	176	158
+LABMALAR	90	0	176	158
+LABSTPCR	90	0	176	169
+LABTBIL	90	1	176	164
+LAB11242R	30	0	175	168
+LABAFBD	1	2	175	175
+LABANTITG	2	0	175	158
+LABANTITG	4	0	175	158
+LABCK	1	3	175	170
+LABCSFTP	7	1	175	99
+LABFDP	30	0	175	41
+LABFDP	90	0	175	41
+LABPLTFUN	1	NULL	175	108
+LABPLTFUN	2	NULL	175	108
+LABPLTFUN	4	NULL	175	108
+LABPLTFUN	30	NULL	175	108
+LABPLTFUN	90	NULL	175	108
+LABFLDC	7	2	175	153
+LABPLTFUN	7	NULL	175	108
+LABSCMV	4	1	175	172
+LABVCUL	2	1	175	169
+LABANTITG	7	0	174	157
+LABFHVIG	7	0	174	148
+LABFHVIG	30	0	174	148
+LABFHVIG	90	0	174	148
+LABRNPABY	1	NULL	174	146
+LABRNPABY	2	NULL	174	146
+LABRNPABY	4	NULL	174	146
+LABRNPABY	30	NULL	174	146
+LABRNPABY	90	NULL	174	146
+LABYZINCS	1	NULL	174	66
+LABYZINCS	2	NULL	174	66
+LABYZINCS	4	NULL	174	66
+LABYZINCS	30	NULL	174	66
+LABYZINCS	90	NULL	174	66
+LABFQFVR	4	0	174	142
+LABHAP	30	2	174	150
+LABLIPS	30	4	174	158
+LABRNPABY	7	NULL	174	146
+LABYFVPA	2	0	174	83
+LABYZINCS	7	NULL	174	66
+LABANER	4	3	173	167
+LABANTITG	30	0	173	156
+LABANTITG	90	0	173	156
+LABBKVPCU	4	0	173	63
+LABFHV12	30	0	173	164
+LABSCLABY	1	NULL	173	171
+LABSCLABY	2	NULL	173	171
+LABSCLABY	4	NULL	173	171
+LABSCLABY	30	NULL	173	171
+LABSCLABY	90	NULL	173	171
+LABFQFVR	7	0	173	142
+LABHIVWBL	1	1	173	172
+LABPCCR	30	1	173	150
+LABSCLABY	7	NULL	173	171
+LABVCULT	7	0	173	168
+LAB11070R	2	0	172	158
+LABDIGL	7	4	172	166
+LABHBSAG	90	2	172	170
+LABFIB	1	4	171	147
+LABFLL	1	0	171	92
+LABFLL	2	0	171	92
+LABKWB	1	NULL	171	115
+LABKWB	2	NULL	171	115
+LABKWB	4	NULL	171	115
+LABKWB	30	NULL	171	115
+LABKWB	90	NULL	171	115
+LABFQFVR	30	0	171	142
+LABKWB	7	NULL	171	115
+LABPB	7	0	171	118
+LABRETIC	4	1	171	111
+LABYVITA	4	0	171	73
+LABYZINCS	1	0	171	65
+LAB11242R	90	0	170	163
+LABCSFCT	4	1	170	98
+LABFQFVR	90	0	170	142
+LABLEGDA	90	1	170	168
+LABORANER	30	1	170	166
+LABUALB	4	0	170	61
+LABADPCRQ	7	NULL	169	157
+LABCD4CT	30	0	169	20
+LABFER	7	1	169	135
+LABFHV12	90	0	169	160
+LABFLL	4	0	169	92
+LABFLL	7	0	169	92
+LABFLL	30	0	169	92
+LABFLL	90	0	169	92
+LABADPCRQ	1	NULL	169	157
+LABADPCRQ	2	NULL	169	157
+LABADPCRQ	4	NULL	169	157
+LABADPCRQ	30	NULL	169	157
+LABADPCRQ	90	NULL	169	157
+LABGNTPOL	2	0	169	26
+LABGNTPRL	2	1	169	139
+LABORANER	90	1	169	165
+LABTEGCP	90	0	169	115
+LABYVITA	7	0	169	72
+LABYZINCS	2	0	169	64
+LABRNPABY	1	0	168	142
+LABRNPABY	2	0	168	142
+LABRNPABY	4	0	168	142
+LABRNPABY	7	0	168	142
+LABTBIL	30	1	168	156
+LABANER	90	4	167	160
+LABCK	4	5	167	161
+LABRNPABY	30	0	167	142
+LABYZINCS	4	0	167	64
+LAB11070R	4	0	166	153
+LABIGM	1	NULL	166	89
+LABIGM	2	NULL	166	89
+LABIGM	4	NULL	166	89
+LABIGM	30	NULL	166	89
+LABIGM	90	NULL	166	89
+LABIGM	7	NULL	166	89
+LABNA	1	5	166	145
+LABORANER	7	1	166	164
+LABRNPABY	90	0	166	141
+LABSCLABY	1	0	166	164
+LABSCLABY	2	0	166	164
+LABSCLABY	4	0	166	164
+LABUALB	7	0	166	59
+LABUALB	30	0	166	59
+LABVCUL	1	1	166	160
+LABBKVPCU	7	0	165	59
+LABORANER	4	1	165	164
+LABRITUX	90	0	165	7
+LABSCLABY	7	0	165	163
+LABSCLABY	30	0	165	163
+LABSCLABY	90	0	165	163
+LABUALB	90	0	165	59
+LABYHGBP	1	0	165	128
+LABYVITA	30	0	165	70
+LABYZINCS	7	0	165	64
+LABCORTS	2	1	164	126
+LABHSVQLP	1	NULL	163	146
+LABHSVQLP	2	NULL	163	146
+LABHSVQLP	4	NULL	163	146
+LABHSVQLP	30	NULL	163	146
+LABHSVQLP	90	NULL	163	146
+LABHIPA	90	1	163	126
+LABHSVQLP	7	NULL	163	146
+LABLIL	4	1	163	137
+LABTOBPRL	30	1	163	152
+LAB11070R	7	0	162	150
+LABDOA	7	1	162	118
+LABORANER	2	1	162	161
+LABPLTFUN	1	0	162	98
+LABVANPRL	7	5	162	119
+LAB11070R	30	0	161	149
+LAB11070R	90	0	161	149
+LAB11081R	7	NULL	161	87
+LABANAS	90	1	161	153
+LAB11081R	1	NULL	161	87
+LAB11081R	2	NULL	161	87
+LAB11081R	4	NULL	161	87
+LAB11081R	30	NULL	161	87
+LAB11081R	90	NULL	161	87
+LABRESPG	1	1	161	130
+LABEBPCRQ	30	1	160	136
+LABMYCPA	90	1	160	59
+LABUNA	7	1	160	131
+LABYFVPA	4	0	160	79
+LABYVITA	90	0	160	66
+LABHSVQLP	1	0	159	142
+LABMETC	90	2	159	55
+LABPB	30	0	159	109
+LABPHEN	7	4	159	113
+LABPLTS	1	1	159	117
+LABVCULT	30	0	159	155
+LAB11199R	7	NULL	158	137
+LABBMT1	30	1	158	14
+LABFCUL	4	3	158	145
+LAB11199R	1	NULL	158	137
+LAB11199R	2	NULL	158	137
+LAB11199R	4	NULL	158	137
+LAB11199R	30	NULL	158	137
+LAB11199R	90	NULL	158	137
+LABYPR3	1	NULL	158	134
+LABYPR3	2	NULL	158	134
+LABYPR3	4	NULL	158	134
+LABYPR3	30	NULL	158	134
+LABYPR3	90	NULL	158	134
+LABORANER	1	1	158	157
+LABTOBPRL	90	1	158	146
+LABUEP	4	1	158	107
+LABYFPHEN	1	0	158	33
+LABYHGBP	2	0	158	122
+LABYPR3	7	NULL	158	134
+LAB11199R	1	0	157	136
+LABCD4CT	90	0	157	20
+LABCRP	90	2	157	114
+LABPLTFUN	2	0	157	96
+LABRETIC	2	1	157	102
+LABSIROL	7	2	157	115
+LABTBIL	7	1	157	142
+LAB11041R	30	1	156	122
+LAB12762R	7	NULL	156	91
+LABAFBC	90	4	156	152
+LABBUN	2	1	156	85
+LABEBVQP	90	1	156	135
+LABFHCT	7	NULL	156	22
+LAB12762R	1	NULL	156	91
+LAB12762R	2	NULL	156	91
+LAB12762R	4	NULL	156	91
+LAB12762R	30	NULL	156	91
+LAB12762R	90	NULL	156	91
+LABFHCT	1	NULL	156	22
+LABFHCT	2	NULL	156	22
+LABFHCT	4	NULL	156	22
+LABFHCT	30	NULL	156	22
+LABFHCT	90	NULL	156	22
+LABYSTOSM	1	NULL	156	81
+LABYSTOSM	2	NULL	156	81
+LABYSTOSM	4	NULL	156	81
+LABYSTOSM	30	NULL	156	81
+LABYSTOSM	90	NULL	156	81
+LABHHV6QT	30	0	156	117
+LABHSVQLP	2	0	156	140
+LABIGM	1	0	156	83
+LABPB	90	0	156	107
+LABYPR3	1	0	156	133
+LABYPR3	2	0	156	133
+LABYSTOSM	7	NULL	156	81
+LAB11199R	2	0	155	134
+LAB11199R	4	0	155	134
+LABCSFTP	4	1	155	94
+LABFOL	30	1	155	135
+LABSIROL	2	2	155	125
+LABYZINCS	30	0	155	58
+LABYCH50	1	NULL	154	133
+LABYCH50	2	NULL	154	133
+LABYCH50	4	NULL	154	133
+LABYCH50	30	NULL	154	133
+LABYCH50	90	NULL	154	133
+LABHSVQLP	4	0	154	139
+LABPHEN	30	4	154	107
+LABPHEN	90	4	154	107
+LABYCH50	7	NULL	154	133
+LABYPR3	4	0	154	132
+LABYPR3	7	0	154	132
+LABYPR3	30	0	154	132
+LAB11036R	7	NULL	153	90
+LAB11041R	90	1	153	119
+LABANAS	30	1	153	145
+LABFIB	90	5	153	122
+LAB11036R	1	NULL	153	90
+LAB11036R	2	NULL	153	90
+LAB11036R	4	NULL	153	90
+LAB11036R	30	NULL	153	90
+LAB11036R	90	NULL	153	90
+LABGNTPRL	4	1	153	124
+LABIGM	2	0	153	83
+LABPCTNI	30	3	153	144
+LABPLTFUN	4	0	153	94
+LABSCMV	2	1	153	150
+LABSIROL	4	2	153	115
+LABVCULT	90	0	153	149
+LABYCP	1	0	153	75
+LABYPR3	90	0	153	131
+LABFER	4	1	152	123
+LABFIB	30	5	152	122
+LABSTFAT	1	NULL	152	88
+LABSTFAT	2	NULL	152	88
+LABSTFAT	4	NULL	152	88
+LABSTFAT	30	NULL	152	88
+LABSTFAT	90	NULL	152	88
+LABSTRED	1	NULL	152	72
+LABSTRED	2	NULL	152	72
+LABSTRED	4	NULL	152	72
+LABSTRED	30	NULL	152	72
+LABSTRED	90	NULL	152	72
+LABSTFAT	7	NULL	152	88
+LABSTRED	7	NULL	152	72
+LAB11199R	7	0	151	130
+LABA1C	30	2	151	136
+LABADPCRQ	1	0	151	141
+LABANER	2	3	151	148
+LABCMVPQ	90	4	151	140
+LABGNTPRL	7	1	151	124
+LABHSVQLP	7	0	151	137
+LABIGM	4	0	151	82
+LABMYCPA	30	1	151	57
+LABPLTFUN	7	0	151	94
+LABYCH50	1	0	151	130
+LABHSCRP	1	NULL	150	35
+LABHSCRP	2	NULL	150	35
+LABHSCRP	4	NULL	150	35
+LABHSCRP	30	NULL	150	35
+LABHSCRP	90	NULL	150	35
+LABLYMES2	1	NULL	150	148
+LABLYMES2	2	NULL	150	148
+LABLYMES2	4	NULL	150	148
+LABLYMES2	30	NULL	150	148
+LABLYMES2	90	NULL	150	148
+LABTOXGM	1	NULL	150	116
+LABTOXGM	2	NULL	150	116
+LABTOXGM	4	NULL	150	116
+LABTOXGM	30	NULL	150	116
+LABTOXGM	90	NULL	150	116
+LABHSCRP	7	NULL	150	35
+LABLYMES2	7	NULL	150	148
+LABOAPE	2	1	150	147
+LABPLTFUN	30	0	150	94
+LABREPT	2	1	150	115
+LABSIROL	90	2	150	104
+LABTEGCP	2	1	150	66
+LABTOXGM	7	NULL	150	116
+LABUAPRN	90	2	150	60
+LABYCH50	2	0	150	129
+LABBXTG	90	2	149	120
+LABCMVP	7	1	149	143
+LABCORTS	30	1	149	114
+LABCORTS	90	1	149	114
+LABFHCT	1	0	149	21
+LABYFFATQ	1	NULL	149	95
+LABYFFATQ	2	NULL	149	95
+LABYFFATQ	4	NULL	149	95
+LABYFFATQ	30	NULL	149	95
+LABYFFATQ	90	NULL	149	95
+LABIGM	7	0	149	81
+LABLIPS	7	4	149	136
+LABPLTFUN	90	0	149	93
+LABYFFATQ	7	NULL	149	95
+LABYZINCS	90	0	149	55
+LAB11041R	7	2	148	128
+LAB11081R	1	0	148	86
+LAB11982R	7	NULL	148	54
+LABAMY	90	4	148	145
+LABCHOL	7	NULL	148	123
+LABCORTS	7	1	148	113
+LAB11982R	1	NULL	148	54
+LAB11982R	2	NULL	148	54
+LAB11982R	4	NULL	148	54
+LAB11982R	30	NULL	148	54
+LAB11982R	90	NULL	148	54
+LABCHOL	1	NULL	148	123
+LABCHOL	2	NULL	148	123
+LABCHOL	4	NULL	148	123
+LABCHOL	30	NULL	148	123
+LABCHOL	90	NULL	148	123
+LABPB	2	1	148	137
+LABTOXGM	1	0	148	114
+LABADPCRQ	2	0	147	138
+LABCORTS	4	1	147	112
+LABCRP	30	2	147	108
+LABDRVVTP	7	NULL	147	89
+LABDRVVTP	1	NULL	147	89
+LABDRVVTP	2	NULL	147	89
+LABDRVVTP	4	NULL	147	89
+LABDRVVTP	30	NULL	147	89
+LABDRVVTP	90	NULL	147	89
+LABGNTPRL	90	1	147	120
+LABHIPA	30	1	147	111
+LABLEGDA	30	1	147	146
+LABORAERO	30	1	147	136
+LABPROCT	1	1	147	113
+LABSTFAT	1	0	147	86
+LABTOXGM	2	0	147	113
+LABYPREAL	90	3	147	117
+LABFHCT	2	0	146	20
+LABFIB	7	5	146	120
+LABLPD	1	1	146	111
+LABMETC	30	2	146	50
+LABORAERO	7	1	146	136
+LABSIROL	30	2	146	103
+LABTCDIF	30	1	146	146
+LABTOXGM	4	0	146	113
+LABTOXGM	7	0	146	113
+LABUSPG	1	5	146	146
+LABVOLTL	7	1	146	117
+LABYCH50	4	0	146	125
+LABYCH50	7	0	146	125
+LABYPREAL	30	3	146	115
+LABYSTOSM	1	0	146	75
+LABBKVPCU	30	0	145	55
+LABRP	1	NULL	145	2
+LABRP	2	NULL	145	2
+LABRP	4	NULL	145	2
+LABRP	30	NULL	145	2
+LABRP	90	NULL	145	2
+LABYUMYO	1	NULL	145	74
+LABYUMYO	2	NULL	145	74
+LABYUMYO	4	NULL	145	74
+LABYUMYO	30	NULL	145	74
+LABYUMYO	90	NULL	145	74
+LABHHV6QT	90	0	145	111
+LABIBIL	2	1	145	138
+LABIGM	30	0	145	80
+LABORAERO	90	1	145	132
+LABRP	7	NULL	145	2
+LABTOXGM	30	0	145	113
+LABYCH50	30	0	145	124
+LABYFVPA	7	0	145	70
+LABYUMYO	7	NULL	145	74
+LAB11199R	30	0	144	124
+LAB11199R	90	0	144	124
+LABCSFCT	2	1	144	87
+LABFCUL	90	4	144	130
+LABFHCT	4	0	144	20
+LABFHCT	7	0	144	20
+LABGGT	2	1	144	130
+LABHSVPAN	1	NULL	144	22
+LABHSVPAN	2	NULL	144	22
+LABHSVPAN	4	NULL	144	22
+LABHSVPAN	30	NULL	144	22
+LABHSVPAN	90	NULL	144	22
+LABLUT	1	NULL	144	139
+LABLUT	2	NULL	144	139
+LABLUT	4	NULL	144	139
+LABLUT	30	NULL	144	139
+LABLUT	90	NULL	144	139
+LABGNTPRL	30	1	144	118
+LABHSVPAN	1	0	144	22
+LABHSVPAN	2	0	144	22
+LABHSVPAN	7	NULL	144	22
+LABLUT	7	NULL	144	139
+LABTCDIF	90	1	144	144
+LABTOXGM	90	0	144	113
+LABURNC	30	4	144	119
+LABYCH50	90	0	144	123
+LABADPCRQ	4	0	143	136
+LABAFBC	2	3	143	142
+LABCHOL	1	0	143	118
+LABDIGL	90	5	143	134
+LABDOA	4	1	143	102
+LABHA1C	1	NULL	143	75
+LABHA1C	2	NULL	143	75
+LABHA1C	4	NULL	143	75
+LABHA1C	30	NULL	143	75
+LABHA1C	90	NULL	143	75
+LABSMABY	1	NULL	143	129
+LABSMABY	2	NULL	143	129
+LABSMABY	4	NULL	143	129
+LABSMABY	30	NULL	143	129
+LABSMABY	90	NULL	143	129
+LABHA1C	1	0	143	75
+LABHA1C	2	0	143	75
+LABHA1C	4	0	143	75
+LABHA1C	7	0	143	75
+LABHA1C	7	NULL	143	75
+LABHSCRP	1	0	143	32
+LABHSVQLP	30	0	143	130
+LABIGM	90	0	143	79
+LABSMABY	7	NULL	143	129
+LABTCDIF	7	1	143	143
+LABYCP	2	0	143	70
+LAB11081R	2	0	142	82
+LABAFBD	90	3	142	142
+LABAMY	30	4	142	139
+LABCHOL	2	0	142	117
+LABCMVQT	4	1	142	125
+LABCORT	1	2	142	94
+LABFACT9	7	NULL	142	71
+LABFHCT	30	0	142	20
+LABFHCT	90	0	142	20
+LABFACT9	1	NULL	142	71
+LABFACT9	2	NULL	142	71
+LABFACT9	4	NULL	142	71
+LABFACT9	30	NULL	142	71
+LABFACT9	90	NULL	142	71
+LABHA1C	30	0	142	74
+LABHA1C	90	0	142	74
+LABHSCRP	2	0	142	32
+LABHSVPAN	4	0	142	22
+LABHSVQLP	90	0	142	129
+LABNH3	2	2	142	107
+LABPCACT	1	0	142	43
+LABSTFAT	2	0	142	82
+LABTRIG	4	4	142	136
+LABYFFATQ	1	0	142	90
+LABDBIL	1	1	141	128
+LABDIGL	7	5	141	132
+LABEPO	7	NULL	141	39
+LABEPO	1	NULL	141	39
+LABEPO	2	NULL	141	39
+LABEPO	4	NULL	141	39
+LABEPO	30	NULL	141	39
+LABEPO	90	NULL	141	39
+LABHSVPAN	7	0	141	22
+LABHSVPAN	30	0	141	22
+LABHSVPAN	90	0	141	22
+LABLUT	1	0	141	136
+LABLUT	2	0	141	136
+LABLYMES2	1	0	141	139
+LABLYMES2	2	0	141	139
+LABNH3	7	2	141	93
+LABORAERO	4	1	141	133
+LABTBIL	4	1	141	132
+LABTOBPRL	7	1	141	131
+LABANAS	7	1	140	133
+LABATTG	7	NULL	140	135
+LABCHOL	4	0	140	115
+LABCHOL	7	0	140	115
+LABDVERV	2	1	140	134
+LABFER	2	1	140	116
+LABATTG	1	NULL	140	135
+LABATTG	2	NULL	140	135
+LABATTG	4	NULL	140	135
+LABATTG	30	NULL	140	135
+LABATTG	90	NULL	140	135
+LABLYMES2	4	0	140	138
+LABLYMES2	7	0	140	138
+LABLYMES2	30	0	140	138
+LABMYCPA	7	1	140	55
+LABPCACT	2	0	140	42
+LABSTFAT	4	0	140	80
+LABBACIDS	7	NULL	139	127
+LABCHOL	30	0	139	114
+LABCHOL	90	0	139	114
+LABDIGL	30	5	139	132
+LABBACIDS	1	NULL	139	127
+LABBACIDS	2	NULL	139	127
+LABBACIDS	4	NULL	139	127
+LABBACIDS	30	NULL	139	127
+LABBACIDS	90	NULL	139	127
+LABUK	1	NULL	139	114
+LABUK	2	NULL	139	114
+LABUK	4	NULL	139	114
+LABUK	30	NULL	139	114
+LABUK	90	NULL	139	114
+LABHSCRP	4	0	139	31
+LABLUT	4	0	139	134
+LABLUT	7	0	139	134
+LABLUT	30	0	139	134
+LABLUT	90	0	139	134
+LABLYMES2	90	0	139	137
+LABSTFAT	7	0	139	79
+LABUK	7	NULL	139	114
+LABYFPHEN	2	0	139	33
+LABYSTOSM	2	0	139	72
+LAB11081R	4	0	138	80
+LABBLCTIP	90	4	138	133
+LABCMVP	4	1	138	133
+LABORAERO	2	1	138	132
+LABSMABY	1	0	138	126
+LABSMABY	2	0	138	126
+LABSMABY	4	0	138	126
+LABSMABY	7	0	138	126
+LABSMABY	30	0	138	126
+LABYFFATQ	2	0	138	88
+LAB11982R	1	0	137	51
+LABATTG	1	0	137	133
+LABATTG	2	0	137	133
+LABATTG	4	0	137	133
+LABBKVPC	7	NULL	137	67
+LABBKVPC	1	NULL	137	67
+LABBKVPC	2	NULL	137	67
+LABBKVPC	4	NULL	137	67
+LABBKVPC	30	NULL	137	67
+LABGRAM	90	5	137	133
+LABBKVPC	90	NULL	137	67
+LABSPE	1	NULL	137	47
+LABSPE	2	NULL	137	47
+LABSPE	4	NULL	137	47
+LABSPE	30	NULL	137	47
+LABSPE	90	NULL	137	47
+LABTHS	1	NULL	137	127
+LABTHS	2	NULL	137	127
+LABTHS	4	NULL	137	127
+LABTHS	30	NULL	137	127
+LABTHS	90	NULL	137	127
+LABHSCRP	7	0	137	30
+LABPCACT	4	0	137	42
+LABSPE	7	NULL	137	47
+LABTHS	7	NULL	137	127
+LABYCP	4	0	137	68
+LABYHGBP	4	0	137	106
+LAB11081R	7	0	136	80
+LABATTG	7	0	136	132
+LABCSFGL	1	1	136	129
+LABDVERO	7	NULL	136	134
+LABEPO	1	0	136	37
+LABFCUL	2	3	136	127
+LABDVERO	1	NULL	136	134
+LABDVERO	2	NULL	136	134
+LABDVERO	4	NULL	136	134
+LABDVERO	30	NULL	136	134
+LABDVERO	90	NULL	136	134
+LABHSCRP	30	0	136	30
+LABIBIL	4	1	136	129
+LABIBIL	90	1	136	126
+LABORAERO	1	1	136	132
+LABPCCG4O	7	1	136	124
+LABSMABY	90	0	136	124
+LABSTFAT	30	0	136	76
+LABSTFAT	90	0	136	76
+LABUNA	4	1	136	114
+LAB11081R	30	0	135	80
+LAB11081R	90	0	135	80
+LABADPCRQ	7	0	135	131
+LABATTG	30	0	135	131
+LABATTG	90	0	135	131
+LABCSFCT	1	1	135	83
+LABFOL	7	1	135	117
+LABGNTPOL	4	0	135	17
+LABHSCRP	90	0	135	30
+LABYCP	7	0	135	66
+LABCMVQT	90	2	134	110
+LABDVERO	1	0	134	132
+LABDVERVP	90	2	134	101
+LABEPO	2	0	134	35
+LABVZVQLP	1	NULL	134	123
+LABVZVQLP	2	NULL	134	123
+LABVZVQLP	4	NULL	134	123
+LABVZVQLP	30	NULL	134	123
+LABVZVQLP	90	NULL	134	123
+LABHFP	90	2	134	81
+LABMETHB	2	0	134	116
+LABMYCPA	4	1	134	54
+LABSCMV	1	1	134	132
+LABSPE	1	0	134	47
+LABSPE	2	0	134	47
+LABVZVQLP	7	NULL	134	123
+LAB11982R	2	0	133	49
+LABDRVVTP	1	0	133	82
+LABDRVVTP	2	0	133	82
+LABDRVVTP	4	0	133	82
+LABDVERO	2	0	133	131
+LABEPO	4	0	133	34
+LABRESP	90	4	133	103
+LABTEGCP	1	1	133	51
+LABVOLTL	4	1	133	108
+LABYCP	30	0	133	64
+LABYUMYO	1	0	133	67
+LABANER	1	3	132	131
+LABBKVPC	1	0	132	65
+LABDRVVTP	7	0	132	82
+LABDRVVTP	30	0	132	82
+LABDRVVTP	90	0	132	82
+LABEPO	7	0	132	34
+LABFIB	2	5	132	115
+LABLIPS	90	5	132	124
+LABMETC	7	2	132	48
+LABNH3	4	2	132	91
+LABRESP	2	2	132	120
+LABTHS	1	0	132	122
+LABVPAL	90	2	132	107
+LABYCP	90	0	132	64
+LABYFVPA	30	0	132	61
+LABDVERO	4	0	131	129
+LABEPO	30	0	131	34
+LABEPO	90	0	131	34
+LABHGHPLC	1	NULL	131	84
+LABHGHPLC	2	NULL	131	84
+LABHGHPLC	4	NULL	131	84
+LABHGHPLC	30	NULL	131	84
+LABHGHPLC	90	NULL	131	84
+LABHGHPLC	7	NULL	131	84
+LABOSM	1	5	131	119
+LABSPE	4	0	131	46
+LABSPE	7	0	131	46
+LABTHS	2	0	131	121
+LABTHS	4	0	131	121
+LABTHS	7	0	131	121
+LABUK	1	0	131	108
+LABVOLTL	2	1	131	107
+LABBKVPCU	90	0	130	52
+LABCSFC	90	3	130	127
+LABDVERO	7	0	130	128
+LABGGT	1	1	130	118
+LABHFP	30	2	130	79
+LABIBIL	30	1	130	120
+LABRETIC	1	1	130	92
+LABSPE	30	0	130	46
+LABSPE	90	0	130	46
+LABTHS	30	0	130	120
+LABYFVPA	90	0	130	60
+LABAMY	7	4	129	126
+LABBKVPC	2	0	129	65
+LABFOL	4	1	129	110
+LABYCHRA	1	NULL	129	54
+LABYCHRA	2	NULL	129	54
+LABYCHRA	4	NULL	129	54
+LABYCHRA	30	NULL	129	54
+LABYCHRA	90	NULL	129	54
+LABPCACT	7	0	129	39
+LABREPT	90	1	129	87
+LABTHS	90	0	129	120
+LABYCHRA	7	NULL	129	54
+LABYFFATQ	4	0	129	83
+LAB11094R	7	NULL	128	110
+LABBACIDS	1	0	128	116
+LABDVERO	30	0	128	126
+LAB11094R	1	NULL	128	110
+LAB11094R	2	NULL	128	110
+LAB11094R	4	NULL	128	110
+LAB11094R	30	NULL	128	110
+LAB11094R	90	NULL	128	110
+LABIBIL	7	1	128	119
+LABPALB	90	2	128	85
+LABREPT	4	1	128	93
+LABUAPRN	1	1	128	56
+LABUK	2	0	128	105
+LABVCUL	90	2	128	121
+LABVZVQLP	1	0	128	117
+LABYPREAL	30	4	128	98
+LABYSTOSM	4	0	128	66
+LAB11102R	7	NULL	127	109
+LAB11982R	4	0	127	48
+LABANER2	90	3	127	116
+LABBLC	7	4	127	121
+LABCMVQT	30	2	127	107
+LABEBVQP	30	1	127	111
+LAB11102R	1	NULL	127	109
+LAB11102R	2	NULL	127	109
+LAB11102R	4	NULL	127	109
+LAB11102R	30	NULL	127	109
+LAB11102R	90	NULL	127	109
+LABOXCMET	1	NULL	127	84
+LABOXCMET	2	NULL	127	84
+LABOXCMET	4	NULL	127	84
+LABOXCMET	30	NULL	127	84
+LABOXCMET	90	NULL	127	84
+LABMETHB	4	0	127	110
+LABOXCMET	7	NULL	127	84
+LABPCACT	30	0	127	38
+LABPCACT	90	0	127	38
+LABAFBCF	90	1	126	125
+LABANAS	4	1	126	121
+LABMETHB	7	0	126	109
+LABPALB	30	2	126	85
+LABREPT	30	1	126	87
+LABUEP	2	1	126	88
+LABUK	4	0	126	103
+LAB11102R	1	0	125	107
+LABBXTG	30	2	125	98
+LABCRP	7	2	125	93
+LABDVERO	90	0	125	123
+LABVZVCSF	1	NULL	125	119
+LABVZVCSF	2	NULL	125	119
+LABVZVCSF	4	NULL	125	119
+LABVZVCSF	30	NULL	125	119
+LABVZVCSF	90	NULL	125	119
+LABKWB	1	0	125	83
+LABVZVCSF	7	NULL	125	119
+LABVZVQLP	2	0	125	114
+LAB11102R	2	0	124	106
+LAB11102R	4	0	124	106
+LABYTESFT	1	NULL	124	33
+LABYTESFT	2	NULL	124	33
+LABYTESFT	4	NULL	124	33
+LABYTESFT	30	NULL	124	33
+LABYTESFT	90	NULL	124	33
+LABMETHB	30	0	124	107
+LABMETHB	90	0	124	107
+LABPCCG4O	4	1	124	116
+LABSTOBGD	90	1	124	33
+LABUK	7	0	124	102
+LABUK	30	0	124	102
+LABUK	90	0	124	102
+LABYCHRA	1	0	124	51
+LABYHGBP	7	0	124	96
+LABYTESFT	7	NULL	124	33
+LAB11094R	1	0	123	105
+LAB11102R	7	0	123	105
+LABCENABY	7	NULL	123	109
+LABFCUL	1	3	123	118
+LABFLDC	30	3	123	113
+LABCENABY	1	NULL	123	109
+LABCENABY	2	NULL	123	109
+LABCENABY	4	NULL	123	109
+LABCENABY	30	NULL	123	109
+LABCENABY	90	NULL	123	109
+LABIGGS4	1	NULL	123	101
+LABIGGS4	2	NULL	123	101
+LABIGGS4	4	NULL	123	101
+LABIGGS4	30	NULL	123	101
+LABIGGS4	90	NULL	123	101
+LABHFP	7	2	123	77
+LABHITAB	1	1	123	120
+LABIGGS4	7	NULL	123	101
+LABPCCG4O	1	1	123	115
+LABRESP	7	3	123	102
+LABVZVCSF	1	0	123	117
+LAB11094R	2	0	122	104
+LAB11095R	7	NULL	122	120
+LABANER	30	4	122	118
+LABBACIDS	2	0	122	111
+LABBACIDS	4	0	122	111
+LABCMVQT	2	1	122	110
+LABCRPLFB	7	NULL	122	119
+LABCSFC	1	1	122	117
+LABFER	1	1	122	102
+LABGNTPOL	7	0	122	16
+LAB11095R	1	NULL	122	120
+LAB11095R	2	NULL	122	120
+LAB11095R	4	NULL	122	120
+LAB11095R	30	NULL	122	120
+LAB11095R	90	NULL	122	120
+LABCRPLFB	1	NULL	122	119
+LABCRPLFB	2	NULL	122	119
+LABCRPLFB	4	NULL	122	119
+LABCRPLFB	30	NULL	122	119
+LABCRPLFB	90	NULL	122	119
+LABOXCMET	1	0	122	81
+LABPCCG4O	2	1	122	114
+LABVPAL	30	2	122	102
+LABVZVCSF	2	0	122	116
+LABVZVQLP	4	0	122	112
+LABYFFATQ	7	0	122	79
+LABYFPHEN	4	0	122	30
+LAB11094R	4	0	121	103
+LAB11102R	30	0	121	103
+LAB11107R	7	NULL	121	109
+LAB12214R	7	NULL	121	118
+LABAFBD	30	3	121	121
+LABBLCSTK	90	4	121	113
+LABCMVP	2	1	121	117
+LABCSFC	30	3	121	118
+LAB11107R	1	NULL	121	109
+LAB11107R	2	NULL	121	109
+LAB11107R	4	NULL	121	109
+LAB11107R	30	NULL	121	109
+LAB11107R	90	NULL	121	109
+LAB12214R	1	NULL	121	118
+LAB12214R	2	NULL	121	118
+LAB12214R	4	NULL	121	118
+LAB12214R	30	NULL	121	118
+LAB12214R	90	NULL	121	118
+LABHIPA	7	1	121	92
+LABKWB	2	0	121	79
+LABVOLTL	1	1	121	97
+LABYFPHEN	7	0	121	30
+LAB11094R	7	0	120	103
+LABBKVPC	4	0	120	64
+LABCENABY	1	0	120	106
+LABCENABY	2	0	120	106
+LABCENABY	4	0	120	106
+LABFIB	4	5	120	99
+LABFOL	2	1	120	104
+LABHAP	90	3	120	98
+LABHGHPLC	1	0	120	76
+LABMETC	4	2	120	42
+LABOXCMET	2	0	120	79
+LABSTRED	1	0	120	60
+LABVZVCSF	4	0	120	114
+LABVZVCSF	7	0	120	114
+LABVZVQLP	7	0	120	112
+LABYCHRA	2	0	120	49
+LABYUMYO	2	0	120	62
+LAB11102R	90	0	119	101
+LABCENABY	7	0	119	106
+LABCENABY	30	0	119	106
+LABCENABY	90	0	119	106
+LABCORTS	1	1	119	91
+LABCRPLFC	7	NULL	119	103
+LABDVERVP	1	1	119	95
+LABFT4	30	2	119	110
+LABCRPLFC	1	NULL	119	103
+LABCRPLFC	2	NULL	119	103
+LABCRPLFC	4	NULL	119	103
+LABCRPLFC	30	NULL	119	103
+LABCRPLFC	90	NULL	119	103
+LABINSL	1	NULL	119	86
+LABINSL	2	NULL	119	86
+LABINSL	4	NULL	119	86
+LABINSL	30	NULL	119	86
+LABINSL	90	NULL	119	86
+LABIGGS4	1	0	119	97
+LABIGGS4	2	0	119	97
+LABIGGS4	4	0	119	97
+LABINSL	7	NULL	119	86
+LABOXCMET	4	0	119	79
+LABREPT	7	1	119	84
+LABRP	1	0	119	2
+LABUPREG	30	1	119	109
+LABYCHRA	4	0	119	48
+LABYSTOSM	7	0	119	62
+LABYTESFT	1	0	119	32
+LAB12214R	1	0	118	115
+LABBACIDS	7	0	118	108
+LABCSFGL	90	3	118	115
+LABIGGS4	7	0	118	97
+LABMETHB	1	1	118	113
+LABVZVCSF	30	0	118	112
+LABYCHRA	7	0	118	47
+LABYFFATQ	30	0	118	75
+LABYHGBP	30	0	118	92
+LAB11094R	30	0	117	101
+LAB11094R	90	0	117	101
+LAB11095R	1	0	117	115
+LAB12214R	2	0	117	114
+LABBMT1	4	1	117	4
+LABBMT1	7	1	117	4
+LABCMVPQ	4	2	117	109
+LABCRPLFB	1	0	117	114
+LABGNTPOL	30	0	117	16
+LABGNTPOL	90	0	117	16
+LABHGBP	4	1	117	110
+LABIGGS4	30	0	117	96
+LABIGGS4	90	0	117	96
+LABKWB	4	0	117	75
+LABKWB	7	0	117	75
+LABORSHOR	7	1	117	107
+LABORSHOR	30	1	117	107
+LABOXCMET	7	0	117	78
+LABYCHRA	30	0	117	47
+LABYCHRA	90	0	117	47
+LABYFFATQ	90	0	117	75
+LABYHGBP	90	0	117	92
+LAB11095R	2	0	116	114
+LAB11107R	1	0	116	105
+LAB11982R	7	0	116	45
+LAB12214R	4	0	116	113
+LAB12214R	7	0	116	113
+LABADPCRQ	30	0	116	113
+LABCSFC	4	2	116	112
+LABHGHPLC	2	0	116	75
+LABHGHPLC	4	0	116	75
+LABLIL	2	1	116	99
+LABVZVCSF	90	0	116	110
+LABWDC	90	1	116	63
+LABYTESFT	2	0	116	31
+LAB11095R	4	0	115	113
+LAB12214R	30	0	115	112
+LAB12214R	90	0	115	112
+LAB12808R	7	NULL	115	37
+LABCRPLFB	2	0	115	112
+LABDOA	2	1	115	90
+LAB12808R	1	NULL	115	37
+LAB12808R	2	NULL	115	37
+LAB12808R	4	NULL	115	37
+LAB12808R	30	NULL	115	37
+LAB12808R	90	NULL	115	37
+LABKWB	30	0	115	74
+LABORSHOR	2	1	115	107
+LABORSHOR	4	1	115	107
+LABORSHOR	90	1	115	105
+LABYSTOSM	30	0	115	61
+LAB11095R	7	0	114	112
+LAB11107R	2	0	114	103
+LABHSVIGG	1	NULL	114	23
+LABHSVIGG	2	NULL	114	23
+LABHSVIGG	4	NULL	114	23
+LABHSVIGG	30	NULL	114	23
+LABHSVIGG	90	NULL	114	23
+LABHSVIGG	1	0	114	23
+LABHSVIGG	2	0	114	23
+LABHSVIGG	7	NULL	114	23
+LABIGG	90	1	114	63
+LABKWB	90	0	114	74
+LABLIPS	30	5	114	106
+LABTCDIF	4	1	114	114
+LABUAS	90	1	114	71
+LABYSTOSM	90	0	114	61
+LABYTESFT	4	0	114	30
+LAB11107R	4	0	113	102
+LAB11107R	7	0	113	102
+LABAMY	90	5	113	111
+LABCRPLFB	4	0	113	110
+LABCRPLFB	7	0	113	110
+LABCRPLFC	1	0	113	100
+LABYLEVT	1	NULL	113	99
+LABYLEVT	2	NULL	113	99
+LABYLEVT	4	NULL	113	99
+LABYLEVT	30	NULL	113	99
+LABYLEVT	90	NULL	113	99
+LABYTRYPT	1	NULL	113	84
+LABYTRYPT	2	NULL	113	84
+LABYTRYPT	4	NULL	113	84
+LABYTRYPT	30	NULL	113	84
+LABYTRYPT	90	NULL	113	84
+LABHSVIGG	4	0	113	23
+LABOAPE	1	1	113	110
+LABORSHOR	1	1	113	107
+LABOXCMET	30	0	113	74
+LABPALB	7	2	113	74
+LABURNC	90	5	113	92
+LABYLEVT	7	NULL	113	99
+LABYTESFT	7	0	113	30
+LABYTRYPT	7	NULL	113	84
+LAB11036R	1	0	112	64
+LABADPCRQ	90	0	112	109
+LABBACIDS	30	0	112	102
+LABCMVPQ	90	5	112	108
+LABCRPLFB	30	0	112	110
+LABCSFGL	30	3	112	109
+LABIGF	1	NULL	112	46
+LABIGF	2	NULL	112	46
+LABIGF	4	NULL	112	46
+LABIGF	30	NULL	112	46
+LABIGF	90	NULL	112	46
+LABHGBP	7	1	112	104
+LABHGHPLC	7	0	112	74
+LABHSVIGG	7	0	112	23
+LABHSVIGG	30	0	112	23
+LABHSVIGG	90	0	112	23
+LABIGF	7	NULL	112	46
+LABOXCMET	90	0	112	73
+LABVZVQLP	30	0	112	105
+LAB11095R	30	0	111	109
+LAB11095R	90	0	111	109
+LAB11107R	30	0	111	100
+LAB11107R	90	0	111	100
+LABAMIPRL	7	NULL	111	82
+LABC4C8	7	NULL	111	6
+LABAMIPRL	1	NULL	111	82
+LABAMIPRL	2	NULL	111	82
+LABAMIPRL	4	NULL	111	82
+LABAMIPRL	30	NULL	111	82
+LABAMIPRL	90	NULL	111	82
+LABC4C8	1	NULL	111	6
+LABC4C8	2	NULL	111	6
+LABC4C8	4	NULL	111	6
+LABC4C8	30	NULL	111	6
+LABC4C8	90	NULL	111	6
+LABMETHB	2	1	111	107
+LABSIROL	4	3	111	90
+LAB11106R	7	NULL	110	84
+LABANER2	30	3	110	99
+LABBACIDS	90	0	110	100
+LABCMVP	1	1	110	107
+LABFCUL	30	4	110	102
+LABHAP	30	3	110	90
+LAB11106R	1	NULL	110	84
+LAB11106R	2	NULL	110	84
+LAB11106R	4	NULL	110	84
+LAB11106R	30	NULL	110	84
+LAB11106R	90	NULL	110	84
+LABHGHPLC	30	0	110	73
+LABSTLCX	90	1	110	99
+LABYUMYO	4	0	110	58
+LABAFBC	30	4	109	106
+LABCTC	90	1	109	102
+LABSTLCP	1	NULL	109	98
+LABSTLCP	2	NULL	109	98
+LABSTLCP	4	NULL	109	98
+LABSTLCP	30	NULL	109	98
+LABSTLCP	90	NULL	109	98
+LABTOXIG	1	NULL	109	87
+LABTOXIG	2	NULL	109	87
+LABTOXIG	4	NULL	109	87
+LABTOXIG	30	NULL	109	87
+LABTOXIG	90	NULL	109	87
+LABHGHPLC	90	0	109	73
+LABSTLCP	7	NULL	109	98
+LABSTSS	90	1	109	106
+LABTOXIG	7	NULL	109	87
+LABYTESFT	30	0	109	29
+LABYTESFT	90	0	109	29
+LAB11106R	1	0	108	83
+LAB11106R	2	0	108	83
+LABC4C8	1	0	108	4
+LABCRFA	7	NULL	108	106
+LABCRPLFB	90	0	108	106
+LABCRPLFC	2	0	108	96
+LABCRFA	1	NULL	108	106
+LABCRFA	2	NULL	108	106
+LABCRFA	4	NULL	108	106
+LABCRFA	30	NULL	108	106
+LABCRFA	90	NULL	108	106
+LABVANPOL	1	NULL	108	44
+LABVANPOL	2	NULL	108	44
+LABVANPOL	4	NULL	108	44
+LABVANPOL	30	NULL	108	44
+LABVANPOL	90	NULL	108	44
+LABSTLCP	1	0	108	97
+LABSTLCP	2	0	108	97
+LABVANPOL	7	NULL	108	44
+LABVZVQLP	90	0	108	101
+LABYFPHEN	30	0	108	26
+LABYUMYO	7	0	108	58
+LABAFBC	1	3	107	106
+LABAMY	30	5	107	105
+LABBKVPC	7	0	107	61
+LABFOL	1	1	107	93
+LABLEGDA	7	1	107	107
+LABLIPS	7	5	107	98
+LABSTLCP	4	0	107	96
+LABSTLCP	7	0	107	96
+LABSTLCP	30	0	107	96
+LABSTLCP	90	0	107	96
+LABTHEOL	2	0	107	25
+LABUNA	2	1	107	87
+LABYLEVT	1	0	107	94
+LABYLEVT	2	0	107	94
+LABYLEVT	4	0	107	94
+LABYTRYPT	1	0	107	81
+LABYUMYO	30	0	107	58
+LABYUMYO	90	0	107	58
+LAB11106R	4	0	106	81
+LAB11106R	7	0	106	81
+LABAMY	7	5	106	105
+LABC4C8	2	0	106	4
+LABFKOH	90	2	106	88
+LABSTOOLC	90	1	106	95
+LABWDCS	90	1	106	69
+LABYFPHEN	90	0	106	26
+LABYLEVT	7	0	106	93
+LAB11106R	30	0	105	80
+LAB11106R	90	0	105	80
+LABAGALA	90	1	105	101
+LABBXTG	7	2	105	85
+LABC4C8	4	0	105	4
+LABC4C8	7	0	105	4
+LABCSFGL	4	2	105	98
+LABEBPCRQ	7	1	105	96
+LABFACT10	7	NULL	105	33
+LABFACT10	1	NULL	105	33
+LABFACT10	2	NULL	105	33
+LABFACT10	4	NULL	105	33
+LABFACT10	30	NULL	105	33
+LABFACT10	90	NULL	105	33
+LABYBSAP	1	NULL	105	80
+LABYBSAP	2	NULL	105	80
+LABYBSAP	4	NULL	105	80
+LABYBSAP	30	NULL	105	80
+LABYBSAP	90	NULL	105	80
+LABPB	1	1	105	99
+LABRESPG	90	2	105	72
+LABRP	2	0	105	2
+LABRP	4	0	105	2
+LABRP	7	0	105	2
+LABRP	30	0	105	2
+LABTOXIG	1	0	105	83
+LABTSH	7	2	105	94
+LABUAPRN	30	2	105	45
+LABVPAL	7	2	105	89
+LABWDC	30	1	105	61
+LABYBSAP	7	NULL	105	80
+LABYLEVT	30	0	105	92
+LABANER2	7	3	104	97
+LABBLCSTK	1	1	104	94
+LABFLDB	90	1	104	96
+LABHAP	4	2	104	94
+LABHFP	4	2	104	66
+LABHIPA	4	1	104	78
+LABIGET	90	1	104	92
+LABIGF	1	0	104	45
+LABPCG3	2	4	104	44
+LABPCG3	90	4	104	43
+LABRP	90	0	104	2
+LABTHEOL	4	0	104	23
+LABTOXIG	2	0	104	83
+LABYLEVT	90	0	104	91
+LAB12238R	7	NULL	103	99
+LABC4C8	30	0	103	4
+LABDATBSI	90	1	103	102
+LAB12238R	1	NULL	103	99
+LAB12238R	2	NULL	103	99
+LAB12238R	4	NULL	103	99
+LAB12238R	30	NULL	103	99
+LAB12238R	90	NULL	103	99
+LABIGF	2	0	103	45
+LABPCG3	4	4	103	43
+LABPCG3	7	4	103	43
+LABPCG3	30	4	103	43
+LABRESP	30	4	103	80
+LABTOXIG	4	0	103	83
+LABTOXIG	7	0	103	83
+LABTOXIG	30	0	103	83
+LABYTRYPT	2	0	103	79
+LABANER2	4	3	102	95
+LABASI	90	3	102	102
+LABBLCTIP	4	2	102	95
+LABFLDC	4	2	102	90
+LABGNTPRL	1	1	102	85
+LABMYCPA	2	1	102	49
+LABPCTNI	90	4	102	98
+LABTHEOL	7	0	102	23
+LABTOXIG	90	0	102	82
+LABYPREAL	1	1	102	79
+LABYTRYPT	4	0	102	79
+LABYTRYPT	7	0	102	79
+LAB12238R	1	0	101	97
+LABCSFTP	2	1	101	59
+LABEBVQP	7	1	101	91
+LABIGF	4	0	101	44
+LABIGF	7	0	101	44
+LABIGF	30	0	101	44
+LABIGF	90	0	101	44
+LABVANPOL	1	0	101	42
+LABWDCS	30	1	101	68
+LABYBSAP	1	0	101	76
+LAB11041R	30	2	100	81
+LAB11097R	7	NULL	100	71
+LABANER2	2	3	100	94
+LABBUN	1	1	100	64
+LABCBCO	90	4	100	73
+LABCRFA	1	0	100	98
+LAB11097R	1	NULL	100	71
+LAB11097R	2	NULL	100	71
+LAB11097R	4	NULL	100	71
+LAB11097R	30	NULL	100	71
+LAB11097R	90	NULL	100	71
+LABSTOBLD	1	NULL	100	63
+LABSTOBLD	2	NULL	100	63
+LABSTOBLD	4	NULL	100	63
+LABSTOBLD	30	NULL	100	63
+LABSTOBLD	90	NULL	100	63
+LABYTOPIR	1	NULL	100	81
+LABYTOPIR	2	NULL	100	81
+LABYTOPIR	4	NULL	100	81
+LABYTOPIR	30	NULL	100	81
+LABYTOPIR	90	NULL	100	81
+LABSTOBGD	30	1	100	28
+LABSTOBLD	1	0	100	63
+LABSTOBLD	2	0	100	63
+LABSTOBLD	4	0	100	63
+LABSTOBLD	7	0	100	63
+LABSTOBLD	7	NULL	100	63
+LABSTOBLD	30	0	100	63
+LABVPAL	2	2	100	84
+LABYTOPIR	7	NULL	100	81
+LABYTRYPT	30	0	100	78
+LABYTRYPT	90	0	100	78
+LAB11041R	90	2	99	80
+LAB12238R	2	0	99	95
+LAB12238R	4	0	99	95
+LABC4C8	90	0	99	4
+LABCRFA	2	0	99	97
+LABCRFA	4	0	99	97
+LABCRPLFC	4	0	99	94
+LABCTC	30	1	99	94
+LABSTOBLD	90	0	99	62
+LABYBSAP	2	0	99	76
+LABYBSAP	4	0	99	76
+LABYBSAP	7	0	99	76
+LABYBSAP	30	0	99	76
+LAB12238R	7	0	98	94
+LABCBCD	90	4	98	66
+LABCBCO	1	2	98	56
+LABDOA	1	1	98	76
+LABGRAM	30	5	98	94
+LABRIST	1	NULL	98	85
+LABRIST	2	NULL	98	85
+LABRIST	4	NULL	98	85
+LABRIST	30	NULL	98	85
+LABRIST	90	NULL	98	85
+LABPCCR	7	1	98	89
+LABRIST	7	NULL	98	85
+LAB11096R	7	NULL	97	78
+LAB12238R	30	0	97	93
+LABANER2	1	3	97	93
+LABBXTG	4	2	97	79
+LABDDIM	90	1	97	38
+LABFACT7	7	NULL	97	35
+LABFKOH	30	2	97	81
+LAB11096R	1	NULL	97	78
+LAB11096R	2	NULL	97	78
+LAB11096R	4	NULL	97	78
+LAB11096R	30	NULL	97	78
+LAB11096R	90	NULL	97	78
+LABFACT7	1	NULL	97	35
+LABFACT7	2	NULL	97	35
+LABFACT7	4	NULL	97	35
+LABFACT7	30	NULL	97	35
+LABFACT7	90	NULL	97	35
+LABYSTFR	1	NULL	97	50
+LABYSTFR	2	NULL	97	50
+LABYSTFR	4	NULL	97	50
+LABYSTFR	30	NULL	97	50
+LABYSTFR	90	NULL	97	50
+LABMETB	90	5	97	59
+LABNH3	4	3	97	75
+LABPHEN	30	5	97	72
+LABPHEN	90	5	97	72
+LABTHEOL	30	0	97	20
+LABYBSAP	90	0	97	76
+LABYSTFR	7	NULL	97	50
+LABCBCO	30	4	96	70
+LABCRFA	7	0	96	94
+LABDDIM	30	1	96	38
+LABFLDB	30	1	96	89
+LABUAMY	1	NULL	96	60
+LABUAMY	2	NULL	96	60
+LABUAMY	4	NULL	96	60
+LABUAMY	30	NULL	96	60
+LABUAMY	90	NULL	96	60
+LABMETB	2	3	96	41
+LABMETC	2	2	96	29
+LABSTSS	30	1	96	93
+LABUAMY	7	NULL	96	60
+LABVANPOL	2	0	96	42
+LABCBCD	1	2	95	28
+LABCBCD	4	4	95	73
+LABCORT	90	3	95	72
+LABCRFA	30	0	95	93
+LABCRPLFC	7	0	95	91
+LABDVERV	1	1	95	95
+LABG6PD	7	NULL	95	82
+LABG6PD	1	NULL	95	82
+LABG6PD	2	NULL	95	82
+LABG6PD	4	NULL	95	82
+LABG6PD	30	NULL	95	82
+LABG6PD	90	NULL	95	82
+LABTBIL	2	1	95	93
+LABTHEOL	90	0	95	19
+LABVANPOL	4	0	95	42
+LABVANPOL	7	0	95	42
+LABVANPOL	30	0	95	42
+LABVANPOL	90	0	95	42
+LABVPAL	4	3	95	79
+LAB11097R	1	0	94	67
+LAB12238R	90	0	94	90
+LABAFBD	7	3	94	94
+LABANAS	2	1	94	91
+LABANERB	7	NULL	94	89
+LABCMVPQ	30	5	94	91
+LABDDIM	4	1	94	38
+LABDDIM	7	1	94	38
+LABANERB	1	NULL	94	89
+LABANERB	2	NULL	94	89
+LABANERB	4	NULL	94	89
+LABANERB	30	NULL	94	89
+LABANERB	90	NULL	94	89
+LABPHEN	7	5	94	70
+LABUAS	30	1	94	57
+LABURNC	2	2	94	72
+LABVCUL	30	2	94	91
+LABYTOPIR	1	0	94	76
+LAB11093R	7	NULL	93	91
+LABACTHO	4	1	93	62
+LABACTHO	90	1	93	61
+LABBLCTIP	30	4	93	90
+LABCBCD	30	4	93	63
+LABCRFA	90	0	93	91
+LABDDIM	2	1	93	37
+LAB11093R	1	NULL	93	91
+LAB11093R	2	NULL	93	91
+LAB11093R	4	NULL	93	91
+LAB11093R	30	NULL	93	91
+LAB11093R	90	NULL	93	91
+LABSIROL	7	3	93	75
+LABSTLCX	30	1	93	83
+LABUUN	90	1	93	77
+LABYSTFR	1	0	93	47
+LAB11093R	1	0	92	90
+LAB11093R	2	0	92	90
+LAB11093R	4	0	92	90
+LAB11093R	7	0	92	90
+LAB11093R	30	0	92	90
+LAB11093R	90	0	92	90
+LAB11097R	2	0	92	65
+LABACTHO	7	1	92	61
+LABACTHO	30	1	92	61
+LABFLDB	7	1	92	87
+LABLIPS	1	2	92	89
+LABYSTFR	2	0	92	47
+LABYSTFR	4	0	92	47
+LABYSTFR	7	0	92	47
+LABACTHO	2	1	91	62
+LABBKVPC	30	0	91	57
+LABHFP	2	2	91	54
+LABMETB	30	5	91	55
+LABUAMY	1	0	91	59
+LABWDC	7	1	91	56
+LABYPREAL	90	4	91	63
+LAB11097R	4	0	90	63
+LABACTHO	1	1	90	59
+LABANERB	1	0	90	85
+LABBLCTIP	90	5	90	83
+LABBMT2	1	1	90	19
+LABCBCO	7	4	90	69
+LABCRPLFC	30	0	90	87
+LABFLDB	4	1	90	87
+LABG6PD	1	0	90	80
+LABGGT	90	1	90	70
+LABGRAM	4	3	90	83
+LABJO1ABY	1	NULL	90	84
+LABJO1ABY	2	NULL	90	84
+LABJO1ABY	4	NULL	90	84
+LABJO1ABY	30	NULL	90	84
+LABJO1ABY	90	NULL	90	84
+LABIGG	30	1	90	59
+LABJO1ABY	7	NULL	90	84
+LABLEGDA	4	1	90	90
+LABLIL	90	2	90	73
+LABSTRED	2	0	90	44
+LABYSTFR	30	0	90	45
+LAB11097R	7	0	89	62
+LAB11097R	30	0	89	62
+LABAGALA	30	1	89	85
+LABCORT	30	3	89	67
+LABDDIM	1	1	89	36
+LABG6PD	2	0	89	79
+LABVWAG	1	NULL	89	34
+LABVWAG	2	NULL	89	34
+LABVWAG	4	NULL	89	34
+LABVWAG	30	NULL	89	34
+LABVWAG	90	NULL	89	34
+LABY25VD	1	NULL	89	25
+LABY25VD	2	NULL	89	25
+LABY25VD	4	NULL	89	25
+LABY25VD	30	NULL	89	25
+LABY25VD	90	NULL	89	25
+LABYFLUZ	1	NULL	89	31
+LABYFLUZ	2	NULL	89	31
+LABYFLUZ	4	NULL	89	31
+LABYFLUZ	30	NULL	89	31
+LABYFLUZ	90	NULL	89	31
+LABLIL	30	2	89	75
+LABPTH	90	1	89	60
+LABSCMV	90	2	89	87
+LABTSH	4	2	89	80
+LABUAMY	2	0	89	58
+LABUNA	1	1	89	74
+LABVWAG	7	NULL	89	34
+LABY25VD	7	NULL	89	25
+LABYFLUZ	7	NULL	89	31
+LAB11096R	1	0	88	73
+LAB11097R	90	0	88	62
+LABANERB	2	0	88	83
+LABANERB	4	0	88	83
+LABANERB	7	0	88	83
+LABANERB	30	0	88	83
+LABANERB	90	0	88	83
+LABCMVQT	1	1	88	79
+LABCR	2	1	88	83
+LABG6PD	4	0	88	79
+LABG6PD	7	0	88	79
+LABG6PD	30	0	88	79
+LABG6PD	90	0	88	79
+LABJO1ABY	1	0	88	83
+LABJO1ABY	2	0	88	83
+LABSTLC	90	1	88	82
+LABSTOOLC	30	1	88	77
+LABYTOPIR	2	0	88	70
+LABAFP	90	1	87	84
+LABAPC	7	NULL	87	67
+LABBKVPC	90	0	87	57
+LABCRPLFC	90	0	87	84
+LABDATBSI	30	1	87	86
+LABDVERVP	30	2	87	65
+LABHAP	2	2	87	82
+LABAPC	1	NULL	87	67
+LABAPC	2	NULL	87	67
+LABAPC	4	NULL	87	67
+LABAPC	30	NULL	87	67
+LABAPC	90	NULL	87	67
+LABHSVQL	1	NULL	87	82
+LABHSVQL	2	NULL	87	82
+LABHSVQL	4	NULL	87	82
+LABHSVQL	30	NULL	87	82
+LABHSVQL	90	NULL	87	82
+LABYCENT	1	NULL	87	79
+LABYCENT	2	NULL	87	79
+LABYCENT	4	NULL	87	79
+LABYCENT	30	NULL	87	79
+LABYCENT	90	NULL	87	79
+LABHSVQL	7	NULL	87	82
+LABJO1ABY	4	0	87	82
+LABSIROL	30	3	87	70
+LABTOBPRL	4	1	87	78
+LABUAMY	4	0	87	57
+LABUUN	30	1	87	72
+LABVPAL	4	2	87	72
+LABYCENT	7	NULL	87	79
+LABYSTFR	90	0	87	42
+LABAPC	1	0	86	66
+LABASI	30	3	86	86
+LABCDAS	1	0	86	62
+LABCDAS	2	0	86	62
+LABCDAS	4	0	86	62
+LABCDAS	7	0	86	62
+LABCDAS	7	NULL	86	62
+LABCDAS	30	0	86	62
+LABCMVPQ	2	2	86	82
+LABFENTA	7	NULL	86	77
+LABGGT	4	1	86	73
+LABGGT	30	1	86	70
+LABGH	7	NULL	86	66
+LABCDAS	1	NULL	86	62
+LABCDAS	2	NULL	86	62
+LABCDAS	4	NULL	86	62
+LABCDAS	30	NULL	86	62
+LABCDAS	90	NULL	86	62
+LABFENTA	1	NULL	86	77
+LABFENTA	2	NULL	86	77
+LABFENTA	4	NULL	86	77
+LABFENTA	30	NULL	86	77
+LABFENTA	90	NULL	86	77
+LABGH	1	NULL	86	66
+LABGH	2	NULL	86	66
+LABGH	4	NULL	86	66
+LABGH	30	NULL	86	66
+LABGH	90	NULL	86	66
+LABJO1ABY	7	0	86	81
+LABJO1ABY	30	0	86	81
+LABJO1ABY	90	0	86	81
+LABSIROL	90	3	86	71
+LABUAMY	7	0	86	56
+LABVANPRL	1	2	86	62
+LABY25VD	1	0	86	24
+LABY25VD	2	0	86	24
+LABYTOPIR	4	0	86	69
+LAB12808R	1	0	85	27
+LABADVDNA	7	NULL	85	83
+LABAPC	2	0	85	65
+LABASTRO	7	NULL	85	71
+LABBXTG	2	2	85	76
+LABCDAS	90	0	85	61
+LABADVDNA	1	NULL	85	83
+LABADVDNA	2	NULL	85	83
+LABADVDNA	4	NULL	85	83
+LABADVDNA	30	NULL	85	83
+LABADVDNA	90	NULL	85	83
+LABASTRO	1	NULL	85	71
+LABASTRO	2	NULL	85	71
+LABASTRO	4	NULL	85	71
+LABASTRO	30	NULL	85	71
+LABASTRO	90	NULL	85	71
+LABPCCR	4	1	85	77
+LABRESPG	30	2	85	55
+LABUAMY	30	0	85	56
+LABUAMY	90	0	85	56
+LABY25VD	4	0	85	23
+LABYCENT	1	0	85	77
+LABYCENT	2	0	85	77
+LABAMIPRL	1	0	84	63
+LABAPC	4	0	84	65
+LABAPC	7	0	84	65
+LABAPC	30	0	84	65
+LABAPC	90	0	84	65
+LABCBCD	7	4	84	60
+LABCBCO	4	4	84	61
+LABDBIL	30	2	84	77
+LABDBIL	90	2	84	77
+LABESRP	90	2	84	59
+LABFACT10	1	0	84	28
+LABSYCT	1	NULL	84	1
+LABSYCT	2	NULL	84	1
+LABSYCT	4	NULL	84	1
+LABSYCT	30	NULL	84	1
+LABSYCT	90	NULL	84	1
+LABHSVQL	1	0	84	79
+LABYAMISO	1	NULL	84	53
+LABYAMISO	2	NULL	84	53
+LABYAMISO	4	NULL	84	53
+LABYAMISO	30	NULL	84	53
+LABYAMISO	90	NULL	84	53
+LABPCG3	1	4	84	33
+LABPT	1	4	84	69
+LABSYCT	7	NULL	84	1
+LABWDCS	7	1	84	62
+LABYAMISO	7	NULL	84	53
+LABYCENT	4	0	84	77
+LABYCENT	7	0	84	77
+LABYCENT	30	0	84	77
+LABYCENT	90	0	84	77
+LABYTOPIR	7	0	84	68
+LAB11096R	2	0	83	68
+LABH1N1PC	7	NULL	83	82
+LABH1N1PC	1	NULL	83	82
+LABH1N1PC	2	NULL	83	82
+LABH1N1PC	4	NULL	83	82
+LABH1N1PC	30	NULL	83	82
+LABH1N1PC	90	NULL	83	82
+LABIGGSUB	1	NULL	83	25
+LABIGGSUB	2	NULL	83	25
+LABIGGSUB	4	NULL	83	25
+LABIGGSUB	30	NULL	83	25
+LABIGGSUB	90	NULL	83	25
+LABIGGSUB	1	0	83	25
+LABIGGSUB	2	0	83	25
+LABIGGSUB	4	0	83	25
+LABIGGSUB	7	0	83	25
+LABIGGSUB	7	NULL	83	25
+LABPB	4	3	83	76
+LABPCCR	2	1	83	75
+LABPCTNI	7	3	83	78
+LABPTH	30	1	83	56
+LABRIST	1	0	83	73
+LABSTOBGD	7	1	83	25
+LABSTSS	7	1	83	83
+LABTBIL	90	2	83	74
+LABTCDIF	2	1	83	83
+LABTEGCP	30	2	83	36
+LABTEGCP	90	2	83	37
+LABTHEOL	2	1	83	68
+LABUEP	1	1	83	58
+LABY25VD	7	0	83	21
+LABANER	90	5	82	79
+LABDBIL	4	2	82	79
+LABDVERV	90	2	82	79
+LABGGT	7	1	82	69
+LABHSVQL	2	0	82	77
+LABIGGSUB	30	0	82	25
+LABPCCR	1	1	82	74
+LABRESPCR	90	1	82	72
+LABSTRED	4	0	82	40
+LABSYCT	1	0	82	1
+LABSYCT	2	0	82	1
+LABSYCT	4	0	82	1
+LABSYCT	7	0	82	1
+LABSYCT	30	0	82	1
+LABSYCT	90	0	82	1
+LABY25VD	30	0	82	21
+LABYTOPIR	30	0	82	66
+LABBXTG	1	2	81	76
+LABCMVQT	30	3	81	71
+LABCMVQT	90	3	81	69
+LABFACT5	7	NULL	81	52
+LABFACT7	1	0	81	25
+LABFPARVD	7	NULL	81	81
+LABGGT	2	2	81	78
+LABHAP	7	3	81	72
+LABHCVA	90	1	81	77
+LABFACT5	1	NULL	81	52
+LABFACT5	2	NULL	81	52
+LABFACT5	4	NULL	81	52
+LABFACT5	30	NULL	81	52
+LABFACT5	90	NULL	81	52
+LABFPARVD	1	NULL	81	81
+LABFPARVD	2	NULL	81	81
+LABFPARVD	4	NULL	81	81
+LABFPARVD	30	NULL	81	81
+LABFPARVD	90	NULL	81	81
+LABNH3	90	3	81	58
+LABVPAL	90	3	81	68
+LABY25VD	90	0	81	21
+LAB11096R	4	0	80	66
+LABAFBD	4	3	80	80
+LABCDTPCR	90	4	80	66
+LABCSFC	7	3	80	77
+LABFACT9	1	0	80	48
+LABGNTPRL	7	2	80	71
+LABGRAM	1	2	80	76
+LABIGGSUB	90	0	80	25
+LABIOPTH	1	0	80	22
+LABIOPTH	2	0	80	22
+LABYTOPIR	90	0	80	64
+LAB11036R	2	0	79	47
+LABAFBC	7	4	79	78
+LABASTRO	1	0	79	67
+LABASTRO	2	0	79	67
+LABASTRO	4	0	79	67
+LABASTRO	7	0	79	67
+LABFCUL	90	5	79	73
+LABFENTA	1	0	79	70
+LABFENTA	2	0	79	70
+LABFENTA	4	0	79	70
+LABHCTX	1	2	79	48
+LABHITAB	90	2	79	66
+LABYANRNP	1	NULL	79	70
+LABYANRNP	2	NULL	79	70
+LABYANRNP	4	NULL	79	70
+LABYANRNP	30	NULL	79	70
+LABYANRNP	90	NULL	79	70
+LABIOPTH	4	0	79	22
+LABWDC	4	1	79	53
+LABWDCS	4	1	79	59
+LABYAMISO	1	0	79	49
+LABYANRNP	7	NULL	79	70
+LABA1C	7	2	78	73
+LABADVDNA	1	0	78	76
+LABBLC2	4	3	78	78
+LABCR	1	1	78	74
+LABCRP	90	3	78	60
+LABHAP	30	4	78	67
+LABHSVQL	4	0	78	73
+LABIOPTH	7	0	78	22
+LABIOPTH	30	0	78	22
+LABPTEG	30	2	78	25
+LABPTEG	90	2	78	25
+LABREPT	1	1	78	61
+LABSTLC	30	1	78	73
+LABSTSS	4	1	78	78
+LABTEGCP	7	2	78	33
+LABVPAL	30	3	78	64
+LAB11096R	7	0	77	63
+LAB11982R	30	0	77	26
+LABAFP	30	1	77	76
+LABASTRO	30	0	77	67
+LABASTRO	90	0	77	67
+LABBLCSTK	90	5	77	70
+LABBRASP	7	NULL	77	28
+LABCLACT	7	1	77	53
+LABDBIL	7	2	77	73
+LABFACT7	2	0	77	24
+LABFENTA	7	0	77	69
+LABFENTA	30	0	77	69
+LABFENTA	90	0	77	69
+LABFIB	1	5	77	72
+LABFLDC	90	4	77	73
+LABFPARVD	1	0	77	77
+LABGNTPRL	90	2	77	69
+LABHITAB	30	2	77	64
+LABHSVQL	7	0	77	72
+LABBRASP	1	NULL	77	28
+LABBRASP	2	NULL	77	28
+LABBRASP	4	NULL	77	28
+LABBRASP	30	NULL	77	28
+LABBRASP	90	NULL	77	28
+LABPCEC4	1	NULL	77	1
+LABPCEC4	2	NULL	77	1
+LABPCEC4	4	NULL	77	1
+LABPCEC4	30	NULL	77	1
+LABPCEC4	90	NULL	77	1
+LABYUHYME	1	NULL	77	59
+LABYUHYME	2	NULL	77	59
+LABYUHYME	4	NULL	77	59
+LABYUHYME	30	NULL	77	59
+LABYUHYME	90	NULL	77	59
+LABIOPTH	90	0	77	22
+LABPCEC4	7	NULL	77	1
+LABPCTNI	4	3	77	73
+LABSTRED	7	0	77	37
+LABUA	7	2	77	39
+LABVWAG	1	0	77	30
+LABYANRNP	1	0	77	68
+LABYANRNP	2	0	77	68
+LABYANRNP	4	0	77	68
+LABYUHYME	7	NULL	77	59
+LABADVDNA	2	0	76	74
+LABAGALA	7	1	76	74
+LABBLCSTK	30	4	76	71
+LABBRASP	1	0	76	27
+LABCA	2	2	76	63
+LABCA	90	2	76	56
+LABFACT7	4	0	76	23
+LABH1N1PC	1	0	76	75
+LABH1N1PC	2	0	76	75
+LABH1N1PC	4	0	76	75
+LABTTM	1	NULL	76	4
+LABTTM	2	NULL	76	4
+LABTTM	4	NULL	76	4
+LABTTM	30	NULL	76	4
+LABTTM	90	NULL	76	4
+LABYU5HIA	1	NULL	76	63
+LABYU5HIA	2	NULL	76	63
+LABYU5HIA	4	NULL	76	63
+LABYU5HIA	30	NULL	76	63
+LABYU5HIA	90	NULL	76	63
+LABLIL	1	1	76	67
+LABPTH	7	1	76	50
+LABTTM	7	NULL	76	4
+LABUAS	7	1	76	44
+LABYANRNP	7	0	76	67
+LABYANRNP	30	0	76	67
+LABYANRNP	90	0	76	67
+LABYU5HIA	1	0	76	63
+LABYU5HIA	2	0	76	63
+LABYU5HIA	7	NULL	76	63
+LABYUHYME	1	0	76	58
+LABBRASP	2	0	75	26
+LABCA	4	2	75	56
+LABCLACT	30	1	75	48
+LABCLACT	90	1	75	48
+LABDVERTV	7	NULL	75	72
+LABFACT7	7	0	75	23
+LABFACT8	1	1	75	57
+LABFPARVD	2	0	75	75
+LABGNTPRL	30	2	75	67
+LABHSVQL	30	0	75	70
+LABHSVQL	90	0	75	70
+LABDVERTV	1	NULL	75	72
+LABDVERTV	2	NULL	75	72
+LABDVERTV	4	NULL	75	72
+LABDVERTV	30	NULL	75	72
+LABDVERTV	90	NULL	75	72
+LABYPROIN	1	NULL	75	65
+LABYPROIN	2	NULL	75	65
+LABYPROIN	4	NULL	75	65
+LABYPROIN	30	NULL	75	65
+LABYPROIN	90	NULL	75	65
+LABNH3	30	3	75	52
+LABPT	2	5	75	64
+LABRESPCR	30	1	75	68
+LABRIST	2	0	75	66
+LABTRIG	7	4	75	70
+LABYPROIN	7	NULL	75	65
+LABYU5HIA	4	0	75	62
+LABADVDNA	4	0	74	72
+LABADVDNA	7	0	74	72
+LABAFBCF	30	1	74	73
+LABANAS	1	1	74	71
+LABCA	30	2	74	56
+LABCDTPCR	30	3	74	57
+LABCR	90	1	74	64
+LABCRP	30	3	74	56
+LABCRPLB	90	1	74	60
+LABDBIL	2	2	74	67
+LABDVERV3	7	NULL	74	55
+LABFACT7	30	0	74	23
+LABFPARVD	4	0	74	74
+LABFPARVD	7	0	74	74
+LABFPARVD	30	0	74	74
+LABFPARVD	90	0	74	74
+LABGGT	4	3	74	72
+LABH1N1PC	7	0	74	73
+LABIBIL	1	1	74	71
+LABDVERV3	1	NULL	74	55
+LABDVERV3	2	NULL	74	55
+LABDVERV3	4	NULL	74	55
+LABDVERV3	30	NULL	74	55
+LABDVERV3	90	NULL	74	55
+LABLEGDA	2	1	74	74
+LABMYCPA	1	1	74	39
+LABSTOBGD	4	1	74	20
+LABTBILFR	7	1	74	56
+LABTRFS	90	1	74	25
+LABYFLUZ	1	0	74	27
+LABYFLUZ	2	0	74	27
+LABYFLUZ	4	0	74	27
+LABYU5HIA	7	0	74	61
+LABYU5HIA	30	0	74	61
+LABYU5HIA	90	0	74	61
+LABYUHYME	2	0	74	56
+LAB11041R	2	1	73	58
+LAB11096R	30	0	73	62
+LAB12808R	2	0	73	23
+LABBRASP	4	0	73	26
+LABCA	7	2	73	55
+LABCRPLC	90	1	73	67
+LABDVERV3	1	0	73	55
+LABFACT10	2	0	73	28
+LABFACT10	4	0	73	28
+LABFACT10	7	0	73	28
+LABFACT10	30	0	73	28
+LABFACT10	90	0	73	28
+LABFACT7	90	0	73	22
+LABYJCPCR	1	NULL	73	71
+LABYJCPCR	2	NULL	73	71
+LABYJCPCR	4	NULL	73	71
+LABYJCPCR	30	NULL	73	71
+LABYJCPCR	90	NULL	73	71
+LABMETB	7	5	73	45
+LABPROCT	90	2	73	64
+LABPTEG	7	2	73	24
+LABTBIL	30	2	73	64
+LABYJCPCR	7	NULL	73	71
+LAB11041R	30	3	72	60
+LAB11086R	7	NULL	72	25
+LABADAMTS	7	NULL	72	27
+LABBRASP	7	0	72	26
+LABBRASP	30	0	72	26
+LABCK	1	4	72	69
+LABCR	30	1	72	62
+LABDVERV3	2	0	72	55
+LABGH	1	0	72	56
+LABHAP	90	4	72	60
+LAB11086R	1	NULL	72	25
+LAB11086R	2	NULL	72	25
+LAB11086R	4	NULL	72	25
+LAB11086R	30	NULL	72	25
+LAB11086R	90	NULL	72	25
+LABADAMTS	1	NULL	72	27
+LABADAMTS	2	NULL	72	27
+LABADAMTS	4	NULL	72	27
+LABADAMTS	30	NULL	72	27
+LABADAMTS	90	NULL	72	27
+LABLWBCS2	1	NULL	72	59
+LABLWBCS2	2	NULL	72	59
+LABLWBCS2	4	NULL	72	59
+LABLWBCS2	30	NULL	72	59
+LABLWBCS2	90	NULL	72	59
+LABUMYOSC	1	NULL	72	23
+LABUMYOSC	2	NULL	72	23
+LABUMYOSC	4	NULL	72	23
+LABUMYOSC	30	NULL	72	23
+LABUMYOSC	90	NULL	72	23
+LABYFRUCM	1	NULL	72	47
+LABYFRUCM	2	NULL	72	47
+LABYFRUCM	4	NULL	72	47
+LABYFRUCM	30	NULL	72	47
+LABYFRUCM	90	NULL	72	47
+LABLWBCS2	7	NULL	72	59
+LABMETHB	2	2	72	71
+LABPB	30	1	72	63
+LABPCTNI	2	3	72	68
+LABPREG	90	1	72	67
+LABROMRS	7	2	72	71
+LABTNI	1	5	72	71
+LABUMYOSC	7	NULL	72	23
+LABYAMISO	2	0	72	46
+LABYFRUCM	7	NULL	72	47
+LABYUHYME	4	0	72	54
+LAB11096R	90	0	71	60
+LABADVDNA	30	0	71	69
+LABALB	90	2	71	50
+LABBRASP	90	0	71	26
+LABCORT	7	3	71	56
+LABCSFC	90	4	71	70
+LABCTC	7	1	71	66
+LABDVERTV	1	0	71	68
+LABDVERV3	4	0	71	55
+LABFACT5	1	0	71	46
+LABFACT5	2	0	71	46
+LABFACT5	4	0	71	46
+LABH1N1PC	30	0	71	70
+LABH1N1PC	90	0	71	70
+LABHCVA	30	1	71	67
+LABLPDPC	1	NULL	71	8
+LABLPDPC	2	NULL	71	8
+LABLPDPC	4	NULL	71	8
+LABLPDPC	30	NULL	71	8
+LABLPDPC	90	NULL	71	8
+LABVWSCN	1	NULL	71	18
+LABVWSCN	2	NULL	71	18
+LABVWSCN	4	NULL	71	18
+LABVWSCN	30	NULL	71	18
+LABVWSCN	90	NULL	71	18
+LABYA1APH	1	NULL	71	39
+LABYA1APH	2	NULL	71	39
+LABYA1APH	4	NULL	71	39
+LABYA1APH	30	NULL	71	39
+LABYA1APH	90	NULL	71	39
+LABLIPS	2	3	71	66
+LABLPDPC	7	NULL	71	8
+LABPB	90	1	71	62
+LABPROCT	30	2	71	63
+LABSIROL	4	4	71	54
+LABTP	90	1	71	53
+LABVWSCN	7	NULL	71	18
+LABYA1APH	7	NULL	71	39
+LABYAMISO	4	0	71	45
+LABYAMISO	7	0	71	45
+LABYAMISO	30	0	71	45
+LABYAMISO	90	0	71	45
+LABYFRUCM	1	0	71	47
+LAB11028R	7	NULL	70	63
+LAB12808R	4	0	70	22
+LABADVDNA	90	0	70	68
+LABBLC2	1	2	70	66
+LABCD34	7	NULL	70	29
+LABDVERTV	2	0	70	67
+LABFACT5	7	0	70	45
+LABFACT5	30	0	70	45
+LABFACT5	90	0	70	45
+LABFLUAPX	90	1	70	69
+LAB11028R	1	NULL	70	63
+LAB11028R	2	NULL	70	63
+LAB11028R	4	NULL	70	63
+LAB11028R	30	NULL	70	63
+LAB11028R	90	NULL	70	63
+LABCD34	1	NULL	70	29
+LABCD34	2	NULL	70	29
+LABCD34	4	NULL	70	29
+LABCD34	30	NULL	70	29
+LABCD34	90	NULL	70	29
+LABUCL	1	NULL	70	39
+LABUCL	2	NULL	70	39
+LABUCL	4	NULL	70	39
+LABUCL	30	NULL	70	39
+LABUCL	90	NULL	70	39
+LABVISC	1	NULL	70	21
+LABVISC	2	NULL	70	21
+LABVISC	4	NULL	70	21
+LABVISC	30	NULL	70	21
+LABVISC	90	NULL	70	21
+LABLPDPC	1	0	70	8
+LABLPDPC	2	0	70	8
+LABLPDPC	4	0	70	8
+LABLPDPC	7	0	70	8
+LABLPDPC	30	0	70	8
+LABLPDPC	90	0	70	8
+LABNTBNP	90	2	70	35
+LABOAPE	90	2	70	69
+LABPTEG	2	2	70	24
+LABPTEG	4	2	70	23
+LABPTH	4	1	70	49
+LABRIST	4	0	70	63
+LABRIST	7	0	70	63
+LABSTLCX	7	1	70	65
+LABTBILFR	4	1	70	54
+LABTBILFR	90	1	70	43
+LABUCL	7	NULL	70	39
+LABUPREG	7	1	70	62
+LABVISC	7	NULL	70	21
+LABYA1APH	1	0	70	39
+LABYA1APH	2	0	70	39
+LABYA1APH	4	0	70	39
+LABYA1APH	7	0	70	39
+LABYA1APH	30	0	70	39
+LABYA1APH	90	0	70	39
+LABYFLUZ	7	0	70	24
+LABYFRUCM	2	0	70	46
+LABYFRUCM	4	0	70	46
+LABYFRUCM	7	0	70	46
+LABYFRUCM	30	0	70	46
+LABYJCPCR	1	0	70	68
+LABYUHYME	7	0	70	52
+LAB11028R	1	0	69	62
+LAB11028R	2	0	69	62
+LAB11036R	4	0	69	42
+LAB11041R	90	3	69	57
+LABDFA	7	NULL	69	41
+LABDVERTV	4	0	69	67
+LABDVERTV	7	0	69	67
+LABDVERTV	30	0	69	67
+LABDVERV3	7	0	69	54
+LABESRP	30	2	69	51
+LABDFA	1	NULL	69	41
+LABDFA	2	NULL	69	41
+LABDFA	4	NULL	69	41
+LABDFA	30	NULL	69	41
+LABDFA	90	NULL	69	41
+LABLEGDA	1	1	69	69
+LABMALAR	30	1	69	63
+LABMALAR	90	1	69	63
+LABMETHB	4	3	69	68
+LABPB	2	2	69	62
+LABRIST	30	0	69	62
+LABRIST	90	0	69	62
+LABUA	90	3	69	29
+LABVANPRL	2	3	69	53
+LABVWAG	2	0	69	26
+LABVWSCN	1	0	69	18
+LABVWSCN	2	0	69	18
+LABYUHYME	30	0	69	52
+LAB11028R	4	0	68	61
+LAB11028R	7	0	68	61
+LAB11086R	1	0	68	25
+LAB11982R	90	0	68	21
+LAB12762R	1	0	68	41
+LAB12808R	7	0	68	22
+LAB12808R	30	0	68	22
+LAB12808R	90	0	68	22
+LABAFBD	2	3	68	68
+LABALB	30	2	68	49
+LABANER	7	4	68	67
+LABDVERV3	30	0	68	53
+LABDVERV3	90	0	68	53
+LABFACT9	2	0	68	45
+LABFACT9	4	0	68	45
+LABFACT9	7	0	68	45
+LABGNTPRL	4	2	68	61
+LABHCTX	4	3	68	53
+LABHEPC	7	NULL	68	55
+LABHGBP	7	2	68	65
+LABHEPC	1	NULL	68	55
+LABHEPC	2	NULL	68	55
+LABHEPC	4	NULL	68	55
+LABHEPC	30	NULL	68	55
+LABHEPC	90	NULL	68	55
+LABLWBCS2	1	0	68	56
+LABMALAR	4	1	68	62
+LABMALAR	7	1	68	62
+LABPB	7	1	68	61
+LABPCEC4	1	0	68	1
+LABPCEC4	2	0	68	1
+LABPCEC4	4	0	68	1
+LABPCEC4	7	0	68	1
+LABPCEC4	30	0	68	1
+LABPCEC4	90	0	68	1
+LABPLTS	90	2	68	54
+LABUCL	1	0	68	37
+LABVWAG	4	0	68	26
+LABWDCG	30	1	68	47
+LABWDCG	90	1	68	46
+LABYJCPCR	2	0	68	66
+LABYJCPCR	4	0	68	66
+LABYUHYME	90	0	68	51
+LAB11028R	30	0	67	60
+LAB11036R	7	0	67	41
+LAB11077R	7	NULL	67	63
+LAB11086R	2	0	67	25
+LABADAMTS	1	0	67	24
+LABADAMTS	2	0	67	24
+LABAFBD	1	3	67	67
+LABCMVP	90	2	67	62
+LABCRPLC	30	1	67	61
+LABDVERTV	90	0	67	65
+LABEBPCRQ	4	1	67	60
+LABFACT2	7	NULL	67	26
+LABFACT9	30	0	67	44
+LABGENC	7	NULL	67	30
+LABHCTX	30	3	67	52
+LABHCTX	90	3	67	52
+LABHEPC	1	0	67	54
+LABHEPC	2	0	67	54
+LAB11077R	1	NULL	67	63
+LAB11077R	2	NULL	67	63
+LAB11077R	4	NULL	67	63
+LAB11077R	30	NULL	67	63
+LAB11077R	90	NULL	67	63
+LABFACT2	1	NULL	67	26
+LABFACT2	2	NULL	67	26
+LABFACT2	4	NULL	67	26
+LABFACT2	30	NULL	67	26
+LABFACT2	90	NULL	67	26
+LABGENC	1	NULL	67	30
+LABGENC	2	NULL	67	30
+LABGENC	4	NULL	67	30
+LABGENC	30	NULL	67	30
+LABGENC	90	NULL	67	30
+LABMALAR	2	1	67	62
+LABPB	4	1	67	60
+LABPTEG	1	2	67	22
+LABSTLCX	4	1	67	62
+LABUAS	4	1	67	40
+LABUUN	7	1	67	57
+LABVWAG	7	0	67	26
+LABVWSCN	4	0	67	18
+LABYFRUCM	90	0	67	43
+LABYJCPCR	7	0	67	65
+LAB11036R	30	0	66	41
+LAB11036R	90	0	66	41
+LABADAMTS	4	0	66	23
+LABADAMTS	7	0	66	23
+LABCOXPNL	1	0	66	63
+LABCOXPNL	2	0	66	63
+LABCOXPNL	7	NULL	66	63
+LABCSFGL	7	3	66	63
+LABFACT11	7	NULL	66	42
+LABFACT9	90	0	66	43
+LABGENC	1	0	66	30
+LABHAP	4	3	66	60
+LABHCTX	7	3	66	51
+LABHEPC	4	0	66	53
+LABHEPC	7	0	66	53
+LABHEPC	30	0	66	53
+LABHEPC	90	0	66	53
+LABIBIL	90	2	66	61
+LABCOXPNL	1	NULL	66	63
+LABCOXPNL	2	NULL	66	63
+LABCOXPNL	4	NULL	66	63
+LABCOXPNL	30	NULL	66	63
+LABCOXPNL	90	NULL	66	63
+LABFACT11	1	NULL	66	42
+LABFACT11	2	NULL	66	42
+LABFACT11	4	NULL	66	42
+LABFACT11	30	NULL	66	42
+LABFACT11	90	NULL	66	42
+LABTHG	1	NULL	66	36
+LABTHG	2	NULL	66	36
+LABTHG	4	NULL	66	36
+LABTHG	30	NULL	66	36
+LABTHG	90	NULL	66	36
+LABVZVQL	1	NULL	66	56
+LABVZVQL	2	NULL	66	56
+LABVZVQL	4	NULL	66	56
+LABVZVQL	30	NULL	66	56
+LABVZVQL	90	NULL	66	56
+LABREPT	4	2	66	53
+LABSTRED	30	0	66	31
+LABTHG	7	NULL	66	36
+LABTSH	2	2	66	59
+LABUCL	2	0	66	35
+LABUCL	4	0	66	35
+LABUCL	7	0	66	35
+LABUCL	30	0	66	35
+LABUCL	90	0	66	35
+LABVISC	1	0	66	20
+LABVWAG	30	0	66	26
+LABVWAG	90	0	66	26
+LABVWSCN	7	0	66	17
+LABVWSCN	30	0	66	17
+LABVWSCN	90	0	66	17
+LABVZVQL	7	NULL	66	56
+LABWDCS	2	1	66	56
+LABYJCPCR	30	0	66	64
+LABYJCPCR	90	0	66	64
+LAB11077R	1	0	65	61
+LABA1C	4	2	65	61
+LABAFBD	90	4	65	65
+LABALB	7	2	65	47
+LABCOXPNL	4	0	65	62
+LABEBPCRQ	90	2	65	58
+LABGENC	2	0	65	30
+LABGENC	4	0	65	30
+LABGENC	7	0	65	30
+LABGENC	30	0	65	30
+LABGENC	90	0	65	30
+LABGH	2	0	65	49
+LABHIPA	2	1	65	43
+LABIBIL	4	2	65	62
+LABIBIL	30	2	65	60
+LABTOTALC	1	NULL	65	12
+LABTOTALC	2	NULL	65	12
+LABTOTALC	4	NULL	65	12
+LABTOTALC	30	NULL	65	12
+LABTOTALC	90	NULL	65	12
+LABLWBCS2	2	0	65	54
+LABORSHOR	30	2	65	61
+LABORSHOR	90	2	65	61
+LABPALB	90	3	65	46
+LABPCTNI	1	3	65	61
+LABRESP	90	5	65	52
+LABSTLC	7	1	65	59
+LABTHG	1	0	65	35
+LABTHG	2	0	65	35
+LABTHG	4	0	65	35
+LABTHG	7	0	65	35
+LABTHG	30	0	65	35
+LABTOTALC	7	NULL	65	12
+LABWDCG	7	1	65	47
+LAB11028R	90	0	64	58
+LAB11041R	30	4	64	54
+LABALB	4	2	64	46
+LABAMIPRL	2	0	64	54
+LABCSFC	30	4	64	62
+LABCTC	4	1	64	61
+LABEP1	2	1	64	35
+LABFLDC	2	2	64	60
+LABGCCR2	1	0	64	57
+LABGCCR2	7	NULL	64	57
+LABHCVA	7	1	64	60
+LABGCCR2	1	NULL	64	57
+LABGCCR2	2	NULL	64	57
+LABGCCR2	4	NULL	64	57
+LABGCCR2	30	NULL	64	57
+LABGCCR2	90	NULL	64	57
+LABUHEMO	1	NULL	64	37
+LABUHEMO	2	NULL	64	37
+LABUHEMO	4	NULL	64	37
+LABUHEMO	30	NULL	64	37
+LABUHEMO	90	NULL	64	37
+LABYSES	1	NULL	64	29
+LABYSES	2	NULL	64	29
+LABYSES	4	NULL	64	29
+LABYSES	30	NULL	64	29
+LABYSES	90	NULL	64	29
+LABNH3	7	3	64	49
+LABORSHOR	4	2	64	61
+LABORSHOR	7	2	64	61
+LABPLTS	30	2	64	50
+LABTBILFR	30	1	64	44
+LABTOBPRL	90	2	64	61
+LABTOTALC	1	0	64	12
+LABTOTALC	2	0	64	12
+LABTOTALC	4	0	64	12
+LABUHEMO	7	NULL	64	37
+LABUMYOSC	1	0	64	20
+LABYSES	7	NULL	64	29
+LAB11101R	1	0	63	34
+LAB11101R	2	0	63	34
+LAB11101R	7	NULL	63	34
+LABAFBC	4	4	63	62
+LABAFBC	90	5	63	62
+LABAGALA	4	1	63	62
+LABBLC	2	3	63	62
+LABCORT	4	3	63	48
+LABDATBSI	7	1	63	63
+LABFCUL	7	4	63	58
+LABFLDB	2	1	63	61
+LABGCCR2	2	0	63	56
+LABGCCR2	4	0	63	56
+LABHIAB	30	1	63	54
+LABINSL	1	0	63	40
+LAB11101R	1	NULL	63	34
+LAB11101R	2	NULL	63	34
+LAB11101R	4	NULL	63	34
+LAB11101R	30	NULL	63	34
+LAB11101R	90	NULL	63	34
+LABYMPA	1	NULL	63	16
+LABYMPA	2	NULL	63	16
+LABYMPA	4	NULL	63	16
+LABYMPA	30	NULL	63	16
+LABYMPA	90	NULL	63	16
+LABLWBCS2	4	0	63	52
+LABMALAR	1	1	63	58
+LABPLTS	7	2	63	50
+LABSCMV	30	2	63	61
+LABSTOOLC	7	1	63	54
+LABTHG	90	0	63	35
+LABTOTALC	7	0	63	12
+LABTOTALC	30	0	63	12
+LABTOTALC	90	0	63	12
+LABUMYOSC	2	0	63	19
+LABYMPA	7	NULL	63	16
+LAB11101R	4	0	62	33
+LABAGALA	2	1	62	61
+LABBLC2	7	4	62	57
+LABCMVPQ	7	3	62	62
+LABCOXPNL	7	0	62	59
+LABCOXPNL	30	0	62	59
+LABCOXPNL	90	0	62	59
+LABCSFGL	90	4	62	61
+LABDFA	1	0	62	36
+LABDFA	2	0	62	36
+LABDVERV	30	2	62	61
+LABEP1	30	1	62	34
+LABEP1	90	1	62	34
+LABESRP	2	2	62	58
+LABF8INH	7	NULL	62	18
+LABFACT8	2	1	62	44
+LABFK506L	2	4	62	59
+LABFLUAPX	30	1	62	61
+LABFT4	7	2	62	59
+LABGCCR2	7	0	62	55
+LABGCCR2	30	0	62	55
+LABGCCR2	90	0	62	55
+LABHGBP	2	1	62	57
+LABHGBSQ	7	NULL	62	50
+LABHHV6QL	1	0	62	42
+LABHHV6QL	7	NULL	62	42
+LABHIAB	7	1	62	52
+LABHIAB	90	1	62	54
+LABIBIL	7	2	62	59
+LABLIL	7	2	62	49
+LABF8INH	1	NULL	62	18
+LABF8INH	2	NULL	62	18
+LABF8INH	4	NULL	62	18
+LABF8INH	30	NULL	62	18
+LABF8INH	90	NULL	62	18
+LABHGBSQ	1	NULL	62	50
+LABHGBSQ	2	NULL	62	50
+LABHGBSQ	4	NULL	62	50
+LABHGBSQ	30	NULL	62	50
+LABHGBSQ	90	NULL	62	50
+LABHHV6QL	1	NULL	62	42
+LABHHV6QL	2	NULL	62	42
+LABHHV6QL	4	NULL	62	42
+LABHHV6QL	30	NULL	62	42
+LABHHV6QL	90	NULL	62	42
+LABMONOSC	1	NULL	62	50
+LABMONOSC	2	NULL	62	50
+LABMONOSC	4	NULL	62	50
+LABMONOSC	30	NULL	62	50
+LABMONOSC	90	NULL	62	50
+LABPROINS	1	NULL	62	42
+LABPROINS	2	NULL	62	42
+LABPROINS	4	NULL	62	42
+LABPROINS	30	NULL	62	42
+LABPROINS	90	NULL	62	42
+LABYHMS	1	NULL	62	57
+LABYHMS	2	NULL	62	57
+LABYHMS	4	NULL	62	57
+LABYHMS	30	NULL	62	57
+LABYHMS	90	NULL	62	57
+LABYUCOP	1	NULL	62	31
+LABYUCOP	2	NULL	62	31
+LABYUCOP	4	NULL	62	31
+LABYUCOP	30	NULL	62	31
+LABYUCOP	90	NULL	62	31
+LABLWBCS2	7	0	62	52
+LABLWBCS2	30	0	62	52
+LABLWBCS2	90	0	62	52
+LABMONOSC	7	NULL	62	50
+LABORSHOR	2	2	62	60
+LABPHEN	2	3	62	47
+LABPLTS	4	2	62	55
+LABPROINS	7	NULL	62	42
+LABQFTB	90	1	62	45
+LABRESP	4	3	62	55
+LABSIROL	7	4	62	42
+LABSTSS	2	1	62	62
+LABTRFS	30	1	62	23
+LABUMYOSC	4	0	62	19
+LABUMYOSC	7	0	62	19
+LABVISC	2	0	62	19
+LABVPAL	7	3	62	50
+LABVZVQL	1	0	62	55
+LABWDCS	1	1	62	55
+LABYHMS	7	NULL	62	57
+LABYSES	1	0	62	29
+LABYSES	2	0	62	29
+LABYUCOP	7	NULL	62	31
+LAB11077R	2	0	61	58
+LAB11101R	7	0	61	32
+LABADGM	7	NULL	61	53
+LABAFP	7	1	61	60
+LABAGALA	1	1	61	60
+LABALB	2	2	61	41
+LABBMTP	7	NULL	61	1
+LABCORTS	30	2	61	45
+LABCORTS	90	2	61	45
+LABCSFTP	90	2	61	36
+LABHCVA	4	1	61	58
+LABIGG	7	1	61	48
+LABADGM	1	NULL	61	53
+LABADGM	2	NULL	61	53
+LABADGM	4	NULL	61	53
+LABADGM	30	NULL	61	53
+LABADGM	90	NULL	61	53
+LABBMTP	1	NULL	61	1
+LABBMTP	2	NULL	61	1
+LABBMTP	4	NULL	61	1
+LABBMTP	30	NULL	61	1
+LABBMTP	90	NULL	61	1
+LABVCULF	1	NULL	61	59
+LABVCULF	2	NULL	61	59
+LABVCULF	4	NULL	61	59
+LABVCULF	30	NULL	61	59
+LABVCULF	90	NULL	61	59
+LABMONOSC	1	0	61	49
+LABMONOSC	2	0	61	49
+LABMONOSC	4	0	61	49
+LABMONOSC	7	0	61	49
+LABMONOSC	30	0	61	49
+LABMONOSC	90	0	61	49
+LABOAPE	30	2	61	60
+LABORSHOR	1	2	61	59
+LABPALB	30	3	61	45
+LABSIROL	90	4	61	38
+LABSTOBGD	2	1	61	17
+LABTP	30	1	61	45
+LABUMYOSC	30	0	61	18
+LABUMYOSC	90	0	61	18
+LABVCULF	7	NULL	61	59
+LABVZVQL	2	0	61	54
+LABWDC	2	1	61	49
+LABYPREAL	4	2	61	50
+LABYUCOP	1	0	61	30
+LAB11086R	4	0	60	23
+LAB11101R	30	0	60	31
+LABANER	30	5	60	57
+LABCMVQT	7	2	60	57
+LABCORTS	4	2	60	44
+LABCORTS	7	2	60	44
+LABCR	7	1	60	52
+LABDFA	4	0	60	36
+LABDFA	7	0	60	36
+LABDFA	30	0	60	36
+LABDFA	90	0	60	36
+LABEP1	7	1	60	33
+LABFACT11	1	0	60	37
+LABFLUAPC	7	NULL	60	44
+LABHGBP	90	1	60	55
+LABHHV6QL	2	0	60	40
+LABINSL	2	0	60	38
+LABFLUAPC	1	NULL	60	44
+LABFLUAPC	2	NULL	60	44
+LABFLUAPC	4	NULL	60	44
+LABFLUAPC	30	NULL	60	44
+LABFLUAPC	90	NULL	60	44
+LABYVB1W	1	NULL	60	35
+LABYVB1W	2	NULL	60	35
+LABYVB1W	4	NULL	60	35
+LABYVB1W	30	NULL	60	35
+LABYVB1W	90	NULL	60	35
+LABYVITB6	1	NULL	60	35
+LABYVITB6	2	NULL	60	35
+LABYVITB6	4	NULL	60	35
+LABYVITB6	30	NULL	60	35
+LABYVITB6	90	NULL	60	35
+LABNH3	4	4	60	54
+LABNTBNP	30	2	60	30
+LABTCDIF	90	2	60	60
+LABUPREG	4	1	60	52
+LABVCULF	1	0	60	58
+LABVCULF	2	0	60	58
+LABVISC	4	0	60	19
+LABYHMS	1	0	60	56
+LABYSES	4	0	60	28
+LABYSES	7	0	60	28
+LABYUCOP	2	0	60	29
+LABYUCOP	4	0	60	29
+LABYVB1W	1	0	60	35
+LABYVB1W	7	NULL	60	35
+LABYVITB6	7	NULL	60	35
+LAB11049R	30	1	59	24
+LAB11049R	90	1	59	24
+LAB11077R	4	0	59	56
+LAB11077R	7	0	59	56
+LAB11077R	30	0	59	56
+LAB11077R	90	0	59	56
+LAB11101R	90	0	59	31
+LAB12762R	2	0	59	35
+LABADAMTS	30	0	59	20
+LABADAMTS	90	0	59	20
+LABBMT2	4	2	59	24
+LABBMT2	7	2	59	18
+LABBMT2	90	2	59	19
+LABBMTP	1	0	59	1
+LABBMTP	2	0	59	1
+LABCK	2	5	59	56
+LABCRPLB	30	1	59	46
+LABEP1	1	1	59	29
+LABEP1	4	1	59	32
+LABFACT8	4	1	59	38
+LABFHTLVQ	7	NULL	59	58
+LABGH	4	0	59	45
+LABGH	7	0	59	45
+LABGH	30	0	59	45
+LABGH	90	0	59	45
+LABHGBP	30	1	59	54
+LABMALSC	7	NULL	59	42
+LABFHTLVQ	1	NULL	59	58
+LABFHTLVQ	2	NULL	59	58
+LABFHTLVQ	4	NULL	59	58
+LABFHTLVQ	30	NULL	59	58
+LABFHTLVQ	90	NULL	59	58
+LABMALSC	1	NULL	59	42
+LABMALSC	2	NULL	59	42
+LABMALSC	4	NULL	59	42
+LABMALSC	30	NULL	59	42
+LABMALSC	90	NULL	59	42
+LABSYDIFF	1	NULL	59	26
+LABSYDIFF	2	NULL	59	26
+LABSYDIFF	4	NULL	59	26
+LABSYDIFF	30	NULL	59	26
+LABSYDIFF	90	NULL	59	26
+LABTOBPOL	1	NULL	59	13
+LABTOBPOL	2	NULL	59	13
+LABTOBPOL	4	NULL	59	13
+LABTOBPOL	30	NULL	59	13
+LABTOBPOL	90	NULL	59	13
+LABYZONIS	1	NULL	59	32
+LABYZONIS	2	NULL	59	32
+LABYZONIS	4	NULL	59	32
+LABYZONIS	30	NULL	59	32
+LABYZONIS	90	NULL	59	32
+LABPTH	2	1	59	43
+LABSIROL	30	4	59	38
+LABSTLC	4	1	59	53
+LABSTRED	90	0	59	27
+LABSYDIFF	1	0	59	26
+LABSYDIFF	2	0	59	26
+LABSYDIFF	4	0	59	26
+LABSYDIFF	7	0	59	26
+LABSYDIFF	7	NULL	59	26
+LABSYDIFF	30	0	59	26
+LABSYDIFF	90	0	59	26
+LABTOBPOL	7	NULL	59	13
+LABUHEMO	1	0	59	35
+LABVCULF	4	0	59	57
+LABWDCG	4	1	59	45
+LABYUCOP	7	0	59	29
+LABYVB1W	2	0	59	34
+LABYVB1W	4	0	59	34
+LABYVITB6	1	0	59	34
+LABYZONIS	1	0	59	32
+LABYZONIS	2	0	59	32
+LABYZONIS	4	0	59	32
+LABYZONIS	7	0	59	32
+LABYZONIS	7	NULL	59	32
+LAB11049R	4	1	58	24
+LAB11049R	7	1	58	24
+LAB11079R	1	0	58	38
+LAB11079R	2	0	58	38
+LAB11079R	4	0	58	38
+LAB11079R	7	0	58	38
+LAB11079R	7	NULL	58	38
+LAB11079R	30	0	58	38
+LAB11079R	90	0	58	38
+LABBKVQU	7	NULL	58	28
+LABBMT2	30	2	58	18
+LABBMTP	4	0	58	1
+LABBMTP	7	0	58	1
+LABCMVQT	30	4	58	51
+LABCR	4	1	58	50
+LABDVEHV	90	1	58	36
+LABDVEVH	90	1	58	47
+LABEBPCRQ	30	2	58	52
+LABFHTLVQ	1	0	58	57
+LABFHTLVQ	2	0	58	57
+LABFLUAPX	7	1	58	56
+LABHIAB	4	1	58	50
+LABHSVM	7	NULL	58	54
+LAB11079R	1	NULL	58	38
+LAB11079R	2	NULL	58	38
+LAB11079R	4	NULL	58	38
+LAB11079R	30	NULL	58	38
+LAB11079R	90	NULL	58	38
+LABBKVQU	1	NULL	58	28
+LABBKVQU	2	NULL	58	28
+LABBKVQU	4	NULL	58	28
+LABBKVQU	30	NULL	58	28
+LABBKVQU	90	NULL	58	28
+LABHSVM	1	NULL	58	54
+LABHSVM	2	NULL	58	54
+LABHSVM	4	NULL	58	54
+LABHSVM	30	NULL	58	54
+LABHSVM	90	NULL	58	54
+LABPANHCT	1	NULL	58	1
+LABPANHCT	2	NULL	58	1
+LABPANHCT	4	NULL	58	1
+LABPANHCT	30	NULL	58	1
+LABPANHCT	90	NULL	58	1
+LABPANHCT	7	NULL	58	1
+LABPHEN	1	2	58	41
+LABPROCT	2	2	58	51
+LABTBPCRS	90	1	58	50
+LABUA	4	2	58	31
+LABVCULF	7	0	58	56
+LABYUCOP	30	0	58	29
+LABYUCOP	90	0	58	29
+LABYVITB6	2	0	58	33
+LABYZONIS	30	0	58	32
+LABYZONIS	90	0	58	32
+LABBMTP	30	0	57	1
+LABBMTP	90	0	57	1
+LABCMVIGG	90	1	57	56
+LABCSFGL	30	4	57	56
+LABDBIL	4	3	57	52
+LABHFP	90	3	57	36
+LABHSVM	1	0	57	53
+LABHSVM	2	0	57	53
+LABTBPCRS	30	1	57	49
+LABTSH	90	3	57	48
+LABVCULF	30	0	57	55
+LABVCULF	90	0	57	55
+LABVZVQL	4	0	57	52
+LABYHMS	2	0	57	53
+LABYHMS	4	0	57	53
+LABYHMS	7	0	57	53
+LABYVB1W	7	0	57	34
+LABYVB1W	30	0	57	34
+LABYVB1W	90	0	57	34
+LAB11250R	7	NULL	56	36
+LABADGM	1	0	56	48
+LABADGM	2	0	56	48
+LABCD34	1	0	56	20
+LABCMVPQ	4	3	56	56
+LABCOHB	7	NULL	56	44
+LABEBVQP	4	1	56	49
+LABFACT11	2	0	56	35
+LABFHTLVQ	4	0	56	55
+LABFHTLVQ	7	0	56	55
+LABFLUAPC	1	0	56	41
+LABFLUAPC	2	0	56	41
+LABFLUAPC	4	0	56	41
+LABFLUAPC	7	0	56	41
+LABFLUAPC	30	0	56	41
+LABFLUAPC	90	0	56	41
+LABGLNF	90	1	56	51
+LABHCVA	2	1	56	54
+LABHHV6QL	4	0	56	38
+LABHHV6QL	7	0	56	38
+LABHSVM	4	0	56	52
+LABHSVM	7	0	56	52
+LABINSL	4	0	56	36
+LABINSL	7	0	56	36
+LABISOF	90	1	56	54
+LAB11250R	1	NULL	56	36
+LAB11250R	2	NULL	56	36
+LAB11250R	4	NULL	56	36
+LAB11250R	30	NULL	56	36
+LAB11250R	90	NULL	56	36
+LABCOHB	1	NULL	56	44
+LABCOHB	2	NULL	56	44
+LABCOHB	4	NULL	56	44
+LABCOHB	30	NULL	56	44
+LABCOHB	90	NULL	56	44
+LABNTBNP	7	2	56	27
+LABPALB	2	1	56	37
+LABTCDIF	30	2	56	56
+LABTOBPRL	2	1	56	49
+LABTRFS	7	1	56	22
+LABTSHFT4	90	1	56	46
+LABUAS	2	1	56	33
+LABUUN	4	1	56	46
+LABVISC	7	0	56	17
+LABYHMS	30	0	56	52
+LABYHMS	90	0	56	52
+LABYPREAL	7	3	56	46
+LABYSES	30	0	56	27
+LABYVITB6	4	0	56	33
+LABYVITB6	7	0	56	33
+LABAFBD	30	4	55	55
+LABAFP	4	1	55	54
+LABALB	1	2	55	36
+LABBUN	90	2	55	33
+LABFACT8	90	1	55	37
+LABFCUL	30	5	55	50
+LABFHTLVQ	30	0	55	54
+LABFT4	90	3	55	51
+LABHSVM	30	0	55	51
+LABPLASMN	1	NULL	55	36
+LABPLASMN	2	NULL	55	36
+LABPLASMN	4	NULL	55	36
+LABPLASMN	30	NULL	55	36
+LABPLASMN	90	NULL	55	36
+LABNTBNP	4	2	55	27
+LABPLASMN	7	NULL	55	36
+LABTRIG	7	5	55	50
+LABYFLUZ	30	0	55	22
+LABYVITB6	30	0	55	32
+LAB11086R	7	0	54	21
+LABCSFTP	30	2	54	33
+LABDVEHVR	7	NULL	54	30
+LABFACT8	7	1	54	36
+LABFACT8	30	1	54	37
+LABFCUL	4	4	54	50
+LABFHTLVQ	90	0	54	53
+LABHFP	30	3	54	35
+LABHSVM	90	0	54	50
+LABINSL	30	0	54	35
+LABINSL	90	0	54	35
+LABDVEHVR	1	NULL	54	30
+LABDVEHVR	2	NULL	54	30
+LABDVEHVR	4	NULL	54	30
+LABDVEHVR	30	NULL	54	30
+LABDVEHVR	90	NULL	54	30
+LABROMRS	30	3	54	54
+LABTOBPOL	1	0	54	13
+LABUHEMO	2	0	54	32
+LABURNC	7	3	54	47
+LABVZVQL	7	0	54	50
+LABYFLUZ	90	0	54	22
+LABBMT1	2	1	53	4
+LABCDTPCR	7	2	53	41
+LABCLACT	4	1	53	38
+LABCRPLB	7	1	53	42
+LABFACT11	4	0	53	33
+LABFACT2	1	0	53	22
+LABFLUAPX	4	1	53	52
+LABGLNF	30	1	53	49
+LABHHV6QL	30	0	53	38
+LABNORW	90	1	53	48
+LABOAPE	7	2	53	53
+LABPREG	30	1	53	48
+LABQFTB	30	1	53	40
+LABRESPG	7	2	53	32
+LABRF	90	1	53	53
+LABTHEOL	1	1	53	40
+LABTRFS	2	1	53	19
+LABUHEMO	4	0	53	32
+LABUHEMO	7	0	53	32
+LABUHEMO	30	0	53	32
+LABUHEMO	90	0	53	32
+LABYVITB6	90	0	53	32
+LAB11250R	1	0	52	34
+LABADGM	4	0	52	44
+LABADGM	7	0	52	44
+LABADGM	30	0	52	44
+LABADGM	90	0	52	44
+LABANER	4	4	52	51
+LABB2GPCO	1	0	52	42
+LABB2GPCO	2	0	52	42
+LABB2GPCO	4	0	52	42
+LABB2GPCO	7	NULL	52	42
+LABCMVP	30	2	52	47
+LABCSFTP	1	1	52	24
+LABDATBSI	4	1	52	52
+LABDBIL	30	3	52	49
+LABDBIL	90	3	52	49
+LABDVEHVR	1	0	52	28
+LABGLWB	7	NULL	52	13
+LABHITPNL	7	NULL	52	41
+LABB2GPCO	1	NULL	52	42
+LABB2GPCO	2	NULL	52	42
+LABB2GPCO	4	NULL	52	42
+LABB2GPCO	30	NULL	52	42
+LABB2GPCO	90	NULL	52	42
+LABGLWB	1	NULL	52	13
+LABGLWB	2	NULL	52	13
+LABGLWB	4	NULL	52	13
+LABGLWB	30	NULL	52	13
+LABGLWB	90	NULL	52	13
+LABHITPNL	1	NULL	52	41
+LABHITPNL	2	NULL	52	41
+LABHITPNL	4	NULL	52	41
+LABHITPNL	30	NULL	52	41
+LABHITPNL	90	NULL	52	41
+LABRF	30	1	52	52
+LABROMRS	90	5	52	52
+LABTBIL	1	1	52	51
+LABTRFS	4	1	52	20
+LABTTM	1	0	52	4
+LABVZVQL	30	0	52	48
+LABVZVQL	90	0	52	48
+LAB11250R	2	0	51	34
+LABAMIPRL	4	0	51	42
+LABB2GPCO	7	0	51	41
+LABBLCSTK	4	2	51	46
+LABCA	1	2	51	44
+LABCBCD	90	5	51	39
+LABCBCO	90	5	51	33
+LABCHOHDL	1	0	51	22
+LABCHOHDL	2	0	51	22
+LABCHOHDL	4	0	51	22
+LABCHOHDL	7	0	51	22
+LABCHOHDL	7	NULL	51	22
+LABCHOHDL	30	0	51	22
+LABCHOHDL	90	0	51	22
+LABCMVQT	90	4	51	47
+LABCORTS	2	2	51	36
+LABCRPLC	4	1	51	46
+LABCRTINE	7	NULL	51	33
+LABDVEVH	30	1	51	43
+LABECHIG	7	NULL	51	48
+LABESRP	7	2	51	42
+LABFACT11	7	0	51	32
+LABFACT11	30	0	51	32
+LABFACT11	90	0	51	32
+LABFKOH	7	2	51	44
+LABHDL	1	0	51	34
+LABHDL	2	0	51	34
+LABHDL	4	0	51	34
+LABHDL	7	0	51	34
+LABHDL	7	NULL	51	34
+LABHDL	30	0	51	34
+LABHDL	90	0	51	34
+LABHFP	4	3	51	31
+LABHHV6QL	90	0	51	37
+LABIGG	4	1	51	43
+LABCHOHDL	1	NULL	51	22
+LABCHOHDL	2	NULL	51	22
+LABCHOHDL	4	NULL	51	22
+LABCHOHDL	30	NULL	51	22
+LABCHOHDL	90	NULL	51	22
+LABCRTINE	1	NULL	51	33
+LABCRTINE	2	NULL	51	33
+LABCRTINE	4	NULL	51	33
+LABCRTINE	30	NULL	51	33
+LABCRTINE	90	NULL	51	33
+LABECHIG	1	NULL	51	48
+LABECHIG	2	NULL	51	48
+LABECHIG	4	NULL	51	48
+LABECHIG	30	NULL	51	48
+LABECHIG	90	NULL	51	48
+LABHDL	1	NULL	51	34
+LABHDL	2	NULL	51	34
+LABHDL	4	NULL	51	34
+LABHDL	30	NULL	51	34
+LABHDL	90	NULL	51	34
+LABVCULS	1	NULL	51	49
+LABVCULS	2	NULL	51	49
+LABVCULS	4	NULL	51	49
+LABVCULS	30	NULL	51	49
+LABVCULS	90	NULL	51	49
+LABWNVRNA	1	NULL	51	49
+LABWNVRNA	2	NULL	51	49
+LABWNVRNA	4	NULL	51	49
+LABWNVRNA	30	NULL	51	49
+LABWNVRNA	90	NULL	51	49
+LABYSEROT	1	NULL	51	39
+LABYSEROT	2	NULL	51	39
+LABYSEROT	4	NULL	51	39
+LABYSEROT	30	NULL	51	39
+LABYSEROT	90	NULL	51	39
+LABYUMETF	1	NULL	51	45
+LABYUMETF	2	NULL	51	45
+LABYUMETF	4	NULL	51	45
+LABYUMETF	30	NULL	51	45
+LABYUMETF	90	NULL	51	45
+LABPANHCT	1	0	51	1
+LABPCG3V	90	2	51	15
+LABVCULS	1	0	51	49
+LABVCULS	7	NULL	51	49
+LABWNVRNA	7	NULL	51	49
+LABYSEROT	7	NULL	51	39
+LABYSES	90	0	51	25
+LABYUMETF	1	0	51	45
+LABYUMETF	2	0	51	45
+LABYUMETF	7	NULL	51	45
+LAB11250R	4	0	50	33
+LAB11265R	7	NULL	50	49
+LABASI	90	4	50	50
+LABB2GPCO	30	0	50	40
+LABB2GPCO	90	0	50	40
+LABBKVQU	1	0	50	25
+LABBXTG	90	3	50	43
+LABCMVQT	4	2	50	49
+LABCRP	4	2	50	40
+LABCRPLC	7	1	50	45
+LABDBIL	7	3	50	46
+LABDVEHV	30	1	50	31
+LABFLDC	1	2	50	48
+LABHAP	30	5	50	42
+LABHAP	90	5	50	42
+LABHITPNL	1	0	50	40
+LABHITPNL	2	0	50	40
+LABISOF	30	1	50	48
+LAB11265R	1	NULL	50	49
+LAB11265R	2	NULL	50	49
+LAB11265R	4	NULL	50	49
+LAB11265R	30	NULL	50	49
+LAB11265R	90	NULL	50	49
+LABSTOBIC	1	NULL	50	32
+LABSTOBIC	2	NULL	50	32
+LABSTOBIC	4	NULL	50	32
+LABSTOBIC	30	NULL	50	32
+LABSTOBIC	90	NULL	50	32
+LABYCYANI	1	NULL	50	37
+LABYCYANI	2	NULL	50	37
+LABYCYANI	4	NULL	50	37
+LABYCYANI	30	NULL	50	37
+LABYCYANI	90	NULL	50	37
+LABPCACTK	90	2	50	3
+LABPCG3V	30	2	50	14
+LABREPT	7	2	50	39
+LABRESP	30	5	50	39
+LABSTOBIC	7	NULL	50	32
+LABSTOOLC	4	1	50	42
+LABTBPCRS	7	1	50	43
+LABTEGCP	4	2	50	21
+LABURIC	1	5	50	50
+LABVCULS	2	0	50	48
+LABVCULS	4	0	50	48
+LABVCULS	7	0	50	48
+LABVISC	30	0	50	16
+LABWDCG	2	1	50	41
+LABYCYANI	7	NULL	50	37
+LABYUMETF	4	0	50	45
+LABA1C	90	3	49	45
+LABAFP	2	1	49	48
+LABCSFC	90	5	49	48
+LABCSFCT	90	2	49	24
+LABDVEHVR	2	0	49	25
+LABFACT2	2	0	49	22
+LABGRAM	7	4	49	44
+LABHITPNL	4	0	49	39
+LABHITPNL	7	0	49	39
+LABHSCUL	7	NULL	49	42
+LABMETHB	4	1	49	45
+LABMETHB	30	1	49	45
+LABMETHB	90	1	49	45
+LABNORW	30	1	49	44
+LABHSCUL	1	NULL	49	42
+LABHSCUL	2	NULL	49	42
+LABHSCUL	4	NULL	49	42
+LABHSCUL	30	NULL	49	42
+LABHSCUL	90	NULL	49	42
+LABYAHA	1	NULL	49	45
+LABYAHA	2	NULL	49	45
+LABYAHA	4	NULL	49	45
+LABYAHA	30	NULL	49	45
+LABYAHA	90	NULL	49	45
+LABPCACTK	2	2	49	2
+LABPCACTK	4	2	49	2
+LABPCACTK	7	2	49	2
+LABPCACTK	30	2	49	2
+LABPCG3V	7	2	49	14
+LABREPT	90	2	49	38
+LABTBIL	7	2	49	43
+LABTBPCRS	4	1	49	42
+LABTCDIF	1	1	49	49
+LABTOBPOL	2	0	49	12
+LABUEOS	90	2	49	45
+LABUPREG	2	1	49	41
+LABURNC	30	5	49	44
+LABVPAL	90	4	49	42
+LABYAHA	7	NULL	49	45
+LABYPREAL	90	5	49	40
+LABYSEROT	1	0	49	37
+LABYSEROT	2	0	49	37
+LABYSEROT	4	0	49	37
+LABYSEROT	7	0	49	37
+LABYSEROT	30	0	49	37
+LABYSEROT	90	0	49	37
+LABYUMETF	7	0	49	44
+LAB11019R	7	NULL	48	15
+LABAHP2	90	1	48	46
+LABCBCO	7	5	48	29
+LABCD34	2	0	48	13
+LABCD34	4	0	48	13
+LABCD34	7	0	48	13
+LABCD34	30	0	48	13
+LABCD34	90	0	48	13
+LABCMVIGG	30	1	48	48
+LABCMVRO	90	1	48	41
+LABCOHB	1	0	48	38
+LABCOHB	2	0	48	38
+LABCOHB	4	0	48	38
+LABCOHB	7	0	48	38
+LABCOHB	30	0	48	38
+LABCOHB	90	0	48	38
+LABDVEHVR	4	0	48	25
+LABDVEHVR	7	0	48	25
+LABDVEHVR	30	0	48	25
+LABEBVQP	90	2	48	44
+LABECHIG	1	0	48	45
+LABECHIG	2	0	48	45
+LABF8INH	1	0	48	17
+LABFACT2	4	0	48	22
+LABFCUL	2	4	48	45
+LABFT4	4	2	48	47
+LABGGT	7	2	48	48
+LABGLNF	7	1	48	42
+LABHSCUL	1	0	48	41
+LABNH3	90	4	48	42
+LABNTBNP	2	2	48	25
+LAB11019R	1	NULL	48	15
+LAB11019R	2	NULL	48	15
+LAB11019R	4	NULL	48	15
+LAB11019R	30	NULL	48	15
+LAB11019R	90	NULL	48	15
+LABPANHCT	2	0	48	1
+LABPCACTK	1	2	48	2
+LABSTSS	1	1	48	48
+LABVISC	90	0	48	16
+LABWDC	1	1	48	42
+LABYUMETF	30	0	48	43
+LABYUMETF	90	0	48	43
+LAB11041R	90	4	47	40
+LABAMY	1	2	47	45
+LABASQ	90	1	47	35
+LABBLCTIP	7	3	47	47
+LABBONC	7	NULL	47	20
+LABCBCD	30	5	47	36
+LABCBCO	30	5	47	29
+LABCRPLB	4	1	47	39
+LABCTC	2	1	47	46
+LABDVEHVR	90	0	47	24
+LABE2O	1	0	47	46
+LABE2O	2	0	47	46
+LABE2O	4	0	47	46
+LABE2O	7	0	47	46
+LABE2O	7	NULL	47	46
+LABESRP	4	2	47	36
+LABFACT2	7	0	47	22
+LABFACT2	30	0	47	22
+LABFACT2	90	0	47	22
+LABFLDB	90	2	47	40
+LABGGT	4	2	47	47
+LABGLNF	1	1	47	43
+LABHEPDAB	7	NULL	47	46
+LABHFP	7	3	47	31
+LABHGBP	1	1	47	43
+LABHSCUL	2	0	47	40
+LABMETHB	7	1	47	43
+LABBONC	1	NULL	47	20
+LABBONC	2	NULL	47	20
+LABBONC	4	NULL	47	20
+LABBONC	30	NULL	47	20
+LABBONC	90	NULL	47	20
+LABE2O	1	NULL	47	46
+LABE2O	2	NULL	47	46
+LABE2O	4	NULL	47	46
+LABE2O	30	NULL	47	46
+LABE2O	90	NULL	47	46
+LABHEPDAB	1	NULL	47	46
+LABHEPDAB	2	NULL	47	46
+LABHEPDAB	4	NULL	47	46
+LABHEPDAB	30	NULL	47	46
+LABHEPDAB	90	NULL	47	46
+LABPANHGB	1	NULL	47	1
+LABPANHGB	2	NULL	47	1
+LABPANHGB	4	NULL	47	1
+LABPANHGB	30	NULL	47	1
+LABPANHGB	90	NULL	47	1
+LABPANHGB	1	0	47	1
+LABPANHGB	7	NULL	47	1
+LABPB	4	2	47	43
+LABPCG3V	4	2	47	13
+LABPTH	1	1	47	38
+LABREPT	30	2	47	37
+LABRETIC	90	2	47	28
+LABRF	7	1	47	47
+LABTRFS	1	1	47	16
+LABUA	2	2	47	28
+LABUAPRN	7	2	47	26
+LAB11265R	1	0	46	45
+LAB12330R	7	NULL	46	31
+LABADVQL	7	NULL	46	32
+LABAHP2	30	1	46	44
+LABAMIPOL	7	NULL	46	10
+LABASPID	7	NULL	46	43
+LABASQ	7	1	46	33
+LABASQ	30	1	46	35
+LABBKVQU	2	0	46	23
+LABCRP	30	4	46	37
+LABCSFC	30	5	46	45
+LABE2O	30	0	46	45
+LABE2O	90	0	46	45
+LABECHIG	4	0	46	43
+LABFLDC	90	5	46	41
+LABGGT	30	2	46	46
+LABGLNF	4	1	46	41
+LABHGBSQ	1	0	46	38
+LABHGBSQ	2	0	46	38
+LABHIPA	1	1	46	28
+LABHITAB	7	2	46	38
+LABHITPNL	30	0	46	38
+LABHITPNL	90	0	46	38
+LABHSCUL	4	0	46	39
+LABHSCUL	7	0	46	39
+LABLPDP	7	NULL	46	3
+LABMALAR	1	2	46	43
+LABMALSC	1	0	46	35
+LAB12330R	1	NULL	46	31
+LAB12330R	2	NULL	46	31
+LAB12330R	4	NULL	46	31
+LAB12330R	30	NULL	46	31
+LAB12330R	90	NULL	46	31
+LABADVQL	1	NULL	46	32
+LABADVQL	2	NULL	46	32
+LABPANHCT	4	0	46	1
+LABADVQL	4	NULL	46	32
+LABADVQL	30	NULL	46	32
+LABADVQL	90	NULL	46	32
+LABAMIPOL	1	NULL	46	10
+LABAMIPOL	2	NULL	46	10
+LABAMIPOL	4	NULL	46	10
+LABAMIPOL	30	NULL	46	10
+LABAMIPOL	90	NULL	46	10
+LABASPID	1	NULL	46	43
+LABASPID	2	NULL	46	43
+LABASPID	4	NULL	46	43
+LABASPID	30	NULL	46	43
+LABASPID	90	NULL	46	43
+LABLPDP	1	NULL	46	3
+LABLPDP	2	NULL	46	3
+LABLPDP	4	NULL	46	3
+LABLPDP	30	NULL	46	3
+LABLPDP	90	NULL	46	3
+LABPCACT	30	1	46	17
+LABPCACT	90	1	46	17
+LABPCINR	90	1	46	24
+LABRESPCR	7	1	46	42
+LABTOBPRL	30	2	46	44
+LABTSHFT4	30	1	46	39
+LABURNC	1	2	46	34
+LABUUN	2	1	46	37
+LABVCUL	90	3	46	43
+LABVPAL	30	4	46	41
+LABYAHA	1	0	46	42
+LAB11019R	1	0	45	13
+LAB11019R	2	0	45	13
+LAB11041R	1	1	45	37
+LAB11265R	2	0	45	44
+LABA1C	2	2	45	43
+LABACTHO	7	2	45	30
+LABACTHO	30	2	45	30
+LABACTHO	90	2	45	30
+LABCTC	1	1	45	44
+LABDVERVP	90	3	45	37
+LABECHIG	7	0	45	42
+LABECHIG	30	0	45	42
+LABECHIG	90	0	45	42
+LABFCUL	1	4	45	43
+LABFLDC	30	4	45	43
+LABGGT	90	2	45	45
+LABHELI	90	1	45	44
+LABHEPDAB	1	0	45	44
+LABHEPDAB	2	0	45	44
+LABHGBP	30	2	45	43
+LABHGBP	90	2	45	43
+LABHIVDS	7	NULL	45	43
+LABLDLD	7	NULL	45	39
+LABMYO	2	1	45	39
+LABPANHGB	2	0	45	1
+LABPANHGB	4	0	45	1
+LABHIVDS	1	NULL	45	43
+LABHIVDS	2	NULL	45	43
+LABHIVDS	4	NULL	45	43
+LABHIVDS	30	NULL	45	43
+LABHIVDS	90	NULL	45	43
+LABLDLD	1	NULL	45	39
+LABLDLD	2	NULL	45	39
+LABLDLD	4	NULL	45	39
+LABLDLD	30	NULL	45	39
+LABLDLD	90	NULL	45	39
+LABYSTCL	1	NULL	45	38
+LABYSTCL	2	NULL	45	38
+LABYSTCL	4	NULL	45	38
+LABPCACT	7	1	45	16
+LABYSTCL	30	NULL	45	38
+LABYSTCL	90	NULL	45	38
+LABPROCT	7	2	45	38
+LABRBCFOL	90	1	45	29
+LABSTOBIC	1	0	45	29
+LABUEOS	30	2	45	43
+LABWNVRNA	1	0	45	43
+LABYSTCL	7	NULL	45	38
+LAB11019R	4	0	44	13
+LAB11019R	7	0	44	13
+LAB11049R	2	1	44	22
+LAB11250R	7	0	44	31
+LAB12762R	4	0	44	25
+LAB12762R	7	0	44	25
+LAB12762R	30	0	44	25
+LAB12762R	90	0	44	25
+LABACA	90	1	44	42
+LABACTHO	4	2	44	29
+LABALT	90	1	44	37
+LABANER	2	4	44	43
+LABBKVQU	4	0	44	22
+LABBONC	1	0	44	20
+LABBONC	2	0	44	20
+LABBONC	4	0	44	20
+LABBONC	7	0	44	20
+LABBONC	30	0	44	20
+LABBONC	90	0	44	20
+LABCBCD	7	5	44	35
+LABHIVDS	1	0	44	43
+LABHIVDS	2	0	44	43
+LABHSCUL	30	0	44	38
+LABLDLD	1	0	44	39
+LABLDLD	2	0	44	39
+LABLDLD	4	0	44	39
+LABLDLD	7	0	44	39
+LABLDLD	30	0	44	39
+LABLDLD	90	0	44	39
+LABLIL	90	3	44	37
+LABMYCPA	90	2	44	26
+LABOAPE	4	2	44	44
+LABPANHGB	7	0	44	1
+LABPANHGB	30	0	44	1
+LABPANHGB	90	0	44	1
+LABRESP	1	2	44	42
+LABSTLC	2	1	44	39
+LABSTLCX	2	1	44	40
+LABTBIL	90	3	44	42
+LABTT	2	1	44	34
+LABVCULS	30	0	44	42
+LABVCULS	90	0	44	42
+LABVPAL	4	4	44	34
+LABWDCG	1	1	44	40
+LABWNVRNA	2	0	44	42
+LABYAHA	2	0	44	40
+LABYMPA	1	0	44	8
+LAB11019R	30	0	43	13
+LAB11265R	4	0	43	42
+LAB11265R	7	0	43	42
+LABAHP2	7	1	43	41
+LABANER2	90	4	43	37
+LABASQ	4	1	43	31
+LABBKVQU	7	0	43	21
+LABBUN	30	2	43	24
+LABCMVRO	30	1	43	37
+LABCORT	2	3	43	32
+LABCRPLB	2	1	43	36
+LABCSFGL	30	5	43	42
+LABEBVQP	30	2	43	40
+LABF8INH	2	0	43	17
+LABFLDB	30	2	43	39
+LABGLNF	2	1	43	38
+LABHBSAG	90	3	43	42
+LABHCVA	1	1	43	42
+LABHGBSQ	4	0	43	36
+LABHSCUL	90	0	43	37
+LABHV6DNA	90	1	43	42
+LABIGG	2	1	43	36
+LABLPDP	1	0	43	3
+LABLPDP	2	0	43	3
+LABLPDP	4	0	43	3
+LABLPDP	7	0	43	3
+LABLPDP	30	0	43	3
+LABLPDP	90	0	43	3
+LABMALSC	2	0	43	32
+LABMALSC	4	0	43	32
+LABMALSC	7	0	43	32
+LABPB	90	2	43	41
+LABPROTSF	1	NULL	43	14
+LABPROTSF	2	NULL	43	14
+LABPROTSF	4	NULL	43	14
+LABPROTSF	30	NULL	43	14
+LABPROTSF	90	NULL	43	14
+LABYPENTO	1	NULL	43	3
+LABYPENTO	2	NULL	43	3
+LABYPENTO	4	NULL	43	3
+LABYPENTO	30	NULL	43	3
+LABYPENTO	90	NULL	43	3
+LABPROTSF	1	0	43	14
+LABPROTSF	2	0	43	14
+LABPROTSF	4	0	43	14
+LABPROTSF	7	0	43	14
+LABPROTSF	7	NULL	43	14
+LABPROTSF	30	0	43	14
+LABPROTSF	90	0	43	14
+LABQFTB	7	1	43	34
+LABSTOBIC	2	0	43	28
+LABTOBPOL	4	0	43	9
+LABTP	7	1	43	35
+LABVCULR	30	1	43	42
+LABVD25H	90	1	43	32
+LABWNVRNA	4	0	43	41
+LABYAHA	4	0	43	39
+LABYAHA	7	0	43	39
+LABYAHA	30	0	43	39
+LABYAHA	90	0	43	39
+LABYCYANI	1	0	43	30
+LABYCYANI	2	0	43	30
+LABYCYANI	4	0	43	30
+LABYPENTO	7	NULL	43	3
+LAB11019R	90	0	42	13
+LABACA	30	1	42	40
+LABACTHO	2	2	42	27
+LABADVQL	1	0	42	29
+LABADVQT	90	1	42	37
+LABAFP	1	1	42	41
+LABAHP2	4	1	42	40
+LABCR	2	2	42	41
+LABDVEVH	7	1	42	37
+LABFKOH	4	2	42	35
+LABHIVDS	4	0	42	41
+LABHIVDS	7	0	42	41
+LABHIVDS	30	0	42	41
+LABHIVDS	90	0	42	41
+LABIBIL	90	3	42	42
+LABLEGDA	90	2	42	42
+LABLIL	30	3	42	35
+LABMALSC	30	0	42	32
+LABMALSC	90	0	42	32
+LABMETC	90	3	42	16
+LABMETHB	4	4	42	42
+LABNH3	30	4	42	38
+LABPCACT	1	1	42	15
+LABPCACT	2	1	42	15
+LABPCACT	4	1	42	15
+LABPCG3	4	5	42	15
+LABPCG3	7	5	42	15
+LABPCG3	30	5	42	15
+LABPCG3	90	5	42	15
+LABPROCT	4	2	42	35
+LABRBCFOL	30	1	42	28
+LABRPRQL	90	1	42	38
+LABSTOBIC	4	0	42	27
+LABSTOBIC	7	0	42	27
+LABVCULT	90	1	42	41
+LABWNVRNA	7	0	42	40
+LABYCYANI	7	0	42	29
+LABYCYANI	30	0	42	29
+LABYCYANI	90	0	42	29
+LABYSTCL	1	0	42	35
+LABYSTCL	2	0	42	35
+LAB11041R	30	5	41	34
+LAB11153R	7	NULL	41	39
+LAB11265R	30	0	41	40
+LABADVQL	2	0	41	28
+LABALT	30	1	41	36
+LABCRP	90	4	41	31
+LABGGT	4	4	41	38
+LABHEPDAB	4	0	41	40
+LABHEPDAB	7	0	41	40
+LABHEPDAB	30	0	41	40
+LABHEPDAB	90	0	41	40
+LABIBIL	30	3	41	41
+LABMYO	1	1	41	33
+LABNH3	7	4	41	37
+LABORANER	90	2	41	38
+LABPALB	30	4	41	32
+LABPANHCT	7	0	41	1
+LABPB	7	2	41	39
+LAB11153R	1	NULL	41	39
+LAB11153R	2	NULL	41	39
+LAB11153R	4	NULL	41	39
+LAB11153R	30	NULL	41	39
+LAB11153R	90	NULL	41	39
+LABPLTS	7	3	41	39
+LABRESPG	4	2	41	21
+LABSTOBIC	30	0	41	26
+LABSTOBIC	90	0	41	26
+LABUPREG	1	1	41	34
+LABVCULT	30	1	41	40
+LABWNVRNA	30	0	41	40
+LABWNVRNA	90	0	41	40
+LAB11049R	1	1	40	21
+LAB11153R	1	0	40	38
+LAB11153R	2	0	40	38
+LAB11153R	4	0	40	38
+LAB11153R	7	0	40	38
+LAB11265R	90	0	40	39
+LAB12809R	7	NULL	40	27
+LABAFBCF	7	1	40	40
+LABAMIPOL	1	0	40	9
+LABAMIPRL	7	0	40	32
+LABANER	1	4	40	40
+LABASQ	2	1	40	29
+LABCRPLC	2	1	40	36
+LABCRYEIA	7	NULL	40	39
+LABDDIML	90	1	40	14
+LABEBPCRQ	2	1	40	34
+LABEBVQP	2	1	40	34
+LABF8INH	4	0	40	17
+LABFACT8	2	2	40	29
+LABHELI	30	1	40	39
+LABMALAR	4	2	40	37
+LABMALAR	7	2	40	37
+LABMALAR	30	2	40	37
+LABMALAR	90	2	40	37
+LABMYCPA	30	2	40	26
+LABPANHCT	30	0	40	1
+LABPB	30	2	40	38
+LAB12809R	1	NULL	40	27
+LAB12809R	2	NULL	40	27
+LAB12809R	4	NULL	40	27
+LAB12809R	30	NULL	40	27
+LAB12809R	90	NULL	40	27
+LABCRYEIA	1	NULL	40	39
+LABCRYEIA	2	NULL	40	39
+LABCRYEIA	4	NULL	40	39
+LABCRYEIA	30	NULL	40	39
+LABCRYEIA	90	NULL	40	39
+LABPCG3	2	5	40	12
+LABQFTB	4	1	40	32
+LABRETIC	30	2	40	24
+LABRF	4	1	40	40
+LABTHEOL	30	1	40	32
+LABTOBPOL	7	0	40	9
+LAB11027R	7	NULL	39	26
+LAB11086R	30	0	39	16
+LAB11250R	30	0	39	31
+LABADVQL	4	0	39	28
+LABADVQL	7	0	39	28
+LABAMY	2	3	39	38
+LABASPID	1	0	39	36
+LABBMC	1	0	39	38
+LABBMC	2	0	39	38
+LABBMC	4	0	39	38
+LABBMC	7	0	39	38
+LABBMC	7	NULL	39	38
+LABBMC	30	0	39	38
+LABBMC	90	0	39	38
+LABBXTG	30	3	39	37
+LABCRYEIA	1	0	39	38
+LABCSFGL	2	2	39	37
+LABDVEHV	7	1	39	24
+LABFPARVO	1	0	39	15
+LABFPARVO	2	0	39	15
+LABFPARVO	4	0	39	15
+LABFPARVO	7	0	39	15
+LABFPARVO	7	NULL	39	15
+LABFPARVO	30	0	39	15
+LABFPARVO	90	0	39	15
+LABFT4	2	2	39	39
+LABGNTPRL	2	2	39	34
+LABGNTPRL	4	3	39	34
+LABHGBSQ	7	0	39	32
+LABHIAB	2	1	39	35
+LABHV6DNA	30	1	39	38
+LABLIL	4	2	39	29
+LABLPA	7	NULL	39	25
+LABMETC	4	3	39	16
+LABMETC	30	3	39	15
+LABMETHB	4	2	39	39
+LABPANHCT	90	0	39	1
+LAB11027R	1	NULL	39	26
+LAB11027R	2	NULL	39	26
+LAB11027R	4	NULL	39	26
+LAB11027R	30	NULL	39	26
+LAB11027R	90	NULL	39	26
+LABBMC	1	NULL	39	38
+LABBMC	2	NULL	39	38
+LABBMC	4	NULL	39	38
+LABBMC	30	NULL	39	38
+LABBMC	90	NULL	39	38
+LABFPARVO	1	NULL	39	15
+LABFPARVO	2	NULL	39	15
+LABFPARVO	4	NULL	39	15
+LABFPARVO	30	NULL	39	15
+LABFPARVO	90	NULL	39	15
+LABLPA	1	NULL	39	25
+LABLPA	2	NULL	39	25
+LABLPA	4	NULL	39	25
+LABLPA	30	NULL	39	25
+LABLPA	90	NULL	39	25
+LABYMGEA	1	NULL	39	32
+LABYMGEA	2	NULL	39	32
+LABYMGEA	4	NULL	39	32
+LABYMGEA	30	NULL	39	32
+LABYMGEA	90	NULL	39	32
+LABRBCFOL	7	1	39	27
+LABRESPCR	4	1	39	36
+LABRPRQL	30	1	39	35
+LABSTOBGD	1	1	39	10
+LABTBPCRS	2	1	39	34
+LABTHEOL	4	1	39	31
+LABTHEOL	4	3	39	32
+LABTHEOL	7	1	39	31
+LABTHEOL	90	1	39	31
+LABTOBPRL	1	1	39	32
+LABUA	30	3	39	16
+LABVCUL	7	2	39	38
+LABVCULT	4	1	39	38
+LABYMGEA	1	0	39	32
+LABYMGEA	2	0	39	32
+LABYMGEA	4	0	39	32
+LABYMGEA	7	0	39	32
+LABYMGEA	7	NULL	39	32
+LABYPREAL	30	5	39	32
+LABYSTCL	4	0	39	32
+LAB12330R	1	0	38	24
+LABACA	7	1	38	36
+LABACTHO	1	2	38	27
+LABADVQT	30	1	38	33
+LABBLCTIP	30	5	38	34
+LABCA	4	3	38	35
+LABCORT	90	4	38	29
+LABCR	4	3	38	38
+LABCRP	2	2	38	29
+LABCRTINE	1	0	38	30
+LABCRTINE	2	0	38	30
+LABCRTINE	4	0	38	30
+LABCRYEIA	2	0	38	37
+LABCRYEIA	4	0	38	37
+LABCRYEIA	7	0	38	37
+LABCRYEIA	30	0	38	37
+LABCRYEIA	90	0	38	37
+LABCSFGL	90	5	38	37
+LABDDIML	1	1	38	13
+LABDDIML	2	1	38	13
+LABDDIML	4	1	38	13
+LABDDIML	7	1	38	13
+LABDDIML	30	1	38	13
+LABDVEHV	4	1	38	24
+LABFLUAPX	2	1	38	37
+LABGNTPRL	30	3	38	29
+LABGNTPRL	90	3	38	29
+LABHGBP	4	2	38	37
+LABHSVG	1	0	38	11
+LABHSVG	2	0	38	11
+LABHSVG	4	0	38	11
+LABHSVG	7	0	38	11
+LABHSVG	7	NULL	38	11
+LABHSVG	30	0	38	11
+LABHSVG	90	0	38	11
+LABMALAR	2	2	38	35
+LABHSVG	1	NULL	38	11
+LABHSVG	2	NULL	38	11
+LABHSVG	4	NULL	38	11
+LABHSVG	30	NULL	38	11
+LABHSVG	90	NULL	38	11
+LABYANTSM	1	NULL	38	33
+LABYANTSM	2	NULL	38	33
+LABYANTSM	4	NULL	38	33
+LABYANTSM	30	NULL	38	33
+LABYANTSM	90	NULL	38	33
+LABYUCORT	1	NULL	38	18
+LABYUCORT	2	NULL	38	18
+LABYUCORT	4	NULL	38	18
+LABYUCORT	30	NULL	38	18
+LABYUCORT	90	NULL	38	18
+LABPREG	7	1	38	33
+LABROMRS	4	2	38	37
+LABTTM	2	0	38	2
+LABVCULR	90	1	38	37
+LABVCULT	7	1	38	37
+LABVD25H	30	1	38	29
+LABYANTSM	7	NULL	38	33
+LABYMGEA	30	0	38	31
+LABYMGEA	90	0	38	31
+LABYUCORT	7	NULL	38	18
+LAB11086R	90	0	37	16
+LAB11250R	90	0	37	31
+LAB11253R	1	0	37	29
+LAB11253R	2	0	37	29
+LAB11253R	4	0	37	29
+LAB11253R	7	NULL	37	29
+LABAFBCF	4	1	37	37
+LABAHP2	2	1	37	36
+LABALT	7	1	37	32
+LABASPID	2	0	37	34
+LABAST	90	1	37	30
+LABCBCO	2	3	37	24
+LABCMVIGG	7	1	37	37
+LABCSFTP	7	2	37	22
+LABDATBSI	2	1	37	37
+LABDVERVP	7	2	37	25
+LABDVEVH	4	1	37	32
+LABEBPCRQ	90	3	37	32
+LABFER	90	2	37	31
+LABFKOH	90	3	37	33
+LABFVARZ	90	1	37	37
+LABHGBP	30	3	37	33
+LABHGBP	90	3	37	33
+LABMYO	90	1	37	30
+LABNORW	7	1	37	33
+LABPCCG4V	90	1	37	2
+LAB11253R	1	NULL	37	29
+LABPCG3V	2	2	37	11
+LAB11253R	2	NULL	37	29
+LAB11253R	4	NULL	37	29
+LAB11253R	30	NULL	37	29
+LAB11253R	90	NULL	37	29
+LABYENTE	1	NULL	37	32
+LABYENTE	2	NULL	37	32
+LABYENTE	4	NULL	37	32
+LABYENTE	30	NULL	37	32
+LABYENTE	90	NULL	37	32
+LABYFCAR	1	NULL	37	27
+LABYFCAR	2	NULL	37	27
+LABYFCAR	4	NULL	37	27
+LABYFCAR	30	NULL	37	27
+LABYFCAR	90	NULL	37	27
+LABYFLUO	1	NULL	37	32
+LABYFLUO	2	NULL	37	32
+LABYFLUO	4	NULL	37	32
+LABYFLUO	30	NULL	37	32
+LABYFLUO	90	NULL	37	32
+LABPSA	90	1	37	36
+LABQFTBG	90	1	37	32
+LABRF	2	1	37	37
+LABRPRQL	7	1	37	33
+LABSCMV	7	2	37	37
+LABTTM	4	0	37	2
+LABYANTSM	1	0	37	32
+LABYENTE	1	0	37	32
+LABYENTE	2	0	37	32
+LABYENTE	4	0	37	32
+LABYENTE	7	NULL	37	32
+LABYFCAR	1	0	37	27
+LABYFCAR	2	0	37	27
+LABYFCAR	7	NULL	37	27
+LABYFLUO	7	NULL	37	32
+LABYUCORT	1	0	37	17
+LABYUCORT	2	0	37	17
+LAB11027R	1	0	36	25
+LAB11027R	2	0	36	25
+LAB11153R	30	0	36	35
+LAB11253R	7	0	36	28
+LAB11253R	30	0	36	28
+LAB11253R	90	0	36	28
+LAB12762R	1	1	36	26
+LABAFBC	30	5	36	36
+LABALT	4	1	36	32
+LABAST	30	1	36	30
+LABBKVQU	30	0	36	17
+LABC3	90	1	36	30
+LABCMVIGG	4	1	36	36
+LABCMVPQ	7	4	36	36
+LABCRTINE	7	0	36	30
+LABCRTINE	30	0	36	30
+LABCRTINE	90	0	36	30
+LABCSFC	2	2	36	36
+LABDIC	2	2	36	9
+LABFLDB	1	1	36	34
+LABGGT	7	3	36	34
+LABGLWB	1	0	36	10
+LABGLWB	2	0	36	10
+LABGLWB	4	0	36	10
+LABGLWB	7	0	36	10
+LABGLWB	30	0	36	10
+LABGLWB	90	0	36	10
+LABGNTPRL	7	3	36	30
+LABLPA	1	0	36	22
+LABMYO	30	1	36	29
+LABNASC	1	0	36	15
+LABNASC	2	0	36	15
+LABNASC	4	0	36	15
+LABNASC	7	0	36	15
+LABNASC	7	NULL	36	15
+LABNASC	30	0	36	15
+LABPALB	90	4	36	27
+LABNASC	1	NULL	36	15
+LABNASC	2	NULL	36	15
+LABNASC	4	NULL	36	15
+LABNASC	30	NULL	36	15
+LABNASC	90	NULL	36	15
+LABPFCATE	1	NULL	36	20
+LABPFCATE	2	NULL	36	20
+LABPFCATE	4	NULL	36	20
+LABPFCATE	7	NULL	36	20
+LABPFCATE	30	NULL	36	20
+LABPFCATE	90	NULL	36	20
+LABYCLON	1	NULL	36	25
+LABYCLON	2	NULL	36	25
+LABYCLON	4	NULL	36	25
+LABYCLON	30	NULL	36	25
+LABYCLON	90	NULL	36	25
+LABYPYRU	1	NULL	36	17
+LABYPYRU	2	NULL	36	17
+LABYPYRU	4	NULL	36	17
+LABYPYRU	30	NULL	36	17
+LABYPYRU	90	NULL	36	17
+LABPSA	30	1	36	35
+LABSTOOLC	2	1	36	31
+LABTBILFR	2	1	36	24
+LABTOBPOL	30	0	36	8
+LABTOBPOL	90	0	36	8
+LABTP	4	1	36	29
+LABTSH	1	2	36	33
+LABTSHFT4	7	1	36	32
+LABTTM	7	0	36	2
+LABTTM	30	0	36	2
+LABTTM	90	0	36	2
+LABVCULT	1	1	36	35
+LABVCULT	2	1	36	35
+LABYANTSM	2	0	36	31
+LABYANTSM	4	0	36	31
+LABYANTSM	7	0	36	31
+LABYANTSM	30	0	36	31
+LABYANTSM	90	0	36	31
+LABYCLON	7	NULL	36	25
+LABYENTE	7	0	36	31
+LABYENTE	30	0	36	31
+LABYENTE	90	0	36	31
+LABYPYRU	7	NULL	36	17
+LABYSTCL	7	0	36	31
+LABYUCORT	4	0	36	17
+LAB11153R	90	0	35	34
+LABADVQL	30	0	35	26
+LABADVQL	90	0	35	26
+LABAMIPRL	30	0	35	28
+LABASPID	4	0	35	32
+LABASPID	7	0	35	32
+LABASPID	30	0	35	32
+LABASPID	90	0	35	32
+LABASQ	1	1	35	25
+LABBLCTIP	2	2	35	32
+LABC3	30	1	35	29
+LABC4	90	1	35	34
+LABCEA	90	1	35	34
+LABCMVPQ	1	2	35	32
+LABCMVQL	90	1	35	28
+LABCMVQT	90	5	35	30
+LABGGT	30	3	35	34
+LABGLNF	1	2	35	31
+LABHELI	1	1	35	34
+LABHELI	2	1	35	34
+LABHELI	4	1	35	34
+LABHELI	7	1	35	34
+LABHGBSQ	30	0	35	30
+LABIGG	1	1	35	30
+LABLPA	2	0	35	21
+LABLPA	4	0	35	21
+LABLPA	7	0	35	21
+LABLPA	30	0	35	21
+LABLPA	90	0	35	21
+LABMETHB	7	2	35	35
+LABMYO	7	1	35	29
+LABNASC	90	0	35	15
+LABNH3	1	2	35	30
+LABORANER	30	2	35	33
+LABPB	4	4	35	34
+LABPCINR	30	1	35	16
+LABPCTNI	30	4	35	34
+LABPFCATE	1	0	35	19
+LABPFCATE	2	0	35	19
+LABPHEN	4	5	35	26
+LABUPBGQT	1	NULL	35	30
+LABUPBGQT	2	NULL	35	30
+LABUPBGQT	4	NULL	35	30
+LABUPBGQT	30	NULL	35	30
+LABUPBGQT	90	NULL	35	30
+LABPLTS	2	2	35	28
+LABRETIC	2	2	35	29
+LABRF	1	1	35	35
+LABTP	2	1	35	27
+LABUAS	1	1	35	23
+LABUPBGQT	7	NULL	35	30
+LABVCUL	4	2	35	34
+LABYCLON	1	0	35	24
+LABYCLON	2	0	35	24
+LABYCLON	4	0	35	24
+LABYFCAR	4	0	35	26
+LABYUCORT	7	0	35	17
+LABYUCORT	30	0	35	17
+LAB11027R	4	0	34	24
+LAB11027R	7	0	34	24
+LAB11027R	30	0	34	24
+LAB11027R	90	0	34	24
+LAB11041R	90	5	34	28
+LABAFBCF	90	2	34	33
+LABAFBD	7	4	34	34
+LABAHP2	1	1	34	33
+LABAMIPRL	90	0	34	27
+LABBMT1	1	1	34	4
+LABCARN	7	NULL	34	20
+LABCEA	30	1	34	33
+LABCMVIGM	90	1	34	32
+LABCRP	4	3	34	30
+LABDIC	30	2	34	7
+LABDIC	90	2	34	7
+LABEBPCRQ	30	3	34	31
+LABGGT	90	3	34	33
+LABGIARS	90	1	34	34
+LABIBIL	7	3	34	34
+LABIGET	30	1	34	32
+LABMYO	4	1	34	28
+LABNH3	90	5	34	27
+LABOAPE	2	2	34	34
+LABCARN	1	NULL	34	20
+LABCARN	2	NULL	34	20
+LABCARN	4	NULL	34	20
+LABCARN	30	NULL	34	20
+LABCARN	90	NULL	34	20
+LABROGC	1	NULL	34	32
+LABROGC	2	NULL	34	32
+LABROGC	4	NULL	34	32
+LABROGC	30	NULL	34	32
+LABROGC	90	NULL	34	32
+LABSHIVAB	1	NULL	34	29
+LABSHIVAB	2	NULL	34	29
+LABSHIVAB	4	NULL	34	29
+LABSHIVAB	30	NULL	34	29
+LABSHIVAB	90	NULL	34	29
+LABPSA	7	1	34	33
+LABRBCFOL	4	1	34	22
+LABREPT	7	3	34	30
+LABRESPG	2	2	34	16
+LABROGC	1	0	34	32
+LABROGC	2	0	34	32
+LABROGC	4	0	34	32
+LABROGC	7	0	34	32
+LABROGC	7	NULL	34	32
+LABROGC	30	0	34	32
+LABROGC	90	0	34	32
+LABSHIVAB	7	NULL	34	29
+LABSIROL	7	5	34	30
+LABSIROL	30	5	34	30
+LABSIROL	90	5	34	30
+LABTEGCP	90	3	34	17
+LABUUN	1	1	34	26
+LABVPAL	30	5	34	22
+LABVPAL	90	5	34	21
+LABYCLON	7	0	34	23
+LABYCLON	30	0	34	23
+LABYCLON	90	0	34	23
+LABYCP	30	1	34	22
+LABYMPA	2	0	34	4
+LABYUCORT	90	0	34	16
+LABAFBC	2	4	33	32
+LABALB	4	3	33	22
+LABB12	90	2	33	33
+LABBKVQU	90	0	33	17
+LABBLC	7	5	33	31
+LABC4	30	1	33	32
+LABCARN	1	0	33	20
+LABCARN	2	0	33	20
+LABCARN	4	0	33	20
+LABCARN	7	0	33	20
+LABCARN	30	0	33	20
+LABCARN	90	0	33	20
+LABCMVQT	4	3	33	32
+LABCMVQT	30	5	33	29
+LABCRYST	90	1	33	19
+LABDIC	4	2	33	7
+LABDIC	7	2	33	7
+LABEBVPAN	90	1	33	33
+LABFVARZ	30	1	33	33
+LABGLF	30	1	33	22
+LABGLF	90	1	33	22
+LABHGBSQ	90	0	33	28
+LABISOF	7	1	33	32
+LABLIPS	4	5	33	32
+LABMETC	7	3	33	13
+LABMETHB	30	2	33	33
+LABMETHB	90	2	33	33
+LABMYCPA	7	2	33	23
+LABNH3	7	5	33	27
+LABNH3	30	5	33	27
+LABPCTNI	90	5	33	31
+LABPENALL	7	NULL	33	23
+LABPENALL	1	NULL	33	23
+LABPENALL	2	NULL	33	23
+LABPENALL	4	NULL	33	23
+LABPENALL	30	NULL	33	23
+LABPENALL	90	NULL	33	23
+LABVZPAN	1	NULL	33	25
+LABVZPAN	2	NULL	33	25
+LABVZPAN	4	NULL	33	25
+LABVZPAN	30	NULL	33	25
+LABVZPAN	90	NULL	33	25
+LABPROCT	4	3	33	30
+LABPSA	2	1	33	32
+LABPSA	4	1	33	32
+LABQFTBG	30	1	33	29
+LABRESPCR	2	1	33	28
+LABRESPG	90	3	33	26
+LABRPRQL	4	1	33	29
+LABSCMV	4	2	33	33
+LABSHIVAB	1	0	33	28
+LABSHIVAB	2	0	33	28
+LABSHIVAB	4	0	33	28
+LABSHIVAB	7	0	33	28
+LABSHIVAB	30	0	33	28
+LABSHIVAB	90	0	33	28
+LABSTLC	1	1	33	30
+LABTBPCRS	1	1	33	32
+LABTEGCP	30	3	33	17
+LABTSHFT4	4	1	33	29
+LABTT	1	1	33	27
+LABUEOS	7	2	33	32
+LABUEP	90	2	33	21
+LABUPBGQT	1	0	33	28
+LABVPAL	7	4	33	31
+LABVZPAN	1	0	33	25
+LABVZPAN	7	NULL	33	25
+LABYCP	4	1	33	20
+LABYCP	7	1	33	20
+LABYCP	90	1	33	21
+LABYPYRU	1	0	33	15
+LABYPYRU	2	0	33	15
+LABYSTCL	30	0	33	29
+LAB12762R	2	1	32	23
+LABALB	1	3	32	26
+LABALB	30	3	32	22
+LABALB	90	3	32	22
+LABAMIPRL	7	1	32	24
+LABBARTPC	1	0	32	31
+LABBARTPC	2	0	32	31
+LABBARTPC	7	NULL	32	31
+LABBLC	4	4	32	30
+LABBMPTP	1	0	32	6
+LABBMPTP	2	0	32	6
+LABBMPTP	4	0	32	6
+LABBMPTP	7	0	32	6
+LABBMPTP	7	NULL	32	6
+LABBMPTP	30	0	32	6
+LABBMPTP	90	0	32	6
+LABCMVP	7	2	32	30
+LABCORT	30	4	32	26
+LABCRPLB	1	1	32	25
+LABCRYST	30	1	32	18
+LABCSFGL	4	3	32	32
+LABDOA	90	2	32	22
+LABDVEVH	2	1	32	27
+LABF8INH	7	0	32	16
+LABFACT12	7	NULL	32	20
+LABFACT8	30	2	32	23
+LABFACT8	90	2	32	23
+LABGLNF	1	3	32	31
+LABHAP	4	4	32	31
+LABISOF	4	1	32	32
+LABLIPS	2	4	32	32
+LABMYCPA	4	2	32	24
+LABPB	7	3	32	29
+LABPFCATE	4	0	32	18
+LABPLASMN	1	0	32	21
+LABBARTPC	1	NULL	32	31
+LABBARTPC	2	NULL	32	31
+LABBARTPC	4	NULL	32	31
+LABBARTPC	30	NULL	32	31
+LABBARTPC	90	NULL	32	31
+LABBMPTP	1	NULL	32	6
+LABBMPTP	2	NULL	32	6
+LABBMPTP	4	NULL	32	6
+LABBMPTP	30	NULL	32	6
+LABBMPTP	90	NULL	32	6
+LABFACT12	1	NULL	32	20
+LABFACT12	2	NULL	32	20
+LABFACT12	4	NULL	32	20
+LABFACT12	30	NULL	32	20
+LABFACT12	90	NULL	32	20
+LABRBCFOL	2	1	32	22
+LABTT	90	1	32	21
+LABUPBGQT	2	0	32	28
+LABUPBGQT	4	0	32	28
+LABUPBGQT	7	0	32	28
+LABUPBGQT	30	0	32	28
+LABVOLTL	90	2	32	29
+LABVZPAN	2	0	32	24
+LABVZPAN	4	0	32	24
+LABVZPAN	7	0	32	24
+LABVZPAN	30	0	32	24
+LABVZPAN	90	0	32	24
+LABYFCAR	7	0	32	23
+LABYMPA	4	0	32	4
+LABYPYRU	4	0	32	14
+LABYPYRU	7	0	32	14
+LABYPYRU	30	0	32	14
+LABYPYRU	90	0	32	14
+LABYSTCL	90	0	32	28
+LAB12762R	4	1	31	23
+LABAFBCF	2	1	31	31
+LABAMIPOL	2	0	31	5
+LABAST	7	1	31	26
+LABCEA	7	1	31	30
+LABDSDNA	90	1	31	28
+LABDVEIAV	7	NULL	31	26
+LABEBVQP	1	1	31	27
+LABFACT8	4	2	31	23
+LABFILAB	7	NULL	31	30
+LABFK506L	1	3	31	30
+LABGLF	7	1	31	20
+LABHSVPCR	90	1	31	23
+LABHSVZL	90	1	31	28
+LABHV6DNA	7	1	31	30
+LABMB	1	5	31	27
+LABPB	90	3	31	28
+LABPFCATE	7	0	31	17
+LABPFCATE	30	0	31	17
+LABPFCATE	90	0	31	17
+LABPLTS	90	3	31	29
+LABDVEIAV	1	NULL	31	26
+LABDVEIAV	2	NULL	31	26
+LABDVEIAV	4	NULL	31	26
+LABDVEIAV	30	NULL	31	26
+LABDVEIAV	90	NULL	31	26
+LABFILAB	1	NULL	31	30
+LABFILAB	2	NULL	31	30
+LABFILAB	4	NULL	31	30
+LABFILAB	30	NULL	31	30
+LABFILAB	90	NULL	31	30
+LABTOTALS	1	NULL	31	10
+LABTOTALS	2	NULL	31	10
+LABTOTALS	4	NULL	31	10
+LABTOTALS	30	NULL	31	10
+LABTOTALS	90	NULL	31	10
+LABVITK	1	NULL	31	23
+LABVITK	2	NULL	31	23
+LABVITK	4	NULL	31	23
+LABVITK	30	NULL	31	23
+LABVITK	90	NULL	31	23
+LABYCHROM	1	NULL	31	7
+LABYCHROM	2	NULL	31	7
+LABYCHROM	4	NULL	31	7
+LABYCHROM	30	NULL	31	7
+LABYCHROM	90	NULL	31	7
+LABYMANG	1	NULL	31	17
+LABYMANG	2	NULL	31	17
+LABYMANG	4	NULL	31	17
+LABYMANG	30	NULL	31	17
+LABYMANG	90	NULL	31	17
+LABPSA	1	1	31	30
+LABTCDIF	7	2	31	31
+LABTOTALS	1	0	31	10
+LABTOTALS	2	0	31	10
+LABTOTALS	4	0	31	10
+LABTOTALS	7	0	31	10
+LABTOTALS	7	NULL	31	10
+LABTOTALS	30	0	31	10
+LABTOTALS	90	0	31	10
+LABTSHFT4	2	1	31	27
+LABTT	30	1	31	20
+LABUEOS	4	2	31	30
+LABUPBGQT	90	0	31	28
+LABVCUL	2	2	31	30
+LABVITK	7	NULL	31	23
+LABYCHROM	7	NULL	31	7
+LABYCP	1	1	31	20
+LABYMANG	1	0	31	17
+LABYMANG	7	NULL	31	17
+LABYMPA	7	0	31	4
+LAB12814R	7	NULL	30	11
+LABAFBD	4	4	30	30
+LABALB	7	3	30	21
+LABBARTPC	4	0	30	29
+LABBARTPC	7	0	30	29
+LABBARTPC	30	0	30	29
+LABBARTPC	90	0	30	29
+LABBLCSTK	30	5	30	26
+LABBXTG	7	3	30	29
+LABCA	7	3	30	26
+LABCA	30	3	30	26
+LABCA	90	3	30	26
+LABCDTPCR	90	5	30	23
+LABCMVRO	7	1	30	27
+LABCTC	90	2	30	28
+LABDVEIAV	1	0	30	25
+LABDVEIAV	2	0	30	25
+LABDVEIAV	4	0	30	25
+LABDVEIAV	7	0	30	25
+LABDVEIAV	30	0	30	25
+LABDVEIAV	90	0	30	25
+LABEBPCRQ	1	1	30	24
+LABFACT8	7	2	30	21
+LABGIARS	30	1	30	30
+LABGTT	7	NULL	30	17
+LABHAP	7	4	30	27
+LABHITAB	4	2	30	29
+LABINSLF	1	1	30	28
+LABINSLF	2	1	30	29
+LABLPDC	90	1	30	22
+LABMPOPR3	7	NULL	30	8
+LABORAERO	90	2	30	26
+LABORANER	4	2	30	29
+LABORANER	7	2	30	29
+LABPB	30	3	30	28
+LABPENALL	1	0	30	21
+LABPENALL	2	0	30	21
+LABPENALL	4	0	30	21
+LABPENALL	7	0	30	21
+LABPORS	1	0	30	29
+LABPORS	2	0	30	29
+LABPORS	4	0	30	29
+LABPORS	7	0	30	29
+LABPORS	7	NULL	30	29
+LABPORS	30	0	30	29
+LABPORS	90	0	30	29
+LABPREG	4	1	30	25
+LABPROL	30	1	30	20
+LABPROL	90	1	30	20
+LAB12814R	1	NULL	30	11
+LAB12814R	2	NULL	30	11
+LAB12814R	4	NULL	30	11
+LAB12814R	30	NULL	30	11
+LAB12814R	90	NULL	30	11
+LABGTT	1	NULL	30	17
+LABGTT	2	NULL	30	17
+LABGTT	4	NULL	30	17
+LABGTT	30	NULL	30	17
+LABGTT	90	NULL	30	17
+LABMPOPR3	1	NULL	30	8
+LABMPOPR3	2	NULL	30	8
+LABMPOPR3	4	NULL	30	8
+LABMPOPR3	30	NULL	30	8
+LABMPOPR3	90	NULL	30	8
+LABPORS	1	NULL	30	29
+LABPORS	2	NULL	30	29
+LABPORS	4	NULL	30	29
+LABPORS	30	NULL	30	29
+LABPORS	90	NULL	30	29
+LABYDESIP	1	NULL	30	9
+LABYDESIP	2	NULL	30	9
+LABYDESIP	4	NULL	30	9
+LABYDESIP	30	NULL	30	9
+LABYDESIP	90	NULL	30	9
+LABRPRQL	2	1	30	26
+LABSTLCX	1	1	30	28
+LABTBIL	30	3	30	29
+LABTHEOL	2	2	30	21
+LABTOBPRL	90	3	30	27
+LABUNA	90	2	30	25
+LABVD25H	7	1	30	23
+LABYDESIP	7	NULL	30	9
+LABYFLUO	1	0	30	25
+LABYMANG	2	0	30	16
+LAB11036R	2	1	29	21
+LAB12762R	7	1	29	21
+LAB12762R	30	1	29	21
+LAB12762R	90	1	29	21
+LAB12814R	1	0	29	11
+LAB12814R	2	0	29	11
+LABADPCRQ	90	1	29	29
+LABAFBCF	1	1	29	29
+LABASI	7	2	29	29
+LABBXTG	4	3	29	29
+LABCA199	90	1	29	22
+LABCLACT	2	1	29	26
+LABCMVP	4	2	29	27
+LABCMVQT	7	3	29	28
+LABCR	4	4	29	29
+LABCRYST	7	1	29	16
+LABEBVPAN	30	1	29	29
+LABFCUL	7	5	29	25
+LABFLDC	7	3	29	28
+LABFT4	30	3	29	27
+LABGLF	2	1	29	19
+LABGLF	4	1	29	19
+LABGLNF	30	2	29	21
+LABGLNF	90	2	29	21
+LABGTT	1	0	29	16
+LABGTT	2	0	29	16
+LABGTT	4	0	29	16
+LABGTT	7	0	29	16
+LABGTT	30	0	29	16
+LABLEVETL	90	1	29	21
+LABMETHB	1	3	29	29
+LABMETHB	7	3	29	28
+LABMETHB	30	3	29	28
+LABMETHB	90	3	29	28
+LABNORW	4	1	29	27
+LABPALB	1	1	29	20
+LABPANAPL	7	NULL	29	12
+LABPCEC4V	90	1	29	14
+LABPENALL	30	0	29	21
+LABPENALL	90	0	29	21
+LABPROL	7	1	29	19
+LABPANAPL	1	NULL	29	12
+LABPANAPL	2	NULL	29	12
+LABPANAPL	4	NULL	29	12
+LABPANAPL	30	NULL	29	12
+LABPANAPL	90	NULL	29	12
+LABUPHOS	1	NULL	29	15
+LABUPHOS	2	NULL	29	15
+LABUPHOS	4	NULL	29	15
+LABUPHOS	30	NULL	29	15
+LABUPHOS	90	NULL	29	15
+LABTT	7	1	29	20
+LABUAPRN	90	3	29	15
+LABUPHOS	7	NULL	29	15
+LABVITK	1	0	29	22
+LABVITK	2	0	29	22
+LABVITK	4	0	29	22
+LABVITK	7	0	29	22
+LABVITK	30	0	29	22
+LABVITK	90	0	29	22
+LABYCHROM	1	0	29	7
+LABYCHROM	2	0	29	7
+LABYDESIP	1	0	29	8
+LABYMPA	30	0	29	3
+LABYMPA	90	0	29	3
+LAB11025R	7	NULL	28	22
+LAB12814R	4	0	28	11
+LAB12814R	7	0	28	11
+LAB12814R	30	0	28	11
+LAB12814R	90	0	28	11
+LABACA	4	1	28	27
+LABAFBD	90	5	28	28
+LABAMIPRL	4	1	28	21
+LABAST	4	1	28	23
+LABBXTG	2	3	28	28
+LABCEA	4	1	28	27
+LABCMVQL	30	1	28	23
+LABCMVRO	4	1	28	26
+LABCORT	1	3	28	22
+LABCR	4	2	28	26
+LABDSDNA	30	1	28	25
+LABDVERV	7	2	28	28
+LABDVERV	90	3	28	26
+LABF8INH	30	0	28	15
+LABFACT12	1	0	28	20
+LABFACT12	2	0	28	20
+LABFACT12	4	0	28	20
+LABFACT12	7	0	28	20
+LABGIARS	7	1	28	28
+LABGTT	90	0	28	16
+LABHELAG	30	1	28	26
+LABHELAG	90	1	28	26
+LABHGBX	30	1	28	11
+LABHGBX	90	1	28	11
+LABIOPTH	90	1	28	18
+LABMEP	7	NULL	28	7
+LABMPOPR3	1	0	28	8
+LABPB	7	5	28	26
+LABPLTS	30	3	28	26
+LAB11025R	1	NULL	28	22
+LAB11025R	2	NULL	28	22
+LAB11025R	4	NULL	28	22
+LAB11025R	30	NULL	28	22
+LAB11025R	90	NULL	28	22
+LABMEP	1	NULL	28	7
+LABMEP	2	NULL	28	7
+LABMEP	4	NULL	28	7
+LABMEP	30	NULL	28	7
+LABMEP	90	NULL	28	7
+LABRPRQT	1	NULL	28	5
+LABRPRQT	2	NULL	28	5
+LABRPRQT	4	NULL	28	5
+LABRPRQT	30	NULL	28	5
+LABRPRQT	90	NULL	28	5
+LABTHIOMT	1	NULL	28	26
+LABTHIOMT	2	NULL	28	26
+LABTHIOMT	4	NULL	28	26
+LABQFTBG	7	1	28	24
+LABTHIOMT	30	NULL	28	26
+LABTHIOMT	90	NULL	28	26
+LABYAT3AG	1	NULL	28	17
+LABYAT3AG	2	NULL	28	17
+LABYAT3AG	4	NULL	28	17
+LABYAT3AG	30	NULL	28	17
+LABYAT3AG	90	NULL	28	17
+LABREPT	4	3	28	24
+LABREPT	30	3	28	25
+LABREPT	90	3	28	25
+LABRPRQT	1	0	28	5
+LABRPRQT	7	NULL	28	5
+LABSCMV	2	2	28	28
+LABTHIOMT	1	0	28	26
+LABTHIOMT	2	0	28	26
+LABTHIOMT	7	NULL	28	26
+LABUPHOS	1	0	28	15
+LABUPHOS	2	0	28	15
+LABVCUL	1	2	28	28
+LABVCUL	30	3	28	27
+LABVPAL	7	5	28	18
+LABYAT3AG	7	NULL	28	17
+LABYCHROM	4	0	28	7
+LABYCHROM	7	0	28	7
+LABYCP	2	1	28	19
+LABYDESIP	2	0	28	8
+LABYFCAR	30	0	28	20
+LABYFCAR	90	0	28	20
+LABYFLUO	2	0	28	24
+LAB11025R	1	0	27	21
+LAB11025R	2	0	27	21
+LAB11025R	4	0	27	21
+LAB11025R	7	0	27	21
+LAB12781R	1	0	27	26
+LAB12781R	2	0	27	26
+LAB12781R	4	0	27	26
+LAB12781R	7	0	27	26
+LAB12781R	7	NULL	27	26
+LAB12781R	30	0	27	26
+LAB12781R	90	0	27	26
+LABADIFF	1	0	27	6
+LABADIFF	2	0	27	6
+LABADIFF	4	0	27	6
+LABADIFF	7	0	27	6
+LABADIFF	7	NULL	27	6
+LABADIFF	30	0	27	6
+LABADIFF	90	0	27	6
+LABADPCRQ	30	1	27	27
+LABAMIPRL	30	1	27	19
+LABANER2	30	4	27	22
+LABBLCA	90	1	27	26
+LABBLCSTK	7	3	27	26
+LABC4	7	1	27	26
+LABCA199	30	1	27	20
+LABCMVIGG	2	1	27	27
+LABCMVPQ	7	5	27	26
+LABCMVQL	7	1	27	22
+LABCRYST	4	1	27	15
+LABCSFC	4	3	27	26
+LABCSFC	7	4	27	27
+LABCSFCT	30	2	27	15
+LABEBVPAN	7	1	27	27
+LABESRP	90	3	27	17
+LABF8INH	90	0	27	15
+LABFACT12	30	0	27	19
+LABFACT12	90	0	27	19
+LABFIBANT	7	NULL	27	16
+LABFILAB	1	0	27	26
+LABFILAB	2	0	27	26
+LABFILAB	4	0	27	26
+LABFILAB	7	0	27	26
+LABG6PDQT	7	NULL	27	19
+LABGLNF	7	2	27	21
+LABHATA	1	0	27	7
+LABHATA	2	0	27	7
+LABHATA	7	NULL	27	7
+LABHGBP	30	4	27	23
+LABHHV6QT	90	1	27	21
+LABIOPTH	1	1	27	18
+LABIOPTH	2	1	27	18
+LABIOPTH	4	1	27	18
+LABIOPTH	7	1	27	18
+LABIOPTH	30	1	27	18
+LABISOF	2	1	27	27
+LABLEGDA	30	2	27	27
+LABOAPE	90	3	27	27
+LABORANER	2	2	27	27
+LABPCEG7	30	1	27	3
+LABPCEG7	90	1	27	3
+LABPROCT	90	3	27	23
+LAB12781R	1	NULL	27	26
+LAB12781R	2	NULL	27	26
+LAB12781R	4	NULL	27	26
+LAB12781R	30	NULL	27	26
+LAB12781R	90	NULL	27	26
+LABADIFF	1	NULL	27	6
+LABADIFF	2	NULL	27	6
+LABADIFF	4	NULL	27	6
+LABADIFF	30	NULL	27	6
+LABADIFF	90	NULL	27	6
+LABFIBANT	1	NULL	27	16
+LABFIBANT	2	NULL	27	16
+LABFIBANT	4	NULL	27	16
+LABFIBANT	30	NULL	27	16
+LABFIBANT	90	NULL	27	16
+LABG6PDQT	1	NULL	27	19
+LABG6PDQT	2	NULL	27	19
+LABG6PDQT	4	NULL	27	19
+LABG6PDQT	30	NULL	27	19
+LABG6PDQT	90	NULL	27	19
+LABHATA	1	NULL	27	7
+LABHATA	2	NULL	27	7
+LABHATA	4	NULL	27	7
+LABHATA	30	NULL	27	7
+LABHATA	90	NULL	27	7
+LABSTOBID	1	NULL	27	11
+LABSTOBID	2	NULL	27	11
+LABSTOBID	4	NULL	27	11
+LABRESP	7	4	27	21
+LABSTOBID	30	NULL	27	11
+LABSTOBID	90	NULL	27	11
+LABYTESFB	1	NULL	27	8
+LABYTESFB	2	NULL	27	8
+LABYTESFB	4	NULL	27	8
+LABYTESFB	30	NULL	27	8
+LABYTESFB	90	NULL	27	8
+LABRESPG	1	2	27	14
+LABSCMV	1	2	27	27
+LABSCMV	90	3	27	26
+LABSIROL	1	2	27	23
+LABSTOBID	1	0	27	11
+LABSTOBID	7	NULL	27	11
+LABTHEOL	4	2	27	23
+LABTHEOL	7	2	27	22
+LABTHEOL	30	2	27	22
+LABTHIOMT	4	0	27	26
+LABTT	4	1	27	19
+LABUEP	30	2	27	18
+LABUPHOS	4	0	27	15
+LABYAT3AG	1	0	27	17
+LABYBOH	90	1	27	19
+LABYCHROM	30	0	27	6
+LABYFLUO	4	0	27	23
+LABYMANG	4	0	27	14
+LABYMANG	7	0	27	14
+LABYTESFB	1	0	27	8
+LABYTESFB	7	NULL	27	8
+LAB11025R	30	0	26	21
+LAB11025R	90	0	26	21
+LAB12330R	2	0	26	16
+LABADAM13	7	NULL	26	3
+LABAGALA	90	2	26	25
+LABAMIPOL	4	0	26	3
+LABAMIPRL	90	1	26	18
+LABB12	30	2	26	26
+LABBLC	1	3	26	25
+LABCMVIGM	30	1	26	25
+LABCMVM	90	1	26	22
+LABCMVP	1	2	26	24
+LABCMVP	2	2	26	24
+LABCMVPQ	4	4	26	26
+LABCMVQL	4	1	26	21
+LABCMVQT	2	2	26	25
+LABCMVRO	1	1	26	25
+LABCMVRO	2	1	26	25
+LABCORT	7	4	26	20
+LABCRP	7	3	26	23
+LABCRYST	2	1	26	14
+LABCYSBLD	7	NULL	26	22
+LABDDIM	90	2	26	8
+LABDIGL	1	2	26	25
+LABDVEHV	2	1	26	15
+LABFHV12	90	1	26	24
+LABFILAB	30	0	26	25
+LABFILAB	90	0	26	25
+LABFLDC	30	5	26	19
+LABFVARZ	7	1	26	26
+LABGLNF	4	2	26	20
+LABGRAM	2	3	26	24
+LABHATA	4	0	26	7
+LABHATA	7	0	26	7
+LABHATA	30	0	26	7
+LABHATA	90	0	26	7
+LABHAVM	90	1	26	25
+LABHGBP	90	4	26	23
+LABHGBX	7	1	26	11
+LABHIAB	1	1	26	23
+LABHSVZL	30	1	26	24
+LABIBIL	2	2	26	25
+LABINSLF	1	3	26	25
+LABLIL	2	2	26	21
+LABMEP	1	0	26	5
+LABMEP	2	0	26	5
+LABMEP	4	0	26	5
+LABMEP	7	0	26	5
+LABMEP	30	0	26	5
+LABMEP	90	0	26	5
+LABORAERO	30	2	26	21
+LABORSHOR	90	3	26	24
+LABPCEG7	7	1	26	3
+LABPCG3V	1	2	26	11
+LABPROL	4	1	26	18
+LABRAAS	7	NULL	26	24
+LABADAM13	1	NULL	26	3
+LABADAM13	2	NULL	26	3
+LABADAM13	4	NULL	26	3
+LABADAM13	30	NULL	26	3
+LABADAM13	90	NULL	26	3
+LABCYSBLD	1	NULL	26	22
+LABCYSBLD	2	NULL	26	22
+LABCYSBLD	4	NULL	26	22
+LABRETIC	7	2	26	20
+LABCYSBLD	30	NULL	26	22
+LABCYSBLD	90	NULL	26	22
+LABRAAS	1	NULL	26	24
+LABRAAS	2	NULL	26	24
+LABRAAS	4	NULL	26	24
+LABRAAS	30	NULL	26	24
+LABRAAS	90	NULL	26	24
+LABYANTJO	1	NULL	26	25
+LABYANTJO	2	NULL	26	25
+LABYANTJO	4	NULL	26	25
+LABYANTJO	30	NULL	26	25
+LABYANTJO	90	NULL	26	25
+LABYASU	1	NULL	26	25
+LABYASU	2	NULL	26	25
+LABYASU	4	NULL	26	25
+LABYASU	30	NULL	26	25
+LABYASU	90	NULL	26	25
+LABYTHI	1	NULL	26	18
+LABYTHI	2	NULL	26	18
+LABYTHI	4	NULL	26	18
+LABYTHI	30	NULL	26	18
+LABYTHI	90	NULL	26	18
+LABYUFCAT	1	NULL	26	14
+LABYUFCAT	2	NULL	26	14
+LABYUFCAT	4	NULL	26	14
+LABYUFCAT	30	NULL	26	14
+LABYUFCAT	90	NULL	26	14
+LABRPRQL	1	1	26	22
+LABRPRQT	2	0	26	5
+LABRPRQT	4	0	26	5
+LABRPRQT	7	0	26	5
+LABRPRQT	30	0	26	5
+LABRPRQT	90	0	26	5
+LABSTOBID	2	0	26	11
+LABSTOBID	4	0	26	11
+LABSTOBID	7	0	26	11
+LABSTRED	30	1	26	9
+LABTBILFR	7	2	26	25
+LABTHEOL	90	2	26	21
+LABTHIOMT	7	0	26	25
+LABTHIOMT	30	0	26	25
+LABTHIOMT	90	0	26	25
+LABTOBPRL	7	2	26	25
+LABUPHOS	7	0	26	15
+LABUPHOS	30	0	26	15
+LABUPHOS	90	0	26	15
+LABYANTJO	1	0	26	25
+LABYANTJO	2	0	26	25
+LABYANTJO	4	0	26	25
+LABYANTJO	7	0	26	25
+LABYANTJO	7	NULL	26	25
+LABYANTJO	30	0	26	25
+LABYANTJO	90	0	26	25
+LABYASU	7	NULL	26	25
+LABYAT3AG	2	0	26	16
+LABYBOH	30	1	26	18
+LABYDESIP	4	0	26	7
+LABYMANG	30	0	26	13
+LABYTHI	7	NULL	26	18
+LABYUFCAT	1	0	26	14
+LABYUFCAT	2	0	26	14
+LABYUFCAT	7	NULL	26	14
+LAB11182R	7	NULL	25	15
+LAB12809R	1	0	25	17
+LABA1C	30	3	25	24
+LABAGAP	1	0	25	22
+LABAGAP	7	NULL	25	22
+LABCA199	7	1	25	18
+LABCBCO	1	3	25	19
+LABCEA	2	1	25	24
+LABCMVIGG	1	1	25	25
+LABCORTS	1	2	25	15
+LABCORTS	4	3	25	17
+LABCR	90	2	25	21
+LABCSFTP	7	3	25	24
+LABCYSBLD	1	0	25	22
+LABCYSBLD	2	0	25	22
+LABCYSBLD	4	0	25	22
+LABCYSBLD	7	0	25	22
+LABCYSBLD	30	0	25	22
+LABCYSBLD	90	0	25	22
+LABCZPL	90	1	25	11
+LABDBIL	90	4	25	21
+LABDDIM	1	2	25	8
+LABDDIM	2	2	25	8
+LABDDIM	4	2	25	8
+LABDDIM	7	2	25	8
+LABDDIM	30	2	25	8
+LABDIGL	2	3	25	25
+LABDIGL	4	5	25	24
+LABEBVQP	90	3	25	21
+LABFACT8	1	2	25	17
+LABFHV12	30	1	25	23
+LABFKOH	2	2	25	22
+LABFVARZ	4	1	25	25
+LABG6PDQT	1	0	25	17
+LABG6PDQT	2	0	25	17
+LABG6PDQT	4	0	25	17
+LABGIARS	4	1	25	25
+LABHAB	1	0	25	13
+LABHAB	2	0	25	13
+LABHAB	4	0	25	13
+LABHAB	7	0	25	13
+LABHAB	7	NULL	25	13
+LABHAB	30	0	25	13
+LABHAB	90	0	25	13
+LABHBCT	90	1	25	24
+LABHCPQ	30	1	25	24
+LABHCTX	2	3	25	18
+LABHGBX	2	1	25	11
+LABHGBX	4	1	25	11
+LABHSVPCR	30	1	25	18
+LABHV6DNA	4	1	25	24
+LABLEVETL	30	1	25	18
+LABMETHB	1	2	25	25
+LABMETHB	30	4	25	25
+LABMETHB	90	4	25	25
+LABMPOPR3	2	0	25	8
+LABORSHOR	30	3	25	23
+LABPCEC4V	30	1	25	12
+LABPCEG7	2	1	25	3
+LABPCEG7	4	1	25	3
+LABQFTB	2	1	25	21
+LABREPT	2	2	25	20
+LAB11182R	1	NULL	25	15
+LABROMRS	2	2	25	24
+LAB11182R	2	NULL	25	15
+LAB11182R	4	NULL	25	15
+LAB11182R	30	NULL	25	15
+LAB11182R	90	NULL	25	15
+LABAGAP	1	NULL	25	22
+LABAGAP	2	NULL	25	22
+LABAGAP	4	NULL	25	22
+LABAGAP	30	NULL	25	22
+LABAGAP	90	NULL	25	22
+LABHAB	1	NULL	25	13
+LABHAB	2	NULL	25	13
+LABHAB	4	NULL	25	13
+LABHAB	30	NULL	25	13
+LABHAB	90	NULL	25	13
+LABSTOBID	30	0	25	10
+LABSTOBID	90	0	25	10
+LABTBILFR	30	2	25	21
+LABTBILFR	90	2	25	22
+LABTSH	30	3	25	21
+LABVD25H	4	1	25	19
+LABYASU	1	0	25	24
+LABYASU	2	0	25	24
+LABYASU	4	0	25	24
+LABYASU	7	0	25	24
+LABYASU	30	0	25	24
+LABYASU	90	0	25	24
+LABYAT3AG	4	0	25	16
+LABYAT3AG	7	0	25	16
+LABYAT3AG	30	0	25	16
+LABYAT3AG	90	0	25	16
+LABYBOH	4	1	25	16
+LABYBOH	7	1	25	16
+LABYFLUO	7	0	25	21
+LABYHGBP	4	1	25	22
+LABYTESFB	2	0	25	7
+LABYTESFB	4	0	25	7
+LABYUFCAT	4	0	25	13
+LABYUFCAT	7	0	25	13
+LABYUFCAT	30	0	25	13
+LAB11036R	4	1	24	15
+LAB11036R	30	1	24	13
+LAB11036R	90	1	24	13
+LAB11041R	4	2	24	20
+LAB11182R	1	0	24	14
+LAB11182R	2	0	24	14
+LAB11182R	4	0	24	14
+LAB11182R	7	0	24	14
+LAB11182R	30	0	24	14
+LAB11182R	90	0	24	14
+LAB12809R	2	0	24	16
+LABAAP	7	NULL	24	1
+LABADAM13	1	0	24	3
+LABADAM13	2	0	24	3
+LABAFBD	2	4	24	24
+LABAGAP	2	0	24	21
+LABAGAP	4	0	24	21
+LABAGAP	7	0	24	21
+LABALB	2	3	24	18
+LABANCA	30	1	24	21
+LABANCA	90	1	24	21
+LABBACIDS	90	1	24	22
+LABBHB	90	1	24	15
+LABBUN	7	2	24	16
+LABBXTG	1	3	24	24
+LABC4	4	1	24	23
+LABCMVQL	1	1	24	19
+LABCMVQL	2	1	24	19
+LABCORTS	7	3	24	16
+LABCORTS	30	3	24	16
+LABCORTS	90	3	24	16
+LABCRYST	1	1	24	13
+LABCSFGL	7	4	24	22
+LABCSFGL	7	5	24	24
+LABCTC	30	2	24	23
+LABCYLEX	1	0	24	13
+LABCYLEX	2	0	24	13
+LABCYLEX	7	NULL	24	13
+LABDBIL	30	4	24	20
+LABDVEVH	1	1	24	20
+LABECL	7	NULL	24	16
+LABFLDB	90	3	24	22
+LABFLUAPX	1	1	24	23
+LABGGT	90	4	24	22
+LABGLNF	2	2	24	19
+LABHAV	7	NULL	24	23
+LABHAVM	30	1	24	23
+LABHBCT	30	1	24	23
+LABHCPQ	90	1	24	23
+LABHGBP	7	3	24	21
+LABHIPA	90	2	24	17
+LABHIVQ2	90	1	24	21
+LABINSLF	90	1	24	21
+LABISOF	1	1	24	24
+LABLPDC	30	1	24	17
+LABMETHB	7	4	24	24
+LABMPOPR3	4	0	24	8
+LABMPOPR3	7	0	24	8
+LABMPOPR3	30	0	24	8
+LABMPOPR3	90	0	24	8
+LABMYO	2	2	24	21
+LABPB	30	4	24	22
+LABPB	90	4	24	22
+LABPCCG4O	90	2	24	22
+LABPCEG7	1	1	24	3
+LABPCEG7V	2	1	24	4
+LABPCEG7V	4	1	24	4
+LABPCEG7V	7	1	24	4
+LABPCEG7V	30	1	24	4
+LABPCEG7V	90	1	24	4
+LABPROCT	4	4	24	24
+LABPROL	2	1	24	17
+LABRAAS	1	0	24	22
+LABRAAS	2	0	24	22
+LABRAAS	4	0	24	22
+LABRAAS	7	0	24	22
+LABRAAS	30	0	24	22
+LABRAAS	90	0	24	22
+LABRESPG	30	3	24	21
+LABRETIC	4	2	24	18
+LABAAP	1	NULL	24	1
+LABAAP	2	NULL	24	1
+LABAAP	4	NULL	24	1
+LABAAP	30	NULL	24	1
+LABAAP	90	NULL	24	1
+LABCYLEX	1	NULL	24	13
+LABCYLEX	2	NULL	24	13
+LABCYLEX	4	NULL	24	13
+LABCYLEX	30	NULL	24	13
+LABCYLEX	90	NULL	24	13
+LABECL	1	NULL	24	16
+LABECL	2	NULL	24	16
+LABECL	4	NULL	24	16
+LABECL	30	NULL	24	16
+LABECL	90	NULL	24	16
+LABHAV	1	NULL	24	23
+LABHAV	2	NULL	24	23
+LABHAV	4	NULL	24	23
+LABHAV	30	NULL	24	23
+LABHAV	90	NULL	24	23
+LABYAVH	1	NULL	24	20
+LABYAVH	2	NULL	24	20
+LABYAVH	4	NULL	24	20
+LABYAVH	30	NULL	24	20
+LABYAVH	90	NULL	24	20
+LABYMEXIL	1	NULL	24	12
+LABYMEXIL	2	NULL	24	12
+LABYMEXIL	4	NULL	24	12
+LABYMEXIL	30	NULL	24	12
+LABYMEXIL	90	NULL	24	12
+LABSTRED	7	1	24	7
+LABSTRED	90	1	24	8
+LABTBIL	4	2	24	22
+LABTCDIF	90	3	24	24
+LABUEOS	2	2	24	23
+LABUPREG	90	2	24	22
+LABVCULR	7	1	24	24
+LABYAVH	7	NULL	24	20
+LABYCOPPR	90	1	24	15
+LABYDESIP	7	0	24	6
+LABYMEXIL	7	NULL	24	12
+LABYPTHRP	90	1	24	23
+LABYTESFB	7	0	24	7
+LABYTESFB	30	0	24	7
+LABYUFCAT	90	0	24	12
+LAB11036R	7	1	23	13
+LAB12809R	4	0	23	16
+LAB12809R	7	0	23	16
+LAB12809R	30	0	23	16
+LAB12809R	90	0	23	16
+LABAAP	1	0	23	1
+LABACA	2	1	23	23
+LABADAM13	4	0	23	3
+LABADAM13	7	0	23	3
+LABADAM13	30	0	23	3
+LABAFBC	7	5	23	23
+LABAFBD	30	5	23	23
+LABAGAP	30	0	23	20
+LABAGAP	90	0	23	20
+LABAMIPOL	7	0	23	3
+LABANCA	7	1	23	20
+LABANER2	2	4	23	19
+LABANER2	4	4	23	19
+LABANER2	7	4	23	19
+LABANTIXA	7	1	23	2
+LABANTIXA	30	1	23	2
+LABANTIXA	90	1	23	2
+LABASI	30	4	23	23
+LABBACIDS	30	1	23	21
+LABBHB	30	1	23	14
+LABBHCG	30	1	23	22
+LABBLCTIP	1	2	23	23
+LABBXTG	90	4	23	18
+LABC3	7	1	23	22
+LABC4	2	1	23	22
+LABCEA	1	1	23	21
+LABCLACT	90	2	23	13
+LABCORT	4	4	23	17
+LABCR	7	5	23	23
+LABCR	30	2	23	19
+LABCRPLC	7	2	23	23
+LABCSFTP	4	2	23	17
+LABCZPL	30	1	23	11
+LABDVERVP	4	2	23	14
+LABEBVPAN	4	1	23	23
+LABESRP	4	4	23	22
+LABESRP	30	3	23	15
+LABFACT8	4	3	23	17
+LABFHHV6	1	0	23	4
+LABFHHV6	2	0	23	4
+LABFHHV6	4	0	23	4
+LABFHHV6	7	0	23	4
+LABFHHV6	7	NULL	23	4
+LABFHHV6	30	0	23	4
+LABFHHV6	90	0	23	4
+LABFLDB	7	2	23	21
+LABFUNIDS	1	0	23	18
+LABFUNIDS	7	NULL	23	18
+LABG6PDQT	7	0	23	16
+LABGGT	30	4	23	21
+LABHAP	1	2	23	23
+LABHAV	1	0	23	22
+LABHAV	2	0	23	22
+LABHAV	4	0	23	22
+LABHAV	7	0	23	22
+LABHAV	30	0	23	22
+LABHAV	90	0	23	22
+LABHAVM	7	1	23	22
+LABHFP	90	4	23	14
+LABHGBX	1	1	23	9
+LABHITAB	90	3	23	19
+LABHIVWBL	90	2	23	23
+LABIBIL	30	4	23	22
+LABIBIL	90	4	23	22
+LABINSLF	30	1	23	20
+LABLIL	7	3	23	19
+LABMETHB	2	3	23	23
+LABMETHB	7	5	23	23
+LABOAPE	30	3	23	23
+LABORAERO	7	2	23	20
+LABORSHOR	1	3	23	23
+LABORSHOR	2	3	23	23
+LABORSHOR	4	3	23	23
+LABORSHOR	7	3	23	23
+LABPLTS	4	3	23	21
+LABPREG	2	1	23	18
+LABPROCT	30	3	23	19
+LABRBCFOL	1	1	23	17
+LABRUB	1	0	23	1
+LABRUB	7	NULL	23	1
+LABFHHV6	1	NULL	23	4
+LABFHHV6	2	NULL	23	4
+LABFHHV6	4	NULL	23	4
+LABFHHV6	30	NULL	23	4
+LABFHHV6	90	NULL	23	4
+LABFUNIDS	1	NULL	23	18
+LABFUNIDS	2	NULL	23	18
+LABFUNIDS	4	NULL	23	18
+LABFUNIDS	30	NULL	23	18
+LABFUNIDS	90	NULL	23	18
+LABRUB	1	NULL	23	1
+LABRUB	2	NULL	23	1
+LABRUB	4	NULL	23	1
+LABRUB	30	NULL	23	1
+LABRUB	90	NULL	23	1
+LABTSHFT4	1	1	23	20
+LABUNA	30	2	23	19
+LABVANPRL	4	5	23	18
+LABYAVH	1	0	23	19
+LABYAVH	2	0	23	19
+LABYAVH	4	0	23	19
+LABYCERU	90	1	23	17
+LABYCHROM	90	0	23	5
+LABYHGBP	7	1	23	20
+LABYTESFB	90	0	23	7
+LABYTHI	1	0	23	15
+LABYTHI	2	0	23	15
+LAB11080R	1	0	22	18
+LAB11080R	7	NULL	22	18
+LAB12815R	1	0	22	21
+LAB12815R	2	0	22	21
+LAB12815R	7	NULL	22	21
+LABAAP	2	0	22	1
+LABAAP	4	0	22	1
+LABAAP	7	0	22	1
+LABAAP	30	0	22	1
+LABAAP	90	0	22	1
+LABADAM13	90	0	22	3
+LABADPCRQ	7	1	22	22
+LABAFBD	1	4	22	22
+LABANER2	90	5	22	19
+LABANTIXA	4	1	22	2
+LABASI	90	5	22	22
+LABBUN	90	3	22	12
+LABCA	2	3	22	20
+LABCA	4	4	22	20
+LABCA199	4	1	22	16
+LABCDTPCR	4	2	22	17
+LABCLACT	30	2	22	13
+LABCMVM	30	1	22	19
+LABCRP	90	5	22	15
+LABCRPLC	1	1	22	19
+LABCSFC	4	4	22	22
+LABCZPL	7	1	22	11
+LABDATBSI	1	1	22	22
+LABDSDNA	7	1	22	19
+LABDVEIBV	1	0	22	20
+LABDVEIBV	2	0	22	20
+LABDVEIBV	4	0	22	20
+LABDVEIBV	7	0	22	20
+LABDVEIBV	7	NULL	22	20
+LABDVEIBV	30	0	22	20
+LABDVEIBV	90	0	22	20
+LABFKOH	30	3	22	20
+LABFLDB	30	3	22	20
+LABFUNIDS	2	0	22	17
+LABFUNIDS	4	0	22	17
+LABG6PDQT	30	0	22	15
+LABG6PDQT	90	0	22	15
+LABGGT	7	4	22	20
+LABGLF	1	1	22	19
+LABHAVM	2	1	22	21
+LABHAVM	4	1	22	21
+LABHBPQ	30	1	22	21
+LABHSVZL	7	1	22	20
+LABINSLF	1	2	22	22
+LABINSLF	7	1	22	19
+LABLIL	90	4	22	18
+LABLPDC	4	1	22	16
+LABLPDC	7	1	22	16
+LABMYCPA	90	3	22	9
+LABPB	7	4	22	20
+LABPCEG7V	1	1	22	4
+LABPCG3	1	5	22	8
+LABPROL	1	1	22	16
+LABRESPCR	90	2	22	17
+LABRUB	2	0	22	1
+LAB11080R	1	NULL	22	18
+LAB11080R	2	NULL	22	18
+LAB11080R	4	NULL	22	18
+LAB11080R	30	NULL	22	18
+LAB11080R	90	NULL	22	18
+LAB12815R	1	NULL	22	21
+LAB12815R	2	NULL	22	21
+LAB12815R	4	NULL	22	21
+LAB12815R	30	NULL	22	21
+LAB12815R	90	NULL	22	21
+LABDVEIBV	1	NULL	22	20
+LABDVEIBV	2	NULL	22	20
+LABDVEIBV	4	NULL	22	20
+LABDVEIBV	30	NULL	22	20
+LABDVEIBV	90	NULL	22	20
+LABUCA	1	NULL	22	17
+LABUCA	2	NULL	22	17
+LABUCA	4	NULL	22	17
+LABUCA	30	NULL	22	17
+LABUCA	90	NULL	22	17
+LABUVH	1	NULL	22	16
+LABUVH	2	NULL	22	16
+LABUVH	4	NULL	22	16
+LABUVH	30	NULL	22	16
+LABUVH	90	NULL	22	16
+LABYC1IN	1	NULL	22	15
+LABYC1IN	2	NULL	22	15
+LABYC1IN	4	NULL	22	15
+LABYC1IN	30	NULL	22	15
+LABYC1IN	90	NULL	22	15
+LABYTAT	1	NULL	22	14
+LABYTAT	2	NULL	22	14
+LABYTAT	4	NULL	22	14
+LABYTAT	30	NULL	22	14
+LABYTAT	90	NULL	22	14
+LABYVD125	1	NULL	22	10
+LABYVD125	2	NULL	22	10
+LABYVD125	4	NULL	22	10
+LABYVD125	30	NULL	22	10
+LABYVD125	90	NULL	22	10
+LABSTOOLC	1	1	22	21
+LABSTRED	2	1	22	8
+LABSTRED	4	1	22	7
+LABTCDIF	4	2	22	22
+LABTP	1	1	22	18
+LABUCA	1	0	22	17
+LABUCA	2	0	22	17
+LABUCA	4	0	22	17
+LABUCA	7	0	22	17
+LABUCA	7	NULL	22	17
+LABUCA	30	0	22	17
+LABUCA	90	0	22	17
+LABUVH	1	0	22	16
+LABUVH	2	0	22	16
+LABUVH	4	0	22	16
+LABUVH	7	NULL	22	16
+LABYA1AS	90	1	22	18
+LABYBOH	2	1	22	16
+LABYC1IN	7	NULL	22	15
+LABYCOPPR	30	1	22	13
+LABYSTOSM	30	1	22	12
+LABYSTOSM	90	1	22	12
+LABYTAT	1	0	22	14
+LABYTAT	2	0	22	14
+LABYTAT	4	0	22	14
+LABYTAT	7	NULL	22	14
+LABYTHI	4	0	22	15
+LABYTHI	7	0	22	15
+LABYTHI	30	0	22	15
+LABYTHI	90	0	22	15
+LABYVD125	1	0	22	10
+LABYVD125	2	0	22	10
+LABYVD125	4	0	22	10
+LABYVD125	7	NULL	22	10
+LAB11042R	7	NULL	21	7
+LAB11060R	90	1	21	15
+LAB11080R	2	0	21	17
+LAB11080R	4	0	21	17
+LAB11080R	7	0	21	17
+LAB11080R	30	0	21	17
+LAB11080R	90	0	21	17
+LAB12811R	7	NULL	21	12
+LABACTHO	7	3	21	15
+LABACTHO	30	3	21	15
+LABACTHO	90	3	21	15
+LABAFBC	1	4	21	21
+LABAMY	4	5	21	21
+LABANCA	4	1	21	18
+LABANER	7	5	21	20
+LABANER2	1	4	21	18
+LABANTIXA	2	1	21	2
+LABAT3	90	1	21	14
+LABBHCG	90	1	21	20
+LABC3	2	1	21	20
+LABC3	4	1	21	20
+LABCLGC2	90	1	21	20
+LABCMVIGM	7	1	21	19
+LABCMVP	90	3	21	19
+LABCMVRC	7	NULL	21	19
+LABCSFTP	30	3	21	17
+LABCSFTP	90	3	21	18
+LABDBIL	7	4	21	19
+LABDSDNA	4	1	21	18
+LABECL	1	0	21	16
+LABFACT8	30	3	21	13
+LABFACT8	90	3	21	13
+LABFCUL	4	5	21	17
+LABFHV12	7	1	21	19
+LABHBPQ	90	1	21	19
+LABHELAG	7	1	21	20
+LABHEPAR	1	5	21	15
+LABHIPA	30	2	21	15
+LABHITAB	30	3	21	16
+LABIBIL	4	3	21	21
+LABIGG	90	2	21	14
+LABINSLF	1	4	21	21
+LABINSLF	2	2	21	21
+LABLAMOL	90	1	21	15
+LABLPD	90	2	21	19
+LABLPDC	2	1	21	17
+LABMYCPA	2	2	21	14
+LABMYO	4	2	21	20
+LABPROCT	7	3	21	19
+LABPROINS	1	0	21	11
+LABPROINS	2	0	21	11
+LABREPT	7	4	21	19
+LABRUB	4	0	21	1
+LABRUB	7	0	21	1
+LABRUB	30	0	21	1
+LABRUB	90	0	21	1
+LABSIROL	2	3	21	20
+LAB11042R	1	NULL	21	7
+LAB11042R	2	NULL	21	7
+LAB11042R	4	NULL	21	7
+LAB11042R	30	NULL	21	7
+LAB11042R	90	NULL	21	7
+LAB12811R	1	NULL	21	12
+LAB12811R	2	NULL	21	12
+LAB12811R	4	NULL	21	12
+LAB12811R	30	NULL	21	12
+LAB12811R	90	NULL	21	12
+LABCMVRC	1	NULL	21	19
+LABCMVRC	2	NULL	21	19
+LABCMVRC	4	NULL	21	19
+LABCMVRC	30	NULL	21	19
+LABCMVRC	90	NULL	21	19
+LABYAMA	1	NULL	21	19
+LABYAMA	2	NULL	21	19
+LABYAMA	4	NULL	21	19
+LABYAMA	30	NULL	21	19
+LABYAMA	90	NULL	21	19
+LABUVH	7	0	21	15
+LABUVH	30	0	21	15
+LABUVH	90	0	21	15
+LABWDC	90	2	21	13
+LABWDCS	90	2	21	16
+LABYAMA	1	0	21	19
+LABYAMA	2	0	21	19
+LABYAMA	4	0	21	19
+LABYAMA	7	0	21	19
+LABYAMA	7	NULL	21	19
+LABYAMA	30	0	21	19
+LABYAMA	90	0	21	19
+LABYAVH	7	0	21	18
+LABYAVH	30	0	21	18
+LABYAVH	90	0	21	18
+LABYC1IN	1	0	21	14
+LABYC1IN	2	0	21	14
+LABYC1IN	4	0	21	14
+LABYC1IN	7	0	21	14
+LABYC1IN	30	0	21	14
+LABYC1IN	90	0	21	14
+LABYCERU	30	1	21	15
+LABYDESIP	30	0	21	4
+LABYFFATQ	7	1	21	15
+LABYFFATQ	30	1	21	16
+LABYMANG	90	0	21	12
+LABYPTHRP	30	1	21	20
+LABYSTOSM	7	1	21	12
+LABYTAT	7	0	21	13
+LABYTAT	30	0	21	13
+LABYTAT	90	0	21	13
+LAB11036R	1	1	20	15
+LAB11042R	1	0	20	7
+LAB11042R	2	0	20	7
+LAB11071R	30	1	20	18
+LAB11071R	90	1	20	18
+LAB11088R	1	0	20	16
+LAB11088R	2	0	20	16
+LAB11088R	4	0	20	16
+LAB11088R	7	0	20	16
+LAB11088R	7	NULL	20	16
+LAB11088R	30	0	20	16
+LAB11088R	90	0	20	16
+LAB12811R	1	0	20	11
+LAB12815R	4	0	20	19
+LAB12815R	7	0	20	19
+LAB12815R	30	0	20	19
+LAB12815R	90	0	20	19
+LABABG	30	2	20	1
+LABABG	90	2	20	1
+LABACA	1	1	20	20
+LABACTHO	4	3	20	14
+LABADVQT	7	1	20	18
+LABALT	2	1	20	18
+LABAMIPOL	30	0	20	3
+LABAMIPRL	2	1	20	13
+LABASI	2	1	20	20
+LABAST	2	1	20	16
+LABB12	7	2	20	20
+LABBHB	4	1	20	13
+LABBHB	7	1	20	13
+LABCBCS	2	1	20	12
+LABCCT	1	0	20	2
+LABCCT	2	0	20	2
+LABCCT	4	0	20	2
+LABCCT	7	0	20	2
+LABCCT	7	NULL	20	2
+LABCCT	30	0	20	2
+LABCCT	90	0	20	2
+LABCMVIGM	4	1	20	18
+LABCORTS	1	3	20	16
+LABCR	7	2	20	18
+LABCZPL	4	1	20	10
+LABDVEFLA	7	NULL	20	19
+LABDVERVR	90	1	20	17
+LABEBVQL	7	NULL	20	9
+LABEBVQPB	1	0	20	0
+LABEBVQPB	2	0	20	0
+LABEBVQPB	4	0	20	0
+LABEBVQPB	7	0	20	0
+LABEBVQPB	7	NULL	20	0
+LABEBVQPB	30	0	20	0
+LABEBVQPB	90	0	20	0
+LABECL	2	0	20	15
+LABEP1	4	2	20	14
+LABEP1	7	2	20	14
+LABEP1	30	2	20	14
+LABEP1	90	2	20	14
+LABESRP	4	3	20	19
+LABFHAN	7	NULL	20	18
+LABFT4	1	2	20	20
+LABFT4ED	7	NULL	20	9
+LABFVARZ	2	1	20	20
+LABHBSAG	30	2	20	19
+LABHELAG	1	1	20	19
+LABHELAG	2	1	20	19
+LABHELAG	4	1	20	19
+LABHGBP	2	2	20	20
+LABHHV6QT	30	1	20	16
+LABHITAB	2	2	20	20
+LABHIVQ2	30	1	20	17
+LABHMCY	90	1	20	19
+LABIBIL	7	4	20	20
+LABINSLF	4	1	20	19
+LABINSLF	4	3	20	20
+LABINSLF	4	4	20	19
+LABLEVETL	4	1	20	16
+LABLIPS	1	3	20	20
+LABMETHB	30	5	20	20
+LABMETHB	90	5	20	20
+LABMYCPA	30	3	20	8
+LABMYO	1	2	20	19
+LABMYO	7	2	20	19
+LABMYO	30	2	20	19
+LABMYO	90	2	20	19
+LABNH3	2	3	20	20
+LABNORW	2	1	20	18
+LABORANER	1	2	20	20
+LABPALB	90	5	20	16
+LABPB	30	5	20	17
+LABPB	90	5	20	17
+LABPCCG4V	30	1	20	1
+LABPLTAGG	7	NULL	20	14
+LABPLTS	1	2	20	17
+LABPROTC	90	1	20	14
+LABPROTS	90	1	20	16
+LABPT	1	5	20	17
+LABQFTBG	4	1	20	17
+LABREPT	30	4	20	19
+LABREPT	90	4	20	19
+LAB11088R	1	NULL	20	16
+LAB11088R	2	NULL	20	16
+LAB11088R	4	NULL	20	16
+LAB11088R	30	NULL	20	16
+LAB11088R	90	NULL	20	16
+LABCCT	1	NULL	20	2
+LABCCT	2	NULL	20	2
+LABCCT	4	NULL	20	2
+LABCCT	30	NULL	20	2
+LABCCT	90	NULL	20	2
+LABDVEFLA	1	NULL	20	19
+LABDVEFLA	2	NULL	20	19
+LABDVEFLA	4	NULL	20	19
+LABDVEFLA	30	NULL	20	19
+LABDVEFLA	90	NULL	20	19
+LABEBVQL	1	NULL	20	9
+LABEBVQL	2	NULL	20	9
+LABEBVQL	4	NULL	20	9
+LABEBVQL	30	NULL	20	9
+LABEBVQL	90	NULL	20	9
+LABEBVQPB	1	NULL	20	0
+LABEBVQPB	2	NULL	20	0
+LABEBVQPB	4	NULL	20	0
+LABEBVQPB	30	NULL	20	0
+LABEBVQPB	90	NULL	20	0
+LABFHAN	1	NULL	20	18
+LABFHAN	2	NULL	20	18
+LABFHAN	4	NULL	20	18
+LABFHAN	30	NULL	20	18
+LABFHAN	90	NULL	20	18
+LABFT4ED	1	NULL	20	9
+LABFT4ED	2	NULL	20	9
+LABFT4ED	4	NULL	20	9
+LABFT4ED	30	NULL	20	9
+LABFT4ED	90	NULL	20	9
+LABPLTAGG	1	NULL	20	14
+LABPLTAGG	2	NULL	20	14
+LABPLTAGG	4	NULL	20	14
+LABPLTAGG	30	NULL	20	14
+LABPLTAGG	90	NULL	20	14
+LABTYPSNI	1	NULL	20	20
+LABTYPSNI	2	NULL	20	20
+LABTYPSNI	4	NULL	20	20
+LABTYPSNI	30	NULL	20	20
+LABTYPSNI	90	NULL	20	20
+LABYAMNOR	1	NULL	20	2
+LABYAMNOR	2	NULL	20	2
+LABYAMNOR	4	NULL	20	2
+LABYAMNOR	30	NULL	20	2
+LABYAMNOR	90	NULL	20	2
+LABTBILFR	1	1	20	15
+LABTEGCP	7	3	20	14
+LABTHEOL	7	3	20	16
+LABTHEOL	30	3	20	16
+LABTHEOL	90	3	20	15
+LABTYPSNI	7	NULL	20	20
+LABUAPRN	30	3	20	9
+LABUTPRAN	90	1	20	6
+LABWDCS	30	2	20	15
+LABYA1AS	30	1	20	16
+LABYAMNOR	1	0	20	2
+LABYAMNOR	2	0	20	2
+LABYAMNOR	7	NULL	20	2
+LABYDESIP	90	0	20	4
+LABYFFATQ	90	1	20	15
+LABYFLUO	30	0	20	17
+LABYFLUO	90	0	20	17
+LAB11041R	7	3	19	15
+LAB11042R	4	0	19	7
+LAB11042R	7	0	19	7
+LAB11042R	30	0	19	7
+LAB11042R	90	0	19	7
+LAB11071R	7	1	19	17
+LAB11982R	30	1	19	15
+LAB12292R	7	NULL	19	12
+LAB12586R	1	0	19	15
+LAB12586R	2	0	19	15
+LAB12586R	4	0	19	15
+LAB12586R	7	0	19	15
+LAB12586R	7	NULL	19	15
+LAB12586R	30	0	19	15
+LAB12586R	90	0	19	15
+LAB12796R	7	NULL	19	8
+LABA1C	1	2	19	17
+LABABG	7	2	19	1
+LABADPCRQ	4	1	19	19
+LABAGALA	30	2	19	19
+LABAT3	2	1	19	12
+LABAT3	4	1	19	12
+LABAT3	7	1	19	12
+LABAT3	30	1	19	12
+LABBLC2	2	3	19	19
+LABBMT1	90	2	19	2
+LABBXTG	30	4	19	15
+LABC153	1	0	19	16
+LABC153	2	0	19	16
+LABC153	4	0	19	16
+LABC153	7	0	19	16
+LABC153	7	NULL	19	16
+LABC153	30	0	19	16
+LABC153	90	0	19	16
+LABCBCD	2	3	19	10
+LABCDIFF	1	0	19	7
+LABCDIFF	2	0	19	7
+LABCDIFF	4	0	19	7
+LABCDIFF	7	0	19	7
+LABCDIFF	7	NULL	19	7
+LABCDIFF	30	0	19	7
+LABCDIFF	90	0	19	7
+LABCLGC2	7	1	19	18
+LABCLGC2	30	1	19	18
+LABCMVM	7	1	19	16
+LABCMVPQ	2	3	19	19
+LABCMVRC	1	0	19	17
+LABCMVRC	2	0	19	17
+LABCMVRC	4	0	19	17
+LABCMVRC	7	0	19	17
+LABCRPLC	30	2	19	19
+LABCSFGL	4	4	19	19
+LABCYLEX	4	0	19	11
+LABDATBSI	90	2	19	18
+LABDVERV	4	2	19	19
+LABEBVQL	1	0	19	9
+LABEBVQL	2	0	19	9
+LABECL	4	0	19	15
+LABECL	7	0	19	15
+LABECL	30	0	19	15
+LABECL	90	0	19	15
+LABFACT8	7	3	19	14
+LABFER	30	2	19	16
+LABFHAN	1	0	19	18
+LABFHAN	2	0	19	18
+LABFHAN	4	0	19	18
+LABFHAN	7	0	19	18
+LABFHAN	30	0	19	18
+LABFHAN	90	0	19	18
+LABFKOH	1	2	19	18
+LABFKOH	90	4	19	17
+LABFLDC	4	3	19	17
+LABFOL	90	2	19	17
+LABFT4ED	1	0	19	9
+LABFT4ED	2	0	19	9
+LABFT4ED	4	0	19	9
+LABFT4ED	7	0	19	9
+LABFUNIDS	7	0	19	16
+LABGLNF	2	4	19	18
+LABHAP	7	5	19	18
+LABHBODY	7	NULL	19	18
+LABHBSQL	30	1	19	17
+LABHBSQL	90	1	19	17
+LABHFP	30	4	19	11
+LABHGBP	90	5	19	18
+LABHIVWBL	30	2	19	19
+LABHMCY	7	1	19	18
+LABHMCY	30	1	19	18
+LABHSVPCR	7	1	19	11
+LABHSVZL	4	1	19	17
+LABLEVETL	7	1	19	15
+LABLIL	4	3	19	17
+LABMETHB	1	4	19	19
+LABOAPE	1	2	19	19
+LABPCINR	7	1	19	6
+LABPLTAGG	1	0	19	13
+LABPLTAGG	2	0	19	13
+LABPREG	1	1	19	16
+LABPROTC	30	1	19	13
+LABPROTS	30	1	19	15
+LABREPT	1	2	19	14
+LABRESPCR	1	1	19	16
+LABSCMV	30	3	19	18
+LABSNA	7	NULL	19	15
+LAB12292R	1	NULL	19	12
+LAB12292R	2	NULL	19	12
+LAB12292R	4	NULL	19	12
+LAB12292R	30	NULL	19	12
+LAB12292R	90	NULL	19	12
+LAB12586R	1	NULL	19	15
+LAB12586R	2	NULL	19	15
+LAB12586R	4	NULL	19	15
+LAB12586R	30	NULL	19	15
+LAB12586R	90	NULL	19	15
+LAB12796R	1	NULL	19	8
+LAB12796R	2	NULL	19	8
+LAB12796R	4	NULL	19	8
+LAB12796R	30	NULL	19	8
+LAB12796R	90	NULL	19	8
+LABC153	1	NULL	19	16
+LABC153	2	NULL	19	16
+LABC153	4	NULL	19	16
+LABC153	30	NULL	19	16
+LABC153	90	NULL	19	16
+LABCDIFF	1	NULL	19	7
+LABCDIFF	2	NULL	19	7
+LABCDIFF	4	NULL	19	7
+LABCDIFF	30	NULL	19	7
+LABCDIFF	90	NULL	19	7
+LABHBODY	1	NULL	19	18
+LABHBODY	2	NULL	19	18
+LABHBODY	4	NULL	19	18
+LABHBODY	30	NULL	19	18
+LABHBODY	90	NULL	19	18
+LABSNA	1	NULL	19	15
+LABSNA	2	NULL	19	15
+LABSNA	4	NULL	19	15
+LABSNA	30	NULL	19	15
+LABSNA	90	NULL	19	15
+LABUCORT	1	NULL	19	16
+LABUCORT	2	NULL	19	16
+LABUCORT	4	NULL	19	16
+LABUCORT	30	NULL	19	16
+LABUCORT	90	NULL	19	16
+LABTBIL	2	2	19	18
+LABTCDIF	30	3	19	19
+LABTEGCP	30	4	19	11
+LABTT	2	2	19	17
+LABTYPSNI	1	0	19	19
+LABTYPSNI	2	0	19	19
+LABTYPSNI	4	0	19	19
+LABTYPSNI	7	0	19	19
+LABTYPSNI	30	0	19	19
+LABTYPSNI	90	0	19	19
+LABUCORT	1	0	19	16
+LABUCORT	7	NULL	19	16
+LABURNC	4	3	19	17
+LABVBG	4	2	19	0
+LABVBG	7	2	19	0
+LABVBG	30	2	19	0
+LABVBG	90	2	19	0
+LABWDCS	7	2	19	14
+LABYA1AS	4	1	19	15
+LABYA1AS	7	1	19	15
+LABYAMNOR	4	0	19	2
+LABYAMNOR	7	0	19	2
+LABYAMNOR	30	0	19	2
+LABYAMNOR	90	0	19	2
+LABYCERU	2	1	19	15
+LABYCERU	4	1	19	15
+LABYCERU	7	1	19	15
+LABYCOPPR	7	1	19	11
+LABYFT3	30	1	19	13
+LABYFT3	90	1	19	12
+LABYPTHRP	7	1	19	19
+LABYVD125	7	0	19	9
+LABYVD125	30	0	19	9
+LABYVD125	90	0	19	9
+LAB11070R	7	1	18	13
+LAB11070R	30	1	18	13
+LAB11070R	90	1	18	13
+LAB11071R	4	1	18	16
+LAB11242R	90	1	18	17
+LAB12330R	4	0	18	11
+LAB12811R	2	0	18	11
+LABABG	4	2	18	0
+LABACTHO	1	3	18	13
+LABADPCRQ	2	1	18	18
+LABAFBC	4	5	18	18
+LABALKP	90	1	18	13
+LABAMIPOL	90	0	18	3
+LABANAS	90	2	18	12
+LABASPPCP	1	0	18	15
+LABASPPCP	2	0	18	15
+LABASPPCP	4	0	18	15
+LABASPPCP	7	0	18	15
+LABASPPCP	7	NULL	18	15
+LABASPPCP	30	0	18	15
+LABASPPCP	90	0	18	15
+LABB12	4	2	18	18
+LABB2GP1	90	1	18	16
+LABBACIDS	7	1	18	16
+LABC4	1	1	18	17
+LABCA199	1	1	18	14
+LABCA199	2	1	18	14
+LABCMVQT	7	4	18	18
+LABCMVRC	30	0	18	16
+LABCMVRC	90	0	18	16
+LABDBIL	4	4	18	16
+LABDIC	1	2	18	2
+LABDVEFLA	1	0	18	17
+LABEBVQP	30	3	18	16
+LABFACT9	1	1	18	9
+LABFACT9	2	1	18	9
+LABFACT9	4	1	18	9
+LABFACT9	7	1	18	9
+LABFACT9	30	1	18	9
+LABFACT9	90	1	18	9
+LABFER	2	2	18	17
+LABFHV12	4	1	18	17
+LABFUNIDS	30	0	18	15
+LABFUNIDS	90	0	18	15
+LABGGT	7	5	18	17
+LABGGT	30	5	18	17
+LABGGT	90	5	18	17
+LABGNTPRL	30	4	18	18
+LABGNTPRL	90	4	18	18
+LABHAP	2	3	18	17
+LABHCPQ	7	1	18	17
+LABHGBP	30	5	18	18
+LABHGBS	7	NULL	18	15
+LABHHV6QT	7	1	18	13
+LABHMCY	4	1	18	17
+LABLPDC	1	1	18	14
+LABMYCPA	4	3	18	7
+LABMYCPA	7	3	18	7
+LABMYO	4	3	18	16
+LABOAPE	7	3	18	18
+LABPLTAGG	4	0	18	12
+LABPLTAGG	7	0	18	12
+LABPLTAGG	30	0	18	12
+LABPLTAGG	90	0	18	12
+LABPROTS	7	1	18	14
+LABQFTBG	2	1	18	16
+LABSNA	1	0	18	14
+LABSNA	2	0	18	14
+LABSNA	4	0	18	14
+LABSNA	7	0	18	14
+LABSNA	30	0	18	14
+LABSNA	90	0	18	14
+LABSTRONG	7	1	18	17
+LABSTRONG	30	1	18	17
+LABSTRONG	90	1	18	17
+LABSWTCL	1	0	18	17
+LABSWTCL	7	NULL	18	17
+LABASPPCP	1	NULL	18	15
+LABASPPCP	2	NULL	18	15
+LABASPPCP	4	NULL	18	15
+LABASPPCP	30	NULL	18	15
+LABASPPCP	90	NULL	18	15
+LABHGBS	1	NULL	18	15
+LABHGBS	2	NULL	18	15
+LABHGBS	4	NULL	18	15
+LABHGBS	30	NULL	18	15
+LABHGBS	90	NULL	18	15
+LABSWTCL	1	NULL	18	17
+LABSWTCL	2	NULL	18	17
+LABSWTCL	4	NULL	18	17
+LABTBILFR	30	3	18	18
+LABSWTCL	30	NULL	18	17
+LABSWTCL	90	NULL	18	17
+LABTOBPRL	2	2	18	14
+LABTT	4	3	18	15
+LABUCORT	2	0	18	16
+LABUEOS	1	2	18	17
+LABVBG	1	2	18	0
+LABVBG	2	2	18	0
+LABVCUL	90	4	18	18
+LABVCULR	4	1	18	18
+LABVD25H	2	1	18	14
+LABWDCS	4	2	18	14
+LABYCOPPR	4	1	18	10
+LABYFFATQ	4	1	18	11
+LABYMEXIL	1	0	18	8
+LABYMEXIL	2	0	18	8
+LABYUMYO	4	1	18	9
+LABYUMYO	7	1	18	9
+LABYUMYO	30	1	18	9
+LABYUMYO	90	1	18	9
+LAB11071R	2	1	17	16
+LAB11198R	1	0	17	5
+LAB11198R	2	0	17	5
+LAB11198R	4	0	17	5
+LAB11198R	7	0	17	5
+LAB11198R	7	NULL	17	5
+LAB11198R	30	0	17	5
+LAB11198R	90	0	17	5
+LAB12292R	1	0	17	10
+LAB12796R	1	0	17	7
+LAB12811R	4	0	17	11
+LAB12811R	7	0	17	11
+LAB12811R	30	0	17	11
+LAB12811R	90	0	17	11
+LABACTHO	2	3	17	12
+LABALKP	30	1	17	13
+LABANER	4	5	17	16
+LABBUN	4	2	17	13
+LABCBCS	90	1	17	9
+LABCMVIGM	2	1	17	15
+LABCRP	7	4	17	14
+LABCRP	30	5	17	12
+LABCYLEX	7	0	17	9
+LABDBIL	1	2	17	15
+LABDSDNA	2	1	17	14
+LABDVEFLA	2	0	17	16
+LABDVEFLA	4	0	17	16
+LABDVEFLA	7	0	17	16
+LABDVEFLA	30	0	17	16
+LABDVEFLA	90	0	17	16
+LABDVEHV	1	1	17	10
+LABEBPCRQ	90	4	17	17
+LABEBVC	1	0	17	11
+LABEBVC	2	0	17	11
+LABEBVC	4	0	17	11
+LABEBVC	7	0	17	11
+LABEBVC	7	NULL	17	11
+LABEBVC	30	0	17	11
+LABEBVC	90	0	17	11
+LABEP1	2	2	17	11
+LABFACT8	2	3	17	13
+LABFLUAPX	90	2	17	17
+LABFT4	90	4	17	17
+LABFT4ED	30	0	17	9
+LABFT4ED	90	0	17	9
+LABFVARZ	1	1	17	17
+LABGNTPRL	7	4	17	17
+LABHBSQL	7	1	17	15
+LABHCPQ	4	1	17	16
+LABHCTX	1	3	17	14
+LABHCTX	30	4	17	13
+LABHCTX	90	4	17	13
+LABHFP	7	4	17	9
+LABHMCY	1	1	17	16
+LABHPVHR2	1	0	17	13
+LABHPVHR2	2	0	17	13
+LABHPVHR2	4	0	17	13
+LABHPVHR2	7	0	17	13
+LABHPVHR2	7	NULL	17	13
+LABHPVHR2	30	0	17	13
+LABHPVHR2	90	0	17	13
+LABHV6DNA	2	1	17	16
+LABISOSR	7	NULL	17	16
+LABLEGDA	90	3	17	17
+LABLEVETL	2	1	17	15
+LABLIL	30	4	17	13
+LABLPD	30	2	17	15
+LABMETB	4	5	17	11
+LABMMAS	90	1	17	16
+LABORAERO	4	2	17	15
+LABPHEN	2	4	17	12
+LABPLASMN	2	0	17	12
+LABPROTC	7	1	17	12
+LABREPT	30	5	17	14
+LABREPT	90	5	17	14
+LABSTOOLC	90	2	17	14
+LABSTRONG	4	1	17	16
+LABSWTCL	2	0	17	16
+LABSWTCL	4	0	17	16
+LABSWTCL	7	0	17	16
+LABSWTCL	30	0	17	16
+LABSWTCL	90	0	17	16
+LABTBIL	4	3	17	17
+LABTBIL	7	3	17	17
+LAB11198R	1	NULL	17	5
+LAB11198R	2	NULL	17	5
+LAB11198R	4	NULL	17	5
+LAB11198R	30	NULL	17	5
+LAB11198R	90	NULL	17	5
+LABEBVC	1	NULL	17	11
+LABEBVC	2	NULL	17	11
+LABEBVC	4	NULL	17	11
+LABTBPCRS	30	2	17	14
+LABEBVC	30	NULL	17	11
+LABTBPCRS	90	2	17	14
+LABEBVC	90	NULL	17	11
+LABHPVHR2	1	NULL	17	13
+LABHPVHR2	2	NULL	17	13
+LABHPVHR2	4	NULL	17	13
+LABHPVHR2	30	NULL	17	13
+LABHPVHR2	90	NULL	17	13
+LABISOSR	1	NULL	17	16
+LABISOSR	2	NULL	17	16
+LABISOSR	4	NULL	17	16
+LABISOSR	30	NULL	17	16
+LABISOSR	90	NULL	17	16
+LABYAMIO	1	NULL	17	10
+LABYAMIO	2	NULL	17	10
+LABYAMIO	4	NULL	17	10
+LABYAMIO	30	NULL	17	10
+LABTEGCP	90	4	17	9
+LABYAMIO	90	NULL	17	10
+LABYTADID	1	NULL	17	12
+LABYTADID	2	NULL	17	12
+LABYTADID	4	NULL	17	12
+LABYTADID	30	NULL	17	12
+LABYTADID	90	NULL	17	12
+LABUCORT	4	0	17	15
+LABUCORT	7	0	17	15
+LABUCORT	30	0	17	15
+LABUCORT	90	0	17	15
+LABUTPRAN	30	1	17	5
+LABWDC	30	2	17	11
+LABWDCG	90	2	17	14
+LABWDCS	2	2	17	14
+LABYAMIO	1	0	17	10
+LABYAMIO	2	0	17	10
+LABYAMIO	4	0	17	10
+LABYAMIO	7	0	17	10
+LABYAMIO	7	NULL	17	10
+LABYAMIO	30	0	17	10
+LABYAMIO	90	0	17	10
+LABYCERU	1	1	17	14
+LABYMETFF	7	1	17	12
+LABYMETFF	30	1	17	12
+LABYMETFF	90	1	17	12
+LABYMEXIL	4	0	17	7
+LABYPREAL	2	2	17	13
+LABYTADID	7	NULL	17	12
+LAB11037R	7	NULL	16	10
+LAB11060R	30	1	16	11
+LAB11068R	4	1	16	15
+LAB11068R	7	1	16	15
+LAB11068R	30	1	16	15
+LAB11068R	90	1	16	15
+LAB11090R	1	0	16	12
+LAB11090R	2	0	16	12
+LAB11090R	4	0	16	12
+LAB11090R	7	0	16	12
+LAB11090R	7	NULL	16	12
+LAB11090R	30	0	16	12
+LAB11090R	90	0	16	12
+LAB11179R	7	NULL	16	12
+LAB11982R	90	1	16	11
+LAB12292R	2	0	16	9
+LAB12762R	4	2	16	11
+LAB12796R	2	0	16	7
+LAB12796R	4	0	16	7
+LABABG	1	2	16	0
+LABABG	2	2	16	0
+LABADPCRQ	1	1	16	16
+LABALT	90	2	16	15
+LABANAS	30	2	16	11
+LABANTIM	90	1	16	15
+LABANTIXA	1	1	16	2
+LABB12	2	2	16	16
+LABBHB	1	1	16	9
+LABBHB	2	1	16	9
+LABC3	1	1	16	15
+LABCA	1	3	16	14
+LABCBCS	4	1	16	9
+LABCBCS	7	1	16	9
+LABCBCS	30	1	16	9
+LABCLGC2	4	1	16	16
+LABCRPLC	90	2	16	16
+LABDBIL	30	5	16	15
+LABDBIL	90	5	16	14
+LABDVEVH	90	2	16	14
+LABEBVPAN	2	1	16	16
+LABEBVQL	4	0	16	9
+LABFCUL	2	5	16	16
+LABFER	4	2	16	15
+LABFER	7	2	16	15
+LABFLUAPX	30	2	16	16
+LABGH	4	1	16	15
+LABGH	7	1	16	15
+LABGH	30	1	16	15
+LABGH	90	1	16	15
+LABGIARS	2	1	16	16
+LABGLNF	2	3	16	16
+LABHCTX	7	4	16	13
+LABHFP	4	4	16	10
+LABHIAB	90	2	16	13
+LABHMCY	2	1	16	15
+LABIGG	30	2	16	11
+LABKWB	2	1	16	14
+LABKWB	4	1	16	14
+LABKWB	7	1	16	14
+LABKWB	30	1	16	14
+LABKWB	90	1	16	14
+LABLAMOL	30	1	16	13
+LABMYCPA	1	2	16	10
+LABORAERO	2	2	16	15
+LABPALB	30	5	16	12
+LABPCINR	1	1	16	4
+LABPCINR	2	1	16	4
+LABPCINR	4	1	16	4
+LABPCTNI	4	4	16	15
+LABPCTNI	7	4	16	15
+LABPLASMN	2	1	16	13
+LABPROINS	4	0	16	8
+LABPROTS	4	1	16	13
+LABREPT	7	5	16	14
+LABRETIC	90	3	16	9
+LABSTLCX	90	2	16	15
+LABTBIL	90	4	16	14
+LABTBILFR	90	3	16	16
+LABTEGCP	2	2	16	7
+LAB11037R	1	NULL	16	10
+LAB11037R	2	NULL	16	10
+LAB11037R	4	NULL	16	10
+LAB11037R	30	NULL	16	10
+LAB11037R	90	NULL	16	10
+LAB11090R	1	NULL	16	12
+LAB11090R	2	NULL	16	12
+LAB11090R	4	NULL	16	12
+LAB11090R	30	NULL	16	12
+LAB11090R	90	NULL	16	12
+LAB11179R	1	NULL	16	12
+LAB11179R	2	NULL	16	12
+LAB11179R	4	NULL	16	12
+LAB11179R	30	NULL	16	12
+LAB11179R	90	NULL	16	12
+LABYUVMA	1	NULL	16	10
+LABYUVMA	2	NULL	16	10
+LABYUVMA	4	NULL	16	10
+LABYUVMA	30	NULL	16	10
+LABYUVMA	90	NULL	16	10
+LABTRIG	1	2	16	15
+LABUA	4	3	16	15
+LABUA	90	4	16	11
+LABVCULR	90	2	16	16
+LABVZVQLP	90	1	16	12
+LABWDCG	30	2	16	13
+LABWNVAB	90	1	16	14
+LABYA1AS	2	1	16	14
+LABYBOH	1	1	16	11
+LABYFT3	7	1	16	11
+LABYFVPA	7	1	16	9
+LABYFVPA	30	1	16	8
+LABYSTOSM	4	1	16	11
+LABYTADID	1	0	16	12
+LABYTADID	2	0	16	12
+LABYTADID	4	0	16	12
+LABYTADID	7	0	16	12
+LABYTADID	30	0	16	12
+LABYTADID	90	0	16	12
+LABYUVMA	1	0	16	10
+LABYUVMA	2	0	16	10
+LABYUVMA	4	0	16	10
+LABYUVMA	7	0	16	10
+LABYUVMA	7	NULL	16	10
+LABYUVMA	30	0	16	10
+LABYUVMA	90	0	16	10
+LAB11041R	2	2	15	10
+LAB11044R	1	0	15	7
+LAB11044R	2	0	15	7
+LAB11044R	4	0	15	7
+LAB11044R	7	0	15	7
+LAB11044R	7	NULL	15	7
+LAB11049R	4	2	15	10
+LAB11049R	7	2	15	10
+LAB11049R	30	2	15	10
+LAB11049R	90	2	15	10
+LAB11068R	2	1	15	14
+LAB11070R	4	1	15	11
+LAB11096R	90	1	15	14
+LAB11179R	1	0	15	11
+LAB11179R	2	0	15	11
+LAB11179R	4	0	15	11
+LAB12050R	1	0	15	13
+LAB12050R	2	0	15	13
+LAB12050R	4	0	15	13
+LAB12050R	7	0	15	13
+LAB12050R	7	NULL	15	13
+LAB12050R	30	0	15	13
+LAB12050R	90	0	15	13
+LAB12330R	7	0	15	9
+LABADVQT	4	1	15	14
+LABAFBCF	30	2	15	15
+LABALT	30	2	15	14
+LABAMY	2	4	15	15
+LABANTIM	30	1	15	14
+LABCBCO	2	4	15	12
+LABCBCS	1	1	15	8
+LABCDTPCR	30	4	15	10
+LABCMVM	4	1	15	12
+LABCMVPQ	4	5	15	15
+LABCO2	90	1	15	14
+LABCRPLC	4	2	15	15
+LABCRYO	90	1	15	14
+LABCSFCT	7	2	15	11
+LABCZPL	2	1	15	6
+LABDVERV	30	3	15	15
+LABDVERVP	90	4	15	13
+LABDVERVR	30	1	15	13
+LABEBVQL	7	0	15	9
+LABEBVQL	30	0	15	9
+LABEBVQL	90	0	15	9
+LABEBVQP	90	4	15	15
+LABESRP	1	2	15	14
+LABESRP	7	3	15	11
+LABFCUL	1	5	15	15
+LABFOL	30	2	15	13
+LABGH	2	1	15	13
+LABGNTPOL	7	1	15	3
+LABGNTPOL	30	1	15	3
+LABGNTPOL	90	1	15	3
+LABGNTPRL	30	5	15	14
+LABGNTPRL	90	5	15	14
+LABHBCT	7	1	15	14
+LABHBODY	1	0	15	14
+LABHBODY	2	0	15	14
+LABHBSQL	4	1	15	14
+LABHGBP	4	3	15	13
+LABHHV6QT	4	1	15	10
+LABHIAB	30	2	15	12
+LABISOSR	1	0	15	15
+LABISOSR	2	0	15	15
+LABISOSR	4	0	15	15
+LABISOSR	7	0	15	15
+LABISOSR	30	0	15	15
+LABISOSR	90	0	15	15
+LABLAMOL	2	1	15	15
+LABLAMOL	4	1	15	15
+LABLAMOL	7	1	15	13
+LABLEVETL	1	1	15	13
+LABMALAR	2	3	15	15
+LABMMAS	7	1	15	14
+LABMMAS	30	1	15	14
+LABORAERO	1	2	15	14
+LABPCTNI	1	4	15	14
+LABPLTFUN	90	1	15	9
+LABPROCT	7	4	15	14
+LABPROCT	90	4	15	13
+LABPROTC	4	1	15	11
+LABQFTB	1	1	15	13
+LABRETIC	4	3	15	9
+LABRETIC	30	3	15	9
+LABSTRONG	1	1	15	15
+LABTBPCRS	7	2	15	12
+LAB11044R	1	NULL	15	7
+LAB11044R	2	NULL	15	7
+LAB11044R	4	NULL	15	7
+LAB11044R	30	NULL	15	7
+LAB11044R	90	NULL	15	7
+LAB12050R	1	NULL	15	13
+LAB12050R	2	NULL	15	13
+LAB12050R	4	NULL	15	13
+LAB12050R	30	NULL	15	13
+LAB12050R	90	NULL	15	13
+LABYTRAB	1	NULL	15	13
+LABYTRAB	2	NULL	15	13
+LABYTRAB	4	NULL	15	13
+LABYTRAB	30	NULL	15	13
+LABYTRAB	90	NULL	15	13
+LABTOBPRL	30	3	15	12
+LABUAPRN	4	2	15	6
+LABUEP	7	2	15	10
+LABUNA	7	2	15	12
+LABVZVQLP	30	1	15	11
+LABWNVAB	30	1	15	13
+LABYCP	90	2	15	12
+LABYFT3	2	1	15	11
+LABYFT3	4	1	15	11
+LABYFVPA	90	1	15	8
+LABYMEXIL	7	0	15	6
+LABYMEXIL	30	0	15	6
+LABYMEXIL	90	0	15	6
+LABYPROIN	1	0	15	11
+LABYTRAB	1	0	15	13
+LABYTRAB	2	0	15	13
+LABYTRAB	4	0	15	13
+LABYTRAB	7	0	15	13
+LABYTRAB	7	NULL	15	13
+LABYTRAB	30	0	15	13
+LABYTRAB	90	0	15	13
+LAB11037R	1	0	14	10
+LAB11037R	2	0	14	10
+LAB11044R	30	0	14	7
+LAB11044R	90	0	14	7
+LAB11096R	7	1	14	13
+LAB11096R	30	1	14	13
+LAB11098R	90	1	14	10
+LAB11240R	7	NULL	14	12
+LAB11242R	30	1	14	13
+LAB12292R	4	0	14	9
+LAB12330R	2	1	14	13
+LAB12796R	7	0	14	7
+LAB12853R	1	0	14	10
+LAB12853R	7	NULL	14	10
+LABA1C	7	3	14	13
+LABA1C	90	4	14	12
+LABAFBD	7	5	14	14
+LABANAS	7	2	14	10
+LABANTIM	7	1	14	13
+LABAPHSAB	30	1	14	13
+LABAPHSAB	90	1	14	13
+LABAST	1	1	14	11
+LABAT3	1	1	14	9
+LABB2GP1	7	1	14	13
+LABB2GP1	30	1	14	13
+LABBACIDS	2	1	14	13
+LABBACIDS	4	1	14	13
+LABBMT2	7	3	14	6
+LABCMVIGM	1	1	14	12
+LABCMVP	30	3	14	13
+LABCO2	30	1	14	13
+LABCRP	4	4	14	10
+LABCRPLFC	90	1	14	14
+LABCSFC	7	5	14	14
+LABCSFCT	90	3	14	12
+LABCTC	7	2	14	14
+LABEBVPAN	1	1	14	14
+LABEBVQP	30	4	14	14
+LABFCULS	1	0	14	9
+LABFCULS	2	0	14	9
+LABFCULS	4	0	14	9
+LABFCULS	7	0	14	9
+LABFCULS	7	NULL	14	9
+LABFCULS	30	0	14	9
+LABFCULS	90	0	14	9
+LABFCYST	7	NULL	14	6
+LABFIBANT	1	0	14	7
+LABFLDC	2	3	14	12
+LABFMYAB	2	1	14	12
+LABFOL	7	2	14	12
+LABGIARS	1	1	14	14
+LABGLNF	2	5	14	14
+LABGLNF	4	3	14	14
+LABGLNF	7	3	14	14
+LABGLNF	30	3	14	14
+LABGLNF	90	3	14	14
+LABHAVM	1	1	14	14
+LABHBODY	4	0	14	13
+LABHBODY	7	0	14	13
+LABHBODY	30	0	14	13
+LABHBODY	90	0	14	13
+LABHGBS	1	0	14	13
+LABHSVQLP	90	1	14	12
+LABHSVZL	2	1	14	12
+LABINSLF	2	3	14	13
+LABINSLF	30	2	14	14
+LABINSLF	90	2	14	14
+LABLEISAB	7	NULL	14	13
+LABLPD	2	2	14	14
+LABLPD	4	2	14	13
+LABLPD	7	2	14	13
+LABMETHB	2	4	14	13
+LABOAPE	4	3	14	14
+LABOF	1	0	14	12
+LABOF	2	0	14	12
+LABOF	4	0	14	12
+LABOF	7	0	14	12
+LABOF	7	NULL	14	12
+LABOF	30	0	14	12
+LABOF	90	0	14	12
+LABORANER	90	3	14	13
+LABPCTNI	2	4	14	13
+LABPLASMN	4	0	14	11
+LABPLASMN	7	0	14	11
+LABPLASMN	30	0	14	11
+LABPLASMN	90	0	14	11
+LABPLTFUN	7	1	14	8
+LABPLTFUN	30	1	14	8
+LABPROINS	7	0	14	8
+LABRBCMOR	1	0	14	12
+LABRBCMOR	2	0	14	12
+LABRBCMOR	4	0	14	12
+LABRBCMOR	7	0	14	12
+LABRBCMOR	7	NULL	14	12
+LABRBCMOR	30	0	14	12
+LABRBCMOR	90	0	14	12
+LABRESP	2	3	14	13
+LABRESPCR	30	2	14	10
+LABTBIL	4	4	14	14
+LABTBPCRS	4	2	14	11
+LAB11240R	1	NULL	14	12
+LAB11240R	2	NULL	14	12
+LAB11240R	4	NULL	14	12
+LAB11240R	30	NULL	14	12
+LAB11240R	90	NULL	14	12
+LAB12853R	1	NULL	14	10
+LAB12853R	2	NULL	14	10
+LAB12853R	4	NULL	14	10
+LAB12853R	30	NULL	14	10
+LAB12853R	90	NULL	14	10
+LABFCULS	1	NULL	14	9
+LABFCULS	2	NULL	14	9
+LABFCULS	4	NULL	14	9
+LABFCULS	30	NULL	14	9
+LABFCULS	90	NULL	14	9
+LABFCYST	1	NULL	14	6
+LABFCYST	2	NULL	14	6
+LABFCYST	4	NULL	14	6
+LABFCYST	30	NULL	14	6
+LABFCYST	90	NULL	14	6
+LABLEISAB	1	NULL	14	13
+LABLEISAB	2	NULL	14	13
+LABLEISAB	4	NULL	14	13
+LABLEISAB	30	NULL	14	13
+LABLEISAB	90	NULL	14	13
+LABOF	1	NULL	14	12
+LABOF	2	NULL	14	12
+LABOF	4	NULL	14	12
+LABOF	30	NULL	14	12
+LABOF	90	NULL	14	12
+LABRBCMOR	1	NULL	14	12
+LABRBCMOR	2	NULL	14	12
+LABRBCMOR	4	NULL	14	12
+LABRBCMOR	30	NULL	14	12
+LABRBCMOR	90	NULL	14	12
+LABTXCAB	1	NULL	14	13
+LABTXCAB	2	NULL	14	13
+LABTXCAB	4	NULL	14	13
+LABTXCAB	30	NULL	14	13
+LABTXCAB	90	NULL	14	13
+LABYACE	1	NULL	14	10
+LABYACE	2	NULL	14	10
+LABYACE	4	NULL	14	10
+LABYACE	30	NULL	14	10
+LABYACE	90	NULL	14	10
+LABYLYSO	1	NULL	14	10
+LABYLYSO	2	NULL	14	10
+LABYLYSO	4	NULL	14	10
+LABYLYSO	30	NULL	14	10
+LABYLYSO	90	NULL	14	10
+LABTSH	7	3	14	12
+LABTSH	90	4	14	13
+LABTXCAB	1	0	14	13
+LABTXCAB	2	0	14	13
+LABTXCAB	4	0	14	13
+LABTXCAB	7	0	14	13
+LABTXCAB	7	NULL	14	13
+LABTXCAB	30	0	14	13
+LABTXCAB	90	0	14	13
+LABUK	7	1	14	12
+LABUK	30	1	14	12
+LABUK	90	1	14	12
+LABUTPRAN	7	1	14	4
+LABVCULR	2	1	14	14
+LABVD25H	1	1	14	10
+LABWDCG	7	2	14	11
+LABWDCS	1	2	14	13
+LABYACE	1	0	14	10
+LABYACE	7	NULL	14	10
+LABYCP	30	2	14	11
+LABYHGBP	7	2	14	13
+LABYHGBP	90	1	14	12
+LABYLYSO	1	0	14	10
+LABYLYSO	2	0	14	10
+LABYLYSO	4	0	14	10
+LABYLYSO	7	0	14	10
+LABYLYSO	7	NULL	14	10
+LABYLYSO	30	0	14	10
+LABYLYSO	90	0	14	10
+LABYMETFF	4	1	14	10
+LABYPROIN	2	0	14	10
+LABYPTHRP	4	1	14	14
+LAB11036R	4	3	13	11
+LAB11049R	2	2	13	8
+LAB11060R	7	1	13	9
+LAB11067R	7	NULL	13	10
+LAB11179R	7	0	13	9
+LAB11179R	30	0	13	9
+LAB11179R	90	0	13	9
+LAB11199R	30	1	13	12
+LAB11199R	90	1	13	12
+LAB11240R	1	0	13	11
+LAB11240R	2	0	13	11
+LAB11240R	4	0	13	11
+LAB11240R	7	0	13	11
+LAB11240R	30	0	13	11
+LAB11240R	90	0	13	11
+LAB11242R	7	1	13	12
+LAB12090R	1	0	13	7
+LAB12090R	2	0	13	7
+LAB12090R	4	0	13	7
+LAB12090R	7	0	13	7
+LAB12090R	7	NULL	13	7
+LAB12292R	7	0	13	9
+LAB12292R	30	0	13	9
+LAB12292R	90	0	13	9
+LAB12330R	4	1	13	12
+LABANCA	2	1	13	12
+LABANER	2	5	13	12
+LABANTIM	2	1	13	12
+LABANTIM	4	1	13	12
+LABAPHSA2	90	1	13	12
+LABAPHSAB	7	1	13	12
+LABASQ	90	2	13	10
+LABAST	90	2	13	10
+LABBLCA	30	1	13	12
+LABBLCTIP	4	3	13	13
+LABBUN	30	3	13	9
+LABCA	30	4	13	11
+LABCA	90	4	13	11
+LABCBCO	4	5	13	5
+LABCLGC2	1	1	13	13
+LABCLGC2	2	1	13	13
+LABCORTS	1	4	13	11
+LABCORTS	2	3	13	8
+LABCRPLFB	90	1	13	13
+LABCRPLFC	30	1	13	12
+LABCSFC	1	2	13	13
+LABCSFCT	4	2	13	10
+LABCYLEX	30	0	13	8
+LABCYSCSF	7	NULL	13	12
+LABDATBSI	30	2	13	13
+LABDVEIAR	1	0	13	8
+LABDVEIAR	2	0	13	8
+LABDVEIAR	4	0	13	8
+LABDVEIAR	7	0	13	8
+LABDVEIAR	7	NULL	13	8
+LABDVEIAR	30	0	13	8
+LABDVEIAR	90	0	13	8
+LABDVEVH	30	2	13	11
+LABESRP	30	4	13	12
+LABFER	4	3	13	13
+LABFHIVQA	1	0	13	11
+LABFHIVQA	2	0	13	11
+LABFHIVQA	4	0	13	11
+LABFHIVQA	7	0	13	11
+LABFHIVQA	7	NULL	13	11
+LABFHIVQA	30	0	13	11
+LABFHIVQA	90	0	13	11
+LABFLDB	30	4	13	13
+LABFOL	4	2	13	12
+LABFUNIDI	1	0	13	10
+LABFUNIDI	2	0	13	10
+LABFUNIDI	4	0	13	10
+LABFUNIDI	7	0	13	10
+LABFUNIDI	7	NULL	13	10
+LABFUNIDI	30	0	13	10
+LABFUNIDI	90	0	13	10
+LABGLF	4	2	13	9
+LABGLF	7	2	13	9
+LABGLF	30	2	13	9
+LABGLF	90	2	13	9
+LABGNTPOL	4	1	13	3
+LABHBCT	4	1	13	12
+LABHBPQ	7	1	13	12
+LABHBSAG	7	2	13	13
+LABHBSQL	2	1	13	12
+LABHGBS	2	0	13	12
+LABHGBS	4	0	13	12
+LABHGBS	7	0	13	12
+LABHGBS	30	0	13	12
+LABHGBS	90	0	13	12
+LABHIVWBL	7	2	13	13
+LABHSVGM	90	1	13	13
+LABHSVPCR	4	1	13	7
+LABHSVQLP	30	1	13	11
+LABHSVZL	1	1	13	11
+LABINSLF	4	2	13	13
+LABINSLF	7	2	13	13
+LABINSLF	7	3	13	13
+LABINSLF	7	4	13	13
+LABINSLF	30	3	13	13
+LABINSLF	30	4	13	13
+LABINSLF	90	3	13	13
+LABINSLF	90	4	13	13
+LABKWB	1	1	13	11
+LABLIDOL	1	5	13	13
+LABMALAR	4	3	13	13
+LABMALAR	7	3	13	13
+LABMALAR	30	3	13	13
+LABMALAR	90	3	13	13
+LABMETB	2	4	13	8
+LABNH3	2	4	13	13
+LABNH3	4	5	13	11
+LABNORW	1	1	13	13
+LABNORW	90	2	13	13
+LABPALB	4	2	13	11
+LABPCCG4V	7	1	13	1
+LABPCEC4V	1	1	13	1
+LABPCEC4V	2	1	13	1
+LABPCEC4V	4	1	13	1
+LABPCEC4V	7	1	13	1
+LABPLTFUN	4	1	13	8
+LABPROINS	30	0	13	8
+LABPROINS	90	0	13	8
+LABPROTS	2	1	13	12
+LABRESPG	90	4	13	12
+LABRETIC	4	4	13	11
+LABRETIC	7	3	13	8
+LABSIROL	4	5	13	11
+LABSTRONG	2	1	13	13
+LABTHEOL	7	4	13	9
+LABTHEOL	30	4	13	9
+LABTHEOL	90	4	13	10
+LABTOBPRL	4	2	13	12
+LABTOBPRL	4	3	13	9
+LABTRFN	1	1	13	12
+LAB11067R	1	NULL	13	10
+LAB11067R	2	NULL	13	10
+LAB11067R	4	NULL	13	10
+LAB11067R	30	NULL	13	10
+LAB11067R	90	NULL	13	10
+LAB12090R	1	NULL	13	7
+LAB12090R	2	NULL	13	7
+LAB12090R	4	NULL	13	7
+LAB12090R	30	NULL	13	7
+LAB12090R	90	NULL	13	7
+LABCYSCSF	1	NULL	13	12
+LABCYSCSF	2	NULL	13	12
+LABCYSCSF	4	NULL	13	12
+LABCYSCSF	30	NULL	13	12
+LABCYSCSF	90	NULL	13	12
+LABDVEIAR	1	NULL	13	8
+LABDVEIAR	2	NULL	13	8
+LABTSH	4	3	13	11
+LABDVEIAR	4	NULL	13	8
+LABDVEIAR	30	NULL	13	8
+LABDVEIAR	90	NULL	13	8
+LABFHIVQA	1	NULL	13	11
+LABFHIVQA	2	NULL	13	11
+LABFHIVQA	4	NULL	13	11
+LABFHIVQA	30	NULL	13	11
+LABFHIVQA	90	NULL	13	11
+LABFUNIDI	1	NULL	13	10
+LABFUNIDI	2	NULL	13	10
+LABFUNIDI	4	NULL	13	10
+LABFUNIDI	30	NULL	13	10
+LABFUNIDI	90	NULL	13	10
+LABUVMA	1	NULL	13	5
+LABUVMA	2	NULL	13	5
+LABUVMA	4	NULL	13	5
+LABUVMA	30	NULL	13	5
+LABUVMA	90	NULL	13	5
+LABYALDO	1	NULL	13	3
+LABYALDO	2	NULL	13	3
+LABYALDO	4	NULL	13	3
+LABYALDO	30	NULL	13	3
+LABYALDO	90	NULL	13	3
+LABYC4F	1	NULL	13	2
+LABYC4F	2	NULL	13	2
+LABYC4F	4	NULL	13	2
+LABYC4F	30	NULL	13	2
+LABYC4F	90	NULL	13	2
+LABYDHEAS	1	NULL	13	8
+LABYDHEAS	2	NULL	13	8
+LABYDHEAS	4	NULL	13	8
+LABYDHEAS	30	NULL	13	8
+LABYDHEAS	90	NULL	13	8
+LABUAS	90	2	13	12
+LABUTPRAN	4	1	13	3
+LABUVMA	1	0	13	5
+LABUVMA	2	0	13	5
+LABUVMA	4	0	13	5
+LABUVMA	7	0	13	5
+LABUVMA	7	NULL	13	5
+LABUVMA	30	0	13	5
+LABVCUL	7	3	13	13
+LABVCUL	30	4	13	13
+LABVCULR	1	1	13	13
+LABVCULR	30	2	13	13
+LABVPAL	1	2	13	11
+LABVSTLC	7	1	13	13
+LABVSTLC	30	1	13	13
+LABVSTLC	90	1	13	13
+LABWDC	7	2	13	9
+LABWDCG	2	2	13	11
+LABWDCG	4	2	13	11
+LABYACE	2	0	13	10
+LABYACE	4	0	13	10
+LABYACE	7	0	13	10
+LABYACE	30	0	13	10
+LABYACE	90	0	13	10
+LABYALDO	1	0	13	3
+LABYALDO	7	NULL	13	3
+LABYC4F	1	0	13	2
+LABYC4F	2	0	13	2
+LABYC4F	4	0	13	2
+LABYC4F	7	0	13	2
+LABYC4F	7	NULL	13	2
+LABYC4F	30	0	13	2
+LABYC4F	90	0	13	2
+LABYCOPPR	2	1	13	7
+LABYCP	1	2	13	11
+LABYCP	1	3	13	10
+LABYCP	2	2	13	11
+LABYCP	4	2	13	11
+LABYCP	7	2	13	11
+LABYDHEAS	1	0	13	8
+LABYDHEAS	2	0	13	8
+LABYDHEAS	4	0	13	8
+LABYDHEAS	7	0	13	8
+LABYDHEAS	7	NULL	13	8
+LABYDHEAS	30	0	13	8
+LABYDHEAS	90	0	13	8
+LABYHGBP	30	1	13	12
+LABYMETFF	2	1	13	12
+LABYPROIN	1	1	13	10
+LABYPROIN	2	1	13	10
+LAB11037R	4	0	12	9
+LAB11037R	7	0	12	9
+LAB11041R	4	3	12	7
+LAB11043R	1	0	12	9
+LAB11043R	2	0	12	9
+LAB11043R	4	0	12	9
+LAB11043R	7	0	12	9
+LAB11043R	7	NULL	12	9
+LAB11043R	30	0	12	9
+LAB11067R	1	0	12	9
+LAB11068R	1	1	12	12
+LAB11096R	4	1	12	11
+LAB11098R	30	1	12	10
+LAB11100R	1	0	12	8
+LAB11100R	2	0	12	8
+LAB11100R	4	0	12	8
+LAB11100R	7	0	12	8
+LAB11100R	7	NULL	12	8
+LAB11100R	30	0	12	8
+LAB11100R	90	0	12	8
+LAB11242R	4	1	12	11
+LAB11607R	1	0	12	6
+LAB11607R	2	0	12	6
+LAB11607R	4	0	12	6
+LAB11607R	7	NULL	12	6
+LAB11858R	1	0	12	7
+LAB11858R	2	0	12	7
+LAB11858R	4	0	12	7
+LAB11858R	7	0	12	7
+LAB11858R	7	NULL	12	7
+LAB11858R	30	0	12	7
+LAB11858R	90	0	12	7
+LAB11860R	1	0	12	8
+LAB11860R	2	0	12	8
+LAB11860R	4	0	12	8
+LAB11860R	7	0	12	8
+LAB11860R	7	NULL	12	8
+LAB11860R	30	0	12	8
+LAB11860R	90	0	12	8
+LAB12762R	7	2	12	8
+LAB12762R	30	2	12	8
+LAB12762R	90	2	12	8
+LAB12808R	2	1	12	8
+LAB12808R	7	1	12	8
+LAB12808R	30	1	12	8
+LAB12808R	90	1	12	8
+LAB12838R	7	NULL	12	6
+LABABC	1	0	12	2
+LABABC	2	0	12	2
+LABABC	4	0	12	2
+LABABC	7	0	12	2
+LABABC	7	NULL	12	2
+LABABC	30	0	12	2
+LABABC	90	0	12	2
+LABACTHO	4	4	12	7
+LABACTHO	7	4	12	7
+LABACTHO	30	4	12	7
+LABACTHO	90	4	12	7
+LABADVDNA	90	1	12	12
+LABADVQT	1	1	12	11
+LABADVQT	2	1	12	11
+LABALB	4	4	12	11
+LABALB	7	4	12	11
+LABALB	30	4	12	11
+LABALB	90	4	12	11
+LABALT	1	1	12	11
+LABAMIPRL	1	1	12	9
+LABASQ	30	2	12	9
+LABAST	30	2	12	9
+LABBHCG	7	1	12	11
+LABBKVPCU	90	1	12	11
+LABBMT2	30	3	12	6
+LABBMT2	90	3	12	6
+LABBXTG	7	4	12	9
+LABC125	30	1	12	11
+LABCA	7	4	12	10
+LABCL	90	1	12	10
+LABCMVM	2	1	12	10
+LABCORT	90	5	12	9
+LABCRPLC	4	3	12	12
+LABCYLEX	90	0	12	8
+LABCYSCSF	1	0	12	11
+LABCYSCSF	2	0	12	11
+LABCYSCSF	4	0	12	11
+LABCYSCSF	7	0	12	11
+LABDHBP	7	1	12	11
+LABDHBP	30	1	12	11
+LABDHBP	90	1	12	11
+LABDPARE	7	NULL	12	10
+LABDVERVP	2	2	12	7
+LABDVERVP	30	3	12	10
+LABEBPCRQ	90	5	12	12
+LABESRP	90	4	12	11
+LABFACT8	7	4	12	11
+LABFCYST	1	0	12	6
+LABFE	90	1	12	9
+LABFHV12	2	1	12	11
+LABFLDB	90	4	12	12
+LABFLDC	1	3	12	10
+LABFMYAB	4	1	12	10
+LABFT4	4	3	12	11
+LABHBSAG	4	2	12	12
+LABHBSQL	1	1	12	10
+LABHCPQ	1	1	12	11
+LABHCPQ	2	1	12	11
+LABHCTX	2	4	12	9
+LABHCTX	4	4	12	10
+LABHFP	1	2	12	8
+LABHGBSQ	7	1	12	11
+LABHGHPLC	30	1	12	9
+LABHGHPLC	90	1	12	9
+LABHSVCSF	90	1	12	10
+LABHSVGM	4	1	12	12
+LABHSVGM	30	1	12	12
+LABIGET	4	1	12	12
+LABIGET	7	1	12	12
+LABINSLF	7	5	12	11
+LABINSLF	30	5	12	11
+LABINSLF	90	5	12	11
+LABLDH	1	4	12	12
+LABLEISAB	1	0	12	11
+LABLEISAB	2	0	12	11
+LABLIL	90	5	12	10
+LABMETB	1	3	12	6
+LABMMAS	4	1	12	12
+LABMYO	7	3	12	10
+LABMYO	30	3	12	10
+LABMYO	90	3	12	10
+LABPCCR	90	2	12	10
+LABPCG3V	90	3	12	6
+LABPLTS	30	4	12	12
+LABPLTS	90	4	12	12
+LABPROCT	30	4	12	11
+LABPROTC	2	1	12	10
+LABPROTS	1	1	12	11
+LABPTH	90	2	12	8
+LABRIST	30	1	12	11
+LABRIST	90	1	12	11
+LABROVRE	1	0	12	3
+LABROVRE	2	0	12	3
+LABROVRE	4	0	12	3
+LABROVRE	7	0	12	3
+LABROVRE	7	NULL	12	3
+LABROVRE	30	0	12	3
+LABROVRE	90	0	12	3
+LABSTLC	90	2	12	11
+LABSTLCX	30	2	12	11
+LABSTPOLY	7	1	12	8
+LABSTPOLY	30	1	12	8
+LABSTPOLY	90	1	12	8
+LABTOBPRL	90	4	12	11
+LABTRIG	2	3	12	12
+LAB11043R	1	NULL	12	9
+LAB11043R	2	NULL	12	9
+LAB11043R	4	NULL	12	9
+LABUA	30	4	12	8
+LAB11043R	30	NULL	12	9
+LAB11043R	90	NULL	12	9
+LAB11100R	1	NULL	12	8
+LAB11100R	2	NULL	12	8
+LAB11100R	4	NULL	12	8
+LAB11100R	30	NULL	12	8
+LAB11100R	90	NULL	12	8
+LAB11607R	1	NULL	12	6
+LAB11607R	2	NULL	12	6
+LAB11607R	4	NULL	12	6
+LAB11607R	30	NULL	12	6
+LAB11607R	90	NULL	12	6
+LAB11858R	1	NULL	12	7
+LAB11858R	2	NULL	12	7
+LAB11858R	4	NULL	12	7
+LAB11858R	30	NULL	12	7
+LAB11858R	90	NULL	12	7
+LAB11860R	1	NULL	12	8
+LAB11860R	2	NULL	12	8
+LAB11860R	4	NULL	12	8
+LAB11860R	30	NULL	12	8
+LAB11860R	90	NULL	12	8
+LAB12838R	1	NULL	12	6
+LAB12838R	2	NULL	12	6
+LAB12838R	4	NULL	12	6
+LABUAS	30	2	12	11
+LAB12838R	30	NULL	12	6
+LAB12838R	90	NULL	12	6
+LABABC	1	NULL	12	2
+LABABC	2	NULL	12	2
+LABABC	4	NULL	12	2
+LABUB2MG	7	NULL	12	6
+LABABC	30	NULL	12	2
+LABABC	90	NULL	12	2
+LABDPARE	1	NULL	12	10
+LABDPARE	2	NULL	12	10
+LABDPARE	4	NULL	12	10
+LABDPARE	30	NULL	12	10
+LABDPARE	90	NULL	12	10
+LABROVRE	1	NULL	12	3
+LABROVRE	2	NULL	12	3
+LABROVRE	4	NULL	12	3
+LABROVRE	30	NULL	12	3
+LABROVRE	90	NULL	12	3
+LABUB2MG	1	NULL	12	6
+LABUB2MG	2	NULL	12	6
+LABUB2MG	4	NULL	12	6
+LABUB2MG	30	NULL	12	6
+LABUB2MG	90	NULL	12	6
+LABUEOS	90	3	12	11
+LABUK	4	1	12	11
+LABUNA	4	2	12	9
+LABUVMA	90	0	12	5
+LABVOLTL	30	2	12	12
+LABWDCG	1	2	12	11
+LABYCP	4	3	12	11
+LABYERYTH	90	1	12	10
+LABYPREAL	4	3	12	9
+LABYUMYO	2	1	12	6
+LAB11036R	4	2	11	9
+LAB11037R	30	0	11	8
+LAB11043R	90	0	11	9
+LAB11049R	1	2	11	7
+LAB11067R	2	0	11	8
+LAB11067R	4	0	11	8
+LAB11067R	7	0	11	8
+LAB11067R	30	0	11	8
+LAB11067R	90	0	11	8
+LAB11086R	30	1	11	5
+LAB11095R	30	1	11	11
+LAB11095R	90	1	11	11
+LAB11607R	7	0	11	6
+LAB11607R	30	0	11	6
+LAB11607R	90	0	11	6
+LAB12090R	30	0	11	6
+LAB12330R	30	0	11	7
+LAB12401R	1	0	11	3
+LAB12401R	2	0	11	3
+LAB12401R	4	0	11	3
+LAB12401R	7	0	11	3
+LAB12401R	7	NULL	11	3
+LAB12401R	30	0	11	3
+LAB12401R	90	0	11	3
+LAB12762R	2	2	11	8
+LAB12796R	30	0	11	7
+LAB12808R	4	1	11	8
+LAB12838R	1	0	11	5
+LAB12838R	2	0	11	5
+LAB12838R	4	0	11	5
+LAB12838R	7	0	11	5
+LAB12838R	30	0	11	5
+LAB12838R	90	0	11	5
+LABADVDNA	4	1	11	11
+LABADVDNA	7	1	11	11
+LABADVDNA	30	1	11	11
+LABAFBD	4	5	11	11
+LABAGALA	90	3	11	11
+LABALB	1	4	11	10
+LABALKP	7	1	11	9
+LABALT	7	2	11	11
+LABANER	1	5	11	10
+LABANTIM	1	1	11	11
+LABAPHSA2	30	1	11	10
+LABB2GP1	4	1	11	11
+LABBKVPCU	30	1	11	9
+LABBLC	4	5	11	10
+LABC125	90	1	11	10
+LABCA	2	4	11	9
+LABCD34	2	1	11	10
+LABCLACT	7	2	11	8
+LABCLCR2	1	0	11	11
+LABCLCR2	2	0	11	11
+LABCLCR2	4	0	11	11
+LABCLCR2	7	0	11	11
+LABCLCR2	7	NULL	11	11
+LABCLCR2	30	0	11	11
+LABCLCR2	90	0	11	11
+LABCORT	30	5	11	8
+LABCR	7	3	11	11
+LABCRDVBG	1	0	11	10
+LABCRDVBG	2	0	11	10
+LABCRDVBG	4	0	11	10
+LABCRDVBG	7	0	11	10
+LABCRDVBG	7	NULL	11	10
+LABCRDVBG	30	0	11	10
+LABCRDVBG	90	0	11	10
+LABCRFA	90	1	11	11
+LABCRPLC	30	3	11	10
+LABCRPLC	90	3	11	10
+LABCRYO	30	1	11	10
+LABCSFCT	30	3	11	8
+LABCSFGL	1	2	11	11
+LABCSFTP	90	4	11	11
+LABCTC	90	3	11	10
+LABCYSCSF	30	0	11	10
+LABCYSCSF	90	0	11	10
+LABDBIL	7	5	11	10
+LABDHBP	2	1	11	11
+LABDPARE	1	0	11	9
+LABDPARE	2	0	11	9
+LABDPARE	4	0	11	9
+LABDPARE	7	0	11	9
+LABDPARE	30	0	11	9
+LABDPARE	90	0	11	9
+LABDSDNA	1	1	11	10
+LABDVERV	90	4	11	11
+LABDVERVR	7	1	11	9
+LABEP1	1	2	11	9
+LABESRP	7	4	11	10
+LABFACT8	30	4	11	11
+LABFACT8	90	4	11	11
+LABFCLAB	1	0	11	8
+LABFCLAB	2	0	11	8
+LABFCLAB	4	0	11	8
+LABFCLAB	7	0	11	8
+LABFCLAB	7	NULL	11	8
+LABFCLAB	30	0	11	8
+LABFCLAB	90	0	11	8
+LABFCYST	2	0	11	6
+LABFCYST	4	0	11	6
+LABFCYST	7	0	11	6
+LABFCYST	30	0	11	6
+LABFCYST	90	0	11	6
+LABFE	30	1	11	9
+LABFER	90	3	11	11
+LABFLUAPX	7	2	11	11
+LABFMYAB	7	1	11	9
+LABFMYAB	30	1	11	9
+LABFMYAB	90	1	11	9
+LABGLF	1	2	11	8
+LABGLF	1	3	11	10
+LABGLF	2	2	11	9
+LABGLNF	1	4	11	10
+LABGRAM	4	4	11	10
+LABHBCT	2	1	11	10
+LABHBSAG	2	2	11	11
+LABHGBP	1	2	11	9
+LABHGBSQ	30	1	11	10
+LABHGHPLC	7	1	11	8
+LABHIVQ2	7	1	11	9
+LABHSVGM	7	1	11	11
+LABHV6DNA	1	1	11	10
+LABIGET	1	1	11	11
+LABIGET	2	1	11	11
+LABIGET	90	2	11	10
+LABINSL	1	1	11	10
+LABINSLF	1	5	11	11
+LABKOHP	90	1	11	11
+LABLAMOL	1	1	11	11
+LABLDL	1	0	11	10
+LABLDL	2	0	11	10
+LABLDL	4	0	11	10
+LABLDL	7	0	11	10
+LABLDL	7	NULL	11	10
+LABLDL	30	0	11	10
+LABLDL	90	0	11	10
+LABLEGDA	7	2	11	11
+LABLEISAB	4	0	11	10
+LABLEISAB	7	0	11	10
+LABLEISAB	30	0	11	10
+LABLEISAB	90	0	11	10
+LABLYMES2	90	1	11	11
+LABMALAR	4	4	11	10
+LABMALAR	7	4	11	10
+LABMALAR	30	4	11	10
+LABMALAR	90	4	11	10
+LABMETC	30	4	11	4
+LABMETC	90	4	11	4
+LABMMAS	2	1	11	11
+LABORAERO	90	3	11	7
+LABORANER	30	3	11	10
+LABPCCG4O	30	2	11	11
+LABPCCG4V	1	1	11	1
+LABPCCG4V	2	1	11	1
+LABPCCG4V	4	1	11	1
+LABPCG3V	4	3	11	6
+LABPCG3V	7	3	11	6
+LABPCG3V	30	3	11	6
+LABPLTFUN	2	1	11	8
+LABPROTC	1	1	11	9
+LABQFTBG	1	1	11	11
+LABREPT	4	4	11	10
+LABRIST	2	1	11	10
+LABRIST	4	1	11	10
+LABRIST	7	1	11	10
+LABSALICY	1	1	11	4
+LABSALICY	2	1	11	4
+LABSALICY	4	1	11	4
+LABSALICY	7	1	11	4
+LABSALICY	30	1	11	4
+LABSALICY	90	1	11	4
+LABSCMV	7	3	11	11
+LABSCMV	90	4	11	11
+LABTBIL	30	4	11	10
+LABTCDIF	2	2	11	11
+LABTESFBT	1	0	11	0
+LABTESFBT	2	0	11	0
+LABTESFBT	7	NULL	11	0
+LABTRFN	2	1	11	9
+LABTT	4	4	11	10
+LABUAS	4	2	11	9
+LABUB2MG	1	0	11	6
+LABUB2MG	2	0	11	6
+LAB12401R	1	NULL	11	3
+LAB12401R	2	NULL	11	3
+LAB12401R	4	NULL	11	3
+LAB12401R	30	NULL	11	3
+LAB12401R	90	NULL	11	3
+LABCLCR2	1	NULL	11	11
+LABCLCR2	2	NULL	11	11
+LABCLCR2	4	NULL	11	11
+LABCLCR2	30	NULL	11	11
+LABCLCR2	90	NULL	11	11
+LABCRDVBG	1	NULL	11	10
+LABCRDVBG	2	NULL	11	10
+LABCRDVBG	4	NULL	11	10
+LABCRDVBG	30	NULL	11	10
+LABCRDVBG	90	NULL	11	10
+LABFCLAB	1	NULL	11	8
+LABFCLAB	2	NULL	11	8
+LABFCLAB	4	NULL	11	8
+LABFCLAB	30	NULL	11	8
+LABFCLAB	90	NULL	11	8
+LABLDL	1	NULL	11	10
+LABLDL	2	NULL	11	10
+LABLDL	4	NULL	11	10
+LABLDL	30	NULL	11	10
+LABLDL	90	NULL	11	10
+LABTESFBT	1	NULL	11	0
+LABTESFBT	2	NULL	11	0
+LABTESFBT	4	NULL	11	0
+LABTESFBT	30	NULL	11	0
+LABTESFBT	90	NULL	11	0
+LABVITB1P	1	NULL	11	2
+LABVITB1P	2	NULL	11	2
+LABVITB1P	4	NULL	11	2
+LABVITB1P	30	NULL	11	2
+LABVITB1P	90	NULL	11	2
+LABYDHEA	1	NULL	11	10
+LABYDHEA	2	NULL	11	10
+LABYDHEA	4	NULL	11	10
+LABYDHEA	30	NULL	11	10
+LABYDHEA	90	NULL	11	10
+LABYFACE	1	NULL	11	0
+LABYFACE	2	NULL	11	0
+LABYFACE	4	NULL	11	0
+LABYFACE	30	NULL	11	0
+LABYFACE	90	NULL	11	0
+LABYGBMGE	1	NULL	11	10
+LABYGBMGE	2	NULL	11	10
+LABYGBMGE	4	NULL	11	10
+LABYGBMGE	30	NULL	11	10
+LABYGBMGE	90	NULL	11	10
+LABVCUL	4	3	11	11
+LABVITB1P	7	NULL	11	2
+LABWDC	4	2	11	8
+LABWNVAB	4	1	11	9
+LABWNVAB	7	1	11	9
+LABYALDO	2	0	11	3
+LABYCP	2	3	11	10
+LABYCP	7	3	11	10
+LABYCP	7	4	11	7
+LABYCP	30	3	11	10
+LABYCP	90	3	11	10
+LABYDHEA	1	0	11	10
+LABYDHEA	7	NULL	11	10
+LABYERYTH	30	1	11	9
+LABYFACE	7	NULL	11	0
+LABYFT3	1	1	11	8
+LABYGBMGE	1	0	11	10
+LABYGBMGE	2	0	11	10
+LABYGBMGE	4	0	11	10
+LABYGBMGE	7	0	11	10
+LABYGBMGE	7	NULL	11	10
+LABYGBMGE	30	0	11	10
+LABYGBMGE	90	0	11	10
+LABYHGBP	2	1	11	10
+LABYPROIN	4	0	11	7
+LABYPROIN	7	0	11	7
+LABYPROIN	30	0	11	7
+LABYPROIN	90	0	11	7
+LAB11023R	90	1	10	10
+LAB11036R	2	2	10	8
+LAB11036R	7	2	10	8
+LAB11036R	30	2	10	8
+LAB11036R	90	2	10	8
+LAB11060R	4	1	10	7
+LAB11069R	90	1	10	5
+LAB11071R	1	1	10	10
+LAB11086R	90	1	10	5
+LAB11096R	2	1	10	9
+LAB11098R	4	1	10	8
+LAB11982R	7	1	10	6
+LAB12090R	90	0	10	5
+LAB12241R	1	0	10	2
+LAB12241R	7	NULL	10	2
+LAB12330R	7	1	10	9
+LAB12330R	30	1	10	8
+LAB12330R	90	0	10	6
+LAB12796R	90	0	10	6
+LAB12853R	2	0	10	7
+LAB13006R	1	0	10	8
+LAB13006R	7	NULL	10	8
+LABACTHO	2	4	10	8
+LABAFBCF	7	2	10	10
+LABAGALA	7	2	10	10
+LABAGALA	30	4	10	10
+LABALB	2	4	10	9
+LABAMIPRL	7	2	10	9
+LABANAS	4	2	10	8
+LABAPHSA2	7	1	10	9
+LABAPHSAB	4	1	10	9
+LABBLCA	4	1	10	9
+LABBLCA	7	1	10	9
+LABBXTG	90	5	10	7
+LABCIGMT	1	0	10	7
+LABCIGMT	2	0	10	7
+LABCIGMT	4	0	10	7
+LABCIGMT	7	0	10	7
+LABCIGMT	7	NULL	10	7
+LABCIGMT	30	0	10	7
+LABCIGMT	90	0	10	7
+LABCL	30	1	10	8
+LABCLACT	1	1	10	10
+LABCMV	30	1	10	6
+LABCMV	90	1	10	6
+LABCMVP	7	3	10	10
+LABCO2	7	1	10	9
+LABCRPLFC	7	1	10	9
+LABCRYO	7	1	10	9
+LABCSTOTA	30	1	10	6
+LABCSTOTA	90	1	10	6
+LABCTC	30	3	10	9
+LABDBIL	2	3	10	9
+LABDHBP	1	1	10	10
+LABDHBP	4	1	10	10
+LABDOA	30	2	10	7
+LABDVERO	90	1	10	10
+LABDVERV	2	2	10	10
+LABEBPCRQ	7	2	10	9
+LABEP1	4	3	10	5
+LABESRP	4	5	10	10
+LABFE	4	1	10	9
+LABFE	7	1	10	9
+LABFIBANT	2	0	10	5
+LABFK506L	2	5	10	10
+LABFLDB	4	2	10	8
+LABFOL	2	2	10	9
+LABFT4	7	3	10	9
+LABGASPH	7	NULL	10	4
+LABGLF	1	5	10	9
+LABGLNF	4	4	10	9
+LABGLNF	7	4	10	9
+LABGLNF	30	4	10	9
+LABGLNF	90	4	10	9
+LABGNTPRL	4	4	10	9
+LABGNTPRL	7	5	10	9
+LABHAP	4	5	10	10
+LABHBPQ	4	1	10	9
+LABHGBSQ	90	1	10	9
+LABHGHPLC	2	1	10	7
+LABHGHPLC	4	1	10	7
+LABHHV6QT	2	1	10	6
+LABHV6DNA	90	2	10	10
+LABIBIL	30	5	10	9
+LABIGM	90	1	10	8
+LABINSL	4	1	10	9
+LABINSL	30	1	10	8
+LABINSL	90	1	10	8
+LABINSLF	2	4	10	10
+LABINSLF	2	5	10	10
+LABINSLF	4	5	10	10
+LABISOF	90	2	10	9
+LABLIL	4	4	10	10
+LABLIPS	1	4	10	10
+LABLYMES2	4	1	10	10
+LABLYMES2	7	1	10	10
+LABLYMES2	30	1	10	10
+LABMALAR	1	3	10	9
+LABMETC	7	4	10	3
+LABMETHB	4	5	10	10
+LABOXCMET	30	1	10	8
+LABOXCMET	90	1	10	8
+LABPANAPL	1	0	10	4
+LABPCCG4O	7	2	10	10
+LABPCEG7O	90	1	10	5
+LABPCG3V	2	3	10	4
+LABPHEN	1	3	10	9
+LABPLASMN	1	1	10	8
+LABPLASMN	4	1	10	6
+LABPLASMN	7	1	10	6
+LABPLASMN	30	1	10	6
+LABPLASMN	90	1	10	6
+LABPLTFUN	1	1	10	7
+LABPORFEC	1	0	10	9
+LABPORFEC	2	0	10	9
+LABPORFEC	4	0	10	9
+LABPORFEC	7	NULL	10	9
+LABREPT	1	4	10	9
+LABREPT	2	3	10	9
+LABRIST	1	1	10	9
+LABSTLC	30	2	10	9
+LABSTLCX	7	2	10	10
+LABSTOBGC	2	1	10	3
+LABSTOBGC	90	1	10	3
+LABSTOBGD	90	2	10	1
+LABSTPOLY	4	1	10	7
+LABSTRED	1	1	10	2
+LABTBILFR	4	2	10	10
+LABTBILFR	90	4	10	10
+LABTBPCRS	2	2	10	8
+LABTESFBT	4	0	10	0
+LABTFPSA	7	NULL	10	6
+LABTOBPRL	7	3	10	7
+LABUA	1	2	10	6
+LABUA	90	5	10	6
+LABUAS	1	2	10	9
+LABUAS	7	2	10	9
+LABUEOS	30	3	10	10
+LABUEP	4	2	10	7
+LABUK	2	1	10	9
+LAB12241R	1	NULL	10	2
+LAB12241R	2	NULL	10	2
+LAB12241R	4	NULL	10	2
+LAB12241R	30	NULL	10	2
+LAB12241R	90	NULL	10	2
+LAB13006R	1	NULL	10	8
+LAB13006R	2	NULL	10	8
+LAB13006R	4	NULL	10	8
+LAB13006R	30	NULL	10	8
+LAB13006R	90	NULL	10	8
+LABCIGMT	1	NULL	10	7
+LABCIGMT	2	NULL	10	7
+LABCIGMT	4	NULL	10	7
+LABCIGMT	30	NULL	10	7
+LABCIGMT	90	NULL	10	7
+LABGASPH	1	NULL	10	4
+LABGASPH	2	NULL	10	4
+LABGASPH	4	NULL	10	4
+LABGASPH	30	NULL	10	4
+LABGASPH	90	NULL	10	4
+LABPORFEC	1	NULL	10	9
+LABPORFEC	2	NULL	10	9
+LABPORFEC	4	NULL	10	9
+LABPORFEC	30	NULL	10	9
+LABPORFEC	90	NULL	10	9
+LABTFPSA	1	NULL	10	6
+LABTFPSA	2	NULL	10	6
+LABTFPSA	4	NULL	10	6
+LABTFPSA	30	NULL	10	6
+LABTFPSA	90	NULL	10	6
+LABUURIC	1	NULL	10	3
+LABUURIC	2	NULL	10	3
+LABUURIC	4	NULL	10	3
+LABUURIC	30	NULL	10	3
+LABUURIC	90	NULL	10	3
+LABYDNBT	1	NULL	10	8
+LABYDNBT	2	NULL	10	8
+LABYDNBT	4	NULL	10	8
+LABYDNBT	30	NULL	10	8
+LABYDNBT	90	NULL	10	8
+LABUTPRAN	2	1	10	3
+LABUURIC	1	0	10	3
+LABUURIC	2	0	10	3
+LABUURIC	4	0	10	3
+LABUURIC	7	0	10	3
+LABUURIC	7	NULL	10	3
+LABUURIC	30	0	10	3
+LABUURIC	90	0	10	3
+LABVCULT	30	2	10	9
+LABVCULT	90	2	10	9
+LABVITB1P	1	0	10	2
+LABVITB1P	2	0	10	2
+LABVP3	30	1	10	9
+LABVP3	90	1	10	9
+LABVPAL	2	3	10	8
+LABVSTLC	4	1	10	10
+LABVZVQLP	7	1	10	7
+LABYALDO	4	0	10	2
+LABYALDO	7	0	10	2
+LABYCCPIG	90	1	10	10
+LABYCP	30	4	10	6
+LABYCP	90	4	10	6
+LABYDHEA	2	0	10	9
+LABYDHEA	4	0	10	9
+LABYDHEA	7	0	10	9
+LABYDHEA	30	0	10	9
+LABYDHEA	90	0	10	9
+LABYDNBT	1	0	10	8
+LABYDNBT	2	0	10	8
+LABYDNBT	4	0	10	8
+LABYDNBT	7	0	10	8
+LABYDNBT	7	NULL	10	8
+LABYDNBT	30	0	10	8
+LABYDNBT	90	0	10	8
+LABYFACE	1	0	10	0
+LABYFACE	2	0	10	0
+LABYFFATQ	2	1	10	6
+LABYFLUO	30	1	10	9
+LABYFLUO	90	1	10	9
+LABYFLUZ	90	1	10	5
+LABYFPHEN	90	1	10	4
+LABYMETFF	1	1	10	9
+LABYPROIN	4	1	10	7
+LABYPROIN	7	1	10	7
+LABYPROIN	30	1	10	7
+LABYPROIN	90	1	10	7
+LABYSTOSM	2	1	10	6
+LAB11037R	90	0	9	6
+LAB11070R	2	1	9	6
+LAB11078R	1	0	9	8
+LAB11078R	2	0	9	8
+LAB11078R	7	NULL	9	8
+LAB11098R	7	1	9	7
+LAB11107R	30	1	9	9
+LAB11107R	90	1	9	9
+LAB11242R	2	1	9	8
+LAB12241R	2	0	9	2
+LAB12241R	4	0	9	2
+LAB13006R	2	0	9	7
+LAB13006R	4	0	9	7
+LAB13006R	7	0	9	7
+LABA1C	4	3	9	9
+LABAAUR	1	0	9	0
+LABAAUR	2	0	9	0
+LABAAUR	4	0	9	0
+LABAAUR	7	0	9	0
+LABAAUR	7	NULL	9	0
+LABAAUR	30	0	9	0
+LABAAUR	90	0	9	0
+LABACYLP	1	0	9	5
+LABACYLP	2	0	9	5
+LABACYLP	4	0	9	5
+LABACYLP	7	0	9	5
+LABACYLP	7	NULL	9	5
+LABACYLP	30	0	9	5
+LABACYLP	90	0	9	5
+LABADVDNA	2	1	9	9
+LABAFBC	2	5	9	9
+LABAFBCF	90	3	9	9
+LABALB	7	5	9	7
+LABALB	30	5	9	7
+LABALB	90	5	9	7
+LABAMIPRL	30	2	9	8
+LABAMIPRL	90	2	9	8
+LABANCA	1	1	9	9
+LABAPHSAB	1	1	9	8
+LABAPHSAB	2	1	9	8
+LABASI	30	5	9	9
+LABASQ	4	2	9	8
+LABASQ	7	2	9	8
+LABB12	1	2	9	9
+LABB2GP1	2	1	9	9
+LABBHCG	4	1	9	8
+LABBLC2	4	4	9	9
+LABBLCTIP	7	4	9	8
+LABBUN	7	3	9	9
+LABBXTG	4	4	9	8
+LABCBCO	2	5	9	5
+LABCIGA	1	0	9	6
+LABCIGA	2	0	9	6
+LABCIGA	4	0	9	6
+LABCIGA	7	0	9	6
+LABCIGA	7	NULL	9	6
+LABCIGA	30	0	9	6
+LABCIGA	90	0	9	6
+LABCMVM	1	1	9	7
+LABCMVQT	7	5	9	9
+LABCMVRO	90	2	9	8
+LABCO2	4	1	9	8
+LABCORT	2	4	9	8
+LABCR	7	4	9	9
+LABCRCL	30	1	9	4
+LABCRCL	90	1	9	4
+LABCRFA	30	1	9	9
+LABCRPLC	7	3	9	8
+LABCRPLFB	4	1	9	9
+LABCRPLFB	7	1	9	9
+LABCRPLFB	30	1	9	9
+LABCRYO	4	1	9	8
+LABDBIL	1	3	9	9
+LABDIC	4	3	9	3
+LABDRVVTP	7	1	9	4
+LABDRVVTP	30	1	9	4
+LABDRVVTP	90	1	9	4
+LABEBPCRQ	30	4	9	9
+LABEP1	7	3	9	5
+LABEP1	30	3	9	5
+LABEP1	90	3	9	5
+LABESRP	30	5	9	9
+LABESRP	90	5	9	9
+LABEVPCR	90	1	9	4
+LABFACT8	1	3	9	6
+LABFE	2	1	9	8
+LABFHVIG	7	1	9	9
+LABFHVIG	30	1	9	9
+LABFHVIG	90	1	9	9
+LABFIBANT	4	0	9	5
+LABFIBANT	7	0	9	5
+LABFIBANT	30	0	9	5
+LABFIBANT	90	0	9	5
+LABFLDB	7	3	9	9
+LABFLUAPX	90	3	9	8
+LABFT4	30	4	9	9
+LABFT4	90	5	9	9
+LABGASPH	1	0	9	4
+LABGGT	1	2	9	9
+LABGLNF	4	5	9	9
+LABGLNF	7	5	9	9
+LABGLNF	30	5	9	9
+LABGLNF	90	5	9	9
+LABGLOB	90	1	9	9
+LABGRAM	7	5	9	8
+LABHBCA	90	1	9	8
+LABHBCC	1	0	9	6
+LABHBCC	2	0	9	6
+LABHBCC	4	0	9	6
+LABHBCC	7	0	9	6
+LABHBCC	7	NULL	9	6
+LABHBCC	30	0	9	6
+LABHBCC	90	0	9	6
+LABHFP	90	5	9	6
+LABHGBP	4	4	9	8
+LABHGBP	7	4	9	7
+LABHGBSQ	1	1	9	8
+LABHGBSQ	2	1	9	8
+LABHGBSQ	4	1	9	8
+LABHITAB	90	4	9	8
+LABHIVQ2	2	1	9	8
+LABHIVQ2	4	1	9	8
+LABHIVWBL	4	2	9	9
+LABIBIL	90	5	9	8
+LABIGA	30	1	9	9
+LABIGA	90	1	9	9
+LABIGM	7	1	9	7
+LABIGM	30	1	9	7
+LABINSL	2	1	9	8
+LABINSL	7	1	9	8
+LABLIL	30	5	9	7
+LABLYMES2	1	1	9	9
+LABLYMES2	2	1	9	9
+LABMALAR	2	4	9	8
+LABMALSC	30	1	9	7
+LABMALSC	90	1	9	7
+LABMMAS	1	1	9	9
+LABMYCPA	2	3	9	3
+LABNH3	1	3	9	9
+LABNORW	30	2	9	9
+LABOAPE	30	4	9	9
+LABOAPE	90	4	9	9
+LABPANAPL	2	1	9	4
+LABPCACT	1	2	9	1
+LABPCACT	2	2	9	1
+LABPCACT	4	2	9	1
+LABPCACT	7	2	9	1
+LABPCACT	30	2	9	1
+LABPCACT	90	2	9	1
+LABPCCG4O	2	2	9	9
+LABPCCG4O	4	2	9	9
+LABPCEG7O	7	1	9	5
+LABPCEG7O	30	1	9	5
+LABPENTO	7	NULL	9	4
+LABPORFEC	7	0	9	8
+LABPTEG	30	3	9	4
+LABPTEG	90	3	9	4
+LABPTH	30	2	9	6
+LABREPT	1	3	9	8
+LABRESPG	7	3	9	7
+LABROMRS	1	2	9	9
+LABSCMV	4	3	9	9
+LABSSBABY	30	1	9	9
+LABSSBABY	90	1	9	9
+LABSTOBGC	4	1	9	3
+LABSTOBGC	7	1	9	3
+LABSTOBGC	30	1	9	3
+LABSTOBGD	30	2	9	0
+LABSTOOLC	30	2	9	8
+LABT3	30	1	9	6
+LABT3	90	1	9	6
+LABTBILFR	30	4	9	8
+LABTCDIF	30	4	9	9
+LABTCDIF	90	4	9	9
+LABTFPSA	1	0	9	5
+LABTFPSA	2	0	9	5
+LABTFPSA	4	0	9	5
+LABTFPSA	7	0	9	5
+LABTFPSA	30	0	9	5
+LABTFPSA	90	0	9	5
+LABTHEOL	4	4	9	4
+LABTHEOL	90	5	9	7
+LABTRFN	4	4	9	9
+LABTT	7	2	9	7
+LABTT	30	2	9	7
+LABTT	90	2	9	7
+LABUA	7	3	9	8
+LABUAS	2	2	9	7
+LABUB2MG	4	0	9	4
+LABUB2MG	7	0	9	4
+LABUB2MG	30	0	9	4
+LABUB2MG	90	0	9	4
+LABUEOS	7	3	9	9
+LABUMAG	1	0	9	0
+LABUMAG	7	NULL	9	0
+LAB11078R	1	NULL	9	8
+LAB11078R	2	NULL	9	8
+LAB11078R	4	NULL	9	8
+LAB11078R	30	NULL	9	8
+LAB11078R	90	NULL	9	8
+LABAAUR	1	NULL	9	0
+LABAAUR	2	NULL	9	0
+LABAAUR	4	NULL	9	0
+LABAAUR	30	NULL	9	0
+LABAAUR	90	NULL	9	0
+LABACYLP	1	NULL	9	5
+LABACYLP	2	NULL	9	5
+LABACYLP	4	NULL	9	5
+LABACYLP	30	NULL	9	5
+LABACYLP	90	NULL	9	5
+LABCIGA	1	NULL	9	6
+LABCIGA	2	NULL	9	6
+LABCIGA	4	NULL	9	6
+LABCIGA	30	NULL	9	6
+LABCIGA	90	NULL	9	6
+LABHBCC	1	NULL	9	6
+LABHBCC	2	NULL	9	6
+LABHBCC	4	NULL	9	6
+LABHBCC	30	NULL	9	6
+LABHBCC	90	NULL	9	6
+LABPENTO	1	NULL	9	4
+LABPENTO	2	NULL	9	4
+LABPENTO	4	NULL	9	4
+LABPENTO	30	NULL	9	4
+LABPENTO	90	NULL	9	4
+LABUMAG	1	NULL	9	0
+LABUMAG	2	NULL	9	0
+LABUMAG	4	NULL	9	0
+LABUMAG	30	NULL	9	0
+LABUMAG	90	NULL	9	0
+LABYC2729	1	NULL	9	5
+LABYC2729	2	NULL	9	5
+LABYC2729	4	NULL	9	5
+LABYC2729	30	NULL	9	5
+LABYC2729	90	NULL	9	5
+LABVCUL	1	3	9	9
+LABVCUL	2	3	9	9
+LABVITB1P	4	0	9	2
+LABVITB1P	7	0	9	2
+LABVP3	4	1	9	8
+LABVP3	7	1	9	8
+LABWDC	2	2	9	7
+LABWNVRNA	30	1	9	8
+LABWNVRNA	90	1	9	8
+LABYA1AS	1	1	9	8
+LABYBOH	30	2	9	7
+LABYBOH	90	2	9	7
+LABYC2729	1	0	9	5
+LABYC2729	2	0	9	5
+LABYC2729	4	0	9	5
+LABYC2729	7	0	9	5
+LABYC2729	7	NULL	9	5
+LABYC2729	30	0	9	5
+LABYC2729	90	0	9	5
+LABYCCPIG	30	1	9	9
+LABYCP	2	4	9	5
+LABYCP	4	4	9	5
+LABYERYTH	2	1	9	7
+LABYERYTH	4	1	9	7
+LABYERYTH	7	1	9	7
+LABYFACE	4	0	9	0
+LABYFACE	7	0	9	0
+LABYFLUO	2	1	9	8
+LABYFLUO	4	1	9	8
+LABYFLUZ	30	1	9	5
+LABYFPHEN	30	1	9	4
+LABYHGBP	30	2	9	7
+LABYMPA	2	1	9	5
+LABYPROIN	2	2	9	9
+LABYPTHRP	2	1	9	9
+LAB11023R	30	1	8	8
+LAB11041R	7	4	8	6
+LAB11069R	30	1	8	5
+LAB11078R	4	0	8	7
+LAB11078R	7	0	8	7
+LAB11078R	30	0	8	7
+LAB11078R	90	0	8	7
+LAB11081R	7	1	8	4
+LAB11081R	30	1	8	4
+LAB11081R	90	1	8	4
+LAB11085R	2	1	8	8
+LAB11085R	90	1	8	8
+LAB11094R	30	1	8	8
+LAB11094R	90	1	8	8
+LAB11095R	7	1	8	8
+LAB11265R	30	1	8	8
+LAB11265R	90	1	8	8
+LAB12241R	7	0	8	1
+LAB12241R	30	0	8	1
+LAB12241R	90	0	8	1
+LAB12330R	90	1	8	7
+LAB12397R	7	NULL	8	5
+LAB12808R	1	1	8	6
+LAB13006R	30	0	8	6
+LAB13006R	90	0	8	6
+LABADPCRQ	30	2	8	8
+LABADPCRQ	90	2	8	8
+LABADVQT	90	2	8	6
+LABAFBD	1	5	8	8
+LABAFBD	2	5	8	8
+LABAFP	90	2	8	8
+LABAGALA	30	3	8	8
+LABALKP	4	1	8	8
+LABAPHSA2	4	1	8	7
+LABASI	1	1	8	8
+LABASQ	2	2	8	7
+LABAST	7	2	8	6
+LABB2GP1	1	1	8	8
+LABBACIDS	1	1	8	8
+LABBHCG	2	1	8	7
+LABBHCG	90	2	8	8
+LABBKVPCU	7	1	8	6
+LABBLC2	1	3	8	8
+LABBLC2	7	5	8	7
+LABBLCA	2	1	8	8
+LABBLCSTK	2	2	8	7
+LABBUN	4	3	8	8
+LABBXTG	1	4	8	7
+LABBXTG	2	4	8	7
+LABC125	7	1	8	7
+LABCBCS	2	2	8	6
+LABCD34	1	1	8	6
+LABCFRESP	30	1	8	4
+LABCFRESP	90	1	8	4
+LABCMVP	90	4	8	8
+LABCR	90	3	8	7
+LABCRDABG	1	0	8	7
+LABCRDABG	2	0	8	7
+LABCRDABG	4	0	8	7
+LABCRDABG	7	0	8	7
+LABCRDABG	7	NULL	8	7
+LABCRDABG	30	0	8	7
+LABCRDABG	90	0	8	7
+LABCRFA	7	1	8	8
+LABCRP	7	5	8	5
+LABCRPLFC	4	1	8	7
+LABCSFGL	2	3	8	8
+LABCSFGL	4	5	8	8
+LABCSFTP	30	4	8	8
+LABCSFTP	90	5	8	7
+LABCTC	4	2	8	8
+LABDGHBP	90	1	8	7
+LABDRVVTP	1	1	8	4
+LABDRVVTP	2	1	8	4
+LABDRVVTP	4	1	8	4
+LABDVERO	30	1	8	8
+LABDVERVR	4	1	8	8
+LABEBVQP	7	2	8	8
+LABEBVQP	90	5	8	8
+LABFACT8	30	5	8	7
+LABFACT8	90	5	8	7
+LABFACT9	2	2	8	2
+LABFACT9	4	2	8	2
+LABFACT9	7	2	8	2
+LABFACT9	30	2	8	3
+LABFACT9	90	2	8	3
+LABFE	1	1	8	7
+LABFENTA	7	1	8	8
+LABFENTA	30	1	8	8
+LABFENTA	90	1	8	8
+LABFER	7	3	8	8
+LABFER	30	3	8	8
+LABFKOH	90	5	8	8
+LABFLUAPX	30	3	8	7
+LABFMYAB	1	1	8	8
+LABGASPH	2	0	8	4
+LABGASPH	4	0	8	4
+LABGASPH	7	0	8	4
+LABGASPH	30	0	8	4
+LABGASPH	90	0	8	4
+LABGGT	1	3	8	8
+LABGH	1	1	8	6
+LABGNTPOL	2	1	8	2
+LABHBCA	30	1	8	7
+LABHBPQ	1	1	8	7
+LABHBPQ	2	1	8	7
+LABHBPQ	90	2	8	8
+LABHGBP	2	3	8	7
+LABHIPA	7	2	8	3
+LABHIVQ2	1	1	8	7
+LABHSVGM	2	1	8	8
+LABHSVQL	7	1	8	8
+LABHSVQL	30	1	8	8
+LABHSVQL	90	1	8	8
+LABHV6DNA	30	2	8	8
+LABIGA	1	1	8	8
+LABIGA	2	1	8	8
+LABIGA	4	1	8	8
+LABIGA	7	1	8	8
+LABIGM	4	1	8	6
+LABINSL	1	3	8	8
+LABISOF	30	2	8	7
+LABKOHP	30	1	8	8
+LABKWB	1	2	8	7
+LABKWB	4	2	8	8
+LABKWB	7	2	8	8
+LABKWB	30	2	8	8
+LABKWB	90	2	8	8
+LABMALSC	2	1	8	7
+LABMALSC	4	1	8	7
+LABMALSC	7	1	8	7
+LABMETC	1	2	8	0
+LABORANER	7	3	8	8
+LABORANER	90	4	8	8
+LABORSHOR	1	4	8	7
+LABORSHOR	2	4	8	7
+LABORSHOR	4	4	8	7
+LABORSHOR	7	4	8	7
+LABORSHOR	30	4	8	7
+LABORSHOR	90	4	8	7
+LABPANAPL	4	1	8	4
+LABPANAPL	7	1	8	4
+LABPANAPL	30	1	8	4
+LABPANAPL	90	1	8	4
+LABPCCG4O	1	2	8	8
+LABPCEG7O	4	1	8	5
+LABPLASMN	4	2	8	6
+LABPLTS	2	3	8	7
+LABPLTS	30	5	8	7
+LABPLTS	90	5	8	8
+LABPORFEC	30	0	8	7
+LABPORFEC	90	0	8	7
+LABPROCT	7	5	8	8
+LABPROCT	30	5	8	8
+LABPROCT	90	5	8	8
+LABPROL	2	2	8	8
+LABPTEG	7	3	8	4
+LABPTH	4	2	8	6
+LABPTH	7	2	8	6
+LABRESPG	90	5	8	6
+LABSCMV	30	4	8	8
+LABSSAABY	30	1	8	8
+LABSSAABY	90	1	8	8
+LABSSBABY	1	1	8	8
+LABSSBABY	2	1	8	8
+LABSSBABY	4	1	8	8
+LABSSBABY	7	1	8	8
+LABSTOBGD	7	2	8	0
+LABSTPOLY	2	1	8	5
+LABSTRED	90	2	8	7
+LABT3	7	1	8	5
+LABTBIL	90	5	8	8
+LABTBILFR	90	5	8	7
+LABTBPCRS	1	2	8	6
+LABTCPSA	1	0	8	5
+LABTCPSA	2	0	8	5
+LABTCPSA	4	0	8	5
+LABTCPSA	7	0	8	5
+LABTCPSA	7	NULL	8	5
+LABTCPSA	30	0	8	5
+LABTCPSA	90	0	8	5
+LABTEGCP	1	2	8	3
+LABTEGCP	30	5	8	5
+LABTHEOL	7	5	8	6
+LABTHEOL	30	5	8	6
+LABTHS	90	1	8	7
+LABTOBPRL	30	4	8	7
+LABTP	90	2	8	5
+LABTRFN	2	2	8	8
+LABTT	4	2	8	7
+LABUALB	7	1	8	5
+LABUAPRN	90	4	8	5
+LABUEOS	4	3	8	8
+LABUNA	2	2	8	7
+LABUPOR	1	0	8	7
+LABUPOR	2	0	8	7
+LABUPOR	4	0	8	7
+LABUPOR	7	0	8	7
+LABUPOR	7	NULL	8	7
+LABUPOR	30	0	8	7
+LABUPOR	90	0	8	7
+LABURAMY	7	NULL	8	4
+LABURNC	7	4	8	8
+LAB12397R	1	NULL	8	5
+LAB12397R	2	NULL	8	5
+LAB12397R	4	NULL	8	5
+LAB12397R	30	NULL	8	5
+LAB12397R	90	NULL	8	5
+LABCRDABG	1	NULL	8	7
+LABCRDABG	2	NULL	8	7
+LABCRDABG	4	NULL	8	7
+LABCRDABG	30	NULL	8	7
+LABVCUL	90	5	8	8
+LABCRDABG	90	NULL	8	7
+LABTCPSA	1	NULL	8	5
+LABTCPSA	2	NULL	8	5
+LABTCPSA	4	NULL	8	5
+LABTCPSA	30	NULL	8	5
+LABTCPSA	90	NULL	8	5
+LABUPOR	1	NULL	8	7
+LABUPOR	2	NULL	8	7
+LABUPOR	4	NULL	8	7
+LABUPOR	30	NULL	8	7
+LABVCULR	90	3	8	8
+LABUPOR	90	NULL	8	7
+LABURAMY	1	NULL	8	4
+LABURAMY	2	NULL	8	4
+LABURAMY	4	NULL	8	4
+LABURAMY	30	NULL	8	4
+LABURAMY	90	NULL	8	4
+LABYCARO	1	NULL	8	4
+LABYCARO	2	NULL	8	4
+LABYCARO	4	NULL	8	4
+LABVCULT	7	2	8	8
+LABYCARO	30	NULL	8	4
+LABYCARO	90	NULL	8	4
+LABYKLFRE	1	NULL	8	0
+LABYKLFRE	2	NULL	8	0
+LABYKLFRE	4	NULL	8	0
+LABYKLFRE	30	NULL	8	0
+LABYKLFRE	90	NULL	8	0
+LABYLAP	1	NULL	8	4
+LABYLAP	2	NULL	8	4
+LABYLAP	4	NULL	8	4
+LABYLAP	30	NULL	8	4
+LABYLAP	90	NULL	8	4
+LABYUARSR	1	NULL	8	7
+LABYUARSR	2	NULL	8	7
+LABYUARSR	4	NULL	8	7
+LABVITB1P	30	0	8	2
+LABYUARSR	30	NULL	8	7
+LABVITB1P	90	0	8	2
+LABYUARSR	90	NULL	8	7
+LABYUOXAL	1	NULL	8	3
+LABYUOXAL	2	NULL	8	3
+LABYUOXAL	4	NULL	8	3
+LABYUOXAL	30	NULL	8	3
+LABYUOXAL	90	NULL	8	3
+LABVP3	1	1	8	8
+LABVP3	2	1	8	8
+LABVSTLC	2	1	8	8
+LABVZVQLP	4	1	8	7
+LABWDC	1	2	8	7
+LABWNVRNA	7	1	8	8
+LABYCARO	1	0	8	4
+LABYCARO	2	0	8	4
+LABYCARO	4	0	8	4
+LABYCARO	7	0	8	4
+LABYCARO	7	NULL	8	4
+LABYCARO	30	0	8	4
+LABYCARO	90	0	8	4
+LABYCH50	4	1	8	8
+LABYCH50	90	1	8	8
+LABYFLUO	7	1	8	7
+LABYHGBP	90	2	8	6
+LABYKLFRE	1	0	8	0
+LABYKLFRE	2	0	8	0
+LABYKLFRE	4	0	8	0
+LABYKLFRE	7	0	8	0
+LABYKLFRE	7	NULL	8	0
+LABYLAP	1	0	8	4
+LABYLAP	2	0	8	4
+LABYLAP	4	0	8	4
+LABYLAP	7	0	8	4
+LABYLAP	7	NULL	8	4
+LABYLAP	30	0	8	4
+LABYLAP	90	0	8	4
+LABYPARVO	4	1	8	8
+LABYPARVO	7	1	8	8
+LABYPARVO	30	1	8	8
+LABYPARVO	90	1	8	8
+LABYPENTO	1	0	8	1
+LABYPENTO	2	0	8	1
+LABYPENTO	4	0	8	1
+LABYPROIN	1	2	8	8
+LABYSTCL	90	1	8	7
+LABYSTOSM	1	1	8	4
+LABYUARSR	1	0	8	7
+LABYUARSR	2	0	8	7
+LABYUARSR	7	NULL	8	7
+LABYUHYME	90	1	8	7
+LABYUOXAL	1	0	8	3
+LABYUOXAL	2	0	8	3
+LABYUOXAL	4	0	8	3
+LABYUOXAL	7	0	8	3
+LABYUOXAL	7	NULL	8	3
+LABYUOXAL	30	0	8	3
+LABYUOXAL	90	0	8	3
+LABYZINCS	90	1	8	2
+LAB11023R	7	1	7	7
+LAB11036R	7	3	7	7
+LAB11036R	30	3	7	7
+LAB11036R	90	3	7	7
+LAB11041R	1	2	7	6
+LAB11053R	1	0	7	4
+LAB11053R	2	0	7	4
+LAB11053R	4	0	7	4
+LAB11053R	7	0	7	4
+LAB11053R	7	NULL	7	4
+LAB11053R	30	0	7	4
+LAB11053R	90	0	7	4
+LAB11069R	2	1	7	5
+LAB11069R	4	1	7	4
+LAB11069R	7	1	7	4
+LAB11081R	4	1	7	4
+LAB11085R	4	1	7	7
+LAB11085R	7	1	7	7
+LAB11085R	30	1	7	7
+LAB11086R	7	1	7	3
+LAB11091R	1	0	7	5
+LAB11091R	7	NULL	7	5
+LAB11094R	4	1	7	7
+LAB11094R	7	1	7	7
+LAB11095R	4	1	7	7
+LAB11098R	2	1	7	5
+LAB11107R	4	1	7	7
+LAB11107R	7	1	7	7
+LAB11199R	7	1	7	7
+LAB11882R	1	0	7	6
+LAB11882R	2	0	7	6
+LAB11882R	4	0	7	6
+LAB11882R	7	0	7	6
+LAB11882R	7	NULL	7	6
+LAB11882R	30	0	7	6
+LAB11882R	90	0	7	6
+LAB12330R	90	2	7	6
+LAB12397R	1	0	7	5
+LAB12397R	2	0	7	5
+LAB12397R	4	0	7	5
+LAB12397R	7	0	7	5
+LAB12397R	30	0	7	5
+LAB12397R	90	0	7	5
+LAB12762R	1	2	7	6
+LAB12762R	4	3	7	7
+LABA1C	30	4	7	7
+LABACTHO	4	5	7	7
+LABACTHO	7	5	7	7
+LABACTHO	30	5	7	7
+LABACTHO	90	5	7	7
+LABADVDNA	1	1	7	7
+LABADVQT	30	2	7	6
+LABAFBCF	1	2	7	7
+LABAFBCF	2	2	7	7
+LABAFBCF	4	2	7	7
+LABAGALA	4	2	7	7
+LABALB	2	5	7	6
+LABALB	4	5	7	6
+LABAMIPRL	4	2	7	6
+LABANER2	30	5	7	6
+LABASQ	1	2	7	6
+LABBHCG	1	1	7	6
+LABBLCA	1	1	7	7
+LABBUN	2	2	7	5
+LABC125	4	1	7	7
+LABCBCD	1	3	7	5
+LABCBCO	1	4	7	6
+LABCD34	4	1	7	6
+LABCD34	7	1	7	6
+LABCD34	30	1	7	6
+LABCD34	90	1	7	6
+LABCFRESP	7	1	7	4
+LABCMVQT	1	2	7	7
+LABCMVRO	30	2	7	6
+LABCO2	2	1	7	7
+LABCORT	1	4	7	5
+LABCORTS	7	4	7	7
+LABCORTS	30	4	7	7
+LABCORTS	90	4	7	7
+LABCR	1	2	7	7
+LABCR	30	3	7	7
+LABCRCL	4	1	7	4
+LABCRCL	7	1	7	4
+LABCRPLB	90	2	7	7
+LABCRPLC	7	4	7	7
+LABCRPLFB	2	1	7	7
+LABCSFC	2	3	7	7
+LABCSFTP	30	5	7	6
+LABCZPL	1	1	7	3
+LABDATBSI	4	2	7	7
+LABDATBSI	7	2	7	7
+LABDFA	4	1	7	5
+LABDFA	7	1	7	5
+LABDFA	30	1	7	5
+LABDFA	90	1	7	5
+LABDGHBP	30	1	7	6
+LABDIC	7	3	7	3
+LABDIC	30	3	7	3
+LABDIC	90	3	7	3
+LABDVERVR	1	1	7	7
+LABDVERVR	2	1	7	7
+LABDVEVH	7	2	7	6
+LABEB	4	1	7	6
+LABEB	7	1	7	6
+LABEB	30	1	7	6
+LABEB	90	1	7	6
+LABEP1	1	3	7	5
+LABEP1	2	3	7	4
+LABEP2	1	0	7	3
+LABEP2	2	0	7	3
+LABEP2	4	0	7	3
+LABEP2	7	0	7	3
+LABEP2	7	NULL	7	3
+LABEP2	30	0	7	3
+LABEP2	90	0	7	3
+LABESRP	7	5	7	6
+LABEVPCR	30	1	7	3
+LABF8INH	1	1	7	1
+LABF8INH	2	1	7	1
+LABF8INH	4	1	7	1
+LABF8INH	7	1	7	1
+LABF8INH	30	1	7	1
+LABF8INH	90	1	7	1
+LABFACT10	1	1	7	3
+LABFACT10	2	1	7	3
+LABFACT10	4	1	7	3
+LABFACT10	7	1	7	3
+LABFACT10	30	1	7	3
+LABFACT10	90	1	7	3
+LABFACT11	2	1	7	5
+LABFACT2	7	1	7	3
+LABFACT2	30	1	7	3
+LABFACT2	90	1	7	3
+LABFACT8	4	4	7	7
+LABFACT8	4	5	7	6
+LABFACT8	7	5	7	6
+LABFENTA	1	1	7	7
+LABFENTA	2	1	7	7
+LABFENTA	4	1	7	7
+LABFHV12	90	2	7	7
+LABFHVIG	1	1	7	7
+LABFHVIG	2	1	7	7
+LABFHVIG	4	1	7	7
+LABFK506L	1	4	7	7
+LABFLDB	90	5	7	7
+LABGGT	1	4	7	7
+LABGGT	2	3	7	7
+LABGIARS	90	2	7	7
+LABGLF	2	3	7	7
+LABGLF	4	3	7	7
+LABGLF	7	3	7	7
+LABGLF	30	3	7	7
+LABGLF	90	3	7	7
+LABGNTUNL	1	0	7	1
+LABGNTUNL	2	0	7	1
+LABGNTUNL	7	NULL	7	1
+LABGRAM	1	3	7	7
+LABHAP	1	3	7	6
+LABHBCA	7	1	7	6
+LABHBCT	1	1	7	6
+LABHBSAG	1	2	7	7
+LABHFP	2	3	7	4
+LABHGBSQ	90	2	7	7
+LABHGHPLC	1	1	7	6
+LABHHV6QT	1	1	7	3
+LABHIAB	7	2	7	7
+LABHITAB	4	3	7	6
+LABHITAB	30	4	7	7
+LABHIVWBL	2	2	7	7
+LABHSVCSF	30	1	7	6
+LABHSVGM	1	1	7	7
+LABHSVPCR	2	1	7	3
+LABHSVQL	4	1	7	7
+LABIGGS2	1	0	7	6
+LABIGGS2	2	0	7	6
+LABIGGS2	4	0	7	6
+LABIGGS2	7	0	7	6
+LABIGGS2	7	NULL	7	6
+LABIGGS2	30	0	7	6
+LABIGGS2	90	0	7	6
+LABIGM	2	1	7	5
+LABINSL	1	2	7	6
+LABINSL	7	2	7	5
+LABINSL	30	2	7	5
+LABINSL	90	2	7	5
+LABKOHP	4	1	7	7
+LABKOHP	7	1	7	7
+LABKWB	90	3	7	5
+LABLEGDA	30	3	7	7
+LABLIL	7	4	7	6
+LABLPD	1	2	7	7
+LABLWBCS2	4	1	7	7
+LABLWBCS2	7	1	7	7
+LABLWBCS2	30	1	7	7
+LABLWBCS2	90	1	7	7
+LABMYCPA	30	4	7	5
+LABMYCPA	90	4	7	5
+LABNEURSP	7	NULL	7	1
+LABORAERO	30	3	7	4
+LABPANAPL	1	1	7	3
+LABPCEG7O	2	1	7	5
+LABPLTS	7	4	7	7
+LABPROINS	30	1	7	6
+LABPROINS	90	1	7	6
+LABPSARX	1	0	7	5
+LABPSARX	2	0	7	5
+LABPSARX	4	0	7	5
+LABPSARX	7	0	7	5
+LABPSARX	7	NULL	7	5
+LABPSARX	30	0	7	5
+LABPSARX	90	0	7	5
+LABPTH	2	2	7	6
+LABREPT	2	4	7	6
+LABRESP	4	4	7	7
+LABRESPCR	7	2	7	3
+LABRETIC	7	4	7	5
+LABRETIC	30	4	7	5
+LABRETIC	90	4	7	5
+LABRSVPCR	7	NULL	7	2
+LABSCLABY	7	1	7	7
+LABSCLABY	30	1	7	7
+LABSCLABY	90	1	7	7
+LABSCMV	2	3	7	7
+LABSEZPNL	1	0	7	0
+LABSEZPNL	2	0	7	0
+LABSEZPNL	4	0	7	0
+LABSEZPNL	7	0	7	0
+LABSEZPNL	7	NULL	7	0
+LABSEZPNL	30	0	7	0
+LABSEZPNL	90	0	7	0
+LABSSAABY	1	1	7	7
+LABSSAABY	2	1	7	7
+LABSSAABY	4	1	7	7
+LABSSAABY	7	1	7	7
+LABSTRED	30	2	7	6
+LABSTRONG	90	2	7	7
+LABT3	4	1	7	4
+LABTBILFR	7	3	7	7
+LABTBILFR	30	5	7	6
+LABTBLYM	4	1	7	0
+LABTBLYM	7	1	7	0
+LABTBLYM	30	1	7	0
+LABTBLYM	90	1	7	0
+LABTBPCRS	30	3	7	7
+LABTBPCRS	90	3	7	7
+LABTEGCP	90	5	7	4
+LABTESFBT	7	0	7	0
+LABTHS	30	1	7	7
+LABTRFS	90	2	7	3
+LABTRICHG	1	0	7	6
+LABTRICHG	2	0	7	6
+LABTRICHG	4	0	7	6
+LABTRICHG	7	0	7	6
+LABTRICHG	7	NULL	7	6
+LABTRICHG	30	0	7	6
+LABTRICHG	90	0	7	6
+LABTSH	4	4	7	6
+LABTSH	30	4	7	7
+LABTSH	90	5	7	4
+LABUA	7	4	7	6
+LABUA	30	5	7	6
+LABUALB	4	1	7	4
+LABUALB	30	1	7	4
+LABUALB	90	1	7	4
+LABUEP	90	3	7	5
+LABUK	1	1	7	6
+LABUMAG	2	0	7	0
+LABUMAG	4	0	7	0
+LABUMAG	7	0	7	0
+LABUMAG	30	0	7	0
+LABUMAG	90	0	7	0
+LABURAMY	1	0	7	4
+LABURAMY	2	0	7	4
+LABURAMY	4	0	7	4
+LABURAMY	7	0	7	4
+LABURAMY	30	0	7	4
+LABURAMY	90	0	7	4
+LABUUN	30	2	7	6
+LABUUN	90	2	7	6
+LABVCULS	30	1	7	7
+LABVCULT	30	3	7	7
+LAB11053R	1	NULL	7	4
+LAB11053R	2	NULL	7	4
+LAB11053R	4	NULL	7	4
+LAB11053R	30	NULL	7	4
+LABVOLTL	90	3	7	7
+LAB11053R	90	NULL	7	4
+LAB11091R	1	NULL	7	5
+LAB11091R	2	NULL	7	5
+LAB11091R	4	NULL	7	5
+LAB11091R	30	NULL	7	5
+LAB11091R	90	NULL	7	5
+LAB11882R	1	NULL	7	6
+LAB11882R	2	NULL	7	6
+LAB11882R	4	NULL	7	6
+LAB11882R	30	NULL	7	6
+LAB11882R	90	NULL	7	6
+LABEP2	1	NULL	7	3
+LABEP2	2	NULL	7	3
+LABEP2	4	NULL	7	3
+LABEP2	30	NULL	7	3
+LABEP2	90	NULL	7	3
+LABGNTUNL	1	NULL	7	1
+LABGNTUNL	2	NULL	7	1
+LABVPAL	4	5	7	6
+LABGNTUNL	4	NULL	7	1
+LABGNTUNL	30	NULL	7	1
+LABGNTUNL	90	NULL	7	1
+LABIGGS2	1	NULL	7	6
+LABIGGS2	2	NULL	7	6
+LABIGGS2	4	NULL	7	6
+LABIGGS2	30	NULL	7	6
+LABIGGS2	90	NULL	7	6
+LABNEURSP	1	NULL	7	1
+LABNEURSP	2	NULL	7	1
+LABVWAG	4	1	7	3
+LABNEURSP	4	NULL	7	1
+LABVWAG	7	1	7	3
+LABVWAG	30	1	7	3
+LABNEURSP	30	NULL	7	1
+LABVWAG	90	1	7	3
+LABNEURSP	90	NULL	7	1
+LABPSARX	1	NULL	7	5
+LABPSARX	2	NULL	7	5
+LABPSARX	4	NULL	7	5
+LABPSARX	30	NULL	7	5
+LABPSARX	90	NULL	7	5
+LABRSVPCR	1	NULL	7	2
+LABRSVPCR	2	NULL	7	2
+LABRSVPCR	4	NULL	7	2
+LABRSVPCR	30	NULL	7	2
+LABRSVPCR	90	NULL	7	2
+LABSEZPNL	1	NULL	7	0
+LABSEZPNL	2	NULL	7	0
+LABSEZPNL	4	NULL	7	0
+LABSEZPNL	30	NULL	7	0
+LABSEZPNL	90	NULL	7	0
+LABTRICHG	1	NULL	7	6
+LABTRICHG	2	NULL	7	6
+LABTRICHG	4	NULL	7	6
+LABTRICHG	30	NULL	7	6
+LABTRICHG	90	NULL	7	6
+LABVZVQLP	2	1	7	7
+LABWNVRNA	2	1	7	7
+LABWNVRNA	4	1	7	7
+LABYALDO	30	0	7	2
+LABYALDO	90	0	7	2
+LABYBOH	4	2	7	6
+LABYCH50	7	1	7	7
+LABYCH50	30	1	7	7
+LABYCOPPR	1	1	7	5
+LABYCP	1	4	7	5
+LABYFCAR	30	1	7	5
+LABYFCAR	90	1	7	5
+LABYFLUO	1	1	7	7
+LABYFPHEN	7	1	7	3
+LABYFVPA	4	1	7	2
+LABYHGBP	1	1	7	7
+LABYJCPCR	30	1	7	7
+LABYJCPCR	90	1	7	7
+LABYKLFRE	30	0	7	0
+LABYKLFRE	90	0	7	0
+LABYLEVT	90	1	7	6
+LABYMPA	1	1	7	4
+LABYMPA	7	1	7	3
+LABYMPA	30	1	7	3
+LABYMPA	90	1	7	3
+LABYPARVO	2	1	7	7
+LABYPROIN	1	3	7	5
+LABYPROIN	2	3	7	5
+LABYPROIN	4	2	7	7
+LABYSTCL	7	1	7	6
+LABYSTCL	30	1	7	6
+LABYUARSR	4	0	7	7
+LABYUARSR	7	0	7	7
+LABYUARSR	30	0	7	7
+LABYUARSR	90	0	7	7
+LABYUHYME	30	1	7	6
+LABYVITA	90	1	7	6
+LAB11023R	4	1	6	6
+LAB11036R	7	4	6	4
+LAB11036R	30	4	6	4
+LAB11036R	90	4	6	4
+LAB11060R	2	1	6	5
+LAB11077R	4	1	6	6
+LAB11077R	7	1	6	6
+LAB11077R	30	1	6	6
+LAB11077R	90	1	6	6
+LAB11086R	4	1	6	2
+LAB11091R	2	0	6	4
+LAB11094R	2	1	6	6
+LAB11095R	2	1	6	6
+LAB11097R	7	1	6	6
+LAB11097R	30	1	6	6
+LAB11097R	90	1	6	6
+LAB11107R	2	1	6	6
+LAB11190R	1	0	6	5
+LAB11190R	2	0	6	5
+LAB11190R	4	0	6	5
+LAB11190R	7	0	6	5
+LAB11190R	7	NULL	6	5
+LAB11190R	30	0	6	5
+LAB11190R	90	0	6	5
+LAB11265R	4	1	6	6
+LAB11265R	7	1	6	6
+LAB11982R	4	1	6	4
+LAB12017R	1	0	6	3
+LAB12017R	7	NULL	6	3
+LAB12330R	4	2	6	6
+LAB12330R	7	2	6	6
+LAB12330R	30	2	6	6
+LAB12330R	90	3	6	5
+LAB12371R	7	NULL	6	1
+LAB12853R	4	1	6	5
+LABACHOO	1	0	6	3
+LABACHOO	2	0	6	3
+LABACHOO	4	0	6	3
+LABACHOO	7	NULL	6	3
+LABACTHO	1	4	6	6
+LABACTHO	2	5	6	6
+LABADGM	4	1	6	6
+LABADGM	7	1	6	6
+LABADGM	30	1	6	6
+LABADGM	90	1	6	6
+LABADPCRQ	30	3	6	6
+LABADVRC	1	0	6	4
+LABADVRC	2	0	6	4
+LABADVRC	4	0	6	4
+LABADVRC	7	0	6	4
+LABADVRC	7	NULL	6	4
+LABADVRC	30	0	6	4
+LABADVRC	90	0	6	4
+LABAGALA	1	2	6	6
+LABAGALA	2	2	6	6
+LABAID	1	0	6	2
+LABAID	2	0	6	2
+LABAID	4	0	6	2
+LABAID	7	0	6	2
+LABAID	7	NULL	6	2
+LABAID	30	0	6	2
+LABAID	90	0	6	2
+LABALKP	2	1	6	6
+LABALPHA2	1	0	6	5
+LABALPHA2	2	0	6	5
+LABALPHA2	4	0	6	5
+LABALPHA2	7	0	6	5
+LABALPHA2	7	NULL	6	5
+LABALPHA2	30	0	6	5
+LABALPHA2	90	0	6	5
+LABALT	30	3	6	5
+LABAMIPOL	7	1	6	1
+LABAMIPOL	30	1	6	1
+LABAMIPOL	90	1	6	1
+LABAMIPRL	90	3	6	6
+LABAMY	1	4	6	6
+LABANERB	2	1	6	6
+LABANERB	4	1	6	6
+LABANERB	7	1	6	6
+LABANERB	30	1	6	6
+LABANERB	90	1	6	6
+LABBKVPC	90	1	6	4
+LABBKVQU	30	1	6	3
+LABBKVQU	90	1	6	3
+LABBLC	2	4	6	5
+LABBUN	30	4	6	5
+LABCA	1	4	6	5
+LABCA	7	5	6	6
+LABCA	30	5	6	6
+LABCA	90	5	6	6
+LABCFRESP	4	1	6	4
+LABCL	7	1	6	4
+LABCLACT	30	3	6	5
+LABCLACT	90	3	6	5
+LABCMVIGG	30	2	6	6
+LABCMVIGG	90	2	6	6
+LABCMVP	1	3	6	6
+LABCMVP	2	3	6	6
+LABCMVP	4	3	6	6
+LABCMVPQ	1	3	6	6
+LABCMVQL	30	2	6	6
+LABCMVQL	90	2	6	6
+LABCMVQT	4	4	6	6
+LABCORT	7	5	6	5
+LABCORTS	4	4	6	6
+LABCR	30	4	6	6
+LABCR	30	5	6	6
+LABCR	90	4	6	6
+LABCR	90	5	6	6
+LABCRCL	2	1	6	3
+LABCRFA	2	1	6	6
+LABCRFA	4	1	6	6
+LABCRPLC	7	5	6	6
+LABCRPLC	30	4	6	6
+LABCRPLC	90	4	6	6
+LABCRPLC	90	5	6	6
+LABCRPLFC	2	1	6	6
+LABCSFCT	2	2	6	6
+LABCSTOTA	7	1	6	4
+LABCYLEX	90	1	6	2
+LABCZPL	30	2	6	2
+LABDATBSI	90	3	6	6
+LABDGHBP	7	1	6	5
+LABDVEHV	90	2	6	4
+LABDVERO	7	1	6	6
+LABDVERTV	4	1	6	5
+LABDVERTV	7	1	6	5
+LABDVERTV	30	1	6	5
+LABDVERTV	90	1	6	5
+LABDVERVP	1	2	6	5
+LABEB	2	1	6	5
+LABEVPCR	7	1	6	3
+LABFACT11	4	1	6	5
+LABFACT11	7	1	6	5
+LABFACT11	30	1	6	5
+LABFACT11	90	1	6	5
+LABFACT2	1	1	6	3
+LABFACT2	2	1	6	3
+LABFACT2	4	1	6	3
+LABFACT5	1	1	6	4
+LABFACT5	2	1	6	4
+LABFACT5	4	1	6	4
+LABFACT5	7	1	6	4
+LABFACT5	30	1	6	4
+LABFACT5	90	1	6	4
+LABFACT7	90	1	6	6
+LABFACT8	2	4	6	5
+LABFER	1	2	6	5
+LABFER	90	4	6	6
+LABFHV12	1	1	6	6
+LABFHV12	30	2	6	6
+LABFIBANT	1	1	6	5
+LABFKOH	7	3	6	6
+LABFLDB	30	5	6	6
+LABFLUAPX	4	2	6	6
+LABFMYAB	2	2	6	6
+LABFPARVD	4	1	6	6
+LABFPARVD	7	1	6	6
+LABFPARVD	30	1	6	6
+LABFPARVD	90	1	6	6
+LABFQFVR	4	1	6	5
+LABFQFVR	7	1	6	5
+LABFQFVR	30	1	6	5
+LABFQFVR	90	1	6	5
+LABFT4	4	4	6	6
+LABFUNDAP	1	0	6	5
+LABFUNDAP	2	0	6	5
+LABFUNDAP	4	0	6	5
+LABFUNDAP	7	0	6	5
+LABFUNDAP	7	NULL	6	5
+LABFUNDAP	30	0	6	5
+LABFUNDAP	90	0	6	5
+LABFVARZ	30	2	6	6
+LABFVARZ	90	2	6	6
+LABGIARS	7	2	6	6
+LABGIARS	30	2	6	6
+LABGLF	1	4	6	5
+LABGLF	2	4	6	5
+LABGLF	4	4	6	5
+LABGLF	7	4	6	5
+LABGLF	30	4	6	5
+LABGLF	90	4	6	5
+LABGNTPRL	1	2	6	4
+LABGNTUNL	4	0	6	1
+LABGNTUNL	7	0	6	1
+LABGNTUNL	30	0	6	1
+LABGNTUNL	90	0	6	1
+LABH1N1PC	7	1	6	6
+LABHCPQ	90	2	6	5
+LABHCTX	2	5	6	6
+LABHCTX	90	5	6	4
+LABHEPDAB	4	1	6	6
+LABHFP	30	5	6	3
+LABHGBFQ	1	0	6	4
+LABHGBFQ	2	0	6	4
+LABHGBFQ	4	0	6	4
+LABHGBFQ	7	0	6	4
+LABHGBFQ	7	NULL	6	4
+LABHGBFQ	30	0	6	4
+LABHGBFQ	90	0	6	4
+LABHGBSQ	30	2	6	6
+LABHHV6QL	90	1	6	3
+LABHIAB	4	2	6	6
+LABHIPA	90	3	6	5
+LABHITAB	1	2	6	6
+LABHITAB	4	4	6	6
+LABHSVQLP	7	1	6	5
+LABIBD	7	1	6	6
+LABIBD	30	1	6	6
+LABIBD	90	1	6	6
+LABIGM	1	1	6	5
+LABINSL	1	4	6	6
+LABINSL	2	2	6	5
+LABINSL	4	2	6	4
+LABISOF	7	2	6	5
+LABKOHP	1	1	6	6
+LABKOHP	2	1	6	6
+LABKWB	2	2	6	6
+LABKWB	30	3	6	5
+LABLAMOL	4	2	6	6
+LABLAMOL	90	2	6	6
+LABLEGDA	2	2	6	6
+LABLEGDA	4	2	6	6
+LABLEGDD	7	NULL	6	5
+LABMALAR	4	5	6	6
+LABMALAR	7	5	6	6
+LABMALAR	30	5	6	6
+LABMALAR	90	5	6	6
+LABMYCPA	7	4	6	5
+LABMYO	2	3	6	6
+LABMYO	4	4	6	6
+LABMYO	7	4	6	6
+LABMYO	30	4	6	6
+LABMYO	90	4	6	6
+LABOAPE	2	3	6	6
+LABOLIG	90	1	6	2
+LABOXCMET	7	1	6	4
+LABPALB	7	3	6	6
+LABPCEG7O	1	1	6	5
+LABPCG3V	1	3	6	3
+LABPENTO	1	0	6	2
+LABPENTO	2	0	6	2
+LABPENTO	4	0	6	2
+LABPLASMN	2	2	6	5
+LABPLASMN	4	3	6	5
+LABPLASMN	7	2	6	4
+LABPLASMN	30	2	6	4
+LABPLASMN	90	2	6	4
+LABPLTS	1	3	6	4
+LABPLTS	4	4	6	6
+LABPROCT	1	2	6	5
+LABPROINS	1	1	6	6
+LABPROINS	2	1	6	6
+LABPROINS	4	1	6	6
+LABPROINS	7	1	6	6
+LABPROL	4	2	6	6
+LABPROL	7	2	6	6
+LABPROL	30	2	6	6
+LABPROL	90	2	6	6
+LABPTEG	4	3	6	4
+LABREPT	4	5	6	6
+LABRESP	7	5	6	5
+LABRESPG	4	3	6	5
+LABRESPG	30	4	6	6
+LABRIST	2	2	6	6
+LABRIST	4	2	6	6
+LABSCLABY	1	1	6	6
+LABSCLABY	2	1	6	6
+LABSCLABY	4	1	6	6
+LABSTLC	7	2	6	6
+LABSTLCX	4	2	6	6
+LABSTOBGC	1	1	6	3
+LABSTOBGD	4	2	6	0
+LABSTPCR	90	1	6	5
+LABSTRED	90	3	6	4
+LABSTRONG	4	2	6	6
+LABSTRONG	7	2	6	6
+LABSTRONG	30	2	6	6
+LABTBPCRS	7	3	6	6
+LABTCDIF	90	5	6	6
+LABTEGCP	4	3	6	5
+LABTHS	2	1	6	6
+LABTHS	4	1	6	6
+LABTHS	7	1	6	6
+LABTOXIG	90	1	6	5
+LABTP	30	2	6	4
+LABTRIG	4	5	6	6
+LABUA	4	4	6	5
+LABUA	7	5	6	6
+LABUEP	2	2	6	5
+LABUNA	1	2	6	5
+LABUTPRAN	1	1	6	2
+LABVCUL	2	4	6	6
+LABVCULS	90	1	6	6
+LABVCULT	4	2	6	6
+LABVP5	1	0	6	1
+LABVP5	2	0	6	1
+LABVP5	4	0	6	1
+LABVP5	7	0	6	1
+LABVP5	7	NULL	6	1
+LABVP5	30	0	6	1
+LABVP5	90	0	6	1
+LABVWAG	2	1	6	3
+LABVZVCSF	90	1	6	6
+LAB11190R	1	NULL	6	5
+LAB11190R	2	NULL	6	5
+LAB11190R	4	NULL	6	5
+LAB11190R	30	NULL	6	5
+LAB11190R	90	NULL	6	5
+LAB12017R	1	NULL	6	3
+LAB12017R	2	NULL	6	3
+LAB12017R	4	NULL	6	3
+LABVZVQL	30	1	6	5
+LAB12017R	30	NULL	6	3
+LABVZVQL	90	1	6	5
+LAB12017R	90	NULL	6	3
+LAB12371R	1	NULL	6	1
+LAB12371R	2	NULL	6	1
+LAB12371R	4	NULL	6	1
+LAB12371R	30	NULL	6	1
+LAB12371R	90	NULL	6	1
+LABACHOO	1	NULL	6	3
+LABACHOO	2	NULL	6	3
+LABACHOO	4	NULL	6	3
+LABACHOO	30	NULL	6	3
+LABACHOO	90	NULL	6	3
+LABADVRC	1	NULL	6	4
+LABADVRC	2	NULL	6	4
+LABADVRC	4	NULL	6	4
+LABADVRC	30	NULL	6	4
+LABWDCG	90	3	6	4
+LABADVRC	90	NULL	6	4
+LABAID	1	NULL	6	2
+LABAID	2	NULL	6	2
+LABAID	4	NULL	6	2
+LABAID	30	NULL	6	2
+LABWDCS	90	3	6	3
+LABAID	90	NULL	6	2
+LABALPHA2	1	NULL	6	5
+LABWNVAB	2	1	6	6
+LABALPHA2	2	NULL	6	5
+LABALPHA2	4	NULL	6	5
+LABALPHA2	30	NULL	6	5
+LABALPHA2	90	NULL	6	5
+LABWNVRNA	1	1	6	6
+LABFUNDAP	1	NULL	6	5
+LABFUNDAP	2	NULL	6	5
+LABFUNDAP	4	NULL	6	5
+LABFUNDAP	30	NULL	6	5
+LABFUNDAP	90	NULL	6	5
+LABHGBFQ	1	NULL	6	4
+LABHGBFQ	2	NULL	6	4
+LABHGBFQ	4	NULL	6	4
+LABHGBFQ	30	NULL	6	4
+LABHGBFQ	90	NULL	6	4
+LABLEGDD	1	NULL	6	5
+LABLEGDD	2	NULL	6	5
+LABLEGDD	4	NULL	6	5
+LABLEGDD	30	NULL	6	5
+LABLEGDD	90	NULL	6	5
+LABVP5	1	NULL	6	1
+LABVP5	2	NULL	6	1
+LABVP5	4	NULL	6	1
+LABVP5	30	NULL	6	1
+LABVP5	90	NULL	6	1
+LABYC5REF	1	NULL	6	4
+LABYC5REF	2	NULL	6	4
+LABYC5REF	4	NULL	6	4
+LABYC5REF	30	NULL	6	4
+LABYC5REF	90	NULL	6	4
+LABYFCHYL	1	NULL	6	5
+LABYFCHYL	2	NULL	6	5
+LABYFCHYL	4	NULL	6	5
+LABYFCHYL	30	NULL	6	5
+LABYFCHYL	90	NULL	6	5
+LABYFELBA	1	NULL	6	2
+LABYFELBA	2	NULL	6	2
+LABYFELBA	4	NULL	6	2
+LABYFELBA	30	NULL	6	2
+LABYFELBA	90	NULL	6	2
+LABYIGF1	1	NULL	6	3
+LABYIGF1	2	NULL	6	3
+LABYIGF1	4	NULL	6	3
+LABYIGF1	30	NULL	6	3
+LABYIGF1	90	NULL	6	3
+LABYIMIPR	1	NULL	6	1
+LABYIMIPR	2	NULL	6	1
+LABYAHA	4	1	6	6
+LABYIMIPR	4	NULL	6	1
+LABYIMIPR	30	NULL	6	1
+LABYIMIPR	90	NULL	6	1
+LABYINSFT	1	NULL	6	3
+LABYINSFT	2	NULL	6	3
+LABYINSFT	4	NULL	6	3
+LABYINSFT	30	NULL	6	3
+LABYINSFT	90	NULL	6	3
+LABYLDISO	1	NULL	6	1
+LABYLDISO	2	NULL	6	1
+LABYLDISO	4	NULL	6	1
+LABYLDISO	30	NULL	6	1
+LABYLDISO	90	NULL	6	1
+LABYTRYPS	1	NULL	6	3
+LABYTRYPS	2	NULL	6	3
+LABYTRYPS	4	NULL	6	3
+LABYTRYPS	30	NULL	6	3
+LABYTRYPS	90	NULL	6	3
+LABYUALDO	1	NULL	6	2
+LABYUALDO	2	NULL	6	2
+LABYUALDO	4	NULL	6	2
+LABYUALDO	30	NULL	6	2
+LABYUALDO	90	NULL	6	2
+LABYUCITR	1	NULL	6	3
+LABYUCITR	2	NULL	6	3
+LABYUCITR	4	NULL	6	3
+LABYUCITR	30	NULL	6	3
+LABYUCITR	90	NULL	6	3
+LABYBOH	2	2	6	5
+LABYBOH	7	2	6	5
+LABYBOH	7	4	6	4
+LABYBSAP	90	1	6	4
+LABYC5REF	1	0	6	4
+LABYC5REF	2	0	6	4
+LABYC5REF	4	0	6	4
+LABYC5REF	7	0	6	4
+LABYC5REF	7	NULL	6	4
+LABYC5REF	30	0	6	4
+LABYC5REF	90	0	6	4
+LABYERYTH	1	1	6	4
+LABYFCHYL	1	0	6	5
+LABYFCHYL	2	0	6	5
+LABYFCHYL	4	0	6	5
+LABYFCHYL	7	0	6	5
+LABYFCHYL	7	NULL	6	5
+LABYFCHYL	30	0	6	5
+LABYFCHYL	90	0	6	5
+LABYFELBA	7	NULL	6	2
+LABYFFATQ	1	1	6	4
+LABYFPHEN	4	1	6	3
+LABYHGBP	30	3	6	5
+LABYHGBP	30	4	6	6
+LABYIGF1	1	0	6	3
+LABYIGF1	2	0	6	3
+LABYIGF1	4	0	6	3
+LABYIGF1	7	0	6	3
+LABYIGF1	7	NULL	6	3
+LABYIGF1	30	0	6	3
+LABYIGF1	90	0	6	3
+LABYIMIPR	1	0	6	1
+LABYIMIPR	7	NULL	6	1
+LABYINSFT	1	0	6	3
+LABYINSFT	2	0	6	3
+LABYINSFT	4	0	6	3
+LABYINSFT	7	0	6	3
+LABYINSFT	7	NULL	6	3
+LABYINSFT	30	0	6	3
+LABYINSFT	90	0	6	3
+LABYJCPCR	7	1	6	6
+LABYLDISO	1	0	6	1
+LABYLDISO	2	0	6	1
+LABYLDISO	4	0	6	1
+LABYLDISO	7	0	6	1
+LABYLDISO	7	NULL	6	1
+LABYLDISO	30	0	6	1
+LABYLDISO	90	0	6	1
+LABYLEVT	30	1	6	5
+LABYMANG	90	1	6	3
+LABYMPA	4	1	6	3
+LABYPREAL	1	2	6	6
+LABYPROIN	7	2	6	6
+LABYPROIN	30	2	6	6
+LABYPROIN	90	2	6	6
+LABYPTHRP	1	1	6	6
+LABYTOPIR	2	1	6	6
+LABYTOPIR	90	1	6	5
+LABYTRYPS	1	0	6	3
+LABYTRYPS	2	0	6	3
+LABYTRYPS	4	0	6	3
+LABYTRYPS	7	0	6	3
+LABYTRYPS	7	NULL	6	3
+LABYTRYPS	30	0	6	3
+LABYTRYPS	90	0	6	3
+LABYTRYPT	2	1	6	5
+LABYTRYPT	4	1	6	5
+LABYTRYPT	7	1	6	5
+LABYTRYPT	30	1	6	5
+LABYTRYPT	90	1	6	5
+LABYUALDO	1	0	6	2
+LABYUALDO	2	0	6	2
+LABYUALDO	4	0	6	2
+LABYUALDO	7	0	6	2
+LABYUALDO	7	NULL	6	2
+LABYUALDO	30	0	6	2
+LABYUALDO	90	0	6	2
+LABYUCITR	1	0	6	3
+LABYUCITR	2	0	6	3
+LABYUCITR	4	0	6	3
+LABYUCITR	7	0	6	3
+LABYUCITR	7	NULL	6	3
+LABYUCITR	30	0	6	3
+LABYUCITR	90	0	6	3
+LABYUHYME	7	1	6	6
+LABYVITA	30	1	6	4
+LABYZINCS	30	1	6	2
+LAB11023R	2	1	5	5
+LAB11041R	1	3	5	4
+LAB11041R	2	3	5	4
+LAB11049R	4	3	5	3
+LAB11051R	1	0	5	2
+LAB11051R	2	0	5	2
+LAB11051R	4	0	5	2
+LAB11051R	7	0	5	2
+LAB11051R	7	NULL	5	2
+LAB11051R	30	0	5	2
+LAB11051R	90	0	5	2
+LAB11055R	7	NULL	5	4
+LAB11058R	1	0	5	2
+LAB11058R	2	0	5	2
+LAB11058R	4	0	5	2
+LAB11058R	7	0	5	2
+LAB11058R	7	NULL	5	2
+LAB11058R	30	0	5	2
+LAB11058R	90	0	5	2
+LAB11070R	1	1	5	4
+LAB11072R	1	0	5	4
+LAB11072R	2	0	5	4
+LAB11072R	4	0	5	4
+LAB11072R	7	0	5	4
+LAB11072R	7	NULL	5	4
+LAB11072R	30	0	5	4
+LAB11072R	90	0	5	4
+LAB11077R	2	1	5	5
+LAB11085R	1	1	5	5
+LAB11091R	4	0	5	3
+LAB11091R	7	0	5	3
+LAB11091R	30	0	5	3
+LAB11091R	90	0	5	3
+LAB11094R	1	1	5	5
+LAB11095R	1	1	5	5
+LAB11096R	1	1	5	4
+LAB11097R	4	1	5	5
+LAB11250R	30	1	5	1
+LAB11250R	90	1	5	1
+LAB11265R	2	1	5	5
+LAB11982R	90	3	5	4
+LAB12017R	2	0	5	2
+LAB12017R	4	0	5	2
+LAB12017R	7	0	5	2
+LAB12017R	30	0	5	2
+LAB12017R	90	0	5	2
+LAB12214R	4	1	5	5
+LAB12214R	7	1	5	5
+LAB12214R	30	1	5	5
+LAB12214R	90	1	5	5
+LAB12238R	90	1	5	5
+LAB12330R	1	1	5	5
+LAB12330R	30	3	5	4
+LAB12371R	1	0	5	1
+LAB12371R	2	0	5	1
+LAB12371R	4	0	5	1
+LAB12371R	7	0	5	1
+LAB12371R	30	0	5	1
+LAB12371R	90	0	5	1
+LAB12399R	7	NULL	5	2
+LAB12645R	7	NULL	5	4
+LAB12762R	2	3	5	5
+LAB12762R	7	3	5	5
+LAB12762R	7	4	5	5
+LAB12762R	30	3	5	5
+LAB12762R	30	4	5	5
+LAB12762R	90	3	5	5
+LAB12762R	90	4	5	5
+LAB12790R	1	0	5	0
+LAB12790R	2	0	5	0
+LAB12790R	4	0	5	0
+LAB12790R	7	0	5	0
+LAB12790R	7	NULL	5	0
+LAB12790R	30	0	5	0
+LAB12790R	90	0	5	0
+LAB12808R	4	2	5	3
+LAB12808R	7	2	5	3
+LAB12808R	30	2	5	3
+LAB12808R	90	2	5	3
+LAB12809R	2	1	5	4
+LAB12809R	4	1	5	4
+LAB12809R	7	1	5	4
+LAB12809R	30	1	5	4
+LAB12809R	90	1	5	4
+LABACHOO	7	0	5	3
+LABACHOO	30	0	5	3
+LABACHOO	90	0	5	3
+LABACTHO	1	5	5	5
+LABADIFFS	1	0	5	1
+LABADIFFS	2	0	5	1
+LABADIFFS	4	0	5	1
+LABADIFFS	7	0	5	1
+LABADIFFS	7	NULL	5	1
+LABADIFFS	30	0	5	1
+LABADIFFS	90	0	5	1
+LABALKP	1	1	5	5
+LABALT	90	3	5	4
+LABAMIPRL	30	3	5	5
+LABAMIPRL	90	4	5	5
+LABAMY	1	3	5	5
+LABAPHSA2	1	1	5	4
+LABAPHSA2	2	1	5	4
+LABASPID	2	1	5	5
+LABBKVPC	7	1	5	4
+LABBKVPC	30	1	5	4
+LABBKVQU	7	1	5	3
+LABBLCTIP	2	3	5	5
+LABBMT1	30	2	5	0
+LABBUN	90	4	5	4
+LABBXTG	30	5	5	3
+LABC125	2	1	5	5
+LABCA	4	5	5	5
+LABCBCS	4	3	5	4
+LABCD4CT	30	1	5	1
+LABCD4CT	90	1	5	1
+LABCDTPCR	7	3	5	5
+LABCHOL	1	1	5	5
+LABCHOL	2	1	5	5
+LABCHOL	4	1	5	5
+LABCHOL	30	1	5	5
+LABCHOL	90	1	5	5
+LABCL	4	1	5	3
+LABCMV	7	1	5	4
+LABCMVP	30	4	5	5
+LABCMVQL	7	2	5	5
+LABCORTS	2	4	5	5
+LABCORTS	2	5	5	5
+LABCORTS	4	5	5	5
+LABCORTS	7	5	5	5
+LABCORTS	30	5	5	5
+LABCORTS	90	5	5	5
+LABCRFA	1	1	5	5
+LABCRPLC	2	2	5	5
+LABCRPLFB	1	1	5	5
+LABCRPLFC	90	2	5	4
+LABCSFC	4	5	5	5
+LABCSFCT	1	2	5	5
+LABCSTOTA	4	1	5	3
+LABCTC	1	2	5	5
+LABCTC	2	2	5	5
+LABCYLEX	4	1	5	2
+LABCYLEX	7	1	5	2
+LABCYLEX	30	1	5	2
+LABCZPL	90	2	5	2
+LABDFA	1	1	5	5
+LABDFA	2	1	5	5
+LABDGHBP	2	1	5	4
+LABDGHBP	4	1	5	4
+LABDIC	2	3	5	1
+LABDVERO	4	1	5	5
+LABDVERTV	2	1	5	5
+LABDVERV	7	3	5	5
+LABDVERV	90	5	5	5
+LABEP1	1	4	5	5
+LABEP1	7	4	5	2
+LABEP1	30	4	5	2
+LABEP1	90	4	5	2
+LABEVPCR	4	1	5	2
+LABFACT11	1	1	5	4
+LABFACT7	2	1	5	5
+LABFACT7	4	1	5	5
+LABFACT7	7	1	5	5
+LABFACT7	30	1	5	5
+LABFACT9	1	2	5	0
+LABFHTLVQ	90	1	5	5
+LABFIBANT	2	1	5	4
+LABFKOH	30	4	5	4
+LABFLCR	30	1	5	2
+LABFLCR	90	1	5	2
+LABFLDB	2	2	5	3
+LABFLDC	7	4	5	4
+LABFLUAPX	2	2	5	5
+LABFMYAB	4	4	5	5
+LABFPARVD	2	1	5	5
+LABFUNIDS	30	1	5	3
+LABFUNIDS	90	1	5	3
+LABG6PD	4	1	5	1
+LABG6PD	7	1	5	1
+LABG6PD	30	1	5	1
+LABG6PD	90	1	5	1
+LABGASBLD	30	1	5	1
+LABGASBLD	90	1	5	1
+LABGGT	2	4	5	5
+LABGGT	4	5	5	4
+LABGIARS	4	2	5	5
+LABGLF	2	5	5	3
+LABGLF	4	5	5	3
+LABGLF	7	5	5	3
+LABGLF	30	5	5	3
+LABGLF	90	5	5	3
+LABGLOB	30	1	5	5
+LABH1N1PC	1	1	5	5
+LABH1N1PC	2	1	5	5
+LABH1N1PC	4	1	5	5
+LABH1N1PC	30	1	5	5
+LABH1N1PC	90	1	5	5
+LABHAVT	30	1	5	5
+LABHAVT	90	1	5	5
+LABHBCA	2	1	5	5
+LABHBSAG	4	3	5	5
+LABHCTX	4	5	5	4
+LABHCTX	7	5	5	4
+LABHCTX	30	5	5	4
+LABHEPDAB	7	1	5	5
+LABHEPDAB	30	1	5	5
+LABHEPDAB	90	1	5	5
+LABHGBP	2	4	5	4
+LABHGBP	7	5	5	5
+LABHGBSQ	7	2	5	5
+LABHHV6QL	30	1	5	2
+LABHHV6QT	90	2	5	3
+LABHIAB	90	3	5	3
+LABHIPA	4	2	5	2
+LABHITAB	30	5	5	5
+LABHITAB	90	5	5	5
+LABHITPNL	30	1	5	3
+LABHITPNL	90	1	5	3
+LABHIVWBL	90	3	5	5
+LABHSVZL	90	2	5	5
+LABIBD	4	1	5	5
+LABIGG	7	2	5	4
+LABIGG	90	3	5	4
+LABIGGS4	30	1	5	5
+LABIGGS4	90	1	5	5
+LABINSL	2	5	5	5
+LABISOF	4	2	5	4
+LABKWB	2	3	5	5
+LABKWB	4	3	5	5
+LABKWB	7	3	5	5
+LABLAMOL	7	2	5	5
+LABLAMOL	30	2	5	5
+LABLEGDA	1	2	5	5
+LABLEVETL	30	2	5	5
+LABLIL	1	2	5	3
+LABLIPS	2	5	5	4
+LABLUT	4	1	5	5
+LABLUT	7	1	5	5
+LABLUT	30	1	5	5
+LABLUT	90	1	5	5
+LABLWBCS2	2	1	5	5
+LABMALAR	2	5	5	5
+LABMALSC	1	1	5	4
+LABMONEBV	1	1	5	1
+LABMONEBV	2	1	5	1
+LABMONEBV	4	1	5	1
+LABMONEBV	7	1	5	1
+LABMONEBV	30	1	5	1
+LABMONEBV	90	1	5	1
+LABMYO	1	3	5	5
+LABNTBNP	4	3	5	5
+LABNTBNP	30	3	5	3
+LABNTBNP	90	3	5	3
+LABOAPE	7	4	5	5
+LABORAERO	7	3	5	4
+LABORANER	30	4	5	5
+LABORSHOR	1	5	5	5
+LABORSHOR	2	5	5	5
+LABORSHOR	4	5	5	5
+LABORSHOR	7	5	5	5
+LABORSHOR	30	5	5	5
+LABORSHOR	90	5	5	5
+LABPANAPL	2	0	5	2
+LABPANAPL	4	0	5	2
+LABPANAPL	7	0	5	2
+LABPANAPL	30	0	5	2
+LABPANAPL	90	0	5	2
+LABPANWBC	1	0	5	0
+LABPANWBC	2	0	5	0
+LABPANWBC	4	0	5	0
+LABPANWBC	7	0	5	0
+LABPANWBC	7	NULL	5	0
+LABPCCG4	1	1	5	1
+LABPCCG4	2	1	5	1
+LABPCCG4	4	1	5	1
+LABPCCG4	7	1	5	1
+LABPCCG4	30	1	5	1
+LABPCCG4	90	1	5	1
+LABPCG3V	30	4	5	3
+LABPCG3V	90	4	5	3
+LABPCTNI	1	5	5	5
+LABPCTNI	30	5	5	5
+LABPENTO	7	0	5	2
+LABPENTO	30	0	5	2
+LABPENTO	90	0	5	2
+LABPLTS	7	5	5	5
+LABPROINS	1	2	5	5
+LABPROINS	1	3	5	5
+LABPROINS	2	2	5	5
+LABPROINS	2	3	5	5
+LABPROINS	4	2	5	4
+LABPROINS	7	2	5	4
+LABPROINS	30	2	5	4
+LABPROINS	90	2	5	4
+LABPSAU	1	0	5	0
+LABPSAU	2	0	5	0
+LABPSAU	4	0	5	0
+LABPSAU	7	0	5	0
+LABPSAU	7	NULL	5	0
+LABPSAU	30	0	5	0
+LABPSAU	90	0	5	0
+LABPTEG	1	3	5	3
+LABPTEG	2	3	5	3
+LABRBCFOL	90	2	5	3
+LABREPT	2	5	5	4
+LABRESPCR	4	2	5	1
+LABRESPG	2	3	5	4
+LABRETIC	7	5	5	5
+LABRETIC	30	5	5	5
+LABRETIC	90	5	5	5
+LABRIST	7	2	5	5
+LABRIST	30	2	5	5
+LABRIST	90	2	5	5
+LABROMRS	30	4	5	5
+LABRSVPCR	1	0	5	1
+LABRSVPCR	2	0	5	1
+LABRSVPCR	4	0	5	1
+LABRSVPCR	7	0	5	1
+LABSCMV	1	3	5	5
+LABSCMV	90	5	5	5
+LABSMABY	90	1	5	5
+LABSTPCR	4	1	5	4
+LABSTPCR	7	1	5	4
+LABSTPCR	30	1	5	4
+LABSTRED	4	2	5	4
+LABSTSS	90	2	5	5
+LABT3	2	1	5	3
+LABTBIL	7	4	5	5
+LABTBIL	30	5	5	5
+LABTBILFR	2	2	5	5
+LABTBILFR	4	3	5	5
+LABTCDIF	7	3	5	5
+LABTCDIF	30	5	5	5
+LABTHS	1	1	5	5
+LABTOBPRL	4	4	5	4
+LABTOBPRL	7	4	5	4
+LABTOXIG	4	1	5	4
+LABTOXIG	7	1	5	4
+LABTOXIG	30	1	5	4
+LABTOXO	30	1	5	4
+LABTOXO	90	1	5	4
+LABTP	4	2	5	4
+LABTP	7	2	5	4
+LABTRFN	4	1	5	3
+LABTRFN	7	1	5	3
+LABTRFN	30	1	5	3
+LABTRFN	90	1	5	3
+LABTRFS	30	2	5	2
+LABTSH	7	4	5	5
+LABTT	7	3	5	4
+LABTT	30	3	5	4
+LABTT	90	3	5	4
+LABUAPRN	2	2	5	3
+LABUAPRN	30	4	5	4
+LABUAS	7	3	5	3
+LABUAS	30	3	5	3
+LABUAS	90	3	5	3
+LABUEOS	2	3	5	5
+LABUEP	1	3	5	5
+LABUEP	30	3	5	3
+LABUNA	90	3	5	4
+LABVANPOL	4	1	5	0
+LABVANPOL	7	1	5	0
+LABVANPOL	30	1	5	0
+LABVANPOL	90	1	5	0
+LABVCUL	4	4	5	5
+LABVCUL	7	4	5	5
+LABVCUL	30	5	5	5
+LABVCULT	1	2	5	5
+LABVCULT	2	2	5	5
+LABVOLTL	7	2	5	5
+LABVZVQLP	1	1	5	5
+LABWDCG	2	3	5	3
+LABWDCG	4	3	5	3
+LABWDCG	7	3	5	3
+LABWDCG	30	3	5	3
+LABWDCS	4	3	5	3
+LABWDCS	7	3	5	3
+LABWDCS	30	3	5	3
+LABYAHA	2	1	5	5
+LABYAHA	7	1	5	5
+LABYAHA	30	1	5	5
+LABYAHA	90	1	5	5
+LABYAMISO	4	1	5	3
+LABYAMISO	7	1	5	3
+LABYAMISO	30	1	5	3
+LABYAMISO	90	1	5	3
+LAB11051R	1	NULL	5	2
+LAB11051R	2	NULL	5	2
+LAB11051R	4	NULL	5	2
+LAB11051R	30	NULL	5	2
+LAB11051R	90	NULL	5	2
+LAB11055R	1	NULL	5	4
+LAB11055R	2	NULL	5	4
+LAB11055R	4	NULL	5	4
+LAB11055R	30	NULL	5	4
+LAB11055R	90	NULL	5	4
+LAB11058R	1	NULL	5	2
+LAB11058R	2	NULL	5	2
+LAB11058R	4	NULL	5	2
+LAB11058R	30	NULL	5	2
+LAB11058R	90	NULL	5	2
+LAB11072R	1	NULL	5	4
+LAB11072R	2	NULL	5	4
+LAB11072R	4	NULL	5	4
+LAB11072R	30	NULL	5	4
+LAB11072R	90	NULL	5	4
+LAB12399R	1	NULL	5	2
+LAB12399R	2	NULL	5	2
+LAB12399R	4	NULL	5	2
+LAB12399R	30	NULL	5	2
+LAB12399R	90	NULL	5	2
+LAB12645R	1	NULL	5	4
+LAB12645R	2	NULL	5	4
+LAB12645R	4	NULL	5	4
+LAB12645R	30	NULL	5	4
+LAB12645R	90	NULL	5	4
+LAB12790R	1	NULL	5	0
+LAB12790R	2	NULL	5	0
+LAB12790R	4	NULL	5	0
+LAB12790R	30	NULL	5	0
+LAB12790R	90	NULL	5	0
+LABADIFFS	1	NULL	5	1
+LABADIFFS	2	NULL	5	1
+LABADIFFS	4	NULL	5	1
+LABADIFFS	30	NULL	5	1
+LABADIFFS	90	NULL	5	1
+LABPANWBC	1	NULL	5	0
+LABPANWBC	2	NULL	5	0
+LABPANWBC	4	NULL	5	0
+LABPANWBC	30	NULL	5	0
+LABPANWBC	90	NULL	5	0
+LABPSAU	1	NULL	5	0
+LABPSAU	2	NULL	5	0
+LABPSAU	4	NULL	5	0
+LABPSAU	30	NULL	5	0
+LABPSAU	90	NULL	5	0
+LABYBOH	1	2	5	4
+LABYGADAB	1	NULL	5	4
+LABYBOH	2	3	5	4
+LABYGADAB	2	NULL	5	4
+LABYBOH	4	3	5	5
+LABYGADAB	4	NULL	5	4
+LABYBOH	7	3	5	5
+LABYBOH	30	3	5	5
+LABYBOH	30	4	5	3
+LABYGADAB	30	NULL	5	4
+LABYBOH	90	3	5	5
+LABYBOH	90	4	5	3
+LABYGADAB	90	NULL	5	4
+LABYPERAB	1	NULL	5	3
+LABYBSAP	2	1	5	4
+LABYPERAB	2	NULL	5	3
+LABYBSAP	4	1	5	4
+LABYPERAB	4	NULL	5	3
+LABYBSAP	7	1	5	4
+LABYBSAP	30	1	5	4
+LABYPERAB	30	NULL	5	3
+LABYPERAB	90	NULL	5	3
+LABYT7P	1	NULL	5	2
+LABYT7P	2	NULL	5	2
+LABYT7P	4	NULL	5	2
+LABYT7P	30	NULL	5	2
+LABYT7P	90	NULL	5	2
+LABYCCPIG	1	1	5	5
+LABYCCPIG	2	1	5	5
+LABYCCPIG	4	1	5	5
+LABYCCPIG	7	1	5	5
+LABYCP	7	5	5	4
+LABYCP	30	5	5	4
+LABYCP	90	5	5	4
+LABYFACE	30	0	5	0
+LABYFACE	90	0	5	0
+LABYFELBA	1	0	5	2
+LABYFELBA	2	0	5	2
+LABYFELBA	4	0	5	2
+LABYFELBA	7	0	5	2
+LABYFELBA	30	0	5	2
+LABYFELBA	90	0	5	2
+LABYFVPA	30	2	5	5
+LABYFVPA	90	2	5	4
+LABYGADAB	7	NULL	5	4
+LABYHGBP	4	2	5	4
+LABYHGBP	7	3	5	3
+LABYHGBP	90	3	5	4
+LABYHMS	30	1	5	5
+LABYHMS	90	1	5	5
+LABYIMIPR	2	0	5	1
+LABYJCPCR	2	1	5	5
+LABYJCPCR	4	1	5	5
+LABYLEVT	1	1	5	4
+LABYLEVT	7	1	5	4
+LABYPENTO	7	0	5	1
+LABYPERAB	1	0	5	3
+LABYPERAB	7	NULL	5	3
+LABYPREAL	7	4	5	5
+LABYPREAL	7	5	5	5
+LABYPROIN	4	3	5	3
+LABYPROIN	7	3	5	3
+LABYPROIN	30	3	5	3
+LABYPROIN	90	3	5	3
+LABYSTCL	4	1	5	5
+LABYT7P	1	0	5	2
+LABYT7P	2	0	5	2
+LABYT7P	4	0	5	2
+LABYT7P	7	0	5	2
+LABYT7P	7	NULL	5	2
+LABYT7P	30	0	5	2
+LABYT7P	90	0	5	2
+LABYTESFT	7	1	5	2
+LABYTESFT	30	1	5	2
+LABYTESFT	90	1	5	2
+LABYTOPIR	7	1	5	4
+LABYTOPIR	30	1	5	4
+LABYUMYO	1	1	5	4
+LABYZINCS	7	1	5	2
+LAB11023R	1	1	4	4
+LAB11028R	90	1	4	4
+LAB11032R	1	0	4	1
+LAB11032R	2	0	4	1
+LAB11032R	7	NULL	4	1
+LAB11036R	7	5	4	4
+LAB11036R	30	5	4	4
+LAB11036R	90	5	4	4
+LAB11037R	90	1	4	2
+LAB11049R	7	3	4	2
+LAB11049R	30	3	4	2
+LAB11049R	90	3	4	2
+LAB11074R	1	0	4	3
+LAB11074R	2	0	4	3
+LAB11074R	4	0	4	3
+LAB11074R	7	0	4	3
+LAB11074R	7	NULL	4	3
+LAB11074R	30	0	4	3
+LAB11074R	90	0	4	3
+LAB11081R	2	1	4	2
+LAB11097R	2	1	4	4
+LAB11098R	1	1	4	4
+LAB11102R	30	1	4	4
+LAB11102R	90	1	4	4
+LAB11106R	30	1	4	4
+LAB11106R	90	1	4	4
+LAB11107R	1	1	4	4
+LAB11242R	1	1	4	3
+LAB11265R	1	1	4	4
+LAB11982R	2	1	4	3
+LAB11982R	90	2	4	4
+LAB12214R	2	1	4	4
+LAB12238R	2	1	4	4
+LAB12399R	1	0	4	2
+LAB12399R	2	0	4	2
+LAB12399R	4	0	4	2
+LAB12399R	7	0	4	2
+LAB12399R	30	0	4	2
+LAB12399R	90	0	4	2
+LAB12505R	1	0	4	3
+LAB12505R	2	0	4	3
+LAB12505R	4	0	4	3
+LAB12505R	7	0	4	3
+LAB12505R	7	NULL	4	3
+LAB12505R	30	0	4	3
+LAB12505R	90	0	4	3
+LAB12548R	1	0	4	3
+LAB12548R	2	0	4	3
+LAB12548R	4	0	4	3
+LAB12548R	7	0	4	3
+LAB12548R	7	NULL	4	3
+LAB12548R	30	0	4	3
+LAB12548R	90	0	4	3
+LAB12762R	7	5	4	3
+LAB12762R	30	5	4	3
+LAB12762R	90	5	4	3
+LAB12808R	2	2	4	3
+LAB12809R	1	1	4	3
+LAB12853R	4	0	4	3
+LAB12853R	7	0	4	3
+LAB12853R	7	1	4	4
+LAB12853R	30	1	4	4
+LAB12853R	90	1	4	4
+LAB13000R	1	0	4	2
+LAB13000R	2	0	4	2
+LAB13000R	4	0	4	2
+LAB13000R	7	0	4	2
+LAB13000R	7	NULL	4	2
+LAB13000R	30	0	4	2
+LAB13000R	90	0	4	2
+LABA1C	4	4	4	4
+LABA1C	7	4	4	4
+LABADAMTS	30	1	4	3
+LABADAMTS	90	1	4	3
+LABADPCRQ	90	3	4	4
+LABADVQL	30	1	4	3
+LABADVQL	90	1	4	3
+LABAFBC	1	5	4	4
+LABAFBCF	30	3	4	4
+LABAFBCF	90	4	4	4
+LABAFP	30	2	4	4
+LABAGALA	90	4	4	4
+LABAHP2	4	2	4	4
+LABAHP2	7	2	4	4
+LABAHP2	30	2	4	4
+LABAHP2	90	2	4	4
+LABALKP	30	2	4	4
+LABALKP	90	2	4	4
+LABAMIPOL	4	1	4	1
+LABAMIPRL	7	3	4	4
+LABAMIPRL	30	4	4	4
+LABANAS	2	2	4	3
+LABANERB	1	1	4	4
+LABANTITG	30	1	4	4
+LABANTITG	90	1	4	4
+LABARIX	1	0	4	2
+LABARIX	2	0	4	2
+LABARIX	4	0	4	2
+LABARIX	7	0	4	2
+LABARIX	7	NULL	4	2
+LABARIX	30	0	4	2
+LABARIX	90	0	4	2
+LABASPID	1	1	4	4
+LABASPID	4	3	4	4
+LABASTRO	30	1	4	3
+LABASTRO	90	1	4	3
+LABATTG	30	1	4	4
+LABATTG	90	1	4	4
+LABB12	30	3	4	4
+LABB12	90	3	4	4
+LABBKVPCU	4	1	4	2
+LABBKVQU	4	1	4	2
+LABBLCA	90	2	4	4
+LABBMT2	90	4	4	2
+LABBUN	4	4	4	2
+LABBUN	7	4	4	3
+LABBUN	7	5	4	3
+LABC125	1	1	4	4
+LABC4	90	2	4	4
+LABCA	2	5	4	4
+LABCBCD	4	5	4	4
+LABCBCS	4	2	4	3
+LABCBCS	4	4	4	3
+LABCBCS	7	2	4	3
+LABCBCS	30	2	4	3
+LABCBCS	90	2	4	3
+LABCD34	2	2	4	2
+LABCD34	4	2	4	2
+LABCD34	4	3	4	4
+LABCD34	7	2	4	2
+LABCD34	30	2	4	2
+LABCD34	90	2	4	2
+LABCD3B	1	0	4	1
+LABCD3B	2	0	4	1
+LABCD3B	4	0	4	1
+LABCD3B	7	0	4	1
+LABCD3B	7	NULL	4	1
+LABCD3B	30	0	4	1
+LABCD3B	90	0	4	1
+LABCFRESP	1	1	4	3
+LABCFRESP	2	1	4	3
+LABCHOL	7	1	4	4
+LABCLGCT2	1	0	4	3
+LABCLGCT2	2	0	4	3
+LABCLGCT2	4	0	4	3
+LABCLGCT2	7	0	4	3
+LABCLGCT2	7	NULL	4	3
+LABCLGCT2	30	0	4	3
+LABCLGCT2	90	0	4	3
+LABCMV	4	1	4	3
+LABCMVIGG	7	2	4	4
+LABCMVIGM	30	2	4	3
+LABCMVIGM	90	2	4	3
+LABCMVP	90	5	4	4
+LABCMVPQ	2	4	4	4
+LABCMVRO	7	2	4	3
+LABCO2	1	1	4	4
+LABCOXPNL	7	1	4	4
+LABCR	2	3	4	4
+LABCR	2	4	4	4
+LABCRCL	1	1	4	3
+LABCRPLC	30	5	4	4
+LABCRYO	2	1	4	3
+LABCRYO	90	2	4	3
+LABCSFCT	90	4	4	4
+LABDATBSI	30	3	4	4
+LABDBGT	7	NULL	4	1
+LABDIC	4	4	4	3
+LABDOA	7	2	4	3
+LABDVEHV	30	2	4	3
+LABDVEHVR	90	1	4	4
+LABDVERTV	1	1	4	4
+LABDVERV	30	4	4	4
+LABEB	1	1	4	4
+LABECHIG	7	1	4	4
+LABECHIG	30	1	4	4
+LABECHIG	90	1	4	4
+LABEP1	4	4	4	2
+LABEPO	30	1	4	3
+LABEPO	90	1	4	3
+LABESRP	2	3	4	4
+LABEVPCR	2	1	4	2
+LABFACT7	1	1	4	4
+LABFACT8	1	4	4	4
+LABFER	7	4	4	4
+LABFER	30	4	4	4
+LABFHTLVQ	30	1	4	4
+LABFIBANT	2	2	4	3
+LABFIBANT	4	1	4	3
+LABFIBANT	7	1	4	3
+LABFIBANT	30	1	4	3
+LABFIBANT	90	1	4	3
+LABFKOH	4	3	4	4
+LABFLCR	7	1	4	1
+LABFLUAPX	30	4	4	4
+LABFMYAB	4	2	4	4
+LABFMYAB	4	3	4	4
+LABFOL	1	2	4	4
+LABFOL	7	3	4	4
+LABFOL	30	3	4	4
+LABFOL	90	3	4	4
+LABFQFVR	1	1	4	4
+LABFQFVR	2	1	4	4
+LABFT4	30	5	4	3
+LABFUNIDS	7	1	4	2
+LABFVARZ	7	2	4	4
+LABG6PD	2	1	4	1
+LABGGT	2	5	4	4
+LABGIARS	30	3	4	4
+LABGIARS	90	3	4	4
+LABGLNF	1	5	4	4
+LABGNTPOL	1	1	4	1
+LABH1N1PC	30	2	4	4
+LABH1N1PC	90	2	4	4
+LABHAP	1	4	4	4
+LABHAP	2	4	4	4
+LABHAP	2	5	4	3
+LABHAVM	90	2	4	4
+LABHAVT	4	1	4	4
+LABHAVT	7	1	4	4
+LABHBCA	4	1	4	4
+LABHBPQ	30	2	4	4
+LABHBSAG	30	3	4	4
+LABHBSAG	90	4	4	4
+LABHCPQ	30	2	4	3
+LABHEAG	1	0	4	3
+LABHEAG	2	0	4	3
+LABHEAG	4	0	4	3
+LABHEAG	7	0	4	3
+LABHEAG	7	NULL	4	3
+LABHEAG	30	0	4	3
+LABHEAG	90	0	4	3
+LABHFP	7	5	4	1
+LABHGBP	1	3	4	4
+LABHGBS	1	1	4	2
+LABHGBS	2	1	4	2
+LABHGBS	4	1	4	2
+LABHGBS	7	1	4	2
+LABHGBS	30	1	4	2
+LABHGBS	90	1	4	2
+LABHGBSQ	4	2	4	4
+LABHGBX	2	2	4	1
+LABHGBX	4	2	4	1
+LABHGBX	7	2	4	1
+LABHGBX	30	2	4	1
+LABHGBX	90	2	4	1
+LABHHV6QL	4	1	4	2
+LABHHV6QL	7	1	4	2
+LABHHV6QT	30	2	4	3
+LABHHV6QT	30	3	4	3
+LABHIPA	30	3	4	3
+LABHITAB	7	3	4	4
+LABHIVWBL	4	3	4	4
+LABHIVWBL	7	3	4	4
+LABHIVWBL	30	3	4	4
+LABHSCRP	90	1	4	3
+LABHSCUL	30	1	4	4
+LABHSVCSF	7	1	4	3
+LABHSVPCR	90	2	4	3
+LABHSVQL	2	1	4	4
+LABHSVQLP	4	1	4	4
+LABHV6DNA	90	3	4	4
+LABIBIL	4	4	4	4
+LABIGGS4	1	1	4	4
+LABIGGS4	2	1	4	4
+LABIGGS4	4	1	4	4
+LABIGGS4	7	1	4	4
+LABINSL	1	5	4	4
+LABINSL	2	3	4	4
+LABINSL	2	4	4	4
+LABINSL	4	3	4	4
+LABINSL	4	4	4	4
+LABINSL	4	5	4	4
+LABINSL	7	3	4	4
+LABINSL	7	4	4	4
+LABINSL	7	5	4	4
+LABINSL	30	3	4	4
+LABINSL	30	4	4	4
+LABINSL	30	5	4	4
+LABINSL	90	3	4	4
+LABINSL	90	4	4	4
+LABINSL	90	5	4	4
+LABISOF	2	2	4	3
+LABKWB	1	3	4	4
+LABLEVETL	4	2	4	4
+LABLEVETL	90	2	4	4
+LABLPA	2	1	4	4
+LABLPA	4	1	4	4
+LABLPA	7	1	4	4
+LABLPA	30	1	4	4
+LABLPA	90	1	4	4
+LABMETB	1	4	4	2
+LABMETC	4	4	4	1
+LABMETC	30	5	4	1
+LABMETC	90	5	4	1
+LABMETHB	2	5	4	4
+LABMOLDS	1	0	4	2
+LABMOLDS	2	0	4	2
+LABMOLDS	4	0	4	2
+LABMOLDS	7	0	4	2
+LABMOLDS	7	NULL	4	2
+LABMOLDS	30	0	4	2
+LABMOLDS	90	0	4	2
+LABMYCPA	1	3	4	1
+LABMYCPA	4	4	4	3
+LABMYCPA	90	5	4	2
+LABNH3	1	4	4	4
+LABNH3	2	5	4	3
+LABOAPE	1	3	4	4
+LABOLIG	7	1	4	2
+LABOLIG	30	1	4	2
+LABORAERO	2	3	4	3
+LABORAERO	4	3	4	3
+LABOXCMET	1	1	4	3
+LABOXCMET	2	1	4	3
+LABOXCMET	4	1	4	3
+LABPALB	2	2	4	3
+LABPANWBC	30	0	4	0
+LABPANWBC	90	0	4	0
+LABPB	2	3	4	4
+LABPCEC4V	90	2	4	2
+LABPCG3V	4	4	4	3
+LABPCG3V	7	4	4	3
+LABPCTNI	2	5	4	4
+LABPCTNI	4	5	4	4
+LABPCTNI	7	5	4	4
+LABPFCATE	4	1	4	2
+LABPFCATE	7	1	4	2
+LABPFCATE	30	1	4	2
+LABPFCATE	90	1	4	2
+LABPLASMN	7	3	4	3
+LABPLASMN	30	3	4	3
+LABPLASMN	90	3	4	3
+LABPLTS	2	4	4	4
+LABPREG	90	2	4	4
+LABPROINS	4	3	4	4
+LABPROINS	7	3	4	4
+LABPROINS	30	3	4	4
+LABPROINS	90	3	4	4
+LABPT12	1	0	4	3
+LABPT12	2	0	4	3
+LABPT12	4	0	4	3
+LABPT12	7	0	4	3
+LABPT12	7	NULL	4	3
+LABPT12	30	0	4	3
+LABPT12	90	0	4	3
+LABPTH	1	2	4	3
+LABQFTB	90	2	4	3
+LABRBCFOL	4	2	4	3
+LABRBCFOL	7	2	4	3
+LABRBCFOL	30	2	4	3
+LABRESP	2	4	4	4
+LABRESPCR	30	3	4	4
+LABRESPG	1	3	4	3
+LABRETIC	1	2	4	2
+LABRF	90	2	4	4
+LABRITUX	90	1	4	0
+LABRNPABY	90	1	4	4
+LABRSVPCR	30	0	4	1
+LABRSVPCR	90	0	4	1
+LABSCMV	1	4	4	4
+LABSCMV	2	4	4	4
+LABSCMV	4	4	4	4
+LABSCMV	7	4	4	4
+LABSHIV12	4	1	4	1
+LABSHIV12	7	1	4	1
+LABSHIV12	30	1	4	1
+LABSHIV12	90	1	4	1
+LABSTAND	1	0	4	0
+LABSTAND	2	0	4	0
+LABSTAND	4	0	4	0
+LABSTAND	7	0	4	0
+LABSTAND	7	NULL	4	0
+LABSTAND	30	0	4	0
+LABSTAND	90	0	4	0
+LABSTLC	4	2	4	4
+LABSTLCX	2	2	4	4
+LABSTLCX	90	3	4	4
+LABSTOBIC	4	1	4	3
+LABSTOBIC	7	1	4	3
+LABSTOBIC	30	1	4	3
+LABSTOBIC	90	1	4	3
+LABSTOOLC	90	3	4	4
+LABSTOOLC	90	4	4	4
+LABSTPCR	2	1	4	3
+LABSTRED	7	2	4	4
+LABSTRED	30	3	4	3
+LABSTRED	90	4	4	2
+LABSTREP	4	1	4	2
+LABSTREP	7	1	4	2
+LABSTREP	30	1	4	2
+LABSTREP	90	1	4	2
+LABSTSS	4	2	4	4
+LABTBIL	7	5	4	4
+LABTBPCRS	4	3	4	4
+LABTEGCP	7	4	4	1
+LABTES	90	1	4	4
+LABTESFBT	30	0	4	0
+LABTESFBT	90	0	4	0
+LABTOBPOL	4	1	4	1
+LABTOBPOL	7	1	4	1
+LABTOBPOL	30	1	4	2
+LABTOBPOL	90	1	4	2
+LABTOBPRL	30	5	4	3
+LABTOBPRL	90	5	4	3
+LABTOXATP	1	0	4	4
+LABTOXATP	2	0	4	4
+LABTOXATP	4	0	4	4
+LABTOXATP	7	0	4	4
+LABTOXATP	7	NULL	4	4
+LABTOXATP	30	0	4	4
+LABTOXATP	90	0	4	4
+LABTOXIG	1	1	4	4
+LABTOXIG	2	1	4	4
+LABTP	4	3	4	3
+LABTRFN	4	3	4	4
+LABTRFS	4	2	4	1
+LABTRFS	7	2	4	1
+LABTT	7	4	4	2
+LABTT	30	4	4	2
+LABTT	90	4	4	2
+LABTT4	1	1	4	4
+LABTT4	2	1	4	4
+LABTT4	4	1	4	4
+LABTT4	7	1	4	4
+LABTT4	30	1	4	4
+LABTT4	90	1	4	4
+LABUA	2	3	4	4
+LABUALB	2	1	4	2
+LABUAMY	7	1	4	3
+LABUAMY	30	1	4	3
+LABUAMY	90	1	4	3
+LABUAPRN	90	5	4	4
+LABUAS	2	5	4	4
+LABUGL	1	0	4	3
+LABUGL	2	0	4	3
+LABUGL	4	0	4	3
+LABUGL	7	0	4	3
+LABUGL	7	NULL	4	3
+LABUGL	30	0	4	3
+LABUGL	90	0	4	3
+LABUMYOSC	1	1	4	3
+LABUNA	2	3	4	4
+LABUNA	4	3	4	3
+LABUNA	7	3	4	3
+LABUNA	30	3	4	3
+LABUPBGQT	90	1	4	2
+LABURNC	2	3	4	4
+LABUUN	7	2	4	3
+LABVANPOL	2	1	4	0
+LABVCUL	1	4	4	4
+LABVCUL	4	5	4	4
+LABVCULF	30	1	4	4
+LABVCULF	90	1	4	4
+LABVCULR	30	3	4	4
+LABVCULT	7	3	4	4
+LABVCULT	90	3	4	4
+LABVD25H	30	2	4	4
+LABVD25H	90	2	4	4
+LABVISC	30	1	4	0
+LABVISC	90	1	4	0
+LABVOLTL	4	2	4	4
+LABVPAL	1	3	4	3
+LABVPAL	1	4	4	4
+LABVSTLC	1	1	4	4
+LABVZVCSF	30	1	4	4
+LABVZVIGG	90	1	4	4
+LABVZVQL	7	1	4	3
+LABWDC	90	3	4	3
+LABWDCS	2	3	4	3
+LABWNVAB	1	1	4	4
+LABYAMISO	2	1	4	2
+LABYBILEA	1	0	4	1
+LABYBILEA	2	0	4	1
+LABYBILEA	4	0	4	1
+LABYBILEA	7	0	4	1
+LABYBILEA	7	NULL	4	1
+LABYBILEA	30	0	4	1
+LABYBILEA	90	0	4	1
+LABYBOH	1	3	4	4
+LABYBOH	4	4	4	2
+LABYBSAP	1	1	4	4
+LAB11032R	1	NULL	4	1
+LAB11032R	2	NULL	4	1
+LAB11032R	4	NULL	4	1
+LAB11032R	30	NULL	4	1
+LAB11032R	90	NULL	4	1
+LAB11074R	1	NULL	4	3
+LAB11074R	2	NULL	4	3
+LAB11074R	4	NULL	4	3
+LAB11074R	30	NULL	4	3
+LAB11074R	90	NULL	4	3
+LABYC2REF	1	0	4	2
+LAB12505R	1	NULL	4	3
+LABYC2REF	2	0	4	2
+LAB12505R	2	NULL	4	3
+LABYC2REF	4	0	4	2
+LAB12505R	4	NULL	4	3
+LABYC2REF	7	0	4	2
+LABYC2REF	7	NULL	4	2
+LABYC2REF	30	0	4	2
+LAB12505R	30	NULL	4	3
+LABYC2REF	90	0	4	2
+LAB12505R	90	NULL	4	3
+LAB12548R	1	NULL	4	3
+LAB12548R	2	NULL	4	3
+LAB12548R	4	NULL	4	3
+LAB12548R	30	NULL	4	3
+LAB12548R	90	NULL	4	3
+LAB13000R	1	NULL	4	2
+LAB13000R	2	NULL	4	2
+LAB13000R	4	NULL	4	2
+LAB13000R	30	NULL	4	2
+LAB13000R	90	NULL	4	2
+LABARIX	1	NULL	4	2
+LABARIX	2	NULL	4	2
+LABARIX	4	NULL	4	2
+LABARIX	30	NULL	4	2
+LABARIX	90	NULL	4	2
+LABCD3B	1	NULL	4	1
+LABCD3B	2	NULL	4	1
+LABCD3B	4	NULL	4	1
+LABCD3B	30	NULL	4	1
+LABCD3B	90	NULL	4	1
+LABCLGCT2	1	NULL	4	3
+LABCLGCT2	2	NULL	4	3
+LABCLGCT2	4	NULL	4	3
+LABCLGCT2	30	NULL	4	3
+LABCLGCT2	90	NULL	4	3
+LABDBGT	1	NULL	4	1
+LABDBGT	2	NULL	4	1
+LABDBGT	4	NULL	4	1
+LABDBGT	30	NULL	4	1
+LABDBGT	90	NULL	4	1
+LABHEAG	1	NULL	4	3
+LABYCH50	2	1	4	4
+LABHEAG	2	NULL	4	3
+LABHEAG	4	NULL	4	3
+LABHEAG	30	NULL	4	3
+LABHEAG	90	NULL	4	3
+LABMOLDS	1	NULL	4	2
+LABYCHRA	2	1	4	4
+LABMOLDS	2	NULL	4	2
+LABMOLDS	4	NULL	4	2
+LABMOLDS	30	NULL	4	2
+LABMOLDS	90	NULL	4	2
+LABPT12	1	NULL	4	3
+LABPT12	2	NULL	4	3
+LABPT12	4	NULL	4	3
+LABPT12	30	NULL	4	3
+LABPT12	90	NULL	4	3
+LABSTAND	1	NULL	4	0
+LABSTAND	2	NULL	4	0
+LABSTAND	4	NULL	4	0
+LABSTAND	30	NULL	4	0
+LABSTAND	90	NULL	4	0
+LABTOXATP	1	NULL	4	4
+LABTOXATP	2	NULL	4	4
+LABTOXATP	4	NULL	4	4
+LABTOXATP	30	NULL	4	4
+LABTOXATP	90	NULL	4	4
+LABUGL	1	NULL	4	3
+LABYCP	2	5	4	3
+LABUGL	2	NULL	4	3
+LABYCP	4	5	4	3
+LABUGL	4	NULL	4	3
+LABUGL	30	NULL	4	3
+LABUGL	90	NULL	4	3
+LABYBILEA	1	NULL	4	1
+LABYBILEA	2	NULL	4	1
+LABYBILEA	4	NULL	4	1
+LABYBILEA	30	NULL	4	1
+LABYBILEA	90	NULL	4	1
+LABYC2REF	1	NULL	4	2
+LABYC2REF	2	NULL	4	2
+LABYC2REF	4	NULL	4	2
+LABYC2REF	30	NULL	4	2
+LABYC2REF	90	NULL	4	2
+LABYHGH	1	NULL	4	3
+LABYHGH	2	NULL	4	3
+LABYHGH	4	NULL	4	3
+LABYHGH	30	NULL	4	3
+LABYHGH	90	NULL	4	3
+LABYSSARO	1	NULL	4	2
+LABYSSARO	2	NULL	4	2
+LABYSSARO	4	NULL	4	2
+LABYSSARO	30	NULL	4	2
+LABYSSARO	90	NULL	4	2
+LABYFCAR	7	1	4	3
+LABYFFATQ	90	2	4	3
+LABYFLUZ	1	1	4	3
+LABYFLUZ	2	1	4	3
+LABYFLUZ	4	1	4	3
+LABYFLUZ	7	1	4	4
+LABYFPHEN	1	1	4	3
+LABYFPHEN	2	1	4	3
+LABYFT3	90	2	4	4
+LABYHGBP	90	4	4	4
+LABYHGH	1	0	4	3
+LABYHGH	2	0	4	3
+LABYHGH	4	0	4	3
+LABYHGH	7	0	4	3
+LABYHGH	7	NULL	4	3
+LABYHGH	30	0	4	3
+LABYHGH	90	0	4	3
+LABYHMS	2	1	4	4
+LABYHMS	4	1	4	4
+LABYHMS	7	1	4	4
+LABYIMIPR	4	0	4	1
+LABYLEVT	2	1	4	3
+LABYLEVT	4	1	4	3
+LABYMEXIL	7	1	4	2
+LABYMEXIL	30	1	4	2
+LABYMEXIL	90	1	4	2
+LABYPARVO	1	1	4	4
+LABYPERAB	2	0	4	2
+LABYPERAB	4	0	4	2
+LABYPERAB	7	0	4	2
+LABYPERAB	30	0	4	2
+LABYPERAB	90	0	4	2
+LABYPR3	90	1	4	3
+LABYPROIN	1	4	4	4
+LABYPROIN	2	4	4	4
+LABYSES	90	1	4	2
+LABYSSARO	1	0	4	2
+LABYSSARO	2	0	4	2
+LABYSSARO	4	0	4	2
+LABYSSARO	7	0	4	2
+LABYSSARO	7	NULL	4	2
+LABYSSARO	30	0	4	2
+LABYSSARO	90	0	4	2
+LABYSTOSM	7	2	4	3
+LABYTESFT	4	1	4	2
+LABYTOPIR	1	1	4	4
+LABYTOPIR	4	1	4	4
+LABYTOPIR	90	2	4	4
+LABYUHYME	4	1	4	4
+LABYVITA	7	1	4	3
+LABYVITB6	90	1	4	2
+LABYZINCS	4	1	4	2
+LAB11027R	4	1	3	2
+LAB11027R	7	1	3	2
+LAB11027R	30	1	3	2
+LAB11027R	90	1	3	2
+LAB11028R	30	1	3	3
+LAB11032R	4	0	3	1
+LAB11032R	7	0	3	1
+LAB11032R	30	0	3	1
+LAB11032R	90	0	3	1
+LAB11036R	1	2	3	3
+LAB11036R	2	3	3	3
+LAB11036R	4	4	3	2
+LAB11037R	30	1	3	1
+LAB11055R	1	0	3	2
+LAB11055R	2	0	3	2
+LAB11069R	1	1	3	2
+LAB11084R	1	0	3	1
+LAB11084R	2	0	3	1
+LAB11084R	4	0	3	1
+LAB11084R	7	0	3	1
+LAB11084R	7	NULL	3	1
+LAB11084R	30	0	3	1
+LAB11084R	90	0	3	1
+LAB11097R	1	1	3	3
+LAB11098R	30	2	3	3
+LAB11098R	90	2	3	3
+LAB11101R	30	1	3	3
+LAB11101R	90	1	3	3
+LAB11102R	7	1	3	3
+LAB11102R	90	2	3	3
+LAB11106R	4	1	3	3
+LAB11106R	7	1	3	3
+LAB11153R	30	1	3	2
+LAB11153R	90	1	3	3
+LAB11199R	2	1	3	3
+LAB11199R	4	1	3	3
+LAB11250R	7	1	3	1
+LAB11982R	30	2	3	3
+LAB11982R	90	4	3	3
+LAB12210R	1	0	3	2
+LAB12210R	2	0	3	2
+LAB12210R	4	0	3	2
+LAB12210R	7	0	3	2
+LAB12210R	7	NULL	3	2
+LAB12210R	30	0	3	2
+LAB12210R	90	0	3	2
+LAB12214R	1	1	3	3
+LAB12238R	7	1	3	3
+LAB12238R	30	1	3	3
+LAB12284R	1	0	3	2
+LAB12284R	2	0	3	2
+LAB12284R	7	NULL	3	2
+LAB12292R	2	1	3	3
+LAB12292R	7	1	3	2
+LAB12292R	30	1	3	2
+LAB12292R	90	1	3	2
+LAB12330R	7	3	3	3
+LAB12330R	90	4	3	3
+LAB12664R	1	0	3	2
+LAB12664R	2	0	3	2
+LAB12664R	4	0	3	2
+LAB12664R	7	0	3	2
+LAB12664R	7	NULL	3	2
+LAB12664R	30	0	3	2
+LAB12664R	90	0	3	2
+LAB12733R	1	0	3	1
+LAB12733R	2	0	3	1
+LAB12733R	4	0	3	1
+LAB12733R	7	0	3	1
+LAB12733R	7	NULL	3	1
+LAB12762R	2	4	3	3
+LAB12762R	4	4	3	3
+LAB12808R	1	2	3	2
+LAB12811R	4	1	3	0
+LAB12811R	7	1	3	0
+LAB12811R	30	1	3	0
+LAB12811R	90	1	3	0
+LAB12853R	7	2	3	1
+LAB12853R	30	0	3	2
+LAB12853R	30	2	3	1
+LAB12853R	90	0	3	2
+LAB12853R	90	2	3	1
+LABA1C	2	3	3	3
+LABA1C	7	5	3	3
+LABA1C	30	5	3	3
+LABA1C	90	5	3	3
+LABAACSF	1	0	3	0
+LABAACSF	2	0	3	0
+LABAACSF	4	0	3	0
+LABAACSF	7	0	3	0
+LABAACSF	7	NULL	3	0
+LABAACSF	30	0	3	0
+LABAACSF	90	0	3	0
+LABADAMTS	4	1	3	3
+LABADAMTS	7	1	3	3
+LABADPCRQ	7	2	3	3
+LABADVQL	4	1	3	2
+LABADVQL	7	1	3	2
+LABADVQT	4	2	3	1
+LABAFBCF	4	3	3	3
+LABAFBCF	7	3	3	3
+LABAFP	4	2	3	3
+LABAFP	7	2	3	3
+LABAHP2	2	2	3	3
+LABALKP	7	2	3	3
+LABALT	4	2	3	3
+LABAMIPOL	2	1	3	1
+LABAMIPRL	2	2	3	3
+LABAMIPRL	30	5	3	2
+LABAMIPRL	90	5	3	2
+LABANAS	1	2	3	2
+LABANCA	30	2	3	3
+LABANCA	90	2	3	3
+LABANDRO	1	0	3	1
+LABANDRO	2	0	3	1
+LABANDRO	4	0	3	1
+LABANDRO	7	0	3	1
+LABANDRO	7	NULL	3	1
+LABANDRO	30	0	3	1
+LABANDRO	90	0	3	1
+LABANER2	4	5	3	2
+LABANER2	7	5	3	2
+LABANTITG	7	1	3	3
+LABASPID	1	2	3	3
+LABASPID	4	2	3	3
+LABASQ	1	3	3	3
+LABASQ	2	3	3	3
+LABASQ	4	3	3	3
+LABASQ	7	3	3	3
+LABASQ	30	3	3	3
+LABASQ	90	3	3	3
+LABAST	2	2	3	2
+LABAST	4	2	3	2
+LABAST	30	3	3	3
+LABAST	90	3	3	3
+LABASTRO	1	1	3	3
+LABASTRO	2	1	3	3
+LABASTRO	4	1	3	3
+LABASTRO	7	1	3	3
+LABATTG	7	1	3	3
+LABB12	4	3	3	3
+LABB12	7	3	3	3
+LABB12	90	4	3	3
+LABB2M	4	1	3	2
+LABB2M	7	1	3	2
+LABB2M	30	1	3	2
+LABB2M	90	1	3	2
+LABBACIDS	90	2	3	3
+LABBHB	1	4	3	3
+LABBHB	4	2	3	1
+LABBHB	7	2	3	1
+LABBHB	30	2	3	1
+LABBHB	90	2	3	1
+LABBHCG	30	2	3	3
+LABBKVQU	2	1	3	1
+LABBKVQU	90	2	3	2
+LABBLCSTK	1	2	3	2
+LABBLCTIP	1	3	3	3
+LABBLCTIP	4	4	3	3
+LABBMT1	4	2	3	0
+LABBMT1	7	2	3	0
+LABBMT2	30	4	3	2
+LABBUN	30	5	3	3
+LABBUN	90	5	3	3
+LABC125	90	2	3	2
+LABC3	90	2	3	2
+LABCBCD	2	5	3	3
+LABCBCO	1	5	3	3
+LABCBCS	7	3	3	3
+LABCBCS	30	3	3	3
+LABCBCS	90	3	3	3
+LABCD34	1	2	3	2
+LABCD4CT	1	1	3	1
+LABCD4CT	2	1	3	1
+LABCD4CT	4	1	3	1
+LABCD4CT	7	1	3	1
+LABCDTPCR	2	2	3	2
+LABCEA	4	2	3	2
+LABCEA	7	2	3	2
+LABCEA	30	2	3	2
+LABCEA	90	2	3	2
+LABCENABY	1	1	3	3
+LABCENABY	2	1	3	3
+LABCENABY	4	1	3	3
+LABCENABY	7	1	3	3
+LABCENABY	30	1	3	3
+LABCENABY	90	1	3	3
+LABCK	1	5	3	3
+LABCL	2	1	3	2
+LABCLACT	4	2	3	3
+LABCLACT	30	4	3	3
+LABCMV	2	1	3	3
+LABCMVIGG	4	2	3	3
+LABCMVP	7	4	3	3
+LABCMVQL	4	2	3	3
+LABCMVQL	90	3	3	3
+LABCMVRC	30	1	3	3
+LABCMVRC	90	1	3	3
+LABCMVRO	2	2	3	2
+LABCMVRO	4	2	3	2
+LABCMVRO	90	3	3	3
+LABCO2	1	3	3	3
+LABCO2	4	2	3	3
+LABCO2	7	2	3	3
+LABCO2	30	2	3	3
+LABCO2	90	2	3	3
+LABCOHB	1	1	3	3
+LABCOHB	2	1	3	3
+LABCOHB	4	1	3	3
+LABCOHB	7	1	3	3
+LABCOHB	30	1	3	3
+LABCOHB	90	1	3	3
+LABCORT	1	5	3	2
+LABCORT	2	5	3	3
+LABCORT	4	5	3	3
+LABCOXPNL	30	1	3	3
+LABCOXPNL	90	1	3	3
+LABCR	1	3	3	3
+LABCR	4	5	3	3
+LABCRFA	7	2	3	3
+LABCRFA	30	2	3	3
+LABCRFA	90	2	3	3
+LABCRPLB	30	2	3	3
+LABCRPLFC	1	1	3	3
+LABCRPLFC	30	2	3	3
+LABCRYO	1	1	3	2
+LABCRYST	90	2	3	1
+LABCSFCT	7	3	3	2
+LABCSFCT	30	4	3	3
+LABCSFCT	90	5	3	2
+LABCSTOTA	2	1	3	2
+LABCTC	30	4	3	3
+LABCTC	90	4	3	3
+LABCZPL	7	2	3	0
+LABDBIL	1	4	3	3
+LABDBIL	4	5	3	3
+LABDGHBP	1	1	3	2
+LABDIC	7	4	3	2
+LABDIC	30	4	3	2
+LABDIC	90	4	3	2
+LABDIGL	2	4	3	3
+LABDOA	90	3	3	3
+LABDVEAV3	1	0	3	2
+LABDVEAV3	2	0	3	2
+LABDVEAV3	4	0	3	2
+LABDVEAV3	7	0	3	2
+LABDVEAV3	7	NULL	3	2
+LABDVEAV3	30	0	3	2
+LABDVEAV3	90	0	3	2
+LABDVEFLA	2	1	3	3
+LABDVEFLA	4	1	3	3
+LABDVEFLA	7	1	3	3
+LABDVEFLA	30	1	3	3
+LABDVEFLA	90	1	3	3
+LABDVEHV	7	2	3	2
+LABDVEHVR	2	1	3	3
+LABDVEHVR	4	1	3	3
+LABDVEHVR	7	1	3	3
+LABDVEHVR	30	1	3	3
+LABDVEMPV	1	0	3	1
+LABDVEMPV	2	0	3	1
+LABDVEMPV	4	0	3	1
+LABDVEMPV	7	0	3	1
+LABDVEMPV	7	NULL	3	1
+LABDVEMPV	30	0	3	1
+LABDVEMPV	90	0	3	1
+LABDVEPI1	7	NULL	3	0
+LABDVERO	2	1	3	3
+LABDVEVH	90	3	3	2
+LABEBPCRQ	4	2	3	3
+LABEBPCRQ	30	5	3	3
+LABEBVQP	30	5	3	3
+LABECHIG	4	1	3	3
+LABEP1	2	4	3	2
+LABEPO	2	1	3	3
+LABEPO	4	1	3	3
+LABEPO	7	1	3	3
+LABEVPCR	1	1	3	2
+LABFACT7	2	2	3	2
+LABFACT7	4	2	3	2
+LABFACT7	7	2	3	2
+LABFACT7	30	2	3	2
+LABFACT7	90	2	3	2
+LABFACT8	2	5	3	3
+LABFCHGA	1	0	3	2
+LABFCHGA	2	0	3	2
+LABFCHGA	4	0	3	2
+LABFCHGA	7	0	3	2
+LABFCHGA	7	NULL	3	2
+LABFCHGA	30	0	3	2
+LABFCHGA	90	0	3	2
+LABFER	7	5	3	3
+LABFER	30	5	3	3
+LABFER	90	5	3	3
+LABFHTLVQ	4	1	3	3
+LABFHTLVQ	7	1	3	3
+LABFHVIG	2	2	3	3
+LABFHVIG	4	2	3	3
+LABFHVIG	7	2	3	3
+LABFHVIG	30	2	3	3
+LABFHVIG	90	2	3	3
+LABFIBANT	4	3	3	2
+LABFKOH	1	3	3	3
+LABFKOH	2	3	3	3
+LABFLDC	4	4	3	2
+LABFLL	4	1	3	2
+LABFLL	7	1	3	2
+LABFLL	30	1	3	2
+LABFLL	90	1	3	2
+LABFLUAPC	1	1	3	3
+LABFLUAPC	2	1	3	3
+LABFLUAPC	4	1	3	3
+LABFLUAPC	7	1	3	3
+LABFLUAPC	30	1	3	3
+LABFLUAPC	90	1	3	3
+LABFLUAPX	90	4	3	3
+LABFMYAB	7	5	3	3
+LABFOL	4	3	3	3
+LABFOL	4	4	3	3
+LABFOL	7	4	3	3
+LABFOL	30	4	3	3
+LABFOL	90	4	3	3
+LABFPARVD	1	1	3	3
+LABFT4	7	4	3	3
+LABFVARZ	4	2	3	3
+LABG6PD	1	1	3	0
+LABG6PDQT	30	1	3	2
+LABG6PDQT	90	1	3	2
+LABGASBLD	2	1	3	0
+LABGASBLD	4	1	3	0
+LABGASBLD	7	1	3	0
+LABGH	4	2	3	2
+LABGH	7	2	3	2
+LABGH	30	2	3	2
+LABGH	90	2	3	2
+LABGIARS	1	2	3	3
+LABGIARS	2	2	3	3
+LABGIARS	7	3	3	3
+LABGLOB	7	1	3	3
+LABGRAM	4	5	3	3
+LABH1N1PC	7	2	3	3
+LABHAVM	4	2	3	3
+LABHAVM	7	2	3	3
+LABHAVM	30	2	3	3
+LABHAVT	1	1	3	3
+LABHAVT	2	1	3	3
+LABHBCA	1	1	3	3
+LABHBODY	1	1	3	3
+LABHBODY	2	1	3	3
+LABHBODY	4	1	3	3
+LABHBODY	7	1	3	3
+LABHBODY	30	1	3	3
+LABHBODY	90	1	3	3
+LABHBSAG	7	3	3	3
+LABHCPQ	7	2	3	2
+LABHCVA	4	2	3	3
+LABHCVA	7	2	3	3
+LABHCVA	30	2	3	3
+LABHCVA	90	2	3	3
+LABHGBP	1	4	3	3
+LABHGBP	4	5	3	3
+LABHGBSQ	1	2	3	3
+LABHGBSQ	2	2	3	3
+LABHGBSQ	30	3	3	3
+LABHGBSQ	90	3	3	3
+LABHIAB	30	3	3	2
+LABHIVQ2	90	2	3	3
+LABHSCRP	7	1	3	3
+LABHSCRP	30	1	3	3
+LABHSCUL	4	1	3	3
+LABHSCUL	7	1	3	3
+LABHSCUL	90	1	3	3
+LABHSVCSF	4	1	3	2
+LABHSVM	30	1	3	3
+LABHSVM	90	1	3	3
+LABHSVPCR	1	1	3	0
+LABHSVPCR	30	2	3	2
+LABHSVQL	1	1	3	3
+LABHSVQLP	1	1	3	3
+LABHSVQLP	2	1	3	3
+LABHV6DNA	7	2	3	3
+LABHYPP	1	0	3	0
+LABHYPP	2	0	3	0
+LABHYPP	4	0	3	0
+LABHYPP	7	0	3	0
+LABHYPP	7	NULL	3	0
+LABHYPP	30	0	3	0
+LABHYPP	90	0	3	0
+LABIGF	4	1	3	2
+LABIGF	7	1	3	2
+LABIGF	30	1	3	2
+LABIGF	90	1	3	2
+LABIGG	30	3	3	2
+LABIOPTH	1	2	3	2
+LABIOPTH	2	2	3	2
+LABIOPTH	4	2	3	2
+LABIOPTH	7	2	3	2
+LABIOPTH	30	2	3	2
+LABIOPTH	90	2	3	2
+LABKWB	1	4	3	3
+LABKWB	2	4	3	2
+LABKWB	4	4	3	2
+LABKWB	7	4	3	2
+LABKWB	30	4	3	2
+LABKWB	90	4	3	2
+LABLAMOL	2	2	3	3
+LABLAMOL	7	3	3	3
+LABLAMOL	30	3	3	3
+LABLAMOL	90	3	3	3
+LABLEGDA	90	4	3	3
+LABLEGDD	1	0	3	2
+LABLEGDD	2	0	3	2
+LABLEGDD	4	0	3	2
+LABLEGDD	7	0	3	2
+LABLEGDD	30	0	3	2
+LABLEGDD	90	0	3	2
+LABLEVETL	7	2	3	3
+LABLEVETL	90	3	3	3
+LABLIL	7	5	3	3
+LABLPA	1	1	3	3
+LABLPD	4	3	3	3
+LABLUT	1	1	3	3
+LABLUT	2	1	3	3
+LABLWBCS2	1	1	3	3
+LABMETC	7	5	3	1
+LABMMIC	1	0	3	0
+LABMMIC	2	0	3	0
+LABMMIC	4	0	3	0
+LABMMIC	7	0	3	0
+LABMMIC	7	NULL	3	0
+LABMMIC	30	0	3	0
+LABMMIC	90	0	3	0
+LABMYCPA	7	5	3	1
+LABMYCPA	30	5	3	1
+LABMYO	2	4	3	3
+LABMYO	7	5	3	3
+LABMYO	30	5	3	3
+LABMYO	90	5	3	3
+LABNTBNP	1	2	3	2
+LABNTBNP	7	3	3	3
+LABORAERO	1	3	3	3
+LABORAERO	90	4	3	3
+LABORANER	1	3	3	3
+LABORANER	2	3	3	3
+LABORANER	4	3	3	3
+LABORANER	90	5	3	3
+LABPB	4	5	3	3
+LABPCEG7	1	2	3	1
+LABPCEG7V	1	2	3	2
+LABPCEG7V	2	2	3	2
+LABPCEG7V	4	2	3	2
+LABPCEG7V	7	2	3	2
+LABPCEG7V	30	2	3	2
+LABPCEG7V	90	2	3	2
+LABPCG3V	1	4	3	3
+LABPCG3V	2	4	3	3
+LABPLASMN	1	2	3	3
+LABPLTS	2	5	3	3
+LABPLTS	4	5	3	3
+LABPROCT	2	3	3	3
+LABPROINS	1	4	3	3
+LABPROINS	2	4	3	3
+LABPROINS	4	4	3	3
+LABPROINS	4	5	3	3
+LABPROINS	7	4	3	3
+LABPROINS	7	5	3	3
+LABPROINS	30	4	3	3
+LABPROINS	30	5	3	3
+LABPROINS	90	4	3	3
+LABPROINS	90	5	3	3
+LABRBCENZ	1	0	3	1
+LABRBCENZ	2	0	3	1
+LABRBCENZ	4	0	3	1
+LABRBCENZ	7	0	3	1
+LABRBCENZ	7	NULL	3	1
+LABRBCENZ	30	0	3	1
+LABRBCENZ	90	0	3	1
+LABREPT	1	5	3	2
+LABRESP	1	3	3	3
+LABRESPCR	90	3	3	2
+LABRESPCR	90	4	3	3
+LABRF	7	2	3	3
+LABRF	30	2	3	3
+LABRITUX	30	1	3	0
+LABRNPABY	1	1	3	3
+LABRNPABY	2	1	3	3
+LABRNPABY	4	1	3	3
+LABRNPABY	7	1	3	3
+LABRNPABY	30	1	3	3
+LABROMRS	7	3	3	3
+LABSCMV	30	5	3	3
+LABSHIV12	1	1	3	1
+LABSHIV12	2	1	3	1
+LABSIROL	2	4	3	3
+LABSMABY	1	1	3	3
+LABSMABY	2	1	3	3
+LABSMABY	4	1	3	3
+LABSMABY	7	1	3	3
+LABSMABY	30	1	3	3
+LABSPE	4	1	3	0
+LABSPE	7	1	3	0
+LABSPE	30	1	3	0
+LABSPE	90	1	3	0
+LABSTFAT	7	1	3	3
+LABSTFAT	30	1	3	3
+LABSTFAT	90	1	3	3
+LABSTLCX	30	3	3	3
+LABSTOBGD	2	2	3	0
+LABSTOBIC	1	1	3	2
+LABSTOBIC	2	1	3	2
+LABSTOOLC	4	2	3	3
+LABSTOOLC	7	2	3	3
+LABSTOOLC	30	3	3	3
+LABSTRED	2	2	3	2
+LABSTRED	4	3	3	2
+LABSTRED	30	4	3	1
+LABSTREP	1	1	3	1
+LABSTREP	2	1	3	1
+LABSTRONG	2	2	3	3
+LABSTSS	2	2	3	3
+LABSTSS	4	3	3	3
+LABSTSS	4	4	3	3
+LABSTSS	7	2	3	3
+LABSTSS	30	2	3	3
+LABSTSS	90	3	3	3
+LABT3	1	1	3	1
+LABTBILFR	7	4	3	3
+LABTBLYM	2	1	3	0
+LABTBPCRS	2	3	3	3
+LABTCDIF	4	3	3	3
+LABTOBPRL	1	2	3	2
+LABTOBPRL	7	5	3	2
+LABTOXO	4	1	3	2
+LABTOXO	7	1	3	2
+LABTP	2	2	3	3
+LABTP	4	4	3	3
+LABTRFS	2	2	3	2
+LABTSH	30	5	3	2
+LABTT	7	5	3	3
+LABTTM	1	1	3	0
+LABTTM	2	1	3	0
+LABTTM	4	1	3	0
+LABTTM	7	1	3	0
+LABTTM	30	1	3	0
+LABTTM	90	1	3	0
+LABUALB	1	1	3	1
+LABUAMY	4	1	3	2
+LABUAPRN	4	3	3	3
+LABUAS	4	3	3	2
+LABUCARN	1	0	3	1
+LABUCARN	2	0	3	1
+LABUCARN	4	0	3	1
+LABUCARN	7	0	3	1
+LABUCARN	7	NULL	3	1
+LABUCARN	30	0	3	1
+LABUCARN	90	0	3	1
+LABUCL	2	1	3	3
+LABUCL	4	1	3	3
+LABUCL	7	1	3	3
+LABUCL	30	1	3	3
+LABUCL	90	1	3	3
+LABUEP	1	2	3	3
+LABUHEMO	4	1	3	1
+LABUHEMO	7	1	3	1
+LABUHEMO	30	1	3	1
+LABUHEMO	90	1	3	1
+LABUMYOSC	2	1	3	2
+LABUNA	4	4	3	3
+LABUNA	7	4	3	3
+LABUNA	30	4	3	3
+LABUNA	90	4	3	3
+LABUPBGQT	2	1	3	2
+LABUPBGQT	4	1	3	2
+LABUPBGQT	7	1	3	2
+LABUPBGQT	30	1	3	2
+LABUTHI	7	NULL	3	1
+LABUUN	4	2	3	2
+LABVANPOL	1	1	3	0
+LABVCUL	7	5	3	3
+LABVCULF	7	1	3	3
+LABVCULR	7	2	3	3
+LABVCULR	90	4	3	3
+LABVCULT	7	4	3	3
+LABVCULT	30	4	3	3
+LABVD25H	4	2	3	3
+LABVD25H	7	2	3	3
+LABVISC	7	1	3	0
+LABVOLTL	2	2	3	3
+LABVP3	4	2	3	2
+LABVPAL	2	4	3	3
+LABVPAL	2	5	3	3
+LABVZVIGG	30	1	3	3
+LABVZVQL	4	1	3	2
+LABVZVQLP	90	2	3	3
+LABWDC	30	3	3	3
+LABWDCG	1	3	3	3
+LABYAHA	1	1	3	3
+LABYANDRO	1	0	3	1
+LABYANDRO	2	0	3	1
+LABYANDRO	4	0	3	1
+LABYANDRO	7	0	3	1
+LABYANDRO	7	NULL	3	1
+LABYANDRO	30	0	3	1
+LABYANDRO	90	0	3	1
+LABYANRNP	7	1	3	3
+LABYANRNP	30	1	3	3
+LABYANRNP	90	1	3	3
+LABYBOH	1	4	3	2
+LABYBOH	2	4	3	2
+LABYCH50	1	1	3	3
+LABYCHRA	1	1	3	3
+LABYCHRA	4	1	3	3
+LABYCHRA	4	2	3	3
+LABYCHRA	7	1	3	3
+LABYCHRA	30	1	3	3
+LABYCHRA	90	1	3	3
+LABYCYANI	1	1	3	3
+LABYCYANI	2	1	3	3
+LABYCYANI	4	1	3	3
+LABYDESIP	7	1	3	1
+LABYDESIP	90	1	3	1
+LAB11084R	1	NULL	3	1
+LAB11084R	2	NULL	3	1
+LAB11084R	4	NULL	3	1
+LAB11084R	30	NULL	3	1
+LAB11084R	90	NULL	3	1
+LABYDIAZE	1	0	3	0
+LAB12210R	1	NULL	3	2
+LABYDIAZE	2	0	3	0
+LAB12210R	2	NULL	3	2
+LABYDIAZE	4	0	3	0
+LAB12210R	4	NULL	3	2
+LABYDIAZE	7	0	3	0
+LABYDIAZE	7	NULL	3	0
+LABYDIAZE	30	0	3	0
+LAB12210R	30	NULL	3	2
+LABYDIAZE	90	0	3	0
+LAB12210R	90	NULL	3	2
+LAB12284R	1	NULL	3	2
+LAB12284R	2	NULL	3	2
+LAB12284R	4	NULL	3	2
+LAB12284R	30	NULL	3	2
+LAB12284R	90	NULL	3	2
+LAB12664R	1	NULL	3	2
+LAB12664R	2	NULL	3	2
+LAB12664R	4	NULL	3	2
+LAB12664R	30	NULL	3	2
+LAB12664R	90	NULL	3	2
+LAB12733R	1	NULL	3	1
+LAB12733R	2	NULL	3	1
+LAB12733R	4	NULL	3	1
+LAB12733R	30	NULL	3	1
+LAB12733R	90	NULL	3	1
+LABAACSF	1	NULL	3	0
+LABAACSF	2	NULL	3	0
+LABAACSF	4	NULL	3	0
+LABAACSF	30	NULL	3	0
+LABAACSF	90	NULL	3	0
+LABANDRO	1	NULL	3	1
+LABANDRO	2	NULL	3	1
+LABANDRO	4	NULL	3	1
+LABANDRO	30	NULL	3	1
+LABANDRO	90	NULL	3	1
+LABDVEAV3	1	NULL	3	2
+LABDVEAV3	2	NULL	3	2
+LABDVEAV3	4	NULL	3	2
+LABDVEAV3	30	NULL	3	2
+LABDVEAV3	90	NULL	3	2
+LABDVEMPV	1	NULL	3	1
+LABDVEMPV	2	NULL	3	1
+LABDVEMPV	4	NULL	3	1
+LABDVEMPV	30	NULL	3	1
+LABDVEMPV	90	NULL	3	1
+LABDVEPI1	1	NULL	3	0
+LABDVEPI1	2	NULL	3	0
+LABDVEPI1	4	NULL	3	0
+LABDVEPI1	30	NULL	3	0
+LABDVEPI1	90	NULL	3	0
+LABFCHGA	1	NULL	3	2
+LABFCHGA	2	NULL	3	2
+LABFCHGA	4	NULL	3	2
+LABYFFATQ	30	2	3	2
+LABFCHGA	30	NULL	3	2
+LABFCHGA	90	NULL	3	2
+LABHYPP	1	NULL	3	0
+LABHYPP	2	NULL	3	0
+LABHYPP	4	NULL	3	0
+LABYFLUO	30	2	3	2
+LABHYPP	30	NULL	3	0
+LABYFLUO	90	2	3	2
+LABHYPP	90	NULL	3	0
+LABMMIC	1	NULL	3	0
+LABMMIC	2	NULL	3	0
+LABMMIC	4	NULL	3	0
+LABMMIC	30	NULL	3	0
+LABMMIC	90	NULL	3	0
+LABRBCENZ	1	NULL	3	1
+LABRBCENZ	2	NULL	3	1
+LABRBCENZ	4	NULL	3	1
+LABYFPHEN	30	2	3	1
+LABRBCENZ	30	NULL	3	1
+LABYFPHEN	90	2	3	1
+LABRBCENZ	90	NULL	3	1
+LABUCARN	1	NULL	3	1
+LABUCARN	2	NULL	3	1
+LABUCARN	4	NULL	3	1
+LABUCARN	30	NULL	3	1
+LABYFRUCM	90	1	3	3
+LABUCARN	90	NULL	3	1
+LABUTHI	1	NULL	3	1
+LABUTHI	2	NULL	3	1
+LABUTHI	4	NULL	3	1
+LABYFT3	7	2	3	3
+LABYFT3	30	2	3	3
+LABUTHI	30	NULL	3	1
+LABUTHI	90	NULL	3	1
+LABYANDRO	1	NULL	3	1
+LABYANDRO	2	NULL	3	1
+LABYANDRO	4	NULL	3	1
+LABYANDRO	30	NULL	3	1
+LABYFVPA	90	3	3	2
+LABYANDRO	90	NULL	3	1
+LABYGADAB	1	0	3	2
+LABYDIAZE	1	NULL	3	0
+LABYGADAB	2	0	3	2
+LABYDIAZE	2	NULL	3	0
+LABYGADAB	4	0	3	2
+LABYDIAZE	4	NULL	3	0
+LABYGADAB	7	0	3	2
+LABYGADAB	30	0	3	2
+LABYDIAZE	30	NULL	3	0
+LABYGADAB	90	0	3	2
+LABYDIAZE	90	NULL	3	0
+LABYGAS	1	0	3	2
+LABYGAS	1	NULL	3	2
+LABYGAS	2	0	3	2
+LABYGAS	2	NULL	3	2
+LABYGAS	4	0	3	2
+LABYGAS	4	NULL	3	2
+LABYGAS	7	0	3	2
+LABYGAS	7	NULL	3	2
+LABYGAS	30	0	3	2
+LABYGAS	30	NULL	3	2
+LABYGAS	90	0	3	2
+LABYGAS	90	NULL	3	2
+LABYPRIMI	1	NULL	3	1
+LABYPRIMI	2	NULL	3	1
+LABYPRIMI	4	NULL	3	1
+LABYPRIMI	30	NULL	3	1
+LABYPRIMI	90	NULL	3	1
+LABYSERTR	1	NULL	3	1
+LABYSERTR	2	NULL	3	1
+LABYSERTR	4	NULL	3	1
+LABYSERTR	30	NULL	3	1
+LABYSERTR	90	NULL	3	1
+LABYSSBLA	1	NULL	3	2
+LABYHGBP	2	2	3	3
+LABYSSBLA	2	NULL	3	2
+LABYSSBLA	4	NULL	3	2
+LABYHGBP	30	5	3	3
+LABYSSBLA	30	NULL	3	2
+LABYHGBP	90	5	3	3
+LABYSSBLA	90	NULL	3	2
+LABYIMIPR	7	0	3	1
+LABYIMIPR	30	0	3	1
+LABYIMIPR	90	0	3	1
+LABYJCPCR	1	1	3	3
+LABYMANG	4	1	3	2
+LABYMANG	7	1	3	2
+LABYMANG	30	1	3	2
+LABYMEXIL	1	1	3	2
+LABYMEXIL	2	1	3	2
+LABYMEXIL	4	1	3	2
+LABYMPA	2	2	3	2
+LABYMPA	4	2	3	2
+LABYMPA	4	3	3	2
+LABYMPA	7	2	3	2
+LABYMPA	30	2	3	2
+LABYMPA	90	2	3	2
+LABYPENTO	30	0	3	1
+LABYPENTO	90	0	3	1
+LABYPR3	4	1	3	2
+LABYPR3	7	1	3	2
+LABYPR3	30	1	3	2
+LABYPREAL	2	3	3	2
+LABYPRIMI	1	0	3	1
+LABYPRIMI	2	0	3	1
+LABYPRIMI	4	0	3	1
+LABYPRIMI	7	0	3	1
+LABYPRIMI	7	NULL	3	1
+LABYPRIMI	30	0	3	1
+LABYPRIMI	90	0	3	1
+LABYPROIN	1	5	3	3
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+LABYPROIN	4	5	3	3
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+LABYPROIN	7	5	3	3
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+LABBMT2	90	5	2	0
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+LABCBCS	30	4	2	2
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+LABCD34	7	3	2	2
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+LABEP1	7	5	2	2
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+LABGDMSCR	90	0	2	1
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+LABGLWB	2	1	2	0
+LABGLWB	4	1	2	0
+LABGLWB	7	1	2	0
+LABGLWB	30	1	2	0
+LABGLWB	90	1	2	0
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+LABGNTPOL	30	2	2	0
+LABGNTPOL	90	2	2	0
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+LABGTOL	2	0	2	1
+LABGTOL	4	0	2	1
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+LABGTOL	90	0	2	1
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+LABHBCA	4	3	2	2
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+LABHBCT	4	3	2	2
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+LABHBODY	7	2	2	2
+LABHBODY	30	2	2	2
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+LABHCPQ	2	2	2	1
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+LABHITAB	7	5	2	2
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+LABHITPNL	7	1	2	2
+LABHIVDS	4	1	2	2
+LABHIVDS	7	1	2	2
+LABHIVDS	30	1	2	2
+LABHIVDS	90	1	2	2
+LABHIVWBL	7	4	2	2
+LABHIVWBL	30	4	2	2
+LABHIVWBL	90	4	2	2
+LABHMCY	2	2	2	2
+LABHMCY	4	2	2	2
+LABHMCY	7	2	2	2
+LABHMCY	30	2	2	2
+LABHMCY	90	2	2	2
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+LABHPVPCR	2	0	2	1
+LABHPVPCR	4	0	2	1
+LABHPVPCR	7	0	2	1
+LABHPVPCR	7	NULL	2	1
+LABHPVPCR	30	0	2	1
+LABHPVPCR	90	0	2	1
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+LABHSCRP	2	1	2	2
+LABHSCRP	4	1	2	2
+LABHSCUL	2	1	2	2
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+LABHSVM	7	1	2	2
+LABHSVZL	30	2	2	2
+LABHV6DNA	4	2	2	2
+LABHV6DNA	30	3	2	2
+LABIBD	2	1	2	2
+LABIBIL	7	5	2	2
+LABICTO	1	0	2	1
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+LABICTO	4	0	2	1
+LABICTO	7	0	2	1
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+LABICTO	30	0	2	1
+LABICTO	90	0	2	1
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+LABIGG	4	2	2	1
+LABIGG	30	4	2	1
+LABIGG	90	4	2	2
+LABIGP	30	1	2	1
+LABIGP	90	1	2	1
+LABIMM1	1	0	2	1
+LABIMM1	2	0	2	1
+LABIMM1	4	0	2	1
+LABIMM1	7	0	2	1
+LABIMM1	7	NULL	2	1
+LABIMM1	30	0	2	1
+LABIMM1	90	0	2	1
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+LABISOSR	4	1	2	1
+LABISOSR	7	1	2	1
+LABISOSR	30	1	2	1
+LABISOSR	90	1	2	1
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+LABKOHP	4	2	2	2
+LABKOHP	7	2	2	2
+LABKOHP	30	2	2	2
+LABKOHP	90	2	2	2
+LABLAMOL	4	3	2	2
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+LABLEVETL	7	3	2	2
+LABLEVETL	30	3	2	2
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+LABLIVER	4	0	2	0
+LABLIVER	7	0	2	0
+LABLIVER	7	NULL	2	0
+LABLIVER	30	0	2	0
+LABLIVER	90	0	2	0
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+LABLPD	7	4	2	2
+LABLPD	30	3	2	2
+LABLPD	30	4	2	2
+LABLPD	90	3	2	2
+LABLPD	90	4	2	2
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+LABLPDC	4	2	2	2
+LABLPDC	4	3	2	2
+LABLPDC	7	2	2	2
+LABLPDC	30	2	2	2
+LABLPDC	90	2	2	2
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+LABMALSC	2	2	2	1
+LABMALSC	4	2	2	1
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+LABMALSC	30	2	2	1
+LABMALSC	90	2	2	1
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+LABMUMRC	7	NULL	2	2
+LABMUMRC	30	0	2	2
+LABMUMRC	90	0	2	2
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+LABNEURSP	2	0	2	0
+LABNEURSP	4	0	2	0
+LABNEURSP	7	0	2	0
+LABNEURSP	30	0	2	0
+LABNEURSP	90	0	2	0
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+LABNTBNP	7	4	2	2
+LABNTBNP	30	4	2	2
+LABNTBNP	90	4	2	2
+LABOLIG	2	1	2	0
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+LABORANER	30	5	2	2
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+LABPANANC	4	0	2	0
+LABPANANC	7	0	2	0
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+LABPANANC	90	0	2	0
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+LABPANAPL	7	2	2	0
+LABPANAPL	30	2	2	0
+LABPANAPL	90	2	2	0
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+LABPCACTK	90	3	2	0
+LABPCCG4O	1	3	2	2
+LABPCCG4O	2	3	2	2
+LABPCCG4O	4	3	2	2
+LABPCCG4O	7	3	2	2
+LABPCCG4O	30	3	2	2
+LABPCCG4O	90	3	2	2
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+LABPCEG7	4	2	2	1
+LABPCEG7	7	2	2	1
+LABPCEG7	30	2	2	1
+LABPCEG7	90	2	2	1
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+LABPCEG7O	4	2	2	2
+LABPCEG7O	7	2	2	2
+LABPCEG7O	30	2	2	2
+LABPCEG7O	90	2	2	2
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+LABPCEG7V	7	3	2	1
+LABPCEG7V	30	3	2	1
+LABPCEG7V	90	3	2	1
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+LABPCG3V	7	5	2	0
+LABPCG3V	30	5	2	0
+LABPCG3V	90	5	2	0
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+LABPENALL	1	1	2	2
+LABPENALL	2	1	2	2
+LABPENALL	4	1	2	2
+LABPENALL	7	1	2	2
+LABPENALL	30	1	2	2
+LABPENALL	90	1	2	2
+LABPENTO	7	1	2	1
+LABPENTO	30	1	2	1
+LABPENTO	90	1	2	1
+LABPHEN	2	5	2	2
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+LABPLASMN	7	4	2	2
+LABPLASMN	7	5	2	2
+LABPLASMN	30	4	2	2
+LABPLASMN	90	4	2	2
+LABPLTAGG	4	1	2	2
+LABPLTAGG	7	1	2	2
+LABPLTAGG	30	1	2	2
+LABPLTAGG	90	1	2	2
+LABPORFEC	30	1	2	2
+LABPORFEC	90	1	2	2
+LABPREG	90	3	2	2
+LABPROINS	1	5	2	2
+LABPROINS	2	5	2	2
+LABPROL	1	2	2	2
+LABPROL	4	3	2	2
+LABPROL	7	3	2	2
+LABPROL	30	3	2	2
+LABPROL	90	3	2	2
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+LABPROPAF	2	0	2	1
+LABPROPAF	4	0	2	1
+LABPROPAF	7	0	2	1
+LABPROPAF	7	NULL	2	1
+LABPROPAF	30	0	2	1
+LABPROPAF	90	0	2	1
+LABPSA	90	2	2	2
+LABPTEG	30	4	2	1
+LABPTEG	90	4	2	1
+LABPTH	90	3	2	2
+LABPTH	90	4	2	0
+LABQFTB	4	2	2	1
+LABQFTB	7	2	2	1
+LABQFTB	30	2	2	1
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+LABRAAS	4	1	2	2
+LABRAAS	7	1	2	2
+LABRAAS	30	1	2	2
+LABRAAS	90	1	2	2
+LABRBCFOL	1	2	2	1
+LABRBCFOL	2	2	2	1
+LABRBP	1	0	2	0
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+LABRBP	4	0	2	0
+LABRBP	7	0	2	0
+LABRBP	7	NULL	2	0
+LABRBP	30	0	2	0
+LABRBP	90	0	2	0
+LABRESP	1	4	2	2
+LABRESP	4	5	2	2
+LABRESPCR	1	2	2	1
+LABRESPCR	2	2	2	1
+LABRESPCR	30	4	2	2
+LABRESPCR	90	5	2	2
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+LABRF	1	2	2	2
+LABRF	2	2	2	2
+LABRF	4	2	2	2
+LABRIST	4	4	2	2
+LABRITUX	2	1	2	0
+LABRITUX	4	1	2	0
+LABRITUX	7	1	2	0
+LABRPRQL	1	2	2	2
+LABRPRQL	2	2	2	2
+LABRPRQL	4	2	2	2
+LABRPRQL	7	2	2	2
+LABRPRQL	30	2	2	2
+LABRPRQL	90	2	2	2
+LABSALCOR	1	0	2	1
+LABSALCOR	2	0	2	1
+LABSALCOR	4	0	2	1
+LABSALCOR	7	0	2	1
+LABSALCOR	7	NULL	2	1
+LABSALCOR	30	0	2	1
+LABSALCOR	90	0	2	1
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+LABSALICY	2	2	2	1
+LABSALICY	4	2	2	1
+LABSALICY	7	2	2	1
+LABSALICY	30	2	2	1
+LABSALICY	90	2	2	1
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+LABSCMV	4	5	2	2
+LABSCMV	7	5	2	2
+LABSPE	1	1	2	0
+LABSPE	2	1	2	0
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+LABSTLC	2	2	2	2
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+LABSTLCP	7	1	2	2
+LABSTLCP	30	1	2	2
+LABSTLCP	90	1	2	2
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+LABSTOOLC	30	4	2	2
+LABSTRED	7	3	2	2
+LABSTRED	7	4	2	1
+LABSTRONG	1	2	2	2
+LABSTRONG	90	3	2	2
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+LABSTSS	7	4	2	2
+LABSTSS	7	5	2	2
+LABSTSS	30	3	2	2
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+LABSTSS	30	5	2	2
+LABSTSS	90	4	2	2
+LABSTSS	90	5	2	2
+LABTBILFR	1	2	2	2
+LABTBILFR	4	4	2	2
+LABTBILFR	7	5	2	2
+LABTBPCRS	1	3	2	2
+LABTBPCRS	1	4	2	2
+LABTBPCRS	2	4	2	2
+LABTBPCRS	4	4	2	2
+LABTBPCRS	7	4	2	2
+LABTBPCRS	30	4	2	2
+LABTBPCRS	90	4	2	2
+LABTCDIF	1	2	2	2
+LABTCDIF	7	4	2	2
+LABTCDIF	7	5	2	2
+LABTES	30	1	2	2
+LABTHIOMT	7	1	2	1
+LABTHIOMT	30	1	2	1
+LABTHIOMT	90	1	2	1
+LABTOBPRL	2	3	2	1
+LABTOXGM	4	1	2	1
+LABTOXGM	7	1	2	1
+LABTOXGM	30	1	2	1
+LABTOXGM	90	1	2	1
+LABTRFS	30	3	2	2
+LABTRFS	90	3	2	2
+LABTSH	7	5	2	1
+LABTSHFT4	90	2	2	2
+LABTT	30	5	2	2
+LABTT	90	5	2	2
+LABUA	2	4	2	2
+LABUALB	30	2	2	2
+LABUALB	90	2	2	2
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+LABUAPRN	7	3	2	1
+LABUAPRN	30	5	2	2
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+LABUAS	4	5	2	2
+LABUAS	7	4	2	2
+LABUAS	7	5	2	2
+LABUAS	30	4	2	2
+LABUAS	30	5	2	2
+LABUAS	90	4	2	2
+LABUAS	90	5	2	2
+LABUB2MG	4	1	2	2
+LABUB2MG	7	1	2	2
+LABUB2MG	30	1	2	2
+LABUB2MG	90	1	2	2
+LABUCL	1	1	2	2
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+LABUCORT	7	1	2	1
+LABUCORT	30	1	2	1
+LABUCORT	90	1	2	1
+LABUEOS	1	3	2	2
+LABUEP	4	3	2	2
+LABUEP	7	3	2	2
+LABUHEMO	2	1	2	1
+LABUMYOSC	2	2	2	2
+LABUMYOSC	4	1	2	2
+LABUMYOSC	4	2	2	2
+LABUMYOSC	7	1	2	2
+LABUMYOSC	7	2	2	2
+LABUMYOSC	30	1	2	2
+LABUMYOSC	30	2	2	2
+LABUMYOSC	90	1	2	2
+LABUMYOSC	90	2	2	2
+LABUNA	1	3	2	2
+LABUNA	2	4	2	2
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+LABUPHOS	7	1	2	0
+LABUPHOS	30	1	2	0
+LABUPHOS	90	1	2	0
+LABUPREG	30	2	2	2
+LABUPREG	90	3	2	2
+LABUPREG	90	5	2	2
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+LABURNC	1	4	2	2
+LABURNC	2	4	2	2
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+LABURNC	7	5	2	2
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+LABUTHI	2	0	2	1
+LABUTHI	4	0	2	1
+LABUTHI	7	0	2	1
+LABUTHI	30	0	2	1
+LABUTHI	90	0	2	1
+LABVANPRL	1	3	2	1
+LABVANPRL	2	4	2	2
+LABVCUL	1	5	2	2
+LABVCUL	2	5	2	2
+LABVCULF	4	1	2	2
+LABVCULR	90	5	2	2
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+LABVCULT	4	3	2	2
+LABVCULT	7	5	2	2
+LABVCULT	30	5	2	2
+LABVCULT	90	4	2	2
+LABVCULT	90	5	2	2
+LABVD25H	2	2	2	2
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+LABVISC	2	1	2	0
+LABVISC	4	1	2	0
+LABVOLTL	1	2	2	2
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+LABVOLTL	90	4	2	2
+LABVOLTL	90	5	2	1
+LABVP3	2	2	2	1
+LABVP3	7	2	2	2
+LABVP3	30	2	2	2
+LABVP3	90	2	2	2
+LABVP4	1	1	2	2
+LABVP4	2	1	2	2
+LABVP4	4	1	2	2
+LABVP4	7	1	2	2
+LABVP4	30	1	2	2
+LABVP4	90	1	2	2
+LABVPAL	1	5	2	2
+LABVWAG	1	1	2	2
+LABVZVCSF	4	1	2	2
+LABVZVCSF	7	1	2	2
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+LABVZVIGG	7	1	2	2
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+LABVZVQL	2	1	2	1
+LABVZVQLP	30	2	2	2
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+LABWDCS	7	4	2	2
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+LABY21HYX	1	0	2	1
+LABY21HYX	2	0	2	1
+LABY21HYX	4	0	2	1
+LABY21HYX	7	0	2	1
+LABY21HYX	7	NULL	2	1
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+LABY21HYX	90	0	2	1
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+LABY5NUC	1	0	2	1
+LABY5NUC	2	0	2	1
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+LABY5NUC	7	0	2	1
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+LABY5NUC	90	0	2	1
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+LABYALUM	2	0	2	0
+LABYALUM	4	0	2	0
+LABYALUM	7	0	2	0
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+LABYALUM	90	0	2	0
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+LABYAMISO	4	2	2	2
+LABYAMISO	7	2	2	2
+LABYAMISO	30	2	2	2
+LABYAMISO	90	2	2	2
+LABYANRNP	1	1	2	2
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+LABYANTSM	4	1	2	2
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+LABYANTSM	30	1	2	2
+LABYANTSM	90	1	2	2
+LABYAT3AG	2	1	2	1
+LABYAVH	7	1	2	2
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+LABYAVH	90	1	2	2
+LABYBOH	30	5	2	2
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+LABYCENT	2	1	2	2
+LABYCENT	4	1	2	2
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+LABYCENT	30	1	2	2
+LABYCENT	90	1	2	2
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+LABYCOPPR	30	2	2	1
+LABYCOPPR	90	2	2	1
+LABYCP	1	5	2	2
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+LABYCYANI	7	2	2	2
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+LABYCYANI	90	1	2	2
+LABYCYANI	90	2	2	2
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+LABYCYSTC	2	0	2	1
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+LABYFVPA	2	1	2	0
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+LABYHALO	1	0	2	1
+LABYHALO	2	0	2	1
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+LABYHALO	7	0	2	1
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+LABYHALO	30	0	2	1
+LABYHALO	90	0	2	1
+LABYHGBP	4	3	2	1
+LAB11089R	1	NULL	2	0
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+LAB11089R	4	NULL	2	0
+LAB11089R	30	NULL	2	0
+LAB11089R	90	NULL	2	0
+LAB11217R	1	NULL	2	0
+LAB11217R	2	NULL	2	0
+LAB11217R	4	NULL	2	0
+LAB11217R	30	NULL	2	0
+LAB11217R	90	NULL	2	0
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+LAB11572R	30	NULL	2	1
+LAB11572R	90	NULL	2	1
+LAB11590R	1	NULL	2	1
+LAB11590R	2	NULL	2	1
+LAB11590R	4	NULL	2	1
+LAB11590R	30	NULL	2	1
+LAB11590R	90	NULL	2	1
+LAB11604R	1	NULL	2	1
+LAB11604R	2	NULL	2	1
+LAB11604R	4	NULL	2	1
+LAB11604R	30	NULL	2	1
+LAB11604R	90	NULL	2	1
+LAB12003R	1	NULL	2	0
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+LAB12003R	4	NULL	2	0
+LAB12003R	30	NULL	2	0
+LAB12003R	90	NULL	2	0
+LAB12043R	1	NULL	2	0
+LAB12043R	2	NULL	2	0
+LAB12043R	4	NULL	2	0
+LAB12043R	30	NULL	2	0
+LAB12043R	90	NULL	2	0
+LAB12373R	1	NULL	2	1
+LAB12373R	2	NULL	2	1
+LAB12373R	4	NULL	2	1
+LAB12373R	30	NULL	2	1
+LAB12373R	90	NULL	2	1
+LAB12395R	1	NULL	2	0
+LAB12395R	2	NULL	2	0
+LAB12395R	4	NULL	2	0
+LAB12395R	30	NULL	2	0
+LAB12395R	90	NULL	2	0
+LAB13008R	1	NULL	2	1
+LABYLEVT	2	2	2	2
+LAB13008R	2	NULL	2	1
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+LABYLEVT	90	2	2	2
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+LABACRBLO	90	NULL	2	1
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+LABACRMOD	90	NULL	2	1
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+LABGTOL	90	NULL	2	1
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+LABHEAB	2	NULL	2	1
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+LABHEAB	90	NULL	2	1
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+LABIMM1	90	NULL	2	1
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+LABADGM	1	4	1	1
+LABADGM	2	2	1	1
+LABADGM	2	3	1	1
+LABADGM	2	4	1	1
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+LABADGM	4	3	1	1
+LABADGM	4	4	1	1
+LABADGM	7	2	1	1
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+LABADGM	7	4	1	1
+LABADGM	30	2	1	1
+LABADGM	30	3	1	1
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+LABADGM	90	2	1	1
+LABADGM	90	3	1	1
+LABADGM	90	4	1	1
+LABADPCRQ	2	2	1	1
+LABADPCRQ	4	2	1	1
+LABADPCRQ	30	4	1	1
+LABADPCRQ	90	5	1	1
+LABADVDNA	30	3	1	1
+LABADVDNA	90	3	1	1
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+LABADVQT	30	3	1	1
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+LABADVQT	90	4	1	1
+LABADVQT	90	5	1	1
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+LABALKP	7	3	1	1
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+LABAPHSAB	30	2	1	1
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+LABASPID	30	4	1	1
+LABASPID	30	5	1	1
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+LABASPID	90	2	1	1
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+LABASQ	7	4	1	1
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+LABASQ	30	4	1	1
+LABASQ	30	5	1	1
+LABASQ	90	4	1	1
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+LABAST	4	4	1	0
+LABAST	7	4	1	0
+LABAST	30	4	1	0
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+LABASTRO	90	2	1	1
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+LABATTG	4	2	1	0
+LABATTG	7	2	1	0
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+LABB12	1	3	1	1
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+LABB2GPCO	30	1	1	1
+LABB2GPCO	90	1	1	1
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+LABBACIDS	1	3	1	1
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+LABBACIDS	7	2	1	1
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+LABBACIDS	30	3	1	1
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+LABBACIDS	90	3	1	1
+LABBACIDS	90	4	1	1
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+LABBHB	1	3	1	1
+LABBHB	1	5	1	1
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+LABBHB	7	3	1	1
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+LABBHB	30	5	1	1
+LABBHB	90	3	1	1
+LABBHB	90	4	1	1
+LABBHB	90	5	1	1
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+LABBLCTIP	7	5	1	1
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+LABDPARE	7	1	1	1
+LABDPARE	30	1	1	1
+LABDPARE	90	1	1	1
+LABDSDNA	7	2	1	1
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+LABFACT10	7	2	1	1
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+LABFIBANT	90	4	1	1
+LABFIBANT	90	5	1	1
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+LABYMPA	30	3	1	1
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+LABYMPA	30	5	1	0
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+LABYSES	4	1	1	1
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+LABYSTCL	30	2	1	1
+LABYSTCL	30	3	1	1
+LABYSTCL	90	2	1	1
+LABYSTCL	90	3	1	1
+LABYSTFR	1	1	1	1
+LABYSTFR	1	2	1	1
+LAB11195R	1	NULL	1	0
+LABYSTFR	2	1	1	1
+LABYSTFR	2	2	1	1
+LAB11195R	2	NULL	1	0
+LABYSTFR	4	1	1	1
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+LAB11195R	4	NULL	1	0
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+LAB11195R	30	NULL	1	0
+LAB11195R	90	NULL	1	0
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+LABYSTOSM	7	3	1	0
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+LAB11592R	30	NULL	1	0
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+LAB11599R	1	NULL	1	0
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+LABYTAT	7	1	1	1
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+LABYTAT	90	1	1	1
+LAB12059R	90	NULL	1	0
+LAB12813R	1	NULL	1	0
+LABYTESFB	2	1	1	1
+LAB12813R	2	NULL	1	0
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+LAB12813R	4	NULL	1	0
+LABYTESFB	7	1	1	1
+LABYTESFB	30	1	1	1
+LAB12813R	30	NULL	1	0
+LAB12813R	90	NULL	1	0
+LAB13010R	1	NULL	1	0
+LAB13010R	2	NULL	1	0
+LABYTESFT	4	2	1	0
+LAB13010R	4	NULL	1	0
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+LAB13010R	30	NULL	1	0
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+LAB13010R	90	NULL	1	0
+LABYTHI	1	2	1	1
+LABCLAMP	1	NULL	1	0
+LABYTHI	2	2	1	1
+LABCLAMP	2	NULL	1	0
+LABYTHI	4	2	1	1
+LABCLAMP	4	NULL	1	0
+LABYTHI	7	2	1	1
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+LABCLOMIP	1	NULL	1	0
+LABCLOMIP	2	NULL	1	0
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+LABCLOMIP	4	NULL	1	0
+LABYTOPIR	7	3	1	1
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+LABCLOMIP	30	NULL	1	0
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+LABHPVHRT	30	NULL	1	0
+LABYTRYPT	90	2	1	1
+LABHPVHRT	90	NULL	1	0
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+LABLEGS	1	NULL	1	0
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+LABMPVPCR	1	NULL	1	1
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+LABYVITB6	1	1	1	1
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+LABYVITB6	2	1	1	1
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+LABYPA	4	NULL	1	0
+LABYPA	30	NULL	1	0
+LABYPA	90	NULL	1	0
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+LABYPTSAB	1	NULL	1	0
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+LABYVITE	4	0	1	0
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+LABYVITE	7	0	1	0
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+LABYVITE	30	0	1	0
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+LABYVITE	90	0	1	0
+LABYPTSAB	90	NULL	1	0
+LABYVIP	1	NULL	1	0
+LABYVIP	2	NULL	1	0
+LABYVIP	4	NULL	1	0
+LABYVIP	30	NULL	1	0
+LABYVIP	90	NULL	1	0
+LABYVITE	1	NULL	1	0
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+LABYVITE	4	NULL	1	0
+LABYVITE	30	NULL	1	0
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diff --git a/scripts/DevWorkshop/sandbox/TestFile.txt b/scripts/DevWorkshop/sandbox/TestFile.txt
new file mode 100644
index 00000000..2af60b72
--- /dev/null
+++ b/scripts/DevWorkshop/sandbox/TestFile.txt
@@ -0,0 +1,54 @@
+Working file to test
+Sandbox folder to test out different functions, for example Git version control commands.
+https://twitter.com/MJAlwajeeh/status/984177298076262401?s=20
+
+# Get a copy/clone of the code repository to work with
+	git clone https://github.com/HealthRex/CDSS.git
+
+# Identify yourself to the GitHub repository
+	git config --global user.email "yourGitHubEmail"
+	git config --global user.name "yourGitHubUserName"
+
+# Try creating a file in the sandbox directory
+	cd CDSS/scripts/DevWorkshop/sandbox
+	vi yourTestFile.txt
+
+	    Quick vi commands
+	    - x: Delete a character
+	    - i: Change to "insert" (i.e., edit mode)
+	    - Escape: Exit out of edit mode
+	    - ":wq": Change to "colon-command" mode, then (w)rite your changes to the file, then (q)uit the editor
+	    - ":q!": Change to "colon-command" mode, (q)uit the editor, WITHOUT saving changes (!)
+
+# Tell Git that you want this file to be tracked
+	git add yourTextFile.txt
+
+# Save the version of the file to your LOCAL repository
+	git commit yourTextFile.txt
+		# It will ask for a commit message. Add a 1 or 2 liner comment describing the major changes you made with this commit
+
+# Save your local repository changes to the master online repository
+	git push
+		# It may ask for your GitHub login info. You can later configure ways to automatically do this
+		# It may complain about your local repository not being up to date if others have already pushed their own changes.
+		# If so, then first update your own local copy...
+
+# Update your local working code with whatever has been pushed to the master online repository
+	git pull
+
+
+# Try making further edits to one of the files (including ones created by others), and manually correcting any merged file conflicts
+	vi yourTestFile.txt
+
+# Review what changes have been made to the file since it was last commit/pushed
+	git diff yourTestFile.txt
+
+# Commit changes to local repository with comment message at the same time
+	git commit -m "Test revision message" yourTestFile.txt
+
+# Push your changes up to the server again
+	git push
+
+# Check a log of all the changes made to the file
+	git log yourTestFile.txt
+	
diff --git a/scripts/DevWorkshop/sandbox/bootstrapTest.py b/scripts/DevWorkshop/sandbox/bootstrapTest.py
new file mode 100644
index 00000000..2ee9ca90
--- /dev/null
+++ b/scripts/DevWorkshop/sandbox/bootstrapTest.py
@@ -0,0 +1,44 @@
+import numpy as np;
+from medinfo.common.Util import ProgressDots;
+
+def calcStat(data):
+	rate = float(sum(data)) / len(data);
+	return rate;
+
+
+def main(nResamples = 1000, sampleSize = 200, positiveCount = 180):
+	
+
+	
+	
+	confidenceInterval = 0.95
+
+	sample = list()
+	for i in range(sampleSize):
+		value = 0;
+		if i < positiveCount:
+			value = 1;
+		sample.append(value);
+	sample = np.array(sample);
+
+	print("Sample Rate: ",calcStat(sample) );
+
+	rng = np.random.RandomState(nResamples);	# Seed with number for consistency
+	bootstrapValues = list();
+
+	prog = ProgressDots();
+	for i in range(nResamples):
+		indices = rng.random_integers(0, sampleSize-1, sampleSize);	# Get indices of resample items (with replacement)
+		resample = sample[indices];
+		bootstrapValues.append( calcStat(resample) );
+		prog.update();
+
+	ciFractionLow = (1.0-confidenceInterval) / 2;
+	ciFractionHigh= confidenceInterval + ciFractionLow;
+
+	bootstrapValues.sort();
+	print("Sample Rate 95%CI Low", bootstrapValues[int(ciFractionLow*nResamples)] )
+	print("Sample Rate 95%CI High", bootstrapValues[int(ciFractionHigh*nResamples)] )
+
+if __name__ == "__main__":
+	main();
diff --git a/scripts/DevWorkshop/sandbox/jonc101.testfile.txt b/scripts/DevWorkshop/sandbox/jonc101.testfile.txt
new file mode 100644
index 00000000..6be1023c
--- /dev/null
+++ b/scripts/DevWorkshop/sandbox/jonc101.testfile.txt
@@ -0,0 +1,3 @@
+Test file contents
+Extra edits after commit
+
diff --git a/scripts/DevWorkshop/screening/associationStats/AssociationStatsExample.py b/scripts/DevWorkshop/screening/associationStats/AssociationStatsExample.py
new file mode 100644
index 00000000..d7d8a403
--- /dev/null
+++ b/scripts/DevWorkshop/screening/associationStats/AssociationStatsExample.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+
+"""
+Download the detailed Medicare Part D Drug Prescription data file from the page below
+https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/PartD2015.html
+
+    Part D Prescriber PUF NPI Drug, CY2015, (Tab Delimited Format) [552MB]
+    http://download.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/PartD_Prescriber_PUF_NPI_DRUG_15.zip
+
+The file represents a large data file tracking individual doctors by "npi" (ID number), 
+along with the drugs they prescribed, identified by "generic_name"
+and total prescriptions by "total_claim_count".
+
+Design and implement application code in this class to
+Scan through the input data file of who prescribed which drugs,
+and determine which other "drug B's" are also prescribed
+for those who prescribe drug A.
+
+Return a list of 2-ples with non-zero values below, sorted in descending order
+    - Number of prescribers prescribing drugs A and B ("Support")
+    - Generic name of drug B
+
+HINT:
+- Data assumed to already be sorted in order by prescriber npi unique identifier
+- zipfile package facilitates reading from ZIP files
+- csv.DictReader can facilitate reading over tab-delimited file
+
+
+Create a respective unit test script/program that verifies correct functionality of your program.
+Complete the code implementation below such that results can be calculated from the command-line. For example, running
+
+    python AssociationStatsExample.py PartD_Prescriber_PUF_NPI_DRUG_15.zip AZITHROMYCIN
+
+Should result in output similar to...
+
+    (180431, 'AZITHROMYCIN')
+    (132454, 'OMEPRAZOLE')
+    (129860, 'LISINOPRIL')
+    (128944, 'AMLODIPINE BESYLATE')
+    (128887, 'LEVOTHYROXINE SODIUM')
+    (128786, 'PREDNISONE')
+    (126266, 'ALBUTEROL SULFATE')
+    (126127, 'ATORVASTATIN CALCIUM')
+    (124496, 'FUROSEMIDE')
+    (123981, 'SIMVASTATIN')
+    (123915, 'METFORMIN HCL')
+    (123578, 'FLUTICASONE PROPIONATE')
+    (120314, 'HYDROCHLOROTHIAZIDE')
+    ...
+
+"""
+import sys, os;
+import zipfile;
+import csv;
+
+class AssociationStatsExample:
+    """Application module with example functions to implement and test.
+    """
+    def __init__(self):
+        """Initialization constructor"""
+        pass;
+        
+    ###################### START CODE HERE ########################
+    ###################### START CODE HERE ########################
+    ###################### START CODE HERE ########################
+    ###################### START CODE HERE ########################
+    
+    ###################### END CODE HERE ########################
+    ###################### END CODE HERE ########################
+    ###################### END CODE HERE ########################
+    ###################### END CODE HERE ########################
+
+if __name__ == "__main__":
+    # Command-line execution should start here and attempt to run the primary function and print results to the console
+    zipFilename = sys.argv[1];
+    drugA = sys.argv[2];
+    
+    instance = AssociationStatsExample();
+    for (support, drugB) in #????:
+        print (support, drugB);
diff --git a/scripts/DevWorkshop/screening/associationStats/PartD_Prescriber_PUF_NPI_DRUG_15.sample.zip b/scripts/DevWorkshop/screening/associationStats/PartD_Prescriber_PUF_NPI_DRUG_15.sample.zip
new file mode 100644
index 00000000..a4735de2
Binary files /dev/null and b/scripts/DevWorkshop/screening/associationStats/PartD_Prescriber_PUF_NPI_DRUG_15.sample.zip differ
diff --git a/scripts/DevWorkshop/screening/associationStats/TestAssociationStatsExample.py b/scripts/DevWorkshop/screening/associationStats/TestAssociationStatsExample.py
new file mode 100644
index 00000000..e1d492e7
--- /dev/null
+++ b/scripts/DevWorkshop/screening/associationStats/TestAssociationStatsExample.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package.
+Fill in test functions such that this can be run from command-line:
+
+    python TestAssociationStatsExample.py
+
+To verify whether the core counting functionality in the respective application code works appropriately.
+"""
+
+import sys, os
+from io import StringIO
+from datetime import datetime;
+import unittest
+import zipfile, gzip;
+
+from .AssociationStatsExample import AssociationStatsExample;
+
+INPUT_FILENAME_ZIP = "PartD_Prescriber_PUF_NPI_DRUG_15.zip";
+INPUT_SAMPLE_FILENAME_ZIP = "PartD_Prescriber_PUF_NPI_DRUG_15.sample.zip";  # Consider using smaller sample file to avoid excessively long test
+TOP_RESULTS = 10;   # How many of the top results to verify
+
+class TestAssociationStatsExample(unittest.TestCase):
+    def setUp(self):
+        """Prepare state for test cases. """
+        self.app = AssociationStatsExample();
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        pass;
+
+    def test_drugAssociationStats(self):
+        """Verify association statistics counts from an example data table"""
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        pass;
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    suite.addTest(unittest.makeSuite(TestAssociationStatsExample));
+    
+    return suite;
+    
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=2).run(suite())
diff --git a/scripts/DevWorkshop/screening/parsing/ParsingExample.py b/scripts/DevWorkshop/screening/parsing/ParsingExample.py
new file mode 100644
index 00000000..cf0a3ce7
--- /dev/null
+++ b/scripts/DevWorkshop/screening/parsing/ParsingExample.py
@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+
+import sys, os
+import time;
+import re;
+from datetime import datetime;
+
+class ParsingExample:
+    """Application module with several functions to implement and test.
+    """
+    def parseLogicStr(self, logicStr):
+        """Parse logic string into computable structured format
+        Examples
+        (SEX)&(AGE)&FI(1)&FF(1)&(FI(5)!FI(6)!FI(7))&FF(2)
+        (SEX)&(AGE)&FI(2)&'FI(3)
+        Assumed interpretation:
+          ' represents NOT
+          ! represents OR
+          & represents AND
+
+
+		Parse it into a computable stack/list of descriptive components, similar to below:
+
+        testStr = "(SEX)&(AGE)&FI(2)&'FI(3)";
+        expectedLogicStack = \
+            [   {"negate": False, "type": "SEX", "index": None },
+                "&",
+                {"negate": False, "type": "AGE", "index": None },
+                "&",
+                {"negate": False, "type": "FI", "index": 2 },
+                "&",
+                {"negate": True, "type": "FI", "index": 3 },
+            ];
+
+
+        """
+        logicStack = list();  # Store components as a stack (list) of items and operators
+
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+
+
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+
+        return logicStack;
diff --git a/scripts/DevWorkshop/screening/parsing/TestParsingExample.py b/scripts/DevWorkshop/screening/parsing/TestParsingExample.py
new file mode 100644
index 00000000..8298f7b7
--- /dev/null
+++ b/scripts/DevWorkshop/screening/parsing/TestParsingExample.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+from io import StringIO
+from datetime import datetime;
+import unittest
+import sqlite3;
+
+from .ParsingExample import ParsingExample;
+
+class TestParsingExample(unittest.TestCase):
+    def setUp(self):
+        # Application instance to test on
+        self.app = ParsingExample();
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        pass;
+
+
+    def test_parseLogicStr(self):
+        """Parsing of logic string into stack of items and operators"""
+        testStr = "(SEX)&(AGE)&FI(1)&FI(2)";
+        expectedLogicStack = \
+            [   {"negate": False, "type": "SEX", "index": None },
+                "&",
+                {"negate": False, "type": "AGE", "index": None },
+                "&",
+                {"negate": False, "type": "FI", "index": 1 },
+                "&",
+                {"negate": False, "type": "FI", "index": 2 },
+            ];
+        logicStack = self.app.parseLogicStr( testStr );
+        self.assertEqual(expectedLogicStack, logicStack);
+
+
+        testStr = "(SEX)&(AGE)&FI(2)&'FI(3)";
+        expectedLogicStack = \
+            [   {"negate": False, "type": "SEX", "index": None },
+                "&",
+                {"negate": False, "type": "AGE", "index": None },
+                "&",
+                {"negate": False, "type": "FI", "index": 2 },
+                "&",
+                {"negate": True, "type": "FI", "index": 3 },
+            ];
+        logicStack = self.app.parseLogicStr( testStr );
+        self.assertEqual(expectedLogicStack, logicStack);
+
+
+        testStr = "FI(1)&FF(1)&(FI(5)!FI(6)!FI(7))&FF(2)";
+        expectedLogicStack = \
+            [   {"negate": False, "type": "FI", "index": 1 },
+                "&",
+                {"negate": False, "type": "FF", "index": 1 },
+                "&",
+                "(",
+                    {"negate": False, "type": "FI", "index": 5 },
+                    "!",
+                    {"negate": False, "type": "FI", "index": 6 },
+                    "!",
+                    {"negate": False, "type": "FI", "index": 7 },
+                ")",
+                "&",
+                {"negate": False, "type": "FF", "index": 2 },
+            ];
+        logicStack = self.app.parseLogicStr( testStr );
+        self.assertEqual(expectedLogicStack, logicStack);
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    suite.addTest(unittest.makeSuite(TestParsingExample));
+    
+    return suite;
+    
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=2).run(suite())
diff --git a/scripts/DevWorkshop/screening/sqlQuery/SQLQueryExample.py b/scripts/DevWorkshop/screening/sqlQuery/SQLQueryExample.py
new file mode 100644
index 00000000..46956f20
--- /dev/null
+++ b/scripts/DevWorkshop/screening/sqlQuery/SQLQueryExample.py
@@ -0,0 +1,240 @@
+#!/usr/bin/env python
+
+# Placeholder character for dynamic SQL queries
+SQL_PLACEHOLDER = "?";  
+
+class SQLQueryExample:
+    """Application module with example function to implement and test.
+    """
+    conn = None; # Database connection to read from
+    
+    def __init__(self, conn):
+        """Initialization constructor to specify database connection source"""
+        self.conn = conn;
+
+    def queryTrackCounts(self, options):
+        """Query sample Chinook database for track counts for artists based on different criteria
+        specified in the options.
+        
+        More information and data schema for Chinook sample database: 
+          http://www.sqlitetutorial.net/sqlite-sample-database/
+        
+        Return columns should be the following:
+        - ArtistId
+        - Name (artist)
+        - Number of Albums for the Artist
+        - Number of Tracks within those Albums
+
+        Query options is a dictionary with key-values including:
+        - artistPrefix: Only retrieve artists whose name starts with this prefix string
+        - invoiceDateStart: Count only tracks which were sold (have a dated invoice) on or after this date
+        - invoiceDateEnd: Count only tracks which were sold (have a dated invoice) before this date
+        - sortField: Sort criteria (name of column to sort by, optionally followed by "desc" for descending order)
+        """
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+        ###################### START CODE HERE ########################
+
+        # queryStr = ???
+        # queryParams = ???
+
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+        ###################### END CODE HERE ########################
+
+        cursor = self.conn.cursor();
+        cursor.execute(queryStr, queryParams);
+        results = cursor.fetchall();
+        cursor.close();
+
+        return results;
+
+class SQLQuery:
+    """Helper class to dynamically build a SQL statement.
+    Unlike just appending to a string, allows for addition
+    of select, from, where, etc. clauses in any order,
+    then generate them in normal order at the end.
+
+    When ready to use, simply convert to a string with placeholders and pass the list of respective parameters
+    For example:
+        query = SQLQuery();
+        query.addSelect("sampleColumn");
+        query.addFrom("sampleTable");
+        query.addWhereOp("sampleColumn",">", 1000);
+
+        conn = <<<DatabaseConnection>>>;    # Environment specific database connection
+        cursor = conn.cursor();
+        cursor.execute( str(query), query.getParams() );
+    """
+    def __init__(self, sqlPlaceholder=SQL_PLACEHOLDER):
+        """Allow specification of specific SQL_PLACEHOLDER character. (Different modules use ? vs. %s, etc.)
+        """
+        self.prefix = None;
+        self.delete = False;    # If set, will ignore the select list and make a delete query instead
+        self.select = [];
+        self.into   = None;
+        self.from_  = [];
+        self.where  = [];
+        self.groupBy= [];
+        self.having = [];
+        self.orderBy= [];
+        self.limit  = -1;
+        self.offset = -1;
+        self.params = [];
+        self.sqlPlaceholder = sqlPlaceholder;
+
+    def setPrefix(self,aPrefix):
+        self.prefix = aPrefix;
+
+    def addSelect(self,aSelect):
+        self.select.append(aSelect)
+    
+    def setInto(self,aInto):
+        self.into = aInto;
+
+    def addFrom(self,aFrom):
+        self.from_.append(aFrom)
+    
+    def addJoin(self, table, criteria, joinType="INNER"):
+        self.from_.append(None);    # Sentinel value to reflect specific join phrase instead of usual comma-separated FROM clause
+        self.from_.append("%s JOIN %s\nON %s" % (joinType, table, criteria) );
+
+    def addWhere(self,aWhere):
+        self.where.append(aWhere);
+    
+    def addWhereEqual(self,field,param):
+        self.where.append("%s = %s" % (field,self.sqlPlaceholder));
+        self.params.append(param);
+
+    def addWhereNotEqual(self,field,param):
+        self.where.append("%s <> %s" % (field,self.sqlPlaceholder));
+        self.params.append(param);
+
+    def addWhereLike(self,field,param):
+        self.where.append("%s like %s" % (field,self.sqlPlaceholder));
+        self.params.append(param);
+
+    def addWhereOp(self,field,op,param):
+        self.where.append("%s %s %s" % (field,op,self.sqlPlaceholder));
+        self.params.append(param);
+
+    def addWhereIn(self,field,paramList):
+        placeholders = generatePlaceholders(len(paramList))
+        self.where.append("%s IN (%s)" % (field,placeholders));
+        self.params.extend(paramList);
+
+    def addWhereNotIn(self,field,paramList):
+        placeholders = generatePlaceholders(len(paramList))
+        self.where.append("%s NOT IN (%s)" % (field,placeholders));
+        self.params.extend(paramList);
+
+    def openWhereOrClause(self):
+        """Signal switch from 'and' conjunctions to 'or', which also requires new paranthetical level"""
+        self.where.append("(");
+    
+    def closeWhereOrClause(self):
+        self.where.append(")");
+    
+    def addGroupBy(self,aGroupBy):
+        self.groupBy.append(aGroupBy)
+
+    def addHaving(self,aHaving):
+        self.having.append(aHaving)
+
+    def addOrderBy(self,aOrderBy,dir=None):
+        if dir != None:
+            aOrderBy = "%s %s" % (aOrderBy, dir);
+        self.orderBy.append(aOrderBy)
+
+    def setLimit(self,limit):
+        self.limit = limit
+    
+    def setOffset(self,offset):
+        self.offset = offset
+    
+    def addParam(self,aParam):
+        self.params.append(aParam);
+
+    def getParams(self):
+        return self.params;
+    
+    def __str__(self):
+        query = []  # Build list of query components, then join into a string at the end
+        
+        if self.prefix is not None:
+            query.append(self.prefix);
+            query.append("\n");
+
+        if self.delete:
+            query.append("DELETE");
+        else:
+            query.append("SELECT");
+            for item in self.select:
+                query.append(item);
+                query.append(",");
+            query.pop(); # Remove the last extraneous comma (",")
+        
+        if self.into is not None:
+            query.append("\nINTO");
+            query.append(self.into);
+
+        query.append("\nFROM");
+        for item in self.from_:
+            if item is None:
+                # Sentinel value indicating the next FROM clause will be an explictly stated join clause, so discard/overwrite the last comma
+                query[-1] = "\n";
+            else:
+                query.append(item);
+                query.append(",");
+        query.pop()
+
+        if self.where:
+            query.append("\nWHERE");
+            conjunction = "\nAND";
+            for item in self.where:
+                if item == "(": # Just opened up a paranthetical section
+                    query.append(item)
+                    conjunction = "\nOR"; # Convert to "or" conjunctions (wouldn't be a point in parantheticals if all "and" conjunctions)
+                elif item == ")":   # Closing a paranthetical section
+                    query.pop();    # Discard last "or" conjunction
+                    query.append(item)
+                    conjunction = "\nAND";    # Revert to default 'and' conjunctions
+                    query.append(conjunction);
+                else: 
+                    query.append(item)
+                    query.append(conjunction)
+            query.pop()
+
+        if self.groupBy:
+            query.append("\nGROUP BY")
+            for item in self.groupBy:
+                query.append(item)
+                query.append(",")
+            query.pop()
+
+        if self.having:
+            query.append("\nHAVING")
+            for item in self.having:
+                query.append(item)
+                query.append("\nAND")
+            query.pop()
+
+        if self.orderBy:
+            query.append("\nORDER BY")
+            for item in self.orderBy:
+                query.append(item)
+                query.append(",")
+            query.pop()
+                
+        if self.limit is not None and self.limit > -1:
+            query.append("\nLIMIT")
+            query.append(str(self.limit))
+        if self.offset is not None and self.offset > -1:
+            query.append("OFFSET")
+            query.append(str(self.offset))
+        
+        query.append(";");  # Closing semi-colon
+        query = str.join(" ",query) # Join list items into a single string, space-separated
+        return query
diff --git a/scripts/DevWorkshop/screening/sqlQuery/TestSQLQueryExample.py b/scripts/DevWorkshop/screening/sqlQuery/TestSQLQueryExample.py
new file mode 100644
index 00000000..783b38ca
--- /dev/null
+++ b/scripts/DevWorkshop/screening/sqlQuery/TestSQLQueryExample.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+"""Test case for respective module in application package"""
+
+import sys, os
+from io import StringIO
+from datetime import datetime;
+import unittest
+import sqlite3;
+
+from .SQLQueryExample import SQLQueryExample;
+
+# Sample SQL database contained in single sqlite file.
+# More information and data schema: 
+#   http://www.sqlitetutorial.net/sqlite-sample-database/
+TEMP_DATABASE_FILENAME = "chinook.db"
+
+class TestSQLQueryExample(unittest.TestCase):
+    def setUp(self):
+        """Prepare state for test cases"""
+        # Create temp (SQLite) database file to work with
+        self.conn = sqlite3.connect(TEMP_DATABASE_FILENAME);
+        
+        # Application instance to test on
+        self.app = SQLQueryExample(self.conn);
+
+    def tearDown(self):
+        """Restore state from any setUp or test steps"""
+        # Close DB connection
+        self.conn.close();
+
+    def test_sqlQueries(self):
+        """Dynamic SQL queries and expected results based on different parameters"""
+        options = {"artistPrefix":"S", "invoiceDateStart":None, "invoiceDateEnd":None, "sortField":"artists.name"}
+        expectedResults = \
+            [
+                (59,"Santana",3,27),
+                (213,"Scholars Baroque Ensemble",1,1),
+                (179,"Scorpions",1,12),
+                (232,"Sergei Prokofiev & Yuri Temirkanov",1,1),
+                (221,"Sir Georg Solti & Wiener Philharmoniker",1,1),
+                (249,"Sir Georg Solti, Sumi Jo & Wiener Philharmoniker",1,1),
+                (130,"Skank",2,23),
+                (131,"Smashing Pumpkins",2,34),
+                (132,"Soundgarden",1,17),
+                (53,"Spyro Gyra",2,21),
+                (133,"Stevie Ray Vaughan & Double Trouble",1,10),
+                (134,"Stone Temple Pilots",1,12),
+                (135,"System Of A Down",1,11),
+            ];
+        actualResults = self.app.queryTrackCounts(options);
+        self.assertEqual(expectedResults, actualResults);
+
+        options = {"artistPrefix":"The", "invoiceDateStart": datetime(2012,1,1), "invoiceDateEnd":datetime(2013,1,1), "sortField":"artists.name"}
+        expectedResults = \
+            [
+                (137,"The Black Crowes",2,4),
+                (138,"The Clash",1,2),
+                (139,"The Cult",2,3),
+                (140,"The Doors",1,1),
+                (156,"The Office",3,13),
+                (141,"The Police",1,2),
+                (200,"The Posies",1,1),
+                (142,"The Rolling Stones",3,5),
+                (143,"The Tea Party",2,2),
+                (144,"The Who",1,9),
+            ];
+        actualResults = self.app.queryTrackCounts(options);
+        self.assertEqual(expectedResults, actualResults);
+
+def suite():
+    """Returns the suite of tests to run for this test class / module.
+    Use unittest.makeSuite methods which simply extracts all of the
+    methods for the given class whose name starts with "test"
+    """
+    suite = unittest.TestSuite();
+    suite.addTest(unittest.makeSuite(TestSQLQueryExample));
+    
+    return suite;
+    
+if __name__=="__main__":
+    unittest.TextTestRunner(verbosity=2).run(suite())
diff --git a/scripts/DevWorkshop/screening/sqlQuery/chinook.db b/scripts/DevWorkshop/screening/sqlQuery/chinook.db
new file mode 100644
index 00000000..38a98b39
Binary files /dev/null and b/scripts/DevWorkshop/screening/sqlQuery/chinook.db differ
diff --git a/scripts/DevWorkshop/statsSimCalc/powerCalculation.R b/scripts/DevWorkshop/statsSimCalc/powerCalculation.R
new file mode 100644
index 00000000..87dc1cb6
--- /dev/null
+++ b/scripts/DevWorkshop/statsSimCalc/powerCalculation.R
@@ -0,0 +1,178 @@
+library(dplyr)  # Will use dplyr package for convenient data table manipulation. If not already, then run: install.packages("dplyr")
+library(reshape2) # Helps for reshaping Pivot Table data. Likely already included as a dplyr dependency. Otherwise: install.packages("reshape2")
+
+# Manually prepare a simulated data frame with one patient/observation per row
+# A binary treatment indicator (e.g., medication) and a resulting binary outcome (e.g., death within 30 days)
+patientId = c(1,2,3,4,5,6,7,8,9,10)
+treated   = c(0,0,0,0,0,1,1,1,1,1)
+outcome   = c(0,0,0,1,1,0,1,1,1,1)
+dfManual = data.frame(patientId, treated, outcome)
+
+# After sourcing this file, try inspecting the contents of the dataframes and doing some basic calculations
+# https://www.datacamp.com/community/tutorials/contingency-tables-r
+# > source("/<filePath>/powerCalculation.R")    # Specify the full path where you stored and run the contents in your console
+# > head(dfManual)  # Look at the top several rows of the dataframe
+# > table(dfManual$treated, dfManual$outcome) # Prepare a 2x2 contingency table out of the treated and outcome columns
+# > chisq.test(dfManual$treated, dfManual$outcome)  # Calculate Chi-square test to assess for independence between the treated and outcome columns (Note the warning that Chi-square is an approximation that may not be correct for small datasets with cell counts <5)
+# > fisher.test(dfManual$treated, dfManual$outcome) # Calculate Fisher exact test to assess for independence between the treated and outcome columns
+# Example to illustrate observed vs. expected cell counts if assume independence between treated and outcome
+# > result = chisq.test(dfManual$treated, dfManual$outcome)
+# > result$observed
+# > result$expected
+
+
+########## For the manually constructed treatment vs. outcome data above, what is p-value for for whether the treatment affects the outcome?
+########## Why does the above not have one answer? (What are the tradeoffs between a Chi-square vs. Fisher exact test?)
+
+
+# Define a function to generate simulated data for binary treatment and outcomes
+# - nPatients: Number of patient rows to generate
+# - nTreated: Number of patient rows to consider treated (rest will be considered untreated)
+# - probOutcomeUntreated: Probability that a patient will have the outcome if they were not treated
+# - probOutcomeTreated: Probability that a patient will have the outcome if they were treated (these parameters are generally unknown in real life, and is what we're trying to estimate in research)
+simulateBinaryTreatmentOutcome = function(nPatients, nTreated, probOutcomeUntreated, probOutcomeTreated)
+{
+  nUntreated = nPatients - nTreated
+
+  patientId = 1:nPatients # Generate a sequence of ID values from 1,2,3,...,nPatients
+  
+  treated = c( rep(0,nUntreated), rep(1,nTreated) ) # Generate repeating sequences of 0,0,0,...,0,1,1,1,...1 to reflect which patients received the treatment
+  
+  untreatedOutcomes = rbinom(nUntreated, 1, probOutcomeUntreated) # Randomly generate binary (binomial of size=1) outcome results for the untreated group
+  treatedOutcomes   = rbinom(nTreated,   1, probOutcomeTreated)
+  outcome = c( untreatedOutcomes, treatedOutcomes )
+  
+  dfSim = data.frame(patientId, treated, outcome)
+  return(dfSim)
+}
+
+# Programmatically generate simulated data for a progressively larger numbers of cases
+# Try repeating some of the inspection and test calculations on this dataframe as in the prior manual example
+dfSample10  = simulateBinaryTreatmentOutcome(nPatients= 10, nTreated= 5, probOutcomeUntreated=0.4, probOutcomeTreated=0.2)
+dfSample40  = simulateBinaryTreatmentOutcome(nPatients= 40, nTreated=20, probOutcomeUntreated=0.4, probOutcomeTreated=0.2)
+dfSample100 = simulateBinaryTreatmentOutcome(nPatients=100, nTreated=50, probOutcomeUntreated=0.4, probOutcomeTreated=0.2)
+
+########## How does the p-value for a treatment effect hypothesis change for the above samples of increasing sample size?
+
+
+
+
+# Define a function to run a batch of simulations (like running the same randomized controlled trial over and over again)
+# and collect the Fisher exact test p.values and estimated odds ratio effect estimates across all trials.
+# This will then allow us to empirically estimate how often (how likely) a simultation/trial will 
+# yield a "significant" result with p.value < 0.05 (alpha value) and the range of effect estimates (odds ratio)
+# - nSims: Number of simulations to run
+# - simParams: List of the parameters needed for each simulation (e.g, simParams$nPatients, simParams$nTreated, ...)
+#
+#   Beware that if you simulate a small number of patients and small probability of outcomes, it's possible
+#   that some simulations will result in nobody in a group having the outcome at all, at which point
+#   the pValue and oddsRatio calculations will fail since they don't make sense (divide by zero), yielding the cryptic error message:
+#     Error in fisher.test(dfSample$treated, dfSample$outcome) : 
+#     'x' and 'y' must have at least 2 levels 
+batchSimulateBinaryTreatmentOutcome = function(nSims, simParams)
+{
+  pValues = vector()
+  oddsRatios = vector()
+  for( i in 1:nSims )
+  {
+    dfSample = simulateBinaryTreatmentOutcome(simParams$nPatients, simParams$nTreated, simParams$probOutcomeUntreated, simParams$probOutcomeTreated)
+    fisherResults = fisher.test(dfSample$treated, dfSample$outcome)
+    pValues = append(pValues, fisherResults$p.value) # This is probably an inefficient repeated vector copy
+    oddsRatios = append(oddsRatios, fisherResults$estimate)
+  }
+  batchSimResults = list("pValues"=pValues, "oddsRatios"=oddsRatios)
+}
+
+
+# Type I error rate (alpha) estimation
+# Run a batch of sims to empirically estimate Type I error rate:
+# How often a "significant" difference is detected when there is none (i.e., probOutcomeUntreated = probOutcomeTreated))
+# In theory, the type1ErroRate should equal the pre-specified alpha, since that is the definition
+nSims = 1000
+simParams = list("nPatients"=40, "nTreated"=20, "probOutcomeUntreated"=0.4, "probOutcomeTreated"=0.4)
+alpha = 0.05  # P-value threshold at which to consider a difference to be "statistically significant" or not
+batchSimResults = batchSimulateBinaryTreatmentOutcome(nSims, simParams)
+type1Errors = (batchSimResults$pValues < alpha) # Since we know there is no (only random) difference between treated and untreated outcome rates, it is a type 1 error if the p-value is less than the alpha significance threshold. Track how often this happens
+type1ErrorRate = mean(type1Errors) # By interpreting the individual errors as binary (0,1) values, the mean value can be interpreted as a percentage rate
+nullOddsRatio95CI = quantile( batchSimResults$oddsRatios, c(0.025,0.975) ) # Empirically estimated 95% confidence interval for odds ratio. In null hypothesis case, the true odds ratio should = 1
+
+# Type II error rate (beta) estimation
+# Run a batch of sims for an example set of parameters to empirically estimate Type II error rate (how often "no difference" is concluded when there actually is one)
+nSims = 1000
+simParams = list("nPatients"=40, "nTreated"=20, "probOutcomeUntreated"=0.4, "probOutcomeTreated"=0.2)
+alpha = 0.05  # P-value threshold at which to consider a difference to be "statistically significant" or not
+batchSimResults = batchSimulateBinaryTreatmentOutcome(nSims, simParams)
+type2Errors = (batchSimResults$pValues >= alpha) # Since we know there is a difference between treated and untreated outcome rates, it is a type 2 error if the p-value is not less than the alpha significance threshold. Track how often this happens
+type2ErrorRate = mean(type2Errors) # By interpreting the individual errors as binary (0,1) values, the mean value can be interpreted as a percentage rate
+nonNullOddsRatio95CI = quantile( batchSimResults$oddsRatios, c(0.025,0.975) ) # Empirically estimated 95% confidence interval for odds ratio
+
+####### For the two sets of simulations above, how would you estimate the alpha, beta, and Power values of the simulated trials?
+####### What 95% confidence interval range of the odds ratio estimates in the above simulations would be considered "significant?" Would you expect that here?
+
+
+
+# Define a function to iterate through combinations of simulation parameters to 
+# batch simulate scenarios and collect the empirically estimated pValues significance rates and oddsRatio confidence intervals.
+# Return the collected results in a long-format data frame with one column per modifiable parameter and one column per result value
+# - nPatientsRange: Increasing values of nPatients in the sample size
+# - nTreatedRange: How many of the total nPatients in a simulation will be assigned to treatment. If a decimal value < 1.0, then interpret as a percentage of the total nPatients
+# - probOutcomeUntreatedRange: Range of untreated outcome rates to simulate
+# - probOutcomeTreatedRange: Range of treated outcome rates to simulate (must be same length as probOutcomeUntreatedRange to match)
+# - nSimsPerCombo: The number of simulations for each parameter combination
+# - alpha: Threshold below which to consider a p-value as "statistically significant" to reject the null hypothesis. Classically use 0.05, corresponding to 95% confidence intervals.
+batchSimulateBinaryTreatmentOutcomeAcrossParameters = function(nPatientsRange, nTreatedRange, probOutcomeUntreatedRange, probOutcomeTreatedRange, nSimsPerCombo, alpha)
+{
+  # Start with data frame, with just column assignments first for parameters and result values of interest
+  resultDF = 
+    data.frame("nPatients"=numeric(0), "percentTreated"=numeric(0), "probOutcomUntreated"=numeric(0), "probOutcomeTreated"=numeric(0), "averageTreatmentEffect"=numeric(0),
+               "nullHypothesisRejectRate"=numeric(0), "nullHypothesisNonRejectRate"=numeric(0), "oddsRatioMean"=numeric(0), "oddsRatioMedian"=numeric(0), "oddsRatioCILow"=numeric(0), "oddsRatioCIHigh"=numeric(0))
+
+  # Iterate through each combination of simulation parameters
+  for (nPatients in nPatientsRange)
+  {
+    for (nTreated in nTreatedRange)
+    {
+      if ( 0.0 < nTreated & nTreated < 1.0 )
+      { # Interpret as a percentage of the total nPatients
+        percentTreated = nTreated
+        nTreated = as.integer(nPatients * percentTreated) # Overwrite with integer count value
+      }
+
+      for (iProbOutcome in 1:length(probOutcomeUntreatedRange)) # Use numeric index to simultaneously iterate through both treated and untreated parameters
+      {
+        probOutcomeUntreated = probOutcomeUntreatedRange[iProbOutcome]
+        probOutcomeTreated = probOutcomeTreatedRange[iProbOutcome]
+        
+        # Now run a batch of simulations for the given combination of parameters
+        simParams = list("nPatients"=nPatients, "nTreated"=nTreated, "probOutcomeUntreated"=probOutcomeUntreated, "probOutcomeTreated"=probOutcomeTreated)
+        message(paste(as.character(simParams), collapse=", "))  # Print message before simulation as a progress indicator
+        batchSimResults = batchSimulateBinaryTreatmentOutcome(nSimsPerCombo, simParams)
+        
+        simParams["averageTreatmentEffect"] = probOutcomeTreated - probOutcomeUntreated
+        simParams["nullHypothesisRejectRate"] = mean(batchSimResults$pValues < alpha) # Classical frequentist statistics, if p-value is less than the alpha threshold, then reject null hypothesis = "statistically significany differnece detected." Track how often this happens
+        simParams["nullHypothesisNonRejectRate"] = 1.0-mean(batchSimResults$pValues < alpha)
+        simParams["oddsRatioMean"] = mean(batchSimResults$oddsRatios)
+        simParams["oddsRatioMedian"] = median(batchSimResults$oddsRatios)
+        simParams["oddsRatioCILow"] = quantile( batchSimResults$oddsRatios, alpha/2 ) # At alpha = 0.05, bottom end of 95% confidence interval corresponds to the 2.5%ile
+        simParams["oddsRatioCIHigh"] = quantile( batchSimResults$oddsRatios, 1.0 - alpha/2 )
+        
+        # Append the parameter and result values to the result data frame
+        resultDF = rbind(resultDF, simParams)
+      }
+    }
+  }
+  return(resultDF)
+}
+
+# Define function to make a quick plot of the first column of the dataframe as the x-axis vs all others as y-axis lines
+# - plotDF: Dataframe with data to plot. First column expected to be the x-axis values
+# - ylab: Y-axis / value label
+# - legendLoc: Where to place the legend. Defaults to top-right of the figure
+quickPlotDF = function(plotDF, ylab, legendLoc="topright")
+{
+  yDF = plotDF[-1] # Remove the "x-axis" column to leave just the "y-axis" value columns
+  matplot(plotDF[1], yDF, type=c("b"), pch="+", xlab=colnames(plotDF)[1], ylab=ylab)  # Multi-line plot
+  legend(legendLoc, legend=colnames(yDF), col=1:ncol(yDF), lty=1:ncol(yDF))  # Add a legend to the plot that matches the color scheme of the plot
+}
+  
+######### See the accompanying powerCalculation.examples.R for example batch simulations
diff --git a/scripts/DevWorkshop/statsSimCalc/powerCalculation.examples.R b/scripts/DevWorkshop/statsSimCalc/powerCalculation.examples.R
new file mode 100644
index 00000000..ddd70b2b
--- /dev/null
+++ b/scripts/DevWorkshop/statsSimCalc/powerCalculation.examples.R
@@ -0,0 +1,50 @@
+# Depends on powerCalculation.R to be sourced first
+
+# Example simulate results where probOutcomeUntreated == probOutcomeTreated 
+#   (i.e., null hypothesis is true, where outcome rates are independent of any treatment effect) 
+nullResultDF = batchSimulateBinaryTreatmentOutcomeAcrossParameters( nPatients=c(32,64,128,256,512,1024), nTreatedRange=c(0.50), probOutcomeUntreatedRange=c(0.4), probOutcomeTreatedRange=c(0.4), nSimsPerCombo=1000, alpha=0.05 )
+#
+########## How likely do you expect to reject the null hypothesis with increasing sample size if you know there is no treatment effect here?
+########## Explain what the "%>% select" command does to the dataframe contents
+# Plot the x-axis (nPatients) and the empiric null hypothesis reject rate. In theory, this should always = alpha, since that's the definition
+# > quickPlotDF( nullResultDF %>% select(nPatients, nullHypothesisRejectRate), "Type I Error Rate = alpha","bottomright")  # Since we know the null hypothesis is true here, the rejection rate is the Type I error rate
+#
+########## What do you expect the median and shape of the 95% confidence intervals to be for the estimated odds ratio with increasing sample size?
+# Plot the x-axis (nPatients) and the oddsRatio estimates and confidence interval ranges to see how they change with respect to data size
+# > quickPlotDF( nullResultDF %>% select(nPatients, oddsRatioCILow, oddsRatioMean, oddsRatioMedian, oddsRatioCIHigh), "Odds Ratio Estimates")
+#
+#
+# Review cases where probOutcomeUntreated != probOutcomeTreated 
+#   (i.e., null hypothesis is false. There is a treatment effect on the outcome) 
+nonNullResultDF = batchSimulateBinaryTreatmentOutcomeAcrossParameters( nPatients=c(32,64,128,256,512,1024), nTreatedRange=c(0.50), probOutcomeUntreatedRange=c(0.2), probOutcomeTreatedRange=c(0.4), nSimsPerCombo=1000, alpha=0.05 )
+#
+########## How can we estimate the statistical power of these simulated studies and how do you expect that to change with increasing sample size?
+# Plot the x-axis (nPatients) and the empiric null hypothesis non-reject rate (i.e., Power). 
+# > quickPlotDF( nonNullResultDF %>% select(nPatients, nullHypothesisRejectRate), "Power = 1-Beta","bottomright")  # Since we know the null hypothesis is false here, the reject rate is the Power = 1 - Beta (Type II error rate)
+#
+########## How do you expect the shape of the 95% confidence intervals to be for the estimated odds ratio with increasing sample size when there is a known treatment effect?
+# Plot the x-axis (nPatients) and the oddsRatio estimates and confidence interval ranges to see how they change with respect to data size
+# > quickPlotDF( nonNullResultDF %>% select(nPatients, oddsRatioCILow, oddsRatioMean, oddsRatioMedian, oddsRatioCIHigh), "Odds Ratio Estimates")
+
+
+# Example simulations with increasing total sample sizes, but same (small) number of treated cases. Illustrate that power depends on your smaller class size, not the total sample size
+imbalancedResultDF = batchSimulateBinaryTreatmentOutcomeAcrossParameters( nPatients=c(32,64,128,256,512,1024), nTreatedRange=c(16), probOutcomeUntreatedRange=c(0.2), probOutcomeTreatedRange=c(0.4), nSimsPerCombo=1000, alpha=0.05 )
+########## Review the two dataframes being "joined" here and explain what the inner_join does
+# Join the imbalanced results with the 50:50 balanced results to compare the progression of Power with sample size
+balancedVsImbalancedDF = inner_join( nonNullResultDF[c("nPatients","nullHypothesisRejectRate")], imbalancedResultDF[c("nPatients","nullHypothesisRejectRate")], by=c("nPatients") )
+colnames(balancedVsImbalancedDF) = c("nPatients","nTreated = 50%","nTreated = 16")  # Rename columns to clarify comparison
+#
+########## Are you better off studying 1000 total patients, 20 of which are treated, or 200 total patients, 100 of which are treated?
+# > quickPlotDF(balancedVsImbalancedDF, "Power = 1-Beta","bottomright")
+
+
+# Example simulations with increasing average treatment effect (larger difference between outcome probabilities). Illustrate that power depends on how small a sample size is to be detected (and very hard to detect small differences)
+treatmentEffectResultDF = batchSimulateBinaryTreatmentOutcomeAcrossParameters( nPatients=c(32,64,128,256,512,1024), nTreatedRange=c(0.50), probOutcomeUntreatedRange=c(0.3,0.4,0.45,0.48,0.50), probOutcomeTreatedRange=c(0.5,0.5,0.5,0.5,0.5), nSimsPerCombo=1000, alpha=0.05 )
+treatmentEffectResultDF$averageTreatmentEffectLabel = paste("ATE",format(treatmentEffectResultDF$averageTreatmentEffect))
+########## Review the "long" vs. "wide" dataframes and explain what the "pivot table" dcast does to the structure of the data (see also https://tidyr.tidyverse.org/articles/pivot.html)
+# PivotTable to reshape-dcast long-format data into wide-format, so can compare effect of multiple parameters simultaneously: https://www.r-bloggers.com/pivot-tables-in-r/
+longTreatmentEffectResultDF = treatmentEffectResultDF %>% select(nPatients,averageTreatmentEffectLabel,nullHypothesisRejectRate)
+wideTreatmentEffectResultDF = dcast(longTreatmentEffectResultDF, nPatients ~ averageTreatmentEffectLabel)
+########## If you needed 100,000 patients to reliably detect a treatment effect, does it still matter?
+# > quickPlotDF(wideTreatmentEffectResultDF,"Power","topleft")
+
diff --git a/scripts/ER_Infection/README.md b/scripts/ER_Infection/README.md
new file mode 100644
index 00000000..36a85026
--- /dev/null
+++ b/scripts/ER_Infection/README.md
@@ -0,0 +1,38 @@
+# Personalized Antibiograms for Machine Learning DrivenAntibiotic Selection
+
+### Abstract
+The Centers for Disease Control and Prevention identify improving antibiotic prescribing stewardship as the most important action to combat the spread of resistant bacteria. Clinicians balance broad empiric antibiotic coverage ensuring activity against serious infections vs. precision coverage targeting only the most likely pathogens. In this retrospective multi-site study, we trained machine learning models to predict which antibiotics infections would be susceptible to (personalized antibiograms) using electronic health record data of 8,342 infection treated at Stanford emergency departments between 2009 to 2019 and 15,806 uncomplicated urinary tract infections from Massachusetts General Hospital and Brigham Women’s Hospital in Boston between 2007 and 2016. We assessed the trade-off of broad-spectrum vs. precise antibiotic prescribing using a novel optimization procedure for antibiotic selection. In the Stanford dataset, personalized antibiograms reallocated existing distributions of empiric antibiotics while achieving a coverage rate (fraction of infections covered by treatment selection) of 85.9%; similar to clinician performance (84.3% p=0.11). In the Boston dataset, personalized antibiograms achieved a coverage rate of 90.4%; a significant improvement over clinician(88.1% p<0.0001) prescribing. Antibiotic selection driven by personalized antibiograms achieved similar coverage rates as seen in actual clinical practice with substantially narrower antibiotics. In the Stanford dataset, personalized antibiograms maintained clinician coverage rates while narrowing 69% of empiric vancomycin+piperacillin/tazobactam prescriptions to piperacillin/tazobactam monotherapy, and 40% of piperacillin/tazobactam prescriptions to cefazolin. In the Boston dataset, personalized antibiograms maintained clinical coverage rates while narrowing 48% of ciprofloxacin to trimethoprim/sulfamethoxazole. Precision empiric antibiotic prescribing supported by personalized antibiogram prediction models could improve patient safety and antibiotic stewardship by reducing unnecessary use of broad spectrum antibiotics that breed a growing tide of resistant organisms.
+
+### Data Availability Statement
+The Stanford cohort was derived from data made available through [STARR](https://starr.stanford.edu/), STAnford medicine Research data Repository. The Boston cohort has been made [publicly available](https://physionet.org/content/antimicrobial-resistance-uti/1.0.0/) through Physionet. 
+
+### Demo
+A demo of the software used for the analysis of this manuscript has been been made publicly available in a jupyter notebook [here](https://github.com/HealthRex/CDSS/blob/master/scripts/ER_Infection/notebooks/demo/Demo%20Personalized%20Antibiogram%20Linear%20Programming.ipynb). 
+
+### Directory Descriptions
+
+#### /ER_Infection/SQL/
+ Contains the set of SQL queries used to derive the Stanford cohort.
+
+####  /ER_Infection/notebooks/decision_alg/
+Contains python code for the linear programming optimizer
+
+####  /ER_Infection/notebooks/eda/
+Contains notebooks with exploratory data analysis
+
+####  /ER_Infection/notebooks/featurizing/
+Contains code used to construct a feature matrix for our personalized antibiogram models
+
+####  /ER_Infection/notebooks/figures_and_tabels/
+Contains code used to construct figures and tables seen in the manuscript and supplement
+
+####  /ER_Infection/notebooks/labelling/
+Contains code used to label observations in the stanford dataset
+
+####  /ER_Infection/notebooks/mit_data_analysis/
+Contains code used for analysis of the Boston cohort
+
+####  /ER_Infection/notebooks/modeling/
+Contains code used to train personalized antibiogram models
+
+
diff --git a/scripts/ER_Infection/SQL/Featurize/Feature_Sequence.sql b/scripts/ER_Infection/SQL/Featurize/Feature_Sequence.sql
new file mode 100644
index 00000000..2ec807b0
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Featurize/Feature_Sequence.sql
@@ -0,0 +1,151 @@
+--- Creates Long Form Patient Feature Timeline From the following tables.  This is an expensive query - costs roughly $1 each time you run it.
+-- order_proc
+-- diagnois_code
+-- order_med
+-- lab_results
+-- flowsheets -- gets first and second value and appends value number to feature name
+-- demographic
+
+CREATE OR REPLACE TABLE `mining-clinical-decisions.abx.feature_timeline_long` AS
+(
+-- Get order proc feautres from previous year
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, lab.order_proc_id_coded as order_id, lab.order_type feature_type, lab.description features, NULL as value, lab.order_time_jittered_utc observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+INNER JOIN shc_core.order_proc as lab
+USING (anon_id)
+WHERE lab.order_time_jittered_utc < labels.index_time
+AND lab.order_time_jittered_utc IS NOT NULL
+AND timestamp_add(lab.order_time_jittered_utc, INTERVAL 24*365 HOUR) >= labels.index_time
+
+UNION DISTINCT
+
+-- Get icd10 codes from full timeline
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, dx.pat_enc_csn_id_jittered order_id, -- fill in bc dx does not have order_id
+'Diagnosis' as feature_type,
+dx.icd10 as features,
+NULL as value,
+CAST(dx.start_date_utc AS TIMESTAMP) observation_time
+FROM
+`mining-clinical-decisions.abx.final_cohort_table` as labels
+INNER JOIN shc_core.diagnosis_code as dx
+USING (anon_id)
+WHERE labels.pat_enc_csn_id_coded <> dx.pat_enc_csn_id_jittered 
+AND timestamp_add(CAST(dx.start_date_utc AS TIMESTAMP), INTERVAL 24*14 HOUR) < labels.index_time 
+AND dx.icd10 is not NULL
+
+UNION DISTINCT
+
+-- Get med orders from order_med from last year
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, med.order_med_id_coded order_id,
+'Meds' as feature_type,
+med.med_description as features,
+NULL as value,
+med.order_inst_utc as observeration_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+INNER JOIN shc_core.order_med as med
+USING (anon_id)
+WHERE med.order_inst_utc < labels.index_time
+AND timestamp_add(med.order_inst_utc, INTERVAL 24*365 HOUR) >= labels.index_time
+AND med.order_inst_utc IS NOT NULL
+
+UNION DISTINCT
+
+-- Lab Results From Previous Year - fills 999999's with corresponding high or low value form ord_num
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, lr.order_id_coded order_id,
+'Lab Results' as feature_type,
+lr.base_name as features,
+CASE WHEN lr.ord_num_value = 9999999 AND lr.ord_value LIKE "%<%"  OR lr.ord_value LIKE "%>%"
+THEN CAST(REGEXP_EXTRACT(lr.ord_value, r'(\d+(?:\.\d+)?)') AS FLOAT64) 
+ELSE lr.ord_num_value END value,
+lr.result_time_utc as observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.lab_result` lr
+USING(anon_id)
+WHERE lr.result_time_utc < labels.index_time
+AND TIMESTAMP_ADD(lr.result_time_utc, INTERVAL 24*365 HOUR) >= labels.index_time
+AND lr.base_name IN ('AG','AGAP','ALB','ALT','AST','BASOAB','BE','BUN','CA','CL','CO2','CR','EOSAB','GLOB','GLU','HCO3','HCO3A', 'HCO3V','HCT','HGB','INR','K','LAC','LACWBL','LYMAB','MG','MONOAB','NEUTAB','O2SATA','O2SATV','PCAGP','PCBUN','PCCL','PCO2A',
+'PCO2V','PH','PHA','PHCAI','PHOS','PHV','PLT','PO2A','PO2V','PT','RBC','TBIL','TCO2A','TCO2V','TNI','UPH','WBC','XLEUKEST','XUKET')
+AND (lr.ord_num_value <> 9999999 OR lr.ord_value LIKE "%<%" OR ord_value LIKE "%>%")
+
+UNION DISTINCT
+
+-- Flowsheets From Previous Year
+SELECT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, fl.line order_id, # not selecting distinct bc no unique identifier for flowsheet entry
+'Flowsheet' feature_type, 
+CONCAT(fl.row_disp_name, "_val_1") features,
+fl.num_value1 value,
+fl.recorded_time_utc observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.flowsheet` fl
+USING (anon_id)
+WHERE fl.recorded_time_utc < labels.index_time
+AND TIMESTAMP_ADD(fl.recorded_time_utc, INTERVAL 24*365 HOUR) >= labels.index_time
+AND fl.row_disp_name IN ("BP", "NIBP", "Resting BP", "Pulse", "Heart Rate", "Resting HR", "Weight", "Height", "Temp", "Resp", 
+"Resp Rate", "Resting RR", "SpO2", "Resting SpO2", "O2 (LPM) Arterial Systolic BP", "Arterial Diastolic BP", "Temp (in Celsius)",
+"Blood Pressure", "Oxygen Saturation", "Glasgow Coma Scale Score", "Altered Mental Status (GCS<15)", "Total GCS Points", "GCS Score")
+AND fl.num_value1 IS NOT NULL
+
+UNION DISTINCT 
+
+SELECT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, fl.line order_id, # not selecting distinct bc no unique identifier for flowsheet entry
+'Flowsheet' feature_type, 
+CONCAT(fl.row_disp_name, "_val_2") features,
+fl.num_value2 value,
+fl.recorded_time_utc observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.flowsheet` fl
+USING (anon_id)
+WHERE fl.recorded_time_utc < labels.index_time
+AND TIMESTAMP_ADD(fl.recorded_time_utc, INTERVAL 24*365 HOUR) >= labels.index_time
+AND fl.row_disp_name IN ("BP", "NIBP", "Resting BP", "Pulse", "Heart Rate", "Resting HR", "Weight", "Height", "Temp", "Resp", 
+"Resp Rate", "Resting RR", "SpO2", "Resting SpO2", "O2 (LPM) Arterial Systolic BP", "Arterial Diastolic BP", "Temp (in Celsius)",
+"Blood Pressure", "Oxygen Saturation", "Glasgow Coma Scale Score", "Altered Mental Status (GCS<15)", "Total GCS Points", "GCS Score")
+AND fl.num_value2 IS NOT NULL
+
+UNION DISTINCT
+
+-- Demographics
+
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, labels.pat_enc_csn_id_coded order_id,
+'Demographics' as feature_type, demo.GENDER as feature, NULL as value, CAST(NULL AS TIMESTAMP) observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.demographic` demo
+ON labels.anon_id = demo.ANON_ID
+
+UNION DISTINCT
+
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, labels.pat_enc_csn_id_coded order_id,
+'Demographics' as feature_type, demo.CANONICAL_RACE as feature, NULL as value,
+CAST(NULL AS TIMESTAMP) observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.demographic` demo
+ON labels.anon_id = demo.ANON_ID
+
+UNION DISTINCT
+
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, labels.pat_enc_csn_id_coded order_id,
+'Demographics' as feature_type, demo.CANONICAL_ETHNICITY feature, NULL as value,
+CAST(NULL AS TIMESTAMP) observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.demographic` demo
+ON labels.anon_id = demo.ANON_ID
+
+UNION DISTINCT
+
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, labels.pat_enc_csn_id_coded order_id,
+'Demographics' as feature_type, demo.INSURANCE_PAYOR_NAME as feature, NULL as value,
+CAST(NULL AS TIMESTAMP) observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.demographic` demo
+ON labels.anon_id = demo.ANON_ID
+
+UNION DISTINCT
+
+SELECT DISTINCT labels.anon_id, labels.pat_enc_csn_id_coded, labels.index_time, labels.pat_enc_csn_id_coded order_id,
+'Demographics' as feature_type,
+'Age' as feature, DATE_DIFF(CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR) as value,
+CAST(NULL AS TIMESTAMP) observation_time
+FROM `mining-clinical-decisions.abx.final_cohort_table` as labels
+LEFT JOIN `shc_core.demographic` demo
+ON labels.anon_id = demo.ANON_ID
+)
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Featurize/feature_counts_long.sql b/scripts/ER_Infection/SQL/Featurize/feature_counts_long.sql
new file mode 100644
index 00000000..027a64dd
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Featurize/feature_counts_long.sql
@@ -0,0 +1,21 @@
+CREATE OR REPLACE TABLE `mining-clinical-decisions.abx.feature_counts_long` AS (
+
+SELECT anon_id, pat_enc_csn_id_coded, index_time, feature_type, features, COUNT(*) value 
+FROM `mining-clinical-decisions.abx.feature_timeline_long` 
+WHERE feature_type NOT IN ("Demographics", "Lab Results", "Flowsheet")
+AND feature_type IS NOT NULL
+AND features IS NOT NULL
+GROUP BY anon_id, pat_enc_csn_id_coded, index_time, order_id, feature_type, features
+
+UNION DISTINCT
+
+SELECT anon_id, pat_enc_csn_id_coded, index_time, feature_type, features, 
+CASE WHEN value IS NOT NULL THEN value
+ELSE 1 END value
+FROM `mining-clinical-decisions.abx.feature_timeline_long` 
+WHERE feature_type = "Demographics"
+AND feature_type IS NOT NULL
+AND features IS NOT NULL
+
+)
+
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped.sql b/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped.sql
new file mode 100644
index 00000000..84f73cdb
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped.sql
@@ -0,0 +1,68 @@
+-- For each abx order, find whether it was given, and an abx stop time. 
+WITH mar_action_interim as (
+SELECT abx.*, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times
+FROM `mining-clinical-decisions.abx.abx_orders_within_24_hrs`  abx
+INNER JOIN `mining-clinical-decisions.abx.culture_orders_within_24_hrs`  cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded) 
+ORDER BY anon_id, order_start_time_utc, order_med_id_coded, taken_time_jittered_utc
+), 
+
+distinct_actions as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM mar_action_interim 
+GROUP BY order_med_id_coded, mar_action),
+
+grouped_actions as (
+SELECT order_med_id_coded, 
+max(taken_time_jittered_utc) last_mar_action_time,
+CASE WHEN STRING_AGG(mar_action) IS NULL THEN NULL
+ELSE STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) END action_list
+FROM distinct_actions
+GROUP BY order_med_id_coded),
+
+orders_with_info as 
+(SELECT DISTINCT abx.anon_id, abx.pat_enc_csn_id_coded, abx.order_time, abx.med_description, ga.order_med_id_coded, ga.action_list, ga.last_mar_action_time,
+-- Was_Given should be 1 if Given, Complete(d), New Bag, or Stopped exists in action_list
+CASE WHEN ga.action_list LIKE "%Given%" OR action_list  LIKE "%Complete%" OR action_list LIKE "%Stopped%"
+OR action_list LIKE "%New Bag%" THEN 1
+-- Should be 1 if was_given flag from mar_action_was_given table is 1 
+WHEN wg.was_given = 1 THEN 1
+ELSE 0 END was_given, -- should handle when action_list is null 
+FROM `mining-clinical-decisions.abx.abx_orders_within_24_hrs`  abx
+INNER JOIN `mining-clinical-decisions.abx.culture_orders_within_24_hrs`  cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN grouped_actions ga
+USING (order_med_id_coded)
+LEFT JOIN `mining-clinical-decisions.abx.mar_was_given`  wg
+USING (action_list))
+
+SELECT DISTINCT owi.*, om.discon_time_jittered_utc,
+-- When no mar actions listed for order than infer wasn't given and abx stop time will be order time
+CASE WHEN was_given = 0 AND action_list IS NULL AND om.discon_time_jittered_utc IS NULL THEN owi.order_time
+-- If not in mar actions but discontinued time listed in order_med then abx stop time is discontinued time
+WHEN was_given = 0 AND action_list IS NULL THEN om.discon_time_jittered_utc 
+-- If in mar but nothing to infer was actually given, then take last known timestamp as stop time. 
+WHEN was_given = 0 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 0 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc
+-- If in mar and was given but not discontinued time listed in order_med, then take timestamp of last mar action
+WHEN was_given = 1 AND discon_time_jittered_utc IS NULL THEN last_mar_action_time
+-- If in mar and was given and there exists a discontinued time, then take latest timestamp between that and last mar action
+WHEN was_given = 1 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 1 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc END abx_stop_time
+FROM orders_with_info owi
+LEFT JOIN (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.pat_enc_csn_id_coded, om.med_description, om.order_med_id_coded, om.discon_time_jittered_utc,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+) om
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+ORDER BY anon_id, pat_enc_csn_id_coded, order_time
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped_2_weeks.sql b/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped_2_weeks.sql
new file mode 100644
index 00000000..2b3df100
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped_2_weeks.sql
@@ -0,0 +1,69 @@
+-- For each abx order ordered within 2 weeks after er_admit - get whether it was started and abx_stop_time
+WITH mar_action_interim as (
+SELECT abx.*, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times
+FROM `mining-clinical-decisions.abx.abx_orders_within_2_weeks`  abx
+INNER JOIN `mining-clinical-decisions.abx.culture_orders_within_24_hrs`  cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded) 
+ORDER BY anon_id, order_start_time_utc, order_med_id_coded, taken_time_jittered_utc
+), 
+
+distinct_actions as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM mar_action_interim 
+GROUP BY order_med_id_coded, mar_action),
+
+grouped_actions as (
+SELECT order_med_id_coded, 
+max(taken_time_jittered_utc) last_mar_action_time,
+CASE WHEN STRING_AGG(mar_action) IS NULL THEN NULL
+ELSE STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) END action_list
+FROM distinct_actions
+GROUP BY order_med_id_coded),
+
+orders_with_info as 
+(SELECT DISTINCT abx.anon_id, abx.pat_enc_csn_id_coded, abx.order_time, abx.med_description, ga.order_med_id_coded, ga.action_list, ga.last_mar_action_time,
+-- Was_Given should be 1 if Given, Complete(d), New Bag, or Stopped exists in action_list
+CASE WHEN ga.action_list LIKE "%Given%" OR action_list  LIKE "%Complete%" OR action_list LIKE "%Stopped%"
+OR action_list LIKE "%New Bag%" THEN 1
+-- Should be 1 if was_given flag from mar_action_was_given table is 1 
+WHEN wg.was_given = 1 THEN 1
+ELSE 0 END was_given, -- should handle when action_list is null 
+FROM `mining-clinical-decisions.abx.abx_orders_within_2_weeks`  abx
+INNER JOIN `mining-clinical-decisions.abx.culture_orders_within_24_hrs`  cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN grouped_actions ga
+USING (order_med_id_coded)
+LEFT JOIN `mining-clinical-decisions.abx.mar_was_given`  wg
+USING (action_list)
+)
+
+SELECT DISTINCT owi.*, om.discon_time_jittered_utc,
+-- When no mar actions listed for order than infer wasn't given and abx stop time will be order time
+CASE WHEN was_given = 0 AND action_list IS NULL AND om.discon_time_jittered_utc IS NULL THEN owi.order_time
+-- If not in mar actions but discontinued time listed in order_med then abx stop time is discontinued time
+WHEN was_given = 0 AND action_list IS NULL THEN om.discon_time_jittered_utc 
+-- If in mar but nothing to infer was actually given, then take last known timestamp as stop time. 
+WHEN was_given = 0 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 0 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc
+-- If in mar and was given but not discontinued time listed in order_med, then take timestamp of last mar action
+WHEN was_given = 1 AND discon_time_jittered_utc IS NULL THEN last_mar_action_time
+-- If in mar and was given and there exists a discontinued time, then take latest timestamp between that and last mar action
+WHEN was_given = 1 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 1 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc END abx_stop_time
+FROM orders_with_info owi
+LEFT JOIN (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.pat_enc_csn_id_coded, om.med_description, om.order_med_id_coded, om.discon_time_jittered_utc,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+) om
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+ORDER BY anon_id, pat_enc_csn_id_coded, order_time
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped_include_oral.sql b/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped_include_oral.sql
new file mode 100644
index 00000000..1a41b2f5
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_orders_given_and_stopped_include_oral.sql
@@ -0,0 +1,70 @@
+-- For each abx order, find whether it was given, and an abx stop time. 
+CREATE OR REPLACE TABLE mining-clinical-decisions.abx.abx_orders_given_and_stopped_include_oral AS (
+WITH mar_action_interim as (
+SELECT abx.*, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times
+FROM `mining-clinical-decisions.abx.abx_orders_within_24_hrs_include_oral`  abx
+INNER JOIN `mining-clinical-decisions.abx.culture_orders_within_24_hrs`  cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded) 
+ORDER BY anon_id, order_start_time_utc, order_med_id_coded, taken_time_jittered_utc
+), 
+
+distinct_actions as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM mar_action_interim 
+GROUP BY order_med_id_coded, mar_action),
+
+grouped_actions as (
+SELECT order_med_id_coded, 
+max(taken_time_jittered_utc) last_mar_action_time,
+CASE WHEN STRING_AGG(mar_action) IS NULL THEN NULL
+ELSE STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) END action_list
+FROM distinct_actions
+GROUP BY order_med_id_coded),
+
+orders_with_info as 
+(SELECT DISTINCT abx.anon_id, abx.pat_enc_csn_id_coded, abx.order_time, abx.med_description, ga.order_med_id_coded, ga.action_list, ga.last_mar_action_time,
+-- Was_Given should be 1 if Given, Complete(d), New Bag, or Stopped exists in action_list
+CASE WHEN ga.action_list LIKE "%Given%" OR action_list  LIKE "%Complete%" OR action_list LIKE "%Stopped%"
+OR action_list LIKE "%New Bag%" THEN 1
+-- Should be 1 if was_given flag from mar_action_was_given table is 1 
+WHEN wg.was_given = 1 THEN 1
+ELSE 0 END was_given, -- should handle when action_list is null 
+FROM `mining-clinical-decisions.abx.abx_orders_within_24_hrs_include_oral`  abx
+INNER JOIN `mining-clinical-decisions.abx.culture_orders_within_24_hrs`  cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN grouped_actions ga
+USING (order_med_id_coded)
+LEFT JOIN `mining-clinical-decisions.abx.mar_was_given`  wg
+USING (action_list))
+
+SELECT DISTINCT owi.*, om.discon_time_jittered_utc,
+-- When no mar actions listed for order than infer wasn't given and abx stop time will be order time
+CASE WHEN was_given = 0 AND action_list IS NULL AND om.discon_time_jittered_utc IS NULL THEN owi.order_time
+-- If not in mar actions but discontinued time listed in order_med then abx stop time is discontinued time
+WHEN was_given = 0 AND action_list IS NULL THEN om.discon_time_jittered_utc 
+-- If in mar but nothing to infer was actually given, then take last known timestamp as stop time. 
+WHEN was_given = 0 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 0 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc
+-- If in mar and was given but not discontinued time listed in order_med, then take timestamp of last mar action
+WHEN was_given = 1 AND discon_time_jittered_utc IS NULL THEN last_mar_action_time
+-- If in mar and was given and there exists a discontinued time, then take latest timestamp between that and last mar action
+WHEN was_given = 1 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 1 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc END abx_stop_time
+FROM orders_with_info owi
+LEFT JOIN (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.pat_enc_csn_id_coded, om.med_description, om.order_med_id_coded, om.discon_time_jittered_utc,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+) om
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+ORDER BY anon_id, pat_enc_csn_id_coded, order_time
+)
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_orders_within_24_hrs.sql b/scripts/ER_Infection/SQL/Refactored/abx_orders_within_24_hrs.sql
new file mode 100644
index 00000000..2564c44d
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_orders_within_24_hrs.sql
@@ -0,0 +1,52 @@
+-- Gets all antibiotic orders that happen within 24 hours
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+er_admits_adults AS (
+SELECT DISTINCT ea.* 
+FROM er_admits ea
+INNER JOIN `shc_core.demographic` demo
+USING (anon_id)
+WHERE DATE_DIFF(CAST(ea.er_admit_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+),
+
+has_inp_code AS (
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM
+  (SELECT DISTINCT ea.pat_enc_csn_id_coded,
+  MAX(CASE WHEN adt.base_pat_class_c = 1 THEN 1 ELSE 0 END) has_inpatient 
+  FROM er_admits_adults ea
+  INNER JOIN shc_core.adt adt
+  USING (pat_enc_csn_id_coded)
+  GROUP BY ea.pat_enc_csn_id_coded) t
+WHERE has_inpatient = 1
+),
+
+include_abx as (
+SELECT med_description
+FROM `mining-clinical-decisions.abx.abx_types` 
+WHERE is_include_abx = 1
+)
+
+SELECT ea.anon_id, ea.pat_enc_csn_id_coded, om.pat_enc_csn_id_coded om_csn, om.order_med_id_coded, om.med_description, om.order_time
+FROM (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.pat_enc_csn_id_coded, om.med_description, om.order_med_id_coded,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+     WHERE om.order_class_name <> "Historical Med"
+) om
+
+INNER JOIN er_admits_adults ea
+USING (anon_id)
+INNER JOIN include_abx
+USING (med_description)
+INNER JOIN has_inp_code
+ON has_inp_code.pat_enc_csn_id_coded = ea.pat_enc_csn_id_coded
+WHERE TIMESTAMP_DIFF(om.order_time, ea.er_admit_time, HOUR) BETWEEN 0 AND 24
+ORDER BY anon_id, om.order_time
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_orders_within_24_hrs_incude_oral.sql b/scripts/ER_Infection/SQL/Refactored/abx_orders_within_24_hrs_incude_oral.sql
new file mode 100644
index 00000000..b1d9cba7
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_orders_within_24_hrs_incude_oral.sql
@@ -0,0 +1,54 @@
+-- Gets all antibiotic orders that happen within 24 hours
+CREATE OR REPLACE TABLE mining-clinical-decisions.abx.abx_orders_within_24_hrs_include_oral AS (
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+er_admits_adults AS (
+SELECT DISTINCT ea.* 
+FROM er_admits ea
+INNER JOIN `shc_core.demographic` demo
+USING (anon_id)
+WHERE DATE_DIFF(CAST(ea.er_admit_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+),
+
+has_inp_code AS (
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM
+  (SELECT DISTINCT ea.pat_enc_csn_id_coded,
+  MAX(CASE WHEN adt.base_pat_class_c = 1 THEN 1 ELSE 0 END) has_inpatient 
+  FROM er_admits_adults ea
+  INNER JOIN shc_core.adt adt
+  USING (pat_enc_csn_id_coded)
+  GROUP BY ea.pat_enc_csn_id_coded) t
+WHERE has_inpatient = 1
+),
+
+include_abx as (
+SELECT med_description
+FROM `mining-clinical-decisions.abx.abx_types` 
+WHERE is_include_abx = 1 OR is_oral = 1
+)
+
+SELECT ea.anon_id, ea.pat_enc_csn_id_coded, om.pat_enc_csn_id_coded om_csn, om.order_med_id_coded, om.med_description, om.order_time
+FROM (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.pat_enc_csn_id_coded, om.med_description, om.order_med_id_coded,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+     WHERE om.order_class_name <> "Historical Med"
+) om
+
+INNER JOIN er_admits_adults ea
+USING (anon_id)
+INNER JOIN include_abx
+USING (med_description)
+INNER JOIN has_inp_code
+ON has_inp_code.pat_enc_csn_id_coded = ea.pat_enc_csn_id_coded
+WHERE TIMESTAMP_DIFF(om.order_time, ea.er_admit_time, HOUR) BETWEEN 0 AND 24
+ORDER BY anon_id, om.order_time
+)
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_orders_within_2_weeks.sql b/scripts/ER_Infection/SQL/Refactored/abx_orders_within_2_weeks.sql
new file mode 100644
index 00000000..787e8e01
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_orders_within_2_weeks.sql
@@ -0,0 +1,52 @@
+-- Gets all antibiotic orders that affect label that happen within First two weeks hours
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+er_admits_adults AS (
+SELECT DISTINCT ea.* 
+FROM er_admits ea
+INNER JOIN `shc_core.demographic` demo
+USING (anon_id)
+WHERE DATE_DIFF(CAST(ea.er_admit_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+),
+
+has_inp_code AS (
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM
+  (SELECT DISTINCT ea.pat_enc_csn_id_coded,
+  MAX(CASE WHEN adt.base_pat_class_c = 1 THEN 1 ELSE 0 END) has_inpatient 
+  FROM er_admits_adults ea
+  INNER JOIN shc_core.adt adt
+  USING (pat_enc_csn_id_coded)
+  GROUP BY ea.pat_enc_csn_id_coded) t
+WHERE has_inpatient = 1
+),
+
+include_abx as (
+SELECT med_description
+FROM `mining-clinical-decisions.abx.abx_types` 
+WHERE affects_not_infected_label = 1
+)
+
+SELECT ea.anon_id, ea.pat_enc_csn_id_coded, om.pat_enc_csn_id_coded om_csn, om.order_med_id_coded, om.med_description, om.order_time
+FROM (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.pat_enc_csn_id_coded, om.med_description, om.order_med_id_coded,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+     WHERE om.order_class_name <> "Historical Med"
+) om
+
+INNER JOIN er_admits_adults ea
+USING (anon_id)
+INNER JOIN include_abx
+USING (med_description)
+INNER JOIN has_inp_code
+ON has_inp_code.pat_enc_csn_id_coded = ea.pat_enc_csn_id_coded
+WHERE TIMESTAMP_DIFF(om.order_time, ea.er_admit_time, HOUR) BETWEEN 0 AND 24*14
+ORDER BY anon_id, om.order_time
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/abx_types.sql b/scripts/ER_Infection/SQL/Refactored/abx_types.sql
new file mode 100644
index 00000000..f3da52f4
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/abx_types.sql
@@ -0,0 +1,62 @@
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+antibiotics as (
+SELECT COUNT (DISTINCT order_med_id_coded) cnt, REGEXP_EXTRACT(med_description, r'([^\s]+)') as simple_abx
+FROM `shc_core.order_med` 
+WHERE thera_class_name LIKE '%ANTIBIOTIC%'
+GROUP BY simple_abx, med_description
+ORDER BY simple_abx)
+
+SELECT om.med_description, COUNT (DISTINCT om.order_med_id_coded) as num_times_ordered_4_hrs_into_ed,
+
+CASE WHEN med_description LIKE "METHYLENE BLUE%" THEN 0 # gets picked up as abx - so this overrides it
+WHEN med_description LIKE "PREDNISOLONE%" THEN 0 # gets picked up as abx - this overrides it
+WHEN med_description LIKE "% IV%" THEN 1 #"IV"
+WHEN med_description = "ZZZ IMS TEMPLATE" THEN 0
+WHEN med_description LIKE "% IVPB %" THEN 1 #"IV"
+WHEN med_description LIKE "%BAG%" THEN 1 #"IV"
+WHEN med_description LIKE "% INJECTION%" THEN 1 #"IM"
+WHEN med_description LIKE "% IM%" THEN 1 # "IM"
+WHEN med_description LIKE "% INJ %" THEN 1 #"IM"
+WHEN med_description LIKE "%VANCOMYCIN PER PHARMACY PROTOCOL%" OR med_description LIKE "%VANCOMYCIN ED ORDER%"  OR med_description LIKE "%VANCOMYCIN ORAL SOLUTION%" THEN 1 
+ELSE 0 END is_include_abx,
+
+CASE WHEN med_description LIKE "METHYLENE BLUE%" THEN 0 # gets picked up as abx - so this overrides it
+WHEN med_description LIKE "PREDNISOLONE%" THEN 0 # gets picked up as abx - this overrides it
+WHEN med_description LIKE "% IV%" THEN 1 #"IV"
+WHEN med_description = "ZZZ IMS TEMPLATE" THEN 1
+WHEN med_description LIKE "% IVPB %" THEN 1 #"IV"
+WHEN med_description LIKE "%BAG%" THEN 1 #"IV"
+WHEN med_description LIKE "% INJECTION%" THEN 1 #"IM"
+WHEN med_description LIKE "% IM%" THEN 1 # "IM"
+WHEN med_description LIKE "% INJ %" THEN 1 #"IM"
+WHEN med_description LIKE "% PO%" THEN 1 # "Oral" 
+WHEN med_description LIKE "%VANCOMYCIN PER PHARMACY PROTOCOL%" OR med_description LIKE "%VANCOMYCIN ED ORDER%"  OR med_description LIKE "%VANCOMYCIN ORAL SOLUTION%" THEN 1
+ELSE 0 END affects_not_infected_label,
+
+CASE WHEN med_description LIKE "METHYLENE BLUE%" THEN 0 # gets picked up as abx - so this overrides it
+WHEN med_description LIKE "PREDNISOLONE%" THEN 0 # gets picked up as abx - this overrides it
+WHEN med_description LIKE "% PO%" THEN 1 # "Oral" 
+ELSE 0 END is_oral
+
+
+FROM (
+     -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+     SELECT om.anon_id, om.med_description, om.order_med_id_coded,
+     CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc ELSE om.order_inst_utc END order_time
+     FROM `shc_core.order_med` om
+     LEFT JOIN `starr_datalake2018.order_med` omm
+     USING (order_med_id_coded)
+) om
+    
+INNER JOIN er_admits
+USING (anon_id)
+INNER JOIN antibiotics
+ON antibiotics.simple_abx =  REGEXP_EXTRACT(om.med_description, r'([^\s]+)')
+WHERE TIMESTAMP_DIFF(om.order_time, er_admits.er_admit_time, HOUR) BETWEEN -24*14 AND 24*14 # two weeks before & two weeks after 
+GROUP BY om.med_description
+ORDER BY med_description desc
diff --git a/scripts/ER_Infection/SQL/Refactored/cohort_not_infected_rules.sql b/scripts/ER_Infection/SQL/Refactored/cohort_not_infected_rules.sql
new file mode 100644
index 00000000..d996ed1c
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/cohort_not_infected_rules.sql
@@ -0,0 +1,400 @@
+# Gets all antibiotic orders that happen within 24 hours
+WITH er_admits AS (
+  SELECT 
+    pat_enc_csn_id_coded,
+    min(effective_time_jittered_utc) as er_admit_time,
+    max(effective_time_jittered_utc) as er_transfer_out_time
+  FROM
+    `shc_core.adt`
+  WHERE
+    pat_class_c = "112" AND pat_service = "Emergency"
+  GROUP BY 
+    pat_enc_csn_id_coded
+),
+
+-- Prelim cohort before filtering
+cohort_and_index_times AS (
+  SELECT 
+    abx.anon_id,
+    abx.pat_enc_csn_id_coded,
+    min(order_time) index_time
+  FROM 
+    `mining-clinical-decisions.abx.abx_orders_within_24_hrs` abx
+  INNER JOIN
+    `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cult 
+  USING
+    (pat_enc_csn_id_coded)
+  GROUP BY
+    anon_id, pat_enc_csn_id_coded
+),
+
+-- CSNS in cohort that have no abx ordered in two weeks prior to er admit time
+has_no_prior_abx_2_weeks AS (
+  SELECT DISTINCT 
+    cit.pat_enc_csn_id_coded
+  FROM
+    cohort_and_index_times cit
+  LEFT JOIN
+    -- Finds all csns' where abx were ordered between 1 hours and 14 weeks befor admit. 12 hours
+    -- gives a little bit of wiggle room because I'm not sure how accurate order_start_time is. 
+      (SELECT DISTINCT
+        cit.pat_enc_csn_id_coded
+       FROM
+        cohort_and_index_times cit
+       LEFT JOIN
+        er_admits ea
+       USING
+        (pat_enc_csn_id_coded)
+       INNER JOIN (
+         -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+         SELECT 
+          om.anon_id, om.pat_enc_csn_id_coded,
+          om.med_description, om.order_med_id_coded,
+          om.discon_time_jittered_utc,
+          CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc
+          ELSE om.order_inst_utc END order_time
+         FROM 
+          `shc_core.order_med` om
+         LEFT JOIN 
+          `starr_datalake2018.order_med` omm
+         USING
+          (order_med_id_coded)
+        ) om
+       USING
+        (anon_id)
+       INNER JOIN
+         `mining-clinical-decisions.abx.abx_types` abx_types
+       USING
+        (med_description)
+       WHERE 
+        TIMESTAMP_DIFF(ea.er_admit_time, om.order_time, HOUR) BETWEEN 1 AND 24*14 
+       AND
+        abx_types.affects_not_infected_label = 1) has_prior_order
+  USING
+    (pat_enc_csn_id_coded)
+  WHERE
+    has_prior_order.pat_enc_csn_id_coded IS NULL
+),
+
+-- CSNs in cohort that have no cultures ordered within 2 weeks leading up until er admit
+has_no_prior_cultures AS (
+  SELECT DISTINCT
+    cit.pat_enc_csn_id_coded
+  FROM
+    cohort_and_index_times cit
+  LEFT JOIN
+    -- Finds all csns where cultures were ordered between 1 hours and 14 weeks before admit
+     (SELECT DISTINCT
+        cit.pat_enc_csn_id_coded
+      FROM
+        cohort_and_index_times cit
+      LEFT JOIN
+        er_admits ea
+      USING
+        (pat_enc_csn_id_coded)
+      INNER JOIN
+        `shc_core.order_proc` op
+      USING
+        (anon_id)
+      INNER JOIN
+        `mining-clinical-decisions.abx.culture_types` culture_types
+      USING
+        (description)
+      WHERE
+        TIMESTAMP_DIFF(ea.er_admit_time, op.order_time_jittered_utc, HOUR)
+      BETWEEN
+        1 AND 24*14
+      AND
+        culture_types.affects_not_infected_label = 1) has_prior_order
+  USING
+    (pat_enc_csn_id_coded)
+  WHERE
+    has_prior_order.pat_enc_csn_id_coded IS NULL
+),
+
+-- Removes examples from cohort where cultures or abx were ordered in 2 weeks prior to er admit time
+filtered_examples AS (
+  SELECT
+    cit.*
+  FROM
+    cohort_and_index_times cit
+  INNER JOIN
+    has_no_prior_abx_2_weeks 
+  USING
+    (pat_enc_csn_id_coded)
+  INNER JOIN
+    has_no_prior_cultures 
+  USING
+    (pat_enc_csn_id_coded)
+),
+
+-- Gets table of max abx_stop_times for abx that were ordered within first two weeks and given
+last_abx_stop_time_if_started as (
+  SELECT
+    anon_id, pat_enc_csn_id_coded, max(abx_stop_time) abx_stop_time
+  FROM 
+    `mining-clinical-decisions.abx.abx_orders_given_and_stopped_2_weeks`
+  WHERE
+    was_given = 1
+  GROUP BY
+    anon_id, pat_enc_csn_id_coded
+),
+
+-- Gets CSN's where all abx ordered within 2 weeks were either not started or were stopped within a 2 days of er admit time. 
+started_abx_stopped_within_day_after_cult_results_or_not_started AS (
+  SELECT DISTINCT
+    fe.pat_enc_csn_id_coded
+  FROM
+    filtered_examples fe
+  LEFT JOIN
+    last_abx_stop_time_if_started las
+  USING
+    (pat_enc_csn_id_coded)
+  LEFT JOIN
+    er_admits ea
+  USING
+    (pat_enc_csn_id_coded)
+  WHERE
+    las.abx_stop_time IS NULL -- its null for csn's where abx were never started
+  OR 
+    -- abx that were started must be stopped 2 days after er admit time
+    las.abx_stop_time <= TIMESTAMP_ADD(ea.er_admit_time, INTERVAL 48 HOUR) 
+),
+
+-- Gets CSNS where we say patients were discharged with oral abx defined as:
+-- There exists an order for oral abx within the interval
+-- of 12 hours before their discharge and 5 days after. 
+discharged_with_orals AS (
+  SELECT DISTINCT
+    fe.pat_enc_csn_id_coded
+  FROM
+    filtered_examples fe
+  INNER JOIN 
+   (SELECT
+      fe.pat_enc_csn_id_coded, max(adt.effective_time_jittered_utc) discharge_time
+    FROM
+      filtered_examples fe
+    INNER JOIN
+      `shc_core.adt` adt
+    USING
+      (pat_enc_csn_id_coded)
+    GROUP BY
+      fe.pat_enc_csn_id_coded) discharge
+  USING
+    (pat_enc_csn_id_coded)
+  LEFT JOIN (
+      -- Gets columns we need from order med and finds order time from 2018 extract when missing in 2020 extract 
+      SELECT
+        om.anon_id, om.pat_enc_csn_id_coded,
+        om.med_description, om.order_med_id_coded,
+        om.discon_time_jittered_utc,
+        CASE WHEN om.order_inst_utc IS NULL THEN omm.order_time_jittered_utc
+        ELSE om.order_inst_utc END order_time
+      FROM
+        `shc_core.order_med` om
+      LEFT JOIN
+        `starr_datalake2018.order_med` omm
+      USING
+        (order_med_id_coded)
+  ) om
+  USING
+    (anon_id)
+  INNER JOIN
+    `mining-clinical-decisions.abx.abx_types` abx_types
+  USING
+    (med_description)
+  WHERE
+    abx_types.is_oral = 1
+  AND 
+    om.order_time BETWEEN TIMESTAMP_SUB(discharge.discharge_time, INTERVAL 12 HOUR)
+  AND
+    TIMESTAMP_ADD(discharge.discharge_time, INTERVAL 24*5 HOUR)
+),
+
+-- Finds csns with a positive culture (that affects label) ordered within 2 weeks of er admit
+has_pos_cult_ordered_2_weeks AS (
+  SELECT DISTINCT
+    fe.pat_enc_csn_id_coded
+  FROM 
+    filtered_examples fe
+  LEFT JOIN
+    er_admits ea
+  USING
+    (pat_enc_csn_id_coded)
+  LEFT JOIN
+    `shc_core.order_proc` op
+  USING
+    (anon_id)
+  LEFT JOIN
+    `mining-clinical-decisions.abx.culture_types` culture_types
+  USING
+    (description)
+  INNER JOIN
+    `shc_core.culture_sensitivity` cs
+  USING
+    (order_proc_id_coded)
+  WHERE
+    culture_types.affects_not_infected_label = 1 
+  AND
+    TIMESTAMP_DIFF(op.order_time_jittered_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 14*24
+  AND
+    cs.organism <> "COAG NEGATIVE STAPHYLOCOCCUS" 
+  AND
+    cs.organism NOT LIKE "%CANDIDA%" 
+  AND
+    cs.organism NOT IN ('HAEMOPHILUS INFLUENZAE', 'HAEMOPHILUS PARAINFLUENZAE')
+),
+
+-- Finds positive cultures from include list of cultures that were
+-- ordered within 24 hours of ER admit
+-- Redundent now, but in case we want to change flag for cultures that affect 
+-- labels later
+has_pos_cult AS (
+SELECT DISTINCT
+  fe.pat_enc_csn_id_coded
+FROM
+  filtered_examples fe
+LEFT JOIN
+  er_admits ea
+USING
+  (pat_enc_csn_id_coded)
+LEFT JOIN
+  `shc_core.order_proc` op
+USING
+  (anon_id)
+LEFT JOIN
+  `mining-clinical-decisions.abx.culture_types` culture_types
+USING 
+  (description)
+INNER JOIN
+  `shc_core.culture_sensitivity` cs
+USING
+  (order_proc_id_coded)
+WHERE
+  culture_types.include = 1 
+AND
+  TIMESTAMP_DIFF(op.order_time_jittered_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 24
+AND
+  cs.organism <> "COAG NEGATIVE STAPHYLOCOCCUS" 
+AND
+  cs.organism NOT LIKE "%CANDIDA%" 
+AND
+  cs.organism NOT IN ('HAEMOPHILUS INFLUENZAE', 'HAEMOPHILUS PARAINFLUENZAE')
+),
+
+-- finds csns where patient died within 2 weeks of admits
+patient_died AS (
+  SELECT DISTINCT
+    fe.pat_enc_csn_id_coded
+  FROM
+    filtered_examples fe
+  LEFT JOIN
+    er_admits ea
+  USING
+    (pat_enc_csn_id_coded)
+  LEFT JOIN
+    `shc_core.demographic` demo
+  USING
+    (anon_id)
+  WHERE
+    demo.DEATH_DATE_JITTERED IS NOT NULL 
+  AND
+    DATE_DIFF(demo.DEATH_DATE_JITTERED , CAST(ea.er_admit_time AS DATE), DAY)
+  BETWEEN 0 AND 14
+),
+
+-- finds csns that have dx codes associated with bacterial infection
+infection_dx_code AS (
+  SELECT DISTINCT
+    fe.pat_enc_csn_id_coded
+  FROM
+    filtered_examples fe
+  LEFT JOIN
+    shc_core.diagnosis_code dx
+  ON 
+    fe.pat_enc_csn_id_coded = dx.pat_enc_csn_id_jittered
+  WHERE 
+    icd9 LIKE "995.91%" -- Sepsis
+    OR icd9 LIKE "995.92%" -- Severe
+    OR icd9 LIKE "481%" -- Pneumococcal pneumonia
+    OR icd9 LIKE "482%" -- Other bacterial pneumonia
+    OR icd9 LIKE "483%" -- Pneumonia due to other specified organism (will include some viral infections probably)
+    OR icd9 LIKE "484%" -- Pneumonia in infectious diseases classified elsewhere (same as above)
+    OR icd9 LIKE "485%" -- Bronchopneumonia org NOS (same)
+    OR icd9 LIKE "486%" -- Pneumonia, organism NOS (same)
+    OR icd9 LIKE "590%" -- Infections of kidney
+    OR icd10 LIKE "A41%" -- Other sepsis
+    OR icd10 LIKE "J13%" -- Pneumonia due to Streptococcus pneumoniae
+    OR icd10 LIKE "J15%" -- Bacterial pneumonia, not elsewhere classified
+    OR icd10 LIKE "J16%" -- Pneumonia due to other infectious organisms, not elsewhere classified (may contain some viral infections)
+    OR icd10 LIKE "J17%" -- Pneumonia in diseases classified elsewhere (same)
+    OR icd10 LIKE "J18%" -- Pneumonia, unspecified organism (same)
+    OR icd10 LIKE "N10%" -- Acute pyelonephritis
+    OR icd10 LIKE "N11%" -- Chronic tubulo-interstitial nephritis
+    OR icd10 LIKE "N12%" -- Tubulo-interstitial nephritis, not specified as acute or chronic
+    OR icd10 LIKE "N39.0%" -- Urinary tract infection, site not specified
+    OR icd10 LIKE "J06%" -- Acute upper respiratory infections of multiple and unspecified sites
+    OR icd10 LIKE "A49%" -- Bacterial infection of unspecified site
+    OR icd10 LIKE "J22%" -- Unspecified acute lower respiratory infection
+    OR icd10 LIKE "R65.2%" -- Severe sepsis (with and without septic shock)
+),
+
+-- Flags whether the patient had an operative note recorded within a window of index time
+-- window defined as [-7 days, +7 days]
+has_operative_note as (
+  SELECT
+      fe.pat_enc_csn_id_coded,
+      MAX (
+          CASE WHEN TIMESTAMP_DIFF(fe.index_time, cdoc.note_date_jittered_utc, DAY)
+          BETWEEN -7 AND 7 AND note_type LIKE 'Operative%' THEN 1
+          ELSE 0 END
+      ) has_op_note,
+  FROM
+      filtered_examples fe
+  LEFT JOIN 
+      `mining-clinical-decisions.starr_datalake2018.clinical_doc_meta` cdoc
+  ON
+      anon_id = jc_uid
+  GROUP BY
+      pat_enc_csn_id_coded
+)
+
+SELECT 
+  fe.*,
+  CASE WHEN abx.pat_enc_csn_id_coded IS NOT NULL THEN 1 ELSE 0 END abx_stopped_2_days,
+  CASE WHEN orals.pat_enc_csn_id_coded IS NULL THEN 1 ELSE 0 END not_discharged_with_orals,
+  CASE WHEN other_cult.pat_enc_csn_id_coded IS NULL THEN 1 ELSE 0 END no_pos_cult_2_weeks_later,
+  CASE WHEN current_cult.pat_enc_csn_id_coded IS NULL THEN 1 ELSE 0 END no_pos_cult_within_day,
+  CASE WHEN dead.pat_enc_csn_id_coded IS NULL THEN 1 ELSE 0 END no_death_within_2_weeks,
+  CASE WHEN dx.pat_enc_csn_id_coded IS NULL THEN 1 ELSE 0 END no_inf_dx_codes,
+  CASE WHEN cdoc.has_op_note = 0 THEN 1 ELSE 0 END no_op_notes
+FROM
+  filtered_examples fe
+LEFT JOIN
+  started_abx_stopped_within_day_after_cult_results_or_not_started abx
+USING
+  (pat_enc_csn_id_coded)
+LEFT JOIN
+  discharged_with_orals orals
+USING
+  (pat_enc_csn_id_coded)
+LEFT JOIN
+  has_pos_cult_ordered_2_weeks other_cult 
+USING
+  (pat_enc_csn_id_coded)
+LEFT JOIN
+  has_pos_cult current_cult
+USING
+  (pat_enc_csn_id_coded)
+LEFT JOIN
+  patient_died dead
+USING
+  (pat_enc_csn_id_coded) 
+LEFT JOIN
+  infection_dx_code dx
+USING
+  (pat_enc_csn_id_coded)
+LEFT JOIN
+    has_operative_note cdoc
+USING 
+    (pat_enc_csn_id_coded)
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/culture_orders_within_24_hrs.sql b/scripts/ER_Infection/SQL/Refactored/culture_orders_within_24_hrs.sql
new file mode 100644
index 00000000..5a432a41
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/culture_orders_within_24_hrs.sql
@@ -0,0 +1,43 @@
+# Adult patients with admit codes from the ER who get blood, urine, csf, or fluid cultures ordered. 
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+er_admits_adults AS (
+SELECT DISTINCT ea.* 
+FROM er_admits ea
+INNER JOIN `shc_core.demographic` demo
+USING (anon_id)
+WHERE DATE_DIFF(CAST(ea.er_admit_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+),
+
+has_inp_code AS (
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM
+  (SELECT DISTINCT ea.pat_enc_csn_id_coded,
+  MAX(CASE WHEN adt.base_pat_class_c = 1 THEN 1 ELSE 0 END) has_inpatient 
+  FROM er_admits_adults ea
+  INNER JOIN shc_core.adt adt
+  USING (pat_enc_csn_id_coded)
+  GROUP BY ea.pat_enc_csn_id_coded) t
+WHERE has_inpatient = 1
+),
+
+include_culture as (
+SELECT description
+FROM `mining-clinical-decisions.abx.culture_types`  
+WHERE include = 1
+)
+
+SELECT ea.anon_id, ea.pat_enc_csn_id_coded, op.pat_enc_csn_id_coded op_csn, op.order_proc_id_coded, op.description, op.order_time_jittered_utc
+FROM `shc_core.order_proc` op
+INNER JOIN er_admits_adults ea
+USING (anon_id)
+INNER JOIN include_culture
+USING (description)
+INNER JOIN has_inp_code
+ON has_inp_code.pat_enc_csn_id_coded = ea.pat_enc_csn_id_coded
+WHERE TIMESTAMP_DIFF(op.order_time_jittered_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 24
+ORDER BY anon_id, op.order_time_jittered_utc
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/culture_types.sql b/scripts/ER_Infection/SQL/Refactored/culture_types.sql
new file mode 100644
index 00000000..a1390500
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/culture_types.sql
@@ -0,0 +1,15 @@
+SELECT DISTINCT description, 
+CASE WHEN description LIKE "BLOOD CULT%" THEN 1 
+WHEN description IN ("URINE CULTURE", "URINE CULTURE (MENLO)", "URINE CULTURE AND GRAM STAIN", "URINE SENSITIVITIES (MENLO)") THEN 1
+WHEN description IN ("FLUID CULTURE / BB GRAM STAIN", "FLUID CULTURE AND GRAM STAIN") THEN 1
+WHEN description IN ("CSF CULTURE AND GRAM STAIN", "CULTURE AND GRAM STAIN, CSF") THEN 1
+ELSE 0 END include,
+# This column may be userful later to see how analysis changes when we decide not to ignore results from all other cultures.
+CASE WHEN description LIKE "BLOOD CULT%" THEN 1 
+WHEN description IN ("URINE CULTURE", "URINE CULTURE (MENLO)", "URINE CULTURE AND GRAM STAIN", "URINE SENSITIVITIES (MENLO)") THEN 1
+WHEN description IN ("FLUID CULTURE / BB GRAM STAIN", "FLUID CULTURE AND GRAM STAIN") THEN 1
+WHEN description IN ("CSF CULTURE AND GRAM STAIN", "CULTURE AND GRAM STAIN, CSF") THEN 1
+ELSE 0 END affects_not_infected_label
+FROM `shc_core.order_proc` 
+WHERE order_type LIKE "Microbiology%"
+ORDER BY description
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/Refactored/final_cohort_table.sql b/scripts/ER_Infection/SQL/Refactored/final_cohort_table.sql
new file mode 100644
index 00000000..5f2af2b8
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/final_cohort_table.sql
@@ -0,0 +1,68 @@
+CREATE OR REPLACE TABLE `mining-clinical-decisions.abx.final_cohort_table` AS
+(
+SELECT anon_id, pat_enc_csn_id_coded, index_time,
+CASE WHEN abx_stopped_2_days = 1
+AND not_discharged_with_orals = 1
+AND no_pos_cult_2_weeks_later = 1
+AND no_pos_cult_within_day = 1
+AND no_inf_dx_codes = 1
+THEN 1 ELSE 0 END not_infected
+FROM `mining-clinical-decisions.abx.cohort_not_infected_rules`
+);
+
+CREATE OR REPLACE TABLE `mining-clinical-decisions.abx.final_cohort_table` AS (
+WITH joined_table AS (
+SELECT c.*, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin, l.Meropenem,
+l.Ampicillin, l.Ciprofloxacin, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Ceftriaxone,
+l.Vancomycin_Cefepime
+FROM `mining-clinical-decisions.abx.final_cohort_table` c
+LEFT JOIN `mining-clinical-decisions.abx.final_ast_labels` l
+USING (pat_enc_csn_id_coded)
+),
+
+labels as (SELECT anon_id, pat_enc_csn_id_coded, index_time,
+CASE WHEN Cefazolin = 1 OR (Cefazolin IS NULL AND not_infected = 1) THEN 1 # if suscept or not infected
+WHEN Cefazolin = 0 THEN 0 # if resistant
+ELSE NULL END Cefazolin, # if no bug growth but not necessarily not infected
+CASE WHEN Ceftriaxone = 1 OR (Ceftriaxone IS NULL AND not_infected = 1) THEN 1
+WHEN Ceftriaxone = 0 THEN 0
+ELSE NULL END Ceftriaxone,
+CASE WHEN Cefepime = 1 OR (Cefepime IS NULL AND not_infected = 1) THEN 1
+WHEN Cefepime = 0 THEN 0
+ELSE NULL END Cefepime,
+CASE WHEN Zosyn = 1 OR (Zosyn IS NULL AND not_infected = 1) THEN 1
+WHEN Zosyn = 0 THEN 0
+ELSE NULL END Zosyn,
+CASE WHEN Vancomycin = 1 OR (Vancomycin IS NULL AND not_infected = 1) THEN 1
+WHEN Vancomycin = 0 THEN 0
+ELSE NULL END Vancomycin,
+CASE WHEN Meropenem = 1 OR (Meropenem IS NULL AND not_infected = 1) THEN 1
+WHEN Meropenem = 0 THEN 0
+ELSE NULL END Meropenem,
+CASE WHEN Ampicillin = 1 OR (Ampicillin IS NULL AND not_infected = 1) THEN 1
+WHEN Ampicillin = 0 THEN 0
+ELSE NULL END Ampicillin,
+CASE WHEN Ciprofloxacin = 1 OR (Ciprofloxacin IS NULL AND not_infected = 1) THEN 1
+WHEN Ciprofloxacin = 0 THEN 0
+ELSE NULL END Ciprofloxacin,
+CASE WHEN Vancomycin_Cefepime = 1 OR (Vancomycin_Cefepime IS NULL AND not_infected = 1) THEN 1
+WHEN Vancomycin_Cefepime = 0 THEN 0
+ELSE NULL END Vancomycin_Cefepime,
+CASE WHEN Vancomycin_Ceftriaxone = 1 OR (Vancomycin_Ceftriaxone IS NULL AND not_infected = 1) THEN 1
+WHEN Vancomycin_Ceftriaxone = 0 THEN 0
+ELSE NULL END Vancomycin_Ceftriaxone,
+CASE WHEN Vancomycin_Meropenem = 1 OR (Vancomycin_Meropenem IS NULL AND not_infected = 1) THEN 1
+WHEN Vancomycin_Meropenem = 0 THEN 0
+ELSE NULL END Vancomycin_Meropenem,
+CASE WHEN Vancomycin_Zosyn = 1 OR (Vancomycin_Zosyn IS NULL AND not_infected = 1) THEN 1
+WHEN Vancomycin_Zosyn = 0 THEN 0
+ELSE NULL END Vancomycin_Zosyn
+
+
+
+
+FROM joined_table)
+
+SELECT l.*, CASE WHEN l.Cefazolin IS NULL THEN 1 ELSE 0 END label_unobserved
+FROM labels l
+)
diff --git a/scripts/ER_Infection/SQL/Refactored/mar_was_given.sql b/scripts/ER_Infection/SQL/Refactored/mar_was_given.sql
new file mode 100644
index 00000000..c8c2da5a
--- /dev/null
+++ b/scripts/ER_Infection/SQL/Refactored/mar_was_given.sql
@@ -0,0 +1,28 @@
+WITH mar_interm AS (
+SELECT abx.med_description, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times, om.order_med_id_coded
+FROM `mining-clinical-decisions.abx.abx_types` abx
+LEFT JOIN `shc_core.order_med` om
+USING (med_description)
+INNER JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+WHERE abx.is_include_abx = 1 OR abx.affects_not_infected_label = 1
+),
+
+t as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM mar_interm 
+GROUP BY order_med_id_coded, mar_action),
+
+tt as (
+SELECT order_med_id_coded, STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) action_list
+FROM t
+GROUP BY order_med_id_coded )
+
+SELECT action_list, COUNT (DISTINCT order_med_id_coded) cnt
+FROM tt
+LEFT JOIN mar_interm mar
+USING (order_med_id_coded)
+WHERE action_list NOT LIKE "%Given%" AND action_list NOT LIKE "%Complete%" AND action_list NOT LIKE "%Stopped%"
+AND action_list NOT LIKE "%New Bag%"
+GROUP BY action_list
+ORDER BY cnt DESC
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_ast_long.sql b/scripts/ER_Infection/SQL/abx_ast_long.sql
new file mode 100644
index 00000000..745ff02e
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_ast_long.sql
@@ -0,0 +1,18 @@
+WITH infected_csns AS (
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids_no_prior` inf
+WHERE infected = 1
+),
+
+culture_ids AS (
+SELECT DISTINCT pat_enc_csn_id_coded, order_proc_id_coded
+FROM `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` cults
+INNER JOIN infected_csns
+USING (pat_enc_csn_id_coded)
+)
+
+SELECT DISTINCT pat_enc_csn_id_coded, order_proc_id_coded, cs.sens_organism_sid, cs.line, cs.description, cs.organism, cs.antibiotic, cs.suscept
+FROM `shc_core.culture_sensitivity` cs
+INNER JOIN culture_ids
+USING (order_proc_id_coded)
+ORDER BY pat_enc_csn_id_coded, order_proc_id_coded, cs.sens_organism_sid, cs.line
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_cohort_mar_interm.sql b/scripts/ER_Infection/SQL/abx_cohort_mar_interm.sql
new file mode 100644
index 00000000..483b4985
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_cohort_mar_interm.sql
@@ -0,0 +1,9 @@
+SELECT abx.*, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times
+FROM `mining-clinical-decisions.conor_db.abx_med_orders_last_4_hours` abx
+INNER JOIN `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded) 
+ORDER BY anon_id, order_start_time_utc, order_med_id_coded, taken_time_jittered_utc
diff --git a/scripts/ER_Infection/SQL/abx_cohort_not_censored_label.sql b/scripts/ER_Infection/SQL/abx_cohort_not_censored_label.sql
new file mode 100644
index 00000000..8357dee6
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_cohort_not_censored_label.sql
@@ -0,0 +1,35 @@
+WITH cult_result as (
+SELECT DISTINCT co.anon_id, co.pat_enc_csn_id_coded, co.order_proc_id_coded, cs.organism
+FROM `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` co
+INNER JOIN 
+  (SELECT DISTINCT pat_enc_csn_id_coded FROM `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) cults_and_abx_csns
+USING (pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.culture_sensitivity` cs
+USING (order_proc_id_coded)
+ORDER BY co.anon_id, co.pat_enc_csn_id_coded
+),
+
+infected as ( -- make sure coag neg staph is deemed a contaminent
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM
+  (SELECT pat_enc_csn_id_coded, MAX(CASE WHEN organism IS NOT NULL AND organism <> "COAG NEGATIVE STAPHYLOCOCCUS" THEN 1 ELSE 0 END) any_positive
+  FROM cult_result
+  GROUP BY pat_enc_csn_id_coded) t
+WHERE any_positive = 1
+)
+
+SELECT ROW_NUMBER() OVER (ORDER BY cr.pat_enc_csn_id_coded) obs_id, cr.anon_id, cr.pat_enc_csn_id_coded, 
+CASE WHEN ni.pat_enc_csn_id_coded IS NOT NULL THEN 1 ELSE 0 END not_infected,
+CASE WHEN i.pat_enc_csn_id_coded IS NOT NULL THEN 1 ELSE 0 END infected,
+CASE WHEN ni.pat_enc_csn_id_coded IS NULL AND i.pat_enc_csn_id_coded IS NULL THEN 0 ELSE 1 END not_censored,  --- this is what we want to estimate P(C = 0 | x)
+min(mo.order_start_time_utc) index_time
+FROM cult_result cr
+LEFT JOIN infected i
+USING (pat_enc_csn_id_coded)
+LEFT JOIN `mining-clinical-decisions.conor_db.abx_not_infected_csns` ni
+USING (pat_enc_csn_id_coded)
+LEFT JOIN `mining-clinical-decisions.conor_db.abx_med_orders_last_4_hours` mo
+USING (pat_enc_csn_id_coded)
+GROUP BY cr.anon_id, cr.pat_enc_csn_id_coded,  not_infected, infected, not_censored
+
+
diff --git a/scripts/ER_Infection/SQL/abx_cohort_not_censored_no_kids.sql b/scripts/ER_Infection/SQL/abx_cohort_not_censored_no_kids.sql
new file mode 100644
index 00000000..c8e5177a
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_cohort_not_censored_no_kids.sql
@@ -0,0 +1,7 @@
+SELECT cens.*, DATE_DIFF(CAST(cens.index_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) age,
+demo.CANONICAL_RACE, demo.CANONICAL_ETHNICITY, demo.GENDER, demo.LANGUAGE, demo.MARITAL_STATUS, demo.INSURANCE_PAYOR_NAME, demo.DEATH_DATE_JITTERED
+FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_label` cens
+INNER JOIN `shc_core.demographic` demo
+USING (anon_id)
+WHERE DATE_DIFF(CAST(cens.index_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+ORDER BY obs_id
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_cohort_not_censored_no_kids_no_prior.sql b/scripts/ER_Infection/SQL/abx_cohort_not_censored_no_kids_no_prior.sql
new file mode 100644
index 00000000..a990d1c7
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_cohort_not_censored_no_kids_no_prior.sql
@@ -0,0 +1,54 @@
+-- Finds subset of csns in mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids 
+-- where cultures/abx have not been ordered for patient within 14 days prior of the admit time for the ER encounter
+-- we are using for our observation. 
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+csns_with_prior_abx AS (
+SELECT DISTINCT c.pat_enc_csn_id_coded
+FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids` c
+INNER JOIN (SELECT pat_enc_csn_id_coded, er_admit_time FROM er_admits) ea
+USING (pat_enc_csn_id_coded)
+LEFT JOIN 
+  (SELECT *
+  FROM `shc_core.order_med` om
+  INNER JOIN `mining-clinical-decisions.conor_db.abx_include` abx
+  USING (med_description)
+  WHERE abx.is_include_abx = 1) abx
+USING (anon_id)
+WHERE TIMESTAMP_DIFF(ea.er_admit_time, abx.order_start_time_utc, MINUTE) BETWEEN 0 AND 14*24*60 
+), 
+
+csns_with_prior_pos_cult AS (
+SELECT DISTINCT c.pat_enc_csn_id_coded
+FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids` c
+INNER JOIN (SELECT pat_enc_csn_id_coded, er_admit_time FROM er_admits) ea
+USING (pat_enc_csn_id_coded)
+LEFT JOIN 
+  (SELECT * 
+  FROM `shc_core.culture_sensitivity` cs
+  INNER JOIN `mining-clinical-decisions.conor_db.abx_include_exclude_cultures` c
+  USING (description)
+  WHERE c.included = 1 OR c.excluded = 1) pc
+USING (anon_id)
+WHERE TIMESTAMP_DIFF(ea.er_admit_time, pc.order_time_jittered_utc, MINUTE) BETWEEN 0 AND 14*24*60
+),
+
+csns_to_remove as (
+SELECT pat_enc_csn_id_coded
+FROM csns_with_prior_abx
+
+UNION DISTINCT
+
+SELECT pat_enc_csn_id_coded
+FROM csns_with_prior_pos_cult
+)
+
+SELECT c.*
+FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids` c
+LEFT JOIN csns_to_remove rm
+USING (pat_enc_csn_id_coded)
+WHERE rm.pat_enc_csn_id_coded IS NULL
diff --git a/scripts/ER_Infection/SQL/abx_culture_orders_within_4_hours.sql b/scripts/ER_Infection/SQL/abx_culture_orders_within_4_hours.sql
new file mode 100644
index 00000000..53ec28de
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_culture_orders_within_4_hours.sql
@@ -0,0 +1,32 @@
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+-- Finds included cultures in order_proc, removes ED stays with excluded cultures,
+-- Forces cultures to appear in lab_results
+culture_orders as (
+SELECT DISTINCT ea.anon_id, ea.pat_enc_csn_id_coded, op.pat_enc_csn_id_coded as op_csn, op.description, op.order_proc_id_coded, op.order_time_jittered_utc,
+ec.included, ec.excluded
+FROM `shc_core.order_proc` op
+INNER JOIN er_admits ea
+USING (anon_id) --- will include overlapping csns assciated with order (if they exist)
+INNER JOIN `shc_core.lab_result` lr -- Forces order to appear in lab results - this is where selection bias could creep in. 
+ON op.order_proc_id_coded = lr.order_id_coded
+INNER JOIN `mining-clinical-decisions.conor_db.abx_include_exclude_cultures` ec 
+USING (description)
+WHERE TIMESTAMP_DIFF(op.order_time_jittered_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 4),
+
+-- HAS AT LEAST ONE INCLUDE CULTURE AND NO EXCLUDE CULTRES
+c_o_include_flag as (
+SELECT pat_enc_csn_id_coded, MAX(included) included, MAX(excluded) excluded
+FROM culture_orders 
+GROUP BY pat_enc_csn_id_coded)
+
+SELECT DISTINCT culture_orders.* 
+FROM culture_orders
+INNER JOIN c_o_include_flag co
+USING (pat_enc_csn_id_coded)
+WHERE co.included = 1 AND co.excluded = 0
+ORDER BY pat_enc_csn_id_coded, order_time_jittered_utc
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_include.sql b/scripts/ER_Infection/SQL/abx_include.sql
new file mode 100644
index 00000000..dbfe985c
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_include.sql
@@ -0,0 +1,32 @@
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+antibiotics as (
+SELECT COUNT (DISTINCT order_med_id_coded) cnt, REGEXP_EXTRACT(med_description, r'([^\s]+)') as simple_abx
+FROM `shc_core.order_med` 
+WHERE thera_class_name LIKE '%ANTIBIOTIC%'
+GROUP BY simple_abx#, med_description
+ORDER BY simple_abx)
+
+SELECT om.med_description, COUNT (DISTINCT om.order_med_id_coded) as num_times_ordered_4_hrs_into_ed,
+CASE WHEN med_description LIKE "% IV%" THEN 1 #"IV"
+WHEN med_description = "ZZZ IMS TEMPLATE" THEN 0
+WHEN med_description LIKE "% IVPB %" THEN 1 #"IV"
+WHEN med_description LIKE "%BAG%" THEN 1 #"IV"
+WHEN med_description LIKE "% INJECTION%" THEN 1 #"IM"
+WHEN med_description LIKE "% IM%" THEN 1 # "IM"
+WHEN med_description LIKE "% INJ %" THEN 1 #"IM"
+WHEN med_description LIKE "% PO%" THEN 1 # "Oral" 
+WHEN med_description LIKE "%VANCOMYCIN PER PHARMACY PROTOCOL%" OR med_description LIKE "%VANCOMYCIN ED ORDER%"  OR med_description LIKE "%VANCOMYCIN ORAL SOLUTION%" THEN 1 # "IV"
+ELSE 0 END is_include_abx
+FROM `shc_core.order_med` om
+INNER JOIN er_admits
+USING (anon_id)
+INNER JOIN antibiotics
+ON antibiotics.simple_abx =  REGEXP_EXTRACT(om.med_description, r'([^\s]+)')
+WHERE TIMESTAMP_DIFF(om.order_start_time_utc, er_admits.er_admit_time, HOUR) BETWEEN 0 AND 4
+GROUP BY om.med_description
+ORDER BY med_description desc
diff --git a/scripts/ER_Infection/SQL/abx_include_exclude_cultures.sql b/scripts/ER_Infection/SQL/abx_include_exclude_cultures.sql
new file mode 100644
index 00000000..e0ec4b2c
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_include_exclude_cultures.sql
@@ -0,0 +1,41 @@
+DECLARE culture_names ARRAY<STRING>;
+
+SET culture_names = ["URINE CULTURE", "BLOOD CULTURE (2 AEROBIC BOTTLES)", "BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)", "BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLE)", "ANAEROBIC CULTURE", "RESPIRATORY CULTURE AND GRAM STAIN", "RESPIRATORY CULTURE", "FLUID CULTURE AND GRAM STAIN", "WOUND CULTURE", "STOOL CULTURE", "CSF CULTURE AND GRAM STAIN", "BLOOD CULT - FIRST SET, VIA PHLEBOTOMY", "BLOOD CULT CENTRAL LINE CATHETER BY NURSE", "WOUND CULTURE AND GRAM STAIN", "WOUND CULTURE AND GRAM STAIN, DEEP / SURGICAL / ASP", "FLUID CULTURE / BB GRAM STAIN", "CYSTIC FIBROSIS RESPIRATORY CULTURE"];
+
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+in_micro AS (
+SELECT DISTINCT op.description 
+FROM `shc_core.order_proc` op
+WHERE op.order_type = "Microbiology"
+),
+
+in_micro_cult AS (
+SELECT DISTINCT op.description 
+FROM `shc_core.order_proc` op
+WHERE op.order_type = "Microbiology Culture"
+),
+
+in_micro_not_micro_cult AS (
+SELECT DISTINCT op.description, op.order_proc_id_coded, CASE WHEN op.order_type = "Microbiology" THEN 1 ELSE 0 END in_micro,
+CASE WHEN op.order_type = "Microbiology Culture" THEN 1 ELSE 0 END in_micro_culture
+FROM `shc_core.order_proc` op
+INNER JOIN er_admits ea
+USING (anon_id)
+WHERE TIMESTAMP_DIFF(op.order_time_jittered_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 4
+AND (op.description IN UNNEST(culture_names) OR op.order_type LIKE "Microbiology%")
+)
+
+
+SELECT description, COUNT (DISTINCT order_proc_id_coded) num_orders_4_hrs_into_stay,
+-- Include cultures from array defined above
+CASE WHEN description IN UNNEST(culture_names) THEN 1 ELSE 0 END included,
+-- Exclude cultures not in array defined above except for descriptions that appear in order_type microbiology but not order_type microbiology_culture - ex: gram stain shouldn't be excluded
+CASE WHEN description NOT IN UNNEST(culture_names) AND NOT (MAX(in_micro) = 1 AND MAX(in_micro_culture) = 0) THEN 1 ELSE 0 END excluded 
+FROM in_micro_not_micro_cult
+GROUP BY description
+ORDER BY description
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_mar_action_was_given_flag.sql b/scripts/ER_Infection/SQL/abx_mar_action_was_given_flag.sql
new file mode 100644
index 00000000..e8de42ac
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_mar_action_was_given_flag.sql
@@ -0,0 +1,22 @@
+-- This query created all unique mar_action lists for a particular order_id using timestamp of first time
+-- a particular action was taken (eg. given can be listed more than once for a particular order_id). 
+-- This table was then manually editted to add was_given and unsure_if_given columsn to state whether
+-- the mar_action sequence implies the abx was actually administered and whether I was confident about
+-- it. 
+WITH t as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM `mining-clinical-decisions.conor_db.abx_cohort_mar_interm` 
+GROUP BY order_med_id_coded, mar_action),
+
+tt as (
+SELECT order_med_id_coded, STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) action_list
+FROM t
+GROUP BY order_med_id_coded )
+
+SELECT action_list, COUNT (DISTINCT order_med_id_coded) cnt
+FROM tt
+LEFT JOIN `mining-clinical-decisions.conor_db.abx_cohort_mar_interm` mar
+USING (order_med_id_coded)
+WHERE action_list NOT LIKE "%Given%" AND action_list NOT LIKE "%Completed%" AND action_list NOT LIKE "%Stopped%"
+GROUP BY action_list
+ORDER BY cnt DESC
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_med_orders_given_and_stopped_info.sql b/scripts/ER_Infection/SQL/abx_med_orders_given_and_stopped_info.sql
new file mode 100644
index 00000000..8ea5765d
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_med_orders_given_and_stopped_info.sql
@@ -0,0 +1,59 @@
+WITH mar_action_interim as (
+SELECT abx.*, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times
+FROM `mining-clinical-decisions.conor_db.abx_med_orders_last_4_hours` abx
+INNER JOIN `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded) 
+ORDER BY anon_id, order_start_time_utc, order_med_id_coded, taken_time_jittered_utc
+), 
+
+distinct_actions as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM mar_action_interim 
+GROUP BY order_med_id_coded, mar_action),
+
+grouped_actions as (
+SELECT order_med_id_coded, 
+max(taken_time_jittered_utc) last_mar_action_time,
+CASE WHEN STRING_AGG(mar_action) IS NULL THEN NULL
+ELSE STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) END action_list
+FROM distinct_actions
+GROUP BY order_med_id_coded),
+
+orders_with_info as 
+(SELECT DISTINCT abx.anon_id, abx.pat_enc_csn_id_coded, abx.order_start_time_utc, abx.med_description, ga.order_med_id_coded, ga.action_list, ga.last_mar_action_time,
+-- Was_Given should be 1 if Given, Completed, or Stopped exists in action_list
+CASE WHEN ga.action_list LIKE "%Given%" OR action_list  LIKE "%Completed%" OR action_list LIKE "%Stopped%" THEN 1
+-- Shoudl be 1 if was_given flag from mar_action_was_given table is 1 
+WHEN wg.was_given = 1 THEN 1
+ELSE 0 END was_given,
+FROM `mining-clinical-decisions.conor_db.abx_med_orders_last_4_hours` abx
+INNER JOIN `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` cult
+USING (anon_id, pat_enc_csn_id_coded)
+LEFT JOIN grouped_actions ga
+USING (order_med_id_coded)
+LEFT JOIN `mining-clinical-decisions.conor_db.abx_mar_actions_was_given_flag` wg
+USING (action_list))
+
+SELECT DISTINCT owi.*, om.discon_time_jittered_utc,
+-- When no mar actions listed for order than infer wasn't given and abx stop time will be order time
+CASE WHEN was_given = 0 AND action_list IS NULL AND om.discon_time_jittered_utc IS NULL THEN owi.order_start_time_utc
+-- If not in mar actions but discontinued time listed in order_med then abx stop time is discontinued time
+WHEN was_given = 0 AND action_list IS NULL THEN om.discon_time_jittered_utc 
+-- If in mar but nothing to infer was actually given, then take last known timestamp as stop time. 
+WHEN was_given = 0 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 0 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc
+-- If in mar and was given but not discontinued time listed in order_med, then take timestamp of last mar action
+WHEN was_given = 1 AND discon_time_jittered_utc IS NULL THEN last_mar_action_time
+-- If in mar and was given and there exists a discontinued time, then take latest timestamp between that and last mar action
+WHEN was_given = 1 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 1 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc END abx_stop_time
+FROM orders_with_info owi
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+ORDER BY anon_id, pat_enc_csn_id_coded, order_start_time_utc
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_med_orders_within_last_4_hours.sql b/scripts/ER_Infection/SQL/abx_med_orders_within_last_4_hours.sql
new file mode 100644
index 00000000..da62c1cd
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_med_orders_within_last_4_hours.sql
@@ -0,0 +1,20 @@
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+include_abx as (
+SELECT med_description
+FROM `mining-clinical-decisions.conor_db.abx_include` 
+WHERE is_include_abx = 1
+)
+
+SELECT ea.anon_id, ea.pat_enc_csn_id_coded, om.pat_enc_csn_id_coded om_csn, om.order_med_id_coded, om.med_description, om.order_start_time_utc
+FROM `shc_core.order_med` om
+INNER JOIN er_admits ea
+USING (anon_id)
+INNER JOIN include_abx
+USING (med_description)
+WHERE TIMESTAMP_DIFF(om.order_start_time_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 4
+ORDER BY anon_id, om.order_start_time_utc
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/abx_not_infected_csns.sql b/scripts/ER_Infection/SQL/abx_not_infected_csns.sql
new file mode 100644
index 00000000..284a9275
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_not_infected_csns.sql
@@ -0,0 +1,59 @@
+WITH cult_result as (
+SELECT DISTINCT co.anon_id, co.pat_enc_csn_id_coded, co.order_proc_id_coded, cs.organism
+FROM `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` co
+INNER JOIN 
+  (SELECT DISTINCT pat_enc_csn_id_coded FROM `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) cults_and_abx_csns
+USING (pat_enc_csn_id_coded)
+LEFT JOIN `shc_core.culture_sensitivity` cs
+USING (order_proc_id_coded)
+ORDER BY co.anon_id, co.pat_enc_csn_id_coded
+),
+
+any_growth as -- make sure coag neg staph is deemed a contaminent
+(SELECT pat_enc_csn_id_coded, MAX(CASE WHEN organism IS NOT NULL AND organism <> "COAG NEGATIVE STAPHYLOCOCCUS" THEN 1 ELSE 0 END) any_positive
+FROM cult_result
+GROUP BY pat_enc_csn_id_coded),
+
+start_stop_time as (
+SELECT anon_id, pat_enc_csn_id_coded, max(was_given) any_abx_given, min(order_start_time_utc) first_abx_order_time, max(abx_stop_time) last_abx_stop_time
+FROM `mining-clinical-decisions.conor_db.abx_temp_14_day_orders` 
+GROUP BY anon_id, pat_enc_csn_id_coded
+ORDER BY anon_id, pat_enc_csn_id_coded),
+
+er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+-- Looks for positive cultures in include/exclude set of cultures that were ordered within 14 days of er_stay, but not within 4 hours. Patients with these should not be labelled not infected.
+pos_cults_outside_4hrs AS (
+SELECT DISTINCT ea.pat_enc_csn_id_coded
+FROM er_admits ea
+INNER JOIN (SELECT pat_enc_csn_id_coded FROM any_growth WHERE any_positive = 0) no_growth_in_4
+USING (pat_enc_csn_id_coded)
+RIGHT JOIN `shc_core.culture_sensitivity` cs
+USING (anon_id)
+INNER JOIN `mining-clinical-decisions.conor_db.abx_include_exclude_cultures` inec
+USING (description)
+WHERE TIMESTAMP_DIFF(cs.order_time_jittered_utc, ea.er_admit_time, DAY) BETWEEN 0 AND 14
+AND (inec.included = 1 OR inec.excluded = 1)
+),
+
+abx_stop_time AS (
+SELECT ss.pat_enc_csn_id_coded, TIMESTAMP_DIFF(ss.last_abx_stop_time, ea.er_admit_time, DAY) time_until_abx_stop
+FROM start_stop_time ss
+INNER JOIN er_admits ea
+USING (pat_enc_csn_id_coded))
+
+SELECT DISTINCT pat_enc_csn_id_coded
+FROM cult_result cr
+INNER JOIN (SELECT DISTINCT pat_enc_csn_id_coded FROM any_growth WHERE any_positive = 0) pos_csns -- must not have positive culture from 4 hr orders
+USING (pat_enc_csn_id_coded)
+LEFT JOIN pos_cults_outside_4hrs pos_cults_14
+USING (pat_enc_csn_id_coded)
+INNER JOIN abx_stop_time abx_stop
+USING (pat_enc_csn_id_coded)
+WHERE time_until_abx_stop = 0 -- must have stopped abx within first hour of admit time (and not have restarted the for 14 days).
+AND pos_cults_14.pat_enc_csn_id_coded IS NULL -- must not be a csn with a positive culture within 14 days. 
+
diff --git a/scripts/ER_Infection/SQL/abx_organism_and_ast_report_freq.sql b/scripts/ER_Infection/SQL/abx_organism_and_ast_report_freq.sql
new file mode 100644
index 00000000..cbb748c1
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_organism_and_ast_report_freq.sql
@@ -0,0 +1,14 @@
+# For each bug, what fraction of the time is the antibiotic suscept listed?
+WITH num_orders_with_bug AS (
+SELECT organism, COUNT (DISTINCT CONCAT(CAST (order_proc_id_coded AS STRING), sens_organism_sid)) total_bugs_grown
+FROM `mining-clinical-decisions.conor_db.abx_ast_long`
+GROUP BY organism
+ORDER BY total_bugs_grown DESC
+)
+
+SELECT organism,  antibiotic, COUNT (DISTINCT CONCAT(CAST (order_proc_id_coded AS STRING), sens_organism_sid)) total_times_ast_reported, total_bugs_grown
+FROM `mining-clinical-decisions.conor_db.abx_ast_long` abx
+INNER JOIN num_orders_with_bug total
+USING (organism)
+GROUP BY organism, antibiotic, total_bugs_grown
+ORDER BY total_bugs_grown desc, organism, antibiotic 
diff --git a/scripts/ER_Infection/SQL/abx_temp_14_day_orders.sql b/scripts/ER_Infection/SQL/abx_temp_14_day_orders.sql
new file mode 100644
index 00000000..a65fb3e8
--- /dev/null
+++ b/scripts/ER_Infection/SQL/abx_temp_14_day_orders.sql
@@ -0,0 +1,84 @@
+-- For each CSN where abx were ordered and cultures were ordered both within 4 hrs of er admit, find all abx that were ordered within 14 days of er admit
+-- and find the time the last antibiotic was stopped. Will use this as as my discontinued time to help build "not_infected" labels. This creates a temp table
+-- that lists each abx order, whether it was administered, and the time it was stopped for all ordered IV, IM, Oral antibiotics within 14 days of er admit.
+-- Need to group by csn, and take the max abx_stop_time to get discontinued time.  Saving this temp file for future sanity checks. 
+WITH er_admits AS (
+SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+FROM `shc_core.adt`
+WHERE pat_class_c = "112" AND pat_service = "Emergency"
+GROUP BY anon_id, pat_enc_csn_id_coded),
+
+include_abx as (
+SELECT med_description
+FROM `mining-clinical-decisions.conor_db.abx_include` 
+WHERE is_include_abx = 1
+),
+
+frteen_day_abx as (
+SELECT ea.anon_id, ea.pat_enc_csn_id_coded, om.pat_enc_csn_id_coded om_csn, om.order_med_id_coded, om.med_description, om.order_start_time_utc
+FROM `shc_core.order_med` om
+INNER JOIN er_admits ea
+USING (anon_id)
+INNER JOIN 
+  (SELECT DISTINCT anon_id FROM
+   `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) has_abx_orders
+USING (anon_id)
+INNER JOIN include_abx
+USING (med_description)
+WHERE TIMESTAMP_DIFF(om.order_start_time_utc, ea.er_admit_time, DAY) BETWEEN 0 AND 14
+ORDER BY anon_id, om.order_start_time_utc),
+
+mar_action_interim as (
+SELECT abx.*, mar.line, mar.mar_action, mar.route, mar_duration, mar.taken_time_jittered_utc, om.discon_time_jittered_utc, om.order_status, om.freq_name, om.number_of_times
+FROM frteen_day_abx abx
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded) 
+ORDER BY anon_id, order_start_time_utc, order_med_id_coded, taken_time_jittered_utc
+), 
+
+distinct_actions as (
+SELECT DISTINCT order_med_id_coded, mar_action, min(taken_time_jittered_utc) taken_time_jittered_utc
+FROM mar_action_interim 
+GROUP BY order_med_id_coded, mar_action),
+
+grouped_actions as (
+SELECT order_med_id_coded, 
+max(taken_time_jittered_utc) last_mar_action_time,
+CASE WHEN STRING_AGG(mar_action) IS NULL THEN NULL
+ELSE STRING_AGG( mar_action ORDER BY taken_time_jittered_utc) END action_list
+FROM distinct_actions
+GROUP BY order_med_id_coded),
+
+orders_with_info as 
+(SELECT DISTINCT abx.anon_id, abx.pat_enc_csn_id_coded, abx.order_start_time_utc, abx.med_description, ga.order_med_id_coded, ga.action_list, ga.last_mar_action_time,
+-- Was_Given should be 1 if Given, Completed, or Stopped exists in action_list
+CASE WHEN ga.action_list LIKE "%Given%" OR action_list  LIKE "%Completed%" OR action_list LIKE "%Stopped%" THEN 1
+-- Shoudl be 1 if was_given flag from mar_action_was_given table is 1 
+WHEN wg.was_given = 1 THEN 1
+ELSE 0 END was_given,
+FROM frteen_day_abx abx
+LEFT JOIN grouped_actions ga
+USING (order_med_id_coded)
+LEFT JOIN `mining-clinical-decisions.conor_db.abx_mar_actions_was_given_flag` wg
+USING (action_list))
+
+SELECT DISTINCT owi.*, om.discon_time_jittered_utc,
+CASE WHEN was_given = 0 AND action_list IS NULL AND om.discon_time_jittered_utc IS NULL THEN owi.order_start_time_utc
+WHEN was_given = 0 AND action_list IS NULL THEN om.discon_time_jittered_utc 
+WHEN was_given = 0 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 0 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc
+WHEN was_given = 1 AND discon_time_jittered_utc IS NULL THEN last_mar_action_time
+WHEN was_given = 1 AND last_mar_action_time > discon_time_jittered_utc THEN last_mar_action_time
+WHEN was_given = 1 AND last_mar_action_time < discon_time_jittered_utc THEN discon_time_jittered_utc END abx_stop_time
+FROM orders_with_info owi
+LEFT JOIN `shc_core.order_med` om
+USING (order_med_id_coded)
+LEFT JOIN `shc_core.mar` mar
+USING (order_med_id_coded)
+INNER JOIN 
+  (SELECT DISTINCT pat_enc_csn_id_coded FROM
+   `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) has_abx_orders
+ON owi.pat_enc_csn_id_coded = has_abx_orders.pat_enc_csn_id_coded  -- should only keep enc's from ER visit that had the cultures taken
+ORDER BY anon_id, pat_enc_csn_id_coded, order_start_time_utc
\ No newline at end of file
diff --git a/scripts/ER_Infection/SQL/positive_urine_cultures_outpatient.sql b/scripts/ER_Infection/SQL/positive_urine_cultures_outpatient.sql
new file mode 100644
index 00000000..f1b0a7a6
--- /dev/null
+++ b/scripts/ER_Infection/SQL/positive_urine_cultures_outpatient.sql
@@ -0,0 +1,121 @@
+-- URINE CULTURE TABLE
+WITH pos_outpatient_urine_cultures AS (
+    SELECT
+        cs.anon_id,
+        op.pat_enc_csn_id_coded,
+        cs.description,
+        cs.order_proc_id_coded,
+        cs.organism,
+        cs.order_time_jittered_utc
+    FROM
+        `mining-clinical-decisions.shc_core.culture_sensitivity` cs
+    INNER JOIN 
+        -- Need csn from order_proc to join to er_admits
+        (SELECT pat_enc_csn_id_coded, order_proc_id_coded
+         FROM `mining-clinical-decisions.shc_core.order_proc`
+         WHERE order_type LIKE "Microbiology%"
+         AND description LIKE "%URINE%"
+         AND ordering_mode = 'Outpatient') op
+    USING
+        (order_proc_id_coded) 
+),
+-- Filter er admits to adult patients
+adult_puc AS (
+    SELECT DISTINCT
+        puc.* 
+    FROM 
+        pos_outpatient_urine_cultures puc
+    INNER JOIN
+        `shc_core.demographic` demo
+    USING
+        (anon_id)
+    WHERE
+        DATE_DIFF(CAST(puc.order_time_jittered_utc as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+),
+
+-- Set of antibiotics that show up in order_med (may not be totally all encompassing) 
+include_abx AS (
+    SELECT
+        med_description
+    FROM
+        `mining-clinical-decisions.abx.abx_types` 
+    WHERE
+        is_include_abx = 1 OR is_oral = 1
+),
+
+-- Antibiotic orders within window of culture order time. window = -4 to 24 hours. 
+abx_orders AS (
+    SELECT 
+        puc.anon_id,
+        puc.pat_enc_csn_id_coded,
+        om.order_med_id_coded,
+        om.med_description,
+        om.order_inst_utc
+    FROM
+        `mining-clinical-decisions.shc_core.order_med` om
+    RIGHT JOIN -- preserve instances when no antibiotic are ordered
+        adult_puc puc 
+    USING
+        (anon_id)
+    INNER JOIN
+        include_abx
+    USING
+        (med_description)
+    WHERE 
+        TIMESTAMP_DIFF(om.order_inst_utc, puc.order_time_jittered_utc, HOUR) BETWEEN -4 AND 24
+),
+
+--Other postive culture types ordered within window of urine culture. window = -4 ot 24 hours
+pos_other_cultures_24_hours AS (
+    SELECT
+        puc.anon_id,
+        puc.pat_enc_csn_id_coded,
+        cs.description,
+        cs.organism,
+    FROM
+        `mining-clinical-decisions.shc_core.culture_sensitivity` cs
+    INNER JOIN 
+        -- Need csn from order_proc to join to er_admits
+        (SELECT pat_enc_csn_id_coded, order_proc_id_coded
+         FROM `mining-clinical-decisions.shc_core.order_proc`
+         WHERE order_type LIKE "Microbiology%"
+         AND description NOT LIKE "%URINE%") op
+    USING
+        (order_proc_id_coded) 
+    INNER JOIN
+        adult_puc puc
+    USING
+        (anon_id)
+    WHERE  
+        TIMESTAMP_DIFF(cs.order_time_jittered_utc, puc.order_time_jittered_utc, HOUR)
+    BETWEEN
+        -4 AND 24
+)
+
+SELECT DISTINCT
+    puc.*,
+    ab.med_description,
+    STRING_AGG(DISTINCT mar.mar_action) mar_actions,
+    STRING_AGG(DISTINCT other.description) other_positive_cultures,
+    STRING_AGG(DISTINCT other.organism) bugs_from_other_cultures
+FROM
+    adult_puc puc
+LEFT JOIN
+    abx_orders ab 
+USING
+    (pat_enc_csn_id_coded)
+LEFT JOIN
+    `mining-clinical-decisions.shc_core.mar` mar
+USING
+    (order_med_id_coded)
+LEFT JOIN
+    pos_other_cultures_24_hours other
+USING 
+    (pat_enc_csn_id_coded)
+GROUP BY 
+    anon_id, pat_enc_csn_id_coded,
+    order_time_jittered_utc, description,
+    order_proc_id_coded, organism,
+    med_description
+ORDER BY 
+    anon_id, order_time_jittered_utc
diff --git a/scripts/ER_Infection/SQL/positive_urine_cultures_with_abx_info.sql b/scripts/ER_Infection/SQL/positive_urine_cultures_with_abx_info.sql
new file mode 100644
index 00000000..fd0e079c
--- /dev/null
+++ b/scripts/ER_Infection/SQL/positive_urine_cultures_with_abx_info.sql
@@ -0,0 +1,143 @@
+-- URINE CULTURE TABLE
+WITH er_admits AS (
+  SELECT 
+    anon_id,
+    pat_enc_csn_id_coded,
+    min(effective_time_jittered_utc) as er_admit_time,
+    max(effective_time_jittered_utc) as er_transfer_out_time
+  FROM
+    `shc_core.adt`
+  WHERE
+    pat_class_c = "112" AND pat_service = "Emergency"
+  GROUP BY 
+    anon_id, pat_enc_csn_id_coded
+),
+
+-- Filter er admits to adult patients
+er_admits_adults AS (
+    SELECT DISTINCT
+        ea.* 
+    FROM 
+        er_admits ea
+    INNER JOIN
+        `shc_core.demographic` demo
+    USING
+        (anon_id)
+    WHERE
+        DATE_DIFF(CAST(ea.er_admit_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+),
+
+-- Set of antibiotics that show up in order_med (may not be totally all encompassing) 
+include_abx AS (
+    SELECT
+        med_description
+    FROM
+        `mining-clinical-decisions.abx.abx_types` 
+    WHERE
+        is_include_abx = 1 OR is_oral = 1
+),
+
+-- Antibiotic orders within 24 hours of er visit (IV/IM/Oral)
+abx_orders_24_hours AS (
+    SELECT 
+        ea.anon_id,
+        ea.pat_enc_csn_id_coded,
+        om.order_med_id_coded,
+        om.med_description,
+        om.order_inst_utc
+    FROM
+        `mining-clinical-decisions.shc_core.order_med` om
+    RIGHT JOIN -- preserve instances when no antibiotic are ordered
+        er_admits_adults ea 
+    USING
+        (anon_id)
+    INNER JOIN
+        include_abx
+    USING
+        (med_description)
+    WHERE 
+        TIMESTAMP_DIFF(om.order_inst_utc, ea.er_admit_time, HOUR) BETWEEN 0 AND 24
+),
+
+-- Positive urine cultures ordered within 24 hours of ER visit
+pos_urine_cultures_24_hours AS (
+    SELECT
+        cs.anon_id,
+        op.pat_enc_csn_id_coded,
+        er.er_admit_time,
+        cs.description,
+        cs.order_proc_id_coded,
+        cs.organism,
+    FROM
+        `mining-clinical-decisions.shc_core.culture_sensitivity` cs
+    INNER JOIN 
+        -- Need csn from order_proc to join to er_admits
+        (SELECT pat_enc_csn_id_coded, order_proc_id_coded
+         FROM `mining-clinical-decisions.shc_core.order_proc`
+         WHERE order_type LIKE "Microbiology%"
+         AND description LIKE "%URINE%") op
+    USING
+        (order_proc_id_coded) 
+    INNER JOIN
+        er_admits_adults er
+    USING
+        (pat_enc_csn_id_coded)
+    WHERE  
+        TIMESTAMP_DIFF(cs.order_time_jittered_utc, er.er_admit_time, HOUR)
+    BETWEEN
+        0 AND 24
+),
+
+--Other postive culture types ordered within 24 hours of er visit
+pos_other_cultures_24_hours AS (
+    SELECT
+        er.pat_enc_csn_id_coded,
+        description,
+        organism,
+    FROM
+        `mining-clinical-decisions.shc_core.culture_sensitivity` cs
+    INNER JOIN 
+        -- Need csn from order_proc to join to er_admits
+        (SELECT pat_enc_csn_id_coded, order_proc_id_coded
+         FROM `mining-clinical-decisions.shc_core.order_proc`
+         WHERE order_type LIKE "Microbiology%"
+         AND description NOT LIKE "%URINE%") op
+    USING
+        (order_proc_id_coded) 
+    INNER JOIN
+        er_admits_adults er
+    USING
+        (pat_enc_csn_id_coded)
+    WHERE  
+        TIMESTAMP_DIFF(cs.order_time_jittered_utc, er.er_admit_time, HOUR)
+    BETWEEN
+        0 AND 24
+)
+
+SELECT DISTINCT
+    uc.*,
+    ab.med_description,
+    STRING_AGG(DISTINCT mar.mar_action) mar_actions,
+    STRING_AGG(DISTINCT other.description) other_positive_cultures,
+    STRING_AGG(DISTINCT other.organism) bugs_from_other_cultures
+FROM
+    pos_urine_cultures_24_hours uc
+LEFT JOIN
+    abx_orders_24_hours ab 
+USING
+    (pat_enc_csn_id_coded)
+LEFT JOIN
+    `mining-clinical-decisions.shc_core.mar` mar
+USING
+    (order_med_id_coded)
+LEFT JOIN
+    pos_other_cultures_24_hours other
+USING 
+    (pat_enc_csn_id_coded)
+GROUP BY 
+    anon_id, pat_enc_csn_id_coded,
+    er_admit_time, description,
+    order_proc_id_coded, organism,
+    med_description
+ORDER BY 
+    anon_id, er_admit_time
diff --git a/scripts/ER_Infection/notebooks/decision_alg/Allocation Distribution Figure.ipynb b/scripts/ER_Infection/notebooks/decision_alg/Allocation Distribution Figure.ipynb
new file mode 100644
index 00000000..3262f018
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/decision_alg/Allocation Distribution Figure.ipynb	
@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Allocation Distribution Figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm\n",
+    "sns.set(style='white', font_scale=2.0)\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "import pdb\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from integer_programming import get_clinician_prescribing_patterns\n",
+    "from DecisionAnalysis import AbxDecisionMaker, load_predictions, plot_distribution_of_allocated_abx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_distribution_of_allocated_abx()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/decision_alg/Antibiotic Sweep Plots.ipynb b/scripts/ER_Infection/notebooks/decision_alg/Antibiotic Sweep Plots.ipynb
new file mode 100644
index 00000000..eaf8b296
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/decision_alg/Antibiotic Sweep Plots.ipynb	
@@ -0,0 +1,338 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sweeps to analyze how budget constraints affect miss rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm\n",
+    "sns.set(style='white', font_scale=1.5)\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "import pdb\n",
+    "from tqdm import tqdm\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../decision_alg\")\n",
+    "from DecisionAnalysis import AbxDecisionMaker, load_predictions, perform_sweep, plot_histogram\n",
+    "from integer_programming import get_clinician_prescribing_patterns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abx_settings = {\"Ceftriaxone\" : 404,\n",
+    "    \"Vancomycin_Zosyn\" :  149,\n",
+    "    \"Zosyn\" : 102,\n",
+    "    \"Vancomycin_Ceftriaxone\" : 31,\n",
+    "    \"Vancomycin_Cefepime\" : 23,\n",
+    "    \"Cefepime\" : 14,\n",
+    "    \"Vancomycin\" : 13,\n",
+    "    \"Vancomycin_Meropenem\" : 9,\n",
+    "    \"Meropenem\" : 9,\n",
+    "    \"Cefazolin\" : 8,\n",
+    "    \"Ciprofloxacin\" : 8,\n",
+    "    \"Ampicillin\" : 0,\n",
+    "    }\n",
+    "\n",
+    "df_predictions = load_predictions()\n",
+    "df_drugs = get_clinician_prescribing_patterns()\n",
+    "opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 8))\n",
+    "os.makedirs('./sweep_results', exist_ok=True)\n",
+    "fname='./sweep_results/test.txt'\n",
+    "settings = perform_sweep(opt, abx_1='Vancomycin_Zosyn', abx_2='Ampicillin', ax=ax[1], fname=fname, legend=True, append=False)\n",
+    "plot_histogram(settings, ax[0])\n",
+    "plt.subplots_adjust(wspace=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Select Sweeps for main body of text\n",
+    "* Vancomycin & Zosyn to Zosyn\n",
+    "* Zosyn to Cefazolin\n",
+    "* Ceftriaxone to Cefazolin\n",
+    "* Ceftriaxone to Ampicillin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig, ax = plt.subplots(5, 2, figsize=(16, 50))\n",
+    "sns.set(style='white', font_scale=1.4)\n",
+    "os.makedirs('./sweep_results', exist_ok=True)\n",
+    "fname='./sweep_results/test.txt'\n",
+    "\n",
+    "# Plot original settings\n",
+    "abx_settings = {\"Ceftriaxone\" : 404,\n",
+    "    \"Vancomycin_Zosyn\" :  149,\n",
+    "    \"Zosyn\" : 102,\n",
+    "    \"Vancomycin_Ceftriaxone\" : 31,\n",
+    "    \"Vancomycin_Cefepime\" : 23,\n",
+    "    \"Cefepime\" : 14,\n",
+    "    \"Vancomycin\" : 13,\n",
+    "    \"Vancomycin_Meropenem\" : 9,\n",
+    "    \"Meropenem\" : 9,\n",
+    "    \"Cefazolin\" : 8,\n",
+    "    \"Ciprofloxacin\" : 8,\n",
+    "    \"Ampicillin\" : 0,\n",
+    "    }\n",
+    "\n",
+    "plt.figure(figsize=(16,20))\n",
+    "gs = gridspec.GridSpec(5, 2, hspace=0.5)\n",
+    "ax1a = plt.subplot(gs[0, :])\n",
+    "\n",
+    "ax2a = plt.subplot(gs[1, 0])\n",
+    "ax2b = plt.subplot(gs[1, 1])\n",
+    "\n",
+    "ax3a = plt.subplot(gs[2, 0])\n",
+    "ax3b = plt.subplot(gs[2, 1])\n",
+    "\n",
+    "ax4a = plt.subplot(gs[3, 0])\n",
+    "ax4b = plt.subplot(gs[3, 1])\n",
+    "\n",
+    "ax5a = plt.subplot(gs[4, 0])\n",
+    "ax5b = plt.subplot(gs[4, 1])\n",
+    "\n",
+    "axes = [[ax1a], [ax2a, ax2b], [ax3a, ax3b], [ax4a, ax4b], [ax5a, ax5b]]\n",
+    "\n",
+    "plot_histogram(abx_settings, axes[0][0])\n",
+    "axes[0][0].set_title(\"Observed Clinician Allocation\")\n",
+    "axes[0][0].set_xlabel('')\n",
+    "\n",
+    "# Vanc zosyn to zosyn\n",
+    "settings, miss_rate, reduction_rate = perform_sweep(opt,\n",
+    "                                      abx_1='Vancomycin_Zosyn',\n",
+    "                                      abx_2='Zosyn',\n",
+    "                                      ax=axes[1][1],\n",
+    "                                      fname='./sweep_results/Vancomycin_Zosyn_to_Zosyn.txt',\n",
+    "                                      legend=True,\n",
+    "                                      append=False)\n",
+    "plot_histogram(settings, axes[1][0], miss_rate, reduction_rate, 'vancomycin & pip/tazo')\n",
+    "axes[1][0].set_title(\"Optimized Allocation\")\n",
+    "\n",
+    "\n",
+    "# Zosyn to Cefazolin\n",
+    "settings, miss_rate, reduction_rate = perform_sweep(opt,\n",
+    "                                    abx_1='Zosyn',\n",
+    "                                    abx_2='Cefazolin',\n",
+    "                                    ax=axes[2][1],\n",
+    "                                    fname='./sweep_results/Zosyn_to_Cefazolin.txt',\n",
+    "                                    legend=False,\n",
+    "                                    append=False)\n",
+    "plot_histogram(settings, axes[2][0], miss_rate, reduction_rate, 'pip/tazo')\n",
+    "axes[2][0].set_title(\"Optimized Allocation\")\n",
+    "\n",
+    "\n",
+    "# Ceftriaxone to Cefazolin\n",
+    "settings, miss_rate, reduction_rate = perform_sweep(opt,\n",
+    "                                    abx_1='Ceftriaxone',\n",
+    "                                    abx_2='Cefazolin',\n",
+    "                                    ax=axes[3][1],\n",
+    "                                    fname='./sweep_results/Ceftriaxone_to_Cefazolin.txt',\n",
+    "                                    legend=False,\n",
+    "                                    append=False)\n",
+    "plot_histogram(settings, axes[3][0], miss_rate, reduction_rate, 'ceftriaxone')\n",
+    "axes[3][0].set_title(\"Optimized Allocation\")\n",
+    "\n",
+    "\n",
+    "settings, miss_rate, reduction_rate = perform_sweep(opt,\n",
+    "                                    abx_1='Ceftriaxone',\n",
+    "                                    abx_2='Ampicillin',\n",
+    "                                    ax=axes[4][1],\n",
+    "                                    fname='./sweep_results/Ceftriaxone_to_Ampicillin.txt',\n",
+    "                                    legend=False,\n",
+    "                                    append=False)\n",
+    "plot_histogram(settings, axes[4][0], miss_rate, reduction_rate, 'ceftriaxone')\n",
+    "axes[4][0].set_title(\"Optimized Allocation\")\n",
+    "plt.savefig('sweeps_for_main_text.png', bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perform de-escalation sweeps\n",
+    "\n",
+    "Here we take every combo of broader to narrower spectrum antibiotic and perform a sweep where the allocation constraint for the broader drug is reduced in favor of the narrower drug.  Save all to a pdf. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abx_rankings = ['Vancomycin_Meropenem',\n",
+    "                'Vancomycin_Cefepime',\n",
+    "                'Vancomycin_Zosyn',\n",
+    "                'Zosyn',\n",
+    "                'Vancomycin_Ceftriaxone',\n",
+    "                'Meropenem',\n",
+    "                'Cefepime',\n",
+    "                'Ceftriaxone',\n",
+    "                'Ciprofloxacin',\n",
+    "                'Cefazolin',\n",
+    "                'Ampicillin',\n",
+    "                'Vancomycin'\n",
+    "                ]\n",
+    "\n",
+    "\n",
+    "def get_narrower_sets(abx_rankings):\n",
+    "    \"\"\"Get a list of tupes for sweeps\"\"\"\n",
+    "    combos = []\n",
+    "    for i, abx in enumerate(abx_rankings):\n",
+    "        combos += [(abx, abx_rankings[j]) for j in range(len(abx_rankings)) if j > i\n",
+    "                   and abx != 'Ampicillin']\n",
+    "    \n",
+    "    print(\"Number of sweep plots %s\" % len(combos))\n",
+    "    return combos\n",
+    "\n",
+    "def create_axes(num_sweeps, first=False):\n",
+    "    gs = gridspec.GridSpec(num_sweeps, 2, hspace=0.5)\n",
+    "    axes = []\n",
+    "    if first:\n",
+    "        axes.append([plt.subplot(gs[0, :])])\n",
+    "        for i in range(num_sweeps-1):\n",
+    "            axes.append([plt.subplot(gs[i+1, 0]), plt.subplot(gs[i+1, 1])])\n",
+    "    else:\n",
+    "        for i in range(num_sweeps):\n",
+    "            axes.append([plt.subplot(gs[i, 0]), plt.subplot(gs[i, 1])])\n",
+    "    return axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.backends.backend_pdf import PdfPages\n",
+    "\n",
+    "pp = PdfPages(\"Sweep_Plots_Broad_to_Narrow.pdf\")\n",
+    "abx_combos = get_narrower_sets(abx_rankings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Lets do 5 rows per page. \n",
+    "combo_index = 0\n",
+    "page = 0\n",
+    "while combo_index < len(abx_combos)-1:\n",
+    "    fig = plt.figure(figsize=(16, 20))\n",
+    "    if combo_index == 0:\n",
+    "        num_sweeps = min(5, len(abx_combos) + 1)\n",
+    "    else:\n",
+    "        num_sweeps = min(5, len(abx_combos) - combo_index)\n",
+    "    plt.figure(figsize=(16, num_sweeps*4))\n",
+    "    \n",
+    "    if combo_index == 0:\n",
+    "        axes = create_axes(num_sweeps, first=True)\n",
+    "    else:\n",
+    "        axes = create_axes(num_sweeps)\n",
+    "    \n",
+    "    for ax in axes:\n",
+    "        if len(ax) == 1:\n",
+    "            plot_histogram(abx_settings, ax[0])\n",
+    "            ax[0].set_title(\"Observed Clinician Allocation\")\n",
+    "            ax[0].set_xlabel('')\n",
+    "        else:\n",
+    "            \n",
+    "            # Perform sweep\n",
+    "            if combo_index == 0:\n",
+    "                legend = True\n",
+    "            else:\n",
+    "                legend = False\n",
+    "            fname = './sweep_results/%s_to_%s.txt' % (abx_combos[combo_index][0], abx_combos[combo_index][1])\n",
+    "            settings, miss_rate, reduction_rate = perform_sweep(opt,\n",
+    "                                                  abx_1=abx_combos[combo_index][0],\n",
+    "                                                  abx_2=abx_combos[combo_index][1],\n",
+    "                                                  ax=ax[1],\n",
+    "                                                  fname=fname,\n",
+    "                                                  legend=legend,\n",
+    "                                                  append=False)\n",
+    "            \n",
+    "            sweep_name = abx_combos[combo_index][0].lower().replace('_', ' & ')\n",
+    "            plot_histogram(settings, ax[0], miss_rate, reduction_rate, sweep_name, abx2=abx_combos[combo_index][1])\n",
+    "            ax[0].set_title(\"Optimized Allocation\")\n",
+    "            \n",
+    "            combo_index += 1\n",
+    "    \n",
+    "    \n",
+    "    #pp.savefig(bbox_inches='tight')\n",
+    "    plt.savefig('sweeps_page_%s.png' % str(page), bbox_inches='tight')\n",
+    "    page += 1\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/decision_alg/DecisionAnalysis.py b/scripts/ER_Infection/notebooks/decision_alg/DecisionAnalysis.py
new file mode 100644
index 00000000..5812c5a0
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/decision_alg/DecisionAnalysis.py
@@ -0,0 +1,1025 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import seaborn as sns
+from tqdm import tqdm
+sns.set(style='white', font_scale=1.5)
+import numpy as np
+from pulp import *
+import os, glob
+import pdb
+from tqdm import tqdm
+
+from integer_programming import get_clinician_prescribing_patterns
+
+def load_predictions():
+    """Helper function that loads predictions from AST classifiers for test set data"""
+    abx_options = ["Vancomycin",
+                   "Ampicillin",
+                   "Cefazolin",
+                   "Ceftriaxone",
+                   "Cefepime",
+                   "Zosyn",
+                   "Ciprofloxacin",
+                   "Meropenem",
+                   "Vancomycin_Meropenem",
+                   "Vancomycin_Zosyn",
+                   "Vancomycin_Cefepime",
+                   "Vancomycin_Ceftriaxone"
+                   ]
+    base_path="/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}"
+    df = pd.DataFrame()
+    for i, abx in enumerate(abx_options):
+        path = base_path.format(abx=abx)
+        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]
+        if i == 0:
+            df = pd.read_csv(f_path)
+            df = df[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]
+            df = df.rename(columns={'label' : '%s_label' % abx,
+                                    'predictions' : '%s_predictions' % abx})
+        else:
+            df_preds = pd.read_csv(f_path)
+            df_preds = df_preds[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]
+            df_preds = df_preds.rename(columns={'label' : '%s_label' % abx,
+                                                'predictions' : '%s_predictions' % abx})
+            df = df.merge(df_preds, how='left', on=['anon_id', 'pat_enc_csn_id_coded'])
+    
+    return df
+
+
+class AbxDecisionMaker():
+
+    def __init__(self, df_predictions, df_drugs, abx_settings):
+        self.df_predictions = df_predictions
+        self.df_drugs = df_drugs
+        self.df = (df_predictions
+            .merge(df_drugs, how='inner', on='pat_enc_csn_id_coded')
+        )
+        self.abx_settings = abx_settings
+        self.abx_options = ["Vancomycin",
+                            "Ampicillin",
+                            "Cefazolin",
+                            "Ceftriaxone",
+                            "Cefepime",
+                            "Zosyn",
+                            "Ciprofloxacin",
+                            "Meropenem",
+                            "Vancomycin_Meropenem",
+                            "Vancomycin_Zosyn",
+                            "Vancomycin_Cefepime",
+                            "Vancomycin_Ceftriaxone"
+                            ]
+
+        self.abx_map = {'Ceftriaxone' : "CEFTRIAXONE",
+                        'Vancomycin_Zosyn' : "PIPERACILLIN-TAZOBACTAM VANCOMYCIN",
+                        'Zosyn' : "PIPERACILLIN-TAZOBACTAM",
+                        'Vancomycin_Ceftriaxone' : "CEFTRIAXONE VANCOMYCIN",
+                        'Vancomycin_Cefepime' : "CEFEPIME VANCOMYCIN",
+                        'Cefepime' : "CEFEPIME",
+                        'Vancomycin' :  "VANCOMYCIN",
+                        'Meropenem' : "MEROPENEM",
+                        'Vancomycin_Meropenem' : "MEROPENEM VANCOMYCIN",
+                        'Cefazolin' : "CEFAZOLIN",
+                        'Ciprofloxacin' : "CIPROFLOXACIN",
+                        'Ampicillin' : 'AMPICILLIN'
+                        }
+        self.abx_map_inverse = {self.abx_map[key] : key for key in self.abx_map}
+        self.abx_map_inverse['CEFTRIAXONE PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'
+        # self.abx_map_inverse['LEVOFLOXACIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'
+        self.abx_map_inverse['AZITHROMYCIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'
+        # self.abx_map_inverse['MEROPENEM PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Meropenem'
+        self.abx_map_inverse['AZITHROMYCIN CEFTRIAXONE'] = 'Ceftriaxone'
+
+        # Assign random med descriptions here. 
+        self.df = (self.df
+            .assign(random_med_description=lambda x: np.random.choice(x.med_description,
+                                                                      size=len(x.med_description),
+                                                                      replace=False))
+        )
+
+        self.df_for_reset = self.df.copy()
+        self.abx_settings_for_reset = {key : self.abx_settings[key] for key in self.abx_settings} # deep copy
+        self.n = len(df_drugs)
+
+
+    def set_abx_settings(self, abx_settings):
+        self.abx_settings = abx_settings
+
+    def reset_df(self):
+        self.df = self.df_for_reset.copy()
+
+    def reset_abx_settings(self):
+        self.abx_settings = {key : self.abx_settings_for_reset[key] for key in self.abx_settings_for_reset}
+
+    def replace_one(self, to_replace, replace_with):
+        """ Randomly takes one allocation of to_replace from the clinician and random
+        allocation and replaces with replace_with
+        """
+
+        # Replace one clinician allocation
+        num_to_replace_before = len(self.df[self.df['med_description'] == to_replace])
+        num_replace_with_before = len(self.df[self.df['med_description'] == replace_with])
+        inds = [i for i in range(len(self.df)) if self.df['med_description'].values[i] == to_replace]
+        ind = np.random.choice(inds)
+        self.df = (self.df
+            .assign(med_description=lambda x: [a if i != ind else replace_with 
+                                              for i, a in enumerate(x.med_description)])
+        )
+        num_to_replace_after = len(self.df[self.df['med_description'] == to_replace])
+        num_replace_with_after = len(self.df[self.df['med_description'] == replace_with])
+
+        assert num_to_replace_before == num_to_replace_after + 1
+        assert num_replace_with_before == num_replace_with_after - 1 
+        
+        # Replace one random allocation
+        num_to_replace_before = len(self.df[self.df['random_med_description'] == to_replace])
+        num_replace_with_before = len(self.df[self.df['random_med_description'] == replace_with])
+        inds = [i for i in range(len(self.df)) if self.df['random_med_description'].values[i] == to_replace]
+        ind = np.random.choice(inds)
+        self.df = (self.df
+            .assign(random_med_description=lambda x: [a if i != ind else replace_with 
+                                                      for i, a in enumerate(x.random_med_description)])
+        )
+        num_to_replace_after = len(self.df[self.df['random_med_description'] == to_replace])
+        num_replace_with_after = len(self.df[self.df['random_med_description'] == replace_with])
+
+        assert num_to_replace_before == num_to_replace_after + 1
+        assert num_replace_with_before == num_replace_with_after - 1 
+
+    def compute_was_covered(self, x, decision_column='med_description'):
+        """
+        Given med description, find appropriate label column and return whether patient was covered during CSN
+        Returns "Not in abx options" if abx regimen isn't in our set of 12 options - useful for filtering later
+        """
+        if decision_column == 'med_description':
+            med_description = x.med_description
+        elif decision_column == 'random_med_description':
+            med_description = x.random_med_description
+        elif decision_column == 'IP_med_description':
+            med_description = x.IP_med_description
+        
+        return x[med_description]
+
+    def get_coverage_rates(self, df=None):
+        """
+        Create flag for whether clinicians covered the patient during the csn, whether a random assignemnt
+        covered patient CSN, and whether optimized assignment covered the patient CSN
+        """
+        if df is None:
+            df = self.df
+
+        df = (df
+            .assign(was_covered_dr=df.apply(lambda x: self.compute_was_covered(x), axis=1))
+            .assign(was_covered_random=df.apply(lambda x: self.compute_was_covered(x, 
+                                                decision_column='random_med_description'),
+                                                axis=1))
+            .assign(was_covered_IP=df.apply(lambda x: self.compute_was_covered(x, 
+                                            decision_column='IP_med_description'),
+                                            axis=1))
+        )
+
+        clin_covered_rate = df['was_covered_dr'].sum() / len(df)
+        random_covered_rate = df['was_covered_random'].sum() / len(df)
+        ip_covered_rate = df['was_covered_IP'].sum() / len(df)
+        
+        return random_covered_rate, clin_covered_rate, ip_covered_rate
+
+    def solve_and_assign(self):
+
+        # Predictions string
+        predictions_string = '%s_predictions'
+        abx_model = LpProblem("Antibiotics", LpMaximize)
+
+        # Create binary indicators for whether treatment is used
+        drug_inds = {}
+        for abx in self.abx_options:
+            drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')
+                            for i in range(len(self.df))]
+
+        # Add objective function to model
+        per_csn_sum = []
+        for i in range(len(self.df)):
+            _sum = 0
+            for abx in self.abx_options:
+                _sum += drug_inds[abx][i] * self.df[predictions_string % abx].values[i]
+            per_csn_sum.append(_sum)
+            
+        abx_model += lpSum(per_csn_sum)
+
+        # Add one selection constraint
+        for i in range(len(self.df)):
+            selections = []
+            for abx in self.abx_options:
+                selections.append(drug_inds[abx][i])
+            abx_model += lpSum(selections) == 1
+
+        for drug in drug_inds:
+            abx_model += lpSum([drug_inds[drug][i] for i in range(len(self.df))]) == self.abx_settings[drug]
+
+        # Solve model
+        abx_model.solve()
+
+        # print("Status:", LpStatus[abx_model.status])
+        # Save selected antibiotic to df
+        abx_decisions = []
+        for i in range(len(self.df)):
+            abx_decision = None
+            for abx in self.abx_options:
+                if drug_inds[abx][i].value() == 1:
+                    abx_decision = abx
+            assert abx_decision is not None
+            abx_decisions.append(abx_decision)
+        self.df['IP_med_description'] = abx_decisions
+
+def perform_sweep(opt, abx_1, abx_2, ax, fname, legend=True, append=True):
+    """
+    Performs one antibiotic sweep where allocations of abx_1 are iterativelly converted to
+    allocations of abx_2.  Creates plots of said sweep, and saves a text file indicating
+    the point on the sweep where the clinician threshold is met (fraction of abx_1 you can convert
+    to abx_2) and maintatin clinician miss rate
+
+    Parameters
+    ----------
+    opt : AbxDecisionMaker object
+    abx_1 : abx to use less of
+    abx_2 : abx to use more of
+    ax : matplotlib axes to plot on
+    """
+    if append:
+        char_ = 'a'
+    else:
+        char_ = 'w'
+    opt.reset_df()
+    opt.reset_abx_settings()
+    abx_1_start_setting = opt.abx_settings[abx_1]
+    abx_settings_perturbed = {key : opt.abx_settings[key] for key in opt.abx_settings} #deep copy
+    reached_clin_error_rate = False
+    print("Performing %s to %s sweep" % (abx_1, abx_2))
+    random_rates, clin_rates, ip_rates = [], [], []
+    c_from_rs, ip_from_rs = [], []
+
+    opt.set_abx_settings(abx_settings_perturbed)
+    opt.solve_and_assign()
+    r, c, i = opt.get_coverage_rates()
+    baseline_clin_miss_rate = 1-c
+    random_rates.append(1-r)
+    ip_rates.append(1-i)
+    clin_rates.append(1-c)
+
+    for iter_ in tqdm(range(abx_1_start_setting)):
+        abx_settings_perturbed[abx_1] -= 1
+        abx_settings_perturbed[abx_2] += 1
+        opt.set_abx_settings(abx_settings_perturbed)
+        opt.replace_one(abx_1, abx_2)
+        opt.solve_and_assign()
+        r, c, i = opt.get_coverage_rates()
+
+        random_rates.append(1-r)
+        ip_rates.append(1-i)
+        clin_rates.append(1-c)
+
+        if 1-i > baseline_clin_miss_rate and not reached_clin_error_rate:
+            # Number of iterations before gettting to baseline clinician error rate
+            max_deescalation = iter_
+            max_deescaltion_miss_rate = ip_rates[-2]
+            settings_to_plot = {key : abx_settings_perturbed[key] for key in abx_settings_perturbed} 
+            settings_to_plot[abx_1] += 1
+            settings_to_plot[abx_2] -= 1
+            d_rate = float(iter_)/abx_1_start_setting
+            with open(fname, char_) as w:
+                w.write("Miss rate of %.2f achieved with %.2f percent fewer %s\n" % ((max_deescaltion_miss_rate)*100,
+                                                                              d_rate*100,
+                                                                              abx_1.replace("_", ' & ')))
+            miss_rate_to_return = max_deescaltion_miss_rate*100
+            reduction_rate = d_rate * 100
+            reached_clin_error_rate = True
+
+
+    if reached_clin_error_rate == False:
+        settings_to_plot = {key : abx_settings_perturbed[key] for key in abx_settings_perturbed}
+        with open(fname, char_) as w:
+                w.write("Miss rate of %.2f achieved with %.2f percent fewer %s\n" % (ip_rates[-1]*100,
+                                                                              100,
+                                                                              abx_1.replace("_", ' & ')))
+        miss_rate_to_return = ip_rates[-1]*100
+        reduction_rate = 100
+        max_deescalation = abx_1_start_setting
+        max_deescaltion_miss_rate = ip_rates[-1]
+
+    deescalations = [i for i in range(len(random_rates))]
+    deescalations.reverse()
+    ax.plot(deescalations, ip_rates, label='Optimized Allocation', linewidth=2.0)
+    ax.plot(deescalations, random_rates, label='Random Allocation', linewidth=2.0)
+    ax.plot(deescalations, [baseline_clin_miss_rate for c in range(len(random_rates))],
+              linestyle='--', color='black', label='Observed Clinician Allocation')
+    if reached_clin_error_rate == False:
+        ax.plot(max_deescalation - abx_1_start_setting, max_deescaltion_miss_rate, marker='x',
+                markersize=15, color='red', linewidth=2.0)
+    else:
+        ax.plot(abx_1_start_setting - max_deescalation, max_deescaltion_miss_rate, marker='x',
+                markersize=15, color='red', linewidth=2.0)
+    ax.invert_xaxis()
+    ax.set_title("%s to %s" % (abx_1.replace("_", ' & ').replace('Zosyn', 'Pip/Tazo'),
+                 abx_2.replace("_", ' & ').replace('Zosyn', 'Pip/Tazo')))#, y=1.04)
+    ax.set_xlabel("# %s Allocated" % abx_1.replace("_", ' & ').replace('Zosyn', 'Pip/Tazo'))
+    ax.set_ylabel("Miss Rate")
+    ax.set_ylim((0.10, 0.40))
+
+    if legend:
+        ax.legend(loc='upper left', fontsize='small')
+
+    return settings_to_plot, miss_rate_to_return, reduction_rate
+
+def plot_histogram(settings, ax, miss_rate=None, reduction_rate=None, abx1=None, abx2=None):
+    """
+    Given antibiotic settings, plot distribution as bar plot
+    """
+    if abx1 == None:
+        text_offset = 8
+        xlim_min = 430
+    else:
+        text_offset = 17
+        xlim_min = 460
+
+    df_settings = pd.DataFrame(data={
+        'med' : [key.replace("_", " & ").replace('Zosyn', 'Pip/Tazo') for key in settings],
+        'value' : [value for key, value in settings.items()]
+    })
+    ax = sns.barplot(x="value", y="med", ci=None, data=df_settings, ax=ax, palette="deep")
+    max_value = np.max([value for key, value in settings.items()])
+    x_max = max(xlim_min, max_value+50)
+    ax.set_xlim([0, x_max])
+    ax.set_ylabel('')
+    ax.set_xlabel('')
+
+    
+    for i, p in enumerate(ax.patches):
+        ax.text(text_offset+p.get_width(), p.get_y()+0.55*p.get_height(),
+                str(p.get_width()),
+                ha='center', va='center',
+                fontsize='x-small')
+        if i == 6 and abx2 not in ('Vancomycin', 'Cefepime'):
+            y_center = p.get_y()
+        
+        if i == 8 and abx2 in ('Vancomycin', 'Cefepime'):
+            y_center = p.get_y()
+
+
+            
+    if miss_rate is not None and reduction_rate is not None:
+        if '&' in abx1:
+            text = "Miss rate {miss_rate}% with\n{red_rate}% fewer {abx}".format(
+                miss_rate=str(round(miss_rate, 1)),
+                red_rate=str(round(reduction_rate, 1)),
+                abx=abx1
+            )
+
+        else:
+            text = "Miss rate {miss_rate}% with {red_rate}% fewer {abx}".format(
+                miss_rate=str(round(miss_rate, 1)),
+                red_rate=str(round(reduction_rate, 1)),
+                abx=abx1
+            )
+    else:
+        text = "Miss rate {miss_rate}% in observed clinician allocation".format(
+                miss_rate=15.7
+                )
+    ax.text(x_max/2., y_center, text, ha="center", va="center", fontsize='small')
+
+
+
+
+def plot_sweeps(opt):
+    """ Perform and plot abx sweeps """
+    ordered_abx = ['Vancomycin_Meropenem',
+                   'Vancomycin_Cefepime',
+                   'Vancomycin_Zosyn',
+                   'Zosyn',
+                   'Vancomycin_Ceftriaxone',
+                   'Meropenem',
+                   'Cefepime',
+                   'Ceftriaxone',
+                   'Ciprofloxacin',
+                   'Cefazolin',
+                   'Ampicillin',
+                   'Vancomycin']
+    
+    # For each abx, get a set of narrower-spectrum antibiotics
+    abx_sets = {}
+    for i, abx in enumerate(ordered_abx):
+        abx_sets[abx] = [a for j, a in enumerate(ordered_abx) if j > i]
+    
+    base_path = "./sweep_plots/%s/"
+    for j, abx in enumerate(abx_sets):
+        path = base_path % abx
+        os.makedirs(path, exist_ok=True)
+        for i, abx_2 in enumerate(abx_sets[abx]):
+            fig, ax = plt.subplots(1, 2, figsize=(16, 8))
+            plt.subplots_adjust(wspace=0.3, hspace=0.3)
+            fname = os.path.join(path, "%s_to_%s.txt" % (abx, abx_2))
+            settings, miss_rate, reduction_rate = perform_sweep(opt,
+                                                                abx_1=abx,
+                                                                abx_2=abx_2,
+                                                                ax=ax[1],
+                                                                fname=fname,
+                                                                legend=True,
+                                                                append=False)
+            plot_histogram(settings, ax[0])
+            plt.savefig(os.path.join(path, '%s_to_%s_sweeps.png' % (abx, abx_2)),
+                        bbox_inches = "tight")
+
+def perform_abx_sweep():
+    """
+    Simulates antibiotic delivery sweeping through different abx prescribing contraints. 
+    For each abx pair, we add the number of times the two were prescribed in total (N) in actual practice
+    and then sweep contraints from the extreme where only the first antbiotic prescribed N times to the other
+    extreme where the other antibiotic is prescribed N times. We show how coverage rate changes as we change 
+    these contraints for both the IP decision and a random decision.
+    """
+    abx_settings = {"Vancomycin" : 13,
+                "Ampicillin" : 0,
+                "Cefazolin" : 8,
+                "Ceftriaxone" : 404,
+                "Cefepime" : 14,
+                "Zosyn" : 102,
+                "Ciprofloxacin" : 8,
+                "Meropenem" : 9,
+                "Vancomycin_Meropenem" : 16,
+                "Vancomycin_Zosyn" :  153,
+                "Vancomycin_Cefepime" : 23,
+                "Vancomycin_Ceftriaxone" : 31
+                }
+    df_predictions = load_predictions()
+    df_drugs = get_clinician_prescribing_patterns()
+    opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)
+    opt.solve_and_assign()
+    random_covered_rate, clin_covered_rate, ip_covered_rate = opt.get_coverage_rates()
+
+    for abx_to_perturb in abx_settings:
+        # plt.figure(figsize=(32,24))
+        # fig, ax = plt.subplots(3, 4, figsize=(32, 24))
+        # gs = gridspec.GridSpec(4, 24, wspace=2.0)
+        # ax1a = plt.subplot(gs[0, 0:6])
+        # ax1b = plt.subplot(gs[0, 6:12])
+        # ax1c = plt.subplot(gs[0, 12:18])
+        # ax1d = plt.subplot(gs[0, 18:24])
+        
+        # ax2a = plt.subplot(gs[1, 3:9])
+        # ax2b = plt.subplot(gs[1, 9:15])
+        # ax2c = plt.subplot(gs[1, 15:21])
+
+        # ax3a = plt.subplot(gs[2, 0:6])
+        # ax3b = plt.subplot(gs[2, 6:12])
+        # ax3c = plt.subplot(gs[2, 12:18])
+        # ax3d = plt.subplot(gs[2, 18:24])
+
+        plt.figure(figsize=(24,32))
+        gs = gridspec.GridSpec(4, 6, wspace=0.5)
+        ax1a = plt.subplot(gs[0, 0:2])
+        ax1b = plt.subplot(gs[0, 2:4])
+        ax1c = plt.subplot(gs[0, 4:6])
+
+        ax2a = plt.subplot(gs[1, 0:2])
+        ax2b = plt.subplot(gs[1, 2:4])
+        ax2c = plt.subplot(gs[1, 4:6])
+
+        ax3a = plt.subplot(gs[2, 0:2])
+        ax3b = plt.subplot(gs[2, 2:4])
+        ax3c = plt.subplot(gs[2, 4:6])
+
+        ax4a = plt.subplot(gs[3, 1:3])
+        ax4b = plt.subplot(gs[3, 3:5])
+
+
+        axes = [ax1a, ax1b, ax1c, ax2a, ax2b, ax2c, ax3a, ax3b, ax3c, ax4a, ax4b]
+
+        skip = 0
+        for ind, abx in enumerate(abx_settings):
+            if abx == abx_to_perturb:
+                skip = 1
+                continue
+            else:
+                abx_settings_perturbed = {key : abx_settings[key] for key in abx_settings}
+                print("Performing %s to %s sweep" % (abx_to_perturb, abx))
+                random_rates, ip_rates = [], []
+                # Total selections to sweep over
+                total_selections = abx_settings[abx] + abx_settings[abx_to_perturb]
+                abx_settings_perturbed[abx_to_perturb] = total_selections
+                abx_settings_perturbed[abx] = 0
+                opt.set_abx_settings(abx_settings_perturbed)
+                opt.solve_and_assign()
+                r, c, i = opt.get_coverage_rates()
+                random_rates.append(r)
+                ip_rates.append(i)
+                if abx_settings_perturbed == abx_settings:
+                    clin_iter = -1 # save point on x axis for clinician performance
+                for iter_ in tqdm(range(total_selections)):
+                    abx_settings_perturbed[abx_to_perturb] -= 1
+                    abx_settings_perturbed[abx] += 1
+                    opt.set_abx_settings(abx_settings_perturbed)
+                    opt.solve_and_assign()
+                    r, c, i = opt.get_coverage_rates()
+                    random_rates.append(r)
+                    ip_rates.append(i)
+
+                    if abx_settings_perturbed == abx_settings:
+                        clin_iter = iter_ # save point on x axis for clinician performance
+                    
+            axes[ind-skip].plot(range(total_selections+1), random_rates, label='Random Assignment')
+            axes[ind-skip].plot(range(total_selections+1), ip_rates, label='Integer Programming')
+            axes[ind-skip].plot(clin_iter+1, clin_covered_rate, marker='o', label='Clinician Benchmark')
+            # forward = lambda x: total_selections - x
+            # backward = lambda x: total_selections - x
+            # secax = axes[ind-skip].secondary_xaxis('top', functions=(forward, backward))
+            axes[ind-skip].set_xlabel("Num %s Administered" % abx)
+            # secaxes[ind-skip].set_xlabel("Num %s Administered" % abx_to_perturb)
+            axes[ind-skip].set_ylabel("Coverage Rate")
+            axes[ind-skip].set_ylim((0.5, 1.))
+            axes[ind-skip].set_title("%s to %s Sweep" % (abx_to_perturb, abx))
+            axes[ind-skip].legend()
+        
+        dir_='./tall_grid/'
+        os.makedirs(dir_, exist_ok=True)
+        fig_name = "./tall_grid/%s_sweeps.jpg" % abx_to_perturb
+        plt.savefig(fig_name)
+
+
+def full_waterfall():
+    abx_settings = {"Vancomycin" : 0,
+                    "Ampicillin" : 0,
+                    "Cefazolin" : 0,
+                    "Ceftriaxone" : 0,
+                    "Cefepime" : 0,
+                    "Zosyn" : 0,
+                    "Ciprofloxacin" : 0,
+                    "Meropenem" : 0,
+                    "Vancomycin_Meropenem" : 697,
+                    "Vancomycin_Zosyn" :  0,
+                    "Vancomycin_Cefepime" : 0,
+                    "Vancomycin_Ceftriaxone" : 0
+                    }
+    abx_rankings = ['Vancomycin_Meropenem',
+                    'Vancomycin_Cefepime',
+                    'Vancomycin_Zosyn',
+                    'Zosyn',
+                    'Vancomycin_Ceftriaxone',
+                    'Meropenem',
+                    'Cefepime',
+                    'Ceftriaxone',
+                    'Ciprofloxacin',
+                    'Cefazolin',
+                    'Ampicillin',
+                    'Vancomycin'
+                    ]
+    
+    if os.path.exists('df_predictions.csv'):
+        df_predictions = pd.read_csv('df_predictions.csv')
+    else:
+        df_predictions = load_predictions()
+        df_predictions.to_csv('df_predictions.csv', index=None)
+
+
+    if os.path.exists('df_drugs.csv'):
+        df_drugs = pd.read_csv('df_drugs.csv')
+    else:
+        df_drugs = get_clinician_prescribing_patterns()
+        df_drugs.to_csv('df_drugs.csv', index=None)
+
+    deplete_idx = 0
+    push_from_idx = 0
+    push_to_idx = 1
+    random_rates = []
+    ip_rates = []
+    counter = 0
+    while abx_settings['Vancomycin'] != 697: # end of sweep
+        opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)
+        opt.solve_and_assign()
+        r, c, i = opt.get_coverage_rates()
+        random_rates.append(r)
+        ip_rates.append(i)
+
+        if counter == 10:
+            print(abx_settings)
+            counter = 0
+
+        # Make more narrow spectrum
+        abx_settings[abx_rankings[push_from_idx]] -= 1
+        abx_settings[abx_rankings[push_to_idx]] += 1
+        
+        if abx_settings[abx_rankings[deplete_idx]] == 0:
+            deplete_idx += 1
+            print("Moving deplete index from %s to %s" % (abx_rankings[deplete_idx-1], abx_rankings[deplete_idx]))
+
+        if push_to_idx == len(abx_rankings) - 1:
+            push_to_idx = deplete_idx + 1
+            push_from_idx = deplete_idx
+        else:
+            push_from_idx += 1
+            push_to_idx += 1
+
+        counter += 1
+    
+    clin_rates = [c for i in range(len(random_rates))]
+    df = pd.DataFrame(data={'clin_rates' : clin_rates,
+                            'random_rates' : random_rates,
+                            'ip_rates' : ip_rates})
+    df.to_csv('summary_sweep.csv')
+    fix, ax = plt.subplots(1, 1, figsize=(8,8))
+    ax.plot(range(len(random_rates)), random_rates, label='Random Assignment')
+    ax.plot(range(len(random_rates)), ip_rates, label='Integer Programming')
+    ax.plot(range(len(random_rates)), c, label='Clinician Benchmark')
+
+    fig_name = "summary_sweep.jpg"
+    plt.savefig(fig_name)
+
+def bootstrap_miss_rates():
+    abx_settings = {"Vancomycin" : 13,
+                    "Ampicillin" : 0,
+                    "Cefazolin" : 8,
+                    "Ceftriaxone" : 404,
+                    "Cefepime" : 14,
+                    "Zosyn" : 102,
+                    "Ciprofloxacin" : 8,
+                    "Meropenem" : 9,
+                    "Vancomycin_Meropenem" : 9,
+                    "Vancomycin_Zosyn" :  149,
+                    "Vancomycin_Cefepime" : 23,
+                    "Vancomycin_Ceftriaxone" : 31
+            }
+
+    # Solve once then bootstrap solutions
+    df_predictions = load_predictions()
+    df_drugs = get_clinician_prescribing_patterns()
+    opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)
+    opt.solve_and_assign()
+
+    rs, cs, ls = [], [], []
+    l_c_relatives, l_r_relatives = [], []
+    for i in tqdm(range(1000)):
+        
+        # Don't stratify, only stratify if bootstrapping the solving procedure as well
+        df_drugs_b = (opt.df
+            .sample(frac=1.0, replace=True)
+        )
+        # # Stratified bootstrap
+        # df_drugs_b = pd.DataFrame()
+        # for abx in abx_settings:
+        #     df_temp = (opt.df
+        #         .query("med_description == @abx", engine='python')
+        #         .sample(frac=1.0, replace=True)
+        #     )
+        #     df_drugs_b = pd.concat([df_drugs_b, df_temp])
+        
+        # Sanity Check
+        # for abx in abx_settings:
+        #     num_allocations = len(df_drugs_b[df_drugs_b['med_description'] == abx])
+        #     assert num_allocations == abx_settings[abx]
+
+        if i == 0:
+            print(opt.n)
+
+        r, c, l = opt.get_coverage_rates(df=df_drugs_b)
+        rs.append(1-r)
+        cs.append(1-c)
+        ls.append(1-l)
+        l_r_relative = ((rs[-1] - ls[-1]) / rs[-1]) * 100
+        l_r_relatives.append(l_r_relative)
+        l_c_relative = ((cs[-1] - ls[-1]) / cs[-1]) * 100
+        l_c_relatives.append(l_c_relative)
+
+    # Miss Rates
+    r_mean = np.mean(rs) * 100
+    r_low = np.percentile(rs, 2.5) * 100
+    r_high = np.percentile(rs, 97.5) * 100
+    r_miss_rate = "%.2f [%.2f, %.2f]" % (r_mean, r_low, r_high)
+
+    c_mean = np.mean(cs) * 100
+    c_low = np.percentile(cs, 2.5) * 100
+    c_high = np.percentile(cs, 97.5) * 100
+    c_miss_rate = "%.2f [%.2f, %.2f]" % (c_mean, c_low, c_high)
+
+    l_mean = np.mean(ls) * 100
+    l_low = np.percentile(ls, 2.5) * 100
+    l_high = np.percentile(ls, 97.5) * 100
+    l_miss_rate = "%.2f [%.2f, %.2f]" % (l_mean, l_low, l_high)
+
+    # Relative Miss Reductions
+    l_c_means = np.mean(l_c_relatives)
+    l_c_low = np.percentile(l_c_relatives, 2.5)
+    l_c_high = np.percentile(l_c_relatives, 97.5)
+    l_c_final = "%.2f [%.2f, %.2f]" % (l_c_means, l_c_low, l_c_high)
+
+    l_r_means = np.mean(l_r_relatives)
+    l_r_low = np.percentile(l_r_relatives, 2.5)
+    l_r_high = np.percentile(l_r_relatives, 97.5)
+    l_r_final = "%.2f [%.2f, %.2f]" % (l_r_means, l_r_low, l_r_high)
+
+    return r_miss_rate, c_miss_rate, l_miss_rate, l_r_final, l_c_final
+
+def for_debugging_plot_select_sweeps(df_drugs, abx_settings):
+    """ Resample df_drugs so that there exist the specified number of allocations in settings """
+    df = pd.DataFrame()
+    for abx in abx_settings:
+        df_temp = (df_drugs
+            .query("med_description == @abx", engine='python')
+            .sample(n=abx_settings[abx])
+        )
+        df = pd.concat([df, df_temp])
+    return df
+
+def plot_select_sweeps():
+    """ Plot Vancomycin & Zosyn to Zosyn 
+        Plot Zosyn to Cefazolin
+    """
+    # abx_settings = {"Vancomycin" : 13,
+    #             "Ampicillin" : 0,
+    #             "Cefazolin" : 8,
+    #             "Ceftriaxone" : 20,#404,
+    #             "Cefepime" : 14,
+    #             "Zosyn" : 20,#102,
+    #             "Ciprofloxacin" : 8,
+    #             "Meropenem" : 9,
+    #             "Vancomycin_Meropenem" : 9,
+    #             "Vancomycin_Zosyn" :  20,#149,
+    #             "Vancomycin_Cefepime" : 23,
+    #             "Vancomycin_Ceftriaxone" : 31
+    #             }
+    abx_settings = {"Ceftriaxone" : 404,
+            "Vancomycin_Zosyn" :  149,
+            "Zosyn" : 102,
+            "Vancomycin_Ceftriaxone" : 31,
+            "Vancomycin_Cefepime" : 23,
+            "Cefepime" : 14,
+            "Vancomycin" : 13,
+            "Vancomycin_Meropenem" : 9,
+            "Meropenem" : 9,
+            "Cefazolin" : 8,
+            "Ciprofloxacin" : 8,
+            "Ampicillin" : 0,
+            }
+    f_name_missrates = 'select_sweep_miss_rates.txt'
+    df_predictions = load_predictions()
+    df_drugs = get_clinician_prescribing_patterns()
+    # df_drugs = for_debugging_plot_select_sweeps(df_drugs, abx_settings)
+    opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)
+    opt.solve_and_assign()
+    random_covered_rate, clin_covered_rate, ip_covered_rate = opt.get_coverage_rates()
+    baseline_clin_miss_rate = 1-clin_covered_rate
+    sweep_one = ("Vancomycin_Zosyn", "Zosyn")
+    sweep_two = ("Zosyn", "Cefazolin")
+    # sweep_three = ("Zosyn", "Ampicillin")
+    sweep_three = ("Ceftriaxone", 'Cefazolin')
+    sweep_four = ("Ceftriaxone", 'Ampicillin')
+    # fig, ax = plt.subplots(1, 3, figsize=(24,8))
+
+    plt.figure(figsize=(36,30))
+    gs = gridspec.GridSpec(5, 6, wspace=0.5, hspace=0.35)
+    ax1a = plt.subplot(gs[0, 2:4])
+
+    ax2a = plt.subplot(gs[1, 1:3])
+    ax2b = plt.subplot(gs[1, 3:5])
+
+    ax3a = plt.subplot(gs[2, 1:3])
+    ax3b = plt.subplot(gs[2, 3:5])
+
+    ax4a = plt.subplot(gs[3, 1:3])
+    ax4b = plt.subplot(gs[3, 3:5])
+
+    ax5a = plt.subplot(gs[4, 1:3])
+    ax5b = plt.subplot(gs[4, 3:5])
+
+    axes = [[ax1a], [ax2a, ax2b], [ax3a, ax3b], [ax4a, ax4b], [ax5a, ax5b]]
+
+    # Plot original clinician distribution
+    plot_histogram(abx_settings, axes[0][0])
+    axes[0][0].set_title("Actual Clinician Allocation") # hard coded miss rate
+    axes[0][0].set_xlabel("Number of Allcations")
+    axes[0][0].set_ylabel('')
+    with open (f_name_missrates, 'w') as w:
+        w.write("Clinician Miss Rate: %.2f\n" % (baseline_clin_miss_rate * 100))
+    # sweep_one = ('Cefepime', 'Cefazolin')
+    for k, sweep in enumerate([sweep_one, sweep_two, sweep_three, sweep_four]):
+        opt.reset_df()
+        abx_settings_perturbed = {key : abx_settings[key] for key in abx_settings} #deep copy
+        max_deescalation = None
+        print("Performing %s to %s sweep" % (sweep[0], sweep[1]))
+        random_rates, clin_rates, ip_rates = [], [], []
+        c_from_rs, ip_from_rs = [], []
+
+        opt.set_abx_settings(abx_settings_perturbed)
+        opt.solve_and_assign()
+        r, c, i = opt.get_coverage_rates()
+        if k ==0:
+            print( (1-c) * 100)
+        random_rates.append(1-r)
+        ip_rates.append(1-i)
+        clin_rates.append(1-c)
+
+        for iter_ in tqdm(range(abx_settings[sweep[0]])):
+            abx_settings_perturbed[sweep[0]] -= 1
+            abx_settings_perturbed[sweep[1]] += 1
+            opt.set_abx_settings(abx_settings_perturbed)
+            opt.replace_one(sweep[0], sweep[1])
+            opt.solve_and_assign()
+            r, c, i = opt.get_coverage_rates()
+
+            random_rates.append(1-r)
+            ip_rates.append(1-i)
+            clin_rates.append(1-c)
+
+            if 1-i > baseline_clin_miss_rate and max_deescalation is None:
+                # Number of iterations before gettting to baseline clinician error rate
+                max_deescalation = abx_settings[sweep[0]] - abx_settings_perturbed[sweep[0]] + 1
+                max_deescaltion_miss_rate = ip_rates[len(ip_rates)-2]
+                settings_to_plot = {key : abx_settings_perturbed[key] for key in abx_settings_perturbed} 
+                settings_to_plot[sweep[0]] += 1
+                settings_to_plot[sweep[1]] -= 1
+                plot_histogram(settings_to_plot, axes[k+1][0])
+                axes[k+1][0].set_title("Optmized Allocation Fewer %s" % sweep[0].replace('_', ' & '))
+                axes[k+1][0].set_xlabel('Number of Allocations')
+                axes[k+1][0].set_ylabel('')
+                print(max_deescalation)
+                d_rate = float(abx_settings[sweep[0]] - (abx_settings_perturbed[sweep[0]] + 1))/abx_settings[sweep[0]]
+                with open(f_name_missrates, 'a') as w:
+                    w.write("Miss rate of %.2f achieve with %.2f fewer %s\n" % ((max_deescaltion_miss_rate)*100,
+                                                                                d_rate*100,
+                                                                                sweep[0].replace("_", ' & ')))
+            # if abx_settings_perturbed == abx_settings:
+            #     clin_iter = iter_ # save point on x axis for clinician performance
+        if max_deescalation is None:
+            max_deescalation = abx_settings[sweep[0]]
+
+        deescalations = [i for i in range(len(random_rates))]
+        deescalations.reverse()
+        axes[k+1][1].plot(deescalations, ip_rates, label='Optimized', linewidth=2.0)
+        axes[k+1][1].plot(deescalations, clin_rates, label='Clinician', linewidth=2.0)
+        axes[k+1][1].plot(deescalations, random_rates, label='Random', linewidth=2.0)
+        axes[k+1][1].plot(deescalations, [baseline_clin_miss_rate for c in range(len(random_rates))], linestyle='--', color='black')
+        axes[k+1][1].invert_xaxis()
+        axes[k+1][1].set_title("Replacing %s with %s" % (sweep[0].replace("_", ' & '), sweep[1]))
+        axes[k+1][1].set_xlabel("Number of %s Allocated" % sweep[0].replace("_", ' & '))
+        axes[k+1][1].set_ylabel("Miss Rate")
+        axes[k+1][1].set_ylim((0.10, 0.40))
+        print("Max Re-allocations of %s to %s: %s" % (sweep[0], sweep[1], str(max_deescalation)))
+        if k == 0:
+            axes[k+1][1].legend(loc='upper left', fontsize='small')
+        
+
+    plt.savefig('select_sweeps.png')
+
+def test_waterfall():
+    abx_settings = {"a" : 2,
+                     "b" : 0,
+                     "c" : 0
+                    }
+    abx_rankings = ['a',
+                    'b',
+                    'c'
+                    ]  
+    true_settings = [{"a" : 2,
+                     "b" : 0,
+                     "c" : 0
+                    },
+                    {"a" : 1,
+                     "b" : 1,
+                     "c" : 0,
+                    },
+                    {"a" : 1,
+                     "b" : 0,
+                     "c" : 1
+                    },
+                    {"a" : 0,
+                     "b" : 1,
+                     "c" : 1
+                    },
+                    {"a" : 0,
+                     "b" : 0,
+                     "c" : 2
+                    }]                 
+
+    settings = []
+    deplete_idx = 0
+    push_from_idx = 0
+    push_to_idx = 1
+    import copy
+    while abx_settings['c'] != 2: # end of sweep
+        settings.append(copy.deepcopy(abx_settings))
+        print("Deplete idx:{d} Push From Idx:{f} Push To Idx:{t}".format(d=deplete_idx,
+                                                                         f=push_from_idx,
+                                                                         t=push_to_idx))
+        # Make more narrow spectrum
+        abx_settings[abx_rankings[push_from_idx]] -= 1
+        abx_settings[abx_rankings[push_to_idx]] += 1
+
+        if abx_settings[abx_rankings[deplete_idx]] == 0:
+            deplete_idx += 1
+
+        if push_to_idx == len(abx_rankings)-1:
+            push_to_idx = deplete_idx + 1
+            push_from_idx = deplete_idx
+        else:
+            push_from_idx += 1
+            push_to_idx += 1
+
+    for i, s in enumerate(settings):
+        try:
+            assert s == true_settings[i]
+        except:
+            pdb.set_trace()
+
+def plot_distribution_of_allocated_abx():
+    """ Bar plots of allocated abx """
+    df = pd.DataFrame()
+    abx_settings = {"Vancomycin" : 13,
+                "Ampicillin" : 0,
+                "Cefazolin" : 8,
+                "Ceftriaxone" : 404,
+                "Cefepime" : 14,
+                "Zosyn" : 102,
+                "Ciprofloxacin" : 8,
+                "Meropenem" : 9,
+                "Vancomycin_Meropenem" : 9,
+                "Vancomycin_Zosyn" :  149,
+                "Vancomycin_Cefepime" : 23,
+                "Vancomycin_Ceftriaxone" : 31
+                }
+
+    df_predictions = load_predictions()
+    df_drugs = get_clinician_prescribing_patterns()
+    opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)
+
+    opt.df = (opt.df
+        .assign(was_covered_dr=opt.df.apply(lambda x: opt.compute_was_covered(x), axis=1))
+    )
+
+    # Get counts
+    df_final = (opt.df
+        .groupby('med_description')
+        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'nunique'),
+             num_times_covered_by_dr=('was_covered_dr', 'sum'))
+        .reset_index()
+        .assign(med_description=lambda x: [m.replace('_', ' & ') + " [%s/%s]" % (nc, nd) 
+                                           for m, nd, nc in zip(x.med_description,
+                                                                x.num_distinct_csns,
+                                                                x.num_times_covered_by_dr) ])
+    )
+
+
+    fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+    df_final = df_final.sort_values('num_distinct_csns', ascending=False)
+    sns.barplot(x="num_distinct_csns", y="med_description", ci=None, data=df_final, ax=ax, color='red' )
+    sns.barplot(x="num_times_covered_by_dr", y="med_description", ci=None, data=df_final, ax=ax, color='blue')
+    ax.set_ylabel("")
+    ax.set_xlabel("Number of Allocations")
+    ax.set_title("Clinician Antibiotic Selections and Fraction of Patients Covered")
+    # ax.set_xlim((0, 550))
+    # Save Figure
+    # plt.gcf().subplots_adjust(left=0.25)
+    # plt.savefig('abx_distribution.png')
+    plt.show()
+
+    # Save Miss Rates For Each antibiotic
+    fname = 'clinicians_miss_rates_by_abx.csv'
+    df_miss_rates = (df_final
+        .assign(num_misses=lambda x: [csn - n_c for csn, n_c in zip(x.num_distinct_csns,
+                                                                    x.num_times_covered_by_dr)])
+    )  
+
+    df_miss_rates.to_csv(fname, index=None)
+
+
+if __name__ == '__main__':
+    # r, c, l, lr, lc = bootstrap_miss_rates()
+    # fname = './bootstrapped_miss_rates.txt'
+    # with open(fname, 'w') as w:
+    #     w.write("Random miss rate:%s\n" % r)
+    #     w.write("Clinician miss rate:%s\n" % c)
+    #     w.write("Optimized miss rate:%s\n" % l)
+    #     w.write("Relative Reduction Miss Rate Optimized to Random:%s\n" % lr)
+    #     w.write("Relative Reduction Miss Rate Optimized to Clinician:%s\n" % lc)
+
+    # plot_select_sweeps()
+    # plot_distribution_of_allocated_abx()
+    # bootstrap_miss_rates()
+
+    abx_settings = {"Ceftriaxone" : 404,
+        "Vancomycin_Zosyn" :  149,
+        "Zosyn" : 102,
+        "Vancomycin_Ceftriaxone" : 31,
+        "Vancomycin_Cefepime" : 23,
+        "Cefepime" : 14,
+        "Vancomycin" : 13,
+        "Vancomycin_Meropenem" : 9,
+        "Meropenem" : 9,
+        "Cefazolin" : 8,
+        "Ciprofloxacin" : 8,
+        "Ampicillin" : 0,
+        }
+
+    f_name_missrates = 'individual_sweep_test.txt'
+    df_predictions = load_predictions()
+    df_drugs = get_clinician_prescribing_patterns()
+    opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)
+    plot_sweeps(opt)
+    # perform_sweep(opt, abx_1='Vancomycin_Meropenem', abx_2='Vancomycin', ax=ax, fname=f_name_missrates, legend=True)
+    # plt.show()
\ No newline at end of file
diff --git a/scripts/ER_Infection/notebooks/decision_alg/Integer Programming Including Not Infected.ipynb b/scripts/ER_Infection/notebooks/decision_alg/Integer Programming Including Not Infected.ipynb
new file mode 100644
index 00000000..77046145
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/decision_alg/Integer Programming Including Not Infected.ipynb	
@@ -0,0 +1,727 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algorithmically Assign Abx To CSNs based on Model Predictions\n",
+    "## Include patients who are phenotyped as not infected "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.calibration import CalibratedClassifierCV, calibration_curve\n",
+    "from sklearn.model_selection import cross_val_predict, StratifiedKFold\n",
+    "from sklearn.metrics import roc_auc_score, brier_score_loss\n",
+    "%matplotlib inline\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Predictions Data For Each Abx Option\n",
+    "\n",
+    "Predictions of coverage for each antibiotic selection are stored in csv files using the following directory schema.  This function reads in predictions for each classifier so that each row is CSN and has an estimated probability of coverage for each antibiotic selection.  We implement a test function that compute the AUROC of each model after the predictions have been read in and cross checks it with the AUROC that was computed and stored in an `auroc.txt` text file during the model training procedure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "def test_load_predictions(df):\n",
+    "    \"\"\"\n",
+    "    Reads in output of df, computes AUROC for each classifier and asserts that it equals the AUROC\n",
+    "    listed in the auroc.txt file associate with the classfier's directory\n",
+    "    \"\"\"\n",
+    "    base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_w_not_infected/testing/{abx}\"\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "    for abx in abx_options:\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_auroc = os.path.join(path, 'auroc.txt')\n",
+    "        with open(f_auroc, 'r') as f:\n",
+    "            auroc = round(float(f.read()), 3)\n",
+    "        \n",
+    "        computed_auroc = round(roc_auc_score(df['%s_label' % abx], df['%s_predictions' % abx]), 3)\n",
+    "        \n",
+    "        assert auroc == computed_auroc\n",
+    "        print(\"%s_auroc: %s\"% (abx, str(auroc)))\n",
+    "\n",
+    "def load_predictions():\n",
+    "    \"\"\"Helper function that loads predictions from AST classifiers for test set data\"\"\"\n",
+    "    \n",
+    "    base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_w_not_infected/testing/{abx}\"\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "    df = pd.DataFrame()\n",
+    "    for i, abx in enumerate(abx_options):\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "        if i == 0:\n",
+    "            df = pd.read_csv(f_path)\n",
+    "            df = df[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df = df.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                    'predictions' : '%s_predictions' % abx})\n",
+    "        else:\n",
+    "            df_preds = pd.read_csv(f_path)\n",
+    "            df_preds = df_preds[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df_preds = df_preds.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                                'predictions' : '%s_predictions' % abx})\n",
+    "            df = df.merge(df_preds, how='left', on=['anon_id', 'pat_enc_csn_id_coded'])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "df = load_predictions()\n",
+    "test_load_predictions(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calibration Classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class IdentityEstimator(LogisticRegression):\n",
+    "    def __init__(self):\n",
+    "        LogisticRegression.__init__(self)\n",
+    "            \n",
+    "    def predict_proba(self, input_array):   \n",
+    "        return input_array*1\n",
+    "\n",
+    "    def decision_function(self, input_array):\n",
+    "        return input_array*1\n",
+    "\n",
+    "def calibrate_probabilities(predictions, labels):\n",
+    "    \"\"\" \n",
+    "    Takes in test set probabilites and does a k-fold cross fitting procedure to recalibrate each model\n",
+    "    \"\"\"\n",
+    "    est = IdentityEstimator()\n",
+    "    X = predictions.values.reshape(-1, 1)\n",
+    "    y = labels\n",
+    "    isotonic_calibrated_predictions = np.array([float(i) for i in range(len(y))])\n",
+    "    sigmoid_calibrated_predictions = np.array([float(i) for i in range(len(y))])\n",
+    "\n",
+    "    # Fit base estimator\n",
+    "    est.fit(X, y) # because we've overloaded predict_proba and decision function this doesn't matter\n",
+    "\n",
+    "    # Calibrated with isotonic calibration\n",
+    "    isotonic = CalibratedClassifierCV(est, cv='prefit', method='isotonic')\n",
+    "\n",
+    "    # Calibrated with sigmoid calibration\n",
+    "    sigmoid = CalibratedClassifierCV(est, cv='prefit', method='sigmoid')\n",
+    "\n",
+    "    cv = StratifiedKFold(n_splits=10)\n",
+    "    for train_inds, val_inds in cv.split(X, y):\n",
+    "        X_train, y_train = X[train_inds], y[train_inds]\n",
+    "        X_val, y_val = X[val_inds], y[val_inds]\n",
+    "        isotonic.fit(X_train, y_train)\n",
+    "        isotonic_predictions = isotonic.predict_proba(X_val)\n",
+    "        isotonic_calibrated_predictions[val_inds] = isotonic_predictions[:, 1]\n",
+    "\n",
+    "        sigmoid.fit(X_train, y_train)\n",
+    "        sigmoid_predictions = sigmoid.predict_proba(X_val)\n",
+    "        sigmoid_calibrated_predictions[val_inds] = sigmoid_predictions[:, 1]\n",
+    "\n",
+    "    \n",
+    "    return sigmoid_calibrated_predictions, isotonic_calibrated_predictions\n",
+    "\n",
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "for abx in abx_options:\n",
+    "    predictions = df['%s_predictions' % abx]\n",
+    "    labels = df['%s_label' % abx]\n",
+    "    s_preds, i_preds = calibrate_probabilities(predictions, labels)\n",
+    "    df['%s_predictions_sigmoid' % abx] = s_preds\n",
+    "    df['%s_predictions_isotonic' % abx] = i_preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sanity check AUROCS\n",
+    "for abx in abx_options:\n",
+    "    auc = roc_auc_score(df['%s_label' % abx], df['%s_predictions' % abx])\n",
+    "    auc_s = roc_auc_score(df['%s_label' % abx], df['%s_predictions_sigmoid' % abx])\n",
+    "    auc_i = roc_auc_score(df['%s_label' % abx], df['%s_predictions_isotonic' % abx])\n",
+    "    console_str = \"{} AUC Uncalibrated:{:.2f} AUC Sigmoid Calibrated:{:.2f} AUC Isotonic Calibrated:{:.2f}\"\n",
+    "    print(console_str.format(abx, auc, auc_s, auc_i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot Calibration Curves\n",
+    "def plot_calibration_curves(df, abx, ax):\n",
+    "\n",
+    "    prob_pos = df['%s_predictions' % abx]\n",
+    "    labels = df['%s_label' % abx]\n",
+    "    clf_score = brier_score_loss(labels, prob_pos)\n",
+    "    print(\"\\tBrier Uncalibrated: %1.5f\" % (clf_score))\n",
+    "\n",
+    "    fraction_of_positives, mean_predicted_value = \\\n",
+    "        calibration_curve(labels, prob_pos, n_bins=10)\n",
+    "\n",
+    "    ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
+    "    ax.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
+    "            label=\"%s (%1.5f)\" % ('Uncalibrated', clf_score))\n",
+    "        \n",
+    "    # Sigmoid \n",
+    "    clf_score = brier_score_loss(labels, df['%s_predictions_sigmoid'% abx])\n",
+    "    print(\"\\t Sigmoid Brier: %1.5f\" % (clf_score))\n",
+    "    fraction_of_positives, mean_predicted_value = \\\n",
+    "        calibration_curve(labels, df['%s_predictions_sigmoid'% abx], n_bins=10)\n",
+    "    ax.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
+    "            label=\"%s (%1.5f)\" % ('Sigmoid', clf_score))\n",
+    "    \n",
+    "    \n",
+    "    # Isotonic curve\n",
+    "    clf_score = brier_score_loss(labels, df['%s_predictions_isotonic'% abx])\n",
+    "    print(\"\\t Isotonic Brier: %1.5f\" % (clf_score))\n",
+    "    fraction_of_positives, mean_predicted_value = \\\n",
+    "        calibration_curve(labels, df['%s_predictions_isotonic'% abx], n_bins=10)\n",
+    "    ax.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
+    "            label=\"%s (%1.5f)\" % ('Isotonic', clf_score))\n",
+    "    \n",
+    "    ax.legend()\n",
+    "    ax.set_title(abx)\n",
+    "    ax.set_xlabel('Estimated Probability')\n",
+    "    ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "fig, ax = plt.subplots(4, 3, figsize=(24, 32))\n",
+    "sns.set(font_scale=1.5)\n",
+    "row, col = 0, 0\n",
+    "for abx in abx_options:\n",
+    "    plot_calibration_curves(df, abx, ax[row, col])\n",
+    "    \n",
+    "    if col == 2:\n",
+    "        col = 0\n",
+    "        row += 1\n",
+    "    else:\n",
+    "        col += 1 \n",
+    "    \n",
+    "plt.show        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get clinician prescribing patterns\n",
+    "This SQL query gathers all abx medications ordered within the first 24 hours of admission that were administered to the patient in long format ( one row per administered med_description ) and then joins to our labels table so that we can cross check whether the administered antibiotic was sufficient to cover the patient. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "WITH adm_abx AS (\n",
+    "    SELECT\n",
+    "        om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded,\n",
+    "        om.med_description, om.order_time\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_orders_given_and_stopped` om\n",
+    "    WHERE\n",
+    "        om.was_given = 1\n",
+    "), \n",
+    "not_infected as (\n",
+    "    SELECT \n",
+    "        anon_id, pat_enc_csn_id_coded, index_time,\n",
+    "        CASE WHEN abx_stopped_2_days = 1\n",
+    "        AND not_discharged_with_orals = 1\n",
+    "        AND no_pos_cult_2_weeks_later = 1\n",
+    "        AND no_pos_cult_within_day = 1\n",
+    "        AND no_inf_dx_codes = 1\n",
+    "        THEN 1 ELSE 0 END not_infected\n",
+    "    FROM \n",
+    "        `mining-clinical-decisions.abx.cohort_not_infected_rules`\n",
+    ")\n",
+    "SELECT \n",
+    "    not_infected.not_infected, om.med_description, om.order_med_id_coded, l.*\n",
+    "FROM \n",
+    "    adm_abx om\n",
+    "RIGHT JOIN \n",
+    "    `mining-clinical-decisions.abx.final_cohort_table` l\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "INNER JOIN \n",
+    "    not_infected \n",
+    "USING \n",
+    "    (pat_enc_csn_id_coded)\n",
+    "WHERE \n",
+    "    l.label_unobserved = 0\n",
+    "ORDER BY \n",
+    "    anon_id, l.pat_enc_csn_id_coded, om.order_time\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_abx = query_job.result().to_dataframe()\n",
+    "df_abx.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_abx[df_abx['med_description'].isna()]\n",
+    "df_abx['med_description'] = df_abx['med_description'].fillna('No Abx Administered')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregate antibiotic orders \n",
+    "Here we aggregate the antibiotic orders so that one row in the desulting dataframe corresponds to a unique CSN. We do this by\n",
+    "1. Grouping by the CSN\n",
+    "2. Grabbing the first word (antibiotic name) from the med description\n",
+    "3. Aggregating the `med_description` column such that it is a single string with all antibiotics admistered to the patient, sorted in alphabetical order and separated by spaces. \n",
+    "4. Only keep CSNs where the set of administered antibiotics is equal to one of the antbiotic selections we've trained classifiers for. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Useful dictionaries to map corresponding. strings for the sameantibiotic selections\n",
+    "abx_map = {'Ceftriaxone' : \"CEFTRIAXONE\",\n",
+    "           'Vancomycin_Zosyn' : \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\",\n",
+    "           'Zosyn' : \"PIPERACILLIN-TAZOBACTAM\",\n",
+    "           'Vancomycin_Ceftriaxone' : \"CEFTRIAXONE VANCOMYCIN\",\n",
+    "           'Vancomycin_Cefepime' : \"CEFEPIME VANCOMYCIN\",\n",
+    "           'Cefepime' : \"CEFEPIME\",\n",
+    "           'Vancomycin' :  \"VANCOMYCIN\",\n",
+    "           'Meropenem' : \"MEROPENEM\",\n",
+    "           'Vancomycin_Meropenem' : \"MEROPENEM VANCOMYCIN\",\n",
+    "           'Cefazolin' : \"CEFAZOLIN\",\n",
+    "           'Ciprofloxacin' : \"CIPROFLOXACIN\",\n",
+    "           'Ampicillin' : 'AMPICILLIN'\n",
+    "           #'No Abx' : 'No Abx Administered'\n",
+    "          }\n",
+    "abx_map_inverse = {abx_map[key] : key for key in abx_map}\n",
+    "\n",
+    "# Lambda that aggregate Antibiotic orders after we've grouped by CSN\n",
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "\n",
+    "# \n",
+    "df_drugs = (df_abx\n",
+    "    .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)\n",
+    "    .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                        .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                       for a in x.med_description])\n",
+    "    .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on\n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'med_description' : concat_abx,\n",
+    "          'not_infected' : 'first',\n",
+    "          'year' : 'first',\n",
+    "          'Ampicillin' : 'first',\n",
+    "          'Ciprofloxacin' : 'first',\n",
+    "          'Cefazolin' : 'first',\n",
+    "          'Ceftriaxone' : 'first',\n",
+    "          'Cefepime' : 'first',\n",
+    "          'Zosyn' : 'first',\n",
+    "          'Vancomycin' : 'first',\n",
+    "          'Meropenem' : 'first',\n",
+    "          'Vancomycin_Ceftriaxone' : 'first',\n",
+    "          'Vancomycin_Cefepime' : 'first',\n",
+    "          'Vancomycin_Zosyn' : 'first',\n",
+    "          'Vancomycin_Meropenem' : 'first'})\n",
+    "    .reset_index()\n",
+    "    # Only look at test set data and CSNs where allowed antibiotic selection was administered\n",
+    "    .query(\"year == 2019 and med_description in @abx_map_inverse\", engine='python') \n",
+    ")\n",
+    "\n",
+    "# Roughly 700 of the 1300 original CSNs in the test set\n",
+    "print(len(df_drugs))\n",
+    "df_drugs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge this dataframe to predictions dataframe\n",
+    "After this step we should have a dataframe that has one row per CSN, each row should have the antibiotic selection actually administered to the patient, along with the predicted probability of said antibiotic selection covering the patient, and the ground truth as to whether it did. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge df_total to df on pat_enc_csn_id_coded\n",
+    "base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_w_not_infected/testing/{abx}\"\n",
+    "df_new = (df\n",
+    "    .merge(df_drugs, how='inner', on='pat_enc_csn_id_coded')\n",
+    ")\n",
+    "\n",
+    "# Sanity check - make sure %abx_label columns are equal to %abx columns\n",
+    "for abx in abx_options:\n",
+    "    for i in range(len(df_new)):\n",
+    "        assert df_new[abx].values[i] == df_new['%s_label' % abx].values[i]\n",
+    "        \n",
+    "# Sanity check 2: compute AUROC of this subset of patients and compare to AUROC on full test set\n",
+    "for abx in abx_options:\n",
+    "    computed_auroc = roc_auc_score(df_new['%s_label' % abx], df_new['%s_predictions' % abx])\n",
+    "    f_auroc = os.path.join(base_path.format(abx=abx), 'auroc.txt')\n",
+    "    with open(f_auroc, 'r') as f:\n",
+    "        auroc = float(f.read())\n",
+    "    print(\"{}: Full test set AUROC:{:.3f} Subset AUROC:{:.3f}\".format(abx, auroc, computed_auroc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Binary Integer Programming Problem Formulation and Solve\n",
+    "Here we specificy the problem formulation of the optimization process we wish to solve. The goal is to maximize the probability of covering the set of patients in the test set with the available antibiotic selections subject to the constraints that we assign each antibiotic selection a prespecified number of times, and that we only assign one antibiotic selection to each patient CSN. \n",
+    "\n",
+    "More technically, Let $N$ be the number of patient CSNs in our test set who were administered one of the 12 abx selections by clinicians, and let $K$ be the number of possible antibiotic selections.  Let $A$ be a matrix in $\\mathbb{R}^{N\\times K}$ such that $a_{ij}$ is 1 if antibiotic selection $j$ is selected for patient CSN $i$ and 0 otherwise. Let $\\Phi$ be a matrix in $\\mathbb{R}^{N \\times K}$ such that $\\phi_{ij}$ is the predicted probability that antibiotic $j$ will cover patient CSN $i$.  Let $C$ be a vector in $\\mathbb{R}^K$ such that $c_j$ specifies the budget for anitbiotic selection $j$ - that is the number of times we are allowed to select antibiotic $j$ across our $N$ patient CSNs. Our problem formulation is as follows. \n",
+    "\n",
+    "$$  \\underset{A}{\\text{maximize}} \\sum_{i=1}^{N} \\sum_{j=1}^K \\phi_{ij} a_{ij} $$\n",
+    "\n",
+    "Subject to the following constraints:\n",
+    "\n",
+    "$$ \\sum_{j=1}^{K} a_{ij} = 1 \\quad i = 1, ..., N $$\n",
+    "\n",
+    "$$ \\sum_{i=1}^{N} a_{ij} = c_j \\quad j = 1, ...,  K $$\n",
+    "\n",
+    "In the following code, we implenent and solve this optimization process using the pulp python package. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get clinician drug counts\n",
+    "(df_drugs\n",
+    "    .groupby('med_description')\n",
+    "    .agg(num_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    "    .reset_index()\n",
+    "    .sort_values('num_csns', ascending=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "\n",
+    "# Create binary indicators for whether treatment is used\n",
+    "drug_inds = {}\n",
+    "for abx in abx_options:\n",
+    "    drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                      for i in range(len(df_new))]\n",
+    "\n",
+    "# Add objective function to model\n",
+    "per_csn_sum = []\n",
+    "for i in range(len(df_new)):\n",
+    "    _sum = 0\n",
+    "    for abx in abx_options:\n",
+    "        _sum += drug_inds[abx][i] * df_new['%s_predictions_isotonic' % abx].values[i]\n",
+    "    per_csn_sum.append(_sum)\n",
+    "    \n",
+    "abx_model += lpSum(per_csn_sum)\n",
+    "\n",
+    "# Add one selection constraint\n",
+    "for i in range(len(df_new)):\n",
+    "    selections = []\n",
+    "    for abx in abx_options:\n",
+    "        selections.append(drug_inds[abx][i])\n",
+    "    abx_model += lpSum(selections) == 1\n",
+    "\n",
+    "# Add max assignment constraints\n",
+    "# abx_assignment_constraints = {\"Vancomycin\" : 13,\n",
+    "#                               \"Ampicillin\" : 0,\n",
+    "#                               \"Cefazolin\" : 8,\n",
+    "#                               \"Ceftriaxone\" : 367,\n",
+    "#                               \"Cefepime\" : 14,\n",
+    "#                               \"Zosyn\" : 102,\n",
+    "#                               \"Ciprofloxacin\" : 8,\n",
+    "#                               \"Meropenem\" : 9,\n",
+    "#                               \"Vancomycin_Meropenem\" : 9,\n",
+    "#                               \"Vancomycin_Zosyn\" :  113,\n",
+    "#                               \"Vancomycin_Cefepime\" : 23,\n",
+    "#                               \"Vancomycin_Ceftriaxone\" : 31\n",
+    "#                              }\n",
+    "\n",
+    "abx_assignment_constraints = {\"Vancomycin\" : 20,\n",
+    "                              \"Ampicillin\" : 54,\n",
+    "                              \"Cefazolin\" : 22,\n",
+    "                              \"Ceftriaxone\" : 462,\n",
+    "                              \"Cefepime\" : 15,\n",
+    "                              \"Zosyn\" : 100, # 154\n",
+    "                              \"Ciprofloxacin\" : 12,\n",
+    "                              \"Meropenem\" : 9,\n",
+    "                              \"Vancomycin_Meropenem\" : 10,\n",
+    "                              \"Vancomycin_Zosyn\" :  101,\n",
+    "                              \"Vancomycin_Cefepime\" : 31,\n",
+    "                              \"Vancomycin_Ceftriaxone\" : 38\n",
+    "                             }\n",
+    "\n",
+    "for drug in drug_inds:\n",
+    "    abx_model += lpSum([drug_inds[drug][i] for i in range(len(df_new))]) == abx_assignment_constraints[drug]\n",
+    "\n",
+    "# Solve model\n",
+    "abx_model.solve()\n",
+    "print(\"Status:\", LpStatus[abx_model.status])\n",
+    "\n",
+    "# Save selected antibiotic to df_new\n",
+    "abx_decisions = []\n",
+    "for i in range(len(df_new)):\n",
+    "    abx_decision = None\n",
+    "    for abx in abx_options:\n",
+    "        if drug_inds[abx][i].value() == 1:\n",
+    "            abx_decision = abx_map[abx]\n",
+    "    assert abx_decision is not None\n",
+    "    abx_decisions.append(abx_decision)\n",
+    "df_new['IP_med_description'] = abx_decisions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare Performance to Clinician Performance\n",
+    "1. Write a function that takes in antibiotic selection and outputs a 1 if that selection covered the patient.  Simple to do, but annoying because of different ways we've named antibiotic selections.\n",
+    "2. Compute fraction of time each patient CSN was covered by the antibiotic selection. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ugly helper function that just does some string mapping\n",
+    "def compute_was_covered(x, decision_column='med_description'):\n",
+    "    \"\"\"\n",
+    "    Given med description, find appropriate label column and return whether patient was covered during CSN\n",
+    "    Returns \"Not in abx options\" if abx regimen isn't in our set of 12 options - useful for filtering later\n",
+    "    \"\"\"\n",
+    "    if decision_column == 'med_description':\n",
+    "        med_description = x.med_description\n",
+    "    elif decision_column == 'random_med_description':\n",
+    "        med_description = x.random_med_description\n",
+    "    elif decision_column == 'IP_med_description':\n",
+    "        med_description = x.IP_med_description\n",
+    "        \n",
+    "    if med_description == \"CEFTRIAXONE\":\n",
+    "        return x.Ceftriaxone\n",
+    "    elif med_description == \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Zosyn\n",
+    "    elif med_description == \"PIPERACILLIN-TAZOBACTAM\":\n",
+    "        return x.Zosyn\n",
+    "    elif med_description == \"CEFTRIAXONE VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Ceftriaxone\n",
+    "    elif med_description == \"CEFEPIME VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Cefepime\n",
+    "    elif med_description == \"CEFEPIME\":\n",
+    "        return x.Cefepime\n",
+    "    elif med_description == \"VANCOMYCIN\":\n",
+    "        return x.Vancomycin\n",
+    "    elif med_description == \"MEROPENEM\":\n",
+    "        return x.Meropenem\n",
+    "    elif med_description == \"MEROPENEM VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Meropenem\n",
+    "    elif med_description == \"CEFAZOLIN\":\n",
+    "        return x.Cefazolin\n",
+    "    elif med_description == \"CIPROFLOXACIN\":\n",
+    "        return x.Ciprofloxacin\n",
+    "    elif med_description == \"AMPICILLIN\":\n",
+    "        return x.Ampicillin\n",
+    "    else:\n",
+    "        return \"Not in abx options\"\n",
+    "\n",
+    "    \n",
+    "# Create flag for whether clinicians covered the patient during the csn, whether a random assignemnt covered patient\n",
+    "# CSN, and whether optimized assignment covered the patient CSN.\n",
+    "\n",
+    "df_new = (df_new\n",
+    "    .assign(random_med_description=lambda x: np.random.choice(x.med_description, size=len(x.med_description), replace=False))\n",
+    ")\n",
+    "df_new = (df_new\n",
+    "    #.sample(frac=1.0, replace=True) # bootstrap each iteration\n",
+    "    .assign(was_covered_dr=df_new.apply(lambda x: compute_was_covered(x), axis=1))\n",
+    "    .assign(was_covered_random=df_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='random_med_description'),\n",
+    "                                                                          axis=1))\n",
+    "    .assign(was_covered_IP=df_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                      decision_column='IP_med_description'),\n",
+    "                                                                      axis=1))\n",
+    ")\n",
+    "\n",
+    "clin_covered_rate = df_new['was_covered_dr'].sum() / len(df_new)\n",
+    "random_covered_rate = df_new['was_covered_random'].sum() / len(df_new)\n",
+    "ip_covered_rate = df_new['was_covered_IP'].sum() / len(df_new)\n",
+    "console_str = \"Random Coverage Rate:{:.4f} Clinician Coverage Rate:{:.4f} Integer Programming Coverage Rate:{:.4f}\"\n",
+    "print(console_str.format(random_covered_rate, clin_covered_rate, ip_covered_rate))\n",
+    "\n",
+    "df_new_random = (df_new\n",
+    "        .groupby('random_med_description')\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_random=('was_covered_random', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .assign(random_covered=lambda x: ['{}/{}'.format(c, t) for c, t in zip(x.num_times_covered_random,\n",
+    "                                                                               x.num_distinct_csns)])\n",
+    "        .rename(columns={'random_med_description' : 'med_description'})\n",
+    ")[['med_description', 'random_covered']]\n",
+    "                 \n",
+    "df_new_clinician = (df_new\n",
+    "        .groupby('med_description')\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_dr=('was_covered_dr', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .assign(dr_covered=lambda x: ['{}/{}'.format(c, t) for c, t in zip(x.num_times_covered_dr,\n",
+    "                                                                               x.num_distinct_csns)])\n",
+    ")[['med_description', 'dr_covered']]\n",
+    "                    \n",
+    "df_new_ip = (df_new\n",
+    "        .groupby('IP_med_description')\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_IP=('was_covered_IP', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .assign(IP_covered=lambda x: ['{}/{}'.format(c, t) for c, t in zip(x.num_times_covered_IP,\n",
+    "                                                                               x.num_distinct_csns)])\n",
+    "        .rename(columns={'IP_med_description' : 'med_description'})\n",
+    ")[['med_description', 'IP_covered']]\n",
+    "\n",
+    "df_new_agg = (df_new_random\n",
+    "    .merge(df_new_clinician, how='outer', on='med_description')\n",
+    "    .merge(df_new_ip, how='outer', on='med_description')\n",
+    ")\n",
+    "\n",
+    "df_new_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note two big limitations here\n",
+    "1. Performing a cross fitting procedure on test set to calibrate each classifier on test set \n",
+    "2. Assuming we have access to the \"budget\" of antibiotics ahead of time.  Realistically this should be a one at a time problem. Would have to \"cross fit\" the abx selections. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/decision_alg/Integer Programming.ipynb b/scripts/ER_Infection/notebooks/decision_alg/Integer Programming.ipynb
new file mode 100644
index 00000000..c104c0a3
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/decision_alg/Integer Programming.ipynb	
@@ -0,0 +1,8872 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algorithmically Assign Abx To CSNs based on Model Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
+      "  %reload_ext google.cloud.bigquery\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/conorcorbin/opt/anaconda3/lib/python3.7/site-packages/google/auth/_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Predictions Data For Each Abx Option\n",
+    "\n",
+    "Predictions of coverage for each antibiotic selection are stored in csv files using the following directory schema.  This function reads in predictions for each classifier so that each row is CSN and has an estimated probability of coverage for each antibiotic selection.  We implement a test function that compute the AUROC of each model after the predictions have been read in and cross checks it with the AUROC that was computed and stored in an `auroc.txt` text file during the model training procedure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vancomycin_auroc: 0.717\n",
+      "Ampicillin_auroc: 0.621\n",
+      "Cefazolin_auroc: 0.674\n",
+      "Ceftriaxone_auroc: 0.688\n",
+      "Cefepime_auroc: 0.649\n",
+      "Zosyn_auroc: 0.641\n",
+      "Ciprofloxacin_auroc: 0.611\n",
+      "Meropenem_auroc: 0.685\n",
+      "Vancomycin_Meropenem_auroc: 0.731\n",
+      "Vancomycin_Zosyn_auroc: 0.699\n",
+      "Vancomycin_Cefepime_auroc: 0.697\n",
+      "Vancomycin_Ceftriaxone_auroc: 0.669\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pdb\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "def test_load_predictions(df):\n",
+    "    \"\"\"\n",
+    "    Reads in output of df, computes AUROC for each classifier and asserts that it equals the AUROC\n",
+    "    listed in the auroc.txt file associate with the classfier's directory\n",
+    "    \"\"\"\n",
+    "    base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "    for abx in abx_options:\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_auroc = os.path.join(path, 'auroc.txt')\n",
+    "        with open(f_auroc, 'r') as f:\n",
+    "            auroc = round(float(f.read()), 3)\n",
+    "        \n",
+    "        computed_auroc = round(roc_auc_score(df['%s_label' % abx], df['%s_predictions' % abx]), 3)\n",
+    "        \n",
+    "        assert auroc == computed_auroc\n",
+    "        print(\"%s_auroc: %s\"% (abx, str(auroc)))\n",
+    "\n",
+    "def load_predictions():\n",
+    "    \"\"\"Helper function that loads predictions from AST classifiers for test set data\"\"\"\n",
+    "    \n",
+    "    base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "    df = pd.DataFrame()\n",
+    "    for i, abx in enumerate(abx_options):\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "        if i == 0:\n",
+    "            df = pd.read_csv(f_path)\n",
+    "            df = df[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df = df.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                    'predictions' : '%s_predictions' % abx})\n",
+    "        else:\n",
+    "            df_preds = pd.read_csv(f_path)\n",
+    "            df_preds = df_preds[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df_preds = df_preds.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                                'predictions' : '%s_predictions' % abx})\n",
+    "            df = df.merge(df_preds, how='left', on=['anon_id', 'pat_enc_csn_id_coded'])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "df = load_predictions()\n",
+    "test_load_predictions(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1320\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem_label</th>\n",
+       "      <th>Meropenem_predictions</th>\n",
+       "      <th>Vancomycin_Meropenem_label</th>\n",
+       "      <th>Vancomycin_Meropenem_predictions</th>\n",
+       "      <th>Vancomycin_Zosyn_label</th>\n",
+       "      <th>Vancomycin_Zosyn_predictions</th>\n",
+       "      <th>Vancomycin_Cefepime_label</th>\n",
+       "      <th>Vancomycin_Cefepime_predictions</th>\n",
+       "      <th>Vancomycin_Ceftriaxone_label</th>\n",
+       "      <th>Vancomycin_Ceftriaxone_predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd29af0          131261001696                 0                0.321022   \n",
+       "4   JCd356bf          131261014293                 1                0.421153   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 0                0.351406                0   \n",
+       "4                 1                0.412029                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.459186                  0                 0.424984  ...   \n",
+       "4               0.528823                  1                 0.724792  ...   \n",
+       "\n",
+       "   Meropenem_label  Meropenem_predictions  Vancomycin_Meropenem_label  \\\n",
+       "0                1               0.896608                           1   \n",
+       "1                0               0.883272                           1   \n",
+       "2                1               0.815785                           1   \n",
+       "3                1               0.554059                           1   \n",
+       "4                1               0.839434                           1   \n",
+       "\n",
+       "   Vancomycin_Meropenem_predictions  Vancomycin_Zosyn_label  \\\n",
+       "0                          0.987670                       1   \n",
+       "1                          0.987744                       1   \n",
+       "2                          0.988870                       1   \n",
+       "3                          0.980191                       1   \n",
+       "4                          0.954408                       1   \n",
+       "\n",
+       "   Vancomycin_Zosyn_predictions  Vancomycin_Cefepime_label  \\\n",
+       "0                      0.964979                          1   \n",
+       "1                      0.966207                          1   \n",
+       "2                      0.965573                          1   \n",
+       "3                      0.947425                          1   \n",
+       "4                      0.963848                          1   \n",
+       "\n",
+       "   Vancomycin_Cefepime_predictions  Vancomycin_Ceftriaxone_label  \\\n",
+       "0                         0.961668                             1   \n",
+       "1                         0.970636                             1   \n",
+       "2                         0.956648                             1   \n",
+       "3                         0.948773                             0   \n",
+       "4                         0.946225                             1   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone_predictions  \n",
+       "0                            0.871461  \n",
+       "1                            0.904466  \n",
+       "2                            0.897293  \n",
+       "3                            0.805817  \n",
+       "4                            0.840946  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get clinician prescribing patterns\n",
+    "This SQL query gathers all abx medications ordered within the first 24 hours of admission that were administered to the patient in long format ( one row per administered med_description ) and then joins to our labels table so that we can cross check whether the administered antibiotic was sufficient to cover the patient. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_med_id_coded</th>\n",
+       "      <th>index_time</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>Ampicillin</th>\n",
+       "      <th>Ciprofloxacin</th>\n",
+       "      <th>Cefazolin</th>\n",
+       "      <th>Ceftriaxone</th>\n",
+       "      <th>Cefepime</th>\n",
+       "      <th>Zosyn</th>\n",
+       "      <th>Vancomycin</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC29f92d0</td>\n",
+       "      <td>131265245074</td>\n",
+       "      <td>603371616</td>\n",
+       "      <td>2019-02-20 10:49:00+00:00</td>\n",
+       "      <td>CEFTRIAXONE 1 GRAM/100 ML NS MINIBAG PLUS</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JC29f92d0</td>\n",
+       "      <td>131265245074</td>\n",
+       "      <td>603374106</td>\n",
+       "      <td>2019-02-20 10:49:00+00:00</td>\n",
+       "      <td>PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 M...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JC29f92d0</td>\n",
+       "      <td>131265245074</td>\n",
+       "      <td>603418477</td>\n",
+       "      <td>2019-02-20 10:49:00+00:00</td>\n",
+       "      <td>ERTAPENEM 1 GRAM/50 ML NS MINIBAG PLUS</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JC29f9afd</td>\n",
+       "      <td>131114292966</td>\n",
+       "      <td>476869102</td>\n",
+       "      <td>2015-09-03 14:08:00+00:00</td>\n",
+       "      <td>VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JC29f9afd</td>\n",
+       "      <td>131114292966</td>\n",
+       "      <td>476869103</td>\n",
+       "      <td>2015-09-03 14:08:00+00:00</td>\n",
+       "      <td>PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  order_med_id_coded  \\\n",
+       "0  JC29f92d0          131265245074           603371616   \n",
+       "1  JC29f92d0          131265245074           603374106   \n",
+       "2  JC29f92d0          131265245074           603418477   \n",
+       "3  JC29f9afd          131114292966           476869102   \n",
+       "4  JC29f9afd          131114292966           476869103   \n",
+       "\n",
+       "                 index_time  \\\n",
+       "0 2019-02-20 10:49:00+00:00   \n",
+       "1 2019-02-20 10:49:00+00:00   \n",
+       "2 2019-02-20 10:49:00+00:00   \n",
+       "3 2015-09-03 14:08:00+00:00   \n",
+       "4 2015-09-03 14:08:00+00:00   \n",
+       "\n",
+       "                                     med_description  Ampicillin  \\\n",
+       "0          CEFTRIAXONE 1 GRAM/100 ML NS MINIBAG PLUS           0   \n",
+       "1  PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 M...           0   \n",
+       "2             ERTAPENEM 1 GRAM/50 ML NS MINIBAG PLUS           0   \n",
+       "3            VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK           1   \n",
+       "4  PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML...           1   \n",
+       "\n",
+       "   Ciprofloxacin  Cefazolin  Ceftriaxone  Cefepime  Zosyn  Vancomycin  \\\n",
+       "0              0          0            0         0      1           0   \n",
+       "1              0          0            0         0      1           0   \n",
+       "2              0          0            0         0      1           0   \n",
+       "3              0          1            1         1      1           1   \n",
+       "4              0          1            1         1      1           1   \n",
+       "\n",
+       "   Meropenem  Vancomycin_Meropenem  Vancomycin_Zosyn  Vancomycin_Cefepime  \\\n",
+       "0          1                     1                 1                    0   \n",
+       "1          1                     1                 1                    0   \n",
+       "2          1                     1                 1                    0   \n",
+       "3          1                     1                 1                    1   \n",
+       "4          1                     1                 1                    1   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone  \n",
+       "0                       0  \n",
+       "1                       0  \n",
+       "2                       0  \n",
+       "3                       1  \n",
+       "4                       1  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded, l.index_time, om.med_description,\n",
+    "    l.Ampicillin, l.Ciprofloxacin, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin,\n",
+    "    l.Meropenem, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Cefepime, l.Vancomycin_Ceftriaxone\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.abx_orders_given_and_stopped` om\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.final_ast_labels` l\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "WHERE\n",
+    "    om.was_given = 1\n",
+    "ORDER BY \n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_time\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_abx = query_job.result().to_dataframe()\n",
+    "df_abx.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregate antibiotic orders \n",
+    "Here we aggregate the antibiotic orders so that one row in the desulting dataframe corresponds to a unique CSN. We do this by\n",
+    "1. Grouping by the CSN\n",
+    "2. Grabbing the first word (antibiotic name) from the med description\n",
+    "3. Aggregating the `med_description` column such that it is a single string with all antibiotics admistered to the patient, sorted in alphabetical order and separated by spaces. \n",
+    "4. Only keep CSNs where the set of administered antibiotics is equal to one of the antbiotic selections we've trained classifiers for. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### List Most Commmon Antibiotic Combinations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>num_csns</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>med_description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CEFTRIAXONE</th>\n",
+       "      <td>1886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PIPERACILLIN-TAZOBACTAM VANCOMYCIN</th>\n",
+       "      <td>985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PIPERACILLIN-TAZOBACTAM</th>\n",
+       "      <td>640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CIPROFLOXACIN</th>\n",
+       "      <td>307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFTRIAXONE PIPERACILLIN-TAZOBACTAM</th>\n",
+       "      <td>225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFTRIAXONE PIPERACILLIN-TAZOBACTAM VANCOMYCIN</th>\n",
+       "      <td>211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LEVOFLOXACIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN</th>\n",
+       "      <td>191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AZITHROMYCIN CEFTRIAXONE</th>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFTRIAXONE VANCOMYCIN</th>\n",
+       "      <td>152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LEVOFLOXACIN</th>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VANCOMYCIN</th>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFEPIME VANCOMYCIN</th>\n",
+       "      <td>114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFTRIAXONE CIPROFLOXACIN</th>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ERTAPENEM</th>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AZITHROMYCIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN</th>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFEPIME</th>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CEFAZOLIN</th>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEROPENEM PIPERACILLIN-TAZOBACTAM VANCOMYCIN</th>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEROPENEM VANCOMYCIN</th>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CIPROFLOXACIN METRONIDAZOLE</th>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 num_csns\n",
+       "med_description                                          \n",
+       "CEFTRIAXONE                                          1886\n",
+       "PIPERACILLIN-TAZOBACTAM VANCOMYCIN                    985\n",
+       "PIPERACILLIN-TAZOBACTAM                               640\n",
+       "CIPROFLOXACIN                                         307\n",
+       "CEFTRIAXONE PIPERACILLIN-TAZOBACTAM                   225\n",
+       "CEFTRIAXONE PIPERACILLIN-TAZOBACTAM VANCOMYCIN        211\n",
+       "LEVOFLOXACIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN       191\n",
+       "AZITHROMYCIN CEFTRIAXONE                              175\n",
+       "CEFTRIAXONE VANCOMYCIN                                152\n",
+       "LEVOFLOXACIN                                          128\n",
+       "VANCOMYCIN                                            115\n",
+       "CEFEPIME VANCOMYCIN                                   114\n",
+       "CEFTRIAXONE CIPROFLOXACIN                             112\n",
+       "ERTAPENEM                                              96\n",
+       "AZITHROMYCIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN        85\n",
+       "CEFEPIME                                               83\n",
+       "CEFAZOLIN                                              79\n",
+       "MEROPENEM PIPERACILLIN-TAZOBACTAM VANCOMYCIN           75\n",
+       "MEROPENEM VANCOMYCIN                                   57\n",
+       "CIPROFLOXACIN METRONIDAZOLE                            55"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "df_common_abx = (df_abx\n",
+    "    .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)\n",
+    "    .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                        .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                       for a in x.med_description])\n",
+    "    .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on\n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'med_description' : concat_abx})\n",
+    "    .reset_index()\n",
+    "    .groupby('med_description')\n",
+    "    .agg(num_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    "    .sort_values('num_csns', ascending=False)\n",
+    "           )\n",
+    "df_common_abx.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "770\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Ampicillin</th>\n",
+       "      <th>Ciprofloxacin</th>\n",
+       "      <th>Cefazolin</th>\n",
+       "      <th>Ceftriaxone</th>\n",
+       "      <th>Cefepime</th>\n",
+       "      <th>Zosyn</th>\n",
+       "      <th>Vancomycin</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6847</th>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6855</th>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6858</th>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6861</th>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6878</th>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pat_enc_csn_id_coded   med_description  year  Ampicillin  Ciprofloxacin  \\\n",
+       "6847          131260812263       Ceftriaxone  2019           0              1   \n",
+       "6855          131260883970  Vancomycin_Zosyn  2019           1              1   \n",
+       "6858          131261001599             Zosyn  2019           0              1   \n",
+       "6861          131261014293       Ceftriaxone  2019           1              1   \n",
+       "6878          131261155365       Ceftriaxone  2019           1              1   \n",
+       "\n",
+       "      Cefazolin  Ceftriaxone  Cefepime  Zosyn  Vancomycin  Meropenem  \\\n",
+       "6847          0            1         1      1           0          1   \n",
+       "6855          0            0         0      1           1          0   \n",
+       "6858          1            1         1      1           0          1   \n",
+       "6861          1            1         1      1           1          1   \n",
+       "6878          1            1         1      1           0          1   \n",
+       "\n",
+       "      Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "6847                       1                    1                 1   \n",
+       "6855                       1                    1                 1   \n",
+       "6858                       1                    1                 1   \n",
+       "6861                       1                    1                 1   \n",
+       "6878                       1                    1                 1   \n",
+       "\n",
+       "      Vancomycin_Meropenem  \n",
+       "6847                     1  \n",
+       "6855                     1  \n",
+       "6858                     1  \n",
+       "6861                     1  \n",
+       "6878                     1  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Useful dictionaries to map corresponding. strings for the sameantibiotic selections\n",
+    "abx_map = {'Ceftriaxone' : \"CEFTRIAXONE\",\n",
+    "           'Vancomycin_Zosyn' : \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\",\n",
+    "           'Zosyn' : \"PIPERACILLIN-TAZOBACTAM\",\n",
+    "           'Vancomycin_Ceftriaxone' : \"CEFTRIAXONE VANCOMYCIN\",\n",
+    "           'Vancomycin_Cefepime' : \"CEFEPIME VANCOMYCIN\",\n",
+    "           'Cefepime' : \"CEFEPIME\",\n",
+    "           'Vancomycin' :  \"VANCOMYCIN\",\n",
+    "           'Meropenem' : \"MEROPENEM\",\n",
+    "           'Vancomycin_Meropenem' : \"MEROPENEM VANCOMYCIN\",\n",
+    "           'Cefazolin' : \"CEFAZOLIN\",\n",
+    "           'Ciprofloxacin' : \"CIPROFLOXACIN\",\n",
+    "           'Ampicillin' : 'AMPICILLIN'\n",
+    "          }\n",
+    "abx_map_inverse = {abx_map[key] : key for key in abx_map}\n",
+    "abx_map_inverse['CEFTRIAXONE PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'\n",
+    "# abx_map_inverse['LEVOFLOXACIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'\n",
+    "abx_map_inverse['AZITHROMYCIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'\n",
+    "# abx_map_inverse['MEROPENEM PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Meropenem'\n",
+    "abx_map_inverse['AZITHROMYCIN CEFTRIAXONE'] = 'Ceftriaxone'\n",
+    "years = [2019]\n",
+    "# Lambda that aggregate Antibiotic orders after we've grouped by CSN\n",
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "\n",
+    "# \n",
+    "df_drugs = (df_abx\n",
+    "    .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)\n",
+    "    .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                        .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                       for a in x.med_description])\n",
+    "    .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on\n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'med_description' : concat_abx,\n",
+    "          'year' : 'first',\n",
+    "          'Ampicillin' : 'first',\n",
+    "          'Ciprofloxacin' : 'first',\n",
+    "          'Cefazolin' : 'first',\n",
+    "          'Ceftriaxone' : 'first',\n",
+    "          'Cefepime' : 'first',\n",
+    "          'Zosyn' : 'first',\n",
+    "          'Vancomycin' : 'first',\n",
+    "          'Meropenem' : 'first',\n",
+    "          'Vancomycin_Ceftriaxone' : 'first',\n",
+    "          'Vancomycin_Cefepime' : 'first',\n",
+    "          'Vancomycin_Zosyn' : 'first',\n",
+    "          'Vancomycin_Meropenem' : 'first'})\n",
+    "    .reset_index()\n",
+    "    # Only look at test set data and CSNs where allowed antibiotic selection was administered\n",
+    "    .query(\"year == @years and med_description in @abx_map_inverse\", engine='python') \n",
+    "    .assign(med_description=lambda x: [abx_map_inverse[a] for a in x.med_description])\n",
+    ")\n",
+    "\n",
+    "# Roughly 700 of the 1300 original CSNs in the test set\n",
+    "print(len(df_drugs))\n",
+    "df_drugs.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge this dataframe to predictions dataframe\n",
+    "After this step we should have a dataframe that has one row per CSN, each row should have the antibiotic selection actually administered to the patient, along with the predicted probability of said antibiotic selection covering the patient, and the ground truth as to whether it did. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ceftriaxone: Full test set AUROC:0.688 Subset AUROC:0.697\n",
+      "Vancomycin_Zosyn: Full test set AUROC:0.699 Subset AUROC:0.672\n",
+      "Zosyn: Full test set AUROC:0.641 Subset AUROC:0.602\n",
+      "Vancomycin_Ceftriaxone: Full test set AUROC:0.669 Subset AUROC:0.674\n",
+      "Vancomycin_Cefepime: Full test set AUROC:0.697 Subset AUROC:0.739\n",
+      "Cefepime: Full test set AUROC:0.649 Subset AUROC:0.661\n",
+      "Vancomycin: Full test set AUROC:0.717 Subset AUROC:0.698\n",
+      "Meropenem: Full test set AUROC:0.685 Subset AUROC:0.678\n",
+      "Vancomycin_Meropenem: Full test set AUROC:0.731 Subset AUROC:0.766\n",
+      "Cefazolin: Full test set AUROC:0.674 Subset AUROC:0.675\n",
+      "Ciprofloxacin: Full test set AUROC:0.611 Subset AUROC:0.615\n",
+      "Ampicillin: Full test set AUROC:0.621 Subset AUROC:0.597\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge df_total to df on pat_enc_csn_id_coded\n",
+    "df_new = (df\n",
+    "    .merge(df_drugs, how='inner', on='pat_enc_csn_id_coded')\n",
+    ")\n",
+    "\n",
+    "# Sanity check - make sure %abx_label columns are equal to %abx columns\n",
+    "for abx in abx_map:\n",
+    "    for i in range(len(df_new)):\n",
+    "        assert df_new[abx].values[i] == df_new['%s_label' % abx].values[i]\n",
+    "        \n",
+    "# Sanity check 2: compute AUROC of this subset of patients and compare to AUROC on full test set\n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "for abx in abx_map:\n",
+    "    computed_auroc = roc_auc_score(df_new['%s_label' % abx], df_new['%s_predictions' % abx])\n",
+    "    f_auroc = os.path.join(base_path.format(abx=abx), 'auroc.txt')\n",
+    "    with open(f_auroc, 'r') as f:\n",
+    "        auroc = float(f.read())\n",
+    "    print(\"{}: Full test set AUROC:{:.3f} Subset AUROC:{:.3f}\".format(abx, auroc, computed_auroc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Binary Integer Programming Problem Formulation and Solve\n",
+    "Here we specificy the problem formulation of the optimization process we wish to solve. The goal is to maximize the probability of covering the set of patients in the test set with the available antibiotic selections subject to the constraints that we assign each antibiotic selection a prespecified number of times, and that we only assign one antibiotic selection to each patient CSN. \n",
+    "\n",
+    "More technically, Let $N$ be the number of patient CSNs in our test set who were administered one of the 12 abx selections by clinicians, and let $K$ be the number of possible antibiotic selections.  Let $A$ be a matrix in $\\mathbb{R}^{N\\times K}$ such that $a_{ij}$ is 1 if antibiotic selection $j$ is selected for patient CSN $i$ and 0 otherwise. Let $\\Phi$ be a matrix in $\\mathbb{R}^{N \\times K}$ such that $\\phi_{ij}$ is the predicted probability that antibiotic $j$ will cover patient CSN $i$.  Let $C$ be a vector in $\\mathbb{R}^K$ such that $c_j$ specifies the budget for anitbiotic selection $j$ - that is the number of times we are allowed to select antibiotic $j$ across our $N$ patient CSNs. Our problem formulation is as follows. \n",
+    "\n",
+    "$$  \\underset{A}{\\text{maximize}} \\sum_{i=1}^{N} \\sum_{j=1}^K \\phi_{ij} a_{ij} $$\n",
+    "\n",
+    "Subject to the following constraints:\n",
+    "\n",
+    "$$ \\sum_{j=1}^{K} a_{ij} = 1 \\quad i = 1, ..., N $$\n",
+    "\n",
+    "$$ \\sum_{i=1}^{N} a_{ij} = c_j \\quad j = 1, ...,  K $$\n",
+    "\n",
+    "In the following code, we implenent and solve this optimization process using the pulp python package. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### How often was each options prescribed by clinicians?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>num_csns</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>med_description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Cefazolin</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cefepime</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ceftriaxone</th>\n",
+       "      <td>404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ciprofloxacin</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meropenem</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <td>149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zosyn</th>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        num_csns\n",
+       "med_description                 \n",
+       "Cefazolin                      8\n",
+       "Cefepime                      14\n",
+       "Ceftriaxone                  404\n",
+       "Ciprofloxacin                  8\n",
+       "Meropenem                      9\n",
+       "Vancomycin                    13\n",
+       "Vancomycin_Cefepime           23\n",
+       "Vancomycin_Ceftriaxone        31\n",
+       "Vancomycin_Meropenem           9\n",
+       "Vancomycin_Zosyn             149\n",
+       "Zosyn                        102"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_new\n",
+    ".groupby('med_description')\n",
+    ".agg(num_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "abx_options = [\"Vancomycin\",\n",
+    "           \"Ampicillin\",\n",
+    "           \"Cefazolin\",\n",
+    "           \"Ceftriaxone\",\n",
+    "           \"Cefepime\",\n",
+    "           \"Zosyn\",\n",
+    "           \"Ciprofloxacin\",\n",
+    "           \"Meropenem\",\n",
+    "           \"Vancomycin_Meropenem\",\n",
+    "           \"Vancomycin_Zosyn\",\n",
+    "           \"Vancomycin_Cefepime\",\n",
+    "           \"Vancomycin_Ceftriaxone\"\n",
+    "           ]\n",
+    "\n",
+    "abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "\n",
+    "# Create binary indicators for whether treatment is used\n",
+    "drug_inds = {}\n",
+    "for abx in abx_options:\n",
+    "    drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                      for i in range(len(df_new))]\n",
+    "\n",
+    "# Add objective function to model\n",
+    "per_csn_sum = []\n",
+    "for i in range(len(df_new)):\n",
+    "    _sum = 0\n",
+    "    for abx in abx_options:\n",
+    "        _sum += drug_inds[abx][i] * df_new['%s_predictions' % abx].values[i]\n",
+    "    per_csn_sum.append(_sum)\n",
+    "    \n",
+    "abx_model += lpSum(per_csn_sum)\n",
+    "\n",
+    "# Add one selection constraint\n",
+    "for i in range(len(df_new)):\n",
+    "    selections = []\n",
+    "    for abx in abx_options:\n",
+    "        selections.append(drug_inds[abx][i])\n",
+    "    abx_model += lpSum(selections) == 1\n",
+    "\n",
+    "# Add max assignment constraints\n",
+    "# abx_assignment_constraints = {\"Vancomycin\" : 13,\n",
+    "#                               \"Ampicillin\" : 0,\n",
+    "#                               \"Cefazolin\" : 8,\n",
+    "#                               \"Ceftriaxone\" : 367,\n",
+    "#                               \"Cefepime\" : 14,\n",
+    "#                               \"Zosyn\" : 102,\n",
+    "#                               \"Ciprofloxacin\" : 8,\n",
+    "#                               \"Meropenem\" : 9,\n",
+    "#                               \"Vancomycin_Meropenem\" : 9,\n",
+    "#                               \"Vancomycin_Zosyn\" :  113,\n",
+    "#                               \"Vancomycin_Cefepime\" : 23,\n",
+    "#                               \"Vancomycin_Ceftriaxone\" : 31\n",
+    "#                              }\n",
+    "abx_assignment_constraints = {\"Vancomycin\" : 13,\n",
+    "                              \"Ampicillin\" : 0,\n",
+    "                              \"Cefazolin\" : 8,\n",
+    "                              \"Ceftriaxone\" : 404,\n",
+    "                              \"Cefepime\" : 14,\n",
+    "                              \"Zosyn\" : 102,\n",
+    "                              \"Ciprofloxacin\" : 8,\n",
+    "                              \"Meropenem\" : 9,\n",
+    "                              \"Vancomycin_Meropenem\" : 9,\n",
+    "                              \"Vancomycin_Zosyn\" :  149,\n",
+    "                              \"Vancomycin_Cefepime\" : 23,\n",
+    "                              \"Vancomycin_Ceftriaxone\" : 31\n",
+    "                             }\n",
+    "\n",
+    "\n",
+    "for drug in drug_inds:\n",
+    "    abx_model += lpSum([drug_inds[drug][i] for i in range(len(df_new))]) == abx_assignment_constraints[drug]\n",
+    "\n",
+    "# Solve model\n",
+    "abx_model.solve()\n",
+    "print(\"Status:\", LpStatus[abx_model.status])\n",
+    "\n",
+    "# Save selected antibiotic to df_new\n",
+    "abx_decisions = []\n",
+    "for i in range(len(df_new)):\n",
+    "    abx_decision = None\n",
+    "    for abx in abx_options:\n",
+    "        if drug_inds[abx][i].value() == 1:\n",
+    "            abx_decision = abx\n",
+    "    assert abx_decision is not None\n",
+    "    abx_decisions.append(abx_decision)\n",
+    "df_new['IP_med_description'] = abx_decisions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare Performance to Clinician Performance\n",
+    "1. Write a function that takes in antibiotic selection and outputs a 1 if that selection covered the patient.  Simple to do, but annoying because of different ways we've named antibiotic selections.\n",
+    "2. Compute fraction of time each patient CSN was covered by the antibiotic selection. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8428571428571429\n",
+      "0.787012987012987\n",
+      "0.8597402597402597\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>random_covered</th>\n",
+       "      <th>dr_covered</th>\n",
+       "      <th>IP_covered</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>6/8</td>\n",
+       "      <td>5/8</td>\n",
+       "      <td>8/8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>12/14</td>\n",
+       "      <td>11/14</td>\n",
+       "      <td>12/14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>278/404</td>\n",
+       "      <td>313/404</td>\n",
+       "      <td>325/404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ciprofloxacin</td>\n",
+       "      <td>7/8</td>\n",
+       "      <td>7/8</td>\n",
+       "      <td>8/8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Meropenem</td>\n",
+       "      <td>8/9</td>\n",
+       "      <td>7/9</td>\n",
+       "      <td>5/9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Vancomycin</td>\n",
+       "      <td>2/13</td>\n",
+       "      <td>9/13</td>\n",
+       "      <td>8/13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Vancomycin_Cefepime</td>\n",
+       "      <td>23/23</td>\n",
+       "      <td>22/23</td>\n",
+       "      <td>22/23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>26/31</td>\n",
+       "      <td>30/31</td>\n",
+       "      <td>29/31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Vancomycin_Meropenem</td>\n",
+       "      <td>9/9</td>\n",
+       "      <td>9/9</td>\n",
+       "      <td>8/9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>143/149</td>\n",
+       "      <td>143/149</td>\n",
+       "      <td>143/149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>92/102</td>\n",
+       "      <td>93/102</td>\n",
+       "      <td>94/102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           med_description random_covered dr_covered IP_covered\n",
+       "0                Cefazolin            6/8        5/8        8/8\n",
+       "1                 Cefepime          12/14      11/14      12/14\n",
+       "2              Ceftriaxone        278/404    313/404    325/404\n",
+       "3            Ciprofloxacin            7/8        7/8        8/8\n",
+       "4                Meropenem            8/9        7/9        5/9\n",
+       "5               Vancomycin           2/13       9/13       8/13\n",
+       "6      Vancomycin_Cefepime          23/23      22/23      22/23\n",
+       "7   Vancomycin_Ceftriaxone          26/31      30/31      29/31\n",
+       "8     Vancomycin_Meropenem            9/9        9/9        8/9\n",
+       "9         Vancomycin_Zosyn        143/149    143/149    143/149\n",
+       "10                   Zosyn         92/102     93/102     94/102"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ugly helper function that just does some string mapping\n",
+    "def compute_was_covered(x, decision_column='med_description'):\n",
+    "    \"\"\"\n",
+    "    Given med description, find appropriate label column and return whether patient was covered during CSN\n",
+    "    Returns \"Not in abx options\" if abx regimen isn't in our set of 12 options - useful for filtering later\n",
+    "    \"\"\"\n",
+    "    if decision_column == 'med_description':\n",
+    "        med_description = x.med_description\n",
+    "    elif decision_column == 'random_med_description':\n",
+    "        med_description = x.random_med_description\n",
+    "    elif decision_column == 'IP_med_description':\n",
+    "        med_description = x.IP_med_description\n",
+    "    elif decision_column == 'guideline_med_description':\n",
+    "        med_description = x.guideline_med_description\n",
+    "    return x[med_description]\n",
+    "#     if med_description == \"CEFTRIAXONE\":\n",
+    "#         return x.Ceftriaxone\n",
+    "#     elif med_description == \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\":\n",
+    "#         return x.Vancomycin_Zosyn\n",
+    "#     elif med_description == \"PIPERACILLIN-TAZOBACTAM\":\n",
+    "#         return x.Zosyn\n",
+    "#     elif med_description == \"CEFTRIAXONE VANCOMYCIN\":\n",
+    "#         return x.Vancomycin_Ceftriaxone\n",
+    "#     elif med_description == \"CEFEPIME VANCOMYCIN\":\n",
+    "#         return x.Vancomycin_Cefepime\n",
+    "#     elif med_description == \"CEFEPIME\":\n",
+    "#         return x.Cefepime\n",
+    "#     elif med_description == \"VANCOMYCIN\":\n",
+    "#         return x.Vancomycin\n",
+    "#     elif med_description == \"MEROPENEM\":\n",
+    "#         return x.Meropenem\n",
+    "#     elif med_description == \"MEROPENEM VANCOMYCIN\":\n",
+    "#         return x.Vancomycin_Meropenem\n",
+    "#     elif med_description == \"CEFAZOLIN\":\n",
+    "#         return x.Cefazolin\n",
+    "#     elif med_description == \"CIPROFLOXACIN\":\n",
+    "#         return x.Ciprofloxacin\n",
+    "#     elif med_description == \"AMPICILLIN\":\n",
+    "#         return x.Ampicillin\n",
+    "#     else:\n",
+    "#         return \"Not in abx options\"\n",
+    "    \n",
+    "    \n",
+    "# Create flag for whether clinicians covered the patient during the csn, whether a random assignemnt covered patient\n",
+    "# CSN, and whether optimized assignment covered the patient CSN.\n",
+    "\n",
+    "df_new = (df_new\n",
+    "    .assign(random_med_description=lambda x: np.random.choice(x.med_description, size=len(x.med_description), replace=False))\n",
+    ")\n",
+    "df_new = (df_new\n",
+    "    #.sample(frac=1.0, replace=True) # bootstrap each iteration\n",
+    "    .assign(was_covered_dr=df_new.apply(lambda x: compute_was_covered(x), axis=1))\n",
+    "    .assign(was_covered_random=df_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='random_med_description'),\n",
+    "                                                                          axis=1))\n",
+    "    .assign(was_covered_IP=df_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                      decision_column='IP_med_description'),\n",
+    "                                                                      axis=1))\n",
+    ")\n",
+    "\n",
+    "clin_covered_rate = df_new['was_covered_dr'].sum() / len(df_new)\n",
+    "random_covered_rate = df_new['was_covered_random'].sum() / len(df_new)\n",
+    "ip_covered_rate = df_new['was_covered_IP'].sum() / len(df_new)\n",
+    "\n",
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "\n",
+    "df_new_random = (df_new\n",
+    "        .groupby('random_med_description')\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_random=('was_covered_random', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .assign(random_covered=lambda x: ['{}/{}'.format(c, t) for c, t in zip(x.num_times_covered_random,\n",
+    "                                                                               x.num_distinct_csns)])\n",
+    "        .rename(columns={'random_med_description' : 'med_description'})\n",
+    ")[['med_description', 'random_covered']]\n",
+    "                 \n",
+    "df_new_clinician = (df_new\n",
+    "        .groupby('med_description')\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_dr=('was_covered_dr', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .assign(dr_covered=lambda x: ['{}/{}'.format(c, t) for c, t in zip(x.num_times_covered_dr,\n",
+    "                                                                               x.num_distinct_csns)])\n",
+    ")[['med_description', 'dr_covered']]\n",
+    "                    \n",
+    "df_new_ip = (df_new\n",
+    "        .groupby('IP_med_description')\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_IP=('was_covered_IP', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .assign(IP_covered=lambda x: ['{}/{}'.format(c, t) for c, t in zip(x.num_times_covered_IP,\n",
+    "                                                                               x.num_distinct_csns)])\n",
+    "        .rename(columns={'IP_med_description' : 'med_description'})\n",
+    ")[['med_description', 'IP_covered']]\n",
+    "\n",
+    "df_new_agg = (df_new_random\n",
+    "    .merge(df_new_clinician, how='inner', on='med_description')\n",
+    "    .merge(df_new_ip, how='inner', on='med_description')\n",
+    ")\n",
+    "\n",
+    "df_new_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Upload this final table to bigquery projet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/conorcorbin/opt/anaconda3/lib/python3.7/site-packages/google/auth/_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "1it [00:04,  4.43s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_final = (df_new\n",
+    "[['anon_id', 'pat_enc_csn_id_coded', 'med_description', 'IP_med_description', 'was_covered_dr', 'was_covered_IP']]\n",
+    ")\n",
+    "\n",
+    "df_final.to_gbq(destination_table='abx.abx_allocation',\n",
+    "                project_id='mining-clinical-decisions',\n",
+    "                if_exists='replace')   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Find cases where optimized procedure orders Zosyn and clinicians prescribe vancomycin and zosyn and zosyn sufficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_str = \"med_description == 'Vancomycin_Zosyn' AND IP_med_description == 'Zosyn' AND Zosyn_label == 1\"\n",
+    "df_zosyn_ok = (df_new\n",
+    "[(df_new['med_description']=='Vancomycin_Zosyn') &\n",
+    "(df_new['IP_med_description']=='Zosyn') &\n",
+    "(df_new['Zosyn_label']==1)]\n",
+    "[['anon_id', 'pat_enc_csn_id_coded', 'med_description', 'IP_med_description', 'Zosyn_label', 'Vancomycin_Zosyn_label']]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>Zosyn_label</th>\n",
+       "      <th>Vancomycin_Zosyn_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>JCe6159a</td>\n",
+       "      <td>131261616681</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>JCd2fc4d</td>\n",
+       "      <td>131261807863</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>JCea0302</td>\n",
+       "      <td>131262039722</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>JCcda7db</td>\n",
+       "      <td>131262420251</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>JCd3d869</td>\n",
+       "      <td>131263011349</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>JCea97b3</td>\n",
+       "      <td>131263078721</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>JCeaaa9a</td>\n",
+       "      <td>131264012380</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>JCec2c71</td>\n",
+       "      <td>131264055666</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>JCdb0553</td>\n",
+       "      <td>131265867822</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>JCe8d86c</td>\n",
+       "      <td>131266988673</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>JCdab0b3</td>\n",
+       "      <td>131267755547</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>JCcc19ea</td>\n",
+       "      <td>131271381182</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>JCd8f729</td>\n",
+       "      <td>131272355945</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>JCe9b234</td>\n",
+       "      <td>131272357028</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>JCe06c2e</td>\n",
+       "      <td>131272467025</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>JCe0591b</td>\n",
+       "      <td>131279472654</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      anon_id  pat_enc_csn_id_coded   med_description IP_med_description  \\\n",
+       "22   JCe6159a          131261616681  Vancomycin_Zosyn              Zosyn   \n",
+       "30   JCd2fc4d          131261807863  Vancomycin_Zosyn              Zosyn   \n",
+       "40   JCea0302          131262039722  Vancomycin_Zosyn              Zosyn   \n",
+       "48   JCcda7db          131262420251  Vancomycin_Zosyn              Zosyn   \n",
+       "74   JCd3d869          131263011349  Vancomycin_Zosyn              Zosyn   \n",
+       "80   JCea97b3          131263078721  Vancomycin_Zosyn              Zosyn   \n",
+       "126  JCeaaa9a          131264012380  Vancomycin_Zosyn              Zosyn   \n",
+       "127  JCec2c71          131264055666  Vancomycin_Zosyn              Zosyn   \n",
+       "207  JCdb0553          131265867822  Vancomycin_Zosyn              Zosyn   \n",
+       "248  JCe8d86c          131266988673  Vancomycin_Zosyn              Zosyn   \n",
+       "283  JCdab0b3          131267755547  Vancomycin_Zosyn              Zosyn   \n",
+       "385  JCcc19ea          131271381182  Vancomycin_Zosyn              Zosyn   \n",
+       "398  JCd8f729          131272355945  Vancomycin_Zosyn              Zosyn   \n",
+       "399  JCe9b234          131272357028  Vancomycin_Zosyn              Zosyn   \n",
+       "407  JCe06c2e          131272467025  Vancomycin_Zosyn              Zosyn   \n",
+       "694  JCe0591b          131279472654  Vancomycin_Zosyn              Zosyn   \n",
+       "\n",
+       "     Zosyn_label  Vancomycin_Zosyn_label  \n",
+       "22             1                       1  \n",
+       "30             1                       1  \n",
+       "40             1                       1  \n",
+       "48             1                       1  \n",
+       "74             1                       1  \n",
+       "80             1                       1  \n",
+       "126            1                       1  \n",
+       "127            1                       1  \n",
+       "207            1                       1  \n",
+       "248            1                       1  \n",
+       "283            1                       1  \n",
+       "385            1                       1  \n",
+       "398            1                       1  \n",
+       "399            1                       1  \n",
+       "407            1                       1  \n",
+       "694            1                       1  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_zosyn_ok"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "schema = [\n",
+    "{'name': 'pat_enc_csn_id_coded', 'type': 'INTEGER'},\n",
+    "{'name': 'feature_type', 'type': 'STRING'},\n",
+    "{'name': 'feature', 'type': 'STRING'},\n",
+    "{'name': 'value', 'type': 'INTEGER'} # bag of words\n",
+    "]\n",
+    "df_zosyn_ok.to_gbq(\n",
+    "    destination_table=\"abx.zosyn_only_okay\",\n",
+    "    project_id='mining-clinical-decisions',\n",
+    "    if_exists='append',\n",
+    "    table_schema=self.feature_matrix_schema\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108161</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.462078</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.215610</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501686</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631382</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.421153</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.412029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.724792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.064201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528883</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.749904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.789198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd356bf          131261014293                 1                0.421153   \n",
+       "4   JCd1c07c          131261155365                 0                0.064201   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 1                0.412029                1   \n",
+       "4                 1                0.528883                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.528823                  1                 0.724792  ...   \n",
+       "4               0.749904                  1                 0.789198  ...   \n",
+       "\n",
+       "   Meropenem  Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0          1                       1                    1                 1   \n",
+       "1          0                       1                    1                 1   \n",
+       "2          1                       1                    1                 1   \n",
+       "3          1                       1                    1                 1   \n",
+       "4          1                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone                   Zosyn   \n",
+       "1                     1         Ceftriaxone                   Zosyn   \n",
+       "2                     1    Vancomycin_Zosyn             Ceftriaxone   \n",
+       "3                     1         Ceftriaxone                   Zosyn   \n",
+       "4                     1         Ceftriaxone             Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   1               1  \n",
+       "1               1                   1               0  \n",
+       "2               1                   1               1  \n",
+       "3               1                   1               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_uti_guidelines --project mining-clinical-decisions\n",
+    "WITH observations_with_pos_urine_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT a.*, cohort.index_time\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.final_cohort_table` cohort\n",
+    "    USING\n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description = \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "observations_with_pos_other_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT pat_enc_csn_id_coded\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description <> \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "only_utis as (\n",
+    "SELECT \n",
+    "    DISTINCT a.*\n",
+    "FROM \n",
+    "    observations_with_pos_urine_cultures a \n",
+    "WHERE\n",
+    "    a.pat_enc_csn_id_coded NOT IN (SELECT DISTINCT pat_enc_csn_id_coded \n",
+    "                                   FROM observations_with_pos_other_cultures)\n",
+    "),\n",
+    "\n",
+    "utis_with_neutra as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN lr.ord_num_value IS NULL THEN 0\n",
+    "            WHEN lr.ord_num_value < 10 THEN 1\n",
+    "            ELSE 0 END)\n",
+    "        OVER(PARTITION BY a.pat_enc_csn_id_coded) NEUTRA\n",
+    "    FROM\n",
+    "        only_utis a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT anon_id, cohort.pat_enc_csn_id_coded, lab_name, base_name, ord_num_value, result_time_utc \n",
+    "         FROM shc_core.lab_result \n",
+    "         INNER JOIN abx.final_cohort_table cohort\n",
+    "         USING (anon_id)\n",
+    "         WHERE base_name in ('NEUTRA', 'NEUTAB', 'ABSBAND', 'ABSNEUTBDYFL')\n",
+    "         AND result_time_utc <= cohort.index_time\n",
+    "         AND TIMESTAMP_ADD(result_time_utc, INTERVAL 24 Hour) >= cohort.index_time) lr\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "\n",
+    "),\n",
+    "\n",
+    "final_table as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%CEFTRIAXONE%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_ceftriaxone_resistance,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%PIPERACILLIN/TAZOBACTAM%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_zosyn_resistance\n",
+    "    FROM\n",
+    "        utis_with_neutra a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT DISTINCT anon_id, cohort.index_time, antibiotic, suscept, result_time_jittered_utc\n",
+    "        FROM shc_core.culture_sensitivity\n",
+    "        INNER JOIN abx.final_cohort_table cohort\n",
+    "        USING (anon_id)\n",
+    "        WHERE result_time_jittered_utc < cohort.index_time) cs\n",
+    "    USING\n",
+    "        (anon_id, index_time)\n",
+    ")\n",
+    "\n",
+    "SELECT\n",
+    "    *,\n",
+    "    CASE \n",
+    "    WHEN NEUTRA = 0 and  prior_ceftriaxone_resistance = 0 THEN 'Ceftriaxone'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_zosyn_resistance = 0 THEN 'Zosyn'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_zosyn_resistance = 1 THEN 'Meropenem'\n",
+    "    WHEN NEUTRA = 1 and prior_zosyn_resistance = 1 THEN 'Meropenem'\n",
+    "    ELSE 'Zosyn'\n",
+    "    END guideline_med_description\n",
+    "FROM final_table\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>index_time</th>\n",
+       "      <th>NEUTRA</th>\n",
+       "      <th>prior_ceftriaxone_resistance</th>\n",
+       "      <th>prior_zosyn_resistance</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC29fd3dc</td>\n",
+       "      <td>131262483875</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-06 02:35:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zosyn</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcd45b3</td>\n",
+       "      <td>131277210163</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-09-29 22:47:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zosyn</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JC2a1bfa5</td>\n",
+       "      <td>131268574477</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-06-30 22:35:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Meropenem</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCdf8793</td>\n",
+       "      <td>131265312214</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-02-27 16:43:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd13ceb</td>\n",
+       "      <td>131272779729</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-07-29 13:49:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zosyn</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded   med_description IP_med_description  \\\n",
+       "0  JC29fd3dc          131262483875  Vancomycin_Zosyn        Ceftriaxone   \n",
+       "1   JCcd45b3          131277210163       Ceftriaxone        Ceftriaxone   \n",
+       "2  JC2a1bfa5          131268574477       Ceftriaxone   Vancomycin_Zosyn   \n",
+       "3   JCdf8793          131265312214       Ceftriaxone        Ceftriaxone   \n",
+       "4   JCd13ceb          131272779729       Ceftriaxone        Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_IP                index_time  NEUTRA  \\\n",
+       "0               1               1 2019-01-06 02:35:00+00:00       1   \n",
+       "1               1               1 2019-09-29 22:47:00+00:00       1   \n",
+       "2               1               1 2019-06-30 22:35:00+00:00       1   \n",
+       "3               1               1 2019-02-27 16:43:00+00:00       0   \n",
+       "4               1               1 2019-07-29 13:49:00+00:00       1   \n",
+       "\n",
+       "   prior_ceftriaxone_resistance  prior_zosyn_resistance  \\\n",
+       "0                             0                       0   \n",
+       "1                             0                       0   \n",
+       "2                             1                       1   \n",
+       "3                             0                       0   \n",
+       "4                             0                       0   \n",
+       "\n",
+       "  guideline_med_description  \n",
+       "0                     Zosyn  \n",
+       "1                     Zosyn  \n",
+       "2                 Meropenem  \n",
+       "3               Ceftriaxone  \n",
+       "4                     Zosyn  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_guidelines.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(476, 46)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC29fd3dc</td>\n",
+       "      <td>131262483875</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.298378</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.517242</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.750544</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcd45b3</td>\n",
+       "      <td>131277210163</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.095530</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.406455</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.712526</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JC2a1bfa5</td>\n",
+       "      <td>131268574477</td>\n",
+       "      <td>Meropenem</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.137741</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.473210</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.581040</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCdf8793</td>\n",
+       "      <td>131265312214</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.154827</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.472927</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.613512</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd13ceb</td>\n",
+       "      <td>131272779729</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.072778</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.426553</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.703634</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded guideline_med_description  \\\n",
+       "0  JC29fd3dc          131262483875                     Zosyn   \n",
+       "1   JCcd45b3          131277210163                     Zosyn   \n",
+       "2  JC2a1bfa5          131268574477                 Meropenem   \n",
+       "3   JCdf8793          131265312214               Ceftriaxone   \n",
+       "4   JCd13ceb          131272779729                     Zosyn   \n",
+       "\n",
+       "   Vancomycin_label  Vancomycin_predictions  Ampicillin_label  \\\n",
+       "0                 0                0.298378                 1   \n",
+       "1                 0                0.095530                 1   \n",
+       "2                 0                0.137741                 0   \n",
+       "3                 0                0.154827                 0   \n",
+       "4                 0                0.072778                 1   \n",
+       "\n",
+       "   Ampicillin_predictions  Cefazolin_label  Cefazolin_predictions  \\\n",
+       "0                0.517242                1               0.750544   \n",
+       "1                0.406455                1               0.712526   \n",
+       "2                0.473210                1               0.581040   \n",
+       "3                0.472927                1               0.613512   \n",
+       "4                0.426553                1               0.703634   \n",
+       "\n",
+       "   Ceftriaxone_label  ...  Meropenem  Vancomycin_Ceftriaxone  \\\n",
+       "0                  1  ...          1                       1   \n",
+       "1                  1  ...          1                       1   \n",
+       "2                  1  ...          1                       1   \n",
+       "3                  1  ...          1                       1   \n",
+       "4                  1  ...          1                       1   \n",
+       "\n",
+       "   Vancomycin_Cefepime  Vancomycin_Zosyn  Vancomycin_Meropenem  \\\n",
+       "0                    1                 1                     1   \n",
+       "1                    1                 1                     1   \n",
+       "2                    1                 1                     1   \n",
+       "3                    1                 1                     1   \n",
+       "4                    1                 1                     1   \n",
+       "\n",
+       "   IP_med_description  random_med_description  was_covered_dr  \\\n",
+       "0         Ceftriaxone             Ceftriaxone               1   \n",
+       "1         Ceftriaxone                   Zosyn               1   \n",
+       "2    Vancomycin_Zosyn  Vancomycin_Ceftriaxone               1   \n",
+       "3         Ceftriaxone                   Zosyn               1   \n",
+       "4         Ceftriaxone        Vancomycin_Zosyn               1   \n",
+       "\n",
+       "   was_covered_random  was_covered_IP  \n",
+       "0                   1               1  \n",
+       "1                   1               1  \n",
+       "2                   1               1  \n",
+       "3                   1               1  \n",
+       "4                   1               1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new = (df_uti_guidelines\n",
+    "    [['anon_id', 'pat_enc_csn_id_coded', 'guideline_med_description']]\n",
+    "    .merge(df_new, how='inner')\n",
+    ")\n",
+    "print(df_uti_new.shape)\n",
+    "df_uti_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_new = (df_uti_new\n",
+    "    .assign(was_covered_guideline=df_uti_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    ")\n",
+    "clin_covered_rate = df_uti_new['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_new['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_new['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_new['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8151260504201681\n",
+      "0.8067226890756303\n",
+      "0.8550420168067226\n",
+      "0.8466386554621849\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We note that this comparison is likely unfair because the the optimizer is using much broader spectrum antibiotics in this subset than what the guidelines recommend\n",
+    "\n",
+    "##### Thus we do another analysis where we constrict the optimizer to the budget used by the guidelines themselves. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(476, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_uti_new.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Zosyn          247\n",
+       "Ceftriaxone    211\n",
+       "Meropenem       18\n",
+       "Name: guideline_med_description, dtype: int64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new['guideline_med_description'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "def solve_lp_problem(df, budgets):\n",
+    "    \"\"\"\n",
+    "    Creates a lp problem formulation with pulp for antibiotic selection and adds\n",
+    "    budget constraints and solves.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "\n",
+    "    abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "\n",
+    "    # Create binary indicators for whether treatment is used\n",
+    "    drug_inds = {}\n",
+    "    for abx in abx_options:\n",
+    "        drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                          for i in range(len(df))]\n",
+    "\n",
+    "    # Add objective function to model\n",
+    "    per_csn_sum = []\n",
+    "    for i in range(len(df)):\n",
+    "        _sum = 0\n",
+    "        for abx in abx_options:\n",
+    "            _sum += drug_inds[abx][i] * df['%s_predictions' % abx].values[i]\n",
+    "        per_csn_sum.append(_sum)\n",
+    "\n",
+    "    abx_model += lpSum(per_csn_sum)\n",
+    "\n",
+    "    # Add one selection constraint\n",
+    "    for i in range(len(df)):\n",
+    "        selections = []\n",
+    "        for abx in abx_options:\n",
+    "            selections.append(drug_inds[abx][i])\n",
+    "        abx_model += lpSum(selections) == 1\n",
+    "    \n",
+    "    for drug in drug_inds:\n",
+    "        abx_model += lpSum([drug_inds[drug][i] for i in range(len(df))]) == budgets[drug]\n",
+    "\n",
+    "    # Solve model\n",
+    "    abx_model.solve()\n",
+    "    print(\"Status:\", LpStatus[abx_model.status])\n",
+    "\n",
+    "    # Save selected antibiotic to df_new\n",
+    "    abx_decisions = []\n",
+    "    for i in range(len(df)):\n",
+    "        abx_decision = None\n",
+    "        for abx in abx_options:\n",
+    "            if drug_inds[abx][i].value() == 1:\n",
+    "                abx_decision = abx\n",
+    "        assert abx_decision is not None\n",
+    "        abx_decisions.append(abx_decision)\n",
+    "    df['IP_med_description'] = abx_decisions\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def boostrap_coverage_rates(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    guideline_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_IP'].sum() / len(df)\n",
+    "    actual_guideline_rate = df['was_covered_guideline'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_guide = (df\n",
+    "            .groupby('guideline_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('IP_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_IP'].sum() / len(df_boot_ip))\n",
+    "        guideline_covered_rates.append(df_boot_guide['was_covered_guideline'].sum() / len(df_boot_guide))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    guide_high = np.percentile(guideline_covered_rates, 97.5)\n",
+    "    guide_low = np.percentile(guideline_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 3)}: [{round(ip_low, 3)}, {round(ip_high, 3)}]\"\n",
+    "    guide_estimate = f\"{round(actual_guideline_rate, 3)}: [{round(guide_low, 3)}, {round(guide_high, 3)}]\"\n",
+    "    \n",
+    "    pval = len(\n",
+    "        [g for g in guideline_covered_rates if g >= actual_ip_rate]\n",
+    "    ) / len(guideline_covered_rates)\n",
+    "    \n",
+    "    return ip_estimate, guide_estimate, pval\n",
+    "    \n",
+    "    \n",
+    "def boostrap_clin_coverage_rates(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    guideline_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_dr'].sum() / len(df)\n",
+    "    actual_guideline_rate = df['was_covered_guideline'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_guide = (df\n",
+    "            .groupby('guideline_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_dr'].sum() / len(df_boot_ip))\n",
+    "        guideline_covered_rates.append(df_boot_guide['was_covered_guideline'].sum() / len(df_boot_guide))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    guide_high = np.percentile(guideline_covered_rates, 97.5)\n",
+    "    guide_low = np.percentile(guideline_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 3)}: [{round(ip_low, 3)}, {round(ip_high, 3)}]\"\n",
+    "    guide_estimate = f\"{round(actual_guideline_rate, 3)}: [{round(guide_low, 3)}, {round(guide_high, 3)}]\"\n",
+    "    \n",
+    "    pval = len(\n",
+    "        [g for g in guideline_covered_rates if g >= actual_ip_rate]\n",
+    "    ) / len(guideline_covered_rates)\n",
+    "    \n",
+    "    return ip_estimate, guide_estimate, pval\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "budgets  = {\n",
+    "  \"Vancomycin\" : 0,\n",
+    "  \"Ampicillin\" : 0,\n",
+    "  \"Cefazolin\" : 0,\n",
+    "  \"Ceftriaxone\" : 211,\n",
+    "  \"Cefepime\" : 0,\n",
+    "  \"Zosyn\" : 247,\n",
+    "  \"Ciprofloxacin\" : 0,\n",
+    "  \"Meropenem\" : 18,\n",
+    "  \"Vancomycin_Meropenem\" : 0,\n",
+    "  \"Vancomycin_Zosyn\" :  0,\n",
+    "  \"Vancomycin_Cefepime\" : 0,\n",
+    "  \"Vancomycin_Ceftriaxone\" : 0\n",
+    "}\n",
+    "df_uti_final = solve_lp_problem(df_uti_new, budgets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_final = (df_uti_final\n",
+    "    .assign(was_covered_guideline=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "    .assign(was_covered_IP=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='IP_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "\n",
+    ")\n",
+    "clin_covered_rate = df_uti_final['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_final['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_final['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_final['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8151260504201681\n",
+      "0.8067226890756303\n",
+      "0.8907563025210085\n",
+      "0.8466386554621849\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ceftriaxone: 0.6900497406229318\n",
+      "Cefepime: 0.6533521109322769\n",
+      "Meropenem: 0.6583880943177426\n",
+      "Zosyn: 0.5792461197339246\n"
+     ]
+    }
+   ],
+   "source": [
+    "for abx in ['Ceftriaxone', 'Cefepime', 'Meropenem', 'Zosyn']:\n",
+    "    print(f\"{abx}: {roc_auc_score(df_uti_final[f'{abx}_label'], df_uti_final[f'{abx}_predictions'])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 69%|██████▉   | 6903/10000 [02:56<01:19, 39.02it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-74-e55247cc109e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mguide\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboostrap_coverage_rates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_uti_final\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-73-1f560e6fd8b6>\u001b[0m in \u001b[0;36mboostrap_coverage_rates\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m     80\u001b[0m         df_boot_guide = (df\n\u001b[1;32m     81\u001b[0m             \u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'guideline_med_description'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m             \u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m             \u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         )\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, n, frac, replace, weights, random_state)\u001b[0m\n\u001b[1;32m   3036\u001b[0m         ]\n\u001b[1;32m   3037\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3038\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    296\u001b[0m     )\n\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m             new_data = concatenate_block_managers(\n\u001b[0;32m--> 521\u001b[0;31m                 \u001b[0mmgrs_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbm_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             )\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/concat.py\u001b[0m in \u001b[0;36mconcatenate_block_managers\u001b[0;34m(mgrs_indexers, axes, concat_axis, copy)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \"\"\"\n\u001b[1;32m     52\u001b[0m     concat_plans = [\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0m_get_mgr_concatenation_plan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmgrs_indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m     ]\n\u001b[1;32m     55\u001b[0m     \u001b[0mconcat_plan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_combine_concat_plans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcat_plans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/concat.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \"\"\"\n\u001b[1;32m     52\u001b[0m     concat_plans = [\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0m_get_mgr_concatenation_plan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmgrs_indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m     ]\n\u001b[1;32m     55\u001b[0m     \u001b[0mconcat_plan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_combine_concat_plans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcat_plans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/concat.py\u001b[0m in \u001b[0;36m_get_mgr_concatenation_plan\u001b[0;34m(mgr, indexers)\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m             \u001b[0mblk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mblkno\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0max0_blk_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblklocs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplacements\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             unit_no_ax0_reindexing = (\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "ip, guide, pval = boostrap_coverage_rates(df_uti_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.891: [0.863, 0.916]'"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.847: [0.813, 0.876]'"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:54<00:00, 18.50it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clin_rate, guide, pval = boostrap_clin_coverage_rates(df_uti_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.815: [0.782, 0.849]'"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clin_rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ceftriaxone               335\n",
+       "Vancomycin_Zosyn           51\n",
+       "Zosyn                      36\n",
+       "Vancomycin_Ceftriaxone     10\n",
+       "Cefepime                    9\n",
+       "Vancomycin_Cefepime         8\n",
+       "Ciprofloxacin               7\n",
+       "Cefazolin                   7\n",
+       "Vancomycin_Meropenem        5\n",
+       "Meropenem                   5\n",
+       "Vancomycin                  3\n",
+       "Name: med_description, dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_final['med_description'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But now do this using cefepime as originally discussed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_uti_guidelines --project mining-clinical-decisions\n",
+    "WITH observations_with_pos_urine_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT a.*, cohort.index_time\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.final_cohort_table` cohort\n",
+    "    USING\n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description = \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "observations_with_pos_other_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT pat_enc_csn_id_coded\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description <> \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "only_utis as (\n",
+    "SELECT \n",
+    "    DISTINCT a.*\n",
+    "FROM \n",
+    "    observations_with_pos_urine_cultures a \n",
+    "WHERE\n",
+    "    a.pat_enc_csn_id_coded NOT IN (SELECT DISTINCT pat_enc_csn_id_coded \n",
+    "                                   FROM observations_with_pos_other_cultures)\n",
+    "),\n",
+    "\n",
+    "utis_with_neutra as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN lr.ord_num_value IS NULL THEN 0\n",
+    "            WHEN lr.ord_num_value < 10 THEN 1\n",
+    "            ELSE 0 END)\n",
+    "        OVER(PARTITION BY a.pat_enc_csn_id_coded) NEUTRA\n",
+    "    FROM\n",
+    "        only_utis a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT anon_id, cohort.pat_enc_csn_id_coded, lab_name, base_name, ord_num_value, result_time_utc \n",
+    "         FROM shc_core.lab_result \n",
+    "         INNER JOIN abx.final_cohort_table cohort\n",
+    "         USING (anon_id)\n",
+    "         WHERE base_name in ('NEUTRA', 'NEUTAB', 'ABSBAND', 'ABSNEUTBDYFL')\n",
+    "         AND result_time_utc <= cohort.index_time\n",
+    "         AND TIMESTAMP_ADD(result_time_utc, INTERVAL 24 Hour) >= cohort.index_time) lr\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "\n",
+    "),\n",
+    "\n",
+    "final_table as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%CEFTRIAXONE%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_ceftriaxone_resistance,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%CEFEPIME%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_cefepime_resistance\n",
+    "    FROM\n",
+    "        utis_with_neutra a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT DISTINCT anon_id, cohort.index_time, antibiotic, suscept, result_time_jittered_utc\n",
+    "        FROM shc_core.culture_sensitivity\n",
+    "        INNER JOIN abx.final_cohort_table cohort\n",
+    "        USING (anon_id)\n",
+    "        WHERE result_time_jittered_utc < cohort.index_time) cs\n",
+    "    USING\n",
+    "        (anon_id, index_time)\n",
+    ")\n",
+    "\n",
+    "SELECT\n",
+    "    *,\n",
+    "    CASE \n",
+    "    WHEN NEUTRA = 0 and  prior_ceftriaxone_resistance = 0 THEN 'Ceftriaxone'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_cefepime_resistance = 0 THEN 'Cefepime'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_cefepime_resistance = 1 THEN 'Meropenem'\n",
+    "    WHEN NEUTRA = 1 and prior_cefepime_resistance = 1 THEN 'Meropenem'\n",
+    "    ELSE 'Cefepime'\n",
+    "    END guideline_med_description\n",
+    "FROM final_table\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>index_time</th>\n",
+       "      <th>NEUTRA</th>\n",
+       "      <th>prior_ceftriaxone_resistance</th>\n",
+       "      <th>prior_cefepime_resistance</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCdb4134</td>\n",
+       "      <td>131279966991</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-11-25 18:27:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCd01f42</td>\n",
+       "      <td>131262173637</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-23 22:22:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd13ceb</td>\n",
+       "      <td>131272779729</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-07-29 13:49:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd79080</td>\n",
+       "      <td>131265141270</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-03-27 20:24:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCdf8793</td>\n",
+       "      <td>131265312214</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-02-27 16:43:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    anon_id  pat_enc_csn_id_coded   med_description IP_med_description  \\\n",
+       "0  JCdb4134          131279966991  Vancomycin_Zosyn   Vancomycin_Zosyn   \n",
+       "1  JCd01f42          131262173637       Ceftriaxone        Ceftriaxone   \n",
+       "2  JCd13ceb          131272779729       Ceftriaxone        Ceftriaxone   \n",
+       "3  JCd79080          131265141270       Ceftriaxone        Ceftriaxone   \n",
+       "4  JCdf8793          131265312214       Ceftriaxone        Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_IP                index_time  NEUTRA  \\\n",
+       "0               1               1 2019-11-25 18:27:00+00:00       0   \n",
+       "1               1               1 2019-01-23 22:22:00+00:00       0   \n",
+       "2               1               1 2019-07-29 13:49:00+00:00       1   \n",
+       "3               1               1 2019-03-27 20:24:00+00:00       1   \n",
+       "4               1               1 2019-02-27 16:43:00+00:00       0   \n",
+       "\n",
+       "   prior_ceftriaxone_resistance  prior_cefepime_resistance  \\\n",
+       "0                             1                          0   \n",
+       "1                             0                          0   \n",
+       "2                             0                          0   \n",
+       "3                             0                          0   \n",
+       "4                             0                          0   \n",
+       "\n",
+       "  guideline_med_description  \n",
+       "0                  Cefepime  \n",
+       "1               Ceftriaxone  \n",
+       "2                  Cefepime  \n",
+       "3                  Cefepime  \n",
+       "4               Ceftriaxone  "
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_guidelines.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(476, 46)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCdb4134</td>\n",
+       "      <td>131279966991</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.231372</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.249809</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.350487</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCd01f42</td>\n",
+       "      <td>131262173637</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.109541</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.465354</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.707414</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd13ceb</td>\n",
+       "      <td>131272779729</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.072778</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.426553</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.703634</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Meropenem</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd79080</td>\n",
+       "      <td>131265141270</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.173595</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.379767</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.637087</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCdf8793</td>\n",
+       "      <td>131265312214</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.154827</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.472927</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.613512</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    anon_id  pat_enc_csn_id_coded guideline_med_description  Vancomycin_label  \\\n",
+       "0  JCdb4134          131279966991                  Cefepime                 0   \n",
+       "1  JCd01f42          131262173637               Ceftriaxone                 0   \n",
+       "2  JCd13ceb          131272779729                  Cefepime                 0   \n",
+       "3  JCd79080          131265141270                  Cefepime                 0   \n",
+       "4  JCdf8793          131265312214               Ceftriaxone                 0   \n",
+       "\n",
+       "   Vancomycin_predictions  Ampicillin_label  Ampicillin_predictions  \\\n",
+       "0                0.231372                 1                0.249809   \n",
+       "1                0.109541                 1                0.465354   \n",
+       "2                0.072778                 1                0.426553   \n",
+       "3                0.173595                 1                0.379767   \n",
+       "4                0.154827                 0                0.472927   \n",
+       "\n",
+       "   Cefazolin_label  Cefazolin_predictions  Ceftriaxone_label  ...  Meropenem  \\\n",
+       "0                1               0.350487                  1  ...          1   \n",
+       "1                1               0.707414                  1  ...          1   \n",
+       "2                1               0.703634                  1  ...          1   \n",
+       "3                1               0.637087                  1  ...          1   \n",
+       "4                1               0.613512                  1  ...          1   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0                       1                    1                 1   \n",
+       "1                       1                    1                 1   \n",
+       "2                       1                    1                 1   \n",
+       "3                       1                    1                 1   \n",
+       "4                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1    Vancomycin_Zosyn             Ceftriaxone   \n",
+       "1                     1         Ceftriaxone             Ceftriaxone   \n",
+       "2                     1         Ceftriaxone    Vancomycin_Meropenem   \n",
+       "3                     1         Ceftriaxone             Ceftriaxone   \n",
+       "4                     1         Ceftriaxone             Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   1               1  \n",
+       "1               1                   1               1  \n",
+       "2               1                   1               1  \n",
+       "3               1                   1               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new = (df_uti_guidelines\n",
+    "    [['anon_id', 'pat_enc_csn_id_coded', 'guideline_med_description']]\n",
+    "    .merge(df_new, how='inner')\n",
+    ")\n",
+    "print(df_uti_new.shape)\n",
+    "df_uti_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_new = (df_uti_new\n",
+    "    .assign(was_covered_guideline=df_uti_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    ")\n",
+    "clin_covered_rate = df_uti_new['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_new['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_new['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_new['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8151260504201681\n",
+      "0.7983193277310925\n",
+      "0.8550420168067226\n",
+      "0.7899159663865546\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We note that this comparison is likely unfair because the the optimizer is using much broader spectrum antibiotics in this subset than what the guidelines recommend\n",
+    "\n",
+    "##### Thus we do another analysis where we constrict the optimizer to the budget used by the guidelines themselves. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(476, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_uti_new.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cefepime       254\n",
+       "Ceftriaxone    211\n",
+       "Meropenem       11\n",
+       "Name: guideline_med_description, dtype: int64"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new['guideline_med_description'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "def solve_lp_problem(df, budgets):\n",
+    "    \"\"\"\n",
+    "    Creates a lp problem formulation with pulp for antibiotic selection and adds\n",
+    "    budget constraints and solves.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "\n",
+    "    abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "\n",
+    "    # Create binary indicators for whether treatment is used\n",
+    "    drug_inds = {}\n",
+    "    for abx in abx_options:\n",
+    "        drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                          for i in range(len(df))]\n",
+    "\n",
+    "    # Add objective function to model\n",
+    "    per_csn_sum = []\n",
+    "    for i in range(len(df)):\n",
+    "        _sum = 0\n",
+    "        for abx in abx_options:\n",
+    "            _sum += drug_inds[abx][i] * df['%s_predictions' % abx].values[i]\n",
+    "        per_csn_sum.append(_sum)\n",
+    "\n",
+    "    abx_model += lpSum(per_csn_sum)\n",
+    "\n",
+    "    # Add one selection constraint\n",
+    "    for i in range(len(df)):\n",
+    "        selections = []\n",
+    "        for abx in abx_options:\n",
+    "            selections.append(drug_inds[abx][i])\n",
+    "        abx_model += lpSum(selections) == 1\n",
+    "    \n",
+    "    for drug in drug_inds:\n",
+    "        abx_model += lpSum([drug_inds[drug][i] for i in range(len(df))]) == budgets[drug]\n",
+    "\n",
+    "    # Solve model\n",
+    "    abx_model.solve()\n",
+    "    print(\"Status:\", LpStatus[abx_model.status])\n",
+    "\n",
+    "    # Save selected antibiotic to df_new\n",
+    "    abx_decisions = []\n",
+    "    for i in range(len(df)):\n",
+    "        abx_decision = None\n",
+    "        for abx in abx_options:\n",
+    "            if drug_inds[abx][i].value() == 1:\n",
+    "                abx_decision = abx\n",
+    "        assert abx_decision is not None\n",
+    "        abx_decisions.append(abx_decision)\n",
+    "    df['IP_med_description'] = abx_decisions\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def boostrap_coverage_rates(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    guideline_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_IP'].sum() / len(df)\n",
+    "    actual_guideline_rate = df['was_covered_guideline'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_guide = (df\n",
+    "            .groupby('guideline_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('IP_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_IP'].sum() / len(df_boot_ip))\n",
+    "        guideline_covered_rates.append(df_boot_guide['was_covered_guideline'].sum() / len(df_boot_guide))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    guide_high = np.percentile(guideline_covered_rates, 97.5)\n",
+    "    guide_low = np.percentile(guideline_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 2)}: [{round(ip_low, 2)}, {round(ip_high, 2)}]\"\n",
+    "    guide_estimate = f\"{round(actual_guideline_rate, 2)}: [{round(guide_low, 2)}, {round(guide_high, 2)}]\"\n",
+    "    \n",
+    "    pval = len(\n",
+    "        [g for g in guideline_covered_rates if g >= actual_ip_rate]\n",
+    "    ) / len(guideline_covered_rates)\n",
+    "    \n",
+    "    return ip_estimate, guide_estimate, pval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "budgets  = {\n",
+    "  \"Vancomycin\" : 0,\n",
+    "  \"Ampicillin\" : 0,\n",
+    "  \"Cefazolin\" : 0,\n",
+    "  \"Ceftriaxone\" : 211,\n",
+    "  \"Cefepime\" : 254,\n",
+    "  \"Zosyn\" : 0,\n",
+    "  \"Ciprofloxacin\" : 0,\n",
+    "  \"Meropenem\" : 11,\n",
+    "  \"Vancomycin_Meropenem\" : 0,\n",
+    "  \"Vancomycin_Zosyn\" :  0,\n",
+    "  \"Vancomycin_Cefepime\" : 0,\n",
+    "  \"Vancomycin_Ceftriaxone\" : 0\n",
+    "}\n",
+    "df_uti_final = solve_lp_problem(df_uti_new, budgets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_final = (df_uti_final\n",
+    "    .assign(was_covered_guideline=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "    .assign(was_covered_IP=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='IP_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "\n",
+    ")\n",
+    "clin_covered_rate = df_uti_final['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_final['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_final['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_final['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8151260504201681\n",
+      "0.7983193277310925\n",
+      "0.7794117647058824\n",
+      "0.7899159663865546\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ceftriaxone: 0.6900497406229318\n",
+      "Cefepime: 0.6533521109322769\n",
+      "Meropenem: 0.6583880943177426\n",
+      "Zosyn: 0.5792461197339246\n"
+     ]
+    }
+   ],
+   "source": [
+    "for abx in ['Ceftriaxone', 'Cefepime', 'Meropenem', 'Zosyn']:\n",
+    "    print(f\"{abx}: {roc_auc_score(df_uti_final[f'{abx}_label'], df_uti_final[f'{abx}_predictions'])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:23<00:00, 42.19it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ip, guide, pval = boostrap_coverage_rates(df_uti_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.78: [0.74, 0.82]'"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.79: [0.75, 0.83]'"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.707"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Repeat but now drop all cases where ENTEROCOCCUS was one of the bugs that grew"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_uti_guidelines --project mining-clinical-decisions\n",
+    "WITH observations_with_pos_urine_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT a.*, cohort.index_time\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.final_cohort_table` cohort\n",
+    "    USING\n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description = \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "observations_with_pos_other_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT pat_enc_csn_id_coded\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description <> \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "observations_with_pos_enterococcus as (\n",
+    "    SELECT DISTINCT\n",
+    "        pat_enc_csn_id_coded\n",
+    "    FROM \n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        UPPER(cs.organism) LIKE \"%ENTEROCOCCUS%\"\n",
+    "),\n",
+    "\n",
+    "only_utis as (\n",
+    "SELECT \n",
+    "    DISTINCT a.*\n",
+    "FROM \n",
+    "    observations_with_pos_urine_cultures a \n",
+    "WHERE\n",
+    "    a.pat_enc_csn_id_coded NOT IN (SELECT DISTINCT pat_enc_csn_id_coded \n",
+    "                                   FROM observations_with_pos_other_cultures)\n",
+    "AND\n",
+    "    a.pat_enc_csn_id_coded NOT IN (SELECT DISTINCT pat_enc_csn_id_coded\n",
+    "                                   FROM observations_with_pos_enterococcus)\n",
+    "),\n",
+    "\n",
+    "utis_with_neutra as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN lr.ord_num_value IS NULL THEN 0\n",
+    "            WHEN lr.ord_num_value < 10 THEN 1\n",
+    "            ELSE 0 END)\n",
+    "        OVER(PARTITION BY a.pat_enc_csn_id_coded) NEUTRA\n",
+    "    FROM\n",
+    "        only_utis a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT anon_id, cohort.pat_enc_csn_id_coded, lab_name, base_name, ord_num_value, result_time_utc \n",
+    "         FROM shc_core.lab_result \n",
+    "         INNER JOIN abx.final_cohort_table cohort\n",
+    "         USING (anon_id)\n",
+    "         WHERE base_name in ('NEUTRA', 'NEUTAB', 'ABSBAND', 'ABSNEUTBDYFL')\n",
+    "         AND result_time_utc <= cohort.index_time\n",
+    "         AND TIMESTAMP_ADD(result_time_utc, INTERVAL 24 Hour) >= cohort.index_time) lr\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "\n",
+    "),\n",
+    "\n",
+    "final_table as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%CEFTRIAXONE%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_ceftriaxone_resistance,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%CEFEPIME%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_cefepime_resistance\n",
+    "    FROM\n",
+    "        utis_with_neutra a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT DISTINCT anon_id, cohort.index_time, antibiotic, suscept, result_time_jittered_utc\n",
+    "        FROM shc_core.culture_sensitivity\n",
+    "        INNER JOIN abx.final_cohort_table cohort\n",
+    "        USING (anon_id)\n",
+    "        WHERE result_time_jittered_utc < cohort.index_time) cs\n",
+    "    USING\n",
+    "        (anon_id, index_time)\n",
+    ")\n",
+    "\n",
+    "SELECT\n",
+    "    *,\n",
+    "    CASE \n",
+    "    WHEN NEUTRA = 0 and  prior_ceftriaxone_resistance = 0 THEN 'Ceftriaxone'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_cefepime_resistance = 0 THEN 'Cefepime'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_cefepime_resistance = 1 THEN 'Meropenem'\n",
+    "    WHEN NEUTRA = 1 and prior_cefepime_resistance = 1 THEN 'Meropenem'\n",
+    "    ELSE 'Cefepime'\n",
+    "    END guideline_med_description\n",
+    "FROM final_table\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>index_time</th>\n",
+       "      <th>NEUTRA</th>\n",
+       "      <th>prior_ceftriaxone_resistance</th>\n",
+       "      <th>prior_cefepime_resistance</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCda81cb</td>\n",
+       "      <td>131267723331</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-05-06 21:57:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcda7db</td>\n",
+       "      <td>131262420251</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-02-09 14:21:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCe09d08</td>\n",
+       "      <td>131277291645</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-10-24 04:41:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCe22085</td>\n",
+       "      <td>131264672428</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-02-18 21:29:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCdec57d</td>\n",
+       "      <td>131264827613</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-03-05 06:21:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    anon_id  pat_enc_csn_id_coded   med_description IP_med_description  \\\n",
+       "0  JCda81cb          131267723331  Vancomycin_Zosyn   Vancomycin_Zosyn   \n",
+       "1  JCcda7db          131262420251  Vancomycin_Zosyn              Zosyn   \n",
+       "2  JCe09d08          131277291645       Ceftriaxone   Vancomycin_Zosyn   \n",
+       "3  JCe22085          131264672428       Ceftriaxone           Cefepime   \n",
+       "4  JCdec57d          131264827613       Ceftriaxone        Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_IP                index_time  NEUTRA  \\\n",
+       "0               1               1 2019-05-06 21:57:00+00:00       0   \n",
+       "1               1               1 2019-02-09 14:21:00+00:00       0   \n",
+       "2               1               1 2019-10-24 04:41:00+00:00       1   \n",
+       "3               0               1 2019-02-18 21:29:00+00:00       1   \n",
+       "4               1               1 2019-03-05 06:21:00+00:00       0   \n",
+       "\n",
+       "   prior_ceftriaxone_resistance  prior_cefepime_resistance  \\\n",
+       "0                             0                          0   \n",
+       "1                             0                          0   \n",
+       "2                             0                          0   \n",
+       "3                             0                          0   \n",
+       "4                             0                          0   \n",
+       "\n",
+       "  guideline_med_description  \n",
+       "0               Ceftriaxone  \n",
+       "1               Ceftriaxone  \n",
+       "2                  Cefepime  \n",
+       "3                  Cefepime  \n",
+       "4               Ceftriaxone  "
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_guidelines.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(403, 46)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCda81cb</td>\n",
+       "      <td>131267723331</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.408740</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.479061</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcda7db</td>\n",
+       "      <td>131262420251</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.075222</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.498187</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630462</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCe09d08</td>\n",
+       "      <td>131277291645</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.191309</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.420485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.289654</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCe22085</td>\n",
+       "      <td>131264672428</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.191883</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.405953</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.590927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCdec57d</td>\n",
+       "      <td>131264827613</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.111572</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.482054</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.632007</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    anon_id  pat_enc_csn_id_coded guideline_med_description  Vancomycin_label  \\\n",
+       "0  JCda81cb          131267723331               Ceftriaxone                 0   \n",
+       "1  JCcda7db          131262420251               Ceftriaxone                 0   \n",
+       "2  JCe09d08          131277291645                  Cefepime                 0   \n",
+       "3  JCe22085          131264672428                  Cefepime                 0   \n",
+       "4  JCdec57d          131264827613               Ceftriaxone                 0   \n",
+       "\n",
+       "   Vancomycin_predictions  Ampicillin_label  Ampicillin_predictions  \\\n",
+       "0                0.408740                 0                0.479061   \n",
+       "1                0.075222                 1                0.498187   \n",
+       "2                0.191309                 0                0.420485   \n",
+       "3                0.191883                 0                0.405953   \n",
+       "4                0.111572                 1                0.482054   \n",
+       "\n",
+       "   Cefazolin_label  Cefazolin_predictions  Ceftriaxone_label  ...  Meropenem  \\\n",
+       "0                1               0.631823                  1  ...          1   \n",
+       "1                1               0.630462                  1  ...          1   \n",
+       "2                0               0.289654                  1  ...          1   \n",
+       "3                0               0.590927                  0  ...          1   \n",
+       "4                1               0.632007                  1  ...          1   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0                       1                    1                 1   \n",
+       "1                       1                    1                 1   \n",
+       "2                       1                    1                 1   \n",
+       "3                       0                    1                 0   \n",
+       "4                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1    Vancomycin_Zosyn             Ceftriaxone   \n",
+       "1                     1               Zosyn             Ceftriaxone   \n",
+       "2                     1    Vancomycin_Zosyn        Vancomycin_Zosyn   \n",
+       "3                     1            Cefepime             Ceftriaxone   \n",
+       "4                     1         Ceftriaxone             Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   1               1  \n",
+       "1               1                   1               1  \n",
+       "2               1                   1               1  \n",
+       "3               0                   0               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new = (df_uti_guidelines\n",
+    "    [['anon_id', 'pat_enc_csn_id_coded', 'guideline_med_description']]\n",
+    "    .merge(df_new, how='inner')\n",
+    ")\n",
+    "print(df_uti_new.shape)\n",
+    "df_uti_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_new = (df_uti_new\n",
+    "    .assign(was_covered_guideline=df_uti_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    ")\n",
+    "clin_covered_rate = df_uti_new['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_new['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_new['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_new['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8933002481389578\n",
+      "0.8610421836228288\n",
+      "0.9106699751861043\n",
+      "0.9330024813895782\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We note that this comparison is likely unfair because the the optimizer is using much broader spectrum antibiotics in this subset than what the guidelines recommend\n",
+    "\n",
+    "##### Thus we do another analysis where we constrict the optimizer to the budget used by the guidelines themselves. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(403, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_uti_new.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cefepime       217\n",
+       "Ceftriaxone    178\n",
+       "Meropenem        8\n",
+       "Name: guideline_med_description, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new['guideline_med_description'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "def solve_lp_problem(df, budgets):\n",
+    "    \"\"\"\n",
+    "    Creates a lp problem formulation with pulp for antibiotic selection and adds\n",
+    "    budget constraints and solves.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "\n",
+    "    abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "\n",
+    "    # Create binary indicators for whether treatment is used\n",
+    "    drug_inds = {}\n",
+    "    for abx in abx_options:\n",
+    "        drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                          for i in range(len(df))]\n",
+    "\n",
+    "    # Add objective function to model\n",
+    "    per_csn_sum = []\n",
+    "    for i in range(len(df)):\n",
+    "        _sum = 0\n",
+    "        for abx in abx_options:\n",
+    "            _sum += drug_inds[abx][i] * df['%s_predictions' % abx].values[i]\n",
+    "        per_csn_sum.append(_sum)\n",
+    "\n",
+    "    abx_model += lpSum(per_csn_sum)\n",
+    "\n",
+    "    # Add one selection constraint\n",
+    "    for i in range(len(df)):\n",
+    "        selections = []\n",
+    "        for abx in abx_options:\n",
+    "            selections.append(drug_inds[abx][i])\n",
+    "        abx_model += lpSum(selections) == 1\n",
+    "    \n",
+    "    for drug in drug_inds:\n",
+    "        abx_model += lpSum([drug_inds[drug][i] for i in range(len(df))]) == budgets[drug]\n",
+    "\n",
+    "    # Solve model\n",
+    "    abx_model.solve()\n",
+    "    print(\"Status:\", LpStatus[abx_model.status])\n",
+    "\n",
+    "    # Save selected antibiotic to df_new\n",
+    "    abx_decisions = []\n",
+    "    for i in range(len(df)):\n",
+    "        abx_decision = None\n",
+    "        for abx in abx_options:\n",
+    "            if drug_inds[abx][i].value() == 1:\n",
+    "                abx_decision = abx\n",
+    "        assert abx_decision is not None\n",
+    "        abx_decisions.append(abx_decision)\n",
+    "    df['IP_med_description'] = abx_decisions\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def boostrap_coverage_rates(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    guideline_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_IP'].sum() / len(df)\n",
+    "    actual_guideline_rate = df['was_covered_guideline'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_guide = (df\n",
+    "            .groupby('guideline_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('IP_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_IP'].sum() / len(df_boot_ip))\n",
+    "        guideline_covered_rates.append(df_boot_guide['was_covered_guideline'].sum() / len(df_boot_guide))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    guide_high = np.percentile(guideline_covered_rates, 97.5)\n",
+    "    guide_low = np.percentile(guideline_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 2)}: [{round(ip_low, 2)}, {round(ip_high, 2)}]\"\n",
+    "    guide_estimate = f\"{round(actual_guideline_rate, 2)}: [{round(guide_low, 2)}, {round(guide_high, 2)}]\"\n",
+    "    \n",
+    "    pval = len(\n",
+    "        [g for g in guideline_covered_rates if g >= actual_ip_rate]\n",
+    "    ) / len(guideline_covered_rates)\n",
+    "    \n",
+    "    return ip_estimate, guide_estimate, pval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "budgets  = {\n",
+    "  \"Vancomycin\" : 0,\n",
+    "  \"Ampicillin\" : 0,\n",
+    "  \"Cefazolin\" : 0,\n",
+    "  \"Ceftriaxone\" : 178,\n",
+    "  \"Cefepime\" : 217,\n",
+    "  \"Zosyn\" : 0,\n",
+    "  \"Ciprofloxacin\" : 0,\n",
+    "  \"Meropenem\" : 8,\n",
+    "  \"Vancomycin_Meropenem\" : 0,\n",
+    "  \"Vancomycin_Zosyn\" :  0,\n",
+    "  \"Vancomycin_Cefepime\" : 0,\n",
+    "  \"Vancomycin_Ceftriaxone\" : 0\n",
+    "}\n",
+    "df_uti_final = solve_lp_problem(df_uti_new, budgets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_final = (df_uti_final\n",
+    "    .assign(was_covered_guideline=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "    .assign(was_covered_IP=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='IP_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "\n",
+    ")\n",
+    "clin_covered_rate = df_uti_final['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_final['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_final['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_final['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8933002481389578\n",
+      "0.8610421836228288\n",
+      "0.9255583126550868\n",
+      "0.9330024813895782\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ceftriaxone: 0.7026796151425231\n",
+      "Cefepime: 0.6910603496897914\n",
+      "Meropenem: 0.8447236180904523\n",
+      "Zosyn: 0.5746481970096746\n"
+     ]
+    }
+   ],
+   "source": [
+    "for abx in ['Ceftriaxone', 'Cefepime', 'Meropenem', 'Zosyn']:\n",
+    "    print(f\"{abx}: {roc_auc_score(df_uti_final[f'{abx}_label'], df_uti_final[f'{abx}_predictions'])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:23<00:00, 42.22it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ip, guide, pval = boostrap_coverage_rates(df_uti_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.93: [0.9, 0.95]'"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.93: [0.91, 0.96]'"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.758"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now repeat zosyn guideline but without the ENTEROCOCCUS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_uti_guidelines --project mining-clinical-decisions\n",
+    "WITH observations_with_pos_urine_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT a.*, cohort.index_time\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.final_cohort_table` cohort\n",
+    "    USING\n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description = \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "observations_with_pos_other_cultures as (\n",
+    "    SELECT \n",
+    "        DISTINCT pat_enc_csn_id_coded\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        cs.description <> \"URINE CULTURE\"\n",
+    "),\n",
+    "\n",
+    "observations_with_pos_enterococcus as (\n",
+    "    SELECT DISTINCT\n",
+    "        pat_enc_csn_id_coded\n",
+    "    FROM \n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE\n",
+    "        UPPER(cs.organism) LIKE \"%ENTEROCOCCUS%\"\n",
+    "),\n",
+    "\n",
+    "\n",
+    "only_utis as (\n",
+    "SELECT \n",
+    "    DISTINCT a.*\n",
+    "FROM \n",
+    "    observations_with_pos_urine_cultures a \n",
+    "WHERE\n",
+    "    a.pat_enc_csn_id_coded NOT IN (SELECT DISTINCT pat_enc_csn_id_coded \n",
+    "                                   FROM observations_with_pos_other_cultures)\n",
+    "AND \n",
+    "    a.pat_enc_csn_id_coded NOT IN (SELECT DISTINCT pat_enc_csn_id_coded \n",
+    "                                   FROM observations_with_pos_enterococcus)\n",
+    "\n",
+    "),\n",
+    "\n",
+    "utis_with_neutra as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN lr.ord_num_value IS NULL THEN 0\n",
+    "            WHEN lr.ord_num_value < 10 THEN 1\n",
+    "            ELSE 0 END)\n",
+    "        OVER(PARTITION BY a.pat_enc_csn_id_coded) NEUTRA\n",
+    "    FROM\n",
+    "        only_utis a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT anon_id, cohort.pat_enc_csn_id_coded, lab_name, base_name, ord_num_value, result_time_utc \n",
+    "         FROM shc_core.lab_result \n",
+    "         INNER JOIN abx.final_cohort_table cohort\n",
+    "         USING (anon_id)\n",
+    "         WHERE base_name in ('NEUTRA', 'NEUTAB', 'ABSBAND', 'ABSNEUTBDYFL')\n",
+    "         AND result_time_utc <= cohort.index_time\n",
+    "         AND TIMESTAMP_ADD(result_time_utc, INTERVAL 24 Hour) >= cohort.index_time) lr\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "\n",
+    "),\n",
+    "\n",
+    "final_table as (\n",
+    "    SELECT DISTINCT\n",
+    "        a.*,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%CEFTRIAXONE%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_ceftriaxone_resistance,\n",
+    "        MAX(CASE WHEN UPPER(cs.antibiotic) LIKE \"%PIPERACILLIN/TAZOBACTAM%\" AND UPPER(cs.suscept) LIKE \"%RESISTANT%\" THEN 1 ELSE 0 END)\n",
+    "        OVER (PARTITION BY a.anon_id) AS prior_zosyn_resistance\n",
+    "    FROM\n",
+    "        utis_with_neutra a\n",
+    "    LEFT JOIN \n",
+    "        (SELECT DISTINCT anon_id, cohort.index_time, antibiotic, suscept, result_time_jittered_utc\n",
+    "        FROM shc_core.culture_sensitivity\n",
+    "        INNER JOIN abx.final_cohort_table cohort\n",
+    "        USING (anon_id)\n",
+    "        WHERE result_time_jittered_utc < cohort.index_time) cs\n",
+    "    USING\n",
+    "        (anon_id, index_time)\n",
+    ")\n",
+    "\n",
+    "SELECT\n",
+    "    *,\n",
+    "    CASE \n",
+    "    WHEN NEUTRA = 0 and  prior_ceftriaxone_resistance = 0 THEN 'Ceftriaxone'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_zosyn_resistance = 0 THEN 'Zosyn'\n",
+    "    WHEN NEUTRA = 0 and prior_ceftriaxone_resistance = 1 and prior_zosyn_resistance = 1 THEN 'Meropenem'\n",
+    "    WHEN NEUTRA = 1 and prior_zosyn_resistance = 1 THEN 'Meropenem'\n",
+    "    ELSE 'Zosyn'\n",
+    "    END guideline_med_description\n",
+    "FROM final_table\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>index_time</th>\n",
+       "      <th>NEUTRA</th>\n",
+       "      <th>prior_ceftriaxone_resistance</th>\n",
+       "      <th>prior_zosyn_resistance</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcde394</td>\n",
+       "      <td>131269348156</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-06-27 02:59:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe4d962</td>\n",
+       "      <td>131269280090</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-05-13 03:38:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zosyn</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCdfd218</td>\n",
+       "      <td>131278102592</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-10-22 17:58:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCe75909</td>\n",
+       "      <td>131265108077</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-04-15 05:45:00+00:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zosyn</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCe0907f</td>\n",
+       "      <td>131277794260</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-11-04 20:06:00+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    anon_id  pat_enc_csn_id_coded   med_description IP_med_description  \\\n",
+       "0  JCcde394          131269348156       Ceftriaxone        Ceftriaxone   \n",
+       "1  JCe4d962          131269280090       Ceftriaxone   Vancomycin_Zosyn   \n",
+       "2  JCdfd218          131278102592       Ceftriaxone              Zosyn   \n",
+       "3  JCe75909          131265108077  Vancomycin_Zosyn        Ceftriaxone   \n",
+       "4  JCe0907f          131277794260       Ceftriaxone        Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_IP                index_time  NEUTRA  \\\n",
+       "0               1               1 2019-06-27 02:59:00+00:00       0   \n",
+       "1               0               1 2019-05-13 03:38:00+00:00       1   \n",
+       "2               1               1 2019-10-22 17:58:00+00:00       0   \n",
+       "3               1               1 2019-04-15 05:45:00+00:00       1   \n",
+       "4               1               1 2019-11-04 20:06:00+00:00       0   \n",
+       "\n",
+       "   prior_ceftriaxone_resistance  prior_zosyn_resistance  \\\n",
+       "0                             0                       0   \n",
+       "1                             0                       0   \n",
+       "2                             0                       0   \n",
+       "3                             0                       0   \n",
+       "4                             0                       0   \n",
+       "\n",
+       "  guideline_med_description  \n",
+       "0               Ceftriaxone  \n",
+       "1                     Zosyn  \n",
+       "2               Ceftriaxone  \n",
+       "3                     Zosyn  \n",
+       "4               Ceftriaxone  "
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_guidelines.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(403, 46)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>guideline_med_description</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcde394</td>\n",
+       "      <td>131269348156</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.129348</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.391012</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.826151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe4d962</td>\n",
+       "      <td>131269280090</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.241762</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.400408</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.431113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCdfd218</td>\n",
+       "      <td>131278102592</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.381317</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.557518</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.681020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCe75909</td>\n",
+       "      <td>131265108077</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.148747</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.459139</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.634669</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCe0907f</td>\n",
+       "      <td>131277794260</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.196151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.507214</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.788909</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    anon_id  pat_enc_csn_id_coded guideline_med_description  Vancomycin_label  \\\n",
+       "0  JCcde394          131269348156               Ceftriaxone                 0   \n",
+       "1  JCe4d962          131269280090                     Zosyn                 0   \n",
+       "2  JCdfd218          131278102592               Ceftriaxone                 0   \n",
+       "3  JCe75909          131265108077                     Zosyn                 0   \n",
+       "4  JCe0907f          131277794260               Ceftriaxone                 0   \n",
+       "\n",
+       "   Vancomycin_predictions  Ampicillin_label  Ampicillin_predictions  \\\n",
+       "0                0.129348                 1                0.391012   \n",
+       "1                0.241762                 0                0.400408   \n",
+       "2                0.381317                 0                0.557518   \n",
+       "3                0.148747                 0                0.459139   \n",
+       "4                0.196151                 1                0.507214   \n",
+       "\n",
+       "   Cefazolin_label  Cefazolin_predictions  Ceftriaxone_label  ...  Meropenem  \\\n",
+       "0                1               0.826151                  1  ...          1   \n",
+       "1                0               0.431113                  0  ...          1   \n",
+       "2                1               0.681020                  1  ...          1   \n",
+       "3                0               0.634669                  1  ...          1   \n",
+       "4                1               0.788909                  1  ...          1   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0                       1                    1                 1   \n",
+       "1                       0                    1                 1   \n",
+       "2                       1                    1                 1   \n",
+       "3                       1                    1                 1   \n",
+       "4                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone             Ceftriaxone   \n",
+       "1                     1    Vancomycin_Zosyn                Cefepime   \n",
+       "2                     1               Zosyn             Ceftriaxone   \n",
+       "3                     1         Ceftriaxone        Vancomycin_Zosyn   \n",
+       "4                     1         Ceftriaxone             Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   1               1  \n",
+       "1               0                   1               1  \n",
+       "2               1                   1               1  \n",
+       "3               1                   1               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new = (df_uti_guidelines\n",
+    "    [['anon_id', 'pat_enc_csn_id_coded', 'guideline_med_description']]\n",
+    "    .merge(df_new, how='inner')\n",
+    ")\n",
+    "print(df_uti_new.shape)\n",
+    "df_uti_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_new = (df_uti_new\n",
+    "    .assign(was_covered_guideline=df_uti_new.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    ")\n",
+    "clin_covered_rate = df_uti_new['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_new['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_new['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_new['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8933002481389578\n",
+      "0.8610421836228288\n",
+      "0.9106699751861043\n",
+      "0.9106699751861043\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We note that this comparison is likely unfair because the the optimizer is using much broader spectrum antibiotics in this subset than what the guidelines recommend\n",
+    "\n",
+    "##### Thus we do another analysis where we constrict the optimizer to the budget used by the guidelines themselves. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(403, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_uti_new.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Zosyn          210\n",
+       "Ceftriaxone    178\n",
+       "Meropenem       15\n",
+       "Name: guideline_med_description, dtype: int64"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uti_new['guideline_med_description'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "def solve_lp_problem(df, budgets):\n",
+    "    \"\"\"\n",
+    "    Creates a lp problem formulation with pulp for antibiotic selection and adds\n",
+    "    budget constraints and solves.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "\n",
+    "    abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "\n",
+    "    # Create binary indicators for whether treatment is used\n",
+    "    drug_inds = {}\n",
+    "    for abx in abx_options:\n",
+    "        drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                          for i in range(len(df))]\n",
+    "\n",
+    "    # Add objective function to model\n",
+    "    per_csn_sum = []\n",
+    "    for i in range(len(df)):\n",
+    "        _sum = 0\n",
+    "        for abx in abx_options:\n",
+    "            _sum += drug_inds[abx][i] * df['%s_predictions' % abx].values[i]\n",
+    "        per_csn_sum.append(_sum)\n",
+    "\n",
+    "    abx_model += lpSum(per_csn_sum)\n",
+    "\n",
+    "    # Add one selection constraint\n",
+    "    for i in range(len(df)):\n",
+    "        selections = []\n",
+    "        for abx in abx_options:\n",
+    "            selections.append(drug_inds[abx][i])\n",
+    "        abx_model += lpSum(selections) == 1\n",
+    "    \n",
+    "    for drug in drug_inds:\n",
+    "        abx_model += lpSum([drug_inds[drug][i] for i in range(len(df))]) == budgets[drug]\n",
+    "\n",
+    "    # Solve model\n",
+    "    abx_model.solve()\n",
+    "    print(\"Status:\", LpStatus[abx_model.status])\n",
+    "\n",
+    "    # Save selected antibiotic to df_new\n",
+    "    abx_decisions = []\n",
+    "    for i in range(len(df)):\n",
+    "        abx_decision = None\n",
+    "        for abx in abx_options:\n",
+    "            if drug_inds[abx][i].value() == 1:\n",
+    "                abx_decision = abx\n",
+    "        assert abx_decision is not None\n",
+    "        abx_decisions.append(abx_decision)\n",
+    "    df['IP_med_description'] = abx_decisions\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def boostrap_coverage_rates(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    guideline_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_IP'].sum() / len(df)\n",
+    "    actual_guideline_rate = df['was_covered_guideline'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_guide = (df\n",
+    "            .groupby('guideline_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('IP_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_IP'].sum() / len(df_boot_ip))\n",
+    "        guideline_covered_rates.append(df_boot_guide['was_covered_guideline'].sum() / len(df_boot_guide))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    guide_high = np.percentile(guideline_covered_rates, 97.5)\n",
+    "    guide_low = np.percentile(guideline_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 2)}: [{round(ip_low, 3)}, {round(ip_high, 3)}]\"\n",
+    "    guide_estimate = f\"{round(actual_guideline_rate, 2)}: [{round(guide_low, 3)}, {round(guide_high, 3)}]\"\n",
+    "    \n",
+    "    pval = len(\n",
+    "        [g for g in guideline_covered_rates if g >= actual_ip_rate]\n",
+    "    ) / len(guideline_covered_rates)\n",
+    "    \n",
+    "    return ip_estimate, guide_estimate, pval\n",
+    "    \n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "budgets  = {\n",
+    "  \"Vancomycin\" : 0,\n",
+    "  \"Ampicillin\" : 0,\n",
+    "  \"Cefazolin\" : 0,\n",
+    "  \"Ceftriaxone\" : 178,\n",
+    "  \"Cefepime\" : 0,\n",
+    "  \"Zosyn\" : 210,\n",
+    "  \"Ciprofloxacin\" : 0,\n",
+    "  \"Meropenem\" : 15,\n",
+    "  \"Vancomycin_Meropenem\" : 0,\n",
+    "  \"Vancomycin_Zosyn\" :  0,\n",
+    "  \"Vancomycin_Cefepime\" : 0,\n",
+    "  \"Vancomycin_Ceftriaxone\" : 0\n",
+    "}\n",
+    "df_uti_final = solve_lp_problem(df_uti_new, budgets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uti_final = (df_uti_final\n",
+    "    .assign(was_covered_guideline=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='guideline_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "    .assign(was_covered_IP=df_uti_final.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='IP_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "\n",
+    ")\n",
+    "clin_covered_rate = df_uti_final['was_covered_dr'].sum() / len(df_uti_new)\n",
+    "random_covered_rate = df_uti_final['was_covered_random'].sum() / len(df_uti_new)\n",
+    "ip_covered_rate = df_uti_final['was_covered_IP'].sum() / len(df_uti_new)\n",
+    "guideline_covered_rate = df_uti_final['was_covered_guideline'].sum() / len(df_uti_new)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8933002481389578\n",
+      "0.8610421836228288\n",
+      "0.9205955334987593\n",
+      "0.9106699751861043\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n",
+    "print(guideline_covered_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ceftriaxone: 0.7026796151425231\n",
+      "Cefepime: 0.6910603496897914\n",
+      "Meropenem: 0.8447236180904523\n",
+      "Zosyn: 0.5746481970096746\n"
+     ]
+    }
+   ],
+   "source": [
+    "for abx in ['Ceftriaxone', 'Cefepime', 'Meropenem', 'Zosyn']:\n",
+    "    print(f\"{abx}: {roc_auc_score(df_uti_final[f'{abx}_label'], df_uti_final[f'{abx}_predictions'])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:25<00:00, 39.88it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ip, guide, pval = boostrap_coverage_rates(df_uti_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.92: [0.89, 0.95]'"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.91: [0.88, 0.94]'"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.275"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Antibiogram comparison\n",
+    "\n",
+    "##### We note this is unfair because here we let the benchmark know the species type (which the lp method does not know). \n",
+    "Assign each observation antibiotic efficacy estimates based on the antibiogram value of the bug(s). Take mean over each unique bug combo. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_biogram_baseline --project mining-clinical-decisions\n",
+    "\n",
+    "WITH observations_and_bugs as (\n",
+    "    SELECT DISTINCT \n",
+    "        a.*, cohort.index_time, cs.organism\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_allocation` a\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.abx.final_cohort_table` cohort\n",
+    "    USING\n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "    USING \n",
+    "        (pat_enc_csn_id_coded)\n",
+    "    INNER JOIN \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs \n",
+    "    USING \n",
+    "        (order_proc_id_coded)\n",
+    "    ORDER BY\n",
+    "        pat_enc_csn_id_coded, organism\n",
+    "\n",
+    ")\n",
+    "SELECT DISTINCT\n",
+    "    pat_enc_csn_id_coded,\n",
+    "    STRING_AGG(organism) bug\n",
+    "FROM\n",
+    "    observations_and_bugs\n",
+    "GROUP BY \n",
+    "    pat_enc_csn_id_coded\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(770, 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>bug</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pat_enc_csn_id_coded                                             bug\n",
+       "0          131260812263  CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS\n",
+       "1          131260883970                            ENTEROCOCCUS SPECIES\n",
+       "2          131261001599                                ESCHERICHIA COLI\n",
+       "3          131261014293                           STAPHYLOCOCCUS AUREUS\n",
+       "4          131261155365                                ESCHERICHIA COLI"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df_biogram_baseline.shape)\n",
+    "df_biogram_baseline.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108161</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.462078</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.215610</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501686</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631382</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.421153</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.412029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.724792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.064201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528883</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.749904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.789198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd356bf          131261014293                 1                0.421153   \n",
+       "4   JCd1c07c          131261155365                 0                0.064201   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 1                0.412029                1   \n",
+       "4                 1                0.528883                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.528823                  1                 0.724792  ...   \n",
+       "4               0.749904                  1                 0.789198  ...   \n",
+       "\n",
+       "   Meropenem  Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0          1                       1                    1                 1   \n",
+       "1          0                       1                    1                 1   \n",
+       "2          1                       1                    1                 1   \n",
+       "3          1                       1                    1                 1   \n",
+       "4          1                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone               Cefazolin   \n",
+       "1                     1         Ceftriaxone             Ceftriaxone   \n",
+       "2                     1    Vancomycin_Zosyn             Ceftriaxone   \n",
+       "3                     1         Ceftriaxone             Ceftriaxone   \n",
+       "4                     1         Ceftriaxone  Vancomycin_Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   0               1  \n",
+       "1               1                   0               0  \n",
+       "2               1                   1               1  \n",
+       "3               1                   1               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(770, 46)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>bug</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108161</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.462078</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.215610</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501686</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631382</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.421153</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.412029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.724792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.064201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528883</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.749904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.789198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd356bf          131261014293                 1                0.421153   \n",
+       "4   JCd1c07c          131261155365                 0                0.064201   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 1                0.412029                1   \n",
+       "4                 1                0.528883                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.528823                  1                 0.724792  ...   \n",
+       "4               0.749904                  1                 0.789198  ...   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0                       1                    1                 1   \n",
+       "1                       1                    1                 1   \n",
+       "2                       1                    1                 1   \n",
+       "3                       1                    1                 1   \n",
+       "4                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone               Cefazolin   \n",
+       "1                     1         Ceftriaxone             Ceftriaxone   \n",
+       "2                     1    Vancomycin_Zosyn             Ceftriaxone   \n",
+       "3                     1         Ceftriaxone             Ceftriaxone   \n",
+       "4                     1         Ceftriaxone  Vancomycin_Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \\\n",
+       "0               1                   0               1   \n",
+       "1               1                   0               0   \n",
+       "2               1                   1               1   \n",
+       "3               1                   1               1   \n",
+       "4               1                   1               1   \n",
+       "\n",
+       "                                              bug  \n",
+       "0  CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS  \n",
+       "1                            ENTEROCOCCUS SPECIES  \n",
+       "2                                ESCHERICHIA COLI  \n",
+       "3                           STAPHYLOCOCCUS AUREUS  \n",
+       "4                                ESCHERICHIA COLI  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new_with_bug = df_new.merge(df_biogram_baseline, how='inner', on='pat_enc_csn_id_coded')\n",
+    "print(df_new_with_bug.shape)\n",
+    "df_new_with_bug.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(770, 45)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_new.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>bug</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>131261167965</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>131261225689</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>131261364764</td>\n",
+       "      <td>MORGANELLA MORGANII</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>131261423740</td>\n",
+       "      <td>STREPTOCOCCUS MITIS GROUP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>131261425594</td>\n",
+       "      <td>MORGANELLA MORGANII</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>131261446761</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>131261464115</td>\n",
+       "      <td>KLEBSIELLA PNEUMONIAE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>131261467739</td>\n",
+       "      <td>STAPH AUREUS {MRSA}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>131261492941</td>\n",
+       "      <td>ENTEROCOCCUS FAECIUM,ENTEROCOCCUS SPECIES,ESCH...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>131261506477</td>\n",
+       "      <td>STREPTOCOCCUS MITIS GROUP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>131261536836</td>\n",
+       "      <td>FUSOBACTERIUM NUCLEATUM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>131261550795</td>\n",
+       "      <td>KLEBSIELLA PNEUMONIAE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>131261558495</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>131261573889</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>131261586041</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES,KLEBSIELLA PNEUMONIAE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    pat_enc_csn_id_coded                                                bug\n",
+       "0           131260812263     CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS\n",
+       "1           131260883970                               ENTEROCOCCUS SPECIES\n",
+       "2           131261001599                                   ESCHERICHIA COLI\n",
+       "3           131261014293                              STAPHYLOCOCCUS AUREUS\n",
+       "4           131261155365                                   ESCHERICHIA COLI\n",
+       "5           131261167965                              STAPHYLOCOCCUS AUREUS\n",
+       "6           131261225689                                   ESCHERICHIA COLI\n",
+       "7           131261364764                                MORGANELLA MORGANII\n",
+       "8           131261423740                          STREPTOCOCCUS MITIS GROUP\n",
+       "9           131261425594                                MORGANELLA MORGANII\n",
+       "10          131261446761                                   ESCHERICHIA COLI\n",
+       "11          131261464115                              KLEBSIELLA PNEUMONIAE\n",
+       "12          131261467739                                STAPH AUREUS {MRSA}\n",
+       "13          131261492941  ENTEROCOCCUS FAECIUM,ENTEROCOCCUS SPECIES,ESCH...\n",
+       "14          131261506477                          STREPTOCOCCUS MITIS GROUP\n",
+       "15          131261536836                            FUSOBACTERIUM NUCLEATUM\n",
+       "16          131261550795                              KLEBSIELLA PNEUMONIAE\n",
+       "17          131261558495                                   ESCHERICHIA COLI\n",
+       "18          131261573889                                   ESCHERICHIA COLI\n",
+       "19          131261586041         ENTEROCOCCUS SPECIES,KLEBSIELLA PNEUMONIAE"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_biogram_baseline.sort_values('pat_enc_csn_id_coded').head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = df_new_with_bug.groupby('bug')['Vancomycin_predictions'].transform('mean')\n",
+    "# for abx in antibiotic_options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assign_kwargs = {\n",
+    "    f\"{abx}_predictions\" : df_new_with_bug.groupby('bug')[f'{abx}_label'].transform('mean')\n",
+    "    for abx in abx_options\n",
+    "}\n",
+    "df_new_with_bug = df_new_with_bug.assign(**assign_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "budgets = {\"Vancomycin\" : 13,\n",
+    "          \"Ampicillin\" : 0,\n",
+    "          \"Cefazolin\" : 8,\n",
+    "          \"Ceftriaxone\" : 404,\n",
+    "          \"Cefepime\" : 14,\n",
+    "          \"Zosyn\" : 102,\n",
+    "          \"Ciprofloxacin\" : 8,\n",
+    "          \"Meropenem\" : 9,\n",
+    "          \"Vancomycin_Meropenem\" : 9,\n",
+    "          \"Vancomycin_Zosyn\" :  149,\n",
+    "          \"Vancomycin_Cefepime\" : 23,\n",
+    "          \"Vancomycin_Ceftriaxone\" : 31\n",
+    " }\n",
+    "df_new_with_bug = solve_lp_problem(df_new_with_bug, budgets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new_with_bug = (df_new_with_bug\n",
+    "    .assign(was_covered_IP=df_new_with_bug.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='IP_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "\n",
+    ")\n",
+    "clin_covered_rate = df_new_with_bug['was_covered_dr'].sum() / len(df_new_with_bug)\n",
+    "random_covered_rate = df_new_with_bug['was_covered_random'].sum() / len(df_new_with_bug)\n",
+    "ip_covered_rate = df_new_with_bug['was_covered_IP'].sum() / len(df_new_with_bug)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8428571428571429\n",
+      "0.787012987012987\n",
+      "0.948051948051948\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>bug</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.942857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.942857</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.488372</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.882353</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.882353</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.488372</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.000000   \n",
+       "1   JCe45a3c          131260883970                 1                0.942857   \n",
+       "2   JCd235bb          131261001599                 0                0.000000   \n",
+       "3   JCd356bf          131261014293                 1                1.000000   \n",
+       "4   JCd1c07c          131261155365                 0                0.000000   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.000000                0   \n",
+       "1                 1                0.942857                0   \n",
+       "2                 0                0.488372                1   \n",
+       "3                 1                0.882353                1   \n",
+       "4                 1                0.488372                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.000000                  1                 1.000000  ...   \n",
+       "1               0.000000                  0                 0.000000  ...   \n",
+       "2               0.813953                  1                 0.883721  ...   \n",
+       "3               1.000000                  1                 0.882353  ...   \n",
+       "4               0.813953                  1                 0.883721  ...   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0                       1                    1                 1   \n",
+       "1                       1                    1                 1   \n",
+       "2                       1                    1                 1   \n",
+       "3                       1                    1                 1   \n",
+       "4                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone                   Zosyn   \n",
+       "1                     1               Zosyn             Ceftriaxone   \n",
+       "2                     1         Ceftriaxone                   Zosyn   \n",
+       "3                     1    Vancomycin_Zosyn                   Zosyn   \n",
+       "4                     1               Zosyn  Vancomycin_Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \\\n",
+       "0               1                   1               1   \n",
+       "1               1                   0               1   \n",
+       "2               1                   1               1   \n",
+       "3               1                   1               1   \n",
+       "4               1                   1               1   \n",
+       "\n",
+       "                                              bug  \n",
+       "0  CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS  \n",
+       "1                            ENTEROCOCCUS SPECIES  \n",
+       "2                                ESCHERICHIA COLI  \n",
+       "3                           STAPHYLOCOCCUS AUREUS  \n",
+       "4                                ESCHERICHIA COLI  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new_with_bug.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108161</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.462078</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.215610</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501686</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631382</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.421153</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.412029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.724792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.064201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528883</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.749904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.789198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd356bf          131261014293                 1                0.421153   \n",
+       "4   JCd1c07c          131261155365                 0                0.064201   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 1                0.412029                1   \n",
+       "4                 1                0.528883                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.528823                  1                 0.724792  ...   \n",
+       "4               0.749904                  1                 0.789198  ...   \n",
+       "\n",
+       "   Meropenem  Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0          1                       1                    1                 1   \n",
+       "1          0                       1                    1                 1   \n",
+       "2          1                       1                    1                 1   \n",
+       "3          1                       1                    1                 1   \n",
+       "4          1                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone                   Zosyn   \n",
+       "1                     1         Ceftriaxone             Ceftriaxone   \n",
+       "2                     1    Vancomycin_Zosyn                   Zosyn   \n",
+       "3                     1         Ceftriaxone                   Zosyn   \n",
+       "4                     1         Ceftriaxone  Vancomycin_Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   1               1  \n",
+       "1               1                   0               0  \n",
+       "2               1                   1               1  \n",
+       "3               1                   1               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "662"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new_with_bug['was_covered_IP'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "662"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new['was_covered_IP'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCe5fd00</td>\n",
+       "      <td>131261425594</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.660258</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.583722</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.569683</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.690373</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>JCd5d810</td>\n",
+       "      <td>131261586041</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.170803</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.218224</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.530841</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.746683</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>JCcfaf6c</td>\n",
+       "      <td>131261698993</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105692</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.486886</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.752132</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>JC2a25c15</td>\n",
+       "      <td>131261798884</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.121255</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.435473</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.702215</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.781805</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "1    JCe45a3c          131260883970                 1                0.483241   \n",
+       "9    JCe5fd00          131261425594                 0                0.660258   \n",
+       "19   JCd5d810          131261586041                 0                0.170803   \n",
+       "25   JCcfaf6c          131261698993                 0                0.105692   \n",
+       "28  JC2a25c15          131261798884                 1                0.121255   \n",
+       "\n",
+       "    Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "1                  1                0.538177                0   \n",
+       "9                  0                0.583722                0   \n",
+       "19                 0                0.218224                0   \n",
+       "25                 0                0.486886                0   \n",
+       "28                 1                0.435473                0   \n",
+       "\n",
+       "    Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "1                0.631024                  0                 0.784364  ...   \n",
+       "9                0.569683                  1                 0.690373  ...   \n",
+       "19               0.530841                  0                 0.746683  ...   \n",
+       "25               0.752132                  0                 0.784198  ...   \n",
+       "28               0.702215                  0                 0.781805  ...   \n",
+       "\n",
+       "    Meropenem  Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "1           0                       1                    1                 1   \n",
+       "9           1                       1                    1                 1   \n",
+       "19          0                       1                    1                 1   \n",
+       "25          1                       0                    1                 1   \n",
+       "28          0                       1                    1                 1   \n",
+       "\n",
+       "    Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "1                      1         Ceftriaxone             Ceftriaxone   \n",
+       "9                      1          Vancomycin                   Zosyn   \n",
+       "19                     1         Ceftriaxone             Ceftriaxone   \n",
+       "25                     1         Ceftriaxone        Vancomycin_Zosyn   \n",
+       "28                     1         Ceftriaxone             Ceftriaxone   \n",
+       "\n",
+       "    was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "1                1                   0               0  \n",
+       "9                1                   1               0  \n",
+       "19               1                   0               0  \n",
+       "25               0                   1               0  \n",
+       "28               1                   0               0  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.query('was_covered_IP == 0', engine='python').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>bug</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.942857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.942857</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCe5fd00</td>\n",
+       "      <td>131261425594</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>MORGANELLA MORGANII</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>JCd5d810</td>\n",
+       "      <td>131261586041</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES,KLEBSIELLA PNEUMONIAE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>JCcfaf6c</td>\n",
+       "      <td>131261698993</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PROTEUS VULGARIS GROUP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>JC2a25c15</td>\n",
+       "      <td>131261798884</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.944444</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENTEROCOCCUS FAECALIS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "1    JCe45a3c          131260883970                 1                0.942857   \n",
+       "9    JCe5fd00          131261425594                 0                0.000000   \n",
+       "19   JCd5d810          131261586041                 0                0.000000   \n",
+       "25   JCcfaf6c          131261698993                 0                0.000000   \n",
+       "28  JC2a25c15          131261798884                 1                0.944444   \n",
+       "\n",
+       "    Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "1                  1                0.942857                0   \n",
+       "9                  0                0.000000                0   \n",
+       "19                 0                0.000000                0   \n",
+       "25                 0                0.000000                0   \n",
+       "28                 1                1.000000                0   \n",
+       "\n",
+       "    Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "1                     0.0                  0                     0.00  ...   \n",
+       "9                     0.0                  1                     0.75  ...   \n",
+       "19                    0.0                  0                     0.00  ...   \n",
+       "25                    0.0                  0                     0.00  ...   \n",
+       "28                    0.0                  0                     0.00  ...   \n",
+       "\n",
+       "    Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "1                        1                    1                 1   \n",
+       "9                        1                    1                 1   \n",
+       "19                       1                    1                 1   \n",
+       "25                       0                    1                 1   \n",
+       "28                       1                    1                 1   \n",
+       "\n",
+       "    Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "1                      1               Zosyn             Ceftriaxone   \n",
+       "9                      1    Vancomycin_Zosyn                   Zosyn   \n",
+       "19                     1               Zosyn             Ceftriaxone   \n",
+       "25                     1               Zosyn        Vancomycin_Zosyn   \n",
+       "28                     1               Zosyn             Ceftriaxone   \n",
+       "\n",
+       "    was_covered_dr  was_covered_random  was_covered_IP  \\\n",
+       "1                1                   0               0   \n",
+       "9                1                   1               0   \n",
+       "19               1                   0               0   \n",
+       "25               0                   1               0   \n",
+       "28               1                   0               0   \n",
+       "\n",
+       "                                           bug  \n",
+       "1                         ENTEROCOCCUS SPECIES  \n",
+       "9                          MORGANELLA MORGANII  \n",
+       "19  ENTEROCOCCUS SPECIES,KLEBSIELLA PNEUMONIAE  \n",
+       "25                      PROTEUS VULGARIS GROUP  \n",
+       "28                       ENTEROCOCCUS FAECALIS  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new_with_bug.query('was_covered_IP == 0', engine='python').head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's do this again but instead create antibiogram values only on the gram stain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "negative    530\n",
+       "positive    175\n",
+       "both         64\n",
+       "unknown       1\n",
+       "Name: gram_stain, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load in gram positive and gram negative text files\n",
+    "with open ('gram_positive.txt', 'r') as f:\n",
+    "    gram_positives = set([l.rstrip() for l in f.readlines()])\n",
+    "with open ('gram_negative.txt', 'r') as f:\n",
+    "    gram_negatives = set([l.rstrip() for l in f.readlines()])\n",
+    "\n",
+    "# We'll drop observations that have more than one bug or whos species we don't identify\n",
+    "def get_gram_stain(a):\n",
+    "    \"\"\"\n",
+    "    Get's gram stain for bugs\n",
+    "    \"\"\"\n",
+    "    bugs = a.split(\",\")\n",
+    "    gram_positive = False\n",
+    "    gram_negative = False\n",
+    "    for bug in bugs:\n",
+    "        if bug in gram_positives:\n",
+    "            gram_positive = True\n",
+    "        if bug in gram_negatives:\n",
+    "            gram_negative = True\n",
+    "    if gram_positive and gram_negative:\n",
+    "        return 'both'\n",
+    "    elif gram_positive: \n",
+    "        return 'positive'\n",
+    "    elif gram_negative:\n",
+    "        return 'negative'\n",
+    "    else:\n",
+    "        return 'unknown'\n",
+    "        \n",
+    "\n",
+    "df_new_gram_stain = (df_new_with_bug\n",
+    "    .assign(gram_stain=lambda x: [get_gram_stain(a) for a in x.bug])\n",
+    ")\n",
+    "df_new_gram_stain['gram_stain'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assign_kwargs = {\n",
+    "    f\"{abx}_predictions\" : df_new_gram_stain.groupby('gram_stain')[f'{abx}_label'].transform('mean')\n",
+    "    for abx in abx_options\n",
+    "}\n",
+    "df_new_gram_stain = df_new_gram_stain.assign(**assign_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: Optimal\n"
+     ]
+    }
+   ],
+   "source": [
+    "budgets = {\"Vancomycin\" : 13,\n",
+    "          \"Ampicillin\" : 0,\n",
+    "          \"Cefazolin\" : 8,\n",
+    "          \"Ceftriaxone\" : 404,\n",
+    "          \"Cefepime\" : 14,\n",
+    "          \"Zosyn\" : 102,\n",
+    "          \"Ciprofloxacin\" : 8,\n",
+    "          \"Meropenem\" : 9,\n",
+    "          \"Vancomycin_Meropenem\" : 9,\n",
+    "          \"Vancomycin_Zosyn\" :  149,\n",
+    "          \"Vancomycin_Cefepime\" : 23,\n",
+    "          \"Vancomycin_Ceftriaxone\" : 31\n",
+    " }\n",
+    "df_new_gram_stain = solve_lp_problem(df_new_gram_stain, budgets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new_gram_stain = (df_new_gram_stain\n",
+    "    .assign(was_covered_IP=df_new_gram_stain.apply(lambda x: compute_was_covered(x, \n",
+    "                                                                          decision_column='IP_med_description'),\n",
+    "                                                                          axis=1)\n",
+    "           )\n",
+    "\n",
+    ")\n",
+    "clin_covered_rate = df_new_gram_stain['was_covered_dr'].sum() / len(df_new_gram_stain)\n",
+    "random_covered_rate = df_new_gram_stain['was_covered_random'].sum() / len(df_new_gram_stain)\n",
+    "ip_covered_rate = df_new_gram_stain['was_covered_IP'].sum() / len(df_new_gram_stain)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8428571428571429\n",
+      "0.787012987012987\n",
+      "0.8935064935064935\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clin_covered_rate)\n",
+    "print(random_covered_rate)\n",
+    "print(ip_covered_rate)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "316    STAPHYLOCOCCUS SPECIES - COAG NEGATIVE, NOT ST...\n",
+       "Name: bug, dtype: object"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# It's the comma\n",
+    "df_new_gram_stain.query(\"gram_stain == 'unknown'\", engine='python').bug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "      <th>bug</th>\n",
+       "      <th>gram_stain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.352830</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.820755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ciprofloxacin</td>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948571</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.868571</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.485714</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ENTEROCOCCUS SPECIES</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.352830</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.820755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948571</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.868571</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.485714</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.514286</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.352830</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.820755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.000000   \n",
+       "1   JCe45a3c          131260883970                 1                0.948571   \n",
+       "2   JCd235bb          131261001599                 0                0.000000   \n",
+       "3   JCd356bf          131261014293                 1                0.948571   \n",
+       "4   JCd1c07c          131261155365                 0                0.000000   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.352830                0   \n",
+       "1                 1                0.868571                0   \n",
+       "2                 0                0.352830                1   \n",
+       "3                 1                0.868571                1   \n",
+       "4                 1                0.352830                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.700000                  1                 0.820755  ...   \n",
+       "1               0.485714                  0                 0.514286  ...   \n",
+       "2               0.700000                  1                 0.820755  ...   \n",
+       "3               0.485714                  1                 0.514286  ...   \n",
+       "4               0.700000                  1                 0.820755  ...   \n",
+       "\n",
+       "   Vancomycin_Cefepime  Vancomycin_Zosyn  Vancomycin_Meropenem  \\\n",
+       "0                    1                 1                     1   \n",
+       "1                    1                 1                     1   \n",
+       "2                    1                 1                     1   \n",
+       "3                    1                 1                     1   \n",
+       "4                    1                 1                     1   \n",
+       "\n",
+       "   IP_med_description  random_med_description  was_covered_dr  \\\n",
+       "0       Ciprofloxacin               Cefazolin               1   \n",
+       "1    Vancomycin_Zosyn             Ceftriaxone               1   \n",
+       "2         Ceftriaxone             Ceftriaxone               1   \n",
+       "3          Vancomycin             Ceftriaxone               1   \n",
+       "4         Ceftriaxone  Vancomycin_Ceftriaxone               1   \n",
+       "\n",
+       "   was_covered_random  was_covered_IP  \\\n",
+       "0                   0               1   \n",
+       "1                   0               1   \n",
+       "2                   1               1   \n",
+       "3                   1               1   \n",
+       "4                   1               1   \n",
+       "\n",
+       "                                              bug  gram_stain  \n",
+       "0  CITROBACTER FREUNDII COMPLEX,PROTEUS MIRABILIS    negative  \n",
+       "1                            ENTEROCOCCUS SPECIES    positive  \n",
+       "2                                ESCHERICHIA COLI    negative  \n",
+       "3                           STAPHYLOCOCCUS AUREUS    positive  \n",
+       "4                                ESCHERICHIA COLI    negative  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new_gram_stain.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "def boostrap_gram_stain_covereage(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_IP'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('IP_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_IP'].sum() / len(df_boot_ip))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 3)}: [{round(ip_low, 3)}, {round(ip_high, 3)}]\"\n",
+    "    \n",
+    "    return ip_estimate\n",
+    "\n",
+    "def boostrap_clin_coverage_covereage(df):\n",
+    "    \"\"\"\n",
+    "    Get's guideline and lp coverage rates given a dataframe with med descriptions and assignments. \n",
+    "    We'll do a stratified bootstrap (stratified by antibiotic) to build up two distributions in coverage\n",
+    "    rates. We'll return the confidence intervals around each estimate and a pvalue for testing the null \n",
+    "    hypothesis that the ip assignment is equal to or less than the guideline assignment\n",
+    "    \"\"\"\n",
+    "    ip_covered_rates = []\n",
+    "    actual_ip_rate = df['was_covered_dr'].sum() / len(df)\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        df_boot_ip = (df\n",
+    "            .groupby('IP_med_description')\n",
+    "            .sample(frac=1., replace=True)\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        ip_covered_rates.append(df_boot_ip['was_covered_dr'].sum() / len(df_boot_ip))\n",
+    "        \n",
+    "    ip_high = np.percentile(ip_covered_rates, 97.5)\n",
+    "    ip_low = np.percentile(ip_covered_rates, 2.5)\n",
+    "    \n",
+    "    ip_estimate = f\"{round(actual_ip_rate, 3)}: [{round(ip_low, 3)}, {round(ip_high, 3)}]\"\n",
+    "    \n",
+    "    return ip_estimate\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:45<00:00, 21.82it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ci = boostrap_gram_stain_covereage(df_new_gram_stain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.894: [0.871, 0.914]'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ci"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:40<00:00, 24.99it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ci = boostrap_gram_stain_covereage(df_new_with_bug)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.948: [0.932, 0.962]'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ci"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:30<00:00, 32.98it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ci = boostrap_gram_stain_covereage(df_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.86: [0.836, 0.883]'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ci"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:28<00:00, 35.12it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0.843: [0.818, 0.869]'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boostrap_clin_coverage_covereage(df_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem</th>\n",
+       "      <th>Vancomycin_Ceftriaxone</th>\n",
+       "      <th>Vancomycin_Cefepime</th>\n",
+       "      <th>Vancomycin_Zosyn</th>\n",
+       "      <th>Vancomycin_Meropenem</th>\n",
+       "      <th>IP_med_description</th>\n",
+       "      <th>random_med_description</th>\n",
+       "      <th>was_covered_dr</th>\n",
+       "      <th>was_covered_random</th>\n",
+       "      <th>was_covered_IP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108161</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.462078</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.215610</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501686</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631382</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Vancomycin_Zosyn</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.421153</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.412029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.724792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd1c07c</td>\n",
+       "      <td>131261155365</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.064201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528883</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.749904</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.789198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>Vancomycin_Ceftriaxone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd356bf          131261014293                 1                0.421153   \n",
+       "4   JCd1c07c          131261155365                 0                0.064201   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 1                0.412029                1   \n",
+       "4                 1                0.528883                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.528823                  1                 0.724792  ...   \n",
+       "4               0.749904                  1                 0.789198  ...   \n",
+       "\n",
+       "   Meropenem  Vancomycin_Ceftriaxone  Vancomycin_Cefepime  Vancomycin_Zosyn  \\\n",
+       "0          1                       1                    1                 1   \n",
+       "1          0                       1                    1                 1   \n",
+       "2          1                       1                    1                 1   \n",
+       "3          1                       1                    1                 1   \n",
+       "4          1                       1                    1                 1   \n",
+       "\n",
+       "   Vancomycin_Meropenem  IP_med_description  random_med_description  \\\n",
+       "0                     1         Ceftriaxone               Cefazolin   \n",
+       "1                     1         Ceftriaxone             Ceftriaxone   \n",
+       "2                     1    Vancomycin_Zosyn             Ceftriaxone   \n",
+       "3                     1         Ceftriaxone             Ceftriaxone   \n",
+       "4                     1         Ceftriaxone  Vancomycin_Ceftriaxone   \n",
+       "\n",
+       "   was_covered_dr  was_covered_random  was_covered_IP  \n",
+       "0               1                   0               1  \n",
+       "1               1                   0               0  \n",
+       "2               1                   1               1  \n",
+       "3               1                   1               1  \n",
+       "4               1                   1               1  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_ampC_producers --project mining-clinical-decisions\n",
+    "WITH all_bugs as (\n",
+    "SELECT DISTINCT\n",
+    "    c.anon_id, c.pat_enc_csn_id_coded, c.order_proc_id_coded, c.description, r.organism\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.shc_core.culture_sensitivity` r\n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "INNER JOIN \n",
+    "     (SELECT DISTINCT pat_enc_csn_id_coded from `mining-clinical-decisions.abx.final_ast_labels`\n",
+    "     WHERE EXTRACT(YEAR FROM index_time) < 2020) f\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    ")\n",
+    "\n",
+    "SELECT DISTINCT \n",
+    "    anon_id, pat_enc_csn_id_coded,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%enterobacter%\" THEN 1 ELSE 0 END) enterobacter,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%serratia%\" THEN 1 ELSE 0 END) serratia,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%citrobacter freundii%\" THEN 1 ELSE 0 END) citrobacter_freundii,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%aeromonas%\" THEN 1 ELSE 0 END) aeromonas,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%proteus%\" THEN 1 ELSE 0 END) proteus,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%providencia%\" THEN 1 ELSE 0 END) providencia,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%morganella morganii%\" THEN 1 ELSE 0 END) morganella_morganii\n",
+    "FROM\n",
+    "    all_bugs\n",
+    "GROUP BY\n",
+    "    anon_id, pat_enc_csn_id_coded\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8342, 9)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ampC_producers.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8342, 2)"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ampC_producers[['anon_id', 'pat_enc_csn_id_coded']].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    7348\n",
+       "1     994\n",
+       "Name: ampC_any, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ampC_producers = df_ampC_producers.assign(ampC_sum=\n",
+    "lambda x: x.enterobacter + x.serratia + x.citrobacter_freundii + x.aeromonas + x.proteus + x.providencia\n",
+    "          + x.morganella_morganii)\n",
+    "df_ampC_producers = df_ampC_producers.assign(ampC_any=lambda x: [1 if a > 0 else 0 for a in x.ampC_sum])\n",
+    "df_ampC_producers['ampC_any'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_ampC_producers_with_ctx --project mining-clinical-decisions\n",
+    "WITH all_bugs as (\n",
+    "SELECT DISTINCT\n",
+    "    c.anon_id, c.pat_enc_csn_id_coded, c.order_proc_id_coded, c.description, r.organism\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.shc_core.culture_sensitivity` r\n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "INNER JOIN \n",
+    "     (SELECT DISTINCT pat_enc_csn_id_coded from `mining-clinical-decisions.abx.final_ast_labels`\n",
+    "     WHERE EXTRACT(YEAR FROM index_time) < 2020 AND Ceftriaxone=1) f\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    ")\n",
+    "\n",
+    "SELECT DISTINCT \n",
+    "    anon_id, pat_enc_csn_id_coded,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%enterobacter%\" THEN 1 ELSE 0 END) enterobacter,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%serratia%\" THEN 1 ELSE 0 END) serratia,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%citrobacter freundii%\" THEN 1 ELSE 0 END) citrobacter_freundii,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%aeromonas%\" THEN 1 ELSE 0 END) aeromonas,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%proteus%\" THEN 1 ELSE 0 END) proteus,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%providencia%\" THEN 1 ELSE 0 END) providencia,\n",
+    "    MAX(CASE WHEN LOWER(organism) LIKE \"%morganella morganii%\" THEN 1 ELSE 0 END) morganella_morganii\n",
+    "FROM\n",
+    "    all_bugs\n",
+    "GROUP BY\n",
+    "    anon_id, pat_enc_csn_id_coded\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5715, 9)"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ampC_producers_with_ctx.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "anon_id                 JC29f9afdJC29f9cb8JC29fadf0JC29fb7b9JC29fcd61J...\n",
+       "pat_enc_csn_id_coded                                      749449248729939\n",
+       "enterobacter                                                          161\n",
+       "serratia                                                               53\n",
+       "citrobacter_freundii                                                   74\n",
+       "aeromonas                                                              15\n",
+       "proteus                                                               352\n",
+       "providencia                                                            21\n",
+       "morganella_morganii                                                    47\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ampC_producers_with_ctx.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    5014\n",
+       "1     701\n",
+       "Name: ampC_any, dtype: int64"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ampC_producers_with_ctx = df_ampC_producers_with_ctx.assign(ampC_sum=\n",
+    "lambda x: x.enterobacter + x.serratia + x.citrobacter_freundii + x.aeromonas + x.proteus + x.providencia\n",
+    "          + x.morganella_morganii)\n",
+    "df_ampC_producers_with_ctx = df_ampC_producers_with_ctx.assign(ampC_any=lambda x: [1 if a > 0 else 0 for a in x.ampC_sum])\n",
+    "df_ampC_producers_with_ctx['ampC_any'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\item \u0007dded[id=CKC]{ICD9 995.92 Sepsis}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 995.92 Severe Sepsis}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 481 Pneumococcal pneumonia}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 482 Other bacterial pneumonia}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 483 Pneumonia due to other specified organism}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 484 Pneumonia in infectious diseases classified elsewhere,\\item ICD9 485 Bronchopneumonia org NOS}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 486 Pneumonia, organism NOS}\n",
+      "\\item \u0007dded[id=CKC]{ICD9 590 Infections of kidney}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 A41 Other sepsis}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J13 Pneumonia due to Streptococcus pneumoniae}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J15 Bacterial pneumonia, not elsewhere classified}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J16 Pneumonia due to other infectious organisms, not elsewhere classified}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J17 Pneumonia in diseases classified elsewhere}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J18 Pneumonia, unspecified organism}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 N10 Acute pyelonephritis}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 N11 Chronic tubulo-interstitial nephritis}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 N12 Tubulo-interstitial nephritis, not specified as acute or chronic}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 N39.0 Urinary tract infection, site not specified}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J06 Acute upper respiratory infections of multiple and unspecified sites}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 A49 Bacterial infection of unspecified site}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 J22 Unspecified acute lower respiratory infection}\n",
+      "\\item \u0007dded[id=CKC]{ICD10 R65.2 Severe sepsis (with and without septic shock)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "test = [   '\\item ICD9 995.92 Sepsis',\n",
+    "    '\\item ICD9 995.92 Severe Sepsis', \n",
+    "    '\\item ICD9 481 Pneumococcal pneumonia',\n",
+    "    '\\item ICD9 482 Other bacterial pneumonia',\n",
+    "    '\\item ICD9 483 Pneumonia due to other specified organism',\n",
+    "    '\\item ICD9 484 Pneumonia in infectious diseases classified elsewhere,'\n",
+    "    '\\item ICD9 485 Bronchopneumonia org NOS',\n",
+    "   '\\item ICD9 486 Pneumonia, organism NOS',\n",
+    "    '\\item ICD9 590 Infections of kidney',\n",
+    "    '\\item ICD10 A41 Other sepsis',\n",
+    "    '\\item ICD10 J13 Pneumonia due to Streptococcus pneumoniae',\n",
+    "   '\\item ICD10 J15 Bacterial pneumonia, not elsewhere classified',\n",
+    "    '\\item ICD10 J16 Pneumonia due to other infectious organisms, not elsewhere classified',\n",
+    "    '\\item ICD10 J17 Pneumonia in diseases classified elsewhere',\n",
+    "    '\\item ICD10 J18 Pneumonia, unspecified organism',\n",
+    "    '\\item ICD10 N10 Acute pyelonephritis',\n",
+    "    '\\item ICD10 N11 Chronic tubulo-interstitial nephritis',\n",
+    "    '\\item ICD10 N12 Tubulo-interstitial nephritis, not specified as acute or chronic',\n",
+    "    '\\item ICD10 N39.0 Urinary tract infection, site not specified',\n",
+    "    '\\item ICD10 J06 Acute upper respiratory infections of multiple and unspecified sites',\n",
+    "    '\\item ICD10 A49 Bacterial infection of unspecified site',\n",
+    "    '\\item ICD10 J22 Unspecified acute lower respiratory infection',\n",
+    "    '\\item ICD10 R65.2 Severe sepsis (with and without septic shock)'\n",
+    "]\n",
+    "for t in test:\n",
+    "    text = t.split()[1:]\n",
+    "    item = t.split()[0]\n",
+    "    print(item + \" \\added[id=CKC]{\" + ' '.join(text) + \"}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/decision_alg/integer_programming.py b/scripts/ER_Infection/notebooks/decision_alg/integer_programming.py
new file mode 100644
index 00000000..2795269a
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/decision_alg/integer_programming.py
@@ -0,0 +1,434 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from pulp import *
+import os, glob
+from sklearn.metrics import roc_auc_score, brier_score_loss
+from sklearn.calibration import CalibratedClassifierCV, calibration_curve
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import cross_val_predict, StratifiedKFold
+
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' 
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' 
+
+from google.cloud import bigquery
+client=bigquery.Client()
+
+import pdb
+
+abx_options = ["Vancomycin",
+                "Ampicillin",
+                "Cefazolin",
+                "Ceftriaxone",
+                "Cefepime",
+                "Zosyn",
+                "Ciprofloxacin",
+                "Meropenem",
+                "Vancomycin_Meropenem",
+                "Vancomycin_Zosyn",
+                "Vancomycin_Cefepime",
+                "Vancomycin_Ceftriaxone"
+                ]
+
+abx_map = {'Ceftriaxone' : "CEFTRIAXONE",
+           'Vancomycin_Zosyn' : "PIPERACILLIN-TAZOBACTAM VANCOMYCIN",
+           'Zosyn' : "PIPERACILLIN-TAZOBACTAM",
+           'Vancomycin_Ceftriaxone' : "CEFTRIAXONE VANCOMYCIN",
+           'Vancomycin_Cefepime' : "CEFEPIME VANCOMYCIN",
+           'Cefepime' : "CEFEPIME",
+           'Vancomycin' :  "VANCOMYCIN",
+           'Meropenem' : "MEROPENEM",
+           'Vancomycin_Meropenem' : "MEROPENEM VANCOMYCIN",
+           'Cefazolin' : "CEFAZOLIN",
+           'Ciprofloxacin' : "CIPROFLOXACIN",
+           'Ampicillin' : 'AMPICILLIN'
+          }
+abx_map_inverse = {abx_map[key] : key for key in abx_map}
+abx_map_inverse['CEFTRIAXONE PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'
+# abx_map_inverse['LEVOFLOXACIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'
+abx_map_inverse['AZITHROMYCIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'
+# abx_map_inverse['MEROPENEM PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Meropenem'
+abx_map_inverse['AZITHROMYCIN CEFTRIAXONE'] = 'Ceftriaxone'
+
+
+
+from sklearn.base import BaseEstimator
+
+class IdentityEstimator(LogisticRegression):
+    def __init__(self):
+        LogisticRegression.__init__(self)
+            
+    def predict_proba(self, input_array):   
+        return input_array*1
+
+    def decision_function(self, input_array):
+        return input_array*1
+
+
+def load_predictions():
+    """Helper function that loads predictions from AST classifiers for test set data"""
+    
+    base_path="/Users/conorcorbin/repos/er_infection/results/ast_models/testing/{abx}"
+    df = pd.DataFrame()
+    for i, abx in enumerate(abx_options):
+        path = base_path.format(abx=abx)
+        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]
+        if i == 0:
+            df = pd.read_csv(f_path)
+            df = df[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]
+            df = df.rename(columns={'label' : '%s_label' % abx,
+                                    'predictions' : '%s_predictions' % abx})
+        else:
+            df_preds = pd.read_csv(f_path)
+            df_preds = df_preds[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]
+            df_preds = df_preds.rename(columns={'label' : '%s_label' % abx,
+                                                'predictions' : '%s_predictions' % abx})
+            df = df.merge(df_preds, how='left', on=['anon_id', 'pat_enc_csn_id_coded'])
+    
+    return df
+
+def simulate_improved_auroc(df, auroc_floor):
+    """
+    Takes in the predictions dataframe and uses the labels to simualate improved auroc. Loops through negative
+    label predicted probabilites and iteratively swaps them with positive label probability if the postive
+    label probability is lower than the negative label probability. Does this until desired auroc is reached.   
+    """
+    for abx in abx_options:
+        predictions = df['%s_predictions' % abx].values
+        labels = df['%s_label' % abx]
+        auc = roc_auc_score(labels, predictions)
+
+        if auc >= auroc_floor:
+            continue
+
+        neg_inds = [i for i in range(len(labels)) if labels[i] == 0]
+        pos_inds = [i for i in range(len(labels)) if labels[i] == 1]
+        for n_ind in neg_inds: 
+            # find a positive ind that is ranked lower than neg ind and flip probabilities
+            for p_ind in pos_inds:
+                if predictions[n_ind] > predictions[p_ind]:
+                    temp = predictions[n_ind]
+                    predictions[n_ind] = predictions[p_ind]
+                    predictions[p_ind] = temp
+
+            auc = roc_auc_score(labels, predictions)
+            if auc >= auroc_floor:
+                df['%s_predictions' % abx] = predictions
+                break
+    return df
+
+
+def calibrate_probabilities(df):
+    """ 
+    Takes in test set probabilites and does a k-fold cross fitting procedure to recalibrate each model
+    """
+    est = IdentityEstimator()
+    for abx in abx_options:
+
+        X = df['%s_predictions' % abx].values.reshape(-1, 1)
+        y = df['%s_label' % abx]
+        isotonic_calibrated_predictions = np.array([float(i) for i in range(len(y))])
+        sigmoid_calibrated_predictions = np.array([float(i) for i in range(len(y))])
+
+        # Fit base estimator
+        est.fit(X, y) # because we've overloaded predict_proba and decision function this doesn't matter
+
+        # Calibrated with isotonic calibration
+        isotonic = CalibratedClassifierCV(est, cv='prefit', method='isotonic')
+
+        # Calibrated with sigmoid calibration
+        sigmoid = CalibratedClassifierCV(est, cv='prefit', method='sigmoid')
+
+        cv = StratifiedKFold(n_splits=10)
+        for train_inds, val_inds in cv.split(X, y):
+            X_train, y_train = X[train_inds], y[train_inds]
+            X_val, y_val = X[val_inds], y[val_inds]
+            isotonic.fit(X_train, y_train)
+            isotonic_predictions = isotonic.predict_proba(X_val)
+            isotonic_calibrated_predictions[val_inds] = isotonic_predictions[:, 1]
+
+            sigmoid.fit(X_train, y_train)
+            sigmoid_predictions = sigmoid.predict_proba(X_val)
+            sigmoid_calibrated_predictions[val_inds] = sigmoid_predictions[:, 1]
+
+
+        df['%s_predictions_isotonic' % abx] = isotonic_calibrated_predictions
+        df['%s_predictions_sigmoid' % abx] = sigmoid_calibrated_predictions
+    
+    return df
+
+
+def get_calibration_plots(df):
+    """ Plots a 4x3 subplot of calibration curves """ 
+    fig, ax = plt.subplots(4, 3, figsize=(24, 32))
+    row, col = 0, 0
+    df = calibrate_probabilities(df)
+
+    for abx in abx_options:
+        ax[row, col].plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")
+        for name in ['', '_isotonic', '_sigmoid']:
+
+            prob_pos = df['%s_predictions%s' % (abx, name)]
+            clf_score = brier_score_loss(df['%s_label' % abx], prob_pos)
+            print("%s:" % name)
+            print("\tBrier: %1.3f" % (clf_score))
+
+            fraction_of_positives, mean_predicted_value = \
+                calibration_curve(df['%s_label' % abx], prob_pos, n_bins=10)
+
+            ax[row, col].plot(mean_predicted_value, fraction_of_positives, "s-",
+                    label="%s (%1.3f)" % (name, clf_score))
+
+        
+        ax[row, col].set_ylabel("Fraction of positives")
+        ax[row, col].set_ylim([-0.05, 1.05])
+        ax[row, col].legend(loc="lower right")
+        ax[row, col].set_title('Calibration plots  (reliability curve)')
+
+        if col == 2:
+            col = 0
+            row +=1
+        else:
+            col += 1
+
+
+    plt.tight_layout()
+
+    plt.savefig('./test2.jpg')
+
+def get_clinician_prescribing_patterns():
+    """
+    Queries med orders actually administered to each patient and filters for CSNs where
+    only abx regimens in our abx_options list were administered
+    """
+
+    query = """
+    SELECT
+        om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded, l.index_time, om.med_description,
+        l.Ampicillin, l.Ciprofloxacin, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin,
+        l.Meropenem, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Cefepime, l.Vancomycin_Ceftriaxone
+    FROM
+        `mining-clinical-decisions.abx.abx_orders_given_and_stopped` om
+    INNER JOIN 
+        `mining-clinical-decisions.abx.final_ast_labels` l
+    USING
+        (pat_enc_csn_id_coded)
+    WHERE
+        om.was_given = 1
+    ORDER BY 
+        om.anon_id, om.pat_enc_csn_id_coded, om.order_time
+    """
+    query_job = client.query(query)
+    df_abx = query_job.result().to_dataframe()
+    df_abx.head()
+
+    # Lambda that aggregate Antibiotic orders after we've grouped by CSN
+    concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))
+
+    # Aggregate abx orders
+    df_drugs = (df_abx
+        .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)
+        .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')
+                                            .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))
+                                        for a in x.med_description])
+        .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on
+        .groupby('pat_enc_csn_id_coded')
+        .agg({'med_description' : concat_abx,
+            'year' : 'first',
+            'Ampicillin' : 'first',
+            'Ciprofloxacin' : 'first',
+            'Cefazolin' : 'first',
+            'Ceftriaxone' : 'first',
+            'Cefepime' : 'first',
+            'Zosyn' : 'first',
+            'Vancomycin' : 'first',
+            'Meropenem' : 'first',
+            'Vancomycin_Ceftriaxone' : 'first',
+            'Vancomycin_Cefepime' : 'first',
+            'Vancomycin_Zosyn' : 'first',
+            'Vancomycin_Meropenem' : 'first'})
+        .reset_index()
+        # Only look at test set data and CSNs where allowed antibiotic selection was administered
+        .query("year == 2019 and med_description in @abx_map_inverse", engine='python') 
+        .assign(med_description=lambda x: [abx_map_inverse[a] for a in x.med_description])
+    )
+
+    # Roughly 700 of the 1300 original CSNs in the test set
+    return df_drugs
+
+
+def get_optimized_abx_selections(df, calibration=None):
+
+    # Predictions string
+    if calibration == None:
+        predictions_string = '%s_predictions'
+    else:
+        predictions_string = '%s_predictions_isotonic'
+    
+    abx_model = LpProblem("Antibiotics", LpMaximize)
+
+    # Create binary indicators for whether treatment is used
+    drug_inds = {}
+    for abx in abx_options:
+        drug_inds[abx] = [LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')
+                        for i in range(len(df))]
+
+    # Add objective function to model
+    per_csn_sum = []
+    for i in range(len(df)):
+        _sum = 0
+        for abx in abx_options:
+            _sum += drug_inds[abx][i] * df[predictions_string % abx].values[i]
+        per_csn_sum.append(_sum)
+        
+    abx_model += lpSum(per_csn_sum)
+
+    # Add one selection constraint
+    for i in range(len(df)):
+        selections = []
+        for abx in abx_options:
+            selections.append(drug_inds[abx][i])
+        abx_model += lpSum(selections) == 1
+
+    # Add max assignment constraints
+    abx_assignment_constraints = {"Vancomycin" : 13,
+                                "Ampicillin" : 0,
+                                "Cefazolin" : 8,
+                                "Ceftriaxone" : 367,
+                                "Cefepime" : 14,
+                                "Zosyn" : 102,
+                                "Ciprofloxacin" : 8,
+                                "Meropenem" : 9,
+                                "Vancomycin_Meropenem" : 9,
+                                "Vancomycin_Zosyn" :  113,
+                                "Vancomycin_Cefepime" : 23,
+                                "Vancomycin_Ceftriaxone" : 31
+                                }
+
+    for drug in drug_inds:
+        abx_model += lpSum([drug_inds[drug][i] for i in range(len(df))]) == abx_assignment_constraints[drug]
+
+    # Solve model
+    abx_model.solve()
+    # print("Status:", LpStatus[abx_model.status])
+
+    # Save selected antibiotic to df
+    abx_decisions = []
+    for i in range(len(df)):
+        abx_decision = None
+        for abx in abx_options:
+            if drug_inds[abx][i].value() == 1:
+                abx_decision = abx_map[abx]
+        assert abx_decision is not None
+        abx_decisions.append(abx_decision)
+    df['IP_med_description'] = abx_decisions
+
+    return df
+
+# Ugly helper function that just does some string mapping
+def compute_was_covered(x, decision_column='med_description'):
+    """
+    Given med description, find appropriate label column and return whether patient was covered during CSN
+    Returns "Not in abx options" if abx regimen isn't in our set of 12 options - useful for filtering later
+    """
+    if decision_column == 'med_description':
+        med_description = x.med_description
+    elif decision_column == 'random_med_description':
+        med_description = x.random_med_description
+    elif decision_column == 'IP_med_description':
+        med_description = x.IP_med_description
+
+    return x[med_description]
+        
+    # if med_description == "CEFTRIAXONE":
+    #     return x.Ceftriaxone
+    # elif med_description == "PIPERACILLIN-TAZOBACTAM VANCOMYCIN":
+    #     return x.Vancomycin_Zosyn
+    # elif med_description == "PIPERACILLIN-TAZOBACTAM":
+    #     return x.Zosyn
+    # elif med_description == "CEFTRIAXONE VANCOMYCIN":
+    #     return x.Vancomycin_Ceftriaxone
+    # elif med_description == "CEFEPIME VANCOMYCIN":
+    #     return x.Vancomycin_Cefepime
+    # elif med_description == "CEFEPIME":
+    #     return x.Cefepime
+    # elif med_description == "VANCOMYCIN":
+    #     return x.Vancomycin
+    # elif med_description == "MEROPENEM":
+    #     return x.Meropenem
+    # elif med_description == "MEROPENEM VANCOMYCIN":
+    #     return x.Vancomycin_Meropenem
+    # elif med_description == "CEFAZOLIN":
+    #     return x.Cefazolin
+    # elif med_description == "CIPROFLOXACIN":
+    #     return x.Ciprofloxacin
+    # elif med_description == "AMPICILLIN":
+    #     return x.Ampicillin
+    # else:
+    #     return "Not in abx options"
+
+def get_coverage_rates(df):
+    """
+    Create flag for whether clinicians covered the patient during the csn, whether a random assignemnt
+    covered patient CSN, and whether optimized assignment covered the patient CSN
+    """
+
+    df = (df
+        .assign(random_med_description=lambda x: np.random.choice(x.med_description, size=len(x.med_description), replace=False))
+    )
+    df = (df
+        .assign(was_covered_dr=df.apply(lambda x: compute_was_covered(x), axis=1))
+        .assign(was_covered_random=df.apply(lambda x: compute_was_covered(x, 
+                                            decision_column='random_med_description'),
+                                            axis=1))
+        .assign(was_covered_IP=df.apply(lambda x: compute_was_covered(x, 
+                                        decision_column='IP_med_description'),
+                                        axis=1))
+    )
+
+    clin_covered_rate = df['was_covered_dr'].sum() / len(df)
+    random_covered_rate = df['was_covered_random'].sum() / len(df)
+    ip_covered_rate = df['was_covered_IP'].sum() / len(df)
+
+    # print(random_covered_rate)
+    # print(clin_covered_rate)
+    # print(ip_covered_rate)
+
+    return random_covered_rate, clin_covered_rate, ip_covered_rate
+
+def main():
+    from tqdm import tqdm
+    df_drugs = get_clinician_prescribing_patterns()
+    ip_covered_rates, ip_covered_rates_isotonic = [], []
+    for i in tqdm(range(62, 100, 1)):
+        df = load_predictions()
+        auc = float(i) / 100
+    # get_calibration_plots(df) 
+        df = simulate_improved_auroc(df, auroc_floor=auc)
+        df = calibrate_probabilities(df)    
+        df = (df
+            .merge(df_drugs, how='inner', on='pat_enc_csn_id_coded')
+        )
+
+        # Uncalibrated predictions
+        df = get_optimized_abx_selections(df)
+        random_covered_rate, clin_covered_rate, ip_covered_rate = get_coverage_rates(df)
+        ip_covered_rates.append(ip_covered_rate)
+        
+        # Calibrated predictions
+        df = get_optimized_abx_selections(df, calibration='isotonic')
+        random_covered_rate, clin_covered_rate, ip_covered_rate = get_coverage_rates(df)
+        ip_covered_rates_isotonic.append(ip_covered_rate)
+
+    fig, ax = plt.subplots(1, 1, figsize=(8, 8))
+    plt.plot([i for i in range(62, 100, 1)], ip_covered_rates, label='IP Selections')
+    plt.plot([i for i in range(62, 100, 1)], ip_covered_rates_isotonic, label='IP Selections Isotonic Calib')
+    plt.plot([i for i in range(62, 100, 1)], [clin_covered_rate for i in range(62, 100, 1)], label='Clin Covered Rate')
+    plt.plot([i for i in range(62, 100, 1)], [random_covered_rate for i in range(62, 100, 1)], label='Random Covered Rate')
+    plt.legend()
+    plt.savefig('./simulated_auroc_improvement_2.jpg')
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/scripts/ER_Infection/notebooks/demo/Demo Personalized Antibiogram Linear Programming.ipynb b/scripts/ER_Infection/notebooks/demo/Demo Personalized Antibiogram Linear Programming.ipynb
new file mode 100644
index 00000000..3269adb1
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/demo/Demo Personalized Antibiogram Linear Programming.ipynb	
@@ -0,0 +1,1215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Demo Personalized Antibiogram Linear Programming\n",
+    "\n",
+    "## Data Description\n",
+    "Here we simulate a dataset of antibiotic prescriptions, antibiotic susceptibilities, and personalized antibiogram predictions; all of which are used to perform an analysis as seen in our manuscript. The code to simulate said dataset is below (step 0 of demo), and generates a dataframe with the following column names. \n",
+    "\n",
+    "* `INFECTION_ID`: infection identifier\n",
+    "* `ABX_RX`: the antibiotic selected by clinicians\n",
+    "* `Random_ABX_RX`: randomly selected antibiotics\n",
+    "* `Pip_Tazo`: whether Pip-Tazo was listed as susceptible against the infection\n",
+    "* `Cefepime`: whether Cefepime was listed as susceptible against the infection\n",
+    "* `Ceftriaxone`: whether Ceftriaxone was listed as susceptible against the infection\n",
+    "* `Cefazolin`: whether Cefazolin was listed as susceptible against the infection\n",
+    "* `Pip_Tazo_predictions`: predicted probability that Pip-Tazo was listed as susceptible\n",
+    "* `Cefepime_predictions`: predicted probability that Cefepime was listed as susceptible\n",
+    "* `Ceftriaxone_predictions`: predicted probability that Ceftriaxone was listed as susceptible\n",
+    "* `Cefazolin_predictions`: predicted probability that Cefazolin was listed as susceptible\n",
+    "\n",
+    "## Demo\n",
+    "Total time to run code ~10 min.  The only time intensive portion of the demo is Step 3 (performing antibiotic sweeps). \n",
+    "\n",
+    "### Package Requirements\n",
+    "This jupyter notebook uses python 3 and requires installation of the following packages. \n",
+    "* `pandas==1.2.4`\n",
+    "* `numpy==1.20.1`\n",
+    "* `sklearn==0.24.2`\n",
+    "* `matplotlib==3.1.3`\n",
+    "* `seaborn==0.10.0`\n",
+    "* `pulp==2.4`\n",
+    "* `tqdm==4.60.0`\n",
+    "\n",
+    "### Step 0: Simulate Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>INFECTION_ID</th>\n",
+       "      <th>Random_ABX_RX</th>\n",
+       "      <th>Pip_Tazo</th>\n",
+       "      <th>Cefepime</th>\n",
+       "      <th>Ceftriaxone</th>\n",
+       "      <th>Cefazolin</th>\n",
+       "      <th>Pip_Tazo_predictions</th>\n",
+       "      <th>Cefepime_predictions</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>ABX_RX</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.571996</td>\n",
+       "      <td>0.706604</td>\n",
+       "      <td>0.648257</td>\n",
+       "      <td>0.533554</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.153900</td>\n",
+       "      <td>0.535348</td>\n",
+       "      <td>0.872395</td>\n",
+       "      <td>0.910824</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.760161</td>\n",
+       "      <td>0.854547</td>\n",
+       "      <td>0.172386</td>\n",
+       "      <td>0.831246</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.805432</td>\n",
+       "      <td>0.559745</td>\n",
+       "      <td>0.613116</td>\n",
+       "      <td>0.766768</td>\n",
+       "      <td>Cefepime</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.934433</td>\n",
+       "      <td>0.869650</td>\n",
+       "      <td>0.978947</td>\n",
+       "      <td>0.573975</td>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   INFECTION_ID Random_ABX_RX  Pip_Tazo  Cefepime  Ceftriaxone  Cefazolin  \\\n",
+       "0             0      Pip_Tazo         1         1            1          1   \n",
+       "1             1      Pip_Tazo         0         1            1          1   \n",
+       "2             2      Pip_Tazo         1         1            0          1   \n",
+       "3             3      Pip_Tazo         1         1            1          1   \n",
+       "4             4      Pip_Tazo         1         1            1          1   \n",
+       "\n",
+       "   Pip_Tazo_predictions  Cefepime_predictions  Ceftriaxone_predictions  \\\n",
+       "0              0.571996              0.706604                 0.648257   \n",
+       "1              0.153900              0.535348                 0.872395   \n",
+       "2              0.760161              0.854547                 0.172386   \n",
+       "3              0.805432              0.559745                 0.613116   \n",
+       "4              0.934433              0.869650                 0.978947   \n",
+       "\n",
+       "   Cefazolin_predictions    ABX_RX  \n",
+       "0               0.533554  Pip_Tazo  \n",
+       "1               0.910824  Pip_Tazo  \n",
+       "2               0.831246  Cefepime  \n",
+       "3               0.766768  Cefepime  \n",
+       "4               0.573975  Pip_Tazo  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Simulate data\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from numpy.random import binomial, uniform, seed\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "seed(42) # for reproducibility\n",
+    "\n",
+    "def simulate_predictions(labels: np.array, auroc: float) -> np.array:\n",
+    "    \"\"\"\n",
+    "    Simulates an array of predictions intended to resemble personalized\n",
+    "    antibiogram predictions of antibiotic susceptibility on test set\n",
+    "    observations. Prediction array is instantiated with a random set\n",
+    "    of floats between 0 and 1.  Low predictions for positive labels\n",
+    "    are then iteratively swapped for high predictions of negative\n",
+    "    labels until the desired AUROC is met. \n",
+    "    \n",
+    "    Args:\n",
+    "        labels: an array of labels\n",
+    "        auroc: desired AUROC of predictions\n",
+    "    Returns:\n",
+    "        predictions: an array of predictions\n",
+    "    \"\"\"\n",
+    "    predictions = uniform(0, 1, size=labels.shape)\n",
+    "    auc = roc_auc_score(labels, predictions)\n",
+    "    pos_labels_low_pred_inds = [\n",
+    "        i for i in range(len(labels)) if labels[i] == 1 and predictions[i] < 0.5\n",
+    "    ]\n",
+    "    neg_labels_high_pred_inds = [\n",
+    "        i for i in range(len(labels)) if labels[i] == 0 and predictions[i] > 0.5\n",
+    "    ]\n",
+    "    \n",
+    "    counter = 0\n",
+    "    while auc < auroc:\n",
+    "        pos_ind = pos_labels_low_pred_inds[counter]\n",
+    "        neg_ind = neg_labels_high_pred_inds[counter]\n",
+    "        temp = predictions[pos_ind]\n",
+    "        predictions[pos_ind] = predictions[neg_ind]\n",
+    "        predictions[neg_ind] = temp\n",
+    "        auc = roc_auc_score(labels, predictions)\n",
+    "        counter += 1\n",
+    "    \n",
+    "    return predictions\n",
+    "\n",
+    "def improve_clinician_abx_selections(df: pd.DataFrame,\n",
+    "    coverage_delta: float) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Simulate clinician abx selections such that coverage rate exceeds\n",
+    "    the random coverage rate by some given delta. Coverage rate is\n",
+    "    the fraction of infections covered by the selected antibiotic\n",
+    "    \n",
+    "    Args:\n",
+    "        df: simulated data with prescriptions and labels\n",
+    "        coverage_delta: clinicians beat random by this amount\n",
+    "    Returns\n",
+    "        df: simulated data with enhanced clinician performance\n",
+    "    \"\"\"\n",
+    "    initial_coverage_rate = np.mean([\n",
+    "        1 if df[df.Random_ABX_RX.values[i]][i] == 1 else 0\n",
+    "        for i in range(len(df)) \n",
+    "    ])\n",
+    "    df = df.assign(ABX_RX=lambda x: [a for a in x.Random_ABX_RX])\n",
+    "    coverage_rate = initial_coverage_rate\n",
+    "    counter = 0\n",
+    "    while coverage_rate < initial_coverage_rate + coverage_delta:\n",
+    "        np.random.shuffle(df.ABX_RX.values)\n",
+    "        coverage_rate = np.mean([\n",
+    "            1 if df[df.ABX_RX.values[i]][i] == 1 else 0\n",
+    "            for i in range(len(df)) \n",
+    "        ])\n",
+    "        counter += 1\n",
+    "        if counter % 1000 == 0:\n",
+    "            print(counter)\n",
+    "            pdb.set_trace()\n",
+    "    return df\n",
+    "        \n",
+    "def simulate_dataset(n=1000) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Simulates a dataset with columns as described above. \n",
+    "    \n",
+    "    Args:\n",
+    "        n: number of observations to simulate\n",
+    "    Returns:\n",
+    "        df: a dataframe of the simulated dataset\n",
+    "    \"\"\"\n",
+    "    INFECTION_ID = [i for i in range(n)]\n",
+    "    \n",
+    "    # Assume abx follow a bernoulli distribution with varying sigma.\n",
+    "    # Higher the sigma, the more broad-spectrum the antibiotic is\n",
+    "    # ie more infections susceptible to it.\n",
+    "    sigma_pip_tazo = 0.95\n",
+    "    sigma_cefepime = 0.90\n",
+    "    sigma_ceftriaxone = 0.85\n",
+    "    sigma_cefazolin = 0.80\n",
+    "    \n",
+    "    # Simulate antibiotic susceptibility labels from specified sigmas\n",
+    "    Pip_Tazo = binomial(n=1, p=sigma_pip_tazo, size=(n,))\n",
+    "    Cefepime = binomial(n=1, p=sigma_cefepime, size=(n,))\n",
+    "    Ceftriaxone = binomial(n=1, p=sigma_ceftriaxone, size=(n,))\n",
+    "    Cefazolin = binomial(n=1, p=sigma_cefazolin, size=(n,))\n",
+    "    \n",
+    "    # Simulate a set of personalized antibiogram predictions with AUROC=0.65\n",
+    "    Pip_Tazo_predictions = simulate_predictions(Pip_Tazo, auroc=0.65)\n",
+    "    Cefepime_predictions = simulate_predictions(Cefepime, auroc=0.65)\n",
+    "    Ceftriaxone_predictions = simulate_predictions(Ceftriaxone, auroc=0.65)\n",
+    "    Cefazolin_predictions = simulate_predictions(Cefazolin, auroc=0.65)\n",
+    "    \n",
+    "    # Simulate Clinician Antibiotic Allocations\n",
+    "    # First randomly assign antibiotics to infections so match desired\n",
+    "    # distribution of antiotic selections, then perturb clinician\n",
+    "    # selections so that clinicians are perform slightly better than\n",
+    "    # random chance.\n",
+    "    try:\n",
+    "        assert n % 8 == 0\n",
+    "    except:\n",
+    "        print(\"n must be divisible by 8\")\n",
+    "        raise\n",
+    "    abx_distribution = {\n",
+    "        'Pip_Tazo' : n//2,\n",
+    "        'Cefepime' : n//4,\n",
+    "        'Ceftriaxone' : n//8,\n",
+    "        'Cefazolin' : n//8\n",
+    "    }\n",
+    "    Random_ABX_RX = ['Pip_Tazo' for i in range(abx_distribution['Pip_Tazo'])]\n",
+    "    Random_ABX_RX += ['Cefepime' for i in range(abx_distribution['Cefepime'])]\n",
+    "    Random_ABX_RX += ['Ceftriaxone' for i in range(abx_distribution['Ceftriaxone'])]\n",
+    "    Random_ABX_RX += ['Cefazolin' for i in range(abx_distribution['Cefazolin'])]\n",
+    "    \n",
+    "    df = pd.DataFrame(data={\n",
+    "        'INFECTION_ID' : INFECTION_ID,\n",
+    "        'Random_ABX_RX' : Random_ABX_RX,\n",
+    "        'Pip_Tazo' : Pip_Tazo,\n",
+    "        'Cefepime' : Cefepime,\n",
+    "        'Ceftriaxone' : Ceftriaxone,\n",
+    "        'Cefazolin' : Cefazolin,\n",
+    "        'Pip_Tazo_predictions' : Pip_Tazo_predictions,\n",
+    "        'Cefepime_predictions' : Cefepime_predictions,\n",
+    "        'Ceftriaxone_predictions' : Ceftriaxone_predictions,\n",
+    "        'Cefazolin_predictions' : Cefazolin_predictions\n",
+    "    })\n",
+    "    df = improve_clinician_abx_selections(df, coverage_delta=0.02)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "df = simulate_dataset()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1:  Plot Distribution of Antiobiotcs Actually Selected to Treat Patient Infections\n",
+    "Here we'll demonstrate how Figure 2A and 3A in the manuscript were generated. They are simply counts of the number of infections treated by particular antibiotic selections. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff9b0a0d3d0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import all necessary modules\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "def plot_abx_distributions(df: pd.DataFrame,\n",
+    "                           ax: plt.axis) -> plt.axis:\n",
+    "    \"\"\"\n",
+    "    Creates a barplot that counts the number of times particular antibiotic were \n",
+    "    selected by patiens to treat infections in the dataset. This function produces\n",
+    "    a figure that is intended to resemble Figure 2a and Figure 3a in our manuscript. \n",
+    "    \n",
+    "    Args:\n",
+    "        df: simulated dataset containing abx prescriptions\n",
+    "        ax: axis to create plot on\n",
+    "    Returns:\n",
+    "        ax: populated axis\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    abx_distribution = df['ABX_RX'].value_counts()\n",
+    "    data = pd.DataFrame(data={\n",
+    "        'Antibiotics' : abx_distribution.index,\n",
+    "        'Number of Prescriptions' : abx_distribution.values\n",
+    "    })\n",
+    "    ax = sns.barplot(\n",
+    "        x='Number of Prescriptions',\n",
+    "        y='Antibiotics',\n",
+    "        ci=None,\n",
+    "        data=data,\n",
+    "        ax=ax,\n",
+    "        palette='deep',\n",
+    "    )\n",
+    "    ax.set_title(\"Clinician Antibiotic Use\")\n",
+    "    ax.set_ylabel('')\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
+    "plot_abx_distributions(df, ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Compute AUROC, Average Precision, and confidence intervals of Personalized Antibiogram Prediction Models\n",
+    "Here we'll demonstrate how Tables 3 and 4 were generated in the manuscript.  Performance of models here are meaningless as they have been simulated to achieve set performance characteristics. However the process of bootstrapping the test set is replicated and a table similar to tables 3 and 4 in the manuscript is created. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:07<00:00, 129.54it/s]\n",
+      "100%|██████████| 1000/1000 [00:07<00:00, 139.73it/s]\n",
+      "100%|██████████| 1000/1000 [00:08<00:00, 124.47it/s]\n",
+      "100%|██████████| 1000/1000 [00:09<00:00, 107.75it/s]\n",
+      "100%|██████████| 1000/1000 [00:08<00:00, 116.23it/s]\n",
+      "100%|██████████| 1000/1000 [00:09<00:00, 102.76it/s]\n",
+      "100%|██████████| 1000/1000 [00:09<00:00, 103.15it/s]\n",
+      "100%|██████████| 1000/1000 [00:07<00:00, 128.67it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Antibiotic</th>\n",
+       "      <th>Prevalance</th>\n",
+       "      <th>AUROC</th>\n",
+       "      <th>Average Precision</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Pip_Tazo</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>0.66 [0.57, 0.73]</td>\n",
+       "      <td>0.97 [0.95, 0.98]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>0.903</td>\n",
+       "      <td>0.65 [0.6, 0.71]</td>\n",
+       "      <td>0.94 [0.92, 0.96]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>0.846</td>\n",
+       "      <td>0.65 [0.61, 0.69]</td>\n",
+       "      <td>0.91 [0.89, 0.93]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.65 [0.61, 0.69]</td>\n",
+       "      <td>0.88 [0.85, 0.9]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Antibiotic  Prevalance              AUROC  Average Precision\n",
+       "0     Pip_Tazo       0.954  0.66 [0.57, 0.73]  0.97 [0.95, 0.98]\n",
+       "1     Cefepime       0.903   0.65 [0.6, 0.71]  0.94 [0.92, 0.96]\n",
+       "2  Ceftriaxone       0.846  0.65 [0.61, 0.69]  0.91 [0.89, 0.93]\n",
+       "3    Cefazolin       0.804  0.65 [0.61, 0.69]   0.88 [0.85, 0.9]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Callable\n",
+    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "def bootstrap_performance_metric(labels: np.array,\n",
+    "                                 predictions: np.array,\n",
+    "                                 get_performance: Callable,\n",
+    "                                 n: int=1000) -> str:\n",
+    "    \"\"\"\n",
+    "    Computes a distribution of a particular performance measure over a\n",
+    "    dataset by bootstrapping and returns realized and 95% confidence interval\n",
+    "    of measurement over said dataset\n",
+    "    \n",
+    "    Args:\n",
+    "        labels: ground truth labels\n",
+    "        predictions: model predictions over same observations\n",
+    "        get_performance: function that takes in labels and predictions\n",
+    "            and returns a performance measure ex: roc_auc_score, average_precsision\n",
+    "        n: number of times to bootstrap the dataset\n",
+    "    Returns:\n",
+    "        measure: a string representing actual measure and 95% confidence interval\n",
+    "    \"\"\"\n",
+    "    actual_measure = get_performance(labels, predictions)\n",
+    "    inds = [i for i in range(len(labels))]\n",
+    "    measures = []\n",
+    "    for i in tqdm(range(n)):\n",
+    "        inds_b = np.random.choice(inds, size=len(inds), replace=True)\n",
+    "        labels_b = [labels[i] for i in inds_b]\n",
+    "        predictions_b = [predictions[i] for i in inds_b]\n",
+    "        measure = get_performance(labels_b, predictions_b)\n",
+    "        measures.append(measure)\n",
+    "\n",
+    "    upper = round(np.percentile(measures, 97.5), 2)\n",
+    "    lower = round(np.percentile(measures, 2.5), 2)\n",
+    "    measure_ci = f\"{round(actual_measure, 2)} [{lower}, {upper}]\"\n",
+    "\n",
+    "    return measure_ci\n",
+    "\n",
+    "def generate_table(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Takes in dataset with labels and predictions for each antibiotic and\n",
+    "    produces a dataframe tabulating the Prevalence, AUROC, Average Precision\n",
+    "    and 95% confidence intervals estimate by bootstrapping the test set 1000\n",
+    "    times. \n",
+    "    \n",
+    "    Args:\n",
+    "        df: dataframe with labels and predictions\n",
+    "    Returns:\n",
+    "        df_results: a dataframe with performance characteristics\n",
+    "    \"\"\"\n",
+    "    abx_options = ['Pip_Tazo', 'Cefepime', 'Ceftriaxone', 'Cefazolin']\n",
+    "    prevs, aucs, aps = [], [], []\n",
+    "    for abx in abx_options:\n",
+    "        prevs.append(df[abx].mean())\n",
+    "        aucs.append(\n",
+    "            bootstrap_performance_metric(df[abx],\n",
+    "                                         df[f\"{abx}_predictions\"],\n",
+    "                                         roc_auc_score)\n",
+    "        )\n",
+    "        aps.append(\n",
+    "            bootstrap_performance_metric(df[abx],\n",
+    "                                         df[f\"{abx}_predictions\"],\n",
+    "                                         average_precision_score)\n",
+    "        )\n",
+    "    \n",
+    "    df_results = pd.DataFrame(data={\n",
+    "        'Antibiotic' : abx_options,\n",
+    "        'Prevalance' : prevs,\n",
+    "        'AUROC' : aucs,\n",
+    "        'Average Precision' : aps\n",
+    "    })\n",
+    "    \n",
+    "    return df_results\n",
+    "        \n",
+    "generate_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Optimize Antibiotic Allocation Across Infections\n",
+    "Here we'll demonstrate our algorithm for optimizing antibiotic selections across a set of patients using linear programming. We create a problem formulation using the `pulp` python package and solve with the CBC solver. We then demonstrate how the antibiotic sweep plots in Figure 2 and 3 in our manuscript were generated. We start with the constraint that the number of times the optimizer can select particular antibiotics must match the number of times clinicians actually selected those particular antibiotics.  Then for every pair of broader and relatively more narrow antibiotics (defined by the fraction of infections listed as susceptible to the antibiotic in the data), we iteratively perturb the optimizer constraints so that more of the narrow spectrum antibiotic and less of the broad spectrum antibiotic are selected. We iterate until all of the broader spectrum antibiotic has been replaced with the more narrow spectrum antibiotic.  We track the coverage rate (fraction of infections covered by the selected antibiotics) of the optimizer at each iteration. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear Programming Problem Formulation\n",
+    "Let $N$ be the number of infections in our test set who were treated with antibiotics, and let $K$ be the number of possible antibiotic selections.  Let $A$ be a matrix in $\\mathbb{R}^{N\\times K}$ such that $a_{ij}$ is 1 if antibiotic selection $j$ is selected to treat infection $i$ and 0 otherwise. Let $\\Phi$ be a matrix in $\\mathbb{R}^{N \\times K}$ such that $\\phi_{ij}$ is the predicted probability that antibiotic $j$ will cover infection $i$.  Let $C$ be a vector in $\\mathbb{R}^K$ such that $c_j$ specifies the budget for anitbiotic selection $j$ - that is the number of times we are allowed to select antibiotic $j$ across our $N$ infections. Our problem formulation is as follows. \n",
+    "\n",
+    "$$  \\underset{A}{\\text{maximize}} \\sum_{i=1}^{N} \\sum_{j=1}^K \\phi_{ij} a_{ij} $$\n",
+    "\n",
+    "Subject to the following constraints:\n",
+    "\n",
+    "$$ \\sum_{j=1}^{K} a_{ij} = 1 \\quad i = 1, ..., N $$\n",
+    "\n",
+    "$$ \\sum_{i=1}^{N} a_{ij} = c_j \\quad j = 1, ...,  K $$\n",
+    "\n",
+    "In the following code, we implenent and solve this optimization process using the pulp python package. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pulp import *\n",
+    "from pulp.apis import PULP_CBC_CMD\n",
+    "\n",
+    "class AbxSolver():\n",
+    "    \"\"\"\n",
+    "    A class for instantiating and iteratively solving optimized antibiotic\n",
+    "    selections problems as formulated above. \n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, df, abx_settings):\n",
+    "        \"\"\"\n",
+    "        Args\n",
+    "            df: dataframe of labels and predictions\n",
+    "            abx_settings: antibiotic budget parameters\n",
+    "        \"\"\"\n",
+    "        self.df = df\n",
+    "        self.abx_settings = abx_settings\n",
+    "        self.abx_options = [key for key in abx_settings]\n",
+    "        self.abx_settings_copy = {\n",
+    "            key : self.abx_settings[key] for key in self.abx_settings\n",
+    "        } \n",
+    "        self.n = len(df)\n",
+    "\n",
+    "    def set_config(self, config_alt):\n",
+    "        \"\"\"\n",
+    "        Alters config based on values in config_alt dict\n",
+    "        \n",
+    "        Args:\n",
+    "            config_alt: antibiotic budget parameters to set\n",
+    "        \"\"\"\n",
+    "        for key in config_alt:\n",
+    "            self.abx_settings[key] += config_alt[key]\n",
+    "\n",
+    "    def reset_config(self):\n",
+    "        \"\"\"\n",
+    "        Resets config to original config\n",
+    "        \"\"\"\n",
+    "        self.abx_settings = {\n",
+    "            key : val for key, val in self.abx_settings_copy.items()\n",
+    "        }\n",
+    "\n",
+    "    def compute_was_covered(self, x, decision_column='ABX_RX'):\n",
+    "        \"\"\"\n",
+    "        Given administered antibiotic, return whether antibiotic covered infection\n",
+    "        Args:\n",
+    "            x: dataframe row\n",
+    "            decision_column: column containing antibiotic selection \n",
+    "        \"\"\"\n",
+    "        if decision_column == 'Random_ABX_RX':\n",
+    "            med_description = x.Random_ABX_RX\n",
+    "        elif decision_column == 'ABX_RX':\n",
+    "            med_description = x.ABX_RX \n",
+    "        elif decision_column == 'Optimized_ABX_RX':\n",
+    "            med_description = x.Optimized_ABX_RX\n",
+    "        else:\n",
+    "            raise\n",
+    "        return x[med_description]\n",
+    "\n",
+    "    def get_coverage_rates(self, df=None):\n",
+    "        \"\"\"\n",
+    "        Create flags for whether random, clinician, and optimized antibiotic\n",
+    "        selections coverered infections in the dataset and return fraction\n",
+    "        of times each covered the infection - coverag rate.\n",
+    "        \"\"\"\n",
+    "        if df is None:\n",
+    "            df = self.df\n",
+    "\n",
+    "        df = (df\n",
+    "            .assign(was_covered_dr=df.apply(\n",
+    "                lambda x: self.compute_was_covered(x), axis=1))\n",
+    "            .assign(was_covered_ip=df.apply(\n",
+    "                lambda x: self.compute_was_covered(x, 'Optimized_ABX_RX'),\n",
+    "                axis=1\n",
+    "                )\n",
+    "            )\n",
+    "            .assign(was_covered_random=df.apply(\n",
+    "                lambda x: self.compute_was_covered(x, 'Random_ABX_RX'),\n",
+    "                axis=1\n",
+    "                )\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        clin_coverage_rate = df['was_covered_dr'].sum() / len(df)\n",
+    "        lp_coverage_rate = df['was_covered_ip'].sum() / len(df)\n",
+    "        random_coverage_rate = df['was_covered_random'].sum() / len(df)\n",
+    "        \n",
+    "        return clin_coverage_rate, lp_coverage_rate, random_coverage_rate\n",
+    "\n",
+    "    def random_assignment(self):\n",
+    "        \"\"\"\n",
+    "        Gets a random assignment of meds given current config\n",
+    "        \"\"\"\n",
+    "        meds = []\n",
+    "        for key in self.abx_settings:\n",
+    "            meds += [key for i in range(self.abx_settings[key])]\n",
+    "        np.random.shuffle(meds)\n",
+    "\n",
+    "        self.df['Random_ABX_RX'] = [m for m in meds]\n",
+    "\n",
+    "    def solve_and_assign(self):\n",
+    "        \"\"\"\n",
+    "        Instantiates a linear programming problem formulation using the pulp\n",
+    "        python package and solves with the CBC solver. Appends optimized\n",
+    "        antibiotic selection in to Optimized_ABX_RX column in dataframe.\n",
+    "        \"\"\"\n",
+    "        # Predictions string\n",
+    "        predictions_string = '%s_predictions'\n",
+    "        abx_model = LpProblem(\"Antibiotics\", LpMaximize)\n",
+    "        pdb.set_trace\n",
+    "        # Create binary indicators for whether treatment is used\n",
+    "        drug_inds = {}\n",
+    "        for abx in self.abx_options:\n",
+    "            drug_inds[abx] = [\n",
+    "                LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')\n",
+    "                for i in range(len(self.df))\n",
+    "            ]\n",
+    "\n",
+    "        # Add objective function to model\n",
+    "        per_infection_sum = []\n",
+    "        for i in range(len(self.df)):\n",
+    "            _sum = 0\n",
+    "            for abx in self.abx_options:\n",
+    "                _sum += drug_inds[abx][i] * self.df[predictions_string % abx].values[i]\n",
+    "            per_infection_sum.append(_sum)\n",
+    "        abx_model += lpSum(per_infection_sum) # this is what we want to maximize\n",
+    "\n",
+    "        # Add one selection constraint (only one abx option per infection)\n",
+    "        for i in range(len(self.df)):\n",
+    "            selections = []\n",
+    "            for abx in self.abx_options:\n",
+    "                selections.append(drug_inds[abx][i])\n",
+    "            abx_model += lpSum(selections) == 1\n",
+    "        \n",
+    "        # Add budget constraints - total number of abx selection for particular abx\n",
+    "        # must match fixed value specified in abx_settings.\n",
+    "        for drug in drug_inds:\n",
+    "            abx_model += lpSum(\n",
+    "                [drug_inds[drug][i] for i in range(len(self.df))]\n",
+    "            ) == self.abx_settings[drug]\n",
+    "\n",
+    "        # Solve model\n",
+    "        abx_model.solve(solver=PULP_CBC_CMD(msg=False, timeLimit=10))\n",
+    "\n",
+    "        # Save selected antibiotic to df\n",
+    "        abx_decisions = []\n",
+    "        for i in range(len(self.df)):\n",
+    "            abx_decision = None\n",
+    "            for abx in self.abx_options:\n",
+    "                if drug_inds[abx][i].value() == 1:\n",
+    "                    abx_decision = abx\n",
+    "            assert abx_decision is not None\n",
+    "            abx_decisions.append(abx_decision)\n",
+    "        self.df['Optimized_ABX_RX'] = abx_decisions\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare performance without perturbing antibiotic budget constraints "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Coverage Rate: 0.893\n",
+      "Clinician Coverage Rate: 0.916\n",
+      "Optimized Coverage Rate: 0.958\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_antibiotic_coverage_rates(df: pd.DataFrame):\n",
+    "    \"\"\"\n",
+    "    Instantiates a AbxSolver, solves using the same number of antiotics\n",
+    "    of varying types as used by clinicians. Computes coverage rate\n",
+    "    of a random selection, the clinician selection, and the optimized\n",
+    "    selection all using same antibiotic budget parameters.\n",
+    "    \n",
+    "    Args:\n",
+    "        df: dataframe of predictions, labels, and random and clinician\n",
+    "            antibiotic selections\n",
+    "    \"\"\"\n",
+    "    abx_settings = df['ABX_RX'].value_counts().to_dict()\n",
+    "    solver = AbxSolver(df, abx_settings)\n",
+    "    solver.solve_and_assign()\n",
+    "    coverage_rates = solver.get_coverage_rates()\n",
+    "    print(f\"Random Coverage Rate: {coverage_rates[2]}\")\n",
+    "    print(f\"Clinician Coverage Rate: {coverage_rates[0]}\")\n",
+    "    print(f\"Optimized Coverage Rate: {coverage_rates[1]}\")\n",
+    "get_antibiotic_coverage_rates(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sweep Budget Parameters So That Optimizer Chooses Fewer Broad-Spectrum Antibiotics\n",
+    "In the last portion of this demo, we'll perform several sweeps of the budget parameters to reproduce plots that resemble Figures 2B and 3B in the manuscript. Specifically, we'll showcase three sweeps where the optimizer is forced to use less of a broader spectrum antibiotic and more of a narrower spectrum antibiotic. The three tuples of (broad-specturm, more narrow-spectrum) antibiotics that we'll simulate are listed below. \n",
+    "* (Pip_Tazo, Cefepime)\n",
+    "* (Pip_Tazo, Ceftriaxone)\n",
+    "* (Pip_Tazo, Cefazolin)\n",
+    "\n",
+    "In each sweep, we start with budget constraints defined again by what was actually used by clinicains. Then at each iteration we force the optimizer to use one less of the broader spectrum antibiotic and one more of the more narrrow spectrum antiotic. The optimizer solves for new antibiotic allocations at each step. We keep track of the optimizers coverage rate at each step, and make sure to record the clician coverage rate and random coverage rate so that we can show where in the sweep the optimizer falls below these thresholds. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 500/500 [03:26<00:00,  2.43it/s]\n",
+      "100%|██████████| 500/500 [03:36<00:00,  2.31it/s]\n",
+      "100%|██████████| 500/500 [03:19<00:00,  2.51it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List, Tuple, Dict\n",
+    "\n",
+    "def sweep_opt(df: pd.DataFrame,\n",
+    "              sweep: Tuple[str, str],\n",
+    "              config: Dict[str, int]) -> Tuple[List[float], List[int],\n",
+    "                                               float, float]:\n",
+    "    \"\"\"\n",
+    "    Performs one sweep simulation. We start using the antibiotc budget settings\n",
+    "    listed in config, and then iteratively perturb the settings for the antibiotics\n",
+    "    listed in sweep such that the optimizer is forced to use less of the broader \n",
+    "    spectrum antibiotic and more of the narrower spectrum antibiotic. We track\n",
+    "    the coverage rate over each iteration and measure the Random, and Clinician coverage\n",
+    "    rate using the initial budget parameters\n",
+    "    \n",
+    "    Args:\n",
+    "        df: dataframe containing predictions and labels\n",
+    "        sweep: antibiotics to sweep over (broader, narrower)\n",
+    "        config: budget paramters\n",
+    "    Returns:\n",
+    "        data: tuple of lists of coverage rates and iteration number\n",
+    "    \"\"\"\n",
+    "    step_size = 1\n",
+    "    opt = AbxSolver(df, config)\n",
+    "    num_replaced, o_rates = [], []\n",
+    "    \n",
+    "    # Solve with initial constraints\n",
+    "    opt.solve_and_assign()\n",
+    "    c_rate, o, r_rate = opt.get_coverage_rates()\n",
+    "    o_rates.append(o)\n",
+    "    num_replaced.append(0)\n",
+    "    \n",
+    "    for j in tqdm(range(0, config[sweep[0]], step_size)):\n",
+    "        opt.reset_config()\n",
+    "        opt.set_config({\n",
+    "            sweep[0] : -j,\n",
+    "            sweep[1] : +j\n",
+    "        })\n",
+    "        opt.solve_and_assign()\n",
+    "        _, o, _ = opt.get_coverage_rates()\n",
+    "        o_rates.append(o)\n",
+    "        num_replaced.append(j+1)\n",
+    "\n",
+    "    data = (\n",
+    "        o_rates,\n",
+    "        num_replaced,\n",
+    "        c_rate,\n",
+    "        r_rate\n",
+    "    )\n",
+    "\n",
+    "    return data\n",
+    "\n",
+    "def perform_sweeps(df: pd.DataFrame, sweeps: List[Tuple[str, str]]) -> Dict:\n",
+    "    \"\"\"\n",
+    "    Performs sweep simulations given a list of tuples representing\n",
+    "    which antibiotics budget parameters to sweep over.\n",
+    "    \n",
+    "    Args:\n",
+    "        df: dataframe with predictions and labels\n",
+    "        sweeps: list of tuples of antibiotics to sweep\n",
+    "    Returns:\n",
+    "        sweep_data: dictionary containing results of the sweeps\n",
+    "    \"\"\"\n",
+    "\n",
+    "    start_config = df['ABX_RX'].value_counts().to_dict()\n",
+    "    sweep_data = {}\n",
+    "    for i, sweep in enumerate(sweeps):\n",
+    "        sweep_data[sweep] = {}\n",
+    "        data = sweep_opt(df, sweep, start_config)\n",
+    "        \n",
+    "        sweep_data[sweep]['o_rates'] = data[0]\n",
+    "        sweep_data[sweep]['num_replaced'] = data[1]\n",
+    "        sweep_data[sweep]['c_rates'] = [data[2] for i in range(len(data[0]))]\n",
+    "        sweep_data[sweep]['r_rates'] = [data[3] for i in range(len(data[0]))]\n",
+    "    \n",
+    "    return sweep_data\n",
+    "\n",
+    "sweeps = [\n",
+    "    ('Pip_Tazo', 'Cefepime'),\n",
+    "    ('Pip_Tazo', 'Ceftriaxone'),\n",
+    "    ('Pip_Tazo', 'Cefazolin')\n",
+    "]\n",
+    "sweep_data = perform_sweeps(df, sweeps)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot Sweep Data\n",
+    "Here we demonstate how we generated Figures 2B and 3B from the results of the sweep plot data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2160x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='white', font_scale=2.0)\n",
+    "\n",
+    "def green(a: np.array, b: np.array, c: np.array) -> np.array:\n",
+    "    \"\"\"\n",
+    "    Helper that returns array of booleans when a has yet to exceed b\n",
+    "    or c.\n",
+    "    \"\"\"\n",
+    "    arr = a < b\n",
+    "    was_false = False\n",
+    "    for i in range(len(arr)):\n",
+    "        if arr[i] == False:\n",
+    "            was_false = True\n",
+    "        \n",
+    "        if was_false and arr[i] == True:\n",
+    "            arr[i] == False\n",
+    "\n",
+    "    return arr\n",
+    "\n",
+    "def yellow(a: np.array, b: np.array, c: np.array) -> np.array:\n",
+    "    \"\"\"\n",
+    "    Helper that returns array of booleans when a has exceeded b but has yet\n",
+    "    to exceed c\n",
+    "    \"\"\"\n",
+    "    arr = np.logical_and(\n",
+    "        a > b,\n",
+    "        a <= c\n",
+    "    )\n",
+    "    ind_first_true, ind_last_true = None, None\n",
+    "    was_true = False\n",
+    "    for i in range(len(arr)):\n",
+    "        if arr[i] == True and was_true == False:\n",
+    "            ind_first_true = i\n",
+    "            was_true = True\n",
+    "        elif arr[i] == True and was_true == True:\n",
+    "            ind_last_true = i\n",
+    "\n",
+    "    if ind_first_true is None:\n",
+    "        ind_first_true = len(arr)\n",
+    "    if ind_last_true is None:\n",
+    "        ind_last_true = len(arr)\n",
+    "\n",
+    "    for i in range(len(arr)):\n",
+    "        if i > ind_first_true-2 and i < ind_last_true:\n",
+    "            arr[i] = True\n",
+    "\n",
+    "    return arr\n",
+    "\n",
+    "def yellow_range(a: np.array, b: np.array, c: np.array) -> Tuple[int, int]:\n",
+    "    \"\"\"\n",
+    "    Helper that returns range of indices where array returned by yellow is true\n",
+    "    \"\"\"\n",
+    "    arr = np.logical_and(\n",
+    "        a > b,\n",
+    "        a <= c\n",
+    "    )\n",
+    "    ind_first_true, ind_last_true = None, None\n",
+    "    was_true = False\n",
+    "    for i in range(len(arr)):\n",
+    "        if arr[i] == True and was_true == False:\n",
+    "            ind_first_true = i-1\n",
+    "            was_true = True\n",
+    "        elif arr[i] == True and was_true == True:\n",
+    "            ind_last_true = i\n",
+    "    \n",
+    "    if ind_last_true is not None:\n",
+    "        if ind_last_true >= len(arr) - 2: # hack for 2nd mit plot\n",
+    "            ind_last_true = None\n",
+    "\n",
+    "    return ind_first_true, ind_last_true\n",
+    "\n",
+    "def red(a: np.array, b: np.array, c: np.array) -> np.array:\n",
+    "    \"\"\"\n",
+    "    Helper that returns array of booleans when as has exceeded b and c\n",
+    "    \"\"\"\n",
+    "    arr = a > c\n",
+    "    was_true = False\n",
+    "    for i in range(len(arr)):\n",
+    "        if arr[i] == True:\n",
+    "            was_true = True\n",
+    "        if was_true and arr[i] == False:\n",
+    "            arr[i] == True\n",
+    "    return arr\n",
+    "\n",
+    "\n",
+    "def sweep_plot(ax: plt.axis,\n",
+    "               sweep: Tuple[str, str],\n",
+    "               num_replaced: List[int],\n",
+    "               o_rates: List[float],\n",
+    "               c_rates: List[float],\n",
+    "               r_rates: List[float],\n",
+    "               params: Dict[str, int]) -> plt.axis:\n",
+    "    \"\"\"\n",
+    "    Makes one sweep plot. Plots the coverage rate of the optimizer at each iteration.\n",
+    "    Starting on the far left of the plot we see the coverage rate where the optimizer\n",
+    "    was allowed to use antibiotics at the same rate clinicians used them. We then\n",
+    "    show how the optimizer's coverage rate changes as it is allowed a smaller and\n",
+    "    smaller budget of the more broad spectrum antibiotic.  As a benchmark, we plot\n",
+    "    the clinicians coverage rate as a dashed black line and the random coverage rate\n",
+    "    as a grey dashed line.  We then highlight the portion of the plot where the optimizers\n",
+    "    coverage rate exceeded the clinicians coverage rate in green, and when it exceeded\n",
+    "    the random coverage rate in yellow. \n",
+    "    \n",
+    "    Args:\n",
+    "        ax: blank axis\n",
+    "        sweep: tuple (abx_1, abx_2)\n",
+    "        num_replaced: np array iteration\n",
+    "        o_rates: optimized coverage rates\n",
+    "        c_rates: clinician coverage rate\n",
+    "        r_rates: random coverage rate\n",
+    "        params: dictionary - original abx prescribing distribution\n",
+    "                 along with site specific data used for plot formatting\n",
+    "\n",
+    "    Returns:\n",
+    "        ax: populated axis\n",
+    "    \"\"\"\n",
+    "    percent_replaced = [\n",
+    "        int(n/params[sweep[0]] * 100)\n",
+    "        for n in num_replaced\n",
+    "    ]\n",
+    "\n",
+    "    r_rates =  np.array(\n",
+    "        [params['random_rate'] for i in range(len(r_rates))]\n",
+    "    )\n",
+    "    c_rates = np.array([\n",
+    "        c_rates[0] for i in range(len(c_rates))\n",
+    "    ])\n",
+    "    o_rates = np.array(o_rates)\n",
+    "\n",
+    "    yellow_first, yellow_last = yellow_range(o_rates, c_rates, r_rates)\n",
+    "    if yellow_first is not None:\n",
+    "        ax.vlines(\n",
+    "            percent_replaced[yellow_first],\n",
+    "            color='black',\n",
+    "            linestyles='dotted',\n",
+    "            ymin=params['ymin'],\n",
+    "            ymax=params['ymax']\n",
+    "        )\n",
+    "    if yellow_last is not None:\n",
+    "        ax.vlines(\n",
+    "            percent_replaced[yellow_last],\n",
+    "            color='black',\n",
+    "            linestyles='dotted',\n",
+    "            ymin=params['ymin'],\n",
+    "            ymax=params['ymax']\n",
+    "        )\n",
+    "    ax.plot(\n",
+    "        percent_replaced,\n",
+    "        r_rates,\n",
+    "        '--',\n",
+    "        label='Random Allocation',\n",
+    "        linewidth=2.0,\n",
+    "        color='grey',\n",
+    "    )\n",
+    "    ax.plot(\n",
+    "        percent_replaced,\n",
+    "        c_rates,\n",
+    "        '--',\n",
+    "        label='Clinician Allocation',\n",
+    "        linewidth=2.0,\n",
+    "        color='black',\n",
+    "    )\n",
+    "    ax.plot(\n",
+    "        percent_replaced,\n",
+    "        o_rates,\n",
+    "        label='Optimized Allocation',\n",
+    "        linewidth=2.0,\n",
+    "        color='black'\n",
+    "    )\n",
+    "    ax.fill_between(\n",
+    "        percent_replaced, o_rates, c_rates,\n",
+    "        where=(green(1-o_rates, 1-c_rates, 1-r_rates)),\n",
+    "        color='green', alpha=0.3,\n",
+    "        interpolate=True,\n",
+    "        label='Greater coverage than clinicians'\n",
+    "    )\n",
+    "    ax.fill_between(\n",
+    "        percent_replaced, o_rates, r_rates,\n",
+    "        where=(\n",
+    "            yellow(1-o_rates, 1-c_rates, 1-r_rates)\n",
+    "        ),\n",
+    "        color='yellow', alpha=0.3,\n",
+    "        interpolate=True,\n",
+    "        label='Greater coverage than random'\n",
+    "    )\n",
+    "    ax.fill_between(\n",
+    "        percent_replaced, o_rates, r_rates,\n",
+    "        where=(red(1-o_rates, 1-c_rates, 1-r_rates)),\n",
+    "        color='red', alpha=0.3,\n",
+    "        interpolate=True,\n",
+    "        label=\"Poorer coverage than random\"\n",
+    "    )\n",
+    "    ax.set_ylim([params['ymin'], params['ymax']])\n",
+    "    ax.set_xlim([0, 100])\n",
+    "    ax.set_title(f\"{sweep[0]} - {sweep[1]}\")\n",
+    "    ax.set_xlabel(\n",
+    "        f\"Percentage Swapped\"\n",
+    "    )\n",
+    "    ax.set_xticklabels([\n",
+    "        f\"{int(n)}%\"\n",
+    "        for n in ax.get_xticks()\n",
+    "    ])\n",
+    "    ax.set_yticklabels([\n",
+    "        f\"{int(n*100)}%\" for n in ax.get_yticks()\n",
+    "    ])\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "params = df['ABX_RX'].value_counts().to_dict()\n",
+    "params['ymin'] = 0.85\n",
+    "params['ymax'] = 1.0\n",
+    "params['random_rate'] = sweep_data[('Pip_Tazo', 'Cefepime')]['r_rates'][0]\n",
+    "params['site'] = 'Synthetic'\n",
+    "fig, axs = plt.subplots(1, 3, figsize=(30, 10))\n",
+    "col = 0\n",
+    "for key, sweep in sweep_data.items():\n",
+    "    axs[col] = sweep_plot(\n",
+    "        axs[col],\n",
+    "        key,\n",
+    "        num_replaced=sweep['num_replaced'],\n",
+    "        o_rates=sweep['o_rates'],\n",
+    "        c_rates=sweep['c_rates'],\n",
+    "        r_rates=sweep['r_rates'],\n",
+    "        params=params\n",
+    "    )\n",
+    "\n",
+    "    if col == 0:\n",
+    "        axs[col].set_ylabel(\"Coverage Rate\")\n",
+    "\n",
+    "    if col == 1:\n",
+    "        axs[col].legend(\n",
+    "            loc=\"lower center\", bbox_to_anchor=(0.5, -0.46),\n",
+    "            borderaxespad=0.\n",
+    "        )\n",
+    "    col += 1      "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/eda/Abx_Start_and_Stop.ipynb b/scripts/ER_Infection/notebooks/eda/Abx_Start_and_Stop.ipynb
new file mode 100644
index 00000000..d574fc9e
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/eda/Abx_Start_and_Stop.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "\n",
+    "### THIS IS MEANT TO RUN ON NERO - NEEDS TO BE CHANGED IF YOU RUN LOCALLY\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ccorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Of our 60k encounters, how many didn't have any abx started\n",
+    "WITH start_stop_time as (\n",
+    "SELECT anon_id, pat_enc_csn_id_coded, max(was_given) any_abx_given, min(order_start_time_utc) first_abx_order_time, max(abx_stop_time) last_abx_stop_time\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_temp_14_day_orders` \n",
+    "GROUP BY anon_id, pat_enc_csn_id_coded\n",
+    "ORDER BY anon_id, pat_enc_csn_id_coded)\n",
+    "\n",
+    "SELECT any_abx_given, COUNT (DISTINCT pat_enc_csn_id_coded) cnt\n",
+    "FROM start_stop_time \n",
+    "GROUP BY any_abx_given "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# How many CSNS have abx that are stopped within given number of days. Shows distribution.  Weird how long the tail is, should investigate further. \n",
+    "# Also note how 1/3 of the CSN's have abx stop within the first day. Note that these will include people sent home with PO abx who were probably\n",
+    "# taking them for the full week. May end up wanting to remove PO abx from this analysis bc we can't get a stop time for them. \n",
+    "WITH start_stop_time as (\n",
+    "SELECT anon_id, pat_enc_csn_id_coded, max(was_given) any_abx_given, min(order_start_time_utc) first_abx_order_time, max(abx_stop_time) last_abx_stop_time\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_temp_14_day_orders` \n",
+    "GROUP BY anon_id, pat_enc_csn_id_coded\n",
+    "ORDER BY anon_id, pat_enc_csn_id_coded),\n",
+    "\n",
+    "er_admits AS (\n",
+    "SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time\n",
+    "FROM `shc_core.adt`\n",
+    "WHERE pat_class_c = \"112\" AND pat_service = \"Emergency\"\n",
+    "GROUP BY anon_id, pat_enc_csn_id_coded)\n",
+    "\n",
+    "SELECT\n",
+    "TIMESTAMP_DIFF(ss.last_abx_stop_time, ea.er_admit_time, DAY) time_until_abx_stop, COUNT (DISTINCT ss.pat_enc_csn_id_coded) cnt\n",
+    "FROM start_stop_time ss\n",
+    "INNER JOIN er_admits ea\n",
+    "USING (pat_enc_csn_id_coded)\n",
+    "GROUP BY time_until_abx_stop\n",
+    "ORDER BY time_until_abx_stop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Counts the number of CSNS that that have any growth in the union of all cultures ordered within 4 hrs of er stay\n",
+    "WITH cult_result as (\n",
+    "SELECT DISTINCT co.anon_id, co.pat_enc_csn_id_coded, co.order_proc_id_coded, cs.organism\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` co\n",
+    "INNER JOIN \n",
+    "  (SELECT DISTINCT pat_enc_csn_id_coded FROM `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) cults_and_abx_csns\n",
+    "USING (pat_enc_csn_id_coded)\n",
+    "LEFT JOIN `shc_core.culture_sensitivity` cs\n",
+    "USING (order_proc_id_coded)\n",
+    "ORDER BY co.anon_id, co.pat_enc_csn_id_coded\n",
+    ")\n",
+    "\n",
+    "SELECT any_growth, COUNT (DISTINCT pat_enc_csn_id_coded) num_csns\n",
+    "FROM\n",
+    "  (SELECT pat_enc_csn_id_coded, MAX(CASE WHEN organism IS NOT NULL THEN 1 ELSE 0 END) any_growth\n",
+    "  FROM cult_result\n",
+    "  GROUP BY pat_enc_csn_id_coded) t\n",
+    "GROUP BY any_growth\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Of all encs with positive cultures, how many different kinds of bugs grew?\n",
+    "WITH cult_result as (\n",
+    "SELECT DISTINCT co.anon_id, co.pat_enc_csn_id_coded, co.order_proc_id_coded, cs.organism\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` co\n",
+    "INNER JOIN \n",
+    "  (SELECT DISTINCT pat_enc_csn_id_coded FROM `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) cults_and_abx_csns\n",
+    "USING (pat_enc_csn_id_coded)\n",
+    "LEFT JOIN `shc_core.culture_sensitivity` cs\n",
+    "USING (order_proc_id_coded)\n",
+    "ORDER BY co.anon_id, co.pat_enc_csn_id_coded\n",
+    "),\n",
+    "\n",
+    "any_growth as \n",
+    "(SELECT pat_enc_csn_id_coded, MAX(CASE WHEN organism IS NOT NULL THEN 1 ELSE 0 END) any_positive\n",
+    "FROM cult_result\n",
+    "GROUP BY pat_enc_csn_id_coded)\n",
+    "\n",
+    "SELECT num_bugs, COUNT (DISTINCT pat_enc_csn_id_coded) num_csns\n",
+    "FROM \n",
+    "  (SELECT cr.pat_enc_csn_id_coded, COUNT (DISTINCT cr.organism) num_bugs\n",
+    "  FROM cult_result cr\n",
+    "  INNER JOIN (SELECT DISTINCT pat_enc_csn_id_coded FROM any_growth WHERE any_positive = 1) pos_csns\n",
+    "  USING (pat_enc_csn_id_coded)\n",
+    "  GROUP BY cr.pat_enc_csn_id_coded) t\n",
+    "GROUP BY num_bugs\n",
+    "ORDER BY num_bugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Kinds of bugs that grow and how often stratified by department (Pediatric ER, Adult ER, Valley Care ER).  Scary thing here is most of these are pediatric.  Which I was going to exclude\n",
+    "# from the cohort... Note the Pediatric and adult ER's are the same building I believe.  \n",
+    "WITH cult_result as (\n",
+    "SELECT DISTINCT co.anon_id, co.pat_enc_csn_id_coded, co.order_proc_id_coded, cs.organism\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_culture_orders_within_4_hours` co\n",
+    "INNER JOIN \n",
+    "  (SELECT DISTINCT pat_enc_csn_id_coded FROM `mining-clinical-decisions.conor_db.abx_med_orders_given_and_stopped_info`) cults_and_abx_csns\n",
+    "USING (pat_enc_csn_id_coded)\n",
+    "LEFT JOIN `shc_core.culture_sensitivity` cs\n",
+    "USING (order_proc_id_coded)\n",
+    "ORDER BY co.anon_id, co.pat_enc_csn_id_coded\n",
+    "),\n",
+    "\n",
+    "any_growth as \n",
+    "(SELECT pat_enc_csn_id_coded, MAX(CASE WHEN organism IS NOT NULL THEN 1 ELSE 0 END) any_positive\n",
+    "FROM cult_result\n",
+    "GROUP BY pat_enc_csn_id_coded), \n",
+    "\n",
+    "dep_id as (\n",
+    "SELECT \n",
+    "adt.pat_enc_csn_id_coded,\n",
+    "FIRST_VALUE(adt.department_id) OVER (PARTITION BY adt.pat_enc_csn_id_coded ORDER BY adt.effective_time_jittered_utc) department_id\n",
+    "FROM cult_result\n",
+    "INNER JOIN `shc_core.adt` adt\n",
+    "USING (pat_enc_csn_id_coded)\n",
+    ")\n",
+    "\n",
+    "SELECT department_name, organism, COUNT (DISTINCT pat_enc_csn_id_coded) num_csns\n",
+    "FROM \n",
+    "  (SELECT DISTINCT cr.pat_enc_csn_id_coded, cr.organism, dm.department_name\n",
+    "  FROM cult_result cr\n",
+    "  INNER JOIN (SELECT DISTINCT pat_enc_csn_id_coded FROM any_growth WHERE any_positive = 1) pos_csns\n",
+    "  USING (pat_enc_csn_id_coded)\n",
+    "  INNER JOIN dep_id\n",
+    "  USING (pat_enc_csn_id_coded)\n",
+    "  INNER JOIN `shc_core.dep_map` dm\n",
+    "  USING (department_id)\n",
+    "  WHERE cr.organism IS NOT NULL) t\n",
+    "GROUP BY department_name, organism\n",
+    "ORDER BY department_name, num_csns DESC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Counts of infected, not infected, and censored patients.  Roughly half of patients are censored according to first round logic.\n",
+    "SELECT sum(not_infected), sum(infected), sum(not_censored)\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_label`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Counts of infected, not infected, and censored patients when kids removed -- still roughly half censored. more infected than uninfected\n",
+    "SELECT sum(not_infected), sum(infected), sum(not_censored)\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Counts of infected, not infected, and censored patients when kids removed -- still roughly half censored. more infected than uninfected\n",
+    "SELECT sum(not_infected), sum(infected), sum(not_censored)\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids_no_prior`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# get dep_names for the Ed for cohort without kids, lot of pediatric even though we're 18+.\n",
+    "WITH adt_dep as (\n",
+    "SELECT pat_enc_csn_id_coded, FIRST_VALUE(department_id) OVER (PARTITION BY pat_enc_csn_id_coded ORDER BY effective_time_jittered_utc) department_id\n",
+    "FROM `shc_core.adt`)\n",
+    "\n",
+    "SELECT DISTINCT cens.*,  dep.department_name,\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_cohort_not_censored_no_kids` cens\n",
+    "INNER JOIN adt_dep \n",
+    "USING (pat_enc_csn_id_coded)\n",
+    "INNER JOIN `shc_core.dep_map` dep\n",
+    "USING (department_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/eda/PositiveCultures.ipynb b/scripts/ER_Infection/notebooks/eda/PositiveCultures.ipynb
new file mode 100644
index 00000000..6aacdedb
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/eda/PositiveCultures.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Exploratory Data Analysis For Positive Growth Cultures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas as pd\n",
+    "# import matplotlib.pyplot as plt\n",
+    "# import numpy as np\n",
+    "import os \n",
+    "\n",
+    "# ### THIS IS MEANT TO RUN ON NERO - NEEDS TO BE CHANGED IF YOU RUN LOCALLY\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ccorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "# from google.cloud import bigquery\n",
+    "# client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Stratify Positive Culture Encounters By The Number of Organisms that Grew in the encounter\n",
+    "SELECT num_bugs, COUNT (DISTINCT pat_enc_csn_id_coded) num_csns\n",
+    "FROM \n",
+    "  (SELECT cr.pat_enc_csn_id_coded, COUNT (DISTINCT cr.sens_organism_sid) num_bugs\n",
+    "   FROM `mining-clinical-decisions.conor_db.abx_ast_long` cr\n",
+    "   GROUP BY cr.pat_enc_csn_id_coded) t\n",
+    "GROUP BY num_bugs\n",
+    "ORDER BY num_bugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery\n",
+    "# Get types of organisms that grow stratified by ER that grew them\n",
+    "# Top 15 per ER\n",
+    "WITH dep_id as (\n",
+    "SELECT \n",
+    "adt.pat_enc_csn_id_coded,\n",
+    "FIRST_VALUE(adt.department_id) OVER (PARTITION BY adt.pat_enc_csn_id_coded ORDER BY adt.effective_time_jittered_utc) department_id\n",
+    "FROM `mining-clinical-decisions.conor_db.abx_ast_long` \n",
+    "INNER JOIN `shc_core.adt` adt\n",
+    "USING (pat_enc_csn_id_coded)\n",
+    "),\n",
+    "\n",
+    "all_bugs as (\n",
+    "SELECT CASE WHEN department_name in ('ADULT EMERGENCY DEPARTMENT', 'PEDIATRIC EMERGENCY DEPARTMENT') THEN \"SHC ED\" ELSE department_name END department_name,\n",
+    "organism, COUNT (DISTINCT pat_enc_csn_id_coded) num_csns\n",
+    "FROM \n",
+    "  (SELECT DISTINCT cr.pat_enc_csn_id_coded, cr.organism, dm.department_name\n",
+    "  FROM `mining-clinical-decisions.conor_db.abx_ast_long` cr\n",
+    "  INNER JOIN dep_id\n",
+    "  USING (pat_enc_csn_id_coded)\n",
+    "  INNER JOIN `shc_core.dep_map` dm\n",
+    "  USING (department_id)) t\n",
+    "GROUP BY department_name, organism\n",
+    "-- ORDER BY department_name, num_csns DESC\n",
+    "),\n",
+    "\n",
+    "topten as (\n",
+    "SELECT *, ROW_NUMBER() \n",
+    "over (\n",
+    "    PARTITION BY department_name \n",
+    "    order by num_csns DESC\n",
+    ") AS RowNo \n",
+    "FROM all_bugs\n",
+    ")\n",
+    "\n",
+    "SELECT * FROM topten WHERE RowNo <= 15\n",
+    "ORDER BY department_name, RowNo\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "unexpected indent (<ipython-input-6-0333222e3593>, line 3)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-6-0333222e3593>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    order_time_jittered_utc\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/featurizing/bin_features.ipynb b/scripts/ER_Infection/notebooks/featurizing/bin_features.ipynb
new file mode 100644
index 00000000..bb4ddc1a
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/featurizing/bin_features.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "from google.cloud import bigquery\n",
+    "from tqdm import tqdm\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chunking the query because I've had issues with the bigquery api timing out when given too many columns. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_bq_to_pandas(query, nrows, chunksize=500000):\n",
+    "    offsets = [i for i in range(0, nrows, chunksize)]\n",
+    "    df = pd.DataFrame()\n",
+    "    for offset in tqdm(offsets):\n",
+    "        query_str = query + \" LIMIT {chunksize} OFFSET {offset}\"\n",
+    "        query_str = query_str.format(chunksize=chunksize, offset=offset)\n",
+    "        query_job = client.query(query_str)\n",
+    "        df_slice = query_job.result().to_dataframe()\n",
+    "        df = pd.concat([df, df_slice])\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get lab results and flowsheets within 2 weeks of index time from `feature_timeline_long`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note this is querying a table (feature_timeline_long) that only has observations from before index time\n",
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "    *\n",
+    "FROM \n",
+    "    `mining-clinical-decisions.abx.feature_timeline_long` \n",
+    "WHERE \n",
+    "    feature_type IN (\"Lab Results\", \"Flowsheet\")\n",
+    "AND \n",
+    "    TIMESTAMP_ADD(observation_time, INTERVAL 14*24 HOUR) > index_time \n",
+    "ORDER BY\n",
+    "    pat_enc_csn_id_coded, order_id, features, value, observation_time -- make sure each slice ordered same way\n",
+    "\"\"\"\n",
+    "# Num rows is number of rows this query should result in\n",
+    "df = read_bq_to_pandas(query, nrows=2337767, chunksize=500000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f'Number of distinct rows: {len(df.drop_duplicates())}') # sanity check this should be nrows\n",
+    "print(f'Number of rows: {len(df)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bin numerical features\n",
+    "Use training set to create a distribution of each feature type, and then use that distribution to map each feature to it's appropriate bin.  This allows us to represent numerical features in bag of words representations (where each numerical feature will have k columns in feature matrix for k bins).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_to_dict(look_up_table):\n",
+    "    \"\"\"Converts df look up table to dictionary for faster look up later\"\"\"\n",
+    "    bin_val_dict = {}\n",
+    "    for feature in look_up_table['features'].unique():\n",
+    "        feature_bin_vals = look_up_table[look_up_table['features'] == feature]\n",
+    "        for _bin in feature_bin_vals['bins'].unique():\n",
+    "            if feature not in bin_val_dict:\n",
+    "                bin_val_dict[feature] = {}\n",
+    "                bin_val_dict[feature]['min'] = []\n",
+    "                bin_val_dict[feature]['max'] = []\n",
+    "\n",
+    "            min_val_for_bin = feature_bin_vals[feature_bin_vals['bins'] == _bin]['value']['min'].values[0]\n",
+    "            max_val_for_bin = feature_bin_vals[feature_bin_vals['bins'] == _bin]['value']['max'].values[0]\n",
+    "\n",
+    "            bin_val_dict[feature]['min'].append(min_val_for_bin)\n",
+    "            bin_val_dict[feature]['max'].append(max_val_for_bin)\n",
+    "    return bin_val_dict\n",
+    "\n",
+    "    \n",
+    "def train_featurizer(df_train):\n",
+    "    \"\"\"\n",
+    "    Compute percent_ranks and generates a look up table of min and max bin values\n",
+    "    Input : long form dataframe with features and value where value are the continuous values of labs / vitals\n",
+    "    Output: look up table - dict of dict of lists (key1 = feature_name, key2 = max or min, values = lists of values)\n",
+    "    \"\"\"\n",
+    "    # Compute percentiles and bins\n",
+    "    df_train['percentiles'] = df_train.groupby('features')['value'].transform(lambda x: x.rank(pct=True))\n",
+    "    df_train['bins'] = df_train['percentiles'].apply(lambda x: int(x * 10))\n",
+    "    \n",
+    "    # Generate look up table and convert to dictionary structure\n",
+    "    look_up_table_df = df_train.groupby(['features', 'bins']).agg({'value' : ['min', 'max']}).reset_index()\n",
+    "    look_up_table = convert_to_dict(look_up_table_df)\n",
+    "    \n",
+    "    ### Sanity Check. Ensure that min vector for each feature is strictly increasing (no ties!)\n",
+    "    # Should be the case because ties are given same percentile rank in default pandas rank function\n",
+    "    for feature in look_up_table:\n",
+    "        mins = look_up_table[feature]['min']\n",
+    "        for i in range(len(mins)-1):\n",
+    "            assert mins[i] < mins[i+1]\n",
+    "    \n",
+    "    return look_up_table\n",
+    "\n",
+    "\n",
+    "def apply_featurizer(df, look_up_table):\n",
+    "    \n",
+    "    def get_appropriate_bin(feature, value, look_up_table):\n",
+    "        \"\"\"Takes in feature, value and look up table and returns appropriate bin\n",
+    "\n",
+    "        Quick Note: For some features, we do not have 10 bins.  This happens when we have many many ties in the \n",
+    "        percent rank - and the percent rank alg returns ties as the average rank within that tie. So for instance\n",
+    "        we're trying to break each feature up into deciles where each bin covers range of 10% of the examples. But if more\n",
+    "        than 10% of the examples take on 1 value - then bins can be skipped. This shouldn't really be a problem\n",
+    "        for downstream tasks - just something to be aware of. This also means 'bins' and 'bins_applied' won't have\n",
+    "        perfect overlap in features that end up having less than 10 bins\n",
+    "\n",
+    "        \"\"\"\n",
+    "        try:\n",
+    "            mins = look_up_table[feature]['min']\n",
+    "        except: # Feature not found in training set\n",
+    "            return 0 # return 0th bin - this will get removed as a feature in downstream processing\n",
+    "        for i in range(len(mins) - 1):\n",
+    "            # If value is smaller than min value of smallest bin (in test time) - then return 0 (smallest bin)\n",
+    "            if i == 0 and value < mins[i]:\n",
+    "                return i\n",
+    "\n",
+    "            if value >= mins[i] and value < mins[i+1] :\n",
+    "                return i\n",
+    "\n",
+    "        # Then in last bin\n",
+    "        return len(mins)-1\n",
+    "    \n",
+    "    df['bins_applied'] = df[['features', 'value']].apply(\n",
+    "        lambda x: get_appropriate_bin(x['features'], x['value'], look_up_table), axis=1)\n",
+    "    \n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Segment by validation split time\n",
+    "df_train = df[df['index_time'] < '2018-01-01']\n",
+    "\n",
+    "# Train featurizer\n",
+    "look_up_table = train_featurizer(df_train)\n",
+    "\n",
+    "# Apply featurizer\n",
+    "df_featurized = apply_featurizer(df, look_up_table)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_featurized.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sanity check in leui of test code for now"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = apply_featurizer(df_train, look_up_table)\n",
+    "look_up_table_df = df_train.groupby(['features', 'bins']).agg({'value' : ['min', 'max']}).reset_index()\n",
+    "\n",
+    "features_with_0_9_bins = []\n",
+    "for feature in look_up_table_df:\n",
+    "    num_bins = len(look_up_table_df[look_up_table_df['features'] == feature]['bins'].values)\n",
+    "    ten_in_bins = 10 in look_up_table_df[look_up_table_df['features'] == feature]['bins'].values\n",
+    "    if num_bins == 10 and not ten_in_bins:\n",
+    "        features_with_0_9_bins.append(feature)\n",
+    "\n",
+    "for feature in features_with_0_9_bins:\n",
+    "    df_test = df_train[df_train['features'] == 'feature']\n",
+    "    for b_real, b_computed in zip(df_test['bins'].values, df_test['bins_applied'].values):\n",
+    "        assert(b_real == b_computed)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### House Cleaning\n",
+    "Rename features by their bin, value column should be none, and feature_type should be set to `_train` if training set was used to generate distribution of values and `_test` if training and validation set used to create distributions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# House Cleaning\n",
+    "columns = ['anon_id', 'pat_enc_csn_id_coded', 'index_time', 'order_id', 'observation_time', 'feature_type', 'features', 'value', 'bins_applied']\n",
+    "df_new = df_featurized[columns]\n",
+    "\n",
+    "# New feature names\n",
+    "df_new['features'] = ['_'.join([x, str(y)]) for x, y in zip(df_new['features'].values, df_new['bins_applied'].values)] \n",
+    "df_new['feature_type'] = [x + '_train' for x in df_new['feature_type'].values]\n",
+    "df_new['value'] = [None for x in df_new['value'].values] \n",
+    "\n",
+    "# df_new.to_csv('lab_results_vitals_binned.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# columns = ['obs_num', 'anon_id', 'pat_enc_csn_id_coded', 'index_time', 'order_id', 'feature_type', 'features', 'value', 'observation_time']\n",
+    "# df_new[columns].to_csv('binned_labs_vitals.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_new[columns].to_csv('binned_labs_vitals.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/featurizing/bin_labs_vitals.ipynb b/scripts/ER_Infection/notebooks/featurizing/bin_labs_vitals.ipynb
new file mode 100644
index 00000000..57cf6416
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/featurizing/bin_labs_vitals.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "from google.cloud import bigquery\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ccorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_to_dict(look_up_table):\n",
+    "    \"\"\"Converts df look up table to dictionary for faster look up later\"\"\"\n",
+    "    bin_val_dict = {}\n",
+    "    for feature in look_up_table['features'].unique():\n",
+    "        feature_bin_vals = look_up_table[look_up_table['features'] == feature]\n",
+    "        for _bin in feature_bin_vals['bins'].unique():\n",
+    "            if feature not in bin_val_dict:\n",
+    "                bin_val_dict[feature] = {}\n",
+    "                bin_val_dict[feature]['min'] = []\n",
+    "                bin_val_dict[feature]['max'] = []\n",
+    "\n",
+    "            min_val_for_bin = feature_bin_vals[feature_bin_vals['bins'] == _bin]['value']['min'].values[0]\n",
+    "            max_val_for_bin = feature_bin_vals[feature_bin_vals['bins'] == _bin]['value']['max'].values[0]\n",
+    "\n",
+    "            bin_val_dict[feature]['min'].append(min_val_for_bin)\n",
+    "            bin_val_dict[feature]['max'].append(max_val_for_bin)\n",
+    "    return bin_val_dict\n",
+    "\n",
+    "    \n",
+    "def train_featurizer(df_train):\n",
+    "    \"\"\"\n",
+    "    Compute percent_ranks and generates a look up table of min and max bin values\n",
+    "    Input : long form dataframe with features and value where value are the continuous values of labs / vitals\n",
+    "    Output: look up table - dict of dict of lists (key1 = feature_name, key2 = max or min, values = lists of values)\n",
+    "    \"\"\"\n",
+    "    # Compute percentiles and bins\n",
+    "    df_train['percentiles'] = df_train.groupby('features')['value'].transform(lambda x: x.rank(pct=True))\n",
+    "    df_train['bins'] = df_train['percentiles'].apply(lambda x: int(x * 10))\n",
+    "    \n",
+    "    # Generate look up table and conver to dictionary stucture\n",
+    "    look_up_table_df = df_train.groupby(['features', 'bins']).agg({'value' : ['min', 'max']}).reset_index()\n",
+    "    look_up_table = convert_to_dict(look_up_table_df)\n",
+    "    \n",
+    "    ### Sanity Check. Ensure that min vector for each feature is strictly increasing (no ties!)\n",
+    "    # Should be the case because ties are given same percentile rank in default pandas rank function\n",
+    "    for feature in look_up_table:\n",
+    "        mins = look_up_table[feature]['min']\n",
+    "        for i in range(len(mins)-1):\n",
+    "            assert mins[i] < mins[i+1]\n",
+    "    \n",
+    "    return look_up_table\n",
+    "\n",
+    "\n",
+    "def apply_featurizer(df, look_up_table):\n",
+    "    \n",
+    "    def get_appropriate_bin(feature, value, look_up_table):\n",
+    "        \"\"\"Takes in feature, value and look up table and returns appropriate bin\n",
+    "\n",
+    "        Quick Note: For some features, we do not have 10 bins.  This happens when we have many many ties in the \n",
+    "        percent rank - and the percent rank alg returns ties as the average rank within that tie. So for instance\n",
+    "        we're trying to break each feature up into deciles where each bin covers range of 10% of the examples. But if more\n",
+    "        than 10% of the examples take on 1 value - then bins can be skipped. This shouldn't really be a problem\n",
+    "        for downstream tasks - just something to be aware of. This also means 'bins' and 'bins_applied' won't have\n",
+    "        perfect overlap in features that end up having less than 10 bins\n",
+    "\n",
+    "        \"\"\"\n",
+    "        mins = look_up_table[feature]['min']\n",
+    "        for i in range(len(mins) - 1):\n",
+    "            # If value is smaller than min value of smallest bin (in test time) - then return 0 (smallest bin)\n",
+    "            if i == 0 and value < mins[i]:\n",
+    "                return i\n",
+    "\n",
+    "            if value >= mins[i] and value < mins[i+1] :\n",
+    "                return i\n",
+    "\n",
+    "        # Then in last bin\n",
+    "        return len(mins)-1\n",
+    "    \n",
+    "    df['bins_applied'] = df[['features', 'value']].apply(\n",
+    "        lambda x: get_appropriate_bin(x['features'], x['value'], look_up_table), axis=1)\n",
+    "    \n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gets vitals and labs from feature timeline that we want to bin\n",
+    "query = \"\"\"SELECT * FROM `mining-clinical-decisions.abx.feature_timeline_long`\n",
+    "           WHERE feature_type IN ('Lab Results', 'Flowsheet')\n",
+    "        \"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.result().to_dataframe()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Segment by validation split time\n",
+    "df_train = df[df['index_time'] < '2018-01-01']\n",
+    "\n",
+    "# Train featurizer\n",
+    "look_up_table = train_featurizer(df_train)\n",
+    "\n",
+    "# Apply featurizer\n",
+    "df_featurized = apply_featurizer(df, look_up_table)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sanity check in leui of test code for now\n",
+    "df_train = apply_featurizer(df_train, look_up_table)\n",
+    "look_up_table_df = df_train.groupby(['features', 'bins']).agg({'value' : ['min', 'max']}).reset_index()\n",
+    "\n",
+    "features_with_0_9_bins = []\n",
+    "for feature in look_up_table_df:\n",
+    "    num_bins = len(look_up_table_df[look_up_table_df['features'] == feature]['bins'].values)\n",
+    "    ten_in_bins = 10 in look_up_table_df[look_up_table_df['features'] == feature]['bins'].values\n",
+    "    if num_bins == 10 and not ten_in_bins:\n",
+    "        features_with_0_9_bins.append(feature)\n",
+    "\n",
+    "for feature in features_with_0_9_bins:\n",
+    "    df_test = df_train[df_train['features'] == 'feature']\n",
+    "    for b_real, b_computed in zip(df_test['bins'].values, df_test['bins_applied'].values):\n",
+    "        assert(b_real == b_computed)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/featurizing/bin_labs_vitals.py b/scripts/ER_Infection/notebooks/featurizing/bin_labs_vitals.py
new file mode 100644
index 00000000..12a12d82
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/featurizing/bin_labs_vitals.py
@@ -0,0 +1,126 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import os 
+from google.cloud import bigquery
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ccorbin/.config/gcloud/application_default_credentials.json' 
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' 
+
+client=bigquery.Client()
+
+
+def convert_to_dict(look_up_table):
+    """Converts df look up table to dictionary for faster look up later"""
+    bin_val_dict = {}
+    for feature in look_up_table['features'].unique():
+        feature_bin_vals = look_up_table[look_up_table['features'] == feature]
+        for _bin in feature_bin_vals['bins'].unique():
+            if feature not in bin_val_dict:
+                bin_val_dict[feature] = {}
+                bin_val_dict[feature]['min'] = []
+                bin_val_dict[feature]['max'] = []
+
+            min_val_for_bin = feature_bin_vals[feature_bin_vals['bins'] == _bin]['value']['min'].values[0]
+            max_val_for_bin = feature_bin_vals[feature_bin_vals['bins'] == _bin]['value']['max'].values[0]
+
+            bin_val_dict[feature]['min'].append(min_val_for_bin)
+            bin_val_dict[feature]['max'].append(max_val_for_bin)
+    return bin_val_dict
+
+    
+def train_featurizer(df_train):
+    """
+    Compute percent_ranks and generates a look up table of min and max bin values
+    Input : long form dataframe with features and value where value are the continuous values of labs / vitals
+    Output: look up table - dict of dict of lists (key1 = feature_name, key2 = max or min, values = lists of values)
+    """
+    # Compute percentiles and bins
+    df_train['percentiles'] = df_train.groupby('features')['value'].transform(lambda x: x.rank(pct=True))
+    df_train['bins'] = df_train['percentiles'].apply(lambda x: int(x * 10))
+    
+    # Generate look up table and conver to dictionary stucture
+    look_up_table_df = df_train.groupby(['features', 'bins']).agg({'value' : ['min', 'max']}).reset_index()
+    look_up_table = convert_to_dict(look_up_table_df)
+    
+    ### Sanity Check. Ensure that min vector for each feature is strictly increasing (no ties!)
+    # Should be the case because ties are given same percentile rank in default pandas rank function
+    for feature in look_up_table:
+        mins = look_up_table[feature]['min']
+        for i in range(len(mins)-1):
+            assert mins[i] < mins[i+1]
+    
+    return look_up_table
+
+
+def apply_featurizer(df, look_up_table):
+    
+    def get_appropriate_bin(feature, value, look_up_table):
+        """Takes in feature, value and look up table and returns appropriate bin
+
+        Quick Note: For some features, we do not have 10 bins.  This happens when we have many many ties in the 
+        percent rank - and the percent rank alg returns ties as the average rank within that tie. So for instance
+        we're trying to break each feature up into deciles where each bin covers range of 10% of the examples. But if more
+        than 10% of the examples take on 1 value - then bins can be skipped. This shouldn't really be a problem
+        for downstream tasks - just something to be aware of. This also means 'bins' and 'bins_applied' won't have
+        perfect overlap in features that end up having less than 10 bins
+
+        """
+        mins = look_up_table[feature]['min']
+        for i in range(len(mins) - 1):
+            # If value is smaller than min value of smallest bin (in test time) - then return 0 (smallest bin)
+            if i == 0 and value < mins[i]:
+                return i
+
+            if value >= mins[i] and value < mins[i+1] :
+                return i
+
+        # Then in last bin
+        return len(mins)-1
+    
+    df['bins_applied'] = df[['features', 'value']].apply(
+        lambda x: get_appropriate_bin(x['features'], x['value'], look_up_table), axis=1)
+    
+    return df
+
+# Gets vitals and labs from feature timeline that we want to bin
+query = """SELECT * FROM `mining-clinical-decisions.abx.feature_timeline_long`
+           WHERE feature_type IN ('Lab Results', 'Flowsheet')
+        """
+query_job = client.query(query)
+df = query_job.result().to_dataframe()
+
+# Segment by validation split time
+df_train = df[df['index_time'] < '2018-01-01']
+
+# Train featurizer
+look_up_table = train_featurizer(df_train)
+
+# Apply featurizer
+df_featurized = apply_featurizer(df, look_up_table)
+
+# Sanity check in leui of test code for now
+df_train = apply_featurizer(df_train, look_up_table)
+look_up_table_df = df_train.groupby(['features', 'bins']).agg({'value' : ['min', 'max']}).reset_index()
+
+features_with_0_9_bins = []
+for feature in look_up_table_df:
+    num_bins = len(look_up_table_df[look_up_table_df['features'] == feature]['bins'].values)
+    ten_in_bins = 10 in look_up_table_df[look_up_table_df['features'] == feature]['bins'].values
+    if num_bins == 10 and not ten_in_bins:
+        features_with_0_9_bins.append(feature)
+
+for feature in features_with_0_9_bins:
+    df_test = df_train[df_train['features'] == 'feature']
+    for b_real, b_computed in zip(df_test['bins'].values, df_test['bins_applied'].values):
+        assert(b_real == b_computed)
+
+# House Cleaning
+columns = ['anon_id', 'pat_enc_csn_id_coded', 'index_time', 'observation_time' 'feature_type', 'features', 'value', 'bins_applied']
+df_new = df_featurized[columns]
+
+# New feature names
+df_new['features'] = ['_'.join([x, str(y)]) for x, y in zip(df_new['features'].values, df_new['bins_applied'].values)] 
+df_new['feature_type'] = [x + '_train' for x in df_new['feature_type'].values]
+
+df_new.to_csv('lab_results_vitals_binned.csv', index=None)
\ No newline at end of file
diff --git a/scripts/ER_Infection/notebooks/featurizing/to_sparse.py b/scripts/ER_Infection/notebooks/featurizing/to_sparse.py
new file mode 100644
index 00000000..36bd5faa
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/featurizing/to_sparse.py
@@ -0,0 +1,100 @@
+from scipy.sparse import csr_matrix, save_npz
+import pdb
+
+def build_vocab(data):
+    """Builds vocabulary for of terms from the data. Assigns each unique term to a monotonically increasing integer."""
+    vocabulary = {}
+    for i, d in enumerate(data):
+        for j, term in enumerate(d):
+            vocabulary.setdefault(term, len(vocabulary))
+    return vocabulary
+
+def create_sparse_feature_matrix(train_data, apply_data):
+    """Creates sparse matrix efficiently from long form dataframe.  We build a vocabulary
+       from the training set, then apply vocab to the apply_set
+       
+       Parameters
+       ----------
+       train_data : long form pandas DataFrame
+           Data to use to build vocabulary
+       apply_data : long form pandas DataFrame
+           Data to transform to sparse matrix for input to ML models
+    
+       Returns
+       -------
+       csr_data : scipy csr_matrix
+           Sparse matrix version of apply_data to feed into ML models. 
+    """
+    
+    train_features = (train_data
+        .groupby('pat_enc_csn_id_coded')
+        .agg({'features' : lambda x: list(x),
+              'values' : lambda x: list(x)})
+        .reset_index()
+    )
+    train_feature_names = [doc for doc in train_features.features.values]
+    train_feature_values = [doc for doc in train_features['values'].values]
+    train_csns = [csn for csn in train_features.pat_enc_csn_id_coded.values]
+    
+    apply_features = (apply_data
+        .groupby('pat_enc_csn_id_coded')
+        .agg({'features' : lambda x: list(x),
+              'values' : lambda x: list(x)})
+        .reset_index()
+    )
+    apply_features_names = [doc for doc in apply_features.features.values]
+    apply_features_values = [doc for doc in apply_features['values'].values]
+    apply_csns = [csn for csn in apply_features.pat_enc_csn_id_coded.values]
+
+    
+    vocabulary = build_vocab(train_feature_names)
+    indptr = [0]
+    indices = []
+    data = []
+    for i, d in enumerate(apply_features_names):
+        for j, term in enumerate(d):
+            if term not in vocabulary:
+                continue
+            else:
+                indices.append(vocabulary[term])
+                data.append(apply_features_values[i][j])
+            if j == 0:
+                # Add zero to data and max index in vocabulary to indices in case max feature indice isn't in apply features.
+                indices.append(len(vocabulary)-1)
+                data.append(0)
+        indptr.append(len(indices))
+    
+    csr_data = csr_matrix((data, indices, indptr), dtype=float)
+    
+    return csr_data, apply_csns, vocabulary
+
+# Query Long Feature Sequence
+q_features = 
+"""
+SELECT f.*, EXTRACT(YEAR from f.index_time) year
+FROM `mining-clinical-decisions.abx.feature_timeline_long` f
+RIGHT JOIN  `mining-clinical-decisions.abx.final_cohort_table` l
+USING (pat_enc_csn_id_coded)
+"""
+
+query_job = client.query(q_features)
+df_features = query_job.result().to_dataframe()
+
+df_features = df_features.sort_values('pat_enc_csn_id_coded')
+df_features_val = df_features[~df_features['feature_type'].isin(['Lab Results_test', 'Flowsheet_test'])]
+#df_features_test = df_features[~df_features['feature_type'].isin(['Lab Results_train', 'Flowsheet_train'])]
+
+train_csr, train_csns, train_vocab = create_sparse_feature_matrix(training_examples, training_examples)
+test_csr, test_csns, test_and_val_vocab = create_sparse_feature_matrix(training_examples, validation_examples)
+
+# Sanity check csn order
+for a, b in zip(train_labels['pat_enc_csn_id_coded'].values, train_csns):
+    assert a == b
+for a, b in zip(validation_labels['pat_enc_csn_id_coded'].values, val_csns):
+    assert a == b
+for a, b in zip(train_and_val_labels['pat_enc_csn_id_coded'].values, train_and_val_csns):
+    assert a == b
+for a, b in zip(test_labels['pat_enc_csn_id_coded'].values, test_csns):
+    assert a == b
+
+    
\ No newline at end of file
diff --git a/scripts/ER_Infection/notebooks/featurizing/to_sparse_ast.ipynb b/scripts/ER_Infection/notebooks/featurizing/to_sparse_ast.ipynb
new file mode 100644
index 00000000..44c52c8a
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/featurizing/to_sparse_ast.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transform Long Form Count Features to Sparse Matrices\n",
+    "### We want one feature matrix for each of\n",
+    "* Training Set\n",
+    "* Validation Set\n",
+    "* Training Set + Validation Set\n",
+    "* Test Set\n",
+    "* We similarily will save multiple labels csv files where rows correspond one to one with rows in csr matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "from google.cloud import bigquery\n",
+    "from tqdm import tqdm\n",
+    "from scipy.sparse import csr_matrix, save_npz\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper to read in big query data in chunks\n",
+    "Without this, queries can take too long to run and/or timeout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_bq_to_pandas(query, nrows, chunksize=500000):\n",
+    "    offsets = [i for i in range(0, nrows, chunksize)]\n",
+    "    df = pd.DataFrame()\n",
+    "    for offset in tqdm(offsets):\n",
+    "        query_str = query + \" LIMIT {chunksize} OFFSET {offset}\"\n",
+    "        query_str = query_str.format(chunksize=chunksize, offset=offset)\n",
+    "        query_job = client.query(query_str)\n",
+    "        df_slice = query_job.result().to_dataframe()\n",
+    "        df = pd.concat([df, df_slice])\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Join long form feature matrix to cohort table\n",
+    "Read in chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cohort_table = 'final_ast_labels'\n",
+    "nrows = 2540179\n",
+    "chunksize = 600000\n",
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "    f.*, EXTRACT(YEAR from f.index_time) year\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.feature_counts_long f\n",
+    "RIGHT JOIN \n",
+    "    mining-clinical-decisions.abx.{cohort_table} c\n",
+    "USING \n",
+    "    (pat_enc_csn_id_coded)\n",
+    "ORDER BY\n",
+    "    pat_enc_csn_id_coded, feature_type, features -- necessary so that we don't get random ordering in our chunks\n",
+    "\"\"\"\n",
+    "df = read_bq_to_pandas(query.format(cohort_table=cohort_table),\n",
+    "                       nrows=nrows,\n",
+    "                       chunksize=chunksize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Sanity check - no duplicate rows (means query executed properly)\n",
+    "assert(len(df) == len(df.drop_duplicates()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functions that create CSR matrices from long form data in memory efficient way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.sparse import csr_matrix, save_npz\n",
+    "\n",
+    "def build_vocab(data):\n",
+    "    \"\"\"\n",
+    "    1. Builds vocabulary for of terms from the data.\n",
+    "    2. Assigns each unique term to a monotonically increasing integer.\n",
+    "    \"\"\"\n",
+    "    vocabulary = {}\n",
+    "    for i, d in enumerate(data):\n",
+    "        for j, term in enumerate(d):\n",
+    "            vocabulary.setdefault(term, len(vocabulary))\n",
+    "    return vocabulary\n",
+    "\n",
+    "def create_sparse_feature_matrix(train_data, apply_data):\n",
+    "    \"\"\"Creates sparse matrix efficiently from long form dataframe.  \n",
+    "       1. Builds a vocabulary from the training set,\n",
+    "       2. Then applies vocab to the training + eval set\n",
+    "       \n",
+    "       We only want to include terms in our feature matrix that exist in \n",
+    "       the training set.  \n",
+    "       \n",
+    "       Parameters\n",
+    "       ----------\n",
+    "       train_data : long form pandas DataFrame\n",
+    "           Data to use to build vocabulary\n",
+    "       apply_data : long form pandas DataFrame\n",
+    "           Data to transform to sparse matrix for input to ML models\n",
+    "    \n",
+    "       Returns\n",
+    "       -------\n",
+    "       csr_data : scipy csr_matrix\n",
+    "           Sparse matrix version of apply_data to feed into ML models. \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Group long form train data by CSN (our unit of observation) and get list of features and values for each\n",
+    "    train_features = (train_data\n",
+    "        .groupby('pat_enc_csn_id_coded')\n",
+    "        .agg({'features' : lambda x: list(x),\n",
+    "              'value' : lambda x: list(x)})\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    train_feature_names = [doc for doc in train_features.features.values]\n",
+    "    train_feature_values = [doc for doc in train_features['value'].values]\n",
+    "    train_csns = [csn for csn in train_features.pat_enc_csn_id_coded.values]\n",
+    "    \n",
+    "    # Group long form apply data by CSN (our unit of observation) and get list of features and values for each\n",
+    "    apply_features = (apply_data\n",
+    "        .groupby('pat_enc_csn_id_coded')\n",
+    "        .agg({'features' : lambda x: list(x),\n",
+    "              'value' : lambda x: list(x)})\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    apply_features_names = [doc for doc in apply_features.features.values]\n",
+    "    apply_features_values = [doc for doc in apply_features['value'].values]\n",
+    "    apply_csns = [csn for csn in apply_features.pat_enc_csn_id_coded.values]\n",
+    "\n",
+    "    # Build vocab from training set feature names\n",
+    "    vocabulary = build_vocab(train_feature_names)\n",
+    "    \n",
+    "    # Build up csr matrix\n",
+    "    indptr = [0]\n",
+    "    indices = []\n",
+    "    data = []\n",
+    "    for i, d in enumerate(apply_features_names):\n",
+    "        for j, term in enumerate(d):\n",
+    "            if term not in vocabulary: # make sure term is in training set\n",
+    "                continue\n",
+    "            else:\n",
+    "                indices.append(vocabulary[term])\n",
+    "                data.append(apply_features_values[i][j])\n",
+    "            if j == 0:\n",
+    "                # Add zero to data and max index in vocabulary to indices in case max feature indice isn't in apply features.\n",
+    "                indices.append(len(vocabulary)-1)\n",
+    "                data.append(0)\n",
+    "        indptr.append(len(indices))\n",
+    "    \n",
+    "    csr_data = csr_matrix((data, indices, indptr), dtype=float)\n",
+    "    \n",
+    "    return csr_data, apply_csns, vocabulary\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create and save sparse matrices and labels for each of validation and testing round"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "for r in [\"validation\", 'testing']:\n",
+    "    \n",
+    "    # Get proper subset of data\n",
+    "    if r == \"validation\":\n",
+    "        print(r)\n",
+    "        not_included = ['Lab_Results_test', 'Flowsheet_test']\n",
+    "        df = (df\n",
+    "            .query(\"feature_type not in @not_included\", engine='python')\n",
+    "        )\n",
+    "        training_examples = df[df['year'] < 2018]\n",
+    "        test_examples = df[df['year'] == 2018]\n",
+    "    else:\n",
+    "        print(r)\n",
+    "        not_included = ['Lab_Results_val', 'Flowsheet_val']\n",
+    "        df = (df\n",
+    "            .query(\"feature_type not in @not_included\", engine='python')\n",
+    "        )\n",
+    "        training_examples = df[df['year'] < 2019]\n",
+    "        test_examples = df[df['year'] == 2019]\n",
+    "    \n",
+    "    # Get sparce matrices for each set\n",
+    "    train_csr, train_csns, train_vocab = create_sparse_feature_matrix(training_examples, training_examples)\n",
+    "    test_csr, test_csns, test_vocab = create_sparse_feature_matrix(training_examples, test_examples)\n",
+    "   \n",
+    "    # Query cohort table for labels\n",
+    "    q_cohort = \"\"\"\n",
+    "    SELECT \n",
+    "        * \n",
+    "    FROM \n",
+    "        mining-clinical-decisions.abx.{cohort_table}\n",
+    "    ORDER BY\n",
+    "        pat_enc_csn_id_coded\n",
+    "    \"\"\"\n",
+    "    query_job = client.query(q_cohort.format(cohort_table=cohort_table))\n",
+    "    df_cohort = query_job.result().to_dataframe()\n",
+    "    if r == 'validation':\n",
+    "        train_labels = df_cohort[df_cohort['index_time'].dt.year < 2018]\n",
+    "        test_labels = df_cohort[df_cohort['index_time'].dt.year == 2018]\n",
+    "    else:\n",
+    "        train_labels = df_cohort[df_cohort['index_time'].dt.year < 2019]\n",
+    "        test_labels = df_cohort[df_cohort['index_time'].dt.year == 2019]\n",
+    "        \n",
+    "    # Sanity check - make sure CSNs from labels and features in same order\n",
+    "    for a, b in zip(train_labels['pat_enc_csn_id_coded'].values, train_csns):\n",
+    "        try:\n",
+    "            assert a == b\n",
+    "        except:\n",
+    "            pdb.set_trace()\n",
+    "    for a, b in zip(test_labels['pat_enc_csn_id_coded'].values, test_csns):\n",
+    "        assert a == b\n",
+    "    \n",
+    "    # Create output path\n",
+    "    path = '/Users/conorcorbin/repos/er_infection/data/ast_models_large_test_set/'\n",
+    "    os.makedirs(path, exist_ok=True)\n",
+    "    \n",
+    "    # Save sparce matrices\n",
+    "    save_npz(os.path.join(path, 'training_examples_round_{r}.npz'.format(r=r)), train_csr)\n",
+    "    save_npz(os.path.join(path, 'test_examples_round_{r}.npz'.format(r=r)), test_csr)\n",
+    "    \n",
+    "    # Save labels\n",
+    "    train_labels.to_csv(os.path.join(path, 'training_labels_round_{r}.csv'.format(r=r)), index=None)\n",
+    "    test_labels.to_csv(os.path.join(path, 'test_labels_round_{r}.csv'.format(r=r)), index=None)\n",
+    "    \n",
+    "    # Save test vocab using train vocab of test round\n",
+    "    if r == 'testing':\n",
+    "        df_train_vocab = pd.DataFrame(data={\n",
+    "            'features' : [t for t in train_vocab],\n",
+    "            'indices' : [train_vocab[t] for t in train_vocab]\n",
+    "        })\n",
+    "        df_train_vocab.to_csv(os.path.join(path, 'feature_vocabulary.csv'))\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### All code below is vestigial   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_csr, train_csns, train_vocab = create_sparse_feature_matrix(training_examples, training_examples)\n",
+    "test_csr, test_csns, test_and_val_vocab = create_sparse_feature_matrix(training_examples, test_examples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q_cohort = \"\"\"\n",
+    "SELECT * \n",
+    "FROM mining-clinical-decisions.abx.final_cohort_table\n",
+    "WHERE label_unobserved = 0\n",
+    "ORDER BY pat_enc_csn_id_coded\n",
+    "\"\"\"\n",
+    "query_job = client.query(q_cohort)\n",
+    "df_cohort = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if ROUND == 'validation':\n",
+    "    train_labels = df_cohort[df_cohort['index_time'].dt.year < 2018]\n",
+    "    test_labels = df_cohort[df_cohort['index_time'].dt.year == 2018]\n",
+    "else:\n",
+    "    train_labels = df_cohort[df_cohort['index_time'].dt.year < 2019]\n",
+    "    test_labels = df_cohort[df_cohort['index_time'].dt.year == 2019]\n",
+    "\n",
+    "for a, b in zip(train_labels['pat_enc_csn_id_coded'].values, train_csns):\n",
+    "    assert a == b\n",
+    "for a, b in zip(test_labels['pat_enc_csn_id_coded'].values, test_csns):\n",
+    "    assert a == b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.sparse import csr_matrix, save_npz\n",
+    "\n",
+    "path = '/home/ccorbin/er_infection/data/'\n",
+    "os.makedirs(path, exist_ok=True)\n",
+    "\n",
+    "# Save feature matrix\n",
+    "save_npz(os.path.join(path, 'training_examples.npz'), train_csr)\n",
+    "save_npz(os.path.join(path, 'test_examples.npz'), test_csr)\n",
+    "\n",
+    "# Save labels\n",
+    "train_labels.to_csv(os.path.join(path, 'training_labels.csv'), index=None)\n",
+    "test_labels.to_csv(os.path.join(path, 'test_labels.csv'), index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/featurizing/to_sparse_c.ipynb b/scripts/ER_Infection/notebooks/featurizing/to_sparse_c.ipynb
new file mode 100644
index 00000000..5892bbda
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/featurizing/to_sparse_c.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transform Long Form Count Features to Sparse Matrices\n",
+    "\n",
+    "Generate train+val and test sets.  Not going to do a model selection procedure for this.  We'll simply use a random forest. We'll then cross fit a calibrator on the test set.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "from google.cloud import bigquery\n",
+    "from tqdm import tqdm\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_bq_to_pandas(query, nrows, chunksize=500000):\n",
+    "    offsets = [i for i in range(0, nrows, chunksize)]\n",
+    "    df = pd.DataFrame()\n",
+    "    for offset in tqdm(offsets):\n",
+    "        query_str = query + \" LIMIT {chunksize} OFFSET {offset}\"\n",
+    "        query_str = query_str.format(chunksize=chunksize, offset=offset)\n",
+    "        query_job = client.query(query_str)\n",
+    "        df_slice = query_job.result().to_dataframe()\n",
+    "        df = pd.concat([df, df_slice])\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "    f.*, EXTRACT(YEAR from f.index_time) year\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.feature_counts_long f\n",
+    "RIGHT JOIN\n",
+    "    mining-clinical-decisions.abx.final_cohort_table c\n",
+    "USING \n",
+    "    (pat_enc_csn_id_coded)\n",
+    "ORDER BY \n",
+    "    pat_enc_csn_id_coded, feature_type, features\n",
+    "\"\"\"\n",
+    "df = read_bq_to_pandas(query, nrows=8770634, chunksize=500000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Sanity check - no duplicate rows\n",
+    "assert(len(df) == len(df.drop_duplicates()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.sparse import csr_matrix, save_npz\n",
+    "\n",
+    "def build_vocab(data):\n",
+    "    \"\"\"\n",
+    "    1. Builds vocabulary for of terms from the data.\n",
+    "    2. Assigns each unique term to a monotonically increasing integer.\n",
+    "    \"\"\"\n",
+    "    vocabulary = {}\n",
+    "    for i, d in enumerate(data):\n",
+    "        for j, term in enumerate(d):\n",
+    "            vocabulary.setdefault(term, len(vocabulary))\n",
+    "    return vocabulary\n",
+    "\n",
+    "def create_sparse_feature_matrix(train_data, apply_data):\n",
+    "    \"\"\"Creates sparse matrix efficiently from long form dataframe.  \n",
+    "       1. Builds a vocabulary from the training set,\n",
+    "       2. Then applies vocab to the training + eval set\n",
+    "       \n",
+    "       We only want to include terms in our feature matrix that exist in \n",
+    "       the training set.  \n",
+    "       \n",
+    "       Parameters\n",
+    "       ----------\n",
+    "       train_data : long form pandas DataFrame\n",
+    "           Data to use to build vocabulary\n",
+    "       apply_data : long form pandas DataFrame\n",
+    "           Data to transform to sparse matrix for input to ML models\n",
+    "    \n",
+    "       Returns\n",
+    "       -------\n",
+    "       csr_data : scipy csr_matrix\n",
+    "           Sparse matrix version of apply_data to feed into ML models. \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Group long form train data by CSN (our unit of observation) and get list of features and values for each\n",
+    "    train_features = (train_data\n",
+    "        .groupby('pat_enc_csn_id_coded')\n",
+    "        .agg({'features' : lambda x: list(x),\n",
+    "              'value' : lambda x: list(x)})\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    train_feature_names = [doc for doc in train_features.features.values]\n",
+    "    train_feature_values = [doc for doc in train_features['value'].values]\n",
+    "    train_csns = [csn for csn in train_features.pat_enc_csn_id_coded.values]\n",
+    "    \n",
+    "    # Group long form apply data by CSN (our unit of observation) and get list of features and values for each\n",
+    "    apply_features = (apply_data\n",
+    "        .groupby('pat_enc_csn_id_coded')\n",
+    "        .agg({'features' : lambda x: list(x),\n",
+    "              'value' : lambda x: list(x)})\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    apply_features_names = [doc for doc in apply_features.features.values]\n",
+    "    apply_features_values = [doc for doc in apply_features['value'].values]\n",
+    "    apply_csns = [csn for csn in apply_features.pat_enc_csn_id_coded.values]\n",
+    "\n",
+    "    # Build vocab from training set feature names\n",
+    "    vocabulary = build_vocab(train_feature_names)\n",
+    "    \n",
+    "    # Build up csr matrix\n",
+    "    indptr = [0]\n",
+    "    indices = []\n",
+    "    data = []\n",
+    "    for i, d in enumerate(apply_features_names):\n",
+    "        for j, term in enumerate(d):\n",
+    "            if term not in vocabulary: # make sure term is in training set\n",
+    "                continue\n",
+    "            else:\n",
+    "                indices.append(vocabulary[term])\n",
+    "                data.append(apply_features_values[i][j])\n",
+    "            if j == 0:\n",
+    "                # Add zero to data and max index in vocabulary to indices in case max feature indice isn't in apply features.\n",
+    "                indices.append(len(vocabulary)-1)\n",
+    "                data.append(0)\n",
+    "        indptr.append(len(indices))\n",
+    "    \n",
+    "    csr_data = csr_matrix((data, indices, indptr), dtype=float)\n",
+    "    \n",
+    "    return csr_data, apply_csns, vocabulary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "    \n",
+    "not_included = ['Lab_Results_val', 'Flowsheet_val']\n",
+    "df = (df\n",
+    "    .query(\"feature_type not in @not_included\", engine='python')\n",
+    ")\n",
+    "training_examples = df[df['year'] < 2019]\n",
+    "test_examples = df[df['year'] == 2019]\n",
+    "\n",
+    "# Get sparce matrices for each set\n",
+    "train_csr, train_csns, train_vocab = create_sparse_feature_matrix(training_examples, training_examples)\n",
+    "test_csr, test_csns, test_vocab = create_sparse_feature_matrix(training_examples, test_examples)\n",
+    "\n",
+    "# Query cohort table for labels\n",
+    "q_cohort = \"\"\"\n",
+    "SELECT \n",
+    "    * \n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.final_cohort_table\n",
+    "ORDER BY\n",
+    "    pat_enc_csn_id_coded\n",
+    "\"\"\"\n",
+    "query_job = client.query(q_cohort)\n",
+    "df_cohort = query_job.result().to_dataframe()\n",
+    "\n",
+    "train_labels = df_cohort[df_cohort['index_time'].dt.year < 2019]\n",
+    "test_labels = df_cohort[df_cohort['index_time'].dt.year == 2019]\n",
+    "\n",
+    "# Sanity check - make sure CSNs from labels and features in same order\n",
+    "for a, b in zip(train_labels['pat_enc_csn_id_coded'].values, train_csns):\n",
+    "    try:\n",
+    "        assert a == b\n",
+    "    except:\n",
+    "        pdb.set_trace()\n",
+    "for a, b in zip(test_labels['pat_enc_csn_id_coded'].values, test_csns):\n",
+    "    assert a == b\n",
+    "\n",
+    "# Create output path\n",
+    "path = '/Users/conorcorbin/repos/er_infection/data/ast_models_c/'\n",
+    "os.makedirs(path, exist_ok=True)\n",
+    "\n",
+    "# Save sparce matrices\n",
+    "save_npz(os.path.join(path, 'training_examples_round_test.npz'), train_csr)\n",
+    "save_npz(os.path.join(path, 'test_examples_round_test.npz'), test_csr)\n",
+    "\n",
+    "# Save labels\n",
+    "train_labels.to_csv(os.path.join(path, 'training_labels_round_test.csv'), index=None)\n",
+    "test_labels.to_csv(os.path.join(path, 'test_labels_round_test.csv'), index=None)\n",
+    "\n",
+    "# Save test vocab using train vocab of test round\n",
+    "df_train_vocab = pd.DataFrame(data={\n",
+    "    'features' : [t for t in train_vocab],\n",
+    "    'indices' : [train_vocab[t] for t in train_vocab]\n",
+    "})\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fold_data  = []\n",
+    "for fold in folds:\n",
+    "    train_csr, train_csns, train_vocab = create_sparse_feature_matrix(fold['training_examples'], fold['training_examples'])\n",
+    "    test_csr, test_csns, test_and_val_vocab = create_sparse_feature_matrix(fold['training_examples'], fold['test_examples'])\n",
+    "    \n",
+    "    fold_data.append({'train_csr' : train_csr, 'train_csns' : train_csns, 'train_vocab': train_vocab,\n",
+    "                       'test_csr' : test_csr, 'test_csns' : test_csns, 'test_and_val_vocab' : test_and_val_vocab})\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q_cohort = \"\"\"\n",
+    "SELECT * \n",
+    "FROM mining-clinical-decisions.abx.final_cohort_table\n",
+    "ORDER BY pat_enc_csn_id_coded\n",
+    "\"\"\"\n",
+    "query_job = client.query(q_cohort)\n",
+    "df_cohort = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(ROUND)\n",
+    "fold_labels = []\n",
+    "for i, year in enumerate(fold_years):\n",
+    "    training_labels = df_cohort[(df_cohort['index_time'].dt.year < end_year) & (df_cohort['index_time'].dt.year != year)]\n",
+    "    test_labels = df_cohort[(df_cohort['index_time'].dt.year == year)]\n",
+    "    fold_labels.append({'training_labels' : training_labels, 'test_labels' : test_labels})\n",
+    "\n",
+    "    # Sanity checks\n",
+    "    for a, b in zip(training_labels['pat_enc_csn_id_coded'].values, fold_data[i]['train_csns']):\n",
+    "        assert a == b\n",
+    "    for a, b in zip(test_labels['pat_enc_csn_id_coded'].values, fold_data[i]['test_csns']):\n",
+    "        assert a == b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.sparse import csr_matrix, save_npz\n",
+    "\n",
+    "base_path = '/home/ccorbin/er_infection/data/censored_task/{}'\n",
+    "\n",
+    "for i, year in enumerate(fold_years):\n",
+    "    path = base_path.format(year)\n",
+    "    os.makedirs(path, exist_ok=True)\n",
+    "    \n",
+    "    # Save feature matrix\n",
+    "    save_npz(os.path.join(path, 'training_examples.npz'), fold_data[i]['train_csr'])\n",
+    "    save_npz(os.path.join(path, 'test_examples.npz'), fold_data[i]['test_csr'])\n",
+    "\n",
+    "    # Save labels\n",
+    "    fold_labels[i]['training_labels'].to_csv(os.path.join(path, 'training_labels.csv'), index=None)\n",
+    "    fold_labels[i]['test_labels'].to_csv(os.path.join(path, 'test_labels.csv'), index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Classier Results Table Bucket 1.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Classier Results Table Bucket 1.ipynb
new file mode 100644
index 00000000..1e9bd19a
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Classier Results Table Bucket 1.ipynb	
@@ -0,0 +1,1420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook that Generates All Tables For Classifier Performance Bucket 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os, glob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test Set Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vancomycin_auroc: 0.717\n",
+      "Ampicillin_auroc: 0.621\n",
+      "Cefazolin_auroc: 0.674\n",
+      "Ceftriaxone_auroc: 0.688\n",
+      "Cefepime_auroc: 0.649\n",
+      "Zosyn_auroc: 0.641\n",
+      "Ciprofloxacin_auroc: 0.611\n",
+      "Meropenem_auroc: 0.685\n",
+      "Vancomycin_Meropenem_auroc: 0.731\n",
+      "Vancomycin_Zosyn_auroc: 0.699\n",
+      "Vancomycin_Cefepime_auroc: 0.697\n",
+      "Vancomycin_Ceftriaxone_auroc: 0.669\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pdb\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "def test_load_predictions(df):\n",
+    "    \"\"\"\n",
+    "    Reads in output of df, computes AUROC for each classifier and asserts that it equals the AUROC\n",
+    "    listed in the auroc.txt file associate with the classfier's directory\n",
+    "    \"\"\"\n",
+    "    base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "    for abx in abx_options:\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_auroc = os.path.join(path, 'auroc.txt')\n",
+    "        with open(f_auroc, 'r') as f:\n",
+    "            auroc = round(float(f.read()), 3)\n",
+    "        \n",
+    "        computed_auroc = round(roc_auc_score(df['%s_label' % abx], df['%s_predictions' % abx]), 3)\n",
+    "        \n",
+    "        try:\n",
+    "            assert auroc == computed_auroc\n",
+    "        except:\n",
+    "            pdb.set_trace()\n",
+    "        print(\"%s_auroc: %s\"% (abx, str(auroc)))\n",
+    "\n",
+    "def load_predictions():\n",
+    "    \"\"\"Helper function that loads predictions from AST classifiers for test set data\"\"\"\n",
+    "    \n",
+    "    base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "    df = pd.DataFrame()\n",
+    "    for i, abx in enumerate(abx_options):\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "        if i == 0:\n",
+    "            df = pd.read_csv(f_path)\n",
+    "            df = df[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df = df.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                    'predictions' : '%s_predictions' % abx})\n",
+    "        else:\n",
+    "            df_preds = pd.read_csv(f_path)\n",
+    "            df_preds = df_preds[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df_preds = df_preds.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                                'predictions' : '%s_predictions' % abx})\n",
+    "            df = df.merge(df_preds, how='left', on=['anon_id', 'pat_enc_csn_id_coded'])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "df = load_predictions()\n",
+    "test_load_predictions(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Table with bootstrapped 95% CI intervals for auroc and average precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bootstrapped_performance(labels, predictions):\n",
+    "    \"\"\"Given labels and predictions, bootstraps auroc and average precision\n",
+    "       and returns mean values along with 95%ci range for both of them\"\"\"\n",
+    "    \n",
+    "    aurocs,  average_precisions = [], []\n",
+    "    inds = [i for i in range(len(labels))]\n",
+    "    for i in range(1000):\n",
+    "        inds_boot = np.random.choice(inds, size=len(inds), replace=True)\n",
+    "        labels_b = labels[inds_boot]\n",
+    "        predictions_b = predictions[inds_boot]\n",
+    "        aurocs.append(roc_auc_score(labels_b, predictions_b))\n",
+    "        average_precisions.append(average_precision_score(labels_b, predictions_b))\n",
+    "    \n",
+    "    mean_auc = np.mean(aurocs)\n",
+    "    auc_low = np.percentile(aurocs, 2.5)\n",
+    "    auc_high = np.percentile(aurocs, 97.5)\n",
+    "    auroc = \"%.2f [%.2f, %.2f]\" % (mean_auc, auc_low, auc_high)\n",
+    "    \n",
+    "    mean_ap = np.mean(average_precisions)\n",
+    "    ap_low = np.percentile(average_precisions, 2.5)\n",
+    "    ap_high = np.percentile(average_precisions, 97.5)\n",
+    "    average_precision = \"%.2f [%.2f, %.2f]\" % (mean_ap, ap_low, ap_high)\n",
+    "    \n",
+    "    prev = str(round(np.average(labels), 2))\n",
+    "    \n",
+    "    return auroc, average_precision, prev\n",
+    "\n",
+    "\n",
+    "df_test_results = pd.DataFrame()\n",
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "df_test_results['Antibiotic Selection'] = [a for a in abx_options]\n",
+    "\n",
+    "\n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "best_model_classes = []\n",
+    "prevs = []\n",
+    "aps = []\n",
+    "aurocs = []\n",
+    "for abx in abx_options:\n",
+    "    path = base_path.format(abx=abx)\n",
+    "    f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "    best_model_class = f_path.split('/')[-1].split('_')[0]\n",
+    "    best_model_classes.append(best_model_class)\n",
+    "    auroc, ap, prev = bootstrapped_performance(df['%s_label' % abx].values,\n",
+    "                                               df['%s_predictions' % abx].values)\n",
+    "    prevs.append(prev)\n",
+    "    aps.append(ap)\n",
+    "    aurocs.append(auroc)\n",
+    "df_test_results['Best Model Class'] = best_model_classes\n",
+    "df_test_results['Positive Class Prevalence'] = prevs\n",
+    "df_test_results['Average Precision'] = aps\n",
+    "df_test_results['AUROC'] = aurocs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Antibiotic Selection</th>\n",
+       "      <th>Best Model Class</th>\n",
+       "      <th>Positive Class Prevalence</th>\n",
+       "      <th>Average Precision</th>\n",
+       "      <th>AUROC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Vancomycin</td>\n",
+       "      <td>lightgbm</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.46 [0.40, 0.52]</td>\n",
+       "      <td>0.72 [0.69, 0.75]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ampicillin</td>\n",
+       "      <td>lightgbm</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>0.54 [0.50, 0.58]</td>\n",
+       "      <td>0.62 [0.59, 0.65]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cefazolin</td>\n",
+       "      <td>lightgbm</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>0.72 [0.69, 0.76]</td>\n",
+       "      <td>0.67 [0.64, 0.70]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ceftriaxone</td>\n",
+       "      <td>lightgbm</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.80 [0.76, 0.82]</td>\n",
+       "      <td>0.69 [0.66, 0.72]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Cefepime</td>\n",
+       "      <td>random</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.87 [0.85, 0.89]</td>\n",
+       "      <td>0.65 [0.61, 0.68]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Antibiotic Selection Best Model Class Positive Class Prevalence  \\\n",
+       "0           Vancomycin         lightgbm                      0.23   \n",
+       "1           Ampicillin         lightgbm                      0.43   \n",
+       "2            Cefazolin         lightgbm                      0.59   \n",
+       "3          Ceftriaxone         lightgbm                      0.66   \n",
+       "4             Cefepime           random                       0.8   \n",
+       "\n",
+       "   Average Precision              AUROC  \n",
+       "0  0.46 [0.40, 0.52]  0.72 [0.69, 0.75]  \n",
+       "1  0.54 [0.50, 0.58]  0.62 [0.59, 0.65]  \n",
+       "2  0.72 [0.69, 0.76]  0.67 [0.64, 0.70]  \n",
+       "3  0.80 [0.76, 0.82]  0.69 [0.66, 0.72]  \n",
+       "4  0.87 [0.85, 0.89]  0.65 [0.61, 0.68]  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test_results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results = df_test_results.sort_values('Positive Class Prevalence')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results.to_csv(\"Table 3: Bucket 1 Classifier Results.csv\", index=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Precision recall curves for each classifier  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>Vancomycin_label</th>\n",
+       "      <th>Vancomycin_predictions</th>\n",
+       "      <th>Ampicillin_label</th>\n",
+       "      <th>Ampicillin_predictions</th>\n",
+       "      <th>Cefazolin_label</th>\n",
+       "      <th>Cefazolin_predictions</th>\n",
+       "      <th>Ceftriaxone_label</th>\n",
+       "      <th>Ceftriaxone_predictions</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Meropenem_label</th>\n",
+       "      <th>Meropenem_predictions</th>\n",
+       "      <th>Vancomycin_Meropenem_label</th>\n",
+       "      <th>Vancomycin_Meropenem_predictions</th>\n",
+       "      <th>Vancomycin_Zosyn_label</th>\n",
+       "      <th>Vancomycin_Zosyn_predictions</th>\n",
+       "      <th>Vancomycin_Cefepime_label</th>\n",
+       "      <th>Vancomycin_Cefepime_predictions</th>\n",
+       "      <th>Vancomycin_Ceftriaxone_label</th>\n",
+       "      <th>Vancomycin_Ceftriaxone_predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC2a03b24</td>\n",
+       "      <td>131260812263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.108161</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.462078</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.896608</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.987670</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.964979</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.961668</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.871461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCe45a3c</td>\n",
+       "      <td>131260883970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.483241</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.538177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.631024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.784364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.883272</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.987744</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.966207</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.970636</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.904466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd235bb</td>\n",
+       "      <td>131261001599</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.215610</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501686</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.631382</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.653581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.815785</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.988870</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.965573</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.956648</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.897293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCd29af0</td>\n",
+       "      <td>131261001696</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.321022</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.351406</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.459186</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.424984</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.554059</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.980191</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.947425</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.948773</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.805817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd356bf</td>\n",
+       "      <td>131261014293</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.421153</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.412029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.528823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.724792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.839434</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.954408</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.963848</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.946225</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.840946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  Vancomycin_label  Vancomycin_predictions  \\\n",
+       "0  JC2a03b24          131260812263                 0                0.108161   \n",
+       "1   JCe45a3c          131260883970                 1                0.483241   \n",
+       "2   JCd235bb          131261001599                 0                0.215610   \n",
+       "3   JCd29af0          131261001696                 0                0.321022   \n",
+       "4   JCd356bf          131261014293                 1                0.421153   \n",
+       "\n",
+       "   Ampicillin_label  Ampicillin_predictions  Cefazolin_label  \\\n",
+       "0                 0                0.462078                0   \n",
+       "1                 1                0.538177                0   \n",
+       "2                 0                0.501686                1   \n",
+       "3                 0                0.351406                0   \n",
+       "4                 1                0.412029                1   \n",
+       "\n",
+       "   Cefazolin_predictions  Ceftriaxone_label  Ceftriaxone_predictions  ...  \\\n",
+       "0               0.664275                  1                 0.798270  ...   \n",
+       "1               0.631024                  0                 0.784364  ...   \n",
+       "2               0.631382                  1                 0.653581  ...   \n",
+       "3               0.459186                  0                 0.424984  ...   \n",
+       "4               0.528823                  1                 0.724792  ...   \n",
+       "\n",
+       "   Meropenem_label  Meropenem_predictions  Vancomycin_Meropenem_label  \\\n",
+       "0                1               0.896608                           1   \n",
+       "1                0               0.883272                           1   \n",
+       "2                1               0.815785                           1   \n",
+       "3                1               0.554059                           1   \n",
+       "4                1               0.839434                           1   \n",
+       "\n",
+       "   Vancomycin_Meropenem_predictions  Vancomycin_Zosyn_label  \\\n",
+       "0                          0.987670                       1   \n",
+       "1                          0.987744                       1   \n",
+       "2                          0.988870                       1   \n",
+       "3                          0.980191                       1   \n",
+       "4                          0.954408                       1   \n",
+       "\n",
+       "   Vancomycin_Zosyn_predictions  Vancomycin_Cefepime_label  \\\n",
+       "0                      0.964979                          1   \n",
+       "1                      0.966207                          1   \n",
+       "2                      0.965573                          1   \n",
+       "3                      0.947425                          1   \n",
+       "4                      0.963848                          1   \n",
+       "\n",
+       "   Vancomycin_Cefepime_predictions  Vancomycin_Ceftriaxone_label  \\\n",
+       "0                         0.961668                             1   \n",
+       "1                         0.970636                             1   \n",
+       "2                         0.956648                             1   \n",
+       "3                         0.948773                             0   \n",
+       "4                         0.946225                             1   \n",
+       "\n",
+       "   Vancomycin_Ceftriaxone_predictions  \n",
+       "0                            0.871461  \n",
+       "1                            0.904466  \n",
+       "2                            0.897293  \n",
+       "3                            0.805817  \n",
+       "4                            0.840946  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Vancomycin',\n",
+       " 'Ampicillin',\n",
+       " 'Cefazolin',\n",
+       " 'Ceftriaxone',\n",
+       " 'Cefepime',\n",
+       " 'Zosyn',\n",
+       " 'Ciprofloxacin',\n",
+       " 'Meropenem',\n",
+       " 'Vancomycin_Meropenem',\n",
+       " 'Vancomycin_Zosyn',\n",
+       " 'Vancomycin_Cefepime',\n",
+       " 'Vancomycin_Ceftriaxone']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abx_options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2160x2880 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set(style='white', font_scale=2.0)\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "abbs = {\n",
+    "    \"Ceftriaxone\" : \"Ceftriaxone\",\n",
+    "    \"Vancomycin_Zosyn\" : \"Vanc/Pip-Tazo\",\n",
+    "    \"Zosyn\" : \"Pip-Tazo\",\n",
+    "    \"Vancomycin_Ceftriaxone\" : \"Vanc/Ceftriaxone\",\n",
+    "    \"Vancomycin_Cefepime\" : \"Vanc/Cefepime\",\n",
+    "    \"Cefepime\" : \"Cefepime\",\n",
+    "    \"Vancomycin\" : \"Vanc\",\n",
+    "    \"Vancomycin_Meropenem\" : \"Vanc/Meropenem\",\n",
+    "    \"Meropenem\" : \"Meropenem\",\n",
+    "    \"Cefazolin\" : \"Cefazolin\",\n",
+    "    \"Ciprofloxacin\" : \"Ciprofloxacin\",\n",
+    "    \"Ampicillin\" : \"Ampicillin\"\n",
+    "}\n",
+    "\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 3, figsize=(30, 40))\n",
+    "row, col = 0, 0\n",
+    "for abx in abx_options:\n",
+    "    precision, recall, _ = precision_recall_curve(df['%s_label' % abx], df['%s_predictions' % abx])\n",
+    "    average_precision = average_precision_score(df['%s_label' % abx], df['%s_predictions' % abx])\n",
+    "    base_prev = round(np.average(df['%s_label' % abx].values), 2)\n",
+    "    axs[row, col].plot(recall, precision, label=f\"Average Precision: {round(average_precision, 2)}\")\n",
+    "    axs[row, col].plot(recall, [base_prev for r in recall], 'k--', label=f\"Prevalence: {base_prev}\")\n",
+    "    axs[row, col].set_xlim([0.0, 1.0])\n",
+    "    axs[row, col].set_ylim([0.0, 1.05])\n",
+    "    axs[row, col].set_title(f\"{abbs[abx]}\")\n",
+    "    axs[row, col].set_xlabel('Recall')\n",
+    "    axs[row, col].set_ylabel('Precision')\n",
+    "    axs[row, col].legend()\n",
+    "    \n",
+    "    \n",
+    "    if col == 2:\n",
+    "        col = 0\n",
+    "        row += 1\n",
+    "    else:\n",
+    "        col +=1\n",
+    "\n",
+    "plt.savefig(\n",
+    "    './precision_recall_curves.png',\n",
+    "    bbox_inches='tight',\n",
+    "    dpi=300\n",
+    ")\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now do this for the Boston Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SXT best auc: 0.60325 best model: gbm\n",
+      "NIT best auc: 0.57413 best model: gbm\n",
+      "CIP best auc: 0.64089 best model: lasso\n",
+      "LVX best auc: 0.63747 best model: lasso\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def load_predictions(base_path):\n",
+    "    best_auc = 0.5\n",
+    "    best_model = None\n",
+    "    label = base_path.split('/')[-1]\n",
+    "    for model in ['ridge', 'lasso', 'gbm', 'random_forest']:\n",
+    "        auc_path = os.path.join(base_path, model, \"auroc.txt\")\n",
+    "        with open(auc_path, 'r') as f:\n",
+    "            auc = float(f.read().rstrip())\n",
+    "        if auc > best_auc:\n",
+    "            # Load predictions for this model\n",
+    "            path = os.path.join(base_path, model, f\"{model}_predictions.csv\")\n",
+    "            df_preds = pd.read_csv(path)\n",
+    "            best_auc = auc\n",
+    "            best_model = model\n",
+    "    print(f\"{label} best auc: {best_auc} best model: {best_model}\")\n",
+    "    return df_preds, best_model\n",
+    "\n",
+    "base_path = '/Users/conorcorbin/data/mit_abx_model_results/'\n",
+    "abbs = {\n",
+    "    'CIP' : 'Ciprofloxacin',\n",
+    "    'LVX' : 'Levofloxacin',\n",
+    "    'NIT' : 'Nitrofurantoin',\n",
+    "    'SXT' : 'Trimethoprim/Sulfamethoxazole'\n",
+    "}\n",
+    "\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(20, 20))\n",
+    "row, col = 0, 0\n",
+    "for abx in ['SXT', 'NIT', 'CIP', 'LVX']:\n",
+    "    df_preds, best_model = load_predictions(os.path.join(base_path, abx))\n",
+    "    labels = [1 if l == 0 else 0 for l in df_preds['label'].values]\n",
+    "    predictions = [1 - p for p in df_preds[\"predictions\"].values]\n",
+    "    precision, recall, _ = precision_recall_curve(labels, predictions)\n",
+    "    average_precision = average_precision_score(labels, predictions)\n",
+    "    base_prev = round(np.mean(labels), 2)\n",
+    "    axs[row, col].plot(recall, precision, label=f\"Average Precision: {round(average_precision, 2)}\")\n",
+    "    axs[row, col].plot(recall, [base_prev for r in recall], 'k--', label=f\"Prevalence: {base_prev}\")\n",
+    "    axs[row, col].set_xlim([0.0, 1.0])\n",
+    "    axs[row, col].set_ylim([0.0, 1.05])\n",
+    "    axs[row, col].set_title(f\"{abbs[abx]}\")\n",
+    "    axs[row, col].set_xlabel('Recall')\n",
+    "    axs[row, col].set_ylabel('Precision')\n",
+    "    axs[row, col].legend()\n",
+    "    \n",
+    "    if col == 1:\n",
+    "        col = 0\n",
+    "        row += 1\n",
+    "    else:\n",
+    "        col +=1\n",
+    "\n",
+    "plt.savefig(\n",
+    "    './precision_recall_curves_boston.png',\n",
+    "    bbox_inches='tight',\n",
+    "    dpi=300\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test Set AUROC For Unseen patients vs patients that have been seen, and the two combined.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query set patients from train + val sets. \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()\n",
+    "\n",
+    "# Read in feature type dataframe so we can map to it\n",
+    "query=\"\"\"\n",
+    "SELECT DISTINCT\n",
+    "    anon_id\n",
+    "FROM \n",
+    "    `mining-clinical-decisions.abx.final_ast_labels`\n",
+    "WHERE \n",
+    "    EXTRACT(YEAR FROM index_time) < 2019\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_training_pats = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_val_pats = set(df_training_pats.anon_id.values)\n",
+    "len(train_val_pats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bootstrapped_auroc(labels, predictions):\n",
+    "    \"\"\"Given labels and predictions, bootstraps auroc and average precision\n",
+    "       and returns mean values along with 95%ci range for both of them\"\"\"\n",
+    "    \n",
+    "    aurocs = []\n",
+    "    inds = [i for i in range(len(labels))]\n",
+    "    print(\"Number of positive labels:{pos} Number of Labels:{total}\".format(pos=str(labels.sum()),\n",
+    "                                                                            total=str(len(labels))))\n",
+    "    for i in range(1000):\n",
+    "        inds_boot = np.random.choice(inds, size=len(inds), replace=True)\n",
+    "        labels_b = labels[inds_boot]\n",
+    "        predictions_b = predictions[inds_boot]\n",
+    "        aurocs.append(roc_auc_score(labels_b, predictions_b))\n",
+    "    \n",
+    "    mean_auc = np.mean(aurocs)\n",
+    "    auc_low = np.percentile(aurocs, 2.5)\n",
+    "    auc_high = np.percentile(aurocs, 97.5)\n",
+    "    auroc = \"%.2f [%.2f, %.2f]\" % (mean_auc, auc_low, auc_high)\n",
+    "\n",
+    "    return auroc\n",
+    "\n",
+    "def stratified_bootstrapped_auroc(labels, predictions):\n",
+    "    \"\"\"Given labels and predictions, bootstraps auroc and average precision\n",
+    "       and returns mean values along with 95%ci range for both of them\"\"\"\n",
+    "    \n",
+    "    df_hats = pd.DataFrame(data={\n",
+    "        'labels' : labels,\n",
+    "        'predictions' : predictions\n",
+    "    })\n",
+    "    \n",
+    "    aurocs = []\n",
+    "    inds = [i for i in range(len(labels))]\n",
+    "    print(\"Number of positive labels:{pos} Number of Labels:{total}\".format(pos=str(labels.sum()),\n",
+    "                                                                            total=str(len(labels))))\n",
+    "    for i in range(1000):\n",
+    "        \n",
+    "        df_hats_boot = pd.DataFrame()\n",
+    "        for l in np.unique(labels):\n",
+    "            df_temp = (df_hats\n",
+    "                .query(\"labels == @l\", engine='python')\n",
+    "                .sample(n=len(df_hats[df_hats['labels']==l]), replace=True)\n",
+    "            )\n",
+    "            df_hats_boot = pd.concat([df_hats_boot, df_temp])\n",
+    "        \n",
+    "        aurocs.append(roc_auc_score(df_hats_boot['labels'], df_hats_boot['predictions']))\n",
+    "    \n",
+    "    mean_auc = np.mean(aurocs)\n",
+    "    auc_low = np.percentile(aurocs, 2.5)\n",
+    "    auc_high = np.percentile(aurocs, 97.5)\n",
+    "    auroc = \"%.2f [%.2f, %.2f]\" % (mean_auc, auc_low, auc_high)\n",
+    "    \n",
+    "    return auroc\n",
+    "\n",
+    "\n",
+    "\n",
+    "df_test_results = pd.DataFrame()\n",
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "df_test_results['Antibiotic Selection'] = [a for a in abx_options]\n",
+    "\n",
+    "\n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "aurocs_total, aurocs_repeat, aurocs_no_repeat = [], [], []\n",
+    "for abx in abx_options:\n",
+    "    print(abx)\n",
+    "    path = base_path.format(abx=abx)\n",
+    "    f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "    df = pd.read_csv(f_path)\n",
+    "    df_no_repeat_pats = (df\n",
+    "        .query(\"anon_id not in @train_val_pats\", engine='python')\n",
+    "    )\n",
+    "    df_repeat_pats = (df\n",
+    "        .query(\"anon_id in @train_val_pats\", engine='python')\n",
+    "    )\n",
+    "    print(\"Full\")\n",
+    "    auroc_t = stratified_bootstrapped_auroc(df['label'].values,\n",
+    "                               df['predictions'].values)\n",
+    "    print(\"Repeat\")\n",
+    "    aurocs_r = stratified_bootstrapped_auroc(df_repeat_pats['label'].values,\n",
+    "                                       df_repeat_pats['predictions'].values)\n",
+    "    print(\"No Repeat\")\n",
+    "    aurocs_nr = stratified_bootstrapped_auroc(df_no_repeat_pats['label'].values,\n",
+    "                                          df_no_repeat_pats['predictions'].values)\n",
+    "    aurocs_total.append(auroc_t)\n",
+    "    aurocs_repeat.append(aurocs_r)\n",
+    "    aurocs_no_repeat.append(aurocs_nr)\n",
+    "    \n",
+    "df_test_results['AUROC All Observations'] = aurocs_total\n",
+    "df_test_results['AUROC Patients Seen In Training'] = aurocs_repeat\n",
+    "df_test_results['AUROC Patients Not Seen In Training'] = aurocs_no_repeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results.to_csv('test_results_repeat_patients.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get Classifier Performance Stratified By Various Demographic Factors\n",
+    "* Age Buckets, Sex, Race, Ethnicity "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Pull in Demographics for patients in the test set\n",
+    "query=\"\"\"\n",
+    "WITH cohort AS (\n",
+    "SELECT DISTINCT\n",
+    "  anon_id, pat_enc_csn_id_coded, index_time\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.final_ast_labels`\n",
+    "WHERE \n",
+    "    EXTRACT(YEAR FROM index_time) = 2019\n",
+    "),\n",
+    "\n",
+    "adt_dep as (\n",
+    "SELECT DISTINCT\n",
+    "  adt.pat_enc_csn_id_coded, \n",
+    "  FIRST_VALUE (dm.department_name) OVER \n",
+    "  (PARTITION BY adt.pat_enc_csn_id_coded ORDER BY adt.effective_time_jittered_utc) department_name,\n",
+    "FROM \n",
+    "  `shc_core.adt` adt\n",
+    "INNER JOIN\n",
+    "  `som-nero-phi-jonc101.shc_core.dep_map` dm\n",
+    "USING\n",
+    "  (department_id)\n",
+    ")\n",
+    "\n",
+    "SELECT DISTINCT \n",
+    "  dep.department_name,\n",
+    "  DATE_DIFF(CAST(c.index_time as DATE), d.BIRTH_DATE_JITTERED, year) age,\n",
+    "  c.pat_enc_csn_id_coded,\n",
+    "  d.ANON_ID, d.GENDER, d.CANONICAL_RACE, d.CANONICAL_ETHNICITY,\n",
+    "  CASE WHEN d.LANGUAGE = \"English\" THEN \"English\"\n",
+    "  ELSE \"Non-English\" END LANGUAGE,\n",
+    "  CASE WHEN d.INSURANCE_PAYOR_NAME = \"MEDICARE\" THEN \"Medicare\"\n",
+    "  WHEN d.INSURANCE_PAYOR_NAME = \"MEDI-CAL\" THEN \"Medi-Cal\"\n",
+    "  ELSE \"Other\" END INSURANCE_PAYOR_NAME,\n",
+    "FROM \n",
+    "  `som-nero-phi-jonc101.shc_core.demographic` d\n",
+    "INNER JOIN\n",
+    "  cohort c\n",
+    "ON\n",
+    "  d.ANON_ID = c.anon_id\n",
+    "INNER JOIN\n",
+    "  adt_dep dep\n",
+    "ON\n",
+    "  c.pat_enc_csn_id_coded = dep.pat_enc_csn_id_coded\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_demographics = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_demographics.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_demographics['age'].plot.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_demographics.groupby('department_name').agg(num_observations=('pat_enc_csn_id_coded', 'nunique'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_demo = (df_demographics\n",
+    "    .assign(department_name=lambda x: ['Stanford ED' if a != 'VCP EMERGENCY DEPARTMENT' else 'Valley Care ED'\n",
+    "                                       for a in x.department_name])\n",
+    ")\n",
+    "df_demo.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_demo.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _get_perf_by_strata(df, aurocs, column_name, name='race'):\n",
+    "    aurocs[name] = {}\n",
+    "    strata = df[column_name].unique()\n",
+    "    for s in strata:\n",
+    "        df_strata = df[df[column_name]==s]\n",
+    "        num_positive = df_strata['label'].sum()\n",
+    "        total = len(df_strata)\n",
+    "        # Check if we have at least one sample from each class\n",
+    "        if len(df_strata['label'].unique()) != 2:\n",
+    "            aurocs[name][s] = (num_positive, total, np.nan) # auroc undefined in this case\n",
+    "        else:\n",
+    "            auc = stratified_bootstrapped_auroc(df_strata['label'].values,\n",
+    "                                                df_strata['predictions'].values)\n",
+    "            aurocs[name][s] = (num_positive, total, auc)\n",
+    "    return aurocs\n",
+    "\n",
+    "def get_performance_by_demographics(df):\n",
+    "    \"\"\"With a pandas dataframe containing labels, predicitons, and variables to stratify over,\n",
+    "       computes performance measures over strata and retuns a table of AUROCS\n",
+    "    \"\"\"\n",
+    "    aurocs = {}\n",
+    "    \n",
+    "    ### Get performance on all observations\n",
+    "    aurocs[''] = {}\n",
+    "    auc = stratified_bootstrapped_auroc(df['label'].values,\n",
+    "                                        df['predictions'].values)\n",
+    "    num_positive = df['label'].sum()\n",
+    "    total = len(df)\n",
+    "    aurocs['']['All Observations'] = (num_positive, total, auc)\n",
+    "    \n",
+    "    ### Get performance stratified by age buckets. We'll do 18-30, 31-50, 51-60, 61-70, 71-80, 81-90\n",
+    "    buckets = [(18, 30), (31, 50), (51, 60), (61, 70), (71, 80), (81, 90)]\n",
+    "    aurocs['Age'] = {}\n",
+    "    for age_bucket in buckets:\n",
+    "        df_age_bucket = (df\n",
+    "            .query(\"age >= @age_bucket[0] and age <= @age_bucket[1]\", engine='python')\n",
+    "        )\n",
+    "        # Check if we have at least one sample from each class\n",
+    "        num_positive = df_age_bucket['label'].sum()\n",
+    "        total = len(df_age_bucket)\n",
+    "        if len(df_age_bucket['label'].unique()) != 2: \n",
+    "            aurocs['Age'][age_bucket] = (num_positive, total, np.nan) # auroc undefined in this case\n",
+    "        else:\n",
+    "            auc = stratified_bootstrapped_auroc(df_age_bucket['label'].values,\n",
+    "                                                df_age_bucket['predictions'].values)\n",
+    "            aurocs['Age'][age_bucket] = (num_positive, total, auc)\n",
+    "        \n",
+    "        \n",
+    "    # Get Performance By Other Demographics\n",
+    "    aurocs = _get_perf_by_strata(df, aurocs, 'CANONICAL_RACE', name='Race')\n",
+    "    aurocs = _get_perf_by_strata(df, aurocs, 'department_name', name='Department ID')\n",
+    "    aurocs = _get_perf_by_strata(df, aurocs, 'GENDER', name='Sex')\n",
+    "    aurocs = _get_perf_by_strata(df, aurocs, 'CANONICAL_ETHNICITY', name='Ethnicity')\n",
+    "    aurocs = _get_perf_by_strata(df, aurocs, 'LANGUAGE', name='Language')\n",
+    "    aurocs = _get_perf_by_strata(df, aurocs, 'INSURANCE_PAYOR_NAME', name='Insurance Payer')\n",
+    "    \n",
+    "    \n",
+    "    return aurocs\n",
+    "    \n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "os.makedirs('./results_by_demographics/', exist_ok=True)\n",
+    "for abx in abx_options:\n",
+    "    df_results = pd.DataFrame()\n",
+    "    print(abx)\n",
+    "    path = base_path.format(abx=abx)\n",
+    "    f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "    df = pd.read_csv(f_path)\n",
+    "    df_merged = df_demo.merge(df[['pat_enc_csn_id_coded', 'label', 'predictions']],\n",
+    "                            how='inner', on='pat_enc_csn_id_coded')\n",
+    "    aurocs = get_performance_by_demographics(df_merged)\n",
+    "\n",
+    "    for strata_type in aurocs:\n",
+    "        df_results_temp = pd.DataFrame()\n",
+    "        df_results_temp['Stratified By'] = [strata_type for a in range(len(aurocs[strata_type]))]\n",
+    "        strata, num_pos, total, aucs = [], [], [], []\n",
+    "        for group in aurocs[strata_type]:\n",
+    "            strata.append(group)\n",
+    "            num_pos.append(aurocs[strata_type][group][0])\n",
+    "            total.append(aurocs[strata_type][group][1])\n",
+    "            aucs.append(aurocs[strata_type][group][2])\n",
+    "\n",
+    "        df_results_temp['Group'] = strata\n",
+    "        df_results_temp['# Positive Examples'] = num_pos\n",
+    "        df_results_temp['# Total Examples'] = total\n",
+    "        df_results_temp['AUROC'] = aucs\n",
+    "        df_results = pd.concat([df_results, df_results_temp])\n",
+    "\n",
+    "    # Write to csv\n",
+    "    df_results.to_csv(os.path.join('./results_by_demographics/', '%s_results_by_demographics.csv' % abx), index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results = pd.DataFrame()\n",
+    "for strata_type in aurocs:\n",
+    "    df_results_temp = pd.DataFrame()\n",
+    "    df_results_temp['Stratified By'] = [strata_type for a in range(len(aurocs[strata_type]))]\n",
+    "    strata, aurocs = [], []\n",
+    "    for group in aurocs[strata_type]:\n",
+    "        strata.append(group)\n",
+    "        aurocs.append(aurocs[strata_type][group])\n",
+    "\n",
+    "    df_results_temp['Group'] = strata\n",
+    "    df_results_temp['AUROC'] = aurocs\n",
+    "    df_results = pd.concat([df_results, df_results_temp])\n",
+    "\n",
+    "df_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validation Set Results\n",
+    "Create a table that has four rows for each antibiotic corresponding to model class.  We'll list AUROC on validation set and chosen hyperparameters.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import glob, os\n",
+    "import pdb\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "def bootstrapped_performance(labels, predictions):\n",
+    "    \"\"\"Given labels and predictions, bootstraps auroc and average precision\n",
+    "       and returns mean values along with 95%ci range for both of them\"\"\"\n",
+    "    \n",
+    "    aurocs = []\n",
+    "    inds = [i for i in range(len(labels))]\n",
+    "    for i in range(1000):\n",
+    "        inds_boot = np.random.choice(inds, size=len(inds), replace=True)\n",
+    "        labels_b = labels[inds_boot]\n",
+    "        predictions_b = predictions[inds_boot]\n",
+    "        aurocs.append(roc_auc_score(labels_b, predictions_b))\n",
+    "    \n",
+    "    mean_auc = np.mean(aurocs)\n",
+    "    auc_low = np.percentile(aurocs, 2.5)\n",
+    "    auc_high = np.percentile(aurocs, 97.5)\n",
+    "    auroc = \"%.2f [%.2f, %.2f]\" % (mean_auc, auc_low, auc_high)\n",
+    "        \n",
+    "    return auroc\n",
+    "\n",
+    "\n",
+    "df_test_results = pd.DataFrame()\n",
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "\n",
+    "\n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/validation/{model}/{abx}\"\n",
+    "model_class = []\n",
+    "prevs = []\n",
+    "aurocs = []\n",
+    "abxs = []\n",
+    "best_params_list = []\n",
+    "for abx in abx_options:\n",
+    "    for model in ['lasso', 'ridge', 'random_forest', 'lightgbm']:\n",
+    "        path = base_path.format(model=model, abx=abx)\n",
+    "        abxs.append(abx)\n",
+    "        model_class.append(model)\n",
+    "        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "        df = pd.read_csv(f_path)\n",
+    "        auroc = bootstrapped_performance(df['label'].values,\n",
+    "                                         df['predictions'].values)\n",
+    "        aurocs.append(auroc)\n",
+    "        \n",
+    "        f_path = glob.glob(os.path.join(path, '*.json'))[0]\n",
+    "        with open(f_path) as f:\n",
+    "            best_params = json.load(f)\n",
+    "        best_params_list.append(best_params)\n",
+    "\n",
+    "\n",
+    "df_test_results['Antibiotic Selection'] = abxs\n",
+    "df_test_results['Model Class'] = model_class\n",
+    "df_test_results['AUROC'] = aurocs\n",
+    "df_test_results['Best Hyperparameters'] = best_params_list\n",
+    "df_test_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results.to_csv(\"Validation_results_bucket1.csv\", index=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature importances for test set classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()\n",
+    "\n",
+    "# Read in feature type dataframe so we can map to it\n",
+    "query=\"\"\"\n",
+    "SELECT DISTINCT\n",
+    "    feature_type, features\n",
+    "FROM \n",
+    "    `mining-clinical-decisions.abx.feature_counts_long`\n",
+    "WHERE \n",
+    "    feature_type NOT IN (\"Lab Results_val\", \"Flowsheet_val\")\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_features = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}/\"\n",
+    "feature_map_path = '/Users/conorcorbin/repos/er_infection/data/ast_models_bucket1/feature_vocabulary.csv'\n",
+    "df_feature_map = pd.read_csv(feature_map_path)\n",
+    "df_feature_imps = pd.DataFrame()\n",
+    "for abx in abx_options:\n",
+    "    path = base_path.format(abx=abx)\n",
+    "    fpath = glob.glob(os.path.join(path, '*importances.csv'))[0]\n",
+    "    df_imps = pd.read_csv(fpath)\n",
+    "    df_imps = (df_imps\n",
+    "        .merge(df_feature_map, how='inner', right_on='indices', left_on='feature_indices')\n",
+    "        .merge(df_features, how='left', on='features')\n",
+    "        .sort_values('feature_importances', ascending=False)\n",
+    "        .head(6) # Take top five only\n",
+    "    )\n",
+    "    df_imps['Antibiotic'] = [abx for i in range(len(df_imps))]\n",
+    "    df_imps['ModelClass'] = [fpath.split('/')[-1].split('_')[0] for i in range(len(df_imps))]\n",
+    "    df_imps = df_imps[['Antibiotic', 'ModelClass', 'features', 'feature_type']]\n",
+    "    df_feature_imps = pd.concat([df_feature_imps, df_imps])\n",
+    "df_feature_imps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_feature_imps.to_csv('feature_importances.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process table to make more clear\n",
+    "df_feature_imps = (df_feature_imps\n",
+    "    .assign(Antibiotic=lambda x: [a.replace('_', ' & ') for a in x.Antibiotic])\n",
+    "    .assign(ModelClass=lambda x: ['Gradient Boosted Tree' if a=='lightgbm' else 'Random Forest'\n",
+    "                                  for a in x.ModelClass])\n",
+    "    .assign(features=lambda x: ['Stanford ED' if a == 'PEDIATRIC EMERGENCY DEPARTMENT' else a\n",
+    "                                for a in x.features]) # pediatric and adult are the same thing and we see it coded as pediatric in our data even though its not\n",
+    ")\n",
+    "df_feature_imps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_feature_imps.to_csv('feature_importances.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Culture Result Times.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Culture Result Times.ipynb
new file mode 100644
index 00000000..75a7919c
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Culture Result Times.ipynb	
@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"\"\"\n",
+    "SELECT DISTINCT\n",
+    "    orders.order_proc_id_coded,\n",
+    "    orders.description,\n",
+    "    EXTRACT(YEAR FROM orders.order_time_jittered_utc) year,\n",
+    "    TIMESTAMP_DIFF(cs.result_time_jittered_utc, orders.order_time_jittered_utc, HOUR) hours_to_abx_sensitivities\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.culture_orders_within_24_hrs orders\n",
+    "INNER JOIN\n",
+    "    mining-clinical-decisions.shc_core.culture_sensitivity cs\n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.result().to_dataframe()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cultures = ['URINE CULTURE', 'BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLE)', 'BLOOD CULTURE (2 AEROBIC BOTTLES)' ]\n",
+    "train_years = ['2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']\n",
+    "test_years = ['2019']\n",
+    "df_time_to_results = (df\n",
+    ".assign(description=lambda x: ['Blood Culture' if \"BLOOD\" in a\n",
+    "                               else 'Urine Culture' if 'URINE' in a\n",
+    "                               else 'Other Fluid Culture' for a in x.description])\n",
+    ".assign(dataset=lambda x: ['Training Set' if str(year) in train_years else 'Test Set' for year in x.year])\n",
+    ".groupby(['description', 'dataset'])\n",
+    ".agg(mean_hours=('hours_to_abx_sensitivities', 'mean'),\n",
+    "     median_hours=('hours_to_abx_sensitivities', 'median'),\n",
+    "     std_hours=('hours_to_abx_sensitivities', 'std')\n",
+    "    )\n",
+    ".reset_index()\n",
+    ".sort_values(['dataset', 'description'], ascending=True)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_time_to_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_time_to_results.to_csv('time_to_results.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Extract method info for each positive culture (kirby bauer or MIC)\n",
+    "query=\"\"\"\n",
+    "SELECT DISTINCT\n",
+    "    orders.order_proc_id_coded,\n",
+    "    orders.description,\n",
+    "    antibiotic,\n",
+    "    sensitivity_value,\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.culture_orders_within_24_hrs orders\n",
+    "INNER JOIN\n",
+    "    mining-clinical-decisions.shc_core.culture_sensitivity cs\n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "WHERE\n",
+    "    UPPER(antibiotic) LIKE \"%METHOD%\"\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_method = query_job.result().to_dataframe()\n",
+    "df_method.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(df_method\n",
+    ".groupby('sensitivity_value')\n",
+    ".agg(num_orders=('order_proc_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Prevalance in trainign set vs test set for each of the twelve classifiers\n",
+    "query=\"\"\"\n",
+    "SELECT\n",
+    "    *, EXTRACT(YEAR FROM index_time) as year\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.final_ast_labels\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_labels = query_job.result().to_dataframe()\n",
+    "df_labels.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abx_columns = ['Ampicillin',\n",
+    "             'Ciprofloxacin',\n",
+    "             'Cefazolin',\n",
+    "             'Ceftriaxone',\n",
+    "             'Cefepime',\n",
+    "             'Zosyn',\n",
+    "             'Vancomycin',\n",
+    "             'Meropenem',\n",
+    "             'Vancomycin_Ceftriaxone',\n",
+    "             'Vancomycin_Cefepime',\n",
+    "             'Vancomycin_Zosyn',\n",
+    "             'Vancomycin_Meropenem']\n",
+    "df_labels[abx_columns].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Figure 2 Abx Selection Patterns.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Figure 2 Abx Selection Patterns.ipynb
new file mode 100644
index 00000000..28bc9518
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Figure 2 Abx Selection Patterns.ipynb	
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Figure 2: ER Doc Antibiotic Selection Patterns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import os \n",
+    "from google.cloud import bigquery\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded, l.index_time, om.med_description,\n",
+    "    l.Ampicillin, l.Ciprofloxacin, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin,\n",
+    "    l.Meropenem, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Cefepime, l.Vancomycin_Ceftriaxone\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.abx_orders_given_and_stopped` om\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.final_ast_labels` l\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "WHERE\n",
+    "    om.was_given = 1\n",
+    "ORDER BY \n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_time\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.result().to_dataframe()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "(df\n",
+    ".assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description])\n",
+    ".assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                    .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                   for a in x.med_description])\n",
+    ".assign(index_time=lambda x: x.index_time.dt.year)\n",
+    ".groupby('pat_enc_csn_id_coded')\n",
+    ".agg(med_description=('med_description', concat_abx),\n",
+    "     year=('index_time', 'first'))\n",
+    ".reset_index()\n",
+    ".groupby(['year', 'med_description'])\n",
+    ".agg(num_distinct_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['year', 'num_distinct_csns'], ascending=False)\n",
+    ".query(\"year == '2019'\")\n",
+    ").head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For now only use CSNs with exact matches to our abx selections\n",
+    "Talk to Jon about this later, can we assume `PIPERACILLIN-TAZOBACTAM CEFTRIAXONE VANCOMYCIN` falls into the `PIPERACILLIN-TAZOBACTAM VANCOMYCIN` category for instance. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "def compute_was_covered(x, random=False):\n",
+    "    \"\"\"\n",
+    "    Given med description, find appropriate label column and return whether patient was covered during CSN\n",
+    "    Returns \"Not in abx options\" if abx regimen isn't in our set of 12 options - useful for filtering later\n",
+    "    \"\"\"\n",
+    "    if not random:\n",
+    "        med_description = x.med_description\n",
+    "    else:\n",
+    "        med_description = x.random_med_description\n",
+    "        \n",
+    "    if med_description == \"CEFTRIAXONE\":\n",
+    "        return x.Ceftriaxone\n",
+    "    elif med_description == \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Zosyn\n",
+    "    elif med_description == \"PIPERACILLIN-TAZOBACTAM\":\n",
+    "        return x.Zosyn\n",
+    "    elif med_description == \"CEFTRIAXONE VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Ceftriaxone\n",
+    "    elif med_description == \"CEFEPIME VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Cefepime\n",
+    "    elif med_description == \"CEFEPIME\":\n",
+    "        return x.Cefepime\n",
+    "    elif med_description == \"VANCOMYCIN\":\n",
+    "        return x.Vancomycin\n",
+    "    elif med_description == \"MEROPENEM\":\n",
+    "        return x.Meropenem\n",
+    "    elif med_description == \"MEROPENEM VANCOMYCIN\":\n",
+    "        return x.Vancomycin_Meropenem\n",
+    "    elif med_description == \"CEFAZOLIN\":\n",
+    "        return x.Cefazolin\n",
+    "    elif med_description == \"CIPROFLOXACIN\":\n",
+    "        return x.Ciprofloxacin\n",
+    "    elif med_description == \"AMPICILLIN\":\n",
+    "        return x.Ampicillin\n",
+    "    else:\n",
+    "        return \"Not in abx options\"\n",
+    "\n",
+    "df_final = pd.DataFrame()\n",
+    "clin_covered_rates = []\n",
+    "random_covered_rates = []\n",
+    "for i in tqdm(range(100)):\n",
+    "    # Aggregate clinician orders\n",
+    "    df_drugs = (df\n",
+    "        .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description])\n",
+    "        .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                            .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                           for a in x.med_description])\n",
+    "        .assign(index_time=lambda x: x.index_time.dt.year)\n",
+    "        .groupby('pat_enc_csn_id_coded')\n",
+    "        .agg({'med_description' : concat_abx,\n",
+    "              'index_time' : 'first',\n",
+    "              'Ampicillin' : 'first',\n",
+    "              'Ciprofloxacin' : 'first',\n",
+    "              'Cefazolin' : 'first',\n",
+    "              'Ceftriaxone' : 'first',\n",
+    "              'Cefepime' : 'first',\n",
+    "              'Zosyn' : 'first',\n",
+    "              'Vancomycin' : 'first',\n",
+    "              'Meropenem' : 'first',\n",
+    "              'Vancomycin_Ceftriaxone' : 'first',\n",
+    "              'Vancomycin_Cefepime' : 'first',\n",
+    "              'Vancomycin_Zosyn' : 'first',\n",
+    "              'Vancomycin_Meropenem' : 'first'})\n",
+    "        .rename(columns={'index_time' : 'year'})\n",
+    "        .reset_index()    \n",
+    "    )\n",
+    "\n",
+    "    # Create flag for whether clinicians covered the patient during the csn, then filter for \n",
+    "    df_drugs = (df_drugs\n",
+    "        .query(\"year == 2019\")\n",
+    "        .sample(frac=1.0, replace=True) # bootstrap each iteration\n",
+    "        .assign(was_covered_dr=df_drugs.apply(lambda x: compute_was_covered(x), axis=1))\n",
+    "        .query(\"was_covered_dr == 1 or was_covered_dr == 0\")\n",
+    "        .assign(random_med_description=lambda x: np.random.choice(x.med_description, size=len(x.med_description), replace=False))\n",
+    "    )\n",
+    "\n",
+    "    # create a column for a randomly shuffled med description and then compute when random assignemnt covers patient\n",
+    "    df_drugs = (df_drugs\n",
+    "        .assign(was_covered_random=df_drugs.apply(lambda x: compute_was_covered(x, random=True), axis=1))\n",
+    "        .groupby(['med_description'])\n",
+    "        .agg(num_distinct_csns=('pat_enc_csn_id_coded', 'count'),\n",
+    "             num_times_covered_dr=('was_covered_dr', 'sum'),\n",
+    "             num_times_covered_random=('was_covered_random', 'sum'))\n",
+    "        .reset_index()\n",
+    "        .sort_values(['num_distinct_csns'], ascending=False)\n",
+    "    )\n",
+    "    \n",
+    "    clin_covered_rate = df_drugs['num_times_covered_dr'].sum() / df_drugs['num_distinct_csns'].sum()\n",
+    "    random_covered_rate = df_drugs['num_times_covered_random'].sum() / df_drugs['num_distinct_csns'].sum()\n",
+    "    clin_covered_rates.append(clin_covered_rate)\n",
+    "    random_covered_rates.append(random_covered_rate)\n",
+    "    \n",
+    "    df_final = pd.concat([df_final, df_drugs])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Clinician Coverage Rate:{:.2f} [{:.2f}-{:.2f}]\".format(np.mean(clin_covered_rates),\n",
+    "                                                              np.percentile(clin_covered_rates, 2.5),\n",
+    "                                                              np.percentile(clin_covered_rates, 97.5))\n",
+    "     )\n",
+    "\n",
+    "print(\"Random Coverage Rate:{:.2f} [{:.2f}-{:.2f}]\".format(np.mean(random_covered_rates),\n",
+    "                                                           np.percentile(random_covered_rates, 2.5),\n",
+    "                                                           np.percentile(random_covered_rates, 97.5))\n",
+    "     )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 2 Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
+    "sns.set(style='white', font_scale=1.5)\n",
+    "df_final = df_final.sort_values('med_description')\n",
+    "sns.barplot(x=\"num_distinct_csns\", y=\"med_description\", ci='sd', data=df_final, ax=ax, color='red' )\n",
+    "sns.barplot(x=\"num_times_covered_dr\", y=\"med_description\", ci=None, data=df_final, ax=ax, color='blue')\n",
+    "ax.set_ylabel(\"\")\n",
+    "ax.set_xlabel(\"ER Encounters\")\n",
+    "ax.set_title(\"Clinician Antibiotic Selections and Coverage\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot\n",
+    "import pdb\n",
+    "year_sets = [[\"2009\", \"2010\", \"2011\", \"2012\"],\n",
+    "             [\"2013\", \"2014\", \"2015\", \"2016\"],\n",
+    "             [\"2017\", \"2018\", \"2019\"]]\n",
+    "\n",
+    "fig, ax = plt.subplots(3, 1, figsize=(8, 24))\n",
+    "sns.set(style='white', font_scale=1.5)\n",
+    "row, col = 0, 0\n",
+    "for i, ys in enumerate(year_sets):\n",
+    "    df_year = (df_drugs\n",
+    "        .query(\"year in @ys\", engine='python')\n",
+    "        .groupby(\"med_description\")\n",
+    "        .agg({'num_distinct_csns' : 'sum',\n",
+    "              'num_times_covered' : 'sum'})\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    sns.barplot(x=\"num_distinct_csns\", y=\"med_description\", data=df_year, ax=ax[i], color='red')\n",
+    "    sns.barplot(x=\"num_times_covered\", y=\"med_description\", data=df_year, ax=ax[i], color='blue')\n",
+    "    ax[i].set_ylabel(\"\")\n",
+    "    ax[i].set_xlabel(\"ER Encounters\")\n",
+    "    ax[i].set_title(\"Clinician Antibiotic Selections %s-%s\" % (ys[0], ys[-1]))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/IPW vs NOT AUROC Estimates.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/IPW vs NOT AUROC Estimates.ipynb
new file mode 100644
index 00000000..da6e3ab5
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/IPW vs NOT AUROC Estimates.ipynb	
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IPW vs Not AUROC Estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import roc_auc_score, brier_score_loss, average_precision_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.calibration import CalibratedClassifierCV, calibration_curve\n",
+    "from sklearn.model_selection import cross_val_predict, StratifiedKFold\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import glob, os\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in probability of observing labels estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = '/Users/conorcorbin/repos/er_infection/data/results/ast_models_c/yhats.csv'\n",
+    "df_p_labels = pd.read_csv(path)\n",
+    "df_p_labels.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calibrate Predictions (Isotonic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class IdentityEstimator(LogisticRegression):\n",
+    "    def __init__(self):\n",
+    "        LogisticRegression.__init__(self)\n",
+    "            \n",
+    "    def predict_proba(self, input_array):   \n",
+    "        return input_array*1\n",
+    "\n",
+    "    def decision_function(self, input_array):\n",
+    "        return input_array*1\n",
+    "\n",
+    "def calibrate_probabilities(predictions, labels):\n",
+    "    \"\"\" \n",
+    "    Takes in test set probabilites and does a k-fold cross fitting procedure to recalibrate each model\n",
+    "    \"\"\"\n",
+    "    est = IdentityEstimator()\n",
+    "    X = predictions.values.reshape(-1, 1)\n",
+    "    y = labels\n",
+    "    isotonic_calibrated_predictions = np.array([float(i) for i in range(len(y))])\n",
+    "    sigmoid_calibrated_predictions = np.array([float(i) for i in range(len(y))])\n",
+    "\n",
+    "    # Fit base estimator\n",
+    "    est.fit(X, y) # because we've overloaded predict_proba and decision function this doesn't matter\n",
+    "\n",
+    "    # Calibrated with isotonic calibration\n",
+    "    isotonic = CalibratedClassifierCV(est, cv='prefit', method='isotonic')\n",
+    "\n",
+    "    # Calibrated with sigmoid calibration\n",
+    "    sigmoid = CalibratedClassifierCV(est, cv='prefit', method='sigmoid')\n",
+    "\n",
+    "    cv = StratifiedKFold(n_splits=10)\n",
+    "    for train_inds, val_inds in cv.split(X, y):\n",
+    "        X_train, y_train = X[train_inds], y[train_inds]\n",
+    "        X_val, y_val = X[val_inds], y[val_inds]\n",
+    "        isotonic.fit(X_train, y_train)\n",
+    "        isotonic_predictions = isotonic.predict_proba(X_val)\n",
+    "        isotonic_calibrated_predictions[val_inds] = isotonic_predictions[:, 1]\n",
+    "\n",
+    "        sigmoid.fit(X_train, y_train)\n",
+    "        sigmoid_predictions = sigmoid.predict_proba(X_val)\n",
+    "        sigmoid_calibrated_predictions[val_inds] = sigmoid_predictions[:, 1]\n",
+    "\n",
+    "    \n",
+    "    return sigmoid_calibrated_predictions, isotonic_calibrated_predictions\n",
+    "\n",
+    "s_preds, i_preds = calibrate_probabilities(df_p_labels['predictions'], df_p_labels['label'])\n",
+    "df_p_labels['s_preds'] = s_preds\n",
+    "df_p_labels['i_preds'] = i_preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p_labels.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(24, 8))\n",
+    "\n",
+    "for i, preds in enumerate(['predictions', 's_preds' , 'i_preds']):\n",
+    "    # Clip and visualize weights \n",
+    "    df_p_labels = (df_p_labels\n",
+    "        .assign(clipped_preds = lambda x: [0.05 if p < 0.05 else p for p in x[preds]])\n",
+    "    )\n",
+    "    df_p_labels['weights_%s' % preds] = [1./p for p in df_p_labels.clipped_preds]\n",
+    "    \n",
+    "    if preds == 'predictions': \n",
+    "        lab = 'Uncalibrated'\n",
+    "    elif preds == 's_preds':\n",
+    "        lab = 'Sigmoid Calibrated'\n",
+    "    else:\n",
+    "        lab = 'Isotonic Calibrated'\n",
+    "    alpha = [0.1, 1.0, 0.1]\n",
+    "    for j in range(3):\n",
+    "        if j == 0:\n",
+    "            alpha = [1.0, 0.2, 0.2]\n",
+    "        elif j == 1:\n",
+    "            alpha = [0.2, 1.0, 0.2]\n",
+    "        else:\n",
+    "            alpha = [0.2, 0.2, 1.0]\n",
+    "        sns.set(font_scale=1.5)\n",
+    "        sns.distplot(df_p_labels[df_p_labels['label']==1]['weights_%s' % preds],\n",
+    "                     kde=False,\n",
+    "                     ax=ax[j],\n",
+    "                     label=lab,\n",
+    "                     hist_kws=dict(alpha=alpha[i]))\n",
+    "        ax[j].set_title(\"Inverse Probability Weights\")\n",
+    "        ax[j].set_xlim([1, 20])\n",
+    "        ax[j].legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in Coverage predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_predictions(base_path):\n",
+    "    \"\"\"Helper function that loads predictions from AST classifiers for test set data\"\"\"\n",
+    "    \n",
+    "    abx_options = [\"Vancomycin\",\n",
+    "                   \"Ampicillin\",\n",
+    "                   \"Cefazolin\",\n",
+    "                   \"Ceftriaxone\",\n",
+    "                   \"Cefepime\",\n",
+    "                   \"Zosyn\",\n",
+    "                   \"Ciprofloxacin\",\n",
+    "                   \"Meropenem\",\n",
+    "                   \"Vancomycin_Meropenem\",\n",
+    "                   \"Vancomycin_Zosyn\",\n",
+    "                   \"Vancomycin_Cefepime\",\n",
+    "                   \"Vancomycin_Ceftriaxone\"\n",
+    "                   ]\n",
+    "    df = pd.DataFrame()\n",
+    "    for i, abx in enumerate(abx_options):\n",
+    "        path = base_path.format(abx=abx)\n",
+    "        f_path = glob.glob(os.path.join(path, '*predictions.csv'))[0]\n",
+    "        if i == 0:\n",
+    "            df = pd.read_csv(f_path)\n",
+    "            df = df[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df = df.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                    'predictions' : '%s_predictions' % abx})\n",
+    "        else:\n",
+    "            df_preds = pd.read_csv(f_path)\n",
+    "            df_preds = df_preds[['anon_id', 'pat_enc_csn_id_coded', 'label', 'predictions']]\n",
+    "            df_preds = df_preds.rename(columns={'label' : '%s_label' % abx,\n",
+    "                                                'predictions' : '%s_predictions' % abx})\n",
+    "            df = df.merge(df_preds, how='left', on=['anon_id', 'pat_enc_csn_id_coded'])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "base_path=\"/Users/conorcorbin/repos/er_infection/results/ast_models_w_not_infected/testing/{abx}\"\n",
+    "df_ipw = load_predictions(baes_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_final = (df\n",
+    "    .merge(df_p_labels, how='left', on='pat_enc_csn_id_coded')\n",
+    ")\n",
+    "df_final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "def bootstrapped_performance(labels, predictions, weights):\n",
+    "    \"\"\"Given labels and predictions, bootstraps auroc and average precision\n",
+    "       and returns mean values along with 95%ci range for both of them\"\"\"\n",
+    "    \n",
+    "    aurocs,  aurocs_ipw = [], []\n",
+    "    inds = [i for i in range(len(labels))]\n",
+    "    for i in range(1000):\n",
+    "        inds_boot = np.random.choice(inds, size=len(inds), replace=True)\n",
+    "        labels_b = labels[inds_boot]\n",
+    "        predictions_b = predictions[inds_boot]\n",
+    "        weights_b = weights[inds_boot]\n",
+    "        aurocs.append(roc_auc_score(labels_b, predictions_b))\n",
+    "        aurocs_ipw.append(roc_auc_score(labels_b, predictions_b, sample_weight=weights_b))\n",
+    "    mean_auc = np.mean(aurocs)\n",
+    "    auc_low = np.percentile(aurocs, 2.5)\n",
+    "    auc_high = np.percentile(aurocs, 97.5)\n",
+    "    auroc = \"%.2f [%.2f, %.2f]\" % (mean_auc, auc_low, auc_high)\n",
+    "    \n",
+    "    mean_auc_ipw = np.mean(aurocs_ipw)\n",
+    "    auc_ipw_low = np.percentile(aurocs_ipw, 2.5)\n",
+    "    auc_ipw_high = np.percentile(aurocs_ipw, 97.5)\n",
+    "    auroc_ipw = \"%.2f [%.2f, %.2f]\" % (mean_auc_ipw, auc_ipw_low, auc_ipw_high)\n",
+    "    \n",
+    "    prev = str(round(np.average(labels), 2))\n",
+    "    \n",
+    "    return auroc, auroc_ipw, prev\n",
+    "\n",
+    "\n",
+    "df_test_results = pd.DataFrame()\n",
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "df_test_results['Antibiotic Selection'] = [a for a in abx_options]\n",
+    "\n",
+    "\n",
+    "base_path = \"/Users/conorcorbin/repos/er_infection/results/ast_models_bucket1/testing/{abx}\"\n",
+    "best_model_classes = []\n",
+    "prevs = []\n",
+    "aurocs_ipw = []\n",
+    "aurocs = []\n",
+    "for abx in abx_options:\n",
+    "    auroc, auroc_ipw, prev = bootstrapped_performance(df_final['%s_label' % abx].values,\n",
+    "                                               df_final['%s_predictions' % abx].values,\n",
+    "                                               df_final['weights_predictions'].values)\n",
+    "    prevs.append(prev)\n",
+    "    aurocs_ipw.append(auroc_ipw)\n",
+    "    aurocs.append(auroc)\n",
+    "    \n",
+    "df_test_results['AUROC'] = aurocs\n",
+    "df_test_results['AUROC IPW'] = aurocs_ipw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_results.to_csv('IPW_weights_results_table.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_final = (df\n",
+    "    .merge(df_p_labels, how='left', on='pat_enc_csn_id_coded')\n",
+    ")\n",
+    "abx_options = [\"Vancomycin\",\n",
+    "               \"Ampicillin\",\n",
+    "               \"Cefazolin\",\n",
+    "               \"Ceftriaxone\",\n",
+    "               \"Cefepime\",\n",
+    "               \"Zosyn\",\n",
+    "               \"Ciprofloxacin\",\n",
+    "               \"Meropenem\",\n",
+    "               \"Vancomycin_Meropenem\",\n",
+    "               \"Vancomycin_Zosyn\",\n",
+    "               \"Vancomycin_Cefepime\",\n",
+    "               \"Vancomycin_Ceftriaxone\"\n",
+    "               ]\n",
+    "\n",
+    "# Compute AUROC for each antibiotic weighted by each of the three weights\n",
+    "aurocs = {}\n",
+    "aurocs['Antibiotic'] = []\n",
+    "aurocs['Unweighted'] = []\n",
+    "aurocs['Weighted Uncalibrated'] = []\n",
+    "aurocs['Weighted Sigmoid Calibrated'] = []\n",
+    "aurocs['Weighted Isotonic Calibrated'] = []\n",
+    "for abx in abx_options:\n",
+    "    aurocs['Antibiotic'].append(abx)\n",
+    "    predictions = df_final['%s_predictions' % abx]\n",
+    "    labels = df_final['%s_label' % abx]\n",
+    "\n",
+    "    for w in ['unweighted', 'predictions', 's_preds' , 'i_preds']:\n",
+    "        if w != 'unweighted':\n",
+    "            weights = df_final[w]\n",
+    "        else:\n",
+    "            weights = [1.0 for i in range(len(df_final))]\n",
+    "        \n",
+    "        auroc = round(roc_auc_score(labels, predictions, sample_weight=weights), 2)\n",
+    "        if w == 'unweighted':\n",
+    "            aurocs['Unweighted'].append(auroc)\n",
+    "        elif w == 'predictions':\n",
+    "            aurocs['Weighted Uncalibrated'].append(auroc)\n",
+    "        elif w == 's_preds':\n",
+    "            aurocs['Weighted Sigmoid Calibrated'].append(auroc)\n",
+    "        else:\n",
+    "            aurocs['Weighted Isotonic Calibrated'].append(auroc)\n",
+    "\n",
+    "df_results = pd.DataFrame(data=aurocs)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "from tqdm import tqdm\n",
+    "def bootstrapped_performance(df, measure):\n",
+    "    \"\"\"\n",
+    "    Given a performance measure, bootstraps test set examples and plots boxplots of said performance measure\n",
+    "    using each of the weighting scheme for each antibiotic label. \n",
+    "    \"\"\"\n",
+    "    df_results = pd.DataFrame()\n",
+    "    for i in tqdm(range(1000)):\n",
+    "        meas = {}\n",
+    "        meas['Antibiotic'] = []\n",
+    "        meas['Unweighted'] = []\n",
+    "        meas['Weighted Uncalibrated'] = []\n",
+    "        meas['Weighted Sigmoid Calibrated'] = []\n",
+    "        meas['Weighted Isotonic Calibrated'] = []\n",
+    "        \n",
+    "        df_sampled = (df\n",
+    "            .sample(n=len(df), replace=True)\n",
+    "        )\n",
+    "        for abx in abx_options:\n",
+    "            meas['Antibiotic'].append(abx)\n",
+    "            predictions = df_sampled['%s_predictions' % abx]\n",
+    "            labels = df_sampled['%s_label' % abx]\n",
+    "\n",
+    "            for w in ['unweighted', 'predictions', 's_preds' , 'i_preds']:\n",
+    "                if w != 'unweighted':\n",
+    "                    weights = df_sampled[w]\n",
+    "                else:\n",
+    "                    weights = [1.0 for i in range(len(df_sampled))]\n",
+    "\n",
+    "                m = measure(labels, predictions, sample_weight=weights)\n",
+    "                if w == 'unweighted':\n",
+    "                    meas['Unweighted'].append(m)\n",
+    "                elif w == 'predictions':\n",
+    "                    meas['Weighted Uncalibrated'].append(m)\n",
+    "                elif w == 's_preds':\n",
+    "                    meas['Weighted Sigmoid Calibrated'].append(m)\n",
+    "                else:\n",
+    "                    meas['Weighted Isotonic Calibrated'].append(m)\n",
+    "\n",
+    "        df_meas = pd.DataFrame(data=meas)\n",
+    "        df_results = pd.concat([df_results, df_meas])\n",
+    "    df_results = (df_results\n",
+    "        .melt(id_vars=['Antibiotic'],\n",
+    "              value_vars=['Unweighted', 'Weighted Uncalibrated',\n",
+    "                          'Weighted Sigmoid Calibrated', 'Weighted Isotonic Calibrated'],\n",
+    "              var_name='Weighting',\n",
+    "              value_name='score')\n",
+    "        .reset_index()\n",
+    "    )\n",
+    "    return df_results\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bootstrap AUROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(32, 8))\n",
+    "df_auroc = bootstrapped_performance(df_final, roc_auc_score)\n",
+    "\n",
+    "sns.set(style='whitegrid', font_scale=2.0)\n",
+    "sns.boxplot(x='Antibiotic',\n",
+    "            y='score',\n",
+    "            hue='Weighting',\n",
+    "            data=df_auroc,\n",
+    "            fliersize=0\n",
+    "            )\n",
+    "plt.xticks(rotation = 45)\n",
+    "ax.set_ylabel('AUROC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bootstrap Average Precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(32, 8))\n",
+    "df_ap = bootstrapped_performance(df_final, average_precision_score)\n",
+    "\n",
+    "sns.set(style='whitegrid', font_scale=2.0)\n",
+    "sns.boxplot(x='Antibiotic',\n",
+    "            y='score',\n",
+    "            hue='Weighting',\n",
+    "            data=df_ap,\n",
+    "            fliersize=0\n",
+    "            )\n",
+    "plt.xticks(rotation = 45)\n",
+    "ax.set_ylabel('Average Precision')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bootstrap Brier Score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(32, 8))\n",
+    "df_ap = bootstrapped_performance(df_final, brier_score_loss)\n",
+    "\n",
+    "sns.set(style='whitegrid', font_scale=2.0)\n",
+    "sns.boxplot(x='Antibiotic',\n",
+    "            y='score',\n",
+    "            hue='Weighting',\n",
+    "            data=df_ap,\n",
+    "            fliersize=0\n",
+    "            )\n",
+    "plt.xticks(rotation = 45)\n",
+    "ax.set_ylabel('Brier Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Table Feature Types And Counts.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Table Feature Types And Counts.ipynb
new file mode 100644
index 00000000..3ef82124
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Table Feature Types And Counts.ipynb	
@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"\"\"\n",
+    "SELECT \n",
+    "    feature_type, COUNT (DISTINCT features) num_features\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.feature_counts_long\n",
+    "WHERE\n",
+    "    EXTRACT(YEAR FROM index_time) < 2019 \n",
+    "AND\n",
+    "    feature_type not LIKE \"%test%\"\n",
+    "GROUP BY\n",
+    "    feature_type\n",
+    "ORDER BY \n",
+    "    num_features DESC\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.result().to_dataframe()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('feature_types.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Table Misses by bug.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Table Misses by bug.ipynb
new file mode 100644
index 00000000..080fc3ad
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Table Misses by bug.ipynb	
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import os, glob\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First We'll find misses on the level of the organism instead of the encounter\n",
+    "This requires us to use code from the label generation notebook, but we won't collapse to the encounter level at the end.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Fill in And Processing Functions\n",
+    "def fill_in_cefazolin(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Cefazolin == 'Susceptible' or x.Cefazolin == 'Resistant':\n",
+    "        return x.Cefazolin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Cefazolin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # If anything resistant to Cefepime or Ceftriaxone and Cefazolin not listed, assume resistant to Cefazolin\n",
+    "    if x.Cefepime == \"Resistant\" or x.Ceftriaxone == 'Resistant':\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # For anything STREPTOCOCCUS related except STREPTOCOCCUS PNEUMONIAE  - refer to penicillin if not listed \n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism) and x.organism != 'STREPTOCOCCUS PNEUMONIAE':\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "    \n",
+    "    # Check For MRSA in STAPH AUREUS if not listed\n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_ceftriaxone(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Ceftriaxone == 'Susceptible' or x.Ceftriaxone == 'Resistant':\n",
+    "        return x.Ceftriaxone\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Ceftriaxone' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # If anything resistant to Cefepime, assume resistant to Ceftriaxone\n",
+    "    if x.Cefepime == \"Resistant\":\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # If susceptible to Cefazolin, assume susceptible to Ceftriaxone\n",
+    "    if x.Cefazolin == \"Susceptible\": \n",
+    "        return \"Susceptible\"\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "\n",
+    "    # For anything STREPTOCOCCUS related - refer to penicillin if not listed\n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "        \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_cefepime(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Cefepime == 'Susceptible' or x.Cefepime == 'Resistant':\n",
+    "        return x.Cefepime\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Cefepime' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # For anything STREPTOCOCCUS related - refer to penicillin \n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "        \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # If susceptible to Cefazolin or Ceftriaxone assume susceptible to Cefepime\n",
+    "    if x.Cefazolin == \"Susceptible\" or x.Ceftriaxone == \"Susceptible\":\n",
+    "        return \"Susceptible\"\n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_zosyn(x):\n",
+    "     \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Zosyn == 'Susceptible' or x.Zosyn == 'Resistant':\n",
+    "        return x.Zosyn\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules\n",
+    "                 .query(\"antibiotic == 'Piperacillin/Tazobactam' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # For anything STREPTOCOCCUS related - refer to penicillin \n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "        \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "        \n",
+    "    # If susceptible to ampicillin, then zosyn susceptible - useless rule because of the following rule\n",
+    "    if x.Ampicillin == \"Susceptible\":\n",
+    "        return x.Ampicillin\n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "\n",
+    "def fill_in_vancomycin(x):\n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Vancomycin == 'Susceptible' or x.Vancomycin == 'Resistant':\n",
+    "        return x.Vancomycin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Vancomycin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # Will apply Nancy Watz Rule Later (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_meropenem(x):\n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Meropenem == 'Susceptible' or x.Meropenem == 'Resistant':\n",
+    "        return x.Meropenem\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Meropenem' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "        \n",
+    "    # If susceptible to ampicillin, then meropenem susceptible\n",
+    "    if x.Ampicillin == \"Susceptible\": # not the case with Enteroccocus, but this should be in alwasys resistant list above. \n",
+    "        return x.Ampicillin # also - useless rule because of rule below\n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_ciprofloxacin(x):\n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Ciprofloxacin == 'Susceptible' or x.Ciprofloxacin == 'Resistant':\n",
+    "        return x.Ciprofloxacin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Ciprofloxacin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # If Resistant to Levofloxacin, resistant to Cipro\n",
+    "    if x.Levofloxacin == \"Resistant\":\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # Check for MRSA - assume same as oxacillin if not listed\n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_ampicillin(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Ampicillin == 'Susceptible' or x.Ampicillin == 'Resistant':\n",
+    "        return x.Ampicillin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Ampicillin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "        \n",
+    "    # Check for MRSA - assume same as oxacillin if not listed\n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "\n",
+    "def combine_labels(arr):\n",
+    "    \"\"\"\n",
+    "    Sometimes organisms have multiple suscept labels. When they do, if any of them are not one of\n",
+    "    Susceptible, Positive, or Susceptible - Dose Dependent then we say organism is resistant to \n",
+    "    said antibiotic\n",
+    "    \"\"\"\n",
+    "    for a in arr:\n",
+    "        if a not in ['Susceptible', 'Positive', 'Susceptible - Dose Dependent']:\n",
+    "            return 'Resistant'\n",
+    "    return 'Susceptible'\n",
+    "\n",
+    "def combine_antibiotic_syns(x):\n",
+    "    \"\"\"\n",
+    "    Antibiotics often are given different names in the AST tables, this function combines\n",
+    "    synnonyms so that we don't have to refer to multiple names in downstream analysis. \n",
+    "    \"\"\"\n",
+    "    if x == 'Aztreonam.':\n",
+    "        return 'Aztreonam'\n",
+    "    elif x == 'Cefazolin..':\n",
+    "        return 'Cefazolin'\n",
+    "    elif x == 'Ceftazidime.':\n",
+    "        return 'Ceftazidime'\n",
+    "    elif x in ('Ceftriaxone (Meningeal)', 'Ceftriaxone (Non-Meningeal)', 'Ceftriaxone.'):\n",
+    "        return 'Ceftriaxone'\n",
+    "    elif x in ('Ciprofloxacin.'):\n",
+    "        return 'Ciprofloxacin'\n",
+    "    elif x == 'Gentamicin 500 mcg/ml.':\n",
+    "        return 'Gentamicin'\n",
+    "    elif x in ('Oxacillin Screen', 'Oxacillin.'):\n",
+    "        return 'Oxacillin'\n",
+    "    elif x in ('PENICILLIN G (MENINGEAL)','PENICILLIN G (NON-MENINGEAL)', 'PENICILLIN V (ORAL)', 'Penicillin..'):\n",
+    "        return 'Penicillin'\n",
+    "    elif x == 'Trimethoprim/Sulfamethoxazole.':\n",
+    "        return 'Trimethoprim/Sulfamethoxazole'\n",
+    "    else:\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "# Query positive culture data\n",
+    "query = \"\"\"\n",
+    "SELECT DISTINCT \n",
+    "    cohort.pat_enc_csn_id_coded, cults.order_proc_id_coded, cults.description,\n",
+    "    cs.sens_organism_sid, cs.line, cs.organism,\n",
+    "    cs.antibiotic, cs.suscept, cs.sensitivity_value, cs.sens_ref_range \n",
+    "FROM \n",
+    "    `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cults\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.cohort_not_infected_rules` cohort\n",
+    "USING \n",
+    "    (pat_enc_csn_id_coded)\n",
+    "INNER JOIN \n",
+    "    `shc_core.culture_sensitivity` cs\n",
+    "USING \n",
+    "    (order_proc_id_coded)\n",
+    "WHERE \n",
+    "    organism not LIKE \"%CANDIDA%\"\n",
+    "AND \n",
+    "    organism not in ('COAG NEGATIVE STAPHYLOCOCCUS', 'HAEMOPHILUS INFLUENZAE', 'HAEMOPHILUS PARAINFLUENZAE')\n",
+    "ORDER BY \n",
+    "    cohort.pat_enc_csn_id_coded, cults.order_proc_id_coded, cs.line\n",
+    "\"\"\"\n",
+    "\n",
+    "query_job = client.query(query)\n",
+    "df=query_job.to_dataframe()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_cols = ['example', 'pat_enc_csn_id_coded', 'organism', 'description', 'order_proc_id_coded', 'sens_organism_sid',\n",
+    "               'Cefepime', 'Ceftriaxone', 'Cefazolin', 'Vancomycin', 'Meropenem', 'Zosyn',\n",
+    "               'Ampicillin', 'Penicillin', 'Oxacillin', 'Ciprofloxacin', 'Levofloxacin']\n",
+    "\n",
+    "df_wide = (df\n",
+    "    .assign(example=lambda x: x.pat_enc_csn_id_coded.map(str) + ', ' \\\n",
+    "            + x.order_proc_id_coded.map(str) + ', ' + x.sens_organism_sid + ', ' + x.organism + ', ' + x.description,\n",
+    "            antibiotic=lambda x: [combine_antibiotic_syns(a) for a in x.antibiotic])\n",
+    "    .groupby(['example', 'antibiotic']) \n",
+    "    .agg({'suscept' : lambda x: combine_labels(x)})          \n",
+    "    .reset_index()\n",
+    "    .pivot(index='example', columns='antibiotic', values='suscept')\n",
+    "    .reset_index()\n",
+    "    .assign(organism = lambda x: [a.split(', ')[3] for a in x.example],\n",
+    "            order_proc_id_coded = lambda x: [a.split(', ')[1] for a in x.example],\n",
+    "            sens_organism_sid = lambda x: [a.split(', ')[2] for a in x.example],\n",
+    "            pat_enc_csn_id_coded = lambda x: [a.split(', ')[0] for a in x.example],\n",
+    "            description = lambda x: [a.split(', ')[4] for a in x.example])\n",
+    "    .rename(columns={'Piperacillin/Tazobactam' : 'Zosyn'})\n",
+    ")\n",
+    "df_wide = df_wide[filter_cols]\n",
+    "df_wide.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wide = (df_wide\n",
+    "    .assign(Ampicillin=lambda x: df_wide.apply(lambda x: fill_in_ampicillin(x), axis=1),\n",
+    "            Cefepime=lambda x: df_wide.apply(lambda x: fill_in_cefepime(x), axis=1),\n",
+    "            Ceftriaxone=lambda x: df_wide.apply(lambda x: fill_in_ceftriaxone(x), axis=1),\n",
+    "            Cefazolin=lambda x: df_wide.apply(lambda x: fill_in_cefazolin(x), axis=1),\n",
+    "            Ciprofloxacin=lambda x: df_wide.apply(lambda x: fill_in_ciprofloxacin(x), axis=1),\n",
+    "            Zosyn=lambda x: df_wide.apply(lambda x: fill_in_zosyn(x), axis=1),\n",
+    "            Vancomycin=lambda x: df_wide.apply(lambda x: fill_in_vancomycin(x), axis=1),\n",
+    "            Meropenem=lambda x: df_wide.apply(lambda x: fill_in_meropenem(x), axis=1),\n",
+    "            Vancomycin_Ceftriaxone=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                              or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                              for a, b in zip(x.Vancomycin, x.Ceftriaxone)],\n",
+    "            Vancomycin_Cefepime=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                           or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                           for a, b in zip(x.Vancomycin, x.Cefepime)],\n",
+    "            Vancomycin_Zosyn=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                        or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                        for a, b in zip(x.Vancomycin, x.Zosyn)],\n",
+    "            Vancomycin_Meropenem=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                            or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                            for a, b in zip(x.Vancomycin, x.Meropenem)])\n",
+    "    .reset_index()\n",
+    ")\n",
+    "df_wide.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fill in any NA's with Susceptible - nancy watz rule\n",
+    "abx_map = {'Ceftriaxone' : \"CEFTRIAXONE\",\n",
+    "           'Vancomycin_Zosyn' : \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\",\n",
+    "           'Zosyn' : \"PIPERACILLIN-TAZOBACTAM\",\n",
+    "           'Vancomycin_Ceftriaxone' : \"CEFTRIAXONE VANCOMYCIN\",\n",
+    "           'Vancomycin_Cefepime' : \"CEFEPIME VANCOMYCIN\",\n",
+    "           'Cefepime' : \"CEFEPIME\",\n",
+    "           'Vancomycin' :  \"VANCOMYCIN\",\n",
+    "           'Meropenem' : \"MEROPENEM\",\n",
+    "           'Vancomycin_Meropenem' : \"MEROPENEM VANCOMYCIN\",\n",
+    "           'Cefazolin' : \"CEFAZOLIN\",\n",
+    "           'Ciprofloxacin' : \"CIPROFLOXACIN\",\n",
+    "           'Ampicillin' : 'AMPICILLIN'\n",
+    "          }\n",
+    "df_wide[list(abx_map.keys())] = df_wide[list(abx_map.keys())].fillna('Susceptible')\n",
+    "df_ast_labels_by_bug = df_wide[['pat_enc_csn_id_coded', 'organism', 'order_proc_id_coded', 'sens_organism_sid'] + list(abx_map.keys())]\n",
+    "df_ast_labels_by_bug.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abx_map = {'Ceftriaxone' : \"CEFTRIAXONE\",\n",
+    "           'Vancomycin_Zosyn' : \"PIPERACILLIN-TAZOBACTAM VANCOMYCIN\",\n",
+    "           'Zosyn' : \"PIPERACILLIN-TAZOBACTAM\",\n",
+    "           'Vancomycin_Ceftriaxone' : \"CEFTRIAXONE VANCOMYCIN\",\n",
+    "           'Vancomycin_Cefepime' : \"CEFEPIME VANCOMYCIN\",\n",
+    "           'Cefepime' : \"CEFEPIME\",\n",
+    "           'Vancomycin' :  \"VANCOMYCIN\",\n",
+    "           'Meropenem' : \"MEROPENEM\",\n",
+    "           'Vancomycin_Meropenem' : \"MEROPENEM VANCOMYCIN\",\n",
+    "           'Cefazolin' : \"CEFAZOLIN\",\n",
+    "           'Ciprofloxacin' : \"CIPROFLOXACIN\",\n",
+    "           'Ampicillin' : 'AMPICILLIN'\n",
+    "          }\n",
+    "abx_map_inverse = {abx_map[key] : key for key in abx_map}\n",
+    "abx_map_inverse['CEFTRIAXONE PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'\n",
+    "# abx_map_inverse['LEVOFLOXACIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'\n",
+    "abx_map_inverse['AZITHROMYCIN PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Zosyn'\n",
+    "# abx_map_inverse['MEROPENEM PIPERACILLIN-TAZOBACTAM VANCOMYCIN'] = 'Vancomycin_Meropenem'\n",
+    "abx_map_inverse['AZITHROMYCIN CEFTRIAXONE'] = 'Ceftriaxone'\n",
+    "\n",
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded, l.index_time, om.med_description,\n",
+    "    l.Ampicillin, l.Ciprofloxacin, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin,\n",
+    "    l.Meropenem, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Cefepime, l.Vancomycin_Ceftriaxone\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.abx_orders_given_and_stopped` om\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.final_ast_labels` l\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "WHERE\n",
+    "    om.was_given = 1\n",
+    "ORDER BY \n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_time\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_abx = query_job.result().to_dataframe()\n",
+    "df_abx.head()\n",
+    "\n",
+    "# Lambda that aggregates Antibiotic orders after we've grouped by CSN\n",
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "\n",
+    "# Aggregate abx orders\n",
+    "df_drugs = (df_abx\n",
+    "    .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)\n",
+    "    .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                        .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                    for a in x.med_description])\n",
+    "    .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on\n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'med_description' : concat_abx,\n",
+    "        'year' : 'first',\n",
+    "        'Ampicillin' : 'first',\n",
+    "        'Ciprofloxacin' : 'first',\n",
+    "        'Cefazolin' : 'first',\n",
+    "        'Ceftriaxone' : 'first',\n",
+    "        'Cefepime' : 'first',\n",
+    "        'Zosyn' : 'first',\n",
+    "        'Vancomycin' : 'first',\n",
+    "        'Meropenem' : 'first',\n",
+    "        'Vancomycin_Ceftriaxone' : 'first',\n",
+    "        'Vancomycin_Cefepime' : 'first',\n",
+    "        'Vancomycin_Zosyn' : 'first',\n",
+    "        'Vancomycin_Meropenem' : 'first'})\n",
+    "    .reset_index()\n",
+    "    # Only look at test set data and CSNs where allowed antibiotic selection was administered\n",
+    "    .query(\"year == 2019 and med_description in @abx_map_inverse\", engine='python') \n",
+    "    .assign(med_description=lambda x: [abx_map_inverse[a] for a in x.med_description])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Solve linear programming formulation so we can stratify by misses of optimized procedure as well\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../decision_alg\")\n",
+    "from DecisionAnalysis import AbxDecisionMaker, load_predictions\n",
+    "from integer_programming import get_clinician_prescribing_patterns\n",
+    "\n",
+    "abx_settings = {\"Ceftriaxone\" : 404,\n",
+    "    \"Vancomycin_Zosyn\" :  149,\n",
+    "    \"Zosyn\" : 102,\n",
+    "    \"Vancomycin_Ceftriaxone\" : 31,\n",
+    "    \"Vancomycin_Cefepime\" : 23,\n",
+    "    \"Cefepime\" : 14,\n",
+    "    \"Vancomycin\" : 13,\n",
+    "    \"Vancomycin_Meropenem\" : 9,\n",
+    "    \"Meropenem\" : 9,\n",
+    "    \"Cefazolin\" : 8,\n",
+    "    \"Ciprofloxacin\" : 8,\n",
+    "    \"Ampicillin\" : 0,\n",
+    "    }\n",
+    "\n",
+    "df_predictions = load_predictions()\n",
+    "df_drugs = get_clinician_prescribing_patterns()\n",
+    "opt = AbxDecisionMaker(df_predictions, df_drugs, abx_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt.solve_and_assign()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map med_description to df_ast_labels_by_bug\n",
+    "df_ast_labels_by_bug = (df_ast_labels_by_bug\n",
+    "    .assign(pat_enc_csn_id_coded = lambda x: x.pat_enc_csn_id_coded.astype(int))\n",
+    "    .merge(opt.df[['pat_enc_csn_id_coded', 'med_description', 'IP_med_description']],\n",
+    "           how='inner',\n",
+    "           on='pat_enc_csn_id_coded')\n",
+    ")\n",
+    "df_ast_labels_by_bug.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Abx labels to binary\n",
+    "to_binary = lambda x: 1 if x == \"Susceptible\" else 0\n",
+    "for key in abx_map.keys():\n",
+    "    df_ast_labels_by_bug[key] = [1 if x == \"Susceptible\" else 0 for x in df_ast_labels_by_bug[key].values]\n",
+    "df_ast_labels_by_bug.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_was_covered(x, decision_column='med_description'):\n",
+    "    \"\"\"\n",
+    "    Given med description, find appropriate label column and return whether patient was covered during CSN\n",
+    "    Returns \"Not in abx options\" if abx regimen isn't in our set of 12 options - useful for filtering later\n",
+    "    \"\"\"\n",
+    "    if decision_column == 'med_description':\n",
+    "        med_description = x.med_description\n",
+    "    elif decision_column == 'random_med_description':\n",
+    "        med_description = x.random_med_description\n",
+    "    elif decision_column == 'IP_med_description':\n",
+    "        med_description = x.IP_med_description\n",
+    "\n",
+    "    return x[med_description]\n",
+    "\n",
+    "df_ast_labels_by_bug = (df_ast_labels_by_bug\n",
+    "    .assign(dr_covered=df_ast_labels_by_bug.apply(lambda x: compute_was_covered(x), axis=1))\n",
+    "    .assign(alg_covered=df_ast_labels_by_bug.apply(lambda x: compute_was_covered(x, 'IP_med_description'), axis=1))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Query culture sensitivity table for culture types\n",
+    "query = \"\"\"\n",
+    "SELECT DISTINCT\n",
+    "    description, order_proc_id_coded \n",
+    "FROM \n",
+    "    `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cult_orders\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_culture_types = query_job.result().to_dataframe()\n",
+    "df_culture_types['order_proc_id_coded'] = df_culture_types['order_proc_id_coded'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ast_labels_by_bug.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ast_labels_by_bug['order_proc_id_coded'] = df_ast_labels_by_bug['order_proc_id_coded'].astype(int)\n",
+    "df_ast_labels_by_bug = (df_ast_labels_by_bug\n",
+    "                        .merge(df_culture_types, on='order_proc_id_coded', how='left')\n",
+    "                       )\n",
+    "df_ast_labels_by_bug.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get most common doctor misses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "        \n",
+    "def pipe_func(df):\n",
+    "    df = df.assign(organism_and_culture = df.apply(lambda x: x['description'] + ' ' + x['organism'], axis=1))\n",
+    "    return df\n",
+    "\n",
+    "def format_description(x):\n",
+    "    if \"URINE\" in x:\n",
+    "        return 'Urine Culture'\n",
+    "    elif \"BLOOD\" in x:\n",
+    "        return \"Blood Culture\"\n",
+    "    else:\n",
+    "        return \"Other Fluid Culture\"\n",
+    "df_frequent_misses = (df_ast_labels_by_bug\n",
+    ".query(\"dr_covered == 0\", engine='python')\n",
+    ".assign(description = lambda x: [format_description(a) for a in x.description])\n",
+    ".assign(organism = lambda x: [a.capitalize() for a in x.organism])\n",
+    ".groupby(['description', 'organism'])\n",
+    ".agg(num_misses=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['description', 'num_misses'], ascending=False)\n",
+    ")\n",
+    "df_frequent_misses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get most common alg misses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_frequent_misses_alg = (df_ast_labels_by_bug\n",
+    ".query(\"alg_covered == 0\", engine='python')\n",
+    ".assign(description = lambda x: [format_description(a) for a in x.description])\n",
+    ".assign(organism = lambda x: [a.capitalize() for a in x.organism])\n",
+    ".groupby(['description', 'organism'])\n",
+    ".agg(num_misses=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['description', 'num_misses'], ascending=False)\n",
+    ")\n",
+    "df_frequent_misses_alg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_high_level_organism = {\n",
+    "    'Escherichia coli' : 'Lactose Fermenting GNRs',\n",
+    "    'Enterococcus species' : 'Enterococcus species',\n",
+    "    'Enterococcus faecalis' : 'Enterococcus species',\n",
+    "    'Enterobacter cloacae complex' : \"Lactose Fermenting GNRs\",\n",
+    "    'Klebsiella pneumoniae' : \"Lactose Fermenting GNRs\",\n",
+    "    'Morganella morganii' : \"Non Lactose Fermenting GNRs\",\n",
+    "    'Enterococcus faecium' : 'Enterococcus species',\n",
+    "    'Citrobacter freundii complex' : \"Lactose Fermenting GNRs\",\n",
+    "    'Mucoid pseudomonas aeruginosa' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Proteus mirabilis' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Proteus vulgaris group' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Zzzcitrobacter amalonaticus' : 'Non Lactose Fermenting GNRs', \n",
+    "    'Elizabethkingia species' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Serratia marcescens' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Stenotrophomonas maltophilia' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Non fermenting gram negative rods' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Streptococcus parasanguinis' : 'Streptococcus species',\n",
+    "    'Streptococcus mitis group' : 'Streptococcus species',\n",
+    "    'Viridans group streptococci not s. pneumoniae' : 'Streptococcus species',\n",
+    "    'Pseudomonas aeruginosa' : 'Non Lactose Fermenting GNRs',\n",
+    "    'Klebsiella aerogenes' : 'Lactose Fermenting GNRs',\n",
+    "    'Klebsiella oxytoca' : 'Lactose Fermenting GNRs',\n",
+    "    'Staph aureus {mrsa}' : 'MRSA',\n",
+    "    'Streptococcus species' : 'Streptococcus species',\n",
+    "    'Enterococcus gallinarum' : 'Enterococcus species',\n",
+    "    'Streptococcus sanguinis' : 'Streptococcus species',\n",
+    "    'Streptococcus salivarius group' : 'Streptococcus species',\n",
+    "    'Citrobacter species' : 'Lactose Fermenting GNRs',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Group doctor misses into high level organisms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_frequent_misses_high_level = (df_ast_labels_by_bug\n",
+    ".query(\"dr_covered == 0\", engine='python')\n",
+    ".assign(description = lambda x: [format_description(a) for a in x.description])\n",
+    ".assign(organism = lambda x: [a.capitalize() for a in x.organism])\n",
+    ".assign(organism=lambda x: [map_high_level_organism[a] for a in x.organism])\n",
+    ".groupby(['description', 'organism'])\n",
+    ".agg(num_misses=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['num_misses'], ascending=False)\n",
+    ".rename(columns={'description' : 'Culture Type',\n",
+    "                 'organism' : 'Infection Type',\n",
+    "                 'num_misses' : 'Number of Misses'})\n",
+    ")\n",
+    "df_frequent_misses_high_level.to_csv('doctor_misses_by_infection_type.csv', index=None)\n",
+    "df_frequent_misses_high_level"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Group Alg misses into high level organisms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_frequent_misses_alg_high_level = (df_ast_labels_by_bug\n",
+    ".query(\"alg_covered == 0\", engine='python')\n",
+    ".assign(description = lambda x: [format_description(a) for a in x.description])\n",
+    ".assign(organism = lambda x: [a.capitalize() for a in x.organism])\n",
+    ".assign(organism=lambda x: [map_high_level_organism[a] for a in x.organism])\n",
+    ".groupby(['description', 'organism'])\n",
+    ".agg(num_misses=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['num_misses'], ascending=False)\n",
+    ".rename(columns={'description' : 'Culture Type',\n",
+    "                 'organism' : 'Infection Type',\n",
+    "                 'num_misses' : 'Number of Misses'})\n",
+    ")\n",
+    "df_frequent_misses_alg_high_level.to_csv('alg_misses_by_infection_type.csv', index=None)\n",
+    "df_frequent_misses_alg_high_level"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dr misses stratified by what antibiotic was delivered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_frequent_misses_dr = (df_ast_labels_by_bug\n",
+    ".query(\"dr_covered == 0\", engine='python')\n",
+    ".assign(description = lambda x: [format_description(a) for a in x.description])\n",
+    ".assign(organism = lambda x: [a.capitalize() for a in x.organism])\n",
+    ".assign(organism=lambda x: [map_high_level_organism[a] for a in x.organism])\n",
+    ".groupby(['description', 'organism', 'med_description'])\n",
+    ".agg(num_misses=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['num_misses'], ascending=False)\n",
+    ".rename(columns={'description' : 'Culture Type',\n",
+    "                 'organism' : 'Infection Type',\n",
+    "                 'med_description' : 'Antibiotics Administered',\n",
+    "                 'num_misses' : 'Number of Misses'})\n",
+    ")\n",
+    "df_frequent_misses_dr.to_csv('doctor_misses_by_infection_type_and_abx.csv', index=None)\n",
+    "df_frequent_misses_dr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alg misses by what antibiotic was given"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_frequent_misses_alg = (df_ast_labels_by_bug\n",
+    ".query(\"alg_covered == 0\", engine='python')\n",
+    ".assign(description = lambda x: [format_description(a) for a in x.description])\n",
+    ".assign(organism = lambda x: [a.capitalize() for a in x.organism])\n",
+    ".assign(organism=lambda x: [map_high_level_organism[a] for a in x.organism])\n",
+    ".groupby(['description', 'organism', 'IP_med_description'])\n",
+    ".agg(num_misses=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values(['num_misses'], ascending=False)\n",
+    ".rename(columns={'description' : 'Culture Type',\n",
+    "                 'organism' : 'Infection Type',\n",
+    "                 'IP_med_description' : 'Antibiotics Administered',\n",
+    "                 'num_misses' : 'Number of Misses'})\n",
+    ")\n",
+    "df_frequent_misses_alg.to_csv('alg_misses_by_infection_type_and_abx.csv', index=None)\n",
+    "df_frequent_misses_alg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IV AND IM Antibiotics Only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded, l.index_time, om.med_description,\n",
+    "    l.Ampicillin, l.Ciprofloxacin, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin,\n",
+    "    l.Meropenem, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Cefepime, l.Vancomycin_Ceftriaxone\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.abx_orders_given_and_stopped` om\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.final_ast_labels` l\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "WHERE\n",
+    "    om.was_given = 1\n",
+    "ORDER BY \n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_time\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_abx = query_job.result().to_dataframe()\n",
+    "df_abx.head()\n",
+    "\n",
+    "# Lambda that aggregate Antibiotic orders after we've grouped by CSN\n",
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "\n",
+    "# Aggregate abx orders\n",
+    "df_drugs = (df_abx\n",
+    "    .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)\n",
+    "    .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                        .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                    for a in x.med_description])\n",
+    "    .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on\n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'med_description' : concat_abx,\n",
+    "        'year' : 'first',\n",
+    "        'Ampicillin' : 'first',\n",
+    "        'Ciprofloxacin' : 'first',\n",
+    "        'Cefazolin' : 'first',\n",
+    "        'Ceftriaxone' : 'first',\n",
+    "        'Cefepime' : 'first',\n",
+    "        'Zosyn' : 'first',\n",
+    "        'Vancomycin' : 'first',\n",
+    "        'Meropenem' : 'first',\n",
+    "        'Vancomycin_Ceftriaxone' : 'first',\n",
+    "        'Vancomycin_Cefepime' : 'first',\n",
+    "        'Vancomycin_Zosyn' : 'first',\n",
+    "        'Vancomycin_Meropenem' : 'first'})\n",
+    "    .reset_index()\n",
+    "    # Only look at test set data and CSNs where allowed antibiotic selection was administered\n",
+    "    .query(\"year == 2019\", engine='python') # and med_description in @abx_map_inverse\", engine='python') \n",
+    "    #.assign(med_description=lambda x: [abx_map_inverse[a] for a in x.med_description])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_drugs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df_drugs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_regimens = (df_drugs\n",
+    ".groupby('med_description')\n",
+    ".agg(num_ed_visits=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".sort_values('num_ed_visits', ascending=False)\n",
+    ".reset_index()\n",
+    ")\n",
+    "unique_regimens.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_regimens.to_csv('unique_regimens.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now Include Oral Abx that were listed as administered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_med_id_coded, l.index_time, om.med_description,\n",
+    "    l.Ampicillin, l.Ciprofloxacin, l.Cefazolin, l.Ceftriaxone, l.Cefepime, l.Zosyn, l.Vancomycin,\n",
+    "    l.Meropenem, l.Vancomycin_Meropenem, l.Vancomycin_Zosyn, l.Vancomycin_Cefepime, l.Vancomycin_Ceftriaxone\n",
+    "FROM\n",
+    "    `mining-clinical-decisions.abx.abx_orders_given_and_stopped_include_oral` om\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.final_ast_labels` l\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "WHERE\n",
+    "    om.was_given = 1\n",
+    "ORDER BY \n",
+    "    om.anon_id, om.pat_enc_csn_id_coded, om.order_time\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_abx = query_job.result().to_dataframe()\n",
+    "df_abx.head()\n",
+    "\n",
+    "# Lambda that aggregate Antibiotic orders after we've grouped by CSN\n",
+    "concat_abx = lambda x : ' '.join(np.unique(sorted([a for a in x])))\n",
+    "\n",
+    "# Aggregate abx orders\n",
+    "df_drugs = (df_abx\n",
+    "    .assign(med_description=lambda x: [a.split(' ')[0] for a in x.med_description]) # Only Take first word (abx)\n",
+    "    .assign(med_description=lambda x: [(a.replace('PIPERACILLIN-TAZOBACTAM-DEXTRS','PIPERACILLIN-TAZOBACTAM')\n",
+    "                                        .replace('VANCOMYCIN-WATER', 'VANCOMYCIN'))\n",
+    "                                    for a in x.med_description])\n",
+    "    .assign(year=lambda x: x.index_time.dt.year) # get year of each CSN - used to filter later on\n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'med_description' : concat_abx,\n",
+    "        'year' : 'first',\n",
+    "        'Ampicillin' : 'first',\n",
+    "        'Ciprofloxacin' : 'first',\n",
+    "        'Cefazolin' : 'first',\n",
+    "        'Ceftriaxone' : 'first',\n",
+    "        'Cefepime' : 'first',\n",
+    "        'Zosyn' : 'first',\n",
+    "        'Vancomycin' : 'first',\n",
+    "        'Meropenem' : 'first',\n",
+    "        'Vancomycin_Ceftriaxone' : 'first',\n",
+    "        'Vancomycin_Cefepime' : 'first',\n",
+    "        'Vancomycin_Zosyn' : 'first',\n",
+    "        'Vancomycin_Meropenem' : 'first'})\n",
+    "    .reset_index()\n",
+    "    # Only look at test set data and CSNs where allowed antibiotic selection was administered\n",
+    "    .query(\"year == 2019\", engine='python') # and med_description in @abx_map_inverse\", engine='python') \n",
+    "    #.assign(med_description=lambda x: [abx_map_inverse[a] for a in x.med_description])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_drugs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df_drugs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_regimens = (df_drugs\n",
+    ".groupby('med_description')\n",
+    ".agg(num_ed_visits=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".sort_values('num_ed_visits', ascending=False)\n",
+    ".reset_index()\n",
+    ")\n",
+    "unique_regimens.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(unique_regimens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_regimens.to_csv('unique_regimens_include_oral.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inspect Misses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"\"\"\n",
+    "SELECT DISTINCT\n",
+    "  a.anon_id, a.pat_enc_csn_id_coded, a.index_time, cs.organism, cs.description\n",
+    "FROM  \n",
+    "  `mining-clinical-decisions.abx.final_ast_labels` a\n",
+    "INNER JOIN\n",
+    "  `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cult_orders\n",
+    "USING\n",
+    "  (pat_enc_csn_id_coded)\n",
+    "INNER JOIN \n",
+    "  `som-nero-phi-jonc101.shc_core.culture_sensitivity` cs\n",
+    "USING\n",
+    "  (order_proc_id_coded)\n",
+    "WHERE \n",
+    "    EXTRACT(YEAR FROM a.index_time) = 2019\n",
+    "AND \n",
+    "    organism NOT LIKE \"%COAG NEGATIVE STAPHYLOCOCCUS%\"\n",
+    "AND \n",
+    "    organism NOT LIKE \"%CANDIDA%\"\n",
+    "\n",
+    "\n",
+    "\"\"\" \n",
+    "query_job = client.query(query)\n",
+    "df_bugs = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bugs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(df_bugs\n",
+    ".groupby('description')\n",
+    ".agg(num_ids=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bugs that are most frequent misses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "### Get organisms corresponding to misses\n",
+    "concat_bugs = lambda x : ' '.join(np.unique(sorted([a.capitalize() for a in x])))\n",
+    "def group_cultures(x):\n",
+    "    if x.split()[0] in (\"BLOOD\", \"URINE\"): \n",
+    "        return x.split()[0].capitalize()\n",
+    "    else:\n",
+    "        return 'Other fluid' \n",
+    "        \n",
+    "def pipe_func(df):\n",
+    "    df = df.assign(organism_and_culture = df.apply(lambda x: x['description'] + ' ' + x['organism'], axis=1))\n",
+    "    return df\n",
+    "\n",
+    "df_bugs_collapsed = (df_bugs\n",
+    ".assign(description = lambda x: [group_cultures(a) for a in x.description]) \n",
+    ".pipe(pipe_func)\n",
+    ".groupby('pat_enc_csn_id_coded')\n",
+    ".agg(organism=('organism_and_culture', concat_bugs))\n",
+    ".reset_index()\n",
+    ")\n",
+    "\n",
+    "\n",
+    "df_bugs_collapsed = (df_bugs_collapsed\n",
+    ".merge(df_drugs, how='inner', on='pat_enc_csn_id_coded')\n",
+    ".query(\"dr_covered == 0\", engine='python')\n",
+    ".groupby('organism')\n",
+    ".agg(num_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values('num_csns', ascending=False)\n",
+    ")\n",
+    "df_bugs_collapsed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bug & Drug Combos of misses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Get organisms corresponding to misses\n",
+    "df_bugs_collapsed = (df_bugs\n",
+    ".assign(description = lambda x: [group_cultures(a) for a in x.description]) \n",
+    ".pipe(pipe_func)\n",
+    ".groupby('pat_enc_csn_id_coded')\n",
+    ".agg(organism=('organism_and_culture', concat_bugs))\n",
+    ".reset_index()\n",
+    ")\n",
+    "\n",
+    "df_misses_with_drugs = (df_bugs_collapsed\n",
+    ".merge(df_drugs, how='inner', on='pat_enc_csn_id_coded')\n",
+    ".query(\"dr_covered == 0\", engine='python')\n",
+    ".groupby(['organism', 'med_description'])\n",
+    ".agg(num_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ".reset_index()\n",
+    ".sort_values('num_csns', ascending=False)\n",
+    ")\n",
+    "\n",
+    "df_misses_with_drugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_misses_with_drugs.to_csv(\"top_misses_with_what_was_given.csv\", index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Table1.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Table1.ipynb
new file mode 100644
index 00000000..2be477ec
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Table1.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from tableone import TableOne\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('table1.sql', 'r') as f:\n",
+    "    query = f.read()\n",
+    "query_job = client.query(query)\n",
+    "df=query_job.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = pd.read_csv('demographics.csv')\n",
+    "# df.groupby('culture_growth').count()\n",
+    "df.sort_values('ANON_ID').tail(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.groupby('department_name').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in demographics table\n",
+    "\n",
+    "#culture_growth=lambda x: ['Positive Cultures' if a == 1 else \"Negative Cultures\"\n",
+    "                                        #for a in x.culture_growth],\n",
+    "df = (df\n",
+    "      .assign(department_name=lambda x: ['Stanford ED' if 'VCP' not in a else \"Valley Care ED\"\n",
+    "                                         for a in x.department_name])\n",
+    "      .assign(split=lambda x: ['Train + Validation (2009-2018)' if year >= 2009 and year < 2019\n",
+    "                               else \"Test (2019)\" for year in x.year])\n",
+    "      .rename(columns={\n",
+    "        'department_name' : 'Emergency Department',\n",
+    "        \"age\" : \"Age\",\n",
+    "        \"GENDER\" : \"Sex\",\n",
+    "        \"CANONICAL_RACE\" : \"Race\",\n",
+    "        \"CANONICAL_ETHNICITY\" : \"Ethnicity\",\n",
+    "        \"LANGUAGE\" : \"Language\",\n",
+    "        \"INSURANCE_PAYOR_NAME\" : \"Insurance Payer\",\n",
+    "        \"split\" : \"Dataset\",\n",
+    "                      })\n",
+    ")\n",
+    "# Group by patient identifier to get average age \n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = [\"Emergency Department\", 'Age', 'Sex', 'Race', 'Ethnicity', \"Language\", \"Insurance Payer\"]\n",
+    "categorical = ['Emergency Department', 'Sex', 'Race', 'Ethnicity', \"Language\", \"Insurance Payer\"]\n",
+    "df = df.sort_values('Dataset', ascending=False)\n",
+    "df.head()\n",
+    "mytable = TableOne(df,\n",
+    "                   columns=columns,\n",
+    "                   categorical=categorical,\n",
+    "                   groupby='Dataset',\n",
+    "                   missing=False,\n",
+    "                   overall=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(mytable.tabulate(tablefmt=\"github\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "mytable.to_html('table1_by_dataset.html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mytable.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mytable.to_latex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mytable.to_csv('table1_by_dataset.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_table = pd.read_csv('table_')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/Table2 Top Bugs.ipynb b/scripts/ER_Infection/notebooks/figure_and_tabels/Table2 Top Bugs.ipynb
new file mode 100644
index 00000000..2366227a
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/Table2 Top Bugs.ipynb	
@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import os \n",
+    "from google.cloud import bigquery\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"\"\"\n",
+    "WITH cohort_bugs AS (\n",
+    "ed), \n",
+    "\n",
+    "adt_dep as (\n",
+    "    SELECT DISTINCT\n",
+    "      adt.pat_enc_csn_id_coded, \n",
+    "      FIRST_VALUE(dm.department_name) OVER \n",
+    "      (PARTITION BY adt.pat_enc_csn_id_coded ORDER BY adt.effective_time_jittered_utc) department_name,\n",
+    "    FROM \n",
+    "      `shc_core.adt` adt\n",
+    "    INNER JOIN\n",
+    "      `som-nero-phi-jonc101.shc_core.dep_map` dm\n",
+    "    USING\n",
+    "      (department_id)\n",
+    ")\n",
+    "\n",
+    "SELECT \n",
+    "    c.*, a.department_name\n",
+    "FROM\n",
+    "    cohort_bugs c\n",
+    "INNER JOIN\n",
+    "    adt_dep a\n",
+    "USING\n",
+    "    (pat_enc_csn_id_coded)\n",
+    "\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.result().to_dataframe()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(df\n",
+    "    .assign(description=lambda x: [\"Blood Culture\" if \"BLOOD\" in a else\n",
+    "                                   \"Urine Culture\" if \"URINE\" in a else\n",
+    "                                   \"Csf or Fluid Culture\" for a in x.description])\n",
+    "    .groupby('description')\n",
+    "    .agg(num_csns=('pat_enc_csn_id_coded', 'nunique'))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Filter out coag neg staph and rename MRSA to Staph Aureus\n",
+    "df = (df\n",
+    "    .assign(description=lambda x: [\"Blood Culture\" if \"BLOOD\" in a else\n",
+    "                                   \"Urine Culture\" if \"URINE\" in a else\n",
+    "                                   \"Csf or Fluid Culture\" for a in x.description])\n",
+    "    .query(\"organism != 'COAG NEGATIVE STAPHYLOCOCCUS'\")\n",
+    "    .assign(organism = lambda x: [a if a != 'STAPH AUREUS {MRSA}' else 'STAPHYLOCOCCUS AUREUS'\n",
+    "                                 for a in x.organism])\n",
+    "    .assign(organism = lambda x: [a if a != 'STREPTOCOCCUS AGALACTIAE {GROUP B}' else 'STREPTOCOCCUS AGALACTIAE (GROUP B)'\n",
+    "                                 for a in x.organism])\n",
+    "    .assign(organism = lambda x: [a.lower() for a in x.organism])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Get Top 5 bugs for each culture type by department where we count by number of encounters bug grew. \n",
+    "df_stanford = pd.DataFrame()\n",
+    "df_valley_care = pd.DataFrame()\n",
+    "for culture_type in ['Blood Culture', 'Urine Culture', 'Csf or Fluid Culture']:\n",
+    "    \n",
+    "    df_stanford_temp = (df\n",
+    "        .query('not department_name.str.contains(\"VCP\") and description==@culture_type', engine='python')\n",
+    "        .groupby('organism')\n",
+    "        .agg({'pat_enc_csn_id_coded' : 'nunique'})\n",
+    "        .reset_index()\n",
+    "        .rename(columns={'organism' : 'Organism', 'pat_enc_csn_id_coded' : \"Count\"})\n",
+    "        .sort_values('Count', ascending=False)\n",
+    "        .head(5)\n",
+    "    )\n",
+    "    df_stanford_temp['Culture Type'] = [culture_type for i in range(len(df_stanford_temp))]\n",
+    "\n",
+    "\n",
+    "    df_valley_care_temp = (df\n",
+    "        .query('department_name.str.contains(\"VCP\") and description==@culture_type', engine='python')\n",
+    "        .groupby('organism')\n",
+    "        .agg({'pat_enc_csn_id_coded' : 'nunique'})\n",
+    "        .reset_index()\n",
+    "        .rename(columns={'organism' : 'Organism', 'pat_enc_csn_id_coded' : \"Count\"})\n",
+    "        .sort_values('Count', ascending=False)\n",
+    "        .head(5)\n",
+    "    )\n",
+    "    df_valley_care_temp['Culture Type'] = [culture_type for i in range(len(df_valley_care_temp))]\n",
+    "\n",
+    "    \n",
+    "    df_stanford = pd.concat([df_stanford, df_stanford_temp])\n",
+    "    df_valley_care = pd.concat([df_valley_care, df_valley_care_temp])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_stanford"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_valley_care"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_stanford['Institution'] = [\"Stanford ED\" for i in range(len(df_stanford))]\n",
+    "df_valley_care['Institution'] = [\"Valley Care ED\" for i in range(len(df_valley_care))]\n",
+    "df_final = pd.concat([df_stanford, df_valley_care])\n",
+    "df_final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_final = df_final[['Institution', 'Culture Type', 'Organism', 'Count']]\n",
+    "df_final['Organism'] = [a.capitalize() for a in df_final['Organism']]\n",
+    "df_final.to_csv('Table 2: Organism by culture type long.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "culture_type = df_stanford['Culture Type'].values\n",
+    "df_stanford = df_stanford.drop('Culture Type', axis=1)\n",
+    "df_valley_care = df_valley_care.drop('Culture Type', axis=1)\n",
+    "\n",
+    "df_valley_care.columns = pd.MultiIndex.from_product([['Valley Care ED'], df_valley_care.columns])\n",
+    "df_stanford.columns = pd.MultiIndex.from_product([['Stanford ED'], df_stanford.columns])\n",
+    "\n",
+    "df_bugs = pd.concat([df_valley_care.reset_index(drop=True), df_stanford.reset_index(drop=True)], axis=1)\n",
+    "df_bugs['Culture Type'] = culture_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bugs = df_bugs[['Culture Type', 'Stanford ED', 'Valley Care ED']]\n",
+    "df_bugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bugs.to_html('table2_top_bugs.html', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bugs.to_csv('table2_top_bugs_by_culture.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bugs_test['test'] = ['test' for i in range(len(df_bugs_test))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bugs_test.drop(\"Culture Type\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/figure2.sql b/scripts/ER_Infection/notebooks/figure_and_tabels/figure2.sql
new file mode 100644
index 00000000..8110b54c
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/figure2.sql
@@ -0,0 +1,69 @@
+# ER admissions between 2009 and 2019 where patient was admitted to the hospital and was above 18 yo
+-- WITH er_admits AS (
+-- SELECT anon_id, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time, max(effective_time_jittered_utc) as er_transfer_out_time
+-- FROM `shc_core.adt`
+-- WHERE pat_class_c = "112" AND pat_service = "Emergency"
+-- GROUP BY anon_id, pat_enc_csn_id_coded),
+
+-- er_admits_adults AS (
+-- SELECT DISTINCT ea.* 
+-- FROM er_admits ea
+-- INNER JOIN `shc_core.demographic` demo
+-- USING (anon_id)
+-- WHERE DATE_DIFF(CAST(ea.er_admit_time as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18
+-- AND EXTRACT(YEAR FROM ea.er_admit_time) BETWEEN 2009 AND 2019
+-- ),
+
+-- has_inp_code AS (
+-- SELECT DISTINCT anon_id, pat_enc_csn_id_coded
+-- FROM
+--   (SELECT DISTINCT ea.anon_id, ea.pat_enc_csn_id_coded,
+--   MAX(CASE WHEN adt.base_pat_class_c = 1 THEN 1 ELSE 0 END) has_inpatient 
+--   FROM er_admits_adults ea
+--   INNER JOIN shc_core.adt adt
+--   USING (pat_enc_csn_id_coded)
+--   GROUP BY ea.anon_id, ea.pat_enc_csn_id_coded) t
+-- WHERE has_inpatient = 1
+-- )
+
+-- SELECT DISTINCT anon_id FROM has_inp_code # Patients
+-- SELECT DISTINCT pat_enc_csn_id_coded FROM has_inp_code # Admissions
+
+
+### Abx ordered and Cultures Ordered Within 24 hours
+# Admissions
+-- SELECT DISTINCT
+--   pat_enc_csn_id_coded
+-- FROM
+--   `mining-clinical-decisions.abx.abx_orders_within_24_hrs` a
+-- INNER JOIN
+--   `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c
+-- USING
+--   (pat_enc_csn_id_coded)
+-- WHERE
+--   EXTRACT(YEAR FROM order_time) BETWEEN 2009 AND 2019 # slight difference than if using ER admit time but fine bc this is what we use in analysis anyway 
+# Patients
+-- SELECT DISTINCT
+--   a.anon_id
+-- FROM
+--   `mining-clinical-decisions.abx.abx_orders_within_24_hrs` a
+-- INNER JOIN
+--   `mining-clinical-decisions.abx.culture_orders_within_24_hrs` c
+-- USING
+--   (pat_enc_csn_id_coded)
+-- WHERE
+--   EXTRACT(YEAR FROM order_time) BETWEEN 2009 AND 2019 # slight difference than if using ER admit time but fine bc this is what we use in analysis anyway 
+
+
+# No Abx or cultures ordered in prior 2 weeks
+# Admissions
+-- SELECT DISTINCT pat_enc_csn_id_coded
+-- FROM `mining-clinical-decisions.abx.final_ast_labels` 
+-- WHERE EXTRACT(YEAR FROM index_time) BETWEEN 2009 AND 2019
+
+# Patients
+SELECT DISTINCT anon_id
+FROM `mining-clinical-decisions.abx.final_cohort_table` 
+WHERE EXTRACT(YEAR FROM index_time) BETWEEN 2009 AND 2019
+
+
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/sweep_plots.py b/scripts/ER_Infection/notebooks/figure_and_tabels/sweep_plots.py
new file mode 100644
index 00000000..d6525916
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/sweep_plots.py
@@ -0,0 +1,642 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set(style='white', font_scale=2.0)
+import os, sys
+from tqdm import tqdm
+import pickle
+
+sys.path.insert(1, '../mit_data_analysis/')
+from linear_programming import sweep_plot, sweep_plot_coverage_rate
+
+import pdb
+
+def plot_abx_distributions(site=None):
+    """
+    Makes bar plots of abx prescription distributions for both the Stanford
+    and Boston datasets
+    """
+    if site == "Stanford":
+        abbs = {
+            "Ceftriaxone" : "Ceftriaxone",
+            "Vancomycin_Zosyn" : "Vanc/Pip-Tazo",
+            "Zosyn" : "Pip-Tazo",
+            "Vancomycin_Ceftriaxone" : "Vanc/Ceftriaxone",
+            "Vancomycin_Cefepime" : "Vanc/Cefepime",
+            "Cefepime" : "Cefepime",
+            "Vancomycin" : "Vanc",
+            "Vancomycin_Meropenem" : "Vanc/Meropenem",
+            "Meropenem" : "Meropenem",
+            "Cefazolin" : "Cefazolin",
+            "Ciprofloxacin" : "Ciprofloxacin",
+            "Ampicillin" : "Ampicillin"
+        }
+
+        stanford_params = {
+            "Ceftriaxone" : 404,
+            "Vanc/Pip-Tazo" :  149,
+            "Pip-Tazo" : 102,
+            "Vanc/Ceftriaxone" : 31,
+            "Vanc/Cefepime" : 23,
+            "Cefepime" : 14,
+            "Vanc" : 13,
+            "Vanc/Meropenem" : 9,
+            "Meropenem" : 9,
+            "Cefazolin" : 8,
+            "Ciprofloxacin" : 8,
+        }
+
+        fig, axs = plt.subplots(1, 1, figsize=(10, 10))
+        df_stanford = pd.DataFrame(data={
+            'med' : [key for key in stanford_params],
+            'value' : [value for key, value in stanford_params.items()]
+        })
+
+        axs = sns.barplot(
+            x='value',
+            y='med',
+            ci=None,
+            data=df_stanford,
+            ax=axs,
+            palette='deep',
+        )
+        axs.set_xlabel("Number of Prescriptions")
+        axs.set_title("Clinician Antibiotic Use 2019")
+        axs.set_ylabel('')
+
+        os.makedirs("./select_sweep_plots/", exist_ok=True)
+        plt.savefig(
+            './select_sweep_plots/stanford_abx_distribution.png',
+            bbox_inches='tight',
+            dpi=300
+        )
+
+    elif site == "Boston":
+        boston_abx = {
+            'Nitrofurantoin' : 1358,
+            'Ciprofloxacin' : 1282,
+            'Trim/Sulfamethoxazole' : 1260,
+            'Levofloxacin' : 41,
+       }
+        fig, axs = plt.subplots(1, 1, figsize=(10, 10))
+        df_boston = pd.DataFrame(data={
+            'med' : [key for key in boston_abx],
+            'value' : [value for key, value in boston_abx.items()]
+        })
+        axs = sns.barplot(
+            x='value',
+            y='med',
+            ci=None,
+            data=df_boston,
+            ax=axs,
+            palette='deep',
+        )
+        axs.set_xlabel("Number of Prescriptions")
+        axs.set_title("Clinician Antibiotic Use 2014-2016")
+        axs.set_ylabel('')
+
+        os.makedirs("./select_sweep_plots/", exist_ok=True)
+        plt.savefig(
+            './select_sweep_plots/boston_abx_distributions.png',
+            bbox_inches='tight',
+            dpi=300
+        )
+
+
+    else:
+
+        boston_abx = {
+            'NIT' : 1358,
+            'CIP' : 1282,
+            'SXT' : 1260,
+            'LVX' : 41
+        }
+
+        abbs = {
+            "Ceftriaxone" : "CTX",
+            "Vancomycin_Zosyn" : "VAN/TZP",
+            "Zosyn" : "TZP",
+            "Vancomycin_Ceftriaxone" : "VAN/CTX",
+            "Vancomycin_Cefepime" : "VAN/CFP",
+            "Cefepime" : "CFP",
+            "Vancomycin" : "VAN",
+            "Vancomycin_Meropenem" : "VAN/MEM",
+            "Meropenem" : "MEM",
+            "Cefazolin" : "CFZ",
+            "Ciprofloxacin" : "CIP",
+            "Ampicillin" : "AMP"
+        }
+
+        stanford_abx = {
+            "CTX" : 404,
+            "VAN/TZP" :  149,
+            "TZP" : 102,
+            "VAN/CTX" : 31,
+            "VAN/CFP" : 23,
+            "CFP" : 14,
+            "VAN" : 13,
+            "VAN/MEM" : 9,
+            "MEM" : 9,
+            "CFZ" : 8,
+            "CIP" : 8,
+        }
+        fig, axs = plt.subplots(1, 2, figsize=(20, 10))
+        df_stanford = pd.DataFrame(data={
+            'med' : [key for key in stanford_abx],
+            'value' : [value for key, value in stanford_abx.items()]
+        })
+        df_boston = pd.DataFrame(data={
+            'med' : [key for key in boston_abx],
+            'value' : [value for key, value in boston_abx.items()]
+        })
+        axs[0] = sns.barplot(
+            x='value',
+            y='med',
+            ci=None,
+            data=df_stanford,
+            ax=axs[0],
+            palette='deep',
+        )
+        axs[1] = sns.barplot(
+            x='value',
+            y='med',
+            ci=None,
+            data=df_boston,
+            ax=axs[1],
+            palette='deep',
+        )
+        axs[0].set_xlabel("Number of Prescriptions")
+        axs[0].set_title("[Stanford] Distribution of Abx Prescriptions")
+        axs[0].set_ylabel('')
+
+        axs[1].set_xlabel("Number of Prescriptions")
+        axs[1].set_title("[Boston] Distribution of Abx Prescriptions")
+        axs[1].set_ylabel('')
+
+        os.makedirs("./select_sweep_plots/", exist_ok=True)
+        plt.savefig(
+            './select_sweep_plots/abx_distributions.png',
+            bbox_inches='tight',
+            dpi=300
+        )
+
+def plot_select_sweeps_boston():
+    """
+    Plot select sweeps from boston data
+    """
+
+    with open("sweep_data_boston.pickle", "rb") as f:
+        boston_data = pickle.load(f)
+
+    boston_sweeps = {
+        1 : ('CIP', 'NIT'),
+        2 : ('CIP', 'SXT'),
+        5 : ('NIT', 'SXT')
+    }
+
+    abbs = {
+        'CIP' : 'Ciprofloxacin',
+        'LVX' : 'Levofloxacin',
+        'NIT' : 'Nitrofurantoin',
+        'SXT' : 'Trimethoprim/Sulfamethoxazole'
+    }
+
+    boston_params = {
+        'Ciprofloxacin' : 1282,
+        'Levofloxacin' : 41,
+        'Nitrofurantoin' : 1358,
+        'Trimethoprim/Sulfamethoxazole' : 1260,
+        'ymin' : 0.84,
+        'ymax' : 0.92,
+        'random_rate' : 0.125,
+        'site' : "[Boston]"
+    }
+    fig, axs = plt.subplots(1, 3, figsize=(30, 10))
+    col = 0
+    for key, sweep in boston_data.items():
+        if key not in [key for key in boston_sweeps]:
+            continue
+        axs[col] = sweep_plot_coverage_rate(
+            axs[col],
+            (abbs[boston_sweeps[key][0]], abbs[boston_sweeps[key][1]]),
+            num_replaced=sweep['num_replaced'],
+            o_rates=sweep['o_rates'],
+            c_rates=sweep['c_rates'],
+            r_rates=sweep['r_rates'],
+            params=boston_params
+        )
+
+        if col == 0:
+            axs[col].set_ylabel("Coverage Rate")
+
+        if col == 1:
+            axs[col].legend(
+                loc="lower center", bbox_to_anchor=(0.5, -0.46),
+                borderaxespad=0.
+            )
+        col += 1      
+
+    os.makedirs("./select_sweep_plots/", exist_ok=True)
+    plt.savefig(
+        './select_sweep_plots/sweep_plot_boston_select_coverage_rate.png',
+        bbox_inches='tight',
+        dpi=300
+    )
+
+
+def plot_select_sweeps_stanford():
+    """
+    Plot select sweeps from stanford data
+    """
+    with open('sweep_data.pickle', 'rb') as f:
+        stanford_data = pickle.load(f)
+
+    abbs = {
+        "Ceftriaxone" : "Ceftriaxone",
+        "Vancomycin_Zosyn" : "Vanc/Pip-Tazo",
+        "Zosyn" : "Pip-Tazo",
+        "Vancomycin_Ceftriaxone" : "Vanc/Ceftriaxone",
+        "Vancomycin_Cefepime" : "Vanc/Cefepime",
+        "Cefepime" : "Cefepime",
+        "Vancomycin" : "Vanc",
+        "Vancomycin_Meropenem" : "Vanc/Meropenem",
+        "Meropenem" : "Meropenem",
+        "Cefazolin" : "Cefazolin",
+        "Ciprofloxacin" : "Ciprofloxacin",
+        "Ampicillin" : "Ampicillin"
+    }
+
+    stanford_params = {
+        "Ceftriaxone" : 404,
+        "Vanc/Pip-Tazo" :  149,
+        "Pip-Tazo" : 102,
+        "Vanc/Ceftriaxone" : 31,
+        "Vanc/Cefepime" : 23,
+        "Cefepime" : 14,
+        "Vanc" : 13,
+        "Vanc/Meropenem" : 9,
+        "Meropenem" : 9,
+        "Cefazolin" : 8,
+        "Ciprofloxacin" : 8,
+        "Ampicillin" : 0,
+        "random_rate" : .208,   
+        "ymin" : 0.77,
+        "ymax" : 0.88,
+        "site" : "[Stanford]"
+    }
+    stanford_sweeps = [
+        ('Vancomycin_Zosyn', 'Zosyn'),
+        ('Vancomycin_Zosyn', 'Cefepime'),
+        ('Vancomycin_Zosyn', 'Ceftriaxone'),
+        ('Vancomycin_Zosyn', 'Ampicillin'),
+        ('Zosyn', 'Ceftriaxone'),
+        ('Zosyn', 'Cefazolin'),
+        ('Zosyn', 'Ampicillin'),
+        ('Ceftriaxone', 'Cefazolin'),
+        ('Ceftriaxone', 'Ampicillin')
+    ]
+
+    fig, axs = plt.subplots(3, 3, figsize=(30, 30))
+    row, col = 0, 0
+    for sweep in stanford_sweeps:
+        num_replaced = [
+                k for k in range(len(stanford_data[sweep]['r_rates']))
+            ]
+        r_rates = stanford_data[sweep]['r_rates']
+        c_rates = stanford_data[sweep]['c_rates']
+        o_rates = stanford_data[sweep]['o_rates']
+        axs[row, col] = sweep_plot_coverage_rate(
+            axs[row, col],
+            (abbs[sweep[0]], abbs[sweep[1]]),
+            num_replaced=num_replaced,
+            o_rates=o_rates,
+            c_rates=c_rates,
+            r_rates=r_rates,
+            params=stanford_params
+        )
+
+        if col == 0:
+            axs[row, col].set_ylabel("Coverage Rate")
+
+        if col == 1 and row == 2:
+            axs[row, col].legend(
+                loc="lower center", bbox_to_anchor=(0.5, -0.55),
+                borderaxespad=0.
+            )
+
+        if col == 2:
+            row += 1
+            col = 0
+        else:
+            col += 1
+
+    os.makedirs("./select_sweep_plots/", exist_ok=True)
+    plt.savefig(
+        './select_sweep_plots/sweep_plot_stanford_select.png',
+        bbox_inches='tight',
+        dpi=300
+    )
+  
+def plot_select_sweeps():
+    """
+    Plot select sweeps from both stanford and boston. This creates main text
+    figure. 
+    """
+    with open("sweep_data_boston.pickle", "rb") as f:
+        boston_data = pickle.load(f)
+
+    with open('sweep_data.pickle', 'rb') as f:
+        stanford_data = pickle.load(f)
+
+    boston_sweeps = {
+        1 : ('CIP', 'NIT'),
+        2 : ('CIP', 'SXT'),
+        5 : ('NIT', 'SXT')
+    }
+
+    boston_params = {
+        'CIP' : 1282,
+        'LVX' : 41,
+        'NIT' : 1358,
+        'SXT' : 1260,
+        'ymin' : 0.09,
+        'ymax' : 0.14,
+        'random_rate' : .125,
+        'site' : "[Boston]"
+    }
+
+    abbs = {
+        "Ceftriaxone" : "CTX",
+        "Vancomycin_Zosyn" : "VAN/TZP",
+        "Zosyn" : "TZP",
+        "Vancomycin_Ceftriaxone" : "VAN/CTX",
+        "Vancomycin_Cefepime" : "VAN/CFP",
+        "Cefepime" : "CFP",
+        "Vancomycin" : "VAN",
+        "Vancomycin_Meropenem" : "VAN/MEM",
+        "Meropenem" : "MEM",
+        "Cefazolin" : "CFZ",
+        "Ciprofloxacin" : "CIP",
+        "Ampicillin" : "AMP"
+    }
+
+    stanford_params = {
+        "CTX" : 404,
+        "VAN/TZP" :  149,
+        "TZP" : 102,
+        "VAN/CTX" : 31,
+        "VAN/CFP" : 23,
+        "CFP" : 14,
+        "VAN" : 13,
+        "VAN/MEM" : 9,
+        "MEM" : 9,
+        "CFZ" : 8,
+        "CIP" : 8,
+        "AMP" : 0,
+        "random_rate" : .208,   
+        "ymin" : 0.12,
+        "ymax" : 0.23,
+        "site" : "[Stanford]"
+    }
+    stanford_sweeps = [
+        ('Vancomycin_Zosyn', 'Zosyn'),
+        ('Vancomycin_Zosyn', 'Cefepime'),
+        ('Vancomycin_Zosyn', 'Ceftriaxone'),
+        ('Vancomycin_Zosyn', 'Ampicillin'),
+        ('Zosyn', 'Ceftriaxone'),
+        ('Zosyn', 'Cefazolin'),
+        ('Zosyn', 'Ampicillin'),
+        ('Ceftriaxone', 'Cefazolin'),
+        ('Ceftriaxone', 'Ampicillin')
+    ]
+        
+    fig, axs = plt.subplots(4, 3, figsize=(30, 40))
+    row, col = 0, 0
+    for sweep in stanford_sweeps:
+        num_replaced = [
+                k for k in range(len(stanford_data[sweep]['r_rates']))
+            ]
+        r_rates = stanford_data[sweep]['r_rates']
+        c_rates = stanford_data[sweep]['c_rates']
+        o_rates = stanford_data[sweep]['o_rates']
+        axs[row, col] = sweep_plot(
+            axs[row, col],
+            (abbs[sweep[0]], abbs[sweep[1]]),
+            num_replaced=num_replaced,
+            o_rates=o_rates,
+            c_rates=c_rates,
+            r_rates=r_rates,
+            params=stanford_params
+        )
+
+        if col == 0:
+            axs[row, col].set_ylabel("Coverage Rate")
+
+        if col == 2 and row == 0:
+            axs[row, col].legend(
+                bbox_to_anchor=(1.05, 1),
+                loc=2, borderaxespad=0.
+            )
+
+        if col == 2:
+            row += 1
+            col = 0
+        else:
+            col += 1
+
+    for key, sweep in boston_data.items():
+        if key not in [key for key in boston_sweeps]:
+            continue
+        axs[row, col] = sweep_plot(
+            axs[row, col],
+            boston_sweeps[key],
+            num_replaced=sweep['num_replaced'],
+            o_rates=sweep['o_rates'],
+            c_rates=sweep['c_rates'],
+            r_rates=sweep['r_rates'],
+            params=boston_params
+        )
+
+        if col == 2 and row == 0:
+            axs[row, col].legend(
+                bbox_to_anchor=(1.05, 1),
+                loc=2, borderaxespad=0.
+            )
+
+        if col == 0:
+            axs[row, col].set_ylabel("Coverage Rate")
+
+        col += 1      
+
+    os.makedirs("./select_sweep_plots/", exist_ok=True)
+    plt.savefig(
+        './select_sweep_plots/sweep_plot.png',
+        bbox_inches='tight',
+        dpi=300
+    )
+  
+
+def plot_all_boston():
+    """
+    Plot all abx sweeps from the boston dataset.  
+    """
+    with open("sweep_data_boston.pickle", "rb") as f:
+        data = pickle.load(f)
+    
+    sweeps = {
+        0 : ('CIP', 'LVX'),
+        1 : ('CIP', 'NIT'),
+        2 : ('CIP', 'SXT'),
+        3 : ('LVX', 'NIT'),
+        4 : ('LVX', 'SXT'),
+        5 : ('NIT', 'SXT')
+    }
+
+    abbs = {
+        'CIP' : 'Ciprofloxacin',
+        'LVX' : 'Levofloxacin',
+        'NIT' : 'Nitrofurantoin',
+        'SXT' : 'Trimethoprim/Sulfamethoxazole'
+    }
+
+    params = {
+        'Ciprofloxacin' : 1282,
+        'Levofloxacin' : 41,
+        'Nitrofurantoin' : 1358,
+        'Trimethoprim/Sulfamethoxazole' : 1260,
+        'ymin' : 0.86,
+        'ymax' : 0.91,
+        'random_rate' : 0.125,
+        'site' : "[Boston]"
+    }
+
+    fig, axs = plt.subplots(2, 3, figsize=(30, 20))
+    row, col = 0, 0
+    for key, sweep in data.items():
+        axs[row, col] = sweep_plot_coverage_rate(
+            axs[row, col],
+            (abbs[sweeps[key][0]], abbs[sweeps[key][1]]),
+            num_replaced=sweep['num_replaced'],
+            o_rates=sweep['o_rates'],
+            c_rates=sweep['c_rates'],
+            r_rates=sweep['r_rates'],
+            params=params
+        )
+
+        if col == 2 and row == 0:
+            axs[row, col].legend(
+                bbox_to_anchor=(1.05, 1),
+                loc=2, borderaxespad=0.
+            )
+
+        if col == 0:
+            axs[row, col].set_ylabel("Coverage Rate")
+
+        if col == 2:
+            row += 1
+            col = 0
+        else:
+            col += 1
+
+    os.makedirs("./sweep_plots_boston", exist_ok=True)
+    plt.savefig(
+        './sweep_plots_boston/boston_coverage_rate.png',
+        bbox_inches='tight',
+        dpi=300
+    )
+
+
+def plot_all_stanford():
+    """
+    Plots all abx sweeps from the stanford dataset. There are 65 of them.
+    We'll make 5 figures of 16 subplots. 
+    """
+
+    abbs = {
+        "Ceftriaxone" : "Ceftriaxone",
+        "Vancomycin_Zosyn" : "Vanc/Pip-Tazo",
+        "Zosyn" : "Pip-Tazo",
+        "Vancomycin_Ceftriaxone" : "Vanc/Ceftriaxone",
+        "Vancomycin_Cefepime" : "Vanc/Cefepime",
+        "Cefepime" : "Cefepime",
+        "Vancomycin" : "Vanc",
+        "Vancomycin_Meropenem" : "Vanc/Meropenem",
+        "Meropenem" : "Meropenem",
+        "Cefazolin" : "Cefazolin",
+        "Ciprofloxacin" : "Ciprofloxacin",
+        "Ampicillin" : "Ampicillin"
+    }
+
+    params = {
+        "Ceftriaxone" : 404,
+        "Vanc/Pip-Tazo" :  149,
+        "Pip-Tazo" : 102,
+        "Vanc/Ceftriaxone" : 31,
+        "Vanc/Cefepime" : 23,
+        "Cefepime" : 14,
+        "Vanc" : 13,
+        "Vanc/Meropenem" : 9,
+        "Meropenem" : 9,
+        "Cefazolin" : 8,
+        "Ciprofloxacin" : 8,
+        "Ampicillin" : 0,
+        "random_rate" : .208,   
+        "ymin" : 0.77,
+        "ymax" : 0.88,
+        "site" : "[Stanford]"
+    }
+
+    with open('sweep_data.pickle', 'rb') as f:
+        sweep_data = pickle.load(f)
+
+    sweeps = [key for key in sweep_data]
+    os.makedirs('./sweep_plots_stanford/', exist_ok=True)
+    for i in tqdm(range(5)):
+        fig, axs = plt.subplots(4, 4, figsize=(40, 40))
+        row, col = 0, 0
+        for j in range(i*16, i*16+16):
+            try: 
+                test = sweeps[j]
+            except: 
+                break
+            num_replaced = [
+                k for k in range(len(sweep_data[sweeps[j]]['r_rates']))
+            ]
+            r_rates = sweep_data[sweeps[j]]['r_rates']
+            c_rates = sweep_data[sweeps[j]]['c_rates']
+            o_rates = sweep_data[sweeps[j]]['o_rates']
+            axs[row, col] = sweep_plot_coverage_rate(
+                axs[row, col],
+                (abbs[sweeps[j][0]], abbs[sweeps[j][1]]),
+                num_replaced=num_replaced,
+                o_rates=o_rates,
+                c_rates=c_rates,
+                r_rates=r_rates,
+                params=params
+            )
+
+            if col == 0:
+                axs[row, col].set_ylabel("Coverage Rate")
+
+            if col == 3 and row == 0:
+                axs[row, col].legend(
+                    bbox_to_anchor=(1.05, 1),
+                    loc=2, borderaxespad=0.
+                )
+
+            if col == 3:
+                row += 1
+                col = 0
+            else:
+                col += 1
+
+        plt.savefig(
+            f"./sweep_plots_stanford/stanford_coverage_rates{i}",
+            bbox_inches='tight',
+            dpi=300
+        )  
+
+
+if __name__ == "__main__":
+    # plot_select_sweeps_stanford()
+    plot_select_sweeps_boston()
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/table1.sql b/scripts/ER_Infection/notebooks/figure_and_tabels/table1.sql
new file mode 100644
index 00000000..0c131779
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/table1.sql
@@ -0,0 +1,43 @@
+WITH cohort AS (
+SELECT DISTINCT
+  anon_id, pat_enc_csn_id_coded, index_time, EXTRACT(YEAR FROM index_time) year
+FROM 
+  `mining-clinical-decisions.abx.final_ast_labels`
+WHERE 
+    EXTRACT(YEAR FROM index_time) BETWEEN 2009 AND 2019
+),
+
+adt_dep as (
+SELECT DISTINCT
+  adt.pat_enc_csn_id_coded, 
+  FIRST_VALUE(dm.department_name) OVER 
+  (PARTITION BY adt.pat_enc_csn_id_coded ORDER BY adt.effective_time_jittered_utc) department_name,
+FROM 
+  `shc_core.adt` adt
+INNER JOIN
+  `som-nero-phi-jonc101.shc_core.dep_map` dm
+USING
+  (department_id)
+)
+
+SELECT DISTINCT 
+  dep.department_name,
+  DATE_DIFF(CAST(c.index_time as DATE), d.BIRTH_DATE_JITTERED, year) age,
+  c.pat_enc_csn_id_coded,
+  c.year,
+  d.ANON_ID, d.GENDER, d.CANONICAL_RACE, d.CANONICAL_ETHNICITY,
+  CASE WHEN d.LANGUAGE = "English" THEN "English"
+  ELSE "Non-English" END LANGUAGE,
+  CASE WHEN d.INSURANCE_PAYOR_NAME = "MEDICARE" THEN "Medicare"
+  WHEN d.INSURANCE_PAYOR_NAME = "MEDI-CAL" THEN "Medi-Cal"
+  ELSE "Other" END INSURANCE_PAYOR_NAME
+FROM 
+  `som-nero-phi-jonc101.shc_core.demographic` d
+INNER JOIN
+  cohort c
+ON
+  d.ANON_ID = c.anon_id
+INNER JOIN
+  adt_dep dep
+ON
+  c.pat_enc_csn_id_coded = dep.pat_enc_csn_id_coded
diff --git a/scripts/ER_Infection/notebooks/figure_and_tabels/table2.sql b/scripts/ER_Infection/notebooks/figure_and_tabels/table2.sql
new file mode 100644
index 00000000..9e7e1d67
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/figure_and_tabels/table2.sql
@@ -0,0 +1,34 @@
+WITH cohort_bugs AS (
+SELECT DISTINCT
+  a.anon_id, a.pat_enc_csn_id_coded, a.index_time, cs.organism, cs.description
+FROM  
+  `mining-clinical-decisions.abx.final_ast_labels` a
+INNER JOIN
+  `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cult_orders
+USING
+  (pat_enc_csn_id_coded)
+INNER JOIN 
+  `som-nero-phi-jonc101.shc_core.culture_sensitivity` cs
+USING
+  (order_proc_id_coded)
+WHERE EXTRACT(YEAR FROM a.index_time) BETWEEN 2009 and 2019
+), 
+
+adt_dep as (
+SELECT DISTINCT
+  adt.pat_enc_csn_id_coded, 
+  FIRST_VALUE(dm.department_name) OVER 
+  (PARTITION BY adt.pat_enc_csn_id_coded ORDER BY adt.effective_time_jittered_utc) department_name,
+FROM 
+  `shc_core.adt` adt
+INNER JOIN
+  `som-nero-phi-jonc101.shc_core.dep_map` dm
+USING
+  (department_id)
+)
+
+SELECT c.*, a.department_name
+FROM cohort_bugs c
+INNER JOIN adt_dep a
+USING (pat_enc_csn_id_coded)
+
diff --git a/scripts/ER_Infection/notebooks/labelling/AST_Processing.ipynb b/scripts/ER_Infection/notebooks/labelling/AST_Processing.ipynb
new file mode 100644
index 00000000..cb73aaf6
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/labelling/AST_Processing.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os \n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Resistant bugs dataframe - manually filled in\n",
+    "df_bugs = pd.read_csv('resistant_bugs.csv')\n",
+    "df_bugs.query(\"antibiotic == 'Meropenem' and `Always Resistant` == 1\")['bug'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Fill in And Processing Functions\n",
+    "def fill_in_cefazolin(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Cefazolin == 'Susceptible' or x.Cefazolin == 'Resistant':\n",
+    "        return x.Cefazolin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Cefazolin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # If anything resistant to Cefepime or Ceftriaxone and Cefazolin not listed, assume resistant to Cefazolin\n",
+    "    if x.Cefepime == \"Resistant\" or x.Ceftriaxone == 'Resistant':\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # For anything STREPTOCOCCUS related except STREPTOCOCCUS PNEUMONIAE  - refer to penicillin if not listed \n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism) and x.organism != 'STREPTOCOCCUS PNEUMONIAE':\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "    \n",
+    "    # Check For MRSA in STAPH AUREUS if not listed\n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_ceftriaxone(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Ceftriaxone == 'Susceptible' or x.Ceftriaxone == 'Resistant':\n",
+    "        return x.Ceftriaxone\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Ceftriaxone' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # If anything resistant to Cefepime, assume resistant to Ceftriaxone\n",
+    "    if x.Cefepime == \"Resistant\":\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # If susceptible to Cefazolin, assume susceptible to Ceftriaxone\n",
+    "    if x.Cefazolin == \"Susceptible\": \n",
+    "        return \"Susceptible\"\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "\n",
+    "    # For anything STREPTOCOCCUS related - refer to penicillin if not listed\n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "        \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_cefepime(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Cefepime == 'Susceptible' or x.Cefepime == 'Resistant':\n",
+    "        return x.Cefepime\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Cefepime' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # For anything STREPTOCOCCUS related - refer to penicillin \n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "        \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # If susceptible to Cefazolin or Ceftriaxone assume susceptible to Cefepime\n",
+    "    if x.Cefazolin == \"Susceptible\" or x.Ceftriaxone == \"Susceptible\":\n",
+    "        return \"Susceptible\"\n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_zosyn(x):\n",
+    "     \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Zosyn == 'Susceptible' or x.Zosyn == 'Resistant':\n",
+    "        return x.Zosyn\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules\n",
+    "                 .query(\"antibiotic == 'Piperacillin/Tazobactam' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # For anything STREPTOCOCCUS related - refer to penicillin \n",
+    "    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):\n",
+    "        if x.Penicillin is not None:\n",
+    "            return x.Penicillin\n",
+    "        \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "        \n",
+    "    # If susceptible to ampicillin, then zosyn susceptible - useless rule because of the following rule\n",
+    "    if x.Ampicillin == \"Susceptible\":\n",
+    "        return x.Ampicillin\n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "\n",
+    "def fill_in_vancomycin(x):\n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Vancomycin == 'Susceptible' or x.Vancomycin == 'Resistant':\n",
+    "        return x.Vancomycin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Vancomycin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # Will apply Nancy Watz Rule Later (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_meropenem(x):\n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Meropenem == 'Susceptible' or x.Meropenem == 'Resistant':\n",
+    "        return x.Meropenem\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Meropenem' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # Check For MRSA in STAPH AUREUS \n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "        \n",
+    "    # If susceptible to ampicillin, then meropenem susceptible\n",
+    "    if x.Ampicillin == \"Susceptible\": # not the case with Enteroccocus, but this should be in alwasys resistant list above. \n",
+    "        return x.Ampicillin # also - useless rule because of rule below\n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_ciprofloxacin(x):\n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Ciprofloxacin == 'Susceptible' or x.Ciprofloxacin == 'Resistant':\n",
+    "        return x.Ciprofloxacin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Ciprofloxacin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # If Resistant to Levofloxacin, resistant to Cipro\n",
+    "    if x.Levofloxacin == \"Resistant\":\n",
+    "        return \"Resistant\"\n",
+    "    \n",
+    "    # Check for MRSA - assume same as oxacillin if not listed\n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "def fill_in_ampicillin(x):\n",
+    "    \n",
+    "    # If not missing just return what it is. \n",
+    "    if x.Ampicillin == 'Susceptible' or x.Ampicillin == 'Resistant':\n",
+    "        return x.Ampicillin\n",
+    "    \n",
+    "    # Read in bug rules\n",
+    "    df_bug_rules = pd.read_csv('resistant_bugs.csv')\n",
+    "    r_bugs = set(df_bug_rules.query(\"antibiotic == 'Ampicillin' and `Always Resistant` == 1\")['bug'].values)\n",
+    "    \n",
+    "    # Uses hand labelled list of resistant bugs\n",
+    "    if x.organism in r_bugs:\n",
+    "        return \"Resistant\"\n",
+    "        \n",
+    "    # Check for MRSA - assume same as oxacillin if not listed\n",
+    "    if x.organism == 'STAPHYLOCOCCUS AUREUS':\n",
+    "        if x.Oxacillin is not None:\n",
+    "            return x.Oxacillin \n",
+    "    \n",
+    "    # Otherwise apply Nancy Watz Rule (assume susc if not listed)\n",
+    "    return \"Susceptible\"\n",
+    "\n",
+    "\n",
+    "def combine_labels(arr):\n",
+    "    \"\"\"\n",
+    "    Sometimes organisms have multiple suscept labels. When they do, if any of them are not one of\n",
+    "    Susceptible, Positive, or Susceptible - Dose Dependent then we say organism is resistant to \n",
+    "    said antibiotic\n",
+    "    \"\"\"\n",
+    "    for a in arr:\n",
+    "        if a not in ['Susceptible', 'Positive', 'Susceptible - Dose Dependent']:\n",
+    "            return 'Resistant'\n",
+    "    return 'Susceptible'\n",
+    "\n",
+    "def combine_antibiotic_syns(x):\n",
+    "    \"\"\"\n",
+    "    Antibiotics often are given different names in the AST tables, this function combines\n",
+    "    synnonyms so that we don't have to refer to multiple names in downstream analysis. \n",
+    "    \"\"\"\n",
+    "    if x == 'Aztreonam.':\n",
+    "        return 'Aztreonam'\n",
+    "    elif x == 'Cefazolin..':\n",
+    "        return 'Cefazolin'\n",
+    "    elif x == 'Ceftazidime.':\n",
+    "        return 'Ceftazidime'\n",
+    "    elif x in ('Ceftriaxone (Meningeal)', 'Ceftriaxone (Non-Meningeal)', 'Ceftriaxone.'):\n",
+    "        return 'Ceftriaxone'\n",
+    "    elif x in ('Ciprofloxacin.'):\n",
+    "        return 'Ciprofloxacin'\n",
+    "    elif x == 'Gentamicin 500 mcg/ml.':\n",
+    "        return 'Gentamicin'\n",
+    "    elif x in ('Oxacillin Screen', 'Oxacillin.'):\n",
+    "        return 'Oxacillin'\n",
+    "    elif x in ('PENICILLIN G (MENINGEAL)','PENICILLIN G (NON-MENINGEAL)', 'PENICILLIN V (ORAL)', 'Penicillin..'):\n",
+    "        return 'Penicillin'\n",
+    "    elif x == 'Trimethoprim/Sulfamethoxazole.':\n",
+    "        return 'Trimethoprim/Sulfamethoxazole'\n",
+    "    else:\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Query BQ Tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pdb\n",
+    "# Query positive culture data\n",
+    "query = \"\"\"\n",
+    "SELECT DISTINCT \n",
+    "    cohort.pat_enc_csn_id_coded, cults.order_proc_id_coded,\n",
+    "    cs.sens_organism_sid, cs.line, cs.organism,\n",
+    "    cs.antibiotic, cs.suscept, cs.sensitivity_value, cs.sens_ref_range \n",
+    "FROM \n",
+    "    `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cults\n",
+    "INNER JOIN \n",
+    "    `mining-clinical-decisions.abx.cohort_not_infected_rules` cohort\n",
+    "USING \n",
+    "    (pat_enc_csn_id_coded)\n",
+    "INNER JOIN \n",
+    "    `shc_core.culture_sensitivity` cs\n",
+    "USING \n",
+    "    (order_proc_id_coded)\n",
+    "WHERE \n",
+    "    organism not LIKE \"%CANDIDA%\"\n",
+    "AND \n",
+    "    organism not in ('COAG NEGATIVE STAPHYLOCOCCUS', 'HAEMOPHILUS INFLUENZAE', 'HAEMOPHILUS PARAINFLUENZAE')\n",
+    "ORDER BY \n",
+    "    cohort.pat_enc_csn_id_coded, cults.order_proc_id_coded, cs.line\n",
+    "\"\"\"\n",
+    "\n",
+    "query_job = client.query(query)\n",
+    "df=query_job.to_dataframe()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make row correspond to unique organism_sid and pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_cols = ['example', 'pat_enc_csn_id_coded', 'organism', 'order_proc_id_coded', 'sens_organism_sid',\n",
+    "               'Cefepime', 'Ceftriaxone', 'Cefazolin', 'Vancomycin', 'Meropenem', 'Zosyn',\n",
+    "               'Ampicillin', 'Penicillin', 'Oxacillin', 'Ciprofloxacin', 'Levofloxacin']\n",
+    "\n",
+    "df_wide = (df\n",
+    "    .assign(example=lambda x: x.pat_enc_csn_id_coded.map(str) + ', ' \\\n",
+    "            + x.order_proc_id_coded.map(str) + ', ' + x.sens_organism_sid + ', ' + x.organism,\n",
+    "            antibiotic=lambda x: [combine_antibiotic_syns(a) for a in x.antibiotic])\n",
+    "    .groupby(['example', 'antibiotic']) \n",
+    "    .agg({'suscept' : lambda x: combine_labels(x)})          \n",
+    "    .reset_index()\n",
+    "    .pivot(index='example', columns='antibiotic', values='suscept')\n",
+    "    .reset_index()\n",
+    "    .assign(organism = lambda x: [a.split(', ')[3] for a in x.example],\n",
+    "            order_proc_id_coded = lambda x: [a.split(', ')[1] for a in x.example],\n",
+    "            sens_organism_sid = lambda x: [a.split(', ')[2] for a in x.example],\n",
+    "            pat_enc_csn_id_coded = lambda x: [a.split(', ')[0] for a in x.example])\n",
+    "    .rename(columns={'Piperacillin/Tazobactam' : 'Zosyn'})\n",
+    ")\n",
+    "df_wide = df_wide[filter_cols]\n",
+    "df_wide.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fill in Missing AST Labels \n",
+    "### Make Unit of Observation a CSN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agg_labels = lambda x: \"Resistant\" if any(x == \"Resistant\") else \"Susceptible\"\n",
+    "df_wide = (df_wide\n",
+    "    .assign(Ampicillin=lambda x: df_wide.apply(lambda x: fill_in_ampicillin(x), axis=1),\n",
+    "            Cefepime=lambda x: df_wide.apply(lambda x: fill_in_cefepime(x), axis=1),\n",
+    "            Ceftriaxone=lambda x: df_wide.apply(lambda x: fill_in_ceftriaxone(x), axis=1),\n",
+    "            Cefazolin=lambda x: df_wide.apply(lambda x: fill_in_cefazolin(x), axis=1),\n",
+    "            Ciprofloxacin=lambda x: df_wide.apply(lambda x: fill_in_ciprofloxacin(x), axis=1),\n",
+    "            Zosyn=lambda x: df_wide.apply(lambda x: fill_in_zosyn(x), axis=1),\n",
+    "            Vancomycin=lambda x: df_wide.apply(lambda x: fill_in_vancomycin(x), axis=1),\n",
+    "            Meropenem=lambda x: df_wide.apply(lambda x: fill_in_meropenem(x), axis=1),\n",
+    "            Vancomycin_Ceftriaxone=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                              or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                              for a, b in zip(x.Vancomycin, x.Ceftriaxone)],\n",
+    "            Vancomycin_Cefepime=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                           or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                           for a, b in zip(x.Vancomycin, x.Cefepime)],\n",
+    "            Vancomycin_Zosyn=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                        or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                        for a, b in zip(x.Vancomycin, x.Zosyn)],\n",
+    "            Vancomycin_Meropenem=lambda x: [\"Susceptible\" if a == \"Susceptible\" \n",
+    "                                            or b == \"Susceptible\" else \"Resistant\"\n",
+    "                                            for a, b in zip(x.Vancomycin, x.Meropenem)])\n",
+    "            \n",
+    "    .groupby('pat_enc_csn_id_coded')\n",
+    "    .agg({'Ampicillin' : agg_labels,\n",
+    "          'Cefazolin' : agg_labels,\n",
+    "          'Ceftriaxone' : agg_labels,\n",
+    "          'Cefepime' : agg_labels,\n",
+    "          'Ciprofloxacin' : agg_labels,\n",
+    "          'Zosyn' : agg_labels,\n",
+    "          'Vancomycin' : agg_labels,\n",
+    "          'Meropenem' : agg_labels,\n",
+    "          'Vancomycin_Ceftriaxone' : agg_labels,\n",
+    "          'Vancomycin_Cefepime' : agg_labels,\n",
+    "          'Vancomycin_Zosyn' : agg_labels,\n",
+    "          'Vancomycin_Meropenem' : agg_labels})\n",
+    "    .reset_index()\n",
+    ")\n",
+    "df_wide.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge with cohort table to get index times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT DISTINCT \n",
+    "    anon_id, pat_enc_csn_id_coded, index_time\n",
+    "FROM \n",
+    "    mining-clinical-decisions.abx.cohort_not_infected_rules\n",
+    "\"\"\"\n",
+    "query_job = client.query(query)\n",
+    "df_cohort = query_job.to_dataframe()\n",
+    "\n",
+    "columns = ['anon_id', 'pat_enc_csn_id_coded', 'index_time',\n",
+    "           'Ampicillin', 'Ciprofloxacin',\n",
+    "           'Cefazolin', 'Ceftriaxone', 'Cefepime',\n",
+    "           'Zosyn', 'Vancomycin', 'Meropenem',\n",
+    "           'Vancomycin_Ceftriaxone', 'Vancomycin_Cefepime',\n",
+    "           'Vancomycin_Zosyn', 'Vancomycin_Meropenem']\n",
+    "df_wide = (df_wide\n",
+    "    .assign(pat_enc_csn_id_coded=lambda x: x.pat_enc_csn_id_coded.astype(int))\n",
+    "    .merge(df_cohort, on='pat_enc_csn_id_coded', how='left')\n",
+    ")[columns]\n",
+    "df_wide.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert labels to binary values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform Susceptible vs Resitant to 1 or 0\n",
+    "abx_cols = ['Ampicillin', 'Ciprofloxacin',\n",
+    "            'Cefazolin', 'Ceftriaxone', 'Cefepime',\n",
+    "            'Zosyn', 'Vancomycin', 'Meropenem',\n",
+    "            'Vancomycin_Ceftriaxone', 'Vancomycin_Cefepime',\n",
+    "            'Vancomycin_Zosyn', 'Vancomycin_Meropenem']\n",
+    "df_wide[abx_cols] = (df_wide[abx_cols]\n",
+    "    .transform(lambda x: x.replace('Susceptible', '1').replace(\"Resistant\", '0'))\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Convert labels to integers\n",
+    "convert_dict = {col : int for col in abx_cols}\n",
+    "df_wide = df_wide.astype(convert_dict) \n",
+    "df_wide.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sanity Checks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show Fraction of CSNs where each abx therapy is susceptible\n",
+    "df_wide[abx_cols].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make Sure Combination therapies are never resistant if either of it's parts are susceptible\n",
+    "for combo, a, b in zip(df_wide.Vancomycin_Ceftriaxone.values, df_wide.Ceftriaxone.values, df_wide.Vancomycin.values):\n",
+    "    if combo == False:\n",
+    "        assert a or b == False\n",
+    "        \n",
+    "for combo, a, b in zip(df_wide.Vancomycin_Cefepime.values, df_wide.Cefepime.values, df_wide.Vancomycin.values):\n",
+    "    if combo == False:\n",
+    "        assert a or b == False\n",
+    "        \n",
+    "for combo, a, b in zip(df_wide.Vancomycin_Meropenem.values, df_wide.Meropenem.values, df_wide.Vancomycin.values):\n",
+    "    if combo == False:\n",
+    "        assert a or b == False\n",
+    "\n",
+    "for combo, a, b in zip(df_wide.Vancomycin_Zosyn.values, df_wide.Zosyn.values, df_wide.Vancomycin.values):\n",
+    "    if combo == False:\n",
+    "        assert a or b == False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make sure Cefazolin is rarely susceptible if Ceftriaxone or Cefepime are Resistant\n",
+    "# This can happen if for instance cefazolin is listed as susceptible, ceftriaxone is not listed but \n",
+    "# is filled in as resistant due to the strepp rule\n",
+    "count = 0\n",
+    "for a, b, c in zip(df_wide.Cefepime.values, df_wide.Ceftriaxone.values, df_wide.Cefazolin.values):\n",
+    "    if c:\n",
+    "        if not a and b:\n",
+    "            count += 1\n",
+    "print(count)\n",
+    "print(len(df_wide))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make sure Ceftriaxone is rarely susceptible if Cefeime is Resistant\n",
+    "# This can happen if for instance Ceftriaxone is listed as susceptible, Cefepime is not listed but \n",
+    "# is filled in as resistant due to one of the rules rule\n",
+    "count = 0\n",
+    "for a, b in zip(df_wide.Cefepime.values, df_wide.Ceftriaxone.values):\n",
+    "    if b:\n",
+    "        if not a:\n",
+    "            count += 1\n",
+    "print(count)\n",
+    "print(len(df_wide))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Upload Labels Table to BQ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Upload table \n",
+    "table_schema = [{'name' : 'anon_id', 'type' : 'STRING'},\n",
+    "                {'name' : 'pat_enc_csn_id_coded', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'index_time', 'type' : 'TIMESTAMP'},\n",
+    "                {'name' : 'Ampicillin', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Cefazolin', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Ceftriaxone', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Cefepime', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Cefepime', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Zosyn', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Vancomycin', 'type' : 'INTEGER'},\n",
+    "                {'name' : 'Meropenem', 'type' : 'INTEGER'}]\n",
+    "\n",
+    "DATASET_NAME = 'abx'\n",
+    "TABLE_NAME = 'final_ast_labels'\n",
+    "df_wide.to_gbq(destination_table='%s.%s' % (DATASET_NAME, TABLE_NAME),\n",
+    "               project_id='mining-clinical-decisions',\n",
+    "               table_schema=table_schema,\n",
+    "               if_exists='replace')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/ER_Infection/notebooks/labelling/fill_in_rules.py b/scripts/ER_Infection/notebooks/labelling/fill_in_rules.py
new file mode 100644
index 00000000..814a75e7
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/labelling/fill_in_rules.py
@@ -0,0 +1,344 @@
+import pandas as pd
+import numpy as np
+import os 
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ccorbin/.config/gcloud/application_default_credentials.json' 
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' 
+
+from google.cloud import bigquery
+client=bigquery.Client()
+
+
+def fill_in_cefazolin(x):
+    
+    # If not missing just return what it is. 
+    if x.Cefazolin == 'Susceptible' or x.Cefazolin == 'Resistant':
+        return x.Cefazolin
+    
+    # Read in bug rules
+    df_bug_rules = pd.read_csv('Bacteria_AST_Rules.csv')
+    r_bugs = set(df_bug_rules.query("cefazolin_r == 1")['Organism'].values)
+    s_bugs = set(df_bug_rules.query("cefazolin_s == 1")['Organism'].values)
+    
+    # Uses hand labelled list of resistant bugs
+    if x.organism in r_bugs:
+        return "Resistant"
+    
+    # Uses hand labelled always susceptible bugs
+    if x.organism in s_bugs:
+        return "Susceptible"
+
+    # For anything STREPTOCOCCUS related except STREPTOCOCCUS PNEUMONIAE  - refer to penicillin 
+    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism) and x.organism != 'STREPTOCOCCUS PNEUMONIAE':
+        if x.Penicillin is not None:
+            return x.Penicillin
+    
+    # Check For MRSA in STAPH AUREUS 
+    if x.organism == 'STAPHYLOCOCCUS AUREUS':
+        if x.Oxacillin is not None:
+            return x.Oxacillin 
+        
+    # If anything resistant to Cefepime or Ceftriaxone, assume resistant to Cefazolin
+    if x.Cefepime == "Resistant" or x.Ceftriaxone == 'Resistant':
+        return "Resistant"
+    
+    # Will apply Nancy Watz Rule Later (assume susc if not listed)
+    return "Susceptible"
+
+def fill_in_ceftriaxone(x):
+    
+    # If not missing just return what it is. 
+    if x.Ceftriaxone == 'Susceptible' or x.Ceftriaxone == 'Resistant':
+        return x.Ceftriaxone
+    
+    # Read in bug rules
+    df_bug_rules = pd.read_csv('Bacteria_AST_Rules.csv')
+    r_bugs = set(df_bug_rules.query("ceftriaxone_r == 1")['Organism'].values)
+    s_bugs = set(df_bug_rules.query("ceftriaxone_s == 1")['Organism'].values)
+    
+    # Uses hand labelled list of resistant bugs
+    if x.organism in r_bugs:
+        return "Resistant"
+    
+    # Uses hand labelled always susceptible bugs
+    if x.organism in s_bugs:
+        return "Susceptible"
+
+    # For anything STREPTOCOCCUS related - refer to penicillin 
+    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):
+        if x.Penicillin is not None:
+            return x.Penicillin
+        
+    # Check For MRSA in STAPH AUREUS 
+    if x.organism == 'STAPHYLOCOCCUS AUREUS':
+        if x.Oxacillin is not None:
+            return x.Oxacillin 
+
+    # If anything resistant to Cefepime, assume resistant to Ceftriaxone
+    if x.Cefepime == "Resistant":
+        return "Resistant"
+    
+    # If susceptible to Cefazolin, assume susceptible to Ceftriaxone
+    if x.Cefazolin == "Susceptible":
+        return "Resistant"
+    
+    # Will apply Nancy Watz Rule Later (assume susc if not listed)
+    return "Susceptible"
+
+def fill_in_cefepime(x):
+    
+    # If not missing just return what it is. 
+    if x.Cefepime == 'Susceptible' or x.Cefepime == 'Resistant':
+        return x.Cefepime
+    
+    # Read in bug rules
+    df_bug_rules = pd.read_csv('Bacteria_AST_Rules.csv')
+    r_bugs = set(df_bug_rules.query("cefepime_r == 1")['Organism'].values)
+    s_bugs = set(df_bug_rules.query("cefepime_s == 1")['Organism'].values)
+    
+    # Uses hand labelled list of resistant bugs
+    if x.organism in r_bugs:
+        return "Resistant"
+    
+    # Uses hand labelled always susceptible bugs
+    if x.organism in s_bugs:
+        return "Susceptible"
+
+    # For anything STREPTOCOCCUS related - refer to penicillin 
+    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):
+        if x.Penicillin is not None:
+            return x.Penicillin
+        
+    # Check For MRSA in STAPH AUREUS 
+    if x.organism == 'STAPHYLOCOCCUS AUREUS':
+        if x.Oxacillin is not None:
+            return x.Oxacillin 
+    
+    # If susceptible to Cefazolin or Ceftriaxone assume susceptible to Cefepime
+    if x.Cefazolin == "Susceptible" or x.Ceftriaxone == "Susceptible":
+        return "Susceptible"
+    
+    # Will apply Nancy Watz Rule Later (assume susc if not listed)
+    return "Susceptible"
+
+def fill_in_zosyn(x):
+     
+    # If not missing just return what it is. 
+    if x.Zosyn == 'Susceptible' or x.Zosyn == 'Resistant':
+        return x.Zosyn
+    
+    # Read in bug rules
+    df_bug_rules = pd.read_csv('Bacteria_AST_Rules.csv')
+    r_bugs = set(df_bug_rules.query("zosyn_r == 1")['Organism'].values)
+    s_bugs = set(df_bug_rules.query("zosyn_s == 1")['Organism'].values)
+    
+    # Uses hand labelled list of resistant bugs
+    if x.organism in r_bugs:
+        return "Resistant"
+    
+    # Uses hand labelled always susceptible bugs
+    if x.organism in s_bugs:
+        return "Susceptible"
+    
+    # For anything STREPTOCOCCUS related - refer to penicillin 
+    if ('STREPTOCOCCUS' in x.organism or 'STREPTOCOCCI' in x.organism):
+        if x.Penicillin is not None:
+            return x.Penicillin
+        
+    # Check For MRSA in STAPH AUREUS 
+    if x.organism == 'STAPHYLOCOCCUS AUREUS':
+        if x.Oxacillin is not None:
+            return x.Oxacillin 
+        
+    # If susceptible to ampicillin, then zosyn susceptible
+    if x.Ampicillin == "Susceptible":
+        return x.Ampicillin
+    
+    # Will apply Nancy Watz Rule Later (assume susc if not listed)
+    return "Susceptible"
+
+
+def fill_in_vancomycin(x):
+    # If not missing just return what it is. 
+    if x.Vancomycin == 'Susceptible' or x.Vancomycin == 'Resistant':
+        return x.Vancomycin
+    
+    # Read in bug rules
+    df_bug_rules = pd.read_csv('Bacteria_AST_Rules.csv')
+    r_bugs = set(df_bug_rules.query("vancomycin_r == 1")['Organism'].values) # Want to make sure I catch all gram negative bugs in here
+    s_bugs = set(df_bug_rules.query("vancomycin_s == 1")['Organism'].values)
+    
+    # Uses hand labelled list of resistant bugs
+    if x.organism in r_bugs:
+        return "Resistant"
+    
+    # Uses hand labelled always susceptible bugs
+    if x.organism in s_bugs:
+        return "Susceptible"
+    
+    
+    # Will apply Nancy Watz Rule Later (assume susc if not listed)
+    return "Susceptible"
+
+def fill_in_meropenem(x):
+    # If not missing just return what it is. 
+    if x.Meropenem == 'Susceptible' or x.Meropenem == 'Resistant':
+        return x.Meropenem
+    
+    # Read in bug rules
+    df_bug_rules = pd.read_csv('Bacteria_AST_Rules.csv')
+    r_bugs = set(df_bug_rules.query("meropenem_r == 1")['Organism'].values) # Want to make sure I catch all gram negative bugs in here
+    s_bugs = set(df_bug_rules.query("meropenem_s == 1")['Organism'].values)
+    
+    # Uses hand labelled list of resistant bugs
+    if x.organism in r_bugs:
+        return "Resistant"
+    
+    # Uses hand labelled always susceptible bugs
+    if x.organism in s_bugs:
+        return "Susceptible"
+    
+     # Check For MRSA in STAPH AUREUS 
+    if x.organism == 'STAPHYLOCOCCUS AUREUS':
+        if x.Oxacillin is not None:
+            return x.Oxacillin 
+        
+    # If susceptible to ampicillin, then meropenem susceptible
+    if x.Ampicillin == "Susceptible": # not the case with Enteroccocus, but this should be in alwasys resistant list above. 
+        return x.Ampicillin
+    
+    # Will apply Nancy Watz Rule Later (assume susc if not listed)
+    return "Susceptible"
+
+def combine_labels(arr):
+    """
+    Sometimes organisms have multiple suscept labels. When they do, if any of them are not one of
+    Susceptible, Positive, or Susceptible - Dose Dependent then we say organism is resistant to 
+    said antibiotic
+    """
+    for a in arr:
+        if a not in ['Susceptible', 'Positive', 'Susceptible - Dose Dependent']:
+            return 'Resistant'
+    return 'Susceptible'
+
+def combine_antibiotic_syns(x):
+    """
+    Antibiotics often are given different names in the AST tables, this function combines
+    synnonyms so that we don't have to refer to multiple names in downstream analysis. 
+    """
+    if x == 'Aztreonam.':
+        return 'Aztreonam'
+    elif x == 'Cefazolin..':
+        return 'Cefazolin'
+    elif x == 'Ceftazidime.':
+        return 'Ceftazidime'
+    elif x in ('Ceftriaxone (Meningeal)', 'Ceftriaxone (Non-Meningeal)', 'Ceftriaxone.'):
+        return 'Ceftriaxone'
+    elif x in ('Ciprofloxacin.'):
+        return 'Ciprofloxacin'
+    elif x == 'Gentamicin 500 mcg/ml.':
+        return 'Gentamicin'
+    elif x in ('Oxacillin Screen', 'Oxacillin.'):
+        return 'Oxacillin'
+    elif x in ('PENICILLIN G (MENINGEAL)','PENICILLIN G (NON-MENINGEAL)', 'PENICILLIN V (ORAL)', 'Penicillin..'):
+        return 'Penicillin'
+    elif x == 'Trimethoprim/Sulfamethoxazole.':
+        return 'Trimethoprim/Sulfamethoxazole'
+    else:
+        return x
+
+
+if __name__ == '__main__':
+    
+    # Query positive culture data
+    query = """
+    SELECT DISTINCT cohort.pat_enc_csn_id_coded, cults.order_proc_id_coded, cs.sens_organism_sid, cs.line, cs.organism, cs.antibiotic, cs.suscept, cs.sensitivity_value, cs.sens_ref_range 
+    FROM `mining-clinical-decisions.abx.culture_orders_within_24_hrs` cults
+    INNER JOIN `mining-clinical-decisions.abx.interm_cohort_with_no_inf_rules` cohort
+    USING (pat_enc_csn_id_coded)
+    INNER JOIN `shc_core.culture_sensitivity` cs
+    USING (order_proc_id_coded)
+    WHERE organism <> "COAG NEGATIVE STAPHYLOCOCCUS"
+    AND organism not LIKE "%CANDIDA%"
+    AND organism not in ('HAEMOPHILUS INFLUENZAE', 'HAEMOPHILUS PARAINFLUENZAE')
+    ORDER BY cohort.pat_enc_csn_id_coded, cults.order_proc_id_coded, cs.line
+    """
+
+    query_job = client.query(query)
+    df=query_job.to_dataframe()
+
+    # Collapses labels for all unique combinations of csns, order_ids, organism_sids, and organisms
+    df[['pat_enc_csn_id_coded', 'order_proc_id_coded', 'sens_organism_sid', 'organism']].fillna(
+        99999, inplace=True)
+    df = (df
+        .assign(example = lambda x: x.pat_enc_csn_id_coded.map(str) + ', ' \
+                + x.order_proc_id_coded.map(str) + ', ' + x.sens_organism_sid + ', ' + x.organism,
+                antibiotic = lambda x: [combine_antibiotic_syns(a) for a in x.antibiotic])
+        .groupby(['example', 'antibiotic']) 
+        .agg({'suscept' : lambda x: combine_labels(x)})          
+        .reset_index()
+    )
+
+    filter_cols = ['example', 'pat_enc_csn_id_coded', 'organism', 'order_proc_id_coded', 'sens_organism_sid',
+               'Cefepime', 'Ceftriaxone', 'Cefazolin', 'Vancomycin', 'Meropenem', 'Piperacillin/Tazobactam',
+               'Ampicillin', 'Penicillin', 'Oxacillin']
+
+    # Pivot so that we get one column per antibiotic. Each row should be a
+    # unique combo of csn, order_id, organims, organism_id
+    df_wide = (df
+        .pivot(index='example', columns='antibiotic', values='suscept')
+        .reset_index()
+        .assign(organism = lambda x: [a.split(', ')[3] for a in x.example],
+                order_proc_id_coded = lambda x: [a.split(', ')[1] for a in x.example],
+                sens_organism_sid = lambda x: [a.split(', ')[2] for a in x.example],
+                pat_enc_csn_id_coded = lambda x: [a.split(', ')[0] for a in x.example])
+    )[filter_cols]
+
+    # Fill in missing values with set of rules defined in functions above
+    df_wide = (df_wide
+        .assign(Cefazolin=lambda x: df_wide.apply(lambda x: fill_in_cefazolin(x), axis=1),
+                Ceftriaxone=lambda x: df_wide.apply(lambda x: fill_in_ceftriaxone(x), axis=1),
+                Cefepime=lambda x: df_wide.apply(lambda x: fill_in_cefepime(x), axis=1),
+                Zosyn=lambda x: df_wide.apply(lambda x: fill_in_zosyn(x), axis=1),
+                Vancomycin=lambda x: df_wide.apply(lambda x: fill_in_vancomycin(x), axis=1),
+                Meropenem=lambda x: df_wide.apply(lambda x: fill_in_meropenem(x), axis=1))
+        .groupby('pat_enc_csn_id_coded')
+        .agg({'Cefazolin' : lambda x: "Resistant" if any(x == "Resistant") else "Susceptible",
+              'Ceftriaxone' : lambda x: "Resistant" if any(x == "Resistant") else "Susceptible",
+              'Cefepime' : lambda x: "Resistant" if any(x == "Resistant") else "Susceptible",
+              'Zosyn' : lambda x: "Resistant" if any(x == "Resistant") else "Susceptible",
+              'Vancomycin' : lambda x: "Resistant" if any(x == "Resistant") else "Susceptible",
+              'Meropenem' : lambda x: "Resistant" if any(x == "Resistant") else "Susceptible"})
+        .reset_index()
+    )
+
+    # Query cohort table and merge to get index time for each csn
+    query = """
+    SELECT DISTINCT anon_id, pat_enc_csn_id_coded, index_time
+    FROM mining-clinical-decisions.abx.interm_cohort_with_no_inf_rules
+    """
+    query_job =client.query(query)
+    df_cohort=query_job.to_dataframe()
+
+    df_wide = (df_wide
+        .join(df_cohort, on='pat_enc_csn_id_coded', how='left')
+    )
+
+    # Upload table 
+    table_schema = [{'name' : 'anon_id', 'type' : 'STRING'},
+                    {'name' : 'pat_enc_csn_id_coded', 'type' : 'INTEGER'},
+                    {'name' : 'index_time', 'type' : 'TIMESTAMP'},
+                    {'name' : 'Cefazolin', 'type' : 'STRING'},
+                    {'name' : 'Ceftriaxone', 'type' : 'STRING'},
+                    {'name' : 'Cefepime', 'type' : 'STRING'},
+                    {'name' : 'Zosyn', 'type' : 'STRING'},
+                    {'name' : 'Vancomycin', 'type' : 'STRING'},
+                    {'name' : 'Meropenem', 'type' : 'STRING'}]
+
+    DATASET_NAME = 'abx'
+    TABLE_NAME = 'final_ast_labels'
+    df_wide.to_gbq(destination_table='%s.%s' % (DATASET_NAME, TABLE_NAME),
+                   project_id='mining-clinical-decisions',
+                   table_schema=table_schema,
+                   if_exists='replace')
+
diff --git a/scripts/ER_Infection/notebooks/mit_data_analysis/CheckDrPerformance.ipynb b/scripts/ER_Infection/notebooks/mit_data_analysis/CheckDrPerformance.ipynb
new file mode 100644
index 00000000..661361b7
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/mit_data_analysis/CheckDrPerformance.ipynb
@@ -0,0 +1,534 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(15806, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>example_id</th>\n",
+       "      <th>prescription</th>\n",
+       "      <th>is_train</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>CIP</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31</td>\n",
+       "      <td>NIT</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>34</td>\n",
+       "      <td>SXT</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>36</td>\n",
+       "      <td>NIT</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48</td>\n",
+       "      <td>LVX</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   example_id prescription  is_train\n",
+       "0           4          CIP         1\n",
+       "1          31          NIT         0\n",
+       "2          34          SXT         1\n",
+       "3          36          NIT         0\n",
+       "4          48          LVX         1"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_prescriptions = pd.read_csv('./data/all_prescriptions.csv')\n",
+    "print(df_prescriptions.shape)\n",
+    "df_prescriptions.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(116902, 7)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>example_id</th>\n",
+       "      <th>NIT</th>\n",
+       "      <th>SXT</th>\n",
+       "      <th>CIP</th>\n",
+       "      <th>LVX</th>\n",
+       "      <th>is_train</th>\n",
+       "      <th>uncomplicated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   example_id  NIT  SXT  CIP  LVX  is_train  uncomplicated\n",
+       "0           0  0.0  0.0  0.0  0.0         0              0\n",
+       "1           1  0.0  0.0  0.0  0.0         1              0\n",
+       "2           2  0.0  0.0  1.0  1.0         1              0\n",
+       "3           3  0.0  0.0  0.0  0.0         1              0\n",
+       "4           4  0.0  0.0  0.0  0.0         1              1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_labels = pd.read_csv('./data/all_uti_resist_labels.csv')\n",
+    "print(df_labels.shape)\n",
+    "df_labels.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(15806, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Join prescriptions to labels\n",
+    "df_prescriptions = (df_prescriptions\n",
+    "    .merge(df_labels, how='inner', on=['example_id', 'is_train'])\n",
+    ")\n",
+    "print(df_prescriptions.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>example_id</th>\n",
+       "      <th>prescription</th>\n",
+       "      <th>is_train</th>\n",
+       "      <th>NIT</th>\n",
+       "      <th>SXT</th>\n",
+       "      <th>CIP</th>\n",
+       "      <th>LVX</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uncomplicated</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15806</td>\n",
+       "      <td>15806</td>\n",
+       "      <td>15806</td>\n",
+       "      <td>15806</td>\n",
+       "      <td>15806</td>\n",
+       "      <td>15806</td>\n",
+       "      <td>15806</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               example_id  prescription  is_train    NIT    SXT    CIP    LVX\n",
+       "uncomplicated                                                                \n",
+       "1                   15806         15806     15806  15806  15806  15806  15806"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# prescriptions only come from uncomplicated uti\n",
+    "df_prescriptions.groupby('uncomplicated').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NIT    1358\n",
+       "CIP    1282\n",
+       "SXT    1260\n",
+       "LVX      41\n",
+       "Name: prescription, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_prescriptions.query(\"is_train==0\")['prescription'].value_counts() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.33570159857904086"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1323 / 3941"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.11925907130170008"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_dr_miss_rate(df):\n",
+    "    \"\"\"\n",
+    "    Given dataframe with prescription and resistance labels,\n",
+    "    will return the fraction of observations not covered by their prescriptions\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    df = (df\n",
+    "        .assign(missed=lambda x: [x[p][i] for i, p in enumerate(x.prescription.values)])\n",
+    "        .query(\"is_train == 0\", engine='python')\n",
+    "    )\n",
+    "    num_missed = df['missed'].sum()\n",
+    "    frac_missed = float(num_missed) / len(df)\n",
+    "    return frac_missed\n",
+    "\n",
+    "get_dr_miss_rate(df_prescriptions)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Randomly shuffle prescription column to build null distribution of miss rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [01:28<00:00, 11.27it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbcd72ed510>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import pdb\n",
+    "random_miss_rates = []\n",
+    "for i in tqdm(range(1000)):\n",
+    "    df_prescriptions['prescription'] = df_prescriptions['prescription'].sample(frac=1.0).values\n",
+    "    random_miss_rates.append(get_dr_miss_rate(df_prescriptions))\n",
+    "\n",
+    "df_miss = pd.DataFrame(data={\n",
+    "    'random_miss_rates' : random_miss_rates\n",
+    "})\n",
+    "\n",
+    "df_miss.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.084\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get a pvalues\n",
+    "actual_miss_rate = 0.11925907130170008\n",
+    "num_fewer = len([r for r in random_miss_rates if r <= actual_miss_rate])\n",
+    "pvalue = float(num_fewer)/1000\n",
+    "print(pvalue)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1248442019791931"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(random_miss_rates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/mit_data_analysis/linear_programming.py b/scripts/ER_Infection/notebooks/mit_data_analysis/linear_programming.py
new file mode 100644
index 00000000..021cd053
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/mit_data_analysis/linear_programming.py
@@ -0,0 +1,808 @@
+from pulp import *
+from sklearn.metrics import roc_auc_score, average_precision_score
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set(style='white', font_scale=2.0)
+from pulp.apis import PULP_CBC_CMD
+import os
+from tqdm import tqdm
+import pickle
+
+import pdb
+
+class AbxDecisionMaker():
+
+    def __init__(self, df, abx_settings):
+        self.df = df
+        self.abx_settings = abx_settings
+        self.abx_options = ['NIT', 'SXT', 'CIP', 'LVX']
+        self.abx_settings_copy = {
+            key : self.abx_settings[key] for key in self.abx_settings
+        } 
+        self.n = len(df)
+
+    def set_config(self, config_alt):
+        """
+        Alters config based on values in config_alt dict
+        """
+        for key in config_alt:
+            self.abx_settings[key] += config_alt[key]
+
+    def reset_config(self):
+        """
+        Resets config to original config
+        """
+        self.abx_settings = {
+            key : val for key, val in self.abx_settings_copy.items()
+        }
+
+    def compute_was_not_covered(self, x, decision_column='prescription'):
+        """
+        Given administered antibiotic, return whether antibiotic covered pateint
+        """
+        if decision_column == 'prescription':
+            med_description = x.prescription
+        elif decision_column == 'ip_prescription':
+            med_description = x.ip_prescription 
+        elif decision_column == 'random_prescription':
+            med_description = x.random_prescription
+        else:
+            raise
+        return x[med_description]
+
+    def get_coverage_rates(self, df=None):
+        """
+        Create flag for whether clinicians covered the patient during the csn,
+        whether a random assignemnt covered patient CSN, and whether optimized
+        assignment covered the patient CSN
+        """
+        if df is None:
+            df = self.df
+
+        df = (df
+            .assign(was_covered_dr=df.apply(
+                lambda x: self.compute_was_not_covered(x), axis=1))
+            .assign(was_covered_ip=df.apply(
+                lambda x: self.compute_was_not_covered(x, 'ip_prescription'),
+                axis=1
+                )
+            )
+            .assign(was_covered_random=df.apply(
+                lambda x: self.compute_was_not_covered(x, 'random_prescription'),
+                axis=1
+                )
+            )
+        )
+
+        clin_miss_rate = df['was_covered_dr'].sum() / len(df)
+        ip_miss_rate = df['was_covered_ip'].sum() / len(df)
+        random_miss_rate = df['was_covered_random'].sum() / len(df)
+        
+        return clin_miss_rate, ip_miss_rate, random_miss_rate
+
+    def random_assignment(self):
+        """
+        Gets a random assignment of meds given current config
+        """
+        meds = []
+        for key in self.abx_settings:
+            meds += [key for i in range(self.abx_settings[key])]
+        np.random.shuffle(meds)
+
+        self.df['random_prescription'] = [m for m in meds]
+
+    def solve_and_assign(self):
+
+        # Predictions string
+        predictions_string = '%s_predictions'
+        abx_model = LpProblem("Antibiotics", LpMinimize)
+
+        # Create binary indicators for whether treatment is used
+        drug_inds = {}
+        for abx in self.abx_options:
+            drug_inds[abx] = [
+                LpVariable('%s_%d' % (abx, i), lowBound=0, upBound=1, cat='Binary')
+                for i in range(len(self.df))
+            ]
+
+        # Add objective function to model
+        per_csn_sum = []
+        for i in range(len(self.df)):
+            _sum = 0
+            for abx in self.abx_options:
+                _sum += drug_inds[abx][i] * self.df[predictions_string % abx].values[i]
+            per_csn_sum.append(_sum)
+            
+        abx_model += lpSum(per_csn_sum)
+
+        # Add one selection constraint
+        for i in range(len(self.df)):
+            selections = []
+            for abx in self.abx_options:
+                selections.append(drug_inds[abx][i])
+            abx_model += lpSum(selections) == 1
+
+        for drug in drug_inds:
+            abx_model += lpSum(
+                [drug_inds[drug][i] for i in range(len(self.df))]
+            ) == self.abx_settings[drug]
+
+        # Solve model
+        abx_model.solve(solver=PULP_CBC_CMD(msg=False, timeLimit=10))
+
+        # Save selected antibiotic to df
+        abx_decisions = []
+        for i in range(len(self.df)):
+            abx_decision = None
+            for abx in self.abx_options:
+                if drug_inds[abx][i].value() == 1:
+                    abx_decision = abx
+            assert abx_decision is not None
+            abx_decisions.append(abx_decision)
+        self.df['ip_prescription'] = abx_decisions
+
+def load_df():
+    """
+    Loads df for input into AbxDecisionMaker
+    """
+    base_path = '/Users/conorcorbin/data/mit_abx_model_results/'
+    df = pd.DataFrame()
+    for label in ['NIT', 'SXT', 'CIP', 'LVX']:
+        df_preds = load_best_model_predictions(
+            os.path.join(base_path, label)
+        )
+        df = pd.concat([df, df_preds])
+    df = (df
+        .pivot(index='pred_id', columns='label', values='predictions')
+        .reset_index()
+        [['NIT', 'SXT', 'CIP', 'LVX']]
+        .rename(columns={
+            'NIT' : 'NIT_predictions',
+            'SXT' : 'SXT_predictions',
+            'CIP' : 'CIP_predictions',
+            'LVX' : 'LVX_predictions'
+        })
+    )
+
+    df_prescriptions = pd.read_csv(
+        ('/Users/conorcorbin/repos/CDSS/scripts/ER_Infection/'
+         'notebooks/mit_data_analysis/data/all_prescriptions.csv')
+    )
+    df_labels = pd.read_csv(
+        ('/Users/conorcorbin/repos/CDSS/scripts/ER_Infection/'
+         'notebooks/mit_data_analysis/data/all_uti_resist_labels.csv')
+    )
+
+    df_prescriptions = (df_prescriptions
+        .merge(df_labels, how='inner', on=['example_id', 'is_train'])
+        .query("is_train == 0", engine='python')
+        .dropna(subset=['uncomplicated'])
+    )
+    df = df.assign(
+        example_id = lambda x: [id_ for id_ in df_prescriptions.example_id]
+    )
+    df = (df
+        .merge(df_prescriptions, how='inner', on='example_id')
+    )
+
+    return df
+
+def stat_test_better_than_random():
+    """
+    Permutation test to see if optimized miss rate is better than the random
+    miss rate
+    """
+    df = load_df()
+
+    start_config = {
+        'CIP' : 1282,
+        'LVX' : 41,
+        'NIT' : 1358,
+        'SXT' : 1260
+    }
+
+    opt = AbxDecisionMaker(df, start_config)
+    opt.solve_and_assign()
+    opt.random_assignment()
+    c, o, r = opt.get_coverage_rates()
+    bootstrapped_r = []
+    for i in range(10000):
+        opt.random_assignment()
+        _, __, r = opt.get_coverage_rates()
+        bootstrapped_r.append(r)
+    
+    pvalue = len([
+        r for r in bootstrapped_r if r <= o
+    ])/len(bootstrapped_r)
+
+    return pvalue
+
+def stat_test_better_than_clinician():
+    """
+    Boostrap data to get a distribution for clinician and random miss rates so 
+    that we can get a pvalue when comparing to the optimized miss rate
+    """
+    
+    df = load_df()
+
+    start_config = {
+        'CIP' : 1282,
+        'LVX' : 41,
+        'NIT' : 1358,
+        'SXT' : 1260
+    }
+
+    opt = AbxDecisionMaker(df, start_config)
+    opt.solve_and_assign()
+    opt.random_assignment()
+    c, o, r = opt.get_coverage_rates()
+    bootstrapped_c = []
+    for i in tqdm(range(10000)):
+        df_sampled  = (opt.df
+            .sample(frac=1., replace=True)
+        )
+        c, _, __ = opt.get_coverage_rates(df=df_sampled)
+        bootstrapped_c.append(c)
+    pvalue = len([
+        c for c in bootstrapped_c if c <= o
+    ])/len(bootstrapped_c)
+
+    return pvalue
+
+def bootstrap_performance(labels, predictions):
+    """
+    Get bootstrapped confidence interval of auc
+    """
+    inds = [i for i in range(len(labels))]
+    aucs = []
+    average_precisions = []
+    for i in tqdm(range(1000)):
+        inds_b = np.random.choice(inds, size=len(inds), replace=True)
+        labels_b = [labels[i] for i in inds_b]
+        predictions_b = [predictions[i] for i in inds_b]
+        auc = roc_auc_score(labels_b, predictions_b)
+        ap = average_precision_score(labels_b, predictions_b)
+        aucs.append(auc)
+        average_precisions.append(ap)
+
+    mean = round(np.mean(aucs), 2)
+    upper = round(np.percentile(aucs, 97.5), 2)
+    lower = round(np.percentile(aucs, 2.5), 2)
+    auc_ci = f"{mean} [{lower}, {upper}]"
+
+    mean = round(np.mean(average_precisions), 2)
+    upper = round(np.percentile(average_precisions, 97.5), 2)
+    lower = round(np.percentile(average_precisions, 2.5), 2)
+    auc_pr = f"{mean} [{lower}, {upper}]"
+
+    return auc_ci, auc_pr
+
+def load_best_model_predictions(base_path):
+    """
+    Loads best model predictions for input into linear programming opt
+    """
+    best_auc = 0.5
+    best_model = None
+    label = base_path.split('/')[-1]
+    for model in ['ridge', 'lasso', 'gbm', 'random_forest']:
+        auc_path = os.path.join(base_path, model, "auroc.txt")
+        with open(auc_path, 'r') as f:
+            auc = float(f.read().rstrip())
+        if auc > best_auc:
+            # Load predictions for this model
+            path = os.path.join(base_path, model, f"{model}_predictions.csv")
+            df_preds = pd.read_csv(path)
+            df_preds = df_preds.assign(
+                pred_id = [i for i in range(len(df_preds))],
+                label = [label for i in range(len(df_preds))]
+            )
+            best_auc = auc
+            best_model = model
+    print(f"{label} best auc: {best_auc} best model: {best_model}")
+    return df_preds
+
+def compute_model_performances():
+    """
+    computes auc cis for each of the 4 best models
+    """
+    base_path = '/Users/conorcorbin/data/mit_abx_model_results/'
+    df_preds = load_best_model_predictions(base_path)
+    for abx in ['SXT', 'NIT', 'CIP', 'LVX']:
+        labels = df_preds[abx].values
+        predictions = df_preds[f"{abx}_predictions"].values
+        auc_ci = bootstrap_auroc(labels, predictions)
+        print(f"{abx}: {auc_ci}")
+
+def perform_sweep_plots():
+    """
+    Starts antibiotics config at what doctors prescribed and then 
+    """
+    df = load_df()
+
+    fig, axs = plt.subplots(2, 3, figsize=(30, 20))
+    sweeps = [
+        ('CIP', 'LVX'),
+        ('CIP', 'NIT'),
+        ('CIP', 'SXT'),
+        ('LVX', 'NIT'),
+        ('LVX', 'SXT'),
+        ('NIT', 'SXT')
+    ]
+
+    start_config = {
+        'CIP' : 1282,
+        'LVX' : 41,
+        'NIT' : 1358,
+        'SXT' : 1260
+    }
+
+    row, col = 0, 0
+    sweep_data = {}
+    for i, sweep in enumerate(sweeps):
+        sweep_data[i] = {}
+        axs, data = sweep_plot(df, sweep, axs, row, col, start_config)
+        if col == 2:
+            row += 1
+            col = 0
+        else:
+            col += 1
+        
+        sweep_data[i]['num_replaced'] = data[0]
+        sweep_data[i]['r_rates'] = data[1]
+        sweep_data[i]['c_rates'] = data[2]
+        sweep_data[i]['o_rates'] = data[3]
+
+    plt.savefig(
+        './abx_sweep.png',
+        bbox_inches='tight',
+        dpi=300
+    )
+
+    with open("sweep_data.pickle", "wb") as f:
+        pickle.dump(sweep_data, f)
+
+def sweep_opt(df, sweep, axs, row, col, config):
+    """
+    Performs one sweep
+    """
+    if sweep[0] in ('CIP', 'NIT'):
+        step_size = 10
+    else:
+        step_size = 1
+    opt = AbxDecisionMaker(df, config)
+    r_rates, o_rates, c_rates = [], [], []
+    num_replaced = []
+    for j in tqdm(range(0, config[sweep[0]], step_size)):
+        opt.reset_config()
+        opt.set_config({
+            sweep[0] : -j,
+            sweep[1] : +j
+        })
+        opt.solve_and_assign()
+        opt.random_assignment()
+        c, i, r = opt.get_coverage_rates()
+        r_rates.append(r)
+        c_rates.append(c)
+        o_rates.append(i)
+        num_replaced.append(j)
+
+    axs[row, col].plot(
+        num_replaced,
+        r_rates,
+        label='Random Allocation',
+        linewidth=2.0
+    )
+    axs[row, col].plot(
+        num_replaced,
+        c_rates,
+        label='Clinician Allocation',
+        linewidth=2.0
+    )
+    axs[row, col].plot(
+        num_replaced,
+        o_rates,
+        label='Optimized Allocation',
+        linewidth=2.0
+    )
+
+    if col == 2:
+        axs[row, col].legend(
+            bbox_to_anchor=(1.05, 1),
+            loc=2, borderaxespad=0.
+        )
+
+    axs[row, col].set_title(f"{sweep[0]} to {sweep[1]}")
+    axs[row, col].set_xlabel(f"Num {sweep[0]} replaced with {sweep[1]}")
+    axs[row, col].set_ylabel(f"Miss Rate")
+
+    data = (
+        num_replaced,
+        r_rates,
+        c_rates,
+        o_rates
+    )
+
+    return axs, data
+
+def green(a, b, c):
+    """
+    array of booleans when a has yet to exceed b or c
+    """
+    arr = a < b
+    was_false = False
+    for i in range(len(arr)):
+        if arr[i] == False:
+            was_false = True
+        
+        if was_false and arr[i] == True:
+            arr[i] == False
+
+    return arr
+
+def yellow(a, b, c):
+    """
+    array of booleans when a has exceeded b but has yet to exceed c
+    """
+    arr = np.logical_and(
+        a > b,
+        a <= c
+    )
+    ind_first_true, ind_last_true = None, None
+    was_true = False
+    for i in range(len(arr)):
+        if arr[i] == True and was_true == False:
+            ind_first_true = i
+            was_true = True
+        elif arr[i] == True and was_true == True:
+            ind_last_true = i
+
+    if ind_first_true is None:
+        ind_first_true = len(arr)
+    if ind_last_true is None:
+        ind_last_true = len(arr)
+
+    for i in range(len(arr)):
+        if i > ind_first_true-2 and i < ind_last_true:
+            arr[i] = True
+
+    return arr
+
+def yellow_range(a, b, c):
+    """
+    returns range of indices where yellow is true
+    """
+    arr = np.logical_and(
+        a > b,
+        a <= c
+    )
+    ind_first_true, ind_last_true = None, None
+    was_true = False
+    for i in range(len(arr)):
+        if arr[i] == True and was_true == False:
+            ind_first_true = i-1
+            was_true = True
+        elif arr[i] == True and was_true == True:
+            ind_last_true = i
+    
+    if ind_last_true is not None:
+        if ind_last_true >= len(arr) - 2: # hack for 2nd mit plot
+            ind_last_true = None
+
+    return ind_first_true, ind_last_true
+
+def red(a, b, c):
+    """
+    array of booleans when as has exceeded b and c
+    """
+    arr = a > c
+    was_true = False
+    for i in range(len(arr)):
+        if arr[i] == True:
+            was_true = True
+        if was_true and arr[i] == False:
+            arr[i] == True
+    return arr
+
+def sweep_plot_coverage_rate(ax, sweep, num_replaced,
+                             o_rates, c_rates, r_rates, params):
+    """
+    Makes on sweep plot (but plots coverage rate instead of miss rate)
+    """
+    x_axis = [n/params[sweep[0]] * 100 for n in num_replaced]
+    percent_replaced = [
+        int(n/params[sweep[0]] * 100)
+        for n in num_replaced
+    ]
+
+    r_rates =  np.array(
+        [params['random_rate'] for i in range(len(r_rates))]
+    )
+    c_rates = np.array([
+        c_rates[0] for i in range(len(c_rates))
+    ])
+    o_rates = np.array(o_rates)
+
+    yellow_first, yellow_last = yellow_range(o_rates, c_rates, r_rates)
+    if yellow_first is not None:
+        ax.vlines(
+            percent_replaced[yellow_first],
+            color='black',
+            linestyles='dotted',
+            ymin=params['ymin'],
+            ymax=params['ymax']
+        )
+    if yellow_last is not None:
+        ax.vlines(
+            percent_replaced[yellow_last],
+            color='black',
+            linestyles='dotted',
+            ymin=params['ymin'],
+            ymax=params['ymax']
+        )
+    if yellow_first is not None:
+        print(
+            f"{sweep[0]} to {sweep[1]}: {round(percent_replaced[yellow_first], 4)}"
+        )
+    else:
+        print(
+            f"{sweep[0]} to {sweep[1]}: 100"
+        )
+    ax.plot(
+        percent_replaced,
+        1-r_rates,
+        '--',
+        label='Random Allocation',
+        linewidth=2.0,
+        color='grey',
+    )
+    ax.plot(
+        percent_replaced,
+        1-c_rates,
+        '--',
+        label='Clinician Allocation',
+        linewidth=2.0,
+        color='black',
+    )
+    ax.plot(
+        percent_replaced,
+        1-o_rates,
+        label='Optimized Allocation',
+        linewidth=2.0,
+        color='black'
+    )
+    ax.fill_between(
+        percent_replaced, 1-o_rates, 1-c_rates,
+        where=(green(o_rates, c_rates, r_rates)),
+        color='#57c4ad', alpha=0.3,
+        interpolate=True,
+        label='Greater coverage rate than clinicians'
+    )
+    if yellow_first is not None:
+        ax.fill_between(
+            percent_replaced, 1-o_rates, 1-r_rates,
+            where=(
+                yellow(o_rates, c_rates, r_rates)
+            ),
+            color='#E6E1BC', alpha=0.3,
+            interpolate=True,
+            label='Greater coverage rate than random'
+        )
+    ax.fill_between(
+        percent_replaced, 1-o_rates, 1-r_rates,
+        where=(red(o_rates, c_rates, r_rates)),
+        color='#EDA247', alpha=0.3,
+        interpolate=True,
+        label="Lower coverage rate than random"
+    )
+    ax.set_ylim([params['ymin'], params['ymax']])
+    ax.set_xlim([0, 100])
+    ax.set_title(f"{sweep[0]} - {sweep[1]}")
+    ax.set_xlabel(
+        f"Percentage Swapped"
+    )
+    ax.set_xticklabels([
+        f"{int(n)}%"
+        for n in ax.get_xticks()
+    ])
+    ax.set_yticklabels([
+        f"{int(n*100)}%" for n in ax.get_yticks()
+    ])
+    import pdb
+    pdb.set_trace()
+    # For supplementary data 
+    df_out = pd.DataFrame(data={
+        'x-axis': x_axis,
+        'y-axis': 1-o_rates
+    })
+    os.makedirs('./supp_data/', exist_ok=True)
+    df_out.to_csv(f"./supp_data/{sweep[0].replace('/', '-')}_{sweep[1].replace('/', '-')}.csv")
+
+    return ax
+
+
+def sweep_plot(ax, sweep, num_replaced, o_rates, c_rates, r_rates, params):
+    """
+    Makes one sweep plot
+    
+    Parameters
+    ----------
+    ax : axes object
+    sweep : tuble (abx_1, abx_2)
+    num_replaced : np array iteration
+    o_rates : np array optimized miss rates
+    c_rates : np array clinician miss rates
+    r_rates : np array random miss rates
+    params : dictionary - original abx prescribing distribution
+             along with site specific data used for plot formatting
+
+    Returns
+    -------
+    ax : axes object
+    """
+    percent_replaced = [
+        int(n/params[sweep[0]] * 100)
+        for n in num_replaced
+    ]
+
+    r_rates =  np.array(
+        [params['random_rate'] for i in range(len(r_rates))]
+    )
+    c_rates = np.array([
+        c_rates[0] for i in range(len(c_rates))
+    ])
+    o_rates = np.array(o_rates)
+
+    yellow_first, yellow_last = yellow_range(o_rates, c_rates, r_rates)
+    if yellow_first is not None:
+        ax.vlines(
+            percent_replaced[yellow_first],
+            color='black',
+            linestyles='dotted',
+            ymin=params['ymin'],
+            ymax=params['ymax']
+        )
+    if yellow_last is not None:
+        ax.vlines(
+            percent_replaced[yellow_last],
+            color='black',
+            linestyles='dotted',
+            ymin=params['ymin'],
+            ymax=params['ymax']
+        )
+    print(
+        f"{sweep[0]} to {sweep[1]}: {round(percent_replaced[yellow_first], 4)}"
+    )
+    ax.plot(
+        percent_replaced,
+        r_rates,
+        '--',
+        label='Random Allocation',
+        linewidth=2.0,
+        color='grey',
+    )
+    ax.plot(
+        percent_replaced,
+        c_rates,
+        '--',
+        label='Clinician Allocation',
+        linewidth=2.0,
+        color='black',
+    )
+    ax.plot(
+        percent_replaced,
+        o_rates,
+        label='Optimized Allocation',
+        linewidth=2.0,
+        color='black'
+    )
+    ax.fill_between(
+        percent_replaced, o_rates, c_rates,
+        where=(green(o_rates, c_rates, r_rates)),
+        color='green', alpha=0.3,
+        interpolate=True,
+        label='Fewer misses than clinicians'
+    )
+    ax.fill_between(
+        percent_replaced, o_rates, r_rates,
+        where=(
+            yellow(o_rates, c_rates, r_rates)
+        ),
+        color='yellow', alpha=0.3,
+        interpolate=True,
+        label='Fewer misses than random'
+    )
+    ax.fill_between(
+        percent_replaced, o_rates, r_rates,
+        where=(red(o_rates, c_rates, r_rates)),
+        color='red', alpha=0.3,
+        interpolate=True,
+        label="More misses than random"
+    )
+    ax.set_ylim([params['ymin'], params['ymax']])
+    ax.set_xlim([0, 100])
+    ax.set_title(f"{sweep[0]} - {sweep[1]}")
+    ax.set_xlabel(
+        f"Percentage Swapped"
+    )
+    ax.set_xticklabels([
+        f"{int(n)}%"
+        for n in ax.get_xticks()
+    ])
+    ax.set_yticklabels([
+        f"{int(n*100)}%" for n in ax.get_yticks()
+    ])
+
+    return ax
+
+def plot_data():
+    """
+    Given saved data from sweeps, makes figures
+    """
+    # Pre-calculated - we know mean random miss rate is 12.5 percent
+    # Clinician miss rate is 11.9 percent
+
+    with open("sweep_data.pickle", "rb") as f:
+        data = pickle.load(f)
+    
+    sweeps = {
+        0 : ('CIP', 'LVX'),
+        1 : ('CIP', 'NIT'),
+        2 : ('CIP', 'SXT'),
+        3 : ('LVX', 'NIT'),
+        4 : ('LVX', 'SXT'),
+        5 : ('NIT', 'SXT')
+    }
+
+    params = {
+        'CIP' : 1282,
+        'LVX' : 41,
+        'NIT' : 1358,
+        'SXT' : 1260,
+        'ymin' : 0.09,
+        'ymax' : 0.14,
+        'random_rate' : .125,
+        'site' : "[Boston]"
+    }
+
+    fig, axs = plt.subplots(2, 3, figsize=(30, 20))
+    row, col = 0, 0
+    for key, sweep in data.items():
+        axs[row, col] = sweep_plot(
+            axs[row, col],
+            sweeps[key],
+            num_replaced=sweep['num_replaced'],
+            o_rates=sweep['o_rates'],
+            c_rates=sweep['c_rates'],
+            r_rates=sweep['r_rates'],
+            params=params
+        )
+
+        if col == 2 and row == 0:
+            axs[row, col].legend(
+                bbox_to_anchor=(1.05, 1),
+                loc=2, borderaxespad=0.
+            )
+
+        if col == 0:
+            axs[row, col].set_ylabel("Miss Rate")
+
+        if col == 2:
+            row += 1
+            col = 0
+        else:
+            col += 1
+
+    plt.savefig(
+        './abx_sweep_with_shade.png',
+        bbox_inches='tight',
+        dpi=300
+    )
+
+
+if __name__ == "__main__":
+    pval_c = stat_test_better_than_clinician()
+    print(pval_c)
+    pval_r = stat_test_better_than_random()
+    print(f"clin pvalue: {pval_c} random pvalue: {pval_r}")
diff --git a/scripts/ER_Infection/notebooks/mit_data_analysis/train_models_mit.py b/scripts/ER_Infection/notebooks/mit_data_analysis/train_models_mit.py
new file mode 100644
index 00000000..e1226942
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/mit_data_analysis/train_models_mit.py
@@ -0,0 +1,292 @@
+import pandas as pd
+import numpy as np
+from sklearn.metrics import roc_auc_score
+import lightgbm as lgb
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import (
+    cross_val_predict,
+    StratifiedKFold
+)
+
+import sys, argparse, os, json
+from tqdm import tqdm
+import pdb
+
+def lasso(X_train, y_train, X_test, y_test):
+    """
+    Trains an l2 penalized logistic regression sweeping over reg strength
+    """
+    grid = {'C' : np.logspace(-8, 8, 17)}
+
+    best_auc = 0.5
+    best_params = {key: None for key in grid}
+    best_preds = None
+
+    for c in tqdm(grid['C']):
+        clf = LogisticRegression(penalty='l1', C=c, solver='liblinear')
+        clf.fit(X_train, y_train)
+        preds = clf.predict_proba(X_test)[:, 1]
+        auc = roc_auc_score(y_test, preds)
+        if auc > best_auc:
+            best_auc = auc
+            best_params['C'] = c
+            best_preds = [p for p in preds]
+                
+    print(f"AUC: {round(best_auc, 2)}")
+    return clf, best_preds, best_auc, best_params 
+
+
+def ridge(X_train, y_train, X_test, y_test):
+    """
+    Trains an l2 penalized logistic regression sweeping over reg strength
+    """
+    grid = {'C' : np.logspace(-8, 8, 17)}
+
+    best_auc = 0.5
+    best_params = {key: None for key in grid}
+    best_preds = None
+
+    for c in tqdm(grid['C']):
+        clf = LogisticRegression(penalty='l2', C=c)
+        clf.fit(X_train, y_train)
+        preds = clf.predict_proba(X_test)[:, 1]
+        auc = roc_auc_score(y_test, preds)
+        if auc > best_auc:
+            best_auc = auc
+            best_params['C'] = c
+            best_preds = [p for p in preds]
+                
+    print(f"AUC: {round(best_auc, 2)}")
+    return clf, best_preds, best_auc, best_params 
+
+
+def random_forest(X_train, y_train, X_test, y_test):
+    """
+    Trains a random forest, sweeps over a few hyperparams
+    """
+    grid = {
+        'min_samples_split' : [2, 10, 50, 100],
+        'max_features' : ['sqrt', 'log2', None]
+    }
+
+    best_auc = 0.5
+    best_params = {key: None for key in grid}
+    best_preds = None
+
+    for mss in tqdm(grid['min_samples_split']):
+        for mf in grid['max_features']:
+            clf = RandomForestClassifier(
+                n_estimators=1000,
+                min_samples_split=mss,
+                max_features=mf
+            )
+            clf.fit(X_train, y_train)
+            preds = clf.predict_proba(X_test)[:, 1]
+            auc = roc_auc_score(y_test, preds)
+            if auc > best_auc:
+                best_auc = auc
+                best_params['min_samples_split'] = mss
+                best_params['max_features'] = mf
+                best_preds = [p for p in preds]
+
+    print(f"AUC: {round(best_auc, 2)}")
+    return clf, best_preds, best_auc, best_params 
+
+def lightgbm(X_train, y_train, X_test, y_test):
+    """
+    Trains a gbm with standard hyperparamters and returns AUROC on test set. 
+    """
+
+    grid = {'learning_rate' : [0.01, 0.05, 0.1, 0.5],
+            'num_leaves' : [2, 8, 16, 32, 64]
+    }
+
+    best_auc = 0.5
+    best_lr, best_num_leaves, best_iter = None, None, None
+    best_preds = None
+
+    for lr in tqdm(grid['learning_rate']):
+        for n in grid['num_leaves']:
+            # Instantiate GBM
+            clf = lgb.LGBMClassifier(
+                objective='binary',
+                n_estimators=1000,
+                learning_rate=lr,
+                num_leaves=n
+            )
+            
+            # Fit model with early stopping
+            clf.fit(
+                X_train,
+                y_train,
+                eval_set= [(X_test, y_test)],
+                eval_metric = 'binary',
+                early_stopping_rounds = 20,
+                verbose=False
+            )
+            
+            boosting_rounds = clf.best_iteration_
+            preds = clf.predict_proba(X_test)[:, 1]
+            auc = roc_auc_score(y_test, preds)
+
+            if auc > best_auc:
+                best_auc = auc
+                best_lr, best_num_leaves = lr, n
+                best_preds = [p for p in preds]
+                best_iter = clf.best_iteration_
+        
+    print(f"Num Boosting Rounds: {best_iter} AUC: {round(best_auc, 2)}")
+    best_params = {
+        'boosting_rounds' : best_iter,
+        'num_leaves' : best_num_leaves,
+        'learning_rate' : best_lr
+    }
+    return clf, best_preds, best_auc, best_params 
+
+def train_ensembled_model():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--label', default="NIT",
+                        type=str,
+                        help='NIT SXT CIP LVX')
+    parser.add_argument('--output_path',
+                        default="/Users/conorcorbin/data/mit_abx_model_results/",
+                        type=str,
+                        help='directory to save outputs')
+    parser.add_argument('--model_classes',
+                        nargs='+',
+                        default=['ridge', 'lasso', 'gbm', 'random_forest'],
+                        help='models to ensemble')
+    args = parser.parse_args()
+
+    df = pd.DataFrame()
+    for model in args.model_classes:
+        path = os.path.join(
+            args.output_path, args.label, model,
+            f"{model}_predictions.csv"
+        )
+        df_preds = pd.read_csv(path)
+        df_preds = df_preds.assign(
+            model=lambda x: [model for i in range(len(df_preds))],
+            pred_id = lambda x: [i for i in range(len(df_preds))]
+        )
+        df = pd.concat([df, df_preds])
+    
+    df_wide = (df
+        .pivot(index='pred_id', columns='model', values='predictions')
+        .reset_index()
+        .merge(df[['pred_id', 'label']], how='inner', on='pred_id')
+        .drop_duplicates()
+    )
+
+    cv = StratifiedKFold(n_splits=5)
+    clf = LogisticRegression(penalty='none')
+    X, y = df_wide[args.model_classes], df_wide['label']
+    predictions = cross_val_predict(clf, X, y, cv=cv, method='predict_proba')[:, 1]
+    auc = roc_auc_score(y, predictions)
+    print(f"{args.label} ensembled AUC: {round(auc, 3)}")
+
+    df_ens_preds = pd.DataFrame(data={
+        'labels' : y,
+        'predictions' : predictions
+    })
+
+    df_ens_preds.to_csv(f"{args.label}_ensembled_preds.csv", index=False)
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_class', default=None,
+                        type=str,
+                        help='ridge/lasso/lightgbm/random_forest/ffnn')
+    parser.add_argument('--label', default="NIT",
+                        type=str,
+                        help='NIT SXT CIP LVX')
+    parser.add_argument('--output_path',
+                        default="/Users/conorcorbin/data/mit_abx_model_results/",
+                        type=str,
+                        help='directory to save outputs')
+    args = parser.parse_args()
+
+    output_path = os.path.join(args.output_path, args.label, args.model_class)
+    os.makedirs(output_path, exist_ok=True)
+    
+    df_features = pd.read_csv('./data/all_uti_features.csv')
+    df_labels = pd.read_csv('./data/all_uti_resist_labels.csv')
+
+    feature_cols = [col for col in df_features.columns 
+                    if col not in ('example_id', 'is_train', 'uncomplicated')]
+
+    df = (df_labels
+        .merge(df_features, how='inner', on=['example_id', 'is_train', 'uncomplicated'])
+        .query("uncomplicated == 1", engine='python')
+    )
+    
+    pdb.set_trace()
+    df_train = df.query("is_train == 1", engine='python')
+    df_test = df.query("is_train == 0", engine='python')
+
+    # DEBUG
+    # df_train = df_train.sample(n=500).reset_index(drop=True)
+
+    model_dict = {
+        'gbm' : lightgbm,
+        'random_forest' : random_forest,
+        'lasso' : lasso,
+        'ridge' : ridge
+    }
+
+    X_train, y_train = df_train[feature_cols], df_train[args.label]
+    X_test, y_test = df_test[feature_cols], df_test[args.label]
+
+    clf, predictions, roc, params = model_dict[args.model_class](
+        X_train.values, y_train.values,
+        X_test.values, y_test.values
+    )
+
+    print("%s AUROC %s: " % (args.model_class, roc))
+
+    # Write AUROC to file so we can read it in later if this is val
+    f_auroc = os.path.join(output_path, 'auroc.txt')
+    with open(f_auroc, 'w') as w:
+        w.write(str(round(roc, 5)))
+
+    # Write optimal hyerparameters to file
+    f_params = os.path.join(output_path, f'{args.model_class}_best_params.json')
+    with open(f_params, 'w') as w:
+        json.dump(params, w) 
+
+    ## Save predictions
+    df_yhats = pd.DataFrame(data={
+        'label' : y_test,
+        'predictions' : predictions
+    })
+    df_yhats.to_csv(
+        os.path.join(output_path, f"{args.model_class}_predictions.csv")
+    )
+
+    # If testing, save top 50 features
+    if args.model_class in ['ridge', 'lasso']:
+        # use coeff parameter and sort by absolute value to get top features
+        inds = [i for i in range(len(clf.coef_))]
+        feature_imps = [abs(c) for c in clf.coef_]
+    else:
+        inds = [i for i in range(len(clf.feature_importances_))]
+        feature_imps = clf.feature_importances_
+
+    df_features = pd.DataFrame()
+    df_features['feature_indices'] = inds
+    df_features['feature_importances'] = feature_imps
+    df_features_top_50 = (df_features
+        .sort_values('feature_importances', ascending=False)
+        .head(50)
+    )
+    df_features_top_50.to_csv(
+        os.path.join(output_path, f"{args.model_class}_feature_importances.csv"),
+        index=False
+    )
+
+
+if __name__ == '__main__':
+    main()
+ 
+
diff --git a/scripts/ER_Infection/notebooks/modeling/FeatureMatrix.py b/scripts/ER_Infection/notebooks/modeling/FeatureMatrix.py
new file mode 100644
index 00000000..30203642
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/modeling/FeatureMatrix.py
@@ -0,0 +1,16 @@
+from google.cloud import bigquery
+import pandas as pd
+import os
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' 
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' 
+
+SQL_SEQUENCE = 
+
+class FeatureGenerator():
+
+	def __init__(self, cohort_table):
+		self.cohort_table = cohort_table
+
+	def getSequence(self):
+		
\ No newline at end of file
diff --git a/scripts/ER_Infection/notebooks/modeling/Train Censored Model.ipynb b/scripts/ER_Infection/notebooks/modeling/Train Censored Model.ipynb
new file mode 100644
index 00000000..64980ae0
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/modeling/Train Censored Model.ipynb	
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train $P(\\text{Label Observed} = 1 \\mid X=x)$ Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from scipy.sparse import load_npz\n",
+    "from sklearn.metrics import roc_curve, auc, average_precision_score, roc_auc_score, brier_score_loss\n",
+    "from sklearn.calibration import CalibratedClassifierCV, calibration_curve\n",
+    "from sklearn.model_selection import cross_val_predict, StratifiedKFold\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in feature matrix and labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = '/Users/conorcorbin/repos/er_infection/data/ast_models_c/'\n",
+    "\n",
+    "X_train = load_npz(os.path.join(data_dir, 'training_examples_round_test.npz'))\n",
+    "df_y_train = pd.read_csv(os.path.join(data_dir, 'training_labels_round_test.csv'))\n",
+    "y_train = df_y_train['label_unobserved']\n",
+    "X_test = load_npz(os.path.join(data_dir, 'test_examples_round_test.npz'))\n",
+    "df_y_test = pd.read_csv(os.path.join(data_dir, 'test_labels_round_test.csv'))\n",
+    "y_test = df_y_test['label_unobserved']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = RandomForestClassifier(n_estimators=1000)\n",
+    "clf.fit(X_train, y_train)\n",
+    "predictions = clf.predict_proba(X_test)[:, 0] # want probability of observing the label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_y_test['label'] = 1-y_test # so that now label is probability of observing the label\n",
+    "df_y_test['predictions'] = predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_dir = '/Users/conorcorbin/repos/er_infection/data/results/ast_models_c/'\n",
+    "os.makedirs(results_dir, exist_ok=True)\n",
+    "df_y_test.to_csv(os.path.join(results_dir, 'yhats.csv'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "auc = roc_auc_score(df_y_test['label'], df_y_test['predictions'])\n",
+    "sns.set(font_scale=1.5)\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
+    "for label in np.unique(df_y_test['label']): \n",
+    "    if label == 1:\n",
+    "        lab = \"Labels Observed\"\n",
+    "    else:\n",
+    "        lab = \"Labels Unobserved\"\n",
+    "    sns.distplot(df_y_test[df_y_test['label'] == label]['predictions'],\n",
+    "                 label=lab,\n",
+    "                 ax=ax)\n",
+    "ax.set_title(\"Observing labels classifier AUROC: {:.2f}\".format(auc))\n",
+    "ax.set_xlabel(\"Estimated Probability of Observing Label\")\n",
+    "ax.legend()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calibration Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class IdentityEstimator(LogisticRegression):\n",
+    "    def __init__(self):\n",
+    "        LogisticRegression.__init__(self)\n",
+    "            \n",
+    "    def predict_proba(self, input_array):   \n",
+    "        return input_array*1\n",
+    "\n",
+    "    def decision_function(self, input_array):\n",
+    "        return input_array*1\n",
+    "    \n",
+    "def calibrate_probabilities(predictions, labels):\n",
+    "    \"\"\" \n",
+    "    Takes in test set probabilites and does a k-fold cross fitting procedure to recalibrate each model\n",
+    "    \"\"\"\n",
+    "    est = IdentityEstimator()\n",
+    "    X = predictions.values.reshape(-1, 1)\n",
+    "    y = labels\n",
+    "    isotonic_calibrated_predictions = np.array([float(i) for i in range(len(y))])\n",
+    "    sigmoid_calibrated_predictions = np.array([float(i) for i in range(len(y))])\n",
+    "\n",
+    "    # Fit base estimator\n",
+    "    est.fit(X, y) # because we've overloaded predict_proba and decision function this doesn't matter\n",
+    "\n",
+    "    # Calibrated with isotonic calibration\n",
+    "    isotonic = CalibratedClassifierCV(est, cv='prefit', method='isotonic')\n",
+    "\n",
+    "    # Calibrated with sigmoid calibration\n",
+    "    sigmoid = CalibratedClassifierCV(est, cv='prefit', method='sigmoid')\n",
+    "\n",
+    "    cv = StratifiedKFold(n_splits=10)\n",
+    "    for train_inds, val_inds in cv.split(X, y):\n",
+    "        X_train, y_train = X[train_inds], y[train_inds]\n",
+    "        X_val, y_val = X[val_inds], y[val_inds]\n",
+    "        isotonic.fit(X_train, y_train)\n",
+    "        isotonic_predictions = isotonic.predict_proba(X_val)\n",
+    "        isotonic_calibrated_predictions[val_inds] = isotonic_predictions[:, 1]\n",
+    "\n",
+    "        sigmoid.fit(X_train, y_train)\n",
+    "        sigmoid_predictions = sigmoid.predict_proba(X_val)\n",
+    "        sigmoid_calibrated_predictions[val_inds] = sigmoid_predictions[:, 1]\n",
+    "\n",
+    "    \n",
+    "    return sigmoid_calibrated_predictions, isotonic_calibrated_predictions\n",
+    "\n",
+    "def plot_calibration_curves():\n",
+    "\n",
+    "    sns.set(font_scale=1.5)\n",
+    "    fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
+    "    prob_pos = df_y_test['predictions']\n",
+    "    labels = df_y_test['label']\n",
+    "    clf_score = brier_score_loss(labels, prob_pos)\n",
+    "    print(\"\\tBrier: %1.3f\" % (clf_score))\n",
+    "\n",
+    "    fraction_of_positives, mean_predicted_value = \\\n",
+    "        calibration_curve(labels, prob_pos, n_bins=10)\n",
+    "\n",
+    "    ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
+    "    ax.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
+    "            label=\"%s (%1.3f)\" % ('Uncalibrated', clf_score))\n",
+    "    \n",
+    "    # Now calibrate\n",
+    "    s_predictions, i_predictions = calibrate_probabilities(prob_pos, labels)\n",
+    "    \n",
+    "    # Sigmoid curve\n",
+    "    clf_score = brier_score_loss(labels, s_predictions)\n",
+    "    print(\"\\t Sigmoid Brier: %1.3f\" % (clf_score))\n",
+    "    fraction_of_positives, mean_predicted_value = \\\n",
+    "        calibration_curve(labels, s_predictions, n_bins=10)\n",
+    "    ax.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
+    "            label=\"%s (%1.3f)\" % ('Sigmoid', clf_score))\n",
+    "    \n",
+    "    \n",
+    "    # Isotonic curve\n",
+    "    clf_score = brier_score_loss(labels, i_predictions)\n",
+    "    print(\"\\t Isotonic Brier: %1.3f\" % (clf_score))\n",
+    "    fraction_of_positives, mean_predicted_value = \\\n",
+    "        calibration_curve(labels, i_predictions, n_bins=10)\n",
+    "    ax.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
+    "            label=\"%s (%1.3f)\" % ('Isotonic', clf_score))\n",
+    "    \n",
+    "    ax.legend()\n",
+    "    ax.set_xlabel('Estimated Probability')\n",
+    "    ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_calibration_curves()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ER_Infection/notebooks/modeling/batch_driver.sh b/scripts/ER_Infection/notebooks/modeling/batch_driver.sh
new file mode 100644
index 00000000..15399b6c
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/modeling/batch_driver.sh
@@ -0,0 +1,34 @@
+# Validation
+data_dir=/home/conorcorbin/repos/er_infection/data/ast_models_bucket1/
+output_dir=/home/conorcorbin/repos/er_infection/results/ast_models_bucket1/validation/
+model_classes=(ridge lightgbm)
+labels=(Ampicillin Ciprofloxacin Cefazolin Ceftriaxone Cefepime Zosyn Vancomycin Meropenem 
+Vancomycin_Ceftriaxone Vancomycin_Cefepime Vancomycin_Zosyn Vancomycin_Meropenem)
+log_dir=/home/conorcorbin/logs_ast_bucket1/
+mkdir -p $log_dir
+
+for mc in ${model_classes[@]}
+    do
+    for l in ${labels[@]}
+        do
+        python train_model.py --model_class $mc \
+                              --data_dir $data_dir \
+                              --label $l \
+                              --output_dir ${output_dir}/${mc}/${l} \
+                              --val &> ${log_dir}${mc}_${l}_validation.txt & 
+        done
+    done
+
+# #Testing
+# data_dir=/home/conorcorbin/repos/er_infection/data/
+# output_dir=/home/conorcorbin/repos/er_infection/results/ast_models/testing/
+# labels=(Ampicillin Ciprofloxacin Cefazolin Ceftriaxone Cefepime Zosyn Vancomycin Meropenem 
+# Vancomycin_Ceftriaxone Vancomycin_Cefepime Vancomycin_Zosyn Vancomycin_Meropenem)
+# log_dir=/home/conorcorbin/logs/
+
+# for l in ${labels[@]}
+#     do
+#     python train_model.py --data_dir $data_dir \
+#                             --label $l \
+#                             --output_dir ${output_dir}/${l}  &> ${log_dir}_${l}_testing.txt & 
+#     done
diff --git a/scripts/ER_Infection/notebooks/modeling/train_model.py b/scripts/ER_Infection/notebooks/modeling/train_model.py
new file mode 100644
index 00000000..876b3ed3
--- /dev/null
+++ b/scripts/ER_Infection/notebooks/modeling/train_model.py
@@ -0,0 +1,411 @@
+import numpy as np
+import pandas as pd
+from scipy.sparse import load_npz
+import lightgbm as lgb
+import datetime
+import pytz
+import joblib
+import os
+import argparse
+import json
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import GridSearchCV, StratifiedKFold
+from sklearn.preprocessing import FunctionTransformer
+from random import randrange
+from datetime import timedelta, datetime
+from sklearn.metrics import roc_curve, auc, average_precision_score, roc_auc_score,plot_precision_recall_curve, plot_roc_curve
+
+import pdb
+
+def ridge(X_train, y_train, X_valid, y_valid):
+
+    """ 1. Splits train set into k fold for hyperparameter search
+        2. Refits on Train
+        3. Evaluate on Val
+        4. Returns Model, hyperparameters, and yhats
+    """
+    grid = {'C' : np.logspace(-8, 8, 17)}
+    cv = StratifiedKFold(n_splits=5)
+    ridge = LogisticRegression(penalty='l2', max_iter= 1000, solver='lbfgs')
+    search = GridSearchCV(ridge, grid, scoring='roc_auc', cv=cv, verbose=2)
+    result = search.fit(X_train, y_train)
+    
+    print("Ridge\n")
+    print("Best CV Score: {score}\n".format(score=result.best_score_))
+    print("Best Hyperparameters: %s\n" % result.best_params_)
+    
+    # Fit model with best params on full train set and get score on val set
+    ridge = LogisticRegression(**result.best_params_)
+    ridge.fit(X_train, y_train)
+    y_predict = ridge.predict_proba(X_valid)[:, 1]
+    auc = roc_auc_score(y_valid, y_predict)
+
+    print("Validation Set AUROC: {auc}\n".format(auc=auc))
+
+    return ridge, y_predict, auc, result.best_params_
+
+def lasso(X_train, y_train, X_valid, y_valid):
+
+    """ 1. Splits train set into k fold for hyperparameter search
+        2. Refits on Train
+        3. Evaluate on Val
+        4. Returns Model, hyperparameters, and yhats
+    """
+    grid = {'C' : np.logspace(-8, 8, 17)}
+    cv = StratifiedKFold(n_splits=5)
+    lasso = LogisticRegression(penalty='l1', max_iter= 1000, solver='liblinear')
+    search = GridSearchCV(lasso, grid, scoring='roc_auc', cv=cv, verbose=2)
+    result = search.fit(X_train, y_train)
+    
+    print("Lasso\n")
+    print("Best CV Score: {score}\n".format(score=result.best_score_))
+    print("Best Hyperparameters: %s\n" % result.best_params_)
+    
+    # Fit model with best params on full train set and get score on val set
+    lasso = LogisticRegression(**result.best_params_)
+    lasso.fit(X_train, y_train)
+    y_predict = lasso.predict_proba(X_valid)[:, 1]
+    auc = roc_auc_score(y_valid, y_predict)
+
+    print("Validation Set AUROC: {auc}\n".format(auc=auc))
+
+    return lasso, y_predict, auc, result.best_params_
+
+def elastic_net(X_train, y_train, X_valid, y_valid):
+    # log_transformer = FunctionTransformer(np.log1p, validate=True)
+    # X_train = log_transformer.transform(X_train)
+    # X_valid = log_transformer.transform(X_valid)
+
+    cv_history = {}
+
+    #Fit on all values of c and then find the classifier with best AUROC 
+    for l1 in [0.2, 0.4, 0.6, 0.8]:
+        for c in np.logspace(-4, 4, 9): 
+            elnet = LogisticRegression(penalty='elasticnet', C = c, l1_ratio= l1, max_iter= 1000, solver='saga')
+            elnet.fit(X_train, y_train)
+
+            #Predict the probability of positive class
+            y_predict = elnet.predict_proba(X_valid)[:,1]
+            cv_roc = roc_auc_score(y_valid, y_predict)
+            print("AUC w/ C = ", c, "and l1_ratio of ", l1, " is ", cv_roc)
+            cv_history[cv_roc] = elnet
+
+    elnet_opt = cv_history[max(cv_history.keys())]
+    y_opt_predict = elnet_opt.predict_proba(X_valid)[:,1]
+    
+    return y_opt_predict, roc_auc_score(y_valid, y_opt_predict), elnet_opt
+
+
+
+def random_forest(X_train, y_train, X_valid, y_valid):
+
+    """ 
+    1. Splits train set into k fold for hyperparameter search
+    2. Refits on Train
+    3. Evaluate on Val
+    4. Returns Model, hyperparameters, and yhats
+    """
+
+    grid = {'min_samples_split' : [2, 10, 50, 100],
+            'max_features' : ['sqrt', 'log2', None]
+           }
+    cv = StratifiedKFold(n_splits=5)
+    rf = RandomForestClassifier(n_estimators=1000, random_state=42)
+    search = GridSearchCV(rf, grid, scoring='roc_auc', cv=cv, verbose=2)
+    result = search.fit(X_train, y_train)
+    
+    
+    print("Random Forest\n")
+    print("Best CV Score: {score}\n".format(score=result.best_score_))
+    print("Best Hyperparameters: %s\n" % result.best_params_)
+    
+    # Fit model with best params on full train set and get score on val set
+    rf = RandomForestClassifier(**result.best_params_)
+    rf.fit(X_train, y_train)
+    y_predict = rf.predict_proba(X_valid)[:, 1]
+    auc = roc_auc_score(y_valid, y_predict)
+
+    print("Validation Set AUROC: {auc}\n".format(auc=auc))
+
+    return rf, y_predict, auc, result.best_params_
+
+def lightgbm(X_train, y_train, X_valid, y_valid): 
+    """ 
+    1. Splits training set into k folds
+    2. Splits random small 5% chunk of training data from each fold for early stopping
+    3. Finds optimal hyperparameters on training set
+    4. Fits on entire training set - again split 5% chunk of training data for early stopping
+    5. Evaluate on Val
+    6. Return model, best hyperparams, yhats
+    """
+
+    grid = {'learning_rate' : [0.01, 0.05, 0.1, 0.5],
+            'num_leaves' : [2, 8, 16, 32, 64]
+    }
+    cv = StratifiedKFold(n_splits=5)
+
+    # Instantiate dictionary of aucs
+    aucs = {}
+    for lr in grid['learning_rate']:
+        aucs[lr] = {}
+        for l in grid['num_leaves']:
+            aucs[lr][l] = []
+    
+    ind=0
+    for train_inds, val_inds in cv.split(X_train, y_train):
+        # Get train, val, test for each fold.  Use 5% of train inds for early stopping
+        e_stopping = int(len(train_inds) * 0.95)
+        x_tr, x_v, = X_train[train_inds[:e_stopping]], X_train[train_inds[e_stopping:]]
+        y_tr, y_v =  y_train[train_inds[:e_stopping]], y_train[train_inds[e_stopping:]]
+        x_test, y_test = X_train[val_inds], y_train[val_inds]
+
+        for lr in grid['learning_rate']:
+            for num_leaves in grid['num_leaves']:
+                # Create GBM model 
+                gbm = lgb.LGBMClassifier(objective='binary',
+                                         n_estimators=1000,
+                                         learning_rate=lr,
+                                         num_leaves=num_leaves
+                )
+
+                # Fit model with early stopping
+                gbm.fit(x_tr,
+                        y_tr,
+                        eval_set= [(x_v, y_v)],
+                        eval_metric = 'binary',
+                        early_stopping_rounds = 10,
+                        verbose=False
+                )
+
+                y_predict = gbm.predict_proba(x_test)[:,1]
+                fold_auc = roc_auc_score(y_test, y_predict)
+                estimators = gbm.best_iteration_
+                out_str = "Learning Rate:{lr} Num Leaves:{num_leaves} Num Estimators:{est} Fold:{ind} AUROC:{roc}"
+                print(out_str.format(lr=lr, num_leaves=num_leaves, est=estimators, ind=ind, roc=fold_auc))  
+                aucs[lr][num_leaves].append(roc_auc_score(y_test, y_predict))
+        ind += 1
+    # Get Best Hyperparameters
+    best_auc = 0.0
+    best_params = {'learning_rate' : None, 'num_leaves' : None}
+    for lr in aucs:
+        for l in aucs[lr]:
+            aucs[lr][l] = np.mean(aucs[lr][l])
+            if aucs[lr][l] > best_auc:
+                best_auc = aucs[lr][l]
+                best_params = {'learning_rate' : lr, 'num_leaves' : num_leaves}
+    print("Optimal Params")
+    print(best_params)
+    print("Best cross validated auroc")
+    print(best_auc)
+
+    print("Retraining on full training set...")
+    # Fit on full training set
+    inds = [i for i in range(len(y_train))]
+    np.random.shuffle(inds)
+    e_s = int(len(inds) * 0.95)
+    x_tr, x_val = X_train[inds[:e_s]], X_train[inds[e_s:]]
+    y_tr, y_val = y_train[inds[:e_s]], y_train[inds[e_s:]]
+    gbm = lgb.LGBMClassifier(objective='binary',
+                             n_estimators=1000,
+                             learning_rate=best_params['learning_rate'],
+                             num_leaves=best_params['num_leaves']
+    )
+
+    gbm.fit(x_tr,
+            y_tr,
+            eval_set = [(x_val, y_val)],
+            eval_metric = 'binary',
+            early_stopping_rounds = 10,
+            verbose = False
+    )
+
+    best_params['boosting_rounds'] = gbm.best_iteration_ # get num boosting rounds
+    y_predict = gbm.predict_proba(X_valid)[:,1]
+    auc = roc_auc_score(y_valid, y_predict)
+    print("Num Estimators:{est} Eval set AUROC:{roc}".format(est=gbm.best_iteration_ , roc=auc))
+
+    return gbm, y_predict, auc, best_params
+
+def write_params_to_json(clf, output_path, model, TAB):
+
+    json_path = output_path + model + TAB + '_params.json'
+    print("Saving hyperparameters to ", json_path)
+
+    if model in ["lasso", "ridge", "elastic_net", "random_forest"]: 
+        json.dump(clf.get_params(), open(json_path, 'w'))
+    else: # lightgbm
+        params = clf.get_params()
+        params["n_estimators"] = clf.best_iteration_ # Add in early stopping round 
+        json.dump(params, open(json_path, 'w'))
+
+def read_from_json(json_file, model):
+
+    with open(json_file) as file:
+        params = json.load(file)
+
+    if model in ["ridge","lasso","elastic_net"]:
+        clf = LogisticRegression(**params)
+    elif model == "random_forest":
+        clf = RandomForestClassifier(**params)
+    elif model == "lightgbm":
+        clf = lgb.LGBMClassifier(**params)
+    elif model == "ffnn":
+        json_file = open(json_file, 'r')
+        loaded_model_json = json_file.read()
+        json_file.close()
+        clf = model_from_json(loaded_model_json)
+    
+    return clf
+
+def retrain_from_model(X_train, y_train, X_test, y_test, clf, model):
+    # Fit with specified hyperparameters
+    clf.fit(X_train, y_train)
+    y_predict = clf.predict_proba(X_test)[:,1]
+    roc = roc_auc_score(y_test, y_predict)
+        
+    return y_predict, roc
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_class', default=None, type=str, help='ridge/lasso/lightgbm/random_forest/ffnn')
+    parser.add_argument('--data_dir', default="", type=str, help='directory with sparce matrices train val test')
+    parser.add_argument('--label', default="", type=str, help='the column name for label in traige_TE.triage_cohort_final_with_labels_complete1vs')
+    parser.add_argument('--output_dir', default="", type=str, help='directory to save outputs')
+    parser.add_argument('--val', action='store_true', help='True if performing validation (i.e. not using test data)')
+
+    args = parser.parse_args()
+    model = args.model_class
+
+    command_str = """python train_model.py --model_class {model_class}
+                                           --data_dir {data_dir}
+                                           --label {label}
+                                           --output_dir {output_dir}
+                                           --val {val}
+                  """
+    command_str = command_str.format(model_class=args.model_class,
+                                     data_dir=args.data_dir,
+                                     label=args.label,
+                                     output_dir=args.output_dir,
+                                     val=args.val == True)
+    print(command_str)
+
+    # features = args.feature_types
+    output_path = args.output_dir
+    val_flag = args.val
+
+    if args.val and model == None:
+        print("No model entered.")
+        exit()
+
+    if not args.val and args.label == "":
+        print("If testing must specify which antibiotic(s)")
+
+    TAB = "_validation" if val_flag else "_test"
+
+    os.makedirs(output_path, exist_ok=True)
+
+    print("Using MODEL: ", model)
+    
+    print("Loading data...")
+    
+    if val_flag:
+        X_train = load_npz(os.path.join(args.data_dir, 'training_examples_round_validation.npz'))
+        y_train = pd.read_csv(os.path.join(args.data_dir, 'training_labels_round_validation.csv'))
+        y_train = y_train[args.label].values
+
+        X_test = load_npz(os.path.join(args.data_dir, 'test_examples_round_validation.npz'))
+        y_test_df = pd.read_csv(os.path.join(args.data_dir, 'test_labels_round_validation.csv'))
+        y_test = y_test_df[args.label].values
+    else:
+        X_train = load_npz(os.path.join(args.data_dir, 'training_examples_round_testing.npz'))
+        y_train = pd.read_csv(os.path.join(args.data_dir, 'training_labels_round_testing.csv'))
+        y_train = y_train[args.label].values
+
+        X_test = load_npz(os.path.join(args.data_dir, 'test_examples_round_testing.npz'))
+        y_test_df = pd.read_csv(os.path.join(args.data_dir, 'test_labels_round_testing.csv'))
+        y_test = y_test_df[args.label].values
+        
+    print("Starting training...")
+    if args.val: # then we manually specify model type
+
+        if model == "ridge":
+            clf, predict, roc, clf_opt = ridge(X_train, y_train, X_test, y_test)
+        elif model == "lasso":
+            clf, predict, roc, clf_opt = lasso(X_train,y_train, X_test, y_test) 
+        elif model == "elastic_net":
+            clf, predict, roc, clf_opt = elastic_net(X_train, y_train, X_test, y_test)
+        elif model == "random_forest":
+            clf, predict, roc, clf_opt = random_forest(X_train, y_train, X_test, y_test)
+        elif model == "lightgbm":
+            clf, predict, roc, clf_opt = lightgbm(X_train, y_train, X_test, y_test)
+        else:
+            print("Model not recognized!")
+    else: # otherwise we use the best model class from validation experiment
+
+        path = '/home/conorcorbin/repos/er_infection/results/ast_models/validation/{model}/{abx}' # hardcoded
+        best_auroc = 0.0
+        best_model_class = None
+        for model in ['lasso', 'ridge', 'lightgbm', 'random_forest']:
+            auroc_path = os.path.join(path.format(model=model, abx=args.label), 'auroc.txt')
+            with open(auroc_path, 'r') as f:
+                auroc = float(f.read())
+                print("Model: {model} Val AUROC:{auc}".format(model=model,auc=str(auroc)))
+            if auroc > best_auroc:
+                best_model_class = model
+                best_auroc = auroc
+        print("Best Model: {model}".format(model=best_model_class))
+        model = best_model_class
+        if model == "ridge":
+            clf, predict, roc, clf_opt = ridge(X_train, y_train, X_test, y_test)
+        elif model == "lasso":
+            clf, predict, roc, clf_opt = lasso(X_train,y_train, X_test, y_test) 
+        elif model == "elastic_net":
+            clf, predict, roc, clf_opt = elastic_net(X_train, y_train, X_test, y_test)
+        elif model == "random_forest":
+            clf, predict, roc, clf_opt = random_forest(X_train, y_train, X_test, y_test)
+        elif model == "lightgbm":
+            clf, predict, roc, clf_opt = lightgbm(X_train, y_train, X_test, y_test)
+        else:  
+            print("Model not recognized!")
+
+
+    print("%s AUROC %s: " % (model, roc))
+    # Write AUROC to file so we can read it in later if this is val
+    f_auroc = os.path.join(output_path, 'auroc.txt')
+    with open(f_auroc, 'w') as w:
+        w.write(str(round(roc, 5)))
+
+    # Write optimal hyerparameters to file
+    f_params = os.path.join(output_path, '%s_best_params.json' % model)
+    with open(f_params, 'w') as w:
+        json.dump(clf_opt, w) 
+
+    ## Save predictions
+    print("Saving predictions to: " + output_path + "/%s_predictions.csv" % model)
+    y_test_df["label"] = y_test
+    y_test_df["predictions"] = predict
+    y_test_df.to_csv(os.path.join(output_path, "%s_predictions.csv" % model))
+
+    # If testing, save top 50 features
+    if model in ['ridge', 'lasso']:
+        # use coeff parameter and sort by absolute value to get top features
+        inds = [i for i in range(len(clf.coef_))]
+        feature_imps = [abs(c) for c in clf.coef_]
+    else:
+        inds = [i for i in range(len(clf.feature_importances_))]
+        feature_imps = clf.feature_importances_
+    df_features = pd.DataFrame()
+    df_features['feature_indices'] = inds
+    df_features['feature_importances'] = feature_imps
+    df_features_top_50 = (df_features
+        .sort_values('feature_importances', ascending=False)
+        .head(50)
+    )
+    print("Saving feature importances to: " + output_path + "/%s_feature_importances.csv" % model)
+    df_features_top_50.to_csv(os.path.join(output_path, "%s_feature_importances.csv" % model), index=None)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/GoogleCloud/BQ/BigQueryConnect.py b/scripts/GoogleCloud/BQ/BigQueryConnect.py
new file mode 100644
index 00000000..cd2d594b
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/BigQueryConnect.py
@@ -0,0 +1,262 @@
+'''
+TODO: This is written for py3, pause on this for now and use _py2 version
+TODO: stream_csv_to_table method not functional
+
+connect to BQ using json token
+create new dataset
+create new table
+create new table by uploading CSV
+create schema/table-type from postgres /d <db> output
+get row count of table
+get query result
+'''
+
+from google.cloud import bigquery
+#from google.cloud import storage
+from google.cloud.exceptions import NotFound
+
+from typing import List
+from pathlib import Path
+import re
+import pandas
+import time
+
+
+class BigQueryConnect():
+    '''
+    setup credentials: export GOOGLE_APPLICATION_CREDENTIALS='<path to json>'
+    '''
+
+    def __init__(self):
+        self.client = bigquery.Client()
+        assert self.client is not None, 'Did not connect to BQ, check credentials env(GOOGLE_APPLICATION_CREDENTIALS)'
+
+    def create_new_dataset(self, dataset_id: str) -> None:
+        '''
+        https://cloud.google.com/bigquery/docs/datasets#create-dataset
+
+        :param dataset_id: dataset name
+        :return: None
+        '''
+
+        dataset_ref = self.client.dataset(dataset_id)
+
+        try:
+            # Check if the dataset with specified ID already exists
+            self.client.get_dataset(dataset_ref)
+            print(f'Dataset {dataset_id} already exists! Skipping create operation.')
+        except NotFound:
+            # Construct a full Dataset object to send to the API.
+            dataset = bigquery.Dataset(dataset_ref)
+            dataset.location = 'US'
+            dataset = self.client.create_dataset(dataset)  # API request
+            print(f'Dataset {dataset.dataset_id} created successfully project: {self.client.project}.')
+
+    def create_new_table_from_schema(self, dataset_id: str, table_id: str,
+                                     schema: List[bigquery.SchemaField]) -> None:
+        '''
+        https://cloud.google.com/bigquery/docs/tables#create-table
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param schema:
+            schema = [
+                bigquery.SchemaField('full_name', 'STRING', mode='REQUIRED', description='blah'),
+                bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
+            ]
+        :return: None
+        '''
+
+        dataset_ref = self.client.dataset(dataset_id)
+        table_ref = dataset_ref.table(table_id)
+
+        try:
+            self.client.get_table(table_ref)
+            print(f'Table {table_id} in dataset {dataset_id} already exists! Skipping create operation.')
+        except NotFound:
+            # Construct a full Table object to send to the API.
+            table = bigquery.Table(table_ref, schema=schema)
+            table = self.client.create_table(table)  # API request
+            print(
+                f'Table {table.table_id} in dataset {dataset_id}'
+                f'created successfully project: {self.client.project}.'
+            )
+
+    def _stream_csv_to_table(self, dataset_id: str, table_id: str, csv_path: str, batch_size: int = 1000):
+        '''
+        FYI: Streaming is NOT free :)
+        https://cloud.google.com/bigquery/pricing#streaming_pricing
+        TODO: 06/03/2019 NOT TESTED, DO NOT USE THIS FUNCTION
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param csv_path: path to CSV from SQL
+        :param batch_size: rows per insert (default 1000)
+        :return: None
+        '''
+
+        table_ref = self.client.dataset(dataset_id).table(table_id)
+        table = self.client.get_table(table_ref)  # API request
+
+        with open(csv_path, 'w') as infile, open(csv_path + '_errors', 'w') as outfile:
+            for line in infile:
+                rows_to_insert = []
+                row = line.split(',')
+                # TODO validate row
+                rows_to_insert.append(row)
+
+                if len(rows_to_insert) == batch_size:
+                    errors = self.client.insert_rows(table, rows_to_insert)  # API request
+                    assert errors == []
+                    rows_to_insert = []
+
+            if rows_to_insert:
+                errors = self.client.insert_rows(table, rows_to_insert)  # API request
+                assert errors == []
+
+    def load_csv_to_table(self, dataset_id: str, table_id: str, csv_path: str, auto_detect_schema: bool = True,
+                          schema: List[bigquery.SchemaField] = [], skip_rows: int = 0) -> None:
+        '''
+        TODO: add functionality for optional schema input
+        TODO: what happens if dataset does not exist?
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param csv_path: path to exported csv file
+        :param auto_detect_schema: auto detect schema types from CSV (default True)
+        :param schema: (default None)
+            schema = [
+                bigquery.SchemaField('full_name', 'STRING', mode='REQUIRED', description='blah'),
+                bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
+            ]
+        :param skip_rows: how many rows in CSV to skip (default 0)
+        :return: None
+
+        Details on loading local data
+        https://cloud.google.com/bigquery/docs/loading-data-local
+        '''
+
+        location = 'US'
+
+        dataset_ref = self.client.dataset(dataset_id)
+        table_ref = dataset_ref.table(table_id)
+        dataset = bigquery.Dataset(dataset_ref)
+        dataset.location = location
+
+        job_config = bigquery.LoadJobConfig()
+        job_config.source_format = bigquery.SourceFormat.CSV
+        # https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv#overwriting_a_table_with_csv_data
+        job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
+        job_config.skip_leading_rows = skip_rows
+        job_config.quote_character = '\"'
+
+        if auto_detect_schema:
+            assert schema == [], 'Auto-detect is False, but schema is specified'
+            job_config.autodetect = True
+        else:
+            job_config.autodetect = False
+            assert schema != [], 'Auto-detect is False, but no schema specified'
+            job_config.schema = schema
+
+
+        with open(csv_path, 'rb') as csv_file:
+            load_table_job = self.client.load_table_from_file(
+                csv_file,
+                table_ref,
+                location=location,
+                job_config=job_config,
+            )  # API request
+
+        print('Uploading table... ')
+        try:
+            load_table_job.result() # Wait for load to complete
+        except:
+            errors = load_table_job.errors
+            return errors
+        #print(load_table_job.error_result)
+        table = self.client.get_table(table_ref)
+
+        print(
+            f'{load_table_job.output_rows} rows loaded into '
+            f'table {table.table_id} in dataset {dataset_id} '
+            f'in project: {self.client.project}.'
+        )
+
+    def read_table_types(self, table_types_path: str) -> List[bigquery.SchemaField]:
+        '''
+        reads in lines, deliminited by "|", e.g.:
+        <column name> | <data type> | <collation> | <nullable> | <default>
+
+        :param table_types_path:
+        :return: list of bigquery.SchemaField
+        '''
+
+        type_mapping = {
+            'TINYINT'   : 'INT64',
+            'SMALLINT'  : 'INT64',
+            'MEDIUMINT' : 'INT64',
+            'INT'       : 'INT64',
+            'INTEGER'   : 'INT64',
+            'BIGINT'    : 'INT64',
+            'DECIMAL'   : 'NUMERIC',
+            'NUMERIC'   : 'NUMERIC',
+            'REAL'      : 'FLOAT64',
+            'DOUBLE PRECISION': 'FLOAT64',
+            'BOOLEAN'   : 'BOOL',
+            'CHAR'      : 'STRING',
+            'CHARACTER': 'STRING',
+            'CHARACTER VARYING': 'STRING',
+            'VARCHAR'   : 'STRING',
+            'BYTEA'     : 'BYTES',
+            'TINYTEXT'  : 'STRING',
+            'TEXT'      : 'STRING',
+            'MEDIUMTEXT': 'STRING',
+            'LONGTEXT'  : 'STRING',
+            'DATE'      : 'DATE',
+            'TIME'      : 'TIME',
+            'TIMESTAMP' : 'TIMESTAMP',
+            'TIMESTAMP WITHOUT TIME ZONE': 'TIMESTAMP'
+        }
+
+        schema_fields = []
+
+        with open(table_types_path, 'r') as type_table_f:
+            for line in type_table_f:
+                column, pg_type, collation, nullable, default = [item.strip() for item in line.split('|')]
+                pg_type_ = re.sub(r'\(.*\)', '', pg_type).upper()
+                bq_type = type_mapping[pg_type_]
+                mode = 'REQUIRED' if nullable == 'not null' else 'NULLABLE'
+                schema_fields.append(bigquery.SchemaField(column, bq_type, mode=mode))
+
+        return schema_fields
+
+    def get_row_count(self, full_table_name: str) -> int:
+
+        query_job = self.client.query('SELECT COUNT(*) FROM ' + full_table_name, location='US')
+        results = query_job.result()
+        results_list = [row for row in results]
+
+        return results_list[0][0]
+
+    def queryBQ(self, query_str: str, location: str ='US', batch_mode: bool = False, dry_run: bool = False,
+                verbose: bool = False) -> bigquery.job.QueryJob:
+
+        job_config = bigquery.QueryJobConfig()
+        job_config.dry_run = dry_run
+
+        if batch_mode:
+            # Run at batch priority, which won't count toward concurrent rate limit.
+            job_config.priority = bigquery.QueryPriority.BATCH
+
+        query_job = self.client.query(query_str, location=location, job_config=job_config)
+
+        if batch_mode: # wait until job is done
+            while query_job.state != 'DONE':
+                query_job = self.client.get_job(query_job.job_id, location=location)
+                print("Job {} is currently in state {}".format(query_job.job_id, query_job.state))
+                time.sleep(5)
+
+        if verbose:
+            print("This query will process {} bytes.".format(query_job.total_bytes_processed))
+
+        return query_job
\ No newline at end of file
diff --git a/scripts/GoogleCloud/BQ/BigQueryConnect_py2.py b/scripts/GoogleCloud/BQ/BigQueryConnect_py2.py
new file mode 100644
index 00000000..19ee3e17
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/BigQueryConnect_py2.py
@@ -0,0 +1,286 @@
+'''
+TODO: fix fstrings for py2
+TODO: stream_csv_to_table method not functional
+
+connect to BQ using json token
+create new dataset
+create new table
+create new table by uploading CSV
+create schema/table-type from postgres /d <db> output
+get row count of table
+get query result
+'''
+
+from google.cloud import bigquery
+#from google.cloud import storage
+from google.cloud.exceptions import NotFound
+
+from medinfo.dataconversion.Util import log
+
+import re
+import pandas
+import time
+
+
+class BigQueryConnect():
+    '''
+    setup credentials: export GOOGLE_APPLICATION_CREDENTIALS='<path to json>'
+    '''
+
+    def __init__(self):
+        self.client = bigquery.Client()
+        assert self.client is not None, 'Did not connect to BQ, check credentials env(GOOGLE_APPLICATION_CREDENTIALS)'
+
+    def create_new_dataset(self, dataset_id):
+        '''
+        https://cloud.google.com/bigquery/docs/datasets#create-dataset
+
+        :param dataset_id: dataset name
+        :return: None
+        '''
+
+        dataset_ref = self.client.dataset(dataset_id)
+
+        try:
+            # Check if the dataset with specified ID already exists
+            self.client.get_dataset(dataset_ref)
+            log.info('Dataset {} already exists! Skipping create operation.'.format(dataset_id))
+            #print(f'Dataset {dataset_id} already exists! Skipping create operation.')
+        except NotFound:
+            # Construct a full Dataset object to send to the API.
+            dataset = bigquery.Dataset(dataset_ref)
+            dataset.location = 'US'
+            dataset = self.client.create_dataset(dataset)  # API request
+            log.info('Dataset {} created successfully project: {}.'.format(dataset.dataset_id, self.client.project))
+            #print(f'Dataset {dataset.dataset_id} created successfully project: {self.client.project}.')
+
+    def create_new_table_from_schema(self, dataset_id, table_id,
+                                     schema):
+        '''
+        https://cloud.google.com/bigquery/docs/tables#create-table
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param schema:
+            schema = [
+                bigquery.SchemaField('full_name', 'STRING', mode='REQUIRED', description='blah'),
+                bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
+            ]
+        :return: None
+        '''
+
+        dataset_ref = self.client.dataset(dataset_id)
+        table_ref = dataset_ref.table(table_id)
+
+        try:
+            self.client.get_table(table_ref)
+            #print(f'Table {table_id} in dataset {dataset_id} already exists! Skipping create operation.')
+        except NotFound:
+            # Construct a full Table object to send to the API.
+            table = bigquery.Table(table_ref, schema=schema)
+            table = self.client.create_table(table)  # API request
+            log.info('''
+                    Table {} in dataset {}
+                    created successfully project: {}.
+                    '''.format(table.table_id, dataset_id, self.client.project))
+            '''
+            print(
+                f'Table {table.table_id} in dataset {dataset_id}'
+                f'created successfully project: {self.client.project}.'
+            )
+            '''
+
+    def _stream_csv_to_table(self, dataset_id, table_id, csv_path, batch_size = 1000):
+        '''
+        FYI: Streaming is NOT free :)
+        https://cloud.google.com/bigquery/pricing#streaming_pricing
+        TODO: 06/03/2019 NOT TESTED, DO NOT USE THIS FUNCTION
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param csv_path: path to CSV from SQL
+        :param batch_size: rows per insert (default 1000)
+        :return: None
+        '''
+
+        table_ref = self.client.dataset(dataset_id).table(table_id)
+        table = self.client.get_table(table_ref)  # API request
+
+        with open(csv_path, 'w') as infile, open(csv_path + '_errors', 'w') as outfile:
+            for line in infile:
+                rows_to_insert = []
+                row = line.split(',')
+                # TODO validate row
+                rows_to_insert.append(row)
+
+                if len(rows_to_insert) == batch_size:
+                    errors = self.client.insert_rows(table, rows_to_insert)  # API request
+                    assert errors == []
+                    rows_to_insert = []
+
+            if rows_to_insert:
+                errors = self.client.insert_rows(table, rows_to_insert)  # API request
+                assert errors == []
+
+    def load_csv_to_table(self, dataset_id, table_id, csv_path, auto_detect_schema = True,
+                          schema = [], skip_rows = 0, append_to_table=False):
+        '''
+        TODO: add functionality for optional schema input
+        TODO: what happens if dataset does not exist?
+
+        :param dataset_id: dataset name
+        :param table_id: table name
+        :param csv_path: path to exported csv file
+        :param auto_detect_schema: auto detect schema types from CSV (default True)
+        :param schema: (default None)
+            schema = [
+                bigquery.SchemaField('full_name', 'STRING', mode='REQUIRED', description='blah'),
+                bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
+            ]
+        :param skip_rows: how many rows in CSV to skip (default 0)
+        :return: None
+
+        Details on loading local data
+        https://cloud.google.com/bigquery/docs/loading-data-local
+        '''
+
+        location = 'US'
+
+        dataset_ref = self.client.dataset(dataset_id)
+        table_ref = dataset_ref.table(table_id)
+        dataset = bigquery.Dataset(dataset_ref)
+        dataset.location = location
+
+        job_config = bigquery.LoadJobConfig()
+        job_config.source_format = bigquery.SourceFormat.CSV
+        # https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv#overwriting_a_table_with_csv_data
+        job_config.skip_leading_rows = skip_rows
+        job_config.quote_character = '\"'
+
+
+        if not append_to_table:
+            job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
+
+            if auto_detect_schema:
+                assert schema == [], 'Auto-detect is False, but schema is specified'
+                job_config.autodetect = True
+            else:
+                job_config.autodetect = False
+                assert schema != [], 'Auto-detect is False, but no schema specified'
+                job_config.schema = schema
+
+
+        with open(csv_path, 'rb') as csv_file:
+            load_table_job = self.client.load_table_from_file(
+                csv_file,
+                table_ref,
+                location=location,
+                job_config=job_config,
+            )  # API request
+
+        log.info('Uploading table... ')
+        #print('Uploading table... ')
+        try:
+            load_table_job.result() # Wait for load to complete
+        except:
+            errors = load_table_job.errors
+            log.error(errors)
+            return errors
+        #print(load_table_job.error_result)
+        table = self.client.get_table(table_ref)
+        log.info('''
+                {} rows loaded into 
+                table {} in dataset {}
+                in project: {}.
+                '''.format(load_table_job.output_rows, table.table_id, dataset_id, self.client.project))
+        '''
+        print(
+            f'{load_table_job.output_rows} rows loaded into '
+            f'table {table.table_id} in dataset {dataset_id} '
+            f'in project: {self.client.project}.'
+        )
+        '''
+
+    def read_table_types(self, table_types_path):
+        '''
+        reads in lines, deliminited by "|", e.g.:
+        <column name> | <data type> | <collation> | <nullable> | <default>
+
+        :param table_types_path:
+        :return: list of bigquery.SchemaField
+        '''
+
+        type_mapping = {
+            'TINYINT'   : 'INT64',
+            'SMALLINT'  : 'INT64',
+            'MEDIUMINT' : 'INT64',
+            'INT'       : 'INT64',
+            'INTEGER'   : 'INT64',
+            'BIGINT'    : 'INT64',
+            'DECIMAL'   : 'NUMERIC',
+            'NUMERIC'   : 'NUMERIC',
+            'REAL'      : 'FLOAT64',
+            'DOUBLE PRECISION': 'FLOAT64',
+            'BOOLEAN'   : 'BOOL',
+            'CHAR'      : 'STRING',
+            'CHARACTER': 'STRING',
+            'CHARACTER VARYING': 'STRING',
+            'VARCHAR'   : 'STRING',
+            'BYTEA'     : 'BYTES',
+            'TINYTEXT'  : 'STRING',
+            'TEXT'      : 'STRING',
+            'MEDIUMTEXT': 'STRING',
+            'LONGTEXT'  : 'STRING',
+            'DATE'      : 'DATE',
+            'TIME'      : 'TIME',
+            'TIMESTAMP' : 'TIMESTAMP',
+            'TIMESTAMP WITHOUT TIME ZONE': 'TIMESTAMP'
+        }
+
+        schema_fields = []
+
+        with open(table_types_path, 'r') as type_table_f:
+            for line in type_table_f:
+                column, pg_type, collation, nullable, default = [item.strip() for item in line.split('|')]
+                pg_type_ = re.sub(r'\(.*\)', '', pg_type).upper()
+                bq_type = type_mapping[pg_type_]
+                mode = 'REQUIRED' if nullable == 'not null' else 'NULLABLE'
+                schema_fields.append(bigquery.SchemaField(column, bq_type, mode=mode))
+
+        return schema_fields
+
+    def get_row_count(self, full_table_name):
+
+        query_job = self.client.query('SELECT COUNT(*) FROM ' + full_table_name, location='US')
+        results = query_job.result()
+        results_list = [row for row in results]
+
+        return results_list[0][0]
+
+    def queryBQ(self, query_str, query_params=None, location ='US', batch_mode = False, dry_run = False,
+                verbose = False):
+
+        job_config = bigquery.QueryJobConfig()
+        job_config.dry_run = dry_run
+
+        if batch_mode:
+            # Run at batch priority, which won't count toward concurrent rate limit.
+            job_config.priority = bigquery.QueryPriority.BATCH
+
+        if query_params:
+            job_config.query_parameters = query_params
+
+        query_job = self.client.query(query_str, location=location, job_config=job_config)
+
+        if batch_mode: # wait until job is done
+            while query_job.state != 'DONE':
+                query_job = self.client.get_job(query_job.job_id, location=location)
+                log.info('Job {} is currently in state {}'.format(query_job.job_id, query_job.state))
+                #print('Job {} is currently in state {}'.format(query_job.job_id, query_job.state))
+                time.sleep(5)
+
+        if verbose:
+            log.info('This query will process {} bytes.'.format(query_job.total_bytes_processed))
+            #print('This query will process {} bytes.'.format(query_job.total_bytes_processed))
+
+        return query_job
\ No newline at end of file
diff --git a/scripts/GoogleCloud/BQ/__init__.py b/scripts/GoogleCloud/BQ/__init__.py
new file mode 100644
index 00000000..9b45adaa
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/__init__.py
@@ -0,0 +1,2 @@
+# Nothing much for now, just enable importing of various modules as a common package
+
diff --git a/scripts/GoogleCloud/BQ/ahrq/ahrq_citation.txt b/scripts/GoogleCloud/BQ/ahrq/ahrq_citation.txt
new file mode 100644
index 00000000..606da9f2
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/ahrq/ahrq_citation.txt
@@ -0,0 +1,2 @@
+https://www.hcup-us.ahrq.gov/toolssoftware/ccsr/ccs_refined.jsp # ICD10 diagnosis
+https://www.hcup-us.ahrq.gov/toolssoftware/ccs10/ccs10.jsp # ICD10 procedural
diff --git a/scripts/GoogleCloud/BQ/ahrq/create_ahrq.sh b/scripts/GoogleCloud/BQ/ahrq/create_ahrq.sh
new file mode 100644
index 00000000..9c368b3b
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/ahrq/create_ahrq.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env python
+python create_ahrq_table.py
+python upload_ahrq_diag.py
+python upload_ahrq.py
diff --git a/scripts/GoogleCloud/BQ/ahrq/create_ahrq_table.py b/scripts/GoogleCloud/BQ/ahrq/create_ahrq_table.py
new file mode 100644
index 00000000..58ddb488
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/ahrq/create_ahrq_table.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python2
+
+import os
+import credentials
+import logging
+import itertools
+import string
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+# Construct a BigQuery client object.
+
+
+bq_client = bigQueryUtil.BigQueryClient()
+dataset_id = "ahrq_ccsr"
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    create_table = input('Create Table? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if create_table == 'Y' or create_table == 'y':
+        bq_client.reconnect_client()
+        bq_client.create_new_dataset(dataset_id)
+    print('data set run finished')
diff --git a/scripts/GoogleCloud/BQ/ahrq/upload_ahrq.py b/scripts/GoogleCloud/BQ/ahrq/upload_ahrq.py
new file mode 100644
index 00000000..77f821eb
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/ahrq/upload_ahrq.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import credentials
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+#os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-2c5b.json'
+
+
+# /Users/jonc101/Downloads/
+#CSV_FILE_PREFIX = '/Users/jonc101/Downloads/ahrq_ccs4.csv'
+csv_path = '/Users/jonc101/Downloads/ahrq_ccs4.csv'
+
+DATASET_NAME = 'ahrq_ccsr'
+TABLE_NAME = 'procedure_code'
+
+ # 'icd_10',
+ # 'ccs_category',
+ # 'icd_code_description',
+ # 'ccs_category_description',
+ # 'multi_ccs_lvl_1',
+ # 'multi_ccs_lvl_1_label',
+ # 'multi_ccs_lvl_2',
+ # 'multi_ccs_lvl_2_label')
+
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('CODE', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('ccs_category', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('ccs_category_description', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('multi_ccs_lvl_1', 'INT64', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('multi_ccs_lvl_1_label', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('CODE_TYPE', 'STRING', 'NULLABLE', None, ())]
+                       #bigquery.SchemaField('multi_ccs_lvl_2', 'FLOAT64', 'NULLABLE', None, ()),
+                       #bigquery.SchemaField('multi_ccs_lvl_2_label', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+#    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+#    if upload == 'Y' or upload == 'y':
+    bq_client.reconnect_client()
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=FINAL_TABLE_SCHEMA,
+                                skip_rows=1)
+    print('upload_ahrq run finished')
+
+
+'''
+
+RSCRIPT TO CLEAN DATA FOR REFERENCE
+
+library(dplyr)
+
+# change directory
+setwd('~/Downloads/')
+ccs <- read.csv('ccs_pr_icd10pcs_2020_1/ccs_pr_icd10pcs_2020_1.csv')
+
+# function to remove commas
+remove_comma <- function(x){
+  return(gsub("," ," ",x))
+}
+
+ccs_colnames <- c('CODE',
+  'ccs_category',
+  'DESCRIPTION',
+  'ccs_category_description',
+  'multi_ccs_lvl_1',
+  'multi_ccs_lvl_1_label',
+  'multi_ccs_lvl_2',
+  'multi_ccs_lvl_2_label')
+
+colnames(ccs) <- ccs_colnames
+
+ccs2 <- ccs %>% select(CODE, ccs_category, DESCRIPTION, ccs_category_description,
+                       multi_ccs_lvl_1, multi_ccs_lvl_1_label)
+
+ccs3 <- sapply(ccs2, remove_comma)
+#colnames(ccs) <- ccs_colnames
+write.csv(ccs3,'ahrq_ccs4.csv')
+
+'''
diff --git a/scripts/GoogleCloud/BQ/ahrq/upload_ahrq_diag.py b/scripts/GoogleCloud/BQ/ahrq/upload_ahrq_diag.py
new file mode 100644
index 00000000..f9c344c7
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/ahrq/upload_ahrq_diag.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import pandas as pd
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+import credentials
+
+#CSV_FILE_PREFIX = '/Users/jonc101/Downloads/ahrq_ccs4.csv'
+DATASET_NAME = 'ahrq_ccsr'
+TABLE_NAME = 'diagnosis_code'
+csv_path = '/Users/jonc101/Downloads/ahrq_diag_save.csv'
+
+#'ICD10'
+#'ICD10_string'
+#'CCSR_CATEGORY_1'
+#'CCSR_CATEGORY_2'
+#'ICD_10_CODE_Description'
+#'Category_Description'
+#'Category_1_Description'
+#'Category_2_Description'
+
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ICD10', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('ICD10_string', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('CCSR_CATEGORY_1', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('CCSR_CATEGORY_2', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('ICD_10_CODE_Description', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('Category_Description', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('Category_1_Description', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('Category_2_Description', 'STRING', 'NULLABLE', None, ())]
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+#    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+#    if upload == 'Y' or upload == 'y':
+    bq_client.reconnect_client()
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=FINAL_TABLE_SCHEMA,
+                                skip_rows=1)
+    print('Done')
+
+'''
+ahrq = pd.read_csv('/Users/jonc101/Downloads/ahrq_icd10_diagnosis.csv')
+ahrq['ICD10_string'] = ahrq["'ICD-10-CM CODE'"]
+ahrq['ICD10_string'] = ahrq['ICD10_string'].str.replace("'", '')
+ahrq_col2 =  []
+num_rows = len(ahrq)
+for row in range(0,num_rows):
+    ahrq_col2.append(ahrq['ICD10_string'][row][0:3] + '.' + ahrq['ICD10_string'][row][3:])
+ahrq['ICD10'] = ahrq_col2
+ahrq['CCSR_CATEGORY_1'] = ahrq["'CCSR CATEGORY 1'"].str.replace("'", '')
+ahrq['CCSR_CATEGORY_2'] = ahrq["'CCSR CATEGORY 2'"].str.replace("'", '')
+ahrq['ICD_10_CODE_Description'] = ahrq["'ICD-10-CM CODE DESCRIPTION'"]
+ahrq['Category_Description'] = ahrq["'Default CCSR CATEGORY DESCRIPTION'"]
+ahrq['Category_1_Description'] = ahrq["'CCSR CATEGORY 1 DESCRIPTION'"]
+ahrq['Category_2_Description'] = ahrq["'CCSR CATEGORY 2 DESCRIPTION'"]
+ahrq_save = ahrq[['ICD10',
+                  'ICD10_string',
+                  'CCSR_CATEGORY_1',
+                  'CCSR_CATEGORY_2',
+                  'ICD_10_CODE_Description',
+                  'Category_Description',
+                  'Category_1_Description',
+                  'Category_2_Description']]
+
+
+csv_path = '/Users/jonc101/Downloads/ahrq_icd10_diagnosis.csv'
+'''
diff --git a/scripts/GoogleCloud/BQ/alert_upload.py b/scripts/GoogleCloud/BQ/alert_upload.py
new file mode 100755
index 00000000..c487f402
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/alert_upload.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python2
+
+import os
+import csv
+import pandas as pd
+import logging
+from datetime import datetime, timedelta
+import pytz
+import itertools
+import string
+
+import LocalEnv
+from medinfo.dataconversion.Util import log
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+class LoadAlert:
+
+    @staticmethod
+    def add_zone_to_timestamp_col(csv_in_path, csv_out_path, timezone_diff='-07'):
+        '''
+        pandas method has some caveats, didn't end up using
+        '''
+        df = pd.read_csv(csv_in_path)
+        df[' update_date_jittered'] = df[' update_date_jittered'].apply(lambda x: x + timezone_diff)
+        df.to_csv(csv_out_path, index=False, sep=',')
+
+    @staticmethod
+    def add_offset_to_timestamp_col(csv_in_path, csv_out_path, timezone_diff=8):
+
+        with open(csv_in_path, 'rb') as csv_in, open(csv_out_path, 'wb') as csv_out:
+            next(csv_in)
+            next(csv_in)    # skip first 2 rows
+            writer = csv.writer(csv_out, lineterminator='\n')
+            reader = csv.reader(csv_in)
+            for row in reader:
+                orig_ts = datetime.strptime(row[3], "%Y-%m-%d %H:%M:%S")
+                #new_ts = orig_ts + timedelta(hours=timezone_diff)
+                new_ts = orig_ts.astimezone(pytz.utc)
+                row.append(row[3])
+                row[3] = new_ts.strftime("%Y-%m-%d %H:%M:%S")
+                writer.writerow(row)
+
+
+    @staticmethod
+    def load_alert_table(csv_path):
+        bq_client = bigQueryUtil.BigQueryClient()
+        assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+        schema = [ bigquery.SchemaField('anon_id',              'STRING', 'REQUIRED', None, ()),
+                   bigquery.SchemaField('alt_id_jittered',      'INT64', 'REQUIRED', None, ()),
+                   bigquery.SchemaField('pat_csn_jittered',     'INT64', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('update_date_jittered', 'TIMESTAMP', 'REQUIRED', None, ()),
+                   bigquery.SchemaField('alert_desc',           'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('general_alt_type_c',   'INT64', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('general_alert_name',   'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('general_alert_title',  'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('general_alert_abbr',   'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('med_alert_type_c',     'INT64', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('med_alert_name',       'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('med_alert_title',      'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('med_alert_abbr',       'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('immun_id',             'INT64', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('immun_name',           'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('immun_abbreviation',   'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('immun_type',           'STRING', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('bpa_locator_id',       'INT64', 'NULLABLE', None, ()),
+                   bigquery.SchemaField('local_update_date_jittered','DATETIME', 'REQUIRED', None, ())]
+
+        bq_client.load_csv_to_table('alert_2019', 'alert', csv_path, auto_detect_schema=False, \
+                              schema=schema, skip_rows=0)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    #csv_in_path = input('Enter csv in path: ')
+    #csv_out_path = input('Enter csv out path: ')
+    #LoadAlert.add_offset_to_timestamp_col(csv_in_path, csv_out_path)
+
+    '''
+    needed to split file instead
+    split -l 2000000 jc_alerts_mod.csv jc_alerts_mod_
+    '''
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading jc_alerts_mod_aa')
+        LoadAlert.load_alert_table('/Users/starli/Downloads/jc_alerts_mod_aa')
+        for fn in ([x+y for x,y in itertools.product('a', string.ascii_lowercase[1:])] +
+                   [x+y for x,y in itertools.product('b', string.ascii_lowercase)] +
+                   [x+y for x,y in itertools.product('c', string.ascii_lowercase[:11])]):
+            print('uploading jc_alerts_mod_%s' % (fn))
+            bq_client.reconnect_client()
+            bq_client.load_csv_to_table('alert_2019', 'alert', '/Users/starli/Downloads/jc_alerts_mod_' + fn, \
+                                        auto_detect_schema=False, \
+                                        schema=None, skip_rows=0, append_to_table=True)
+    print('Done')
+
+    '''
+    expecting 62,364,707 lines from original table
+    '''
+
+
diff --git a/scripts/GoogleCloud/BQ/auth_cloud.py b/scripts/GoogleCloud/BQ/auth_cloud.py
new file mode 100644
index 00000000..40d0d69f
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/auth_cloud.py
@@ -0,0 +1,61 @@
+
+'''
+request .json api key
+get project id
+write sql
+profit
+
+Setting Up Virtual Environment
+
+https://www.google.com/search?client=firefox-b-1-d&q=create+a+new+virtual+environment
+
+#! [mac]        python -m virtualenv env_gcp
+#! [windows]    py -m virtualenv env_gcp
+#! [mac]        source env_gcp/bin/activate
+#! [windows]    .\env_gcp\Scripts\activate
+
+# [installing pip requirements]
+
+#!              pip install -r  requirements.txt
+
+Authentication for Big Query
+https://cloud.google.com/docs/authentication/getting-started
+https://cloud.google.com/bigquery/docs/authentication/
+https://cloud.google.com/python/setup
+'''
+
+from google.cloud import bigquery
+
+# PATH TO BOX SYNC: PLEASE CHANGE FOR LOCAL
+print("DID YOU CHANGE YOUR PATH TO LOCAL BOX SYNC FOLDER ")
+quiver_path = "/Users/jonc101/Box Sync/Jonathan Chiang's Files/quiver_test/"
+
+
+# CONFIGURATION
+
+client = bigquery.Client.from_service_account_json('gcp_key.json')
+project_id = 'mining-clinical-decisions'
+print("before sql")
+sql = '''
+
+select * from `datalake_47618_sample.encounter`
+where appt_time_jittered > "2015-12-31" and appt_time_jittered < "2017-01-01"
+
+    '''
+
+print("sql saved")
+"""
+select patient_item_id, external_id, clinical_item_id, item_date, encounter_id, text_value, num_value, source_id from `clinical_inpatient.patient_item` where item_date >= timestamp('2014-01-01 00:00:00')
+"""
+# Run a Standard SQL query using the environment's default project
+# df = client.query(sql).to_dataframe()
+print("df")
+df = client.query(sql, project=project_id).to_dataframe()
+print("print df.head and df.shape")
+print(df.shape)
+print(df.head())
+#df.to_csv("encounter_2016.csv")
+print("df_csv")
+print("quiver_sync_environment")
+df.to_csv(quiver_path + "quiver_save2.csv")
+print("check quiver save ")
diff --git a/scripts/GoogleCloud/BQ/google_auth.R b/scripts/GoogleCloud/BQ/google_auth.R
new file mode 100644
index 00000000..40e30f2d
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/google_auth.R
@@ -0,0 +1,82 @@
+######## bigquery way 
+# install bigrquery
+# install DBI
+# install dplyr 
+# Get the name of your project (mining-clinical-decisions)
+# Get the name of your dataset (datalake_47618)
+# authenticate online with google 
+# Can use tidyverse querying or standard sql 
+# 
+
+
+
+library(bigrquery)
+project <- "mining-clinical-decisions" # put your project ID here
+
+sql <- "
+        SELECT
+          jc_uid, order_type,  description
+        FROM
+          `datalake_47618.order_proc` 
+        WHERE
+          jc_uid = 'JCe6e161' 
+        LIMIT 5 
+      "
+
+sql2 <- "SELECT 
+  jc_uid, contact_date_jittered, department_id, visit_type, enc_type
+FROM 
+`datalake_47618.encounter`
+WHERE
+enc_type = 'Office Visit' 
+and 
+visit_type like 'RETURN P%' 
+and 
+contact_date_jittered > '2014-01-01'
+ORDER BY 
+contact_date_jittered"
+
+
+tb <- bq_project_query(project, sql)
+tb2 <- bq_project_query(project, sql2)
+test3 <- bq_table_download(tb2)
+new_p <- test2 
+ret_p <- test3
+
+
+##### TIDYVERSE WAY 
+
+
+library(DBI)
+library(dplyr)
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+
+
+
+
+data_tables <- dbListTables(con)
+encounter <- tbl(con, "encounter")
+head(encounter)
+test2 <- encounter %>% 
+            select(pat_enc_csn_id_coded, inpatient_data_id_coded) %>% 
+            head(5)
+
+######
+# create a new dataset 
+
+library(DBI)
+library(dplyr)
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+
diff --git a/scripts/GoogleCloud/BQ/load_stride_tables.py b/scripts/GoogleCloud/BQ/load_stride_tables.py
new file mode 100755
index 00000000..ad561d04
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/load_stride_tables.py
@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+
+import os
+from pathlib import Path
+
+from .BigQueryConnect import BigQueryConnect
+
+class LoadStride:
+
+    @staticmethod
+    def load_stride_tables(csv_dir, type_dir, tables_file):
+        bqc = BigQueryConnect()
+        assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+        p = Path(os.environ['GOOGLE_APPLICATION_CREDENTIALS'])
+        assert p.exists(), '{os.environ["GOOGLE_APPLICATION_CREDENTIALS"]} does not exist.'
+
+        tables = [line.rstrip('\n') for line in open(tables_file)]
+
+        for table in tables:
+            print(f'Loading {table}')
+            schema = bqc.read_table_types(type_dir + '/' + table + '.types')
+            bqc.load_csv_to_table('stride_2008_2017', table, csv_dir + '/' + table + '.csv', auto_detect_schema=False,\
+                              schema=schema, skip_rows=1)
+            print(f'Done loading {table}')
+        return
+
+if __name__ == '__main__':
+    csv_dir = input('Enter csv directory: ')
+    type_table_dir = input('Enter type table directory: ')
+    table_list_file = input('Enter table list file: ')
+    LoadStride.load_stride_tables(csv_dir, type_table_dir, table_list_file)
+
+
+
diff --git a/scripts/GoogleCloud/BQ/lpch/date_utc_change.sql b/scripts/GoogleCloud/BQ/lpch/date_utc_change.sql
new file mode 100644
index 00000000..7428a9a1
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/date_utc_change.sql
@@ -0,0 +1,3 @@
+update lpch.clinical_note_meta
+set FILING_DATE_JITTERED_UTC = case FILING_DATE_JITTERED when null then null else datetime(timestamp(FILING_DATE_JITTERED), 'America/Los_Angeles') end
+where true;
diff --git a/scripts/GoogleCloud/BQ/lpch/directory_flowsheet_setup.sh b/scripts/GoogleCloud/BQ/lpch/directory_flowsheet_setup.sh
new file mode 100644
index 00000000..e5e15f83
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/directory_flowsheet_setup.sh
@@ -0,0 +1,106 @@
+
+#MANUAL SETUP
+flowsheet_path='/Users/jonc101/Documents/lpch_auto/flowsheets/'
+github_path='/Users/jonc101/Documents/github/CDSS/'
+#export PYTHONPATH='/Users/jonc101/Documents/github/CDSS/'
+
+export PYTHONPATH=$github_path
+
+flowsheet2011='flowsheet2011'
+flowsheet2012='flowsheet2012'
+flowsheet2013='flowsheet2013'
+flowsheet2014='flowsheet2014'
+flowsheet2015='flowsheet2015'
+flowsheet2016='flowsheet2016'
+flowsheet2017='flowsheet2017'
+flowsheet2018='flowsheet2018'
+flowsheet2019='flowsheet2019'
+
+
+upload_script_path='scripts/GoogleCloud/BQ/lpch/flowsheets/'
+
+flowsheet2012py='upload_flowsheet2012.py'
+flowsheet2013py='upload_flowsheet2013.py'
+flowsheet2014py='upload_flowsheet2014.py'
+flowsheet2015py='upload_flowsheet2015.py'
+flowsheet2016py='upload_flowsheet2016.py'
+flowsheet2017py='auto_flowsheet_2017.py'
+flowsheet2018py='upload_flowsheet2018.py'
+flowsheet2019py='upload_flowsheet2019.py'
+
+
+#cut_inline_comma=awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1
+#remove_quotes_from_numbers=sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'
+#remove_double_quotes_from_string=sed 's/\([^",]\)"\([^",]\)/\1""\2/g'
+#remove_non_numeric_col_6=awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=','
+#remove_non_numeric_col_8=awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','
+
+
+
+#cd $flowsheet_path$flowsheet2011
+#split -l 2000000 lpch_flowsheet2011.csv flowsheet_
+
+cd $flowsheet_path$flowsheet2012
+#sed 's/\"//g' lpch_flowsheet2012.csv > lpch_flowsheet2012_v2.csv
+split -l 2000000 lpch_flowsheet2012_v2.csv flowsheet_
+
+
+
+#cd $flowsheet_path$flowsheet2012
+#awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' lpch_flowsheet2012.csv  | sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | awk -F, '{ gsub(/[^0-9]/,"",$6) }1' OFS=','  > flowsheet.csv
+
+#awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' lpch_flowsheet2012.csv  | awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  > flowsheet.csv
+#split -l 2000000 lpch_flowsheet2012.csv flowsheet_
+#rm flowsheet.csv
+#python2 $github_path$upload_script_path$flowsheet2012py
+
+#cd $flowsheet_path$flowsheet2013
+#awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' lpch_flowsheet2013.csv  | awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  > flowsheet.csv
+#split -l 2000000 lpch_flowsheet2013.csv flowsheet_
+#rm flowsheet.csv
+#python2 $github_path$upload_script_path$flowsheet2013py
+
+#cd $flowsheet_path$flowsheet2014
+#awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' lpch_flowsheet2014.csv  | awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  > flowsheet.csv
+#split -l 2000000 lpch_flowsheet2014.csv flowsheet_
+
+
+#rm flowsheet.csv
+#python2 $github_path$upload_script_path$flowsheet2014py
+
+#cd $flowsheet_path$flowsheet2015
+
+#sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'  lpch_flowsheet2015.csv | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'  | awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','> flowsheet.csv
+#awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' $csv_name  | awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'| awk -F, '{ gsub(/[^0-9.-]/,"",$14) }1' OFS=','|awk -F, '{ gsub(/[^0-9.-]/,"",$15) }1' OFS=','| awk -F, '{ gsub(/[^0-9.-]/,"",$23) }1' OFS=','| awk -F, '{ gsub(/[^0-9.-]/,"",$24) }1' OFS=','| sed 's/\([^",]\)"\([^",]\)/\1""\2/g'  > $csv_write_name
+#basis# awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' lpch_flowsheet2015.csv  | awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=',' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' > flowsheet.csv
+#awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' lpch_flowsheet2015.csv  |sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g'|  awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'| awk -F, '{ gsub(/[^0-9.-/]/,"",$6) }1' OFS=',' |  awk -F, '{ gsub(/[^0-9.-/]/,"",$8) }1' OFS=','|  sed 's/\([^",]\)"\([^",]\)/\1""\2/g' > flowsheet.csv
+#split -l 2000000 lpch_flowsheet2015.csv flowsheet_
+#rm flowsheet.csv
+#python2 $github_path$upload_script_path$flowsheet2015py
+
+#cd $flowsheet_path$flowsheet2019
+#cat lpch_flowsheet2016.csv |  awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' |awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'| awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  | awk -F, '{ gsub(/"/,"",$5) }1' OFS=','| sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'  | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' > flowsheet.csv
+#split -l 2000000  lpch_flowsheet2019.csv flowsheet_
+
+#split -l 150000 flowsheet.csv flowsheet_
+#rm flowsheet.csv
+#python2 $github_path$upload_script_path$flowsheet2016py
+
+
+#cd $flowsheet_path$flowsheet2017
+
+#split -l 5000000 flowsheet.csv flowsheet_
+
+
+#cd $flowsheet_path$flowsheet2018
+#cat lpch_flowsheet2018.csv |  awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' |awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'| awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  | awk -F, '{ gsub(/"/,"",$5) }1' OFS=','| sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'  | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | sed 's/\([^",]\)"\([^",]\)/#\1""\2/g' | awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | awk -F, '{ gsub(/[^0-9.:-]/," ",$7) }1' OFS=','  | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','   > flowsheet.csv
+#split -l 1000000 lpch_flowsheet2018.csv flowsheet_
+
+
+#cd $flowsheet_path$flowsheet2019
+#cat lpch_flowsheet2018.csv |  awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' |awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'| awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  | awk -F, '{ gsub(/"/,"",$5) }1' OFS=','| sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'  | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' > flowsheet.csv
+#cat lpch_flowsheet2019.csv |  awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1' |awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1'| awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=',' | awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','  | awk -F, '{ gsub(/"/,"",$5) }1' OFS=','| sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'  | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' | sed 's/\([^",]\)"\([^",]\)/#\1""\2/g' | awk '{if (NR!=1) {print}}' > flowsheet.csv
+
+
+#  HOW TO LOG / AND PRINT
+# sh [SHELL_FILE.sh] 2>&1 | tee -a [NAME_OF_LOG_FILE.log]
diff --git a/scripts/GoogleCloud/BQ/lpch/preprocess_flowsheet.py b/scripts/GoogleCloud/BQ/lpch/preprocess_flowsheet.py
new file mode 100644
index 00000000..33b81704
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/preprocess_flowsheet.py
@@ -0,0 +1,86 @@
+import os
+import glob
+import os
+import logging
+import itertools
+import string
+import  numpy  as np
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+import pandas as pd
+from os import listdir
+from os.path import isfile, join
+import re
+
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+def count_alphabet(input_row):
+    d = []
+    for i in  string.ascii_lowercase:
+        d.append(input_row.count(i))
+    return sum(d)
+
+def non_numeric_sub(string):
+    return re.sub("[^0-9.-]", "", string)
+
+
+file_match = glob.glob('/Users/jonc101/Documents/lpch_auto/flowsheets/flowsheet2012/flowsheet_*')
+file_prefix = '/Users/jonc101/Documents/lpch_auto/flowsheets/flowsheet2012/flowsheet_'
+
+file_prefix_list = []
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+for file in file_match:
+	file_prefix_list.append(remove_prefix(file, file_prefix))
+file_prefix_list = sorted(file_prefix_list)
+print(file_prefix_list)
+
+for file in file_prefix_list:
+    print('-----------------')
+    print('reading file in:')
+    print('-----------------')
+    print(file_prefix + file)
+    print('-----------------')
+    if file == 'aa':
+
+        c  = pd.read_csv(file_prefix + file, sep='\t')
+        c['meas_value'] = c['meas_value'].apply(str)
+        c['meas_value'] = c['meas_value'].apply(non_numeric_sub)
+        c['entry_user_id'] = c['entry_user_id'].apply(str)
+        c['entry_user_id'] = c['entry_user_id'].apply(non_numeric_sub)
+        # remove potential numbers:
+        c['meas_length'] = c['meas_value'].apply(len)
+        c[(c.meas_length < 10)]
+        c = c.drop('meas_length',1)
+        c.to_csv(file_prefix + file, encoding='utf-8', index=False)
+
+
+    else:
+        c  = pd.read_csv(file_prefix + file, sep='\t',header = None)
+        c[5] = c[5].apply(str)
+        c[5] = c[5].apply(non_numeric_sub)
+        c[7] = c[7].apply(str)
+        c[7] = c[7].apply(non_numeric_sub)
+        # remove potential numbers:
+        c['meas_length'] = c[5].apply(len)
+        c[(c.meas_length < 10)]
+        c = c.drop('meas_length',1)
+        c.to_csv(file_prefix + file, encoding='utf-8', index=False)
+    print('-----------------')
+    print('processed file')
+    print('-----------------')
+    print(file_prefix + file)
+    print('-----------------')
diff --git a/scripts/GoogleCloud/BQ/lpch/preprocess_upload_flowsheet.py b/scripts/GoogleCloud/BQ/lpch/preprocess_upload_flowsheet.py
new file mode 100644
index 00000000..5f578eef
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/preprocess_upload_flowsheet.py
@@ -0,0 +1,75 @@
+import os
+import glob
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+import pandas as pd
+from os import listdir
+from os.path import isfile, join
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+def count_alphabet(input_row):
+    d = []
+    for i in  string.ascii_lowercase:
+        d.append(input_row.count(i))
+    return sum(d)
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+file_match = glob.glob('/Users/jonc101/Documents/lpch_auto/flowsheets/flowsheet2012/flowsheet_*')
+file_prefix = '/Users/jonc101/Documents/lpch_auto/flowsheets/flowsheet2012/flowsheet_'
+CSV_FILE_PREFIX = file_prefix
+file_prefix_list = []
+
+for file in file_match:
+	file_prefix_list.append(remove_prefix(file, file_prefix))
+file_prefix_list = sorted(file_prefix_list)
+print(file_prefix_list)
+
+
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'flowsheet'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('INPATIENT_DATA_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('LINE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('TEMPLATE', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ROW_DISP_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MEAS_VALUE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECORDED_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ENTRY_USER_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+def load_table(csv_path):
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=FINAL_TABLE_SCHEMA, skip_rows=1, append_to_table = True)
+
+
+if __name__ == '__main__':
+  logging.basicConfig()
+  #upload = input('Upload? ("y"/"n"): ')
+  bq_client = bigQueryUtil.BigQueryClient()
+  print('uploading {}aa'.format(CSV_FILE_PREFIX))
+
+  load_table(CSV_FILE_PREFIX + 'aa')
+  file_prefix_list.remove('aa')
+
+  for fn in (file_prefix_list):
+      print('uploading {}'.format(fn))
+      bq_client.reconnect_client()
+      bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                          auto_detect_schema=False,
+                                          schema=FINAL_TABLE_SCHEMA, skip_rows=1, append_to_table=True)
+      print('uploaded {}'.format(fn))
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_clinical_note_meta.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_clinical_note_meta.py
new file mode 100644
index 00000000..18de88a2
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_clinical_note_meta.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [1] "lpch_clinical_note_meta_121619.csv"
+# [1] "ANON_ID"                 "PAT_ENC_CSN_ID_CODED"
+# [3] "FILING_DATE_JITTERED"    "NOTE_DATE_JITTERED"
+# [5] "ACTIVITY_DATE_JITTERED"  "AUTHOR_PROV_MAP_ID"
+# [7] "EFFECTIVE_DEPT_ID"       "NOTE_STATUS_C"
+# [9] "NOTE_STATUS"             "AMBULATORY"
+# [11] "LTR_STATUS_C"            "LETTER_STATUS"
+# [13] "NOTE_TYPE"               "NOTE_TYPE_DESC"
+# [15] "EFFECTIVE_TIME_JITTERED" "AUTH_LNKED_PROV_MAP_ID"
+# [17] "COSIGN_PROV_MAP_ID"      "DATA_SOURCE"
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_clinical_note_meta_121619.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_clinical_note_meta_121619.csv'
+
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'clinical_note_meta'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('FILING_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ACTIVITY_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHOR_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EFFECTIVE_DEPT_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_STATUS_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AMBULATORY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LTR_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LETTER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_TYPE_DESC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EFFECTIVE_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTH_LNKED_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('COSIGN_PROV_MAP_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('FILING_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ACTIVITY_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHOR_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EFFECTIVE_DEPT_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AMBULATORY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LTR_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LETTER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTE_TYPE_DESC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EFFECTIVE_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTH_LNKED_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('COSIGN_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed heading and trailing lines in vim
+    - added header line
+
+    split every 2 mln lines:
+    split -l 2000000 alert_history_012420.csv alert_history_012420_
+    '''
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
+
+    '''
+    expecting 167,058,216 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace
+table alert_2019.alert_history_20200124
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_coded_s,
+    alt_status_c_s,
+    was_shown_c_s,
+    bpa_trgr_action_c_s,
+    shown_place_c_s,
+    patient_dep_id_s,
+    contact_date_time
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_coded_s = '' then NULL else cast(alt_csn_id_coded_s as INT64) end as alt_csn_id_coded,
+case when alt_status_c_s = '' then NULL else cast(alt_status_c_s as INT64) end as alt_status_c,
+case when was_shown_c_s = '' then NULL else cast(was_shown_c_s as INT64) end as was_shown_c,
+case when bpa_trgr_action_c_s = '' then NULL else cast(bpa_trgr_action_c_s as INT64) end as bpa_trgr_action_c,
+case when shown_place_c_s = '' then NULL else cast(shown_place_c_s as INT64) end as shown_place_c,
+case when patient_dep_id_s = '' then NULL else cast(patient_dep_id_s as INT64) end as patient_dep_id,
+cast(contact_date_time as DATE) as contact_date,
+timestamp(alt_action_inst, 'America/Los_Angeles') as alt_action_inst_utc
+from alert_2019.alert_history_20200124;
+    '''
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_demographics.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_demographics.py
new file mode 100644
index 00000000..5cd875c9
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_demographics.py
@@ -0,0 +1,127 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [2] "lpch_demographics_121119.csv"
+# [1] "ANON_ID"                  "BIRTH_DATE_JITTERED"
+# [3] "DEATH_DATE_JITTERED"      "GENDER"
+# [5] "PRIMARY_RACE"             "ETHNICITY"
+# [7] "MARITAL_STATUS"           "RELIGION"
+# [9] "LANGUAGE"                 "INTRPTR_NEEDED_YN"
+# [11] "INSURANCE_PAYOR_NAME"     "CUR_PCP_PROV_MAP_ID"
+# [13] "RECENT_CONF_ENC_JITTERED" "RECENT_HT_IN_CMS"
+# [15] "RECENT_WT_IN_KGS"         "BMI"
+# [17] "CHARLSON_SCORE"           "N_HOSPITALIZATIONS"
+# [19] "DAYS_IN_HOSPITAL"
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_demographics_121119.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_demographics_121119.csv'
+
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'demographics'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('BIRTH_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DEATH_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('GENDER', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRIMARY_RACE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ETHNICITY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MARITAL_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RELIGION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LANGUAGE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('INTRPTR_NEEDED_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('INSURANCE_PAYOR_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CUR_PCP_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECENT_CONF_ENC_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECENT_HT_IN_CMS', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECENT_WT_IN_KGS', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BMI', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CHARLSON_SCORE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('N_HOSPITALIZATIONS', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DAYS_IN_HOSPITAL', 'INT64', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA =[bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('BIRTH_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DEATH_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('GENDER', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRIMARY_RACE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ETHNICITY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MARITAL_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RELIGION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LANGUAGE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('INTRPTR_NEEDED_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('INSURANCE_PAYOR_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CUR_PCP_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECENT_CONF_ENC_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECENT_HT_IN_CMS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RECENT_WT_IN_KGS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BMI', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CHARLSON_SCORE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('N_HOSPITALIZATIONS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DAYS_IN_HOSPITAL', 'STRING', 'NULLABLE', None, ())]
+
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed heading and trailing lines in vim
+    - added header line
+
+    split every 2 mln lines:
+    split -l 2000000 alert_history_012420.csv alert_history_012420_
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
+    '''
+    expecting 167,058,216 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace
+table alert_2019.alert_history_20200124
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_coded_s,
+    alt_status_c_s,
+    was_shown_c_s,
+    bpa_trgr_action_c_s,
+    shown_place_c_s,
+    patient_dep_id_s,
+    contact_date_time
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_coded_s = '' then NULL else cast(alt_csn_id_coded_s as INT64) end as alt_csn_id_coded,
+case when alt_status_c_s = '' then NULL else cast(alt_status_c_s as INT64) end as alt_status_c,
+case when was_shown_c_s = '' then NULL else cast(was_shown_c_s as INT64) end as was_shown_c,
+case when bpa_trgr_action_c_s = '' then NULL else cast(bpa_trgr_action_c_s as INT64) end as bpa_trgr_action_c,
+case when shown_place_c_s = '' then NULL else cast(shown_place_c_s as INT64) end as shown_place_c,
+case when patient_dep_id_s = '' then NULL else cast(patient_dep_id_s as INT64) end as patient_dep_id,
+cast(contact_date_time as DATE) as contact_date,
+timestamp(alt_action_inst, 'America/Los_Angeles') as alt_action_inst_utc
+from alert_2019.alert_history_20200124;
+    '''
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_diagnosis.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_diagnosis.py
new file mode 100644
index 00000000..81b52563
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_diagnosis.py
@@ -0,0 +1,147 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+
+# [3] "lpch_diagnosis_101119.csv"
+# [1] "ANON_ID"                   "LINE"
+# [3] "PAT_ENC_CSN_ID_CODED"      "DX_ID"
+# [5] "DX_NAME"                   "ICD9"
+# [7] "ICD10"                     "START_DATE_JITTERED"
+# [9] "NOTED_DATE_JITTERED"       "HX_DATE_OF_ENTRY_JITTERED"
+# [11] "RESOLVED_DATE_JITTERED"    "END_DATE_JITTERED"
+# [13] "PERF_PROV_MAP_ID"          "BILLING_PROV_MAP_ID"
+# [15] "ENTRY_PROV_MAP_ID"         "DEPT_ID"
+# [17] "PRIMARY"                   "CHRONIC"
+# [19] "PRINCIPAL"                 "HOSPITAL_PL"
+# [21] "PROBLEM_STATUS"            "ED"
+# [23] "POA"                       "PRESENT_ON_ADM"
+# [25] "SOURCE"                    "DATA_SOURCE"
+
+
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_diagnosis_101119.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_diagnosis_101119.csv'
+
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'diagnosis'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LINE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DX_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DX_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ICD9', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ICD10', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('START_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTED_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HX_DATE_OF_ENTRY_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESOLVED_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('END_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ENTRY_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DEPT_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRIMARY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CHRONIC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRINCIPAL', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HOSPITAL_PL', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROBLEM_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('POA', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRESENT_ON_ADM', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SOURCE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LINE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DX_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DX_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ICD9', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ICD10', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('START_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NOTED_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HX_DATE_OF_ENTRY_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESOLVED_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('END_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ENTRY_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DEPT_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRIMARY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CHRONIC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRINCIPAL', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HOSPITAL_PL', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROBLEM_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('POA', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRESENT_ON_ADM', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SOURCE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed heading and trailing lines in vim
+    - added header line
+
+    split every 2 mln lines:
+    split -l 2000000 alert_history_012420.csv alert_history_012420_
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    #print('Done')
+    '''
+    expecting 167,058,216 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace
+table alert_2019.alert_history_20200124
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_coded_s,
+    alt_status_c_s,
+    was_shown_c_s,
+    bpa_trgr_action_c_s,
+    shown_place_c_s,
+    patient_dep_id_s,
+    contact_date_time
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_coded_s = '' then NULL else cast(alt_csn_id_coded_s as INT64) end as alt_csn_id_coded,
+case when alt_status_c_s = '' then NULL else cast(alt_status_c_s as INT64) end as alt_status_c,
+case when was_shown_c_s = '' then NULL else cast(was_shown_c_s as INT64) end as was_shown_c,
+case when bpa_trgr_action_c_s = '' then NULL else cast(bpa_trgr_action_c_s as INT64) end as bpa_trgr_action_c,
+case when shown_place_c_s = '' then NULL else cast(shown_place_c_s as INT64) end as shown_place_c,
+case when patient_dep_id_s = '' then NULL else cast(patient_dep_id_s as INT64) end as patient_dep_id,
+cast(contact_date_time as DATE) as contact_date,
+timestamp(alt_action_inst, 'America/Los_Angeles') as alt_action_inst_utc
+from alert_2019.alert_history_20200124;
+    '''
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_drg_code.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_drg_code.py
new file mode 100644
index 00000000..626432be
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_drg_code.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+
+# [4] "lpch_drg_code.csv"
+# [1] "ANON_ID"              "PAT_ENC_CSN_ID_CODED" "DRG_MPI_CODE"
+# [4] "DRG_NAME"             "DRG_WEIGHT"           "DATA_SOURCE"
+
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_drg_code.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_drg_code.csv'
+
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'drg_code'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('DRG_MPI_CODE', 'INT64', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DRG_NAME', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DRG_WEIGHT', 'FLOAT64', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+
+# [1] "ANON_ID"              "PAT_ENC_CSN_ID_CODED" "DRG_MPI_CODE"
+# [4] "DRG_NAME"             "DRG_WEIGHT"           "DATA_SOURCE"
+
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('DRG_MPI_CODE', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DRG_NAME', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DRG_WEIGHT', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_lab_result.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_lab_result.py
new file mode 100644
index 00000000..9572e1a4
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_lab_result.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [5] "lpch_lab_result_121219.csv"
+# [1] "ANON_ID"                "PAT_ENC_CSN_ID_CODED"
+# [3] "ORDER_ID_CODED"         "ORDER_TIME_JITTERED"
+# [5] "TAKEN_TIME_JITTERED"    "RESULT_TIME_JITTERED"
+# [7] "COMPONENT_ID"           "LINE"
+# [9] "ORDER_TYPE"             "PROC_CODE"
+# [11] "GROUP_LAB_NAME"         "LAB_NAME"
+# [13] "BASE_NAME"              "ORD_VALUE"
+# [15] "ORD_NUM_VALUE"          "REFERENCE_LOW"
+# [17] "REFERENCE_HIGH"         "REFERENCE_UNIT"
+# [19] "RESULT_IN_RANGE_YN"     "RESULT_FLAG"
+# [21] "AUTH_PROV_MAP_ID"       "ORDERING_MODE"
+# [23] "EXTENDED_VALUE_COMMENT" "EXTENDED_COMP_COMMENT"
+# [25] "DATA_SOURCE"
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_lab_result_121219.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_lab_result_121219.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'lab_result'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('TAKEN_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('COMPONENT_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LINE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('GROUP_LAB_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BASE_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORD_VALUE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORD_NUM_VALUE', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERENCE_LOW', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERENCE_HIGH', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERENCE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_IN_RANGE_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_FLAG', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTH_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_MODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EXTENDED_VALUE_COMMENT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EXTENDED_COMP_COMMENT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('TAKEN_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('COMPONENT_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LINE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('GROUP_LAB_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BASE_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORD_VALUE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORD_NUM_VALUE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERENCE_LOW', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERENCE_HIGH', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERENCE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_IN_RANGE_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_FLAG', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTH_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_MODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EXTENDED_VALUE_COMMENT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EXTENDED_COMP_COMMENT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    #print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_mar.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_mar.py
new file mode 100644
index 00000000..8b47c9c5
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_mar.py
@@ -0,0 +1,103 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [6] "lpch_mar_121319.csv"
+
+# [1] "ANON_ID"                 "PAT_ENC_CSN_ID_CODED"
+# [3] "ORDER_MED_ID_CODED"      "TAKEN_TIME_JITTERED"
+# [5] "SCHEDULED_TIME_JITTERED" "MAR_TIME_SOURCE_C"
+# [7] "MAR_ACTION_C"            "MAR_ACTION"
+# [9] "SIG"                     "ROUTE_C"
+# [11] "ROUTE"                   "REASON_C"
+# [13] "REASON"                  "SITE_C"
+# [15] "SITE"                    "INFUSION_RATE"
+# [17] "MAR_INF_RATE_UNIT_C"     "MAR_INF_RATE_UNIT"
+# [19] "DOSE_UNIT_C"             "DOSE_UNIT"
+# [21] "MAR_DURATION"            "MAR_DURATION_UNIT_C"
+# [23] "MAR_DURATION_UNIT"       "DATA_SOURCE"
+
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_mar_121319.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_mar_121319.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'mar'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_MED_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('TAKEN_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SCHEDULED_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_TIME_SOURCE_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_ACTION_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_ACTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIG', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ROUTE_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ROUTE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REASON_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REASON', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SITE_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SITE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('INFUSION_RATE', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_INF_RATE_UNIT_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_INF_RATE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_DURATION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_DURATION_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_DURATION_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_MED_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('TAKEN_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SCHEDULED_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_TIME_SOURCE_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_ACTION_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_ACTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIG', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ROUTE_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ROUTE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REASON_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REASON', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SITE_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SITE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('INFUSION_RATE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_INF_RATE_UNIT_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_INF_RATE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_DURATION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_DURATION_UNIT_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAR_DURATION_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_ndc_code.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_ndc_code.py
new file mode 100644
index 00000000..9a97fa2e
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_ndc_code.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [7] "lpch_ndc_code.csv"
+
+# [1] "MEDICATION_ID"    "LINE"             "NDC_CODE"
+# [4] "NDC_ID"           "NDC_FORMAT"       "RAW_11_DIGIT_NDC"
+# [7] "MFG_LONG_NAME"    "MFG_CODE"         "PACKAGE_SIZE"
+# [10] "MED_UNIT_C"       "UNIT"             "SIMPLE_GENERIC_C"
+# [13] "SIMPLE_GENERIC"   "DATA_SOURCE"
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_ndc_code.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_ndc_code.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'ndc_code'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('MEDICATION_ID', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('LINE', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('NDC_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NDC_ID', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('NDC_FORMAT', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RAW_11_DIGIT_NDC', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MFG_LONG_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MFG_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PACKAGE_SIZE', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_UNIT_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIMPLE_GENERIC_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIMPLE_GENERIC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('MEDICATION_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('LINE', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('NDC_CODE', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('NDC_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NDC_FORMAT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RAW_11_DIGIT_NDC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MFG_LONG_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MFG_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PACKAGE_SIZE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_UNIT_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIMPLE_GENERIC_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIMPLE_GENERIC', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_order_med.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_order_med.py
new file mode 100644
index 00000000..01396dba
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_order_med.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [8] "lpch_order_med_121319.csv"
+# [1] "ANON_ID"                 "PAT_ENC_CSN_ID_CODED"
+# [3] "ORDER_MED_ID_CODED"      "ORDER_TIME_JITTERED"
+# [5] "START_TIME_JITTERED"     "END_TIME_JITTERED"
+# [7] "MEDICATION_ID"           "MED_DESCRIPTION"
+# [9] "AMB_MED_DISP_NAME"       "ORDER_CLASS_C"
+# [11] "ORDER_CLASS"             "ORDERING_MODE_C"
+# [13] "ORDERING_MODE"           "SIG"
+# [15] "QUANTITY"                "REFILLS"
+# [17] "PRESC_PROV_MAP_ID"       "AUTHR_PROV_MAP_ID"
+# [19] "MED_ROUTE_C"             "MED_ROUTE"
+# [21] "DISCON_TIME_JITTERED"    "CHNG_ORDER_MED_ID_CODED"
+# [23] "HV_DISCR_FREQ_ID"        "FREQ_NAME"
+# [25] "FREQ_DISPLAY_NAME"       "FREQ_TYPE"
+# [27] "NUMBER_OF_TIMES"         "TIME_UNIT"
+# [29] "PRN_YN"                  "FREQ_PERIOD"
+# [31] "HV_DISCRETE_DOSE"        "HV_DOSE_UNIT_C"
+# [33] "HV_DOSE_UNIT"            "ORDER_STATUS_C"
+# [35] "ORDER_STATUS"            "MIN_DISCRETE_DOSE"
+# [37] "MAX_DISCRETE_DOSE"       "DOSE_UNIT_C"
+# [39] "DOSE_UNIT"               "LASTDOSE"
+# [41] "SERV_AREA_ID"            "EQUIP_STATUS_YN"
+# [43] "PHARM_CLASS_NAME"        "THERA_CLASS_NAME"
+# [45] "PHARM_CLASS_ABBR"        "THERA_CLASS_ABBR"
+# [47] "IS_ADMINISTERED"         "DATA_SOURCE"
+
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_order_med_121319.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_order_med_121319.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'order_med'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_MED_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('START_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('END_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MEDICATION_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AMB_MED_DISP_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_CLASS_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_CLASS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_MODE_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_MODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIG', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('QUANTITY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFILLS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRESC_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHR_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_ROUTE_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_ROUTE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DISCON_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CHNG_ORDER_MED_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DISCR_FREQ_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_DISPLAY_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_TYPE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NUMBER_OF_TIMES', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('TIME_UNIT', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRN_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_PERIOD', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DISCRETE_DOSE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DOSE_UNIT_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DOSE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MIN_DISCRETE_DOSE', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAX_DISCRETE_DOSE', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT_C', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LASTDOSE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SERV_AREA_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EQUIP_STATUS_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PHARM_CLASS_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('THERA_CLASS_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PHARM_CLASS_ABBR', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('THERA_CLASS_ABBR', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('IS_ADMINISTERED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA =[bigquery.SchemaField('ANON_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_MED_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('START_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('END_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MEDICATION_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AMB_MED_DISP_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_CLASS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_CLASS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_MODE_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_MODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SIG', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('QUANTITY', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFILLS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRESC_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHR_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_ROUTE_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MED_ROUTE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DISCON_TIME_JITTERED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CHNG_ORDER_MED_ID_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DISCR_FREQ_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_DISPLAY_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('NUMBER_OF_TIMES', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('TIME_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PRN_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('FREQ_PERIOD', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DISCRETE_DOSE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DOSE_UNIT_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('HV_DOSE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MIN_DISCRETE_DOSE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('MAX_DISCRETE_DOSE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DOSE_UNIT', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LASTDOSE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SERV_AREA_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('EQUIP_STATUS_YN', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PHARM_CLASS_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('THERA_CLASS_NAME', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PHARM_CLASS_ABBR', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('THERA_CLASS_ABBR', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('IS_ADMINISTERED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_path_report.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_path_report.py
new file mode 100644
index 00000000..349db25e
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_path_report.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+
+# $lpch_path_report_meta.csv
+# [1] "ANON_ID"                   "PAT_ENC_CSN_ID_CODED"
+# [3] "ORDER_ID_CODED"            "PROC_CODE"
+# [5] "DESCRIPTION"               "ORDERING_DATE_JITTERING"
+# [7] "PROC_START_TIME_JITTERING" "PROC_END_TIME_JITTERING"
+# [9] "RESULT_TIME_JITTERING"     "ANON_ACCESSION_NUMBER"
+# [11] "AUTHRZING_PROV_MAP_ID"     "RPT_PRELIM_PROV_MAP_ID"
+# [13] "RPT_FINAL_PROV_MAP_ID"     "BILLING_PROV_MAP_ID"
+# [15] "REFERRING_PROV_MAP_ID"     "PROC_PERF_PROV_MAP_ID"
+# [17] "LAB_STATUS_C"              "LAB_STATUS"
+# [19] "ORDER_STATUS_C"            "ORDER_STATUS"
+# [21] "DATA_SOURCE"
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_path_report_meta.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_path_report_meta.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'path_report'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_ID_CODED', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PROC_CODE', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_DATE_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_START_TIME_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_END_TIME_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_TIME_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ANON_ACCESSION_NUMBER', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHRZING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_PRELIM_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_FINAL_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERRING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA =[bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('ORDER_ID_CODED', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('PROC_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_DATE_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_START_TIME_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_END_TIME_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_TIME_JITTERING', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ANON_ACCESSION_NUMBER', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHRZING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_PRELIM_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_FINAL_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERRING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_procedure.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_procedure.py
new file mode 100644
index 00000000..8f33326d
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_procedure.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+
+# $lpch_procedure_101219.csv
+# [1] "ANON_ID"                "LINE"
+# [3] "PAT_ENC_CSN_ID_CODED"   "PX_ID"
+# [5] "CODE"                   "DESCRIPTION"
+# [7] "CODE_TYPE"              "START_DATE_JITTERED"
+# [9] "PROC_DATE_JITTERED"     "ADM_DATE_TIME_JITTERED"
+# [11] "PERF_PROV_MAP_ID"       "BILLING_PROV_MAP_ID"
+# [13] "ENTRY_PROV_MAP_ID"      "DEP_MAP_ID"
+# [15] "SOURCE"                 "DATA_SOURCE"
+
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_procedure_101219.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_procedure_101219.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'procedure'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('LINE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PX_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CODE_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('START_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ADM_DATE_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ENTRY_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DEP_MAP_ID', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SOURCE', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('LINE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PX_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CODE_TYPE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('START_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ADM_DATE_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ENTRY_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DEP_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('SOURCE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_lpch_radiology_meta.py b/scripts/GoogleCloud/BQ/lpch/upload_lpch_radiology_meta.py
new file mode 100644
index 00000000..2080fc7a
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_lpch_radiology_meta.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [11] "lpch_radiology_meta.csv"
+
+# [1] "ANON_ID"                  "PAT_ENC_CSN_ID_CODED"
+# [3] "ORDER_ID_CODED"           "PROC_CODE"
+# [5] "CODE"                     "DESCRIPTION"
+# [7] "ORDERING_DATE_JITTERED"   "PROC_START_TIME_JITTERED"
+# [9] "PROC_END_TIME_JITTERED"   "RPT_PRELIM_DTTM_JITTERED"
+# [11] "RPT_FINAL_DTTM_JITTERED"  "RESULT_TIME_JITTERED"
+# [13] "ACCESSION_NUMBER_CODED"   "AUTHRZING_PROV_MAP_ID"
+# [15] "RPT_PRELIM_PROV_MAP_ID"   "RPT_FINAL_PROV_MAP_ID"
+# [17] "BILLING_PROV_MAP_ID"      "REFERRING_PROV_MAP_ID"
+# [19] "PROC_PERF_PROV_MAP_ID"    "LAB_STATUS_C"
+# [21] "LAB_STATUS"               "ORDER_STATUS_C"
+# [23] "ORDER_STATUS"             "DATA_SOURCE"
+
+# /Users/jonc101/Downloads/
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/Users/jonc101/Downloads/lpch_radiology_meta.csv'
+csv_path = '/Users/jonc101/Downloads/lpch_radiology_meta.csv'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'radiology_meta'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_ID_CODED', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_START_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_END_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_PRELIM_DTTM_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_FINAL_DTTM_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ACCESSION_NUMBER_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHRZING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_PRELIM_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_FINAL_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERRING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS_C', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('ANON_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PAT_ENC_CSN_ID_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_ID_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('CODE', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DESCRIPTION', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDERING_DATE_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_START_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_END_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_PRELIM_DTTM_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_FINAL_DTTM_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RESULT_TIME_JITTERED', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ACCESSION_NUMBER_CODED', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('AUTHRZING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_PRELIM_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('RPT_FINAL_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERRING_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_PERF_PROV_MAP_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('LAB_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS_C', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ORDER_STATUS', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('DATA_SOURCE', 'STRING', 'NULLABLE', None, ())]
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        bq_client.reconnect_client()
+        bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                    schema=FINAL_TABLE_SCHEMA, skip_rows=1)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_new_date_column.py b/scripts/GoogleCloud/BQ/lpch/upload_new_date_column.py
new file mode 100644
index 00000000..6e09670f
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_new_date_column.py
@@ -0,0 +1,66 @@
+from google.cloud import bigquery
+
+# Construct a BigQuery client object.
+client = bigquery.Client()
+
+
+
+'''
+# TODO(developer): Set table_id to the ID of the table
+#                  to add an empty column.
+#table_id = "mining-clinical-decisions.lpch.allergy"
+table_id = "mining-clinical-decisions.lpch.family_hx"
+table = client.get_table(table_id)  # Make an API request.
+original_schema = table.schema
+new_schema = original_schema[:]  # Creates a copy of the schema.
+new_schema.append(bigquery.SchemaField("CONTACT_DATE_JITTERED_UTC", "DATETIME"))
+table.schema = new_schema
+table = client.update_table(table, ["schema"])  # Make an API request.
+
+if len(table.schema) == len(original_schema) + 1 == len(new_schema):
+    print("A new column has been added.")
+else:
+    print("The column has not been added.")
+
+table_id_lda = "mining-clinical-decisions.lpch.lda"
+table_lda = client.get_table(table_id_lda)  # Make an API request.
+original_schema_lda = table_lda.schema
+new_schema_lda = original_schema_lda[:]  # Creates a copy of the schema.
+new_schema_lda.append(bigquery.SchemaField("PLACEMENT_INSTANT_JITTERED_UTC", "DATETIME"))
+new_schema_lda.append(bigquery.SchemaField("REMOVAL_INSTANT_JITTERED_UTC", "DATETIME"))
+table_lda.schema = new_schema_lda
+table_lda = client.update_table(table_lda, ["schema"])  # Make an API request.
+
+if len(table_lda.schema) == len(original_schema_lda) + 2 == len(new_schema_lda):
+    print("A new column has been added to LDA")
+else:
+    print("The column has not been added LDA")
+
+table_id = "mining-clinical-decisions.lpch.treatment_team"
+table = client.get_table(table_id)  # Make an API request.
+original_schema = table.schema
+new_schema = original_schema[:]  # Creates a copy of the schema.
+new_schema.append(bigquery.SchemaField("TRTMNT_TM_BEGIN_DT_JITTERED_UTC", "DATETIME"))
+new_schema.append(bigquery.SchemaField("TRTMNT_TM_END_DT_JITTERED_UTC", "DATETIME"))
+table.schema = new_schema
+table = client.update_table(table, ["schema"])  # Make an API request.
+
+if len(table_lda.schema) == len(original_schema_lda) + 2 == len(new_schema_lda):
+    print("A new column has been added to treatment_team")
+else:
+    print("The column has not been added treatment")
+
+'''
+
+table_id = "mining-clinical-decisions.lpch.flowsheets"
+table = client.get_table(table_id)  # Make an API request.
+original_schema = table.schema
+new_schema = original_schema[:]  # Creates a copy of the schema.
+new_schema.append(bigquery.SchemaField("RECORDED_TIME_JITTERED_UTC", "DATETIME"))
+table.schema = new_schema
+table = client.update_table(table, ["schema"])  # Make an API request.
+
+if len(table.schema) == len(original_schema) + 2 == len(new_schema):
+    print("A new column has been added to treatment_team")
+else:
+    print("The column has not been added treatment")
diff --git a/scripts/GoogleCloud/BQ/lpch/upload_template.py b/scripts/GoogleCloud/BQ/lpch/upload_template.py
new file mode 100644
index 00000000..1f36614e
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/lpch/upload_template.py
@@ -0,0 +1,147 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+# files names:
+
+# [1] "lpch_clinical_note_meta_121619.csv"
+# [2] "lpch_demographics_121119.csv"
+# [3] "lpch_diagnosis_101119.csv"
+# [4] "lpch_drg_code.csv"
+# [5] "lpch_lab_result_121219.csv"
+# [6] "lpch_mar_121319.csv"
+# [7] "lpch_ndc_code.csv"
+# [8] "lpch_order_med_121319.csv"
+# [9] "lpch_path_report_meta.csv"
+# [10] "lpch_procedure_101219.csv"
+# [11] "lpch_radiology_meta.csv"
+
+# table names
+
+# lpch_clinical_note_meta
+# lpch_demographics
+# lpch_diagnosis
+# lpch_drg_code
+# lpch_lab_result
+# lpch_mar
+# lpch_ndc_code
+# lpch_order_med_121319
+# lpch_path_report_meta
+# lpch_procedure
+# lpch_radiology_meta
+
+# /Users/jonc101/Downloads/
+CSV_FILE_PREFIX = '/path/to/alert_history_012420_'
+DATASET_NAME = 'lpch'
+TABLE_NAME = 'alert_history_20200124'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_id_jittered', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_csn_id_coded', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_status_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alt_status_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('was_shown_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('was_shown_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('bpa_trgr_action_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('bpa_trgr_action_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('shown_place_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('shown_place_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('contact_date', 'DATE', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alt_action_inst', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('user_id', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('patient_dep_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('department_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alt_group_info', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_id_jittered_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_csn_id_coded_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_status_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alt_status_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('was_shown_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('was_shown_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('bpa_trgr_action_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('bpa_trgr_action_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('shown_place_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('shown_place_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('contact_date_time', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alt_action_inst', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('user_id', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('patient_dep_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('department_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alt_group_info', 'STRING', 'NULLABLE', None, ())]
+
+
+def load_alert_table(csv_path):
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=UPLOAD_TABLE_SCHEMA, skip_rows=1)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed heading and trailing lines in vim
+    - added header line
+
+    split every 2 mln lines:
+    split -l 2000000 alert_history_012420.csv alert_history_012420_
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading {}aa'.format(CSV_FILE_PREFIX))
+        load_alert_table(CSV_FILE_PREFIX + 'aa')
+        for fn in ([x + y for x, y in itertools.product('a', string.ascii_lowercase[1:])] +
+                   [x + y for x, y in itertools.product('b', string.ascii_lowercase)] +
+                   [x + y for x, y in itertools.product('c', string.ascii_lowercase)] +
+                   [x + y for x, y in itertools.product('d', string.ascii_lowercase[:6])]):
+            print('uploading {}'.format(CSV_FILE_PREFIX + fn))
+            bq_client.reconnect_client()
+            bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                        auto_detect_schema=False,
+                                        schema=None, skip_rows=0, append_to_table=True)
+
+    print('Done')
+
+    '''
+    expecting 167,058,216 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace
+table alert_2019.alert_history_20200124
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_coded_s,
+    alt_status_c_s,
+    was_shown_c_s,
+    bpa_trgr_action_c_s,
+    shown_place_c_s,
+    patient_dep_id_s,
+    contact_date_time
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_coded_s = '' then NULL else cast(alt_csn_id_coded_s as INT64) end as alt_csn_id_coded,
+case when alt_status_c_s = '' then NULL else cast(alt_status_c_s as INT64) end as alt_status_c,
+case when was_shown_c_s = '' then NULL else cast(was_shown_c_s as INT64) end as was_shown_c,
+case when bpa_trgr_action_c_s = '' then NULL else cast(bpa_trgr_action_c_s as INT64) end as bpa_trgr_action_c,
+case when shown_place_c_s = '' then NULL else cast(shown_place_c_s as INT64) end as shown_place_c,
+case when patient_dep_id_s = '' then NULL else cast(patient_dep_id_s as INT64) end as patient_dep_id,
+cast(contact_date_time as DATE) as contact_date,
+timestamp(alt_action_inst, 'America/Los_Angeles') as alt_action_inst_utc
+from alert_2019.alert_history_20200124;
+    '''
diff --git a/scripts/GoogleCloud/BQ/readme.md b/scripts/GoogleCloud/BQ/readme.md
new file mode 100644
index 00000000..7848482d
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/readme.md
@@ -0,0 +1,34 @@
+# TODO 
+##  in progress January 23rd 2019 -
+
+#### queries saved on google queries 
+
+## Module 1 
+
+1) How many “Initial Consult Visit” encounters 
+happen in our dataset/healthcare system 
+per year in the last five years?
+    
+    #### first_encounter_with_year_count_SAVE
+
+2) How many new patients per year in the last five years?
+
+    #### new patients per year in the last five years_mod2
+
+3) What are the top five most common specialty initial consult visit types?
+
+
+4) Figure out the top 5-10 diagnosis codes associated with 
+each of the most common specialty initial consults
+
+
+## Module 2 
+
+1) If you can find orders for consultation referral 
+and the respective initial consult visit that follows for the respective specialty
+
+2) Quantify how many days between the request vs. the visit, 
+and sum up the distribution 
+of how long these waits usually are for 
+different specialties and different diagnoses.
+
diff --git a/scripts/GoogleCloud/BQ/requirements.txt b/scripts/GoogleCloud/BQ/requirements.txt
new file mode 100644
index 00000000..688218aa
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/requirements.txt
@@ -0,0 +1,27 @@
+cachetools==3.1.0
+certifi==2018.11.29
+chardet==3.0.4
+futures==3.2.0
+google-api-core==1.7.0
+google-auth==1.6.2
+google-auth-oauthlib==0.2.0
+google-cloud-bigquery==1.8.1
+google-cloud-core==0.29.1
+google-resumable-media==0.3.2
+googleapis-common-protos==1.5.6
+idna==2.8
+numpy==1.16.1
+oauthlib==3.0.1
+pandas==0.24.0
+pandas-gbq==0.9.0
+protobuf==3.6.1
+pyasn1==0.4.5
+pyasn1-modules==0.2.4
+pydata-google-auth==0.1.1
+python-dateutil==2.7.5
+pytz==2018.9
+requests==2.21.0
+requests-oauthlib==1.2.0
+rsa==4.7
+six==1.12.0
+urllib3==1.26.5
diff --git a/scripts/GoogleCloud/BQ/shc/directory_shc_setup.sh b/scripts/GoogleCloud/BQ/shc/directory_shc_setup.sh
new file mode 100644
index 00000000..ee8daa6b
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/shc/directory_shc_setup.sh
@@ -0,0 +1,43 @@
+
+#MANUAL SETUP
+sheet_path='/Users/jonc101/Downloads/shc/'
+github_path='/Users/jonc101/Documents/github2/CDSS/'
+#export PYTHONPATH='/Users/jonc101/Documents/github/CDSS/'
+
+export PYTHONPATH=$github_path
+
+flowsheet2011='flowsheet2011'
+
+flowsheet2019='flowsheet2019'
+
+
+upload_script_path='scripts/GoogleCloud/BQ/lpch/flowsheets/'
+
+flowsheet2012py='upload_flowsheet2012.py'
+
+
+
+#cut_inline_comma=awk -F'"' -v OFS='' '{ for (i=2; i<=NF; i+=2) gsub(",", "", $i) } 1
+#remove_quotes_from_numbers=sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g'
+#remove_double_quotes_from_string=sed 's/\([^",]\)"\([^",]\)/\1""\2/g'
+#remove_non_numeric_col_6=awk -F, '{ gsub(/[^0-9.-]/,"",$6) }1' OFS=','
+#remove_non_numeric_col_8=awk -F, '{ gsub(/[^0-9.-]/,"",$8) }1' OFS=','
+
+
+
+#cd $flowsheet_path$flowsheet2011
+#split -l 2000000 lpch_flowsheet2011.csv flowsheet_
+
+cd $sheet_path
+#sed 's/\"//g' lpch_flowsheet2012.csv > lpch_flowsheet2012_v2.csv
+split -l 250000 shc_order_proc_2020.csv sheet_
+
+
+
+#cd $flowsheet_path$flowsheet2012
+#split -l 2000000 lpch_flowsheet2012.csv flowsheet_
+#rm flowsheet.csv
+#python2 $github_path$upload_script_path$flowsheet2012py
+
+#  HOW TO LOG / AND PRINT
+# sh [SHELL_FILE.sh] 2>&1 | tee -a [NAME_OF_LOG_FILE.log]
diff --git a/scripts/GoogleCloud/BQ/shc/preprocess_shc_order_proc.py b/scripts/GoogleCloud/BQ/shc/preprocess_shc_order_proc.py
new file mode 100644
index 00000000..97b19c37
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/shc/preprocess_shc_order_proc.py
@@ -0,0 +1,79 @@
+import os
+import glob
+import os
+import logging
+import itertools
+import string
+import  numpy  as np
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+import pandas as pd
+from os import listdir
+from os.path import isfile, join
+import re
+
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+def count_alphabet(input_row):
+    d = []
+    for i in  string.ascii_lowercase:
+        d.append(input_row.count(i))
+    return sum(d)
+
+def non_numeric_sub(string):
+    return re.sub("[^0-9.-]", "", string)
+
+
+file_match = glob.glob('/Users/jonc101/Downloads/shc/sheet_*')
+file_prefix = '/Users/jonc101/Downloads/shc/sheet_'
+
+file_prefix_list = []
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+for file in file_match:
+	file_prefix_list.append(remove_prefix(file, file_prefix))
+file_prefix_list = sorted(file_prefix_list)
+print(file_prefix_list)
+
+for file in file_prefix_list:
+    print('-----------------')
+    print('reading file in:')
+    print('-----------------')
+    print(file_prefix + file)
+    print('-----------------')
+    if file == 'aa':
+        c  = pd.read_csv(file_prefix + file, sep='\t',header=None,skiprows=1)
+        pd.options.display.float_format = '{:,.0f}'.format
+        pd.set_option('precision', 0)
+        c[2] = c[2].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c[5] = c[5].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c[34] = c[34].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c[41] = c[41].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c.to_csv(file_prefix + file, encoding='utf-8', index=False)
+    else:
+        c  = pd.read_csv(file_prefix + file, sep='\t',header = None)
+        pd.options.display.float_format = '{:,.0f}'.format
+        pd.set_option('precision', 0)
+        c[2] = c[2].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c[5] = c[5].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c[34] = c[34].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c[41] = c[41].astype(str).replace(".0", "", regex=True).replace('nan','',regex=True)
+        c.to_csv(file_prefix + file, encoding='utf-8', index=False)
+    print('-----------------')
+    print('processed file')
+    print('-----------------')
+    print(file_prefix + file)
+    print('-----------------')
diff --git a/scripts/GoogleCloud/BQ/shc/upload_shc_order_proc.py b/scripts/GoogleCloud/BQ/shc/upload_shc_order_proc.py
new file mode 100644
index 00000000..de98bc31
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/shc/upload_shc_order_proc.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+#import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+import glob
+from medinfo.db.bigquery import bigQueryUtil
+import pandas as pd
+from google.cloud import bigquery
+
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"]='/Users/jonc101/Downloads/Mining Clinical Decisions-58be3d782c5b.json'
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+def count_alphabet(input_row):
+    d = []
+    for i in  string.ascii_lowercase:
+        d.append(input_row.count(i))
+    return sum(d)
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix):]
+    return text
+
+file_match = glob.glob('/Users/jonc101/Downloads/shc/order_processed/sheet_*')
+file_prefix = '/Users/jonc101/Downloads/shc/order_processed/sheet_'
+
+CSV_FILE_PREFIX = file_prefix
+file_prefix_list = []
+
+for file in file_match:
+	file_prefix_list.append(remove_prefix(file, file_prefix))
+file_prefix_list = sorted(file_prefix_list)
+print(file_prefix_list)
+
+
+
+
+
+
+DATASET_NAME = 'shc_test'
+TABLE_NAME = 'order_proc'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('order_proc_id_jittered', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('pat_enc_csn_id_jittered', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ordering_date_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('order_type_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_id', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_code', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('description', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('display_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('cpt_code', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('order_class_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('authrzing_prov_id', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('BILLING_PROV_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('REFERRING_PROV_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('PROC_PERF_PROV_ID', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('abnormal_yn', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('lab_status_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('order_status_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('quantity', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('future_or_stand', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('standing_exp_date', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('standing_occurs', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('standing_orig_occur', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('radiology_sts_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_bgn_time', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_end_time', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('order_inst', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('prov_status_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('stand_interval', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('discrete_interval_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('instantiated_time', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('order_time', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('result_time', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_start_time', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('problem_list_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_ending_time', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('pat_class_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('proc_date', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('chng_order_proc_id', 'FLOAT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('last_stand_perf_dt', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('last_stand_perf_tm', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('parent_ce_order_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('initiated_time', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('ordering_mode_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('duration', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('frequency', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('intervention', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('after_order_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('before_order_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('data_source', 'STRING', 'NULLABLE', None, ())]
+
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+    bq_client = bigQueryUtil.BigQueryClient()
+    bq_client.reconnect_client()
+    for fn in (file_prefix_list):
+         print('uploading {}'.format(fn))
+         bq_client.reconnect_client()
+         bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                             auto_detect_schema=False,
+                                             schema=FINAL_TABLE_SCHEMA, skip_rows=1, append_to_table=True)
+
+    print('Done')
diff --git a/scripts/GoogleCloud/BQ/test/check_row_counts.py b/scripts/GoogleCloud/BQ/test/check_row_counts.py
new file mode 100644
index 00000000..2f46727d
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/test/check_row_counts.py
@@ -0,0 +1,83 @@
+'''
+setup credentials: export GOOGLE_APPLICATION_CREDENTIALS='<path to json>'
+'''
+
+import psycopg2 as pg
+
+from scripts.GoogleCloud.BQ.BigQueryConnect import BigQueryConnect
+
+class LocalStrideConnect:
+
+    def __init__(self, db_name, user_name, password):
+        self.conn = pg.connect(dbname=db_name, user=user_name, password=password)
+
+    def get_row_count(self, table_name):
+        cur = self.conn.cursor()
+        cur.execute(f'SELECT COUNT(*) from {table_name};')
+        count = cur.fetchone()[0]
+        return count
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.conn.close()
+
+def compare_table_row_counts(table_list, pg_db_name, pg_user, pg_pw):
+    local_stride = LocalStrideConnect(pg_db_name, pg_user, pg_pw)
+    bq_stride = BigQueryConnect()
+
+    print('table_name\tlocal_count\tbq_count\tmatch')
+
+    for table_name in table_list:
+        local_count = local_stride.get_row_count(table_name)
+        bq_count = bq_stride.get_row_count(table_name)
+
+        count_good = 'YES' if local_count == bq_count else 'MISMATCH'
+
+        print(f'{table_name}\t{local_count}\t{bq_count}\t{count_good}')
+
+    return
+
+
+if __name__ == '__main__':
+    stride_table_list = [   'stride_2008_2017.backup_link_patient_item',
+                            'stride_2008_2017.clinical_item',
+                            'stride_2008_2017.clinical_item_association',
+                            'stride_2008_2017.clinical_item_category',
+                            'stride_2008_2017.clinical_item_link',
+                            'stride_2008_2017.collection_type',
+                            'stride_2008_2017.data_cache',
+                            'stride_2008_2017.item_collection',
+                            'stride_2008_2017.item_collection_item',
+                            'stride_2008_2017.order_result_stat',
+                            'stride_2008_2017.patient_item',
+                            'stride_2008_2017.patient_item_collection_link',
+                            'stride_2008_2017.stride_admit',
+                            'stride_2008_2017.stride_adt',
+                            'stride_2008_2017.stride_chargemaster',
+                            'stride_2008_2017.stride_culture_micro',
+                            'stride_2008_2017.stride_drg',
+                            'stride_2008_2017.stride_dx_list',
+                            'stride_2008_2017.stride_flowsheet',
+                            'stride_2008_2017.stride_icd9_cm',
+                            'stride_2008_2017.stride_income',
+                            'stride_2008_2017.stride_io_flowsheet',
+                            'stride_2008_2017.stride_mapped_meds',
+                            'stride_2008_2017.stride_medication_mpi',
+                            'stride_2008_2017.stride_note',
+                            'stride_2008_2017.stride_order_med',
+                            'stride_2008_2017.stride_order_medmixinfo',
+                            'stride_2008_2017.stride_order_proc',
+                            'stride_2008_2017.stride_order_results',
+                            'stride_2008_2017.stride_orderset_order_med',
+                            'stride_2008_2017.stride_orderset_order_proc',
+                            'stride_2008_2017.stride_patient',
+                            'stride_2008_2017.stride_patient_encounter',
+                            'stride_2008_2017.stride_preadmit_med',
+                            'stride_2008_2017.stride_treatment_team'
+                         ]
+    #stride_table_list = ['stride_2008_2017.order_result_stat', 'stride_2008_2017.collection_type']
+
+    # stride_db_name = input('Enter STRIDE db name: ')
+    # stride_db_user = input('Enter STRIDE db user: ')
+    # stride_db_pw = input('Enter STRIDE db user: ')
+    stride_db_name = stride_db_user = stride_db_pw = 'postgres'
+    compare_table_row_counts(stride_table_list, stride_db_name, stride_db_user, stride_db_pw)
\ No newline at end of file
diff --git a/scripts/GoogleCloud/BQ/test/testBigQueryConnect.py b/scripts/GoogleCloud/BQ/test/testBigQueryConnect.py
new file mode 100644
index 00000000..42aa7fa0
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/test/testBigQueryConnect.py
@@ -0,0 +1,29 @@
+'''
+need a delete list to remove from GCP after testing (do this in web GUI)
+'''
+import os
+from pathlib import Path
+
+from scripts.GoogleCloud.BQ.BigQueryConnect import BigQueryConnect
+
+def test_creating_from_csv(csv_path, type_path):
+    bqc = BigQueryConnect()
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+    p = Path(os.environ['GOOGLE_APPLICATION_CREDENTIALS'])
+    assert p.exists(), '{os.environ["GOOGLE_APPLICATION_CREDENTIALS"]} does not exist.'
+
+    bqc.create_new_dataset('test_dataset_delete_me')
+    schema = bqc.read_table_types(type_table_path)
+
+    bqc.load_csv_to_table('test_dataset_delete_me', 'test_table_delete_me', csv_path, auto_detect_schema=False,\
+                          schema=schema, skip_rows=1)
+    return
+
+if __name__ == '__main__':
+    CSV_path = input('Enter CSV_path: ')
+    type_table_path = input('Enter type table path: ')
+    test_creating_from_csv(CSV_path, type_table_path)
+
+
+
+
diff --git a/scripts/GoogleCloud/BQ/test/testSTARRDemo.py b/scripts/GoogleCloud/BQ/test/testSTARRDemo.py
new file mode 100755
index 00000000..2399cc42
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/test/testSTARRDemo.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+'''
+setup credentials: export GOOGLE_APPLICATION_CREDENTIALS='<path to json>'
+'''
+
+from medinfo.dataconversion.starr_conv.STARRDemographicsConversion import STARRDemographicsConversion
+from medinfo.db import DBUtil
+from stride.clinical_item.ClinicalItemDataLoader import ClinicalItemDataLoader
+
+TEST_SOURCE_TABLE = 'starr_datalake2018.demographic'
+
+if __name__ == '__main__':
+    ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+    STARR_demo_conv = STARRDemographicsConversion()
+    print('testing')
+    #STARR_demo_conv.querySourceItems(patientIds=['JCcdf815', 'JCe99f38', 'JCea259c'], debug=True)
+
+    patientIds = ['JCd5ef6e', 'JCce317d', 'JCe83f82']
+
+    STARR_demo_conv.convertSourceItems(patientIds)
+
+    testQuery = \
+        """
+        select 
+            pi.external_id,
+            pi.patient_id,
+            pi.encounter_id,
+            cic.description,
+            ci.external_id,
+            ci.name,
+            ci.description,
+            pi.item_date
+        from
+            patient_item as pi,
+            clinical_item as ci,
+            clinical_item_category as cic
+        where
+            pi.clinical_item_id = ci.clinical_item_id and
+            ci.clinical_item_category_id = cic.clinical_item_category_id and
+            cic.source_table = '%s'
+        order by
+            pi.patient_id desc, ci.name
+        """ % TEST_SOURCE_TABLE
+
+    actualData = DBUtil.execute(testQuery)
+    print(actualData)
+    print('done')
+
diff --git a/scripts/GoogleCloud/BQ/upload_alert_history.py b/scripts/GoogleCloud/BQ/upload_alert_history.py
new file mode 100644
index 00000000..11a17d0b
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/upload_alert_history.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+CSV_FILE_PREFIX = '/path/to/alert_history_012420_'
+DATASET_NAME = 'alert_2019'
+TABLE_NAME = 'alert_history_20200228'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_id_jittered', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_csn_id_coded', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_status_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alt_status_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('was_shown_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('was_shown_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('bpa_trgr_action_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('bpa_trgr_action_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('shown_place_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('shown_place_c_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('contact_date', 'DATE', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alt_action_inst', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('user_id', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('patient_dep_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('department_name', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alt_group_info', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_id_jittered_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_csn_id_coded_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_status_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alt_status_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('was_shown_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('was_shown_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('bpa_trgr_action_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('bpa_trgr_action_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('shown_place_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('shown_place_c_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('contact_date_time', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alt_action_inst', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('user_id', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('patient_dep_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('department_name', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alt_group_info', 'STRING', 'NULLABLE', None, ())]
+
+
+def load_alert_table(csv_path):
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=UPLOAD_TABLE_SCHEMA, skip_rows=1)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed heading and trailing lines in vim
+    - added header line
+    
+    split every 2 mln lines:
+    split -l 2000000 alt_history_022820.csv alert_history_022820_
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading {}aa'.format(CSV_FILE_PREFIX))
+        load_alert_table(CSV_FILE_PREFIX + 'aa')
+        for fn in ([x + y for x, y in itertools.product('a', string.ascii_lowercase[1:])] +
+                   [x + y for x, y in itertools.product('b', string.ascii_lowercase)] +
+                   [x + y for x, y in itertools.product('c', string.ascii_lowercase)] +
+                   [x + y for x, y in itertools.product('d', string.ascii_lowercase[:6])]):
+            print('uploading {}'.format(CSV_FILE_PREFIX + fn))
+            bq_client.reconnect_client()
+            bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                        auto_detect_schema=False,
+                                        schema=None, skip_rows=0, append_to_table=True)
+
+    print('Done')
+
+    '''
+    expecting 167,056,690 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace 
+table alert_2019.alert_history_20200228
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_coded_s,
+    alt_status_c_s,
+    was_shown_c_s,
+    bpa_trgr_action_c_s,
+    shown_place_c_s,
+    patient_dep_id_s,
+    contact_date_time
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_coded_s = '' then NULL else cast(alt_csn_id_coded_s as INT64) end as alt_csn_id_coded,
+case when alt_status_c_s = '' then NULL else cast(alt_status_c_s as INT64) end as alt_status_c,
+case when was_shown_c_s = '' then NULL else cast(was_shown_c_s as INT64) end as was_shown_c,
+case when bpa_trgr_action_c_s = '' then NULL else cast(bpa_trgr_action_c_s as INT64) end as bpa_trgr_action_c,
+case when shown_place_c_s = '' then NULL else cast(shown_place_c_s as INT64) end as shown_place_c,
+case when patient_dep_id_s = '' then NULL else cast(patient_dep_id_s as INT64) end as patient_dep_id,
+cast(contact_date_time as DATE) as contact_date,
+timestamp(alt_action_inst, 'America/Los_Angeles') as alt_action_inst_utc
+from alert_2019.alert_history_20200228;
+    '''
diff --git a/scripts/GoogleCloud/BQ/upload_alerts_orders.py b/scripts/GoogleCloud/BQ/upload_alerts_orders.py
new file mode 100644
index 00000000..05ad1e13
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/upload_alerts_orders.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+CSV_FILE_PREFIX = '/path/to/jc_alerts_orders_120619_head_tail_removed_reformatted_quotes_escaped_'
+DATASET_NAME = 'alert_2019'
+TABLE_NAME = 'alerts_orders'
+TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                bigquery.SchemaField('alt_id_coded', 'INT64', 'REQUIRED', None, ()),
+                bigquery.SchemaField('order_id_coded', 'INT64', 'NULLABLE', None, ()),
+                bigquery.SchemaField('alt_csn_id_coded', 'INT64', 'REQUIRED', None, ()),
+                bigquery.SchemaField('line', 'INT64', 'REQUIRED', None, ()),
+                bigquery.SchemaField('med_alerts_actn_c', 'INT64', 'NULLABLE', None, ()),
+                bigquery.SchemaField('med_alerts_actn_c_name', 'STRING', 'NULLABLE', None, ()),
+                bigquery.SchemaField('medication_id', 'INT64', 'NULLABLE', None, ()),
+                bigquery.SchemaField('medication_name', 'STRING', 'NULLABLE', None, ())]
+
+
+def load_alert_table(csv_path):
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=TABLE_SCHEMA, skip_rows=0)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    CSV remove SQL commands head and tail:
+    tail -n +3 jc_alerts_orders_120619.csv | head -n -1 > jc_alerts_orders_120619_head_tail_removed.csv
+    
+    CSV check line nums are as expected:
+    wc -l jc_alerts_orders_120619_head_tail_removed.csv
+    
+    CSV cleanup command:
+    cat jc_alerts_orders_120619_head_tail_removed.csv | sed -e 's/^\(".*"\),"\(.*\)","\(.*\)","\(.*\)","\(.*\)","\(.*\)",\(".*"\),"\(.*\)",\(".*"\)$/\1,\2,\3,\4,\5,\6,\7,\8,\9/g' > jc_alerts_orders_120619_head_tail_removed_reformatted.csv
+    
+    CSV escape quotes command:
+    cat jc_alerts_orders_120619_head_tail_removed_reformatted.csv | sed 's/\([^",]\)"\([^",]\)/\1""\2/g' > jc_alerts_orders_120619_head_tail_removed_reformatted_quotes_escaped.csv
+     
+    file needs to be split:
+    split -l 2000000 jc_<TABLE_NAME>_reformatted.csv jc_<TABLE_NAME>_reformatted_
+
+    example of above:
+    split -l 2000000 jc_alerts_orders_120619_head_tail_removed_reformatted_quotes_escaped.csv jc_alerts_orders_120619_head_tail_removed_reformatted_quotes_escaped_
+    '''
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading {}aa'.format(CSV_FILE_PREFIX))
+        load_alert_table(CSV_FILE_PREFIX + 'aa')
+        for fn in ([x + y for x, y in itertools.product('a', string.ascii_lowercase[1:])] +
+                   [x + y for x, y in itertools.product('b', string.ascii_lowercase)] +
+                   [x + y for x, y in itertools.product('c', string.ascii_lowercase[:2])]):
+            print('uploading {}'.format(CSV_FILE_PREFIX + fn))
+            bq_client.reconnect_client()
+            bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                        auto_detect_schema=False,
+                                        schema=None, skip_rows=0, append_to_table=True)
+
+    print('Done')
+
+    '''
+    expecting 107,202,804 lines from original table
+    '''
diff --git a/scripts/GoogleCloud/BQ/upload_alt_com_action.py b/scripts/GoogleCloud/BQ/upload_alt_com_action.py
new file mode 100644
index 00000000..c660fb60
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/upload_alt_com_action.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+CSV_FILE_PREFIX = '/path/to/alt_com_action_020320_'
+DATASET_NAME = 'alert_2019'
+TABLE_NAME = 'alt_com_action'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_id_jittered', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_csn_id_jittered', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('line', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('contact_date_jittered', 'DATE', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('cm_ct_owner_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('action_taken_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('action_taken_c_name', 'STRING', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_id_jittered_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_csn_id_jittered_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('line_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('contact_date_time_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('cm_ct_owner_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('action_taken_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('action_taken_c_name', 'STRING', 'NULLABLE', None, ())]
+
+
+def load_alert_table(csv_path):
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=UPLOAD_TABLE_SCHEMA, skip_rows=1)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed heading and trailing lines in vim
+    - added header line
+    
+    split every 2 mln lines:
+    split -l 2000000 alt_com_action_020320.csv alt_com_action_020320_
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading {}aa'.format(CSV_FILE_PREFIX))
+        load_alert_table(CSV_FILE_PREFIX + 'aa')
+        for fn in ([x + y for x, y in itertools.product('a', string.ascii_lowercase[1:])] +
+                   [x + y for x, y in itertools.product('b', string.ascii_lowercase[:9])]):
+            print('uploading {}'.format(CSV_FILE_PREFIX + fn))
+            bq_client.reconnect_client()
+            bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                        auto_detect_schema=False,
+                                        schema=None, skip_rows=0, append_to_table=True)
+
+    print('Done')
+
+    '''
+    expecting 69,884,092 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace 
+table alert_2019.alt_com_action
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_jittered_s,
+    line_s,
+    cm_ct_owner_id_s,
+    action_taken_c_s,
+    contact_date_time_jittered
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_jittered_s = '' then NULL else cast(alt_csn_id_jittered_s as INT64) end as alt_csn_id_jittered,
+case when line_s = '' then NULL else cast(line_s as INT64) end as line,
+case when cm_ct_owner_id_s = '' then NULL else cast(cm_ct_owner_id_s as INT64) end as cm_ct_owner_id,
+case when action_taken_c_s = '' then NULL else cast(action_taken_c_s as INT64) end as action_taken_c,
+cast(contact_date_time_jittered as DATE) as contact_date_jittered
+from alert_2019.alt_com_action;
+    '''
diff --git a/scripts/GoogleCloud/BQ/upload_alt_drug_allergy.py b/scripts/GoogleCloud/BQ/upload_alt_drug_allergy.py
new file mode 100644
index 00000000..b788b5f2
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/upload_alt_drug_allergy.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+CSV_FILE_PREFIX = '/path/to/alt_drug_allergy_020320_'
+DATASET_NAME = 'alert_2019'
+TABLE_NAME = 'alt_drug_allergy'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_id_jittered', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('alt_csn_id_jittered', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('medication_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('allergen_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('allergy_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('drug_intrct_lvl_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('cm_ct_owner_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('inact_ingre_ind_yn', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('inact_review_yn', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alert_root_aller_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('alert_med_class_id', 'INT64', 'NULLABLE', None, ())]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('anon_id', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_id_jittered_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('alt_csn_id_jittered_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('medication_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('allergen_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('allergy_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('drug_intrct_lvl_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('cm_ct_owner_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('inact_ingre_ind_yn', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('inact_review_yn', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alert_root_aller_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('alert_med_class_id_s', 'STRING', 'NULLABLE', None, ())]
+
+
+def load_alert_table(csv_path):
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=UPLOAD_TABLE_SCHEMA, skip_rows=1)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    - removed trailing line in vim
+    
+    split every 2 mln lines:
+    split -l 2000000 alt_drug_allergy_020320.csv alt_drug_allergy_020320_
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading {}aa'.format(CSV_FILE_PREFIX))
+        load_alert_table(CSV_FILE_PREFIX + 'aa')
+        for fn in ([x + y for x, y in itertools.product('a', string.ascii_lowercase[1:8])]):
+            print('uploading {}'.format(CSV_FILE_PREFIX + fn))
+            bq_client.reconnect_client()
+            bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, CSV_FILE_PREFIX + fn,
+                                        auto_detect_schema=False,
+                                        schema=None, skip_rows=0, append_to_table=True)
+
+    print('Done')
+
+    '''
+    expecting 15,734,195 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace 
+table alert_2019.alt_drug_allergy
+as
+select * except(
+    alt_id_jittered_s,
+    alt_csn_id_jittered_s,
+    medication_id_s,
+    allergen_id_s,
+    allergy_id_s,
+    drug_intrct_lvl_c_s,
+    cm_ct_owner_id_s,
+    alert_root_aller_id_s,
+    alert_med_class_id_s
+),
+case when alt_id_jittered_s = '' then NULL else cast(alt_id_jittered_s as INT64) end as alt_id_jittered,
+case when alt_csn_id_jittered_s = '' then NULL else cast(alt_csn_id_jittered_s as INT64) end as alt_csn_id_jittered,
+case when medication_id_s = '' then NULL else cast(medication_id_s as INT64) end as medication_id,
+case when allergen_id_s = '' then NULL else cast(allergen_id_s as INT64) end as allergen_id,
+case when allergy_id_s = '' then NULL else cast(allergy_id_s as INT64) end as allergy_id,
+case when drug_intrct_lvl_c_s = '' then NULL else cast(drug_intrct_lvl_c_s as INT64) end as drug_intrct_lvl_c,
+case when cm_ct_owner_id_s = '' then NULL else cast(cm_ct_owner_id_s as INT64) end as cm_ct_owner_id,
+case when alert_root_aller_id_s = '' then NULL else cast(alert_root_aller_id_s as INT64) end as alert_root_aller_id,
+case when alert_med_class_id_s = '' then NULL else cast(alert_med_class_id_s as INT64) end as alert_med_class_id
+from alert_2019.alt_drug_allergy;
+    '''
diff --git a/scripts/GoogleCloud/BQ/upload_culture_sensitivity.py b/scripts/GoogleCloud/BQ/upload_culture_sensitivity.py
new file mode 100644
index 00000000..e47d1880
--- /dev/null
+++ b/scripts/GoogleCloud/BQ/upload_culture_sensitivity.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python2
+
+import os
+import logging
+import itertools
+import string
+import LocalEnv     # used for setting GOOGLE_APPLICATION_CREDENTIALS
+
+from medinfo.db.bigquery import bigQueryUtil
+from google.cloud import bigquery
+
+CSV_FILE = '/path/to/culture_sensitivity_021320.csv'
+DATASET_NAME = 'test_dataset'
+TABLE_NAME = 'culture_sensitivity'
+FINAL_TABLE_SCHEMA = [bigquery.SchemaField('rit_uid', 'STRING', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('order_proc_id_coded', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('line', 'INT64', 'REQUIRED', None, ()),
+                      bigquery.SchemaField('order_time_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('result_time_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sens_obs_inst_tm_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sens_anl_inst_tm_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('description', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sens_organism_sid', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('organism', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('antibiotic', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('suscept', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sensitivity_value', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sensitivity_units', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sens_status_c', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('sens_ref_range', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('antibio_lnc_id', 'INT64', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('specimen_source', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('specimen_type', 'STRING', 'NULLABLE', None, ()),
+                      bigquery.SchemaField('source', 'STRING', 'NULLABLE', None, ()),
+                      ]
+
+# Final schema is what we want at the end, however, regexp used to process the csv can't handle matching more than 9 fragments (\1 - \9).
+# So upload everything as string and process in bigquery - this will take care of string to int and datetime to date conversions
+UPLOAD_TABLE_SCHEMA = [bigquery.SchemaField('rit_uid', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('order_proc_id_coded_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('line_s', 'STRING', 'REQUIRED', None, ()),
+                       bigquery.SchemaField('order_time_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('result_time_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sens_obs_inst_tm_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sens_anl_inst_tm_jittered', 'DATETIME', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('description', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sens_organism_sid', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('organism', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('antibiotic', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('suscept', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sensitivity_value', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sensitivity_units', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sens_status_c_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('sens_ref_range', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('antibio_lnc_id_s', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('specimen_source', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('specimen_type', 'STRING', 'NULLABLE', None, ()),
+                       bigquery.SchemaField('source', 'STRING', 'NULLABLE', None, ()),
+                       ]
+
+
+def load_alert_table(csv_path):
+    assert 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ, 'GOOGLE_APPLICATION_CREDENTIALS is not set.'
+
+    bq_client.load_csv_to_table(DATASET_NAME, TABLE_NAME, csv_path, auto_detect_schema=False,
+                                schema=UPLOAD_TABLE_SCHEMA, skip_rows=1)
+
+
+if __name__ == '__main__':
+    logging.basicConfig()
+
+    '''
+    No preprocessing necessary
+    '''
+
+    upload = input('Upload? ("y"/"n"): ')
+    bq_client = bigQueryUtil.BigQueryClient()
+    if upload == 'Y' or upload == 'y':
+        print('uploading {}'.format(CSV_FILE))
+        load_alert_table(CSV_FILE)
+
+    print('Done')
+
+    '''
+    expecting 2,110,392 lines from original table
+    '''
+
+    '''
+    Conversion script in SQL:
+create or replace 
+table test_dataset.culture_sensitivity
+as
+select * except(
+    order_proc_id_coded_s,
+    line_s,
+    sens_status_c_s,
+    antibio_lnc_id_s
+),
+case when order_proc_id_coded_s = '' then NULL else cast(order_proc_id_coded_s as INT64) end as order_proc_id_coded,
+case when line_s = '' then NULL else cast(line_s as INT64) end as line,
+case when sens_status_c_s = '' then NULL else cast(sens_status_c_s as INT64) end as sens_status_c,
+case when antibio_lnc_id_s = '' then NULL else cast(antibio_lnc_id_s as INT64) end as antibio_lnc_id,
+timestamp(order_time_jittered, 'America/Los_Angeles') as order_time_jittered_utc,
+timestamp(result_time_jittered, 'America/Los_Angeles') as result_time_jittered_utc,
+timestamp(sens_obs_inst_tm_jittered, 'America/Los_Angeles') as sens_obs_inst_tm_jittered_utc,
+timestamp(sens_anl_inst_tm_jittered, 'America/Los_Angeles') as sens_anl_inst_tm_jittered_utc
+from test_dataset.culture_sensitivity;
+    '''
diff --git a/scripts/GoogleCloud/Sheets/sheets_to_df.py b/scripts/GoogleCloud/Sheets/sheets_to_df.py
new file mode 100644
index 00000000..2da1f8aa
--- /dev/null
+++ b/scripts/GoogleCloud/Sheets/sheets_to_df.py
@@ -0,0 +1,77 @@
+'''
+for JSON file, go to https://console.developers.google.com/apis/credentials for your project
+
+useful references:
+https://towardsdatascience.com/how-to-access-google-sheet-data-using-the-python-api-and-convert-to-pandas-dataframe-5ec020564f0e
+https://developers.google.com/sheets/api/samples/
+'''
+
+
+import pickle
+import os.path
+from googleapiclient.discovery import build
+from google_auth_oauthlib.flow import InstalledAppFlow
+from google.auth.transport.requests import Request
+import pandas as pd
+
+
+class GoogleSheetsConnect:
+    '''
+    modified from Google Sheets API example
+    '''
+
+    def __init__(self, json_file):
+        # Authenticate and connect
+
+        # If modifying these scopes, delete the file token.pickle.
+        SCOPES = ['https://www.googleapis.com/auth/spreadsheets.readonly']
+
+        creds = None
+        # The file token.pickle stores the user's access and refresh tokens, and is
+        # created automatically when the authorization flow completes for the first
+        # time.
+        if os.path.exists('token.pickle'):
+            with open('token.pickle', 'rb') as token:
+                creds = pickle.load(token)
+        # If there are no (valid) credentials available, let the user log in.
+        if not creds or not creds.valid:
+            if creds and creds.expired and creds.refresh_token:
+                creds.refresh(Request())
+            else:
+                flow = InstalledAppFlow.from_client_secrets_file(
+                    json_file,
+                    SCOPES)
+                creds = flow.run_local_server()
+            # Save the credentials for the next run
+            with open('token.pickle', 'wb') as token:
+                pickle.dump(creds, token)
+
+        self.service = build('sheets', 'v4', credentials=creds)
+
+    def get_sheet(self, sheet_id, range_name):
+        # Get sheet result
+
+        sheet_hook = self.service.spreadsheets()
+        sheet_result = list(sheet_hook.values()).get(spreadsheetId=sheet_id, range=range_name).execute()
+        return sheet_result
+
+    def get_df(self, sheet_result, verbose=False, first_line_header=True):
+        # Get DF from sheet result
+
+        if first_line_header:
+            # Assumes first line is headers
+            headers = sheet_result.get('values', [])[0]
+            values = sheet_result.get('values', [])[1:]
+        else:
+            values = sheet_result.get('values', [])
+            headers = list(range(len(sheet_result.get('values', [])[0])))
+
+        df = pd.DataFrame()
+
+        for i, series_name in enumerate(headers):
+            if verbose:
+                print('Preparing {}/{} {}'.format(i + 1, len(headers), series_name))
+            series_data = [row[i] for row in values]
+            df[series_name] = series_data
+
+        return df
diff --git a/scripts/GoogleCloud/Sheets/test_sheets_to_df.py b/scripts/GoogleCloud/Sheets/test_sheets_to_df.py
new file mode 100644
index 00000000..beefcc7e
--- /dev/null
+++ b/scripts/GoogleCloud/Sheets/test_sheets_to_df.py
@@ -0,0 +1,28 @@
+from pathlib import Path
+
+from scripts.GoogleCloud.Sheets.sheets_to_df import GoogleSheetsConnect
+
+
+def test_creating_df_from_sheets(json_path, sheet_id, range_name):
+    p = Path(json_path)
+    assert p.exists(), json_path + ' does not exist.'
+
+    sheet_connect = GoogleSheetsConnect(json_path)
+
+    sheet_result = sheet_connect.get_sheet(sheet_id, range_name)
+
+    df = sheet_connect.get_df(sheet_result, verbose=True, first_line_header=False)
+
+    print(df)
+
+    return
+
+
+if __name__ == '__main__':
+    json = input('Enter json_path: ')
+
+    # The ID and range of a sample spreadsheet.
+    SAMPLE_SPREADSHEET_ID = '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms'
+    SAMPLE_RANGE_NAME = 'Class Data!A2:E'
+
+    test_creating_df_from_sheets(json, SAMPLE_SPREADSHEET_ID, SAMPLE_RANGE_NAME)
\ No newline at end of file
diff --git a/scripts/GoogleCloud/StrokeCohort/ministudy.sql b/scripts/GoogleCloud/StrokeCohort/ministudy.sql
new file mode 100644
index 00000000..3e84e846
--- /dev/null
+++ b/scripts/GoogleCloud/StrokeCohort/ministudy.sql
@@ -0,0 +1,10 @@
+# mini study: 
+# look at ordersets 
+
+# create a cohort who recieved a very specific order set 
+
+SELECT rit_uid, ss_sg_name, pat_enc_csn_id_coded, ss_section_name, protocol_name  	
+FROM datalake_47618.med_orderset
+WHERE ss_sg_name in ( 'IV Thrombolytic Therapy Stroke')
+
+# 
diff --git a/scripts/GoogleCloud/StrokeCohort/readme.md b/scripts/GoogleCloud/StrokeCohort/readme.md
new file mode 100644
index 00000000..1c01b142
--- /dev/null
+++ b/scripts/GoogleCloud/StrokeCohort/readme.md
@@ -0,0 +1,46 @@
+[Google Big Query Stroke](https://console.cloud.google.com/bigquery?project=mining-clinical-decisions&j=bq:US:bquxjob_341287ce_16997f447ea&page=queryresults)
+
+## Diagnostic Pathways 
+
+
+
+
+## Emergency Department 
+
+SQL Query 2 <br/>
+		- given those people describe them  <br/>
+		- look at diagnosis codes (top 5 diagnosis codes)  <br/>
+		- demographics <br/>
+		- common prescribed 24 hours <br/>
+		- top 10 lab tests (lab_result)  <br/>
+
+##
+Patient can have multiple diagnoses in one encounter, though one is usally designated "Primary" <br/>
+
+
+Make Med list looks like specific medications instead of categories <br/>
+
+Looks like you got the top ten lab results, but many of these (sodium, glucose, creatinine, potassium, etc.) come from a single panel order (Basic metabolic panel). <br/>
+
+Go to the Order_Proc table instead to look for most common orders to type laboratory and imaging <br/>
+
+All of the above, mo' better if can report the counts as well as the "baseline" counts (how often that item occurs for any random admission to the hospital) so that you can calculate relative risks / lift / interest /TF-IDF.  <br/>
+
+Consult Song <br/>
+
+## Communicating with Susan Weber to Get Data 
+
+#### April 15, 2019 
+
+[ ]	Figure out Timestamp for google query (https://stackoverflow.com/questions/38837868/add-five-hours-to-timestamp-field)
+
+
+#### BIG QUERY CHEAT SHEET 
+
+- TIMESTAMP 
+
+/*
+select TIMESTAMP(effective_time_jittered) as timestamp, TIMESTAMP_ADD(TIMESTAMP(effective_time_jittered), INTERVAL 60 MINUTE) as one_hour_later
+from `datalake_47618_sample.adt`
+*/
+
diff --git a/scripts/GoogleCloud/StrokeCohort/stroke_aggregate.R b/scripts/GoogleCloud/StrokeCohort/stroke_aggregate.R
new file mode 100644
index 00000000..f8f03cb2
--- /dev/null
+++ b/scripts/GoogleCloud/StrokeCohort/stroke_aggregate.R
@@ -0,0 +1,145 @@
+library(bigrquery)
+library(DBI)
+library(dplyr)
+library(lubridate)
+library(stringr)
+library(data.table)
+library(ggplot2)
+
+path <- "/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read"
+setwd(path)
+
+# Assigning GCP Project Name
+project <- "mining-clinical-decisions" # put your project ID here
+
+#https://console.cloud.google.com/bigquery?project=mining-clinical-decisions&page=savedqueries&sq=103208666750:607c18a7b9254ec4b1543533d242d46d
+
+# Creating DBI connection to Google BigQuery 1
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+# converting a date to numeric from bq string date
+
+date_to_numeric_val <- function(date_column){
+  day <- str_split_fixed(date_column, " ",2)[,1]
+  time <- str_split_fixed(date_column, " ",2)[,2]
+  year <- str_split_fixed(day, "-", 3)[,1]
+  month <- str_split_fixed(day, "-", 3)[,2]
+  day_time <- str_split_fixed(day, "-", 3)[,3]
+  numeric_date <- paste0(year,month,day_time)
+  return(numeric_date)  
+}
+
+df_convert_date_to_numeric <- function(df){
+  df$date_num <- date_to_numeric_val(df$date)
+  return(df)
+}
+# ------------------------------------------------------
+# get number of rows through dim()
+# accepts dataframe as object
+get_row <- function(df){
+  rows <- dim(df)[1]
+  return(rows)
+}
+
+# write a function to remove duplicates in lapply 
+remove_duplicate <- function(dt){
+  dtx <- dt[!duplicated(dt), ]
+  return(dtx)
+}
+
+
+
+
+aggregate_event <- function(df){
+  df$sub_date<- as.POSIXct(df$order_time_jittered,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  testing <- aggregate( df , by = list(df$Day) , length )
+  return(testing)
+}
+
+aggregate_name <- function(dfx){
+  require(dplyr)
+  dt <- dfx %>% select(Group.1, order_type) 
+  return(dt)
+}
+
+list_aggregate_rename <- function(listx){
+  # accepts list 
+  t <- names(listx)
+  
+}
+
+bargg <- function(df){
+  p4 <- ggplot() + geom_bar(aes(y = order_type, x = as.Date(Group.1), fill = order_event), data = df,
+                            stat="identity")
+  p4
+}
+
+bargg.02 <- function(df){
+  p4 <- ggplot() + geom_bar(aes(y = order_type, 
+                                x = as.Date(Group.1), 
+                                fill = order_event), 
+                                data = df,
+                                stat="identity") + 
+                                xlab("Date") +
+                                ylab("Count") +
+                                ggtitle( paste0("Count of Stroke Orders Type: ")
+                                        ) +
+                                theme(plot.title = element_text(hjust = 0.5))
+  p4
+}
+
+convert_aggregate<- function(df){
+  df$sub_date<- as.POSIXct(df$order_time_jittered,format="%Y-%m-%d %H:%M:%S")
+  df$Day <- as.character( round(df$sub_date , "day" ) )
+  day_split <- split(df, df$order_type)
+  df_aggregate_order <- lapply(day_split, aggregate_event)
+  df_aggregate <- lapply(df_aggregate_order, aggregate_name)
+  event <- bind_rows(df_aggregate, .id = "order_event")
+  return(event)
+}
+
+#--------------------------------------------------------
+
+# -------------------------------------------------------
+
+# Big Computation: 
+# Goal Pull all Referral and Save 
+
+# Sql Call that Pulls the All the Patients that had a description of referral
+sql <- "select * from `clinical_inpatient.stroke_ministudy`"
+
+# Project to To Use SQL Query with BQ 
+tb <- bq_project_query(project, sql)
+
+# Downloads the data to local environment 
+tb.dtx <- bq_table_download(tb)
+
+tb.dtx2 <- tb.dtx[!duplicated(tb.dtx), ]
+tb.dtx2$date <- tb.dtx2$contact_date_jittered
+tb.dt <- df_convert_date_to_numeric(tb.dtx2)
+
+# Splits the Data into different description 
+# Each entry of list is a different referral 
+
+dt.listx <- split(tb.dt, tb.dt$jc_uid)
+dt.list <- lapply(dt.listx, remove_duplicate)
+
+# Returns the number of rows in each referral
+num_rows <- lapply(dt.list, get_row)
+
+agg_list <- lapply(dt.list, convert_aggregate)
+#cplot <- lapply(agg_list, bargg)
+cplot.02 <- lapply(agg_list, bargg.02)
+
+
+cplot.02$JCcb6780
+cplot.02$JCcb69b7
+cplot.02$JCcb92a7
+cplot.02$JCcbf918
+cplot.02$JCcc820c
diff --git a/scripts/GoogleCloud/__init__.py b/scripts/GoogleCloud/__init__.py
new file mode 100644
index 00000000..9b45adaa
--- /dev/null
+++ b/scripts/GoogleCloud/__init__.py
@@ -0,0 +1,2 @@
+# Nothing much for now, just enable importing of various modules as a common package
+
diff --git a/scripts/GoogleCloud/cloud_sdk/readme.md b/scripts/GoogleCloud/cloud_sdk/readme.md
new file mode 100644
index 00000000..a6f8c072
--- /dev/null
+++ b/scripts/GoogleCloud/cloud_sdk/readme.md
@@ -0,0 +1,15 @@
+#### Cloud SDK 
+
+#### [Link to Documentation](https://cloud.google.com/sdk/docs/quickstart-macos)
+
+#### [Python API (BQ Github)](https://github.com/googleapis/google-cloud-python/tree/master/bigquery)
+
+### High Level Instruction: 
+
+1) install cloud sdk 
+
+2) authenticate your service and project 
+
+3) python (may have to specify schema of table) 
+
+
diff --git a/scripts/GoogleCloud/readme.md b/scripts/GoogleCloud/readme.md
new file mode 100644
index 00000000..fa6ce59e
--- /dev/null
+++ b/scripts/GoogleCloud/readme.md
@@ -0,0 +1,6 @@
+:: TODO :: 12/29/2018 --
+ 
+ document and implement
+ 
+# Big Query 
+# Bit Bucket
diff --git a/scripts/GoogleCloud/specialty_referral/cardiology_referral.sql b/scripts/GoogleCloud/specialty_referral/cardiology_referral.sql
new file mode 100644
index 00000000..128c1e3d
--- /dev/null
+++ b/scripts/GoogleCloud/specialty_referral/cardiology_referral.sql
@@ -0,0 +1,98 @@
+/*
+Sub-Aim: 
+Estimate patient days and in-person specialty visits consumed while patients await a specialty visits that could be addressed through non-in-person consultations.
+
+Objective: Quantify the relative potential opportunity to expedite patient care and reduce specialty clinic need with virtual (non-in-person) clinical consultation options.
+Cohort / Participants: All available pairs of requests for specialty consultation and a respective specialty clinic visit, which excludes cases where the visit includes a billable procedure (e.g., surgical or counseling) are likely not reproducible in the primary referring clinic even with virtual consultation.
+
+Design: Simple descriptive analysis, summing up the outcome quantities, stratified by specialty type and referral diagnosis. For referrals that yield a single,  specialty clinic visit, count the number of days between the referral request and specialty visit towards the total patient days and the one specialty clinic visit. For referrals that yield a specialty clinic visit followed by subsequent visits to the same specialty clinic (e.g., where initial diagnostic workup must be ordered in the first visit, and then followed up for treatment in the subsequent visits), this implies subsequent specialty management was required either way, so the delay until initial specialty care cannot be directly eliminated. This potentially could still have been expedited if the diagnostic workup was initiated in the primary referring clinic. In these cases, count the number of days between the first specialty clinic visit and the subsequent one towards the total patient days and one extra specialty clinic visit consumed.
+
+Potential problems and alternatives: Some clinical orders in specialty clinics would not be reasonable to expect a primary referring clinician to reproduce, even with guidance (e.g., chemotherapy). In addition to excluding cases that yield a billable surgical or counseling procedure, we will exclude cases that generate a clinical order that essentially never arises from within a primary care clinic.
+
+Expected impacts/Products: Estimates of the potential opportunity to benefit from virtual consult systems, and a methodology for estimating wait times for specialty/diagnosis referrals that could further be incorporated into integrated decision support systems.
+*/
+
+# Create a Population of Referral: 
+
+# resulting Table: z_sub_aim_referral_table
+
+/*
+      SELECT
+        a.jc_uid, a.pat_enc_csn_id_coded, a.ordering_date_jittered, a.description,
+        b.appt_time_jittered, b.appt_type, b.enc_type, b.visit_type, b.department_id
+      FROM
+        `datalake_47618.order_proc` as a
+      INNER JOIN 
+        `datalake_47618.encounter` as b
+      ON
+        a.pat_enc_csn_id_coded = b.pat_enc_csn_id_coded
+      WHERE 
+        a.description like 'REFERRAL%' 
+*/
+
+# Create a count of each referral 
+
+# resulting table: z1_sub_aim_referral_table
+
+/*
+    select count(description) count, description  
+    from  `datalake_47618.z_subaim_referral_table` 
+    group by description 
+    order by count desc
+*/
+
+# Create A subcohort study on Cardiology 
+
+# resulting table: z2_subaim_referral_table
+
+/*
+    select *
+    from  `datalake_47618.z_subaim_referral_table`
+    where description like "REFERRAL_TO_CARDIOLOGY"
+    and appt_time_jittered > '2014' 
+*/ 
+
+# Create a Clinical Department Timeline for Cardiology Referral Patients 
+
+# resulting table: z3_subaim_referral_table
+
+/*
+select jc_uid, pat_enc_csn_id_coded, appt_time_jittered as date, department_id, visit_type
+from `datalake_47618.encounter`
+WHERE jc_uid in 
+  (
+  select distinct(jc_uid) jc_uid 
+  from `datalake_47618.z2_subaim_referral_table`
+  )
+*/
+
+
+# Create a Clinical Department Timeline for Cardiology Referral Patients joined on description post 2014
+
+# resulting table: z4_subaim_referral_table
+
+/*
+      SELECT
+        a.description,
+        b.date, b.visit_type, b.jc_uid, b.pat_enc_csn_id_coded, b.department_id
+      FROM `datalake_47618.order_proc` as a
+      INNER JOIN `datalake_47618.z3_sub_aim_referral_table` as b
+      ON a.pat_enc_csn_id_coded = b.pat_enc_csn_id_coded
+      where date > '2014'
+*/
+
+# New Event Timeline for New Patient from a Cohort of Cardiology 
+
+# resulting table: z5_subaim_referral_table
+/*
+select * 
+from `datalake_47618.z4_subaim_referral_table`
+where visit_type like "NEW PATIENT%"
+*/
+      
+# Create Department Map to ID Table 
+
+# resulting table: z6_subaim_referral_table
+
+select department_id, department_name 
+from `datalake_47618.dep_map`
diff --git a/scripts/GoogleCloud/specialty_referral/cardiology_referral_script.R b/scripts/GoogleCloud/specialty_referral/cardiology_referral_script.R
new file mode 100644
index 00000000..3732f291
--- /dev/null
+++ b/scripts/GoogleCloud/specialty_referral/cardiology_referral_script.R
@@ -0,0 +1,127 @@
+library(bigrquery)
+library(DBI)
+library(dplyr)
+library(lubridate)
+
+# Assigning GCP Project Name
+project <- "mining-clinical-decisions" # put your project ID here
+
+# Creating DBI connection to Google BigQuery 
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+# converting a date to numeric from bq string date
+
+date_to_numeric_val <- function(date_column){
+  day <- str_split_fixed(date_column, " ",2)[,1]
+  time <- str_split_fixed(date_column, " ",2)[,2]
+  year <- str_split_fixed(day, "-", 3)[,1]
+  month <- str_split_fixed(day, "-", 3)[,2]
+  day_time <- str_split_fixed(day, "-", 3)[,3]
+  numeric_date <- paste0(year,month,day_time)
+  return(numeric_date)  
+}
+
+df_convert_date_to_numeric <- function(df){
+  df$date_num <- date_to_numeric_val(df$date)
+  return(df)
+}
+# ------------------------------------------------------
+
+# Listing Datatables 
+data_tables <- dbListTables(con)
+
+# Creating Connection to Cardiology Referral Patients 
+cardiology_referral <- tbl(con, "z2_subaim_referral_table")
+
+# Creating Connection to Event Table of Cardiology Patients with Description New Patient 
+cardiology_referral_event <- tbl(con, "z5_subaim_referral_table")
+
+department_map <- tbl(con, "dep_map")
+
+
+# reassigning variable names for ease of use
+# pulls data from GCP BQ (download)
+cdf <- as_tibble(cardiology_referral)
+cdn <- as_tibble(cardiology_referral_event)
+dep.df <- as_tibble(department_map)
+
+# View Data 
+
+# Compare Population between both data sets  
+a1 <- unique(cdf$jc_uid)  
+a2 <- unique(cdn$jc_uid)
+# write test 
+
+# Creating an Intersecting Patient Population 
+same_cohort <- intersect(a1,a2)
+# same_cohort2 <- intersect(a2,a1)
+# write test 
+
+cdf2 <- cdf %>% filter(jc_uid %in% same_cohort)
+cdn2 <- cdn %>% filter(jc_uid %in% same_cohort)
+
+cdf2$date <- cdf2$appt_time_jittered
+
+
+cdf3 <- df_convert_date_to_numeric(cdf2)
+cdn3 <- df_convert_date_to_numeric(cdn2)
+
+cdf4 <- cdf3 %>% select(jc_uid, description, pat_enc_csn_id_coded, description, visit_type, department_id, date_num) %>% arrange(date_num)
+cdn4 <- cdn3 %>% select(jc_uid, description, pat_enc_csn_id_coded, description, visit_type, department_id, date_num) %>% arrange(date_num)
+
+cdf5 <- merge(cdf4, dep.df)
+cdn5 <- merge(cdn4, dep.df)
+
+cardiology_list <- c("Cardiology", "Cardiothoracic Surgery", "Cardiology Lab", "Cardiovascular Medicine", "Vascular Surgery")
+cdn6 <- cdn5 %>% filter(specialty %in% cardiology_list)
+
+# create new dataframe 
+dt2 <- rbind(cdf5, cdn6)
+
+# create a list from  ID 
+patient_split <- split(dt2, dt2$jc_uid)
+
+# return a dataframe only if the rows is greater one 
+get_row <- function(c){
+  t <- dim(c)[1]
+  return(t)
+}
+
+cond <- sapply(patient_split, function(x) get_row(x) > 1)
+rc <- patient_split[cond]
+
+
+sort_date_num <- function(df){
+  return(df %>% arrange(date_num))
+}
+
+rc2 <- lapply(rc, sort_date_num)
+
+# update function 
+# needs to cover instance where the first occurence is not the same date 
+
+visit_difference <- function(df1){
+  df1$dateval <- as.numeric(df1$date_num)
+  dfx <- df1 %>% filter(description == "REFERRAL TO CARDIOLOGY")
+  mini_df <- dfx[1,] 
+  initial_date <- as.numeric(mini_df$date_num[1]) 
+  mini_df2 <- df1 %>% filter(description != "REFERRAL TO CARDIOLOGY") %>% filter(dateval > initial_date)
+  df3 <- rbind(mini_df, mini_df2)  
+  df3$date_num2 <- ymd(df3$date_num)
+  return(df3$date_num2[2] - df3$date_num2[1])
+}
+
+
+
+vis_list <- lapply(rc2, visit_difference)
+t <- do.call(rbind.data.frame, vis_list)
+colnames(t) <- "visit_difference"
+t2 <- t %>% filter(t < 365)
+summary(t2)
+
+
diff --git a/scripts/GoogleCloud/specialty_referral/gastroenterology_referral.sql b/scripts/GoogleCloud/specialty_referral/gastroenterology_referral.sql
new file mode 100644
index 00000000..9b04881e
--- /dev/null
+++ b/scripts/GoogleCloud/specialty_referral/gastroenterology_referral.sql
@@ -0,0 +1,148 @@
+/*
+Sub-Aim: 
+Estimate patient days and in-person specialty visits consumed while patients await a specialty visits that could be addressed through non-in-person consultations.
+
+Objective: Quantify the relative potential opportunity to expedite patient care and reduce specialty clinic need with virtual (non-in-person) clinical consultation options.
+Cohort / Participants: All available pairs of requests for specialty consultation and a respective specialty clinic visit, which excludes cases where the visit includes a billable procedure (e.g., surgical or counseling) are likely not reproducible in the primary referring clinic even with virtual consultation.
+
+Design: Simple descriptive analysis, summing up the outcome quantities, stratified by specialty type and referral diagnosis. For referrals that yield a single,  specialty clinic visit, count the number of days between the referral request and specialty visit towards the total patient days and the one specialty clinic visit. For referrals that yield a specialty clinic visit followed by subsequent visits to the same specialty clinic (e.g., where initial diagnostic workup must be ordered in the first visit, and then followed up for treatment in the subsequent visits), this implies subsequent specialty management was required either way, so the delay until initial specialty care cannot be directly eliminated. This potentially could still have been expedited if the diagnostic workup was initiated in the primary referring clinic. In these cases, count the number of days between the first specialty clinic visit and the subsequent one towards the total patient days and one extra specialty clinic visit consumed.
+
+Potential problems and alternatives: Some clinical orders in specialty clinics would not be reasonable to expect a primary referring clinician to reproduce, even with guidance (e.g., chemotherapy). In addition to excluding cases that yield a billable surgical or counseling procedure, we will exclude cases that generate a clinical order that essentially never arises from within a primary care clinic.
+
+Expected impacts/Products: Estimates of the potential opportunity to benefit from virtual consult systems, and a methodology for estimating wait times for specialty/diagnosis referrals that could further be incorporated into integrated decision support systems.
+*/
+
+# Create a Population of Referral: 
+
+# resulting Table: z_sub_aim_referral_table
+
+/*
+      SELECT
+        a.jc_uid, a.pat_enc_csn_id_coded, a.ordering_date_jittered, a.description,
+        b.appt_time_jittered, b.appt_type, b.enc_type, b.visit_type, b.department_id
+      FROM
+        `datalake_47618.order_proc` as a
+      INNER JOIN 
+        `datalake_47618.encounter` as b
+      ON
+        a.pat_enc_csn_id_coded = b.pat_enc_csn_id_coded
+      WHERE 
+        a.description like 'REFERRAL%' 
+*/
+
+# Create a count of each referral 
+
+# resulting table: z1_sub_aim_referral_table
+
+/*
+    select count(description) count, description  
+    from  `datalake_47618.z_subaim_referral_table` 
+    group by description 
+    order by count desc
+*/
+
+# Create A subcohort study on Cardiology 
+# resulting table: z2_subaim_referral_table_cardio 
+
+
+/*
+    select *
+    from  `datalake_47618.z_subaim_referral_table`
+    where description like "REFERRAL_TO_CARDIOLOGY"
+    and appt_time_jittered > '2014' 
+*/ 
+
+
+# Create A subcohort study on Gastroenterology 
+
+# resulting table: z2_subaim_referral_table_gastro
+
+/*
+    select *
+    from  `datalake_47618.z_subaim_referral_table`
+    where description like "REFERRAL_TO_GASTROENTEROLOGY"
+    and appt_time_jittered > '2014'
+*/
+
+
+
+# Create a Clinical Department Timeline for Cardiology Referral Patients 
+
+# resulting table: z3_subaim_referral_table
+
+/*
+select jc_uid, pat_enc_csn_id_coded, appt_time_jittered as date, department_id, visit_type
+from `datalake_47618.encounter`
+WHERE jc_uid in 
+  (
+  select distinct(jc_uid) jc_uid 
+  from `datalake_47618.z2_subaim_referral_table`
+  )
+*/
+
+# Create a Clinical Department Timeline for Gastroenterology Referral Patients 
+
+# resulting table: z3_subaim_referral_table_gastro
+
+/*
+select jc_uid, pat_enc_csn_id_coded, appt_time_jittered as date, department_id, visit_type
+from `datalake_47618.encounter`
+WHERE jc_uid in 
+  (
+  select distinct(jc_uid) jc_uid 
+  from `datalake_47618.z2_subaim_referral_table_gastro` 
+)
+*/
+
+# Create a Clinical Department Timeline for Cardiology Referral Patients joined on description post 2014
+
+# resulting table: z4_subaim_referral_table
+
+
+
+
+
+/*
+      SELECT
+        a.description,
+        b.date, b.visit_type, b.jc_uid, b.pat_enc_csn_id_coded, b.department_id
+      FROM `datalake_47618.order_proc` as a
+      INNER JOIN `datalake_47618.z3_sub_aim_referral_table` as b
+      ON a.pat_enc_csn_id_coded = b.pat_enc_csn_id_coded
+      where date > '2014'
+*/
+
+/*
+      SELECT
+        a.description,
+        b.date, b.visit_type, b.jc_uid, b.pat_enc_csn_id_coded, b.department_id
+      FROM `datalake_47618.order_proc` as a
+      INNER JOIN `datalake_47618.z3_subaim_referral_table_gastro` as b
+      ON a.pat_enc_csn_id_coded = b.pat_enc_csn_id_coded
+      where date > '2014'
+*/
+
+
+# New Event Timeline for New Patient from a Cohort of Cardiology 
+
+# resulting table: z5_subaim_referral_table
+/*
+select * 
+from `datalake_47618.z4_subaim_referral_table`
+where visit_type like "NEW PATIENT%"
+*/
+
+/*
+select * 
+from `datalake_47618.z4_subaim_referral_table_gastro`
+where visit_type like "NEW PATIENT%"
+ */
+ 
+# Create Department Map to ID Table 
+
+# resulting table: z6_subaim_referral_table
+
+/*
+select department_id, department_name 
+from `datalake_47618.dep_map`
+*/
diff --git a/scripts/GoogleCloud/specialty_referral/gastroenterology_referral_script.R b/scripts/GoogleCloud/specialty_referral/gastroenterology_referral_script.R
new file mode 100644
index 00000000..688cdbfa
--- /dev/null
+++ b/scripts/GoogleCloud/specialty_referral/gastroenterology_referral_script.R
@@ -0,0 +1,139 @@
+library(bigrquery)
+library(DBI)
+library(dplyr)
+library(lubridate)
+library(stringr)
+# Assigning GCP Project Name
+project <- "mining-clinical-decisions" # put your project ID here
+
+#https://console.cloud.google.com/bigquery?project=mining-clinical-decisions&page=savedqueries&sq=103208666750:607c18a7b9254ec4b1543533d242d46d
+
+# Creating DBI connection to Google BigQuery 1
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+# converting a date to numeric from bq string date
+
+date_to_numeric_val <- function(date_column){
+  day <- str_split_fixed(date_column, " ",2)[,1]
+  time <- str_split_fixed(date_column, " ",2)[,2]
+  year <- str_split_fixed(day, "-", 3)[,1]
+  month <- str_split_fixed(day, "-", 3)[,2]
+  day_time <- str_split_fixed(day, "-", 3)[,3]
+  numeric_date <- paste0(year,month,day_time)
+  return(numeric_date)  
+}
+
+df_convert_date_to_numeric <- function(df){
+  df$date_num <- date_to_numeric_val(df$date)
+  return(df)
+}
+# ------------------------------------------------------
+
+
+# Listing Datatables 
+data_tables <- dbListTables(con)
+
+# Creating Connection to Cardiology Referral Patients 
+cardiology_referral <- tbl(con, "z2_subaim_referral_table_gastro")
+
+# Creating Connection to Event Table of Cardiology Patients with Description New Patient 
+cardiology_referral_event <- tbl(con, "z5_subaim_referral_table_gastro")
+
+department_map <- tbl(con, "dep_map")
+
+
+# reassigning variable names for ease of use
+# pulls data from GCP BQ (download)
+cdf <- as_tibble(cardiology_referral)
+cdn <- as_tibble(cardiology_referral_event)
+dep.df <- as_tibble(department_map)
+
+# View Data 
+
+# Compare Population between both data sets  
+a1 <- unique(cdf$jc_uid)  
+a2 <- unique(cdn$jc_uid)
+# write test 
+
+# Creating an Intersecting Patient Population 
+same_cohort <- intersect(a1,a2)
+# same_cohort2 <- intersect(a2,a1)
+# write test 
+
+cdf2 <- cdf %>% filter(jc_uid %in% same_cohort)
+cdn2 <- cdn %>% filter(jc_uid %in% same_cohort)
+
+cdf2$date <- cdf2$appt_time_jittered
+
+## exploratory 
+# look at unique department places 
+visit_department <- unique(cdn2$department_id)
+list_of_department <- dep.df %>% filter(department_id %in% visit_department)
+unique_speciality <- unique(list_of_department$specialty)
+
+##
+cdf3 <- df_convert_date_to_numeric(cdf2)
+cdn3 <- df_convert_date_to_numeric(cdn2)
+
+cdf4 <- cdf3 %>% select(jc_uid, description, pat_enc_csn_id_coded, description, visit_type, department_id, date_num) %>% arrange(date_num)
+cdn4 <- cdn3 %>% select(jc_uid, description, pat_enc_csn_id_coded, description, visit_type, department_id, date_num) %>% arrange(date_num)
+
+cdf5 <- merge(cdf4, dep.df)
+cdn5 <- merge(cdn4, dep.df)
+
+#cardiology_list <- c("Cardiology", "Cardiothoracic Surgery", "Cardiology Lab", "Cardiovascular Medicine", "Vascular Surgery")
+
+cardiology_list <- c("Gastrointestinal", "Gastroenterology")
+cdn6 <- cdn5 %>% filter(specialty %in% cardiology_list)
+
+# create new dataframe 
+dt2 <- rbind(cdf5, cdn6)
+
+
+# create a list from  ID 
+patient_split <- split(dt2, dt2$jc_uid)
+
+# return a dataframe only if the rows is greater one 
+
+
+get_row <- function(c){
+  t <- dim(c)[1]
+  return(t)
+}
+cond <- sapply(patient_split, function(x) get_row(x) > 1)
+rc <- patient_split[cond]
+
+
+sort_date_num <- function(df){
+  return(df %>% arrange(date_num))
+}
+
+rc2 <- lapply(rc, sort_date_num)
+
+# update function 
+# needs to cover instance where the first occurence is not the same date 
+
+visit_difference <- function(df1){
+  df1$dateval <- as.numeric(df1$date_num)
+  dfx <- df1 %>% filter(description == "REFERRAL TO GASTROENTEROLOGY")
+  mini_df <- dfx[1,] 
+  initial_date <- as.numeric(mini_df$date_num[1]) 
+  mini_df2 <- df1 %>% filter(description != "REFERRAL TO GASTROENTEROLOGY") %>% filter(dateval > initial_date)
+  df3 <- rbind(mini_df, mini_df2)  
+  df3$date_num2 <- ymd(df3$date_num)
+  return(df3$date_num2[2] - df3$date_num2[1])
+}
+
+
+
+vis_list <- lapply(rc2, visit_difference)
+t <- do.call(rbind.data.frame, vis_list)
+colnames(t) <- "visit_difference"
+t2 <- t %>% filter(t < 365)
+summary(t2)
+
diff --git a/scripts/GoogleCloud/specialty_referral/nested_referral_script.R b/scripts/GoogleCloud/specialty_referral/nested_referral_script.R
new file mode 100644
index 00000000..b2fa69c6
--- /dev/null
+++ b/scripts/GoogleCloud/specialty_referral/nested_referral_script.R
@@ -0,0 +1,169 @@
+library(bigrquery)
+library(DBI)
+library(dplyr)
+library(lubridate)
+library(stringr)
+library(data.table)
+
+# Assigning GCP Project Name
+project <- "mining-clinical-decisions" # put your project ID here
+
+#https://console.cloud.google.com/bigquery?project=mining-clinical-decisions&page=savedqueries&sq=103208666750:607c18a7b9254ec4b1543533d242d46d
+
+# Creating DBI connection to Google BigQuery 1
+con <- dbConnect(
+  bigrquery::bigquery(),
+  project = "mining-clinical-decisions",
+  dataset = 'datalake_47618' ,
+  billing = project
+)
+
+# converting a date to numeric from bq string date
+
+date_to_numeric_val <- function(date_column){
+  day <- str_split_fixed(date_column, " ",2)[,1]
+  time <- str_split_fixed(date_column, " ",2)[,2]
+  year <- str_split_fixed(day, "-", 3)[,1]
+  month <- str_split_fixed(day, "-", 3)[,2]
+  day_time <- str_split_fixed(day, "-", 3)[,3]
+  numeric_date <- paste0(year,month,day_time)
+  return(numeric_date)  
+}
+
+df_convert_date_to_numeric <- function(df){
+  df$date_num <- date_to_numeric_val(df$date)
+  return(df)
+}
+# ------------------------------------------------------
+# get number of rows through dim()
+# accepts dataframe as object
+get_row <- function(df){
+  rows <- dim(df)[1]
+  return(rows)
+}
+
+path <- "/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read"
+setwd(path)
+
+
+#--------------------------------------------------------
+
+# --------------------------------------------------------------------------------------------------
+# Big Computation: 
+# Goal Pull all Referral and Save 
+
+# Sql Call that Pulls the All the Patients that had a description of referral
+sql <- "select * from `datalake_47618.z_subaim_referral_table` where appt_time_jittered > '2014' "
+
+# Project to To Use SQL Query with BQ 
+tb <- bq_project_query(project, sql)
+
+# Downloads the data to local environment 
+tb.dt <- bq_table_download(tb)
+
+# Splits the Data into different description 
+# Each entry of list is a different referral 
+referral_list <- split(tb.dt, tb.dt$description)
+
+# Returns the number of rows in each referral
+num_rows <- lapply(referral_list, get_row)
+
+# Returns the number of rows in each referral list df 
+row_t <- do.call(rbind.data.frame, num_rows)
+
+# write a function to get unique users from each dataframe 
+
+unique_patients <- function(df){
+  u.df <- unique(df$jc_uid)
+  return(u.df)
+}
+
+
+unique_patients <- lapply(referral_list, unique_patients)
+# [1] "JCd4c94e" "JCdce3e5" "JCcbd887" "JCe3e60c" "JCd2638b"
+# [6] "JCe659bd"
+
+# SAVED AS CSV FILE: BQ COMPUTING "LOCAL CACHE" 
+
+#   encounter <- tbl(con, "encounter")
+#   subaim_encounter <- tbl(con, "z_subaim_")
+#   encounter_df <- as_tibble(subaim_encounter)
+
+#   path <- "/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read"
+#   write.csv(encounter_df, "encounter.csv")
+
+encounter_df <- fread('encounter.csv')
+
+
+# <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
+# WRITE A FUNCTION TO FILTER BY ENCOUNTER 
+
+# takes the dataframe of 
+encounter_referral_unique <- function(unique.list, df){
+  return(df %>% filter(jc_uid %in% unique.list))
+}
+
+
+# maybe use future.apply 
+
+#t <- sys.time()
+encounter_by_referral <- lapply(unique_patients, encounter_referral_unique, df = encounter_df)
+#(c <- sys.time() - t)
+
+# need to bind two dataframes together in a list 
+
+## colnames(referral_list[[1]]) equals colnames(encounter_by_referral[[1]])
+# clean referral_list 
+# convert description to visit_type 
+clean_referral_list <- function(df){
+  df2 <- df %>% select(jc_uid, pat_enc_csn_id_coded, appt_time_jittered, department_id)
+  df2$visit_type <- df$description
+  return(df2)
+  
+  
+}
+
+# helper function to remove extra columns 
+clean_encounter_by_referral <- function(df){
+  return( df %>% select(jc_uid, pat_enc_csn_id_coded, appt_time_jittered, department_id, visit_type) )
+}
+
+# helper function to return visits that were NEW PATIENTS 
+new_patient_encounter_by_referral <- function(df){
+  ptn <- '^NEW.*'
+  ndx <- grep(ptn, df$visit_type, perl = T)
+  return(df[ndx,])
+}
+
+
+# removes columns and shortens referral list to match encounter by referall
+cat_referral_list <- lapply(referral_list, clean_referral_list)
+cat_trim_encounter_by_referral <- lapply(encounter_by_referral, clean_encounter_by_referral)
+cat_encounter_by_referral <- lapply(cat_trim_encounter_by_referral, new_patient_encounter_by_referral)
+
+unique_encounter_map <- Map(rbind, cat_referral_list, cat_encounter_by_referral)
+
+
+encounter_referral_patient_list <- lapply(unique_encounter_map, function(x) split(x, x$jc_uid))
+# reassignign name to something easier to write
+
+epl <- encounter_referral_patient_list
+
+path <-  "/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_read"
+
+# quiver path 
+setwd("/Users/jonc101/Box Sync/Jonathan Chiang's Files/sub_aim_referral")
+
+batch1 <- unique_encounter_map
+
+mapply(
+  write.csv,
+  x=unique_encounter_map, file=paste(names(unique_encounter_map), "csv", sep="."),
+  MoreArgs=list(row.names=FALSE, sep=",")
+)
+
+setwd(path)
+# make a referral directory:    
+
+
+
diff --git a/scripts/GoogleCloud/specialty_referral/readme.md b/scripts/GoogleCloud/specialty_referral/readme.md
new file mode 100644
index 00000000..ed444cc9
--- /dev/null
+++ b/scripts/GoogleCloud/specialty_referral/readme.md
@@ -0,0 +1,23 @@
+ ### Cardiology Referral Approximated Numbers Summary Table 
+ 
+ #### visit_difference
+ Min.   :  1.00  
+ 1st Qu.: 15.00  
+ Median : 32.00  
+ Mean   : 49.43  
+ 3rd Qu.: 57.00  
+ Max.   :364.00 
+
+
+ ### Gastroenterology Referral Approximated Numbers Summary Table 
+
+ #### visit_difference
+ Min.   :  1.00  
+ 1st Qu.: 18.25  
+ Median : 44.00  
+ Mean   : 70.29  
+ 3rd Qu.: 98.00  
+ Max.   :364.00 
+
+[Link to Google Cloud Drive for Intermediary Inputs](https://drive.google.com/drive/folders/1dKVN5yKF093c_EjZJjMyWrRVkoFG6zqn)
+
diff --git a/scripts/Heme-Stamp/__init__.py b/scripts/Heme-Stamp/__init__.py
new file mode 100644
index 00000000..5e54265e
--- /dev/null
+++ b/scripts/Heme-Stamp/__init__.py
@@ -0,0 +1,49 @@
+import os
+import json
+import orderInfo
+from datetime import date
+import cosmos
+import order_action
+
+def main():
+    with open(os.path.join(os.sys.path[0], "live_HS.confg"), "r") as creds_json_file:
+        credentials = json.load(creds_json_file)
+
+    order_date = date.today()
+
+    ENV = 'https://epic-ic-background.stanfordmed.org/Interconnect-Cloud/'
+    # CID taken out for privacy
+
+    a = cosmos.getcosmosbetweendate("2022-06-18", "2022-07-10")
+    # a = cosmos.getcosmosbydate("2022-06-19")
+
+    # a = cosmos.getcosmos()
+    FHIR_ID_set = set()
+    if len(a) > 0:
+        for i in range(len(a)):
+            if 'Error' not in a[i]['patient'].keys():
+                # print(a[i]['patient'])
+                # print("--------")
+                FHIR_ID_set.add(a[i]['patient']['FHIR ID'])
+
+    # phys_list = ['Rondeep Singh Brar, MD', 'David Joseph Iberri, MD', 'William Elias Shomali, MD']
+    phys_list = ['Rondeep Singh Brar', 'David Joseph Iberri', 'William Elias Shomali']
+    order_list = []
+    for FHIR_ID in FHIR_ID_set:
+        order = orderInfo.Order(credentials, FHIR_ID, order_date, ENV, CID)
+        order_dict, active_order = order()
+        # print("active_order: ", active_order)
+        # print(order_dict)
+        # print("-----------\n")
+        if order_dict["Physician"] in phys_list and active_order:
+            order_list.append(order_dict)
+    print(order_list)
+    for order_dict in order_list:
+        order_dict["Msg_sent"] = False
+        order_action.upload_order(order_dict)
+    #
+    # # we don't want to generate emails until all recent orders have been uploaded to bigquery
+    order_action.send_email()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/scripts/Heme-Stamp/cosmos.py b/scripts/Heme-Stamp/cosmos.py
new file mode 100644
index 00000000..581f8abd
--- /dev/null
+++ b/scripts/Heme-Stamp/cosmos.py
@@ -0,0 +1,174 @@
+import azure.cosmos.documents as documents
+import azure.cosmos.cosmos_client as cosmos_client
+import azure.cosmos.exceptions as exceptions
+from azure.cosmos.partition_key import PartitionKey
+import datetime
+import logging
+
+# import config
+settings = {
+    'host': 'https://machinelearningoutput.documents.azure.com:443/',
+    'master_key': 'LufjB7LUP4aRaDGIRs3NTTdIU440Gv1hlhVV7453HcL1UeJs3oXoj55lPsNmckzZMNEFXSu3oFTe2K3DsxLpZg==',
+    'database_id': 'machinelearningdb',
+    'container_id': 'machinelearningcontainer'
+}
+HOST = settings['host']
+MASTER_KEY = settings['master_key']
+DATABASE_ID = settings['database_id']
+CONTAINER_ID = settings['container_id']
+
+
+def query_items(container):
+    print('\nQuerying for an  Item by Partition Key\n')
+
+    # Including the partition key value of account_number in the WHERE filter results in a more efficient query
+    items = list(container.query_items(
+        query="SELECT * FROM c WHERE c.partitionKey = 'HemeStamp'",
+    ))
+    return items
+    # for item in items:
+    #    print(f"Item: {item}")
+
+
+def query_items_bydate(container, qdate):
+    print('\nQuerying for an  Item by Partition Key\n')
+
+    # Including the partition key value of account_number in the WHERE filter results in a more efficient query
+    items = list(container.query_items(
+        query=f"SELECT * FROM c WHERE c.partitionKey = 'HemeStamp' AND TimestampToDateTime(c._ts*1000) > '{qdate}'",
+    ))
+    return items
+
+
+def query_items_between_dates(container, date1, date2):
+    print('\nQuerying for an  Item by Partition Key\n')
+
+    # Including the partition key value of account_number in the WHERE filter results in a more efficient query
+    items = list(container.query_items(
+        query=f"SELECT * FROM c WHERE c.partitionKey = 'HemeStamp' AND TimestampToDateTime(c._ts*1000) >= '{date1}' AND TimestampToDateTime(c._ts*1000) <= '{date2}' ",
+    ))
+    return items
+
+    # for item in items:
+    #    print(f"Item: {item}")
+
+
+def get_epic_order(patient):
+    patient['DOB'] = ''
+    order1 = {
+        'id': datetime.datetime.now().strftime('%c'),
+        'partitionKey': 'HemeStamp',
+        'orderid': 'None',
+        'order_date': datetime.datetime.now().strftime('%c'),
+        'patient': patient
+    }
+    logging.info(f"order1: {order1} End order1")
+    return order1
+
+
+def create_item(container, patient):
+    # farts
+
+    epic_patient = get_epic_order(patient)
+    logging.info(epic_patient)
+
+    container.create_item(body=epic_patient)
+
+
+def getcosmosbetweendate(date1, date2):
+    client = cosmos_client.CosmosClient(
+        HOST, {'masterKey': MASTER_KEY}, user_agent="CosmosDBPythonQuickstart", user_agent_overwrite=True)
+    # setup database for this sample
+    try:
+        db = client.create_database(id=DATABASE_ID)
+        print('Database with id \'{0}\' created'.format(DATABASE_ID))
+
+    except exceptions.CosmosResourceExistsError:
+        db = client.get_database_client(DATABASE_ID)
+        print('Database with id \'{0}\' was found'.format(DATABASE_ID))
+
+    # setup container for this sample
+    try:
+        container = db.create_container(id=CONTAINER_ID, partition_key=PartitionKey(path='/partitionKey'))
+        print('Container with id \'{0}\' created'.format(CONTAINER_ID))
+
+    except exceptions.CosmosResourceExistsError:
+        container = db.get_container_client(CONTAINER_ID)
+        print('Container with id \'{0}\' was found'.format(CONTAINER_ID))
+
+    return query_items_between_dates(container, date1, date2)
+
+
+def getcosmosbydate(qdate):
+    client = cosmos_client.CosmosClient(HOST, {'masterKey': MASTER_KEY}, user_agent="CosmosDBPythonQuickstart",
+                                        user_agent_overwrite=True)
+    # setup database for this sample
+    try:
+        db = client.create_database(id=DATABASE_ID)
+        print('Database with id \'{0}\' created'.format(DATABASE_ID))
+
+    except exceptions.CosmosResourceExistsError:
+        db = client.get_database_client(DATABASE_ID)
+        print('Database with id \'{0}\' was found'.format(DATABASE_ID))
+
+    # setup container for this sample
+    try:
+        container = db.create_container(id=CONTAINER_ID, partition_key=PartitionKey(path='/partitionKey'))
+        print('Container with id \'{0}\' created'.format(CONTAINER_ID))
+
+    except exceptions.CosmosResourceExistsError:
+        container = db.get_container_client(CONTAINER_ID)
+        print('Container with id \'{0}\' was found'.format(CONTAINER_ID))
+
+    return query_items_bydate(container, qdate)
+
+
+def getcosmos():
+    client = cosmos_client.CosmosClient(HOST, {'masterKey': MASTER_KEY}, user_agent="CosmosDBPythonQuickstart",
+                                        user_agent_overwrite=True)
+    # setup database for this sample
+    try:
+        db = client.create_database(id=DATABASE_ID)
+        print('Database with id \'{0}\' created'.format(DATABASE_ID))
+
+    except exceptions.CosmosResourceExistsError:
+        db = client.get_database_client(DATABASE_ID)
+        print('Database with id \'{0}\' was found'.format(DATABASE_ID))
+
+    # setup container for this sample
+    try:
+        container = db.create_container(id=CONTAINER_ID, partition_key=PartitionKey(path='/partitionKey'))
+        print('Container with id \'{0}\' created'.format(CONTAINER_ID))
+
+    except exceptions.CosmosResourceExistsError:
+        container = db.get_container_client(CONTAINER_ID)
+        print('Container with id \'{0}\' was found'.format(CONTAINER_ID))
+
+    return query_items(container)
+
+
+def cosmoswrite(patient):
+    client = cosmos_client.CosmosClient(HOST, {'masterKey': MASTER_KEY}, user_agent="CosmosDBPythonQuickstart",
+                                        user_agent_overwrite=True)
+
+    # setup database for this sample
+    try:
+        db = client.create_database(id=DATABASE_ID)
+        print('Database with id \'{0}\' created'.format(DATABASE_ID))
+
+
+    except exceptions.CosmosResourceExistsError:
+        db = client.get_database_client(DATABASE_ID)
+        print('Database with id \'{0}\' was found'.format(DATABASE_ID))
+
+    # setup container for this sample
+    try:
+        container = db.create_container(id=CONTAINER_ID, partition_key=PartitionKey(path='/partitionKey'))
+        print('Container with id \'{0}\' created'.format(CONTAINER_ID))
+
+
+    except exceptions.CosmosResourceExistsError:
+        container = db.get_container_client(CONTAINER_ID)
+        print('Container with id \'{0}\' was found'.format(CONTAINER_ID))
+
+    create_item(container, patient)
\ No newline at end of file
diff --git a/scripts/Heme-Stamp/emailParser.py b/scripts/Heme-Stamp/emailParser.py
new file mode 100644
index 00000000..f02950e5
--- /dev/null
+++ b/scripts/Heme-Stamp/emailParser.py
@@ -0,0 +1,97 @@
+import glob
+import email
+from email import policy
+from email.parser import BytesParser
+import os
+from bs4 import BeautifulSoup
+from google.cloud import bigquery
+
+all_emails = []
+
+for filepath in glob.glob('/Users/g0123/Downloads/emails/*.eml'):
+    with open(filepath, 'rb') as fp:
+        name = fp.name
+        msg = BytesParser(policy=policy.default).parse(fp)
+    text = msg.get_body(preferencelist='html').get_content()
+    soup = BeautifulSoup(text, 'html.parser')
+    table_body = soup.find('tbody')
+    table_head = soup.find('thead')
+    data = []
+    dataHead = []
+    for row in table_body.findAll("tr"):
+        cols = row.find_all("td")
+        cols1 = [ele.text.strip() for ele in cols[:6]]
+        cols2 = [ele.text for ele in cols[6:]]
+        cols = cols1 + cols2
+        # cols = [ele.text.strip() for ele in cols]
+        # data.append([ele for ele in cols if ele])
+        data.append(cols)
+
+    print(table_head.findAll("tr"))
+    for row in table_head.findAll("tr"):
+        cols = row.find_all("th") #use 'td' to parse received emails. Else use 'th'
+        if not cols:
+            cols = row.find_all("td")
+        cols = [ele.text.strip() for ele in cols]
+        dataHead.append([ele for ele in cols if ele])
+
+    print(dataHead)
+    print(data)
+
+    for row in data:
+        vals_dict = {}
+        for k, v in zip(dataHead[0], row):
+            vals_dict[k] = v
+        all_emails.append(vals_dict)
+    fp.close()
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (
+    '/Users/g0123/.config/gcloud/application_default_credentials.json'
+)
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101-secure'
+
+client = bigquery.Client("som-nero-phi-jonc101")
+table_id = "som-nero-phi-jonc101-secure.grace_db.test_table"
+
+response_set = set(('[x]', '[X]', 'x', 'X'))
+response_headers = set(('<10%', '10-30%', '30-50%', '50-70%', '70-90%', '>90%'))
+
+for email in all_emails:
+    response = None
+    for k, v in email.items():
+        #if k in response_headers and (v.strip() in response_set or v.strip() != ""):
+        if k in response_headers and v.strip() != "":
+            response = k
+    print("email: ", email)
+
+    email["Notes"] = email["Notes"].strip()
+
+    if response:
+        if email["Notes"]:
+            update_query = """
+            UPDATE `som-nero-phi-jonc101-secure.grace_db.test_table`
+            SET Phys_estimate = {0}, Notes = {1}, Response = True
+            WHERE MRN = {2}
+            """.format("'"+response+"'", "'''"+email["Notes"]+"'''", email["MRN"])
+            print(update_query)
+        else:
+            update_query = """
+            UPDATE `som-nero-phi-jonc101-secure.grace_db.test_table`
+            SET Phys_estimate = {0}, Response = True
+            WHERE MRN = {1}
+            """.format("'" + response + "'", email["MRN"])
+            print(update_query)
+
+    elif email["Notes"]:
+        update_query = """
+        UPDATE `som-nero-phi-jonc101-secure.grace_db.test_table`
+        SET Notes = {0}, Response = True
+        WHERE MRN = {1}
+        """.format("'''" + email["Notes"] + "'''", email["MRN"])
+        print(update_query)
+
+
+    client.query(update_query)
+
+
+
diff --git a/scripts/Heme-Stamp/generateEmail.py b/scripts/Heme-Stamp/generateEmail.py
new file mode 100644
index 00000000..8834f3c1
--- /dev/null
+++ b/scripts/Heme-Stamp/generateEmail.py
@@ -0,0 +1,88 @@
+import json
+import os
+from tabulate import tabulate
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+import smtplib
+
+class email(object):
+    def __init__(self, email, patient_data):
+        self.email = email
+        self.patient_data = patient_data
+
+        with open(os.path.join(os.sys.path[0], "emailAuth.confg"), "r") as json_file:
+            creds = json.load(json_file)
+
+        self.sender = creds["sender"]
+        self.password = creds["password"]
+        self.server = 'smtp.stanford.edu'
+        self.text_content = """
+            Hello, thank you for participating in our study. To respond, please click the Reply button, press "..." to show the
+            email chain, and directly edit the table below. Please mark X in the corresponding column to indicate your
+            estimation of the likelihood that the Heme-STAMP test will result in at least one pathogenic variant for each patient.  
+            Please try to avoid  adding anything other than an X in these cells to ensure that we can automatically parse responses. 
+            Please feel free to utilize the Notes column to add any additional comments.
+            
+            This data was pulled from Epic and based on orders that have been submitted in the last week. If one of these orders
+            have since been canceled or you believe was mistakenly included, please write Canceled in the Notes section.
+            
+            We have added the latest WBC and Hgb count for your convenience. 
+            
+            Thank you!
+            {table} 
+        """
+        self.html_content = """
+            <html>
+            <head>
+                <style> 
+                  table, th, td {{ border: 1px solid black; border-collapse: collapse; overflow: hidden; white-space: nowrap;}}
+                  th, td, tr {{ padding: 5px; overflow: hidden; white-space: nowrap;}}
+                </style>
+                </head>
+            <body><p>Hello, thank you for participating in our study. 
+            <br>
+            <br>
+            To respond, please click the Reply button, press "..." to show the
+            email chain, and directly edit the table below. Please mark X in the corresponding column to indicate your
+            estimation of the likelihood that the Heme-STAMP test will result in at least one pathogenic variant for each patient.
+            <br>
+            <br>  
+            Please try to avoid  adding anything other than an X in these cells to ensure that we can automatically parse responses. 
+            Please feel free to utilize the Notes column to add any additional comments.
+            <br>
+            <br>
+            This data was pulled from Epic and based on orders that have been submitted in the last week. If one of these orders
+            have since been canceled or you believe was mistakenly included, please mark Canceled in the Notes section.
+            <br>
+            <br>
+            We have added the latest WBC and Hgb count for your convenience. 
+            <br>
+            <br>
+            Thank you! </p>
+            {table}
+            </body>
+            </html>
+        """
+
+
+    def send_email(self):
+        data = self.patient_data
+        col_names = data.columns.tolist()
+        #data.set_index("MRN", inplace=True)
+        text = self.text_content.format(table=tabulate(data, showindex=False, headers=col_names, tablefmt="fancy_grid"))
+        html = self.html_content.format(table=tabulate(data, showindex=False, headers=col_names, tablefmt="html"))
+
+        message = MIMEMultipart(
+            "alternative", None, [MIMEText(text), MIMEText(html,'html')])
+
+        message['Subject'] = "Secure: Heme-STAMP Estimates"
+        message['From'] = self.sender
+        message['To'] = self.email
+        message['CC'] = self.sender
+        server = smtplib.SMTP(self.server)
+        server.ehlo()
+        server.starttls()
+        server.login(self.sender, self.password)
+        server.sendmail(self.sender, ([self.email] + [self.sender]), message.as_string())
+        server.quit()
+        print("email sent")
\ No newline at end of file
diff --git a/scripts/Heme-Stamp/orderInfo.py b/scripts/Heme-Stamp/orderInfo.py
new file mode 100644
index 00000000..bd986aa9
--- /dev/null
+++ b/scripts/Heme-Stamp/orderInfo.py
@@ -0,0 +1,213 @@
+import requests
+from requests.auth import HTTPBasicAuth
+import json
+import os
+import xmltodict
+from datetime import date, datetime
+from dateutil.parser import parse
+
+class Order(object):
+    def __init__(self, credentials, fhir_id, order_date, env, cid):
+        self.credentials = credentials
+        self.FHIR_ID = fhir_id
+        self.date = order_date
+        self.client_id = cid
+        self.api_prefix = env
+
+    def __call__(self):
+        order_dict = {}
+        order_dict['FHIR_ID'] = self.FHIR_ID
+        order_dict['Physician'], order_dict['Ordered'], active_order = self.get_ordering_phys()
+        if active_order:
+            lab_names = ['WBC', 'Hgb']
+            for lab in lab_names:
+                order_dict[lab] = self.get_lab_result(lab)
+            age, sex, name = self.get_demographics()
+            age_sex = str(age) + sex[0]
+            order_dict['Age'], order_dict['Name'] = age_sex, name
+            order_dict['MRN'] = self.get_other_identifiers()
+            order_dict['Physician'] = order_dict['Physician'][:-4]
+            order_dict['Ordered'] = order_dict['Ordered'].split("T")[0]
+        return (order_dict, active_order)
+
+    def get_ordering_phys(self):
+        active_order = True
+        physician = ""
+        ordered_date = ""
+        procedure_request = requests.get(
+            f"{self.api_prefix}/api/FHIR/STU3/ProcedureRequest",
+            params={'patient': self.FHIR_ID, 'status': 'canceled'},  # , 'status': 'completed'
+            headers={'Content-Type': 'application/json; charset=utf-8',
+                     'Epic-Client-ID': self.client_id},
+            auth=HTTPBasicAuth(self.credentials["username"],
+                               self.credentials["password"])
+        )
+        patient_orders = xmltodict.parse(procedure_request.text)
+        pt_json = json.loads(json.dumps(patient_orders))
+        if 'Bundle' not in pt_json:
+            active_order = False
+            print('1', pt_json['OperationOutcome']['@xmlns'])
+            print('2', pt_json['OperationOutcome']['issue'].keys())
+        else:
+            pt_json = pt_json['Bundle']['entry']
+
+            HS_names = {"STANFORD HEME-STAMP NGS PANEL BLOOD",
+                        "STANFORD HEME-STAMP NGS PANEL, NON-BLOOD"}
+
+
+            phys_names = {'Rondeep Singh Brar, MD', 'David Joseph Iberri, MD', 'William Elias Shomali, MD'}
+
+            i_list = []
+            for x in range(len(pt_json)):
+                # print("json keys: ", pt_json[x].keys())
+                i_val = pt_json[x]['resource']
+                if 'ProcedureRequest' in i_val.keys():
+                    i_val = i_val['ProcedureRequest']
+                else:
+                    print('3', i_val.keys())
+                    print(i_val['OperationOutcome'])
+                    print(self.FHIR_ID)
+                i_list.append(i_val)
+
+            all_HS_orders = []
+            HS_orders = []
+
+            for v in range(len(i_list)):
+                if 'code' in i_list[v].keys() and 'coding' in i_list[v]['code'].keys():
+                    if isinstance(i_list[v]['code']['coding'], list):
+                        for a in range(len(i_list[v]['code']['coding'])):
+                            lab = i_list[v]['code']['coding'][a]['display']['@value']
+                            phys = i_list[v]['requester']['agent']['display']['@value']
+                            authored_date = i_list[v]
+                            if 'authoredOn' in authored_date.keys():
+                                authored_date = authored_date['authoredOn']['@value']
+                            else:
+                                # authored_date = authored_date['requester']['agent']['display']['@value']
+                                authored_date = authored_date['occurrenceTiming']['repeat']['boundsPeriod']['start']['@value']
+
+                            if lab in HS_names:
+                                HS_orders.append((lab, phys, authored_date))
+                                # if phys in phys_names:
+                                #     HS_orders.append((lab, phys, authored_date))
+
+                    else:
+                        lab = i_list[v]['code']['coding']['display']['@value']
+                        phys = i_list[v]['requester']['agent']['display']['@value']
+                        authored_date = i_list[v]
+                        if 'authoredOn' in authored_date.keys():
+                            authored_date = authored_date['authoredOn']['@value']
+                        else:
+                            # authored_date = authored_date['requester']['agent']['display']['@value']
+                            authored_date = authored_date['occurrenceTiming']['repeat']['boundsPeriod']['start'][
+                                '@value']
+
+                        if lab in HS_names:
+                            HS_orders.append((lab, phys, authored_date))
+                            # if phys in phys_names:
+                            #     HS_orders.append((lab, phys, authored_date))
+                else:
+                    # print('4', i_list[v].keys())
+                    if 'code' in i_list[v].keys():
+                        print('5', i_list[v]['code'].keys())
+                        print(i_list[v]['code']['text'])
+                        print(self.FHIR_ID)
+                    else:
+                        print('4', i_list[v].keys())
+
+            #print("all HS orders: ", all_HS_orders)
+            print("HS orders: ", HS_orders)
+
+            if len(HS_orders) > 0:
+                physician = HS_orders[-1][1]
+                ordered_date = HS_orders[-1][2]
+        return (physician, ordered_date, active_order)
+
+    def get_lab_result(self, base_name):
+        lab_results_dict = {}
+        lab_result_packet = {
+            "PatientID": self.FHIR_ID,
+            "PatientIDType": "FHIR STU3",
+            "UserID": "Z0002000",
+            "UserIDType": "External",
+            "MaxNumberOfResults": 200,
+            "FromInstant": "",
+            "ComponentTypes":
+                [{"Value": base_name, "Type": "base-name"}]
+        }
+        lab_component_packet = json.dumps(lab_result_packet)
+        lab_component_response = requests.post(
+            (f'{self.api_prefix}api/epic/2014/Results/Utility/'
+             'GETPATIENTRESULTCOMPONENTS/ResultComponents'),
+            headers={
+                'Content-Type': 'application/json; charset=utf-8',
+                'Epic-Client-ID': self.client_id
+            },
+            auth=HTTPBasicAuth(self.credentials["username"],
+                               self.credentials["password"]),
+            data=lab_component_packet
+        )
+        lab_response = json.loads(lab_component_response.text)
+
+
+        date_found = False
+        lab_result = 0
+
+        if lab_response["ResultComponents"]:
+            for i in range(len(lab_response["ResultComponents"])):
+                resp = lab_response["ResultComponents"][i]
+                #print("resp: ", resp)
+                value = resp["Value"]
+                if value is not None:
+                    value = value[0]
+                result_date = resp['ResultDate']
+                lab_results_dict[result_date] = value
+                if datetime.strptime(result_date, '%m/%d/%Y').date() <= self.date and not date_found and value is not None:
+                    recent_date = result_date
+                    # print("recent date: ", recent_date)
+                    date_found = True
+                    lab_result = value
+                # print("-----------")
+                # print("value: ", value)
+                # print("date: ", result_date)
+
+        return lab_result
+
+    def get_demographics(self):
+        request_results = requests.get(
+            (f'{self.api_prefix}api/epic/2010/Common/Patient/'
+             'GETPATIENTDEMOGRAPHICS/Patient/Demographics'),
+            params={'PatientID': self.FHIR_ID,
+                    'PatientIDType': 'FHIR STU3'},
+            headers={'Content-Type': 'application/json; charset=utf-8',
+                     'Epic-Client-ID': self.client_id},
+            auth=HTTPBasicAuth(self.credentials["username"],
+                               self.credentials["password"])
+        )
+
+        demographic_json = json.loads(request_results.text)
+        #print(demographic_json)
+        age = self.calculate_age(parse(demographic_json["DateOfBirth"]))
+        sex = demographic_json['Gender']
+        name = demographic_json['Name']['FirstName'] + " " + demographic_json['Name']['LastName']
+        demo_info = (age, sex, name)
+        return demo_info
+
+    def calculate_age(self, date_born):
+        today = date.today()
+        return today.year - date_born.year - ((today.month, today.day) < (date_born.month, date_born.day))
+
+    def get_other_identifiers(self):
+        ReqPatientIDs = requests.get(
+            (f"{self.api_prefix}api/epic/2010/Common/Patient/"
+             "GETPATIENTIDENTIFIERS/Patient/Identifiers"),
+            params={'PatientID': self.FHIR_ID, 'PatientIDType': 'FHIR STU3'},
+            headers={'Content-Type': 'application/json; charset=utf-8',
+                     'Epic-Client-ID': self.client_id},
+            auth=HTTPBasicAuth(self.credentials["username"], self.credentials["password"])
+        )
+        patient_json = json.loads(ReqPatientIDs.text)
+        patient_dict = {}
+        for id_load in patient_json["Identifiers"]:
+            patient_dict[id_load["IDType"]] = id_load["ID"]
+
+        return patient_dict['SHCMRN']
diff --git a/scripts/Heme-Stamp/order_action.py b/scripts/Heme-Stamp/order_action.py
new file mode 100644
index 00000000..41d6eddd
--- /dev/null
+++ b/scripts/Heme-Stamp/order_action.py
@@ -0,0 +1,75 @@
+import os
+from google.cloud import bigquery
+import generateEmail
+
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (
+    '/Users/g0123/.config/gcloud/application_default_credentials.json'
+)
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101-secure'
+
+client = bigquery.Client("som-nero-phi-jonc101")
+table_id = "som-nero-phi-jonc101-secure.grace_db.test_table"
+
+bkt_list = ["<10%", "10-30%", "30-50%", "50-70%", "70-90%", ">90%"]
+
+query = """
+SELECT MRN, Name, Ordered, Age, WBC, Hgb, Physician, Notes
+FROM `som-nero-phi-jonc101-secure.grace_db.test_table`
+WHERE Msg_sent is not TRUE
+"""
+
+
+
+email_dict = {
+    'Rondeep Singh Brar': 'rbrar@stanford.edu',
+    'David Joseph Iberri': 'diberri@stanford.edu',
+    'William Elias Shomali': 'wshomali@stanford.edu'
+}
+
+# email_dict = {
+#     'Rondeep Singh Brar': 'yek1354@stanford.edu',
+#     'David Joseph Iberri': 'yek1354@stanford.edu',
+#     'William Elias Shomali': 'yek1354@stanford.edu'
+# }
+
+def upload_order(new_order_dict):
+    print("new order dict: ", new_order_dict)
+    rows_to_insert = [new_order_dict]
+
+    errors = client.insert_rows_json(table_id, rows_to_insert)
+    if errors == []:
+        print("New rows have been added.")
+    else:
+        print("Encountered errors while inserting rows: {}".format(errors))
+
+def send_email():
+    df = (
+        client.query(query).result().to_dataframe()
+    )
+    print(df)
+    for bkt in bkt_list:
+        df[bkt] = ""
+
+    for phys in email_dict.keys():
+        phys_df = df.loc[df['Physician'] == phys]
+        del phys_df["Physician"]
+        cols = phys_df.columns.tolist()
+        cols = cols[:6] + cols[7:] + [cols[6]]
+        phys_df = phys_df[cols]
+        print(phys_df)
+        if len(phys_df) > 0:
+            print(phys_df)
+            email = generateEmail.email(email=email_dict[phys],
+                                        patient_data=phys_df)
+            email.send_email()
+            for index, row in phys_df.iterrows():
+                print(row)
+                mrn_val = row['MRN']
+                update_msg_status_query = """
+                UPDATE `som-nero-phi-jonc101-secure.grace_db.test_table`
+                SET Msg_sent = True
+                WHERE MRN = {0}
+                """.format(mrn_val)
+                print(update_msg_status_query)
+                client.query(update_msg_status_query)
diff --git a/scripts/InpatientDiabetes/BigQuery Access.ipynb b/scripts/InpatientDiabetes/BigQuery Access.ipynb
new file mode 100644
index 00000000..5411b876
--- /dev/null
+++ b/scripts/InpatientDiabetes/BigQuery Access.ipynb	
@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Ivana/opt/miniconda3/lib/python3.7/site-packages/google/auth/_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "import pandas as pd\n",
+    "\n",
+    "##Use correct path based on whether you are local or Nero \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/Ivana/.config/gcloud/application_default_credentials.json' \n",
+    "\n",
+    "##set correct Nero project\n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101'\n",
+    "#os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "\n",
+    "##load bq magic command\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Ivana/opt/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: PyarrowMissingWarning: Converting to a dataframe without pyarrow installed is often slower and will become unsupported in the future. Please install the pyarrow package.\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    }
+   ],
+   "source": [
+    "#How to save results of a query into a table using python (pandas)\n",
+    "query = 'select pat_enc_csn_id_coded from starr_datalake2018.adt limit 10'\n",
+    "query_job = client.query(query)\n",
+    "df_cohort = query_job.result().to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>131252359745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>131248767001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>131250104967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>131256032446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>131260217397</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pat_enc_csn_id_coded\n",
+       "0          131252359745\n",
+       "1          131248767001\n",
+       "2          131250104967\n",
+       "3          131256032446\n",
+       "4          131260217397"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cohort.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>131248255400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>131251163697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>131249233204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>131247873288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>131250281284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>131249233204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>131250719981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>131257678868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>131256499078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>131249935974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>131258013615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>131256927474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>131249649529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>131249751857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>131260778399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>131253289598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>131254912522</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    pat_enc_csn_id_coded\n",
+       "0           131248255400\n",
+       "1           131251163697\n",
+       "2           131249233204\n",
+       "3           131247873288\n",
+       "4           131250281284\n",
+       "5           131249233204\n",
+       "6           131250719981\n",
+       "7           131257678868\n",
+       "8           131256499078\n",
+       "9           131249935974\n",
+       "10          131258013615\n",
+       "11          131256927474\n",
+       "12          131249649529\n",
+       "13          131249751857\n",
+       "14          131260778399\n",
+       "15          131253289598\n",
+       "16          131254912522"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#\"%%bigquery\" runs SQL queries, does not save data\n",
+    "%%bigquery\n",
+    "select pat_enc_csn_id_coded from starr_datalake2018.adt limit 17"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/InpatientDiabetes/Data_Getting.ipynb b/scripts/InpatientDiabetes/Data_Getting.ipynb
new file mode 100644
index 00000000..06924db4
--- /dev/null
+++ b/scripts/InpatientDiabetes/Data_Getting.ipynb
@@ -0,0 +1,2642 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery;\n",
+    "from google.cloud.bigquery import dbapi;\n",
+    "import pandas as pd;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/google/auth/_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "client = bigquery.Client(\"mining-clinical-decisions\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn = dbapi.connect(client);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Query to make sure things are working"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"SELECT * FROM `mining-clinical-decisions.starr_datalake2018.adt` LIMIT 10\"; \n",
+    "resultsDF = pd.read_sql_query(query, conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>jc_uid</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type_c</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>event_subtype_c</th>\n",
+       "      <th>event_subtype</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_class_c</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>...</th>\n",
+       "      <th>seq_num_in_enc</th>\n",
+       "      <th>seq_num_in_bed_min</th>\n",
+       "      <th>labor_status_c</th>\n",
+       "      <th>first_ip_in_ip_yn</th>\n",
+       "      <th>loa_reason_c</th>\n",
+       "      <th>data_source</th>\n",
+       "      <th>effective_time_jittered_utc</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>event_time_jittered_utc</th>\n",
+       "      <th>event_time_jittered</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCe4b0f8</td>\n",
+       "      <td>131248255400</td>\n",
+       "      <td>26794006</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Leave of Absence Out</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>43</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-05-08 16:54:00+00:00</td>\n",
+       "      <td>2018-05-08 09:54:00</td>\n",
+       "      <td>2018-05-08 16:55:00+00:00</td>\n",
+       "      <td>2018-05-08 09:55:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCd33188</td>\n",
+       "      <td>131251163697</td>\n",
+       "      <td>27554470</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Leave of Absence Return</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-05-12 22:15:00+00:00</td>\n",
+       "      <td>2018-05-12 15:15:00</td>\n",
+       "      <td>2018-05-12 22:39:00+00:00</td>\n",
+       "      <td>2018-05-12 15:39:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCeadc76</td>\n",
+       "      <td>131249233204</td>\n",
+       "      <td>26700504</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Leave of Absence Return</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-03-15 15:30:00+00:00</td>\n",
+       "      <td>2018-03-15 08:30:00</td>\n",
+       "      <td>2018-03-15 16:04:00+00:00</td>\n",
+       "      <td>2018-03-15 09:04:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCe00dd9</td>\n",
+       "      <td>131247873288</td>\n",
+       "      <td>26274432</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Leave of Absence Out</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-02-11 17:04:00+00:00</td>\n",
+       "      <td>2018-02-11 09:04:00</td>\n",
+       "      <td>2018-02-11 17:06:00+00:00</td>\n",
+       "      <td>2018-02-11 09:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCe5d92e</td>\n",
+       "      <td>131250281284</td>\n",
+       "      <td>27000067</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Leave of Absence Return</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-05-17 01:24:00+00:00</td>\n",
+       "      <td>2018-05-16 18:24:00</td>\n",
+       "      <td>2018-05-17 01:24:00+00:00</td>\n",
+       "      <td>2018-05-16 18:24:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCeadc76</td>\n",
+       "      <td>131249233204</td>\n",
+       "      <td>26699522</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Leave of Absence Out</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-03-15 14:18:00+00:00</td>\n",
+       "      <td>2018-03-15 07:18:00</td>\n",
+       "      <td>2018-03-15 14:18:00+00:00</td>\n",
+       "      <td>2018-03-15 07:18:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCe0a8cc</td>\n",
+       "      <td>131250719981</td>\n",
+       "      <td>27264341</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Leave of Absence Return</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-06-07 23:10:00+00:00</td>\n",
+       "      <td>2018-06-07 16:10:00</td>\n",
+       "      <td>2018-06-07 23:32:00+00:00</td>\n",
+       "      <td>2018-06-07 16:32:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCceb968</td>\n",
+       "      <td>131257678868</td>\n",
+       "      <td>29156948</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Leave of Absence Out</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-10-28 12:15:00+00:00</td>\n",
+       "      <td>2018-10-28 05:15:00</td>\n",
+       "      <td>2018-10-28 15:21:00+00:00</td>\n",
+       "      <td>2018-10-28 08:21:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCd1e561</td>\n",
+       "      <td>131256499078</td>\n",
+       "      <td>28900775</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Leave of Absence Return</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-10-15 10:00:00+00:00</td>\n",
+       "      <td>2018-10-15 03:00:00</td>\n",
+       "      <td>2018-10-15 10:05:00+00:00</td>\n",
+       "      <td>2018-10-15 03:05:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCdd60ca</td>\n",
+       "      <td>131249935974</td>\n",
+       "      <td>27076627</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Leave of Absence Return</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Original</td>\n",
+       "      <td>120301002</td>\n",
+       "      <td>172</td>\n",
+       "      <td>SNF</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CLARITY_SHC</td>\n",
+       "      <td>2018-05-23 21:14:00+00:00</td>\n",
+       "      <td>2018-05-23 14:14:00</td>\n",
+       "      <td>2018-05-23 21:14:00+00:00</td>\n",
+       "      <td>2018-05-23 14:14:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     jc_uid  pat_enc_csn_id_coded  event_id_coded  event_type_c  \\\n",
+       "0  JCe4b0f8          131248255400        26794006             8   \n",
+       "1  JCd33188          131251163697        27554470             9   \n",
+       "2  JCeadc76          131249233204        26700504             9   \n",
+       "3  JCe00dd9          131247873288        26274432             8   \n",
+       "4  JCe5d92e          131250281284        27000067             9   \n",
+       "5  JCeadc76          131249233204        26699522             8   \n",
+       "6  JCe0a8cc          131250719981        27264341             9   \n",
+       "7  JCceb968          131257678868        29156948             8   \n",
+       "8  JCd1e561          131256499078        28900775             9   \n",
+       "9  JCdd60ca          131249935974        27076627             9   \n",
+       "\n",
+       "                event_type  event_subtype_c event_subtype  department_id  \\\n",
+       "0     Leave of Absence Out                1      Original      120301002   \n",
+       "1  Leave of Absence Return                1      Original      120301002   \n",
+       "2  Leave of Absence Return                1      Original      120301002   \n",
+       "3     Leave of Absence Out                1      Original      120301002   \n",
+       "4  Leave of Absence Return                1      Original      120301002   \n",
+       "5     Leave of Absence Out                1      Original      120301002   \n",
+       "6  Leave of Absence Return                1      Original      120301002   \n",
+       "7     Leave of Absence Out                1      Original      120301002   \n",
+       "8  Leave of Absence Return                1      Original      120301002   \n",
+       "9  Leave of Absence Return                1      Original      120301002   \n",
+       "\n",
+       "  pat_class_c pat_class         ...         seq_num_in_enc seq_num_in_bed_min  \\\n",
+       "0         172       SNF         ...                     43                  1   \n",
+       "1         172       SNF         ...                     32                  1   \n",
+       "2         172       SNF         ...                     10                  1   \n",
+       "3         172       SNF         ...                     29                  1   \n",
+       "4         172       SNF         ...                      4                  1   \n",
+       "5         172       SNF         ...                      9                  1   \n",
+       "6         172       SNF         ...                     15                  1   \n",
+       "7         172       SNF         ...                     14                  1   \n",
+       "8         172       SNF         ...                     17                  1   \n",
+       "9         172       SNF         ...                     26                  1   \n",
+       "\n",
+       "  labor_status_c first_ip_in_ip_yn loa_reason_c  data_source  \\\n",
+       "0           None              None          4.0  CLARITY_SHC   \n",
+       "1           None              None          NaN  CLARITY_SHC   \n",
+       "2           None              None          NaN  CLARITY_SHC   \n",
+       "3           None              None          4.0  CLARITY_SHC   \n",
+       "4           None              None          NaN  CLARITY_SHC   \n",
+       "5           None              None          4.0  CLARITY_SHC   \n",
+       "6           None              None          NaN  CLARITY_SHC   \n",
+       "7           None              None          4.0  CLARITY_SHC   \n",
+       "8           None              None          NaN  CLARITY_SHC   \n",
+       "9           None              None          NaN  CLARITY_SHC   \n",
+       "\n",
+       "  effective_time_jittered_utc effective_time_jittered  \\\n",
+       "0   2018-05-08 16:54:00+00:00     2018-05-08 09:54:00   \n",
+       "1   2018-05-12 22:15:00+00:00     2018-05-12 15:15:00   \n",
+       "2   2018-03-15 15:30:00+00:00     2018-03-15 08:30:00   \n",
+       "3   2018-02-11 17:04:00+00:00     2018-02-11 09:04:00   \n",
+       "4   2018-05-17 01:24:00+00:00     2018-05-16 18:24:00   \n",
+       "5   2018-03-15 14:18:00+00:00     2018-03-15 07:18:00   \n",
+       "6   2018-06-07 23:10:00+00:00     2018-06-07 16:10:00   \n",
+       "7   2018-10-28 12:15:00+00:00     2018-10-28 05:15:00   \n",
+       "8   2018-10-15 10:00:00+00:00     2018-10-15 03:00:00   \n",
+       "9   2018-05-23 21:14:00+00:00     2018-05-23 14:14:00   \n",
+       "\n",
+       "    event_time_jittered_utc event_time_jittered  \n",
+       "0 2018-05-08 16:55:00+00:00 2018-05-08 09:55:00  \n",
+       "1 2018-05-12 22:39:00+00:00 2018-05-12 15:39:00  \n",
+       "2 2018-03-15 16:04:00+00:00 2018-03-15 09:04:00  \n",
+       "3 2018-02-11 17:06:00+00:00 2018-02-11 09:06:00  \n",
+       "4 2018-05-17 01:24:00+00:00 2018-05-16 18:24:00  \n",
+       "5 2018-03-15 14:18:00+00:00 2018-03-15 07:18:00  \n",
+       "6 2018-06-07 23:32:00+00:00 2018-06-07 16:32:00  \n",
+       "7 2018-10-28 15:21:00+00:00 2018-10-28 08:21:00  \n",
+       "8 2018-10-15 10:05:00+00:00 2018-10-15 03:05:00  \n",
+       "9 2018-05-23 21:14:00+00:00 2018-05-23 14:14:00  \n",
+       "\n",
+       "[10 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultsDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TDD  by day, by patient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-- TDD by day by patient. Insulin not pumps or U500 or rare. Patient without AKI during that encounter. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('Inpatient_info.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>jc_uid</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>units</th>\n",
+       "      <th>mar_action</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>route</th>\n",
+       "      <th>medication_id</th>\n",
+       "      <th>taken_time_jittered</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcc0c77</td>\n",
+       "      <td>131257485179</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Given</td>\n",
+       "      <td>INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE F...</td>\n",
+       "      <td>Subcutaneous</td>\n",
+       "      <td>541453</td>\n",
+       "      <td>2018-09-19 11:44:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     jc_uid  pat_enc_csn_id_coded units mar_action  \\\n",
+       "0  JCcc0c77          131257485179     1      Given   \n",
+       "\n",
+       "                                     med_description         route  \\\n",
+       "0  INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE F...  Subcutaneous   \n",
+       "\n",
+       "   medication_id taken_time_jittered  \n",
+       "0         541453 2018-09-19 11:44:00  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/InsulinDosing.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TDD where BG was from 100-180"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-- TDD on days where BG was 100-180 and at least 3 glucose readings in range"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('patient_date_numBG_minBG_maxBG_TDD.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>jc_uid</th>\n",
+       "      <th>patientDate</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>numBG</th>\n",
+       "      <th>minBG</th>\n",
+       "      <th>maxBG</th>\n",
+       "      <th>TDD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCd5154f</td>\n",
+       "      <td>2013-05-22</td>\n",
+       "      <td>131024269168</td>\n",
+       "      <td>4</td>\n",
+       "      <td>158.0</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     jc_uid patientDate  pat_enc_csn_id_coded  numBG  minBG  maxBG  TDD\n",
+       "0  JCd5154f  2013-05-22          131024269168      4  158.0  169.0  5.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/patient_date_numBG_minBG_maxBG_TDD.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age and Sex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DOB and Sex Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('age_and_sex.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rit_uid</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>dob</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcb6582</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>1930-03-22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    rit_uid  gender         dob\n",
+       "0  JCcb6582  Female  1930-03-22"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/age_and_sex.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A1C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for a1c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('a1c.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rit_uid</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>taken_time_jittered</th>\n",
+       "      <th>ord_value</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcb657e</td>\n",
+       "      <td>6648732</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2004-04-15 09:25:00</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcb657e</td>\n",
+       "      <td>6648732</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2005-09-20 10:11:00</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCcb657e</td>\n",
+       "      <td>131008252292</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2010-01-11 10:13:00</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCcb65a9</td>\n",
+       "      <td>4764750</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2004-07-15 05:10:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCcb65d7</td>\n",
+       "      <td>14536711</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2000-08-26 12:40:00</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCcb65d7</td>\n",
+       "      <td>14536711</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2001-07-21 14:05:00</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>6641909</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2000-07-03 16:00:00</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2017-11-07 23:27:00</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCcb65e4</td>\n",
+       "      <td>131063755584</td>\n",
+       "      <td>Hemoglobin A1c (Manual Entry) See EMR for details</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>% (See scan or EMR data for detail)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCcb65ee</td>\n",
+       "      <td>14489527</td>\n",
+       "      <td>Hemoglobin A1c</td>\n",
+       "      <td>2006-05-02 08:38:00</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    rit_uid  pat_enc_csn_id_coded  \\\n",
+       "0  JCcb657e               6648732   \n",
+       "1  JCcb657e               6648732   \n",
+       "2  JCcb657e          131008252292   \n",
+       "3  JCcb65a9               4764750   \n",
+       "4  JCcb65d7              14536711   \n",
+       "5  JCcb65d7              14536711   \n",
+       "6  JCcb65e1               6641909   \n",
+       "7  JCcb65e1          131239638605   \n",
+       "8  JCcb65e4          131063755584   \n",
+       "9  JCcb65ee              14489527   \n",
+       "\n",
+       "                                            lab_name taken_time_jittered  \\\n",
+       "0                                     Hemoglobin A1c 2004-04-15 09:25:00   \n",
+       "1                                     Hemoglobin A1c 2005-09-20 10:11:00   \n",
+       "2                                     Hemoglobin A1c 2010-01-11 10:13:00   \n",
+       "3                                     Hemoglobin A1c 2004-07-15 05:10:00   \n",
+       "4                                     Hemoglobin A1c 2000-08-26 12:40:00   \n",
+       "5                                     Hemoglobin A1c 2001-07-21 14:05:00   \n",
+       "6                                     Hemoglobin A1c 2000-07-03 16:00:00   \n",
+       "7                                     Hemoglobin A1c 2017-11-07 23:27:00   \n",
+       "8  Hemoglobin A1c (Manual Entry) See EMR for details                 NaT   \n",
+       "9                                     Hemoglobin A1c 2006-05-02 08:38:00   \n",
+       "\n",
+       "  ord_value                       reference_unit result_in_range_yn  result  \n",
+       "0       5.4                                    %               None     NaN  \n",
+       "1       5.3                                    %               None     NaN  \n",
+       "2       5.6                                    %               None     NaN  \n",
+       "3       7.0                                    %               None     NaN  \n",
+       "4       6.9                                    %               None     NaN  \n",
+       "5       7.4                                    %               None     NaN  \n",
+       "6       5.7                                    %                  Y     NaN  \n",
+       "7       6.2                                    %               None     NaN  \n",
+       "8       5.7  % (See scan or EMR data for detail)               None     NaN  \n",
+       "9       5.7                                    %               None     NaN  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/a1c.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Diet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('diet.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>jc_uid</th>\n",
+       "      <th>order_proc_id_coded</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>proc_start_time_jittered</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcb6601</td>\n",
+       "      <td>376862154</td>\n",
+       "      <td>131011126327</td>\n",
+       "      <td>2010-09-28 12:30:00</td>\n",
+       "      <td>DIET NOURISHMENTS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcb6932</td>\n",
+       "      <td>451275286</td>\n",
+       "      <td>131045874945</td>\n",
+       "      <td>2014-10-18 00:00:00</td>\n",
+       "      <td>DIET NPO AFTER MIDNIGHT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCcb6b7c</td>\n",
+       "      <td>414240353</td>\n",
+       "      <td>131021834984</td>\n",
+       "      <td>2012-11-12 18:30:00</td>\n",
+       "      <td>BMT DIET</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCcb6fa2</td>\n",
+       "      <td>584585310</td>\n",
+       "      <td>131258217311</td>\n",
+       "      <td>2018-11-12 00:00:00</td>\n",
+       "      <td>DIET NPO AFTER MIDNIGHT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCcb7169</td>\n",
+       "      <td>369520458</td>\n",
+       "      <td>131008898162</td>\n",
+       "      <td>2010-04-05 17:30:00</td>\n",
+       "      <td>DIET NOURISHMENTS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCcb74ce</td>\n",
+       "      <td>363665884</td>\n",
+       "      <td>131007907827</td>\n",
+       "      <td>2010-01-09 17:30:00</td>\n",
+       "      <td>DIET CARBOHYDRATE CONTROLLED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCcb79c7</td>\n",
+       "      <td>413123750</td>\n",
+       "      <td>131020887135</td>\n",
+       "      <td>2012-10-31 12:30:00</td>\n",
+       "      <td>DIET NUTRITIONAL SUPPLEMENTS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCcb7d8d</td>\n",
+       "      <td>373369533</td>\n",
+       "      <td>131010248235</td>\n",
+       "      <td>2010-08-29 10:30:00</td>\n",
+       "      <td>BMT DIET</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCcb7d8d</td>\n",
+       "      <td>373417625</td>\n",
+       "      <td>131010272940</td>\n",
+       "      <td>2010-08-30 09:30:00</td>\n",
+       "      <td>BMT DIET</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCcb7d8d</td>\n",
+       "      <td>374116001</td>\n",
+       "      <td>131010388819</td>\n",
+       "      <td>2010-09-15 07:45:00</td>\n",
+       "      <td>BMT DIET</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     jc_uid  order_proc_id_coded  pat_enc_csn_id_coded  \\\n",
+       "0  JCcb6601            376862154          131011126327   \n",
+       "1  JCcb6932            451275286          131045874945   \n",
+       "2  JCcb6b7c            414240353          131021834984   \n",
+       "3  JCcb6fa2            584585310          131258217311   \n",
+       "4  JCcb7169            369520458          131008898162   \n",
+       "5  JCcb74ce            363665884          131007907827   \n",
+       "6  JCcb79c7            413123750          131020887135   \n",
+       "7  JCcb7d8d            373369533          131010248235   \n",
+       "8  JCcb7d8d            373417625          131010272940   \n",
+       "9  JCcb7d8d            374116001          131010388819   \n",
+       "\n",
+       "  proc_start_time_jittered                   description  \n",
+       "0      2010-09-28 12:30:00             DIET NOURISHMENTS  \n",
+       "1      2014-10-18 00:00:00       DIET NPO AFTER MIDNIGHT  \n",
+       "2      2012-11-12 18:30:00                      BMT DIET  \n",
+       "3      2018-11-12 00:00:00       DIET NPO AFTER MIDNIGHT  \n",
+       "4      2010-04-05 17:30:00             DIET NOURISHMENTS  \n",
+       "5      2010-01-09 17:30:00  DIET CARBOHYDRATE CONTROLLED  \n",
+       "6      2012-10-31 12:30:00  DIET NUTRITIONAL SUPPLEMENTS  \n",
+       "7      2010-08-29 10:30:00                      BMT DIET  \n",
+       "8      2010-08-30 09:30:00                      BMT DIET  \n",
+       "9      2010-09-15 07:45:00                      BMT DIET  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/diet.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BMT DIET                                                  2090\n",
+       "DIET NPO AFTER MIDNIGHT                                    451\n",
+       "DIET REGULAR                                               420\n",
+       "DIET LOW MICROBIAL                                         366\n",
+       "DIET NUTRITIONAL SUPPLEMENTS                               311\n",
+       "DIET NOURISHMENTS                                          289\n",
+       "DIET NPO                                                   160\n",
+       "DIET CARBOHYDRATE CONTROLLED                                78\n",
+       "DIET TUBE FEEDING AND MEAL TRAY                             69\n",
+       "DIET CLEAR LIQUID                                           58\n",
+       "DIET NPO AFTER MIDNIGHT FOR SURGERY                         41\n",
+       "DIET REGULAR (SVC)                                          31\n",
+       "DIET LOW SODIUM                                             26\n",
+       "DIET GUEST TRAY                                             21\n",
+       "DIET NPO (SVC)                                              15\n",
+       "DIET CLEAR LIQUID (SVC)                                     13\n",
+       "RD COMMUNICATION TO DIET OFFICE (SVC)                       11\n",
+       "DIET LOW SODIUM LOW CHOLESTEROL LOW SAT FAT                  9\n",
+       "DIET CARB CONTROL/LOW SODIUM/LOW SAT FAT                     9\n",
+       "DIET POST SURGICAL - STEP ONE                                9\n",
+       "DIET DYSPHAGIA TEST TRAY                                     8\n",
+       "DIET TUBE FEEDING NO TRAY (SVC)                              8\n",
+       "DIET COMBINATION                                             8\n",
+       "DIET CARB CONTROL/LOW SODIUM/LOW CHOL/LOW SAT FAT            8\n",
+       "DIET FULL LIQUID                                             8\n",
+       "DIET POST GASTRIC SURGERY                                    8\n",
+       "DIET SODIUM RESTRICTED                                       7\n",
+       "DIET POST SURGICAL - STEP TWO                                7\n",
+       "DIET NPO AFTER MIDNIGHT (SVC)                                7\n",
+       "DIET RENAL                                                   7\n",
+       "                                                          ... \n",
+       "ADVANCE DIET AS TOLERATED (RN COMMUNICATION)                 6\n",
+       "DIET CARBOHYDRATE CONTROLLED/LOW SODIUM/LOW FAT (SVC)        6\n",
+       "DIET CARBOHYDRATE CONTROLLED (SVC)                           6\n",
+       "DIET LOW SODIUM/LOW FAT (SVC)                                5\n",
+       "DIET COMBINATION (SVC)                                       5\n",
+       "DIET VEGETARIAN                                              5\n",
+       "DIET GLUTEN FREE                                             4\n",
+       "DIET LOW SODIUM LOW SATURATED FAT                            3\n",
+       "COMBINATION DIET                                             3\n",
+       "DIET RENAL (SVC)                                             3\n",
+       "DIET LOW CHOLESTEROL LOW SATURATED FAT                       3\n",
+       "DIET WELL BABY (SVC)                                         3\n",
+       "DIET LOW POTASSIUM                                           2\n",
+       "DIET CARBOHYDRATE CONTROLLED RENAL (SVC)                     2\n",
+       "DIET CALORIE CONTROLLED                                      2\n",
+       "PKU DIET MONITORING                                          1\n",
+       "DIET LOW FAT                                                 1\n",
+       "DIET FULL LIQUID (SVC)                                       1\n",
+       "DIET LOW FIBER LOW RESIDUE                                   1\n",
+       "DIET FAT RESTRICTED                                          1\n",
+       "DISCHARGE DIET                                               1\n",
+       "DIET GRAFT VERSUS HOST DISEASE, LOW MICROBIAL                1\n",
+       "DIET TUBE FEEDING WITH TRAY (SVC)                            1\n",
+       "DIET LACTOSE RESTRICTED                                      1\n",
+       "DIET SWEET SUCCESS (SVC)                                     1\n",
+       "BARIATRIC ADVANCE DIET AS TOLERATED (RN COMMUNICATION)       1\n",
+       "DIET HIGH CALORIE HIGH PROTEIN                               1\n",
+       "DIET LOW SODIUM (SVC)                                        1\n",
+       "DIET LOW NA/SATFAT/CHOL 1600-1800CAL SM MEAL                 1\n",
+       "DIET LOW FIBER                                               1\n",
+       "Name: description, Length: 61, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data[\"description\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['DIET NOURISHMENTS' 'DIET NPO AFTER MIDNIGHT' 'BMT DIET'\n",
+      " 'DIET CARBOHYDRATE CONTROLLED' 'DIET NUTRITIONAL SUPPLEMENTS'\n",
+      " 'DIET TUBE FEEDING AND MEAL TRAY' 'DIET REGULAR'\n",
+      " 'DIET POST SURGICAL - STEP TWO' 'DIET COMBINATION' 'DIET REGULAR (SVC)'\n",
+      " 'DIET CARBOHYDRATE CONTROLLED/LOW SODIUM/LOW FAT (SVC)'\n",
+      " 'DIET DYSPHAGIA TEST TRAY' 'DIET GUEST TRAY'\n",
+      " 'DIET CARB CONTROL/LOW SODIUM/LOW CHOL/LOW SAT FAT' 'DIET NPO'\n",
+      " 'DIET CLEAR LIQUID' 'DIET LOW MICROBIAL' 'DIET LOW FIBER LOW RESIDUE'\n",
+      " 'DIET FULL LIQUID' 'DIET NPO AFTER MIDNIGHT FOR SURGERY'\n",
+      " 'DIET LOW SODIUM/LOW FAT (SVC)' 'DIET RENAL' 'DIET SODIUM RESTRICTED'\n",
+      " 'DIET LOW SODIUM LOW CHOLESTEROL LOW SAT FAT' 'DIET POST GASTRIC SURGERY'\n",
+      " 'DIET LOW SODIUM' 'DIET VEGETARIAN' 'DIET TUBE FEEDING ONLY'\n",
+      " 'DIET GLUTEN FREE' 'COMBINATION DIET' 'DIET TUBE FEEDING NO TRAY (SVC)'\n",
+      " 'DIET POST SURGICAL - STEP ONE' 'DIET CLEAR LIQUID (SVC)'\n",
+      " 'RD COMMUNICATION TO DIET OFFICE (SVC)' 'DIET NPO (SVC)'\n",
+      " 'DIET NPO AFTER MIDNIGHT (SVC)'\n",
+      " 'ADVANCE DIET AS TOLERATED (RN COMMUNICATION)' 'DIET RENAL (SVC)'\n",
+      " 'DIET CARB CONTROL/LOW SODIUM/LOW SAT FAT' 'DIET SWEET SUCCESS (SVC)'\n",
+      " 'DIET COMBINATION (SVC)'\n",
+      " 'BARIATRIC ADVANCE DIET AS TOLERATED (RN COMMUNICATION)'\n",
+      " 'DIET CARBOHYDRATE CONTROLLED RENAL (SVC)'\n",
+      " 'DIET CARBOHYDRATE CONTROLLED (SVC)' 'DIET LOW POTASSIUM'\n",
+      " 'DIET WELL BABY (SVC)' 'DISCHARGE DIET'\n",
+      " 'DIET LOW SODIUM LOW SATURATED FAT' 'DIET HIGH CALORIE HIGH PROTEIN'\n",
+      " 'DIET LOW CHOLESTEROL LOW SATURATED FAT' 'DIET CALORIE CONTROLLED'\n",
+      " 'DIET TUBE FEEDING WITH TRAY (SVC)' 'DIET FULL LIQUID (SVC)'\n",
+      " 'DIET LOW NA/SATFAT/CHOL 1600-1800CAL SM MEAL' 'DIET LOW FIBER'\n",
+      " 'PKU DIET MONITORING' 'DIET LACTOSE RESTRICTED'\n",
+      " 'DIET GRAFT VERSUS HOST DISEASE, LOW MICROBIAL' 'DIET FAT RESTRICTED'\n",
+      " 'DIET LOW FAT' 'DIET LOW SODIUM (SVC)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tdd_subqueries_data[\"description\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TUBE FEEDING AND MEAL TRAY; DIET TUBE FEEDING ONLY; "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Common Labs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Common Labs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('commonLabs.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>224051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Potassium, Ser/Plas</td>\n",
+       "      <td>222685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Sodium, Ser/Plas</td>\n",
+       "      <td>222329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Chloride, Ser/Plas</td>\n",
+       "      <td>221903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CO2, Ser/Plas</td>\n",
+       "      <td>221201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Calcium, Ser/Plas</td>\n",
+       "      <td>220861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>220252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Hematocrit</td>\n",
+       "      <td>219534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>WBC</td>\n",
+       "      <td>217221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>RBC</td>\n",
+       "      <td>216993</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               lab_name   total\n",
+       "0  Creatinine, Ser/Plas  224051\n",
+       "1   Potassium, Ser/Plas  222685\n",
+       "2      Sodium, Ser/Plas  222329\n",
+       "3    Chloride, Ser/Plas  221903\n",
+       "4         CO2, Ser/Plas  221201\n",
+       "5     Calcium, Ser/Plas  220861\n",
+       "6     Glucose, Ser/Plas  220252\n",
+       "7            Hematocrit  219534\n",
+       "8                   WBC  217221\n",
+       "9                   RBC  216993"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/commonLabs.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creatinine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Creatinine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('creatinine.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rit_uid</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>taken_time_jittered</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2001-07-08 10:08:00</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2002-05-04 16:52:00</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2002-06-10 11:24:00</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2002-09-21 16:36:00</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2004-03-28 16:46:00</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2005-05-05 14:32:00</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2006-06-04 13:04:00</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>6684962</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2007-10-15 12:25:00</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>131009207572</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2010-05-24 12:20:00</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCcb657d</td>\n",
+       "      <td>131079498600</td>\n",
+       "      <td>Creatinine, Ser/Plas</td>\n",
+       "      <td>2015-04-10 17:45:00</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    rit_uid  pat_enc_csn_id_coded              lab_name taken_time_jittered  \\\n",
+       "0  JCcb657d               6684962  Creatinine, Ser/Plas 2001-07-08 10:08:00   \n",
+       "1  JCcb657d               6684962  Creatinine, Ser/Plas 2002-05-04 16:52:00   \n",
+       "2  JCcb657d               6684962  Creatinine, Ser/Plas 2002-06-10 11:24:00   \n",
+       "3  JCcb657d               6684962  Creatinine, Ser/Plas 2002-09-21 16:36:00   \n",
+       "4  JCcb657d               6684962  Creatinine, Ser/Plas 2004-03-28 16:46:00   \n",
+       "5  JCcb657d               6684962  Creatinine, Ser/Plas 2005-05-05 14:32:00   \n",
+       "6  JCcb657d               6684962  Creatinine, Ser/Plas 2006-06-04 13:04:00   \n",
+       "7  JCcb657d               6684962  Creatinine, Ser/Plas 2007-10-15 12:25:00   \n",
+       "8  JCcb657d          131009207572  Creatinine, Ser/Plas 2010-05-24 12:20:00   \n",
+       "9  JCcb657d          131079498600  Creatinine, Ser/Plas 2015-04-10 17:45:00   \n",
+       "\n",
+       "   ord_num_value reference_unit result_in_range_yn  \n",
+       "0           0.80          mg/dL                  Y  \n",
+       "1           0.80          mg/dL                  Y  \n",
+       "2           0.80          mg/dL                  Y  \n",
+       "3           0.80          mg/dL                  Y  \n",
+       "4           0.70          mg/dL               None  \n",
+       "5           0.80          mg/dL               None  \n",
+       "6           0.83          mg/dL               None  \n",
+       "7           0.78          mg/dL               None  \n",
+       "8           0.90          mg/dL               None  \n",
+       "9           0.90          mg/dL               None  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/creatinine.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Steroids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Steroids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('steroidQuery.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>jc_uid</th>\n",
+       "      <th>order_med_id_coded</th>\n",
+       "      <th>sig</th>\n",
+       "      <th>dose_unit</th>\n",
+       "      <th>route</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>taken_time_jittered</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>freq_display_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcb6582</td>\n",
+       "      <td>454499543</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Intravenous</td>\n",
+       "      <td>131060083874</td>\n",
+       "      <td>2014-11-19 12:25:00</td>\n",
+       "      <td>DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN</td>\n",
+       "      <td>PRN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>541848146</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Intravenous</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-08 07:45:00</td>\n",
+       "      <td>METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ...</td>\n",
+       "      <td>Once</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>541922794</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Feeding Tube</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-09 13:43:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>541922794</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Feeding Tube</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-10 09:12:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>541922794</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Feeding Tube</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-11 08:32:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>541922794</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Feeding Tube</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-12 09:09:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>542264105</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Feeding Tube</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-13 08:07:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>542453808</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Oral</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-14 09:16:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>542453808</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Oral</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-15 08:18:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCcb65e1</td>\n",
+       "      <td>542453808</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>mg</td>\n",
+       "      <td>Oral</td>\n",
+       "      <td>131239638605</td>\n",
+       "      <td>2017-11-16 12:25:00</td>\n",
+       "      <td>PREDNISONE 20 MG PO TABS</td>\n",
+       "      <td>DAILY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     jc_uid  order_med_id_coded    sig dose_unit         route  \\\n",
+       "0  JCcb6582           454499543    4.0        mg   Intravenous   \n",
+       "1  JCcb65e1           541848146  125.0        mg   Intravenous   \n",
+       "2  JCcb65e1           541922794   40.0        mg  Feeding Tube   \n",
+       "3  JCcb65e1           541922794   40.0        mg  Feeding Tube   \n",
+       "4  JCcb65e1           541922794   40.0        mg  Feeding Tube   \n",
+       "5  JCcb65e1           541922794   40.0        mg  Feeding Tube   \n",
+       "6  JCcb65e1           542264105   20.0        mg  Feeding Tube   \n",
+       "7  JCcb65e1           542453808   20.0        mg          Oral   \n",
+       "8  JCcb65e1           542453808   20.0        mg          Oral   \n",
+       "9  JCcb65e1           542453808   20.0        mg          Oral   \n",
+       "\n",
+       "   pat_enc_csn_id_coded taken_time_jittered  \\\n",
+       "0          131060083874 2014-11-19 12:25:00   \n",
+       "1          131239638605 2017-11-08 07:45:00   \n",
+       "2          131239638605 2017-11-09 13:43:00   \n",
+       "3          131239638605 2017-11-10 09:12:00   \n",
+       "4          131239638605 2017-11-11 08:32:00   \n",
+       "5          131239638605 2017-11-12 09:09:00   \n",
+       "6          131239638605 2017-11-13 08:07:00   \n",
+       "7          131239638605 2017-11-14 09:16:00   \n",
+       "8          131239638605 2017-11-15 08:18:00   \n",
+       "9          131239638605 2017-11-16 12:25:00   \n",
+       "\n",
+       "                                     med_description freq_display_name  \n",
+       "0    DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN               PRN  \n",
+       "1  METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ...              Once  \n",
+       "2                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "3                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "4                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "5                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "6                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "7                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "8                           PREDNISONE 20 MG PO TABS             DAILY  \n",
+       "9                           PREDNISONE 20 MG PO TABS             DAILY  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/steroids.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weight"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('weight.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/weight.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Height"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('height.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/height.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Glucoses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for Glucose values (first value in admission)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('glucoses.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rit_uid</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>ord_value</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>taken_time_jittered</th>\n",
+       "      <th>result_time_jittered</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCd59f64</td>\n",
+       "      <td>131248252494</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>123</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>2018-03-20 13:30:00</td>\n",
+       "      <td>2018-03-20 14:36:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCd4df71</td>\n",
+       "      <td>131240996220</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>150</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>2017-12-26 07:49:00</td>\n",
+       "      <td>2017-12-26 08:11:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCe4e1e8</td>\n",
+       "      <td>131178145935</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>2016-02-07 04:45:00</td>\n",
+       "      <td>2016-02-07 05:17:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCdc0cfa</td>\n",
+       "      <td>131240421000</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>131</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>2017-11-25 02:46:00</td>\n",
+       "      <td>2017-11-25 03:10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd66cc3</td>\n",
+       "      <td>131237394594</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>126</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>2017-07-23 16:31:00</td>\n",
+       "      <td>2017-07-23 17:36:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCd20376</td>\n",
+       "      <td>131234429925</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>115</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>2017-06-24 14:54:00</td>\n",
+       "      <td>2017-06-24 15:39:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCdb84b4</td>\n",
+       "      <td>131228966152</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>183</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>2017-05-09 05:40:00</td>\n",
+       "      <td>2017-05-09 06:26:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCe003bf</td>\n",
+       "      <td>131239633350</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>84</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2017-11-10 18:53:00</td>\n",
+       "      <td>2017-11-10 19:33:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCeb4cb9</td>\n",
+       "      <td>131193965421</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>97</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>2016-07-30 09:29:00</td>\n",
+       "      <td>2016-07-30 10:09:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCccc72a</td>\n",
+       "      <td>131202703847</td>\n",
+       "      <td>Glucose, Ser/Plas</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>2016-09-26 11:18:00</td>\n",
+       "      <td>2016-09-26 13:04:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    rit_uid  pat_enc_csn_id_coded           lab_name ord_value  ord_num_value  \\\n",
+       "0  JCd59f64          131248252494  Glucose, Ser/Plas       123          123.0   \n",
+       "1  JCd4df71          131240996220  Glucose, Ser/Plas       150          150.0   \n",
+       "2  JCe4e1e8          131178145935  Glucose, Ser/Plas       187          187.0   \n",
+       "3  JCdc0cfa          131240421000  Glucose, Ser/Plas       131          131.0   \n",
+       "4  JCd66cc3          131237394594  Glucose, Ser/Plas       126          126.0   \n",
+       "5  JCd20376          131234429925  Glucose, Ser/Plas       115          115.0   \n",
+       "6  JCdb84b4          131228966152  Glucose, Ser/Plas       183          183.0   \n",
+       "7  JCe003bf          131239633350  Glucose, Ser/Plas        84           84.0   \n",
+       "8  JCeb4cb9          131193965421  Glucose, Ser/Plas        97           97.0   \n",
+       "9  JCccc72a          131202703847  Glucose, Ser/Plas       110          110.0   \n",
+       "\n",
+       "  taken_time_jittered result_time_jittered  \n",
+       "0 2018-03-20 13:30:00  2018-03-20 14:36:00  \n",
+       "1 2017-12-26 07:49:00  2017-12-26 08:11:00  \n",
+       "2 2016-02-07 04:45:00  2016-02-07 05:17:00  \n",
+       "3 2017-11-25 02:46:00  2017-11-25 03:10:00  \n",
+       "4 2017-07-23 16:31:00  2017-07-23 17:36:00  \n",
+       "5 2017-06-24 14:54:00  2017-06-24 15:39:00  \n",
+       "6 2017-05-09 05:40:00  2017-05-09 06:26:00  \n",
+       "7 2017-11-10 18:53:00  2017-11-10 19:33:00  \n",
+       "8 2016-07-30 09:29:00  2016-07-30 10:09:00  \n",
+       "9 2016-09-26 11:18:00  2016-09-26 13:04:00  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/glucoses.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Larry's Queries to Write:\n",
+    "- Weight, BMI, Admission BG\n",
+    "- Lab Results: CBC, ESR (erthrocyte), CRP (c reactive protein), CHEM 10, Liver function test\n",
+    "- Medications (steroid or not)\n",
+    "- # of diagnoses\n",
+    "- Procedures in the last 48 hours"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TPN/PPN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query for TPN/PPN people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = open('TPN_PPN.sql', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data = pd.read_sql_query(query.read(), conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>jc_uid</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>med_description</th>\n",
+       "      <th>medication_id</th>\n",
+       "      <th>order_time_jittered</th>\n",
+       "      <th>start_time_jittered</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JCcc0528</td>\n",
+       "      <td>16624126</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>550000</td>\n",
+       "      <td>2008-10-10 19:10:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JCce2fbc</td>\n",
+       "      <td>131007070249</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>550000</td>\n",
+       "      <td>2009-10-15 14:43:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd4b533</td>\n",
+       "      <td>131181914407</td>\n",
+       "      <td>TPN ADULT CYCLIC</td>\n",
+       "      <td>3232</td>\n",
+       "      <td>2016-03-19 16:40:00</td>\n",
+       "      <td>2016-03-19 21:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCce469c</td>\n",
+       "      <td>16426096</td>\n",
+       "      <td>TPN BMT</td>\n",
+       "      <td>27617</td>\n",
+       "      <td>2008-09-03 11:04:00</td>\n",
+       "      <td>2008-09-03 20:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCcba0a4</td>\n",
+       "      <td>131010279813</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>27617</td>\n",
+       "      <td>2010-09-17 10:56:00</td>\n",
+       "      <td>2010-09-17 20:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>JCcd3a24</td>\n",
+       "      <td>131014406974</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>3232</td>\n",
+       "      <td>2011-09-21 11:05:00</td>\n",
+       "      <td>2011-09-21 20:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>JCd7105d</td>\n",
+       "      <td>131041353367</td>\n",
+       "      <td>TPN ADULT CYCLIC</td>\n",
+       "      <td>27617</td>\n",
+       "      <td>2014-09-22 11:33:00</td>\n",
+       "      <td>2014-09-22 21:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>JCdb5074</td>\n",
+       "      <td>16875291</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>3232</td>\n",
+       "      <td>2008-10-11 08:08:00</td>\n",
+       "      <td>2008-10-11 20:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>JCdf68e7</td>\n",
+       "      <td>131024625892</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>27617</td>\n",
+       "      <td>2013-07-07 11:49:00</td>\n",
+       "      <td>2013-07-07 20:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>JCe25c8e</td>\n",
+       "      <td>131030378700</td>\n",
+       "      <td>TPN ADULT STANDARD</td>\n",
+       "      <td>27617</td>\n",
+       "      <td>2014-04-22 14:17:00</td>\n",
+       "      <td>2014-04-22 21:00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     jc_uid  pat_enc_csn_id_coded     med_description  medication_id  \\\n",
+       "0  JCcc0528              16624126  TPN ADULT STANDARD         550000   \n",
+       "1  JCce2fbc          131007070249  TPN ADULT STANDARD         550000   \n",
+       "2  JCd4b533          131181914407    TPN ADULT CYCLIC           3232   \n",
+       "3  JCce469c              16426096             TPN BMT          27617   \n",
+       "4  JCcba0a4          131010279813  TPN ADULT STANDARD          27617   \n",
+       "5  JCcd3a24          131014406974  TPN ADULT STANDARD           3232   \n",
+       "6  JCd7105d          131041353367    TPN ADULT CYCLIC          27617   \n",
+       "7  JCdb5074              16875291  TPN ADULT STANDARD           3232   \n",
+       "8  JCdf68e7          131024625892  TPN ADULT STANDARD          27617   \n",
+       "9  JCe25c8e          131030378700  TPN ADULT STANDARD          27617   \n",
+       "\n",
+       "  order_time_jittered start_time_jittered  \n",
+       "0 2008-10-10 19:10:00                 NaT  \n",
+       "1 2009-10-15 14:43:00                 NaT  \n",
+       "2 2016-03-19 16:40:00 2016-03-19 21:00:00  \n",
+       "3 2008-09-03 11:04:00 2008-09-03 20:00:00  \n",
+       "4 2010-09-17 10:56:00 2010-09-17 20:00:00  \n",
+       "5 2011-09-21 11:05:00 2011-09-21 20:00:00  \n",
+       "6 2014-09-22 11:33:00 2014-09-22 21:00:00  \n",
+       "7 2008-10-11 08:08:00 2008-10-11 20:00:00  \n",
+       "8 2013-07-07 11:49:00 2013-07-07 20:00:00  \n",
+       "9 2014-04-22 14:17:00 2014-04-22 21:00:00  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdd_subqueries_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tdd_subqueries_data.to_csv(\"./Data/tpn_ppn.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/InpatientDiabetes/Inpatient diabetes cohort b/scripts/InpatientDiabetes/Inpatient diabetes cohort
new file mode 100644
index 00000000..f7bc18f7
--- /dev/null
+++ b/scripts/InpatientDiabetes/Inpatient diabetes cohort	
@@ -0,0 +1,157 @@
+
+Cohort of patients where subQ, inpatient insulin was ordered and given (>0 units), attempting to exclude pump insulin, excluding any insulin ordered <10x total in the dataset, and excluding any patient encounters in which the creatinine rose >2 
+
+	Number of insulin doses recorded as given & 0 units: 123
+	Number of unique patients: 39083 
+		- 39087 when dose of insulin given 0 or greater
+	Number of unique patient encounters: 61973 
+		- 61979 when dose of insulin given 0 or greater
+
+	Number of unique insulin orders: 192049
+	Number of insulin doses given where units > 0: 1014575
+		- Number of insulin doses marked as given (including 0 units given): 1014698
+	Range of insulin doses: 1 (exlcuded anything <1)- 50000 (incorrectly recorded heparin) --> Will exclude doses > 100 (Church & Haines 2016, can't absorb more than ~80U)
+
+	Excluding U-500 insulin:
+		Number of doses: 1014542
+		Number of unique patients: 39082
+		Number of unique encounters: 61972
+
+	Excluding single doses >100:
+		Number of doses: 1014221
+		Number of unique patients:39077
+		Number of unique encounters: 61947
+
+	Number of patient encounters for which short-acting insulin was ordered: 19041
+	Number of pt encounters for which "scale" alone was ordered: 10175 (16.4%) 
+		-- note that this is only after 2015, other "scale" will have to be found in orderset data
+	Percent of pt encounters in which (1-100u) insulin was given & Cr < 2 for which ISS was ordered: 19041/61947 = 30.73%
+	Number of pt encounters for which NPH was ordered: 12788
+	Number of pt encounters for which Glargine was ordered: 10238
+	Number of pt encounters for which Detemir was ordered: 3320
+	Number of pt encounters for which Degludec was ordered: 49
+
+	Number of patient encounters for which basal was NOT ordered: 38031 (61.39%)
+
+	Max date: 12/30/2018
+	Min date: 03/26/2008
+
+Average BG of patient cohort: 164
+Average BG of patients who weren't ordered for basal: 152
+
+2008: 3/26-12/31/2008
+	Number of patient encounters for which basal was NOT ordered: 2772 
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 3536
+	Percent of pt encounters with no basal: 78.39%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2009: 
+	Number of patient encounters for which basal was NOT ordered: 4022
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5459
+	Percent of pt encounters with no basal: 73.68%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2010:
+	Number of patient encounters for which basal was NOT ordered: 3621
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5126
+	Percent of pt encounters with no basal: 70.64%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2011:
+	Number of patient encounters for which basal was NOT ordered: 3667
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5233
+	Percent of pt encounters with no basal: 70.01%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2012:
+	Number of patient encounters for which basal was NOT ordered: 3567
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5403
+	Percent of pt encounters with no basal: 66.02%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2013:
+	Number of patient encounters for which basal was NOT ordered: 3386
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5730
+	Percent of pt encounters with no basal: 59.10%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2014:
+	Number of patient encounters for which basal was NOT ordered: 3307
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5870
+	Percent of pt encounters with no basal: 56.34%
+
+	Number of pt encounters with "scale" alone ordered: 0
+
+2015:
+	Number of patient encounters for which basal was NOT ordered: 3162
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 5816
+	Percent of pt encounters with no basal: 54.37%
+
+	Number of pt encounters with "scale" alone ordered: 652
+
+2016:
+ 	Number of patient encounters for which basal was NOT ordered: 3346
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 6199
+	Percent of pt encounters with no basal: 53.98%
+
+	Number of pt encounters with "scale" alone ordered: 3126
+
+	Total no of BG checks w/ basal: 97564
+	Total no of BGs 80-200 w/basal: 66982
+	No of BGs <70 w/basal: 1071
+	No of BGs >200 w/basal: 28470
+
+	Total no of BG checks no basal:  52077
+	Total no of BGs 80-200 no basal: 42345
+	No of BGs <70 no basal: 247
+	No of BGs >200 no basal: 9019
+
+2017:
+	Number of patient encounters for which basal was NOT ordered: 3585
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 6559
+	Percent of pt encounters with no basal: 54.66%
+
+	Number of pt encounters with "scale" alone ordered: 3366
+
+	Total no of BG checks: 183078
+
+	Total no of BG checks w/ basal: 116447
+	Total no of BGs 80-200 w/basal: 80676
+	No of BGs <70 w/basal: 1154
+	No of BGs >200 w/basal: 33425
+
+	Total no of BG checks no basal:  59841
+	Total no of BGs 80-200 no basal: 48763
+	No of BGs <70 no basal: 308
+	No of BGs >200 no basal: 10204
+
+2018:
+ 	Number of patient encounters for which basal was NOT ordered: 3968
+	Number of pt encounters where insulin was given (excluding pumps and rare orders) and without AKI: 7553
+	Percent of pt encounters with no basal: 52.54%
+
+	Number of pt encounters with "scale" alone ordered: 3113
+
+	Average BG total: 172
+	Average BG no basal: 157
+
+	
+	Total no of BG checks w/ basal:  120183
+	Total no of BGs 80-200 w/basal: 80141
+	No of BGs <70 w/basal: 1327
+	No of BGs >200 w/basal: 37426
+
+	Total no of BG checks no basal:  61200
+	Total no of BGs 80-200 no basal: 50204
+	No of BGs <70 no basal: 277
+	No of BGs >200 no basal: 10132
+
+
+
+
diff --git a/scripts/InpatientDiabetes/Inpatientdiabetesreadme.md b/scripts/InpatientDiabetes/Inpatientdiabetesreadme.md
new file mode 100644
index 00000000..07fe004e
--- /dev/null
+++ b/scripts/InpatientDiabetes/Inpatientdiabetesreadme.md
@@ -0,0 +1,3 @@
+Inpatient diabetes readme
+
+Scripts for inpatient diabetes cohort
\ No newline at end of file
diff --git a/scripts/InpatientDiabetes/TDD prediction/Except_pts_TT_ICU_vs_ADT_ICU.sql b/scripts/InpatientDiabetes/TDD prediction/Except_pts_TT_ICU_vs_ADT_ICU.sql
new file mode 100644
index 00000000..059a7f04
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD prediction/Except_pts_TT_ICU_vs_ADT_ICU.sql	
@@ -0,0 +1,35 @@
+SELECT distinct anon_id, pat_enc_csn_id_coded 
+
+FROM `som-nero-phi-jonc101.shc_core.treatment_team`
+
+WHERE prov_map_id IN ( -- Removed NSurg, ICU, CCU, removed anything with <100 patients 
+'S176', 
+'S358',
+'SUNK00008',
+'S400',
+'SG1000003',
+'SG1000007',
+'SG1000005',
+'SG1000009',
+'S1398',
+'S3395',
+'S3399',
+'S3396',
+'SG1000041',
+'SG1000044',
+'S224',
+'SG1000046',
+'SG1000031',
+'SG1000027',
+'S19626') 
+
+AND name = 'Primary Team'
+
+EXCEPT DISTINCT SELECT anon_id, pat_enc_csn_id_coded  FROM `som-nero-phi-jonc101.shc_core.adt`
+
+
+WHERE pat_service IN ('Neurocritical Care', 'ICU Trauma/GenSurg', 'Cardiovascular ICU', 'Critical Care', 'Emergency Critical Care')
+
+
+
+LIMIT 1000
\ No newline at end of file
diff --git a/scripts/InpatientDiabetes/TDD prediction/Patients_with_TxTeam_as_ICU.sql b/scripts/InpatientDiabetes/TDD prediction/Patients_with_TxTeam_as_ICU.sql
new file mode 100644
index 00000000..95d4e408
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD prediction/Patients_with_TxTeam_as_ICU.sql	
@@ -0,0 +1,31 @@
+SELECT extract(year from trtmnt_tm_begin_dt_jittered) as asgnment_yr, count(distinct anon_id) as num_pt, count(anon_id) as tot_assignments_icu, count(distinct pat_enc_csn_id_coded) as num_enc 
+
+FROM `som-nero-phi-jonc101.shc_core.treatment_team`
+
+WHERE prov_map_id IN ( -- Removed NSurg, removed anything with <100  
+	'S176',
+	'S358',
+	'SUNK00008',
+	'S400',
+	'SG1000003',
+	'SG1000007',
+	'SG1000005',
+	'SG1000009',
+	'S1398',
+	'S3395',
+	'S3399',
+	'S3396',
+	'SG1000041',
+	'SG1000044',
+	'S224',
+	'SG1000046',
+	'SG1000031',
+	'SG1000027',
+	'S19626') 
+
+AND name = 'Primary Team'
+
+GROUP BY asgnment_yr
+ORDER BY asgnment_yr
+
+LIMIT 1000
\ No newline at end of file
diff --git a/scripts/InpatientDiabetes/TDD prediction/TDD cohort b/scripts/InpatientDiabetes/TDD prediction/TDD cohort
new file mode 100644
index 00000000..89e41e02
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD prediction/TDD cohort	
@@ -0,0 +1,17 @@
+TDD cohort
+
+MAR inclusion criteria: 
+	- Received insulin
+	- Inpatient
+	- SubQ
+	- Did not receive U500, pump, partial units, or insulins ordered <25x during the admission
+	- Not > 2 during hospitalization
+	- No insulin dose >100U
+
+Day inclusion criteria:
+	- >/= 3 BG checks
+	- No value </=100 or > 180
+
+
+Number of patient-days with good control: 63,736
+Distinct number of patients with good control: 20,551
\ No newline at end of file
diff --git a/scripts/InpatientDiabetes/TDD prediction/TDD_insulin.sql b/scripts/InpatientDiabetes/TDD prediction/TDD_insulin.sql
new file mode 100644
index 00000000..72cf0b2d
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD prediction/TDD_insulin.sql	
@@ -0,0 +1,414 @@
+-- TDD by day by patient. Insulin not pumps or U500 or rare. Patient without AKI during that encounter. 
+
+SELECT mar.jc_uid, (SUM(CAST(mar.sig AS float64))) as TDD, DATE(mar.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+    )
+    
+GROUP BY date, mar.jc_uid
+ORDER BY mar.jc_uid, date 
+
+
+-- 
+-- Average BG on days when every BG between 100-180
+SELECT a.rit_uid, DATE(a.taken_time_jittered) as date  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+
+WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+AND a.lab_name = "Glucose by Meter"
+AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+-- AND a.ord_num_value NOT BETWEEN 100 AND 180 
+
+AND a.pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)  
+)
+
+GROUP BY a.rit_uid, date
+
+EXCEPT DISTINCT SELECT b.rit_uid, DATE(b.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+AND b.lab_name = "Glucose by Meter"
+AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+GROUP BY b.rit_uid, date
+
+ORDER BY rit_uid, date
+
+
+-- 
+-- TDD on days where BG was 100-180
+SELECT jc_uid, TDD, date FROM
+(SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as date, (SUM(CAST(mar.sig AS float64))) as TDD, FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+    )
+    
+GROUP BY date, mar.jc_uid
+ORDER BY mar.jc_uid, date 
+)
+
+
+WHERE CONCAT(jc_uid,date)  IN
+
+(SELECT CONCAT(rit_uid,date) FROM
+
+(SELECT a.rit_uid, DATE(a.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+
+WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+AND a.lab_name = "Glucose by Meter"
+AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+-- AND a.ord_num_value NOT BETWEEN 100 AND 180 
+
+AND a.pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+    
+)
+
+GROUP BY a.rit_uid, date
+
+EXCEPT DISTINCT SELECT b.rit_uid, DATE(b.taken_time_jittered) as date  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+
+WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+AND b.lab_name = "Glucose by Meter"
+AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+GROUP BY b.rit_uid, date
+
+)
+)
+
+
+ORDER BY jc_uid, date
+
+--
+-- Avg BG on days where all checks between 100-180 and at least 3 checks
+SELECT jc_uid, TDD, date FROM
+(SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as date, (SUM(CAST(mar.sig AS float64))) as TDD, FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+    )
+    
+GROUP BY date, mar.jc_uid
+ORDER BY mar.jc_uid, date 
+)
+
+
+WHERE CONCAT(jc_uid,date)  IN
+
+(SELECT CONCAT(rit_uid,date) FROM
+
+(SELECT a.rit_uid, DATE(a.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+
+WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+AND a.lab_name = "Glucose by Meter"
+AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+-- AND a.ord_num_value NOT BETWEEN 100 AND 180 
+
+AND a.pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+    
+)
+
+GROUP BY a.rit_uid, date
+
+EXCEPT DISTINCT SELECT b.rit_uid, DATE(b.taken_time_jittered) as date  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+
+WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+AND b.lab_name = "Glucose by Meter"
+AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+GROUP BY b.rit_uid, date having (count(b.lab_name) >2) -- at least 3 lab checks on that date
+
+)
+)
+
+ORDER BY jc_uid desc, date 
+
+
+
+
+---
+WITH patientDatesWithAtLeast3POCGlucoseWithinRange AS
+(
+  SELECT rit_uid as jc_uid, DATE(taken_time_jittered) AS patientDate, count(ord_num_value) AS numBG, min(ord_num_value) AS minBG, max(ord_num_value) AS maxBG
+  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result`
+  WHERE proc_code = 'LABPOCGLU' -- Point of care glucose
+  AND base_name = 'GLU' -- Glucose by Meter (lab_name)
+  AND ord_num_value < 9999999
+  GROUP BY rit_uid, patientDate
+  HAVING numBG >= 3
+  AND minBG >= 100
+  AND maxBG < 180
+),
+--- Make separate query to eliminate patient days WHere ever used pump or other of the undesired insulin types
+patientDatesTotalDailyDoseSubQInsulinNoAKI AS
+(
+  SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as patientDate, SUM(CAST(mar.sig AS float64)) as TDD
+  FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded = medord.order_med_id_coded
+  WHERE UPPER(medord.med_description) LIKE '%INSULIN%'-- insulin ordered)
+  AND medord.ordering_mode_c = 2 -- inpatient
+  AND medord.med_route = 'Subcutaneous' --and subQ
+  AND mar.mar_action = 'Given' --medication actually given
+  AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+  AND mar.sig <> "0-10"
+  AND CAST(mar.sig AS float64) > 0
+  AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+  AND mar.sig IS NOT NULL
+  
+  -- Exclude other types of insulin don't want to capture
+  AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med`
+       WHERE UPPER(med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND med_description NOT IN
+       (   "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG"
+      ) -- removes anything ordered <10 times
+      AND med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+      AND med_description NOT LIKE "%U-500%" -- exlcudes U-500
+   )
+     AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar`
+      WHERE (mar_action) NOT IN 
+        ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push'
+        )
+      AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+    )
+  -- Exclude any encounter where patient EVER had an elevated creatinine > 2 (even if it was days later or before)
+  AND mar.pat_enc_csn_id_coded NOT IN
+  ( SELECT lab.pat_enc_csn_id_coded
+    FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+  )
+
+  GROUP BY patientDate, mar.jc_uid
+  ORDER BY mar.jc_uid, patientDate
+)
+SELECT a.*, b.TDD
+from patientDatesWithAtLeast3POCGlucoseWithinRange as a
+join patientDatesTotalDailyDoseSubQInsulinNoAKI as b on (a.jc_uid = b.jc_uid and a.patientDate = b.patientDate)
+
+
+
+--
+-- Average and STDev of TDD
+WITH patientDatesWithAtLeast3POCGlucoseWithinRange AS
+(
+  SELECT rit_uid as jc_uid, DATE(taken_time_jittered) AS patientDate, count(ord_num_value) AS numBG, min(ord_num_value) AS minBG, max(ord_num_value) AS maxBG
+  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result`
+  WHERE proc_code = 'LABPOCGLU' -- Point of care glucose
+  AND base_name = 'GLU' -- Glucose by Meter (lab_name)
+  AND ord_num_value < 9999999
+  GROUP BY rit_uid, patientDate
+  HAVING numBG >= 3
+  AND minBG >= 100
+  AND maxBG < 180
+),
+--- Make separate query to eliminate patient days WHere ever used pump or other of the undesired insulin types
+patientDatesTotalDailyDoseSubQInsulinNoAKI AS
+(
+  SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as patientDate, SUM(CAST(mar.sig AS float64)) as TDD
+  FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded = medord.order_med_id_coded
+  WHERE UPPER(medord.med_description) LIKE '%INSULIN%'-- insulin ordered)
+  AND medord.ordering_mode_c = 2 -- inpatient
+  AND medord.med_route = 'Subcutaneous' --and subQ
+  AND mar.mar_action = 'Given' --medication actually given
+  AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+  AND mar.sig <> "0-10"
+  AND CAST(mar.sig AS float64) > 0
+  AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+  AND mar.sig IS NOT NULL
+  --******* TEST CODE
+  ---AND mar.jc_uid IN ('JCcb65e1', 'JCcb65ee', 'JCcb6601', 'JCcb6e1b', 'JCcb6e75')
+  -- Exclude other types of insulin don't want to capture
+  AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med`
+       WHERE UPPER(med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND med_description NOT IN
+       (   "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG"
+      ) -- removes anything ordered <10 times
+       AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+      AND med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+      AND med_description NOT LIKE "%U-500%" -- exlcudes U-500
+   )
+     AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar`
+      WHERE (mar_action) NOT IN 
+        ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push'
+        )
+      AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+    )
+  -- Exclude any encounter where patient EVER had an elevated creatinine > 2 (even if it was days later or before)
+  AND mar.pat_enc_csn_id_coded NOT IN
+  ( SELECT lab.pat_enc_csn_id_coded
+    FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+  )
+
+  GROUP BY patientDate, mar.jc_uid
+  ORDER BY mar.jc_uid, patientDate
+)
+
+SELECT AVG(TDD) as MeanTDD, STDDEV(TDD) as StDevTDD FROM
+  (
+SELECT a.*, b.TDD
+from patientDatesWithAtLeast3POCGlucoseWithinRange as a
+join patientDatesTotalDailyDoseSubQInsulinNoAKI as b on (a.jc_uid = b.jc_uid and a.patientDate = b.patientDate)
+
+ORDER BY jc_uid, patientDate
+  )
+
+
+
+
+
diff --git a/scripts/InpatientDiabetes/TDD prediction/TDD_subqueries.sql b/scripts/InpatientDiabetes/TDD prediction/TDD_subqueries.sql
new file mode 100644
index 00000000..fe98c12e
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD prediction/TDD_subqueries.sql	
@@ -0,0 +1,187 @@
+
+-- 
+-- Patients receiving 1-100u per dose inpatient subQ insulin that is not a pump, U500, or a rare order (<25 orders in dataset)
+-- Verified that dose was given, dose 1-100, insulins as expected when removing pumps, partial units, insulins ordered <25x
+-- If ORDER BY units DESC, top 10 rows should be 98-95U and time 2012 (1) & 2011 (10)
+WITH inpt_insulin_given AS ( 
+  SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, mar.taken_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+    LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+    WHERE mar.order_med_id_coded in 
+    (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+         WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+         AND (medord2.ordering_mode_c) = 2 -- inpatient
+         AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+         AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+         AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+         AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+        AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+        AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+        AND medord2.med_route = 'Subcutaneous'
+        ) --and subQ
+      AND (mar.mar_action) IN ('Given') --medication actually given
+      AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+      AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+      AND mar.sig <> "0-10"
+      AND CAST(mar.sig AS float64) > 0
+      AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+      AND mar.sig IS NOT NULL 
+      AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+    ),
+
+--
+-- Pt encounters with creatinine > 2 (will use to exclude hospitalizations where pt had Cr > 2)
+-- If DISTINCT pt encounters & ORDER BY pt encounter, top 5 will end in 6-6-6-0-9
+cr_over_2 AS (
+  SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- patient encounters with creatinine >2 
+      WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+      AND ord_num_value != 9999999
+      AND taken_time_jittered IS NOT null
+      AND ord_num_value > 2
+      ),
+
+-- 
+-- 
+inpt_insulin_given_noAKI AS ( 
+  SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, mar.taken_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+    LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+    WHERE mar.order_med_id_coded in 
+    (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+         WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+         AND (medord2.ordering_mode_c) = 2 -- inpatient
+         AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+         AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+         AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+         AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+        AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+        AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+        AND medord2.med_route = 'Subcutaneous'
+        ) --and subQ
+      AND (mar.mar_action) IN ('Given') --medication actually given
+      AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+      AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+      AND mar.sig <> "0-10"
+      AND CAST(mar.sig AS float64) > 0
+      AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+      AND mar.sig IS NOT NULL 
+      AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+      AND mar.pat_enc_csn_id_coded NOT IN 
+        (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+        WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+        AND ord_num_value != 9999999
+        AND taken_time_jittered IS NOT null
+        AND ord_num_value > 2
+        )
+      ),
+
+
+--
+-- Glucose by meter
+gluc_by_meter AS (
+      SELECT a.rit_uid, DATE(a.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+        WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+        AND a.lab_name = "Glucose by Meter"
+        AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+      ),
+
+--
+-- patient + date where BG <100 or >180
+-- Date 10 should be 9/24, Date 76 should be 3/17
+days_outofrange AS (
+  SELECT b.rit_uid, DATE(b.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+    WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+    AND b.lab_name = "Glucose by Meter"
+    AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+    AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+  GROUP BY b.rit_uid, date
+  ORDER BY rit_uid, date
+  ),
+
+--
+-- patiet encounters excluding insulins ordered <25x/pumps/U500, where basal insulin was ordered subQ and inpatient
+-- Distinct order by pt encounter top 5 end with 7-8-9-4-0
+basal_ordered AS (
+  SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2 -- excludes patients who had basal insulin ordered * except for 2 orders of NPH that are excluded above
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered <10-25 times, conveniently excluding any U200 or U300
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP", "NPH INSULIN HUMAN RECOMB 100 UNIT/ML SC SUSP", "INSULIN GLARGINE 100 UNIT/ML SC SOLN", "INSULIN DETEMIR 100 UNIT/ML SC SOLN", "INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP", "INSULIN DETEMIR U-100 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70/30) SUBCUTANEOUS VIAL", "INSULIN DETEMIR 100 UNIT/ML (3 ML) SC INPN", "INSULIN DETEMIR U-100 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (50-50) SC SUSP", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP","INSULIN DETEMIR 100 UNIT/ML SC INPN", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC INPN") -- patients ordered for basal)
+),
+
+--
+-- Weight converted from oz to Kg for pts getting inpatient insulin subQ not on pump/u500/ordered <25x 
+-- Note that "meas value" in the flowsheet contains real dates **do not download**, num_value2 may have some data but appears unreliable 
+weight_pt_getting_insulin AS (
+SELECT rit_uid, row_disp_name, num_value1, (num_value1*0.028) AS KG, recorded_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet`
+-- SELECT (num_value1*0.028) AS KG, count(meas_value) as count  FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` 
+  WHERE rit_uid IN (SELECT mar.jc_uid FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+    LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+    WHERE mar.order_med_id_coded in 
+    (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+         WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+         AND (medord2.ordering_mode_c) = 2 -- inpatient
+         AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+         AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+         AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+         AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+        AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+        AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+        AND medord2.med_route = 'Subcutaneous'
+        ) --and subQ
+    AND (mar.mar_action) IN ('Given') --medication actually given
+    AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+    AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+    AND mar.sig <> "0-10"
+    AND CAST(mar.sig AS float64) > 0
+    AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+    AND mar.sig IS NOT NULL 
+    AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+    AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+  )
+
+AND row_disp_name LIKE "Weight"
+AND num_value1 IS NOT NULL --numeric value exists
+)
+
+--
+-- wt, time, age table for adults who got subQ insulin with wt > 45kg and < doubling in weight between time in flowsheet and "most recent wt"
+recent_wt_flowsht_insulin AS (
+SELECT dem.rit_uid, DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) as ageYears, flow.row_disp_name, flow.meas_value, flow.recorded_time_jittered, (flow.num_value1*0.028) AS KG, dem.recent_wt_in_kgs, (dem.recent_wt_in_kgs-(flow.num_value1*0.028)) as wt_diff, ((dem.recent_wt_in_kgs-(flow.num_value1*0.028))/dem.recent_wt_in_kgs) as wt_diff_percent, bmi FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` AS flow
+INNER JOIN `som-nero-phi-jonc101.starr_datalake2018.demographic` AS dem ON dem.rit_uid = flow.rit_uid 
+WHERE dem.rit_uid IN 
+  (SELECT rit_uid,FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` where inpatient_data_id_coded IN
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar` WHERE order_med_id_coded in --patient received subQ insulin
+      (SELECT order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` 
+       WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+    AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND sig <> "0-10"
+   AND sig IS NOT NULL 
+   AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   )
+  )
+AND row_disp_name LIKE "Weight"
+AND UPPER(meas_value) NOT LIKE "%W%" -- removes "XweeksXdays"-- presumed misentered data
+AND UPPER(meas_value) NOT LIKE "%/%" -- Removes fractions of lbs
+AND UPPER(meas_value) NOT LIKE "%GMS%" --Removes gms
+AND UPPER(meas_value) NOT LIKE "%LBS%" -- Removes lbs
+AND UPPER(meas_value) NOT LIKE "% G%" -- Removes grams 
+AND num_value1 IS NOT NULL --numeric value exists
+AND (flow.num_value1*0.028) > 45 -- wt > 45kg (99lbs)
+AND DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) >= 18 -- ADULTS ONLY
+AND dem.recent_wt_in_kgs != 0 -- recent wt not equal zero (there are no pts with recent wt = 0 but if I don't do this the percent returns an error)
+AND ((dem.recent_wt_in_kgs-(flow.num_value1*0.028))/dem.recent_wt_in_kgs) < 0.5 -- Pt did not > double in wt (Quality control)
+)
+
+
diff --git a/scripts/InpatientDiabetes/TDD_insulin.sql b/scripts/InpatientDiabetes/TDD_insulin.sql
new file mode 100644
index 00000000..a16e3191
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD_insulin.sql
@@ -0,0 +1,413 @@
+-- TDD by day by patient. Insulin not pumps or U500 or rare. Patient without AKI during that encounter. 
+
+SELECT mar.jc_uid, (SUM(CAST(mar.sig AS float64))) as TDD, DATE(mar.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+    )
+    
+GROUP BY date, mar.jc_uid
+ORDER BY mar.jc_uid, date 
+
+
+-- 
+-- Average BG on days when every BG between 100-180
+SELECT a.rit_uid, DATE(a.taken_time_jittered) as date  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+
+WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+AND a.lab_name = "Glucose by Meter"
+AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+-- AND a.ord_num_value NOT BETWEEN 100 AND 180 
+
+AND a.pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)  
+)
+
+GROUP BY a.rit_uid, date
+
+EXCEPT DISTINCT SELECT b.rit_uid, DATE(b.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+AND b.lab_name = "Glucose by Meter"
+AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+GROUP BY b.rit_uid, date
+
+ORDER BY rit_uid, date
+
+
+-- 
+-- TDD on days where BG was 100-180
+SELECT jc_uid, TDD, date FROM
+(SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as date, (SUM(CAST(mar.sig AS float64))) as TDD, FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+    )
+    
+GROUP BY date, mar.jc_uid
+ORDER BY mar.jc_uid, date 
+)
+
+
+WHERE CONCAT(jc_uid,date)  IN
+
+(SELECT CONCAT(rit_uid,date) FROM
+
+(SELECT a.rit_uid, DATE(a.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+
+WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+AND a.lab_name = "Glucose by Meter"
+AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+-- AND a.ord_num_value NOT BETWEEN 100 AND 180 
+
+AND a.pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+    
+)
+
+GROUP BY a.rit_uid, date
+
+EXCEPT DISTINCT SELECT b.rit_uid, DATE(b.taken_time_jittered) as date  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+
+WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+AND b.lab_name = "Glucose by Meter"
+AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+GROUP BY b.rit_uid, date
+
+)
+)
+
+
+ORDER BY jc_uid, date
+
+--
+-- Avg BG on days where all checks between 100-180 and at least 3 checks
+SELECT jc_uid, TDD, date FROM
+(SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as date, (SUM(CAST(mar.sig AS float64))) as TDD, FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+    )
+    
+GROUP BY date, mar.jc_uid
+ORDER BY mar.jc_uid, date 
+)
+
+
+WHERE CONCAT(jc_uid,date)  IN
+
+(SELECT CONCAT(rit_uid,date) FROM
+
+(SELECT a.rit_uid, DATE(a.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+
+WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+AND a.lab_name = "Glucose by Meter"
+AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+-- AND a.ord_num_value NOT BETWEEN 100 AND 180 
+
+AND a.pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+    
+)
+
+GROUP BY a.rit_uid, date
+
+EXCEPT DISTINCT SELECT b.rit_uid, DATE(b.taken_time_jittered) as date  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+
+WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+AND b.lab_name = "Glucose by Meter"
+AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+GROUP BY b.rit_uid, date having (count(b.lab_name) >2) -- at least 3 lab checks on that date
+
+)
+)
+
+ORDER BY jc_uid desc, date 
+
+
+
+
+---
+WITH patientDatesWithAtLeast3POCGlucoseWithinRange AS
+(
+  SELECT rit_uid as jc_uid, DATE(taken_time_jittered) AS patientDate, count(ord_num_value) AS numBG, min(ord_num_value) AS minBG, max(ord_num_value) AS maxBG
+  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result`
+  WHERE proc_code = 'LABPOCGLU' -- Point of care glucose
+  AND base_name = 'GLU' -- Glucose by Meter (lab_name)
+  AND ord_num_value < 9999999
+  GROUP BY rit_uid, patientDate
+  HAVING numBG >= 3
+  AND minBG >= 100
+  AND maxBG < 180
+),
+--- Make separate query to eliminate patient days WHere ever used pump or other of the undesired insulin types
+patientDatesTotalDailyDoseSubQInsulinNoAKI AS
+(
+  SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as patientDate, SUM(CAST(mar.sig AS float64)) as TDD
+  FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded = medord.order_med_id_coded
+  WHERE UPPER(medord.med_description) LIKE '%INSULIN%'-- insulin ordered)
+  AND medord.ordering_mode_c = 2 -- inpatient
+  AND medord.med_route = 'Subcutaneous' --and subQ
+  AND mar.mar_action = 'Given' --medication actually given
+  AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+  AND mar.sig <> "0-10"
+  AND CAST(mar.sig AS float64) > 0
+  AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+  AND mar.sig IS NOT NULL
+  
+  -- Exclude other types of insulin don't want to capture
+  AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med`
+       WHERE UPPER(med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND med_description NOT IN
+       (   "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG"
+      ) -- removes anything ordered <10 times
+      AND med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+      AND med_description NOT LIKE "%U-500%" -- exlcudes U-500
+   )
+     AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar`
+      WHERE (mar_action) NOT IN 
+        ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push'
+        )
+      AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+    )
+  -- Exclude any encounter where patient EVER had an elevated creatinine > 2 (even if it was days later or before)
+  AND mar.pat_enc_csn_id_coded NOT IN
+  ( SELECT lab.pat_enc_csn_id_coded
+    FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+  )
+
+  GROUP BY patientDate, mar.jc_uid
+  ORDER BY mar.jc_uid, patientDate
+)
+SELECT a.*, b.TDD
+from patientDatesWithAtLeast3POCGlucoseWithinRange as a
+join patientDatesTotalDailyDoseSubQInsulinNoAKI as b on (a.jc_uid = b.jc_uid and a.patientDate = b.patientDate)
+
+
+
+--
+-- Average and STDev of TDD
+WITH patientDatesWithAtLeast3POCGlucoseWithinRange AS
+(
+  SELECT rit_uid as jc_uid, DATE(taken_time_jittered) AS patientDate, count(ord_num_value) AS numBG, min(ord_num_value) AS minBG, max(ord_num_value) AS maxBG
+  FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result`
+  WHERE proc_code = 'LABPOCGLU' -- Point of care glucose
+  AND base_name = 'GLU' -- Glucose by Meter (lab_name)
+  AND ord_num_value < 9999999
+  GROUP BY rit_uid, patientDate
+  HAVING numBG >= 3
+  AND minBG >= 100
+  AND maxBG < 180
+),
+--- Make separate query to eliminate patient days WHere ever used pump or other of the undesired insulin types
+patientDatesTotalDailyDoseSubQInsulinNoAKI AS
+(
+  SELECT mar.jc_uid, DATE(mar.taken_time_jittered) as patientDate, SUM(CAST(mar.sig AS float64)) as TDD
+  FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded = medord.order_med_id_coded
+  WHERE UPPER(medord.med_description) LIKE '%INSULIN%'-- insulin ordered)
+  AND medord.ordering_mode_c = 2 -- inpatient
+  AND medord.med_route = 'Subcutaneous' --and subQ
+  AND mar.mar_action = 'Given' --medication actually given
+  AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+  AND mar.sig <> "0-10"
+  AND CAST(mar.sig AS float64) > 0
+  AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+  AND mar.sig IS NOT NULL
+  --******* TEST CODE
+  ---AND mar.jc_uid IN ('JCcb65e1', 'JCcb65ee', 'JCcb6601', 'JCcb6e1b', 'JCcb6e75')
+  -- Exclude other types of insulin don't want to capture
+  AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med`
+       WHERE UPPER(med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND med_description NOT IN
+       (   "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG"
+      ) -- removes anything ordered <10 times
+      AND med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+      AND med_description NOT LIKE "%U-500%" -- exlcudes U-500
+   )
+     AND mar.pat_enc_csn_id_coded IN 
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar`
+      WHERE (mar_action) NOT IN 
+        ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push'
+        )
+      AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+    )
+  -- Exclude any encounter where patient EVER had an elevated creatinine > 2 (even if it was days later or before)
+  AND mar.pat_enc_csn_id_coded NOT IN
+  ( SELECT lab.pat_enc_csn_id_coded
+    FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2
+  )
+
+  GROUP BY patientDate, mar.jc_uid
+  ORDER BY mar.jc_uid, patientDate
+)
+
+SELECT AVG(TDD) as MeanTDD, STDDEV(TDD) as StDevTDD FROM
+  (
+SELECT a.*, b.TDD
+from patientDatesWithAtLeast3POCGlucoseWithinRange as a
+join patientDatesTotalDailyDoseSubQInsulinNoAKI as b on (a.jc_uid = b.jc_uid and a.patientDate = b.patientDate)
+
+ORDER BY jc_uid, patientDate
+  )
+
+
+
+
+
diff --git a/scripts/InpatientDiabetes/TDD_subqueries.sql b/scripts/InpatientDiabetes/TDD_subqueries.sql
new file mode 100644
index 00000000..fe98c12e
--- /dev/null
+++ b/scripts/InpatientDiabetes/TDD_subqueries.sql
@@ -0,0 +1,187 @@
+
+-- 
+-- Patients receiving 1-100u per dose inpatient subQ insulin that is not a pump, U500, or a rare order (<25 orders in dataset)
+-- Verified that dose was given, dose 1-100, insulins as expected when removing pumps, partial units, insulins ordered <25x
+-- If ORDER BY units DESC, top 10 rows should be 98-95U and time 2012 (1) & 2011 (10)
+WITH inpt_insulin_given AS ( 
+  SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, mar.taken_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+    LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+    WHERE mar.order_med_id_coded in 
+    (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+         WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+         AND (medord2.ordering_mode_c) = 2 -- inpatient
+         AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+         AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+         AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+         AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+        AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+        AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+        AND medord2.med_route = 'Subcutaneous'
+        ) --and subQ
+      AND (mar.mar_action) IN ('Given') --medication actually given
+      AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+      AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+      AND mar.sig <> "0-10"
+      AND CAST(mar.sig AS float64) > 0
+      AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+      AND mar.sig IS NOT NULL 
+      AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+    ),
+
+--
+-- Pt encounters with creatinine > 2 (will use to exclude hospitalizations where pt had Cr > 2)
+-- If DISTINCT pt encounters & ORDER BY pt encounter, top 5 will end in 6-6-6-0-9
+cr_over_2 AS (
+  SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- patient encounters with creatinine >2 
+      WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+      AND ord_num_value != 9999999
+      AND taken_time_jittered IS NOT null
+      AND ord_num_value > 2
+      ),
+
+-- 
+-- 
+inpt_insulin_given_noAKI AS ( 
+  SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, mar.taken_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+    LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+    WHERE mar.order_med_id_coded in 
+    (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+         WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+         AND (medord2.ordering_mode_c) = 2 -- inpatient
+         AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+         AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+         AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+         AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+        AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+        AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+        AND medord2.med_route = 'Subcutaneous'
+        ) --and subQ
+      AND (mar.mar_action) IN ('Given') --medication actually given
+      AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+      AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+      AND mar.sig <> "0-10"
+      AND CAST(mar.sig AS float64) > 0
+      AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+      AND mar.sig IS NOT NULL 
+      AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+      AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+      AND mar.pat_enc_csn_id_coded NOT IN 
+        (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+        WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+        AND ord_num_value != 9999999
+        AND taken_time_jittered IS NOT null
+        AND ord_num_value > 2
+        )
+      ),
+
+
+--
+-- Glucose by meter
+gluc_by_meter AS (
+      SELECT a.rit_uid, DATE(a.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as a 
+        WHERE UPPER(a.lab_name) LIKE '%GLUCOSE%' 
+        AND a.lab_name = "Glucose by Meter"
+        AND UPPER(a.ordering_mode) = 'INPATIENT' AND a.ord_num_value BETWEEN 0 AND 9999998
+      ),
+
+--
+-- patient + date where BG <100 or >180
+-- Date 10 should be 9/24, Date 76 should be 3/17
+days_outofrange AS (
+  SELECT b.rit_uid, DATE(b.taken_time_jittered) as date FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as b
+    WHERE UPPER(b.lab_name) LIKE '%GLUCOSE%' 
+    AND b.lab_name = "Glucose by Meter"
+    AND UPPER(b.ordering_mode) = 'INPATIENT' AND b.ord_num_value BETWEEN 0 AND 9999998
+    AND b.ord_num_value NOT BETWEEN 100 AND 180 
+
+  GROUP BY b.rit_uid, date
+  ORDER BY rit_uid, date
+  ),
+
+--
+-- patiet encounters excluding insulins ordered <25x/pumps/U500, where basal insulin was ordered subQ and inpatient
+-- Distinct order by pt encounter top 5 end with 7-8-9-4-0
+basal_ordered AS (
+  SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2 -- excludes patients who had basal insulin ordered * except for 2 orders of NPH that are excluded above
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered <10-25 times, conveniently excluding any U200 or U300
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP", "NPH INSULIN HUMAN RECOMB 100 UNIT/ML SC SUSP", "INSULIN GLARGINE 100 UNIT/ML SC SOLN", "INSULIN DETEMIR 100 UNIT/ML SC SOLN", "INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP", "INSULIN DETEMIR U-100 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70/30) SUBCUTANEOUS VIAL", "INSULIN DETEMIR 100 UNIT/ML (3 ML) SC INPN", "INSULIN DETEMIR U-100 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (50-50) SC SUSP", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP","INSULIN DETEMIR 100 UNIT/ML SC INPN", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC INPN") -- patients ordered for basal)
+),
+
+--
+-- Weight converted from oz to Kg for pts getting inpatient insulin subQ not on pump/u500/ordered <25x 
+-- Note that "meas value" in the flowsheet contains real dates **do not download**, num_value2 may have some data but appears unreliable 
+weight_pt_getting_insulin AS (
+SELECT rit_uid, row_disp_name, num_value1, (num_value1*0.028) AS KG, recorded_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet`
+-- SELECT (num_value1*0.028) AS KG, count(meas_value) as count  FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` 
+  WHERE rit_uid IN (SELECT mar.jc_uid FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+    LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+    WHERE mar.order_med_id_coded in 
+    (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+         WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+         AND (medord2.ordering_mode_c) = 2 -- inpatient
+         AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+         AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+         AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+         AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+        AND (medord2.med_description) NOT IN ("INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING (BEDTIME)", "INSULIN GLULISINE 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", " INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN") -- removes any insulin ordered < 10-25 times, conveniently excluding any U200 or U300
+        AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+        AND medord2.med_route = 'Subcutaneous'
+        ) --and subQ
+    AND (mar.mar_action) IN ('Given') --medication actually given
+    AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+    AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+    AND mar.sig <> "0-10"
+    AND CAST(mar.sig AS float64) > 0
+    AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+    AND mar.sig IS NOT NULL 
+    AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+    AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+  )
+
+AND row_disp_name LIKE "Weight"
+AND num_value1 IS NOT NULL --numeric value exists
+)
+
+--
+-- wt, time, age table for adults who got subQ insulin with wt > 45kg and < doubling in weight between time in flowsheet and "most recent wt"
+recent_wt_flowsht_insulin AS (
+SELECT dem.rit_uid, DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) as ageYears, flow.row_disp_name, flow.meas_value, flow.recorded_time_jittered, (flow.num_value1*0.028) AS KG, dem.recent_wt_in_kgs, (dem.recent_wt_in_kgs-(flow.num_value1*0.028)) as wt_diff, ((dem.recent_wt_in_kgs-(flow.num_value1*0.028))/dem.recent_wt_in_kgs) as wt_diff_percent, bmi FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` AS flow
+INNER JOIN `som-nero-phi-jonc101.starr_datalake2018.demographic` AS dem ON dem.rit_uid = flow.rit_uid 
+WHERE dem.rit_uid IN 
+  (SELECT rit_uid,FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` where inpatient_data_id_coded IN
+    (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar` WHERE order_med_id_coded in --patient received subQ insulin
+      (SELECT order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` 
+       WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+    AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND sig <> "0-10"
+   AND sig IS NOT NULL 
+   AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   )
+  )
+AND row_disp_name LIKE "Weight"
+AND UPPER(meas_value) NOT LIKE "%W%" -- removes "XweeksXdays"-- presumed misentered data
+AND UPPER(meas_value) NOT LIKE "%/%" -- Removes fractions of lbs
+AND UPPER(meas_value) NOT LIKE "%GMS%" --Removes gms
+AND UPPER(meas_value) NOT LIKE "%LBS%" -- Removes lbs
+AND UPPER(meas_value) NOT LIKE "% G%" -- Removes grams 
+AND num_value1 IS NOT NULL --numeric value exists
+AND (flow.num_value1*0.028) > 45 -- wt > 45kg (99lbs)
+AND DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) >= 18 -- ADULTS ONLY
+AND dem.recent_wt_in_kgs != 0 -- recent wt not equal zero (there are no pts with recent wt = 0 but if I don't do this the percent returns an error)
+AND ((dem.recent_wt_in_kgs-(flow.num_value1*0.028))/dem.recent_wt_in_kgs) < 0.5 -- Pt did not > double in wt (Quality control)
+)
+
+
diff --git a/scripts/InpatientDiabetes/flowsheetglucoses.sql b/scripts/InpatientDiabetes/flowsheetglucoses.sql
new file mode 100644
index 00000000..8406ee19
--- /dev/null
+++ b/scripts/InpatientDiabetes/flowsheetglucoses.sql
@@ -0,0 +1,17 @@
+SELECT rit_uid, row_disp_name, meas_value  FROM `mining-clinical-decisions.starr_datalake2018.flowsheet`
+
+WHERE (lower(row_disp_name) IN ('bedside fs glucose (mg/dl)', 'diabetes', 'diabetic control', 'diabetic type', 'glucose'))
+
+ORDER BY row_disp_name, rit_uid 
+
+LIMIT 10000
+
+-- Bedside FS Glucose (mg/dL)
+-- Blood sugar range: useless 
+-- Diabetes Mellitus: useless 
+-- Diabetes
+-- Diabetic Control: "controlled" or "uncontrolled"
+-- Diabetic Type: Type 1, Type 2, null, Type 2 Simultaneous filing, Type 2 Added and corrected by GA   
+-- Glu: 19 values
+-- Glucose: urinary (trace, null, negative, 0, 1+), also serum values w/ nursing comments
+-- Glucose > 249 : useless 
diff --git a/scripts/InpatientDiabetes/inpt_glucose_labs.sql b/scripts/InpatientDiabetes/inpt_glucose_labs.sql
new file mode 100644
index 00000000..95f0bcb4
--- /dev/null
+++ b/scripts/InpatientDiabetes/inpt_glucose_labs.sql
@@ -0,0 +1,99 @@
+--
+-- Glucose lab values by person
+SELECT rit_uid, pat_enc_csn_id_coded, lab_name, ord_value, ord_num_value, taken_time_jittered, result_time_jittered FROM `mining-clinical-decisions.starr_datalake2018.lab_result` 
+-- SELECT lab_name, COUNT(lab_name) as number FROM `mining-clinical-decisions.starr_datalake2018.lab_result` 
+WHERE UPPER(lab_name) LIKE '%GLUCOSE%' 
+AND (lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose urine', 'Glucose/Creat ratio', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose", "Glucose,GDM 3HR", "Glucose,GDM 2HR", 
+"Glucose,GDM 1HR", "Glucose, 2 HR", "Glucose, 1 HR", "Glucose, Fasting (GDMF)", "Glucose,GTT 30 min", "Glucose,GTT 1HR", "Glucose,GTT 2HR", "Glucose,2HR - 75g", "Glucose, GDM Screen", "GLUCOSE", "Glucose") -- excludes glucoses not for insulin dose; also "GLUCOSE" and "Glucose" only used n=3 times
+AND UPPER(ordering_mode) = 'INPATIENT' AND ord_num_value BETWEEN 0 AND 9999998
+-- GROUP BY lab_name 
+-- ORDER BY number
+
+--
+-- Glucose by meter lab values
+SELECT rit_uid, pat_enc_csn_id_coded, lab_name, base_name, ordering_mode, ord_value, ord_num_value, taken_time_jittered, result_time_jittered FROM `mining-clinical-decisions.starr_datalake2018.lab_result` 
+-- SELECT ord_num_value, count(ord_num_value) as number FROM `mining-clinical-decisions.starr_datalake2018.lab_result`
+-- SELECT lab_name, COUNT(lab_name) as number FROM `mining-clinical-decisions.starr_datalake2018.lab_result` 
+WHERE UPPER(lab_name) LIKE '%GLUCOSE%' 
+--AND (lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose urine', 'Glucose/Creat ratio', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose", "Glucose,GDM 3HR", "Glucose,GDM 2HR", "Glucose,GDM 1HR", "Glucose, 2 HR", "Glucose, 1 HR", "Glucose, Fasting (GDMF)", "Glucose,GTT 30 min", "Glucose,GTT 1HR", "Glucose,GTT 2HR", "Glucose,2HR - 75g", "Glucose, GDM Screen", "GLUCOSE", "Glucose") -- excludes glucoses not for insulin dose; also "GLUCOSE" and "Glucose" only used n=3 times
+AND lab_name = "Glucose by Meter"
+AND UPPER(ordering_mode) = 'INPATIENT' AND ord_num_value BETWEEN 0 AND 9999998
+AND ord_num_value BETWEEN 80 AND 200
+order by rit_uid, taken_time_jittered, result_time_jittered 
+
+--
+-- Creatinine
+SELECT rit_uid, pat_enc_csn_id_coded, lab_name, ord_num_value, taken_time_jittered    FROM `mining-clinical-decisions.datalake_47618.lab_result` 
+WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+AND ord_num_value != 9999999
+AND taken_time_jittered IS NOT null
+
+--
+-- Patient encounters with AKI/CKD
+SELECT rit_uid, pat_enc_csn_id_coded, lab_name, ord_num_value, taken_time_jittered    FROM `mining-clinical-decisions.datalake_47618.lab_result` as lab
+WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+AND ord_num_value != 9999999
+AND taken_time_jittered IS NOT null
+AND ord_num_value > 2 -- patient with AKI/CKD, rough approximation 
+
+--
+-- Labs for the patient cohort of non-pump pts with no AKI who received insulin
+-- SELECT AVG(ord_num_value) as average FROM `mining-clinical-decisions.starr_datalake2018.lab_result` 
+SELECT COUNT(ord_num_value) as count FROM `mining-clinical-decisions.starr_datalake2018.lab_result` 
+-- SELECT rit_uid, pat_enc_csn_id_coded, lab_name, base_name, ordering_mode, ord_value, ord_num_value, taken_time_jittered, result_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` 
+-- SELECT ord_num_value, count(ord_num_value) as number FROM `mining-clinical-decisions.starr_datalake2018.lab_result`
+WHERE UPPER(lab_name) LIKE '%GLUCOSE%' 
+--AND (lab_name) NOT IN ( 'Glucose, Urine', 'Glucose Urine', 'Glucose urine', 'Glucose/Creat ratio', 'Glucose, Fluid', "Est. Mean Glucose", "Glucose, CSF", "Collection Period Glucose", "Glucose Excretion", "Glucose Body Fluid", "Volume Glucose", "Glucose,GDM 3HR", "Glucose,GDM 2HR", "Glucose,GDM 1HR", "Glucose, 2 HR", "Glucose, 1 HR", "Glucose, Fasting (GDMF)", "Glucose,GTT 30 min", "Glucose,GTT 1HR", "Glucose,GTT 2HR", "Glucose,2HR - 75g", "Glucose, GDM Screen", "GLUCOSE", "Glucose") -- excludes glucoses not for insulin dose; also "GLUCOSE" and "Glucose" only used n=3 times
+AND lab_name = "Glucose by Meter"
+AND UPPER(ordering_mode) = 'INPATIENT' AND ord_num_value BETWEEN 0 AND 9999998
+-- Checking BG by range 
+	--    AND ord_num_value BETWEEN 80 AND 200 -- at goal
+	--  AND ord_num_value < 70 -- hypoglycemia
+ 	-- AND ord_num_value > 200 -- hyperglycemia
+
+AND pat_enc_csn_id_coded IN (SELECT DISTINCT(mar.pat_enc_csn_id_coded) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+	WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+	     WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below *(see below)
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+    
+ --    /* 
+    AND mar.pat_enc_csn_id_coded NOT IN (SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2 -- excludes patients who had basal insulin ordered * except for 2 orders of NPH that are excluded above
+	     WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP", "NPH INSULIN HUMAN RECOMB 100 UNIT/ML SC SUSP", "INSULIN GLARGINE 100 UNIT/ML SC SOLN", "INSULIN DETEMIR 100 UNIT/ML SC SOLN", "INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP", "INSULIN DETEMIR U-100 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70/30) SUBCUTANEOUS VIAL", "INSULIN DETEMIR 100 UNIT/ML (3 ML) SC INPN", "INSULIN DETEMIR U-100 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (50-50) SC SUSP", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP","INSULIN DETEMIR 100 UNIT/ML SC INPN", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC INPN") -- patients ordered for basal
+       ) 
+  --     */
+       
+    
+)
+
+AND taken_time_jittered BETWEEN "2015-01-01T00:00:00" AND "2015-12-31T23:59:59"      -- Date range
+
+
diff --git a/scripts/InpatientDiabetes/inpt_subQ_insulin.sql b/scripts/InpatientDiabetes/inpt_subQ_insulin.sql
new file mode 100644
index 00000000..8b953322
--- /dev/null
+++ b/scripts/InpatientDiabetes/inpt_subQ_insulin.sql
@@ -0,0 +1,367 @@
+
+--
+--Patients ordered for and receiving subQ insulin inpatient (n=51,887 patients in 88,372 encounters)
+SELECT jc_uid, pat_enc_csn_id_coded, order_med_id_coded, medication_id, med_description, ordering_mode, med_route from starr_datalake2018.order_med
+	WHERE order_med_id_coded in 
+  (SELECT order_med_id_coded FROM starr_datalake2018.mar --insulin given
+	   WHERE order_med_id_coded IN (SELECT order_med_id_coded FROM `starr_datalake2018.order_med`
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered
+     	AND (ordering_mode_c) = 2 -- inpatient
+      AND med_route = 'Subcutaneous')) --and subQ
+	ORDER BY jc_uid, med_description  ASC 
+
+
+-- 
+-- MAR insulin
+SELECT jc_uid, pat_enc_csn_id_coded, order_med_id_coded, infusion_rate, sig, dose_unit_c, dose_unit, mar_action, route, route_c FROM `mining-clinical-decisions.starr_datalake2018.mar` 
+-- SELECT  mar_action FROM `mining-clinical-decisions.starr_datalake2018.mar` 
+	WHERE order_med_id_coded in 
+  (SELECT order_med_id_coded FROM starr_datalake2018.order_med 
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+   AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push', 'Refused', 'Held', 'Stopped', 'Missed') 
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+--   AND (mar_action) IN ('Bolus', 'PUMP Bolus', 'Pump Check', 'Restarted', 'Stopped', 'Given')
+   AND (mar_action) IN ('Completed', 'Given', 'Push', 'Refused', 'Held', 'Stopped', 'Missed')       
+-- GROUP BY mar_action   
+ORDER BY mar_action, jc_uid 
+
+
+--
+-- Evaluating if "sig" is the insulin dose given
+SELECT sig, COUNT(sig) as number FROM `mining-clinical-decisions.starr_datalake2018.mar` 
+	WHERE order_med_id_coded in 
+  (SELECT order_med_id_coded FROM starr_datalake2018.order_med 
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+   AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push', 'Refused', 'Held', 'Stopped', 'Missed') 
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+--   AND (mar_action) IN ('Bolus', 'PUMP Bolus', 'Pump Check', 'Restarted', 'Stopped', 'Given')
+   AND sig <> "0-10" 
+   AND sig IS NOT NULL 
+GROUP BY sig  
+ORDER BY CAST(sig AS float64) --converts string to number
+
+
+--
+-- MAR insulin given, excluding pump dosing (assumed that partial units = pump)
+SELECT jc_uid, pat_enc_csn_id_coded, order_med_id_coded, sig, dose_unit, mar_action, route FROM `mining-clinical-decisions.starr_datalake2018.mar` 
+-- SELECT  infusion_rate  FROM `mining-clinical-decisions.starr_datalake2018.mar` 
+-- SELECT sig, COUNT(sig) as number FROM `mining-clinical-decisions.starr_datalake2018.mar` 
+	WHERE order_med_id_coded in 
+  (SELECT order_med_id_coded FROM starr_datalake2018.order_med 
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+     AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+--   AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push', 'Refused', 'Held', 'Stopped', 'Missed') 
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+--   AND (mar_action) IN ('Bolus', 'PUMP Bolus', 'Pump Check', 'Restarted', 'Stopped', 'Given')
+   AND sig <> "0-10"
+   AND sig IS NOT NULL 
+   AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+-- GROUP BY sig  
+ORDER BY CAST(sig AS float64) --converts string to number
+
+
+--
+-- MAR insulin given joined to name of insulin 
+SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.order_med_id_coded,  medord.med_description, mar.sig,  mar.dose_unit, mar.mar_action, mar.route FROM `mining-clinical-decisions.starr_datalake2018.mar` as mar 
+  LEFT JOIN starr_datalake2018.order_med as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+	WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM starr_datalake2018.order_med as medord2
+	     WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+--   AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push', 'Refused', 'Held', 'Stopped', 'Missed') 
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+--   AND (mar_action) IN ('Bolus', 'PUMP Bolus', 'Pump Check', 'Restarted', 'Stopped', 'Given')
+   AND mar.sig <> "0-10"
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+-- GROUP BY sig  
+ORDER BY CAST(mar.sig AS float64) --converts string to number
+
+
+--
+-- MAR insulin given w/ name of insulin, excluding insulin cartridges and types ordered <10x
+SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.order_med_id_coded,  medord.med_description, mar.sig,  mar.dose_unit, mar.mar_action, mar.route, mar.taken_time_jittered FROM `mining-clinical-decisions.starr_datalake2018.mar` as mar 
+-- SELECT  medord.med_description, count(medord.med_description) as number FROM `mining-clinical-decisions.starr_datalake2018.mar` as mar 
+  LEFT JOIN starr_datalake2018.order_med as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+	WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM starr_datalake2018.order_med as medord2
+	     WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+ORDER BY CAST(mar.sig AS float64) --converts string to number
+-- ORDER BY mar.taken_time_jittered 
+
+
+-- 
+-- NERO version of MAR insulin given w/ name of insulin, excluding insulin cartridges and types ordered <10x
+SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.order_med_id_coded,  medord.med_description, mar.sig,  mar.dose_unit, mar.mar_action, mar.route, mar.taken_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+-- SELECT  medord.med_description, count(medord.med_description) as number FROM `mining-clinical-decisions.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+ORDER BY CAST(mar.sig AS float64) --converts string to number
+-- ORDER BY mar.taken_time_jittered 
+
+-- 
+-- Units subQ insulin given where creatinine <2 during the patient encounter
+SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+--
+-- Units subQ insulin given (>0units) where creatinine <2 during the patient encounter
+SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+
+-- 
+-- Units subQ insulin given (>0units) where creatinine <2 during the patient encounter, excluding U500
+SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+
+
+-- 
+-- Patient encounters for which short-acting insulin was ordered
+-- SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+SELECT count(distinct(mar.pat_enc_csn_id_coded)) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+   AND mar.pat_enc_csn_id_coded IN (SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) LIKE "%SCALE%"
+       )
+
+-- Patient encounters for which ISS (explicitly "scale") alone was ordered (basal excluded)
+SELECT medord.med_description, count(medord.med_description) as count FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+-- SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+-- SELECT count(distinct(mar.pat_enc_csn_id_coded)) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND UPPER(medord2.med_description) LIKE '%SCALE%'
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+   AND mar.pat_enc_csn_id_coded IN (SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) LIKE "%SCALE%")
+  -- EXCLUDING PATIENTS ORDERED FOR BASAL
+    AND mar.pat_enc_csn_id_coded NOT IN (SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2 -- excludes patients who had basal insulin ordered * except for 2 orders of NPH that are excluded above
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP", "NPH INSULIN HUMAN RECOMB 100 UNIT/ML SC SUSP", "INSULIN GLARGINE 100 UNIT/ML SC SOLN", "INSULIN DETEMIR 100 UNIT/ML SC SOLN", "INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP", "INSULIN DETEMIR U-100 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70/30) SUBCUTANEOUS VIAL", "INSULIN DETEMIR 100 UNIT/ML (3 ML) SC INPN", "INSULIN DETEMIR U-100 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (50-50) SC SUSP", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP","INSULIN DETEMIR 100 UNIT/ML SC INPN", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC INPN") -- patients ordered for basal
+       ) 
+   --  */
+GROUP BY medord.med_description       
+ORDER BY count ASC
+-- AND taken_time_jittered BETWEEN "2008-01-01T00:00:00" AND "2008-12-31T23:59:59"
+
+
+
+
+--
+-- Patient encounters for which basal insulin was not ordered
+-- SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+-- SELECT mar.jc_uid, mar.pat_enc_csn_id_coded, mar.sig as units, mar.mar_action, medord.med_description, mar.route, medord.medication_id, medord.sig, mar.taken_time_jittered as instructions FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+-- SELECT count(distinct(mar.pat_enc_csn_id_coded)) FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+SELECT medord.med_description, COUNT(medord.med_description) as count FROM `som-nero-phi-jonc101.starr_datalake2018.mar` as mar 
+  LEFT JOIN `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord on mar.order_med_id_coded=medord.order_med_id_coded 
+  WHERE mar.order_med_id_coded in 
+  (SELECT medord2.order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_description NOT IN ("INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP") -- removes 2 basal doses that were not excluded by basal clause below
+       AND medord2.med_route = 'Subcutaneous') --and subQ
+     AND (mar.mar_action) IN ('Given') --medication actually given
+   AND (mar.mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed', 'Bolus', 'Complete', 'Completed', 'Push')
+   AND mar.dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND mar.sig <> "0-10"
+   AND CAST(mar.sig AS float64) > 0
+   -- AND CAST(mar.sig AS float64) < 100 --set maximum insulin at 100 to minimize recording errors
+   AND mar.sig IS NOT NULL 
+   AND mar.sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND mar.infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT lab.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.lab_result` as lab -- excludes patient encounters with creatinine >2 
+    WHERE (lab_name) LIKE "Creatinine, Ser/Plas" --most common creatinine order
+    AND ord_num_value != 9999999
+    AND taken_time_jittered IS NOT null
+    AND ord_num_value > 2)
+   AND mar.pat_enc_csn_id_coded NOT IN (SELECT medord2.pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` as medord2
+       WHERE UPPER(medord2.med_description) LIKE '%INSULIN%'-- insulin ordered
+       AND (medord2.ordering_mode_c) = 2 -- inpatient
+       AND UPPER(medord2.med_description) NOT LIKE '%PUMP%' --excludes pumps
+       AND (medord2.med_description) NOT IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC CRTG", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (50-50) SC SUSP", "INSULIN GLARGINE 300 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC CRTG", "INSULIN NPH-REGULAR HUM S-SYN 100 UNIT/ML (70-30) SC CRTG", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC INPH", "INSULIN NPH HUMAN SEMI-SYN 100 UNIT/ML SC CRTG", "INSULIN ASPART PROTAMINE-ASPART (70/30) 100 UNIT/ML SUBCUTANEOUS PEN", "INSULIN LISPRO PROTAM-LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN GLULISINE 100 UNIT/ML SC CRTG", "INSULIN DEGLUDEC-LIRAGLUTIDE 100 UNIT-3.6 MG /ML (3 ML) SC INPN", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC SUSP", "INSULIN REGULAR HUM U-500 CONC 500 UNIT/ML SC SOLN", "INSULIN LISPRO 100 UNIT/ML SC CRTG", "INSULIN ASPART 100 UNIT/ML SC CRTG") -- removes anything ordered <10 times
+       AND medord2.med_description NOT LIKE "%CRTG%" -- excludes anything ordered as a cartridge to remove pumps
+       AND medord2.med_description NOT LIKE "%U-500%" -- exlcudes U-500
+       AND medord2.med_route = 'Subcutaneous' --and subQ
+       AND UPPER(medord2.med_description) IN ("INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP", "NPH INSULIN HUMAN RECOMB 100 UNIT/ML SC SUSP", "INSULIN GLARGINE 100 UNIT/ML SC SOLN", "INSULIN DETEMIR 100 UNIT/ML SC SOLN", "INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP", "INSULIN DETEMIR U-100 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70/30) SUBCUTANEOUS VIAL", "INSULIN DETEMIR 100 UNIT/ML (3 ML) SC INPN", "INSULIN DETEMIR U-100 100 UNIT/ML SC SOLN", "INSULIN DEGLUDEC 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH & REGULAR HUMAN 100 UNIT/ML (50-50) SC SUSP", "INSULIN LISPRO PROTAM & LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN LISPRO PROTAMIN-LISPRO 100 UNIT/ML (75-25) SC INPN", "INSULIN GLARGINE 300 UNIT/3 ML SC INPN", "INSULIN GLARGINE 300 UNIT/ML (1.5 ML) SC INPN", "INSULIN DEGLUDEC 200 UNIT/ML (3 ML) SC INPN", "INSULIN GLARGINE 100 UNIT/ML (3 ML) SC INPN", "INSULIN NPH AND REGULAR HUMAN 100 UNIT/ML (70-30) SC SUSP", "INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP","INSULIN DETEMIR 100 UNIT/ML SC INPN", "INSULIN ASP PRT-INSULIN ASPART 100 UNIT/ML (70-30) SC INPN") -- patients ordered for basal
+       )
+--ORDER BY medord.med_description 
+GROUP BY medord.med_description
+ORDER BY count desc
+
+
+
+
diff --git a/scripts/InpatientDiabetes/inpt_weight.sql b/scripts/InpatientDiabetes/inpt_weight.sql
new file mode 100644
index 00000000..499a6552
--- /dev/null
+++ b/scripts/InpatientDiabetes/inpt_weight.sql
@@ -0,0 +1,90 @@
+--
+-- Weight 
+SELECT template, row_disp_name, line, num_value1, num_value2 FROM `mining-clinical-decisions.starr_datalake2018.flowsheet` 
+WHERE (row_disp_name) LIKE "Weight"
+AND num_value1 IS NOT NULL
+ORDER BY num_value1 DESC
+LIMIT 1000
+
+--
+-- Weights converted from oz to kg for pts who received insulin
+SELECT rit_uid, row_disp_name, meas_value, num_value1, (num_value1*0.028) AS KG, num_value2, recorded_time_jittered FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet`
+-- SELECT (num_value1*0.028) AS KG, count(meas_value) as count  FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` 
+WHERE rit_uid IN (select jc_uid from `som-nero-phi-jonc101.starr_datalake2018.mar` WHERE order_med_id_coded in --patient received subQ insulin
+  (SELECT order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` 
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+     AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND sig <> "0-10"
+   AND sig IS NOT NULL 
+   AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+)
+AND row_disp_name LIKE "Weight"
+AND num_value1 IS NOT NULL --numeric value exists
+--GROUP BY KG 
+--ORDER BY count desc
+Limit 1000
+
+--
+-- Wts converted from oz to kgs for adults, compared to their "most recent weight" from demographics
+SELECT dem.rit_uid, flow.row_disp_name, flow.meas_value, flow.num_value1, (flow.num_value1*0.028) AS KG, dem.recent_wt_in_kgs, dem.bmi, flow.num_value2, flow.recorded_time_jittered, DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) as ageYears FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` AS flow
+INNER JOIN `som-nero-phi-jonc101.starr_datalake2018.demographic` AS dem ON dem.rit_uid = flow.rit_uid 
+--DATEDIFF(DATE(flow.recorded_time_jittered), DATE(dem.birth_date_jittered), year)  as ageYears
+WHERE dem.rit_uid IN (SELECT rit_uid,FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` where inpatient_data_id_coded IN
+  (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar` WHERE order_med_id_coded in --patient received subQ insulin
+  (SELECT order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` 
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+     AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND sig <> "0-10"
+   AND sig IS NOT NULL 
+   AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+))
+AND row_disp_name LIKE "Weight"
+AND num_value1 IS NOT NULL --numeric value exists
+AND DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) >= 18
+-- AND num_value1*0.028 < 45
+ORDER BY KG
+
+
+--
+-- wt, time, age table for adults who got subQ insulin with wt > 45kg and < doubling in weight between time in flowsheet and "most recent wt"
+SELECT dem.rit_uid, DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) as ageYears, flow.row_disp_name, flow.meas_value, flow.recorded_time_jittered, (flow.num_value1*0.028) AS KG, dem.recent_wt_in_kgs, (dem.recent_wt_in_kgs-(flow.num_value1*0.028)) as wt_diff, ((dem.recent_wt_in_kgs-(flow.num_value1*0.028))/dem.recent_wt_in_kgs) as wt_diff_percent, bmi FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` AS flow
+INNER JOIN `som-nero-phi-jonc101.starr_datalake2018.demographic` AS dem ON dem.rit_uid = flow.rit_uid 
+WHERE dem.rit_uid IN (SELECT rit_uid,FROM `som-nero-phi-jonc101.starr_datalake2018.flowsheet` where inpatient_data_id_coded IN
+  (SELECT pat_enc_csn_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.mar` WHERE order_med_id_coded in --patient received subQ insulin
+  (SELECT order_med_id_coded FROM `som-nero-phi-jonc101.starr_datalake2018.order_med` 
+	     WHERE UPPER(med_description) LIKE '%INSULIN%'-- insulin ordered)
+       AND (ordering_mode_c) = 2 -- inpatient
+       AND med_route = 'Subcutaneous') --and subQ
+     AND (mar_action) IN ('Bolus', 'Complete', 'Completed', 'Given', 'Push')
+   AND (mar_action) NOT IN ('Bag Removal', 'Canceled Entry', 'Due', 'Existing Bag', 'Infusion Restarted', 'Infusion Started', 'Infusion Stopped', 'New Bag', 'Patch Removal', 'Patient\'s Own Med', 'Patient/Family Admin', 'Paused', 'Pending', 'Rate Changed', 'Rate Verify', 'Pump%', 'See Anesthesia Record', 'Self Administered Med', 'See Override Pull','Refused', 'Held', 'Stopped', 'Missed')
+   AND dose_unit_c = 5 -- "units", not an infusion (not units/hr)
+   AND sig <> "0-10"
+   AND sig IS NOT NULL 
+   AND sig NOT LIKE "%.%" -- removes any partial unit injections (assumed to be pump)
+   AND infusion_rate IS NULL -- infusion_rate assumed to signify pump pt
+))
+AND row_disp_name LIKE "Weight"
+AND UPPER(meas_value) NOT LIKE "%W%" -- removes "XweeksXdays"-- presumed misentered data
+AND UPPER(meas_value) NOT LIKE "%/%" -- Removes fractions of lbs
+AND UPPER(meas_value) NOT LIKE "%GMS%" --Removes gms
+AND UPPER(meas_value) NOT LIKE "%LBS%" -- Removes lbs
+AND UPPER(meas_value) NOT LIKE "% G%" -- Removes grams 
+AND num_value1 IS NOT NULL --numeric value exists
+AND (flow.num_value1*0.028) > 45 -- wt > 45kg (99lbs)
+AND DATE_DIFF(DATE(flow.recorded_time_jittered), (dem.birth_date_jittered), YEAR) >= 18 -- ADULTS ONLY
+AND dem.recent_wt_in_kgs != 0 -- recent wt not equal zero (there are no pts with recent wt = 0 but if I don't do this the percent returns an error)
+AND ((dem.recent_wt_in_kgs-(flow.num_value1*0.028))/dem.recent_wt_in_kgs) < 0.5 -- Pt did not > double in wt 
+ORDER BY wt_diff_percent desc
+
+
+
diff --git a/scripts/InpatientVariability/clinicalFeatures.R b/scripts/InpatientVariability/clinicalFeatures.R
new file mode 100644
index 00000000..22828951
--- /dev/null
+++ b/scripts/InpatientVariability/clinicalFeatures.R
@@ -0,0 +1,141 @@
+library(tidyverse)
+library(bigrquery)
+
+# library(DBI)
+# library(scales)
+# library(sqldf) # https://www.r-bloggers.com/in-between-a-rock-and-a-conditional-join/
+# library(fuzzyjoin)
+# library(rio)
+
+# warning = TRUE # default
+# options(warn = 0) # default
+warning = FALSE
+options(warn = -1)
+options(scipen = 20)
+
+##### filepaths
+dir <- "/Users/kushgupta/Documents/StanfordMD/Projects/Mentors/Chen/Inpatient\ Variability\ Cost\ Reduction/code/"
+filepath1 <- paste0(dir,"clinicalFeatures.sql")
+
+##### import data
+sqlQuery <- readChar(filepath1, file.info(filepath1)$size)
+project <- "som-nero-phi-jonc101-secure" # "mining-clinical-decisions"
+results <- bq_project_query(project, sqlQuery)
+tb1 <- as_tibble(bq_table_download(results, bigint = c("integer64"))) # bigint needed to properly download full PT_ENC_CSN_ID
+rm(results)
+
+##### identify cost categories that have highest relative variance (coefficient of variance): respiratory therapy, implants (many NA's), supplies, ICU, IICU, Accom, Pharmacy
+# coefVar <- function(x){
+#   stdev = sd(x,na.rm=TRUE)
+#   avg = mean(x,na.rm=TRUE)
+#   return(stdev/avg)
+# }
+# cv <- tb %>% summarise(across(starts_with("Cost_"), coefVar)) # %>% t()
+# cv <- pivot_longer(cv, cols=starts_with("Cost_"), names_to="category", values_to="coefVar") %>% arrange(desc(coefVar))
+# cv
+# 
+# tb %>% summarise(across(starts_with("Cost_"), sum(is.na))) # %>% t()
+
+##### cleaning table
+#mrn, AdmitTRUE, DischTRUE, LOS, Cost_Direct, Cost_Total, anon_id, enc, admsnTimeJTR, dischTimeJTR, drg, drg_c,
+#GENDER, CANONICAL_ETHNICITY, CANONICAL_RACE, INTRPTR_NEEDED_YN, INSURANCE_PAYOR_NAME, BMI, CHARLSON_SCORE, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL,
+#firstRT_daysb4dc, firstPT_daysb4dc, firstOT_daysb4dc, firstSLP_daysb4dc, firstSW_daysb4dc,
+#medID, med_description, med_route, dose_unit, totalDose, lastAdminJTR, lastOpioidAdminJTR
+#ordersetSmartGroup, ordersetProtocol, ordersetTime
+
+cohort <- tb %>% select(enc, Cost_Total) %>% distinct()
+
+patientFactors <- tb %>% select(enc, GENDER, CANONICAL_ETHNICITY, CANONICAL_RACE, INTRPTR_NEEDED_YN, INSURANCE_PAYOR_NAME, BMI, CHARLSON_SCORE, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL) %>% distinct()
+patientFactors[,8:10] <- lapply(patientFactors[,8:10],as.numeric)
+
+### analyze therapy orders
+therapyOrders <- tb %>% select(enc, Cost_Total, LOS, firstRT_daysb4dc, firstPT_daysb4dc, firstOT_daysb4dc, firstSLP_daysb4dc, firstSW_daysb4dc) %>% distinct()
+therapyOrders <- therapyOrders %>% mutate(across(c(1:ncol(therapyOrders)), as.numeric)) # across(starts_with("first"), as.numeric)
+ggplot(data = therapyOrders) + geom_point(mapping = aes(x = firstOT_daysb4dc, y = LOS))
+ggplot(data = therapyOrders) + geom_point(mapping = aes(x = firstPT_daysb4dc, y = LOS))
+fit1 = lm(Cost_Total ~ firstOT_daysb4dc, data=therapyOrders)
+summary(fit1) 
+fit2 = lm(Cost_Total ~ firstPT_daysb4dc, data=therapyOrders)
+summary(fit2) 
+fit3 = lm(Cost_Total ~ firstOT_daysb4dc*firstPT_daysb4dc, data=therapyOrders)
+summary(fit3)
+par(mfrow=c(2,2))
+plot(fit1) # 4 plots get produced by a linear models
+plot(fit2)
+plot(fit3)
+fit1 = lm(LOS ~ firstOT_daysb4dc, data=therapyOrders)
+summary(fit1) 
+fit2 = lm(LOS ~ firstPT_daysb4dc, data=therapyOrders)
+summary(fit2) 
+fit3 = lm(LOS ~ firstOT_daysb4dc*firstPT_daysb4dc, data=therapyOrders)
+summary(fit3)
+
+### analyze opioids (only considering columns that are mostly NA <- inspected manually to do this)
+opioids <- tb %>% select(enc, Cost_Total, dischTimeJTR, med_description, totalDose, lastOpioidAdminJTR) %>% distinct()
+opioids$oralMME <- NA
+opioids <- opioids %>% mutate(oralMME = ifelse(med_description=="HYDROMORPHONE 1 MG/ML INJ SYRG",totalDose * 20,oralMME)) # convert to oral MME
+opioids <- opioids %>% mutate(oralMME = ifelse(med_description=="OXYCODONE 5 MG PO TABS",totalDose * 1.5,oralMME)) # convert to oral MME
+opioids <- opioids %>% mutate(oralMME = ifelse(med_description=="OXYCODONE 5 MG/5 ML PO SOLN",totalDose * 1.5,oralMME)) # convert to oral MME
+opioids <- opioids %>% mutate(oralMME = ifelse(med_description=="FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN (WRAPPER RECORD)",totalDose * 0.3,oralMME)) # convert to oral MME
+opioids <- opioids %>% mutate(oralMME = ifelse(med_description=="TRAMADOL 50 MG PO TABS",totalDose * 0.1,oralMME)) # convert to oral MME
+ggplot(data = opioids) + 
+  geom_point(mapping = aes(x = (oralMME), y = Cost_Total, color = med_description)) +
+  geom_smooth(method='lm', se=FALSE, mapping = aes(x = (oralMME), y = Cost_Total, color= med_description)) +
+  xlim(0,1000)
+opioids <- opioids %>% group_by(enc) %>% mutate(totalOralMME = sum(oralMME))
+ggplot(data = opioids) + 
+  geom_point(mapping = aes(x = totalOralMME, y = Cost_Total)) +
+  geom_smooth(method='lm', se=TRUE, mapping = aes(x = totalOralMME, y = Cost_Total))
+  xlim(0,1000)
+opioids <- pivot_wider(opioids, names_from = med_description, values_from = oralMME)
+opioids$lastOpioidAdmin_daysb4dc <- as.numeric(difftime(opioids$dischTimeJTR, opioids$lastOpioidAdminJTR, units="days")) # a$admsnTimeJTR - as.difftime(as.integer(a$JITTER),units="days")
+opioids <- opioids %>% select(-dischTimeJTR,-lastOpioidAdminJTR)
+opioids <- opioids %>% select(-enc)
+fit1 = lm(Cost_Total ~ `HYDROMORPHONE 1 MG/ML INJ SYRG`, data=opioids)
+fit2 = lm(Cost_Total ~ `OXYCODONE 5 MG PO TABS`, data=opioids)
+fit3 = lm(Cost_Total ~ `FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN (WRAPPER RECORD)`, data=opioids)
+fit4 = lm(Cost_Total ~ `HYDROMORPHONE 1 MG/ML INJ SYRG` * `OXYCODONE 5 MG PO TABS` * `FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN (WRAPPER RECORD)`, data=opioids)
+summary(fit1) 
+summary(fit2) 
+summary(fit3) 
+summary(fit4) 
+print(paste("significant predictors are:", "HYDROMORPHONE 1 MG/ML INJ SYRG"))
+
+ordersets <- tb %>% select(enc, ordersetProtocol, ordersetTime) %>% distinct()
+ordersets <- ordersets %>% group_by(enc, ordersetProtocol) %>% summarise(lastOrdered = max(ordersetTime)) %>% filter(!is.na(lastOrdered))
+ordersets <- pivot_wider(ordersets, names_from = ordersetProtocol, values_from = lastOrdered)
+ordersets <- cbind(ordersets[,1], as_tibble(!is.na(ordersets[,2:ncol(ordersets)])))
+
+features <- cohort %>% 
+              left_join(patientFactors) %>%
+              left_join(therapyOrders) %>%
+              left_join(opioids) %>%
+              inner_join(ordersets)
+features <- features %>% select(-c(enc))
+
+fit7 = lm(Cost_Total ~ `ANE PACU `, data=features)
+summary(fit7) 
+
+fit1 = lm(
+  as.formula(paste(colnames(features)[1], "~",
+             paste(colnames(features)[c(2:10,12,13)], collapse = "+"), #don't work: 8,9,10
+             sep = ""
+  )),
+  data=features,
+  na.action=na.exclude #,na.action=na.omit
+)
+summary(fit1) 
+
+missing <- function(x) { sum(is.na(x))}
+
+####
+opioids
+SQL coalesce --> if value is NULL, replace with 0. if not NULL, give back actual value. 
+Jon thoughts: why do we have NAs for opioids... unexpected
+convert to morphine equivalents --> if we control for ME, does IV vs PO make difference?
+see if epidurals / spinals / IV lidocaine reduced costs
+
+PT/OT is interesting, positive coefficient is curious
+do both LOS and total Cost --> expect LOS to go down and Cost to perhaps stay even as PT_daysb4dc goes up
+
+ask Rocky about ask Rocky about ask Rocky about plots
\ No newline at end of file
diff --git a/scripts/InpatientVariability/clinicalFeatures.sql b/scripts/InpatientVariability/clinicalFeatures.sql
new file mode 100644
index 00000000..4d1682fc
--- /dev/null
+++ b/scripts/InpatientVariability/clinicalFeatures.sql
@@ -0,0 +1,249 @@
+WITH
+
+selectDRGs AS
+(
+    SELECT * FROM `som-nero-phi-jonc101-secure.kush_db.selectDRGs`
+),
+
+-- determine which sub-categories of cost were (1) biggest drivers, (2) most variable
+costBreakdown AS
+(
+    SELECT b.*
+    FROM selectDRGs a
+    JOIN `som-nero-phi-jonc101-secure.shc_cost.costUB` b
+    ON a.mrn = b.MRN AND a.AdmitTRUE = b.AdmitDate
+),
+
+-- patient age, comorbidities, demographic
+patientFactors AS
+(
+    SELECT ANON_ID, GENDER, CANONICAL_ETHNICITY, CANONICAL_RACE, INTRPTR_NEEDED_YN, INSURANCE_PAYOR_NAME, BMI, CHARLSON_SCORE, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL, BIRTH_DATE_JITTERED
+    FROM
+        (SELECT DISTINCT anon_id FROM selectDRGs) a
+    JOIN `mining-clinical-decisions.shc_core.demographic`
+    USING(anon_id)
+),
+
+-- collect orders (placed) for RT
+respiratoryTherapy AS
+(
+    SELECT DISTINCT anon_id, order_proc_id_coded, pat_enc_csn_id_coded as enc, order_type, proc_id, description, display_name, authrzing_prov_id,
+                    order_inst_jittered as rt_orderInstJTR, proc_start_time_jittered as rt_procStartJTR, last_stand_perf_tm_jittered as rt_procFinalJTR -- order_time_jittered redundant? seems equivalent to order_inst_jittered
+    FROM `mining-clinical-decisions.shc_core.order_proc` -- other potential tables to consider: order_proc, procedure_code, proc_orderset
+    WHERE (LOWER(order_class) LIKE '%hospital%' OR LOWER(order_class) LIKE '%stanford%' OR LOWER(order_class) LIKE '%point of care%' OR LOWER(order_class) LIKE '%normal%')
+    AND ordering_mode = 'Inpatient'
+
+    -- respiratory therapy
+    AND proc_id IN (931,1196,1962,542,5039,1769,425922)
+    AND order_type IN ('Respiratory Care') # order_type 'PFT' is unrelated to RT in my opinion
+),
+
+-- collect orders (placed) for OT
+occupationalTherapy AS
+(
+    SELECT DISTINCT anon_id, order_proc_id_coded, pat_enc_csn_id_coded as enc, order_type, proc_id, description, display_name, authrzing_prov_id,
+                    order_inst_jittered as ot_orderInstJTR, proc_start_time_jittered as ot_procStartJTR, last_stand_perf_tm_jittered as ot_procFinalJTR -- order_time_jittered redundant? seems equivalent to order_inst_jittered
+    FROM `mining-clinical-decisions.shc_core.order_proc` -- other potential tables to consider: order_proc, procedure_code, proc_orderset
+    WHERE (LOWER(order_class) LIKE '%hospital%' OR LOWER(order_class) LIKE '%stanford%' OR LOWER(order_class) LIKE '%point of care%' OR LOWER(order_class) LIKE '%normal%')
+    AND ordering_mode = 'Inpatient'
+
+    -- occupational therapy
+    AND order_type IN ('OT','REHAB')
+    AND proc_id IN (199493,1575,507087,420225,419445,17587,172763,1577,172765,399680)
+),
+
+-- collect orders (placed) for PT
+physicalTherapy AS
+(
+    SELECT DISTINCT anon_id, order_proc_id_coded, pat_enc_csn_id_coded as enc, order_type, proc_id, description, display_name, authrzing_prov_id,
+                    order_inst_jittered as pt_orderInstJTR, proc_start_time_jittered as pt_procStartJTR, last_stand_perf_tm_jittered as pt_procFinalJTR -- order_time_jittered redundant? seems equivalent to order_inst_jittered
+    FROM `mining-clinical-decisions.shc_core.order_proc` -- other potential tables to consider: order_proc, procedure_code, proc_orderset
+    WHERE (LOWER(order_class) LIKE '%hospital%' OR LOWER(order_class) LIKE '%stanford%' OR LOWER(order_class) LIKE '%point of care%' OR LOWER(order_class) LIKE '%normal%')
+    AND ordering_mode = 'Inpatient'
+
+    -- physical therapy
+    AND order_type IN ('PT','REHAB')
+    AND proc_id IN (37225,1717,175908,189238,439060,467954,420233,389602,425090,432201,219820,13123,461330,119293,13121,259884)
+
+),
+
+-- collect orders (placed) for ST / SLP
+speechTherapy AS
+(
+    SELECT DISTINCT anon_id, order_proc_id_coded, pat_enc_csn_id_coded as enc, order_type, proc_id, description, display_name, authrzing_prov_id,
+                    order_inst_jittered as slp_orderInstJTR, proc_start_time_jittered as slp_procStartJTR, last_stand_perf_tm_jittered as slp_procFinalJTR -- order_time_jittered redundant? seems equivalent to order_inst_jittered
+    FROM `mining-clinical-decisions.shc_core.order_proc` -- other potential tables to consider: order_proc, procedure_code, proc_orderset
+    WHERE (LOWER(order_class) LIKE '%hospital%' OR LOWER(order_class) LIKE '%stanford%' OR LOWER(order_class) LIKE '%point of care%' OR LOWER(order_class) LIKE '%normal%')
+    AND ordering_mode = 'Inpatient'
+
+    -- speech therapy
+    AND order_type IN ('SLP','REHAB')
+    AND proc_id IN (198308,172773,172775,17588,1869,412517,172770,1868,1867,467380,493867,493288)
+
+),
+
+-- collect orders (placed) for SW
+socialWork AS
+(
+    SELECT DISTINCT anon_id, order_proc_id_coded, pat_enc_csn_id_coded as enc, order_type, proc_id, description, display_name, authrzing_prov_id,
+                    order_inst_jittered as sw_orderInstJTR, proc_start_time_jittered as sw_procStartJTR, last_stand_perf_tm_jittered as sw_procFinalJTR -- order_time_jittered redundant? seems equivalent to order_inst_jittered
+    FROM `mining-clinical-decisions.shc_core.order_proc` -- other potential tables to consider: order_proc, procedure_code, proc_orderset
+    WHERE (LOWER(order_class) LIKE '%hospital%' OR LOWER(order_class) LIKE '%stanford%' OR LOWER(order_class) LIKE '%point of care%' OR LOWER(order_class) LIKE '%normal%')
+    AND ordering_mode = 'Inpatient'
+
+    -- social work / case management
+    AND proc_id IN (25226,35508,48129,189235) -- social work
+
+),
+
+-- answering question: ("Did PT/ST get ordered on 1st day or 4th day? SW consult early or late?")
+therapyOrders AS
+(
+    SELECT  a.enc,
+            DATETIME_DIFF(dischTimeJTR, min(rt_orderInstJTR), DAY) as firstRT_daysb4dc,
+            DATETIME_DIFF(dischTimeJTR, min(pt_orderInstJTR), DAY) as firstPT_daysb4dc,
+            DATETIME_DIFF(dischTimeJTR, min(ot_orderInstJTR), DAY) as firstOT_daysb4dc,
+            DATETIME_DIFF(dischTimeJTR, min(slp_orderInstJTR), DAY) as firstSLP_daysb4dc,
+            DATETIME_DIFF(dischTimeJTR, min(sw_orderInstJTR), DAY) as firstSW_daysb4dc,
+    FROM selectDRGs a
+    LEFT JOIN respiratoryTherapy b USING(enc)
+    LEFT JOIN physicalTherapy c USING(enc)
+    LEFT JOIN occupationalTherapy d USING(enc)
+    LEFT JOIN speechTherapy e USING(enc)
+    LEFT JOIN socialWork f USING(enc)
+
+    GROUP BY a.anon_id, a.enc, admsnTimeJTR, dischTimeJTR, drg -- mrn, AdmitTRUE, DischTRUE, LOS, Cost_Direct, Cost_Total,
+
+
+),
+
+-- opioids? total amount of opiates / pain meds consumed? needed opioid withdrawal agents?
+opioidOrders AS
+(
+    SELECT DISTINCT a.anon_id, enc, order_med_id_coded as orderID,
+                    order_inst, # order_start_time, order_end_time
+                    medication_id as medID, med_description, med_presc_prov_id as prescriber, freq_name,
+                    med_route, dose_unit -- use mar::sig for dosage instead of order_med::hv_discrete_dose (range of allowed doses) or order_med::min_discrete_dose (minimum dose allowable)
+                    -- total dosage administered
+                    -- whether given or not EARLY due to patient request
+    FROM selectDRGs a
+    LEFT JOIN `mining-clinical-decisions.shc_core.order_med` b
+        ON enc = pat_enc_csn_id_coded
+
+    WHERE LOWER(pharm_class_name) LIKE '%opioid%'
+    AND LOWER(pharm_class_name) NOT LIKE '%non-opioid%'
+    AND thera_class_name IN ('ANALGESICS')
+    AND thera_class_name != 'ANTIDOTES' -- remove narcan, naloxone, etc.
+    AND med_route_c IN (15,11) -- ('Oral','Intravenous','Transdermal','Feeding Tube','Intramuscular') = (15,11,20,123,6)
+    # WHERE LOWER(pharm_class_name) NOT LIKE '%opioid withdrawal%' -- buprenorphine, suboxone
+
+    ORDER BY enc, order_inst
+),
+
+-- link to MAR to find total dose administered (over encounter) for order
+opioidTotalDose AS
+(
+    #SELECT infusion_rate, mar_inf_rate_unit, count(*) as n
+    SELECT  enc, medID, med_description, med_route, b.dose_unit as dose_unit, sum(CAST(sig as NUMERIC)) as totalDose, max(taken_time_jittered) as lastAdminJTR
+    FROM opioidOrders a
+    LEFT JOIN `mining-clinical-decisions.shc_core.mar` b
+        ON enc = b.mar_enc_csn_coded
+        AND a.orderID = b.order_med_id_coded
+    WHERE mar_action = 'Given'
+    GROUP BY enc, medID, med_description, med_route, b.dose_unit
+
+    ORDER BY enc, med_description, totalDose
+),
+
+-- pick the most commonly used opioids; column for last administration of any kind of opioid
+opioidsFinal AS
+(
+
+    SELECT b.*, a.lastOpioidAdminJTR
+    FROM
+        (
+        SELECT enc, max(lastAdminJTR) as lastOpioidAdminJTR
+        FROM opioidTotalDose
+        GROUP BY enc
+        ) a
+    JOIN opioidTotalDose b
+    ON a.enc = b.enc
+
+    WHERE medID IN (112586,10814,540106,14632,10813) ##### 80/20 rule --> contains the majority of opioid orders (for drg=001)
+),
+
+-- PACU orderset used?
+ordersets AS
+(
+    SELECT DISTINCT enc, SS_SG_NAME as ordersetSmartGroup, protocol_name as ordersetProtocol, min(order_inst) as ordersetTime
+                    -- PACU orderset used? Y/N (day of admission)
+                    -- ICU PAIN o/s used? Y/N (day of admission)
+                    -- PCA o/s used? Y/N (day of admission)
+                    -- good idea to pull day of surgery (as day of admission) to help contextualize the above
+    FROM SelectDRGs a
+    JOIN `mining-clinical-decisions.shc_core.med_orderset` b ON a.enc = b.pat_enc_csn_id_coded
+    JOIN `mining-clinical-decisions.shc_core.order_med` c ON a.enc = c.pat_enc_csn_id_coded AND b.order_med_id_coded = c.order_med_id_coded
+    WHERE ((LOWER(ss_section_name) LIKE '%pain%' OR LOWER(SS_SG_NAME) LIKE '%pain%' OR LOWER(protocol_name) LIKE '%pain%')
+            OR (LOWER(ss_section_name) LIKE '%analg%' OR LOWER(SS_SG_NAME) LIKE '%analg%' OR LOWER(protocol_name) LIKE '%analg%'))
+    GROUP BY enc, b.order_med_id_coded, ordersetSmartGroup, ordersetProtocol
+    ORDER BY enc, ordersetTime
+
+    /* -- better to use LIKE clauses above which capture many disparate protocols
+    AND ss_prl_id IN (4380, -- 'IP ICU PAIN MANAGEMENT'
+                      625, --  'ANE PACU'
+                      3862, -- 'IP CTS ICU CARDIAC SURGERY POSTOP'
+                      946 -- 'IP PAIN PATIENT CONTROLLED ANALGESIA (PCA)'
+    )*/
+
+    #GROUP BY ss_section_name, SS_SG_NAME, protocol_name, ss_prl_id ORDER BY n DESC
+
+),
+
+-- acute pain service consulted?
+painService AS
+(
+    SELECT NULL
+),
+
+-- OR characteristics?
+-- surgical approach, supplies/devices used, etc.
+operation AS
+(
+    SELECT NULL
+),
+
+-- Choice of attending?
+provider AS
+(
+    SELECT NULL
+),
+
+-- Guideline Adherence for XYZ diagnosis
+guidelineXYZ AS
+(
+    SELECT NULL
+),
+
+aggregator AS
+(
+    SELECT
+        mrn, AdmitTRUE, DischTRUE, JITTER, LOS, Cost_Direct, Cost_Total, a.anon_id, a.enc, admsnTimeJTR, dischTimeJTR, drg, drg_c,
+        GENDER, CANONICAL_ETHNICITY, CANONICAL_RACE, INTRPTR_NEEDED_YN, INSURANCE_PAYOR_NAME, BMI, CHARLSON_SCORE, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL,
+        firstRT_daysb4dc, firstPT_daysb4dc, firstOT_daysb4dc, firstSLP_daysb4dc, firstSW_daysb4dc,
+        medID, med_description, med_route, dose_unit, totalDose, lastAdminJTR, lastOpioidAdminJTR,
+        ordersetSmartGroup, ordersetProtocol, ordersetTime
+    # selectDRG: mrn, AdmitTRUE, DischTRUE, LOS, Cost_Direct, Cost_Total, anon_id, enc, admsnTimeJTR, dischTimeJTR, drg, drg_c
+    # patientFactors: ANON_ID, GENDER, CANONICAL_ETHNICITY, CANONICAL_RACE, INTRPTR_NEEDED_YN, INSURANCE_PAYOR_NAME, BMI, CHARLSON_SCORE, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL
+    # therapyOrders: enc, firstRT_daysb4dc, firstPT_daysb4dc, firstOT_daysb4dc, firstSLP_daysb4dc, firstSW_daysb4dc,
+    # opioidsFinal: enc, medID, med_description, med_route, dose_unit, totalDose, lastAdminJTR, lastOpioidAdminJTR
+    # ordersets: enc, ordersetSmartGroup, ordersetProtocol, ordersetTime
+    FROM selectDRGs a
+    LEFT JOIN patientFactors b ON a.anon_id = b.ANON_ID
+    LEFT JOIN therapyOrders c ON a.enc = c.enc
+    LEFT JOIN opioidsFinal d ON a.enc = d.enc
+    LEFT JOIN ordersets e ON a.enc = e.enc
+
+    ORDER BY a.enc, lastAdminJTR, ordersetTime
+)
+
+SELECT * FROM aggregator
diff --git a/scripts/InpatientVariability/cohortSelection.sql b/scripts/InpatientVariability/cohortSelection.sql
new file mode 100644
index 00000000..f96ab879
--- /dev/null
+++ b/scripts/InpatientVariability/cohortSelection.sql
@@ -0,0 +1,194 @@
+-- Reconstruct cohort of inpatient cost data against clinical EMR data
+-- Temp versions of some result tables saved to kush_db dataset.
+
+WITH
+
+-- Identify records to analyze; 56,149 records -- Inpatient encounter cost data available
+# Q: how to handle scenarios where encounters overlap? ignoring for now
+cohortCostAnon AS
+(
+    SELECT DISTINCT 
+      MRN, 
+      AdmitDate, DischargeDate, LOS, 
+      MSDRGweight,
+      Cost_Direct, Cost_Indirect, Cost_Total,
+      Cost_Breakdown_Blood,
+      Cost_Breakdown_Cardiac,
+      Cost_Breakdown_ED,
+      Cost_Breakdown_ICU,
+      Cost_Breakdown_IICU,
+      Cost_Breakdown_Imaging,
+      Cost_Breakdown_Labs,
+      Cost_Breakdown_Implants,
+      Cost_Breakdown_Supplies,
+      Cost_Breakdown_OR,
+      Cost_Breakdown_OrganAcq,
+      Cost_Breakdown_Other,
+      Cost_Breakdown_PTOT,
+      Cost_Breakdown_Resp,
+      Cost_Breakdown_Accom,
+      Cost_Breakdown_Pharmacy
+    FROM `som-nero-phi-jonc101-secure.shc_cost.costUB`
+    WHERE Inpatient_c = 'I' -- Inpatient
+    AND DischargeDate IS NOT NULL
+    --ORDER BY MRN DESC, AdmitTRUE
+),
+
+-- Match to anonymized ID to link to STARR anonymized database
+-- Returns 44,205 records
+cohortCostIdentified AS
+(
+    SELECT DISTINCT 
+      map.MRN, map.MRN_INT, ANON_ID,
+      JITTER,
+      cca.* -- Capture all of the original info from the cost cohort table
+    FROM cohortCostAnon as cca
+        JOIN `som-nero-phi-jonc101-secure.starr_map.shc_map` as map
+        # ON (a.MRN = CAST(b.MRN AS INT64)) -- MRN stored as INT64 vs. STRING; can't CAST to INT b/c some entries of format "<E12345789>"
+        # ON (CAST(a.MRN AS STRING) LIKE b.MRN) -- JOINING using strings and 'LIKE' is slow, not scalable
+        ON cca.MRN = map.MRN_INT
+    --ORDER BY MRN_INT, ANON_ID, AdmitTRUE, DischTRUE
+),
+
+-- Encounters table, obtain relevant columns, join with departments
+-- Returns 1672043 records
+encountersAnonymized AS
+(
+    SELECT DISTINCT 
+      anon_id, pat_enc_csn_id_coded, 
+      hosp_admsn_time_jittered, hosp_dischrg_time_jittered,
+      inpatient_data_id_coded , contact_date_jittered --, department_name, specialty
+    FROM `mining-clinical-decisions.shc_core.encounter`
+      --LEFT JOIN `mining-clinical-decisions.shc_core.dep_map`
+      --  USING (department_id)
+    WHERE enc_type = 'Hospital Encounter'
+    AND appt_type IN ('Admission (Discharged)') -- many (1600+) app_type for enc_type='Hospital Encounter' such as radiology, infusion, etc.
+    #AND LOWER(appt_type) LIKE '%admission%'
+    #AND appt_type NOT IN ('Admission (Hospital Outpatient Visit)','Lab')
+    #AND anon_id LIKE '%JCde8830%'
+),
+
+-- Link cost cohort with Encounters data via reidentified ID linkage
+--  Trying to match based on same MRN and Date of admission
+-- returned 12749 rows if exact match by admit date. 
+--    15,389 if match by +/- 1 day of admit date, 17982 within +/- 7 days, 25567 +/- 30 days
+-- >49K if match by same year, too blunt. Maybe should match by nearest matching date???? Or Manually reconstruct why some records not finding a match?
+cohortEncounterIdentified AS
+(
+    SELECT DISTINCT
+        cci.*,
+        enc.pat_enc_csn_id_coded, hosp_admsn_time_jittered, hosp_dischrg_time_jittered
+    FROM cohortCostIdentified cci
+      JOIN encountersAnonymized as enc
+      ON  (  cci.ANON_ID = enc.anon_id
+            
+            -- Join by exact date match
+            -- AND AdmitDate + jitter = EXTRACT(DATE FROM enc.hosp_admsn_time_jittered) 
+            
+            -- Join by date match within +/- 7 day
+            AND (DATE_DIFF(AdmitDate + JITTER, EXTRACT(DATE FROM enc.hosp_admsn_time_jittered), DAY) >= -0) 
+            AND (DATE_DIFF(AdmitDate + JITTER, EXTRACT(DATE FROM enc.hosp_admsn_time_jittered), DAY) <= +0)
+          )
+    #AND enc = 131276303665
+    --WHERE (enc IS NOT NULL) -- returned 31544 records
+    --ORDER BY mrn, AdmitTRUE, DischTRUE, enc
+),
+
+-- Find all DRGs per encounter
+-- Q: multiple records for same ENC & DRG that vary by drg_weight. Currently ignoring column. Should drg_weight be summed and taken into account?
+-- drg_weight ∆= (per data dictionary): "From the list of DRGs filed to the hospital account, the weight for the DRG in this row."
+-- 25849 records (avg 2.05 per encounter)
+cohortDRG AS
+(
+    SELECT DISTINCT 
+      cei.*,
+      drg.drg_name, drg.drg_mpi_code --, drg.drg_weight
+    FROM cohortEncounterIdentified AS cei
+      JOIN `mining-clinical-decisions.shc_core.drg_code` AS drg
+      USING (anon_id, pat_enc_csn_id_coded)
+    -- ORDER BY MRN, enc, AdmitTRUE, DischTRUE, drg
+),
+
+-- Goal: Find top DRG by greatest A) volume, B) total cost, C) cost variation, D) LOS
+-- option (1): take each DRG (multiple per enc) and associate with full admission cost. (2): take only 1 DRG per enc (idea: max(DRG_WEIGHT). idea: link with CMS DRG reimbursement, pick max)
+-- CURRENTLY --> option (1), don't know how to do option (2) scalably
+-- could also do this with GROUP BY, but practicing window functions
+costVarPerDRG AS
+(
+    SELECT DISTINCT
+      drg_mpi_code, drg_name, 
+      count(distinct anon_id) AS nPatients,
+      COUNT(distinct pat_enc_csn_id_coded) AS nEncounters,
+      ROUND(AVG(LOS),2) AS LOSAverage,
+      ROUND(STDDEV_SAMP(LOS),2) AS LOSStdDevS,
+      --SUM(LOS) AS LOSTotal,
+      ROUND(AVG(Cost_Direct)) AS costDirectAverage,
+      SUM(Cost_Direct) AS costDirectTotal,
+      ROUND(STDDEV_SAMP(Cost_Direct)) AS costDirectStdDevS,
+      ROUND(AVG(Cost_Direct/LOS)) AS costDirectOverLOSAverage,
+      ROUND(STDDEV_SAMP(Cost_Direct/LOS)) AS costDirectOverLOSStdDevS,  -- If most variability explained by LOS, try scaling by it to see what's lingering
+      ROUND(STDDEV_SAMP(Cost_Direct) * COUNT(distinct pat_enc_csn_id_coded)) AS costVariabilityScore,
+      ROUND(STDDEV_SAMP(Cost_Direct/LOS) * COUNT(distinct pat_enc_csn_id_coded)) AS costVariabilityOverLOSScore
+    FROM cohortDRG
+    GROUP BY drg_mpi_code, drg_name
+    ORDER BY costVariabilityOverLOSScore DESC
+),
+
+/*
+-- Goal: Find top DRG by greatest A) volume, B) total cost, C) cost variation, D) LOS
+-- option (1): take each DRG (multiple per enc) and associate with full admission cost. (2): take only 1 DRG per enc (idea: max(DRG_WEIGHT). idea: link with CMS DRG reimbursement, pick max)
+-- CURRENTLY --> option (1), don't know how to do option (2) scalably
+-- 908 DRGs records
+topDRG AS
+(
+    SELECT a.*, cms_drg_weights.MS_DRG_NAME
+    FROM
+    (
+        SELECT DISTINCT
+          COUNT(enc) OVER drg_window AS nEnc,
+          SUM(LOS) OVER drg_window AS totLOS,
+          ROUND(SUM(Cost_Direct) OVER drg_window /1000,0) AS costDirectThou,
+          ROUND(STDDEV_SAMP(Cost_Direct) OVER drg_window /1000,0) AS stdevDirectCostThou,
+          ROUND((STDDEV_SAMP(Cost_Direct) OVER drg_window) / (SUM(Cost_Direct) OVER drg_window) * 100, 1) as stDevPctDirect,
+          drg, drg_c
+        FROM cohortDRG
+        WINDOW drg_window AS (PARTITION BY drg)--ORDER BY drg)
+        ORDER BY (stdevDirectCostThou) * nEnc DESC
+        #GROUP BY dx
+    ) as 
+    LEFT JOIN `som-nero-phi-jonc101-secure.CMS_reimbursement.cms_drg_weights_2022` AS cms_drg_weights
+    ON (CAST(a.drg_c AS INT64) = cms_drg_weights.MS_DRG_ID)
+    -- ORDER BY (stdevDirectCostThou) * nEnc DESC
+),
+
+
+selectDRGs AS
+(
+
+    SELECT *
+    FROM cohortDRG # topDRG
+
+    WHERE (drg_c IN ("870","871", "872")) -- SEVERE SEPSIS
+    # WHERE (drg_c IN ("469","470") AND drg LIKE '%JOINT REPLACEMENT%') -- MAJOR HIP AND KNEE JOINT REPLACEMENT OR REATTACHMENT
+
+    # WHERE (drg_c = "014") -- ALLOGENEIC BONE MARROW TRANSPLANT
+    # WHERE (drg_c IN ("216","217","218","219","220","221") AND drg LIKE '%CARDIAC%') -- CARDIAC VALVE AND OTHER MAJOR CARDIOTHORACIC PROCEDURES: W VS W/O CARDIAC CATHETERIZATION; W VS W/O CC,MCC
+    # WHERE (drg_c IN ("344","345","346") AND drg LIKE '%BOWEL%') -- MINOR SMALL & LARGE BOWEL PROCEDURES: W VS W/O CC,MCC
+    # WHERE (drg_c IN ("456","457","458","459","460") AND drg LIKE '%SPINAL%') -- SPINAL FUSION EXCEPT CERVICAL
+    # WHERE (drg_c IN ("881")) -- DEPRESSIVE NEUROSES
+    # WHERE (drg_c IN ("885")) -- PSYCHOSES
+    # WHERE (drg_c IN ("001") AND drg LIKE '%HEART TRANSPLANT%') -- HEART TRANSPLANT OR IMPLANT OF HEART ASSIST SYSTEM
+    # WHERE (drg_c IN ("002") AND drg LIKE '%HEART TRANSPLANT%') -- HEART TRANSPLANT OR IMPLANT OF HEART ASSIST SYSTEM
+
+    ORDER BY MRN, enc, AdmitTRUE, DischTRUE, drg, drg_c
+
+),
+*/
+placeholder AS (select 1 from `som-nero-phi-jonc101.shc_core_2021.demographic`)
+
+SELECT *
+from costVarPerDRG
+
+limit 1000
+
+
diff --git a/scripts/InpatientVariability/comorbiditiesOnAdmission.sql b/scripts/InpatientVariability/comorbiditiesOnAdmission.sql
new file mode 100644
index 00000000..f46c7101
--- /dev/null
+++ b/scripts/InpatientVariability/comorbiditiesOnAdmission.sql
@@ -0,0 +1,61 @@
+
+
+cohort AS
+(
+    SELECT * FROM selectDRGs
+),
+
+-- Goal: A) Find top diagnoses by greatest volume / tottal cost / cost variation / LOS. B) find comorbidities on admission
+-- avg 24 records per encounter
+-- Expect exactly 1 'primary' dx per enc, --> HOWEVER of the 12665 enc: # of 'primary' dx per encounter: (0,1,2) = (1627,11036,2)
+diagnosesOnAdmission AS
+(
+    SELECT DISTINCT enc, admsnTimeJTR,
+            icd10, dx_name, start_date, primary,
+            DATETIME_DIFF(a.admsnTimeJTR, b.start_date, DAY) as daysDocumentedBeforeAdmsn,
+            if(DATETIME_DIFF(b.start_date, a.admsnTimeJTR, HOUR) >= 0, 1, 0) as withinFirst24H
+            # the following are almost entirely missing data: hospital_pl, problem_status, poa, present_on_adm, noted_date, hx_date_of_entry, end_date, resolved_date
+            #count(case primary when 'Y' then 1 else null end) as numPrimary
+            #REGEXP_EXTRACT(icd10, r'[A-Z]\d+') as icd10prefix
+    FROM cohort a
+    JOIN `mining-clinical-decisions.shc_core.diagnosis_code` b
+    ON (a.anon_id = b.anon_id)
+        AND DATETIME_DIFF(b.start_date, a.admsnTimeJTR, HOUR) <= 24 -- start_date exists for every record in ::diagnosis_code
+),
+
+
+icd10_matching AS
+(
+    -- filter out redundancies: only 1 unique ICD10 per patient encounter corresponding to its earliest documented instance
+    SELECT DISTINCT enc, admsnTimeJTR, ICD10_string,
+                min(start_date) as earliestNoted,
+                if(DATETIME_DIFF(min(start_date), admsnTimeJTR, HOUR) >= 0, 1, 0) as withinFirst24H,
+                DATETIME_DIFF(admsnTimeJTR,min(start_date),DAY) as daysFirstDocumentedBeforeAdmsn
+    FROM
+    (
+        -- The following captures almost all ICD10s in the diagnosis_code table. Only thing left are rows with multiple ICD10's separated by commas --> small minority, excluding for now
+        SELECT enc, admsnTimeJTR, start_date, ICD10_string
+        FROM diagnosesOnAdmission a
+        JOIN `mining-clinical-decisions.ahrq_ccsr.diagnosis_code` b
+        ON a.icd10 = b.ICD10
+        UNION DISTINCT
+        SELECT enc, admsnTimeJTR, start_date, ICD10_string
+        FROM diagnosesOnAdmission a
+        JOIN `mining-clinical-decisions.ahrq_ccsr.diagnosis_code` b
+        ON CONCAT(a.icd10,".") = b.ICD10
+    ) x
+    GROUP BY enc, admsnTimeJTR, ICD10_string
+    ORDER BY enc, daysFirstDocumentedBeforeAdmsn
+),
+
+-- match to CCSR categories
+icd10_CCSR AS
+(
+    SELECT DISTINCT enc, admsnTimeJTR, earliestNoted, withinFirst24H, daysFirstDocumentedBeforeAdmsn, b.*
+    FROM icd10_matching a
+    JOIN `som-nero-phi-jonc101-secure.codes.diagnoses_AHRQ_CCSR` b
+    ON ICD10_string = b.ICD10
+    ORDER BY enc, earliestNoted
+)
+
+SELECT * FROM icd10_CCSR
diff --git a/scripts/InpatientVariability/hospitalLengthOfStayVariability.sql b/scripts/InpatientVariability/hospitalLengthOfStayVariability.sql
new file mode 100644
index 00000000..852e1f41
--- /dev/null
+++ b/scripts/InpatientVariability/hospitalLengthOfStayVariability.sql
@@ -0,0 +1,171 @@
+-- (Elective Ortho Surgery) Admission as Example
+WITH
+
+-- Identify Cases by Diagnosis Related Group
+cohortDiagnosisRelatedGroup AS
+(
+    select *
+    from `mining-clinical-decisions.shc_core.drg_code`
+    /*where drg_mpi_code in 
+    (   '301' -- Hip Joint Replacement
+        --'470',    -- Hip and Knee Joint Replacement or Reattachment w/o MCC    14K encounters
+        --'301',    -- Hip Joint Replacement    9.7K encounters
+        --'302'     -- Knee Joint Replacement   8.6K encounters
+    )
+    */
+),
+
+-- Find all Specialty Primary cohort Assignments
+cohortPrimaryEncounter AS
+(
+    select distinct anon_id, pat_enc_csn_id_coded
+    from `mining-clinical-decisions.shc_core.treatment_team`
+    where name = 'Primary Team'
+    --and prov_name like 'ORTHO ARTHRITIS%'
+    -- Up to 1,000 cases per year
+),
+
+-- Exclude all those that passed through Emergency Department (only capture "elective" cases)
+emergencyAdmissionEncounter AS
+(
+    select distinct anon_id, pat_enc_csn_id_coded
+    from `mining-clinical-decisions.shc_core.adt`
+    where pat_service_c in ('100','187') -- Emergency and Emergency Services
+    and in_event_type_c = 1 -- Admission
+),
+cohortNonEmergencyEncounter AS
+(
+    select anon_id, pat_enc_csn_id_coded
+    from cohortPrimaryEncounter 
+    EXCEPT DISTINCT
+    select anon_id, pat_enc_csn_id_coded
+    from emergencyAdmissionEncounter 
+),
+cohortNonEmergencyEncounterByYear AS
+(
+    select EXTRACT(YEAR FROM trtmnt_tm_begin_dt_jittered) as assignYear, count(distinct anon_id) nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    from cohortNonEmergencyEncounter join shc_core.treatment_team using (anon_id, pat_enc_csn_id_coded)
+    group by assignYear
+    order by assignYear desc
+    -- Close to 1,000 patients per year, confirming most of these admissions are elective / non-emergency
+),
+
+-- Find top Admission Diagnoses for those Encounters
+cohortDiagnosisCounts AS
+(  
+    select source, icd9, icd10, dx_name, count(distinct snee.anon_id) as nPatients, count(distinct snee.pat_enc_csn_id_coded) as nEncounters, count(*) as nDiagnoses
+    from `mining-clinical-decisions.shc_core.diagnosis_code` as dc 
+        join cohortNonEmergencyEncounter as snee on (snee.anon_id = dc.anon_id and snee.pat_enc_csn_id_coded = dc.pat_enc_csn_id_jittered)
+    group by source, icd9, icd10, dx_name
+    order by nPatients desc
+),
+diagnosisRelatedGroupCounts AS
+(
+    select drg_mpi_code, drg_name, count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters, count(*) as nDiagnoses
+    from `mining-clinical-decisions.shc_core.drg_code`
+    group by drg_mpi_code, drg_name
+    order by nEncounters desc
+),
+
+-- Calculate Length of Stay Distribution for Top Admission Diagnosis
+cohortDiagnosisRelatedGroupLengthOfStay AS
+(
+    select 
+        drg_mpi_code, drg_name, anon_id, pat_enc_csn_id_coded, 
+        admn_prov_map_id, visit_prov_map_id,
+        min(event_time_jittered) as firstEventTime,
+        DATETIME_DIFF(max(event_time_jittered), min(event_time_jittered), HOUR) as lengthOfStayHour, 
+        ROUND(DATETIME_DIFF(max(event_time_jittered), min(event_time_jittered), HOUR) / 24, 2) as lengthOfStayDay,
+        ROUND(DATETIME_DIFF(max(event_time_jittered), min(event_time_jittered), HOUR) / 24) as lengthOfStayDayFloor
+    from cohortDiagnosisRelatedGroup
+        join cohortNonEmergencyEncounter using (anon_id, pat_enc_csn_id_coded)
+        join shc_core.adt using (anon_id, pat_enc_csn_id_coded) -- Join ADT data for first and last event time to estimate length of stay
+        join shc_core.encounter using (anon_id, pat_enc_csn_id_coded)
+    group by 
+        drg_mpi_code, drg_name, anon_id, pat_enc_csn_id_coded,
+        admn_prov_map_id, visit_prov_map_id
+),
+-- Calculate summary stats per diagnosis 
+cohortDRGLengthOfStaySummary AS
+(
+    select 
+        drg_mpi_code, drg_name, 
+        AVG(lengthOfStayDay) AS avgLengthOfStayDay, STDDEV(lengthOfStayDay) as stddevLengthOfStayDay,
+        COUNT(distinct anon_id) as nPatients, COUNT(distinct pat_enc_csn_id_coded) as nEncounters,
+        STDDEV(lengthOfStayDay) * COUNT(distinct pat_enc_csn_id_coded) AS variabilityImpact,
+        SUM(lengthOfStayDay) as totalDays
+    from cohortDiagnosisRelatedGroupLengthOfStay
+    group by 
+        drg_mpi_code, drg_name
+    order by 
+        stddevLengthOfStayDay * nEncounters desc -- Potential impact? Volume * variability
+),
+-- Break out diagnosis summary stats by admitting provider for top diagnoses...
+cohortDRGLengthOfStaySummaryByProvider AS
+(
+    select 
+        drg_mpi_code, drg_name, 
+        admn_prov_map_id,
+        AVG(lengthOfStayDay) AS avgLengthOfStayDay, STDDEV(lengthOfStayDay) as stddevLengthOfStayDay,
+        COUNT(distinct anon_id) as nPatients, COUNT(distinct pat_enc_csn_id_coded) as nEncounters,
+        STDDEV(lengthOfStayDay) * COUNT(distinct pat_enc_csn_id_coded) AS variabilityImpact,
+        SUM(lengthOfStayDay) as totalDays
+    from cohortDiagnosisRelatedGroupLengthOfStay
+    -- where drg_mpi_code = '021' -- Restrict to one diagnosis at a time (e.g., Craniotomy) to make it easier to parse
+    group by 
+        drg_mpi_code, drg_name,
+        admn_prov_map_id
+    having nPatients > 10 -- Only inspect providers who've had a non-trival number of admissions
+    order by 
+        --avgLengthOfStayDay DESC
+        drg_mpi_code, drg_name,
+        stddevLengthOfStayDay * nEncounters desc -- Potential impact? Volume * variability
+),
+-- Join above two so able to compare total DRG averages vs per provider average
+cohortDRGLengthOfStaySummaryByProviderAndTotal AS
+(
+    select 
+        drg_mpi_code, drg_name, 
+        admn_prov_map_id,
+        byProv.avgLengthOfStayDay, byProv.stddevLengthOfStayDay, byProv.nPatients, byProv.nEncounters, byProv.variabilityImpact, byProv.totalDays,
+        byDRG.avgLengthOfStayDay, byDRG.stddevLengthOfStayDay, byDRG.nPatients, byDRG.nEncounters, byDRG.variabilityImpact, byDRG.totalDays
+    from 
+        cohortDRGLengthOfStaySummaryByProvider as byProv
+        join cohortDRGLengthOfStaySummary as byDRG using (drg_mpi_code, drg_name) 
+    order by 
+        byDRG.variabilityImpact desc,
+        byProv.totalDays desc
+
+),
+-- Break out data by length of stay values so can plot as a histogram
+cohortDRGLengthOfStayHistogram AS
+(
+    select 
+        --EXTRACT(YEAR FROM firstEventTime) as admitYear,
+        drg_mpi_code, drg_name,
+        admn_prov_map_id,
+        FLOOR(lengthOfStayHour/24) as lengthOfStayDayMin, FLOOR(lengthOfStayHour/24)+1 as lengthOfStayDayMax,
+        count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    from cohortDiagnosisRelatedGroupLengthOfStay
+    where drg_mpi_code = '021' and drg_name like 'CRANIOTOMY%' -- Restrict to diagnosis to facilitate visualization
+    group by 
+        --admitYear, 
+        drg_mpi_code, drg_name, admn_prov_map_id, lengthOfStayDayMin, lengthOfStayDayMax
+    order by 
+        --admitYear desc, 
+        drg_mpi_code, drg_name, admn_prov_map_id, lengthOfStayDayMin
+),
+
+spacer AS ( select * from `mining-clinical-decisions.shc_core.demographic`)
+
+
+
+-- Break out LoS Distribution by Primary Attending
+
+
+-- Correlate Discharge Order time Relative to Discharge Time vs. Length of Stay
+
+
+select *
+from cohortDRGLengthOfStayHistogram
+--limit 1000
\ No newline at end of file
diff --git a/scripts/InpatientVariability/inptCostVar_lm_v1.R b/scripts/InpatientVariability/inptCostVar_lm_v1.R
new file mode 100644
index 00000000..1e00338e
--- /dev/null
+++ b/scripts/InpatientVariability/inptCostVar_lm_v1.R
@@ -0,0 +1,287 @@
+library(tidyverse)
+library(bigrquery)
+library(caret)
+library(mlbench)
+library(tidyverse)
+library(glmnet)
+
+# library(DBI)
+# library(scales)
+# library(sqldf) # https://www.r-bloggers.com/in-between-a-rock-and-a-conditional-join/
+# library(fuzzyjoin)
+# library(rio)
+
+## warning = TRUE # default
+## options(warn = 0) # default
+# warning = FALSE
+# options(warn = -1)
+# options(scipen = 20)
+
+##############################################################################
+##### filepaths
+dir <- "/Users/kushgupta/Documents/StanfordMD/Projects/Mentors/Chen/Inpatient\ Variability\ Cost\ Reduction/"
+filepath0 <- paste0(dir,"code/cohortSelection.sql")
+filepath1 <- paste0(dir,"code/comorbiditiesOnAdmission.sql")
+filepath2 <- paste0(dir,"code/objectiveFeatures.sql")
+
+##############################################################################
+##### import data: comorbidities
+sqlQuery0 <- readChar(filepath0, file.info(filepath0)$size)
+sqlQuery1 <- readChar(filepath1, file.info(filepath1)$size)
+sqlQuery1 <- paste0(sqlQuery0, sqlQuery1)
+project <- "som-nero-phi-jonc101-secure" # "mining-clinical-decisions"
+results <- bq_project_query(project, sqlQuery1)
+tb <- as_tibble(bq_table_download(results, bigint = c("integer64"))) # bigint needed to properly download full PT_ENC_CSN_ID
+rm(results)
+
+print(paste0(tb %>% distinct(enc) %>% nrow(), " distinct patient encounters"))
+
+categories <- read_csv(paste0(dir,"data/AHRQ_CCSR_2022/categories.csv"), col_names=c("CCSR", "CCSR_modified", "category"))
+categories <- categories %>% select(-CCSR_modified)
+
+##############################################################################
+df <- tb %>% select(-c(withinFirst24H, daysFirstDocumentedBeforeAdmsn, ICD10, ICD10_description))
+
+# for each encounter, keep only a single element per unique CCSR corresponding to the earliest that it (meaning any of the ICD10s that map to that given CCSR) was documented
+df <- df %>% pivot_longer(cols = starts_with("CCSR"), names_to = "category", values_to = "CCSR", values_drop_na = TRUE)
+df <- df %>% distinct(enc, admsnTimeJTR, CCSR, earliestNoted) %>%
+        group_by(enc, admsnTimeJTR, CCSR) %>%
+        summarise(earliestDocumented = min(earliestNoted)) %>%
+        pivot_wider(id_cols = c(enc, admsnTimeJTR), names_from = CCSR, values_from = earliestDocumented, names_sort=TRUE)
+
+# take each CCSR column (values = earliest it was noted for any given pt) and convert to a 3 factor column: 
+# (1) POA (present on admission, ie. was documented prior to current encounter's admission time) (2) First24H (earliest documented was w/in 24h of encounter admit time), (3) FALSE (if N/A, meaning never previously documented in pt)
+poa_freq <- vector("integer", ncol(df))
+first24_freq <- vector("integer", ncol(df))
+names(poa_freq) <- names(df)
+names(first24_freq) <- names(df)
+for (i in 3:ncol(df)) {
+  df[[i]] <-ifelse(difftime(df[[i]], df[[2]], units = "days") >= 0, "First24H", "POA")
+  df[[i]] <- ifelse(is.na(df[[i]]), "FALSE", df[[i]])
+  poa_freq[[i]] <- sum(df[[i]] == "POA")
+  first24_freq[[i]] <- sum(df[[i]] == "First24H")
+}
+ccsr_frequencies <- as_tibble(as.list(poa_freq)) %>% select(-enc,-admsnTimeJTR) %>% 
+                      pivot_longer(everything(), names_to="CCSR", values_to="nEncPOA")
+ccsr_frequencies <- as_tibble(as.list(first24_freq)) %>% select(-enc,-admsnTimeJTR) %>% 
+                      pivot_longer(everything(), names_to="CCSR", values_to="nEncFirst24") %>%
+                      inner_join(ccsr_frequencies) %>% 
+                      inner_join(categories) %>%
+                      arrange(desc(nEncFirst24 * nEncPOA))
+
+
+##############################################################################
+##### import data: objective features
+sqlQuery2 <- readChar(filepath2, file.info(filepath2)$size)
+sqlQuery2 <- paste0(sqlQuery0, sqlQuery2)
+project <- "som-nero-phi-jonc101-secure" # "mining-clinical-decisions"
+results <- bq_project_query(project, sqlQuery2)
+tb2 <- as_tibble(bq_table_download(results, bigint = c("integer64"))) # bigint needed to properly download full PT_ENC_CSN_ID
+rm(results)
+
+print(paste0(tb2 %>% distinct(enc) %>% nrow(), " distinct patient encounters"))
+##############################################################################
+
+df2 <- tb2 %>% inner_join(df_topN)
+features <- df2 %>% select(-c(enc,admsnTimeJTR,mrn,AdmitTRUE,DischTRUE,JITTER,anon_id,dischTimeJTR,drg))
+features <- features %>% select(-drg_c, -LOS)
+colNames <- as_tibble(names(features))
+
+## ensure no blanks
+# features %>% summarise(across(everything(), sum(is.na))) %>% t()
+blanks <- vector("integer", ncol(features))
+distincts <- vector("integer", ncol(features))
+names(blanks) <- names(features)
+names(distincts) <- names(features)
+for (i in seq_along(features)) {
+  blanks[[i]] = sum(is.na(features[[i]]))
+  nDistinct <- length(unique(features[[i]]))
+  distincts[[i]] <- nDistinct
+  if (nDistinct < 10) { 
+    features[[i]] <- as.factor(features[[i]])
+  }
+}
+nBlank <- as_tibble(as.list(blanks)) %>% pivot_longer(everything(), names_to="col", values_to="numBlanks") %>% arrange(desc(numBlanks))
+nDistinct <- as_tibble(as.list(distincts)) %>% pivot_longer(everything(), names_to="col", values_to="numDistinct") %>% arrange(desc(numDistinct))
+
+
+# topN <- unlist(c(ccsr_frequencies[1:n,1]))
+# df_topN <- df[,c("enc","admsnTimeJTR",topN)] # df <- df[, (colnames(df) %in% topN)]
+# df_topN <- df
+
+##############################################################################
+
+## delete following
+# patientFactors[,8:10] <- lapply(patientFactors[,8:10],as.numeric)
+# cols <- unlist(lapply(features, as.numeric))  
+# cols <- as.numeric(features$cr)
+# temp <- features %>% mutate(across(c(1:ncol(features)), as.numeric)) # across(starts_with("first"), as.numeric)
+
+features <- features1
+
+test<-"/Users/kushgupta/Downloads/cost_var_new_features.csv"
+test<-read_csv(test)
+features<-test[,-1]
+
+
+num <- 1000
+rSquared <- vector("double", num)
+numNonZeroCoef <- vector("integer", num)
+nonZeroCoef <- vector("list", num)
+for (i in 1:num) {
+    set.seed(NULL)
+    print(paste0("Iteration: ", i))
+    
+    train.indices <- features$Cost_Direct %>% createDataPartition(p = 0.8, list = FALSE)
+    train.data  <- features[train.indices, ]
+    test.data <- features[-train.indices, ]
+    
+    testing <- train.data %>% select(matches("[A-Z]{3}\\d{3}", ignore.case=FALSE))
+    nPOA = vector("integer", ncol(testing))
+    nFirst24 = vector("integer", ncol(testing))
+    nMissing = vector("integer", ncol(testing))
+    names(nPOA) <- names(testing)
+    names(nFirst24) <- names(testing)
+    names(nMissing) <- names(testing)
+    for (j in seq_along(testing)) {
+      nPOA[[j]] = sum("POA"==(testing[[j]]))
+      nFirst24[[j]] = sum("First24H"==testing[[j]])
+      nMissing[[j]] = sum("FALSE"==testing[[j]])
+    }
+    nPOA <- as_tibble(as.list(nPOA)) %>% pivot_longer(everything(), names_to="col", values_to="nPOA") %>% arrange(desc(nPOA))
+    nFirst24 <- as_tibble(as.list(nFirst24)) %>% pivot_longer(everything(), names_to="col", values_to="nFirst24") %>% arrange(desc(nFirst24))
+    nMissing <- as_tibble(as.list(nMissing)) %>% pivot_longer(everything(), names_to="col", values_to="nMissing") %>% arrange(desc(nMissing))
+    frequencies <- nPOA %>% left_join(nMissing, by=c("col")) %>% left_join(nFirst24, by=c("col"))
+    n <- 50
+    # remove <- frequencies %>% mutate(mult = nPOA * nFirst24 * nMissing) %>% arrange(desc(mult)) %>% filter(row_number() > n) %>% distinct(col)
+    remove <- frequencies %>% arrange(nMissing) %>% filter(row_number() > n) %>% distinct(col)
+    
+    t <- unlist(c(remove))
+    features_cleaned <- features %>% select(-all_of(t)) # preferred over select(-t) or select(-c(t))
+    
+    train.data  <- features_cleaned[train.indices, ]
+    test.data <- features_cleaned[-train.indices, ]
+    
+    ## this doesn't work -> x.test ends up with different ncol as x
+    # x <- model.matrix(Cost_Direct~., train.data)[,-1]
+    # y <- train.data$Cost_Direct
+    # x.test <- model.matrix(Cost_Direct~., test.data)[,-1] # predict test
+    
+    ## trying to make training and test model.matrix same # features
+    modelMatrix <- model.matrix(Cost_Direct~., features_cleaned)[,-1]
+    x <- modelMatrix[train.indices,]
+    y <- features_cleaned[train.indices,]$Cost_Direct
+    x.test <- modelMatrix[-train.indices,]
+    
+    # # glmnet(x, y, alpha = 1, lambda = NULL)
+    # 
+    # #RIDGE
+    # cv <- cv.glmnet(x, y, alpha = 0) # find best lambda using C-V
+    # cv$lambda.min # display best lambda
+    # model <- glmnet(x, y, alpha = 0, lambda = cv$lambda.min) # fit model
+    # #coef(model) # display coeff
+    # #x.test <- model.matrix(Cost_Direct~., test.data)[,-1] # predict test
+    # predictions <- model %>% predict(x.test) %>% as.vector() # predict test
+    # data.frame(
+    #   RMSE = RMSE(predictions, test.data$Cost_Direct),
+    #   Rsquare = R2(predictions, test.data$Cost_Direct)
+    # )
+    
+    #LASSO
+    cv <- cv.glmnet(x, y, alpha = 1) # find best lambda using C-V
+    cv$lambda.min # display best lambda
+    model <- glmnet(x, y, alpha = 1, lambda = cv$lambda.min) # fit model
+    #coef(model) # display coeff
+    modelCoefficients <- coef(model)[1:length(coef(model)),]
+    temp <- tibble(colname = names(modelCoefficients), val = as.numeric(modelCoefficients)) %>% filter(val != 0) %>% arrange(desc(val))
+    temp$extract <- str_extract(temp$colname, pattern='[A-Z]{3}\\d{3}')
+    temp <- temp %>% left_join(categories, by=c("extract"="CCSR"))
+    #x.test <- model.matrix(Cost_Direct~., test.data)[,-1] # predict test
+    predictions <- model %>% predict(x.test) %>% as.vector() # predict test
+    # data.frame(
+    #   RMSE = RMSE(predictions, test.data$Cost_Direct),
+    #   Rsquare = R2(predictions, test.data$Cost_Direct)
+    # )
+    rSq <- R2(predictions, test.data$Cost_Direct)
+    print(paste0("R Squared: ", rSq))
+    rSquared[[i]] <- rSq
+    numNonZeroCoef[[i]] <- nrow(temp)
+    nonZeroCoef[[i]] <- temp
+    
+    # rSquared
+    # numNonZeroCoef
+    # nonZeroCoef
+
+}
+AAmissing <- tibble(round(rSquared,2), numNonZeroCoef)
+
+coeff <- nonZeroCoef[[1]]
+for (i in 2:num) {
+  coeff <- rbind(coeff, nonZeroCoef[[i]])
+}
+
+coeff$val <- as.integer(round(coeff$val,0))
+coeff <- coeff %>%
+  arrange(colname, val) %>% 
+  group_by(colname) %>% 
+  mutate(value = paste0("val",row_number()),
+         numOccurrences = n(),
+         avgVal = as.integer(round(mean(val),0)))
+coeff <- pivot_wider(coeff, id_cols=c(colname, extract, category, numOccurrences, avgVal), names_from=value, values_from=val) %>% arrange(desc(numOccurrences))
+
+summary(AAmissing$`round(rSquared, 2)`)
+summary(AAmissing$numNonZeroCoef)
+
+# R Squared: 95% CI: [0.00, 0.27], mean: 0.1021
+# # non-zero coefficients: 95% CI: [4, 34], mean: 15.21
+
+
+View(coeff)
+View(AAmissing)
+
+
+##############################################################################
+
+# /*buckets AS
+
+  #CCSR AHRQ mappings
+  #CCSR IN ('CIR007','CIR008') -- hypertension ± associated secondary complications
+  #CCSR IN ('CIR011') -- Coronary atherosclerosis and other heart disease
+  #CCSR IN ('CIR017') -- cardiac dysrhythmias
+  #CCSR IN ('CIR019') -- heart failure
+  #CCSR IN ('CIR020','CIR021','CIR022','CIR023','CIR024','CIR025') -- cerebrovascular disease, infarction / ischemia, sequelae
+  #CCSR IN ('CIR026') -- peripheral / visceral vascular disease
+  #CCSR IN ('DIG012') -- Intestinal obstruction and ileus
+  #CCSR IN ('END002',  'END003',  'END004',  'END005',  'END006',  'END007',  'END008',  'END009') -- Diabetes, Obesity
+  #CCSR IN ('FAC002','FAC007') -- mental health related encounter
+  #CCSR IN ('FAC019') -- Socioeconomic/psychosocial factors
+  #CCSR IN ('GEN001') -- Nephritis; nephrosis; renal sclerosis
+  #CCSR IN ('GEN003') -- chronic kidney disease
+  #CCSR IN ('INF006') -- HIV infection
+  #CCSR IN ('INF007') -- Hepatitis
+  #CCSR IN ('INF012') -- Coronavirus disease – 2019 (COVID-19)
+  #CCSR IN ('MUS006') -- Osteoarthritis
+  #CCSR IN ('MUS013') -- Osteoporosis
+  #CCSR IN ('MBD001','MBD003') -- Schizo, Bipolar
+  #CCSR IN ('MBD002') -- Depressive disorders
+  #CCSR IN ('MBD010') -- Feeding and eating disorders
+  #CCSR IN ('MBD012', 'MBD027') -- Suicidal ideation/attempt/intentional self-harm
+  #CCSR IN ('MBD017','MBD018','MBD019','MBD020','MBD021','MBD022','MBD023','MBD024','MBD025','MBD026','MBD028','MBD029','MBD030','MBD031','MBD032','MBD033') -- substance abuse: EtOH, opioids, cannabis, sedatives, etc. 
+  #CCSR LIKE "%NVS%" -- all neuro conditions
+  #CCSR IN ('NEO001','NEO002','NEO003','NEO004','NEO005','NEO006','NEO007','NEO008','NEO009','NEO010') -- cancer - ENT
+  #CCSR IN ('NEO011') -- cancer - heart
+  #CCSR IN ('NEO012','NEO013','NEO014','NEO015','NEO016','NEO017','NEO018','NEO019','NEO020','NEO021') -- cancer - GI
+  #CCSR IN ('NEO022') -- cancer - pulm
+  #CCSR IN ('NEO023','NEO024') -- cancer - bone
+  #CCSR IN ('NEO025','NEO026','NEO027','NEO028') -- cancer - skin
+  #CCSR IN ('NEO029','NEO030') -- cancer - breast
+  #CCSR IN ('NEO031','NEO032','NEO033','NEO034','NEO035','NEO036','NEO037','NEO038') -- cancer - female repro
+  #CCSR IN ('NEO039','NEO040','NEO041','NEO042') -- cancer - male repro
+  #CCSR IN ('NEO043','NEO044','NEO045','NEO046','NEO047') -- cancer - urinary
+  #CCSR IN ('NEO048','NEO049') -- cancer - nervous
+  #CCSR IN ('NEO050','NEO051','NEO052','NEO053','NEO054','NEO055','NEO056') -- cancer - endocrine
+  #CCSR IN ('NEO057','NEO058') -- cancer - lymphoma
+  #CCSR IN ('NEO059','NEO060','NEO061','NEO062','NEO063','NEO064') -- cancer - leukemia
+  #CCSR IN ('NEO065','NEO066','NEO067','NEO068','NEO069','NEO070','NEO071','NEO072') -- cancer - other
+  
+  
diff --git a/scripts/InpatientVariability/lacticAcidosisOnAdmission.sql b/scripts/InpatientVariability/lacticAcidosisOnAdmission.sql
new file mode 100644
index 00000000..8f258088
--- /dev/null
+++ b/scripts/InpatientVariability/lacticAcidosisOnAdmission.sql
@@ -0,0 +1,128 @@
+-- Find admissions with lactic acidosis within 24 hours of admission, but not in problem list / diagnosis codes???
+-- Group by admission service line and DRG or diagnosis
+
+WITH
+-- Admission Times and service admitted with Inpatient designation
+-- This likely includes Valley Care as well as SHC. May want to separate...
+admissionServiceTime AS
+(
+    select
+      anon_id, pat_enc_csn_id_coded, 
+      pat_service_c, pat_service,
+      event_time_jittered AS admission_time,
+      EXTRACT(YEAR FROM event_time_jittered) as admission_year
+    from `shc_core_2021.adt`
+    where in_event_type_c = 1 -- Admission
+    and base_pat_class_c = 1 -- Inpatient
+),
+-- 343,361 distinct pat_enc_csn_id_coded
+
+-- Find all lactic acid results
+lacticAcidResult AS
+(
+    select 
+      lr.anon_id, lr.pat_enc_csn_id_coded,
+      op.order_proc_id_coded, lr.order_time, lr.result_time,
+      lr.base_name, lr.lab_name, 
+      lr.ord_num_value, lr.result_flag
+    from 
+      `shc_core_2021.lab_result` as lr
+        inner join `shc_core_2021.order_proc` as op on (lr.order_id_coded = op.order_proc_id_coded)
+    where
+      base_name in ('LACWBL','LAC','LACTATEPOC','SPLACW','PCLAM','LACC')
+),
+
+-- Find all Lactic Acidosis Diagnosis Code
+lacticAcidDiagnosisCode AS
+(  
+    select 
+      anon_id, pat_enc_csn_id_jittered
+      noted_date, start_date,
+      poa, present_on_adm, hospital_pl
+    from `shc_core_2021.diagnosis` as dc
+    where
+      icd9 in ('276.2','790.6')
+      or
+      icd10 in ('E87.2','R79.89')
+),
+
+-- Find all admission encounters where lactic acid result is available within 24 hours of admission
+-- Consider making this an outer join without the time-range filter, so can apply in a subsequent step
+--  to do a single summary query that generates total encounters as well as "lactate checked" encounters.
+cohortLacticAcidResult AS
+(
+  select
+      ast.anon_id, ast.pat_enc_csn_id_coded, 
+      ast.pat_service_c, ast.pat_service,
+      ast.admission_time, ast.admission_year,
+
+      lar.order_proc_id_coded, lar.order_time, lar.result_time,
+      lar.base_name, lar.lab_name, 
+      lar.ord_num_value, lar.result_flag,
+      -- IF(lar.result_time is null, null, DATETIME_DIFF(lar.result_time, ast.admission_time, HOUR)) as hours_until_lactic_acid_result  -- Null check if outer join
+      DATETIME_DIFF(lar.result_time, ast.admission_time, HOUR) as hours_until_lactic_acid_result
+  from
+    admissionServiceTime AS ast
+      INNER JOIN lacticAcidResult AS lar 
+        USING (anon_id)
+  where
+    DATETIME_DIFF(lar.result_time, ast.admission_time, HOUR) >= -24 and -- Find admissions where lactic acid result available within +/- 24 hours of admission time
+    DATETIME_DIFF(lar.result_time, ast.admission_time, HOUR) <  +24 
+),
+-- 86,067 distinct pat_enc_csn_ud_coded
+-- 26,758 distinct pat_enc_csn_id_coded where result_flag is not null (i.e., abnormal lactate)
+
+-- One row per encounter, with indicator of whether any lactic acid result exists within 24 hours, and if so, if it's abnormal
+cohortLacticAcidIndicator AS
+(
+  select
+      anon_id, pat_enc_csn_id_coded, 
+      pat_service_c, pat_service,
+      admission_time, admission_year,
+
+      min(result_time) as firstLactateResultTime,
+      max(result_flag) as anyLactateResultFlag,
+  from
+    cohortLacticAcidResult
+  group by
+    anon_id, pat_enc_csn_id_coded, 
+    pat_service_c, pat_service,
+    admission_time, admission_year
+),
+
+-- Summarize counts for admissions with a lactate result within +/- 24 hours and how many of those with abnormal result flags
+cohortLacticAcidSummary AS
+(
+  select
+    count(distinct anon_id) as nPatients,
+    count(distinct pat_enc_csn_id_coded) as nEncounters,
+    countif(firstLactateResultTime is not null) as nEncountersLactateChecked,
+    countif(anyLactateResultFlag is not null) as nEncountersLactateAbnormal
+  from
+    cohortLacticAcidIndicator
+),
+cohortLacticAcidSummaryByYear AS
+(
+  select
+    admission_year,
+    count(distinct anon_id) as nPatients,
+    count(distinct pat_enc_csn_id_coded) as nEncounters,
+    countif(firstLactateResultTime is not null) as nEncountersLactateChecked,
+    countif(anyLactateResultFlag is not null) as nEncountersLactateAbnormal
+  from
+    cohortLacticAcidIndicator
+  group by admission_year
+  order by admission_year desc
+),
+-- 2020 - 8,259 patients, 10,368 encounters where lactate checked within +/- 24 hours of admission. 4,028 of those with an abnormal lactate result.
+
+placeholder AS
+(
+  select * 
+  from `som-nero-phi-jonc101.shc_core_2021.lab_result`
+  limit 1
+)
+
+select * -- count(distinct pat_enc_csn_id_coded)
+from cohortLacticAcidSummaryByYear
+limit 1000
\ No newline at end of file
diff --git a/scripts/InpatientVariability/objectiveFeatures.sql b/scripts/InpatientVariability/objectiveFeatures.sql
new file mode 100644
index 00000000..ca421ed7
--- /dev/null
+++ b/scripts/InpatientVariability/objectiveFeatures.sql
@@ -0,0 +1,508 @@
+
+
+/*
+selectDRGs AS
+(
+    SELECT *
+    #SELECT anon_id, enc, admsnTimeJTR
+    FROM `som-nero-phi-jonc101-secure.kush_db.selectDRGs` # selectDRGs_sepsis
+), */
+
+-- From: Charlson, Apache, SOFA, etc.
+features AS
+(
+    SELECT NULL
+-- comorbidities / AHRQ CCSR
+    -- MI: History of definite or probable MI (EKG changes and/or enzyme changes)
+    -- CHF: DOE or PND that has responded to digitalis, diuretics, or afterload reducing agents
+    -- PVD: Intermittent claudication or past bypass for chronic arterial insufficiency, history of gangrene or acute arterial insufficiency, or untreated thoracic or abdominal aneurysm (≥6 cm)
+    -- TIA / CVD: History of a cerebrovascular accident with minor or no residua and transient ischemic attacks
+    -- Dementia: Chronic cognitive deficit
+    -- COPD
+    -- Connective Tissue Disease
+    -- PUD: Any history of treatment for ulcer disease or history of ulcer bleeding
+    -- Liver Dz: Mild = chronic hepatitis (or cirrhosis without portal hypertension). Severe = cirrhosis and portal hypertension with variceal bleeding history, moderate = cirrhosis and portal hypertension but no variceal bleeding history
+    -- Uncomplicated Diabetes
+    -- Hemiplegia
+    -- CKD: Severe = on dialysis, status post kidney transplant, uremia, moderate = creatinine >3 mg/dL (0.27 mmol/L)
+    -- Localized Solid Tumor
+    -- Metastatic Solid Tumor
+    -- Leukemia
+    -- Lymphoma
+    -- AIDS
+    -- ? Chronic renal failure?
+    -- ? H/o severe organ insufficiency?
+    -- ? Immunocompromised?
+
+-- labs
+    -- Platelets: >150, 100-149, 50-99, 20-49, <20
+    -- PaO2 / FiO2: >=400, 300-399, 200-299, 100-199, <100
+    -- Cr: <1.2, 1.2-1.9, 2.0-3.4, 3.5-4.9, >5.0
+    -- Hct: cutoffs: >60, 50, 46, 30, 20, <20
+    -- WBC: cutoffs: >40, 20, 15, 3, 1, <1
+    -- PaO2: cutoffs: >70, 60, 55, <55
+    -- Arterial pH: cutoffs: >7.7, 7.6, 7.5, 7.33, 7.25, 7.15, <7.15
+    -- Sodium: cutoffs: >180, 160, 155, 150, 130, 120, 111, <111
+    -- Potassium: cutoffs: >7, 6, 5.5, 3.5, 3, 2.5, <2.5
+    -- Cr: cutoffs: >3.5, 2, 1.5, 0.6, <0.6
+    -- Bilirubin: mg/dL: <1.2, 1.2-1.9, 2.0-5.9, 6.0-11.9, >12.0 (umol/L: <20, 20-32, 33-101, 102-204, >204)
+    -- BUN: cutoffs: >84, <28
+    -- Bicarbonate: cutoffs: <15, >20
+    -- Acute renal failure?
+
+-- vitals
+    -- FiO2
+    -- SpO2: >96, 94-95, 92-93, 88-92, 86-87, 84-85, <83
+    -- Temperature: >41, 39-41, 38.5-39, 36-38.5, 34-36, 32-34, 30-32, <30
+    -- MAP: cutoffs: >159, 129, 109, 69, 49, <49
+    -- HR: cutoffs: >180, 140, 110, 70, 55, 40, <40
+
+-- other
+    -- Age: <44, 45-54, 55-64, 65-74, >74
+    -- Urine Output 24H: cutoffs: <500, >1000
+
+
+-- TO DO:
+    -- Dopamine <5, >5
+    -- Dobutamine (y/n)
+    -- Epinephrine <0.1, >0.1
+    -- Norepinephrne <0.1, >0.1
+    -- Type of O2 Delivery: NC, simple face mask, non-rebreather, HFNC
+    -- On mechanical ventilation
+    -- On room air or supplemental?
+    -- RR: cutoffs: >50, 35, 25, 12, 10, 6, <6
+    -- Recent surgery
+    -- Type of Admission: scheduled surgical, medical, unscheduled surgical
+    -- GCS: (glasgow) 15, 13-14, 10-12, 6-9, <6
+    -- Confusion?
+
+
+),
+
+-- Charlson Comorbidity Index (CCI): predicts 10y survival
+charlson AS
+(
+    SELECT NULL
+    -- MI: History of definite or probable MI (EKG changes and/or enzyme changes)
+    -- CHF: DOE or PND that has responded to digitalis, diuretics, or afterload reducing agents
+    -- PVD: Intermittent claudication or past bypass for chronic arterial insufficiency, history of gangrene or acute arterial insufficiency, or untreated thoracic or abdominal aneurysm (≥6 cm)
+    -- TIA / CVD: History of a cerebrovascular accident with minor or no residua and transient ischemic attacks
+    -- Dementia: Chronic cognitive deficit
+    -- COPD
+    -- Connective Tissue Disease
+    -- PUD: Any history of treatment for ulcer disease or history of ulcer bleeding
+    -- Liver Dz: Mild = chronic hepatitis (or cirrhosis without portal hypertension). Severe = cirrhosis and portal hypertension with variceal bleeding history, moderate = cirrhosis and portal hypertension but no variceal bleeding history
+    -- Uncomplicated Diabetes
+    -- Hemiplegia
+    -- CKD: Severe = on dialysis, status post kidney transplant, uremia, moderate = creatinine >3 mg/dL (0.27 mmol/L)
+    -- Localized Solid Tumor
+    -- Metastatic Solid Tumor
+    -- Leukemia
+    -- Lymphoma
+    -- AIDS
+),
+
+
+-- Sequential Organ Failure Assessment (SOFA) Score: predicts ICU mortality
+sofa AS
+(
+    SELECT NULL
+    -- PaO2 / FiO2: >=400, 300-399, 200-299, 100-199, <100
+    -- On mechanical ventilation
+    -- Platelets: >150, 100-149, 50-99, 20-49, <20
+    -- GCS: (glasgow) 15, 13-14, 10-12, 6-9, <6
+    -- Bilirubin: mg/dL: <1.2, 1.2-1.9, 2.0-5.9, 6.0-11.9, >12.0 (umol/L: <20, 20-32, 33-101, 102-204, >204)
+    -- MAP: <70, >70
+    -- Dopamine <5, >5
+    -- Dobutamine (y/n)
+    -- Epinephrine <0.1, >0.1
+    -- Norepinephrne <0.1, >0.1
+    -- Cr: <1.2, 1.2-1.9, 2.0-3.4, 3.5-4.9, >5.0
+    -- Type of O2 Delivery: NC, simple face mask, non-rebreather, HFNC
+),
+
+-- APACHE2 (acute physiology and chronic health evaluation): predicts ICU mortality
+apache2 AS
+(
+    SELECT NULL
+    -- Age: <44, 45-54, 55-64, 65-74, >74
+    -- H/o severe organ insufficiency
+    -- Immunocompromised?
+    -- Recent surgery
+    -- Temperature: >41, 39-41, 38.5-39, 36-38.5, 34-36, 32-34, 30-32, <30
+    -- MAP: cutoffs: >159, 129, 109, 69, 49, <49
+    -- HR: cutoffs: >180, 140, 110, 70, 55, 40, <40
+    -- RR: cutoffs: >50, 35, 25, 12, 10, 6, <6
+    -- PaO2: cutoffs: >70, 60, 55, <55
+    -- Arterial pH: cutoffs: >7.7, 7.6, 7.5, 7.33, 7.25, 7.15, <7.15
+    -- Sodium: cutoffs: >180, 160, 155, 150, 130, 120, 111, <111
+    -- Potassium: cutoffs: >7, 6, 5.5, 3.5, 3, 2.5, <2.5
+    -- Cr: cutoffs: >3.5, 2, 1.5, 0.6, <0.6
+    -- Acute renal failure?
+    -- Chronic renal failure?
+    -- Hct: cutoffs: >60, 50, 46, 30, 20, <20
+    -- WBC: cutoffs: >40, 20, 15, 3, 1, <1
+),
+
+-- NEWS2 (National Early Warning Score), SAPS (Simplified Acute Physiology Score)
+other AS
+(
+    SELECT NULL
+    -- SpO2: >96, 94-95, 92-93, 88-92, 86-87, 84-85, <83
+    -- On room air or supplemental?
+    -- Confusion?
+    -- BUN: cutoffs: >84, <28
+    -- Urine Output 24H: cutoffs: <500, >1000
+    -- Bicarbonate: cutoffs: <15, >20
+    -- Type of Admission: scheduled surgical, medical, unscheduled surgical
+),
+
+-- patient age, comorbidities, demographic
+patientFactors AS
+(
+    SELECT enc, admsnTimeJTR, GENDER, CANONICAL_ETHNICITY, CANONICAL_RACE, INTRPTR_NEEDED_YN, INSURANCE_PAYOR_NAME, BMI, # CHARLSON_SCORE, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL,
+            case when DATETIME_DIFF(admsnTimeJTR, BIRTH_DATE_JITTERED, YEAR) >= 75 then ">=75"
+                when DATETIME_DIFF(admsnTimeJTR, BIRTH_DATE_JITTERED, YEAR) between 65 and 75 then "65-75"
+                when DATETIME_DIFF(admsnTimeJTR, BIRTH_DATE_JITTERED, YEAR) between 55 and 65 then "55-65"
+                when DATETIME_DIFF(admsnTimeJTR, BIRTH_DATE_JITTERED, YEAR) between 45 and 55 then "45-55"
+                when DATETIME_DIFF(admsnTimeJTR, BIRTH_DATE_JITTERED, YEAR) < 45 then "<45"
+                else null
+                end as age
+    FROM selectDRGs a
+    JOIN `mining-clinical-decisions.shc_core.demographic`
+    -- should LEFT JOIN, but this loses some data (current use case: 4 out of 521 rows), don't want to deal with missing data imputation right now
+    USING(anon_id)
+),
+
+-- Gather select labs that were collected within the first 24 hours of admission
+-- Glucose, AST/ALT, PaO2/FiO2, Plt, Bili, Cr, pHA, Na, K, Hct, WBC, BUN, HCO3, + vitals (SpO2, HR, RR, MAP, temp)
+-- UNITS?! GLUCOSE (base_name = "GLU") AST/ALT (base_name IN "AST","ALT"), PaO2/FiO2 (base_name IN "FIO2", "PO2A")
+labs_all AS
+(
+    SELECT  enc, admsnTimeJTR, result_time,
+            DATETIME_DIFF(result_time, admsnTimeJTR, HOUR) as hrsAfterAdmit,
+            lab_name, base_name, ord_num_value, result_flag, -- component_id, order_type, proc_code
+            -- reference_unit # reference units for most are either mg/dL (standardized) or, when they vary, mEq/L vs. mmol/L (which are both on same scale, a.k.a. interchangeable)
+            #COUNT(result_time) OVER (PARTITION BY enc, base_name ORDER BY result_time DESC) AS latest, -- filter by "latest=1" to take the latest value for any given lab in 24h period
+            case when base_name = "CR" then ord_num_value else null end as cr, -- creatinine
+            case when base_name IN ("LAC","LACWBL") then ord_num_value else null end as lactate, -- lactate
+            case when base_name = "PO2V" then ord_num_value else null end as pO2v, -- pO2 (venous); arterial = PO2A
+            case when base_name = "PCO2V" then ord_num_value else null end as pCO2v, -- pCO2 (venous); arterial = PCO2A
+            case when base_name = "O2SATV" then ord_num_value else null end as spO2v, -- O2 saturation (venous); arterial = O2SATA
+            case when base_name = "PHV" then ord_num_value else null end as pHv, -- pH (venous); arterial = PHA
+            case when base_name = "HCO3V" then ord_num_value else null end as hco3v, -- HCO3 (venous); arterial = HCO3A
+            case when base_name = "BUN" then ord_num_value else null end as bun, -- BUN
+            case when base_name = "TNI" then ord_num_value else null end as trop, -- troponin
+            case when base_name = "TBIL" then ord_num_value else null end as tbili, -- total bilirubin ("DBIL" = direct bili; "IBIL" = indirect bili)
+            case when base_name = "PLT" then ord_num_value else null end as plt, -- platelets
+            case when base_name = "NA" then ord_num_value else null end as na, -- sodium
+            case when base_name = "K" then ord_num_value else null end as k, -- potassium
+            case when base_name = "WBC" AND reference_unit = "/uL" then ord_num_value/1000
+                 when base_name = "WBC" then ord_num_value else null end as wbc, -- WBC count
+            #case when base_name = "XUWBC" then ord_num_value else null end as u_wbc, -- urine WBC
+            case when base_name = "HCT" then ord_num_value else null end as hct -- hematocrit
+
+    FROM selectDRGs a
+
+    LEFT JOIN `mining-clinical-decisions.shc_core.lab_result` b
+        ON a.enc = b.pat_enc_csn_id_coded
+        AND DATETIME_DIFF(result_time, admsnTimeJTR, HOUR) <= 24
+        AND base_name IN ("CR", "LAC", "LACWBL", "PO2V", "PCO2V", "O2SATV", "PHV", "HCO3V", "BUN", "TNI", "TBIL", "PLT", "NA", "K", "WBC", "XUWBC", "HCT")
+        AND ord_num_value != 9999999 -- discard junk values
+
+    ORDER BY enc, hrsAfterAdmit DESC
+    #ORDER BY latest DESC
+
+    #missing data imputation -> avoid for now
+    #use result_flag (lump HIGH + HIGH PANIC, and LOW + LOW PANIC)
+),
+
+-- Unclear how to process instances where multiple timepoints within 24h range are outside reference.... what if taking the most recent timepoint is wrong (ie. transfusions to correct for low HCT)
+-- ex: SELECT * FROM labs_all WHERE enc=131275787339 ORDER BY base_name, hrsAfterAdmit DESC -- this query shows instance with MULTIPLE labs (ie. HCT) drawn for same pt in given 24h period
+labs_processed AS
+(
+    SELECT enc, max(cr) as cr, max(lactate) as lactate, min(pO2v) as pO2v, max(pCO2v) as pCO2v, min(spO2v) as spO2v,
+            max(bun) as bun, max(tbili) as tbili, max(trop) as trop, min(hct) as hct,
+            max(wbc) as wbc, min(na) as na, min(k) as k, min(plt) as plt, min(hco3v) as hco3v, min(pHv) as pHv -- this row of features could have MIN + MAX per feature (as WBC, for instance, that's too low is just as bad as too high. unclear what to do.
+            -- min(wbc) as wbc, max(na) as na, max(k) as k, max(plt) as plt, max(hco3v) as hco3v, max(pHv) as pHv -- what if both min(wbc) and max(wbc) are outside range (ie. WBC 3, 17) --> take the latest value
+    FROM labs_all
+    GROUP BY enc
+),
+
+labs_5buckets AS
+(
+    SELECT
+        enc,
+        case when cr >= 5.0 then ">=5.0"
+            when cr between 3.5 and 5.0 then "3.5-5.0"
+            when cr between 2.0 and 3.5 then "2.0-3.5"
+            when cr between 1.5 and 2.0 then "1.5-2.0"
+            when cr between 0.6 and 1.5 then "0.6-1.5"
+            when cr < 0.6 then "<0.6"
+            else "unmeasured"
+            end as cr,
+        case when lactate >= 3.5 then ">=3.5"
+            when lactate between 2.5 and 3.5 then "2.5-3.5"
+            when lactate between 1.5 and 2.5 then "1.5-2.5"
+            when lactate between 1.0 and 1.5 then "1.0-1.5"
+            when lactate < 1.0 then "<1.0"
+            else "unmeasured"
+            end as lactate,
+        case when pO2v >= 45 then ">=45"
+            when pO2v between 35 and 45 then "35-45"
+            when pO2v between 25 and 35 then "25-35"
+            when pO2v < 25 then "<25"
+            else "unmeasured"
+            end as pO2v,
+        case when pCO2v >= 60 then ">=60"
+            when pCO2v between 50 and 60 then "50-60"
+            when pCO2v between 40 and 50 then "40-50"
+            when pCO2v between 30 and 40 then "30-40"
+            when pCO2v < 30 then "<30"
+            else "unmeasured"
+            end as pCO2v,
+        case when spO2v >= 85 then ">=85"
+            when spO2v between 75 and 85 then "75-85"
+            when spO2v between 65 and 75 then "65-75"
+            when spO2v between 55 and 65 then "55-65"
+            when spO2v < 55 then "<55"
+            else "unmeasured"
+            end as spO2v,
+        case when bun >= 60 then ">=60"
+            when bun between 30 and 60 then "30-60"
+            when bun between 5 and 30 then "5-30"
+            when bun < 5 then "<5"
+            else "unmeasured"
+            end as bun,
+        case when tbili >= 3.5 then ">=3.5"
+            when tbili between 1.2 and 3.5 then "1.2-3.5"
+            when tbili between 0.3 and 1.2 then "0.3-1.2"
+            when tbili < 0.3 then "<0.3"
+            else "unmeasured"
+            end as tbili,
+        case when trop >= 0.5 then ">=0.5"
+            when trop between 0.1 and 0.5 then "0.1-0.5"
+            when trop < 0.1 then "<0.1"
+            else "unmeasured"
+            end as trop,
+        case when hct >= 47 then ">=47"
+            when hct between 33 and 47 then "33-47"
+            when hct between 24 and 33 then "24-33"
+            when hct between 18 and 24 then "18-24"
+            when hct < 18 then "<18"
+            else "unmeasured"
+            end as hct,
+        case when wbc >= 18.0 then ">=18.0"
+            when wbc between 11.0 and 18.0 then "11.0-18.0"
+            when wbc between 4.0 and 11.0 then "4.0-11.0"
+            when wbc between 2.5 and 4.0 then "2.5-4.0"
+            when wbc < 2.5 then "<2.5"
+            else "unmeasured"
+            end as wbc,
+        case when na >= 155 then ">=155"
+            when na between 145 and 155 then "145-155"
+            when na between 130 and 145 then "130-145"
+            when na between 120 and 130 then "120-130"
+            when na < 120 then "<120"
+            else "unmeasured"
+            end as na,
+        case when k >= 6.5 then ">=6.5"
+            when k between 5.0 and 6.5 then "5.0-6.5"
+            when k between 3.5 and 5.0 then "3.5-5.0"
+            when k between 2.5 and 3.5 then "2.5-3.5"
+            when k < 2.5 then "<2.5"
+            else "unmeasured"
+            end as k,
+        case when plt >= 600 then ">=600"
+            when plt between 450 and 600 then "450-600"
+            when plt between 150 and 450 then "150-450"
+            when plt between 50 and 150 then "50-150"
+            when plt < 50 then "<50"
+            else "unmeasured"
+            end as plt,
+        case when hco3v >= 38.0 then ">=38.0"
+            when hco3v between 33.0 and 38.0 then "33.0-38.0"
+            when hco3v between 23.0 and 33.0 then "23.0-33.0"
+            when hco3v between 17.0 and 23.0 then "17.0-23.0"
+            when hco3v < 17.0 then "<17.0"
+            else "unmeasured"
+            end as hco3v,
+        case when pHv >= 7.55 then ">=7.55"
+            when pHv between 7.45 and 7.55 then "7.45-7.55"
+            when pHv between 7.30 and 7.45 then "7.30-7.45"
+            when pHv between 7.20 and 7.30 then "7.20-7.30"
+            when pHv < 7.20 then "<7.20"
+            else "unmeasured"
+            end as pHv,
+    FROM labs_processed
+    ORDER BY enc
+),
+
+labs_2buckets AS
+(
+    SELECT
+        enc,
+        case when cr >= 3.0 then ">=3.0"
+            when cr < 3.0 then "<3.0"
+            else "unmeasured"
+            end as cr,
+        case when lactate >= 2.5 then ">=2.5"
+            when lactate < 2.5 then "<2.5"
+            else "unmeasured"
+            end as lactate,
+        case when pO2v >= 30 then ">=30"
+            when pO2v < 30 then "<30"
+            else "unmeasured"
+            end as pO2v,
+        case when pCO2v >= 50 then ">=50"
+            when pCO2v < 50 then "<50"
+            else "unmeasured"
+            end as pCO2v,
+        case when spO2v >= 60 then ">=60"
+            when spO2v < 60 then "<60"
+            else "unmeasured"
+            end as spO2v,
+        case when bun >= 40 then ">=40"
+            when bun < 40 then "<40"
+            else "unmeasured"
+            end as bun,
+        case when tbili >= 1.5 then ">=1.5"
+            when tbili < 1.5 then "<1.5"
+            else "unmeasured"
+            end as tbili,
+        case when trop >= 0.1 then ">=0.1"
+            when trop < 0.1 then "<0.1"
+            else "unmeasured"
+            end as trop,
+        case when hct >= 27 then ">=27"
+            when hct < 27 then "<27"
+            else "unmeasured"
+            end as hct,
+        case when wbc >= 14.0 then ">=14.0"
+            when wbc < 14.0 then "<14.0"
+            else "unmeasured"
+            end as wbc,
+        case when na >= 125 then ">=125"
+            when na < 125 then "<125"
+            else "unmeasured"
+            end as na,
+        case when k >= 3.0 then ">=3.0"
+            when k < 3.0 then "<3.0"
+            else "unmeasured"
+            end as k,
+        case when plt >= 100 then ">=100"
+            when plt < 100 then "<100"
+            else "unmeasured"
+            end as plt,
+        case when hco3v >= 35.0 then ">=35.0"
+            when hco3v < 35.0 then "<35.0"
+            else "unmeasured"
+            end as hco3v,
+        case when pHv >= 7.50 then ">=7.50"
+            when pHv < 7.50 then "<7.50"
+            else "unmeasured"
+            end as pHv,
+    FROM labs_processed
+    ORDER BY enc
+
+    #CR > 3 --> YES, NO (include MISSING for data extraction, but for regression, convert all MISSING -> NO ... there would be negative interaction btwn MISSING & NO)
+),
+
+-- Note: not sure if I can trust values... many temperatures are in 60s... doesn't make sense for either Celsius or Fahrenheit. Furthermore, some temperatures labeled as Fahrenheit are in mid/high 30s (a.k.a. clearly in Celsius). Tabling this for now
+flowsheet_all AS
+(
+
+    SELECT DISTINCT
+      enc, admsnTimeJTR, template, row_disp_name, num_value1, num_value2, units, recorded_time, DATETIME_DIFF(b.recorded_time, a.admsnTimeJTR, HOUR) AS difference,
+      case when row_disp_name IN ("Urine Output (mL)", "Last 24Hrs - Total Urine Output") then num_value1 else null end as uop, -- urine output
+      case when row_disp_name IN ("Heart Rate", "Pulse") then num_value1 else null end as hr, -- heart rate
+      case when row_disp_name IN ("FiO2","FiO2 (%)") then num_value1 else null end as fiO2, -- fractional inspired O2
+      case when row_disp_name IN ("SpO2") then num_value1 else null end as spO2, -- pulse ox
+      --case when row_disp_name IN ("") then num_value1 else null end as rr, -- respiratory rate
+      case when row_disp_name IN ("In last 6 hours temperature < 36 C or > 38.3 C","Temp < 36 C (96.8 F) or >38.3C (100.9 F)") then TRUE
+           when (row_disp_name="Temp" and num_value1 < 96.8) then TRUE
+           when (row_disp_name="Temp" and num_value1 > 100.9) then TRUE
+           when (row_disp_name="Temp (in Celsius)" and num_value1 < 36) then TRUE
+           when (row_disp_name="Temp (in Celsius)" and num_value1 > 38.3) then TRUE
+           else FALSE end as tempAbnormal, -- temperature abnormal
+      case when row_disp_name = "Temp" then (num_value1-32)*5/9
+           when row_disp_name = "Temp (in Celsius)" then num_value1
+           # "RLE Temperature/Condition","LLE Temperature/Condition","LUE Temperature/Condition","RUE Temperature/Condition","Temperature"
+           else null end as temp, -- temperature
+      case when row_disp_name IN ("MAP","MAP ","Mean Arterial Pressure (Calculated)","Mean Arterial Pressure") then num_value1 else null end as map,
+      case when row_disp_name IN ("MAP","MAP ","Mean Arterial Pressure (Calculated)","Mean Arterial Pressure") AND num_value1 <65 then TRUE else FALSE end as mapBelow65,
+      case when row_disp_name IN ("MAP","MAP ","Mean Arterial Pressure (Calculated)","Mean Arterial Pressure") AND num_value1 >130 then TRUE else FALSE end as mapAbove130
+      --case when row_disp_name IN ("Vent Mode","Ventilation support mode","O2 Delivery","O2 Delivery Method","Ventilation device","Ventilation device status","Ventilation type","Ventilation mode","Ventilator on") then num_value1 else null end as vent -- breath support, oxygenation delivery, ventilator mode
+      #COUNT(result_time) OVER (PARTITION BY enc, base_name ORDER BY result_time DESC) AS latest, -- filter by "latest=1" to take the latest value for any given lab in 24h period
+    FROM selectDRGs a
+    LEFT JOIN `mining-clinical-decisions.shc_core.flowsheet` b
+        ON a.anon_id = b.anon_id
+        AND DATETIME_DIFF(recorded_time, admsnTimeJTR, HOUR) >= 0
+        AND DATETIME_DIFF(recorded_time, admsnTimeJTR, HOUR) <= 24
+        -- use inpatient_data_id_coded?
+
+        AND row_disp_name IN ("Urine Output (mL)", "Last 24Hrs - Total Urine Output",
+                                "Heart Rate", "Pulse",
+                                "FiO2","FiO2 (%)",
+                                "SpO2",
+                                "In last 6 hours temperature < 36 C or > 38.3 C","Temp < 36 C (96.8 F) or >38.3C (100.9 F)",
+                                "Temp","Temp (in Celsius)", -- "RLE Temperature/Condition","LLE Temperature/Condition","LUE Temperature/Condition","RUE Temperature/Condition","Temperature",
+                                "MAP","MAP ","Mean Arterial Pressure (Calculated)","Mean Arterial Pressure") -- "BP","NIBP","Blood Pressure","Arterial Systolic BP ","Arterial Diastolic BP ","In last 6 hours SBP < 90 or MAP < 65"
+                                -- "Vent Mode","Ventilation support mode","O2 Delivery","O2 Delivery Method","Ventilation device","Ventilation device status","Ventilation type","Ventilation mode","Ventilator on")
+    ORDER BY enc, recorded_time
+
+    /*
+    # smoothing --> ensure not capturing just an outlier / error decreased SpO2 (due to artifact) but rather a true, sustained deterioration
+    # message Morteza, Conor, Grace
+    # highest HR w/in 24h
+
+    */
+),
+
+
+flowsheet_2buckets AS
+(
+  SELECT enc, admsnTimeJTR,
+          case when max(uop) <= 500 then "<= 500cc"
+               else ">500cc"
+               end as uop,
+               -- SpO2: >96, 94-95, 92-93, 88-92, 86-87, 84-85, <83
+               -- Temperature: >41, 39-41, 38.5-39, 36-38.5, 34-36, 32-34, 30-32, <30
+               -- MAP: cutoffs: >159, 129, 109, 69, 49, <49
+               -- HR: cutoffs: >180, 140, 110, 70, 55, 40, <40
+          case when max(hr) >= 140 then ">=140bpm"
+               else "<140"
+               end as hr_max,
+          case when avg(fiO2) >= 75 then ">=75%"
+               else "<75%"
+               end as fiO2_avg,
+          case when min(spO2) <= 85 then "<=85%"
+               else ">85%"
+               end as spO2_Min,
+          max(tempAbnormal) as tempEverAbnormal,
+          max(mapBelow65) as mapEverBelow65,
+          max(mapAbove130) as mapEverAbove130,
+          case when avg(map) >= 115 then ">=115"
+               when avg(map) between 75 and 115 then "between 75 and 115"
+               else "<75"
+               end as map_Avg
+
+  FROM flowsheet_all # `som-nero-phi-jonc101-secure.kush_db.vitals`
+  GROUP BY enc, admsnTimeJTR
+),
+
+
+-- aggregate lab values + vitals flowchart data
+gatherObjectiveFeatures AS
+(
+    SELECT *
+    FROM selectDRGs a
+    JOIN patientFactors USING (enc,admsnTimeJTR)
+    LEFT JOIN labs_2buckets USING(enc)
+    LEFT JOIN flowsheet_2buckets USING (enc,admsnTimeJTR)
+
+)
+
+SELECT * FROM gatherObjectiveFeatures
+
+-- SELECT * FROM labs_all WHERE enc=131275787339 ORDER BY base_name, hrsAfterAdmit DESC -- this query shows instance with MULTIPLE labs (ie. HCT) drawn for same pt in given 24h period
diff --git a/scripts/InpatientVariability/objectiveFindings.R b/scripts/InpatientVariability/objectiveFindings.R
new file mode 100644
index 00000000..93c3ef9c
--- /dev/null
+++ b/scripts/InpatientVariability/objectiveFindings.R
@@ -0,0 +1,30 @@
+library(tidyverse)
+library(bigrquery)
+
+# library(DBI)
+# library(scales)
+# library(sqldf) # https://www.r-bloggers.com/in-between-a-rock-and-a-conditional-join/
+# library(fuzzyjoin)
+# library(rio)
+
+## warning = TRUE # default
+## options(warn = 0) # default
+# warning = FALSE
+# options(warn = -1)
+# options(scipen = 20)
+
+##############################################################################
+##### filepaths
+dir <- "/Users/kushgupta/Documents/StanfordMD/Projects/Mentors/Chen/Inpatient\ Variability\ Cost\ Reduction/"
+filepath1 <- paste0(dir,"code/comorbiditiesOnAdmission.sql")
+
+##############################################################################
+##### import data
+sqlQuery <- readChar(filepath1, file.info(filepath1)$size)
+project <- "som-nero-phi-jonc101-secure" # "mining-clinical-decisions"
+results <- bq_project_query(project, sqlQuery)
+tb <- as_tibble(bq_table_download(results, bigint = c("integer64"))) # bigint needed to properly download full PT_ENC_CSN_ID
+rm(results)
+
+
+##############################################################################
\ No newline at end of file
diff --git a/scripts/InpatientVariability/obsAdmitMismatch.sql b/scripts/InpatientVariability/obsAdmitMismatch.sql
new file mode 100644
index 00000000..08a852b6
--- /dev/null
+++ b/scripts/InpatientVariability/obsAdmitMismatch.sql
@@ -0,0 +1,241 @@
+-- Find Observation Admits vs. Inpatient Admits
+-- Count how often conversions happening or times until discharge
+WITH
+
+procCodesForAdmitVsObs AS
+(
+    select proc_id, proc_code, description, display_name, count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    from `som-nero-phi-jonc101.shc_core_2021.order_proc`
+    where description like 'DISCHARGE%'
+    and order_Type = 'ADT Discharge' -- 'Admission'
+
+    -- and proc_code in ('ADT20','ADT13','ADT15')   -- PLACE IN OBSERVATION or PLACE IN OBSERVATION-CDA/CDU or -OTHER
+    -- and proc_code in ('ADT22')   -- CONVERT INPATIENT TO OBSERVATION
+    -- and proc_code in ('ADT1','ADT100')   -- ADMIT TO INPATIENT or ADMIT/PLACE PATIENT (this is not perfectly clean, as some comments reference place/convert to observation)
+    -- and proc_code in ('ADT12')   -- DISCHARGE PATIENT
+
+    group by proc_id, proc_code, description, display_name
+    order by nEncounters desc
+),
+
+obsOrderSample AS
+(
+    select *
+    from `som-nero-phi-jonc101.shc_core_2021.order_proc`
+    where proc_code in ('ADT22')
+    and order_status <> 'Canceled'
+),
+
+adtSample AS
+(
+    select *
+    from `som-nero-phi-jonc101.shc_core_2021.adt`
+    where pat_enc_csn_id_coded = 131297273359
+    order by event_time_jittered
+),
+admitOrderSample AS
+(
+    select *
+    from `som-nero-phi-jonc101.shc_core_2021.order_proc`
+    where order_type in ('Admission','ADT Discharge')
+    and pat_enc_csn_id_coded = 131297273359
+
+
+    order by order_inst_jittered
+),
+
+
+adtPatClassOptions AS
+(
+    select pat_class, count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    from `som-nero-phi-jonc101.shc_core_2021.adt`
+
+    -- and pat_class in ('Outpatient','Emergency Services','Inpatient','Observation')
+    -- and pat_class_c in               112 = Emergency Services, 126 = Inpatient, 128 = Observation
+    group by pat_class
+    order by nEncounters desc
+),
+
+-- Find all Place in Observation Orders
+-- Find all Admit (to Inpatient) Orders
+-- Find all Discharge Orders
+-- Find all Convert Inpatient to Observation Orders
+
+-- Find all Encounters and their earliest ADT records for Admission, Discharge and Inpatient, Observation status
+encounterEarliestAdmissionADT AS
+(
+    select anon_id, pat_enc_csn_id_coded, min(event_time_jittered) as admitTime
+    from `som-nero-phi-jonc101.shc_core_2021.adt` as admissionEvent
+    where  admissionEvent.event_type_c = 1 -- Admission
+    group by anon_id, pat_enc_csn_id_coded
+),
+encounterEarliestDischargeADT AS
+(
+    select anon_id, pat_enc_csn_id_coded, min(event_time_jittered) as dischargeTime
+    from `som-nero-phi-jonc101.shc_core_2021.adt` as admissionEvent
+    where  admissionEvent.event_type_c = 2 -- Discharge
+    group by anon_id, pat_enc_csn_id_coded
+),
+encounterEarliestInpatientADT AS
+(
+    select anon_id, pat_enc_csn_id_coded, min(event_time_jittered) as earliestInpatientTime
+    from `som-nero-phi-jonc101.shc_core_2021.adt` as inpatientADT
+    where inpatientADT.pat_class_c = '126' -- Inpatient
+    group by anon_id, pat_enc_csn_id_coded
+),
+encounterEarliestObservationADT AS
+(
+    select anon_id, pat_enc_csn_id_coded, min(event_time_jittered) as earliestObservationTime
+    from `som-nero-phi-jonc101.shc_core_2021.adt` as inpatientADT
+    where inpatientADT.pat_class_c = '128' -- Observation
+    group by anon_id, pat_enc_csn_id_coded
+),
+
+-- Find all Encounters in ADT Records with an Inpatient pat_class_c = 126
+    -- Calculate Admission duration: Final event_type = Discharge (event_type_c = 2) minus event_tye = Admission (event_type_c = 1)
+    -- Add columns for whether time within 24hr, 48hr, 72hr
+encounterInpatientDuration AS
+(
+    select anon_id, pat_enc_csn_id_coded, 
+        admitTime, earliestInpatientTime, dischargeTime,
+        dischargeTime-admitTime as encounterDuration,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) as encounterDurationHour,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <24 as encounterDurationWithin24hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <48 as encounterDurationWithin48hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <72 as encounterDurationWithin72hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR)>=72 as encounterDurationBeyond72hr,
+    from encounterEarliestInpatientADT
+        inner join encounterEarliestAdmissionADT using (anon_id, pat_enc_csn_id_coded)
+        inner join encounterEarliestDischargeADT using (anon_id, pat_enc_csn_id_coded)
+    where admitTime <= dischargeTime -- Filter out abberant results where have out of order admit-discharge times that likely reflect some missing data
+),
+-- Find all Encounters in ADT Records with an Observation pat_class_c = 128
+    -- Calculate Admission duration: Final event_type = Discharge (event_type_c = 2) minus event_tye = Admission (event_type_c = 2)
+    -- Add columns for whether time within 24hr, 48hr, 72hr
+encounterObservationDuration AS
+(
+    select anon_id, pat_enc_csn_id_coded, 
+        admitTime, earliestObservationTime, dischargeTime,
+        dischargeTime-admitTime as encounterDuration,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) as encounterDurationHour,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <24 as encounterDurationWithin24hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <48 as encounterDurationWithin48hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <72 as encounterDurationWithin72hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR)>=72 as encounterDurationBeyond72hr,
+    from encounterEarliestObservationADT
+        inner join encounterEarliestAdmissionADT using (anon_id, pat_enc_csn_id_coded)
+        inner join encounterEarliestDischargeADT using (anon_id, pat_enc_csn_id_coded)
+    where admitTime <= dischargeTime -- Filter out abberant results where have out of order admit-discharge times that likely reflect some missing data
+),
+
+-- Find Encounters where had both an Observation and Inpatient period (join above results)
+    -- Calculate earliest Inpatient time minus earliest Observation time
+        -- If positive or negative, indicates which way the conversion was made
+    -- Add columns for whether Inpatient to Observation time within 24hr, 48hr, 72hr
+    -- Add columns for whether Observation to Inpatient time within 24hr, 48hr, 72hr
+encounterObservationAndInpatientDuration AS
+(
+    select anon_id, pat_enc_csn_id_coded, 
+        admitTime, earliestObservationTime, earliestInpatientTime, dischargeTime,
+        dischargeTime-admitTime as encounterDuration,
+        earliestObservationTime-admitTime as timeUntilObservation,
+        earliestInpatientTime-admitTime as timeUntilInpatient,
+        earliestInpatientTime-earliestObservationTime as timeFromObservationToInpatient,    -- Could be negative if started Inpatient then converted back to Obs
+
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) as encounterDurationHour,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <24 as encounterDurationWithin24hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <48 as encounterDurationWithin48hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR) <72 as encounterDurationWithin72hr,
+        DATETIME_DIFF(dischargeTime, admitTime, HOUR)>=72 as encounterDurationBeyond72hr,
+
+        DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) as observationToInpatientHours,
+        DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) >= 0 and DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) <24 as observationToInpatientWithin24hr,
+        DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) >= 0 and DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) <48 as observationToInpatientWithin48hr,
+        DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) >= 0 and DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) <72 as observationToInpatientWithin72hr,
+        DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) >= 0 and DATETIME_DIFF(earliestInpatientTime, earliestObservationTime, HOUR) >=72 as observationToInpatientBeyond72hr,
+
+        DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) as inpatientToObservationHours, -- Just flip the above for convenience to avoid negative number interpretation
+        DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) >= 0 and DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) <24 as inpatientToObservationWithin24hr,
+        DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) >= 0 and DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) <48 as inpatientToObservationWithin48hr,
+        DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) >= 0 and DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) <72 as inpatientToObservationWithin72hr,
+        DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) >= 0 and DATETIME_DIFF(earliestObservationTime, earliestInpatientTime, HOUR) >=72 as inpatientToObservationBeyond72hr,
+
+    from encounterEarliestObservationADT
+        inner join encounterEarliestInpatientADT using (anon_id, pat_enc_csn_id_coded)
+        inner join encounterEarliestAdmissionADT using (anon_id, pat_enc_csn_id_coded)
+        inner join encounterEarliestDischargeADT using (anon_id, pat_enc_csn_id_coded)
+    where admitTime <= dischargeTime -- Filter out abberant results where have out of order admit-discharge times that likely reflect some missing data
+),
+
+-- Summarize Inpatient Durations (includes those who convert in or out of Observation, so may need to subtract some from joint counts)
+--  2020 - 32,947 encounters in 2020. 1,219 (3.7%) discharged within 24 hours, 6,527 (20%) discharged within 48 hours
+summaryInpatientDuration AS
+(
+    select 
+        EXTRACT(YEAR from admitTime) as admitYear,
+        count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters,
+        avg(encounterDurationHour) as encounterDurationHourAvg, stddev_samp(encounterDurationHour) as encounterDurationHourStdDevS,
+        countif(encounterDurationWithin24hr) as nEncounterDurationWithin24hr,
+        countif(encounterDurationWithin48hr) as nEncounterDurationWithin48hr,
+        countif(encounterDurationWithin72hr) as nEncounterDurationWithin72hr,
+        countif(encounterDurationBeyond72hr) as nEncounterDurationBeyond72hr
+    from encounterInpatientDuration
+    group by admitYear
+    order by admitYear desc
+),
+-- Summarize Observation Durations (includes those who convert in or out of Inpatient, so may need to subtract some from joint counts)
+--  2020 - 12,717 encounters in 2020. 3,646 (29%) discharged within 24 hours, 8,545 (67%) discharged within 48 hours
+summaryObservationDuration AS
+(
+    select 
+        EXTRACT(YEAR from admitTime) as admitYear,
+        count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters,
+        avg(encounterDurationHour) as encounterDurationHourAvg, stddev_samp(encounterDurationHour) as encounterDurationHourStdDevS,
+        countif(encounterDurationWithin24hr) as nEncounterDurationWithin24hr,
+        countif(encounterDurationWithin48hr) as nEncounterDurationWithin48hr,
+        countif(encounterDurationWithin72hr) as nEncounterDurationWithin72hr,
+        countif(encounterDurationBeyond72hr) as nEncounterDurationBeyond72hr
+    from encounterObservationDuration
+    group by admitYear
+    order by admitYear desc
+),
+-- Summarize Durations when both an Observation and Inpatient period (indicating a conversion in one direction or the other)
+summaryInpatientAndObservationDuration AS
+(
+    select 
+        EXTRACT(YEAR from admitTime) as admitYear,
+        count(distinct anon_id) as nPatients, 
+
+        -- 2020 - 4,439 encounters, 337 (7.6%) discharge within 24 hours, 1166 (26%) discharge within 48 hours
+        count(distinct pat_enc_csn_id_coded) as nEncounters,
+        avg(encounterDurationHour) as encounterDurationHourAvg, stddev_samp(encounterDurationHour) as encounterDurationHourStdDevS,
+        countif(encounterDurationWithin24hr) as nEncounterDurationWithin24hr,
+        countif(encounterDurationWithin48hr) as nEncounterDurationWithin48hr,
+        countif(encounterDurationWithin72hr) as nEncounterDurationWithin72hr,
+        countif(encounterDurationBeyond72hr) as nEncounterDurationBeyond72hr,
+
+        -- 2020 - 3719 encounters where observation before inpatient, 1923 (52%) converted to inpatient within 24 hours, 3156 (85%) within 48 hours
+        countif(observationToInpatientHours >=0 ) as nEncounterObservationBeforeInpatient,
+        avg(observationToInpatientHours) as observationToInpatientHourAvg, stddev_samp(observationToInpatientHours) as observationToInpatientStdDevS,
+        countif(observationToInpatientWithin24hr) as observationToInpatientWithin24hr,
+        countif(observationToInpatientWithin48hr) as observationToInpatientWithin48hr,
+        countif(observationToInpatientWithin72hr) as observationToInpatientWithin72hr,
+        countif(observationToInpatientBeyond72hr) as observationToInpatientBeyond72hr,
+
+        -- 2020 - 1006 encounters where inpatient converted to observation, 923 (92%) converted to observation with 24 hours, 992 (98%) within 48 hours
+        countif(inpatientToObservationHours >=0 ) as nEncounterInpatientBeforeObservation,
+        avg(inpatientToObservationHours) as inpatientToObservationHourAvg, stddev_samp(inpatientToObservationHours) as inpatientToObservationStdDevS,
+        countif(inpatientToObservationWithin24hr) as inpatientToObservationWithin24hr,
+        countif(inpatientToObservationWithin48hr) as inpatientToObservationWithin48hr,
+        countif(inpatientToObservationWithin72hr) as inpatientToObservationWithin72hr,
+        countif(inpatientToObservationBeyond72hr) as inpatientToObservationBeyond72hr
+    from encounterObservationAndInpatientDuration
+    group by admitYear
+    order by admitYear desc
+),
+placeholder AS (select 1 from `som-nero-phi-jonc101.shc_core_2021.demographic`)
+
+select * 
+from summaryInpatientAndObservationDuration
+limit 1000
+
diff --git a/scripts/InpatientVariability/readme.txt b/scripts/InpatientVariability/readme.txt
new file mode 100644
index 00000000..b10b4987
--- /dev/null
+++ b/scripts/InpatientVariability/readme.txt
@@ -0,0 +1,897 @@
+README.txt
+
+Inpatient Cost Variability
+
+# loading data into BigQuery: TechIPbyGL_CostCategory (FY20, FY21)
+1. Modify raw data: remove last row (totals)
+2. Modify raw data: all columns A through BE: replace " -   " with ""
+3. Modify raw data: all columns A through BE: format cells as "General" (ensure no commas delimiting thousands for #'s)
+4. Upload parameters: Header rows to skip: 2
+5. Upload instructions: upload to BigQuery (use schema below). NOTE: columns (and schema) vary between years, see below.
+6. Upload instructions: First upload FY20 data. Check schema with raw data to ensure columns match.
+7. Upload instructions: Now go into table on BigQuery and edit schema, add any missing columns from FY21 data (ie. VCCPharmacy)
+8. Upload instructions: Now upload FY21 data with its respective schema (ensure schema matches data)
+
+
+# loading data into BigQuery: TechIPbyUB_CostBucket (FY20, FY21) (UB = universal billing)
+1. Modify raw data: format first two columns (admit & discharge dates) in Excel as YYYY-MM-DD (req for BigQuery "date" field)
+2. Modify raw data: columns L through AD (costs): replace " -   " with ""
+3. Modify raw data: columns L through AD (costs): format cells as "General" (ensure no commas delimiting thousands for #'s)
+4. Modify raw data: column D (MRN): replace "<E" and ">" with ""
+5. Modify raw data: Split .XLSX files containing multiple sheets into multiple .csv files (size <100MB each)
+6. Upload parameters: upload to BigQuery (use schema below)
+7. Upload parameters: Header rows to skip: 1
+8. Upload parameters: Number of errors allowed: 500. Skip extraneous outlier rows that have unsupported decimals (>9 decimals, which exceed BigQuery "NUMERIC" datatype restrictions). NUMERIC will suffice for >99.99% of data rows. We COULD circumvent this by altering the table schema to cast all Cost columns as BIGNUMERIC, however table alterations aren't supported in BigQuery and require workarounds (ie. copying data into new, redefined table). Better to exclude the rare edge cases.
+9. Upload parameters: Write preferences: Append to pre-existing table (if already created)
+
+# table schema: TechIPbyGL (FY21)
+[
+  {
+    "name": "Account",
+    "type": "INTEGER",
+    "mode": "NULLABLE"
+  },
+  {
+    "name": "Depreciation_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Depreciation of 500P. Total Direct Cost."
+  },
+  {
+    "name": "Benefits_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Clinical_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "COVID19_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "COVID19vaccine_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "CV_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"CVH APP\" (cardiovascular health). Total Direct Cost."
+  },
+  {
+    "name": "Facilities_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Housestaff_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Inpatient_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"IP APP\" (inpatient). Total Direct Cost."
+  },
+  {
+    "name": "Management_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Supplies_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Medical Supplies\". Total Direct Cost."
+  },
+  {
+    "name": "NonClinical_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Nursing_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Oncology_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Onc APP\". Total Direct Cost."
+  },
+  {
+    "name": "OtherExpense_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Expense\". Total Direct Cost."
+  },
+  {
+    "name": "OtherRevenue_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Revenue\" Total Direct Cost."
+  },
+  {
+    "name": "OtherSupplies_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Pharmacy_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Physician_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Physician Services. Total Direct Cost."
+  },
+  {
+    "name": "ProductLine_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Product Line\". Total Direct Cost."
+  },
+  {
+    "name": "Prosthesis_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "PurchasedServices_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Purchased Services\". Total Direct Cost."
+  },
+  {
+    "name": "Physician_Tier1_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Tier 1 Physician Services\". Total Direct Cost."
+  },
+  {
+    "name": "Transplant_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "VCCImplant_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCImplant\". Total Direct Cost."
+  },
+  {
+    "name": "VCCMedSupp_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCMedSupp\". Total Direct Cost."
+  },
+  {
+    "name": "VCCPharmacy_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Depreciation_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Depreciation of 500P. Total Indirect Cost."
+  },
+  {
+    "name": "Benefits_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Clinical_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "COVID19_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "COVID19vaccine_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "CV_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"CVH APP\" (cardiovascular health). Total Indirect Cost."
+  },
+  {
+    "name": "Facilities_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Housestaff_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Inpatient_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"IP APP\" (inpatient). Total Indirect Cost."
+  },
+  {
+    "name": "Management_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Supplies_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Medical Supplies\". Total Indirect Cost."
+  },
+  {
+    "name": "NonClinical_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Nursing_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Oncology_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Onc APP\". Total Indirect Cost."
+  },
+  {
+    "name": "OtherExpense_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Expense\". Total Indirect Cost."
+  },
+  {
+    "name": "OtherRevenue_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Revenue\" Total Indirect Cost."
+  },
+  {
+    "name": "OtherSupplies_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Pharmacy_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Physician_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Physician Services. Total Indirect Cost."
+  },
+  {
+    "name": "ProductLine_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Product Line\". Total Indirect Cost."
+  },
+  {
+    "name": "Prosthesis_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "PurchasedServices_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Purchased Services\". Total Indirect Cost."
+  },
+  {
+    "name": "Physician_Tier1_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Tier 1 Physician Services\". Total Indirect Cost."
+  },
+  {
+    "name": "Transplant_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "VCCImplant_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCImplant\". Total Indirect Cost."
+  },
+  {
+    "name": "VCCMedSupp_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCMedSupp\". Total Indirect Cost."
+  },
+  {
+    "name": "VCCPharmacy_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "TotalCost_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Sum of Total Direct Costs."
+  },
+  {
+    "name": "TotalCost_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Sum of Total Indirect Costs."
+  }
+]
+
+# table schema: TechIPbyGL (FY20)
+[
+  {
+    "name": "Account",
+    "type": "INTEGER",
+    "mode": "NULLABLE"
+  },
+  {
+    "name": "Depreciation_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Depreciation of 500P. Total Direct Cost."
+  },
+  {
+    "name": "BadDebt_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Benefits_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Clinical_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "COVID19_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "COVID19vaccine_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "CV_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"CVH APP\" (cardiovascular health). Total Direct Cost."
+  },
+  {
+    "name": "Facilities_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Housestaff_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Inpatient_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"IP APP\" (inpatient). Total Direct Cost."
+  },
+  {
+    "name": "Management_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Supplies_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Medical Supplies\". Total Direct Cost."
+  },
+  {
+    "name": "NonClinical_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Nursing_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Oncology_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Onc APP\". Total Direct Cost."
+  },
+  {
+    "name": "OtherExpense_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Expense\". Total Direct Cost."
+  },
+  {
+    "name": "OtherRevenue_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Revenue\" Total Direct Cost."
+  },
+  {
+    "name": "OtherSupplies_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Pharmacy_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "Physician_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Physician Services. Total Direct Cost."
+  },
+  {
+    "name": "ProductLine_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Product Line\". Total Direct Cost."
+  },
+  {
+    "name": "Prosthesis_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "PurchasedServices_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Purchased Services\". Total Direct Cost."
+  },
+  {
+    "name": "Physician_Tier1_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Tier 1 Physician Services\". Total Direct Cost."
+  },
+  {
+    "name": "Transplant_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Direct Cost."
+  },
+  {
+    "name": "VCCImplant_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCImplant\". Total Direct Cost."
+  },
+  {
+    "name": "VCCMedSupp_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCMedSupp\". Total Direct Cost."
+  },
+  {
+    "name": "Depreciation_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Depreciation of 500P. Total Indirect Cost."
+  },
+  {
+    "name": "BadDebt_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Benefits_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Clinical_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "COVID19_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "COVID19vaccine_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "CV_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"CVH APP\" (cardiovascular health). Total Indirect Cost."
+  },
+  {
+    "name": "Facilities_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Housestaff_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Inpatient_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"IP APP\" (inpatient). Total Indirect Cost."
+  },
+  {
+    "name": "Management_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Supplies_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Medical Supplies\". Total Indirect Cost."
+  },
+  {
+    "name": "NonClinical_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Nursing_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Oncology_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Onc APP\". Total Indirect Cost."
+  },
+  {
+    "name": "OtherExpense_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Expense\". Total Indirect Cost."
+  },
+  {
+    "name": "OtherRevenue_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Other Revenue\" Total Indirect Cost."
+  },
+  {
+    "name": "OtherSupplies_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Pharmacy_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "Physician_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Physician Services. Total Indirect Cost."
+  },
+  {
+    "name": "ProductLine_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Product Line\". Total Indirect Cost."
+  },
+  {
+    "name": "Prosthesis_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "PurchasedServices_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Purchased Services\". Total Indirect Cost."
+  },
+  {
+    "name": "Physician_Tier1_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"Tier 1 Physician Services\". Total Indirect Cost."
+  },
+  {
+    "name": "Transplant_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Total Indirect Cost."
+  },
+  {
+    "name": "VCCImplant_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCImplant\". Total Indirect Cost."
+  },
+  {
+    "name": "VCCMedSupp_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "\"VCCMedSupp\". Total Indirect Cost."
+  },
+  {
+    "name": "TotalCost_Direct",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Sum of Total Direct Costs."
+  },
+  {
+    "name": "TotalCost_Indirect",
+    "type": "INTEGER",
+    "mode": "NULLABLE",
+    "description": "Sum of Total Indirect Costs."
+  }
+]
+
+# table schema: TechIPbyUB
+[
+    {
+        "name": "AdmitDate",
+        "type": "DATE",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "DischargeDate",
+        "type": "DATE",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "LOS",
+        "type": "INTEGER",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "MRN",
+        "type": "INTEGER",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "VisitCount",
+        "type": "INTEGER",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "MSDRGweight",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Weighting for Medical Severity (MS) DRG per encounter"
+    },
+    {
+        "name": "Account",
+        "type": "INTEGER",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Inpatient_C",
+        "type": "STRING",
+        "mode": "NULLABLE",
+        "description": "O (outpatient), I (inpatient)"
+    },
+    {
+        "name": "Inpatient",
+        "type": "STRING",
+        "mode": "NULLABLE",
+        "description": "Outpatient, Inpatient"
+    },
+    {
+        "name": "ServiceCategory_C",
+        "type": "STRING",
+        "mode": "NULLABLE",
+        "description": "P (professional), T (technical)"
+    },
+    {
+        "name": "ServiceCategory",
+        "type": "STRING",
+        "mode": "NULLABLE",
+        "description": "Professional, Technical"
+    },
+    {
+        "name": "Cost_Direct",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs (UB = universal billing): Total Direct"
+    },
+    {
+        "name": "Cost_Indirect",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs (UB = universal billing): Total Indirect"
+    },
+    {
+        "name": "Cost_Total",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs (UB = universal billing): Grand Total"
+    },
+    {
+        "name": "Cost_Breakdown_Blood",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_Cardiac",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_ED",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_ICU",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_IICU",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_Imaging",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_Labs",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_Implants",
+        "type": "NUMERIC",
+        "mode": "NULLABLE"
+    },
+    {
+        "name": "Cost_Breakdown_Supplies",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "UB M/S Supplies w/o Implants"
+    },
+    {
+        "name": "Cost_Breakdown_OR",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Operating Room services"
+    },
+    {
+        "name": "Cost_Breakdown_OrganAcq",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Organ acquisition"
+    },
+    {
+        "name": "Cost_Breakdown_Other",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs: Other (alternate)"
+    },
+    {
+        "name": "Cost_Breakdown_PTOT",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs: PT/OT/ST"
+    },
+    {
+        "name": "Cost_Breakdown_Resp",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs: Resp therapy"
+    },
+    {
+        "name": "Cost_Breakdown_Accom",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs: Routine Accommodation"
+    },
+    {
+        "name": "Cost_Breakdown_Pharmacy",
+        "type": "NUMERIC",
+        "mode": "NULLABLE",
+        "description": "Costs d/t RX & IV therapy"
+    },
+    {
+        "name": "BillingStatus_C",
+        "type": "STRING",
+        "mode": "NULLABLE",
+        "description": "F (final billed)"
+    },
+    {
+        "name": "BillingStatus",
+        "type": "STRING",
+        "mode": "NULLABLE"
+    }
+]
+
diff --git a/scripts/KFREReferralRate/AUROC_initial code_AC.R b/scripts/KFREReferralRate/AUROC_initial code_AC.R
new file mode 100644
index 00000000..f586c289
--- /dev/null
+++ b/scripts/KFREReferralRate/AUROC_initial code_AC.R	
@@ -0,0 +1,22 @@
+install.packages('pROC')
+
+library(tidyverse)
+library(pROC)
+
+#my_data <- select(data_7.9.20, Needs_Neph_Consult, Num_Neph_Consults_Actual)
+#df <- data.frame(my_data)
+#df$Any_Neph_Actual <- ifelse(df$Num_Neph_Consults_Actual > 1, 1, 0)
+#df1 <- select(df, Needs_Neph_Consult, Any_Neph_Actual)
+#roc1 <- roc(df1$Any_Neph_Actual, df1$Needs_Neph_Consult)
+#plot(roc1)
+#auc(roc1)
+
+my_data2 <- select(data_7.9.20, KFRE_Score, Num_Neph_Consults_Actual)
+df2 <- data.frame(my_data2)
+df2$Any_Neph_Actual <- ifelse(df2$Num_Neph_Consults_Actual > 1, 1, 0)
+df3 <- select(df2, KFRE_Score, Any_Neph_Actual)
+roc2 <- roc(df3$Any_Neph_Actual, df3$KFRE_Score)
+auc(roc2)
+plot(roc2, print.auc=TRUE)
+
+##
diff --git a/scripts/KFREReferralRate/Notes.txt b/scripts/KFREReferralRate/Notes.txt
new file mode 100644
index 00000000..8897ef24
--- /dev/null
+++ b/scripts/KFREReferralRate/Notes.txt
@@ -0,0 +1,3 @@
+Workspace for KFRE referral quick project
+
+Starting to add some sample code 
\ No newline at end of file
diff --git a/scripts/KFREReferralRate/referral_gaps.sql b/scripts/KFREReferralRate/referral_gaps.sql
new file mode 100644
index 00000000..aaa6b84f
--- /dev/null
+++ b/scripts/KFREReferralRate/referral_gaps.sql
@@ -0,0 +1,276 @@
+/* SQL Query to determine proportion of patients receiving Nephrology specialty care across KFRE levels*/ 
+
+--subquery that identifies all patients who have a history of dialysis or kidney transplant
+WITH Dialysis_Patients as 
+(
+SELECT distinct enc.anon_id, dx_name, dx_id
+FROM
+  `shc_core.encounter` as enc 
+    JOIN `shc_core.diagnosis_code` as dc on (pat_enc_csn_id_coded=pat_enc_csn_id_jittered)
+where 
+  UPPER(dc.dx_name ) like '%DIALYSIS%' OR
+  (UPPER(dc.dx_name) like '%TRANSPLANT%' and UPPER(dc.dx_name) like '%KIDNEY%')
+),
+
+--subquery that queries database to only include patients that do not have a history of dialysis and/or kidney transplant. This list of patients is then used for further queries.
+Excluding_Dialysis as
+(
+SELECT distinct enc.anon_id, 
+FROM `shc_core.encounter` as enc 
+  LEFT OUTER JOIN Dialysis_Patients as dial USING(ANON_ID)
+WHERE 
+  dial.anon_id is null 
+),
+
+
+-- Subquery that finds all patients with no history of dialysis/ kidney transplant that received an albumin to creatinine ratio lab result in the outpatient setting
+AlbuminLabs as 
+(
+  SELECT 
+    dem.ANON_ID, 
+    DATE_DIFF(DATE(labs.result_time), dem.BIRTH_DATE_JITTERED  , YEAR) as AgeYears, 
+    dem.GENDER,
+    dem.CANONICAL_RACE,
+    dem.INSURANCE_PAYOR_NAME,
+    labs.proc_code,
+    labs.group_lab_name, 
+    labs.result_time , 
+    labs.pat_enc_csn_id_coded,
+    max(labs.ord_num_value) as MaxAlbValue, --there are sometimes multiple Alb to Creatinine ratios during an encounter so this selects for maximum within an encounter
+    labs.order_id_coded, 
+  EXTRACT (YEAR from labs.result_time) as Year,
+  FROM 
+  Excluding_Dialysis 
+    JOIN 
+    `shc_core.demographic` as dem USING(ANON_ID)
+      JOIN `shc_core.lab_result` as labs on dem.ANON_ID=labs.anon_id 
+  WHERE 
+    labs.proc_code='LABUALB' and 
+    labs.ordering_mode='Outpatient' and 
+    (DATE_DIFF(DATE(dem.DEATH_DATE_JITTERED ), DATE(labs.result_time), DAY) >365 or dem.DEATH_DATE_JITTERED is NULL) and -- selecting for patients that are alive 1 year post ACR lab result
+    labs.ord_num_value !=9999999 and labs.ord_num_value>0 and 
+    EXTRACT(year from labs.result_time)>2014 and EXTRACT(year from labs.result_time)<2020
+  GROUP by
+  ANON_ID, result_time, dem.BIRTH_DATE_JITTERED , dem.GENDER , dem.CANONICAL_RACE , dem.INSURANCE_PAYOR_NAME , labs.proc_code , labs.group_lab_name , 
+  labs.pat_enc_csn_id_coded , labs.order_id_coded 
+),
+
+-- subquery that finds all GFR labs; also converts GFR values based on race 
+GFRlabs AS 
+
+(
+SELECT 
+  dem.ANON_ID, 
+  DATE_DIFF(DATE(labs.result_time), dem.BIRTH_DATE_JITTERED, YEAR) as AgeYears, 
+  dem.GENDER, 
+  labs.proc_code, 
+  labs.base_name , 
+  labs.result_time, 
+  labs.order_id_coded,
+  labs.pat_enc_csn_id_coded,
+  (case when canonical_race='Black' and lab_name='GFR' THEN ord_num_value*1.21  -- when race is 'Black' and lab_name is 'GFR' multiplies by 1.21 to get MDRD Study GFR equation of African Americans
+        when canonical_race!='Black' and lab_name like '%eGFR for African American%' THEN ord_num_Value/1.21 -- when race is not 'Black' but lab_name is 'eGFR for African American', divides by 1.21 to calculate 'GFR'
+        else ord_num_value END) as ord_num_value, -- 'For instances when race is 'Black' and lab_name is 'eGFR for African American' the original ord_num_value is kept. When race is not 'Black' and lab_name is 'GFR' the original ord_num_value is kept
+  
+FROM 
+  `shc_core.demographic` as dem
+  JOIN `shc_core.lab_result`  as labs on dem.ANON_ID = labs.anon_id
+
+WHERE 
+  UPPER(labs.base_name) LIKE '%GFR%' AND 
+  ordering_mode='Outpatient' AND
+  ord_num_value <10000 AND
+  ord_num_value>0 AND 
+  ord_num_value is not null
+), 
+
+--subquery that finds all patients that received albumin to creatinine ratio and eGFR within 90 days of each other (indicating that they have a calculable KFRE)
+GFR_ALB_JOIN AS 
+(
+SELECT 
+  DISTINCT alb.ANON_ID , 
+  alb.gender, 
+  alb.AgeYears , 
+  alb.pat_enc_csn_id_coded , 
+  alb.order_id_coded,
+  MIN(alb.result_time) as result_time, 
+  alb.MaxAlbValue, 
+  min(gfr.ord_num_value) as MinGFRvalue, --for when there are multiple GFR values within 90 days of ACR, selects the minimum GFR
+  CASE WHEN alb.gender ='Male' then 1 else 0 end AS male, 
+  
+FROM 
+  Albuminlabs as alb
+  JOIN GFRlabs as gfr 
+    USING(ANON_ID)
+WHERE 
+  (DATE_DIFF(DATE(alb.result_time), DATE(gfr.result_time), DAY) <90 and DATE_DIFF(DATE(alb.result_time), DATE(gfr.result_time), DAY) >-90) AND
+    gfr.ord_num_value < 10000 AND --excludes lab results that are recorded in the EHR as 99999
+    alb.MaxAlbValue  < 10000 
+    
+GROUP BY 
+  ANON_ID, 
+  alb.GENDER , 
+  alb.AgeYears , 
+  alb.result_time , 
+  alb.MaxAlbValue , 
+  alb.order_id_coded , 
+  alb.pat_enc_csn_id_coded 
+),
+
+--subquery that calculates the KFRE betacoefficient based on the Age, Sex, GFR, and ACR
+Betacoeff AS 
+(
+SELECT
+  *,
+(0.2694*0.56422)+(-0.2167*7.0355)+(-0.55418*7.2216)+(0.45608*5.2774) as beta_X_bar_Sum, --creates a new column that takes the sum of beta coefficients of each variable multiplied by the average value for each risk factor
+(0.2694*MALE)+(-0.2167*AgeYears/10)+(-0.55418*MinGFRValue/5)+(0.45608*LN(GFR_ALB_JOIN.MaxAlbValue )) as beta_X_Sum -- takes the sum of beta coefficients of each risk factor multiplied the individual patient values for each risk factor (Age is divided by 10, GFR is divided by 5, natural log of ACR is taken)
+
+FROM GFR_ALB_JOIN 
+), 
+
+--subquery that determines KFRE score based on betacoefficient
+KFREScore As 
+(
+SELECT
+  ANON_ID, Betacoeff.pat_enc_csn_id_coded,order_id_coded, result_time,gender, AgeYears, Betacoeff.MaxAlbValue , MinGFRvalue,male, 
+  1-POWER(0.924,EXP(beta_x_sum - beta_x_bar_sum)) as KFRE_Score
+
+FROM Betacoeff
+),
+
+--subquery that looks at all encounters within 1 year of date from albumin to creatinine ratio used for KFRE calculation, and determines which KFRE encounters had a follow-up encounter in Nephrology.
+NephrologyConsultin1yr as 
+
+(
+SELECT kfre.*,enc.appt_when_jittered , dep.specialty, DATETIME_DIFF(enc.appt_when_jittered, kfre.result_time, DAY) as TimeLag,
+CASE WHEN dep.specialty='Nephrology' then kfre.order_id_coded end as Matching_order_id_coded -- For KFRE encounters that have been seen by nephrology specialty care within +/- 1 year, 
+
+FROM KFREScore as kfre
+  JOIN `shc_core.encounter` as enc USING(ANON_ID)
+    JOIN `shc_core.dep_map` as dep USING(department_id)
+
+WHERE
+  (DATETIME_DIFF(enc.appt_when_jittered, kfre.result_time, DAY)<365 and DATETIME_DIFF(enc.appt_when_jittered, kfre.result_time, DAY)>-365)
+  AND dep.specialty='Nephrology'
+  
+),
+
+--subquery that combines the 'KFREScore' subquery with the 'NephrologyConsultin1yr' subquery to determine which KFRE encounters received a nephrology consult within +/-1 year of calculable KFRE
+--when specialty column is 'Nephrology', this indicates that KFRE encounter received nephrology care within 1 year. when specialty column is 'null', this indicates that KFRE encounter did not receive nephrology specialty care within 1 year of calculable KFRE. 
+Calculable_KFRE_Left_Joined_With_Nephrology_Referral as 
+(
+SELECT kfre.*,
+neph.appt_when_jittered , 
+neph.specialty, neph.Matching_order_id_coded
+FROM KFREScore as kfre 
+  LEFT JOIN NephrologyConsultin1yr as neph on (kfre.order_id_coded=neph.Matching_order_id_coded)                               
+),
+
+--subquery that assigns all KFRE scores a KFRE Risk Category in 0.02 increments (i.e. all KFRE encounters between 0 and 0.02 will be assigned KFRE Risk Category of 0; all KFRE encounters between 0.02 and 0.04 will be assigned KFRE risk category of 0.02 etc)
+Referral_Status as 
+(
+
+SELECT 
+  *, EXTRACT (YEAR from result_time) as Year, 
+  case     
+    when (KFRE_Score>=0 and KFRE_Score<=0.02) then 0.00
+    when (KFRE_Score>0.02 and KFRE_Score<=0.04) then 0.02
+    when (KFRE_Score>0.04 and KFRE_Score<=0.06) then 0.04
+    when (KFRE_Score>0.06 and KFRE_Score<=0.08) then 0.06
+    when (KFRE_Score>0.08 and KFRE_Score<=0.10) then 0.08
+    when (KFRE_Score>0.10 and KFRE_Score<=0.12) then 0.10
+    
+    when (KFRE_Score>0.12 and KFRE_Score<=0.14) then 0.12
+    when (KFRE_Score>0.14 and KFRE_Score<=0.16) then 0.14
+    when (KFRE_Score>0.16 and KFRE_Score<=0.18) then 0.16
+    when (KFRE_Score>0.18 and KFRE_Score<=0.20) then 0.18
+    when (KFRE_Score>0.20 and KFRE_Score<=0.22) then 0.20
+    
+    when (KFRE_Score>0.22 and KFRE_Score<=0.24) then 0.22
+    when (KFRE_Score>0.24 and KFRE_Score<=0.26) then 0.24
+    when (KFRE_Score>0.26 and KFRE_Score<=0.28) then 0.26
+    when (KFRE_Score>0.28 and KFRE_Score<=0.30) then 0.28
+    when (KFRE_Score>0.30 and KFRE_Score<=0.32) then 0.30
+    
+    when (KFRE_Score>0.32 and KFRE_Score<=0.34) then 0.32
+    when (KFRE_Score>0.34 and KFRE_Score<=0.36) then 0.34
+    when (KFRE_Score>0.36 and KFRE_Score<=0.38) then 0.36
+    when (KFRE_Score>0.38 and KFRE_Score<=0.40) then 0.38
+    when (KFRE_Score>0.40 and KFRE_Score<=0.42) then 0.40
+    
+    when (KFRE_Score>0.42 and KFRE_Score<=0.44) then 0.42
+    when (KFRE_Score>0.44 and KFRE_Score<=0.46) then 0.44
+    when (KFRE_Score>0.46 and KFRE_Score<=0.48) then 0.46
+    when (KFRE_Score>0.48 and KFRE_Score<=0.50) then 0.48
+    when (KFRE_Score>0.50 and KFRE_Score<=0.52) then 0.50
+    
+    when (KFRE_Score>0.52 and KFRE_Score<=0.54) then 0.52
+    when (KFRE_Score>0.54 and KFRE_Score<=0.56) then 0.54
+    when (KFRE_Score>0.56 and KFRE_Score<=0.58) then 0.56
+    when (KFRE_Score>0.58 and KFRE_Score<=0.60) then 0.58
+    when (KFRE_Score>0.60 and KFRE_Score<=0.62) then 0.60
+    
+    when (KFRE_Score>0.62 and KFRE_Score<=0.64) then 0.62
+    when (KFRE_Score>0.64 and KFRE_Score<=0.66) then 0.64
+    when (KFRE_Score>0.66 and KFRE_Score<=0.68) then 0.66
+    when (KFRE_Score>0.68 and KFRE_Score<=0.70) then 0.68
+    when (KFRE_Score>0.70 and KFRE_Score<=0.72) then 0.70
+    
+    when (KFRE_Score>0.72 and KFRE_Score<=0.74) then 0.72
+    when (KFRE_Score>0.74 and KFRE_Score<=0.76) then 0.74
+    when (KFRE_Score>0.76 and KFRE_Score<=0.78) then 0.76
+    when (KFRE_Score>0.78 and KFRE_Score<=0.80) then 0.78
+    when (KFRE_Score>0.80 and KFRE_Score<=0.82) then 0.80
+    
+    when (KFRE_Score>0.82 and KFRE_Score<=0.84) then 0.82
+    when (KFRE_Score>0.84 and KFRE_Score<=0.86) then 0.84
+    when (KFRE_Score>0.86 and KFRE_Score<=0.88) then 0.86
+    when (KFRE_Score>0.88 and KFRE_Score<=0.90) then 0.88
+    when (KFRE_Score>0.90 and KFRE_Score<=0.92) then 0.90
+    
+    when (KFRE_Score>0.92 and KFRE_Score<=0.94) then 0.92
+    when (KFRE_Score>0.94 and KFRE_Score<=0.96) then 0.94
+    when (KFRE_Score>0.96 and KFRE_Score<=0.98) then 0.96
+    when (KFRE_Score>0.98 ) then 0.98
+    end as KFRE_Risk_Category,
+  case 
+    when specialty is null then 0 
+    when specialty='Nephrology' then 1
+      end as Neph_Referral_Status --for each calculable KFRE encounter, determines if an additional nephrology specialty encounter occurred within 1 year
+from Calculable_KFRE_Left_Joined_With_Nephrology_Referral
+where
+((DATETIME_DIFF(appt_when_jittered, result_time, DAY)<365 and appt_when_jittered>result_time) 
+or appt_when_jittered is null)  
+order by appt_when_jittered desc, Neph_Referral_Status desc
+),
+
+Referral_Status_by_Risk_Category as
+(
+SELECT 
+  distinct pat_enc_csn_id_coded ,ANON_ID, 
+  KFRE_Risk_Category, Year,  
+  max(Neph_Referral_Status) as Received_Nephrology_Referral 
+                      --each KFRE encounter will have either a '0' or '1' in the 'Neph_Referral_Status' column depending on whether or not a nephrology encounter occured within 1 year of KFRE encounter
+                      --some KFRE encounters may have multiple Nephrology encounters within 1 year while others will have 0
+                      --the 'max()' function is used here to indicate that if the KFRE encounter has atleast 1 nephrology encounter within 1 year, they are classified as receiving a nephrology referral.
+                      --those KFRE encounters that did have atleast one nephrology encounter within 1 year will be given a '1' in the 'Received_Nephrology_Referral' column, and those without will be given a '0' 
+FROM 
+  Referral_Status 
+Group by
+  pat_enc_csn_id_coded , KFRE_Risk_Category, ANON_ID, Year
+  
+)
+
+--Final query that counts distinct patient encounters at each Kidney Failure Risk level and sums the 'Received_Nephrology_Referral' from previous subquery for each Kidney Failure Risk Level
+SELECT KFRE_Risk_Category as Kidney_Failure_Risk, 
+  COUNT(distinct pat_enc_csn_id_coded ) as nEncounters, 
+  SUM(Received_Nephrology_Referral) as Total_Nephrology_Referrals, 
+  ROUND(SUM(Received_Nephrology_Referral)/COUNT(distinct pat_enc_csn_id_coded ), 3) as Proportion_Of_Nephrology_Referral,
+FROM Referral_Status_by_Risk_Category 
+WHERE
+  Year>2014 and Year<2020
+GROUP By
+  KFRE_Risk_Category 
+Order by  
+  KFRE_Risk_Category asc, 
+  nEncounters desc
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/AntiBiogramMatrix.py b/scripts/LabCulturePrediction/AntiBiogramMatrix.py
new file mode 100644
index 00000000..2805f28f
--- /dev/null
+++ b/scripts/LabCulturePrediction/AntiBiogramMatrix.py
@@ -0,0 +1,394 @@
+#!/usr/bin/python
+"""
+Class for generating LabCultureMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+import pandas as pd
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+
+class AntiBiogramMatrix(FeatureMatrix):
+    def __init__(self, bug_panel, num_episodes, random_state=None):
+        FeatureMatrix.__init__(self, bug_panel, num_episodes)
+
+        # Parse arguments.
+        self._bug_panel = bug_panel
+        self._lab_panel = ['LABBLC', 'LABBLC2', 'LABURNC'] # we will use prior blood and urine cultures as features
+
+        self._med_panel = [['Cefepime (Oral)', 'Cefepime (Intravenous)'],
+                            ['Cefazolin (Oral)', 'Cefazolin (Intravenous)'],
+                            ['Ceftriaxone (Oral)', 'Ceftriaxone (Intravenous)'],
+                            ['Meropenem (Oral)', 'Meropenem (Intravenous)'],
+                            ['Vancomycin (Oral)', 'Vancomycin (Intravenous)'],
+                            ['Linezolid (Oral)', 'Linezolid (Intravenous)'],
+                            ['Daptomycin (Oral)', 'Daptomycin (Intravenous)'],
+                            ['Levofloxacin (Oral)', 'Levofloxacin (Intravenous)'],
+                            ['Ciprofloxacin (Oral)', 'Ciprofloxacin (Intravenous)'],
+                            ['Ampicillin (Oral)', 'Ampicillin (Intravenous)'],
+                            ['Metronidazole (Oral)', 'Metronidazole (Intravenous)'],
+                            ['Caspofungin (Oral)', 'Caspofungin (Intravenous)']]
+        susceptibility_df = pd.read_csv('/Users/conorcorbin/repos/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        # susceptibility_df = pd.read_csv('/home/ec2-user/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        self._susceptibility_names = susceptibility_df['name'].values        
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+    # def _get_components_in_lab_panel(self):
+    #     # Initialize DB connection.
+    #     cursor = self._connection.cursor()
+
+    #     # Doing a single query results in a sequential scan through
+    #     # stride_order_results. To avoid this, break up the query in two.
+
+    #     # First, get all the order_proc_ids for proc_code.
+    #     query = SQLQuery()
+    #     query.addSelect('order_proc_id')
+    #     query.addFrom('stride_order_proc')
+    #     query.addWhereIn('proc_code', self._lab_panel)
+    #     query.addGroupBy('order_proc_id')
+    #     log.debug('Querying order_proc_ids for... %s' % ','.join(self._lab_panel))
+    #     results = DBUtil.execute(query)
+    #     lab_order_ids = [row[0] for row in results]
+
+    #     # Second, get all base_names from those orders.
+    #     query = SQLQuery()
+    #     query.addSelect('base_name')
+    #     query.addFrom('stride_order_results')
+    #     query.addWhereIn('order_proc_id', lab_order_ids)
+    #     query.addGroupBy('base_name')
+    #     log.debug('Querying base_names for order_proc_ids...')
+    #     results = DBUtil.execute(query)
+    #     components = [row[0] for row in results]
+
+    #     return components
+
+    # def _get_average_orders_per_patient(self):
+    #     # Initialize DB cursor.
+    #     cursor = self._connection.cursor()
+
+    #     # Get average number of results for this lab test per patient.
+    #     query = SQLQuery()
+    #     query.addSelect('pat_id')
+    #     query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
+    #     query.addFrom('stride_order_proc AS sop')
+    #     query.addFrom('stride_order_results AS sor')
+    #     query.addWhere('sop.order_proc_id = sor.order_proc_id')
+    #     query.addWhereIn("proc_code", self._lab_panel)
+    #     components = self._get_components_in_lab_panel()
+    #     query.addWhereIn("base_name", components)
+    #     query.addGroupBy('pat_id')
+    #     log.debug('Querying median orders per patient...')
+    #     results = DBUtil.execute(query)
+    #     order_counts = [ row[1] for row in results ]
+    #     if len(order_counts) == 0:
+    #         error_msg = '0 orders for lab panel %s' % ','.join(self._lab_panel)
+    #         log.critical(error_msg)
+    #         sys.exit('[ERROR] %s' % error_msg)
+    #     else:
+    #         return numpy.median(order_counts)
+
+    # def _get_random_patient_list(self):
+    #     # Initialize DB cursor.
+    #     cursor = self._connection.cursor()
+
+    #     # Get average number of results for this lab test per patient.
+    #     avg_orders_per_patient = self._get_average_orders_per_patient()
+    #     log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
+    #     # Based on average # of results, figure out how many patients we'd
+    #     # need to get for a feature matrix of requested size.
+    #     self._num_patients = int(numpy.max([self._num_requested_episodes / \
+    #         avg_orders_per_patient, 1]))
+
+    #     # Get numPatientsToQuery random patients who have gotten test.
+    #     # TODO(sbala): Have option to feed in a seed for the randomness.
+    #     query = SQLQuery()
+    #     query.addSelect('pat_id')
+    #     query.addFrom('stride_order_proc AS sop')
+    #     query.addWhereIn('proc_code', self._lab_panel)
+    #     query.addGroupBy('pat_id') # this should be a unique list of patients
+    #     query.addOrderBy('RANDOM()')
+    #     query.setLimit(self._num_patients)
+    #     log.debug('Querying random patient list...')
+    #     results = DBUtil.execute(query)
+
+    #     # Get patient list.
+    #     random_patient_list = [ row[0] for row in results ]
+
+    #     return random_patient_list
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        # self._lab_components = self._get_components_in_lab_panel()
+        # random_patient_list = self._get_random_patient_list()
+
+        #print("RANDOM PATIENT LIST", random_patient_list)
+
+        # Build SQL query for list of patient episodes.
+        # Note that for 2008-2014 data, result_flag can take on any of the
+        # following values:  High, Low, High Panic, Low Panic,
+        # Low Off-Scale, Negative, Positive, Resistant, Susceptible, Abnormal, *
+        # (NONE): Only 27 lab components can have this flag. None has this
+        #           value for more than 5 results, so ignore it.
+        # *: Only 10 lab components can have this flag. Only 6 have it for
+        #           >10 tests, and each of them is a microbiology test for which
+        #           a flag is less meaningful, e.g. Gram Stain, AFB culture.
+        # Susceptible: Only 15 lab components can have this flag. All of those
+        #           only have this value for 2 results, so ignore it.
+        # Resistant: Only 1 lab component can have this flag. Only two results
+        #           have this value, so ignore it.
+        # Abnormal: 1462 lab components can have this flag. Many (e.g. UBLOOD)
+        #           have this value for thousands of results, so include it.
+        # Negative: Only 10 lab components can have this flag, and all for
+        #           less than 5 results, so ignore it.
+        # Positive: Only 3 lab components can have this flag, and all for
+        #           only 1 result, so ignore it.
+        # Low Off-Scale: Only 1 lab component can have this flag, and only for
+        #           3 results, so ignore it.
+        # Low Panic: 1401 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        # High Panic: 8084 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        query = SQLQuery()
+        query.addSelect('CAST(pat_anon_id AS BIGINT) AS pat_id')
+        query.addSelect('CAST(shifted_order_time AS TIMESTAMP)')
+        query.addSelect('stride_culture_micro.proc_code')
+        query.addSelect('organism_name') #one for the result
+
+        # Experimenting
+        # susceptibility_flags = ['Trimethoprim/Sulfamethoxazole', 'Vancomycin', 'Penicillin', 'Levofloxacin',
+        #                         'Clindamycin', 'Ceftriaxone', 'Erythromycin', 'Ampicillin',
+        #                         'Meropenem', 'Ciprofloxacin', 'Cefepime', 'Aztreonam.',
+        #                         'Ampicillin/Sulbactam', 'Piperacillin/Tazobactam',
+        #                         'Linezolid', 'Oxacillin.', 'Cefazolin', 'Daptomycin']
+        susceptibility_flags = ['Piperacillin/Tazobactam', 'Meropenem', 'Imipenem', 'Ceftriaxone', 'Cefazolin', 'Aztreonam.']
+
+        for med in susceptibility_flags:
+            med_col = med.replace('/', '_').replace('.', '')
+            if med == 'Cefazolin':
+                query.addSelect("MAX(CASE WHEN (antibiotic_name = '%s' or antibiotic_name = '%s.') AND (suseptibility = 'Susceptible' OR suseptibility = 'Positive') THEN 1 ELSE 0 END) AS %s_Susc" % (med, med, med_col))
+                query.addSelect("MAX(CASE WHEN (antibiotic_name = '%s' or antibiotic_name = '%s.') THEN 1 ELSE 0 END) as %s_tested" % (med, med, med_col))
+            else:
+                query.addSelect("MAX(CASE WHEN antibiotic_name = '%s' AND (suseptibility = 'Susceptible' OR suseptibility = 'Positive') THEN 1 ELSE 0 END) AS %s_Susc" % (med, med_col))
+                query.addSelect("MAX(CASE WHEN antibiotic_name = '%s' THEN 1 ELSE 0 END) as %s_tested" % (med, med_col))
+
+        
+        query.addFrom('stride_culture_micro')
+ 
+        query.addWhereIn("stride_culture_micro.organism_name", [self._bug_panel])
+        query.addWhereIn("stride_culture_micro.antibiotic_name", ['Piperacillin/Tazobactam', 'Meropenem', 'Imipenem', 'Ceftriaxone', 'Cefazolin', 'Cefazolin.', 'Aztreonam.'])
+        query.addGroupBy('pat_anon_id')
+        query.addGroupBy('shifted_order_time')
+        query.addGroupBy('stride_culture_micro.proc_code')
+        query.addGroupBy('organism_name')
+        query.addOrderBy('pat_anon_id')
+        query.addOrderBy('shifted_order_time')
+        query.addOrderBy('stride_culture_micro.proc_code')
+        query.addOrderBy('organism_name')
+
+
+        self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query, pat_id_col='pat_id', index_time_col='shifted_order_time')
+
+    def _add_features(self):
+        # Add past susceptibility readings
+        # self._add_susc_features()
+
+        # Add past antibiotic use as features
+        # self._add_med_features()
+
+        # Add lab panel order features.
+        self._factory.addClinicalItemFeatures(self._lab_panel, features="pre")
+
+        # Add lab component result features, for a variety of time deltas.
+        # LAB_PRE_TIME_DELTAS = [datetime.timedelta(-14)]
+        # LAB_POST_TIME_DELTA = datetime.timedelta(0)
+        # log.info('Adding lab component features...')
+        # for pre_time_delta in LAB_PRE_TIME_DELTAS:
+        #     log.info('\t%s' % pre_time_delta)
+        #     self._factory.addLabResultFeatures(self._lab_components, False, pre_time_delta, LAB_POST_TIME_DELTA)
+
+        FeatureMatrix._add_features(self, index_time_col='shifted_order_time')
+
+    def _add_susc_features(self):
+        for susc_name in self._susceptibility_names:
+            log.debug('Adding %s feature...' % susc_name)
+            self._factory.addClinicalItemFeatures([susc_name], column='name',
+                                                  label=susc_name, features="pre")
+
+    def _add_med_features(self):
+        # Adds all prior antibiotic use as features
+        for med_set in self._med_panel:
+            med_label = med_set[0].split()[0] # Takes name of antibiotic
+            log.debug('Adding %s medication features...' % med_label)
+            self._factory.addClinicalItemFeatures(med_set, column="description",
+                                                  label="Med." + med_label, features="pre")
+
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab panel across %s inpatients from Stanford hospital.' % (','.join(self._lab_panel), self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab panel orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % ','.join(self._lab_panel)
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        # line = 'Also included %s panel components: %s' % (','.join(self._lab_panel), self._lab_components)
+        # summary.append(line)
+
+        return summary
+
+if __name__ == "__main__":
+    log.level = logging.DEBUG
+    start_time = time.time()
+    # Initialize lab test matrix.
+    ltm = LabCultureMatrix("LABBLC", 5)
+    # Output lab test matrix.
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    ltm.write_matrix("LABBLC-panel-5-episodes-%s-sec.tab" % str(elapsed_time))
diff --git a/scripts/LabCulturePrediction/AntiBiogramPredictionPipeline.py b/scripts/LabCulturePrediction/AntiBiogramPredictionPipeline.py
new file mode 100644
index 00000000..161640a1
--- /dev/null
+++ b/scripts/LabCulturePrediction/AntiBiogramPredictionPipeline.py
@@ -0,0 +1,317 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of Antibiotic Suseptibiltiy prediction.
+"""
+
+import argparse
+import inspect
+import os
+import pandas as pd
+from pandas import DataFrame, Series
+from sklearn.externals import joblib
+from sklearn.metrics import make_scorer, average_precision_score
+import logging
+
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from .AntiBiogramMatrix import AntiBiogramMatrix
+
+
+class AntibiogramPredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, bug_panel, drug, num_episodes, use_cache=None, random_state=None):
+        SupervisedLearningPipeline.__init__(self, bug_panel, num_episodes, use_cache, random_state)
+
+        self.drug = drug
+        self._build_raw_feature_matrix()
+        self._build_processed_feature_matrix()
+        self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-normality-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+
+        slugified_var = '-'.join(self._var.split())
+        model_dump_name = template % (slugified_var)
+
+        # Build path.
+        data_dir = self._fetch_data_dir_path(pipeline_file_name)
+        model_dump_path = '/'.join([data_dir, self.drug, model_dump_name])
+
+        return model_dump_path
+
+
+    def _build_raw_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+
+        slugified_var = '-'.join(self._var.split())
+        raw_matrix_name = template % (slugified_var, self._num_rows)
+
+        data_dir = self._fetch_data_dir_path(pipeline_file_name)
+        drug_path = '/'.join([data_dir, self.drug])
+        if not os.path.exists(drug_path):
+            os.makedirs(drug_path)
+        raw_matrix_path = '/'.join([data_dir, self.drug, raw_matrix_name])
+
+        return raw_matrix_path
+
+    def _build_composite_raw_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_name)
+
+    def _build_composite_raw_feature_matrix(self):
+        composite_raw_matrix_path = self._build_composite_raw_matrix_path()
+        matrix_class = AntiBiogramMatrix
+        SupervisedLearningPipeline._build_raw_feature_matrix(self, matrix_class, \
+            composite_raw_matrix_path)
+
+   
+    def _build_raw_feature_matrix(self):
+        # We first build the composite feature matrix if it doesn't exist
+        # And then filter it down to the bug and drug we care about. 
+
+        raw_matrix_path = self._build_raw_matrix_path()
+        self._raw_matrix_params = {}
+        if os.path.exists(raw_matrix_path):
+            pass
+        else:
+            # Build composite raw matrix if does not exist
+            composite_raw_matrix_path = self._build_composite_raw_matrix_path()
+            if not os.path.exists(composite_raw_matrix_path):
+                self._build_composite_raw_feature_matrix()
+
+            # Load in LabCultureMatrix, filter on susceptibiliy tested column
+            comp_raw_matrix = pd.read_csv(composite_raw_matrix_path, sep='\t')
+            outcome_tested = '%s_tested' % self.drug
+            raw_matrix = comp_raw_matrix[comp_raw_matrix[outcome_tested] == 1]
+            
+            # Write Filtered Matrix to raw matrix path
+            raw_matix_path = self._build_raw_matrix_path()
+            raw_matrix.to_csv(raw_matrix_path, sep='\t', index=False)
+
+
+    def _build_processed_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-processed.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+
+        slugified_var = '-'.join(self._var.split())
+        processed_matrix_name = template % (slugified_var, self._num_rows)
+
+        # Build Path
+        data_dir = self._fetch_data_dir_path(pipeline_file_path)
+        processed_matrix_path = '/'.join([data_dir, self.drug, processed_matrix_name])
+
+        return processed_matrix_path
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path()
+        features_to_add = {}
+        imputation_strategies = {
+        }
+
+        features_to_remove = [
+            'pat_id', 'shifted_order_time',
+            'proc_code', 'abnormal_panel', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays',
+            'Death.post',
+            'Death.postTimeDays',
+            'organism_name'
+        ]
+
+        antibiotic_flags = ['ceftriaxone',
+                            'meropenem',
+                            'piperacillin_tazobactam',
+                            'cefazolin',
+                            'imipenem',
+                            'aztreonam'
+        ]
+
+
+        features_to_remove += ['%s_susc' % flag for flag in antibiotic_flags if flag != self.drug]
+        features_to_remove += ['%s_tested' % flag for flag in antibiotic_flags]
+
+        features_to_keep = ['LABBLC-LABBLC2-LABURNC.pre']
+            # Keep the # of times it's been ordered in past, even if low info.
+            # '%s.pre' % '-'.join(self._var.split())
+        
+        outcome_label = '%s_susc' % self.drug
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 0.05
+        matrix_class = AntiBiogramMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        random_state = self._random_state
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether all components
+            '%s is a boolean indicator which summarizes whether all components ' % outcome_label,
+            # in the lab panel order represented by a given row are normal.
+            'in the lab panel order represented by a given row are normal.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+        ]
+
+        # Bundle parameters into single object to be unpacked in SLP.
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+        params['random_state'] = random_state
+
+        # Defer processing logic to SupervisedLearningPipeline.
+        SupervisedLearningPipeline._build_processed_feature_matrix(self, params)
+
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, self.drug, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+            hyperparams['random_state'] = self._random_state
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'bug_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'bug_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabCulturePredictionPipeline("%s", 1000000)' % self._var]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-normality-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-normality-prediction-%s' % (self._var, algorithm)
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabCulturePredictionPipeline("%s", 1000000)' % self._var]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-normality-prediction-report.tab' % self._var]), header)
+
+
+
+
+        
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-bug_name', required=True)
+    parser.add_argument('-drug_name', required=True)
+    args = parser.parse_args()
+
+    AntibiogramPredictionPipeline(args.bug_name, args.drug_name, 1000000, use_cache=True, random_state=123456789)
+
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/DemographicFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/DemographicFeatures.sql
new file mode 100644
index 00000000..8e31b463
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/DemographicFeatures.sql
@@ -0,0 +1,11 @@
+select distinct demo.gender,
+                demo.canonical_race,
+                demo.canonical_ethnicity,
+                demo.birth_date_jittered,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.demographic as demo
+where
+  labels.jc_uid = demo.rit_uid
+order by pat_enc_csn_id_coded, organism
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/DxFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/DxFeatures.sql
new file mode 100644
index 00000000..cb8009fb
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/DxFeatures.sql
@@ -0,0 +1,13 @@
+select distinct dx.dx_id,
+                dx.dx_name,
+                dx.timestamp_utc as dx_time,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.diagnosis_code as dx
+where
+  labels.jc_uid = dx.jc_uid
+  and labels.pat_enc_csn_id_coded <> dx.pat_enc_csn_id_coded
+  and dx.timestamp_utc < labels.order_time_jittered_utc
+  and timestamp_add(dx.timestamp_utc, INTERVAL 24*365 HOUR) >= labels.order_time_jittered_utc
+order by pat_enc_csn_id_coded, organism, dx_time
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/ImagingFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/ImagingFeatures.sql
new file mode 100644
index 00000000..c111363a
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/ImagingFeatures.sql
@@ -0,0 +1,15 @@
+select distinct proc.order_proc_id_coded,
+                proc.order_type,
+                proc.proc_code,
+                proc.description,
+                proc.order_time_jittered_utc as proc_order_time,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.order_proc as proc
+where
+  proc.order_type = 'Imaging'
+  and labels.jc_uid = proc.jc_uid
+  and proc.order_time_jittered_utc < labels.order_time_jittered_utc
+  and timestamp_add(proc.order_time_jittered_utc, INTERVAL 24*365 HOUR) >= labels.order_time_jittered_utc
+order by pat_enc_csn_id_coded, organism, proc_order_time
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/LabFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/LabFeatures.sql
new file mode 100644
index 00000000..a5fa6ec0
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/LabFeatures.sql
@@ -0,0 +1,13 @@
+select distinct lab.order_proc_id_coded,
+                lab.order_type,
+                lab.proc_code,
+                lab.order_time_jittered_utc as lab_order,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.order_proc as lab
+where
+  lab.order_type = 'Lab'
+  and labels.jc_uid = lab.jc_uid
+  and lab.order_time_jittered_utc < labels.order_time_jittered_utc
+  and timestamp_add(lab.order_time_jittered_utc, INTERVAL 24*365 HOUR) >= labels.order_time_jittered_utc
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/MedFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/MedFeatures.sql
new file mode 100644
index 00000000..7b248b4b
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/MedFeatures.sql
@@ -0,0 +1,14 @@
+select distinct med.order_med_id_coded,
+                med.med_description,
+                med.pharm_class_name,
+                med.thera_class_name,
+                med.order_class,
+                med.order_time_jittered_utc as med_order_time,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.order_med as med
+where
+  labels.jc_uid = med.jc_uid
+  and med.order_time_jittered_utc < labels.order_time_jittered_utc
+  and timestamp_add(med.order_time_jittered_utc, INTERVAL 24*365 HOUR) >= labels.order_time_jittered_utc
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/MicrobiologyOrderFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/MicrobiologyOrderFeatures.sql
new file mode 100644
index 00000000..103aac98
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/MicrobiologyOrderFeatures.sql
@@ -0,0 +1,15 @@
+select distinct proc.order_proc_id_coded,
+                proc.order_type,
+                proc.proc_code,
+                proc.description,
+                proc.order_time_jittered_utc as proc_order_time,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.order_proc as proc
+where
+  proc.jc_uid = labels.jc_uid
+  and proc.order_type = 'Microbiology Culture'
+  and proc.order_time_jittered_utc < labels.order_time_jittered_utc
+  and timestamp_add(proc.order_time_jittered_utc, INTERVAL 24*365 HOUR) >= labels.order_time_jittered_utc
+order by pat_enc_csn_id_coded, organism, proc_order_time
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/SQL/ProcedureFeatures.sql b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/ProcedureFeatures.sql
new file mode 100644
index 00000000..36019f10
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/SQL/ProcedureFeatures.sql
@@ -0,0 +1,15 @@
+select distinct proc.order_proc_id_coded,
+                proc.order_type,
+                proc.proc_code,
+                proc.description,
+                proc.order_time_jittered_utc as proc_order_time,
+                labels.*
+from
+  conor_db.er_empiric_treatment as labels,
+  starr_datalake2018.order_proc as proc
+where
+  proc.order_type = 'Procedures'
+  and labels.jc_uid = proc.jc_uid
+  and proc.order_time_jittered_utc < labels.order_time_jittered_utc
+  and timestamp_add(proc.order_time_jittered_utc, INTERVAL 24*365 HOUR) >= labels.order_time_jittered_utc
+order by pat_enc_csn_id_coded, organism, proc_order_time
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/UtilityModel.py b/scripts/LabCulturePrediction/DecisionMakingClass/UtilityModel.py
new file mode 100644
index 00000000..b4205834
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/UtilityModel.py
@@ -0,0 +1,226 @@
+from scipy.optimize import (minimize, differential_evolution, fsolve,
+broyden1, newton_krylov, leastsq, basinhopping)
+import numpy as np
+import pdb
+
+class UtilityModel():
+    
+    def __init__(self, data, C_no_cover=-100, C_meropenem=-1, C_vancomycin=-1,
+                 C_piptazo=-1, C_cefepime=-1, C_ceftriaxone=-1,
+                 C_cefazolin=-1, C_vanc_meropenem=-1, C_vanc_piptazo=-1,
+                 C_vanc_cefepime = -1, C_vanc_ceftriaxone = -1):
+        self.data = data
+
+        self.C_no_cover = C_no_cover
+        self.drug_cost = {}
+        self.drug_cost['meropenem'] = C_meropenem
+        self.drug_cost['vancomycin'] = C_vancomycin
+        self.drug_cost['piptazo'] = C_piptazo
+        self.drug_cost['cefepime'] = C_cefepime
+        self.drug_cost['ceftriaxone'] = C_ceftriaxone
+        self.drug_cost['cefazolin'] =  C_cefazolin
+        
+        self.drug_cost['vanc_meropenem'] = C_vanc_meropenem
+        self.drug_cost['vanc_piptazo'] = C_vanc_piptazo
+        self.drug_cost['vanc_cefepime'] = C_vanc_cefepime
+        self.drug_cost['vanc_ceftriaxone'] = C_vanc_ceftriaxone
+    
+    
+    def compute_best_action(self, x):
+        """ 
+        Given model outputs and utility values, return the optimal
+        treatment based on expected utility.
+        """
+
+        drugs = ['ceftriaxone', 'cefepime', 
+                 'piptazo', 'vancomycin', 'vanc_ceftriaxone',
+                 'vanc_cefepime', 'vanc_piptazo',
+                 'meropenem', 'vanc_meropenem']
+
+        # drugs = ['cefazolin', 'ceftriaxone', 'cefepime', 'piptazo', 'vancomycin', 'meropenem']
+
+        drugs.reverse() # Looks at broadest spectrum first
+        
+        best_drug = None
+        EU_best = -99999999
+        for drug in drugs:
+            EU = (1-x["p_of_c_"+drug])*(self.C_no_cover + self.drug_cost[drug]) + \
+                      x["p_of_c_"+drug] * self.drug_cost[drug]
+            if EU > EU_best:
+                EU_best = EU
+                best_drug = drug
+                
+        if best_drug == 'vanc_meropenem':
+            best_drug = 'vancomycin meropenem'
+        if best_drug == 'vanc_piptazo':
+            best_drug = 'vancomycin piperacillin-tazobactam'
+        if best_drug == 'vanc_cefepime':
+            best_drug = 'vancomycin cefepime'
+        if best_drug == 'vanc_ceftriaxone':
+            best_drug = 'vancomycin ceftriaxone'
+        if best_drug == 'piptazo':
+            best_drug = 'piperacillin-tazobactam'
+        return best_drug
+
+    def f(self, vars):
+        C_meropenem, C_vancomycin, C_piptazo, C_cefepime, C_ceftriaxone, C_cefazolin = vars
+        equations = []
+        drugs = ['cefazolin', 'ceftriaxone', 'cefepime', 
+             'piptazo', 'vancomycin', 'meropenem']
+        relative_ns = np.array([0, 0, 8, 6, 4, 0])
+        p_res = 1 - self.data[['p_of_c_' + t for t in drugs]].values
+        for i, drug in enumerate(drugs):
+          eq = np.sum([1 if np.argmax(p_res[j, :] * -100 + np.asarray([C_meropenem,
+                                                                 C_vancomycin,
+                                                                 C_piptazo,
+                                                                 C_cefepime,
+                                                                 C_ceftriaxone,
+                                                                 C_cefazolin]))
+                  == i else 0 for j in range(p_res.shape[0])]) - relative_ns[i]
+
+          if i != 5:
+              equations.append(eq)
+
+        equations.append(C_meropenem + C_vancomycin + C_piptazo + C_cefepime + C_ceftriaxone + C_cefazolin)
+        
+        return equations
+    # def _generate_equations(self, data,
+    #                         n_meropenem,
+    #                         n_vanc,
+    #                         n_piptazo,
+    #                         n_cefepime,
+    #                         n_ceftriaxone,
+    #                         n_cefazolin):
+
+    #     equations = [] 
+    #     single_drugs = ['cefazolin', 'ceftriaxone', 'cefepime', 
+    #              'piptazo', 'vancomycin', 'meropenem']
+
+    #     treatments = ['cefazolin', 'ceftriaxone', 'cefepime', 
+    #                   'piptazo', 'vancomycin', 'vanc_ceftriaxone',
+    #                   'vanc_cefepime', 'vanc_piptazo',
+    #                   'meropenem', 'vanc_meropenem']
+
+
+    #     for drug in single_drugs:
+    #         p_res = 1 - data[['p_of_c_' + t for t in single_drugs]].values
+    #         np.sum([1 if np.argmax(pres[j, :] * -100 + C_)])
+
+
+
+    def f_optimize(self, vars):
+        self.drug_cost['meropenem'] = vars[0]
+        self.drug_cost['vancomycin'] = vars[1]
+        self.drug_cost['piptazo'] = vars[2]
+        self.drug_cost['cefepime'] = vars[3]
+        self.drug_cost['ceftriaxone'] = vars[4]
+
+        self.data['alg_meds'] = self.data.apply(self.compute_best_action, axis=1)
+
+        alg_pres_counts = self.data.groupby('alg_meds').count(
+            )['pat_enc_csn_id_coded'].reset_index()
+
+        cost = 0
+        for drug in alg_pres_counts['alg_meds'].values:
+            count = alg_pres_counts[alg_pres_counts['alg_meds'] == drug]['pat_enc_csn_id_coded'].values[0]
+
+        cost += np.square(np.abs(count - self.n[drug]))
+
+        print(cost)
+    
+        return cost
+
+
+    def f_new(self, vars):
+        self.drug_cost['ceftriaxone'] = vars[0]
+        self.drug_cost['cefepime'] = vars[1]
+        self.drug_cost['piptazo'] = vars[2]
+        self.drug_cost['vancomycin'] = vars[3]
+        self.drug_cost['meropenem'] = vars[4]
+
+        self.drug_cost['vanc_meropenem'] = vars[5]
+        self.drug_cost['vanc_piptazo'] = vars[6]
+        self.drug_cost['vanc_cefepime'] = vars[7]
+        self.drug_cost['vanc_ceftriaxone'] = vars[8]
+
+        self.data['alg_meds'] = self.data.apply(self.compute_best_action, axis=1)
+
+        alg_pres_counts = self.data.groupby('alg_meds').count(
+            )['pat_enc_csn_id_coded'].reset_index()
+
+
+        # drugs = ['cefazolin', 'ceftriaxone', 'cefepime', 
+        #          'piperacillin-tazobactam', 'vancomycin', 'vancomycin ceftriaxone',
+        #          'vancomycin cefepime', 'vancomycin piperacillin-tazobactam',
+        #          'meropenem', 'vancomcyin meropenem']
+
+        drugs = ['ceftriaxone', 'cefepime',
+                 'piperacillin-tazobactam', 'vancomycin',
+                 'meropenem', 'vancomycin meropenem',
+                 'vancomycin piperacillin-tazobactam',
+                 'vancomycin cefepime', 'vancomycin ceftriaxone']
+        equations = []
+        total_count = 0
+        for i, drug in enumerate(drugs):
+            count = len(self.data[self.data['alg_meds'] == drug])
+            print("Count %s: %f" % (drug, count))
+
+            if (drug == 'meropenem' or drug == 'vancomycin meropenem') and \
+                (count > self.n[drug]) :
+                equations.append(np.power(count - self.n[drug], 4))
+            else:
+                equations.append(np.square(count - self.n[drug]))
+        print()
+        print()
+        # for key in ['cefazolin', 'ceftriaxone', 'cefepime',
+        #             'piptazo', 'vancomycin', 'meropenem']:
+        #     total_cost += self.drug_cost[key]
+
+        # for key in self.drug_cost:
+        #     total_cost += self.drug_cost[key]
+
+        # equations.append(np.square(total_cost))
+
+        # print(vars)
+        # For optimization
+        return np.sum([e for e in equations])
+        # return equations
+
+
+
+    def fit_drug_parameters(self, n):
+        """ This function solves a system of equations to fit cost values
+            for each of the six antibiotics. 
+        """
+
+        self.n = n
+        drug_costs = basinhopping(self.f_new, (self.drug_cost['ceftriaxone'],
+                                         self.drug_cost['cefepime'],
+                                         self.drug_cost['piptazo'],
+                                         self.drug_cost['vancomycin'],
+                                         self.drug_cost['meropenem'],
+                                         self.drug_cost['vanc_meropenem'],
+                                         self.drug_cost['vanc_piptazo'],
+                                         self.drug_cost['vanc_cefepime'],
+                                         self.drug_cost['vanc_ceftriaxone']), T=1000, niter=10)
+        
+
+        self.drug_cost['ceftriaxone'] = drug_costs['x'][0]
+        self.drug_cost['cefepime'] = drug_costs['x'][1]
+        self.drug_cost['piptazo'] = drug_costs['x'][2]
+        self.drug_cost['vancomycin'] = drug_costs['x'][3]
+        self.drug_cost['meropenem'] = drug_costs['x'][4]    
+
+        self.drug_cost['vanc_meropenem'] = drug_costs['x'][5]
+        self.drug_cost['vanc_piptazo'] = drug_costs['x'][6]
+        self.drug_cost['vanc_cefepime'] = drug_costs['x'][7]
+        self.drug_cost['vanc_ceftriaxone'] = drug_costs['x'][8]
+
+        self.data['alg_meds'] = self.data.apply(self.compute_best_action, axis=1)
+        print(self.data.groupby('alg_meds').count())
+
+        return self.data
+        
+
+
+
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/batch_driver.sh b/scripts/LabCulturePrediction/DecisionMakingClass/batch_driver.sh
new file mode 100644
index 00000000..f08a40ff
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/batch_driver.sh
@@ -0,0 +1,61 @@
+time python ./run_experiments.py --n_vanc_mero 3 \
+								 --n_mero 15 \
+								 --n_vanc_piptazo 436 \
+								 --n_vanc_cefepime 183 \
+								 --n_vanc_ceftriaxone 58 \
+								 --n_vanc 1 \
+								 --n_piptazo 186 \
+								 --n_cefepime 56 \
+								 --n_ceftriaxone 568 \
+								 --out_path ./param_files/cefepime/100/ \
+								 &> ./log_utility_param_experiments/cefepime_100.log
+
+time python ./run_experiments.py --n_vanc_mero 3 \
+								 --n_mero 15 \
+								 --n_vanc_piptazo 336 \
+								 --n_vanc_cefepime 283 \
+								 --n_vanc_ceftriaxone 58 \
+								 --n_vanc 1 \
+								 --n_piptazo 186 \
+								 --n_cefepime 56 \
+								 --n_ceftriaxone 568 \
+								 --out_path ./param_files/cefepime/200/ \
+								 &> ./log_utility_param_experiments/cefepime_200.log
+
+time python ./run_experiments.py --n_vanc_mero 3 \
+								 --n_mero 15 \
+								 --n_vanc_piptazo 236 \
+								 --n_vanc_cefepime 383 \
+								 --n_vanc_ceftriaxone 58 \
+								 --n_vanc 1 \
+								 --n_piptazo 186 \
+								 --n_cefepime 56 \
+								 --n_ceftriaxone 568 \
+								 --out_path ./param_files/cefepime/300/ \
+								 &> ./log_utility_param_experiments/cefepime_300.log
+
+time python ./run_experiments.py --n_vanc_mero 3 \
+								 --n_mero 15 \
+								 --n_vanc_piptazo 136 \
+								 --n_vanc_cefepime 483 \
+								 --n_vanc_ceftriaxone 58 \
+								 --n_vanc 1 \
+								 --n_piptazo 186 \
+								 --n_cefepime 56 \
+								 --n_ceftriaxone 568 \
+								 --out_path ./param_files/cefepime/400/ \
+								 &> ./log_utility_param_experiments/cefepime_400.log
+
+time python ./run_experiments.py --n_vanc_mero 3 \
+								 --n_mero 15 \
+								 --n_vanc_piptazo 36 \
+								 --n_vanc_cefepime 583 \
+								 --n_vanc_ceftriaxone 58 \
+								 --n_vanc 1 \
+								 --n_piptazo 186 \
+								 --n_cefepime 56 \
+								 --n_ceftriaxone 568 \
+								 --out_path ./param_files/cefepime/500/ \
+								 &> ./log_utility_param_experiments/cefepime_500.log
+
+
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/cohort_queries.py b/scripts/LabCulturePrediction/DecisionMakingClass/cohort_queries.py
new file mode 100644
index 00000000..59154d0a
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/cohort_queries.py
@@ -0,0 +1,24 @@
+from google.cloud import bigquery
+
+
+def test():
+	query = """select distinct er_admits.*, labs.order_id_coded, labs.proc_code,
+	           labs.group_lab_name, labs.result_flag, labs.order_time_jittered_utc,
+	           labs.result_time_jittered_utc
+			from
+			    starr_datalake2018.lab_result as labs,
+			    (select jc_uid, pat_enc_csn_id_coded, min(effective_time_jittered_utc) as er_admit_time,
+			     		max(effective_time_jittered_utc) as er_transfer_out_time 
+			     from starr_datalake2018.adt
+			     where pat_class_c = "112"
+			     and pat_service = "Emergency"
+			     group by jc_uid, pat_enc_csn_id_coded
+			     order by pat_enc_csn_id_coded) er_admits
+			    
+			where er_admits.pat_enc_csn_id_coded = labs.pat_enc_csn_id_coded
+			and labs.order_type = "Microbiology Culture"
+			and labs.order_time_jittered_utc <= er_admits.er_transfer_out_time
+			and labs.order_time_jittered_utc >= er_admits.er_admit_time
+			order by pat_enc_csn_id_coded, labs.order_time_jittered_utc"""
+	query_job = client.query(query)
+	return query_job.result().to_dataframe()
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/cost_analysis.py b/scripts/LabCulturePrediction/DecisionMakingClass/cost_analysis.py
new file mode 100644
index 00000000..daa37f94
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/cost_analysis.py
@@ -0,0 +1,245 @@
+import pandas as pd
+import numpy as np
+import os
+from sklearn.metrics import roc_auc_score
+from random import random, randint
+
+from UtilityModel import UtilityModel
+
+import pdb
+
+test_year = 2013
+
+
+def read_model_outputs(data, model_dir):
+    """ Read in coverage predictions output by each of our models
+        and attaches them to dataframe appropriately. 
+    """
+    drugs = ['ceftriaxone', 'cefepime',
+             'piptazo', 'vancomycin', 'meropenem',
+             'vanc_meropenem', 'vanc_piptazo',
+             'vanc_cefepime', 'vanc_ceftriaxone']
+
+    for drug in drugs:
+        prediction_file = os.path.join(model_dir, drug, 'predictions.csv')
+        with open(prediction_file, 'r') as f:
+            data['p_of_c_' + drug] = np.array([float(prob.split(',')[1].rstrip())  +  random() / 1000
+                                                     for prob in f])
+    # 
+    return data
+
+def sanity_check_aurocs(data):
+    """ Once we've attached model probabilites, generate aurocs
+        and make sure they are the same as what we got before """
+
+    drugs = ['ceftriaxone', 'cefepime',
+             'piptazo', 'vancomycin', 'meropenem',
+             'vanc_meropenem', 'vanc_piptazo',
+             'vanc_cefepime', 'vanc_ceftriaxone']
+
+    for drug in drugs:
+        labels = data[drug]
+        predictions = data['p_of_c_' + drug]
+        auroc = roc_auc_score(labels, predictions)
+        print(drug)
+        print(auroc)
+
+def _assume_med_orders(meds):
+    if meds == 'piperacillin-tazobactam vancomycin levofloxacin':
+        return "vancomycin piperacillin-tazobactam"
+    if meds == 'ceftriaxone vancomycin piperacillin-tazobactam': 
+        return "vancomycin piperacillin-tazobactam"
+    if meds == 'vancomycin cefepime levofloxacin':
+        return 'vancomycin cefepime'
+    if meds == 'ceftriaxone piperacillin-tazobactam':
+        return 'piperacillin-tazobactam'
+    if meds == 'vancomycin ciprofloxacin piperacillin-tazobactam':
+        return 'vancomycin piperacillin-tazobactam'
+    
+    return meds
+
+def _filter_admits_by_med_orders(data):
+    data['med'] = data['med'].apply(lambda x: _assume_med_orders(x))
+    data = data[(data['med'] == 'ceftriaxone') | \
+                (data['med'] == 'vancomycin piperacillin-tazobactam') | \
+                (data['med'] == 'piperacillin-tazobactam') | \
+                (data['med'] == 'vancomycin') | \
+                (data['med'] == 'vancomycin cefepime') | \
+                (data['med'] == 'ceftriaxone vancomycin') | \
+                (data['med'] == 'cefepime') | \
+                (data['med'] == 'vancomycin meropenem') | \
+                (data['med'] == 'meropenem')]
+    return data
+    
+
+def filter_encounters_by_med_order(data):
+    """ We only want to analyze patient encounters where one of the ten forms 
+        of empiric treatment are actually administered. We will also make some
+        assumptions to reassign drug orders for example ceftriaxone, pip tazo 
+        and vancomycin order we'll assume was physician orginally going on ceftriaxone
+        and immediatley upping to zosyn. Refers to two helper functions provided above.
+    """
+    original_number_of_encounters = len(data)
+    data = data[(data['med'].str.contains('ceftriaxone')) | \
+                (data['med'].str.contains('cefepime')) | \
+                (data['med'].str.contains('piperacillin-tazobactam')) | \
+                (data['med'].str.contains('vancomycin')) | \
+                (data['med'].str.contains('meropenem'))]
+
+    data = _filter_admits_by_med_orders(data)
+    new_number_of_encounters = len(data)
+
+    print("Originally had %d encounters" % original_number_of_encounters)
+    print("Now have %d encounters" % new_number_of_encounters)
+
+    return data
+
+def compute_clin_covered_flag(x):
+    """ Returns flag as to whether patient was covered adequately """
+    meds = x.med.split()
+    covered = 0
+    for med in meds:
+        if med == 'ceftriaxone':
+            if x.ceftriaxone == 1:
+                covered = 1
+        elif med == 'cefepime':
+            if x.cefepime == 1:
+                covered = 1
+        elif med == 'piperacillin-tazobactam':
+            if x.piptazo == 1:
+                covered = 1
+        elif med == 'vancomycin':
+            if x.vancomycin == 1:
+                covered = 1
+        elif med == 'meropenem':
+            if x.meropenem == 1:
+                covered = 1
+    return covered
+
+def compute_alg_covered_flag(x):
+    meds = x.alg_meds.split()
+    covered = 0
+    for med in meds:
+        if med == 'ceftriaxone':
+            if x.ceftriaxone == 1:
+                covered = 1
+        elif med == 'cefepime':
+            if x.cefepime == 1:
+                covered = 1
+        elif med == 'piperacillin-tazobactam':
+            if x.piptazo == 1:
+                covered = 1
+        elif med == 'vancomycin':
+            if x.vancomycin == 1:
+                covered = 1
+        elif med == 'meropenem':
+            if x.meropenem == 1:
+                covered = 1
+    return covered
+
+def get_presciption_profile(data, alg=True):
+    """ After medications chosen, return a dataframe that counts number of times each
+        treatment given """
+    if alg:
+        col = 'alg_meds'
+    else:
+        col = 'med'
+    p_profile = data.groupby(col).count()['pat_enc_csn_id_coded'].reset_index(
+        ).rename(columns = {'pat_enc_csn_id_coded' : 'count'})
+    return p_profile
+
+def floor_single_drug_probabilites(data):
+    """ Function that makes sure p_of_vanco never exceeds p_of_vanc_zosyn 
+        and similar logic """
+
+    def vanc_combos(x):
+        min_num = np.min([x.p_of_c_vanc_piptazo, x.p_of_c_vanc_cefepime,
+                          x.p_of_c_vanc_meropenem, x.p_of_c_vanc_ceftriaxone])
+        if x.p_of_c_vancomycin > min_num:
+            return min_num
+        else:
+            return x.p_of_c_vancomycin
+
+    data['p_of_c_vancomycin'] = data.apply(vanc_combos, axis=1)
+    data['p_of_c_meropenem'] = data.apply(lambda x: x.p_of_c_meropenem if x.p_of_c_meropenem <
+                                x.p_of_c_vanc_meropenem else x.p_of_c_vanc_meropenem, axis=1)
+    data['p_of_c_piptazo'] = data.apply(lambda x: x.p_of_c_piptazo if x.p_of_c_piptazo <
+                                x.p_of_c_vanc_piptazo else x.p_of_c_vanc_piptazo, axis=1)
+    data['p_of_c_ceftriaxone'] = data.apply(lambda x: x.p_of_c_ceftriaxone if x.p_of_c_ceftriaxone <
+                                x.p_of_c_vanc_ceftriaxone else x.p_of_c_vanc_ceftriaxone, axis=1)
+    data['p_of_c_cefepime'] = data.apply(lambda x: x.p_of_c_cefepime if x.p_of_c_cefepime <
+                                x.p_of_c_vanc_cefepime else x.p_of_c_vanc_cefepime, axis=1)
+    return data
+
+if __name__ == '__main__':
+
+    data = pd.read_csv("./data/labels.csv")
+    data_unchanged = data
+    data = data[data['year'] == test_year]
+    data = read_model_outputs(data, './baseline_models')
+    data['med'] = data['med'].apply(lambda x: x.lower()) 
+
+    # Filter encounters by what meds were ordered
+    data = filter_encounters_by_med_order(data)
+
+    # Test this out
+    # data['p_of_c_cefazolin'] = data.apply(lambda x: x.p_of_c_cefazolin if x.p_of_c_cefazolin <= \
+    #                                 x.p_of_c_ceftriaxone else x.p_of_c_ceftriaxone, axis=1)
+    # data['p_of_c_ceftriaxone'] = data.apply(lambda x: x.p_of_c_ceftriaxone if x.p_of_c_ceftriaxone <= \
+    #                                 x.p_of_c_cefepime else x.p_of_c_cefepime, axis=1)
+
+    # Get Flag For Whether patients were adequately covered 
+    data['clin_covered'] = data.apply(compute_clin_covered_flag, axis=1)
+
+    # data = data.head(1530)
+
+    umodel = UtilityModel(data=data,
+                          C_meropenem = -1,
+                          C_vancomycin = -1,
+                          C_piptazo = -1,
+                          C_cefepime = -1,
+                          C_ceftriaxone = 1,
+                          C_cefazolin = -1,
+                          C_vanc_meropenem = 0,
+                          C_vanc_piptazo = -1,
+                          C_vanc_cefepime = -1,
+                          C_vanc_ceftriaxone = -1)
+
+    # data = floor_single_drug_probabilites(data)
+
+    data['alg_meds'] = data.apply(umodel.compute_best_action, axis=1)
+
+    # Get Flag for Whether alg covered pateints adequately 
+    data['alg_covered'] = data.apply(compute_alg_covered_flag, axis=1)
+
+
+    # # Profile
+    # alg_profile = get_presciption_profile(data)
+    # clin_profile = get_presciption_profile(data, alg=False)
+    # alg_profile.to_csv("./data/no_weights_profile.csv", index=None)
+    # clin_profile.to_csv('./data/clin_profile.csv', index=False)
+
+    n = {}
+    n['vancomycin meropenem'] = 14
+    n['vancomycin piperacillin-tazobactam'] = 513
+    n['vancomycin'] = 94
+    n['piperacillin-tazobactam'] = 174
+    n['cefepime'] = 35
+    n['ceftriaxone'] = 561
+    n['cefazolin'] = 0
+    n['vancomycin cefepime'] = 57
+    n['vancomycin ceftriaxone'] = 45
+    n['meropenem'] = 13
+
+    data = umodel.fit_drug_parameters(n)
+
+    with open('./data/model_util_vals.txt', 'w') as w:
+        for key in umodel.drug_cost:
+            w.write("%s,%.2f\n" % (key, umodel.drug_cost[key]))
+
+
+    data['alg_covered'] = data.apply(compute_alg_covered_flag, axis=1)
+
+    print("Number of mismatches %d" % len(data[data['alg_covered'] == 0]))
+
+    
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/feature_engineering.py b/scripts/LabCulturePrediction/DecisionMakingClass/feature_engineering.py
new file mode 100644
index 00000000..aea9012b
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/feature_engineering.py
@@ -0,0 +1,208 @@
+from google.cloud import bigquery
+import pandas as pd
+import os
+
+client = bigquery.Client()
+
+def featurize_lab_orders(query):
+    """ Given a SQL query that joins a cohort table with order_procs labs,
+        execute the query and keep in long form """
+    query_job = client.query(query)
+    df_lab_features = query_job.result().to_dataframe()
+
+    df_labs = df_lab_features[['pat_enc_csn_id_coded',
+                               'proc_code',
+                               'order_proc_id_coded']].drop_duplicates().groupby(
+                               ['pat_enc_csn_id_coded', 'proc_code']).agg(
+                               'count').reset_index().rename(columns={
+                               'order_proc_id_coded' : 'values',
+                               'proc_code' : 'features'})
+    return df_labs
+
+
+def featurize_med_orders(query):
+    """ Given a SQL query that joins cohort table and order_med,
+        execute query, make historical meds its own feature, and
+        save in long form
+    """
+    query_job = client.query(query)
+    df_med_features = query_job.result().to_dataframe()
+
+    df_med_features['order_class'] = df_med_features['order_class'].apply(
+        lambda x: 'Normal' if x != 'Historical Med' else 'Historical')
+    df_med_features['med_description'] = df_med_features[['med_description', 'order_class']].apply(
+        lambda x: '_'.join([x.med_description, x.order_class]), axis=1)
+
+    df_meds = df_med_features[['pat_enc_csn_id_coded',
+                               'med_description',
+                               'order_med_id_coded']].drop_duplicates().groupby(
+                               ['pat_enc_csn_id_coded', 'med_description']).agg(
+                               'count').reset_index().rename(columns={
+                               'order_med_id_coded' : 'values',
+                               'med_description' : 'features'})
+    return df_meds
+
+
+def featurize_imaging_orders(query):
+    """ 
+    Query imaging orders and return in long format
+    """
+    query_job = client.query(query)
+    df_imaging_features = query_job.result().to_dataframe()
+    df_images = df_imaging_features[['pat_enc_csn_id_coded',
+                                     'description',
+                                     'order_proc_id_coded']].drop_duplicates().groupby(
+                                     ['pat_enc_csn_id_coded', 'description']).agg(
+                                     'count').reset_index().rename(columns={
+                                     'order_proc_id_coded' : 'values',
+                                     'description' : 'features'})
+
+    return df_images
+
+
+def featurize_microbiology_orders(query):
+    """
+    Query micorbiology orders and return in long format
+    """
+
+    query_job = client.query(query)
+    df_micro_features = query_job.result().to_dataframe()
+    df_micro = df_micro_features[['pat_enc_csn_id_coded',
+                                  'description',
+                                  'order_proc_id_coded']].drop_duplicates().groupby(
+                                  ['pat_enc_csn_id_coded', 'description']).agg(
+                                  'count').reset_index().rename(columns={
+                                  'order_proc_id_coded' : 'values',
+                                  'description' : 'features'})
+
+    return df_micro
+
+def featurize_procedure_orders(query):
+    """
+    Query procedure orders and return in long format
+    """
+    query_job = client.query(query)
+    df_proc_features = query_job.result().to_dataframe()
+
+    df_proc = df_proc_features[['pat_enc_csn_id_coded',
+                                'description',
+                                'order_proc_id_coded']].drop_duplicates().groupby(
+                                ['pat_enc_csn_id_coded', 'description']).agg(
+                                'count').reset_index().rename(columns={
+                                'order_proc_id_coded' : 'values',
+                                'description' : 'features'})
+
+    return df_proc
+
+def featurize_dx_codes(query):
+    """
+    Query dx codes and return in long format
+    """
+    query_job = client.query(query)
+    df_dx_features = query_job.result().to_dataframe()
+    df_dx = df_dx_features[['pat_enc_csn_id_coded',
+                            'dx_name',
+                            'dx_id']].drop_duplicates().groupby(
+                            ['pat_enc_csn_id_coded', 'dx_name']).agg(
+                            'count').reset_index().rename(columns={
+                            'dx_id' : 'values',
+                            'dx_name' : 'features'})
+
+    return df_dx
+
+def featurize_demographics(query):
+    """
+    Query demographics and get features for age, gender, race, and ethnicity
+    """
+
+    query_job = client.query(query)
+    df_demo = query_job.result().to_dataframe()
+
+    df_demo['order_time_jittered_utc'] = pd.to_datetime(df_demo['order_time_jittered_utc'])
+    df_demo['birth_date_jittered'] = pd.to_datetime(df_demo['birth_date_jittered'])
+    df_demo = df_demo.assign(
+        age=lambda x: (x.order_time_jittered_utc.dt.date - x.birth_date_jittered.dt.date).dt.days)
+    df_demo = df_demo[['pat_enc_csn_id_coded', 
+                       'age', 'gender',
+                       'canonical_race',
+                       'canonical_ethnicity']]
+    df_demo['canonical_ethnicity'] = df_demo['canonical_ethnicity'].apply(
+        lambda x: 'Unknown Ethnicity' if x == 'Unknown' else x)
+    df_demo['canonical_race'] = df_demo['canonical_race'].apply(
+        lambda x: 'Unknown Race' if x == 'Unknown' else x)
+    df_demo = pd.concat([df_demo,
+                         pd.get_dummies(df_demo.gender),
+                         pd.get_dummies(df_demo.canonical_race),
+                         pd.get_dummies(df_demo.canonical_ethnicity)], axis=1)
+    df_demo = df_demo.drop(['gender',
+                            'canonical_race',
+                            'Female',
+                            'canonical_ethnicity'], axis=1).drop_duplicates()
+
+    df_demo = pd.melt(df_demo, id_vars=['pat_enc_csn_id_coded'],
+                      var_name = 'features',
+                      value_name = 'values')
+
+    return df_demo
+
+
+def featurize_cohort(out_file = './data/feature_matrix.csv', dx_codes = True,
+                     lab_orders = True, image_orders = True, med_orders = True,
+                     procedure_orders = True, micro_orders = True, demos=True):
+    """
+    Featurize Cohort, return and save long form dataframe
+    """
+
+    data_frames = []
+    sql_path = './SQL'
+    if dx_codes:
+        f = os.path.join(sql_path, 'DxFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_dx_codes(query))
+
+    if lab_orders:
+        f = os.path.join(sql_path, 'LabFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_lab_orders(query))
+
+    if image_orders:
+        f = os.path.join(sql_path, 'ImagingFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_imaging_orders(query))
+
+    if procedure_orders:
+        f = os.path.join(sql_path, 'ProcedureFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_procedure_orders(query))
+
+    if micro_orders:
+        f = os.path.join(sql_path, 'MicrobiologyOrderFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_microbiology_orders(query))
+
+    if med_orders:
+        f = os.path.join(sql_path, 'MedFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_med_orders(query))
+
+    if demos:
+        f = os.path.join(sql_path, 'DemographicFeatures.sql')
+        with open(f, 'r') as file:
+            query = file.read()
+        data_frames.append(featurize_demographics(query))
+
+    feature_matrix = pd.concat(data_frames)
+    feature_matrix.to_csv(out_file, index=None)
+
+    return feature_matrix
+
+if __name__ == "__main__":
+    df = featurize_cohort()
+
+
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/label_generation.py b/scripts/LabCulturePrediction/DecisionMakingClass/label_generation.py
new file mode 100644
index 00000000..979357e1
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/label_generation.py
@@ -0,0 +1,102 @@
+import pandas as pd
+
+def collapse_labels(arr):
+    """ TODO **** This function is wrong when infection has two or more bugs
+    """
+    """If anything in arr is 3 (ie contradiction encountered in labelling function),
+         then label is unknown -1.
+       else If anything in arr is -1, label is unknown -1
+       Else if anything in arr is 0, label is 0
+       Else label is 1
+    """
+    three_detected = False
+    unk_detected = False
+    zero_detected = False
+    for val in arr:
+        if val == 3:
+            three_detected = True
+        elif val == -1:
+            unk_detected = True
+        elif val == 0:
+            zero_detected = True
+    
+    if three_detected or unk_detected:
+        return -1
+    elif zero_detected:
+        return 0
+    else:
+        return 1
+    
+def collapse_organism(arr):
+    x = []
+    for a in arr:
+        if isinstance(a, str):
+            x.append(a)
+        else:
+            x.append('Nothing')
+    return ' '.join(x)
+    
+
+
+if __name__ == '__main__':
+    # Read in cohort
+    df_cohort = pd.read_csv('./data/er_empiric_treatment.csv')
+
+    df_cohort[['cefazolin',
+               'ceftriaxone',
+               'vancomycin',
+               'cefepime', 
+               'piptazo', 
+               'meropenem']] = df_cohort[['cefazolin',
+                                          'ceftriaxone', 
+                                          'vancomycin',
+                                          'cefepime',
+                                          'piptazo',
+                                          'meropenem']].fillna(1.0)
+
+    # Collapse labels - we want one label per drug per encounter
+    labels = df_cohort[['pat_enc_csn_id_coded', 'organism',
+              'cefazolin', 'ceftriaxone',
+              'vancomycin', 'cefepime',
+              'piptazo', 'meropenem']].sort_values('organism').groupby(
+             'pat_enc_csn_id_coded').agg({
+             'cefazolin' : collapse_labels,
+             'ceftriaxone' : collapse_labels,
+             'cefepime' : collapse_labels,
+             'piptazo' : collapse_labels,
+             'vancomycin' : collapse_labels,
+             'meropenem' : collapse_labels,
+             'organism' : collapse_organism
+         }).reset_index()
+
+    # Only keep samples where we know each label - one of the biggest 
+    # limitations in this study.... 
+    labels_small =        labels[(labels['cefazolin'] != -1) & \
+                             (labels['ceftriaxone'] != -1) & \
+                             (labels['cefepime'] != -1) & \
+                             (labels['piptazo'] != -1) & \
+                             (labels['vancomycin'] != -1) & \
+                             (labels['meropenem'] != -1)]
+
+    # Merge labels_small back onto data
+    df_cohort = df_cohort.drop(['cefazolin', 'ceftriaxone', 'vancomycin',
+                      'cefepime', 'piptazo', 'meropenem', 'organism'], axis=1)
+
+    df_cohort = pd.merge(df_cohort,
+                          labels_small, 
+                          on='pat_enc_csn_id_coded', 
+                          how='inner').groupby(
+                            'pat_enc_csn_id_coded').first().reset_index()
+
+    # Create combo labels
+    df_cohort['vanc_meropenem'] = df_cohort.apply(
+        lambda x: 1 if x.vancomycin == 1 or x.meropenem == 1 else 0, axis=1)
+    df_cohort['vanc_piptazo'] = df_cohort.apply(
+        lambda x: 1 if x.vancomycin == 1 or x.piptazo == 1 else 0, axis=1)
+    df_cohort['vanc_cefepime'] = df_cohort.apply(
+        lambda x: 1 if x.vancomycin == 1 or x.cefepime == 1 else 0, axis=1)
+    df_cohort['vanc_ceftriaxone'] = df_cohort.apply(
+        lambda x: 1 if x.vancomycin == 1 or x.ceftriaxone == 1 else 0, axis=1)
+
+
+    df_cohort.to_csv('./data/labels.csv', index=None)
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/predictive_models.py b/scripts/LabCulturePrediction/DecisionMakingClass/predictive_models.py
new file mode 100644
index 00000000..32889810
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/predictive_models.py
@@ -0,0 +1,102 @@
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+import scipy
+import os
+import numpy as np
+from sklearn.externals import joblib
+import pdb
+
+up_to_year = 2014
+
+def fit_baseline_models(x_train, labels_train,
+                        x_test, labels_test):
+    """
+    Fits first pass random forest models to each drug combo
+    No hyperparamter tuning (trees == 1000, features = sqrt)
+    Prints AUROC with CIs
+    Saves y_hats and trained model to output directory 
+    """
+
+    drugs = ['cefazolin', 'ceftriaxone', 'cefepime',
+             'piptazo', 'vancomycin', 'meropenem',
+             'vanc_meropenem', 'vanc_piptazo',
+             'vanc_cefepime', 'vanc_ceftriaxone']
+    
+
+    for drug in drugs:
+        
+        out_dir = './baseline_models/'
+        y_train = labels_train[drug]
+        y_test = labels_test[drug]
+
+        rf = RandomForestClassifier(n_estimators=1000, random_state=42)
+        rf.fit(x_train, y_train)
+    
+        predictions = rf.predict_proba(x_test)
+        auroc = roc_auc_score(y_test, predictions[:, 1])
+        
+        out_dir = os.path.join(out_dir, drug)
+        os.makedirs(out_dir, exist_ok=True)
+
+        np.savetxt(os.path.join(out_dir, "predictions.csv"), predictions, delimiter=",")
+        joblib.dump(rf, os.path.join(out_dir, 'model.pkl')) 
+        
+        p_res = 1 - float(labels_small[drug].sum())/len(labels_small[drug])
+        print(drug)
+        print("Percent Resistant %.4f" % p_res)
+        print("AUROC: %.2f" % auroc)
+
+
+if __name__ == '__main__':
+    labels = pd.read_csv('./data/labels.csv')
+    labels_small = labels[labels['year'] < up_to_year]
+
+    # Load in feature matrix, pivot, and join
+    features = pd.read_csv('./data/feature_matrix.csv')
+    features = features.pivot(index='pat_enc_csn_id_coded',
+                              columns='features',
+                              values='values').fillna(0.0).reset_index()
+    data = pd.merge(labels_small,
+                    features,
+                    on='pat_enc_csn_id_coded',
+                    how='left')
+
+    data = data.drop(['pat_enc_csn_id_coded',
+                      'jc_uid',
+                      'med',
+                      'order_time_jittered_utc',
+                      'med_sens',
+                      'organism'], axis=1)
+
+    data_train = data[data['year'] < 2013]
+    data_test = data[data['year'] == 2013]
+
+    labels_train = data_train[['cefazolin', 'ceftriaxone', 'cefepime',
+                               'piptazo', 'vancomycin', 'meropenem',
+                               'vanc_meropenem', 'vanc_piptazo',
+                               'vanc_cefepime', 'vanc_ceftriaxone']]
+
+    labels_test = data_test[['cefazolin', 'ceftriaxone', 'cefepime',
+                             'piptazo', 'vancomycin', 'meropenem',
+                             'vanc_meropenem', 'vanc_piptazo',
+                             'vanc_cefepime', 'vanc_ceftriaxone']]
+
+    data_train = data_train.drop(['cefazolin', 'ceftriaxone', 'cefepime',
+                               'piptazo', 'vancomycin', 'meropenem',
+                               'vanc_meropenem', 'vanc_piptazo',
+                               'vanc_cefepime', 'vanc_ceftriaxone'], axis=1)
+
+    data_test = data_test.drop(['cefazolin', 'ceftriaxone', 'cefepime',
+                                'piptazo', 'vancomycin', 'meropenem',
+                                'vanc_meropenem', 'vanc_piptazo',
+                                'vanc_cefepime', 'vanc_ceftriaxone'], axis=1)
+
+    data_train = scipy.sparse.csr_matrix(data_train.values)
+    data_test = scipy.sparse.csr_matrix(data_test.values)
+
+    pdb.set_trace()
+    fit_baseline_models(data_train, labels_train,
+                        data_test, labels_test)
+
+
diff --git a/scripts/LabCulturePrediction/DecisionMakingClass/run_experiments.py b/scripts/LabCulturePrediction/DecisionMakingClass/run_experiments.py
new file mode 100644
index 00000000..54aa8d3e
--- /dev/null
+++ b/scripts/LabCulturePrediction/DecisionMakingClass/run_experiments.py
@@ -0,0 +1,74 @@
+import pandas as pd
+import os
+import argparse
+
+
+from UtilityModel import UtilityModel 
+from cost_analysis import compute_alg_covered_flag, get_presciption_profile
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--n_vanc_mero', type=int, required=True)
+parser.add_argument('--n_mero', type=int, required=True)
+parser.add_argument('--n_vanc_piptazo', type=int, required=True)
+parser.add_argument('--n_vanc_cefepime', type=int, required=True)
+parser.add_argument('--n_vanc_ceftriaxone', type=int, required=True)
+parser.add_argument('--n_vanc', type=int, required=True)
+parser.add_argument('--n_piptazo', type=int, required=True)
+parser.add_argument('--n_cefepime', type=int, required=True)
+parser.add_argument('--n_ceftriaxone', type=int, required=True)
+parser.add_argument('--out_path', type=str, required=True)
+
+
+args = parser.parse_args()
+
+data = pd.read_csv('./data/labels_with_probs.csv')
+
+n = {}
+n['vancomycin meropenem'] = args.n_vanc_mero
+n['vancomycin piperacillin-tazobactam'] = args.n_vanc_piptazo
+n['vancomycin'] = args.n_vanc
+n['piperacillin-tazobactam'] = args.n_piptazo
+n['cefepime'] = args.n_cefepime
+n['ceftriaxone'] = args.n_ceftriaxone
+n['cefazolin'] = 0
+n['vancomycin cefepime'] = args.n_vanc_cefepime
+n['vancomycin ceftriaxone'] = args.n_vanc_ceftriaxone
+n['meropenem'] = args.n_mero
+
+fpath = './data/model_util_vals.txt'
+
+
+with open(fpath, 'r') as f:
+	lines = f.readlines()
+
+utility_vals = [u.split(',')[1] for u in lines]
+
+umodel = UtilityModel(data,
+					  C_meropenem = utility_vals[0],
+                      C_vancomycin = utility_vals[1],
+                      C_piptazo = utility_vals[2],
+                      C_cefepime = utility_vals[3],
+                      C_ceftriaxone = utility_vals[4],
+                      C_cefazolin = utility_vals[5],
+                      C_vanc_meropenem = utility_vals[6],
+                      C_vanc_piptazo = utility_vals[7],
+                      C_vanc_cefepime = utility_vals[8],
+                      C_vanc_ceftriaxone = utility_vals[9])
+
+
+data = umodel.fit_drug_parameters
+profile = get_presciption_profile(data)
+
+f_u_params = os.path.join(args.outpath, 'utility_params.txt')
+f_profile = os.path.join(args.outpath, 'profile.csv')
+
+profile.to_csv(f_profile, index=None)
+
+with open(args.outpath, 'w') as w:
+	for key in umodel.drug_cost:
+	    w.write("%s,%.2f\n" % (key, umodel.drug_cost[key]))
+
+
+data['alg_covered'] = data.apply(compute_alg_covered_flag, axis=1)
+print("Number of mismatches %d" % len(data[data['alg_covered'] == 0]))
+
diff --git a/scripts/LabCulturePrediction/LabCultureMatrix.py b/scripts/LabCulturePrediction/LabCultureMatrix.py
new file mode 100644
index 00000000..d4f4172f
--- /dev/null
+++ b/scripts/LabCulturePrediction/LabCultureMatrix.py
@@ -0,0 +1,399 @@
+#!/usr/bin/python
+"""
+Class for generating LabCultureMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+import pandas as pd
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+class LabCultureMatrix(FeatureMatrix):
+    def __init__(self, lab_panel, num_episodes, random_state=None):
+        FeatureMatrix.__init__(self, lab_panel, num_episodes)
+
+        # Parse arguments.
+        self._lab_panel = lab_panel.split()
+
+        self._med_panel = [['Cefepime (Oral)', 'Cefepime (Intravenous)'],
+                            ['Cefazolin (Oral)', 'Cefazolin (Intravenous)'],
+                            ['Ceftriaxone (Oral)', 'Ceftriaxone (Intravenous)'],
+                            ['Meropenem (Oral)', 'Meropenem (Intravenous)'],
+                            ['Vancomycin (Oral)', 'Vancomycin (Intravenous)'],
+                            ['Linezolid (Oral)', 'Linezolid (Intravenous)'],
+                            ['Daptomycin (Oral)', 'Daptomycin (Intravenous)'],
+                            ['Levofloxacin (Oral)', 'Levofloxacin (Intravenous)'],
+                            ['Ciprofloxacin (Oral)', 'Ciprofloxacin (Intravenous)'],
+                            ['Ampicillin (Oral)', 'Ampicillin (Intravenous)'],
+                            ['Metronidazole (Oral)', 'Metronidazole (Intravenous)'],
+                            ['Caspofungin (Oral)', 'Caspofungin (Intravenous)']]
+        # susceptibility_df = pd.read_csv('/Users/conorcorbin/repos/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        susceptibility_df = pd.read_csv('/home/ec2-user/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        self._susceptibility_names = susceptibility_df['name'].values        
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+    def _get_components_in_lab_panel(self):
+        # Initialize DB connection.
+        cursor = self._connection.cursor()
+
+        # Doing a single query results in a sequential scan through
+        # stride_order_results. To avoid this, break up the query in two.
+
+        # First, get all the order_proc_ids for proc_code.
+        query = SQLQuery()
+        query.addSelect('order_proc_id')
+        query.addFrom('stride_order_proc')
+        query.addWhereIn('proc_code', self._lab_panel)
+        query.addGroupBy('order_proc_id')
+        log.debug('Querying order_proc_ids for... %s' % ','.join(self._lab_panel))
+        results = DBUtil.execute(query)
+        lab_order_ids = [row[0] for row in results]
+
+        # Second, get all base_names from those orders.
+        query = SQLQuery()
+        query.addSelect('base_name')
+        query.addFrom('stride_order_results')
+        query.addWhereIn('order_proc_id', lab_order_ids)
+        query.addGroupBy('base_name')
+        log.debug('Querying base_names for order_proc_ids...')
+        results = DBUtil.execute(query)
+        components = [row[0] for row in results]
+
+        return components
+
+    def _get_average_orders_per_patient(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        query = SQLQuery()
+        query.addSelect('pat_id')
+        query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
+        query.addFrom('stride_order_proc AS sop')
+        query.addFrom('stride_order_results AS sor')
+        query.addWhere('sop.order_proc_id = sor.order_proc_id')
+        query.addWhereIn("proc_code", self._lab_panel)
+        components = self._get_components_in_lab_panel()
+        query.addWhereIn("base_name", components)
+        query.addGroupBy('pat_id')
+        log.debug('Querying median orders per patient...')
+        results = DBUtil.execute(query)
+        order_counts = [ row[1] for row in results ]
+        if len(order_counts) == 0:
+            error_msg = '0 orders for lab panel %s' % ','.join(self._lab_panel)
+            log.critical(error_msg)
+            sys.exit('[ERROR] %s' % error_msg)
+        else:
+            return numpy.median(order_counts)
+
+    def _get_random_patient_list(self):
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Get average number of results for this lab test per patient.
+        avg_orders_per_patient = self._get_average_orders_per_patient()
+        log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
+        # Based on average # of results, figure out how many patients we'd
+        # need to get for a feature matrix of requested size.
+        self._num_patients = int(numpy.max([self._num_requested_episodes / \
+            avg_orders_per_patient, 1]))
+
+        # Get numPatientsToQuery random patients who have gotten test.
+        # TODO(sbala): Have option to feed in a seed for the randomness.
+        query = SQLQuery()
+        query.addSelect('pat_id')
+        query.addFrom('stride_order_proc AS sop')
+        query.addWhereIn('proc_code', self._lab_panel)
+        query.addGroupBy('pat_id') # this should be a unique list of patients
+        query.addOrderBy('RANDOM()')
+        query.setLimit(self._num_patients)
+        log.debug('Querying random patient list...')
+        results = DBUtil.execute(query)
+
+        # Get patient list.
+        random_patient_list = [ row[0] for row in results ]
+
+        return random_patient_list
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        self._lab_components = self._get_components_in_lab_panel()
+        random_patient_list = self._get_random_patient_list()
+
+        #print("RANDOM PATIENT LIST", random_patient_list)
+
+        # Build SQL query for list of patient episodes.
+        # Note that for 2008-2014 data, result_flag can take on any of the
+        # following values:  High, Low, High Panic, Low Panic,
+        # Low Off-Scale, Negative, Positive, Resistant, Susceptible, Abnormal, *
+        # (NONE): Only 27 lab components can have this flag. None has this
+        #           value for more than 5 results, so ignore it.
+        # *: Only 10 lab components can have this flag. Only 6 have it for
+        #           >10 tests, and each of them is a microbiology test for which
+        #           a flag is less meaningful, e.g. Gram Stain, AFB culture.
+        # Susceptible: Only 15 lab components can have this flag. All of those
+        #           only have this value for 2 results, so ignore it.
+        # Resistant: Only 1 lab component can have this flag. Only two results
+        #           have this value, so ignore it.
+        # Abnormal: 1462 lab components can have this flag. Many (e.g. UBLOOD)
+        #           have this value for thousands of results, so include it.
+        # Negative: Only 10 lab components can have this flag, and all for
+        #           less than 5 results, so ignore it.
+        # Positive: Only 3 lab components can have this flag, and all for
+        #           only 1 result, so ignore it.
+        # Low Off-Scale: Only 1 lab component can have this flag, and only for
+        #           3 results, so ignore it.
+        # Low Panic: 1401 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        # High Panic: 8084 lab components can have this flag, many core
+        #           metabolic components. Include it.
+        query = SQLQuery()
+        query.addSelect('CAST(pat_anon_id AS BIGINT) AS pat_id')
+        query.addSelect('CAST(shifted_order_time AS TIMESTAMP)')
+        query.addSelect('stride_culture_micro.proc_code')
+        query.addSelect('organism_name') #one for the result
+
+        # Experimenting
+        susceptibility_flags = ['Trimethoprim/Sulfamethoxazole', 'Vancomycin', 'Penicillin', 'Levofloxacin',
+                                'Clindamycin', 'Ceftriaxone', 'Erythromycin', 'Ampicillin',
+                                'Meropenem', 'Ciprofloxacin', 'Cefepime', 'Aztreonam.',
+                                'Ampicillin/Sulbactam', 'Piperacillin/Tazobactam',
+                                'Linezolid', 'Oxacillin.', 'Cefazolin', 'Daptomycin']
+
+        for med in susceptibility_flags:
+            med_col = med.replace('/', '_').replace('.', '')
+            query.addSelect("MAX(CASE WHEN antibiotic_name = '%s' AND (suseptibility = 'Susceptible' OR suseptibility = 'Positive') THEN 1 ELSE 0 END) AS %s_Susc" % (med, med_col))
+            query.addSelect("MAX(CASE WHEN antibiotic_name = '%s' THEN 1 ELSE 0 END) as %s_tested" % (med, med_col))
+
+
+        # Let us look at top 10 commonly occuring bacteria
+        query.addSelect("CASE WHEN organism_name IS NULL THEN 0 ELSE 1 END AS BACTERIA_PRESENT")
+        query.addSelect("CASE WHEN organism_name = 'ESCHERICHIA COLI' THEN 1 ELSE 0 END AS ESCHERICHIA_COLI")
+        query.addSelect("CASE WHEN organism_name = 'STAPHYLOCOCCUS AUREUS' THEN 1 ELSE 0 END AS STAPHYLOCOCCUS_AUREUS")
+        query.addSelect("CASE WHEN organism_name = 'ENTEROCOCCUS SPECIES' THEN 1 ELSE 0 END AS ENTEROCOCCUS_SPECIES")
+        query.addSelect("CASE WHEN organism_name = 'KLEBSIELLA PNEUMONIAE' THEN 1 ELSE 0 END AS KLEBSIELLA_PNEUMONIAE")
+        query.addSelect("CASE WHEN organism_name = 'PSEUDOMONAS AERUGINOSA' THEN 1 ELSE 0 END AS PSEUDOMONAS_AERUGINOSA")
+        query.addSelect("CASE WHEN organism_name = 'COAG NEGATIVE STAPHYLOCOCCUS' THEN 1 ELSE 0 END AS COAG_NEGATIVE_STAPHYLOCOCCUS")
+        query.addSelect("CASE WHEN organism_name = 'ENTEROCOCCUS FAECALIS' THEN 1 ELSE 0 END AS ENTEROCOCCUS_FAECALIS")
+        query.addSelect("CASE WHEN organism_name = 'PROTEUS MIRABILIS' THEN 1 ELSE 0 END AS PROTEUS_MIRABILIS")
+        query.addSelect("CASE WHEN organism_name = 'CANDIDA ALBICANS' THEN 1 ELSE 0 END AS CANDIDA_ALBICANS")
+        
+        query.addFrom('stride_culture_micro')
+ 
+        query.addWhereIn("stride_culture_micro.proc_code", self._lab_panel)
+        query.addWhereIn("pat_anon_id", random_patient_list)
+        query.addGroupBy('pat_anon_id')
+        query.addGroupBy('shifted_order_time')
+        query.addGroupBy('stride_culture_micro.proc_code')
+        query.addGroupBy('organism_name')
+        query.addOrderBy('pat_anon_id')
+        query.addOrderBy('shifted_order_time')
+        query.addOrderBy('stride_culture_micro.proc_code')
+        query.addOrderBy('organism_name')
+        query.setLimit(self._num_requested_episodes)
+
+        self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query, pat_id_col='pat_id', index_time_col='shifted_order_time')
+
+    def _add_features(self):
+        # Add past susceptibility readings
+        self._add_susc_features()
+
+        # Add past antibiotic use as features
+        self._add_med_features()
+
+        # Add lab panel order features.
+        self._factory.addClinicalItemFeatures(self._lab_panel, features="pre")
+
+        # Add lab component result features, for a variety of time deltas.
+        LAB_PRE_TIME_DELTAS = [datetime.timedelta(-14)]
+        LAB_POST_TIME_DELTA = datetime.timedelta(0)
+        log.info('Adding lab component features...')
+        for pre_time_delta in LAB_PRE_TIME_DELTAS:
+            log.info('\t%s' % pre_time_delta)
+            self._factory.addLabResultFeatures(self._lab_components, False, pre_time_delta, LAB_POST_TIME_DELTA)
+
+        FeatureMatrix._add_features(self, index_time_col='shifted_order_time')
+
+    def _add_susc_features(self):
+        for susc_name in self._susceptibility_names:
+            log.debug('Adding %s feature...' % susc_name)
+            self._factory.addClinicalItemFeatures([susc_name], column='name',
+                                                  label=susc_name, features="pre")
+
+    def _add_med_features(self):
+        # Adds all prior antibiotic use as features
+        for med_set in self._med_panel:
+            med_label = med_set[0].split()[0] # Takes name of antibiotic
+            log.debug('Adding %s medication features...' % med_label)
+            self._factory.addClinicalItemFeatures(med_set, column="description",
+                                                  label="Med." + med_label, features="pre")
+
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab panel across %s inpatients from Stanford hospital.' % (','.join(self._lab_panel), self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab panel orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % ','.join(self._lab_panel)
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        line = 'Also included %s panel components: %s' % (','.join(self._lab_panel), self._lab_components)
+        summary.append(line)
+
+        return summary
+
+if __name__ == "__main__":
+    log.level = logging.DEBUG
+    start_time = time.time()
+    # Initialize lab test matrix.
+    ltm = LabCultureMatrix("LABBLC", 5)
+    # Output lab test matrix.
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    ltm.write_matrix("LABBLC-panel-5-episodes-%s-sec.tab" % str(elapsed_time))
diff --git a/scripts/LabCulturePrediction/LabCulturePredictionPipeline.py b/scripts/LabCulturePrediction/LabCulturePredictionPipeline.py
new file mode 100644
index 00000000..f913ebdb
--- /dev/null
+++ b/scripts/LabCulturePrediction/LabCulturePredictionPipeline.py
@@ -0,0 +1,302 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of LabCulture prediction.
+"""
+
+import inspect
+import os
+from pandas import DataFrame, Series
+from sklearn.externals import joblib
+from sklearn.metrics import make_scorer, average_precision_score
+import logging
+
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from .LabCultureMatrix import LabCultureMatrix
+
+class LabCulturePredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, lab_panel, num_episodes, use_cache=None, random_state=None):
+        SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state)
+
+        self._build_raw_feature_matrix()
+        self._build_processed_feature_matrix()
+        self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-normality-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_model_dump_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_feature_matrix(self):
+        raw_matrix_path = self._build_raw_matrix_path()
+        matrix_class = LabCultureMatrix
+        SupervisedLearningPipeline._build_raw_feature_matrix(self, matrix_class, \
+            raw_matrix_path)
+
+    def _build_processed_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-processed.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_path)
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path()
+        features_to_add = {}
+        imputation_strategies = {
+        }
+
+        features_to_remove = [
+            'pat_id', 'shifted_order_time',
+            'proc_code', 'abnormal_panel', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays',
+            'Death.post',
+            'Death.postTimeDays',
+            'escherichia_coli',
+            "organism_name",
+            "staphylococcus_aureus",
+            "enterococcus_species", 
+            "klebsiella_pneumoniae",
+            "pseudomonas_aeruginosa", 
+            "coag_negative_staphylococcus", 
+            "enterococcus_faecalis", 
+            "proteus_mirabilis", 
+            "candida_albicans"
+        ]
+
+        antibiotic_flags = ['trimethoprim_sulfamethoxazole', 'vancomycin',
+                            'penicillin', 'levofloxacin',
+                            'clindamycin', 'ceftriaxone',
+                            'erythromycin', 'ampicillin',
+                            'meropenem', 'ciprofloxacin',
+                            'cefepime', 'aztreonam',
+                            'ampicillin_sulbactam', 'piperacillin_tazobactam',
+                            'linezolid', 'oxacillin',
+                            'cefazolin', 'daptomycin'
+        ]
+
+        features_to_remove += ['%s_susc' % flag for flag in antibiotic_flags]
+        features_to_remove += ['%s_tested' % flag for flag in antibiotic_flags]
+
+        features_to_keep = [
+            # Keep the # of times it's been ordered in past, even if low info.
+            '%s.pre' % '-'.join(self._var.split())
+        ]
+        outcome_label = 'bacteria_present'
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 0.05
+        matrix_class = LabCultureMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        random_state = self._random_state
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether all components
+            '%s is a boolean indicator which summarizes whether all components ' % outcome_label,
+            # in the lab panel order represented by a given row are normal.
+            'in the lab panel order represented by a given row are normal.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+        ]
+
+        # Bundle parameters into single object to be unpacked in SLP.
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+        params['random_state'] = random_state
+
+        # Defer processing logic to SupervisedLearningPipeline.
+        SupervisedLearningPipeline._build_processed_feature_matrix(self, params)
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+            hyperparams['random_state'] = self._random_state
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'lab_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'lab_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabCulturePredictionPipeline("%s", 10000)' % self._var]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-normality-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-normality-prediction-%s' % (self._var, algorithm)
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabCulturePredictionPipeline("%s", 10000)' % self._var]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-normality-prediction-report.tab' % self._var]), header)
+
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    TOP_LAB_PANELS_BY_CHARGE_VOLUME = set([
+        "LABA1C", "LABABG", "LABBLC", "LABBLC2", "LABCAI",
+        "LABCBCD", "LABCBCO", "LABHFP", "LABLAC", "LABMB",
+        "LABMETB", "LABMETC", "LABMGN", "LABNTBNP", "LABPCG3",
+        "LABPCTNI", "LABPHOS", "LABPOCGLU", "LABPT", "LABPTT",
+        "LABROMRS", "LABTNI","LABTYPSNI", "LABUA", "LABUAPRN",
+        "LABURNC", "LABVANPRL", "LABVBG"
+    ])
+    TOP_NON_PANEL_TESTS_BY_VOLUME = set([
+        "LABPT", "LABMGN", "LABPTT", "LABPHOS", "LABTNI",
+        "LABBLC", "LABBLC2", "LABCAI", "LABURNC", "LABLACWB",
+        "LABA1C", "LABHEPAR", "LABCDTPCR", "LABPCTNI", "LABPLTS",
+        "LABLAC", "LABLIPS", "LABRESP", "LABTSH", "LABHCTX",
+        "LABLDH", "LABMB", "LABK", "LABGRAM", "LABFCUL",
+        "LABNTBNP", "LABCRP", "LABFLDC", "LABSPLAC", "LABANER",
+        "LABCK", "LABESRP", "LABBLCTIP", "LABBLCSTK", "LABNA",
+        "LABFER", "LABUSPG", "LABB12", "LABURNA", "LABFT4",
+        "LABFIB", "LABURIC", "LABPALB", "LABPCCR", "LABTRFS",
+        "LABUOSM", "LABAFBD", "LABSTOBGD", "LABCSFGL", "LABCSFTP",
+        "LABNH3", "LABAFBC", "LABCMVQT", "LABCSFC", "LABUCR",
+        "LABTRIG", "LABFE",
+        # "LABNONGYN", # No base names.
+        "LABALB", "LABLIDOL",
+        "LABUPREG", "LABRETIC", "LABHAP", "LABBXTG", "LABHIVWBL"
+    ])
+    NON_PANEL_TESTS_WITH_GT_500_ORDERS = [
+        'LABA1C', 'LABAFBC', 'LABAFBD', 'LABALB', 'LABANER', 'LABB12', 'LABBLC', 'LABBLC2',
+        'LABBLCSTK', 'LABBLCTIP', 'LABBUN', 'LABBXTG', 'LABCA', 'LABCAI', 'LABCDTPCR', 'LABCK',
+        'LABCMVQT', 'LABCORT', 'LABCRP', 'LABCSFC', 'LABCSFGL', 'LABCSFTP', 'LABDIGL', 'LABESRP',
+        'LABFCUL', 'LABFE', 'LABFER', 'LABFIB', 'LABFLDC', 'LABFOL', 'LABFT4', 'LABGRAM',
+        'LABHAP', 'LABHBSAG', 'LABHCTX', 'LABHEPAR', 'LABHIVWBL', 'LABK', 'LABLAC', 'LABLACWB',
+        'LABLDH', 'LABLIDOL', 'LABLIPS', 'LABMB', 'LABMGN', 'LABNA', 'LABNH3', 'LABNONGYN',
+        'LABNTBNP', 'LABOSM', 'LABPALB', 'LABPCCG4O', 'LABPCCR', 'LABPCTNI', 'LABPHOS', 'LABPLTS',
+        'LABPROCT', 'LABPT', 'LABPTEG', 'LABPTT', 'LABRESP', 'LABRESPG', 'LABRETIC', 'LABSPLAC',
+        'LABSTLCX', 'LABSTOBGD', 'LABTNI', 'LABTRFS', 'LABTRIG', 'LABTSH', 'LABUCR', 'LABUOSM',
+        'LABUA', 'LABUAPRN', 'LABUPREG', 'LABURIC', 'LABURNA', 'LABURNC', 'LABUSPG'
+    ]
+
+    # only blood and urine cultures
+    CULTURE_MICRO_ORDERS = ["LABBLC", "LABBLC2", "LABURNC"]
+
+    labs_to_test = CULTURE_MICRO_ORDERS
+    panel = "LABBLC LABBLC2"
+    # for panel in labs_to_test:
+    LabCulturePredictionPipeline(panel, 1000000, use_cache=True, random_state=123456789)
diff --git a/scripts/LabCulturePrediction/MRSAMatrix.py b/scripts/LabCulturePrediction/MRSAMatrix.py
new file mode 100644
index 00000000..c5040773
--- /dev/null
+++ b/scripts/LabCulturePrediction/MRSAMatrix.py
@@ -0,0 +1,297 @@
+#!/usr/bin/python
+"""
+Class for generating MRSAMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+import pandas as pd
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+import pdb
+class MRSAMatrix(FeatureMatrix):
+    def __init__(self, lab_panel, lab_features, num_episodes, random_state=None):
+        FeatureMatrix.__init__(self, lab_panel, num_episodes)
+
+        # Parse arguments.
+        self._lab_panel = lab_panel
+        self._lab_features = lab_features
+
+        self._med_panel = [['Cefepime (Oral)', 'Cefepime (Intravenous)'],
+                            ['Cefazolin (Oral)', 'Cefazolin (Intravenous)'],
+                            ['Ceftriaxone (Oral)', 'Ceftriaxone (Intravenous)'],
+                            ['Meropenem (Oral)', 'Meropenem (Intravenous)'],
+                            ['Vancomycin (Oral)', 'Vancomycin (Intravenous)'],
+                            ['Linezolid (Oral)', 'Linezolid (Intravenous)'],
+                            ['Daptomycin (Oral)', 'Daptomycin (Intravenous)'],
+                            ['Levofloxacin (Oral)', 'Levofloxacin (Intravenous)'],
+                            ['Ciprofloxacin (Oral)', 'Ciprofloxacin (Intravenous)'],
+                            ['Ampicillin (Oral)', 'Ampicillin (Intravenous)'],
+                            ['Metronidazole (Oral)', 'Metronidazole (Intravenous)'],
+                            ['Caspofungin (Oral)', 'Caspofungin (Intravenous)']]
+        # susceptibility_df = pd.read_csv('/Users/conorcorbin/repos/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        susceptibility_df = pd.read_csv('/home/ec2-user/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        self._susceptibility_names = susceptibility_df['name'].values        
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        
+        query = """SELECT DISTINCT ON(labels.pat_id, labels.pat_enc_csn_id)
+                    labels.pat_id,
+                    labels.pat_enc_csn_id,
+                    labels.order_time,
+                    labels.MRSA_PRESENT
+                   FROM
+                    (SELECT 
+                        vanco_zosyn.pat_id,
+                        vanco_zosyn.pat_enc_csn_id, 
+                        vanco_zosyn.order_time,
+                        MAX(CASE WHEN micro.organism_name = 'STAPHYLOCOCCUS AUREUS' AND 
+                                      (micro.antibiotic_name = 'Oxacillin' OR
+                                       micro.antibiotic_name = 'Oxacillin.') AND
+                                      micro.suseptibility = 'Resistant' 
+                                 THEN 1
+                                 ELSE 0
+                            END) as MRSA_PRESENT          
+                    FROM
+                      (SELECT 
+                           pip_tazo.pat_id, 
+                           pip_tazo.pat_enc_csn_id,
+                           pip_start_time AS order_time 
+                       FROM 
+                         (SELECT 
+                              pat_id, 
+                              pat_enc_csn_id, 
+                              start_taking_time AS pip_start_time,
+                              end_taking_time AS pip_end_time,
+                              description AS pip_description
+                           FROM stride_order_med
+                           WHERE description LIKE '%PIPERACILLIN-TAZOBACTAM%') pip_tazo
+                       INNER JOIN
+                          (SELECT 
+                            pat_id, 
+                            pat_enc_csn_id,
+                            start_taking_time AS vanco_start_time,
+                            end_taking_time AS vanco_end_time,
+                            description AS vanco_desciption
+                           FROM stride_order_med
+                           WHERE description LIKE '%VANCOMYCIN%') vanco
+                       ON pip_tazo.pat_enc_csn_id = vanco.pat_enc_csn_id AND pip_start_time = vanco_start_time) vanco_zosyn
+                    INNER JOIN
+                     (SELECT 
+                          pat_anon_id,
+                          pat_enc_csn_anon_id,
+                          proc_code,
+                          description,
+                          organism_name,
+                          antibiotic_name,
+                          suseptibility,
+                          shifted_order_time
+                      FROM stride_culture_micro) micro
+                   ON vanco_zosyn.pat_id = micro.pat_anon_id AND vanco_zosyn.pat_enc_csn_id = micro.pat_enc_csn_anon_id
+                     WHERE 
+                        DATE_PART('day', vanco_zosyn.order_time - CAST(micro.shifted_order_time AS timestamp)) * 24 + DATE_PART('hour', vanco_zosyn.order_time - CAST(micro.shifted_order_time AS timestamp)) >= 0 AND 
+                        DATE_PART('day', vanco_zosyn.order_time - CAST(micro.shifted_order_time AS timestamp)) * 24 + DATE_PART('hour', vanco_zosyn.order_time - CAST(micro.shifted_order_time AS timestamp)) <= 24
+                     GROUP BY pat_id, pat_enc_csn_id, order_time) as labels
+                   ORDER BY pat_id, pat_enc_csn_id, order_time
+                   """
+        self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query, pat_id_col='pat_id', index_time_col='order_time')
+
+    def _add_features(self):
+        # Add past susceptibility readings
+        self._add_susc_features()
+
+        # Add past antibiotic use as features
+        self._add_med_features()
+
+        # Add lab panel order features.
+        for panel in self._lab_features:
+            self._factory.addClinicalItemFeatures([panel], features="pre")
+
+        FeatureMatrix._add_features(self, index_time_col='order_time')
+
+    def _add_susc_features(self):
+        for susc_name in self._susceptibility_names:
+            log.debug('Adding %s feature...' % susc_name)
+            self._factory.addClinicalItemFeatures([susc_name], column='name',
+                                                  label=susc_name, features="pre")
+
+    def _add_med_features(self):
+        # Adds all prior antibiotic use as features
+        for med_set in self._med_panel:
+            med_label = med_set[0].split()[0] # Takes name of antibiotic
+            log.debug('Adding %s medication features...' % med_label)
+            self._factory.addClinicalItemFeatures(med_set, column="description",
+                                                  label="Med." + med_label, features="pre")
+
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab panel across %s inpatients from Stanford hospital.' % (','.join(self._lab_panel), self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab panel orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % ','.join(self._lab_panel)
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        # line = 'Also included %s panel components: %s' % (','.join(self._lab_panel), self._lab_components)
+        # summary.append(line
+
+        return summary
+
+if __name__ == "__main__":
+    log.level = logging.DEBUG
+    start_time = time.time()
+    # Initialize lab test matrix.
+    ltm = LabCultureMatrix("LABBLC", 5)
+    # Output lab test matrix.
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    ltm.write_matrix("LABBLC-panel-5-episodes-%s-sec.tab" % str(elapsed_time))
diff --git a/scripts/LabCulturePrediction/MRSAPredictionPipeline.py b/scripts/LabCulturePrediction/MRSAPredictionPipeline.py
new file mode 100644
index 00000000..aea3168a
--- /dev/null
+++ b/scripts/LabCulturePrediction/MRSAPredictionPipeline.py
@@ -0,0 +1,298 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of MRSA prediction.
+"""
+
+import inspect
+import os
+from pandas import DataFrame, Series
+from sklearn.externals import joblib
+from sklearn.metrics import make_scorer, average_precision_score
+import logging
+import sys
+
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from .MRSAMatrix import MRSAMatrix
+
+class MRSAPredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, lab_panel, microcultures,  num_episodes, use_cache=None, random_state=None):
+        SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state)
+
+        self.panel = microcultures
+        self._build_raw_feature_matrix()
+        self._build_processed_feature_matrix()
+        self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-MRSA-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_model_dump_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_matrix_path(self):
+        template = '%s-MRSA-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_feature_matrix(self):
+        raw_matrix_path = self._build_raw_matrix_path()
+        matrix_class = MRSAMatrix
+        params = None
+        if params is None:
+            self._raw_matrix_params = {}
+        else:
+            self._raw_matrix_params = params
+        if os.path.exists(raw_matrix_path) and not self._flush_cache:
+            pass
+        else:
+            # Each matrix class may have a custom set of parameters which should
+            # be passed on directly to matrix_class, but we expect them to have
+            # at least 1 primary variables and # of rows.
+            # Ensure that random_state is [-1, 1]
+            random_state = float(self._random_state)/float(sys.maxsize)
+            if self._isLabNormalityPredictionPipeline:
+                matrix = matrix_class(self._var, self.panel, self._num_rows, random_state=random_state,
+                                  isLabPanel=self._isLabPanel, timeLimit=self._timeLimit,
+                                      notUsePatIds=self.notUsePatIds)
+            else:
+                matrix = matrix_class(self._var, self.panel, self._num_rows, random_state=random_state)
+            matrix.write_matrix(raw_matrix_path)
+
+    def _build_processed_matrix_path(self):
+        template = '%s-MRSA-matrix-%d-episodes-processed.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_path)
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path()
+        features_to_add = {}
+        imputation_strategies = {
+        }
+
+        features_to_remove = [
+            'pat_id', 'order_time', 'pat_enc_csn_id',
+            'proc_code', 'abnormal_panel', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays',
+            'Death.post',
+            'Death.postTimeDays'
+        ]
+
+        features_to_keep = [
+            # Keep the # of times it's been ordered in past, even if low info.
+            "LABBLC.pre" # placeholder - one element at least needed for pipeline to function - weird
+        ]
+        outcome_label = 'mrsa_present'
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 0.05
+        matrix_class = MRSAMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        random_state = self._random_state
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether all components
+            '%s is a boolean indicator which summarizes whether all components ' % outcome_label,
+            # in the lab panel order represented by a given row are normal.
+            'in the lab panel order represented by a given row are normal.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+        ]
+
+        # Bundle parameters into single object to be unpacked in SLP.
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+        params['random_state'] = random_state
+
+        # Defer processing logic to SupervisedLearningPipeline.
+        SupervisedLearningPipeline._build_processed_feature_matrix(self, params)
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+            hyperparams['random_state'] = self._random_state
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'lab_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'lab_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabCulturePredictionPipeline("%s", 10000)' % self._var]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-MRSA-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-MRSA-prediction-%s' % (self._var, algorithm)
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabCulturePredictionPipeline("%s", 10000)' % self._var]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-MRSA-prediction-report.tab' % self._var]), header)
+
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    TOP_LAB_PANELS_BY_CHARGE_VOLUME = set([
+        "LABA1C", "LABABG", "LABBLC", "LABBLC2", "LABCAI",
+        "LABCBCD", "LABCBCO", "LABHFP", "LABLAC", "LABMB",
+        "LABMETB", "LABMETC", "LABMGN", "LABNTBNP", "LABPCG3",
+        "LABPCTNI", "LABPHOS", "LABPOCGLU", "LABPT", "LABPTT",
+        "LABROMRS", "LABTNI","LABTYPSNI", "LABUA", "LABUAPRN",
+        "LABURNC", "LABVANPRL", "LABVBG"
+    ])
+    TOP_NON_PANEL_TESTS_BY_VOLUME = set([
+        "LABPT", "LABMGN", "LABPTT", "LABPHOS", "LABTNI",
+        "LABBLC", "LABBLC2", "LABCAI", "LABURNC", "LABLACWB",
+        "LABA1C", "LABHEPAR", "LABCDTPCR", "LABPCTNI", "LABPLTS",
+        "LABLAC", "LABLIPS", "LABRESP", "LABTSH", "LABHCTX",
+        "LABLDH", "LABMB", "LABK", "LABGRAM", "LABFCUL",
+        "LABNTBNP", "LABCRP", "LABFLDC", "LABSPLAC", "LABANER",
+        "LABCK", "LABESRP", "LABBLCTIP", "LABBLCSTK", "LABNA",
+        "LABFER", "LABUSPG", "LABB12", "LABURNA", "LABFT4",
+        "LABFIB", "LABURIC", "LABPALB", "LABPCCR", "LABTRFS",
+        "LABUOSM", "LABAFBD", "LABSTOBGD", "LABCSFGL", "LABCSFTP",
+        "LABNH3", "LABAFBC", "LABCMVQT", "LABCSFC", "LABUCR",
+        "LABTRIG", "LABFE",
+        # "LABNONGYN", # No base names.
+        "LABALB", "LABLIDOL",
+        "LABUPREG", "LABRETIC", "LABHAP", "LABBXTG", "LABHIVWBL"
+    ])
+    NON_PANEL_TESTS_WITH_GT_500_ORDERS = [
+        'LABA1C', 'LABAFBC', 'LABAFBD', 'LABALB', 'LABANER', 'LABB12', 'LABBLC', 'LABBLC2',
+        'LABBLCSTK', 'LABBLCTIP', 'LABBUN', 'LABBXTG', 'LABCA', 'LABCAI', 'LABCDTPCR', 'LABCK',
+        'LABCMVQT', 'LABCORT', 'LABCRP', 'LABCSFC', 'LABCSFGL', 'LABCSFTP', 'LABDIGL', 'LABESRP',
+        'LABFCUL', 'LABFE', 'LABFER', 'LABFIB', 'LABFLDC', 'LABFOL', 'LABFT4', 'LABGRAM',
+        'LABHAP', 'LABHBSAG', 'LABHCTX', 'LABHEPAR', 'LABHIVWBL', 'LABK', 'LABLAC', 'LABLACWB',
+        'LABLDH', 'LABLIDOL', 'LABLIPS', 'LABMB', 'LABMGN', 'LABNA', 'LABNH3', 'LABNONGYN',
+        'LABNTBNP', 'LABOSM', 'LABPALB', 'LABPCCG4O', 'LABPCCR', 'LABPCTNI', 'LABPHOS', 'LABPLTS',
+        'LABPROCT', 'LABPT', 'LABPTEG', 'LABPTT', 'LABRESP', 'LABRESPG', 'LABRETIC', 'LABSPLAC',
+        'LABSTLCX', 'LABSTOBGD', 'LABTNI', 'LABTRFS', 'LABTRIG', 'LABTSH', 'LABUCR', 'LABUOSM',
+        'LABUA', 'LABUAPRN', 'LABUPREG', 'LABURIC', 'LABURNA', 'LABURNC', 'LABUSPG'
+    ]
+
+    # only blood and urine cultures
+    CULTURE_MICRO_ORDERS = ["LABBLC", "LABBLC2", "LABURNC"]
+
+    labs_to_test = CULTURE_MICRO_ORDERS
+    panel = NON_PANEL_TESTS_WITH_GT_500_ORDERS
+    # for panel in labs_to_test:
+    MRSAPredictionPipeline("Microcultures", panel, 1000000, use_cache=True, random_state=123456789)
diff --git a/scripts/LabCulturePrediction/PersonalizedAntibiogramMatrix.py b/scripts/LabCulturePrediction/PersonalizedAntibiogramMatrix.py
new file mode 100644
index 00000000..e430ada7
--- /dev/null
+++ b/scripts/LabCulturePrediction/PersonalizedAntibiogramMatrix.py
@@ -0,0 +1,300 @@
+#!/usr/bin/python
+"""
+Class for generating PersonalizedAntibiogramMatrix.
+"""
+
+import datetime
+import inspect
+import logging
+import os
+import sys
+import time
+import numpy
+import pandas as pd
+
+from medinfo.common.Util import log
+from medinfo.dataconversion.FeatureMatrixFactory import FeatureMatrixFactory
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery
+from medinfo.dataconversion.FeatureMatrix import FeatureMatrix
+
+import pdb
+class PersonalizedAntibiogramMatrix(FeatureMatrix):
+    def __init__(self, lab_panel, lab_features, num_episodes, random_state=None):
+        FeatureMatrix.__init__(self, lab_panel, num_episodes)
+
+        # Parse arguments.
+        self._lab_panel = lab_panel
+        self._lab_features = lab_features
+
+        self._med_panel = [['Cefepime (Oral)', 'Cefepime (Intravenous)'],
+                            ['Cefazolin (Oral)', 'Cefazolin (Intravenous)'],
+                            ['Ceftriaxone (Oral)', 'Ceftriaxone (Intravenous)'],
+                            ['Meropenem (Oral)', 'Meropenem (Intravenous)'],
+                            ['Vancomycin (Oral)', 'Vancomycin (Intravenous)'],
+                            ['Linezolid (Oral)', 'Linezolid (Intravenous)'],
+                            ['Daptomycin (Oral)', 'Daptomycin (Intravenous)'],
+                            ['Levofloxacin (Oral)', 'Levofloxacin (Intravenous)'],
+                            ['Ciprofloxacin (Oral)', 'Ciprofloxacin (Intravenous)'],
+                            ['Ampicillin (Oral)', 'Ampicillin (Intravenous)'],
+                            ['Metronidazole (Oral)', 'Metronidazole (Intravenous)'],
+                            ['Caspofungin (Oral)', 'Caspofungin (Intravenous)']]
+        # susceptibility_df = pd.read_csv('/Users/conorcorbin/repos/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        susceptibility_df = pd.read_csv('/home/ec2-user/CDSS/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv')
+        self._susceptibility_names = susceptibility_df['name'].values        
+        self._num_requested_episodes = num_episodes
+        self._num_reported_episodes = 0
+
+        # Query patient episodes.
+        self._query_patient_episodes()
+        episodes = self._factory.getPatientEpisodeIterator()
+        patients = set()
+        for episode in episodes:
+            patient_id = int(episode[self._factory.patientEpisodeIdColumn])
+            patients.add(patient_id)
+        self._num_patients = len(patients)
+
+        # Add features.
+        self._add_features()
+
+        # Build matrix.
+        FeatureMatrix._build_matrix(self)
+
+
+    def _query_patient_episodes(self):
+        log.info('Querying patient episodes...')
+        # Initialize DB cursor.
+        cursor = self._connection.cursor()
+
+        # Build parameters for query.
+        
+        query = """SELECT
+                    pat_anon_id as pat_id,
+                    pat_enc_csn_anon_id,
+                    shifted_order_time,
+                    order_proc_anon_id,
+                    organism_name,
+                    proc_code,
+                    
+                    MAX(CASE WHEN antibiotic_name = 'Ampicillin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as ampicillin_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Ampicillin/Sulbactam' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as ampicillin_sulbactam_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Piperacillin/Tazobactam' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as piperacillin_tazobactam_susc,
+
+                    MAX(CASE WHEN antibiotic_name in ('Cefazolin', 'Cefazolin..') and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as cefazolin_susc,
+                    MAX(CASE WHEN antibiotic_name in ('Ceftriaxone', 'Ceftriaxone.') and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as ceftriaxone_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Cefepime' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as cefepime_susc,
+
+                    MAX(CASE WHEN antibiotic_name in ('Aztreonam', 'Aztreonam.') and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as aztreonam_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Imipenem' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as imipenem_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Meropenem' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as meropenem_susc,
+
+                    MAX(CASE WHEN antibiotic_name = 'Gentamicin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as gentamicin_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Tobramycin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as tobramycin_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Amikacin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as amikacin_susc,
+
+                    MAX(CASE WHEN antibiotic_name = 'Ciprofloxacin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as ciprofloxacin_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Levofloxacin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as levofloxacin_susc,
+                    MAX(CASE WHEN antibiotic_name = 'Trimethoprim/Sulfamethoxazole.' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as trimethoprim_sulfamethoxazole_susc,
+
+                    MAX(CASE WHEN antibiotic_name = 'Nitrofurantoin' and suseptibility in ('Positive', 'Susceptible') then 1 else 0 end) as nitrofurantoin_susc,
+
+                    MAX(CASE WHEN antibiotic_name = 'Ampicillin' then 1 else 0 end) as ampicillin_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Ampicillin/Sulbactam' then 1 else 0 end) as ampicillin_sulbactam_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Piperacillin/Tazobactam' then 1 else 0 end) as piperacillin_tazobactam_tested,
+
+                    MAX(CASE WHEN antibiotic_name in ('Cefazolin', 'Cefazolin..') then 1 else 0 end) as cefazolin_tested,
+                    MAX(CASE WHEN antibiotic_name in ('Ceftriaxone', 'Ceftriaxone.') then 1 else 0 end) as ceftriaxone_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Cefepime' then 1 else 0 end) as cefepime_tested,
+
+                    MAX(CASE WHEN antibiotic_name in ('Aztreonam', 'Aztreonam.') then 1 else 0 end) as aztreonam_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Imipenem' then 1 else 0 end) as imipenem_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Meropenem' then 1 else 0 end) as meropenem_tested,
+
+                    MAX(CASE WHEN antibiotic_name = 'Gentamicin' then 1 else 0 end) as gentamicin_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Tobramycin' then 1 else 0 end) as tobramycin_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Amikacin' then 1 else 0 end) as amikacin_tested,
+
+                    MAX(CASE WHEN antibiotic_name = 'Ciprofloxacin' then 1 else 0 end) as ciprofloxacin_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Levofloxacin' then 1 else 0 end) as levofloxacin_tested,
+                    MAX(CASE WHEN antibiotic_name = 'Trimethoprim/Sulfamethoxazole.' then 1 else 0 end) as trimethoprim_sulfamethoxazole_tested,
+
+                    MAX(CASE WHEN antibiotic_name = 'Nitrofurantoin' then 1 else 0 end) as nitrofurantoin_tested
+                FROM 
+                    stride_culture_micro
+                WHERE
+                    antibiotic_name in ('Ampicillin', 'Ampicillin/Sulbactam', 'Piperacillin/Tazobactam', 'Cefazolin', 'Cefazolin..',
+                                       'Ceftriaxone', 'Ceftriaxone.', 'Cefepime', 'Aztreonam', 'Aztreonam.', 'Imipenem', 'Meropenem',
+                                       'Gentamicin', 'Tobramycin', 'Amikacin', 'Ciprofloxacin', 'Levofloxacin', 'Trimethoprim/Sulfamethoxazole.',
+                                       'Nitrofurantoin')
+                    AND
+                    organism_name in ('ESCHERICHIA COLI', 'KLEBSIELLA PNEUMONIAE', 'PSEUDOMONAS AERUGINOSA', 'PROTEUS MIRABILIS')
+                GROUP BY pat_id, pat_enc_csn_anon_id, shifted_order_time, order_proc_anon_id, organism_name, proc_code
+                ORDER BY pat_id, pat_enc_csn_anon_id, shifted_order_time
+                """
+        self._num_reported_episodes = FeatureMatrix._query_patient_episodes(self, query, pat_id_col='pat_id', index_time_col='shifted_order_time')
+
+    def _add_features(self):
+        # # Add past susceptibility readings
+        self._add_susc_features()
+
+        # # Add past antibiotic use as features
+        self._add_med_features()
+
+        # # Add lab panel order features.
+        for panel in self._lab_features:
+            self._factory.addClinicalItemFeatures([panel], features="pre")
+        FeatureMatrix._add_features(self, index_time_col='shifted_order_time')
+
+    def _add_susc_features(self):
+        for susc_name in self._susceptibility_names:
+            log.debug('Adding %s feature...' % susc_name)
+            self._factory.addClinicalItemFeatures([susc_name], column='name',
+                                                  label=susc_name, features="pre")
+
+    def _add_med_features(self):
+        # Adds all prior antibiotic use as features
+        for med_set in self._med_panel:
+            med_label = med_set[0].split()[0] # Takes name of antibiotic
+            log.debug('Adding %s medication features...' % med_label)
+            self._factory.addClinicalItemFeatures(med_set, column="description",
+                                                  label="Med." + med_label, features="pre")
+
+
+    def write_matrix(self, dest_path):
+        log.info('Writing %s...' % dest_path)
+        header = self._build_matrix_header(dest_path)
+        FeatureMatrix.write_matrix(self, dest_path, header)
+
+    def _build_matrix_header(self, matrix_path):
+        params = {}
+
+        params['matrix_path'] = matrix_path
+        params['matrix_module'] = inspect.getfile(inspect.currentframe())
+        params['data_overview'] = self._build_data_overview()
+        params['field_summary'] = self._build_field_summary()
+        params['include_lab_suffix_summary'] = True
+        params['include_clinical_item_suffix_summary'] = True
+
+        return FeatureMatrix._build_matrix_header(self, params)
+
+    def _build_data_overview(self):
+        overview = list()
+        # Overview:\n\
+        line = 'Overview:'
+        overview.append(line)
+        # This file contains %s data rows, representing %s unique orders of
+        line = 'This file contains %s data rows, representing %s unique orders of' % (self._num_reported_episodes, self._num_reported_episodes)
+        overview.append(line)
+        # the %s lab panel across %s inpatients from Stanford hospital.
+        line = 'the %s lab panel across %s inpatients from Stanford hospital.' % (','.join(self._lab_panel), self._num_patients)
+        overview.append(line)
+        # Each row contains columns summarizing the patient's demographics,
+        line = "Each row contains columns summarizing the patient's demographics,"
+        overview.append(line)
+        # inpatient admit date, prior vitals, prior lab panel orders, and
+        line = 'inpatient admit date, prior vitals, prior lab panel orders, and'
+        overview.append(line)
+        # prior lab component results. Note the distinction between a lab
+        line = 'prior lab component results. NOte the distinction between a lab'
+        overview.append(line)
+        # panel (e.g. LABMETB) and a lab component within a panel (e.g. Na, RBC).
+        line = 'panel (e.g. LABMETB) and a lab component within a panel (e.g. Na).'
+        overview.append(line)
+        # Most cells in matrix represent a count statistic for an event's
+        line = "Most cells in matrix represent a count statistic for ran event's"
+        overview.append(line)
+        # occurrence or the difference between an event's time and order_time.
+        line = "occurrence or the difference between an event's time and order_time."
+        overview.append(line)
+
+        return overview
+
+    def _build_field_summary(self):
+        summary = list()
+
+        # Fields:
+        line = 'Fields:'
+        summary.append(line)
+        #   pat_id - ID # for patient in the STRIDE data set. \n\
+        line = 'pat_id - ID # for patient in the STRIDE data set.'
+        summary.append(line)
+        #   order_proc_id - ID # for clinical order. \n\
+        line = 'order_proc_id - ID # for clinical order.'
+        summary.append(line)
+        #   proc_code - code representing ordered lab panel. \n\
+        line = 'proc_code - code representing ordered lab panel.'
+        summary.append(line)
+        #   order_time - time at which lab panel was ordered. \n\
+        line = 'order_time - time at which lab panel was ordered.'
+        summary.append(line)
+        #   abnormal_panel - were any components in panel abnormal (binary)? \n\
+        line = 'abnormal_panel - were any components in panel abnoral (binary)?'
+        summary.append(line)
+        #   num_components - # of unique components in lab panel. \n\
+        line = 'num_components - # of unique components in lab panel.'
+        summary.append(line)
+        #   num_normal_components - # of normal component results in panel. \n\
+        line = 'num_normal_components - # of normal component results in panel.'
+        summary.append(line)
+        #   all_components_normal - inverse of abnormal_panel (binary). \n\
+        line = 'all_components_normal - inverse of abnormal_panel (binary).'
+        summary.append(line)
+        #   AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.\n\
+        line = 'AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
+        summary.append(line)
+        #   order_time.[month|hour] - when was the lab panel ordered? \n\
+        line = 'order_time.[month|hour] - when was the lab panel ordered?'
+        summary.append(line)
+        #   Birth.preTimeDays - patient's age in days.\n\
+        line = "Birth.preTimeDays - patient's age in days."
+        summary.append(line)
+        #   [Male|Female].pre - is patient male/female (binary)?\n\
+        line = '[Male|Female].pre - is patient male/female (binary)?'
+        summary.append(line)
+        #   [RaceX].pre - is patient race [X]?\n\
+        line = '[RaceX].pre - is patient race [X]?'
+        summary.append(line)
+        #   Team.[specialty].[clinical_item] - specialist added to treatment team.\n\
+        line = 'Team.[specialty].[clinical_item] - specialist added to treatment team.'
+        summary.append(line)
+        #   Comorbidity.[disease].[clinical_item] - disease added to problem list.\n\
+        line = 'Comorbidity.[disease].[clinical_item] - disease added to problem list.'
+        summary.append(line)
+        #   ___.[flowsheet] - measurements for flowsheet biometrics.\n\
+        line = '___.[flowsheet] - measurements for flowsheet biometrics.'
+        summary.append(line)
+        #       Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,\n\
+        line = '    Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,'
+        summary.append(line)
+        #           Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.\n\
+        line = '        Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.'
+        summary.append(line)
+        #   %s.[clinical_item] - orders of the lab panel of interest.\n\
+        line = '%s.[clinical_item] - orders of the lab panel of interest.' % ','.join(self._lab_panel)
+        summary.append(line)
+        #   ___.[lab_result] - lab component results.\n\
+        line = '__.[lab_result] - lab component results.'
+        summary.append(line)
+        #       Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,\n\
+        line = '    Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
+        summary.append(line)
+        #           CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,\n\
+        line = '        CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,'
+        summary.append(line)
+        #           PHV, PO2V, PCO2V\n\
+        line = '        PHV, PO2V, PCO2V'
+        summary.append(line)
+        #       Also included %s panel components: %s\n\
+        # line = 'Also included %s panel components: %s' % (','.join(self._lab_panel), self._lab_components)
+        # summary.append(line
+
+        return summary
+
+if __name__ == "__main__":
+    log.level = logging.DEBUG
+    start_time = time.time()
+    # Initialize lab test matrix.
+    ltm = LabCultureMatrix("LABBLC", 5)
+    # Output lab test matrix.
+    elapsed_time = numpy.ceil(time.time() - start_time)
+    ltm.write_matrix("LABBLC-panel-5-episodes-%s-sec.tab" % str(elapsed_time))
diff --git a/scripts/LabCulturePrediction/PersonalizedAntibiogramPredictionPipeline.py b/scripts/LabCulturePrediction/PersonalizedAntibiogramPredictionPipeline.py
new file mode 100644
index 00000000..fadb944a
--- /dev/null
+++ b/scripts/LabCulturePrediction/PersonalizedAntibiogramPredictionPipeline.py
@@ -0,0 +1,298 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of MRSA prediction.
+"""
+
+import inspect
+import os
+from pandas import DataFrame, Series
+from sklearn.externals import joblib
+from sklearn.metrics import make_scorer, average_precision_score
+import logging
+import sys
+
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from .PersonalizedAntibiogramMatrix import PersonalizedAntibiogramMatrix
+
+class PersonalizedAntibiogramPredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, lab_panel, microcultures,  num_episodes, use_cache=None, random_state=None):
+        SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state)
+
+        self.panel = microcultures
+        self._build_raw_feature_matrix()
+        self._build_processed_feature_matrix()
+        self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-PA-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_model_dump_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_matrix_path(self):
+        template = '%s-PA-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_name)
+
+    def _build_raw_feature_matrix(self):
+        raw_matrix_path = self._build_raw_matrix_path()
+        matrix_class = PersonalizedAntibiogramMatrix
+        params = None
+        if params is None:
+            self._raw_matrix_params = {}
+        else:
+            self._raw_matrix_params = params
+        if os.path.exists(raw_matrix_path) and not self._flush_cache:
+            pass
+        else:
+            # Each matrix class may have a custom set of parameters which should
+            # be passed on directly to matrix_class, but we expect them to have
+            # at least 1 primary variables and # of rows.
+            # Ensure that random_state is [-1, 1]
+            random_state = float(self._random_state)/float(sys.maxsize)
+            if self._isLabNormalityPredictionPipeline:
+                matrix = matrix_class(self._var, self.panel, self._num_rows, random_state=random_state,
+                                  isLabPanel=self._isLabPanel, timeLimit=self._timeLimit,
+                                      notUsePatIds=self.notUsePatIds)
+            else:
+                matrix = matrix_class(self._var, self.panel, self._num_rows, random_state=random_state)
+            matrix.write_matrix(raw_matrix_path)
+
+    def _build_processed_matrix_path(self):
+        template = '%s-PA-matrix-%d-episodes-processed.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_path)
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path()
+        features_to_add = {}
+        imputation_strategies = {
+        }
+
+        features_to_remove = [
+            'pat_id', 'shifted_order_time', 'organism_name', 'pat_enc_csn_id',
+            'proc_code', 'abnormal_panel', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays',
+            'Death.post',
+            'Death.postTimeDays'
+        ]
+
+        features_to_keep = [
+            # Keep the # of times it's been ordered in past, even if low info.
+            "LABBLC.pre" # placeholder - one element at least needed for pipeline to function - weird
+        ]
+        outcome_label = 'ampicillin_susc' # dummy bc all i care about it feature matrix
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 1.0
+        matrix_class = PersonalizedAntibiogramMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        random_state = self._random_state
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether all components
+            '%s is a boolean indicator which summarizes whether all components ' % outcome_label,
+            # in the lab panel order represented by a given row are normal.
+            'in the lab panel order represented by a given row are normal.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+        ]
+
+        # Bundle parameters into single object to be unpacked in SLP.
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+        params['random_state'] = random_state
+
+        # Defer processing logic to SupervisedLearningPipeline.
+        SupervisedLearningPipeline._build_processed_feature_matrix(self, params)
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+            hyperparams['random_state'] = self._random_state
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'lab_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'lab_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabCulturePredictionPipeline("%s", 10000)' % self._var]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-MRSA-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-MRSA-prediction-%s' % (self._var, algorithm)
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabCulturePredictionPipeline("%s", 10000)' % self._var]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-MRSA-prediction-report.tab' % self._var]), header)
+
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    TOP_LAB_PANELS_BY_CHARGE_VOLUME = set([
+        "LABA1C", "LABABG", "LABBLC", "LABBLC2", "LABCAI",
+        "LABCBCD", "LABCBCO", "LABHFP", "LABLAC", "LABMB",
+        "LABMETB", "LABMETC", "LABMGN", "LABNTBNP", "LABPCG3",
+        "LABPCTNI", "LABPHOS", "LABPOCGLU", "LABPT", "LABPTT",
+        "LABROMRS", "LABTNI","LABTYPSNI", "LABUA", "LABUAPRN",
+        "LABURNC", "LABVANPRL", "LABVBG"
+    ])
+    TOP_NON_PANEL_TESTS_BY_VOLUME = set([
+        "LABPT", "LABMGN", "LABPTT", "LABPHOS", "LABTNI",
+        "LABBLC", "LABBLC2", "LABCAI", "LABURNC", "LABLACWB",
+        "LABA1C", "LABHEPAR", "LABCDTPCR", "LABPCTNI", "LABPLTS",
+        "LABLAC", "LABLIPS", "LABRESP", "LABTSH", "LABHCTX",
+        "LABLDH", "LABMB", "LABK", "LABGRAM", "LABFCUL",
+        "LABNTBNP", "LABCRP", "LABFLDC", "LABSPLAC", "LABANER",
+        "LABCK", "LABESRP", "LABBLCTIP", "LABBLCSTK", "LABNA",
+        "LABFER", "LABUSPG", "LABB12", "LABURNA", "LABFT4",
+        "LABFIB", "LABURIC", "LABPALB", "LABPCCR", "LABTRFS",
+        "LABUOSM", "LABAFBD", "LABSTOBGD", "LABCSFGL", "LABCSFTP",
+        "LABNH3", "LABAFBC", "LABCMVQT", "LABCSFC", "LABUCR",
+        "LABTRIG", "LABFE",
+        # "LABNONGYN", # No base names.
+        "LABALB", "LABLIDOL",
+        "LABUPREG", "LABRETIC", "LABHAP", "LABBXTG", "LABHIVWBL"
+    ])
+    NON_PANEL_TESTS_WITH_GT_500_ORDERS = [
+        'LABA1C', 'LABAFBC', 'LABAFBD', 'LABALB', 'LABANER', 'LABB12', 'LABBLC', 'LABBLC2',
+        'LABBLCSTK', 'LABBLCTIP', 'LABBUN', 'LABBXTG', 'LABCA', 'LABCAI', 'LABCDTPCR', 'LABCK',
+        'LABCMVQT', 'LABCORT', 'LABCRP', 'LABCSFC', 'LABCSFGL', 'LABCSFTP', 'LABDIGL', 'LABESRP',
+        'LABFCUL', 'LABFE', 'LABFER', 'LABFIB', 'LABFLDC', 'LABFOL', 'LABFT4', 'LABGRAM',
+        'LABHAP', 'LABHBSAG', 'LABHCTX', 'LABHEPAR', 'LABHIVWBL', 'LABK', 'LABLAC', 'LABLACWB',
+        'LABLDH', 'LABLIDOL', 'LABLIPS', 'LABMB', 'LABMGN', 'LABNA', 'LABNH3', 'LABNONGYN',
+        'LABNTBNP', 'LABOSM', 'LABPALB', 'LABPCCG4O', 'LABPCCR', 'LABPCTNI', 'LABPHOS', 'LABPLTS',
+        'LABPROCT', 'LABPT', 'LABPTEG', 'LABPTT', 'LABRESP', 'LABRESPG', 'LABRETIC', 'LABSPLAC',
+        'LABSTLCX', 'LABSTOBGD', 'LABTNI', 'LABTRFS', 'LABTRIG', 'LABTSH', 'LABUCR', 'LABUOSM',
+        'LABUA', 'LABUAPRN', 'LABUPREG', 'LABURIC', 'LABURNA', 'LABURNC', 'LABUSPG'
+    ]
+
+    # only blood and urine cultures
+    CULTURE_MICRO_ORDERS = ["LABBLC", "LABBLC2", "LABURNC"]
+
+    labs_to_test = CULTURE_MICRO_ORDERS
+    panel = NON_PANEL_TESTS_WITH_GT_500_ORDERS
+    # for panel in labs_to_test:
+    PersonalizedAntibiogramPredictionPipeline("PersonalizedAntibiogram", panel, 1000000, use_cache=True, random_state=123456789)
diff --git a/scripts/LabCulturePrediction/README.md b/scripts/LabCulturePrediction/README.md
new file mode 100644
index 00000000..11a7fbd1
--- /dev/null
+++ b/scripts/LabCulturePrediction/README.md
@@ -0,0 +1,141 @@
+# Table of Contents
+- [Goal](#goal)
+- [Background](#background)
+   - [Motivation](#background-motivation)
+   - [Terms](#background-terms)
+- [Data](#data)
+- [How tos](#howtos)
+- [ML tasks](#mltasks)
+- [Further Investigation](#further-investigation)
+- [Suggested Readings](#readings)
+
+# <a name="goal"></a>Goal
+
+The goal of this project is to predict the result of blood culture lab results. As a corollary,
+we also seek to predict the susceptibility/resistance of certain cultures to certain antibiotics.
+
+# <a name="background"></a>Background
+## <a name="background-motivation"></a>Motivation
+
+Understanding accurate predictors of blood culture labs could help physicians choose the appropriate
+spectrum antibiotic and help reduce the need to perform certain lab tests thereby reducing waste. 
+Additionally, choosing the right spectrum antibiotic is becoming more imperative as bacteria start to 
+develop resistance to broad spectrum antibiotics.
+
+## <a name="background-terms"></a>Terms
+
+### Blood culture
+A test of a blood sample to find organisms that can cause infection.
+
+### Antibiotic
+A class of drugs that inhibit/destroys the growth of microorganisms. Examples you will encounter in this project include ciprofloxacin, amoxicillin, etc.
+
+### Bacteria
+Unicellular microorganisms known to cause diseases. Examples you will encounter in the data include Streptococcus pneumoniae, Escherichia coli.
+
+### Bacteremia
+Presence of bacteria in the blood.
+
+### Antibiotic resistance
+Change in bacteria to be less susceptible to antibiotics.
+
+
+# <a name="data"></a>Data
+The core data for this project is in the stride_culture_micro table. This table provides a series of blood
+culture tests.
+Note: we create a new column in our matrix for our presence/absence label  using
+the organism_name column from stride_culture_micro. See LabCultureMatrix for code sample.
+
+# <a name="howtos"></a>How tos
+## Loading the data needed for this project
+It is recommended that you load the data for this project from the data dumps. This is a much faster way to
+have the data in your PSql DB as the dumps are already processed from the raw data.
+- First download the dumps from our box folder at CDSS/Stride/data
+- Run the script `stride/psql/dump_stride.sh`. Note: create a copy of this file and edit the appropriate env variables
+You can now access the data via your psql server using command line or a desktop client such as PSequel.
+
+## Running the project
+There are two main files in this project:
+1. LabCultureMatrix.py - creates a feature matrix for the analysis
+2. LabCulturePredictionPipeline.py - performs analysis on the feature matrix generated by LabCultureMatrix
+
+Run the appropriate script depending on which stage of the project you are working on.
+
+## Adding features to feature matrix
+The base feature matrix includes features such as vitals, blood pressure and blood cell counts out of the box.
+For this project, LabCultureMatrix extends this class to include additional features we are interested in. 
+For example, we add a column for absence/presence of bacteria.
+See `_query_patient_episodes` method of LabCultureMatrix for an example of this.
+
+## Choosing which lab panels
+The call to LabCultureMatrix takes as an argument which lab panel you would like to build a matrix for. So far
+we have only worked with LABBLC (aerobic blood culture). If running only LabCultureMatrix.py, you can change this
+in the `main function` check.
+
+## Performing analysis
+The LabCulturePredictionPipeline.py file runs a series of training, testing and analysis on a feature matrix.
+Its input is the output of LabCultureMatrix.py. Note: if you run LabCulturePredictionPipeline.py it will generate
+a feature matrix using LabCultureMatrix and run analysis on this generated matrix.
+
+Currently supported algorithms are:
+- Decision Tree
+- Logistic Regression
+- Random Forest
+- Regress and Round
+- AdaBoost
+- SVM
+- Gaussian Naive Bayes
+
+Output reports will be generated for each of these algorithms.
+
+
+# <a name="mltasks"></a>ML Tasks
+This project defines two main ML tasks.
+1. Determine presence of infection
+    * Binary classification
+2. What strain of bacteria
+    * Binary classification
+
+Results of the above tasks are reproducible by running the current LabCulturePredictionPipeline script.
+
+
+# <a name="further-investigation"></a>Further Investigation
+This project could benefit from including additional features such as:
+
+#### Adding previous medications as a feature
+Previous medications of a patient could give us an indication of whether the patient has previously
+grown a culture and serve as a proxy for the likelihood the patient will regrow the same culture.
+Below is a list of antibiotics curated from interviews with domain experts that could serve as additional features:
+   - cefepime
+   - cefazolin
+   - ceftriaxone
+   - meropenem
+   - piperacillin-tazobactam
+   - vancomycin 
+   - linezolid
+   - daptomycin
+   - levofloxacin
+   - ciprofloxacin
+   - ampicillin
+   - metronidazole
+   - tobramycin
+   - caspofungin
+   
+Suggested implementation: We can treat each medication as a clinical item feature and include it in our
+feature matrix. We can accomplish this via a call to the `addClinicalItemFeature` function of LabCultureMatrix.py.
+
+#### Select biomarkers curated from domain experts
+The next phase of this project could also benefit from further refining the list of features by curating other biomarkers
+not related to medications. Further interviews with domain experts should be conducted to investigate biomarkers
+such as Alanine aminotransferase (ALT) and Alkaline phosphatase (ALP).
+
+
+# <a name="readings"></a>Suggested Readings
+* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5632149/
+* https://www.sciencedirect.com/science/article/pii/S1532046418301291
+* https://www.shmabstracts.com/abstract/predicting-bacteremia-in-hospitalized-patients-an-analysis-of-electronic-health-record-data/
+* https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-017-0550-1
+* https://www.sciencedirect.com/science/article/pii/S0924857907003445?via%3Dihub
+* https://www.ncbi.nlm.nih.gov/pubmed/28268133
+* https://academic.oup.com/cid/article/65/10/1607/3950071
+* https://journals.lww.com/co-infectiousdiseases/Citation/2017/12000/Machine_learning___novel_bioinformatics_approaches.2.aspx
diff --git a/scripts/LabCulturePrediction/SusceptibilityPredictionPipeline.py b/scripts/LabCulturePrediction/SusceptibilityPredictionPipeline.py
new file mode 100644
index 00000000..5d917374
--- /dev/null
+++ b/scripts/LabCulturePrediction/SusceptibilityPredictionPipeline.py
@@ -0,0 +1,321 @@
+#!/usr/bin/python
+"""
+Pipeline class for managing end to end training, testing,
+and analysis of Antibiotic Suseptibiltiy prediction.
+"""
+
+import argparse
+import inspect
+import os
+import pandas as pd
+from pandas import DataFrame, Series
+from sklearn.externals import joblib
+from sklearn.metrics import make_scorer, average_precision_score
+import logging
+
+from medinfo.common.Util import log
+from medinfo.ml.FeatureSelector import FeatureSelector
+from medinfo.dataconversion.FeatureMatrixTransform import FeatureMatrixTransform
+from medinfo.dataconversion.FeatureMatrixIO import FeatureMatrixIO
+from medinfo.ml.BifurcatedSupervisedClassifier import BifurcatedSupervisedClassifier
+from medinfo.ml.SupervisedClassifier import SupervisedClassifier
+from medinfo.ml.SupervisedLearningPipeline import SupervisedLearningPipeline
+from .LabCultureMatrix import LabCultureMatrix
+
+class SusceptibilityPredictionPipeline(SupervisedLearningPipeline):
+    def __init__(self, lab_panel, drug, num_episodes, use_cache=None, random_state=None):
+        SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state)
+
+        self.drug = drug
+        self._build_raw_feature_matrix()
+        self._build_processed_feature_matrix()
+        self._train_and_analyze_predictors()
+
+    def _build_model_dump_path(self, algorithm):
+        template = '%s' + '-normality-%s-model.pkl' % algorithm
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+
+        slugified_var = '-'.join(self._var.split())
+        model_dump_name = template % (slugified_var)
+
+        # Build path.
+        data_dir = self._fetch_data_dir_path(pipeline_file_name)
+        model_dump_path = '/'.join([data_dir, self.drug, model_dump_name])
+
+        return model_dump_path
+
+
+    def _build_raw_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+
+        slugified_var = '-'.join(self._var.split())
+        raw_matrix_name = template % (slugified_var, self._num_rows)
+
+        data_dir = self._fetch_data_dir_path(pipeline_file_name)
+        drug_path = '/'.join([data_dir, self.drug])
+        if not os.path.exists(drug_path):
+            os.makedirs(drug_path)
+        raw_matrix_path = '/'.join([data_dir, self.drug, raw_matrix_name])
+
+        return raw_matrix_path
+
+    def _build_composite_raw_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-raw.tab'
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        return SupervisedLearningPipeline._build_matrix_path(self, template, \
+            pipeline_file_name)
+   
+    def _build_raw_feature_matrix(self):
+        # This pipeline should only be run AFTER raw LabCultureMatrix has been
+        # created. To build our raw feature matrix, we load the labculture matrix
+        # into memory and filter down to rows where the antibiotic
+        # we care about was tested. 
+        raw_matrix_path = self._build_raw_matrix_path()
+        self._raw_matrix_params = {}
+        if os.path.exists(raw_matrix_path):
+            pass
+        else:
+            # Assert LabCultureMatrix already built
+            composite_raw_matrix_path = self._build_composite_raw_matrix_path()
+            assert os.path.exists(composite_raw_matrix_path)
+
+            # Load in LabCultureMatrix, filter on susceptibiliy tested column
+            comp_raw_matrix = pd.read_csv(composite_raw_matrix_path, sep='\t')
+            outcome_tested = '%s_tested' % self.drug
+            raw_matrix = comp_raw_matrix[comp_raw_matrix[outcome_tested] == 1]
+            
+            # Write Filtered Matrix to raw matrix path
+            raw_matix_path = self._build_raw_matrix_path()
+            raw_matrix.to_csv(raw_matrix_path, sep='\t', index=False)
+
+
+    def _build_processed_matrix_path(self):
+        template = '%s-normality-matrix-%d-episodes-processed.tab'
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+
+        slugified_var = '-'.join(self._var.split())
+        processed_matrix_name = template % (slugified_var, self._num_rows)
+
+        # Build Path
+        data_dir = self._fetch_data_dir_path(pipeline_file_path)
+        processed_matrix_path = '/'.join([data_dir, self.drug, processed_matrix_name])
+
+        return processed_matrix_path
+
+    def _build_processed_feature_matrix(self):
+        # Define parameters for processing steps.
+        params = {}
+        raw_matrix_path = self._build_raw_matrix_path()
+        processed_matrix_path = self._build_processed_matrix_path()
+        features_to_add = {}
+        imputation_strategies = {
+        }
+
+        features_to_remove = [
+            'pat_id', 'shifted_order_time',
+            'proc_code', 'abnormal_panel', 'Birth.pre',
+            'Male.preTimeDays', 'Female.preTimeDays',
+            'RaceWhiteHispanicLatino.preTimeDays',
+            'RaceWhiteNonHispanicLatino.preTimeDays',
+            'RaceHispanicLatino.preTimeDays',
+            'RaceAsian.preTimeDays',
+            'RaceBlack.preTimeDays',
+            'RacePacificIslander.preTimeDays',
+            'RaceNativeAmerican.preTimeDays',
+            'RaceOther.preTimeDays',
+            'RaceUnknown.preTimeDays',
+            'Death.post',
+            'Death.postTimeDays',
+            'escherichia_coli',
+            "organism_name",
+            "staphylococcus_aureus",
+            "enterococcus_species", 
+            "klebsiella_pneumoniae",
+            "pseudomonas_aeruginosa", 
+            "coag_negative_staphylococcus", 
+            "enterococcus_faecalis", 
+            "proteus_mirabilis", 
+            "candida_albicans"
+        ]
+
+        antibiotic_flags = ['trimethoprim_sulfamethoxazole', 'vancomycin',
+                            'penicillin', 'levofloxacin',
+                            'clindamycin', 'ceftriaxone',
+                            'erythromycin', 'ampicillin',
+                            'meropenem', 'ciprofloxacin',
+                            'cefepime', 'aztreonam',
+                            'ampicillin_sulbactam', 'piperacillin_tazobactam',
+                            'linezolid', 'oxacillin',
+                            'cefazolin', 'daptomycin'
+        ]
+
+
+        features_to_remove += ['%s_susc' % flag for flag in antibiotic_flags if flag != self.drug]
+        features_to_remove += ['%s_tested' % flag for flag in antibiotic_flags]
+
+        features_to_keep = [
+            # Keep the # of times it's been ordered in past, even if low info.
+            '%s.pre' % '-'.join(self._var.split())
+        ]
+        outcome_label = '%s_susc' % self.drug
+        selection_problem = FeatureSelector.CLASSIFICATION
+        selection_algorithm = FeatureSelector.RECURSIVE_ELIMINATION
+        percent_features_to_select = 0.05
+        matrix_class = LabCultureMatrix
+        pipeline_file_path = inspect.getfile(inspect.currentframe())
+        random_state = self._random_state
+        data_overview = [
+            # Overview:
+            'Overview',
+            # The outcome label is ___.
+            'The outcome label is %s.' % outcome_label,
+            # %s is a boolean indicator which summarizes whether all components
+            '%s is a boolean indicator which summarizes whether all components ' % outcome_label,
+            # in the lab panel order represented by a given row are normal.
+            'in the lab panel order represented by a given row are normal.',
+            # Each row represents a unique lab panel order.
+            'Each row represents a unique lab panel order.',
+            # Each row contains fields summarizing the patient's demographics,
+            "Each row contains fields summarizing the patient's demographics",
+            # inpatient admit date, prior vitals, and prior lab results.
+            'inpatient admit date, prior vitals, and prior lab results.',
+            # Most cells in matrix represent a count statistic for an event's
+            "Most cells in matrix represent a count statistic for an event's",
+            # occurrence or a difference between an event's time and index_time.
+            "occurrence or a difference between an event's time and index_time.",
+        ]
+
+        # Bundle parameters into single object to be unpacked in SLP.
+        params['raw_matrix_path'] = raw_matrix_path
+        params['processed_matrix_path'] = processed_matrix_path
+        params['features_to_add'] = features_to_add
+        params['features_to_keep'] = features_to_keep
+        params['imputation_strategies'] = imputation_strategies
+        params['features_to_remove'] = features_to_remove
+        params['outcome_label'] = outcome_label
+        params['selection_problem'] = selection_problem
+        params['selection_algorithm'] = selection_algorithm
+        params['percent_features_to_select'] = percent_features_to_select
+        params['matrix_class'] = matrix_class
+        params['pipeline_file_path'] = pipeline_file_path
+        params['data_overview'] = data_overview
+        params['random_state'] = random_state
+
+        # Defer processing logic to SupervisedLearningPipeline.
+        SupervisedLearningPipeline._build_processed_feature_matrix(self, params)
+
+
+    def _train_and_analyze_predictors(self):
+        log.info('Training and analyzing predictors...')
+        problem = SupervisedLearningPipeline.CLASSIFICATION
+        meta_report = None
+        fm_io = FeatureMatrixIO()
+
+        # Build paths for output.
+        pipeline_file_name = inspect.getfile(inspect.currentframe())
+        data_dir = SupervisedLearningPipeline._fetch_data_dir_path(self, pipeline_file_name)
+
+        # Test BifurcatedSupervisedClassifier and SupervisedClassifier.
+        algorithms_to_test = list()
+        algorithms_to_test.extend(SupervisedClassifier.SUPPORTED_ALGORITHMS)
+        # for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
+        #     algorithms_to_test.append('bifurcated-%s' % algorithm)
+        log.debug('algorithms_to_test: %s' % algorithms_to_test)
+
+        # Train and analyse algorithms.
+        for algorithm in algorithms_to_test:
+            log.info('Training and analyzing %s...' % algorithm)
+            # If report_dir does not exist, make it.
+            report_dir = '/'.join([data_dir, self.drug, algorithm])
+            if not os.path.exists(report_dir):
+                os.makedirs(report_dir)
+
+            # Define hyperparams.
+            hyperparams = {}
+            hyperparams['algorithm'] = algorithm
+            hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
+            hyperparams['max_iter'] = 1024
+            hyperparams['random_state'] = self._random_state
+
+            # If bifurcated algorithm, define bifurcator.
+            if 'bifurcated' in algorithm:
+                # bifrucator = LAB.pre == 0
+                hyperparams['bifurcator'] = '%s.pre' % self._var
+                hyperparams['bifurcation_strategy'] = BifurcatedSupervisedClassifier.EQUAL
+                hyperparams['bifurcation_value'] = 0
+                hyperparams['bifurcated'] = True
+
+            # Train classifier.
+            predictor_path = self._build_model_dump_path(algorithm)
+            if os.path.exists(predictor_path) and 'bifurcated' not in algorithm:
+                log.debug('Loading model from disk...')
+                # TODO(sbala): Fix loblib.load so that it works for bifurcated
+                # supervised classifiers.
+                self._predictor = joblib.load(predictor_path)
+                self._features = self._X_train.columns
+                status = SupervisedClassifier.TRAINED
+            else:
+                status = SupervisedLearningPipeline._train_predictor(self, problem, [0, 1], hyperparams)
+
+            # If failed to train, write an error report.
+            y_train_counts = self._y_train[self._y_train.columns[0]].value_counts()
+            y_test_counts = self._y_test[self._y_test.columns[0]].value_counts()
+            if status == SupervisedClassifier.INSUFFICIENT_SAMPLES:
+                # Skip all analysis and reporting.
+                # This will be true for all algorithms, so just return.
+                # Build error report.
+                algorithm_report = DataFrame(
+                    {
+                    'lab_panel': [self._var],
+                    'algorithm': [algorithm],
+                    'error': [status],
+                    'y_train.value_counts()': [y_train_counts.to_dict()],
+                    'y_test.value_counts()': [y_test_counts.to_dict()]
+                    },
+                    columns=[
+                        'lab_panel', 'algorithm', 'error',
+                        'y_train.value_counts()', 'y_test.value_counts()'
+                    ]
+                )
+                header = ['LabCulturePredictionPipeline("%s", 1000000)' % self._var]
+                # Write error report.
+                fm_io.write_data_frame_to_file(algorithm_report, \
+                    '/'.join([report_dir, '%s-normality-prediction-report.tab' % (self._var)]), \
+                    header)
+            # If successfully trained, append to a meta report.
+            elif status == SupervisedClassifier.TRAINED:
+                pipeline_prefix = '%s-normality-prediction-%s' % (self._var, algorithm)
+                SupervisedLearningPipeline._analyze_predictor(self, report_dir, pipeline_prefix)
+                if meta_report is None:
+                    meta_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                else:
+                    algorithm_report = fm_io.read_file_to_data_frame('/'.join([report_dir, '%s-report.tab' % pipeline_prefix]))
+                    log.debug('algorithm_report: %s' % algorithm_report)
+                    meta_report = meta_report.append(algorithm_report)
+                # Write predictor to disk.
+                predictor = SupervisedLearningPipeline.predictor(self)
+                predictor_path = self._build_model_dump_path(algorithm)
+                joblib.dump(predictor, predictor_path)
+
+        # After building per-algorithm reports, write to meta report.
+        # Note that if there were insufficient samples to build any of the
+        # algorithms, then meta_report will still be None.
+        if meta_report is not None:
+            header = ['LabCulturePredictionPipeline("%s", 1000000)' % self._var]
+            fm_io.write_data_frame_to_file(meta_report, \
+                '/'.join([data_dir, '%s-normality-prediction-report.tab' % self._var]), header)
+
+
+
+
+        
+if __name__ == '__main__':
+    log.level = logging.DEBUG
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-antibiotic_name', required=True)
+    args = parser.parse_args()
+
+    panel = "LABBLC LABBLC2"
+    SusceptibilityPredictionPipeline(panel, args.antibiotic_name, 1000000, use_cache=True, random_state=123456789)
+
diff --git a/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv b/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv
new file mode 100644
index 00000000..a6257b8f
--- /dev/null
+++ b/scripts/LabCulturePrediction/Susceptibility_Feature_Names.csv
@@ -0,0 +1,173 @@
+name,description
+5-Flucytosine:Not_Susceptible,Not_Susceptible TO 5-Flucytosine
+5-Flucytosine:Susceptible,Susceptible TO 5-Flucytosine
+ACYCLOVIR:Not_Susceptible,Not_Susceptible TO ACYCLOVIR
+ACYCLOVIR:Susceptible,Susceptible TO ACYCLOVIR
+Amikacin:Not_Susceptible,Not_Susceptible TO Amikacin
+Amikacin:Susceptible,Susceptible TO Amikacin
+Amoxicillin-Clavulanic Acid:Not_Susceptible,Not_Susceptible TO Amoxicillin-Clavulanic Acid
+Amoxicillin-Clavulanic Acid:Susceptible,Susceptible TO Amoxicillin-Clavulanic Acid
+Amphotericin B:Not_Susceptible,Not_Susceptible TO Amphotericin B
+Ampicillin-Sulbactam:Not_Susceptible,Not_Susceptible TO Ampicillin-Sulbactam
+Ampicillin-Sulbactam:Susceptible,Susceptible TO Ampicillin-Sulbactam
+Ampicillin:Not_Susceptible,Not_Susceptible TO Ampicillin
+Ampicillin:Susceptible,Susceptible TO Ampicillin
+Anidulafungin:Not_Susceptible,Not_Susceptible TO Anidulafungin
+Anidulafungin:Susceptible,Susceptible TO Anidulafungin
+Aztreonam.:Not_Susceptible,Not_Susceptible TO Aztreonam.
+Aztreonam.:Susceptible,Susceptible TO Aztreonam.
+Aztreonam:Not_Susceptible,Not_Susceptible TO Aztreonam
+Aztreonam:Susceptible,Susceptible TO Aztreonam
+Beta Lactamase:Not_Susceptible,Not_Susceptible TO Beta Lactamase
+Beta Lactamase:Susceptible,Susceptible TO Beta Lactamase
+Carbapenemase:Not_Susceptible,Not_Susceptible TO Carbapenemase
+Carbapenemase:Susceptible,Susceptible TO Carbapenemase
+Caspofungin:Not_Susceptible,Not_Susceptible TO Caspofungin
+Caspofungin:Susceptible,Susceptible TO Caspofungin
+Cefazolin..:Not_Susceptible,Not_Susceptible TO Cefazolin..
+Cefazolin..:Susceptible,Susceptible TO Cefazolin..
+Cefazolin:Not_Susceptible,Not_Susceptible TO Cefazolin
+Cefazolin:Susceptible,Susceptible TO Cefazolin
+Cefepime:Not_Susceptible,Not_Susceptible TO Cefepime
+Cefepime:Susceptible,Susceptible TO Cefepime
+Cefotaxime:Not_Susceptible,Not_Susceptible TO Cefotaxime
+Cefotaxime:Susceptible,Susceptible TO Cefotaxime
+Cefotetan:Not_Susceptible,Not_Susceptible TO Cefotetan
+Cefotetan:Susceptible,Susceptible TO Cefotetan
+Cefoxitin:Not_Susceptible,Not_Susceptible TO Cefoxitin
+Cefoxitin:Susceptible,Susceptible TO Cefoxitin
+Cefpodoxime:Susceptible,Susceptible TO Cefpodoxime
+Ceftaroline:Susceptible,Susceptible TO Ceftaroline
+Ceftazidime.:Not_Susceptible,Not_Susceptible TO Ceftazidime.
+Ceftazidime.:Susceptible,Susceptible TO Ceftazidime.
+Ceftazidime:Not_Susceptible,Not_Susceptible TO Ceftazidime
+Ceftazidime:Susceptible,Susceptible TO Ceftazidime
+Ceftriaxone (Meningeal):Not_Susceptible,Not_Susceptible TO Ceftriaxone (Meningeal)
+Ceftriaxone (Meningeal):Susceptible,Susceptible TO Ceftriaxone (Meningeal)
+Ceftriaxone.:Not_Susceptible,Not_Susceptible TO Ceftriaxone.
+Ceftriaxone.:Susceptible,Susceptible TO Ceftriaxone.
+Ceftriaxone:Not_Susceptible,Not_Susceptible TO Ceftriaxone
+Ceftriaxone:Susceptible,Susceptible TO Ceftriaxone
+Cefuroxime (IV):Not_Susceptible,Not_Susceptible TO Cefuroxime (IV)
+Cefuroxime (IV):Susceptible,Susceptible TO Cefuroxime (IV)
+Cefuroxime (PO):Not_Susceptible,Not_Susceptible TO Cefuroxime (PO)
+Cefuroxime (PO):Susceptible,Susceptible TO Cefuroxime (PO)
+Cephalexin-Cephalothin:Not_Susceptible,Not_Susceptible TO Cephalexin-Cephalothin
+Cephalexin-Cephalothin:Susceptible,Susceptible TO Cephalexin-Cephalothin
+Cephalothin:Susceptible,Susceptible TO Cephalothin
+Chloramphenicol:Not_Susceptible,Not_Susceptible TO Chloramphenicol
+Chloramphenicol:Susceptible,Susceptible TO Chloramphenicol
+Ciprofloxacin:Not_Susceptible,Not_Susceptible TO Ciprofloxacin
+Ciprofloxacin:Susceptible,Susceptible TO Ciprofloxacin
+Clarithromycin:Not_Susceptible,Not_Susceptible TO Clarithromycin
+Clarithromycin:Susceptible,Susceptible TO Clarithromycin
+Clindamycin:Not_Susceptible,Not_Susceptible TO Clindamycin
+Clindamycin:Susceptible,Susceptible TO Clindamycin
+Colistin:Not_Susceptible,Not_Susceptible TO Colistin
+Colistin:Susceptible,Susceptible TO Colistin
+D-Test:Susceptible,Susceptible TO D-Test
+DORIPENEM:Not_Susceptible,Not_Susceptible TO DORIPENEM
+DORIPENEM:Susceptible,Susceptible TO DORIPENEM
+Daptomycin:Not_Susceptible,Not_Susceptible TO Daptomycin
+Daptomycin:Susceptible,Susceptible TO Daptomycin
+Doxycycline:Not_Susceptible,Not_Susceptible TO Doxycycline
+Doxycycline:Susceptible,Susceptible TO Doxycycline
+ESBL confirmation test:Not_Susceptible,Not_Susceptible TO ESBL confirmation test
+ESBL confirmation test:Susceptible,Susceptible TO ESBL confirmation test
+Ertapenem:Not_Susceptible,Not_Susceptible TO Ertapenem
+Ertapenem:Susceptible,Susceptible TO Ertapenem
+Erythromycin:Not_Susceptible,Not_Susceptible TO Erythromycin
+Erythromycin:Susceptible,Susceptible TO Erythromycin
+Esbl Check:Not_Susceptible,Not_Susceptible TO Esbl Check
+Esbl Check:Susceptible,Susceptible TO Esbl Check
+Ethambutol 5.0 mcg-ml:Susceptible,Susceptible TO Ethambutol 5.0 mcg-ml
+Ethambutol:Not_Susceptible,Not_Susceptible TO Ethambutol
+FOSFOMYCIN:Not_Susceptible,Not_Susceptible TO FOSFOMYCIN
+FOSFOMYCIN:Susceptible,Susceptible TO FOSFOMYCIN
+Fluconazole:Not_Susceptible,Not_Susceptible TO Fluconazole
+Fluconazole:Susceptible,Susceptible TO Fluconazole
+Foscarnet:Not_Susceptible,Not_Susceptible TO Foscarnet
+Foscarnet:Susceptible,Susceptible TO Foscarnet
+Ganciclovir:Not_Susceptible,Not_Susceptible TO Ganciclovir
+Ganciclovir:Susceptible,Susceptible TO Ganciclovir
+Gentamicin 500 mcg-ml.:Not_Susceptible,Not_Susceptible TO Gentamicin 500 mcg-ml.
+Gentamicin 500 mcg-ml.:Susceptible,Susceptible TO Gentamicin 500 mcg-ml.
+Gentamicin:Not_Susceptible,Not_Susceptible TO Gentamicin
+Gentamicin:Susceptible,Susceptible TO Gentamicin
+INH 0.1 mcg-ml:Susceptible,Susceptible TO INH 0.1 mcg-ml
+Imipenem:Not_Susceptible,Not_Susceptible TO Imipenem
+Imipenem:Susceptible,Susceptible TO Imipenem
+Itraconazole:Not_Susceptible,Not_Susceptible TO Itraconazole
+Itraconazole:Susceptible,Susceptible TO Itraconazole
+Levofloxacin:Not_Susceptible,Not_Susceptible TO Levofloxacin
+Levofloxacin:Susceptible,Susceptible TO Levofloxacin
+Linezolid:Not_Susceptible,Not_Susceptible TO Linezolid
+Linezolid:Susceptible,Susceptible TO Linezolid
+Meropenem:Not_Susceptible,Not_Susceptible TO Meropenem
+Meropenem:Susceptible,Susceptible TO Meropenem
+Metronidazole:Not_Susceptible,Not_Susceptible TO Metronidazole
+Metronidazole:Susceptible,Susceptible TO Metronidazole
+Micafungin:Not_Susceptible,Not_Susceptible TO Micafungin
+Micafungin:Susceptible,Susceptible TO Micafungin
+Minocycline:Not_Susceptible,Not_Susceptible TO Minocycline
+Minocycline:Susceptible,Susceptible TO Minocycline
+Moxifloxacin.:Susceptible,Susceptible TO Moxifloxacin.
+Moxifloxacin:Not_Susceptible,Not_Susceptible TO Moxifloxacin
+Moxifloxacin:Susceptible,Susceptible TO Moxifloxacin
+Nalidixic Acid.:Not_Susceptible,Not_Susceptible TO Nalidixic Acid.
+Negative Culture,Microculture Grew No Bacteria
+Nitrofurantoin:Not_Susceptible,Not_Susceptible TO Nitrofurantoin
+Nitrofurantoin:Susceptible,Susceptible TO Nitrofurantoin
+Oxacillin.:Not_Susceptible,Not_Susceptible TO Oxacillin.
+Oxacillin.:Susceptible,Susceptible TO Oxacillin.
+Oxacillin:Not_Susceptible,Not_Susceptible TO Oxacillin
+Oxacillin:Susceptible,Susceptible TO Oxacillin
+PENICILLIN G (MENINGEAL):Not_Susceptible,Not_Susceptible TO PENICILLIN G (MENINGEAL)
+PENICILLIN G (MENINGEAL):Susceptible,Susceptible TO PENICILLIN G (MENINGEAL)
+PENICILLIN G (NON-MENINGEAL):Susceptible,Susceptible TO PENICILLIN G (NON-MENINGEAL)
+PENICILLIN V (ORAL):Not_Susceptible,Not_Susceptible TO PENICILLIN V (ORAL)
+PENICILLIN V (ORAL):Susceptible,Susceptible TO PENICILLIN V (ORAL)
+Penicillin-Ampicillin:Not_Susceptible,Not_Susceptible TO Penicillin-Ampicillin
+Penicillin..:Not_Susceptible,Not_Susceptible TO Penicillin..
+Penicillin..:Susceptible,Susceptible TO Penicillin..
+Penicillin:Not_Susceptible,Not_Susceptible TO Penicillin
+Penicillin:Susceptible,Susceptible TO Penicillin
+Piperacillin-Tazobactam:Not_Susceptible,Not_Susceptible TO Piperacillin-Tazobactam
+Piperacillin-Tazobactam:Susceptible,Susceptible TO Piperacillin-Tazobactam
+Piperacillin:Not_Susceptible,Not_Susceptible TO Piperacillin
+Piperacillin:Susceptible,Susceptible TO Piperacillin
+Polymixin B:Not_Susceptible,Not_Susceptible TO Polymixin B
+Polymixin B:Susceptible,Susceptible TO Polymixin B
+Posaconazole:Not_Susceptible,Not_Susceptible TO Posaconazole
+Pyrazinamide 100 mcg-ml:Not_Susceptible,Not_Susceptible TO Pyrazinamide 100 mcg-ml
+Pyrazinamide 100 mcg-ml:Susceptible,Susceptible TO Pyrazinamide 100 mcg-ml
+Quinupristin-Dalfopristin:Not_Susceptible,Not_Susceptible TO Quinupristin-Dalfopristin
+Quinupristin-Dalfopristin:Susceptible,Susceptible TO Quinupristin-Dalfopristin
+Rifabutin:Not_Susceptible,Not_Susceptible TO Rifabutin
+Rifampin 1.0 mcg-ml:Susceptible,Susceptible TO Rifampin 1.0 mcg-ml
+Rifampin:Not_Susceptible,Not_Susceptible TO Rifampin
+Rifampin:Susceptible,Susceptible TO Rifampin
+Streptomycin 1000 mcg-ml:Not_Susceptible,Not_Susceptible TO Streptomycin 1000 mcg-ml
+Streptomycin 1000 mcg-ml:Susceptible,Susceptible TO Streptomycin 1000 mcg-ml
+Streptomycin 2000 mcg-ml:Not_Susceptible,Not_Susceptible TO Streptomycin 2000 mcg-ml
+Streptomycin:Not_Susceptible,Not_Susceptible TO Streptomycin
+Telavancin:Not_Susceptible,Not_Susceptible TO Telavancin
+Telavancin:Susceptible,Susceptible TO Telavancin
+Tetracycline:Not_Susceptible,Not_Susceptible TO Tetracycline
+Tetracycline:Susceptible,Susceptible TO Tetracycline
+Ticacarcillin-Clavulanic Acid:Not_Susceptible,Not_Susceptible TO Ticacarcillin-Clavulanic Acid
+Ticacarcillin-Clavulanic Acid:Susceptible,Susceptible TO Ticacarcillin-Clavulanic Acid
+Ticarcillin:Not_Susceptible,Not_Susceptible TO Ticarcillin
+Ticarcillin:Susceptible,Susceptible TO Ticarcillin
+Tigecycline:Not_Susceptible,Not_Susceptible TO Tigecycline
+Tigecycline:Susceptible,Susceptible TO Tigecycline
+Tobramycin:Not_Susceptible,Not_Susceptible TO Tobramycin
+Tobramycin:Susceptible,Susceptible TO Tobramycin
+Trimethoprim-Sulfamethoxazole.:Not_Susceptible,Not_Susceptible TO Trimethoprim-Sulfamethoxazole.
+Trimethoprim-Sulfamethoxazole.:Susceptible,Susceptible TO Trimethoprim-Sulfamethoxazole.
+Trimethoprim-Sulfamethoxazole:Not_Susceptible,Not_Susceptible TO Trimethoprim-Sulfamethoxazole
+Trimethoprim-Sulfamethoxazole:Susceptible,Susceptible TO Trimethoprim-Sulfamethoxazole
+Vancomycin:Not_Susceptible,Not_Susceptible TO Vancomycin
+Vancomycin:Susceptible,Susceptible TO Vancomycin
+Voriconazole:Not_Susceptible,Not_Susceptible TO Voriconazole
+Voriconazole:Susceptible,Susceptible TO Voriconazole
\ No newline at end of file
diff --git a/scripts/LabCulturePrediction/__init__.py b/scripts/LabCulturePrediction/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/chargemaster.csv b/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/chargemaster.csv
deleted file mode 100644
index 1cedc388..00000000
--- a/scripts/LabTestAnalysis/lab_statistics/data_summary_stats/chargemaster.csv
+++ /dev/null
@@ -1,45093 +0,0 @@
-billing_code,price_description,price
-19100601,HC MEDICATION ADMIN INITIAL, 357 
-19100809,HC OXYGEN DAILY SERVICE, 361 
-19100866,HC IP VENT MGMT INV/NON-INV INIT," 6,184 "
-19101112,HC PEP DEVICE(RESISTEX)-LPCH, 78 
-19101146,HC MDI SUBSEQUENT-LPCH, 357 
-19101153,HC VENT ASSIST & MGMT-INIT-LPCH," 6,184 "
-19101161,HC HAND NEBULIZER SUBSEQNT-LPCH, 357 
-19101187,HC IPPB TRMNT:SUBSEQUENT-LPCH, 357 
-19101195,HC CONT MED AERSL 1STHR-LPCH, 704 
-19101203,HC HAND NEBULIZER INITIAL-LPCH, 357 
-19101237,HC CPAP INITIAL & MANAGE-LPCH," 1,665 "
-19101245,HC BIPAP INIT & MGMT 1ST DAY-LPCH," 1,665 "
-19101260,HC CARDIOPULMONARY RESUS -LPCH," 2,129 "
-19101286,HC MDI-DEMO/EVAL-LPCH, 592 
-19101351,HC SPUTUM INDUCTION/AER MED SUBSQ, 357 
-19101385,HC STD HHN AER MED THR SUBSQ, 357 
-19102250,HC CARDIOPULMONARY RESUSCIT," 2,129 "
-19102557,HC CPAP-POS AIRWAY PRES DAILY CHG," 1,665 "
-19102607,HC RESUSC BAG SETUP/REPLACE, 155 
-19102706,HC THERPST PERC TRTMNT SUB, 480 
-19102714,HC CHEST PHYSIOTHERAPY INITIAL, 480 
-19102854,HC BEDSIDE PULM FUNCTION TEST, 806 
-19103050,HC IP VENT MGMT INV/NON-INV EA AD," 3,924 "
-19103357,HC BRONCH DIAG W/WO CELL WASH," 4,446 "
-19103449,HC MDI THERAPY - SUBSEQUENT, 357 
-19103506,HC PENTAMIDINE AEROSOL TRTMNT, 734 
-19103605,HC CONT SET-UP/DAILY SVC, 404 
-19103753,HC ARTERIAL PUNCTURE, 355 
-19103845,HC BRONCH FB REMOVAL-SINGLE," 5,713 "
-19103886,HC BRONCH/TRANSBR BX 1ST LOBE," 5,733 "
-19103902,HC BRONCH W NDL BX UPP AIRWAY," 6,173 "
-19103928,HC IPV SUBSEQUENT, 357 
-19160027,HC BRONCHOSCOPY/BRUSH SINGLE," 4,936 "
-19160035,HC BRONCHOSCOPY/LAVAGE SINGLE," 5,854 "
-19160043,HC BRONCHOSCOPY/BRUSH BILATERAL," 6,170 "
-19160050,HC BRONCH/TRANSBR BX ADD LOBE," 2,152 "
-19160076,HC BRONCHOSCOPY/LAVAGE BILATERAL," 7,318 "
-19160100,HC BRONCHOSCOPY/FB REMOVAL BILAT," 7,141 "
-21130018,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-21130026,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-21130034,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-21130042,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-21130059,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-21130067,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-21130075,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-21130083,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-21130091,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-21130117,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-21130125,HC IV INFUSION-THER-ADDTL HRS, 607 
-21130133,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-21130141,HC IV PUSH INJECTION, 542 
-21190111,HC B1 MED/SURG PRIV," 8,897 "
-21190129,HC B1 MED/SURG PRIV," 8,897 "
-21190137,HC B1 MED/SURG PRIV," 8,897 "
-21190145,HC B1 MED/SURG PRIV," 8,897 "
-21190210,HC B1 MED/SURG SP," 8,397 "
-21190228,HC B1 MED/SURG SP," 8,397 "
-21190236,HC B1 MED/SURG SP," 8,397 "
-21190244,HC B1 MED/SURG SP," 8,397 "
-21190319,HC B1 MED/SURG W," 8,397 "
-21190327,HC B1 MED/SURG," 8,397 "
-21190335,HC B1 MED/SURG," 8,397 "
-21190343,HC B1 MED/SURG," 8,397 "
-22290118,HC G2P PSYCH PRIV," 6,201 "
-22290126,HC G2P PSYCH LEVEL 2 PRIVATE," 6,201 "
-22290134,HC G2P PSYCH PRIV," 6,201 "
-22290142,HC G2P PSYCH PRIV," 6,201 "
-22290217,HC G2P PSYCH," 5,770 "
-22290225,HC G2P PSYCH SP," 5,770 "
-22290233,HC G2P PSYCH SP," 5,770 "
-22290241,HC G2P PSYCH SP," 5,770 "
-23130040,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-23130057,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-23130065,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-23130073,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-23130081,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-23130099,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-23130107,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-23130115,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-23130123,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-23130149,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-23130156,HC IV INFUSION-THER-ADDTL HRS, 607 
-23130164,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-23130172,HC IV PUSH INJECTION, 542 
-23230014,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-23230022,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-23230030,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-23230048,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-23230055,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-23230063,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-23230071,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-23230089,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-23230097,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-23230139,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-23230147,HC IV INFUSION-THER-ADDTL HRS, 607 
-23230154,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-23230162,HC IV PUSH INJECTION, 542 
-23290117,HC FGR ONC/BMT LEVEL 1 PVT," 8,897 "
-23290125,HC FGR ONC/BMT LEVEL 2 PVT," 8,897 "
-23290133,HC FGR ONC/BMT LEVEL 3 PVT," 15,479 "
-23290141,HC FGR ONC/BMT LEVEL 4 PVT," 15,479 "
-23290216,HC FGR ONC/BMT LEVEL 1 SP," 8,897 "
-23290224,HC FGR ONC/BMT LEVEL 2 SP," 8,397 "
-23290232,HC FGR ONC/BMT LEVEL 3 SP," 15,479 "
-23290240,HC FGR ONC/BMT LEVEL 4 SP," 15,479 "
-23290315,HC FGR ONC/BMT LEVEL 1 W," 8,013 "
-23290323,HC FGR ONC/BMT LEVEL 2 W," 8,653 "
-23290331,HC FGR ONC/BMT LEVEL 3 W," 14,466 "
-23290349,HC FGR ONC/BMT LEVEL 4 W," 12,182 "
-23730039,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-23730047,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-23730054,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-23730062,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-23730070,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-23730088,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-23730096,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-23730104,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-23730112,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-23730138,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-23730146,HC IV INFUSION-THER-ADDTL HRS, 607 
-23730153,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-23730161,HC IV PUSH INJECTION, 542 
-23790116,HC B2 ICC LEVEL 1 PRIVATE," 14,466 "
-23790124,HC B2 IMC ICU," 15,479 "
-23790132,HC B2 IMC ICU," 15,479 "
-23790215,HC B2 IMC ICU," 14,466 "
-23790223,HC B2 IMC ICU," 15,479 "
-23790231,HC B2 IMC ICU," 15,479 "
-23790314,HC B2 IMC ICU," 14,466 "
-23790322,HC B2 IMC ICU," 15,479 "
-23790330,HC B2 IMC ICU," 15,479 "
-23830029,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-23830037,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-23830045,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-23830052,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-23830060,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-23830078,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-23830086,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-23830094,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-23830102,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-23830128,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-23830136,HC IV INFUSION-THER-ADDTL HRS, 607 
-23830144,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-23830151,HC IV PUSH INJECTION, 542 
-23890114,HC F3 MED/SURG PRIV," 8,897 "
-23890122,HC F3 MED/SURG PRIV," 8,897 "
-23890130,HC F3 MED/SURG PRIV," 8,897 "
-23890148,HC F3 MED/SURG PRIV," 8,897 "
-23890213,HC F3 MED/SURG SP," 8,397 "
-23890221,HC F3 MED/SURG SP," 8,397 "
-23890239,HC F3 MED/SURG SP," 8,397 "
-23890247,HC F3 MED/SURG SP," 8,397 "
-23890312,HC F3 M/S LEVEL I W," 5,769 "
-23890320,HC F3 M/S LEVEL II W," 8,653 "
-23890338,HC F3 MED/SURG W," 8,397 "
-23890346,HC F3 MED/SURG," 8,397 "
-24890121,HC H2 PSYCH PRIV," 6,201 "
-24890139,HC H2 PSYCH PRIV," 6,201 "
-24890147,HC H2 PSYCH PRIV," 6,201 "
-24890212,HC H2 PSYCH," 5,770 "
-24890220,HC H2 PSYCH," 5,770 "
-24890238,HC H2 PSYCH SP," 5,770 "
-24890246,HC H2 PSYCH SP," 5,770 "
-25030057,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-25030065,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25030073,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25030081,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25030099,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25030107,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25030115,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25030123,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25030131,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-25030172,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-25030180,HC IV INFUSION-THER-ADDTL HRS, 607 
-25030198,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-25030206,HC IV PUSH INJECTION, 542 
-25130071,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-25130089,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25130097,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25130105,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25130113,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25130121,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25130139,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25130147,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25130154,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-25130188,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-25130196,HC IV INFUSION-THER-ADDTL HRS, 607 
-25130204,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-25130212,HC IV PUSH INJECTION, 542 
-25190018,HC D1CC CCU," 29,167 "
-25190026,HC D1CC CCU," 29,167 "
-25190034,HC D1CC CCU," 29,167 "
-25190042,HC D1CC CCU," 29,167 "
-25290115,HC D3 ICC LEVEL 1 PRIVATE," 14,466 "
-25290123,HC D3 IMC ICU," 15,479 "
-25290131,HC D3 IMC ICU," 15,479 "
-25290214,HC D3 IMC ICU," 14,466 "
-25290222,HC D3 IMC ICU," 15,479 "
-25290230,HC D3 IMC ICU," 15,479 "
-25290313,HC D3 ICC LEVEL 1 WARD," 8,013 "
-25290321,HC D3 IMC ICU," 15,479 "
-25290339,HC D3 IMC ICU," 14,466 "
-25330028,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-25330036,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25330044,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25330051,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25330069,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25330077,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25330085,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25330093,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25330101,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-25330127,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-25330135,HC IV INFUSION-THER-ADDTL HRS, 607 
-25330143,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-25330150,HC IV PUSH INJECTION, 542 
-25430018,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-25430026,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25430034,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25430042,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25430059,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25430067,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25430075,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25430083,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25430091,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-25430117,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-25430125,HC IV INFUSION-THER-ADDTL HRS, 607 
-25430133,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-25430141,HC IV PUSH INJECTION, 542 
-25490111,HC D1CS IMC CCU," 15,479 "
-25490129,HC D1CS IMC CCU," 15,479 "
-25490137,HC D1CS IMC CCU," 15,479 "
-25490210,HC D1CS IMC CCU," 15,479 "
-25490228,HC D1CS IMC CCU," 15,479 "
-25490236,HC D1CS IMC CCU," 15,479 "
-25490319,HC D1CS ICC LEVEL 1 WARD," 8,013 "
-25490327,HC D1CS IMC ICU," 14,466 "
-25490335,HC D1CS IMC ICU," 14,466 "
-25530080,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-25530098,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25530106,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25530114,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25530122,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25530130,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25530148,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25530155,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25530163,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-25530197,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-25530205,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-25530213,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-25530221,HC IV PUSH INJECTION, 542 
-25590019,HC NORTH ICU," 28,088 "
-25590027,HC NORTH ICU," 30,054 "
-25590035,HC NORTH ICU," 30,054 "
-25590043,HC NORTH ICU," 30,054 "
-26190116,HC E1 ONC/BMT LEVEL 1 PVT," 8,897 "
-26190124,HC E1 ONC/BMT LEVEL 2 PVT," 8,897 "
-26190132,HC E1 IMC ICU," 15,479 "
-26190140,HC E1 ONC/BMT LEVEL 4 PVT," 15,479 "
-26190215,HC E1 ONC/BMT LEVEL 1 SP," 8,397 "
-26190223,HC E1 ONC/BMT LEVEL 2 SP," 8,397 "
-26190231,HC E1 ONC/BMT LEVEL 3 SP," 15,479 "
-26190249,HC E1 ONC/BMT LEVEL 4 SP," 15,479 "
-26190314,HC E1 ONC/BMT LEVEL 1 W," 8,013 "
-26190322,HC E1 ONC/BMT LEVEL 2 W," 8,653 "
-26190330,HC E1 ONC/BMT LEVEL 3 W," 12,182 "
-26190348,HC E1 ONC/BMT LEVEL 4 W," 12,182 "
-26290015,HC E2 ICU," 28,088 "
-26290023,HC E2 ICU," 30,054 "
-26290031,HC E2 ICU," 30,054 "
-26290049,HC E2 ICU," 30,054 "
-26390112,HC C2 MED/SURG PRIV," 8,897 "
-26390120,HC C2 MED/SURG PRIV," 8,897 "
-26390138,HC C2 MED/SURG PRIV," 8,897 "
-26390146,HC C2 MED/SURG PRIV," 8,897 "
-26390211,HC C2 MED/SURG SP," 8,397 "
-26390229,HC C2 MED/SURG SP," 8,397 "
-26390237,HC C2 MED/SURG SP," 8,397 "
-26390245,HC C2 MED/SURG SP," 8,397 "
-26390310,HC C2 M/S LEVEL I W," 5,769 "
-26390328,HC C2 MED/SURG W," 8,397 "
-26390336,HC C2 M/S LEVEL III W," 8,397 "
-26390344,HC C2 MED/SURG," 8,397 "
-27390111,HC E3 MED/SURG PRIV," 8,897 "
-27390129,HC E3 MED/SURG PRIV," 8,897 "
-27390137,HC E3 MED/SURG PRIV," 8,897 "
-27390145,HC E3 MED/SURG PRIV," 8,897 "
-27390210,HC E3 MED/SURG," 8,397 "
-27390228,HC E3 MED/SURG SP," 8,397 "
-27390236,HC E3 MED/SURG SP," 8,397 "
-27390244,HC E3 MED/SURG SP," 8,397 "
-27390319,HC E3 M/S LEVEL I W," 5,769 "
-27390327,HC E3 MED/SURG W," 8,397 "
-27390335,HC E3 MED/SURG WARD," 8,397 "
-27390343,HC E3 MED/SURG," 8,397 "
-30000038,HC MAJOR SURGERY-15 MINUTES," 7,045 "
-30000046,HC MAJOR SURGERY-ADD 15 MIN," 2,400 "
-30000053,HC OPEN HEART OR 15 MIN," 3,720 "
-30000061,HC OR MAJOR 30 MIN," 17,122 "
-30000087,HC OR MAJOR 45 MIN," 18,963 "
-30000103,HC OR MAJOR 1HR," 21,439 "
-30000152,HC OR MAJOR 1HR15," 23,839 "
-30000202,HC OR MAJOR 1HR30," 26,239 "
-30000251,HC OR MAJOR 1HR45," 28,639 "
-30000301,HC OR MAJOR 2HR," 31,039 "
-30000350,HC OR MAJOR 2HR15," 33,439 "
-30000400,HC OR MAJOR 2HR30," 35,839 "
-30000459,HC OR MAJOR 2HR45," 38,239 "
-30000509,HC OR MAJOR 3HR," 40,639 "
-30000558,HC OR MAJOR 3HR15," 43,039 "
-30000608,HC OR MAJOR 3HR30," 45,439 "
-30000657,HC OR MAJOR 3HR45," 47,839 "
-30000707,HC OR MAJOR 4HR," 50,239 "
-30000756,HC OR MAJOR 4HR15," 52,639 "
-30000806,HC OR MAJOR 4HR30," 55,039 "
-30000855,HC OR MAJOR 4HR45," 57,439 "
-30000905,HC OR MAJOR 5HR," 59,839 "
-30000954,HC OR MAJOR 5HR15," 62,239 "
-30001002,HC OR MAJOR 5HR30," 64,639 "
-30001051,HC OR MAJOR 5HR45," 67,039 "
-30001101,HC OR MAJOR 6HR," 69,439 "
-30001150,HC OR MAJOR 6HR15," 71,839 "
-30001200,HC OR MAJOR 6HR30," 74,239 "
-30001259,HC OR MAJOR 6HR45," 76,639 "
-30001309,HC OR MAJOR 7HR," 79,039 "
-30001358,HC OR MAJOR 7HR15," 81,439 "
-30001408,HC OR MAJOR 7HR30," 83,839 "
-30001457,HC OR MAJOR 7HR45," 86,239 "
-30001507,HC OR MAJOR 8HR," 88,639 "
-30001556,HC OR MAJOR 8HR15," 91,039 "
-30001606,HC OR MAJOR 8HR30," 93,439 "
-30001655,HC OR MAJOR 8HR45," 95,839 "
-30001705,HC OR MAJOR 9HR," 98,239 "
-30001754,HC OR MAJOR 9HR15," 100,639 "
-30001804,HC OR MAJOR 9HR30," 103,039 "
-30001853,HC OR MAJOR 9HR45," 105,439 "
-30001903,HC OR MAJOR 10HR," 107,839 "
-30001952,HC OR MAJOR 10HR15," 110,239 "
-30002000,HC OR MAJOR 10HR30," 112,639 "
-30002059,HC OR MAJOR 10HR45," 115,039 "
-30002109,HC OR MAJOR 11HR," 117,439 "
-30002158,HC OR MAJOR 11HR15," 119,839 "
-30002208,HC OR MAJOR 11HR30," 122,239 "
-30002257,HC OR MAJOR 11HR45," 124,639 "
-30002307,HC OR MAJOR 12HR," 127,039 "
-30002356,HC OR MAJOR 12HR15," 129,439 "
-30002406,HC OR MAJOR 12HR30," 131,839 "
-30002455,HC OR 2MAJOR 1HR45," 28,639 "
-30002505,HC OPEN HEART OR 2HR," 49,831 "
-30002554,HC OPEN HEART OR 2HR 15 MIN," 53,549 "
-30002604,HC OR 2MAJOR 2HR30," 57,269 "
-30002653,HC OR 2MAJOR 2HR45," 60,989 "
-30002703,HC OR 2MAJOR 3HR," 64,705 "
-30002752,HC OR 2MAJOR 3HR15," 38,804 "
-30002802,HC OR 2MAJOR 3HR30," 72,147 "
-30002851,HC OR 2MAJOR 3HR45," 75,865 "
-30002901,HC OR 2MAJOR 4HR," 79,585 "
-30002950,HC OR 2MAJOR 4HR15," 83,303 "
-30003008,HC OR 2MAJOR 4HR30," 87,023 "
-30003057,HC OR 2MAJOR 4HR45," 90,742 "
-30003107,HC OR 2MAJOR 5HR," 94,460 "
-30003156,HC OR 2MAJOR 5HR15," 98,181 "
-30003206,HC OR 2MAJOR 5HR30," 101,902 "
-30003255,HC OR 2MAJOR 5HR45," 105,621 "
-30003305,HC OR 2MAJOR 6HR," 109,341 "
-30003354,HC OR 2MAJOR 6HR15," 113,060 "
-30003404,HC OR 2MAJOR 6HR30," 116,778 "
-30003453,HC OR 2MAJOR 6HR45," 120,498 "
-30003503,HC OR 2MAJOR 7HR," 124,218 "
-30003552,HC OR 2MAJOR 7HR15," 127,937 "
-30003602,HC OR 2MAJOR 7HR30," 131,657 "
-30003651,HC OR 2MAJOR 7HR45," 135,378 "
-30003701,HC OR 2MAJOR 8HR," 139,095 "
-30003800,HC OR 2MAJOR 8HR15," 142,815 "
-30003859,HC OR 2MAJOR 8HR30," 146,536 "
-30003909,HC OR 2MAJOR 8HR45," 150,254 "
-30003958,HC OR 2MAJOR 9HR," 153,974 "
-30004006,HC OPEN HEART OR 9HR 15 MIN," 157,689 "
-30004055,HC OR 2MAJOR 9HR30," 161,410 "
-30004105,HC OR 2MAJOR 9HR45," 165,132 "
-30004154,HC OR 2MAJOR 10HR," 168,851 "
-30005037,HC MINOR SURGERY-ADD 15 MIN," 1,517 "
-30005045,HC MINOR SURGERY-15 MINUTES," 1,517 "
-30005052,HC OR MINOR 30 MIN," 10,303 "
-30005102,HC OR MINOR 45 MIN," 12,803 "
-30005128,HC MINOR SURGERY-FIRST HOUR," 15,303 "
-30005151,HC OR MINOR 1HR," 15,303 "
-30005201,HC OR MINOR 1HR15MIN," 17,803 "
-30005219,HC OR MINOR 1HR30MIN," 20,303 "
-30005250,HC OR MINOR 1HR45MIN," 22,803 "
-30005300,HC OR MINOR 2HR," 25,303 "
-30005359,HC OR MINOR 2HR15MIN," 27,803 "
-30005367,HC OR MINOR 2HR30MIN," 30,303 "
-30005409,HC OR MINOR 2HR45MIN," 32,803 "
-30005458,HC OR MINOR 3HR," 35,303 "
-30005508,HC OR MINOR 3HR15MIN," 37,803 "
-30005516,HC OR MINOR 3HR30MIN," 40,303 "
-30005557,HC OR MINOR 3HR45MIN," 42,803 "
-30005607,HC OR MINOR 4HR," 45,303 "
-30005656,HC OR MINOR 4HR15MIN," 47,803 "
-30005664,HC OR MINOR 4HR30MIN," 50,303 "
-30005706,HC OR MINOR 4HR45MIN," 52,803 "
-30005755,HC OR MINOR 5HR," 55,303 "
-30005805,HC OR MINOR 5HR15MIN," 57,803 "
-30005813,HC OR MINOR 5HR30MIN," 60,303 "
-30005854,HC CPB SURGERY-FIRST HR," 27,775 "
-30005862,HC CPB SURGERY-ADD 15 MIN," 3,720 "
-30006001,HC ROBOT ASSISTED OR 30 MIN," 44,797 "
-30006019,HC ROBOT ASSISTED OR 45 MIN," 46,636 "
-30006027,HC ROBOT ASSISTED OR 1 HR," 48,479 "
-30006035,HC ROBOT ASSISTED OR 1:15," 50,320 "
-30006043,HC ROBOT ASSISTED OR 1:30," 52,158 "
-30006050,HC ROBOT ASSISTED OR 1:45," 54,000 "
-30006068,HC ROBOT ASSISTED OR 2 HRS," 55,838 "
-30006076,HC ROBOT ASSISTED OR 2:15," 57,679 "
-30006084,HC ROBOT ASSISTED OR 2:30," 59,518 "
-30006092,HC ROBOT ASSISTED OR 2:45," 61,358 "
-30006100,HC ROBOT ASSISTED OR 3 HRS," 63,198 "
-30006118,HC ROBOT ASSISTED OR 3:15," 65,037 "
-30006126,HC ROBOT ASSISTED OR 3:30," 66,879 "
-30006134,HC ROBOT ASSISTED OR 3:45," 68,718 "
-30006142,HC ROBOT ASSISTED OR 4 HRS," 70,558 "
-30006159,HC ROBOT ASSISTED OR 4:15," 72,399 "
-30006167,HC ROBOT ASSISTED OR 4:30," 74,239 "
-30006175,HC ROBOT ASSISTED OR 4:45," 76,078 "
-30006183,HC ROBOT ASSISTED OR 5 HRS," 77,918 "
-30006191,HC ROBOT ASSISTED OR 5:15," 79,757 "
-30006209,HC ROBOT ASSISTED OR 5:30," 81,600 "
-30006217,HC ROBOT ASSISTED OR 5:45," 83,438 "
-30006225,HC ROBOT ASSISTED OR 6 HRS," 85,277 "
-30006233,HC ROBOT ASSISTED OR 6:15," 87,120 "
-30006241,HC ROBOT ASSISTED OR 6:30," 88,959 "
-30006258,HC ROBOT ASSISTED OR 6:45," 90,800 "
-30006266,HC ROBOT ASSISTED OR 7 HRS," 92,639 "
-30006274,HC ROBOT ASSISTED OR 7:15," 94,480 "
-30006282,HC ROBOT ASSISTED OR 7:30," 96,320 "
-30006290,HC ROBOT ASSISTED OR 7:45," 98,159 "
-30006308,HC ROBOT ASSISTED OR 8 HRS," 99,996 "
-30006316,HC ROBOT ASSISTED OR 8:15," 101,840 "
-30006324,HC ROBOT ASSISTED OR 8:30," 103,678 "
-30006332,HC ROBOT ASSISTED OR 8:45," 105,516 "
-30006340,HC ROBOT ASSISTED OR 9 HRS," 107,357 "
-30006357,HC ROBOT ASSISTED OR 9:15," 109,196 "
-30006365,HC ROBOT ASSISTED OR 9:30," 111,039 "
-30006373,HC ROBOT ASSISTED OR 9:45," 112,877 "
-30006381,HC ROBOT ASSISTED OR 10 HRS," 114,720 "
-30006399,HC ROBOT ASSISTED OR 10:15," 116,560 "
-30006407,HC ROBOT ASSISTED OR 10:30," 118,397 "
-30006415,HC ROBOT ASSISTED OR 10:45," 120,237 "
-30006423,HC ROBOT ASSISTED OR:15MIN," 17,265 "
-30010094,HC GENERAL ANESTHESIA, 783 
-30020051,HC MINOR PROCEDURE 15 MIN," 1,517 "
-30030167,HC DENTAL SURGERY-UP TO ONE HOUR," 3,599 "
-30030217,HC VASOVASOSTOMY," 6,876 "
-30030423,HC ABDOMINOPLASTY," 2,475 "
-30030431,HC BREAST AUGMENTATION X 1, 675 
-30030449,HC SCAR REVISION < 1 HR," 2,621 "
-30030456,HC SCAR REVISION 1-2 HRS," 3,044 "
-30030464,HC SUBMENTAL LIPECTOMY," 2,666 "
-30030472,HC VEIN STRIPPING-UNILATERAL," 3,044 "
-30030480,HC VEIN STRIPPING-BILATERAL," 3,423 "
-30030498,HC ADDITIONAL PLASTIC PROCEDURE," 1,168 "
-30030589,HC BREAST AUGMENTATION X 2," 1,575 "
-30030597,HC BREAST AUGMENT REVISE 1," 2,939 "
-30030605,HC BREAST AUGMENT REVISE 2," 3,552 "
-30030613,HC BLEPHAROPLASTY-1 LID," 2,302 "
-30030621,HC BLEPHAROPLASTY-2 LIDS," 2,821 "
-30030639,HC BLEPHAROPLASTY-4 LIDS," 3,044 "
-30030647,HC BREAST IMPL REMOVAL X 1," 2,350 "
-30030654,HC BREAST IMPL REMOVAL X 2," 2,667 "
-30030662,HC BROW LIFT," 3,104 "
-30030670,HC CLOSED CAPSILOTOMY X 1," 1,487 "
-30030688,HC CLOSED CAPSILOTOMY X2," 1,710 "
-30030696,HC DERMABRASION-PARTIAL," 2,562 "
-30030704,HC DERMABRASION-FULL," 2,917 "
-30030712,HC FOREHEAD LIFT," 3,104 "
-30030779,HC NOSE IMPLANT PROCEDURE," 2,144 "
-30030787,HC LESION EXCISION," 2,478 "
-30030795,HC LESION EXCISION GRAFT," 2,738 "
-30030803,HC LIPOSUCTION UNDER 90 MIN," 1,240 "
-30030811,HC LIPOSUCTION OVER 90 MINUTES," 5,370 "
-30030829,HC MASTOPEXY-UNILATERAL," 2,475 "
-30030837,HC MASTOPEXY-BILATERAL," 2,475 "
-30030845,HC MELOPLASTY," 4,507 "
-30030852,HC MELOPLASTY W/BLEPHAROPLASTY," 5,370 "
-30030860,HC MELOPLASTY W/FOREHEAD/LIDS," 6,784 "
-30030878,HC MENTOPLASTY," 3,423 "
-30030886,HC NOSE TIP PROCEDURE," 2,230 "
-30030894,HC OTOPLASTY," 2,892 "
-30030902,HC BREAST REDUCTION X 1," 6,774 "
-30030910,HC BREAST REDUCTION X 2," 7,909 "
-30030928,HC RHINOPLASTY W/O GRAFT," 2,475 "
-30030936,HC RHINOPLASTY W/GRAFT," 2,475 "
-30031249,HC LASER RESURFACE FULL," 2,963 "
-30031256,HC RESURFACING LASER EQUIP., 704 
-30031264,HC LASER RESURFACE PART," 1,609 "
-30063812,HC HYPAQUE 30CC, 61 
-30079826,HC LIPOSUCTION 90-180 MIN.," 4,962 "
-30079834,HC LIPOSUCTION 180-300 MIN.," 5,706 "
-30090336,HC MANDIBLE DISTRCTION," 3,661 "
-30100903,HC VENIPUNCTURE, 115 
-30110019,HC ACUTE POST ANES CARE 15 MIN, 631 
-30110027,HC INTENSE POST ANES CARE-15 MIN," 1,142 "
-30110035,HC CRITICAL POST ANES CARE-15 MIN," 1,356 "
-30200059,HC MAJOR SURGERY-ADD 15 MIN," 2,400 "
-30200067,HC OR MAJOR 30 MINUTES," 17,122 "
-30200083,HC OR MAJOR 45 MINUTES," 18,963 "
-30200109,HC OR MAJOR 1HR," 21,439 "
-30200158,HC OR MAJOR 1HR15," 23,839 "
-30200208,HC OR MAJOR 1HR30," 26,239 "
-30200257,HC OR MAJOR 1HR45," 28,639 "
-30200307,HC OR MAJOR 2HR," 31,039 "
-30200356,HC OR MAJOR 2HR15," 33,439 "
-30200406,HC OR MAJOR 2HR30," 35,839 "
-30200455,HC OR MAJOR 2HR45," 38,239 "
-30200505,HC OR MAJOR 3HR," 40,639 "
-30200554,HC OR MAJOR 3HR15," 43,039 "
-30200604,HC OR MAJOR 3HR30," 45,439 "
-30200653,HC OR MAJOR 3HR45," 47,839 "
-30200703,HC OR MAJOR 4HR," 50,239 "
-30200752,HC OR MAJOR 4HR15," 52,639 "
-30200802,HC OR MAJOR 4HR30," 55,039 "
-30200851,HC OR MAJOR 4HR45," 57,439 "
-30200901,HC OR MAJOR 5HR," 59,839 "
-30200950,HC OR MAJOR 5HR15," 62,239 "
-30201008,HC OR MAJOR 5HR30," 64,639 "
-30201057,HC OR MAJOR 5HR45," 67,039 "
-30201107,HC OR MAJOR 6HR," 69,439 "
-30201156,HC OR MAJOR 6HR15," 71,839 "
-30201206,HC OR MAJOR 6HR30," 74,239 "
-30201255,HC OR MAJOR 6HR45," 76,639 "
-30201305,HC OR MAJOR 7HR," 79,039 "
-30201354,HC OR MAJOR 7HR15," 81,439 "
-30201404,HC OR MAJOR 7HR30," 83,839 "
-30201453,HC OR MAJOR 7HR45," 86,239 "
-30201503,HC OR MAJOR 8HR," 88,639 "
-30201552,HC OR MAJOR 8HR15," 91,039 "
-30201602,HC OR MAJOR 8HR30," 93,439 "
-30201651,HC OR MAJOR 8HR45," 95,839 "
-30201701,HC OR MAJOR 9HR," 98,239 "
-30201750,HC OR MAJOR 9HR15," 100,639 "
-30201800,HC OR MAJOR 9HR30," 103,039 "
-30201859,HC OR MAJOR 9HR45," 105,439 "
-30201909,HC OR MAJOR 10HR," 107,839 "
-30202006,HC ROBOT-ASSISTED OR 15 MIN," 16,871 "
-30202014,HC ROBOT-ASSISTED OR 30 MIN," 42,558 "
-30202022,HC ROBOT-ASSISTED OR 45MIN," 44,272 "
-30202030,HC ROBOT-ASSISTED OR 1 HR," 45,991 "
-30202048,HC ROBOT-ASSISTED OR 1:15," 47,709 "
-30202055,HC ROBOT-ASSISTED OR 1:30," 49,423 "
-30202063,HC ROBOT-ASSISTED OR 1:45," 51,141 "
-30202071,HC ROBOT-ASSISTED OR 2HRS," 52,857 "
-30202089,HC ROBOT-ASSISTED OR 2:15," 54,574 "
-30202097,HC ROBOT-ASSISTED OR 2:30," 56,290 "
-30202105,HC ROBOT-ASSISTED OR 2:45," 58,006 "
-30202113,HC ROBOT-ASSISTED OR 3HRS," 59,723 "
-30202121,HC ROBOT-ASSISTED OR 3:15," 61,439 "
-30202139,HC ROBOT-ASSISTED OR 3:30," 63,157 "
-30202147,HC ROBOT-ASSISTED OR 3:45," 64,873 "
-30202154,HC ROBOT-ASSISTED OR 4HRS," 66,590 "
-30202162,HC ROBOT-ASSISTED OR 4:15," 68,307 "
-30202170,HC ROBOT-ASSISTED OR 4:30," 70,025 "
-30202188,HC ROBOT-ASSISTED OR 4:45," 71,739 "
-30202196,HC ROBOT-ASSISTED OR 5HRS," 73,455 "
-30202204,HC ROBOT-ASSISTED OR 5:15," 75,170 "
-30202212,HC ROBOT-ASSISTED OR 5:30," 76,890 "
-30202220,HC ROBOT-ASSISTED OR 5:45," 78,605 "
-30202238,HC ROBOT-ASSISTED OR 6HRS," 80,320 "
-30202246,HC ROBOT-ASSISTED OR 6:15," 82,040 "
-30202253,HC ROBOT-ASSISTED OR 6:30," 83,755 "
-30202261,HC ROBOT-ASSISTED OR 6:45," 85,473 "
-30202279,HC ROBOT-ASSISTED OR 7HRS," 87,188 "
-30202287,HC ROBOT-ASSISTED OR 7:15," 88,906 "
-30202295,HC ROBOT-ASSISTED OR 7:30," 90,623 "
-30202303,HC ROBOT-ASSISTED OR 7:45," 92,337 "
-30202311,HC ROBOT-ASSISTED OR 8HRS," 94,052 "
-30205009,HC OR 15 MIN FLAT RATE," 1,517 "
-30205017,HC MINOR SURGERY-ADD 15 MIN," 1,517 "
-30205058,HC OR MINOR 30 MIN," 10,303 "
-30205108,HC OR MINOR 45 MIN," 12,803 "
-30205157,HC OR MINOR 1HR," 15,303 "
-30205165,HC MINOR SURGERY-FIRST HOUR," 15,303 "
-30205207,HC OR MINOR 1HR15MIN," 17,803 "
-30205215,HC OR MINOR 1HR30MIN," 20,303 "
-30205256,HC OR MINOR 1HR45MIN," 22,803 "
-30205306,HC OR MINOR 2HR," 25,303 "
-30205355,HC OR MINOR 2HR15MIN," 27,803 "
-30205363,HC OR MINOR 2HR30MIN," 30,303 "
-30205405,HC OR MINOR 2HR45MIN," 32,803 "
-30205454,HC OR MINOR 3HR," 35,303 "
-30205504,HC OR MINOR 3HR15MIN," 37,803 "
-30205512,HC OR MINOR 3HR30MIN," 40,303 "
-30205553,HC OR MINOR 3HR45MIN," 42,803 "
-30205603,HC OR MINOR 4HR," 45,303 "
-30205652,HC OR MINOR 4HR15MIN," 47,803 "
-30205660,HC OR MINOR 4HR30MIN," 50,303 "
-30205702,HC OR MINOR 4HR45MIN," 52,803 "
-30205751,HC OR MINOR 5HR," 55,303 "
-30205801,HC OR MINOR 5HR15MIN," 57,803 "
-30205819,HC OR MINOR 5HR30MIN," 60,303 "
-30210108,HC MINOR PROCEDURE 15 MIN," 1,517 "
-30210157,HC MINOR PROCEDURE 30 MIN," 2,567 "
-30210207,HC MINOR PROCEDURE 45 MIN," 3,617 "
-30210256,HC MINOR PROCEDURE 1 HR," 4,667 "
-30210306,HC MINOR PROCEDURE 1 HR 15 MIN," 5,717 "
-30210355,HC MINOR PROCEDURE 1 HR 30 MIN," 6,767 "
-30210405,HC MINOR PROCEDURE 1 HR 45 MIN," 7,417 "
-30210454,HC MINOR PROCEDURE 2 HR," 8,067 "
-30210504,HC MINOR PROCEDURE 2 HR 15 MIN," 8,717 "
-30210553,HC MINOR PROCEDURE 2 HR 30 MIN," 9,367 "
-30210603,HC MINOR PROCEDURE 2 HR 45 MIN," 10,017 "
-30210652,HC MINOR PROCEDURE 3 HR," 10,667 "
-30210850,HC LIPOSUCTION UNDER 90 MIN," 1,575 "
-30210876,HC LIPOSUCTION 90-180 MIN.," 4,962 "
-30210884,HC LIPOSUCTION 180-300 MIN.," 5,706 "
-30210892,HC LIPOSUCTION HOURLY CHG., 356 
-30227102,HC BLPHLSTY-2 LIDS UP/LO BILAT OR," 2,821 "
-30227300,HC BLPHLSTY-4 LIDS UP&LO BILAT OR," 3,044 "
-30227508,HC BLPHLSTY-1 LID UP OR LOW UNILA," 2,302 "
-30227714,HC BLPHLSTY UP&LOW UNILAT OR," 2,821 "
-30227904,HC BROWLIFT OR," 3,105 "
-30228100,HC FOREHEAD LIFT OR," 3,104 "
-30228308,HC MELOPLASTY OR," 4,507 "
-30228506,HC MELOPLASTY W/BLEPHLSTY OR," 5,370 "
-30228704,HC MELOPLAST W/FOREHEAD & LO LIDS," 6,784 "
-30228902,HC MENTOPLASTY UP TO 2HRS," 3,423 "
-30229108,HC RHINO W/ BONE GRAFT UP TO 2HRS," 2,475 "
-30229306,HC RHINOPLASTY W/O BONE GRAFT OR," 2,475 "
-30229504,HC NOSE TIP ONLY OR," 2,230 "
-30229702,HC IMPLANT IN NOSE OR," 2,144 "
-30229900,HC MASTOPEXY UNILATERAL OR," 2,475 "
-30230106,HC MASTOPEXY BILATERAL OR," 2,475 "
-30230304,HC AUG MAMMOPLASTY UNILATERAL OR," 2,939 "
-30230346,HC REDUCTION MAMMOPLASTY -UNILAT," 6,775 "
-30230353,HC REDUCTION MAMMOPLASTY-BILAT," 7,908 "
-30230502,HC AUG REVISION UNILATERAL OR," 2,939 "
-30230700,HC AUG REVISION BILATERAL OR," 3,552 "
-30230908,HC BREAST IMPLANT REM UNILAT OR," 2,349 "
-30231112,HC BREAST IMPLANT REM BILAT OR," 2,666 "
-30231310,HC CLOSED CAPSILOTOMY UNILAT OR," 1,225 "
-30231518,HC CLOSED CAPSILOTOMY BILAT OR," 1,409 "
-30231716,HC SUBMENTAL LIPECTOMY OR," 2,666 "
-30231914,HC LESION WITH GRAFT OR," 2,738 "
-30232003,HC LESION EXCISION OR," 2,478 "
-30232110,HC DERMABRASION PARTIAL OR," 2,562 "
-30232318,HC DERMABRASION FULL OR," 2,917 "
-30232706,HC GENERAL ANESTHESIA ADD ON OR, 687 
-30232904,HC 2ND PROC ADD ON UP TO 2HR," 1,217 "
-30232912,HC 2ND PROCEDURE ADD ON OR," 1,217 "
-30233316,HC AUG MAMMOPLASTY BILAT OR," 3,552 "
-30233514,HC OTOPLASTY OR," 2,892 "
-30233530,HC ABDOMINOPLASTY," 2,475 "
-30236103,HC UNILATERAL VEIN STRIP/LIGATION," 3,044 "
-30236129,HC BILATERAL VEIN STRIP/LIGATION," 3,423 "
-30236301,HC DENTAL SURGERY-1 HOUR OR LESS," 3,599 "
-30236319,HC DENTAL SURGERY OVER ONE HOUR," 3,737 "
-30236327,HC SCAR REVISION (LESS THAN 1 HR)," 2,621 "
-30236335,HC SCAR REVISION (ONE TO TWO HRS)," 3,044 "
-30236368,HC PHLEBOTOMY CHARGE, 115 
-30236418,HC PHAKIC LENS PROCEDURE," 2,018 "
-30237184,HC VASOVASOSTOMY PROCEDURE," 6,876 "
-30240436,HC ACUTE POST ANES CARE 15 MIN, 631 
-30240444,HC INTENSE POST ANES CARE 15 MIN," 1,142 "
-30240451,HC CRITICAL POST ANES CARE 15 MIN," 1,356 "
-30275135,HC LASER RESURFACING-FACE," 2,995 "
-30275143,HC RESURFACING LASER EQUIP., 704 
-30275150,HC LASER RESURFACING PART.," 2,475 "
-30290332,HC MANDIBLE DISTR PROC," 3,661 "
-30301006,HC ANES SPINAL-CAUD-EPI," 3,160 "
-30301055,HC BRACHIAL BLOCK," 2,948 "
-30301097,HC ANESTHESIA-ADDITIONAL 15MIN, 295 
-30301105,HC ANES TIME 30 MIN," 3,336 "
-30301154,HC ANES TIME 45 MIN," 3,756 "
-30301204,HC ANESTHESIA-FIRST HOUR," 4,176 "
-30301253,HC ANES TIME 1 HR 15," 4,596 "
-30301303,HC ANES TIME 1 HR 30," 5,016 "
-30301352,HC ANES TIME 1 HR 45," 5,436 "
-30301402,HC ANES TIME 2 HR," 5,856 "
-30301451,HC ANES TIME 2 HR 15," 6,276 "
-30301501,HC ANES TIME 2 HR 30," 6,696 "
-30301550,HC ANES TIME 2 HR 45," 7,116 "
-30301600,HC ANES TIME 3 HR," 7,536 "
-30301659,HC ANES TIME 3 HR 15," 7,956 "
-30301709,HC ANES TIME 3 HR 30," 8,376 "
-30301758,HC ANES TIME 3 HR 45," 8,796 "
-30301808,HC ANES TIME 4 HR," 9,216 "
-30301857,HC ANES TIME 4 HR 15," 9,636 "
-30301907,HC ANES TIME 4 HR 30," 10,056 "
-30301956,HC ANES TIME 4 HR 45," 10,476 "
-30302004,HC ANES TIME 5 HR," 10,896 "
-30302053,HC ANES TIME 5 HR 15," 11,316 "
-30302103,HC ANES TIME 5 HR 30," 11,736 "
-30302152,HC ANES TIME 5 HR 45," 12,156 "
-30302202,HC ANES TIME 6 HR," 12,576 "
-30302251,HC ANES TIME 6 HR 15," 12,996 "
-30302301,HC ANES TIME 6 HR 30," 13,416 "
-30302350,HC ANES TIME 6 HR 45," 13,836 "
-30302400,HC ANES TIME 7 HR," 14,256 "
-30302459,HC ANES TIME 7 HR 15," 14,551 "
-30302509,HC ANES TIME 7 HR 30," 14,846 "
-30302558,HC ANES TIME 7 HR 45," 15,141 "
-30302608,HC ANES TIME 8 HR," 15,436 "
-30302657,HC ANES TIME 8 HR 15," 15,731 "
-30302707,HC ANES TIME 8 HR 30," 16,026 "
-30302756,HC ANES TIME 8 HR 45," 16,321 "
-30302806,HC ANES TIME 9 HR," 16,616 "
-30302855,HC ANES TIME 9 HR 15," 16,911 "
-30302905,HC ANES TIME 9 HR 30," 17,206 "
-30302954,HC ANES TIME 9 HR 45," 17,501 "
-30303002,HC ANES TIME 10 HR," 17,796 "
-30303051,HC ANES TIME 10 HR 15," 18,091 "
-30303101,HC ANES TIME 10 HR 30," 18,386 "
-30303150,HC ANES TIME 10 HR 45," 18,681 "
-30303200,HC ANES TIME 11 HR," 18,976 "
-30303259,HC ANES TIME 11 HR 15," 19,271 "
-30303309,HC ANES TIME 11 HR 30," 19,566 "
-30303358,HC ANES TIME 11 HR 45," 19,861 "
-30303408,HC ANES TIME 12 HR," 20,156 "
-30303457,HC ANES TIME 12 HR 15," 20,451 "
-30303507,HC ANES TIME 12 HR 30," 20,746 "
-30303523,HC ANES TIME 12 HR 45," 21,041 "
-30303549,HC ANES TIME 13 HR," 21,336 "
-30303564,HC ANES TIME 13 HR 15," 21,631 "
-30303580,HC ANES TIME 13 HR 30," 21,926 "
-30303606,HC ANES TIME 13 HR 45," 22,221 "
-30303622,HC ANES TIME 14 HR," 22,516 "
-30303648,HC ANES TIME 14 HR 15," 22,811 "
-30303663,HC ANES TIME 14 HR 30," 23,106 "
-30303689,HC ANES TIME 14 HR 45," 23,401 "
-30303705,HC ANES TIME 15 HR," 23,696 "
-30303721,HC ANES TIME 15 HR 15," 23,991 "
-30303747,HC ANES TIME 15 HR 30," 24,286 "
-30303762,HC ANES TIME 15 HR 45," 24,581 "
-30303788,HC ANES TIME 16 HR," 24,876 "
-30303804,HC ANES TIME 16 HR 15," 25,171 "
-30327118,HC ANES BLPHSTY UP OR LOW BILAT, 693 
-30327316,HC ANES BLPHSTY UP&LOW BILAT, 406 
-30327514,HC ANES BLPHSTY UP OR LOW UNILAT, 406 
-30327712,HC ANES BLPHSTY UP&LOW UNILAT, 591 
-30327910,HC ANES BROWLIFT, 759 
-30328116,HC ANES FOREHEAD LIFT, 736 
-30328314,HC ANES MELOPLASTY, 738 
-30328512,HC ANES MELOPLASTY W/BLEPHPLSTY, 842 
-30328710,HC ANES MLPLSTY W/FORHD& LOW LID, 565 
-30328918,HC ANES MENTOPLASTY, 570 
-30329114,HC ANES RHNPLSTY W/BONE GRFT," 1,475 "
-30329312,HC ANES RHNPLSTY W/O BONE GRFT, 704 
-30329510,HC ANES NOSE TIP ONLY, 677 
-30329718,HC ANES IMPLANT NOSE, 622 
-30329916,HC ANES MASTOPEXY UNILATERAL, 826 
-30330112,HC ANES MASTOPEXY BILATERAL," 1,005 "
-30330310,HC ANES AUG MAMMPLSTY UNILAT, 800 
-30330518,HC ANES AUG REVISION UNILAT, 779 
-30330716,HC ANES AUG REVISION BILATERAL, 660 
-30330914,HC ANES BREAST IMPL REM UNILAT, 971 
-30331110,HC BREAST IMPL REM BILAT ANES, 736 
-30331714,HC ANES SUBMENTAL LIPECTOMY, 733 
-30331912,HC ANES LESION EXCISION WITH GRAF, 674 
-30332019,HC ANES LESION EXCISION," 1,041 "
-30332118,HC ANES PARTIAL DERMABRASION, 426 
-30332316,HC ANES FULL DERMABRASION, 478 
-30332712,HC GENERAL ANESTHESIA ADD ON ANES, 818 
-30332910,HC 2ND PROCEDURE ADD ON ANES, 196 
-30333116,HC 3RD PROCEDURE ADD ON ANES, 591 
-30333314,HC ANES AUGMENT MAMOPLASTY BILAT, 570 
-30333512,HC ANES OTOPLASTY, 710 
-30333520,HC ANES ABDOMINOPLASTY LEVEL 1," 1,340 "
-30333538,HC ABDOMINOPLASTY, 787 
-30334916,HC ANES IOL SECONDARY," 1,959 "
-30335111,HC GENERAL ANESTHESIA ADD ON ANES, 175 
-30335160,HC ANES DIAGNOSTIC LAPARASCOPY, 904 
-30335186,HC ANES LAPAROS TUBAL LIGATION, 904 
-30335301,HC ANES DILIATION & CURETTAGE, 702 
-30337182,HC ANES VASOVASOSTOMY PROCEDURE, 970 
-30340004,HC ANES DENTAL SURGERY < 1 HOUR," 1,096 "
-30340046,HC ANES DENTAL SURGERY -1-2 HRS, 754 
-30340053,HC ANES VASOVASOSTOMY, 591 
-30340152,HC ANES CONE BIOPSY, 874 
-30340269,HC ABDOMINOPLASTY," 1,091 "
-30340277,HC ANES AUGMENT MAMMOPLASTY-UNILA, 683 
-30340285,HC ANES SCAR REVISION<1 HOUR, 610 
-30340293,HC ANES SCAR REVISION-1-2 HRS, 635 
-30340301,HC ANES SUBMENTAL LIPECTOMY, 417 
-30340319,HC ANES VEIN STRIPPING-UNILATERAL," 1,273 "
-30340327,HC ANES VEIN STRIPPING-BILATERAL, 537 
-30340335,HC ANES ADDITIONAL PLASTIC PROCED, 401 
-30340426,HC ANES AUGMENT MAMMOPLASTY-BILAT, 776 
-30340434,HC ANES AUGMENTATION REVISION-UNI, 875 
-30340442,HC ANES AUGMENTATION REVISION-BIL, 745 
-30340459,HC ANES BLEPHAROPLASTY-1 LID," 1,084 "
-30340467,HC ANES BLEPHAROPLASTY-2 LIDS, 706 
-30340475,HC ANES BLEPHAROPLASTY - 4 LIDS, 970 
-30340483,HC ANES BREAST IMPLANT REMVL-UNIL, 708 
-30340491,HC ANES BREAST IMPL REMOVAL-BILAT, 838 
-30340509,HC ANES BROW LIFT, 608 
-30340517,HC ANES CLOSED CAPSILOTOMY-UNILAT, 717 
-30340525,HC ANES CLOSED CAPSILOTOMY-BILAT, 685 
-30340533,HC ANES DERMABRASION-PARTIAL, 603 
-30340541,HC ANES DERMABRASION-FULL, 612 
-30340558,HC ANES FOREHEAD LIFT," 1,163 "
-30340616,HC ANES NOSE IMPLANT PROCEDURE, 693 
-30340624,HC ANES LESION EXCISION W/O GRAFT, 897 
-30340632,HC ANES LESION EXCISION W/GRAFT," 1,121 "
-30340640,HC ANES LIPOSCUTION UNDER 90 MINU," 1,595 "
-30340657,HC ANES LIPOSUCTION OVER 90 MIN, 831 
-30340665,HC ANES MASTOPEXY-UNILATERAL30340," 1,426 "
-30340673,HC ANES MASTOPEXY-BILATERAL, 744 
-30340681,HC ANES MELOPLASTY, 611 
-30340699,HC ANES MELOPLASTY W/BLEPHAROPLAS, 879 
-30340707,HC ANES MELOPLASTY W/FOREHEAD/LID, 727 
-30340715,HC ANES MENTOPLASTY, 719 
-30340723,HC ANES NOSE TIP PROCEDURE, 818 
-30340731,HC ANES OTOPLASTY," 1,160 "
-30340749,HC ANES REDUCTION MAMMOPLASTY-UNI, 921 
-30340756,HC ANES REDUCTION MAMMOPLASTY-BIL," 1,117 "
-30340764,HC ANES RHINOPLASTY W/O GRAFT," 1,522 "
-30340772,HC ANES RHINOPLASTY W/GRAFT," 1,242 "
-30361356,HC SET STERILE LINE/CVP/PUMP," 6,163 "
-30380570,HC HYPOTHERMIA CATH L41," 13,125 "
-30402028,HC ACUTE POST ANES CARE 15 MIN, 631 
-30402044,HC INTENSE POST ANES CARE 15 MIN," 1,142 "
-30402069,HC CRITICAL POST ANES CARE 15 MIN," 1,356 "
-30430169,HC DENTAL SURGERY-UP TO ONE HOUR, 103 
-30430201,HC DENTAL SURGERY-ONE TO TWO HOUR, 472 
-30430219,HC VASOVASOSTOMY, 117 
-30430425,HC ABDOMINOPLASTY-LEVEL 2, 97 
-30430433,HC AUGMENT MAMMOPLASTY-UNILATERAL, 97 
-30430441,HC SCAR REVISION-UP TO ONE HOUR, 411 
-30430458,HC SCAR REVISION-ONE TO TWO HRS, 450 
-30430466,HC SUBMENTAL LIPECTOMY, 246 
-30430474,HC VEIN STRIPPING-UNILATERAL, 253 
-30430482,HC VEIN STRIPPING-BILATERAL, 270 
-30430490,HC ADDITIONAL PLASTIC PROCEDURE, 219 
-30430581,HC AUGMENTATION MAMMOPLASTY-BILAT, 160 
-30430599,HC AUGMENTATION REVISION-UNILATER, 172 
-30430607,HC AUGMENTATION REVISION-BILAT, 212 
-30430615,HC BLEPHAROPLASTY-1 LID, 97 
-30430623,HC BLEPHAROPLASTY-2 LIDS, 279 
-30430631,HC BLEPHAROPLASTY-4 LIDS, 278 
-30430649,HC BREAST IMPLANT REMVL-UNILAT, 140 
-30430656,HC BREAST IMPLANT REMVL-BILAT, 122 
-30430664,HC BROW LIFT, 103 
-30430672,HC CLOSED CAPSILOTOMY-UNILAT, 102 
-30430680,HC CLOSED CAPSILOTOMY-BILAT, 169 
-30430698,HC DERMABRASION-PARTIAL, 69 
-30430706,HC DERMABRASION-FULL, 93 
-30430714,HC FOREHEAD LIFT, 99 
-30430771,HC NOSE IMPLANT PROCEDURE, 111 
-30430789,HC LESION EXCISION W/O GRAFT, 123 
-30430797,HC LESION EXCISION W/GRAFT, 222 
-30430805,HC LIPOSUCTION UNDER 90 MINUTES, 159 
-30430813,HC LIPOSUCTION OVER 90 MINUTES, 141 
-30430821,HC MASTOPEXY-UNILATERAL, 121 
-30430839,HC MASTOPEXY-BILATERAL, 177 
-30430847,HC MELOPLASTY, 183 
-30430854,HC MELOPLASTY W/BLEPHAROPLASTY, 519 
-30430862,HC MELOPLASTY W/FOREHEAD/LIDS, 654 
-30430870,HC MENTOPLASTY, 334 
-30430888,HC NOSE TIP PROCEDURE, 217 
-30430896,HC OTOPLASTY, 280 
-30430904,HC REDUCTION MAMMOPLASTY-UNILAT, 654 
-30430912,HC REDUCTION MAMMOPLASTY-BILAT, 764 
-30430920,HC RHINOPLASTY W/O GRAFT, 282 
-30430938,HC RHINOPLASTY W/GRAFT, 318 
-30561112,HC LIVER PERFUSION PACK," 1,615 "
-30568265,HC FILTER LEOKOCYTE RED, 213 
-30568281,HC TUBING CPB PUMP SASH, 213 
-30568299,HC SOL CARDIOPLEGIA MEDTRONIC, 237 
-30568307,HC ADAPTOR CARDIOPLEGIA INFANT, 213 
-30568315,HC PLEGISOL SOLUTION, 237 
-30568323,HC TUBING VENTR ASSIST 1/4, 383 
-30568331,HC TUBING VENTR ASSIST 3/8, 425 
-30568349,HC CUVETTE VENOUS CDI 1/2, 425 
-30568356,HC RESERVOIR VENOUS 1000ML, 510 
-30568364,HC FILTER LEUKOCYTE, 723 
-30568372,HC FILTER ARTERIAL PEDI, 723 
-30568380,HC TUBING PACK CPB 3/8 X 1/4, 723 
-30568398,HC HEMO-CONCENTRATOR 1000TS, 723 
-30568406,HC HEMO-CONCENTRATOR 400TS, 808 
-30568414,HC SENSOR CDI ARTERIAL, 850 
-30568422,HC TUBING CELLSAVER," 1,105 "
-30568430,HC FILTER REDUCTION LGB," 1,615 "
-30568448,HC TUBING PACK 1/2 ADULT," 1,913 "
-30568455,HC TUBING PACK CPS," 6,588 "
-30568463,HC TUBING CARMEDA COATED DISP," 7,013 "
-30568471,HC OXYGENATOR CARMEDA COATED," 3,188 "
-30568489,HC OXYGENATOR PEDI," 3,188 "
-30568497,HC OXYGENATOR MICRO RESERVOIR," 3,613 "
-30568505,HC TUBING PEDI 1/4," 2,763 "
-30568513,HC OXYGENATOR ADULT MEDTRONIC," 2,338 "
-30570022,HC PERFUSION SUPPLY LEVEL 3, 85 
-30570030,HC PERFUSION SUPPLY LEVEL 4, 149 
-30570048,HC PERFUSION SUPPLY LEVEL 5, 213 
-30570055,HC PERFUSION SUPPLY LEVEL 6, 298 
-30570063,HC PERFUSION SUPPLY LEVEL 7, 383 
-30570071,HC PERFUSION SUPPLY LEVEL 8, 425 
-30570089,HC PERFUSION SUPPLY LEVEL 9, 510 
-30570097,HC PERFUSION SUPPLY LEVEL 10, 595 
-30570105,HC PERFUSION SUPPLY LEVEL 11, 723 
-30570113,HC PERFUSION SUPPLY LEVEL 12, 808 
-30570121,HC PERFUSION SUPPLY LEVEL 13, 850 
-30570139,HC PERFUSION SUPPLY LEVEL 14," 1,105 "
-30570147,HC PERFUSION SUPPLY LEVEL 15," 1,275 "
-30570154,HC PERFUSION SUPPLY LEVEL 16," 1,445 "
-30570162,HC PERFUSION SUPPLY LEVEL 17," 1,615 "
-30570170,HC PERFUSION SUPPLY LEVEL 18," 1,913 "
-30570188,HC PERFUSION SUPPLY LEVEL 19," 2,338 "
-30570196,HC PERFUSION SUPPLY LEVEL 20," 2,763 "
-30570204,HC PERFUSION SUPPLY LEVEL 21," 3,188 "
-30570212,HC PERFUSION SUPPLY LEVEL 22," 3,613 "
-30570220,HC PERFUSION SUPPLY LEVEL 23," 4,038 "
-30570238,HC PERFUSION SUPPLY LEVEL 24," 4,463 "
-30570246,HC PERFUSION SUPPLY LEVEL 25," 4,888 "
-30570253,HC PERFUSION SUPPLY LEVEL 26," 5,313 "
-30570261,HC PERFUSION SUPPLY LEVEL 27," 5,738 "
-30570279,HC PERFUSION SUPPLY LEVEL 28," 6,163 "
-30570287,HC PERFUSION SUPPLY LEVEL 29," 6,588 "
-30570295,HC PERFUSION SUPPLY LEVEL 30," 7,013 "
-30570303,HC PERFUSION SUPPLY LEVEL 31," 7,438 "
-30570311,HC PERFUSION SUPPLY LEVEL 32," 7,863 "
-30570329,HC PERFUSION SUPPLY LEVEL 33," 8,288 "
-30570337,HC PERFUSION SUPPLY LEVEL 34," 7,875 "
-30591192,HC BIO-PUMP INSERT," 1,913 "
-30591200,HC PRESSURE DOME, 149 
-30591226,HC IABP INSERT KIT L 12, 808 
-30600464,HC RECTAL MANOMETRY," 2,671 "
-30600480,HC ESOPHAGEAL MANOMTRY," 1,902 "
-30600514,HC ERCP WITH/WITHOUT BRUSHING," 7,961 "
-30600555,HC COLONOSCOPY W/LASER," 7,724 "
-30600563,HC ESOPHOGASCPY W/LASR," 6,547 "
-30600605,HC COLONOSCOPY DIAGNOSTIC," 1,700 "
-30600654,HC GASTROSCOPY," 5,391 "
-30600753,HC FLEXBLE SIGMOIDSCPY," 2,395 "
-30600852,HC LIVER BIOPSY," 3,429 "
-30601504,HC UP ENDOSC/POLYPECTM," 5,514 "
-30601520,HC ENDSCPY SAVRY DILAT," 5,934 "
-30601546,HC UP ENDOSCPY/SCLEROT," 5,934 "
-30601561,HC UP ENDOSCPY/TUB PLC," 6,979 "
-30601587,HC FLEX SIG/GALL DILAT," 4,586 "
-30601603,HC FLEX SIG W/HEMOSTASIS," 5,009 "
-30601629,HC FLEX SIG/POLYPECTMY," 3,647 "
-30601645,HC FLEX SIG/LASER," 5,461 "
-30601686,HC FLEX SIG/SCLEROTHRP," 5,009 "
-30601702,HC PERCTN ENDSC GASTR**," 7,285 "
-30601728,HC GASTRIC BUTTN INSRT," 2,087 "
-30601801,HC ENDOSCOPY/BALLOON," 7,690 "
-30601827,HC EGD W/HEMOSTASIS," 6,547 "
-30601843,HC ERCP/BALLOON," 11,923 "
-30601868,HC ERCP/DRAIN," 11,923 "
-30601900,HC ERCP/STONE EXTRACTN," 10,625 "
-30601926,HC ERCP/SPHINCTEROTOMY," 11,923 "
-30601967,HC COLONSCPY/BALN DILA," 6,267 "
-30601983,HC COLONOSCOPY W/HEMOSTASIS," 7,724 "
-30602007,HC COLONSCPY/POLYPCTMY," 6,267 "
-30602023,HC COLONSCPY/SCLERTHRP," 7,724 "
-30602049,HC COLONSCPY/TUBE PLAC," 6,267 "
-30602064,HC PEG/JEJUNAL TUB INS," 5,625 "
-30602114,HC PARACENTESIS," 2,323 "
-30602288,HC EGD ULTRASOUND," 7,459 "
-30602296,HC COLONSCOPY ULTRASOUND," 7,762 "
-30602304,HC FLEX SIG ULTRASOUND," 4,851 "
-30602361,HC AMB ESOPH PH MONITR," 2,532 "
-30602379,HC SM BOWEL ENDOS DIAG," 5,640 "
-30602387,HC SM BOWEL ENDO HEMO," 5,865 "
-30602429,HC PYLORITEK TEST, 206 
-30680649,HC ERCP W/ BIOPSY," 7,961 "
-30680664,HC ERCP W/ MANOMETRY," 11,923 "
-30680672,HC EGD W/ BIOPSY," 5,934 "
-30680680,HC EGD W/ FOREIGN BODY," 5,934 "
-30680698,HC EGD W/ BANDING," 7,176 "
-30680714,HC EGD W/ SAVARY AND BALLOON DILA," 7,690 "
-30680722,HC FLEX SIGMOIDOSCOPY W/ BIOPSY," 2,760 "
-30680748,HC JEJUNAL TUBE PLCMNT," 5,674 "
-30680755,HC COLONOSCOPY W/BIOPSY," 5,982 "
-30680797,HC SMALL BOWEL MOTILITY," 10,073 "
-30680904,HC EGD WITH STENT PLACEMENT," 6,622 "
-30680912,HC ERCP + LASER," 7,093 "
-30680938,HC UGI ENDO STRETTA PROC," 7,607 "
-30683023,HC EGD W/EUS/FNA," 6,977 "
-30683031,HC ERCP W/STENT PLACEMENT," 10,317 "
-30683049,HC ERCP W/LITHOTRIPSY," 16,280 "
-30683056,HC FLEX SIG W/STENT PLACEMENT," 5,136 "
-30683064,HC CAPSULE ENDOSCOPY," 6,712 "
-30683171,HC ESOPHAGUS MOTILITY STUDY," 2,108 "
-30683189,HC MODERATE SEDATION," 1,066 "
-30683197,HC TRANSFUSION," 3,300 "
-30683205,HC EGD W/DRAIN CYST," 4,657 "
-30683213,HC EGD W/HOT BIOPSY," 4,657 "
-30683221,HC ERCP W/STENT CHANGE OR RMVL," 15,697 "
-30683247,HC SB ENDO W/LESION RMVL," 5,064 "
-30683254,HC SB ENDO W/HOT BIOPSY," 5,064 "
-30683262,HC SB ENDO W/ABL LESION," 5,064 "
-30683270,HC SB ENDO DIAGNOSTIC," 1,700 "
-30683296,HC SB ENDO W/CONTROL BLEEDING," 5,064 "
-30683304,HC ILEOSCOPY W/WOUT BRUSHING," 5,064 "
-30683312,HC ILEOSCOPY W/BIOPSY," 5,064 "
-30683320,HC COLONOSCOPY W/WOUT BRUSHING," 5,064 "
-30683338,HC COLONOSCOPY W/BIOPSY," 5,064 "
-30683346,HC COLONOSCOPY W/CONTROL BLEEDING," 5,064 "
-30683353,HC COLONOSCOPY W/HOT BIOPSY," 5,064 "
-30683361,HC COLONOSCOPY W/LESION RMVL," 2,532 "
-30683379,HC COLONOSCOPY W/LESION RMVL," 4,657 "
-30683387,HC FLEX SIG W/FB REMOVAL," 4,657 "
-30683395,HC FLEX SIG W/HOT BIOPSY," 5,064 "
-30683403,HC FLEX SIGM W/DECOMPRESSION," 5,064 "
-30683411,HC FLEX SIG W/EUS & FNA," 4,657 "
-30683429,HC COLONOSCOPY W/FB REMOVAL," 5,064 "
-30683437,HC COLONOSCOPY W/HOT BIOPSY," 5,064 "
-30683551,HC ESOPH W/ABLATION LSN," 16,252 "
-30683569,HC SMALL BOWEL ENDO W/BX," 4,998 "
-30683577,HC SB ENDO W/BX," 6,037 "
-30683585,HC COLONOSCOPY W/EUS/FNA," 5,962 "
-30683593,HC 24 HR PH STUDY W/ BRAVO," 5,475 "
-30683601,HC ESO CAPS ENDO," 5,711 "
-35100106,HC PT EVALUATION INIT 30 MIN, 692 
-35100122,HC PT RE-EVALUATION, 431 
-35100221,HC XPT CASE CONF + RPT INIT 30, 214 
-35100247,HC XPT CASE CONF + RPT ADDL 15, 68 
-35100270,HC PT DIRECT THER ACT EA 15, 309 
-35100304,HC PT EXER/STR/ROM EA 15, 320 
-35100395,HC XPT COMB MOD/PR INIT 30, 214 
-35100403,HC XPT COMB MOD PR EA ADL 15, 68 
-35100429,HC XPT PROC 1 AREA INIT 30, 214 
-35100445,HC XPT PROC 1 AREA EA ADDL 15, 102 
-35100452,HC XPT SNGL MOD 1 AREA 30MIN, 172 
-35100478,HC XPT MOD 1 AREA EA ADDL 15, 43 
-35110006,HC PT CONTRAST BATH EA 15, 126 
-35111483,HC PT APPLY E-STIM UNATTND, 160 
-35111533,HC PT MASSAGE EA 15 MIN, 198 
-35111541,HC PT TX GROUP 2 OR MORE, 133 
-35111566,HC PT APPLY IONTO EA 15, 233 
-35111582,HC PT APPLY ULTRASOUND EA 15, 190 
-35111590,HC PT MANUAL THERAPY TECH 15, 249 
-35111632,HC PT ORTHOTIC MGMT/TRAIN EA 15, 255 
-35111640,HC PT PROSTHETIC TRAINING EA 15, 221 
-35120211,HC PT NEUROMUSC RE-EDUC EA 15, 311 
-35120302,HC PT ATTENDED E-STIM EA 15, 225 
-35120310,HC PT GAIT TRAINING EA 15 MIN, 298 
-35120377,HC XPT ORTH MGMT/TRAIN U/L 30, 214 
-35120385,HC XPT PROSTH TRAIN U/L INIT 30, 214 
-35120393,HC PT SELF CARE MGMT TRNG EA 15, 279 
-35120427,HC XPT CASE CONSULT&RPT, 214 
-35160068,HC PT DEBRIDEMENT SEL<20CM, 302 
-35160548,HC XPT JOINT MOBILIZATION 30, 249 
-35160571,HC PT WHEELCHAIR MGMT 15 MIN, 275 
-35200104,HC OT EVALUATION, 692 
-35200112,HC OT RE-EVALUATION, 431 
-35200336,HC OT COMM/WORK RETRAIN EA 15, 131 
-35200369,HC OT TX ACTIVITIES DIRECT EA 15, 309 
-35200393,HC OT TX PROC GRP 2 OR MORE, 133 
-35220268,HC OT STRENGTH/ROM TX EA 15, 320 
-35220284,HC OT NEUROMUSC RE-ED EA 15, 311 
-35220342,HC OT MANUAL THERAPY EA 15, 249 
-35220367,HC OT ORTHOTIC FIT/TRAIN EA 15, 255 
-35220383,HC OT SELF CARE TRNG EA 15, 279 
-35220391,HC OT EVAL SWALLOW/ORAL FUNCT, 933 
-35220409,HC OT TX SWALLOW/ORAL FUNCT, 225 
-35220417,HC OT CKOUT PROSTH ESTL PT EA 15, 195 
-35220425,HC OT WHEELCHR TRN EA 15, 275 
-35220441,HC OT SPLINT, 89 
-35220532,HC XOT EVAL INIT 30 PLUS REPORT, 336 
-35220573,HC XOT TREATMENT INITIAL 30, 214 
-35220581,HC XOT TREATMENT EA ADDL 15, 84 
-35220599,HC XOT CASE CONSULT & RPT, 214 
-35220607,HC OT DEV COG SKILL DIRECT EA 15, 242 
-35220631,HC OT SENSORY INTEGRATION EA 15, 93 
-35600964,HC SP ASSESS APHASIA W/RPT 60MIN, 473 
-35600972,HC SP EVALUATION SPEECH VOICE, 873 
-35600980,HC SP EVAL SWALLOW/ORAL FUNCT, 933 
-35600998,HC SP DEV COGNITIVE SKILL DIR 15, 242 
-35601004,HC SP GROUP SPEECH TREATMENT, 271 
-35601012,HC SP INDIV SPEECH RX PER TX, 848 
-35601020,HC SP SWALLOW EVAL W/XRAY, 960 
-35601038,HC SP TX SWALLW/ORAL FNCTN, 225 
-35601046,HC XSP SPEECH EVALUATION, 636 
-35601053,HC XSP LANGUAGE EVALUATION, 636 
-35601061,HC XSP INDIV SLP THERAPY PER HOUR, 440 
-35601079,HC XSP GRP THERAPY EA PATIENT, 257 
-35601087,HC XSP INDIV SLP THERAPY 1/2 HR, 220 
-35601137,HC SP EVAL VOICE PROS/AUG DEVICE, 442 
-35601145,HC SP MOD VOICE PROS/AUG DEV, 512 
-35601160,HC SP TX EXER/STR/ROM/FLEX 15, 320 
-35601186,HC SP SELF CARE HM MGMT 15, 279 
-35601194,HC SP COMM WORK REINTEG EA 15, 131 
-35601319,HC SP SPEECH GROUP TX, 271 
-35601418,HC VOICE PROSTHESIS, 143 
-35601426,"HC INDWEL V. PROSTH, STARTER KIT", 371 
-35601434,HC INDWEL V. PROSTH- REPLACE, 383 
-35601467,HC SP TEP VALVE HUMID CAP, 117 
-35601483,HC TEP DILATOR, 197 
-35601517,HC SP FFE FIBEROP SWL EVAL," 1,178 "
-35601525,HC SP FFE FIBEROP SENS EVAL, 465 
-35700004,HC PHYSICAL THERAPY EVALUATION, 692 
-35700012,HC PHYSICAL THERAPY RE-EVAL, 431 
-35700103,HC MCAL PT PROC EA ADD 15MIN, 102 
-35700194,HC PT ORTHOTIC FIT/TRAIN EA 15, 255 
-35700244,HC PT TX/STRNG/ROM/FLEX EA 15, 320 
-35700327,HC PT APPLY TRACTION MECH, 311 
-35700350,HC PT APPLY PARAFFIN BATH, 136 
-35700384,HC PT ATTENDED ELECT STIM EA 15, 225 
-35700467,HC PT APPLY IONTOPHORESIS EA15, 233 
-35700509,HC PT APPLY ULTRASOUND EA 15, 190 
-35700624,HC PT NEUROMUSC RE ED EA 15, 311 
-35700764,HC PT MANUAL THERAPY TECH EA 15, 249 
-35701002,HC PT PROSTHETIC TRNG EA 15, 221 
-35701127,HC PT SELFCARE MNGT EA 15, 279 
-35701333,HC OCCUPATIONAL THERAPY RE-EVAL, 431 
-35701341,HC OCCUPATIONAL THERAPY EVAL, 692 
-35701366,HC OT ORTHOTIC FIT/TRN EA 15MIN, 255 
-35701416,HC OT EXER STRNG/ROM/FLEX EA 15, 320 
-35701515,HC OT PARAFFIN BATH, 136 
-35701549,HC OT ATTEND E STIM EA 15MIN, 225 
-35701556,HC OT ATTEND IONTOPHOR EA 15MIN, 233 
-35701564,HC OT ATTEND ULTRASND EA 15MIN, 190 
-35701580,HC OT NEUROMUSC RE-ED EA 15, 311 
-35701622,HC OT MANUAL THERAPY TECH EA 15, 249 
-35701630,HC OT PROSTHETIC TRAINING EA 15, 221 
-35701648,HC OT TX ACTIVITIES DIRECT EA 15, 309 
-35701655,HC OT SELF CARE MNGT EA 15, 279 
-35701945,HC MCAL PT PROCEDURE INIT 30MIN, 214 
-35701952,HC MCAL PT PROC & MOD INIT 30MIN, 214 
-35701960,HC MCAL PT PROC & MOD ADD 15 MIN, 68 
-35701978,HC MCAL PT EVAL INIT 30 MIN, 692 
-35701986,HC MCAL PT RE-EVAL ADD 15 MIN, 172 
-35701994,HC MCAL OT TRMT INIT 30MIN, 214 
-35702000,HC MCAL OT TRMT EA ADD 15MIN, 84 
-35702109,HC OT WND CARE/NS DEBRIDE, 361 
-35702133,HC PT WND CARE/NS DEBRIDE INIT, 361 
-35702141,HC PT ATTEND CONTRST BATHS EA 15, 126 
-35702158,HC OT CONTRAST BATH EACH 15 MIN, 126 
-36405009,HC PETCT LIMITED BONE METS IN CLINICAL STUDY," 10,566 "
-56000052,HC ECG COMMUNITY INST, 914 
-56000342,HC TELE EKG HOOKUP, 640 
-56000359,HC TELE EKG TECHNICAL, 579 
-56100050,HC S ECG HOSP, 914 
-56100357,HC EX STRESS TEST INST," 2,736 "
-56100381,HC STRESS TEST HOSP," 2,736 "
-56100555,HC EXTER ECG <48HR SCAN W REPRT," 1,331 "
-56100563,HC EXTER ECG <48HR RECORDING, 819 
-56101207,HC SGNL AVRGD ECG," 1,542 "
-56101504,HC 24HR BP SCAN W/REPORT, 781 
-56101777,HC PULM STRESS TEST CPLX," 2,378 "
-56101827,HC 24HR BP MONITOR RECORD ONLY, 938 
-48002000,HC HHN TREATMENT, 357 
-48001705,HC ARTERIAL CATHETRZN-ABG," 3,241 "
-48001754,HC ARTERIAL PUNCTURE **, 355 
-48001150,HC BRONCH PROVOC METHAC CHALLENGE," 1,759 "
-48001457,HC EX INDUC ASTHMA," 1,759 "
-48001200,HC EXERCISE STRESS SIMPLE," 1,618 "
-48001903,HC FLO-VOLUME LOOP, 613 
-48001358,HC HAST HI ALT SIM TEST W/RPT," 3,665 "
-48001853,HC EXERC OXIMETRY MULT READS, 439 
-48001804,HC OXIMTRY-SNGL MSRMNT, 105 
-48002125,HC PENTAMIDINE TREATMT, 734 
-48000301,HC SPIRO B/A BRONKODLTR," 1,470 "
-48000202,HC SPIROMETRY W/SVC MVV, 806 
-48002166,HC OP MDI ADMIN/SI/PCP INITIAL, 357 
-32000010,HC EMERGENCY CARE LEVEL 1, 686 
-32000028,HC EMERGENCY CARE LEV2," 1,267 "
-32000036,HC EMERGENCY CARE LEV3," 2,299 "
-32000044,HC EMERGENCY CARE LEV4," 8,061 "
-32000051,HC EMERGENCY CARE LEV5," 9,886 "
-32000218,HC EMERGENCY CARE LEVEL 1 PROC, 686 
-32000226,HC EMERGENCY CARE LEVEL 2 PROC," 1,267 "
-32000234,HC EMERGENCY CARE LEVEL 3 PROC," 2,299 "
-32000242,HC EMERGENCY CARE LEVEL 4 PROC," 8,061 "
-32000259,HC EMERGENCY CARE LEVEL 5 PROC," 9,886 "
-32001018,HC ED MEDICAL CRITICAL CARE," 12,847 "
-32001026,HC TRAUMA CENTER CRITICAL," 12,847 "
-32001034,HC TRAUMA CENTER HIGH," 9,886 "
-32001042,HC TRAUMA CENTER MODERATE," 9,886 "
-32000424,HC RETURN VISIT TO EMERGENCY, 686 
-32001000,HC EMPLOYEE HEALTH PROTOCOL, 956 
-32091464,HC TRAUMA ACTIVATION LVL 99," 15,000 "
-32091480,HC TRAUMA NOTIFICATION LVL 95," 9,000 "
-32091472,HC TRAUMA ACTIVATION 97," 11,500 "
-32500019,HC OBVS INITIAL DIFF DATE LEV I, 582 
-32500027,HC OBVS INITIAL DIFF DATE LEV II, 582 
-32500035,HC OBVS INITIAL DIFF DATE LEV III, 582 
-32500043,HC OBVS INITIAL SAME DATE LEV 1, 582 
-32500050,HC OBVS INITIAL SAME DATE LEV II, 582 
-32500068,HC OBVS INITIAL INITIAL DAY LVIII, 582 
-32500076,HC OBSV EA ADD'L HR, 582 
-92200302,HC CARDIOVASCULAR STRE," 2,736 "
-92200344,HC CONTRAST-DEFINITY 2ML VIAL, 894 
-92200062,HC DOPPLER COLOR FLOW VELCITY MAP," 1,182 "
-92200047,HC DOPPLER/PULSED WAVE," 1,177 "
-92200013,HC ECHOCARDIOGRAPHY 2D," 2,761 "
-92200260,HC STRS TTE W/WORX W I&R," 4,747 "
-92200278,HC TRANSESOPH INTRA-OP," 6,578 "
-92200021,HC SERIAL/FOLLOWUP ECHO," 2,853 "
-92200054,HC SERIAL/FOLLOW-UP DOPPLER, 859 
-92200252,HC TRANSESOPHAGEAL ECH," 6,578 "
-92200310,HC TRANSESOPHAGEAL-CON," 1,644 "
-92200328,HC TRANSTHORACIC CONG COMPLETE," 2,970 "
-92200336,HC TRANSTHORA CONG LIMITED F-UP," 1,033 "
-26100313,HC 7041 BUSULFAN LEVEL, 251 
-30603997,HC EGD SUBMUCOSAL INJ," 3,934 "
-30604607,HC OP VISIT LEVEL 1-NEW-W PX, 334 
-30604615,HC OP VISIT LEVEL 2-NEW-W PX, 392 
-30604623,HC OP VISIT LEVEL 3-NEW-W PX, 453 
-30604631,HC OP VISIT LEVEL 4-NEW-W PX, 500 
-30604649,HC OP VISIT LEVEL 5-NEW-W PX, 601 
-30604656,HC OP VISIT LEVEL 1-EST-W PX, 160 
-30604664,HC OP VISIT LEVEL 2-EST-W PX, 361 
-30604672,HC OP VISIT LEVEL 3-EST-W PX, 413 
-30604680,HC OP VISIT LEVEL-4-EST-W-PX, 463 
-30604698,HC OP VISIT LEVEL 5-EST-W PX, 593 
-32000879,HC PULSE OXIMTR MONITR, 105 
-32001067,HC CLSD TRT CLAV DI W/MA," 1,124 "
-32001083,"HC INJ,EPI,BLD/CLOT PATCH"," 2,381 "
-32001109,HC SUCTION CURRETAGE POSTPARTUM," 5,508 "
-32001117,HC EPISIOTOMY," 4,097 "
-32003006,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-32003014,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-32003022,HC IV INFUSN-THER-ADDTL SEQL NEW DRUG, 796 
-32003048,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-32003063,HC LUMBAR PUNCTURE DISCONTINUED A," 2,210 "
-32003501,HC HERNIA INGUINAL INITIAL," 7,044 "
-32003527,HC HERNIA UMBILICAL," 6,841 "
-32003568,HC ENDOMETRIAL BIOPSY, 334 
-32003964,HC FOREIGN BODY REMOVAL KNEE," 4,678 "
-32003972,HC KIT CATH PRESEPT CV OXIMETRY," 1,913 "
-32003980,HC RPR TNL CATH W/O PUMP," 1,239 "
-32003998,HC BLOOD DRAW CNTRL/PERIPH CATH, 576 
-32004004,HC TRAUMA ACTIV W CRIT CARE," 15,000 "
-32050007,HC I&D OF PILONIDAL CYST:SIMPLE, 354 
-32050015,HC REMOVE FB SUBQ SIMPLE," 1,343 "
-32050023,HC I&D HEMATOMA," 2,774 "
-32050031,HC I&D OF ABSCESS:SIMPLE OR SINGL, 937 
-32050049,HC PUNCT ASPIR CYST ABSCESS OR HEMATOMA," 1,423 "
-32050056,HC DEBRIDE SKIN EPID 1ST 20CM, 302 
-32050064,HC DEBRIDE SKIN DERM 1ST 20CM, 302 
-32050072,HC REMOVE NAIL PLATE SNGL," 1,742 "
-32050080,HC REMOVE NAIL PLATE EA ADDL, 450 
-32050098,HC EVAC SUBUNGAL HEMATOMA, 506 
-32050106,"HC EXCISE NAIL & MATRIX, PERM"," 1,012 "
-32050114,HC REPAIR NAIL BED, 874 
-32050122,HC EXCISE NAIL FOLD INGROWN, 447 
-32050130,HC REPAIR SUPFCL WND <2.6CM," 1,399 "
-32050148,HC REPAIR SUPFCL WND 2.5-7.5CM," 1,854 "
-32050155,HC REPAIR SUPFCL WND 7.6-12.5CM," 2,310 "
-32050163,HC REPAIR SUPFCL WND 12.6-20.0CM," 3,126 "
-32050171,HC REPAIR SUPFCL WND 20.1-30.0CM," 3,815 "
-32050189,HC REPAIR SUPFCL WND >30.0CM," 4,694 "
-32050197,HC REPAIR SUPFCL FACE <2.6CM," 2,582 "
-32050205,HC REPAIR SUPFCL FACE 2.6-5.0CM," 2,879 "
-32050213,HC REPAIR SUPFCL FACE 5.1-7.5CM," 3,661 "
-32050221,HC REPAIR SUPFCL FACE 7.6-12.5CM," 4,553 "
-32050239,HC REPAIR SUPFCL FACE 12.6-20.0CM," 6,227 "
-32050247,HC REPAIR SUPFCL FACE 20.1-30.0 - ED, 362 
-32050254,HC REPAIR SUPFCL FACE >30.0 - ED," 1,062 "
-32050262,HC REPAIR INTERMED SCALP <2.6CM," 1,920 "
-32050270,HC REPAIR INTERMED SCALP 2.6-7.5CM," 2,451 "
-32050288,HC REPAIR INTERMED SCALP 7.6-12.5CM," 2,586 "
-32050296,HC REPAIR INTERMED SCALP 12.6-20CM," 2,998 "
-32050304,HC REPAIR INTERMED SCALP 20.1-30CM," 3,525 "
-32050312,HC LAYER CLOSURE WND FACE >30.0 - ED," 1,062 "
-32050320,HC REPAIR INTERMED EXT WND <2.6, 587 
-32050338,HC REPAIR INTERMED EXT WND 2.6-7.5CM, 752 
-32050346,HC REPAIR INTERMED EXT WND 7.6-12.5CM, 803 
-32050353,HC REPAIR INTERMED EXT WND 12.6-20CM," 1,008 "
-32050361,HC LAYR CLOS EXT WND 20.1-30.0 - ED," 1,141 "
-32050379,HC REPAIR INTERMED EXT WND >30CM," 1,281 "
-32050387,HC REPAIR INTERMED FACE <2.6CM," 2,658 "
-32050395,HC REPAIR INTERMED FACE 2.6-5.0CM," 3,150 "
-32050403,HC REPAIR INTERMED FACE 5.1-7.5CM," 3,362 "
-32050411,HC REPAIR INTERMED FACE 7.6-12.5CM," 3,528 "
-32050429,HC REPAIR INTERMED FACE 12.6-20.0CM," 4,658 "
-32050437,HC REPAIR INTERMED FACE 20.1-30.0CM," 5,896 "
-32050452,"HC INTL TX, 1ST DEGREE BURN", 962 
-32050460,HC DRESS/DEBRIDE BURN SMALL W/O ANES," 1,025 "
-32050486,HC DRAIN SMALL JOINT/BURSA, 864 
-32050494,HC DRAIN INTERMED JOINT/BURSA, 875 
-32050502,HC DRAIN MAJOR JOINT/BURSA," 1,193 "
-32050510,HC CLSD TX NASAL FX W/O MANIP, 256 
-32050528,HC CLSD TX CLAV FX W/O MANIP, 743 
-32050536,HC CLSD TX CLAV FX W MANIP," 3,939 "
-32050544,HC CLSD TX SCAP FX W/O MANIP, 627 
-32050551,HC CLSD TX HUM FX W/O MANIP," 1,321 "
-32050569,HC CLSD TX HUM FX W/MANIP," 2,353 "
-32050577,HC CLSD TX HUM FX W/O MANIP, 580 
-32050593,HC CLSD TX SHLDR DISL W/MANIP," 1,403 "
-32050601,HC CLSD TX SHLDR DISL W/FX," 1,124 "
-32050619,HC CLSD TS HUM SHFT FX W/O MAN, 729 
-32050627,HC CLSD TX HUM SHFT FX W/MANIP, 990 
-32050635,HC CLSD TX HUM FX W/O MANIP, 864 
-32050643,HC CLSD TRT HUM FX W/MANIP," 1,229 "
-32050650,HC CLSD TRT HUM FX W/O MANIP, 648 
-32050668,HC TX CLOS ELB DISLOC:W/O ANESTHE," 1,147 "
-32050676,HC CLSD TX RAD SUBLUX W/MANIP," 1,010 "
-32050684,HC CLSD TX RAD HEAD FX W/O MAN, 892 
-32050692,HC CLOS TX RADIAL HEAD/NCK FX:W/M," 1,726 "
-32050700,HC CLSD TX ULNAR FX W/O MANIP, 631 
-32050718,HC CLSD TX RAD SHFT FX W/O MAN, 562 
-32050726,HC CLSD TX RAD SHFT FX W/MANIP," 1,157 "
-32050734,HC CLSD TX ULNAR SHFT FX W/O M, 540 
-32050742,HC CLSD TX ULNAR SHFT FX W/MAN," 1,105 "
-32050759,HC CLD TX RAD&ULN SFT FX W/O M, 566 
-32050767,HC CLD TX RAD&ULN SFT FX W/MA," 1,268 "
-32050775,HC CLSD TX DIST RAD FX W/O MAN," 1,055 "
-32050783,HC CLSD TX DIST RAD FX W/MANIP," 3,545 "
-32050791,HC CLOS TX CARPAL SCAPHOID FX:WO, 627 
-32050809,HC CLSD TX CARPAL BONE FX W/O, 625 
-32050817,HC CLSD TX CARPAL BONE FX W/M, 886 
-32050833,HC REP EXT TENDON FINGER EACH TENDON," 4,795 "
-32050841,HC CLOS TX MC FX 1:W/O MANIP EA B, 631 
-32050858,HC CLD TX METACARP FX EA W/MA," 1,136 "
-32050866,HC CLD TX METACARP DISLOCATION, 918 
-32050874,HC CLD TX FING FX EA W/O MANIP, 951 
-32050882,"HC CLD TX FING FX, EA W/MANIP"," 1,327 "
-32050890,"HC CLD TX ARTIC FX, EA W/O MANI", 487 
-32050908,"HC CLD TX ARTIC FX, EA W/MANIP - ED", 728 
-32050916,HC CLD TX DIST PHALANG FX W/O M," 1,160 "
-32050924,HC OPEN TX DIS PHALANG FX W/MA," 7,557 "
-32050932,HC CLOS TX IP DISLOC W/MANIP:W/O," 1,421 "
-32050940,HC CLOS TX HIP DISLOC TRAUMA:WO A, 464 
-32050957,HC CLD TX HIP DISLOC POST ARTH WO, 875 
-32050965,HC CLD TX FEMORAL FX W/MANIP," 1,448 "
-32050973,HC CLD TX KNEECAP FX W/O MANIP, 665 
-32050981,HC CLSD TRT TIBIAL FX W/O MANIP, 638 
-32050999,HC CLSD TRT KNEE DISLOC W/O ANES, 999 
-32051005,HC CLOS TX PATR DISLOC:W/O ANESTH, 794 
-32051013,HC CLSD TRT TIBIAL SHFT FX W/O M, 712 
-32051021,HC CLSD TRT TIBIAL SHFT FX W/MA," 5,579 "
-32051039,HC CLD TX MED MALL FX W/O MANIP," 1,105 "
-32051047,HC CLD TX MED MALL FX W/MANIP," 2,353 "
-32051054,HC CLD TX DIST FIB FX W/O MANIP, 765 
-32051062,HC CLD TX DIST FIB FX W/MANIP," 1,013 "
-32051070,HC CLD TX ANKLE FX W/O MANIP, 681 
-32051088,HC CLD TX ANKLE FX W/MANIP," 1,260 "
-32051096,HC CLD TX TRI ANKLE FX W/O MAN, 642 
-32051104,HC CLD TX TRI ANKLE FX W/MANIP," 1,124 "
-32051112,HC CLOS TX FX ARTIC DIST TIB:W/O, 678 
-32051120,HC CLOS TX ANK DISLOC:W/O ANESTHE, 963 
-32051138,HC CLD TX CALCANEAL FX W/O MANIP, 524 
-32051146,HC CLD TX TALUS FX W/O MANIP, 475 
-32051153,HC CLD TX MIDFOOT FX W/O MANIP, 438 
-32051161,HC CLD TX METATARSAL FX W/O M, 670 
-32051179,HC CLD TX BIG TOE FX W/O MANIP, 398 
-32051187,HC CLSD TRT TOE FX W/O MANIP, 377 
-32051195,HC CLSD TRT TOE FX W/MANIP, 583 
-32051211,HC CLSD TRT TOE DISLOC W/O ANES, 611 
-32051229,HC CLD TX INTPHAL JNT DISLOC W/O ANES, 769 
-32051237,HC APPLY SHORT ARM SPLINT STATIC, 711 
-32051245,HC APPLY FINGER SPLINT STATIC, 827 
-32051252,HC APPLY LONG LEG SPLINT, 776 
-32051260,HC APPLY SHORT LEG SPLINT, 782 
-32051286,HC REMOVE NASAL FB, 548 
-32051294,HC CNTRL NASL HEMORR ANT SMPL ANY," 1,865 "
-32051302,HC CNTRL NASL HEM POST INITIAL, 317 
-32051310,"HC ENDOTRACH INTUB,EMERGENT"," 1,427 "
-32051328,"HC LARYNGOSCOPY, INDIR DX", 218 
-32051336,"HC LARYNGOSCOPY, DIR FOR ASPIR"," 3,808 "
-32051344,"HC LARYNGOSCOPY, FIBEROP DX"," 1,320 "
-32051351,HC LARYNG FIBEROP W/FB REM," 1,465 "
-32051369,HC TRACHE EMERG CRICOTH," 1,405 "
-32051377,"HC THORACENTESIS, PLEURAL NO GUIDANCE"," 1,658 "
-32051385,"HC THORACENTESIS, PLEURAL WITH GUIDANCE"," 3,897 "
-32051393,HC TUBE THORACOSTOMY," 1,512 "
-32051401,HC PERICARDIOCENTESIS INITIAL - ED," 1,600 "
-32051419,HC INSERT TEMP TRANSV PCMKR S," 5,539 "
-32051427,HC INSERT TEMP TRANSV PCMKR D," 7,836 "
-32051443,HC INSERT CVC < 5YRS," 5,677 "
-32051450,HC INSERT NON-TUNNEL CVC >5YRS," 7,725 "
-32051476,HC ARTERIAL BLOOD DRAW, 355 
-32051484,HC ART CATHTRZATION PERC," 3,241 "
-32051500,HC REPAIR LIP FULL THICK VERMILION," 2,172 "
-32051518,HC REP MOUTH/TONGUE<=2.5CM," 1,367 "
-32051526,HC DRAIN DENTAL ABSCESS/CYST, 916 
-32051534,HC I&D TONSILL ABSCESS, 993 
-32051542,HC REMOVE FB PHARYNX, 340 
-32051559,HC FLEX EGD W/PERC G-TUBE," 2,610 "
-32051567,HC CHANGE GASTROSTOMY TUBE, 760 
-32051575,HC I&D RECTAL ABSCESS," 3,602 "
-32051583,HC I&D PERIANAL ABSCESS," 1,346 "
-32051591,HC INCISE EXTERNAL HEMORRHOID," 1,337 "
-32051609,"HC ANOSCOPY, DIAGNOSTIC"," 2,842 "
-32051617,HC ANOSCOPY W/FB REMOVAL - ED," 3,190 "
-32051633,HC CATH FOLEY INSERTION," 1,300 "
-32051641,HC I&D BARTHOLINS GLAND ABSCESS, 522 
-32051658,HC LUMBAR PUNCTURE DIAGNOSTIC," 2,210 "
-32051666,HC REMOVE FB EXTERNAL EYE, 653 
-32051674,HC REMOVE FB CORNEA, 708 
-32051682,HC REM FB CORNEA W/SLIT LMP, 777 
-32051690,HC REM FB EXT AUDITORY CANAL, 591 
-32051708,HC REM IMPACT CERUMEN W INST, 417 
-32051716,HC HEART/LUNG RESUSCITATION," 2,129 "
-32051740,HC INSERT SWAN-GANZ CATH," 3,857 "
-32051757,HC GASTRIC LAVAGE MD SKILL, 566 
-32051765,HC THORACOTOMY W/CARD MASSG," 1,611 "
-32051773,HC APPLY LONG ARM SPLINT, 798 
-32051781,"HC TX INCMPLT ABORTION, SUCTIO"," 4,097 "
-32051799,HC WND CLSE BY TISSUE ADHESIVE <2.6CM," 1,399 "
-32061400,HC CENTRAL VEIN CATH KIT, 85 
-32061608,HC TRAY- LAVAGE, 298 
-32062424,HC LEVEL ONE D-100 TUBING, 327 
-32062861,HC PROBE PULSE OXIMETER I20INFANT, 85 
-32063281,HC YAUNKER SUCT DISP, 85 
-32065211,HC MORGAN LENS, 85 
-32065435,HC EXTERNAL PACING PATCHES ADULT, 213 
-32065997,HC TRAY CATHETER TRIPLE LUMEN, 808 
-32091167,HC DERMABOND SKIN ADHESIVE, 213 
-32091498,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-32091506,HC THRP PROPH OR DX SQ IM, 344 
-32091514,"HC THRP,PROPH OR DX INTRA ART", 125 
-32091522,HC IV PUSH INJECTION INITIAL, 542 
-32091530,HC THRP IM ANTIBIOTIC, 344 
-32091555,HC CATH STRAIGHT INSERTION, 742 
-32091563,HC DIGITAL BLOCK," 3,781 "
-32091571,HC IV INFUSION-THER-ADDTL HRS, 607 
-32091589,HC I&D OF ABSCESS: COMPLIC OR MULT," 1,079 "
-32091605,HC THORACIC VENT TRAY," 1,615 "
-32091613,HC CLSD TRT JAW DISLOCATION," 1,252 "
-32091621,HC TRIGGER PNT INJ 1-2 MUSCLES, 515 
-32091639,HC TRIGGER PNT INJ 3+ MUSCLES, 597 
-32091654,HC SIGMOIDOSCOPY FLEX," 1,184 "
-32500084,HC IV HYDRATION INITIAL 1ST HR," 1,035 "
-32500092,HC IV HYDRA EA ADD'L HR, 607 
-32500100,HC THRP INF INITIAL 1ST HR," 1,226 "
-32500118,HC THRP INF EA ADD'L HR, 607 
-32500126,HC THERAP INF ADDL SEQ NEW DRUG, 796 
-32500134,HC THRP INF CONCURRENT, 821 
-32500142,HC THRP PROPH DX SQ IM, 344 
-32500159,HC IM INJ ANTIBIOTIC, 344 
-32500167,HC THRP PROPH DX INTRA ART, 125 
-32500175,HC THERAP PROPH DX IVP SGL INIT, 542 
-32500183,HC IVP INJ ADDL SEQ NEW DRUG, 400 
-32500191,HC THERAP PROPH DX IV INJ UNLIST, 256 
-32500209,HC THRP PROPH IVP SAME DRUG, 405 
-35160688,HC XPT WHEELCHAIR MGMT 30 MIN, 214 
-35160696,HC XPT TX GROUP 2 OR MORE, 133 
-35260603,HC OT ATTEND E-STIM EA 15, 225 
-35601590,HC XSP SWALLOW EVAL W/XRAY, 960 
-35601608,HC XSP THER SGD W/PROG & MOD, 440 
-35601632,HC XSP EVAL SGD VOICE PROSTH AUG, 923 
-35601640,HC XSP FLEX FFE EVAL SWL, 157 
-35601657,HC XSP FLEX FFE SWL/LARYNX EVAL, 157 
-35601665,HC XSP LARYNGEAL FX STUDY, 372 
-35703065,HC WRIST COCKUP SPLINT PREFAB L3, 128 
-35703073,HC WRIST COCKUP SPLINT CUSTOM L3, 159 
-35703081,HC HAND BASED SPLINT LEVEL4, 179 
-35703099,HC FINGER SPLINT, 9 
-35703107,HC PIP EXTNSN SPLNT LMB PREFAB L5, 204 
-35703115,HC HAND THMB SPICA SPLNT L5, 204 
-35703123,HC ULNAR GUTTER SPLINT, 639 
-35703131,HC UPPR EXTRMTY SPLNT WRST/HAND, 444 
-35703149,HC UPPER EXTREM WRIST/FA SPLINT, 541 
-35703164,HC STATIC FINGER SPLINT LEVEL3, 100 
-35703180,HC PT ORTH/PROS FUP EST PT EA15, 195 
-35703248,HC MCAL OT EVAL INIT 30'+REPORT, 692 
-35703255,HC MCAL OT RE EVAL EAADD15' +RPT, 431 
-35703347,HC FINGER PREFABRICATED LEVEL3, 122 
-35703354,HC HAND PREFABRICATED LEVEL5, 204 
-35703362,HC WRIST PREFABRICATED LEVEL5, 128 
-35703370,HC FOREARM PREFABRICATED LEVEL5, 213 
-35703388,HC ELBOW PREFABRICATED LEVEL5, 281 
-35703396,HC FINGER CUSTOM STATIC LEVEL5, 239 
-35703404,HC FINGER CUSTOM DYNAMIC LEVEL5, 170 
-35703412,HC HAND BASED CUSTOM LEVEL4, 178 
-35703420,HC FOREARM CUSTOM STATIC LEVEL5, 239 
-35703438,HC FOREARM CUSTOM DYNAMIC, 321 
-35703446,HC ELBOW CUSTOM SPLINT L3, 123 
-35703453,HC WRIST CUSTOM L4, 179 
-36400216,HC PET MYOCARDIAL METABOLIC EVALUATION," 10,566 "
-36400240,HC FDG- PET RADIOPHARMACEUTICAL," 2,156 "
-36400307,"HC PET SCAN, BRAIN METABOLIC"," 10,566 "
-36400315,HC PETCT LIMITED," 10,566 "
-36400331,HC PETCT WHOLE BODY," 10,566 "
-36400539,HC PETCT SKULL-THIGH," 10,566 "
-36400620,HC PET SCAN BRAIN," 4,521 "
-36400687,HC PET BREAST INITIAL DX STAGING," 10,566 "
-36405017,HC PETCT SKULL-THIGH BONE METS IN CLINICAL STUDY," 10,566 "
-36405025,HC PETCT WHOLE BODY BONE METS IN CLINICAL STUDY," 10,566 "
-36405033,HC PET MCI PRFSN 1 STDY RST/STRS," 10,566 "
-36405041,HC PET MCI PRFSN MULT STDY RST/ST," 10,566 "
-3402427,"HC MED NUTR THER INIT 15""", 127 
-3402435,HC MNT REASSESS 15 SAME DX, 101 
-3402443,HC MNT GRP:EA 30 MIN SAME DX, 105 
-24130122,HC IV INFUSION-THER-ADDTL HRS, 607 
-24130114,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-24130148,HC IV PUSH INJECTION, 542 
-24130130,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-24130015,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-24130023,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-24130031,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-24130049,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-24130056,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-24130064,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-24130072,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-24130080,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-24130098,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-26130211,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-26130203,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-26130237,HC IV PUSH INJECTION, 542 
-26130229,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-26130054,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-26130062,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-26130070,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-26130088,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-26130096,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-26130104,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-26130112,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-26130120,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-26130138,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-22800015,HC IP ADULT HEMODIALYSIS," 4,707 "
-22800106,HC ADULT IP CCPD," 4,383 "
-22800205,HC ADULT IP CAPD," 2,494 "
-22800551,HC HEMOPERFUSION 1 TO 4 HOURS," 4,707 "
-22800700,HC CANNULA DECLOTTING, 550 
-22800759,HC PERITONEAL DIALYSIS CULTURES," 1,276 "
-22801054,HC OP HEMODIALYSIS EMER/UNSCHED," 4,715 "
-22801104,HC OP TRANSFUSION BLOOD ADMIN 1ST," 3,300 "
-22801203,HC OP CCPD," 4,715 "
-22801252,HC OP CAPD," 4,715 "
-22801302,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-22801401,HC O/P CANNULA DECLOTTING, 550 
-22801609,HC OP VENIPUNCTURE, 115 
-22801658,HC I/P VASCULAR ACCESS-BLOOD DRAW, 571 
-22801864,HC CONT RENAL REPLACMT TX ICU," 4,383 "
-22801906,"HC TREATMENT RM,NEW PT,EACH 15""", 334 
-22801914,HC TREATMENT RM EST PT EA 15, 160 
-22801922,HC IV PUSH INJECTION, 542 
-35601822,HC CASE CONF PT 30 MIN OR MORE, 279 
-70000104,HC NCPT EVALUATION, 692 
-70000112,HC NCPT RE-EVALUATION, 431 
-70000120,HC NCPT DIRECT THER ACT EA 15, 309 
-70000138,HC NCPT EXER/STR/ROM EA 15, 320 
-70000146,HC NCPT NEUROMUSC RE-EDUC EA 15, 311 
-70000153,HC NCPT ATTENDED ESTIM EA 15, 225 
-70000161,HC NCPT GAIT TRNG EA 15 MIN, 298 
-70000179,HC NCPT MASSAGE EA 15 MIN, 198 
-70000187,HC NCPT TX GROUP 2 OR MORE, 133 
-70000195,HC NCPT MANUAL THERAPY TECH 15, 249 
-70000203,HC NCPT ORTHOTIC MGMT/TRAIN EA 15, 255 
-70000211,HC NCPT PROSTHETIC TRAINING EA 15, 221 
-70000229,HC NCPT DEBRIDEM 1ST 20CM, 302 
-70000237,HC NCPT DEBRIDEMENT NON SELECTIVE, 361 
-70000245,HC NCPT APPLY ULTRASOUND EA 15, 190 
-70000252,HC NCPT APPLY WHIRLPOOL, 100 
-70000260,HC NCPT HOT/COLD PACKS, 179 
-70000278,HC NCPT APPLY TRACTION MECH, 311 
-70000286,HC NCPT APPLY E STIM UNATTND, 160 
-70000294,HC NCPT APPLY PARAFIN BATH, 136 
-70000302,HC NCPT APPLY VASOPNEUMATIC, 230 
-70000310,HC NCPT SELF CARE MGMT TRNG EA 15, 279 
-70000328,HC NCPT APPLY IONTO EA 15, 233 
-70000336,HC NCPT CONTRAST BATH EA 15, 126 
-70000344,HC NCPT WHEELCHAIR MGMT 15 MIN, 275 
-70000351,HC NCXPT ORTH MGMT/TRAIN INITIAL 30 MIN, 214 
-70000369,HC NCXPT PROSTH TRAIN EA U/L 30, 214 
-70000385,HC NCXPT CASE CONF+RPT ADDL 15, 68 
-70000393,HC NCXPT COMB MOD/PR INIT 30, 214 
-70000401,HC NCXPT COMB MOD/PR EA ADL 15, 68 
-70000419,HC NCXPT PROC 1 AREA INIT 30, 214 
-70000427,HC NCXPT PROC 1 AREA EA ADDL 15, 102 
-70000435,HC NCXPT SNGL MOD 1 AREA 30MN, 172 
-70000443,HC NCXPT MOD 1 AREA EA ADDL 15, 43 
-70000450,HC NCXPT CASE CONSULT&RPT 30, 214 
-70000468,HC NCXPT JOINT MOBILIZATION 30, 249 
-70000476,HC NCXPT TX GROUP 2 OR MORE, 68 
-70000484,HC NCXPT WHEELCHAIR MGMT 15 MIN, 214 
-70000765,HC NCPT SPLINT, 89 
-58000035,HC EMG ONE EXTREM," 1,141 "
-58000050,HC EMG TWO EXTREMITIES," 1,230 "
-58000068,HC OR-EMG-THREE EXTREMITIES - NEURODX LAB," 1,386 "
-58000084,HC EMG CRANIAL NRV UNILAT, 761 
-58000126,HC EMG CRANIAL NRV BILAT," 1,016 "
-58001009,HC HEALTH & BEHAV 15 MIN FACE W P, 107 
-58001017,HC REASSESS 15 MIN FACE W PATIENT, 105 
-58001025,HC INTERVENTION 15 MIN FACE TO FA, 64 
-58001033,HC INTERVENTION 15MIN FACE TO FAC, 105 
-58001041,HC INTRVN 15MN FACE FAM W/PT, 180 
-58001058,HC INTERVENTION 15 MIN FACE FAMIL, 194 
-58001066,HC MIDDLE FOSSA ELECTRODES, 325 
-58001074,HC PSYCHOLOGICAL TESTING PER HR, 522 
-58001082,HC PSYCH TESTING PER HR OF TECH T, 330 
-58001090,HC NEUROPSYCH TESTING PER HR OF P, 949 
-58001108,HC NEUROPSYCH TESTING PER HR OF T, 825 
-58001116,HC NEUROBEHAVIORAL STATUS EXAM, 949 
-58001124,HC NEUROPSYCH TESTING ADMIN BY CO, 825 
-58001132,HC ELECTRICAL STIM CHEMODENERVATI, 232 
-58001140,HC NEEDLE EMG FOR GUID CHEMODENER, 235 
-58001157,HC PSYCHOLOGICAL TESTING BY COMPU, 330 
-58010000,HC ECOG AT SURGERY," 4,153 "
-58010042,HC WADA TS EEG MNTRG," 1,941 "
-58010059,HC EPM-SPHENOIDAL ELECTRODES PAIR - NEURODX LAB, 376 
-58010067,HC EEG VDO EA 24 HRS," 5,801 "
-58010083,HC EEG VIDEO EA 24 HR PH2," 5,801 "
-58010091,HC ELCTRD DEPTH 8 CONTACT," 3,667 "
-58010109,HC ELCTRD STRIP 6 CONTACT," 4,888 "
-58010117,HC ELCTRD STRIP 8 CONTACT," 5,313 "
-58010125,HC 20 CONTACT SR ELTR," 7,013 "
-58010133,HC ELCTRD GRID 32 CONTACT," 7,863 "
-58010141,HC ELCTRD GRID 64 CONTACT," 7,875 "
-58010158,HC *EPM-CORTICAL STIM W/EEG 1ST HR. - NEURODX LAB," 2,054 "
-58010166,HC CRTCL ST/EEG EA A H, 544 
-58010224,"HC CHEMODNERV FACIAL MUSCLES, UNILATERAL", 519 
-58010240,HC CHEMODNERV CRVICAL SPINE MUS, 484 
-58010265,"HC BOTULINUM TOXIN TYPEA,PER UNIT", 34 
-58010307,HC 16 CONTACT INTERHEM ELECTRODE," 6,588 "
-58010323,HC EEG MONITORING 24HR," 2,710 "
-58010349,HC EEG W/ NONINTRACRAN, 700 
-58010356,HC MONOPOLAR STIM PROBE, 850 
-58010364,HC EEG-DIGITAL ANALYS, 383 
-58030008,HC EEG-AWAKE," 1,996 "
-58030057,HC EEG SLP ONLY," 2,503 "
-58030107,HC EEG AWK/SLP W/T1-T2," 2,062 "
-58030388,HC EEG/VDO TO 12-24 HRS ST," 5,801 "
-58030404,HC EEG/VDO TO 8-12 HRS ST," 2,900 "
-58031659,HC AMBULATORY EEG RCDG," 4,213 "
-58031808,HC AUDITORY BRNSTM E.R.," 2,609 "
-58031857,HC SMTSRY EVKD UPPER," 2,518 "
-58031865,HC SMTSRY EVKD LOWER," 2,518 "
-58031915,HC SMTSRY TRUNK/HEAD, 632 
-58032665,HC EEG EXTEND UPTO 1HR," 3,273 "
-58032681,HC EEG EXTNDED > 1 HR," 3,912 "
-58032723,"HC PSYCHOTHERAPY, 45-50 MIN", 275 
-58032947,"HC PSYCHOTHERAPY, 20-30 MIN", 189 
-58032954,HC EMG HOOK WIRES, 298 
-58032962,HC ONE CONTACT STRIP ELECTRODE," 1,105 "
-58032970,HC ELECTRD SPINAL 2 CONTACT, 723 
-58032988,HC ELECTRD SPINAL 3 CONTACT, 808 
-58033002,HC PSYCHOLOGICAL DIAGNOSIS INTERV, 949 
-58033119,"HC MYOBLOC, PER 100 UNITS", 53 
-58033127,HC CHEMODNERV XTREM MUSCLES, 505 
-58033135,"HC EMG, LMTD ONE EXT/NONLIMB", 383 
-58033143,HC CHEMODNERV PERIPHERAL NERVE," 2,176 "
-58033283,HC ANS TEST-PARASYMPTH, 616 
-58033291,HC ANS TEST-SYMPATHETIC, 616 
-58033309,HC ANS TEST-SUDOMOTOR, 616 
-58033317,HC ELECTRD SET SUBDML NEEDLE, 213 
-58033333,HC BIPOLAR STIM PROBE, 425 
-58033341,"HC MOTOR EP, UPPER EXT", 616 
-58033358,"HC MOTOR EP, LOWER EXT", 616 
-58100033,HC S EMG 1 XTRM," 1,141 "
-58100058,HC S EMG 2 XTRM," 1,230 "
-58100066,HC S EMG 3 XTRMS," 1,386 "
-58100074,HC S EMG 4 XTRM," 1,480 "
-58100082,HC EMG CRNL ULTR, 761 
-58100124,HC S EMG CRNL BLAT," 1,016 "
-58100140,HC NERV CNDCT MOTO INS, 303 
-58100165,HC S NRV CNDT SENS INS, 263 
-58100215,HC S BLNK RFLX TEST IN, 323 
-58100256,HC S REPETITIVE STIM TS, 544 
-58100272,HC NM JITTER TEST, 616 
-58100280,HC VISUAL EVOKED RES, 760 
-58100298,HC AUDITRY BRINSTM EV," 2,609 "
-58100306,HC SMTSRY EVKD LOWER," 2,480 "
-58100314,HC TRANSCRN DOPPLER CMPLTE," 2,485 "
-58100322,HC TRANSCRN DOPPLER LMTD," 1,596 "
-58100330,HC SMTSRY EVKD UPPER," 2,518 "
-58100355,HC MOTOR WITH F-WAVE, 589 
-22600316,"HC SCOTCH CAST PLUS ""5""", 63 
-22601116,HC SPLINT ARM SHORT PLASTER, 711 
-22601124,HC SHORT LEG CAST, 798 
-22601157,HC SPLINT SHORT LEG, 782 
-22601165,HC CAST- LONG LEG SPL, 776 
-22603054,HC DISP TRCTN SUPP/ONE LEG, 291 
-22603088,HC CERVICAL TRAC C HALT, 319 
-22603138,HC OHF TRAP 1 LEG SLING, 378 
-22603237,HC BALANCED SKIN TRACTION, 565 
-22604094,HC UNIVERSAL SLING, 54 
-22604128,HC SPLINT CLAVICLE, 76 
-22604144,HC CERVICAL COLLAR, 63 
-22604151,HC CRUTCHES, 139 
-22604193,HC CANE, 76 
-22604219,HC SPLINT HAND COCK UP, 69 
-22605125,HC BIAS STOCKIN 6X3 YD, 44 
-22605604,HC ABDUCTION PILLOW, 460 
-22605653,HC KNEE IMMOBILIZER, 314 
-22606016,HC SHORT ARM-STCHCST, 795 
-22606024,HC LONG ARM-STCHCST, 533 
-22606040,HC SHORT LEG-STCHSCT, 702 
-22606065,HC LONG LEG-STCHCST, 533 
-22606107,HC POST-OP SHOE, 99 
-22606123,HC HIP SPICA 1 LEG SCST, 552 
-22606289,HC HIP SPICA 2 LEG-SCST, 533 
-22606347,HC BLEDSOE BRACE, 815 
-22606354,HC SLING SWATHE, 46 
-22606362,HC WEDGE SHLDER, 383 
-22606370,HC WALKER FX BT, 659 
-22606412,HC 4 ARM CRUTCHES, 249 
-22606420,HC AIRCAST, 175 
-22606537,HC SUGAR TONG/SPLINT DISP SUPPY, 279 
-22606545,HC SKI BOX SUPPLY, 383 
-22606552,HC LEG TRACTION DISP SUPPLIES, 383 
-22660203,HC KIT SLIT CATHETER (STRYKER), 931 
-22660328,HC SHOULDER IMMOBOLIZER, 65 
-22660427,HC MIAMI J COLLAR, 206 
-22660575,HC FULL ARM OR FULL LE, 267 
-22660609,HC WINDOW OF CAST, 267 
-19101435,HC BRONCH ENDOBR BX SGL/MULTI," 5,161 "
-19101492,HC NEB SPCL GAS ADMIN-SUBSQNT, 357 
-22660443,HC BREMER HALO CROWN," 2,338 "
-22660468,HC BREMER HALO VEST," 4,038 "
-35601954,HC XSP EVAL SWALLOW/ORAL FXN, 933 
-81200057,HC HEART ACQ CTDN," 78,500 "
-81400053,HC HRT/LUNG ACQISITIN CTDN," 126,200 "
-81500050,HC SINGLE LUNG ACQ CTDN," 77,286 "
-81500076,HC DOUBLE LUNG ACQ CTDN," 106,500 "
-82200080,HC KIDNEY ACQ CAD ADULT RECIP CTD," 73,300 "
-82200114,HC KIDNEY ACQ LRD ADULT RECIP," 68,800 "
-85200038,HC KIDNEY/PANCREAS ACQ CTDN," 147,510 "
-85200020,HC PANCREAS ACQ CTDN," 74,210 "
-84200021,HC LIVER ACQ CAD ADULT RECIP CTDN," 83,436 "
-84200039,HC LIVER ACQ LRD ADULT REC," 86,216 "
-84200047,HC LIVER ACQ SPLIT ADULT REC CTDN," 83,436 "
-35160845,HC OP PT CLINIC EVAL 15MIN, 692 
-35260769,HP OT CLINIC EVAL, 692 
-35260777,HC COG PERF TESTING PER HOUR, 844 
-35601962,HC COG PERF TESTING PER HOUR, 844 
-58002007,HC SUBQ/IM INJECTION, 300 
-58002106,HC INJ COSYNTROPIN .25MG, 286 
-58033200,HC BACLOFEN KIT--500MCG," 1,734 "
-58033226,"HC REFILL PUMP,STRAIGHTFORWARD", 782 
-58033234,"HC REFILL PUMP,MEDIUM DIFFICULTY- NEURODX LAB", 665 
-35703016,HC COMPRESSION GLOVES, 149 
-35703024,HC COMPRESSION KINESIOTAPE, 85 
-19101559,HC CONT NEB 1ST HR, 704 
-19101567,HC CONT NEB ADDL HR, 529 
-22600423,HC BLEDSOE ARM BRACE," 1,322 "
-58033101,HC AMBULATORY EEG W/VIDEO- NEURODX LAB," 4,000 "
-30605174,HC ENDO CAN PAPILLA W DV OF CBD," 11,173 "
-22802037,HC CONT RENAL REPLACMT TX OR," 4,383 "
-3402484,HC MNT REASSESS 15 DIFF DX, 60 
-3402492,HC MNT GRP:EA 30 MIN DIFF DX, 35 
-70000856,HC NCPT EVAL 15 MIN, 692 
-30800908,HC PHLEBOTOMY, 115 
-40510075,HC AUDIOMETRY TYMPANO, 172 
-52290954,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-62021233,HC REVISE/REPR EYELID>1/4 LID MAR," 2,618 "
-35160852,HC PT MED TEAM CONF W/PAT >30 MIN, 279 
-35160860,HC PT MED TEAM CONF W/O PAT >30 M, 184 
-35260785,HC OT MED TEAM CONF W/PAT >30 MIN, 279 
-35260793,HC OT MED TEAM CONF W/O PAT >30 M, 184 
-35601970,HC SLP MED TEAM CONF W/PAT >30 MN, 279 
-35601988,HC SLP MED TEAM CONF WO PAT >30 M, 184 
-19101591,HC BRONCH THERAPEUTIC ASPIRATION," 5,950 "
-19101609,HC BRONCH THERAPEUTIC ASP SUB," 2,152 "
-70000864,HC NCXPT EVAL ADDL 15MIN, 172 
-70000872,HC NCXPT EVAL INIT 30MIN, 336 
-62800024,HC PT RE-EVALUATION, 431 
-62800032,HC PT APPLY TRACTION MECH, 311 
-62800040,HC PT APPLY E-STIM UNATTND, 160 
-62800057,HC PT ATTENDED E-STIM EA 15, 225 
-62800065,HC PT APPLY IONTO EA 15, 233 
-62800073,HC PT APPLY ULTRASOUND EA 15, 190 
-62800081,HC PT EXER/STR/ROM EA 15, 320 
-62800099,HC PT NEUROSMUSC RE-EDUC EA 15, 311 
-62800107,HC PT GAIT TRAINING EA 15 MIN, 298 
-62800115,HC PT MASSAGE EA 15 MIN, 198 
-62800123,HC PT MANUAL THERAPY TECH 15, 249 
-62800131,HC PT DIRECT THER ACT EA 15, 309 
-62800149,HC PT SELF CARE MGMT TRNG EA 15, 279 
-62800156,HC PT WHEELCHAIR MGMT 15 MIN, 275 
-62800164,HC PT ORTHOTIC MGMT/TRAIN EA 15, 255 
-62800172,HC PT PROSTHETIC TRAINING EA 15, 221 
-62800180,HC XPT JOINT MOBILIZATION 30, 249 
-62800198,HC XPT SNGL MOD 1 AREA 30MIN, 172 
-62800206,HC XPT MOD 1 AREA EA ADDL 15, 43 
-62800214,HC XPT PROC 1 AREA INIT 30, 214 
-62800222,HC XPT WHEELCHAIR MGMT 15MIN, 214 
-62800230,HC XPT PROC 1 AREA EA ADDL 15, 102 
-62800248,HC XPT COMB MOD/PR INIT 30, 214 
-62800255,HC XPT COMB MOD PR EA ADL 15, 68 
-62800263,HC XPT CASE CONT + RPT INIT 15, 214 
-62800271,HC XPT CASE CONSULT&RPT 30, 214 
-62800289,HC OT TX SWALLOW/ORAL FUNCT, 225 
-62800297,HC OT EVAL SWALLOW/ORAL FUNCT, 933 
-62800305,HC OT EVALUATION, 692 
-62800313,HC OT RE-EVALUATION, 431 
-62800321,HC OT MODALITY:E-STIM UNATTND, 160 
-62800339,HC OT ATTEND E-STIM EA 15, 225 
-62800347,HC OT CONTRAST BATHS EA 15, 126 
-62800354,HC OT ATTND ULTRASND EA 15, 190 
-62800362,HC OT STRENGTH/ROM TX EA 15, 320 
-62800370,HC OT NEUROMUSC RE-ED EA 15, 311 
-62800388,HC OT MANUAL THERAPY EA 15, 249 
-62800396,HC OT TX ACTIVITIES DIRECT EA 15, 309 
-62800404,HC OT DEV COG SKILL DIRECT EA 15, 242 
-62800412,HC OT SENSORRY INTEGRATION EA 15, 93 
-62800420,HC OT SELF CARE TRNG EA 15, 279 
-62800438,HC OT COMM/WORK RETRAIN EA 15, 131 
-62800446,HC OT WHEELCHR TRN EA 15, 275 
-62800453,HC OT ORTHOTIC FIT/TRAIN EA 15, 255 
-62800461,HC OT PROSTHETIC TRN EA 15, 221 
-62800479,HC OT CKOUT PROSTH ESTLPT EA 15, 195 
-62800487,HC OT COG PERF TESTING PER HOUR, 844 
-62800495,HC XOT EVAL INIT 30 PLUS REPORT, 336 
-62800503,HC XOT EVAL EA ADDL 15M+RPT, 172 
-62800511,HC XOT CASE CONF&RPT INIT 30, 214 
-62800529,HC XOT CASE CONF&RPT EA ADDL 15, 68 
-62800537,HC XOT TREATMENT INITIAL 30, 214 
-62800545,HC XOT TREATMENT EA ADDL 15, 84 
-62800552,HC XOT CASE CONSULT & RPT, 214 
-62800560,HC SP EVAL LARYNX W/SCSPE," 4,129 "
-62800578,HC SP EVALUATION SPEECH VOICE, 873 
-62800586,HC SP INDIV SPEECH RX PER TX, 848 
-62800594,HC SP SPEECH GROUP TX, 271 
-62800602,HC SP LARYNGEAL FXN STUDY, 372 
-62800610,HC SP TX SWALLW/ORAL FNCTN 30MIN, 225 
-62800628,HC SP EVAL VOICE PROS/AUG DEVICE, 442 
-62800636,HC SP MOD VOICE PROS/AUG DEV, 512 
-62800644,HC SP EVAL SWALLOW/ORAL FUNCT, 933 
-62800651,HC SP SWALLOW EVAL W/XRAY, 960 
-62800669,HC SP FFE FIBEROP SWL EVAL," 1,178 "
-62800677,HC SP FFE FIBEROP SENS EVAL, 465 
-62800685,HC SP ASSESS APHASIA W/RPT 60MIN, 473 
-62800693,HC SP TX EXER/STR/ROM/FLEX 15, 320 
-62800701,HC SP DEV COGNITIVE SKILL DIR 15, 242 
-62800719,HC SP SELF CARE HM MGMT 15, 279 
-62800727,HC SP COMM WORK REINTEG EA 15, 131 
-62800735,HC SP CHK ORTH/PROSTH EA 15, 195 
-62800743,HC COG PERF TESTING PER HOUR, 844 
-62800750,HC XSP EVAL SWALLOW/ORAL FXN, 933 
-62800768,HC EVAL AUDITORY REHAB 1ST HR, 459 
-62800776,HC EVAL AUDITORY REHAB ADD 15MIN, 101 
-62800784,HC AUD REHAB PRELINGUAL LOSS, 292 
-62800800,HC CI COUNSELING PER HR, 173 
-62800818,HC MAP/PROGRAMMING PER HR, 362 
-62800826,HC INITIAL EVAL 2-5 YEARS, 173 
-62800834,HC RECHECK/TROUBLE SHOOTING, 173 
-62800859,HC SUBSEQUENT EVAL<2YRS, 173 
-62800867,HC SUBSEQUENT EVAL2-5YRS, 173 
-62800875,HC SUBSQUENT EVAL 6-20YRS, 173 
-62800883,HC EVAL DIFFICLT 2 TEST<7YRS, 362 
-62800891,HC CASE CONF PT 1/4 HR, 127 
-62800917,HC XSP SWALLOW EVAL W/XRAY, 157 
-62800925,HC XSP FLEX FFE EVAL SWL, 157 
-62800933,HC XSP FLEX FFE SWL/LARYNX EVAL, 192 
-62800941,HC XSP LARYNGEAL FX STUDY, 173 
-62800958,HC XSP LANGUAGE EVALUATION, 636 
-62800966,HC XSP SPEECH EVALUATION, 636 
-62800974,HC XSP GRP THERAPY EA PATIENT, 257 
-62800982,HC XSP INDIV SLP THERAPY PER HOUR, 440 
-62800990,HC XSP INDIV SLP THERAPY 1/2 HR, 220 
-62801006,HC XSP THER SGD W/PROG & MOD, 440 
-62801014,HC XSP EVAL SGD VOICE PROSTH AUG, 923 
-62801147,HC VOICE PROSTHESIS, 143 
-62801154,HC INDWELL V PROSTH STARTER KIT, 371 
-62801162,HC INDWELL V PROSTH REPLACE, 383 
-62801170,HC OPSP TEP VALVE HUMID CAP, 117 
-62801196,HC TEP DILATOR, 197 
-62800016,HC PT EVALUATION, 692 
-62800792,HC AUD REHAB POSTLINGUAL LOSS, 207 
-30605216,HC FINE NDL INJ CELIAC PLEXUS," 3,964 "
-62801220,HC OP OT CLINIC EVAL 15MIN, 692 
-30009955,HC DERMAMATRIX 6X16 L48," 19,600 "
-30009948,HC DERMAMATRIX 8X16 L54," 28,000 "
-30206031,HC DERMAMATRIX 6X16 L48," 19,600 "
-62801303,HC XPT EVAL INIT 30 MIN, 336 
-62801311,HC XPT EVAL ADDL 15 MIN, 172 
-62801212,HC OP PT EVAL 15MIN, 692 
-62801253,HC WRITTEN REPORT 2 HR MAX, 173 
-62801261,HC WRITTEN REPORT EA 15 MIN, 173 
-62801295,HC AURAL REHAB POST COCH IMPL, 207 
-62801287,HC AURAL REHAB RELATED TO COCH IMPL, 292 
-30210082,HC PRC/PRSRV/TRNST CORNEAL TISL44," 22,400 "
-30605240,HC ESOPHAGEAL IMPEDANCE TEST," 2,179 "
-30605257,HC PYLORIC MANOMETRY," 2,179 "
-30605265,HC ANTRODUODERNAL MANOMETRY," 2,179 "
-30605273,HC COLONIC MANOMETRY, 927 
-30605281,HC ELECTRICAL GASTROGRAM, 927 
-30605299,HC LACTULOSE BREATH TEST, 927 
-30605307,HC PUDENDAL NERVE LATENCY TEST," 1,402 "
-30605315,HC GASTRIC BAROSTAT," 2,179 "
-22606438,HC QPM NEEDLE, 187 
-32003105,HC BLOOD DRAW IMPLANT VAD, 571 
-30096150,HC SKIN SUBSTITUTE NOS, 303 
-30296487,HC SKIN SUBSTITUTE NOS, 303 
-19100833,HC BIPAP-POS AIRWAY PRESSURE," 1,665 "
-38200408,HC FACIAL BONES 2V, 915 
-38200507,HC NASAL BONES MIN 3V, 896 
-38200705,HC ORBITS MIN 4V, 671 
-38200853,HC SINUSES PARANASAL MIN 3V," 1,671 "
-38200986,HC SKULL <4 VIEWS, 806 
-38201059,HC SKULL MIN 4 VIEWS, 984 
-38201877,HC L SPINE BEND ONLY 2/3 VIEWS," 1,304 "
-38201901,"HC C-SPINE, 3 VIEWS OR LESS"," 1,015 "
-38201950,HC SPINE CERV 4 OR 5 VIEWS," 1,284 "
-38202008,HC SPINE CERV 6 OR MORE VIEWS," 1,465 "
-38202057,HC T-SPINE 2 VIEWS," 1,012 "
-38202065,HC T-SPINE THREE VIEWS," 1,185 "
-38202107,HC THORACOLUMBAR 2 VIEWS," 1,113 "
-38202156,HC L-SPINE 2 OR 3 VIEWS," 1,043 "
-38202206,HC L-SPINE MIN FOUR VIEWS," 1,413 "
-38202214,HC L SPINE COMP W/BEND," 2,003 "
-38202354,HC SACRUM COCCYX MIN 2," 1,054 "
-38202552,HC PELVIS <3 VIEWS, 804 
-38202602,HC PELVIS MIN 3V, 953 
-38202651,HC SACROILIAC JOINTS MIN 3V, 984 
-38202958,HC HIP 1 VIEW, 885 
-38203006,HC HIP MIN 2V," 1,012 "
-38203055,HC HIP BIL W/PELVIS," 1,393 "
-38203204,HC SHOULDER 1 VIEW, 761 
-38203352,HC SHOULDER MIN 2/V, 959 
-38203402,HC HUMERUS MIN 2V, 771 
-38203451,HC ELBOW AP + LAT, 705 
-38203501,HC ELBOW MIN 3V, 826 
-38203550,HC FOREARM AP + LAT, 769 
-38203600,HC WRIST AP + LAT, 753 
-38203659,HC WRIST MIN 3V, 907 
-38203758,HC HAND 2 VIEW, 806 
-38203808,HC HAND MIN 3 VIEWS, 896 
-38203857,HC FINGER(S) MIN 2V, 592 
-38204004,HC FEMUR AP + LAT, 879 
-38204053,"HC KNEE, ONE OR TWO VIEWS-UNLAT", 732 
-38204103,HC KNEE THREE VIEWS-UNILAT, 815 
-38204152,HC KNEE 4 OR MORE VIEWS," 1,110 "
-38204202,HC TIBIA + FIBULA AP, 864 
-38204251,HC ANKLE AP + LAT, 689 
-38204301,HC ANKLE MIN 3V, 909 
-38204350,HC FOOT AP + LAT, 722 
-38204400,HC FOOT MIN 3V, 950 
-38204509,HC CALCANEUS MIN 2V, 707 
-38204558,HC TOE(S) MIN 2V, 586 
-38204707,HC CHEST 1 VIEW, 593 
-38204756,HC CHEST 2V FRONT + LA, 765 
-38204855,HC CHEST MIN 4V," 1,232 "
-38205258,HC RIBS UNI 2V," 1,024 "
-38205266,HC RIBS UNILAT W/CHEST 3+ V," 1,053 "
-38205308,HC RIBS BILAT 3V," 1,365 "
-38205316,HC RIBSBILAT W/CHEST 4+ V," 1,885 "
-38205357,HC STAND KNEES AP BILT, 672 
-38205407,HC STERNUM MIN 2V, 832 
-38205456,HC STERNOCLAVICULAR JT MIN 3V," 1,006 "
-38205506,HC CLAVICLE, 812 
-38205555,HC ACROMIOCLAV JTS BILAT," 1,054 "
-38205605,HC SCAPULA," 1,049 "
-38206108,HC NEPHROTOMOGRAPHY," 1,300 "
-38206652,HC NEPHROSTOGRAM," 1,036 "
-38206678,HC 36 LEG LENGTH," 1,084 "
-38207304,HC ABDOMEN AP ONLY, 718 
-38207353,HC ABD CPL W/DECUB+ERECT," 1,272 "
-38207403,HC ABD CPL W/DECUB+1V CHEST," 1,541 "
-38209706,HC BONE SRVY METS AXIA COMPL," 2,083 "
-38210209,HC FLUOROSCOPY UP 1HR," 1,849 "
-38210217,HC FLUOROSCOPY OVER 1 HR," 3,786 "
-38210803,HC ARTHROGRAM-KNEE," 1,238 "
-38210811,HC ARTHROGRAPHY HIP," 1,810 "
-38210829,HC ARTHROGRAM SHOULDER," 1,929 "
-38210837,HC ARTHROGRAM WRIST," 1,296 "
-38210852,HC ARTHROGRAPHY ANKLE," 1,168 "
-38216016,HC LOCM 200 - 299 MG/ML - OMNPQ, 20 
-38216131,HC NOSE 2 RECTUM CHILD SNGL VW, 682 
-38221313,HC SPINE SINGLE VIEW, 510 
-38245288,HC ORBITS FOREIGN BODY, 544 
-38245411,HC INJ KNEE ARTHROGRAM, 380 
-38245429,HC INJ HIP ARTHROGRAM," 2,941 "
-38245437,HC INJ SHOULDER ARTHRO," 2,775 "
-38245445,HC INJ WRIST ARTHROGRM, 725 
-38245460,HC INJ ANKLE ARTHROGRM," 4,836 "
-38268504,HC STERILE TRAY, 300 
-38271714,HC FLUORO GUIDED NDL PLC SPINE," 1,593 "
-38280111,HC CHEST 2V W/OBLIQUE," 1,275 "
-38280129,HC CHEST LAT DECUB, 821 
-38305009,HC US: PREG UTER TRANSVAGINAL," 1,150 "
-38305017,HC ASPIRATION MAJOR JOINT," 1,193 "
-38305025,HC ASPIRATION MEDIUM JOINT, 875 
-38305033,HC INJ(S); SNGL TNDN SHTH OR LGM, 515 
-38305041,HC US SCREEN AAA," 1,762 "
-38305058,"HC US: PREG UTRS W/EVAL, 1ST TRI"," 1,778 "
-38305066,HC ASPIRATION SMALL JOINT, 864 
-38305108,HC US GUID PARENCH TISS ABLAT, 712 
-38305116,"HC US INFANT HIPS, LIMITED"," 1,173 "
-38305702,HC US SUPPLY LEVEL 3, 85 
-38305769,HC US SUPPLY LEVEL 6, 298 
-38311213,HC US GUIDANCE NEEDLE PLCMNT," 1,896 "
-38328621,HC US: SOFT TISSUE OF HEAD OR NEC," 1,554 "
-38328647,HC US CHEST," 1,529 "
-38328654,HC US PREG UTERUS W/EVAL >1TRI," 1,928 "
-38328662,HC US:PREG UTER LMTD," 1,257 "
-38328688,HC US:TRANSVAGINAL," 1,456 "
-38328696,HC US:PELVIC NON-OB," 1,809 "
-38328704,HC US: PELV NON-OB LTD," 1,237 "
-38328712,HC US:SCROTUM," 1,470 "
-38328720,HC US: EXTREMITY," 1,189 "
-38328761,HC USL GUIDANCE INTRA-OPERATIVE," 1,715 "
-38328787,HC US: ABDOMEN," 2,294 "
-38328795,HC US:ABDOMEN LIMITED," 1,584 "
-38328803,HC US: RETROPERITONEAL," 1,822 "
-38328811,HC US:RETROPERITON LTD," 1,321 "
-38328878,HC US GUIDED COMPRESSN RP, 469 
-38328886,HC US:TRANSVAGINALLIMITED, 728 
-38330015,HC CAROTID DPLX BILAT," 3,055 "
-38330023,HC CAROTID DPLX UNILAT," 1,756 "
-38330056,HC UP+LWR EXT 1LVL BIL," 1,276 "
-38330080,HC DPLX LWR EXT ART/GRFT BIL," 3,076 "
-38330098,HC DPLX LWR EXT ART/GRFT UNIL," 2,177 "
-38330106,HC DPLX UP EXT ART/GRFT UNIL," 1,939 "
-38330114,HC DPLX UP EXT ART/GRFT BIL," 2,151 "
-38330130,HC DPLX VENOUS EX IMAN BIL," 2,966 "
-38330148,HC DPLX VEN EXT LTD UNIL," 2,072 "
-38330155,HC DPLX IN/OUT ABD/PEL/SCR/RETRO," 2,995 "
-38330163,HC AB/ART/ISCR/PLV/RETROP LTMD ST," 1,982 "
-38330171,HC DPLX AORT/IVC/ILIAC CMPL," 1,665 "
-38330189,HC DPLX AORT/IVC/ILIAC LTD," 1,627 "
-38330205,HC DPLX ART/VEN PENILE LTD OR F-U, 436 
-38330213,HC US:TPLNT KIDNEY W/WO DPLX/DOP," 1,578 "
-38345377,HC INJ HYSTEROSAPINGOGRAM," 1,241 "
-38371662,HC FINE NEEDLE ASPIRATION W/IMAGING," 2,086 "
-38380002,HC BX BONE TROC/NDL SUP," 3,880 "
-38380085,HC BX ABD/RETRO MASS PERC NDL," 2,277 "
-38380093,HC BX RENAL NDL," 2,637 "
-38380176,HC HYSTEROSONOGRAM, 893 
-38380192,HC BX LYMPHNODE SUPERFICIAL," 3,219 "
-38380234,HC PUNCTURE ASPIRATION," 1,423 "
-38400206,HC NUCLEAR MED THERAPY," 1,336 "
-38400255,HC RADIODN TX/HYPRTHYR," 2,995 "
-38400305,HC INTERCAVITARY THRPY, 939 
-38400347,HC RADIOIODIN TX ABLAT," 2,995 "
-38400354,HC RADIOPHARM TX:ORAL," 2,995 "
-38400404,HC POLYCYTHEM VER THRP," 1,336 "
-38400487,HC KIDNY FNCTN CLEARNC," 2,099 "
-38400503,HC CARD ANGIOGRM STRES," 5,038 "
-38400552,HC CARD BLD PL FRST PS TCHNQ-SIN, 871 
-38400651,HC BRAIN SCN W/VSC FLW," 1,291 "
-38400701,HC BONE SCAN WHOLE BODY," 4,580 "
-38400750,HC BONE MARROW SCAN," 1,305 "
-38400800,HC CAVITARY SCAN," 1,601 "
-38401154,HC LIVR/SPLEEN IMAGING, 886 
-38401170,HC LYMPHTC/LYMPHGL IMG," 4,375 "
-38401303,HC LUNG PERFUSION STDY," 3,596 "
-38401451,HC STRESS/REST REDIST MYOCARD," 2,790 "
-38401527,HC MYOCARD TOMO MULTI," 7,246 "
-38401535,HC MYOCARD TOMO SINGLE," 5,014 "
-38401550,HC BOWL IMAG MCKLS LCL," 3,192 "
-38401600,HC RBC GI BLEED," 4,521 "
-38401915,HC PARATHYROID IMAGING," 3,656 "
-38401956,HC 1-131 BODY METS SURVEY," 4,645 "
-38402145,HC CSF LEAK IMAGING," 3,679 "
-38402152,HC CSF KINETICS," 1,804 "
-38402178,HC RADPHARM LOC INFLAM WHOLE BODY," 3,523 "
-38402202,HC TUMOR LOCALIZATION," 5,209 "
-38402350,HC RBC SURVIVAL/SEQUES," 1,228 "
-38402400,HC BLOOD VOLUME," 1,228 "
-38402459,HC GASTROINT PROT LOSS, 831 
-38403135,HC BONE DENSITY DEXA AXIAL," 1,077 "
-38403309,HC P-32 INTRAVEN THRPY," 1,336 "
-38403424,HC GASTRC EMPT TIM STD," 4,131 "
-38403507,HC RENAL PERFUS/EXCRT," 2,332 "
-38403515,HC DMSA RENAL TOMO," 1,903 "
-38403564,HC RADPHARM LOC INFLAM TOMO-SPECT," 3,616 "
-38403663,HC CSF SHUNT EVAL," 1,559 "
-38403705,HC BONE SCAN THREE PHASE," 4,984 "
-38403721,HC RESTING HEART FUNCT," 4,164 "
-38403747,HC MYOC PRF/AT REST OR STRS-SGL," 1,915 "
-38403804,HC R TO L SHUNT," 2,178 "
-38403820,HC RENAL V-CYSTO," 2,193 "
-38403846,HC BLOOD+PLASMA VOLUME," 1,228 "
-38403887,HC BONE IMAG TOMO SPECT," 4,065 "
-38403937,HC LIVER IMAGING (SPECT)," 3,991 "
-38403945,HC GAST ESOPH REFL STD," 1,653 "
-38404000,HC BRAIN SCN - SPECT," 4,212 "
-38405015,HC TC99M DISOFENIN <15MCI, 118 
-38405023,HC 1-123-CAPSULE PER 100 UCI, 176 
-38405031,HC TX 1-131 CAPSULE PER MCI, 57 
-38405049,HC CERETEC HMPAO PER DOSE<25MCI," 1,896 "
-38405064,HC DX 1-131 CAPSULE PER MCI, 256 
-38405072,HC TC-99M SULFUR COLLOID <20MCI, 132 
-38405080,HC IN 111 OXYQUINOLINE/0.5 MCI," 1,681 "
-38405098,HC IN 111 DTPA PER 0.5 MCI," 2,968 "
-38405106,HC DMSA PER DOSE <10MCI, 419 
-38405114,HC NEUROLITE PER DOSE <25MCI," 2,029 "
-38405122,HC MAG-3 PER DOSE, 631 
-38405130,HC DTPA AEROSOL PER DOSE <75MCI, 150 
-38405148,HC MAA PER DOS <10MCI, 55 
-38405155,HC TC-99M LABELED RBC <30 MCI, 313 
-38405163,HC ADENOSINE DX <30 MG," 1,065 "
-38405189,HC RADPHARM LOC INFLAM: LIMITED," 1,911 "
-38405197,HC INDIUM IN-111," 3,600 "
-38410023,HC IOFETAMIN HC1-I-123," 1,573 "
-38410049,HC THALLIUM TL-201 DX PER MCI, 66 
-38410056,HC TC99M MEDRONATE DX <30MCI, 40 
-38410098,HC FUROSEMIDE < 20 MG, 117 
-38410122,HC TC-99M MEBROFENIN UP TO 15 MC, 118 
-38410130,HC GA 67 GALLIUM/MCI, 88 
-38410205,HC TC99M PERTECHNETATE DX PER MCI, 39 
-38410213,HC P32 SODIUM PHOSPHATE/MCI," 3,085 "
-38410221,HC CR51 SODIUM CHROMATE/250 MCI, 461 
-38410254,HC P32 CHROMIC PHOSPHATE SUSP/MCI," 7,455 "
-38410304,HC ADENOSINE THER OR DIAG USE/6 M," 1,363 "
-38410312,HC IN 111 PENTETREOTIDE DX <6 MCI," 9,041 "
-38410353,HC TC-99M PENETATE UP TO 25 MCI, 40 
-38410502,HC TC99M SESTAMIBI DX <40MCI, 662 
-38410585,HC TC99M TETROFOSMIN DX <40 MCI, 287 
-38410601,HC GA-67 GALLIUM CITRATE/MCI, 680 
-38410627,HC PROSTASCINT DOSE UP TO 10MCI," 8,975 "
-38420345,HC IN-111 SATUMOMAB PENDETIDE<6MC," 8,269 "
-38420428,HC TUMOR LOC SPECT," 6,146 "
-38420568,HC ESOPH MOT STUDY," 1,505 "
-38420907,HC CIST/LAT PUNC W/ IN," 1,663 "
-38420931,HC THYROGEN 1.1MG," 6,652 "
-38421103,HC MYOCARD PERF QUAL/QUAN," 5,014 "
-38421129,HC I-125 SODIUM IOTHALAMATE/10 UC," 2,205 "
-38421202,HC SAMARIUM SM-153 PER 50 MCI," 8,265 "
-38421434,HC RADIOPHARM I-123 MIBG," 5,733 "
-38421459,HC ZEVALIN IN-111 UP TO 5 MCI," 15,487 "
-38421467,HC ZEVALIN Y-90 UP TO 40 MCI," 134,073 "
-38421483,HC 1-123 IODINE WB METS SURVEY," 4,645 "
-38421491,HC SINCALIDE <5 UG, 172 
-38421509,HC BEXXAR I-131 TOSITUMOMAB/DIAG," 15,487 "
-38421517,HC BEXXAR I-131 TOSITUMOMAB/THER," 134,073 "
-38421525,HC WHOLE BODY SCAN-2 OR MORE," 9,741 "
-38421541,HC RADIO PHARM INF:THERAPY DOSE," 2,116 "
-38421558,HC RADIATION DOSIMETRY CALCULATIO," 1,428 "
-38421590,HC TC-99M FANOLESOMAB UP TO 25 M," 5,038 "
-38500005,HC MR HEAD PEFFERBAUM," 3,858 "
-38500138,HC MRI ABDOMEN WO CONTRAST," 5,769 "
-38500336,HC MRI UPR EXTREM JNT WO CONTRAST," 5,950 "
-38500401,HC MRI LWR EXTREM JNT WO CONTRAST," 4,973 "
-38500435,HC MRI BRAIN W/O CONTRAST," 6,610 "
-38500443,HC MRI BRAIN W/CONTRAST," 7,269 "
-38500484,HC MRI CHEST WO CONTRAST," 5,887 "
-38500500,HC MRI C-SPINE W/CONTRAST," 7,335 "
-38500534,HC MRI C SPINE WO CONTRAST," 5,727 "
-38500740,HC MRI LWR EXTREM NON-JT WO/W CON," 7,969 "
-38500781,HC MRI UPR EXTREM NON-JT WO/W CON," 7,794 "
-38501425,HC MRI T-SPINE WO CONTRAST," 5,732 "
-38501433,HC MRI T-SPINE W/CONTRAST," 6,419 "
-38501524,HC MRI L-SPINE WO CONTRAST," 5,647 "
-38501532,HC MRI L-SPINE W/CONTRAST," 6,681 "
-38505004,HC MRI GAD CONTRAST /ML, 29 
-38505129,HC MRI BRAIN FUNCTION NO MD," 1,641 "
-38505137,HC MRI BRAIN FUNCTION W/MD," 2,477 "
-38510004,HC MRI ORBITS FACE NECK W/CONT," 6,253 "
-38510012,HC MRI ORBIT FACE NECK WO/W CONT," 8,855 "
-38510020,HC MRI ANGIO HEAD WO CONTRAST," 5,681 "
-38510038,HC MRI ANGIO HEAD W/CONTRST," 7,199 "
-38510046,HC MRI ANGIO HEAD WO/W CONT," 8,699 "
-38510053,HC MRI ANGIO NECK WO CONTRAST," 5,295 "
-38510061,HC MRI ANGIO NECK W/CONTRST," 6,350 "
-38510079,HC MRI ANGIO NECK W/W/O CONT," 7,957 "
-38510087,HC MRI CHEST W/CONTRAST," 6,458 "
-38510095,HC MRI CHEST WO/W CONTRAST," 8,190 "
-38510103,HC MRI PELVIS WO CONTRAST," 5,566 "
-38510111,HC MRI PELVIS WO/W CONTRAST," 7,794 "
-38510129,HC MRI UPR EXTREM NON-JT WO CONTRAST," 5,594 "
-38510137,HC MRI UPR EXTREM NON-JT W/CONT," 6,120 "
-38510145,HC MRI UPR EXTREM JNT W/CONTRAST," 6,565 "
-38510152,HC MRI UPR EXTREM JNT WO/W CONTRA," 8,002 "
-38510160,HC MRI LWR EXTREM NON-JT WO CONTRAST," 5,898 "
-38510178,HC MRI LWR EXTREM NON-JT W/CONT," 6,629 "
-38510186,HC MRI LWR EXTREM JNT W/CONTRAST," 6,152 "
-38510194,HC MRI LWR EXTREM JNT WO/W CONT," 8,324 "
-38510202,HC MRI ABD W/CONTRAST," 6,681 "
-38510210,HC MRI ABD WO/W CONTRAST," 8,002 "
-38510228,HC MRI TMJ(S)," 4,252 "
-38530085,HC MRI PELVIS W/CONTRAST," 6,681 "
-38531448,HC MR RESEARCH CHARGE," 1,721 "
-38531620,HC MRI ORBITS FACE NECK W/O CONT," 5,554 "
-38531661,HC MRA WO FOL W CONT CHEST," 8,190 "
-38531687,HC MRA SPINAL CANAL W OR W/O CONT," 8,263 "
-38531703,HC MRA WO FOL W CONT PELVIS," 7,794 "
-38531729,HC MRA UPPER EXT WO/W CONTRAST," 7,794 "
-38531745,HC MRA W/O FOL W CONT ABD," 8,001 "
-38531760,HC MR HEART MORPH WO/W CONTRAST," 7,610 "
-38531828,NC MRI WO FOL W CONT BREAST UNI," 3,233 "
-38531844,HC W CONT BREAST BI," 5,351 "
-38531893,HC MR SPECTROSCOPY," 2,499 "
-38560256,HC MR SYRINGE&TRAY COS, 199 
-38560330,HC MRI BRAIN WO/W CONTRST," 8,276 "
-38560348,HC MR C SPINE WO/W CONT," 8,423 "
-38560355,HC MR T SPINE WO/W CONT," 7,998 "
-38560363,HC MR L SPINE WO/W CONT," 8,733 "
-38560397,HC MRA WO FOL W CONT LWR EXT," 7,969 "
-38560421,HC MR HEART W/O CONTRAST," 6,268 "
-38560439,HC MR HEART VELOCITY FLOW," 4,171 "
-38560454,HC MRA W/CONT CHEST," 6,458 "
-38560462,HC MRA W/O CONT CHEST," 5,886 "
-38560470,HC MRA W/CONT PELVIS," 6,681 "
-38560488,HC MRA W/O CONT PELVIS," 5,566 "
-38560496,HC MRA W/CONT LWR EXT," 6,283 "
-38560504,HC MRA W/O CONT LWR EXT," 5,566 "
-38560512,HC MRA W/CONT ABD," 6,681 "
-38560520,HC MRA W/O CONT ABD," 5,769 "
-38560538,HC MRI W/CONT BREAST UNI," 2,676 "
-38560546,HC MRI W/O CONT BREAST UNI," 1,993 "
-38560553,HC MRI W/O CONT BREAST BI," 3,985 "
-38560561,HC MRI W/O FOL W/CONT BREAST BI," 6,466 "
-38616033,HC LOCM 300-399 MG/ML-OMNPQ, 2 
-38616058,HC LOCM 300-399 MG/ML-VSPQ, 5 
-38616116,HC LOCM 300-399 MG/ML, 3 
-38616140,HC CTA CARD W/QUAN EVAL OF CORO C," 7,284 "
-38616264,HC CT CEREBRAL PERFUSION W CONTRAST," 6,996 "
-38616272,HC CT COLONGRAPHY - SCREEN," 2,250 "
-38616280,"HC CT COLONGRAPHY,DIAG WO CONTR"," 1,885 "
-38621363,HC CT GUIDANCE FOR NDL PLCMNT," 4,373 "
-38621389,HC CT GUIDANCE ABSCESS DRAIN," 2,894 "
-38623104,HC CT HEAD W/O CONTRAST," 3,850 "
-38623203,HC CT HEAD W/CNTRST," 4,505 "
-38623302,HC CT HEAD WO/W CON," 5,077 "
-38623344,HC CT MAXILLOFACIAL W/O CONTRAST," 3,954 "
-38623385,HC CT MAXFACIAL W/CNTRST," 4,848 "
-38623401,HC CT ORB SELL/PST FOS WO CONT," 4,780 "
-38623500,HC CT ORB SEL/PFOS W/CON," 5,656 "
-38623609,HC CT ORB SEL/PFOS WO/W CON," 6,355 "
-38623625,HC CT MAXFACL AREA W/O - SINUS," 1,100 "
-38624706,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-38624755,HC CT THORAX W/O CONTRAST," 4,426 "
-38624763,HC CT UPPER EXTREMITY W/O CONTRAST," 4,016 "
-38624771,HC CT UP EXT W/CONTRAST," 4,387 "
-38624789,HC CT UP EXT WO/W CONTRAST," 5,620 "
-38624805,HC CT PELVIS W/O CONTRAST," 4,373 "
-38624854,HC CT LOWER EXTREMITY W/O CONTRAST," 3,592 "
-38624862,HC CT LWR EXTRMTY WO/W CONTRAST," 5,943 "
-38624870,HC CT LWR EXTRMTY W/CONTRAST," 4,503 "
-38624904,HC CT SOFT TISS NECK W/O CONT," 4,347 "
-38624912,HC CT SFT TIS NCK W/CON," 4,628 "
-38624920,HC CT SFT NCK WO/W CON," 5,609 "
-38624953,HC CT ABDOMEN W/CONTRAST," 6,285 "
-38625000,HC CT THORAX W/CNTRST," 5,564 "
-38625059,HC CT PELVIS W/CONTRAST," 7,439 "
-38625109,HC CT ABDOMEN WO/W CONTRAST," 6,956 "
-38625158,HC CT THRX W+W/O CNTRS," 6,921 "
-38625208,HC CT PELVIS WO/W CONTRAST," 7,944 "
-38625307,HC CT ABDOMEN LIMITED W/O CONTRAST," 2,814 "
-38625828,HC CT PELVIMETRY, 200 
-38625869,HC CT C-SPINE W/O CONTRAST," 5,262 "
-38625877,HC CT T-SPINE W/O CONTRAST," 5,180 "
-38625885,HC CT L-SPINE W/O CONTRAST," 5,155 "
-38625935,HC CT C-SPINE W/CONTRAST," 6,554 "
-38625950,HC CT T-SPINE W/CONTRAST," 6,523 "
-38625976,HC CT L-SPINE W/ CONTRAST," 6,510 "
-38626453,CT CORONARY CALCIUM W/O CONTRAST, 690 
-38644068,HC CT GUIDE STEREOTAC LOCAL," 4,470 "
-38644449,HC CT ANGIO HEAD WO/W CONTRAST," 8,725 "
-38644456,HC CT ANGIO NECK WO/W CONTRAST," 9,345 "
-38644464,HC CT ANGIO CHEST WO/W CONTR," 7,745 "
-38644472,HC CT ANGIO PELVIS WO/W CONTR," 8,238 "
-38644480,HC CT ANGIO UP EXT WO/W CONTRAST," 7,717 "
-38644498,HC CT ANGIO LOW EXT WO/W CONTRAST," 6,960 "
-38644506,HC CT ANGIO ABD WO/W CONTRAST," 8,709 "
-38644514,HC CT ANGIO AORTA/LOW EXT," 10,739 "
-38644522,HC CT GUIDED PERC VRTEBROP," 2,785 "
-38661955,HC CT IV START KIT, 204 
-38672630,HC CT THORAX LIMITED W/O CONTRAST," 2,124 "
-38672671,HC CT GUIDANCE FOR RF ABLATION," 4,302 "
-38672697,HC 3D RENDERING - MODALITY, 775 
-38672804,HC CT GUIDANCE FOR RADIATION FIEL," 3,582 "
-38672812,HC CT HEAD W/O CONT (W XEN) 2ND," 3,850 "
-38672820,HC 3D RENDERING (2ND)-MODALITY, 775 
-38700019,HC OSC OR LEVEL1INIT 30M," 2,680 "
-38700027,HC OSC OR LVL2 INIT 30M," 6,422 "
-38700035,HC OSC OR LEVEL3 INIT 30M," 7,647 "
-38700043,HC OSC OR LEVEL4 INIT 30M," 8,874 "
-38700050,HC OSC OR LEVEL1 EA ADD 5M, 350 
-38700068,HC OSC OR LEVEL2 EA ADD 5M, 608 
-38700076,HC OSC OR LVL3 EA ADD 5M, 862 
-38700084,HC OSC OR LVL4 EA ADD 5M, 872 
-38700092,HC OSC CONSCIOUS SEDATION," 2,203 "
-38700100,HC OSC LOCAL ANESTHESIA, 311 
-38700118,HC OSC MONITORED ANESTHESIA," 2,973 "
-38700126,HC OSC GENERAL ANESTHESIA," 5,005 "
-38700134,HC OSC PACU INIT 30M," 2,057 "
-38700142,HC OSC PACU EA ADD 5M, 177 
-38702395,HC HOCM UP TO 149MG/ML L2, 50 
-38702601,HC LOCM 100 199MG/ML L4, 146 
-38405171,HC DEXA BODY COMPOSITION STDY," 1,077 "
-38505996,HC GADOXETATE DISODIUM- PER ML, 161 
-92200369,HC 3D ECHO," 1,983 "
-22802045,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-22802078,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-22802094,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-22802102,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-22802110,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-22802052,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-22802060,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-22802086,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-38202305,HC SCOLIOSIS INC SUP& ERECT," 1,662 "
-38271623,HC STRESS VIEWS ANY JOINT, 349 
-38305124,HC US PREGTRNSABD ADD GEST1TRI, 270 
-62801329,HC PT TEAM CONF W/PAT >30, 279 
-62801345,HC OT TEAM CONF W/PAT >30, 279 
-62801352,HC OT TEAM CONF W/O PAT >30, 184 
-62801360,HC SLP TEAM CONF W/PAT>30, 279 
-62801378,HC SLP TEAM CONF W/O PAT >30, 184 
-38305132,HC US PREG TRNSABD GEST 2 TRI, 951 
-62801337,HC PT TEAM CONF W/O PAT>30, 184 
-38700993,HC LOCM 200-299 MG/ML L3, 100 
-38305918,"HC PHLEBOTOMY, TX", 658 
-56101850,HC NON INVASIVE CO, 398 
-38305900,HC VENIPUNCTURE, 115 
-19160209,HC CONT AEROSOL EA ADDL HR LPCH, 529 
-56269699,HC PED TRNCTH INTR STENT," 8,001 "
-32091704,HC IMMUN ADM ONE VACC, 263 
-30206155,HC ELECTROCONVULSIVE THERAPY," 3,273 "
-50120526,HC LIQUID PLASMA, 751 
-50120534,HC GRANULOCYTES," 6,944 "
-51564540,HC (DBQ)DIRECT LIPID FRACTIONATIO, 278 
-58564543,HC (DBQ)DIRECT LIPID FRACTIONATIO, 278 
-51564532,HC (HDQ)HDL CHOLESTEROL FRACTION, 278 
-58564535,HC (HDQ)HDL CHOLESTEROL FRACTION, 278 
-50501964,HC 1 HR NORM + BUFFER PTT, 92 
-51401966,HC 1 HR NORM + BUFFER PTT, 92 
-51401941,HC 1 HR PT + BUFFER PTT, 92 
-58401936,HC 1 HR PT + NORMAL PTT, 92 
-51401933,HC 1 HR PT +BUFFER PTT, 92 
-58401944,HC 1 HR PT +BUFFER PTT, 92 
-37021482,HC 1-123 IODINE WB METS SURVEY," 4,645 "
-37005022,HC 1-123-CAPSULE PER 100 UCI, 298 
-37001955,HC 1-131 BODY METS SURVEY," 4,645 "
-50750801,HC 6956 11087R 17HYDXYPRGNOLON, 111 
-50750819,HC 6956 11067R 17D HYDPRGSTRNE, 21 
-50721281,HC 6959 U17OHC 17-OHCS UR, 36 
-58401969,HC 1HR NORMAL+BUFFER PTT, 92 
-41151820,HC 1ST ORDER CATH PLMT," 6,196 "
-92801711,HC 2ND AUDIOLOGIST (TEST ASSIST), 192 
-26403097,HC 2ND ENC CHEMO INFUS EA ADD HR, 641 
-26503094,HC 2ND ENC CHEMO INFUS EA ADD HR, 641 
-26603092,HC 2ND ENC CHEMO INFUS EA ADD HR, 641 
-26403071,HC 2ND ENC CHEMO IV INFUS UP TO 1," 1,444 "
-26503078,HC 2ND ENC CHEMO IV INFUS UP TO 1," 1,444 "
-26603076,HC 2ND ENC CHEMO IV INFUS UP TO 1," 1,444 "
-26503086,HC 2ND ENC CHEMO-IVINFUS-ADDL SEQ," 1,043 "
-26603084,HC 2ND ENC CHEMO-IVINFUS-ADDL SEQ," 1,043 "
-26503060,HC 2ND ENC CHEMO-IVPUSH-ADDL DRUG, 801 
-26603068,HC 2ND ENC CHEMO-IVPUSH-ADDL DRUG, 801 
-26403204,HC 2ND ENC IVP INJ +SEQ SAME DRUG, 256 
-26503219,HC 2ND ENC IVP INJ +SEQ SAME DRUG, 405 
-26603217,HC 2ND ENC IVP INJ +SEQ SAME DRUG, 405 
-26703223,HC 2ND ENC IVP INJ +SEQ SAME DRUG, 405 
-26403048,HC 2ND ENC IVP INJ+SEQ NEW DRUG, 400 
-26503045,HC 2ND ENC IVP INJ+SEQ NEW DRUG, 400 
-26603043,HC 2ND ENC IVP INJ+SEQ NEW DRUG, 400 
-26703041,HC 2ND ENC IVP INJ+SEQ NEW DRUG, 400 
-26403113,HC 2ND ENC-IV INF-THER-1ST HR," 1,226 "
-26503110,HC 2ND ENC-IV INF-THER-1ST HR," 1,226 "
-26603118,HC 2ND ENC-IV INF-THER-1ST HR," 1,226 "
-26703116,HC 2ND ENC-IV INF-THER-1ST HR," 1,226 "
-26503128,HC 2ND ENC-IV INF-THER-ADDTL SEQ, 796 
-26603126,HC 2ND ENC-IV INF-THER-ADDTL SEQ, 796 
-26703124,HC 2ND ENC-IV INF-THER-ADDTL SEQ, 796 
-26403014,HC 2ND ENC-IV INF-THER-EA ADD HR, 607 
-26503011,HC 2ND ENC-IV INF-THER-EA ADD HR, 607 
-26603019,HC 2ND ENC-IV INF-THER-EA ADD HR, 607 
-26703017,HC 2ND ENC-IV INF-THER-EA ADD HR, 607 
-26503052,HC 2ND ENC CHEMO IV PUSH INITIAL, 888 
-26603050,HC 2ND ENC CHEMO IV PUSH INITIAL, 888 
-26403006,HC 2ND ENCTR HYDRA INFUS EA ADDL, 607 
-26503003,HC 2ND ENCTR HYDRA INFUS EA ADDL, 607 
-26603001,HC 2ND ENCTR HYDRA INFUS EA ADDL, 607 
-26703009,HC 2ND ENCTR HYDRA INFUS EA ADDL, 607 
-26403105,HC 2ND ENCTR HYDRA INFUS UP TO 1H," 1,035 "
-26503102,HC 2ND ENCTR HYDRA INFUS UP TO 1H," 1,035 "
-26603100,HC 2ND ENCTR HYDRA INFUS UP TO 1H," 1,035 "
-26703108,HC 2ND ENCTR HYDRA INFUS UP TO 1H," 1,035 "
-26403022,HC 2ND ENCTR IV INFUS THERA CONCU, 821 
-26503029,HC 2ND ENCTR IV INFUS THERA CONCU, 821 
-26603027,HC 2ND ENCTR IV INFUS THERA CONCU, 821 
-26703025,HC 2ND ENCTR IV INFUS THERA CONCU, 821 
-26403030,HC 2ND ENCTR TX/DX INJECTION: INITIAL, 542 
-26503037,HC 2ND ENC TX/DX IVP INITIAL, 542 
-26603035,HC 2ND ENC TX/DX IVP INJECTION, 542 
-26703033,HC 2ND ENC TX/DX IVP INJECTION, 542 
-41151838,HC 2ND ORDER CATH PLMT," 6,939 "
-94100518,HC 3 OZ WOUND GEL DRESSING, 85 
-41006677,HC 36 LEG LENGTH," 1,084 "
-41306671,HC 36 LEG LENGTH," 1,084 "
-93606671,HC 36 LEG LENGTH," 1,084 "
-41100447,"HC 3D RENDERING W IMAGE POSTPROC, CONC SUPRV"," 1,984 "
-38072823,HC 3D RENDERING (2ND)-MODALITY, 775 
-42072827,HC 3D RENDERING (2ND)-MODALITY, 775 
-40300022,HC 3D RENDERING - 3D LAB," 1,984 "
-40300048,HC 3D RENDERING - 3D LAB," 1,984 "
-38072690,HC 3D RENDERING - MODALITY, 775 
-42072694,HC 3D RENDERING - MODALITY, 775 
-94372695,HC 3D RENDERING - MODALITY, 775 
-40300030,HC 3D RENDERING ADD'L - 3D LAB," 1,984 "
-40300014,HC 3D RENDERING ADD'L-3D LAB," 1,984 "
-51401867,HC 5 MIN NORM+BUFFER PT, 125 
-58401860,HC 5 MIN NORM+BUFFER PT, 125 
-51401925,HC 5 MIN NORM+BUFFER PTT, 92 
-58401928,HC 5 MIN NORM+BUFFER PTT, 92 
-51401842,HC 5 MIN PT + BUFFER INHITR, 125 
-51401917,HC 5 MIN PT + BUFFER PTT, 92 
-51401859,HC 5 MIN PT + NORM PRO, 125 
-58401852,HC 5 MIN PT + NORM-PRO, 125 
-58401910,HC 5 MIN PT+BUFFER PTT, 92 
-50750827,HC 6956 YU5HIA 5 HIAA UR, 13 
-31900103,HC 5FR DUAL LUM POWER PICC," 1,190 "
-31900111,HC 5FR NITINOL 5 CM MICROINTRO, 383 
-31903511,HC 6 FR MST KIT FOR TRIPLE," 1,360 "
-50749555,HC 6911 DNR CHAGAS W/REPL, 64 
-50774009,HC 6911 DNR HEP BS AG, 54 
-50773829,HC 6911 DNR HIVI/HCV NAT PROCLEIX, 370 
-50749563,HC 6911 DONOR CHAGAS RIPA, 400 
-50726462,HC 6911 DONOR HIVI/HCV, 410 
-50790807,HC 6911 FREE T3 DIALYSIS, 27 
-50773977,HC 6911DNR CMV TOTAL AB, 100 
-50773985,HC 6911DNR HEP B CORE TTL AB, 63 
-50773993,HC 6911DNR HEPC AB W/REFLX RIBA, 70 
-50774017,HC 6911DNR HIV 1/2 AB, 71 
-50774033,HC 6911DNR RPR W/RFLX SYPH, 75 
-50774041,HC 6911DNR WEST NILE VIRUS, 290 
-50774025,HC 6911HTLV 1/2 AB SCREEN, 65 
-50753680,HC 6912 NBS/17HYDROXYPROG, 16 
-50773415,HC 6912 NBS/BIOTINIDASE, 16 
-50753664,HC 6912 NBS/HEMOGLOBIN CHROMAT, 16 
-50773423,HC 6912 NBS/IRT, 16 
-50753631,HC 6912 NBS/TANDEM MASS SPECT, 16 
-50753649,HC 6912 NBS/TRANSFERASE-SCREEN, 16 
-50753656,HC 6912 NBS/TSH, 16 
-50730415,HC 6922 COCCI CF SERUM, 40 
-50773852,HC 6922 COCCI IMMUNODIFF, 34 
-50730423,HC 6922 COCCIDIODOMYCOSIS, 40 
-50730969,HC 6937 CYSTICERCOSIS WEST BLOT, 90 
-55034276,HC 6939 AFB ID WALLACE LAB, 88 
-59034272,HC 6939 AFB ID WALLACE LAB, 88 
-55034250,HC 6939 AGAR DIFFUSION SENS WALL, 122 
-59034256,HC 6939 AGAR DIFFUSION SENS WALL, 122 
-50731397,HC 6939 CASPOFUNGIN LVL, 95 
-55033880,"HC 6939 FNGL ID, RNALDI LAB", 237 
-55032122,HC 6939 FUNGAL MIC ADDL, 254 
-59032128,HC 6939 FUNGAL MIC ADDL, 254 
-55032130,HC 6939 FUNGAL SYNERGY, 93 
-55034268,HC 6939 MIC BY WALL LAB, 254 
-59034264,HC 6939 MIC WALLACE LAB, 254 
-50731348,HC 6939 11131R VORI QNT SNG AN, 120 
-47202304,HC 6940 IGA ANTIGEN/AB, 213 
-50773837,HC 6942 A PHAGOCYTOPHILUM AB, 77 
-50730290,HC 6942 AB SEROLOGY 2, 122 
-50773217,HC 6942 ADENOVIRUS AB IGG/M, 14 
-50730407,HC 6942 ADENOVIRUS AG BY EIA, 52 
-50773233,HC 6942 ADENOVIRUS DNA QNT PCR, 152 
-50773100,HC 6942 ADENOVIRUS DNA QUAL PCR, 142 
-55033781,HC 6942 AFB ID CONV. MRL, 88 
-59033787,HC 6942 AFB ID CONV. MRL, 88 
-55033807,HC 6942 AFB MIC PAN 1 DRG, 58 
-59033803,HC 6942 AFB MIC PAN 1 DRG, 58 
-50773126,HC 6942 AFB MTB PCR, 224 
-50730332,HC 6942 ARBOVIRUS TITER, 474 
-50731280,HC 6942 ASO TITER, 23 
-50730902,HC 6942 ASPERGILLUS-CF, 34 
-50730910,HC 6942 ASPGLUS IMMUNODIFSN, 35 
-50731330,HC 6942 BARTONELLA AB PAN, 20 
-50730779,HC 6942 BARTPC BRTNL HNSL&QNTN, 224 
-50730571,HC 6942 BK VIRUS DNA PCR, 147 
-50730654,HC 6942 BK VIRUS DNA QUANT, 150 
-50731298,HC 6942 BLASTOMYCOSES AB ID, 34 
-50730928,HC 6942 BLASTOMYCOSIS, 48 
-50730936,HC 6942 CANDIDA PRECIPITANS, 44 
-50772946,HC 6942 CGD FC, 66 
-50773308,HC 6942 CHLAMYDIA PNEUM CULT, 84 
-50773316,HC 6942 CHLAMYDIA PNEUM CULT, 24 
-50773662,HC 6942 CHLAMYDIA TRACH IGG AB, 73 
-50773670,HC 6942 CHLAMYDIA TRACH IGM AB, 79 
-50730514,HC 6942 CHLAMYDIA TRACHOMATIS CU, 58 
-50773043,HC 6942 CMV UL97 UL54 MUT ANALY, 10 
-50773050,HC 6942 CMV UL97 UL54 MUT ANALY, 41 
-50773068,HC 6942 CMV UL97 UL54 MUT ANALY, 10 
-50773076,HC 6942 CMV UL97 UL54 MUT ANALY, 41 
-50773084,HC 6942 CMV UL97 UL54 MUT ANALY, 10 
-50730498,HC 6942 COCCIIMMUNODIFFUSION, 23 
-50731306,HC 6942 COCCIDIODES AB CF, 53 
-50730381,HC 6942 COXSACKIE AB PANEL, 17 
-50731439,HC 6942 COXSACKIE B AB, 17 
-50730944,HC 6942 CYSTICERCOSIS CSF, 86 
-50730951,HC 6942 CYSTICERCOSIS SERUM, 83 
-50773027,HC 6942 CYSTICERCUS AB ELISA, 83 
-50773266,HC 6942 CYSTICERCUS IGG AB, 107 
-50730282,HC 6942 DELTA HEPAT AGENT, 62 
-50773035,HC 6942 DENGUE FEVER AB ELISA, 30 
-50730613,HC 6942 EB EBNA, 53 
-59160291,HC 6942 EB EBNA, 218 
-50730621,HC 6942 EB VCA IGA, 51 
-50730597,HC 6942 EB VCA IGG, 53 
-59160275,HC 6942 EB VCA IGG, 343 
-50730605,HC 6942 EB VCA IGM, 24 
-59160283,HC 6942 EB VCA IGM, 343 
-50730795,HC 6942 EB VCAM IGM, 47 
-50774082,HC 6942 EBV EA, 14 
-50730589,HC 6942 EBV EARLY AB (D&R), 53 
-59160267,HC 6942 EBV EARLY AB (D&R), 191 
-50774090,HC 6942 EBV EBNA, 16 
-50774108,HC 6942 EBV VCA, 19 
-50773324,HC 6942 ECHOVIRUS AB PNL CF, 21 
-50773282,HC 6942 EHRLICHIA CHAFFEENIS, 74 
-50773332,HC 6942 ENTAMOEGA HISTOLYTICA AB, 61 
-50730399,HC 6942 ENTEROVIRUS AB PANL, 19 
-50730803,HC 6942 EPSTEIN BARR EA, 48 
-50731389,HC 6942 F TULARENSIS AB, 100 
-50730993,HC 6942 FASCIOLIASIS, 65 
-50731009,HC 6942 FILARIASIS, 157 
-50731074,HC 6942 FSFTA TRPNMA PALDM IFA, 26 
-50773241,HC 6942 HANTAVIRUS AB, 47 
-50772953,HC 6942 HCV GENOTYPE, 242 
-50772961,HC 6942 HEP B GENO, 76 
-50772979,HC 6942 HEP B GENO, 38 
-50772987,HC 6942 HEP B GENO, 9 
-50772995,HC 6942 HEP B GENO, 9 
-50773001,HC 6942 HEB B GENO INT AND RPT, 9 
-50730266,HC 6942 HEPATITIS BE AB, 20 
-59100222,HC 6942 HEPATITIS BE ANTBDY, 40 
-50730274,HC 6942 HEPATITIS BE ANTGEN, 18 
-50773407,HC 6942 HERPES 6 IGG/M AB, 46 
-50773498,HC 6942 HERPES SELECT 1 IGG, 30 
-50773506,HC 6942 HERPES SELECT 2 IGG, 45 
-50773514,HC 6942 HERPES VIRUS 7AB IGG/M, 117 
-50730530,HC 6942 HHV-6 DNA PCR, 107 
-50774074,HC 6942 HHV-6 QUANT PCR, 224 
-50731314,HC 6942 HISTOPLASMA AB ID, 34 
-50730894,HC 6942 HISTOPLASMOSIS CF, 33 
-50730506,HC 6942 HIV-1 DNA PCR, 127 
-50730324,HC 6942 HIV P24, 150 
-50730704,HC 6942 HIV-1 DIRECT AG, 110 
-50730845,HC 6942 HS AB NON SPEC TYPE, 32 
-50730852,HC 6942 HS AB TYPE 1, 29 
-50730860,HC 6942 HS AB TYPE 2, 43 
-50773480,HC 6942 HSV 1/2 PCR NONCSF, 63 
-50730720,HC 6942 HSV-1 IGM AB IFA, 20 
-50730738,HC 6942 HSV-2 IGM AB IFA, 29 
-50730449,HC 6942 HTLV 1 AB DIFFENT, 89 
-50730431,HC 6942 HTLV 1/2 ANT WEST B, 112 
-50730472,HC 6942 HTLV 2 AB DIFFENT, 89 
-50773365,HC 6942 HTLV-I/II DNA QUANT, 112 
-50731173,HC 6942 HUMORAL IMMUN PANEL, 375 
-50730563,HC 6942 JC VIRUS DNA PCR, 147 
-50731025,HC 6942 LEGIONELLA AG URINE, 45 
-50731033,HC 6942 LEISHMANIASIS, 10 
-50731264,HC 6942 LYME B BURGDORFERI WB, 32 
-50731272,HC 6942 LYME CSF WESTERN BLOT, 81 
-50731371,HC 6942 LYME SCREEN, 55 
-50774710,HC 6942 LYMPH MITOGEM SCREEN, 98 
-50748599,"HC 6942 LYMPH. MITOGEN SCREEN, TR", 89 
-50748607,"HC 6942 LYMPH. MITOGEN SCREEN, TR", 89 
-50748581,"HC 6942 LYMPH. MITOGEN SCREEN, TR", 89 
-50752989,HC 6942 LYMPHOCYTE FUNCTION I, 77 
-50752997,HC 6942 LYMPHOCYTE FUNCTION II, 80 
-50731041,HC 6942 MALARIA, 145 
-59034207,HC 6942 MRL ID PROBE MGOR/KAN, 69 
-50773118,HC 6942 MYCO FAST PATH CULT, 230 
-50773688,HC 6942 MYCOPLASMA PNEUM IGG/M AB, 52 
-50774702,HC 6942 NATURAL KILLER CELLS ENUM, 49 
-50730811,HC 6942 NOROVIRUS-STOOL, 98 
-50731058,HC 6942 PARAGONIMIASIS, 75 
-50730548,HC 6942 PARVOVIR (B19) PCR, 127 
-50773522,HC 6942 PARVOVIRUS AB IGG/M, 29 
-50731157,HC 6942 PNEUMOCOCCAL TITER, 11 
-50731363,HC 6942 PNEUMOVAX 1 OF 12, 13 
-50773142,HC 6942 POLIOVIRUS AB NEUTRALZTN, 68 
-50730357,HC 6942 FPOLIO POLIOVIRUS AB, 26 
-50730308,HC 6942 Q FEVER ANTIBODY, 16 
-50731405,HC 6942 RUBELLA IGM, 52 
-50773019,HC 6942 S PNEUMONIAE AG DETECT LA, 55 
-50731066,HC 6942 SCHISTOSOMIASIS, 77 
-50731082,HC 6942 STRONGYLOIDIASIS, 66 
-50731132,HC 6942 TOXOCARIASIS, 92 
-50731124,HC 6942 TRICHINOSIS, 52 
-50730555,HC 6942 VZ DNA PCR, 147 
-50774058,HC 6942 VZV AB PANEL, 42 
-50774066,HC 6942 VZV AB PANEL, 42 
-50773134,HC 6942 W NILE AB PNL ELISA CSF, 54 
-50730340,HC 6942 WEST BLOT CONFM SEN, 102 
-50773092,HC 6942 WESTNILE VIRUS RNA RT-PCR, 224 
-50731140,HC 6942 YERSINIOSIS, 88 
-50731181,HC 6944 NUC MOLEC ISOL PCR, 63 
-50731108,HC 6944 TOXO DYE TEST IGG, 114 
-50730688,HC 6944 TOXO IGA ELISA, 126 
-50731116,HC 6944 TOXO IGM ELISA, 121 
-50791268,HC 6949 CYLX IMM CEL FNCT, 102 
-50773472,HC 6949 DONOR CMV AB LIVING, 25 
-50773464,HC 6949 DONOR HBC LIVING, 35 
-50749571,HC 6949 DRPRS DONOR RPR, 29 
-50730464,HC 6949 HTLV 1/2 AB, 27 
-59131243,HC 6949 HTLV-1 AB, 29 
-50727007,HC 6949 11136R HTLVS 1, 15 
-50727015,HC 6949 11136R HTLVS 2, 26 
-50774157,HC 6956 11017R BRUCELLA AB, 40 
-50770825,"HC 6956 17-KETOSTEROID, U", 63 
-50770361,HC 6956 5-FLUCYTOSINE, 66 
-50770908,HC 6956 5'NUCLEOTIDASE, 15 
-50726470,HC 6956 AB ENA EVAL, 4 
-50751437,HC 6956 ACETYLCHOLINESTERASE RBC, 61 
-50770957,HC 6956 ACID PHOSPHATASE, 36 
-50751999,HC 6956 ACYLGLYCINE, 135 
-50754043,HC 6956 AFP CSF, 40 
-50770247,"HC 6956 ALA, URINE", 28 
-50760115,HC 6956 ALBUMIN CSF, 13 
-50770197,"HC 6956 ALDOSTERONE, URINE", 13 
-50748623,HC 6956 ALKALINE PHOSPHATASE, 4 
-50770213,HC 6956 ALPHA-1-ANTITRYPSIN, 12 
-50752450,HC 6956 ALPHA-1-ANTITRYPSIN STL, 31 
-50770726,HC 6956 ALPHA-SUBUNIT OF PGH, 33 
-50770221,HC 6956 ALUMINUM, 20 
-50753003,HC 6956 AMIODARONE, 26 
-50772052,HC 6956 AMP MPLX NA 1-2, 94 
-50772318,HC 6956 AMP MPLX NA 1-2, 100 
-50772417,HC 6956 AMP MPLX NA 1-2, 47 
-50772466,HC 6956 AMP MPLX NA 1-2, 90 
-50760040,HC 6956 AMP NA EA, 48 
-50772011,HC 6956 AMP NA EA, 113 
-50753201,HC 6956 YUAMC AMPHET CONF QNUR, 23 
-50770106,HC 6956 AMPHOTERICIN B, 152 
-50772797,"HC 6956 AMYLASE, NON ASCITES", 39 
-50770254,"HC 6956 AMYLASE, PANCREATIC", 41 
-50770262,HC 6956 ANDROSTANEDIOL GLUC, 65 
-50773175,HC 6956 ANF, 56 
-50772904,HC 6956 ANTI-DNASE B TITER, 82 
-50771526,HC 6956 ANTI-HCV BY RIBA, 26 
-50771187,HC 6956 ANTITHROMBIN III AG, 17 
-50751486,HC 6956 APOLIPOPROTEIN A, 45 
-50751494,HC 6956 APOLIPOPROTEIN B, 18 
-50771146,HC 6956 ARGININE VASOPRESSIN, 24 
-50771880,HC 6956 ARSENIC, 8 
-50770288,"HC 6956 ARSENIC, BLOOD", 11 
-50770296,"HC 6956 ARSENIC FRACT, URINE", 55 
-50770270,"HC 6956 ARSENIC, RANDOM URINE", 21 
-50752344,"HC 6956 AUTO AB TO JOL, SERUM", 17 
-50771716,"HC 6956 B. PERTUSSIS AB, IGG/M", 6 
-50771781,HC 6956 BABESIA MICROTI IGG/M, 33 
-50773357,HC 6956 BACTERIAL AG DETECT PNL, 68 
-50753219,HC 6956 BARBITURATE CONFIRM, 14 
-50771344,HC 6956 BCA AG (CA 27.29), 23 
-50753227,HC 6956 BENZODIAZEPINE CONFIRM, 7 
-50754050,HC 6956 BETA HCG QUANT, 42 
-50770189,HC 6956 BETA-HYDROXYBUTYRATE, 27 
-50770312,"HC 6956 BILE ACIDS, TOTAL", 50 
-50772102,"HC 6956 BILIRUBIN, TOTAL", 9 
-50771708,HC 6956 BLASTOMYCES AB, 22 
-50752286,"HC 6956 BONE ALK PHOSP,SERUM", 52 
-50754092,"HC 6956 BROMIDE, BLOOD", 46 
-50771724,"HC 6956 BRUCELLA AB, IGG/M", 26 
-50770148,HC 6956 BUPROPION, 57 
-50752377,"HC 6956 C1 ESTERASE INHBTR,PLSMA", 20 
-50771260,HC 6956 C2 COMPLEMENT AG, 132 
-50753037,HC 6956 C4 FUNCTION, 60 
-50771252,HC 6956 C5 COMPLEMENT AG, 49 
-50760008,HC 6956 CADMIUM, 14 
-50771914,HC 6956 CADMIUM, 10 
-50770320,"HC 6956 CADMIUM, URINE", 36 
-50772144,HC 6956 CALCIUM UR QUANT, 11 
-50771351,HC 6956 CANCER ANTIGEN 15-3, 12 
-50771377,HC 6956 CANCER ANTIGEN 72-4, 52 
-50772094,"HC 6956 CARBAMAZEPINE, FREE", 31 
-50772086,"HC 6956 CARBAMAZEPINE, TOT", 34 
-50751981,HC 6956 CARBOHYDRATE DEF, 44 
-50771245,"HC 6956 CARDIOLIPIN AB, IGA", 19 
-50770338,HC 6956 CAROTENE, 11 
-50770346,"HC 6956 CATECHOLAMINE FREE, P", 27 
-50749548,HC 6956 CEA, 100 
-50772748,HC 6956 CEA SPINAL FLUID, 76 
-50752328,"HC 6956 CENTROMERE AB,SERUM", 18 
-50752195,"HC 6956 CERULOPLASMIN, SERUM", 10 
-50771823,HC 6956 CHLAMYDIA, 6 
-50771831,HC 6956 CHLAMYDIA IGM, 6 
-50772839,"HC 6956 CHLORIDE, FLUID", 27 
-50772821,"HC 6956 CHLORIDE, STOOL", 34 
-50772151,"HC 6956 CHLORIDE, URINE", 9 
-50772490,HC 6956 CHROMATOGRAPHY, 16 
-50770395,HC 6956 CHROMIUM, 24 
-50771369,HC 6956 CHROMOGRANIN A, 14 
-50772177,HC 6956 CITRATE, 52 
-50770049,HC 6956 CLONAZEPAM, 24 
-50753235,HC 6956 COCAINE CONFIRM URINE, 16 
-50731447,HC 6956 COCCIDIODES AB CSF, 18 
-50734987,HC 6956 COCCIDIOIDES AB SERUM, 3 
-50771807,HC 6956 COLORADO TICK FEVER, 42 
-50752203,"HC 6956 COMPLEMENT, TOTAL, SERUM", 20 
-50770411,"HC 6956 COPPER, URINE", 11 
-50754027,HC 6956 CPKI CK ISOENSYMES, 35 
-50754019,HC 6956 CPKI TOTAL, 35 
-50772169,"HC 6956 CREATININE, URINE", 10 
-50771641,HC 6956 CUTANEOUS IMMFLR IGG, 21 
-50770445,"HC 6956 CYANIDE, BLOOD", 45 
-50771278,HC 6956 CYCLIC CITRULL PEPT, 14 
-50770239,"HC 6956 CYSTINURIA PROF, QT", 60 
-50770460,HC 6956 DEOXYCORTICOSTEROIDS, 35 
-50770056,HC 6956 DESIPRAMINE, 27 
-50770452,HC 6956 DHEAS, 8 
-50772664,HC 6956 DIAZEPAM/NORDIAZEPAM, 28 
-50770403,HC 6956 DIGITOXIN, 66 
-50770478,HC 6956 DIHYDROTESTOSTERONE, 23 
-50771435,HC 6956 DIPHTH TOXOID IGG AB, 32 
-50770080,HC 6956 DISOPYRAMIDE, 38 
-50760081,HC 6956 DRUG SCREEN, 27 
-50760099,HC 6956 DRUG SCREEN 12, 33 
-50770015,"HC 6956 DRUG SCREEN, FLUIDS", 69 
-50752237,"HC 6956 DRUG SCREEN, PLASME", 28 
-50772912,HC 6956 DRUG SCREEN PRESC/OTC, 55 
-50751114,HC 6956 DESIPRAMINE, 19 
-50771468,HC 6956 E. HISTOLYTICA AB, 27 
-50771575,HC 6956 EBV BY PCR, 85 
-50772367,"HC 6956 EBV, EBNA", 6 
-50772375,"HC 6956 EBV, VCA", 7 
-50772383,HC 6956 EHRLICHIA, 4 
-50772110,HC 6956 ELECTROPHORETIC, 60 
-50771310,HC 6956 ENA AG, 24 
-50771567,"HC 6956 ENTEROVIRUS, PCR", 62 
-50771997,HC 6956 ENZYMATIC DIGESTION, 27 
-50772573,HC 6956 ESTRADIOL, 9 
-50770486,"HC 6956 ESTRIOL, FREE", 18 
-50770494,HC 6956 ESTRONE, 9 
-50772565,HC 6956 ESTRONE, 8 
-50770502,"HC 6956 FAT, FECES", 44 
-50770510,"HC 6956 FAT, FECES, RANDOM", 6 
-50773209,HC 6956 FATTY ACID ESSENTIAL, 71 
-50752013,HC 6956 FATTY ACID FLUX--OXIDA, 189 
-50752021,HC 6956 FATTY ACID FLUX-FIBRO, 181 
-50770528,"HC 6956 FATTY ACID PROF,PEROX", 54 
-50770114,HC 6956 FELBAMATE, 38 
-50773159,HC 6956 FETAL LUNG AF, 74 
-50773167,HC 6956 FETAL LUNG AF, 55 
-50772276,"HC 6956 FLOW CYTO, 1ST MKR", 67 
-50772284,"HC 6956 FLOW CYTO, EA ADDTL", 33 
-50770163,HC 6956 FLUCONAZOLE, 51 
-50770940,HC 6956 FLUPHENAZINE, 75 
-50770551,HC 6956 FRUCTOSAMINE, 13 
-50770536,"HC 6956 FRUCTOSE, SEMEN", 55 
-50751072,HC 6956 GABAPENTIN, 19 
-50770775,"HC 6956 GBM AB, IGG, ELISA", 28 
-50760123,HC 6956 GC/MS - VIT D2, 12 
-50752211,"HC 6956 GLIADIN AB, SERUM", 14 
-50731413,HC  6956 GLIADIN DEAMIDATED IGA, 18 
-50770700,HC 6956 GLIADIN IGA, 12 
-50770544,HC 6956 GLUCAGON, 19 
-50772789,"HC 6956 GLUCOSE, FLUID", 34 
-50751445,HC 6956 GLUTAMIC AC DECARBOXY AB, 14 
-50770833,HC 6956 H PLACENTAL LACTOGEN, 118 
-50771443,"HC 6956 H. INFLU B AB, IGG", 57 
-50770064,HC 6956 HALOPERIDOL, 52 
-50752294,"HC 6956 HEAVY METALS SCR,BL", 11 
-50771385,HC 6956 HEP B SURFACE AB, 10 
-50752229,"HC 6956 HEREDITARY HEMO GENE, BL", 11 
-50770627,HC 6956 HISTAMINE, 46 
-50770676,HC 6956 HISTONE AUTOANTIBODIES, 23 
-50771815,HC 6956 HISTOPLASMA AB SCRN, 20 
-50754076,"HC 6956 HISTOPLASMA AG,CSF", 109 
-50754068,"HC 6956 HISTOPLASMA AG,SRM", 109 
-50754084,"HC 6956 HISTOPLASMA AG,U", 109 
-50770635,"HC 6956 HOMOCYSTEINE, TOTAL, U", 65 
-50771583,HC 6956 HUMAN METAPNEUMO RNA, 247 
-50790286,HC 6956 HYDROXYLASE AB SR, 38 
-50771732,HC 6956 IBD PANEL, 40 
-50753029,HC 6956 IBUPROFEN, 51 
-50772524,HC 6956 IFN-B AB, 427 
-50772532,HC 6956 IFN-B-AB, 357 
-50770791,HC 6956 IGF BINDING PROTEIN-3, 14 
-50770734,HC 6956 IGF II, 59 
-50752260,HC 6956 IGG, 23 
-50760107,HC 6956 IMIPRAMINE, 19 
-50771625,HC 6956 IMMUNO FR LIGHT CHAINS, 13 
-50760065,"HC 6956 IMMUNOASSAY, ANALYTE, QUA", 28 
-50771740,HC 6956 INFLUENZA AB PANEL, 12 
-50770817,HC 6956 INHIBIN B, 162 
-50772623,HC 6956 INSULIN ANTIBODIES, 13 
-50772615,"HC 6956 INSULIN, FREE", 17 
-50772607,"HC 6956 INSULIN, TOTAL", 15 
-50760032,HC 6956 INTERP AND REPORT, 11 
-50772029,HC 6956 INTERP/REPORT, 27 
-50772060,HC 6956 INTERP/REPORT, 11 
-50772326,HC 6956 INTERP/REPORT, 12 
-50772425,HC 6956 INTERP/REPORT, 12 
-50772474,HC 6956 INTERP/REPORT, 11 
-50771401,HC 6956 INTRINSIC FACT BLK AB, 20 
-50771989,HC 6956 ISOL NUC ACID CONF, 27 
-50770130,HC 6956 ITRACONAZOLE, 38 
-50770718,"HC 6956 LACTOFERRIN DETECT,STL", 91 
-50770122,HC 6956 LAMOTRIGINE, 12 
-50771211,HC 6956 LAP SCORE, 14 
-50752468,HC 6956 YUCLAX CHRM QL TLNES U, 105 
-50752484,HC 6956 YUCLAX PHOS INORG, 27 
-50771930,HC 6956 LD ISOENZYMES, 14 
-50771922,HC 6956 LDH, 6 
-50760016,HC 6956 LEAD, 7 
-50771898,HC 6956 LEAD, 5 
-50752583,"HC 6956 LEAD, CHDP", 7 
-50773530,HC 6956 LEFLUNOMIDE METABOLITE, 194 
-50771757,HC 6956 LEGIONELLA PNEUMO, 11 
-50770767,HC 6956 LEPTIN, 103 
-50771450,HC 6956 LEPTOSPIRA AB, 45 
-50773183,HC 6956 LEVETIRACETAM, 22 
-50752351,"HC 6956 LYSOZYME(MURA),PLASMA", 23 
-50752476,HC 6956 YUCLAX LAX MAGNESIUM U, 38 
-50772185,HC 6956 MAGNESIUM, 13 
-50772847,"HC 6956 MAGNESIUM, FLUID", 34 
-50770858,"HC 6956 MAGNESIUM, QUANT, FECES", 38 
-50770866,HC 6956 MANGANESE, 29 
-50760057,HC 6956 MASS SPEC/TNDM MASS SPECT, 10 
-50771492,"HC 6956 MEASLES AB, IGM", 12 
-50752310,HC 6956 MERCURY BLOOD QUANT, 11 
-50760024,"HC 6956 MERCURY, QUANT", 10 
-50771906,"HC 6956 MERCURY, QUANT", 7 
-50770874,"HC 6956 MERCURY, URINE", 11 
-50790294,HC 6956 METANEPHRINES FREE PL, 68 
-50752245,"HC 6956 METANEPHRINES,FR,24,UR", 19 
-50753250,HC 6956 METHADONE CONFIRM, 75 
-50772508,HC 6956 YMETHS METHSUXIMIDE, 65 
-50770098,HC 6956 MEXILETINE, 90 
-50772037,HC 6956 MOLECULAR EXTRACT, 11 
-50772292,HC 6956 MOLECULAR EXTRACT, 12 
-50772391,HC 6956 MOLECULAR EXTRACT, 12 
-50772441,HC 6956 MOLECULAR EXTRACT, 11 
-50771765,HC 6956 MUMPS VIRUS AB IGG/M, 13 
-50772078,HC 6956 MUT ID ENZ EA, 47 
-50772334,HC 6956 MUT ID ENZ EA, 50 
-50772433,HC 6956 MUT ID ENZ EA, 51 
-50772482,HC 6956 MUT ID ENZ EA, 45 
-50752138,HC 6956 MYASTH GRA EVAL-ADULT, 29 
-50771773,HC 6956 MYCO PNEUMO AB IGG/M, 15 
-50772698,HC 6956 MYCO/UREAPLASMA, 20 
-50771542,HC 6956 MYCOPLASMA CULT, 75 
-50752302,"HC 6956 MYELINE BSC PROT,CSF", 26 
-50751452,HC 6956 MYELOPEROXIDASE AB, 28 
-50751460,HC 6956 MYOGLOBIN URINE, 19 
-50770890,HC 6956 NICKEL, 11 
-50773191,HC 6956 NMO-IGG, 161 
-50760073,HC 6956 NORTRIPTYLINE, 9 
-50772045,HC 6956 NUC ACID PROBE, 11 
-50772300,HC 6956 NUC ACID PROBE, 84 
-50772409,HC 6956 NUC ACID PROBE, 12 
-50772458,HC 6956 NUC ACID PROBE, 11 
-50772706,HC 6956 OSMOLALITY STOOL, 41 
-50772193,"HC 6956 OSMOLALITY, URINE", 13 
-50770916,HC 6956 OSTEOCALCIN, 60 
-50772201,HC 6956 OXALATE, 24 
-50771633,HC 6956 PARAINFLUENZA AB, 23 
-50772730,HC 6956 PARVOVIRUS B19 AB, 10 
-50753284,HC 6956 PCP CONFIRM, 16 
-50770304,HC 6956 PENTOBARBITAL, 48 
-50772219,"HC 6956 PH, URINE", 7 
-50771872,HC 6956 PHENOBARBITAL, 5 
-50748698,HC 6956 PHENYTOIN TOTAL, 12 
-50771070,HC 6956 PHOSPHOLIPIDS, 36 
-50772227,HC 6956 PHOSPHORS INORGANIC UR, 10 
-50770601,HC 6956 PLASMA HEMOGLOBIN, 36 
-50773225,HC 6956 PLATELET AB, 144 
-50772755,"HC 6956 POTASSIUM, FLUID", 27 
-50772235,"HC 6956 POTASSIUM, URINE", 8 
-50772813,"HC 6956 POTSSIUM, STOOL", 34 
-50770692,HC 6956 PR3 AUTOANTIBODIES, 34 
-50752187,"HC 6956 PREALBUMIN(PAB), SERUM", 16 
-50770973,HC 6956 PREGNENOLONE, 106 
-50771864,HC 6956 PRIMIDONE, 7 
-50770999,"HC 6956 PROINSULIN, P", 30 
-50771195,HC 6956 PROTEIN C AG, 29 
-50771203,HC 6956 PROTEIN S AG, 36 
-50770353,"HC 6956 PTH-RELATED PEPTIDE, P", 14 
-50752005,HC 6956 PURINE&PRYRMIDINE PANEL, 85 
-50771021,"HC 6956 PYRUVATE, CSF", 49 
-50771013,HC 6956 PYRUVIC ACID, 21 
-50771393,HC 6956 RABIES ANTIBODY, 54 
-50771336,HC 6956 RETICULIN AB, 7 
-50771328,HC 6956 RIBOSOME P AB, 40 
-50771799,HC 6956 RICKETTSIA AB PANEL, 15 
-50770429,HC 6956 YRISPM COL CHRM MS QNT, 76 
-50772672,"HC 6956 RSV AB, IGG/M", 23 
-50771658,HC 6956 S. PNEUMO AB 23 SERO, 3 
-50771039,HC 6956 SELENIUM, 26 
-50772003,HC 6956 SEP ELECTROPH, 27 
-50771047,HC 6956 SEROTONIN, 45 
-50770155,HC 6956 YSERTR SERTRALINE MSQN, 71 
-50771054,HC 6956 SEX HORMONE BIND GLOB, 21 
-50771302,HC 6956 SM AUTO AB, 12 
-50772771,"HC 6956 SODIUM, FLUID", 26 
-50772805,"HC 6956 SODIUM, STOOL", 34 
-50772243,"HC 6956 SODIUM, URINE", 9 
-50790328,HC 6956 SOLUBLE TRANSFERRIN, 29 
-50771062,HC 6956 SOMATOSTATIN, 235 
-50772128,HC 6956 SPECTROPHOTOMETRY, 12 
-50771609,HC 6956 SPERM AB, 111 
-50772714,HC 6956 STREPTOCCAL AB, 8 
-50772722,HC 6956 STREPTOCCAL AB, 15 
-50770783,HC 6956 STRIATIONAL (SM) AB, 7 
-50772250,"HC 6956 SULFATE, URINE", 9 
-50770437,HC 6956 SYNTH GLUCOCORTIC SCR, 65 
-50771112,HC 6956 T-UPTAKE, 7 
-50771120,"HC 6956 T3, FREE", 4 
-50772540,HC 6956 T3 UPTAKE, 24 
-50770809,HC 6956 TAT COMPLEX, 178 
-50752336,"HC 6956 TC(TRIIODOT),REVERSE", 22 
-50770031,HC 6956 TCA SCREEN, 96 
-50752153,"HC 6956 TESTERONE, T&F, SERUM", 11 
-50772342,"HC 6956 TESTOSTERONE, FREE", 10 
-50772359,"HC 6956 TESTOSTERONE, TOTAL", 17 
-50771500,HC 6956 TETANUS TOXOID IGG AB, 28 
-50771088,HC 6956 THIAMIN (VIT B1), 31 
-50771096,HC 6956 THIOCYANATE, 33 
-50770924,HC 6956 THIORIDAZINE, 28 
-50770742,HC 6956 THYROTROPIN RECEPT AB, 42 
-50772557,"HC 6956 THYROXINE, TOTAL", 26 
-50771104,HC 6956 THYROXINE-BINDING GLOB, 29 
-50770072,HC 6956 TOPIRAMATE, 28 
-50772136,HC 6956 TRIGLYCERIDES, 10 
-50771476,"HC 6956 TRYPAN CRUZI AB, IGG", 43 
-50771138,"HC 6956 TRYPSIN, FECAL", 45 
-50770759,HC 6956 TRYPSIN-LKE IMMNREACT, 62 
-50752369,"HC 6956 TRYPTASE, SERUM", 63 
-50770684,"HC 6956 TTG AB, IGA", 69 
-50771617,"HC 6956 TTG AB, IGG/M", 11 
-50771294,HC 6956 U1RNP AUTO AB, 17 
-50772854,"HC 6956 UREA NITROGEN, FLUID", 30 
-50772268,"HC 6956 URIC ACID, URINE", 9 
-50752278,"HC 6956 VALPROIS ACID,T&F,PLSMA", 52 
-50752252,"HC 6956 VANILLYLMANDELIC(VMA),UR", 18 
-50771427,"HC 6956 VDRL, CSF", 7 
-50771153,HC 6956 VITAMIN A, 16 
-50771005,HC 6956 VITAMIN B6, 15 
-50753672,HC 6956 VON WILLEBR MULTIMERS, 19 
-50771674,"HC 6956 WEST NILE VIRUS, IGM", 47 
-50771179,HC 6956 ZINC, 11 
-50770171,HC 6956 ZONISAMIDE, 46 
-50749530,HC 6956CLOZAPINE SERUM, 12 
-50731421,HC 6956GLIADIN DEMIDATED AB EVAL, 14 
-50753276,HC 6956OPIATE  URINE, 24 
-50747807,HC 6958 BETA-2-GLYCOPROTEIN, 40 
-50749035,HC 6958 PAI-1 ACTIVITY, 135 
-50749043,HC 6958 PAI-1 ANTIGEN, 135 
-50749050,HC 6958 TPA ACTIVITY, 165 
-50749068,HC 6958 TPA ANTIGEN, 135 
-50773845,HC 6959 ACETYCHOLINESTERASE, 135 
-50773431,HC 6959 AFP AMNIOTIC FLUID, 14 
-50753342,HC 6959 ANTI-NEUTROPHIL AB, 92 
-50772862,"HC 6959 BILIRUBIN, FLUID", 9 
-50772870,"HC 6959 CHOLESTEROL, FLUID", 9 
-50790393,HC 6959 CREATINE SERUM, 68 
-50790401,HC 6959 ECP ESNPHL CTNC PRTN, 88 
-50721232,HC 6959 FLECAINIDE, 26 
-50773381,HC 6959 FREE T4 EQUILIB DIALYSIS, 40 
-50774694,HC 6959 GMBIFA AB IGA IGG, 64 
-50754001,HC 6959 HISTAMINE, 34 
-50773399,HC 6959 HOMOVANILLIC ACID URINE, 22 
-50746841,"HC 6959 IGA, CSF", 9 
-50724574,HC 6959 IMMUNOFIXATION IGD IGE, 151 
-50753722,HC 6959 INHIBIN A, 36 
-50748680,"HC 6959 KIDNEY STONE PANEL, CR", 7 
-50753391,HC 6959 LIVER KID MICROSOME-1, 19 
-50790427,HC 6959 MEAT FIBERS FECAL, 12 
-50773456,HC 6959 NMP22 URINE, 94 
-50773449,HC 6959 OXICARBAZEPINE METAB, 41 
-50746817,HC 6959 PHENOBARBITAL, 6 
-50746809,HC 6959 PRIMIDONE & METABOLITE, 10 
-50741271,HC 6959 PURKINJE CELL CYTOPLASMIC, 22 
-50753433,HC 6959 RETINOL BINDING PROT, 20 
-50753441,HC 6959 UTHI THIOCYANATE URINE, 34 
-50753458,HC 6959 TISS. TRANSGLUT IGG, 47 
-50772888,"HC 6959 TRIGLYCERIDES, NON PLEU", 9 
-50772896,"HC 6959 URIC ACID, FLUID", 9 
-50790377,HC 6959 VITAMIN KI, 74 
-50726215,HC 6961 ABCC8 DNA SEQ, 23 
-50748953,"HC 6961 ATAXIA COMPLETE, AMP.", 92 
-50748961,"HC 6961 ATAXIA COMPLETE, AMP.", 92 
-50748979,"HC 6961 ATAXIA COMPLETE, AMP.", 92 
-50748987,"HC 6961 ATAXIA COMPLETE, AMP", 92 
-50748995,"HC 6961 ATAXIA COMPLETE, AMP.", 92 
-50749001,"HC 6961 ATAXIA COMPLETE, AMP.", 92 
-50749019,"HC 6961 ATAXIA COMPLETE, AMP.", 92 
-50749027,"HC 6961 ATAXIA COMPLETE, INTERP.", 93 
-50749217,"HC 6961 ATAXIA COMPLETE, LSOL.", 92 
-50748938,"HC 6961 ATAXIA COMPLETE, SEP. GEL", 92 
-50748946,"HC 6961 ATAXIA COMPLETE, SEP. GEL", 92 
-50749225,"HC 6961 ATAXIA COMPLETE, SEP. GEL", 92 
-50749233,"HC 6961 ATAXIA COMPLETE, SEP. GEL", 92 
-50749241,"HC 6961 ATAXIA COMPLETE, SEP. GEL", 92 
-50749266,"HC 6961 ATAXIA COMPLETE, SEP. GEL", 92 
-50749142,"HC 6961 ATAXIA STANDARD, AMP.", 85 
-50749159,"HC 6961 ATAXIA STANDARD, AMP.", 85 
-50749167,"HC 6961 ATAXIA STANDARD, AMP.", 85 
-50749175,"HC 6961 ATAXIA STANDARD, AMP.", 85 
-50749183,"HC 6961 ATAXIA STANDARD, AMP.", 85 
-50749191,"HC 6961 ATAXIA STANDARD, AMP.", 85 
-50749209,"HC 6961 ATAXIA STANDARD, INTERP.", 85 
-50749076,"HC 6961 ATAXIA STANDARD, LSOL.", 85 
-50749084,"HC 6961 ATAXIA STANDARD, SEP. GEL", 85 
-50749092,"HC 6961 ATAXIA STANDARD, SEP. GEL", 85 
-50749100,"HC 6961 ATAXIA STANDARD, SEP. GEL", 85 
-50749118,"HC 6961 ATAXIA STANDARD, SEP. GEL", 85 
-50749126,"HC 6961 ATAXIA STANDARD, SEP. GEL", 85 
-50749134,"HC 6961 ATAXIA STANDARD, SEP. GEL", 85 
-50726140,HC 6961 FGF23 DNA SEQ, 15 
-50726157,HC 6961 FGF23 DNA SEQ, 63 
-50726165,HC 6961 FGF23 DNA SEQ, 63 
-50726173,HC 6961 FGF23 DNA SEQ, 63 
-50726181,HC 6961 FGF23 DNA SEQ INT/RPT, 15 
-50725845,HC 6961 GCK DNA SEQ, 9 
-50725852,HC 6961 GCK DNA SEQ, 36 
-50725860,HC 6961 GCK DNA SEQ, 36 
-50725878,HC 6961 GCK DNA SEQ, 36 
-50725886,HC 6961 GCK DNA SEQ, 9 
-50725944,HC 6961 IPF1 DNA SEQ, 12 
-50725951,HC 6961 IPF1 DNA SEQ, 52 
-50725969,HC 6961 IPFI DNA SEQ, 52 
-50725977,HC 6961 IPFI DNA SEQ, 52 
-50725985,HC 6961 IPFI DNA SEQ INT/RPT, 12 
-50774769,HC 6961 MELAS ENZYMATIC DIG, 29 
-50774801,HC 6961 MELAS INTERP AND REPORT, 29 
-50774777,HC 6961 MELAS MUT AMP EA NA SEQ, 123 
-50774785,HC 6961 MELAS MUT ID EA SEG, 123 
-50774793,HC 6961 MELAS SEP ELECTRO CAP, 123 
-50725746,HC 6961 MODY EVALUATION, 5 
-50725753,HC 6961 MODY EVALUATION, 22 
-50725761,HC 6961 MODY EVALUATION, 22 
-50725779,HC 6961 MODY EVALUATION, 22 
-50725787,HC 6961 MODY EVAL INT/RPT, 5 
-50774728,HC 6961 MOTOR NP PROFILE, 142 
-50774736,HC 6961 MOTOR NP PROFILE, 142 
-50726041,HC 6961 OSTEOGENESIS IMPERFECTA, 4 
-50726058,HC 6961 OSTEOGENESIS IMPERFECTA, 18 
-50726066,HC 6961 OSTEOGENESIS IMPERFECTA, 18 
-50726074,HC 6961 OSTEOGENESIS IMPERFECTA, 18 
-50726082,HC 6961 OSTEOGEN IMPERF INT/RT, 4 
-50748227,HC 6961 SMALL FIBER PAIN.AXONAL, 64 
-50748235,HC 6961 SMALL FIBER PAIN.AXONAL, 64 
-50748243,HC 6961 SMALL FIBER PAIN.AXONAL, 64 
-50748250,HC 6961 SMALL FIBER PAIN.AXONAL, 64 
-50748276,"HC 6961 SMALL FIBER PAIN.AXONAL,", 64 
-50748284,"HC 6961 SMALL FIBER PAIN.AXONAL,", 64 
-50748318,"HC 6961 SMALL FIBER PAIN.AXONAL,", 64 
-50748326,"HC 6961 SMALL FIBER PAIN.AXONAL,", 64 
-50748292,"HC 6961 SMALL FIBER PAIN.AXONAL,A", 64 
-50748268,"HC 6961 SMALL FIBER PAIN.AXONAL,I", 64 
-50748334,"HC 6961 SMALL FIBER PAIN.AXONAL,I", 64 
-50748342,"HC 6961 SMALL FIBER PAIN.AXONAL,I", 64 
-50748300,"HC 6961 SMALL FIBER PAIN.AXONAL,M", 64 
-50748359,"HC 6961 SMALL FIBER PAIN.AXONAL,W", 64 
-50774744,HC 6961 SULFATIDE AUTO ELISA, 203 
-50725894,HC 6961 TCF1 DNA SEQ, 8 
-50725902,HC 6961 TCF1 DNA SEQ, 32 
-50725910,HC 6961 TCF1 DNA SEQ, 32 
-50725928,HC 6961 TCF1 DNA SEQ, 32 
-50725936,HC 6961 TCF1 DNA SEQ INT/RPT, 8 
-50725993,HC 6961 TCF2 DNA SEQ, 9 
-50726009,HC 6961 TCF2 DNA SEQ, 36 
-50726017,HC 6961 TCF2 DNA SEQ, 36 
-50726025,HC 6961 TCF2 DNA SEQ, 36 
-50726033,HC 6961 TCF2 DNA SEQ INT/RPT, 9 
-50748409,"HC 6961 TTR AMYLOIDOSIS DNA,AMP", 29 
-50748417,"HC 6961 TTR AMYLOIDOSIS DNA,AMP", 29 
-50748425,"HC 6961 TTR AMYLOIDOSIS DNA,AMP", 29 
-50748433,"HC 6961 TTR AMYLOIDOSIS DNA,INT", 29 
-50748367,"HC 6961 TTR AMYLOIDOSIS DNA,ISOL", 29 
-50748375,"HC 6961 TTR AMYLOIDOSIS DNA,MUT", 29 
-50748383,"HC 6961 TTR AMYLOIDOSIS DNA,MUT", 29 
-50748391,"HC 6961 TTR AMYLOIDOSIS DNA,MUT", 29 
-50774850,HC 6961 TTRMET INTERP AND REPORT, 24 
-50774827,HC 6961 TTRMET MUT AMP EA NA SEQ, 99 
-50774835,HC 6961 TTRMET MUT ID EA SEG, 99 
-50774819,HC 6961 TTRMET MUT ISOL NA, 24 
-50774843,HC 6961 TTRMET SEP ELECTRO CAP, 99 
-50725795,HC 6961HNF4A DNA SEQ, 8 
-50725803,HC 6961HNF4A DNA SEQ, 32 
-50725811,HC 6961HNF4A DNA SEQ, 32 
-50725829,HC 6961HNF4A DNA SEQ, 32 
-50725837,HC 6961 HVF4A DNA SEQ INT/RPT, 8 
-50774751,HC 6961MELAS MUT ISOL NA, 29 
-50790468,HC 6964-DEHYDROCHOLESTEROL, 150 
-50730985,HC 6972 ECHINOCOCCOSIS, 128 
-52050317,HC 6973 HISTOCHEM STN/CHLDRNS, 573 
-50730639,HC 6976 PHENOSENSE HIV COMP, 855 
-50752963,HC 6996GALACTOSE-1-PHOSPHATE, 82 
-50731454,HC 6997 ACETYLCHOLINESTERASE, 330 
-50731462,HC 6997 AFAFPN AFP AMNIOTC FLD, 63 
-50752088,HC 6997 FANCONI ANEMIA, 105 
-50753755,HC 6997 Y CHROM MICRODELETIONS, 160 
-50753763,HC 6997 Y CHROM MICRODELETIONS, 38 
-50753771,HC 6997 Y CHROM MICRODELETIONS IN, 38 
-50730647,HC 7008 HSV WESTERN BLOT, 156 
-50753052,HC 7016 CELIAC DISEASE SYS I, 18 
-50753060,HC 7016 CELIAC DISEASE SYS II, 50 
-50753078,HC 7016 CELIAC DISEASE SYS III, 86 
-50753086,HC 7016 CELIAC DISEASE SYS IV, 16 
-50753110,HC 7016 IBD DIAGNOSTIC SYS I, 30 
-50753128,HC 7016 IBD DIAGNOSTIC SYS II, 92 
-50753144,HC 7016 PRO-PREDICT ENAACT I, 83 
-50753151,HC 7016 PRO-PREDICT ENAACT II, 83 
-50753169,HC 7016 PROPREDICTTPMT I, 15 
-50753177,HC 7016 PROPREDICTTPMT II, 59 
-50753185,HC 7016 PROPREDICTTPMT III, 15 
-50753193,HC 7016 PROPREDICTTPMT IV, 15 
-50773696,HC 7026 CELIAC PLUS, 11 
-50773704,HC 7026 CELIAC PLUS, 15 
-50773712,HC 7026 CELIAC PLUS, 5 
-50773720,HC 7026 CELIAC PLUS NA PROBE, 5 
-50773738,HC 7026 CELIAC PLUS AMP MPLX, 39 
-50773746,HC 7026 CELIAC PLUS INT/RPT, 5 
-50773753,HC 7026 CELIAC PLUS, 45 
-50753094,HC 7026 FIBROSPECT LIVERFIBRO I, 83 
-50753102,HC 7026 FIBROSPECT LIVERFIBRO II, 98 
-50773548,HC 7026 THIOPURINE METABOLITES, 51 
-50752591,HC 7031 11-DEXOXYCORTISOL/SPEC C, 47 
-50752609,HC 7031 17-A-HYDROXYPROGESTERONE, 33 
-50752617,HC 7031 17-OH-PREGNENOLONE, 35 
-50752625,HC 7031 ALDOSTERONE, 47 
-50752633,HC 7031 ANDROSTENEDIONE, 35 
-50753789,HC 7031 ANTI-ADRENAL 21 OH AB, 28 
-50752955,HC 7031 XGAD65 IMMANYT QNT RIA, 40 
-50726397,HC 7031 CAHDETXTM BY PCR, 10 
-50726405,HC 7031 CAHDETXTM BY PCR, 40 
-50726413,HC 7031 CAHDETXTM BY PCR, 10 
-50726421,HC 7031 CAHDETXTM BY PCR, 10 
-50726439,HC 7031 CAHDETXTM BY PCR, 10 
-50726447,HC 7031 CAHDETXTM BY PCR, 6 
-50726454,HC 7031 CAHDETXTM BY PCR, 39 
-50752682,HC 7031 CATECHOLAMINES, 52 
-50752641,HC 7031 CATECHOLAMINES FRACT I, 49 
-50752658,HC 7031 CATECHOLAMINES FRACT II, 5 
-50752666,HC 7031 CATECHOLAMINE TOT URIN, 25 
-50752674,HC 7031 CATECHOLAMINES TOTAL II, 5 
-50752690,HC 7031 DEHYDROEPINANDROSTERONE, 42 
-50752708,HC 7031 DHEA SULFATE, 29 
-50752716,HC 7031 DIHYDROTESTOSTERONE, 39 
-50752724,HC 7031 ESTERADIOL, 45 
-50752732,HC 7031 ESTROGENS TOTAL, 30 
-50752740,HC 7031 ESTRONE, 49 
-50752757,HC 7031 FOLLICLE STIM HORMONE, 24 
-50752765,HC 7031 FREE CORTISOL I, 35 
-50752773,HC 7031 FREE CORTISOL II, 5 
-50752781,HC 7031 FREE TESTOSTERONE I, 32 
-50752799,HC 7031 FREE TESTOSTERONE II, 11 
-50752807,HC 7031 FREE TESTOSTERONE III, 21 
-50752815,HC 7031 GROWTH HORMONE, 21 
-50752823,HC 7031 ICA-512 AUTOAB, 40 
-50752831,HC 7031 IGF BINDING PROTEIN 3, 30 
-50752849,HC 7031 IGF-I/SOMATOMEDIN-C, 33 
-50790435,HC 7031 INSULIN ANTIBODY, 38 
-50752856,HC 7031 LUTEINIZING HORMONE, 24 
-50752864,HC 7031 MULLERIAN INHIBITING SUBS, 37 
-50752872,HC 7031 PROGESTERONE, 29 
-50752880,HC 7031 RENIN PLASMA, 37 
-50752898,HC 7031 RESIN T3 UPTAKE AND FTI, 4 
-50752906,HC 7031 REVERSE T3, 36 
-50752914,HC 7031 TESTOSTERONE TOTAL, 40 
-50752922,HC 7031 THYROXIN BINDING GLOBULIN, 22 
-50752948,HC 7031 VITAMIN D 25-HYDROXY, 46 
-50752930,HC 7031 VITAMIN D1 25-DIHYDROXY, 60 
-50751874,HC 7031 VWF PROTEASE ACTIVITY, 195 
-50773910,HC 703117 HYDROXYPREGNENOLONE, 34 
-50773860,HC 7031ANDROSTENEDIONE, 44 
-50773951,HC 7031CORTISOL SERUM, 17 
-50773886,HC 7031CORTISOL TOTAL, 24 
-50773969,HC 7031DEOXYCORTICOTESTERONE 11, 62 
-50773878,HC 7031DEOXYCORTISOL 11, 44 
-50773902,HC 7031DESOXYCORTICOSTERONE 11, 46 
-50773894,HC 7031DHEA, 38 
-50773936,HC 7031HYDROXYPROGESTERONE 17D, 41 
-50773928,HC 7031PROGESTERONE, 31 
-50773944,HC 7031TESTOSTERONE:TOTAL, 39 
-50790443,HC 7032 ELASTASE-1, 55 
-50753706,HC 7038 VAP CHOLESTEROL, 20 
-50753714,HC 7038 VAP TRIGLYCERIDES, 5 
-50753797,HC 7039 MECONIUM TOX SCREEN, 112 
-50753805,HC 7041 11824R BUSULFAN MSQNT, 233 
-50753821,HC 7042 CHLA IMMUNO CYTO, 5 
-50753839,HC 7042 CHLA IMMUNO CYTO, 29 
-50753847,HC 7042 CHLA IMMUNO CYTO, 63 
-50753854,HC 7042 CHLA IMMUNO CYTO, 17 
-50753862,HC 7042 CHLA IMMUNO CYTO, 7 
-50753870,HC 7042 CHLA IMMUNO CYTO, 21 
-50753888,HC 7042 CHLA IMMUNO CYTO, 27 
-50753896,HC 7042 CHLA IMMUNO CYTO, 5 
-50753904,HC 7042 CHLA IMMUNO CYTO, 57 
-50753912,HC 7042 CHLA IMMUNO CYTO, 37 
-50753938,HC 7042 CHLA IMMUNO CYTO, 23 
-50753946,HC 7042 CHLA IMMUNO CYTO 1ST AB, 65 
-50753953,HC 7042 CHLA IMMUNO CYTO 1ST AB, 57 
-50753961,HC 7042 CHLA IMMUNO CYTO, 65 
-50753979,HC 7042 CHLA IMMUNO CYTO, 27 
-50753987,HC 7042 CHLA IMMUNO CYTO, 10 
-50773613,HC 7045 HEP B CORE/PRE MUTATION, 36 
-50773621,HC 7045 HEP B CORE/PRE MUTATION, 36 
-50773639,HC 7045 HEP B CORE/PRE MUTATION, 36 
-50773647,HC 7045 HEP B CORE/PRE MUTATION, 152 
-50773654,HC 7045 HEP B CORE/PRE MUTATION, 36 
-50773555,HC 7045 HEPATITIS B GENOTYPE, 24 
-50773563,HC 7045 HEPATITIS B GENOTYPE, 24 
-50773571,HC 7045 HEPATITIS B GENOTYPE, 24 
-50773589,HC 7045 HEPATITIS B GENOTYPE, 102 
-50773597,HC 7045 HEPATITIS B GENOTYPE, 102 
-50773605,HC 7045 HEPATITIS B GENOTYPE, 24 
-50773761,HC 7046 OLIGONUCLEOTIDE MICRO ARR, 266 
-50773779,HC 7046OLIGONUCLEOTIDE MICRO ARRY," 1,462 "
-50773290,HC 7047 GHRELIN TOTAL, 275 
-36200509,HC 3D RADIOTHRPY PLAN W/HD HIST," 12,192 "
-36300507,HC 3D RADIOTHRPY PLAN W/HD HIST," 12,192 "
-56240526,HC A-V NODE ABLATION," 21,643 "
-41154196,HC AAA RPR MOD BIF PROST," 10,616 "
-47203146,"HC AB ELUTION RBC, EA", 535 
-47204805,HC AB ID PANEL - ADDTL, 570 
-50774140,HC AB ID PLATELETS, 18 
-40930166,HC AB/ART/ISCR/PLV/RETROP LTMD ST," 1,982 "
-50749431,"HC AB42 PEPTIDE, CSF", 195 
-50393834,HC ABBOTT SAMPLE BF, 23 
-59393835,HC ABBOTT SAMPLE BF, 23 
-50383843,HC ABBOTT SAMPLE BL, 17 
-59393843,HC ABBOTT SAMPLE BL, 15 
-41127853,HC ABCESS DRAIN GUIDE," 2,894 "
-41116039,HC ABD AORT/BIL ILEOFM," 6,252 "
-41126350,HC ABD AORTOGRAPHY," 6,252 "
-41007402,HC ABD CPL W/DECUB+1V CHEST," 1,541 "
-93607406,HC ABD CPL W/DECUB+1V CHEST," 1,541 "
-41007352,HC ABD CPL W/DECUB+ERECT," 1,272 "
-93607356,HC ABD CPL W/DECUB+ERECT," 1,272 "
-41151093,HC ABD PARACENTESIS:INIT, 863 
-41530155,HC ABD/RENAL/MES/RTR COMPL," 2,995 "
-41530163,HC ABD/RENAL/MES/RTR LIMITED," 1,982 "
-41007303,HC ABDOMEN AP ONLY, 718 
-41307307,HC ABDOMEN AP ONLY, 718 
-93607307,HC ABDOMEN AP ONLY, 718 
-41307356,HC ABD CPL W/DECUB+ERECT," 1,272 "
-41307406,HC ABDOMEN COMP INCL +1V CHEST," 1,541 "
-40980229,HC ABDOMINAL PARACENTESIS, 863 
-48002257,HC ABG W COMPLEX STRESS TEST," 3,241 "
-41530056,HC ABI/UP&LW EXT 1-2 LVL BIL," 1,276 "
-47203047,HC ABID R/O, 570 
-40105033,HC ABLATION PROSTATE W/MRI GUIDE," 2,130 "
-42053579,"HC ABLATION,BONE TUMOR INC CT"," 12,895 "
-56209844,HC ABLT ARYTH;AT VT;S/C," 22,625 "
-47204839,HC ABO CONFIRM PT TYPE, 260 
-47204813,HC ABO CONFIRM UNIT TYPE, 260 
-47200654,HC ABO TYPING, 260 
-47201801,HC ABSORPTION, 415 
-52290087,"HC ABTS:HLA-A,B TYPE(SER)", 120 
-82390089,"HC ABTS:HLA-A,B TYPE(SER)", 120 
-93605558,HC AC JOINTS BILAT W/WO WTS," 1,054 "
-50722842,HC 6959 11028R ACE CSF, 24 
-51860054,HC ACETAMINOPHEN LEVEL, 432 
-56860059,HC ACETAMINOPHEN LEVEL, 432 
-59460055,HC ACETAMINOPHEN LEVEL, 432 
-50460054,HC ACETAMINOPHEN LVL, 432 
-59400101,HC ACETONE QUANT, 175 
-50400100,HC ACETONE SEMI QUANT, 243 
-51800100,HC ACETONE QUANT, 243 
-56800105,HC ACETONE QUAL, 243 
-50750843,HC 6956 11074R ACH BIND AB QN, 29 
-50743574,HC 6959 ACRMOD ACTYLCHL MOD AB, 85 
-50672765,HC ACGH ADD 251-500 PROBES," 1,462 "
-50672757,HC ACGH GEN 251-500 PROBES," 1,462 "
-62010004,HC ACNE SURGERY, 165 
-62310354,HC ACOUST REFLEX DECAY TEST, 128 
-92800101,HC ACOUST REFLEX DECAY TEST, 128 
-43100122,HC ACOUSTIC REFLEX TESTING, 170 
-62310222,HC ACOUSTIC REFLEX TESTING, 124 
-92800119,HC ACOUSTIC REFLEX TESTING, 124 
-50742154,HC 6959 ACRBLO IMM QN RIA, 48 
-41005554,HC ACROMIOCLAV JTS BILAT," 1,054 "
-41305558,HC ACROMIOCLAV JTS BILAT," 1,054 "
-56202229,HC ACT AUTOMATED COAG, 250 
-58700246,HC ACT CLOTT TIME(ACT+), 250 
-51403350,HC ACT PROTEIN C RESIST, 285 
-58403353,HC ACT PROTEIN C RESIST, 285 
-51550010,HC ACTH, 551 
-58550013,HC ACTH, 551 
-94101003,HC ACTICOAT 5X5, 85 
-50774116,HC ACTINOMYCES ANTIBODY, 16 
-83500058,HC ACTVN DNR SRCH," 1,885 "
-52751674,HC ACYLCARNITINE PROFILE QNT, 187 
-46093308,HC ADA, 324 
-51493708,HC ADAMTS-13 ACT AG TEST, 427 
-58493701,HC ADAMTS-13 ACT AG TEST, 427 
-41151531,HC ADD VESSEL STENT," 18,985 "
-50411065,HC ADDL KIDNEY PNL C02, 95 
-59411066,HC ADDL KIDNEY PNL C02, 95 
-50404565,HC ADDL KIDNEY PNL CO2, 95 
-62700844,HC ADD'L SEQ'L IVP SAME DRUG, 405 
-83500850,HC ADDL SMPL IDM, 644 
-55020960,HC ADDNL CONC. MTHDS, 153 
-59020966,HC ADDNL CONC. MTHDS, 153 
-59080531,HC ADDN'L CUTL METHODS, 226 
-52009859,HC ADDNLT FROZEN SECT, 202 
-55034367,HC ADDTL METHODS CULT, 226 
-59034363,HC ADDTL METHODS CULT, 226 
-59140210,HC ADENO ANTIGEN, 284 
-46140216,HC ADENO VIRUS ANTIGEN, 284 
-50741636,HC 6959 FADA ADNSN DMNS FLD, 40 
-94100500,HC ADH SPRAY CAN EACH, 85 
-62026273,HC CLOSED TREATMENT NASAL FRACTURE W/O MANIPULATION, 344 
-50749480,"HC ADMARK APO GENOTYPE, AMP.", 70 
-50749456,"HC ADMARK APO GENOTYPE, ISOL.", 70 
-50749464,"HC ADMARK APO GENOTYPE, ISOL.", 70 
-50749472,"HC ADMARK APO GENOTYPE, SEP.", 70 
-40510471,HC ADMIN EACH ADDL VACCINE, 135 
-40510646,HC ADMIN HEPATITIS B VACC, 263 
-26502179,HC ADMIN INFLUENZA VACC IM, 77 
-40510489,HC ADMIN INFLUENZA VACCINE, 77 
-62025986,HC ADMIN PNEUMOCOCCAL VACCINE, 49 
-26702159,HC ADMIN PNEUMOCOCCAL VAC IM, 295 
-40510497,HC ADMIN PNEUMOCOCCAL VACCINE, 295 
-40510463,HC ADMINISTRATION OF VACCINE, 77 
-81091233,HC ADP: ADSORPTION, 73 
-81191231,HC ADP: ADSORPTION, 73 
-82391236,HC ADP: ADSORPTION, 73 
-50791235,HC ADP:ADSORPTION, 73 
-52291234,HC ADP:ADSORPTION, 73 
-81391237,HC ADP:ADSORPTION, 73 
-81791238,HC ADP:ADSORPTION, 73 
-84391234,HC ADP:ADSORPTION, 73 
-85391233,HC ADP:ADSORPTION, 73 
-50723766,HC 6959 11021R 21OH AB QN RIA, 61 
-55090153,HC AFB (SMR ONLY), 162 
-55090005,HC AFB CLT/CONC TBRCL, 337 
-59090001,HC AFB CLT/CONC TBRCL, 337 
-55034359,HC AFB CULT BLOOD, 337 
-59034355,HC AFB CULT BLOOD, 337 
-55034334,HC AFB CULT DIR TBRCL, 337 
-59034330,HC AFB CULT DIR TBRCL, 337 
-55092100,HC AFB ID RRNA SEQUENCE," 1,653 "
-59092106,HC AFB ID RRNA SEQUENCE, 121 
-55092084,HC AFB PCR ID CULTURE, 578 
-59092080,HC AFB PCR ID CULTURE, 566 
-55092076,HC AFB PCR ID SPECIMAN, 566 
-59092072,HC AFB PCR ID SPECIMEN, 566 
-55034342,HC AFB SMEAR, 162 
-59034348,HC AFB SMEAR, 162 
-55092118,HC AFB SUSCEP E-TEST, 122 
-59092114,HC AFB SUSCEP E-TEST, 122 
-55092126,HC AFB SUSCEP MGIT, 93 
-59092122,HC AFB SUSCEP MGIT, 93 
-46130191,HC AGGLUTIN+TITRA NONTREP QNT, 117 
-59130195,HC AGGLUTINATN+TITRATN, 117 
-81200164,HC AIR/GRND TRANSP-HEART LVL1," 14,700 "
-81200172,HC AIR/GRND TRANSP-HEART LVL2," 16,800 "
-81200180,HC AIR/GRND TRANSP-HEART LVL3," 18,900 "
-81200214,HC AIR/GRND TRANSP-HEART LVL4," 21,000 "
-81200222,HC AIR/GRND TRANSP-HEART LVL5," 23,100 "
-81200230,HC AIR/GRND TRANSP-HEART LVL6," 25,200 "
-81400160,HC AIR/GRND TRANSP-HRT/LNG LVL1," 9,450 "
-81400178,HC AIR/GRND TRANSP-HRT/LNG LVL2," 1,050 "
-81400186,HC AIR/GRND TRANSP-HRT/LNG LVL3," 13,650 "
-81400210,HC AIR/GRND TRANSP-HRT/LNG LVL4," 15,750 "
-81400228,HC AIR/GRND TRANSP-HRT/LNG LVL5," 17,850 "
-81400236,HC AIR/GRND TRANSP-HRT/LNG LVL6," 19,950 "
-82200163,HC AIR/GRND TRANSP-KIDNEY LVL1," 9,450 "
-82200171,HC AIR/GRND TRANSP-KIDNEY LVL2," 10,500 "
-82200189,HC AIR/GRND TRANSP-KIDNEY LVL3," 13,650 "
-82200213,HC AIR/GRND TRANSP-KIDNEY LVL4," 15,750 "
-82200221,HC AIR/GRND TRANSP-KIDNEY LVL5," 17,850 "
-82200239,HC AIR/GRND TRANSP-KIDNEY LVL6," 19,950 "
-84200187,HC AIR/GRND TRANSP-LIVER LVL1," 9,450 "
-84200195,HC AIR/GRND TRANSP-LIVER LVL2," 1,050 "
-84200203,HC AIR/GRND TRANSP-LIVER LVL3," 13,650 "
-84200211,HC AIR/GRND TRANSP-LIVER LVL4," 15,750 "
-84200229,HC AIR/GRND TRANSP-LIVER LVL5," 17,850 "
-84200237,HC AIR/GRND TRANSP-LIVER LVL6," 19,950 "
-81500183,HC AIR/GRND TRANSP-LUNG LVL1," 9,450 "
-81500191,HC AIR/GRND TRANSP-LUNG LVL2," 1,050 "
-81500209,HC AIR/GRND TRANSP-LUNG LVL3," 13,650 "
-81500217,HC AIR/GRND TRANSP-LUNG LVL4," 15,750 "
-81500225,HC AIR/GRND TRANSP-LUNG LVL5," 17,850 "
-81500233,HC AIR/GRND TRANSP-LUNG LVL6," 19,950 "
-59400267,HC ALBMN BDY FL, 126 
-50400258,HC ALBUMIN, 138 
-51300259,HC ALBUMIN, 138 
-59400259,HC ALBUMIN, 138 
-50400266,HC ALBUMIN BODY FLUID QUANT, 126 
-50722651,HC 6959 CSFALB ALBUMIN CSF QN, 8 
-50405364,HC ALBUMIN CUSTOM PANEL, 138 
-59405365,HC ALBUMIN CUSTOM PNL, 138 
-50464767,HC ALCOHOL, 399 
-51864767,HC ALCOHOL, 399 
-56864762,HC ALCOHOL, 399 
-59464768,HC ALCOHOL, 399 
-56202013,HC ALCOHOL ABLATION, 319 
-50750868,HC 6956 11069R ALDOLASE, 9 
-50750876,HC 6956 11070R ALDOSTERONE, 33 
-50405349,HC ALK PHOS CUSTOM PNL, 155 
-59405340,HC ALK PHOS CUSTOM PNL, 155 
-50400407,HC ALK PHOS TOTAL, 155 
-51300408,HC ALK PHOS TOTAL, 155 
-59400408,HC ALK PHOS TOTAL, 155 
-50741297,HC ALKALINE PHOSPHATASE ISOENZYME, 49 
-51564086,HC ALLERGEN IGE, 79 
-58564089,HC ALLERGEN IGE, 79 
-51505600,HC ALLERGEN;AMOXICILLIN IGE, 79 
-58505603,HC ALLERGEN;AMOXICILLIN IGE, 79 
-51505618,HC ALLERGEN;AMPICILLIN IGE, 79 
-58505611,HC ALLERGEN;AMPICILLIN IGE, 79 
-51505519,HC ALLERGEN;CANARIES IGE, 79 
-51505527,HC ALLERGEN;FINCHES IGE, 79 
-51505550,HC ALLERGEN;FORMALDEHYDE IGE, 79 
-51505568,HC ALLERGEN;HDI IGE, 79 
-51564326,HC ALLERGEN;HONEY BEE IGE, 79 
-58564329,HC ALLERGEN;HONEY BEE IGE, 79 
-51564359,HC ALLERGEN;HORNET IGE, 79 
-58564352,HC ALLERGEN;HORNET IGE, 79 
-51505576,HC ALLERGEN;MDI IGE, 79 
-51505535,HC ALLERGEN;PARAKEETS IGE, 79 
-51505543,HC ALLERGEN;PARROTS IGE, 79 
-51505626,HC ALLERGEN; PENICILLIN G IGE, 79 
-58505629,HC ALLERGEN; PENICILLIN G IGE, 79 
-58505637,HC ALLERGEN; PENICILLIN V IGE, 79 
-51505634,HC ALLERGEN; PENICILLIN V IGE, 79 
-51505584,HC ALLERGEN;PHTHALIC ANHYD IGE, 79 
-51505592,HC ALLERGEN;RDI TMA IGE, 79 
-51564474,HC ALLERGEN;TDI IGE, 79 
-51564342,HC ALLERGEN;WASP IGE, 79 
-58564345,HC ALLERGEN;WASP IGE, 79 
-51564334,HC ALLERGEN;YELLOW JACKET IGE, 79 
-58564337,HC ALLERGEN;YELLOW JACKET IGE, 79 
-26401307,HC 41.03 ALLO BMT W/O PURGING," 13,718 "
-26400291,HC 41.03 ALLO BMT W/O PURGING," 13,718 "
-26401323,HC 41.05 ALLO HSC TRANSPLANT W/O PURGING," 13,718 "
-26401356,HC 41.08 ALLO HSC TRANSPLANT W/PURGING," 13,718 "
-26410852,"HC ALLO THERAPEUTIC CELLS, FRESH PRODUCT"," 2,678 "
-51500452,HC ALPHA FETOPROTEIN, 280 
-58500455,HC ALPHA FETOPROTEIN, 280 
-59400457,HC ALPHA FETOPROTEIN, 280 
-50751387,HC 6956 YA1APH A1A PHENOTYPE, 12 
-51400034,HC ALPHA-2-ANTIPLASMIN, 86 
-58400037,HC ALPHA-2-ANTIPLASMIN, 86 
-50751718,HC 6956 YA1APH A1A TOTAL, 11 
-51563930,HC ALRGY TST: SOY IGE, 79 
-51563971,HC ALRGY TST: WHEAT IGE, 79 
-51563690,HC ALRGY TST: ALDER TREE IGE, 79 
-58563693,HC ALRGY TST: ALDER TREE IGE, 79 
-51563708,HC ALRGY TST:ALTERNARI MLD IGE, 79 
-58563701,HC ALRGY TST:ALTERNARI MLD IGE, 79 
-51563716,HC ALRGY TST:ASPERGILL MLD IGE, 79 
-58563719,HC ALRGY TST:ASPERGILL MLD IGE, 79 
-51563724,HC ALRGY TST:BERMUDA GRASS IGE, 79 
-58563727,HC ALRGY TST:BERMUDA GRASS IGE, 79 
-51563732,HC ALRGY TST:CAT DANDER IGE, 79 
-58563735,HC ALRGY TST:CAT DANDER IGE, 79 
-51563740,HC ALRGY TST:CHICKEN IGE, 79 
-58563743,HC ALRGY TST:CHICKEN IGE, 79 
-51563757,HC ALRGY TST:CLADOSPOR MLD IGE, 79 
-58563750,HC ALRGY TST:CLADOSPOR MLD IGE, 79 
-51563765,HC ALRGY TST:COCKROACH IGE, 79 
-58563768,HC ALRGY TST:COCKROACH IGE, 79 
-51563773,HC ALRGY TST:CORN IGE, 79 
-58563776,HC ALRGY TST:CORN IGE, 79 
-51563781,HC ALRGY TST:DOG DANDER IGE, 79 
-58563784,HC ALRGY TST:DOG DANDER IGE, 79 
-51563799,HC ALRGY TST:DUCK WEED IGE, 79 
-58563792,HC ALRGY TST:DUCK WEED IGE, 79 
-51563807,HC ALRGY TST:DUST MITE-DF IGE, 79 
-58563800,HC ALRGY TST:DUST MITE-DF IGE, 79 
-58563818,HC ALRGY TST:EGG WHITE IGE, 79 
-51563815,HC ALRGY TST:EGG WHITE IGE, 79 
-51563823,HC ALRGY TST:FISH IGE, 79 
-58563826,HC ALRGY TST:FISH IGE, 79 
-51563831,HC ALRGY TST:MILK IGE, 79 
-58563834,HC ALRGY TST:MILK IGE, 79 
-51563849,HC ALRGY TST:NUT IGE, 79 
-58563842,HC ALRGY TST:NUT IGE, 79 
-51563856,HC ALRGY TST:OAK TREE IGE, 79 
-58563859,HC ALRGY TST:OAK TREE IGE, 79 
-51563864,HC ALRGY TST:OAT IGE, 79 
-58563867,HC ALRGY TST:OAT IGE, 79 
-51563872,HC ALRGY TST:OLIVE TREE IGE, 79 
-58563875,HC ALRGY TST:OLIVE TREE IGE, 79 
-51563880,HC ALRGY TST:ORANGE IGE, 79 
-58563883,HC ALRGY TST:ORANGE IGE, 79 
-51563898,HC ALRGY TST:PEANUT IGE, 79 
-58563891,HC ALRGY TST:PEANUT IGE, 79 
-51563906,HC ALRGY TST:RYE GRASS IGE, 79 
-58563909,HC ALRGY TST:RYE GRASS IGE, 79 
-51563914,HC ALRGY TST:SAGE WD MUGWORT IGE, 79 
-58563917,HC ALRGY TST:SAGE WD MUGWORT IGE, 79 
-51563922,HC ALRGY TST:SHRIMP IGE, 79 
-58563925,HC ALRGY TST:SHRIMP IGE, 79 
-58563933,HC ALRGY TST:SOY IGE, 79 
-51563948,HC ALRGY TST:TIMOTHY GRASS IGE, 79 
-58563941,HC ALRGY TST:TIMOTHY GRASS IGE, 79 
-51563955,HC ALRGY TST:TOMATO IGE, 79 
-58563958,HC ALRGY TST:TOMATO IGE, 79 
-51563963,HC ALRGY TST:WEST RAGWEED IGE, 79 
-58563966,HC ALRGY TST:WEST RAGWEED IGE, 79 
-58563974,HC ALRGY TST:WHEAT IGE, 79 
-50411107,HC ALT CUSTOM, 190 
-59411108,HC ALT CUSTOM, 190 
-50405273,HC ALT CUSTOM PNL, 190 
-59405274,HC ALT CUSTOM PNL, 190 
-50462605,HC AMIKACIN, 191 
-51562601,HC AMIKACIN, 191 
-51862605,HC AMIKACIN, 191 
-56862600,HC AMIKACIN, 191 
-59462606,HC AMIKACIN, 191 
-52753647,HC AMINO ACIDS BLD SPOT QUANT, 307 
-52751617,HC AMINO ACUDS CSF QUANT, 307 
-52751609,HC AMINO ACIDS PLASMA QUANT, 307 
-52751625,HC AMINO ACIDS URINE QUANT, 307 
-50750884,HC 6956 YAMNOR AMITRIPTYLINE, 13 
-50465269,HC AMMONIA PLASMA, 334 
-51365260,HC AMMONIA PLASMA, 334 
-59465260,HC AMMONIA PLASMA, 334 
-50803014,HC AMP MPLX CF POLY-T ADTL, 90 
-50803626,HC AMP MPLX NA 1-2, 172 
-50802776,HC AMP MPLX NA ADD, 90 
-50802768,HC AMP NA EA, 188 
-50803279,HC AMP NA EA CRANIO, 188 
-50803295,HC AMP NA EA PFEIFFER, 188 
-50802990,HC AMP NA EA QUANT, 188 
-50803550,HC AMP NA EA SYN SARC, 188 
-32001075,"HC AMPUTATION,FGR/THUMB"," 3,339 "
-59462101,HC AMYLASE BDY FLD, 395 
-50462100,HC AMYLASE BODY FLUID, 395 
-50400605,HC AMYLASE TEST, 395 
-51500601,HC AMYLASE TEST, 395 
-51800605,HC AMYLASE TEST, 395 
-56800600,HC AMYLASE TEST, 395 
-58500604,HC AMYLASE TEST, 395 
-50462126,HC AMYLASE URINE, 395 
-51862126,HC AMYLASE URINE, 395 
-56862121,HC AMYLASE URINE, 395 
-59462127,HC AMYLASE URINE, 395 
-59400606,HC AMYLASE TEST, 395 
-59090910,HC ANA MIC SUSCEPTBLTY, 254 
-55090914,HC ANA MIC SUSCPTBTY, 254 
-51565216,HC ANA TITER, 255 
-58565219,HC ANA TITER, 255 
-55060016,HC ANAEROBIC CLTR WKUP, 226 
-55060008,HC ANAEROBIC CULTR, 544 
-59060004,HC ANAEROBIC CULTURE, 544 
-55070015,HC ANAEROBIC LIMITED CULTURE, 165 
-59070011,HC ANAEROBIC LIMITED CULTURE, 165 
-62025325,HC ANALYZ NEUROSTIM BRAIN ADDON, 188 
-62025317,HC ANALYZE NEUROSTIM BRAIN/1H, 409 
-62023171,HC ELEC ALYS NSTIM W/REPRG, 337 
-62023163,HC ELEC ALYS NSTIM PERPH W/O REPRG, 233 
-62026067,HC ADJACENT TISSUE TRANSFER/REARGMT TRUNK 10 CM/<," 1,624 "
-59060012,HC ANARBC CULTURE WKUP, 226 
-50722230,HC 6959 ANCIGG ANCA IGG, 35 
-50751726,HC 6959 ANCA INFLAM BOWEL, 90 
-50751908,HC 6959 BANCAT ANCA FL AB TTR, 54 
-50750900,HC 6956 YANDRO ANDROSTENEDIONE, 28 
-41144353,HC ANG ADRENAL BILAT," 6,252 "
-41144346,HC ANG ADRENAL UNILAT," 6,252 "
-41144676,HC ANG CRVICOCERE ARCH," 14,463 "
-41126558,HC ANGGRPH EACH AD VSL," 7,328 "
-41144395,HC ANGIO CAROTID CERBRL BIL," 13,443 "
-41144437,HC ANGIO CAROTID CERV BIL," 10,589 "
-41144429,HC ANGIO CAROTID CERV UNI," 8,791 "
-41144460,HC ANGIO EXT CAROTID BIL," 20,784 "
-41144452,HC ANGIO EXT CAROTID UNI," 10,392 "
-41144700,HC ANGIO EXTREM BILAT," 10,431 "
-41144304,HC ANGIO FLU W/ TRANS," 4,292 "
-41145103,HC ANGIO PULM NO-SLCT," 2,017 "
-41144502,HC ANGIO SPINAL 1 VSL," 7,229 "
-41151622,HC ANGIO-BRACH RETRO," 6,252 "
-41144601,HC ANGIOGRPH AV SHUNT," 2,017 "
-41130048,HC ANGIOPLST VISCERAL," 8,209 "
-41130006,HC ANGIOPLSTY PERIPH," 9,456 "
-41130089,HC ANGIOPLSTY VENOUS," 12,081 "
-50750918,HC 6956 11068R ACE, 15 
-58504069,HC ANIT-THYRGLB ANTBDY, 227 
-41004250,HC ANKLE AP + LAT, 689 
-41304254,HC ANKLE AP + LAT, 689 
-93604254,HC ANKLE AP + LAT, 689 
-93604304,HC ANKLE COMPLETE MIN 3V, 909 
-41004300,HC ANKLE MIN 3V, 909 
-41304304,HC ANKLE MIN 3V, 909 
-50751890,HC 6959 ANCA FLR NONINF EA AB, 32 
-62018163,HC ANOSCOPY ABLATE LSN," 2,307 "
-62018080,HC ANOSCOPY DIAGNOSTIC, 255 
-62018122,HC ANOSCOPY REMOVE LSN SNGL," 2,307 "
-62018106,HC ANOSCOPY W/BIOPSY, 604 
-59060053,HC ANRBC CLT ID ONLY, 226 
-41127655,HC ANTEGRADE PYELOGRM," 1,036 "
-51565224,HC ANTI DNA, 281 
-58565227,HC ANTI DNA, 281 
-50724707,HC 6959 11048R NEURON AB QN, 73 
-50747633,HC 6959 CANNAB ANNA FL AB CSF, 95 
-51402154,HC ANTI XA/LMWH 85520, 466 
-58402157,HC ANTI XZ/LMWH, 466 
-50743103,HC ANTI-68(HSP-70) ANTIBODIES, 165 
-50463579,HC ANTI-NUC AB (FANA), 261 
-51563575,HC ANTI-NUC AB (FANA), 261 
-58563578,HC ANTI-NUC AB (FANA), 261 
-51504066,HC ANTI-THYROGLBL ATBD, 227 
-51504058,HC ANTI-THYROID MICROSOMAL AB, 270 
-58504051,HC ANTI-THYROID MICROSOMAL AB, 270 
-59404053,HC ANTI-THYROID MICROSOMAL AB., 270 
-47200837,HC ANTIB SCRN-3 CELL, 325 
-51500759,HC ANTIB SEROL 1.2, 280 
-58500752,HC ANTIB SEROL 1.2, 280 
-47201454,HC ANTIBODY ID PANEL, 570 
-51402352,HC ANTICARDIOLIPIN IGG AB, 243 
-58402355,HC ANTICARDIOLIPIN IGG AB, 243 
-51402360,HC ANTICARDIOLIPIN IGM AB, 243 
-58402363,HC ANTICARDIOLIPIN IGM AB, 243 
-47200902,HC BLD TYP ANTG SCRN DNR EA, 375 
-47200514,HC RBC ANTIGEN TYPE PATIENT, 245 
-51400554,HC ANTITHROBIN III, 581 
-58400557,HC ANTITHROMBIN 111, 581 
-41152299,HC AORTA CATH PLCMT," 6,438 "
-41530171,HC AORTA/IVC/ILIAC DPLX COMPL," 1,665 "
-41530189,HC AORTA/IVC/ILIAC DPLX LIMITED," 1,627 "
-26730564,HC APHERESIS DISP SUPPLY, 723 
-26730366,HC APHERESIS-ADSORP/REINFUSE," 8,135 "
-26730358,HC APHERESIS-PLASMA," 11,254 "
-26730341,HC APHERESIS-PLATELETS," 7,171 "
-26730333,HC APHERESIS-RBC," 6,752 "
-26730325,HC APHERESIS-WBC," 6,998 "
-62026240,HC APP CYLNDER CAST THIGH TO ANKL, 498 
-62015813,HC APPLY FINGER SPLINT STATIC, 219 
-62015797,HC APPLY FOREARM SPLINT STATIC, 219 
-40400012,HC APPLY HOT/COLD PACK, 102 
-62015755,HC APPLY LONG ARM CAST, 498 
-62015789,HC APPLY LONG ARM SPLINT, 219 
-62015888,HC APPLY LONG LEG CAST, 498 
-62015995,HC APPLY LOWER LEG SPLINT, 219 
-62015763,HC APPLY SHORT ARM CAST, 498 
-62015920,HC APPLY SHORT LEG CAST, 299 
-62015938,HC APPLY SHORT LEG CAST WALKING, 283 
-50393560,HC APT TEST - FETAL HGB QUAL, 114 
-58700253,HC APTT, 240 
-55061006,HC ARBC ORG ID NOT SUH, 226 
-59061002,HC ARBC ORGSM ID N SUH, 226 
-50743095,HC 6956 ARSAL ARLY SULFATASE, 86 
-41530080,HC ART DPLX LOWER EXT BILAT," 3,076 "
-41530098,HC ART DPLX LOWER EXT UNILAT," 2,177 "
-41530114,HC ART DPLX UPPER EXT BILAT," 2,151 "
-41530106,HC ART DPLX UPPER EXT UNILAT," 1,939 "
-41153727,HC ARTERY XRAY CHEST S&I," 2,017 "
-41010828,HC ARTHROGRAM SHOULDER," 1,929 "
-41010836,HC ARTHROGRAM WRIST," 1,296 "
-41010802,HC ARTHROGRAM-KNEE," 1,238 "
-41010851,HC ARTHROGRAPHY ANKLE," 1,168 "
-41010844,HC ARTHROGRAPHY ELBOW, 957 
-41010810,HC ARTHROGRAPHY HIP," 1,810 "
-50774165,HC 6942 FUNAB ASPERGILLUS AB, 48 
-51571008,HC ASO TITER, 186 
-58571001,HC ASO TITER, 186 
-50742261,HC 6959 ASPER ASPERGILLS AB CF, 13 
-50742279,HC 6959 ASPERP ASPRGLLS AB ID, 13 
-50774124,HC ASPERGILLUS ANTIBODY, 23 
-55091953,HC ASPERGILLUS BY GALACTOMANNAN, 105 
-59091959,HC ASPERGILLUS BY GALACTOMANNAN, 105 
-51564383,HC ASPERGILLUS IGE, 179 
-58564386,HC ASPERGILLUS IGE, 179 
-51564391,HC ASPERGILLUS IGG, 179 
-58564394,HC ASPERGILLUS IGG, 179 
-40140006,HC ASPIR BREAST CYST PUNCT," 1,332 "
-41100843,HC ASPIR INVERT DISC, 974 
-41151366,HC ASPIR RENAL CYST," 2,010 "
-40905010,HC ASPIRATION MAJOR JOINT," 1,193 "
-40905028,HC ASPIRATION MEDIUM JOINT, 875 
-62026638,HC ASPIRATION OF GANGLION CYST, 253 
-40905069,HC ASPIRATION SMALL JOINT, 864 
-82300062,HC ASR:HLA AB SCR(RAPID), 61 
-41152497,HC ASSESS CYST CONT," 2,240 "
-62024765,HC ASSESS: HEALTH & BEHAVIOR EA 1, 80 
-91900134,HC ASSIST FERTILIZAT>10(ICSI)," 1,848 "
-91900126,HC ASSIST FERTILZAT<10(ICSI)," 1,848 "
-50405356,HC AST CUSTOM PANEL, 180 
-59405357,HC AST CUSTOM PNL, 180 
-62310032,HC AUD EVAL HANDICAPPED>7 YRS, 420 
-92801588,HC AUD REHAB POST LINGUAL LOSS, 183 
-92801570,HC AUD REHAB PRELINGUAL LOSS, 183 
-92800184,HC AUDIOLOGIC COUNSELING-COMP, 75 
-40510067,HC AUDIOMETRY COMPREH, 509 
-40510083,HC AUDIOMETRY REFLEX, 170 
-62310255,HC AUDITORY EVOKED POTENTIALS COM," 2,609 "
-40450439,HC AUGEMENTIN TAB 875, 50 
-83500504,HC AUTO BLOOD, 982 
-26400309,HC 41.01 AUTO BMT W/O PURGING," 13,681 "
-26401364,HC 41.09 AUTO BMT W/PURGING," 13,681 "
-26401315,HC 41.04 AUTO HSC TRANSPLANT W/O PURGING," 13,681 "
-26401349,HC 41.07 AUTO HSC TRANSPLANT W/ PURGING," 13,681 "
-52110616,HC AUTO THIN LAYER PAP DIAG, 294 
-52110665,HC AUTO THIN LAYER PAP SCRN, 376 
-47200126,HC AUTOLOGOUS PROCESSING," 1,158 "
-41152786,HC AV FISTUAL CLOT REMOVAL," 6,262 "
-39000138,HC AV OPTIM W/REPROG, 639 
-81090847,HC AXB:HLA AUTO XM:B-CELL, 82 
-82390840,HC AXB:HLA AUTO XM:B-CELL, 82 
-82390980,HC AXBDT:HLA AUTO XM:B-CELL(DTT), 82 
-50790260,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 516 
-52290269,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 82 
-81090268,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 82 
-81190266,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 82 
-81390262,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 82 
-82390261,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 82 
-85390268,HC AXBF:HLA AUTO XM:B-CELL(FLOW), 82 
-50790864,HC AXT:HLA AUTO XM:T-CELL, 200 
-81090862,HC AXT:HLA AUTO XM:T-CELL, 82 
-81190860,HC AXT:HLA AUTO XM:T-CELL, 82 
-81390866,HC AXT:HLA AUTO XM:T-CELL, 82 
-82390865,HC AXT:HLA AUTO XM:T-CELL, 82 
-81090995,HC AXTDT:HLA AUTO XM:T-CELL(DTT), 82 
-81390999,HC AXTDT:HLA AUTO XM:T-CELL(DTT), 82 
-82390998,HC AXTDT:HLA AUTO XM:T-CELL(DTT), 82 
-50790252,HC AXTF:HLA AUTO XM:T-CELL(FLOW), 375 
-81090250,HC AXTF:HLA AUTO XM:T-CELL(FLOW), 82 
-81190258,HC AXTF:HLA AUTO XM:T-CELL(FLOW), 82 
-81390254,HC AXTF:HLA AUTO XM:T-CELL(FLOW), 82 
-82390253,HC AXTF:HLA AUTO XM:T-CELL(FLOW), 82 
-85390250,HC AXTF:HLA AUTO XM:T-CELL(FLOW), 82 
-52290251,HC AXTF:HLA SUTO XM:T-CELL(FLOW), 82 
-55091912,"HC B, PARAPERTUSSIS DFA", 173 
-55091904,HC B PERTUSSIS DFA, 173 
-59091900,HC B PERTUSSIS DFA, 173 
-92000041,HC B-HCG QUANTITATIVE, 198 
-59091918,HC B. PARAPERTUSSIS DFA, 173 
-55091896,HC B.PARAPERTUSSIS PCR, 388 
-59091892,HC B.PARAPERTUSSIS PCR, 388 
-21191119,HC B1 ONC/BMT LEVEL 1 PVT," 8,897 "
-21191218,HC B1 ONC/BMT LEVEL 1 SP," 8,397 "
-21191127,HC B1 ONC/BMT LEVEL 2 PVT," 8,897 "
-21191226,HC B1 ONC/BMT LEVEL 2 SP," 8,397 "
-21191135,HC B1 ONC/BMT LEVEL 3 PVT," 15,479 "
-21191234,HC B1 IMC ICU," 15,479 "
-21191143,HC B1 ONC/BMT LEVEL 4 PVT," 14,466 "
-21191242,HC B1 ONC/BMT LEVEL 4 SP," 14,466 "
-51503829,HC B12-SERUM, 281 
-58503822,HC B12-SRM, 281 
-59403824,HC B12-SRM, 281 
-23791114,HC B2 MED/SURG PRIV," 8,897 "
-23791213,HC B2 MED/SURG," 8,397 "
-23791312,HC B2 MED/SURG," 8,397 "
-23791122,HC B2 MED/SURG PRIV," 8,897 "
-23791221,HC B2 MED/SURG SP," 8,397 "
-23791320,HC B2 MED/SURG WARD," 8,397 "
-23791130,HC B2 MED/SURG PRIV," 8,897 "
-23791239,HC B2 MED/SURG SP," 8,397 "
-23791338,HC B2 MED/SURG WARD," 8,397 "
-23791148,HC B2 MED/SURG PRIV," 8,897 "
-23791247,HC B2 MED/SURG SP," 8,397 "
-23791346,HC B2 MED/SURG W," 8,397 "
-50791177,HC B27:HLA B27 ANTIGEN TYPE, 121 
-52291176,HC B27:HLA B27 ANTIGEN TYPE, 53 
-25790114,HC B3 IMC ICU," 15,479 "
-25790213,HC B3 IMC ICU," 15,479 "
-25790312,HC B3 IMC ICU," 15,479 "
-25790122,HC B3 IMC ICU," 15,479 "
-25790221,HC B3 IMC ICU," 15,479 "
-25790320,HC B3 IMC ICU," 15,479 "
-25790130,HC B3 IMC ICU," 15,479 "
-25790239,HC B3 IMC ICU," 15,479 "
-25790338,HC B3 IMC ICU," 15,479 "
-25791112,HC B3 MED/SURG PRIV," 8,897 "
-25791211,HC B3 M/S LEVEL I SP," 8,397 "
-25791310,HC B3 MED/SURG," 8,397 "
-25791120,HC B3 M/S LEVEL II PVT," 8,897 "
-25791229,HC B3 MED/SURG SP," 8,397 "
-25791328,HC B3 MED/SURG W," 8,397 "
-25791138,HC B3 MED/SURG PRIV," 8,897 "
-25791237,HC B3 MED/SURG SP," 8,397 "
-25791336,HC B3 MED/SURG WARD," 8,397 "
-25791146,HC B3 MED/SURG PRIV," 8,897 "
-25791245,HC B3 MED/SURG SP," 8,397 "
-25791344,HC B3 M/S LEVEL IV W," 8,397 "
-55080063,HC BACTERIAL ID METHDS, 226 
-59080069,HC BACTERIAL ID METHDS, 226 
-41100173,HC BALL ANGIO INTRACRAN," 3,413 "
-41007808,HC BARIUM ENEMA W/AIR," 3,283 "
-41007758,HC BARIUM ENEMA W/WO KUB," 2,360 "
-36060408,HC BARIUM/HYPAQUE/PREPCAT, 60 
-55050009,HC BARTLETT CATH CULT, 302 
-36200087,HC BASIC RAD DOSI CALC," 1,428 "
-36300085,HC BASIC RAD DOSIM CLC," 1,428 "
-50790948,HC BCFRZ:CELL FREEZING, 21 
-52290947,BCFRZ:CELL FREEZING, 21 
-81090946,HC BCFRZ:CELL FREEZING, 21 
-81190944,HC BCFRZ:CELL FREEZING, 21 
-81390940,HC BCFRZ:CELL FREEZING, 21 
-82390949,HC BCFRZ:CELL FREEZING, 21 
-84390947,HC BCFRZ:CELL FREEZING, 21 
-50803758,HC BCRKDM ISOL NA, 146 
-59061044,HC BDY FL CLT-BACTC SP, 302 
-62026331,HC CLOSED TX ULNAR FRACTURE PROXIMAL END W MNPJ, 485 
-62026349,HC EXC TUM/VASC MALFRMJ SOFT TISS HAND/FNGR SUBQ," 2,142 "
-92801679,HC BEHAVIORAL AUDIOMETRIC, 420 
-51563112,HC BETA 2 MICROGLOBULN, 178 
-58563115,HC BETA 2 MICROGLOBULN, 178 
-51800621,HC BHCG BETA HCG QUANT, 335 
-56800626,HC BETA HCG - TUMOR MRKR, 335 
-59400622,HC BETA HCG-TMR MRKR, 335 
-50400621,HC BETA HCG-TUMOR MRKR, 335 
-51500627,HC BETA HCG-TUMOR MRKR, 335 
-58500620,HC BETA HCG-TUMOR MRKR, 335 
-55020937,HC BETA LACTAMASE, 125 
-59020933,HC BETA LACTAMASE, 125 
-50323021,HC BETA STREP THROAT SCREEN, 239 
-50725043,HC 6959 UB2MG BETA-2 MCRGLBN U, 16 
-37021508,HC BEXXAR I-131 TOSITUMOMAB/DIAG," 15,487 "
-37021516,HC BEXXAR I-131 TOSITUMOMAB/THER," 134,073 "
-50723733,HC 6959 UBICAR BICARBONAT HCO3, 9 
-41151564,HC BIL STONE EXTRACT," 6,818 "
-41151135,HC BIL STRCT W/ STENT," 5,711 "
-41151580,HC BIL STRICTURE W/ ST," 5,711 "
-59060103,HC BILE CLT, 302 
-55060107,HC BILE CULT, 302 
-41152273,HC BILI ENDO W/ STENT," 11,396 "
-41127150,HC BILIARY DRAINAGE," 1,161 "
-41108291,HC BILIARY STENT PLACEMENT," 1,689 "
-41151127,HC BILIARY TRACK DIL," 6,123 "
-50400654,"HC BILIRUBIN, DIRECT", 165 
-51300655,HC BILIRUBIN DIRECT, 165 
-59400655,HC BILIRUBIN DIRECT, 165 
-50400704,"HC BILIRUBIN, TOTAL", 150 
-51300705,HC BILIRUBIN TOTAL, 150 
-57300709,HC BILIRUBIN TOTAL, 150 
-59400705,HC BILIRUBIN TOTAL, 150 
-50790955,HC BIMMIS:IMMUNOBEAD ISOLATION, 17 
-81090953,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-81190951,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-81390957,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-81790958,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-82390956,HC BIMMIS:IMMUNOBEAD ISOLATION, 390 
-84390954,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-85390953,HC BIMMIS:IMMUNOBEAD ISOLATION, 15 
-62700828,HC BIO PATCH ATIC, 45 
-55060255,HC BIOPSY/TISS (GEN) CULT, 302 
-62022165,HC BIOFEEDBACK TRAINING, 46 
-26402172,HC BIOPATCH, 50 
-32712457,HC BIOPATCH, 50 
-62012471,HC BIOPSY BONE/TROCAR/NDL SUPER," 1,079 "
-62012307,HC BIOPSY BREAST PERC, 773 
-37205978,HC BIOPSY GUN," 2,841 "
-42005975,HC BIOPSY GUN," 2,841 "
-94305976,HC BIOPSY GUN," 2,583 "
-62014246,HC BIOPSY LEG/ANKL TISSUE SUPER," 1,213 "
-62012463,HC BIOPSY MUSCLE PERC," 1,409 "
-62013172,HC BIOPSY NECK/CHEST," 1,649 "
-62016845,HC BIOPSY NEEDLE LYMPH NODE," 1,545 "
-62010210,HC BIOPSY OF ADDT'L LESION, 125 
-62019351,HC BIOPSY OF CERVIX, 656 
-62021266,HC BIOPSY OF CONJUCTIVA," 1,489 "
-62020458,HC BIOPSY OF CORNEA," 3,223 "
-62021514,HC BIOPSY OF EXTERNAL EAR, 474 
-62021522,HC BIOPSY OF EXTERNAL EAR CANAL," 3,276 "
-62020953,HC BIOPSY OF EYELID, 682 
-62010202,HC BIOPSY OF LESION, 250 
-62016860,HC BIOPSY OF LIP, 338 
-62016969,HC BIOPSY OF MOUTH LESION, 302 
-62018940,HC BIOPSY OF PENIS," 3,302 "
-62019120,HC BIOPSY OF PROSTATE," 2,687 "
-62019443,HC BIOPSY OF UTERUS LINING, 236 
-62019252,HC BIOPSY OF VAGINA," 1,060 "
-62017272,HC BIOPSY ROOF OF MOUTH, 723 
-42072744,HC BIOPSY SALIVARY GLAND," 1,761 "
-55050058,HC BIOPSY TISSUE (RESP) CULT, 302 
-37205994,HC BIOPSY TRAY," 1,638 "
-42005991,HC BIOPSY TRAY," 1,638 "
-94305992,HC BIOPSY TRAY," 1,489 "
-62017546,HC BIOPSY UPPER NOSE SMPL, 765 
-62019187,HC BIOPSY VULVA/PERINEUM, 677 
-41153123,"HC BIOPSY,MUSCLE,PERCUT"," 2,367 "
-52751682,HC BIOTINIDASE, 274 
-32704009,HC BIPHASIC CARDIO PADS, 213 
-50774173,HC 6942 FUNAB BLASTOMYCES AB, 41 
-50400753,HC BLD AMMONIA TEST, 334 
-51865269,HC BLD AMMONIA TEST, 334 
-56865264,HC BLD AMMONIA TEST, 334 
-59400754,HC BLD AMMONIA TEST, 334 
-31900012,HC BLD DRAW CTRL/PERIPH VEN, 576 
-31900095,HC BLD DRAW ESTAB CATH, 576 
-59300152,HC BLD SMEAR INTERP, 51 
-55080006,HC BLOOD CULTURE, 499 
-59080002,HC BLOOD CULTURE, 499 
-32702508,HC BLOOD DRAW (VAD), 571 
-62700679,HC BLOOD DRAW, 558 
-62700661,HC BLOOD DRAW IMPLANT VAD ATIC, 571 
-32702458,HC BLOOD DRAW VENIPUNCTURE, 115 
-58700048,HC BLOOD GAS EG7, 815 
-50460237,HC BLOOD GASES," 1,161 "
-59460238,HC BLOOD GASES," 1,161 "
-47200027,HC BLOOD IRRADIATION EA UNIT, 278 
-55020051,HC BLOOD SMEAR EXAM, 379 
-59020057,HC BLOOD SMEAR EXAM, 379 
-32500506,HC BLOOD TRANSFUSION," 3,300 "
-37002409,HC BLOOD VOLUME MULT SAMPLES," 1,228 "
-37003845,HC BLOOD+PLASMA VOLUME," 1,228 "
-50790096,HC BLX:HLA XM:B-CELL, 249 
-81090094,HC BLX:HLA XM:B-CELL, 82 
-81190092,HC BLX:HLA XM:B-CELL, 82 
-81390098,HC BLX:HLA XM:B-CELL, 82 
-82390097,HC BLX:HLA XM:B-CELL, 82 
-50411230,HC BMT I ALBUMIN, 138 
-59411231,HC BMT I ALBUMIN, 138 
-50411271,HC BMT I ALK PHOS, 155 
-59411264,HC BMT I ALK PHOS, 155 
-59411272,HC BMT I ALK PHOS, 155 
-50411222,HC BMT I ALT, 190 
-59411223,HC BMT I ALT, 190 
-50411214,HC BMT I AST, 180 
-59411215,HC BMT I AST, 180 
-59411256,HC BMT I CREATININE, 179 
-50411255,HC BMT I CREATININE, 179 
-50411248,HC BMT I GLUCOSE, 159 
-59411249,HC BMT I GLUCOSE, 159 
-50411198,HC BMT I MAGNESIUM, 280 
-59411199,HC BMT I MAGNESIUM, 280 
-50411206,HC BMT I POTASSIUM, 204 
-59411207,HC BMT I POTASSIUM, 204 
-50411180,HC BMT I SODIUM, 219 
-59411181,HC BMT I SODIUM, 219 
-50411263,HC BMT I T BILI, 150 
-59411173,HC BMT II C02, 95 
-50411164,HC BMT II CREATININE, 179 
-59411165,HC BMT II CREATININE, 179 
-50411156,HC BMT II GLUCOSE, 159 
-59411157,HC BMT II GLUCOSE, 159 
-50404110,HC BMT II PNL SODIUM, 219 
-59404111,HC BMT II PNL SODIUM, 219 
-50411149,HC BMT II POTASSIUM, 204 
-59411140,HC BMT II POTASSIUM, 204 
-50411131,HC BMT II SODIUM, 219 
-59411132,HC BMT II SODIUM, 219 
-50411172,HC BMT IIC02, 95 
-59404095,HC BMT PANEL 1 SODIUM, 219 
-50404094,HC BMT PANEL I SODIUM, 219 
-50324797,HC BODY FLUID CELL COUNT, 174 
-50300359,HC BODY FLUID CELL COUNT & DIFF, 252 
-55061048,HC BODY FLUID CULT, 302 
-41151879,HC BONE BX DEEP," 5,930 "
-59060202,HC BONE CLT, 302 
-55060206,HC BONE CULTURE, 302 
-37003134,HC BONE DENSITY DEXA AXIAL," 1,077 "
-43100114,HC BONE DENSITY DEXA AXIAL," 1,077 "
-37003886,HC BONE IMAG TOMO SPECT," 4,065 "
-26400275,HC BONE MARROW ASPIRATION," 2,869 "
-26500272,HC BONE MARROW ASPIRATION," 2,869 "
-26600270,HC BONE MARROW ASPIRATION," 2,869 "
-62024732,HC BONE MARROW ASPIRATION, 693 
-62024740,HC BONE MARROW BIOPSY, 396 
-26402230,HC BONE MARROW BX W/ASPIRATION, 415 
-26503227,HC BONE MARROW BX W/ASPIRATION, 415 
-26400283,HC BONE MARROW NEEDLE BIOPSY," 2,867 "
-26500280,HC BONE MARROW NEEDLE BIOPSY," 2,867 "
-26600288,HC BONE MARROW NEEDLE BIOPSY," 2,867 "
-37000759,HC BONE MARROW SCAN WB," 1,305 "
-55091789,HC BONE MARROW STERLTY CULT, 302 
-59091785,HC BONE MARROW STERLTY, 302 
-26400853,HC BONE MARROW TRAY, 268 
-26500850,HC BONE MARROW TRAY, 268 
-55060156,HC BONE MRO CULT, 302 
-59060152,HC BONE MRW CLT, 302 
-37003704,HC BONE SCAN THREE PHASE," 4,984 "
-37000700,HC BONE SCAN WHOLE BODY," 4,580 "
-41309709,HC BONE SRVY METS AXIA COMPL," 2,083 "
-41009705,HC BONE SRVY METS AXIA COMPL," 2,083 "
-55091888,HC BORDETELLA PERTUSSIS PCR, 388 
-59091884,HC BORDETELLA PERTUSSIS PCR, 388 
-37001559,HC BOWL IMAG MCKLS LCL," 3,192 "
-59060251,HC BPSY/TIS (GEN), 302 
-59050054,HC BPSY/TISS (RESP), 302 
-36079051,HC BRACHY INTRA DOSMTR," 1,516 "
-36033447,HC BRACHYTX NON-STR HDR IR 192, 858 
-37000601,HC BRAIN SCAN 4+VIEWS, 744 
-37004009,HC BRAIN SCN - SPECT," 4,212 "
-37000650,HC BRAIN SCN W/VSC FLW," 1,291 "
-52751930,HC BRANCHED-CHAIN AMINO QUANT, 274 
-40180143,HC BREAST BX VACUUM ASSIST W IMAGING," 5,302 "
-40809980,HC BREAST PUNC ASP-CYST," 1,332 "
-40870529,HC BREAST PUNC ASP-CYST-ADDL, 346 
-40510679,HC BREATH ALCOHOL TEST, 103 
-41100595,HC BRONCHGRAPHY SEGM W/ CONTRAST," 3,301 "
-62016555,HC BRONCHOSCOPY DX," 1,135 "
-40828501,HC BRST NDL LOC WIRE PLCMT," 3,466 "
-40828519,HC BRST NDL LOC WIRE PLCMT-ADDL," 1,695 "
-36079309,HC BSC RADIAT DOSIMTRY," 1,428 "
-40100570,HC BX ABD/RETRO MASS PERC NDL," 2,277 "
-40980088,HC BX ABD/TETRO MASS PERC NDL," 2,277 "
-42062265,HC BX ABD/RETRO MASS PERC NDL," 2,277 "
-42062034,HC BX BONE TROC/NDL DEEP," 5,930 "
-40980005,HC BX BONE TROC/NDL SUP," 3,880 "
-42062026,HC BX BONE TROC/NDL SUP," 3,880 "
-40828451,"HC BX BREAST NDL CORE, WITH GUIDE"," 3,418 "
-40132052,HC BX BRST PERC NDL W/IMAGING," 3,418 "
-40828469,HC BX BRST PERC VACUUM W/IMAGING," 5,302 "
-40100257,HC BX LIVER NDL PERC," 4,317 "
-42062208,HC BX LIVER NDL PERC," 4,317 "
-42062133,HC BX LUNG/MEDIAST PERC NDL," 3,287 "
-41100678,HC BX LYMPHNODE," 3,219 "
-42080192,HC BX LYMPH NODE PERC SUPERVICIAL," 3,219 "
-40980195,"HC BX LYMPHNODE, SUPERFICIAL"," 3,219 "
-42072645,HC BX MUSCLE PERC NEEDLE," 2,367 "
-40980070,HC BX PANCREAS PERC NDL," 2,295 "
-42062224,HC BX PANCREAS PERC NDL," 2,295 "
-41100710,HC BX PLEURA," 2,384 "
-42062125,HC BX PLEURA PERC NDL," 2,384 "
-40105025,"HC BX, PROSTATE"," 2,724 "
-40100414,HC BX RENAL NDL," 2,637 "
-40980096,HC BX RENAL NDL," 2,637 "
-42062315,HC BX RENAL NDL," 2,637 "
-91900142,HC BX-OOCYTE<=5(PGD)," 2,090 "
-91900159,HC BX-OOCYTE>5(PGD)," 2,090 "
-46122875,HC C DIFF TC ADDL, 349 
-59122879,HC C DIFF TOX CON ADDL, 349 
-46122867,HC C DIFF TOX CONF, 106 
-59122861,HC C DIFF TOX CONF, 106 
-51504587,HC C PEPTIDE, 220 
-58504580,HC C PEPTIDE, 220 
-50463488,HC C REACTIVE PROTEIN, 179 
-51563484,HC C REACTIVE PROTEIN, 179 
-51863488,HC C REACTIVE PROTEIN, 179 
-56863483,HC C REACTIVE PROTEIN, 179 
-58563487,HC C REACTIVE PROTEIN, 179 
-59463489,HC C REACTIVE PROTEIN, 179 
-50723014,HC 6959 11026R C1 AG FNCT ACT, 36 
-50721547,HC 6959 11025R C1INH CMPL ATGN, 19 
-50725316,HC 6959 11031R ONCPRTN HER2 QN, 165 
-50803717,HC C-KIT MUT AMP NA, 188 
-50803733,HC C-KIT MUT ID SEQ EA, 54 
-50803741,HC C-KIT MUT INTERP REPORT, 138 
-50803691,HC C-KIT MUT ISOL NA, 146 
-50803725,HC C-KIT MUT REVERSE TRANS, 140 
-50803709,HC C-KIT MUT SEP GEL ELECT, 103 
-50751023,HC 6956 C PEPTIDE, 14 
-93601904,"HC C-SPINE, 3 VIEWS OR LESS"," 1,015 "
-41301904,"HC C-SPINE, 3 VIEWS OR LESS"," 1,015 "
-50741545,HC 6959 C1 ESTERASE INHIB AG, 7 
-50741560,HC 6959 C1 ESTRS INHIB FUNCACT, 21 
-50741552,HC 6959 C1 ESTERASE INHIB, 7 
-62801279,HC COCH IMPL ORIENTATION, 173 
-26391110,HC C2 ONC/BMT LEVEL 1 PVT," 8,897 "
-26391219,HC C2 ONC/BMT LEVEL 1 SP," 8,397 "
-26391128,HC C2 ONC/BMT LEVEL 2 PVT," 8,897 "
-26391227,HC C2 ONC/BMT LEVEL 2 SP," 8,397 "
-26391136,HC C2 IMC ICU," 15,479 "
-26391235,HC C2 ONC/BMT LEVEL 3 SP," 15,479 "
-26391144,HC C2 ONC/BMT LEVEL 4 PVT," 14,466 "
-26391243,HC C2 ONC/BMT LEVEL 4 SP," 14,466 "
-25091117,HC C3 MED/SURG PRIV," 8,897 "
-25091216,HC C3 MED/SURG SP," 8,397 "
-25091315,HC C3 MED/SURG," 8,397 "
-25091125,HC C3 MED/SURG PRIV," 8,897 "
-25091224,HC C3 MED/SURG SP," 8,397 "
-25091323,HC C3 MED/SURG," 8,397 "
-25091133,HC C3 MED/SURG PRIV," 8,897 "
-25091232,HC C3 MED/SURG SP," 8,397 "
-25091331,HC C3 MED/SURG," 8,397 "
-25091141,HC C3 MED/SURG PRIV," 8,897 "
-25091240,HC C3 MED/SURG SP," 8,397 "
-25091349,HC C3 MED/SURG W," 8,397 "
-25092115,HC C3 ONC/BMT LEVEL 1 PVT," 8,897 "
-25093212,HC C3 ONC/BMT LEVEL 1 SP," 8,397 "
-25092123,HC C3 ONC/BMT LEVEL 2 PVT," 8,897 "
-25093220,HC C3 ONC/BMT LEVEL 2 SP," 8,397 "
-25092131,HC C3 IMC ICU," 15,479 "
-25093238,HC C3 ONC/BMT LEVEL 3 SP," 15,479 "
-25092149,HC C3 ONC/BMT LEVEL 4 PVT," 14,466 "
-25093246,HC C3 ONC/BMT LEVEL 4 SP," 14,466 "
-58510090,HC CA125, 386 
-51510097,HC CA125 (C125), 386 
-81600124,HC CAD INTESTINE ORGAN ACQ CTDN," 65,426 "
-40880148,HC CAD SCREENING MAMMO, 139 
-50725167,HC 6959 11092R CADMIUM, 20 
-50750926,HC 6956 11076R CAFFEINE QNT, 20 
-41004508,HC CALCANEUS MIN 2V, 707 
-41304502,HC CALCANEUS MIN 2V, 707 
-93604502,HC CALCANEUS MIN 2V, 707 
-50750934,HC 6956 11077R CALCITONIN, 44 
-50400985,HC CALCIUM IONIZED, 399 
-50411032,HC CALCIUM IONIZED, 399 
-59411033,HC CALCIUM IONIZED, 399 
-59400960,"HC CALCIUM, QUANT. URINE", 149 
-50400902,HC CALCIUM TOTAL, 143 
-51300903,HC CALCIUM TOTAL, 143 
-59400903,HC CALCIUM TOTAL, 143 
-50400969,HC CALCIUM URINE TIMED, 149 
-62310289,HC CALORIC VESTIBULAR TEST IRRIG, 307 
-92800671,HC CALORIC VESTIBULAR TEST IRRIG, 307 
-51593820,HC CANCER AG 19-9, 351 
-58593823,HC CANCER AG 19-9, 351 
-51580017,HC CANCER ANTIGEN 15-3, 336 
-58580010,HC CANCER ANTIGEN 15-3, 336 
-50750959,HC 6956 YCA199 CA 19-9, 15 
-50725290,HC 6959 CANPPT CANDIDA AB, 13 
-26400325,HC DECLOT THROMB IMP VAD/CATH," 2,257 "
-26500322,HC DECLOT IMP VAD OR CATH," 2,257 "
-26610238,HC DECLOT IMP VAD OR CATH," 2,257 "
-26730416,HC CANNULA DECLOT IMP VAD OR CATH," 2,257 "
-46060026,HC CAP - HBG EP, 250 
-46060018,HC CAP - HGHPLC, 257 
-46060034,HC CAP - SICKLEDEX, 92 
-50465137,HC CAPILLARY BLOOD GASES," 1,161 "
-50725308,"HC CARBAMAZEPINE, FREE", 11 
-50460104,"HC CARBAMAZEPINE, TOTAL", 352 
-50743053,"HC CARBAMAZEPINE, TOTAL", 11 
-51560100,"HC CARBAMAZEPINE, TOTAL", 352 
-51860104,HC CARBAMAZEPINE TOTAL, 352 
-56860109,HC CARBAMAZEPINE TOTAL, 352 
-59460105,"HC CARBAMAZEPINE, TOTAL", 352 
-50462209,HC FCO2 CO2 BICARB FLUID, 95 
-50401603,HC CARBON DIOXIDE, 95 
-50470061,HC CARBON DIOXIDE, 95 
-51370062,HC CARBON DIOXIDE, 95 
-59401604,HC CARBON DIOXIDE, 95 
-59470062,HC CARBON DIOXIDE, 95 
-37000502,HC CARD BLD PL REST AND STRESS," 5,038 "
-37000551,"HC CARD BLD PL, FRST PS TCHNQ-SIN", 871 
-56209018,HC MAP TACHYCARDIA SITES," 15,182 "
-56202203,HC CARDIOASSIST DEVICE INTERNL, 619 
-32704017,HC EXT CARDIOVERSION SCHEDULED," 5,446 "
-56200959,HC CARDIOVERSION ELECTIVE," 5,446 "
-56269152,HC CARDIOVERSION-INTERNAL," 1,346 "
-52751526,HC CARNITINE FREE/TOTAL QNT, 187 
-52753621,HC CARNITINE URINE FREE/TOT QNT, 187 
-41118357,HC CAROTID HC ANG UNILAT," 8,802 "
-40930018,HC CAROTID DPLX BILAT," 3,055 "
-41530015,HC CAROTID DPLX BILAT," 3,055 "
-41530023,HC CAROTID DPLX LIMITED," 1,756 "
-40930026,HC CAROTID DPLX UNILAT," 1,756 "
-92801760,HC CASE CONF PT 1/4 HR, 192 
-40450025,HC CASTING KIT, 298 
-41151200,"HC CAT SCAN, NDL BX"," 4,373 "
-50750967,HC 6956 YUFCAT CTCHLMNS FRAC U, 23 
-41153586,HC CATH INJ PORT CHECK, 716 
-56269202,HC CATH PLCE CRD BRCHYTX," 3,257 "
-41151788,HC CATH PLMT MAIN PULM," 1,099 "
-41152216,HC CATH PLMT RENAL/PEL," 3,186 "
-41151812,"HC INTRO CATHETER, SUPER OR INFER VENA CAVA"," 6,297 "
-32700429,HC CATH TRAY W/FOLEY, 85 
-50721216,HC 6959 UFCATE CATCHL FRC U FR, 31 
-32712275,HC CATHETER 6 EYE 24 FR, 149 
-41530288,HC CATHETER LVL 28," 5,950 "
-55060354,HC CATHR TIP CULTURE, 302 
-62016282,HC CAUTERIZE INTRAMURAL," 1,637 "
-62016274,HC CAUTERIZE SUPERFICIAL," 1,181 "
-37000809,HC CAVITARY SCAN," 1,601 "
-50393529,HC CBC AUTOMATED, 285 
-51393528,HC CBC AUTOMATED, 285 
-59393520,HC CBC AUTOMATED, 285 
-50393438,HC CBC W/AUTO DIFF, 300 
-51393437,HC CBC W/AUTO DIFF, 300 
-57393431,HC CBC W/AUTO DIFF, 300 
-59393439,HC CBC W/AUTO DIFF, 300 
-50300235,HC CBC/PLAT/AUTO DIFF, 300 
-59300236,HC CBC/PLAT/AUTO DIFF, 300 
-51593846,HC CCP ANTIBODY, 122 
-51593879,HC CCP ANTIBODY, 122 
-58593849,HC CCP ANTIBODY, 122 
-58593872,HC CCP ANTIBODY, 122 
-51420735,HC CD 13 AB EA ADD MARKER, 380 
-58420738,HC CD 13 ANTIBODY, 380 
-51421287,HC CD 7, 570 
-58421280,HC CD 7 CELL MARKER, 570 
-51424521,HC CD126 AB, 570 
-58424524,HC CD126 AB, 570 
-51420594,HC CD14 AB EA ADD MARKER, 380 
-58420597,HC CD14, 380 
-51493146,HC CD16, 570 
-58493149,HC CD16, 570 
-58421181,HC CD19 AB, 380 
-59321182,HC CD19 AB EA ADD MARKER, 380 
-51420669,HC CD20, 570 
-58420662,HC CD20, 570 
-50320613,HC CD3, 380 
-51420610,HC CD3 AB EA ADD MARKER, 380 
-58420613,HC CD3, 380 
-59320614,HC CD3 AB EA ADD MARKER, 380 
-50790245,HC CD31:HLA CD31 TESTING, 342 
-52290244,HC CD31:HLA CD31 TESTING, 133 
-58421306,HC CD34 AB, 380 
-51420628,HC CD5, 570 
-58420621,HC CD5, 570 
-59320622,HC CD5 FIRST MARKER, 700 
-58493156,HC CD56, 380 
-59393157,HC CD56 AB EA ADD MARKER, 380 
-51501104,HC CEA, 490 
-58501107,HC CEA, 490 
-59401109,HC CEA, 490 
-47400296,HC ALLO CELL CONCENTRATION," 7,160 "
-59300350,"HC CELL COUNT AND DIFF, BODY FLD", 252 
-51424794,HC CELL COUNT BODY FLUID, 174 
-58424797,HC CELL COUNT BODY FLUID, 174 
-59324798,HC CELL COUNT-BODY FLUID, 174 
-51593796,HC CERULOPLASMIN, 197 
-58593799,HC CERULOPLASMIN, 197 
-36001543,HC CESIUM 137, 238 
-41152281,HC CHANGE BILI CATH," 4,733 "
-62018577,HC CHANGE BLADDER TUBE SMPL, 464 
-62026422,HC CHANGE G-TUBE TO G-J PERC," 1,227 "
-62024328,HC CHANGE GASTROSTOMY TUBE, 501 
-62012000,HC CHEMICAL CAUTERY OF WOUND, 210 
-41152794,HC CHEMICAL PLEURODESIS," 1,162 "
-26400408,HC CHEMO ADMIN IV PUSH INITIAL, 888 
-26500405,HC CHEMO ADMIN IV PUSH INITIAL, 888 
-26610097,HC CHEMO ADMIN IV PUSH INITIAL, 888 
-26402180,HC CHEMO ADMIN PERITONEAL CAVITY," 1,635 "
-26502161,HC CHEMO ADM PERIT CAVITY," 1,635 "
-26602169,HC CHEMO ADM PERIT CAVITY," 1,635 "
-26602011,HC CHEMO ADMIN SQ/OR IV; HORMONAL, 786 
-26402016,HC CHEMO ADMIN SQ/OR IV;HORMONAL, 786 
-26502005,HC CHEMO ADMIN SQ/OR IV;HORMONAL, 786 
-62700620,HC CHEMO INF EA ADD HR RITX ATIC, 641 
-62700638,HC CHEMO INF EA ADDL HR CYTX ATIC, 641 
-62700646,HC CHEMO INF EA ADDL HR REMI ATIC, 641 
-26402024,HC CHEMO INFSN > 8HRS W PUMP," 5,335 "
-26502013,HC CHEMO INFSN > 8HRS W PUMP," 5,335 "
-26602029,HC CHEMO INFSN > 8HRS W PUMP," 5,335 "
-26400424,HC CHEMO INFUSION EA ADDTL HR, 641 
-26500421,HC CHEMO INFUSION EA ADDTL HR, 641 
-26610113,HC CHEMO INFUSION EA ADDTL HR, 641 
-26400416,HC CHEMO INFUSION UP TO 1HR," 1,444 "
-26500413,HC CHEMO INFUSION UP TO 1HR," 1,444 "
-26610105,HC CHEMO INFUSION UP TO 1HR," 1,444 "
-62700562,HC CHEMO INFUSN 1HR ATIC," 1,444 "
-62700596,HC CHEMO INFUSN 1HR CYTX ATIC," 1,444 "
-62700604,HC CHEMO INFUSN 1HR REMI ATIC," 1,444 "
-62700588,HC CHEMO INFUSN 1HR RITX ATIC," 1,444 "
-62700612,HC CHEMO INFUSN EA ADDL HR ATIC, 641 
-26402032,HC CHEMO SUBCUTANEOUS RESERVOIR, 629 
-26502021,HC CHEMO SUBCUTANEOUS RESERVOIR, 629 
-26402123,HC CHEMO-IV INFUSION-ADDTL SEQ," 1,043 "
-26502120,HC CHEMO-IV INFUSION-ADDTL SEQ," 1,043 "
-26602110,HC CHEMO-IV INFUSION-ADDTL SEQ," 1,043 "
-26702076,HC CHEMO-IV INFUSION-ADDTL SEQ," 1,043 "
-26402107,HC CHEMO-IV PUSH-ADDTL DRUG, 801 
-26502096,HC CHEMO-IV PUSH-ADDTL DRUG, 801 
-26602094,HC CHEMO-IV PUSH-ADDTL DRUG, 801 
-26702050,HC CHEMO-IV PUSH-ADDTL DRUG, 801 
-26420141,HC CHEMO SUBCUT/IM NON-HORMONAL, 903 
-26520148,HC CHEMO SQ/IM NON-HORMONAL, 903 
-26620146,HC CHEMO SQ/IM NON-HORMONAL, 903 
-62026075,HC SPLIT AGRFT T/A/L EA 100 CM/EA 1% BDY INFT/CHLD," 2,069 "
-62026083,HC EPIDERMAL AGRFT F/S/N/H/F/G/M/D GT EA 100 CM/EA, 663 
-62025960,HC CHEMODENERV OF ANAL SPHINC," 4,738 "
-41004706,HC CHEST 1 VIEW, 593 
-41304700,HC CHEST 1 VIEW, 593 
-93604700,HC CHEST 1 VIEW, 593 
-41004755,HC CHEST 2V FRONT + LA, 765 
-41304759,HC CHEST 2V FRONT + LA, 765 
-93604759,HC CHEST 2V FRONT + LA, 765 
-93604809,HC CHEST 2V W/APICAL, 944 
-41080110,"HC CHEST, 2V W/OBLIQUE"," 1,275 "
-41380114,"HC CHEST, 2V W/OBLIQUE"," 1,275 "
-93680114,HC CHEST 2V W/OBLIQUE," 1,275 "
-41080128,"HC CHEST, LAT DECUB", 821 
-41380122,"HC CHEST, LAT DECUB", 821 
-93680122,HC CHEST LAT DECUB, 821 
-41004854,HC CHEST MIN 4V," 1,232 "
-41304858,HC CHEST MIN 4V," 1,232 "
-93604858,HC CHEST MIN 4V," 1,232 "
-62026489,HC CLTX METATARSOPHLNGL JT DISLC W/O ANES, 344 
-56330053,HC CHL.TRACH AMP PROBE, 566 
-46310348,HC CHLAMYDIA TRACH MOLECULAR, 566 
-56310345,HC CHLAMYDIA MOLECULAR, 566 
-50401223,HC CHLORIDE BODY FLUID, 199 
-50411016,HC CHLORIDE CUSTOM, 129 
-59411017,HC CHLORIDE CUSTOM, 129 
-50405372,HC CHLORIDE CUSTOM PNL, 129 
-50401207,HC CHLORIDE SERUM/PLA, 129 
-51301208,HC CHLORIDE SERUM/PLA, 129 
-59401208,HC CHLORIDE SERUM/PLA, 129 
-50401249,"HC CHLORIDE, URINE", 131 
-59401240,HC CHLORIDE URINE, 131 
-59401224,HC CHLORIDE BODY FLD, 199 
-59401158,HC CHOLESTEROL TS PA, 145 
-41070632,HC CHNG PERC TUBE/DRAIN W CONT," 2,465 "
-50405281,HC CHOL-CUSTOM PNL, 145 
-41108200,HC CHOLANG PERCUTAN," 1,871 "
-41008053,HC CHOLANGIOGRAM O/R, 419 
-41070103,HC CHOLANGIOGRAM OR VIA TUBE," 1,141 "
-41108101,HC CHOLANGIOGRAM T-TB," 1,141 "
-50401157,HC CHOLESTEROL, 145 
-51301158,HC CHOLESTEROL, 145 
-51501153,HC CHOLESTEROL, 145 
-58501156,HC CHOLESTEROL, 145 
-50411115,HC CHOLESTEROL CUSTOM, 145 
-59411116,HC CHOLESTEROL CUSTOM, 145 
-50401173,HC CHOLESTEROL SRM/BLD TTL, 145 
-59401174,HC CHOLESTEROL SRM/BLD TTL, 145 
-50411057,HC CHOLESTEROL TOTAL, 145 
-59411058,HC CHOLESTEROL TOTAL, 145 
-50642123,HC CHRM ANAL 15-20 CELLS 2KAR," 1,000 "
-50642149,HC CHRM ANAL 20-25 CELLS," 1,365 "
-50642206,HC CHRM ANAL ADD CELLS, 66 
-50642214,HC CHRM ANAL ADD HIGH RES, 153 
-50642164,HC CHRM ANAL ADD KARYOTYPE, 295 
-50642172,HC CHRM ANAL ADD STAIN, 236 
-50642156,HC CHRM ANAL AF 15 CELLS 1KAR," 1,970 "
-50642107,HC CHRM ANAL BASE BREAK 50-100 CE," 1,898 "
-50642115,HC CHRM ANAL CLAST BRK 100 C," 1,898 "
-50641778,HC CHRM ISH ANG 10-30 CELL, 353 
-50641729,HC CHRM ISH MAR 3-5 CELL, 295 
-50672658,HC CHRM ISH MDK 10-30 CELL, 353 
-50641802,HC CHRM ISH NOS 10-30 CELL, 353 
-50641760,HC CHRM ISH PWS 10-30 CELL, 353 
-50641794,HC CHRM ISH SMS 10-30 CELL, 353 
-50642230,HC CHRM ISH SUBTELOMERE 3-5 CELLS, 295 
-50641752,HC CHRM ISH VCF 10-30 CELL, 353 
-50672641,HC CHRM ISH WCP 3-5 CELL, 295 
-50641786,HC CHRM ISH WMS 10-30 CELL, 353 
-50725688,HC 6959 11058R TRZDN QNT DRG, 26 
-92801687,HC CI COUNSELING PER HR, 192 
-62311030,HC CI NEURO RESP ASSESS, 467 
-92800192,HC CI SP PROC REPLACE L48," 19,600 "
-40450447,HC CIPRO TAB 500MG(CIPROFLOXACIN), 10 
-50723881,HC 6959 11027R C1Q IMM CPLX AS, 24 
-50465129,HC CIRCUIT BLOOD GASES," 1,161 "
-37020906,HC CIST/LAT PUNC W/ INJ," 1,663 "
-41100744,HC CIST/LAT PUNC W/INJ," 1,663 "
-50750983,HC 6956 YUCITR CITRATE EXCR UR, 24 
-50401546,HC CK-MB, 446 
-51501542,HC CK-MB, 446 
-51801546,HC CK-MB, 446 
-56801541,HC CK-MB, 446 
-59401547,HC CK-MB, 446 
-41005505,HC CLAVICLE, 812 
-41305509,HC CLAVICLE, 812 
-93605509,HC CLAVICLE COMPLETE, 812 
-62021589,HC CLEAN OUT MASTOID CAVITY CMPLX," 1,637 "
-62021571,HC CLEAN OUT MASTOID CAVITY SMPL, 734 
-62024039,HC DEBRIDE DERMIS/SUBQ/MUSCLE +/- FASCIA 1ST 20 CM," 1,943 "
-40510000,HC CLINIC ADMIN FEE, 64 
-62023825,HC CLINIC USE LVL 1 ESTAB PT, 75 
-62023775,HC CLINIC USE LVL 1 NEW PT, 101 
-62023833,HC CLINIC USE LVL 2 ESTAB PT, 101 
-62023783,HC CLINIC USE LVL 2 NEW PT, 119 
-62023841,HC CLINIC USE LVL 3 ESTAB PT, 109 
-62023791,HC CLINIC USE LVL 3 NEW PT, 172 
-62023858,HC CLINIC USE LVL 4 ESTAB PT, 159 
-62023809,HC CLINIC USE LVL 4 NEW PT, 222 
-62023866,HC CLINIC USE LVL 5 ESTAB PT, 195 
-62023817,HC CLINIC USE LVL 5 NEW PT, 257 
-50722677,HC 6959 CLOMIP CLMPRMN&META QN, 12 
-62011143,HC CLOSE SPLIT WOUND SMPL, 625 
-62011150,HC CLOSE SPLIT WOUND W/PACK, 474 
-62021407,HC CLOSE TEAR DUCT OPENING, 620 
-62021415,HC CLOSE TEAR DUCT OPENING PLUG, 619 
-46130654,HC CLOST DIFF TOX A, 93 
-59130658,HC CLOST DIFF TOX A, 93 
-62021019,HC CLOSURE OF EYELID BY SUTURE, 744 
-41530312,HC CLOSURE PACK LVL 11, 723 
-50750991,HC 6956 YCLOZP CLOZAPINE QUANT, 24 
-62026364,HC CLSD TX POST ANKLE FX WO/MANIP, 482 
-36079127,HC CM BRAC ISOD PN INT," 4,469 "
-36200103,HC CMP TX DEV HALO STN," 2,439 "
-36070118,HC CMPL TRT IMPLNT DEV," 2,439 "
-36079440,HC CMPLX BRACHYTHR SIM," 4,747 "
-62026091,HC DERMAL AUTOGRAFT F/S/N/H/F/G/M/D GT 1ST 100," 1,624 "
-36079416,HC CMPLX EXTR BEAM SIM," 4,747 "
-36079275,HC CMPLX MOLDED BLOCK," 2,439 "
-62025879,HC ELEC ALYS NSTIM PLS GEN CPLX CRNL NRV EA 30 MIN, 744 
-46310694,HC CMV CULT ID/PCR, 70 
-56310691,HC CMV CULT ID/PCR, 70 
-56300502,HC CMV PCR QUANT, 849 
-46300505,HC CMV PCR QUANTIFICATION, 849 
-46300489,HC CMV QUAL PCR AMP, 651 
-56300486,HC CMV QUAL PCR AMP, 651 
-47220124,HC CMV TOTAL AB, 268 
-46100012,HC CMV-IGM, 314 
-59100016,HC CMV-IGM, 314 
-41100058,"HC CNTRST INJCT, EVL CVC", 487 
-41895327,HC LOCM 200-299 L5, 215 
-41892787,HC LOCM 200-299 L8, 523 
-41896093,HC CNTRST MED 300-349 L3, 110 
-41892860,HC CNTRST MED 300-349 L6, 322 
-41892795,HC CNTRST MED 300-349 L8, 523 
-41895319,HC CNTRST MED 350-399 L3, 110 
-41892852,HC CNTRST MED 350-399 L5, 215 
-41892878,HC CNTRST MED 350-399 L7, 418 
-50405398,HC CO2 CUSTOM PNL, 95 
-59405399,HC CO2 CUSTOM PNL, 95 
-51864858,HC COHGB(CARBOXYHEMOGL), 334 
-56864853,HC COHGB(CARBOXYHEMOGL), 334 
-36001527,HC COIL:VORTX 18 2-3-23-3.3, 850 
-36301018,HC COIL:VORTX 18 2-3-23-3.3, 850 
-47201553,HC COLD SCREEN/PANEL, 156 
-47201017,HC COLD TITER, 191 
-26402164,HC COLLECT BLOOD IMPL VAD, 571 
-26502146,HC COLLECT BLOOD IMPL VAD, 571 
-26602144,HC COLLECT BLOOD IMPL VAD, 571 
-26730960,HC COLLECT BLOOD IMPL VAD, 571 
-62024369,HC COLONOSCOPY," 1,133 "
-62026570,HC COLPOSCOPY OF VAGINA W/BIOPSY, 517 
-62026612,HC COLPOSCOPY OF VAGINA W/CERVIX, 338 
-62026620,HC COLPOSCOPY OF VULVA, 156 
-62026588,HC COLPOSCOPY OF VULVA W BIOPSY, 209 
-62026596,HC COLPOSCOPY W BIOPSY OF CERVIX, 377 
-62026653,HC PRIM PRQ TRLUML MCHNL THRMBC SBSQ VSL," 3,981 "
-40450454,HC COMBIVIR TAB 150MG, 89 
-92800200,HC COMP AUDIO EV/SP REC DISABLED, 420 
-92800218,HC COMP AUDIO THRESH EVAL/SP RECO, 420 
-62310339,HC COMP DYN POSTUREOGRAPHY, 222 
-92800721,HC COMP DYN POSTUREOGRAPHY, 222 
-47200555,HC COMPATIBILITY TEST:AHG, 440 
-51563468,HC COMPLEMENT C3 SERUM, 199 
-58563461,HC COMPLEMENT C3 SERUM, 199 
-51563476,HC COMPLEMENT C4 SERUM, 199 
-58563479,HC COMPLEMENT C4 SERUM, 199 
-50393420,HC COMPLETE BLOOD COUNT, 285 
-51393429,HC COMPLETE BLOOD COUNT, 285 
-57393423,HC COMPLETE BLOOD COUNT, 285 
-59393421,HC COMPLETE BLOOD COUNT, 285 
-62023189,HC ELEC ALYS NSTIM CPLX PERPH 1ST HR, 663 
-62023197,HC ELEC ALYS NSTIM EA 30 MIN, 415 
-62024021,HC COMPLEX DRAINAGE," 1,994 "
-36079226,HC COMPLEX TX DEVICE," 2,439 "
-62011671,HC COMPOSITE SKIN GRAFT," 2,122 "
-40880163,"HC COMPUTER AIDED DETECTION, DIAG", 208 
-55091532,HC CONC FOR PNEUMOCYSTIS, 153 
-59091538,HC CONC FOR PNEUMOCYSTIS, 153 
-62310115,HC CONDITIONING PLAY AUDIO, 246 
-92800226,HC CONDITIONING PLAY AUDIO, 246 
-83500157,HC CONF TYPE, 644 
-52014107,HC CONSULT II W/SLIDE PREP," 1,220 "
-62110796,HC CONSULTATION II-SPC," 1,220 "
-41101270,HC CONTRAST INJECTION - TUBE(S)," 2,544 "
-62016290,HC CONTROL NOSEBLEED SMPL, 331 
-51402428,HC CONTROL PT INH SCRN, 190 
-58402421,HC CONTROL PT INH SCRN, 190 
-52110590,HC CONV PAP DIAG, 42 
-52110632,HC CONV PAP SCREENING, 63 
-52102399,HC CONV. PAP SMEAR, 63 
-41101221,HC CONVERT G-TUBE TO GJ TUBE," 4,775 "
-47201157,HC COOMBS AB TITER, 259 
-50751007,HC 6956 YCOPPR COPPER, 24 
-50465111,HC CORD BLOOD GASES, 815 
-56269038,HC CORN STENT SNGL VES," 14,970 "
-56269046,HC CORN STNT EA AD VES," 23,682 "
-62010939,HC CORRECT SKIN COLOR 6.1-20.0 SQ," 1,045 "
-62010921,HC CORRECT SKIN COLOR <6.1 SQ CM, 892 
-50740612,HC 6959 11056R CORTISOL FREE, 100 
-51501419,HC CORTISOL TOTAL, 374 
-58501412,HC CORTISOL TOTAL, 374 
-59401414,HC CORTISOL TOTAL, 374 
-50751015,HC 6956 YUCORT CORTISOL FREE U, 9 
-59401505,HC CPK TTL, 254 
-56270093,HC CPR," 2,129 "
-37010220,HC CR51 SODIUM CHROMATE/250 MCI, 461 
-51301554,HC CREATININE, 179 
-59401554,HC CREATININE, 179 
-50462159,HC CREATININE BDY FL, 174 
-50401553,HC CREATININE BLOOD, 179 
-59401570,HC CREATININE CLRN TS, 256 
-50401579,HC CREATININE CLRNC TS, 256 
-50405323,HC CREATININE CUSTOM PANEL, 179 
-59405324,HC CREATININE CUSTOM PNL, 179 
-50470046,HC CREATININE KINASE TOTAL, 254 
-51370047,HC CREATININE KINASE, 254 
-59470047,HC CREATININE KINASE, 254 
-58700162,HC CREATININE POC, 179 
-50462175,HC CREATININE URINE, 174 
-59462176,HC CREATININE URINE, 174 
-59462150,HC CREATININE BDY FLD, 174 
-26400531,"HC CRITICAL CARE, EA 30 MIN"," 2,434 "
-26600536,"HC CRITICAL CARE, EA 30 MIN"," 2,434 "
-32711707,"HC CRITICAL CARE, EA ADDL 30 MIN"," 2,434 "
-26600528,HC CRITICAL CARE INITIAL," 12,847 "
-26400523,"HC CRITICAL CARE, INITIAL HOUR"," 12,847 "
-26500520,"HC CRITICAL CARE, INITIAL HOUR"," 12,847 "
-26500538,"HC CRITICAL CARE,EA 30 MIN"," 2,434 "
-26730051,"HC CRITICAL CARE,EA 30 MIN"," 2,434 "
-32702540,"HC CRITICAL CARE,FIRST HOUR"," 12,847 "
-26730044,"HC CRITICAL CARE,INITIAL HOUR"," 12,847 "
-47400023,HC AUTO CRYO & STOR. OF PROD," 9,662 "
-47204169,HC CRYO POOLING," 1,701 "
-40180176,HC CRYO PROBE," 5,799 "
-42080176,HC CRYO PROBE L34," 8,264 "
-59320267,HC CRYOGLOBULIN QUAL SEMIQT, 229 
-50320266,HC CRYOGLOBIN QUAL SEMIQNT, 229 
-50113505,HC CRYOPOOR PLASMA, 751 
-50113901,HC CRYOPRECIPITATE, 275 
-50120294,HC CRYOPRECIPITATE-PREPOOLED, 275 
-50120302,HC CRYOPRECIPITATE EACH UNIT, 275 
-50120310,HC CRYOPRECIPITATE EACH UNIT, 275 
-50120328,HC CRYOPRECIPITATE EACH UNIT, 275 
-50120336,HC CRYOPRECIPITATE EACH UNIT, 275 
-50642081,HC CRYOPRES FRZ/STORE EA LINE, 195 
-50642099,HC CRYOPRES THAW EA LINE, 35 
-91900209,HC CRYOPRES-TISS/TESTICULAR, 550 
-91900290,HC CRYOPRES:TISSUE OVARIAN," 1,276 "
-91900076,HC CRYOPRESER-EMBRYO(S)," 1,276 "
-91900084,HC CRYOPRESER-SPERM, 400 
-91900308,HC CRYOPRESERVATION:OOCYTE," 1,276 "
-50320258,HC CRYOPROTEIN QUAL SEMIQNT, 229 
-59320259,HC CRYOPROTEIN, 229 
-62025556,HC CRYOSURG ABLATE FA EACH FIBR," 7,992 "
-62012240,HC CRYOTHERAPY OF SKIN, 130 
-46140455,HC CRYPTO AB TITER, 318 
-59140459,HC CRYPTO AG TITER, 318 
-46130126,HC CRYPTOC. AB (BLD), 309 
-55020309,HC CRYPTOSPORIDIUM DFA, 209 
-59020305,HC CRYPTOSPORIDIUM DFA, 209 
-50301837,HC CRYSTAL ID, 244 
-59301838,HC CRYSTAL ID, 244 
-59060301,HC CSF CLT, 302 
-55060305,HC CSF CULTURE, 302 
-37002151,HC CSF KINETICS," 1,804 "
-37002144,HC CSF LEAK IMAGING," 3,679 "
-37003662,HC CSF SHUNT EVAL," 1,559 "
-37224706,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-38024709,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-42024703,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-94324704,HC CT ABDOMEN," 5,733 "
-38025300,HC CT ABDOMEN LIMITED W/O CONTRAST," 2,814 "
-37224953,HC CT ABDOMEN W/CONTRAST," 6,285 "
-38024956,HC CT ABDOMEN W/CONTRAST," 6,285 "
-42024950,HC CT ABDOMEN W/CONTRAST," 6,285 "
-94324951,HC CT ABDOMEN W/CONTRAST," 6,285 "
-37225109,HC CT ABDOMEN WO/W CONTRAST," 6,956 "
-38025102,HC  CT ABDOMEN WO/W CONTRAST," 6,956 "
-42025106,HC CT ABDOMEN WO/W CONTRAST," 6,956 "
-94325107,HC CT ABDOMEN WO/W CONTRAST," 6,956 "
-38044509,HC CT ANGIO ABD WO/W CONTRAST," 8,709 "
-42044503,HC CT ANGIO ABD WO/W CONTRAST," 8,709 "
-94344504,HC CT ANGIO ABD WO/W CONTRAST," 8,709 "
-38044517,HC CT ANGIO AORTA/LOW EXT," 10,739 "
-42044511,HC CT ANGIO AORTA/LOW EXT," 10,739 "
-94344512,HC CT ANGIO AORTA/LOW EXT," 10,739 "
-42044461,HC CT ANGIO CHEST W/WO," 7,745 "
-94344462,HC CT ANGIO CHEST W/WO," 7,745 "
-38044467,HC CT ANGIO CHEST WO/W CONTR," 7,745 "
-38044442,HC CT ANGIO HEAD WO/W CONTRAST," 8,725 "
-42044446,HC CT ANGIO HEAD WO/W CONTRAST," 8,725 "
-94344447,HC CT ANGIO HEAD WO/W CONTRAST," 8,725 "
-38044491,HC CT ANGIO LOW EXT WO/W CONTRAST," 6,960 "
-42044495,HC CT ANGIO LOW EXT WO/W CONTRAST," 6,960 "
-94344496,HC CT ANGIO LOW EXT WO/W CONTRAST," 6,960 "
-38044459,HC CT ANGIO NECK WO/W CONTRAST," 9,345 "
-42044453,HC CT ANGIO NECK WO/W CONTRAST," 9,345 "
-94344454,HC CT ANGIO NECK WO/W CONTRAST," 9,345 "
-38044475,HC CT ANGIO PELVIS WO/W CONTR," 8,238 "
-42044479,HC CT ANGIO PELVIS WO/W CONTR," 8,238 "
-94344470,HC CT ANGIO PELVIS WO/W CONTR," 8,238 "
-38044483,HC CT ANGIO UP EXT WO/W CONTRAST," 7,717 "
-42044487,HC CT ANGIO UP EXT WO/W CONTRAST," 7,717 "
-94344488,HC CT ANGIO UP EXT WO/W CONTRAST," 7,717 "
-42017012,HC BIOPSY NEEDLE - L10, 732 
-42017020,HC BIOPSY NEEDLE  L11, 814 
-42017038,HC CT BIOPSY NEEDLE - LVL12, 891 
-42017004,HC CT BIOPSY NEEDLE - LVL9, 627 
-42016998,HC BIOPSY NEEDLE L3, 85 
-42016733,HC DRAINAGE BAG L6, 298 
-42016923,HC BIOPSY NEEDLE L6, 298 
-38025862,HC CT C-SPINE W/O CONTRAST," 5,262 "
-42025866,HC CT C-SPINE W/O CONTRAST," 5,262 "
-94325867,HC CT C-SPINE W/O CONTRAST," 5,262 "
-38025938,HC CT C-SPINE W/CONTRAST," 6,554 "
-42025932,HC CT C-SPINE W/CONTRAST," 6,554 "
-94325933,HC CT C-SPINE W/CONTRAST," 6,554 "
-38016267,HC CT CEREBRAL PERFUSION W CONTRAST," 6,996 "
-42016261,HC CT CEREBRAL PERFUSION W CONTRAST," 6,996 "
-94316262,HC CT CEREBRAL PERFUSION W CONTRAST," 6,996 "
-38016283,"HC CT COLONGRAPHY,DIAG WO CONTR"," 1,885 "
-38016275,HC CT COLONGRAPHY - SCREEN," 2,250 "
-42016279,HC CT COLONOGRAPHY - SCREEN," 2,250 "
-42016287,"HC CT COLONGRAPHY,DIAG WO CONTR"," 1,885 "
-38026456,CT CORONARY CALCIUM W/O CONTRAST, 690 
-42026450,CT CORONARY CALCIUM W/O CONTRAST, 690 
-94326451,CT CORONARY CALCIUM W/O CONTRAST, 690 
-42016824,HC CT DRAIN SUPPLY - LVL 4, 149 
-42016774,HC DRAINAGE CATHETER L11, 906 
-42016790,HC DRAINAGE CATHETER, 723 
-41153206,HC CT GUIDANCE ABLTN PARENCHYMAL," 4,302 "
-38021382,HC CT GUIDANCE ABSCESS DRAIN," 2,894 "
-42021386,HC CT GUIDANCE ABSCESS DRAIN," 2,894 "
-38021366,HC CT GUIDANCE FOR NDL PLCMNT," 4,373 "
-42021360,HC CT GUIDANCE FOR NDL PLCMNT," 4,373 "
-38072807,HC CT GUIDANCE FOR RADIATION FIEL," 3,582 "
-38072674,HC CT GUIDANCE FOR RF ABLATION," 4,302 "
-42072678,HC CT GUIDANCE FOR RF ABLATION," 4,302 "
-36033280,HC CT GUIDANCE/PCM RAD," 3,582 "
-38044061,HC CT GUIDE STEREOTAC LOCAL," 4,470 "
-42044065,HC CT GUIDE STEREOTAC LOCAL," 4,470 "
-36001535,HC CT GUIDE STEROTAC LOCAL," 4,470 "
-36201507,HC CT GUIDE STEROTAC LOCAL," 4,470 "
-36301505,HC CT GUIDE STEROTAC LOCAL," 4,470 "
-38044525,HC CT GUIDED PERC VRTEBROP," 2,785 "
-42016964,HC CT GUIDEWIRE   L4, 179 
-42016741,HC CT GUIDEWIRE SUPPLY LVL 7, 465 
-37223104,HC CT HEAD W/O CONTRAST," 3,850 "
-38023107,HC CT HEAD W/O CONTRAST," 3,850 "
-42023101,HC CT HEAD W/O CONTRAST," 3,850 "
-94323102,HC CT HEAD W/O CONTRAST," 3,850 "
-42023317,HC CT HEAD W/O CONTRAST (WITH XENON)," 3,850 "
-38072815,HC CT HEAD W/O CONT (W XENON) 2ND SCAN," 3,850 "
-42072819,HC CT HEAD (WITH XENON)-2ND SCAN," 3,850 "
-41154295,HC CT HEAD W CONTRAST," 4,505 "
-38023206,HC CT HEAD W/CNTRST," 4,505 "
-42023200,HC CT HEAD W/CNTRST," 4,505 "
-94323201,HC CT HEAD W/CNTRST," 4,505 "
-41154287,HC CT HEAD W/O CONTRAST," 3,850 "
-41154303,HC CT HEAD W/O FOL W/C," 5,077 "
-94323300,HC CT HEAD WO/W CNTRS," 5,077 "
-37223302,HC CT HEAD WO/W CON," 5,077 "
-38023305,HC CT HEAD WO/W CON," 5,077 "
-42023309,HC CT HEAD WO/W CON," 5,077 "
-37261955,HC CT IV START KIT, 228 
-38061958,HC CT IV START KIT, 204 
-42061952,HC CT IV START KIT, 224 
-94361953,HC CT IV START KIT, 168 
-38025888,HC CT L-SPINE W/O CONTRAST," 5,155 "
-42025882,HC CT L-SPINE W/O CONTRAST," 5,155 "
-94325883,HC CT L-SPINE W/O CONTRAST," 5,155 "
-38025979,HC CT L-SPINE W/ CONTRAST," 6,510 "
-42025973,HC CT L-SPINE W/ CONTRAST," 6,510 "
-94325974,HC CT L-SPINE W/CONTRAST," 6,510 "
-38024857,HC CT LOWER EXTREMITY W/O CONTRAST," 3,592 "
-42024851,HC CT LOWER EXTREMITY W/O CONTRAST," 3,592 "
-94324852,HC CT LOWER EXTREMITY W/O CONTRAST," 3,592 "
-38024873,HC CT LWR EXTRMTY W/CONTRAST," 4,503 "
-42024877,HC CT LWR EXTRMTY W/CONTRAST," 4,503 "
-94324878,HC CT LWR EXTRMTY W/CONTRAST," 4,503 "
-38024865,HC CT LWR EXTRMTY WO/W CONTRAST," 5,943 "
-42024869,HC CT LWR EXTRMTY WO/W CONTRAST," 5,943 "
-94324860,HC CT LWR EXTRMTY WO/W CONTRAST," 5,943 "
-38023388,HC CT MAXFACIAL W/CNTRST," 4,848 "
-42023382,HC CT MAXFACIAL W/CNTRST," 4,848 "
-94323383,HC CT MAXFACIAL W/CNTRST," 4,848 "
-38023628,HC CT MAXFACL AREA W/O - SINUS," 1,100 "
-42023622,HC CT MAXFACL AREA W/O - SINUS," 1,100 "
-94323623,HC CT MAXFACL AREA W/O - SINUS," 1,100 "
-38023347,HC CT MAXILLOFACIAL W/O CONTRAST," 3,954 "
-42023341,HC CT MAXILLOFACIAL W/O CONTRAST," 3,954 "
-94323342,HC CT MAXILLOFACIAL W/O CONTRAST," 3,954 "
-42016956,HC NEEDLE INTRODUCER L5, 213 
-41154329,HC CT NECK W/CONTRAST," 4,628 "
-41154311,HC CT NECK W/O CONTRAST," 4,347 "
-41154337,HC CT NECK W/O FOL W/CONTRAST," 5,609 "
-42023606,HC CT ORB SEL PFOS WO/W CON," 6,355 "
-94323607,HC CT ORB SEL PFOS WO/W CON," 6,355 "
-38023503,HC CT ORB SEL/PFOS W/CON," 5,656 "
-42023507,HC CT ORB SEL/PFOS W/CON," 5,656 "
-94323508,HC CT ORB SEL/PFOS W/CON," 5,656 "
-38023602,HC CT ORB SEL/PFOS WO/W CON," 6,355 "
-38023404,HC CT ORB SELL/PST FOS WO CONT," 4,780 "
-42023408,HC CT ORB SELL/PST FOS WO CONT," 4,780 "
-94323409,HC CT ORB SELL/PST FOS WO CONT," 4,780 "
-38025821,HC CT PELVIMETRY, 200 
-42025825,HC CT PELVIMETRY, 200 
-37224805,HC CT PELVIS W/O CONTRAST," 4,373 "
-38024808,HC CT PELVIS W/O CONTRAST," 4,373 "
-42024802,HC CT PELVIS W/O CONTRAST," 4,373 "
-94324803,HC CT PELVIS W/O CONTRAST," 4,373 "
-37225059,HC CT PELVIS W/CONTRAST," 7,439 "
-38025052,HC CT PELVIS W/CONTRAST," 7,439 "
-42025056,HC CT PELVIS W/CONTRAST," 7,439 "
-94325057,HC CT PELVIS W/CONTRAST," 7,439 "
-37225208,HC CT PELVIS WO/W CONTRAST," 7,944 "
-38025201,HC CT PELVIS WO/W CONTRAST," 7,944 "
-42025205,HC CT PELVIS WO/W CONTRAST," 7,944 "
-94325206,HC CT PELVIS WO/W CONTRAST," 7,944 "
-42016766,HC CT RF SUPPLY LVL 32," 7,863 "
-38024923,HC CT SFT NCK WO/W CON," 5,609 "
-42024927,HC CT SFT NCK WO/W CON," 5,609 "
-94324928,HC CT SFT NCK WO/W CON," 5,609 "
-37224912,HC CT SFT TIS NCK W/CON," 4,628 "
-38024915,HC CT SFT TIS NCK W/CON," 4,628 "
-42024919,HC CT SFT TIS NCK W/CON," 4,628 "
-94324910,HC CT SFT TIS NCK W/CON," 4,628 "
-37224904,HC CT SOFT TISS NECK W/O CONT," 4,347 "
-38024907,HC CT SOFT TISS NECK W/O CONT," 4,347 "
-42024901,HC CT SOFT TISS NECK W/O CONT," 4,347 "
-94324902,HC CT SOFT TISS NECK W/O CONT," 4,347 "
-42016907,HC BIOPSY NEEDLE L8, 425 
-42016816,HC DRAINAGE BAG L3, 85 
-42016972,HC CT BIOPSY TRAY L5, 213 
-42016782,HC DRAINAGE CATHETER L12, 808 
-42016758,HC BIOPSY NEEDLE L20," 2,763 "
-42016881,HC CT SUPPLY LVL 33," 8,288 "
-42016840,"HC CT SUPPLY, LVL 34"," 7,875 "
-42016857,HC ABLATION PROBE L35," 8,625 "
-42016865,HC ABLATION PROBE L37," 10,125 "
-42016709,HC CT SUPPLY LVL 4, 149 
-42016717,HC CT SUPPLY LVL 4, 149 
-42016873,HC ABLATION PROBE L40," 12,375 "
-42016832,HC BIOPSY NEEDLE L5, 213 
-42016725,HC BIOPSY NEEDLE L7, 383 
-40300097,HC CT SURGICAL PLANNING," 3,122 "
-38025870,HC CT T-SPINE W/O CONTRAST," 5,180 "
-42025874,HC CT T-SPINE W/O CONTRAST," 5,180 "
-94325875,HC CT T-SPINE W/O CONTRAST," 5,180 "
-38025953,HC CT T-SPINE W/CONTRAST," 6,523 "
-42025957,HC CT T-SPINE W/CONTRAST," 6,523 "
-94325958,HC CT T-SPINE W/CONTRAST," 6,523 "
-37224755,HC CT THORAX W/O CONTRAST," 4,426 "
-38024758,HC CT THORAX W/O CONTRAST," 4,426 "
-42024752,HC CT THORAX W/O CONTRAST," 4,426 "
-94324753,HC CT THORAX W/O CONTRAST," 4,426 "
-38072633,HC CT THORAX LIMITED W/O CONTRAST," 2,124 "
-42072637,HC CT THORAX LIMITED W/O CONTRAST," 2,124 "
-94372638,HC CT THORAX LIMITED," 2,124 "
-37225000,HC CT THORAX W/CNTRST," 5,564 "
-38025003,HC CT THORAX W/CNTRST," 5,564 "
-42025007,HC CT THORAX W/CNTRST," 5,564 "
-94325008,HC CT THORAX W/CNTRST," 5,564 "
-37225158,HC CT THRX W+W/O CNTRS," 6,921 "
-38025151,HC CT THRX W+W/O CNTRS," 6,921 "
-42025155,HC CT THRX W+W/O CNTRS," 6,921 "
-94325156,HC CT THRX W+W/O CNTRS," 6,921 "
-38024774,HC CT UP EXT W/CONTRAST," 4,387 "
-42024778,HC CT UP EXT W/CONTRAST," 4,387 "
-94324779,HC CT UP EXT W/CONTRAST," 4,387 "
-38024782,HC CT UP EXT WO/W CONTRAST," 5,620 "
-94324787,HC CT UP EXT WO/W CONTRAST," 5,620 "
-38024766,HC CT UPPER EXTREMITY W/O CONTRAST," 4,016 "
-42024760,HC CT UPPER EXTREMITY W/O CONTRAST," 4,016 "
-94324761,HC CT UPPER EXTREMITY W/O CONTRAST," 4,016 "
-37216140,HC CTA CARD W/QUAN EVAL OF CORO C," 7,284 "
-38016143,HC CTA CARD W/QUAN EVAL OF CORO C," 7,284 "
-42016147,HC CTA CARD W/QUAN EVAL OF CORO C," 7,284 "
-94316148,HC CTA CARD W/QUAN EVAL OF CORO C," 7,284 "
-59060350,HC CTH TIP CLT, 302 
-41894841,HC CTRST MED 300-349 L13, 990 
-91900019,HC CULTR OOCYTE/EMBRYO<4DAY," 2,090 "
-55091797,HC CULTURE VRE, 165 
-36301000,HC CYBERKNIFE MLT 2-5BODY ONLY," 27,992 "
-36300200,HC CYBERKNIFE MULTI 2-5 TX-BRAIN," 27,992 "
-50463660,"HC CYCLOSPORINE, WHOLE BLOOD", 437 
-51563666,"HC CYCLOSPORINE, WHOLE BLOOD", 437 
-51863660,HC CYCLOSPORINE WHOLE BLOOD, 437 
-56863665,HC CYCLOSPORINE WHOLE BLOOD, 437 
-59463661,"HC CYCLOSPORINE, WHOLE BLOOD", 437 
-51565067,HC CYSTATIN C, 56 
-58565060,HC CYSTATIN C, 56 
-41151176,HC CYSTOGRAM," 1,666 "
-41151184,HC CYSTOGRAM," 1,561 "
-41006156,HC CYSTOGRAM MIN 3V," 1,561 "
-62018619,HC CYSTOSCOPY, 709 
-62018718,HC CYSTOSCOPY REMOVE FB SMPL," 3,396 "
-62018635,HC CYSTOSCOPY W/BX," 2,549 "
-40106205,"HC CYSTOURETH, VOID"," 2,291 "
-41006206,HC CYSTOURETH VOID," 2,291 "
-52102316,HC CYTO CELL BLOCK-LV4, 875 
-52110681,HC CYTO MISC SOURCE, 371 
-52101664,HC CYTO MISC SOURCES, 371 
-50672740,HC CYTOGEN ENZYM DIGEST, 89 
-50672732,HC CGHGEN CYTOGEN ISOL EA NA, 123 
-52101623,HC CYTOSPIN, 408 
-50503366,HC D DIMER LIA, 438 
-59503367,HC D DIMER LIA, 438 
-91000067,"HC D&C,TX MISSED ABORTION 1ST TRI"," 1,770 "
-59500082,HC D-DIMER, 82 
-50502954,"HC D-DIMER, ELISA", 438 
-59502955,"HC D-DIMER, ELISA", 438 
-24192114,HC D2G2 IMC ICU," 15,479 "
-24193211,HC D2G2 IMC ICU," 15,479 "
-24192122,HC D2G2 IMC ICU," 15,479 "
-24193229,HC D2G2 IMC ICU," 15,479 "
-24192130,HC D2G2 IMC ICU," 15,479 "
-24193237,HC D2G2 IMC ICU," 15,479 "
-24191116,HC D2G2 MED/SURG PRIV," 8,897 "
-24191215,HC ZZD2G2 MED/SURG," 8,397 "
-24191124,HC ZZD2G2 MED/SURG PRIV," 8,897 "
-24191223,HC ZZD2G2 MED/SURG SP," 8,397 "
-24191132,HC ZZD2G2 MED/SURG PRIV," 8,897 "
-24191231,HC D2G2 MED/SURG SP," 8,397 "
-24191140,HC D2G2 M/S LEVEL IV PVT," 8,897 "
-24191249,HC ZZD2G2 MED/SURG SP," 8,397 "
-25291113,HC D3 MED/SURG PRIV," 8,897 "
-25291212,HC D3 MED/SURG," 8,397 "
-25291311,HC D3 MED/SURG," 8,397 "
-25291121,HC D3 MED/SURG PRIV," 8,897 "
-25291220,HC D3 MED/SURG SP," 8,397 "
-25291329,HC D3 MED/SURG," 8,397 "
-25291139,HC D3 MED/SURG PRIV," 8,897 "
-25291238,HC D3 MED/SURG," 8,397 "
-25291337,HC D3 MED/SURG," 8,397 "
-25291147,HC D3 MED/SURG PRIV," 8,897 "
-25291246,HC D3 MED/SURG SP," 8,397 "
-25291345,HC D3 MED/SURG W," 8,397 "
-47204862,HC DAT NEG INVEST-ARC, 133 
-62010137,HC DEBRIDE SKIN EPID 1ST 20CM, 310 
-62010160,"HC DEBRIDE SKIN,SQ TISSUE,MUSCLE 1ST 20 CM", 813 
-62010152,HC DEBRIDE SKN & SQ TISSUE 1ST 20 CM, 620 
-52032109,HC DECALCIFICATION, 167 
-62110747,HC DECALCIFICATION-SPC, 167 
-62700877,HC DECLOT THRMB AGNT IMPL VAD G1," 2,257 "
-32003097,HC DECLOT THROMB AGNT IMPL VAD," 2,257 "
-31903982,HC DECLT THRM AGNT IMPLTD VAD/CAT," 2,257 "
-62700851,HC DECLT THROM AGNT IMPL VAD ATIC," 2,257 "
-40450462,HC DEPO-MEDROL 40MG INJ, 113 
-40450470,HC DEPO-MEDROL 80MG INJ, 155 
-52032174,HC DERM:IMNOFLUOR-DIR, 586 
-62026174,HC DEST NEUROAGENT 2-3 DIV," 1,831 "
-62026182,HC DEST NEUROAGENT 2-3DIV/RAD MON," 2,610 "
-41100827,HC DESTR VASC LESIONS 10-50, 762 
-41100819,HC DESTR VASC LESIONS<10, 592 
-41100835,HC DESTR VASC LESIONS>50," 1,121 "
-62020862,HC DESTROY NERVE OF EYE MUSCLE, 465 
-62018189,HC DESTRUCT ANAL LESION(S) CHEM, 488 
-62018205,HC DESTRUCT ANAL LESION(S)CRYOSUR, 513 
-62018239,HC DESTRUCT ANAL LESION(S)EXTENSI," 2,101 "
-62020656,HC DESTRUCT CILIARY BODY CYCLOPHO," 1,069 "
-62020516,HC DESTRUCT CORNEA LSN CRYOTHERAP, 538 
-62021001,HC DESTRUCT EYELID LESION," 1,134 "
-62011986,HC DESTRUCT FLT WARTS <15 LSNS, 146 
-62011994,HC DESTRUCT FLT WARTS >14 LSNS, 179 
-62011929,HC DESTRUCT LESION ANY METH 1ST, 117 
-62011937,HC DESTRUCT LESION ANY METH 2-14, 38 
-62011945,HC DESTRUCT LESION ANY METH > 15, 409 
-62012141,HC DESTRUCT MALG LSN F/E/E/N/L<0., 268 
-62012018,HC DESTRUCT MALG LSN TRNK/EXT <0., 291 
-62012034,HC DESTRUCT MALG LSN TRNK/EXT1.1, 347 
-62012042,HC DESTRUCT MALG LSN TRNK/EXT2.1, 384 
-62012026,HC DESTRUCT MALG LSN TRNK/EXT<0.6, 273 
-62018908,HC DESTRUCT PENIS LSN CRYOSURG," 1,353 "
-62020821,HC DESTRUCT RETINA LSN PHOTOCOAGU," 1,202 "
-62019237,HC DESTRUCT VAGINA LSN SMPL," 2,003 "
-37005170,HC DEXA BODY COMPOSITION STDY," 1,077 "
-25391111,HC DGR MED/SURG PRIV," 8,897 "
-25391210,HC DGR MED/SURG," 8,397 "
-25391319,HC DGR MED/SURG," 8,397 "
-25391129,HC DGR MED/SURG PRIV," 8,897 "
-25391228,HC DGR MED/SURG SP," 8,397 "
-25391327,HC DGR MED/SURG," 8,397 "
-25391137,HC DGR MED/SURG PRIV," 8,897 "
-25391236,HC DGR MED/SURG SP," 8,397 "
-25391335,HC DGR MED/SURG W," 8,397 "
-25391145,HC DGR MED/SURG PRIV," 8,897 "
-25391244,HC DGR MED/SURG SP," 8,397 "
-25391343,HC DGR MED/SURG," 8,397 "
-25392119,HC DGR ONC/BMT LEVEL 1 PVT," 8,897 "
-25393216,HC DGR ONC/BMT LEVEL 1 SP," 8,397 "
-25392127,HC DGR ONC/BMT LEVEL 2 PVT," 8,897 "
-25393224,HC DGR ONC/BMT LEVEL 2 SP," 8,397 "
-25392135,HC DGR IMC ICU," 15,479 "
-25393232,HC DGR ONC/BMT LEVEL 3 SP," 14,466 "
-25392143,HC DGR ONC/BMT LEVEL 4 PVT," 14,466 "
-25393240,HC DGR ONC/BMT LEVEL 4 SP," 14,466 "
-50751031,HC 6956 YDHEA DHEA, 22 
-51593788,HC DHEA-S, 317 
-58593781,HC DHEA-S, 317 
-62310370,HC DIAG ANALYSIS-C1<7 YRS, 612 
-62310396,HC DIAG ANALYSIS-C1>7 YRS, 624 
-92800234,HC DIAG ANALYSIS-CI<7YRS W/PROG, 612 
-92800242,HC DIAG ANALYSIS-CI>7YRS W/PROG, 624 
-62310016,HC DIAG AUDIOLOGIC EVAL, 420 
-43100098,HC DIAG AUDIOLOGICAL EVAL, 509 
-41151507,HC DIAG SINOGRAM," 2,549 "
-62024344,HC DIAGNOSTIC COLONOSCOPY," 1,819 "
-62021829,HC DIAGNOSTIC INTERVIEW, 271 
-55060370,HC DIAL CULT/LRG VOL, 302 
-59090258,HC DIAL MAC/OTHR WTR, 302 
-55090203,HC DIALYSIS FLD CULT (PT), 302 
-40510364,HC DIATHERMY - OCCUP THERAPY, 121 
-25491119,HC DICS MED/SURG PRIV," 8,897 "
-25491218,HC DICS MED/SURG SP," 8,397 "
-25491317,HC DICS MED/SURG W," 8,397 "
-25491127,HC DICS MED/SURG PRIV," 8,897 "
-25491226,HC DICS MED/SURG," 8,397 "
-25491325,HC DICS MED/SURG," 8,397 "
-25491135,HC DICS MED/SURG PRIV," 8,897 "
-25491234,HC DICS MED/SURG," 8,397 "
-25491333,HC DICS MED/SURG," 8,397 "
-25491143,HC DICS MED/SURG PRIV," 8,897 "
-25491242,HC DICS MED/SURG," 8,397 "
-25491341,HC DICS MED/SURG," 8,397 "
-52025319,HC DIF ANTIBODY (EACH), 355 
-40880262,HC DIGITAL DIAG MAMMOGRAPHY BILAT, 626 
-40880254,HC DIGITAL DIAG MAMMOGRAPHY UNILAT, 501 
-40880155,HC DIGITAL SCREENING MAMMOGRAM, 400 
-50460302,HC DIGOXIN LEVEL, 389 
-51560308,HC DIGOXIN LEVEL, 389 
-51860302,HC DIGOXIN LEVEL, 389 
-56860307,HC DIGOXIN LEVEL, 389 
-58560301,HC DIGOXIN LVL, 389 
-59460303,HC DIGOXIN LVL, 389 
-58401951,HC DIL RUSEL VIPR VENM, 240 
-51401958,HC DIL RUSSELL VPR VNM, 240 
-41100199,HC DILAT INTRCRAN VASO," 4,361 "
-62018791,HC DILATE URETHRA STRICTURE INITI, 619 
-62018809,HC DILATE URETHRA STRICTURE REPEA, 257 
-50741586,HC 6959 DIPHTHERIA AB, 30 
-55020150,HC DIR PARASIT EXAM, 132 
-59020156,HC DIR PRASIT EXAM, 132 
-62700836,HC DIRECT ADMIT TO OBV, 821 
-47200803,HC DIRECT COOMBS:BRD SPECTRM, 133 
-47200811,HC DIRECT COOMBS:C3, 133 
-47201355,HC DIRECT COOMBS:GAMMA, 133 
-41100215,HC DL VAS ADD IN DIFF VF," 1,698 "
-41100207,HC DL VAS ADD IN SAME VF, 900 
-83501122,HC T-CELL DNR PE," 2,477 "
-83501148,HC T CELL W/U CNCL," 2,035 "
-37003514,HC DMSA RENAL TOMO," 1,903 "
-51563625,HC DNA ANTIBODY, 281 
-58563628,HC DNA ANTIBODY, 281 
-50790021,HC DNAEX:DNA EXTRACTION/STORAGE, 109 
-81090029,HC DNAEX:DNA EXTRACTION/STORAGE, 8 
-81190027,HC DNAEX:DNA EXTRACTION/STORAGE, 8 
-81390023,HC DNAEX:DNA EXTRACTION/STORAGE, 8 
-82390022,HC DNAEX:DNA EXTRACTION/STORAGE, 8 
-84390020,HC DNAEX:DNA EXTRACTION/STORAGE, 8 
-52290020,HC DNAEX:DNA EXTRACTIONS/STORAGE, 8 
-83500405,HC DNR MARROW PROC," 47,073 "
-47201421,HC DONATH-LANDSTEINER, 91 
-83501817,HC THERAPEUTIC CELL SHIP," 18,545 "
-83501056,HC THERAPEUTIC CELL COUR," 21,229 "
-43100049,HC DOPPLER COLOR FLOW VELCITY MAP," 1,375 "
-43100031,HC DOPPLER/PULSED WAVE," 1,469 "
-36033181,"HC DOSIMETRY, ISODOSE"," 4,469 "
-50725373,HC 6959 DOXEPIN, 23 
-40930174,HC DPLX AORT/IVC/ILIAC CMPL," 1,665 "
-40930182,HC DPLX AORT/IVC/ILIAC LTD," 1,627 "
-40930208,HC DPLX ART/VEN PENILE LTD OR F-U, 436 
-40930158,HC DPLX IN/OUT ABD/PEL/SCR/RETRO," 2,995 "
-40930083,HC DPLX LWR EXT ART/GRFT BIL," 3,076 "
-40930091,HC DPLX LWR EXT ART/GRFT UNIL," 2,177 "
-40930117,HC DPLX UP EXT ART/GRFT BIL," 2,151 "
-40930109,HC DPLX UP EXT ART/GRFT UNIL," 1,939 "
-40930141,HC DPLX VEN EXT LTD UNIL," 2,072 "
-40930133,HC DPLX VENOUS EX IMAN BIL," 2,966 "
-62018502,HC DRAIN BLADDER NEEDLE, 530 
-62018528,HC DRAIN BLADDER W/INSERT CATH," 1,983 "
-62013826,HC DRAIN FINGER ABSCESS SMPL, 948 
-62012547,HC DRAIN JOINT/BURSA INTERMED, 507 
-62012554,HC DRAIN JOINT/BURSA MAJOR, 507 
-62012539,HC DRAIN JOINT/BURSA SML, 507 
-62014204,HC DRAIN LEG/ANKLE LSN DEEP," 3,992 "
-62016159,HC DRAIN NOSE LESION, 508 
-62017959,HC DRAIN PERIANAL ABSCESS, 767 
-42062232,HC DRAIN PERIT ABSCS PERC," 3,150 "
-42062307,HC DRAIN RENAL ABSCS PERC," 3,662 "
-42062257,HC DRAIN RETROPERIT ABSCS," 5,215 "
-62013297,HC DRAIN SHOULDER LSN," 1,994 "
-62010020,HC DRAIN SKIN ABSCESS CMPLX, 264 
-62010012,HC DRAIN SKIN ABSCESS SMPL, 142 
-62019823,HC DRAIN SPINAL FLUID, 780 
-42062240,HC DRAIN SUBDIAPH ABSCS PER," 3,156 "
-62019146,HC DRAIN VULVA ABSCESS, 440 
-62024047,HC DRAINAGE OF BREAST LESION, 485 
-62010079,HC DRAINAGE OF HEMATOMA," 2,548 "
-81190126,"HC DRDQ:HLA-DR,DQ TYPE(PCR)", 133 
-56209026,HC EP PROG STIM/PACE AFTER INF," 15,497 "
-50790138,HC DRHR:HLA-DR TYPE(HI-RES), 625 
-52290137,HC DRHR:HLA-DR TYPE(HI-RES), 58 
-81090136,HC DRHR:HLA-DR TYPE(HI-RES), 58 
-81390130,HC DRHR:HLA-DR TYPE(HI-RES), 58 
-82390139,HC DRHR:HLA-DR TYPE(HI-RES), 58 
-41152554,HC DRN APP ABSC PERCUT," 3,662 "
-41152430,HC DRN PANCR PSEUDOCYS," 6,439 "
-94100708,HC DRSG AQUACEL AG ROPE SILVER, 122 
-50790237,HC DRSSP:HLA-DR TYPE(HI-RES SSP), 247 
-52290236,HC DRSSP:HLA-DR TYPE(HI-RES SSP), 58 
-81090235,HC DRSSP:HLA-DR TYPE(HI-RES SSP), 58 
-81390239,HC DRSSP:HLA-DR TYPE(HI-RES SSP), 58 
-82390238,HC DRSSP:HLA-DR TYPE(HI-RES SSP), 58 
-50722693,HC 6956 DRUG ABUSE CONF THC, 2 
-40510687,HC DRUG TEST SALIVA, 37 
-40510695,HC DRUG TEST URINE, 50 
-36060200,HC DRUG: ATIVAN, 12 
-36060226,HC DRUG: DECADRON (PO), 12 
-36060242,HC DRUG: DILANTIN, 75 
-50803600,HC DRVVT, 240 
-50791029,HC DTTXB:HLA XM:B-CELL(DTT), 249 
-81091027,HC DTTXB:HLA XM:B-CELL(DTT), 82 
-81191025,HC DTTXB:HLA XM:B-CELL(DTT), 82 
-81391021,HC DTTXB:HLA XM:B-CELL(DTT), 82 
-82391020,HC DTTXB:HLA XM:B-CELL(DTT), 82 
-50791045,HC DTTXT:HLA XM:T-CELL(DTT), 249 
-81091043,HC DTTXT:HLA XM:T-CELL(DTT), 82 
-81191041,HC DTTXT:HLA XM:T-CELL(DTT), 82 
-81391047,HC DTTXT:HLA XM:T-CELL(DTT), 82 
-82391046,HC DTTXT:HLA XM:T-CELL(DTT), 82 
-39000112,HC DUAL CHMBR TELE ANAL, 352 
-26401034,HC DUODERM DRESSING, 102 
-26610618,HC DUODERM DRESSING, 103 
-40510638,HC DUODERM DRESSING 8X8 IN, 79 
-41530247,HC DUPLX HEMODIALYS ACCESS," 2,223 "
-37005063,HC DX 1-131 CAPSULE PER MCI, 256 
-50743186,HC DYSTONIA, 295 
-26191114,HC E1 MED/SURG PRIV," 14,466 "
-26191213,HC E1 M/S LEVEL I SP," 8,397 "
-26191312,HC E1 MED/SURG," 14,466 "
-26191122,HC E1 MED/SURG PRIV," 8,897 "
-26191221,HC E1 MED/SURG SP," 8,397 "
-26191320,HC E1 MED/SURG," 14,466 "
-26191130,HC E1 M/S LEVEL III PVT," 8,897 "
-26191239,HC E1 MED/SURG SP," 8,397 "
-26191338,HC E1 MED/SURG," 14,466 "
-26191148,HC E1 M/S LEVEL IV PVT," 8,897 "
-26191247,HC E1 M/S LEVEL IV SP," 14,466 "
-26191346,HC E1 MED/SURG," 14,466 "
-27391119,HC E3 ONC/BMT LEVEL 1 PVT," 8,897 "
-27391218,HC E3 ONC/BMT LEVEL 1 SP," 8,397 "
-27391127,HC E3 ONC/BMT LEVEL 2 PVT," 8,897 "
-27391226,HC E3 ONC/BMT LEVEL 2 SP," 8,397 "
-27391135,HC E3 ONC/BMT LEVEL 3 PVT," 14,466 "
-27391234,HC E3 IMC ICU," 15,479 "
-27391143,HC E3 ONC/BMT LEVEL 4 PVT," 14,466 "
-27391242,HC E3 ONC/BMT LEVEL 4 SP," 14,466 "
-59060400,HC EAR CLT, 302 
-55060404,HC EAR CULT, 302 
-62022421,HC EAR MICROSCOPY EXAMINATION, 105 
-92800788,HC EAR PROTECTOR ATTEN MEAS, 158 
-92811009,HC EAR WAX REMOVAL UNIL, 190 
-46160297,HC 6942 EB EBNA, 218 
-46160271,HC 6942 EB VCA IGG, 343 
-46160289,HC 6942 EB VCA IGM, 343 
-46160263,HC 6942 EBNPC EBV EARLY AB, 191 
-56201908,HC ECG 12-LEAD, 914 
-43100007,HC ECG 12L TRACING WO 1&R, 769 
-56269236,HC ECHO GUIDE CARDCENTS, 498 
-56202195,HC ECHO TEE 2D," 1,574 "
-56202211,HC ECHO TRANSTHORACIC 2D," 2,853 "
-43100056,HC ECHOCARDIOGRAM," 2,736 "
-43100015,HC ECHOCARDIOGRAPHY 2D," 3,529 "
-41154162,HC WADA TEST INCL EEG," 2,836 "
-91000059,HC EGG RETRIEVAL," 1,132 "
-25690116,HC EGR M/S LEVEL I PVT," 8,897 "
-25690215,HC EGR M/S LEVEL I SP," 8,397 "
-25690124,HC EGR M/S LEVEL II PVT," 8,897 "
-25690223,HC EGR M/S LEVEL II SP," 8,397 "
-25690132,HC EGR M/S LEVEL III PVT," 8,897 "
-25690231,HC EGR M/S LEVEL III SP," 8,397 "
-25690140,HC EGR M/S LEVEL IV PVT," 8,897 "
-25690249,HC EGR M/S LEVEL IV SP," 8,397 "
-25691114,HC EGR ONC/BMT LEVEL 1 PVT," 8,897 "
-25691213,HC EGR ONC/BMT LEVEL 1 SP," 8,397 "
-25691122,HC EGR ONC/BMT LEVEL 2 PVT," 8,897 "
-25691221,HC EGR ONC/BMT LEVEL 2 SP," 8,397 "
-25691130,HC EGR ONC/BMT LEVEL 3 PVT," 13,491 "
-25691239,HC EGR ONC/BMT LEVEL 3 SP," 13,491 "
-25691148,HC EGR ONC/BMT LEVEL 4 PVT," 13,491 "
-25691247,HC EGR ONC/BMT LEVEL 4 SP," 13,491 "
-41003450,HC ELBOW AP + LAT, 705 
-41303454,HC ELBOW AP + LAT, 705 
-93603454,HC ELBOW AP + LAT, 705 
-93603504,HC ELBOW COMPLETE MIN 3V, 826 
-41003500,HC ELBOW MIN 3V, 826 
-41303504,HC ELBOW MIN 3V, 826 
-62026059,HC REPAIR INTERMEDIATE S/A/T/E 20.1-30.0 CM, 775 
-62023502,HC ELECTRIC STIMULATION/15 MIN, 22 
-40400038,HC ELECTRICAL STIM-15MIN OT, 211 
-52012200,HC ELECTRN MICROSCOPY," 3,868 "
-62310206,HC ELECTROACOUSTIC EVAL (BIN), 226 
-92800275,HC ELECTROACOUSTIC EVAL HA MON, 97 
-62310347,HC ELECTROCOCHLEOGRAPHY, 467 
-62026026,HC ELECTROCONVULSIVE THERAPY, 580 
-42070003,HC ELECTRODE ABLATION NDL," 2,338 "
-50401629,HC ELECTROLYTE PANEL, 342 
-51301620,HC ELECTROLYTE PANEL, 342 
-59401620,HC ELECTROLYTE PANEL, 342 
-47204060,HC ELECTRONIC CROSSMATCH, 339 
-92800283,HC ELECTROOCHLEOGRAPHY, 467 
-40450488,HC ELIMITE CREME 60GM, 256 
-52012309,HC EM THICK SCTN ONLY," 3,868 "
-41152059,HC EMBOL NON-CNS," 30,629 "
-41152042,HC EMBOLIZATION-CNS," 11,265 "
-91900324,HC EMBRYO GLUE, 312 
-91900035,HC EMBRYO HATCHING, 660 
-41145228,HC ENDO CATH BIL DUCT," 2,036 "
-41008616,HC ENDO DIL BILI SYST," 2,036 "
-41100686,HC ENDO PROS BY GROIN-OPEN," 4,818 "
-62019369,HC ENDOCERVICAL CURETTAGE, 656 
-56200702,HC ENDOMYOCARDIAL BIOP," 4,650 "
-50751395,HC 6956 YEMA ENDOMYSIAL AB IGA, 29 
-62026117,HC FTH/GFT FR W/DIR CLSR TRNK EA 20 CM," 1,624 "
-62018593,HC ENDOSCOPIC INJECT IMPLNT MATER," 3,134 "
-62017595,HC FLEX ENDOSCOPY ESOPHAGUS, 866 
-62018395,HC ENDOSCOPY KIDNEY W/CATH," 2,549 "
-41100025,"HC ENDOVAS PROS, DLY SETL"," 5,073 "
-41152646,HC ENDOVAS REPR AAA W/MOD PRSTHES," 10,300 "
-41152927,HC ENDOVASC GRAFT RPLCE ILLC ART," 3,931 "
-41152729,HC ENDOVASC OCCLUSIVE DEVICE, 591 
-41100116,"HC ENDOVASC PROSTH, DLY"," 3,876 "
-41152679,HC ENDOVASCULAR REPAIR AAA," 1,252 "
-41152851,HC ENDVASC GRFT RPLCE ILLC ART," 8,554 "
-50802719,HC ENZYMATIC DIGESTION, 78 
-47204730,HC ENZYME TREATMENT, 380 
-56209216,HC EP W IND/LAP/REC SINUS/ATR," 6,691 "
-56209810,HC EP EVAL;W/O IND ARYTH," 22,209 "
-56203102,EP EVAL ICD LEADS AT IMP/REPL," 6,693 "
-56210040,HC EP ICD EVAL W/PROG ADJUSTMENT," 9,007 "
-32712432,HC EPIDURAL TRAY, 149 
-56209232,HC EPS VT/SVT F/U," 11,380 "
-46160479,HC EPSTEIN BARR VIRUS PCR QUANT, 650 
-59160473,HC EPSTEIN BARR VIRUS PCR QUANT, 650 
-50751056,HC 6956 YERYTH ERTHROPOIETIN, 14 
-51593804,HC ERYTHROPOIETIN, 208 
-58593807,HC ERYTHROPOIETIN, 208 
-56209778,HC ESFGL RCRD G," 4,163 "
-41151234,HC ESOPH DIL," 1,361 "
-37020567,HC ESOPH MOT STUDY," 1,505 "
-41151242,HC ESOPH STENT PLMT," 4,138 "
-41128513,HC ESOPHAGEAL DILATION, 200 
-41007501,HC ESOPHOGRAM," 1,492 "
-56209786,HC ESP A ELC W/WO V;W/PC," 2,292 "
-50393743,HC ESR AUTOMATED, 75 
-59393744,HC ESR AUTOMATED, 75 
-62024260,HC ESTABLISH ACCESS TO VEIN, 77 
-62300116,HC ESTABLISHED PT LEVEL 1, 75 
-92000108,HC ESTRADIOL, 149 
-51510048,HC ESTRADIOL (ESTDL), 499 
-58510041,HC ESTRADIOL (ESTDL), 499 
-55091615,HC ETEST MIC, 122 
-59091611,HC ETEST MIC, 122 
-50741602,HC 6959 11034R ETHOSUXIMIDE, 16 
-58400755,HC EUGLOBULN CLOT LYSI, 172 
-51400752,HC EUGLOBULN CLOT LYSS, 172 
-92801125,HC EVAL AUDITORY REHAB 1ST HR, 459 
-92801562,HC  EVAL AUDITORY REHAB ADD 15MIN, 115 
-62311105,HC EVAL AUDITORY STATUS 1ST HR, 459 
-92801752,HC EVAL DIFFICLT 2 TEST <7YRS, 192 
-56263882,HC EVAL ICD LEADS + GENERATOR," 11,500 "
-62310875,HC EVAL OF APD W/REPRT 1ST HR, 391 
-62310867,HC EVAL OF AR TININITUS, 213 
-92800291,HC EVAL/ORAL REHAB STATUS 1ST HR, 391 
-62019328,HC EXAMINATION OF VAGINA, 311 
-62019336,HC EXAMINATION VAGINA W/BX, 463 
-62019344,HC EXAMINATION VAGINA W/LEEP CERV," 2,003 "
-62026257,HC IMPRESSION&PREPJ PALATAL AUGMENTATION PROSTHESIS," 4,383 "
-41151499,HC EXCH CATH THROM TX," 7,242 "
-41152489,HC EXCHANGE DRN CATH," 6,666 "
-62021274,HC EXCISE CONJUNCTIVA LSN <1.0CM," 3,197 "
-62021282,HC EXCISE CONJUNCTIVA LSN >1.0CM," 3,861 "
-62017280,HC EXCISE LSN ROOF OF MOUTH W/O C," 1,637 "
-62016191,HC EXCISE NASAL LESION," 2,387 "
-62014261,HC EXCISE TUMOR LEG/ANKL SUBQ," 1,649 "
-62013180,HC EXCISE TUMOR NECK/CHEST," 1,649 "
-62017314,HC EXCISE UVULA," 1,131 "
-62016985,HC EXCISE/REPR MOUTH LSN SMPL," 2,499 "
-40100349,HC ABLATION RF LIVER TUMOR(S)," 13,579 "
-91900118,HC EXT CULT OOCYTES>3DAY," 2,090 "
-41124058,HC EXTR ANGIOG UNILAT," 9,202 "
-41125451,HC EXTR VENOGRAM UNILA," 2,856 "
-52101854,HC EXTRAGENITAL SMEAR, 117 
-59060459,HC EYE CLT, 302 
-55060453,HC EYE CULT, 302 
-62022215,HC EYE EVAL/TREAT COMPRE ESTAB PT, 152 
-62022199,HC EYE EVAL/TREAT COMPRE NEW PT, 274 
-62022207,HC EYE EVAL/TREAT INTERMED ESTAB, 101 
-62022181,HC EYE EVAL/TREAT INTERMED NEW PT, 132 
-37100245,"HC FDG FLUORODEOXYGLUCOSE,45"," 2,156 "
-41152067,HC F-B RETRIEVAL," 7,875 "
-23891112,HC F3 ONC/BMT LEVEL 1 PVT," 8,897 "
-23891211,HC F3 ONC/BMT LEVEL 1 SP," 8,397 "
-23891120,HC F3 ONC/BMT LEVEL 2 PVT," 8,897 "
-23891229,HC F3 ONC/BMT LEVEL 2 SP," 8,397 "
-23891138,HC F3 IMC ICU," 15,479 "
-23891237,HC F3 ONC/BMT LEVEL 3 SP," 14,466 "
-23891146,HC F3 ONC/BMT LEVEL 4 PVT," 14,466 "
-23891245,HC F3 ONC/BMT LEVEL 4 SP," 14,466 "
-41000407,HC FACIAL BONES 2V, 915 
-93600401,HC FACIAL BONES 2V, 915 
-41300401,HC FACIAL BONES 2V OR, 915 
-41000456,HC FACIAL BONES MIN 3," 1,277 "
-41300450,HC FACIAL BONES MIN 3," 1,277 "
-93600450,HC FACIAL BONES MIN 3," 1,277 "
-92801158,HC FACIAL NERVE FUNCTION, 318 
-51401065,HC FACT VIII INH PORCI, 251 
-51400802,HC FACTOR II ASSAY, 346 
-58400805,HC FACTOR II ASSAY, 346 
-51401057,HC FACTOR IX ASSAY, 406 
-58401050,HC FACTOR IX ASSAY, 406 
-51400851,HC FACTOR V ASSAY, 375 
-58400854,HC FACTOR V ASSAY, 375 
-58400904,HC FACTOR V11 ASSAY, 285 
-58401001,HC FACTOR V111 INHIBIT, 251 
-51400950,HC FACTOR VIII ASSAY, 375 
-58400953,HC FACTOR VIII ASSAY, 375 
-59500959,HC FACTOR VIII ASSAY, 375 
-51401008,HC FACTOR VIII INHIBTO, 251 
-51401107,HC FACTOR X ASSAY, 364 
-58401100,HC FACTOR X ASSAY, 364 
-51401156,HC FACTOR XI ASSAY, 375 
-58401159,HC FACTOR XI ASSAY, 375 
-51401206,HC FACTOR XII ASSAY, 214 
-58401209,HC FACTOR XII ASSAY, 214 
-51401214,HC FACTOR XIII SCREEN, 195 
-58401217,HC FACTOR XIII SCREEN, 195 
-51400901,HC FACTOR VII ASSAY, 285 
-36000024,HC FDG PET RADIOPHARM," 2,415 "
-50393404,HC FECAL FAT, 303 
-59393405,HC FECAL FAT, 303 
-41151259,HC FEED/GASTRO TUBE RE," 2,016 "
-41152158,HC FEM/POP ATHERECT," 18,994 "
-41100322,HC FEM/POP ATHERECT OPEN," 9,020 "
-41004003,HC FEMUR AP + LAT, 879 
-41304007,HC FEMUR AP + LAT, 879 
-93604007,HC FEMUR AP + LAT, 879 
-51501716,HC FERRITIN, 375 
-58501719,HC FERRITIN, 375 
-59401711,HC FERRITIN, 375 
-51870012,HC FETAL FIBRONECTIN," 1,161 "
-56870017,HC FETAL FIBRONECTIN," 1,161 "
-50464627,HC FETAL LUNG MATURITY, 106 
-51864627,HC FETAL LUNG MATURITY, 106 
-56864622,HC FETAL LUNG MATURITY, 106 
-50465251,HC FETAL SCALP PH, 63 
-59465252,HC FETAL SCALP PH, 63 
-50100825,"HC FFP,SPLIT", 751 
-23291115,HC FGR MED/SURG PRIV," 8,897 "
-23291214,HC FGR MED/SURG," 8,397 "
-23291123,HC FGR MED/SURG PRIV," 8,897 "
-23291222,HC FGR MED/SURG SP," 8,397 "
-23291131,HC FGR MED/SURG PRIV," 8,897 "
-23291230,HC FGR MED/SURG SP," 8,397 "
-23291149,HC FGR MED/SURG PRIV," 8,897 "
-23291248,HC FGR MED/SURG W," 8,397 "
-50503333,HC FIBRIN DEGRADATION PROD, 107 
-59503334,HC FIBRIN DEGRADATION PROD, 107 
-50723220,HC 6959 11036R FIBRINOGEN AG, 52 
-50500305,HC FIBRINOGEN SEMIQUAN, 285 
-51300309,HC FIBRINOGEN SEMIQUAN, 285 
-59500306,HC FIBRINOGEN SEMIQUANT, 285 
-42016915,HC FIDUCIAL SEED - LEVEL 13, 740 
-42072793,"HC FIDUCIAL SEED IMPLANTS, ABDOME"," 3,978 "
-42072769,"HC FIDUCIAL SEED IMPLANTS, LIVER"," 3,978 "
-42072777,"HC FIDUCIAL SEED IMPLANTS, LUNG"," 2,773 "
-42072785,"HC FIDUCIAL SEED IMPLANTS, PANCRE"," 3,978 "
-83501189,HC FILGR INJS," 7,755 "
-26730549,HC FILTER 40 MICRONS, 99 
-26730556,HC FILTER PALL PXL 8, 455 
-92800309,HC FILTERED SPEECH TEST, 95 
-40871667,HC FINE NEEDLE ASPIRATION W/IMAGING," 2,086 "
-40971665,HC FINE NEEDLE ASPIRATION W/IMAGING," 2,086 "
-41154188,HC FINE NEEDLE ASPIRATION," 2,086 "
-42016238,HC FINE NEEDLE ASPIRATION W/IMAGING," 2,086 "
-41003856,HC FINGER(S) MIN 2V, 592 
-41303850,HC FINGER(S) MIN 2V, 592 
-93603850,HC FINGER(S) MIN 2V, 592 
-41151671,HC FIRST ORDER VESSEL," 5,773 "
-41151713,HC FIRST ORDER VESSEL," 4,547 "
-41008301,HC FISTULA/SINUS TRACT," 3,012 "
-41144759,HC FISTULOGRAM," 3,012 "
-58401845,HC FIVE MIN PT + BUFFE, 125 
-62026463,HC CLOSED TX METATARSAL FRACTURE W/O MANIPULATION, 516 
-50405653,HC FK506:TACROLIMUS, 345 
-51505659,HC FK506:TACROLIMUS, 345 
-51805653,HC FK506:TACROLIMUS, 345 
-56805658,HC FK506:TACROLIMUS, 345 
-58505652,HC FK506:TACROLIMUS, 345 
-59405654,HC FK506:TACROLIMUS, 345 
-58424912,HC FLOW CD 45 RA AB, 570 
-51424919,HC FLOW CD45 RA AB, 570 
-51424927,HC FLOW CD45 RD AB, 570 
-58424920,HC FLOW CD45 RD AB, 570 
-51493682,HC FLOW CYTOMETRY - 1ST MARKER, 700 
-58493685,HC FLOW CYTOMETRY - 1ST MARKER, 700 
-51493690,HC FLOW CYTOMETRY ADDTLT MARKER, 380 
-58493693,HC FLOW CYTOMETRY ADDTLT MARKER, 380 
-59393496,HC FLOW CYTOMETRY CONCENTRATION, 408 
-51493492,HC FLOW CYTOSPIN, 408 
-58493495,HC FLOW CYTOSPIN, 408 
-58493644,"HC FLOW:ABSLTE CD4-CD8,CNT,RATIO", 575 
-59393645,"HC FLOW:ABSLTE CD4-CD8,CNT,RATIO", 575 
-51493641,HC FLOW:ABSLUTE CD4-CD8 CNTRATIO, 575 
-51493658,HC FLOW:ABSOLUTE CD4 COUNT, 522 
-58493651,HC FLOW:ABSOLUTE CD4 COUNT, 522 
-51493625,"HC FLOW:B CELLS, TOTAL COUNT", 548 
-58493628,HC FLOW:B CELLS TOTAL COUNT, 548 
-59393629,HC FLOW:B CELLS TOTAL COUNT, 548 
-51493666,"HC FLOW:NK CELLS, TOTAL COUNT", 422 
-58493669,"HC FLOW:NK CELLS, TOTAL COUNT", 422 
-59393660,"HC FLOW:NK CELLS, TOTAL COUNT", 422 
-58493677,HC FLOW:STEM CELLS(CD34)TOTAL CNT, 699 
-51493674,HC FLOW:STEM CELLS(CD34)TTL CNT, 699 
-58493636,"HC FLOW:T CELLS, TOTAL COUNT", 538 
-50393636,"HC FLOW:T-CELLS,TOTAL COUNT", 538 
-51493633,"HC FLOW:T-CELLS,TOTAL COUNT", 538 
-46140182,HC FLU A ANTIGEN, 221 
-59140186,HC FLU A ANTIGEN, 221 
-46140505,HC FLU B DFA, 165 
-59140509,HC FLU B DFA, 165 
-59060509,HC FLUID CLT, 302 
-55060503,HC FLUID CULT, 302 
-55091821,HC FLUID STERILITY TEST, 302 
-59091827,HC FLUID STERILITY TEST, 302 
-62026471,HC CLTX FX PHLX/PHLG OTH/THN GRT TOE W/MNPJ, 282 
-41153495,HC FLUORO GUIDANCE FOR VEIN DEVIC," 2,530 "
-41152604,HC FLUORO GUIDANCE SPINE INJ," 1,593 "
-41130121,HC FLUORO GUIDE NDL PLACEMENT," 2,021 "
-41071713,HC FLUORO GUIDED NDL PLC SPINE," 1,593 "
-41152695,HC FLUORO GUIDED VERTEBROPLASTY," 3,360 "
-41126954,HC FLUORO-SEP PROC<1HR," 1,849 "
-41010216,HC FLUOROSCOPY OVER 1 HR," 3,786 "
-41010208,HC FLUOROSCOPY UP 1HR," 1,849 "
-50741651,HC 6959 FLUOX FLXTN & MTBLITE, 23 
-50324847,HC FLW GLYCOPHORINA AB, 537 
-62026604,HC FLX FBROPT ENDO EVAL SWALLOW, 494 
-52102100,HC FNA EVAL, 899 
-62026224,HC FNA WITHOUT IMAGE, 256 
-62150925,HC FNA;INTERP AND REPORT, 899 
-58503848,HC FOLATE RBC, 246 
-59403840,HC FOLATE RBC, 246 
-51503845,HC FOLATE-RBC, 246 
-51503878,HC FOLATE-SERUM, 274 
-32712267,HC FOLEY CATH 22FR.3 WAY, 85 
-26400721,HC FOLEY CATHETER TRAY, 70 
-58503871,"HC FOLIC ACID, SERUM", 274 
-59403873,"HC FOLIC ACID, SERUM", 274 
-41004359,HC FOOT AP + LAT, 722 
-41304353,HC FOOT AP + LAT, 722 
-93604353,HC FOOT AP + LAT, 722 
-93604403,HC FOOT COMPLETE MIN 3V, 950 
-41004409,HC FOOT MIN 3V, 950 
-41304403,HC FOOT MIN 3V, 950 
-41003559,HC FOREARM AP + LAT, 769 
-41303553,HC FOREARM AP + LAT, 769 
-93603553,HC FOREARM AP + LAT, 769 
-62025549,HC FOREHEAD FLAP W/VASC PEDICLE," 2,069 "
-41118159,HC FOREIGN BODY RETRIE," 8,815 "
-50803451,HC FREE PROTEIN S, 282 
-51403459,HC FREE PROTEIN S, 282 
-58403452,HC FREE PROTEIN S, 282 
-51510030,HC FREE T3, 285 
-58510033,HC FREE T3, 285 
-51504025,HC FREE T4, 246 
-58504028,HC FREE T4, 246 
-59404020,HC FREE T4, 246 
-50113208,HC FFP 8-24 HRS SINCE COLLECTION, 437 
-47204821,"HC FRESH FROZEN PLASMA, THAWING", 77 
-52009800,HC FROZEN SECTION FIRST/SINGLE, 544 
-92000165,HC FSH, 110 
-51510022,HC FSH (FSHOR), 345 
-58510025,HC FSH (FSHOR), 345 
-40400137,HC FUNCTIONAL ACT-15MIN, 309 
-50774132,HC FUNGUS AB NOT SPECIFIED, 19 
-55090302,HC FUNGUS CULTURE, 309 
-59090308,HC FUNGUS CULTURE, 309 
-55092019,"HC FUNGUS CULTURE-SKIN,NAIL", 307 
-59092015,"HC FUNGUS CULTURE-SKIN,NAIL", 307 
-50791052,HC FXB:HLA XM:B-CELL(FLOW), 516 
-81091050,HC FXB:HLA XM:B-CELL(FLOW), 82 
-81191058,HC FXB:HLA XM:B-CELL(FLOW), 82 
-81391054,HC FXB:HLA XM:B-CELL(FLOW), 82 
-82391053,HC FXB:HLA XM:B-CELL(FLOW), 82 
-84391051,HC FXB:HLA XM:B-CELL(FLOW), 82 
-50790179,HC FXMAT:HLA XM:T-CELL(FLOW), 516 
-81090177,HC FXMAT:HLA XM:T-CELL(FLOW), 82 
-81190175,HC FXMAT:HLA XM:T-CELL(FLOW), 82 
-81390171,HC FXMAT:HLA XM:T-CELL(FLOW), 82 
-82390170,HC FXMAT:HLA XM:T-CELL(FLOW), 82 
-84390178,HC FXMAT:HLA XM:T-CELL(FLOW), 82 
-59320408,HC G-6PD H SCR, 176 
-50320407,HC G-6PD H SCREEN, 176 
-23191216,HC G1 MED/SURG SP," 8,397 "
-23191315,HC G1 MED/SURG," 8,397 "
-23191125,HC G1 MED/SURG PRIV," 8,897 "
-23191224,HC G1 MED/SURG SP," 8,397 "
-23191323,HC G1 MED/SURG W," 8,397 "
-23191133,HC G1 MED/SURG PRIV," 8,897 "
-23191232,HC G1 MED/SURG SP," 8,397 "
-23191331,HC G1 MED/SURG WARD," 8,397 "
-23191141,HC G1 MED/SURG PRIV," 8,897 "
-23191240,HC G1 MED/SURG SP," 8,397 "
-23191349,HC G1 MED/SURG WARD," 8,397 "
-23193212,HC G1 NCOR IMC ICU," 14,466 "
-23193220,HC G1 NCOR IMC ICU," 15,479 "
-23193238,HC G1 IMC ICU," 15,479 "
-50393271,HC G6 PD QUANT, 199 
-59393272,HC G6 PD, 199 
-46093274,HC G6 PD QUANT, 199 
-37010139,HC GA 67 GALLIUM/MCI, 289 
-38105995,"HC GADOXETATE DISODIUM, PER ML", 161 
-94205994,"HC GADOXETATE DISODIUM, PER ML", 161 
-40400145,HC KENALOG 10MG INJ, 269 
-50749274,HC GALOP AUTO AB 1, 108 
-50749290,HC GALOP AUTO AB 3, 108 
-50749282,HC GALOP AUTO AB2, 108 
-50401850,HC GAMMA GLUTAMYL TRAN, 204 
-51301851,HC GAMMA GLUTAMYL TRAN, 204 
-57301855,HC GAMMA GLUTAMYL TRAN, 204 
-59401851,HC GAMMA GLUTAMYL TRAN, 204 
-50751759,HC 6959 GHBUR DRGSCN QL CHR UR, 212 
-37003944,HC GAST ESOPH REFL STD," 1,653 "
-37003423,HC GASTRC EMPT TIM STD," 4,131 "
-55090450,HC GASTRIC CLTR, 302 
-50325125,HC GASTRIC PH, 50 
-59325126,HC GASTRIC PH, 50 
-50751080,HC 6956 11081R GASTRIN, 16 
-37002458,HC GASTROINT PROT LOSS, 831 
-50742287,HC 6959 GBMIGA FL AB SCR IGA, 68 
-50740695,HC 6959 11039R GBM AB IGG IFA, 64 
-50740711,HC GBM AB PNL BY IFA-IGA, 56 
-50740703,HC GBM AB PNL BY IFA-IGA, 56 
-46310314,HC GC AMP PROBE, 566 
-56310311,HC GC AMP PROBE, 566 
-56310352,HC GC LCR AMPLIFIED, 566 
-46330056,HC GC/CHLAMYDIA TRACH MOLEC DET, 566 
-58564758,HC GDM CONFIRM (100G GLU), 789 
-51564755,HC GDM CONFIRM(100G GLU), 789 
-94100674,HC GEL IODOSORB, 298 
-94100682,HC GEL SILVASORB 1.5 TUBE, 149 
-46330064,HC GEN PCR SINGLE LOPA, 388 
-56330061,HC GEN PCR SINGLE LOPA, 388 
-55060552,HC GENITAL CULT, 302 
-50462621,HC GENTAMICIN, 319 
-51862621,HC GENTAMICIN, 319 
-56862626,HC GENTAMICIN, 319 
-55020200,HC GIARDIA SCREEN, 282 
-59020206,HC GIARDIA SCREEN, 282 
-62026299,HC EXCISION SOFT TISSUE TUMOR SHOULDER SUBCUTANEOUS," 1,649 "
-59464735,HC GLUCOS TOLER(75G GLU), 298 
-50462274,HC GLUCOSE BODY FLUID, 250 
-50405315,HC GLUCOSE CUSTOM PANEL, 159 
-59405316,HC GLUCOSE CUSTOM PANEL, 159 
-59464727,"HC GLUCOSE, GDM(50G GLU)", 298 
-58700238,HC GLUCOSE POC, 200 
-58700139,HC GLUCOSE QUANT, 159 
-36033298,HC GLUCOSE SCREEN, 176 
-32702326,HC GLUCOSE FINGERSTICK BY STRIP, 90 
-59462259,"HC GLUCOSE, SERUM PLASMA", 159 
-50401884,"HC GLUCOSE, SERUM/PLASMA", 159 
-50462258,"HC GLUCOSE, SERUM/PLASMA", 159 
-51301885,HC GLUCOSE SERUM/PLASMA, 159 
-51362259,HC GLUCOSE SERUM/PLASMA, 159 
-59401885,"HC GLUCOSE, SERUM/PLASMA", 159 
-50462266,HC GLUCOSE SPINAL FLD, 250 
-58564733,HC GLUCOSE TOLER (75G GLU), 298 
-50464734,HC GLUCOSE TOLER(75G GLU), 298 
-51564730,HC GLUCOSE TOLER(75G GLU), 298 
-50462290,HC GLUCOSE URINE, 250 
-59462291,HC GLUCOSE URINE, 250 
-50464726,"HC GLUCOSE,GDM(50G GLU)", 298 
-59462267,HC GLUCS SPNL FLD, 250 
-50405661,HC GLYCOLATED HEMOGLOBIN, 145 
-51505667,HC GLYCOLATED HEMOGLOBIN, 145 
-58505660,HC GLYCOLATED HEMOGLOBIN, 145 
-59060558,HC GNTL CLT, 302 
-36000511,HC GOLD SEEDS L2, 61 
-46093290,HC GPI, 211 
-59393298,HC GPI, 211 
-50393750,HC GRAM STAIN, 150 
-55090401,HC GRAM STAIN, 150 
-59393751,HC GRAM STAIN, 150 
-59090407,HC GRAM STN, 150 
-50115344,HC GRANULOCYTES," 6,200 "
-94100732,HC COLOPLAST IRRIGATION SET, 383 
-81400145,HC GRND TRANSPRT-HRT/LNG LVL1, 525 
-81400152,HC GRND TRANSPRT-HRT/LNG LVL2, 735 
-31950009,HC GROSHONG CATH TRAY 4FR," 5,908 "
-31950074,HC GROSHONG CATH TRAY 5FR," 1,360 "
-52005105,HC GROSS EXAM-LEVEL I, 265 
-62110721,HC GROSS/MICRO LVL IV-SPC, 875 
-81200149,HC GROUND TRANSPRT-HEART LVL1, 525 
-81200156,HC GROUND TRANSPRT-HEART LVL2, 735 
-82200148,HC GROUND TRANSPRT-KIDNEY LVL1, 525 
-82200155,HC GROUND TRANSPRT-KIDNEY LVL2, 735 
-84200161,HC GROUND TRANSPRT-LIVER LVL1, 525 
-84200179,HC GROUND TRANSPRT-LIVER LVL2, 735 
-81500167,HC GROUND TRANSPRT-LUNG LVL1, 525 
-81500175,HC GROUND TRANSPRT-LUNG LVL2, 735 
-50751098,HC 6956 YHGH GROWTH HORMONE, 5 
-51571024,HC GROWTH HORMONE, 238 
-58571027,HC GROWTH HORMONE, 238 
-55092134,HC GRP B STREP PCR ID, 578 
-59092130,HC GRP B STREP PCR ID, 393 
-41530304,HC GUIDEWIRE LVL 3, 85 
-51393619,HC GYCATED HGB BY DCA 2000, 145 
-92800333,HC HA CHECK BINAURAL, 310 
-92800341,HC HA CHECK MONAURAL, 170 
-62310990,HC HA IH REPAIR/MODIFICATION, 101 
-92801109,HC HA ORIENTATION - GRP, 602 
-92801091,HC HA ORIENTATION - IND, 602 
-92800317,HC HA REPAIR/MODIFICATION, 101 
-92801133,HC HA SO REPAIR/MODIFICATION, 101 
-92801117,HC HA VERIFICATION, 602 
-92800325,HC HA VERIFY CONFORMITY EVAL, 602 
-41003757,HC HAND 2 VIEW, 806 
-41303751,HC HAND 2 VIEW, 806 
-93603751,HC HAND 2 VIEW, 806 
-93603702,HC HAND 2V LIMITED 73120, 806 
-41003807,HC HAND MIN 3 VIEWS, 896 
-41303801,HC HAND MIN 3 VIEWS, 896 
-93603801,HC HAND MIN 3 VIEWS, 896 
-52291242,HC HAPLO: HAPLO PREP, 82 
-82391244,HC HAPLO: HAPLO PREP, 82 
-50791243,HC HAPLO:HAPLO PREP, 151 
-81091241,HC HAPLO:HAPLO PREP, 82 
-81191249,HC HAPLO:HAPLO PREP, 82 
-81391245,HC HAPLO:HAPLO PREP, 82 
-81791246,HC HAPLO:HAPLO PREP, 82 
-84391242,HC HAPLO:HAPLO PREP, 82 
-85391241,HC HAPLO:HAPLO PREP, 82 
-50463496,HC HAPTOGLOBIN QNT, 258 
-51563492,HC HAPTOGLOBIN, 258 
-58563495,HC HAPTOGLOBIN, 258 
-59465021,"HC HAV AB, TOTAL (HATA)", 231 
-51565026,HC HAV AB TOTAL(HATA), 231 
-58565029,HC HAV AB TOTAL(HATA), 231 
-51565034,HC HAV IGM (HAV), 268 
-58565037,HC HAV IGM (HAV), 268 
-51565042,HC HBV CORE IGM (HBC), 248 
-58565045,HC HBV CORE IGM (HBC), 248 
-46310397,HC HBV PCR QUANTIFICATION, 794 
-46093365,HC HCB S QUANT, 257 
-46160420,HC HCV RNA QUANT ASSAY, 874 
-59160424,HC HCV RNA QUANT ASSAY, 874 
-50401181,HC HDL CHOLESTEROL, 192 
-51501187,HC HDL CHOLESTEROL, 192 
-59401182,HC HDL CHOLESTEROL, 192 
-50411040,HC HDL CHOLESTEROL, 192 
-59411041,HC HDL CHOLESTEROL, 192 
-50742956,HC HDL GGE, 62 
-36001550,HC HDR 1 CHANNEL," 6,915 "
-36001568,HC HDR 2-12 CHANNELS," 9,263 "
-36001576,HC HDR OVER 12 CHANNELS," 13,210 "
-62310180,HC HEARING AID CHECK (BIN), 310 
-62310172,HC HEARING AID CHECK(MON), 170 
-62310156,HC HEARING AID EVAL (MON), 170 
-92800358,HC HEARING AID EVAL BINAURAL, 354 
-92800366,HC HEARING AID EVAL MONAURAL, 170 
-62310164,HC HEARING AID EVAL(BIN), 354 
-62310941,HC HEARING AID FITTING - INITIAL, 602 
-92800374,HC HEARING REHAB GROUP, 41 
-62310040,HC HEARING SCREEN TEST, 63 
-92800382,HC HEARING SCREEN TEST, 63 
-81200289,HC HEART MNT GRP:EA 30 DIFF DX, 35 
-81200263,HC HEART MNT GRP:EA 30 SAME DX, 73 
-81200248,HC HEART MNT INIT 15, 118 
-81200271,HC HEART MNT RE 15 DIFF DX, 46 
-81200255,HC HEART MNT RE 15 SAME DX, 101 
-81400244,HC HEART/LUNG MNT INIT 15, 118 
-81400251,HC HEART/LUNG MNT RE 15 SAME DX, 101 
-40510174,HC HEB B VIR VACC RECOMB, 334 
-50300573,HC HEINZ BODY PREP, 66 
-55091599,HC HELI PYL AG STOOL, 376 
-59091595,HC HELI PYL AG, 376 
-55091581,HC HELICOBACTER EIA, 206 
-59091587,HC HELICOBACTER EIA, 206 
-50303080,HC HEMATOCRIT, 217 
-51303089,HC HEMATOCRIT, 217 
-59303081,HC HEMATOCRIT, 217 
-59300715,"HC HEMATOCRIT, FLUID/URINE", 55 
-58700147,HC HEMATOCRIT POC, 217 
-50300714,HC HEMATOCRIT-FLUID, 55 
-50303072,HC HEMOGLOBIN, 48 
-51303071,HC HEMOGLOBIN, 48 
-59303073,HC HEMOGLOBIN, 48 
-50300755,HC HEMOGLOBIN H PREP, 546 
-59300756,HC HEMOGLOBIN H PREP, 546 
-46093217,HC HEMOGLOBIN QUANT AND FRACT, 257 
-50725217,HC 6959 11041R HEMOGLBN SPCTRO, 20 
-50725225,HC 6959 UHGB HEMOGLOBIN URINE, 20 
-50300805,HC HEMOSIDERIN QUAL, 145 
-59300806,HC HEMOSIDERIN, 145 
-51565158,HC HEP B CR TOTAL AB, 189 
-58565151,HC HEP B CR TOTAL AB, 189 
-59465153,HC HEP B CR TOTAL AB, 189 
-56310394,HC HEP B PCR QUANT, 794 
-46160438,HC HEP C VIRUS GENOTYPE," 2,847 "
-59160432,HC HEP C VIRUS GENOTYPE," 2,847 "
-51401016,HC HEP IND PLT AGGR 1, 469 
-58401019,HC HEP IND PLT AGGR 1, 469 
-51401024,HC HEP IND PLT AGGR 2, 469 
-58401027,HC HEP IND PLT AGGR 2, 469 
-51401032,HC HEP IND PLT AGGR 3, 469 
-58401035,HC HEP IND PLT AGGR 3, 469 
-51401040,HC HEP IND PLT AGGR 4, 469 
-58401043,HC HEP IND PLT AGGR 4, 469 
-26410712,HC HEPA FILTER MASK, 419 
-26510719,HC HEPA FILTER MASK, 419 
-26602235,HC HEPA FILTER MASK, 419 
-50740729,HC 6959 11040R HEP COF CHRM AS, 187 
-50502202,HC HEPARIN CONCENTRATN, 466 
-59502203,HC HEPARIN CONCENTRATN, 466 
-58402967,HC HEPARIN-INDUCED ANTIBODY, 231 
-51402964,HC HEPARIN-INDUCED ANTIBODYT, 231 
-51565174,HC HEPAT BS AB-EIA, 197 
-58565177,HC HEPAT BS AB-EIA, 197 
-51565166,HC HEPAT BS AG-EIA, 220 
-58565169,HC HEPAT BS AG-EIA, 220 
-46100152,HC HEPAT BS ANTIGEN, 220 
-46160578,HC HEPAT BS ANTIGEN, 220 
-59160572,HC HEPAT BS ANTIGEN, 220 
-50465160,HC HEPAT BS ATGN-EIA, 220 
-59465161,HC HEPAT BS ATGN-EIA, 220 
-50463439,HC HEPATIC FXN PANEL, 342 
-51363430,HC HEPATIC FXN PANEL, 342 
-57363434,HC HEPATIC FXN PANEL, 342 
-59463430,HC HEPATIC FXN PANEL, 342 
-41144809,HC HEPATIC VENOGRAM," 6,252 "
-40510109,HC HEPATITIS A VACCINE, 365 
-40510505,HC HEPATITIS B IMMUNE GLOBULIN, 690 
-46100228,HC HEPATITIS BE ANTIBODY, 40 
-46160586,HC HEPATITIS C AB, 193 
-50465186,HC HEPATITIS C AB, 193 
-59160580,HC HEPATITIS C AB, 193 
-51565182,HC HEPATITIS CONFIRM, 220 
-58565185,HC HEPATITIS CONFIRM, 220 
-41145061,HC HEPT VENO W/O HEMO," 6,252 "
-46093233,HC HEXOKINASE, 324 
-52753639,HC HEXOSAMINIDASE A & TOTAL, 86 
-50393370,HC HGB ELECTRO CELLULOSE, 250 
-59393371,HC HGB ELECTRO CELLULOSE, 250 
-46093357,HC HGB F QUANT, 257 
-46093373,HC HGB FRACT/QUANT ELECTROPH, 250 
-50303098,HC HGB MED EMERGENCY, 48 
-26400994,HC HICKMAN BOOSTER KIT, 78 
-26500991,HC HICKMAN BOOSTER KIT, 82 
-26610808,HC HICKMAN CATHETER SUPPLIES, 199 
-50464577,HC HIGH SENSITIVE CRP, 190 
-51564573,HC HIGH SENSITIVE CRP, 190 
-58564576,HC HIGH SENSITIVE CRP, 190 
-59464578,HC HIGH SENSITIVE CRP, 190 
-51503969,HC HIGHLY SENSITIV TSH, 380 
-58503962,HC HIGHLY SENSITIVE TSH, 380 
-59403964,HC HIGHLY SENSITVE TSH, 380 
-41002957,HC HIP 1 VIEW, 885 
-41302951,HC HIP 1 VIEW, 885 
-93602951,HC HIP 1 VIEW, 885 
-41003054,HC HIP BIL W/PELVIS," 1,393 "
-41303058,HC HIP BIL W/PELVIS," 1,393 "
-93603058,HC HIP BIL W/PELVIS MIN 2V," 1,393 "
-93603009,HC HIP COMPLETE MIN 2V," 1,012 "
-41003005,HC HIP MIN 2V," 1,012 "
-41303009,HC HIP MIN 2V," 1,012 "
-50722594,HC 6959 UHIST HISTAMINE URINE, 34 
-52032133,HC HISTOCHEM ENZYME," 1,220 "
-52050135,HC HISTOCHEMICAL STN/FZN SECTION, 573 
-50774181,HC 6942 FUNAB HISTPLSM AB SCRN, 40 
-56330012,HC HIV PCR QUANT AVRT DIS," 1,377 "
-46160503,HC HIV RNA PCR QUANT," 1,377 "
-59160507,HC HIV-1 RNA PCR QUANT," 1,377 "
-50790047,HC HLAA:HLA-A TYPE(PCR), 280 
-52290046,HC HLAA:HLA-A TYPE(PCR), 58 
-81090045,HC HLAA:HLA-A TYPE(PCR), 58 
-81190043,HC HLAA:HLA-A TYPE(PCR), 58 
-81390049,HC HLAA:HLA-A TYPE(PCR), 58 
-82390048,HC HLAA:HLA-A TYPE(PCR), 58 
-50790039,"HC HLAAB:HLA-A, B TYPE(PCR)", 521 
-81090037,"HC HLAAB:HLA-A, B TYPE(PCR)", 133 
-81390031,"HC HLAAB:HLA-A, B TYPE(PCR)", 133 
-82390030,"HC HLAAB:HLA-A, B TYPE(PCR)", 133 
-84390038,"HC HLAAB:HLA-A, B TYPE(PCR)", 133 
-52290038,"HC HLAAB:HLA-A,B TYPE(PCR)", 133 
-81190035,"HC HLAAB:HLA-A,B TYPE(PCR)", 133 
-50791060,"HC HLAABH:HLA-A,B TYPE(HI RES)", 551 
-52291069,"HC HLAABH:HLA-A,B TYPE(HI RES)", 133 
-81091068,"HC HLAABH:HLA-A,B TYPE(HI RES)", 133 
-82391061,"HC HLAABH:HLA-A,B TYPE(HI RES)", 133 
-50791078,HC HLAAH:HLA-A TYPE(HI RES), 404 
-52291077,HC HLAAH:HLA-A TYPE(HI RES), 58 
-81191074,HC HLAAH:HLA-A TYPE(HI RES), 58 
-81391070,HC HLAAH:HLA-A TYPE(HI RES), 58 
-82391079,HC HLAAH:HLA-A TYPE(HI RES), 58 
-84391077,HC HLAAH:HLA-A TYPE(HI RES), 58 
-50790054,HC HLAB:HLA-B TYPE(PCR), 280 
-52290053,HC HLAB:HLA-B TYPE(PCR), 58 
-81090052,HC HLAB:HLA-B TYPE(PCR), 58 
-81190050,HC HLAB:HLA-B TYPE(PCR), 58 
-81390056,HC HLAB:HLA-B TYPE(PCR), 58 
-82390055,HC HLAB:HLA-B TYPE(PCR), 58 
-84390053,HC HLAB:HLA-B TYPE(PCR), 58 
-50791086,HC HLABH:HLA-B TYPE(HI RES), 300 
-52291085,HC HLABH:HLA-B TYPE(HI RES), 58 
-81191082,HC HLABH:HLA-B TYPE(HI RES), 58 
-81391088,HC HLABH:HLA-B TYPE(HI RES), 58 
-82391087,HC HLABH:HLA-B TYPE(HI RES), 58 
-50790112,HC HLAC:HLA-C TYPE(PCR), 272 
-52290111,HC HLAC:HLA-C TYPE(PCR), 58 
-81090110,HC HLAC:HLA-C TYPE(PCR), 58 
-81190118,HC HLAC:HLA-C TYPE(PCR), 58 
-81390114,HC HLAC:HLA-C TYPE(PCR), 58 
-82390113,HC HLAC:HLA-C TYPE(PCR), 58 
-84390111,HC HLAC:HLA-C TYPE(PCR), 58 
-50791094,HC HLACH:HLA-C TYPE(HI RES), 331 
-52291093,HC HLACH:HLA-C TYPE(HI RES), 58 
-81091092,HC HLACH:HLA-C TYPE(HI RES), 58 
-82391095,HC HLACH:HLA-C TYPE(HI RES), 58 
-50791250,HC HLADP: DP1 LOCUS TYPE PCR, 502 
-52291259,HC HLADP: DP1 LOCUS TYPE PCR, 82 
-82391251,HC HLADP: DP1 LOCUS TYPE PCR, 82 
-81091258,HC HLADP:DP1 LOCUS TYPE PCR, 82 
-81191256,HC HLADP:DP1 LOCUS TYPE PCR, 82 
-81391252,HC HLADP:DP1 LOCUS TYPE PCR, 82 
-81791253,HC HLADP:DP1 LOCUS TYPE PCR, 82 
-84391259,HC HLADP:DP1 LOCUS TYPE PCR, 82 
-85391258,HC HLADP:DP1 LOCUS TYPE PCR, 82 
-50791219,HC HLADQA:DQA TYPING (PCR), 228 
-52291218,HC HLADQA:DQA TYPING (PCR), 58 
-81091217,HC HLADQA:DQA TYPING (PCR), 58 
-81191215,HC HLADQA:DQA TYPING (PCR), 58 
-81391211,HC HLADQA:DQA TYPING (PCR), 58 
-81791212,HC HLADQA:DQA TYPING (PCR), 58 
-82391210,HC HLADQA:DQA TYPING (PCR), 58 
-84391218,HC HLADQA:DQA TYPING (PCR), 58 
-85391217,HC HLADQA:DQA TYPING (PCR), 58 
-50791128,HC HLAQ:HLA MONTHLY AB SCRN, 441 
-82391129,HC HLAQ:HLA MONTHLY AB SCRN, 61 
-84391127,HC HLAQ:HLA MONTHLY AB SCRN, 61 
-85391126,HC HLAQ:HLA MONTHLY AB SCRN, 61 
-81091126,HC HLAQ:HLA MONTHLY AB SCRN, 61 
-50790161,HC HLATX:HLA XM:T-CELL, 249 
-81090169,HC HLATX:HLA XM:T-CELL, 82 
-81190167,HC HLATX:HLA XM:T-CELL, 82 
-81390163,HC HLATX:HLA XM:T-CELL, 82 
-82390162,HC HLATX:HLA XM:T-CELL, 82 
-84390160,HC HLATX:HLA XM:T-CELL, 82 
-36034460,HC HNDL & LOAD RADIOELEMENT, 238 
-59300574,HC HNZ BDY PRP, 66 
-58562877,HC HOMOCYSTEINE, 399 
-50462878,HC HOMOCYSTINE, 399 
-51562874,HC HOMOCYSTEINE, 399 
-59462879,HC HOMOCYSTEINE, 399 
-46160321,HC HPVHR2 DNA HPV APT HRISK CV, 372 
-50790906,HC HRDQ:HLA-DQ TYPE(HI-RES), 331 
-52290905,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-81090904,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-81190902,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-81390908,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-82390907,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-84390905,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-56202054,HC HRT FLW RSRV MEAS ADD," 1,185 "
-81400285,HC HRT/LNG MNT GRP:EA 30 DIFF DX, 35 
-81400269,HC HRT/LNG MNT GRP:EA 30 SAME DX, 73 
-81400277,HC HRT/LNG MNT RE 15 DIFF DX, 46 
-46140158,HC HSV ANTIGEN, 319 
-59140152,HC HSV ANTIGEN, 319 
-46330197,HC HSV DNA TYPE 1, 186 
-56330194,HC HSV DNA TYPE 1, 186 
-46330205,HC HSV DNA TYPE 2, 186 
-56330202,HC HSV DNA TYPE 2, 186 
-46140489,HC HSV-2 AB IMMUNOBLOT, 240 
-59140483,HC HSV-2 AB IMMUNOBLOT, 240 
-46140471,HC HSV-I AB IMMUNOBLOT, 226 
-59140475,HC HSV-I AB IMMUNOBLOT, 226 
-41003401,HC HUMERUS MIN 2V, 771 
-41303405,HC HUMERUS MIN 2V, 771 
-93603405,HC HUMERUS MIN 2V, 771 
-50749332,"HC HUNTINGTONS DNA, AMP.", 108 
-50749316,"HC HUNTINGTONS DNA, ISOL.", 108 
-50749324,"HC HUNTINGTONS DNA, SEP.", 108 
-26400440,HC HYDRATION INFUSION EA ADDTL HR, 607 
-26500447,HC HYDRATION INFUSION EA ADDTL HR, 607 
-26610063,HC HYDRATION INFUSION EA ADDTL HR, 607 
-26730077,HC HYDRATION INFUSION EA ADDTL HR, 607 
-26400432,HC HYDRATION INFUSION UP TO 1HR," 1,035 "
-26500439,HC HYDRATION INFUSION UP TO 1HR," 1,035 "
-26610055,HC HYDRATION INFUSION UP TO 1HR," 1,035 "
-26730069,HC HYDRATION INFUSION UP TO 1HR," 1,035 "
-40106213,HC HYPAQUE CYSTO CONTRAST, 86 
-91000026,HC HYST W/BX ENDOMET +/- POLYPS," 1,970 "
-41006750,HC HYSTEROSALPINGOGRAM," 1,726 "
-41128554,HC HYSTEROSALPINGOGRM," 1,726 "
-62019526,HC HYSTEROSCOPY," 4,466 "
-91000018,"HC HYSTEROSCOPY, DX"," 1,970 "
-91000083,"HC HYSTEROSCOPY, REMOVE MYOM"," 3,278 "
-91000042,"HC HYSTEROSCOPY, RESECTION SEPTUM"," 3,278 "
-91000034,HC HYSTEROSCOPY W/LYSIS ADHESION," 1,970 "
-40980179,HC HYSTEROSONOGRAM, 893 
-41100439,HC I&D ABCESS HEMATOMA," 3,715 "
-37021128,HC I-125 SODIUM IOTHALAMATE/10 UC, 663 
-50463413,HC I/IP PTH EACH SPECIMEN, 786 
-56222276,HC IABP REM W REPAIR FEM ART," 4,269 "
-39000070,HC ICD CHCK SNGL CHMB W/OUT REPRG, 843 
-39000088,HC ICD CHK DUAL W/REPROG, 642 
-39000179,HC ICD CHK MULTI W/REPROG, 639 
-39000062,HC ICD CHK S/D/MLT WO REPRG, 843 
-39000054,HC ICD CHK SINGLE W/REPROG, 623 
-39000187,"HC ICD INTROGATE, WEARABLE", 165 
-56210032,HC ICD PCKT RMV GEN ON," 17,827 "
-56210016,HC ICD POCKET REVISION," 8,533 "
-39000203,HC ICM INTR&ANLYS, 66 
-55020754,HC ID WORM, 167 
-59020750,HC ID WORM, 167 
-91900092,HC SPERM IDENTIFY (TESE), 825 
-62110812,"HC IF STN, DIRECT-SPC", 586 
-51593895,HC IGA ANTI-TTG, 273 
-58593898,HC IGA ANTI-TTG, 273 
-51563500,HC IGA SERUM, 213 
-58563503,HC IGA SERUM, 213 
-51593911,HC IGA SERUM CDAS, 213 
-58593914,HC IGA SERUM CDAS, 213 
-50725175,HC 6959 11043R IGD SERUM, 25 
-50751106,HC 6956 YIGF1 IGF1 SOMATOMEDIN, 28 
-51564425,HC IGG 1, 186 
-58564428,HC IGG 1, 186 
-51564433,HC IGG 2, 186 
-58564436,HC IGG 2, 186 
-51564441,HC IGG 3, 186 
-58564444,HC IGG 3, 186 
-51564458,HC IGG 4, 186 
-58564451,HC IGG 4, 186 
-51593903,HC IGG ANTI-DGP, 273 
-58593906,HC IGG ANTI-DGP, 273 
-51563518,HC IGG SERUM, 213 
-58563511,HC IGG SERUM, 213 
-50746833,HC 6959 CIGMT IGM CSF, 9 
-51563526,HC IGM SERUM, 213 
-58563529,HC IGM SERUM, 213 
-40850000,HC II GAUGE MAMMOTOME NEEDLE, 931 
-39000229,HC ILR INTR&ANLYS W/REPROG, 207 
-39000146,HC ILR INTR&ANLYS WO REPRG, 189 
-40880247,"HC IMAGE GUIDANCE, ABSCESS DRAINA"," 2,894 "
-40828477,HC IMAGE GUIDED PLCMNT CLIP," 2,542 "
-40180150,"HC IMAGE GUIDED,CLIP PLACEMENT"," 2,542 "
-52110582,HC IMAGED PAP DIAG, 416 
-52110640,HC IMAGED PAP SCREENING, 490 
-26610295,HC IMED PUMP TUBING W/FILTER, 226 
-41100660,HC IMG BARIUM ENEMA, 656 
-41100751,HC IMG MYELOGRAPHY CERVICAL," 1,920 "
-41100793,HC IMG US BONE DESITY, 156 
-41100736,HC IMG US SFT TSSUE NECK/HEAD," 1,554 "
-26500603,HC IMM ADMIN 1 VACC SING/COMBO, 77 
-26610253,"HC IMM ADMIN,1 VACC,SING/COMBO", 77 
-26500611,HC IMM ADMIN EA ADD SNG/COMB, 135 
-26610261,"HC IMM ADMIN,EA ADDL VACCINE", 135 
-47203823,HC IMMED-SPIN X-MATCH, 395 
-32500621,HC IMMUNIZATION ADMIN, 263 
-26402198,HC IMMUN ADMIN EA ADDL VACCINE, 135 
-26702134,HC IMMUN ADMIN EA ADDL VACCINE, 135 
-26702126,HC IMMUN ADMIN 1 VACCINE SNG/COMB, 77 
-50751122,HC 6956 YICP IMMUNE CMPLX ASSY, 69 
-21140165,HC IMMUNIZATION ADMIN SINGLE, 77 
-21140207,HC IMMUNIZATION ADMIN SINGLE, 77 
-23140163,HC IMMUNIZATION ADMIN SINGLE, 77 
-23140205,HC IMMUNIZATION ADMIN SINGLE, 77 
-23240161,HC IMMUNIZATION ADMIN SINGLE, 77 
-23240203,HC IMMUNIZATION ADMIN SINGLE, 77 
-23740160,HC IMMUNIZATION ADMIN SINGLE, 77 
-23740202,HC IMMUNIZATION ADMIN SINGLE, 77 
-23840168,HC IMMUNIZATION ADMIN SINGLE, 77 
-23840200,HC IMMUNIZATION ADMIN SINGLE, 77 
-24140162,HC IMMUNIZATION ADMIN SINGLE, 77 
-24140204,HC IMMUNIZATION ADMIN SINGLE, 77 
-25040163,HC IMMUNIZATION ADMIN SINGLE, 77 
-25040205,HC IMMUNIZATION ADMIN SINGLE, 77 
-25240169,HC IMMUNIZATION ADMIN SINGLE, 77 
-25240201,HC IMMUNIZATION ADMIN SINGLE, 77 
-25340167,HC IMMUNIZATION ADMIN SINGLE, 77 
-25440165,HC IMMUNIZATION ADMIN SINGLE, 77 
-25740168,HC IMMUNIZATION ADMIN SINGLE, 263 
-25740200,HC IMMUNIZATION ADMIN SINGLE, 77 
-26140160,HC IMMUNIZATION ADMIN SINGLE, 77 
-26340166,HC IMMUNIZATION ADMIN SINGLE, 77 
-26402156,"HC IMMUNIZATION ADMIN, SINGLE", 263 
-27340165,HC IMMUNIZATION ADMIN SINGLE, 77 
-62110804,HC IMMUNOH'CHEM STN-SPC 1ST AB, 999 
-52022050,HC IMMUNOSTAIN (FIRST ANTIBODY), 999 
-62600051,HC IMMUNOTHERAPY INCL EXTRACT 2+, 130 
-62600044,HC IMMUNTHERPY INCL EXTRCT 1 INJ, 103 
-56269251,HC IMPLNT PAT ACTV RECRD," 21,749 "
-36033413,HC IMRT DAILY TREATMENT 15X," 7,929 "
-36033439,HC IMRT DAILY TREATMENT 4X," 7,929 "
-36033421,HC IMRT DAILY TREATMENT 6X," 7,929 "
-36033405,HC IMRT TREATMENT PLAN," 19,230 "
-37005089,HC IN 111 OXYQUINOLINE/0.5 MCI," 6,756 "
-37005097,HC IN 111 DTPA PER 0.5 MCI," 4,106 "
-37010311,HC IN 111 PENTETREOTIDE DX <6 MCI," 21,749 "
-62111026,HC IN SITU HYBRIDIZATION-SPC, 771 
-37020344,HC IN-111 SATUMOMAB PENDETIDE<6MC," 8,269 "
-52032158,HC IN-SITU HYBRDIZATN, 771 
-55091466,HC INCDNL YEAST ID NO DR, 237 
-59091462,HC INCDNTL YEAST ID, 237 
-62026216,HC INCIS/DRAIN ISCHIORECTAL ABSCS," 2,367 "
-62020706,HC INCISE 2NDARY CATARACT STAB," 1,194 "
-62021639,HC INCISE EARDRUM W/ASP/INFLATE, 303 
-62020912,HC INCISE EYELID," 1,539 "
-41100389,HC INCISE TISSUE ABSCESS SUPER, 579 
-56209034,HC INDCT ARYTH;ELCT PCG," 4,144 "
-55090500,HC INDIA INK, 270 
-59090506,HC INDIA INK, 270 
-47203286,"HC INDIRECT COOMBS, QUAL", 191 
-37005196,HC INDIUM IN-111 LABELED WBC," 10,538 "
-62021845,HC PSYCHOTHERAPY 30 MIN, 118 
-62021852,HC PSYCHOTHERAPY 45MIN, 156 
-83500355,HC INF DIS MRKR, 514 
-41118100,HC INF VEN CAVA FLTR," 9,933 "
-41122656,HC INF VENA CAVOGRAM," 7,970 "
-50323005,HC INFECT MONO SCRN, 199 
-51523009,HC INFECT MONO SCRN, 199 
-51823003,HC INFECT MONO SCRN, 199 
-56823008,HC INFECT MONO SCRN, 199 
-58523002,HC INFECT MONO SCRN, 199 
-59323006,HC INFECTIOS MONO SCRN, 199 
-26602177,HC INFLUENZA ADMIN IM, 77 
-26702142,HC INFLUENZA ADMIN IM, 77 
-26402206,HC INFLUENZA ADMINISTRATION IM, 77 
-40510141,HC INFLUENZA VACCINE, 39 
-21140181,HC INFLUENZA VACCINE ADMIN, 77 
-23140189,HC INFLUENZA VACCINE ADMIN, 77 
-23240187,HC INFLUENZA VACCINE ADMIN, 77 
-23740186,HC INFLUENZA VACCINE ADMIN, 77 
-23840184,HC INFLUENZA VACCINE ADMIN, 77 
-24140188,HC INFLUENZA VACCINE ADMIN, 77 
-25040189,HC INFLUENZA VACCINE ADMIN, 77 
-25240185,HC INFLUENZA VACCINE ADMIN, 77 
-25340183,HC INFLUENZA VACCINE ADMIN, 77 
-25440181,HC INFLUENZA VACCINE ADMIN, 77 
-25740184,HC INFLUENZA VACCINE ADMIN, 77 
-26140186,HC INFLUENZA VACCINE ADMIN, 77 
-26340182,HC INFLUENZA VACCINE ADMIN, 77 
-27340181,HC INFLUENZA VACCINE ADMIN, 77 
-41151408,HC INFUS THROMBO THER," 4,778 "
-41151663,HC INITIAL 2ND ORDER," 6,436 "
-41151705,HC INITIAL 2ND ORDER," 4,084 "
-41151697,HC INITIAL 3RD ORDER," 7,294 "
-41152372,HC INITIAL 3RD ORDER," 11,086 "
-92801695,HC INITIAL EVAL 2-5 YRS, 192 
-62026158,HC INJ 1-2 TRIGGER PTS/3 MUSCLES, 505 
-41100272,HC INJ AGT LUM/SAC 1 LVL," 1,594 "
-40100521,HC INJ ANESTH INTERCOSTAL, 822 
-41045469,HC INJ ANKLE ARTHROGRM," 4,836 "
-41151432,HC INJ ANTEGRADE PYELO, 391 
-41100280,HC INJ ART OCC SPINAL," 1,594 "
-41152265,HC INJ CHOLANGIO/EXIST," 2,435 "
-40145336,HC INJ CYSTO OR VOID URTHRCYSTO," 1,666 "
-40845406,HC INJ DUCTOGRAM, 301 
-41045451,HC INJ ELBOW ARTHROGRM, 896 
-41100769,HC INJ EPIDURAL BLD/CLOT," 2,381 "
-41152208,HC INJ FOR UROGRAPHY, 417 
-62024427,HC INJ FORAMEN EPIDURAL CERV/THOR W GUIDANCE SNGL LEVEL," 1,609 "
-62024443,HC INJ FORAMEN EPIDURAL LUMB/SACR W GUIDANCE SNGL LEVEL," 1,339 "
-62024450,HC INJ FORAMEN EPIDURAL W GUIDANCE LIMB/SACR- ADD-ON, 620 
-62024435,HC INJ FORAMEN EPIDURAL W GUIDANCE CERV/THOR- ADD-ON, 783 
-40100158,HC INJ HIP ARTHROGRAM," 2,941 "
-41045428,HC INJ HIP ARTHROGRAM," 2,941 "
-41045378,HC INJ HYSTEROSALPINGO," 1,241 "
-40945370,HC INJ HYSTEROSAPINGOGRAM," 1,241 "
-41045410,HC INJ KNEE ARTHROGRAM, 380 
-41045394,HC INJ MYELOGRAM," 2,473 "
-62026190,HC INJ NEURO SUBST SUBARACHNOID, 830 
-21140157,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-23140155,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-23240153,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-23740152,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-23840150,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-24140154,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-25040155,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-25240151,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-25340159,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-25440157,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-25740150,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-26140152,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-26340158,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-27340157,HC INJ OR IV THRP/PROPH/DX UNLIST, 256 
-41100868,HC INJ PARAVERT LUM/SAC," 1,594 "
-41151358,HC INJ PERC TRANSHEP, 604 
-41152869,HC INJ PERC TX OF EXT PSEUDOANEUR, 436 
-41151481,HC INJ PROC FOR HSG," 1,241 "
-42016253,HC INJECT SACROILIAC JOINT," 4,533 "
-41151168,HC INJ PROC URETER VIA, 393 
-62026166,HC INJ PX CERVICAL OR THORACIC," 1,215 "
-40105009,"HC INJ SCL SOL, 1 VEIN", 302 
-40105017,"HC INJ SCL SOL, MULT VEINS", 344 
-41100314,"HC INJ SCL SOL,MULT VEIN,", 344 
-41045436,HC INJ SHOULDER ARTHRO," 2,775 "
-41045642,"HC INJ SINOGRAM/FISTUL, DX"," 2,549 "
-41100801,HC INJ SPINAL PUNCTURE, 822 
-21140124,HC INJ THER/PROPH/DX INTRA ART, 125 
-23140122,HC INJ THER/PROPH/DX INTRA ART, 125 
-23240120,HC INJ THER/PROPH/DX INTRA ART, 125 
-23740129,HC INJ THER/PROPH/DX INTRA ART, 125 
-23840127,HC INJ THER/PROPH/DX INTRA ART, 125 
-24140121,HC INJ THER/PROPH/DX INTRA ART, 125 
-25040122,HC INJ THER/PROPH/DX INTRA ART, 125 
-25240128,HC INJ THER/PROPH/DX INTRA ART, 125 
-25340126,HC INJ THER/PROPH/DX INTRA ART, 125 
-25440124,HC INJ THER/PROPH/DX INTRA ART, 125 
-25740127,HC INJ THER/PROPH/DX INTRA ART, 125 
-26140129,HC INJ THER/PROPH/DX INTRA ART, 125 
-26340125,HC INJ THER/PROPH/DX INTRA ART, 125 
-27340124,HC INJ THER/PROPH/DX INTRA ART, 125 
-41151804,HC INJ VENOGRAPHY," 4,831 "
-41045337,HC INJ VOID URETHROCYS," 1,666 "
-41045444,HC INJ WRIST ARTHROGRM, 725 
-40510414,HC INJ(S) SNGL TNDN SHTH OR LGM, 385 
-40905036,"HC INJ(S); SNGL TNDN SHTH, OR LGM", 515 
-41153271,HC INJ:DSKOGRPHY LUMBAR, 796 
-62019831,HC INJECT EPIDURAL BLD PATCH, 725 
-62020730,HC INJECT EYE DRUG, 441 
-62024401,HC INJECT FOR SPINE," 1,259 "
-62019039,HC INJECT PENIS W/DRUGS, 190 
-62010905,HC INJECT SKN LSNS 1-7LSNS, 88 
-62010913,HC INJECT SKN LSNS > 7LSNS, 136 
-62019880,HC NJX DX/THER SBST EPIDURAL/SUBRACH CERV/THORACIC," 1,066 "
-62019898,HC NJX DX/THER SBST EPIDURAL/SUBARACH LUMB/SACRAL, 725 
-62021308,HC INJECT SUBCONJUNCTIVAL, 95 
-62020177,HC INJECT/NERVE BLOCK CELIAC, 958 
-62020045,HC INJECT/NERVE BLOCK ILIOLINGUIN, 489 
-62020029,HC INJECT/NERVE BLOCK INTERCOSTAL, 466 
-62020037,HC INJECT/NERVE BLOCK INTERCOSTAL, 725 
-62020169,HC INJECT/NERVE BLOCK LUM/THOR, 834 
-62020086,HC INJECT/NERVE BLOCK OTHER PERIP, 702 
-62020052,HC INJECT/NERVE BLOCK PUDENDAL, 725 
-62020151,HC INJECT/NERVE BLOCK STELLATE, 759 
-62020011,HC INJECT/NERVE BLOCK SUPRASCAPUL, 579 
-62020599,HC INJECT/TREAT EYE AIR/LIQUID," 1,855 "
-62020607,HC INJECT/TREAT EYE MEDICATION," 1,610 "
-62024385,HC INJECTION FOR BLADDER, 122 
-40450033,HC INJECTION TRAY, 85 
-62012521,HC INJECTION TREATMENT, 507 
-26340208,HC INJECTION: SUBQ/IM, 344 
-27340207,HC INJECTION: SUBQ/IM, 344 
-56269269,HC INS HRT ELCTR PRV DVC," 17,855 "
-56221567,HC INS OR REPL PCR GEN," 5,389 "
-56209927,HC INS/RPL TMP DL ELECTDS," 7,836 "
-91900100,HC INSEMIN-OOCYTES, 385 
-56269079,HC INSERT ADD. DRUG ELUTING STENT," 11,496 "
-56269061,HC INSERT DRUG ELUT STENT SNGL," 32,831 "
-41101205,HC INSERT DUODEN OR JEJUNO TUBE," 2,610 "
-56269103,HC INSERT ICD-SNGL OR DUAL," 10,081 "
-41153339,"HC INSERT NON-TUNNEL CVC,5 YO+"," 7,725 "
-62019278,HC INSERT PESSARY DEVICE, 209 
-41152877,HC INSERT TIPS SHUNT," 7,908 "
-41100421,HC INSERT TUNNELED CATH," 6,426 "
-41153362,"HC INSERT TUNNELED CVAD PORT,5 YO"," 9,772 "
-41153354,"HC INSERT TUNNELED CVC,5 YO+"," 5,382 "
-41153347,"HC INSERT TUNNELED CVC,UNDER 5 YO"," 5,231 "
-41153388,"HC INSERT TUNNELED PICC,5 YO+"," 3,045 "
-41153370,"HC INSERT TUNNELED PICC,UNDER 5 Y"," 3,628 "
-62018833,HC INSERT URINARY CATHETER SMPL, 331 
-56269129,HC INSERT/REPOS LEAD W GEN INS," 13,902 "
-41128505,HC INSERTION GASTROSTOMY TUBE," 6,450 "
-56209968,HC INSRT PCMKR/ICD DUAL LEAD," 14,458 "
-56200900,HC INSRT PCMKR/ICD SNGL LEAD," 5,849 "
-56269137,HC INSRT PCNG ELECT CVS-LV," 24,867 "
-41100371,HC INSRT PLEURAL CATH W/CUFF," 7,314 "
-62026646,HC INSRT TEMP INDWEL BLADDER CATH, 425 
-41153321,"HC INSTER NON-TUNNEL CVC,UNDER 5"," 5,677 "
-51510105,HC INSULIN (FAST/UNSPEC), 181 
-58510108,HC INSULIN (FAST/UNSPEC), 181 
-51564821,HC INSULIN FASTING 2HR, 181 
-58564824,HC INSULIN FASTING 2HR, 181 
-51571032,HC INSULIN LIKE GROWTH FACT1, 304 
-58571035,HC INSULIN LIKE GROWTH FACT1, 304 
-36070134,HC INT BRCH ISO PN INT," 1,516 "
-41145319,HC INTEG PUNC/ASP PROC," 1,423 "
-37000304,HC INTERCAVITARY THRPY, 939 
-50725605,HC 6959 11044R IL6 CHMLMNSCNT, 90 
-50791367,HC INTERP 16 OR MORE MARKERS, 145 
-50791391,HC INTERP 16 OR MORE MARKERS, 145 
-52291366,HC INTERP 16 OR MORE MARKERS, 145 
-52291390,HC INTERP 16 OR MORE MARKERS, 145 
-81091365,HC INTERP 16 OR MORE MARKERS, 145 
-81091399,HC INTERP 16 OR MORE MARKERS, 145 
-81191363,HC INTERP 16 OR MORE MARKERS, 145 
-81191397,HC INTERP 16 OR MORE MARKERS, 145 
-81391369,HC INTERP 16 OR MORE MARKERS, 145 
-81391393,HC INTERP 16 OR MORE MARKERS, 145 
-81791360,HC INTERP 16 OR MORE MARKERS, 145 
-81791394,HC INTERP 16 OR MORE MARKERS, 145 
-82391368,HC INTERP 16 OR MORE MARKERS, 145 
-82391392,HC INTERP 16 OR MORE MARKERS, 145 
-84391366,HC INTERP 16 OR MORE MARKERS, 145 
-84391390,HC INTERP 16 OR MORE MARKERS, 145 
-85391365,HC INTERP 16 OR MORE MARKERS, 145 
-85391399,HC INTERP 16 OR MORE MARKERS, 145 
-50791300,HC 6949 H095 PATH CONSULT COMP, 8 
-52291309,HC 6949 H095 PATH CONSULT COMP, 8 
-81091308,HC 6949 H095 PATH CONSULT COMP, 8 
-81191306,HC INTERP AND REPORT, 8 
-81391302,HC 6949 H095 PATH CONSULT COMP, 8 
-81791303,HC INTERP AND REPORT, 8 
-82391301,HC 6949 H095 PATH CONSULT COMP, 8 
-84391309,HC 6949 H095 PATH CONSULT COMP, 8 
-85391308,HC INTERP AND REPORT, 8 
-50791334,HC 6949 H002 PATH CONSULT COMP, 8 
-52291333,HC 6949 H002 PATH CONSULT COMP, 8 
-81091332,HC 6949 H002 PATH CONSULT COMP, 8 
-81191330,HC INTERP AND REPORT, 8 
-81391336,HC 6949 H002 PATH CONSULT COMP, 8 
-81791337,HC INTERP AND REPORT, 8 
-82391335,HC 6949 H002 PATH CONSULT COMP, 8 
-84391333,HC 6949 H002 PATH CONSULT COMP, 8 
-85391332,HC INTERP AND REPORT, 8 
-50803667,HC INTERP AND REPORT CDH1, 138 
-50802818,HC INTERP AND REPORT MOLECULAR, 138 
-50803642,"HC INTERP AND REPORT, VH MUTATION", 138 
-36099943,HC INTERSTITIAL CMPLX," 2,466 "
-62024781,HC INTERVENT:HEALTH & BEHAVIOR EA, 64 
-50672526,HC INTPH ISH 13Q 100-300 CELL, 441 
-50672682,HC INTPH ISH 20Q 100-300 CELL, 441 
-50672781,HC INTPH ISH 20Q 100-300 CELL, 441 
-50672500,HC INTPH ISH 5Q 100-300 CELL, 441 
-50672518,HC INTPH ISH 7QS 100-300 CELL, 441 
-50642404,HC INTPH ISH ALK 100-300 CELL, 441 
-50642370,HC INTPH ISH BCL2 100-300 CELL, 441 
-50642362,HC INTPH ISH BCL6 100-300 CELL, 441 
-50641810,HC INTPH ISH BCR 100-300 CELL, 441 
-50672484,HC INTPH ISH CBFB 100-300 CELL, 441 
-50642396,HC INTPH ISH CCND1 100-300 CELL, 441 
-50672674,HC INTPH ISH CHIC2 100-300 CELL, 441 
-50672773,HC INTPH ISH CHIC2 100-300 CELL, 441 
-50672468,HC INTPH ISH CLL 100-300 CELL, 441 
-50642388,HC INTPH ISH CMYC 100-300 CELL, 441 
-50641869,HC INTPH ISH ENUM 100-300 CELL, 441 
-50672492,HC INTPH ISH ETO 100-300 CELL, 441 
-50642347,HC INTPH ISH EWS 100-300 CELL, 441 
-50672716,HC INTPH ISH FGFR3 100-300 CELL, 441 
-50672724,HC INTPH ISH MAF 100-300 CELL, 441 
-50642354,HC INTPH ISH MALT 100-300 CELL, 441 
-50641844,HC INTPH ISH MLL 100-300 CELL, 441 
-50641851,HC INTPH ISH NOS 100-300 CELL, 441 
-50641828,HC INTPH ISH PML 100-300 CELL, 441 
-50641877,HC INTPH ISH PNTL 100-300 CELL, 441 
-50672476,HC INTPH ISH SYT 100-300 CELL, 441 
-50641836,HC INTPH ISH TEL 100-300 CELL, 441 
-50672690,HC INTPH ISH TELBAP 100-300 CELL, 441 
-50642248,HC INTPH ISH UROVY 25-99 CELL, 383 
-56203367,HC INSERT INTRA AORT BALLOON," 15,492 "
-36071041,HC INTRA OPERATIVE TX, 693 
-41100181,HC INTRA STENT INTRACRAN," 4,881 "
-56209174,HC INTRA-VENT PACG," 4,144 "
-56270101,HC INTRACARD EP 3D MAP," 15,402 "
-36099976,HC INTRACAVITARY CMPL," 1,556 "
-36099992,HC INTRACAVITARY INT," 1,157 "
-62600036,HC INTRADERM ALLERGY ITR, 40 
-41100611,HC INTRAPERI CATH DIALYSIS," 5,474 "
-62600028,HC INTRAQ ALLERGY TEST W/EXTRACTS, 131 
-41145368,HC INTRAV. US NON-COR, 889 
-41145376,HC INTRAV. US NON-COR, 155 
-41144825,HC INTRAVAS OCCL NON," 16,094 "
-41144908,HC INTRAVASC STENT NON COR," 8,001 "
-56269277,HC IVUS EA ADD VESSEL," 4,069 "
-41151606,HC INTRAVASC US NON-CO," 3,311 "
-41151614,HC INTRAVASC US NON-CO EA ADDTL," 2,498 "
-56240641,HC IVUS INITIAL VESSEL," 11,731 "
-36079473,HC INTRM EXTR BEAM SIM," 2,807 "
-41151390,HC INTRO CATH BILI," 5,711 "
-41151630,HC INTRO CATH RETRO BR, 588 
-41152885,HC INTRO NEEDLE OR INTRACATH VEIN, 151 
-41151440,HC INTRO URET CATH/STE," 5,376 "
-58700113,HC IONIZED CALCIUM, 399 
-59400986,HC IONIZED CALCIUM, 399 
-40400160,HC IONTOPHORESIS-15MIN, 225 
-94100872,HC IP NEGPRESSURE WND THER>50CM2, 560 
-94100864,HC IP NEGPRESSURE WNDTHER<=50CM2, 202 
-94100856,HC IP NON-SELECTIVE DEBRIDEMENT, 361 
-94100880,HC IPPARING/CUTTING SINGLE LESION, 202 
-50400365,HC IRON OD SCREEN, 165 
-50300250,HC IRON STAIN, 546 
-59300251,HC IRON STAIN, 546 
-50402304,HC IRON TTL SERUM, 165 
-51502300,HC IRON TTL SERUM, 165 
-58502303,HC IRON TTL SERUM, 165 
-59402305,HC IRON TTL SRM, 165 
-26402115,HC IRRIGATION IMPLANTED VAD DRUG, 672 
-62018569,HC IRRIGATION OF BLADDER, 318 
-26502112,HC IRRIGATION OF IMPLANTED VAD DR, 672 
-26602102,HC IRRIGATION OF IMPLANTED VAD FO, 672 
-26702068,HC IRRIGATION OF IMPLANTED VENOUS, 672 
-50672567,HC ISG EWS MRPHMET MAN," 1,383 "
-50672542,HC ISH ADD MRPHMET MAN," 1,383 "
-50672617,HC ISH ALK MRPHMET MAN," 1,383 "
-50672591,HC ISH CMYC MRPHMET MAN," 1,383 "
-50672583,HC ISH FKHD MRPHMET MAN," 1,383 "
-50672625,HC ISH GLIOMA MRPHMET MAN," 1,383 "
-50672559,HC ISH HER2 MRPHMET MAN," 1,383 "
-50672609,HC ISH MALT MRPHMET MAN," 1,383 "
-50672633,HC ISH NOS MRPHMET MAN," 1,383 "
-50672575,HC ISH SYT MRPHMET MAN," 1,383 "
-62011663,HC ISLAND PEDICLE FLAP," 1,639 "
-50722396,HC 6959 11022R ISLET CELL AB, 30 
-50803097,HC ISO NUC ACID FRAGILE X, 146 
-36079036,HC ISODOSE PLAN 3D," 3,983 "
-36200095,HC ISODOSE PLAN 3D," 3,983 "
-36300093,HC ISODOSE PLAN 3D," 3,983 "
-36079028,HC ISODOSE PLAN/COMPLX," 3,983 "
-36070126,HC ISODOSE PLAN/INTERM," 1,856 "
-36079010,HC ISODOSE PLAN/SIMPLE, 762 
-50803105,HC ISOL A-THAL, 146 
-50803402,HC ISOL NA ASHPLEX 1, 146 
-50803444,HC ISOL NA ASHPLEX EXT, 146 
-50803139,HC ISOL NA BTHAL SEQ, 146 
-50803261,HC ISOL NA CCND1/LGH, 146 
-50803162,HC ISOL NA CHONDRO, 146 
-50803113,HC ISOL NA HG C SPRING, 146 
-50803592,HC ISOL NA JAK2, 146 
-50803394,HC ISOL NA MSAT INSTAB, 146 
-50803287,HC ISOL NA MUNENKE, 146 
-50803469,HC ISOL NA PENDRED, 146 
-50803386,HC ISOL NA T(15;17), 146 
-50802594,HC ISOL NUC ACID B-CLONE PCR, 146 
-50802602,HC ISOL NUC ACID BCR ABL, 146 
-50803006,HC ISOL NUC ACID CF, 146 
-50803352,HC ISOL NUC ACID CON30, 146 
-50803055,HC ISOL NUC ACID DBMD, 146 
-50802628,HC ISOL NUC ACID FCTR V LEIDEN, 146 
-50803345,HC ISOL NUC ACID MCC, 146 
-50802636,HC ISOL NUC ACID MTHFR, 146 
-50802644,HC ISOL NUC ACID PRADER WILLI, 146 
-50802651,HC ISOL NUC ACID PT20210A, 146 
-50802974,HC ISOL NUC ACID QUANT BCR ABL, 146 
-50802669,HC ISOL NUC ACID T(14:18), 146 
-50802677,HC ISOL NUC ACID T-CLONE BY PCR, 146 
-50791409,HC ISOL OR EXT EA NA, 8 
-52291408,HC ISOL OR EXT EA NA, 8 
-81091407,HC ISOL OR EXT EA NA, 8 
-81191405,HC ISOL OR EXT EA NA, 8 
-81391401,HC ISOL OR EXT EA NA, 8 
-81791402,HC ISOL OR EXT EA NA, 8 
-82391400,HC ISOL OR EXT EA NA, 8 
-84391408,HC ISOL OR EXT EA NA, 8 
-85391407,HC ISOL OR EXT EA NA, 8 
-50803030,HC ISOLATION NUC ACID COENXIN SEQ, 146 
-50802693,HC ISOLATION NUCLEIC ACID, 146 
-55080204,HC ISOLATOR-BRUCELLA, 499 
-55080261,HC ISOLATOR-FUNGUS, 513 
-59080267,HC ISOLATOR-FUNGUS, 513 
-55080451,HC ISOLATOR-SPEC REG, 499 
-59080457,HC ISOLATOR-SPEC REQ, 499 
-62700141,HC IV HYDRA EA ADDIT HR, 607 
-62700158,HC IV HYDRA INIT TO 1 HR," 1,035 "
-62700455,HC IV HYDRA INIT TO 1 HR ATIC," 1,035 "
-62700448,HC IV HYDRATION EA ADDL HR ATIC, 607 
-21140116,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-23140114,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-23240112,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-23740111,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-23840119,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-24140113,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-25040114,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-25240110,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-25340118,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-25440116,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-25740119,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-26140111,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-26340117,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-27340116,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-26502062,HC IV INFUSION THER SEQ UP TO 1H, 796 
-26402073,HC IV INFUSION THERAPY ADDTL SEQU, 796 
-26702027,HC IV INFUSION THERAPY ADDTL SEQU, 796 
-26631010,HC IV INFUSION THERAPY EA ADDL HR, 607 
-26702019,HC IV INFUSION THERAPY EA ADDL HR, 607 
-26702001,HC IV INFUSION THERAPY UP TO 1 HR," 1,226 "
-26402040,HC IV INFUSION THERAPY UP TO 1HR," 1,226 "
-26502039,HC IV INFUSION UP TO 1 HR," 1,226 "
-21140215,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-23140213,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-23240211,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-23740210,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-23840218,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-24140212,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-25040213,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-25240219,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-25340217,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-25440215,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-25740218,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-26340216,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-32705014,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-36060366,HC IV INFUSION-HYDRA-EA ADDTL HR, 607 
-27340215,HC IV INFUSION-HYDRA=EA ADDTL HR, 607 
-21140223,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-23140221,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-23740228,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-23840226,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-24140220,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-25040221,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-25240227,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-25340225,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-25440223,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-25740226,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-26340224,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-27340223,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-32705006,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-36060358,HC IV INFUSION-HYDRATION-1ST HR," 1,035 "
-26140210,HC IV INFUSION-THER-ADDTL HR, 607 
-26340232,HC IV INFUSION-THER-ADDTL HR, 607 
-27340231,HC IV INFUSION-THER-ADDTL HR, 607 
-25230178,HC IV INFUSION-THER-ADDTL HRS, 607 
-25730177,HC IV INFUSION-THER-ADDTL HRS, 607 
-26430991,HC IV INFUSION-THER-ADDTL HRS, 607 
-26531004,HC IV INFUSION-THER-ADDTL HRS, 607 
-32711699,HC IV INFUSION-THER-ADDTL HRS, 607 
-26602003,HC IV INFUSION-THER-ADDTL SEQUTL, 796 
-32705022,HC IV INFUSION-THER-ADDTL SEQUTL, 796 
-23240229,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-25230160,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-25730169,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-26140228,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-26340240,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-26602045,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-27340249,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-32711681,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-56202310,HC IV INFUSN-THERAP-1ST HR," 1,226 "
-26601534,HC IV PUMP TAXOL TUBING, 298 
-62700463,HC IV PUSH INIT OR SGL ATIC, 542 
-62700166,HC IV PUSH INITIAL/SINGLE, 542 
-25230194,HC IV PUSH INJECTION, 542 
-25730193,HC IV PUSH INJECTION, 542 
-26340257,HC IV PUSH INJECTION, 542 
-27340256,HC IV PUSH INJECTION, 542 
-32712473,HC IV PUSH INJECTION INITIAL, 542 
-62700208,HC IV THERAP INFUSION 1ST HR," 1,226 "
-62700513,HC IV THERAP INFUSION 1ST HR ATIC," 1,226 "
-62700182,HC IV  INFUS THER ADDL SEQ1HR NEW DRUG, 796 
-62700216,HC IV THERAP INFUSN EA ADDL HR, 607 
-62700521,HC IV THERAP INFUSN EA ADDL HR AT, 607 
-62700224,HC IV THERAP INFUSN OR IVP UNL, 256 
-62700232,HC IV THERAP PROPH INTRA ART, 125 
-62700547,HC IV THERAP PROPH INTRA ART, 125 
-62700539,HC IV THRP INFUSN IVP UNLIST ATIC, 256 
-26730994,HC IV UNLISTED THER INFUSION, 256 
-26402099,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-26502088,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-26602086,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-26702043,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-32705055,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-26431015,HC IVP INJ-ADDTL SEQ SAME DRUG, 405 
-26503201,HC IVP INJ-ADDTL SEQ SAME DRUG, 405 
-26603209,HC IVP INJ-ADDTL SEQ SAME DRUG, 405 
-26703215,HC IVP INJ-ADDTL SEQ SAME DRUG, 405 
-62700190,HC IVP THRP EA + SEQ NEW DRUG, 400 
-62700505,HC IVP THRP EA+SEQ NEW DRUG, 400 
-21140140,HC IVP THRP/PROPH SAME DRUG, 405 
-23140148,HC IVP THRP/PROPH SAME DRUG, 405 
-23240146,HC IVP THRP/PROPH SAME DRUG, 405 
-23740145,HC IVP THRP/PROPH SAME DRUG, 405 
-23840143,HC IVP THRP/PROPH SAME DRUG, 405 
-24140147,HC IVP THRP/PROPH SAME DRUG, 405 
-25040148,HC IVP THRP/PROPH SAME DRUG, 405 
-25240144,HC IVP THRP/PROPH SAME DRUG, 405 
-25340142,HC IVP THRP/PROPH SAME DRUG, 405 
-25440140,HC IVP THRP/PROPH SAME DRUG, 405 
-25740143,HC IVP THRP/PROPH SAME DRUG, 405 
-26140145,HC IVP THRP/PROPH SAME DRUG, 405 
-26340141,HC IVP THRP/PROPH SAME DRUG, 405 
-27340140,HC IVP THRP/PROPH SAME DRUG, 405 
-21140132,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-23140130,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-23240138,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-23740137,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-23840135,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-24140139,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-25040130,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-25240136,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-25340134,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-25440132,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-25740135,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-26140137,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-26340133,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-27340132,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-21140173,HC IZ ADMIN EACH ADTL VACCINE, 135 
-23140171,HC IZ ADMIN EACH ADTL VACCINE, 135 
-23240179,HC IZ ADMIN EACH ADTL VACCINE, 135 
-23740178,HC IZ ADMIN EACH ADTL VACCINE, 135 
-23840176,HC IZ ADMIN EACH ADTL VACCINE, 135 
-24140170,HC IZ ADMIN EACH ADTL VACCINE, 135 
-25040171,HC IZ ADMIN EACH ADTL VACCINE, 135 
-25240177,HC IZ ADMIN EACH ADTL VACCINE, 135 
-25340175,HC IZ ADMIN EACH ADTL VACCINE, 135 
-25440173,HC IZ ADMIN EACH ADTL VACCINE, 135 
-25740176,HC IZ ADMIN EACH ADTL VACCINE, 135 
-26140178,HC IZ ADMIN EACH ADTL VACCINE, 135 
-26340174,HC IZ ADMIN EACH ADTL VACCINE, 135 
-27340173,HC IZ ADMIN EACH ADTL VACCINE, 135 
-26400960,HC JAMSHIDI NEEDLE, 163 
-26500967,HC JAMSHIDI NEEDLE, 163 
-26610543,HC JAMSHIDI NEEDLE, 163 
-59060608,HC JNT FL/SITE, 302 
-55060602,HC JOINT FLUID/SITE CULT, 302 
-40132060,"HC JOINT INJECTION, MAJOR"," 1,193 "
-41009408,HC JT SURVEY 1V 2 OR MORE JT, 140 
-50100775,HC JUMBO FP 1, 437 
-50120518,HC JUMBO FP 2, 437 
-40450512,HC KENALOG 10MG INJ, 122 
-50749373,"HC KENNEDY'S DNA, AMP.", 108 
-50749357,"HC KENNEDY'S DNA, ISOL", 108 
-50749365,"HC KENNEDY'S DNA, SEP.", 108 
-82200288,HC KIDNEY MNT GRP:EA 30 DIFF DX, 35 
-82200262,HC KIDNEY MNT GRP:EA 30 SAME DX, 73 
-82200247,HC KIDNEY MNT INIT 15, 118 
-82200270,HC KIDNEY MNT RE 15 DIFF DX, 46 
-82200254,HC KIDNEY MNT RE15 SAME DX, 101 
-50722974,"HC KIDNEY STONE PANEL, CAL", 42 
-37000486,HC KIDNY FNCTN CLEARNC," 2,099 "
-59325084,HC KIEIHAUER-BETKE, 255 
-50790146,HC 6449 HLA KIT SUP MAIL, 285 
-52290145,HC 6949 HLA KIT SUP MAIL, 285 
-81090144,HC 6949 HLA KIT SUP MAIL, 285 
-81190142,HC 6949 HLA KIT SUP MAIL, 285 
-81790149,HC 6949 HLA KIT SUP MAIL, 285 
-82390147,HC 6949 H019 HLA KIT SUP MAIL, 285 
-84390145,HC 6949 HLA KIT SUP MAIL, 285 
-85390144,HC 6949 HLA KIT SUP MAIL, 285 
-50325083,HC KLEIHAUER-BETKE, 255 
-40450082,HC KNEE IMMOB 19IN UNIV, 179 
-40450066,HC KNEE IMMOB TRIMFIT 16, 149 
-41004052,"HC KNEE, ONE OR TWO VIEWS-UNLAT", 732 
-41304056,"HC KNEE, ONE OR TWO VIEWS-UNLAT", 732 
-93604056,"HC KNEE, ONE OR TWO VIEWS-UNLAT", 732 
-41004102,HC KNEE THREE VIEWS-UNILAT, 815 
-41304106,HC KNEE THREE VIEWS-UNILAT, 815 
-93604106,HC KNEE THREE VIEWS-UNILAT, 815 
-93604155,HC KNEE 4 OR MORE VIEWS," 1,110 "
-41004151,HC KNEE 4 OR MORE VIEWS," 1,110 "
-41304155,HC KNEE 4 OR MORE VIEWS," 1,110 "
-55090559,HC KOH PREP, 270 
-59090555,HC KOH PREP, 270 
-50803808,HC KRAS AMP MPLX NA 1-2, 172 
-50803816,HC KRAS AMP MPLX NA ADD, 90 
-50803782,HC KRAS ENZYMATIC DIGEST, 78 
-50803840,HC KRAS INTERP AND RPT, 138 
-50803824,HC KRAS LYSIS PRE NA, 66 
-50803766,HC KRAS MOL ISOL EA NA, 125 
-50803790,HC KRAS MUT AMP NA, 188 
-50803857,HC KRAS MUT ID EA, 71 
-50803774,HC KRAS MUT ISOL NA, 146 
-50803832,HC KRAS SEP ELECTRO CAP, 112 
-41001876,HC L SPINE BEND ONLY 2/3 VIEWS," 1,304 "
-93601870,HC L SPINE BEND ONLY 2/3 VIEWS," 1,304 "
-41301870,HC L SPINE BEND ONLY 2/3 VIEWS," 1,304 "
-41002213,HC L SPINE COMP W/BEND," 2,003 "
-41302217,HC L SPINE COMP W/BEND," 2,003 "
-93602217,HC L SPINE COMP W/BENDING V," 2,003 "
-41002155,HC L-SPINE 2 OR 3 VIEWS," 1,043 "
-41302159,HC L-SPINE 2 OR 3 VIEWS," 1,043 "
-93602159,HC L-SPINE 2 OR 3 VIEWS," 1,043 "
-41002205,HC L-SPINE MIN FOUR VIEWS," 1,413 "
-41302209,HC L-SPINE MIN FOUR VIEWS," 1,413 "
-93602209,HC L-SPINE MIN FOUR VIEWS," 1,413 "
-55000004,HC LAB BACTERIOLOGY BR, 737 
-59000000,HC LAB BCTRY BR, 737 
-31900087,HC LAB DRAW - VENIPUNCTURE, 115 
-40450017,HC LACERATION TRAY, 213 
-50462571,HC LACTATE, 330 
-51362572,HC LACTATE, 330 
-51562577,HC LACTATE, 330 
-58562570,HC LACTATE, 330 
-59462572,HC LACTATE, 330 
-94100757,HC LARGE WOUND DRAIN, 149 
-62022504,HC LARYNGEAL FUNCTION STUDIES, 297 
-62016480,HC LARYNGOSCOPY FIBEROPTIC DX, 349 
-62016498,HC LARYNGOSCOPY FIBEROPTIC W/BX," 4,144 "
-62016522,HC LARYNGOSCOPY FIBEROPTIC W/STRO, 509 
-62016472,HC LARYNGOSCOPY W/INJECT VOCAL CO," 4,144 "
-50722735,HC LASA P, 90 
-62020573,HC LASER TRABECULOPLASTY, 838 
-46130142,HC LATX CRYPTL AG(CSF), 309 
-62011291,HC LAYER CLSE F/E/E/N/L 2.6-5.0CM, 713 
-62011309,HC LAYER CLSE F/E/E/N/L 5.1-7.5CM, 762 
-62011283,HC LAYER CLSE F/E/E/N/L <2.6CM, 602 
-62011234,HC LAYER CLSE NK/HN/FT/GT 2.6-7.5, 500 
-62011226,HC LAYER CLSE NK/HN/FT/GT <2.6CM, 390 
-62011176,HC LAYER CLSE SC/AX/TRK/EXT 2.6-7, 539 
-62011184,HC LAYER CLSE SC/AX/TRK/EXT 7.6-1, 569 
-62011168,HC LAYER CLSE SC/AX/TRK/EXT <2.6C, 422 
-50411123,HC LDH CUSTOM, 193 
-59411124,HC LDH CUSTOM, 193 
-50405299,HC LDH CUSTOM PNL, 193 
-50402569,HC LDH FLUID, 193 
-59402560,HC LDH FLUID, 193 
-50402551,HC LDH TOTAL, 193 
-51302552,HC LDH TOTAL, 193 
-57302556,HC LDH TOTAL, 193 
-59402552,HC LDH TOTAL, 193 
-50742964,HC LDL GGE, 45 
-51565232,HC LDL PHENOTYPE, 278 
-58565235,HC LDL PHENOTYPE, 278 
-50464569,HC LDLD - DIRECT LDL CHOLESTEROL, 219 
-59464560,HC LDLD - DIRECT LDL CHOLESTEROL, 219 
-51580025,HC LEAD WHOLE BLOOD, 172 
-58580028,HC LEAD WHOLE BLOOD, 172 
-55090666,HC LEGIONEL ADDL MTHDS, 226 
-55090658,HC LEGIONELL/ACID CULT, 165 
-55090757,HC LEGIONELLA CULT/D+A, 165 
-55090807,HC LEGNLA CLTR/D+D, 165 
-55090708,HC LEGNLA CULT/DIR, 165 
-50743160,HC LEMS AUTO ANTIBODY, 327 
-33200049,HC LEVEL 1 ESTABLISHED PT, 160 
-40510208,HC LEVEL 1 EXAM ESTAB, 160 
-33200031,HC LEVEL 1 NEW PT, 334 
-40510018,HC LEVEL 1 NEW PT EXAM, 334 
-40510216,HC LEVEL 2 EXAM ESTAB, 361 
-40510026,HC LEVEL 2 NEW PT EXAM, 392 
-40510224,HC LEVEL 3 EXAM ESTAB, 453 
-40510034,HC LEVEL 3 NEW PT EXAM, 453 
-26401158,HC LEVEL III ESTB PT, 413 
-26501155,HC OP VISIT LEVEL 3-EST, 413 
-26601096,HC LEVEL III ESTB PT, 413 
-26401166,HC LEVEL IV ESTB PT, 463 
-26501163,HC OP VISIT LEVEL 4-EST, 463 
-26501171,HC OP VISIT LEVEL 5-EST, 593 
-26601153,HC LEVEL IV ESTB PT, 463 
-26401174,HC LEVEL V ESTB PT, 593 
-26601161,HC LEVEL V ESTB PT, 593 
-59090753,HC LGNLA CLT/D+A, 165 
-59090704,HC LGNLA CLT/DIR, 165 
-92000173,HC LH, 110 
-51510014,HC LH (LHOR), 344 
-58510017,HC LH (LHOR), 344 
-50460708,HC LIDOCAINE LEVEL, 264 
-51560704,HC LIDOCAINE LEVEL, 264 
-51860708,HC LIDOCAINE LEVEL, 264 
-56860703,HC LIDOCAINE LEVEL, 264 
-58560707,HC LIDOCN LVL, 264 
-40450520,HC LIDODERM PATCH, 46 
-62018007,HC LIGATION OF HEMORRHOID(S)," 1,080 "
-62016803,HC LIGATION/BX TEMPORAL ARTERY," 3,695 "
-50464601,HC LIPASE, 213 
-51564607,HC LIPASE, 213 
-51864601,HC LIPASE, 213 
-56864606,HC LIPASE, 213 
-58564600,HC LIPASE, 213 
-59464602,HC LIPASE, 213 
-59464552,HC LIPID (LPD), 415 
-50402403,HC LIPID PANEL - CALCULATED LD, 415 
-59402404,HC LIPID PANEL WITH CALCULATED LD, 415 
-50464551,HC LIPID(LPD), 415 
-50464783,HC LIPID(LPDPC), 415 
-59464784,HC LIPID(LPDPC), 415 
-52110699,HC LIQUID BASED SLIDE PREP, 513 
-50120468,HC LIQUID PLASMA, 751 
-62111323,HC LIQUID PREP INTERP&REPT, 513 
-52110541,"HC LIQUID-BASED PREP,INTERP, RPT", 513 
-59402859,HC LITHIUM QNT, 188 
-51802858,HC LITHIUM QNT, 188 
-56802853,HC LITHIUM QNT, 188 
-50402858,HC LITHIUM QAN, 188 
-51502854,HC LITHIUM QNT, 188 
-37003936,HC LIVER IMAGING (SPECT)," 3,991 "
-84200286,HC LIVER MNT GRP:EA 30 DIFF DX, 35 
-84200260,HC LIVER MNT GRP:EA 30 SAME DX, 73 
-84200245,HC LIVER MNT INIT 15, 118 
-84200278,HC LIVER MNT RE 15 DIFF DX, 46 
-84200252,HC LIVER MNT RE 15 SAME DX, 101 
-50743236,HC 6959 11045R LIV KID IGG AB, 19 
-37001153,HC LIVR/SPLEEN IMAGING, 886 
-50791193,HC LMXSI:AB LUMINEX SA I, 426 
-52291192,HC LMXSI:AB LUMINEX SA I, 82 
-81091191,HC LMXSI:AB LUMINEX SA I, 82 
-81191199,HC LMXSI:AB LUMINEX SA I, 82 
-81391195,HC LMXSI:AB LUMINEX SA I, 82 
-81791196,HC LMXSI:AB LUMINEX SA I, 82 
-82391194,HC LMXSI:AB LUMINEX SA I, 82 
-84391192,HC LMXSI:AB LUMINEX SA I, 82 
-85391191,HC LMXSI:AB LUMINEX SA I, 82 
-50791201,HC LMXSII:AB LUMINEX SA II, 368 
-52291200,HC LMXSII:AB LUMINEX SA II, 82 
-81091209,HC LMXSII:AB LUMINEX SA II, 82 
-81191207,HC LMXSII:AB LUMINEX SA II, 82 
-81391203,HC LMXSII:AB LUMINEX SA II, 82 
-81791204,HC LMXSII:AB LUMINEX SA II, 82 
-82391202,HC LMXSII:AB LUMINEX SA II, 82 
-84391200,HC LMXSII:AB LUMINEX SA II, 82 
-85391209,HC LMXSII:AB LUMINEX SA II, 82 
-62026521,HC BIOPSY INTRANASAL, 723 
-62026539,HC SINUSOTOMY MAXILLARY ANTROTOMY INTRANASAL," 3,030 "
-52110566,HC LOC GUIDE SCRN PAP W/REV, 490 
-41016007,HC LOCM 100 - 199 MG/ML - OMNPQ, 10 
-41016015,HC LOCM 200 - 299 MG/ML - OMNPQ, 20 
-41895624,HC LOCM 200-299," 1,188 "
-41016023,HC LOCM 300 - 399 MG/ML - OMNPQ, 20 
-37216116,HC LOCM 300-399 MG/ML, 3 
-38016119,HC LOCM 300-399 MG/ML, 3 
-42016113,HC LOCM 300-399 MG/ML, 3 
-94316114,HC LOCM 300-399 MG/ML, 3 
-37216033,HC LOCM 300-399 MG/ML-OMNPQ, 2 
-38016036,HC LOCM 300-399 MG/ML-OMNPQ, 2 
-42016030,HC LOCM 300-399 MG/ML-OMNPQ, 2 
-94316031,HC LOCM 300-399 MG/ML-OMNPQ, 2 
-37216058,HC LOCM 300-399 MG/ML-VSPQ, 5 
-38016051,HC LOCM 300-399 MG/ML-VSPQ, 5 
-42016055,HC LOCM 300-399 MG/ML-VSPQ, 5 
-94316056,HC LOCM 300-399 MG/ML-VSPQ, 5 
-41006669,HC LOOPOGRAM," 1,036 "
-50751155,HC 6956 11084R CHROM QNT SNGL, 64 
-41530072,HC LOW EXT POST TRDML BL," 2,401 "
-41016122,"HC LOW EXTREMITY, PED, MIN 2V", 908 
-59393199,HC LOW SUBSTRATE PYRUVATE KINASE, 165 
-50464528,HC LP(A), 194 
-51564524,HC LP(A), 194 
-58564527,HC LP(A), 194 
-59464529,HC LP(A), 194 
-50465244,HC LP-PLA2, 117 
-51565240,HC LP-PLA2, 324 
-58565243,HC LP-PLA2, 324 
-59465245,HC LP-PLA2, 117 
-59130120,HC LTX CRPTC AG (BLD), 309 
-59130146,HC LTX CRYTCL AG (CSF), 309 
-26431023,HC LUMBAR PUNCTURE DIAGNOSTIC," 2,210 "
-26530998,HC LUMBAR PUNCTURE DIAGNOSTIC," 2,210 "
-26630996,HC LUMBAR PUNCTURE," 2,210 "
-41152026,HC LUMBAR PUNCTURE," 2,210 "
-62019815,HC LUMBAR PUNCTURE DX, 541 
-26400861,HC LUMBAR TRAY, 313 
-26500868,HC LUMBAR TRAY, 313 
-81500282,HC LUNG MNT GRP:EA 30 DIFF DX, 35 
-81500266,HC LUNG MNT GRP:EA 30 SAME DX, 73 
-81500241,HC LUNG MNT INIT 15, 118 
-81500274,HC LUNG MNT RE 15 DIFF DX, 46 
-81500258,HC LUNG MNT RE 15 SAME DX, 101 
-37001302,HC LUNG PERFUSION STDY," 3,596 "
-36060382,HC LUPRON DEPO INJECTION," 1,407 "
-51402477,HC LUPUS ANTICOAG PTT, 92 
-51402485,HC LUPUS ANTICOAG PTT, 92 
-51402493,HC LUPUS ANTICOAG PTT, 92 
-51402501,HC LUPUS ANTICOAG PTT, 92 
-51402519,HC LUPUS ANTICOAG PTT, 92 
-51402527,HC LUPUS ANTICOAG PTT, 92 
-51402535,HC LUPUS ANTICOAG PTT, 240 
-51402543,HC LUPUS ANTICOAG PTT, 240 
-51402550,HC LUPUS ANTICOAG PTT, 240 
-51402568,HC LUPUS ANTICOAG PTT, 240 
-58402470,HC LUPUS ANTICOAG PTT, 92 
-58402488,HC LUPUS ANTICOAG PTT, 92 
-58402496,HC LUPUS ANTICOAG PTT, 92 
-58402504,HC LUPUS ANTICOAG PTT, 92 
-58402512,HC LUPUS ANTICOAG PTT, 92 
-58402520,HC LUPUS ANTICOAG PTT, 92 
-58402538,HC LUPUS ANTICOAG PTT, 240 
-58402546,HC LUPUS ANTICOAG PTT, 240 
-58402553,HC LUPUS ANTICOAG PTT, 240 
-58402561,HC LUPUS ANTICOAG PTT, 240 
-37001179,HC LYMPHTC/LYMPHGL IMG," 4,375 "
-50791417,HC LYSIS PRE NA EA SPEC, 24 
-52291416,HC LYSIS PRE NA EA SPEC, 24 
-81091415,HC LYSIS PRE NA EA SPEC, 24 
-81191413,HC LYSIS PRE NA EA SPEC, 24 
-81391419,HC LYSIS PRE NA EA SPEC, 24 
-81791410,HC LYSIS PRE NA EA SPEC, 24 
-82391418,HC LYSIS PRE NA EA SPEC, 24 
-84391416,HC LYSIS PRE NA EA SPEC, 24 
-85391415,HC LYSIS PRE NA EA SPEC, 24 
-58700055,HC LYTES BLOOD GAS G3, 815 
-55090252,HC MAC/OTHR WTR CULT, 302 
-50749407,HC MAG DUAL ANTIGEN AUTO AB, 123 
-50749399,HC MAG DUAL INTIGEN AUTO AB, 232 
-50411073,HC MAGNESIUM CUSTOM, 280 
-59411074,HC MAGNESIUM CUSTOM, 280 
-50405240,HC MAGNESIUM CUSTOM PANEL, 280 
-59405241,HC MAGNESIUM CUSTOM PANEL, 280 
-50402908,HC MAGNESIUM SERUM/PL, 280 
-51302909,HC MAGNESIUM SERUM/PL, 280 
-57302903,HC MAGNESIUM SERUM/PL, 280 
-59402909,HC MAGNESIUM SERUM/PL, 280 
-59393785,HC MALARIA PREP, 379 
-50393784,HC MALARIA PREP, 379 
-40809113,HC MAMMO DUCT SNG, 720 
-40871642,HC MAMMO GUIDE NDL PLCMNT BRST EA," 1,577 "
-41000258,HC MANDIBLE MIN 4 VIEW," 1,076 "
-41300252,HC MANDIBLE MIN 4 VIEW," 1,076 "
-93600252,HC MANDIBLE MIN 4 VIEW," 1,076 "
-50725472,HC 6959 MANGANESE, 20 
-62026356,HC MANIPULATION TMJ W/ANEST, 723 
-31950108,HC MAPCATH WIRE ONLY, 255 
-40450538,HC MARCAINE 0.5%, 86 
-50751163,HC 6956 YUTHC THC MARJN CONF U, 13 
-41100850,HC MASTOTOMY W/DRIAN," 3,715 "
-62024500,HC MEASURE BLOOD OXYGEN LEVEL, 62 
-55092142,HC MECA PCR ID CULT, 578 
-59092148,HC MECA PCR ID CULT, 153 
-41152935,HC MECH RMVL INTRALUMIN OBSTR CNT, 480 
-41152901,HC MECH RMVL PERICATH OBSTR CNT," 3,207 "
-41152943,HC MECH RMVL PERICATH OBSTR CNT, 384 
-41153545,HC MECH THROMBECTOMY NON-DIAL GRAFT," 9,793 "
-50411321,HC MED EMERG CALCIUM IO, 399 
-59411322,HC MED EMERG CALCIUM IO, 399 
-50411313,HC MED EMERG CHLORIDE, 129 
-59411314,HC MED EMERG CHLORIDE, 129 
-50411305,HC MED EMERG GLUCOSE, 159 
-59411306,HC MED EMERG GLUCOSE, 159 
-50411339,HC MED EMERG HCT, 217 
-59411330,HC MED EMERG HCT, 217 
-50411297,HC MED EMERG POTASSIUM, 204 
-59411298,HC MED EMERG POTASSIUM, 204 
-50411289,HC MED EMERG SODIUM, 219 
-59411280,HC MED EMERG SODIUM, 219 
-43100080,HC MED NUTR THER INIT 15, 118 
-50401645,HC MEDICAL EMERGENCY PANEL SODIUM, 219 
-62022157,HC MEDICAL HYPNOTHERAPY, 153 
-50743756,HC MELAS MT DNA MUTATE INT & RPT, 55 
-50743731,HC MELAS MT DNA MUTATE PCR 1, 55 
-50743749,HC MELAS MT DNA MUTATE PCR 2, 55 
-50743715,HC MELAS MT DNA MUTATION DIGEST, 55 
-50743707,HC MELAS MT DNA MUTATION EXTRACTN, 55 
-50743723,HC MELAS MT DNA MUTATION SEPARATN, 55 
-40510448,HC MENINGOCOCCAL VACCINE, 801 
-94100658,HC MEPILEX BORDER 6X6, 50 
-94100781,HC MEPITEL SILICON DRSG 4X8, 50 
-50450014,HC METABOLIC PANEL - BASIC W/CAI, 487 
-59450015,HC METABOLIC PANEL - BASIC W/CAI, 487 
-50463447,HC METABOLIC PNL BASIC, 350 
-51363448,HC METABOLIC PNL BASIC, 350 
-57363442,HC METABOLIC PNL BASIC, 350 
-59463448,HC METABOLIC PNL BASIC 8, 350 
-50463454,HC METABOLIC PNL COMP, 435 
-51363455,HC METABOLIC PNL COMP, 435 
-57363459,HC METABOLIC PNL COMP, 435 
-59463455,HC METABOLIC PNL COMP, 435 
-46160743,HC METAPNEUMOVIRUS DFA, 190 
-59160747,HC METAPNEUMOVIRUS DFA, 190 
-50464841,HC METHGB(METHHEMOGLOB) QNT, 81 
-51864841,HC METHGB(METHHEMOGLOB), 81 
-56864846,HC METHGB(METHHEMOGLOB), 81 
-50463678,HC METHOTREXATE, 291 
-51563674,HC METHOTREXATE, 291 
-51863678,HC METHOTREXATE, 291 
-56863673,HC METHOTREXATE, 291 
-58563677,HC METHOTREXATE, 291 
-59463679,HC METHOTREXATE, 291 
-52725504,HC METHYLMALONIC ACID QUANT, 200 
-50803907,HC MGMT INTERP AND RPT, 138 
-50803899,HC MGMT LYSIS PRE NA, 66 
-50803881,HC MGMT MUT AMP NA, 188 
-50803865,HC MGMT MUT ISOL NA, 146 
-50803873,HC MGMT SEP GEL ELECT, 103 
-55090906,HC MIC BATTERY, 254 
-59090902,HC MIC BATTERY, 254 
-51564995,"HC ALBUMIN, URINE/OTHER, QUANT", 126 
-58564998,"HC ALBUMIN, URINE/OTHER QUAN", 126 
-59464990,"HC MICROALBUMIN, URINE", 154 
-55020820,HC MICROSPORIDIUM, 379 
-59020826,HC MICROSPORIDIUM STAIN, 379 
-51563997,HC MISC ALLERGEN 1 IGE, 79 
-58563990,HC MISC ALLERGEN 1 IGE, 79 
-51564003,HC MISC ALLERGEN 2 IGE, 79 
-58564006,HC MISC ALLERGEN 2 IGE, 79 
-51564011,HC MISC ALLERGEN 3 IGE, 79 
-58564014,HC MISC ALLERGEN 3 IGE, 79 
-51564029,HC MISC ALLERGEN 4 IGE, 79 
-58564022,HC MISC ALLERGEN 4 IGE, 79 
-51564037,HC MISC ALLERGEN 5 IGE, 79 
-58564030,HC MISC ALLERGEN 5 IGE, 79 
-51564045,HC MISC ALLERGEN 6 IGE, 79 
-58564048,HC MISC ALLERGEN 6 IGE, 79 
-51564052,HC MISC ALLERGEN 7 IGE, 79 
-58564055,HC MISC ALLERGEN 7 IGE, 79 
-51564060,HC MISC ALLERGEN 8 IGE, 79 
-58564063,HC MISC ALLERGEN 8 IGE, 79 
-51564078,HC MISC ALLERGEN 9 IGE, 79 
-58564071,HC MISC ALLERGEN 9 IGE, 79 
-50751171,HC 6956 11071R MITCHNDRL M2 AB, 13 
-40510182,HC MMR VACCINE, 397 
-52005204,HC MNR SPC/TIS ID-LVII, 590 
-62012190,HC MOHS 1 STAGE H/N/HF/G STG1<6, 755 
-62025515,HC MOHS 1 STATE T/A/L<6," 1,176 "
-62012216,HC MOHS ADDL STAGE T/A/L<6, 646 
-62012208,HC MOHS H/N/HF/G ADDL STAGE <6, 716 
-62012232,HC MOHS SURG ADDL BLOCK>5, 984 
-55091425,HC MOLD ID, 237 
-59091421,HC MOLD ID, 237 
-52051356,HC MORPH ANALYSIS;MANUAL, 562 
-62150875,HC MORPH TUMOR ANALYSIS-SPC, 616 
-50743780,HC MOTOR NP PROFILE 6ML ELISA, 145 
-50743798,HC MOTOR NP PROFILE ASIALO-6ML EL, 145 
-50743764,HC MOTOR NP PROFILE MAG DUAL AG, 135 
-50743814,HC MOTOR NP PROFILE: GQIB ELISA, 145 
-50743806,HC MOTOR NP PROFILE:GDLB ELISA, 145 
-50751916,HC 6959 BMPOR MPO IMM QLSQ MLT, 26 
-94231420,HC MR AVM ST ADDNL CPY," 1,068 "
-94260338,HC MR BRAIN WO/W CONTRST," 8,276 "
-38160347,HC MR C SPINE WO/W CONT," 8,423 "
-94260346,HC MR C SPINE WO/W CONT," 8,423 "
-38100004,HC MR HEAD PEFFERBAUM," 3,858 "
-38131769,HC MR HEART MORPH WO/W CONTRAST," 7,610 "
-94231768,HC MR HEART MORPH WO/W CONTRAST," 7,610 "
-38160438,HC MR HEART VELOCITY FLOW," 4,171 "
-94260437,HC MR HEART VELOCITY FLOW," 4,171 "
-38160420,HC MR HEART W/O CONTRAST," 6,268 "
-94260429,HC MR HEART W/O CONTRAST," 6,268 "
-38160362,HC MR L SPINE WO/W CONT," 8,733 "
-94260361,HC MR L SPINE WO/W CONT," 8,733 "
-38131447,HC MR RESEARCH CHARGE," 2,088 "
-38131892,HC MR SPECTROSCOPY," 2,499 "
-94231891,HC MR SPECTROSCOPY," 2,499 "
-40300105,HC MR SURGICAL PLANNING," 3,858 "
-38160255,HC MR SYRINGE&TRAY COS, 199 
-94260254,HC MR SYRINGE&TRAY COS, 199 
-38160354,HC MR T SPINE WO/W CONT," 7,998 "
-94260353,HC MR T SPINE WO/W CONT," 7,998 "
-38131744,HC MRA WO FOL W CONT ABD," 8,001 "
-94231743,HC MRA ABDOMEN WITH AND WITHOUT CONTRAST," 8,001 "
-38131660,HC MRA WO FOL W CONT CHEST," 8,190 "
-94231669,HC MRA CHEST WITH AND WITHOUT CONTRAST," 8,190 "
-38160396,HC MRA WO FOL W CONT LWR EXT," 7,969 "
-94260395,HC MRA LWR EXTRM W AND WO CONTRAST," 7,969 "
-38131702,HC MRA WO FOL W CONT PELVIS," 7,794 "
-94231701,HC MRA PELVIS W AND WO CONTRAST," 7,794 "
-38131686,HC MRA SPINAL CANAL W OR W/O CONT," 8,263 "
-94231685,HC MRA SPINAL CANAL W OR W/O CONT," 8,263 "
-38131728,HC MRA UPPER EXT WO/W CONTRAST," 7,794 "
-94231727,HC MRA UPPER EXT WO/W CONTRAST," 7,794 "
-38160511,HC MRA W/CONT ABD," 6,681 "
-38160453,HC MRA W/CONT CHEST," 6,458 "
-38160495,HC MRA W/CONT LWR EXT," 6,283 "
-38160479,HC MRA W/CONT PELVIS," 6,681 "
-38160529,HC MRA W/O CONT ABD," 5,769 "
-38160461,HC  MRA W/O CONT CHEST," 5,886 "
-38160503,HC MRA W/O CONT LWR EXT," 5,566 "
-38160487,HC MRA W/O CONT PELVIS," 5,566 "
-38110201,HC MRI ABD W/CONTRAST," 6,681 "
-94210200,HC MRI ABD W/CONTRAST," 6,681 "
-38110219,HC MRI ABD WO/W CONTRAST," 8,002 "
-94210218,HC MRI ABD WO/W CONTRAST," 8,002 "
-38100137,HC MRI ABDOMEN WO CONTRAST," 5,769 "
-94200136,HC MRI ABDOMEN WO CONTRAST," 5,769 "
-38110029,HC MRI ANGIO HEAD WO CONTRAST," 5,681 "
-94210028,HC MRI ANGIO HEAD WO CONTRAST," 5,681 "
-38110037,HC MRI ANGIO HEAD W/CONTRST," 7,199 "
-38110045,HC  MRI ANGIO HEAD WO/W CONT," 8,699 "
-94210044,HC MRI ANGIO HEAD.WO/W CONT," 8,699 "
-94210036,"HC MRI ANGIO HEAD,W/CONTRST"," 7,199 "
-38110052,HC MRI ANGIO NECK WO CONTRAST," 5,295 "
-94210051,HC MRI ANGIO NECK WO CONTRAST," 5,295 "
-38110060,HC MRI ANGIO NECK W/CONTRST," 6,350 "
-38110078,HC MRI ANGIO NECK W/W/O CONT," 7,957 "
-94210069,"HC MRI ANGIO NECK,W/CONTRST"," 6,350 "
-94210077,"HC MRI ANGIO NECK,W/W/O CONT"," 7,957 "
-38100400,HC MRI LWR EXTREM JNT WO CONTRAST," 4,973 "
-94200409,HC MRI LWR EXTREM JNT WO CONTRAST," 4,973 "
-38100434,HC MRI BRAIN W/O CONTRAST," 6,610 "
-94200433,HC MRI BRAIN W/O CONTRAST," 6,610 "
-38105128,HC MRI BRAIN FUNCTION NO MD," 1,641 "
-94205127,HC MRI BRAIN FUNCTION NO MD," 1,641 "
-38105136,HC MRI BRAIN FUNCTION W/MD," 2,477 "
-94205135,HC MRI BRAIN FUNCTION W/MD," 2,477 "
-38100442,HC MRI BRAIN W/CONTRAST," 7,269 "
-94200441,HC MRI BRAIN W/CONTRAST," 7,269 "
-38160339,HC MRI BRAIN WO/W CONTRST," 8,276 "
-38131843,HC MRI W CONT BREAST BI," 6,466 "
-94231842,HC MRI W CONTRAST BREAST BILAT," 5,351 "
-38131827,HC MRI W FOL W CONT BREAST UNI," 3,233 "
-94231826,HC MRI BRST UNI W AND WO CONTRAST," 3,233 "
-38100533,HC MRI C SPINE WO CONTRAST," 5,727 "
-94200532,HC MRI C SPINE WO CONTRAST," 5,727 "
-38100509,HC MRI C-SPINE W/CONTRAST," 7,335 "
-94200508,HC MRI C-SPINE W/CONTRAST," 7,335 "
-38100483,HC MRI CHEST WO CONTRAST," 5,887 "
-94200482,HC MRI CHEST WO CONTRAST," 5,887 "
-38110086,HC MRI CHEST W/CONTRAST," 6,458 "
-94210085,HC MRI CHEST W/CONTRAST," 6,458 "
-38110094,HC MRI CHEST WO/W CONTRAST," 8,190 "
-94210093,HC MRI CHEST WO/W CONTRAST," 8,190 "
-38105003,HC MRI GAD CONTRAST /ML, 29 
-94205002,HC MRI GAD CONTRAST /ML, 29 
-38101523,HC MRI L-SPINE WO CONTRAST," 5,647 "
-94201522,HC MRI L-SPINE WO CONTRAST," 5,647 "
-38101531,HC MRI L-SPINE W/CONTRAST," 6,681 "
-94201530,HC MRI L-SPINE W/CONTRAST," 6,681 "
-38110169,HC MRI LWR EXTREM NON-JT WO CONTRAST," 5,898 "
-94210168,HC MRI LWR EXTREM NON-JT WO CONTRAST," 5,898 "
-38110185,HC MRI LWR EXTREM JNT W/CONTRAST," 6,152 "
-94210184,HC MRI LWR EXTREM JNT W/CONTRAST," 6,152 "
-38110193,HC MRI LWR EXTREM JNT WO/W CONT," 8,324 "
-94210192,HC MRI LWR EXTREM JNT WO/W CONT," 8,324 "
-38110177,HC MRI LWR EXTREM NON-JT W/CONT," 6,629 "
-94210176,HC MRI LWR EXTREM NON-JT W/CONT," 6,629 "
-38100749,HC MRI LWR EXTREM NON-JT WO/W CON," 7,969 "
-94200748,HC MRI LWR EXTREM NON-JT WO/W CON," 7,969 "
-38110011,HC MRI ORBIT FACE NECK WO/W CONT," 8,855 "
-94210010,"HC MRI ORBIT,FACE,NECK,WO/W CONT"," 8,855 "
-38131629,HC MRI ORBITS FACE NECK W/O CONT," 5,554 "
-94231628,HC MRI ORBITS FACE NECK W/O CONT," 5,554 "
-38110003,HC MRI ORBITS FACE NECK W/CONT," 6,253 "
-94210002,"HC MRI ORBITS,FACE,NECK,W/CONT"," 6,253 "
-38110102,HC MRI PELVIS WO CONTRAST," 5,566 "
-94210101,HC MRI PELVIS WO CONTRAST," 5,566 "
-94230083,HC MRI PELVIS W/CONTRAST," 6,681 "
-38130084,HC MRI PELVIS W/CONTRAST," 6,681 "
-38110110,HC MRI PELVIS WO/W CONTRAST," 7,794 "
-94210119,HC MRI PELVIS WO/W CONTRAST," 7,794 "
-38101424,HC MRI T-SPINE WO CONTRAST," 5,732 "
-94201423,HC MRI T-SPINE WO CONTRAST," 5,732 "
-38101432,HC MRI T-SPINE W/CONTRAST," 6,419 "
-94201431,HC MRI T-SPINE W/CONTRAST," 6,419 "
-38110227,HC MRI TMJ(S)," 4,252 "
-94210226,HC MRI TMJ(S)," 4,252 "
-38100335,HC MRI UPR EXTREM JNT WO CONTRAST," 5,950 "
-94200334,HC MRI UPR EXTREM JNT WO CONTRAST," 5,950 "
-38110144,HC MRI UPR EXTREM JNT W/CONTRAST," 6,565 "
-94210143,HC MRI UPR EXTREM JNT W/CONTRAST," 6,565 "
-38110151,HC MRI UPR EXTREM JNT WO/W CONTRA," 8,002 "
-94210150,HC MRI UPR EXTREM JNT WO/W CONTRA," 8,002 "
-38110128,HC MRI UPR EXTREM NON-JT WO CONTRAST," 5,594 "
-94210127,HC MRI UPR EXTREM NON-JT WO CONTRAST," 5,594 "
-38110136,HC MRI UPR EXTREM NON-JT W/CONT," 6,120 "
-94210135,HC MRI UPR EXTREM NON-JT W/CONT," 6,120 "
-38100780,HC MRI UPR EXTREM NON-JT WO/W CON," 7,794 "
-94200789,HC MRI UPR EXTREM NON-JT WO/W CON," 7,794 "
-38160537,HC MRI W/CONT BREAST UNI," 2,676 "
-38160552,HC MRI W/O CONT BREAST BI," 3,985 "
-38160545,HC MRI W/O CONT BREAST UNI," 1,993 "
-38160560,HC MRI W/O FOL W/CONT BREAST BI," 6,466 "
-55091813,HC MRSA BY MECA LA, 309 
-59091819,HC MRSA BY MECA LA, 309 
-40180119,HC MRT GUIDANCE NEEDLE PLCMNT," 4,256 "
-83500207,HC MRW DNR INFO SESS, 858 
-83500256,HC MRW DNR PHYS EXM," 2,951 "
-83500959,HC MRW DNR W/U CNCL," 2,977 "
-52751971,HC MUCOPOLYSACCHA U TLC, 317 
-52751955,HC MUCOPOLYSACCARIDES U QNT, 322 
-46160735,HC MUMPS AB, 212 
-59160739,HC MUMPS AB, 212 
-62023122,HC MUSCLE TEST MANUAL BODY W/HNDS, 155 
-50791292,HC MUT AMP EA NA SEQ, 30 
-52291291,HC  MUT AMP EA NA SEQ, 30 
-81091290,HC  MUT AMP EA NA SEQ, 30 
-81191298,HC MUT AMP EA NA SEQ, 30 
-81391294,HC MUT AMP EA NA SEQ, 30 
-81791295,HC MUT AMP EA NA SEQ, 30 
-82391293,HC  MUT AMP EA NA SEQ, 30 
-84391291,HC  MUT AMP EA NA SEQ, 30 
-85391290,HC MUT AMP EA NA SEQ, 30 
-50791326,HC MUT AMP EA NA SEQ, 30 
-52291325,HC MUT AMP EA NA SEQ, 30 
-81091324,HC MUT AMP EA NA SEQ, 30 
-81191322,HC MUT AMP EA NA SEQ, 30 
-81391328,HC MUT AMP EA NA SEQ, 30 
-81791329,HC MUT AMP EA NA SEQ, 30 
-82391327,HC MUT AMP EA NA SEQ, 30 
-84391325,HC MUT AMP EA NA SEQ, 30 
-85391324,HC MUT AMP EA NA SEQ, 30 
-50803634,"HC MUT ID ENZ LIG/PRM EXT, EA", 71 
-50802800,HC MUT ID SEQUN EA SEG, 54 
-50791318,HC MUT SEP BY BEL ELCT EA NA, 8 
-52291317,HC MUT SEP BY BEL ELCT EA NA, 8 
-81091316,HC MUT SEP BY BEL ELCT EA NA, 8 
-81191314,HC MUT SEP BY BEL ELCT EA NA, 8 
-81391310,HC MUT SEP BY BEL ELCT EA NA, 8 
-81791311,HC MUT SEP BY BEL ELCT EA NA, 8 
-82391319,HC MUT SEP BY BEL ELCT EA NA, 8 
-84391317,HC MUT SEP BY BEL ELCT EA NA, 8 
-85391316,HC MUT SEP BY BEL ELCT EA NA, 8 
-85391274,HC MUT SEP BY GEL ELCT EA NA, 8 
-50791276,HC MUT SEP BY GEL ELECT EA NA, 8 
-52291275,HC MUT SEP BY GEL ELECT EA NA, 8 
-81091274,HC MUT SEP BY GEL ELECT EA NA, 8 
-81191272,HC MUT SEP BY GEL ELECT EA NA, 8 
-81391278,HC MUT SEP BY GEL ELECT EA NA, 8 
-81791279,HC MUT SEP BY GEL ELECT EA NA, 8 
-82391277,HC MUT SEP BY GEL ELECT EA NA, 8 
-84391275,HC MUT SEP BY GEL ELECT EA NA, 8 
-50802792,HC MUTATION SCAN HETERODUPLEX, 84 
-50751478,HC 6956 YMPA MYCO ACD QNT NES, 19 
-41008905,HC MYELOGRAM CERVICAL," 1,920 "
-41008954,HC MYELOGRAM THORACIC," 1,751 "
-41009002,HC MYELOGRAPHY LUMBAR," 1,866 "
-37003746,HC MYOC PRF/AT REST OR STRS-SGL," 1,915 "
-37021102,"HC MYOCARD PERF, QUAL/QUAN"," 5,014 "
-37001526,HC MYOCARD TOMO MULTI," 7,246 "
-37001534,HC MYOCARD TOMO SINGLE," 5,014 "
-50404524,HC MYOGLOBIN, 265 
-51504520,HC MYOGLOBIN, 265 
-51804524,HC MYOGLOBIN, 265 
-56804529,HC MYOGLOBIN, 265 
-59404525,HC MYOGLOBIN, 265 
-50751197,HC 6956 YUNTX NTXTPD CRS LNK U, 46 
-50751775,HC 6956 NAPROXEN, 97 
-50301100,HC NAS SMEAR EO CT, 30 
-41000506,HC NASAL BONES MIN 3V, 896 
-41300500,HC NASAL BONES MIN 3V, 896 
-62016365,HC NASAL ENDOSCOPY DX, 300 
-55050108,HC NASAL NP CULTURE, 302 
-62016399,HC NASAL/SINUS ENDOSCOPY W/BIOPSY," 4,144 "
-62016407,HC NASAL/SINUS ENDOSCOPY W/CON BL," 2,244 "
-62022470,HC NASOPHARYNGOSCOPY, 209 
-50747872,HC NATURAL KILLER CELLS ENUM., 43 
-50747864,HC NATURAL KILLER CELLS ENUM., 89 
-50393578,HC NBT NITROBLUE TETRAZOL, 69 
-59393579,HC NBT NITROBLUE TETRAZOL, 69 
-36033397,HC NEEDLE BRACHY MENTOR, 27 
-26730614,"HC NEEDLE, HUBER", 99 
-37205986,HC NEEDLE INTRODUCER," 1,444 "
-42005983,HC NEEDLE INTRODUCER," 1,444 "
-94305984,HC NEEDLE INTRODUCER," 1,313 "
-62026323,HC CLTX SPRCNDYLR/TRANSCNDYLR HUMERAL FX W/MNPJ, 795 
-94100534,HC NEG PRESSURE WOUND THER<=50CM, 202 
-94100542,HC NEG PRESSURE WOUND THERE>50CM2, 560 
-46310355,"HC NEISSERIA GONORRHOEAE, GC LCR", 566 
-51571016,HC NEPH 1MM FR LIGHT CHAINGS, 189 
-58571019,HC NEPH IMM FR LIGHT CHAINS, 189 
-41151416,HC NEPHRO TUBE CHANGE," 2,578 "
-41100009,HC NEPHRO TUBE REMOVAL," 1,436 "
-41127713,HC NEPHROSTMY TRCT DIL," 3,186 "
-41006651,HC NEPHROSTOGRAM," 1,036 "
-40400129,HC NEUROMUSC REEDUC-15MIN, 261 
-50723303,HC 6959 11049R NSE IMMUN AG QN, 39 
-52032042,HC NEUROPTH NRV TEASING, 739 
-47202205,HC NEUTRALIZATION BR, 580 
-62300017,HC NEW PATIENT LEVEL 1, 101 
-94100609,HC NEW PATIENT VISIT LEVEL 1, 334 
-94100617,HC NEW PATIENT VISIT LEVEL 2, 392 
-62012380,HC NIPPLE RECONSTRUCTION," 4,362 "
-41151739,HC INTRO NEEDLE OR INTRACATHETER; EXTR ARTERY, 610 
-94100526,HC NON-SELECTIVE DEBRIDEMENT, 361 
-59091579,HC NON-SHS STRAIN TYP, 98 
-50751189,HC 6956 11086R NORTRIPTYLINE, 18 
-41016130,"HC NOSE 2 RECTUM, CHILD, SNGL VW", 682 
-59050104,HC NSL/NP CLT, 302 
-50465004,HC NT-PROBNP, 821 
-51865004,HC NT-PROBNP, 821 
-56865009,HC NT-PROBNP, 821 
-58565003,HC NT-PROBNP, 821 
-59465005,HC NT-PROBNP, 821 
-50802982,HC NUC ACID PROBE PCR, 53 
-50802743,HC NUC. ACID PROBE BLOT, 53 
-37000205,HC NUCLEAR MED THERAPY IV," 1,336 "
-50791284,HC NUCLEIC ACID PROBE EA, 8 
-52291283,HC NUCLEIC ACID PROBE EA, 8 
-81091282,HC NUCLEIC ACID PROBE EA, 8 
-81191280,HC NUCLEIC ACID PROBE EA, 8 
-81391286,HC NUCLEIC ACID PROBE EA, 8 
-81791287,HC NUCLEIC ACID PROBE EA, 8 
-82391285,HC NUCLEIC ACID PROBE EA, 8 
-84391283,HC NUCLEIC ACID PROBE EA, 8 
-85391282,HC NUCLEIC ACID PROBE EA, 8 
-50802750,HC NUCLEIC ACID TRANSFER, 65 
-48002265,HC O2 TITRATION STUDY, 267 
-62700315,HC OBV EA HOUR, 582 
-41153297,HC OCCLUSIVE DEVICE-VEIN," 2,473 "
-50393792,HC OCCULT BLD GUAIAC COL SCRN, 101 
-59393793,HC OCCULT BLD GUAIAC COL SCRN, 101 
-50393800,HC OCCULT BLD GUAIAC DIAG, 130 
-59393801,HC OCCULT BLD GUAIAC DIAG, 130 
-50393818,HC OCCULT BLD IMMUNO COL SCRN, 623 
-59393819,HC OCCULT BLD IMMUNO COL SCRN, 623 
-50393826,HC OCCULT BLD IMMUNO DIAG, 425 
-59393827,HC OCCULT BLD IMMUNO DIAG, 425 
-50325133,"HC OCCULT BLOOD, GASTRIC FLUID", 101 
-59325134,"HC OCCULT BLOOD, GASTRIC FLUID", 101 
-50393735,"HC OCCULT BLOOD, GUAIAC, STOOL", 101 
-51893733,HC OCCULT BLOOD GUAIAC STOOL, 101 
-56893738,HC OCCULT BLOOD GUAIAC STOOL, 101 
-59393736,"HC OCCULT BLOOD, GUAIAC, STOOL", 101 
-51893725,HC OCCULT BLOOD IMMUNO METH, 425 
-56893720,HC OCCULT BLOOD IMMUNO METH, 425 
-59393728,"HC OCCULT BLOOD,IMMUNO METH", 425 
-51564771,HC OD450, 121 
-51864775,HC OD450, 121 
-56864770,HC OD450, 121 
-58564774,HC OD450, 121 
-50741248,HC 6959 11050R OKT3 LKCYT AB, 223 
-50751791,HC 6959 11051R OLNZPIN QN DRUG, 92 
-58502949,HC OLIGCLNL BNDG, 374 
-51593812,HC OLIGOCLONAL IMMUNE, 374 
-58593815,HC OLIGOCLONAL IMMUNE, 374 
-52751963,HC OLIGOSACCHARIDES U TLC, 317 
-51502946,HC OLOGOCLONAL BANDING, 374 
-41810961,HC OMNIAQUE 300 (100ML), 2 
-41810979,HC OMNIPAQUE 300 (200ML), 1.0 
-41810987,HC OMNIPAQUE 300 (50ML), 1.0 
-36301521,HC OMNIPAQUE 300-399MG PER ML, 2 
-41811027,HC OMNIPAQUE 350 (100ML), 600 
-41811035,HC OMNIPAQUE 350 (200ML), 2 
-41811043,HC OMNIPAQUE 350 (50ML), 2 
-41810953,HC OMNISCAN (50ML), 2 
-91900043,HC OOCYTE IDENTIFICATION," 1,276 "
-94100559,HC OP VISIT LEVEL 1 EST PT, 160 
-26400549,HC OP VISIT LEVEL 1-EST, 160 
-26500546,HC OP VISIT LEVEL 1-EST, 160 
-26610006,HC OP VISIT LEVEL 1-EST, 160 
-26730028,HC OP VISIT LEVEL 1-EST, 160 
-62700729,HC OP VISIT LEVEL 1-EST, 160 
-26403139,HC OP VISIT LEVEL 1-EST-W PX, 160 
-26503136,HC OP VISIT LEVEL 1-EST-W PX, 160 
-26603134,HC OP VISIT LEVEL 1-EST-W PX, 160 
-26703165,HC OP VISIT LEVEL 1-EST-W PX, 160 
-62700778,HC OP VISIT LEVEL 1-EST-W PX, 160 
-94100906,HC OP VISIT LEVEL 1-EST-W-PX, 160 
-94100955,HC OP VISIT LEVEL 1-NEW W/PROC, 334 
-94100567,HC OP VISIT LEVEL 2 EST PT, 361 
-26400556,HC OP VISIT LEVEL 2-EST, 361 
-26500553,HC OP VISIT LEVEL 2-EST, 361 
-26610246,HC OP VISIT LEVEL 2-EST, 361 
-26730036,HC OP VISIT LEVEL 2-EST, 361 
-62700737,HC OP VISIT LEVEL 2-EST, 361 
-26403147,HC OP VISIT LEVEL 2-EST-W PX, 361 
-26503144,HC OP VISIT LEVEL 2-EST-W PX, 361 
-26603142,HC OP VISIT LEVEL 2-EST-W PX, 361 
-26703173,HC OP VISIT LEVEL 2-EST-W PX, 361 
-62700786,HC OP VISIT LEVEL 2-EST-W PX, 361 
-94100914,HC OP VISIT LEVEL 2-EST-W PX, 361 
-94100963,HC OP VISIT LEVEL 2-NEW W/PROC, 392 
-26703132,HC OP VISIT LEVEL 3-EST, 413 
-62700745,HC OP VISIT LEVEL 3-EST, 413 
-94100575,HC OP VISIT LEVEL 3-EST, 413 
-26403154,HC OP VISIT LEVEL 3-EST-W PX, 413 
-26503151,HC OP VISIT LEVEL 3-EST-W PX, 413 
-26603159,HC OP VISIT LEVEL 3-EST-W PX, 413 
-26703181,HC OP VISIT LEVEL 3-EST-W PX, 413 
-62700794,HC OP VISIT LEVEL 3-EST-W PX, 413 
-94100922,HC OP VISIT LEVEL 3-EST-W PX, 413 
-94100625,HC OP VISIT LEVEL 3-NEW, 453 
-94100971,HC OP VISIT LEVEL 3-NEW W/PROC, 453 
-26703140,HC OP VISIT LEVEL 4-EST, 463 
-40510307,HC OP VISIT LEVEL 4-EST, 463 
-62700752,HC OP VISIT LEVEL 4-EST, 463 
-94100583,HC OP VISIT LEVEL 4-EST, 463 
-26403162,HC OP VISIT LEVEL 4-EST-W PX, 463 
-26503169,HC OP VISIT LEVEL 4-EST-W PX, 463 
-26603167,HC OP VISIT LEVEL 4-EST-W PX, 463 
-26703199,HC OP VISIT LEVEL 4-EST-W PX, 463 
-62700802,HC OP VISIT LEVEL 4-EST-W PX, 463 
-94100930,HC OP VISIT LEVEL 4-EST-W PX, 463 
-40510281,HC OP VISIT LEVEL 4-NEW, 500 
-94100633,HC OP VISIT LEVEL 4-NEW, 500 
-94100989,HC OP VISIT LEVEL 4-NEW W/PROC, 500 
-26703157,HC OP VISIT LEVEL 5-EST, 593 
-40510315,HC OP VISIT LEVEL 5-EST, 593 
-62700760,HC OP VISIT LEVEL 5-EST, 593 
-94100591,HC OP VISIT LEVEL 5-EST, 593 
-26403170,HC OP VISIT LEVEL 5-EST-W PX, 593 
-26503177,HC OP VISIT LEVEL 5-EST-W PX, 593 
-26603175,HC OP VISIT LEVEL 5-EST-W PX, 593 
-26703207,HC OP VISIT LEVEL 5-EST-W PX, 593 
-62700810,HC OP VISIT LEVEL 5-EST-W PX, 593 
-94100948,HC OP VISIT LEVEL 5-EST-W PX, 593 
-40510299,HC OP VISIT LEVEL 5-NEW, 601 
-94100641,HC OP VISIT LEVEL 5-NEW, 601 
-94100997,HC OP VISIT LEVEL 5-NEW W/PROC, 601 
-62310297,HC OPTO NYSTAGMUS TEST, 318 
-92800689,HC OPTO NYSTAGMUS TEST, 318 
-41045287,HC ORBITS FOREIGN BODY, 544 
-93645281,HC ORBITS FOREIGN BODY, 544 
-41000704,HC ORBITS MIN 4V, 671 
-41300708,HC ORBITS MIN 4V, 671 
-93600708,HC ORBITS MIN 4V, 671 
-52751591,HC ORGANIC ACID SCR URI, 195 
-52752045,HC OROTIC ACID URINE QUANT, 200 
-62310305,HC OSCILLATING TRACKING TEST, 318 
-92800697,HC OSCILLATING TRACKING TEST, 318 
-50393339,HC OSMOTIC FRAGILITY INCUBATED, 200 
-59393330,HC OSMOTIC FRAGILITY INCUBATED, 200 
-46093332,HC OSMOTIC FRAGILITY RBC INCUBATED, 200 
-50464965,"HC OSMOLALITY, SERUM", 209 
-51564961,"HC OSMOLALITY, SERUM", 209 
-51864965,HC OSMOLALITY SERUM, 209 
-56864960,HC OSMOLALITY SERUM, 209 
-59464966,"HC OSMOLALITY, SERUM", 209 
-50464973,"HC OSMOLALITY, URINE", 255 
-51864973,HC OSMOLALITY URINE, 255 
-56864978,HC OSMOLALITY URINE, 255 
-59464974,"HC OSMOLALITY, URINE", 255 
-40400335,HC OT JOINT MOBILIZATION-15MIN, 249 
-62310248,HC OTOACOUSTIC EMISSIONS COMP, 423 
-92800408,HC OTOACOUSTIC EMISSIONS COMPLETE, 423 
-92800390,HC OTOACOUSTIC EMISSIONS LIMITED, 278 
-62310230,HC OTOACOUSTIC EMISSIONS LTD, 278 
-92801653,HC OTORHINOLAR SERV OR PROC, 192 
-50741255,HC 6959 11052R OVARY FL AB SCN, 138 
-55020929,HC OX/METH FOR MSRA, 122 
-59020925,HC OX/METH FOR MSRA, 122 
-50751205,HC 6956 YUOXAL OXALATE UR, 11 
-37003308,HC P-32 INTRAVEN THRPY," 1,336 "
-37010253,HC P32 CHROMIC PHOS SUSP PER MCI," 3,343 "
-37010212,HC P32 SODIUM PHOSPH TX PER MCI," 8,962 "
-56209117,HC PACG RT ATRIUM," 7,636 "
-47203369,HC PACK PLATELETS, 282 
-50751213,HC 6956 11088R PP IMM QN RIA, 89 
-26500876,HC PARACENTESIS TRAY, 448 
-46140513,HC PARAFLU DFA, 324 
-59140517,HC PARAFLU DFA, 324 
-46140547,HC PARAFLU DFA POOL, 319 
-59140541,HC PARAFLU DFA POOL, 319 
-37001914,HC PARATHYROID IMAGING," 3,656 "
-50751809,HC 6956 11090R PCAB IGG, 24 
-81391138,HC PATDA:DRUG ASSOC PLTLT AB, 33 
-82391137,HC PATDA:DRUG ASSOC PLTLT AB, 33 
-52051448,HC PATH CONSULT SURG CYTO EA ADD, 217 
-52051430,HC PATH CONSULT SURG CYTO INIT, 348 
-52005600,HC PATH EXM-CMPLX LVL6," 2,000 "
-52005402,HC PATH EXM-COMPL-LV4, 875 
-52005501,HC PATH EXM-EXTND LV5," 1,653 "
-51402410,HC PATIENT PT INH SCRN, 190 
-58402413,HC PATIENT PT INH SCRN, 190 
-50742378,HC 6959 11053R PBGD ENZY ACT, 71 
-83501411,HC PBSC DNR INFO SESS, 832 
-83501445,HC PBSC DNR PHYS EXM," 2,906 "
-83501478,HC PBSC DNR W/U CNCL," 3,075 "
-58700303,HC PC02-02 (CG4), 815 
-58700295,HC PCLACT-LACTATE (CG4), 330 
-58700311,HC PCPCO2-CO2 (CG4), 815 
-58700329,HC PCPH-PH (CG4), 815 
-50791144,HC PCPR:HLA ANTIBODY ID(RAPID), 463 
-81091142,HC PCPR:HLA ANTIBODY ID(RAPID), 61 
-81191140,HC PCPR:HLA ANTIBODY ID(RAPID), 61 
-81391146,HC PCPR:HLA ANTIBODY ID(RAPID), 61 
-82391145,HC PCPR:HLA ANTIBODY ID(RAPID), 61 
-50790898,HC PCRDQ:HLA-DQ TYPE(PCR), 280 
-52290897,HC PCRDQ:HLA-DQ TYPE(PCR), 58 
-81090896,HC PCRDQ:HLA-DQ TYPE(PCR), 58 
-81190894,HC PCRDQ:HLA-DQ TYPE(PCR), 58 
-81390890,HC PCRDQ:HLA-DQ TYPE(PCR), 58 
-82390899,HC PCRDQ:HLA-DQ TYPE(PCR), 58 
-50790880,HC PCRDR:HLA-DR TYPE(PCR), 342 
-52290889,HC PCRDR:HLA-DR TYPE(PCR), 120 
-81090888,HC PCRDR:HLA-DR TYPE(PCR), 120 
-81190886,HC PCRDR:HLA-DR TYPE(PCR), 120 
-81390882,HC PCRDR:HLA-DR TYPE(PCR), 120 
-82390881,HC PCRDR:HLA-DR TYPE(PCR), 120 
-84390889,HC PCRDR:HLA-DR TYPE(PCR), 120 
-50790203,HC PCRPL:PLT ANTIGEN TYPE(PCR), 342 
-52290202,HC PCRPL:PLT ANTIGEN TYPE(PCR), 133 
-58700337,HC PCTN1-TROPONIN 1, 520 
-56269343,HC PED AIRWAY INHALE TRT, 357 
-56269350,HC PED ASD/PFO CLOSURE W IMP," 36,899 "
-56269368,HC PED BIOPSY HEART LNG," 4,650 "
-56269384,HC PED CNT VEN LN (<5YR)," 5,677 "
-56269392,HC CNT VEN LN >=5YR," 7,725 "
-56269418,HC PED US GD ENDMYCD BIOPSY," 1,896 "
-56269426,HC PED ECHO GDE CRDCNTS, 498 
-56269434,HC FLUOROSCOPE EXAM," 1,849 "
-56269442,HC PED HRT FLW RSRV MEAS INIT," 1,967 "
-56269459,HC PED ICE ECG," 10,926 "
-56269475,HC PED TUBE PERICOSTOMY," 1,155 "
-56269541,HC ART CATH SAMPLING/MONITORNG," 3,241 "
-56269947,HC PED IVUS INITIAL VESSEL," 11,731 "
-56269954,HC PED IVUS EA ADD VESSEL," 4,069 "
-56269962,HC PED IVUS NONCOR ADD VESS," 2,498 "
-56269970,HC PED IVUS NONCOR INIT VESS," 3,311 "
-56269988,HC PED LT HEART CATH," 18,234 "
-56269996,HC PED FLUORO GD NDL PLACEMENT," 2,021 "
-56202047,HC PED PERICARD WINDOW," 1,617 "
-56269400,HC PED PERICARDIOCENTESIS INIT," 3,756 "
-56270028,HC PED PICC FLR ULTS<5YR," 3,628 "
-56270036,HC PED PICC FLRO ULT >=5," 3,045 "
-56270010,HC PED PTPA BALLOON EA ADDTL," 13,577 "
-56270002,HC PED PTPA BALLOON SINGLE," 13,577 "
-56270044,HC PED R HRT/RTRO L HRT CTH CNGTL," 20,809 "
-56270051,HC PED R HRT/TRNS L HRT CTH CNGTL," 16,225 "
-56270069,HC PED R&L HRT CTH W/TRNS L CNGTL," 16,225 "
-56270077,HC PED TRANSLUM PLASTY VENOUS," 12,081 "
-56270085,HC PED REPSTN CVL FLOURO," 2,214 "
-56269558,HC PED RETRV BROKE CATH," 8,815 "
-56269566,HC PED REVISE AORTIC VLV," 15,755 "
-56269582,HC PED REVISE PLMNRY VLV," 15,755 "
-56269574,HC PED REVSN MITRAL VLVE," 8,209 "
-56269608,HC PED RMV TNLD CATH NO PORT," 3,651 "
-56269616,HC PED PTA AORTIC," 12,870 "
-56269624,HC PED PERICARDIOCENTESIS SUB," 1,314 "
-56269632,HC PED RT HEART CATH," 16,567 "
-56269640,HC PED RT HRT CTH CNGNTL," 15,802 "
-56269657,HC PED R/LHC TRANSSEPTAL INTACT," 18,234 "
-56269665,HC PED RT/LT HEART CATH," 23,873 "
-56269673,HC R/LHC EXIST SEPTAL OPENING," 16,225 "
-56269590,HC PED RVS HEART CHMBER," 20,101 "
-56269681,HC PED TRANSCATH STENT INIT," 22,982 "
-56269871,HC PED TRNSCATH RETRIEVE," 7,875 "
-56269889,HC PED TRNSCTH OCCLUSION," 20,399 "
-56269897,HC PED TRNSCTH STENT ADD," 18,985 "
-56269913,HC VENOGRAPHY SUPR," 8,166 "
-56269921,HC VENOGRAPHY INF," 7,970 "
-56269939,HC PED EMBOL TRNCTH THRPY," 16,094 "
-41117292,HC PEDS INSERT FLOW DIR CATH," 3,857 "
-41002551,HC PELVIS <3 VIEWS, 804 
-41126418,HC PELVIS ANGIOGRAPHY," 6,252 "
-41002601,HC PELVIS MIN 3V, 953 
-41302605,HC PELVIS MIN 3V, 953 
-93602605,HC PELVIS MIN 3V, 953 
-93602555,HC PELVIS ONE OR TWO VIEWS, 804 
-41302555,"HC PELVIS,ONE OR TWO VIEWS", 804 
-56268147,HC PERC AVR PARTNERS," 21,478 "
-41151143,HC PERC BX ABD/RETROPE," 2,277 "
-56269285,HC PERC CORNRY THRMECTMY MECH," 11,952 "
-62026505,HC ESWT HI NRG PFRMD PHYS W/US GDN INVG PLNTAR FSCA," 3,143 "
-41145202,HC PERC DIL BIL STRICK," 5,995 "
-41152448,HC PERC DRN ABD ABSC," 3,156 "
-41152422,HC PERC DRN LVR LESION," 3,150 "
-41152471,"HC PERC DRN, PELVIC"," 4,097 "
-41152562,HC PERC DRN PEROTONEAL," 3,150 "
-41152463,HC PERC DRN RENAL ABS," 3,662 "
-41152455,HC PERC DRN RETROP ABS," 5,215 "
-41151887,HC PERC NDL BX LIVER," 4,317 "
-41151895,HC PERC NDL BX LIVER:W/OTH MAJ PR," 4,360 "
-41151374,HC PERC NDL BX LUNG/ME," 3,287 "
-41127507,HC PERC NEPH TUBE CHANGE," 2,465 "
-41127556,HC PERC NEPHROSTOMY," 5,094 "
-41145145,HC PERC PLCE ENTCLY TB, 435 
-42016246,HC PERC PLCMNT OF GASTROSTOMY TUB," 2,610 "
-42062323,HC PERC RF ABLATION LIVER," 13,579 "
-42072686,HC THORACIC UNLISTED PROCEDURE," 2,278 "
-41152620,HC PERC VPL 1LVL LUMB UNIL/BIL," 6,497 "
-41152612,HC PERC VPL 1LVL THOR UNIL/BIL," 8,927 "
-41152638,HC PERC VRTEBROPLSTY EA ADDL LVL," 9,436 "
-41152547,HC PERCU DRAIN LUNG," 1,613 "
-41153149,HC PERCUT CHOLECYSTOSTOMY," 8,494 "
-41100165,"HC PERCUT VERT AUG, ADD"," 16,510 "
-41100157,"HC PERCUT VERT AUG, LUMB"," 16,510 "
-56200801,HC PERICARDIOCENTESIS INIT," 3,756 "
-39000195,HC PERIPROC ICD EVAL, 122 
-39000161,HC PERIPROC PM EVAL, 116 
-56209067,HC PERM PCMKR IMP ATRIAL LEADS," 35,800 "
-56209075,HC PERM PCMKR IMP VENT LEADS," 38,945 "
-56209083,HC PERM PCMKR IMP A/V LEADS," 7,686 "
-62600010,HC PERQ ALLERGY TEST W/EXTRACTS, 88 
-62025580,HC PERQ RF ABLATE TX PUL TUMOR," 5,669 "
-37105046,HC PET MCI PRFSN MTLP STDY RST/ST," 10,566 "
-37105038,HC PET MCI PRFSN SNGL STDY RST/ST," 10,566 "
-37100211,"HC PET MYOCARDIAL,METABOLIC EVA"," 10,566 "
-37100302,"HC PET SCAN BRAIN,METABOLIC"," 4,521 "
-37105004,HC PETCT LIMITED - NOPR," 8,254 "
-37100534,HC PETCT SKULL-THIGH," 10,566 "
-37100625,HC PET IMAGE W/CT LMTD," 4,521 "
-37105012,HC PETCT SKULL-THIGH - NOPR," 10,566 "
-37100336,HC PETCT WHOLE BODY," 10,566 "
-37105020,HC PETCT WHOLE BODY - NOPR," 10,566 "
-50403005,HC PH BY METER, 50 
-51803005,HC PH BY METER, 50 
-56803000,HC PH BY METER, 50 
-59403006,HC PH BY METER, 50 
-52751948,HC PHENLYLALAINE &TYROS QUANT, 274 
-50460906,HC PHENOBARITAL LEVEL, 223 
-51860906,HC PHENOBARITAL LEVEL, 223 
-56860901,HC PHENOBARITAL LEVEL, 223 
-50751403,HC 6956 YFPHEN PHENYTOIN FREE, 12 
-50722214,HC 6959 FDILAN PHENYTOIN FREE, 18 
-50461003,HC PHENYTOIN LEVEL, 346 
-51861003,HC PHENYTOIN LEVEL, 346 
-56861008,HC PHENYTOIN LEVEL, 346 
-50200203,HC PHLEBOTOMY MICRO, 115 
-58900200,HC PHLEBOTOMY MICRO, 115 
-26500348,"HC PHLEBOTOMY, THERAPEUTIC", 658 
-26510701,HC PHLEBOTOMY TUBING, 183 
-40905911,"HC PHLEBOTOMY, TX", 658 
-59460907,HC PHENOBARBITAL LVL, 223 
-59461004,HC PHENYTOIN LVL, 346 
-50751221,HC 6956 YPTSAB PHOSPHOLIPID AB, 75 
-50405265,HC PHOSPHORUS CUSTOM PANEL, 225 
-50403104,HC PHOSPHORUS SERUM/, 225 
-51303105,HC PHOSPHORUS SERUM/, 225 
-57303109,HC PHOSPHORUS SERUM/, 225 
-59403105,HC PHOSPHORUS SERUM/, 225 
-50403120,HC PHOSPHORUS URINE, 115 
-59403121,HC PHOSPHORUS URINE, 115 
-50411099,HC PHOSPORUS CUSTOM, 225 
-59411090,HC PHOSPORUS CUSTOM, 225 
-26730390,HC PHOTOPHERESIS," 13,619 "
-26730515,HC PHOTOPHERESIS KIT," 3,266 "
-36200129,HC PHYS CONSLT STEREOT," 5,752 "
-36070100,HC PHYSIC CONSULT/QA," 1,424 "
-43100072,HC PHYSICAL THERAPY EVAL, 692 
-36033249,HC PHYSICS CONSULT INC," 1,424 "
-31900020,HC PICC INSERTION OLDER THAN 5YRS," 3,045 "
-31950082,HC PICC INTRODUCER KIT," 1,241 "
-31950090,HC PICC PREP PACK, 149 
-31950041,HC PICC PROTECTOR - LG, 85 
-31950025,HC PICC PROTECTOR - MED, 85 
-31950033,HC PICC PROTECTOR - SMALL, 85 
-26410720,"HC PICC PROTECTOR SLEEVE, DISP", 298 
-26510727,"HC PICC PROTECTOR SLEEVE, DISP", 298 
-26610725,"HC PICC PROTECTOR SLEEVE, DISP", 298 
-31950058,HC PICC REPAIR KIT, 425 
-31900004,HC PICC REPLACE THRU SAME ACCESS," 4,020 "
-31950017,HC PICC TRAY, 383 
-55020101,HC PINWORM PREP, 76 
-59020107,HC PINWORM PREP, 76 
-62026406,HC PLACE DUOD/JEJ TUBE PERC," 1,227 "
-62026398,HC PLACE GASTRONOMY TUBE PERC," 1,227 "
-62025200,HC PLACE PO BREAST CATH FOR RAD," 9,981 "
-62025606,HC PLACE RT DEVICE/MARKER-PROS," 2,646 "
-56202088,HC PLACEMNT OF TEE PROBE," 1,100 "
-47400288,HC ALLO PLASMA DEPLETION," 2,913 "
-51400380,HC PLASMINOGEN, 90 
-58400383,HC PLASMINOGEN, 90 
-50393446,"HC PLATELET COUNT, AUTOMATED", 138 
-51393445,HC PLATELET COUNT AUTOMATED, 138 
-59393447,"HC PLATELET COUNT, AUTOMATED", 138 
-50502905,HC PLATELET FUNCTION SCRN ADP, 469 
-51402907,HC PLATELET FUNCTION SCRN ADP, 469 
-58402900,HC PLATELET FUNCTION SCRN ADP, 469 
-59502906,HC PLATELET FUNCTION SCRN ADP, 469 
-50502913,HC PLATELET FUNCTION SCRN EPINEPH, 469 
-51402915,HC PLATELET FUNCTION SCRN EPINEPH, 469 
-59502914,HC PLATELET FUNCTION SCRN EPINEPH, 469 
-51403376,HC PLATELET MAPPING - ADP, 469 
-58403379,HC PLATELET MAPPING - ADP, 469 
-58403387,HC PLATELET MAPPING - ARACHIDONIC, 469 
-51403384,HC PLATELET MAPPING-ARACHIDONIC A, 469 
-50112929,HC PLATELET PHERESIS EACH UNIT," 2,035 "
-50120476,HC PLATELETS - PARTIAL UNIT, 751 
-62700695,HC PLATELETS PER INFUSION ATIC," 3,300 "
-40100034,HC PLC NDL LOCLZ WIR EA ADD," 1,695 "
-40100026,HC PLC NDL LOCLZ WIRE," 3,466 "
-41152687,HC PLCMNT EXT PROSTHESIS AAA REPR, 363 
-41152661,HC PLCMNT EXT PRSTHES AAA EA ADD, 611 
-41152653,HC PLCMNT EXT PRSTHES AAA INTIAL," 2,048 "
-41153164,HC PLCMNT-CHOLEDOCHAL STENT," 2,852 "
-41127754,HC PLCMT-URET STENT," 5,094 "
-51401636,HC PLT AGG-ADP, 469 
-50503341,HC PLT AGG-ARACH ACID, 469 
-51403343,HC PLT AGG-ARACH ACID, 469 
-58403346,HC PLT AGG-ARACH ACID, 469 
-51401610,HC PLT AGG-COLLAG, 469 
-51401602,HC PLT AGG-EPINEPH, 469 
-51401628,HC PLT AGG-RISTOC, 469 
-50115542,HC PLT PHERE LR HLAXM EACH UNIT," 2,936 "
-50115435,HC PLT PHERESIS IRRAD EACH UNIT," 1,932 "
-50115427,HC PLT PHERESIS LR IRRAD EACH UNIT," 2,867 "
-81090011,HC PLTAB:PLT ALLOANTIBODY, 33 
-82390014,HC PLTAB:PLT ALLOANTIBODY, 33 
-58401621,HC PLTL AGG-RISTOCETIN, 469 
-58401639,HC PLTLT AGG-ADP, 469 
-58401613,HC PLTLT AGG-COLLAGEN, 469 
-58401605,HC PLTLT AGG-EPINEP, 469 
-39000021,HC PM CHK DUAL W/REPROG, 595 
-39000153,HC PM CHK MULTI W/REPROG, 357 
-39000013,HC PM CHK S/D/MULT WO REPROG, 435 
-39000047,HC PM CHK SINGLE W/REPROG, 517 
-56209943,HC PM GEN ONL-DUAL CHA," 5,653 "
-56209976,HC PM LEAD REPAIR SINGLE," 4,740 "
-56209984,HC PM LEAD REPAIR TWO/DUAL," 4,740 "
-56209869,HC PM REMOVAL GEN ONLY," 4,740 "
-56209885,HC PM RMV LEAD DUAL," 4,740 "
-56209877,HC PM RMV LEAD SNG," 8,287 "
-50740216,HC 6959 11054R PM1 ENAAB EA AB, 72 
-26402214,HC PNEUMOCOCCAL ADMIN IM, 295 
-26502187,HC PNEUMOCOCCAL ADMIN IM, 295 
-26602185,HC PNEUMOCOCCAL ADMIN IM, 295 
-40510455,HC PNEUMOCOCCAL VACCINE, 155 
-21140199,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-23140197,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-23240195,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-23740194,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-23840192,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-24140196,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-25040197,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-25240193,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-25340191,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-25440199,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-25740192,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-26140194,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-26340190,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-27340199,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-55091508,HC PNEUMOCYSTIS STAIN, 289 
-59091504,HC PNEUMOCYSTIS STAIN, 289 
-50502574,HC POC-PROTHROMBIN TIME, 190 
-59502575,HC POC-PROTHROMBIN TIME, 190 
-37000403,HC POLYCYTHEM VER THRP IV," 1,336 "
-50722867,HC 6959 UPBGQT PBG UR QNT, 41 
-50740240,HC 6959 UPOR PRPHYRN QN&FRAC U, 36 
-50742006,HC 6959 PORFEC PRPHYRNS QN FEC, 168 
-50742014,HC 6959 PORS PRYRNS SPECT NES, 26 
-41151846,HC PORTAL VEIN CATH PL," 2,226 "
-62310271,HC POSIT NYSTAG(MUS) TEST, 318 
-92800663,HC POSIT NYSTAG(MUS) TEST, 318 
-50403153,HC POTASSIUM, 204 
-51303154,HC POTASSIUM, 204 
-59403154,HC POTASSIUM, 204 
-50411024,HC POTASSIUM CUSTOM, 204 
-59411025,HC POTASSIUM CUSTOM, 204 
-59405308,HC POTASSIUM CUSTOM PANEL, 204 
-50403161,HC POTASSIUM FLUID, 550 
-58700105,HC POTASSIUM POC, 204 
-50403187,HC POTASSIUM URINE, 158 
-59403188,HC POTASSIUM URINE, 158 
-50405307,HC POTASSIUM-CUSTOM PANEL, 204 
-94100799,HC POUCH W/CONVEX WAFER 2 PIECES, 85 
-94100690,HC POWDER ARGLAES 5MG, 85 
-50751924,HC 6959 BPR3R PR3 IMM QLSQ MLT, 26 
-62801238,HC PRE-IMPLANT ORAL MOTOR EVAL, 207 
-51560068,HC PREALBUMIN, 246 
-58560061,HC PREALBUMIN, 246 
-91900050,HC PREPARE EMBRYO TRANSFER, 275 
-40510265,"HC PREVENTIVE MED, GROUP, 30 MIN", 153 
-40510273,"HC PREVENTIVE MED, GROUP, 60 MIN", 146 
-40510240,"HC PREVENTIVE MED, INDIV, 30 MIN", 153 
-40510257,"HC PREVENTIVE MED, INDIV, 60 MIN", 302 
-83501031,HC PRI PBSC COL," 43,232 "
-50641893,HC PROBE MOL CYTOG EA, 426 
-62026513,HC STRAPPING ANKLE &/FOOT, 219 
-62021464,HC PROBE TEAR DUCTS, 432 
-50751700,HC PROCAINAMIDE + NAPA, 48 
-62017744,HC PROCTOSIGMOIDOSCOPY, 730 
-62017769,HC PROCTOSIGMOIDOSCOPY W/BX," 1,074 "
-41100652,HC PROCTOSIGMOIDOSCOPY W/STENT," 6,972 "
-92000140,HC PROGESTERONE, 149 
-51510055,HC PROGESTERONE (PGN), 273 
-51503209,HC PROLACTIN, 328 
-58503202,HC PROLACTIN, 328 
-59403204,HC PROLACTIN, 328 
-92000595,HC PROLACTIN, 148 
-50740257,HC 6959 11055R PROPAFEN QN DRG, 39 
-26730523,HC PROSORBA COLUMN," 18,900 "
-36099950,HC PROSTATE IMPLANT QA," 1,424 "
-50751247,HC 6956 11089R ACD PHOS PRSTIC, 4 
-59403253,HC PROT ELCTR SRM, 180 
-50403419,HC PROT TOTAL URINE, 103 
-50503416,HC PROTEIN C ACT (LILIY), 362 
-51400620,HC PROTEIN C FUNCTNAL, 362 
-58400623,HC PROTEIN C FUNCTNL, 362 
-50741339,HC 6959 CSTOTA PROTEIN C AG, 20 
-51400646,HC PROTEIN S FUNCTIONAL, 282 
-58400649,HC PROTEIN S FUNCTIONAL, 269 
-59403352,HC PROTEIN TOTAL CSF, 229 
-59403360,HC PROTEIN TOTAL FLUID, 229 
-50403401,"HC PROTEIN, TOTAL SERUM/PLASMA", 75 
-51303402,HC PROTEIN TOTAL SERUM/PLASMA, 75 
-59403402,"HC PROTEIN, TOTAL SERUM/PLASMA", 75 
-50742428,HC PROTHROMBIN FRAGMNT, 120 
-50500107,HC PROTHROMBIN TIME, 190 
-50570019,HC PROTHROMBIN TIME, 190 
-51370013,HC PROTHROMBIN TIME, 190 
-59500108,HC PROTHROMBIN TIME, 190 
-59570010,HC PROTHROMBIN TIME, 190 
-50748870,"HC PROTHROMBIN-20210A, DIGESTION", 13 
-58700279,HC PROTIME, 190 
-50403351,HC PROTN TOTAL CSF, 229 
-50403369,HC PROTN TOTL BDY FLUD, 229 
-50751833,HC 6959 ZINC PRTPTPHYRN RBC QN, 12 
-59091447,HC PRSMPTV FNG ID, 309 
-55091441,HC PRSMPTV FNGL ID, 309 
-41152992,HC PRSTHSIS DTAA," 9,980 "
-41153032,HC PRSTHSIS DTAA S&I," 5,837 "
-59403410,HC PRTN TTL URN, 103 
-51503985,HC PSA TOTAL, 342 
-58503988,HC PSA TOTAL, 342 
-59403980,HC PSA TOTAL, 342 
-51565109,"HC PSA, FREE", 281 
-58565102,"HC PSA, FREE", 281 
-51505402,HC PSA ULTRASENSITIVE, 342 
-58505405,HC PSA ULTRASENSITIVE, 342 
-50740265,HC 6959 11032R PSEUDOCHOLINEST, 13 
-41145186,HC PST-OP BIL STN RMVL, 812 
-62026034,HC PSYCH TEST ADMIN PSYCHIATRIST, 250 
-41130022,HC PTA ADDL PERIPH VSL," 6,036 "
-41130063,HC PTA ADDL VISC VSL," 1,479 "
-41152091,HC PTA AORTIC," 12,870 "
-41152125,HC PTA BRACHIOCEPHALI," 16,243 "
-41100405,HC PTA DIAL ACCESS ART," 16,243 "
-41100520,HC PTA FEMPOP," 12,440 "
-41152117,HC PTA HEMODIAL ACCESS ART," 16,243 "
-41152109,HC PTA ILIAC," 12,440 "
-41100504,HC PTA ILIAC OPEN," 12,440 "
-41152083,HC PTA RENAL/VISC ART," 17,039 "
-41100512,HC PTA TIBIO OPEN," 12,081 "
-41152075,HC PTA TIBIOPERONEAL," 12,440 "
-41152133,HC PTA VENOUS," 17,478 "
-41100413,HC PTA VENOUS," 17,478 "
-56201809,HC PTCA; SINGLE MAJOR CORONARY ARTERY OR BRANCH," 16,995 "
-56240161,HC PTCA EA ADD CORN ART," 16,995 "
-51510089,HC PTH (PTH), 786 
-58510082,HC PTH (PTH), 786 
-52005303,HC PTH EXM-UNCMP-LVIII, 626 
-58510058,HC PTOGESTERONE (PGN), 273 
-51402469,HC PTT INH 1 HR PT, 240 
-58402462,HC PTT INH 1 HR PT, 240 
-51402444,HC PTT INH 5 MIN PT, 240 
-58402447,HC PTT INH 5 MIN PT., 240 
-58401902,HC PTT INH 5 MIN PTT, 92 
-51401909,HC PTT INH 5 MIN PTT +, 92 
-51402451,HC PTTINH 1 HR NORMAL PLASMA, 240 
-58402454,HC PTTINH 1 HR NORMAL PLASMA, 240 
-51402436,HC PTTINH 5 MIN NORM PLASMA, 240 
-58402439,HC PTTINH 5 MIN NORM PLASMA, 240 
-41144866,HC PULM ARTERIOG UNILT," 6,252 "
-48002224,HC PULM STRESS TEST-COMPLEX," 2,378 "
-41119850,HC PULMONARY ANGIO BIL," 8,275 "
-40880239,"HC PUNC ASP-ABS,HEMA,BULLA,CYST"," 1,423 "
-40980237,HC PUNCTURE ASPIRATION," 1,423 "
-62010087,HC PUNCTURE DRAINAGE OF LESION, 301 
-92800424,HC PURE TONE AUDIO A/B, 392 
-92800432,HC PURE TONE THRESH (AC), 120 
-62310057,HC PURE TONE THRESH(AC), 120 
-62310065,HC PURE TONE THRESH(AC/BC), 392 
-46093324,HC PURINE NUCLEOSIDE PHOSPHORYL, 324 
-46093282,HC PYRUVATE KINASE, 165 
-59393280,HC PYRUVATE KINASE, 165 
-36000057,HC QUALITY ASSURANCE," 1,424 "
-55092027,HC QUANTIFERON," 1,250 "
-59092023,HC QUANTIFERON," 1,250 "
-50751692,HC QUINIDINE, 41 
-37003803,HC R TO L SHUNT," 2,178 "
-59091108,HC R/O GC CLT, 165 
-55091102,HC R/O GC CULT, 165 
-55020945,HC R/O MRSA, 165 
-59020941,HC R/O MRSA, 165 
-55020804,HC R/O PAR/MOD ACD STN, 162 
-59020800,HC R/O PAR/MOD ACD STN, 162 
-55091557,HC R/O STREP BETA GPB, 165 
-59091553,HC R/O STREP BETA GPB, 165 
-32003550,HC RABIES VAC INTRADERM, 609 
-41145160,HC RAD EXAM ESOPHAGUS," 1,492 "
-40868457,HC RAD EXAM MAMMO SURG SPEC, 907 
-36300127,HC RADIAT PHYSICS STEREOTCT," 5,752 "
-37021557,HC RADIATION DOSIMETRY CALCULATIO," 1,428 "
-37021540,HC RADIO PHARM INF:THERAPY DOSE," 2,116 "
-37000254,HC RADIODN TX/HYPRTHYR," 2,995 "
-41530262,HC RF ABLATION 1ST VEIN," 9,611 "
-41530270,HC RF ABLATION EA SUBSQ VEIN," 5,516 "
-37000346,HC RADIOIODIN TX ABLAT," 2,995 "
-37021433,RC RADPHARM 1-123 MIBG STDY DOSE," 7,650 "
-37000353,HC RADIOPHARM TX:ORAL," 2,995 "
-37003563,HC RADPHARM LOC INFLAM TOMO-SPECT," 3,616 "
-37002177,HC RADPHARM LOC INFLAM WHOLE BODY," 3,523 "
-50393776,HC RAPID FLU A OR B EIA, 111 
-51393775,HC RAPID FLU A OR B EIA EACH, 111 
-51893766,HC RAPID FLU A OR B EIA EACH, 111 
-56893761,HC RAPID FLU A OR B EIA EACH, 111 
-59393777,HC RAPID FLU A OR B EIA EACH, 111 
-50393719,HC RAPID HIV-1 ANTIBODY, 121 
-59393710,HC RAPID HIV-1 ANTIBODY, 121 
-40510398,HC RAPID STREP A, 239 
-51323020,HC RAPID STREP A, 239 
-51823029,HC RAPID STREP A, 239 
-56823024,HC RAPID STREP A, 239 
-58523028,HC RAPID STREP A, 239 
-59323022,HC RAPID STREP A, 239 
-40510406,HC RAPID STREP B, 310 
-37001609,HC RBC GI BLEED," 4,521 "
-47203625,"HC RBC LEUKOPOOR FILTER, BEDSIDE", 154 
-50111509,"HC RBC, PARTIAL UNIT", 751 
-47400213,HC AUTO RBC REMOVAL," 3,420 "
-37002359,HC RBC SURVIVAL/SEQUES," 1,228 "
-47204748,HC RBC TREATMENT, 350 
-41154261,HC RDIOPHRM PART ADMIN(SIRS)," 2,466 "
-37005188,HC RDPHARM LOC INFLAM: LIMITED," 1,911 "
-92801703,HC RECHECK/TROUBLE SHOOTING, 192 
-56209133,HC RECORD BNDL OF HIS," 2,368 "
-56209125,HC RECORD RT ATRIUM," 1,963 "
-32711616,HC RECOVERY LVL2-EA ADD .5HR, 393 
-50110808,HC RED BLOOD CELLS EACH UNIT, 665 
-50111301,HC RED BLOOD CELLS LR EACH UNIT, 909 
-50114933,HC RBC DEGLYC EACH UNIT," 1,622 "
-50115492,HC RBC LRIRRCMV EACH UNIT," 1,253 "
-50115393,"HC RED CELLS;DRY,LR EA UNIT", 909 
-50115401,"HC RBC DRY,LR,IRR EA UNIT"," 1,253 "
-50115369,HC RBC IRRAD EACH UNIT, 953 
-50115351,HC RBC LR IRRAD EACH UNIT," 1,152 "
-50115591,HC RBC PHERE IRR LR EACH UNIT," 1,152 "
-50115609,HC RBC PHERE LR EACH UNIT, 909 
-50115484,HC RBC WASHED EACH UNIT," 1,314 "
-50114651,HC RBC AUTOLOGOUS EACH UNIT, 665 
-50115385,"HC REDCELLS;DRY,IRR,CMB"," 1,253 "
-50325075,HC REDUCING SUBSTANCES, 115 
-50740273,HC 6959 11035R REDCSUB QL FECL, 10 
-26500371,HC REFIL/MAIN OF PORTABLE PUMP, 726 
-26400374,HC REFIL/MAINT OF PORTABLE PUMP, 726 
-26730978,HC REFIL/MAINT OF PORTABLE PUMP, 726 
-26610071,HC REFILL/MAIN PORTABLE PUMP, 726 
-26602128,HC REFILL/MAINT IMPLANT PUMP/RESE, 590 
-26702084,HC REFILL/MAINT IMPLANT PUMP/RESE, 590 
-62010707,HC REM MALG SK LSN S/N/H/F/G3.1-4," 1,841 "
-62010632,HC REM MALG SKIN LSN TRNK/EXT2.1-," 1,056 "
-62010731,HC REM MALG SKN LSN F/E/E/N/L0.6, 617 
-62010749,HC REM MALG SKN LSN F/E/E/N/L1.1, 727 
-62010756,HC REM MALG SKN LSN F/E/E/N/L2.1, 912 
-62010723,HC REM MALG SKN LSN F/E/E/N/L<0.5, 493 
-62010699,HC REM MALG SKN LSN S/H/N/F/G2.1," 3,302 "
-62010673,HC REM MALG SKN LSN S/N/H/F/G0.6," 1,149 "
-62010681,HC REM MALG SKN LSN S/N/H/F/G1.1," 1,315 "
-62010715,HC REM MALG SKN LSN S/N/H/F/G>4.0," 2,256 "
-62010616,HC REM MALG SKN LSN TRNK/EXT 0.6, 806 
-62010624,HC REM MALG SKN LSN TRNK/EXT 1.1, 885 
-62010640,HC REM MALG SKN LSN TRNK/EXT3.1-4," 1,163 "
-62010608,HC REM MALG SKN LSN TRNK/EXT<0.5C, 802 
-39000245,HC REMOTE ICD INTR S/D/MULT 90 DAYS, 905 
-39000211,HC REMOVE ICM INTR 30 DAYS, 252 
-39000252,HC REMOVE ILR INTR 30 DAYS, 252 
-39000237,HC REMOTE PM INTR S/D/MULT 90 DAYS, 905 
-62010657,HC REMOV MALG SKN LSN TRNK/EXT>4.," 1,722 "
-41151226,"HC REMOVAL, ACCESS POR"," 3,651 "
-62026281,HC CLOSED TX MANDIBULAR FX W/INTERDENTAL FIXATION," 4,383 "
-41153479,HC REMOVAL TUNNELD CVAD PORT," 5,402 "
-41153461,HC REMOVAL TUNNELED CVC," 3,651 "
-62010236,HC REMOVE ADD SKN TAGS+10 LSNS, 87 
-62024062,HC REMOVE BONE FIXATION," 2,141 "
-62012349,HC REMOVE BREAST LSN," 2,473 "
-62010996,HC REMOVE CONTRACEPTIVE CAP, 485 
-62026315,HC BIOPSY SOFT TISSUE UPPER ARM/ELBOW AREA DEEP," 4,005 "
-62021548,HC REMOVE EAR CANAL LESION," 1,650 "
-62020441,HC REMOVE EYE LSN CORNEA," 2,012 "
-62020466,HC REMOVE EYE LSN W/O GRAFT," 1,929 "
-62020995,HC REMOVE EYELID LESION," 1,672 "
-62020920,HC REMOVE CHALAZION SNGL, 666 
-62020375,HC REMOVE FB EYE CONJ IMBEDDED, 186 
-62020391,HC REMOVE FB EYE CORNEAL W/SLIT, 205 
-62014964,HC REMOVE FB FOOT SUBQ, 809 
-62017215,HC REMOVE FB GUM BONE," 3,775 "
-62021555,HC REMOVE FB OUTER EAR CANAL W/O, 386 
-62021381,HC REMOVE FB TEAR DUCT, 996 
-62010053,HC REMOVE FOREIGN BODY SMPL, 403 
-62012505,HC REMOVE FOREIGN BODY SMPL, 563 
-62021563,HC REMOVE IMPACTED EAR WAX UNIL, 128 
-62310263,HC RMV IMPACT EAR WAX EAR W/TEST, 277 
-92800655,HC REMOVE IMPACTED EAR WAX UNIL, 128 
-62012588,HC REMOVE IMPLANT SUPER," 1,649 "
-62019468,HC REMOVE INTRAUTERINE DEVICE, 327 
-62010814,HC REMOVE NAIL PLATE SMPL, 263 
-62016241,HC REMOVE NASAL FOREIGN BODY, 405 
-62017421,HC REMOVE SALIVARY STONE CMPLX," 2,477 "
-62010491,HC REMOVE SKN LSN F/E/E/N/L0.6-1., 620 
-62010509,HC REMOVE SKN LSN F/E/E/N/L1.1-2.," 1,606 "
-62010483,HC REMOVE SKN LSN F/E/E/N/L<0.5CM, 485 
-62010533,HC REMOVE SKN LSN F/E/E/N/L>4.0CM," 2,257 "
-62010434,HC REMOVE SKN LSN S/N/H/F/G0.6-1.," 1,100 "
-62010442,HC REMOVE SKN LSN S/N/H/F/G1.1-2.," 1,344 "
-62010459,HC REMOVE SKN LSN S/N/H/F/G2.1-3.," 1,650 "
-62010467,HC REMOVE SKN LSN S/N/H/F/G3.1-4.," 1,650 "
-62010376,HC REMOVE SKN LSN TRNK/EXT 0.6-1., 567 
-62010392,HC REMOVE SKN LSN TRNK/EXT 2.1-3.," 1,606 "
-62010384,HC REMOVE SKN LSN TRNK/EXT1.1-2.0, 622 
-62010400,HC REMOVE SKN LSN TRNK/EXT3.1-4.0," 3,302 "
-62010368,HC REMOVE SKN LSN TRNK/EXT<0.5CM, 433 
-62010228,HC REMOVE SKN TAGS 1-15 LSNS, 150 
-62026497,HC OPEN TREATMENT INTERPHALANGEAL JOINT DISLOCATION," 2,538 "
-62025093,HC REMOVE TUNNELED CV CATH," 1,206 "
-62025101,HC REMOVE TUNNELED CV CATH," 1,179 "
-62016068,HC REMOVE BIVALVING BOOT/CAST, 202 
-56269293,HC IABP REMOVAL PERC, 124 
-56269301,HC REMVE PAT ACTV RECRD," 2,193 "
-40180184,HC RENAL ABLATION CRYO UNILATERAL," 12,190 "
-42080184,HC RENAL ABLATION CRYO UNILAT," 12,190 "
-41126392,HC RENAL ANGIO UNILAT," 9,694 "
-56202294,HC RENAL ANGIO-NON-SEL," 6,811 "
-41152364,HC RENAL CYST TRANSL, 720 
-50464817,HC RENAL FUNCTION PANEL, 170 
-51364818,HC RENAL FUNCTION PANEL, 170 
-57364812,HC RENAL FUNCTION PANEL, 170 
-59464818,HC RENAL FUNCTION PANEL, 170 
-41151911,HC RENAL NDL BX," 2,637 "
-37003506,HC RENAL PERFUS/EXCRT," 2,332 "
-41151275,HC RENAL STONE REMOVE," 9,184 "
-37003829,HC RENAL V-CYSTO," 2,193 "
-50751270,HC 6956 11092R RENIN ACTIVITY, 16 
-62021670,HC REPAIR OF EARDRUM," 2,416 "
-41153404,HC REPAIR TUNNELD CVAD PORT," 2,214 "
-41153420,HC REPLACE COMPLETE NON-TUN CVC," 2,214 "
-41153446,HC REPLACE COMPLETE TUN CVAD PORT," 9,370 "
-41153438,HC REPLACE COMPLETE TUNNELED CVC," 5,223 "
-41153453,HC REPLACE COMPLETE TUNNELED PICC," 4,020 "
-62026448,HC REPLACE DUOD/JEJ TUBE PERC, 603 
-62026455,HC RPR NON/MAL METAR +-B1 GRF," 5,659 "
-62026430,HC REPLACE G/C TUBE PERC, 603 
-41153412,HC REPLACE PARTIAL TUNNELED CVAD," 4,674 "
-41101247,HC REPLC DUODEN OR J-TUBE," 1,446 "
-41101239,HC REPLC G OR CECOSTOMY TUBE," 1,239 "
-41101254,HC REPLC GJ-TUBE," 4,852 "
-56269319,HC REPOSIT CARD VEN ELECTRODE," 4,740 "
-42016303,HC REPOSITION GASTRIC FEEDING TUB," 2,016 "
-56269327,HC REPOSITION PPM OR ICD LEAD," 4,740 "
-41153487,HC REPOSITION VENOUS CATHETER," 2,214 "
-62011457,HC REPR CMPLX E/N/E/L 1.1-2.5CM, 807 
-62011465,HC REPR CMPLX E/N/E/L 2.6-7.5CM," 1,077 "
-62011440,HC REPR CMPLX E/N/E/L <1.1CM, 703 
-62011416,HC REPR CMPLX F/A/G/H/F 1.1-2.5CM, 602 
-62011424,HC REPR CMPLX F/A/G/H/F 2.6-7.5CM, 780 
-62011390,HC REPR CMPLX SCLP/EXT + 5CM EA, 482 
-62011408,HC REPR CMPLX SCLP/EXT + 5CM EA, 127 
-62011382,HC REPR CMPLX SCLP/EXT 1.1-2.5CM, 361 
-62011374,HC REPR CMPLX TRNK + 5CM EA, 632 
-62011366,HC REPR CMPLX TRNK 2.6-7.5CM, 749 
-62020433,HC REPR EYE WOUND TISSUE GLUE, 760 
-62021068,HC REPR EYELID DEFECT EXT APPROAC," 3,731 "
-62021134,HC REPR ECTROPION EXTENSIVE," 3,861 "
-62021175,HC REPR EYELID DEFECT EXTENSIVE," 2,858 "
-62012984,HC REPR NOSE FX W/STAB," 2,002 "
-92800739,"HC REPR,REPL DEV/MINOR PARTS", 314 
-50500503,HC REPTILASE TIME, 203 
-59500504,HC REPTILASE TIME, 203 
-52032562,HC RES COMP SPECIAL STAIN, 31 
-52032547,HC RES DELCALCIFICATION, 10 
-52032463,HC RES GROSS PREP WET TISS, 11 
-52032521,HC RES H AND E FURN SLIDE, 11 
-52032513,HC RES H AND E STAIN RECUT, 12 
-52032612,HC RES INSITU LAB FURN PROBE, 75 
-52032588,HC RES IPOX INVEST SUPPLIED, 28 
-52032570,HC RES IPOX ROUTINE, 36 
-52032638,HC RES PCR TUBE ROLL, 7 
-50393859,HC RES PLATELET COUNT, 138 
-59393850,HC RES PLATELET COUNT, 138 
-52032505,HC RES PROC H AND E SLIDE, 16 
-52032497,HC RES PROC UNSTAINED SLIDE, 12 
-52032489,HC RES PROCESS AND EMBED, 9 
-52032554,HC RES SPECIAL STAIN ROUTINE, 23 
-52032471,HC RES TISSUE PROCESS ONLY, 7 
-52032539,HC RES UNSTAINED SLIDE BLOCK, 8 
-55050256,HC RESPIR CULT/SPUTUM, 302 
-55020887,HC RESPIRATORY FULL ORG ID, 226 
-36001584,HC RESPIRATORY GATING, 703 
-55050306,HC RESPIRATRY CULTR, 302 
-59020883,HC RESPRTRY FUL ORG ID, 226 
-37003720,HC CARD BLD PL REST OR STRESS," 4,164 "
-56101843,HC PHARM STRESS TEST," 2,736 "
-50323047,HC RETIC COUNT BY FLOW, 179 
-51323046,HC RETIC COUNT BY FLOW, 179 
-59323048,HC RETIC COUNT BY FLOW, 179 
-56221575,HC REV OF PCR PCKT," 5,235 "
-50802784,HC REVERSE TRANSCRIPTION, 140 
-62020961,HC REVISE EYELASHES FORCEPS, 205 
-62020987,HC REVISE EYELASHES INCISION," 1,160 "
-62020979,HC REVISE EYELASHES O/T FORCEPS, 630 
-62021035,HC REVISE EYELID W/TRANSPOSITION," 4,296 "
-62020672,HC LASER IRIS PER SESSION," 1,273 "
-62024468,HC REVISE MEDIAN NERVE," 1,832 "
-56269335,HC REVISE MITRAL VALVE," 8,209 "
-41152919,HC REVISE TIPS SHUNT," 13,506 "
-62011820,HC REVISE UPPER EYE W/WEIGHT," 4,212 "
-62020235,REVJ/RMVL HC PERIPH N-STIM HC ELECTR," 2,316 "
-62021225,HC REVISE/REPR EYELID 1/4 LID MAR," 3,773 "
-62021027,HC REVISION OF EYELID," 3,710 "
-41153156,HC REVISION-BILIARY DRAIN," 2,578 "
-42072751,"HC RF ABLATION, RENAL"," 11,396 "
-26402131,HC RFLL/MNT IMPLNT PUMP/RES, 590 
-26502104,HC RFLL/MNT IMPLNT PUMP/RES, 590 
-46310256,HC RFLX VIR NA QUANT, 388 
-56310261,HC RFLX VIR NA QUANT, 186 
-56310279,HC RFLX VIR NA QUANT, 222 
-56310253,HC RFLX VIR NA QUANT, 388 
-47204847,HC RH CONFIRM PT TYPE, 247 
-50101153,HC RH IMMUNE GLOBULIN, 324 
-47200704,HC RH TYPING, 247 
-50463587,HC RHEUMATOID FACTOR SERUM QNT, 129 
-51563583,HC RHEUMATIOD FACTOR SERUM, 129 
-51593853,HC RHEUMATOID FACTOR QUANT, 129 
-51593861,HC RHEUMATOID FACTOR QUANT, 129 
-58593856,HC RHEUMATOID FACTOR QUANT, 129 
-58593864,HC RHEUMATOID FACTOR QUANT, 129 
-58563586,HC RHEUMATOID FACTOR SERUM, 129 
-41005307,HC RIBS BILAT 3V," 1,365 "
-41305301,HC RIBS BILAT 3V," 1,365 "
-93605301,HC RIBS BILAT 3V," 1,365 "
-41305319,HC RIBS BILAT W/CHEST 4+," 1,885 "
-41005257,HC RIBS UNI 2V," 1,024 "
-41305251,HC RIBS UNI 2V," 1,024 "
-93605251,HC RIBS UNI 2V," 1,024 "
-41005265,HC RIBS UNILAT W/CHEST 3+ V," 1,053 "
-41305269,HC RIBS UNILAT W/CHEST 3+ V," 1,053 "
-93605269,HC RIBS UNILAT W/CHEST 3+ V," 1,053 "
-41005315,HC RIBSBILAT W/CHEST 4+ V," 1,885 "
-93605319,HC RIBSBILAT W/CHEST 4+ V," 1,885 "
-51401511,HC RISTOCETIN COFACTOR, 496 
-58401514,HC RISTOCETIN COFACTOR, 496 
-56269145,HC RMV ELECTRODE BY TV EXT," 9,053 "
-41106659,HC RNL ANGIOGRAPH BIL," 10,872 "
-46140208,HC ROTA VIRUS ANTIGEN, 181 
-59140202,HC ROTAVIRUS ANTIGEN, 181 
-26400333,HC ROUTINE VENIPUNCTURE, 115 
-26500330,HC ROUTINE VENIPUNCTURE, 115 
-26610162,HC ROUTINE VENIPUNCTURE, 115 
-26730002,HC ROUTINE VENIPUNCTURE, 115 
-41152976,HC RPR DTAA COV LSA," 9,980 "
-41153016,HC RPR DTAA COV LSA S&I," 10,263 "
-41152984,HC RPR DTAA NO COV LSA," 8,493 "
-41153024,HC RPR DTAA NO COV LSA S&I," 8,755 "
-51560076,HC RPR QUAL, 238 
-58560079,HC RPR QUAL, 238 
-51560084,HC RPR QUANT, 117 
-58560087,HC RPR QUANT, 117 
-32003543,HC RPR CV DEVICE W PORT/PUMP," 2,214 "
-59050252,HC RPSRTY CLT/SPUT, 302 
-59050302,HC RSPRTY CLT, 302 
-50393768,HC RSV AG BY EIA, 66 
-51393767,HC RSV AG BY EIA, 66 
-51593770,HC RSV AG BY EIA, 66 
-51893774,HC RSV AG BY EIA, 66 
-56893779,HC RSV AG BY EIA, 66 
-59393769,HC RSV AG BY EIA, 66 
-46140174,HC RSV ANTIGEN, 66 
-59140178,HC RSV ANTIGEN, 66 
-46160750,HC RSV DFA, 249 
-59160754,HC RSV DFA, 249 
-41151747,HC RT OR LT PULM ART," 3,040 "
-56209141,HC RT VENT RECORDING," 2,255 "
-56203300,HC RT VENTR MAPG-OR," 1,766 "
-46100251,HC RUBELL ANTIBODY, 268 
-51593887,HC RUBELLA AB, 268 
-58593880,HC RUBELLA AB, 268 
-59100255,HC RUBELLA ANTIBODY, 268 
-40510133,HC RUBELLA VACCINE, 182 
-40510125,HC RUBEOLA VACCINE, 140 
-41002650,HC SACROILIAC JOINTS MIN 3V, 984 
-41302654,HC SACROILIAC JOINTS MIN 3V, 984 
-93602654,HC SACROILIAC JOINTS MIN 3V, 984 
-41002353,HC SACRUM COCCYX MIN 2," 1,054 "
-93602357,HC SACRUM COCCYX MIN 2," 1,054 "
-41302357,HC SACRUM/COCCYX MIN 2," 1,054 "
-50461706,HC SALICYLATES LEVEL, 264 
-51861706,HC SALICYLATES LEVEL, 264 
-56861701,HC SALICYLATES LEVEL, 264 
-37021201,HC SAMARIUM SM-153 PER TX DOSE," 8,265 "
-41154204,HC SAPH VEIN ABL 1ST VN," 5,516 "
-41005604,HC SCAPULA," 1,049 "
-41305608,HC SCAPULA," 1,049 "
-93605608,HC SCAPULA COMPLETE," 1,049 "
-50751288,HC 6956 11093R ENA SCL70 EA AB, 17 
-41002304,HC SCOLIOSIS INCL SUP&ERECT," 1,662 "
-41302308,HC SCOLIOSIS INC SUP& ERECT," 1,662 "
-62020482,HC SCRAPING CORNEA DX SMEAR, 236 
-55092167,HC SCREEN FOR MRSA BY PCR, 393 
-59092163,HC SCREEN FOR MRSA BY PCR, 393 
-50464635,HC SCREEN: AMPHETAMINES, 250 
-51564631,HC SCRN: AMPH NON MC EA, 296 
-51864635,HC SCRN: AMPH NON MC EA, 296 
-56864630,HC SCREEN: AMPHETAMINES, 250 
-58564634,HC SCREEN: AMPHETAMINES, 250 
-59464636,HC SCREEN: AMPHETAMINES, 250 
-50464643,HC SCREEN: BARBITURATES, 250 
-51564649,HC SCRN: BARB NON MC EA, 296 
-51864643,HC SCRN: BARB NON MC EA, 296 
-56864648,HC SCREEN: BARBITURATES, 250 
-58564642,HC SCREEN: BARBITURATES, 250 
-59464644,HC SCREEN: BARBITURATES, 250 
-51864650,HC SCRN: BENZO NON MC EA, 296 
-56864655,HC SCREEN: BENZODIAZEPINES, 250 
-58564659,HC SCREEN: BENZODIAZEPINES, 250 
-59464651,HC SCREEN: BENZODIAZEPINES, 250 
-50464668,HC SCREEN: COCAINE, 250 
-51564664,HC SCRN: COCA NON MC EA, 296 
-51864668,HC SCRN: COCA NON MC EA, 296 
-56864663,HC SCREEN: COCAINE, 250 
-58564667,HC SCREEN: COCAINE, 250 
-59464669,HC SCREEN: COCAINE, 250 
-50464676,HC SCREEN: METHADONE, 250 
-51864676,HC SCREEN: M-DONE NON MC EA, 296 
-56864671,HC SCREEN: METHADONE, 250 
-58564675,HC SCREEN: METHADONE, 250 
-59464677,HC SCREEN: METHADONE, 250 
-50464684,HC SCREEN: OPIATES, 250 
-51564680,HC SCREEN: OPIATES NON MC EA, 296 
-51864684,HC SCREEN: OPIATES NON MC EA, 296 
-56864689,HC SCREEN: OPIATES, 250 
-58564683,HC SCREEN: OPIATES, 250 
-59464685,HC SCREEN: OPIATES, 250 
-50464692,HC SCREEN: PCP, 250 
-51564698,HC SCREEN: PCP NON MC EA, 296 
-51864692,HC SCREEN: PCP NON MC EA, 296 
-56864697,HC SCREEN: PCP, 250 
-58564691,HC SCREEN: PCP, 250 
-59464693,HC SCREEN: PCP, 250 
-50464700,HC SCREEN: THC, 250 
-51564706,HC SCREEN: TNON MC EA, 296 
-51864700,HC SCREEN: TNON MC EA, 296 
-56864705,HC SCREEN: THC, 250 
-58564709,HC SCREEN: THC, 250 
-59464701,HC SCREEN: THC, 250 
-50464718,HC SCREEN: TRICYCLICS, 250 
-51864718,HC SCREEN: TRICYC NON MC EA, 296 
-56864713,HC SCREEN: TRICYCLICS, 250 
-58564717,HC SCREEN: TRICYCLICS, 250 
-59464719,HC SCREEN: TRICYCLICS, 250 
-50464650,HC SCREEN:BENZODIAZEPINES, 250 
-51564656,HC SCRN: BENZO NON MC EA, 296 
-83501163,HC SEC PBSC COL," 43,232 "
-51301653,HC SED RATE-WESTERGREN, 130 
-57301657,HC SED RATE-WESTERGREN, 130 
-50301654,HC SED RATE-WESTERGREN N-AUTO, 130 
-59301655,HC SED RATE-WESTERGREN N-AUTO, 130 
-36001519,HC SEED 1-125 NONSTRND PR SRCE L4, 127 
-36099083,HC SEED IODINE 125 STRND PER SRCE, 340 
-41530064,HC SEG/UP&LW EXT MULT BILA," 2,048 "
-41151689,HC SEL CATH PLMT ADD," 1,883 "
-41151721,HC SEL CATH PLMT EA AD," 2,002 "
-41151770,HC SEL CATH PLMT RT OR," 4,631 "
-91900332,HC SELFPAY:DRY SHIP EMBRYO/SPERM, 304 
-92000033,HC SEMEN ANALYSIS, 248 
-55000905,HC SENSITIVITY STUDIES, 226 
-59000901,HC SENSITIVITY STUDIES, 226 
-50744119,HC SENSORY NEURO PROFILE GALOP  1, 88 
-50744127,HC SENSORY NEURO PROFILE GALOP 2, 88 
-50744135,HC SENSORY NEURO PROFILE GALOP 3, 88 
-50744143,HC SENSORY NEURO PROFILE GML TRIA, 88 
-50744150,HC SENSORY NEURO PROFILE GML TRIA, 88 
-50744069,HC SENSORY NEURO PROFILE MAG BY R, 88 
-50744051,HC SENSORY NEURO PROFILE MAG BY T, 103 
-50744077,HC SENSORY NEURO PROFILE SULFATID, 88 
-50744085,HC SENSORY NEURO PROFILE SULFATID, 88 
-50744093,HC SENSORY NEURO PROFILE SULFATID, 88 
-50744101,HC SENSORY NEURO PROFILE SULFATID, 88 
-50744176,HC SENSORY NEUROPROFILE HU, 88 
-50744168,HC SENSORY PROFILE GM1 TRIAD 3, 88 
-50803089,HC SEP ELECTROPH CAPILLARY, 112 
-50802735,HC SEPARATION ELECTROPHORESIS, 103 
-92000058,HC SERUM B HCG QUAL, 54 
-50301456,HC SERUM PREGNANCY SCR, 332 
-51301455,HC SERUM PREGNANCY SCR, 332 
-51501450,HC SERUM PREGNANCY SCR, 332 
-51801454,HC SERUM PREGNANCY SCR, 332 
-56801459,HC SERUM PREGNANCY SCR, 332 
-58501453,HC SERUM PREGNANCY SCR, 332 
-59301457,HC SERUM PREGNANCY SCR, 332 
-50464585,HC SERUM PROTEIN IMMUNOFIX ELECT, 497 
-51564581,HC SERUM PROTEIN IMMUNOFIX ELECT, 497 
-58564584,HC SERUM PROTEIN IMMUNOFIX ELECT, 497 
-55080568,HC SERUM SCREEN-RPR, 238 
-59080564,HC SERUM SCREEN-RPR, 238 
-50301704,HC SEZARY CELL, 116 
-59301705,HC SEZARY CELL, 116 
-50403450,HC SGOT/AST, 180 
-51303451,HC SGOT/AST, 180 
-59403451,HC SGOT/AST, 180 
-50403500,HC SGPT/ALT, 190 
-51303501,HC SGPT/ALT, 190 
-59403501,HC SGPT/ALT, 190 
-62010327,HC SHAVE SKN LSN F/E/E/N/L<0.5CM, 164 
-62010293,HC SHAVE SKN LSN S/N/H/F/G 0.6-1., 229 
-62010285,HC SHAVE SKN LSN S/N/H/F/G<0.5CM, 139 
-62010251,HC SHAVE SKN LSN TRNK/EXT 0.6-1.0, 120 
-62010269,HC SHAVE SKN LSN TRNK/EXT 1.1-2.0, 150 
-62010277,HC SHAVE SKN LSN TRNK/EXT >2.0CM, 176 
-62010244,HC SHAVE SKN LSN TRNK/EXT<0.5CM, 171 
-41530296,HC SHEATH LVL 5, 170 
-31903990,HC SHERLOC TRIPL LUM BASIC TRAY," 2,143 "
-31903503,HC SHERLOCK 5FR DUAL LUM PWR PICC," 1,445 "
-50790104,HC SHLA:SPECIFIC HLA ANTIGEN TYPE, 147 
-52290103,HC SHLA:SPECIFIC HLA ANTIGEN TYPE, 53 
-41003203,HC SHOULDER 1 VIEW, 761 
-41303207,HC SHOULDER 1 VIEW, 761 
-93603207,HC SHOULDER 1 VIEW, 761 
-93603355,HC SHOULDER COMPLETE MIN 2V, 959 
-41003351,HC SHOULDER MIN 2/V, 959 
-41303355,HC SHOULDER MIN 2/V, 959 
-51401750,HC SICKLE CELL PREP, 92 
-58401753,HC SICKLE CELL PREP, 92 
-50393263,HC SICKLEDEX, 92 
-59393264,HC SICKLEDEX, 92 
-46093266,HC SICKLING OF RBC-REDUCTION, 92 
-62017835,HC SIGMOIDOSCOPY DX," 1,209 "
-36033124,HC 3D RADIOTHRPY PLAN W/HD HIST," 12,192 "
-36079408,HC SIM ELCTRN BEAM SIM," 2,050 "
-36300515,HC SIMULATION SIMPLE," 2,050 "
-36033207,HC SIMULATION COMPLEX," 4,747 "
-36060390,HC SIMULATION TRAY, 165 
-36200079,HC SIMULTN/STEROTACTIC," 4,747 "
-36300077,HC SIMULATION STEREOTAC COMPLX," 4,747 "
-62111265,HC SINGLE FROZEN SECTION, 544 
-41300856,HC SINUSES PARANASAL MIN 3V," 1,671 "
-93680296,HC SINUSES PARANASAL <3V, 260 
-41000852,HC SINUSES PARANASAL MIN 3V," 1,671 "
-93600856,HC SINUSES PARANASAL MIN 3V," 1,671 "
-50465079,HC SIROLIMUS, 293 
-51865079,HC SIROLIMUS, 293 
-56865074,HC SIROLIMUS, 293 
-58565078,HC SIROLIMUS, 293 
-59465070,HC SIROLIMUS, 293 
-48002240,HC SIX-MIN WALK W MEASURES," 1,618 "
-59060806,HC SKIN CLT, 302 
-55060800,HC SKIN CULT, 302 
-62011580,HC SKN FULL GRFT FCE/HND/FT <21SQ," 2,130 "
-62011606,HC SKN FULL GRFT N/E/E/L +20SQCM," 1,639 "
-62011556,HC SKN SPLT GRFT FC/SCL/HN/FT," 2,069 "
-62011515,HC SKN TISSUE REARRAN EY/NS/ER/LP," 1,639 "
-62011507,HC SKN TISSUE REARRAN OTHER 10.1," 2,008 "
-62011499,HC SKN TISSUE REARRAN OTHER <10.1," 1,639 "
-62011481,HC SKN TISSUE REARRAN SQ/EXT<10.1," 1,624 "
-41000985,HC SKULL <4 VIEWS, 806 
-41300989,HC SKULL <4 VIEWS, 806 
-93600989,HC SKULL <4 VIEWS, 806 
-41001058,HC SKULL MIN 4 VIEWS, 984 
-93601052,HC SKULL MIN 4 VIEWS, 984 
-50393461,HC SLIDE REVIEW, 116 
-51393460,HC SLIDE REVIEW, 116 
-59393462,HC SLIDE REVIEW, 116 
-59092155,HC SLUG PCR ID CULT, 388 
-41007865,HC SM BWL ENMA RTROGD," 2,360 "
-41007709,HC SMALL BOWEL STUDY," 1,977 "
-55090039,HC SMEAR AFB, 162 
-50751411,HC 6956 11075R SMTH MUS AB IGG, 15 
-59090035,HC SMR AFB, 162 
-39000039,HC SNGL CHMB PCR ANAL W/OUT REPRG, 435 
-50403559,HC SODIUM, 219 
-51303550,HC SODIUM, 219 
-58700097,HC SODIUM, 219 
-59403550,HC SODIUM, 219 
-50463082,HC SODIUM ARTERIAL LYTES, 219 
-50411008,HC SODIUM ARTERIAL LYTES, 219 
-59411009,HC SODIUM ARTERIAL LYTES, 219 
-50403567,HC SODIUM FLUID, 55 
-50403583,HC SODIUM URINE, 134 
-59403584,HC SODIUM URINE, 134 
-41001850,HC SOFT TISSUE NECK, 727 
-41301854,HC SOFT TISSUE NECK, 727 
-93601854,HC SOFT TISSUE NECK, 727 
-92800457,HC SP AUD - SELECT PICTURES, 158 
-62310081,HC SP AUD THRESH W/SP RECOG, 129 
-92800465,HC SP AUD THRESH W/SP RECOG, 129 
-62310099,HC SP AUD- SELECT PICTURES, 158 
-52025244,HC SP BREAST BIOPSY-RA, 907 
-59301804,HC SPCFC GRVTY, 115 
-36070142,HC SPCL RX PRC/TBI/HEM," 6,226 "
-36079200,HC SPEC DOSM/TLD MICRO," 1,412 "
-50301811,HC SPEC GRAVITY-FLUID, 53 
-52020005,HC SPEC STAIN CLASS 1, 700 
-52020104,HC SPEC STAIN CLASS 2, 546 
-52100138,HC SPEC STAIN CLASS 2, 546 
-52100120,HC SPEC STAIN CLASS I, 700 
-36079358,HC SPEC TELTHRP PO PLN, 820 
-36099968,HC SPEC. RX PROC PROST," 6,226 "
-62022231,HC SPECIAL EYE EVALUATION, 77 
-62022116,HC SPECIAL GROUP THERAPY, 79 
-36071082,HC SPECIAL PHYSC CNSLT," 5,752 "
-36071108,HC SPECIAL PHYSICS CONSULT," 5,752 "
-40510323,HC SPECIAL REPORTS, 191 
-62110762,HC SPECIAL STAIN CLASS 2-SPC, 546 
-62110754,HC SPECIAL STAIN CLASS I-SPC, 700 
-36200137,HC SPECIAL TX PROCEDR," 6,226 "
-36300135,HC SPECIAL TX PROCEDUR," 6,226 "
-50301803,HC SPECIFIC GRAVITY, 115 
-50730050,HC SPECIMEN PROCESSING, 39 
-50230051,HC SPECIMEN PROCESSING**, 210 
-62022439,HC SPEECH & HEARING EVALUATION, 213 
-92800473,HC SPEECH AUDIOMETRY (THRESH), 116 
-62310073,HC SPEECH AUDIOMETRY(THRESH), 116 
-92000116,HC SPERM COUNT, 88 
-92000538,HC SPERM CRYOPRESERVATION, 400 
-91900068,HC SPERM IDENTIFIC (PESA), 550 
-92000223,HC SPERM WASH COMPLEX, 275 
-41016148,HC SPINAL PUNCTURE LUMBAR DX," 2,210 "
-41001900,"HC C-SPINE, 3 VIEWS OR LESS"," 1,015 "
-41302001,HC SPINE CERV 6 OR MORE VIEWS," 1,465 "
-41002007,HC SPINE CERV 6 OR MORE VIEWS," 1,465 "
-93602001,HC SPINE CERV 6 OR MORE VIEWS," 1,465 "
-41001959,HC SPINE CERV 4 OR 5 VIEWS," 1,284 "
-41301953,HC SPINE CERV 4 OR 5 VIEWS," 1,284 "
-93601953,HC SPINE CERV 4 OR 5 VIEWS," 1,284 "
-41021312,HC SPINE SINGLE VIEW, 510 
-41321316,HC SPINE SINGLE VIEW, 510 
-93621316,HC SPINE SINGLE VIEW, 510 
-41301805,HC SPINE TOTAL AP/LAT, 235 
-93601805,HC SPINE TOTAL AP/LAT, 235 
-40510232,HC SPIROMETRY, 806 
-43100064,HC SPIROMETRY W MIP/MEP, 806 
-48002232,HC SPIROMETRY W MIP/MEP, 806 
-40450132,HC SPLINT PAD COLLE LG LT, 85 
-40450108,HC SPLINT PAD COLLE LG RT, 85 
-40450140,HC SPLINT PAD COLLE MD LT, 85 
-40450116,HC SPLINT PAD COLLE MD RT, 85 
-40450157,HC SPLINT PAD COLLE SM LT, 85 
-40450124,HC SPLINT PAD COLLE SM RT, 85 
-40450173,HC SPLINT WRIST LT LGE, 85 
-40450181,HC SPLINT WRIST LT MED, 85 
-40450199,HC SPLINT WRIST LT SML, 85 
-40450207,HC SPLINT WRIST RT LGE, 85 
-40450215,HC SPLINT WRIST RT MED, 85 
-40450223,HC SPLINT WRIST RT SML, 85 
-50791425,HC SPLIT BLOOD EACH UNIT, 76 
-52291424,HC SPLIT BLOOD EACH UNIT, 76 
-81091423,HC SPLIT BLOOD EACH UNIT, 76 
-81191421,HC SPLIT BLOOD EACH UNIT, 76 
-81391427,HC SPLIT BLOOD EACH UNIT, 76 
-81791428,HC SPLIT BLOOD EACH UNIT, 76 
-82391426,HC SPLIT BLOOD EACH UNIT, 76 
-84391424,HC SPLIT BLOOD EACH UNIT, 76 
-85391423,HC SPLIT BLOOD EACH UNIT, 76 
-47200233,HC SPLIT BLOOD UNIT, 125 
-62311014,HC SPONT NYSTAGMUS TEST, 318 
-62010954,HC SQ INJ FILL MATER 1.1-5.0CC, 507 
-50320902,HC SRM VSCSTY, 217 
-59320903,HC SRM VSCSTY, 217 
-36300168,HC SRS CYBERKNIFE 1 SESSION-BODY," 27,992 "
-36300192,HC SRS CYBERKNIFE 1 SESSION-BRAIN," 27,992 "
-36200152,HC SRS LINAC 1 SESSION BRAIN," 15,364 "
-36200202,HC SRS LINAC MULTI<6 TX-BODY," 27,992 "
-36200160,HC SRS LINAC MULTI<6 TX-BRAIN," 27,992 "
-92801661,HC SRT/SDT, 116 
-50751296,HC 6956 11094R ENA SSA RO IGG, 14 
-50751304,HC 6956 11095R ENA SSB LA IGG, 24 
-50791227,HC 6949 H097 SERUM ALIQ & STOR, 210 
-52291226,HC 6949 H097 SERUM ALIQ & STOR, 210 
-81191223,HC 6949 H097 SERUM ALIQ & STOR, 210 
-81391229,HC 6949 H097 SERUM ALIQ & STOR, 210 
-81791220,HC 6949 H097 SERUM ALIQ & STOR, 210 
-82391228,HC 6949 H097 SERUM ALIQ & STOR, 210 
-84391226,HC 6949 H097 SERUM ALIQ & STOR, 210 
-85391225,HC 6949 H097 SERUM ALIQ & STOR, 210 
-81091225,HC 6949 H097 SERUM ALIQ & STOR, 210 
-51403400,HC STACLOT LA TEST, 268 
-58403403,HC STACLOT LA TEST, 268 
-41005356,HC STAND KNEES AP BILT, 672 
-41305350,HC STAND KNEES AP BILT, 672 
-93605350,HC STAND KNEES AP BILT, 672 
-26730382,HC STEM CELL APHERESIS AUTOLOGOUS," 6,971 "
-92801166,HC STENGER TEST - PURE TONE, 158 
-92801174,HC STENGER TEST - SPEECH, 529 
-41154220,HC STENT CERV CAR W/PROT," 4,843 "
-41154238,HC STENT CERV CAROTID W/O PROTECT," 3,509 "
-41100538,HC STENT NC OPEN," 23,772 "
-41100546,HC STENT NC OPEN ADD," 13,506 "
-40818106,HC STEREO GUIDE BRST BX/NDL EA," 3,340 "
-36000503,HC STEREOSCOPIC X-RAY GUIDANCE TA," 1,723 "
-36300119,HC STEREOTCT QUAL ASSR," 1,424 "
-41068503,HC STERILE TRAY, 300 
-41305459,HC STERNOCLAVICULAR JT MIN 3V," 1,006 "
-41005455,HC STERNOCLAVICULAR JT MIN 3V," 1,006 "
-93605459,HC STERNOCLAVICULAR JT MIN 3V," 1,006 "
-41005406,HC STERNUM MIN 2V, 832 
-41305400,HC STERNUM MIN 2V, 832 
-93605400,HC STERNUM MIN 2V, 832 
-36200111,HC STEROTCT QUAL ASSUR," 1,424 "
-40450231,HC STIRRUP AIR/GEL ANKLE, 170 
-41154246,HC STNT EXT VRT ITHR CAR," 1,155 "
-40450264,HC STOCKINETTE BIAS 6IN, 85 
-50751312,HC STONE ANALYSIS INFRARED SPECT, 21 
-50722990,HC STONE FORMATION INFRARED SPECT, 239 
-55061055,HC STOOL ADDL SETUP, 227 
-59061051,HC STOOL ADDL SETUP, 227 
-55091250,HC STOOL CLTR, 384 
-59091256,HC STOOL CULTURE, 384 
-59020651,HC STOOL PARASITES, 457 
-55020655,HC STOOL PARASITES CMPLX STAIN, 457 
-50393503,HC STOOL POLYS & EOS, 154 
-59393504,HC STOOL POLYS & EOS, 154 
-50393396,HC STOOL REDUCING SUBSTANCE, 128 
-59393397,HC STOOL REDUCING SUBSTANCE, 128 
-50240191,HC STORAGE - SPECIMEN, 23 
-91900373,HC STORAGE SPERM 6 MNTHS, 248 
-92000637,HC STORAGE SPERM 1 YEAR, 248 
-91900340,"HC STORAGE: EMBRYO, YEAR 1", 450 
-91900357,"HC STORAGE: EMBRYO, YEAR 2", 550 
-91900365,"HC STORAGE: EMBRYO, YEAR 3", 700 
-91900241,HC STORAGE: OOCYTE, 200 
-91900233,HC STORAGE: TISS TESTIC/OVAR, 200 
-37001450,HC STRESS/REST REDIST MYOCARD," 2,790 "
-56202070,HC STRESS TEST W/O I&R," 2,736 "
-41071622,HC STRESS VIEWS ANY JOINT, 349 
-50790914,HC STRID:HLA STR/VNTR IDENTITY, 245 
-52290913,HC STRID:HLA STR/VNTR IDENTITY, 257 
-92200088,HC STRS TTEW/WORX I&R W MD," 2,462 "
-50790922,HC STRSU:HLA STR/VNTR SUBSETS, 270 
-52290921,HC STRSU:HLA STR/VNTR SUBSETS, 257 
-82390923,HC STRSU:HLA STR/VNTR SUBSETS, 257 
-26610139,HC SUB Q/IM INJECTION, 344 
-62310388,HC SUB REANALYSIS-C1<7 YRS, 347 
-62310404,HC SUB REANALYSIS-C1>7 YRS, 345 
-92800499,HC SUB REANALYSIS-CI->7YRS, 345 
-92800481,HC SUB REANALYSIS-CI<7YRS, 347 
-62700414,HC SUBQ/IM INJECTION, 344 
-62700489,HC SUBQ/IM INJECTION ATIC, 344 
-92801737,HC SUBSEQUENT EVAL 2-5YRS, 192 
-92801745,HC SUBSEQUENT EVAL 6-20YRS, 192 
-92801729,HC SUBSEQUENT EVAL <2YRS, 192 
-50743665,HC SULFATIDE AUTO AB1 ELISA, 83 
-50743673,HC SULFATIDE AUTO AB2 ELISA, 83 
-50743681,HC SULFATIDE AUTO AB3 ELISA, 83 
-41122607,HC SUP VENACAVOGRAM," 8,166 "
-62600069,HC SUPERVISE PREP/PROV SDV, 148 
-62600077,HC SUPERVISE PREP/PROV EACH ANTIGEN, 118 
-62010095,HC SURGICAL CLEANSING OF SKIN, 285 
-40450272,HC SURGICEL 2X3, 213 
-31950066,HC SUTURE REMOVAL SET, 85 
-41007519,HC SWALLOWING STUDY," 1,678 "
-56200207,HC SWAN GANZ PLCMENT," 3,857 "
-50464619,HC SWEAT CHLORIDES, 403 
-59464610,HC SWEAT CHLORIDES, 403 
-51560092,HC SYPHILIS TREPONEMAL SCR, 219 
-58560095,HC SYPHILIS TREPONEMAL SCR, 143 
-50405331,HC T BILI CUSTOM PNL, 150 
-59405332,HC T BILI CUSTOM PNL, 150 
-47400148,HC T CELL DEPLETION," 35,198 "
-41002056,HC T-SPINE 2 VIEWS," 1,012 "
-41302050,HC T-SPINE 2 VIEWS," 1,012 "
-93602050,HC T-SPINE 2 VIEWS," 1,012 "
-93602019,HC T-SPINE STANDING SCOLIOSIS, 564 
-41002064,HC T-SPINE THREE VIEWS," 1,185 "
-41302068,HC T-SPINE THREE VIEWS," 1,185 "
-93602068,HC T-SPINE THREE VIEWS," 1,185 "
-51504009,HC T3 TOTAL, 290 
-58504002,HC T3 TOTAL, 290 
-59404004,HC T3 TOTAL, 290 
-62026265,HC AGMNTJ MNDBLR BODY/ANGLE PROSTHETIC MATERIAL," 7,996 "
-50749423,"HC TAU PROTEIN, CSF", 195 
-40400558,HC TB INTRADERM TEST, 23 
-40510166,HC TB INTRADERM TEST, 23 
-37005113,HC NEUROLITE PER DOSE <25MCI," 8,400 "
-37005154,HC TC-99M LABELED RBC <30 MCI," 1,020 "
-37005147,HC MAA PER DOS <10MCI, 289 
-37005121,HC MAG-3 PER STUDY DOSE," 2,550 "
-37010121,HC TC-99M MEBROFENIN UP TO 15 MC, 340 
-37005139,HC DTPA AEROSOL PER DOSE <75MCI, 799 
-37010352,HC TC-99M PENETATE UP TO 25 MCI, 289 
-37005071,HC TC-99M SULFUR COLLOID <20MCI, 468 
-37005014,HC TC99M DISOFENIN <15MCI, 118 
-37005048,HC CERETEC HMPAO PER DOSE<25MCI," 8,378 "
-37010055,HC TC99M MEDRONATE DX <30MCI, 145 
-37010204,HC TC99M PERTECHNETATE DX PER MCI, 39 
-37010501,HC TC99M SESTAMIBI DX STDY DSE, 493 
-37005105,HC DMSA PER DOSE <10MCI," 3,366 "
-37010584,HC TC99M TETROFOSMIN DX STDY DS, 298 
-40510430,HC TDAP (TET/DIPTH/PERTUS), 147 
-50791359,HC TECH COMP ONLY ADDTL MRKR, 80 
-50791383,HC TECH COMP ONLY ADDTL MRKR, 80 
-52291358,HC TECH COMP ONLY ADDTL MRKR, 80 
-52291382,HC TECH COMP ONLY ADDTL MRKR, 80 
-81091357,HC TECH COMP ONLY ADDTL MRKR, 80 
-81091381,HC TECH COMP ONLY ADDTL MRKR, 80 
-81191355,HC TECH COMP ONLY ADDTL MRKR, 80 
-81191389,HC TECH COMP ONLY ADDTL MRKR, 80 
-81391351,HC TECH COMP ONLY ADDTL MRKR, 80 
-81391385,HC TECH COMP ONLY ADDTL MRKR, 80 
-81791352,HC TECH COMP ONLY ADDTL MRKR, 80 
-81791386,HC TECH COMP ONLY ADDTL MRKR, 80 
-82391350,HC TECH COMP ONLY ADDTL MRKR, 80 
-82391384,HC TECH COMP ONLY ADDTL MRKR, 80 
-84391358,HC TECH COMP ONLY ADDTL MRKR, 80 
-84391382,HC TECH COMP ONLY ADDTL MRKR, 80 
-85391357,HC TECH COMP ONLY ADDTL MRKR, 80 
-85391381,HC TECH COMP ONLY ADDTL MRKR, 80 
-50791342,HC 6949C1Q1 FLOW 1ST MKR, 131 
-50791375,HC TECH COMP ONLY FIRST MRKR, 131 
-52291341,HC TECH COMP ONLY FIRST MRKR, 131 
-52291374,HC TECH COMP ONLY FIRST MRKR, 131 
-81091340,HC TECH COMP ONLY FIRST MRKR, 131 
-81091373,HC TECH COMP ONLY FIRST MRKR, 131 
-81191348,HC TECH COMP ONLY FIRST MRKR, 131 
-81191371,HC TECH COMP ONLY FIRST MRKR, 131 
-81391344,HC TECH COMP ONLY FIRST MRKR, 131 
-81391377,HC TECH COMP ONLY FIRST MRKR, 131 
-81791345,HC TECH COMP ONLY FIRST MRKR, 131 
-81791378,HC TECH COMP ONLY FIRST MRKR, 131 
-82391343,HC TECH COMP ONLY FIRST MRKR, 131 
-82391376,HC TECH COMP ONLY FIRST MRKR, 131 
-84391341,HC TECH COMP ONLY FIRST MRKR, 131 
-84391374,HC TECH COMP ONLY FIRST MRKR, 131 
-85391340,HC TECH COMP ONLY FIRST MRKR, 131 
-85391373,HC TECH COMP ONLY FIRST MRKR, 131 
-52102340,HC TECHNICAL CHRG THIN, 376 
-52102050,"HC TECHN IMMED EVAL, 1ST EA", 360 
-56200850,HC TEMP PCMKR INSERTN," 5,539 "
-56203680,HC TEMP TRANSCUTANEOUS PACING, 453 
-62024716,"HC TENDON, ORIGIN/INSERTION", 262 
-51510063,HC TESTOSTERONE (TES) TOTAL, 290 
-58510066,HC TESTOSTERONE (TES) TOTAL, 290 
-59410068,HC TESTOSTERONE (TES) TOTAL, 290 
-50721505,HC 6956 TESTOSTERONE FREE RIA, 11 
-48002216,HC TGV THORACIC GAS VOLUME, 564 
-37010048,HC THALLIUM TL-201 DX PER MCI, 111 
-91900258,HC THAW CRYO:EMBRYO(S)," 1,001 "
-47400122,HC AUTO THAW PREV. FROZ. PROD," 7,583 "
-91900266,HC THAW-SPERM/EA ALIQ, 55 
-91900282,HC THAW: OOCYTES EA ALIQUOT," 1,001 "
-91900274,HC THAW:TISS TESTICULAR/OVARN," 1,001 "
-50120419,HC FFP WITHIN 8 HRS OF COLLECTION, 437 
-50464791,HC THEOPHYLLINE, 275 
-51864791,HC THEOPHYLLINE, 275 
-56864796,HC THEOPHYLLINE, 275 
-59464792,HC THEOPHYLLINE, 275 
-40510422,HC THER/DIAG/PROPH INJ SUBQ/IM, 300 
-62024708,"HC THERAP INJECTION, CARPAL TUNNE", 254 
-26730218,HC THERAP INJECTION-SUBCUT/IM, 344 
-32705063,HC THERAP PROPH DX IV INJ INF UNL, 256 
-32705048,HC THERAP PROPH INTRA-ARTERIAL, 125 
-25230186,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-25730185,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-32710451,HC THERAP PROPH/DX INJ:SUBQ/IM, 344 
-26500587,"HC THERAP/DIAG INJECT,SUBCUT/IM", 344 
-26400580,"HC THERAP/DIAGINJECTION,SUBCUT/IM", 344 
-40400111,HC THERAPEUTIC EXERCISE-15MIN, 254 
-62700570,HC THERAPEUTIC INFSN 1HR OREN ATI," 1,226 "
-36033389,HC THERAPEUTIC INJECTION, 344 
-36099935,HC THERAPTIC RAD PORT, 800 
-47200985,HC THERMAL AMPLITUDE, 191 
-50723378,HC 6959 11060R THIAMINE VIT B1, 30 
-32712358,HC THIGH COMPRESSION S1, 383 
-26630988,HC THORACENTESIS FOR ASPIRATION, 947 
-32711590,"HC THORACENTESIS, PLEURAL NO GUIDANCE"," 1,658 "
-62016605,HC THORACENTESIS, 581 
-26500884,HC THORACENTESIS TRAY, 625 
-26620005,HC THORACENTESIS TRAY, 149 
-32712036,"HC THORACENTESIS TRAY , DISP", 85 
-41151119,HC PNEUMOTHOR CHEST TUBE," 3,897 "
-41126335,HC THORACIC AORTOGRAM," 7,302 "
-41002106,HC THORACOLUMBAR 2 VIEWS," 1,113 "
-41302100,HC THORACOLUMBAR 2 VIEWS," 1,113 "
-93602100,HC THORACOLUMBAR 2 VIEWS," 1,113 "
-41131020,HC THRM THY ART CTH EX," 13,659 "
-41100074,"HC THRMBCTY, ART, SCND"," 8,731 "
-41100090,"HC THRMBCTY, ARTER, ADD"," 8,731 "
-41100108,"HC THRMBCTY, ARTER, INIT"," 9,846 "
-41100066,"HC THRMBCTY, VEIN, SCND"," 7,892 "
-41100082,"HC THRMBCTY, VENOUS"," 11,952 "
-55050355,HC THROAT SCREEN, 165 
-50500404,HC THROMBIN TIME, 167 
-59500405,HC THROMBIN TIME, 167 
-51403392,HC THROMBOELASTOGRAPHY, 747 
-58403395,HC THROMBOELATROGRAPHY, 747 
-41127408,HC THROMBOLYTIC THRPY," 14,154 "
-50500206,HC THROMPLAS PTT, 240 
-50570027,HC THROMPLAS PTT, 240 
-51370021,HC THROMPLAS PTT, 240 
-59500207,HC THROMPLAS PTT, 240 
-59570028,HC THROMPLAS PTT, 240 
-21140017,HC THRP INF CONCURRENT, 821 
-23140015,HC THRP INF CONCURRENT, 821 
-23240013,HC THRP INF CONCURRENT, 821 
-23740012,HC THRP INF CONCURRENT, 821 
-23840010,HC THRP INF CONCURRENT, 821 
-24140014,HC THRP INF CONCURRENT, 821 
-25040015,HC THRP INF CONCURRENT, 821 
-25240011,HC THRP INF CONCURRENT, 821 
-25340019,HC THRP INF CONCURRENT, 821 
-25440017,HC THRP INF CONCURRENT, 821 
-25740010,HC THRP INF CONCURRENT, 821 
-26340018,HC THRP INF CONCURRENT, 821 
-27340017,HC THRP INF CONCURRENT, 821 
-62700497,HC IV  INFUS THER ADDL SEQ1HR NEW DRUG, 796 
-32001133,HC THRP PROPH IVP SAME DRUG, 405 
-62700869,HC THRP PROPH IVP SAME DRUG G1, 405 
-32003089,HC THRP PROPH OR DX SQ IM EA+DRUG, 344 
-59050351,HC THRT SCRN, 165 
-51504074,HC THYROGLOBULIN (TG), 226 
-58504077,HC THYROGLOBULIN (TG), 226 
-59404079,HC THYROGLOBULIN (TG), 226 
-50723774,HC 6959 THYROI BIND INH IGG QN, 239 
-50751320,HC 6956 11097R THY STIM IMM GL, 12 
-58504036,HC THYROXINE (T4) TOTAL, 158 
-51504033,HC THYROXINE(T4) TOTAL, 158 
-59404038,HC THYROXINE(T4) TOTAL, 158 
-41004201,HC TIBIA + FIBULA AP, 864 
-93604205,HC TIBIA + FIBULA AP, 864 
-41304205,HC TIBIA + FIBULA AP +, 864 
-41152174,HC TIBIOPERON ATHER," 18,994 "
-92200351,HC TILT TABLE EXAM," 2,511 "
-40180127,HC TIPS INSERTION," 7,908 "
-50642016,HC TIS CULT AMNIO FLD," 1,613 "
-50641968,HC TIS CULT ATAX BLD NONNEO," 1,000 "
-50642040,HC TIS CULT BLOOD NEO," 1,385 "
-50642032,HC TIS CULT BONE MRW NEO," 1,385 "
-50642289,HC TIS CULT CVS," 1,613 "
-50641950,HC TIS CULT FANC BLD NONNEO," 1,000 "
-50641943,HC TIS CULT HRES BLD NONNEO," 1,000 "
-50641935,HC TIS CULT ROUT BLD NONNEO," 1,000 "
-50641984,HC TIS CULT SKIN/TISS KAR," 1,542 "
-50641992,HC TIS CULT SKIN/TISS REF," 1,542 "
-50642065,HC TIS CULT TUMOR NEO," 1,616 "
-40868333,HC TISSUE MARKER IMPLANTABLE, 329 
-55091649,HC TISSUE PREP ADDL, 182 
-59091645,HC TISSUE PREP ADDL, 182 
-56209802,HC TLT TBL TST," 2,511 "
-41001306,HC TMJ OPEN + CLOSE BI, 650 
-41301300,HC TMJ OPEN + CLOSED BILATERAL, 650 
-50462647,HC TOBRAMYCIN, 314 
-51862647,HC TOBRAMYCIN, 314 
-56862642,HC TOBRAMYCIN, 314 
-59462648,HC TOBRAMYCIN, 314 
-41004557,HC TOE(S) MIN 2V, 586 
-41304551,HC TOE(S) MIN 2V, 586 
-93604551,HC TOE(S) MIN 2V, 586 
-51564748,HC TOLERANCE W/INSULIN, 181 
-58564741,HC TOLERANCE W/INSULIN, 181 
-93607059,HC TOMOGRAPHY SGL PLANE NO URO, 238 
-51564409,HC TOTAL IGE, 379 
-58564402,HC TOTAL IGE, 379 
-51402741,HC TOTAL PROTEIN C, 133 
-58402744,HC TOTAL PROTEIN C, 133 
-51402758,HC TOTAL PROTEIN S, 269 
-58402751,HC TOTAL PROTEIN S, 269 
-46140463,HC TOXOPLASMA IGG, 231 
-59140467,HC TOXOPLASMA IGG, 231 
-41151044,HC TRACH/BRONCH DIL W/ TR STENT," 7,007 "
-41151036,HC TRACH/BRONCH DIL," 6,142 "
-58501933,HC TRAD GLUC TOLER (100G), 789 
-51501930,HC TRAD GLUC TOLER(100G), 789 
-26420281,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-26520288,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-26620286,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-32701286,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-26420299,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-26520296,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-26620294,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-26420307,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-26520304,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-26620302,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-26420315,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-26520312,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-26620310,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-26420323,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-26520320,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-26620328,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-26520338,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-26620336,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-26420349,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-26520346,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-26620344,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-26420356,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-26520353,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-26620351,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-26420364,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-26520361,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-26620369,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-41151523,HC TRANS CATH INTRAVAS STENT," 22,982 "
-41151515,HC TRANS CATH OCCUL," 20,399 "
-41152331,HC TRANSC INTRO FALLOP," 4,097 "
-41152190,HC TRANSCATH BX," 5,152 "
-41152034,HC TRANSCATH TX INFUS," 4,778 "
-41144957,HC TRANSCATHETER BIOPS," 7,830 "
-50403690,HC TRANSFERRIN, 420 
-51503696,HC TRANSFERRIN, 420 
-58503699,HC TRANSFERRIN, 420 
-59403691,HC TRANSFERRIN, 420 
-25230061,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-25730060,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-26340265,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-26730226,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-27340264,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-32001141,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-32500514,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-62701107,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-32001232,HC TRANSFUSION BLOOD-10 HR," 5,100 "
-32500605,HC TRANSFUSION BLOOD-10 HR," 5,100 "
-25230079,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25730078,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-26340273,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-26730234,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-27340272,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-32001158,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-32500522,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-62701115,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-25230087,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25730086,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-26340281,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-26730242,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-27340280,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-32001166,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-32500530,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-62701123,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-25230095,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25730094,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-26340299,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-26730259,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-27340298,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-32001174,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-32500548,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-62701131,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-25230103,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25730102,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-26340307,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-26730267,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-27340306,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-32001182,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-32500555,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-62701149,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-25230111,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25730110,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-26340315,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-26420331,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-26730275,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-27340314,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-32001190,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-32500563,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-62701156,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-25230129,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25730128,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-26340323,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-27340322,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-32001208,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-32500571,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-62701164,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-25230137,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25730136,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-26340331,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-27340330,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-32001216,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-32500589,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-62701172,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-25230145,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-25730144,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-26340349,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-27340348,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-32001224,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-32500597,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-41151853,HC TRANSHEP PORTOGRAM," 6,252 "
-41152380,HC TRANSHEP PORTOGRAM," 2,025 "
-41151192,HC TRANSLUMINAL BX," 1,616 "
-41530213,HC TRANSPLANTED KIDNEY TX ECHO," 1,578 "
-56202286,HC TRANSSEPTAL PUNCTURE, 319 
-43100023,HC TRANSTHORACIC CONG," 4,683 "
-32710741,"HC TRAY, PARACENTESIS", 85 
-26610444,HC TRAY-BONE MARROW, 268 
-26610451,HC TRAY-LUMBAR, 313 
-62018601,HC TREAT BLADDER LSN INSTILL ANTI, 447 
-62011895,HC TREAT BURN(S) W/O ANES SML, 285 
-62026380,HC TREAT LUNG LINING CHEMICALLY, 665 
-62022447,HC TREAT SPEECH/HEARING INDIVIDUA, 87 
-62019260,HC TREAT VAGINA INFECTION, 234 
-59403717,HC TRGLYRCYD FLD, 175 
-41153677,HC TRIAL BALLN OCC HEAD/NCK," 30,629 "
-55061063,HC TRICHOMONAS EXAM, 270 
-59061069,HC TRICHOMONAS EXAM, 165 
-50403716,HC TRIGLYCERIDES FLUID, 175 
-50403708,HC TRIGLYCERIDES-BLOOD, 175 
-51303709,HC TRIGLYCERIDES-BLOOD, 175 
-59403709,HC TRIGLYCERIDES-BLOOD, 175 
-62010194,HC TRIM OVER 4 SKIN LESIONS, 255 
-62010178,HC TRIM SKIN LESION, 138 
-41145244,HC TRLUM ATHER EA ADD," 4,175 "
-41145236,HC TRLUM ATHERECT PERC," 8,617 "
-41128562,HC TRNSCERV CATH FALLO," 3,991 "
-51563427,HC TROPONIN QUANT T, 520 
-50463421,HC TROPONIN T, 520 
-51863421,HC TROPONIN T QUANT, 520 
-56863426,HC TROPONIN T QUANT, 520 
-58563420,HC TROPONIN T QUANT, 520 
-59463422,HC TROPONIN T QUANT, 520 
-36079234,HC TRTMNT DEVICE/INTER," 1,347 "
-36079242,HC TRTMNT DEVICE/SIMPL, 996 
-36040004,HC TRUS-GUIDANCE PROSTATE IMPLANT, 574 
-92200070,HC TTE W DOPPLER W COLOR," 5,966 "
-36070159,HC TTL BDY IRRADTN-BMT," 6,226 "
-36070167,HC TTL BDY IRRADTN-MF," 6,226 "
-92000488,HC TTL TESTOSTERONE, 149 
-39000096,HC TTM EVAL PM 90 DAYS, 352 
-41151101,"HC TUBE THORACOSTOMY H20 SEAL, OPEN"," 1,512 "
-42016220,HC TUBE THORACOSTOMY W/WO WATER S," 1,512 "
-26730689,"HC TUBING,BLOOD WARMER", 151 
-47400163,HC TUMOR CELL DEPLETION," 39,287 "
-37020427,HC TUMOR LOC SPECT," 6,146 "
-37002201,HC TUMOR LOCALIZATION," 5,209 "
-56202302,HC TUNNEL CVC 2 CATH/2SITES W PORT," 6,732 "
-37005030,HC TX 1-131 CAPSULE PER MCI, 57 
-62310891,HC TX APD AR TININITUS 60MIN, 87 
-92800515,HC TX AURAL INDIVIDUAL, 87 
-36071199,HC TX D CM ETRON BM20M," 2,765 "
-36071173,HC TX DE CM ETR BM6-10," 2,446 "
-36300101,HC TREATMENT DEVICE COMPLEX," 2,439 "
-36071181,HC TX DL CM ET BM11-19," 2,756 "
-36070753,HC TX DLV SMPL UPTO 5M, 381 
-36070936,HC TX DLVR CMPL 11-19M," 2,756 "
-36070944,HC TX DLVR CMPLX 20+MV," 2,765 "
-36070910,HC TX DLVR CMPLX 6-10M," 2,446 "
-36070928,HC TX DLVR CMPLX <5MEV," 2,485 "
-36070811,HC TX DLVR INT UPTO 5M, 555 
-36070837,HC TX DLVR INTR 11-19M, 722 
-36070829,HC TX DLVR INTRM 6-10M, 630 
-36070779,HC TX DLVR SIMP 11-19M," 1,383 "
-36070761,HC TX DLVRY SIMP 6-10M," 1,259 "
-36034478,HC TX IMPLANT DEVICE - SIMPLE, 996 
-91000091,"HC TX INCMPL ABORTION, ANY TRI"," 4,097 "
-26400598,HC TX/DX INJECTION IV INITIAL, 542 
-26500595,HC TX/DX IVP INJ INITIAL, 542 
-26610170,HC TX/DX IVP INJECTION INITIAL, 542 
-26710178,HC TX/DX IVP INJECTION, 542 
-43100106,HC TYMPANOMETRY, 172 
-62310214,HC TYMPANOMETRY, 143 
-92800523,HC TYPANOMETRY, 143 
-62025598,HC UFE PERC INCL RAD STL," 8,089 "
-41100355,HC UFE PERCUTANEOUS APPROACH," 23,302 "
-41007600,HC UGI W/KUB," 2,383 "
-41007832,HC UGI/SB W/AIR, 579 
-41007816,HC UGI/SM BOWEL," 3,317 "
-41128463,HC ULTRASOUND GD/NDL," 1,896 "
-40400202,HC ULTRASOUND-15 MIN, 190 
-41100264,HC UNLIST FLURO PROC, 645 
-41100256,HC UNLIST HEMIC OR LYMPH," 2,100 "
-41100579,HC UNLISTED BILIARY," 9,739 "
-56202252,HC UNLISTED CARD SURG PROC," 5,839 "
-26402222,HC UNLISTED CHEMO PROCEDURE, 77 
-26502195,HC UNLISTED CHEMO PROCEDURE, 77 
-26602193,HC UNLISTED CHEMO PROCEDURE, 77 
-56202260,HC UNLISTED CVS PROCEDURE, 319 
-41153214,HC UNLISTED DX IR PROC," 4,171 "
-91000075,HC UNLISTED HYST PROC. UTERUS," 1,970 "
-41100702,"HC UNLISTED PROC, ABDOMEN"," 7,498 "
-41100462,"HC UNLISTED PROC, BILIARY TRACT"," 9,739 "
-41100629,"HC UNLISTED PROCEDURE, RECTUM"," 2,020 "
-41153222,"HC UNLISTED PROC,INTESTINE"," 7,498 "
-41100553,HC UNLISTED STOMACH," 4,118 "
-41100561,HC UNLISTED URINARY, 502 
-41153511,HC UNLISTED US PROCEDURE, 546 
-41152414,HC UNLISTED VASCULAR PROCEDURE," 10,333 "
-41100454,"HC UNLISTED,LIVER PROC"," 1,910 "
-41016114,"HC UP EXTREMITY, PED, MIN 2V", 946 
-40930059,HC UP+LWR EXT 1-2 LVL BIL," 1,276 "
-56269087,HC UPGRADE PACEMAKER-SNGL OR DUAL," 39,758 "
-47400379,HC URD PRODUCT PREP FOR TRANSPORT," 1,562 "
-50403757,HC UREA NITROGEN, 157 
-51303758,HC UREA NITROGEN, 157 
-59403758,HC UREA NITROGEN, 157 
-50403781,HC UREA NITROGEN URINE, 93 
-59403782,HC UREA NITROGEN URINE, 93 
-41006354,HC URETHROGRAM RETRO," 1,638 "
-50411081,HC URIC ACID CUSTOM, 135 
-59411082,HC URIC ACID CUSTOM, 135 
-50405257,HC URIC ACID CUSTOM PANEL, 135 
-50403807,HC URIC ACID SERUM/PL, 135 
-51303808,HC URIC ACID SERUM/PL, 135 
-59403808,HC URIC ACID SERUM/PL, 135 
-50462324,HC URIC ACID URINE, 89 
-59462325,HC URIC ACID URINE, 89 
-50302231,HC URINALYSIS AUTO, 108 
-59302232,HC URINALYSIS AUTO, 108 
-59463109,HC URINE CHLORIDE, 131 
-50463108,HC URINE ELECTROLYTE PANEL CL, 131 
-50463090,HC URINE ELECTROLYTE PANEL K, 158 
-50301878,HC URINE EOSINOPHIS, 174 
-55020861,HC URINE FULL ORG ID, 226 
-59020867,HC URINE FULL ORG ID, 226 
-92000025,HC URINE HCG QUAL, 138 
-50401637,HC URINE LYTES PANEL SODIUM, 134 
-59401638,HC URINE LYTES SODIUM, 134 
-50464809,HC URINE MAGNESIUM, 280 
-59464800,HC URINE MAGNESIUM, 280 
-50393172,HC URINE MICRO ONLY, 60 
-50302116,HC URINE MYOGLOBIN SCREEN, 115 
-59463091,HC URINE POTASSIUM, 158 
-51302230,HC URINE PRE-SURG SCRN, 115 
-59301465,HC URINE PREGNANCY SCN, 102 
-51801462,HC URINE PREGNANCY SCR, 102 
-56801467,HC URINE PREGNANCY SCR, 102 
-51370039,HC URINE PREGNANCY SCREEN, 102 
-51870038,HC URINE PREGNANCY SCREEN, 102 
-56870033,HC URINE PREGNANCY SCREEN, 102 
-50301464,HC URINE PREGNANCY SCRN, 102 
-51564599,HC URINE PROTEIN IMMUNOFIX ELECT, 376 
-58564592,HC URINE PROTEIN IMMUNOFIX ELECT, 376 
-50302355,HC URINE ROUTINE-MICRO, 196 
-51302354,HC URINE ROUTINE-MICRO NONAUTO, 196 
-57302358,HC URINE ROUTINE-MICRO, 196 
-59302356,HC URINE ROUTINE-MICRO, 196 
-59020875,HC URINE SPOT DRG ID, 209 
-55020879,HC URINE SPOT ORG ID, 209 
-50722263,HC 6959 URINE VMA PANEL, 11 
-59070003,HC URN CLT, 317 
-55070007,HC URN CULT, 317 
-59301879,HC URN EOSOFLS, 174 
-41006057,HC UROGRAM RETRO W/WO KUB, 798 
-41100017,"HC URT EXT STNT, REM/REP"," 3,620 "
-41100041,"HC URT INT STNT, REM/REP"," 5,376 "
-41100132,"HC URT INT STNT, REMOVAL"," 3,186 "
-41100587,HC US ABDOMEN LIMITED," 1,584 "
-40928525,HC US BRST (UNI OR BIL), 948 
-40828527,HC US BRST(U OR B)MAMMO AREA, 948 
-40928640,HC US CHEST," 1,529 "
-56222235,HC US GD ENDMYCD BPSY, 431 
-40880288,HC US GUID NDL PLC:MAMMO AREA," 1,896 "
-40911216,HC US GUIDANCE NEEDLE PLCMNT," 1,896 "
-40905101,HC US GUIDANCE TISSUE ABLATION, 712 
-41153503,"HC US GUIDANCE,VASCULAR ACCESS"," 1,813 "
-40928871,HC US GUIDED COMPRESSN RP, 469 
-40905119,"HC US INFANT HIPS, LIMITED"," 1,173 "
-40905127,HC US PREGTRNSABD ADD GEST1TRI, 270 
-40905044,HC US SCREEN AAA," 1,762 "
-40905705,"HC US SUPPLY, LEVEL 3", 85 
-40905762,"HC US SUPPLY, LEVEL 6", 298 
-41153859,HC US TISSUE PARENCHYMAL ABLATE, 712 
-40905135,HC US PREG TRANSABD GEST 2 TRI, 951 
-40928780,HC US: ABDOMEN," 2,294 "
-40928723,HC US: EXTREMITY," 1,189 "
-40928707,HC US: PELV NON-OB LTD," 1,237 "
-40905002,"HC US: PREG UTER, TRANSVAGINAL"," 1,150 "
-40905051,"HC US: PREG UTRS W/EVAL, 1ST TRI"," 1,778 "
-40928657,HC US PREG UTERUS W/EVAL >1TRI," 1,928 "
-40928806,HC US: RETROPERITONEAL," 1,822 "
-40928624,HC US: SOFT TISSUE OF HEAD OR NEC," 1,554 "
-40928798,HC US:ABDOMEN LIMITED," 1,584 "
-40928699,HC US:PELVIC NON-OB," 1,809 "
-40928665,HC US:PREG UTER LMTD," 1,257 "
-40928814,HC US:RETROPERITON LTD," 1,321 "
-40928715,HC US:SCROTUM," 1,470 "
-40930216,HC US:TPLNT KIDNEY W/WO DPLX/DOP," 1,578 "
-40928681,HC US:TRANSVAGINAL," 1,456 "
-40928889,"HC US:TRANSVAGINAL,LIMITED", 728 
-40928764,HC USL GUIDANCE INTRA-OPERATIVE," 1,715 "
-62025366,HC ADMIN INFLUENZA VACCINE, 55 
-32003204,HC VACCINE ADMIN PNEUMO, 295 
-62700992,HC VACCINE GEN ADMIN 1 VACCINE, 77 
-36060309,HC VACCINES:PNEUMOCOC, 171 
-50462050,HC VALPROATE LEVEL, 391 
-51562056,HC VALPROATE LEVEL, 391 
-51862050,HC VALPROATE LEVEL, 391 
-56862055,HC VALPROATE LEVEL, 391 
-59462051,HC VALPROATE LVL, 391 
-55060859,HC VALVE CULT, 302 
-56222003,HC VALVULOPLASTY," 18,146 "
-56222029,HC VALVULOPLASTY AO PERC," 15,755 "
-50462662,HC VANCOMYCIN, 263 
-51562668,HC VANCOMYCIN, 263 
-51862662,HC VANCOMYCIN, 263 
-56862667,HC VANCOMYCIN, 263 
-59462663,HC VANOCOMYCIN, 263 
-40510117,HC VARICELLA VACCINE, 581 
-62019104,HC VASECTOMY UNILAT/BILAT," 4,581 "
-50751338,HC 6956 11099R VASOACT INT PEP, 50 
-41144973,HC VENGRM ADREN UNILT," 6,252 "
-41145020,HC VENGRM EXTRM BILAT," 3,492 "
-36060325,HC VENIPUNCTURE, 115 
-40510372,HC VENIPUNCTURE, 75 
-56202245,HC VENIPUNCTURE, 115 
-58900101,HC VENIPUNCTURE, 115 
-40905903,HC VENIPUNCTURE, 115 
-92000629,HC VENIPUNCTURE, 115 
-50200104,HC VENIPUNCTURE **, 115 
-62700430,HC VENIPUNCTURE  36415, 115 
-62700653,HC VENIPUNCTURE ATIC, 115 
-41152349,HC VENOG SINUS OR JUG," 5,737 "
-41152356,HC VENOG SUP SAGIT SI," 2,017 "
-41152323,HC VENOGRAM ORBITAL," 2,017 "
-41106683,HC VENOGRAM RNL BILAT," 8,998 "
-41106675,HC VENOGRM RNL UNILAT," 7,730 "
-56269186,"HC VENOG,SINUS OR JUGULAR"," 5,737 "
-50462829,HC VENOUS BLOOD GASES," 1,161 "
-59462820,HC VENOUS BLOOD GASES," 1,161 "
-41530130,HC VENOUS DPLX UP&LW BILAT," 2,966 "
-41530148,HC VENOUS DPLX UP&LW LIMITED," 2,072 "
-41127051,HC VENOUS SAMPLING," 11,880 "
-41151382,HC VENOUS SMPL CATH," 10,704 "
-41126251,HC VERTEBRAL ANGGRPH," 9,683 "
-62310313,HC VERTICAL AXIS ROTATION TEST, 598 
-92800705,HC VERTICAL AXIS ROTATION TEST, 598 
-62310321,HC VERTICAL ELECTRODES, 758 
-92800713,HC VERTICAL ELECTRODES, 758 
-56310667,HC VIR GENE RT/PRO SEQ," 2,847 "
-46100467,HC VIR ID ADDL STUDIES, 349 
-46310660,HC VIR. GENE RT/PRO SEQ.," 2,847 "
-46120762,HC VIRAL AB MEASLES, 240 
-59120766,HC VIRAL AB MEASLES, 240 
-46120887,HC VIRAL ANTIBODY CMV, 268 
-46140075,HC VIRAL EIA SINGLE TEST, 38 
-59140004,HC VIRAL ID DFA, 190 
-46140000,HC VIRAL ID PAN - IFA, 190 
-46160719,HC VIROTYPE HIV1 ANALYSIS, 448 
-59160713,HC VIROTYPE HIV1 ANALYSIS, 448 
-46100475,HC VIRUS ISOL HERPES, 484 
-46100459,HC VIRUS ISOLATN CULTR, 484 
-46100848,HC VIRUS VIAL CULTURE, 363 
-41145046,HC VISCERAL ANGIOGRPH," 9,432 "
-41810938,HC VISIPAQUE 270 (100ML), 5 
-41810946,HC VISIPAQUE 270 (50ML), 4 
-36001501,HC VISIPAQUE 300-399MG PER ML, 9 
-36301513,HC VISIPAQUE 300-399MG PER ML, 9 
-41810995,HC VISIPAQUE 320 (100ML), 5 
-41811019,HC VISIPAQUE 320 (200ML), 5 
-41811001,HC VISIPAQUE 320 (50ML), 4 
-92800531,HC VISUAL REINFOR AUDIO (VRA), 253 
-62310107,HC VISUAL REINFOR AUDIO(VRA), 253 
-62016548,HC VISUALIZE THRU ESTAB TRACH INC, 754 
-50725712,HC 6959 VITB1W VITB1 THIAMINE, 20 
-50751858,HC 6956 11100R VITB2 RIBOFLAVN, 35 
-50751866,HC 6956 11101R VITC ASCRBC ACD, 30 
-50751346,"HC 6956 11098R VITD 1,25 DIHYD", 43 
-50751353,HC 6956 VIT D 25 HYDROXY, 32 
-50751361,HC 6956 11102R VIT E TOCO ALPA, 25 
-50725720,HC 6959 UVMA VMA URINE, 24 
-50790187,HC VNTR:HLA STR/VNTR TESTING, 283 
-52290186,HC VNTR:HLA STR/VNTR TESTING, 257 
-47220041,HC VOLUME REDUCTION-BLOOD PROD, 282 
-59120881,HC VRL AT CMV, 268 
-59100453,HC VRL ISLTN CLT, 484 
-59100479,HC VRL ISOL HRPS, 484 
-59100461,"HC VRS ID, ADL ST", 349 
-59100842,HC VRS VIAL CLT, 363 
-56270119,HC VSD CLOSURE W/IMPLANT," 37,862 "
-56209919,HC VT ABLATION," 22,579 "
-56209828,HC EP W IND/LT VENT PACING/REC," 8,719 "
-56209224,HC EP W IND/AVPACING/RECD/HIS," 29,068 "
-51401701,HC VW FACTOR ANTIGEN, 328 
-58401704,HC VW FACTOR ANTIGEN, 328 
-50751882,HC VWF PROTEASE INHIBITOR, 184 
-46140497,HC VZ VIRUS AB IGG, 251 
-59140491,HC VZ VIRUS AB IGG, 251 
-46140166,HC VZV ANTIGEN, 369 
-59140160,HC VZV ANTIGEN, 369 
-47400270,HC ALLO WASHING OF HARVEST," 3,467 "
-50110402,HC WHOLE BLOOD EACH UNIT," 1,216 "
-50101518,HC WHOLE BLOOD AUTOLOGOUS," 1,216 "
-50115641,HC WHOLE BLOOD;LR," 1,216 "
-50115518,"HC WHOLE BLOOD;LR, IRRAD"," 1,216 "
-37021524,HC WHOLE BODY SCAN-2 OR MORE," 9,741 "
-62025531,HC WND PREP F/N/HF/G ADDL 100CM, 482 
-62025507,HC WND PREP F/N/HF/G FRST 100CM, 797 
-62025523,HC WND PREP TR/A/L ADDL 100CM, 482 
-62011523,HC WND PREP TR/A/L FRST 100CM, 653 
-59060954,HC WND-DEEP/SURG/ASP, 302 
-40510653,HC WORK RELATED DISABILITY EXAM, 403 
-55060909,HC WOUND CLT, 302 
-59060905,HC WOUND CULTURE, 302 
-55060958,HC WOUND DEEP SURG/ASP CULT, 302 
-55020903,HC WOUND FULL ORG ID, 226 
-59020909,HC WOUND FULL ORG ID, 226 
-41003609,HC WRIST AP + LAT, 753 
-41303603,HC WRIST AP + LAT, 753 
-93603603,HC WRIST AP + LAT, 753 
-41003658,HC WRIST MIN 3V, 907 
-41303652,HC WRIST MIN 3V, 907 
-93603652,HC WRIST MIN 3V, 907 
-62801246,HC WRITTEN TEST REPORT 1 HOUR, 173 
-40450579,HC XYLOCAINE 2%, 62 
-40450587,HC XYLOCAINE 2%, 61 
-55091805,HC YEAST MIC, 254 
-59091801,HC YEAST MIC, 254 
-50743202,HC YO AUTO ANTIBODY, 215 
-37021458,"HC ZEVALIN IN-111, UP TO 5 MCI"," 15,487 "
-37021466,"HC ZEVALIN Y-90, UP TO 40 MCI"," 103,175 "
-40450603,HC ZITHROMAX 250MG ZPACK, 71 
-23191117,HC G1 MED/SURG PRIV," 8,897 "
-50470079,HC PROTEIN TOTAL, 75 
-59470070,HC PROTEIN TOTAL, 75 
-58000332,HC COMP BASED MOTION ANALYSIS, 632 
-58000142,HC CTCL ST/EEG M 1ST H, 632 
-58000159,HC CRTCL ST/EEG EA ADD H, 544 
-58000167,HC ANALYZE NEUROSTIM BRAIN/1H, 569 
-58000175,HC ANALYZ NEUROSTIM BRAIN ADDON, 382 
-58000183,HC COMP DYN POSTUREOGRAPHY, 708 
-58000191,HC PHYS PER TEST W/REPORT EA 15 M, 176 
-58000209,HC ROM MEAS&REPORT HAND, 141 
-58000217,HC ROM MEAS&RPRT EA EXTRMTY OR SP, 142 
-58000233,HC THER PROC EA 15 ROM FLEX, 254 
-58000241,HC NEUROMUSC REEDUC-15MIN, 261 
-58000258,HC GAIT TRAININ-15MIN, 269 
-58000266,HC DIRECT FUNCTIONAL ACT-15MIN, 309 
-58000274,HC SENSORY INTEGRATION EA 15, 102 
-58000282,HC SELF CARE MGMT TRNG EA 15, 279 
-58000290,HC BIOFEEDBACK TRAINING, 173 
-58000316,HC MUSCLE TEST MANUAL BODY W/HNDS, 287 
-58000324,HC COMPT MOTION ANA W/PLAN PRESS, 632 
-50774934,HC 6942 EBV EA IGG, 47 
-50774942,HC 6942 EBV EBNA, 47 
-50774918,HC 6942 EBV VCA IGG, 32 
-50774926,HC 6942 EBV VCA IGM, 47 
-50774868,HC 6956 BEAT2 TRSFRN BDY FLD, 78 
-50774629,HC 6956 LIPASE BODY FLUID, 27 
-50722354,HC ALDOLASE, 7 
-50751601,"HC AMINO ACIDS, PLASMA", 165 
-50804103,HC AMLP INTERP AND RPT, 138 
-50804095,HC AMLP MUT AMP NA, 188 
-50722180,HC C-PEPTIDE PLASMA, 7 
-50751510,HC CHORIONIC VILLI STUDY, 425 
-50774983,HC 6942 11265R ENTMB HSTLYT AG, 65 
-51571040,HC MYCOPHENOLIC ACID, 291 
-38884250,HC 8425FENTANYL 0.1MG, 60 
-50776236,HC 7026 INFLIX RIA QUANT, 169 
-40510711,HC NASOPHARYN SWAB H1N1, 355 
-50776327,HC 7053 GCDH MUT ISOL NA, 6 
-26531038,HC CHEMO INTRATHECAL INTO CNS," 5,090 "
-41100926,HC BRONCHOSCOPY W/REV STENT," 4,428 "
-50774645,HC 6956 VITAMIN E, 25 
-50774652,HC 6956 VITAMIN A, 21 
-50775550,HC 7046 TRAPS MULT ISOL NA, 8 
-50775568,HC 7046 TRAPS ENZ DIG EA, 8 
-50775576,HC 7046 TRAPS SEP GEL EP, 8 
-50775584,HC 7046 TRAPS MUT AMP NA SEQ, 34 
-50775592,HC 7046 TRAPS MUT ID SEQ EA, 34 
-50775600,HC 7046 TRAPS INTRP REPORT, 8 
-50775642,HC 7051 CUI HISTAMINE, 85 
-50775659,HC 7051 CUI TSH, 48 
-50775667,HC 7051 LEUKOCYTE AB, 39 
-50775675,HC 7051 CUI LHR, 36 
-50775683,HC 7051 CUI MICROSOMAL AB, 42 
-50775691,HC 7051 CUI THYROGLOB AB, 46 
-50775709,HC 7051 MACADAMIA NUT, 12 
-50776103,HC 7061 BH4/NEOPTERIN PROF, 190 
-50776111,HC 7061 5-METHYLTETRAHYDRO, 180 
-50776129,HC 6976 PHENTYPE GENTYP BIO, 126 
-50776137,HC 6976 HIV 1 TRANS PROT, 249 
-50776145,HC 6976 DRUG SUSP PHENO 10 TSTD, 473 
-50776152,HC 6976 DRUG SUSP PHENO EA ADDT, 25 
-50776160,HC 6976 TROFIE CO-RECEPTOR," 1,960 "
-50776194,HC 7026 CELIAC MUT ISOL/EXT, 3 
-50776202,HC 7026 CELIAC NA PROBE EA, 4 
-50776210,HC 7026 CELIAC AMP MPLX 2 NA, 31 
-50776228,HC 7026 CELIAC INTRP/REPORT, 4 
-50776244,HC 7026 CHEMILUM ANCA, 20 
-50776251,HC 7026 ANCA RIA QUANT, 18 
-50776319,HC 6911 T4 FREE DIALYSIS, 94 
-50776699,HC 6997 CHITO ENZ ACT, 141 
-50776707,HC 6997 CHITO PROTIEN TTL, 141 
-58493727,HC NOV FLOW 1ST MARKER, 538 
-50776335,HC 7053 GCDH SEP GEL EP, 5 
-50776343,HC 7053 GCDH AMP NA, 22 
-50776350,HC 7053 GCDG MUT ID SEQ, 22 
-50776368,HC 7053 GCDH SEP ID HR, 22 
-50776376,HC 7053 GCDH INT AND REPORT, 5 
-50776384,HC 7057 CVID MUT ISOL NA, 12 
-50776392,HC 7057 CVID ENZ DIG EA, 12 
-50776400,HC 7057 CVID MUT AMP NA SEQ, 51 
-50776418,HC 7057 CVID MUT ID SEQ, 51 
-50776426,HC 7057 CVID SEP ELEC CAP, 51 
-50776434,HC 7057 CVID INT AND REPORT, 12 
-50776442,HC 7024 NSD1 ISOL OR EXT, 9 
-50776459,HC 7024 NSD1 ENZ DIG EA, 9 
-50776467,HC 7024 NSD1 AMP MPLX 2 NA, 73 
-50776475,HC 7024 NSD1 AMP MPLX EA ADT, 36 
-50776483,HC 7024 NSD1 SEP ID HR, 36 
-50776491,HC 7024 NSD1 INT AND REPORT, 9 
-50776509,HC 6932 LYSO ENZ ACT NON RA, 316 
-50776517,HC 6932 LYSO ENZ ACT RA, 284 
-50774959,HC 7056 FLOW 1ST MARKER, 40 
-50776558,HC 7056 FLOW EA ADD MARKER, 40 
-50776566,HC 7056 LEUK/LYMPH INTERP 9-15, 83 
-50776574,HC 7058 NF1 MUT ISOL/EXT NA, 5 
-50776582,HC 7058 NF1 MUT SCAN DGGE, 21 
-50776590,HC 7058 NF1 RNA STABLIZATION, 17 
-50776608,HC 7058 NF1 SEP ELEC CAP NA, 21 
-50776616,HC 7058 NF1 SEP GEL EP, 5 
-50776624,HC 7058 NF1 NA PROBE EA, 5 
-50776632,HC 7058 NF1 MUT AMP NA SEQ, 21 
-50776640,HC 7058 NF1 AMP MPLX 1 2, 42 
-50776657,HC 7058 NF1 REVERSE TRANS, 18 
-50776665,HC 7058 NF1 MUT ID EA SEG, 21 
-50776673,HC 7058 NF1 INT AND REPRT, 5 
-50776681,HC 7058 NF1 TISS CULT LYMPHO, 147 
-50775253,HC 7052 MUT ISOL/EXT EA NA, 58 
-50775261,HC 7052 ENZ DIG EA, 58 
-50775279,HC 7052 SEP GEL EP, 58 
-50775287,7052 NUCLEIC ACID RPOBE, 58 
-50775295,7052 NA TRANSFER, 58 
-50775303,7052 INT AND REPRT, 58 
-50775311,HC 7052 CREA/GUAN MS QUANT U, 70 
-50775329,HC 7052 CREATININE URINE, 25 
-50775337,HC 7052 CREA/GUAN MS QUANT U, 87 
-50775345,HC 7052 POLYOLS MS URINE, 187 
-50775352,HC 7052 CREATININE URINE, 53 
-50775360,HC 6957 ADAMTS-13, 121 
-50775378,HC 6957 FACTOR INHIBITOR TST, 121 
-50775386,HC 6957 AB ID PLATELETS, 148 
-50775808,HC 7051 ABALONE IGE, 38 
-50775907,HC 7051 AMOXICILLIN IGE, 23 
-50775964,HC 7051 BASS BLACK IGE, 15 
-50775923,HC 7051 BEAN BLACK IGE, 23 
-50775774,HC 7051 BEAN KIDNEY IGE, 38 
-50775857,HC 7051 BEAN LIMA IGE, 23 
-50775717,HC 7051 BEAN PINTO IGE, 15 
-50775873,HC 7051 BLUEBERRY IGE, 38 
-50775956,HC 7051 CATFISH IGE, 12 
-50775949,HC 7051 CAULIFLOWER IGE, 38 
-50775998,HC 7051 CEPHALOSPORIN IGE, 23 
-50775915,HC 7051 CUMIN SEED IGE, 15 
-50776004,HC 7051 ERYTHROMYCIN IGE, 23 
-50775790,HC 7051 FLAXSEED/LINSEED IGE, 23 
-50775725,HC 7051 GARBANZO BEAN IGE, 23 
-50775741,HC 7051 GREEN BEAN IGE, 12 
-50775824,HC 7051 HALIBUT IGE, 12 
-50775980,HC 7051 LATEX IGE, 23 
-50775758,HC 7051 NECTARINE IGE, 23 
-50775840,HC 7051 PENICILLIN II IGE, 23 
-50776012,HC 7051 PENICILLIN I IGE, 23 
-50775733,HC 7051 PINE NUT IGE, 23 
-50775816,HC 7051 PUMPKIN SEED IGE, 38 
-50775972,HC 7051 RABBIT HAIR IGE, 15 
-50775865,HC 7051 RASPBERRY IGE, 12 
-50775931,HC 7051 SNAIL IGE, 23 
-50775881,HC 7051 SOLE IGE, 38 
-50775766,HC 7051 SQUASH SUMMER IGE, 15 
-50775782,HC 7051 SULFAMETHOXAZOL IGE, 23 
-50775832,HC 7051 ZUCCHINI IGE, 15 
-50775428,HC 7046 FMF MUT ISOL/EXT NA, 8 
-50775436,HC 7046 FMF ENZ DIG EA, 8 
-50775444,HC 7046 FMF SEP GEL EP, 8 
-50775451,HC 7046 FMF MUT AMPNA SEQ, 34 
-50775469,HC 7046 FMF MUT ID BY SQ SEG, 34 
-50775477,HC 7046 INT AND REPRT, 8 
-50776897,HC 7046 MUT ISOL/EXT EA NA, 11 
-50776905,HC 7046 ENZ DIG EA, 11 
-50776913,HC 7046 SEP GEL EP, 11 
-50776921,HC 7046 MUT AMP EA NA SEQ, 44 
-50776939,HC 7046 MUT ID BY SEQ EA, 44 
-50776947,HC 7046 INTERP AND REPORT, 11 
-50776954,HC 7046 CONF MUT ISOL/EXT NA, 11 
-50776962,HC 7046 CONF ENZ DIG EA, 11 
-50776970,HC 7046 CONF SEP GEL EP, 11 
-50776988,HC 7046 CONF MUT AMP NA SEQ, 44 
-50776996,HC 7046 CONF MUT ID BY SQ EA, 44 
-50777002,HC 7046 CONF INT AND REPRT, 11 
-50775618,HC 7051 HISTAMINE URTICARIA, 69 
-50775626,HC 7051 AB ID URTICARIA, 32 
-50775634,HC 7051 LHR URTICARIA, 29 
-50776756,HC 7051 HIST PENICILLIN, 53 
-50776020,HC 7060 FBN1 ISOL/EXT EA NA, 6 
-50776038,HC 7060 FBN1 MUT AMP NA SEQ, 24 
-50776046,HC 7060 FBN1 MUT ID SEQ EA, 24 
-50776053,HC 7060 FBN1 INT AND REPRT, 6 
-50776061,HC 7060 COL1A1/2 ISOL/EXT EA, 6 
-50776079,HC 7060 COLIA1/2 MUT AMP EA, 24 
-50776087,HC 7060 COL1A1/2 MUT ID SEQ, 25 
-50776095,HC 7060 COL1A1/2 INTRP RPRT, 6 
-50776269,HC 6911 CULT CHLAMYDIA, 76 
-50776277,HC 6911 CULT TYPING PCR EA, 21 
-50776285,HC 6911 CHLAMYDIA APT, 60 
-50776293,HC 6911 NEISSERIA GH APT, 60 
-50779537,HC 6942 BLASTOMYCES AB, 49 
-50779545,HC 6942 CMV AB, 48 
-50779552,HC 6942 CMV AB IGM, 56 
-50779560,HC 6942 BRUCELLA AB IGG, 42 
-50780014,HC 6942 BRUCELLA AB IGM, 42 
-50779578,HC 6942 HISTOPLASMA AB, 72 
-50779586,HC 6942 LEGIONELLA AB PNL, 34 
-50779594,HC 6942 LEPTOSPIRA AB, 72 
-50779602,HC 6942 LYMPH CHORIOMEN AB, 50 
-50779610,HC 6942 BABESIA AB, 48 
-50780022,HC 6942 BABESIA AB, 48 
-50779628,HC 6942 PARVOVIRUS AB, 24 
-50779636,HC 6942 PARVO B19 DNA QUANT, 262 
-50779644,HC 6942 ENTEROVIRUS RNA QUAL, 244 
-50779651,HC 6942 HTLV 1 2 AB CSF, 81 
-50779669,HC 6942 BRUCELLA AB CSF, 42 
-50780030,HC 6942 BRUCELLA AB CSF, 42 
-50777036,HC 7053 CF MUT/ISOL EXT NA, 2 
-50777044,HC 7053 CF SEP GEL EP, 3 
-50777051,HC 7053 CF MUT AMP NA SEQ, 10 
-50777069,HC 7053 CF MUT SCAN DGGE, 10 
-50777077,HC 7053 CF MUT ID BY SEQ, 10 
-50777085,HC 7053 CF SEP ELEC CAP NA, 10 
-50777093,HC 7053 CF INTRP AND REPRT, 3 
-50777101,HC 7053 CFTG MUT ISOL/EXT NA, 7 
-50777119,HC 7053 CFTG SEP GEL EP, 7 
-50777127,HC 7053 CFTG MUT AMP NA, 30 
-50777135,HC 7053 CFTG MUT ID BY SEQ, 30 
-50777143,HC 7053 CFTG SEP ELEC CAP NA, 30 
-50777150,HC 7053 CFTG INTRP AND REPRT, 7 
-50777168,HC 7053 POMPE MUT ISOL/EX NA, 5 
-50777176,HC 7053 POMPE SEP GEL EP, 5 
-50777184,HC 7053 POMPE MUT AMP NA, 22 
-50777192,HC 7053 POMPE MUT ID BY SEQ, 22 
-50777200,HC 7053 POMPE SEP ELC CAP NA, 22 
-50777218,HC 7053 POMPE INTRP AND RPT, 5 
-50777226,HC 7053 PKU MUT ISOL/EXT NA, 5 
-50777234,HC 7053 PKU SEP GEL EP, 4 
-50777242,HC 7053 PKU MUT AMP NA, 19 
-50777259,HC 7053 PKU MUT ID BY SEQ, 19 
-50777267,HC 7053 PKU SEP ELEC CAP NA, 19 
-50777275,HC 7053 PKU INTRP AND REPRT, 4 
-50777283,HC 7053 CFTR MUT ISOL/EXT NA, 3 
-50777291,HC 7053 CFTR SEP GEL EP, 3 
-50777309,HC 7053 CFTR MUT AMP NA, 14 
-50777317,HC 7053 CFTR MUT SCAN DGGE, 14 
-50777325,HC 7053 CFTR MUT ID BY SEQ, 14 
-50777333,HC 7053 CFTR SEP ELEC CAP NA, 14 
-50777341,HC 7053 CFTR INTRP AND REPRT, 3 
-50777358,HC 7053 DHAH5 MUT ISOL/EXT, 3 
-50777366,HC 7053 DHAH5 SEP GEL EP, 3 
-50777374,HC 7053 DHAH5 MUT AMP NA, 14 
-50777382,HC 7053 DHAH5 MUT ID BY SEQ, 14 
-50777390,HC 7053 DHAH5 SEP ELEC CAP, 14 
-50777408,HC 7053 DHAH5 INTRP AND RPT, 3 
-50777416,HC 6959 BCRABL MUT ISOL/EXT, 17 
-50777424,HC 6959 BCRABL SEP GEL EP, 17 
-50777432,HC 6959 BCRABL MUT AMP NA, 72 
-50777440,HC 6959 BCRABL REVERSE TRANS, 61 
-50777457,HC 6959 BCRABL INTRP REPRT, 17 
-50777465,HC 6959 CATECHOLOMINES FRAC, 35 
-50777473,HC 6959 GALACTOSE  QUANT EA, 154 
-50777481,HC 6961 DM1 MUT ISOL/EXT, 59 
-50777499,HC 6961 DM1 SEP GEL EP, 59 
-50777507,HC 6961 DM1 MUT AMP NA, 247 
-50777515,HC 6961 DM1 INTRP REPRT, 59 
-50777523,HC 6961 ANTI HU NES, 169 
-50777531,HC 6961 GQ1B AB NES, 248 
-50777549,HC 6961 NEUROPATHY PROF NES, 114 
-50780048,HC 6961 NEUROPATHY PROF NES, 114 
-50777556,HC 6961 MUSK AB QUANT RIA, 774 
-50777564,HC 6961 ANTI VGKC AB QUT RIA, 391 
-50777572,HC 6961 SPAD MUT ISOL/EXT, 31 
-50777580,HC 6961 SPAD NA PROBE, 31 
-50777598,HC 6961 SPAD AMP MPLX NA SEQ, 263 
-50777606,HC 6961 SPAD SEP ELEC CAP NA, 131 
-50777614,HC 6961 SPAD INT AND REPORT, 31 
-50777622,HC 6961 PERIPH NEUR SCRN NES, 170 
-50780055,HC 6961 PERIPH NEUR SCRN NES, 171 
-50777630,HC 6961 ENCEPH SCRN NES, 209 
-50777648,HC 6961 ENCEPH SCRN WB, 291 
-50777655,HC 6961 ENCEPH SCRN RIA, 218 
-50777663,HC 6961 ENCEPH SCRN WB, 275 
-50777671,HC 6961 CEREB PARANEO NES, 186 
-50777689,HC 6961 CEREB PARANEO WB, 245 
-50777697,HC 6961 CEREB PARANEO WB RPT, 259 
-50777705,HC 6961 PARANEO AB RIA, 183 
-50777713,HC 6961 PARANEO AB NES, 175 
-50777721,HC 6961 PARANEO AB WB, 231 
-50777739,HC 6961 PARANEO AB WB RPT, 244 
-50777747,HC 6961 MEE MUT ISOL/EXT, 18 
-50777754,HC 6961 MEE ENZ DIG EA, 18 
-50777762,HC 6961 MEE SEP GEL EP, 18 
-50777770,HC 6961 MEE NA PROBE, 18 
-50777788,HC 6961 MEE NA TRANSFER, 18 
-50777796,HC 6961 MEE MUT AMP NA, 75 
-50777804,HC 6961 MEE MUT ID BY SEQ, 75 
-50777812,HC 6961 MEE SEP ELEC CAP NA, 75 
-50777820,HC 6961 MEE INTRP REPRT, 18 
-50777838,HC 6961 SENSORY MOTOR NES, 215 
-50777846,HC 6961 SENSORY MOTOR WB, 282 
-50777853,HC 6961 SENSORY MOTOR WB RPT, 298 
-50777861,HC 7052 MTDNA MUT ISOL/EXT, 14 
-50777879,HC 7052 MTDNA ENZ DIG EA, 14 
-50777887,HC 7052 MTDNA BLOT PROD EA, 14 
-50777895,HC 7052 MTDNA SEP GEL EP, 14 
-50777903,HC 7052 MTDNA NA PROBE, 14 
-50777911,HC 7052 MTDNA NA TRANSFER, 14 
-50777929,HC 7052 MTDNA AMP MPLX NA, 115 
-50777937,HC 7052 MTDNA AMP MPLX ADD, 58 
-50777945,HC 7052 MTDNA INT AND REPRT, 14 
-50777952,HC 7052 POLG MUT ISOL/EXT, 7 
-50777960,HC 7052 POLG SEP GEL EP, 7 
-50777978,HC 7052 POLG MUT AMP NA, 28 
-50777986,HC 7052 POLG MUT ID BY SEQ, 28 
-50777994,HC 7052 POLG INT AND REPRT, 7 
-50778000,HC 7052 TK2 MUT ISOL/EXT, 10 
-50778018,HC 7052 TK2 SEP GEL EP, 10 
-50778026,HC 7052 TK2 MUT AMP NA, 42 
-50778034,HC 7052 TK2 MUT ID BY SEQ, 42 
-50778042,HC 7052 TK2 INT AND REPRT, 10 
-50778059,HC 7052 UBE3A MUT ISOL/EXT, 16 
-50778067,HC 7052 UBE3A SEP GEL EP, 16 
-50778075,HC 7052 UBE3A MUT AMP NA, 67 
-50778083,HC 7052 UBE3A MUT ID BY SEQ, 67 
-50778091,HC 7052 UBE3A SEP ELEC CAP, 67 
-50778109,HC 7052 UBE3A INT AND REPRT, 16 
-50778117,HC 7052 DMPK MUT ISOL/EXT, 24 
-50778125,HC 7052 DMPK ENZ DIG EA, 24 
-50778133,HC 7052 DMPK SEP GEL EP, 24 
-50778141,HC 7052 DMPK NA PROBE, 24 
-50778158,HC 7052 DMPK NA TRANSFER, 24 
-50778166,HC 7052 DMPK MUT AMP NA, 99 
-50778174,HC 7052 DMPK INT AND REPRT, 24 
-50778182,HC 7052 MITOCHOND ENZ ACT, 60 
-50778190,HC 7052 MITOCHOND ISOL/EXT, 13 
-50778208,HC 7052 MITOCHOND INT REPORT, 13 
-50778216,HC 7052 MITOCHOND SPECT NES, 23 
-50778224,HC 7052 MITOCHOND TISS CULT, 471 
-50778232,HC 70052 MITOCHOND CRYOPRES, 34 
-50778307,HC 7069 WASP ISOL/EXT, 25 
-50778315,HC 7069 WASP MUT ISOL/EXT, 25 
-50778323,HC 7069 WASP SEP GEL EP, 25 
-50778331,HC 7069 WASP MUT AMP NA, 105 
-50778349,HC 7069 WASP MUT ID BY SEQ, 105 
-50778356,HC 7069 WASP INT AND REPORT, 25 
-50778364,HC 7069 NK FUNCTION, 582 
-50778240,HC 6998 SCAD ISOL/EXT, 32 
-50778257,HC 6998 SCAD MUT AMP NA, 133 
-50778265,HC 6998 SCAD MUT ID BY SEQ, 133 
-50778273,HC 6998 SCAD INT AND REPORT, 32 
-50778281,HC 6998 SULFOCYSTEINE URINE, 102 
-50778299,HC 6998 CREATININE URINE, 38 
-50778372,HC 6997 SMAC MUT ISOL/EXT EA, 22 
-50778380,HC 6997 SMAC ENZ DIG EA, 22 
-50778398,HC 6997 SMAC AMP MPLX NA SEQ, 186 
-50778406,HC 6997 SMAC AMP MPLX ADD, 93 
-50778414,HC 6997 SMAC INT AND REPORT, 22 
-50778448,HC 6997 CFAMP MPLX NA SEQ, 3 
-50778455,HC 6997 CFAMP MPLX ADD, 2 
-50778463,HC 6997 CFSEP ELEC CAP, 2 
-50778489,HC 6997 CF MUT ID BY ENZ LIG, 2 
-50778497,HC 7046 CCN MUT ISOL/EXT EA, 7 
-50778505,HC 7046 CCN ENZ DIG EA, 7 
-50778513,HC 7046 CCN SEP GEL EP, 7 
-50778521,HC 7046 CCN MUT AMP NA, 28 
-50778539,HC 7046 CCN MUT ID BY SEQ, 28 
-50778547,HC 7046 CCNINT AND REPORT, 7 
-50778554,HC 7046 HRAS MUT ISOL/EXT EA, 8 
-50778562,HC 7046 HRAS ENZ DIG EA, 8 
-50778570,HC 7046 HRAS SEP GEL EP, 8 
-50778588,HC 7046 HRAS MUT AMP NA, 32 
-50778596,HC 7046 HRAS MUT ID BY SEQ, 32 
-50778604,HC 7046 HRAS INT AND REPORT, 8 
-50778612,HC 7046 CB1CMUT ISOL/EXT EA, 7 
-50778620,HC 7046 CB1C ENZ DIG EA, 7 
-50778638,HC 7046 CB1 CSEP GEL EP, 7 
-50778646,HC 7046 CB1C MUT AMP NA, 28 
-50778653,HC 7046 CB1C MUT ID BY SEQ, 28 
-50778661,HC 7046 CB1C INT AND REPORT, 7 
-50778679,HC 7046 JAG1 MUT ISOL/EXT EA, 5 
-50778687,HC 7046 JAG1 ENZ DIG EA, 5 
-50778695,HC 7046 JAG1 SEP GEL EP, 5 
-50778703,HC 7046 JAG1 MUT AMP NA, 20 
-50778711,HC 7046 JAG1 MUT ID BY SEQ, 20 
-50778729,HC 7046 JAG1 INT AND REPORT, 5 
-50778737,HC 7046 JAG1 TO 500 PROBES," 1,462 "
-50778810,HC 7046 CHD7 MUT ISOL/EXT EA, 8 
-50778828,HC 7046 CHD7 ENZ DIG EA, 8 
-50778836,HC 7046 CHD7 SEP GEL EP, 8 
-50778844,HC 7046 CHD7 MUT AMP NA, 34 
-50778851,HC 7046 CHD7 MUT ID BY SEQ, 34 
-50778869,HC 7046 CHD7 INT AND REPORT, 8 
-50778877,HC 7046 SHH MUT ISOL/EXT EA, 8 
-50778885,HC 7046 SHH ENZ DIG EA, 8 
-50778893,HC 7046 SHH SEP GEL EP, 8 
-50778901,HC 7046 SHHMUT AMP NA, 33 
-50778919,HC 7046 SHH MUT ID BY SEQ, 33 
-50778927,HC 7046 SHH INT AND REPORT, 8 
-50778935,HC 7046 SHH TO 500 PROBES," 1,462 "
-50778943,HC 7046 MUT MUT ISOL/EXT EA, 7 
-50778950,HC 7046 MUT ENZ DIG EA, 7 
-50778968,HC 7046 MUT SEP GEL EP, 7 
-50778976,HC 7046 MUT MUT AMP NA, 29 
-50778984,HC 7046 MUT MUT ID BY SEQ, 29 
-50778992,HC 7046 MUT INT AND REPORT, 7 
-50779693,HC 7046 PTPN11 MUT ISOL/EXT, 5 
-50779701,HC 7046 PTPN11 ENZ DIG EA, 5 
-50779719,HC 7046 PTPN11 SEP GEL EP, 5 
-50779727,HC 7046 PTPN11 MUT AMP NA, 19 
-50779735,HC 7046 PTPN11 MUT ID BY SEQ, 19 
-50779743,HC 7046 PTPN11 INT AND RPT, 5 
-50778745,HC 7046 BRAF MUT ISOL/EXT EA, 1.5 
-50778752,HC 7046 BRAF ENZ DIG EA, 1.5 
-50778760,HC 7046 BRAF AMP MPLX NA SEQ, 14 
-50778778,HC 7046 BRAF AMP MPLX ADD, 7 
-50778786,HC 7046 BRAF SEP ELEC CAP, 7 
-50778794,HC 7046 BRAF INT AND REPORT, 1.4 
-50779008,HC 7024 MECP2 ISOL/EXT, 16 
-50779016,HC 7024 MECP2 MUT AMP NA, 67 
-50779024,HC 7024 MECP2 MUT ID BY SEQ, 67 
-50779032,HC 7024 MECP2 SEP ELEC CAP, 67 
-50779040,HC 7024 MECP2 INT AND REPORT, 16 
-50779057,HC 7024 FRAGX ISOL/EXT, 28 
-50779065,HC 7024 FRAGX ENZ DIG EA, 28 
-50779073,HC 7024 FRAGX SEP GEL EP, 28 
-50779081,HC 7024 FRAGX MUT AMP NA, 118 
-50779099,HC 7024 FRAGX SEP ELEC CAP, 118 
-50779107,HC 7024 FRAGX INT AND REPORT, 28 
-50779115,HC 7024 MPSII ISOL/EXT, 17 
-50779123,HC 7024 MPSII MUT AMP NA, 70 
-50779131,HC 7024 MPSII MUT ID BY SEQ, 70 
-50779149,HC 7024 MPSII SEP ELEC CAP, 70 
-50779156,HC 7024 MPSII INT AND REPORT, 17 
-50778802,HC 6956 TAY SACHS A, 51 
-50780063,HC 6956 TAY SACHS B, 50 
-50779164,HC 6956 NPC1 MUT ISOL/EXT EA, 4 
-50779172,HC 6956 NPC1 ENZ DIG EA, 4 
-50779180,HC 6956 NPC1 SEP GEL EP, 4 
-50779198,HC 6956 NPC1 MUT AMP NA, 15 
-50779206,HC 6956 NPC1 AMP MPLX NA SEQ, 31 
-50779214,HC 6956 NPC1 AMP MPLX ADD, 15 
-50779222,HC 6956 NPC1 SEP ELEC CAP, 15 
-50779230,HC 6956 NPC1 INT AND REPORT, 4 
-50779248,HC 6956 NPC1 MUT ID ENZ LIG, 15 
-50779255,HC 6956 ARSENIC LEVEL, 46 
-50779263,HC 6956 COPPER LEVEL, 61 
-50779271,HC 6956 ETHYLENE GLYCO LEVEL, 64 
-50779289,HC 6956 HURLER ENZ ACT, 106 
-50779297,HC 6956 LYMPHOCYTE TRANSFORM, 107 
-50779305,HC 6956 NOC PROT TLP, 917 
-50779313,HC 6956 BWM MUT ISOL/EXT EA, 47 
-50779321,HC 6956 BWM ENZ DIG EA, 47 
-50779339,HC 6956 BWM BLOT PROD EA, 47 
-50779347,HC 6956 BWM SEP GEL EP, 47 
-50779354,HC 6956 BWM NA PROBE, 47 
-50779362,HC 6956 BWM NA TRANSFER, 47 
-50779370,HC 6956 BWM INT AND REPORT, 47 
-50779388,HC 6956 GANICICLOVIR LVL, 65 
-50779396,HC 6956 IL5 RIA QUANT, 46 
-50779404,HC 6956 PD450 ENZ DIG EA, 3 
-50779412,HC 6956 PD450 AMP MPLX NA, 25 
-50779420,HC 6956 PD450 AMP MPLX ADD, 13 
-50779438,HC 6956 PD450 INT AND REPORT, 3 
-50779446,HC 6956 PD450 MUT ID ENZ LIG, 13 
-50779453,HC 6956 Q10CHROM ANAL QUANT, 123 
-50779461,HC 6956 FIBROBLAST CULTURE, 262 
-50779479,HC 6956 CRYOPRESERVATION, 194 
-50779487,HC 6956 RAPID DNA EXTRACT, 150 
-50779495,HC 6956 FIBRO CULT GENE, 221 
-50779503,HC 6956 CRYOPRESERVATION, 16 
-50779511,HC 6956 AMNIOTIC FL CULT, 236 
-50779529,HC 6956 CRYOPRESERVATION, 16 
-50779677,HC 7070 NEURONAL CEROID ACT, 204 
-50779685,HC 7070 CLN2 ENZ ACTIVITY, 204 
-50779750,HC 7073 HEP C CONFIRM, 117 
-50779784,HC 7030 MSUD MUT ISOL/EXT, 6 
-50779792,HC 7030 MSUD SEP GEL EP, 6 
-50779800,HC 7030 MSUD MUT AMP NA, 25 
-50779818,HC 7030 MSUD MUT ID BY SEQ, 25 
-50779826,HC 7030 MSUD SEP ELEC CAP, 25 
-50779834,HC 7030 MSUD INT AND REPORT, 6 
-50779842,HC 7060 COL2A1 ISOL/EXT, 4 
-50779859,HC 7060 COL2A1 SEP GEL EP, 4 
-50779867,HC 7060 COL2A1 MUT AMP NA, 16 
-50779875,HC 7060 COL2A1 MUT SCAN DGGE, 16 
-50779883,HC 7060 COL2A1 MUT ID BY SEQ, 16 
-50779891,HC 7060 COL2A1 INT AND RPT, 4 
-50779909,HC 7061 CSF CHROMOTOGR QUANT, 190 
-50779917,HC 6911 DONOR HTLVS QNS, 65 
-50779925,HC 7065 NA BENZOATE GC/MS, 150 
-50779933,HC 7065 GC/MS GLUTARIC, 200 
-50779941,HC 7064 INTREACYSTINE QUANT, 395 
-50779958,HC 6957 NAT MUT ISOL/EXT EA, 5 
-50779966,HC 6957 NAT SEP GEL EP, 5 
-50779974,HC 6957 NAT AMP MPLX NA SEQ, 40 
-50779982,HC 6957 NAT AMP MPLX ADD, 20 
-50779990,HC 6957 NAT INT AND REPORT, 5 
-50780006,HC 6957 NAT PLAT AB ID, 22 
-50776541,HC 6964 SLO PRENAT COL CHROM, 200 
-26431049,HC ALLO THERAPEUTIC CELLS CRYOPRESERVED PRODUCT," 2,678 "
-32704314,HC COMPRESSION DEVICE-L10, 595 
-32704322,HC HEMOSTASIS PAD-L6, 298 
-36200194,HC SIM ELCTRN BEAM SIM," 2,050 "
-36301539,HC GOLD SEEDS L2, 62 
-40400566,HC OSELTAMIVIR 75MG CAPSULE, 8 
-41100876,HC DESTRUCT VULVA LESION(S)," 3,993 "
-41100884,HC BRONCHOSCOPY W/STENT PLCMT," 6,786 "
-41100892,HC BRONCHOGRAPHY BILAT," 1,189 "
-41100900,HC CREATE SHUNT PERC," 9,948 "
-41100918,HC US GUIDANCE-INTRA OP," 1,715 "
-46310280,HC VIRAL SUBTYPE ANALYSIS, 87 
-50403252,HC PROT ELECTRO SERUM, 180 
-50570035,HC RES FIBRINIGEN SEMIQ, 285 
-50740810,HC 6959 APOE APOLPRTN E ISOL, 89 
-50774199,HC 6959 11047R CHRM QN CL SNGL, 168 
-50774207,HC 6959 11057R TNF IMM QN NOS, 95 
-50774215,HC ISOL BLOT PROD EA NA PREP, 13 
-50774223,HC MUT SEP BY GEL ELCT EA NA, 13 
-50774231,HC NUCLEIC ACIDE PROBE EA, 13 
-50774249,HC MUT AMP EA NA SEQ, 54 
-50774256,HC ISOL BLOT EA NA PREP, 13 
-50774264,HC MUT SEP BY GEL ELCT EA NA, 13 
-50774272,HC NUCLEIC ACID PROBE EA, 13 
-50774280,HC MUT AMP EA NA SEQ, 54 
-50774298,HC MUT SEP BY GEL ELCT EA NA, 2 
-50774306,HC AMP MPLX FIRST 2 NA SEQ, 19 
-50774314,HC AMP MPLX EA ADTL NA BYND2, 9 
-50774322,HC INTERP AND REPORT, 2 
-50774330,HC IMM INF AGNT AB QUANT, 75 
-50774348,HC MIC SUSCEPTIBILITY EA, 65 
-50774355,HC HEPATITIS BS AG EIA, 35 
-50774363,HC HEPATITIS C AB, 63 
-50774371,HC PHENO DNA HIV WIT CULT, 812 
-50774389,HC PHENO DNA HIV CLT EA ADTL, 43 
-50774397,HC 6959 FLUORESCENT AB TITER, 48 
-50774405,HC 6959 N TELOPEPTD CRS LINK, 159 
-50774413,HC 6959 ALCOHOL, 22 
-50774421,HC 6959 11269R IMM ANLY QN RIA, 42 
-50774439,HC 6959 NICOTINE URINE, 52 
-50774447,HC 6959 DRUGS OF ABUSE PAN 9, 8 
-50774454,HC 6959 TESTOS TOT NON MALE, 33 
-50774462,HC 6959 SEX HORM BIND GLOB, 28 
-50774470,HC 6959 ALCOHOL QUAL URINE, 22 
-50774488,HC 6959 ALLERGEN ASPER FUN, 7 
-50774496,HC 6959 PROCLOGN INTACT N, 180 
-50774504,HC 6959 MAGNESIUM, 54 
-50774512,HC 6959 11299R SCN SULFON HYPO, 82 
-50774520,HC 6959 AFP TOTAL L3 PRCNT, 150 
-50774538,HC 6959 IGA, 13 
-50774546,HC 6959 SOL LIV AG AB IGG, 18 
-50774553,HC 6959 TESTOSTERONE TOTAL, 17 
-50774561,HC 6959 SEX HORM BIND GLOB, 14 
-50774579,HC 6959 SCREEN CANNABINOIDS, 69 
-50774587,HC 6956 ESTRADIOL, 38 
-50774595,HC 6956 NICOTINE, 51 
-50774603,HC 6956 PREGABALIN, 107 
-50774611,HC 6956 IODINE QUANT, 41 
-50774637,HC 6956 NICTN AND MTABLIT UR, 23 
-50774660,HC 6956 IMM ANLYT QUANT RIA, 27 
-50774678,HC 6956 IMM ANLYT QUANT, 26 
-50774686,HC 6956 FLUOR AB TITER, 24 
-50774876,HC 6956 IODINE URINE QUANT, 36 
-50774884,HC 6956 IRON LIVER TISSUE, 60 
-50774892,HC 6956 LEAD URINE, 8 
-50774900,HC 6956 METANEPH FRAC RAN UR, 54 
-50774975,HC 6942 M AVIUM COMPLEX MIC, 121 
-50774991,HC 6942HUMNA HERPESVIRUS 8 IGG, 116 
-50775006,HC 6942 MEASLES IGG AB TIT, 51 
-50775014,HC 6942 VZV DNA PCR QUANT, 262 
-50775022,HC 6942 VZV AB IMM SCRN, 66 
-50775030,HC 6942 11250R HHV8 DNA QN PCR, 262 
-50775048,HC 6942 H PYLORI IGA AB, 107 
-50775055,HC 6942 11188R MYCO PNM DNA QL, 250 
-50775063,HC 6942 BORRELIA HERMIT AB, 85 
-50775071,HC 6961 SCN1A MUT ISOL NA, 10 
-50775089,HC 6961 SCN1A AMP EA NA SEQ, 40 
-50775097,HC 6961 SCN1A MUT ID EA SEG, 40 
-50775105,HC 6961 SCN1A SEP ELEC CAP, 40 
-50775113,HC 6961 SCN1A INTRP AND RPT, 10 
-50775121,HC 6961 COGMI IMMUNO QUANT, 143 
-50775139,HC MUT ISOL OR EXT EA NA, 9 
-50775147,HC MUT AMP EA NA SEQ, 40 
-50775154,HC MUT ID BY SEQ EA SEG, 40 
-50775162,HC SEP ELEC CAP EA NA, 40 
-50775170,HC INTERP AND REPORT, 10 
-50775188,HC MUT ISOL OR EXT EA NA, 4 
-50775196,HC NUCLEIC ACID PROBE EA, 4 
-50775204,HC MUT AMP EA NA SEQ, 15 
-50775212,HC AMP MPLX FIRST 2 NA SEQ, 30 
-50775220,HC AMP MPLX EA ADTL NA BYND2, 15 
-50775238,HC MUT ID BY SEQ EA SEG, 15 
-50775246,HC INTERP AND REPORT, 4 
-50776178,HC 6956 6 MAM URINE, 135 
-50776186,HC 6959 MYELIN BAS PROT CSF, 38 
-50777010,HC 6961 COGMI IMMUNO QUANT, 143 
-50791433,HC TECH COMP ONLY FIRST MRKR, 131 
-50791441,HC TECH COMP ONLY ADDTL MRKR, 80 
-50791458,HC INTERP 16 OR MORE MARKERS, 145 
-50791466,HC TECH COMP ONLY FIRST MRKR, 131 
-50791474,HC TECH COMP ONLY ADDTL MRKR, 80 
-50791482,HC INTERP 16 OR MORE MARKERS, 145 
-50791490,HC MUT SEP BY GEL ELCT EA NA, 8 
-50791508,HC AMP MPLX FIRST 2 NA SEQ, 59 
-50791516,HC AMP MPLX EA ADTL NA BYND2, 30 
-50791524,HC MUT ID BY SEQ EA SEG, 30 
-50791532,HC SEP ELEC CAP EA NA, 30 
-50791540,HC 6949 H059 PATH CONSULT COMP, 8 
-50791557,HC MUT ISOL OR EXT EA NA, 8 
-50791565,HC MUT ID EA OLA SBCE ASPE, 30 
-50803915,HC EGFRP MUT ISOL NA, 146 
-50803923,HC EGFRP ENZYMATIC DIGESTION, 78 
-50803931,HC EGFRP MUT AMP NA, 188 
-50803949,HC EGFRP LYSIS PRE NA EXTR, 66 
-50803956,HC EGFRP SEP ELECTRO CAP, 112 
-50803964,HC EGFRP INTERP AND RPT, 138 
-50803972,HC BCDHPX ISOL OR EXT EA NA, 125 
-50803980,HC BCDHPX MUT SEP GEL ELCT, 103 
-50803998,HC BCDHPX MUT AMP EA NA SEQ, 188 
-50804004,HC BCDHPX MUT ID EA SEG, 54 
-50804012,HC BCDHPX LYSIS PRE NA EXTR, 66 
-50804020,HC BCDHPX INTERP AND RPT, 138 
-50804038,HC BCDHPT ISOL OR EXT EA NA, 125 
-50804046,HC BCDHPT MUT SEP GEL ELCT, 103 
-50804053,HC BCDHPT MUT AMP EA NA SEQ, 188 
-50804061,HC BCDHPT MUT ID EA SEQ, 54 
-50804079,HC BCDHPT LYSIS PRE NA EXTR, 66 
-50804087,HC BCDHPT INTERP AND RPT, 138 
-50804111,HC CEPBA ISOL NA, 146 
-50804129,HC CEPBA MUT SEP GEL ELECT, 103 
-50804137,HC CEPBA MUT SEQ AMP NA, 188 
-50804145,HC CEPBA  MUT ID SEQ EA, 54 
-50804152,HC CEPBA INT AND REPORT, 138 
-51493716,HC NOV FLOW EA ADDTL MARKER, 58 
-51493724,HC NOV FLOW 1ST MARKER, 538 
-51493732,HC FLOW T CELLS TOTAL COUNT, 538 
-52032695,HC NOV PROCESS AND EMBED, 17 
-52032703,HC NOV DECALCIFICATION, 20 
-52032711,HC NOV GROSS PREP WET TISS, 22 
-52032729,HC NOV H AND E STAIN, 22 
-52032737,HC NOV SPECIAL STAIN ROUTINE, 29 
-52032745,HC NOV COMP SPECIAL STAIN, 41 
-52032752,HC NOV IPOX ROUTINE, 67 
-52032760,HC NOV UNSTAINED SLIDES, 10 
-52291432,HC TECH COMP ONLY FIRST MRKR, 131 
-52291440,HC TECH COMP ONLY ADDTL MRKR, 80 
-52291457,HC INTERP 16 OR MORE MARKERS, 145 
-52291465,HC TECH COMP ONLY FIRST MRKR, 131 
-52291473,HC TECH COMP ONLY ADDTL MRKR, 80 
-52291481,HC INTERP 16 OR MORE MARKERS, 145 
-52291499,HC MUT SEP BY GEL ELCT EA NA, 8 
-52291507,HC AMP MPLX FIRST 2 NA SEQ, 59 
-52291515,HC AMP MPLX EA ADTL NA BYND2, 30 
-52291523,HC MUT ID BY SEQ EA SEG, 30 
-52291531,HC SEP ELEC CAP EA NA, 30 
-52291549,HC 6949 H059 PATH CONSULT COMP, 8 
-52291556,HC MUT ISOL OR EXT EA NA, 8 
-52291564,HC MUT ID EA OLA SBCE ASPE, 30 
-55092175,HC CLOSTIRIDIUM DIFFICILE, 486 
-56330251,HC HCV GENOTYPE," 2,847 "
-58000134,HC TCD EMBOLI DETECTION," 2,041 "
-58493719,HC RES GERON SAMPLE, 94 
-58493735,HC FLOW T CELLS TOTAL COUNT, 538 
-58571043,HC MYCOPHENOLIC ACID, 291 
-59092171,HC CLOSTIRIDIUM DIFFICILE, 388 
-59462671,HC CO-OXIMETRY O2 SAT, 97 
-59570036,HC RES FIBRINOGEN SEMIQ, 285 
-62019450,HC DILATION & CURETTAGE NONOB," 4,008 "
-62021191,HC REPR EYELID WOUND FULL THICKNE," 3,814 "
-81091431,HC TECH COMP ONLY FIRST MRKR, 131 
-81091449,HC TECH COMP ONLY ADDTL MRKR, 80 
-81091456,HC INTERP 16 OR MORE MARKERS, 145 
-81091464,HC TECH COMP ONLY FIRST MRKR, 131 
-81091472,HC TECH COMP ONLY ADDTL MRKR, 80 
-81091480,HC INTERP 16 OR MORE MARKERS, 145 
-81091498,HC MUT SEP BY GEL ELCT EA NA, 8 
-81091506,HC AMP MPLX FIRST 2 NA SEQ, 59 
-81091514,HC AMP MPLX EA ADTL NA BYND2, 30 
-81091522,HC MUT ID BY SEQ EA SEG, 30 
-81091530,HC SEP ELEC CAP EA NA, 30 
-81091548,HC 6949 H059 PATH CONSULT COMP, 8 
-81091555,HC MUT ISOL OR EXT EA NA, 8 
-81091563,HC MUT ID EA OLA SBCE ASPE, 30 
-81191439,HC TECH COMP ONLY FIRST MRKR, 131 
-81191447,HC TECH COMP ONLY ADDTL MRKR, 80 
-81191454,HC INTERP 16 OR MORE MARKERS, 145 
-81191462,HC TECH COMP ONLY FIRST MRKR, 131 
-81191470,HC TECH COMP ONLY ADDTL MRKR, 80 
-81191488,HC INTERP 16 OR MORE MARKERS, 145 
-81191496,HC MUT SEP BY GEL ELCT EA NA, 8 
-81191504,HC AMP MPLX FIRST 2 NA SEQ, 59 
-81191512,HC AMP MPLX EA ADTL NA BYND2, 30 
-81191520,HC MUT ID BY SEQ EA SEG, 30 
-81191538,HC SEP ELEC CAP EA NA, 30 
-81191546,HC INTERP AND REPORT, 8 
-81191553,HC MUT ISOL OR EXT EA NA, 8 
-81191561,HC MUT ID EA OLA SBCE ASPE, 30 
-81390148,HC 6949 HLA KIT SUP MAIL, 285 
-81391435,HC TECH COMP ONLY FIRST MRKR, 131 
-81391443,HC TECH COMP ONLY ADDTL MRKR, 80 
-81391450,HC INTERP 16 OR MORE MARKERS, 145 
-81391468,HC TECH COMP ONLY FIRST MRKR, 131 
-81391476,HC TECH COMP ONLY ADDTL MRKR, 80 
-81391484,HC INTERP 16 OR MORE MARKERS, 145 
-81391492,HC MUT SEP BY GEL ELCT EA NA, 8 
-81391500,HC AMP MPLX FIRST 2 NA SEQ, 59 
-81391518,HC AMP MPLX EA ADTL NA BYND2, 30 
-81391526,HC MUT ID BY SEQ EA SEG, 30 
-81391534,HC SEP ELEC CAP EA NA, 30 
-81391542,HC 6949 H059 PATH CONSULT COMP, 8 
-81391559,HC MUT ISOL OR EXT EA NA, 8 
-81391567,HC MUT ID EA OLA SBCE ASPE, 30 
-81791436,HC TECH COMP ONLY FIRST MRKR, 131 
-81791444,HC TECH COMP ONLY ADDTL MRKR, 80 
-81791451,HC INTERP 16 OR MORE MARKERS, 145 
-81791469,HC TECH COMP ONLY FIRST MRKR, 131 
-81791477,HC TECH COMP ONLY ADDTL MRKR, 80 
-81791485,HC INTERP 16 OR MORE MARKERS, 145 
-81791493,HC MUT SEP BY GEL ELCT EA NA, 8 
-81791501,HC AMP MPLX FIRST 2 NA SEQ, 59 
-81791519,HC AMP MPLX EA ADTL NA BYND2, 30 
-81791527,HC MUT ID BY SEQ EA SEG, 30 
-81791535,HC SEP ELEC CAP EA NA, 30 
-81791543,HC INTERP AND REPORT, 8 
-81791550,HC MUT ISOL OR EXT EA NA, 8 
-81791568,HC MUT ID EA OLA SBCE ASPE, 30 
-82391434,HC TECH COMP ONLY FIRST MRKR, 131 
-82391442,HC TECH COMP ONLY ADDTL MRKR, 80 
-82391459,HC INTERP 16 OR MORE MARKERS, 145 
-82391467,HC TECH COMP ONLY FIRST MRKR, 131 
-82391475,HC TECH COMP ONLY ADDTL MRKR, 80 
-82391483,HC INTERP 16 OR MORE MARKERS, 145 
-82391491,HC MUT SEP BY GEL ELCT EA NA, 8 
-82391509,HC AMP MPLX FIRST 2 NA SEQ, 59 
-82391517,HC AMP MPLX EA ADTL NA BYND2, 30 
-82391525,HC MUT ID BY SEQ EA SEG, 30 
-82391533,HC SEP ELEC CAP EA NA, 30 
-82391541,HC 6949 H059 PATH CONSULT COMP, 8 
-82391558,HC MUT ISOL OR EXT EA NA, 8 
-82391566,HC MUT ID EA OLA SBCE ASPE, 30 
-84391432,HC TECH COMP ONLY FIRST MRKR, 131 
-84391440,HC TECH COMP ONLY ADDTL MRKR, 80 
-84391457,HC INTERP 16 OR MORE MARKERS, 145 
-84391465,HC TECH COMP ONLY FIRST MRKR, 131 
-84391473,HC TECH COMP ONLY ADDTL MRKR, 80 
-84391481,HC INTERP 16 OR MORE MARKERS, 145 
-84391499,HC MUT SEP BY GEL ELCT EA NA, 8 
-84391507,HC AMP MPLX FIRST 2 NA SEQ, 59 
-84391515,HC AMP MPLX EA ADTL NA BYND2, 30 
-84391523,HC MUT ID BY SEQ EA SEG, 30 
-84391531,HC SEP ELEC CAP EA NA, 30 
-84391549,HC 6949 H059 PATH CONSULT COMP, 8 
-84391556,HC MUT ISOL OR EXT EA NA, 8 
-84391564,HC MUT ID EA OLA SBCE ASPE, 30 
-85390052,HC HLAB:HLA-B TYPE(PCR), 58 
-85390110,HC HLAC:HLA-C TYPE(PCR), 58 
-85390888,HC PCRDR:HLA-DR TYPE(PCR), 120 
-85390904,HC HRDQ:HLA-DQ TYPE(HI-RES), 58 
-85390946,HC BCFRZ:CELL FREEZING, 21 
-85391431,HC TECH COMP ONLY FIRST MRKR, 131 
-85391449,HC TECH COMP ONLY ADDTL MRKR, 80 
-85391456,HC INTERP 16 OR MORE MARKERS, 145 
-85391464,HC TECH COMP ONLY FIRST MRKR, 131 
-85391472,HC TECH COMP ONLY ADDTL MRKR, 80 
-85391480,HC INTERP 16 OR MORE MARKERS, 145 
-85391498,HC MUT SEP BY GEL ELCT EA NA, 8 
-85391506,HC AMP MPLX FIRST 2 NA SEQ, 59 
-85391514,HC AMP MPLX EA ADTL NA BYND2, 30 
-85391522,HC MUT ID BY SEQ EA SEG, 30 
-85391530,HC SEP ELEC CAP EA NA, 30 
-85391548,HC INTERP AND REPORT, 8 
-85391555,HC MUT ISOL OR EXT EA NA, 8 
-85391563,HC MUT ID EA OLA SBCE ASPE, 30 
-94316270,HC CT COLONOGRAPHY - SCREEN," 2,250 "
-94316288,"HC CT COLONGRAPHY,DIAG WO CONTR"," 1,885 "
-50751841,HC 6959 TISSUE T TG IGA ANTIBODIE, 26 
-46330254,HC HCV GENOTYPE," 2,847 "
-26730986,HC IV INFUSION CONCURRENT, 821 
-41100975,HC TUNNELED CVA W/PORT UNDER 5," 9,118 "
-41100967,HC LYMPHANGIOGRAPHY S&I, 907 
-41100942,HC INJ LYMPHANGIOGRAPHY, 703 
-55091565,HC IN-HOUSE STRAIN TYP, 98 
-55091573,HC NON-SHS STRAIN TYP, 98 
-40980062,HC BX LIVER NDL PERC," 4,317 "
-50722107,HC 6959 ESTRADIOL, 7 
-50780188,HC 7052 COX MUT ISOL NA, 9 
-50780196,HC 7052 COX SEP GEL ELECT, 9 
-50780204,HC 7052 COX AMP EA NA SEQ, 37 
-50780212,HC 7052 COX MUT ID EA SEG, 37 
-50780220,HC 7052 COX INTRP AND RPT, 9 
-50780337,HC 6956 FREE FATTY ACIDS, 40 
-26431056,HC CHEMO INTRATHECAL INTO CNS," 5,090 "
-26631036,HC CHEMO INTRATHECAL INTO CNS," 5,090 "
-38305991,HC PRP INJECT KIT __ LEVEL 28," 6,163 "
-40905994,HC PRP INJECT KIT __ LEVEL 28," 6,163 "
-50780071,HC 6961 ATAXIC MUT ISOL NA, 10 
-40510703,HC TYPHOID VACCINE, 181 
-46330247,HC AVIN GNTYP NA HIV1 INTGRSE," 4,147 "
-50774967,HC 6942 CHLAMYDIA, 18 
-50777028,HC 6942 CHLAMYDIA IGM, 19 
-50780089,HC 6961 ATAXIC SEP GEL ELCT, 10 
-50780097,HC 6961 ATAXIC AMP EA NA SEQ, 41 
-50780105,HC 6961 ATAXIC MUT ID EA SEQ, 41 
-50780113,HC 6961 ATAXIC SEP ELEC CAP, 41 
-50780121,HC 6961 ATAXIC INTRP AND RPT, 10 
-50804160,HC ATHAL NA PROBE EA, 53 
-37224920,HC CT SFT NCK WO/W CON," 5,609 "
-41145129,HC INTRO GASTRO TUBE," 1,192 "
-41151549,HC INTRO LONG GI TUBE," 1,809 "
-26431064,HC 41.02 ALLO BMT W/PURGING," 13,718 "
-26431072,HC 41.03 ALLO BMT CRYOPRESERVED," 6,574 "
-26431080,HC 41.05 ALLO HSC TRANSPLANT W/O PURGING," 13,718 "
-26431098,HC 41.05 ALLO HSC TRANSPLANT W/O PURGING," 13,718 "
-26431106,HC REMOVE CVC CATH W/O SUBQ P/P," 3,651 "
-26431122,HC BLOOD COLLECT CVC/PICC LINE, 576 
-26431130,HC REMOVE CVC W/SUBQ P/P," 5,402 "
-26531046,HC REMOVE CVC CATH W/O SUBQ P/P," 3,651 "
-26531053,HC REMOVE CVC W/SUBQ P/P," 5,402 "
-26531061,HC BLOOD COLLECT CVC/PICC LINE, 576 
-26631044,HC REMOVE CVC CATH W/O SUBQ P/P," 3,651 "
-26631051,HC REMOVE CVC W/SUBQ P/P," 5,402 "
-26631069,HC BLOOD COLLECT CVC/PICC LINE, 576 
-26731018,HC BLOOD COLLECT CVC/PICC LINE, 576 
-33200056,HC DEMO 1X TRNG DEVICE OPERATION, 321 
-33200064,HC PRACTIONER RVW EA 4 PST, 26 
-33200072,HC ANTI COAG MGMT INIT 90 DAYS, 839 
-33200080,HC ANTI COAG MGMT SUB 90 DAYS, 321 
-33200122,HC ONE TIME TRNG DEVICE OPER, 321 
-41100959,HC FOREIGN BODY REMOVAL," 3,159 "
-41100983,"HC CANNULATION, THORACIC DUCT"," 1,618 "
-41530320,HC APLIGRAF SQ CM, 106 
-42016311,HC INJECT/NERVE BLOCK CELIAC," 1,594 "
-46160768,HC FLUAPC FLU A RNA RT PCR AMP, 651 
-50804178,HC ATHAL AMP EA NA SEQ, 188 
-50804186,HC BCRABL KDM MUT ISOL NA, 146 
-50804194,HC BCRABL KDM INTRP AND RPT, 138 
-51565273,HC PSA COMPLEXED, 276 
-55092183,HC AFB CONCENTRATION, 153 
-62016118,HC WINDOWING OF CAST, 213 
-62019609,HC INSERT CERVICAL DILATOR, 338 
-62019922,HC INJECT/NERVE BLOCK TRIGEMINAL, 419 
-62023973,HC STRAPPING OF ELBOW/WRIST, 109 
-40805913,HC BREAST NEEDLE LVL 18," 1,913 "
-40805939,HC TISSUE MARKERS IMPLANTABLE LVL 12, 765 
-40805947,"HC VACUUM PACKAGE, LVL 5", 213 
-40805954,HC BIOPSY TRAY LVL 4, 149 
-40805962,HC BREAST NEEDLE LVL 9, 510 
-40805970,HC BREAST NEEDLE LVL 6, 298 
-40805988,HC JABCZENSKI DUCTO CANNULA LVL 5, 213 
-62026679,HC EXC LES MUCOSA&SBMCSL VESTIBULE MOUTH W/O RPR," 1,637 "
-62026687,HC CLSR LAC VESTIBULE MOUTH 2.5 CM/<, 537 
-62026695,HC VESTIBULOPLASTY PST BI," 3,446 "
-40510737,HC ADACEL VACCINE, 130 
-40510745,HC YELLOW FEVER VACCINE, 297 
-40510752,HC MENACTRA VACCINE, 130 
-52291267,HC 6949 CYLEX, 101 
-81091266,HC 6949 CYLEX, 101 
-81191264,HC 6949 CYLEX, 101 
-81391260,HC 6949 CYLEX, 101 
-81791261,HC 6949 CYLEX, 101 
-82391269,HC 6949 CYLEX, 101 
-84391267,HC 6949 CYLEX, 101 
-85391266,HC 6949 CYLEX, 101 
-50791573,HC 6949 DRHR SEP GEL ELCT EA, 8 
-50791581,HC 6949 DRHR AMP EA NA SEQ, 30 
-50791599,HC 6949 DRHR MUT ID EA SEG, 30 
-50791607,HC 6949 DRHR SEP ELEC CAP, 30 
-50791615,HC 6949 H003 PATH CONSULT COMP, 8 
-52291572,HC 6949 DRHR SEP GEL ELCT EA, 8 
-52291580,HC 6949 DRHR AMP EA NA SEQ, 30 
-52291598,HC 6949 DRHR MUT ID EA SEG, 30 
-52291606,HC 6949 DRHR SEP ELEC CAP, 30 
-52291614,HC 6949 H003 PATH CONSULT COMP, 8 
-81091571,HC 6949 DRHR SEP GEL ELCT EA, 8 
-81091589,HC 6949 DRHR AMP EA NA SEQ, 30 
-81091597,HC 6949 DRHR MUT ID EA SEG, 30 
-81091605,HC 6949 DRHR SEP ELEC CAP, 30 
-81091613,HC 6949 H003 PATH CONSULT COMP, 8 
-81191579,HC 6949 DRHR SEP GEL ELCT EA, 8 
-81191587,HC 6949 DRHR AMP EA NA SEQ, 30 
-81191595,HC 6949 DRHR MUT ID EA SEG, 30 
-81191603,HC 6949 DRHR SEP ELEC CAP, 30 
-81191611,HC 6949 DRHR INTRP AND RPT, 8 
-81391575,HC 6949 DRHR SEP GEL ELCT EA, 8 
-81391583,HC 6949 DRHR AMP EA NA SEQ, 30 
-81391591,HC 6949 DRHR MUT ID EA SEG, 30 
-81391609,HC 6949 DRHR SEP ELEC CAP, 30 
-81391617,HC 6949 H003 PATH CONSULT COMP, 8 
-81791576,HC 6949 DRHR SEP GEL ELCT EA, 8 
-81791584,HC 6949 DRHR AMP EA NA SEQ, 30 
-81791592,HC 6949 DRHR MUT ID EA SEG, 30 
-81791600,HC 6949 DRHR SEP ELEC CAP, 30 
-81791618,HC 6949 DRHR INTRP AND RPT, 8 
-82391574,HC 6949 DRHR SEP GEL ELCT EA, 8 
-82391582,HC 6949 DRHR AMP EA NA SEQ, 30 
-82391590,HC 6949 DRHR MUT ID EA SEG, 30 
-82391608,HC 6949 DRHR SEP ELEC CAP, 30 
-82391616,HC 6949 H003 PATH CONSULT COMP, 8 
-84391572,HC 6949 DRHR SEP GEL ELCT EA, 8 
-84391580,HC 6949 DRHR AMP EA NA SEQ, 30 
-84391598,HC 6949 DRHR MUT ID EA SEG, 30 
-84391606,HC 6949 DRHR SEP ELEC CAP, 30 
-84391614,HC 6949 H003 PATH CONSULT COMP, 8 
-85391571,HC 6949 DRHR SEP GEL ELCT EA, 8 
-85391589,HC 6949 DRHR AMP EA NA SEQ, 30 
-85391597,HC 6949 DRHR MUT ID EA SEG, 30 
-85391605,HC 6949 DRHR SEP ELEC CAP, 30 
-85391613,HC 6949 DRHR INTRP AND RPT, 8 
-50791623,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-50791631,HC 6949 HRDQ AMP EA NA SEQ, 30 
-50791649,HC 6949 HRDQ MUT ID EA SEG, 30 
-50791656,HC 6949 HRDQ SEP ELEC CAP, 30 
-50791664,HC 6949 H072 PATH CONSULT COMP, 8 
-52291622,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-52291630,HC 6949 HRDQ AMP EA NA SEQ, 30 
-52291648,HC 6949 HRDQ MUT ID EA SEG, 30 
-52291655,HC 6949 HRDQ SEP ELEC CAP, 30 
-52291663,HC 6949 H072 PATH CONSULT COMP, 8 
-81091621,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-81091639,HC 6949 HRDQ AMP EA NA SEQ, 30 
-81091647,HC 6949 HRDQ MUT ID EA SEG, 30 
-81091654,HC 6949 HRDQ SEP ELEC CAP, 30 
-81091662,HC 6949 H072 PATH CONSULT COMP, 8 
-81191629,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-81191637,HC 6949 HRDQ AMP EA NA SEQ, 30 
-81191645,HC 6949 HRDQ MUT ID EA SEG, 30 
-81191652,HC 6949 HRDQ SEP ELEC CAP, 30 
-81191660,HC 6949 HRDQ INTRP AND RPT, 8 
-81391625,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-81391633,HC 6949 HRDQ AMP EA NA SEQ, 30 
-81391641,HC 6949 HRDQ MUT ID EA SEG, 30 
-81391658,HC 6949 HRDQ SEP ELEC CAP, 30 
-81391666,HC 6949 H072 PATH CONSULT COMP, 8 
-81791626,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-81791634,HC 6949 HRDQ AMP EA NA SEQ, 30 
-81791642,HC 6949 HRDQ MUT ID EA SEG, 30 
-81791659,HC 6949 HRDQ SEP ELEC CAP, 30 
-81791667,HC 6949 HRDQ INTRP AND RPT, 8 
-82391624,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-82391632,HC 6949 HRDQ AMP EA NA SEQ, 30 
-82391640,HC 6949 HRDQ MUT ID EA SEG, 30 
-82391657,HC 6949 HRDQ SEP ELEC CAP, 30 
-82391665,HC 6949 H072 PATH CONSULT COMP, 8 
-84391622,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-84391630,HC 6949 HRDQ AMP EA NA SEQ, 30 
-84391648,HC 6949 HRDQ MUT ID EA SEG, 30 
-84391655,HC 6949 HRDQ SEP ELEC CAP, 30 
-84391663,HC 6949 H072 PATH CONSULT COMP, 8 
-85391621,HC 6949 HRDQ SEP GEL ELCT EA, 8 
-85391639,HC 6949 HRDQ AMP EA NA SEQ, 30 
-85391647,HC 6949 HRDQ MUT ID EA SEG, 30 
-85391654,HC 6949 HRDQ SEP ELEC CAP, 30 
-85391662,HC 6949 HRDQ INTRP AND RPT, 8 
-50791672,HC 6949 KITFDX KIT FEE FEDX, 495 
-52291671,HC 6949 KITFDX KIT FEE FEDX, 495 
-81091670,HC 6949 KITFDX KIT FEE FEDX, 495 
-81191678,HC 6949 KITFDX KIT FEE FEDX, 495 
-81391674,HC 6949 KITFDX KIT FEE FEDX, 495 
-81791675,HC 6949 KITFDX KIT FEE FEDX, 495 
-82391673,HC 6949 KITFDX KIT FEE FEDX, 495 
-84391671,HC 6949 KITFDX KIT FEE FEDX, 495 
-85391670,HC 6949 KITFDX KIT FEE FEDX, 495 
-50791680,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-50791698,HC 6949 LMXSI FLOW ADTL MKR, 80 
-50791706,HC 6949 LMXSI INTRP 16 PLUS, 145 
-52291689,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-52291697,HC 6949 LMXSI FLOW ADTL MKR, 80 
-52291705,HC 6949 LMXSI INTRP 16 PLUS, 145 
-81091688,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-81091696,HC 6949 LMXSI FLOW ADTL MKR, 80 
-81091704,HC 6949 LMXSI INTRP 16 PLUS, 145 
-81191686,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-81191694,HC 6949 LMXSI FLOW ADTL MKR, 80 
-81191702,HC 6949 LMXSI INTRP 16 PLUS, 145 
-81391682,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-81391690,HC 6949 LMXSI FLOW ADTL MKR, 80 
-81391708,HC 6949 LMXSI INTRP 16 PLUS, 145 
-81791683,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-81791691,HC 6949 LMXSI FLOW ADTL MKR, 80 
-81791709,HC 6949 LMXSI INTRP 16 PLUS, 145 
-82391681,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-82391699,HC 6949 LMXSI FLOW ADTL MKR, 80 
-82391707,HC 6949 LMXSI INTRP 16 PLUS, 145 
-84391689,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-84391697,HC 6949 LMXSI FLOW ADTL MKR, 80 
-84391705,HC 6949 LMXSI INTRP 16 PLUS, 145 
-85391688,HC 6949 LMXSI FLOW 1ST MRKR, 131 
-85391696,HC 6949 LMXSI FLOW ADTL MKR, 80 
-85391704,HC 6949 LMXSI INTRP 16 PLUS, 145 
-50791714,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-50791722,HC 6949 LMXSII FLOW ADTL MKR, 80 
-50791730,HC 6949 LMXSII INTRP 16 PLUS, 145 
-52291713,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-52291721,HC 6949 LMXSII FLOW ADTL MKR, 80 
-52291739,HC 6949 LMXSII INTRP 16 PLUS, 145 
-81091712,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-81091720,HC 6949 LMXSII FLOW ADTL MKR, 80 
-81091738,HC 6949 LMXSII INTRP 16 PLUS, 145 
-81191710,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-81191728,HC 6949 LMXSII FLOW ADTL MKR, 80 
-81191736,HC 6949 LMXSII INTRP 16 PLUS, 145 
-81391716,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-81391724,HC 6949 LMXSII FLOW ADTL MKR, 80 
-81391732,HC 6949 LMXSII INTRP 16 PLUS, 145 
-81791717,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-81791725,HC 6949 LMXSII FLOW ADTL MKR, 80 
-81791733,HC 6949 LMXSII INTRP 16 PLUS, 145 
-82391715,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-82391723,HC 6949 LMXSII FLOW ADTL MKR, 80 
-82391731,HC 6949 LMXSII INTRP 16 PLUS, 145 
-84391713,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-84391721,HC 6949 LMXSII FLOW ADTL MKR, 80 
-84391739,HC 6949 LMXSII INTRP 16 PLUS, 145 
-85391712,HC 6949 LMXSII FLOW 1ST MRKR, 131 
-85391720,HC 6949 LMXSII FLOW ADTL MKR, 80 
-85391738,HC 6949 LMXSII INTRP 16 PLUS, 145 
-50791748,HC 6949 HLAB SEP GEL ELECT, 8 
-50791755,HC 6949 HLAB AMP EA NA SEQ, 30 
-50791763,HC 6949 H062 PATH CONSULT COMP, 8 
-52291747,HC 6949 HLAB SEP GEL ELECT, 8 
-52291754,HC 6949 HLAB AMP EA NA SEQ, 30 
-52291762,HC 6949 H062 PATH CONSULT COMP, 8 
-81091746,HC 6949 HLAB SEP GEL ELECT, 8 
-81091753,HC 6949 HLAB AMP EA NA SEQ, 30 
-81091761,HC 6949 H062 PATH CONSULT COMP, 8 
-81191744,HC 6949 HLAB SEP GEL ELECT, 8 
-81191751,HC 6949 HLAB AMP EA NA SEQ, 30 
-81191769,HC 6949 HLAB INTRP AND RPT, 8 
-81391740,HC 6949 HLAB SEP GEL ELECT, 8 
-81391757,HC 6949 HLAB AMP EA NA SEQ, 30 
-81391765,HC 6949 H062 PATH CONSULT COMP, 8 
-81791741,HC 6949 HLAB SEP GEL ELECT, 8 
-81791758,HC 6949 HLAB AMP EA NA SEQ, 30 
-81791766,HC 6949 HLAB INTRP AND RPT, 8 
-82391749,HC 6949 HLAB SEP GEL ELECT, 8 
-82391756,HC 6949 HLAB AMP EA NA SEQ, 30 
-82391764,HC 6949 H062 PATH CONSULT COMP, 8 
-84391747,HC 6949 HLAB SEP GEL ELECT, 8 
-84391754,HC 6949 HLAB AMP EA NA SEQ, 30 
-84391762,HC 6949 H062 PATH CONSULT COMP, 8 
-85391746,HC 6949 HLAB SEP GEL ELECT, 8 
-85391753,HC 6949 HLAB AMP EA NA SEQ, 30 
-85391761,HC 6949 HLAB INTRP AND RPT, 8 
-50791771,HC 6949 LTB SEP GEL ELECT, 8 
-50791789,HC 6949 LTB NA PROBE EA, 8 
-50791797,HC 6949 LTB AMP EA NA SEQ, 30 
-50791805,HC 6949 H114 PATH CONSULT COMP, 8 
-52291770,HC 6949 LTB SEP GEL ELECT, 8 
-52291788,HC 6949 LTB NA PROBE EA, 8 
-52291796,HC 6949 LTB AMP EA NA SEQ, 30 
-52291804,HC 6949 H114 PATH CONSULT COMP, 8 
-81091779,HC 6949 LTB SEP GEL ELECT, 8 
-81091787,HC 6949 LTB NA PROBE EA, 8 
-81091795,HC 6949 LTB AMP EA NA SEQ, 30 
-81091803,HC 6949 H114 PATH CONSULT COMP, 8 
-81191777,HC 6949 LTB SEP GEL ELECT, 8 
-81191785,HC 6949 LTB NA PROBE EA, 8 
-81191793,HC 6949 LTB AMP EA NA SEQ, 30 
-81191801,HC 6949 LTB INTRP AND RPT, 8 
-81391773,HC 6949 LTB SEP GEL ELECT, 8 
-81391781,HC 6949 LTB NA PROBE EA, 8 
-81391799,HC 6949 LTB AMP EA NA SEQ, 30 
-81391807,HC 6949 H114 PATH CONSULT COMP, 8 
-81791774,HC 6949 LTB SEP GEL ELECT, 8 
-81791782,HC 6949 LTB NA PROBE EA, 8 
-81791790,HC 6949 LTB AMP EA NA SEQ, 30 
-81791808,HC 6949 LTB INTRP AND RPT, 8 
-82391772,HC 6949 LTB SEP GEL ELECT, 8 
-82391780,HC 6949 LTB NA PROBE EA, 8 
-82391798,HC 6949 LTB AMP EA NA SEQ, 30 
-82391806,HC 6949 H114 PATH CONSULT COMP, 8 
-84391770,HC 6949 LTB SEP GEL ELECT, 8 
-84391788,HC 6949 LTB NA PROBE EA, 8 
-84391796,HC 6949 LTB AMP EA NA SEQ, 30 
-84391804,HC 6949 H114 PATH CONSULT COMP, 8 
-85391779,HC 6949 LTB SEP GEL ELECT, 8 
-85391787,HC 6949 LTB NA PROBE EA, 8 
-85391795,HC 6949 LTB AMP EA NA SEQ, 30 
-85391803,HC 6949 LTB INTRP AND RPT, 8 
-50791813,HC 6949 LTC SEP GEL ELECT, 8 
-50791821,HC 6949 LTC NA PROBE EA, 8 
-50791839,HC 6949 LTC AMP EA NA SEQ, 30 
-50791847,HC 6949 H115 PATH CONSULT COMP, 8 
-52291812,HC 6949 LTC SEP GEL ELECT, 8 
-52291820,HC 6949 LTC NA PROBE EA, 8 
-52291838,HC 6949 LTC AMP EA NA SEQ, 30 
-52291846,HC 6949 H115 PATH CONSULT COMP, 8 
-81091811,HC 6949 LTC SEP GEL ELECT, 8 
-81091829,HC 6949 LTC NA PROBE EA, 8 
-81091837,HC 6949 LTC AMP EA NA SEQ, 30 
-81091845,HC 6949 H115 PATH CONSULT COMP, 8 
-81191819,HC 6949 LTC SEP GEL ELECT, 8 
-81191827,HC 6949 LTC NA PROBE EA, 8 
-81191835,HC 6949 LTC AMP EA NA SEQ, 30 
-81191843,HC 6949 LTC INTRP AND RPT, 8 
-81391815,HC 6949 LTC SEP GEL ELECT, 8 
-81391823,HC 6949 LTC NA PROBE EA, 8 
-81391831,HC 6949 LTC AMP EA NA SEQ, 30 
-81391849,HC 6949 H115 PATH CONSULT COMP, 8 
-81791816,HC 6949 LTC SEP GEL ELECT, 8 
-81791824,HC 6949 LTC NA PROBE EA, 8 
-81791832,HC 6949 LTC AMP EA NA SEQ, 30 
-81791840,HC 6949 LTC INTRP AND RPT, 8 
-82391814,HC 6949 LTC SEP GEL ELECT, 8 
-82391822,HC 6949 LTC NA PROBE EA, 8 
-82391830,HC 6949 LTC AMP EA NA SEQ, 30 
-82391848,HC 6949 H115 PATH CONSULT COMP, 8 
-84391812,HC 6949 LTC SEP GEL ELECT, 8 
-84391820,HC 6949 LTC NA PROBE EA, 8 
-84391838,HC 6949 LTC AMP EA NA SEQ, 30 
-84391846,HC 6949 H115 PATH CONSULT COMP, 8 
-85391811,HC 6949 LTC SEP GEL ELECT, 8 
-85391829,HC 6949 LTC NA PROBE EA, 8 
-85391837,HC 6949 LTC AMP EA NA SEQ, 30 
-85391845,HC 6949 LTC INTRP AND RPT, 8 
-50791854,HC 6949 PCRDQ SEP GEL ELECT, 8 
-50791862,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-50791870,HC 6949 H004 PATH CONSULT COMP, 8 
-52291853,HC 6949 PCRDQ SEP GEL ELECT, 8 
-52291861,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-52291879,HC 6949 H004 PATH CONSULT COMP, 8 
-81091852,HC 6949 PCRDQ SEP GEL ELECT, 8 
-81091860,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-81091878,HC 6949 H004 PATH CONSULT COMP, 8 
-81191850,HC 6949 PCRDQ SEP GEL ELECT, 8 
-81191868,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-81191876,HC 6949 PCRDQ INTRP AND RPT, 8 
-81391856,HC 6949 PCRDQ SEP GEL ELECT, 8 
-81391864,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-81391872,HC 6949 H004 PATH CONSULT COMP, 8 
-81791857,HC 6949 PCRDQ SEP GEL ELECT, 8 
-81791865,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-81791873,HC 6949 PCRDQ INTRP AND RPT, 8 
-82391855,HC 6949 PCRDQ SEP GEL ELECT, 8 
-82391863,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-82391871,HC 6949 H004 PATH CONSULT COMP, 8 
-84391853,HC 6949 PCRDQ SEP GEL ELECT, 8 
-84391861,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-84391879,HC 6949 H004 PATH CONSULT COMP, 8 
-85391852,HC 6949 PCRDQ SEP GEL ELECT, 8 
-85391860,HC 6949 PCRDQ AMP EA NA SEQ, 30 
-85391878,HC 6949 PCRDQ INTRP AND RPT, 8 
-50791888,HC 6949 PCRDR SEP GEL ELECT, 8 
-50791896,HC 6949 PCRDR AMP EA NA SEQ, 30 
-50791904,HC 6949 H029 PATH CONSULT COMP, 8 
-52291887,HC 6949 PCRDR SEP GEL ELECT, 8 
-52291895,HC 6949 PCRDR AMP EA NA SEQ, 30 
-52291903,HC 6949 H029 PATH CONSULT COMP, 8 
-81091886,HC 6949 PCRDR SEP GEL ELECT, 8 
-81091894,HC 6949 PCRDR AMP EA NA SEQ, 30 
-81091902,HC 6949 H029 PATH CONSULT COMP, 8 
-81191884,HC 6949 PCRDR SEP GEL ELECT, 8 
-81191892,HC 6949 PCRDR AMP EA NA SEQ, 30 
-81191900,HC 6949 PCRDR INTRP AND RPT, 8 
-81391880,HC 6949 PCRDR SEP GEL ELECT, 8 
-81391898,HC 6949 PCRDR AMP EA NA SEQ, 30 
-81391906,HC 6949 H029 PATH CONSULT COMP, 8 
-81791881,HC 6949 PCRDR SEP GEL ELECT, 8 
-81791899,HC 6949 PCRDR AMP EA NA SEQ, 30 
-81791907,HC 6949 PCRDR INTRP AND RPT, 8 
-82391889,HC 6949 PCRDR SEP GEL ELECT, 8 
-82391897,HC 6949 PCRDR AMP EA NA SEQ, 30 
-82391905,HC 6949 H029 PATH CONSULT COMP, 8 
-84391887,HC 6949 PCRDR SEP GEL ELECT, 8 
-84391895,HC 6949 PCRDR AMP EA NA SEQ, 30 
-84391903,HC 6949 H029 PATH CONSULT COMP, 8 
-85391886,HC 6949 PCRDR SEP GEL ELECT, 8 
-85391894,HC 6949 PCRDR AMP EA NA SEQ, 30 
-85391902,HC 6949 PCRDR INTRP AND RPT, 8 
-50791912,HC 6949 HLACH SEP GEL ELECT, 8 
-50791920,HC 6949 HLACH AMP EA NA SEQ, 30 
-50791938,HC 6949 HLACH MUT ID EA SEG, 30 
-50791946,HC 6949 HLACH SEP ELEC CAP, 30 
-50791953,HC 6949 H069 PATH CONSULT COMP, 8 
-52291911,HC 6949 HLACH SEP GEL ELECT, 8 
-52291929,HC 6949 HLACH AMP EA NA SEQ, 30 
-52291937,HC 6949 HLACH MUT ID EA SEG, 30 
-52291945,HC 6949 HLACH SEP ELEC CAP, 30 
-52291952,HC 6949 H069 PATH CONSULT COMP, 8 
-81091910,HC 6949 HLACH SEP GEL ELECT, 8 
-81091928,HC 6949 HLACH AMP EA NA SEQ, 30 
-81091936,HC 6949 HLACH MUT ID EA SEG, 30 
-81091944,HC 6949 HLACH SEP ELEC CAP, 30 
-81091951,HC 6949 H069 PATH CONSULT COMP, 8 
-81191918,HC 6949 HLACH SEP GEL ELECT, 8 
-81191926,HC 6949 HLACH AMP EA NA SEQ, 30 
-81191934,HC 6949 HLACH MUT ID EA SEG, 30 
-81191942,HC 6949 HLACH SEP ELEC CAP, 30 
-81191959,HC 6949 HLACH INTRP AND RPT, 8 
-81391914,HC 6949 HLACH SEP GEL ELECT, 8 
-81391922,HC 6949 HLACH AMP EA NA SEQ, 30 
-81391930,HC 6949 HLACH MUT ID EA SEG, 30 
-81391948,HC 6949 HLACH SEP ELEC CAP, 30 
-81391955,HC 6949 H069 PATH CONSULT COMP, 8 
-81791915,HC 6949 HLACH SEP GEL ELECT, 8 
-81791923,HC 6949 HLACH AMP EA NA SEQ, 30 
-81791931,HC 6949 HLACH MUT ID EA SEG, 30 
-81791949,HC 6949 HLACH SEP ELEC CAP, 30 
-81791956,HC 6949 HLACH INTRP AND RPT, 8 
-82391913,HC 6949 HLACH SEP GEL ELECT, 8 
-82391921,HC 6949 HLACH AMP EA NA SEQ, 30 
-82391939,HC 6949 HLACH MUT ID EA SEG, 30 
-82391947,HC 6949 HLACH SEP ELEC CAP, 30 
-82391954,HC 6949 H069 PATH CONSULT COMP, 8 
-84391911,HC 6949 HLACH SEP GEL ELECT, 8 
-84391929,HC 6949 HLACH AMP EA NA SEQ, 30 
-84391937,HC 6949 HLACH MUT ID EA SEG, 30 
-84391945,HC 6949 HLACH SEP ELEC CAP, 30 
-84391952,HC 6949 H069 PATH CONSULT COMP, 8 
-85391910,HC 6949 HLACH SEP GEL ELECT, 8 
-85391928,HC 6949 HLACH AMP EA NA SEQ, 30 
-85391936,HC 6949 HLACH MUT ID EA SEG, 30 
-85391944,HC 6949 HLACH SEP ELEC CAP, 30 
-85391951,HC 6949 HLACH INTRP AND RPT, 8 
-50791961,HC 6949 HLAAH SEP GEL ELECT, 8 
-50791979,HC 6949 HLAAH AMP EA NA SEQ, 30 
-50791987,HC 6949 HLAAH MUT ID EA SEG, 30 
-50791995,HC 6949 HLAAH SEP ELEC CAP, 30 
-50792001,HC 6949 H075 PATH CONSULT COMP, 8 
-52291960,HC 6949 HLAAH SEP GEL ELECT, 8 
-52291978,HC 6949 HLAAH AMP EA NA SEQ, 30 
-52291986,HC 6949 HLAAH MUT ID EA SEG, 30 
-52291994,HC 6949 HLAAH SEP ELEC CAP, 30 
-52292000,HC 6949 H075 PATH CONSULT COMP, 8 
-81091969,HC 6949 HLAAH SEP GEL ELECT, 8 
-81091977,HC 6949 HLAAH AMP EA NA SEQ, 30 
-81091985,HC 6949 HLAAH MUT ID EA SEG, 30 
-81091993,HC 6949 HLAAH SEP ELEC CAP, 30 
-81092009,HC 6949 H075 PATH CONSULT COMP, 8 
-81191967,HC 6949 HLAAH SEP GEL ELECT, 8 
-81191975,HC 6949 HLAAH AMP EA NA SEQ, 30 
-81191983,HC 6949 HLAAH MUT ID EA SEG, 30 
-81191991,HC 6949 HLAAH SEP ELEC CAP, 30 
-81192007,HC 6949 HLAAH INTRP AND RPT, 8 
-81391963,HC 6949 HLAAH SEP GEL ELECT, 8 
-81391971,HC 6949 HLAAH AMP EA NA SEQ, 30 
-81391989,HC 6949 HLAAH MUT ID EA SEG, 30 
-81391997,HC 6949 HLAAH SEP ELEC CAP, 30 
-81392003,HC 6949 H075 PATH CONSULT COMP, 8 
-81791964,HC 6949 HLAAH SEP GEL ELECT, 8 
-81791972,HC 6949 HLAAH AMP EA NA SEQ, 30 
-81791980,HC 6949 HLAAH MUT ID EA SEG, 30 
-81791998,HC 6949 HLAAH SEP ELEC CAP, 30 
-81792004,HC 6949 HLAAH INTRP AND RPT, 8 
-82391962,HC 6949 HLAAH SEP GEL ELECT, 8 
-82391970,HC 6949 HLAAH AMP EA NA SEQ, 30 
-82391988,HC 6949 HLAAH MUT ID EA SEG, 30 
-82391996,HC 6949 HLAAH SEP ELEC CAP, 30 
-82392002,HC 6949 H075 PATH CONSULT COMP, 8 
-84391960,HC 6949 HLAAH SEP GEL ELECT, 8 
-84391978,HC 6949 HLAAH AMP EA NA SEQ, 30 
-84391986,HC 6949 HLAAH MUT ID EA SEG, 30 
-84391994,HC 6949 HLAAH SEP ELEC CAP, 30 
-84392000,HC 6949 H075 PATH CONSULT COMP, 8 
-85391969,HC 6949 HLAAH SEP GEL ELECT, 8 
-85391977,HC 6949 HLAAH AMP EA NA SEQ, 30 
-85391985,HC 6949 HLAAH MUT ID EA SEG, 30 
-85391993,HC 6949 HLAAH SEP ELEC CAP, 30 
-85392009,HC 6949 HLAAH INTRP AND RPT, 8 
-50792019,HC 6949 HLABH SEP GEL ELECT, 8 
-50792027,HC 6949 HLABH AMP EA NA SEQ, 30 
-50792035,HC 6949 HLABH MUT ID EA SEG, 30 
-50792043,HC 6949 HLABH SEP ELEC CAP, 30 
-50792050,HC 6949 H076 PATH CONSULT COMP, 8 
-52292018,HC 6949 HLABH SEP GEL ELECT, 8 
-52292026,HC 6949 HLABH AMP EA NA SEQ, 30 
-52292034,HC 6949 HLABH MUT ID EA SEG, 30 
-52292042,HC 6949 HLABH SEP ELEC CAP, 30 
-52292059,HC 6949 H076 PATH CONSULT COMP, 8 
-81092017,HC 6949 HLABH SEP GEL ELECT, 8 
-81092025,HC 6949 HLABH AMP EA NA SEQ, 30 
-81092033,HC 6949 HLABH MUT ID EA SEG, 30 
-81092041,HC 6949 HLABH SEP ELEC CAP, 30 
-81092058,HC 6949 H076 PATH CONSULT COMP, 8 
-81192015,HC 6949 HLABH SEP GEL ELECT, 8 
-81192023,HC 6949 HLABH AMP EA NA SEQ, 30 
-81192031,HC 6949 HLABH MUT ID EA SEG, 30 
-81192049,HC 6949 HLABH SEP ELEC CAP, 30 
-81192056,HC 6949 HLABH INTRP AND RPT, 8 
-81392011,HC 6949 HLABH SEP GEL ELECT, 8 
-81392029,HC 6949 HLABH AMP EA NA SEQ, 30 
-81392037,HC 6949 HLABH MUT ID EA SEG, 30 
-81392045,HC 6949 HLABH SEP ELEC CAP, 30 
-81392052,HC 6949 H076 PATH CONSULT COMP, 8 
-81792012,HC 6949 HLABH SEP GEL ELECT, 8 
-81792020,HC 6949 HLABH AMP EA NA SEQ, 30 
-81792038,HC 6949 HLABH MUT ID EA SEG, 30 
-81792046,HC 6949 HLABH SEP ELEC CAP, 30 
-81792053,HC 6949 HLABH INTRP AND RPT, 8 
-82392010,HC 6949 HLABH SEP GEL ELECT, 8 
-82392028,HC 6949 HLABH AMP EA NA SEQ, 30 
-82392036,HC 6949 HLABH MUT ID EA SEG, 30 
-82392044,HC 6949 HLABH SEP ELEC CAP, 30 
-82392051,HC 6949 H076 PATH CONSULT COMP, 8 
-84392018,HC 6949 HLABH SEP GEL ELECT, 8 
-84392026,HC 6949 HLABH AMP EA NA SEQ, 30 
-84392034,HC 6949 HLABH MUT ID EA SEG, 30 
-84392042,HC 6949 HLABH SEP ELEC CAP, 30 
-84392059,HC 6949 H076 PATH CONSULT COMP, 8 
-85392017,HC 6949 HLABH SEP GEL ELECT, 8 
-85392025,HC 6949 HLABH AMP EA NA SEQ, 30 
-85392033,HC 6949 HLABH MUT ID EA SEG, 30 
-85392041,HC 6949 HLABH SEP ELEC CAP, 30 
-85392058,HC 6949 HLABH INTRP AND RPT, 8 
-50792068,HC 6949 STRSU SEP GEL ELECT, 8 
-50792084,HC 6949 STRSU AMP MPLX 1 2, 59 
-50792092,HC 6949 STRSU AMP MPLX ADTL, 30 
-50792100,HC 6949 STRSU MUT ID EA SEG, 30 
-50792118,HC 6949 STRSU SEP ELEC CAP, 30 
-50792126,HC 6949 H058 PATH CONSULT COMP, 8 
-52292067,HC 6949 STRSU SEP GEL ELECT, 8 
-52292083,HC 6949 STRSU AMP MPLX 1 2, 59 
-52292091,HC 6949 STRSU AMP MPLX ADTL, 30 
-52292109,HC 6949 STRSU MUT ID EA SEG, 30 
-52292117,HC 6949 STRSU SEP ELEC CAP, 30 
-52292125,HC 6949 H058 PATH CONSULT COMP, 8 
-81092066,HC 6949 STRSU SEP GEL ELECT, 8 
-81092082,HC 6949 STRSU AMP MPLX 1 2, 59 
-81092090,HC 6949 STRSU AMP MPLX ADTL, 30 
-81092108,HC 6949 STRSU MUT ID EA SEG, 30 
-81092116,HC 6949 STRSU SEP ELEC CAP, 30 
-81092124,HC 6949 H058 PATH CONSULT COMP, 8 
-81192064,HC 6949 STRSU SEP GEL ELECT, 8 
-81192080,HC 6949 STRSU AMP MPLX 1 2, 59 
-81192098,HC 6949 STRSU AMP MPLX ADTL, 30 
-81192106,HC 6949 STRSU MUT ID EA SEG, 30 
-81192114,HC 6949 STRSU SEP ELEC CAP, 30 
-81192122,HC 6949 STRSU INTRP AND RPT, 8 
-81392060,HC 6949 STRSU SEP GEL ELECT, 8 
-81392086,HC 6949 STRSU AMP MPLX 1 2, 59 
-81392094,HC 6949 STRSU AMP MPLX ADTL, 30 
-81392102,HC 6949 STRSU MUT ID EA SEG, 30 
-81392110,HC 6949 STRSU SEP ELEC CAP, 30 
-81392128,HC 6949 H058 PATH CONSULT COMP, 8 
-81792061,HC 6949 STRSU SEP GEL ELECT, 8 
-81792087,HC 6949 STRSU AMP MPLX 1 2, 59 
-81792095,HC 6949 STRSU AMP MPLX ADTL, 30 
-81792103,HC 6949 STRSU MUT ID EA SEG, 30 
-81792111,HC 6949 STRSU SEP ELEC CAP, 30 
-81792129,HC 6949 STRSU INTRP AND RPT, 8 
-82392069,HC 6949 STRSU SEP GEL ELECT, 8 
-82392085,HC 6949 STRSU AMP MPLX 1 2, 59 
-82392093,HC 6949 STRSU AMP MPLX ADTL, 30 
-82392101,HC 6949 STRSU MUT ID EA SEG, 30 
-82392119,HC 6949 STRSU SEP ELEC CAP, 30 
-82392127,HC 6949 H058 PATH CONSULT COMP, 8 
-84392067,HC 6949 STRSU SEP GEL ELECT, 8 
-84392083,HC 6949 STRSU AMP MPLX 1 2, 59 
-84392091,HC 6949 STRSU AMP MPLX ADTL, 30 
-84392109,HC 6949 STRSU MUT ID EA SEG, 30 
-84392117,HC 6949 STRSU SEP ELEC CAP, 30 
-84392125,HC 6949 H058 PATH CONSULT COMP, 8 
-85392066,HC 6949 STRSU SEP GEL ELECT, 8 
-85392082,HC 6949 STRSU AMP MPLX 1 2, 59 
-85392090,HC 6949 STRSU AMP MPLX ADTL, 30 
-85392108,HC 6949 STRSU MUT ID EA SEG, 30 
-85392116,HC 6949 STRSU SEP ELEC CAP, 30 
-85392124,HC 6949 STRSU INTRP AND RPT, 8 
-50792134,HC 6949 HLAC SEP GEL ELECT, 8 
-50792142,HC 6949 HLAC AMP EA NA SEQ, 30 
-50792159,HC 6949 H022 PATH CONSULT COMP, 8 
-52292133,HC 6949 HLAC SEP GEL ELECT, 8 
-52292141,HC 6949 HLAC AMP EA NA SEQ, 30 
-52292158,HC 6949 H022 PATH CONSULT COMP, 8 
-81092132,HC 6949 HLAC SEP GEL ELECT, 8 
-81092140,HC 6949 HLAC AMP EA NA SEQ, 30 
-81092157,HC 6949 H022 PATH CONSULT COMP, 8 
-81192130,HC 6949 HLAC SEP GEL ELECT, 8 
-81192148,HC 6949 HLAC AMP EA NA SEQ, 30 
-81192155,HC 6949 HLAC INTRP AND RPT, 8 
-81392136,HC 6949 HLAC SEP GEL ELECT, 8 
-81392144,HC 6949 HLAC AMP EA NA SEQ, 30 
-81392151,HC 6949 H022 PATH CONSULT COMP, 8 
-81792137,HC 6949 HLAC SEP GEL ELECT, 8 
-81792145,HC 6949 HLAC AMP EA NA SEQ, 30 
-81792152,HC 6949 HLAC INTRP AND RPT, 8 
-82392135,HC 6949 HLAC SEP GEL ELECT, 8 
-82392143,HC 6949 HLAC AMP EA NA SEQ, 30 
-82392150,HC 6949 H022 PATH CONSULT COMP, 8 
-84392133,HC 6949 HLAC SEP GEL ELECT, 8 
-84392141,HC 6949 HLAC AMP EA NA SEQ, 30 
-84392158,HC 6949 H022 PATH CONSULT COMP, 8 
-85392132,HC 6949 HLAC SEP GEL ELECT, 8 
-85392140,HC 6949 HLAC AMP EA NA SEQ, 30 
-85392157,HC 6949 HLAC INTRP AND RPT, 8 
-50792167,HC 6949 AXTF AUTOS PROC, 73 
-50792175,HC 6949 AXTF FLOW 1ST MRKR, 131 
-50792183,HC 6949 AXTF FLOW ADTL MKR, 80 
-50792191,HC 6949 AXTF INTERP 2 TO 8, 87 
-52292166,HC 6949 AXTF AUTOS PROC, 73 
-52292174,HC 6949 AXTF FLOW 1ST MRKR, 131 
-52292182,HC 6949 AXTF FLOW ADTL MKR, 80 
-52292190,HC 6949 AXTF INTERP 2 TO 8, 87 
-81092165,HC 6949 AXTF AUTOS PROC, 73 
-81092173,HC 6949 AXTF FLOW 1ST MRKR, 131 
-81092181,HC 6949 AXTF FLOW ADTL MKR, 80 
-81092199,HC 6949 AXTF INTERP 2 TO 8, 87 
-81192163,HC 6949 AXTF AUTOS PROC, 73 
-81192171,HC 6949 AXTF FLOW 1ST MRKR, 131 
-81192189,HC 6949 AXTF FLOW ADTL MKR, 80 
-81192197,HC 6949 AXTF INTERP 2 TO 8, 87 
-81392169,HC 6949 AXTF AUTOS PROC, 73 
-81392177,HC 6949 AXTF FLOW 1ST MRKR, 131 
-81392185,HC 6949 AXTF FLOW ADTL MKR, 80 
-81392193,HC 6949 AXTF INTERP 2 TO 8, 87 
-81792160,HC 6949 AXTF AUTOS PROC, 73 
-81792178,HC 6949 AXTF FLOW 1ST MRKR, 131 
-81792186,HC 6949 AXTF FLOW ADTL MKR, 80 
-81792194,HC 6949 AXTF INTERP 2 TO 8, 87 
-82392168,HC 6949 AXTF AUTOS PROC, 73 
-82392176,HC 6949 AXTF FLOW 1ST MRKR, 131 
-82392184,HC 6949 AXTF FLOW ADTL MKR, 80 
-82392192,HC 6949 AXTF INTERP 2 TO 8, 87 
-84392166,HC 6949 AXTF AUTOS PROC, 73 
-84392174,HC 6949 AXTF FLOW 1ST MRKR, 131 
-84392182,HC 6949 AXTF FLOW ADTL MKR, 80 
-84392190,HC 6949 AXTF INTERP 2 TO 8, 87 
-85392165,HC 6949 AXTF AUTOS PROC, 73 
-85392173,HC 6949 AXTF FLOW 1ST MRKR, 131 
-85392181,HC 6949 AXTF FLOW ADTL MKR, 80 
-85392199,HC 6949 AXTF INTERP 2 TO 8, 87 
-50792209,HC 6949 AXBF AUTOS PROC, 73 
-50792217,HC 6949 AXBF FLOW 1ST MRKR, 131 
-50792225,HC 6949 AXBF FLOW ADTL MKR, 80 
-50792233,HC 6949 AXBF INTERP 2 TO 8, 87 
-52292208,HC 6949 AXBF AUTOS PROC, 73 
-52292216,HC 6949 AXBF FLOW 1ST MRKR, 131 
-52292224,HC 6949 AXBF FLOW ADTL MKR, 80 
-52292232,HC 6949 AXBF INTERP 2 TO 8, 87 
-81092207,HC 6949 AXBF AUTOS PROC, 73 
-81092215,HC 6949 AXBF FLOW 1ST MRKR, 131 
-81092223,HC 6949 AXBF FLOW ADTL MKR, 80 
-81092231,HC 6949 AXBF INTERP 2 TO 8, 87 
-81192205,HC 6949 AXBF AUTOS PROC, 73 
-81192213,HC 6949 AXBF FLOW 1ST MRKR, 131 
-81192221,HC 6949 AXBF FLOW ADTL MKR, 80 
-81192239,HC 6949 AXBF INTERP 2 TO 8, 87 
-81392201,HC 6949 AXBF AUTOS PROC, 73 
-81392219,HC 6949 AXBF FLOW 1ST MRKR, 131 
-81392227,HC 6949 AXBF FLOW ADTL MKR, 80 
-81392235,HC 6949 AXBF INTERP 2 TO 8, 87 
-81792202,HC 6949 AXBF AUTOS PROC, 73 
-81792210,HC 6949 AXBF FLOW 1ST MRKR, 131 
-81792228,HC 6949 AXBF FLOW ADTL MKR, 80 
-81792236,HC 6949 AXBF INTERP 2 TO 8, 87 
-82392200,HC 6949 AXBF AUTOS PROC, 73 
-82392218,HC 6949 AXBF FLOW 1ST MRKR, 131 
-82392226,HC 6949 AXBF FLOW ADTL MKR, 80 
-82392234,HC 6949 AXBF INTERP 2 TO 8, 87 
-84392208,HC 6949 AXBF AUTOS PROC, 73 
-84392216,HC 6949 AXBF FLOW 1ST MRKR, 131 
-84392224,HC 6949 AXBF FLOW ADTL MKR, 80 
-84392232,HC 6949 AXBF INTERP 2 TO 8, 87 
-85392207,HC 6949 AXBF AUTOS PROC, 73 
-85392215,HC 6949 AXBF FLOW 1ST MRKR, 131 
-85392223,HC 6949 AXBF FLOW ADTL MKR, 80 
-85392231,HC 6949 AXBF INTERP 2 TO 8, 87 
-50792241,HC 6949 FXMAT AUTOS PROC, 73 
-50792258,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-50792266,HC 6949 FXMAT FLOW ADTL MKR, 80 
-50792274,HC 6949 FXMAT INTERP 2 TO 8, 87 
-52292240,HC 6949 FXMAT AUTOS PROC, 73 
-52292257,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-52292265,HC 6949 FXMAT FLOW ADTL MKR, 80 
-52292273,HC 6949 FXMAT INTERP 2 TO 8, 87 
-81092249,HC 6949 FXMAT AUTOS PROC, 73 
-81092256,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-81092264,HC 6949 FXMAT FLOW ADTL MKR, 80 
-81092272,HC 6949 FXMAT INTERP 2 TO 8, 87 
-81192247,HC 6949 FXMAT AUTOS PROC, 73 
-81192254,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-81192262,HC 6949 FXMAT FLOW ADTL MKR, 80 
-81192270,HC 6949 FXMAT INTERP 2 TO 8, 87 
-81392243,HC 6949 FXMAT AUTOS PROC, 73 
-81392250,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-81392268,HC 6949 FXMAT FLOW ADTL MKR, 80 
-81392276,HC 6949 FXMAT INTERP 2 TO 8, 87 
-81792244,HC 6949 FXMAT AUTOS PROC, 73 
-81792251,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-81792269,HC 6949 FXMAT FLOW ADTL MKR, 80 
-81792277,HC 6949 FXMAT INTERP 2 TO 8, 87 
-82392242,HC 6949 FXMAT AUTOS PROC, 73 
-82392259,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-82392267,HC 6949 FXMAT FLOW ADTL MKR, 80 
-82392275,HC 6949 FXMAT INTERP 2 TO 8, 87 
-84392240,HC 6949 FXMAT AUTOS PROC, 73 
-84392257,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-84392265,HC 6949 FXMAT FLOW ADTL MKR, 80 
-84392273,HC 6949 FXMAT INTERP 2 TO 8, 87 
-85392249,HC 6949 FXMAT AUTOS PROC, 73 
-85392256,HC 6949 FXMAT FLOW 1ST MRKR, 131 
-85392264,HC 6949 FXMAT FLOW ADTL MKR, 80 
-85392272,HC 6949 FXMAT INTERP 2 TO 8, 87 
-35260801,HC  PT TX PROC GRP 2 OR MORE, 133 
-36099133,HC  ISODOSE BRACHY SMP, 797 
-36099141,HC  STEREO BODY RAD THER MGMT," 1,243 "
-36200210,HC  CT GUIDE RAD THERAPY," 3,582 "
-36200228,HC  SET RAD THRPY FIELD INTERMED," 2,807 "
-36200236,HC  INTENSITY MOD TX PLAN," 19,230 "
-36200244,HC  ISODOSE TELETHRPY SMP, 762 
-36200251,HC  ISODOSE TELETHRPY INTERMED," 1,856 "
-36200269,HC  PORT PLAN SPECIAL, 820 
-36200277,HC  ISODOSE BRACHY SMP, 797 
-36200285,HC  ISODOSE BRACHY CPLX," 4,469 "
-36200293,HC  DOSIMETRY CALCULATION SPECIAL," 1,412 "
-36200301,HC  TREATMENT DEVICE SIMPLE, 996 
-36200319,HC  TREATMENT DEVICE INTERMED," 1,347 "
-36200327,HC  STEREO BODY RAD THER MGMT," 1,243 "
-36200335,HC  INTRACAVITARY BRACHY INTERMED," 1,157 "
-36200343,HC  INTERSTITIAL BRACHY CPLX," 2,466 "
-36200350,HC  RADIOELEMENT HANDLING/LOADING, 238 
-36200368,HC  STEREOTACTIC XR GUIDANCE," 1,723 "
-36300218,HC CT GUIDE PLC RAD TX FIELD," 3,582 "
-36300226,HC SIMULATION INTERMED," 2,807 "
-36300233,HC  INTENSITY MOD TX PLAN," 19,230 "
-36300242,HC  ISODOSE TELETHRPY SMP, 762 
-36300259,HC  ISODOSE TELETHRPY INTERMED," 1,856 "
-36300267,HC  PORT PLAN SPECIAL, 820 
-36300275,HC  ISODOSE BRACHY SMP, 797 
-36300283,HC  ISODOSE BRACHY CPLX," 4,469 "
-36300291,HC  DOSIMETRY CALCULATION SPECIAL," 1,412 "
-36300309,HC  TREATMENT DEVICE SIMPLE, 996 
-36300317,HC  TREATMENT DEVICE INTERMED," 1,347 "
-36300325,HC  STEREO BODY RAD THER MGMT," 1,243 "
-36300333,HC  INTRACAVITARY BRACHY INTERMED," 1,157 "
-36300341,HC  INTERSTITIAL BRACHY CPLX," 2,466 "
-36300358,HC  RADIOELEMENT HANDLING/LOADING, 238 
-36300366,HC  STEREOTACTIC XR GUIDANCE," 1,723 "
-62011564,HC  SKN FULL GRFT SCLP/EXT<21 SQCM," 1,639 "
-62016837,HC  BIOPSY/REMOVE LYMPH NODE OPEN," 5,585 "
-62018148,HC  ANOSCOPY W/SNARE MLTPL," 2,307 "
-62019906,HC NJXS INFUS/BOLUS DX/SBST EDRL/SUBARACH CRV/THRC," 1,289 "
-62020243,HC CHEMODENERV SALIV GLANDS, 254 
-62020276,HC  DESTRUCT BY NEUROLYTIC AGENT I," 1,251 "
-62020334,HC  DESTRUCT BY NEUROLYTIC AGENT O, 909 
-62021217,HC  RECONSTRUCT CANTHUS," 3,861 "
-62024153,HC  INJECT SACROILIAC JOINT," 1,451 "
-62024492,HC  POS AIRWAY PRESSURE, 273 
-62024773,HC  REASSESS: HEALTH & BEHAVIOR EA, 76 
-51565281,HC VIT D 25-HYDROXY, 415 
-50470087,HC PROCAINAMIDE, 186 
-50470095,HC PROCAINAMIDE PLUS NAPA, 186 
-55092191,HC CYSTIC FIB RESP CULT, 302 
-50780139,HC 7052 MTDNA MUT ISOL NA, 6 
-50780147,HC 7052 MTDNA SEP GEL ELECT, 6 
-50780154,HC 7052 MTDNA AMP EA NA SEQ, 23 
-50780162,HC 7052 MTDNA MUT ID EA SEG, 23 
-50780170,HC 7052 MTDNA INTRP AND RPT, 6 
-50780238,HC 7052 MCAD MUT ISOL NA, 8 
-50780246,HC 7052 MCAD SEP GEL ELECT, 8 
-50780253,HC 7052 MCAD AMP EA NA SEQ, 33 
-50780261,HC 7052 MCAD MUT ID EA SEG, 33 
-50780279,HC 7052 MCAD INTRP AND RPT, 8 
-50780287,HC 7052 OTC MUT ISOL NA, 6 
-50780295,HC 7052 OTC SEP GEL ELECT, 6 
-50780303,HC 7052 OTC AMP EA NA SEQ, 25 
-50780311,HC 7052 OTC MUT ID EA SEG, 25 
-50780329,HC 7052 OTC INTRP AND RPT, 6 
-50775394,HC 7031 C-TELOPEPTIDE MS, 66 
-41154352,HC RADIOFRQ ABLAT EXT 2ND VEIN," 5,516 "
-41154360,"HC EXPL EXT PO HEMM,TH,INF"," 6,336 "
-41154378,HC UNLISTED FEMALE GENITAL," 13,896 "
-41154386,"HC U/S, SCROTOM"," 1,470 "
-41154394,HC URETHROCYSTOGRAPH," 2,291 "
-41154402,HC ASP BLADDER;INSRT S/P CATH," 3,970 "
-41154410,HC FBR PERITONEAL CAVITY," 5,218 "
-41154428,"HC TEE W/WO M MODE, S&I", 715 
-41154436,"HC U/S, CHEST"," 1,529 "
-19160241,HC OVERNIGHT PULSE OXIMETRY, 941 
-30096226,HC MASTOPEXY UNILATERAL UP TO 2 HRS," 2,475 "
-38703823,HC MASTOPEXY UNILATERAL UP TO 2 HRS," 2,475 "
-30096234,HC  MASTOPEXY BILATERAL UP TO 2 HRS," 2,475 "
-38703849,HC  MASTOPEXY BILATERAL UP TO 2 HRS," 2,475 "
-30096242,HC RHYTIDECTOMY FACE," 3,375 "
-30296552,HC RHYTIDECTOMY FACE," 3,375 "
-38703864,HC RHYTIDECTOMY FACE," 3,375 "
-30096259,HC RHYTIDECTOMY FACE/NECK," 4,275 "
-30296560,HC RHYTIDECTOMY FACE/NECK," 4,275 "
-38703872,HC RHYTIDECTOMY FACE/NECK," 4,275 "
-30096267,"HC FULL FACE (BROW, UP/LOW BLEPH, FACE/NECK)"," 6,075 "
-30296578,"HC FULL FACE (BROW, UP/LOW BLEPH, FACE/NECK)"," 6,075 "
-38703880,"HC FULL FACE (BROW, UP/LOW BLEPH, FACE/NECK)"," 6,075 "
-30096275,HC SEPTORHINOPLASTY," 3,375 "
-30296602,HC SEPTORHINOPLASTY," 3,375 "
-38703914,HC SEPTORHINOPLASTY," 3,375 "
-30096283,HC LIPO UNDER 90 MINUTES," 1,575 "
-38703930,HC LIPO UNDER 90 MINUTES," 1,575 "
-30296495,HC BREAST AUGMENTATION BILATERAL," 1,575 "
-38703781,HC BREAST AUGMENTATION BILATERAL," 1,575 "
-30296503,HC BREAST IMPLANT EXCHANGE X2," 1,575 "
-38703799,HC BREAST IMPLANT EXCHANGE X2," 1,575 "
-30296511,HC BREAST IMPLANT EXCHANGE X1, 675 
-38703807,HC BREAST IMPLANT EXCHANGE X1, 675 
-30296529,HC MASTOPEXY UNILATERAL," 2,475 "
-38703815,HC MASTOPEXY UNILATERAL," 2,475 "
-30296610,HC ABDOMINOPLASTY," 2,475 "
-38703922,HC ABDOMINOPLASTY," 2,475 "
-30296586,HC RHINOPLASTY W / O GRAFT," 2,475 "
-38703898,HC RHINOPLASTY W / O GRAFT," 2,475 "
-30296594,HC RHINOPLASTY W / GRAFT," 2,475 "
-38703906,HC RHINOPLASTY W / GRAFT," 2,475 "
-30296537,HC MASTOPEXY BILATERAL," 2,475 "
-38703831,HC MASTOPEXY BILATERAL," 2,475 "
-30296545,HC UPPER/LOWER BLEPH 2 LIDS," 1,575 "
-38703856,HC UPPER/LOWER BLEPH 2 LIDS," 1,575 "
-36099174,HC ADMIN PNEUMOCOCCAL VACCINE, 295 
-62026711,HC EXC LES TONGUE W/CLSR ANT 2-3RD," 3,100 "
-62026729,HC EXC LES/TUM XCP LISTED ABOVE DENTALVLR," 1,637 "
-62026737,HC EXC LES PALATE UVULA W/LOCAL FLAP CLSR," 4,049 "
-62026745,HC BX SALIVARY GLND NDL, 791 
-62026752,HC APPL MLTLAYR COMPRES LEG BELOW KNEE W/ANKLE FOOT, 109 
-62026760,"HC BRON PLCMNT FID MRKR,S OR MULT"," 2,805 "
-62026778,"HC BRONCH, RIG OR FLEX W CT", 731 
-62026786,HC REM IND TUN PLRL CATH CUFF, 205 
-62026794,"HC PLC INTR DEV RT PERC,S OR MULT"," 1,322 "
-62026802,"HC INSTILL,CHEST TUBE INT DAY", 597 
-62026810,"HC INSTILL,CHEST TUBE SUBS DAY", 534 
-62026828,"HC INTRO NDL/CATH,DIAL INT ACC", 914 
-62026836,"HC INTRO NDL/CATH,DIAL ADDL ACC", 240 
-62026844,"HC PLCMNT ID RT PERC, IN AB,S OR MULT"," 1,322 "
-62026851,HC EXCHNG ABSC/CST DRG CATH RAD GID SPX," 1,576 "
-62026869,HC NJX PX PLOG THRU TUBE/CATH, 205 
-62026877,HC LITHOTRP XTRCORP SHOCK WAVE," 5,052 "
-62026885,HC NJX RTRGR URETHROCSTOGRAPY, 486 
-62026893,"HC SCOPE BLADDER, INSERT TUBE FOR INJECTION"," 2,549 "
-62026901,HC SCOPE BLADD REMVL LESIONS SML," 5,101 "
-62026919,HC DILAT URTL STRIX FILIFORM&FOLLWR MALE 1ST," 1,983 "
-62026927,HC TRURL DSTRJ PRST8 TISS MICROWAVE THERMOTH," 14,704 "
-62026935,HC DSTRJ LES PNS X10SV," 2,102 "
-62026943,HC PNXR ASPIR HYDROCELE TUNICA VAGIS +-NJX MED, 723 
-62026950,HC VAD INTER ANALYSIS & PROG, 156 
-62311113,HC BASIC VESTIBULAR EVAL," 1,270 "
-62311121,HC TYMPANO RFLX THRSHLD MSRMNT, 134 
-92811017,HC BASIC VESTIBULAR EVAL," 1,270 "
-92811025,HC TYMPANO RFLX THRSHLD MSRMNT, 134 
-41154444,HC INJ PARAVERT CERV/THOR," 1,622 "
-41154451,"HC LYSE CHST,INIT DAY"," 1,162 "
-41154469,"HC LYSE CHST,SUBQ DAY"," 1,162 "
-41154477,"HC CATH AV SHUNT, PROC; W S&I", 349 
-41154485,"HC CATH AV SHUNT, EVAL; W S&I"," 2,017 "
-56270127,HC REPLACE VAD PUMP," 1,638 "
-38616322,"HC CT COLONGRAPHY,DIAG W/CONTR"," 2,199 "
-38016325,"HC CT COLONGRAPHY,DIAG W/CONTR"," 2,199 "
-42016329,"HC CT COLONGRAPHY,DIAG W/CONTR"," 2,199 "
-94316320,"HC CT COLONGRAPHY,DIAG W/CONTR"," 2,199 "
-37005212,"HC ECG,STRESS TEST,TRACING ONLY"," 2,736 "
-94205143,"HC MRI CARD WO&W CON,STRESS"," 8,794 "
-38105144,"HC MRI CARD WO&W CON,STRESS"," 8,794 "
-38505145,"HC MRI CARD WO&W CON,STRESS"," 8,794 "
-37005220,HC NM 1-125 SERUM ALBUMIN 5 UCI," 1,575 "
-36099158,HC MCL FOR IMRT DESIGN&CONSTR," 13,988 "
-55092209,HC AFB CULTURE, 337 
-51302404,HC LIPID PANEL - CALC LDL, 415 
-55092217,HC FUNGAL ID YEAST, 237 
-50780428,HC 6911 MAGNESIUM RBC, 42 
-50780436,HC 6911 LIPID FECES TOTAL QUAN, 44 
-50780444,HC 6911 HEP C VIRAL RNA QUANT, 396 
-50780451,HC 6911 HEPTIMAX (R) HCV RNA, 422 
-50780469,HC 6911 VASC ENDOTHELIAL GF, 169 
-50780477,HC 6911 ASP RES CREATININE U, 38 
-50780485,HC 6911 ASP RES 11DEHY TBX B2 U, 88 
-50780493,HC 6911 C3A DESARG FRAG, 123 
-50780501,HC 6911 HCV RNA PCR QUAL APT, 221 
-50780519,HC 6911 HIV2 AB IMMUNOBLOT, 100 
-50780527,HC 6911 CORTISOL BIND GLOB, 77 
-50780535,HC 6911 C4D FRAGMENT EIA QUANT, 48 
-50780543,HC 6911 C TRACH RNA TMA RECTAL, 60 
-50780550,HC 6911 C TRACH RNA TMA THROAT, 60 
-50804202,HC BTCRB AMP MPLX 1 2, 172 
-50804210,HC BTCRB AMP MPLX ADTL, 90 
-50804228,HC BTCRB SEP ELEC CAP, 112 
-50804236,HC BTCRB INTRP AND RPT, 138 
-50804244,HC BIGK AMP MPLX 1 2, 172 
-50804251,HC BIGK AMP MPLX ADTL, 90 
-50804269,HC BIGK SEP ELEC CAP, 112 
-50804277,HC BIGK INTRP AND RPT, 138 
-46160776,HC FLUAPX FLU A RNA RT PCR AMP, 651 
-46160784,HC 2009 H1N1 PCR H274Y MUT EA, 388 
-46160792,HC HCV GENOTYPE," 2,847 "
-50776301,HC 6911 H PYLORI BREATH, 180 
-50780402,HC 6942 EB EBNA AB, 44 
-50780410,HC 6942 EB VCA AB, 52 
-22802136,"HC IV INFUSION IVIG,IST HR"," 1,226 "
-22802144,HC IV INFUSION IVIG; EA ADDL HR, 607 
-26431163,HC PARACENTESIS TRAY, 448 
-26431171,HC THORACENTESIS, 947 
-26431189,HC THORACENTESIS TRAY, 625 
-26531087,HC THORACENTESIS FOR ASPIRATION, 947 
-26631093,HC BONE MARROW BX W/ASPIRATION, 415 
-31950132,HC PICC SHER 5F TRPL LUMEN," 6,758 "
-36201515,HC MCL FOR IMRT DESIGN & CONSTR," 13,988 "
-50393867,HC LAMELLAR BODIES CNT FLD, 156 
-50804285,HC JAK2 MUT ISOL HP EA NA, 146 
-50804293,HC JAK2 NA PROBE EA, 53 
-50804301,HC JAK2 AMP EA NA SEQ, 188 
-50804319,HC JAK2 INTRP AND RPT, 138 
-51565299,HC ZINC, 126 
-55092233,HC M TUBERCULOSIS PCR APT SPEC, 566 
-55092241,HC AFB ID FROM ISOLATE APT, 566 
-55092258,HC BACTERIA ID BY SEQ SPEC," 1,653 "
-55092266,HC BACTERIA ID BY SEQ ISOLATE," 1,653 "
-55092274,HC FUNGI ID BY SEQ ISOLATE," 1,653 "
-82200296,HC IMPORTED EXTRA RENAL ORG," 8,500 "
-50792282,HC 6949 FXB AUTOS PROC, 73 
-50792290,HC 6949 FXB FLOW 1ST MRKR, 131 
-50792308,HC 6949 FXB FLOW ADTL MKR, 80 
-50792316,HC 6949 FXB INTERP 2 TO 8, 87 
-52292281,HC 6949 FXB AUTOS PROC, 73 
-52292299,HC 6949 FXB FLOW 1ST MRKR, 131 
-52292307,HC 6949 FXB FLOW ADTL MKR, 80 
-52292315,HC 6949 FXB INTERP 2 TO 8, 87 
-81092280,HC 6949 FXB AUTOS PROC, 73 
-81092298,HC 6949 FXB FLOW 1ST MRKR, 131 
-81092306,HC 6949 FXB FLOW ADTL MKR, 80 
-81092314,HC 6949 FXB INTERP 2 TO 8, 87 
-81192288,HC 6949 FXB AUTOS PROC, 73 
-81192296,HC 6949 FXB FLOW 1ST MRKR, 131 
-81192304,HC 6949 FXB FLOW ADTL MKR, 80 
-81192312,HC 6949 FXB INTERP 2 TO 8, 87 
-81392284,HC 6949 FXB AUTOS PROC, 73 
-81392292,HC 6949 FXB FLOW 1ST MRKR, 131 
-81392300,HC 6949 FXB FLOW ADTL MKR, 80 
-81392318,HC 6949 FXB INTERP 2 TO 8, 87 
-81792285,HC 6949 FXB AUTOS PROC, 73 
-81792293,HC 6949 FXB FLOW 1ST MRKR, 131 
-81792301,HC 6949 FXB FLOW ADTL MKR, 80 
-81792319,HC 6949 FXB INTERP 2 TO 8, 87 
-82392283,HC 6949 FXB AUTOS PROC, 73 
-82392291,HC 6949 FXB FLOW 1ST MRKR, 131 
-82392309,HC 6949 FXB FLOW ADTL MKR, 80 
-82392317,HC 6949 FXB INTERP 2 TO 8, 87 
-84392281,HC 6949 FXB AUTOS PROC, 73 
-84392299,HC 6949 FXB FLOW 1ST MRKR, 131 
-84392307,HC 6949 FXB FLOW ADTL MKR, 80 
-84392315,HC 6949 FXB INTERP 2 TO 8, 87 
-85392280,HC 6949 FXB AUTOS PROC, 73 
-85392298,HC 6949 FXB FLOW 1ST MRKR, 131 
-85392306,HC 6949 FXB FLOW ADTL MKR, 80 
-85392314,HC 6949 FXB INTERP 2 TO 8, 87 
-50792324,HC 6949 HLAAB SEP GEL ELECT, 8 
-50792332,HC 6949 HLAAB AMP EA NA SEQ, 30 
-50792340,HC 6949 H063 PATH CONSULT COMP, 8 
-52292323,HC 6949 HLAAB SEP GEL ELECT, 8 
-52292331,HC 6949 HLAAB AMP EA NA SEQ, 30 
-52292349,HC 6949 H063 PATH CONSULT COMP, 8 
-81092322,HC 6949 HLAAB SEP GEL ELECT, 8 
-81092330,HC 6949 HLAAB AMP EA NA SEQ, 30 
-81092348,HC 6949 H063 PATH CONSULT COMP, 8 
-81192320,HC 6949 HLAAB SEP GEL ELECT, 8 
-81192338,HC 6949 HLAAB AMP EA NA SEQ, 30 
-81192346,HC 6949 HLAAB INTRP AND RPT, 8 
-81392326,HC 6949 HLAAB SEP GEL ELECT, 8 
-81392334,HC 6949 HLAAB AMP EA NA SEQ, 30 
-81392342,HC 6949 H063 PATH CONSULT COMP, 8 
-81792327,HC 6949 HLAAB SEP GEL ELECT, 8 
-81792335,HC 6949 HLAAB AMP EA NA SEQ, 30 
-81792343,HC 6949 HLAAB INTRP AND RPT, 8 
-82392325,HC 6949 HLAAB SEP GEL ELECT, 8 
-82392333,HC 6949 HLAAB AMP EA NA SEQ, 30 
-82392341,HC 6949 H063 PATH CONSULT COMP, 8 
-84392323,HC 6949 HLAAB SEP GEL ELECT, 8 
-84392331,HC 6949 HLAAB AMP EA NA SEQ, 30 
-84392349,HC 6949 H063 PATH CONSULT COMP, 8 
-85392322,HC 6949 HLAAB SEP GEL ELECT, 8 
-85392330,HC 6949 HLAAB AMP EA NA SEQ, 30 
-85392348,HC 6949 HLAAB INTRP AND RPT, 8 
-50792357,HC 6949 HLAA SEP GEL ELECT, 8 
-50792365,HC 6949 HLAA AMP EA NA SEQ, 30 
-50792373,HC 6949 H061 PATH CONSULT COMP, 8 
-52292356,HC 6949 HLAA SEP GEL ELECT, 8 
-52292364,HC 6949 HLAA AMP EA NA SEQ, 30 
-52292372,HC 6949 H061 PATH CONSULT COMP, 8 
-81092355,HC 6949 HLAA SEP GEL ELECT, 8 
-81092363,HC 6949 HLAA AMP EA NA SEQ, 30 
-81092371,HC 6949 H061 PATH CONSULT COMP, 8 
-81192353,HC 6949 HLAA SEP GEL ELECT, 8 
-81192361,HC 6949 HLAA AMP EA NA SEQ, 30 
-81192379,HC 6949 HLAA INTRP AND RPT, 8 
-81392359,HC 6949 HLAA SEP GEL ELECT, 8 
-81392367,HC 6949 HLAA AMP EA NA SEQ, 30 
-81392375,HC 6949 H061 PATH CONSULT COMP, 8 
-81792350,HC 6949 HLAA SEP GEL ELECT, 8 
-81792368,HC 6949 HLAA AMP EA NA SEQ, 30 
-81792376,HC 6949 HLAA INTRP AND RPT, 8 
-82392358,HC 6949 HLAA SEP GEL ELECT, 8 
-82392366,HC 6949 HLAA AMP EA NA SEQ, 30 
-82392374,HC 6949 H061 PATH CONSULT COMP, 8 
-84392356,HC 6949 HLAA SEP GEL ELECT, 8 
-84392364,HC 6949 HLAA AMP EA NA SEQ, 30 
-84392372,HC 6949 H061 PATH CONSULT COMP, 8 
-85392355,HC 6949 HLAA SEP GEL ELECT, 8 
-85392363,HC 6949 HLAA AMP EA NA SEQ, 30 
-85392371,HC 6949 HLAA INTRP AND RPT, 8 
-50792381,HC 6949 HLADP SEP GEL ELECT, 8 
-50792399,HC 6949 HLADP AMP EA NA SEQ, 30 
-50792407,HC 6949 H079 PATH CONSULT COMP, 8 
-52292380,HC 6949 HLADP SEP GEL ELECT, 8 
-52292398,HC 6949 HLADP AMP EA NA SEQ, 30 
-52292406,HC 6949 H079 PATH CONSULT COMP, 8 
-81092389,HC 6949 HLADP SEP GEL ELECT, 8 
-81092397,HC 6949 HLADP AMP EA NA SEQ, 30 
-81092405,HC 6949 H079 PATH CONSULT COMP, 8 
-81192387,HC 6949 HLADP SEP GEL ELECT, 8 
-81192395,HC 6949 HLADP AMP EA NA SEQ, 30 
-81192403,HC 6949 HLADP INTRP AND RPT, 8 
-81392383,HC 6949 HLADP SEP GEL ELECT, 8 
-81392391,HC 6949 HLADP AMP EA NA SEQ, 30 
-81392409,HC 6949 H079 PATH CONSULT COMP, 8 
-81792384,HC 6949 HLADP SEP GEL ELECT, 8 
-81792392,HC 6949 HLADP AMP EA NA SEQ, 30 
-81792400,HC 6949 HLADP INTRP AND RPT, 8 
-82392382,HC 6949 HLADP SEP GEL ELECT, 8 
-82392390,HC 6949 HLADP AMP EA NA SEQ, 30 
-82392408,HC 6949 H079 PATH CONSULT COMP, 8 
-84392380,HC 6949 HLADP SEP GEL ELECT, 8 
-84392398,HC 6949 HLADP AMP EA NA SEQ, 30 
-84392406,HC 6949 H079 PATH CONSULT COMP, 8 
-85392389,HC 6949 HLADP SEP GEL ELECT, 8 
-85392397,HC 6949 HLADP AMP EA NA SEQ, 30 
-85392405,HC 6949 HLADP INTRP AND RPT, 8 
-50792415,HC 6949 CD31 SEP GEL ELECT, 8 
-50792423,HC 6949 CD31 NA PROBE EA, 8 
-50792431,HC 6949 CD31 AMP SIG EA NA, 30 
-50792449,HC 6949 H064 PATH CONSULT COMP, 8 
-52292414,HC 6949 CD31 SEP GEL ELECT, 8 
-52292422,HC 6949 CD31 NA PROBE EA, 8 
-52292430,HC 6949 CD31 AMP SIG EA NA, 30 
-52292448,HC 6949 H064 PATH CONSULT COMP, 8 
-81092413,HC 6949 CD31 SEP GEL ELECT, 8 
-81092421,HC 6949 CD31 NA PROBE EA, 8 
-81092439,HC 6949 CD31 AMP SIG EA NA, 30 
-81092447,HC 6949 H064 PATH CONSULT COMP, 8 
-81192411,HC 6949 CD31 SEP GEL ELECT, 8 
-81192429,HC 6949 CD31 NA PROBE EA, 8 
-81192437,HC 6949 CD31 AMP SIG EA NA, 30 
-81192445,HC 6949 CD31 INTRP AND RPT, 8 
-81392417,HC 6949 CD31 SEP GEL ELECT, 8 
-81392425,HC 6949 CD31 NA PROBE EA, 8 
-81392433,HC 6949 CD31 AMP SIG EA NA, 30 
-81392441,HC 6949 H064 PATH CONSULT COMP, 8 
-81792418,HC 6949 CD31 SEP GEL ELECT, 8 
-81792426,HC 6949 CD31 NA PROBE EA, 8 
-81792434,HC 6949 CD31 AMP SIG EA NA, 30 
-81792442,HC 6949 CD31 INTRP AND RPT, 8 
-82392416,HC 6949 CD31 SEP GEL ELECT, 8 
-82392424,HC 6949 CD31 NA PROBE EA, 8 
-82392432,HC 6949 CD31 AMP SIG EA NA, 30 
-82392440,HC 6949 H064 PATH CONSULT COMP, 8 
-84392414,HC 6949 CD31 SEP GEL ELECT, 8 
-84392422,HC 6949 CD31 NA PROBE EA, 8 
-84392430,HC 6949 CD31 AMP SIG EA NA, 30 
-84392448,HC 6949 H064 PATH CONSULT COMP, 8 
-85392413,HC 6949 CD31 SEP GEL ELECT, 8 
-85392421,HC 6949 CD31 NA PROBE EA, 8 
-85392439,HC 6949 CD31 AMP SIG EA NA, 30 
-85392447,HC 6949 CD31 INTRP AND RPT, 8 
-50792456,HC 6949 H007 PATH CONSULT COMP, 8 
-50792464,HC 6949 HLATX LYMPH TITRAT, 90 
-50792472,HC 6949 HLATX AUTOS PROC, 73 
-50792480,HC 6949 HLATX SPLIT BLD EA, 76 
-52292455,HC 6949 H007 PATH CONSULT COMP, 8 
-52292463,HC 6949 HLATX LYMPH TITRAT, 90 
-52292471,HC 6949 HLATX AUTOS PROC, 73 
-52292489,HC 6949 HLATX SPLIT BLD EA, 76 
-81092454,HC 6949 H007 PATH CONSULT COMP, 8 
-81092462,HC 6949 HLATX LYMPH TITRAT, 90 
-81092470,HC 6949 HLATX AUTOS PROC, 73 
-81092488,HC 6949 HLATX SPLIT BLD EA, 76 
-81192452,HC 6949 HLATX INTRP AND RPT, 8 
-81192460,HC 6949 HLATX LYMPH TITRAT, 90 
-81192478,HC 6949 HLATX AUTOS PROC, 73 
-81192486,HC 6949 HLATX SPLIT BLD EA, 76 
-81392458,HC 6949 H007 PATH CONSULT COMP, 8 
-81392466,HC 6949 HLATX LYMPH TITRAT, 90 
-81392474,HC 6949 HLATX AUTOS PROC, 73 
-81392482,HC 6949 HLATX SPLIT BLD EA, 76 
-81792459,HC 6949 HLATX INTRP AND RPT, 8 
-81792467,HC 6949 HLATX LYMPH TITRAT, 90 
-81792475,HC 6949 HLATX AUTOS PROC, 73 
-81792483,HC 6949 HLATX SPLIT BLD EA, 76 
-82392457,HC 6949 H007 PATH CONSULT COMP, 8 
-82392465,HC 6949 HLATX LYMPH TITRAT, 90 
-82392473,HC 6949 HLATX AUTOS PROC, 73 
-82392481,HC 6949 HLATX SPLIT BLD EA, 76 
-84392455,HC 6949 H007 PATH CONSULT COMP, 8 
-84392463,HC 6949 HLATX LYMPH TITRAT, 90 
-84392471,HC 6949 HLATX AUTOS PROC, 73 
-84392489,HC 6949 HLATX SPLIT BLD EA, 76 
-85392454,HC 6949 HLATX INTRP AND RPT, 8 
-85392462,HC 6949 HLATX LYMPH TITRAT, 90 
-85392470,HC 6949 HLATX AUTOS PROC, 73 
-85392488,HC 6949 HLATX SPLIT BLD EA, 76 
-50792498,HC 6949 H008 PATH CONSULT COMP, 8 
-50792506,HC 6949 BLX LYMPH TITRAT, 90 
-50792514,HC 6949 BLX AUTOS PROC, 73 
-50792522,HC 6949 BLX SPLIT BLD EA, 76 
-52292497,HC 6949 H008 PATH CONSULT COMP, 8 
-52292505,HC 6949 BLX LYMPH TITRAT, 90 
-52292513,HC 6949 BLX AUTOS PROC, 73 
-52292521,HC 6949 BLX SPLIT BLD EA, 76 
-81092496,HC 6949 H008 PATH CONSULT COMP, 8 
-81092504,HC 6949 BLX LYMPH TITRAT, 90 
-81092512,HC 6949 BLX AUTOS PROC, 73 
-81092520,HC 6949 BLX SPLIT BLD EA, 76 
-81192494,HC 6949 BLX INTRP AND RPT, 8 
-81192502,HC 6949 BLX LYMPH TITRAT, 90 
-81192510,HC 6949 BLX AUTOS PROC, 73 
-81192528,HC 6949 BLX SPLIT BLD EA, 76 
-81392490,HC 6949 H008 PATH CONSULT COMP, 8 
-81392508,HC 6949 BLX LYMPH TITRAT, 90 
-81392516,HC 6949 BLX AUTOS PROC, 73 
-81392524,HC 6949 BLX SPLIT BLD EA, 76 
-81792491,HC 6949 BLX INTRP AND RPT, 8 
-81792509,HC 6949 BLX LYMPH TITRAT, 90 
-81792517,HC 6949 BLX AUTOS PROC, 73 
-81792525,HC 6949 BLX SPLIT BLD EA, 76 
-82392499,HC 6949 H008 PATH CONSULT COMP, 8 
-82392507,HC 6949 BLX LYMPH TITRAT, 90 
-82392515,HC 6949 BLX AUTOS PROC, 73 
-82392523,HC 6949 BLX SPLIT BLD EA, 76 
-84392497,HC 6949 H008 PATH CONSULT COMP, 8 
-84392505,HC 6949 BLX LYMPH TITRAT, 90 
-84392513,HC 6949 BLX AUTOS PROC, 73 
-84392521,HC 6949 BLX SPLIT BLD EA, 76 
-85392496,HC 6949 BLX INTRP AND RPT, 8 
-85392504,HC 6949 BLX LYMPH TITRAT, 90 
-85392512,HC 6949 BLX AUTOS PROC, 73 
-85392520,HC 6949 BLX SPLIT BLD EA, 76 
-50792530,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-50792548,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-52292539,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-52292547,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-81092538,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-81092546,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-81192536,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-81192544,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-81392532,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-81392540,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-81792533,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-81792541,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-82392531,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-82392549,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-84392539,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-84392547,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-85392538,HC 6949 BIMMIS H134 AUTOS PROC, 73 
-85392546,HC 6949 BIMMISH134 SPLT BLD EA, 76 
-50792555,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-50792563,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-52292554,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-52292562,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-81092553,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-81092561,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-81192551,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-81192569,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-81392557,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-81392565,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-81792558,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-81792566,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-82392556,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-82392564,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-84392554,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-84392562,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-85392553,HC 6949 BIMMIS H056 AUTOS PROC, 73 
-85392561,HC 6949 BIMMISH056 SPLT BLD EA, 76 
-50792571,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-50792589,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-52292570,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-52292588,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-81092579,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-81092587,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-81192577,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-81192585,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-81392573,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-81392581,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-81792574,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-81792582,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-82392572,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-82392580,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-84392570,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-84392588,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-85392579,HC 6949 HAPLO H127 MUT ISOL NA, 8 
-85392587,HC 6949 HAPLOH127 MUT ID EA SG, 30 
-50792597,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-50792605,HC 6949 HLAQ FLOW ADTL MKR, 80 
-50792613,HC 6949 HLAQ INTRP 16 PLUS, 145 
-52292596,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-52292604,HC 6949 HLAQ FLOW ADTL MKR, 80 
-52292612,HC 6949 HLAQ INTRP 16 PLUS, 145 
-81092595,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-81092603,HC 6949 HLAQ FLOW ADTL MKR, 80 
-81092611,HC 6949 HLAQ INTRP 16 PLUS, 145 
-81192593,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-81192601,HC 6949 HLAQ FLOW ADTL MKR, 80 
-81192619,HC 6949 HLAQ INTRP 16 PLUS, 145 
-81392599,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-81392607,HC 6949 HLAQ FLOW ADTL MKR, 80 
-81392615,HC 6949 HLAQ INTRP 16 PLUS, 145 
-81792590,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-81792608,HC 6949 HLAQ FLOW ADTL MKR, 80 
-81792616,HC 6949 HLAQ INTRP 16 PLUS, 145 
-82392598,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-82392606,HC 6949 HLAQ FLOW ADTL MKR, 80 
-82392614,HC 6949 HLAQ INTRP 16 PLUS, 145 
-84392596,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-84392604,HC 6949 HLAQ FLOW ADTL MKR, 80 
-84392612,HC 6949 HLAQ INTRP 16 PLUS, 145 
-85392595,HC 6949 HLAQ FLOW 1ST MRKR, 131 
-85392603,HC 6949 HLAQ FLOW ADTL MKR, 80 
-85392611,HC 6949 HLAQ INTRP 16 PLUS, 145 
-50792621,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-50792639,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-50792647,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-52292620,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-52292638,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-52292646,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-81092629,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-81092637,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-81092645,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-81192627,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-81192635,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-81192643,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-81392623,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-81392631,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-81392649,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-81792624,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-81792632,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-81792640,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-82392622,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-82392630,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-82392648,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-84392620,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-84392638,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-84392646,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-85392629,HC 6949 LDSAG1 FLOW 1ST MRKR, 131 
-85392637,HC 6949 LDSAG1 FLOW ADTL MKR, 80 
-85392645,HC 6949 LDSAG1 INTRP 16 PLUS, 145 
-50792654,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-50792662,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-50792670,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-52292653,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-52292661,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-52292679,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-81092652,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-81092660,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-81092678,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-81192650,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-81192668,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-81192676,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-81392656,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-81392664,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-81392672,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-81792657,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-81792665,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-81792673,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-82392655,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-82392663,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-82392671,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-84392653,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-84392661,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-84392679,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-85392652,HC 6949 LDSAG2 FLOW 1ST MRKR, 131 
-85392660,HC 6949 LDSAG2 FLOW ADTL MKR, 80 
-85392678,HC 6949 LDSAG2 INTRP 16 PLUS, 145 
-50792688,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-50792696,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-50792704,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-52292687,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-52292695,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-52292703,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-81092686,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-81092694,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-81092702,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-81192684,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-81192692,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-81192700,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-81392680,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-81392698,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-81392706,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-81792681,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-81792699,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-81792707,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-82392689,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-82392697,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-82392705,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-84392687,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-84392695,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-84392703,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-85392686,HC 6949 LDSAQ1 FLOW 1ST MRKR, 131 
-85392694,HC 6949 LDSAQ1 FLOW ADTL MKR, 80 
-85392702,HC 6949 LDSAQ1 INTRP 16 PLUS, 145 
-50792712,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-50792720,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-50792738,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-52292711,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-52292729,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-52292737,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-81092710,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-81092728,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-81092736,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-81192718,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-81192726,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-81192734,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-81392714,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-81392722,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-81392730,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-81792715,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-81792723,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-81792731,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-82392713,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-82392721,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-82392739,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-84392711,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-84392729,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-84392737,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-85392710,HC 6949 LDSAQ2 FLOW 1ST MRKR, 131 
-85392728,HC 6949 LDSAQ2 FLOW ADTL MKR, 80 
-85392736,HC 6949 LDSAQ2 INTRP 16 PLUS, 145 
-50792746,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-50792753,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-50792761,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-52292745,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-52292752,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-52292760,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-81092744,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-81092751,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-81092769,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-81192742,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-81192759,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-81192767,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-81392748,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-81392755,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-81392763,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-81792749,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-81792756,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-81792764,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-82392747,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-82392754,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-82392762,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-84392745,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-84392752,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-84392760,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-85392744,HC 6949 LMONQ1 FLOW 1ST MRKR, 131 
-85392751,HC 6949 LMONQ1 FLOW ADTL MKR, 80 
-85392769,HC 6949 LMONQ1 INTRP 16 PLUS, 145 
-50792779,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-50792787,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-50792795,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-52292778,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-52292786,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-52292794,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-81092777,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-81092785,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-81092793,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-81192775,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-81192783,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-81192791,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-81392771,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-81392789,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-81392797,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-81792772,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-81792780,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-81792798,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-82392770,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-82392788,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-82392796,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-84392778,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-84392786,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-84392794,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-85392777,HC 6949 LMONQ2 FLOW 1ST MRKR, 131 
-85392785,HC 6949 LMONQ2 FLOW ADTL MKR, 80 
-85392793,HC 6949 LMONQ2 INTRP 16 PLUS, 145 
-50792803,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-50792811,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-50792829,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-52292802,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-52292810,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-52292828,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-81092801,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-81092819,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-81092827,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-81192809,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-81192817,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-81192825,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-81392805,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-81392813,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-81392821,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-81792806,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-81792814,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-81792822,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-82392804,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-82392812,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-82392820,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-84392802,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-84392810,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-84392828,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-85392801,HC 6949 LSAG1A FLOW 1ST MRKR, 131 
-85392819,HC 6949 LSAG1A FLOW ADTL MKR, 80 
-85392827,HC 6949 LSAG1A INTRP 16 PLUS, 145 
-50792837,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-50792845,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-50792852,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-52292836,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-52292844,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-52292851,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-81092835,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-81092843,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-81092850,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-81192833,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-81192841,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-81192858,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-81392839,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-81392847,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-81392854,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-81792830,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-81792848,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-81792855,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-82392838,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-82392846,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-82392853,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-84392836,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-84392844,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-84392851,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-85392835,HC 6949 LSAG2A FLOW 1ST MRKR, 131 
-85392843,HC 6949 LSAG2A FLOW ADTL MKR, 80 
-85392850,HC 6949 LSAG2A INTRP 16 PLUS, 145 
-50792860,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-50792878,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-50792886,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-52292869,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-52292877,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-52292885,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-81092868,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-81092876,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-81092884,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-81192866,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-81192874,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-81192882,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-81392862,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-81392870,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-81392888,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-81792863,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-81792871,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-81792889,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-82392861,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-82392879,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-82392887,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-84392869,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-84392877,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-84392885,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-85392868,HC 6949 LMONG1 FLOW 1ST MRKR, 131 
-85392876,HC 6949 LMONG1 FLOW ADTL MKR, 80 
-85392884,HC 6949 LMONG1 INTRP 16 PLUS, 145 
-50792894,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-50792902,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-50792910,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-52292893,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-52292901,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-52292919,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-81092892,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-81092900,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-81092918,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-81192890,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-81192908,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-81192916,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-81392896,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-81392904,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-81392912,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-81792897,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-81792905,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-81792913,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-82392895,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-82392903,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-82392911,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-84392893,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-84392901,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-84392919,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-85392892,HC 6949 LMONG2 FLOW 1ST MRKR, 131 
-85392900,HC 6949 LMONG2 FLOW ADTL MKR, 80 
-85392918,HC 6949 LMONG2 INTRP 16 PLUS, 145 
-50792928,HC 6949 HLDQA SEP GEL ELECT, 8 
-50792936,HC 6949 HLDQA AMP EA NA SEQ, 30 
-50792944,HC 6949 H080 PATH CONSULT COMP, 8 
-52292927,HC 6949 HLDQA SEP GEL ELECT, 8 
-52292935,HC 6949 HLDQA AMP EA NA SEQ, 30 
-52292943,HC 6949 H080 PATH CONSULT COMP, 8 
-81092926,HC 6949 HLDQA SEP GEL ELECT, 8 
-81092934,HC 6949 HLDQA AMP EA NA SEQ, 30 
-81092942,HC 6949 H080 PATH CONSULT COMP, 8 
-81192924,HC 6949 HLDQA SEP GEL ELECT, 8 
-81192932,HC 6949 HLDQA AMP EA NA SEQ, 30 
-81192940,HC 6949 HLDQA INTRP AND RPT, 8 
-81392920,HC 6949 HLDQA SEP GEL ELECT, 8 
-81392938,HC 6949 HLDQA AMP EA NA SEQ, 30 
-81392946,HC 6949 H080 PATH CONSULT COMP, 8 
-81792921,HC 6949 HLDQA SEP GEL ELECT, 8 
-81792939,HC 6949 HLDQA AMP EA NA SEQ, 30 
-81792947,HC 6949 HLDQA INTRP AND RPT, 8 
-82392929,HC 6949 HLDQA SEP GEL ELECT, 8 
-82392937,HC 6949 HLDQA AMP EA NA SEQ, 30 
-82392945,HC 6949 H080 PATH CONSULT COMP, 8 
-84392927,HC 6949 HLDQA SEP GEL ELECT, 8 
-84392935,HC 6949 HLDQA AMP EA NA SEQ, 30 
-84392943,HC 6949 H080 PATH CONSULT COMP, 8 
-85392926,HC 6949 HLDQA SEP GEL ELECT, 8 
-85392934,HC 6949 HLDQA AMP EA NA SEQ, 30 
-85392942,HC 6949 HLDQA INTRP AND RPT, 8 
-50792951,HC 6949 DRSSP SEP GEL ELECT, 8 
-50792969,HC 6949 DRSSP AMP EA NA SEQ, 30 
-50792977,HC 6949 H060 PATH CONSULT COMP, 8 
-52292950,HC 6949 DRSSP SEP GEL ELECT, 8 
-52292968,HC 6949 DRSSP AMP EA NA SEQ, 30 
-52292976,HC 6949 H060 PATH CONSULT COMP, 8 
-81092959,HC 6949 DRSSP SEP GEL ELECT, 8 
-81092967,HC 6949 DRSSP AMP EA NA SEQ, 30 
-81092975,HC 6949 H060 PATH CONSULT COMP, 8 
-81192957,HC 6949 DRSSP SEP GEL ELECT, 8 
-81192965,HC 6949 DRSSP AMP EA NA SEQ, 30 
-81192973,HC 6949 DRSSP INTRP AND RPT, 8 
-81392953,HC 6949 DRSSP SEP GEL ELECT, 8 
-81392961,HC 6949 DRSSP AMP EA NA SEQ, 30 
-81392979,HC 6949 H060 PATH CONSULT COMP, 8 
-81792954,HC 6949 DRSSP SEP GEL ELECT, 8 
-81792962,HC 6949 DRSSP AMP EA NA SEQ, 30 
-81792970,HC 6949 DRSSP INTRP AND RPT, 8 
-82392952,HC 6949 DRSSP SEP GEL ELECT, 8 
-82392960,HC 6949 DRSSP AMP EA NA SEQ, 30 
-82392978,HC 6949 H060 PATH CONSULT COMP, 8 
-84392950,HC 6949 DRSSP SEP GEL ELECT, 8 
-84392968,HC 6949 DRSSP AMP EA NA SEQ, 30 
-84392976,HC 6949 H060 PATH CONSULT COMP, 8 
-85392959,HC 6949 DRSSP SEP GEL ELECT, 8 
-85392967,HC 6949 DRSSP AMP EA NA SEQ, 30 
-85392975,HC 6949 DRSSP INTRP AND RPT, 8 
-50792985,HC 6949 H012 PATH CONSULT COMP, 8 
-50792993,HC 6949 AXT LYMPH TITRAT, 90 
-50793009,HC 6949 AXT AUTOS PROC, 73 
-50793017,HC 6949 AXT SPLIT BLD EA UN, 76 
-52292984,HC 6949 H012 PATH CONSULT COMP, 8 
-52292992,HC 6949 AXT LYMPH TITRAT, 90 
-52293008,HC 6949 AXT AUTOS PROC, 73 
-52293016,HC 6949 AXT SPLIT BLD EA UN, 76 
-81092983,HC 6949 H012 PATH CONSULT COMP, 8 
-81092991,HC 6949 AXT LYMPH TITRAT, 90 
-81093007,HC 6949 AXT AUTOS PROC, 73 
-81093015,HC 6949 AXT SPLIT BLD EA UN, 76 
-81192981,HC 6949 AXT INTRP AND RPT, 8 
-81192999,HC 6949 AXT LYMPH TITRAT, 90 
-81193005,HC 6949 AXT AUTOS PROC, 73 
-81193013,HC 6949 AXT SPLIT BLD EA UN, 76 
-81392987,HC 6949 H012 PATH CONSULT COMP, 8 
-81392995,HC 6949 AXT LYMPH TITRAT, 90 
-81393001,HC 6949 AXT AUTOS PROC, 73 
-81393019,HC 6949 AXT SPLIT BLD EA UN, 76 
-81792988,HC 6949 AXT INTRP AND RPT, 8 
-81792996,HC 6949 AXT LYMPH TITRAT, 90 
-81793002,HC 6949 AXT AUTOS PROC, 73 
-81793010,HC 6949 AXT SPLIT BLD EA UN, 76 
-82392986,HC 6949 H012 PATH CONSULT COMP, 8 
-82392994,HC 6949 AXT LYMPH TITRAT, 90 
-82393000,HC 6949 AXT AUTOS PROC, 73 
-82393018,HC 6949 AXT SPLIT BLD EA UN, 76 
-84392984,HC 6949 H012 PATH CONSULT COMP, 8 
-84392992,HC 6949 AXT LYMPH TITRAT, 90 
-84393008,HC 6949 AXT AUTOS PROC, 73 
-84393016,HC 6949 AXT SPLIT BLD EA UN, 76 
-85392983,HC 6949 AXT INTRP AND RPT, 8 
-85392991,HC 6949 AXT LYMPH TITRAT, 90 
-85393007,HC 6949 AXT AUTOS PROC, 73 
-85393015,HC 6949 AXT SPLIT BLD EA UN, 76 
-50793025,HC 6949 B27 SEP GEL ELECT, 8 
-50793033,HC 6949 B27 NA PROBE EA, 8 
-50793041,HC 6949 B27 AMP EA NA SEQ, 30 
-50793058,HC 6949 H005 PATH CONSULT COMP, 8 
-52293024,HC 6949 B27 SEP GEL ELECT, 8 
-52293032,HC 6949 B27 NA PROBE EA, 8 
-52293040,HC 6949 B27 AMP EA NA SEQ, 30 
-52293057,HC 6949 H005 PATH CONSULT COMP, 8 
-81093023,HC 6949 B27 SEP GEL ELECT, 8 
-81093031,HC 6949 B27 NA PROBE EA, 8 
-81093049,HC 6949 B27 AMP EA NA SEQ, 30 
-81093056,HC 6949 H005 PATH CONSULT COMP, 8 
-81193021,HC 6949 B27 SEP GEL ELECT, 8 
-81193039,HC 6949 B27 NA PROBE EA, 8 
-81193047,HC 6949 B27 AMP EA NA SEQ, 30 
-81193054,HC 6949 B27 INTRP AND RPT, 8 
-81393027,HC 6949 B27 SEP GEL ELECT, 8 
-81393035,HC 6949 B27 NA PROBE EA, 8 
-81393043,HC 6949 B27 AMP EA NA SEQ, 30 
-81393050,HC 6949 H005 PATH CONSULT COMP, 8 
-81793028,HC 6949 B27 SEP GEL ELECT, 8 
-81793036,HC 6949 B27 NA PROBE EA, 8 
-81793044,HC 6949 B27 AMP EA NA SEQ, 30 
-81793051,HC 6949 B27 INTRP AND RPT, 8 
-82393026,HC 6949 B27 SEP GEL ELECT, 8 
-82393034,HC 6949 B27 NA PROBE EA, 8 
-82393042,HC 6949 B27 AMP EA NA SEQ, 30 
-82393059,HC 6949 H005 PATH CONSULT COMP, 8 
-84393024,HC 6949 B27 SEP GEL ELECT, 8 
-84393032,HC 6949 B27 NA PROBE EA, 8 
-84393040,HC 6949 B27 AMP EA NA SEQ, 30 
-84393057,HC 6949 H005 PATH CONSULT COMP, 8 
-85393023,HC 6949 B27 SEP GEL ELECT, 8 
-85393031,HC 6949 B27 NA PROBE EA, 8 
-85393049,HC 6949 B27 AMP EA NA SEQ, 30 
-85393056,HC 6949 B27 INTRP AND RPT, 8 
-50793066,HC 6949 H116 SEP GEL ELEC, 8 
-50793074,HC 6949 H116 NA PROBE EA, 8 
-50793082,HC 6949 H116 AMP EA NA SQ, 30 
-50793090,HC 6949 H116 PATH CONSULT COMP, 8 
-52293065,HC 6949 H116 SEP GEL ELEC, 8 
-52293073,HC 6949 H116 NA PROBE EA, 8 
-52293081,HC 6949 H116 AMP EA NA SQ, 30 
-52293099,HC 6949 H116 PATH CONSULT COMP, 8 
-81093064,HC 6949 H116 SEP GEL ELEC, 8 
-81093072,HC 6949 H116 NA PROBE EA, 8 
-81093080,HC 6949 H116 AMP EA NA SQ, 30 
-81093098,HC 6949 H116 PATH CONSULT COMP, 8 
-81193062,HC 6949 H116 SEP GEL ELEC, 8 
-81193070,HC 6949 H116 NA PROBE EA, 8 
-81193088,HC 6949 H116 AMP EA NA SQ, 30 
-81193096,HC 6949 H116 INTRP AND RPT, 8 
-81393068,HC 6949 H116 SEP GEL ELEC, 8 
-81393076,HC 6949 H116 NA PROBE EA, 8 
-81393084,HC 6949 H116 AMP EA NA SQ, 30 
-81393092,HC 6949 H116 PATH CONSULT COMP, 8 
-81793069,HC 6949 H116 SEP GEL ELEC, 8 
-81793077,HC 6949 H116 NA PROBE EA, 8 
-81793085,HC 6949 H116 AMP EA NA SQ, 30 
-81793093,HC 6949 H116 INTRP AND RPT, 8 
-82393067,HC 6949 H116 SEP GEL ELEC, 8 
-82393075,HC 6949 H116 NA PROBE EA, 8 
-82393083,HC 6949 H116 AMP EA NA SQ, 30 
-82393091,HC 6949 H116 PATH CONSULT COMP, 8 
-84393065,HC 6949 H116 SEP GEL ELEC, 8 
-84393073,HC 6949 H116 NA PROBE EA, 8 
-84393081,HC 6949 H116 AMP EA NA SQ, 30 
-84393099,HC 6949 H116 PATH CONSULT COMP, 8 
-85393064,HC 6949 H116 SEP GEL ELEC, 8 
-85393072,HC 6949 H116 NA PROBE EA, 8 
-85393080,HC 6949 H116 AMP EA NA SQ, 30 
-85393098,HC 6949 H116 INTRP AND RPT, 8 
-50793108,HC 6949 H117 SEP GEL ELEC, 8 
-50793116,HC 6949 H117 NA PROBE EA, 8 
-50793124,HC 6949 H117 AMP EA NA SQ, 30 
-50793132,HC 6949 H117 PATH CONSULT COMP, 8 
-52293107,HC 6949 H117 SEP GEL ELEC, 8 
-52293115,HC 6949 H117 NA PROBE EA, 8 
-52293123,HC 6949 H117 AMP EA NA SQ, 30 
-52293131,HC 6949 H117 PATH CONSULT COMP, 8 
-81093106,HC 6949 H117 SEP GEL ELEC, 8 
-81093114,HC 6949 H117 NA PROBE EA, 8 
-81093122,HC 6949 H117 AMP EA NA SQ, 30 
-81093130,HC 6949 H117 PATH CONSULT COMP, 8 
-81193104,HC 6949 H117 SEP GEL ELEC, 8 
-81193112,HC 6949 H117 NA PROBE EA, 8 
-81193120,HC 6949 H117 AMP EA NA SQ, 30 
-81193138,HC 6949 H117 INTRP AND RPT, 8 
-81393100,HC 6949 H117 SEP GEL ELEC, 8 
-81393118,HC 6949 H117 NA PROBE EA, 8 
-81393126,HC 6949 H117 AMP EA NA SQ, 30 
-81393134,HC 6949 H117 PATH CONSULT COMP, 8 
-81793101,HC 6949 H117 SEP GEL ELEC, 8 
-81793119,HC 6949 H117 NA PROBE EA, 8 
-81793127,HC 6949 H117 AMP EA NA SQ, 30 
-81793135,HC 6949 H117 INTRP AND RPT, 8 
-82393109,HC 6949 H117 SEP GEL ELEC, 8 
-82393117,HC 6949 H117 NA PROBE EA, 8 
-82393125,HC 6949 H117 AMP EA NA SQ, 30 
-82393133,HC 6949 H117 PATH CONSULT COMP, 8 
-84393107,HC 6949 H117 SEP GEL ELEC, 8 
-84393115,HC 6949 H117 NA PROBE EA, 8 
-84393123,HC 6949 H117 AMP EA NA SQ, 30 
-84393131,HC 6949 H117 PATH CONSULT COMP, 8 
-85393106,HC 6949 H117 SEP GEL ELEC, 8 
-85393114,HC 6949 H117 NA PROBE EA, 8 
-85393122,HC 6949 H117 AMP EA NA SQ, 30 
-85393130,HC 6949 H117 INTRP AND RPT, 8 
-50793140,HC 6949 H125 SEP GEL ELEC, 8 
-50793157,HC 6949 H125 AMP EA NA SEQ, 30 
-50793165,HC 6949 H125 PATH CONSULT COMP, 8 
-52293149,HC 6949 H125 SEP GEL ELEC, 8 
-52293156,HC 6949 H125 AMP EA NA SEQ, 30 
-52293164,HC 6949 H125 PATH CONSULT COMP, 8 
-81093148,HC 6949 H125 SEP GEL ELEC, 8 
-81093155,HC 6949 H125 AMP EA NA SEQ, 30 
-81093163,HC 6949 H125 PATH CONSULT COMP, 8 
-81193146,HC 6949 H125 SEP GEL ELEC, 8 
-81193153,HC 6949 H125 AMP EA NA SEQ, 30 
-81193161,HC 6949 H125 INTP AND RPT, 8 
-81393142,HC 6949 H125 SEP GEL ELEC, 8 
-81393159,HC 6949 H125 AMP EA NA SEQ, 30 
-81393167,HC 6949 H125 PATH CONSULT COMP, 8 
-81793143,HC 6949 H125 SEP GEL ELEC, 8 
-81793150,HC 6949 H125 AMP EA NA SEQ, 30 
-81793168,HC 6949 H125 INTP AND RPT, 8 
-82393141,HC 6949 H125 SEP GEL ELEC, 8 
-82393158,HC 6949 H125 AMP EA NA SEQ, 30 
-82393166,HC 6949 H125 PATH CONSULT COMP, 8 
-84393149,HC 6949 H125 SEP GEL ELEC, 8 
-84393156,HC 6949 H125 AMP EA NA SEQ, 30 
-84393164,HC 6949 H125 PATH CONSULT COMP, 8 
-85393148,HC 6949 H125 SEP GEL ELEC, 8 
-85393155,HC 6949 H125 AMP EA NA SEQ, 30 
-85393163,HC 6949 H125 INTP AND RPT, 8 
-50793173,HC 6949 H136 SEP GEL ELEC, 8 
-50793181,HC 6949 H136 NA PROBE EA, 8 
-50793199,HC 6949 H136 AMP EA NA SEQ, 30 
-50793207,HC 6949 H136 PATH CONSULT COMP, 8 
-52293172,HC 6949 H136 SEP GEL ELEC, 8 
-52293180,HC 6949 H136 NA PROBE EA, 8 
-52293198,HC 6949 H136 AMP EA NA SEQ, 30 
-52293206,HC 6949 H136 PATH CONSULT COMP, 8 
-81093171,HC 6949 H136 SEP GEL ELEC, 8 
-81093189,HC 6949 H136 NA PROBE EA, 8 
-81093197,HC 6949 H136 AMP EA NA SEQ, 30 
-81093205,HC 6949 H136 PATH CONSULT COMP, 8 
-81193179,HC 6949 H136 SEP GEL ELEC, 8 
-81193187,HC 6949 H136 NA PROBE EA, 8 
-81193195,HC 6949 H136 AMP EA NA SEQ, 30 
-81193203,HC 6949 H136 INTP AND RPT, 8 
-81393175,HC 6949 H136 SEP GEL ELEC, 8 
-81393183,HC 6949 H136 NA PROBE EA, 8 
-81393191,HC 6949 H136 AMP EA NA SEQ, 30 
-81393209,HC 6949 H136 PATH CONSULT COMP, 8 
-81793176,HC 6949 H136 SEP GEL ELEC, 8 
-81793184,HC 6949 H136 NA PROBE EA, 8 
-81793192,HC 6949 H136 AMP EA NA SEQ, 30 
-81793200,HC 6949 H136 INTP AND RPT, 8 
-82393174,HC 6949 H136 SEP GEL ELEC, 8 
-82393182,HC 6949 H136 NA PROBE EA, 8 
-82393190,HC 6949 H136 AMP EA NA SEQ, 30 
-82393208,HC 6949 H136 PATH CONSULT COMP, 8 
-84393172,HC 6949 H136 SEP GEL ELEC, 8 
-84393180,HC 6949 H136 NA PROBE EA, 8 
-84393198,HC 6949 H136 AMP EA NA SEQ, 30 
-84393206,HC 6949 H136 PATH CONSULT COMP, 8 
-85393171,HC 6949 H136 SEP GEL ELEC, 8 
-85393189,HC 6949 H136 NA PROBE EA, 8 
-85393197,HC 6949 H136 AMP EA NA SEQ, 30 
-85393205,HC 6949 H136 INTP AND RPT, 8 
-30605463,HC ENDOSCOPIC SECRETIN FUNCTION TESTING, 710 
-50740778,HC 6959 MPO PR3 ANCA AB, 19 
-41301052,HC SKULL MIN 4 VIEWS, 984 
-41154519,HC RMVL PLEUR CATH W CUFF," 1,162 "
-38704151,HC MINOR PROCEDURE 15 MIN," 1,517 "
-50804327,HC BTCRB MUT ISOL NA, 146 
-50804335,HC BTCRG MUT ISOL NA, 146 
-50804343,HC BTCRG AMP MPLX 1 2, 172 
-50804350,HC BTCRG AMP MPLX ADTL BYND2, 90 
-50804368,HC BTCRG SEP ELEC CAP, 112 
-50804376,HC BTCRG INTRP AND RPT, 138 
-50804384,HC BTCRGB MUT ISOL NA, 146 
-50804392,HC BTCRGB AMP MPLX 1 2, 172 
-50804400,HC BTCRGB AMP MPLX ADTL BYND2, 90 
-50804418,HC BTCRGB SEP ELEC CAP, 112 
-50804426,HC BTCRGB INTRP AND RPT, 138 
-62801386,HC PT TX GROUP 2 OR MORE, 133 
-62801394,HC OT TX PROC GRP 2 OR MORE, 133 
-42016931,HC RESEARCH NURSING TIME EA 15 MIN, 225 
-51393742,HC ESR AUTOMATED, 75 
-62023999,HC SIGMOIDOSCOPY W/BX, 722 
-62023247,HC NEUROBEHAVIOR STATUS EXAM, 257 
-62012455,HC BIOPSY MUSCLE SUPER," 1,649 "
-50793215,HC 6949 H118 SEP GEL ELEC, 8 
-50793223,HC 6949 H118 NA PROBE EA, 8 
-50793231,HC 6949 H118 AMP EA NA SEQ, 30 
-50793249,HC 6949 H118 PATH CONSULT COMP, 8 
-50793256,HC 6949 H120 FLOW 1ST MARKER, 131 
-50793264,HC 6949 H120 FLOW ADTL MARKER, 80 
-50793272,HC 6949 H120 INTRP 16 PLUS, 145 
-50793280,HC 6949 H129 SEP GEL ELECT, 8 
-50793298,HC 6949 H129 AMP MPLX 1 2, 59 
-50793306,HC 6949 H129 AMP MPLX ADTL, 30 
-50793314,HC 6949 H129 MUT ID EA SEG, 30 
-50793322,HC 6949 H129 SEP ELEC CAP, 30 
-50793330,HC 6949 H129 PATH CONSULT COMP, 8 
-50793348,HC 6949 H131 SEP GEL ELECT, 8 
-50793355,HC 6949 H131 AMP EA NA SEQ, 30 
-50793363,HC 6949 H131 MUT ID EA SEG, 30 
-50793371,HC 6949 H131 SEP ELEC CAP, 30 
-50793389,HC 6949 H131 PATH CONSULT COMP, 8 
-50793397,HC 6949 H010 PATH CONSULT COMP, 8 
-50793405,HC 6949 H010 LYMPH TITRAT, 90 
-50793413,HC 6949 H010 AUTOS PROC, 73 
-50793421,HC 6949 H010 SPLIT BLD EA, 76 
-50793439,HC 6949 H011 PATH CONSULT COMP, 8 
-50793447,HC 6949 H011 LYMPH TITRAT, 90 
-50793454,HC 6949 H011 AUTOS PROC, 73 
-50793462,HC 6949 H011 SPLIT BLD EA, 76 
-50793470,HC 6949 H013 PATH CONSULT COMP, 8 
-50793488,HC 6949 H013 LYMPH TITRAT, 90 
-50793496,HC 6949 H013 AUTOS PROC, 73 
-50793504,HC 6949 H013 SPLIT BLD EA, 76 
-50793512,HC 6949 H023 PATH CONSULT COMP, 8 
-50793520,HC 6949 H023 LYMPH TITRAT, 90 
-50793538,HC 6949 H023 AUTOS PROC, 73 
-50793546,HC 6949 H023 SPLIT BLD EA, 76 
-50793553,HC 6949 H024 PATH CONSULT COMP, 8 
-50793561,HC 6949 H024 LYMPH TITRAT, 90 
-50793579,HC 6949 H024 AUTOS PROC, 73 
-50793587,HC 6949 H024 SPLIT BLD EA, 76 
-50793603,HC 6949 H057 SEP GEL ELECT, 8 
-50793611,HC 6949 H057 AMP MPLX 1 2, 59 
-50793629,HC 6949 H057 AMP MPLX ADTL, 30 
-50793637,HC 6949 H057 MUT ID EA SEG, 30 
-50793645,HC 6949 H057 SEP ELEC CAP, 30 
-50793652,HC 6949 H057 PATH CONSULT COMP, 8 
-50793660,HC 6949 H088 PATH CONSULT COMP, 8 
-50793678,HC 6949 H088 LYMPH TITRAT, 90 
-50793686,HC 6949 H088 AUTOS PROC, 73 
-50793694,HC 6949 H088 SPLIT BLD EA, 76 
-50793702,HC 6949 H089 PATH CONSULT COMP, 8 
-50793710,HC 6949 H089 LYMPH TITRAT, 90 
-50793728,HC 6949 H089 AUTOS PROC, 73 
-50793736,HC 6949 H089 SPLIT BLD EA, 76 
-52293214,HC 6949 H118 SEP GEL ELEC, 8 
-52293222,HC 6949 H118 NA PROBE EA, 8 
-52293230,HC 6949 H118 AMP EA NA SEQ, 30 
-52293248,HC 6949 H118 PATH CONSULT COMP, 8 
-52293255,HC 6949 H120 FLOW 1ST MARKER, 131 
-52293263,HC 6949 H120 FLOW ADTL MARKER, 80 
-52293271,HC 6949 H120 INTRP 16 PLUS, 145 
-52293289,HC 6949 H129 SEP GEL ELECT, 8 
-52293297,HC 6949 H129 AMP MPLX 1 2, 59 
-52293305,HC 6949 H129 AMP MPLX ADTL, 30 
-52293313,HC 6949 H129 MUT ID EA SEG, 30 
-52293321,HC 6949 H129 SEP ELEC CAP, 30 
-52293339,HC 6949 H129 PATH CONSULT COMP, 8 
-52293347,HC 6949 H131 SEP GEL ELECT, 8 
-52293354,HC 6949 H131 AMP EA NA SEQ, 30 
-52293362,HC 6949 H131 MUT ID EA SEG, 30 
-52293370,HC 6949 H131 SEP ELEC CAP, 30 
-52293388,HC 6949 H131 PATH CONSULT COMP, 8 
-52293396,HC 6949 H010 PATH CONSULT COMP, 8 
-52293404,HC 6949 H010 LYMPH TITRAT, 90 
-52293412,HC 6949 H010 AUTOS PROC, 73 
-52293420,HC 6949 H010 SPLIT BLD EA, 76 
-52293438,HC 6949 H011 PATH CONSULT COMP, 8 
-52293446,HC 6949 H011 LYMPH TITRAT, 90 
-52293453,HC 6949 H011 AUTOS PROC, 73 
-52293461,HC 6949 H011 SPLIT BLD EA, 76 
-52293479,HC 6949 H013 PATH CONSULT COMP, 8 
-52293487,HC 6949 H013 LYMPH TITRAT, 90 
-52293495,HC 6949 H013 AUTOS PROC, 73 
-52293503,HC 6949 H013 SPLIT BLD EA, 76 
-52293511,HC 6949 H023 PATH CONSULT COMP, 8 
-52293529,HC 6949 H023 LYMPH TITRAT, 90 
-52293537,HC 6949 H023 AUTOS PROC, 73 
-52293545,HC 6949 H023 SPLIT BLD EA, 76 
-52293552,HC 6949 H024 PATH CONSULT COMP, 8 
-52293560,HC 6949 H024 LYMPH TITRAT, 90 
-52293578,HC 6949 H024 AUTOS PROC, 73 
-52293586,HC 6949 H024 SPLIT BLD EA, 76 
-52293602,HC 6949 H057 SEP GEL ELECT, 8 
-52293610,HC 6949 H057 AMP MPLX 1 2, 59 
-52293628,HC 6949 H057 AMP MPLX ADTL, 30 
-52293636,HC 6949 H057 MUT ID EA SEG, 30 
-52293644,HC 6949 H057 SEP ELEC CAP, 30 
-52293651,HC 6949 H057 PATH CONSULT COMP, 8 
-52293669,HC 6949 H088 PATH CONSULT COMP, 8 
-52293677,HC 6949 H088 LYMPH TITRAT, 90 
-52293685,HC 6949 H088 AUTOS PROC, 73 
-52293693,HC 6949 H088 SPLIT BLD EA, 76 
-52293701,HC 6949 H089 PATH CONSULT COMP, 8 
-52293719,HC 6949 H089 LYMPH TITRAT, 90 
-52293727,HC 6949 H089 AUTOS PROC, 73 
-52293735,HC 6949 H089 SPLIT BLD EA, 76 
-81093213,HC 6949 H118 SEP GEL ELEC, 8 
-81093221,HC 6949 H118 NA PROBE EA, 8 
-81093239,HC 6949 H118 AMP EA NA SEQ, 30 
-81093247,HC 6949 H118 PATH CONSULT COMP, 8 
-81093254,HC 6949 H120 FLOW 1ST MARKER, 131 
-81093262,HC 6949 H120 FLOW ADTL MARKER, 80 
-81093270,HC 6949 H120 INTRP 16 PLUS, 145 
-81093288,HC 6949 H129 SEP GEL ELECT, 8 
-81093296,HC 6949 H129 AMP MPLX 1 2, 59 
-81093304,HC 6949 H129 AMP MPLX ADTL, 30 
-81093312,HC 6949 H129 MUT ID EA SEG, 30 
-81093320,HC 6949 H129 SEP ELEC CAP, 30 
-81093338,HC 6949 H129 PATH CONSULT COMP, 8 
-81093346,HC 6949 H131 SEP GEL ELECT, 8 
-81093353,HC 6949 H131 AMP EA NA SEQ, 30 
-81093361,HC 6949 H131 MUT ID EA SEG, 30 
-81093379,HC 6949 H131 SEP ELEC CAP, 30 
-81093387,HC 6949 H131 PATH CONSULT COMP, 8 
-81093395,HC 6949 H010 PATH CONSULT COMP, 8 
-81093403,HC 6949 H010 LYMPH TITRAT, 90 
-81093411,HC 6949 H010 AUTOS PROC, 73 
-81093429,HC 6949 H010 SPLIT BLD EA, 76 
-81093437,HC 6949 H011 PATH CONSULT COMP, 8 
-81093445,HC 6949 H011 LYMPH TITRAT, 90 
-81093452,HC 6949 H011 AUTOS PROC, 73 
-81093460,HC 6949 H011 SPLIT BLD EA, 76 
-81093478,HC 6949 H013 PATH CONSULT COMP, 8 
-81093486,HC 6949 H013 LYMPH TITRAT, 90 
-81093494,HC 6949 H013 AUTOS PROC, 73 
-81093502,HC 6949 H013 SPLIT BLD EA, 76 
-81093510,HC 6949 H023 PATH CONSULT COMP, 8 
-81093528,HC 6949 H023 LYMPH TITRAT, 90 
-81093536,HC 6949 H023 AUTOS PROC, 73 
-81093544,HC 6949 H023 SPLIT BLD EA, 76 
-81093551,HC 6949 H024 PATH CONSULT COMP, 8 
-81093569,HC 6949 H024 LYMPH TITRAT, 90 
-81093577,HC 6949 H024 AUTOS PROC, 73 
-81093585,HC 6949 H024 SPLIT BLD EA, 76 
-81093601,HC 6949 H057 SEP GEL ELECT, 8 
-81093619,HC 6949 H057 AMP MPLX 1 2, 59 
-81093627,HC 6949 H057 AMP MPLX ADTL, 30 
-81093635,HC 6949 H057 MUT ID EA SEG, 30 
-81093643,HC 6949 H057 SEP ELEC CAP, 30 
-81093650,HC 6949 H057 PATH CONSULT COMP, 8 
-81093668,HC 6949 H088 PATH CONSULT COMP, 8 
-81093676,HC 6949 H088 LYMPH TITRAT, 90 
-81093684,HC 6949 H088 AUTOS PROC, 73 
-81093692,HC 6949 H088 SPLIT BLD EA, 76 
-81093700,HC 6949 H089 PATH CONSULT COMP, 8 
-81093718,HC 6949 H089 LYMPH TITRAT, 90 
-81093726,HC 6949 H089 AUTOS PROC, 73 
-81093734,HC 6949 H089 SPLIT BLD EA, 76 
-81193211,HC 6949 H118 SEP GEL ELEC, 8 
-81193229,HC 6949 H118 NA PROBE EA, 8 
-81193237,HC 6949 H118 AMP EA NA SEQ, 30 
-81193245,HC 6949 H118 INTP AND RPT, 8 
-81193252,HC 6949 H120 FLOW 1ST MARKER, 131 
-81193260,HC 6949 H120 FLOW ADTL MARKER, 80 
-81193278,HC 6949 H120 INTRP 16 PLUS, 145 
-81193286,HC 6949 H129 SEP GEL ELECT, 8 
-81193294,HC 6949 H129 AMP MPLX 1 2, 59 
-81193302,HC 6949 H129 AMP MPLX ADTL, 30 
-81193310,HC 6949 H129 MUT ID EA SEG, 30 
-81193328,HC 6949 H129 SEP ELEC CAP, 30 
-81193336,HC 6949 H129 INTRP AND RPT, 8 
-81193344,HC 6949 H131 SEP GEL ELECT, 8 
-81193351,HC 6949 H131 AMP EA NA SEQ, 30 
-81193369,HC 6949 H131 MUT ID EA SEG, 30 
-81193377,HC 6949 H131 SEP ELEC CAP, 30 
-81193385,HC 6949 H131 INTRP AND RPT, 8 
-81193393,HC 6949 H010 INTRP AND RPT, 8 
-81193401,HC 6949 H010 LYMPH TITRAT, 90 
-81193419,HC 6949 H010 AUTOS PROC, 73 
-81193427,HC 6949 H010 SPLIT BLD EA, 76 
-81193435,HC 6949 H011 INTRP AND RPT, 8 
-81193443,HC 6949 H011 LYMPH TITRAT, 90 
-81193450,HC 6949 H011 AUTOS PROC, 73 
-81193468,HC 6949 H011 SPLIT BLD EA, 76 
-81193476,HC 6949 H013 INTRP AND RPT, 8 
-81193484,HC 6949 H013 LYMPH TITRAT, 90 
-81193492,HC 6949 H013 AUTOS PROC, 73 
-81193500,HC 6949 H013 SPLIT BLD EA, 76 
-81193518,HC 6949 H023 INTRP AND RPT, 8 
-81193526,HC 6949 H023 LYMPH TITRAT, 90 
-81193534,HC 6949 H023 AUTOS PROC, 73 
-81193542,HC 6949 H023 SPLIT BLD EA, 76 
-81193559,HC 6949 H024 INTRP AND RPT, 8 
-81193567,HC 6949 H024 LYMPH TITRAT, 90 
-81193575,HC 6949 H024 AUTOS PROC, 73 
-81193583,HC 6949 H024 SPLIT BLD EA, 76 
-81193609,HC 6949 H057 SEP GEL ELECT, 8 
-81193617,HC 6949 H057 AMP MPLX 1 2, 59 
-81193625,HC 6949 H057 AMP MPLX ADTL, 30 
-81193633,HC 6949 H057 MUT ID EA SEG, 30 
-81193641,HC 6949 H057 SEP ELEC CAP, 30 
-81193658,HC 6949 H057 INTRP AND RPT, 8 
-81193666,HC 6949 H088 INTRP AND RPT, 8 
-81193674,HC 6949 H088 LYMPH TITRAT, 90 
-81193682,HC 6949 H088 AUTOS PROC, 73 
-81193690,HC 6949 H088 SPLIT BLD EA, 76 
-81193708,HC 6949 H089 INTRP AND RPT, 8 
-81193716,HC 6949 H089 LYMPH TITRAT, 90 
-81193724,HC 6949 H089 AUTOS PROC, 73 
-81193732,HC 6949 H089 SPLIT BLD EA, 76 
-81393217,HC 6949 H118 SEP GEL ELEC, 8 
-81393225,HC 6949 H118 NA PROBE EA, 8 
-81393233,HC 6949 H118 AMP EA NA SEQ, 30 
-81393241,HC 6949 H118 PATH CONSULT COMP, 8 
-81393258,HC 6949 H120 FLOW 1ST MARKER, 131 
-81393266,HC 6949 H120 FLOW ADTL MARKER, 80 
-81393274,HC 6949 H120 INTRP 16 PLUS, 145 
-81393282,HC 6949 H129 SEP GEL ELECT, 8 
-81393290,HC 6949 H129 AMP MPLX 1 2, 59 
-81393308,HC 6949 H129 AMP MPLX ADTL, 30 
-81393316,HC 6949 H129 MUT ID EA SEG, 30 
-81393324,HC 6949 H129 SEP ELEC CAP, 30 
-81393332,HC 6949 H129 PATH CONSULT COMP, 8 
-81393340,HC 6949 H131 SEP GEL ELECT, 8 
-81393357,HC 6949 H131 AMP EA NA SEQ, 30 
-81393365,HC 6949 H131 MUT ID EA SEG, 30 
-81393373,HC 6949 H131 SEP ELEC CAP, 30 
-81393381,HC 6949 H131 PATH CONSULT COMP, 8 
-81393399,HC 6949 H010 PATH CONSULT COMP, 8 
-81393407,HC 6949 H010 LYMPH TITRAT, 90 
-81393415,HC 6949 H010 AUTOS PROC, 73 
-81393423,HC 6949 H010 SPLIT BLD EA, 76 
-81393431,HC 6949 H011 PATH CONSULT COMP, 8 
-81393449,HC 6949 H011 LYMPH TITRAT, 90 
-81393456,HC 6949 H011 AUTOS PROC, 73 
-81393464,HC 6949 H011 SPLIT BLD EA, 76 
-81393472,HC 6949 H013 PATH CONSULT COMP, 8 
-81393480,HC 6949 H013 LYMPH TITRAT, 90 
-81393498,HC 6949 H013 AUTOS PROC, 73 
-81393506,HC 6949 H013 SPLIT BLD EA, 76 
-81393514,HC 6949 H023 PATH CONSULT COMP, 8 
-81393522,HC 6949 H023 LYMPH TITRAT, 90 
-81393530,HC 6949 H023 AUTOS PROC, 73 
-81393548,HC 6949 H023 SPLIT BLD EA, 76 
-81393555,HC 6949 H024 PATH CONSULT COMP, 8 
-81393563,HC 6949 H024 LYMPH TITRAT, 90 
-81393571,HC 6949 H024 AUTOS PROC, 73 
-81393589,HC 6949 H024 SPLIT BLD EA, 76 
-81393605,HC 6949 H057 SEP GEL ELECT, 8 
-81393613,HC 6949 H057 AMP MPLX 1 2, 59 
-81393621,HC 6949 H057 AMP MPLX ADTL, 30 
-81393639,HC 6949 H057 MUT ID EA SEG, 30 
-81393647,HC 6949 H057 SEP ELEC CAP, 30 
-81393654,HC 6949 H057 PATH CONSULT COMP, 8 
-81393662,HC 6949 H088 PATH CONSULT COMP, 8 
-81393670,HC 6949 H088 LYMPH TITRAT, 90 
-81393688,HC 6949 H088 AUTOS PROC, 73 
-81393696,HC 6949 H088 SPLIT BLD EA, 76 
-81393704,HC 6949 H089 PATH CONSULT COMP, 8 
-81393712,HC 6949 H089 LYMPH TITRAT, 90 
-81393720,HC 6949 H089 AUTOS PROC, 73 
-81393738,HC 6949 H089 SPLIT BLD EA, 76 
-81793218,HC 6949 H118 SEP GEL ELEC, 8 
-81793226,HC 6949 H118 NA PROBE EA, 8 
-81793234,HC 6949 H118 AMP EA NA SEQ, 30 
-81793242,HC 6949 H118 INTP AND RPT, 8 
-81793259,HC 6949 H120 FLOW 1ST MARKER, 131 
-81793267,HC 6949 H120 FLOW ADTL MARKER, 80 
-81793275,HC 6949 H120 INTRP 16 PLUS, 145 
-81793283,HC 6949 H129 SEP GEL ELECT, 8 
-81793291,HC 6949 H129 AMP MPLX 1 2, 59 
-81793309,HC 6949 H129 AMP MPLX ADTL, 30 
-81793317,HC 6949 H129 MUT ID EA SEG, 30 
-81793325,HC 6949 H129 SEP ELEC CAP, 30 
-81793333,HC 6949 H129 INTRP AND RPT, 8 
-81793341,HC 6949 H131 SEP GEL ELECT, 8 
-81793358,HC 6949 H131 AMP EA NA SEQ, 30 
-81793366,HC 6949 H131 MUT ID EA SEG, 30 
-81793374,HC 6949 H131 SEP ELEC CAP, 30 
-81793382,HC 6949 H131 INTRP AND RPT, 8 
-81793390,HC 6949 H010 INTRP AND RPT, 8 
-81793408,HC 6949 H010 LYMPH TITRAT, 90 
-81793416,HC 6949 H010 AUTOS PROC, 73 
-81793424,HC 6949 H010 SPLIT BLD EA, 76 
-81793432,HC 6949 H011 INTRP AND RPT, 8 
-81793440,HC 6949 H011 LYMPH TITRAT, 90 
-81793457,HC 6949 H011 AUTOS PROC, 73 
-81793465,HC 6949 H011 SPLIT BLD EA, 76 
-81793473,HC 6949 H013 INTRP AND RPT, 8 
-81793481,HC 6949 H013 LYMPH TITRAT, 90 
-81793499,HC 6949 H013 AUTOS PROC, 73 
-81793507,HC 6949 H013 SPLIT BLD EA, 76 
-81793515,HC 6949 H023 INTRP AND RPT, 8 
-81793523,HC 6949 H023 LYMPH TITRAT, 90 
-81793531,HC 6949 H023 AUTOS PROC, 73 
-81793549,HC 6949 H023 SPLIT BLD EA, 76 
-81793556,HC 6949 H024 INTRP AND RPT, 8 
-81793564,HC 6949 H024 LYMPH TITRAT, 90 
-81793572,HC 6949 H024 AUTOS PROC, 73 
-81793580,HC 6949 H024 SPLIT BLD EA, 76 
-81793606,HC 6949 H057 SEP GEL ELECT, 8 
-81793614,HC 6949 H057 AMP MPLX 1 2, 59 
-81793622,HC 6949 H057 AMP MPLX ADTL, 30 
-81793630,HC 6949 H057 MUT ID EA SEG, 30 
-81793648,HC 6949 H057 SEP ELEC CAP, 30 
-81793655,HC 6949 H057 INTRP AND RPT, 8 
-81793663,HC 6949 H088 INTRP AND RPT, 8 
-81793671,HC 6949 H088 LYMPH TITRAT, 90 
-81793689,HC 6949 H088 AUTOS PROC, 73 
-81793697,HC 6949 H088 SPLIT BLD EA, 76 
-81793705,HC 6949 H089 INTRP AND RPT, 8 
-81793713,HC 6949 H089 LYMPH TITRAT, 90 
-81793721,HC 6949 H089 AUTOS PROC, 73 
-81793739,HC 6949 H089 SPLIT BLD EA, 76 
-82393216,HC 6949 H118 SEP GEL ELEC, 8 
-82393224,HC 6949 H118 NA PROBE EA, 8 
-82393232,HC 6949 H118 AMP EA NA SEQ, 30 
-82393240,HC 6949 H118 PATH CONSULT COMP, 8 
-82393257,HC 6949 H120 FLOW 1ST MARKER, 131 
-82393265,HC 6949 H120 FLOW ADTL MARKER, 80 
-82393273,HC 6949 H120 INTRP 16 PLUS, 145 
-82393281,HC 6949 H129 SEP GEL ELECT, 8 
-82393299,HC 6949 H129 AMP MPLX 1 2, 59 
-82393307,HC 6949 H129 AMP MPLX ADTL, 30 
-82393315,HC 6949 H129 MUT ID EA SEG, 30 
-82393323,HC 6949 H129 SEP ELEC CAP, 30 
-82393331,HC 6949 H129 PATH CONSULT COMP, 8 
-82393349,HC 6949 H131 SEP GEL ELECT, 8 
-82393356,HC 6949 H131 AMP EA NA SEQ, 30 
-82393364,HC 6949 H131 MUT ID EA SEG, 30 
-82393372,HC 6949 H131 SEP ELEC CAP, 30 
-82393380,HC 6949 H131 PATH CONSULT COMP, 8 
-82393398,HC 6949 H010 PATH CONSULT COMP, 8 
-82393406,HC 6949 H010 LYMPH TITRAT, 90 
-82393414,HC 6949 H010 AUTOS PROC, 73 
-82393422,HC 6949 H010 SPLIT BLD EA, 76 
-82393430,HC 6949 H011 PATH CONSULT COMP, 8 
-82393448,HC 6949 H011 LYMPH TITRAT, 90 
-82393455,HC 6949 H011 AUTOS PROC, 73 
-82393463,HC 6949 H011 SPLIT BLD EA, 76 
-82393471,HC 6949 H013 PATH CONSULT COMP, 8 
-82393489,HC 6949 H013 LYMPH TITRAT, 90 
-82393497,HC 6949 H013 AUTOS PROC, 73 
-82393505,HC 6949 H013 SPLIT BLD EA, 76 
-82393513,HC 6949 H023 PATH CONSULT COMP, 8 
-82393521,HC 6949 H023 LYMPH TITRAT, 90 
-82393539,HC 6949 H023 AUTOS PROC, 73 
-82393547,HC 6949 H023 SPLIT BLD EA, 76 
-82393554,HC 6949 H024 PATH CONSULT COMP, 8 
-82393562,HC 6949 H024 LYMPH TITRAT, 90 
-82393570,HC 6949 H024 AUTOS PROC, 73 
-82393588,HC 6949 H024 SPLIT BLD EA, 76 
-82393604,HC 6949 H057 SEP GEL ELECT, 8 
-82393612,HC 6949 H057 AMP MPLX 1 2, 59 
-82393620,HC 6949 H057 AMP MPLX ADTL, 30 
-82393638,HC 6949 H057 MUT ID EA SEG, 30 
-82393646,HC 6949 H057 SEP ELEC CAP, 30 
-82393653,HC 6949 H057 PATH CONSULT COMP, 8 
-82393661,HC 6949 H058 PATH CONSULT COMP, 8 
-82393679,HC 6949 H088 LYMPH TITRAT, 90 
-82393687,HC 6949 H088 AUTOS PROC, 73 
-82393695,HC 6949 H088 SPLIT BLD EA, 76 
-82393703,HC 6949 H089 PATH CONSULT COMP, 8 
-82393711,HC 6949 H089 LYMPH TITRAT, 90 
-82393729,HC 6949 H089 AUTOS PROC, 73 
-82393737,HC 6949 H089 SPLIT BLD EA, 76 
-84393214,HC 6949 H118 SEP GEL ELEC, 8 
-84393222,HC 6949 H118 NA PROBE EA, 8 
-84393230,HC 6949 H118 AMP EA NA SEQ, 30 
-84393248,HC 6949 H118 PATH CONSULT COMP, 8 
-84393255,HC 6949 H120 FLOW 1ST MARKER, 131 
-84393263,HC 6949 H120 FLOW ADTL MARKER, 80 
-84393271,HC 6949 H120 INTRP 16 PLUS, 145 
-84393289,HC 6949 H129 SEP GEL ELECT, 8 
-84393297,HC 6949 H129 AMP MPLX 1 2, 59 
-84393305,HC 6949 H129 AMP MPLX ADTL, 30 
-84393313,HC 6949 H129 MUT ID EA SEG, 30 
-84393321,HC 6949 H129 SEP ELEC CAP, 30 
-84393339,HC 6949 H129 PATH CONSULT COMP, 8 
-84393347,HC 6949 H131 SEP GEL ELECT, 8 
-84393354,HC 6949 H131 AMP EA NA SEQ, 30 
-84393362,HC 6949 H131 MUT ID EA SEG, 30 
-84393370,HC 6949 H131 SEP ELEC CAP, 30 
-84393388,HC 6949 H131 PATH CONSULT COMP, 8 
-84393396,HC 6949 H010 PATH CONSULT COMP, 8 
-84393404,HC 6949 H010 LYMPH TITRAT, 90 
-84393412,HC 6949 H010 AUTOS PROC, 73 
-84393420,HC 6949 H010 SPLIT BLD EA, 76 
-84393438,HC 6949 H011 PATH CONSULT COMP, 8 
-84393446,HC 6949 H011 LYMPH TITRAT, 90 
-84393453,HC 6949 H011 AUTOS PROC, 73 
-84393461,HC 6949 H011 SPLIT BLD EA, 76 
-84393479,HC 6949 H013 PATH CONSULT COMP, 8 
-84393487,HC 6949 H013 LYMPH TITRAT, 90 
-84393495,HC 6949 H013 AUTOS PROC, 73 
-84393503,HC 6949 H013 SPLIT BLD EA, 76 
-84393511,HC 6949 H023 PATH CONSULT COMP, 8 
-84393529,HC 6949 H023 LYMPH TITRAT, 90 
-84393537,HC 6949 H023 AUTOS PROC, 73 
-84393545,HC 6949 H023 SPLIT BLD EA, 76 
-84393552,HC 6949 H024 PATH CONSULT COMP, 8 
-84393560,HC 6949 H024 LYMPH TITRAT, 90 
-84393578,HC 6949 H024 AUTOS PROC, 73 
-84393586,HC 6949 H024 SPLIT BLD EA, 76 
-84393602,HC 6949 H057 SEP GEL ELECT, 8 
-84393610,HC 6949 H057 AMP MPLX 1 2, 59 
-84393628,HC 6949 H057 AMP MPLX ADTL, 30 
-84393636,HC 6949 H057 MUT ID EA SEG, 30 
-84393644,HC 6949 H057 SEP ELEC CAP, 30 
-84393651,HC 6949 H057 PATH CONSULT COMP, 8 
-84393669,HC 6949 H088 PATH CONSULT COMP, 8 
-84393677,HC 6949 H088 LYMPH TITRAT, 90 
-84393685,HC 6949 H088 AUTOS PROC, 73 
-84393693,HC 6949 H088 SPLIT BLD EA, 76 
-84393701,HC 6949 H089 PATH CONSULT COMP, 8 
-84393719,HC 6949 H089 LYMPH TITRAT, 90 
-84393727,HC 6949 H089 AUTOS PROC, 73 
-84393735,HC 6949 H089 SPLIT BLD EA, 76 
-85393213,HC 6949 H118 SEP GEL ELEC, 8 
-85393221,HC 6949 H118 NA PROBE EA, 8 
-85393239,HC 6949 H118 AMP EA NA SEQ, 30 
-85393247,HC 6949 H118 INTP AND RPT, 8 
-85393254,HC 6949 H120 FLOW 1ST MARKER, 131 
-85393262,HC 6949 H120 FLOW ADTL MARKER, 80 
-85393270,HC 6949 H120 INTRP 16 PLUS, 145 
-85393288,HC 6949 H129 SEP GEL ELECT, 8 
-85393296,HC 6949 H129 AMP MPLX 1 2, 59 
-85393304,HC 6949 H129 AMP MPLX ADTL, 30 
-85393312,HC 6949 H129 MUT ID EA SEG, 30 
-85393320,HC 6949 H129 SEP ELEC CAP, 30 
-85393338,HC 6949 H129 INTRP AND RPT, 8 
-85393346,HC 6949 H131 SEP GEL ELECT, 8 
-85393353,HC 6949 H131 AMP EA NA SEQ, 30 
-85393361,HC 6949 H131 MUT ID EA SEG, 30 
-85393379,HC 6949 H131 SEP ELEC CAP, 30 
-85393387,HC 6949 H131 INTRP AND RPT, 8 
-85393395,HC 6949 H010 INTRP AND RPT, 8 
-85393403,HC 6949 H010 LYMPH TITRAT, 90 
-85393411,HC 6949 H010 AUTOS PROC, 73 
-85393429,HC 6949 H010 SPLIT BLD EA, 76 
-85393437,HC 6949 H011 INTRP AND RPT, 8 
-85393445,HC 6949 H011 LYMPH TITRAT, 90 
-85393452,HC 6949 H011 AUTOS PROC, 73 
-85393460,HC 6949 H011 SPLIT BLD EA, 76 
-85393478,HC 6949 H013 INTRP AND RPT, 8 
-85393486,HC 6949 H013 LYMPH TITRAT, 90 
-85393494,HC 6949 H013 AUTOS PROC, 73 
-85393502,HC 6949 H013 SPLIT BLD EA, 76 
-85393510,HC 6949 H023 INTRP AND RPT, 8 
-85393528,HC 6949 H023 LYMPH TITRAT, 90 
-85393536,HC 6949 H023 AUTOS PROC, 73 
-85393544,HC 6949 H023 SPLIT BLD EA, 76 
-85393551,HC 6949 H024 INTRP AND RPT, 8 
-85393569,HC 6949 H024 LYMPH TITRAT, 90 
-85393577,HC 6949 H024 AUTOS PROC, 73 
-85393585,HC 6949 H024 SPLIT BLD EA, 76 
-85393601,HC 6949 H057 SEP GEL ELECT, 8 
-85393619,HC 6949 H057 AMP MPLX 1 2, 59 
-85393627,HC 6949 H057 AMP MPLX ADTL, 30 
-85393635,HC 6949 H057 MUT ID EA SEG, 30 
-85393643,HC 6949 H057 SEP ELEC CAP, 30 
-85393650,HC 6949 H057 INTRP AND RPT, 8 
-85393668,HC 6949 H088 INTRP AND RPT, 8 
-85393676,HC 6949 H088 LYMPH TITRAT, 90 
-85393684,HC 6949 H088 AUTOS PROC, 73 
-85393692,HC 6949 H088 SPLIT BLD EA, 76 
-85393700,HC 6949 H089 INTRP AND RPT, 8 
-85393718,HC 6949 H089 LYMPH TITRAT, 90 
-85393726,HC 6949 H089 AUTOS PROC, 73 
-85393734,HC 6949 H089 SPLIT BLD EA, 76 
-51493740,HC RES LUPUS ANTICOAG PTT, 240 
-51493765,HC RES PTT INH SCREEN, 240 
-51493773,HC RES PTT, 240 
-30605489,HC FIDUCIAL SEED IMPLANTS PANCREAS," 3,966 "
-32091746,"HC CARDIOVERSION, ELECTIVE EXTERNAL"," 5,446 "
-32091753,HC CLSD TX DISTAL TIB FX W MAN," 6,642 "
-32500639,"HC CARDIOVERSION, ELECTRICAL"," 5,446 "
-35160878,HC OT ORTH/PROS FUP EST PT EA15, 431 
-35703461,HC OT ORTH/PROS FUP EST PT EA15, 195 
-40105041,HC BREAST PUNC ASP-CYST ADDT'L, 346 
-41016155,"HC MYELOGRAPHY, 2 OR MORE REGIONS"," 2,675 "
-41016163,HC INJ PYELO VIA EXIST TUBE/CATH, 391 
-41016171,HC INJ RETRO URETHROCYSTOGRAPHY," 2,751 "
-41154501,HC DRAIN SPINAL CORD CYST," 1,594 "
-46160800,HC BK VIRUS NA PCR QUANT URINE, 650 
-46160818,HC BK VIRUS NA PCR QUANT, 650 
-50775493,HC 7046 COWDEN ENZ DIG EA, 6 
-50775543,HC 7046 COWDEN 251-500 PROBES," 1,462 "
-62600085,"HC RAPID DESENSITATION,EA HR", 541 
-92603000,HC LARYNGOSCOPY FIBEROPTIC, 349 
-92603018,HC ULTRASOUND  TRANSRECTAL, 551 
-92603026,HC CYSTOURETHROSCOPY, 709 
-92603034,HC BIOPSY  PROSTATE," 1,343 "
-92603042,HC BONE MARROW; BIOPSY, 396 
-92603059,HC HEMORRHOIDECTOMY, 539 
-92603067,HC PLACE RT DEVICE MARKER," 1,322 "
-92603075,HC CYSTOURETHROSCOPY W/FB," 1,697 "
-92603083,HC ENDOMETRIAL BIOPSY, 236 
-92603091,HC NIPPLE RECONSTRUCTION," 4,362 "
-92603109,HC COLPOSCOPY, 311 
-92603117,HC NASAL/SINUS ENDOSCOPY," 2,070 "
-92603125,HC BONE MARROW; ASPIRATION, 346 
-92603133,HC SIGMOIDOSCOPY, 604 
-92603141,HC DILATION OF URETHRAL  MALE, 309 
-92603158,HC COLPOSCOPY OF THE CERVIX," 2,003 "
-92603166,HC BIOPSY OF BREAST, 773 
-92603174,HC COLPOSCOPY  W/ BIOPSY, 463 
-92603190,HC PUNCT  PERITONEAL CAVITY SUBS," 2,102 "
-92603208,HC EXAM/BIOPSY VAG W/SCOPE, 517 
-92603216,HC BLADDER IRRIGATION, 318 
-92603232,HC LARYNGO  FLEX FIBEROPTIC," 2,070 "
-92603240,HC CAPSULOTOMY  BREAST," 4,509 "
-92603257,HC BONE MARROW  ALLOGENIC," 10,470 "
-92603273,HC CAUTERY OF CERVIX," 6,627 "
-92603281,HC ENDOCERVICAL CURETTAGE, 656 
-92603307,HC SPINAL PUNCTURE  LUMBAR, 541 
-92603315,HC BIOPSY TISSUE NECK/THORAX," 1,649 "
-92603323,HC BIOPSY; OROPHARYNX, 754 
-92603331,HC BIOPSY OF TONGUE," 1,637 "
-92603349,HC BIOPSY/EXC LYMPH NODE," 9,320 "
-92603364,HC LARYNGO  WITH STROBOSCOPY, 509 
-92603372,HC PLACEMNT RADIOTHERAPY BREAST," 9,981 "
-92603380,HC PROCTOSIGMOIDOSCOPY," 1,251 "
-92603398,HC PROCTOSIGMOIDOSCOPY W/BIOPSY," 1,074 "
-92603406,HC EXC EXT THROMBOTIC HEMORRHOID," 3,979 "
-92603414,HC U/S GUIDANCE-NEEDLE BIOPSY," 1,128 "
-92603422,HC DILATION OF FEMALE URETHRA, 270 
-92603463,HC NASOPHARYNGOSCOPY, 209 
-92603471,HC INSERT URINARY CATHETER, 331 
-92603497,HC COLPOSCOPY OF VAGINA W/CERVIX, 338 
-92603505,HC BIOPSY OF CERVIX, 656 
-92603513,HC DILATN URTHRL STRUCTURE M RPT, 257 
-94260536,HC MRI W/CONT BREAST UNI," 2,676 "
-94260544,HC MRI W/O CONT BREAST UNI," 1,993 "
-94260551,HC MRI W/O CONT BREAST BI," 3,985 "
-94260569,HC MRI W/O FOL W/CONT BREAST BI," 6,466 "
-50775485,HC 7046 COWDEN MUT ISOL/EXT NA, 6 
-50775501,HC 7046 COWDEN SEP GEL EP, 6 
-50775519,HC 7046 COWDEN MUT AMP NA SEQ, 24 
-50775527,HC 7046 COWDEN MUT BY SEQ EA, 24 
-50775535,HC 7046 COWDEN INT AND REPRT, 6 
-38105151,HC MRI 3D REND W/INTERP W/O PSTPR, 775 
-38105987,HC SECRETIN VL 1MCG, 195 
-38505152,HC MRI 3D REND W/INTERP W/O PSTPR, 775 
-38505988,HC SECRETIN VL 1MCG, 195 
-50570043,HC HC PROCALCITONIN, 318 
-62010764,HC REM MALG SKN LSN F/E/E/N/L3.1," 3,302 "
-62016043,HC STRAPPING UNNA BOOT, 221 
-62017389,HC DRAIN SUBMAXILLARY EXTERNAL, 677 
-62018197,HC BIOPSY MUSCLE SUPER," 2,101 "
-91900399,HC STORAGE EMBRYO (CANCER), 450 
-94205150,HC MRI 3D REND W/INTERP W/O PSTPR, 775 
-62701214,HC CHEMO IV PUSH EACH ADDT., 801 
-62701222,HC CHEMO IV PUSH INITIAL/SINGLE, 888 
-30605372,HC RECTAL SENSATION TONE COM, 477 
-36000032,HC CYLINDER COVER SLEEVES L5, 213 
-41016213,HC XR DILATION STRICTURE, 200 
-42016337,HC ABLATION TUMR PULMONARY UNI 1+," 11,396 "
-94205986,HC SECRETIN VL 1MCG, 195 
-82200304,HC CTDN FLYOUT FEE," 5,500 "
-32091761,HCAMPUT FINGER/THUMB W/FLAP," 4,301 "
-32091779,HC CLOSE LAC MOUTH >2.5CM CPL," 2,212 "
-62701230,HC CHEMO SQ/IM NON-HORMONAL, 903 
-62701248,HC CHEMO CONCURRENT INF, 821 
-92000645,HC SPECIMEN HANDLING, 210 
-62311139,HC HEADPIECE, 805 
-62311147,HC MICROPHONE, 172 
-62311154,HC TRANSMITTING COIL, 150 
-62311170,HC ZINC AIR BATTERIES, 328 
-62311196,HC LITHIUM ION BATTERIES, 104 
-62311204,HC LITH ION BATT EAR SPEECH PROC, 265 
-92811033,HC HEADPIECE, 805 
-92811041,HC MICROPHONE, 172 
-92811058,HC TRANSMITTING COIL, 150 
-92811074,HC ZINC AIR BATTERIES, 328 
-92811090,HC LITHIUM ION BATTERIES, 104 
-92811108,HC LITH ION BATT EAR SPEECH PROC, 265 
-38105169,HC MRI GUIDE NEEDLE PLACE," 4,256 "
-38305140,HC INJ SGL TENDON ORIGIN/INSERT, 533 
-38305157,HC INJ TRIGGER PT 1-2 MUSCLE(S), 515 
-38305165,HC INJ TRIGGER PT 3+ MUSC GROUP, 597 
-38505160,HC MRI GUIDE NEEDLE PLACE," 4,256 "
-40905143,HC INJ SGL TENDON ORIGIN/INSERT, 533 
-40905168,HC INJ TRIGGER PT 3+ MUSC GROUP, 597 
-94205168,HC MRI GUIDE NEEDLE PLACE," 4,256 "
-37105962,HC RESEARCH FDG RADIOPHARMACEUTICAL," 1,667 "
-37105970,HC RESEARCH PET-CT SCAN (CORP)," 10,798 "
-37105988,HC RESEARCH PET-CT SCAN (NIH )," 8,082 "
-37105996,HC RESEARCH PET-CT SCAN (PILOT)," 6,065 "
-41016239,HC XR TEETH PARTIAL, 130 
-41016247,HC XR TEETH CMPL, 179 
-41016262,HC INJ ARTHROGRAM HIP ANES," 2,842 "
-51565315,HC LEFLUNOMIDE METABOLITE, 200 
-62010947,HC SQ INJ FILL MATER 1CC OR LESS, 318 
-62019534,HC HYSTEROSCOPY W/BX," 4,466 "
-32091787,HC ER LARYNGOSC DIRECT DX," 3,808 "
-91000109,HC LEUPRON ACETATE 7.5MG," 2,261 "
-91000117,HC METHOTREXATE SODIUM 5 MG, 142 
-91000125,HC METHOTREXATE SODIUM PER 50 MG, 142 
-91000133,HC THER/PROPHY/DIAG IM INJ, 76 
-35260819,HC OT ORTH/PROS FUP EST PT EA15, 195 
-40980021,HC BX PLEURA PERC NDL," 2,384 "
-38380028,HC BX PLEURA PERC NDL," 2,384 "
-40905150,HC INJ TRIGGER PT 1-2 MUSCLE(S), 515 
-42025304,HC CT ABDOMEN LIMITED," 2,814 "
-22802151,HC IVP THRP/PROPH/DX NEW DRUG, 400 
-22802169,HC IVP THRP/PROPH/DX SAME DRUG, 405 
-50780568,HC 6911 T3 TOTAL, 33 
-50780576,HC 6956 POSACONAZOLE QUAN, 76 
-50780584,HC 6956 OXYGEN DISSOC P50 RBC, 143 
-50780592,HC 6956 11608R ENZY DIGEST EA, 2 
-50780600,HC 6956 11608R AMP EA NA SEQ, 10 
-50780618,HC 6956 11608R SEP&ID HRT EANA, 10 
-50780626,HC 6956 11608R INTRP AND RPT, 2 
-50780634,HC 6959 INTERLEUKIN 5 QUAN, 95 
-50781053,HC 6956 11191R DNA EXT NPL EA, 166 
-50781061,HC 6956 NIACIN VITAMIN B3, 75 
-50781079,HC 6959 DRUGS OF ABUSE MEC 9, 10 
-50781087,HC 6959 DRUGS OF ABUSE MEC 9, 10 
-50804434,HC BHEAV MUT ISOL NA, 146 
-50804442,HC BHEAV AMP MPLX 1ST 2 NA SEQ, 172 
-50804459,HC BHEAV AMP MPLX ADTL BYND2, 90 
-50804467,HC BHEAV SEP ELEC CAP, 112 
-50804475,HC BHEAV INTRP AND RPT, 138 
-50804483,HC BIGKH MUT ISOL NA, 146 
-50804491,HC BIGKH AMP MPLX 1ST 2 NA SEQ, 172 
-50804509,HC BIGKH AMP MPLX ADTL BYND2, 90 
-50804517,HC BIGKH SEP ELEC CAP, 112 
-50804525,HC BIGKH INTRP AND RPT, 138 
-62026703,HC INC LNGL FRENUM FREXOMY," 1,131 "
-32091803,HC ER INFLUENZA VACCINE ADMIN, 77 
-32091811,HC IMMUN ADMIN EA ADD VACC, 135 
-32712531,HC TRANSFUS FFP," 3,300 "
-32712549,HC INFUS ALBUMIN 25% PER 50 ML," 1,839 "
-32712556,HC DERMABOND ADHES LVL 5, 213 
-30605513,HC DILAT ESOPH UNGD SND/BOUG," 2,493 "
-40510760,HC BRACE ANKLE AIR, 127 
-40510786,HC BRACE NECK VISTA UNIV, 340 
-40510778,HC BRACE NECK PERFIT ADJUST, 85 
-46010005,HC RBC REDUCED GLUTATHIONE GSH, 109 
-46010013,HC PYRIMIDINE 5 NUCLEOT NONRAD, 324 
-50780691,HC 6942 EB EARLY ANTIGEN AB, 14 
-50780709,HC 6942 EB NUCLEAR ANTIGEN AB, 16 
-50780717,HC 6942 EB VIRAL CAPSID AB, 19 
-50780725,HC 6942 VZV AB PNL TOT AND IGM, 42 
-50780733,HC 6942 HHV-6 AB IGG, 68 
-50780790,HC 6942 VZV AB PNL TOT AND IGM, 42 
-50780873,HC 6942 Q FEVER ANTIBODY, 16 
-50780980,HC 6942 LYME AB PNL IGG IGM WB, 32 
-50781095,HC 6959 AMPHETAMINES CONF QN, 136 
-50781103,HC 6959 BARBITURATES CONF QN, 76 
-50781111,HC 6959 BENZODIAZEPINES CNF QN, 103 
-50781129,HC 6959 COCAINE CONF QUAN, 80 
-50781137,HC 6959 MARIJUANA CONF QUAN, 142 
-50781145,HC 6959 METHADONE CONF QUAN, 59 
-50781152,HC 6959 OPIATES CONF QUAN, 150 
-50781160,HC 6959 PHENCYCLIDINE CONF QN, 173 
-50781178,HC 6959 PROPOXYPHENE CONF QN, 71 
-50804533,HC BRAF MUT ISOL HP EA NA, 146 
-50804541,HC BRAF MUT SEP GEL ELECT, 103 
-50804558,HC BRAF MUT AMP EA NA SEQ, 188 
-50804566,HC BRAF LYSIS PRE NA EA EXTRCT, 66 
-50804574,HC BRAF MOL INTRP AND RPT, 138 
-22802177,HC THERAP/PRO/DX INJ SQ/IM, 344 
-41154527,HC UNLISTED PROC NERVOUS SYSTEM," 1,971 "
-50393875,HC CELL CNT AND DIFF APH BF, 252 
-50780808,HC 6942 BARTONELLA IGG AB IFA, 53 
-50780816,HC 6942 BARTONELLA IGG AB IFA, 53 
-50781186,HC 6942 EBV EA IGG IFA, 37 
-50781194,HC 6942 EBV EBNA AB ACIF, 44 
-50781202,HC 6942 EBV VCA IGG IGM AB IFA, 52 
-50781210,HC 6942 HSV AB NON SPEC TYPE, 41 
-50781228,HC 6942 HSV AB TYPE 1, 38 
-50781236,HC 6942 HSV AB TYPE 2, 55 
-50781244,HC 6942 VZV TOT AND IGM AB PNL, 37 
-50781251,HC 6942 HHV-6 AB IGG, 37 
-50781269,HC 6942 HHV-6 QUANT PCR, 119 
-50781277,HC 6942 RICKETTSIA AB, 36 
-50781285,HC 6942 RICKETTSIA AB, 36 
-62010335,HC SHAVE SKN LSN F/E/E/N/L 0.6-1, 238 
-62011028,HC REPR WND SUPER NONFACE 2.6-7.5, 260 
-62013164,HC I&D DEEP DRAIN NECK/CHEST LSN," 1,994 "
-62017819,HC PROCTOSIGMOIDOSCOPY CNTRL BLEE," 1,600 "
-62018700,HC CYSTOSCOPY FEMALE URETH SYNDRO," 1,697 "
-51565307,HC IGA ANTI-DGP, 273 
-50804582,HC CFTRS CF38 CONFIRM STATE, 651 
-50804590,HC CFTRPC CF38 PARENT CNFRM ST, 95 
-32091795,HC ENDO NASAL W/CONTRL BLEEDING," 3,808 "
-48003002,HC AIRWAY RESISTANCE CHARGE, 314 
-94700259,HC MANDIBLE MIN 4 VIEW," 1,076 "
-94700408,HC FACIAL BONES 2V, 915 
-94700457,HC FACIAL BONES MIN 3," 1,277 "
-94700507,HC NASAL BONES MIN 3V, 896 
-94700705,HC ORBITS MIN 4V, 671 
-94700853,HC SINUSES PARANASAL MIN 3V," 1,671 "
-94700986,HC SKULL <4 VIEWS, 806 
-94701059,HC SKULL MIN 4 VIEWS, 984 
-94701307,HC TMJ OPEN + CLOSE BI, 650 
-94701802,HC SPINE TOTAL AP/LAT, 235 
-94701851,HC SOFT TISSUE NECK, 727 
-94701877,HC L SPINE BEND ONLY 2/3 VIEWS," 1,304 "
-94701901,"HC C-SPINE, 3 VIEWS OR LESS"," 1,015 "
-94701950,HC SPINE CERV 4 OR 5 VIEWS," 1,284 "
-94702008,HC SPINE CERV 6 OR MORE VIEWS," 1,465 "
-94702057,HC T-SPINE 2 VIEWS," 1,012 "
-94702065,HC T-SPINE THREE VIEWS," 1,185 "
-94702107,HC THORACOLUMBAR 2 VIEWS," 1,113 "
-94702156,HC L-SPINE 2 OR 3 VIEWS," 1,043 "
-94702206,HC L-SPINE MIN FOUR VIEWS," 1,413 "
-94702214,HC L SPINE COMP W/BEND," 2,003 "
-94702305,HC SCOLIOSIS INCL SUPL," 1,662 "
-94702354,HC SACRUM COCCYX MIN 2," 1,054 "
-94702552,HC PELVIS <3 VIEWS, 804 
-94702602,HC PELVIS MIN 3V, 953 
-94702651,HC SACROILIAC JOINTS MIN 3V, 984 
-94702958,HC HIP 1 VIEW, 885 
-94703006,HC HIP MIN 2V," 1,012 "
-94703055,HC HIP BIL W/PELVIS," 1,393 "
-94703204,HC SHOULDER 1 VIEW, 761 
-94703352,HC SHOULDER MIN 2/V, 959 
-94703402,HC HUMERUS MIN 2V, 771 
-94703451,HC ELBOW AP + LAT, 705 
-94703501,HC ELBOW MIN 3V, 826 
-94703550,HC FOREARM AP + LAT, 769 
-94703600,HC WRIST AP + LAT, 753 
-94703659,HC WRIST MIN 3V, 907 
-94703758,HC HAND 2 VIEW, 806 
-94703808,HC HAND MIN 3 VIEWS, 896 
-94703857,HC FINGER(S) MIN 2V, 592 
-94704004,HC FEMUR AP + LAT, 879 
-94704053,HC KNEE ONE OR TWO VIEWS, 732 
-94704103,HC KNEE THREE VIEWS, 815 
-94704152,HC KNEES FOUR OR MORE VIEWS," 1,110 "
-94704202,HC TIBIA + FIBULA AP, 864 
-94704251,HC ANKLE AP + LAT, 689 
-94704301,HC ANKLE MIN 3V, 909 
-94704350,HC FOOT AP + LAT, 722 
-94704400,HC FOOT MIN 3V, 950 
-94704509,HC CALCANEUS MIN 2V, 707 
-94704558,HC TOE(S) MIN 2V, 586 
-94704707,HC CHEST 1 VIEW, 593 
-94704756,HC CHEST 2V FRONT + LA, 765 
-94704855,HC CHEST MIN 4V," 1,232 "
-94705258,HC RIBS UNI 2V," 1,024 "
-94705266,HC RIBS UNILAT W/CHEST 3+ V," 1,053 "
-94705308,HC RIBS BILAT 3V," 1,365 "
-94705316,HC RIBSBILAT W/CHEST 4+ V," 1,885 "
-94705357,HC STAND KNEES AP BILT, 672 
-94705407,HC STERNUM MIN 2V, 832 
-94705456,HC STERNOCLAVICULAR JT MIN 3V," 1,006 "
-94705506,HC CLAVICLE, 812 
-94705605,HC SCAPULA," 1,049 "
-94706678,HC 36 LEG LENGTH," 1,084 "
-94707304,HC ABDOMEN AP ONLY, 718 
-94707353,HC ABD CPL W/DECUB+ERECT," 1,272 "
-94707403,HC ABD CPL W/DECUB+1V CHEST," 1,541 "
-94716156,"HC MYELOGRAPHY, 2 OR MORE REGIONS"," 2,675 "
-94721313,HC SPINE SINGLE VIEW, 510 
-94745288,HC ORBITS FOREIGN BODY, 544 
-94768504,HC STERILE TRAY, 300 
-94770104,HC CHOLANGIOGRAM O/R ADDL SET," 1,141 "
-94770633,HC CHNG PERC TUBE/DRAI," 2,465 "
-94771623,HC STRESS VIEWS ANY JOINT, 349 
-94771714,HC FLUORO GUIDED NDL PLC SPINE," 1,593 "
-94780111,HC CHEST 2V W/OBLIQUE," 1,275 "
-94780129,HC CHEST LAT DECUB, 821 
-50793744,HC 6949 H128 MUT SEP GEL ELCT, 8 
-50793751,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-50793769,HC 6949 H128 AMP MPLX EA ADDL, 30 
-50793777,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-50793785,HC 6949 H128 SEP ELEC CAP EA, 30 
-50793801,HC 6949 H128 PATH CONSULT COMP, 8 
-52293743,HC 6949 H128 MUT SEP GEL ELCT, 8 
-52293750,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-52293768,HC 6949 H128 AMP MPLX EA ADDL, 30 
-52293776,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-52293784,HC 6949 H128 SEP ELEC CAP EA, 30 
-52293800,HC 6949 H128 PATH CONSULT COMP, 8 
-81093742,HC 6949 H128 MUT SEP GEL ELCT, 8 
-81093759,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-81093767,HC 6949 H128 AMP MPLX EA ADDL, 30 
-81093775,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-81093783,HC 6949 H128 SEP ELEC CAP EA, 30 
-81093809,HC 6949 H128 PATH CONSULT COMP, 8 
-81193740,HC 6949 H128 MUT SEP GEL ELCT, 8 
-81193757,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-81193765,HC 6949 H128 AMP MPLX EA ADDL, 30 
-81193773,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-81193781,HC 6949 H128 SEP ELEC CAP EA, 30 
-81193807,HC 6949 H128 INTRP AND RPT, 8 
-81393746,HC 6949 H128 MUT SEP GEL ELCT, 8 
-81393753,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-81393761,HC 6949 H128 AMP MPLX EA ADDL, 30 
-81393779,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-81393787,HC 6949 H128 SEP ELEC CAP EA, 30 
-81393803,HC 6949 H128 PATH CONSULT COMP, 8 
-81793747,HC 6949 H128 MUT SEP GEL ELCT, 8 
-81793754,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-81793762,HC 6949 H128 AMP MPLX EA ADDL, 30 
-81793770,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-81793788,HC 6949 H128 SEP ELEC CAP EA, 30 
-81793804,HC 6949 H128 INTRP AND RPT, 8 
-82393745,HC 6949 H128 MUT SEP GEL ELCT, 8 
-82393752,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-82393760,HC 6949 H128 AMP MPLX EA ADDL, 30 
-82393778,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-82393786,HC 6949 H128 SEP ELEC CAP EA, 30 
-82393802,HC 6949 H128 PATH CONSULT COMP, 8 
-84393743,HC 6949 H128 MUT SEP GEL ELCT, 8 
-84393750,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-84393768,HC 6949 H128 AMP MPLX EA ADDL, 30 
-84393776,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-84393784,HC 6949 H128 SEP ELEC CAP EA, 30 
-84393800,HC 6949 H129 PATH CONSULT COMP, 8 
-85393742,HC 6949 H128 MUT SEP GEL ELCT, 8 
-85393759,HC 6949 H128 AMP MPLX NA 1ST 2, 59 
-85393767,HC 6949 H128 AMP MPLX EA ADDL, 30 
-85393775,HC 6949 H128 MUT ID SEQ EA SEG, 30 
-85393783,HC 6949 H128 SEP ELEC CAP EA, 30 
-85393809,HC 6949 H128 INTRP AND RPT, 8 
-50781004,HC 6942 MYCOPLASMA PNM AB IGG, 53 
-50781046,HC 6959 12017R ECHOVRS AB EA, 36 
-50780931,HC 6942 HERPESVIRUS 6 AB IGM, 68 
-55092308,HC STOOL CLTR, 384 
-55092316,HC STOOL ADDL SETUP EA PLT, 227 
-55092324,HC STOOL CLTR ID NA PCR EA, 578 
-50804608,HC CF38 STATE CONTRACT, 100 
-55092290,HC STOOL CLT SHIGA TXN 1&2 PCR, 578 
-50780907,HC 6942 CMV AB IGG, 52 
-50780923,HC 6942 EHRLICHIA CHAFFEENIS, 74 
-50780949,HC 6942 HERPESVIRUS 7 IGG AB, 116 
-50780998,HC 6942 LYME B BURGDORFERI IGG, 74 
-50781020,HC 6942 PARVOVIRUS B19 AB IGG, 24 
-50120559,HC FFP WITHIN 8 HRS COL EA, 390 
-81200300,HC HM SYS CONTROLLER," 30,800 "
-81200305,HC HM EMR PWR PK," 7,101 "
-81200310,HC HM 14 V LITHIUM BATT," 5,100 "
-81200320,HC HM UNIVRSL BATT CHRGR," 29,039 "
-81200325,HC HM BATTERY CLIPS," 7,724 "
-81200330,HC HM SHWR CVR BOX," 1,913 "
-81200355,HC HM BATTERY MDL SYS CNTRL," 2,602 "
-81200360,HC HM PWR MODL DC CAR INPUT CBL," 2,143 "
-81200365,HC HM PATIENT PWR MODL CBL," 7,852 "
-50672799,HC INTPH ISH MDS 100-300 CELL, 441 
-50672807,HC ISH BCL2 MRPHMET MAN," 1,383 "
-50672815,HC ISH BCL6 MRPHMET MAN," 1,383 "
-50672823,HC ISH TFE3 MRPHMET MAN," 1,383 "
-50780642,HC 7031 17 OHP NEO NATAL, 101 
-62019245,HC BIOPSY TONGUE POST1/3," 1,637 "
-62017140,HC DESTR VAGINAL LESN EXTENSIVE," 3,500 "
-41897855,HC THORACIC ANGIO GRAFT," 46,400 "
-48002270,HC INDIRECT CALORIMETERY, 886 
-62026011,HC FINE NEEDLE ASPIRATION WITH IMAGING GUIDANCE, 459 
-62026013,HC INCISION & DRAINAGE PILONIDAL CYST COMPLICATED," 1,273 "
-62026015,HC DBRDMT W/RMVL FM FX&/DISLC SKN&SUBQ TISS, 919 
-62026017,HC PARING/CUTTING BENIGN HYPERKERATOTIC LESION 2-4<, 160 
-62026019,HC SHAVING SKIN LESION 1 S/N/H/F/G DIAM >2.0 CM, 313 
-62026021,HC SHVG SKIN LESION 1 F/E/E/N/L/M DIAM 1.1-2.0 CM, 275 
-62026023,HC SHAVING SKIN LESION 1 F/E/E/N/L/M DIAM >2.0 CM, 325 
-62026025,HC EXC B9 LES MRGN XCP SK TG S/N/H/F/G 0.5 CM/<, 809 
-62026027,HC EXCISION HIDRADENITIS AXILLARY COMPLEX REPAIR," 2,256 "
-62026029,HC EXCISION HIDRADENITIS INGUINAL SMPL/INTRM RPR," 2,256 "
-62026031,HC EXCISION H/P/P/U SIMPLE/INTERMEDIATE REPAIR," 2,256 "
-62026033,HC EXCISION MALIGNANT LESION S/N/H/F/G 0.5 CM/<, 923 
-62026035,HC DEBRIDEMENT NAIL ANY METHOD 1-5, 87 
-62026037,HC DEBRIDEMENT NAIL ANY METHOD 6/>, 146 
-62026039,HC AVULSION NAIL PLATE PARTIAL/COMPLETE SMPL EA, 105 
-62026041,HC EXC NAIL MATRIX PRM RMVL W/AMP TUFT DSTL PHALANX," 2,256 "
-62026043,HC BIOPSY NAIL UNIT SPX, 485 
-62026045,HC REPAIR NAIL BED, 354 
-62026047,HC EXCISION PILONIDAL CYST/SINUS SIMPLE," 2,256 "
-62026049,HC EXCISION PILONIDAL CYST/SINUS EXTENSIVE," 5,319 "
-62026051,HC REMOVAL TISS EXPANDER W/O INSERTION PROSTHESIS," 2,256 "
-62026053,HC RMVL W/RINSJ NON-BIODEGRADABLE DRUG DLVR IMPLT, 209 
-62026055,HC SIMPLE REPAIR SCALP/NECK/AX/GENIT/TRUNK >30.0CM, 658 
-62026057,HC SIMPLE REPAIR F/E/E/N/L/M 12.6CM-20.0CM, 379 
-62026061,HC REPAIR INTERMEDIATE S/A/T/E > 30.0 CM, 901 
-62026063,HC REPAIR INTERMEDIATE F/E/E/N/L/M&/MUC > 30.0 CM," 1,425 "
-62026065,HC REPAIR COMPLEX F/C/C/M/N/AX/G/H/F EA 5 CM/<, 632 
-62026069,HC ATT/REARRANGEMENT SCALP/ARM/LEG 10.1-30.0 CM," 2,050 "
-62026071,HC ATT/REARGMT EYELID/NOSE/EAR/LIP 10.1-30.0 CM," 2,021 "
-62026073,HC HARVEST SKIN TISSUE CLTR SKIN AGRFT 100 CM/<, 439 
-62026077,HC EPIDRM AGRFT T/A/L 1ST 100 CM/</1% BDY INFT/CHLD," 1,294 "
-62026079,HC EPIDRM AGRFT T/A/L EA 100 CM/EA 1% BDY INFT/CHLD, 482 
-62026081,HC EPIDERMAL AGRFT F/S/N/H/F/G/M/D GT 1ST 100 CM," 1,200 "
-62026085,HC SPLIT AGRFT F/S/N/H/F/G/M/D GT EA 100 CM/EA 1 %," 2,069 "
-62026087,HC DERMAL AUTOGRAFT TRUNK/ARM/LEG 1ST 100 CM," 9,819 "
-62026089,HC DERMAL AUTOGRAFT TRUNK/ARM/LEG EA 100 CM/EA," 1,624 "
-62026093,HC DERMAL AGRFT F/S/N/H/F/G/M/D GT EA 100 CM/EA," 1,624 "
-62026095,HC HC CLTR SKIN AUTOGRFT T/A/L 1ST 25 CM/<," 1,045 "
-62026097,HC CLTR SKIN AGRFT T/A/L ADDL 1 CM-75 CM, 316 
-62026099,HC CLTR SKIN AGRFT T/A/L EA 100 CM/EA 1%BODY AREA, 316 
-62026101,HC CLTR SKIN AGRFT FACE+ 1ST 25CM/<," 1,045 "
-62026103,HC CLTR SKIN AGRFT FACE+ ADDL 1-75CM, 316 
-62026105,HC CLTR SKIN AGRFT FACE+ EA 100 EA, 316 
-62026115,HC FTH/GFT FREE W/DIRECT CLOSURE TRUNK 20 CM/<," 1,634 "
-62026119,HC FTH/GFT FR W/DIR CLSR S/A/L EA 20 CM, 865 
-62026121,HC FTH/GFT FR W/DIR CLSR F/C/C/M/N/AX/G/H/F EA 20CM, 494 
-62026149,HC TISS CLTR ALGC DRM F/S/N/H/F/G/M/D EA 100 CM, 130 
-62026163,HC FRMJ DIRECT/TUBED PEDICLE +-TRANSFER TRUNK," 2,069 "
-62026165,HC FRMJ DIRECT/TUBED PEDICLE +-TR SCALP ARMS/LEGS," 2,096 "
-62026167,HC FRMJ DIR/TUBED PEDCL +-TR FT/CH/CH/M/N/AX/G/H/F," 2,156 "
-62026169,HC FRMJ DIRECT/TUBED PEDICLE +-TR E/N/E/L/NTRORAL," 2,069 "
-62026171,HC DELAY FLAP/SECTIONING FLAP SCALP ARMS/LEGS," 2,069 "
-62026173,HC DELAY FLAP/SECTIONING FLAP F/C/C/N/AX/G/H/F," 2,069 "
-62026175,HC DELAY FLAP/SCTJ FLAP EYELIDS NOSE EARS/LIPS," 2,069 "
-62026177,HC MUSC MYOCUTANEOUS/FASCIOCUTANEOUS FLAP HEAD&NCK," 2,676 "
-62026179,HC MUSC MYOCUTANEOUS/FASCIOCUTANEOUS FLAP TRUNK," 3,140 "
-62026181,HC MUSC MYOCUTANEOUS/FASCIOCUTANEOUS FLAP UXTR," 2,730 "
-62026183,HC MUSC MYOCUTANEOUS/FASCIOCUTANEOUS FLAP LXTR," 2,946 "
-62026185,HC PUNCH GRAFT HAIR TRANSPLANT >15 PUNCH GRAFTS, 383 
-62026187,HC CHEMICAL PEEL FACIAL DERMAL, 507 
-62026189,HC CHEMICAL PEEL NONFACIAL DERMAL, 440 
-62026193,HC EXCISION EXCESSIVE SKIN&SUBQ TISSUE OTHER AREA," 1,649 "
-62026195,HC REMOVAL SUTURES UNDER ANESTHESIA OTHER SURGEON, 268 
-62026197,HC INITIAL TX 1ST DEGREE BURN LOCAL TX, 89 
-62026199,HC DESTRUCTION CUTANEOUS VASC PROLIFERATIVE <10CM, 322 
-62026201,HC DESTRUCTION CUTANEOUS VASCULAR PRLF 10.0-50.0CM, 411 
-62026203,HC DESTRUCTION MAL LESION TRUNK/ARM/LEG 3.1-4.0CM, 410 
-62026205,HC DESTRUCTION MALIGNANT LESION S/N/H/F/G 0.5 CM/<, 213 
-62026207,HC DESTRUCTION MALIGNANT LESION S/N/H/F/G 0.6-1.0CM, 237 
-62026209,HC DESTRUCTION MALIGNANT LESION S/N/H/F/G 1.1-2.0CM, 273 
-62026211,HC DESTRUCTION MALIGNANT LESION S/N/H/F/G 2.1-3.0CM, 309 
-62026213,HC DSTRJ MAL LES S/N/H/F/G LES DIAM > 4.0 CM, 433 
-62026215,HC DESTRUCTION MALIGNANT LESION F/E/E/N/L/M 0.5CM/<, 185 
-62026217,HC DESTRUCTION MAL LESION F/E/E/N/L/M 1.1-2.0CM, 294 
-62026219,HC DESTRUCTION MAL LESION F/E/E/N/L/M > 4.0 CM, 583 
-62026221,HC BIOPSY BREAST OPEN INCISIONAL," 2,473 "
-62026223,HC BIOPSY BREAST NEEDLE CORE W/IMAGING GUIDANCE," 1,110 "
-62026225,HC BREAST BIOPSY VACUUM ASSISTED/ROTATING DEVICE," 2,087 "
-62026227,HC EXC BRST LES PREOP PLMT RAD MARKER OPN 1 LES," 2,473 "
-62026233,HC BIOPSY MUSCLE DEEP," 2,640 "
-62026235,HC BIOPSY BONE TROCAR/NEEDLE DEEP," 1,649 "
-62026237,HC INJECTION SINUS TRACT THERAPEUTIC SPX, 723 
-62026239,HC ASPIRATION&INJECTION TREATMENT BONE CYST, 444 
-62026241,HC INSERTION WIRE/PIN W/APPL SKELETAL TRACTION SPX," 3,143 "
-62026243,HC REMOVAL TONG/HALO APPLIED BY ANOTHER INDIVIDUAL, 209 
-62026245,HC BONE GRAFT ANY DONOR AREA MINOR/SMALL," 3,143 "
-62026247,HC FASCIA LATA GRAFT INCISION & AREA EXPOSURE," 1,624 "
-62026249,HC MNTR INTERSTITIAL FLUID PRESSURE CMPRT SYNDROME, 456 
-62026251,HC EXC BENIGN TUMOR/CYST MAXL/ZYGOMA ENCL & CURTG," 2,416 "
-62026253,HC EXCISION TORUS MANDIBULARIS," 2,416 "
-62026255,HC EXCISION MALIGNANT TUMOR MAXILLA/ZYGOMA," 4,383 "
-62026259,HC APPL INTERDENTAL FIXATION DEVICE NON-FX/DISLC," 1,143 "
-62026261,HC INJECTION TEMPOROMANDIBULAR JOINT ARTHROGRAPHY, 444 
-62026263,HC GENIOPLASTY SLIDING OSTEOTOMY SINGLE PIECE," 2,416 "
-62026267,HC OSTEOPLASTY FACIAL BONES AUGMENTATION," 5,648 "
-62026269,HC OSTEOPLASTY FACIAL BONES REDUCTION," 4,383 "
-62026271,HC MALAR AUGMENTATION PROSTHETIC MATERIAL," 4,383 "
-62026275,HC OPEN TX DEPRESSED ZYGOMATIC ARCH FRACTURE," 2,416 "
-62026277,HC CLTX MANDIBULAR/MAXILLARY ALVEOLAR RIDGE FX SPX," 2,416 "
-62026279,HC CLOSED TX MANDIBULAR FRACTURE W/MANIPULATION," 1,540 "
-62026283,HC OPEN TX MANDIBULAR FX W/INTERDENTAL FIXATION," 4,383 "
-62026285,HC CLOSED TX RIB FRACTURE UNCOMPLICATED EACH, 162 
-62026287,HC BIOPSY SOFT TISSUE BACK/FLANK DEEP," 2,256 "
-62026289,HC CL TX VRT FX +/-DISLC + CAST/BRACE/MANIP/TRACTION," 1,858 "
-62026291,HC PERC VPLSTY THOR W/WO BONE BX," 5,763 "
-62026293,HC PERC VPLASTY LUMB W/WO BNE BX," 5,499 "
-62026295,HC BIOPSY SOFT TISSUE SHOULDER SUPERFICIAL, 809 
-62026297,HC BIOPSY SOFT TISSUE SHOULDER DEEP," 2,256 "
-62026301,HC INJECTION SHOULDER ARTHROGRAPHY/ CT/MRI ARTHG, 354 
-62026303,HC CLSD TX ACROMIOCLAVICULAR DISLC W/O MANIPULATION, 396 
-62026305,HC CLOSED TX SCAPULAR FRACTURE W/O MANIPULATION, 281 
-62026307,HC CLTX PROX HUMRL FX W/MANIPULATION +-SKEL TRACJ," 1,858 "
-62026309,HC CLTX GREATER HUMERAL TUBEROSITY FX W/O MNPJ, 482 
-62026311,HC INCISION&DRAINAGE UPPER ARM/ELBOW BURSA," 1,994 "
-62026313,HC BIOPSY SOFT TISSUE UPPER ARM/ELBOW SUPERFICIAL," 1,649 "
-62026317,HC INJECTION ELBOW ARTHROGRAPHY, 633 
-62026319,HC CLSD TX HUMERAL SHAFT FRACTURE W/O MANIPULATION, 380 
-62026321,HC CLTX SPRCNDYLR/TRANSCNDYLR HUMERAL FX+-MNPJ, 482 
-62026325,HC CLTX HUMERAL EPICONDYLAR FX MEDIAL/LAT W/O MNPJ, 482 
-62026327,HC TREATMENT CLOSED ELBOW DISLOCATION W/O ANES, 522 
-62026329,HC CLOSED TX RADIAL HEAD/NECK FX W/O MANIPULATION, 441 
-62026333,HC BIOPSY SOFT TISSUE FOREARM&/WRIST SUPERFICIAL, 802 
-62026335,HC EXCISION TUMOR SOFT TISSUE FOREARM&/WRIST SUBQ," 1,649 "
-62026337,HC INJECTION WRIST ARTHROGRAPHY, 624 
-62026339,HC CLOSED TX ULNAR SHAFT FRACTURE W/O MANIPULATION, 455 
-62026341,HC CLOSED TX CARPAL SCAPHOID FRACTURE W/O MNPJ, 482 
-62026343,HC CLOSED TREATMENT ULNAR STYLOID FRACTURE, 482 
-62026345,HC DRAINAGE FINGER ABSCESS COMPLICATED," 1,273 "
-62026347,HC TENDON SHEATH INCISION," 2,328 "
-62026351,HC EXC LES TDN SHTH/JT CAPSL HAND/FNGR," 2,296 "
-62026353,HC CLTX CARP/MTCRPL FX DISLC THMB W/MNPJ, 546 
-62026355,HC CLTX CARP/MTCRPL DISLC THMB MNPJ EA JT REQ ANES, 482 
-62026357,HC CLTX MTCARPHLNGL DISLC 1 W/MNPJ W/O ANES, 417 
-62026359,HC CLTX MTCARPHLNGL DISLC 1 W/MNPJ REQ ANES, 524 
-62026361,HC CLTX DSTL PHLNGL FX FNGR/THMB W/MNPJ EA, 413 
-62026363,HC CLTX IPHAL JT DISLC 1 W/MNPJ W/O ANES," 1,094 "
-62026365,HC CLTX IPHAL JT DISLC 1 W/MNPJ REQ ANES," 1,499 "
-62026367,HC BIOPSY SOFT TISSUE PELVIS&HIP AREA SUPERFICIAL, 802 
-62026369,HC RMVL FOREIGN BODY PELVIS/HIP SUBCUTANEOUS TISS, 802 
-62026371,HC INJECTION HIP ARTHROGRAPHY W/O ANESTHESIA, 493 
-62026373,HC INJECTION HIP ARTHROGRAPHY W/ANESTHESIA, 577 
-62026375,HC CLOSED TREATMENT COCCYGEAL FRACTURE, 255 
-62026377,HC CLTX ACETABULUM HIP/SOCKT FX W/O MNPJ, 601 
-62026379,HC CLTX GRTER TRCHNTRIC FX W/O MNPJ, 524 
-62026381,HC TX SPON HIP DISLC ABDCT SPLNT/TRCJ W/O ANES, 482 
-62026383,HC BIOPSY SOFT TISSUE THIGH/KNEE AREA SUPERFICIAL, 802 
-62026385,HC INJECTION KNEE ARTHROGRAPHY, 358 
-62026387,HC REMOVAL FOREIGN BODY DEEP THIGH/KNEE," 2,256 "
-62026389,HC CLTX TIBL FX PROX W/O MNPJ, 482 
-62026391,HC CLOSED TX KNEE DISLOCATION W/O ANESTHESIA, 527 
-62026393,HC TENOTOMY PRQ ACHILLES TENDON SPX LOCAL ANES," 2,144 "
-62026395,HC EXCISION LESION TENDON SHEATH/CAPSULE LEG&/ANKLE," 2,141 "
-62026397,HC INJECTION ANKLE ARTHROGRAPHY, 356 
-62026399,HC REPAIR FASCIAL DEFECT LEG," 2,141 "
-62026401,HC LNGTH/SHRT TDN LEG/ANKLE 1 TDN SPX," 3,143 "
-62026403,HC CLTX TIBL SHFT FX W/O MNPJ, 712 
-62026405,HC CLTX TIBL SHFT FX W/MNPJ +-SKEL TRACJ," 1,858 "
-62026407,HC CLTX DSTL FIBULAR FX LAT MALLS W/MNPJ, 670 
-62026409,HC CLOSED TX BIMALLEOLAR ANKLE FRACTURE W MNPJ, 498 
-62026411,HC CLTX TRIMAL ANKLE FX W/O MNPJ, 359 
-62026413,HC CLTX TRIMAL ANKLE FX W/MNPJ, 547 
-62026415,HC RELEASE TARSAL TUNNEL," 1,831 "
-62026417,HC EXC LESION TENDON SHEATH/CAPSULE W/SYNVCT FOOT," 2,144 "
-62026419,HC EXCISION/CURETTAGE CYST/TUMOR TALUS/CALCANEUS," 2,144 "
-62026421,HC EXC/CURTG CST/B9 TUM TARSAL/METAR," 2,144 "
-62026423,HC OSTECTOMY COMPLETE OTHER METATARSAL HEAD 2/3/4," 2,144 "
-62026425,HC OSTECTOMY COMPLETE 5TH METATARSAL HEAD," 2,917 "
-62026427,HC OSTC COMPL ALL METAR HEADS W/PRTL PROX PHALANGC," 5,745 "
-62026429,HC PRTL EXC B1 TARSAL/METAR B1 XCP TALUS/CALCANEUS," 2,884 "
-62026431,HC PRTL EXC B1 PHALANX TOE," 2,144 "
-62026433,HC RESECTION PARTIAL/COMPLETE PHALANGEAL BASE EACH," 2,144 "
-62026435,HC METATARSECTOMY," 2,144 "
-62026437,HC RESECTION CONDYLE DISTAL END PHALANX EACH TOE," 2,144 "
-62026439,HC TENOLYSIS FLEXOR FOOT SINGLE TENDON," 2,144 "
-62026441,HC TENOLYSIS EXTENSOR FOOT MULTIPLE TENDON," 2,144 "
-62026443,HC TENOTOMY OPEN EXTENSOR FOOT/TOE EACH TENDON," 2,144 "
-62026445,HC CAPSULOTOMY MIDTARSAL," 5,659 "
-62026447,HC CORRECTION HAMMERTOE," 2,144 "
-62026449,HC OSTC PRTL EXOSTC/CONDYLC METAR HEAD," 2,144 "
-62026451,HC HALLUX RGDUS CORRJ W/CHEILC," 2,144 "
-62026453,HC CORRJ HALLUX VALGUS +-SESMDC LAPIDUS-TYP PX," 3,172 "
-62026457,HC CLOSED TX CALCANEAL FRACTURE W/O MANIPULATION, 522 
-62026459,HC CLOSED TX TALUS FRACTURE W/O MANIPULATION, 423 
-62026461,HC TX TARSAL B1 FX XCP TALUS&CALCN W/O MNPJ, 387 
-62026465,HC CLTX FX GRT TOE PHLX/PHLG W/O MNPJ, 237 
-62026467,HC CLTX FX GRT TOE PHLX/PHLG W/MNPJ, 287 
-62026469,HC OPEN TX FRACTURE GREAT TOE/PHALANX/PHALANGES," 2,538 "
-62026473,HC OPEN TX FRACTURE PHALANX/PHALANGES NOT GREAT TOE," 2,538 "
-62026475,HC OPEN TX SESAMOID FRACTURE +-INTERNAL FIXATION," 2,538 "
-62026477,HC CLTX TARSAL DISLC OTH/THN TALOTARSAL W/ANES," 2,538 "
-62026479,HC PRQ SKEL FIXJ TARSL DISLC XCP TALOTARSAL W/MNPJ," 2,538 "
-62026481,HC OPEN TREATMENT TARSAL BONE DISLOCATION," 4,841 "
-62026483,HC CLOSED TX TALOTARSAL JOINT DISLOCATION W/ANES," 1,858 "
-62026485,HC OPEN TREATMENT TALOTARSAL JOINT DISLOCATION," 2,538 "
-62026487,HC CLOSED TX TARSOMETATARSAL DISLOCATION W/ANES, 339 
-62026491,HC CLTX METATARSOPHLNGL JT DISLC REQ ANES," 1,499 "
-62026493,HC OPEN TX METATARSOPHALANGEAL JOINT DISLOCATION," 2,538 "
-62026495,HC CLTX IPHAL JT DISLC REQ ANES," 1,499 "
-62026499,HC ARTHRODESIS GREAT TOE METATARSOPHALANGEAL JOINT," 5,659 "
-62026501,HC AMPUTATION TOE METATARSOPHALANGEAL JOINT," 2,286 "
-62026503,HC AMPUTATION TOE INTERPHALANGEAL JOINT," 2,144 "
-62026507,HC APPLICATION BODY CAST SHOULDER HIPS, 407 
-62026509,HC APPLICATION BODY CAST SHOULDER HIPS W/ONE THIGH, 476 
-62026511,HC APPLICATION HIP SPICA CAST 1 LEG, 498 
-62026515,HC STRAPPING TOES, 118 
-62026517,HC ARTHROSCOPY KNEE DIAGNOSTIC +-SYNOVIAL BX SPX," 2,876 "
-62026519,HC ENDOSCOPIC PLANTAR FASCIOTOMY," 2,144 "
-62026523,HC EXCISION NASAL POLYP SIMPLE," 1,637 "
-62026525,HC EXCISION/SURGICAL PLANING SKIN NOSE RHINOPHYMA," 2,416 "
-62026527,HC INJECTION TURBINATE THERAPEUTIC, 723 
-62026529,HC INSERTION NASAL SEPTAL PROSTHESIS BUTTON, 723 
-62026531,HC LYSIS INTRANASAL SYNECHIA, 481 
-62026533,HC REPAIR FISTULA OROMAXILLARY," 5,079 "
-62026535,HC REPAIR FISTULA ORONASAL," 4,383 "
-62026537,HC CTRL NSL HEMRRG PST PST NSL PACKS&/CAUT 1ST, 595 
-62026541,HC SINUSOTOMY MAXILLARY RAD W/O RMVL ANTROCH POLYPS," 4,383 "
-62026543,HC NSL/SINUS NDSC DX MAX SINUSC, 502 
-62026545,HC LARYNGOSCOPY FLX FIBEROPTIC RMVL FOREIGN BODY, 678 
-62026549,HC BRNCHSC NDL BX TRACHEA MAIN STEM&/BRONCHUSI," 1,576 "
-62026551,HC BRNCHSC W/REMOVAL FOREIGN BODY," 1,459 "
-62026553,HC BRNCHSC W/THER ASPIR TRACHEOBRNCL TREE 1ST," 1,245 "
-62026555,HC BRNCHSC W/THER ASPIR TRACHEOBRNCL TREE SBSQ," 1,080 "
-62026559,HC SURG CLSR TRACHS/FSTL W/O PLSTC RPR," 2,416 "
-62026561,HC SURG CLSR TRACHS/FSTL W/PLSTC RPR," 3,528 "
-62026563,HC REVISION TRACHEOSTOMY SCAR," 2,416 "
-62026565,HC PNEUMONOSTOMY PERCUTANEOUS DRAINAGE ABSCESS/CYST," 1,768 "
-62026569,HC TRLUML BALO ANGIOP PRQ RNL/VISC ART," 9,225 "
-62026571,HC TRLUML BALO ANGIOP PRQ AORTIC," 6,404 "
-62026577,HC TRLUML BALO ANGIOP PRQ BRCH/CPHLC TRNK/BRNCH EA," 8,510 "
-62026579,HC TRLUML BALO ANGIOP PRQ VEN," 6,404 "
-62026581,HC NJX PX PRQ TX XTR PSEUDOARYSM, 367 
-62026583,HC NJX PX XTR VNGRPH W/INTRO NDL/INTRACATH, 668 
-62026585,HC INTRO CATH SUPRIOR/IVC, 381 
-62026587,HC SLCTV CATH PLMT VEN SYS 1ST ORDER BRANCH, 566 
-62026589,HC SLCTV CATH PLMT VEN SYS 2ND ORDER/> SLCTV BRANCH, 634 
-62026591,HC INTRO CATH R HRT/MAIN P-ART," 1,129 "
-62026593,HC SLCTV CATH PLMT L/R P-ART, 613 
-62026595,HC SLCTV CATH PLMT SGMTL/SUBSGMTL P-ART," 2,206 "
-62026597,HC INTRO NDL/INTRACATH CRTD/VRT ART," 1,206 "
-62026599,HC INTRO NDL/INTRACATH RTRGR BRACH ART, 754 
-62026601,HC INTRO NDL/INTRACATH XTR ART, 659 
-62026603,HC INTRO NDL/INTRACATH AORTIC TRANSLMBR," 1,294 "
-62026615,HC SLCTV CATHJ EA 1ST ORD ABDL PEL/LXTR ART BRNCH, 754 
-62026617,HC SLCTV CATHJ 2ND ORDER ABDL PEL/LXTR ART BRNCH, 841 
-62026619,HC SLCTV CATHJ 3RD+ ORD SLCTV ABDL PEL/LXTR BRNCH," 1,003 "
-62026621,HC SLCTV CATHJ EA 2ND+ ORD ABDL PEL/LXTR ART BRNCH, 157 
-62026623,HC VNPNXR <3 YEARS PHYS SKILL FEM/JUG VEIN, 154 
-62026625,HC VNPNXR <3 YEARS PHYS SKILL SCALP VEIN, 133 
-62026627,HC VNPNXR <3 YEARS PHYS SKILL OTHER VEIN, 66 
-62026629,HC NJX SCLRSG SLN 1 VEIN, 204 
-62026631,HC NJX SCLRSG SLN MLT VEINS SM LEG, 243 
-62026633,HC PRQ PORTAL VEIN CATHJ ANY METH," 1,180 "
-62026635,HC THER APHERESIS PLSM PHERESIS," 1,130 "
-62026637,HC THER APHERESIS W/XTRCORP IMMUNODSPTJ&PLSM RENFS," 3,163 "
-62026639,HC THER APHRS XTRCORP SLCTV ADSRPJ/FILTRJ&RENFS," 3,163 "
-62026641,HC PHOTOPHERESIS XTRCORP," 3,163 "
-62026643,HC CNTRST NJX RAD EVAL CTR VAD FLUOR IMG&REPRT, 231 
-62026645,HC XTRNL CANNULA DECLTNG SPX W/O BALO CATH, 331 
-62026647,HC THRMBC PRQ ARVEN FSTL AUTOG/NONAUTOG GRF," 4,223 "
-62026649,HC REVJ TRANSVNS INTRAHEPATC PORTOSYSIC SHUNT," 11,213 "
-62026651,HC PRIM PRQ TRLUML MCHNL THRMBC 1ST VSL," 5,655 "
-62026655,HC SEC PRQ TRLUML THRMBC," 3,981 "
-62026657,HC PRQ TRLUML MCHNL THRMBC VEIN," 6,749 "
-62026659,HC PRQ TRLUML MCHNL THRMBC VEIN REPEAT TX," 4,795 "
-62026667,HC DRG LYMPH NODE ABSC/LYMPHADENITIS SMPL," 1,273 "
-62026669,HC BX/EXC LYMPH NODE OPN DP CRV NODE," 9,320 "
-62026671,HC EXC LIP TRANSVRS WEDGE EXC W/PRIM CLSR," 2,416 "
-62026673,HC RPR LIP FULL THKNS UP HALF VER H8, 927 
-62026675,HC RPR LIP FULL THKNS > ONE-HALF VER H8/CPLX," 1,109 "
-62026677,HC INC LABIAL FRENUM FREXOMY, 302 
-62026681,HC EXC MUCOSA VESTIBULE MOUTH AS DON GRF, 589 
-62026683,HC EXC FRENUM LABIAL/BUCCAL, 723 
-62026685,HC DSTRJ LES/SCAR VESTIBULE MOUTH PHYSICAL METHS," 1,637 "
-62026689,HC CLSR LAC VESTIBULE MOUTH > 2.5 CM/CPLX, 723 
-62026691,HC VESTIBULOPLASTY ANT," 2,416 "
-62026693,HC VESTIBULOPLASTY PST UNI," 2,528 "
-62026697,HC VESTIBULOPLASTY ENTIRE ARCH," 4,383 "
-62026699,HC VESTIBULOPLASTY CPLX W/RIDGE XTN MUSC RPSG," 8,831 "
-62026701,HC INTRAORAL I&D TONGUE/FLOOR LNGL," 1,637 "
-62026705,HC BX TONGUE ANT 2-3RD," 1,448 "
-62026707,HC BX FLOOR MOUTH, 485 
-62026709,HC EXC LES TONGUE W/O CLSR," 1,637 "
-62026713,HC EXC LNGL FRENUM FRENECTOMY, 723 
-62026715,HC EXC LES FLOOR MOUTH," 1,637 "
-62026717,HC RPR LAC 2.5 CM/< FLOOR MOUTH&/ANT 2-3RD TONGUE, 258 
-62026719,HC FRENOPLASTY SURG REVJ FRENUM EG W/Z-PLASTY, 723 
-62026721,HC SUBMUCOSAL ABLTJ TONGUE RF 1/> SITES PR SESSION," 2,416 "
-62026723,HC DRG ABSC CST HMTMA FROM DENTOALVEOLAR STRUXS, 294 
-62026725,HC RMVL EMBEDDED FB FROM DENTALVLR STRUXS SOFT TISS," 2,416 "
-62026727,HC EXC OSS TUBEROSITIES DENTOALVEOLAR STRUXS," 2,416 "
-62026731,HC EXC LES/TUM XCP LISTED ABOVE DENTALVLR SMPL RPR," 2,830 "
-62026733,HC EXC LES/TUM XCP LISTED ABOVE DENTALVLR CPLX RPR," 4,121 "
-62026735,HC ALVEOLECTOMY W/CURTG OSTEITIS/SEQUESTRECTOMY," 1,637 "
-62026739,HC RPR LAC PALATE > 2 CM/CPLX," 4,383 "
-62026741,HC RPR NASOLABIAL FSTL," 2,416 "
-62026743,HC DRG ABSC SUBMAX/SUBLNGL INTRAORAL, 481 
-62026747,HC BX SALIVARY GLND INCAL," 3,259 "
-62026749,HC EXC SUBLNGL SALIVARY CST RANULA," 1,637 "
-62026751,HC MARSUPIALIZATION SUBLNGL SALIVARY CST RANULA," 1,637 "
-62026753,HC EXC SUBLNGL GLND," 2,416 "
-62026755,HC PLSTC RPR SALIVARY DUX SIALODOCHOPLASTY PRIM," 3,327 "
-62026757,HC PLSTC RPR SALIVARY DUX SIALODOCHOPLASTY SEC/COMP," 4,383 "
-62026759,HC NJX SIALOGRAPY, 783 
-62026761,HC DILAT SALIVARY DUX, 723 
-62026763,HC BX OROPHARYNX, 754 
-62026765,HC ESPH FLX RMVL TUM HOT BX/CAUT," 2,517 "
-62026767,HC ESPHGSC FLX W/RMVL TUM SNARE," 1,403 "
-62026771,HC UPPER STOMACH-INTESTINE SCOPE FOR DIAGNOSIS, 870 
-62026773,HC UPPER STOMACH-INTESTINE SCOPE FOR BIOPSY," 1,026 "
-62026775,HC DILAT ESOPH > GD WIRE, 639 
-62026777,HC DILAT ESOPH BALO/DILATOR RTRGR, 882 
-62026779,HC DILAT ESOPH BALO 30 MM DIAM/LGR ACHALASIA," 1,227 "
-62026781,HC REPOSITION GASTRIC FEEDING TUBE THRU DUODENUM, 820 
-62026783,HC SCOPE OF COLON THRU OSTOMY FOR DIAGNOSIS, 908 
-62026785,HC SCOPE OF COLON WITH BIOPSY THRU OSTOMY," 1,011 "
-62026787,HC COLSC THRU STOMA W/RMVL FB," 1,186 "
-62026789,HC COLSC THRU STOMA RMVL LES CAUT," 1,201 "
-62026791,HC COLSC THRU STOMA ABLTJ LES," 1,517 "
-62026793,HC COLSC THRU STOMA W/RMVL TUM POLYP/OTH LES SNARE," 1,395 "
-62026795,HC I&D APPENDICEAL ABSC PRQ," 1,489 "
-62026797,HC PROCTOSGMDSC RGD W/DILAT," 1,242 "
-62026799,HC SGMDSC FLX RMVL FB," 1,059 "
-62026801,HC SGMDSC FLX ABLTJ LES," 1,768 "
-62026803,HC SCOPE OF COLON WITH BIOPSY," 1,085 "
-62026805,HC COLSC FLX PROX SPLENIC FLXR ABLTJ LES," 1,406 "
-62026807,HC COLSC FLX PROX SPLENIC FLXR RMVL LES SNARE TQ," 1,287 "
-62026809,HC RMVL ANAL SETON OTH MARKER, 539 
-62026811,HC INC THROMBOSED HEMORRHOID XTRNL, 303 
-62026813,HC FISSURECTOMY +-SPHNCTROTOMY," 2,367 "
-62026815,HC PAPILLECTOMY/EXC 1 TAG ANUS SPX," 1,630 "
-62026817,HC EXC XTRNL HEMORRHOID TAGS&/MLT PAPILLAE," 2,367 "
-62026819,HC HRHC XTRNL COMPL," 2,367 "
-62026821,HC HRHC SMPL," 2,662 "
-62026823,HC SURG TX ANAL FSTL 2ND STG," 2,367 "
-62026825,HC ANOSC DILAT," 1,074 "
-62026827,HC DESTRUCTION INTERNAL HEMORRHOID THERMAL ENERGY, 539 
-62026829,HC BX LVR NDL PRQ, 938 
-62026831,HC ABLTJ 1+ LVR TUM PRQ RF," 5,669 "
-62026833,HC CHNG PRQ BILIARY DRG CATH," 1,576 "
-62026835,HC REVJ&/RINSJ TRANSHEPATC TUBE," 1,576 "
-62026837,HC XTRNL DRG PSEUDOCST PNCRS PRQ," 1,489 "
-62026839,HC DRG PRTL ABSC/LOCLZD PRITONITIS PRQ," 1,489 "
-62026841,HC DRG SUBDIPHRG/SUBPHRENIC ABSC PRQ," 1,766 "
-62026843,HC DRG RPR ABSC PRQ," 1,630 "
-62026847,HC BX ABDL/RPR MASS PRQ NDL, 938 
-62026849,HC NJX AIR/CNTRST IN PRTL CAVITY SPX, 213 
-62026853,HC CNTRST NJX ASSMT ABSC/CST VIA DRG CATH/TUBE SPX, 359 
-62026855,HC INSERT CECOSTOMY/OTHER COLONIC TUBE PERCUTANEOUS," 2,367 "
-62026857,HC DRG PRIRNL/RNL ABSC PRQ," 2,028 "
-62026859,HC RNL BX PRQ TROCAR/NDL, 939 
-62026861,HC RMVL&RPLCMT INTLY DWELLING URTRL STENT," 2,908 "
-62026863,HC RMVL INTLY DWELLING URTRL STENT," 2,817 "
-62026865,HC RMVL&RPLCMT XTRNLLY ACCESSIBLE URTRL STENT," 1,577 "
-62026867,HC RMVL NFROS TUBE REQ FLUOR GID, 983 
-62026871,HC CHNG NFROS/PYELOSTOMY TUBE," 1,577 "
-62026873,HC RNL NDSC NFROS/PYELOSTOMY," 2,373 "
-62026875,HC RNL NDSC NFROS/PYELOSTOMY BX," 2,751 "
-62026879,HC ABLTJ 1+ RNL TUM PRQ UNI RF," 6,663 "
-62026881,HC NJX URTRG/URTROPLOG THRU URTROST/URTRL CATH, 466 
-62026883,HC NDSC THRU URTROST RMVL FB/ST1," 2,549 "
-62026887,HC INSJ TEMP NDWELLG BLDR CATH COMP," 1,153 "
-62026889,HC CHNG CSTOST TUBE COMP," 1,206 "
-62026891,HC CSTO W/IRRG&EVAC MLT OBSTRUCTING CLOTS," 1,697 "
-62026895,HC CSTO W/URTRL CATHJ BRUSH BX URTR&/RNL PELVIS," 2,549 "
-62026897,HC CSTO W/EJACULATORY DUX CATHJ, 932 
-62026899,"HC SCOPE BLADDER, DESTRUCTION OF LESIONS"," 2,549 "
-62026903,HC CSTO CALIBRATION DILAT URTL STRIX/STENOSIS," 1,697 "
-62026905,"HC SCOPE BLADDER, COMPLEX REMOVAL STONE, STENT"," 3,065 "
-62026907,HC LITHOLAPAXY SMPL/SM < 2.5 CM," 2,854 "
-62026909,HC CSTO MNPJ W/O RMVL URTRL ST1," 2,549 "
-62026911,"HC SCOPE BLADDER & URETER, INSERT STENT INTO URETER"," 2,549 "
-62026913,HC LASER COAGULATION OF PROSTATE FOR URINE FLOW," 6,159 "
-62026915,HC LASER VAPORIZATION OF PROSTATE FOR URINE FLOW," 4,443 "
-62026917,HC BX URT," 2,032 "
-62026921,HC DILAT URTL STRIX FILIFORM&FOLLWR MALE SBSQ," 1,630 "
-62026923,HC DILAT FEMALE URT W/SUPPOSITORY&/INSTLJ 1ST, 270 
-62026925,HC DILAT FEMALE URT W/SUPPOSITORY&/INSTLJ SBSQ, 261 
-62026929,HC TRURL DSTRJ PRST8 TISS RF THERMOTH," 16,098 "
-62026931,HC DSTRJ LES PNS SMPL CHEM," 1,262 "
-62026933,HC DSTRJ LES PNS SMPL ELTRDSICCATION," 2,102 "
-62026937,HC LSS/EXC PEN POST-CIRCUMCISION ADS," 2,289 "
-62026939,HC IRRG C/P/A CAVERNOSA F/PRIAPISM, 571 
-62026941,HC NJX F/C/P/A CAVERNOSOGRAPY, 185 
-62026945,HC DRG SCROTAL WALL ABSC," 1,273 "
-62026947,HC VASOTOMY CANNULIZATION +-INC VAS UNI/BI SPX," 2,289 "
-62026949,HC ELECTROEJACULATION, 338 
-62026951,HC CRYOSURG ABLTJ PRST8," 11,029 "
-62026953,HC I&D VULVA/PRNL ABSC, 214 
-62026955,HC LSS LABIAL ADS," 2,003 "
-62026957,HC DSTRJ LES VULVA SMPL," 2,102 "
-62026959,HC DSTRJ LES VULVA X10SV," 3,657 "
-62026961,HC BX VULVA/PR SPX EA SEP ADDL LES, 164 
-62026963,HC BX VAG MUCOSA X10SV REQ SUTR," 2,003 "
-62026965,HC EXC VAG SEPTUM," 2,003 "
-62026967,HC EXC VAG CST/TUM," 2,003 "
-62026969,HC CAUT CERVIX CRYOCAUT 1ST/REPEAT," 7,604 "
-62026971,HC CONIZATION CERVIX +-D&C RPR ELTRD EXC," 2,003 "
-62026973,HC DILAT CRV CANAL INSTRUMENTAL SPX," 2,003 "
-62026975,HC ENDOMETRIAL BX CONJUNCT W/COLPOSCOPY, 110 
-62026979,HC CATHJ&INTRO SALINE NFS SHG/HSG, 353 
-62026981,HC ENDOMETRIAL ABLTJ THERMAL W/O HYSTEROSCOPIC GID," 7,278 "
-62026983,HC HYSTSC RMVL IMPACTED FB," 3,137 "
-62026985,HC HYSTSC ENDOMETRIAL ABLTJ," 3,714 "
-62026987,HC DRG PEL ABSC TRVG/TRANSRCT PRQ," 2,003 "
-62026989,HC FOLLICLE PNXR OOCYTE RETRIEVAL ANY METH, 913 
-62026993,HC TX INCOMPL AB ANY TRI COMPLD SURGLY," 2,003 "
-62026995,HC INDUCED AB DILAT&CURTG," 4,008 "
-62026997,HC BX THYR PRQ CORE NDL, 444 
-62027001,HC PRQ ASPIR PULPOSUS/INTERVERTEBRAL DISC/PVRT TISS, 444 
-62027003,HC PRQ ASPIR SPI CORD CST/SYRINX," 1,788 "
-62027005,HC BX SPI CORD PRQ NDL," 1,130 "
-62027007,HC INJECTION PROCEDURE MYELOGRAPHY/CT SPINAL, 546 
-62027009,HC NJX INFUS/BOLUS DX/SBST EDRL/SUBARACH LUM/SACRAL," 1,251 "
-62027011,HC ELEC ALYS PRGRBL PMP W/O REPRGRMG, 233 
-62027013,HC STRTCTC STIMJ SPI CORD PRQ SPX N/FLWD OTH SURG," 3,490 "
-62027015,HC NJX ANES VAGUS NRV, 254 
-62027017,HC NJX ANES PHRENIC NRV, 254 
-62027019,HC NJX ANES AX NRV, 431 
-62027021,HC NJX ANES PARACRV NRV, 397 
-62027023,HC NJX ANES&/STEROID PLANTAR COMMON DIGITAL NERVE, 254 
-62027027,HC PRQ IMPLTJ N-STIM ELTRD CRANIAL NERVE," 6,184 "
-62027031,HC PRQ IMPLTJ N-STIM ELTRD SACRAL NERVE," 6,184 "
-62027035,HC REVISION/RMVL PERIPHERAL/GASTRIC NPGR," 3,003 "
-62027037,"HC CHEMODNRVTJ MUSC MUSC INNERVATED FACIAL NRV, UNILATERAL", 519 
-62027039,HC CHEMODNRVTJ MUSC NCK MUSC, 484 
-62027041,HC DSTRJ NEUROLYTIC PLANTAR COMMON DIGITAL NERVE, 367 
-62027043,HC DSTRJ NULYT +-RAD MNTR SUPRIOR HYPOGSTR PLEXUS," 1,668 "
-62027045,HC RMVL FB XTRNL EYE CJNCL SUPFC, 205 
-62027047,HC RMVL FB XTRNL EYE CRNL W/O SLIT LAMP, 418 
-62027049,HC REPAIR LACERATION CONJUNCTIVA W/OUT HOSP," 2,258 "
-62027051,HC RMVL CRNL EPITHE +-CHEMOCAUT, 744 
-62027053,HC RMVL CRNL EPITHE W/APPL CHELATING AGT," 3,959 "
-62027055,HC MLT PNXRS ANT CRN," 1,906 "
-62027057,HC CRNL RELAXING INC CORRJ INDUCED ASTIGMATISM," 1,611 "
-62027059,HC PCNTS EYE SPX W/REMOVAL AQUEOUS, 893 
-62027063,HC EXC LES SCL," 2,258 "
-62027065,HC CILIARY BDY DSTRJ CRTX," 1,611 "
-62027067,HC CILIARY BDY DSTRJ CYCLODIAL," 2,258 "
-62027069,HC NJX VTS SUB PARS PLNA/LIMBAL SPX," 3,975 "
-62027071,HC SEVERING VTS STRANDS LASER 1+ STGS, 630 
-62027073,HC RPR RTA DTCHMNT 1+ SESS PC +-DRG SUBRTA," 1,088 "
-62027075,HC RPR RTA DTCHMNT NJX AIR/OTH GAS," 4,189 "
-62027077,HC PROPH RTA DTCHMNT W/O DRG 1+ SESS CRTX DTHRM," 1,170 "
-62027079,HC DSTRJ LES CHOROID PDT, 919 
-62027081,HC DSTRJ LES CHOROID PDT 2ND EYE 1 SESS, 467 
-62027083,HC RETROBULBAR NJX MED SPX, 264 
-62027085,HC RETROBULBAR NJX ALCOHOL, 301 
-62027087,HC NJX MED/OTHER SBST TENON'S CAPSULE, 301 
-62027089,HC CANTHOTOMY SPX," 1,906 "
-62027091,HC EXC CHALAZION MLT DIFF LIDS, 630 
-62027093,HC RPR BLPOS FRNTIS MUSC SUTR/OTH MATRL," 3,608 "
-62027095,HC RPR ECTROPION SUTR," 3,814 "
-62027097,HC RPR ECTROPION THERMOCAUT," 1,906 "
-62027099,HC RMVL EMBEDDED FB EYELID, 432 
-62027101,HC EXC LES CJNC W/ADJ SCL," 7,630 "
-62027103,HC CJP CJNCL GRF/X10SV REARGMT," 3,265 "
-62027105,HC INC DRG LACRIMAL SAC," 1,906 "
-62027107,HC SNIP INC LACRIMAL PUNCTUM, 620 
-62027109,HC CORRJ EVERTED PUNCTUM CAUT, 396 
-62027111,HC NJX CNTRST MEDIUM DACRYOCSTOGRAPY, 945 
-62027113,HC DRG XTRNL EAR ABSC/HMTMA SMPL, 417 
-62027115,HC DRG XTRNL AUD CANAL ABSC, 285 
-62027117,HC OTOPLASTY PROTRUDING EAR +-SIZE RDCTJ," 2,416 "
-62027119,HC CLIN PATH CONSLTJ LIMITED, 33 
-62027121,HC CLIN PATH CONSLTJ COMPRE, 115 
-62027125,HC B1 MARROW SMR INTERPJ, 188 
-62027127,HC BLD BANK PHYS SVCS DIFFC CROSS MATCH&/EVAL REPRT, 55 
-62027129,HC BLD BANK PHYS SVCS INVSTGJ TFUJ RXN REPRT, 55 
-62027131,HC BLD BANK PHYS SVCS AUTHJ DEVIJ STANDARD REPRT, 55 
-62027133,HC CONSLTJ&REPRT SLIDES PREPARED ELSEWHERE, 98 
-62027135,HC CONSLTJ COMPRE REVIEW REPRT REFERRED MATRL, 188 
-62027137,HC PATH CONSLTJ SURG, 58 
-62027139,HC CAFFEINE HALOTHANE CONTRCURE, 72 
-62027167,"HC ADM INTRANSL/ORAL VACC, EA ADDL", 35 
-62027171,HC PSYCHOALYS, 152 
-62027173,"HC PSYCHOTHERAPY,FAM,PHD W/OUT PATIENT PRESENT", 176 
-62027177,HC FAM PSYCHOTX PRO DISC PT PRESNT, 353 
-62027179,HC GSTR INTUBAJ&ASPIR/LVG, 63 
-62027183,HC SRL TNMTRY SPX MLT MEAS IO PRESS, 146 
-62027187,HC OPSCPY EXTND RTA DRAWING I&R SBSQ, 87 
-62027189,HC RX&FITG C-LENS SUPVJ CRNL LENS APHK 1O, 133 
-62027191,HC RX&FITG C-LENS SUPVJ CRNL LENS APHK OU, 206 
-62027193,HC RX&FITG C-LENS SUPVJ CRNLSCLRL LENS, 218 
-62027195,HC RX C-LENS SUPVJ FITG TECH CRNL APHK 1O, 173 
-62027197,HC RX C-LENS SUPVJ FITG TECH CRNL APHK OU, 289 
-62027199,HC RX C-LENS SUPVJ FITG TECH CRNLSCLRL, 156 
-62027203,"HC TREATMENT OF SPEECH AND/OR AUDIT DISORD,GRP", 41 
-62027205,HC EVAL&/FITG VOICE PROSTC DEV SUPPLEMENT O-SP, 146 
-62027225,HC EVAL AUD RHAB STATUS 1ST HR, 459 
-62027227,HC EVAL AUD RHAB STATUS EA 15 MIN, 115 
-62027231,HC CARDIOPULM RESUSCITATION, 371 
-62027233,HC CARDIOVERSION ELECTIVE ARRHYT XTRNL, 612 
-62027249,HC RAPID DESENSITIZATION PX EA HR, 541 
-62027251,HC INSJ BY PHYS OF SPHENOIDAL ELTRDS F/EEG REC, 263 
-62027253,HC MUSC TSTG MNL W/REPRT XTR EX HAND/TRNK, 32 
-62027255,HC CHOLIN INHIB CH TEST M GRAV, 117 
-62027257,HC DEVEL TESTING W/INT & REP, 254 
-62027261,HC CHEMOTX ADMN ILESN UP&W/7 <, 155 
-62027263,HC CHEMOTX ADMN ILESN > 7, 208 
-62027265,HC CHEMOTX ADMN PLEURAL CAVITY REQ&W/THORACNTS, 575 
-62027267,HC CHEMOTX ADMN PRTL CAVITY REQ&W/PRITONEOCNTS, 770 
-62027269,HC CHEMOTX ADMN CNS REQ&W/SPI PNXR, 639 
-62027271,HC CHEMOTX NJX SUBARACHND/INTRAVENTR RSVR 1+ AGENTS, 310 
-62027275,HC RMVL DEVITAL TISS SLCTV DBRDMT W/O ANES > 20 CM, 146 
-62027277,HC NEG PRESS WND THER > 50 SQ CM, 291 
-62027281,HC MED NUTR THER RE-ASSMT&IVNTJ INDIV EA 15 MIN, 32 
-62027283,HC CMT SPI 1-2 REGIONS, 61 
-62027285,HC CMT SPI 3-4 REGIONS, 77 
-62027287,HC CMT SPI 5 REGIONS, 95 
-62027291,HC CC E/M CRITICALLY ILL/INJURED 1ST 30-74 MIN, 646 
-62027293,HC CC E/M CRITICALLY ILL/INJURED EA 30 MIN, 324 
-62027295,HC PROLNG SVC O/P DIR CONTACT 1ST HR, 137 
-62027297,HC PROLNG SVC O/P DIR CONTACT EA 30 MIN, 113 
-62027299,HC 1ST CARE PR DAY NML NB XCPT HOSP/BIRTHING CENTER, 121 
-62027301,HC PROSTATE CA SCREENING; DRE, 36 
-62027303,HC CA SCREEN;FLEXI SIGMOIDSCOPE, 525 
-62027305,HC COLORECTAL SCRN; HI RISK IND, 806 
-62027307,HC COLON CA SCRN NOT HI RSK IND, 806 
-62027323,HC BONE MARROW ASPIRATE &BIOPSY, 287 
-62027337,HC OBTAINING SCREEN PAP SMEAR, 22 
-62026229,HC CORRECTION INVERTED NIPPLES," 3,240 "
-37216348,HC CT HRT W/3D IMAGE W/CON," 6,377 "
-38016341,HC CT HRT W/3D IMAGE W/CON," 6,377 "
-38105979,"HC GADOFOSVESET, PER 1 ML", 108 
-38505970,"HC GADOFOSVESET, PER 1 ML", 108 
-38616348,HC CT HRT W/3D IMAGE W/CON," 6,377 "
-38704190,HC FINGER JOINT IMPLNT L27," 4,875 "
-40805889,HC BREAST BINDER  LVL 4, 149 
-40805897,HC BREAST MARKER  LVL 11, 723 
-40805905,HC BREAST NEEDLE LVL 5, 213 
-40805921,HC BREAST NEEDLE LVL 19," 2,338 "
-40805996,HC NEEDLE INTRO LVL 3, 146 
-42016345,HC CT HRT W/3D IMAGE W/CON," 6,377 "
-42016352,HC BX/EXC LYMPH NODES CERV DP," 8,585 "
-46160826,HC FLU B ADENO VIRUS ANTIGEN, 284 
-46160834,HC FLU B DFA, 165 
-46160842,HC FLU A ANTIGEN, 221 
-46160859,HC PARAFLU DFA ANTIGEN, 324 
-46160867,HC RSV DFA / ANTIGEN, 249 
-46160875,HC METAPNEUMOVIRUS DFA, 190 
-46160883,HC FLU A ANTIGEN, 221 
-94205978,"HC GADOFOSVESET, PER ML", 108 
-94316346,HC CT HRT W/3D IMAGE W/CON," 6,377 "
-19160242,HC HOSPITAL INDIRECT CALORIMETERY, 886 
-41154550,HC HYBRID HI CMPLX INIT 30 MIN," 21,874 "
-41154551,HC HYBRID HI CMPLX ADDL 15 M," 2,500 "
-41897845,HC IDE SHEATH-GUIDING L42," 10,350 "
-46160891,HC TOXOPLASMA AB IGM, 227 
-46160909,HC TOXOPLASMA AB IGG, 231 
-46160917,HC TOXOPLASMA AB IGM, 227 
-46160925,HC TOXOPLASMA AB IGG, 231 
-46160933,HC TOXOPLASMA AB IGM, 227 
-50781392,HC 6944 TOXO AB IGG DYE TEST, 113 
-50781400,HC 6944 TOXO AB IGM ISAGA, 113 
-50781418,HC 6944 TOXO AB IGA ELISA, 113 
-50781426,HC 6944 PRT AGG TIT EA AB ACHS, 65 
-50781434,HC 6944 TOXO AB AVIDITY, 351 
-50781442,HC 6944 TOXO AB IGM ISAGA, 121 
-50781459,HC 6944 TOXO AB IGG DYE TEST, 101 
-50781467,HC 6944 TOXO AB IGM ELISA, 101 
-50781475,HC 6944 PRT AGG TIT EA AB ACHS, 75 
-38105177,HC FUNCTIONAL BRAIN MAPPING, 550 
-38505178,HC FUNCTIONAL BRAIN MAPPING, 550 
-38704692,HC BONE FEMORAL HEAD L36," 7,800 "
-40105843,HC BIOPSY HANDPIECE LVL 12," 2,800 "
-40105850,HC BIOPSY HANDPIECE LVL 21," 3,188 "
-40105868,HC BREAST MARKER LVL 11, 723 
-40105884,HC BREAST BINDER LVL 4, 149 
-40105918,HC MRI KNIFE, 111 
-40105926,HC SUCTION CANNISTER LVL 3, 85 
-40105934,HC NEEDLE INTRO LVL 18," 1,913 "
-40105942,HC CRYO PROBE LVL 38," 10,875 "
-40105959,HC BIOPSY TRAY, 149 
-40105967,HC BREAST NEEDLE LVL 3, 85 
-40105975,HC BREAST NEEDLE LVL 6, 298 
-40105983,HC BREAST NEEDLE LVL 9, 510 
-40105991,HC BREAST NEEDLE LVL 11, 723 
-41154526,HC ASPIRATION MJR JNT IMAGE GUIDED," 1,193 "
-42016360,HC ABLATION PROSTATE CRYOSURGICAL," 18,411 "
-50781293,HC 6961 MUT ISOL HP EA NA, 15 
-50781301,HC 6961 MUT SEP GEL ELCT, 15 
-50781319,HC 6961 NA PROBE EA, 15 
-50781327,HC 6961 MUT AMP EA NA SEQ, 63 
-50781335,HC 6961 AMP MPLX NA SEQ 1 2, 126 
-50781343,HC 6961 AMP MPLX NA EA ADL SEQ, 63 
-50781350,HC 6961 MUT SCAN SNGL SEG EA, 63 
-50781368,HC 6961 MUT ID SEQ EA SEG, 63 
-50781376,HC 6961 SEP ID HIRES EA NA PRP, 63 
-50781384,HC 6961 INTRP AND RPT, 15 
-50781483,HC 6961 KENDNA ISOL HP EA NA, 49 
-50781491,HC 6961 KENDNA SEP GEL ELCT, 49 
-50781509,HC 6961 KENDNA AMP EA NA SEQ, 203 
-50781517,HC 6961 KENDNA INTRP AND RPT, 49 
-51493781,HC ADAMTS FACTOR INHIBITOR, 251 
-51493799,HC ADAMTS 13 ACT EA ANALYTE, 427 
-94205176,HC FUNCTIONAL BRAIN MAPPING, 550 
-41154552,HC HYBRID COMPLX INIT 30 MIN," 17,188 "
-41154553,HC HYBRID COMPLX ADDTL 15 MIN," 2,000 "
-56270220,HC HYBRID HI COMPLX INIT 30 MIN," 21,874 "
-56270221,HC HYBRID HI COMPLX ADDTL 15 MIN," 2,500 "
-56270222,HC HYBRID COMPLX INIT 30 MIN," 17,188 "
-56270223,HC HYBRID COMPLX ADDTL 15 MIN," 2,000 "
-94101030,HC WAFFLE CUSHION-ADULT, 149 
-94101042,HC WAFFLE CUSHION-PEDIATRIC, 149 
-94101054,HC WAFFLE CUSHION-BARIATRIC, 383 
-94101061,HC DRSG MEPILEX SACRAL 23X23CM, 85 
-94101063,HC DRSG MEPILEX TRSF 18X22, 298 
-94101065,"HC INTERDRY 36""", 298 
-94101067,HC INTERDRY 12 FOOT ROLL, 383 
-94101074,HC AQUACEL AG NARROW ROPE, 85 
-94805975,HC BREAST NEEDLE LVL 6, 298 
-94850005,HC II GAUGE MAMMOTOME NEEDLE, 931 
-94805926,HC BREAST NEEDLE LVL 19," 2,338 "
-94805967,HC BREAST NEEDLE LVL 9, 510 
-94805918,HC BREAST NEEDLE LVL 18," 1,913 "
-94805959,HC BIOPSY TRAY LVL 4, 149 
-94805900,HC BREAST NEEDLE LVL 5, 213 
-94805991,HC NEEDLE INTRO LVL 3, 85 
-94805892,HC BREAST MARKER  LVL 11, 723 
-94805942,"HC VACUUM PACKAGE, LVL 5", 213 
-94805983,HC JABCZENSKI DUCTO CANNULA LVL 5, 213 
-94805884,HC BREAST BINDER  LVL 4, 149 
-94805934,HC BREAST MARKER LVL 12, 808 
-94871662,HC FINE NEEDLE ASPIRATION," 2,086 "
-94880234,"HC PUNC ASP-ABS,HEMA,BULLA,CYST"," 1,423 "
-94809985,HC BREAST PUNC ASP-CYST," 1,332 "
-94870524,HC BREAST PUNC ASP-CYST-ADDL, 346 
-94845401,HC INJ DUCTOGRAM, 301 
-94828456,"HC BX BREAST NDL CORE, WITH GUIDE"," 3,418 "
-94828464,HC BX BRST PERC VACUUM W/IMAGING," 5,302 "
-94828506,HC BRST NDL LOC WIRE PLCMT," 3,466 "
-94828514,HC BRST NDL LOC WIRE PLCMT-ADDL," 1,695 "
-94828472,HC IMAGE GUIDED PLCMNT CLIP," 2,542 "
-94880242,"HC IMAGE GUIDANCE, ABSCESS DRAINA"," 2,894 "
-94868452,HC RAD EXAM MAMMO SURG SPEC, 907 
-94828522,HC US BRST(U OR B)MAMMO AREA, 948 
-94880283,HC US GUID NDL PLC:MAMMO AREA," 1,896 "
-94818101,HC STEREO GUIDE BRST BX/NDL EA," 3,340 "
-94871647,HC MAMMO GUIDE NDL PLCMNT BRST EA," 1,577 "
-94880168,"HC COMPUTER AIDED DETECTION, DIAG", 208 
-94880143,HC CAD SCREENING MAMMO, 139 
-94809118,HC MAMMO DUCT SNG, 720 
-94868338,HC TISSUE MARKER, 329 
-94880150,HC DIGITAL SCREENING MAMMOGRAM, 400 
-94880267,HC DIGITAL MAMMOGRAPHY BILAT, 626 
-94880259,HC DIGITAL MAMMOGRAPHY UNILAT, 501 
-94905155,HC INJ TRIGGER PT 1-2 MUSCLE(S), 515 
-94905015,HC ASPIRATION MAJOR JOINT," 1,193 "
-94980075,HC BX PANCREAS PERC NDL," 2,295 "
-94945375,HC INJ HYSTEROSAPINGOGRAM," 1,241 "
-94928645,HC US CHEST," 1,529 "
-94928785,HC US: ABDOMEN," 2,294 "
-94980026,HC BX PLEURA PERC NDL," 2,384 "
-94905056,"HC US: PREG UTRS W/EVAL, 1ST TRI"," 1,778 "
-94928686,HC US:TRANSVAGINAL," 1,456 "
-94928876,HC US GUIDED COMPRESSN RP, 469 
-94905106,HC US GUIDANCE TISSUE ABLATION, 712 
-94930096,HC DPLX LWR EXT ART/GRFT UNIL," 2,177 "
-94930146,HC DPLX VEN EXT LTD UNIL," 2,072 "
-94905916,"HC PHLEBOTOMY, TX", 658 
-94905767,"HC US SUPPLY, LEVEL 6", 298 
-94980067,HC BX LIVER NDL PERC," 4,317 "
-94905007,"HC US: PREG UTER, TRANSVAGINAL"," 1,150 "
-94930187,HC DPLX AORT/IVC/ILIAC LTD," 1,627 "
-94905148,HC INJ SGL TENDON ORIGIN/INSERT, 533 
-94905908,HC VENIPUNCTURE, 115 
-94928728,HC US: EXTREMITY," 1,189 "
-94930088,HC DPLX LWR EXT ART/GRFT BIL," 3,076 "
-94930138,HC DPLX VENOUS EX IMAN BIL," 2,966 "
-94928629,HC US: SOFT TISSUE OF HEAD OR NEC," 1,554 "
-94928819,HC US:RETROPERITON LTD," 1,321 "
-94928769,HC USL GUIDANCE INTRA-OPERATIVE," 1,715 "
-94930179,HC DPLX AORT/IVC/ILIAC CMPL," 1,665 "
-94905999,HC PRP INJECT KIT V LEVEL 28," 6,163 "
-94905049,HC US SCREEN AAA," 1,762 "
-94905700,"HC US SUPPLY, LEVEL 3", 85 
-94971660,HC FINE NEEDLE ASPIRATION," 2,086 "
-94980000,HC BX BONE TROC/NDL SUP," 3,880 "
-94980190,"HC BX LYMPHNODE, SUPERFICIAL"," 3,219 "
-94928520,HC US BRST (UNI OR BIL), 948 
-94905130,HC US TRNSABD ADD GEST 2TRI, 951 
-94928660,HC US:PREG UTER LMTD," 1,257 "
-94928710,HC US:SCROTUM," 1,470 "
-94905031,"HC INJ(S); SNGL TNDN SHTH, OR LGM", 515 
-94980091,HC BX RENAL NDL," 2,637 "
-94928801,HC US: RETROPERITONEAL," 1,822 "
-94930211,HC US:TPLNT KIDNEY W/WO DPLX/DOP," 1,578 "
-94911211,HC US GUIDANCE NEEDLE PLCMNT," 1,896 "
-94930021,HC CAROTID DPLX UNILAT," 1,756 "
-94930161,HC AB/ART/ISCR/PLV/RETROP LTMD ST," 1,982 "
-94980232,HC PUNCTURE ASPIRATION," 1,423 "
-94905122,HC US PREGTRNSABD ADD GEST1TRI, 270 
-94928652,HC US: PREGNANT UTERUS W/EVAL," 1,928 "
-94928702,HC US: PELV NON-OB LTD," 1,237 "
-94930112,HC DPLX UP EXT ART/GRFT BIL," 2,151 "
-94905163,HC INJ TRIGGER PT 3+ MUSC GROUP, 597 
-94905023,HC ASPIRATION MEDIUM JOINT, 875 
-94980083,HC BX ABD/TETRO MASS PERC NDL," 2,277 "
-94928793,HC US:ABDOMEN LIMITED," 1,584 "
-94930013,HC CAROTID DPLX BILAT," 3,055 "
-94930153,HC DPLX IN/OUT ABD/PEL/SCR/RETRO," 2,995 "
-94930203,HC DPLX ART/VEN PENILE LTD OR F-U, 436 
-94905064,HC ASPIRATION SMALL JOINT, 864 
-94928884,"HC US:TRANSVAGINAL,LIMITED", 728 
-94980174,HC HYSTEROSONOGRAM, 893 
-94928694,HC US:PELVIC NON-OB," 1,809 "
-94905114,"HC US INFANT HIPS, LIMITED"," 1,173 "
-94930054,HC UP+LWR EXT 1LVL BIL," 1,276 "
-94930104,HC DPLX UP EXT ART/GRFT UNIL," 1,939 "
-38305173,HC INJ PLSM IMAGE GUIDE HRVST/ PR," 2,768 "
-40905176,HC INJ PLSM IMAGE GUIDE HRVST/," 2,768 "
-50781574,HC 7046 11989R ISOL HP EA NA, 7 
-50781582,HC 7046 11989R AMP NA SQ 1ST 2, 56 
-50781590,HC 7046 11989R AMP EA ADTL, 28 
-50781608,HC 7046 11989R MUT ID EA SEG, 28 
-50781616,HC 7046 11989R SEP&ID HRT EA, 28 
-50781624,HC 7046 11989R INTRP AND RPT, 7 
-50781632,HC 6911 11899R GC RNA APT RCTL, 60 
-50781640,HC 6911 11901R GC RNA APT THRT, 60 
-50781657,HC 6911 11903R HIV1 WB DONOR, 110 
-50781665,HC 6911 11906R HIV2 AB DONOR, 99 
-50781673,HC 6911 11909R HIV2 WB DONOR, 105 
-50781681,HC 6911 11912R CHAGAS RIPA DNR, 400 
-50781699,HC 6911 11914R HBSAG CNFRM DNR, 195 
-50781707,HC 6911 11921R HEPC RIBA DNR, 150 
-50781715,HC 6911 11923R HCV NA APT DNR, 221 
-50781723,HC 6911 11925R HIV1 NA APT DNR, 221 
-50781731,HC 6911 11927R HBV NA APT DNR, 221 
-50781749,HC 6911 11929R RPR IGG T PLDM, 45 
-50781756,HC 6911 11942R HTLV I&II WB, 130 
-95000139,HC MRI ABDOMEN," 5,769 "
-95000337,HC MRI UPR EXTREM JNT," 5,950 "
-95000402,HC MRI ANY JOINT LWR EXTREMITY," 4,973 "
-95000436,HC MRI BRAIN," 6,610 "
-95000444,HC MRI BRAIN W/CONTRAST," 7,269 "
-95000485,HC MRI CHEST," 5,887 "
-95000501,HC MRI C-SPINE W/CONTRAST," 7,335 "
-95000535,HC MRI C SPINE," 5,727 "
-95000741,HC MRI LWR EXTREM NON-JT WO/W CON," 7,969 "
-95000782,HC MRI UPR EXTREM NON-JT WO/W CON," 7,794 "
-95001426,HC MRI T-SPINE," 5,732 "
-95001434,HC MRI T-SPINE W/CONTRAST," 6,419 "
-95001525,HC MRI L-SPINE," 5,647 "
-95001533,HC MRI L-SPINE W/CONTRAST," 6,681 "
-95005005,HC MRI GAD CONTRAST /ML, 29 
-95005120,HC MRI BRAIN FUNCTION NO MD," 1,641 "
-95005138,HC MRI BRAIN FUNCTION W/MD," 2,477 "
-95005146,"HC MRI CARD WO&W CON,STRESS"," 8,794 "
-95005153,HC MRI 3D REND W/INTERP W/O PSTPR, 775 
-95005161,HC MRI GUIDE NEEDLE PLACE," 4,256 "
-95005179,HC FUNCTIONAL BRAIN MAPPING, 550 
-95005971,"HC GADOFOSVESET, PER ML", 108 
-95005989,HC SECRETIN VL 1MCG, 195 
-95005997,"HC GADOXETATE DISODIUM, PER ML", 161 
-95010005,"HC MRI ORBITS,FACE,NECK,W/CONT"," 6,253 "
-95010013,"HC MRI ORBIT,FACE,NECK,WO/W CONT"," 8,855 "
-95010021,HC MRI ANGIO HEAD," 5,681 "
-95010039,"HC MRI ANGIO HEAD,W/CONTRST"," 7,199 "
-95010047,HC MRI ANGIO HEAD.WO/W CONT," 8,699 "
-95010054,HC MRI ANGIO NECK," 5,295 "
-95010062,"HC MRI ANGIO NECK,W/CONTRST"," 6,350 "
-95010070,"HC MRI ANGIO NECK,W/W/O CONT"," 7,957 "
-95010088,HC MRI CHEST W/CONTRAST," 6,458 "
-95010096,HC MRI CHEST WO/W CONTRAST," 8,190 "
-95010104,HC MRI PELVIS," 5,566 "
-95010112,HC MRI PELVIS WO/W CONTRAST," 7,794 "
-95010120,HC MRI UPR EXTREM NON-JT," 5,594 "
-95010138,HC MRI UPR EXTREM NON-JT W/CONT," 6,120 "
-95010146,HC MRI UPR EXTREM JNT W/CONTRAST," 6,565 "
-95010153,HC MRI UPR EXTREM JNT WO/W CONTRA," 8,002 "
-95010161,HC MRI LOWER EXTREMITY NON-JOINT," 5,898 "
-95010179,HC MRI LWR EXTREM NON-JT W/CONT," 6,629 "
-95010187,HC MRI LWR EXTREM JNT W/CONTRAST," 6,152 "
-95010195,HC MRI LWR EXTREM JNT WO/W CONT," 8,324 "
-95010203,HC MRI ABD W/CONTRAST," 6,681 "
-95010211,HC MRI ABD WO/W CONTRAST," 8,002 "
-95010229,HC MRI TMJ(S)," 4,252 "
-95030086,HC MRI PELVIS," 6,681 "
-95031423,HC MR AVM ST ADDNL CPY," 1,068 "
-95031621,HC MRI ORBITS FACE NECK," 5,554 "
-95031662,HC MRA CHEST W OR WO CONTRAST," 8,190 "
-95031688,HC MRA SPINAL CANAL W OR W/O CONT," 8,263 "
-95031704,HC MRA PELVIS W OR WO CONTRAST," 7,794 "
-95031720,HC MRA UPPER EXT WO/W CONTRAST," 7,794 "
-95031746,HC MRA ABDOMEN W OR WO CONTRAST," 8,001 "
-95031761,HC MR HEART MORPHOLOGY W/CONTRAST," 7,610 "
-95031829,HC MRI BRST UNI W&OR WO CONTRAST," 3,233 "
-95031845,HC MRI BRST BILAT W CONTRAST," 5,351 "
-95031894,HC MR SPECTROSCOPY," 2,499 "
-95060257,HC MR SYRINGE&TRAY COS, 199 
-95060331,HC MR BRAIN WO/W CONTRST," 8,276 "
-95060349,HC MR C SPINE WO/W CONT," 8,423 "
-95060356,HC MR T SPINE WO/W CONT," 7,998 "
-95060364,HC MR L SPINE WO/W CONT," 8,733 "
-95060398,HC MRA LWR EXTRM W OR WO CONTRAST," 7,969 "
-95060422,HC MR HEART W/O CONTRAST," 6,268 "
-95060430,HC MR HEART VELOCITY FLOW," 4,171 "
-95060539,HC MRI W/CONT BREAST UNI," 2,676 "
-95060547,HC HC MRI W/O CONT BREAST UNI," 1,993 "
-95060554,HC MRI W/O CONT BREAST BI," 3,985 "
-95060562,HC MRI W/O FOL W/CONT BREAST BI," 6,466 "
-40510498,HC POLIOVIRUS VACC INACT SQ/IM, 63 
-58002107,HC OCTREOTIDE AMP 0.05MG/ML, 6 
-94100252,HC PARING LESN BENIGN 2-4, 247 
-94100260,HC PARING LESN BENIGN >4, 276 
-32050070,HC CHEMICAL CAUTERIZATION OF TISSUE, 322 
-41309402,HC JT SURVEY 1V 2 OR MORE JT, 140 
-42016378,HC PLMT NTRSTL DEV PRQ IABDL IPEL," 3,978 "
-62020854,HC TX RETINOPATHY W/PHOTOCOAG," 1,216 "
-62021092,HC REDUCE OVERCORRCT PTOSIS," 2,858 "
-41316308,HC KNEE THREE VIEWS-BILAT," 1,630 "
-41316316,"HC KNEE, ONE OR TWO VIEWS - BILAT"," 1,464 "
-81200373,HC HOLSTER VEST LRG LIPK REPLACEMENT," 2,976 "
-81200374,HC HM MODLR BELT," 2,550 "
-81200375,HC SYS MONITOR CABLE," 3,400 "
-81200376,HC SYS DISPLAY CABLE," 3,122 "
-81200379,HC HM TRAVEL CASE," 1,190 "
-81200372,HC HOLSTER VEST MED LIPK," 2,976 "
-81200371,HC HOLSTER VEST SML LIPK," 2,976 "
-81200369,HC HM ER POWER CABLE," 1,530 "
-81200370,HC HOLSTER VEST ADJSTBL," 2,976 "
-81200368,HC PWR MODL AC CORD, 255 
-81200367,HC POWER MODULE," 49,400 "
-38216305,HC KNEE THREE VIEWS-BILAT," 1,630 "
-38216313,"HC KNEE, ONE OR TWO VIEWS - BILAT"," 1,464 "
-41016304,HC KNEE THREE VIEWS-BILAT," 1,630 "
-41016312,"HC KNEE, ONE OR TWO VIEWS - BILAT"," 1,464 "
-93616308,HC KNEE THREE VIEWS-BILAT," 1,630 "
-93616316,"HC KNEE, ONE OR TWO VIEWS - BILAT"," 1,464 "
-50781525,HC 7014 ISOL EXT EA NA, 31 
-50781533,HC 7014 ENZY DIGEST EA TRTMT, 31 
-50781541,HC 7014 11620R SEP GEL ELCT, 45 
-50781558,HC 7014 11620R AMP EA NA SEQ, 187 
-50781566,HC 7014 11620R INTRP AND RPT, 45 
-50781764,HC 7051 BASO ACT CELL STIM, 94 
-50781772,HC 7051 11408R BF HRT CEPHAL, 79 
-50781781,HC 7051 11409R BF HRT PEN MIN, 79 
-50781798,HC 7051 11410R BF HRT AMOX, 79 
-50781806,HC 7051 11411R BF HRT CEFRXM, 79 
-50781814,HC 7051 11425R BF HRT PEN MAJ, 79 
-50781822,HC 7051 11426R BF HRT TETRCYLN, 79 
-50781831,HC 7051 11427R BF HRT AMPCLLN, 79 
-50781848,HC 7051 11428R BF HRT TRMTHPRM, 79 
-50780659,HC 6949 DHIVB HIV 1/2 AB, 51 
-42016386,HB ADRENAL BIOPSY WITH CT GUIDANCE," 19,718 "
-62011192,HC LAYER CLOS WND 12.6-20.0, 659 
-62011978,HC DESTR VASC LESIONS>50, 618 
-62020078,INJ ANES NERVE SCIATIC SGL," 1,451 "
-62020342,"HC  DESTRUCTION NEURLYTIC AGENT, CELIAC PLEXUS"," 1,425 "
-62022298,"HC OPHTHALMOSCOPY, EXT I&R", 100 
-81200377,HC HM CONSL BAG," 3,122 "
-81200378,HC HM STABLZ BELT," 1,102 "
-50780667,HC 6941 12095R DARUNAVIR QUANT, 84 
-50780675,HC 6941 12096R LOPINAVIR QUANT, 84 
-50780683,HC 6941 12097R ATAZANAVIR QNT, 84 
-50781855,HC 6976 12098R TROFILE DNA," 1,960 "
-46160958,HC MPVPCR BY NA RNA APT EA, 388 
-46160966,HC RMPCR BY NA RNA APT EA, 388 
-46160941,HC RSVPCR BY NA RNA APT EA, 388 
-81200366,HC DISPLAY MODULE," 18,200 "
-32051528,HC REIMPLANTATION OF TOOTH, 348 
-32092000,HC REIMPLANTATION OF TOOTH, 348 
-38216321,HC XR DISKOGRAM L-SPINE," 4,875 "
-41016320,HC XR DISKOGRAM L-SPINE," 4,875 "
-56269351,HC ADULT ASD/PFO CLOSURE," 36,899 "
-56269460,HC ADULT ICE ECG," 10,926 "
-62023403,HC LASER TX SKIN 250-500 CM, 481 
-30602050,HC COLONOSCOPY WITH SUBMUC INJECT, 350 
-50781863,HC 6997 12080R IS0L HP EA NA, 7 
-50781871,HC 6997 12080R DOT BLOT PRD EA, 7 
-50781889,HC 6997 12080R SEP GEL ECLT EA, 7 
-50781897,HC 6997 12080R NA PROBE EA, 7 
-50781905,HC 6997 12080R AMP MPLX NA 1&2, 59 
-50781913,HC 6997 12080R AMP MPLX ADTL, 29 
-50782077,HC 6911 12090R HE4, 256 
-46160990,HC HPVPCR HPV 16 DNA APT, 555 
-46161006,HC HPVPCR HPV 18 DNA APT, 555 
-32050068,HC EXC LES BGN SCLP/HND .6-1.0," 1,644 "
-91900410,HC PICKED ASSTD OOCYTE FERT 10 OR LESS, 300 
-62010025,HC EXC MALIG FACE OVER 4.0CM," 2,256 "
-62010027,HC S/T AUTOGR TRNK EXTR 100CM," 2,069 "
-62010029,HC US MEASURE POST VOID, 127 
-62027590,HC DEBR SUBQ TISS EA ADD 20 SQ CM, 268 
-62027592,HC DEB MUS/FAS EA ADD 20 SQ CM, 283 
-62027594,HC DEBR BONE EACH ADD 20 SQ CM, 802 
-62027506,HC BRONCH W/BALLOON OCCLUSION," 1,652 "
-62027536,HC DX GASTR INTUB W/ASP SPEC, 209 
-62027538,HC DX GASTR INTUB W/ASP SPECS, 379 
-62027540,HC DX DUOD INTUB W/ASP SPEC, 404 
-62027542,HC DX DUOD INTUB W/ASP SPECS, 613 
-62027548,HC INS VAG BRACHYTX DEVICE, 690 
-62027552,HC COVER EYE W/MEMBRANE," 3,223 "
-62027554,HC COVER EYE W/MEMBRANE; SUTURE," 6,351 "
-62027556,HC XM ARCHIVE TISSUE MOLEC ANAL, 32 
-62027558,HC ESOPH MOTIL STD W/I&R STIM/HC PERF, 459 
-62027400,HC GE REFLUX TEST NASAL ELECTR, 719 
-62027403,HC GE REFLUX TEST MUCOSAL," 1,848 "
-62027406,HC GE REFLUX TEST IMPED ELECTR, 320 
-62027409,HC GE REFLUX TEST IMPED ELEC PROL, 923 
-62027412,HC ESOPH BALLOON DISTEN PROVOC, 546 
-51564710,HC SCRN: 7 DRUGS MODER COMPLEX, 536 
-51864720,HC SCRN: 9 DRUGS MODER COMPLEX, 536 
-51865722,HC SCRN: 9 DRUG HI COMPLEX," 2,664 "
-51565323,HC GDMC GTT EA ADL BYN 3, 176 
-46160982,HC H1N1PC H275Y MUT RT AMP," 1,180 "
-52110707,HC CYPT URN 3-5 MOL PRB EA MAN," 1,387 "
-52110715,HC CYPT URN 3-5 MOL PRB EA CAT," 1,253 "
-52110723,HC CYTP EVL FNA EA ADL EVL EP, 36 
-52051463,HC EXM&SLCT ARCHV TIS MOL ANLY, 136 
-41160001,"HC I&D ABSCESS, SINGLE, SIMPLE", 937 
-41160100,"HC ILIAC, REVASC W PLASTY UNIL"," 8,209 "
-41160103,"HC  ILIAC, REVASC W STENT UNIL"," 22,813 "
-41160106,"HC ILIAC, ADD-ON EA ADDL"," 8,209 "
-41160109,"HC ILIAC, ADD-ON W STENT"," 10,320 "
-41160111,HC FEM-POP REVASC W PLASTY UNI," 10,300 "
-41160117,HC FEM-POP REVASC W STENT," 22,218 "
-41160120,HC FEM-POP REVASC W BOTH," 20,167 "
-41160123,HC TIB-PERO REVASC W PLSTY UNI," 8,361 "
-41160126,HC TIB-PERO REVASC W ATHER," 14,695 "
-41160129,HC TIB-PERO REVASC W STENT," 13,506 "
-41160131,HC TIB-PERO REVASC W BOTH," 19,217 "
-41160133,HC TIB-PERO EA ADDL PLASTY," 8,209 "
-41160136,HC TIB-PERO EA ADDL ATHEREC," 13,506 "
-41160139,HC TIB-PERO EA ADDL STENT," 8,209 "
-41160141,HC TIB-PERO EA ADDL BOTH," 8,209 "
-56264005,HC RIGHT HEART CATH," 16,567 "
-56264010,HC LEFT HEART CATH," 18,234 "
-56264015,HC LEFT HEART CATH TRANS-SEPTAL," 7,637 "
-56264020,HC ADD - PHARMACOLOGIC AGENTS," 8,095 "
-56264025,HC  ADD - PHYSIOL EXERC STUDY, 531 
-56264030,HC ADD - INJECTION COR ART," 3,053 "
-56264035,HC ADD INJECT CONGEN BYPASSES," 2,100 "
-56264040,HC ADD-INJECT L ATRIA/ VENTR," 2,026 "
-56264045,HC ADD - INJECT R ATRIA/ VENTR," 2,149 "
-56264050,HC ADD - SUPRAVALV AORTOGR," 2,424 "
-56264055,HC ADD - PULMON ANGIOGRAPHY," 2,568 "
-56264060,"HC L&R HEART CATH, W S&I"," 23,873 "
-56264065,HC CATH PLACE COR S&I," 18,796 "
-56264070,HC CATH PLACE BYPASS S&I," 21,899 "
-56264075,HC CATH PLACE W R HEART CATH," 23,359 "
-56264080,HC CATH PLACE BYPASS & RHC," 26,433 "
-56264085,HC CATH PLACE L& VGRAM," 22,338 "
-56264090,HC CATH PL BYPASS LVGRAM," 24,476 "
-56264095,HC CATH PL L&R CATH L V-GRAM," 25,803 "
-56264100,"HC CATH PL R&L CATH, ALL"," 30,023 "
-41164105,HC RENAL ATHERECTOMY S&I," 15,710 "
-41164110,HC VISCERAL ATHERECTOMY EACH," 15,710 "
-41164115,HC ABDOM AORTA ATHERECTOMY," 15,710 "
-41164125,HC ILIAC ATHERECTOMY EACH," 15,710 "
-37216405,HC CT ABDOMEN-PELVIS," 6,470 "
-37216413,HC CT ABDOMEN-PELVIS W CONTRAST," 7,711 "
-37216421,HC CT ABDOMEN-PELVIS WO/W CONTRAST," 8,993 "
-38016408,HC CT ABDOMEN-PELVIS NO CONTRAST," 6,470 "
-38016416,HC CT ABDOMEN-PELVIS W CONTRAST," 7,711 "
-38016424,HC CT ABDOMEN-PELVIS WO/W CONTRAST," 8,993 "
-38305181,"HC US, EXTREMITY, NONVASC - COMPLETE"," 1,132 "
-38305199,"HC US, EXTREMITY, NONVASC - LIMITED", 952 
-38616413,HC CT ABDOMEN-PELVIS W CONTRAST," 7,711 "
-38616421,HC CT ABDOMEN-PELVIS WO/W CONTRAST," 8,993 "
-40905184,"HC US, EXTREMITY, NONVASC - COMPLETE"," 1,132 "
-40905192,"HC US, EXTREMITY, NONVASC - LIMITED", 952 
-42016402,HC CT ABDOMEN-PELVIS NO CONTRAST," 6,470 "
-42016410,HC CT ABDOMEN-PELVIS W CONTRAST," 7,711 "
-42016428,HC CT ABDOMEN-PELVIS WO/W CONTRAST," 8,993 "
-56200050,HC I&D HEMATOMA," 2,774 "
-56200054,HC INC ABSC SOFT TISS SUBFASC," 1,644 "
-56200058,HC INS TUN CATH INTP COMPLETE," 6,421 "
-94316411,HC CT ABDOMEN-PELVIS W CONTRAST," 7,711 "
-94316429,HC CT ABDOMEN-PELVIS WO/W CONTRAST," 8,993 "
-94905189,"HC US, EXTREMITY, NONVASC - COMPLETE"," 1,132 "
-94905197,"HC US, EXTREMITY, NONVASC - LIMITED", 952 
-38616405,HC CT ABD-PELVIS NO CONTR," 6,470 "
-94316403,HC CT ABDOMEN-PELVIS NO CONTR," 6,470 "
-41100014,"HC TEST STAT CATH LAB PROC, 1ST 30 MIN"," 22,000 "
-41100015,"HC TEST STAT CATHLAB PX, EA ADDL 15 MIN"," 5,500 "
-41100401,HC INTRAVASC DOPP COLOR FLOW 1ST," 1,967 "
-62025168,HC INJ ANES HYPOGASTRIC PLEXUS," 1,304 "
-41897865,HC IDE INTRACRANIAL EMOBLZN DEVC LVL 71," 29,600 "
-62019989,HC INJ ANES CERVICAL PLEXUS, 379 
-41100403,HC NITRIC OXIDE ADULT," 8,095 "
-32001128,HC ER REMOVAL OF IUD, 334 
-36079000,HC INS UTER TNDM/VAGINA OVOID," 6,328 "
-91900420,HC CALCIUM IONOPHORE, 312 
-50393883,HC CDTPCR C DIFF TOXIN AMP STL, 486 
-50782085,HC 6911 12091R PMT DRUG SCRN U, 14 
-50782093,HC 6911 12091R CREATININE URN, 5 
-50782101,HC 6911 12091R PH BODY FLUID U, 4 
-50782119,HC 6911 12091R OXIDANT SPCTM U, 7 
-50782127,HC 6911 12092R FENTANYL QNT U, 40 
-50782135,HC 6911 12093R SCREEN HEROIN U, 30 
-50782143,HC 6911 12094R SCRN MDMA MDA U, 30 
-50393891,HC SHIV12 HIV 1 AND 2 AB, 280 
-50804616,HC T1517 AMP EA NA SEQ, 188 
-41016288,HC XR ANKLE 1 VIEW - RESEARCH, 376 
-26500620,HC IMMUN ADMIN ONE VACC, 263 
-42016204,HC CT LIVER NANOKNIFE PROC," 1,910 "
-42016212,HC CT LUNG NANOKNIFE PROC," 2,278 "
-42016683,HC ABLATION PROBE L44," 15,750 "
-42016691,HC NANO PROBE SPACER LVL 17," 1,615 "
-92603046,HC ASP BONE MARROW W/BX SAME DAY, 287 
-62020805,HC PROPHYL RETINAL PHOTOCOAG," 1,267 "
-94101084,HC POLYMEM 6.5X 7.5CM, 85 
-46161014,HC HPV6PC HPV 6 AND 11 DNA APT, 555 
-50721083,HC 6959 DHEAS DHEA SULFATE, 16 
-94101085,HC MEDIHONEY, 85 
-94101086,HC FISTULA POUCH MEDI, 383 
-94101087,HC FISTULA POUCH MAXI, 383 
-51565331,HC HMET HEAVY METAL QUAN EA, 249 
-50781921,HC 6997 12082R ENZY DIG EA, 10 
-50781939,HC 6997 12082R NA PROBE EA, 10 
-50781947,HC 6997 12082R AMP EA NA SEQ, 44 
-50781954,HC 6997 12084R ENZY DIG EA, 14 
-50781962,HC 6997 12084R NA PROBE EA, 14 
-50781971,HC 6997 12084R AMP EA NA SEQ, 58 
-50781988,HC 6997 12086R DOT BLOT PRD EA, 8 
-50781996,HC 6997 12086R SEP GEL ECLT EA, 8 
-50782002,HC 6997 12086R NA PROBE EA, 8 
-50782011,HC 6997 12086R AMP MPLX NA 1&2, 63 
-50782028,HC 6997 12088R DOT BLOT PRD EA, 5 
-50782036,HC 6997 12088R SEP GEL ECLT EA, 5 
-50782044,HC 6997 12088R NA PROBE EA, 5 
-50782051,HC 6997 12088R AMP MPLX NA 1&2, 45 
-50782069,HC 6997 12088R AMP MPLX ADTL, 22 
-37223203,HC CT HEAD/BRAIN W/CONTRAST," 4,505 "
-62801205,HC PROVOX TRACH PLUG L19," 2,338 "
-62801206,HC PROVOX TRACH PLUG L18," 1,913 "
-62801207,HC TRACH STARTER KIT L36," 9,375 "
-40928673,HC US FETAL BIO PROFILE W/NST, 314 
-40930190,HC DUPLEX PENILE VESSELS CMPL, 540 
-40945511,"HC THORACENTESIS, PLEURAL WITH IMAGING"," 3,897 "
-40980013,HC BX BONE TROC/NDL DEEP," 5,930 "
-40980039,HC BX LUNG/MEDIAST PERC NDL," 3,287 "
-39000255,HC GENETIC COUNSELING 30 MIN, 458 
-40450605,HC FOAM RLR EXCR EQUIP L3, 85 
-37001104,HC NM DACROCYSTOGRAM, 744 
-41016379,HC XR DISKOGRAM C- OR T-SPINE," 4,875 "
-62025330,HC ELEC ALYS NSTIM PLS GEN CPLX CRNL NRV 1ST HR," 1,312 "
-55092332,HC ROSAUR STAPH AUREUS SCRN, 165 
-55092340,HC ROSTRO STRONGLDS CUL STOOL, 165 
-55092357,HC ROACAN ACANTHAMOEBA CUL, 165 
-55092365,HC 6942 STRPNE STREP PHEUM, 30 
-55092373,HC 6942 STRPNE STREP PHEUM, 30 
-55092381,HC 6942 SYNERG ANTIMIC SYNERGY, 122 
-55092399,HC 6942 HAESTA HMPHLS INFLNZ, 30 
-55092407,HC 6942 HAESTB H INFL TY B, 309 
-55092415,HC 6942 STSMIC ANTI SUSC MIC, 254 
-55092423,HC 6942 ANTSUS ANTISUSC MIC1, 254 
-38216370,HC XR DISKOGRAM C- OR T-SPINE," 4,875 "
-32051685,HC DRAIN ABSC/HEMA EAR EXT SMP, 354 
-26730380,HC PBSC HARVESTING ALLOGENIC," 6,795 "
-41151500,HC INJECT SINUS TRACK THERAP," 3,820 "
-50782648,HC 6959 UVH HVA HMVNLLC ACD UR, 26 
-50782549,HC 6959 11518R BCRABL1 ISL EA, 14 
-50782556,HC 6959 11518R BCRABL1 SEP GEL, 14 
-50782564,HC 6959 11518R BCRABL1 AMP EA, 59 
-50782572,HC 6959 11518R BCRABL1 REV TR, 50 
-50782581,HC 6959 11518R BCRABL1 I & R, 14 
-50782655,HC 6959 UVH VMA VNLYMDLC AC UR, 21 
-50782598,HC 6959 C1INHP CMP C1 C4 AG EA, 23 
-50782606,HC 6959 C1INHP CMP FNCT ACT EA, 23 
-50782622,HC 6959 IBD FLRST NONINF AB EA, 40 
-50782473,HC 6942 PNAB STRP PNEU IGG AB, 17 
-50782465,HC 6942 PNAB STRP PNEU IGG AB, 18 
-50782614,HC 6959 DT DIPHTH TETNS AB IGG, 20 
-50782499,HC 6942 PNET2 STRP PNEU IGG AB, 26 
-50782481,HC 6942 PNET2 STRP PNEU IGG AB, 26 
-50782366,HC 6942 HUMP STRP PNEU IGG AB, 45 
-50782416,HC 6942 LAS LYMPHOCYTE MIT&AG, 57 
-50782408,HC 6942 LAS LYMPHOCYTE MIT&AG, 57 
-50782457,HC 6942 PBAN POLIOVRS AB NEUT, 68 
-50782192,HC 6942 ASPID ASPGLUS IMMDIF, 19 
-50782201,HC 6942 ASPID ASPGLUS IMMDIF, 19 
-50782218,HC 6942 BARSAB BARTNLA IGG IGM, 28 
-50782226,HC 6942 BARSAB BARTNLA IGG IGM, 28 
-50782374,HC 6942 HUMP DIPHTHERIA ANTXD, 46 
-50782267,HC 6942 FABRO ENCPHLTS CA LCR, 63 
-50782275,HC 6942 FABRO ENCPHLTS E EQN, 63 
-50782283,HC 6942 FABRO ENCPHLTS STLOUIS, 63 
-50782291,HC 6942 FABRO ENCPHLTS W EQN, 63 
-50782309,HC 6942 FABRO ENCPHLTS W EQN, 63 
-50782317,HC 6942 FENTP ENTEROVIRUS, 20 
-50782664,HC 6942 FCHGA ANPLS AB EHRLCH, 77 
-50782631,HC 6959 IBD FUNGUS AB SEMIQNT, 40 
-50782325,HC 6942 HELPM H PYLORI IGM, 80 
-50782382,HC 6942 HUMP HAEMOPHILUS INF B, 48 
-50782432,HC 6942 LEGS LEGIONELLA AB IFA, 51 
-50782424,HC 6942 LEGS LEGIONELLA AB IFA, 51 
-50782441,HC 6942 LEISAB LEISHMANIA AB, 10 
-50782151,HC 6942 12055R RCKTSA RMSF IGG, 83 
-50782176,HC 6942 12059R RCKTSA TYPH IGG, 83 
-50782168,HC 6942 12057R RCKTSA RMSF IGM, 90 
-50782184,HC 6942 12061R RCKTSA TYPH IGM, 90 
-50782391,HC 6942 HUMP TETANUS ANTIXD AB, 45 
-50782507,HC 6942 WNVAB WESTNILE IGM CSF, 58 
-50782515,HC 6942 WNVAB WESTNILE IGG CSF, 49 
-50782259,HC 6942 DFA DENGUE FEV AB, 30 
-50782341,HC 6942 HEVG HEP E AB IGG, 35 
-50782358,HC 6942 HEVM HEP E VRS AB IGM, 52 
-50782531,HC 6942 YENTE YERSINIA AB IGA, 47 
-50782523,HC 6942 YENTE YERSINIA AB IGG, 47 
-50782234,HC 6942 CHLAM CHLYM TRACHOMTS, 33 
-50782242,HC 6942 CHLAM PSITTACI CULT, 9 
-50782333,HC 6942 HEPDG HEP D ANTG EIA, 101 
-30683621,HC GI TRACT EXM WIRELESS CAP," 2,644 "
-32051132,HC  REM FB FOOT SUBQ," 1,644 "
-32091776,HC CLOSE LAC MOUTH 2.5CM OR LS, 648 
-32712558,HC CAPR IR DC SUPPYKIT, 425 
-38704844,HC BURN DRS SKINSUB SQ CM L42," 10,350 "
-38704852,HC BURN DRS SKINSUB SQCM L48," 14,000 "
-38704860,HC MESH SKINSUB SQCM L54," 17,000 "
-38704868,HC MESH SKINSUB SQCM L62," 22,400 "
-41151167,HC URETEROT INSERT STENT," 1,987 "
-41154385,HC US EXTR NON VASCULAR LTD, 952 
-50470129,HC OD450 DELTA BODY FLUID, 121 
-81200290,"HC CTDN MNGMT FEE,OUT OF AREA"," 10,500 "
-81200291,HC CTDN FLYOUT FEE OUT OF AREA," 5,500 "
-81400294,"HC CTDN MNGMT FEE,OUT OF AREA"," 10,500 "
-81400295,HC CTDN FLYOUT FEE OUT OF AREA," 5,500 "
-81500291,"HC CTDN MANAGEMENT FEE, OUT OF AREA"," 10,500 "
-81500292,"HC CTDN FLY OUT FEE, OUT OF AREA"," 5,500 "
-82200305,"HC CTDN MANAGEMENT FEE,OUT OF AREA"," 10,500 "
-84200300,"HC CTDN MNGEMT FEE,OUT OF AREA"," 10,500 "
-84200301,HC CTDN FLYOUT FEE OUT OF AREA," 5,500 "
-94716305,HC KNEE THREE VIEWS-BILAT," 1,630 "
-94716313,"HC KNEE, ONE OR TWO VIEWS - BILAT"," 1,464 "
-32050050,HC DEBR SKN/SUBQ TISSUE 1-20CM, 591 
-62600090,HC VOICE PRTHS PROF ISRT L16," 1,360 "
-62600091,HC VOICE PRTHS PROF ISRT L17," 1,530 "
-62600092,HC VOICE PRTHS PROF ISRT L18," 2,143 "
-62600093,HC VOICE PRTHS PROF ISRT L19," 2,602 "
-62600094,HC VOICE PRTHS PROF ISRT L20," 2,550 "
-62600095,HC VOICE PROTH STARTER KIT," 9,375 "
-50470111,HC PALB PREALBUMIN, 246 
-50570050,HC ANTIXA LMW HEPARIN ACT, 466 
-50804624,HC CFDS ISOL HP EA NA DNA, 146 
-50804632,HC CFDS SEP GEL ELCT EA NA, 103 
-50804640,HC CFDS AMP EA NA SEQ, 188 
-50804657,HC CFDS MUT ID SEQ EA SEG, 54 
-50804665,HC CFDS INTRP AND RPT, 138 
-51565349,HC CAD HVY MTL CADMIUM QUAN, 249 
-51565356,HC MERC HVY MTL MERCURY QUAN, 249 
-51565364,HC ARS HVY MTL ARSENIC QUAN, 249 
-92900020,HC LUPRON LEUPROLIDE 3.75MG," 2,190 "
-92900030,HC LEUPROLIDE ACETATE /3.75 MG," 2,190 "
-92900040,HC RHO D IMMUNE GLOB 300 MCG, 339 
-92900050,HC IUD COPPER, 679 
-92900070,HC ETONOGESTREL IMPLANT SYSTEM," 1,569 "
-92900080,HC LEUPRON ACETATE, 829 
-92900090,HC IM OR SUBCU INJECTION DRUG, 193 
-62600105,HC IM OR SUBCU INJECTION DRUG, 344 
-62600100,HC BOTULINUM TOXIN A PER UNIT, 28 
-26632000,HC TRIAMCINOLONE ACET INJ NOS, 52 
-40400001,HC IM OR SUBCU INJECTION DRUG, 193 
-92000700,HC LEUPROLIDE ACETATE /3.75 MG," 2,190 "
-92000705,HC IM OR SUBCU INJECTION DRUG, 193 
-32050515,HC TX FX BONE NASAL W/O STABL," 4,914 "
-32710455,HC THERAPYPRO//DX IV PUSH SAME, 405 
-36099101,HC BRACHY NON-STRANDED  I-125 PER," 9,000 "
-36200128,HC MCL FOR IMRT DESIGN&CONSTR," 13,988 "
-50470137,HC GDMTOL GTT 1 TO 3 SPEC, 789 
-50804673,HC CFPT SEP GEL ELCT EA NA PRP, 103 
-50804681,HC CFPT AMP EA NA SEQ, 188 
-50804699,HC CFPT MUT ID SEQ EA SEG, 54 
-50804707,HC CFPT INTRP AND RPT, 138 
-50804715,HC BCFDSX ISOL HP EA NA DNA, 146 
-50804723,HC BCFDSX SEP GEL ELCT EA NA, 103 
-50804731,HC BCFDSX AMP EA NA SEQ, 188 
-50804749,HC BCFDSX MUT ID SEQ EA SEG, 54 
-50804756,HC BCFDSX INTRP AND RPT, 138 
-62025376,HC DEST LESN CHOROID PHOTOCOAG," 1,545 "
-32051510,HC ER RPR LIP UP 1/2 VERTICAL," 1,405 "
-37225869,HC CT C-SPINE W/O CONTRAST," 5,262 "
-37225877,HC CT T-SPINE W/O CONTRAST," 5,180 "
-37225885,HC CT L-SPINE W/O CONTRAST," 5,155 "
-41100273,HC PERC SACRAL AUGMT BILAT INJ," 8,294 "
-41100743,HC PUNCT C1-C2 W/O INJ," 1,594 "
-41151469,HC ERCP W/PLACE STENT," 4,138 "
-41898222,HC CATH IVUS L30," 5,800 "
-50782671,HC 6959 DOXEPI DOXEPIN TDA, 19 
-50782689,HC 6959 DOXEPI QUANT DRUG NES, 17 
-50782697,HC 7052 11535R SEP HRT EA NA, 28 
-50782705,HC 7052 11536R SEP HRT EA NA, 42 
-50782713,HC 7052 11538R SEP HRT EA NA, 37 
-50782721,HC 7052 11539R SEP HRT EA NA, 33 
-50782739,HC 7052 11540R SEP HRT EA NA, 25 
-50793819,HC 6949 H138 SEP GEL ELCT EANA, 8 
-50793827,HC 6949 H138 NA PROBE EA, 8 
-50793835,HC 6949 H138 AMP EA NA SEQ, 30 
-50793843,HC 6949 H138 PATH CONSULT COMP, 8 
-50793850,HC 6949 H139 SEP GEL ELCT EANA, 8 
-50793868,HC 6949 H139 NA PROBE EA, 8 
-50793876,HC 6949 H139 AMP EA NA SEQ, 30 
-50793884,HC 6949 H139 PATH CONSULT COMP, 8 
-50793892,HC 6949 H117 SEP GEL ELCT EANA, 6 
-50793900,HC 6949 H117 NA PROBE EA, 6 
-50793918,HC 6949 H117 AMP EA NA SEQ, 27 
-50793926,HC 6949 H117 INTERP AND RPT, 6 
-52293818,HC 6949 H138 SEP GEL ELCT EANA, 8 
-52293826,HC 6949 H138 NA PROBE EA, 8 
-52293834,HC 6949 H138 AMP EA NA SEQ, 30 
-52293842,HC 6949 H138 PATH CONSULT COMP, 8 
-52293859,HC 6949 H139 SEP GEL ELCT EANA, 8 
-52293867,HC 6949 H139 NA PROBE EA, 8 
-52293875,HC 6949 H139 AMP EA NA SEQ, 30 
-52293883,HC 6949 H139 PATH CONSULT COMP, 8 
-52293891,HC 6949 H117 SEP GEL ELCT EANA, 8 
-52293909,HC 6949 H117 NA PROBE EA, 8 
-52293917,HC 6949 H117 AMP EA NA SEQ, 30 
-52293925,HC 6949 H117 INTERP AND RPT, 8 
-62062252,HC REPROGRAM SHUNT CSF, 233 
-62095950,HC EEG SEIZURE FOCUS," 1,102 "
-81093817,HC 6949 H138 SEP GEL ELCT EANA, 8 
-81093825,HC 6949 H138 NA PROBE EA, 8 
-81093833,HC 6949 H138 AMP EA NA SEQ, 30 
-81093841,HC 6949 H138 PATH CONSULT COMP, 8 
-81093858,HC 6949 H139 SEP GEL ELCT EANA, 8 
-81093866,HC 6949 H139 NA PROBE EA, 8 
-81093874,HC 6949 H139 AMP EA NA SEQ, 30 
-81093882,HC 6949 H139 PATH CONSULT COMP, 8 
-81093890,HC 6949 H117 SEP GEL ELCT EANA, 8 
-81093908,HC 6949 H117 NA PROBE EA, 8 
-81093916,HC 6949 H117 AMP EA NA SEQ, 30 
-81093924,HC 6949 H117 INTERP AND RPT, 8 
-81393811,HC 6949 H138 SEP GEL ELCT EANA, 8 
-81393829,HC 6949 H138 NA PROBE EA, 8 
-81393837,HC 6949 H138 AMP EA NA SEQ, 30 
-81393845,HC 6949 H138 PATH CONSULT COMP, 8 
-81393852,HC 6949 H139 SEP GEL ELCT EANA, 8 
-81393860,HC 6949 H139 NA PROBE EA, 8 
-81393878,HC 6949 H139 AMP EA NA SEQ, 30 
-81393886,HC 6949 H139 PATH CONSULT COMP, 8 
-81393894,HC 6949 H117 SEP GEL ELCT EANA, 8 
-81393902,HC 6949 H117 NA PROBE EA, 8 
-81393910,HC 6949 H117 AMP EA NA SEQ, 30 
-32040021,HC ED BURR HOLE W/ DRAIN," 1,152 "
-81393928,HC 6949 H117 INTERP AND RPT, 8 
-82393810,HC 6949 H138 SEP GEL ELCT EANA, 8 
-32051776,HC BIOPSY INTRANASAL," 2,110 "
-82393828,HC 6949 H138 NA PROBE EA, 8 
-82393836,HC 6949 H138 AMP EA NA SEQ, 30 
-82393844,HC 6949 H138 PATH CONSULT COMP, 8 
-82393851,HC 6949 H139 SEP GEL ELCT EANA, 8 
-62028853,HC PREPARE FECAL MICROBIOTA FOR I, 70 
-82393869,HC 6949 H139 NA PROBE EA, 8 
-82393877,HC 6949 H139 AMP EA NA SEQ, 30 
-32051886,HC INTRO LONG GI TUBE," 1,809 "
-82393885,HC 6949 H139 PATH CONSULT COMP, 8 
-82393893,HC 6949 H117 SEP GEL ELCT EANA, 8 
-62028854,HC SMOKE/TOBACCO ASYMP 3-10, 32 
-82393901,HC 6949 H117 NA PROBE EA, 8 
-82393919,HC 6949 H117 AMP EA NA SEQ, 30 
-82393927,HC 6949 H117 INTERP AND RPT, 8 
-84393818,HC 6949 H138 SEP GEL ELCT EANA, 8 
-62028855,HC SMOKE/TOBACCO ASYMP >10, 64 
-84393826,HC 6949 H138 NA PROBE EA, 8 
-84393834,HC 6949 H138 AMP EA NA SEQ, 30 
-42005967,HC BIOPSY NEEDLE," 5,057 "
-84393842,HC 6949 H138 PATH CONSULT COMP, 8 
-84393859,HC 6949 H139 SEP GEL ELCT EANA, 8 
-62028856,HC SMOK CESS COUN INTEN >10 MIN, 32 
-84393867,HC 6949 H139 NA PROBE EA, 8 
-84393875,HC 6949 H139 AMP EA NA SEQ, 30 
-84393883,HC 6949 H139 PATH CONSULT COMP, 8 
-84393891,HC 6949 H117 SEP GEL ELCT EANA, 8 
-62028857,HC EUA PELVIC EXAM," 4,097 "
-84393909,HC 6949 H117 NA PROBE EA, 8 
-84393917,HC 6949 H117 AMP EA NA SEQ, 30 
-81800120,HC VAD SPLY-SORBAVIEWSHLD DRESSING, 27 
-84393925,HC 6949 H117 INTERP AND RPT, 8 
-30600610,HC PREP/ASSESS FECAL MICROBIOTA, 210 
-30600615,HC ENDOSCOP MICRBIOTA INSTILL," 7,825 "
-81800130,HC VAD SPLY-DRIVELINE SEC DVC, 42 
-30600620,HC ENEMA/ORO-NASL MCRBIOTA INSTILL, 519 
-62099906,HC COS: BROWLIFT, 965 
-62028858,HC DUPLEX PENILE VESSELS CMPL, 436 
-32096353,HC CNTRL NOSEBLEED ANT CPLX, 410 
-42016529,"HC IMAGE GUIDED,CLIP PLACEMENT"," 2,542 "
-62028899,HC DEST LESN PENIS SMP EXCISION," 3,993 "
-32091557,HC CYSTOURETHROSCOPY SIMPLE," 1,436 "
-32001260,HC IRRIG CORPORA CAVER PRIAPRI, 431 
-38305983,HC PRP INJECT KIT - LEVEL 22," 3,400 "
-40905986,HC PRP INJECT KIT - LEVEL 22," 3,400 "
-41100840,HC FLUID ASPIRATN INGUIN DRAIN, 45 
-41152640,HC PERC SACRL AUGMT BILAT 2NDL," 8,294 "
-41152431,HC DIG SUBTRN ARTERGRPHY PANCR, 480 
-40905978,HC PRP INJECT KIT - LEVEL 15," 1,275 "
-38305975,HC PRP INJECT KIT - LEVEL 15," 1,275 "
-32051630,HC DRAIN ABSC SCROTAL WALL," 3,819 "
-38105953,HC CONTRAST FERUMOXSIL 100ML, 98 
-38505954,HC CONTRAST FERUMOXSIL 100ML, 98 
-41177777,HC REPLACEMENT OF PORT HUB," 5,724 "
-41188830,HC PLCMT FID NON-RAD INTRAVASCLR," 3,966 "
-94205952,HC CONTRAST FERUMOXSIL 100ML, 98 
-95005955,HC CONTRAST FERUMOXSIL 100ML, 98 
-62028550,HC PUNCTAL PLUGS TEMPORARY, 140 
-26140153,HC CHEMO INFUSION 1 HR," 1,444 "
-26140154,HC CHEMO IV INFUSN EA ADDL HR, 641 
-50784842,HC 7026 12507R CHMLMNSCNT ASSY, 22 
-50784859,HC 7026 12507R IMMUNO ALTY QNT, 20 
-50784867,HC 7026 12507R NA PROBE EA, 6 
-50784875,HC 7026 12507R AMP TRG EANA SQ, 26 
-50784883,HC 7026 12507R LYSIS PRE NAEXT, 20 
-50784891,HC 7026 12507R INTRP AND RPT, 6 
-50784909,HC 7026 12507R C REACT PROTEIN, 8 
-50784917,HC 7026 12507R IMM EA AB INDRT, 55 
-50784925,HC 7046 12510R ISL EXT HP EANA, 8 
-50784933,HC 7046 12510R AMP MPLX NA 1&2, 69 
-50784941,HC 7046 12510R AMP MPLX ADTL, 35 
-50784958,HC 7046 12510R MUT ID SQ EA SG, 35 
-50784966,HC 7046 12510R SEP ID HRT EANA, 35 
-50784974,HC 7046 12510R INTRP AND RPT, 8 
-50784982,HC 7046 12512R ISL EXT HP EANA, 13 
-50784991,HC 7046 12512R ENZY DIG EA TRT, 13 
-50785005,HC 7046 12512R SEP GEL ELCT EA, 13 
-50785013,HC 7046 12512R AMP TRG EANA SQ, 55 
-50785021,HC 7046 12512R MUT ID SQ EA SG, 55 
-50785039,HC 7046 12512R INTRP AND RPT, 13 
-50785047,HC 7046 12512R ARR 251-500 PRB," 1,462 "
-50785054,HC 7046 12518R ISL EXT HP EANA, 6 
-50785062,HC 7046 12518R ENZY DIG EA TRT, 6 
-50785071,HC 7046 12518R SEP GEL ELCT EA, 6 
-50785088,HC 7046 12518R AMP TRG EANA SQ, 24 
-50785096,HC 7046 12518R MUT ID SQ EA SG, 24 
-50785104,HC 7046 12518R INTRP AND RPT, 6 
-50785112,HC 7046 12520R ISL EXT HP EANA, 8 
-50785121,HC 7046 12520R ENZY DIG EA TRT, 8 
-50785138,HC 7046 12520R SEP GEL ELCT EA, 8 
-50785146,HC 7046 12520R AMP TRG EANA SQ, 32 
-50785153,HC 7046 12520R MUT ID SQ EA SG, 32 
-50785161,HC 7046 12520R INTRP AND RPT, 8 
-30604188,HC H-RHOIDECTOMY W BANDING," 1,447 "
-32050926,HC ER INS WIRE/PIN SKELETAL TRACT," 6,497 "
-50393909,HC EVPCR ENTEROVIRUS PCR CSF, 504 
-50804764,HC BCFDS2 ISOL HP EA NA DNA, 146 
-50804772,HC BCFDS2 SEP GEL ELCT EA NA, 103 
-50804780,HC BCFDS2 AMP EA NA SEQ, 188 
-50804798,HC BCFDS2 MUT ID SEQ EA SEG, 54 
-50804806,HC BCFDS2 INTRP AND RPT, 138 
-51565372,HC TGWASH TYROGLOBULIN WO FLD, 226 
-51565380,HC SSAABY EXT NA SSA ANTIRO AB, 152 
-51565398,HC SSBABY EXT NA SSB ANTILA AB, 152 
-51565406,HC SMABY EXT NA SM ANTISM AB, 152 
-51565414,HC RNPABY EXT NA RNP ANTIRNPAB, 152 
-51565422,HC SCLABY EXT NA SCL70 AB, 152 
-51565430,HC CENABY EXT NA CNTRMR AB, 152 
-51565448,HC JO1ABY EXT NA JO1 ANTIJO1AB, 152 
-51565463,HC TSHFT4 TSH, 380 
-52051521,HC RES STRL TECH UNSTND SLD PB, 12 
-55092431,HC AETEST SUSC MTHD AGAR DIL, 122 
-55092449,HC MOLDE SUSC MTHD AGAR DIL, 122 
-55092456,HC MOLDB SUSC MTHD MIC PERPLT, 254 
-55092464,HC BLAPCR BLAZ CULT APT EA ORG, 578 
-92900100,HC H PAPILLOMA VACC 3 DOSE IM, 184 
-41199999,HC DRAKE JUGULAR STENT STUDY PX, 550 
-42016451,HC ORBIT BIOPSY CT GUIDED," 1,364 "
-42016436,HC BX THYROID NEEDLE PERC, 974 
-42016469,HC CT GUIDED BX TISSUE NECK/THOR," 3,159 "
-50804814,HC RES NCCF CFTR GRANT ONLY, 667 
-35700351,HC OT INFRARED THERAPY, 115 
-35700352,HC PT INFRARED THERAPY, 115 
-50672955,HC FUS GENE REARRANGEMENT," 1,383 "
-50672963,HC CCND1 GENE REARRANGEMENT," 1,383 "
-50672971,HC MYC GEME AMPLIFICATION," 1,383 "
-41100004,HC SYMPLICITY DENERVATION," 17,039 "
-37105080,"HC FLORBETAPIR F18 INJ, AMYVID, NO CHARGE", 1.0 
-37010214,"HC P32 SODIUM PHOSPH TX, ADDL MCI", 816 
-62099903,HC COS: BLEPHAROPLSTY LOWER, 993 
-62099904,HC COS: BLEPHAROPLSTY UPPER, 951 
-62099905,HC COS: BLEPHAROPLSTY UPPER/LOWER," 1,022 "
-62099907,HC COS: PTOSIS REPAIR-UNILAT, 625 
-62099908,HC COS: PTOSIS REPAIR-BILAT, 951 
-62099910,HC COS: CANTHOPLASTY, 625 
-62099915,HC COS: FEMTOSECOND," 1,210 "
-62099916,HC COS: SPECIALITY IOL-PREMIUM TORIC, 345 
-62099917,HC COS: SPECIALITY IOL-PREMIUM CRYSTALENS, 800 
-62099920,HC COS: EYE ELECTIVE INJECTION PROCEDURE, 50 
-62099921,HC COS: BOTOX PER UNIT, 10 
-62099922,HC COS: XEOMIN PER UNIT, 10 
-62099923,HC COS: DYSPORT PER UNIT, 15 
-62099924,HC COS: FILLER  RESTYLANE 0.5ML, 150 
-62099925,HC COS: FILLER  RESTYLANE 1.0ML, 240 
-62099926,HC COS: FILLER  RESTYLANE 2.0ML, 430 
-62099927,HC COS: FILLER  PERLANE 1.0ML, 265 
-62099928,HC COS: FILLER  PERLANE 2.0ML, 477 
-62099929,HC COS: FILLER  BELOTERO (MERZ) 1.0ML, 170 
-62099930,HC COS: FILLER  RADIESSE 0.8CC, 199 
-62099931,HC COS: FILLER  RADIESSE 1.5CC, 295 
-62099932,HC COS: FILLER  JUVADERM ULTRA XC, 275 
-62099933,HC COS: FILLER  JUVADERM ULTRAPLUS XC, 650 
-50785179,HC 6956 12445R ISL EXT HP EANA, 166 
-50785187,HC 6956 12470R STN I&R GRP2 TS, 144 
-50785195,HC 6956 12494R C&R SLD PREPREP, 181 
-50785203,HC 6956 12496R C&R SLIDE PREP, 181 
-50785211,HC 6956 12498R CNSLT COMP RVW, 181 
-50785229,HC 6956 12503R K 24HR FCS UNLS, 34 
-50785237,HC 6956 12505R SODIUM 24HR FCS, 34 
-42080200,HC ABLATION PROBE L41," 12,750 "
-40105759,HC BIOPSY HANDPIECE LVL17," 1,615 "
-40105768,HC NEEDLE INTRO LVL17," 1,615 "
-40105777,HC BREAST MARKER LVL16," 1,445 "
-40105786,HC BREAST NEEDLE, 900 
-40105795,HC BREAST MARKER LVL12, 808 
-40105804,HC BIOPSY GRID LVL 7, 383 
-40105813,HC BREAST WIRE LVL7, 383 
-40105822,HC BREAST MARKER, 85 
-32093000,HC ECHO 2D W/W/O M-MO LTD," 2,853 "
-32093002,HC US MEASURE POST VOID, 140 
-32093004,HC US GUIDED VASCULAR ACCES," 1,813 "
-32093008,HC US GUIDED PERICARDIO, 498 
-32093010,HC US EXT NON VAS LIMITED, 952 
-32093012,HC US BREAST(S) UNI OR BI, 948 
-32093014,"HC US INFANT HIPS, LIMITED"," 1,173 "
-32093016,HC US GUIDANCE NEEDLE PLCMNT," 1,896 "
-32093018,HC US SOFT TISSUE HEAD/NECK," 1,554 "
-32093020,HC US CHEST," 1,529 "
-32093022,HC US PREG LTD," 1,257 "
-32093024,HC US PELVIS NON-OB LTD," 1,237 "
-32093026,HC US ABDOMEN LTD," 1,584 "
-32093028,HC US RETROPER LTD," 1,321 "
-32093030,HC DUPX VEIN EXT UNI/LMTD," 2,072 "
-32093031,HC CHEST 1 VIEW, 593 
-40105831,HC SUCTION CANNISTER LVL2, 85 
-41100170,HC TRANSFUSION BLOOD," 3,300 "
-62028575,HC PROLONG SERV B/A PT CARE 1ST, 216 
-62028576,HC PROLONG SERV B/A PT CARE, 105 
-62028577,HC MED TEAM CONF 30 MIN NON MD, 123 
-62028579,HC PREVENT MED NEW <1 YR, 131 
-62028580,HC PREVENT MED NEW 1-4 YRS, 136 
-62028581,HC PREVENT MED NEW 5-11 YRS, 142 
-62028582,HC PREVENT MED NEW 12-17 YRS, 160 
-62028583,HC PREVENT MED NEW 18-39 YRS, 156 
-62028584,HC PREVENT MED NEW 40-64 YRS, 181 
-62028585,HC PREVENT MED NEW > 65 YRS, 196 
-62028586,HC PREVENT MED ESTAB <1 YR, 116 
-62028587,HC PREVENT MED ESTAB 1-4 YRS, 124 
-62028588,HC PREVENT MED ESTAB 5-11 YRS, 123 
-62028589,HC PREVENT MED ESTAB 12-17 YRS, 134 
-62028590,HC PREVENT MED ESTAB 18-39 YRS, 137 
-62028591,HC PREVENT MED ESTAB 40-64 YRS, 147 
-62028593,HC ADJUST GASTRIC BAND," 1,641 "
-62028596,HC AMNTIC MEM SURG RECON PER PX," 1,739 "
-62028597,HC XEOMIN INJ TYPE A PER UNIT, 28 
-62028598,HC CLINDAMYCIN INTRAOCULAR 1MG, 170 
-62023726,HC OSTEOPATH MANIP 3-4 BDY REG, 90 
-62010038,HC DRAINAGE OF PILONIDAL CYST, 579 
-62010061,HC INC&REMOVE FB SUBQ, 714 
-62010103,HC DEBRIDE INFECTED SKIN ADD-ON, 85 
-62010541,HC RMVL SWEAT GLAND LESION, 952 
-62010830,HC DRAIN BLOOD FROM UNDER NAIL, 100 
-62010848,HC RMVL OF NAIL BED, 429 
-62011036,HC REPAIR SUPERFIC 7.6-12.5CM, 683 
-62011044,HC REPAIR SUPERFIC 12.6-20CM, 662 
-62011051,HC REPAIR SUPERFIC 20.1-30CM, 799 
-62011093,HC REPAIR SUPERFIC 5.1-7.5CM, 662 
-62011101,HC REPAIR SUPERFIC 7.6-12.5CM, 812 
-62011531,HC SKIN PINCH GRAFT 1/M 2CM, 474 
-62011705,HC ABRASION-TOTAL FACE," 2,256 "
-62011713,HC ABRASION-SEGMENTAL FACE," 1,056 "
-62011721,HC ABRASION-REGION NOT FACE," 1,338 "
-62011739,HC ABRASION-SUPERF ANY SITE, 535 
-62011747,HC ABRASION LESION SINGLE, 633 
-62011754,HC ABRZ LESIONS EA ADDL 4 OR LESS, 170 
-62011788,HC CHEMICAL PEEL NONFACIAL, 291 
-62012067,HC DESTR MALIG > 4.0 LESIONS, 535 
-62012117,HC DESTR MALIG LESIONS 3.1-4.0CM, 535 
-62012166,HC DESTR MALIG LESIONS 2.1-3.0CM, 535 
-62012265,HC SKIN PEEL THERAPY, 145 
-32003210,HC ADMIN HEPATITIS B VACC, 242 
-62012299,HC INJECT FOR BREAST X-RAY, 663 
-62012364,HC PLACE NEEDLE WIRE BREAST, 625 
-32011110,"HC EXPLORE, WOUND, NECK"," 1,406 "
-62012372,HC PLACE NDLE WIRE BREST EA ADDL, 278 
-62012497,HC INJECT SINUS TRACT FOR X-RAY, 647 
-32011115,"HC EXPLORE, WOUND, CHEST"," 6,378 "
-62012620,HC US BONE STIMULATION, 127 
-62012711,HC PREPARE SURG OBTURAT PROSTH," 3,020 "
-32011120,"HC EXPLORE, WOUND, ABD/FLANK/BACK"," 6,378 "
-62012729,HC PREPARE ORBITAL PROSTH," 6,575 "
-62012737,HC PREPARE INTERIM OBTUR PROSTH," 3,318 "
-32050055,HC DBRDMT W/RMVL FM FX&/DISLC SKN&SUBQ TISS," 1,422 "
-62012745,HC PREPARE DEFIN OBTUR PROSTH," 3,779 "
-62012752,HC PREPARE MANDIB RESECT PROSTH," 3,506 "
-32050060,HC EXC B9 LES MRGN XCP SK TG S/N/H/F/G 0.5 CM/<," 2,463 "
-62012778,HC PREPARE PALATAL LIFT PROSTH," 3,270 "
-62012786,HC PREPARE SPEECH AID PROSTH," 3,711 "
-32050075,HC DESTRUCT LESION ANY METH 1ST, 593 
-62012794,HC PREPARE ORAL SURG SPLINT," 1,637 "
-62012802,HC PREPARE AURICULAR PROSTH," 3,556 "
-32052020,HC SPLINT SUPPLY, 50 
-62012810,HC PREPARE NASAL PROSTH," 3,563 "
-62012976,HC TREAT OF NOSE FRACT, 842 
-32052025,HC LACERATION TRAY, 213 
-62028565,HC TREAT NASAL SEPTAL FRACT, 987 
-62013016,HC TREAT CHEEK BONE FRACT," 1,032 "
-32096155,HC REPAIR EYELID WOUND," 4,022 "
-62013057,HC TREAT EYE SOCKET FRACT, 723 
-62028566,HC TREAT MOUTH ROOF FRACT," 1,746 "
-32096215,HC ASP BLADDER;INSRT S/P CATH," 3,952 "
-62028567,HC TREAT CRANIOFACIAL FRACT," 1,101 "
-62013073,HC TREAT MANDIB FRACT W/O MANIP," 2,330 "
-32096245,HC RETROBULBAR NJX MED SPX," 2,879 "
-62013123,HC RESET DISLOCATED JAW 1ST/SUBS, 513 
-62013131,HC RESET DISL JAW 1ST/SUBS COMPL," 1,637 "
-32096375,HC DILAT LACRIMAL PUNCTUM +-IRRG," 1,840 "
-62013222,HC TREAT STERNUM FRACT, 146 
-62013230,HC BIOPSY SFT TISS BACK/FLANK, 802 
-62013255,HC TREAT SPINE PROCESS FRACT, 340 
-62013263,HC TREAT SPINE FRACT CAST/BRACE, 494 
-62013305,HC I&D INFECT SHOULDER BURSA," 1,250 "
-62013347,HC REMOVE SHOULDER FB, 802 
-62013362,HC TREAT CLAVICLE FRACT, 615 
-62013370,HC TREAT CLAVICLE DISLOC, 494 
-40105833,HC ELECTRODE CARBON EKG, 300 
-62024104,HC TREAT SHOULDER DISLOC, 624 
-62013511,HC EXC ARM/ELBOW LES SC < 3 CM," 1,649 "
-40105845,HC BREAST BINDER, 148 
-62013529,HC RMVL OF ARM FB, 429 
-62013594,HC TREAT HUMERAL CONDYL FRACT, 468 
-40105961,HC BIOPSY SITE IDENTIFIER," 1,275 "
-62028568,HC TREAT MONTEGGIA FRACT," 1,090 "
-62013628,HC TREAT NURSEMAIDS ELBOW DISL, 363 
-91600744,HC MINOR PROCEDURE 1 HR 15 MIN," 2,917 "
-62013651,HC TREAT ULNAR PROX END FRACT, 612 
-62013685,HC TREAT FRACT OF RADIAL SHFT, 381 
-62013735,HC TREAT FRACT RADIUS & ULNA, 552 
-62013784,HC TREAT CARPAL B1 FRACT EA, 639 
-62028569,HC TREAT WRIST LUNATE DISLOC, 717 
-62024120,HC TREAT MCP FRACT1 B1, 375 
-94325982,HC CT L-SPINE W&W/O CONTRAST," 5,879 "
-62013875,HC TREAT CARP/MCP DISLOC EA, 351 
-62024138,HC TREAT FINGER PROX/MID FRAC EA, 382 
-62013925,HC TREAT FINGER MCP FRACT EA, 291 
-62013941,HC TREAT PHALAN FINGER FRACT EA, 334 
-62013990,HC EXC HIP/PELVIS LES SC < 3 CM," 1,052 "
-62014063,HC TREAT FEM FX PROX END NECK, 291 
-62028570,HC TREAT HIP DISLOC NO ANES, 930 
-62028571,HC CLTX TX FEM FX PROX HEAD, 291 
-62014089,HC EXC THIGH/KNEE LES SC < 3 CM," 1,162 "
-62014113,HC TREAT OF FEM FRAC W/O MANIP, 741 
-62014121,HC TREAT OF FEM FRACT SUPRACON," 1,122 "
-62014139,HC TREAT OF FEM DISTAL END FRACT, 411 
-62014147,HC TREAT THIGH FX GROWTH PLATE, 442 
-62014196,HC TREAT KNEECAP DISLOC, 219 
-62014410,HC CLTX MEDIAL ANKLE FX, 291 
-62014451,HC TREAT OF ANKLE FRACT, 709 
-62014477,HC TREAT LOWER LEG FRACT, 291 
-62014493,HC TREAT LOWER LEG DISLOC, 291 
-62028572,HC TREAT ANKLE DISLOC NO ANES, 614 
-62014501,HC DRAINAGE OF BURSA OF FOOT, 731 
-62014600,HC EXC FOOT/TOE TUM SC < 1.5 CM, 979 
-62015490,HC TREAT OF TOE FRACT, 219 
-62015508,HC TREAT SESAMOID BONE FRACT, 219 
-62015516,HC TREAT FOOT DISLOC OTH TARSAL, 291 
-62015524,HC TREAT FOOT DISLOC TALOTARS JNT, 291 
-62015532,HC TREAT FOOT DISLOC TMT, 291 
-62015573,HC TREAT TOE DISLOC NO ANES, 291 
-62016167,HC DRAINAGE OF NOSE LESION, 604 
-62016225,HC NASAL SINUS THERAPY, 723 
-62016308,HC CONTROL OF NOSEBLEED ANTER, 453 
-62016324,HC REPEAT CONTROL OF NOSEBLEED, 903 
-62016332,HC IRRIGATION MAXILLARY SINUS, 302 
-62016381,HC NASAL/SINUS ENDOSCOPY DX," 1,263 "
-62024203,HC DIAGN INDIR LARYNGOSCOPY, 288 
-62016423,HC INDIR LARYNGOSCOPY W BIOPSY, 620 
-62016431,HC REMOVE FB LARYNX INDIRECT, 674 
-62016449,HC RMVL OF LARYNX LESION, 924 
-62016456,HC LARYNGOSCOPY FOR ASPIRATION, 819 
-62016464,HC DX LARYNGOSCOPY EXCL NB," 1,203 "
-62016514,HC RMVL OF LARYNX LESION," 2,544 "
-62016530,HC PUNCTURE/CLEAR WINDPIPE, 298 
-62024252,HC PLACE NEEDLE IN VEIN, 129 
-62025267,HC ENDOVENOUS LASER 1ST VEIN," 5,213 "
-62025275,HC ENDOVENOUS LASER VEIN ADDON," 1,390 "
-62024294,HC WITHDRAWAL OF ARTERIAL BLOOD, 123 
-62016944,HC RMVL FB MOUTH SIMPLE, 135 
-62016951,HC RMVL FB MOUTH COMPLEX, 723 
-62017041,HC INTRAORAL I&D MOUTH LESION, 453 
-62017090,HC EXTRAORAL I&D MOUTH LESION, 745 
-62017579,HC REMOVE PHARYNX FB, 606 
-62017702,HC DRAINAGE OF RECTAL ABSCESS, 754 
-62017777,HC PROCTSIGM-SCOPY FB," 1,415 "
-62017785,HC PROCTSIGM-SCOPY RMVL 1 CAUT," 1,611 "
-62017793,HC PROCTSIGM-SCOPY RMVL 1 SNARE," 1,611 "
-62017801,HC PROCTSIGM-SCOPY RMVL MULT," 1,611 "
-62017827,HC PROCTSIGM-SCOPY ABLATE," 1,482 "
-62017900,HC TREAT OF RECTAL PROLAPSE, 291 
-62024377,HC REMOVE RECTAL OBSTRUCTION, 802 
-62018064,HC RMVL OF HEMORRHOID CLOT," 4,260 "
-62018072,HC INJECT INTO HEMORRHOID(S), 539 
-62018114,HC ANOSCOPY REMOVE FOR BODY, 604 
-62018130,HC ANOSCOPY SNARE, 556 
-62018155,HC ANOSCOPY CONTROL BLEEDING, 604 
-32001030,HC MEDICAL CRITICAL CARE W PROC," 11,679 "
-62018171,HC RMVL OF SUTURE FROM ANUS, 662 
-62018213,HC LASER SURGERY ANAL LESIONS," 4,202 "
-62018221,HC EXCISION OF ANAL LESION(S)," 3,152 "
-62018296,HC TREAT OF ANAL FISSURE 1ST, 586 
-32051240,HC APPLIC SHORT LEG-STCHSCT, 702 
-62018304,HC TREAT OF ANAL FISSURE SUBSQ, 539 
-62018312,HC LIGAT INT HEM GRP 1 PX, 717 
-32096355,HC PROBE NASOLACRIMAL DUX +-IRRG," 2,879 "
-62018320,HC LIGAT INT HEM GRPS MULT PX, 740 
-62025283,HC INSTLL RX AGNT INTO RNAL TUB, 215 
-62018916,HC LASER SURG PENIS LESION(S)," 1,298 "
-62018999,HC TREAT OF PENIS LESION, 189 
-62019047,HC PREPUTIAL STRETCHING, 309 
-62019112,HC LIGATION OF SPERM DUCT, 899 
-40105876,HC BREAST BIOPSY MARKER, 680 
-62019229,HC DRAINAGE OF PELVIC FLUID, 181 
-62019294,HC TREAT VAGINAL BLEEDING, 307 
-42016535,"HC BIOPSY KIT, ACKERMAN"," 3,638 "
-62019377,HC CAUTERIZATION OF CERVIX," 6,635 "
-62019393,HC LASER SURGERY OF CERVIX," 7,521 "
-42016675,HC DRAINAGE CATHETER, 650 
-62019401,HC CONIZATION OF CERVIX," 3,003 "
-62019435,HC D&C OF CERVICAL STUMP, 223 
-42017046,HC CT  BIOPSY NEEDLE, 556 
-62019476,HC ARTIFICIAL INSEMINATION, 995 
-62019492,HC SPERM WASHING, 338 
-42017053,HC NEEDLE INTRODUCER, 585 
-62019575,HC AMNIOCENTESIS DIAGNOSTIC, 507 
-62019583,HC CHORION BIOPSY, 507 
-62028737,HC GANCYCLOVIR 2MG/0.1ML INTRAVITR, 206 
-62028799,HC EMG W/CHEMODENERVATION, 242 
-62028555,"HC D&C,TX MISSED ABORTION 1ST TRI"," 4,008 "
-62028859,HC ACTIGRAPHY TESTING 72HRS TO 14, 225 
-62019724,HC DRAIN THYROID/TONGUE CYST, 306 
-62019732,HC ASPIR/INJ THYROID CYST, 444 
-62028878,HC MARSUP CYST BARTHOLIN\'S GLAND," 4,096 "
-62019930,HC N BLOCK INJ FACIAL, 507 
-62019971,HC N BLOCK INJ SPINAL ACCESSOR, 378 
-32040005,HC RPR LAC CPLX TRNK 1.1-2.5CM," 1,662 "
-62020144,HC N BLOCK CAROTID SINUS S/P, 380 
-62020185,HC APPLY NEUROSTIMULATOR, 106 
-32040010,HC REPR CMPLX TRNK 2.6-7.5CM," 1,662 "
-62020201,HC IMPLANT NEUROELECTRODES, 456 
-62020227,HC IMPLANT NEUROELECTRODES, 439 
-32040015,HC REPR CMPLX TRNK + 5CM EA," 1,062 "
-62020847,HC TREAT OF RETINAL LESION," 1,623 "
-62020904,HC DRAINAGE OF EYELID ABSCESS, 451 
-32040020,HC REPR CMPLX SCLP/EXT 1.1-2.5CM," 1,062 "
-62021183,HC REPAIR EYELID WOUND," 1,224 "
-62021241,HC INCISE/DRAIN EYELID LINING, 301 
-32040025,HC REPR CMPLX SCLP/EXT + 5CM EA," 1,062 "
-62021290,HC REMOVE EYELID LINING LESION, 744 
-62021340,HC INCISE/DRAIN TEAR GLAND, 872 
-32040030,HC REPR CMPLX SCLP/EXT + 5CM EA, 362 
-62021456,HC PROBE NASOLACRIMAL DUCT," 3,814 "
-32040035,HC REPAIR COMPLEX F/C/C/M/N/AX/G/H/F EA 5 CM/<," 1,062 "
-32040045,HC REPR CMPLX E/N/E/L 1.1-2.5CM," 1,662 "
-62022108,HC MULTIPLE FAMILY GROUP PSYTX, 118 
-32040050,HC RPR LAC CPLX EYE ADD 5CM," 1,062 "
-62022280,HC GLAUCOMA PROVOCATIVE TESTS, 125 
-32050170,HC IP NEGPRESSURE WNDTHER<=50CM2, 295 
-62022314,HC EYE EXAM WITH PHOTOS, 205 
-62022322,HC OPHTH-SCOPY/DYNAMOMETRY, 125 
-32095010,HC INJECT EYE DRUG, 978 
-62022330,HC ANT SGM IMAGING I&R W/FLURO, 349 
-62022488,HC NASAL FUNCTION STUDIES, 192 
-36405074,"HC FLORBETAPIR F18 INJ, AMYVID, 10 MCI"," 3,746 "
-62022496,HC FACIAL NERVE FUNCTION TEST, 434 
-62022512,HC ORAL FUNCTION THERAPY, 126 
-62022520,HC COMPREHENSIVE HEARING TEST, 118 
-62022546,HC TYMPANOMETRY, 95 
-62022603,HC VISUAL AUDIOMETRY (VRA), 118 
-62022702,HC CARDIAC REHAB NO MONITOR, 97 
-32040199,HC SUGAR TONG/SPLINT DISP SUPPLY, 279 
-62022710,HC CARDIAC REHAB/MONITOR, 97 
-62022991,HC ANTIGEN THER/SERV 1 DOSE VIAL, 163 
-62023007,HC ANTIGEN THER/SERV 1 INSECT, 48 
-62023015,HC ANTIGEN THER/SERV 2 INSECT, 101 
-62023023,HC ANTIGEN THER/SERV 3 INSECT, 101 
-62023031,HC ANTIGEN THER/SERV 4 INSECT, 97 
-62023049,HC ANTIGEN THER/SERV 5 INSECT, 97 
-62023056,HC ANTIGEN THER/SERV 1 MLT AGS, 132 
-62028879,HC BIOIMPEDANCE-DERIVED CV ANALYS, 75 
-62023064,HC ANTIGEN THER/SERV BDY INSECT, 48 
-62023106,HC HAND MUSCLE TESTING MANUAL, 69 
-62028880,HC PNEUMOCOCCAL VACC 13 VAL IM, 246 
-62023114,HC BODY MUSCLE TESTING MANUAL, 146 
-62023130,HC ROM MEASURMNTS EXC HAND, 87 
-62023148,HC ROM MEASUREMENTS HAND, 72 
-62023221,HC PSYCH TESTING BY COMPUTR I&R, 268 
-32096055,HC LACERATION TRAY, 213 
-62023718,HC OSTEOPATH MANIP 1-2 BDY REG, 73 
-62023734,HC OSTEOPATH MANIP 5-6 BDY REG, 95 
-62023742,HC OSTEOPATH MANIP 7-8 BDY REG, 85 
-62023759,HC OSTEOPATH MANIP 9-10 BDY REG, 70 
-62023767,HC ANOGENITAL EXAM CHILD, 215 
-41188888,HC US PELV NON-OB S&I LIMITOR FU," 1,237 "
-42062273,HC BX GALLBLADDER PERC NEEDLE," 5,663 "
-62028600,HC VORICONAZOLE(VFEND) 200MCG/0.1MG EYE, 312 
-62028601,HC FUNGIZONE PER 5MCG/0.1MG EYE, 163 
-62028602,HC ALTEPLASE (ACTIVASE) 10MCG/0.1MG EYE, 243 
-62028603,HC DEXAMETHASONE PER 0.1 MG, 22 
-30332920,HC TRANSESOPH ECHO INTRA-OP," 6,578 "
-41134567,HC STERNOPLASTY," 9,429 "
-32040040,HC REPR CMPLX E/N/E/L <1.1CM," 1,662 "
-32055010,HC PCNTS EYE SPX W/REMOVAL AQUEOUS," 2,800 "
-32056055,HC FINE NEEDLE ASPIRATION W/IMAGING," 2,032 "
-42016899,HC ABLATION CRYO PROBE," 9,096 "
-46161253,HC DVEHVR VZV ANTIGEN, 369 
-46161261,HC DVEHVR DET IMM EA PLY ASRM, 319 
-81800400,HC HW VAD CONTROLLER RPLMT," 25,600 "
-81800410,HC HW LITHIUM ION BATT RPLMT," 6,375 "
-81800420,HC HW BATT CHRGR RPLMT," 39,650 "
-81800430,HC HW VAD SHWR CVR RPLMT," 2,550 "
-81800460,HC HW DC ADAPTER RPLMT," 13,500 "
-81800465,HC HW AC ADAPTER RPLMT," 3,300 "
-81800478,HC HW ALARM ADAPTER RPLMT," 1,225 "
-30005050,HC HEARTWARE IMPLANT SET," 140,000 "
-32051042,HC CLD TX FIB SHAFT FX W/MAN," 2,353 "
-32096000,HC CANALITH REPOSITIONING, 212 
-32096010,HC MANIP FORESKIN STRETCH, 655 
-36033387,HC THER INJECT HORMONL ANTINEOPL, 786 
-36033388,HC THER INJECT NON HORMONAL, 903 
-62028608,HC CEFAZOLIN (KEFZOL) INJ 500MG, 2 
-62028609,HC FENTANYL CITRATE 0.1MG, 1.0 
-62028611,HC METHYLERGON MALEATE UP TO 0.2MG, 16 
-62028612,HC NALOXONE (NARCAN) PER 1MG, 24 
-62028613,HC ONDANSETRON (ZOFRAN) PER 1MG, 4 
-62028614,HC ROMAZICON .05MG/5ML, 14 
-62028615,HC DIPHENHYD (BENADRYL) INJ 50MG, 2 
-62028619,HC AMPICILLIN INJ PER 500MG, 6 
-62028626,HC DEVICE IUD PARAGUARD," 1,073 "
-81800500,HC HEARTWARE HOME SUPPLIES," 51,000 "
-62028607,HC BUPIVICAINE HYDROCHL 30ML, 9 
-62028623,HC LIDOCAINE HCL PER ML, 2 
-62028627,HC ETONOGESTREL IMPLANT SYSTEM," 1,192 "
-62028640,HC ROPIVACAINE INJ 1MG, 26 
-37216470,HC XR ABDOMEN AP, 718 
-38016473,HC XR ABDOMEN AP, 718 
-38616470,HC XR ABDOMEN AP, 718 
-32096020,HC DRAIN FINGER ABSCESS SMPL, 723 
-32096070,HC TREAT CARP/MCP DISLOC EA, 654 
-40300113,"HC CT TREATMENT PLANNING,NON AORTA"," 3,122 "
-42016477,HC XR ABDOMEN AP, 718 
-94316478,HC XR ABDOMEN AP, 718 
-51565513,HC GTTINS GTT 1-3 SPEC FASTING, 789 
-51565521,HC GTTINS GTT EA ADDL BYND 3, 176 
-62013008,HC CLTX NASOMAX CMPX FX," 4,832 "
-62013032,HC CLTX FX ORB EXC BLOW W/MAN," 3,274 "
-62013636,HC CLTX RDL HD/NK FX W/MAN, 988 
-62013701,HC CLTX GALEAZZI RADSHFT ULN JT, 988 
-62013727,HC CLTX ULR SFT FX W/MANIP, 292 
-62013818,HC CLTX DTL RDLUNR DIS W/MAN, 292 
-62013933,HC CLTX ART FX MET/INTERPH W/MAN, 292 
-62014188,HC CLTX KNEE SPINE/TUBER +/- MAN, 292 
-62015425,HC CLTX CALC FRAC W/MANIP," 3,716 "
-62015466,HC CLTX METAR FX W/MANIP, 292 
-62028628,HC TUBE TRACH-CUFFED, 340 
-62028629,HC TUBE TRACH-UNCUFFED, 340 
-62028631,HC TUBE TRACH-CUFFED KIT (PORTEX), 340 
-62028632,HC VOICE PROTHESIS PROVOX 2," 1,700 "
-62028633,HC VOICE PROTHESIS PROVOX VEGA," 1,700 "
-62028634,HC VOICE PROTHESIS STARTER KIT," 9,000 "
-62028635,HC ELECTRODE-PUDENDAL," 1,445 "
-62028636,HC CATHETER-MANOMETRIC," 4,038 "
-30210895,HC TRANSFUSION," 3,300 "
-32096030,HC CYSTO W/CATH URETERAL," 5,376 "
-32096040,HC CHANGE BLADDER TUBE SMPL, 431 
-32096050,HC FNA WITHOUT IMAGE, 346 
-32096090,HC BX LESN SKIN/SUBQ/MM, 438 
-32096092,HC BX LESN SKIN EA ADDL, 448 
-32096110,HC TX FX PHALANX DIST W/MAN CL, 604 
-42080217,"HC THORACENTESIS, PLEURAL WITH IMAGING"," 3,897 "
-32051043,HC CLD TX FIB SHFT FX W/O MAN, 631 
-32096060,HC INJ ANES NRV TRIGEMINAL, 826 
-32096121,HC INJ SPINAL PUNCTURE, 822 
-32096130,HC RPR TENDON EXTNS FOOT EA," 4,461 "
-40805012,HC US EXTREMITY NON VASCULAR LTD, 952 
-94805017,HC US EXTREMITY NON VASCULAR LTD, 952 
-42016948,HC VORTEX DIAMOND COILS, 680 
-62028560,HC ABOBOTULINUM TOXIN A VL 5U, 14 
-62028637,HC PERQ ALLERGY TEST W/EXTRACTS, 88 
-62028638,HC IMMUNTHERPY INCL EXTRCT 1 INJ, 103 
-32096100,HC BX BONE MARROW NDL/TROC," 2,867 "
-32096120,HC TX FX ULNA PROX W/MAN CLSD, 913 
-32003025,HC IV CONCURRENT INFUS EA ADDL HR, 821 
-40180200,HC BREAST BX VACUUM ASSIST W IMAGING," 5,302 "
-41100752,HC MYELOGRAPHY THORACIC S&I," 1,751 "
-42016773,HC DRAINAGE CATHETER L10, 595 
-41100753,HC MYELOGRAPHY LUMBOSAC S&I," 1,866 "
-42016858,HC ABLATION PROBE L36," 9,375 "
-41100885,HC STENT BRONCHUS EA ADD," 2,152 "
-42016866,HC ABLATION PROBE L38," 10,875 "
-41151506,HC INJ TRACT SINUS THPY," 3,820 "
-42016876,HC ABLATION PROBE L43," 14,625 "
-41151555,HC CLOSURE EC FISTULA," 2,819 "
-42016944,HC NEEDLE INTRODUCER L13, 850 
-41153375,HC TUNNEL INTRAPERITONEAL CATH W/ S&I," 6,421 "
-42016945,HC NEEDLE INTRODUCER L12, 808 
-41164130,HC REM TUN INTRAPERITONEAL CATH," 4,740 "
-42016980,HC CONNECTING TUBE L3, 85 
-42016966,HC CT GUIDEWIRE L6, 255 
-32096140,HC TX FX METATAR W/MAN CLSD, 765 
-94030294,HC APLIGRAF PER SQ CM, 359 
-38216271,HC XR FLUORO NEEDLE PLACEMT," 2,021 "
-41016270,HC XR FLUORO NEEDLE PLACEMT," 2,021 "
-41156565,"HC BX, PELVIS/HIP; SUBFASCIAL/IM"," 1,644 "
-41177569,"HC US, ABDOMEN;COMPLETE"," 2,294 "
-62025607,HC TISSUE MARKER IMPLANTABLE," 1,607 "
-55092472,HC STPCR STREP CULT APT EA ORG, 578 
-50782754,HC 6941 12208R FLOW TC 1ST MRK, 38 
-50782762,HC 6941 12208R FLOW TC EA ADL, 38 
-46161022,HC EAIGG EBV EA IGG, 191 
-46161030,HC EBNABY EBV EBNA IGG, 218 
-46161048,HC VCAIGM EBV VCA IGM, 343 
-46161055,HC VCAIGG EBV VCA IGG, 343 
-46161063,HC EBVPAN EBV EA IGG, 191 
-46161071,HC EBVPAN EBV EBNA IGG, 218 
-46161089,HC EBVPAN EBV VCA IGG IGM, 343 
-50782771,HC 6942 CGD ENZ ACT NONRAD EA, 133 
-50393917,HC AROM PAMG1 PP12 QL CERVICOVAG, 408 
-37021565,HC IOFLUPANE I-123 DIAG TO 5 MCG," 10,450 "
-50782788,HC 6959 12210R LP PLA2, 125 
-50782796,HC 6942 12212R PRTZ NES WA1 AB, 88 
-50782804,HC 6997 12216R FDMA ENZ DIG EA, 10 
-50782812,HC 6997 12216R FDMA NA PRB EA, 10 
-40805020,HC LOCM 300-399 MG/ML, 3 
-50782821,HC 6997 12216R AMP EA NA SEQ, 41 
-62028641,HC PESSARY IMPLT SILCN L8, 425 
-50782838,HC 6997 12218R BLT PROD EA NA, 7 
-50782846,HC 6997 12218R SEP GEL ELCT, 7 
-50782853,HC 6997 12218R NA PROBE EA, 7 
-62028642,HC PESSARY IMPLT SILCN L7, 340 
-50782861,HC 6997 12218R AMP EA NA SEQ, 28 
-50782879,HC 6997 12218R AMP MPLX NA 1-2, 56 
-62028643,HC PESSARY IMPLT SILCN L6, 255 
-50782887,HC 6997 12220R MSUD ENZ DIG EA, 21 
-41156575,HC PERITONEOGRAM; CATH/OTHER, 191 
-50782895,HC 6997 12220R MSUD NA PRB EA, 21 
-50782903,HC 6997 12220R AMP EA NA SEQ, 87 
-50782911,HC 7052 12222R ISOL HP EA NA, 4 
-50782929,HC 7052 12222R SEP GEL ELCT, 4 
-50782937,HC 7052 12222R AMP EA NA SEQ, 18 
-50782945,HC 7052 12222R MUT ID SQ EA SG, 18 
-50782952,HC 7052 12222R SEP HRT EA NA, 18 
-50782961,HC 7052 12222R MCCC1 INT&RPT, 4 
-50782978,HC 7052 12224R ISOL HP EA NA, 9 
-50782986,HC 7052 12224R SEP GEL ELCT, 9 
-41156789,"HC XRAY, L/S SPINE; 2 OR 3 VIEWS"," 1,043 "
-50782994,HC 7052 12224R AMP EA NA SEQ, 38 
-41199430,HC PERITONEOGRAM, 720 
-50783001,HC 7052 12224R MUT ID SQ EA SG, 38 
-50783018,HC 7052 12224R SEP HRT EA NA, 38 
-50783026,HC 7052 12224R MTDNA INT&RPT, 9 
-50783034,HC 6956 12284R LYMP TRNS BLST, 137 
-62028645,HC THYROTROPIN ALFA (THYROGEN) INJ 1.1MG," 2,094 "
-50783042,HC 7051 12226R ALRGN QNT EA, 14 
-62028646,HC SYNVISC PER 1MG (48MG/6ML DOSE), 48 
-50783232,HC 7051 12226R ALRGN QNT EA, 14 
-50783059,HC 7051 12228R ALRGN QNT EA, 23 
-50783067,HC 7069 12230R SOL IL2R IMM QN, 322 
-50783075,HC 7074 12232R THYROGLOBULIN, 38 
-50783083,HC 7074 12232R THYRGLBLN AB, 37 
-50783091,HC 7075 12234R VOLATILES, 48 
-50783109,HC 6959 12236R IBUPROFEN, 34 
-50783117,HC 6959 11312R IMM QL MLT SM, 18 
-50783125,HC 6956 12238R LCSMD CHRM SPC, 96 
-50783133,HC 6956 12241R B CELL TOT CNT, 34 
-50783141,HC 6956 12241R MONO CL AG QN, 24 
-50783158,HC 6956 12241R NK CELL TOT CNT, 34 
-50783166,HC 6956 12241R T CELL TOT CNT, 34 
-50783174,HC 6956 12241R TCL ABS CD4 CD8, 42 
-50783182,HC 6956 12241R FC INTRP 2-8, 15 
-50783191,HC 6956 12241R FC INTRP 9-15, 15 
-50783208,HC 6956 12279R FC INTRP 2-8, 75 
-50783216,HC 6956 12279R FC INTRP 9-15, 75 
-50783224,HC 6942 12214R B BRGDFRI LYME, 80 
-50805811,HC NPM1 ISL EXT HP EA NA, 146 
-62027609,"HC INIT PREV EXAM, NEW BENE", 141 
-62027610,HC CERVICAL/VAG CANCER SCREEN, 75 
-50805829,HC NPM1 ENZY DIG EA TRT, 78 
-62027611,HC VISUAL ACUITY SCREEN, 37 
-62027612,HC ORTHOTIC MGMT AND TR, 55 
-50805837,HC NPM1 AMP TRG EA NA SQ, 188 
-62027613,HC INTERV HLTH/BEHAV W/FAM, 100 
-62027616,HC INCISE INNER EAR," 3,640 "
-50805845,HC NPM1 SEP ID HRT EA NA PR, 112 
-62027618,HC BRAIN CANAL SHUNT PROC, 603 
-50805852,HC NPM1 INTRP AND RPT, 138 
-62027619,HC INSERT IUD, 173 
-62027620,HC UNLIS MALE GENITAL SYS PROC, 159 
-50805861,HC BMNPM1 ISL EXT HP EA NA, 146 
-62027622,HC RESECT INFERIOR TURB," 2,416 "
-62027623,"HC MANIP FINGER W/ANES, EA JNT", 786 
-50805878,HC BMNPM1 ENZY DIG EA TRT, 78 
-62027624,HC CLSD TRT CLAV FRACT W/O MAN, 384 
-50805886,HC BMNPM1 AMP TRG EA NA SQ, 188 
-50805894,HC BMNPM1 SEP ID HRT EA NA PR, 112 
-32061510,HC REP TEND/MUSCLE EXT FOREARM," 9,750 "
-50805902,HC BMNPM1 INTRP AND RPT, 138 
-50805911,HC CFSS ISL EXT HP EA NA, 146 
-32061520,HC REPAIR TEND/MUSC FLEXOR FOREARM," 9,750 "
-50805928,HC CFSS SEP GEL ELCT EA, 103 
-50805936,HC CFSS AMP TRG EA NA SQ, 188 
-32061530,HC PERIPHERAL NERVE REPAIR," 10,050 "
-50805944,HC CFSS MUT ID SQ EA SG, 54 
-50805951,HC CFSS INTRP AND RPT, 138 
-32061540,"HC REPAIR BLOOD VESSEL, ARM"," 9,100 "
-50805969,HC CSMPB ISOL EXT HP EA NA, 146 
-50805977,HC CSMPB ENZY DIGEST EA TRMT, 78 
-62099950,HC COS: UNI VARICOCELE," 1,575 "
-50805985,HC CSMPB AMP MPLX 1ST 2 NA SEQ, 172 
-50805993,HC CSMPB AMP MPLX EA ADL NA SQ, 90 
-62099951,HC COS: BIL VARICOCELE," 2,025 "
-50806009,HC CSMPB SEP ID HRT EA NA PREP, 112 
-50806012,HC CSMPB INTRP AND RPT, 138 
-62099952,HC COS: MICRO (TESE)," 2,025 "
-50806020,HC CSMPB MUTID ENZ PRIM EA SEG, 71 
-50806038,HC CSMPBM ISOL EXT HP EA NA, 146 
-62099953,HC COS: VASECTOMY REV," 2,475 "
-50806046,HC CSMPBM ENZY DIGEST EA TRMT, 78 
-50806053,HC CSMPBM AMP MPLX 1ST 2 NA SEQ, 172 
-36405090,HC PET N-13 AMMN PER DOS UP TO 40," 3,400 "
-50806061,HC CSMPBM AMP MPLX EA ADL NA SQ, 90 
-50806079,HC CSMPBM SEP ID HRT EA NA PREP, 112 
-37100310,HC PETCT LIMITED," 10,566 "
-50806087,HC CSMPBM INTRP AND RPT, 138 
-50806095,HC CSMPBM MUTID ENZ PRIM EA SEG, 71 
-37006012,HC THYROID IMAG SING/MULT QUAN MEASURE, 483 
-37006020,HC THYROID IMAG VASC FLOW, 636 
-37006038,"HC PARATHYROID PLANAR, SPECT CONC CT"," 1,362 "
-37010238,"HC P32 SODIUM PHOSPH TX, ADDL MCI", 816 
-36405082,"HC FLORBETAPIR F18 INJ, AMYVID, NO CHARGE", 1.0 
-37105095,HC PET N-13 AMMN PER DOS UP TO 40," 3,400 "
-37216603,HC CT LUNG CANCER SCREENING, 698 
-38316485,HC ASPIRATION MAJOR JOINT," 1,072 "
-40806002,HC TISSUE MARKERS IMPLANTABLE, 705 
-40916488,HC ASPIRATION MAJOR JOINT," 1,072 "
-94916483,HC ASPIRATION MAJOR JOINT," 1,072 "
-50787652,HC 6997 11115R BLM SYND ANAL, 105 
-41898209,HC CRIMPER ELS, 1.0 
-50787660,HC 6997 11116R FANCONI ANAL, 105 
-50787678,HC 6997 11117R Y CHROM MICRO, 237 
-50470145,HC LACWB LACTATE, 330 
-50787686,HC 7053 11318R GCDH GENE SEQ," 1,020 "
-32096150,"HC HEPATITIS A VACCINE,ADULT", 99 
-50787694,HC 11320R SCN1A DNA SEQ TST," 2,941 "
-51565471,HC LAMOL QNT DRG NES, 291 
-91000140,HC CATHJ&INTRO SALINE NFS SHG/HSG, 353 
-51565489,HC LEVETL QNT DRG NES, 291 
-91000143,HC HYSTEROSONOGRAM," 1,351 "
-91000145,HC HYSTEROSALPINGOGRAM," 1,726 "
-91000136,HC THERAPYPRO/DX IVP INITIAL, 97 
-26610255,HC IMMUN ADMIN ONE VACC, 263 
-50793934,HC 6949 H132 SEP GEL ELC EA NA, 8 
-50793942,HC 6949 H132 AMP EA NA SEQ, 30 
-50793959,HC 6949 H132 PATH CONSULT COMP, 8 
-52293933,HC 6949 H132 SEP GEL ELC EA NA, 8 
-52293941,HC 6949 H132 AMP EA NA SEQ, 30 
-52293958,HC 6949 H132 PATH CONSULT COMP, 8 
-81093932,HC 6949 H132 SEP GEL ELC EA NA, 8 
-81093940,HC 6949 H132 AMP EA NA SEQ, 30 
-81093957,HC 6949 H132 PATH CONSULT COMP, 8 
-81393936,HC 6949 H132 SEP GEL ELC EA NA, 8 
-81393951,HC 6949 H132 PATH CONSULT COMP, 8 
-82393935,HC 6949 H132 SEP GEL ELC EA NA, 8 
-82393943,HC 6949 H132 AMP EA NA SEQ, 30 
-82393950,HC 6949 H132 PATH CONSULT COMP, 8 
-84393933,HC 6949 H132 SEP GEL ELC EA NA, 8 
-84393941,HC 6949 H132 AMP EA NA SEQ, 30 
-42016196,HC CT URIN NANOKNIFE PROC," 1,736 "
-42016196,HC CT URIN NANOKNIFE PROC," 1,736 "
-23240147,HC CHEMO INFUSN 1ST HR," 1,444 "
-50787702,HC 11324R CADASIL EVAL," 2,841 "
-23240148,HC CHEMO IV PUSH EA ADDL, 801 
-36516037,HC LOCM 300-399 MG/ML-OMNPQ, 2 
-36516052,HC LOCM 300-399 MG/ML-VSPQ, 5 
-36516110,HC LOCM 300-399 MG/ML, 3 
-50787710,HC 11326R TSC1 DELETION ANAL, 849 
-36516144,HC CTA CARD W/QUAN EVAL OF CORO C," 7,284 "
-62028650,"HC SCOPE BLADDER, REM TUMOR SMALL"," 2,549 "
-36516342,HC CT HRT W/3D IMAGE W/CON," 6,377 "
-36516409,HC CT ABDOMEN-PELVIS WO CONTRAST," 6,470 "
-62028651,HC XR VENOGRAM EPIDURAL, 915 
-36516417,HC CT ABDOMEN-PELVIS W CONTRAST," 7,711 "
-50787728,HC 11326R TSC2 DUP-DEL ANAL, 849 
-36516425,HC CT ABDOMEN-PELVIS WO/W CONTRAST," 8,993 "
-62028652,HC US GUIDED NEEDLE BX," 1,128 "
-36523108,HC CT HEAD WO CON," 3,850 "
-36523207,HC CT HEAD/BRAIN W/CONTRAST," 4,505 "
-62028653,HC US GUIDED RAD THRPY FIELDS, 250 
-36523306,HC CT HEAD WO/W CON," 5,077 "
-50787736,HC 111326R TSC2 FULL GENE SEQ, 849 
-36524700,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-62028654,HC CHEMO SQ/IM ANTI-NEOPLASTIC, 315 
-36524759,HC CT THORAX WO CONTRAST," 4,426 "
-36524809,HC CT PELVIS WO CONTRAST," 4,373 "
-62028655,HC INJ TRIGGER PT 1-2 MUSCLE(S), 448 
-36524908,HC CT SOFT TISS NECK WO CONTRAST," 4,347 "
-50787744,HC 7052 11328R ANGEL MET, 350 
-36524916,HC CT SFT TIS NCK W/CON," 4,628 "
-62028656,HC CYSTOMETROGRAM/CMG CPLX, 426 
-36524924,HC CT SFT NCK WO/W CON," 5,609 "
-36524957,HC CT ABDOMEN W/CONTRAST," 6,285 "
-62028657,HC COMPLEX CYSTOMETROGRAM W/UP, 498 
-36525004,HC CT THORAX W/CNTRST," 5,564 "
-50787751,HC 7046 11357R FAM MEDI FEV, 650 
-36525053,HC CT PELVIS W/CONTRAST," 7,439 "
-62028658,HC CYSTOMETROGRAM W/VP, 496 
-36525103,HC CT ABDOMEN WO/W CONTRAST," 6,956 "
-36525152,HC CT THRX W+W/O CNTRS," 6,921 "
-62028659,HC CYSTOMETROGRAM W/VP&UP, 541 
-36525202,HC CT PELVIS WO/W CONTRAST," 7,944 "
-50787769,HC 7046 11361R COWDEN PTEN, 900 
-62028660,HC UROFLOWMETRY CPLX, 131 
-62028661,HC EMG ANAL/URETHRAL SPHINCTER, 264 
-50787777,HC 7046 11361R DUP/DEL VARI, 900 
-42024786,HC CT UP EXT WO/W CONTRAST," 5,620 "
-62028662,HC VOIDING PPRESS STDS INTRA ABD, 190 
-50783364,HC 6956 12292R TCELL TOT CNT, 230 
-50783372,HC 6956 12292R TCELL CD4 CNT, 163 
-62028663,HC PST TIBL NEUROSTIMULATION 1 TX, 508 
-50783381,HC 6956 12292R FLOW 1ST MRKR, 100 
-50787785,HC 7046 11365R FAM HIBER FEV, 650 
-81393944,HC 6949 H132 AMP EA NA SEQ, 30 
-62028664,HC XR CYSTOGRAM 3+V," 1,017 "
-84393958,HC 6949 H132 PATH CONSULT COMP, 8 
-62028665,HC XR CYSTOURETHROGRAM VOIDING, 510 
-21120000,HC OBS PROC SIMPLE, 766 
-50787793,HC 7060 11373R MARF FBN1 SEQ," 1,400 "
-21120001,HC OBS PROC INTMED," 1,277 "
-62028666,HC US TRANSRECTAL, 551 
-21120002,HC OBS PROC COMP," 5,433 "
-23120000,HC OBS PROC SIMPLE, 766 
-62028667,HC BIOFEEDBACK PRNL MUSC, 637 
-23120001,HC OBS PROC INTMED," 1,277 "
-50787806,"HC 7060 OI(COLIA1,COLIA2) ANL", 900 
-23120002,HC OBS PROC COMP," 5,433 "
-62028668,HC ANORECTAL MANOMETRY TEST, 852 
-23220000,HC OBS PROC SIMPLE, 766 
-23220001,HC OBS PROC INTMED," 1,277 "
-62028669,HC ANLYS PAIN PUMP W/PROG, 345 
-23220002,HC OBS PROC COMP," 5,433 "
-50787814,HC 7053 11514R  POMPE GAA SEQ," 2,130 "
-23720000,HC OBS PROC SIMPLE, 766 
-62028670,HC INJ ANES NRV GREAT OCCIPITAL, 423 
-23720001,HC OBS PROC INTMED," 1,277 "
-23720002,HC OBS PROC COMP," 5,433 "
-62028671,HC DEST NRV MUSCLE EXTREM/TRUNK, 505 
-23820000,HC OBS PROC SIMPLE, 766 
-50787822,HC 7053 11516R PANCRE FULL SEQ, 655 
-23820001,HC OBS PROC INTMED," 1,277 "
-62028672,HC XR FLUORO NEEDLE LOCALIZATION, 929 
-23820002,HC OBS PROC COMP," 5,433 "
-24120000,HC OBS PROC SIMPLE, 766 
-62028673,HC AUD EVOKED POTENTIAL COMP, 257 
-24120001,HC OBS PROC INTMED," 1,277 "
-50787830,HC 7053 11516R  PANCR 8 POLY-T, 655 
-24120002,HC OBS PROC COMP," 5,433 "
-25020000,HC OBS PROC SIMPLE, 766 
-25020001,HC OBS PROC INTMED," 1,277 "
-25020002,HC OBS PROC COMP," 5,433 "
-50787848,HC 7053 11516R PRSSI GENE SEQ, 655 
-25120000,HC OBS PROC SIMPLE, 766 
-25120001,HC OBS PROC INTMED," 1,277 "
-25120002,HC OBS PROC COMP," 5,433 "
-62028681,HC PUMP MNT SPNL/BRAIN REQ DR ELE, 430 
-25130197,HC IV INFUSION HYDRA 1ST HR," 1,035 "
-50787863,HC 6961 11517R DM1 DNA, 674 
-25130198,HC IV INFUSION HYDRA ADDL HR, 607 
-62028682,HC CHEMO IV INFUSION SGL/INIT<1H, 289 
-25130199,HC IV INF THER ADDTL SEQ NEW DRUG, 796 
-25130200,HC IV INFUSION THER CONCURRENT, 821 
-62028683,HC CHEMO IV INFUSION ADD HR, 49 
-25130201,HC IVP THER/PROPH NEW DRUG, 400 
-50787871,HC 6961 11527R SPINL MUSC ATR, 611 
-25130202,HC IVP THER/PROPH SAME DRUG, 405 
-62028685,HC US FACET INJ LMBR OR SAC 1 LEV, 725 
-25130203,HC IV THER/PROPH UNLISTED, 256 
-25220000,HC OBS PROC SIMPLE, 766 
-62028686,HC US FACET INJ LMBR OR SAC 2 LEV, 254 
-25220001,HC OBS PROC INTMED," 1,277 "
-50787889,HC 7052 11534R MTDNA SCRN, 190 
-25220002,HC OBS PROC COMP," 5,433 "
-62028687,HC US FACET INJ LMBR OR SAC 3 LEV, 254 
-25320000,HC OBS PROC SIMPLE, 766 
-25320001,HC OBS PROC INTMED," 1,277 "
-62028639,HC IMMUNOTHRPY 2+ INJ W/O EXTRACT, 130 
-25320002,HC OBS PROC COMP," 5,433 "
-25420000,HC OBS PROC SIMPLE, 766 
-25420001,HC OBS PROC INTMED," 1,277 "
-25420002,HC OBS PROC COMP," 5,433 "
-50787897,HC 7052 11535R POLG SEQ ANAL," 1,750 "
-25720000,HC OBS PROC SIMPLE, 766 
-25720001,HC OBS PROC INTMED," 1,277 "
-25720002,HC OBS PROC COMP," 5,433 "
-26120000,HC OBS PROC SIMPLE, 766 
-26120001,HC OBS PROC INTMED," 1,277 "
-26120002,HC OBS PROC COMP," 5,433 "
-26320000,HC OBS PROC SIMPLE, 766 
-26320001,HC OBS PROC INTMED," 1,277 "
-26320002,HC OBS PROC COMP," 5,433 "
-50787905,HC 7052 11540R OTC SEQ ANAL, 710 
-27320000,HC OBS PROC SIMPLE, 766 
-27320001,HC OBS PROC INTMED," 1,277 "
-27320002,HC OBS PROC COMP," 5,433 "
-32050820,HC TX DISL LUNATE W/MAN CLSD," 2,353 "
-36099099,HC BRACHY NON-STRANDED  I-125 PER," 8,500 "
-62720001,HC OBS PROC INTMED," 1,277 "
-50787913,HC 7052 11544R SYDM UBE3A," 2,120 "
-50787921,HC 7052 11545R DMPK DETECT, 360 
-50787939,HC 7052 11546R MITOCH ENZ, 53 
-50786038,HC 6956 12573R FLR NONIN ABSCR, 31 
-50786046,HC 7081 12571R ISL EXT HP EANA, 15 
-50787947,HC 7052 11546R SPECTROP ANA, 53 
-50786053,HC 7081 12571R ENZY DIG EA TRT, 15 
-50786061,HC 7081 12571R AMP TRG EANA SQ, 64 
-50787954,HC 7052 11546R MITOCH BIOP, 53 
-50786079,HC 7081 12571R SEP ID HRT EANA, 64 
-50786087,HC 7081 12571R INTRP AND RPT, 15 
-41100851,HC CAUT CHEM TISSUE GRANULATION, 322 
-50787962,HC 7052 11546R MITO CRYOP CHN, 53 
-41153588,HC NEEDLE/CATH JUGULAR VEIN," 2,482 "
-62027829,HC BACLOFEN 1000MCG/ML 40CC, 680 
-62027830,HC BACLOFEN 1000MCG/ML 20CC," 1,278 "
-50787970,HC 6957 11548 NATP IMMU TST, 51 
-56202190,HC ECHO TEE ANOMALY CMPL W/PF," 1,644 "
-62027831,HC BACLOFEN 2000MCG/ML 20CC," 1,278 "
-56202196,HC OPTIC COHERENCE TOMO CORON," 11,731 "
-62027833,HC BACLOFEN 2000MCG/ML 40CC," 2,516 "
-50787988,HC 6957 11548R NATP AB, 51 
-62027834,HC BACLOFEN 500MCG/ML 20CC, 360 
-62027835,HC BACLOFEN 500MCG/ML 40CC, 680 
-50787996,HC 6997 11570R SMA CARRIER, 875 
-62027838,HC FENT/CLON COMPOUND 20CC, 284 
-50788004,HC 7046 11573R CCN MUT ISOL/ET, 732 
-62027841,HC MORPHINE 10MG/ML 20ML, 284 
-62027842,HC MORPHINE 10MG/ML 40CC, 529 
-50788012,HC 7046 11576R MMACHC DEFIC, 665 
-62027843,HC MORPHINE 20MG/ML 20CC, 454 
-50788020,HC 7046 11577R JAG1 MUTATION," 1,530 "
-62028688,HC SUPARTZ 25MG/2.5ML, 322 
-62028689,HC EUFLEXXA 20MG/2.5ML, 484 
-50788038,HC 7046 11579R PTPN11 MUT," 1,235 "
-62028690,HC XIAFLEX 0.01MG, 74 
-62028691,HC ORTHOVISC 15MG/2ML, 563 
-50788046,HC 7046 11580R CDH7 GENE SEQ," 3,510 "
-62027845,HC MORP/ CLON COMPOUND 20CC, 520 
-62028707,HC PUMP MAINT INCLUDES ELEC ANLY, 430 
-62028712,HC SUMATRIPTAN INJ 6MG (SUMAVEL), 240 
-23240149,HC CHEMO IV INFUSN EA ADDL HR, 641 
-23240150,HC CHEMO IV INFUSION ADDTL SEQ," 1,043 "
-38202016,HC T-SPINE STANDING SCOLIOSIS, 564 
-41002015,HC T-SPINE STANDING SCOLIOSIS, 564 
-41302019,HC T-SPINE STANDING SCOLIOSIS, 564 
-94702016,HC T-SPINE STANDING SCOLIOSIS, 564 
-50788053,HC 7024 11585R FRAGX CARRIER, 350 
-50785504,HC 7046 12542R ISL EXT HP EANA, 7 
-50788061,HC 6956 11606R P450 GENOTYPE, 477 
-50785512,HC 7046 12542R ENZY DIG EA TRT, 7 
-50785520,HC 7046 12542R SEP GEL ELCT EA, 7 
-50788079,HC 7014 11617R CMV AMP PROB, 146 
-50785538,HC 7046 12542R AMP TRG EANA SQ, 29 
-50785546,HC 7046 12542R MUT ID SQ EA SG, 29 
-50788087,HC 7014 11618R HSV 1/2 PCR AQ, 531 
-50785553,HC 7046 12542R INTRP AND RPT, 7 
-50785561,HC 7046 12544R ISL EXT HP EANA, 6 
-50788095,HC 7014 11619R VZV PCR AQUE, 146 
-50785579,HC 7046 12544R ENZY DIG EA TRT, 6 
-50785587,HC 7046 12544R SEP GEL ELCT EA, 6 
-50788103,HC 7014 11619R TOXO GONDII AQ, 146 
-50785595,HC 7046 12544R AMP TRG EANA SQ, 26 
-50785603,HC 7046 12544R MUT ID SQ EA SG, 26 
-50788111,HC 7046 11989R QT SYN ANAL," 3,038 "
-50785611,HC 7046 12544R INTRP AND RPT, 6 
-50785629,HC 7046 12540R ISL EXT HP EANA, 7 
-50788129,HC 6997 12080R CANA DISEAS ANL, 158 
-50785637,HC 7046 12540R ENZY DIG EA TRT, 7 
-50785645,HC 7046 12540R SEP GEL ELCT EA, 7 
-50788137,HC 6997 12082R GSD1A MUTA ANAL, 150 
-50785652,HC 7046 12540R AMP TRG EANA SQ, 29 
-50785660,HC 7046 12540R MUT ID SQ EA SG, 29 
-50788145,HC 6997 12084R MLIV MUTA ANAL, 140 
-50785678,HC 7046 12540R INTRP AND RPT, 7 
-50785686,HC 7077 12534R ISOL EXT EA NA, 3 
-50788152,HC 6997 12086R NI-PICK TYPE A, 123 
-50785694,HC 7077 12534R SEP GEL ELCT EA, 3 
-50785702,HC 7077 12534R AMP TRG EANA SQ, 12 
-50788160,HC 6997 12088R TAY-SACHS MUT, 158 
-50785710,HC 7077 12534R MUT ID SQ EA SG, 12 
-50785728,HC 7077 12534R INTRP AND RPT, 3 
-50788178,HC 6997 12216R FAM DYSAU MUT, 140 
-41188321,HC PRSTHSIS DTAA EA ADDL PROXIMAL," 9,980 "
-50788186,HC 6997 12218R GAU DISEA MUT, 158 
-50785245,HC 6956 12548R SIROLIMUS TDA, 19 
-50785252,HC 7046 12526R ISL EXT HP EANA, 7 
-50788194,HC 6997 12220R MSUD GENE ANLY, 278 
-50785261,HC 7046 12526R ENZY DIG EA TRT, 7 
-50785278,HC 7046 12526R SEP GEL ELCT EA, 7 
-50788202,HC 7052 12224R MTDNA SEQ ANLYS, 78 
-50785286,HC 7046 12526R AMP TRG EANA SQ, 29 
-50785294,HC 7046 12526R MUT ID SQ EA SG, 29 
-50788210,HC 7052 12224R MTDNA ANLYS, 78 
-50785306,HC 7046 12526R INTRP AND RPT, 7 
-50785314,HC 7046 12528R ISL EXT HP EANA, 9 
-50788228,HC 6956 12331R MLH1 HYPER ANL, 453 
-50785322,HC 7046 12528R ENZY DIG EA TRT, 9 
-50785330,HC 7046 12528R SEP GEL ELCT EA, 9 
-50788236,HC 6956 12332R KIT MUT EXON 11, 766 
-50785348,HC 7046 12528R AMP TRG EANA SQ, 36 
-50785355,HC 7046 12528R MUT ID SQ EA SG, 36 
-50788244,HC 6956 12333R KIT MUT 9 EXON, 766 
-50785363,HC 7046 12528R INTRP AND RPT, 9 
-50785371,HC 7046 12530R ISL EXT HP EANA, 5 
-50788251,HC 7053 12334R XLID ANAL, 62 
-50785389,HC 7046 12530R ENZY DIG EA TRT, 5 
-50785397,HC 7046 12530R SEP GEL ELCT EA, 5 
-50788269,HC 7053 12334R MECP2 ANAL, 62 
-50785405,HC 7046 12530R AMP TRG EANA SQ, 19 
-50785413,HC 7046 12530R MUT ID SQ EA SG, 19 
-50788277,HC 7053 12334R DUP/DEL VARI, 62 
-50785421,HC 7046 12530R INTRP AND RPT, 5 
-50785439,HC 7046 12530R ARR 251-500 PRB," 1,462 "
-50788285,HC 7053 12334R DET MUT VARI, 62 
-50785447,HC 7046 12532R ISL EXT HP EANA, 7 
-50785454,HC 7046 12532R ENZY DIG EA TRT, 7 
-50788293,HC 7053 12334R 1 EXON BY DNA, 63 
-50785462,HC 7046 12532R SEP GEL ELCT EA, 7 
-50785470,HC 7046 12532R AMP TRG EANA SQ, 29 
-50788306,HC 7053 12334R 2-5 EXONS DNA, 62 
-50785488,HC 7046 12532R MUT ID SQ EA SG, 29 
-50785496,HC 7046 12532R INTRP AND RPT, 7 
-50788314,HC 7053 12334R 6-10 EXONS DNA, 62 
-62028716,HC PSG 4+ PARAMETERS AGE 6+," 8,150 "
-50788322,HC 7053 11-25 EXONS BY DNA, 62 
-62028719,HC MULTIPLE SLP LAT TST," 2,724 "
-62028720,HC PSG 4+ PARAMETERS W/CPAP AGE 6+," 8,358 "
-50788330,HC 7053 26-50 EXONS BY DNA, 62 
-62028721,HC SLEEP STUDY UNATTENDED," 1,768 "
-62028722,HC PHOTOCHEMOTHRPY UVB, 302 
-50788348,HC 7053 12334R EXONS BY DNA, 62 
-62028723,HC PHOTOCHEMOTHRPY UVA, 389 
-62028724,HC PHOTODYNAMIC SKIN SESSION, 413 
-50788355,HC 7052 12380R SCO1 SEQ ANAL, 590 
-62028725,HC BURN DRSG SKIN SUB SQCM L56," 17,510 "
-62028726,HC TEST ALLERGY PATCH, 76 
-50788363,HC 7052 12382R SCO2 SEQ ANAL, 410 
-62028727,HC IV INFUSION-THERAPEUTIC-1ST HR, 663 
-62028728,HC PHYSICAL THERAPY EVAL, 360 
-50788371,HC 7052 12384R SLC22A5 SEQ ANL," 1,120 "
-62028729,HC PT GAIT TRAINING 15 MIN, 192 
-62028730,"HC TX ACT-DIRECT EA 15""", 221 
-50788389,HC 7053 12386R MECP2 GENE ANAL, 51 
-62028731,"HC SELF-CARE MGMT TRNG EA 15""", 262 
-62028732,"HC TX PROC (ROM) EA 15""", 238 
-50788397,HC 7053 12386R DET  DYNMC MUT, 51 
-62028733,"HC APPLY ULTRASOUND EA 15""", 140 
-62028734,HC IV INFUSION-THER-ADDTL-HRS, 281 
-50788405,HC 7053 12386R 1 EXON DNA SEQ, 51 
-62028735,HC NEEDLE EMG W/WO PRSINL AREA, 475 
-50788413,HC 7053 12386R 2-5 EXON DNA SQ, 51 
-62028713,HC TEST RECTAL SENSATION CMPLT, 468 
-62028714,HC APLIGRAF PER SQ CM, 78 
-50788421,HC 7053 12386R 6-10 EXONS DNA, 51 
-50788439,HC 7053 12386R 11-25 EXONS DNA, 51 
-50788447,HC 7053 12386R 26-50 EXONS DNA, 51 
-50788454,HC 7053 12386R XLMR NXT GN PNL, 51 
-50788462,HC 7052 ACADVL SEQ ANLYS," 1,220 "
-50788470,HC 6959 ACVRL1 SEQ DEL/DUP, 868 
-50788488,HC 7052 12435R MECP2 SEQ ANAL, 670 
-50788496,HC 7052 12435R DUP/DELETION, 670 
-50788504,HC 7052 MTDNA FULL GENE SEQ," 1,000 "
-50788512,HC 7052 MTDNA MASS PARALEL SEQ," 1,000 "
-50788520,HC 7046 12510R PERIOD FEV SYND, 697 
-50788538,HC 7046 12510R PERIOD FEV PNL, 697 
-50788546,HC 7046 12510R SPECTROPHOTOM, 697 
-50788553,HC 7046 12512R VHL DEL/DUP ANA, 450 
-50788561,HC 7046 12512R VHL FUL GNE SEQ, 450 
-50788579,HC 7077 12536R MARFAN DNA SEQ," 1,570 "
-50788587,HC 7077 12538R TCS1 DNA SEQ," 1,830 "
-50788595,HC 7046 12540R SCAD DEFICIENCY," 1,064 "
-50788603,HC 7046 GLUTARIC ACIDURIA TYPE," 1,116 "
-50788611,HC 7046 12544R ACADVL GENE ANL," 1,431 "
-50788629,HC 7030 12546R NEIMA DIS TYP C," 3,100 "
-50788637,HC 7079 12556R PNH SEQ DELETE," 1,818 "
-50788905,HC 7080  12562R MYH3 GENE SEQ," 1,930 "
-50788645,HC 7080 TRIMETHYLAMINURIA FMO3, 690 
-50788652,HC 7082 12566R PTT HOPKIN SYND," 1,895 "
-50788660,HC 7082 KABUKI SYNDRM (MLL2)," 2,995 "
-50788678,HC 7081 12571R FLT3 GENE ANAL, 150 
-50788686,HC 7081 12571R FLT3-ITD/D835, 150 
-50788694,HC 7046 12599R DILATED CARD SQ, 759 
-50788702,HC 7046 12599R DCM DIL CARDMYP, 759 
-50788710,HC 7046 12599R DCM/LVNC SEQ, 759 
-50788728,HC 7046 SPECTROPHOTO ANALYTE, 759 
-50788736,HC 6956 12603R TREC IMMUN RECN, 272 
-50788744,HC 7069 OTOFERLIN KNOWN MUT, 479 
-50788751,HC 7069 12641R OTOFERLIN COMP," 1,915 "
-50788769,HC 7046 12651R HCM PANEL, 759 
-50788777,HC 7046 12651R HCM 6-10 EN DNA, 759 
-50788785,HC 7046 12651R 11-25 EXONS DNA, 759 
-50788793,HC 7046 SPECTROPHOTO ANALYTE, 759 
-50788806,HC 6961 ADMARK APOE GENOTYPE, 381 
-50788814,HC 6942 CMV GENOTYPE, 638 
-50788822,HC 6961 PALSY PMP22 DUP/DELETE, 740 
-50788830,HC 6961 PALSY PMP22 FULL ANLYS, 740 
-50788848,HC 6961 HUNTINGTON'S DISEASE, 446 
-50788855,HC 6961 KENNEDY'S DISEASE DNA, 446 
-50788863,HC 6961 MELAS MTDNA PROFILE," 1,411 "
-50788871,HC 6961 TTR AMYLOIDOSIS DNA, 948 
-50788889,HC 6956 APLOPOPROTEIN GEN TYP, 220 
-42005348,HC ABDOM PARACENT W/GUIDANCE," 1,648 "
-50808368,HC MYD88 MUTATION BLOOD," 2,195 "
-50808376,HC NMYD88 MUTATION NON BLOOD," 2,195 "
-55092654,HC ORTHO AERO CUL W/GRAM STAIN, 276 
-55092662,HC SMEAR PRIM SOURCE W/INTERP, 150 
-55092670,HC ANAERO CUL FOR ORTHO, 544 
-55092688,HC CUL FOR MULTI ORTHO SPECI, 544 
-32003510,HC I&D ABSC VULVA/PERINEAL, 334 
-32003546,HC PLACE NDL INFS INTRAOSSEO, 346 
-32051148,HC TX FX TALUS W/MAN CLSD, 714 
-38616256,HC INJECT SACROILIAC JOINT," 4,533 "
-41100920,HC XR LMPHANGIO EXTREMITY UNI, 907 
-41100963,HC LYMPHANGIO EXT BIL W/ S&I, 938 
-41100969,HC LYMPHANGIO PEL/ABDOMEN BIL, 907 
-41151615,HC INJ SPLENOPORTOGRAM, 280 
-41152420,HC PERCT MESOCAVAL SHUNT PLCMT," 5,181 "
-41153140,HC RPR FISTULA ANAL W/ GLUE," 7,867 "
-41164150,HC DIRECT IVC PORTOSYST SHUNT," 5,181 "
-56269461,HC VENOUS PRESSURE DETERM," 2,256 "
-81600290,"HC CTDN MNGMT FEE,OUT OF AREA"," 10,500 "
-81600291,HC CTDN FLYOUT FEE OUT OF AREA," 5,500 "
-81800310,HC HM 14 V LITHIUM BATT," 5,693 "
-81800320,HC HM UNIVRSL BATT CHRGR," 29,039 "
-81800325,HC HM BATTERY CLIPS," 7,724 "
-81800330,HC HM SHWR CVR BOX," 2,143 "
-81800355,HC HM BATTERY MDL SYS CNTRL," 2,602 "
-81800360,HC HM PWR MODL DC CAR INPUT CBL," 2,143 "
-81800365,HC HM PATIENT PWR MODL CBL," 7,852 "
-81800367,HC POWER MODULE," 30,652 "
-81800371,HC HOLSTER VEST SML LIPK," 2,976 "
-81800372,HC HOLSTER VEST MED LIPK," 3,536 "
-81800373,HC HOLSTER VEST LRG LIPK REPLACEMENT," 3,536 "
-81800377,HC HM CONSL BAG," 2,763 "
-81800379,HC HM TRAVEL CASE," 1,190 "
-21150041,HC DIRECT ADMIT TO OBS, 821 
-22250013,HC DIRECT ADMIT TO OBS, 821 
-23150049,HC DIRECT ADMIT TO OBS, 821 
-23250047,HC DIRECT ADMIT TO OBS, 821 
-23750046,HC DIRECT ADMIT TO OBS, 821 
-23850044,HC DIRECT ADMIT TO OBS, 821 
-24150147,HC DIRECT ADMIT TO OBS, 821 
-24850014,HC DIRECT ADMIT TO OBS, 821 
-25050049,HC DIRECT ADMIT TO OBS, 821 
-25150015,HC DIRECT ADMIT TO OBS, 821 
-25250045,HC DIRECT ADMIT TO OBS, 821 
-25350043,HC DIRECT ADMIT TO OBS, 821 
-25450041,HC DIRECT ADMIT TO OBS, 821 
-25750044,HC DIRECT ADMIT TO OBS, 821 
-26150046,HC DIRECT ADMIT TO OBS, 821 
-26350042,HC DIRECT ADMIT TO OBS, 821 
-36300319,HC MCL FOR IMRT DESIGN&CONSTR," 13,988 "
-36301540,HC THERAPTIC RAD PORT FILM(S), 800 
-36301541,HC IMRT DAILY TREATM 15X," 7,929 "
-36301550,HC RESPIR MOTION TRACK GATING, 703 
-52051539,HC RES RPM RESEARCH FEE, 50 
-91900645,HC SPECIMEN HANDLING, 250 
-50788897,HC 6956 HEMOCHROMA HFE GENE, 119 
-38023396,HC CT MAX/FACIAL W&W/O CONTRAST," 4,207 "
-38025987,HC CT C-SPINE W&W/O CONTRAST," 5,879 "
-38025995,HC CT L-SPINE W&W/O CONTRAST," 5,879 "
-38026001,HC CT T-SPINE W&W/O CONTRAST," 5,879 "
-38623393,HC CT MAX/FACIAL W&W/O CONTRAST," 4,207 "
-38625984,HC CT C-SPINE W&W/O CONTRAST," 5,879 "
-38625992,HC CT L-SPINE W&W/O CONTRAST," 5,879 "
-38626008,HC CT T-SPINE W&W/O CONTRAST," 5,879 "
-41068529,"HC CONTRAST, EZ PASTE BARIUM", 297 
-41068560,HC PROCEDURE TRAY, 95 
-41068578,HC LUMBAR TRAY, 139 
-42023390,HC CT MAX/FACIAL W&W/O CONTRAST," 4,207 "
-42025981,HC CT C-SPINE W&W/O CONTRAST," 5,879 "
-42025999,HC CT L-SPINE W&W/O CONTRAST," 5,879 "
-42026005,HC CT T-SPINE W&W/O CONTRAST," 5,879 "
-94323391,HC CT MAX/FACIAL W&W/O CONTRAST," 4,207 "
-94325990,HC CT L-SPINE W&W/O CONTRAST," 5,879 "
-94326006,HC CT T-SPINE W&W/O CONTRAST," 5,879 "
-42080226,HC ABLATION BONE TUMR PERC W/CT," 13,822 "
-62010010,HC GENETIC COUNSELING 30 MIN, 458 
-62028881,HC NCXPT COMB MOD/PR INIT 30, 392 
-62028882,HC NCXPT COMB MOD/PR EA ADL 15, 253 
-62028883,HC NCXPT PROC 1 AREA INIT 30, 457 
-62028884,HC NCXPT PROC 1 AREA EA ADDL 15, 230 
-94265311,HC MRI SYRINGE W/DBL VALVE, 100 
-50788913,HC  6910 AMINO ACID 24HR URINE, 89 
-55092704,HC E TEST MIC X7, 113 
-41600255,HC MANDIBLE MIN 4 VIEW," 1,076 "
-41600404,HC FACIAL BONES 2V OR, 791 
-41600453,HC FACIAL BONES MIN 3," 1,107 "
-41600503,HC NASAL BONES MIN 3V, 896 
-41600701,HC ORBITS MIN 4V, 383 
-41600859,HC SINUSES PARANASAL MIN 3V," 1,671 "
-41600982,HC SKULL <4 VIEWS, 691 
-41601055,HC SKULL MIN 4 VIEWS," 1,387 "
-41601303,HC TMJ OPEN + CLOSED BILATERAL, 371 
-41601808,HC SPINE TOTAL AP/LAT, 325 
-41601857,HC SOFT TISSUE NECK, 705 
-41601873,HC L SPINE BEND ONLY 2/3 VIEWS, 940 
-41601907,"HC C-SPINE, 3 VIEWS OR LESS", 951 
-41601956,HC SPINE CERV 4 OR 5 VIEWS," 1,255 "
-41602004,HC SPINE CERV 6 OR MORE VIEWS," 1,465 "
-41602012,HC T-SPINE STANDING SCOLIOSIS, 564 
-41602053,HC T-SPINE 2 VIEWS, 873 
-41602061,HC T-SPINE THREE VIEWS, 960 
-41602103,HC THORACOLUMBAR 2 VIEWS, 960 
-41602152,HC L-SPINE 2 OR 3 VIEWS, 805 
-41602202,HC L-SPINE MIN FOUR VIEWS," 1,090 "
-41602210,HC L SPINE COMP W/BEND," 1,433 "
-41602301,HC SCOLIOSIS INC SUP& ERECT," 1,013 "
-41602350,HC SACRUM/COCCYX MIN 2, 829 
-41602558,"HC PELVIS,ONE OR TWO VIEWS", 515 
-41602608,HC PELVIS MIN 3V, 843 
-41602657,HC SACROILIAC JOINTS MIN 3V, 984 
-41602954,HC HIP 1 VIEW, 865 
-41603002,HC HIP MIN 2V, 972 
-41603051,HC HIP BIL W/PELVIS," 1,333 "
-41603200,HC SHOULDER 1 VIEW, 752 
-41603358,HC SHOULDER MIN 2/V, 823 
-41603408,HC HUMERUS MIN 2V, 744 
-41603457,HC ELBOW AP + LAT, 705 
-41603507,HC ELBOW MIN 3V, 800 
-41603556,HC FOREARM AP + LAT, 744 
-41603606,HC WRIST AP + LAT, 753 
-41603655,HC WRIST MIN 3V, 898 
-41603754,HC HAND 2 VIEW, 742 
-41603804,HC HAND MIN 3 VIEWS, 867 
-41603853,HC FINGER(S) MIN 2V, 592 
-41604000,HC FEMUR AP + LAT, 879 
-41604059,"HC KNEE, ONE OR TWO VIEWS-UNLAT", 685 
-41604109,HC KNEE THREE VIEWS-UNILAT, 815 
-41604158,HC KNEE 4 OR MORE VIEWS," 1,110 "
-41604208,HC TIBIA + FIBULA AP +, 836 
-41604257,HC ANKLE AP + LAT, 673 
-41604307,HC ANKLE MIN 3V, 849 
-41604356,HC FOOT AP + LAT, 722 
-41604406,HC FOOT MIN 3V, 908 
-41604505,HC CALCANEUS MIN 2V, 548 
-41604554,HC TOE(S) MIN 2V, 586 
-41604703,HC CHEST 1 VIEW, 593 
-41604752,HC CHEST 2V FRONT + LA, 765 
-41604851,HC CHEST MIN 4V," 1,160 "
-41605254,HC RIBS UNI 2V, 837 
-41605262,HC RIBS UNILAT W/CHEST 3+ V," 1,010 "
-41605304,HC RIBS BILAT 3V," 1,044 "
-41605312,HC RIBS BILAT W/CHEST 4+," 1,363 "
-41605353,HC STAND KNEES AP BILT, 672 
-41605403,HC STERNUM MIN 2V, 689 
-41605452,HC STERNOCLAVICULAR JT MIN 3V, 808 
-41605502,HC CLAVICLE, 677 
-41605551,HC ACROMIOCLAV JTS BILAT," 1,054 "
-41605601,HC SCAPULA, 746 
-41606674,HC 36 LEG LENGTH, 890 
-41607300,HC ABDOMEN AP ONLY, 682 
-41607359,HC ABD CPL W/DECUB+ERECT," 1,127 "
-41607409,HC ABDOMEN COMP INCL +1V CHEST," 1,345 "
-41609702,HC BONE SRVY METS AXIA COMPL," 2,032 "
-41616301,HC KNEE THREE VIEWS-BILAT," 1,630 "
-41616319,"HC KNEE, ONE OR TWO VIEWS - BILAT"," 1,369 "
-41621319,HC SPINE SINGLE VIEW, 291 
-41680117,"HC CHEST, 2V W/OBLIQUE", 744 
-41680125,"HC CHEST, LAT DECUB", 665 
-23230177,HC CHEMO ADMIN SQ/OR IV; HORMONAL, 643 
-32051515,HC OCCULT BLD GUAIAC DIAG, 74 
-32061515,HC POC URINE PREGNANCY SREEN, 102 
-32062525,HC INC ABSC SOFT TISS SUBFASC," 1,974 "
-32071515,HC GLUCOSE POC, 200 
-32091515,"HC TREATMENT, THIGH FX"," 3,528 "
-38165312,HC MRI SYRINGE W/DBL VALVE, 100 
-42016543,HC XR SOFT TISSUE NECK, 705 
-50788921,HC 12772R ENCEPHALITIS AB SCRN, 302 
-50788939,HC 12272R QUANTITATIVE BY RIA, 302 
-50788947,HC 12272R BLOT W/INTERP, 302 
-50788954,HC 12272R WEST W/PROBE INDENT, 302 
-50788962,HC 12772R NONINFECT AB SCRN, 302 
-55092639,HC MALPCR MALARIA PCR, 388 
-62010036,HC GENETIC COUNSELING 15 MIN, 229 
-32072525,HC ECG REC>48HRS/21DAYS RECORDING, 404 
-69828881,HC NCXPT COMB MOD/PR INIT 30, 392 
-69828882,HC NCXPT COMB MOD/PR EA ADL 15, 253 
-50393933,HC HEMATOCRIT MANUAL, 54 
-50808384,HC HEMOCHROMATOSIS GENO ANA, 872 
-69828883,HC NCXPT PROC 1 AREA INIT 30, 457 
-69828885,HC NCXPT SNGL MOD 1 AREA 30MN, 257 
-69828887,HC NCXPT EVAL ADDL 15MIN, 273 
-69828888,HC NCXPT EVAL INIT 30MIN, 546 
-55092712,HC CRYPTO ANTI LATERAL ASSAY, 132 
-55092720,HC CRYPTO ANTI FLOW ASSAY CSF, 132 
-94260262,HC MR INJECTOR KIT, 118 
-50788970,HC 12558R SNP MICROARRAY," 2,100 "
-25040170,HC IMMUNIZATION ADMIN SINGLE, 35 
-62028890,HC NCXPT COMB MOD/PR INIT 30, 392 
-62028891,HC NCXPT COMB MOD/PR EA ADL 15, 253 
-62028892,HC NCXPT PROC 1 AREA INIT 30, 457 
-62028893,HC NCXPT PROC 1 AREA EA ADDL 15, 230 
-62028894,HC NCXPT SNGL MOD 1 AREA 30MN, 257 
-62028895,HC NCXPT MOD 1 AREA EA ADDL 15, 128 
-62028896,HC NCXPT EVAL INIT 30MIN, 546 
-62028897,HC NCXPT EVAL ADDL 15MIN, 273 
-40110009,HC HIFU TREATMENT KIT," 1,700 "
-40110017,HC HIFU COUPLING GEL BOWL ASSY," 1,060 "
-40110025,"HC HIFU DEGASSED/TREATED WATER, 1 L", 255 
-40110033,HC HIFU DEGASSD/TREATD H2O 500 ML, 128 
-40110041,HC HIFU GEL PAD ASSY," 1,060 "
-40119992,HC SPECIAL PHYSICS CONSULT," 5,752 "
-40120016,HC BASIC DOSIMETRY CALCULATION," 1,369 "
-40120024,HC TREATMENT DEVICE COMPLEX," 2,439 "
-40120032,HC SIM BY 3-D RECONSTRUCTION," 11,883 "
-40120081,HC MR HIFU BONE METS TRMT," 27,992 "
-40905770,HC NDL BIOP NEEDLE, 272 
-38160263,HC MR INJECTOR KIT, 118 
-50789004,HC 6956 REDUCE SUBSTANCE FECES, 45 
-50789012,"HC 6942 ECHIG AB IGG, EIA", 192 
-50808392,HC DEL/DUP ANAL CDH1-ECADHERIN," 1,845 "
-37000361,HC NUC MED IV INFUSION," 1,526 "
-37021607,"HC RADIUM 223, DICHLORIDE"," 69,000 "
-41004805,HC CHEST 2V W/APICAL, 953 
-50789020,HC 6922 COCCI AB IMMUNO CSF, 34 
-58010275,"HC DYSPORT, PER 5 UNITS", 45 
-58033280,HC TILT TABLE EVAL," 2,511 "
-32051182,HC CLTX FX GRT TOE PHLX/PHLG W/MNPJ, 565 
-37003472,HC SHUNT PATENCY STUDY," 5,014 "
-37106101,HC RESEARCH PET-CT SCAN (SPONSORED)," 10,566 "
-41163425,HC ERCP W/REMOVE FB," 8,943 "
-42025908,"HC BX BREAST NDL CORE, WITH GUIDE"," 3,418 "
-50789038,HC 6959 12788R OVARY AB TITER, 82 
-50808400,HC HM PATIENT PWR MODL CBL, 439 
-50808418,HC DEL/DUP ANAL AMD-BMD-BBMD," 1,968 "
-62028898,HC SEMEN PRESENCE/MOTILITY, 33 
-62028900,HC INJ ANES NERVE FEMORAL SGL, 822 
-41164200,HC CT PELVIS W/O CONTRAST," 4,373 "
-41100665,HC COLON BARIUM W/CONTRAST," 2,360 "
-41152645,HC E/V RPR AAA W/SMALL TUBE," 9,304 "
-26432010,HC INJECT/ASPIR SHUNT TUBE RESRV," 1,809 "
-26602032,HC CHEMO INJECT SQ RESERVOIR, 629 
-26702032,HC CHEMO INJ SUBQ PORT, 629 
-32701287,HC TRANSFUSION BLOOD - 2HR," 3,500 "
-32701288,HC TRANSFUSION BLOOD - 3HR," 3,700 "
-32701289,HC TRANSFUS BLOOD/COMP 4H," 3,900 "
-32701290,HC TRANSFUSION BLOOD - 5HR," 4,100 "
-32701291,HC TRANSFUSION BLOOD - 6HR," 4,300 "
-32701292,HC TRANSFUSION BLOOD - 7HR," 4,500 "
-32701293,HC TRANSFUSION BLOOD - 8HR," 4,700 "
-32701294,HC TRANSFUSION BLOOD - 9HR," 4,900 "
-36033390,HC IMMUN ADMIN ONE VACC, 263 
-26532010,HC INJECT/ASPIR SHUNT TUBE RESRV," 1,809 "
-26632010,HC INJECT/ASPIR SHUNT TUBE RESRV," 1,809 "
-26732010,HC INJECT/ASPIR SHUNT TUBE RESRV," 1,809 "
-41164190,"HC CT LTD, LOCAL, F/U", 472 
-62700830,HC SOFT SET INFUSION SET," 1,190 "
-62027628,HC IMMUN ADMIN ONE VACC, 94 
-62027632,HC IMMUN ADMIN EA ADD VACC, 49 
-62027636,HC IMMUN ADMIN ONE VACC ORAL, 41 
-62027640,HC HEPATITIS A VACC ADULT IM DOSE, 99 
-62027644,HC HEPATITIS A VAC PED/ADOL D-2, 55 
-62027648,HC HEPATITIS A&B ADULT VACC IM, 179 
-62027652,HC HAEMOPH B CONJ HBOC IM D-4, 86 
-62027656,HC HEMOPHIL B VACC PRP-D IM, 148 
-62027660,HC HUM PAPILLOMA VIRUS VACC D-3, 237 
-62027664,"HC INFLUENZA VIR VAC PF 6-35 MNTH, TRIVALENT", 27 
-62027668,"HC INFLUENZA VACC SPLT PF >3YRS, TRIVALENT", 27 
-62027672,"HC INFLUENZA VACC SPLT 6-35 MNTH, TRIVALENT", 27 
-62027676,HC PNEUMOCOCCAL VACC 7 VALENT <5Y, 166 
-62027680,HC RABIES VACC IM VL 2.5IU/ML 1ML, 374 
-62027684,HC ROTAVIRUS VACC ORAL PENTA, 147 
-62027688,HC TYPHOID VACC VI CAP (VICPS), 100 
-62027692,HC DTAP VACC <7 YR IM, 50 
-62027696,HC TETANUS TOX ADS IM, 52 
-62027700,HC MEASLES VIRUS VACC LIVE SQ, 39 
-62027704,HC RUBELLA VIRUS VACC LIVE SQ VL, 93 
-62027708,HC MMR VIRUS VACC LIVE SQ 0.5ML, 93 
-62027712,HC MEASL/MUMPS/RUB/VARIC VACC, 53 
-62027716,HC POLIOVIRUS VACC INACT SQ/IM, 63 
-62027720,HC DIP/TET PF VACC >7YR IM DOSE, 37 
-62027724,HC DIP/TET/ACE PERT VACC >7YR IM, 65 
-62027728,HC VARICELLA VIRUS VACC SQ, 178 
-62027732,HC YELLOW FEVER VACC LIVE SQ, 162 
-62027740,HC DTAP-HEPB-IPV VACC IM DOSE, 129 
-62027744,HC PNEUMOC VACC 23 >5YR SQ/IM, 95 
-62027748,HC MENINGOCOCCAL POLY VACC SQ, 204 
-62027752,HC MENING CON VACC ACY W135 IM, 206 
-62027756,HC JAPANESE ENCEPH VIRUS VACC SQ, 212 
-62027760,HC VARICELLA ZOSTER LIVE SQ, 322 
-62027764,HC HEPAT B VACC PED/AD 3-D IM, 195 
-62027768,"HC HEPATITIS B VACC ADULT IM VL, 3 DOSE", 179 
-62027772,HC HAEMOPH B/HEPAT B VACC IM, 79 
-62027776,HC THERAP/PRO/DX INJ SQ/IM, 78 
-62027780,HC THERAPYPRO//DX IV PUSH ADDL, 241 
-62027784,HC BUPIVACAINE HCL-0.5% (5MG/ML), 3 
-62027788,HC LIDOCAINE 0.5% 1CC, 25 
-62027792,HC LIDOCAINE 2% PER 10ML, 26 
-62027804,HC DEHYDRATED ALCOHOL 1 ML, 24 
-62027808,HC DEHYDRATED ALCOHOL 5 ML, 126 
-62027816,HC ADMIN HEPATITIS B VACC, 88 
-62027828,HC BACLOFEN INJ 10MG (ANY CONC), 591 
-62027832,HC PCN G BENZ INJ 2.4MILLU 4ML, 184 
-62027836,HC BOTOX TYPE A PER 1UNIT, 34 
-62027840,HC BOTOX TYPE B PER 100 UNITS, 53 
-62027844,HC CEFRIAXONE INJ PER 250MG, 20 
-62027848,HC CLONIDINE INJ 1MG, 117 
-62027852,HC COSYNTROPIN INJ 0.25MG NOS, 200 
-62027856,HC COSYNTROPIN VL 0.25MG, 138 
-62027860,HC METHYLPRED ACE VL 20MG/ML 1ML, 23 
-62027864,HC METHYLPRED ACE VL 40MG/ML 1ML, 39 
-62027868,HC METHYLPRED ACE VL 80MG/ML 1ML, 71 
-62027876,HC DIHYDROERGOTAMINE AMP 1MG, 238 
-62027880,HC HYDROMORPHONE INJ 4MG (ANY CONG), 59 
-62027884,HC GENTAMICIN UP TO 80MG, 74 
-62027888,HC KETOROLAC INJ PER 15MG, 17 
-62027892,HC LEUPROLIDE DEPOT KIT 3.75MG," 1,318 "
-62027896,HC MEPERIDINE INJ PER 100MG, 6 
-62027900,HC MIDAZOLAM VL 1MG/ML 1ML, 26 
-62027904,HC MORPHINE PF INJ 10MG (ANY CONC), 244 
-62027908,HC ZICONOTIDE IT 1MCG, 255 
-62027912,HC OCTREOTIDE INJ PER 25MCG, 141 
-62027916,HC OMALIZUMAB PWDR INJ 150MG," 1,187 "
-62027920,HC PROMETHAZINE INJ UP TO 50MG, 19 
-62027924,HC RANIBIZUMAB(LUCENTIS).1MG, 780 
-62027928,HC RHO (D) IMM GLOB IV/IM 1500IU, 170 
-62027932,HC TESTOSTERONE INTRAMUSCULAR, 206 
-62027936,HC TRIAMCINOLONE ACE PF INJ 1MG, 74 
-62027940,HC TRIAMCINOLONE ACE INJ PER 10MG, 25 
-62027944,HC USTEKINUMAB PER MG (1MG), 315 
-62027952,HC VERTEPORFIN VL 0.1MG, 189 
-62027956,HC VIT B12(CYANOCOBALAMIN) INJ, 1.0 
-62027960,HC KETAMINE 100MG/ML, 62 
-62027964,HC KETAMINE 500MG/ML, 62 
-62027968,HC LIDOCAINE 1% PER 10ML, 26 
-62027972,HC VANCOMYCIN 1MG, 164 
-62027976,HC CEFTAZIDIME 2.25MG/0.1ML, 182 
-62027980,HC RITUXAN 1 MG /0.5ML 1MG INTRAVITRL SOLN," 1,207 "
-62027984,HC HEALON 1MG/0.55ML, 163 
-62027988,HC BEVACIZUMAB(AVASTIN).25MG, 69 
-62027992,HC NACL 0.9% 1000ML, 87 
-62027996,HC NACL 0.9% 500ML, 81 
-62028000,HC DEVICE IUD COPPER T," 1,136 "
-62028004,HC LEVONORGESTREL IUD SYSTEM 52 MG," 1,406 "
-62028008,HC LEVONORGESTREL IU CONTRACEPT," 1,406 "
-62028012,HC LEVONORGESTREL IMPLANT SYS," 1,250 "
-62028016,HC AMINOLEVULINIC ACID 20% TOP, 300 
-62028020,HC CAPSAICIN PATCH 8% (1)," 1,647 "
-62028024,HC ALBUTER/IPRATROP INH, 96 
-62028028,HC FLUOROURACIL VL 50MG/ML 10ML, 6 
-62028032,HC LEUPROLIDE DEPOT KIT 7.5MG," 2,261 "
-62028036,HC METHOTREXATE VL 2.5MG/ML 2ML, 284 
-62028040,HC METHOTREXATE PER 50MG, 284 
-62028044,HC SCULPTRA VL 0.1ML, 480 
-62028048,HC AFLURIA VACC SPLT >3 YR IM 5ML, 27 
-62028052,HC FLUZONE VACC SPLT >3 YRS IM, 27 
-62028056,HC INFLUENZA VACC SPLT >3 YRS IM, 27 
-62028060,"HC MIFEPRISTONE, ORAL, 200 MG", 184 
-32711617,HC ACUTE POST ANES CARE 15 MIN, 631 
-32711618,HC INTENSE POST ANES CARE 15 MIN," 1,142 "
-32711619,HC CRITICAL POST ANES CARE 15 MIN," 1,356 "
-39000259,HC OP VISIT LEVEL 1 EST PT, 160 
-39000263,HC OP VISIT LEVEL 2 EST PT, 361 
-39000267,HC OP VISIT LEVEL 3 EST PT, 413 
-39000271,HC OP VISIT LEVEL 4 EST PT, 463 
-39000275,HC OP VISIT LEVEL 5 EST PT, 593 
-39000279,HC OP VISIT LEVEL 1 EST W PX, 160 
-39000283,HC OP VISIT LEVEL 2 EST W PX, 361 
-39000287,HC OP VISIT LEVEL 3 EST W PX, 413 
-39000291,HC OP VISIT LEVEL 4 EST W PX, 463 
-39000295,HC OP VISIT LEVEL 5 EST W PX, 593 
-39000299,HC ADMIN OF INFLUENZA VACC, 77 
-39000303,HC ADMIN OF PNEUMO VACC, 295 
-39000311,HC PRESERVE FREE FLU VACCINE, 62 
-39000319,HC PNEUMO IMMUNIZATION, 171 
-41165900,HC THROMBOENDART FEMORAL COMM," 16,673 "
-50793967,HC 6949 H130 SEP GEL ELC EA NA, 8 
-50793975,HC 6949 H130 AMP MPLX NA SEQ, 59 
-50793983,HC 6949 H130 AMP MPLX EA ADTL, 30 
-50793991,HC 6949 H130 MUT ID SEQ EA SEG, 30 
-50794007,HC 6949 H130 SEP ID CAP EA NA, 30 
-50794015,HC 6949 H130 PATH CONSULT COMP, 8 
-50794023,HC 6949 H133 SEP GEL ELC EA NA, 8 
-50794031,HC 6949 H133 NA PROBE EA, 8 
-50794049,HC 6949 H133 AMP EA NA SEQ, 30 
-50794056,HC 6949 H133 PATH CONSULT COMP, 8 
-50794064,HC 6949 H140 SEP GEL ELC EA NA, 8 
-50794072,HC 6949 H140 NA PROBE EA, 8 
-50794080,HC 6949 H140 AMP EA NA SEQ, 30 
-50794098,HC 6949 H140 PATH CONSULT COMP, 8 
-50794106,HC 6949 H141 SEP GEL ELC EA NA, 8 
-50794114,HC 6949 H141 AMP EA NA SEQ, 30 
-50794122,HC 6949 H131 PATH CONSULT COMP, 8 
-52293966,HC 6949 H130 SEP GEL ELC EA NA, 8 
-52293974,HC 6949 H130 AMP MPLX NA SEQ, 59 
-52293982,HC 6949 H130 AMP MPLX EA ADTL, 30 
-52293990,HC 6949 H130 MUT ID SEQ EA SEG, 30 
-52294006,HC 6949 H130 SEP ID CAP EA NA, 30 
-52294014,HC 6949 H130 PATH CONSULT COMP, 8 
-52294022,HC 6949 H133 SEP GEL ELC EA NA, 8 
-52294030,HC 6949 H133 NA PROBE EA, 8 
-52294048,HC 6949 H133 AMP EA NA SEQ, 30 
-52294055,HC 6949 H133 PATH CONSULT COMP, 8 
-52294063,HC 6949 H140 SEP GEL ELC EA NA, 8 
-52294071,HC 6949 H140 NA PROBE EA, 8 
-52294089,HC 6949 H140 AMP EA NA SEQ, 30 
-52294097,HC 6949 H140 PATH CONSULT COMP, 8 
-52294105,HC 6949 H141 SEP GEL ELC EA NA, 8 
-52294113,HC 6949 H141 AMP EA NA SEQ, 30 
-52294121,HC 6949 H141 PATH CONSULT COMP, 8 
-81093965,HC 6949 H130 SEP GEL ELC EA NA, 8 
-81093973,HC 6949 H130 AMP MPLX NA SEQ, 59 
-81093981,HC 6949 H130 AMP MPLX EA ADTL, 30 
-81093999,HC 6949 H130 MUT ID SEQ EA SEG, 30 
-81094005,HC 6949 H130 SEP ID CAP EA NA, 30 
-81094013,HC 6949 H130 PATH CONSULT COMP, 8 
-81094021,HC 6949 H133 SEP GEL ELC EA NA, 8 
-81094039,HC 6949 H133 NA PROBE EA, 8 
-81094047,HC 6949 H133 AMP EA NA SEQ, 30 
-81094054,HC 6949 H133 PATH CONSULT COMP, 8 
-81094062,HC 6949 H140 SEP GEL ELC EA NA, 8 
-81094070,HC 6949 H140 NA PROBE EA, 8 
-81094088,HC 6949 H140 AMP EA NA SEQ, 30 
-81094096,HC 6949 H140 PATH CONSULT COMP, 8 
-81094104,HC 6949 H141 SEP GEL ELC EA NA, 8 
-81094112,HC 6949 H141 AMP EA NA SEQ, 30 
-81094120,HC 6949 H141 PATH CONSULT COMP, 8 
-81393969,HC 6949 H130 SEP GEL ELC EA NA, 8 
-81393977,HC 6949 H130 AMP MPLX NA SEQ, 59 
-81393985,HC 6949 H130 AMP MPLX EA ADTL, 30 
-81393993,HC 6949 H130 MUT ID SEQ EA SEG, 30 
-81394009,HC 6949 H130 SEP ID CAP EA NA, 30 
-81394017,HC 6949 H130 PATH CONSULT COMP, 8 
-81394025,HC 6949 H133 SEP GEL ELC EA NA, 8 
-81394033,HC 6949 H133 NA PROBE EA, 8 
-81394041,HC 6949 H133 AMP EA NA SEQ, 30 
-81394058,HC 6949 H133 PATH CONSULT COMP, 8 
-81394066,HC 6949 H140 SEP GEL ELC EA NA, 8 
-81394074,HC 6949 H140 NA PROBE EA, 8 
-81394082,HC 6949 H140 AMP EA NA SEQ, 30 
-81394090,HC 6949 H140 PATH CONSULT COMP, 8 
-81394108,HC 6949 H141 SEP GEL ELC EA NA, 8 
-81394116,HC 6949 H141 AMP EA NA SEQ, 30 
-81394124,HC 6949 H141 PATH CONSULT COMP, 8 
-82393968,HC 6949 H130 SEP GEL ELC EA NA, 8 
-82393976,HC 6949 H130 AMP MPLX NA SEQ, 59 
-82393984,HC 6949 H130 AMP MPLX EA ADTL, 30 
-82393992,HC 6949 H130 MUT ID SEQ EA SEG, 30 
-82394008,HC 6949 H130 SEP ID CAP EA NA, 30 
-82394016,HC 6949 H130 PATH CONSULT COMP, 8 
-82394024,HC 6949 H133 SEP GEL ELC EA NA, 8 
-82394032,HC 6949 H133 NA PROBE EA, 8 
-82394040,HC 6949 H133 AMP EA NA SEQ, 30 
-82394057,HC 6949 H133 PATH CONSULT COMP, 8 
-82394065,HC 6949 H140 SEP GEL ELC EA NA, 8 
-82394073,HC 6949 H140 NA PROBE EA, 8 
-82394081,HC 6949 H140 AMP EA NA SEQ, 30 
-82394099,HC 6949 H140 PATH CONSULT COMP, 8 
-82394107,HC 6949 H141 SEP GEL ELC EA NA, 8 
-82394115,HC 6949 H141 AMP EA NA SEQ, 30 
-82394123,HC 6949 H141 PATH CONSULT COMP, 8 
-84393966,HC 6949 H130 SEP GEL ELC EA NA, 8 
-84393974,HC 6949 H130 AMP MPLX NA SEQ, 59 
-84393982,HC 6949 H130 AMP MPLX EA ADTL, 30 
-84393990,HC 6949 H130 MUT ID SEQ EA SEG, 30 
-84394006,HC 6949 H130 SEP ID CAP EA NA, 30 
-84394014,HC 6949 H130 PATH CONSULT COMP, 8 
-84394022,HC 6949 H133 SEP GEL ELC EA NA, 8 
-84394030,HC 6949 H133 NA PROBE EA, 8 
-84394048,HC 6949 H133 AMP EA NA SEQ, 30 
-84394055,HC 6949 H133 PATH CONSULT COMP, 8 
-84394063,HC 6949 H140 SEP GEL ELC EA NA, 8 
-84394071,HC 6949 H140 NA PROBE EA, 8 
-84394089,HC 6949 H140 AMP EA NA SEQ, 30 
-84394097,HC 6949 H140 PATH CONSULT COMP, 8 
-84394105,HC 6949 H141 SEP GEL ELC EA NA, 8 
-84394113,HC 6949 H141 AMP EA NA SEQ, 30 
-84394121,HC 6949 H141 PATH CONSULT COMP, 8 
-30210085,HC PRC/PRSRV/TRNST CORNEAL TISL53," 17,609 "
-32051634,HC ER FIST CORP-GLANS PENIS," 7,817 "
-50794130,HC 6949 H090 PATH CONSULT COMP, 8 
-50794148,HC 6949 H090 LYMPH TITRAT, 90 
-50794155,HC 6949 H090 AUTOS PROC, 73 
-50794163,HC 6949 H090 SPLIT BLD EA UNIT, 76 
-52294139,HC 6949 H090 PATH CONSULT COMP, 8 
-52294147,HC 6949 H090 LYMPH TITRAT, 90 
-52294154,HC 6949 H090 AUTOS PROC, 73 
-52294162,HC 6949 H090 SPLIT BLD EA UNIT, 76 
-81094138,HC 6949 H090 PATH CONSULT COMP, 8 
-81094146,HC 6949 H090 LYMPH TITRAT, 90 
-81094153,HC 6949 H090 AUTOS PROC, 73 
-81094161,HC 6949 H090 SPLIT BLD EA UNIT, 76 
-81394132,HC 6949 H090 PATH CONSULT COMP, 8 
-81394140,HC 6949 H090 LYMPH TITRAT, 90 
-81394157,HC 6949 H090 AUTOS PROC, 73 
-81394165,HC 6949 H090 SPLIT BLD EA UNIT, 76 
-82394131,HC 6949 H090 PATH CONSULT COMP, 8 
-82394149,HC 6949 H090 LYMPH TITRAT, 90 
-82394156,HC 6949 H090 AUTOS PROC, 73 
-82394164,HC 6949 H090 SPLIT BLD EA UNIT, 76 
-84394139,HC 6949 H090 PATH CONSULT COMP, 8 
-84394147,HC 6949 H090 LYMPH TITRAT, 90 
-84394154,HC 6949 H090 AUTOS PROC, 73 
-84394162,HC 6949 H090 SPLIT BLD EA UNIT, 76 
-50794171,HC 6949 H091 PATH CONSULT COMP, 8 
-50794189,HC 6949 H091 LYMPH TITRAT, 90 
-50794197,HC 6949 H091 AUTOS PROC, 73 
-50794205,HC 6949 H091 SPLIT BLD EA UNIT, 76 
-52294170,HC 6949 H091 PATH CONSULT COMP, 8 
-52294188,HC 6949 H091 LYMPH TITRAT, 90 
-52294196,HC 6949 H091 AUTOS PROC, 73 
-52294204,HC 6949 H091 SPLIT BLD EA UNIT, 76 
-81094179,HC 6949 H091 PATH CONSULT COMP, 8 
-81094187,HC 6949 H091 LYMPH TITRAT, 90 
-81094195,HC 6949 H091 AUTOS PROC, 73 
-81094203,HC 6949 H091 SPLIT BLD EA UNIT, 76 
-81394173,HC 6949 H091 PATH CONSULT COMP, 8 
-81394181,HC 6949 H091 LYMPH TITRAT, 90 
-81394199,HC 6949 H091 AUTOS PROC, 73 
-81394207,HC 6949 H091 SPLIT BLD EA UNIT, 76 
-82394172,HC 6949 H091 PATH CONSULT COMP, 8 
-82394180,HC 6949 H091 LYMPH TITRAT, 90 
-82394198,HC 6949 H091 AUTOS PROC, 73 
-82394206,HC 6949 H091 SPLIT BLD EA UNIT, 76 
-84394170,HC 6949 H091 PATH CONSULT COMP, 8 
-84394188,HC 6949 H091 LYMPH TITRAT, 90 
-84394196,HC 6949 H091 AUTOS PROC, 73 
-84394204,HC 6949 H091 SPLIT BLD EA UNIT, 76 
-50794213,HC 6949 H119 TECH ONLY 1ST MRK, 131 
-50794221,HC 6949 H119 TECH EA ADDL MRKR, 80 
-50794239,HC 6949 H119 FLW INTRP 16 PLUS, 145 
-52294212,HC 6949 H119 TECH ONLY 1ST MRK, 131 
-52294220,HC 6949 H119 TECH EA ADDL MRKR, 80 
-52294238,HC 6949 H119 FLW INTRP 16 PLUS, 145 
-81094211,HC 6949 H119 TECH ONLY 1ST MRK, 131 
-81094229,HC 6949 H119 TECH EA ADDL MRKR, 80 
-81094237,HC 6949 H119 FLW INTRP 16 PLUS, 145 
-81394215,HC 6949 H119 TECH ONLY 1ST MRK, 131 
-81394223,HC 6949 H119 TECH EA ADDL MRKR, 80 
-81394231,HC 6949 H119 FLW INTRP 16 PLUS, 145 
-82394214,HC 6949 H119 TECH ONLY 1ST MRK, 131 
-82394222,HC 6949 H119 TECH EA ADDL MRKR, 80 
-82394230,HC 6949 H119 FLW INTRP 16 PLUS, 145 
-84394212,HC 6949 H119 TECH ONLY 1ST MRK, 131 
-84394220,HC 6949 H119 TECH EA ADDL MRKR, 80 
-84394238,HC 6949 H119 FLW INTRP 16 PLUS, 145 
-50794247,HC 6949 H121 TECH ONLY 1ST MRK, 131 
-50794254,HC 6949 H121 TECH EA ADDL MRKR, 80 
-50794262,HC 6949 H121 FLW INTRP 16 PLUS, 145 
-52294246,HC 6949 H121 TECH ONLY 1ST MRK, 131 
-52294253,HC 6949 H121 TECH EA ADDL MRKR, 80 
-52294261,HC 6949 H121 FLW INTRP 16 PLUS, 145 
-81094245,HC 6949 H121 TECH ONLY 1ST MRK, 131 
-81094252,HC 6949 H121 TECH EA ADDL MRKR, 80 
-81094260,HC 6949 H121 FLW INTRP 16 PLUS, 145 
-81394249,HC 6949 H121 TECH ONLY 1ST MRK, 131 
-81394256,HC 6949 H121 TECH EA ADDL MRKR, 80 
-81394264,HC 6949 H121 FLW INTRP 16 PLUS, 145 
-82394248,HC 6949 H121 TECH ONLY 1ST MRK, 131 
-82394255,HC 6949 H121 TECH EA ADDL MRKR, 80 
-82394263,HC 6949 H121 FLW INTRP 16 PLUS, 145 
-84394246,HC 6949 H121 TECH ONLY 1ST MRK, 131 
-84394253,HC 6949 H121 TECH EA ADDL MRKR, 80 
-84394261,HC 6949 H121 FLW INTRP 16 PLUS, 145 
-30210086,HC PRC/PRSRV/TRNST CORNEAL TISL54," 18,365 "
-30210087,HC PRC/PRSRV/TRNST CORNEAL TISL55," 5,719 "
-36516607,HC CT LUNG CANCER SCREENING, 698 
-37216600,HC CT LUNG CANCER SCREENING, 698 
-38016606,HC CT LUNG CANCER SCREENING, 698 
-38616603,HC CT LUNG CANCER SCREENING, 698 
-42016600,HC CT LUNG CANCER SCREENING, 698 
-92000070,HC VITAL STAIN (SPEC STAIN I), 60 
-94316601,HC CT LUNG CANCER SCREENING, 698 
-50783406,HC 6956 YUETHC ETHL CONF QN UR, 51 
-37010502,HC NM IP STAY RADIOLABEL ITEM, 1.0 
-50783398,HC 6956 YUCDA DRG SCR QL EA UR, 12 
-62028280,"HC BLEPHAROPLASTY, LOWER EYELID"," 4,244 "
-62028286,"HC BLEPH, LOW LID; W EXT HERN FAT PAD"," 4,244 "
-62028292,"HC BLEPHAROPLASTY, UPPER EYELID"," 4,244 "
-62028064,HC EXC/TRPOS PTERYGIUM W/GRF," 4,651 "
-62028070,HC PCNTS EYE SPX RMVL BLD," 4,651 "
-62028082,HC CILIARY BDY DSTRJ CYCLOPC NDSC," 3,410 "
-62028088,HC DSTRJ LES CHOROID PC 1+ SESS," 1,113 "
-62028094,HC EXC CHALAZION MLT SM LID," 1,672 "
-62028100,HC RPR ENTROPION SUTR," 3,811 "
-62028106,HC BX LACRIMAL GLND," 3,811 "
-62028112,HC PROBE NASOLACRIMAL DUX +-IRRG, 619 
-62028118,HC OPH US DX B-SCAN&QUAN A-SCAN SM PT ENCTR, 486 
-62028124,HC OPH US DX QUAN A-SCAN ONLY, 267 
-62028130,HC OPH US DX B-SCAN +-A-SCAN, 346 
-62028136,HC OPH US DX ANT SGM US IMMERSION B-SCAN/HR BIOM, 267 
-62028142,HC OPH US DX CRNL PACHYMETRY UNI/BI, 51 
-62028148,HC OPH BMTRY US ECHOGRAPY A-SCAN IO LENS PWR CAL, 346 
-62028154,HC COMPUTERIZED CORNEAL TOPOGRAPHY UNI/BI, 186 
-62028160,HC VIS FLD XM UNI/BI I&R INTRM XM, 201 
-62028166,HC VIS FLD XM UNI/BI I&R EXTND XM, 201 
-62028172,HC CMPTR OPHTH DX IMG ANT SEGMT, 130 
-62028178,HC CMPTR OPHTH IMG OPTIC NERVE, 130 
-62028184,HC CPTR OPHTH DX IMG POST SEGMT, 130 
-62028190,HC OPH BMTRY PRTL COHER INTRFRMTRY IO LENS PWR CAL, 186 
-62028196,HC FLUORESCEIN ANGRPH I&R, 499 
-62028202,HC INDOCYA9-GREEN ANGRPH I&R, 554 
-62028208,HC FUNDUS PHTGRPHY I&R, 201 
-62028214,HC ELECTRO-OCULOGRAPY I&R, 168 
-62028220,HC ELECTRORETINOGRAPY I&R, 554 
-62028226,HC COLOR VIS XM EXTND ANOMALOSCOPE/EQUIV, 168 
-62028232,HC DARK ADAPTATION XM I&R, 270 
-62028238,HC XTRNL OC PHTGRPHY I&R PROGRESS, 200 
-62028244,HC ANT SGM IMAGING I&R SPECLR ENDOTHELIAL, 499 
-62028256,HC VISUAL EP TSTG CNS CHECKERBOARD/FLASH, 760 
-62028262,HC ASPIR/RLS FLU PARS PLNA," 7,801 "
-62028268,HC DILAT LACRIMAL PUNCTUM +-IRRG, 185 
-62028274,HC DETERMINATION REFRACTIVE STATE, 67 
-41164140,HC ABL. BONE TMR PERC RF WO GUID," 12,895 "
-41164142,HC ABL BONE TMR/METS RF PERC W/CT," 12,895 "
-41164160,HC WHITAKER TEST, 431 
-41164170,"HC INTRO NDL/CATH, TLA", 688 
-46161097,HC HPVHRT DNA HPV APT HRISK CV, 555 
-56270140,HC INS CANN ECMO," 14,000 "
-41165000,HC EXPOSE ART ILIAC THRPY BII," 10,000 "
-56270144,HC ILIAC/FEM ANGIO W CAR CATH," 3,468 "
-50672831,HC ISH MDM2 MRPHMET MAN EA PRB," 1,383 "
-50672849,HC ISH ROS1 MRPHMET MAN EA PRB," 1,383 "
-50672856,HC ISH EGFR MRPHMET MAN EA PRB," 1,383 "
-51493807,HC B2GP1 BETA2 GLYPRTN I AB EA, 243 
-50783414,HC 6959 12320R THIOCYANATE, 34 
-50783422,HC 6957 12323R VON WIL FAC MLT, 143 
-36099102,HC I-125 BRACHY SRC NS HIGH ATV, 992 
-37105053,HC F-18 NA FLOURIDE DX UP TO 30MCI," 2,356 "
-37105061,HC RESEARCH F-18 RADIOPHARCEUT," 1,667 "
-38616488,HC ARTHROCNT ASP/INJ JT MAJ BILAT," 1,193 "
-41164161,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-42016485,HC ARTHROCNT ASP/INJ JT MAJ BILAT," 1,193 "
-42016493,HC ABD MUSCULOSKELETAL PROC," 6,473 "
-62028068,HC SKIN TEST TB INTRADERMAL, 45 
-62028084,HC ALBU INH SOL 0.083% 3 ML, 16 
-40905309,HC PERC RF ABLATION LIVER," 13,579 "
-40905325,"HC RF ABLATION, RENAL"," 11,396 "
-40905333,HC RENAL ABLATION CRYO UNILAT," 12,190 "
-41175005,HC NG TUBE BY PHYS. INCLUDE FLUORO, 322 
-41175010,HC XR CHEST 2V W/FLUORO," 1,849 "
-42016501,HC CT TUMOR ABLATION MICROWAVE PROC," 1,910 "
-50804822,HC CFMLPA ISOL EXT HPA EA NA, 146 
-50804830,HC CFMLPA NA PROBE EACH, 53 
-50804848,HC CFMLPA AMP MPLX 1ST 2 NASEQ, 172 
-50804855,HC CFMLPA AMP MPLX EA ADL NASQ, 90 
-50804863,HC CFMLPA SEP ID HRT EA NA PRP, 112 
-50804889,HC CSMP ISOL EXT HPA EA NA, 146 
-50804897,HC CSMP ENZY DIGEST EA TRMT, 78 
-50804905,HC CSMP AMP MPLX 1ST 2 NA SEQ, 172 
-50804913,HC CSMP AMP MPLX EA ADL NA SQ, 90 
-50804921,HC CSMP SEP ID HRT EA NA PREP, 112 
-50804939,HC CSMP INTRP AND RPT, 138 
-50804947,HC CSMP MUTID ENZ PRIM EA SEG, 71 
-50804954,HC BIOSA ISOL EXT HPA EA NA, 146 
-50804962,HC BIOSA ENZY DIGEST EA TRMT, 78 
-50804970,HC BIOSA SEP GEL ELCT EANA PRP, 103 
-50804988,HC BIOSA AMP EA NA SEQ, 188 
-50804996,HC BIOSA MUTID BY SEQ SS EA SG, 54 
-50805001,HC BIOSA INTRP AND RPT, 138 
-50805019,HC CFMLPA INTRP AND RPT, 138 
-50805027,HC MGMTB ISOL EXT HPA EA NA, 146 
-50805035,HC MGMTB SEP GEL ELCT EANA PRP, 103 
-50805043,HC MGMTB AMP EA NA SEQ, 188 
-50805050,HC MGMTB LYSIS CELL PRE NA EXT, 66 
-50805068,HC MGMTB INTRP AND RPT, 138 
-50804871,HC CFMLPA MUTID ENZ PRIM EA SG, 71 
-62028092,HC METERED DOSE INHAL TX INITIAL, 325 
-62028096,HC METERED DOSE INHAL TX SUBSQ, 325 
-36099167,HC ADMIN INFLUENZA VACCINE, 77 
-32051683,HC CANTHOTOMY," 3,853 "
-46161105,HC ADVQT ADENOVRS DNA PCR QNT, 650 
-46161113,HC ADVQL ADENOVRS DNA PCR QL, 388 
-46161121,HC HPVHR2 DNA HPV APT HRISK CV, 555 
-41898318,HC TRANSCATH AORTIC HEART VALV L108," 50,616 "
-41898319,HC TRANSCATH AORTIC HEART VLV L116," 72,000 "
-46161139,HC EBVQP EBV DNA PCR QUANT, 650 
-46161147,HC EBVQL EBV DNA PCR APT QUAL, 388 
-46161154,HC MUMPSG MUMPS AB IGG, 212 
-51565497,HC UCORT CORTISOL FREE UR, 235 
-51565505,HC SALCOR CORTISOL TOT SALIVA, 374 
-50783431,HC 6956 12330R EVEROLMS TMS QN, 149 
-50783448,HC 6956 12331R AMP EA NA SEQ, 102 
-50783455,HC 6956 12331R LYSIS PRE NA EX, 82 
-50783463,HC 6956 12331R SEP ID HRT EANA, 102 
-50783471,HC 6956 12331R INTRP AND RPT, 24 
-50783489,HC 6959 12336R IMMUN ALYT QNT, 15 
-50783497,HC 6959 12336R LYMPH TRANSFORM, 55 
-50783505,HC 6956 12355R ISL EXT HPNA EA, 14 
-50783513,HC 6956 12355R NA PROBE EA, 14 
-50783521,HC 6956 12355R AMP TGT EA NA, 59 
-50783539,HC 6956 12355R REVERSE TRASCRP, 50 
-50783547,HC 6956 12355R INTRP AND RPT, 14 
-50783554,HC 6956 12355R RNA STABILIZATN, 47 
-50783562,HC 6956 12358R PROPXYPHN QNT U, 46 
-50783571,HC 6956 12371R COBALT TMS QN, 55 
-50783588,HC 6956 12373R CARBAMAZEPN TOT, 17 
-50783596,HC 6956 12373R CARB 10 11 QN, 16 
-41175012,HC KYPHOPL THOR SINGL UNI/BIL," 16,510 "
-41175013,HC FIDUC INTRA-THORA SGL/MULT," 2,773 "
-41175014,HC ABLATION PULM RF PERC UNIL," 11,396 "
-41175015,HC INS PERIPH CV ACC W/PORT <5," 5,386 "
-41175016,HC INS PERIPH CV ACC W/PORT 5>," 5,985 "
-41175017,HC REPL PERIPH CV ACC DEV W/PORT," 7,802 "
-41175018,HC PLMT INTERSTL DEV FOR RAD TX," 3,978 "
-41175019,HC TUNNELED IP CATH W/SQ PORT," 6,047 "
-41175020,HC INS OTH COLONIC TUBE W/FL CONTR," 3,602 "
-41175022,HC RMVL OBSTR ANY COLONIC TUBE," 1,239 "
-41175023,HC INTRO RENAL GUIDE NEPHROSTOMY," 5,376 "
-41175024,HC ABLAT 1+ RENAL RF PERC UNILAT," 11,396 "
-41175025,HC FIDUC PROSTATE NDLE ANY APPR," 2,773 "
-41175026,HC HNP DECOMP ANY METHOD 1+ LUMB," 6,989 "
-41175027,HC DISCOGRAPHY CERVICAL/THORACIC EA, 768 
-41175028,HC INJ ANESTH LUMBAR/THOR NERVE," 1,594 "
-41175029,HC INJ ANESTH CELIAC PLEXUS NERVE," 1,594 "
-41175030,HC DISCOGR CERV/THOR S&I EACH," 4,875 "
-41175031,HC VERT/KYPHOPLAS PER LEVL W/CT," 2,785 "
-41175032,HC DISCOGRAPHY LUMBAR S&I EACH," 4,875 "
-41175033,HC AORTOGR THORACIC SNGL IMAGE," 6,252 "
-41175036,HC FIDUC RAD GUIDANCE HOSP," 2,773 "
-41175037,HC CT ABDOMEN W/O CONTRAST," 5,733 "
-41175038,HC SPLENOPORTOGRAPHY S&I," 8,407 "
-41175039,HC SHUNTOGRAM NONVASCULAR; S&I, 390 
-41898320,HC CATHTR BALLOON DILAT PTCA L31," 5,950 "
-50783653,HC 6956 12362R FL NONIN TIT EA, 38 
-41167771,HC CT PELVIS W/CONTRAST," 7,439 "
-41167772,HC XR VAGINOGRAM, 591 
-50783604,HC 7053 12334R ISL EXT HPNA EA, 29 
-50783612,HC 7053 12334R ENZY DIG EA TRT, 29 
-50783621,HC 7053 12334R SEP GEL ELCT EA, 29 
-50783638,HC 7053 12334R INTRP AND RPT, 29 
-50783646,HC 7053 12334R ARR EVL 51-250, 261 
-50783661,HC 6956 12328R MS ANLY QUAN EA, 72 
-50783679,HC 6956 12328R SP STN GRP2 I&R, 66 
-50783687,HC 6956 12328R MICRODIS LZ CAP, 615 
-50783695,HC 7053 12334R ARR EVL 251-500," 1,462 "
-56202053,HC HEART FLO RESV MEAS 1ST VES," 1,967 "
-56202087,HC ECHO TEE CMPL W/PF PORT," 6,578 "
-56263932,HC INJ CONGENTL CARD CATH," 3,053 "
-56269278,HC THROMBOLYSIS CORON IV INFS, 487 
-56269279,HC THROMBOLYSIS INCL ANGIO," 6,108 "
-56269336,HC ATRIAL SEPTOSTOMY BALLOON," 20,101 "
-56269337,HC ANGIPLSTY PULM ART 1ST VESL," 13,577 "
-56269338,HC ANGIOPLAS PULM ART ADD VESL," 13,577 "
-56269340,HC CARD OUTPUT/THERMODILUTN, 997 
-56269341,HC CARD OUTPUT/THERMODILU SUBS, 695 
-56270011,HC PEDS CARD OUTPUT/THERMODIL, 997 
-56270012,HC PEDS CARD OUTPUT/THER SUBS, 695 
-56270013,HC PEDS INJ LT ATR/VENTR ANGIO," 2,026 "
-56270014,HC PEDS INJ RT ATRL/VENT ANGIO," 2,149 "
-56270015,HC PEDS INJ SUPRAVALV AORTOGRPH," 2,424 "
-56270016,HC PEDS INJ PULM ANGIO W CC," 2,568 "
-56270086,HC PEDS CORO ARTERY ANGIO S&I," 18,796 "
-56270087,HC PEDS COR ART/GRAFT ANGIO," 21,899 "
-56270088,HC PEDS RT HRT CORO ART ANGIO," 23,359 "
-56270089,HC PEDS RT HRT ART/GRAFT ANGIO," 26,433 "
-56270090,HC PEDS LT HRT ART/VENTR ANGIO," 22,338 "
-56270091,HC PEDS RT< HRT ART/VENT ANGIO," 25,803 "
-56270092,HC PEDS DRUG ADMIN/HEMODYN MEAS," 8,095 "
-58000070,HC EMG LARYNX, 814 
-58000071,HC EMG HEMIDIAPHRAGM, 814 
-58000072,HC EMG THORACIC PARASPINAL, 739 
-62026046,HC CORRECTION LID RETRACTION," 3,811 "
-62026054,HC REPAIR BROW PTOSIS," 5,066 "
-62026074,HC PLASTIC RPR CANALICULI," 3,811 "
-62026086,HC RPR BLPH LVTR RESCJ/ADVMT INT," 3,811 "
-62026090,HC CRCT LAGOPH IMPLT UPR LID LOAD," 3,811 "
-62026106,HC ORBT W/O FLP EXPL W/WO BPSY," 3,811 "
-62026110,HC RPR ENTROPION EXC TRSL WDGE," 3,811 "
-41175034,HC MRI NEEDLE PLCMNT S&I," 4,256 "
-41175035,HC MRI PARENCHYML TISSUE ABLAT," 2,081 "
-56269339,HC CATH RT/LT TRANS CONGEN OPEN," 16,225 "
-56270095,HC CATH RT HEART CONGEN ANOM," 15,802 "
-56270096,HC CATH RT/LT TRANS CONG ANOM," 16,225 "
-56270097,HC CATH RT/LT RETRO CONGE ANOM," 20,809 "
-41175045,HC INSRT/ RPLC OF  PRMNT PCNG CARDEFB, 1.0 
-37001807,HC NM SALIVARY GLAND W/SER, 724 
-32051627,HC ABD PARACENTESIS W/O IMAGING," 1,162 "
-36071042,"HC INTRA-OPER TX X-RAY, SINGLE SESSION"," 2,755 "
-36071043,HC INTRA-OPER TX ELECT SINGLE SESSION," 2,755 "
-36516516,HC CT ANGIO ABD-PELVIS WO/W CONTR," 11,576 "
-37005998,HC HEPATOBILI SCAN W/WO GB," 2,839 "
-37005964,HC PULM VENTILATION (AERO OR GAS)," 10,658 "
-37005972,HC PULM VENT & PERF (AERO OR GAS)," 2,900 "
-37005980,HC QUANTITATIVE DIFFERENTIAL PULMONARY PERFUSION," 1,243 "
-37216512,HC CT ANGIO ABD-PELVIS WO/W CONTR," 11,576 "
-38016515,HC CT ANGIO ABD-PELVIS WO/W CONTR," 11,576 "
-38305348,HC ABDOM PARACENT W/GUIDANCE," 2,568 "
-38405964,HC PULM VENTILATION (AERO OR GAS)," 3,382 "
-38405972,HC PULM VENT & PERF (AERO OR GAS)," 2,900 "
-38616512,HC CT ANGIO ABD-PELVIS WO/W CONTR," 11,576 "
-40980230,HC ABD PARACENTESIS W/IMAGING," 2,568 "
-40905341,HC ABDOM PARACENT W/GUIDANCE," 2,568 "
-42016519,HC CT ANGIO ABD-PELVIS WO/W CONTR," 11,576 "
-47400046,HC ALLO CRYO & STOR. OF PROD.," 9,662 "
-47400144,HC ALLO THAW PREV. FROZ. PROD.," 7,583 "
-47400235,HC AUTO WASHING OF HARVEST," 3,467 "
-47400226,HC ALLO RBC REMOVAL," 3,420 "
-47400244,HC AUTO PLASMA DEPLETION," 2,913 "
-47400248,HC AUTO CELL CONCENTRATION," 7,160 "
-48000054,HC PULM FUNC TEST PLESTHYSMOG, 604 
-48000055,HC PULM FUNC TEST BY GAS, 758 
-48000056,HC PULM FUNC TEST OSCILLOMETRY, 194 
-48000059,HC C02/MEMBR DIFFU CAPAC ADD-ON, 691 
-58000040,HC EMG W/NERVE TEST LTD EA EXT, 746 
-58000045,HC EMG W/NERVE TEST COMP EA EXT, 746 
-58033136,HC EMG W/NERVE TEST NON-EXT, 739 
-58031858,HC SMTSRY EVKD UP & LOW EXTS," 4,128 "
-58033342,HC MOTOR EP UP & LOW EXTS, 684 
-62028400,HC SKIN SUB GRAFT TRNK/ARM/LEG, 628 
-62028403,HC SKIN SUB GRAFT T/A/L ADD-ON, 231 
-62028406,HC SKIN SUB GRFT T/ARM/LG CHILD, 956 
-62028409,HC SKN SUB GRFT T/A/L CHILD ADD, 471 
-62028412,HC SKIN SUB GRAFT FACE/NK/HF/G, 628 
-62028415,HC SKIN SUB GRAFT F/N/HF/G ADDL, 231 
-62028418,HC SKN SUB GRFT F/N/HF/G CHILD, 956 
-62028421,HC SKN SUB GRFT F/N/HF/G CH ADD, 471 
-62028424,HC ABD PARACENTESIS W/O IMAGING, 802 
-62028427,HC ABD PARACENTESIS W/IMAGING," 1,069 "
-62028430,HC PERITONEAL LAVAGE, 534 
-62028433,HC FIT LENS TX OCLR SRFCE DIS, 65 
-62028436,HC FIT LENS MGMT KERATO 1ST FIT, 237 
-94316510,HC CT ANGIO ABD-PELVIS WO/W CONTR," 11,576 "
-94905346,HC ABDOM PARACENT W/GUIDANCE," 2,568 "
-41199366,"HC IVP, W/WO KUB, W/WO TOMO", 602 
-41199367,HC VAGINOGRAM, 591 
-41199368,"HC ESOPH, BALLOON DILATION <30 MM"," 2,610 "
-62028301,HC EXC LESN B TRNK >4.0CM," 3,302 "
-62028305,HC EXC LESN B SCLP >4.0CM, 861 
-62028309,HC EXC LESN B FACE 2.1-3.0, 809 
-62028313,HC EXC LESN B FACE 3.1-4.0, 809 
-62028317,HC RPR LAC INT NECK 7.6-12.5CM, 254 
-62028321,HC RPR LAC INT NECK 12.6-20.0CM, 602 
-62028325,HC RPR LAC INT NECK 20.1-30.0CM, 602 
-62028329,HC RPR LAC INT NECK >30.0CM, 602 
-62028333,HC RPR LAC INT FACE 7.6-12.5CM, 254 
-62028337,HC RPR LAC INT FACE 12.6-20.0CM, 602 
-62028341,HC RPR LAC INT FACE 20.1-30.0CM, 602 
-62028345,HC RPR LAC CPLX TRNK 1.1-2.5CM, 884 
-62028349,HC RPR LAC CPLX EYE ADD 5CM, 602 
-62028353,HC TRNSF TISS TRNK 10.1-30.0CM," 1,803 "
-62028357,HC TRNSF TISS ANY AREA 30.1-60," 2,122 "
-62028361,HC TRNSF TISS ANY AREA EA ADD, 607 
-62028365,HC GFT FULL EYE/EAR ADD 20, 819 
-62028369,HC GFT DERMA-FAT-FASCIA," 1,061 "
-62028377,HC GFT CART EAR AUTOGENOUS," 1,629 "
-62028439,HC INJ ENZ PALM FASCIAL CORD, 506 
-62028442,HC MANI PLR FSCL CRD POST INJ SING CORD, 490 
-62028445,HC APPL MLTLAYR COMP THGH LEG ANK FT, 218 
-62028448,HC APP MLTLYR COMP SYS UPP & LOW ARM, 218 
-62028451,HC APP MLTLYR COMP SYS UPARM LWARM HND&FING, 218 
-62028463,HC SUPSLCTV CATH 2ND+ORD RENL&ACCES ARTER/S&I BILAT," 5,770 "
-62028475,HC ELEC ANL IMPLT ITHCL/EDRL PMP W/RPRG&RFL, 465 
-62028478,HC ELEC ANL IMPLT ITHCL/EDRL PMP W/RPR&REF PHYS, 465 
-62028481,HC DSTR NRLYTC AGNT PARV FCT SNGL CRVCL/THORA," 1,447 "
-62028484,HC DSTR NRLYTC AGNT PARV FCT ADDL CRVCL/THORA, 506 
-62028487,HC DSTR NRLYTC AGNT PARV FCT SNGL LMBR/SACRAL," 1,269 "
-62028490,HC DSTR NRLYTC AGNT PARV FCT ADDL LMBR/SACRAL," 1,085 "
-62028493,HC REPT TMS TX SUBSQ MOTR THRSHLD W/DEL & MNGT, 233 
-62015631,HC APPL OF HALO TYPE BODY CAST, 219 
-62015649,HC APPL RISSER JACKET LOCALIZER BODY ONLY, 498 
-62015664,HC APPL OF TURNBUCK JACKET BODY ONLY, 219 
-62015722,HC APPL CAST FIGURE OF EIGHT, 219 
-62015730,HC APPL CAST; SHOULDER SPICA, 498 
-62015748,HC APPL CAST; PLASTER VELPEAU, 219 
-62015771,HC APPL CAST; HAND AND LOWER FOREARM (GAUNTLET), 219 
-62015839,HC STRAPPING; THORAX, 219 
-62015854,"HC STRAPPING; SHOULDER (EG, VELPEAU)", 219 
-62015862,HC STRAPPING; HAND OR FINGER, 219 
-62015904,HC APPL OF LONG LEG CAST BRACE, 498 
-62015946,HC APPL OF PAT TENDON BEARING (PTB) CAST, 498 
-62015953,HC ADDING WALKER TO PTB, 219 
-62015961,HC APPL OF RIGID TOTAL CONTACT LEG CAST, 498 
-62015979,HC APPL OF CLUBFT CAST W/WO MANIP L/S LEG, 219 
-62015987,HC APPL LONG LEG SPLINT THIGH ANKLE/TOES (3 WAY), 219 
-62016001,HC STRAPPING; HIP, 219 
-62016019,HC STRAPPIN; KNEE, 219 
-62024724,"HC APPL CAST; FINGER (EG,CONTRACTURE)", 219 
-62028308,HC NEG PRESS WND THER </EQUAL 50 SQ CM, 170 
-32700500,HC PARACENTESIS WO IMAGE," 1,162 "
-32700502,HC PARACENTESIS W IMAGE," 2,568 "
-32700504,HC PERITON LAVAG W/WO IMAGE GUID," 1,162 "
-41100425,HC SLCTV CATH RENAL 1STORD BIL," 11,304 "
-41100429,HC SUPSEL CATH 2ND+RENL ART BIL," 9,363 "
-41100431,HC INS VC FILTR ENDOVASC W IMAGE," 12,864 "
-41100434,HC SLCTV CATH RENAL 1STORD UNIL," 8,679 "
-41100427,HC SUPSEL CATH 2ND+RENL ART UNIL," 6,252 "
-41100433,HC PARACENTESIS WO IMAGE," 1,162 "
-41100436,HC PARACENTESIS W IMAGE," 2,568 "
-41100438,HC PERITON LAVAG W/WO IMAGE GUID," 1,162 "
-38105961,HC GADAVIST PER ML (NDC 50419-325-12), 38 
-38505962,HC GADAVIST PER ML (NDC 50419-325-12), 38 
-50783703,HC 6956 12332R ENZY DIG EA TRT, 52 
-50783711,HC 6956 12332R NA PROBE EA, 52 
-50783729,HC 6956 12332R AMP TRG EANA SQ, 217 
-50783737,HC 6956 12332R MUTID SQ EA SEG, 217 
-50783745,HC 6956 12332R LYSIS PRE NA EX, 173 
-50783752,HC 6956 12332R INTRP AND RPT, 52 
-50783761,HC 6956 12333R ENZY DIG EA TRT, 52 
-50783778,HC 6956 12333R NA PROBE EA, 52 
-50783786,HC 6956 12333R AMP TRG EANA SQ, 217 
-50783794,HC 6956 12333R MUTID SQ EA SEG, 217 
-50783802,HC 6956 12333R LYSIS PRE NA EX, 173 
-50783811,HC 6956 12333R INTRP AND RPT, 52 
-55092480,HC AFBD SMEAR FLUR ACID FAST, 162 
-56100561,HC ECG REC>48H/21DAYS ANALY&RPT, 707 
-56100566,HC ECG REC>48HRS/21DAYS RECORDING, 707 
-94205960,HC GADAVIST PER ML (NDC 50419-325-12), 37 
-95005963,HC GADAVIST PER ML (NDC 50419-325-12), 38 
-50783828,HC 7076 12324R HRP B VIR MNKY, 60 
-50721034,HC 6959 17OHPR 17OH PROGESTERN, 26 
-41199370,HC REPOS VC FILTR ENDOVASC W IMAGE," 8,831 "
-41199371,HC RETRVL VC FILTR ENDOVAS W IMAGE," 8,831 "
-46161162,HC HSVZL HSV AMP PCR LESION, 637 
-46161170,HC HSVZL IAD NA APT EA ORG LSN, 388 
-51493815,HC FIBANT FIBRINOGEN ANTIGEN, 255 
-51493823,HC BDRVC RUS VPR VNM DLT CONF, 240 
-51493831,HC BDRVMX RUS VPR VNM DLT MIX, 240 
-62028496,HC THERAPYPRO/DX IVP INITIAL, 97 
-62028497,HC INFLU VAC SPLT PF INTRADE, 27 
-62028498,HC FLUVIRIN VAC SPLT >3 Y IM 5ML, 14 
-62028499,HC HEPAT A VAC PED/ADOL D-3, 55 
-62028500,HC POLIOVIRUS VACC LIVE ORAL, 63 
-62028501,HC DIP/TET/PERT-HIB VACC IM, 129 
-62028502,HC TYPHOID VACC LIVE ORAL, 100 
-24193240,HC ZZD2G2 MED/SURG," 8,397 "
-24193241,HC ZZD2G2 MED/SURG," 8,397 "
-24193242,HC D2G2 MED/SURG W," 8,397 "
-24193243,HC ZZD2G2 MED/SURG," 8,397 "
-41175040,HC XR CHEST SPECIAL VIEWS, 821 
-41175041,HC CT THORAX W/O CONTRAST," 4,426 "
-41175042,"HC SCLEROTHERAPY, AVM, SKIN", 880 
-42123349,HC CT MAXILLOFACIAL W/O CONTRAST," 3,954 "
-42123406,HC CT ORB SELL/PST FOS WO CONT," 4,780 "
-42123620,HC CT MAXFACL AREA W/O - SINUS," 1,100 "
-42144063,HC CT GUIDE STEREOTAC LOCAL," 4,470 "
-62028604,HC DIAB SELF MGT INDIVID OP 30 M, 132 
-38025980,HC CT L-SPINE W&W/O CONTRAST," 8,066 "
-38025986,HC CT C-SPINE W&W/O CONTRAST," 8,123 "
-38025988,HC T T-SPINE W&W/O CONTRAST," 8,109 "
-38625983,HC CT C-SPINE W&W/O CONTRAST," 8,123 "
-38625985,HC CT T-SPINE W&W/O CONTRAST," 8,109 "
-38625987,HC CT L-SPINE W&W/O CONTRAST," 8,066 "
-62028718,HC NOCT PENILE TUMESCENCE, 626 
-62028706,HC KIT REFILL INTRATHEC LIORESAL, 170 
-50786095,HC 6961 12443R IMM QL SQNT MSM, 305 
-50786103,HC 6961 12443R IMM ANLY QNTRIA, 305 
-50786111,HC 6961 12443R PRTN WB W/INTRP, 305 
-50786129,HC 6961 12443R PROT PRB ID EA, 305 
-50786137,HC 6961 12443R FL SCRN EA AB, 305 
-50785736,HC 7077 12536R ISOL EXT EA NA, 3 
-50785744,HC 7077 12536R SEP GEL ELCT EA, 3 
-50785751,HC 7077 12536R AMP TRG EANA SQ, 12 
-50785769,HC 7077 12536R MUT ID SQ EA SG, 12 
-50785777,HC 7077 12536R INTRP AND RPT, 3 
-50785785,HC 7077 12538R ISOL EXT EA NA, 6 
-50785793,HC 7077 12538R SEP GEL ELCT EA, 6 
-50785806,HC 7077 12538R AMP TRG EANA SQ, 26 
-50785814,HC 7077 12538R MUT ID SQ EA SG, 26 
-50785822,HC 7077 12538R INTRP AND RPT, 6 
-50785830,HC 7061 12560R CHR QN MLT ANLY, 190 
-50785848,HC 7079 12556R ISL EXT HP EANA, 23 
-50785855,HC 7079 12556R SEP GEL ELCT EA, 23 
-50785863,HC 7079 12556R AMP TRG EANA SQ, 97 
-50785871,HC 7079 12556R MUT ID SQ EA SG, 97 
-50785889,HC 7079 12556R INTRP AND RPT, 23 
-50785897,HC 7080 12562R MOL ISL EXT EA, 6 
-50785905,HC 7080 12562R ISL EXT HP EANA, 6 
-50785913,HC 7080 12562R SEP GEL ELCT EA, 6 
-50785921,HC 7080 12562R AMP TRG EANA SQ, 25 
-50785939,HC 7080 12562R MUT ID SQ EA SG, 25 
-50785947,HC 7080 12562R INTRP AND RPT, 6 
-50785954,HC 7080 12564R MOL ISL EXT EA, 8 
-50785962,HC 7080 12564R ISL EXT HP EANA, 8 
-50785970,HC 7080 12564R SEP GEL ELCT EA, 8 
-50785988,HC 7080 12564R AMP TRG EANA SQ, 32 
-50785996,HC 7080 12564R MUT ID SQ EA SG, 32 
-50786004,HC 7080 12564R INTRP AND RPT, 8 
-50786012,HC 7030 12522R ANTI I EZYM ACE, 151 
-50786020,HC 7030 12522R ENZAC NONR EASP, 187 
-35160886,HC PT TESTS AND MEASURES EA 15 M, 176 
-35260817,HC OT TESTS AND MEASURES EA 15 M, 176 
-35601996,HC SP TESTS AND MEASURES EA 15 M, 176 
-35703479,HC PT TESTS AND MEASURES EA 15 M, 176 
-35703487,HC OT TESTS AND MEASURES EA 15 M, 176 
-35703495,HC PT DIRECT THER ACT EA 15 M, 309 
-41199887,HC US RETROPERITONEAL; LIMITED," 1,321 "
-62801402,HC PT TESTS AND MEASURES EA 15 M, 176 
-62801410,HC OT TESTS AND MEASURES EA 15 M, 176 
-62801428,HC SP TESTS AND MEASURES EA 15 M, 176 
-62801436,HC PT ASSIST TECH ASSESS EA 15 M, 189 
-62801444,HC OT ASSIST TECH ASSESS EA 15 M, 189 
-62801452,HC SP ASSIST TECH ASSESS EA 15 M, 189 
-70000880,HC PT TESTS AND MEASURES EA 15 M, 176 
-70000898,HC PT ASSIST TECH ASSESS EA 15 M, 189 
-91600009,HC EYE CONSCIOUS SEDATION," 2,203 "
-91600010,HC EYE LOCAL ANESTHESIA, 311 
-32096160,HC REPR EYELID WOUND FULL THICKNE," 5,721 "
-91000150,HC INSEM ARTIFIC INTRAUTER, 677 
-36033250,HC INITIAL TREATMENT OF BURN (S), 962 
-36033251,HC WOUND(S) CARE NON-SELECTIVE, 361 
-36033252,HC NEG PRESS WOUND TX <50CM, 202 
-36033253,HC RMVL DEVITAL TIS 20CM/<, 302 
-36033254,HC RMVL DEITAL TIS ADDL 20 CM/<, 437 
-36033255,HC NEG PRESS WOUND TX > 50 CM, 560 
-36033256,HC DEB SUBQ TISSUE 20 SQ CM/<, 591 
-36033257,HC DEB MUSC/FASCIA 20 SQ CM/<, 591 
-36033258,HC DEB SUB Q TISSUE ADD-ON, 591 
-36033259,HC DEB MUSC/FASCIA ADD-ON, 591 
-50786145,HC 7030 12524R ANGTSN I CE ACE, 76 
-50786152,HC 7030 12524R ENZ ACT NONRAD, 94 
-50786160,HC 7030 12546R ISL EXT HP EANA, 4 
-50786178,HC 7030 12546R SEP GEL ELCT EA, 4 
-50786186,HC 7030 12546R AMP TRG EANA SQ, 16 
-50786194,HC 7030 12546R MUT ID SQ EA SG, 16 
-50786202,HC 7030 12546R SEP ID HRT EANA, 16 
-50786210,HC 7030 12546R INTRP AND RPT, 4 
-91600031,HC CORNEAL TISS BNK PRC," 4,500 "
-32051635,HC SHUNT CORP CAV-COR SPONG," 10,269 "
-22801303,HC IV INF THER ADDL SEQ NEW, 796 
-50783836,HC 6961 12441R ISL EXT HP EANA, 6 
-50783844,HC 6961 12441R AMP TRG EANA SQ, 22 
-50783851,HC 6961 12441R AMP MPLX NA 1&2, 44 
-50783869,HC 6961 12441R AMP MPLX ADTL, 22 
-50783877,HC 6961 12441R MUT ID SQ EA SG, 22 
-50783885,HC 6961 12441R SEP ID HRT EANA, 22 
-50783893,HC 6961 12441R INTRP AND RPT, 6 
-50783901,HC 6961 12441R MUTID EZ PRM EA, 22 
-50783919,HC 6956 12390R IAD NA APT EA, 103 
-50783927,HC 6959 12401R ALK PHOS TOTAL, 7 
-50783935,HC 6959 12401R ALK PHOS ISOENZ, 20 
-50783943,HC 6959 12408R ISL EXT HP EANA, 12 
-50783951,HC 6959 12408R AMP TRG EANA SQ, 48 
-50783968,HC 6959 12408R MUT ID SQ EA SG, 48 
-50783976,HC 6959 12408R SEP ID HRT EANA, 48 
-50783984,HC 6959 12408R INTRP AND RPT, 12 
-56269940,HC RMV PERM PMKR W RPLCMT SGL LEAD," 6,317 "
-56269941,HC RMV & RPLC PERM PMKR DUAL LEAD," 30,063 "
-56269942,HC RMV & RPLC PERM PMKR GEN MULT LEAD," 30,063 "
-56269943,HC INSRT PULSE GEN W DUAL LEADS," 11,205 "
-56269944,HC INS PULSE GEN W MULT LEADS," 11,446 "
-56269945,HC RMV RPLC CVD GEN SGL LEAD," 100,928 "
-56269946,HC RMV REPLC CVD GEN DUAL LEAD," 100,928 "
-56269948,HC REMV REPLC CVD GEN MULT LEADS," 100,928 "
-41199375,BRONCHSCPY RG/FLX W FLUORO W THRMPLSTY 1 LB," 8,414 "
-41199376,BRONCH RGD/FLX  W FLUORO GD W  THRMPLSTY 2 + LOBES," 8,414 "
-50783992,HC 7052 12382R ISL EXT HP EANA, 13 
-50784008,HC 7052 12382R SEP GEL ELCT EA, 13 
-50784016,HC 7052 12382R AMP TRG EANA SQ, 53 
-50784024,HC 7052 12382R MUT ID SQ EA SG, 53 
-50784032,HC 7052 12382R SEP ID HRT EANA, 53 
-50784041,HC 7052 12382R INTRP AND RPT, 13 
-50784057,HC 7052 12380R ISL EXT HP EANA, 7 
-50784065,HC 7052 12380R SEP GEL ELCT EA, 7 
-50784073,HC 7052 12380R AMP TRG EANA SQ, 30 
-50784081,HC 7052 12380R MUT ID SQ EA SG, 30 
-50784099,HC 7052 12380R SEP ID HRT EANA, 30 
-50784107,HC 7052 12380R INTRP AND RPT, 7 
-50784115,HC 7052 12388R ISL EXT HP EANA, 8 
-50784123,HC 7052 12388R SEP GEL ELCT EA, 8 
-50784131,HC 7052 12388R AMP TRG EANA SQ, 32 
-50784149,HC 7052 12388R MUT ID SQ EA SG, 32 
-50784156,HC 7052 12388R SEP ID HRT EANA, 32 
-50784164,HC 7052 12388R INTRP AND RPT, 8 
-50784172,HC 7053 12386R ISL EXT HP NAEA, 5 
-50784181,HC 7053 12386R ENZY DIG EA TRT, 5 
-50784198,HC 7053 12386R SEP GEL ELCT EA, 5 
-50784206,HC 7053 12386R AMP TRG EANA SQ, 20 
-50784222,HC 7053 12386R AMP MPLX ADTL, 20 
-50784255,HC 7053 12386R INTRP AND RPT, 5 
-56200070,HC PLACEMENT OF BAKRI BALLOON, 461 
-41199420,"HC MYELOGRAM, 2+ SPINAL REGIONS"," 2,675 "
-41199425,HC PLACEMENT BAKRI BALLOON, 461 
-50784214,HC 7053 12386R AMP MPLX NA 1&2, 39 
-50784231,HC 7053 12386R MUT ID SQ EA SG, 20 
-50784248,HC 7053 12386R SEP ID HRT EANA, 20 
-32051628,HC ABD PARACENTESIS W/IMAGE," 2,568 "
-36033391,HC IMMUN. ADMIN ADDIT VAC, 135 
-40410050,HC BIOMETRIC SCREENING EA 15 MIN, 107 
-41196541,HC EMBOLIZN PERCUT W GLUE AAA REPR," 5,724 "
-41199350,HC BX BONE NEEDLE SUPERFIC," 3,880 "
-41199351,HC CHOLEDOCHOSCOPY W/BIOPSY," 5,711 "
-41199352,HC REM URETERAL STENT TRANSUR," 1,436 "
-41199353,HC SCLEROTHER PERC AVM HEAD/NCK," 19,337 "
-42016527,HC BRST NDL LOC WIRE PLCMT," 3,466 "
-46161188,HC HHV6QT IAD NA PCR EA QNT, 650 
-46161196,HC HHV6QL IAD NA APT EA QUAL, 388 
-46161204,HC VZVQL IAD NA APT EA QUAL, 388 
-46161212,HC CMVIGT CMV IGG AB TS ONLY, 268 
-46161220,HC HSVQL HSV DNA PCR QUAL CSF, 637 
-46161238,HC TOXIG TOXOPLASMA AB IGG, 231 
-46161246,HC HSVPCR HSV DNA PCR QL CSF, 637 
-50805076,HC ND816V ISOL EXT HPA EANA NB, 146 
-50805084,HC ND816V AMP EA NA SEQ NONBLD, 188 
-50805092,HC ND816V SEP ID HRT EANA PREP, 112 
-50805118,HC D816V ISOL EXT HPA EA NA, 146 
-50805126,HC D816V AMP EA NA SEQ, 188 
-50805134,HC D816V SEP ID HRT EA NA PREP, 112 
-55092498,HC MECAPC ID CULT APT EA ORG, 578 
-55092506,HC BLAZPC ID CULT APT EA ORG, 578 
-55092514,HC NFPCR ID CULT APT EA ORG, 578 
-55092522,HC FUNIDS FUNGUS ID NA SEQ EA," 1,653 "
-58010285,"HC XEOMIN, PER UNIT", 28 
-50805100,HC ND816V INTRP AND RPT NONBLD, 138 
-50805142,HC D816V INTRP AND RPT, 138 
-62028505,HC SIMP REPR FACE ETC 20.1-30.0CM, 232 
-62028506,HC SIMP REPR FACE ETC >30.0CM, 632 
-62028507,HC LARGOSCPY W VC INJ W M/TSCOPE," 5,905 "
-62028508,HC CYSTOSTOMY CYSTOT W DRNG," 2,548 "
-62028509,HC SIMPLE CYSTOMETROGRAM, 618 
-62028510,HC SIMPLE UROFLOMETRY, 127 
-62028511,HC ENDOCERV CURETTAGE W/SCOPE, 507 
-62028512,HC PREC IMPL NSTIM ELECT RA EPI," 1,422 "
-62028513,HC INS/REPLC SPNL PULSE GEN/RECVR," 1,591 "
-62028514,HC INJ PARAVERT FAC JNT C/T 1 LVL," 1,450 "
-62028515,HC INJ PARAVERT FAC JNT C/T 2 LVL, 507 
-62028516,HC INJ PARAVER FAC JNT C/T 3+ LVL, 507 
-62028517,HC INJ PARAVER FAC JNT L/S 1 LVL," 1,450 "
-62028518,HC INJ PARAVER FAC JNT L/S 2 LVL, 507 
-62028519,HC INJ PARAVER FAC JNT L/S 3+ LVL, 507 
-62028520,HC INJECTION TREATMT OF NERVE," 1,876 "
-62028521,HC CHEMODENERV ECCRINE GLANDS, 507 
-62028522,HC TMS INITIAL TX W PLAN, 233 
-62028523,HC TMS SUBSEQ TX W PLAN, 233 
-62028524,HC BASIC VESTIBULR EVAL W RECORD," 1,270 "
-62028525,HC COMPUTR DYNAM POSTUROGRPY, 361 
-62028526,HC NDL EMG 1 XTR +-PARASP AREAS, 233 
-62028527,HC NDL EMG 2 XTR +-PARASP AREAS, 233 
-62028528,HC NDL EMG LMTD STUDY MUSC EXCL, 123 
-62028529,HC NDL EMG GUID W CHEMODNRVTJ, 235 
-62028530,HC INTERV HLTH/BEHAVE GRP EA 15M, 50 
-62028531,HC LASER TX SKIN < 250 SQ CM, 705 
-62028532,HC LASER TX SKIN > 500 SQ CM, 705 
-62028533,HC MODSED SAME PHYS 5+YR 1ST 30MIN, 357 
-62028534,HC MODSED SAME PHYS SVC EA 15 MIN, 328 
-62028536,HC ANNUAL WELLNESS VISIT-SUBSEQ, 175 
-62028537,HC APPLI RISSER JACKET INCL HEAD, 498 
-62028538,HC APPL TURNBCKL JACKET INCL HEAD, 219 
-62028539,HC APPL HIP SPICA 1&1/2 OR 2 LEGS, 498 
-62028540,HC REPAIR SPICA BODY CAST JACKET, 219 
-62028541,HC WEDGING OF CAST EXCEP CLUBFOOT, 219 
-50784263,HC 7052 12384R ISL EXT HP EANA, 8 
-50784271,HC 7052 12384R SEP GEL ELCT EA, 8 
-50784289,HC 7052 12384R AMP TRG EANA SQ, 32 
-50784297,HC 7052 12384R MUT ID SQ EA SG, 32 
-50784305,HC 7052 12384R SEP ID HRT EANA, 32 
-50784313,HC 7052 12384R INTRP AND RPT, 8 
-50784321,HC 7052 12435R ISL EXT HP EANA, 10 
-50784339,HC 7052 12435R ENZY DIG EA TRT, 10 
-50784347,HC 7052 12435R SEP GEL ELCT EA, 10 
-50784354,HC 7052 12435R NA PROBE EA, 10 
-50784362,HC 7052 12435R NA XFER EA PREP, 10 
-50784371,HC 7052 12435R AMP TRG EANA SQ, 40 
-50784388,HC 7052 12435R MUT ID SQ EA SG, 40 
-50784396,HC 7052 12435R SEP ID HRT EANA, 40 
-50784404,HC 7052 12435R INTRP AND RPT, 10 
-50784412,HC 7052 12433R ISL EXT HP EANA, 5 
-50784421,HC 7052 12433R SEP GEL ELCT EA, 5 
-50784438,HC 7052 12433R AMP TRG EANA SQ, 20 
-50784446,HC 7052 12433R MUT ID SQ EA SG, 20 
-50784453,HC 7052 12433R SEP ID HRT EANA, 20 
-50784461,HC 7052 12433R INTRP AND RPT, 5 
-50784479,HC 7052 12437R ISL EXT HP EANA, 4 
-50784487,HC 7052 12437R SEP GEL ELCT EA, 4 
-50784495,HC 7052 12437R AMP TRG EANA SQ, 18 
-50784503,HC 7052 12437R AMP MPLX NA 1&2, 36 
-50784511,HC 7052 12437R AMP MPLX ADTL, 18 
-50784529,HC 7052 12437R MUT ID SQ EA SG, 18 
-50784537,HC 7052 12437R SEP ID HRT EANA, 18 
-50784545,HC 7052 12437R INTRP AND RPT, 4 
-50784552,HC 7052 12437R MUT ID EA SEG, 18 
-50784578,HC 6961 12439R ISL EXT HP EANA, 5 
-50784586,HC 6961 12439R AMP TRG EANA SQ, 18 
-50784594,HC 6961 12439R AMP MPLX NA 1&2, 36 
-50784602,HC 6961 12439R AMP MPLX ADTL, 18 
-50784611,HC 6961 12439R MUT ID SQ EA SG, 18 
-50784628,HC 6961 12439R SEP ID HRT EANA, 18 
-50784636,HC 6961 12439R INTRP AND RPT, 5 
-50784644,HC 6961 12439R MUTID EZ PRM EA, 18 
-50784776,HC 7030 12514R ENZACT NONR EA, 242 
-50784784,HC 7030 12516R ISL EXT HP EANA, 5 
-50784792,HC 7030 12516R SEP GEL ELCT EA, 5 
-50784801,HC 7030 12516R AMP TRG EANA SQ, 21 
-50784818,HC 7030 12516R MUT ID SQ EA SG, 21 
-50784826,HC 7030 12516R SEP ID HRT EANA, 21 
-50784834,HC 7030 12516R INTRP AND RPT, 5 
-50784561,HC 6956 12352R NMO IMM QNT NOS, 161 
-50784651,HC 6959 12395R 5 NUCLEOTINDASE, 20 
-50784669,HC 6959 12397R ACHR IMM QN RIA, 42 
-50784677,HC 6959 12399R ACHR IMM QN RIA, 85 
-50784685,HC 6959 12406R GLCTS1PH UT QN, 113 
-50784693,HC 6959 12415R ISL EXT HP EANA, 8 
-50784701,HC 6959 12415R NA PROBE EA, 8 
-50784719,HC 6959 12415R AMP TRG EANA SQ, 32 
-50784727,HC 6959 12415R MUT ID SQ EA SG, 32 
-50784735,HC 6959 12415R SEP ID HRT EANA, 32 
-50784743,HC 6959 12415R INTRP AND RPT, 8 
-50784751,HC 6959 12415R MUT ID EA SEG, 32 
-50784768,HC 6959 12418R MS NES QL EA SP, 115 
-48000400,HC BLD GAS W/O2 SAT SHUNT STUDY," 1,161 "
-62010129,HC DEBR/REM FB SKIN/SUBQ/MUSCLE," 1,021 "
-62010301,HC SHV LESN SKN SCALP 1.1-2.0CM, 183 
-62010780,HC TRIMMING NAILS NONDYSTROPHIC, 22 
-62010889,HC EXC WEDGE SKIN NAIL FOLD, 207 
-62010962,HC INJ SUBQ FILL MATERIAL 5.0-10., 466 
-62010970,HC INJ SUBQ FILL MATERIAL >10CC, 232 
-62011002,HC REM IMPL DRUG DELIV NON-BIODEG, 234 
-62011003,HC IMPLANT PELLET HORMONE SUBQ, 132 
-62011010,HC RPR LAC SMP SCALP <2.5 CM, 252 
-62011077,HC RPR LAC SMP FACE <2.5 CM, 269 
-62011085,HC RPR LAC SMP FACE 2.6-5.0CM, 291 
-62011722,HC CHEMICAL PEEL FACIAL EPIDERMAL, 999 
-62012273,HC ASP CYST BREAST EA ADD, 123 
-62012612,HC STIM BONE ELECT NONINVASIVE, 376 
-62014022,HC TX FX PELVIC RING W/O MAN CLSD, 663 
-62014436,HC TX FX TIB SHAFT W/O MAN CLSD, 528 
-62016928,HC DRAIN ABSC/CYST MOUTH SMP, 332 
-62018981,HC CIRCUMCISION W/O CLAMP NEONATE, 908 
-62019286,HC FITTING DIAPHAGM/CERV CAP, 183 
-62019520,HC DRAIN CYST OVARIAN VAG APPR," 1,294 "
-62019708,HC ABORTION INDUC D&E," 4,008 "
-62019856,HC INJ NEUROLYTIC LUMB/CAUD," 1,251 "
-62019997,HC INJ ANES BRACHIAL PLEXUS SGL, 378 
-62020813,HC DEST LESN RETINAL CRYO/DIATH," 1,343 "
-62024088,HC EXC TUMR BACK/FLANK <3CM," 3,302 "
-62024096,HC TX FX HUMER PROX W/O MAN CLSD, 583 
-62024179,HC TX FX PATELLAR W/O MAN CLSD, 580 
-62024195,HC TX FX FIB DIST W/O MAN CLSD, 564 
-62024302,HC EXC LIP V-EXC LINEAR," 1,410 "
-62024799,HC BEHAVIOR TX FAMILY W/PT 15 MIN, 46 
-62025143,HC TRANSABDOMINA AMNIOINFUSN W/US, 686 
-40806010,HC BREAST BX NEEDLE," 1,828 "
-41000266,HC CISTERNOGRAPHY," 2,144 "
-41158326,HC BRONCHOSC W/REM FB," 3,227 "
-42072835,"HC FIDUCIAL SEED IMPLANTS, LYMPHATIC SYS"," 3,969 "
-46161584,HC TOXOPLASMA, 351 
-46161592,"HC  TOXOPLASMA, IGM", 227 
-50788988,HC 6942 12781R BRUCELLA AB, 42 
-50788996,HC 6942 BRUCELLA AB W/RFLX, 68 
-50789046,HC 6961 12774R MOTOR NEURO AB, 218 
-50789053,HC 6997 TAY SACHS ENZYME ANAL, 91 
-50789061,HC 6956 CRMP 5 IGG WEST BLOT, 115 
-52032563,HC RES EM EMBED, 90 
-52032565,HC RES EM THINS, 420 
-52032566,HC RES EM THICK /THIN, 510 
-52032567,HC RES EM SCOPE ONLY, 200 
-52032568,HC RES THIN/SCOPE, 620 
-52032569,HC RES EM THICK/THIN/SCOPE, 600 
-55092738,HC CRYPTO AG TITER, 132 
-62016762,HC RADIOFRQ ABLAT EXT 1ST VEIN," 5,492 "
-26571212,HC POWR BONE MARROW BX NEEDLE, 612 
-32095151,HC RMVL FB MOUTH COMPLEX," 7,603 "
-41183456,HC US TRANSRECTAL, 420 
-62028684,HC APPLIC QUTENZA PATCH, 514 
-32081776,HC TX DISL(L-P)HAND W/ANES/MAN," 4,390 "
-32091212,HC REMOVE CVC W/SUBQ P/P," 5,402 "
-32096170,HC REPROGRAM SHUNT CSF, 540 
-50786228,HC 7082 12566R MOL IS EX EA NA, 4 
-50786236,HC 7082 12566R ISL EXT HP EANA, 4 
-50786244,HC 7082 12566R ENZY DIG EA TRT, 4 
-50786251,HC 7082 12566R SEP GEL ELCT EA, 4 
-50786269,HC 7082 12566R AMP TRG EANA SQ, 18 
-50786277,HC 7082 12566R MUT ID SQ EA SG, 18 
-50786285,HC 7082 12566R SEP ID HRT EANA, 18 
-50786293,HC 7082 12566R INTRP AND RPT, 4 
-50786306,HC 7082 12568R MOL IS EX EA NA, 2 
-50786314,HC 7082 12568R ISL EXT HP EANA, 2 
-50786322,HC 7082 12568R ENZY DIG EA TRT, 2 
-50786330,HC 7082 12568R SEP GEL ELCT EA, 2 
-50786348,HC 7082 12568R AMP TRG EANA SQ, 7 
-50786355,HC 7082 12568R MUT ID SQ EA SG, 7 
-50786363,HC 7082 12568R SEP ID HRT EANA, 7 
-50786371,HC 7082 12568R INTRP AND RPT, 2 
-32096180,HC DILATE URETHRA STRIC MALE, 927 
-41111222,"HC BX, SOFT TISSUE, BACK/FLANK; DEEP"," 6,775 "
-46161360,HC ADVRC SHL VL ID IMM EA VRS, 363 
-46161378,HC MUMRC SHL VL ID IMM EA VRS, 363 
-46161386,HC MEARC SHL VL ID IMM EA VRS, 363 
-50786389,HC 7055 12580R TSH, 27 
-50786397,HC 7055 12580R CELFNCT STM DET, 219 
-50786405,HC 7055 12580R MICROSAB IGG, 23 
-50786413,HC 7055 12580R THYROGLOBAB IGG, 26 
-50806103,HC FFGFR1 ISL EXT HP EA NA FL, 146 
-50806111,HC FFGFR1 SEP GEL ELC EANA FL, 103 
-50806129,HC FFGFR1 AMP TRG EA NA SQ FL, 188 
-50806137,HC FFGFR1 MUT ID SEQ EA SG FL, 54 
-50806145,HC FFGFR1 INTRP AND RPT FLUID, 138 
-50806152,HC FFGFR2 ISL EXT HP EA NA FL, 146 
-50806160,HC FFGFR2 SEP GEL ELC EANA FL, 103 
-50806178,HC FFGFR2 AMP TRG EA NA SQ FL, 188 
-50806186,HC FFGFR2 MUT ID SEQ EA SG FL, 54 
-50806194,HC FFGFR2 INTRP AND RPT FLUID, 138 
-50806202,HC FFGFR3 ISL EXT HP EA NA FL, 146 
-50806210,HC FFGFR3 ENZY DIG EA TRMT FL, 78 
-50806228,HC FFGFR3 SEP GEL ELC EANA FL, 103 
-50806236,HC FFGFR3 AMP TRG EA NA SQ FL, 188 
-50806244,HC FFGFR3 INTRP AND RPT FLUID, 138 
-50806251,HC BPDX1 ISL EXT HP EA NA FL, 146 
-50806269,HC BPDX1 SEP GEL ELC EANA FL, 103 
-50806277,HC BPDX1 AMP TRG EA NA SQ FL, 188 
-50806285,HC BPDX1 MUT ID SEQ EA SG FL, 54 
-50806293,HC BPDX1 INTRP AND RPT FLUID, 138 
-50806301,HC BPDX2 ISL EXT HP EA NA FL, 146 
-50806319,HC BPDX2 SEP GEL ELC EANA FL, 103 
-50806327,HC BPDX2 AMP TRG EA NA SQ FL, 188 
-50806335,HC BPDX2 MUT ID SEQ EA SG FL, 54 
-50806343,HC BPDX2 INTRP AND RPT FLUID, 138 
-62024265,HC VENIPUNCTURE, 72 
-32091550,HC BLAD IRRIG SMPL LAV/ INSTIL, 669 
-23240151,HC CHEMO IV INFUS PRO W/PUMP," 1,583 "
-40809121,HC MAMMARY DUCTOGRAM MULTI, 993 
-46161279,HC VZVCSF IAD NA APT EA QL CSF, 388 
-46161287,HC VZVQLP IAD NA APT EA QUAL, 388 
-46161295,HC HSVCSF HSV DNA PCR QUAL CSF, 637 
-46161303,HC HSVQLP HSV DNA PCR QUAL, 637 
-46161311,HC CMVRO SHL VL ID IMM EA VRS, 363 
-46161329,HC CMVRO CMV QUAL PCR AMP, 651 
-46161337,HC CMVRC SHL VL ID IMM EA VRS, 363 
-46161345,HC EBVQPB EBV IAD NA EA ORG, 650 
-46161352,HC EBVBAM EBV IAD NA EA ORG, 650 
-50786421,HC 6956 12586R THIAMIN VIT B1, 31 
-62311226,HC HA VERIFY CONFORMITY EVAL, 602 
-62311227,HC HA VERIFICATION, 602 
-62311228,HC  EVAL AUDITORY REHAB ADD 15MIN, 115 
-62311229,HC AUD REHAB POST LINGUAL LOSS, 183 
-62311235,HC 2ND AUDIOLOGIST (TEST ASSIST), 192 
-62311330,HC OTORHINOLAR SERV OR PROC, 192 
-62311331,HC BEHAVIORAL AUDIOMETRIC, 420 
-62311332,HC CI COUNSELING PER HR, 192 
-62311333,HC INITIAL EVAL 2-5 YRS, 192 
-62311334,HC RECHECK/TROUBLE SHOOTING, 192 
-62311335,HC 2ND AUDIOLOGIST (TEST ASSIST), 192 
-62311336,HC SUBSEQUENT EVAL <2YRS, 192 
-62311337,HC SUBSEQUENT EVAL 2-5YRS, 192 
-62311338,HC SUBSEQUENT EVAL 6-20YRS, 192 
-62311339,HC EVAL DIFFICLT 2 TEST <7YRS, 192 
-62311225,HC ELECTROACOUSTIC EVAL HA MON, 97 
-32096190,HC INJECT/ASPIR SHUNT TUBE," 1,809 "
-41898411,HC ICY-CRYOCATH L40," 9,600 "
-50786439,HC 6956 12099R BRUCELLA TOT AB, 139 
-41158346,INJECT VASCULAR UNLSTD PROC," 3,835 "
-52032564,HC RES EM THICK, 90 
-50570076,HC RIVAROXOBAN PLASMA LVL, 291 
-50789145,HC 6959 12796R FLUCONAZOLE, 54 
-50789152,HC 6956 12798R CALPROTECTIN, 197 
-50789178,HC 7026 CEPLUS AB IGA/IGG & IGA, 87 
-50789186,HC 7026 CEPLUS ANTI-ENDOM IGA AB, 87 
-50789194,HC 7026 CEPLUS TOTAL SERUM IGA, 87 
-50789202,HC 7026 CEPLUS HLA-DQA1/DQB1, 87 
-40119943,HC US ABLAT UTER LEIOMYOM <200," 16,500 "
-40119950,HC US ABLAT UTER LEIOMYOM >200," 17,000 "
-81805120,HC HM II WEARABLE ACC KIT," 1,445 "
-81805130,HC HM II STABLZ BELT MED/LG, 850 
-37105111,HC 68GA DOTA TATE," 8,801 "
-58010250,HC LEVEL 1 EXAM ESTAB, 254 
-62700648,HC CHEMO-IV INFUSION-ADDTL SEQ," 1,043 "
-81801776,HC HM II CONSL BAG LT," 2,763 "
-81801886,HC HM II VAD PKT CONTROL RPLMT," 37,500 "
-19160249,HC BRONCH W/PLCMT FIDUCIAL MKR," 6,641 "
-46161394,HC VCULF VRS ISL CLT OB ID FL, 484 
-46161402,HC VCULO VRS ISL CLT OB ID OC, 484 
-46161410,HC VCULR VRS ISL CLT OBID RES, 484 
-46161428,HC VCULS VRS ISL CLT OB ID SW, 484 
-46161436,HC VCULT VRS ISL CLT OB ID TS, 484 
-46161444,HC VCULU VRS ISL CLT OB ID UR, 484 
-62027738,HC AFLIBERCEPT OP INJ PER 1MG," 1,850 "
-19160248,HC ENDOBRONCHIAL ULTRASOUND," 5,195 "
-19160250,HC BRONCH W/ COMP ASSIST NAV," 4,000 "
-19160251,HC BRONCH W/ TB NDL ASP," 3,227 "
-19160252,HC THORACENTESIS W/O IMAGING," 1,658 "
-19160253,HC THORACENTESIS W/ IMAGING," 3,897 "
-19160254,HC BRONCH W/ THRMPLSTY 1 LOBE," 8,414 "
-19160255,HC BRONCH W/THRMPLSTY 2+ LOBES," 8,414 "
-19160256,HC TUBE THORACOSTOMY, 864 
-19160257,HC INSRT PLEURAL CATH W/CUFF," 4,757 "
-62028901,HC US ABDOMEN LIMITED, 280 
-62028902,"HC US PREG UTRS W/EVAL,1ST TRI", 280 
-62028903,"HC US PREG UTRS ADD GEST,1TRI", 182 
-62028904,"HC US PREG UTRS W/EVAL,>1TRI", 280 
-62028905,HC US PREG UTRS ADD GEST >1TRI, 280 
-62028906,"HC US PREG UTRS, LIMITED", 182 
-62028907,"HC US PREG UTRS, FOLLOW UP", 182 
-62028908,"HC US PREG UTRS, TRANSVAG", 182 
-62028909,"HC US TRANSVAG, NON OB", 280 
-62028910,"HC US TRANSVAG, NON OB, LTD", 140 
-62028911,HC HYSTEROSONOGRAM, 436 
-62028912,HC US PELVIC NON OB, 280 
-62028913,HC US PELVIC NON OB LTD, 182 
-62028914,HC US GUIDE AMNIOCENTESIS, 273 
-62028915,HC US GUIDE INTRAOP, 273 
-62028916,HC CSF KINETICS," 1,291 "
-37223401,HC CT ORB SELL/PST FOS WO CONT," 3,992 "
-56283456,HC ADENOSINE CHALLENGE, 747 
-81801256,HC HEARTWARE PATIENT PACK," 3,613 "
-32031977,HC DELIVERY OF PLACENTA," 6,144 "
-32041977,HC REPAIR JEJUNOSTOMY TUBE," 7,498 "
-32051960,HC DRESS/DEBRIDE BURN LRG W/O ANES," 1,025 "
-81821976,HC HW BATTERY," 6,375 "
-32713011,HC CAPR SHORT STAY >= 8 HOURS, 426 
-32011999,HC CSTO W/IR&EVAC MLT OBSTR CLOTS," 4,779 "
-32021999,HC CLTX MCP DISLC 1 W/MNPJ W ANES, 564 
-32032000,HC CLSD TX HUM FX W/MANIP," 3,529 "
-32042000,HC TREAT FOOT DISLOC OTH TARSAL, 565 
-32052000,HC WINDOW OF CAST, 269 
-50789160,HC 6956 12790R CYST C W/GFR, 7 
-62028918,HC SKYLA IUD," 1,301 "
-50786454,HC 7083 12609R AMP MPLX NA 1&2, 142 
-50786462,HC 7083 12609R AMP MPLX EA ADL, 71 
-50786470,HC 7083 12609R SEP ID HRT EANA, 71 
-50786488,HC 7083 12609R INTRP AND RPT, 17 
-50786496,HC 6956 12584R TIAG QNT DRGNOS, 51 
-47220140,HC THAMPS THRML CLD AGGLUT SCR, 156 
-50786447,HC 7083 12609R ISL EXT HP EANA, 17 
-50786504,HC 6959 12605R FL NONI SRN EA, 208 
-50786512,HC 6959 12607R FL NONI SRN EA, 208 
-42080218,HC PLEUR DRAIN W/INSRT CATH W/IMG," 3,200 "
-62751960,HC SQ THERAP INFUSN EA ADDL HR AT, 577 
-62751999,HC SQ THERAP INFUSION 1ST HR ATIC," 1,094 "
-62028919,HC EMG EA EXTREM W/NERVE COND LTD, 225 
-62028920,HC EMG EA EXTREMITY W/NERVE COND, 225 
-50789095,HC 6944 12787R TOXOPLASMA IGG, 104 
-50789103,HC 6942 12871R BRUCELLA AB IGG, 42 
-50789129,HC 6944  TOXO IGM IGA IGE, 104 
-50789137,HC 6944 12787R TITER EACH AB, 104 
-50789285,HC 6942 12786R BRUCELLA AB AGG, 68 
-32511976,HC I&D PERIANAL ABSCESS, 769 
-50789210,HC 7026 12507R AB BIO MARKER, 29 
-50789228,HC 7026 12507R ICAM/VCAM/SAA, 29 
-50789236,HC 7026 12507R CRP, 29 
-50789244,HC 7026 12507R IBD DNA IGG, 29 
-50789251,HC 7026 12507R IBD SGI SNP, 29 
-50789269,HC 7026 PROTPM TPMT GENETICS, 296 
-50789277,HC 7026 11435R HLA DQA1/DQB1, 211 
-32096200,HC INJ ANES NERVE FACIAL, 817 
-32096210,HC ASPIRATE BLADDER NEEDLE, 994 
-32096220,HC LAP BAND ADJUSTMENT," 4,118 "
-32096230,HC ASPIRATION FLUID VITREOUS," 8,206 "
-50786538,HC 7046 12599R ISL EXT HP EANA, 7 
-50786546,HC 7046 12599R AMP MPLX NA 1&2, 57 
-50786553,HC 7046 12599R AMP MPLX ADTL, 28 
-50786579,HC 7046 12599R SEP ID HRT EANA, 28 
-50786587,HC 7046 12599R INTERP AND RPT, 7 
-51493849,HC BPF4IG PF4 ID PLATLT AB IGG, 231 
-91600035,HC ANES TIME 30 MIN," 3,336 "
-91600036,HC ANES TIME 45 MIN," 3,756 "
-91600037,HC ANESTHESIA-FIRST HOUR," 4,176 "
-91600038,HC ANES TIME 1 HR 15," 4,596 "
-91600039,HC ANES TIME 1 HR 30," 5,016 "
-91600040,HC ANES TIME 1 HR 45," 5,436 "
-91600041,HC ANES TIME 2 HR," 5,856 "
-91600042,HC ANES TIME 2 HR 15," 6,276 "
-91600043,HC ANES TIME 2 HR 30," 6,696 "
-91600044,HC ANES TIME 2 HR 45," 7,116 "
-91600045,HC ANES TIME 3 HR," 7,536 "
-91600046,HC ANES TIME 3 HR 15," 7,956 "
-91600047,HC ANES TIME 3 HR 30," 8,376 "
-91600048,HC ANES TIME 3 HR 45," 8,796 "
-91600049,HC ANES TIME 4 HR," 9,216 "
-91600050,HC ANES TIME 4 HR 15," 9,636 "
-91600051,HC ANES TIME 4 HR 30," 10,056 "
-91600052,HC ANES TIME 4 HR 45," 10,476 "
-91600053,HC ANES TIME 5 HR," 10,896 "
-91600054,HC ANES TIME 5 HR 15," 11,316 "
-91600058,HC OR MAJOR 30 MINUTES," 17,122 "
-91600059,HC OR MAJOR 45 MINUTES," 18,963 "
-91600060,HC OR MAJOR 1HR," 21,439 "
-91600061,HC OR MAJOR 1HR15," 23,839 "
-91600062,HC OR MAJOR 1HR30," 26,239 "
-91600063,HC OR MAJOR 1HR45," 28,639 "
-91600064,HC OR MAJOR 2HR," 31,039 "
-91600065,HC OR MAJOR 2HR15," 33,439 "
-91600066,HC OR MAJOR 2HR30," 35,839 "
-91600067,HC OR MAJOR 2HR45," 38,239 "
-91600068,HC OR MAJOR 3HR," 40,639 "
-91600069,HC OR MAJOR 3HR15," 43,039 "
-91600070,HC OR MAJOR 3HR30," 45,439 "
-91600071,HC OR MAJOR 3HR45," 47,839 "
-91600072,HC OR MAJOR 4HR," 50,239 "
-91600073,HC OR MAJOR 4HR15," 52,639 "
-91600074,HC OR MAJOR 4HR30," 55,039 "
-91600075,HC OR MAJOR 4HR45," 57,439 "
-91600076,HC OR MAJOR 5HR," 59,839 "
-91600077,HC OR MINOR 30 MIN," 10,303 "
-91600078,HC OR MINOR 45 MIN," 12,803 "
-91600079,HC OR MINOR 1HR," 15,303 "
-91600080,HC OR MINOR 1HR15MIN," 17,803 "
-91600081,HC OR MINOR 1HR30MIN," 20,303 "
-91600082,HC OR MINOR 1HR45MIN," 22,803 "
-91600083,HC OR MINOR 2HR," 25,303 "
-91600084,HC OR MINOR 2HR15MIN," 27,803 "
-91600085,HC OR MINOR 2HR30MIN," 30,303 "
-91600086,HC OR MINOR 3HR," 35,303 "
-91600087,HC GENERAL ANESTHESIA ADD ON OR, 687 
-91600091,HC 2ND PROCEDURE ADD ON OR," 1,217 "
-91600092,HC PHLEBOTOMY CHARGE, 115 
-91600101,HC VENIPUNCTURE, 115 
-91600105,HC ACUTE POST ANES CARE 15 MIN, 631 
-91600106,HC INTENSE POST ANES CARE 15 MIN," 1,142 "
-50786561,HC 7046 12599R MUT ID SQ EA SG, 28 
-35160894,HC XPT EVAL INIT 30MIN, 336 
-35160902,HC XPT EVAL ADDL 15MIN, 172 
-38016474,HC XR CHEST 1V AP, 593 
-38616471,HC XR CHEST 1V AP, 593 
-42016478,HC XR CHEST 1V AP, 593 
-92000035,"HC SEMEN ANALYSIS, MANUAL", 298 
-94316479,HC XR CHEST 1V AP, 593 
-32096240,HC PROCTOSIGMOID W/REM FB," 6,926 "
-32096250,HC TRNS TISS SCALP/ARM 10.1-30," 3,131 "
-32096255,HC TRNSF TISS  EA ADDL," 6,211 "
-32096260,HC RPR TENDON/MUSCLE ARM/ELBOW," 9,681 "
-32096265,HC INJ ANES NERVE SCIATIC," 1,594 "
-32096270,HC CHG TUBE TRACHEOTOMY, 671 
-26602015,HC CHEMOTX ADMN ILESN UP TO 7, 145 
-26602016,HC CHEMOTX ADMN ILESN > 7, 412 
-32096275,HC REM FB EMBED MOUTH SMP, 314 
-32096280,HC TX FX ELBOW/MONTEGGIA W/MAN," 2,353 "
-30688721,HC ENDO GI PRC LVL 1 INT 30 MIN," 2,680 "
-30688722,HC ENDO GI PRC LVL 1 ADDTL 5 MIN, 214 
-30688723,HC ENDO GI PRC LVL 2 INT 30 MIN," 6,422 "
-30688724,HC ENDO GI PRC LVL  2 ADDTL 5 MIN, 515 
-30688725,HC ENDO GI PRC LVL  3 INT 30 MIN," 7,647 "
-30688726,HC ENDO GI PRC LVL  3 ADDTL 5 MIN, 611 
-30688727,HC ENDO GI PRC LVL 4  INT 30 MIN," 8,874 "
-30688729,HC ENDO GI PRC LVL 4 ADDTL 5 MIN, 710 
-30688730,HC ENDO GI MNTR ANESTH, 500 
-30688731,HC ENDO GI GEN ANESTH, 300 
-32096285,HC NASOPHARYNGOSCOPY, 218 
-32096290,HC REM IMPACT FECAL W/ANES," 4,503 "
-56293533,HC STRESS TEST TRACING ONLY," 2,736 "
-50470152,HC BHB BETAHDRXYBTRT KETON QNT, 175 
-50470160,HC SPLAC LACTATE SEPSIS PROTCL, 330 
-50786595,HC 6956 12601R OXALATE PED URN, 11 
-50786603,HC 6956 12603R NA PROBE EA, 16 
-50786611,HC 6956 12603R AMP TRG EANA SQ, 68 
-50786629,HC 6956 12603R LYSIS NA EA SP, 54 
-50786637,HC 6956 12603R INTRP AND RPT, 16 
-50786645,HC 6956 12652R TRYPTASE CH PRC, 100 
-50786652,HC 6956 12631R VORI QNT DR NES, 58 
-62700984,HC PNEUMOCOCCAL VACCINE ADMIN, 295 
-50786660,HC 7069 12633R FLOW 1ST MRKR, 224 
-50786678,HC 7069 12633R FLOW ADTL MRKR, 224 
-50786686,HC 7069 12633R FLOW INTRP 2-8, 710 
-41155223,HC INJ SPINE C/T SGL," 3,808 "
-41178789,HC INJ CONTRAST DACRYOCYST," 1,463 "
-41178910,HC XR DACROCYSTOGRAM NASOLACRIMAL, 984 
-32096295,HC ENDO NASAL DIAGNOSTIC, 543 
-32096300,HC REPAIR LIP COMPLEX," 1,620 "
-32096305,HC INTRO HEMOSTATIC AGENT, 770 
-42080225,HC ABLATION BONE TUMR PERC W/CT," 12,895 "
-50393925,HC MALSC ANTG DIR OPT OBS NOS, 132 
-50786694,HC 7084 12597R B CELLS TOT CNT, 72 
-50786702,HC 7084 12597R NK CELLS TOTCNT, 72 
-50786710,HC 7084 12597R T CELLS TOT CNT, 72 
-50786728,HC 7084 12597R TCELL ABS CD4&8, 90 
-50786736,HC 7084 12597R FLOW TC 1ST MRK, 32 
-50786744,HC 7084 12597R FLOW TC EA ADDL, 32 
-50786751,HC 7084 12597R FLOW INTRP 9-15, 127 
-55092548,HC MYET3 SUSC MTHD AGAR DIL, 122 
-55092555,HC MYET8 SUSC MTHD AGAR DIL, 122 
-81800480,HC VAD SPLY-BIOPATCH (EA), 50 
-81800485,HC VAD SPLY-TEGADERM (EA), 50 
-32096310,HC OBSERVATION PER HR, 582 
-62028738,HC POC URINE DIPSTICK, 55 
-62028739,HC POC URE PREGNANCY SREEN, 55 
-62028740,HC POC GLYCOLATED HEMOGLOBIN, 255 
-62028741,HC POC RAPID STREP A TEST, 143 
-30098021,HC ABLTR PEN LTHRMLFLW  L46," 12,600 "
-30098022,HC ABLTN PEN LNRCLRAIL L51," 15,300 "
-30098023,HC  ABLTN CLMP ISLTR CRVD LFT L55," 17,300 "
-30098024,HC ABLTN CLAMP RT CRV L55," 17,300 "
-30098025,HC ISOLTR CLAMP L33," 6,413 "
-30098026,HC ABLTN ISLTR LPEN L41," 9,775 "
-46161451,HC HSVPAN HSV AB NON SPECIFIC, 204 
-46161469,HC HSVPAN HSV AB TYPE 1, 226 
-46161477,HC HSVPAN HSV AB TYPE 2, 240 
-46161485,HC HSVIGG HSV AB TYPE 1, 226 
-46161493,HC HSVIGG HSV AB TYPE 2, 240 
-50786769,HC 7069 11550R FLOW TC 1ST MRK, 469 
-50786777,HC 6942 12582R LYMPH MIT ANTGN, 154 
-51565539,HC HBSQT HEP B SURF AB QUANT, 197 
-62028605,HC DIAB SELF MGMT GROUP OP PER 30MIN, 90 
-32096315,HC SUTURE EYELID FULL THCK," 5,721 "
-50786785,HC 6956 12664R NTXTPD XLNK U, 46 
-62028761,HC TESTOPEL PELLET, 200 
-37105079,"HC FLORBETAPIR F18 INJ, AMYVID, 10 MCI"," 3,746 "
-50470178,HC OGTT GLUCOSE POST DOSE, 298 
-62028762,HC BETAMETH NA PHOS/ACET INJ 3MG, 25 
-62028763,HC TRIAMCINOLONE HEX INJ 5MG, 4 
-91600513,HC PRC/PRSRV/TRNST CORNEAL TISL53," 17,609 "
-32096312,HC BLEPH EYE DRN ABSC, 653 
-51565547,HC TFPSA PSA TOTAL, 342 
-51565554,HC TFPSA PSA FREE, 281 
-51565562,HC PSARX PSA TOTAL, 342 
-51565570,HC FRPSA PSA FREE, 281 
-62027866,HC DERMAL INJ PROC FOR FACIAL LDS, 724 
-62028764,HC DEXAMETH SOD PHOS 1MG, 1.0 
-62311342,HC SRT/SDT, 116 
-62311343,HC AUD EVOKED POTENTIAL LTD, 233 
-41898450,HC ABLATION CATH CT L43," 14,625 "
-32096320,HC RPR LAC TONGUE <2.5CM, 829 
-62026010,HC SPIROMETRY, 806 
-41100225,HC DRAIN CYST OVARIAN VAG APPR," 4,097 "
-50786793,HC 7014 11617R SEPGEL ELCT FL, 45 
-50786806,HC 7014 11617R AMP EANA SEQ FL, 187 
-50786814,HC 7014 11617R INTRP & RPT FLD, 45 
-50786822,HC 7014 11618R SEPGEL ELCT FL, 45 
-50786830,HC 7014 11618R AMP EANA SEQ FL, 187 
-50786848,HC 7014 11618R INTRP & RPT FLD, 45 
-50786855,HC 7014 11619R SEPGEL ELCT FL, 45 
-50786863,HC 7014 11619R AMP EANA SEQ FL, 187 
-50786871,HC 7014 11619R INTRP & RPT FLD, 45 
-58010200,HC CHEMODENERV SALIV GLANDS BILAT, 900 
-62028767,HC TEST ALLERGY PERC W/DRUG IMMED, 81 
-62028769,HC TEST PHOTOSENSITIVITY, 274 
-41166666,HC DRAIN ABSC SUBMAXILLARY EXT, 648 
-51565588,HC HCVA HEPATITIS C AB, 193 
-62311344,HC AUD TREAT NON-SPEECH DEVICE, 325 
-50570068,HC PTEG CLOT ASS I&R PER DAY, 747 
-50786889,HC 6956 12733R ALPHA2 ANTIPSM, 68 
-50786897,HC 6956 12635R B CELL TOT CNT, 47 
-50786905,HC 6956 12635R NAT KIL TOT CNT, 47 
-50786913,HC 6956 12635R T CELL TOT CNT, 47 
-50786921,HC 6956 12635R ABS CD4&8 CNTRT, 58 
-50786939,HC 6956 12635R FLOW 1ST MRKR, 21 
-50786947,HC 6956 12635R CELL 1ST MRKR, 21 
-50786954,HC 6956 12635R CD40 1ST MRKR, 21 
-50786962,HC 6956 12635R EA ADDL MRKR, 21 
-50786970,HC 6956 12643R BTK FC 1ST MRKR, 87 
-50786988,HC 6956 12645R COMP FUNCACT EA, 50 
-50786996,HC 6959 12721R SPCTMNES PL FL, 40 
-50787004,HC 6959 12723R SPCTMNES PE FL, 40 
-50787012,HC 6959 12725R SPCTM NES CSF, 40 
-92200405,HC TEE W/WO CNT FOL W/CNT 2D ST," 2,620 "
-62028771,HC PSG 1-3 PARAMETERS," 2,205 "
-62028770,HC SURG TX ABORT MISSED 2ND TRI," 4,008 "
-92200413,HC TTE W/WO FOL W/ CONT 2D CMP," 5,060 "
-92200397,HC TTE W/WO FOL W/CONT 2D RCRD," 1,038 "
-32096325,HC LARYNGOSCOPY OP REM FB," 6,215 "
-32096330,HC I&D EPIDYMIS/TESTIS/SCROTAL," 6,872 "
-62700994,HC IMMUN ADMIN ONE VACC ATIC, 263 
-32713010,HC CAPR ICU," 30,054 "
-50787020,HC 6911 CYLEX CELLFNCT DETBIO, 275 
-55092589,HC ENVCX ENV CULT, 60 
-55092597,HC ENVIR# LPCHENV ID, 20 
-62028772,HC GLUCAGON VIAL 1MG, 7 
-62028773,HC XOPENEX (LEVALUTEROL) 1.25MG/3ML, 14 
-62028774,HC SODIUM CHLORIDE 0.9% 10ML, 1.0 
-62028775,HC EPIPEN 0.3MG 0.3ML 1:1M 2PK, 505 
-62028777,"HC BUPIVACAINE HC+EPI 1:2000,000 PER 50ML", 7 
-62028778,"HC LIDOCAINE HCE 1%+EPI 1:100,000 PER 20ML", 3 
-62028779,HC XYLOCAINE HCL PER 1ML, 2 
-62028780,"HC MARCAINE .25%+EPI 1:200,000 PER 50ML", 9 
-62028782,HC MAXALT-MLT TABLET 10MG, 50 
-62028783,HC PROMETHAZINE HCI TABLET 25MG, 1.0 
-62028784,HC PROPRANOLOL HCI TABLET 10MG, 1.0 
-36033285,HC  ACQUISN OF PET FOR RT TX PLAN, 454 
-36099182,HC PLMT NTRSTL DEV PRQ IABDL IPEL," 3,978 "
-36099190,HC PL NDLE/CTH PLV/GNTL," 5,218 "
-36099208,HC INS VAG RAD AFTLD APPARATUS," 1,500 "
-50787095,HC 7069 12639R MOL IS EX EA NA, 39 
-50787103,HC 7069 12639R ISL EXT HP EANA, 39 
-50787111,HC 7069 12639R SEP GEL ELCT EA, 39 
-50787129,HC 7069 12639R AMP TRG EANA SQ, 162 
-50787137,HC 7069 12639R MUT ID SQ EA SG, 162 
-50787145,HC 7069 12639R INTRP AND RPT, 39 
-51305662,HC A1C GLYCOSYLATED HEMOGLOBIN, 145 
-62028776,HC ALBUTEROL SGL 0.5%/0.5ML 30ML VIAL, 65 
-62028785,HC DIGOXIN 250MG TABLET (.25MG), 1.0 
-62028786,HC FUL-GLO 1.0MG STRIP, 1.0 
-62028787,HC BUPIVACAINE HCL .25% (2.5MG/ML), 3 
-62028788,HC LIDOCAINE OINT 5% TUBE 1.25OZ, 121 
-62028789,HC VICODIN 5-500MG TAB, 1.0 
-62028790,HC SUFENTA 0.5MG/ML 1ML AMP, 11 
-62028791,HC SUFENTA 0.5MG/ML 2ML AMP, 18 
-50787152,HC 7069 12641R MOL IS EX EA NA, 3 
-50787160,HC 7069 12641R ISL EXT HP EANA, 3 
-50787178,HC 7069 12641R SEP GEL ELCT EA, 3 
-50787186,HC 7069 12641R AMP TRG EANA SQ, 12 
-50787194,HC 7069 12641R MUT ID SQ EA SG, 12 
-50787202,HC 7069 12641R INTRP AND RPT, 3 
-50787038,HC 7046 12651R ISL EXT HP EANA, 7 
-50787046,HC 7046 12651R AMP MPLX NA 1&2, 57 
-50787053,HC 7046 12651R AMP MPLX ADTL, 28 
-50787061,HC 7046 12651R MUT ID SQ EA SG, 28 
-50787079,HC 7046 12651R SEP ID HRT EANA, 28 
-50787210,HC 6942 12581R IMM AB IGG QNT, 19 
-50787228,HC 6942 12581R IMM AB IGG QNT, 18 
-50787236,HC 6942 12583R ENZAC NONR EASP, 168 
-50787244,HC 6942 12583R LEUK PHAGOCYTOS, 74 
-50787251,HC 6942 12585R IAD RNAAPT EAQL, 298 
-50787269,HC 6942 12637R IMM AB IGG QNT, 31 
-50787277,HC 6942 12637R IMM AB IGG QNT, 31 
-50787285,HC 6942 12711R IMM AB IGG QNT, 18 
-50787293,HC 6942 12711R IMM AB IGG QNT, 18 
-32096321,HC ED INPT HOLDING SP," 8,397 "
-32500640,HC CDU INPT HOLDING SP," 8,397 "
-46161501,HC RESPCR IAD NA APT 1ST2 TYPS," 1,180 "
-46161519,HC RESPCR IAD NA APT ADL BYND2, 294 
-46161527,HC RESPCR IAD NA NOS APT EAORG, 388 
-46161535,HC HHV6QL IAD NA HV6 APTPCR QL, 497 
-46161543,HC HHV6QT IAD NA HRP VRS6 QNT, 591 
-50787087,HC 7046 12651R INTRP AND RPT, 7 
-50787306,HC 7084 12611R LYMPH TRAN, 298 
-50787314,HC 7084 12611R FC INTRP 2 TO 8, 322 
-50787322,HC 7084 12655R LEUK PHAGOCYTOS, 57 
-50787330,HC 7084 12655R FC INTRP 2 TO 8, 380 
-50787348,HC 7084 12657R B CELLS TOT CNT, 89 
-50787355,HC 7084 12657R NK CELLS TOTCNT, 89 
-50787363,HC 7084 12657R T CELLS TOT CNT, 89 
-50787371,HC 7084 12657R TCELL ABS CD4&8, 111 
-50787389,HC 7084 12657R FLOW TC 1ST MRK, 39 
-50787397,HC 7084 12657R FLOW TC EA ADDL, 39 
-50787405,HC 7084 12657R FLOW INTRP 2-8, 125 
-50787413,HC 7084 12659R B CELLS TOT CNT, 79 
-50787421,HC 7084 12659R NK CELLS TOTCNT, 79 
-50787439,HC 7084 12659R T CELLS TOT CNT, 79 
-50787447,HC 7084 12659R TCELL ABS CD4&8, 99 
-50787454,HC 7084 12659R FLOW TC 1ST MRK, 35 
-50787462,HC 7084 12659R FLOW TC EA ADDL, 35 
-50787470,HC 7084 12659R FLOW INTRP 9-15, 140 
-50787488,HC 7026 12689R CHEM IFXATI LVL," 2,500 "
-50787496,HC 7069 12647R IMM ANLYT QNT, 234 
-50787504,HC 7069 12649R IMM ANL QNT CSF, 234 
-50787512,HC 7055 12680R ALRGN IGE QNTEA, 16 
-50787520,HC 7055 12680R ALRGN IGE QNTEA, 16 
-62028792,HC REFILL/MAINT IMPLANT PUMP/RESE, 351 
-91600057,HC BRACHIAL BLOCK," 2,948 "
-62800300,HC EVAL APD W/REPORT 1ST HR, 346 
-62800301,HC EVAL APD W/REPORT ADD 15MIN, 86 
-19160243,HC RETRIEVAL EXT DV L18," 1,913 "
-19160244,HC RETRIEVAL EXT DV L 16," 1,445 "
-19160245,HC VENT SUPPLY," 1,275 "
-37106400,"HC RESEARCH PET-CT DYNAMIC SCAN, EA 10 MIN"," 1,132 "
-62028793,HC INS IMPL DRUG DELIV NON-BIODEG, 127 
-32096340,HC PACK WOUND DEHISCENCE, 709 
-32050438,HC ER RPR LAC CPLX FOREHD 1.1-2.5," 1,054 "
-46161550,HC RESPCB IAD NA PCR ADL BYND2, 294 
-41155346,HC VENOGRAM ADRENAL BILAT," 7,491 "
-62028794,HC SMELL TEST KIT L1, 229 
-62028795,HC STERILIZATION COIL ESS05-L2," 12,000 "
-22801305,HC THRP INF CONCURRENT, 821 
-50787538,HC 6911 12761R ISL EXT HP EANA, 11 
-50787546,HC 6911 12761R SEP GEL ELCT EA, 11 
-50787553,HC 6911 12761R AMP MPLX NA 1&2, 88 
-50787561,HC 6911 12761R MUT ID SQ EA SG, 44 
-50787579,HC 6911 12761R INRTP AND RPT, 11 
-50787587,HC 6956 12756R NA PROBE EA, 23 
-50787595,HC 6956 12756R AMP TRG EANA SQ, 96 
-50787603,HC 6956 12756R REVERSE TRANS, 81 
-50787611,HC 6956 12756R INTRP AND RPT, 23 
-50787629,HC 6956 12756R RNA STABILIZATN, 77 
-52110731,HC US GUIDANCE NEEDLE PLCMT TC," 1,896 "
-55092605,HC CRYEIA CRYPTSPDM AG EIAMSM, 231 
-51565596,HC ALLMC ALL SPEC IGE QNT SMQ, 79 
-35220533,HC XOT EVAL ADDL 15 MIN, 172 
-41167890,HC THROMBOLYSIS CEREBRAL IV INFUSION, 672 
-56234561,HC REM LEAD EPICARDIAL DUAL," 7,567 "
-41100852,HC REV FISTULA A-V OPEN," 11,952 "
-32591589,HC I&D OF ABSCESS COMPLIC," 1,079 "
-32096345,HC RPR LAC CPLX EYE 2.6-7.5CM," 1,653 "
-32050439,HC RPR LAC CPLX FORHD 2.6-7.5," 1,365 "
-55092530,HC DNASE2 ID DNA SQ EA ISOL," 1,653 "
-62701210,HC PHLEBOTOMY, 658 
-62701235,HC IMMUN ADMIN EA ADD VACC, 135 
-62028800,HC CYSTOSCOPY W BOTOX INJECT," 3,134 "
-41152896,HC ANGIO EXT/INTCRAN/VERT; THOR ARCH," 6,252 "
-41152897,HC ANGIO UNIL EXTRACRAN; COMM CART," 6,252 "
-41152898,HC ANGIO UNIL INTRACRAN; COMM CART," 6,252 "
-41152899,HC ANGIO INTRACRAN; INTER CAROTID," 15,335 "
-41152900,HC ANGIO UNIL VERTBL; INOM ART," 6,252 "
-41152902,HC ANGIO VERTEBRAL; VERTBL ART," 15,335 "
-41152903,HC ADD ANGIO EXT CART UNIL; EXT CART," 10,392 "
-41152904,HC ANGIO EA INTRACRANAL; INTRACRAN BRCH," 3,244 "
-41156289,"HC THORACENTESIS, W/ IMAGING"," 3,897 "
-41157231,HC PLEURAL DRAINAGE W/CATH W/O IMAGE," 1,743 "
-41157232,HC PLEURAL DRAINAGE W/CATH W/IMAGE," 3,200 "
-41162845,RMVL PT F-B INTRAVAS INCL S&I," 8,815 "
-41178452,"HC THORACENTESIS, W/O IMAGING"," 1,658 "
-41185432,HC INFUS THROMBO THER INT; ATRIAL," 2,214 "
-41185433,HC INFUS THROMBO THER INT; VENOUS," 2,214 "
-41185434,HC INFUS THROMBO SUBQ DAY," 3,207 "
-41185435,HC CESSATION THROMBO," 3,207 "
-41196352,HC ATHRCTMY PT W/S&I; RENAL," 15,710 "
-41196353,HC ATHRCTMY PT W/S&I; VISERAL," 15,710 "
-41196354,HC ATHRCTMY PT W/S&I A AORTA," 15,710 "
-41196355,HC ATHRCTMY PT W/S&I BRACH," 15,710 "
-41198653,HC 3D IMAGING MRI/CT/US W/O SEP WS, 775 
-56202197,HC ADD OPTIC TOMO CORON," 11,731 "
-56222289,HC COMLETE EP STUDY/ABLT; SUPRA TACHY," 47,371 "
-56222290,HC COMLETE EP STUDY/ABLT; VENT TACHY," 47,371 "
-56222291,HC ABLATION DISCRT MECH ARRHYTHMIA," 47,371 "
-56222292,HC EPS & ABLT W/PULM VEIN ISOL; AF," 47,371 "
-56222293,HC ADD ABLT L/R ATRIUM; AF," 47,371 "
-56275896,"HC INSERT VENT ASSIST DEV, S&I, ATRIAL ACCESS"," 14,844 "
-56275897,"HC INSERT VENT ASSIST DEV, ATRIAL/VENT, TRAN PUN"," 14,844 "
-56275898,HC REMOVE VENTRCULAR ASSIST DEVICE," 14,844 "
-56275899,HC REPOSITION VENT ASSIST DEVICE," 14,844 "
-56278454,HC PT ATHRCTMY W/PTCA CORN ART," 32,404 "
-56278455,HC ADD PT ATHRCTMY W/PTCA CORN ART," 32,404 "
-56278456,HC PT STENT W/PTCA CORN ART," 25,812 "
-56278457,HC ADD PT STENT W/PTA CORON ART," 25,812 "
-56278458,HC STENT W/ATHRCTOMY CORON ART," 25,812 "
-56278459,HC ADD STENT W/ATHRCTMY CORON ART," 25,812 "
-56278460,HC REVASCLTION ANY METHOD; BYPASS GRFT," 25,812 "
-56278461,HC ADD REVASCLTION; BYPASS GRAFT," 25,812 "
-56278462,HC REVASCLTION ANY METH; CHRONIC OCCL," 25,812 "
-56278463,HC ADD REVASC; CHRONIC OCCL," 25,812 "
-58022361,HC PSYCHOTHERAPY FOR CRISIS; FIRST 60 MINUTES, 514 
-58033359,CHEMODEHC NERVATION OF MUSCLE(S); BILATERAL, 310 
-58033360,"HC PSYCHOTHERAPY, 60 MINUTES WITH PATIENT AND/OR FAMILY", 310 
-58033362,HC PSYCHOTHERAPY FOR CRISIS; EA ADDL 30 MINUTES, 310 
-58033363,HC NERVE CONDUCTION STUDIES; 1-2 STUDIES," 1,069 "
-58033364,HC NERVE CONDUCTION STUDIES; 3-4 STUDIES," 1,602 "
-58033365,HC NERVE CONDUCTION STUDIES; 5-6 STUDIES," 2,400 "
-58033366,HC NERVE CONDUCTION STUDIES; 7-8 STUDIES," 3,099 "
-58033367,HC NERVE CONDUCTION STUDIES; 9-10 STUDIES," 3,999 "
-58033373,HC NERVE CONDUCTION STUDIES; 11-12 STUDIES," 4,386 "
-58033368,HC NERVE CONDUCTION STUDIES; 13 OR MORE STUDIES," 4,483 "
-58033369,HC TESTING OF ANS; COMBINED W/ AT LEAST 5 MIN PASSIVE TILT, 225 
-58033370,"HC CONTIN INTRAOP N-PHYS MONITORING IN OR, EA 15 MIN", 504 
-58033371,"HC CONTIN INTRAOP N-PHYS MONIT, OUTSIDE THE OR PER HOUR"," 1,516 "
-58033372,"HC SIMUL INDEP QUANT MEASURE, BASED ON TIME-FREQ ANALY", 121 
-38400100,"HC THORACENTESIS, PLEURAL WITHOUT IMAGING"," 1,658 "
-40901000,"HC THORACENTESIS, PLEURAL WITHOUT IMAGING"," 1,658 "
-42001000,"HC THORACENTESIS, PLEURAL WITHOUT IMAGING"," 1,658 "
-37006000,HC THYROID IMAG SING/MULT QUAN MEASURE, 330 
-37006002,HC THYROID IMAG VASC FLOW, 636 
-37006004,HC THROID IMAG SINGL/MULT QUAN MEAS & VASC FLOW," 1,722 "
-38400102,HC THYROID IMAG SING/MULT QUAN MEASURE, 330 
-38400104,HC THYROID IMAG VASC FLOW, 636 
-37006006,"PARATHYROID PLANAR, SPECT CONC CT"," 1,362 "
-38400108,"PARATHYROID PLANAR, SPECT CONC CT"," 1,362 "
-38400106,HC THROID IMAG SINGL/MULT QUAN MEAS & VASC FLOW," 1,722 "
-56200040,HC PT REVASC CORO TOT OCCLUS REVASC MI ONE VSL," 23,682 "
-26431099,HC HPC BOOST," 4,016 "
-26431172,HC THORACENTESIS W/O IMAGING," 1,658 "
-26431173,HC THORACENTESIS W/ IMAGING," 3,897 "
-26531198,HC HPC BOOST," 4,016 "
-26531206,HC THORACENTESIS W/O IMAGING," 1,658 "
-26561214,HC THORACENTESIS W/ IMAGING," 3,897 "
-26632028,HC HPC BOOST," 4,016 "
-26632036,HC THORACENTESIS W/O IMAGING," 1,658 "
-26632044,HC THORACENTESIS W/ IMAGING," 3,897 "
-26730383,HC HPC BOOST," 4,016 "
-92603521,HC HPC BOOST," 4,016 "
-92603539,HC THORACENTESIS W/O IMAGING," 1,742 "
-92603547,HC THORACENTESIS W/ IMAGING," 1,742 "
-46161576,HC RESPCR RNA RSV 6-11 TARGETS," 2,950 "
-50672864,HC ACGH CYTOGEN MICRAR COPY NO," 6,150 "
-50806350,HC ATHAL ALPHA THALASSEMIA," 1,303 "
-50806368,HC BCDHPT L7 CDH1 FULL GN SEQ," 4,789 "
-50806376,HC BCDHPX CDH1 SPEC UNLSTD MOP," 1,026 "
-50806384,HC BCFDS CFTR FULL GENE SEQ," 4,995 "
-50806392,HC BCFDS2 CFTR 2 GENESQ KNOWN, 980 
-50806400,HC BCFDSX CFTR 1 GENESQ KNOWN, 683 
-50806418,HC BCRABL T922 MAJ BRKPNT QUAL, 395 
-50806426,HC BCRABL T922 MIN BRKPNT QUAL, 395 
-50806434,HC BCRKDM ABL1 KINASE DOM MUT, 932 
-50806442,HC BHEAV BCLON IGH AMP PCR," 1,264 "
-50806459,HC BIGK BCLON IGK EVAL ABN CLN," 1,187 "
-50806467,HC BIGKH BCLON IGH AMP PCR," 1,201 "
-50806475,HC BIGKH BCLON IGK EVAL ABN CL," 1,251 "
-50806483,HC BIOSA L5 BTD FULL GENE SEQ," 3,384 "
-50806491,HC BISOL NA ISOL SPC UNLST MOP, 146 
-50806509,HC BM1114 L2 T1114 MAJ BRK BM, 871 
-50806517,HC BM1418 T1418 UNLST MOP BM, 932 
-50806525,HC BM1517 T1517 CMN BRK QN BM," 1,387 "
-50806533,HC BMBCR T922 MAJ BRKPNT QUAL, 395 
-50806541,HC BMBCR T922 MIN BRKPNT QUAL, 395 
-50806558,HC BMCEBP L4 CEBPA FULL GN BM," 1,881 "
-50806566,HC BMNPM1 NPM1 EXON 12 BM, 662 
-50806574,HC BPDX1 1 EXON UNLST MOPATH, 629 
-50806582,HC BPDX2 2 EXON UNLST MOPATH, 871 
-50806590,HC BRAF GENE ANLY V600E VAR, 641 
-50806608,HC BTCRB TCLON TRB EVAL PCR," 1,187 "
-50806616,HC BTCRG TCLON TRG EVL ABNL," 1,332 "
-50806624,HC BTCRGB TCLON TRB EVAL PCR," 1,260 "
-50806632,HC BTCRGB TCLON TRG EVL ABNL," 1,260 "
-50806640,HC BTHSQ HBB THLSMIA FULL SEQ," 1,067 "
-50806665,HC CDHEXB SPC GENE UNLST MOP, 683 
-50806673,HC CEBPA FULL GENE SEQ," 1,881 "
-50806681,HC CF32 GENE ANLY COMMON VARS, 909 
-50806699,HC CFMLPA CFTR DEL DUP VARS," 1,120 "
-50806707,HC CFPT CFTR INTRN8 POLYT ANLY, 538 
-50806715,HC CFSS CFTR FULL GENE SEQ," 4,259 "
-50806723,HC CHON L2 FGFR3 ACHN HYPOCHN," 1,088 "
-50806731,HC CKITMU L5 KIT EXONS 8&17," 1,223 "
-50806749,HC CSMP CANCR PNL UNLST MOP TS," 4,389 "
-50806756,HC CSMPB CANCER PAN UNLST MOP," 4,389 "
-50806764,HC CSMPBM CNCR PNL UNLSTMOP BM," 4,389 "
-50806772,HC CX26 GJB2 FULL GENE SEQ, 792 
-50806780,HC CX30 GJB6 GENE ANLY COM VAR, 575 
-50806798,HC D816V L3 KIT COMMON VARNTS, 771 
-50806806,HC DBMD DMD BMD UNLST MOPATH," 2,925 "
-50806814,HC DNAISO DNA ISOL UNLST MOP, 146 
-50806822,HC EGFRP GN ANLY CMN VARNTS, 840 
-50806830,HC FATHAL ALPHA THLSSMIA FLUID," 1,303 "
-50806848,HC FBTHSQ L5 HBB FUL SEQ FLD," 1,067 "
-50806855,HC FCHON L2 FGFR3 A/HYPOCHN FL," 1,088 "
-50806863,HC FCX26S GJB2 FUL GENE SEQ FL, 792 
-50806871,HC FFGFR1 CRNSYNSTS UNLSMOP FL, 683 
-50806889,HC FFGFR2 L5 CRNSYNSTSIS FLD," 1,083 "
-50806897,HC FFGFR3 L1 MUENKE FLUID, 756 
-50806905,HC FGFR1 CRNSYNSTS UNLS MOPATH, 683 
-50806913,HC FGFR2 L5 CRNSYNSTSIS," 1,083 "
-50806921,HC FGFR3 L1 MUENKE, 756 
-50806939,HC FMCC STR PT&COMP SPEC FLD," 1,461 "
-50806947,HC FT1114 L2 T1114 MAJ BRK FLD, 871 
-50806954,HC FT1418 UNLSTD MOPATH FLUID, 932 
-50806962,HC FT1517 PMLRAR CMN BRK QN FL," 1,387 "
-50806970,HC JAK2 JAN KINASE V617F VRNT, 765 
-50806988,HC KRAS VRNTS CONDONS 12AND13," 2,556 "
-50806996,HC LEID F5 LEIDEN VARIENT, 578 
-50807001,HC MGMT METHYLN UNLS MOP PETMB, 829 
-50807012,HC MGMTB METHYLN UNLS MOP, 829 
-50807020,HC MTHFR CMN VAR 677 1298, 668 
-50807038,HC NCF32 CFTR CMN VARNT NONBLD, 909 
-50807046,HC ND816V L3 KIT CMN VR NONBLD, 771 
-50807053,HC NJAK2 JAN KINAS V617F NONBL, 765 
-50807061,HC NPDS L7 SLC26A4 NONBLD," 3,211 "
-50807079,HC NPM1 GN ANLY EXON 12 VARNTS, 662 
-50807087,HC NVHHA IGH VAR RGN SMUT NONB," 1,778 "
-50807095,HC P20210 F2 GN ANLY PROTHMBN, 578 
-50807103,HC PDS L7 SLC26A4 FULL SEQ," 3,211 "
-50807111,HC PMCC STR PT&COMP SPEC POC," 1,461 "
-50807129,HC PWS SNRPN UBE3A MTHYLTN ANL, 715 
-50807137,HC RNAISO RNA ISOL UNLSTD MOP, 146 
-50807145,HC T1114 L2 MAJOR BRKPNT, 871 
-50807152,HC T1418 UNLSTD MOPATH, 932 
-50807160,HC T1517 PMLRAR CMN BRKPT QN," 1,387 "
-50807178,HC TATHAL ALPHA THLSSMIA TISS," 1,303 "
-50807186,HC TBCR T922 MAJ BRKPNT QUAL, 395 
-50807194,HC TBCR T922 MIN BRKPNT QUAL, 395 
-50807202,HC TMCC STR PT&COMP SPEC TIS," 1,371 "
-50807210,HC TMSI MRK MRD NEO & NRM TIS," 1,538 "
-50807228,HC TT1114 L2 MAJOR BRKPNT TIS, 871 
-50807236,HC TT1418 UNLST MOPATH TIS, 932 
-50807244,HC TT1517 PMLRAR CMN BRK QN TS," 1,387 "
-50807251,HC VHHA IGH VAR RGN SOM MUT," 1,778 "
-62028801,"HC PERC PLEURAL DRAIN, PERC CATH W IMAGE"," 1,161 "
-62028802,HC CYSTO CHEMODENERVATION," 1,593 "
-62028803,HC CHEMODENERV MIGRANE BILAT, 676 
-62028805,HC FLU VACCINE-QUAD, 60 
-62028811,HC INTERACTIVE COMPLEXITY, 34 
-62028812,HC DIAG INTERVIEW W/MED SVCS, 284 
-62028813,HC PSYCHOTHERAPY 30MIN W/E&M, 118 
-62028814,HC PSYCHOTHERPY 45MIN W/E&M, 156 
-62028815,HC PSYCHOTHERPY 60MIN, 156 
-62028816,HC PSYCHOTHERPY 60MIN W/E&M, 205 
-62028817,HC PSYCHOTHERPY CRISIS 1ST 60MIN, 269 
-62028818,HC PSYCHOTHERPY CRISIS EA ADD 30MIN, 156 
-62028820,HC GI WIRELESS CAPSULE W/I&R, 427 
-62028821,HC NERVE CONDUCTION STUDIES; 1-2 STUDIES," 1,069 "
-62028822,HC NERVE CONDUCTION STUDIES; 3-4 STUDIES," 1,602 "
-62028823,HC NERVE CONDUCTION STUDIES; 5-6 STUDIES," 2,400 "
-62028824,HC NERVE CONDUCTION STUDIES; 7-8 STUDIES," 3,099 "
-62028825,HC NERVE CONDUCTION STUDIES; 9-10 STUDIES," 3,999 "
-62028826,HC NERVE CONDUCTION STUDIES; 11-12 STUDIES," 4,386 "
-62028827,HC NERVE CONDUCTION STUDIES; 13 OR MORE STUDIES," 4,483 "
-62028830,HC MITOMYCIN OPTHALMIC PER 0.2MG, 359 
-62028831,HC THORACENTIS W/IMAGE GUIDE, 605 
-62028832,HC THORACENTIS W/O IMAGE GUIDE," 1,161 "
-62028833,"HC PERC PLEURAL DRAIN, PERC CATH WO IMAGE"," 1,161 "
-62028835,HC PSG 4+ PARAM <6YRS," 2,271 "
-62028836,HC PSG 4+ PARAM W/CPAP <6YRS," 2,271 "
-50787637,HC 6956 12762R TACROLIMUS, 23 
-50807285,HC BMAJOR BCRQT MAJ BRKPNT QNT, 905 
-50807293,HC BMINOR BCRQT MIN BRKPNT QNT, 905 
-50807269,HC FRAGX FMR1 EVAL DTCT ABNORM," 1,143 "
-50807277,HC BXBLOT FMR1 CHAR OF ALLELES, 378 
-50794437,HC 6949 H058 STR PT AND COMP, 103 
-50794611,HC 6949 H057 STR PT AND COMP," 2,407 "
-50794628,HC 6949 H128 STR PT AND COMP, 103 
-50794677,HC 6949 H130 STR PT AND COMP, 103 
-52294436,HC 6949 H058 STR PT AND COMP, 103 
-52294619,HC 6949 H057 STR PT AND COMP," 2,406 "
-52294627,HC 6949 H128 STR PT AND COMP, 103 
-52294676,HC 6949 H130 STR PT AND COMP, 103 
-81094435,HC 6949 H058 STR PT AND COMP, 103 
-81094618,HC 6949 H057 STR PT AND COMP," 2,406 "
-81094626,HC 6949 H128 STR PT AND COMP, 103 
-81094675,HC 6949 H130 STR PT AND COMP, 103 
-81394439,HC 6949 H058 STR PT AND COMP, 103 
-81394612,HC 6949 H057 STR PT AND COMP," 2,406 "
-81394621,HC 6949 H128 STR PT AND COMP, 103 
-81394679,HC 6949 H130 STR PT AND COMP, 103 
-82394438,HC 6949 H058 STR PT AND COMP, 103 
-82394611,HC 6949 H057 STR PT AND COMP," 2,406 "
-82394629,HC 6949 H128 STR PT AND COMP, 103 
-82394678,HC 6949 H130 STR PT AND COMP, 103 
-84394436,HC 6949 H058 STR PT AND COMP, 103 
-84394619,HC 6949 H057 STR PT AND COMP," 2,406 "
-84394627,HC 6949 H128 STR PT AND COMP, 103 
-84394676,HC 6949 H130 STR PT AND COMP, 103 
-50794445,HC 6949 H058 STR PT&CMP EA ADL," 2,304 "
-50794636,HC 6949 H128 STR PT&CMP EA ADL," 2,304 "
-50794685,HC 6949 H130 STR PT&CMP EA ADL," 2,304 "
-52294444,HC 6949 H058 STR PT&CMP EA ADL," 2,303 "
-52294635,HC 6949 H128 STR PT&CMP EA ADL," 2,303 "
-52294684,HC 6949 H130 STR PT&CMP EA ADL," 2,303 "
-81094443,HC 6949 H058 STR PT&CMP EA ADL," 2,303 "
-81094634,HC 6949 H128 STR PT&CMP EA ADL," 2,303 "
-81094683,HC 6949 H130 STR PT&CMP EA ADL," 2,303 "
-81394447,HC 6949 H058 STR PT&CMP EA ADL," 2,303 "
-81394638,HC 6949 H128 STR PT&CMP EA ADL," 2,303 "
-81394687,HC 6949 H130 STR PT&CMP EA ADL," 2,303 "
-82394446,HC 6949 H058 STR PT&CMP EA ADL," 2,303 "
-82394637,HC 6949 H128 STR PT&CMP EA ADL," 2,303 "
-82394686,HC 6949 H130 STR PT&CMP EA ADL," 2,303 "
-84394444,HC 6949 H058 STR PT&CMP EA ADL," 2,303 "
-84394635,HC 6949 H128 STR PT&CMP EA ADL," 2,303 "
-84394684,HC 6949 H130 STR PT&CMP EA ADL," 2,303 "
-50794312,HC 6949 H059 CHM CELL SEL EA," 2,407 "
-50794594,HC 6949 H129 CHM CELL SEL EA," 2,407 "
-52294311,HC 6949 H059 CHM CELL SEL EA," 2,406 "
-52294593,HC 6949 H129 CHM CELL SEL EA," 2,406 "
-81094311,HC 6949 H059 CHM CELL SEL EA," 2,406 "
-81094592,HC 6949 H129 CHM CELL SEL EA," 2,406 "
-81394314,HC 6949 H059 CHM CELL SEL EA," 2,406 "
-81394596,HC 6949 H129 CHM CELL SEL EA," 2,406 "
-82394313,HC 6949 H059 CHM CELL SEL EA," 2,406 "
-82394595,HC 6949 H129 CHM CELL SEL EA," 2,406 "
-84394311,HC 6949 H059 CHM CELL SEL EA," 2,406 "
-84394593,HC 6949 H129 CHM CELL SEL EA," 2,406 "
-50794288,HC 6949 H095 HLACI EA LOC LMXA," 1,429 "
-50794353,HC 6949 H062 HLACI EA LOC PCRB," 2,170 "
-50794361,HC 6949 H114 HLACI EA LOC LMXB," 2,005 "
-50794379,HC 6949 H115 HLACI EA LOC LMXC," 1,213 "
-50794452,HC 6949 H022 HLACI EA LOC PCRC," 4,122 "
-50794461,HC 6949 H063 HLACI EA PCR A&B," 3,960 "
-50794478,HC 6949 H061 HLACI EA LOC PCRA," 2,170 "
-50794536,HC 6949 H005 HLACI EA LOC B27," 2,005 "
-50794693,HC 6949 H133 HLACI EA LOC MICA," 1,015 "
-52294287,HC 6949 H095 HLACI EA LOC LMXA," 1,429 "
-52294352,HC 6949 H062 HLACI EA LOC PCRB," 2,170 "
-52294361,HC 6949 H114 HLACI EA LOC LMXB," 2,005 "
-52294378,HC 6949 H115 HLACI EA LOC LMXC," 1,213 "
-52294451,HC 6949 H022 HLACI EA LOC PCRC," 4,122 "
-52294469,HC 6949 H063 HLACI EA PCR A&B," 3,959 "
-52294477,HC 6949 H061 HLACI EA LOC PCRA," 2,170 "
-52294535,HC 6949 H005 HLACI EA LOC B27," 2,005 "
-52294692,HC 6949 H133 HLACI EA LOC MICA," 1,015 "
-81094286,HC 6949 H095 HLACI EA LOC LMXA," 1,429 "
-81094351,HC 6949 H062 HLACI EA LOC PCRB," 2,170 "
-81094369,HC 6949 H114 HLACI EA LOC LMXB," 2,005 "
-81094377,HC 6949 H115 HLACI EA LOC LMXC," 1,213 "
-81094451,HC 6949 H022 HLACI EA LOC PCRC," 4,122 "
-81094468,HC 6949 H063 HLACI EA PCR A&B," 3,959 "
-81094476,HC 6949 H061 HLACI EA LOC PCRA," 2,170 "
-81094534,HC 6949 H005 HLACI EA LOC B27," 2,005 "
-81094691,HC 6949 H133 HLACI EA LOC MICA," 1,015 "
-81394281,HC 6949 H095 HLACI EA LOC LMXA," 1,429 "
-81394355,HC 6949 H062 HLACI EA LOC PCRB," 2,170 "
-81394363,HC 6949 H114 HLACI EA LOC LMXB," 2,005 "
-81394371,HC 6949 H115 HLACI EA LOC LMXC," 1,213 "
-81394454,HC 6949 H022 HLACI EA LOC PCRC," 4,122 "
-81394462,HC 6949 H063 HLACI EA PCR A&B," 3,959 "
-81394471,HC 6949 H061 HLACI EA LOC PCRA," 2,170 "
-81394538,HC 6949 H005 HLACI EA LOC B27," 2,005 "
-81394695,HC 6949 H133 HLACI EA LOC MICA," 1,015 "
-82394289,HC 6949 H095 HLACI EA LOC LMXA," 1,429 "
-82394354,HC 6949 H062 HLACI EA LOC PCRB," 2,170 "
-82394362,HC 6949 H114 HLACI EA LOC LMXB," 2,005 "
-82394371,HC 6949 H115 HLACI EA LOC LMXC," 1,213 "
-82394453,HC 6949 H022 HLACI EA LOC PCRC," 4,122 "
-82394461,HC 6949 H063 HLACI EA PCR A&B," 3,959 "
-82394479,HC 6949 H061 HLACI EA LOC PCRA," 2,170 "
-82394537,HC 6949 H005 HLACI EA LOC B27," 2,005 "
-82394694,HC 6949 H133 HLACI EA LOC MICA," 1,015 "
-84394287,HC 6949 H095 HLACI EA LOC LMXA," 1,429 "
-84394352,HC 6949 H062 HLACI EA LOC PCRB," 2,170 "
-84394361,HC 6949 H114 HLACI EA LOC LMXB," 2,005 "
-84394378,HC 6949 H115 HLACI EA LOC LMXC," 1,213 "
-84394451,HC 6949 H022 HLACI EA LOC PCRC," 4,122 "
-84394469,HC 6949 H063 HLACI EA PCR A&B," 3,959 "
-84394477,HC 6949 H061 HLACI EA LOC PCRA," 2,170 "
-84394535,HC 6949 H005 HLACI EA LOC B27," 2,005 "
-84394692,HC 6949 H133 HLACI EA LOC MICA," 1,015 "
-50794296,HC 6949 H002 HLACII DRB1345DQB," 2,170 "
-52294295,HC 6949 H002 HLACII DRB1345DQB," 2,170 "
-81094294,HC 6949 H002 HLACII DRB1345DQB," 2,170 "
-81394298,HC 6949 H002 HLACII DRB1345DQB," 2,170 "
-82394297,HC 6949 H002 HLACII DRB1345DQB," 2,170 "
-84394295,HC 6949 H002 HLACII DRB1345DQB," 2,170 "
-50794387,HC 6949 H004 HLAC2 LOW EA DQB1," 1,520 "
-50794395,HC 6949 H029 HLAC2 LOW EA DRB1," 1,520 "
-50794486,HC 6949 H079 HLAC2 LOW EA DPB1," 2,170 "
-50794511,HC 6949 H080 HLAC2 LOW EA DQA1," 1,194 "
-50794544,HC 6949 H116 HLAC2 LOW EA DRB," 1,483 "
-50794551,HC 6949 H117 HLAC2 LOW EA DQB," 1,969 "
-50794561,HC 6949 H125 HLACII DRB1345DQB," 2,170 "
-50794578,HC 6949 H136 HLAC2LOW EA DQA&B," 1,969 "
-50794586,HC 6949 H118 HLACII DRB1345DQB, 925 
-50794643,HC 6949 H138 HLAC2LOW EA DPA&B," 2,041 "
-50794651,HC 6949 H139 HLAC2 LOW EA DQA," 1,969 "
-50794669,HC 6949 H132 HLAC2 LOW EA DQA, 869 
-52294386,HC 6949 H004 HLAC2 LOW EA DQB1," 1,519 "
-52294394,HC 6949 H029 HLAC2 LOW EA DRB1," 1,519 "
-52294485,HC 6949 H079 HLAC2 LOW EA DPB1," 2,170 "
-52294519,HC 6949 H080 HLAC2 LOW EA DQA1," 1,194 "
-52294543,HC 6949 H116 HLAC2 LOW EA DRB," 1,483 "
-52294551,HC 6949 H117 HLAC2 LOW EA DQB," 1,969 "
-52294569,HC 6949 H125 HLACII DRB1345DQB," 2,170 "
-52294577,HC 6949 H136 HLAC2LOW EA DQA&B," 1,969 "
-52294585,HC 6949 H118 HLACII DRB1345DQB, 925 
-52294642,HC 6949 H138 HLAC2LOW EA DPA&B," 2,041 "
-52294651,HC 6949 H139 HLAC2 LOW EA DQA," 1,969 "
-52294668,HC 6949 H132 HLAC2 LOW EA DQA, 869 
-81094385,HC 6949 H004 HLAC2 LOW EA DQB1," 1,519 "
-81094393,HC 6949 H029 HLAC2 LOW EA DRB1," 1,519 "
-81094484,HC 6949 H079 HLAC2 LOW EA DPB1," 2,170 "
-81094518,HC 6949 H080 HLAC2 LOW EA DQA1," 1,194 "
-81094542,HC 6949 H116 HLAC2 LOW EA DRB," 1,483 "
-81094559,HC 6949 H117 HLAC2 LOW EA DQB," 1,969 "
-81094568,HC 6949 H125 HLACII DRB1345DQB," 2,170 "
-81094576,HC 6949 H136 HLAC2LOW EA DQA&B," 1,969 "
-81094584,HC 6949 H118 HLACII DRB1345DQB, 925 
-81094641,HC 6949 H138 HLAC2LOW EA DPA&B," 2,041 "
-81094659,HC 6949 H139 HLAC2 LOW EA DQA," 1,969 "
-81094667,HC 6949 H132 HLAC2 LOW EA DQA, 869 
-81394389,HC 6949 H004 HLAC2 LOW EA DQB1," 1,519 "
-81394397,HC 6949 H029 HLAC2 LOW EA DRB1," 1,519 "
-81394488,HC 6949 H079 HLAC2 LOW EA DPB1," 2,170 "
-81394512,HC 6949 H080 HLAC2 LOW EA DQA1," 1,194 "
-81394546,HC 6949 H116 HLAC2 LOW EA DRB," 1,483 "
-81394553,HC 6949 H117 HLAC2 LOW EA DQB," 1,969 "
-81394562,HC 6949 H125 HLACII DRB1345DQB," 2,170 "
-81394571,HC 6949 H136 HLAC2LOW EA DQA&B," 1,969 "
-81394588,HC 6949 H118 HLACII DRB1345DQB, 925 
-81394645,HC 6949 H138 HLAC2LOW EA DPA&B," 2,041 "
-81394653,HC 6949 H139 HLAC2 LOW EA DQA," 1,969 "
-81394661,HC 6949 H132 HLAC2 LOW EA DQA, 869 
-82394388,HC 6949 H004 HLAC2 LOW EA DQB1," 1,519 "
-82394396,HC 6949 H029 HLAC2 LOW EA DRB1," 1,519 "
-82394487,HC 6949 H079 HLAC2 LOW EA DPB1," 2,170 "
-82394511,HC 6949 H080 HLAC2 LOW EA DQA1," 1,194 "
-82394545,HC 6949 H116 HLAC2 LOW EA DRB," 1,483 "
-82394552,HC 6949 H117 HLAC2 LOW EA DQB," 1,969 "
-82394561,HC 6949 H125 HLACII DRB1345DQB," 2,170 "
-82394579,HC 6949 H136 HLAC2LOW EA DQA&B," 1,969 "
-82394587,HC 6949 H118 HLACII DRB1345DQB, 925 
-82394644,HC 6949 H138 HLAC2LOW EA DPA&B," 2,041 "
-82394652,HC 6949 H139 HLAC2 LOW EA DQA," 1,969 "
-82394661,HC 6949 H132 HLAC2 LOW EA DQA, 869 
-84394386,HC 6949 H004 HLAC2 LOW EA DQB1," 1,519 "
-84394394,HC 6949 H029 HLAC2 LOW EA DRB1," 1,519 "
-84394485,HC 6949 H079 HLAC2 LOW EA DPB1," 2,170 "
-84394519,HC 6949 H080 HLAC2 LOW EA DQA1," 1,194 "
-84394543,HC 6949 H116 HLAC2 LOW EA DRB," 1,483 "
-84394551,HC 6949 H117 HLAC2 LOW EA DQB," 1,969 "
-84394569,HC 6949 H125 HLACII DRB1345DQB," 2,170 "
-84394577,HC 6949 H136 HLAC2LOW EA DQA&B," 1,969 "
-84394585,HC 6949 H118 HLACII DRB1345DQB, 925 
-84394642,HC 6949 H138 HLAC2LOW EA DPA&B," 2,041 "
-84394651,HC 6949 H139 HLAC2 LOW EA DQA," 1,969 "
-84394668,HC 6949 H132 HLAC2 LOW EA DQA, 869 
-50794403,HC 6949 H069 HLAC1 HI EA HLAC," 1,438 "
-50794411,HC 6949 H075 HLAC1 HI EA HLAA," 1,438 "
-50794429,HC 6949 H076 HLAC1 HI EA HLAB," 1,438 "
-52294402,HC 6949 H069 HLAC1 HI EA HLAC," 1,438 "
-52294411,HC 6949 H075 HLAC1 HI EA HLAA," 1,438 "
-52294428,HC 6949 H076 HLAC1 HI EA HLAB," 1,438 "
-81094401,HC 6949 H069 HLAC1 HI EA HLAC," 1,438 "
-81094419,HC 6949 H075 HLAC1 HI EA HLAA," 1,438 "
-81094427,HC 6949 H076 HLAC1 HI EA HLAB," 1,438 "
-81394405,HC 6949 H069 HLAC1 HI EA HLAC," 1,438 "
-81394413,HC 6949 H075 HLAC1 HI EA HLAA," 1,438 "
-81394421,HC 6949 H076 HLAC1 HI EA HLAB," 1,438 "
-82394404,HC 6949 H069 HLAC1 HI EA HLAC," 1,438 "
-82394412,HC 6949 H075 HLAC1 HI EA HLAA," 1,438 "
-82394421,HC 6949 H076 HLAC1 HI EA HLAB," 1,438 "
-84394402,HC 6949 H069 HLAC1 HI EA HLAC," 1,438 "
-84394411,HC 6949 H075 HLAC1 HI EA HLAA," 1,438 "
-84394428,HC 6949 H076 HLAC1 HI EA HLAB," 1,438 "
-50794338,HC 6949 H003 HLAC2 HI RES DRB1, 636 
-50794346,HC 6949 H072 HLAC2 HI RES DQB1," 1,032 "
-50794602,HC 6949 H131 HLAC2 HI RES DPB, 828 
-52294337,HC 6949 H003 HLAC2 HI RES DRB1, 636 
-52294345,HC 6949 H072 HLAC2 HI RES DQB1," 1,031 "
-52294601,HC 6949 H131 HLAC2 HI RES DPB, 828 
-81094336,HC 6949 H003 HLAC2 HI RES DRB1, 636 
-81094344,HC 6949 H072 HLAC2 HI RES DQB1," 1,031 "
-81094601,HC 6949 H131 HLAC2 HI RES DPB, 828 
-81394331,HC 6949 H003 HLAC2 HI RES DRB1, 636 
-81394348,HC 6949 H072 HLAC2 HI RES DQB1," 1,031 "
-81394604,HC 6949 H131 HLAC2 HI RES DPB, 828 
-82394339,HC 6949 H003 HLAC2 HI RES DRB1, 636 
-82394347,HC 6949 H072 HLAC2 HI RES DQB1," 1,031 "
-82394603,HC 6949 H131 HLAC2 HI RES DPB, 828 
-84394337,HC 6949 H003 HLAC2 HI RES DRB1, 636 
-84394345,HC 6949 H072 HLAC2 HI RES DQB1," 1,031 "
-84394601,HC 6949 H131 HLAC2 HI RES DPB, 828 
-50794528,HC 6949 H060 HLAC2 HI ALL DRB1," 1,520 "
-52294527,HC 6949 H060 HLAC2 HI ALL DRB1," 1,519 "
-81094526,HC 6949 H060 HLAC2 HI ALL DRB1," 1,519 "
-81394521,HC 6949 H060 HLAC2 HI ALL DRB1," 1,519 "
-82394529,HC 6949 H060 HLAC2 HI ALL DRB1," 1,519 "
-84394527,HC 6949 H060 HLAC2 HI ALL DRB1," 1,519 "
-50806657,HC CDH1B L7 FULL GENE SEQ," 2,565 "
-50794304,HC 6949 H031 DNAEXTSTR UNLSMOP, 363 
-50794321,HC 6949 H085 HAPLO C1 UNLS MOP, 217 
-50794494,HC 6949 H064 CD31 MIN UNLS MOP, 648 
-50794502,HC 6949 H127 HAPLO CII UNLSMOP, 217 
-50794701,HC 6949 H140 LMX KIR UNLS MOP," 1,321 "
-50794719,HC 6949 H141 PCR KIR UNLS MOP," 1,113 "
-52294303,HC 6949 H031 DNAEXTSTR UNLSMOP, 363 
-52294329,HC 6949 H085 HAPLO C1 UNLS MOP, 217 
-52294493,HC 6949 H064 CD31 MIN UNLS MOP, 647 
-52294501,HC 6949 H127 HAPLO CII UNLSMOP, 217 
-52294701,HC 6949 H140 LMX KIR UNLS MOP," 1,321 "
-52294718,HC 6949 H141 PCR KIR UNLS MOP," 1,113 "
-81094302,HC 6949 H031 DNAEXTSTR UNLSMOP, 363 
-81094328,HC 6949 H085 HAPLO C1 UNLS MOP, 217 
-81094492,HC 6949 H064 CD31 MIN UNLS MOP, 647 
-81094501,HC 6949 H127 HAPLO CII UNLSMOP, 217 
-81094709,HC 6949 H140 LMX KIR UNLS MOP," 1,321 "
-81094717,HC 6949 H141 PCR KIR UNLS MOP," 1,113 "
-81394306,HC 6949 H031 DNAEXTSTR UNLSMOP, 363 
-81394322,HC 6949 H085 HAPLO C1 UNLS MOP, 217 
-81394496,HC 6949 H064 CD31 MIN UNLS MOP, 647 
-81394504,HC 6949 H127 HAPLO CII UNLSMOP, 217 
-81394703,HC 6949 H140 LMX KIR UNLS MOP," 1,321 "
-81394711,HC 6949 H141 PCR KIR UNLS MOP," 1,113 "
-82394305,HC 6949 H031 DNAEXTSTR UNLSMOP, 363 
-82394321,HC 6949 H085 HAPLO C1 UNLS MOP, 217 
-82394495,HC 6949 H064 CD31 MIN UNLS MOP, 647 
-82394503,HC 6949 H127 HAPLO CII UNLSMOP, 217 
-82394702,HC 6949 H140 LMX KIR UNLS MOP," 1,321 "
-82394711,HC 6949 H141 PCR KIR UNLS MOP," 1,113 "
-84394303,HC 6949 H031 DNAEXTSTR UNLSMOP, 363 
-84394329,HC 6949 H085 HAPLO C1 UNLS MOP, 217 
-84394493,HC 6949 H064 CD31 MIN UNLS MOP, 647 
-84394501,HC 6949 H127 HAPLO CII UNLSMOP, 217 
-84394701,HC 6949 H140 LMX KIR UNLS MOP," 1,321 "
-84394718,HC 6949 H141 PCR KIR UNLS MOP," 1,113 "
-51565612,HC TACROLIMUS (FK-506), 345 
-62090001,HC SNUG PLUG PUNCTUM EA #S2-4002U, 298 
-62090002,HC MINI MONOKO EA #S1-1500U," 1,403 "
-62090003,HC GOLD EYE LID WEIGHT 1.2GM #51795," 2,253 "
-62090004,HC SET RING PC CRYPRSVRD 16MM #51795," 8,067 "
-62090005,HC BLINKEZE 1.6 #LL3016," 2,678 "
-62090006,HC BLINKEZE 1.8 #LL3018," 2,933 "
-62090007,HC TRANQUILEYE W/THERMOEYE BEAD#77113, 255 
-41100315,HC CYSTOURETHROSCOPY SIMPLE," 1,436 "
-56200888,HC XR CINE/VIDEO RAD, 483 
-42005993,HC NEEDLE INTRODUCER, 161 
-42005573,HC CT DUAL SYRINGE KIT, 100 
-40805903,HC BREAST BIOPSY NEEDLE, 191 
-40805990,HC STEROTACTIC ULTRASOUND PROBE," 1,832 "
-40905764,HC NDL BIOP MONOPTY 18GAX16CM, 365 
-40905766,HC BIOPSY TRAY, 110 
-40105870,HC BREAST BIOPSY MARKERS, 695 
-41068505,HC HYSTEROSALPINO CATHETER, 468 
-41068507,HC GASTROGRAFIN 120 ML, 1.0 
-41068511,"HC CONTRAST, OMNI 240 10ML", 11 
-41068513,"HC CONTRAST, OMNI 300 10ML", 13 
-41068515,"HC CONTRAST, CYSTOGRAFIN 300 ML 18%", 0.5 
-41068517,"HC CONTRAST, CYSTOGRAFIN 300 ML 30%", 0.5 
-41068519,HC PROCEDURE TRAY (MYELOGRAM/LUMBAR), 212 
-50807301,HC CSMP BRAF GENE ANLY V600E, 239 
-50807319,HC CSMP EGFR GN ANLY CMN VAR, 479 
-50807327,HC CSMP KRAS VAR CODONS 12&13," 1,197 "
-50807335,HC CSMP L4 IDH1 CMN EX 4 VAR, 239 
-50807343,HC CSMP L4 IDH2 CMN EX 4 VAR, 239 
-50807350,HC CSMPB BRAF GENE ANLY V600E, 239 
-50807368,HC CSMPB EGFR GN ANLY CMN VAR, 479 
-50807376,HC CSMPB KRAS VAR CODNS 12&13," 1,197 "
-50807384,HC CSMPB L4 IDH1 CMN EX 4 VAR, 239 
-50807392,HC CSMPB L4 IDH2 CMN EX 4 VAR, 239 
-50807400,HC CSMPBM BRAF GN ANL V600E BM, 239 
-50807418,HC CSMPBM EGFR ANL CMN VAR BM, 479 
-50807426,HC CSMPBM KRAS CDNS 12&13 BM," 1,197 "
-50807434,HC CSMPBM L4 IDH1 CMN 4VAR BM, 239 
-50807442,HC CSMPBM L4 IDH2 CMN 4VAR BM, 239 
-62801460,HC PT CANALITH REPOSITIONING, 212 
-32050010,HC NASAL ENDO W/BX," 3,808 "
-50787645,HC 7052 12767R ARX SEQ ANALY, 730 
-62701240,HC BLOOD DRAW AV FISTULA, 571 
-83502000,HC CUSTOM SEARCH SUPPORT," 1,960 "
-83502005,HC CORD HIGH RES CT," 2,815 "
-83502010,HC CORD SEARCH ACTIVATION," 2,900 "
-91600107,HC CRITICAL POST ANES CARE 15 MIN," 1,356 "
-55092613,HC NFPC2 ID CULT APT 2 ORG, 578 
-55092621,HC NFPC3 ID CULT APT 3 ORG, 578 
-32003190,HC PCNTS EYE SPX RMVL," 4,651 "
-32011122,"HC EXPLORE, WOUND, EXTREMITY"," 3,063 "
-32511122,HC LUMBAR PUNCTURE DIAGNOSTIC," 2,210 "
-62310265,HC REMOV IMPACT EAR WAX UNIL, 417 
-94305574,HC CT DUAL SYRINGE KIT, 100 
-91600606,HC OR MINOR 4HR," 35,357 "
-91600604,HC OR MINOR 3HR45MIN," 33,357 "
-91600600,HC OR MINOR 3HR30MIN," 31,357 "
-91600598,HC OR MINOR 3HR15MIN," 29,357 "
-41155008,HC REVASC OCCLUSN CHRONIC ADD," 32,900 "
-41155007,HC REVASC OCCLUSN CHRONIC SGL," 32,900 "
-41155006,HC REVASC OCCLUSN ACUTE MI SGL," 32,900 "
-41155005,HC REVASC COR BYP GRAFT ANY ADD," 32,900 "
-41155004,HC REVASC COR BYP GRAFT ANY SGL," 32,900 "
-41155003,HC ATHERECT COR STNT W/PL ADD," 32,900 "
-41155002,HC ATHERECT COR STNT W/PL SGL," 32,900 "
-41155001,HC PL STNT DRG ELUT I/C W/PL ADD," 32,900 "
-41155000,HC PL STNT DRG ELUT I/C W/PL SGL," 32,900 "
-32077123,"HC CRITICAL CARE, EA ADDTL 30 MIN"," 2,434 "
-19160247,HC EGD W PLACE G-TUBE PERC," 2,804 "
-19160246,HC TRACHEOSTOMY," 7,603 "
-37105800,HC RESEARCH CLINIC VISIT-LUTATHERA," 7,340 "
-32051025,HC CLTX MANDIBULAR/MAXILLARY ALVEOLAR RIDGE FX SPX," 2,416 "
-32061122,HC REMOVE IMPLANT SUPER," 1,649 "
-62028837,HC TATTOO MICROPIGMENT ADD 20.0, 708 
-62028838,HC TX FX PHALANX PROX W/MAN CLSD, 377 
-62028839,HC TX FX RADIAL DIST W/O MAN CLSD, 377 
-62028840,HC TYMPANOSTOMY W/ANES LOCAL," 1,404 "
-62028841,HC LARYNGOSC OP EXC/STRIP W/MI/TE," 5,708 "
-62028842,HC CHEMODENERVATION ECC GLND BOTH, 515 
-62028843,HC DEST LESN MAL FACE/EAR 3.1-4.0, 591 
-62028844,HC SMOK CESS COUN INTER >3MIN, 32 
-62028845,HC PSY OT COG SKILL DEVELOP 15MIN, 222 
-62028846,HC JETREA .5MG/0.2ML-2.5MG/ML EYE INJ," 7,900 "
-91600612,HC BLPHLSTY-1 LID UP OR LOW UNILA," 2,302 "
-91600614,HC BLPHLSTY-2 LIDS UP/LO BILAT OR," 2,821 "
-91600616,HC BLPHLSTY-4 LIDS UP&LO BILAT OR," 3,044 "
-39000320,HC INTERROG VAD WITH PHYS/ QUAL, 314 
-32003551,HC LIG ARTERY EXTREM MAJ," 3,700 "
-32050944,HC TX FX ACETAB W/MANIP CLSD, 920 
-32051380,HC THORACENTESIS TRAY, 625 
-32051620,HC PARACENTESIS TRAY, 448 
-32051632,HC FOLEY CATHETER TRAY, 70 
-32051660,HC TRAY-LUMBAR, 313 
-32051745,HC RMVL PLEUR CATH W CUFF, 252 
-40805022,"HC CONTRAST, OMNI 240 10ML", 38 
-42016476,HC XR PELVIS ONE OR TWO VIEWS, 515 
-62028847,HC FVA NON-OPHTHALMIC, 932 
-62028848,HC INJ IV AGENT CK VASC FLOW FLAP, 140 
-62028849,HC SNAP <=50 CM, 590 
-62090008,HC LUNA KIT FOR SPY ELITE SYSTEM," 2,508 "
-62311355,HC AUD SENSORINEURAL ACUITY LEVEL, 102 
-62311356,HC AUD FILTERED SPEECH TEST, 102 
-62311357,HC AUD STENGER TEST PURE TONE, 102 
-62311358,HC AUD STENGER TEST SPEECH, 330 
-62311359,HC AUD FACIAL NERVE FUNC STUDY, 184 
-91600620,HC OR MAJOR 5HR15," 53,685 "
-91600622,HC OR MAJOR 5HR30," 55,685 "
-91600624,HC OR MAJOR 5HR45," 57,685 "
-91600626,HC OR MAJOR 6HR," 59,685 "
-91600630,HC OR MAJOR 6HR15," 61,685 "
-91600632,HC OR MAJOR 6HR30," 63,685 "
-91600634,HC OR MAJOR 6HR45," 65,685 "
-91600638,HC OR MINOR 4HR15MIN," 37,357 "
-91600640,HC OR MINOR 4HR30MIN," 39,357 "
-91600642,HC OR MINOR 4HR45MIN," 41,357 "
-91600644,HC OR MINOR 5HR," 43,357 "
-91600648,HC ANES TIME 5 HR 30," 10,904 "
-91600650,HC ANES TIME 5 HR 45," 11,301 "
-91600652,HC ANES TIME 6 HR," 11,700 "
-37106004,HC NEPET UDALL," 4,283 "
-41010218,HC FLUORO GUIDED VERTEBROPLASTY," 2,041 "
-42016834,"HC NEEDLE, LOCALIZATION", 191 
-94205170,HC MRI GUIDANCE FOR HIFU PROCEDURE," 6,384 "
-30605298,HC FRUCTOSE BREATH TEST, 927 
-62028850,HC MON GLUCOSE CONTINUOUS <72 HR, 273 
-62028851,HC BX TESTIS NEEDLE," 3,150 "
-62028852,HC US SCROTUM, 280 
-62090021,HC SPIRACUR SNAP DEVICE," 1,700 "
-30605297,HC D-XYLOSE BREATH TEST, 927 
-42017061,HC DISPOSABLE BREATHING CIRCUIT, 230 
-40100018,HC RESEARCH NURSING TIME EA 15 MIN, 200 
-32501976,HC IMMUN ADMIN EA ADD VACC, 135 
-32061960,HC PLEURAL DRAINAGE W/CATH W/O IMAGE," 1,743 "
-32061977,HC LONG LEG-STCHCST, 533 
-32511999,HC INJ SPINAL PUNCTURE," 1,759 "
-41068610,HC STRAIGHT TIP GUIDEWIRE, 244 
-46161600,HC CMV IGG AB AVIDITY, 268 
-50789293,HC 6959 12802R HEP COFACT II, 174 
-50789306,HC 6942 12815R LYME AB IGG IGM, 55 
-50789330,HC 6956 12808R CK TOTAL, 9 
-50789355,HC 6911 12817R DONOR HCV AB, 210 
-50789363,HC 6911 12818R DONOE 2 HCV AB, 70 
-50789371,HC 6942 12813R PARVO B19 ABIGG, 24 
-50789389,HC 6956 12792R CITRATE EXCR UR, 24 
-50789397,HC 6956 12811R MYOGLOBIN URINE, 19 
-50789405,HC 6956 12810R CHLORIDE, 13 
-50789413,HC 6956 12810R MAGNESIUM, 13 
-50789421,HC 6956 12810R SODIUM, 13 
-50789439,HC 6956 12810R PHOSPHORUS, 13 
-50789447,HC 6956 12810R OSMOLALITY POTAS, 13 
-50808426,HC  SF3B1 MUTATION ANAL BLOOD, 949 
-50808434,"HC SF3B1 MUTATION, NON BLOOD", 949 
-51493856,HC HEPARIN (HIT) ASSAY, 203 
-62751977,HC SC THER INFUSION RESET PUMP, 165 
-19160258,HC BRONCHOSCOPY W/STENT PLCMT," 6,786 "
-19160259,HC STENT BRONCHUS EA ADD," 2,299 "
-19160260,HC BRONCHOSCOPY W/REV STENT," 7,818 "
-19160261,HC TRACH/BRONCH DILATION," 6,142 "
-41007840,HC XR ENEMA THERAPEUTIC," 2,360 "
-50789462,HC 7060 11589R COL2A1 DNA SEQ," 1,400 "
-50789470,HC 6961 ADMARK TAU/AB42 CSF, 348 
-50789488,HC 6961 ADMARK PHOSPHO TAU/TOT, 348 
-62028921,HC SLEEP STUDY LTD <6 HRS," 2,270 "
-50789496,HC 6961 DYSTON (DYT1) DNA, 600 
-41050790,HC NG TUBE BY PHYS. INCLUDE FLUORO, 483 
-32045150,HC INTRAORAL I&D TONGUE/FLOOR LNGL," 2,106 "
-51565620,HC SHBG BY ARCHITECT, 308 
-51565638,HC TESTOTERONE BIO/FREE, 361 
-51565646,HC TESTOSTERONE TOTAL, 369 
-46161618,HC HSVNEO HSV DNA PCR NEONATAL, 637 
-50789079,HC 7060 11589R COL2A1 ANAL DNA," 1,400 "
-50789454,HC 6956 11608R FBN1 FULL GENE," 2,093 "
-50789520,HC 7075 12836R EG ASSAY, 50 
-46161634,HC HSV DNA AMP PROBE, 637 
-46161642,HC VZV IGG & IGM, 251 
-37021615,"HC TC-99M LYMPHOSEEK, UP TO 0.5 MCI", 638 
-41137695,HC CTA CHEST W&W/O CONTRAST," 2,880 "
-41152389,HC CHG TUBE CYSTOSTOMY SMP, 645 
-50789314,HC 6944 TOXPCR TOXOPLASMA PCR, 389 
-50789322,HC 6956 12756R PCA3 ASSAY, 500 
-50789546,HC 6911 12761R HEP B VIRUS GEN, 396 
-50789553,HC 7029 CAHD CAUDETYTM PCR, 420 
-50789561,HC 7052 11537R MTDNA DEPLETION," 1,780 "
-50789579,HC 7052 11537R MTDNA SEQ ANLYS," 1,780 "
-50789587,HC 7058 11443R 26-50 EXONS DNA, 400 
-50789595,HC 7058 11443R NF1 NONNEO CULT, 400 
-56243796,HC INS PCMKR GEN W/EXISTING MLT," 32,586 "
-50808491,HC AMLP GENE ANLYS DUP VAR," 1,120 "
-50808509,HC AMLP GENE ANLYS 12 EXON, 662 
-50808517,HC BMAML GENE ANLYS DUP VAR," 1,120 "
-50808525,HC BMAML GENE ANLYS 12 EXON, 662 
-50672989,HC MYB GENE REARRANGEMENT," 1,383 "
-50672997,HC INTPH ISH 1Q1P 100-300 CELL, 441 
-50673003,HC INTPH ISH FGFR1 100-300 CELL, 441 
-50673011,HC INTPH ISH IGH 100-300 CELL, 441 
-62028922,HC REM/REV SLING STRESS INCONT," 7,433 "
-62028923,HC REVISE PROST VAG GRAFT," 4,096 "
-36033282,HC ACQUISN OF MRI FOR RT TX PLAN," 2,525 "
-41003567,HC FOREARM - PEDIATRIC 1 VIEW, 769 
-41178326,HC BRONCHOSC W/BX TRANSBRONCH SGL," 5,733 "
-41178327,HC BRNCHSC W/IMAGE-GUID," 4,000 "
-50789603,HC 7046 12530R NDS1 11-25 EXON," 1,350 "
-50789611,HC 7045 12530R NDS1 26-50 EXON," 1,350 "
-52110730,HC FINE NEEDLE ASPIRATION W/IMAGING," 2,086 "
-46161626,HC VZV DNA PCR WITH & W/O LES, 388 
-50570084,HC QUANT ASSAY DRUG, 291 
-50789645,HC 6956 12838R IGF1 SOMATOMED, 28 
-50808541,HC BMAML ISL EXT HP EA NA, 210 
-50808558,HC BMAML AMP NA EA, 262 
-50808566,HC BMAML SEP ELECTRO CAP, 161 
-50808574,HC BMAML INTERP REPORT, 184 
-50808582,HC BMAML ENZ DIG EA, 117 
-50789629,HC 6942 HBV HEP B GENO, 254 
-50789637,HC 6942 12814R MYCO PNM IGG IGM, 52 
-39000325,HC GENETIC COUNSELING 15 MIN, 229 
-50789678,HC 7024 11586R MPS II ENZ ANAL, 200 
-46010021,HC RBC ASSAY GLUTATHIONE  GSH, 196 
-42016667,HC WATER SEAL CHEST DRAIN, 331 
-50789652,HC 7024 11367R NDS1 11-25-EXON," 2,400 "
-50789660,HC 7024 11584R MECP2 SEQ ANAL, 900 
-50789686,HC 7052 12433R PAH DEFIC SEQ, 810 
-50789694,HC 7052 12222R MCCC1 SEQ ANAL," 1,020 "
-50789702,HC 7052 11538R COX IV PANEL, 555 
-50789710,HC 7052 11539R MCAD DEFIC SEQ, 820 
-58700304,HC CHLORIDE POC, 129 
-58700305,HC UREA NITROGEN (BUN) POC, 157 
-32050818,HC ER TX DISL WRIST W/MANIP CLSD, 565 
-62028924,HC IMPLANT COAPTITE," 1,854 "
-50808442,HC HUNTINGTON'S DISEASE ANALYSIS, 871 
-50789728,HC 6956 11583R NPC2 GENE SEQ," 1,048 "
-50789736,HC 6956 11583R NPC1 GENE SEQ," 1,048 "
-50789744,HC 7053 11517R PCD GENE SEQ, 321 
-50789751,HC 7053 11517R PCD 8 POLY-T ANAL, 321 
-50789769,HC 7053 11517R PCD MOLE PATH, 321 
-50789777,HC 7046 11359R KNOWN FAM MUTAT, 350 
-50789785,HC 7046 11574R HRAS GENE SEQ, 675 
-50789793,HC 7046 11578R CFC BRAF GENE SEQ," 1,425 "
-50789806,HC 7052 11536R TK2-RELAT MTDNA," 1,320 "
-50789814,HC 7046 12518R NPC1 GENE SEQ," 2,651 "
-50789822,HC 7046 12520R NPC2 GENE SEQ, 665 
-50789830,HC 7046 12526R VDWS IRF6 SEQ, 941 
-50789848,HC 7046 12528R TBS SALL1 SEQ," 1,653 "
-50789855,HC 7046 12532R PJS STK11, 150 
-50789863,HC 7046 12532R PJS STK11 EA DNA, 150 
-50789871,HC 7046 12532R INTERP REPORT, 150 
-50789889,HC 7046 11581R HPE SEQ, 188 
-50789897,HC 7046 11581R HPE EA DNA PROBE, 188 
-50789905,HC 7046 11581R INTERP REPORT, 188 
-50789921,HC 7053 11515R PKU (PAH) SEQ," 1,170 "
-50789939,HC 7053 11512R CF FULL GENE SEQ, 665 
-50789954,HC 6959 11518R BCR-ABL1 QUANT, 330 
-50789962,HC 7077 12534R STL COL9A SEQ, 890 
-50789996,HC 6961 11528R PPN AB ANALYTE, 213 
-50789997,HC 6961 11528R PPN QUANT, 213 
-50789998,HC 6961 12439R 2-5 EXON DNA SEQ, 808 
-50789999,HC 6961 12439R 6-10 EXON DNA, 808 
-50790000,HC 6961 12439R 11-25 EXON DNA, 83 
-50790001,HC 6961 12439R DYSF GEN SEQ, 808 
-50790002,HC 6961 12439R LGMD, 808 
-51300358,HC BODY FLUID CELL COUNT & DIFF, 252 
-51393924,HC MALSC ANTG DIR OPT OBS NOS, 132 
-51393932,HC HEMATOCRIT MANUAL, 54 
-41100010,HC REPAIR INTERMED EXT WND <2.6, 587 
-41100011,HC REMOVE SKN LSN TRNK/EXT<0.5CM, 433 
-91500010,HC NUTRITION THRPY EVAL 15 MIN, 118 
-91500011,HC NUTRITION THRPY RE-EVAL 15 MIN, 101 
-38106001,GLUCAGON VL 1MG, 893 
-38506002,GLUCAGON VL 1MG, 893 
-50789913,HC 7053 11513R CF TG ANALYSIS, 400 
-50789947,HC 7053 11512R CF 8 POLY-T ANAL, 665 
-94206000,GLUCAGON VL 1MG, 893 
-95006003,GLUCAGON VL 1MG, 893 
-50789512,HC 6956 12809R CK ISOENZ ELECT, 20 
-50786520,HC 6959 12602R FL NONI TITR EA, 144 
-36033290,HC PROSTATE VOLUME STUDY, 654 
-36301500,HC ACQUISN OF PET FOR RT TX PLAN, 454 
-36301501,HC ACQUISN OF MRI FOR RT TX PLAN," 2,525 "
-37223310,HC CT HEAD W/O CONTRAST (WITH XENON)," 3,850 "
-41152736,HC MUSCULOSKELETAL UNLSTD PROC," 4,666 "
-41179653,HC BRONCHOSC W/ASP TRANSBRONCH," 6,173 "
-42061945,HC BX TISSUE PELVIS/HIP SUPERFI," 2,465 "
-41186743,E/V RPR AAA UNIFEMORAL," 9,304 "
-43100081,HC NUTRITION THRPY RE-EVAL 15 MIN, 101 
-50470186,HC ICTO TEST, 75 
-51565661,HC PROT ELECTRO SERUM, 180 
-51364561,HC LDLD - DIRECT LDL CHOLESTEROL, 168 
-51565653,HC PROTEIN TOTAL, 75 
-32521977,HC TRAY-LUMBAR, 313 
-32581961,HC DRAIN INTERMED JOINT/BURSA, 875 
-32011956,HC REM F/B EMB EYELID," 1,055 "
-40880346,HC SCREENING MAMMOGRAM 3D TOMOSYNTHESIS, 400 
-40880353,HC DIAG MAMMO BILAT 3D TOMOSYNTHESIS, 626 
-62099954,HC WET MOUNT, 270 
-32021977,"HC REMOVAL, ACCESS POR"," 3,651 "
-37106416,HC BRAIN AMYVID SCAN," 4,521 "
-41176345,HC BX TISSUE NECK/THORAX," 3,159 "
-30098037,HC  ORA ANALYSIS, 125 
-32081960,"HC CLSD TRT SHLDR DL W/MA, ANE"," 4,390 "
-32581965,HC CATH FOLEY INSERTION," 1,300 "
-62023352,HC ACTINOTHRPY/UV LIGHT, 302 
-40880361,HC DIAG MAMMO UNILAT 3D TOMOSYNTHESIS, 501 
-41123419,HC PANCREATIC DUCT EMBOLIZ W/ETHIODOL," 20,399 "
-95400001,HC ROUTINE VENIPUNCTURE, 115 
-95400002,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-95400003,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-95400004,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-95400005,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-95400006,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-95400007,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-95400008,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-95400009,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-95400010,HC REMOVE CVC CATH W/O SUBQ P/P," 3,651 "
-95400011,HC COLLECT BLOOD IMPL VAD, 571 
-95400012,HC BLOOD COLLECT CVC/PICC LINE, 576 
-95400013,HC DECLOT THROMB IMP VAD/CATH," 2,257 "
-95400014,HC BONE MARROW ASPIRATION," 2,869 "
-95400015,HC BONE MARROW NEEDLE BIOPSY," 2,867 "
-95400016,HC BONE MARROW BX W/ASPIRATION, 415 
-95400017,HC 41.03 ALLO BMT W/O PURGING," 13,718 "
-95400018,HC 41.05 ALLO HSC TRANSPLANT W/O PURGING," 13,718 "
-95400019,HC 41.04 AUTO HSC TRANSPLANT W/O PURGING," 13,681 "
-95400020,"HC ALLO THERAPEUTIC CELLS, FRESH PRODUCT"," 2,678 "
-95400021,HC ALLO THERAPEUTIC CELLS CRYOPRESERVED PRODUCT," 2,678 "
-95400022,HC INJECT/ASPIR SHUNT TUBE RESRV," 1,809 "
-95400023,HC LUMBAR PUNCTURE DIAGNOSTIC," 2,210 "
-95400024,"HC IMMUNIZATION ADMIN, SINGLE", 263 
-95400025,HC IMMUN ADMIN EA ADDL VACCINE, 135 
-95400026,HC HYDRATION INFUSION UP TO 1HR," 1,035 "
-95400027,HC 2ND ENCTR HYDRA INFUS UP TO 1H," 1,035 "
-95400028,HC HYDRATION INFUSION EA ADDTL HR, 607 
-95400029,HC 2ND ENCTR HYDRA INFUS EA ADDL, 607 
-95400030,HC IV INFUSION THERAPY UP TO 1HR," 1,226 "
-95400031,HC 2ND ENC-IV INF-THER-1ST HR," 1,226 "
-95400032,HC IV INFUSION-THER-ADDTL HRS, 607 
-95400033,HC 2ND ENC-IV INF-THER-EA ADD HR, 607 
-95400034,HC IV INFUSION THERAPY ADDTL SEQU, 796 
-95400035,HC IV INFUSION THERAPY CONCURRENT, 821 
-95400036,HC SQ THERAP INFUSION 1ST HR," 1,094 "
-95400037,HC SQ THERAP INFUSN EA ADDL HR, 577 
-95400038,HC SQ THER INFUSION RESET PUMP, 165 
-95400039,"HC TX/DX INJECTION,SUBCUT/IM", 344 
-95400040,HC TX/DX INJECTION IV INITIAL, 542 
-95400041,HC 2ND ENCTR TX/DX INJECTION: INITIAL, 542 
-95400042,HC IVP INJ-ADDTL SEQ NEW DRUG, 400 
-95400043,HC 2ND ENC IVP INJ+SEQ NEW DRUG, 400 
-95400044,HC IVP INJ-ADDTL SEQ SAME DRUG, 405 
-95400045,HC 2ND ENC IVP INJ +SEQ SAME DRUG, 256 
-95400046,HC CHEMO SUBCUT/IM NON-HORMONAL, 903 
-95400047,HC CHEMO ADMIN SQ/OR IV;HORMONAL, 786 
-95400048,HC CHEMO ADMIN IV PUSH INITIAL, 888 
-95400049,HC CHEMO-IV PUSH-ADDTL DRUG, 801 
-95400050,HC CHEMO INFUSION UP TO 1HR," 1,444 "
-95400051,HC 2ND ENC CHEMO IV INFUS UP TO 1," 1,444 "
-95400052,HC CHEMO INFUSION EA ADDTL HR, 641 
-95400053,HC 2ND ENC CHEMO INFUS EA ADD HR, 641 
-95400054,HC CHEMO-IV INFUSION-ADDTL SEQ," 1,043 "
-95400055,HC CHEMO ADMIN PERITONEAL CAVITY," 1,635 "
-95400056,HC CHEMO INTRATHECAL INTO CNS," 5,090 "
-95400057,HC CHEMO INFSN > 8HRS W PUMP," 5,335 "
-95400058,HC REFIL/MAINT OF PORTABLE PUMP, 726 
-95400059,HC RFLL/MNT IMPLNT PUMP/RES, 590 
-95400060,HC IRRIGATION IMPLANTED VAD DRUG, 672 
-95400061,HC CHEMO SUBCUTANEOUS RESERVOIR, 629 
-95400062,HC UNLISTED CHEMO PROCEDURE, 77 
-95400063,"HC PHLEBOTOMY, THERAPEUTIC", 658 
-95400064,HC OP VISIT LEVEL 1-EST, 160 
-95400065,HC OP VISIT LEVEL 1-EST-W PX, 160 
-95400066,HC OP VISIT LEVEL 2-EST, 361 
-95400067,HC OP VISIT LEVEL 2-EST-W PX, 361 
-95400068,HC OP VISIT LEVEL 3-EST, 413 
-95400069,HC OP VISIT LEVEL 3-EST-W PX, 413 
-95400070,HC OP VISIT LEVEL 4-EST, 463 
-95400071,HC OP VISIT LEVEL 4-EST-W PX, 463 
-95400072,HC OP VISIT LEVEL 5-EST, 593 
-95400073,HC OP VISIT LEVEL 5-EST-W PX, 593 
-95400074,"HC CRITICAL CARE, INITIAL HOUR"," 12,847 "
-95400075,"HC CRITICAL CARE, EA 30 MIN"," 2,434 "
-95400076,HC BIO PATCH, 45 
-95400077,HC BONE MARROW TRAY, 268 
-95400078,HC HEPA FILTER MASK, 419 
-95400079,HC HICKMAN BOOSTER KIT, 78 
-95400080,HC JAMSHIDI NEEDLE, 163 
-95400081,HC LUMBAR TRAY, 313 
-95400082,"HC PICC PROTECTOR SLEEVE, DISP", 298 
-95400083,HC IMED PUMP TUBING W/FILTER, 226 
-95400084,HC IV PUMP TAXOL TUBING, 298 
-95400085,HC POWR BONE MARROW BX NEEDLE, 612 
-95400086,HC INFLUENZA ADMINISTRATION IM, 77 
-95400087,HC PNEUMOCOCCAL ADMIN IM, 295 
-40145310,HC BX TISSUE PELVIS/HIP DEEP," 1,644 "
-94205184,HC RT TREATMENT PLANNING," 2,525 "
-32028102,HC STRAPPIN; KNEE, 219 
-32528100,"HC INJ,EPI,BLD/CLOT PATCH"," 2,381 "
-32581977,HC INJECT/ASPIR SHUNT TUBE," 1,809 "
-32010060,HC DESTRUCT LESION ANY METH 2-14, 120 
-41152735,HC DESTRUCT NRV TRIGEMINAL," 3,619 "
-41152737,HC DESTRUCT  NRV TRIGEMINAL 2-3 BR," 5,680 "
-41152738,HC DESTRUCT NRV TRIGEMINAL 2-3 BR W/," 5,680 "
-41780149,HC CAD SCREENING MAMMO, 139 
-41780156,HC DIGITAL SCREENING MAMMOGRAM, 400 
-41780164,"HC COMPUTER AIDED DETECTION, DIAG", 208 
-41780255,HC DIGITAL DIAG MAMMOGRAPHY UNILAT, 501 
-41780263,HC DIGITAL DIAG MAMMOGRAPHY BILAT, 626 
-41780347,HC SCREENING MAMMOGRAM 3D TOMOSYNTHESIS, 400 
-41780354,HC DIAG MAMMO BILAT 3D TOMOSYNTHESIS, 626 
-62028049,HC FLULAVAL VACC SPLT >3 YRS IM, 27 
-40805582,HC BNDG HMSTC DRY D-STAT PATCH, 331 
-32591999,"HC NERVE BLCK, BRACH PLEXUS"," 2,391 "
-50790003,HC 7046 11582R MMA GENE SEQ," 1,606 "
-32081956,"HC DRAINAGE EXT EAR, COMPL"," 3,066 "
-62700325,HC LUMBAR PUNCTURE DIAGNOSTIC," 2,210 "
-46161667,HC HCV TRANSCRIPTION & QUANT, 874 
-50470194,HC CORTISOL TOTAL, 374 
-46161659,HC HCV GENOTYPE GENMARK," 2,847 "
-62028925,HC AMIKACIN INTRAVITREAL PER 100MG, 22 
-32531977,HC I&D OF ABSCESS:SIMPLE OR SINGL, 937 
-32522013,HC INITIAL CRITICAL CARE," 12,847 "
-32061516,HC POC URINE DIPSTICK, 115 
-50790004,HC 7058 11443R NF1 RNA/CDNA SEQ, 400 
-50790005,HC 6961 11062R HNF4A DNA SEQ, 455 
-50790006,HC 6961 11062R HNF4A MOLE PATH, 455 
-50790007,HC 6961 11063R GCK DNA SEQ, 590 
-50790008,HC  6961 11063R GCK MOLE PATH, 590 
-50790009,HC 6961 11064R TCF1 MOLE PROCED, 600 
-50790010,HC 6961 11066R TCF2 2-5 EXONS DNA, 418 
-50790011,HC 6961 MODY 2-5 EXONS DNA, 571 
-50790012,HC 6961 MODY 11-25 EXONS DNA, 571 
-50790014,HC 6961 MODY 6-10 EXONS DNA, 571 
-50790015,HC 6961 MODY MOLE PROCED, 571 
-50790016,HC 6961 11064R TCF1 6-10 EXONS DNA, 600 
-50790017,HC 6961 11066R TCF2 6-10 EXONS DNA, 418 
-50790018,HC 6956 12849R AMPHIPHYSIN WB, 270 
-50790019,HC 6956 12847R PARANEO AB WBLOT, 141 
-50790020,HC 6956 12853R EVEROLIMUS BLOOD, 170 
-50790022,HC 6956 12851R NMO/AQPA IGG CBA, 161 
-62028926,HC ACTIWATCH, 700 
-19160262,HC PNEUMOTHORAX KIT, 806 
-32021985,HC INJ ANES NERVE FEMORAL SGL," 1,232 "
-32511111,HC CNTRL NASL HEMORR ANT SMPL ANY," 1,865 "
-32071999,"HC PALATE REPAIR, SUTURE", 971 
-32581999,HC NEPHRO TUBE CHANGE," 2,578 "
-62771999,HC IRRIGATION/ACCESS  OF VAD, 672 
-42061978,HC BX MUSCLE DEEP," 4,952 "
-42072652,HC BX TISSUE PELVIS/HIP DEEP," 1,644 "
-41045535,HC INJ MYELOGRAM CERVICAL (C1-C2)," 1,663 "
-42061911,"HC NDL LOC WIRE PLCMT, SHOULDER"," 3,466 "
-37105087,HC PET N-13 AMMN PER DOS UP TO 40," 3,400 "
-41008624,HC XR ERCP CATH BILIARY&PANCREAS," 3,847 "
-42017079,HC FOLEY TRAY WITH SILICONE, 116 
-30250337,HC MESH SURIGMEND PRS 7 X 17CM," 19,159 "
-42072850,HC CT CHEMICAL ABLATION," 12,513 "
-62028927,HC MCAL PT RE-EVAL ADD 15 MIN, 172 
-70000106,HC MCAL PT RE-EVAL ADD 15 MIN, 172 
-32510281,HC STRAPPIN; KNEE, 219 
-56202463,HC DEBRIDE PACE/ICD POCKET TISSUE, 591 
-55092746,HC STOOL CLTR IS NA DAN/RNA EA, 578 
-40106015,HC FOLEY TRAY WITH SILICONE, 116 
-40805814,HC BREAST WIRE LVL7, 383 
-50794577,HC 6949 H064 CD31 MIN AG TYP, 648 
-52294576,HC 6949 H064 CD31 MIN AG TYP, 648 
-81094575,HC 6949 H064 CD31 MIN AG TYP, 648 
-81394579,HC 6949 H064 CD31 MIN AG TYP, 648 
-82394578,HC 6949 H064 CD31 MIN AG TYP, 648 
-84394576,HC 6949 H064 CD31 MIN AG TYP, 648 
-35100108,HC MCAL PT RE-EVAL ADD 15 MIN, 172 
-38245585,"HC UNLIST PROCEDURE, PELVIS OR HIP JOINT"," 2,941 "
-41045584,"HC UNLIST PROCEDURE, PELVIS OR HIP JOINT"," 2,941 "
-35601997,HC SP EVAL SPEECH FLUENCY, 873 
-35601998,HC SP EVAL SPEECH PRODUCTION, 873 
-35601999,HC SP EVAL SND PRD W/LANG EVAL, 873 
-35602000,HC SP BEHAV QUAL ANAL VOICE, 873 
-62801461,HC SP EVAL SPEECH FLUENCY, 873 
-62801462,HC SP EVAL SPEECH PRODUCTION, 873 
-62801463,HC SP EVAL SND PRD W/LANG EVAL, 873 
-62801464,HC SP BEHAV QUAL ANAL VOICE, 873 
-62022440,HC EVAL SPEECH FLUENCY, 873 
-62022441,HC EVAL SPEECH PRODUCTION, 873 
-62022442,HC EVAL SND PRD W/LANG EVAL, 873 
-62022443,HC BEHAV QUAL ANAL VOICE, 873 
-62025581,HC  VASC EMBOLIZE/OCCLUDE ORGAN," 26,526 "
-62026764,HC ESOPHAGOSCOPY RETRO BALLOO," 3,039 "
-62026766,HC  ESOPHAGOSC DILATE BALLOON 30," 4,252 "
-62026768,HC  EGD BALLOON DIL ESOPH30 MM/>," 4,252 "
-62027040,HC  CHEMODENERV MUSC NECK DYSTON, 404 
-62028942,HC CHEMODENERV 1 EXTREMITY 1-4, 505 
-62028928,HC  CHEMODENERV 1 EXTREM 1-4 EA, 253 
-62028929,HC  CHEMODENERV 1 EXTREM 5/> MUS, 657 
-62028930,HC  CHEMODENERV 1 EXTREM 5/> EA, 328 
-62028931,HC  CHEMODENERV TRUNK MUSC 1-5, 505 
-62028932,HC  CHEMODENERV TRUNK MUSC 6/>, 657 
-41160112,HC OPEN/PERQ TRANSCATH PLMT STENT INITAIL ART," 39,282 "
-41160113,HC OPEN/PERQ PLACE STENT EA ADD ARTERY," 25,248 "
-41160130,HC OPEN/PERQ PLACE STENT INITIAL VEIN," 39,282 "
-41160115,HC OPEN/PERQ PLACE STENT EA ADD VEIN," 25,248 "
-41160116,HC VASC EMBOLIZE/OCCLUDE VENOUS," 52,746 "
-41160135,HC VASC EMBOLIZE/OCCLUDE ARTERY," 52,746 "
-41160118,HC VASC EMBOLIZE/OCCLUDE ORGAN," 52,746 "
-41160119,HC VASC EMBOLIZE/OCCLUDE ART/VEN HEMO," 52,746 "
-41160138,HC ESOPHAGOSCOP STENT PLACEMENT," 9,953 "
-41160121,HC ERCP DUCT STENT PLACEMENT," 8,116 "
-41160122,HC ERCP REMOVE FORGN BODY DUCT," 8,116 "
-41160140,HC ERCP STENT EXCHANGE W/DILATE," 8,116 "
-41160124,HC IMAGE CATH FLUID COLXN VISC," 5,134 "
-41160125,HC IMAGE CATH FLUID PERI/RETRO," 5,134 "
-56293534,HC PED OPEN/PERQ TRANSCATH PLMT STENT INITAIL ART," 39,282 "
-56293535,HC PED OPEN/PERQ PLACE STENT EA ADD ARTERY," 25,248 "
-56293536,HC PED OPEN/PERQ PLACE STENT INITIAL VEIN," 39,282 "
-56293537,HC PED OPEN/PERQ PLACE STENT EA ADD VEIN," 25,248 "
-56293538,HC PED VASC EMBOLIZE/OCCLUDE VENOUS," 52,746 "
-56293539,HC PED VASC EMBOLIZE/OCCLUDE ARTERY," 52,746 "
-56293540,HC PED VASC EMBOLIZE/OCCLUDE ORGAN," 52,746 "
-56293541,HC PED VASC EMBOLIZE/OCCLUDE ART/VEN HEMO," 52,746 "
-40830002,"HC BREAST BX, 1ST LESION, BIL, STEREO GUIDE"," 13,087 "
-40830010,"HC BREAST BX, 1ST LESION,STEREO GUIDE"," 8,724 "
-40830028,"HC BREAST BX, EA ADDL LESION, STEREO GUIDE"," 7,799 "
-40830044,"HC BREAST BX, 1ST LESION, BIL, US GUIDE"," 12,013 "
-40830051,"HC BREAST BX, 1ST LESION, US GUIDE"," 8,008 "
-40830069,"HC BREAST BX, EA ADDL LESION, US GUIDE"," 6,063 "
-40830085,"HC BREAST LOC, 1ST LESION, BIL, MAMMO GUIDE"," 9,330 "
-40830093,"HC BREAST LOC, 1ST LESION, MAMMO GUIDE"," 6,220 "
-40830101,"HC BREAST LOC, EA ADDL LESION, MAMMO GUIDE"," 3,164 "
-40830168,"HC BREAST LOC, 1ST LESION,BIL,  US GUIDE"," 6,908 "
-40830176,"HC BREAST LOC, 1ST LESION, US GUIDE"," 4,605 "
-40830184,"HC BREAST LOC, EA ADDL LESION, US GUIDE"," 4,155 "
-40130007,"HC BREAST BX, 1ST LESION, BIL, MRI GUIDANCE"," 13,120 "
-40130015,"HC BREAST BX, 1ST LESION, MRI GUIDANCE"," 8,747 "
-40130023,"HC BREAST BX, EA ADDL LESION, MRI GUIDANCE"," 6,669 "
-40130056,"HC BREAST LOC, 1ST LESION, BIL, MRI GUIDANCE"," 11,583 "
-40130064,"HC BREAST LOC, 1ST LESION, MRI GUIDANCE"," 7,722 "
-40130072,"HC BREAST LOC, EA ADDL LESION, MRI GUIDANCE"," 5,594 "
-42022356,HC IMAGE CATH FLUID COLXN VISC," 5,134 "
-42062364,HC IMAGE CATH FLUID PERI/RETRO," 5,134 "
-32050032,HC GUIDE CATHET FLUID DRAINAGE, 670 
-32051543,HC ESOPHAGOSCP RIG TRNSO REM FB," 4,252 "
-32051544,HC ESOPHAGOSCOPY RIGID BALLOON," 4,252 "
-58010241,HC CHEMODENERV MUSC NECK DYSTON, 404 
-58010242,HC CHEMODENER MUSCLE LARYNX EMG, 505 
-58033128,HC CHEMODENERV 1 EXTREMITY 1-4, 505 
-58033129,HC CHEMODENERV 1 EXTREM 1-4 EA, 253 
-58033130,HC CHEMODENERV 1 EXTREM 5/> MUS, 657 
-58033131,HC CHEMODENERV 1 EXTREM 5/> EA, 328 
-58033132,HC CHEMODENERV TRUNK MUSC 1-5, 505 
-58033133,HC CHEMODENERV TRUNK MUSC 6/>, 657 
-62027042,HC CHEMODENER MUSCLE LARYNX EMG, 505 
-62021234,HC REVISION OF EYELID," 9,234 "
-62021235,HC REVISION OF EYELID," 3,231 "
-50808616,HC MGMTB METHYLN UNLS MOP, 829 
-50808624,HC MGMT METHYLNUNLS MOP PETMB, 829 
-52032571,HC IMMUNOHISTO AB ADD SLIDE, 537 
-36060350,HC IV PUSH INJECTION, 542 
-36060351,HC IV PUSH INJCTN OF NEW SUBST., 400 
-36060352,HC IVP INJCTN-SEQ SAME DRUG, 405 
-42016568,HC BREAST LOC DEVICE," 2,542 "
-62310868,HC EVAL SPEECH FLUENCY, 655 
-62310869,HC EVAL SPEECH PRODUCTION, 655 
-62310870,HC EVAL SND PRD W/LANG EVAL, 655 
-62310871,HC BEHAV QUAL ANAL VOICE, 655 
-62010048,HC INJECTION NERUOMA, 514 
-36033126,HC RESP MOTION MGMT SIMUL, 879 
-36300506,HC RESP MOTION MGMT SIMUL, 879 
-30699910,HC HOOK KNIFE LONG OLYMPUS," 4,941 "
-32042013,HC CLSD TX CARPAL FX W/MANIP," 1,884 "
-41898796,HC IMPL EMBLZTN COIL RETRACTA 7X8X35," 4,760 "
-41898804,HC IMPL IR DISCHARGE KIT B KIT, 488 
-41898805,HC IMPL STNT GFT ZENITH RENU 28X37X125," 42,341 "
-41898806,HC IMPL CUST CRDIAC CTH PACK TRAY, 803 
-41898807,HC IMPL LEAD DURATA ICD DIFIB 65CM," 37,375 "
-41898808,HC IMPL STNT GRFT ZENITH RENU 28X59," 42,341 "
-41898809,"HC IMPL CTH ATTAIN SLCT DELVRY 63,130"," 1,700 "
-41898811,HC IMPL BLN TRUE DILATATION 20X4.5X10F," 6,783 "
-41898812,HC IMPL CATH SOFIA DISTAL ACCESS 5X125," 9,375 "
-41898815,HC IMPL CTH ATTAIN SUREVLVE OUTR GD 45," 1,632 "
-41898816,HC IMPL CABLE MEDTRONIC 12FT, 731 
-41898817,HC IMPL STNT VALIANT DELV SY 44X40X150," 88,725 "
-41898818,HC IMPL STNT VALIANT DELV SY 40X40X150," 88,725 "
-41898819,HC IMPL SNT GRFT ZENITH FLX AAA 24X56," 21,595 "
-50790052,HC 6952 12845R HMVNLLC ACD UR, 26 
-50790053,HC 6952 12845R VMA URINE, 26 
-30042131,HC VISUALASE NEURO LASER T10 KIT," 76,980 "
-30042132,HC VISUALASE NEURO LASER T03 KIT," 87,483 "
-41160110,HC RADIOPAHARM THRPY INTRA-ART," 1,074 "
-41160127,HC FLUID COLLXN TRANSREC/VAG," 3,180 "
-56270070,HC PEDS PTC CLOS PAT DUCT ARTERIOUSUS," 53,670 "
-56278464,HC PTC CLOS PAT DUCT ARTERIOUSUS," 53,670 "
-62028694,HC HYALGAN 10MG/ML, 191 
-62028933,"HC OCCULT BLOOD, FEC IMMUNO", 425 
-91400013,HC EMBR STRND ADHESIVE SIL DRSG 16CM, 25 
-91400014,HC EMBR STRND ADHESIVE SIL DRSG 06CM, 25 
-36099200,HC TRANSPERI NEEDLE PLACE PROS," 12,192 "
-41898820,HC IMPL CBLE REVERSE MED DETACH SET, 842 
-41898821,HC IMPL GRF GORE PROPATN TW RR 8 X 40X50," 13,305 "
-41898823,HC IMPL CATH ABLTN SAFIRE 7X5," 5,525 "
-41898824,HC IMPL CATH TORCON NB ADV 4.1X80, 119 
-41898825,HC IMPL CTH ENDOVAS EKOSONIC 30CM," 17,963 "
-41898826,HC IMPL CATH ATTAIN SUREVLVE OUTER GD," 1,632 "
-58001109,HC NEUROPSY TECH TEST SAMEDAY PHD, 825 
-91600527,HC  ORA ANALYSIS, 125 
-23630017,HC TRANSFUSION BLOOD-1 HR," 3,300 "
-23630025,HC TRANSFUSION BLOOD-2 HR," 3,500 "
-23630033,HC TRANSFUSION BLOOD-3 HR," 3,700 "
-23630041,HC TRANSFUSION BLOOD-4 HR," 3,900 "
-23630058,HC TRANSFUSION BLOOD-5 HR," 4,100 "
-23630066,HC TRANSFUSION BLOOD-6 HR," 4,300 "
-23630074,HC TRANSFUSION BLOOD-7 HR," 4,500 "
-23630082,HC TRANSFUSION BLOOD-8 HR," 4,700 "
-23630090,HC TRANSFUSION BLOOD-9 HR," 4,900 "
-23630116,HC IV INFUSION-THERAPEUTIC-1ST HR," 1,226 "
-23630124,HC IV INFUSION-THER-ADDTL HRS, 607 
-23630132,HC THERAP PROPH/DX INJ: SUBQ/IM, 344 
-23630140,HC IV PUSH INJECTION, 542 
-23690136,HC HI MED/SURG PRIV," 8,897 "
-23690235,HC HI MED/SURG SP," 8,397 "
-23690252,HC H1 MED/SURG W," 8,397 "
-23690352,HC H1 IICU," 15,479 "
-30042133,HC IMPL CANNULA CARDIO AO ROOT W/VENT," 1,281 "
-30042134,HC IMPL CANNULA RETROGRADE EDWARDS," 8,916 "
-30042135,HC IMPL CANNULA CARDIO UNEVEN," 1,281 "
-30042136,HC IMPL CANNULA CARDIO AO ROOT + 3 VES," 2,433 "
-30042137,HC IMPL CANNULA 2-STAGE EDWARDS," 3,579 "
-30042138,HC IMPL CANNULA CARDIO EDWARDS, 888 
-30042139,HC CANNULA VENT  SORIN 20FR," 1,530 "
-30042140,HC IMPL CANNULA HEARTPORT VENOUS 22," 3,613 "
-30042141,HC IMPL CANNULA HEARTPORT VENOUS 25," 3,613 "
-30042142,HC IMPL CANNULA EDWARDS ARTERIAL 16FR," 2,083 "
-30042143,HC IMPL CANNULA EDWARDS ARTERIAL 18FR," 2,083 "
-30042144,HC IMPL CANNULA EDWARDS ARTERIAL 20FR," 2,083 "
-30042145,HC IMPL CANNULA EDWARDS ARTERIAL 22FR," 2,083 "
-41898828,HC IMPL BLLN CATH RAPIDCROSS 2.5X210," 3,919 "
-41898829,HC IMPL BLLN CATH RAPIDCROSS 4X120," 3,919 "
-51600252,HC ALBUMIN, 138 
-51600609,HC AMYLASE TEST, 395 
-51600658,"HC BILIRUBIN, DIRECT", 165 
-51600906,HC CALCIUM TOTAL, 143 
-51601151,HC CHOLESTEROL, 145 
-51601540,HC CK-MB, 446 
-51601557,HC CREATININE BLOOD, 179 
-51601854,HC GAMMA GLUTAMYL TRAN, 204 
-51601888,"HC GLUCOSE, SERUM/PLASMA", 159 
-51602233,HC URINALYSIS AUTO, 108 
-51602308,HC IRON TTL SERUM, 165 
-51602357,HC URINE ROUTINE-MICRO, 196 
-51602407,HC LIPID PANEL - CALCULATED LD, 415 
-51602555,HC LDH TOTAL, 193 
-51602902,HC MAGNESIUM SERUM/PL, 280 
-51603074,HC HEMOGLOBIN, 48 
-51603082,HC HEMATOCRIT, 217 
-51603108,HC PHOSPHORUS SERUM/PLASMA, 225 
-51603157,HC POTASSIUM, 204 
-51603454,HC SGOT/AST, 180 
-51603504,HC SGPT/ALT, 190 
-51603694,HC TRANSFERRIN, 420 
-51603702,HC TRIGLYCERIDES-BLOOD, 175 
-51603751,HC UREA NITROGEN, 157 
-51603801,HC URIC ACID SERUM/PL, 135 
-51623049,HC RETIC COUNT BY FLOW, 179 
-51662252,"HC GLUCOSE, SERUM/PLASMA", 159 
-51663433,HC HEPATIC FXN PANEL, 342 
-51663441,HC METABOLIC PNL BASIC, 350 
-51663482,HC C REACTIVE PROTEIN, 179 
-51664563,HC LDLD - DIRECT LDL CHOLESTEROL, 219 
-51664605,HC LIPASE, 213 
-51665263,HC BLD AMMONIA TEST, 334 
-51670040,HC CREATININE KINASE TOTAL, 254 
-51693422,HC COMPLETE BLOOD COUNT, 285 
-51693430,HC CBC W/AUTO DIFF, 300 
-51693448,"HC PLATELET COUNT, AUTOMATED", 138 
-51693463,HC SLIDE REVIEW, 116 
-91600529,HC SPECIALITY IOL-PREMIUM TORIC, 345 
-91600531,HC SPECIALITY IOL-PREMIUM CRYSTALENS, 800 
-23690354,HC H1 IICU SEMI PRIVATE," 8,897 "
-23690355,HC H1 IICU WARD," 8,397 "
-19160263,HC BRONCH W/DEST TMR/STENOSIS," 8,396 "
-30042146,HC CANNULA 34/38 OVAL 2 STAGE 3/8," 2,293 "
-30042147,HC CANNULA 12G PLEGIA NEEDLE 31CM," 2,331 "
-30042150,HC CANNULA 20FR X 15," 2,139 "
-30042152,HC CANNULA WITH LUER  6.0 18FR," 4,250 "
-40905804,"HC DEFINITY INJECTION, PER ML", 478 
-41898830,HC IMPL STNT GRF ZENITH TX2 34X157," 72,564 "
-41898831,HC IMPL STNT GFT ZENITH FLX AAA 22X96," 54,977 "
-41898832,HC IMPL STNT ZILVER PTX 6X40," 13,463 "
-41898833,HC IMPL STNT ZILVER PTX 7X40," 13,463 "
-41898834,HC IMPL MIC-KEY FDNG 18X1.7X30 KIT," 18,865 "
-41898835,HC IMPL CATH EKOSONIC ENDOVSC 40X106," 2,338 "
-41898836,HC IMPL BLLN CATH ADVANCE 18LP 8X12," 2,668 "
-41898837,HC IMPL BLLN CATH ADVANCE 18LP 8X15," 2,338 "
-62800173,HC PT CKOUT PROSTH ESTPT EA 15, 195 
-56202055,HC PERC TRANSCATH  SEPTAL REDUCTION THERAPY," 36,899 "
-50795141,HC 6949 H007 LYMPH TITRAT, 247 
-52295141,HC 6949 H007 LYMPH TITRAT, 247 
-81095141,HC 6949 H007 LYMPH TITRAT, 247 
-81395141,HC 6949 H007 LYMPH TITRAT, 247 
-82395141,HC 6949 H007 LYMPH TITRAT, 247 
-84395141,HC 6949 H007 LYMPH TITRAT, 247 
-50795158,HC 6949 H008 LYMPH TITRAT, 247 
-52295158,HC 6949 H008 LYMPH TITRAT, 247 
-81095158,HC 6949 H008 LYMPH TITRAT, 247 
-81395158,HC 6949 H008 LYMPH TITRAT, 247 
-82395158,HC 6949 H008 LYMPH TITRAT, 247 
-84395158,HC 6949 H008 LYMPH TITRAT, 247 
-50795166,HC 6949 H009 FLOW 1ST MARKER, 113 
-52295166,HC 6949 H009 FLOW 1ST MARKER, 113 
-81095166,HC 6949 H009 FLOW 1ST MARKER, 113 
-81395166,HC 6949 H009 FLOW 1ST MARKER, 113 
-82395166,HC 6949 H009 FLOW 1ST MARKER, 113 
-84395166,HC 6949 H009 FLOW 1ST MARKER, 113 
-50795174,HC 6949 H009 FLOW ADTL MKR, 113 
-52295174,HC 6949 H009 FLOW ADTL MKR, 113 
-81095174,HC 6949 H009 FLOW ADTL MKR, 113 
-81395174,HC 6949 H009 FLOW ADTL MKR, 113 
-82395174,HC 6949 H009 FLOW ADTL MKR, 113 
-84395174,HC 6949 H009 FLOW ADTL MKR, 113 
-50795182,HC 6949 H009 INTERP 2 TO 8, 112 
-52295182,HC 6949 H009 INTERP 2 TO 8, 112 
-81095182,HC 6949 H009 INTERP 2 TO 8, 112 
-81395182,HC 6949 H009 INTERP 2 TO 8, 112 
-82395182,HC 6949 H009 INTERP 2 TO 8, 112 
-84395182,HC 6949 H009 INTERP 2 TO 8, 112 
-50795191,HC 6949 H010 LYMPH TITRAT, 247 
-52295191,HC 6949 H010 LYMPH TITRAT, 247 
-81095191,HC 6949 H010 LYMPH TITRAT, 247 
-81395191,HC 6949 H010 LYMPH TITRAT, 247 
-82395191,HC 6949 H010 LYMPH TITRAT, 247 
-84395191,HC 6949 H010 LYMPH TITRAT, 247 
-50795208,HC 6949 H011 LYMPH TITRAT, 247 
-52295208,HC 6949 H011 LYMPH TITRAT, 247 
-81095208,HC 6949 H011 LYMPH TITRAT, 247 
-81395208,HC 6949 H011 LYMPH TITRAT, 247 
-82395208,HC 6949 H011 LYMPH TITRAT, 247 
-84395208,HC 6949 H011 LYMPH TITRAT, 247 
-50795216,HC 6949 H012 LYMPH TITRAT, 247 
-52295216,HC 6949 H012 LYMPH TITRAT, 247 
-81095216,HC 6949 H012 LYMPH TITRAT, 247 
-81395216,HC 6949 H012 LYMPH TITRAT, 247 
-82395216,HC 6949 H012 LYMPH TITRAT, 247 
-84395216,HC 6949 H012 LYMPH TITRAT, 247 
-50795223,HC 6949 H013 LYMPH TITRAT, 247 
-52295223,HC 6949 H013 LYMPH TITRAT, 247 
-81095223,HC 6949 H013 LYMPH TITRAT, 247 
-81395223,HC 6949 H013 LYMPH TITRAT, 247 
-82395223,HC 6949 H013 LYMPH TITRAT, 247 
-84395223,HC 6949 H013 LYMPH TITRAT, 247 
-50795231,HC 6949 H016 IMMUNOASSAY NON AB, 723 
-52295231,HC 6949 H016 IMMUNOASSAY NON AB, 723 
-81095231,HC 6949 H016 IMMUNOASSAY NON AB, 723 
-81395231,HC 6949 H016 IMMUNOASSAY NON AB, 723 
-82395231,HC 6949 H016 IMMUNOASSAY NON AB, 723 
-84395231,HC 6949 H016 IMMUNOASSAY NON AB, 723 
-50795589,HC 6949 H016 IMMUNOASSAY NON AB, 726 
-52295589,HC 6949 H016 IMMUNOASSAY NON AB, 726 
-81095589,HC 6949 H016 IMMUNOASSAY NON AB, 726 
-81395589,HC 6949 H016 IMMUNOASSAY NON AB, 726 
-82395589,HC 6949 H016 IMMUNOASSAY NON AB, 726 
-84395589,HC 6949 H016 IMMUNOASSAY NON AB, 726 
-50795249,HC 6949 H023 LYMPH TITRAT, 247 
-52295249,HC 6949 H023 LYMPH TITRAT, 247 
-81095249,HC 6949 H023 LYMPH TITRAT, 247 
-81395249,HC 6949 H023 LYMPH TITRAT, 247 
-82395249,HC 6949 H023 LYMPH TITRAT, 247 
-84395249,HC 6949 H023 LYMPH TITRAT, 247 
-50795257,HC 6949 H024 LYMPH TITRAT, 247 
-52295257,HC 6949 H024 LYMPH TITRAT, 247 
-81095257,HC 6949 H024 LYMPH TITRAT, 247 
-81395257,HC 6949 H024 LYMPH TITRAT, 247 
-82395257,HC 6949 H024 LYMPH TITRAT, 247 
-84395257,HC 6949 H024 LYMPH TITRAT, 247 
-50795265,HC 6949 H056 STR PT AND COMP, 149 
-52295265,HC 6949 H056 STR PT AND COMP, 149 
-81095265,HC 6949 H056 STR PT AND COMP, 149 
-81395265,HC 6949 H056 STR PT AND COMP, 149 
-82395265,HC 6949 H056 STR PT AND COMP, 149 
-84395265,HC 6949 H056 STR PT AND COMP, 149 
-50795273,HC 6949 H058 STR PT AND COMP," 1,203 "
-52295273,HC 6949 H058 STR PT AND COMP," 1,203 "
-81095273,HC 6949 H058 STR PT AND COMP," 1,203 "
-81395273,HC 6949 H058 STR PT AND COMP," 1,203 "
-82395273,HC 6949 H058 STR PT AND COMP," 1,203 "
-84395273,HC 6949 H058 STR PT AND COMP," 1,203 "
-50795281,HC 6949 H058 CHM CELL SEL EA," 1,204 "
-52295281,HC 6949 H058 CHM CELL SEL EA," 1,204 "
-81095281,HC 6949 H058 CHM CELL SEL EA," 1,204 "
-81395281,HC 6949 H058 CHM CELL SEL EA," 1,204 "
-82395281,HC 6949 H058 CHM CELL SEL EA," 1,204 "
-84395281,HC 6949 H058 CHM CELL SEL EA," 1,204 "
-50795299,HC 6949 H060 HLAC2 HI RES DRB1," 1,520 "
-52295299,HC 6949 H060 HLAC2 HI RES DRB1," 1,520 "
-81095299,HC 6949 H060 HLAC2 HI RES DRB1," 1,520 "
-81395299,HC 6949 H060 HLAC2 HI RES DRB1," 1,520 "
-82395299,HC 6949 H060 HLAC2 HI RES DRB1," 1,520 "
-84395299,HC 6949 H060 HLAC2 HI RES DRB1," 1,520 "
-50795307,HC 6949 H061 HLAC2 LOW EA PCRA," 2,170 "
-52295307,HC 6949 H061 HLAC2 LOW EA PCRA," 2,170 "
-81095307,HC 6949 H061 HLAC2 LOW EA PCRA," 2,170 "
-81395307,HC 6949 H061 HLAC2 LOW EA PCRA," 2,170 "
-82395307,HC 6949 H061 HLAC2 LOW EA PCRA," 2,170 "
-84395307,HC 6949 H061 HLAC2 LOW EA PCRA," 2,170 "
-50795315,HC 6949 H062 HLAC2 LOW EA PCRB," 2,170 "
-52295315,HC 6949 H062 HLAC2 LOW EA PCRB," 2,170 "
-81095315,HC 6949 H062 HLAC2 LOW EA PCRB," 2,170 "
-81395315,HC 6949 H062 HLAC2 LOW EA PCRB," 2,170 "
-82395315,HC 6949 H062 HLAC2 LOW EA PCRB," 2,170 "
-84395315,HC 6949 H062 HLAC2 LOW EA PCRB," 2,170 "
-50795322,HC 6949 H066 TECH ONLY 1ST MRK, 113 
-52295322,HC 6949 H066 TECH ONLY 1ST MRK, 113 
-81095322,HC 6949 H066 TECH ONLY 1ST MRK, 113 
-81395322,HC 6949 H066 TECH ONLY 1ST MRK, 113 
-82395322,HC 6949 H066 TECH ONLY 1ST MRK, 113 
-84395322,HC 6949 H066 TECH ONLY 1ST MRK, 113 
-50795331,HC 6949 H066 FLOW ADTL MARKER, 113 
-52295331,HC 6949 H066 FLOW ADTL MARKER, 113 
-81095331,HC 6949 H066 FLOW ADTL MARKER, 113 
-81395331,HC 6949 H066 FLOW ADTL MARKER, 113 
-82395331,HC 6949 H066 FLOW ADTL MARKER, 113 
-84395331,HC 6949 H066 FLOW ADTL MARKER, 113 
-50795348,HC 6949 H066 INTERP 2 TO 8, 112 
-52295348,HC 6949 H066 INTERP 2 TO 8, 112 
-81095348,HC 6949 H066 INTERP 2 TO 8, 112 
-81395348,HC 6949 H066 INTERP 2 TO 8, 112 
-82395348,HC 6949 H066 INTERP 2 TO 8, 112 
-84395348,HC 6949 H066 INTERP 2 TO 8, 112 
-50795356,HC 6949 H067 TECH ONLY 1ST MRK, 113 
-52295356,HC 6949 H067 TECH ONLY 1ST MRK, 113 
-81095356,HC 6949 H067 TECH ONLY 1ST MRK, 113 
-81395356,HC 6949 H067 TECH ONLY 1ST MRK, 113 
-82395356,HC 6949 H067 TECH ONLY 1ST MRK, 113 
-84395356,HC 6949 H067 TECH ONLY 1ST MRK, 113 
-50795364,HC 6949 H067 FLOW ADTL MARKER, 113 
-52295364,HC 6949 H067 FLOW ADTL MARKER, 113 
-81095364,HC 6949 H067 FLOW ADTL MARKER, 113 
-81395364,HC 6949 H067 FLOW ADTL MARKER, 113 
-82395364,HC 6949 H067 FLOW ADTL MARKER, 113 
-84395364,HC 6949 H067 FLOW ADTL MARKER, 113 
-50795372,HC 6949 H067 INTERP 2 TO 8, 112 
-52295372,HC 6949 H067 INTERP 2 TO 8, 112 
-81095372,HC 6949 H067 INTERP 2 TO 8, 112 
-81395372,HC 6949 H067 INTERP 2 TO 8, 112 
-82395372,HC 6949 H067 INTERP 2 TO 8, 112 
-84395372,HC 6949 H067 INTERP 2 TO 8, 112 
-50795381,HC 6949 H068 TECH ONLY 1ST MRK, 113 
-52295381,HC 6949 H068 TECH ONLY 1ST MRK, 113 
-81095381,HC 6949 H068 TECH ONLY 1ST MRK, 113 
-81395381,HC 6949 H068 TECH ONLY 1ST MRK, 113 
-82395381,HC 6949 H068 TECH ONLY 1ST MRK, 113 
-84395381,HC 6949 H068 TECH ONLY 1ST MRK, 113 
-50795398,HC 6949 H068 FLOW ADTL MARKER, 113 
-52295398,HC 6949 H068 FLOW ADTL MARKER, 113 
-81095398,HC 6949 H068 FLOW ADTL MARKER, 113 
-81395398,HC 6949 H068 FLOW ADTL MARKER, 113 
-82395398,HC 6949 H068 FLOW ADTL MARKER, 113 
-84395398,HC 6949 H068 FLOW ADTL MARKER, 113 
-50795406,HC 6949 H068 9 TO 15 MARKERS, 112 
-52295406,HC 6949 H068 9 TO 15 MARKERS, 112 
-81095406,HC 6949 H068 9 TO 15 MARKERS, 112 
-81395406,HC 6949 H068 9 TO 15 MARKERS, 112 
-82395406,HC 6949 H068 9 TO 15 MARKERS, 112 
-84395406,HC 6949 H068 9 TO 15 MARKERS, 112 
-50795414,HC 6949 H086 IMMUNOASSAY NON AB, 723 
-52295414,HC 6949 H086 IMMUNOASSAY NON AB, 723 
-81095414,HC 6949 H086 IMMUNOASSAY NON AB, 723 
-81395414,HC 6949 H086 IMMUNOASSAY NON AB, 723 
-82395414,HC 6949 H086 IMMUNOASSAY NON AB, 723 
-84395414,HC 6949 H086 IMMUNOASSAY NON AB, 723 
-50795597,HC 6949 H086 IMMUNOASSAY NON AB, 726 
-52295597,HC 6949 H086 IMMUNOASSAY NON AB, 726 
-81095597,HC 6949 H086 IMMUNOASSAY NON AB, 726 
-81395597,HC 6949 H086 IMMUNOASSAY NON AB, 726 
-82395597,HC 6949 H086 IMMUNOASSAY NON AB, 726 
-84395597,HC 6949 H086 IMMUNOASSAY NON AB, 726 
-50795421,HC 6949 H087 IMMUNOASSAY NON AB, 635 
-52295421,HC 6949 H087 IMMUNOASSAY NON AB, 635 
-81095421,HC 6949 H087 IMMUNOASSAY NON AB, 635 
-81395421,HC 6949 H087 IMMUNOASSAY NON AB, 635 
-82395421,HC 6949 H087 IMMUNOASSAY NON AB, 635 
-84395421,HC 6949 H087 IMMUNOASSAY NON AB, 635 
-50795646,HC 6949 H087 IMMUNOASSAY NON AB, 641 
-52295646,HC 6949 H087 IMMUNOASSAY NON AB, 641 
-81095646,HC 6949 H087 IMMUNOASSAY NON AB, 641 
-81395646,HC 6949 H087 IMMUNOASSAY NON AB, 641 
-82395646,HC 6949 H087 IMMUNOASSAY NON AB, 641 
-84395646,HC 6949 H087 IMMUNOASSAY NON AB, 641 
-50795431,HC 6949 H088 LYMPH TITRAT, 247 
-52295431,HC 6949 H088 LYMPH TITRAT, 247 
-81095431,HC 6949 H088 LYMPH TITRAT, 247 
-81395431,HC 6949 H088 LYMPH TITRAT, 247 
-82395431,HC 6949 H088 LYMPH TITRAT, 247 
-84395431,HC 6949 H088 LYMPH TITRAT, 247 
-50795448,HC 6949 H089 LYMPH TITRAT, 247 
-52295448,HC 6949 H089 LYMPH TITRAT, 247 
-81095448,HC 6949 H089 LYMPH TITRAT, 247 
-81395448,HC 6949 H089 LYMPH TITRAT, 247 
-82395448,HC 6949 H089 LYMPH TITRAT, 247 
-84395448,HC 6949 H089 LYMPH TITRAT, 247 
-50795456,HC 6949 H090 LYMPH TITRAT, 247 
-52295456,HC 6949 H090 LYMPH TITRAT, 247 
-81095456,HC 6949 H090 LYMPH TITRAT, 247 
-81395456,HC 6949 H090 LYMPH TITRAT, 247 
-82395456,HC 6949 H090 LYMPH TITRAT, 247 
-84395456,HC 6949 H090 LYMPH TITRAT, 247 
-50795464,HC 6949 H091 LYMPH TITRAT, 247 
-52295464,HC 6949 H091 LYMPH TITRAT, 247 
-81095464,HC 6949 H091 LYMPH TITRAT, 247 
-81395464,HC 6949 H091 LYMPH TITRAT, 247 
-82395464,HC 6949 H091 LYMPH TITRAT, 247 
-84395464,HC 6949 H091 LYMPH TITRAT, 247 
-50795472,HC 6949 H104 IMMUNOASSAY NON AB, 723 
-52295472,HC 6949 H104 IMMUNOASSAY NON AB, 723 
-81095472,HC 6949 H104 IMMUNOASSAY NON AB, 723 
-81395472,HC 6949 H104 IMMUNOASSAY NON AB, 723 
-82395472,HC 6949 H104 IMMUNOASSAY NON AB, 723 
-84395472,HC 6949 H104 IMMUNOASSAY NON AB, 723 
-50795605,HC 6949 H104 IMMUNOASSAY NON AB, 726 
-52295605,HC 6949 H104 IMMUNOASSAY NON AB, 726 
-81095605,HC 6949 H104 IMMUNOASSAY NON AB, 726 
-81395605,HC 6949 H104 IMMUNOASSAY NON AB, 726 
-82395605,HC 6949 H104 IMMUNOASSAY NON AB, 726 
-84395605,HC 6949 H104 IMMUNOASSAY NON AB, 726 
-50795481,HC 6949 H105 IMMUNOASSAY NON AB, 635 
-52295481,HC 6949 H105 IMMUNOASSAY NON AB, 635 
-81095481,HC 6949 H105 IMMUNOASSAY NON AB, 635 
-81395481,HC 6949 H105 IMMUNOASSAY NON AB, 635 
-82395481,HC 6949 H105 IMMUNOASSAY NON AB, 635 
-84395481,HC 6949 H105 IMMUNOASSAY NON AB, 635 
-50795654,HC 6949 H105 IMMUNOASSAY NON AB, 641 
-52295654,HC 6949 H105 IMMUNOASSAY NON AB, 641 
-81095654,HC 6949 H105 IMMUNOASSAY NON AB, 641 
-81395654,HC 6949 H105 IMMUNOASSAY NON AB, 641 
-82395654,HC 6949 H105 IMMUNOASSAY NON AB, 641 
-84395654,HC 6949 H105 IMMUNOASSAY NON AB, 641 
-50795498,HC 6949 H110 IMMUNOASSAY NON AB, 723 
-52295498,HC 6949 H110 IMMUNOASSAY NON AB, 723 
-81095498,HC 6949 H110 IMMUNOASSAY NON AB, 723 
-81395498,HC 6949 H110 IMMUNOASSAY NON AB, 723 
-82395498,HC 6949 H110 IMMUNOASSAY NON AB, 723 
-84395498,HC 6949 H110 IMMUNOASSAY NON AB, 723 
-50795612,HC 6949 H110 IMMUNOASSAY NON AB, 726 
-52295612,HC 6949 H110 IMMUNOASSAY NON AB, 726 
-81095612,HC 6949 H110 IMMUNOASSAY NON AB, 726 
-81395612,HC 6949 H110 IMMUNOASSAY NON AB, 726 
-82395612,HC 6949 H110 IMMUNOASSAY NON AB, 726 
-84395612,HC 6949 H110 IMMUNOASSAY NON AB, 726 
-50795506,HC 6949 H111 IMMUNOASSAY NON AB, 635 
-52295506,HC 6949 H111 IMMUNOASSAY NON AB, 635 
-81095506,HC 6949 H111 IMMUNOASSAY NON AB, 635 
-81395506,HC 6949 H111 IMMUNOASSAY NON AB, 635 
-82395506,HC 6949 H111 IMMUNOASSAY NON AB, 635 
-84395506,HC 6949 H111 IMMUNOASSAY NON AB, 635 
-50795662,HC 6949 H111 IMMUNOASSAY NON AB, 641 
-52295662,HC 6949 H111 IMMUNOASSAY NON AB, 641 
-81095662,HC 6949 H111 IMMUNOASSAY NON AB, 641 
-81395662,HC 6949 H111 IMMUNOASSAY NON AB, 641 
-82395662,HC 6949 H111 IMMUNOASSAY NON AB, 641 
-84395662,HC 6949 H111 IMMUNOASSAY NON AB, 641 
-50795513,HC 6949 H112 IMMUNOASSAY NON AB, 723 
-52295513,HC 6949 H112 IMMUNOASSAY NON AB, 723 
-81095513,HC 6949 H112 IMMUNOASSAY NON AB, 723 
-81395513,HC 6949 H112 IMMUNOASSAY NON AB, 723 
-82395513,HC 6949 H112 IMMUNOASSAY NON AB, 723 
-84395513,HC 6949 H112 IMMUNOASSAY NON AB, 723 
-50795621,HC 6949 H112 IMMUNOASSAY NON AB, 726 
-52295621,HC 6949 H112 IMMUNOASSAY NON AB, 726 
-81095621,HC 6949 H112 IMMUNOASSAY NON AB, 726 
-81395621,HC 6949 H112 IMMUNOASSAY NON AB, 726 
-82395621,HC 6949 H112 IMMUNOASSAY NON AB, 726 
-84395621,HC 6949 H112 IMMUNOASSAY NON AB, 726 
-50795521,HC 6949 H113 IMMUNOASSAY NON AB, 635 
-52295521,HC 6949 H113 IMMUNOASSAY NON AB, 635 
-81095521,HC 6949 H113 IMMUNOASSAY NON AB, 635 
-81395521,HC 6949 H113 IMMUNOASSAY NON AB, 635 
-82395521,HC 6949 H113 IMMUNOASSAY NON AB, 635 
-84395521,HC 6949 H113 IMMUNOASSAY NON AB, 635 
-50795671,HC 6949 H113 IMMUNOASSAY NON AB, 641 
-52295671,HC 6949 H113 IMMUNOASSAY NON AB, 641 
-81095671,HC 6949 H113 IMMUNOASSAY NON AB, 641 
-81395671,HC 6949 H113 IMMUNOASSAY NON AB, 641 
-82395671,HC 6949 H113 IMMUNOASSAY NON AB, 641 
-84395671,HC 6949 H113 IMMUNOASSAY NON AB, 641 
-50795539,HC 6949 H119 IMMUNOASSAY NON AB, 723 
-52295539,HC 6949 H119 IMMUNOASSAY NON AB, 723 
-81095539,HC 6949 H119 IMMUNOASSAY NON AB, 723 
-81395539,HC 6949 H119 IMMUNOASSAY NON AB, 723 
-82395539,HC 6949 H119 IMMUNOASSAY NON AB, 723 
-84395539,HC 6949 H119 IMMUNOASSAY NON AB, 723 
-50795638,HC 6949 H119 IMMUNOASSAY NON AB, 726 
-52295638,HC 6949 H119 IMMUNOASSAY NON AB, 726 
-81095638,HC 6949 H119 IMMUNOASSAY NON AB, 726 
-81395638,HC 6949 H119 IMMUNOASSAY NON AB, 726 
-82395638,HC 6949 H119 IMMUNOASSAY NON AB, 726 
-84395638,HC 6949 H119 IMMUNOASSAY NON AB, 726 
-50795547,HC 6949 H120 IMMUNOASSAY NON AB, 635 
-52295547,HC 6949 H120 IMMUNOASSAY NON AB, 635 
-81095547,HC 6949 H120 IMMUNOASSAY NON AB, 635 
-81395547,HC 6949 H120 IMMUNOASSAY NON AB, 635 
-82395547,HC 6949 H120 IMMUNOASSAY NON AB, 635 
-84395547,HC 6949 H120 IMMUNOASSAY NON AB, 635 
-50795696,HC 6949 H120 IMMUNOASSAY NON AB, 641 
-52295696,HC 6949 H120 IMMUNOASSAY NON AB, 641 
-81095696,HC 6949 H120 IMMUNOASSAY NON AB, 641 
-81395696,HC 6949 H120 IMMUNOASSAY NON AB, 641 
-82395696,HC 6949 H120 IMMUNOASSAY NON AB, 641 
-84395696,HC 6949 H120 IMMUNOASSAY NON AB, 641 
-50795555,HC 6949 H121 IMMUNOASSAY NON AB, 635 
-52295555,HC 6949 H121 IMMUNOASSAY NON AB, 635 
-81095555,HC 6949 H121 IMMUNOASSAY NON AB, 635 
-81395555,HC 6949 H121 IMMUNOASSAY NON AB, 635 
-82395555,HC 6949 H121 IMMUNOASSAY NON AB, 635 
-84395555,HC 6949 H121 IMMUNOASSAY NON AB, 635 
-50795688,HC 6949 H121 IMMUNOASSAY NON AB, 641 
-52295688,HC 6949 H121 IMMUNOASSAY NON AB, 641 
-81095688,HC 6949 H121 IMMUNOASSAY NON AB, 641 
-81395688,HC 6949 H121 IMMUNOASSAY NON AB, 641 
-82395688,HC 6949 H121 IMMUNOASSAY NON AB, 641 
-84395688,HC 6949 H121 IMMUNOASSAY NON AB, 641 
-50795563,HC 6949 H129 CHIM ANAL NO CELL," 2,407 "
-52295563,HC 6949 H129 CHIM ANAL NO CELL," 2,407 "
-81095563,HC 6949 H129 CHIM ANAL NO CELL," 2,407 "
-81395563,HC 6949 H129 CHIM ANAL NO CELL," 2,407 "
-82395563,HC 6949 H129 CHIM ANAL NO CELL," 2,407 "
-84395563,HC 6949 H129 CHIM ANAL NO CELL," 2,407 "
-50795571,HC 6949 H134 STR PT AND COMP, 149 
-52295571,HC 6949 H134 STR PT AND COMP, 149 
-81095571,HC 6949 H134 STR PT AND COMP, 149 
-81395571,HC 6949 H134 STR PT AND COMP, 149 
-82395571,HC 6949 H134 STR PT AND COMP, 149 
-84395571,HC 6949 H134 STR PT AND COMP, 149 
-50790071,HC 6942 12866R B HENSELAE IGG, 14 
-50790121,HC6942 12867R B QUINTANA IGG, 14 
-50790153,HC 6942 12868R B HENSELAE IGM, 14 
-50790229,HC6942 12869R B QUINTANA IGM, 14 
-40905382,HC US: ELASTROGRAPHY, 106 
-41898838,HC IMPL STNT WLLFLXCOLONIC 30X25X9X135," 17,963 "
-41898839,HC IMPL GUIDW ROADRUNNER 0.35X180, 9 
-41898840,HC IMPL STNT GFT GORE 16X4X7," 24,500 "
-41898841,HC IMPL STNT GFT GORE 23X14X10," 45,500 "
-41898842,HC IMPL STNF GFT ENDO ZENITH FLX 28X58," 14,573 "
-51565687,HC MYCOPENOLIC ACID, 246 
-51565695,HC LAMOL QNT DRG NES, 181 
-51565703,HC  LEVETL QNT DRG NES, 181 
-58900119,HC SPECIMEN PROCESSING, 77 
-41898843,HC IMPL FLTR VENA CAVA OPTION 6F," 8,625 "
-41898844,HC IMPL GUIDW PRIMEWIRE 185CM," 5,908 "
-81810282,HC HM II PWR MODULE BATTERY, 765 
-81801779,HC HM II CONSL BAG RT," 2,763 "
-62028674,HC URETHROGRAM RETRO," 1,638 "
-62028675,HC ANTEGRADE PYELOGRM," 1,036 "
-50790261,HC 6957 11013R AB ID PLATELETS, 217 
-41100012,HC REPAIR INTERMED SCALP <2.6CM," 1,920 "
-30042566,HC IMPL CAGE X-CORE 2 16X16-22MM," 62,070 "
-30042567,HC IMPL ENDCAP X-CORE 2 16MM," 13,763 "
-30042568,HC IMPL CAGE AVS UNILIF 13X25X0X11," 38,587 "
-30042569,HC IMPL CAGE UNILIF 11X25X88X0'," 38,587 "
-30042570,HC IMPL SCREW XIA POLYAXIAL 7.0 X 55MM," 7,863 "
-30042571,HC IMPL SCREW SPINE 7.0 X 45MM," 8,843 "
-30042573,HC IMPL SCREW LOCKING 2.0 X 6MM," 1,097 "
-30042574,HC IMPL SCREW LOCKING 2.0 X 8MM," 1,716 "
-30042575,HC IMPL PLATE 2.0 Y-PLATE 247.350," 4,197 "
-30042576,HC IMPL SPACER SPINAL SYNFIX 30X38X12X8\'," 55,200 "
-30042577,HC IMPL PLATE HUMERUS 3.5MM X 90MM," 13,039 "
-30042578,HC IMPL NAIL TROCH CANN 12MM X 380MM," 15,071 "
-30042579,HC IMPL PLATE LCP 2.0 X 31MM 4HOLE," 2,371 "
-30042580,HC IMPL SCREW CORTEX 2.0 X 24MM, 341 
-30042581,HC IMPL SPIRAL BLADE FOR HUMERAL NAIL," 5,576 "
-30042582,HC IMPL K-WIRE 2.5MM W/TROCAR POINT," 1,056 "
-30042583,HC IMPL ROD HARD 6.0MM X 150MM," 2,136 "
-30042584,HC IMPL NAIL TIBIAL CANN 10MM X 345MM," 12,359 "
-30042585,HC IMPL SCREW LOCKING 5.0 X 40MM," 1,927 "
-30042586,HC IMPLSCREW LOCKING 5.0 X 50MM," 1,927 "
-30042587,HC IMPL SCREW LOCKING 5.0 X 65MM," 1,927 "
-30042588,HC IMPL SCREW POLYAXIAL 5.5MM X 45MM," 9,844 "
-30042589,HC IMPL NAIL TIBIAL CANN 10MM X 300MM," 12,359 "
-30042590,HC IMPL SCREW LOCKING 5.0MM X 45MM," 1,927 "
-30042591,HC IMPL SCREW LOCKING 5.0MM X 70MM," 1,927 "
-30603811,HC IMPL ANCHOR SUT GASTRO 17GA 12CM 75CM SET, 743 
-30605563,HC IMPL CLIP SYSTEM 12/6GC OVESCO," 4,582 "
-41898845,HC IMPL BLLN CTH DILAT STERLING 9X20," 3,412 "
-41898846,HC IMPL BLLN CTH ATLAS GOLD OTW 14X6," 2,933 "
-41898847,HC IMPL BLLN CTH ATLAS GOLD OTW 12X6," 2,933 "
-41898848,HC IMPL BLLN CTH ATLAS GOLD OTW 14X4," 2,933 "
-41898849,HC IMPLBLLN CTH ATLAS GOLD OTW 12X4," 2,933 "
-41898850,HC IMPL DIFIB QUADRA ASSURA CRDIAC DEV," 84,000 "
-41898851,HC IMPL CTH BLLN DILAT BIB 7X1.5," 6,375 "
-41898852,HC IMPL GRFT OVATN PRM 13X14X14X100," 28,350 "
-41898853,HC IMPL STNT BILI PROTEGE GPS 14X60X80," 10,140 "
-30042593,HC IMPL ROD TEMPLATE DEPUY/SNTHES," 1,700 "
-32051770,HC APPLY SHORT ARM CAST, 795 
-38204806,HC CHEST 2V W/APICAL, 953 
-41304805,HC CHEST 2V W/APICAL, 953 
-41604802,HC CHEST 2V W/APICAL, 953 
-41898854,HC IMPL CTH BLLN THERMODILUTION 7X110," 1,304 "
-94905387,HC US: ELASTROGRAPHY, 106 
-41898855,HC IMPL PORT VORTEX 6.6F," 7,800 "
-41898856,HC IMPL BLLN CTH DILAT STERLING 8X20," 3,215 "
-62028647,HC SYNVISC PER 1MG (16MG/2ML DOSE), 48 
-50673037,HC RES PROBE MOL CYTOG EA, 94 
-50673045,HC RES CHRM ISH MAR 3-5 CELL, 65 
-50673052,HC RES CHRM ISH NOS 10-30 CELL, 78 
-50673060,HC RES INTPH ISH  100-300 CELL, 97 
-50673078,HC RES ISH ADD MRPHMET MAN, 304 
-50673086,HC RES TIS CULT ROUT BLD NONNE, 220 
-50673094,HC RES TIS CULT SKIN/TISS KAR, 339 
-50673102,HC RES TIS CULT BONE MRW NEO, 305 
-50673110,HC RES CHRM 15-20 CELLS 2KAR, 220 
-50673128,HC RES CHRM ANAL 20-25 CELLS, 300 
-50673136,HC RES CHRM ANAL ADD KARYOTYPE, 65 
-50673144,HC RES CRYOPRES FRZ/STORE EA, 43 
-50673151,HC RES CRYOPRES THAW EA LINE, 8 
-50673169,HC RES CHROM ANAL NON-HUMAN, 600 
-50673177,HC RES CULT HAR SLIDE PREP, 100 
-50673185,HC RES IN STU HYB NO ANALY, 50 
-50673193,HC RES IN STU HYB QUAL ANALY, 80 
-26191347,HC E1 MED/SURG W," 8,397 "
-30042594,HC IMPL NAIL FEMORAL 11MM X 360MM," 14,110 "
-30042595,HC IMPL SCREW LOCKING 5.0 X 72MM," 1,709 "
-30042597,HC IMPL ORACLE SPACER 55MMX22MMX15MM," 36,582 "
-30042600,HC IMPL PLATE DISTAL TIBIA 3.5MM LCP," 13,327 "
-32500600,HC ART CATHTRZATION PERC," 3,241 "
-32500601,HC INSERT NON-TUNNEL CVC >5YRS," 7,725 "
-38706180,HC IMPL PEG CORICIAL 2.3 X 20MM, 663 
-38706188,HC IMPL SCREW HEADLESS 4.3 X 46MM," 2,695 "
-38706196,HC IMPL SCREW TRILOCK 2.0X9MM," 1,010 "
-38706204,"HC IMPL WIRE GUIDE 0.62 X 6""", 94 
-38706236,HC IMPL SCREW HEADLESS 4.3 X 6.0," 2,695 "
-23290233,HC FGR IMC ICU," 15,479 "
-30042596,HC IMPL ROD CURVED 5.5MM X 100MM," 2,562 "
-30042598,HC IMPL SCREW METAPHYSEAL 2.7MM X 50MM, 446 
-30042599,HC IMPL PLATE FEMUR HOOKLCP  4.5MM," 1,852 "
-30042601,HC IMPL MINIPLATE DOUBLE BEND 2.0," 6,657 "
-30042602,HC IMPL SCREW CORTEX ST 2.0MM X 4MM, 436 
-30042604,HC IMPL NAIL FEMORAL 10MM X 340MM," 14,110 "
-30042606,HC IMPL SCREW CORTEX ST 3.5MM X 28MM, 293 
-30042607,HC IMPL SCREW CORTEX ST 3.5MM X 30MM, 293 
-30042608,HC IMPL SCREW CORTEX ST 3.5MM X 32MM, 293 
-30042609,HC IMPL SCREW CORTEX ST 3.5MM X 42MM, 293 
-30042610,HC IMPL SCREW LOCKING VAR ANGLE 3.5MM," 1,466 "
-30042611,HC IMPL SCREW LOCKING VAR ANGLE 3.5MM," 1,466 "
-30042612,HC IMPL PLATE DISTAL HUMERUS 3.5MM," 14,769 "
-30042613,HC IMPL SCREW REDUCTION POLY 4.5 X 30," 7,013 "
-30042614,HC IMPL TRANSVERSE LINK 40/53 AEGIS," 7,225 "
-30042615,HC IMPL CAGE ALIF PEEK 29X38X8X16 AEGI," 33,600 "
-30042616,HC IMPL SCREW POLY AXIAL 6.5 X 45MM," 7,013 "
-30042617,HC IMPL SCREW POLY REDUCTION 7.5 X 45," 7,013 "
-30042618,HC IMPL SCREW POLY REDUCTION 7.5 X 55," 7,013 "
-30042619,HC IMPL SCREW STRYKER 4.0 X 18MM," 8,075 "
-30042621,HC IMPL NAIL FEMORAL 11MM X 360MM," 15,992 "
-30042622,HC IMPL SCREW LOCKING 5.0 X 72MM," 1,709 "
-30042623,HC IMPL PLATE FEMUR HOOK 4.5MM LCP," 14,825 "
-30042605,HC IMPL PLATE DISTAL TIBIA 2.7/3.5MM," 13,931 "
-38706212,HC IMPL SCREW BONE CORTICAL 2 X 10 MM," 1,010 "
-30042625,HC IMPL SCREW LOCKING VAR ANGLE 3.5MM," 1,466 "
-30042627,HC IMPL PLATE DISTAL TIBIA 3.5MM LCP," 13,327 "
-30042628,HC IMPL LEAD INTRAMUSCULAR ENTERRA," 16,500 "
-30605432,HC IMPL CATH ABLATION BARRX RFA ENDO," 11,250 "
-38706220,HC IMPL SCREW 2.0X11MM," 1,010 "
-38706244,HC IMPL SCREW 2.0X11MM, 628 
-38706252,HC IMPL SCREW 2.0X12MM, 628 
-41898858,HC IMPL CATH GDNG ATTAIN SUREVLV 45CM," 1,632 "
-62028573,"HC LORAZEPAM 2 MG/ML,1 ML", 122 
-42018002,"HC WIRE LOCALIZATION, BREAST"," 3,466 "
-42018010,"HC WIRE LOCALIZATION, SPINE"," 3,466 "
-30042629,HC IMPL NEUROSTIMULATOR GASTRIC ENTERA," 54,925 "
-30042630,HC IMPL SCREW MAS 8.5 X 40MM," 6,163 "
-30042631,HC IMPL SCREW MEDTRONIC 4.5 X 40MM," 7,863 "
-30042632,HC IMPL OFFSET LONG MEDTRONIC," 7,655 "
-30042633,HC IMPL ROD JJ 3.5MM X 80MM," 2,550 "
-30042634,HC IMPL SCREW CORTICAL FIX 4.0 X 30MM," 11,385 "
-38706284,HC IMPL SCREW 2.0X13MM," 1,010 "
-41898859,HC IMPL BLLN CATH ATTAIN 6X10X80, 850 
-41898860,HC IMPL CATH ATTAIN DLVRY SYS 65X130," 1,700 "
-30042635,HC IMPL CAGE COROENT 8X10X30MM 5'," 28,000 "
-30042636,HC IMPL SCREW HELIX ST 4.0 X 13MM," 2,338 "
-30250562,HC IMPL PLATE VOLR LFT FIX ANGL3HL-7PG," 5,525 "
-30250563,HC IMPL PLATE VOLR LFT FIX ANGL5HL-7PG," 6,375 "
-30250564,HC IMPL PLATE VOLR RT FIX ANGL5HL-7PG," 6,375 "
-30250565,HC IMPL PLATE RADIAL LFT 10PEG," 9,300 "
-30250566,HC IMPL PLATE RADIAL LFT 7PEG," 7,905 "
-30250567,HC IMPL PLATE RADIAL RT 10PEG," 9,300 "
-30250568,HC IMPL PLATE RADIAL RT 7PEG," 7,905 "
-30250569,HC IMPL PLATE VOLR BEARING LT 11HL-7PG," 14,963 "
-30250570,HC IMPL PLATE VOLR BEARING LT 5HL-7PG," 8,513 "
-41898864,HC IMPL STNT GFT ZENITH FLX AAA 14X30," 14,145 "
-41898865,HC IMPL STNT GFT ZENITH FLX AAA 16X30," 14,145 "
-41898866,HC IMPL DELV SYST RETROFLEX3 26MM," 81,250 "
-41179586,HC FLUID COLLECT SFT TISSUE PERC W/IMAGE, 670 
-50711209,HC 6952 13020R SLIDE REVIEW, 73 
-30250571,HC IMPL PLATE VOLR BEARING LT 9HL-7PG," 12,188 "
-30250572,HC IMPL PLATE VOLR BEARING RT 11HL-7PG," 14,963 "
-30250573,HC IMPL PLATE VOLR BEARING RT 3HL-7PG," 7,763 "
-30250574,HC IMPL PLATE VOLR BEARING RT 5HL-7PG," 8,513 "
-30250575,HC IMPL PLATE VOLR BEARING RT 7HL-7PG," 9,338 "
-30250576,HC IMPL PLATE VOLR BEARING RT 9HL-7PG," 12,188 "
-30250577,HC IMPL PLATE WIRE FORM 3HL," 3,485 "
-30250578,HC IMPL PLATE WIRE FORM 5HL," 3,570 "
-30250579,HC IMPL SCREW CORT HEX 3.2X11MM, 706 
-30250580,HC IMPL SCREW CORT HEX 3.2X13MM, 706 
-30250581,HC IMPL SCREW CORT HEX 3.2X15MM, 706 
-30250598,HC IMPL PLATE TUBULR LKNG 6HL," 2,040 "
-30250601,HC IMPL SCREW CORT LOW-PROFIL 3.5X12MM, 278 
-30250602,HC IMPL SCREW CORT LOW-PROFIL 3.5X12MM, 298 
-30250603,HC IMPL SCREW CORT LOW-PROFIL 3.5X24MM, 298 
-30250604,HC IMPL SCREW CANCLUS LO-PROFIL 4X14MM, 298 
-30250605,HC IMPL SCREW CANCLUS LO-PROFIL 4X16MM, 298 
-30250606,HC IMPL SCRW LO-PROFL SHRT THRED 4X50M, 298 
-30250607,HC IMPL SCRW LOW-PROFL SRT THRED 4X46M, 298 
-30250608,HC IMPL TALUS ANKLE COMPONENT LFT XS," 39,722 "
-30250611,HC IMPL TALUS COMPONENT ANKLE LFT MED," 39,722 "
-30250612,HC IMPL SCREW NON-LOK T8 3.5X16MM," 1,615 "
-30250613,HC IMPL PLATE HTO LFT 4HL 11.25MM," 9,450 "
-30250615,HC IMPL STEM TIB TOP ANKLE INBONE 14MM," 5,279 "
-30250616,HC IMPL TIBIAL TRAY ANKLE RT 3LONG," 13,133 "
-30250617,HC IMPL TALAR DOME ANKLE INBONE SIZE 2," 3,119 "
-30250618,HC IMPL TIBL STEM MID ANKL INBONE 14MM," 5,279 "
-30250619,HC IMPL TIB STEM BASE ANKL INBONE 16MM," 5,746 "
-30250620,HC IMPL TALAR STEM ANKLE INBONE 10MM," 9,555 "
-30250621,HC IMPL POLY INSRT ANKL INBONE SIZE 2+," 13,463 "
-30250622,HC IMPL PIN STEINMAN 2.4, 230 
-30637810,HC IMPL SNARE OVAL CAPTIFLEX SMALL," 1,105 "
-30637820,HC IMPL SNARE HEX CAPTIVATOR SMALL," 1,275 "
-30250582,HC IMPL K-WIRE OLIVE 1.6X100MM, 680 
-30250597,HC IMPL PUMP PENILE 15CMX12MM," 53,898 "
-30250609,HC IMPL POLY INSERT ANKLE 8MM," 14,603 "
-30250610,HC IMPL TALUS COMPONENT ANKLE LFT SM," 39,722 "
-30637800,HC IMPL TUBE FEEDING LOW PROFILE 18X6," 1,320 "
-41898867,HC IMPL NDL ACCUSTICK 21GA, 83 
-41898641,HC IMPL GDE SHTH SAFT SHEATH CSG 9 X 50," 3,273 "
-41898861,HC IMPL BLLN CATH RAPIDCROSS 2.5X150," 3,919 "
-41898862,HC IMPL BLLN CATH RAPIDCROSS 2X150," 3,919 "
-41898863,HC IMPL GUIDW NITREX 0.14X300, 935 
-51565711,HC HAV ANITBODY IGG, 231 
-32713018,HC OFFICE OUTPATIENT VISIT EST, 160 
-23630104,HC H1 IV INFUSION-HYDRATION-1ST HR," 1,035 "
-23630105,HC H1 IV INFUSION-HYDRA-EA ADDTL HR, 607 
-23630106,HC H1 IMMUNIZATION ADMIN SINGLE, 77 
-23630108,HC H1 IZ ADMIN EACH ADTL VACCINE, 135 
-23630109,HC H1 IMMUNIZATION ADMIN SINGLE, 77 
-23630110,HC H1 INJ OR IV THRP/PROPH/DX UNLIST, 256 
-23630111,HC H1 IVP THRP/PROPH SAME DRUG, 405 
-23630112,HC H1 IVP THRP/PROPH/DX NEW DRUG, 400 
-23630113,HC H1 INJ THER/PROPH/DX INTRA ART, 125 
-23630114,HC H1 IV INF THER ADDTL SEQ NEW DRUG, 796 
-23630115,HC H1 THRP INF CONCURRENT, 821 
-23630107,HC H1 PNEUMOCOCCAL VACCINE ADMIN, 295 
-38106027,HC GADAVIST PER 0.1 ML (NDC 50419-325-12), 4 
-38506028,HC GADAVIST PER 0.1 ML (NDC 50419-325-12), 4 
-94206026,HC GADAVIST PER 0.1 ML (NDC 50419-325-12), 4 
-95006029,HC GADAVIST PER 0.1 ML (NDC 50419-325-12), 4 
-32020143,HC INJ SPINE C/T SGL," 3,808 "
-32500310,"HC LARYNGOSCOPY, FIBEROP DX"," 1,320 "
-50711233,HC 6956 13032R LACTO FECAL  ELISA, 38 
-50711225,HC 6952 13024R KIT MUT EXON 13, 766 
-50808632,HC NCKIT KIT EXONS 8&17," 1,223 "
-51565729,HC HIV-1 ANTIGEN W/HIV 1-2 AB, 331 
-62095962,HC TOE(S) MIN 2V, 161 
-62095953,HC WRIST AP + LAT, 161 
-62095954,HC WRIST MIN 3V, 161 
-62095952,HC ELBOW MIN 3V, 161 
-62095955,HC HAND 2 VIEW, 161 
-62095956,HC HAND MIN 3 VIEWS, 161 
-62095957,HC FINGER(S) MIN 2V, 161 
-62095958,HC ANKLE AP + LAT, 161 
-62095959,HC ANKLE MIN 3V, 161 
-62095960,HC FOOT AP + LAT, 161 
-62095961,HC FOOT MIN 3V, 161 
-62095951,HC ELBOW AP + LAT, 161 
-50470202,HC CORTISOL TOTAL, 374 
-51664811,HC RP RENAL FUNCTION PANEL, 170 
-62028934,HC NCXPT EVAL ADDL 15MIN, 172 
-38706268,HC IMPL SCREW MINI ACUTRAK 28.0MM," 3,451 "
-50711191,HC 6952 12804R KIT MUT EXON 17, 766 
-50711183,HC 6942 12868R B HENSELAE IGM, 14 
-50711175,HC 6942 12869R B QUINTANA IGM, 14 
-51600401,HC ALK PHOS TOTAL, 155 
-51600708,"HC BILIRUBIN, TOTAL", 150 
-51601185,HC HDL CHOLESTEROL, 192 
-51601201,HC CHLORIDE SERUM/PLA, 129 
-51603405,"HC PROTEIN, TOTAL SERUM/PLASMA", 75 
-51603553,HC SODIUM, 219 
-51670065,HC CARBON DIOXIDE, 95 
-40105322,HC BIOPSY SITE IDENTIFIER, 638 
-40105330,"HC ASPIRATION BIOPSY MRI, TITANIUM"," 1,105 "
-81821956,HC HM I BATTERY HOLSTER(2)," 3,358 "
-81821960,HC HM II STABLZ SMALL, 850 
-81821962,HC HM II BATTERY HOLSTER(2)," 3,358 "
-51663458,HC METABOLIC PNL COMP, 435 
-62095963,HC EMBR STRND ADHESIVE SIL DRSG 16CM, 25 
-62095964,HC EMBR STRND ADHESIVE SIL DRSG 06CM, 25 
-32022334,HC NG TUBE BY PHYS. INCLUDE FLUORO, 483 
-91600906,HC COS: BLEPHAROPLSTY UPPER/LOWER," 2,793 "
-31180001,HC URINE MICRO NONAUTO, 196 
-31180002,HC POC URINE DIPSTICK, 55 
-31180005,HC POC URINE PREGNANCY, 55 
-31180006,HC POC FOBT, 130 
-31180009,HC POC PH MEASUREMENT, 50 
-31180011,HC POC HEMOGLOBIN, 48 
-31180013,HC POC TRICHOPHYTON SKIN TEST, 79 
-31180014,HC POC CANDIDA SKIN TEST, 79 
-31180015,HC POC KOH WET PREP, 270 
-31180016,HC POC KOH SLIDE, 79 
-50711282,HC 6949 12854R DNR WNV NAT, 155 
-50711291,HC 6949 12856R DNR T CRUZI AB, 41 
-91600907,HC COS: BROWLIFT," 3,104 "
-91600909,HC COS: BLEPHAROPLSTY LOWER," 1,575 "
-91600910,HC COS: BLEPHAROPLSTY UPPER," 1,575 "
-40105348,HC DISPOSABLE CUFF, 165 
-40105363,"HC TEMP PROBE, DISPOSABLE", 375 
-40105355,HC BIOPSY GUN," 1,105 "
-51601805,HC USPG SPECIFIC GRAVITY, 115 
-81801366,HC HM SYSTEM MONITOR II," 61,750 "
-81801377,HC HM CONSL BAG BEIGE," 2,763 "
-81802120,HC HM MOD TO PWR MODL CBL," 2,550 "
-81802150,HC HM II DISPLAY MODULE," 19,600 "
-30092751,HC LAP REM ADNEXAL STRUCT," 12,500 "
-30092752,HC LAP OVARY UNLSTD PROC," 17,500 "
-50711258,HC 6949 12858R DNR HBV PROBE, 105 
-50711266,HC 6949 12858R DNR HCV PROB, 105 
-50711274,HC 6949 12858R DNR NAT HIV, 105 
-50808640,HC CALR CALRETICULIN ASSAY," 2,195 "
-19160264,HC NITINOL COIL FIDUCIAL MRKR," 1,341 "
-41156437,HC CVD CUFF REMOVAL," 1,343 "
-32022445,HC INJ ANES NERVE FEMORAL SGL," 1,486 "
-62028935,HC PT NEUROMUSC RE-ED EA 15, 311 
-62028936,HC PT MANUAL THERAPY TECH 15, 249 
-62024343,HC LACTULOSE BREATH TEST, 927 
-62027945,HC USTEKINUMAB PER MG (45 MG), 315 
-62027946,HC USTEKINUMAB PER MG (90 MG), 315 
-32028103,HC STRAPPIN; ANKLE&/FOOT, 219 
-58033389,HC VISUAL EVOKED RES, 760 
-58033390,HC TRANSCRN DOPPLER CMPLTE," 2,485 "
-58033391,HC TRANSCRN DOPPLER LMTD," 1,596 "
-58033382,HC ELCTRD DEPTH 10 CONTACT," 1,032 "
-58033383,HC ELCTRD DEPTH 12 CONTACT," 1,143 "
-58033384,HC ELCTRD DEPTH 14 CONTACT," 1,200 "
-58033380,HC ELCTRD GRID 2X2X8 32CNTCT 2TLS," 4,191 "
-58033378,HC ELCTRD GRID 2X8 16 CONTACT PLAT," 1,965 "
-58033379,HC ELCTRD GRID 4X6 24 CONTACT," 2,601 "
-58033381,HC ELCTRD GRID 8X6 48 CONTACTS," 3,255 "
-58033374,HC ELCTRD PLATINUM CURVED STRIP," 1,827 "
-58033375,HC ELCTRD PLATINUM T-SHAPED," 1,695 "
-58033376,HC ELCTRD STRIP PLATINM 10CONTCT," 1,827 "
-58033377,HC ELCTRD STRIP PLATINM 14CONTCT," 1,200 "
-58033385,HC EMG 4 EXTREMITIES," 1,480 "
-58033386,HC NM JITTER TEST, 616 
-58033388,HC S BLNK RFLX TEST IN, 323 
-58033387,HC S REPETITIVE STIM TS, 544 
-58033392,HC ELCTRD STRIP 12 CONTACT (DUAL SIDE)," 2,784 "
-42123331,HC PANORAMIC XRAY, 320 
-31950174,HC OP VISIT LEVEL 2-EST, 361 
-62095965,HC FOSCARNET INJ 2.4MG/0.1CC, 166 
-31950175,HC BIOPATCH WHITE 1 IN DISK W/4, 45 
-31950176,HC PATCH HEMCON 1.5IN X 1.5IN, 177 
-37005246,HC CA 1-123 CAPSULE PER 100 UCI, 115 
-38106035,HC ACETAZOLAMIDE VL 500MG (DIAMOX), 195 
-38506036,HC ACETAZOLAMIDE VL 500MG (DIAMOX), 195 
-40106031,HC ACETAZOLAMIDE VL 500MG (DIAMOX), 195 
-62781111,HC PROTECTR PICC SMALL 8 IN, 75 
-62781112,HC PROTECTOR LARGE MID ARM 38-56CM, 85 
-62781114,HC PATCH HEMCON 1.5 IN X 1.5 IN, 177 
-94206034,HC ACETAZOLAMIDE VL 500MG (DIAMOX), 195 
-36099100,"HC CATHETER, BRACHY SEED ADMIN", 230 
-42072702,HC ABLATION TUMR PULMONARY UNI 1+," 11,396 "
-50711241,HC 6911 CGD ENZ ACT NONRAD EA, 141 
-50808665,HC NCALR NONBL CALRETICULIN," 2,195 "
-62028117,HC FLUOROSCOPE EXAM, 440 
-62090019,HC HOME SLEEP TEST (HST)/TYPE 2; MIN 7 CH," 1,768 "
-62090020,HC HOME SLEEP TEST (HST)/TYPE 3; MIN 4 CH," 1,768 "
-62095966,HC TRUE-TEST 3 PANELS, 260 
-62024121,HC TREAT MCP W MNP FRACT1 B1, 486 
-62099793,HC MICRO AUTOGRAFTING KIT," 1,278 "
-62028240,HC VISUAL EP TSTG VIS ACU CHECKERBOARD/FLASH, 760 
-62028239,HC XTRNL OC VIDEOGRAPHY I&R PROGRESS, 200 
-62095971,HC ESOPHAGEAL MANOMTRY," 1,902 "
-50711316,HC 6932 11377R LYSO STORG  DISEA, 700 
-50711324,HC 6911 13039R MIT LYMPH PNL, 150 
-50711332,HC 6959 13046R C10/RAJI IMM PNL, 110 
-55092761,HC PFGE IDENTI PLUSE GEL TYP, 98 
-62781115,HC PICC PROTECTOR - MED, 85 
-32003545,HC ER INS PICC W/O PORT >5," 3,045 "
-46161683,HC HIVABX HIV 1/2 AB CONFM, 266 
-51364560,HC LDLD - DIRECT LDL CHOLESTEROL, 219 
-51565737,HC CPIE ALBUMIN, 138 
-51565752,"HC CPIE IGA, IGD,IGG, IGM EA", 213 
-55092779,HC DRMPCR DRG RESIS MUT ANA PCR, 578 
-94880341,HC SCREENING MAMMOGRAM 3D TOMOSYNTHESIS, 400 
-94880358,HC DIAG MAMMO BILAT 3D TOMOSYNTHESIS, 626 
-94880366,HC DIAG MAMMO UNILAT 3D TOMOSYNTHESIS, 501 
-24193244,HC EXTRACORPOREAL HEPATIC ASSISTANCE," 8,135 "
-26290050,HC EXTRACORPOREAL HEPATIC ASSISTANCE," 8,135 "
-51565745,HC CPIE ALBUM URN OTHER QUANT, 126 
-40880189,"HC DBT, SCREENING", 159 
-40880197,"HC DBT, DIAGNOSTIC", 159 
-94880184,"HC DBT, SCREENING", 159 
-94880192,"HC DBT, DIAGNOSTIC", 159 
-25420003,HC CARDIOVERSION ELECTIVE," 5,446 "
-32050550,HC ER I&D BURSA ARM/ELBOW DEEP," 3,545 "
-62095970,HC IMAGE GI TRACT ESOPH W/REP," 2,928 "
-30480009,HC BLOOD TRANSFUSION," 3,300 "
-32050670,HC ER TX DISL ELBOW W/ANES CLSD," 4,845 "
-23220003,HC CHEMO ADMIN PERITONEAL CAVITY," 1,635 "
-32020146,HC CLSD TX HIP IMP DISL W ANE," 4,845 "
-36033260,HC US GUIDE PLCMNT OF RT FIELDS, 250 
-91600911,HC COS: ADD\'L EYE AESTHETIC PROCEDURE," 1,168 "
-23720003,HC CARDIOVERSION ELECTIVE," 5,446 "
-58700307,HC METABOLIC PANEL, 487 
-28000001,HC HANDPIECE LIGASURE IMPACT," 5,525 "
-28000002,HC IMP PIN HALF 5X20MM," 1,557 "
-28000003,HC IMP WIRE EXTERNAL FIXATION BOLT, 329 
-28000004,HC IMP SLEEVE CENTER COMPRESSION 5MM, 988 
-28000005,HC  CATHETER BALLOON PTA 2X30MM 150CM," 1,445 "
-28000006,HC  CATHETER BALLOON PTA 2X40X150CM," 2,338 "
-28000007,HC  CATHETER BALLOON PTA 2X60MM 150CM," 2,593 "
-28000008,HC  CATHETER BALLOON PTA 2.5X30MM 150CM," 1,445 "
-28000009,HC  CATHETER BALLOON PTA 2.5X60MM 150CM," 1,445 "
-28000010,HC  CATHETER BALLOON PTA 150CM 20X3MM," 2,338 "
-28000011,HC  CATHETER BALLOON PTA 3X60MM 150CM," 2,338 "
-28000012,HC IMP STENT GRAFT VIABAHN 5MM10CM," 21,350 "
-28000013,HC IMP STENT GRAFT VIABAHN 6MMX10CM," 21,665 "
-28000014,HC IMP STENT GRAFT VIABAHN 7MMX10CM," 21,350 "
-28000015,HC IMP STENT GRAFT VIABAHN 8MMX10CM," 21,350 "
-28000016,HC IMP STENT GRAFT VIABAHN 5MMX2.5CM," 18,865 "
-28000017,HC IMP STENT GRAFT VIABAHN 5MMX5CM," 18,865 "
-28000018,HC IMP STENT GRAFT VIABAHN 6MMX2.5CM," 18,865 "
-28000019,HC IMP STENT GRAFT VIABAHN 6MMX5CM," 18,865 "
-28000020,HC IMP STENT GRAFT VIABAHN 7MMX5CM," 18,865 "
-28000021,HC IMP STENT GRAFT VIABAHN 8MMX2.5CM," 18,340 "
-28000022,HC IMP STENT GRAFT VIABAHN 5MMX15CM," 25,165 "
-28000023,HC IMP STENT GRAFT VIABAHN 6MMX15CM," 25,165 "
-28000024,HC IMP STENT GRAFT VIABAHN 7MMX15CM," 22,995 "
-28000025,HC IMP STENT GRAFT VIABAHN 8MMX15CM," 24,920 "
-28000026,HC IMPL COIL EMBOLIZATION 14CM 8MM 0.035IN, 730 
-28000027,HC IMPL COIL EMBOLIZATION 14CM 6MM 0.035IN, 730 
-28000028,HC IMP EXTERNAL FIXATOR RING FULL," 7,718 "
-28000029,HC IMP WIRE CORTICAL ILIZAROV TI 1.8X400MM," 2,653 "
-28000030,HC IMP ROD THREADED 300MM, 93 
-28000031,HC IMP EXTERNAL FIXATOR RANCHO CUBE 4 HOLE," 1,480 "
-28000032,HC IMP PIN HALF 5X40MM," 1,557 "
-28000033,HC IMP PIN HALF 5X35MM," 1,557 "
-28000034,HC IMP SEALANT HEMOS FLOSEAL 10ML KIT," 2,706 "
-28000036,HC  CATHETER BALLOON PTA 2X80MM 150CM," 3,188 "
-28000037,HC  CATHETER DILAT PTA FOXCROSS PLUS 120X2X150," 3,188 "
-28000038,HC  CATHETER BALLOON PTA 80X2.5MM 150CM," 3,188 "
-28000039,HC  CATHETER BALLOON PTA 2.5X100MM 150CM," 3,188 "
-28000040,HC  CATHETER DILAT PTA FOXCROSS PLUS 2.5X120X150," 3,188 "
-28000041,HC  CATHETER BALLOON PTA 3X100MM 150CM," 3,188 "
-28000042,HC IMP SPACER X STOP 6MM," 27,650 "
-28000043,HC IMP SPACER X STOP 14MM," 27,650 "
-28000044,HC  CATHETER TIP 2.7F 2.9F 17X130CM," 2,720 "
-28000047,HC IMPL PLATE CONDYLAR 95DEG 70MM 9HOLE," 5,245 "
-28000048,HC PROBE EFLEX TAC C ABLATOR," 4,593 "
-28000049,HC PROBE LIGAMENT CHISEL EFLEX," 3,697 "
-28000052,HC PACK HIP ARTHROSCOPY," 2,525 "
-28000054,HC  CATHETER BALLOON ACHALASIA 35," 6,707 "
-28000055,HC IMPL PLATE 135DEG 4 HOLE," 4,909 "
-28000057,HC IMPL PLATE DCP NARROW 5 HOLE 4.5X87MM," 1,114 "
-28000058,HC  CATHETER INFUSION UNIFUSE 90X10CM 5F," 1,165 "
-28000059,HC  CATHETER HEMODIALYSIS CHRONIC BASIC 36CM," 2,975 "
-28000060,HC  CATHETER HEMODIALYSIS CHRONIC BASIC 40CM," 3,929 "
-28000061,HC  CATHETER HEMODIALYSIS CHRONIC BASIC 55CM, 559 
-28000062,HC  CATHETER DILATATION PTA 4X40X80CM," 2,338 "
-28000063,HC  CATHETER DILATATION PTA 5X40X80CM," 2,338 "
-28000064,HC  CATHETER DILATATION PTA 6X40X80CM," 2,338 "
-28000065,HC  CATHETER DILATATION PTA 7X40X80CM," 3,825 "
-28000066,HC IMP PORT POWER SINGLE 8F," 4,038 "
-28000067,HC IMPL COIL EMBOLIZATION 14CM 4MM 0.035IN, 730 
-28000068,HC IMP STENT GRAFT ICAST 5X16X120," 18,750 "
-28000069,HC IMP STENT GRAFT ICAST 5X22X120," 16,875 "
-28000070,HC IMP STENT GRAFT ICAST 6X38X120," 18,750 "
-28000078,HC CATHETER CENTRL VEIN INTERMEDIATE TRAY," 2,040 "
-28000079,HC  CATHETER BROVIAC SINGLE LUMEN 5F," 1,913 "
-28000080,HC CATHETER HICKMAN DUAL LUMEN 6F," 2,528 "
-28000081,HC  CATHETER CENTRAL VENOUS SINGLE LUMEN 5CM 8F," 2,525 "
-28000082,HC  CATHETER DELIVERY 115X12CM 6F 5F," 5,058 "
-28000083,HC  CATHETER DELIVERY 115X6CM 6F 5F," 5,058 "
-28000084,HC  CATHETER DELIVERY MULTI PURPOSE 6X115CM 5F 6F," 4,208 "
-28000085,HC  CATHETER DELIVERY 6X105CM 6F," 4,208 "
-28000086,HC  CATHETER DELIVERY MULTI PURPOSE 105X6CM 6F 6F," 5,058 "
-28000088,HC IMPL COIL NEURO DETACHABLE 3MM 6CM," 11,378 "
-28000089,HC IMPL COIL DETACHABLE 2.5MM 2.5X175CM," 11,378 "
-28000090,HC IMPL COIL DETACHABLE 2.5MM 3.5X175CM," 11,378 "
-28000091,HC IMPL COIL DETACHABLE 2.5MM 4.5X175CM," 11,378 "
-28000092,HC IMPL COIL DETACHABLE 3.5MM 5X175CM," 11,378 "
-28000093,HC IMPL COIL DETACHABLE 3.5MM 7.5X175CM," 11,378 "
-28000094,HC IMPL COIL DETACHABLE 6MM 15X175CM," 11,550 "
-28000095,HC IMPL COIL DETACHABLE 7MM 21X175CM," 11,550 "
-28000096,HC IMPL COIL DETACHABLE 9MM 25X175CM," 11,550 "
-28000097,HC IMPL COIL DETACHABLE 10MM 30X175CM," 11,550 "
-28000100,HC IMPL COIL DETACHABLE 8MM 24X175CM," 11,550 "
-28000101,HC IMPL COIL DETACHABLE 4MM 10X175CM," 11,963 "
-28000102,HC IMP STENT CORONARY INTEGRITY RX BMS 2.25X8," 6,800 "
-28000103,HC IMP STENT CORONARY INTEGRITY RX BMS 2.25X12," 7,650 "
-28000104,HC IMP STENT CORONARY INTEGRITY RX BMS 2.25X14," 6,800 "
-28000105,HC IMP STENT CORONARY INTEGRITY RX BMS 2.25X18," 6,800 "
-28000106,HC IMP STENT CORONARY INTEGRITY RX BMS 2.5X8," 6,800 "
-28000107,HC IMP STENT CORONARY INTEGRITY RX BMS 2.5X12," 6,800 "
-28000108,HC IMP STENT CORONARY INTEGRITY RX BMS 2.5X14," 6,800 "
-28000109,HC IMP STENT CORONARY INTEGRITY RX BMS 2.5X18," 6,800 "
-28000110,HC IMP STENT CORONARY INTEGRITY RX BMS 2.75X12," 7,875 "
-28000111,HC IMP STENT CORONARY INTEGRITY RX BMS 2.75X14," 6,800 "
-28000112,HC IMP STENT CORONARY INTEGRITY RX BMS 2.75X18," 6,800 "
-28000113,HC IMPL COIL PERIPHERAL EMBOLIZATION 8CM 10MM 0.038IN, 231 
-28000114,HC IMPL COIL PERIPHERAL EMBOLIZATION .038IN 15MM 8CM, 231 
-28000115,HC IMPL COIL PERIPHERAL EMBOLIZATION 15CM 15MM 0.038IN, 595 
-28000116,HC IMPL COIL EMBOLIZATION 4MM 14CM 0.018IN, 730 
-28000117,HC IMPL COIL EMBOLIZATION 6MM 14CM 0.018IN, 730 
-28000118,HC IMPL COIL EMBOLIZATION 14CM 8MM 0.018IN, 730 
-28000119,HC IMPL COIL EMBOLIZATION 14CM 10MM 0.018IN, 730 
-28000120,HC IMPL COIL EMBOLIZATION 5CM 5MM 0.035IN, 578 
-28000121,HC  CATHETER DILATATION 4MM 10X110CM 5F," 1,517 "
-28000122,HC  GUIDEWIRE NITREX STF SHFT 35IN 180, 298 
-28000123,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 7FX45DX80CM," 4,930 "
-28000124,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 8FX45DX80CM," 5,134 "
-28000125,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 6FX180DX80CM," 4,930 "
-28000126,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 7FX180DX80CM," 4,930 "
-28000127,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 180DX80CM," 4,250 "
-28000128,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 6FX45DX60CM," 5,134 "
-28000129,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 7FX45DX60CM," 4,930 "
-28000130,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 8FX45DX60CM," 4,250 "
-28000131,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 9FX45DX80CM," 5,134 "
-28000132,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 10FX45DX80CM," 5,134 "
-28000133,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 12FX45DX80CM," 4,930 "
-28000134,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 5FX180DX60CM," 4,250 "
-28000135,HC IMP SYSTEM DELIVERY AMPLATZER 6FX180X60CM," 4,250 "
-28000136,HC IMP SYSTEM DELIVERY AMPLATZER TORQVUE 9FX180DX80CM," 4,250 "
-28000140,HC  CATHETER DILATATION 4MM 2X135CM 4F," 2,907 "
-28000141,HC  CATHETER DILATATION 6MM 2X135CM 5F," 2,907 "
-28000142,HC  CATHETER DILATATION 6MM 4X135CM 5F," 2,907 "
-28000143,HC IMP STENT BILIARY SMART OTW 6X20X80," 11,100 "
-28000144,HC  CATHETER DILATATION 2X75CM 16MM," 2,785 "
-28000145,HC  CATHETER BALLOON PTA XXL 2XX8X75CM," 2,785 "
-28000146,HC  CATHETER DILATATION PTCA 4.5X15MM," 2,117 "
-28000147,HC  CATHETER DILATATION PTCA 5X15MM," 2,117 "
-28000148,HC  CATHETER DILATATION PTCA RX 2.25X9MM," 2,117 "
-28000149,HC  CATHETER DILATATION PTCA RX 2.25X15MM," 2,128 "
-28000150,HC IMP LEAD DEFIBRILLATOR ENDOTAK ARRAY 70CM," 35,000 "
-28000151,HC IMP CEMENT BONE DELIVERY AVAMAX PLUS TRAY," 6,728 "
-28000156,HC NEEDLE INJECT AVAFLEX 11G KIT," 4,468 "
-28000157,HC  TUBE JEJUNAL LOW PROFILE 5ML 14F 4.0CM, 838 
-28000158,HC IMP WIRE SUTURE BEADED 18G, 97 
-28000159,HC  INTROD PERC TRAY, 265 
-28000160,HC  CATHETER BALLOON PTA 6MM 4X475CM 5F," 1,581 "
-28000161,HC  CATHETER BALLOON PTA 8MM 4X75CM 5F," 1,411 "
-28000162,HC  CATHETER DRAINAGE GENERAL ABSCESSION 30CM 8F, 893 
-28000163,HC  CATHETER DRAINAGE GENERAL ABSCESSION 30CM 10F, 893 
-28000164,HC  CATHETER DRAINAGE GENERAL ABSCESSION 30CM 12F, 893 
-28000165,HC  CATHETER DRAINAGE GENERAL ABSCESSION 30CM 14F, 955 
-28000166,HC DRAIN NEURO EVACUATION SEPS 6MM," 13,125 "
-28000167,HC  CATHETER BALLOON SINUS RELIEVA 5X16MM," 5,814 "
-28000168,HC  CATHETER BALLOON SINUS RELIEVA 6X16MM," 5,814 "
-28000169,HC  CATHETER BALLOON SINUS RELIEVA 7X16MM," 5,814 "
-28000172,HC IMP BONE VITOSS FOAM 25X100X8MM," 24,948 "
-28000173,HC HEMOSTATIC VITAGEL 4.5ML," 4,157 "
-28000174,HC  CATHETER BALLOON SINUS RELIEVA XL 7MM," 6,129 "
-28000175,HC  CATHETER RELIEVA FLEX SINUS GUIDE F70," 3,145 "
-28000178,HC IMP ADAPTER LEAD DEFIBRILLATOR EXTENSION," 8,500 "
-28000179,HC IMP LEAD PACEMAKER DEXTRUS 53CM," 4,675 "
-28000180,HC IMP STENT BILIARY VISIPRO 5X12X80," 8,458 "
-28000181,HC IMP STENT BILIARY VISIPRO 6X12X80," 8,458 "
-28000182,HC IMP STENT BILIARY VISIPRO 6X17X80," 8,458 "
-28000183,HC IMP STENT BILIARY VISIPRO 6X27X80," 7,958 "
-28000184,HC IMP STENT BILIARY VISIPRO 6X37X80," 7,958 "
-28000185,HC IMP STENT BILIARY VISIPRO 6X57X80," 8,458 "
-28000186,HC IMP STENT BILIARY VISIPRO 7X12X80," 7,958 "
-28000187,HC IMP STENT BILIARY VISIPRO 7X27X80," 8,458 "
-28000188,HC IMP STENT BILIARY VISIPRO 7X37X80," 8,458 "
-28000189,HC IMP STENT BILIARY VISIPRO 7X57X80," 8,458 "
-28000190,HC IMP STENT BILIARY VISIPRO 8X27X80," 7,958 "
-28000191,HC IMP STENT BILIARY VISIPRO 8X37X80," 7,958 "
-28000192,HC IMP STENT BILIARY VISIPRO PX17X80," 7,958 "
-28000193,HC IMP STENT BILIARY VISIPRO 9X27X80," 7,958 "
-28000194,HC IMP STENT BILIARY VISIPRO 10X17X80," 7,958 "
-28000195,HC IMP STENT BILIARY VISIPRO 10X27X80," 7,958 "
-28000196,HC IMP STENT BILIARY VISIPRO 10X7X80," 7,958 "
-28000197,HC IMP STENT BILIARY VISIPRO 10X57X80," 8,458 "
-28000198,HC  CATHETER REPERFUSION 150X154CM 2.8F," 8,925 "
-28000199,HC  CATHETER REPERFUSION 150X154CM 3.4F," 10,425 "
-28000200,HC  CATHETER REPERFUSION 137X141CM 4.1F," 10,425 "
-28000201,HC SEPARATOR CATH 0.022INX200CM," 17,175 "
-28000202,HC  CATHETER THROMBECTOMY SEPERATOR 3MAX 158CM," 20,930 "
-28000203,HC  RETRIEVER CATH THROMBECTOMY 35X200CM," 20,930 "
-28000206,HC IMP DEVICE OCCLUSION MESH 6X6X100CM," 7,072 "
-28000207,HC PLUG CATH VENTRICULAR 8X7MM 100CM," 6,800 "
-28000208,HC PLUG CATH GUIDING VASCLR 10X7MM," 6,800 "
-28000209,HC IMP PLUG VASCULAR AMPLATZER II 12MM," 6,800 "
-28000210,HC IMP PLUG VASCULAR AMPLATZER II 14MM," 7,072 "
-28000211,HC IMP PLUG VASCULAR AMPLATZER II 16MM," 7,072 "
-28000212,HC PLUG CATH GUIDING 18X14MM 100CM," 6,800 "
-28000213,HC IMP PLUG VASCULAR AMPLATZER II 20MM," 6,800 "
-28000214,HC IMP PLUG VASCULAR AMPLATZER II 22MM," 6,800 "
-28000216,HC IMP LEAD VENTRICULAR BIPOLAR 10MM 52CM," 4,675 "
-28000217,HC  CATHETER RELIEVA FLEX SINUS GUIDE F30," 3,145 "
-28000218,HC  CATHETER RELIEVA FLEX SINUS GUIDE F00," 3,145 "
-28000219,HC SPINEJET MIS 20 DEG," 3,358 "
-28000220,HC SPINEJET MIS 30 DEG," 3,060 "
-28000221,HC SPINEJET XLS 20 DEG," 3,145 "
-28000222,HC SPINEJET XLS 75 DEG," 2,550 "
-28000223,HC SPINEJET MICRORESECT 2.5," 7,013 "
-28000224,HC SPINEJET BLUE CONNECTOR," 3,528 "
-28000225,HC CATHETER DUAL LUMEN TRAY 6F," 2,210 "
-28000226,HC  CATHETER BILIARY SILICONE SH ADAPTER LL 16F, 706 
-28000227,HC  CATHETER BILIARY SILICONE SH ADAPTER LL 18F, 706 
-28000229,HC GASTROJEJENOSTOMY SHETTY SET," 4,455 "
-28000230,HC CATHETER GASTROJEJUNOSTOMY SET 16.5F," 3,751 "
-28000231,HC  GUIDEWIRE PERCUTANEOUS NEFF 20CM, 571 
-28000232,HC  CATHETER DRAINAGE ABSCESSION JCRVD 41CM 16F, 706 
-28000233,HC  CATHETER DRAINAGE ABSCESSION THL-QK SET, 706 
-28000234,HC  CATHETER DRAINAGE 25.5X50CM 10.2F, 783 
-28000236,HC  CATHETER DIAGNOSTIC 65CM 0.035IN 4F, 113 
-28000237,HC  CATHETER MICRO PROWLER 45D .0165IN 1.9FX150X50," 6,834 "
-28000238,HC  TUBE GASTROSTOMY LOW PROFILE 18F 4.5, 838 
-28000239,HC  TUBE JEJUNAL LOW PROFILE 5ML 18F 2.7CM," 1,326 "
-28000240,HC  TUBE JEJUNAL LOW PROFILE 5ML 18F 4.5CM," 1,462 "
-28000241,HC  GUIDEWIRE HYDROPHILIC COATED 18IN 180CM," 1,462 "
-28000242,HC  GUIDEWIRE SELECTIVE 90D 18IN 180CM," 1,462 "
-28000243,HC  CATHETER THRMBOLYTC PERCUTANEUS 5FX65," 3,461 "
-28000245,HC IMP STENT NEPHROSTOMY ULTRATHANE 10.2X22, 866 
-28000246,HC IMP STENT BILIARY VIADUCT 10FX7CM, 578 
-28000247,HC IMP STENT BILIARY VIADUCT 10FX9CM, 578 
-28000248,HC IMP STENT BILIARY VIADUCT 10FX12CM, 578 
-28000249,HC IMP STENT BILIARY VIADUCT 10FX15CM, 578 
-28000250,HC  GUIDEWIRE CORONARY STRAIGHT 14IN 3X180CM, 680 
-28000251,HC IMP OCCLUDER SEPTAL HELEX 20MM," 37,369 "
-28000252,HC  CATHETER INTRAVASCULAR 135CM 6F," 5,100 "
-28000253,HC IMP STENT GRAFT ICAST 6X22X120," 16,875 "
-28000254,HC  CATHETER DILATATION PTA 3MM 4X80CM 5F," 2,002 "
-28000255,HC  CATHETER DILATATION PTA 2X80CM 4MM 5F," 2,002 "
-28000256,HC  CATHETER DILATATION PTA 4X80CM 4MM 5F," 2,001 "
-28000257,HC  CATHETER DILATATION PTA 2X80CM 5MM 5F," 2,002 "
-28000258,HC  CATHETER DILATATION PTA 4X80CM 5MM 5F," 2,002 "
-28000259,HC  CATHETER DILATATION PTA 5X8X80CM 5F," 1,573 "
-28000260,HC  CATHETER DILATATION PTA 10X80CM 5MM 5F," 2,841 "
-28000261,HC  CATHETER DILATATION PTA 2X80CM 6MM 5F," 2,002 "
-28000262,HC  CATHETER DILATATION PTA 4X80CM 6MM 5F," 2,002 "
-28000263,HC  CATHETER DILATATION PTA 6X120CM 6MM 5F," 2,001 "
-28000264,HC  CATHETER DILATATION PTA DORADO 6X10X80MM," 2,002 "
-28000265,HC  CATHETER DILATATION PTA 2X80CM 7MM 6F," 2,002 "
-28000266,HC  CATHETER DILATATION PTA 4X80CM 7MM 5F," 2,002 "
-28000267,HC  CATHETER DILATATION PTA ANGOPLASTY 7X8X80MM," 1,573 "
-28000268,HC  CATHETER DILATATION PTA 2X80CM 8MM 6F," 2,002 "
-28000269,HC  CATHETER DILATATION PTA 4X80CM 8MM 6F," 2,002 "
-28000270,HC  CATHETER DILATATION PTA 4X80CM 10MM 6F," 1,573 "
-28000271,HC  CATHETER DILATATION PTA 4X120CM 5MM 5F," 1,573 "
-28000272,HC  CATHETER DILATATION PTA 10X120CM 5MM 5F," 2,087 "
-28000273,HC  CATHETER DILATATION PTA 4X120CM 6MM 5F," 2,086 "
-28000274,HC  CATHETER DILATATION PTA DORADO 6X10X120CM," 3,187 "
-28000275,HC  CATHETER DILATATION PTA 7X4X120CM 5F," 2,086 "
-28000276,HC  CATHETER DILATATION PTA 10X120CM 7MM 6F," 3,187 "
-28000277,HC  CATHETER DILATATION PTA 4X120CM 8MM 6F," 1,573 "
-28000278,HC  CATHETER DILATATION PTA 10X120CM 8MM 6F," 2,758 "
-28000279,HC  CATHETER DILATATION PTA 2X135CM 5MM 5F," 2,087 "
-28000280,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.0X22," 2,668 "
-28000282,HC IMP RECORDER CARD EVENT REVEAL DX," 28,000 "
-28000283,HC IMP ICD DUAL TELMTRY 36J," 81,000 "
-28000284,HC IMP LEAD DEFIBRILLATOR DUTRA 2PLR 11MM 65CM," 37,375 "
-28000285,HC  CATHETER DRAINAGE 38IN 25CM 12F, 623 
-28000286,HC IMP PORT MRI 6.6F KIT," 2,019 "
-28000287,HC IMP STENT NEURO ENTERPRISE HDE 4.5X14," 33,800 "
-28000288,HC IMP STENT NEURO ENTERPRISE HDE 4.5X28," 33,800 "
-28000289,HC IMP STENT NEURO ENTERPRISE HDE 4.5X37," 33,800 "
-28000290,HC  CATHETER MICRO PROWLER 0.021IN 150X5," 6,834 "
-28000293,HC IMP HOOK TSRH OFFSET LEFT," 7,725 "
-28000294,HC SHEARS COAGULATING HARMONIC CVD 9CM," 4,330 "
-28000295,HC IMP PERICARDIUM CORMATRIX 7X1," 8,288 "
-28000296,HC IMP STENT NEPHROSTOMY ULTRATHANE 8.5FX20, 866 
-28000299,HC  CATH POWERPORT ISP MRI IMP 8F," 4,165 "
-28000300,HC IMP OCCLUDER AMPLATZ CRBRFRM 30MM," 35,100 "
-28000301,HC IMP OCCLUDER AMPLATZ CRBRFRM 35MM," 35,000 "
-28000303,HC IMP CLOSURE VASCULAR DEVICE VSL 6F," 1,785 "
-28000304,HC  CATHETER DRAINAGE 15CM 35IN 10.2F, 623 
-28000305,HC IMP OCCLUDER AMPLATZ CRBRFRM 25MM," 36,504 "
-28000306,HC IMP OCCLUDER AMPLATZ CRBRFRM 18MM," 35,100 "
-28000307,HC  CATHETER BIDEFLECTABLE 80X110CM," 8,458 "
-28000311,HC  INTROD PEEL AWAY CUST SET, 621 
-28000312,HC IMP PARTICLE EMBOLIZATION FM PUR 300-500U, 831 
-28000313,HC IMP PARTICLE EMBOLIZATION PVOH 500-710U, 831 
-28000314,HC  CATHETER DRAINAGE 10.2F 25CM 18G, 623 
-28000315,HC  CATHETER RELIEVA FLEX SINUS GUIDE F110," 3,774 "
-28000316,HC SINUS RELIEVA ILLUMINATION SYSTEM," 3,647 "
-28000317,HC CABLE LIGHT GUIDE RELIEVA LUMA," 3,647 "
-28000318,HC  CATHETER BALLOON SINUS RELIEVA 5X24MM," 6,129 "
-28000319,HC  CATHETER RELIEVA FLEX SINUS GUIDE F90," 3,145 "
-28000320,HC  CATHETER DRAINAGE BILIARY RING DUCT 12F, 783 
-28000321,HC IMP STENT CORONARY XIENCV RX DES 2.5X8," 13,125 "
-28000322,HC IMP STENT CORONARY XIENCV RX DES 2.5X12," 13,125 "
-28000323,HC IMP STENT CORONARY XIENCV RX DES 2.5X15," 13,125 "
-28000324,HC IMP STENT CORONARY XIENCV RX DES 2.5X18," 13,125 "
-28000325,HC IMP STENT CORONARY XIENCV RX DES 2.5X23," 13,125 "
-28000326,HC IMP STENT CORONARY XIENCV RX DES 2.5X28," 15,188 "
-28000327,HC IMP STENT CORONARY XIENCV RX DES 2.75X8," 13,125 "
-28000328,HC IMP STENT CORONARY XIENCV RX DES 2.75X12," 13,125 "
-28000329,HC IMP STENT CORONARY XIENCV RX DES 2.75X15," 13,125 "
-28000330,HC IMP STENT CORONARY XIENCV RX DES 2.75X18," 13,125 "
-28000331,HC IMP STENT CORONARY XIENCV RX DES 2.75X23," 13,125 "
-28000332,HC IMP STENT CORONARY XIENCV RX DES 2.75X28," 15,188 "
-28000333,HC IMP STENT CORONARY XIENCV RX DES 3X8," 13,125 "
-28000334,HC IMP STENT CORONARY XIENCV RX DES 3X12," 13,125 "
-28000335,HC IMP STENT CORONARY XIENCV RX DES 3X15," 13,125 "
-28000336,HC IMP STENT CORONARY XIENCV RX DES 3X18," 13,125 "
-28000337,HC IMP STENT CORONARY XIENCV RX DES 3X23," 13,125 "
-28000338,HC IMP STENT CORONARY XIENCV RX DES 3X28," 15,188 "
-28000339,HC IMP STENT CORONARY XIENCV RX DES 3.5X8," 13,125 "
-28000340,HC IMP STENT CORONARY XIENCV RX DES 3.5X12," 13,125 "
-28000341,HC IMP STENT CORONARY XIENCV RX DES 3.5X15," 13,125 "
-28000342,HC IMP STENT CORONARY XIENCV RX DES 3.5X18," 13,125 "
-28000343,HC IMP STENT CORONARY XIENCV RX DES 3.5X23," 13,125 "
-28000344,HC IMP STENT CORONARY XIENCE RX DES 3.5X28," 15,188 "
-28000345,HC IMP STENT CORONARY XIENCE RX DES 4X8," 15,750 "
-28000346,HC IMP STENT CORONARY XIENCV RX DES 4X12," 13,125 "
-28000347,HC IMP STENT CORONARY XIENCV RX DES 4X15," 15,188 "
-28000348,HC IMP STENT CORONARY XIENCV RX DES 4X18," 13,125 "
-28000349,HC IMP STENT CORONARY XIENCV RX DES 4X23," 13,125 "
-28000350,HC IMP VALVE NEURO MULTIPURPOSE," 9,662 "
-28000351,HC IMP STENT CORONARY XIENCV RX DES 4X28," 13,688 "
-28000352,HC IMP STENT CORONARY PROMUS RX DES 2.5X8," 15,375 "
-28000353,HC IMP STENT CORONARY PROMUS RX DES 2.75X8," 15,417 "
-28000354,HC IMP STENT CORONARY PROMUS RX DES 3X8," 15,375 "
-28000355,HC IMP STENT CORONARY PROMUS RX DES 3.5X8," 15,375 "
-28000356,HC IMP STENT CORONARY PROMUS RX DES 4X8," 15,375 "
-28000357,HC IMP STENT CORONARY PROMUS RX DES 2.5X12," 26,417 "
-28000358,HC IMP STENT CORONARY PROMUS RX DES 2.75X12," 15,375 "
-28000359,HC IMP STENT CORONARY PROMUS RX DES 3X12," 15,417 "
-28000360,HC IMP STENT CORONARY PROMUS RX DES 3.5X12," 15,375 "
-28000361,HC IMP STENT CORONARY PROMUS RX DES 4X12," 15,375 "
-28000362,HC IMP STENT CORONARY PROMUS RX DES 2.5X15," 15,417 "
-28000363,HC IMP STENT CORONARY PROMUS RX DES 2.75X15," 15,375 "
-28000364,HC IMP STENT CORONARY PROMUS RX DES 3X15," 26,417 "
-28000365,HC IMP STENT CORONARY PROMUS RX DES 3.5X15," 24,689 "
-28000366,HC IMP STENT CORONARY PROMUS RX DES 4X15," 15,375 "
-28000367,HC IMP STENT CORONARY PROMUS RX DES 2.5X18," 15,417 "
-28000368,HC IMP STENT CORONARY PROMUS RX DES 2.75X18," 15,417 "
-28000369,HC IMP STENT CORONARY PROMUS RX DES 3X18," 26,417 "
-28000370,HC IMP STENT CORONARY PROMUS RX DES 3.5X18," 15,417 "
-28000371,HC IMP STENT CORONARY PROMUS RX DES 4X18," 15,375 "
-28000372,HC IMP STENT CORONARY PROMUS RX DES 2.5X23," 15,417 "
-28000373,HC IMP STENT CORONARY PROMUS RX DES 2.75X23," 15,375 "
-28000374,HC IMP STENT CORONARY PROMUS RX DES 3X23," 15,417 "
-28000375,HC IMP STENT CORONARY PROMUS RX DES 3.5X23," 15,417 "
-28000376,HC IMP STENT CORONARY PROMUS RX DES 4X23," 15,417 "
-28000377,HC IMP STENT CORONARY PROMUS RX DES 2.5X28," 15,417 "
-28000378,HC IMP STENT CORONARY PROMUS RX DES 2.75X28," 15,417 "
-28000379,HC IMP STENT CORONARY PROMUS RX DES 3X28," 15,417 "
-28000380,HC IMP STENT CORONARY PROMUS RX DES 3.5X28," 15,375 "
-28000381,HC IMP STENT CORONARY PROMUS RX DES 4X28," 15,375 "
-28000382,HC IMP RESERVOIR OMMAYA CONVERTIBLE 2.5CM," 2,791 "
-28000383,HC  INTRODUCER SHEATH 025 5F 10CM, 675 
-28000384,HC  INTRODUCER SHEATH 025 6F 10CM, 675 
-28000385,HC  CATHETER CORONARY DIAG OPTITORQUE TIGER 5F, 255 
-28000386,HC  CATHETER CORONARY DIAG OPTITORQUE JACKY 5F 4.5, 255 
-28000387,HC  CATHETER CORONARY DIAG OPTITORQUE JACKY 5F 3.5, 255 
-28000388,HC  CATHETER CORONARY DIAG OPTITORQUE JACKY 5F 4.0, 255 
-28000390,HC  CATHETER CORONARY DIAG OPTITORQUE TIGER 6F 4.5, 255 
-28000391,HC  CATHETER CORONARY DIAG OPTITORQUE TIGER 6F 3.5, 255 
-28000392,HC  CATHETER CORONARY DIAG OPTITORQUE TIGER 6F 4.0, 255 
-28000396,HC IMP MESH PARIETEX SKIRT 8INX6IN," 7,859 "
-28000397,HC IMP MESH PARIETEX SKIRT 6INX4IN," 5,100 "
-28000398,HC IMP MESH PARIETEX SKIRT 10INX8IN," 11,583 "
-28000399,HC  CATHETER BALLOON THERMODILUTION HEPARIN 6FX110, 788 
-28000400,HC  CATHETER BALLOON THERMODILUTION 6FX110CM, 453 
-28000401,HC PLATELET SEPARATION GPS MINI," 5,610 "
-28000403,HC  CATHETER BALLOON PTA 150CM 40X3MM," 2,338 "
-28000404,HC  CATHETER GUIDING VISTA BRITE TIP BARBEAU 5F, 442 
-28000406,HC PACK PERFUSION PEDI," 3,851 "
-28000407,HC  CATHETER PEZZAR 18F, 128 
-28000408,HC IMP STENT BILIARY GENESIS 35 OTW 10X59X135," 9,000 "
-28000409,HC IMP STENT BILIARY GENESIS 35 OTW 10X80X135," 9,000 "
-28000410,HC  INTRODUCER SHEATH 6F 10CM 45CM, 340 
-28000411,HC  INTRODUCER SHEATH 5F 10CM 45CM, 321 
-28000412,HC  CATHETER PEZZAR 20F, 128 
-28000413,HC  CATHETER PEZZAR 22F, 128 
-28000414,HC CATHETER PLEURAL COMPLETE 15.5F," 8,250 "
-28000415,HC IMP FILTER VENA CAVA FEMORAL DELIVERY KIT," 10,838 "
-28000416,HC IMP FILTER VENA CAVA JUGULAR DELV SUBC," 10,838 "
-28000417,HC IMP PARTICLE EMBOLIZATION PVOH 90-180U, 785 
-28000418,HC IMP PARTICLE EMBOLIZATION PVOH 180-300U, 785 
-28000419,HC  CATHETER PEZZAR 24F, 128 
-28000421,HC  CATHETER PEZZAR 26F, 128 
-28000422,HC  DEVICE ENDOVASCULAR EKOSONIC 106CMX06CM," 18,165 "
-28000423,HC  CATHETER PEZZAR 28F, 128 
-28000424,HC  DEVICE ENDOVASCULAR EKOSONIC 106X18CM," 18,165 "
-28000425,HC  DEVICE ENDOVASCULAR EKOSONIC 106CMX24CM," 18,165 "
-28000426,HC  DEVICE ENDOVASCULAR EKOSONIC 106CMX30CM," 18,165 "
-28000427,HC  DEVICE ENDOVASCULAR EKOSONIC 135X12CM," 18,165 "
-28000428,HC  DEVICE ENDOVASCULAR EKOSONIC 135CMX40CM," 18,165 "
-28000429,HC  DEVICE ENDOVASCULAR EKOSONIC 135CMX50CM," 18,165 "
-28000430,HC IMPL COIL NEURO HELICAL FINISHING SZ10 2MM 3CM," 7,833 "
-28000431,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 2MMX1CM," 8,363 "
-28000432,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 2MMX2CM," 7,980 "
-28000433,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 2MMX3CM," 8,835 "
-28000434,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 2MMX4CM," 8,835 "
-28000435,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 2MMX6CM," 9,679 "
-28000436,HC IMPL COIL NEURO SRE LOOP HELICAL SZ10 2MM 8CM," 10,545 "
-28000437,HC IMPL COIL NEURO HELICAL FINISHING SZ10 3MM 4CM," 8,835 "
-28000438,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 3MMX6CM," 9,690 "
-28000439,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 3MMX8CM," 10,545 "
-28000440,HC IMPL COIL NEURO HELICAL FINISHING SZ10 4MM 4CM," 8,835 "
-28000441,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 4MMX6CM," 9,690 "
-28000442,HC IMPL COIL NEURO COMPLEX SRE SZ10 4MM 8MM," 11,663 "
-28000443,HC IMPL COIL HELICAL SYS SZ10 4MM 10CM," 11,970 "
-28000444,HC IMPL COIL NEURO EMBOLIZATION HLCL 5MM 6CM," 10,189 "
-28000445,HC IMPL COIL NEURO EMBOLIZATION HLCL 5MM 8CM," 10,545 "
-28000446,HC IMPL COIL NEURO HELICAL SZ10 5MM 10CM VTRAK," 11,970 "
-28000447,HC IMPL COIL NEURO HELICAL SZ10 5MM 15CM VTRAK," 12,683 "
-28000448,HC IMPL COIL NEURO EMBOLIZATION 5MM 20CM," 13,181 "
-28000449,HC IMPL COIL NEURO EMBOLIZATION HLCL 6MM 6CM," 9,690 "
-28000450,HC IMPL COIL NEURO EMBOLIZATION HLCL 6MM 8CM," 10,545 "
-28000451,HC IMPL COIL NEURO EMBOLIZATION 6MM 10CM," 11,970 "
-28000452,HC IMPL COIL NEURO EMBOLIZATION 6MM 15CM," 12,683 "
-28000453,HC IMPL COIL NEURO HELICAL SZ10 6MM 20CM VTRAK," 13,181 "
-28000454,HC  CATHETER DIAGNOSTIC 6F 1X5.2X2MM," 2,465 "
-28000455,HC  GUIDEWIRE BSD 14IN 180CM 4MM," 14,213 "
-28000456,HC  TUBE JEJUNAL LOW PROFILE 5ML 18F 2.3CM," 1,326 "
-28000457,HC  TUBE JEJUNAL LOW PROFILE 5ML 18F 3.5CM," 1,326 "
-28000458,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 1.2X30," 2,738 "
-28000459,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 1.5X30," 2,607 "
-28000460,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.7X30," 2,738 "
-28000462,HC  CATHETER BALLOON THERMODILUTION 4F 75CM," 1,134 "
-28000463,HC  INTROD MICKEY GASTRO 18F," 1,692 "
-28000464,HC  CATHETER EXPORT ASPIRATION 6F 0.014IN," 3,400 "
-28000465,HC IMP SCREW BONE LOCKING 2.0X6MM, 859 
-28000466,HC IMP SCREW BONE LOCKING 2.0X8MM, 944 
-28000467,HC IMPL PLATE COMPRESSION 6HOLE," 2,243 "
-28000468,HC IMPL PLATE MINI STRAIGHT 1.0MM 6HOLE," 1,392 "
-28000469,HC IMPL SCREW AUTO DRIVE 1.2MMX5MM, 488 
-28000470,HC IMPL PLATE BSSO MEDIUM 6HOLE," 2,066 "
-28000471,HC IMPL PLATE LOCKING FRACTURE 2.4MM," 3,450 "
-28000472,HC IMPL PLATE LOCKING FRACTURE 2.0MM," 3,875 "
-28000473,HC IMPL SCREW LOW PROFILE SELF TAPPING 1.5X6MM," 1,604 "
-28000474,HC IMP SCREW BONE SELF DRILLING 1.7X4MM, 469 
-28000476,HC  INTROD TRANSRADIAL 21X11X4, 213 
-28000477,HC  INTROD TRANSRADIAL 5X11, 213 
-28000478,HC IMPL PLATE MIDFACE LOCKING STRAIGHT," 2,825 "
-28000479,HC IMPL PLATE MIDFACE LOCKING CURVED 8 HOLE," 1,366 "
-28000480,HC IMPL PLATE MIDFACE LOCKING L SHPD W/BAR 5MM 5 HOLE," 1,698 "
-28000481,HC IMPL PLATE MIDFACE LOCKING L SHPD W/BAR 12MM 6 HOLE," 1,604 "
-28000482,HC IMPL PLATE FRACTURE MINI 4 HOLE, 665 
-28000483,HC IMPL PLATE FRACTURE W/BAR 4 HOLE," 2,731 "
-28000484,HC IMPL PLATE FRACTURE ANGLE FRACTURE 6HOLE 115," 4,696 "
-28000485,HC IMPL SCREW NEURO MATRIX TI SELF DRILL 4MM, 425 
-28000486,HC  CATHETER BILIARY TOTAL ABCESSION 10F, 937 
-28000487,HC  RETRIEVAL SYSTEM 2.0 2X5MM 18L," 22,365 "
-28000488,HC  CATHETER GUIDING DAC .044IN 4.3FX130CM," 8,458 "
-28000489,HC  RETRIEVAL SOFT SYSTEM 180CM 2.5X6MM," 22,365 "
-28000490,HC  RETRIEVAL FIRM SYSTEM 180CM 2.5X6MM," 22,365 "
-28000491,HC  RETRIEVAL SOFT SYSTEM 180CM 3X7MM," 22,365 "
-28000492,HC IMP STENT GRAFT ICAST 5X16X80," 18,750 "
-28000493,HC IMP STENT GRAFT ICAST 5X22X80," 16,875 "
-28000494,HC IMP STENT GRAFT ICAST 6X22X80," 16,875 "
-28000495,HC IMP STENT GRAFT ICAST 8X38X80," 18,750 "
-28000496,HC IMP STENT GRAFT ICAST 9X59X80," 18,750 "
-28000497,HC IMP STENT GRAFT ICAST 10X38X80," 18,750 "
-28000498,HC IMP STENT GRAFT ICAST 8X38X120," 18,750 "
-28000500,HC IMP STENT NEPHROSTOMY ULTRATHANE 10.2X20, 866 
-28000501,HC IMPL SCREW CORTEX TITANIUM 3.5X12MM, 132 
-28000509,HC IMP STENT NEPHROSTOMY AMPLATZ 8.5FX12," 1,055 "
-28000511,HC IMP STENT NEPHROSTOMY AMPLATZ 8.5FX20," 1,055 "
-28000512,HC IMP STENT NEPHROSTOMY AMPLATZ 8.5FX22," 1,250 "
-28000513,HC IMP STENT NEPHROSTOMY AMPLATZ 8.5FX24," 1,055 "
-28000515,HC IMP STENT NEPHROSTOMY AMPLATZ 8.5FX26," 1,055 "
-28000516,HC IMP STENT NEPHROSTOMY AMPLATZ 8.5FX28," 1,055 "
-28000521,HC IMPL SCREW CORTEX TITANIUM 3.5X40MM, 119 
-28000524,HC IMPL PLATE RECONSTRUCTION TI 3.5X58MM 5HOLE," 3,052 "
-28000525,HC IMPL PLATE CALCANEAL TITANIUM 60MM," 4,420 "
-28000526,HC IMPL PLATE CALCANEAL TITANIUM 70MM," 4,794 "
-28000527,HC IMPL PLATE Y CALCANEAL TITANIUM 87MM," 4,794 "
-28000528,HC IMPL PLATE CLOVERLEAF TITANIUM THIN 152MM," 3,961 "
-28000529,HC IMPL PLATE CLOVERLEAF TITANIUM THIN 168MM," 4,174 "
-28000530,HC IMPL PLATE LC DCP TITANIUM 3.5X64MM 5HOLE," 1,292 "
-28000531,HC IMPL PLATE TUBULAR 1/3 W/COLLAR TI 145MM, 718 
-28000533,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X95MM," 2,420 "
-28000534,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X20MM," 2,091 "
-28000535,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X25MM," 2,091 "
-28000536,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X30MM," 2,091 "
-28000537,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X35MM," 2,091 "
-28000538,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X40MM," 2,091 "
-28000539,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X45MM," 2,091 "
-28000540,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X50MM," 2,091 "
-28000541,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X55MM," 2,091 "
-28000542,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X60MM," 2,091 "
-28000543,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X65MM," 2,091 "
-28000544,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X70MM," 2,091 "
-28000545,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X75MM," 2,091 "
-28000546,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X80MM," 2,091 "
-28000547,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X85MM," 2,091 "
-28000548,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X90MM," 2,091 "
-28000549,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X95MM," 2,091 "
-28000550,HC IMPL SCREW CANNULATED FULLY THREAD TI 6.5X100MM," 2,091 "
-28000551,HC IMPL SCREW CANNULATED FULLY THREAD TI 6.5X105MM," 2,091 "
-28000552,HC IMPL SCREW CANNULATED FULLY THREAD TI 6.5X110MM," 2,091 "
-28000553,HC IMPL SCREW CANNULATED FULLY THREAD TI 6.5X120MM," 2,091 "
-28000554,HC IMPL SCREW CANNULATED FULLY THREAD TI 6.5X125MM," 2,091 "
-28000555,HC IMPL SCREW CANNULATED FULLY THREAD TI 6.5X130MM," 2,091 "
-28000556,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X9MM," 1,598 "
-28000557,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X10MM," 1,743 "
-28000558,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X11MM," 1,743 "
-28000559,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X12MM," 1,743 "
-28000560,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X13MM," 1,743 "
-28000561,HC IMPL PLATE VOLAR DISTAL RADIUS SHT LEFT 2.4 3 HOLE," 7,038 "
-28000562,HC IMPL PLATE VOLAR DISTAL RADIUS SHT LEFT 2.4 5 HOLE," 7,038 "
-28000563,HC IMPL PLATE VOLAR DISTAL RADIUS SHT RIGHT 2.4 3 HOLE," 7,487 "
-28000564,HC IMPL PLATE VOLAR DISTAL RADIUS SHT RIGHT 2.4 5 HOLE," 7,932 "
-28000565,HC IMPL PLATE VOLAR DISTAL RADIUS 2.4MM 3 HOLE," 4,199 "
-28000566,HC IMPL PLATE VOLAR DISTAL RADIUS 2.4MM 4 HOLE," 3,944 "
-28000567,HC IMPL PLATE VOLAR DISTAL RADIUS STR 2.4MM 5 HOLE," 4,047 "
-28000568,HC IMPL PLATE VOLAR DISTAL RADIUS STR 2.4MM 6 HOLE," 3,774 "
-28000569,HC IMPL PLATE DISTAL RADIUS LEFT ANG 2.4MM 3X2 HOLE," 4,063 "
-28000570,HC IMPL PLATE DISTAL RADIUS LEFT ANG 2.4MM 4X2 HOLE," 4,338 "
-28000571,HC IMPL PLATE DISTAL RADIUS RIGHT ANG 2.4MM 3X2 HOLE," 3,876 "
-28000572,HC IMPL PLATE DISTAL RADIUS RIGHT ANG 2.4MM 4X2 HOLE," 3,842 "
-28000573,HC IMPL PLATE DISTAL RADIUS RIGHT ANG 2.4MM 3X3 HOLE," 3,655 "
-28000574,HC IMPL PLATE DISTAL RADIUS RIGHT ANG 2.4MM 4X3 HOLE," 3,842 "
-28000575,HC IMPL PLATE DISTAL RADIUS LEFT ANG 2.4MM 3X3 HOLE," 3,655 "
-28000576,HC IMPL PLATE DISTAL RADIUS LEFT ANG 2.4MM 4X3 HOLE," 4,080 "
-28000577,HC IMPL PLATE DISTAL RADIUS OBLIQUE RIGHT ANG 2.4 3X3," 4,063 "
-28000578,HC IMPL PLATE DISTAL RADIUS OBLIQUE RIGHT ANG 2.4 4X3," 3,842 "
-28000579,HC IMPL PLATE DISTAL RADIUS OBLIQUE LEFT ANG 2.4 3X3," 3,876 "
-28000580,HC IMPL PLATE DISTAL RADIUS OBLIQUE LEFT ANG 2.4 4X3," 3,842 "
-28000581,HC  CATHETER HICKMAN DUAL LUMEN 9F," 1,324 "
-28000582,HC NEEDLE RADIOFREQUENCY TRANSSEPTAL STANDARD 71," 4,208 "
-28000583,HC IMPL COIL PERIPHERAL EMBOLIZATION 4CM 3MM," 1,870 "
-28000584,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC RIGHT 3X5 HOLE," 7,749 "
-28000585,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC RIGHT 5X5 HOLE," 7,038 "
-28000586,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC LEFT 3X5 HOLE," 7,487 "
-28000587,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC LEFT 5X5 HOLE," 7,038 "
-28000588,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC RIGHT 4X3 HOLE," 7,749 "
-28000589,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC RIGHT 5X3 HOLE," 7,038 "
-28000590,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC LEFT 4X3 HOLE," 7,487 "
-28000591,HC IMPL PLATE VOLAR DISTAL RADIUS XARTIC LEFT 4X5 HOLE," 7,463 "
-28000592,HC IMPL PLATE LCP DISTAL RADIUS RIGHT 2.4MM 9X3HOLE," 7,859 "
-28000593,HC IMPL PLATE LCP DISTAL RADIUS LEFT 2.4MM 9X3HOLE," 6,928 "
-28000594,HC IMPL PLATE LCP DISTAL RADIUS RIGHT 2.4MM 9X4HOLE," 7,140 "
-28000595,HC IMPL PLATE LCP DISTAL RADIUS LEFT 2.4MM 9X4HOLE," 7,140 "
-28000596,HC IMPL PLATE VOLAR DISTAL RADIUS COLUMN 8X3HOLE," 7,859 "
-28000597,HC IMPL PLATE VOLAR DISTAL RADIUS COLUMN LEFT 8X3HOLE," 8,134 "
-28000598,HC IMPL PLATE VOLAR DISTAL RADIUS COLUMN RIGHT 8X4HOLE," 7,140 "
-28000599,HC IMPL PLATE VOLAR DISTAL RADIUS COLUMN LEFT 8X4HOLE," 7,140 "
-28000600,HC IMPL PLATE VOLAR DISTAL RADIUS COLUMN RIGHT 8X5HOLE," 7,353 "
-28000601,HC IMPL PLATE VOLAR DISTAL RADIUS COLUMN LEFT 8X5HOLE," 7,353 "
-28000602,HC IMPL PLATE COMPRESSION ULNAR OSTEOTOMY 7HOLE," 5,908 "
-28000603,HC IMP SCREW BONE CORTICAL HEX 3.2X10MM, 638 
-28000604,HC IMP SCREW BONE CORTICAL HEX 3.2X12MM, 638 
-28000605,HC IMP SCREW BONE CORTICAL HEX 3.2X14MM, 638 
-28000606,HC IMP SCREW BONE CORTICAL HEX 3.2X16MM, 638 
-28000607,HC IMP SCREW BONE CORTICAL HEX 3.2X18MM, 638 
-28000608,HC IMP SCREW BONE CORTICAL LAG 3.2X14MM, 723 
-28000609,HC IMP SCREW BONE CORTICAL LAG 3.2X18MM, 723 
-28000610,HC IMP SCREW BONE CORTICAL LAG 3.2X20MM, 723 
-28000611,HC IMP SCREW BONE CORTICAL LAG 3.2X22MM, 723 
-28000612,HC IMP SCREW BONE CORTICAL LAG 3.2X24MM, 723 
-28000613,HC IMP SCREW BONE CORTICAL LAG 3.2X26MM, 723 
-28000619,HC IMPL PLATE LOCKING VOLAR BEARING RIGHT LEFT 4," 7,608 "
-28000620,HC IMPL PLATE LOCKING VOLAR BEARING LEFT 5-4," 8,075 "
-28000621,HC IMPL PLATE LOCKING VOLAR BEARING LEFT 7-4," 7,875 "
-28000622,HC IMPL PLATE LOCKING VOLAR BEARING RIGHT 3-4," 8,458 "
-28000623,HC IMPL PLATE LOCKING VOLAR BEARING RIGHT 5-4," 8,075 "
-28000624,HC IMPL PLATE LOCKING VOLAR BEARING RIGHT 7-4," 7,875 "
-28000625,HC IMPL PLATE VOLAR PEG LEFT 6PEG 3HOLE," 4,463 "
-28000626,HC IMPL PLATE VOLAR PEG LEFT 6PEG 5HOLE," 5,313 "
-28000627,HC IMPL PLATE VOLAR PEG RIGHT 6PEG 3HOLE," 4,463 "
-28000628,HC IMPL PLATE VOLAR PEG RIGHT 6PEG 5HOLE," 6,163 "
-28000629,HC IMPL PLATE LOCKING VOLAR BEARING LEFT 5," 8,213 "
-28000630,HC IMPL PLATE LOCKING VOLAR BEARING RIGHT 5," 8,075 "
-28000631,HC IMPL PLATE RADIAL PIN 7 HOLE," 5,950 "
-28000632,HC IMPL PLATE ULNAR PIN 7 HOLE," 5,950 "
-28000633,HC IMPL PLATE WIRE 2 HOLE," 3,188 "
-28000634,HC IMPL PLATE WIRE 3 HOLE," 3,358 "
-28000635,HC IMPL PLATE WIRE 5 HOLE," 3,443 "
-28000636,HC IMPL PLATE L LEFT 6 HOLE," 3,953 "
-28000637,HC IMPL PLATE L RIGHT 6 HOLE," 3,953 "
-28000638,HC IMP PEG THREAD VOLAR 14MM, 638 
-28000639,HC IMP PEG THREAD VOLAR 16MM, 638 
-28000640,HC IMP PEG THREAD VOLAR 18MM, 638 
-28000641,HC IMP PEG THREAD VOLAR 20MM, 638 
-28000642,HC IMP PEG THREAD VOLAR 22MM, 638 
-28000643,HC IMP PEG THREAD VOLAR 24MM, 638 
-28000644,HC IMP PEG THREAD VOLAR 26MM, 638 
-28000645,HC IMP PEG THREAD VOLAR 28MM, 638 
-28000646,HC IMP PEG UNTHREAD VOLAR 14MM, 638 
-28000647,HC IMP PEG UNTHREAD VOLAR 16MM, 638 
-28000648,HC IMP PEG UNTHREAD VOLAR 18MM, 638 
-28000649,HC IMP PEG UNTHREAD VOLAR 20MM, 638 
-28000650,HC IMP PEG UNTHREAD VOLAR 22MM, 638 
-28000651,HC IMP PEG UNTHREAD VOLAR 24MM, 638 
-28000652,HC IMP PEG UNTHREAD VOLAR 26MM, 638 
-28000653,HC IMP PEG UNTHREAD VOLAR 28MM, 638 
-28000654,HC IMP CLAMP SMALL FRAGMENT OUTER COMPONENT 20MM," 1,658 "
-28000655,HC IMP CLAMP SMALL FRAGMENT OUTER COMPONENT 24MM," 1,658 "
-28000656,HC IMP CLAMP SMALL FRAGMENT OUTER COMPONENT 28MM," 1,488 "
-28000657,HC IMP CLAMP SMALL FRAGMENT OUTER COMPONENT 32MM," 1,658 "
-28000658,HC IMP CLAMP SMALL FRAGMENT INNER COMPONENT 16X8MM," 2,125 "
-28000659,HC IMP CLAMP SMALL FRAGMENT INNER COMPONENT 20X10MM," 2,125 "
-28000660,HC IMP CLAMP SMALL FRAGMENT INNER COMPONENT 20X14MM," 2,125 "
-28000661,HC IMP CLAMP SMALL FRAGMENT INNER COMPONENT 24X10MM," 1,955 "
-28000662,HC IMP CLAMP SMALL FRAGMENT INNER COMPONENT 24X14MM," 2,125 "
-28000663,HC IMP CLAMP SMALL FRAGMENT INNER COMPONENT 24X18MM," 2,125 "
-28000664,HC IMP CLAMP SMALL FRAGMENT BUTTRESS PIN 16X8MM," 2,635 "
-28000665,HC IMP CLAMP SMALL FRAGMENT BUTTRESS PIN 20X10MM," 2,635 "
-28000666,HC IMP CLAMP SMALL FRAGMENT BUTTRESS PIN 24X10MM," 2,635 "
-28000667,HC IMP CLAMP SMALL FRAGMENT BUTTRESS PIN 24X14MM," 2,465 "
-28000668,HC IMP CLAMP SMALL FRAGMENT BUTTRESS PIN 27X14MM," 2,465 "
-28000669,HC IMP PIN BUTTRESS 16MM," 2,125 "
-28000670,HC IMP PIN BUTTRESS 20MM," 2,125 "
-28000671,HC IMP PIN BUTTRESS 24MM," 2,125 "
-28000673,HC IMP PIN BUTTRESS 20MM," 2,125 "
-28000674,HC IMP PIN BUTTRESS VOLAR 32MM," 2,635 "
-28000675,HC IMP PIN BUTTRESS VOLAR 42MM," 2,465 "
-28000676,HC IMP SHOULDER SLED ULNAR 38MM," 2,635 "
-28000678,HC IMPL COIL EMBOLIZATION 7-3MM 0.018IN, 767 
-28000679,HC IMPL COIL PERIPHERAL EMBOLIZATION 2.6CM 4MM 0.035IN, 767 
-28000680,HC IMPL COIL EMBOLIZATION 14CM 12MM 0.035IN, 730 
-28000681,HC  GUIDEWIRE HYDROPHILIC TRAXCESS 14IN," 3,103 "
-28000682,HC  CATHETER BALLOON 10F 40MM 120CM 40CC," 3,171 "
-28000683,HC  CATHETER DILATATION 22MM 4CM 12F 9F," 4,697 "
-28000684,HC  CATHETER DILATATION 4CM 23MM 9F 14F," 4,565 "
-28000685,HC  CATHETER DILATATION 4CM 25MM 9F 14F," 4,565 "
-28000686,HC IMP STENT NEPHROSTOMY ULTRATHANE 10.2X24, 866 
-28000687,HC IMP STENT NEPHROSTOMY ULTRATHANE 8.5FX22, 866 
-28000688,HC IMP STENT NEPHROSTOMY ULTRATHANE 8.5FX24, 866 
-28000689,HC IMP STENT NEPHROSTOMY ULTRATHANE 8.5FX26, 866 
-28000690,HC IMP STENT NEPHROSTOMY ULTRATHANE 8.5FX28, 866 
-28000691,HC IMP SCREW CORTICAL 3.5X15MM, 865 
-28000692,HC IMP SCREW CORTICAL 3.5X20MM, 816 
-28000693,HC IMP SCREW CORTICAL 3.5X13MM, 865 
-28000694,HC IMP PEG THREAD 2.5X26MM LONG DVR, 865 
-28000695,HC IMPL PLATE SMALL FRAG LOCKING HS Y," 5,423 "
-28000696,HC IMPL PLATE SMALL FRAG LOCKING HS RIGHT," 5,423 "
-28000697,HC IMPL PLATE SMALL FRAG LOCK HF STR," 5,423 "
-28000698,HC IMPL PLATE SMALL FRAG LOCKING HF Y," 5,748 "
-28000716,HC IMPL CANNULA FAST FIX REVERSED CURVED," 2,769 "
-28000717,HC IMPL CANNULA FAST FIX CURVED," 2,865 "
-28000718,HC IMP GRAFT STRETCH RING 8X80," 10,095 "
-28000719,HC BUR M4 HIGH SPEED CVD CUTTING 4MM," 2,188 "
-28000720,HC BUR M4 HIGH SPEED CVD DIAMOND 15 DEG 3.2MM," 2,188 "
-28000721,HC BUR M4 HIGH SPEED CVD DIAMOND 15 DEG 5MM," 2,349 "
-28000722,HC BUR HIGH SPEED CVD DIAMOND 40 DEG 3.2MM," 2,440 "
-28000723,HC BUR M4 HIGH SPEED CVD DIAMOND 70 DEG 5MM," 2,188 "
-28000724,HC BLADE ROTATABLE SKIMMER 13CM," 2,950 "
-28000726,HC HOOK SEALER LAP ENDOSCOPIC 2MM," 8,458 "
-28000727,HC IMP STAPLE RELOAD ENDO GIA 60 3.5," 2,422 "
-28000728,HC IMP STAPLE RELOAD ENDO GIA 60 4.8," 2,543 "
-28000729,HC IMP STENT CORONARY ENDEAVOR RX DES 2.5X8," 11,963 "
-28000730,HC IMP STENT CORONARY ENDEAVOR RX DES 2.5X14," 11,963 "
-28000731,HC IMP STENT CORONARY ENDEAVOR RX DES 2.5X18," 11,963 "
-28000732,HC IMP STENT CORONARY ENDEAVOR RX DES 2.5X30," 11,963 "
-28000733,HC IMP STENT CORONARY ENDEAVOR RX DES 3X9," 11,963 "
-28000734,HC IMP STENT CORONARY ENDEAVOR RX DES 3X12," 11,963 "
-28000735,HC IMP STENT CORONARY ENDEAVOR RX DES 3X15," 11,963 "
-28000736,HC IMP STENT CORONARY ENDEAVOR RX DES 3X18," 11,963 "
-28000737,HC IMP STENT CORONARY ENDEAVOR RX DES 3X24," 11,963 "
-28000738,HC IMP STENT CORONARY ENDEAVOR RX DES 3X30," 11,963 "
-28000739,HC IMP STENT CORONARY ENDEAVOR RX DES 3.5X9," 11,963 "
-28000740,HC IMP STENT CORONARY ENDEAVOR RX DES 3.5X12," 11,963 "
-28000741,HC IMP STENT CORONARY ENDEAVOR RX DES 3.5X15," 11,963 "
-28000742,HC IMP STENT CORONARY ENDEAVOR RX DES 3.5X18," 11,963 "
-28000743,HC IMP STENT CORONARY ENDEAVOR RX DES 3.5X24," 13,463 "
-28000744,HC IMP STENT CORONARY ENDEAVOR RX DES 3.5X30," 11,963 "
-28000745,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.5X12MM," 1,488 "
-28000746,HC IMP STENT CORONARY DRIVER RX BMS 2.25X8," 7,650 "
-28000747,HC IMP STENT CORONARY DRIVER RX BMS 2.25X12," 7,650 "
-28000748,HC IMP STENT CORONARY DRIVER RX BMS 2.25X14," 7,875 "
-28000749,HC IMP STENT CORONARY DRIVER RX BMS 2.25X18," 7,875 "
-28000750,HC IMP STENT CORONARY DRIVER RX BMS 2.25X24," 7,650 "
-28000751,HC IMP STENT CORONARY DRIVER RX BMS 2.5X8," 7,875 "
-28000752,HC IMP STENT CORONARY DRIVER RX BMS 2.75X8," 7,650 "
-28000753,HC IMP STENT CORONARY DRIVER RX BMS 2.75X14," 7,650 "
-28000754,HC IMP STENT CORONARY DRIVER RX BMS 2.75X18," 7,875 "
-28000755,HC IMP STENT CORONARY DRIVER RX BMS 2.75X24," 7,875 "
-28000756,HC IMP STENT CORONARY DRIVER RX BMS 3X15," 7,875 "
-28000757,HC IMP STENT CORONARY DRIVER RX BMS 3X24," 7,875 "
-28000758,HC IMP STENT CORONARY DRIVER RX BMS 3.5X15," 7,875 "
-28000759,HC IMP STENT CORONARY DRIVER RX BMS 4X18," 7,650 "
-28000760,HC IMP STENT CORONARY DRIVER RX BMS 4X24," 7,650 "
-28000761,HC IMP STENT CORONARY DRIVER RX BMS 4X30," 7,875 "
-28000762,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.5X15MM," 1,726 "
-28000763,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.5X20MM," 1,488 "
-28000764,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2X12MM," 1,488 "
-28000765,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2X15MM," 1,488 "
-28000766,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2X20MM," 1,785 "
-28000767,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2.5X12MM," 1,488 "
-28000768,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2.5X15MM," 1,488 "
-28000769,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2.5X20MM," 1,785 "
-28000770,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3X15MM," 1,726 "
-28000771,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3X20MM," 1,785 "
-28000772,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3.5X10MM," 1,726 "
-28000773,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3.5X12MM," 1,488 "
-28000774,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3.5X15MM," 1,785 "
-28000775,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3.5X20MM," 1,785 "
-28000776,HC  CATHETER DILATATION NC SPRINTER RX 6MMX3.5MM," 1,785 "
-28000777,HC  CATHETER DILATATION NC SPRINTER RX 9MMX2.5MM," 1,488 "
-28000778,HC  CATHETER DILATATION NC SPRINTER RX 9MMX3.0MM," 1,488 "
-28000779,HC  CATHETER DILATATION NC SPRINTER RX 9MMX3.5MM," 1,488 "
-28000780,HC  CATHETER DILATATION NC SPRINTER RX 9MMX4.0MM," 1,488 "
-28000781,HC  CATHETER DILATATION NC SPRINTER RX 12MMX2.5MM," 1,785 "
-28000782,HC  CATHETER DILATATION NC SPRINTER RX 12MMX3.0MM," 1,488 "
-28000783,HC  CATHETER DILATATION NC SPRINTER RX 12MMX3.5MM," 1,488 "
-28000784,HC  CATHETER DILATATION NC SPRINTER RX 12MMX4.0MM," 1,488 "
-28000785,HC  CATHETER DILATATION NC SPRINTER RX 15MMX2.5MM," 1,488 "
-28000786,HC  CATHETER DILATATION NC SPRINTER RX 15MMX3.0MM," 1,488 "
-28000787,HC  CATHETER DILATATION NC SPRINTER RX 15MMX3.5MM," 1,488 "
-28000788,HC  CATHETER DILATATION NC SPRINTER RX 15MMX4.0MM," 1,785 "
-28000789,HC  CATHETER DILATATION 20MM 4CM 12F 8F," 4,697 "
-28000790,HC  CATHETER DELIVERY STRAIGHT 95X6CM 6F," 4,208 "
-28000791,HC IMP CLIP ANEURYSM RIGHT ANGLE KEES SLIMLINE 5MM," 3,426 "
-28000792,HC  CATHETER DELIVERY MULTI PURPOSE 95X6CM 6F," 5,058 "
-28000793,HC  CATHETER ELECTROPHYSIOLOGY MAP DEL .070IN 8CM 6," 5,058 "
-28000794,HC IMP KNEE LINER ACETABULAR BIPOLAR 28ID 47MM," 2,758 "
-28000795,HC IMP PIN RUSH 4.8X298MM, 557 
-28000796,HC SHEAR COAGULATING CURVED ACE HANDLE 36CM," 4,098 "
-28000797,HC SHEAR COAGULATING CURVED ACE HANDLE 5X23CM," 3,500 "
-28000799,HC IMP GRAFT DURAL MATRIX DURAGN SUTURABLE 2X2IN," 2,613 "
-28000800,HC IMP GRAFT DURAL MATRIX DURAGN SUTURABLE 3X3IN," 4,975 "
-28000801,HC IMP GRAFT DURAL MATRIX DURAGEN XS 1X3IN," 2,734 "
-28000802,HC IMP GRAFT DURAL MATRIX DURAGEN XS 2X2IN," 2,870 "
-28000803,HC IMP GRAFT DURAL MATRIX DURAGEN XS 3X3IN," 5,214 "
-28000804,HC IMP GRAFT DURAL MATRIX DURAGEN XS 4X5IN," 8,518 "
-28000805,HC IMP GRAFT DURAL MATRIX DURAGEN XS 5X7IN," 12,028 "
-28000806,HC IMP ROD SPINAL XIA ALLOY TI 3 6X480MM," 3,999 "
-28000809,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3X10MM," 1,726 "
-28000810,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2X6MM," 1,726 "
-28000811,HC IMP BLOCKER SPINAL TI XIA 3," 1,680 "
-28000812,HC IMPL SCR SPINAL XIA3 POLYAXIAL TI 5.5X40MM," 7,863 "
-28000813,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6.5X40MM," 7,863 "
-28000814,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6.5X45MM," 7,863 "
-28000815,HC IMP VALVE MITRAL MASTERS 23MM," 30,674 "
-28000816,HC  CATHETER BASIC PICC DUAL LUMEN MICRO INTROD 4F," 1,190 "
-28000817,HC  GUIDEWIRE CORONARY STRAIGHTAIGHT 0.014IN 180CM, 986 
-28000818,HC IMP ICD DUAL IC MAXIMO II," 68,400 "
-28000820,HC IMP ANCHOR CORKSCREW FULLY THREAD II 4.5MM, 391 
-28000821,HC IMP ANCHOR CORKSCREW FULLY THREAD III 6.5MM, 417 
-28000822,HC IMP ANCHOR SUTURE SWIVEL 5.5MMX15MM," 3,018 "
-28000823,HC IMP ANCHOR SUTURE SWIVEL 4.5X19.1MM, 604 
-28000824,HC IMP ANCHOR BIOSWIVEL 4.75X24.5MM, 646 
-28000825,HC IMP ANCHOR SUTURE PUSHLOCK PEEK 2.9MM," 2,890 "
-28000826,HC IMP ANCHOR SUTURE PUSHLOCK 3.5X19.5MM, 578 
-28000827,HC IMP ANCHOR SUTURE BIO PUSHLOCK 4.5X28MM," 3,145 "
-28000828,HC IMP ANCHOR SUTURE BIO PUSHLOCK 4.5X24MM," 2,890 "
-28000829,HC IMP ANCHOR BIO SUTURETAK 3X14MM, 442 
-28000839,HC IMP DART MENISCAL CHONDRAL 1.3X18MM," 1,029 "
-28000842,HC IMP ANCHOR SUTURE BIO PUSHLOCK 2.5X8," 2,890 "
-28000843,HC IMP ANCHOR SUTURE PUSHLOCK PEEK 2.5MM, 578 
-28000844,HC IMP ANCHOR BIO SUTURETAK MICRO ARTHROSCOPIC FIX," 1,488 "
-28000845,HC IMP ANCHOR BIO SUTURETAK 2.4MM KIT," 1,488 "
-28000846,HC IMP ANCHOR BIO SUTURETAK 2.4X6.5MM," 2,083 "
-28000849,HC IMP ANCHOR SUTURE PANALOK 2 CP2," 3,525 "
-28000850,HC IMP ANCHOR SUTURE PANACRYL 2 CP2," 3,683 "
-28000855,HC  CATHETER NEURO RENEGADE 10X135CM 2.8F," 5,433 "
-28000856,HC IMP STENT BILIARY LUMINEXX 7X40X80," 11,243 "
-28000857,HC IMP STENT BILIARY LUMINEXX 8X40X80," 11,250 "
-28000858,HC IMP STENT BILIARY LUMINEXX 8X60X80," 11,250 "
-28000859,HC IMP STENT BILIARY LUMINEXX 6X10X80," 11,243 "
-28000860,HC IMPL COIL PERIPHERAL EMBOLIZATION PUSHABLE 2CM 2MM," 1,403 "
-28000861,HC IMPL COIL PERIPHERAL EMBOLIZATION PUSHABLE 6CM 4MM," 1,870 "
-28000862,HC IMPL COIL PERIPH EMBLZTN PUSHABLE XPNDNG SYS 3COIL," 2,805 "
-28000864,HC HOOK MONOPOLAR REALHAND 34CM," 3,613 "
-28000865,HC GRASPER DEBAKEY REALHAND 45CM," 3,613 "
-28000866,HC  CATHETER VENTRICULAR UNLOADING 2.5L," 87,500 "
-28000867,HC IMPL COIL NEURO MICRO LOOP CDF10 16CM 6MM," 9,713 "
-28000868,HC  CATHETER BASIC STRAIGHT HEMODIALYSIS 20CM KIT," 3,953 "
-28000869,HC CATHETER BASIC STRAIGHT INTRODUCER 22CM," 3,944 "
-28000870,HC CATHETER BASIC STRAIGHT INTRODUCER 24CM," 3,477 "
-28000871,HC CATHETER EMBOLIZATION PERIPHERAL 4X150CM," 2,742 "
-28000872,HC  INTROD TRANSRADIAL 21X11X6, 383 
-28000873,HC  CATHETER DILATATION 4X150MM 150CM 4F," 3,324 "
-28000874,HC IMPL PLATE FAN REINFORCED SYNPOR THCK TI 0.8MM," 8,610 "
-28000875,HC IMPL PLATE FAN REINFORCED SYNPOR THCK FIXED HOLE 0.8," 8,370 "
-28000876,HC IMPL PLATE FAN REINFORCED SYNPOR THCK TI TEAL 1.5MM," 8,610 "
-28000877,HC IMPL PLATE FAN REINF SYNPR THCK FIX HOLE TEAL 1.5MM," 8,370 "
-28000878,HC  CATHETER INTERVENTIONAL 130CM 2.4MM 2.9F," 2,720 "
-28000879,HC IMP ANCHOR CORKSCREW 3.5MM, 357 
-28000880,HC CLAMP COMBINATION MR SAFE LARGE," 6,928 "
-28000881,HC IMPL COIL NEURO MICRO 2CM 1.5MM," 9,713 "
-28000882,HC IMPL COIL NEURO MICRO 4CM 1.5MM," 9,713 "
-28000883,HC IMPL COIL NEURO MICRO LOOP 6CM 1.5MM," 9,713 "
-28000884,HC IMPL COIL NEURO MICRO 2CM 2MM," 9,713 "
-28000885,HC IMPL COIL NEURO MICRO 3CM 2MM," 9,713 "
-28000886,HC IMPL COIL NEURO MICRO 4CM 2MM," 9,713 "
-28000887,HC IMPL COIL NEURO MICRO 6CM 2MM," 9,713 "
-28000888,HC IMPL COIL NEURO MICRO 8CM 2MM," 9,713 "
-28000889,HC IMPL COIL NEURO MICRO 10CM 2MM," 9,713 "
-28000890,HC IMPL COIL NEURO MICRO 4CM 3MM," 9,713 "
-28000891,HC IMPL COIL NEURO MICRO 6CM 3MM," 9,713 "
-28000892,HC IMPL COIL NEURO MICRO 8CM 3MM," 9,713 "
-28000893,HC IMPL COIL NEURO MICRO 10CM 3MM," 9,713 "
-28000894,HC IMPL COIL NEURO MICRO 6CM 4MM," 9,713 "
-28000895,HC IMPL COIL NEURO MICRO 8CM 4MM," 9,713 "
-28000896,HC IMPL COIL NEURO MICRO 10CM 4MM," 9,713 "
-28000897,HC IMPL COIL NEURO MICRO 10CM 5MM," 9,713 "
-28000898,HC IMPL COIL NEURO MICRO 15CM 5MM," 9,713 "
-28000899,HC IMPL COIL NEURO MICRO 12CM 6MM," 9,713 "
-28000900,HC IMPL COIL NEURO MICRO 16CM 7MM," 9,713 "
-28000901,HC IMPL COIL NEURO MICRO 20CM 7MM," 9,713 "
-28000902,HC IMPL COIL NEURO MICRO 20CM 8MM," 9,713 "
-28000903,HC IMPL COIL NEURO MICRO 25CM 9MM," 9,713 "
-28000904,HC IMPL COIL NEURO MICRO 25CM 10MM," 9,713 "
-28000905,HC  CATHETER NEURO GUIDING TIP MPC 90CM 5F," 2,259 "
-28000906,HC  CATHETER NEURO GUIDING TIP MPD 90CM 5F," 2,259 "
-28000907,HC  CATHETER NEURO GUIDING MPC VRTBRL 90CM 6F," 2,259 "
-28000908,HC  CATHETER ELECTROPHYSIOLOGY MAP GUID TIP 90CM 6F," 2,259 "
-28000911,HC IMPL SCR SPINAL XIA3 POLYAXIAL TI 5.5X45MM," 8,895 "
-28000912,HC IMPL SCREW MAXIOMANDIBULAR FIXATION 2.4X12MM, 774 
-28000913,HC IMPL SCREW MAXIOMANDIBULAR FIXATION 2.4X14MM, 774 
-28000914,HC ELECTRODE SPATULA PLASMA 33CM," 3,060 "
-28000915,HC  CATHETER DILATATION CORONARY OTW 1.5X12MM," 1,488 "
-28000916,HC  CATHETER DILATATION PTA 3X15MM 150CM 4F," 3,324 "
-28000917,HC  CATHETER DILATATION 5X150MM," 3,324 "
-28000918,HC  CATHETER DILATATION 5X220MM," 3,324 "
-28000919,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.5X6MM," 1,488 "
-28000920,HC  CATHETER NEURO SELECTIVE 5F 130X9CM," 1,063 "
-28000921,HC  CATHETER ACCESS SIM NEURON SELECT 130," 1,063 "
-28000922,HC IMP PERISTRIP REINFORCEMENT VERITAS 45MM," 1,456 "
-28000923,HC IMP PERISTRIP REINFORCEMENT VERITAS 60MM," 1,555 "
-28000924,HC IMP EAR PROSTHESIS OSSICULAR 4MM, 927 
-28000926,HC  CATHETER APEX OTW PUSH 1.5X8MM," 2,117 "
-28000927,HC  CATHETER DILATATION OTW 1.5X8MM," 2,117 "
-28000928,HC  CATHETER DILATATION OTW 2X8MM," 2,117 "
-28000929,HC  CATHETER DILATATION OTW 2.25X8MM," 2,117 "
-28000930,HC  CATHETER DILATATION OTW 2.5X8MM," 2,117 "
-28000931,HC  CATHETER DILATATION OTW 3X8MM," 2,117 "
-28000932,HC  CATHETER APEX OTW PUSH 1.5X12MM," 2,117 "
-28000933,HC  CATHETER DILATATION OTW 12X1.5MM," 2,117 "
-28000934,HC  CATHETER DILATATION OTW 2X12MM," 2,117 "
-28000935,HC  CATHETER DILATATION OTW 2.25X12MM," 2,117 "
-28000936,HC  CATHETER DILATATION OTW 2.5X12MM," 2,117 "
-28000937,HC  CATHETER DILATATION OTW 3X12MM," 2,117 "
-28000938,HC  CATHETER APEX OTW PUSH 1.5X15MM," 2,117 "
-28000939,HC  CATHETER DILATATION OTW 1.5X15MM," 2,117 "
-28000940,HC  CATHETER DILATATION OTW 2X15MM," 2,117 "
-28000941,HC  CATHETER DILATATION OTW 2.25X15MM," 2,117 "
-28000942,HC  CATHETER DILATATION OTW 2.5X15MM," 2,117 "
-28000943,HC  CATHETER DILATATION OTW 3X15MM," 2,117 "
-28000944,HC  CATHETER DILATATION OTW 3.5X15MM," 2,117 "
-28000945,HC  CATHETER DILATATION OTW 4X15MM," 2,117 "
-28000946,HC  CATHETER DILATATION OTW 4.5X15MM," 2,117 "
-28000947,HC  CATHETER DILATATION OTW 5X15MM," 2,117 "
-28000948,HC  CATHETER DILATATION OTW 2X20MM," 2,117 "
-28000949,HC  CATHETER DILATATION OTW 2.25X20MM," 2,117 "
-28000950,HC  CATHETER DILATATION OTW 2.5X20MM," 2,117 "
-28000951,HC  CATHETER DILATATION OTW 3X20MM," 2,117 "
-28000952,HC  CATHETER DILATATION OTW 3.5X20MM," 2,117 "
-28000953,HC  CATHETER DILATATION OTW 4X20MM," 2,117 "
-28000954,HC  CATHETER DILATATION OTW 4.5X20MM," 2,117 "
-28000955,HC  CATHETER DILATATION OTW 5X20MM," 2,117 "
-28000956,HC  CATHETER DILATATION OTW 2.5X30MM," 2,117 "
-28000957,HC  CATHETER DILATATION OTW 3X30MM," 2,117 "
-28000958,HC  CATHETER DILATATION OTW 2.5X40MM," 2,117 "
-28000959,HC  CATHETER DILATATION OTW 3X40MM," 2,117 "
-28000960,HC  CATHETER DILATATION PTCA 1.5X8MM," 2,117 "
-28000961,HC  CATHETER DILATATION RX 1.5X8MM," 2,117 "
-28000962,HC  CATHETER DILATATION RX 2X8MM," 2,117 "
-28000963,HC  CATHETER DILATATION RX 2.25X8MM," 2,117 "
-28000964,HC  CATHETER DILATATION RX 2.5X8MM," 2,117 "
-28000965,HC  CATHETER DILATATION RX 2.75X8MM," 2,117 "
-28000966,HC  CATHETER DILATATION RX 3X8MM," 2,117 "
-28000967,HC  CATHETER DILATATION RX 3.5X8MM," 2,117 "
-28000968,HC  CATHETER DILATATION RX 4X8MM," 2,117 "
-28000969,HC  CATHETER DILATATION PTCA 1.5X12MM," 2,117 "
-28000970,HC  CATHETER DILATATION RX 1.5X12MM," 2,117 "
-28000971,HC  CATHETER DILATATION RX 2X12MM," 2,117 "
-28000972,HC  CATHETER DILATATION RX 2.25X12MM," 2,117 "
-28000973,HC  CATHETER DILATATION RX 2.5X12MM," 2,117 "
-28000974,HC  CATHETER DILATATION RX 2.75X12MM," 2,117 "
-28000975,HC  CATHETER DILATATION RX 3X12MM," 2,117 "
-28000976,HC  CATHETER DILATATION RX 3.5X12MM," 2,117 "
-28000977,HC  CATHETER DILATATION RX 4X12MM," 2,117 "
-28000978,HC  CATHETER DILATATION PTCA 1.5X15MM," 2,117 "
-28000979,HC  CATHETER DILATATION RX 1.5X15MM," 2,117 "
-28000980,HC  CATHETER DILATATION RX 2X15MM," 2,117 "
-28000981,HC  CATHETER DILATATION RX 2.25X15MM," 2,117 "
-28000982,HC  CATHETER DILATATION RX 2.5X15MM," 2,117 "
-28000983,HC  CATHETER DILATATION RX 2.75X15MM," 2,117 "
-28000984,HC IMP STENT NEUROFORM3 2.5X30," 33,649 "
-28000985,HC  CATHETER DILATATION RX 3X15MM," 2,117 "
-28000986,HC  CATHETER DILATATION RX 3.5X15MM," 2,117 "
-28000987,HC  CATHETER DILATATION RX 4X15MM," 2,117 "
-28000988,HC  CATHETER DILATATION RX 4.5X15MM," 2,117 "
-28000989,HC  CATHETER DILATATION RX 5X15MM," 2,117 "
-28000990,HC  CATHETER DILATATION PTCA 1.5X20MM," 2,117 "
-28000991,HC  CATHETER DILATATION RX 1.5X20MM," 2,117 "
-28000992,HC  CATHETER DILATATION RX 2X20MM," 2,117 "
-28000993,HC  CATHETER DILATATION RX 2.25X20MM," 2,117 "
-28000994,HC  CATHETER DILATATION RX 2.5X20MM," 2,117 "
-28000995,HC  CATHETER DILATATION RX 2.75X20MM," 2,117 "
-28000996,HC  CATHETER DILATATION RX 3X20MM," 2,117 "
-28000997,HC IMPL SCREW DHS LAG 95MM," 3,125 "
-28000998,HC  CATHETER DILATATION RX 3.5X20MM," 2,117 "
-28000999,HC  CATHETER DILATATION 4X20MM," 2,117 "
-28001000,HC  CATHETER DILATATION 4.50X20MM," 2,117 "
-28001001,HC  CATHETER DILATATION 5X20MM," 2,117 "
-28001002,HC  CATHETER DILATATION 2X30MM," 2,117 "
-28001003,HC  CATHETER DILATATION 2.5X30MM," 2,117 "
-28001004,HC  CATHETER DILATATION 3X30MM," 2,117 "
-28001005,HC  CATHETER DILATATION 3.5X30MM," 2,117 "
-28001006,HC  CATHETER DILATATION 4X30MM," 2,117 "
-28001007,HC  CATHETER DILATATION 3X40MM," 2,117 "
-28001008,HC  CATHETER DILATATION 3.50X40MM," 2,117 "
-28001009,HC  CATHETER DILATATION 4X40MM," 2,117 "
-28001010,HC IMPL SCREW GLENOID LOCKING 5X26MM, 782 
-28001011,HC  CATHETER HEMODIALYSIS CHRONIC 18CM SET," 2,499 "
-28001012,HC  CATHETER HEMODIALYSIS CHRONIC 16CM SET," 2,890 "
-28001013,HC  CATHETER DILATATION 1.5X10MM 100CM," 1,658 "
-28001014,HC  CATHETER DILATATION 1.5X10MM 2.6F," 1,658 "
-28001015,HC  CATHETER DILATATION FIRE STAR 1 MB 2X10MM 100," 1,658 "
-28001016,HC  CATHETER DILATATION FIRE STAR 2 MB 2X10MM 100," 1,658 "
-28001017,HC  CATHETER DILATATION 2.25X10MM 100CM," 1,658 "
-28001018,HC  CATHETER DILATATION 2.5X10MM 100CM," 1,658 "
-28001019,HC  CATHETER DILATATION 2.75X10MM 2.6F," 1,658 "
-28001020,HC  CATHETER DILATATION 3X10MM 100CM," 1,658 "
-28001021,HC  CATHETER DILATATION 3.25X10MM 100CM," 1,658 "
-28001022,HC  CATHETER DILATATION 3.5X10MM 2.6F," 1,658 "
-28001023,HC  CATHETER DILATATION 1.5X15MM 2.6F," 1,658 "
-28001024,HC  CATHETER DILATATION 1.5X15MM 100CM," 1,658 "
-28001025,HC  CATHETER DILATATION 2X15MM 2.6F," 1,658 "
-28001026,HC  CATHETER DILATATION 2X15MM 100CM," 1,658 "
-28001027,HC  CATHETER DILATATION 2.25X15MM 100CM," 1,658 "
-28001028,HC  CATHETER DILATATION 2.5X15MM 100CM," 1,658 "
-28001029,HC  CATHETER DILATATION 2.75X15MM 100CM," 1,658 "
-28001030,HC  CATHETER DILATATION 3X15MM 100CM," 1,658 "
-28001031,HC  CATHETER DILATATION 3.25X15MM 100CM," 1,658 "
-28001032,HC  CATHETER DILATATION 3.5X15MM 100CM," 1,658 "
-28001033,HC  CATHETER DILATATION FIRE STAR 1BM 1.5X20MM 100," 1,658 "
-28001034,HC  CATHETER DILATATION FIRE STAR RX 2BM 1.5X20 100," 1,658 "
-28001035,HC  CATHETER DILATATION 2X20MM 100CM," 1,658 "
-28001036,HC  CATHETER DILATATION 2X20MM 2.6F," 1,658 "
-28001037,HC  CATHETER DILATATION 2.25X20MM 100CM," 1,658 "
-28001038,HC  CATHETER DILATATION 2.5X20MM 100CM," 1,658 "
-28001039,HC  CATHETER DILATATION 2.75X20MM 2.6F," 1,658 "
-28001040,HC  CATHETER DILATATION 3X20MM 100CM," 1,658 "
-28001041,HC  CATHETER DILATATION 3.25X20MM 2.6F," 1,658 "
-28001042,HC  CATHETER DILATATION 3.5X20MM 100CM," 1,658 "
-28001043,HC  CATHETER DILATATION DURA STAR 2.25X10MM 100CM," 1,658 "
-28001044,HC  CATHETER DILATATION DURA STAR 2.50X10MM 100CM," 1,658 "
-28001045,HC  CATHETER DILATATION 2.75X10MM 100CM," 1,658 "
-28001046,HC  CATHETER DILATATION 3X10MM 2.7F," 1,658 "
-28001047,HC  CATHETER DILATATION 3.25X10MM 2.7F," 1,658 "
-28001048,HC  CATHETER DILATATION 3.5X10MM 100CM," 1,658 "
-28001049,HC  CATHETER DILATATION 3.75X10MM 100CM," 1,658 "
-28001050,HC  CATHETER DILATATION 4X10MM 100CM," 1,658 "
-28001051,HC  CATHETER DILATATION DURA STAR 2.25X15MM 100CM," 1,658 "
-28001052,HC  CATHETER DILATATION 2.5X15MM 2.7F," 1,658 "
-28001053,HC  CATHETER DILATATION DURA STAR 2.75X15MM 100CM," 1,658 "
-28001054,HC  CATHETER DILATATION 3X15MM 2.7F," 1,658 "
-28001055,HC  CATHETER DILATATION DURA STAR 3.25X15MM 100CM," 1,658 "
-28001056,HC  CATHETER DILATATION DURA STAR 3.5X15MM 100CM," 1,658 "
-28001057,HC  CATHETER DILATATION 3.75X15MM 2.7F," 1,658 "
-28001058,HC  CATHETER DILATATION 4X15MM 100CM," 1,658 "
-28001059,HC  CATHETER DILATATION DURA STAR 2.25X20MM 100CM," 1,658 "
-28001060,HC  CATHETER DILATATION DURA STAR 2.5X20MM 100CM," 1,658 "
-28001061,HC  CATHETER DILATATION 2.75X20MM 2.7F," 1,658 "
-28001062,HC  CATHETER DILATATION DURA STAR 3X20MM 100CM," 1,658 "
-28001063,HC  CATHETER DILATATION 3.25X20MM 100CM," 1,658 "
-28001064,HC  CATHETER DILATATION 3.5X20MM 2.7F," 1,658 "
-28001065,HC  CATHETER DILATATION 3.75X20MM 2.7F," 1,658 "
-28001066,HC  CATHETER DILATATION 4X20MM 100CM," 1,658 "
-28001067,HC  CATHETER BALLOON ANORECTAL EXPULSION 60CM, 160 
-28001068,HC  SHEATH ESOPHAGEAL MANOSHIELD, 213 
-28001069,HC  CATHETER SHEATH ANORECTAL MANOMETRY, 595 
-28001070,HC IMP STENT BILIARY LUMINEXX 12X80X80," 14,993 "
-28001071,HC IMP STENT BILIARY LUMINEXX 12X80X135," 14,993 "
-28001072,HC IMP STENT BILIARY LUMINEXX 14X80X80," 10,493 "
-28001073,HC IMP STENT BILIARY LUMINEXX 14X80X135," 14,993 "
-28001074,HC IMP STENT BILIARY LUMINEXX 14X60X80," 11,243 "
-28001075,HC IMP STENT BILIARY LUMINEXX 12X60X135," 11,250 "
-28001077,HC  GUIDEWIRE DIAGNOSTIC PRIMEWRE 14IN 185," 5,100 "
-28001078,HC  GUIDEWIRE CORONARY STRAIGHT TIP 300X11CM," 1,275 "
-28001079,HC IMP WALLFLEX ESOPHAGEAL 23X103MM," 16,125 "
-28001080,HC IMP WALLFLEX ESOPHAGEAL 23X123MM," 16,125 "
-28001081,HC IMP WALLFLEX ESOPHAGEAL 23X153MM," 16,125 "
-28001082,HC IMP WALLFLEX ESOPHAGEAL 28X105MM," 16,125 "
-28001083,HC IMP WALLFLEX ESOPHAGEAL 28X125MM," 16,125 "
-28001084,HC IMP WALLFLEX ESOPHAGEAL 28X155MM," 16,125 "
-28001086,HC CATHETER DISCYPHOR DIRECT SYSTEM," 5,738 "
-28001088,HC IMP SPEEDBRIDGE SYSTEM," 13,763 "
-28001091,HC  CATHETER DRAINAGE 14F 38IN 25CM, 667 
-28001092,HC IMP FILTER VENA CAVA JUGULAR DELIVERY 48X65MM," 9,375 "
-28001093,HC IMP FILTER VENA CAVA JUGULAR DELIVERY 30X48MM," 8,963 "
-28001095,HC IMPL SCREW BIO TENODESIS 4.75X15MM," 2,423 "
-28001096,HC PROBE EFLEX TAC C CHISEL," 4,227 "
-28001097,HC PROBE EFLEX TAC C W/ CABLE," 4,606 "
-28001098,HC IMP ANCHOR SUTURE DEVICE FIXATION KNOTLESS," 2,346 "
-28001099,HC IMP ANCHOR SUTURE QUICKANCHOR ORTHOCORD SZ2," 3,496 "
-28001100,HC IMP ANCHOR BIORAPTOR LONG SHAFT 2.9MM," 2,340 "
-28001101,HC IMP STENT GRAFT TAA TALENT 22X116," 74,400 "
-28001102,HC IMP STENT GRAFT TAA TALENT 24X24X116," 74,400 "
-28001103,HC IMP STENT GRAFT TAA TALENT 26X26X116," 74,400 "
-28001104,HC IMP STENT GRAFT TAA TALENT 30X30X115," 74,400 "
-28001105,HC IMP STENT GRAFT TAA TALENT 34X34X115," 74,400 "
-28001106,HC IMP STENT GRAFT TAA TALENT 36X144," 74,400 "
-28001107,HC IMP STENT GRAFT TAA TALENT 40X40X114," 74,400 "
-28001108,HC IMP STENT GRAFT TAA TALENT XCELERANT 42X42X113," 78,120 "
-28001109,HC IMP STENT GRAFT TAA TALENT 44X44X113," 78,120 "
-28001111,HC IMP STENT GRAFT TAA TALENT 28X28X113," 72,000 "
-28001112,HC IMP STENT GRAFT TAA TALENT 30X26X113," 72,000 "
-28001113,HC IMP STENT GRAFT TAA TALENT 34X30X113," 72,000 "
-28001114,HC IMP STENT GRAFT TAA TALENT 34X38X112," 72,000 "
-28001115,HC IMP STENT GRAFT TAA TALENT 40X35X112," 72,000 "
-28001116,HC IMP STENT GRAFT TAA TALENT 40X44X111," 74,400 "
-28001117,HC IMP STENT GRAFT TAA TALENT 46X42X110," 74,400 "
-28001118,HC IMP STENT GRAFT TAA TALENT XCELERANT 46X44X110," 74,400 "
-28001119,HC CATHETER ACCUFET DUAL CHANNEL 15CM," 5,780 "
-28001120,HC FIBER LASER BEAMPATH ENT-L 150CM," 6,375 "
-28001121,HC IMP SCREW CORTICAL LOW PROFILE 4.5X34MM, 714 
-28001122,HC IMP SCREW CORTICAL LOW PROFILE 4.5X36MM, 714 
-28001123,HC IMP SCREW CORTICAL LOW PROFILE 4.5X40MM, 714 
-28001124,HC IMP SCREW CORTICAL LOW PROFILE 4.5X52MM, 714 
-28001125,HC IMP SCREW CORTICAL LOW PROFILE 4.5X54MM, 714 
-28001126,HC IMP WASHER SPIKED 1.3X14MM," 1,445 "
-28001127,HC IMP WASHER SPIKED 1.3X17MM," 1,445 "
-28001128,HC IMP WASHER SPIKED 1.3X20MM," 1,445 "
-28001129,HC IMP WASHER SPIKED 2.5X14MM," 1,445 "
-28001130,HC IMP WASHER SPIKED 2.5X17MM," 1,445 "
-28001131,HC IMP WASHER SPIKED 2.5X20MM," 1,445 "
-28001132,HC IMPL SCREW PROPEL TAPER TIP 7X20MM, 978 
-28001133,HC IMPL SCREW PROPEL TAPER TIP 7X25MM, 978 
-28001134,HC IMPL SCREW PROPEL TAPER TIP 9X20MM, 978 
-28001135,HC IMPL SCREW PROPEL TAPER TIP 9X25MM, 978 
-28001136,HC IMPL SCREW PROPEL TAPER TIP 9X30MM, 978 
-28001137,HC IMP ANCHOR SUTURE PITON 3.5MM," 3,868 "
-28001138,HC IMP ANCHOR SUTURE PITON KNOTL SZ2," 3,868 "
-28001140,HC CATH BALLON SYS, 425 
-28001142,HC  GUIDEWIRE NITNOL MANDREL WIRE PLAT COIL 018X80, 294 
-28001143,HC IMP GENERATOR NEUROSTIMULATOR DEMIP VARGUS NER," 99,887 "
-28001144,HC IMP ANCHOR CORKSCREW BIOCOMPOSITE 6.5X15MM," 2,763 "
-28001147,HC  INTRODUCER SHEATH HEMOSTASIS 12CM 14F, 204 
-28001148,HC  INTRODUCER SHEATH HEMOSTASIS 30CM 14F, 255 
-28001149,HC IMPL SCREW LOCKING 4X22MM," 1,335 "
-28001150,HC IMPL SCREW LOCKING 4X30MM," 1,335 "
-28001151,HC IMPL SCREW LOCKING 4X34MM," 1,382 "
-28001152,HC IMPL SCREW LOCKING 4X46MM," 1,003 "
-28001153,HC IMPL SCREW LOCKING 4X54MM," 1,382 "
-28001154,HC IMPL SCREW LOCKING 5X80MM," 1,635 "
-28001155,HC IMPL SCREW LOCKING 7.3X55MM," 2,576 "
-28001156,HC IMP SCREW CORTEX 2.7X32MM, 322 
-28001157,HC IMP SCREW CORTEX 2.7X34MM, 322 
-28001158,HC IMP SCREW CORTEX 2.7X36MM, 322 
-28001159,HC IMP SCREW CORTEX 2.7X38MM, 322 
-28001160,HC IMP SCREW CORTEX 2.7X40MM, 322 
-28001161,HC IMP SCREW CORTEX 2.7X45MM, 322 
-28001162,HC IMP SCREW CORTEX 2.7X50MM, 322 
-28001163,HC IMP SCREW CORTEX 2.7X55MM, 322 
-28001166,HC IMPL SCREW CANNULATED 6.5X140MM," 2,261 "
-28001167,HC IMPL SCREW CANNULATED 6.5X150MM," 2,505 "
-28001168,HC IMPL SCREW CANNULATED 6.5X160MM," 2,505 "
-28001169,HC IMPL SCREW CANNULATED 6.5X170MM," 2,372 "
-28001170,HC IMPL SCREW CANNULATED 6.5X180MM," 2,505 "
-28001171,HC IMP SCREW CORTEX SELF TAPPING 4.5X115MM, 295 
-28001172,HC IMP SCREW CORTEX SELF TAPPING 4.5X120MM, 295 
-28001173,HC IMP SCREW CORTEX SELF TAPPING 4.5X125MM, 295 
-28001174,HC IMP SCREW CORTEX SELF TAPPING 4.5X130MM, 270 
-28001175,HC IMP SCREW CORTEX SELF TAPPING 4.5X140MM, 295 
-28001176,HC IMP SCREW CORTEX SELF TAPPING 4.5X145MM, 310 
-28001179,HC IMP EXTERNAL FIXATOR RING 1/4 165MM," 3,196 "
-28001185,HC SLING SMALL," 1,063 "
-28001186,HC SLING MEDIUM," 1,063 "
-28001187,HC SLING LARGE," 1,063 "
-28001189,HC IMPL PLATE TI LCP 5 HOLES 3.5MM," 2,431 "
-28001190,HC IMPL PLATE TI LCP 6 HOLES 3.5MM," 2,978 "
-28001191,HC IMPL PLATE TI LCP 8 HOLES 3.5MM," 3,262 "
-28001192,HC IMPL PLATE TI LCP 9 HOLES 3.5MM," 3,341 "
-28001193,HC IMPL PLATE TI LCP 10 HOLES 3.5MM," 3,496 "
-28001194,HC IMPL PLATE TI LCP 12 HOLES 3.5MM," 3,853 "
-28001195,HC IMPL PLATE TI LCP 14 HOLES 3.5MM," 5,432 "
-28001196,HC  CATHETER CORONARY 1300MM 1.8F 2.6F," 3,613 "
-28001197,HC  CATHETER CORONARY 1500MM 1.8F 2.6F," 3,613 "
-28001198,HC IMPL PLATE RECONSTRUCTION 5H 3.5X70MM," 3,579 "
-28001199,HC IMPL PLATE RECONSTRUCTION 6H 3.5X84MM," 4,775 "
-28001200,HC IMPL PLATE RECONSTRUCTION 7H 3.5X98MM," 5,018 "
-28001201,HC IMPL PLATE RECONSTRUCTION 8H 3.5X112MM," 5,420 "
-28001202,HC IMPL PLATE RECONSTRUCTION 10H 3.5X140MM," 5,423 "
-28001203,HC  CATHETER PEZZAR 30F, 128 
-28001204,HC IMPL PLATE RECONSTRUCTION 12H 3.5X168MM," 6,098 "
-28001205,HC  CATHETER PEZZAR 32F, 128 
-28001206,HC  CATHETER PEZZAR 34F, 128 
-28001207,HC IMPL SCREW MILAGRO 7X23MM," 3,383 "
-28001208,HC IMPL SCREW MILAGRO 8X23MM," 3,383 "
-28001212,HC IMPL SCREW MILAGRO INTERFERENCE 11X30MM," 3,383 "
-28001213,HC IMP SUTURE POLYMER INSYTE 5MM, 340 
-28001214,HC IMP ANCHORS PITON, 531 
-28001215,HC IMP EXTERNAL FIXATOR DIGET WIDGET SYSTEM," 11,063 "
-28001222,HC IMP NERVE COLLAGEN NEUROFLEX 2.5CM," 12,405 "
-28001223,HC IMP ANCHOR BIO SUTURETAK SMALL KIT," 1,063 "
-28001227,HC  GUIDEWIRE CORONARY PTCA J TIP 190CM," 1,275 "
-28001228,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2.5X10MM," 1,488 "
-28001229,HC IMP CARTILAGE COSTAL 2.9CM," 3,222 "
-28001231,HC IMP TENDON TIBIALIS ANTERIOR," 12,638 "
-28001235,HC IMP BUTTON FIXATION LOOP 15MM," 2,550 "
-28001236,HC IMP BUTTON SUTURE ENDO LOOP 20MM," 2,550 "
-28001237,HC IMP BUTTON SUTURE ENDO LOOP 25MM," 1,658 "
-28001238,HC IMP BUTTON SUTURE ENDO LOOP 30MM," 2,550 "
-28001239,HC IMP BUTTON SUTURE ENDO LOOP 35MM," 2,550 "
-28001240,HC IMP BUTTON SUTURE ENDO LOOP 40MM," 2,550 "
-28001241,HC IMP BUTTON SUTURE ENDO LOOP 45MM," 2,550 "
-28001242,HC IMP BUTTON SUTURE ENDO LOOP 50MM," 2,550 "
-28001244,HC IMP PORT INFUSION 9.5F KIT," 4,973 "
-28001245,HC  SNARE GUIDEWIRE BSD 14IN 180CM 2MM," 14,213 "
-28001246,HC CATHETER DIALYSIS STRAIGHT 27CM 12CM, 319 
-28001248,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 3MMX10CM," 12,600 "
-28001249,HC  CATHETER STEERABLE ARTISAN DISP," 13,875 "
-28001253,HC IMP PUTTY BONE MATRIX DBX 5CC," 7,038 "
-28001254,HC IMP PUTTY BONE MATRIX DBM 20CC," 22,246 "
-28001256,HC IMP STENT GRAFT AAA ZENITH RENU 32X43," 33,898 "
-28001257,HC  CATHETER BALLOON 20MM 4CM 85CM," 5,657 "
-28001258,HC  CATHETER DILATATION NC SPRINTER RX 12X2.75MM," 1,488 "
-28001259,HC  CATHETER DILATATION NC SPRINTER RX 15X2.75MM," 1,488 "
-28001260,HC  CATHETER DILATATION NC SPRINTER RX 9X3.25MM," 1,488 "
-28001261,HC  CATHETER DILATATION NC SPRINTER RX 12X3.25MM," 1,488 "
-28001262,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND2X10MM," 1,488 "
-28001263,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 2.5X6MM," 1,726 "
-28001264,HC  GUIDEWIRE CORONARY 14IN 3CM 180CM, 697 
-28001266,HC IMP BREAST MODERATE PROFILE STYLE 10 240CC," 6,758 "
-28001267,HC  CATHETER FJ CURVE 7.5FX115CM," 15,998 "
-28001268,HC IMPL COIL EMBOLIZATION 10MM 14CM 0.038IN," 1,275 "
-28001269,HC  INTRODUCER GUIDING SWARTZ 8X63," 1,318 "
-28001270,HC  INTRODUCER GUIDING SWARTZ 8.5X63," 1,318 "
-28001271,HC IMPL SCREW PEEK TENODESIS 4.75X15MM," 2,423 "
-28001272,HC IMPL SCREW PEEK TENODESIS 5.5X15MM," 2,423 "
-28001273,HC IMPL SCREW PEEK TENODESIS 6.25X15MM," 2,423 "
-28001274,HC IMPL SCREW PEEK TENODESIS 7X10MM," 2,423 "
-28001276,HC IMP STENT CAROTID PRECISE 9X30X135," 16,500 "
-28001277,HC IMP STENT CAROTID PRECISE 10X30X135," 16,875 "
-28001278,HC IMP STENT CAROTID PRECISE 10X40X135," 16,500 "
-28001279,HC  CATHETER DILATATION 6MM 4X120CM 4F," 2,668 "
-28001280,HC IMP STENT BILIARY SMART OTW 10X30X120," 11,100 "
-28001281,HC IMP STENT GRAFT AAA ZENITH FLEX 18X73," 21,595 "
-28001282,HC IMP STENT GRAFT AAA ZENITH FLEX 22X73," 21,595 "
-28001284,HC IMP SPACER ARTELON CMC 10X20X5MM," 17,250 "
-28001285,HC ARTHROSCOPIC TIGHTROPE MINI KIT," 4,208 "
-28001287,HC AUTOGRAFT HARVESTER BONE KIT 10MM," 4,420 "
-28001288,HC IMP STENT ABSOLUTE PRO 10X40X135," 10,313 "
-28001289,HC IMP GRAFT INFUSE BONE EXTRA SMALL," 12,945 "
-28001290,HC IMP GRAFT INFUSE BONE EXTRA EXTRA SMALL," 7,446 "
-28001291,HC IMP ORBITAL MEDPORE TITAN 35X50X.85MM," 7,863 "
-28001292,HC IMP ORBITAL MEDPORE TITAN 38X50X1.00MM," 8,160 "
-28001293,HC IMP ORBITAL MEDPORE TITAN LEFT 41X42X1.00MM," 8,160 "
-28001294,HC IMP ORBITAL MEDPORE TITAN RIGHT 41X42X1.00MM," 8,160 "
-28001295,HC IMP HIP SPACER FEMORAL TAPER -3MM, 638 
-28001297,HC IMPL PLATE CONDYLAR 95DEG 60MM 5HOLE," 4,743 "
-28001298,HC  CATHETER FOGARTY CHOLANGIOGRAM 4F, 228 
-28001299,HC  CATHETER FOGARTY CHOLANGIOGRAM 6F, 228 
-28001301,HC KIT CUSTOM TUBING P AND T," 2,406 "
-28001302,HC IMP PIN RUSH 2.4X64MM, 517 
-28001304,HC IMPL SCREW DHS LAG 36MM, 398 
-28001305,HC IMP PIN RUSH 2.4X57MM, 315 
-28001308,HC IMPL FINGER JOINT SWANSON GROMMET SIZE 1," 8,273 "
-28001309,HC IMP STAPLE BLOUNT EPIPHYSEAL 16X22," 1,410 "
-28001311,HC IMP SHUNT CAROTID PRUITT 9FX31CM," 4,242 "
-28001312,HC IMP CLIP ANEURYSM STRAIGHT KEES SLIMLINE12MM," 2,933 "
-28001313,HC IMP CLIP ANEURYSM STRAIGHT KEES SLIMLINE 15MM," 3,261 "
-28001314,HC IMPL SCREW CANNULATED LARGE 90MM," 2,505 "
-28001317,HC IMP STAPLE BONE SPIKED XSMALL 6.35X23.22MM, 867 
-28001318,HC IMP STAPLE FIXATION SPIKE LARGE 14.29MM," 1,428 "
-28001319,HC IMPL SCREW CANNULATED 16MM 7.3X40MM," 2,355 "
-28001320,HC IMP PIN STEINMAN 5/32X9IN, 202 
-28001321,HC IMP BAND SCLERAL BUCKLING STYLE 40, 102 
-28001322,HC IMP STRIP SCLERAL SILI STY 41 3.5MM, 111 
-28001323,HC IMP STRIP SCLERAL SILI STY 42 4MM, 111 
-28001324,HC IMP EYE MERIDIONAL RETINAL STRIP STY106 8X10.5MM, 162 
-28001325,HC IMPL SCREW CANNULATED LARGE 95MM," 2,505 "
-28001326,HC IMPL SCREW CANNULATED LONG THREAD 40MM," 1,837 "
-28001327,HC IMPL SCREW CANNULATED LONG THREAD 42MM," 1,902 "
-28001333,HC  CATHETER DILATATION ACHALASIA 40MM," 6,707 "
-28001334,HC IMP STAPLE FIXATION SPIKE MEDIUM 11.11MM," 2,040 "
-28001335,HC IMP HIP SHELL ACETABULAR CLUSTER 54MM," 10,050 "
-28001336,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 58MM," 10,050 "
-28001337,HC IMP HIP SHELL ACETABULAR MULTI HOLE 58MM," 10,050 "
-28001338,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ4," 18,550 "
-28001339,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ2," 18,750 "
-28001340,HC PACK PERFUSION COMPLETE," 5,228 "
-28001341,HC IMP HIP STEM PROSTALAC R 200MM SZ3," 29,190 "
-28001342,HC IMPL SCREW HIP CANCELLOUS 6.5X15MM, 901 
-28001343,HC IMP SCREW BONE 6.5X20MM, 821 
-28001344,HC IMP KNEE LPS FLEX GSF OPTION SZ E," 16,980 "
-28001345,HC IMP KNEE LPS FLEX GSF OPTION SZ G," 16,980 "
-28001346,HC IMP KNEE LPS FLEX GSF OPTION SZ D," 16,980 "
-28001348,HC PACK KNEE PIN ODYSSEY," 2,652 "
-28001349,HC IMP KNEE PATELLA ALL POLY 38MM," 2,754 "
-28001350,HC IMP KNEE STEM TIBIAL SIGMA CEMENTED SZ 4/5/6 30MM," 7,813 "
-28001352,HC IMP KNEE INSERT TIBIAL STAB SZ2 10MM," 8,745 "
-28001353,HC IMP KNEE INSERT TIBIAL STAB SZ4 10MM," 13,604 "
-28001354,HC TUBE BLAKEMORE ADULT 1.5 BALLOON 20F," 3,156 "
-28001355,HC IMPL PLATE TIBIAL STEMMED PRECOAT 41X62MM," 7,710 "
-28001356,HC IMPL PLATE TIBIAL STEMMED PRECOAT 42X66MM," 7,710 "
-28001357,HC IMPL PLATE TIBIAL STEMMED PRECOAT 50X74MM," 7,710 "
-28001358,HC IMPL PLATE TIBIAL STEMMED PRECOAT 46X74MM," 7,710 "
-28001359,HC IMPL PLATE TIBIAL UNICOMPARTMENAL PRECOAT," 9,563 "
-28001366,HC IMPL COIL PERIPHERAL EMBOLIZATION 3MM 3CM 0.035IN, 723 
-28001367,HC IMPL COIL PERIPHERAL EMBOLIZATION 4MM 3CM 0.035IN, 723 
-28001368,HC IMPL COIL PERIPHERAL EMBOLIZATION 3MM 5CM 0.038IN, 723 
-28001369,HC IMPL COIL PERIPHERAL EMBOLIZATION 5MM 3CM, 723 
-28001370,HC IMP STENT ABSOLUTE PRO 5X30X135," 10,313 "
-28001371,HC IMP STENT ABSOLUTE PRO 6X60X135," 10,313 "
-28001372,HC IMP STENT ABSOLUTE PRO 7X20X135," 10,313 "
-28001373,HC IMP STENT ABSOLUTE PRO 7X30X135," 10,313 "
-28001374,HC IMP STENT ABSOLUTE PRO 7X60X135," 10,313 "
-28001375,HC IMP STENT ABSOLUTE PRO 7X80X135," 10,313 "
-28001376,HC IMP STENT ABSOLUTE PRO 8X20X135," 10,313 "
-28001377,HC IMP STENT ABSOLUTE PRO 8X30X135," 10,313 "
-28001378,HC IMP STENT ABSOLUTE PRO 10X20X135," 10,313 "
-28001379,HC IMP STENT ABSOLUTE PRO 10X60X135," 10,313 "
-28001380,HC  CATHETER BALLOON PTA 150CM 20X2MM," 2,338 "
-28001381,HC  CATHETER BALLOON PTA 2X100MM 150CM," 3,188 "
-28001382,HC  CATHETER BALLOON PTA 2.5X20MM 150CM," 2,338 "
-28001383,HC  CATHETER BALLOON PTA 150CM 40X2.5MM," 2,338 "
-28001384,HC  CATHETER BALLOON PTA 150CM 120X2.5MM," 3,188 "
-28001385,HC  CATHETER BALLOON PTA 150CM 120X3MM," 3,188 "
-28001386,HC  CATHETER BALLOON PTA 150CM 20X4MM," 2,338 "
-28001387,HC  CATHETER BALLOON PTA 4X40MM 150CM," 2,338 "
-28001388,HC  CATHETER BALLOON PTA 150CM 100X4MM," 3,188 "
-28001389,HC  CATHETER GUIDING VISTA BRITE TIP BARBEAU 6F, 442 
-28001390,HC  SNARE VASC ELITE 35X150MM," 3,613 "
-28001391,HC IMP GRAFT ENDOVASCULAR 18F 22X39MM," 14,153 "
-28001392,HC  GUIDEWIRE CORONARY STRAIGHT XSUPP 190X3CM, 697 
-28001393,HC  GUIDEWIRE CORONARY STRAIGHT TIP 14IN 180," 1,275 "
-28001395,HC  CATHETER GUIDING CVS 6F, 468 
-28001397,HC PLATELET FOUR TUBE KIT," 4,208 "
-28001398,HC PLATELET TWO TUBE KIT," 2,805 "
-28001399,HC IMP SPACER ARTELON 5X14X20MM 1CM," 17,250 "
-28001400,HC IMP ROD PASSIVE TENDON 5X25CM," 9,263 "
-28001401,HC PLATELET CONCENTRATION GPS KIT," 5,610 "
-28001402,HC IMP LEAD QUAD PISCES 6MX33CM KIT," 9,375 "
-28001403,HC IMP LEAD NEUROSTIMULATOR RESTORE OCTAD KIT 60CM," 13,500 "
-28001405,HC IMP STENT GRAFT AAA ZENITH FLEX 18X56," 21,595 "
-28001406,HC  GUIDEWIRE COMBOWIRE XT 1.5CM," 8,625 "
-28001407,HC PLATELET CONCEN BIOCUE KIT," 13,500 "
-28001409,HC  INTRODUCER SHEATH 5F 25X11CM, 230 
-28001410,HC  INTRODUCER SHEATH 2MM 11CM 6F, 230 
-28001411,HC IMP STENT URETERAL DOUBLE J ACMI 8.5FX12," 1,862 "
-28001413,HC IMP THUMB SPACER ARTELON," 13,500 "
-28001417,HC IMP ENDOPROSTHESIS 31X14.5MM 13CM," 64,968 "
-28001418,HC IMP STENT GRAFT AAA EXCLUDER C3 32X4.5," 19,530 "
-28001419,HC IMP STENT ABSOLUTE PRO 8X40X135," 10,313 "
-28001420,HC IMP GENERATOR NEUROSTIMULATOR INTERSTIM II INS," 64,770 "
-28001421,HC IMP CONNECTOR SPINAL MOUNTAINEER LATERAL," 4,565 "
-28001422,HC IMPL SCREW MOUNTINEER INNERSET," 1,148 "
-28001424,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 752CC," 6,800 "
-28001425,HC IMP BREAST SILICONE STYLE 20 500ML," 6,758 "
-28001426,HC  CATHETER CORONARY IMGNG IVUS ICROSS," 5,313 "
-28001427,HC  CATHETER DILATATION 2X120MM 150CM 4F," 2,703 "
-28001428,HC  CATHETER DILATATION 2X150MM 150CM 4F," 3,324 "
-28001429,HC  CATHETER DILATATION 2X220MM 150CM 4F," 3,324 "
-28001430,HC  CATHETER DILATATION 2.5X120MM 150CM," 2,703 "
-28001431,HC  CATHETER DILATATION 2.5X150MM 150CM," 3,324 "
-28001432,HC  CATHETER DILATATION 2.5X220MM 150CM," 2,506 "
-28001433,HC  CATHETER DILATATION 3X120MM 150CM 4F," 2,703 "
-28001434,HC  CATHETER DILATATION 3.5X120MM 150CM," 2,703 "
-28001435,HC  CATHETER DILATATION 3.5X150MM 150CM," 2,506 "
-28001436,HC  CATHETER DILATATION 3.5X220MM 150CM," 2,506 "
-28001437,HC  CATHETER DILATATION 4X120MM 150CM 4F," 2,703 "
-28001438,HC  CATHETER DILATATION 4X220MM 150CM 4F," 3,324 "
-28001439,HC  CATHETER DILATATION 5MM 12X120CM 4F," 2,703 "
-28001440,HC IMP STENT GRAFT VIABAHN 10MMX5CM," 18,865 "
-28001441,HC  CATHETER DILATATION 6MM 12X120CM 5F," 2,703 "
-28001442,HC  CATHETER DILATATION 6MM 15X120CM 5F," 3,324 "
-28001443,HC  CATHETER DILATATION 6MM 22X120CM 5F," 3,324 "
-28001444,HC IMP STENT GRAFT VIABAHN 13MM 5CM," 18,865 "
-28001445,HC IMP STENT GRAFT VIABAHN 13MM 10CM," 21,665 "
-28001446,HC IMP BREAST EXPANDER STYLE 133MX 400ML," 8,963 "
-28001447,HC IMP BREAST EXPANDER STYLE 133MX 500ML," 8,963 "
-28001448,HC IMP BREAST ROUND SALINE STYLE 68LP 175CC," 2,508 "
-28001449,HC  CATHETER INFUSION BIFURCATED SYS 35CM 8F," 13,463 "
-28001450,HC IMP PUMP INF 40CM," 13,463 "
-28001451,HC  CATHETER ELECTROPHYSIOLOGY 4PLR FXDCRV 120CM 5F," 1,700 "
-28001452,HC  CATH ABLAT MLTCRV DIST ELECTRO," 4,888 "
-28001453,HC NEEDLE RADIOFREQUENCY TRANSSEPTAL CURVED 71CM," 4,208 "
-28001454,HC NEEDLE RADIOFREQUENCY TRANSSEPTAL 98CM," 4,208 "
-28001455,HC IMP STENT TRACHEAL ZSTENT 15X5," 6,375 "
-28001456,HC  CATHETER DILATATION CORONARY 2X8MM," 1,785 "
-28001457,HC  CATHETER DILATATION CORONARY 2X12MM," 1,615 "
-28001458,HC  CATHETER DILATATION CORONARY 2X20MM," 1,785 "
-28001459,HC  CATHETER DILATATION CORONARY 2.5X8MM," 1,488 "
-28001460,HC  CATHETER DILATATION CORONARY 2.5X12MM," 1,488 "
-28001461,HC  CATHETER DILATATION CORONARY 2.5X15MM," 1,488 "
-28001462,HC  CATHETER DILATATION CORONARY 2.5X20MM," 1,488 "
-28001463,HC  CATHETER DILATATION CORONARY 2.75X8MM," 1,488 "
-28001464,HC  CATHETER DILATATION CORONARY 2.75X12MM," 1,488 "
-28001465,HC  CATHETER DILATATION CORONARY 2.75X15MM," 1,488 "
-28001466,HC  CATHETER DILATATION CORONARY 2.75X20MM," 1,488 "
-28001467,HC  CATHETER DILATATION PTCA TREK NC RX 3.00X8MM," 1,488 "
-28001468,HC  CATHETER DILATATION PTCA TREK NC RX 3.00X12MM," 1,488 "
-28001469,HC  CATHETER DILATATION CORONARY 3X15MM," 1,488 "
-28001470,HC  CATHETER DILATATION CORONARY 3X20MM," 1,488 "
-28001471,HC  CATHETER DILATATION CORONARY 3.25X12MM," 1,488 "
-28001472,HC  CATHETER DILATATION CORONARY 3.25X15MM," 1,488 "
-28001473,HC  CATHETER DILATATION CORONARY 3.25X20MM," 1,488 "
-28001474,HC  CATHETER DILATATION CORONARY 3.5X8MM," 1,488 "
-28001475,HC  CATHETER DILATATION CORONARY 3.5X12MM," 1,488 "
-28001476,HC  CATHETER DILATATION CORONARY 3.5X15MM," 1,488 "
-28001477,HC  CATHETER DILATATION CORONARY 3.5X20MM," 1,488 "
-28001478,HC  CATHETER DILATATION CORONARY 3.75X12MM," 1,488 "
-28001479,HC  CATHETER DILATATION CORONARY 3.75X15MM," 1,488 "
-28001480,HC  CATHETER DILATATION CORONARY 3.75X20MM," 1,615 "
-28001481,HC  CATHETER DILATATION CORONARY 4X8MM," 1,488 "
-28001482,HC  CATHETER DILATATION CORONARY 4X12MM," 1,488 "
-28001483,HC  CATHETER DILATATION CORONARY 4X15MM," 1,488 "
-28001484,HC  CATHETER DILATATION CORONARY 4X20MM," 1,615 "
-28001485,HC  CATHETER DILATATION CORONARY 5X8MM," 1,615 "
-28001486,HC  CATHETER DILATATION CORONARY 5X12MM," 1,488 "
-28001487,HC  CATHETER DILATATION CORONARY 2X15MM," 1,785 "
-28001488,HC  CATHETER DILATATION 3X220MM 150CM 4F," 2,506 "
-28001489,HC  CATHETER DILATATION PTA 5X15MM 6F," 2,338 "
-28001490,HC  CATHETER DILATATION PTA 6X15MM 6F," 2,635 "
-28001491,HC  CATHETER DILATATION PTA 6X20X135CM 5F," 2,635 "
-28001492,HC  CATHETER DILATATION PTA 7X15MM 6F," 2,635 "
-28001493,HC  CATHETER DILATATION PTA 7X20X135CM 5F," 2,635 "
-28001494,HC IMP PROTECTION EMBOLIC SYSTEM 5MMX190CM," 13,463 "
-28001495,HC IMP PROTECTION EMBOLIC SYSTEM 7.2MM 190CM," 13,463 "
-28001496,HC IMPL PLATE MATRIXRIB RIGHT RIB 4/5 16HOLE," 8,934 "
-28001497,HC IMPL PLATE UNIVERSAL MATRIXRIB 8 HOLE," 6,895 "
-28001498,HC  CATHETER DILAT PTA FOXCROSS PLUS 5X120X80," 1,615 "
-28001499,HC  CATHETER DILAT PTA FOXCROSS PLUS 2.5X120X135," 1,615 "
-28001500,HC  CATHETER DILAT PTA FOXCROSS PLUS 2.55X120X135," 1,615 "
-28001501,HC  CATHETER DILAT PTA FOXCROSS PLUS 4X120X100," 1,615 "
-28001502,HC  CATHETER DILAT PTA FOXCROSS PLUS 7X120X80," 1,615 "
-28001503,HC  CATHETER DILAT PTA FOXCROSS PLUS 6X120X80," 1,615 "
-28001504,HC  CATHETER DILAT PTA FOXCROSS PLUS 4X120X80," 1,615 "
-28001506,HC IMP STENT ABSOLUTE PRO 8X80X135," 10,313 "
-28001507,HC IMP STENT ABSOLUTE PRO 8X60X135," 10,313 "
-28001508,HC IMP STENT ABSOLUTE PRO 9X40X135," 10,313 "
-28001509,HC IMP STENT ABSOLUTE PRO 9X60X135," 10,313 "
-28001510,HC IMP STENT ABSOLUTE PRO 9X80X135," 10,313 "
-28001511,HC IMP STENT ABSOLUTE PRO 10X80X135," 10,313 "
-28001512,HC IMP STENT ABSOLUTE PRO 7X40X135," 10,313 "
-28001513,HC FORCEP SPELZER MALIS 7IN 0.5MM," 3,390 "
-28001514,HC FORCEP SPELZER MALIS 9IN 0.5MM," 3,390 "
-28001515,HC  CATHETER BALLOON TRILOBE THORC 26-42MM," 4,973 "
-28001516,HC IMP ICD DUAL IC MODEL E110," 81,000 "
-28001517,HC IMP ICD DUAL IC SECURA RR," 80,280 "
-28001518,HC IMP ICD SINGLE IC MODEL E102," 61,750 "
-28001519,HC IMP ICD SINGLE IC SECURA VR," 70,200 "
-28001520,HC IMP ICD SINGLE IC MAXIMO VR," 61,750 "
-28001521,HC IMP LEAD VENTRICULAR 58CM," 35,000 "
-28001523,HC IMPL PACEMAKER DUAL ALTRUA," 29,750 "
-28001524,HC IMPL PACEMAKER SINGLE ALTRUA 60," 18,750 "
-28001525,HC IMPL PACEMAKER SINGLE 42X52X6MM," 20,300 "
-28001526,HC IMP ICD OTHER IC RV IS-1 0.99CM," 71,750 "
-28001527,HC IMP ICD OTHER CARDIAC RESYNCRZTN," 91,500 "
-28001528,HC IMP ICD OTHER IC CRT DVC MAXIMO," 82,000 "
-28001529,HC  CATHETER DILATATION NC SPRINTER RX 6MMX3.25MM," 1,726 "
-28001530,HC DEVICE LEAD LOCKING EZ," 4,321 "
-28001531,HC IMP ICD OTHER RESYNCRZTN THRPY," 71,750 "
-28001532,HC IMP PISTON OTOLOGICAL SCHUKNECHT 5X6, 977 
-28001533,HC IMP SEALANT FIBRIN EVICEL 5ML," 4,752 "
-28001535,HC  CATHETER DUAL LUMEN POWERLINE 6F," 2,210 "
-28001536,HC IMPL PLATE L LEFT SHORT," 1,394 "
-28001537,HC IMPL PLATE L RIGHT SHORT," 1,469 "
-28001538,HC  CATHETER DILATATION NC SPRINTER RX 6MMX3.75MM," 1,488 "
-28001539,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X20MM," 1,407 "
-28001540,HC IMPL SCREW SPINAL PEDICLE 6.2X55MM," 5,100 "
-28001541,HC IMP COLLAR SPINAL USS TITANIUM 6MM," 1,063 "
-28001542,HC  CATHETER INFUSION BENEPHIT XT SYSTEM 5F 35CM," 13,463 "
-28001543,HC  INTRODUCER VASCULAR CHECK-FLO 3FX7CM, 289 
-28001544,HC  INTRODUCER CHECK FLO, 204 
-28001545,HC  CATHETER DRAINAGE SHORT MINI PIGTAIL LKNG 6F 12, 740 
-28001546,HC  CATHETER DRAINAGE SHORT MINI PIGTAIL LKNG 8F 12, 740 
-28001547,HC IMP STENT GRAFT ICAST 6X16X80," 16,875 "
-28001548,HC PORT SILS CANNULA 5-12MM," 3,653 "
-28001549,HC  CATHETER DIAGNOSTIC ULTRASOUND 10F 90CM," 9,375 "
-28001550,HC  CATHETER ELECTROPHYSIOLOGY DIAGNSTC POLE 20X6MM," 2,886 "
-28001551,HC IMP TRANSSPHENOIDAL SELLAR 0.45X2.5X20X20MM," 3,279 "
-28001552,HC IMPL PLATE DVRANS-R," 7,574 "
-28001553,HC IMPL PLATE DVRANS-L," 7,508 "
-28001557,HC IMP LEAD VENTRICULAR ATTAIN OTW 2 ELCTRODE 88CM," 17,963 "
-28001558,HC IMP VALVE AORTIC MAGNA EASE 19MM," 40,652 "
-28001559,HC IMP VALVE AORTIC MAGNA EASE 21MM," 40,652 "
-28001560,HC IMP VALVE AORTIC MAGNA EASE 23MM," 40,652 "
-28001561,HC IMP VALVE AORTIC MAGNA EASE 25MM," 40,652 "
-28001562,HC IMP VALVE AORTIC MAGNA EASE 27MM," 41,872 "
-28001563,HC IMP VALVE AORTIC MAGNA EASE 29MM," 40,652 "
-28001564,HC IMP STENT GRAFT VIABAHN 9MMX5CM," 17,955 "
-28001565,HC  INTRODUCER SHEATH MULLINS 7FX32X45CM, 723 
-28001566,HC SEALER BIPOLAR AQUAMANTYS 6MM," 4,463 "
-28001567,HC IMP CARTILAGE COSTAL FRZ 15X10-30MM," 3,222 "
-28001568,HC IMPL SCREW TENODESIS PEEK 8X12MM," 2,423 "
-28001569,HC  CATHETER PERIPHERAL POWERPICC 6F," 1,598 "
-28001571,HC IMP FILTER VENA CAVA OPTION 6.5FX70," 7,875 "
-28001572,HC IMP TENDON POST TIBIALIS," 12,638 "
-28001573,HC  CATHETER BLAZER DUODECAPOLAR LARGE 7F 2-10-2MM," 7,875 "
-28001574,HC  CATHETER BLAZER DUODECAPOLAR MEDIUM 7F," 7,875 "
-28001575,HC  CATHETER CORONARY PTA 15X135CM 5MM," 1,275 "
-28001576,HC IMPL PLATE FRACTURE MINI 4 HOLE SHORT BAR, 759 
-28001577,HC IMP STENT GRAFT TAA TAG PTFE 40X10," 74,250 "
-28001578,HC  CATHETER ELECTROPHYSIOLOGY MAP 150CM 2.5F," 7,688 "
-28001579,HC  INTRODUCER SHEATH HEMOSTASIS 12CM 10F, 187 
-28001580,HC IMP STENT PERIPHERAL LIFESTENT 6X120X130," 14,250 "
-28001581,HC IMPL FINGER MCP ELASTOMER SZ30," 6,979 "
-28001582,HC IMPL FINGER MCP ELASTOMER SZ10," 6,979 "
-28001583,HC IMP GRAFT NERVE 1-2X15MM," 9,375 "
-28001584,HC IMP GRAFT NERVE 2-3X15MM," 10,875 "
-28001585,HC IMP GRAFT NERVE 3-4X15MM," 10,875 "
-28001586,HC IMP GRAFT NERVE 4-5X15MM," 10,875 "
-28001587,HC IMP GRAFT NERVE 1-2X30MM," 13,875 "
-28001588,HC IMP GRAFT NERVE 2-3X30MM," 13,125 "
-28001589,HC IMP GRAFT NERVE 3-4X30MM," 13,125 "
-28001590,HC IMP GRAFT NERVE 4-5X30MM," 13,125 "
-28001591,HC IMP GRAFT NERVE 1-2X50MM," 18,375 "
-28001592,HC IMP GRAFT NERVE 2-3X50MM," 19,775 "
-28001593,HC IMP GRAFT NERVE 3-4X50MM," 18,375 "
-28001594,HC IMP GRAFT NERVE 4-5X50MM," 18,375 "
-28001595,HC IMPL SCREW FEMORAL INTRAFIX 7MM," 4,208 "
-28001598,HC IMPL SCREW FEMORAL 10X23MM," 4,208 "
-28001599,HC IMP SCREW BONE INTRAFIX 10X23MM," 4,208 "
-28001601,HC IMPL PACEMAKER DUAL ACCENT RF," 33,250 "
-28001602,HC IMP SCLERAL BUCKLE SILICONE 7X3.5MM, 162 
-28001604,HC IMPL SCREW TAPER INTRAFIX 8-10X30MM," 2,057 "
-28001607,HC IMP GRAFT ENDOVASCULAR THORACIC 28MM 15CM," 76,500 "
-28001614,HC  GUIDEWIRE SF 25 50, 202 
-28001617,HC  GUIDEWIRE PED J WIRE 18IN 145CM 3MM, 111 
-28001618,HC IMP CEMENT BONE SIMPLEX FULL DOSE, 825 
-28001619,HC  GUIDEWIRE T-J CURVED 35IN 40CM 3MM, 165 
-28001623,HC STAPLER LINEAR CUTTER 60MM," 2,940 "
-28001625,HC ELECTRODE VELOCITY ENSITE," 7,838 "
-28001626,HC WAND SPINE ABLATION 17GX6IN," 13,500 "
-28001627,HC IMPL PACEMAKER DUAL ALTRUA 8X43X49CM," 33,020 "
-28001628,HC PROBE DOPPLER BLOOD FLOW," 3,689 "
-28001632,HC IMP STENT FLEXI FEEMAN 5FX3CM, 374 
-28001633,HC  CATHETER ABLATION 18MM," 10,830 "
-28001634,HC  CATHETER ABLATION 22MM," 11,805 "
-28001635,HC  CATHETER ABLATION 25MM," 10,830 "
-28001636,HC  CATHETER ABLATION 28MM," 10,830 "
-28001637,HC  CATHETER ABLATION 31MM," 10,830 "
-28001638,HC  CATHETER BALLOON SIZING HALO 360 33.7MM," 3,069 "
-28001639,HC  CATHETER ABLATION HALO 90," 8,550 "
-28001640,HC  GUIDEWIRE HALO 38IN 260CM," 1,009 "
-28001642,HC TIGHTROPE ARTHROSCOPIC SS KIT," 4,208 "
-28001643,HC  GUIDEWIRE PERIPHERAL SOFT 14IN 35X300CM," 4,914 "
-28001644,HC  GUIDEWIRE PERIPHERAL STANDARD 14IN 35X300CM," 5,034 "
-28001646,HC IMP STAPLE REINFORCEMENT SEAMGUARD ECHELON 60," 1,331 "
-28001647,HC  CATHETER X FORCE N30 10X22CM," 2,593 "
-28001648,HC  CATHETER DILATATION NC SPRINTER RX 15MMX4.5MM," 1,726 "
-28001649,HC  GUIDEWIRE THREAD GUIDE 3.2MM, 649 
-28001650,HC  CATHETER BALLOON PTA 150CM 20X5MM," 2,338 "
-28001651,HC  CATHETER BALLOON PTA 150CM 40X5MM," 2,338 "
-28001652,HC  CATHETER BALLOON PTA 5X100MM 150CM," 3,188 "
-28001653,HC  CATHETER BALLOON PTA 5X120MM 150CM," 3,188 "
-28001654,HC  CATHETER BALLOON PTA 6X100MM 150CM," 3,188 "
-28001655,HC  CATHETER BALLOON PTA 6X120MM 150CM," 3,188 "
-28001656,HC IMPL PLATE TI MATRIXMIDFACE ORBITAL," 2,802 "
-28001657,HC IMPL COIL NEURO COMPLEX LOOP 10 5X15," 16,725 "
-28001658,HC IMP VALVE CHECKFLO FLXR ACCS 80CM, 740 
-28001659,HC IMPL PLATE L OBLIQUE LEFT 2X3 HOLE .7MM," 2,305 "
-28001660,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT 2X3 HOLE .8MM," 2,292 "
-28001661,HC IMP LEAD DEFIBRILLATOR 11MM 58CM 6.8F," 37,375 "
-28001662,HC IMPL PLATE ORBITAL PREFORMED LARGE LEFT," 11,496 "
-28001663,HC IMPL PACEMAKER SINGLE ACC 52X52X6MM," 20,300 "
-28001664,HC IMP STENT GRAFT TAA TAG 34X10 PTFE," 76,500 "
-28001665,HC IMPL COIL NEURO COMPLEX LOOP 10 10X5," 14,715 "
-28001666,HC IMPL COIL NEURO EMBOLIZATION V-TRAK 6X12," 15,675 "
-28001667,HC IMPL COIL NEURO COMPLEX LOOP 10 7X15," 15,889 "
-28001668,HC IMP BONE SUBSTITUTE INJECTIBLE HYDROSET 10ML," 20,376 "
-28001669,HC  CATHETER HEMODIALYSIS CHRONIC 20CM SET," 2,627 "
-28001671,HC IMP ANCHOR SUTURE POPLOK 3.5MM," 3,876 "
-28001672,HC IMP ANCHOR SUTURE POPLOK 4.5MM," 3,060 "
-28001673,HC CARTILAGE CORONARY 4MM KIT," 6,902 "
-28001674,HC CARTILAGE CORONARY 6MM KIT," 6,902 "
-28001675,HC CARTILAGE CORONARY 8MM KIT," 6,902 "
-28001676,HC CARTILAGE CORONARY 10MM KIT," 6,902 "
-28001677,HC CARTILAGE CORONARY PERPEN 6MM KIT," 8,356 "
-28001681,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML16F 1.5X22," 2,738 "
-28001682,HC  TUBE TRANSG JEJNL LP 7-10ML 22F 3.5X45CM," 2,668 "
-28001683,HC IMP STENT GRAFT VIABAHN 11MMX5CM," 18,865 "
-28001684,HC  CATHETER ASAHI CORSAIR 2.6FX13.5CM," 8,458 "
-28001685,HC IMP STENT GRAFT VIABAHN 9MMX10CM," 21,665 "
-28001686,HC IMP STENT GRAFT VIABAHN 9MMX15CM," 25,165 "
-28001687,HC IMP STENT GRAFT VIABAHN 10MMX15CM," 25,165 "
-28001688,HC IMP STENT GRAFT VIABAHN 11MMX10CM," 21,665 "
-28001692,HC IMP FILTER VENA CAVA FEMORAL DELIVERY," 10,838 "
-28001693,HC  CANNULA CARDIAC ASSIST 21F 62CM," 5,259 "
-28001694,HC  CATH CRYOABLAT 28MM," 33,600 "
-28001695,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 30MM," 9,363 "
-28001696,HC  CATHETER RETRIEVAL 6F 100CM," 3,295 "
-28001697,HC  SNARE ENDOVASCULAR SYSTEM 120CM 18X30," 3,295 "
-28001698,HC IMP ICD OTHER IC DF-1 CNCTR," 95,000 "
-28001699,HC DRILL PIN MENISCAL 3.2MM," 2,474 "
-28001700,HC BUR DRILL BIT MENISCAL 8MM," 5,471 "
-28001702,HC IMPL SCREW BIOZIP 6.5MM," 3,001 "
-28001704,HC  CANNULA TRNSEPTAL 21F 72CM SET," 14,625 "
-28001705,HC TANDEM HEART PROC 75IN 93 KIT," 97,500 "
-28001706,HC  CATHETER INFUSION MICRO INTRAVASCULAR 165CM," 6,163 "
-28001707,HC IMP HEARTMATE II LVAD," 131,250 "
-28001712,HC IMPL COIL NEURO MICRO 3MM 6.0CM," 19,530 "
-28001713,HC IMP PARTICLE EMBOLIZATION EXPAND 25MG," 10,688 "
-28001714,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.0X15," 2,738 "
-28001715,HC IMP STENT BILIARY WALLFLEX COVERED 8X60," 18,375 "
-28001716,HC IMP STENT BILIARY WALLFLEX COVERED 8X80," 18,375 "
-28001717,HC IMP STENT BILIARY WALLFLEX COVERED 10X40," 18,375 "
-28001718,HC IMP STENT BILIARY WALLFLEX COVERED 10X60," 18,375 "
-28001719,HC IMP STENT BILIARY WALLFLEX COVERED 10X80," 18,375 "
-28001720,HC IMPL SCREW ANCHORAGE 1.7X12MM, 711 
-28001721,HC IMPL PLATE ANCHOR STD STR SHT," 1,197 "
-28001722,HC IMPL PLATE ANCHOR STRAIGHT SAS," 1,758 "
-28001724,HC IMPL COIL NEURO MICRO 2.00MM 4.0CM," 10,463 "
-28001725,HC IMP VALVE AORTIC TISSUE MITROFLOW 19MM," 37,050 "
-28001726,HC IMP VALVE AORTIC TISSUE MITROFLOW 21MM," 37,050 "
-28001727,HC IMP VALVE AORTIC TISSUE MITROFLOW 23MM," 37,050 "
-28001728,HC IMP VALVE AORTIC TISSUE MITROFLOW 25MM," 37,050 "
-28001729,HC IMP VALVE AORTIC TISSUE MITROFLOW 27MM," 37,050 "
-28001730,HC  CATHETER CLOSURE FAST 7FX60CM," 7,542 "
-28001731,HC  CATHETER CLOSURE FAST 7FX100CM," 7,183 "
-28001732,HC IMP ARTERY PULMONARY ALLOGRAFT," 37,700 "
-28001733,HC IMP MESH PARIETEX 4INX7IN," 4,697 "
-28001734,HC IMP CONNECTOR CROSS POLARIS SMALL," 9,770 "
-28001735,HC IMPL PLATE DVR NARROW SHORT L," 8,028 "
-28001736,HC IMPL PLATE DVR NARROW SHORT R," 7,508 "
-28001738,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X12MM, 802 
-28001739,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.4X10MM, 734 
-28001740,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.0X6MM, 510 
-28001741,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X10MM, 510 
-28001745,HC IMP ROD HARD CURVED TITANIUM 3.5X40MM," 1,752 "
-28001746,HC IMP ROD HARD CURVED TITANIUM 3.5X80MM," 1,752 "
-28001747,HC IMPL PLATE TIBIAL LOW BEND RIGHT 4HOLE," 11,476 "
-28001748,HC  CATHETER BALLOON INSPIRA AIR 10X40MM," 5,908 "
-28001749,HC  CATHETER BALLOON INSPIRA AIR 14X40MM," 5,908 "
-28001750,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X10MM," 8,250 "
-28001751,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X12MM," 8,250 "
-28001752,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X16MM," 8,250 "
-28001753,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X18MM," 10,661 "
-28001754,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X20MM," 10,661 "
-28001755,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X22MM," 10,661 "
-28001756,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X24MM," 8,250 "
-28001757,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X26MM," 10,661 "
-28001758,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X28MM," 10,661 "
-28001759,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X30MM," 10,661 "
-28001760,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X32MM," 10,661 "
-28001761,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X34MM," 10,661 "
-28001762,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X36MM," 10,661 "
-28001763,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X38MM," 10,661 "
-28001764,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X40MM," 10,661 "
-28001765,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X42MM," 10,661 "
-28001766,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X44MM," 10,661 "
-28001767,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X46MM," 10,661 "
-28001768,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X48MM," 10,661 "
-28001769,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X50MM," 10,661 "
-28001770,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X8MM," 10,661 "
-28001771,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X10MM," 10,661 "
-28001772,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X12MM," 10,661 "
-28001773,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X14MM," 10,661 "
-28001774,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X16MM," 8,250 "
-28001775,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X18MM," 10,661 "
-28001776,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X20MM," 10,661 "
-28001778,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X22MM," 10,661 "
-28001779,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X24MM," 10,661 "
-28001780,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X26MM," 10,661 "
-28001781,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X28MM," 10,661 "
-28001782,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X30MM," 10,661 "
-28001783,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X32MM," 10,661 "
-28001784,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X34MM," 10,661 "
-28001785,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X36MM," 10,661 "
-28001786,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X38MM," 10,661 "
-28001787,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X40MM," 10,661 "
-28001788,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X42MM," 10,661 "
-28001789,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X44MM," 10,661 "
-28001790,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X46MM," 10,661 "
-28001791,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X48MM," 10,661 "
-28001792,HC IMPL SCREW POLYAXIAL TITANIUM 4.0X50MM," 10,661 "
-28001793,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X20MM," 11,040 "
-28001794,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X22MM," 11,040 "
-28001795,HC  GUIDEWIRE LUNDERQUIST CVD 035X260, 918 
-28001796,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X24MM," 8,250 "
-28001797,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X26MM," 11,040 "
-28001798,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X28MM," 11,040 "
-28001799,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X30MM," 8,250 "
-28001800,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X32MM," 11,040 "
-28001801,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X34MM," 11,040 "
-28001802,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X36MM," 11,040 "
-28001803,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X38MM," 11,040 "
-28001804,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X40MM," 11,040 "
-28001805,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X42MM," 11,040 "
-28001806,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X44MM," 11,040 "
-28001807,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X46MM," 11,040 "
-28001808,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X48MM," 11,040 "
-28001809,HC IMPL SCREW POLYAXIAL TITANIUM 4.5X50MM," 11,040 "
-28001810,HC IMP HOOK LAMINA SYNAPSE RIGHT SHRT," 8,062 "
-28001811,HC IMP HOOK LAMINA SYNAPSE LEFT SHRT," 8,062 "
-28001812,HC IMP HOOK LAMINA SYNAPSE RIGHT LONG," 8,062 "
-28001813,HC IMP HOOK LAMINA SYNAPSE LEFT LONG," 8,062 "
-28001814,HC IMP HOOK LAMINA TOP LOAD TITANIUM SHORT RIGHT," 7,562 "
-28001815,HC IMP HOOK LAMINA TOP LOAD TITANIUM SHORT LEFT," 7,562 "
-28001816,HC IMP HOOK LAMINA TOP LOAD TITANIUM LONG RIGHT," 7,562 "
-28001817,HC IMP HOOK LAMINA TOP LOAD TITANIUM LONG LEFT," 7,562 "
-28001818,HC IMP ROD HARD TITANIUM 3.5/5.0MMX120/17," 8,195 "
-28001819,HC IMP ROD HARD TITANIUM 3.5/6.0MMX120/17," 8,195 "
-28001820,HC CONNECTOR ROD SYNAPSE 3.5X60MM," 11,206 "
-28001821,HC CONNECTOR ROD SYNAPSE 3.5X75MM," 11,206 "
-28001822,HC IMP HOOK LAMINA LOAD STRAIGHT SHORT," 7,562 "
-28001823,HC IMP HOOK LAMINA TOP LOAD STRAIGHT LONG," 7,562 "
-28001824,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X14MM," 8,250 "
-28001825,HC IMP SCREW BONE SPINAL SYNAPSE TITANIUM POLYAXIAL," 1,275 "
-28001826,HC IMPL SCREW POLYAXIAL TITANIUM 3.5X8MM," 10,661 "
-28001827,HC IMP SCREW FIXED 6.5X55MM," 8,401 "
-28001828,HC IMP SCREW FIXED 7.5X30MM," 8,401 "
-28001830,HC IMP STENT GRAFT TAA TAG 35X15 PTFE," 76,500 "
-28001832,HC IMP ANCHOR SUTURE BIOCOMPOSITE 5.5X19.1," 3,230 "
-28001833,HC IMP ANCHOR SUTURE BIOCOMPOSITE 4.75X19.1," 3,230 "
-28001834,HC IMP PEEK SWIVELOCK ANCHORS 5.5X24.5MM, 646 
-28001835,HC IMP SCREW FIXED 7.5X35MM," 8,401 "
-28001836,HC IMP SCREW FIXED 7.5X40MM," 8,401 "
-28001837,HC IMP SCREW FIXED 7.5X45MM," 8,401 "
-28001838,HC IMP SCREW FIXED 7.5X50MM," 8,401 "
-28001839,HC IMP SCREW FIXED 7.5X55MM," 8,401 "
-28001840,HC IMP SCREW FIXED 8.5X30MM," 8,401 "
-28001841,HC IMP SCREW FIXED 8.5X35MM," 8,401 "
-28001842,HC IMP SCREW FIXED 8.5X40MM," 8,401 "
-28001843,HC IMP SCREW FIXED 8.5X45MM," 8,401 "
-28001844,HC IMP SCREW FIXED 8.5X50MM," 8,401 "
-28001845,HC IMP SCREW FIXED 8.5X55MM," 8,401 "
-28001846,HC IMP HOOK PEDICLE SMALL," 6,134 "
-28001847,HC IMP HOOK ANGLED LEFT SMALL," 6,134 "
-28001848,HC IMP HOOK ANGLED RIGHT SMALL," 6,134 "
-28001849,HC IMP HOOK ANGLED RIGHT MEDIUM," 5,063 "
-28001850,HC IMP HOOK LAMINA NARROW SMALL," 6,134 "
-28001851,HC IMP HOOK LAMINA NARROW MEDIUM," 6,573 "
-28001852,HC IMP HOOK LAMINA NARROW LARGE," 6,134 "
-28001853,HC IMP HOOK LAMINA NARROW REDUCED SMALL," 6,134 "
-28001854,HC IMP HOOK LAMINA NARROW REDUCED MEDIUM," 5,063 "
-28001855,HC IMP HOOK LAMINA NARROW REDUCED LARGE," 6,134 "
-28001856,HC IMP HOOK LAMINA WIDE SMALL," 6,134 "
-28001857,HC IMP HOOK LAMINA WIDE MEDIUM," 6,134 "
-28001858,HC IMP HOOK LAMINA WIDE LARGE," 6,134 "
-28001859,HC IMP HOOK OFFSET LEFT SMALL," 6,134 "
-28001860,HC IMP HOOK OFFSET RIGHT SMALL," 6,134 "
-28001861,HC IMP HOOK OFFSET LEFT MEDIUM," 6,134 "
-28001862,HC IMP HOOK OFFSET RIGHT MEDIUM," 6,134 "
-28001863,HC IMP HOOK OFFSET LEFT LARGE," 6,134 "
-28001864,HC IMP HOOK OFFSET RIGHT LARGE," 6,134 "
-28001865,HC IMP HOOK ANGLED BLADE SMALL," 6,134 "
-28001866,HC IMP HOOK ANGLED BLADE MEDIUM," 6,134 "
-28001867,HC IMP HOOK ANGLED BLADE LARGE," 6,134 "
-28001868,HC IMP HOOK EXTENDED SMALL," 6,134 "
-28001869,HC IMP HOOK EXTENDED MEDIUM," 6,134 "
-28001870,HC IMP HOOK EXTENDED LARGE," 6,134 "
-28001871,HC IMPL SCREW MULTI AXIAL 6.5X60MM," 8,741 "
-28001872,HC IMPL SCREW MULTI AXIAL 6.5X70MM," 8,741 "
-28001874,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 5.5X50MM," 8,741 "
-28001875,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 5.5X55MM," 8,741 "
-28001876,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X30MM," 8,741 "
-28001877,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X55MM," 8,741 "
-28001878,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X30MM," 8,741 "
-28001879,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X35MM," 8,741 "
-28001880,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X50MM," 8,741 "
-28001881,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X55MM," 8,741 "
-28001882,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 8.5X30MM," 8,741 "
-28001883,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 8.5X35MM," 8,741 "
-28001884,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 8.5X40MM," 8,741 "
-28001885,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 8.5X45MM," 8,741 "
-28001886,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 8.5X50MM," 8,741 "
-28001887,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 8.5X55MM," 8,741 "
-28001888,HC IMPL SCREW EXTENDED 5.5X30MM," 10,600 "
-28001889,HC IMPL SCREW EXTENDED 5.5X35MM," 10,600 "
-28001890,HC IMPL SCREW EXTENDED 5.5X40MM," 10,600 "
-28001891,HC IMPL SCREW EXTENDED 5.5X45MM," 10,600 "
-28001892,HC IMPL SCREW EXTENDED 5.5X50MM," 10,600 "
-28001893,HC IMPL SCREW EXTENDED 6.5X30MM," 10,600 "
-28001894,HC IMPL SCREW EXTENDED 6.5X35MM," 10,600 "
-28001895,HC IMPL SCREW EXTENDED 6.5X40MM," 10,600 "
-28001896,HC IMPL SCREW EXTENDED 6.5X45MM," 7,863 "
-28001897,HC IMPL SCREW EXTENDED 6.5X50MM," 7,863 "
-28001898,HC IMPL SCREW EXTENDED 7.5X35MM," 10,600 "
-28001899,HC IMPL SCREW EXTENDED 7.5X40MM," 10,600 "
-28001900,HC IMPL SCREW EXTENDED 7.5X45MM," 10,600 "
-28001901,HC IMPL SCREW EXTENDED 7.5X50MM," 10,600 "
-28001902,HC IMPL SCREW EXTENDED 7.5X55MM," 10,600 "
-28001903,HC IMP ROD ALLOY TI PRE CURVED 30MM," 3,005 "
-28001904,HC IMP ROD ALLOY TI PRE CURVED 35MM," 3,005 "
-28001905,HC IMP ROD ALLOY TI PRE CURVED 40MM," 3,005 "
-28001906,HC IMP ROD ALLOY TI PRE CURVED 45MM," 2,125 "
-28001907,HC IMP ROD ALLOY TI PRE CURVED 50MM," 3,005 "
-28001908,HC IMP ROD ALLOY TI PRE CURVED 55MM," 3,005 "
-28001909,HC IMP ROD ALLOY TI PRE CURVED 60MM," 2,125 "
-28001910,HC IMP ROD ALLOY TI PRE CURVED 65MM," 2,125 "
-28001911,HC IMP ROD ALLOY TI PRE CURVED 70MM," 2,125 "
-28001912,HC IMP ROD ALLOY TI PRE CURVED 75MM," 3,005 "
-28001913,HC IMP ROD ALLOY TI PRE CURVED 80MM," 2,125 "
-28001914,HC IMP ROD ALLOY TI PRE CURVED 90MM," 3,005 "
-28001915,HC IMP ROD ALLOY TI PRE CURVED 100MM," 2,125 "
-28001916,HC IMP ROD ALLOY TI PRE CURVED 105MM," 3,005 "
-28001917,HC IMP ROD ALLOY TI PRE CURVED 110MM," 3,005 "
-28001918,HC IMP ROD ALLOY TI PRE CURVED 115MM," 3,005 "
-28001919,HC IMP ROD ALLOY TI PRE CURVED 120MM," 3,005 "
-28001920,HC IMPL SCREW MULTI AXIAL 6.5X80MM," 8,741 "
-28001921,HC IMPL SCREW MULTI AXIAL 6.5X90MM," 8,741 "
-28001922,HC IMPL SCREW MULTI AXIAL 7.5X60MM," 8,741 "
-28001923,HC IMPL SCREW MULTI AXIAL 7.5X70MM," 8,741 "
-28001924,HC IMPL SCREW MULTI AXIAL 7.5X80MM," 8,741 "
-28001925,HC IMPL SCREW MULTI AXIAL 7.5X90MM," 8,741 "
-28001926,HC IMPL SCREW MULTI AXIAL 8.5X60MM," 8,741 "
-28001927,HC IMPL SCREW MULTI AXIAL 8.5X70MM," 8,741 "
-28001928,HC IMPL SCREW MULTI AXIAL 8.5X80MM," 8,741 "
-28001929,HC IMPL SCREW MULTI AXIAL 8.5X90MM," 8,741 "
-28001930,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4X20MM," 8,741 "
-28001931,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4X25MM," 8,741 "
-28001932,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4X30MM," 8,741 "
-28001933,HC IMPL PLATE TIBIAL LOW BEND LEFT 3.5," 11,476 "
-28001935,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4X35MM," 8,741 "
-28001937,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4X40MM," 8,741 "
-28001938,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4X45MM," 8,741 "
-28001939,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 5.5X25MM," 8,741 "
-28001942,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 5.5X45MM," 7,013 "
-28001945,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X30 MM," 8,820 "
-28001946,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X35 MM," 8,820 "
-28001947,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X40 MM," 8,820 "
-28001948,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X45 MM," 8,820 "
-28001949,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X50 MM," 8,820 "
-28001950,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X55 MM," 8,820 "
-28001951,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X60 MM," 8,820 "
-28001955,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X45MM," 8,820 "
-28001958,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X60MM," 8,820 "
-28001959,HC IMP ROD ALLOY TI PRE CURVED 125MM," 2,125 "
-28001960,HC IMP ROD ALLOY TI PRE CURVED 130MM," 3,005 "
-28001961,HC IMP ROD SPINAL EXTRA HARD COBALT CHROME 510MM," 5,355 "
-28001962,HC IMP CONNECTOR FIXED CROSS 35MM," 7,949 "
-28001963,HC IMP CONNECTOR LATERAL 50MM," 7,949 "
-28001964,HC IMP CONNECTOR LATERAL 75MM," 7,949 "
-28001965,HC IMP CONNECTOR FIXED CROSS 12MM," 9,770 "
-28001966,HC IMP CONNECTOR FIXED CROSS 14MM," 9,770 "
-28001967,HC IMP CONNECTOR FIXED CROSS 16MM," 9,770 "
-28001968,HC IMP CONNECTOR FIXED CROSS 18MM," 9,770 "
-28001969,HC IMP CONNECTOR FIXED CROSS 20MM," 9,770 "
-28001970,HC IMP CONNECTOR FIXED CROSS 22MM," 9,770 "
-28001971,HC IMP SCREW FIXED 4.0X20MM," 8,401 "
-28001972,HC IMP SCREW FIXED 4.0X25MM," 8,401 "
-28001973,HC IMP SCREW FIXED 4.0X30MM," 8,401 "
-28001974,HC IMP WIRE KIRSCHNER 2X150MM, 159 
-28001975,HC IMP WIRE KIRSCHNER SPADE 2.8X200MM, 340 
-28001976,HC IMP SCREW FIXED 4.0X35MM," 8,401 "
-28001977,HC IMP SCREW FIXED 4.0X40MM," 8,401 "
-28001978,HC IMP SCREW FIXED 4.0X45MM," 8,401 "
-28001979,HC IMP SCREW FIXED 4.75X20MM," 8,401 "
-28001980,HC IMP SCREW FIXED 4.75X35MM," 7,013 "
-28001981,HC IMP SCREW FIXED 4.75X40MM," 8,401 "
-28001982,HC IMP SCREW FIXED 4.75X45MM," 8,401 "
-28001984,HC IMP SCREW FIXED 5.5X30MM," 7,013 "
-28001985,HC IMP SCREW FIXED 5.5X50MM," 8,401 "
-28001986,HC IMP SCREW FIXED 5.5X55MM," 8,401 "
-28001988,HC IMP SCREW FIXED 6.5X35MM," 8,401 "
-28001992,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ10D," 11,070 "
-28001993,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ10P," 11,070 "
-28001994,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ20D," 11,070 "
-28001995,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ20P," 11,070 "
-28001996,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ30D," 11,070 "
-28001997,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ30P," 11,070 "
-28001998,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ40D," 11,070 "
-28001999,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ40P," 11,070 "
-28002001,HC IMPL PLATE PEANUT STEPPED 16MM," 3,009 "
-28002002,HC IMPL PLATE CLAVICLE LATERAL ANTERIOR LAT 8HOLE," 6,333 "
-28002003,HC IMPL PLATE CLAVICLE LATERAL ANTERIOR MEDIUM 8HOLE," 6,333 "
-28002004,HC IMPL PLATE CLAVICLE LATERAL ANTERIOR MEDIUM 6HOLE," 6,333 "
-28002005,HC IMPL PLATE CLAVICLE LATERAL ANTERIOR 10HOLE," 6,333 "
-28002006,HC IMPL PLATE CLAVICLE LATERAL ANTERIOR LAT 6HOLE," 6,333 "
-28002007,HC IMP COLLAR TI FOR 5.0MM RODS," 1,525 "
-28002008,HC IMP NUT POINT TITANIUM 12X10.5MM, 618 
-28002009,HC IMPL SCREW CANCELLOUS 4.0X12MM, 272 
-28002010,HC IMPL SCREW CANCELLOUS 4.0X14MM, 272 
-28002011,HC IMPL SCREW CANCELLOUS 4.0X16MM, 272 
-28002012,HC IMPL SCREW CANCELLOUS 4.0X18MM, 272 
-28002013,HC IMPL SCREW CANCELLOUS 4.0X20MM, 272 
-28002014,HC IMPL SCREW CANCELLOUS 4.0X22MM, 272 
-28002015,HC IMPL SCREW CANCELLOUS 4.0X24MM, 272 
-28002016,HC IMPL SCREW CANCELLOUS 4.0X26MM, 272 
-28002017,HC IMP SCREW CORTICAL 2.7X8MM, 255 
-28002018,HC IMP SCREW CORTICAL 2.7X10MM, 255 
-28002019,HC IMP SCREW CORTICAL 2.7X12MM, 255 
-28002020,HC IMP SCREW CORTICAL 2.7X14MM, 255 
-28002021,HC IMP SCREW CORTICAL 2.7X16MM, 255 
-28002022,HC IMP SCREW CORTICAL 2.7X18MM, 255 
-28002023,HC IMP SCREW CORTICAL 2.7X20MM, 255 
-28002024,HC IMP SCREW CORTICAL 2.7X22MM, 255 
-28002025,HC IMP SCREW CORTICAL 2.7X24MM, 255 
-28002026,HC IMP SCREW CORTICAL 2.7X26MM, 255 
-28002037,HC IMP SCREW CORTICAL LOCKING 3.5X8MM, 125 
-28002038,HC IMP SCREW CORTICAL LOCKING NS 2.3X8MM, 133 
-28002039,HC IMP SCREW CORTICAL LOCKING NS 2.3X10MM, 133 
-28002040,HC IMP SCREW CORTICAL LOCKING NS 2.3X12MM, 133 
-28002041,HC IMP SCREW CORTICAL LOCKING NS 2.3X14MM, 133 
-28002042,HC IMP SCREW CORTICAL LOCKING NS 2.3X16MM, 133 
-28002043,HC IMP SCREW CORTICAL LOCKING NS 2.3X18MM, 133 
-28002044,HC IMP SCREW CORTICAL LOCKING NS 2.3X20MM, 133 
-28002045,HC IMPL SCREW NON TOGGLING 8MM, 128 
-28002046,HC IMPL SCREW NON TOGGLING 10MM, 133 
-28002047,HC IMPL SCREW NON TOGGLING 12MM, 133 
-28002048,HC IMPL SCREW NON TOGGLING 14MM, 133 
-28002049,HC IMPL SCREW NON TOGGLING 16MM, 133 
-28002050,HC IMPL SCREW NON TOGGLING 18MM, 133 
-28002051,HC IMPL SCREW NON TOGGLING 20MM, 133 
-28002056,HC  GUIDEWIRE TROCAR SINGLE 59X5IN, 94 
-28002057,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LATERAL 7HOLE," 5,908 "
-28002059,HC  CATHETER BALLOON OCCLUSION CODA 10FX40MM," 3,545 "
-28002060,HC  CATHETER BALLOON OCCLUSION CODA 10FX32MM," 3,545 "
-28002061,HC  INTRODUCER CHECK FLO FLEXOR 9FX55CM, 425 
-28002062,HC IMP STENT BILIARY LUMINEXX 12X100X135," 10,493 "
-28002064,HC CATHETER EPIDURAL, 340 
-28002065,HC IMP SHOULDER STEM AEQUALIS PARTIAL 6.5MM," 27,195 "
-28002066,HC IMP SHOULDER STEM AEQUALIS PARTIAL 9.0MM," 27,195 "
-28002067,HC IMP SHOULDER STEM AEQUALIS PARTIAL 12.0MM," 27,195 "
-28002068,HC DEVICE VASCULAR LIGASURE 5MM," 4,038 "
-28002069,HC BLADE SHAVER CURVED 60X4MM," 2,193 "
-28002070,HC CATHETER MICRO CRANIOPLASTY," 6,758 "
-28002071,HC STAPLER ENDOS ECHELON LNG 60MM," 2,940 "
-28002073,HC IMP STENT PERIPHERAL LIFESTENT 6X80X80," 10,500 "
-28002074,HC IMP STAPLE BONE COMPRESS QUICK 9MM," 1,972 "
-28002075,HC IMP WIRE KIRSCHNER TROCAR POINT 1.6X150MM, 264 
-28002076,HC IMP GRAFT MIX POWER MINI IGNITE 4ML," 14,250 "
-28002081,HC IMP ANCHOR BIO SUTURETAK 3.7X14MM," 2,083 "
-28002082,HC IMPL PACEMAKER DUAL ACCENT PM2110," 33,250 "
-28002083,HC  INTRODUCER GUID CPS DIRECT XWIDE 47X50.7X2.44X3," 5,908 "
-28002086,HC IMP SCREW FIXED 5.5 TITANIUM 5.0X25MM," 8,408 "
-28002087,HC IMP HOOK ANGLED LEFT," 11,288 "
-28002088,HC IMP HOOK ANGLED RIGHT," 11,288 "
-28002089,HC IMP ROD SPINAL 5.5X300MM," 5,279 "
-28002091,HC  SHEATH URETERAL ACCESS FLEXOR AQ 9.5FX13 CM," 1,505 "
-28002092,HC IMP SHUNT EXTERNAL SUNDT 3X4MM," 2,890 "
-28002093,HC IMP SHUNT EXTERNAL SUNDT 4X5MM," 3,035 "
-28002094,HC IMP SHUNT EXTERNAL SUNDT 3X5MM," 2,890 "
-28002095,HC IMP PATCH BOVINE VERITAS 8CM," 1,335 "
-28002096,HC IMP ANCHOR SUTURE BIO SUTURETAK SZ1," 2,508 "
-28002097,HC IMP STENT GRAFT TAA TAG 37X10 PTFE," 74,250 "
-28002098,HC IMP STENT GRAFT TAA TAG 37X15 PTFE," 76,500 "
-28002099,HC IMP STENT GRAFT TAA TAG 37X20 PTFE," 74,250 "
-28002100,HC IMP STENT GRAFT TAA TAG PTFE 40X20," 76,500 "
-28002101,HC PROCESSOR SOUND PONTO 3MM," 8,186 "
-28002102,HC IMP ABUTMENT CONNECTION SCREW 6MM," 9,788 "
-28002103,HC IMP LAP BAND GASTRIC LAPAROSCOPIC 10CM," 23,149 "
-28002104,HC IMP LAP BAND GASTRIC LAPAROSCOPIC 11CM," 23,149 "
-28002105,HC IMP LAP BAND GASTRIC LAP ADJUSTABLE SMALL," 22,652 "
-28002106,HC IMP LAP BAND GASTRIC LAP ADJUSTABLE LARGE," 22,652 "
-28002107,HC LAP BAND REALIZE KIT," 21,908 "
-28002108,HC  SHEATH VASCULAR FLEXOR ANSEL 7FX.35INX55CM, 553 
-28002109,HC  SHEATH VASCULAR FLEXOR ANSEL 7FX.35INX110CM," 1,386 "
-28002110,HC  SHEATH VASCULAR FLEXOR ANSEL 8FX.35INX55CM2, 553 
-28002111,HC  SHEATH VASCULAR FLEXOR ANSEL 10FX.35INX45CM, 553 
-28002112,HC  INTRODUCER CHECK FLO FLEXOR 12F, 553 
-28002113,HC  SHEATH VASCULAR FLEXOR ANSEL 5FX.35INX65CM, 315 
-28002114,HC CLAMP ADJUSTABLE OPEN LARGE," 5,705 "
-28002115,HC  GUIDEWIRE FATHOM 200CM," 2,579 "
-28002116,HC IMP SCREW FIXED 5.5 TITANIUM 5.0X35MM," 8,408 "
-28002117,HC IMPL SCREW POLYAXIAL 5.5 TI 5.0X30MM," 8,500 "
-28002118,HC IMPL SCREW POLYAXIAL 5.5 TITANIUM 5.5X40MM," 8,500 "
-28002119,HC IMPL TISSUE ALLOGRAFT 11MM," 24,689 "
-28002120,HC IMP ROD 35MM," 3,674 "
-28002121,HC IMPL SCREW POLYAXIAL 5.5 TITANIUM 5.5X35MM," 8,500 "
-28002122,HC IMP CONNECTOR CROSS FIXED 30MM," 10,373 "
-28002123,HC  CATHETER DILATATION POWERFLEX PTA 7X4X135, 978 
-28002124,HC  CATHETER GUIDING ATTAIN SHEATH STR 50CM," 1,632 "
-28002125,HC  CATHETER GUIDING ATTAIN SELECT II," 1,360 "
-28002126,HC IMPL POWERPORT MRI 8 FR," 3,713 "
-28002127,HC IMP SPACER ARTELON LARGE," 17,250 "
-28002128,HC PROCESSOR SOUND PONTO 4MM," 15,525 "
-28002129,HC IMPL FINGER PROXIMAL INTERPHALANGEAL SZ 1," 7,980 "
-28002130,HC IMPL FINGER PROXIMAL INTERPHALANGEAL SZ2," 8,459 "
-28002131,HC IMPL FINGER PROXIMAL INTERPHALANGEAL SZ3," 7,980 "
-28002132,HC IMPL FINGER PROXIMAL INTERPHALANGEAL SZ4," 7,980 "
-28002133,HC IMPL FINGER PROXIMAL INTERPHALANGEAL SZ5," 7,980 "
-28002134,HC IMPL FINGER METACARPOPHALANGEAL SZ0," 7,980 "
-28002135,HC IMPL FINGER METACARPOPHALANGEAL SZ10," 7,980 "
-28002136,HC IMPL FINGER METACARPOPHALANGEAL SZ20," 7,980 "
-28002137,HC IMPL FINGER METACARPOPHALANGEAL SZ30," 7,980 "
-28002138,HC IMPL FINGER METACARPOPHALANGEAL SZ40," 7,980 "
-28002139,HC IMPL FINGER METACARPOPHALANGEAL SZ50," 7,980 "
-28002140,HC IMPL COIL EMBOLIZATION MICRO NESTER 3X2MM, 674 
-28002141,HC IMPL COIL EMBOLIZATION MICRO NESTER 5X2MM, 674 
-28002142,HC IMPL COIL EMBOLIZATION MICRO NESTER 7X2MM, 691 
-28002143,HC IMPL COIL EMBOLIZATION MICRO NESTER 20X18MM, 956 
-28002144,HC IMPL COIL EMBOLIZATION MICRO NESTER 20X20MM, 913 
-28002145,HC  CATHETER SUPPORT CXI STRAIGHT TIP 2.6FX150CM," 1,403 "
-28002146,HC  CATHETER SUPPORT CXI ANGLED TIP 2.6FX150CM," 1,403 "
-28002147,HC  CATHETER SUPPORT CXI STRAIGHT TIP 4.0X90CM," 1,403 "
-28002148,HC  CATHETER SUPPORT CXI STRAIGHT TIP 4.0X135CM," 1,403 "
-28002149,HC IMP GRAFT BONE OP1," 35,000 "
-28002150,HC  CATHETER DILATATION POWERFLEX PTA 6X4X13," 1,517 "
-28002151,HC CARTRIDGE CEMENT CORTOSS 5 ML," 6,800 "
-28002155,HC IMPL SCREW SAG CD HORIZON 6.5X30MM," 8,273 "
-28002156,HC IMP PLATE ORBITAL RESORBABLE 31X106MM," 11,175 "
-28002157,HC IMPL PLATE DOUBLE Y SHAPED RESORBABLE," 2,168 "
-28002158,HC  CATHETER BALLOON PTA 12X20X135," 1,615 "
-28002159,HC IMP FIXATOR MANDIBULAR CRANIO," 30,793 "
-28002160,HC IMP PIN BI DIRECTIONAL 2.7X62MM," 1,981 "
-28002161,HC IMPL SCREW 2.0MMX5MM, 459 
-28002162,HC IMP EMBOLIC TRUFILL NBCA 1 GRAM KIT," 18,455 "
-28002163,HC  CATHETER GUIDING ATTAIN MPR 45CM," 2,040 "
-28002164,HC  INTRODUCER SHEATH 5F 25CM, 131 
-28002165,HC  INTRODUCER SHEATH PINNACLE 6F 25CM, 131 
-28002166,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 3X12MM," 1,488 "
-28002169,HC IMP PLUG FISTULA ENTEROCUTANEOU 7MM," 23,800 "
-28002170,HC BUR DRILL BIT 6MM," 3,078 "
-28002171,HC BUR DRILL BIT 8MM," 3,078 "
-28002172,HC BUR DRILL BIT 10MM," 3,078 "
-28002173,HC BUR DRILL BIT 12 MM," 3,078 "
-28002174,HC CARTRIDGE ARTICULATING DUET 45 4.8MM," 3,464 "
-28002175,HC IMP CARTRIDGE ARTICULATING TRS 45MM," 3,464 "
-28002195,HC COCHLEAR PROCESSOR SOUND NUCLEUS BLACK," 52,000 "
-28002231,HC IMPL SCREW POLYAXIAL EXPEDIUM 5X6X40MM," 8,641 "
-28002234,HC IMPL SCREW POLYAXIAL EXPEDIUM 5X5.5X40MM," 14,058 "
-28002236,HC IMP HOOK REDUCED 10MM," 6,543 "
-28002252,HC  CATHETER GUIDING ATTAIN EHXL 57.5CM," 1,632 "
-28002253,HC  CATHETER BALLOON 5X20MM," 1,615 "
-28002254,HC  CATHETER DILATATION PTA FOXCROSS 3X20MX135CM," 1,530 "
-28002255,HC GUIDE PIN FLEXIBLE ACL," 3,307 "
-28002256,HC  CATHETER BALLOON 5X40MM," 1,615 "
-28002258,HC  CATHETER BALLOON 6X40MM," 1,615 "
-28002259,HC  CATHETER BALLOON 7X20MM," 1,615 "
-28002260,HC  CATHETER BALLOON 7X40MM," 1,615 "
-28002261,HC  CATHETER BALLOON 3X20MM," 1,615 "
-28002262,HC  CATHETER DILATATION PTA FOXCROSS 3X40MX80CM," 1,530 "
-28002263,HC  CATHETER DILATATION PTA FOXCROSS 3X80MMX80CM," 1,615 "
-28002264,HC  CATHETER DILATATION PTA FOXCROSS 4X20MMX80CM," 1,530 "
-28002265,HC  CATHETER DILATATION PTA FOXCROSS 4X40MMX80CM," 1,530 "
-28002266,HC  CATHETER DILATATION PTA FOXCROSS 4X60MX80CM," 1,530 "
-28002267,HC  CATHETER DILATATION PTA FOXCROSS 4X80MMX80CM," 1,530 "
-28002268,HC  CATHETER DILATATION PTA FOXCROSS 4X100MMX80CM," 1,615 "
-28002269,HC  CATHETER DILATATION PTA FOXCROSS 4X120MMX80CM," 1,530 "
-28002270,HC  CATHETER DILATATION PTA FOXCROSS 5X20MMX80CM," 1,530 "
-28002271,HC  CATHETER DILATATION PTA FOXCROSS 5X30MMX80CM," 1,615 "
-28002272,HC  CATHETER DILATATION PTA FOXCROSS 5X40MMX80CM," 1,530 "
-28002273,HC  CATHETER DILATATION PTA FOXCROSS 5X60MMX80CM," 1,530 "
-28002274,HC  CATHETER DILATATION PTA FOXCROSS 5X80MMX80CM," 1,530 "
-28002275,HC  CATHETER DILATATION PTA FOXCROSS 5X100MMX80CM," 1,530 "
-28002276,HC  CATHETER DILATATION PTA FOXCROSS 5X120MMX80CM," 1,615 "
-28002277,HC  CATHETER DILATATION PTA FOXCROSS 6X20MMX80CM," 1,530 "
-28002278,HC  CATHETER DILATATION PTA FOXCROSS 6X30MMX80CM," 1,615 "
-28002279,HC  CATHETER DILATATION PTA FOXCROSS 6X40MMX80CM," 1,530 "
-28002280,HC  CATHETER DILATATION PTA FOXCROSS 6X60MMX80CM," 1,530 "
-28002281,HC  CATHETER DILATATION PTA FOXCROSS 6X80MMX80CM," 1,530 "
-28002282,HC  CATHETER DILATATION PTA FOXCROSS 6X100MMX80CM," 1,530 "
-28002283,HC  CATHETER DILATATION PTA FOXCROSS 6X120MMX80CM," 1,615 "
-28002284,HC  CATHETER DILATATION PTA FOXCROSS 7X20MMX80CM," 1,530 "
-28002285,HC  CATHETER DILATATION PTA FOXCROSS 7X30MMX80CM," 1,615 "
-28002286,HC  CATHETER DILATATION PTA FOXCROSS 7X40MMX80CM," 1,530 "
-28002287,HC  CATHETER DILATATION PTA FOXCROSS 7X60MMX80CM," 1,530 "
-28002288,HC  CATHETER DILATATION PTA FOXCROSS 7X80MMX80CM," 1,530 "
-28002289,HC  CATHETER DILATATION PTA FOXCROSS 7X100MMX80CM," 1,530 "
-28002290,HC  CATHETER DILATATION PTA FOXCROSS 7X120MMX80CM," 1,530 "
-28002291,HC  CATHETER DILATATION PTA FOXCROSS 8X20MMX80CM," 1,530 "
-28002292,HC  CATHETER DILATATION PTA FOXCROSS 8X30MMX80CM," 1,615 "
-28002293,HC  CATHETER DILATATION PTA FOXCROSS 8X40MMX80CM," 1,530 "
-28002294,HC  CATHETER DILATATION PTA FOXCROSS 8X60MMX80CM," 1,530 "
-28002295,HC  CATHETER DILATATION PTA FOXCROSS 8X80MMX80CM," 1,530 "
-28002296,HC  CATHETER DILATATION PTA FOXCROSS 9X20MMX80CM," 1,615 "
-28002297,HC  CATHETER DILATATION PTA FOXCROSS 9X30MMX80CM," 1,615 "
-28002298,HC  CATHETER DILATATION PTA FOXCROSS 9X40MMX80CM," 1,530 "
-28002299,HC  CATHETER DILATATION PTA FOXCROSS 9X60MMX80CM," 1,530 "
-28002300,HC  CATHETER DILATATION PTA FOXCROSS 3X40MMX135CM," 1,615 "
-28002301,HC  CATHETER DILATATION PTA FOXCROSS 3X80MMX135CM," 1,615 "
-28002302,HC  CATHETER DILATATION PTA FOXCROSS 4X20MMX135CM," 1,615 "
-28002303,HC  CATHETER DILATATION PTA FOXCROSS 4X40MMX135CM," 1,530 "
-28002304,HC  CATHETER DILATATION PTA FOXCROSS 4X60MMX135CM," 1,615 "
-28002305,HC  CATHETER DILATATION PTA FOXCROSS 4X80MMX135CM," 1,615 "
-28002306,HC  CATHETER DILATATION PTA FOXCROSS 4X100MMX135CM," 1,615 "
-28002307,HC  CATHETER DILATATION PTA FOXCROSS 4X120MMX135CM," 1,530 "
-28002308,HC  CATHETER DILATATION PTA FOXCROSS 5X20MMX135CM," 1,530 "
-28002309,HC  CATHETER DILATATION PTA FOXCROSS 5X30MMX135CM," 1,615 "
-28002310,HC  CATHETER DILATATION PTA FOXCROSS 5X40MMX135CM," 1,615 "
-28002311,HC  CATHETER DILATATION PTA FOXCROSS 5X60MMX135CM," 1,530 "
-28002312,HC  CATHETER DILATATION PTA FOXCROSS 5X80MMX135CM," 1,530 "
-28002313,HC  CATHETER DILATATION PTA FOXCROSS 5X100MMX135CM," 1,615 "
-28002314,HC  CATHETER DILATATION PTA FOXCROSS 5X120MMX135CM," 1,530 "
-28002315,HC  CATHETER BALLOON 6X20X50," 1,615 "
-28002316,HC  CATHETER DILATATION PTA FOXCROSS 6X30MMX135CM," 1,615 "
-28002317,HC  CATHETER DILATATION PTA FOXCROSS 6X40MMX135CM," 1,530 "
-28002318,HC  CATHETER DILATATION PTA FOXCROSS 6X60MMX135CM," 1,615 "
-28002319,HC  CATHETER DILATATION PTA FOXCROSS 6X80MMX135CM," 1,615 "
-28002320,HC  CATHETER BALLOON 6X100MM," 1,615 "
-28002321,HC  CATHETER DILATATION PTA FOXCROSS 6X120MX135CM," 1,530 "
-28002322,HC  CATHETER DILATATION PTA FOXCROSS 7X20MMX135CM," 1,615 "
-28002323,HC  CATHETER DILATATION PTA FOXCROSS 7X30MMX135CM," 1,615 "
-28002324,HC  CATHETER DILATATION PTA FOXCROSS 7X40MMX135CM," 1,530 "
-28002325,HC  CATHETER DILATATION PTA FOXCROSS 7X60MMX135CM," 1,615 "
-28002326,HC  CATHETER DILATATION PTA FOXCROSS 7X100MMX135CM," 1,615 "
-28002327,HC  CATHETER DILATATION PTA FOXCROSS 7X120MMX135CM," 1,530 "
-28002328,HC IMP CABLE EP INTERFACE 9FT," 1,530 "
-28002329,HC  CATHETER DILATATION PTA FOXCROSS 8X30MMX135CM," 1,615 "
-28002330,HC  CATHETER DILATATION PTA FOXCROSS 8X40MMX135CM," 1,530 "
-28002331,HC  CATHETER DILATATION PTA FOXCROSS 9X20MMX135CM," 1,530 "
-28002332,HC  CATHETER DILATATION PTA FOXCROSS 9X30MMX135CM," 1,615 "
-28002333,HC IMP SCREW FIXED JONES 5.5X45MM," 10,215 "
-28002334,HC IMP MESHPLATE 51MMX51MM," 7,514 "
-28002335,HC IMPL SCREW VUE POINT SET, 850 
-28002336,HC IMP ROD VUE POINT 240X3.5MM," 2,822 "
-28002337,HC IMP SCREW FIXED ARMADA 6.5X35MM," 8,408 "
-28002338,HC IMP CONNECTOR CROSS FIXED 40MM," 10,373 "
-28002339,HC IMPL SCREW UNI EXPEDIUM 6X40MM," 11,808 "
-28002340,HC IMPL SCREW UNI EXPEDIUM 5X35MM," 11,808 "
-28002341,HC IMPL SCREW UNI EXPEDIUM 5X30MM," 11,808 "
-28002342,HC IMPL SCREW FIXED ANGLED TI 4.5X30MM," 7,250 "
-28002343,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X24MM," 6,732 "
-28002344,HC IMPL SCREW NON BREAK OFF SET, 850 
-28002345,HC  CATHETER DILATATION PTA FOXCROSS 10X20MMX80CM," 1,530 "
-28002346,HC  CATHETER DILATATION PTA FOXCROSS 10X30MMX80CM," 1,615 "
-28002347,HC  CATHETER DILATATION PTA FOXCROSS 10X40MMX80CM," 1,530 "
-28002348,HC  CATHETER DILATATION PTA FOXCROSS 10X60MMX80CM," 1,530 "
-28002349,HC  CATHETER DILATATION PTA FOXCROSS 12X20MMX80CM," 1,615 "
-28002350,HC  CATHETER DILATATION PTA FOXCROSS 12X30MMX80CM," 1,615 "
-28002351,HC  CATHETER DILATATION PTA FOXCROSS 12X40MMX80CM," 1,530 "
-28002352,HC  CATHETER DILATATION PTA FOXCROSS 12X60MM," 1,530 "
-28002353,HC  CATHETER DILATATION PTA FOXCROSS 14X20MMX80CM," 1,615 "
-28002354,HC  CATHETER DILATATION PTA FOXCROSS 14X40MMX80CM," 1,530 "
-28002355,HC IMPL PACEMAKER ANTHEM CRT-P 6X52X58MM," 60,775 "
-28002356,HC CARDIAC POSITIONER LEAD SYSTEM KIT," 8,458 "
-28002357,HC  CATHETER DILATATION PTA FOXCROSS 9X40MMX135CM," 1,530 "
-28002358,HC  CATHETER DILATATION PTA FOXCROSS 10X20MMX135CM," 1,530 "
-28002359,HC  CATHETER DILATATION PTA FOXCROSS 10X30MX135CM," 1,615 "
-28002360,HC  CATHETER DILATATION PTA FOXCROSS 10X40MX135CM," 1,615 "
-28002361,HC  CATHETER DILATATION PTA FOXCROSS 12X20MX135CM," 1,615 "
-28002362,HC  CATHETER DILATATION PTA FOXCROSS 12X40MX135CM," 1,530 "
-28002363,HC  CATHETER DILATATION PTA FOXCROSS 14X20MMX135CM," 1,530 "
-28002364,HC  CATHETER DILATATION PTA FOXCROSS 14X40MX135CM," 1,615 "
-28002365,HC IMP ADAPTER LEAD EXTENSION BRADY," 2,975 "
-28002366,HC IMP GRAFT PROPATEN 6X6X70," 17,723 "
-28002369,HC  INTRODUCER SHEATH DRYSEAL 24F," 2,763 "
-28002370,HC PLUNGER DELIVERY SYSTEM," 3,400 "
-28002376,HC  CATHETER FEM 8FX80 VAN ANDEL SZ5, 374 
-28002377,HC  SHEATH VASCULAR MULLINS GUIDING 9FX.38INX53CM, 646 
-28002378,HC  CATHETER GROSHONG NXT SINGLE LUMEN 4F," 1,165 "
-28002379,HC ELECTRODE RADIOFREQUENCY STARBURST XL 25CM," 14,055 "
-28002380,HC STAPLER ENDO ENDOPATH WIDE 340MM," 2,640 "
-28002381,HC STAPLER ENDOPATH ECHELON 45MM," 2,640 "
-28002382,HC BUR DRILL TRAPEZIUM CANNULATED 4.8X155MM," 3,324 "
-28002383,HC IMP WIRE KIRSCHNER STAINLESS STEEL 1.1MM, 170 
-28002384,HC IMP PIN BUTTRESS 19MM," 2,125 "
-28002389,HC IMP PEG UNTHREAD VOLAR 14MM, 723 
-28002390,HC IMP PEG UNTHREAD VOLAR 16MM, 723 
-28002391,HC IMP PEG UNTHREAD VOLAR 18MM, 723 
-28002392,HC IMP PEG UNTHREAD VOLAR 20MM, 723 
-28002393,HC IMP PEG UNTHREAD VOLAR 22MM, 723 
-28002394,HC IMP PEG UNTHREAD TRX 24MM, 723 
-28002395,HC IMP PEG UNTHREAD TRX 26MM, 638 
-28002396,HC IMP PEG VOLAR THREAD 1.75X16MM, 638 
-28002397,HC IMP PEG THREAD TRX 14MM, 723 
-28002398,HC IMP PEG THREAD VOLAR 16MM, 723 
-28002399,HC IMP PEG THREAD VOLAR 18MM, 723 
-28002401,HC IMP PEG THREAD VOLAR 22MM, 723 
-28002402,HC IMP PEG THREAD VOLAR 24MM, 723 
-28002403,HC IMP PEG THREAD VOLAR 26MM, 638 
-28002404,HC IMP PEG THREAD TRX 24MM, 638 
-28002405,HC IMP SCREW CORTICAL 2.3X10MM, 638 
-28002406,HC IMP SCREW CORTICAL 2.3X12MM, 638 
-28002407,HC IMP SCREW CORTICAL 2.3X14MM, 638 
-28002408,HC IMP SCREW CORTICAL 2.3X16MM, 638 
-28002409,HC IMP SCREW CORTICAL 2.3X18MM, 638 
-28002410,HC IMP SCREW CORTICAL 2.3X20MM, 638 
-28002411,HC IMP SCREW CORTICAL 2.3X22MM, 638 
-28002412,HC IMP SCREW CORTICAL 2.3X24MM, 638 
-28002413,HC IMP SCREW CORTICAL 2.3X26MM, 638 
-28002414,HC IMP SCREW CORTICAL 2.3X28MM, 595 
-28002415,HC IMP SCREW CORTICAL 2.3X30MM, 595 
-28002416,HC IMP SCREW CORTICAL 2.3X32MM, 595 
-28002417,HC IMP SCREW BONE CORTICAL HEX 3.2X20MM, 638 
-28002418,HC IMPL PLATE VOLAR PEG RIGHT PEG 5HOLE," 5,313 "
-28002419,HC IMPL PLATE VOLAR SHEAR LEFT," 3,400 "
-28002420,HC IMPL PLATE VOLAR SHEAR RIGHT," 3,400 "
-28002421,HC IMPL PLATE WIRE FORM 5 HOLE," 3,103 "
-28002422,HC IMPL PLATE WIRE FORM 3 HOLE," 4,463 "
-28002424,HC IMPL PLATE RADIAL COLUMN PIN 3 HOLE," 4,463 "
-28002425,HC IMPL PLATE RADIAL COLUMN PIN 5 HOLE," 4,463 "
-28002426,HC IMPL PLATE RADIAL COLUMN PIN 7 HOLE," 5,653 "
-28002427,HC IMP WIRE KIRSCHNER 0.72X80MM, 128 
-28002428,HC IMP WIRE KIRSCHNER 8X120MM, 128 
-28002429,HC IMP WIRE KIRSCHNER 1.1X120MM, 128 
-28002434,HC IMPL SCREW CANNULATED COMPRESSION 8MM," 2,508 "
-28002435,HC IMPL SCREW CANNULATED COMPRESSION 9MM," 2,508 "
-28002436,HC IMP VEIN SAPHENOUS 3MMX43CM," 37,180 "
-28002437,HC  CATHETER DIAGNOSTIC 2X2X10X157MM 7F," 7,863 "
-28002439,HC IMP STENT NEUROFORM EZ 2.5X15," 34,385 "
-28002440,HC IMP STENT NEUROFORM EZ 2.5X20," 35,133 "
-28002441,HC IMP STENT NEUROFORM EZ 3X15," 34,385 "
-28002442,HC IMP STENT NEUROFORM EZ 3X20," 34,385 "
-28002443,HC IMP STENT NEUROFORM EZ 3.5X15," 36,171 "
-28002444,HC IMP STENT NEUROFORM EZ 3.5X20," 35,133 "
-28002445,HC IMP STENT NEUROFORM EZ 4X20," 35,133 "
-28002446,HC IMP STENT NEUROFORM EZ 4X30," 34,385 "
-28002447,HC IMP STENT NEUROFORM EZ 4.5X20," 35,133 "
-28002448,HC IMP STENT NEUROFORM EZ 4.5X30," 35,133 "
-28002449,HC IMP PROSTHESIS TESTICULAR SMALL," 12,375 "
-28002450,HC IMP SCREW CORTEX BONE 2.0X4MM, 628 
-28002451,HC IMP SCREW CORTEX BONE 2.0X5MM, 628 
-28002452,HC IMP SCREW CORTEX BONE 2.0X6MM, 628 
-28002453,HC IMP SCREW CORTEX BONE 2.0X7MM, 628 
-28002454,HC IMP SCREW CORTEX BONE 2.0X8MM, 628 
-28002455,HC IMP SCREW CORTEX BONE 2.0X9MM, 628 
-28002456,HC IMP SCREW CORTEX BONE 2.0X10MM, 628 
-28002457,HC IMP SCREW CORTEX BONE 2.0X11MM, 628 
-28002458,HC IMP SCREW CORTEX BONE 2.0X12MM, 628 
-28002459,HC IMP SCREW CORTEX BONE 2.0X13MM, 628 
-28002460,HC IMP SCREW CORTEX BONE 2.0X14MM, 628 
-28002461,HC IMP SCREW CORTEX BONE 2.0X15MM, 628 
-28002462,HC IMP SCREW CORTEX BONE 2.0X16MM, 628 
-28002463,HC IMP SCREW CORTEX BONE 2.0X17MM, 628 
-28002464,HC IMP SCREW CORTEX BONE 2.0X18MM, 628 
-28002465,HC IMP SCREW CORTEX BONE 2.0X19MM, 628 
-28002466,HC IMP SCREW CORTEX BONE 2.0X20MM, 628 
-28002467,HC IMP SCREW CORTEX BONE 2.0X21MM, 628 
-28002468,HC IMP SCREW CORTEX BONE 2.0X22MM, 628 
-28002469,HC IMP SCREW CORTEX BONE 2.0X23MM, 628 
-28002470,HC IMP SCREW CORTEX BONE 2.0X24MM, 628 
-28002471,HC IMPL SCREW LOCKING 2.0X6MM, 978 
-28002472,HC IMPL SCREW LOCKING 2.0X7MM, 978 
-28002473,HC IMPL SCREW LOCKING 2.0X8MM, 978 
-28002474,HC IMPL SCREW LOCKING 2.0X9MM, 978 
-28002475,HC IMPL SCREW LOCKING 2.0X10MM," 1,013 "
-28002476,HC IMPL SCREW LOCKING 2.0X11MM," 1,013 "
-28002477,HC IMPL SCREW LOCKING 2.0X12MM," 1,010 "
-28002478,HC IMPL SCREW LOCKING 2.0X13MM," 1,010 "
-28002479,HC IMP GRAFT MENISCAL," 30,520 "
-28002480,HC IMPL SCREW LOCKING 2.0X14MM," 1,010 "
-28002481,HC IMPL SCREW LOCKING 2.0X15MM, 978 
-28002482,HC IMPL SCREW LOCKING 2.0X16MM," 1,010 "
-28002483,HC IMPL SCREW LOCKING 2.0X17MM, 978 
-28002484,HC IMPL SCREW LOCKING 2.0X18MM," 1,010 "
-28002485,HC IMPL SCREW LOCKING 2.0X19MM, 978 
-28002486,HC IMPL SCREW LOCKING 2.0X20MM," 1,013 "
-28002487,HC IMP SCREW CORTEX BONE 2.3X6MM, 628 
-28002488,HC IMP SCREW CORTEX BONE 2.3X7MM, 628 
-28002489,HC IMP SCREW CORTEX BONE 2.3X8MM, 628 
-28002490,HC IMP SCREW CORTEX BONE 2.3X9MM, 628 
-28002491,HC IMP SCREW CORTEX BONE 2.3X10MM, 680 
-28002492,HC IMP SCREW CORTEX BONE 2.3X11MM, 628 
-28002493,HC IMP SCREW CORTEX BONE 2.3X12MM, 628 
-28002494,HC IMP SCREW CORTEX BONE 2.3X13MM, 628 
-28002495,HC IMP SCREW CORTEX BONE 2.3X14MM, 628 
-28002496,HC IMP SCREW CORTEX BONE 2.3X15MM, 628 
-28002497,HC IMP SCREW CORTEX BONE 2.3X16MM, 628 
-28002498,HC IMP SCREW CORTEX BONE 2.3X17MM, 628 
-28002499,HC IMP SCREW CORTEX BONE 2.3X18MM, 628 
-28002500,HC IMP SCREW CORTEX BONE 2.3X19MM, 628 
-28002501,HC IMP SCREW CORTEX BONE 2.3X20MM, 628 
-28002502,HC IMP SCREW CORTEX BONE 2.3X21MM, 628 
-28002503,HC IMP SCREW CORTEX BONE 2.3X22MM, 628 
-28002504,HC IMP SCREW CORTEX BONE 2.3X23MM, 628 
-28002505,HC IMP SCREW CORTEX BONE 2.3X24MM, 628 
-28002506,HC IMPL PLATE LOCKING GRID 2.0 2.3 2X2," 5,500 "
-28002507,HC IMPL PLATE LOCKING GRID 2.0 2.3 3X2," 5,670 "
-28002508,HC IMP GRAFT FRESH FEMORAL HEMI CONDY," 64,200 "
-28002509,HC IMP SCREW CORTEX SELF DRILLING 2MM, 491 
-28002510,HC IMP CONNECTOR SPINAL CROSS VERIABLE 45-50," 14,018 "
-28002511,HC IMPL SCREW POLYAXIAL 5.5 TITANIUM 5.5X45MM," 8,500 "
-28002512,HC IMP SCREW FIXED TITANIUM 4.5 5.0X30MM," 8,408 "
-28002513,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6X45MM," 9,488 "
-28002514,HC IMP ROD XIA RADIUS 6X60," 3,374 "
-28002515,HC IMPL PLATE LOCKING GRID 2.0 2.3 5X2," 6,231 "
-28002516,HC IMPL PLATE LOCKING GRID 2.0 2.3 4X2," 5,959 "
-28002517,HC IMPL PLATE LOCKING 2.0 2.3 CURVED 6," 5,500 "
-28002518,HC IMPL PLATE LOCKING 2.0 2.3 CURVED 8," 5,814 "
-28002519,HC IMPL PLATE METACARPAL COMPRESSION 2.0 2.3 4HOLE," 2,355 "
-28002520,HC IMPL PLATE METACARPAL COMPRESSION 2.0 2.3 5HOLE," 2,355 "
-28002521,HC IMPL PLATE METACARPAL COMPRESSION 2.0 2.3 6HOLE," 2,355 "
-28002522,HC IMPL PLATE METACARPAL COMPRESSION 2.0 2.3 2/4," 2,125 "
-28002523,HC IMPL PLATE METACARPAL COMPRESSION 2.0 2.3 2/8," 3,077 "
-28002524,HC IMPL PLATE METACARPAL COMPRESSION 2.0 2.3 8 HOLE," 2,644 "
-28002525,HC IMPL PLATE LOCKING 2.0 6 HOLE STRAI," 3,417 "
-28002526,HC IMPL PLATE LOCKING 2.0 T SHAPE 2/4," 3,545 "
-28002527,HC IMPL PLATE LOCKING 2.0 T SHAPE 3/4," 3,706 "
-28002528,HC IMPL PLATE LOCKING 2.0 Y SHAPE 3/4," 3,647 "
-28002529,HC IMPL PLATE LOCKING 2.0 2X2 HOLE REC," 4,114 "
-28002530,HC IMPL PLATE LOCKING 2.0 3X2 HOLE TRA," 4,413 "
-28002531,HC IMPL PLATE LOCKING 2.0 GRID 4X2 HOL," 4,488 "
-28002532,HC IMPL PLATE LOCKING 2.0 GRID 6X2 HOL," 4,855 "
-28002533,HC IMPL PLATE LOCKING 2.0 2.3 CURVED 4," 4,760 "
-28002534,HC IMPL PLATE LOCKING 2.0 2.3 CURVED 5," 5,707 "
-28002535,HC  CATHETER BALLOON PTA 8X40MMX135CM," 1,445 "
-28002537,HC  CATHETER BALLOON PTA 5MMX4CM," 2,040 "
-28002541,HC IMP WIRE KIRSCHNER 91X80MM, 153 
-28002543,HC IMPL SCREW 2.0MM TWIN PITCH COMP SRE," 2,066 "
-28002544,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 4H RIGHT," 8,256 "
-28002545,HC IMPL PLATE CLAVICLE ANTERIOR 6H 94MM RIGHT," 7,905 "
-28002546,HC IMPL PLATE CLAVICLE ANTERIOR 6H 94MM LEFT," 7,905 "
-28002547,HC IMPL PLATE CLAVICLE ANTERIOR 7H 110MM RIGHT," 7,523 "
-28002548,HC IMPL PLATE CLAVICLE ANTERIOR 7H 110MM LEFT," 8,118 "
-28002549,HC IMPL PLATE CLAVICLE ANTERIOR 8H 120MM RIGHT," 8,330 "
-28002550,HC IMPL PLATE CLAVICLE ANTERIOR 8H 120MM LEFT," 8,330 "
-28002551,HC IMPL PLATE CLAVICLE SUPERIOR RIGHT 6H 85MM," 8,305 "
-28002552,HC IMPL PLATE CLAVICLE SUPERIOR LEFT 6H 85MM," 8,305 "
-28002553,HC IMPL PLATE CLAVICLE SUPERIOR RIGHT 7H 100MM," 7,953 "
-28002554,HC IMPL PLATE CLAVICLE SUPERIOR LEFT 7H 100MM," 7,684 "
-28002555,HC IMPL PLATE CLAVICLE SUPERIOR RIGHT 8H 115MM," 7,718 "
-28002556,HC IMPL PLATE CLAVICLE SUPERIOR LEFT 8H 115MM," 8,160 "
-28002557,HC IMPL PLATE CLAVICLE SUPERIOR RIGHT 3.5MM," 7,754 "
-28002558,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.0X22," 2,738 "
-28002559,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 8H 73MM," 5,568 "
-28002560,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 8H 73MM," 5,931 "
-28002561,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 4H 86MM," 6,023 "
-28002562,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 4H 86MM," 5,819 "
-28002563,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 5H 99MM," 6,107 "
-28002564,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 5H 99MM," 5,738 "
-28002565,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 6H 112MM," 5,823 "
-28002566,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 6H 112MM," 5,989 "
-28002567,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 7H 125MM," 6,291 "
-28002568,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 9H 158MM," 5,993 "
-28002569,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 9H 151MM," 5,993 "
-28002570,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 11H 177MM," 6,078 "
-28002571,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 11H 177MM," 6,078 "
-28002572,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 3H 77MM," 5,730 "
-28002573,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 3H 77MM," 5,931 "
-28002574,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 4H 90MM," 6,023 "
-28002575,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 4H 90MM," 5,819 "
-28002576,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 5H 103MM," 5,900 "
-28002577,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 5H 103MM," 5,900 "
-28002578,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 6H 116MM," 5,823 "
-28002579,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 6H 116MM," 5,823 "
-28002580,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 7H 129MM," 5,908 "
-28002581,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 7H 129MM," 6,291 "
-28002582,HC IMP GRAFT ENDOPROSTHESIS TAG THORACIC 45X10CM," 74,250 "
-28002583,HC IMP GRAFT ENDOPROSTHESIS TAG THORACIC 45X15CM," 76,500 "
-28002584,HC IMP GRAFT ENDOPROSTHESIS TAG THORACIC 45X20CM," 76,500 "
-28002585,HC IMPL SCREW UNIPLANER 5.5X35MM," 7,013 "
-28002586,HC IMPL SCREW UNIPLANER 5.5X40MM," 7,013 "
-28002587,HC IMPL SCREW UNIPLANER 6.5X40MM," 7,013 "
-28002588,HC IMPL SCREW POLYAXIAL 5.0X35MM," 13,823 "
-28002589,HC IMP SCREW TWIN PITCH COMP 2.5MM 12MM," 2,244 "
-28002590,HC IMP SCREW TWIN PITCH COMP 2.5MM 13MM," 2,066 "
-28002591,HC IMP SCREW TWIN PITCH COMP 2.5MM 14MM," 2,244 "
-28002592,HC IMP SCREW TWIN PITCH COMP 2.5MM 15MM," 2,066 "
-28002593,HC IMP SCREW TWIN PITCH COMP 2.5MM 16MM," 2,066 "
-28002594,HC IMP SCREW TWIN PITCH COMP 2.5MM 17MM," 2,066 "
-28002595,HC IMP SCREW TWIN PITCH COMP 2.5MM 18MM," 2,244 "
-28002596,HC IMP SCREW TWIN PITCH COMP 12.5MM 9MM," 2,244 "
-28002597,HC IMP SCREW TWIN PITCH COMP 2.5MM 20MM," 2,355 "
-28002598,HC IMP SCREW TWIN PITCH COMP 2.5MM 21MM," 2,355 "
-28002599,HC IMP SCREW TWIN PITCH COMP 2.5MM 22MM," 2,176 "
-28002600,HC IMP SCREW TWIN PITCH COMP 2.5MM 23MM," 2,355 "
-28002601,HC IMP SCREW TWIN PITCH COMP2.5MM 24MM," 2,244 "
-28002602,HC IMP SCREW TWIN PITCH COMP 2.5MM 25MM," 2,244 "
-28002603,HC IMP SCREW TWIN PITCH COMP 2.5MM 26MM," 2,066 "
-28002604,HC IMP SCREW TWIN PITCH COMP 2.5MM 27MM," 2,066 "
-28002605,HC IMP SCREW TWIN PITCH COMP 2.5MM 28MM," 2,066 "
-28002606,HC IMP SCREW TWIN PITCH COMP 2.5MM 29MM," 2,244 "
-28002607,HC IMP SCREW TWIN PITCH COMP 2.5MM 30MM," 2,244 "
-28002610,HC IMP WIRE KIRSCHNER 1.6MM, 247 
-28002611,HC IMPL PLATE LOCKING CORRECTION RADIUS LEFT 12HOLE," 7,370 "
-28002612,HC IMPL PLATE LOCKING CORRECTION RADIUS RIGHT 12HOLE," 7,370 "
-28002613,HC IMPL PLATE LOCKING CORRECTION RADIUS LEFT LONG 12H," 7,616 "
-28002614,HC IMPL PLATE LOCKING CORRECTION RADIUS RIGHT LONG 12H," 7,616 "
-28002615,HC IMPL PLATE LOCKING CORRECTION RADIUS LEFT LONG 15H," 7,838 "
-28002616,HC IMPL PLATE LOCKING CORRECTION RADIUS RIGHT LONG 15H," 7,775 "
-28002617,HC IMPL PLATE LOCKING CORRECTION RADIUS LEFT LONG 13H," 7,817 "
-28002618,HC IMPL PLATE LOCKING CORRECTION RADIUS RIGHT LONG 13H," 7,817 "
-28002619,HC IMPL PLATE LOCKING 2.5 STRAIGHT 6 HOLE," 5,245 "
-28002620,HC IMPL PLATE LOCKING RADIUS 2.5 TRILOCK L LEFT 6 HOLE," 5,445 "
-28002621,HC IMPL PLATE LOCKING RADIUS 2.5 TRILOCK L RIGHT 6 HOLE," 5,445 "
-28002622,HC IMPL PLATE LOCKING 2.5 CURVED LEFT 5 HOLE," 5,245 "
-28002623,HC IMPL PLATE LOCKING 2.5 CURVED RIGHT 5 HOLE," 5,245 "
-28002624,HC IMPL PLATE LOCKING WIDE LEFT 2.5," 7,370 "
-28002625,HC IMPL PLATE LOCKING WIDE RIGHT 2.5," 7,370 "
-28002626,HC IMPL PLATE LOCKING H LEFT," 7,616 "
-28002627,HC IMPL PLATE LOCKING H RIGHT," 7,616 "
-28002628,HC IMPL PLATE LOCKING FX 2.5 NARROW LEFT SHORT 10HOLE," 7,888 "
-28002629,HC IMPL PLATE LOCKING FX 2.5 NARROW RIGHT SHORT 10HOLE," 7,370 "
-28002630,HC IMPL PLATE LOCKING FX 2.5 NARROW LEFT 11HOLE," 7,370 "
-28002631,HC IMPL PLATE LOCKING FX 2.5 NARROW RIGHT 11HOLE," 7,370 "
-28002632,HC IMP SCREW CORTICAL 2.5X8MM, 595 
-28002633,HC IMP SCREW CORTICAL 2.5X10MM, 628 
-28002634,HC IMP SCREW CORTICAL 2.5X12MM, 628 
-28002635,HC IMP SCREW CORTICAL 2.5X14MM, 628 
-28002636,HC IMP SCREW CORTICAL 2.5X16MM, 628 
-28002637,HC IMP SCREW CORTICAL 2.5X18MM, 628 
-28002638,HC IMP SCREW CORTICAL 2.5X20MM, 628 
-28002639,HC IMP SCREW CORTICAL 2.5X22MM, 628 
-28002640,HC IMP SCREW CORTICAL 2.5X24MM, 628 
-28002641,HC IMP SCREW CORTICAL 2.5X26MM, 628 
-28002642,HC IMP SCREW CORTICAL 2.5X28MM, 595 
-28002643,HC IMP SCREW CORTICAL 2.5X30MM, 595 
-28002644,HC IMP SCREW CORTICAL 2.5X32MM, 595 
-28002645,HC IMP SCREW CORTICAL 2.5X34MM, 628 
-28002646,HC IMPL SCREW LOCKING 2.5X8MM, 978 
-28002647,HC IMPL SCREW LOCKING 2.5X10MM," 1,013 "
-28002648,HC IMPL SCREW LOCKING 2.5X12MM," 1,013 "
-28002649,HC IMPL SCREW LOCKING 2.5X14MM," 1,013 "
-28002650,HC IMPL SCREW LOCKING 2.5X16MM," 1,013 "
-28002651,HC IMPL SCREW LOCKING 2.5X18MM," 1,010 "
-28002652,HC IMPL SCREW LOCKING 2.5X20MM," 1,013 "
-28002653,HC IMPL SCREW LOCKING 2.5X22MM," 1,013 "
-28002654,HC IMPL SCREW LOCKING 2.5X24MM," 1,013 "
-28002655,HC IMPL SCREW LOCKING 2.5X26MM," 1,013 "
-28002656,HC IMPL SCREW LOCKING 2.5X28MM," 1,013 "
-28002657,HC IMPL SCREW LOCKING 2.5X30MM, 978 
-28002658,HC IMPL SCREW LOCKING 2.5X32MM, 978 
-28002659,HC IMPL SCREW LOCKING 2.5X34MM, 978 
-28002670,HC CATH EPIRDURAL 19G 12 IN, 510 
-28002671,HC IMPL SCREW EMERGENCY 1.2X5MM, 629 
-28002672,HC IMPL SCREW EMERGENCY 1.2X6MM, 629 
-28002674,HC IMP ALLOGRAFT MENISCUS LATERAL LEFT," 33,250 "
-28002675,HC IMP ALLOGRAFT MENISCUS MEDIAL RIGHT," 33,250 "
-28002678,HC  GUIDEWIRE THREAD SINGLE 054X6IN, 94 
-28002679,HC IMPL PLATE OSTEOTOMY SHORTENING ULNA 7HOLE," 5,398 "
-28002684,HC IMPL SCREW BONE CORTICAL LAG 3.2MMX18MM, 723 
-28002685,HC IMPL SCREW BONE CORTICAL LAG 3.2MMX20MM, 723 
-28002686,HC IMPL SCREW BONE CORTICAL LAG 3.2MMX22MM, 723 
-28002687,HC IMPL SCREW BONE CORTICAL LAG 3.2MMX24MM, 723 
-28002688,HC IMPL SCREW BONE CORTICAL LAG 3.2MMX26MM, 723 
-28002689,HC IMPL PLATE ULNAR OSTEOTOMY COMPRESS," 7,863 "
-28002691,HC BUR DRILL BIT FLEXIBLE HEX 2.5MM," 3,273 "
-28002695,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE LEFT 8HOLE," 6,333 "
-28002697,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE LEFT 6HOLE," 7,557 "
-28002698,HC IMPL PLATE CLAVICLE LOW PROFILE SMALL LEFT 8HOLE," 7,557 "
-28002699,HC IMPL PLATE CLAVICLE LOW PROFILE MEDIUM LEFT 8HOLE," 6,333 "
-28002700,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE LEFT 8HOLE," 6,333 "
-28002701,HC  GUIDEWIRE NEUROSCOUT STD 14IN 205," 3,251 "
-28002702,HC  GUIDEWIRE NEUROSCOUT STD 14IN 300CM," 3,791 "
-28002703,HC  GUIDEWIRE NEUROSCOUT STEER 14IN 205CM," 3,069 "
-28002704,HC  GUIDEWIRE NEUROSCOUT STEER 14IN 300CM," 3,791 "
-28002705,HC IMPL PLATE CLAVICLE NARROW STR LEFT 8HOLE," 7,557 "
-28002706,HC IMPL SCREW NON TOGGLING 22MM, 663 
-28002707,HC IMPL SCREW NON TOGGLING 24MM, 663 
-28002708,HC IMPL SCREW NON TOGGLING 26MM, 663 
-28002709,HC IMP SCREW CORTICAL LOCKING NS 2.3X24MM, 638 
-28002710,HC IMP SCREW CORTICAL LOCKING NS 2.3X22MM, 663 
-28002711,HC IMP SCREW CORTICAL LOCKING NS 2.3X26MM, 638 
-28002712,HC IMPL SCREW CANCELLOUS 4.0X28MM, 544 
-28002713,HC IMPL SCREW CANCELLOUS 4.0X30MM, 544 
-28002714,HC IMPL SCREW CANCELLOUS 4.0X35MM, 544 
-28002715,HC IMPL SCREW CANCELLOUS 4.0X40MM, 544 
-28002716,HC IMPL SCREW CANCELLOUS 4.0X45MM, 544 
-28002717,HC IMPL SCREW CANCELLOUS 4.0X50MM, 680 
-28002719,HC  INTRODUCER SHEATH DRYSEAL 12F," 2,125 "
-28002720,HC  INTRODUCER SHEATH DRYSEAL 14F," 2,125 "
-28002721,HC  INTRODUCER SHEATH DRYSEAL 16F," 2,125 "
-28002722,HC  INTRODUCER SHEATH DRYSEAL 18F," 2,508 "
-28002723,HC  INTRODUCER SHEATH DRYSEAL 20F," 2,508 "
-28002724,HC  INTRODUCER SHEATH DRYSEAL 22F," 2,763 "
-28002725,HC  INTRODUCER SHEATH DRYSEAL 26F," 2,763 "
-28002726,HC  CATHETER GASTROSTOMY BALLOON LOW PROFILE 14X2.5, 765 
-28002727,HC IMP STENT GRAFT AAA ZENITH 29X96," 54,951 "
-28002728,HC IMP STENT GRAFT AAA ZENITH FLEX 10X22," 21,595 "
-28002729,HC IMP STENT GRAFT AAA ZENITH FLEX 14X73," 21,595 "
-28002730,HC IMP LEAD QUAD 45CM KIT," 13,500 "
-28002731,HC IMPL FINGER SILICONE JOINT SZ 40," 6,979 "
-28002732,HC IMP CEMENT HYDROSET INJECTABLE 03CC," 6,403 "
-28002733,HC IMP CEMENT HYDROSET INJECTABLE 05CC," 9,231 "
-28002734,HC DEVICE ABLATION CARDIOBLATE BP2," 18,570 "
-28002735,HC PEN ABLATION CARDIOBLATE XL," 11,970 "
-28002736,HC IMP PERMACOL BIOLOGICAL 15X20CM," 29,400 "
-28002738,HC IMP SHUNT DRAINAGE GLAUCOMA 175MM," 5,653 "
-28002739,HC IMP SHUNT DRAINAGE GLAUCOMA 230MM," 3,953 "
-28002740,HC IMP RESERVOIR CSF VENTRICULAR 28CM," 2,999 "
-28002741,HC IMP VALVE CSF FLOW CONTROL," 5,297 "
-28002742,HC IMPL SCREW CANCELLOUS 4.0X60MM, 680 
-28002743,HC IMPL SCREW LOCKING EXPRESS 2.5 14MM, 808 
-28002744,HC  CATHETER HEMODIALYSIS 23CM 14.5F," 2,720 "
-28002745,HC CATHETER SPLIT TOP INTRODUCER 19CM," 2,720 "
-28002746,HC IMPL SCREW LOCKING EXPRESS 2.5 16MM, 808 
-28002747,HC IMPL SCREW LOCKING EXPRESS 2.5 18MM, 808 
-28002748,HC IMPL SCREW LOCKING EXPRESS 2.5 20MM, 808 
-28002749,HC IMPL SCREW LOCKING EXPRESS 2.5 22MM, 808 
-28002750,HC IMPL SCREW LOCKING EXPRESS 2.5 24MM, 808 
-28002751,HC IMPL SCREW LOCKING EXPRESS 2.5 26MM, 808 
-28002752,HC IMPL SCREW LOCKING EXPRESS 2.5 28MM, 808 
-28002753,HC IMP ANCHOR SUTURE BIOZIP 5MM," 3,171 "
-28002757,HC IMPL FINGER PROXIMAL INTERPHALANGEAL SZ20," 6,979 "
-28002758,HC IMP MENISCAL REPAIR OMNISPAN 0 DEGREE," 2,542 "
-28002759,HC IMP MENISCAL REPAIR OMNISPAN 12 DEGREE," 2,542 "
-28002760,HC IMP MENISCAL REPAIR OMNISPAN 27 DEGREE," 2,542 "
-28002768,HC CATH EPIDURAL 21G, 468 
-28002770,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X20MM," 1,069 "
-28002772,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X24MM," 1,445 "
-28002775,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X30MM," 1,445 "
-28002776,HC IMP WIRE KIRSCHNER VARIAX OLIVE STOP 17MM, 321 
-28002779,HC IMP SCREW BONE T7 2.3X10MM," 1,063 "
-28002780,HC IMP SCREW BONE T7 2.3X12MM," 1,063 "
-28002781,HC IMP SCREW BONE T7 2.3X14MM," 1,063 "
-28002782,HC IMP SCREW BONE T7 2.3X16MM," 1,063 "
-28002783,HC IMP SCREW BONE T7 2.3X18MM," 1,063 "
-28002784,HC IMP SCREW BONE T7 2.3X20MM," 1,063 "
-28002785,HC IMP SCREW BONE T7 2.3X22MM," 1,063 "
-28002786,HC IMP SCREW BONE T7 2.3X24MM," 1,063 "
-28002787,HC IMP SCREW BONE T7 2.3X26MM," 1,063 "
-28002788,HC IMP SCREW BONE T7 2.3X28MM," 1,063 "
-28002789,HC IMP SCREW BONE T7 2.3X32MM," 1,063 "
-28002790,HC IMP SCREW BONE T7 2.3X34MM," 1,063 "
-28002791,HC IMP SCREW BONE T7 2.3X36MM," 1,063 "
-28002792,HC IMP SCREW BONE T7 2.3X38MM," 1,063 "
-28002793,HC IMP SCREW BONE T7 2.7X10MM," 1,063 "
-28002794,HC IMP SCREW BONE T7 2.7X12MM, 928 
-28002795,HC IMP SCREW BONE T7 2.7X14MM, 928 
-28002796,HC IMP SCREW BONE T7 2.7X16MM, 928 
-28002797,HC IMP SCREW BONE T7 2.7X18MM, 928 
-28002798,HC IMP SCREW BONE T7 2.7X22MM, 928 
-28002799,HC IMP SCREW BONE T7 2.7X24MM, 928 
-28002800,HC IMP SCREW BONE T7 2.7X26MM, 928 
-28002801,HC IMPL SCREW LOCKING VARIAX T7 2.0X16MM," 1,063 "
-28002802,HC IMPL SCREW LOCKING VARIAX T7 2.0X18MM," 1,063 "
-28002803,HC IMPL SCREW LOCKING VARIAX T7 2.0X20MM," 1,063 "
-28002804,HC IMPL SCREW LOCKING VARIAX T7 2.0X22MM," 1,063 "
-28002805,HC IMPL SCREW LOCKING VARIAX T7 2.0X24MM," 1,063 "
-28002806,HC IMPL SCREW LOCKING VARIAX T7 2.0X26MM," 1,063 "
-28002807,HC IMPL SCREW LOCKING 2.3MMX10MM," 1,063 "
-28002808,HC IMPL SCREW LOCKING 2.3MMX12MM," 1,172 "
-28002809,HC IMPL SCREW LOCKING 2.3MMX14MM," 1,172 "
-28002810,HC IMPL SCREW LOCKING 2.3MMX16MM," 1,172 "
-28002811,HC IMPL SCREW LOCKING 2.3MMX18MM," 1,063 "
-28002812,HC IMPL SCREW LOCKING 2.3MMX20MM," 1,172 "
-28002813,HC IMPL SCREW LOCKING 2.3MMX22MM," 1,063 "
-28002814,HC IMPL SCREW LOCKING 2.3MMX24MM," 1,063 "
-28002815,HC IMPL SCREW LOCKING 2.3MMX26MM," 1,063 "
-28002816,HC IMPL LEAD HEART QUICKFLEX LEFT 86CM," 15,000 "
-28002817,HC  CATHETER GUIDING SLITTER UNIVERSAL CPS," 5,908 "
-28002818,HC IMP SHEETING SILASTIC REINFORCED 6X8X0.10 SILAS, 417 
-28002819,HC IMPL SCREW LOCKING 2.3MMX28MM," 1,063 "
-28002820,HC IMPL SCREW LOCKING 2.3MMX30MM," 1,063 "
-28002821,HC IMPL SCREW LOCKING 2.3MMX32MM," 1,063 "
-28002822,HC IMPL SCREW LOCKING 2.3MMX34MM," 1,063 "
-28002823,HC IMPL SCREW LOCKING 2.3MMX36MM," 1,063 "
-28002824,HC IMPL SCREW LOCKING 2.3MMX38MM," 1,063 "
-28002825,HC IMPL SCREW LOCKING 2.7MMX10MM," 1,233 "
-28002826,HC IMPL SCREW LOCKING 2.7MMX12MM," 1,233 "
-28002827,HC IMPL SCREW LOCKING 2.7MMX14MM," 1,233 "
-28002828,HC IMPL SCREW LOCKING 2.7MMX16MM," 1,172 "
-28002829,HC IMPL SCREW LOCKING 2.7MMX18MM," 1,172 "
-28002830,HC IMPL SCREW LOCKING 2.7MMX20MM," 1,172 "
-28002831,HC IMPL SCREW LOCKING 2.7MMX22MM," 1,172 "
-28002832,HC IMPL SCREW LOCKING 2.7MMX24MM," 1,172 "
-28002833,HC  CATHETER GUIDING ANGIOGRAPHY CROSSBOSS 3X135CM," 7,438 "
-28002834,HC  CATHETER PEZZAR 14F, 128 
-28002835,HC IMPL PLATE ULNAR COLUMN LONG RIGHT," 6,027 "
-28002836,HC IMPL PLATE ULNAR COLUMN SHORT RIGHT," 6,027 "
-28002837,HC IMPL PLATE ULNAR COLUMN SHORT LEFT," 6,027 "
-28002838,HC IMPL PLATE ULNAR COLUMN LONG LEFT," 6,027 "
-28002839,HC IMPL PLATE RADIAL COLUMN SHORT RIGHT," 6,027 "
-28002840,HC IMPL PLATE RADIAL COLUMN SHORT," 6,027 "
-28002841,HC IMPL PLATE DORSAL DR WIDE LEFT XLON," 8,100 "
-28002842,HC IMPL PLATE DORSAL DR WIDE LEFT," 8,075 "
-28002843,HC IMPL PLATE DORSAL DR STANDARD LEFT," 8,100 "
-28002844,HC IMPL PLATE DORSAL DR WIDE RIGHT XLO," 8,100 "
-28002845,HC NEEDLE BIOPSY LIVER 16G," 2,380 "
-28002846,HC PEN ABLATION LINEAR 30MM," 24,500 "
-28002847,HC BUR DRILL BIT POPLOK 2.8X3.3MM," 2,290 "
-28002848,HC IMPL FINGER METACARPOPHALANGEAL SZ60," 7,980 "
-28002850,HC IMP ANCHOR SUTURE POPLOK 2.8MM," 2,572 "
-28002851,HC IMP ANCHOR SUTURE POPLOK 3.3MM," 2,615 "
-28002852,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ10," 6,979 "
-28002853,HC DRILL POPLOK GUIDE," 2,713 "
-28002854,HC IMP EXTERNAL FIXATOR MINIRAIL GEARED M2," 24,346 "
-28002856,HC IMPL SCREW 70/25 3MM, 825 
-28002857,HC IMPL SCREW MAXIOMANDIBULAR FIX AUTO DRIVE 2.0X8MM, 975 
-28002858,HC  CATHETER MALECOT 18F, 173 
-28002859,HC IMP STENT GRAFT ICAST 7X16X80," 18,750 "
-28002860,HC IMP OCULAR RING CAPSULAR MORCHER 14CM," 1,522 "
-28002862,HC IMP SCREW BONE FIXATION SELF TAPPING 2.7X15MM," 2,542 "
-28002863,HC  CATHETER MALECOT SILICONE 20F, 201 
-28002864,HC  SNARE ENDOVASCULAR SYSTEM 6-10MM 120CM 6F," 3,295 "
-28002865,HC VALVE PULMONARY TRANSCATH 18X22MM," 84,000 "
-28002866,HC  DELIVERY TRANSCATH ENSEMBLE SYS 18MM," 42,250 "
-28002867,HC  DELIVERY TRANSCATH ENSEMBLE SYS 20MM," 42,250 "
-28002868,HC  DELIVERY TRANSCATH ENSEMBLE SYS 22MM," 42,250 "
-28002870,HC CATHETER 21G X 18, 468 
-28002871,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X11MM, 995 
-28002872,HC IMPL SCREW LOCKING 2.4MMX14MM, 995 
-28002873,HC IMP STENT ADVANIX DUODENAL BEND 7X5CM, 442 
-28002874,HC IMP STENT ADVANIX DUODENAL BEND 7X7CM, 442 
-28002875,HC IMP STENT ADVANIX DUODENAL BEND 7X9CM, 442 
-28002876,HC IMP STENT ADVANIX DUODENAL BEND 7X12CM, 442 
-28002877,HC IMP STENT ADVANIX DUODENAL BEND 7X15CM, 442 
-28002878,HC IMP STENT ADVANIX DUODENAL BEND 8.5X5CM, 442 
-28002879,HC IMP STENT ADVANIX DUODENAL BEND 8.5X7CM, 442 
-28002880,HC IMP STENT ADVANIX DUODENAL BEND 8.5X9CM, 442 
-28002881,HC IMP STENT ADVANIX DUODENAL BEND 8.5X12CM, 442 
-28002882,HC IMP STENT ADVANIX DUODENAL BEND 8.5X15CM, 442 
-28002883,HC IMP STENT ADVANIX DUODENAL BEND 10X5CM, 442 
-28002884,HC IMP STENT ADVANIX DUODENAL BEND 10X7CM, 442 
-28002885,HC IMP STENT ADVANIX DUODENAL BEND 10X9CM, 442 
-28002886,HC IMP STENT ADVANIX DUODENAL BEND 10X12CM, 442 
-28002887,HC IMP STENT ADVANIX DUODENAL BEND 10X15CM, 442 
-28002888,HC IMP STENT ADVANIX DOUBLE PIGTAIL 10X3CM, 442 
-28002889,HC IMP STENT ADVANIX DOUBLE PIGTAIL 10X5CM, 442 
-28002890,HC  STENT NAVIFLEX RX DELIVERY 8.5F, 672 
-28002891,HC  STENT NAVIFLEX RX DELIVERY 10F, 672 
-28002892,HC IMP STENT ADVANIX CENTER BEND 8.5X7CM, 442 
-28002893,HC IMP STENT ADVANIX CENTER BEND 8.5X9CM, 442 
-28002894,HC IMP STENT ADVANIX CENTER BEND 8.5X12CM, 442 
-28002895,HC IMPL SCREW TENODESIS 4MMX15MM," 4,888 "
-28002897,HC  CATHETER MALECOT 22F, 173 
-28002898,HC  INTRODUCER GUID CPS DIRECT 135DEG 54X57.7X7X9," 5,908 "
-28002899,HC IMP LEAD HEART QUICKFLEX 16MM 75CM 4.3F 4F," 15,000 "
-28002900,HC IMP STENT GRAFT ICAST 6X59X120," 18,750 "
-28002901,HC IMP ICD OTHER UNIFY DF-1 40 J," 77,000 "
-28002902,HC IMPL TISSUE ALLOGRAFT PERICARDIUM 7X15CM," 9,713 "
-28002903,HC  CATHETER MALECOT 24F, 173 
-28002904,HC IMPL COIL HYDROFRAME 18 12MMX43CM," 18,113 "
-28002905,HC  CATHETER EP WEBSTER QUAD ACRV 6X5, 978 
-28002906,HC  CATHETER ELECTROPHYSIOLOGY DEFLECT DCRV 92X6," 4,106 "
-28002907,HC  CATHETER BI DIRECTIONAL DF 7F 12PIN," 4,692 "
-28002908,HC  CATHETER LASSO 4F 10POLES," 12,420 "
-28002909,HC CLAMP ISOLATOR RIGHT," 28,000 "
-28002910,HC  CATHETER MALECOT 28F, 173 
-28002913,HC IMP STENT CORONARY INTEGRITY RX BMS 2.5X22," 6,800 "
-28002914,HC IMP STENT GRAFT AAA ZENITH FLEX 20X56," 21,595 "
-28002915,HC IMP STENT GRAFT AAA ZENITH FLEX 20X73," 21,595 "
-28002916,HC IMP STENT GRAFT AAA ZENITH FLEX 20X90," 21,595 "
-28002917,HC IMP STENT GRAFT TAA ZENITH TX2 32X120MM," 72,564 "
-28002918,HC IMP STENT GRAFT TAA TAG 26X10 PTFE," 76,500 "
-28002919,HC IMP ENDOPROSTHESIS SYS 31MM 15CM," 76,500 "
-28002920,HC IMP PROSTHESIS ENDO CONFIG TAG 31MM 10CM," 76,500 "
-28002921,HC IMP STENT GRAFT TAA TAG 34X20 PTFE," 74,250 "
-28002922,HC IMP STENT GRAFT TAA TAG PTFE 40X15," 74,250 "
-28002923,HC  INTRODUCER GUID CPS DIRECT RIGHT 47X50.7X7X9," 5,908 "
-28002924,HC  CATHETER GUIDING CPS AIM INNER OBTUS 5 7F 59X62," 2,508 "
-28002925,HC  CATHETER GUIDING CPS AIM INNER ACUTE 5 7F 59X62," 2,508 "
-28002931,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 65/16," 35,000 "
-28002932,HC IMPL PACEMAKER DUAL EVIA DR," 29,750 "
-28002933,HC IMP LEAD ACTIVE FIXATION PACING 60CM," 4,675 "
-28002934,HC  CATHETER MALECOT 36F, 173 
-28002935,HC IMPL SCREW SNAP OFF 2.7X13MM," 2,593 "
-28002937,HC IMP STENT GRAFT AAA ZENITH FLEX 16X73," 21,595 "
-28002938,HC  CATHETER MALECOT 32F, 173 
-28002939,HC IMP STENT GRAFT ICAST 7X59X120," 18,750 "
-28002940,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X20," 77,400 "
-28002941,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X18," 75,000 "
-28002942,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X20," 75,000 "
-28002943,HC IMP DEVICE EMBOLIZATION PIPELINE 4X35," 80,400 "
-28002944,HC IMP WASHER SPIKED 4X13.5MM," 1,207 "
-28002945,HC IMPL PLATE 1.7 4 HOLE PLATE," 1,998 "
-28002946,HC IMPL PACEMAKER SINGLE ACC 6X42X52MM," 20,300 "
-28002947,HC  CATHETER MALECOT 40F, 173 
-28002948,HC IMP GRAFT ENDOVASCULAR 30X62MM," 33,898 "
-28002949,HC  CATHETER GUIDING CORONARY 7F 20X145CM," 3,383 "
-28002950,HC  CATHETER GUIDING CORONARY 8F 20X145CM," 3,383 "
-28002951,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X16," 75,000 "
-28002952,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X20," 75,000 "
-28002955,HC IMP STENT GRAFT AAA ZENITH FLEX 16X56," 21,595 "
-28002956,HC CATH ABLAT CRYOBALLOON 23MM," 33,600 "
-28002957,HC  SHEATH STEERABLE INTRAVASCLR 12F," 9,000 "
-28002958,HC IMP FIXATION TIGHTROPE KIT," 4,208 "
-28002959,HC  CATHETER MALECOT 26F, 173 
-28002960,HC IMPL SCREW PEEK TENODESIS 3X8MM," 2,848 "
-28002962,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML16F 1.5X15," 2,738 "
-28002963,HC IMP STENT GRAFT AAA ENDURANT 28X16X166," 64,838 "
-28002964,HC IMP STENT GRAFT AAA ENDURANT 32X16X166," 64,838 "
-28002965,HC IMP STENT GRAFT AAA ENDURANT 16X13X124," 34,825 "
-28002966,HC IMP STENT GRAFT AAA ENDURANT 16X13X93," 34,475 "
-28002967,HC  SNARE ENDOVASCULAR SYSTEM 7FX100CM," 3,295 "
-28002968,HC IMP STENT GRAFT AAA ZENITH FLEX 14X90," 21,595 "
-28002969,HC IMP STENT GRAFT AAA ZENITH FLEX 14X56," 21,595 "
-28002970,HC IMP STENT GRAFT AAA ZENITH FLEX 12X73," 21,595 "
-28002971,HC IMP STENT GRAFT AAA ZENITH FLEX 16X12X90," 21,595 "
-28002972,HC IMPL MNTR CRDC IMLPNTBL REVEAL XT," 31,500 "
-28002973,HC  CATHETER DRAINAGE BILIARY 32SP 40CM 16F, 867 
-28002974,HC  CATHETER MALECOT 30F, 173 
-28002975,HC  CATHETER DILATATION CORONARY RX 1.5X8MM," 1,488 "
-28002976,HC  CATHETER DILATATION CORONARY RX 5MM 15MM," 1,488 "
-28002977,HC  CATHETER DILATATION CORONARY RX 1.5X20MM," 1,488 "
-28002978,HC  CATHETER DILATATION CORONARY OTW 1.5X6MM," 1,488 "
-28002979,HC  CATHETER DILATATION CORONARY OTW 2.5X12MM," 1,488 "
-28002980,HC  CATHETER DILATATION CORONARY OTW 1.5X20MM," 1,488 "
-28002987,HC IMP STENT FLEXI FEEMAN 3FX7CM, 374 
-28002988,HC IMPL PLATE T 6 HL 84MM," 3,284 "
-28002989,HC IMP STENT GRAFT AAA ZENITH FLEX 14X107," 21,595 "
-28002990,HC IMPL PLATE DCP NARROW 12 HOLE 4.5X199MM," 1,975 "
-28002991,HC IMPL PLATE DCP BROAD 9 HOLE 4.5X135MM," 2,117 "
-28002992,HC  CATHETER MALECOT 38F, 173 
-28002993,HC IMPL PLATE DCP BROAD 10 HOLE 4.5X167MM," 2,329 "
-28002995,HC IMP ANCHOR SUTURE PITON 2.8MM," 3,103 "
-28002997,HC IMPL PLATE DCP BROAD 12 HOLE 4.5X199MM," 2,236 "
-28002998,HC IMPL PLATE DCP BROAD 14 HOLE 4.5X231MM," 2,236 "
-28002999,HC IMPL SCREW BIOCOMPOSITE 7MMX23MM," 1,913 "
-28003000,HC IMPL SCREW BIOCOMPOSITE 8MMX23MM," 1,913 "
-28003001,HC IMPL PLATE SEMI TUBULAR 4 HOLE, 582 
-28003002,HC IMPL SCREW BIOCOMPOSITE 9MMX23MM," 1,913 "
-28003003,HC IMPL SCREW BIOCOMPOSITE 10MMX23MM," 1,913 "
-28003004,HC IMP SCREW BIO INTEFERENCE 8MMX28MM," 1,913 "
-28003005,HC IMP SCREW BIO INTEFERENCE 9MMX28MM," 1,913 "
-28003006,HC IMP SCREW BIO INTEFERENCE 10MMX28MM," 1,913 "
-28003007,HC IMP SCREW BIO INTEFERENCE 11MMX28MM," 1,913 "
-28003008,HC IMP SCREW BIO INTEFERENCE 9MMX35MM," 1,913 "
-28003009,HC IMP SCREW BIO INTEFERENCE 10MMX35MM," 1,913 "
-28003010,HC IMP SCREW BIO INTEFERENCE 11MMX35MM," 1,913 "
-28003011,HC IMP SCREW BIO INTEFERENCE 12MMX35MM," 1,913 "
-28003012,HC IMPL PLATE SEMI TUBULAR 5HOLE, 527 
-28003013,HC IMPL SCREW BIO RETRO 7MMX20MM," 1,913 "
-28003014,HC IMPL SCREW BIO RETRO 8MMX20MM," 1,913 "
-28003015,HC IMPL PLATE SEMI TUBULAR 6 HOLE, 557 
-28003016,HC IMPL PLATE SEMI TUBULAR 7 HOLE, 604 
-28003017,HC IMPL PLATE DCP NARROW 6 HOLE 4.5X103MM," 1,550 "
-28003018,HC IMPL PLATE DCP NARROW 7 HOLE 4.5X119MM," 1,497 "
-28003019,HC IMPL PLATE DCP NARROW 8 HOLE 4.5X135MM," 1,550 "
-28003020,HC IMPL PLATE DCP NARROW 10 HOLE 4.5X167MM," 1,666 "
-28003021,HC IMP SCREW CANCELLOUS FULLY THREAD SS 25MM, 316 
-28003022,HC IMP SCREW CANCELLOUS FULLY THREAD SS 30MM, 316 
-28003023,HC  CATHETER DILATATION CORONARY OTW 2X12MM," 1,488 "
-28003024,HC IMP LEAD NEUROSTIMULATOR OCTAD STD 1X8 KIT," 13,500 "
-28003025,HC IMP SCREW CANCELLOUS FULLY THREAD SS 35MM, 316 
-28003027,HC SPHINCTEROTOME DREAM 44X20X260," 2,805 "
-28003028,HC IMPL PACEMAKER DUAL MULTI PROGRAMABLE," 33,150 "
-28003029,HC IMP CLIP ANEURYSM STRAIGHT SUNDT 4.5MM," 3,195 "
-28003030,HC IMP CLIP ANEURYSM SUNDT 6.5MM," 3,732 "
-28003031,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.7X15," 2,738 "
-28003032,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.7X22," 2,738 "
-28003033,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.7X30," 2,738 "
-28003034,HC IMP PIN RUSH 3.2X203MM, 459 
-28003036,HC IMP PIN RUSH 3.2X216MM, 557 
-28003037,HC IMP PIN RUSH 3.2X229MM, 557 
-28003038,HC IMP LEAD CAPSURE FIX MRI SURE 45CM," 8,458 "
-28003040,HC LIGATOR MULTI BAND MUCOSECTOMY DUETTE 5X9.5-13," 2,240 "
-28003041,HC IMP STENT GRAFT VIABAHN .018 6X10X120," 22,365 "
-28003042,HC VERSACLOSE KIT," 8,458 "
-28003043,HC IMPL COIL NEURO EMBOLIZATION 11MM 35CM," 18,563 "
-28003044,HC IMPL COIL NEURO EMBOLIZATION 11MM 45CM," 17,675 "
-28003045,HC IMPL COIL NEURO EMBOLIZATION STANDARD 12MM 35CM," 18,563 "
-28003046,HC IMPL COIL NEURO EMBOLIZATION 12MM 45CM," 17,675 "
-28003047,HC IMPL COIL NEURO EMBOLIZATION 15MM 50CM," 18,095 "
-28003048,HC IMPL COIL NEURO EMBOLIZATION 16MM 60CM," 18,200 "
-28003049,HC IMPL COIL NEURO EMBOLIZATION 18MM 57CM," 18,095 "
-28003050,HC IMPL COIL NEURO EMBOLIZATION 20MM 60CM," 18,200 "
-28003051,HC IMPL COIL NEURO EMBOLIZATION 22MM 60CM," 18,200 "
-28003052,HC IMPL COIL NEURO EMBOLIZATION 24MM 57CM," 18,095 "
-28003053,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 4MM 12CM," 18,525 "
-28003054,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 5MM 9CM," 18,300 "
-28003055,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 5MM 13CM," 18,600 "
-28003056,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 6MM 10CM," 18,375 "
-28003057,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 6MM 15CM," 18,750 "
-28003058,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 7MM 15CM," 18,750 "
-28003059,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 7MM 20CM," 17,850 "
-28003060,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 8MM 20CM," 17,850 "
-28003061,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 9MM 25CM," 18,200 "
-28003062,HC IMPL COIL NEURO EMBOLIZATION 10MM 20CM," 17,850 "
-28003063,HC IMPL COIL NEURO EMBOLIZATION 10CM," 17,640 "
-28003064,HC IMPL COIL NEURO EMBOLIZATION 15CM," 18,340 "
-28003065,HC IMPL COIL NEURO EMBOLIZATION 2MM 1CM," 12,863 "
-28003066,HC IMPL COIL NEURO EMBOLIZATION 2MM 3CM," 14,438 "
-28003067,HC IMPL COIL NEURO EMBOLIZATION 2MM 4CM," 15,225 "
-28003068,HC IMPL COIL NEURO EMBOLIZATION 3MM 3CM," 14,438 "
-28003069,HC IMPL COIL NEURO EMBOLIZATION 3MM 4CM," 15,225 "
-28003070,HC IMPL COIL NEURO EMBOLIZATION 3MM 8CM," 18,375 "
-28003071,HC IMPL COIL NEURO EMBOLIZATION 4MM 6CM," 16,800 "
-28003073,HC IMPL LEAD SUBCOMPACT 1X8 KIT," 13,500 "
-28003074,HC IMP STENT GRAFT AAA ZENITH FLEX 32X96," 54,977 "
-28003075,HC IMP STENT GRAFT AAA ZENITH FLEX 12X56," 21,595 "
-28003076,HC IMP CLIP ANEURYSM SUNDT 4X7MM," 5,525 "
-28003077,HC IMP STENT GRAFT AAA ZENITH FLEX 12X107," 21,595 "
-28003078,HC IMP STENT GRAFT AAA ZENITH FLEX 16X90," 21,595 "
-28003079,HC IMP STENT GRAFT AAA ZENITH FLEX 18X90," 21,595 "
-28003080,HC IMP STENT GRAFT AAA ZENITH FLEX 24X73," 21,595 "
-28003081,HC IMP STENT GRAFT AAA ZENITH FLEX 24X90," 21,595 "
-28003082,HC IMP STENT GRAFT ENDOVASCULAR TAA W PROFORM 30X120," 72,564 "
-28003083,HC IMP STENT GRAFT TAA ZENITH TX2 36X127MM," 72,564 "
-28003084,HC IMP STENT GRAFT TAA ZENITH TX2 38X127MM," 72,564 "
-28003085,HC IMP STENT GRAFT TAA ZENITH TX2 40X135MM," 72,564 "
-28003086,HC IMP STENT GRAFT TAA ZENITH TX2 42X135MM," 72,564 "
-28003087,HC IMP STENT GRAFT TAA ZENITH TX2 36X77MM," 33,787 "
-28003088,HC IMP STENT GRAFT TAA ZENITH TX2 40X81MM," 33,787 "
-28003089,HC  GUIDEWIRE PERIPHERAL 35IN 180CM 3MM, 368 
-28003090,HC  GUIDEWIRE AMPLATZ 35IN 260CM 3MM, 385 
-28003091,HC  CATHETER MICRO STR TIP 0.025IN," 7,905 "
-28003092,HC  CATHETER SYSTEM 45D S PX400," 7,905 "
-28003093,HC  CATHETER SYSTEM 90D S PX400," 7,905 "
-28003094,HC  CATHETER MICRO JTIP SYS 150CM," 7,905 "
-28003095,HC IMP STENT GRAFT VIABAHN .018 6X5X120," 19,565 "
-28003097,HC IMP ICD DUAL IC LUMAX 540 DR-T," 81,000 "
-28003098,HC IMPL COIL NEURO EMBOLIZATION 15MM 45CM," 17,675 "
-28003099,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 6MM 20CM," 17,850 "
-28003100,HC IMP STENT GRAFT AAA ENDURANT 16X10X124," 34,825 "
-28003101,HC IMPL COIL NEURO PLATINUM MICRO 5MM 15CM," 8,025 "
-28003102,HC IMPL COIL NEURO PLATINUM MICRO 2CM 2MM," 8,025 "
-28003103,HC IMPL COIL NEURO PLATINUM MICRO 3CM 2MM," 8,025 "
-28003104,HC IMPL COIL NEURO PLATINUM MICRO 4CM 2MM," 8,025 "
-28003105,HC IMPL COIL NEURO PLATINUM MICRO 2MM 6CM," 8,025 "
-28003106,HC IMPL COIL NEURO MICRO SRE LOOP 1CM 1.5MM," 8,213 "
-28003107,HC IMPL COIL NEURO EMBOLIC SRE 1.5MM 2CM," 8,213 "
-28003108,HC IMPL COIL NEURO MICRO SPHERICAL 3CM 1.5MM," 8,213 "
-28003109,HC IMPL COIL NEURO EMBOLIC SRE MICRO PT 2MM 1CM," 7,969 "
-28003110,HC IMPL COIL NEURO EMBOLIC SRE 2MM 2CM," 7,969 "
-28003111,HC IMPL COIL NEURO EMBOLIC SRE 2MM 3CM," 7,969 "
-28003112,HC IMPL COIL NEURO EMBOLIC SRE 2MM 4CM," 7,969 "
-28003113,HC IMPL COIL NEURO EMBOLIC SRE 2MM 6CM," 8,213 "
-28003114,HC IMPL COIL NEURO MICRO SRE LOOP 2CM 2.5MM," 8,213 "
-28003115,HC IMPL COIL NEURO MICRO SRE LOOP 4CM 2.5MM," 8,213 "
-28003116,HC IMPL COIL NEURO MICRO SRE LOOP 6CM 2.5MM," 8,213 "
-28003117,HC IMPL COIL NEURO MICRO SRE LOOP 6CM 3MM," 7,969 "
-28003118,HC IMPL COIL NEURO MICRO RE PT 4X4CM," 7,969 "
-28003119,HC IMPL COIL NEURO MICRO SRE LOOP 6CM 4MM," 7,965 "
-28003120,HC IMPL COIL NEURO MICRO LOOP 2CM 1.5MM," 10,463 "
-28003121,HC IMPL COIL NEURO MICRO LOOP 3CM 1.5MM," 10,463 "
-28003122,HC IMPL COIL NEURO MICRO 2.00MM 2.0CM," 10,463 "
-28003123,HC IMPL COIL NEURO MICRO 2MM 3.0CM," 10,463 "
-28003124,HC IMPL COIL NEURO MICRO LOOP 2CM 2.50MM," 10,463 "
-28003125,HC IMPL COIL NEURO MICRO LOOP 4CM 2.50MM," 10,463 "
-28003126,HC IMPL COIL NEURO MICRO LOOP 4CM 3MM," 10,463 "
-28003127,HC IMPL COIL NEURO MICRO DELTAPLUSH 4MM 4CM," 10,463 "
-28003128,HC IMP ANCHOR SUTURE SWIVEL 4.75X24.5," 3,400 "
-28003130,HC IMP STENT GRAFT VIABAHN .018 6X15X120," 25,865 "
-28003131,HC IMP LEAD NEUROSTIMULATOR 45CM KIT," 13,500 "
-28003132,HC IMPL COIL NEURO EMBOLIZATION COSMOS 2MM 2CM," 10,350 "
-28003133,HC IMPL COIL NEURO VASCULAR FRAMING INNVTV 2.5," 10,500 "
-28003134,HC IMPL COIL NEURO EMBOLIZATION COMPLEX SZ10 3MM 6CM," 10,650 "
-28003135,HC IMPL COIL NEURO EMBOLIZATION 4MM 8CM," 10,800 "
-28003136,HC IMPL COIL NEURO EMBOLIZATION 4MM 12CM," 10,950 "
-28003137,HC IMPL COIL NEURO EMBOLIZATION 5MM 15CM," 11,100 "
-28003138,HC IMPL COIL NEURO COSMOS SZ10 5MM 22MM," 11,700 "
-28003139,HC IMPL COIL NEURO COSMO 6MM 18CM," 11,400 "
-28003140,HC IMPL COIL NEURO EMBOLIC HELICAL SZ10 26CM 6MM," 12,000 "
-28003141,HC IMPL COIL NEURO COMPLEX EMS 22CM SZ10 7MM," 11,700 "
-28003142,HC IMPL COIL NEURO EMBOLIZATION SZ10 MICRO OPLEX," 12,300 "
-28003143,HC IMPL COIL NEURO EMBOLIC HELICAL SZ10 25CM 8MM," 12,000 "
-28003144,HC IMPL COIL NEURO EMBOLIZATION 8MM 37CM," 12,750 "
-28003145,HC IMPL COIL NEURO EMBOLIC HELICAL SZ10 33CM 9MM," 12,600 "
-28003146,HC IMPL COIL NEURO EMBOLIZATION SZ10 36CM 10MM," 12,750 "
-28003147,HC IMPL COIL NEURO EMBOLIZATION 7MM 23CM," 10,800 "
-28003148,HC IMPL COIL NEURO EMBOLIZATION SZ18 8MM 27CM," 11,100 "
-28003149,HC IMPL COIL NEURO EMBOLIZATION 9MM 31CM," 11,400 "
-28003150,HC IMPL COIL NEURO EMBOLIZATION 10MM 36CM," 11,775 "
-28003151,HC IMPL COIL NEURO EMBOLIZATION 11MM 39CM," 12,000 "
-28003152,HC IMPL COIL NEURO EMBOLIC HELICAL SZ18 43CM 12MM," 12,300 "
-28003153,HC IMPL COIL NEURO EMBOLIZATION 13MM 47CM," 12,600 "
-28003154,HC IMPL COIL NEURO EMBOLIZATION 14MM 51CM," 12,900 "
-28003155,HC IMPL COIL NEURO EMBOLIZATION 16MM 52CM," 12,975 "
-28003156,HC IMPL COIL NEURO EMBOLIZATION 18MM 59CM," 13,500 "
-28003157,HC IMPL COIL NEURO EMBOLIZATION 20MM 65CM," 13,950 "
-28003158,HC IMPL COIL NEURO EMBOLIZATION 22MM 63CM," 13,800 "
-28003159,HC IMPL COIL NEURO EMBOLIZATION 24MM 68CM," 14,175 "
-28003160,HC IMP STENT GRAFT VIABAHN .018 5X5X120," 19,565 "
-28003161,HC IMP STENT GRAFT VIABAHN .018 5X2.5X120," 19,565 "
-28003162,HC IMP STENT GRAFT VIABAHN .018 5X10X120," 22,890 "
-28003163,HC IMP STENT GRAFT VIABAHN .018 5X15X120," 25,865 "
-28003164,HC IMP STENT GRAFT VIABAHN .018 6X2.5X120," 19,565 "
-28003165,HC IMP STENT GRAFT VIABAHN .018 7X5X120," 19,565 "
-28003166,HC IMP STENT GRAFT VIABAHN .018 7X10X120," 22,365 "
-28003167,HC IMP STENT GRAFT VIABAHN .018 7X15X120," 25,865 "
-28003168,HC IMP STENT GRAFT VIABAHN .018 8X5X120," 19,565 "
-28003169,HC IMP STENT GRAFT VIABAHN .018 8X10X120," 22,365 "
-28003170,HC IMP ELBOW STEM RADIAL HEAD EVOLVE," 17,318 "
-28003171,HC IMP ELBOW MODULAR RADIAL HEAD 20MM," 14,250 "
-28003172,HC IMP TISSUE GRAFT CARTILAGE DENOVO 110CM," 30,800 "
-28003173,HC IMP TENDON PATELLA PRESHAPED," 23,100 "
-28003174,HC IMP TENDON PATELLA HEMI 1.5X5CM," 23,100 "
-28003175,HC IMP TENDON PATELLA HEMI," 23,100 "
-28003176,HC IMP TISSUE TENDON PATELLA BTB 10MM," 23,100 "
-28003177,HC IMP TENDON ACHILLES W/CALC," 20,685 "
-28003178,HC IMP ICD OTHER CRT RESYNCRZTN THRPY," 91,500 "
-28003179,HC  CATHETER GUIDING ATTAIN MP02 50CM," 2,040 "
-28003180,HC  GUIDEWIRE VASCULAR STEERABLE 300X10CM," 2,975 "
-28003181,HC IMP ICD DUAL IC VR PROTECT," 80,280 "
-28003182,HC IMPL COIL NEURO HELICAL 3MX8CM," 5,058 "
-28003183,HC  GUIDEWIRE METRO TRACER 21IN 480CM," 1,221 "
-28003184,HC LIGASURE JAW SMALL," 3,740 "
-28003185,HC GASPER ENDO DISP 36CM," 2,304 "
-28003188,HC  GUIDEWIRE DOUBLE END 1.6MM, 102 
-28003189,HC IMPL SCREW CANNULATED LG 16MMX75," 2,505 "
-28003190,HC IMPL SCREW LAG 4.0X20MM," 5,525 "
-28003191,HC IMPL SCREW LAG 4.0X22MM," 5,525 "
-28003192,HC IMPL SCREW LAG 4.0X24MM," 5,525 "
-28003193,HC IMPL SCREW LAG 4.0X26MM," 5,525 "
-28003194,HC IMPL SCREW LAG 4.0X28MM," 5,525 "
-28003195,HC IMPL SCREW LAG 4.0X30MM," 5,908 "
-28003196,HC IMPL SCREW LAG 4.0X32MM," 5,908 "
-28003197,HC IMPL SCREW LAG 4.0X34MM," 5,908 "
-28003198,HC IMPL SCREW LAG 4.0X36MM," 5,908 "
-28003199,HC IMPL SCREW LAG SOLID 4.0X20MM," 5,525 "
-28003200,HC IMPL SCREW LAG SOLID 4.0X22MM," 5,525 "
-28003201,HC IMPL SCREW LAG SOLID 4.0X24MM," 5,525 "
-28003202,HC IMPL SCREW LAG SOLID 4.0X26MM," 5,525 "
-28003203,HC IMPL SCREW LAG SOLID 4.0X28MM," 5,525 "
-28003204,HC IMPL SCREW LAG SOLID 4.0X30MM," 5,525 "
-28003205,HC IMPL SCREW LAG SOLID 4.0X32MM," 5,525 "
-28003206,HC IMPL SCREW LAG SOLID 4.0X34MM," 5,525 "
-28003207,HC IMPL SCREW LAG SOLID 4.0X36MM," 5,525 "
-28003208,HC IMP METACARPAL S 4.0X30MM," 9,375 "
-28003209,HC IMP METACARPAL M 5.0X30MM," 9,375 "
-28003210,HC IMP METACARPAL L 6.0X30MM," 9,375 "
-28003211,HC IMP STENT GRAFT AAA ZENITH FLEX 25X56," 21,595 "
-28003213,HC IMP STRAP SECURE FIX DEVICE 5MM," 4,154 "
-28003214,HC IMPL COIL NEURO HELICAL 5MMX20CM," 5,058 "
-28003216,HC IMPL COIL NEURO HELICAL 4MMX12CM," 5,058 "
-28003218,HC IMPL COIL NEURO DETACHABLE 1.5MMX2CM," 6,435 "
-28003219,HC IMPL COIL NEURO DETACHABLE 2MMX1CM," 6,435 "
-28003220,HC IMPL COIL NEURO DETACHABLE 2MMX2CM," 6,435 "
-28003221,HC IMPL COIL NEURO DETACHABLE 2MMX3CM," 6,435 "
-28003222,HC IMPL COIL NEURO DETACHABLE 2MMX4CM," 6,435 "
-28003223,HC IMPL COIL NEURO DETACHABLE 2MMX6CM," 6,435 "
-28003224,HC IMPL COIL NEURO DETACHABLE 2MMX8CM," 6,435 "
-28003225,HC IMPL COIL NEURO DETACHABLE 3MMX4CM," 6,435 "
-28003226,HC IMPL COIL NEURO DETACHABLE 3MMX6CM," 6,435 "
-28003227,HC IMPL COIL NEURO DETACHABLE 3MMX8CM," 6,435 "
-28003228,HC IMPL COIL NEURO DETACHABLE 4MMX8CM," 6,435 "
-28003229,HC IMPL COIL NEURO DETACHABLE 4MMX10CM," 6,435 "
-28003230,HC IMPL COIL NEURO DETACHABLE 5MMX15CM," 6,435 "
-28003231,HC IMPL COIL NEURO DETACHABLE 5MMX20CM," 6,435 "
-28003232,HC IMPL COIL NEURO DETACHABLE 6MMX20CM," 6,435 "
-28003233,HC IMPL COIL NEURO DETACHABLE CONCERTO 7MMX30CM," 5,058 "
-28003234,HC IMPL COIL NEURO DETACHABLE 3D 2MMX2CM," 8,152 "
-28003235,HC IMPL COIL NEURO DETACHABLE 3D 2MMX4CM," 8,152 "
-28003236,HC IMPL COIL NEURO DETACHABLE 3D 2MMX6CM," 8,152 "
-28003237,HC IMPL COIL NEURO DETACHABLE 3D 3MMX4CM," 8,152 "
-28003238,HC IMPL COIL NEURO DETACHABLE 3D 3MMX6CM," 8,152 "
-28003239,HC IMP STENT GRAFT ICAST 9X38X120," 18,750 "
-28003240,HC IMP STENT GRAFT ICAST 9X59X120," 18,750 "
-28003241,HC IMP STENT GRAFT ICAST 10X38X120," 18,750 "
-28003242,HC IMP STENT GRAFT ICAST 6X16X120," 18,750 "
-28003243,HC IMPL COIL NEURO DETACHABLE 3D 3MMX8CM," 8,152 "
-28003244,HC IMPL COIL NEURO DETACHABLE 3D 4MMX8CM," 8,152 "
-28003245,HC IMPL COIL NEURO DETACHABLE 3D 4MMX12CM," 8,152 "
-28003246,HC IMPL COIL NEURO DETACHABLE 3D 5MMX10CM," 8,152 "
-28003247,HC IMPL COIL NEURO DETACHABLE 3D 5MMX15CM," 8,152 "
-28003248,HC IMPL COIL NEURO DETACHABLE 3D 6MMX15CM," 8,152 "
-28003249,HC IMPL COIL NEURO DETACHABLE 3D 6MMX20CM," 8,152 "
-28003250,HC IMPL COIL NEURO DETACHABLE 3D 7MMX20CM," 8,152 "
-28003251,HC IMPL COIL NEURO DETACHABLE 3D 8MMX30CM," 8,152 "
-28003252,HC IMPL COIL NEURO DETACHABLE 3D 9MMX20CM," 8,152 "
-28003253,HC IMPL COIL NEURO DETACHABLE 3D 10MMX30CM," 8,152 "
-28003254,HC IMPL COIL NEURO DETACHABLE 3D 12MMX40CM," 8,152 "
-28003255,HC IMPL COIL NEURO DETACHABLE PGLA 2MMX3CM," 8,407 "
-28003256,HC IMPL COIL NEURO DETACHABLE PGLA 2MMX4CM," 8,407 "
-28003257,HC IMPL COIL NEURO DETACHABLE PGLA 2MMX6CM," 8,407 "
-28003258,HC IMPL COIL NEURO DETACHABLE PGLA 2MMX8CM," 8,407 "
-28003259,HC IMPL COIL NEURO DETACHABLE PGLA 3MMX4CM," 8,407 "
-28003260,HC IMPL COIL NEURO DETACHABLE PGLA 3MMX6CM," 8,407 "
-28003261,HC IMPL COIL NEURO DETACHABLE PGLA 3MMX8CM," 8,407 "
-28003262,HC IMPL COIL NEURO DETACHABLE PGLA 4MMX8CM," 7,688 "
-28003263,HC IMPL COIL NEURO DETACHABLE PGLA 4MMX10CM," 7,688 "
-28003264,HC IMPL COIL NEURO DETACHABLE PGLA 5MMX15CM," 7,688 "
-28003265,HC IMPL COIL NEURO DETACHABLE PGLA 6MMX20CM," 7,688 "
-28003266,HC IMPL COIL NEURO DETACHABLE PGLA 7MMX30CM," 7,988 "
-28003267,HC IMPL COIL NEURO DETACHABLE PGLA 8MMX30CM," 7,988 "
-28003268,HC IMPL COIL NEURO DETACHABLE PGLA 9MMX30CM," 7,988 "
-28003269,HC IMPL COIL NEURO XL10 2MMX2.5CM," 9,420 "
-28003270,HC IMPL COIL NEURO XL10 2.5MMX3CM," 9,420 "
-28003271,HC IMPL COIL NEURO XL10 3MMX5CM," 9,420 "
-28003272,HC IMPL COIL NEURO XL10 3.5MMX7CM," 9,420 "
-28003273,HC IMPL COIL NEURO XL10 4MMX8CM," 9,420 "
-28003274,HC IMPL COIL NEURO XL10 5MMX14CM," 9,420 "
-28003275,HC IMPL COIL NEURO XL10 6MMX12CM," 9,420 "
-28003276,HC IMPL COIL NEURO XL10 6MMX20CM," 9,386 "
-28003277,HC IMPL COIL NEURO XL10 7MMX14CM," 9,420 "
-28003278,HC IMPL COIL NEURO XL10 7MMX20CM," 9,386 "
-28003279,HC IMPL COIL NEURO XL10 8MMX16CM," 9,420 "
-28003280,HC IMPL COIL NEURO XL10 8MMX25CM," 9,420 "
-28003281,HC  TUBE FEEDING CUST 18F 4.5X45CM," 3,163 "
-28003282,HC IMPL COIL NEURO CASHMERE 14 3MMX3CM," 8,370 "
-28003283,HC IMPL COIL NEURO CASHMERE 14 3MMX4CM," 8,408 "
-28003284,HC IMPL COIL NEURO CASHMERE 14 3MMX6CM," 8,438 "
-28003285,HC IMPL COIL NEURO CASHMERE 14 4MMX4.5CM," 8,438 "
-28003286,HC IMPL COIL NEURO CASHMERE 14 4MMX6CM," 8,505 "
-28003287,HC IMPL COIL NEURO CASHMERE 14 4MMX8CM," 8,543 "
-28003288,HC IMPL COIL NEURO CASHMERE 14 5MMX7CM," 8,543 "
-28003289,HC IMPL COIL NEURO CASHMERE 14 5MMX12CM," 8,760 "
-28003290,HC IMPL COIL NEURO CASHMERE 14 6MMX9CM," 8,573 "
-28003291,HC IMPL COIL NEURO CASHMERE 14 6MMX15CM," 8,708 "
-28003292,HC IMPL COIL NEURO CASHMERE 14 7MMX11CM," 8,610 "
-28003293,HC IMPL COIL NEURO CASHMERE 14 7MMX17CM," 8,760 "
-28003294,HC IMPL COIL NEURO CASHMERE 14 8MMX12CM," 8,745 "
-28003295,HC IMPL COIL NEURO CASHMERE 14 8MMX20CM," 8,760 "
-28003296,HC IMPL COIL NEURO CASHMERE 14 9MMX22CM," 8,760 "
-28003297,HC IMPL COIL NEURO CASHMERE 14 10MMX25CM," 9,248 "
-28003298,HC IMPL COIL NEURO CASHMERE 14 11MMX27CM," 9,353 "
-28003299,HC IMPL COIL NEURO CASHMERE 14 12MMX30CM," 8,760 "
-28003306,HC IMP TISSUE BONE ILIAC WEDGE 19-21MM," 8,273 "
-28003307,HC IMP TISSUE BONE ILIAC WEDGE 16-18MM," 7,515 "
-28003308,HC IMP ANCHOR SUTURE INSITE TITANIUM 5MM," 1,913 "
-28003309,HC IMP ANCHOR SUTURE INSITE TITANIUM 6.5MM," 1,913 "
-28003310,HC IMP SHOULDER HEAD HUMERAL OSTEOCHONDRAL LEFT," 35,752 "
-28003312,HC IMP ICD OTHER PROTECA XT," 71,050 "
-28003313,HC IMP STENT CORONARY XIENCE NANO RX DES 2.25X8," 13,125 "
-28003314,HC IMP STENT CORONARY XINANO RX DES 2.25X12," 13,125 "
-28003315,HC IMP STENT CORONARY XINANO RX DES 2.25X15," 13,125 "
-28003316,HC IMP STENT CORONARY XINANO RX DES 2.25X18," 13,125 "
-28003317,HC IMP STENT CORONARY XINANO RX DES 2.25X23," 13,125 "
-28003318,HC IMP STENT CORONARY XINANO RX DES 2.25X28," 15,188 "
-28003320,HC IMPL COIL NEURO DELTAPAQ 4MMX10CM," 8,025 "
-28003321,HC IMPL COIL NEURO DELTAPAQ 55MMX10CM," 8,025 "
-28003322,HC IMP SHOULDER STEM HUMERAL PRIMARY PRESS FIT 11MM," 16,457 "
-28003323,HC IMP SHOULDER GLENOSPHERE REVERSE 38MM," 11,387 "
-28003324,HC IMP SHOULDER ADAPTER HUMERAL REV EQUINOXE TRAY +0MM," 14,772 "
-28003325,HC IMPL PLATE GLENOID EQUINOXE REVERSE TI," 12,264 "
-28003326,HC IMP GRAFT IMPRA STPPD 4-7MMX45CM," 5,899 "
-28003327,HC IMP NEUROSTIMULATOR EXTENSION LINEAR 1X84X80CM KIT," 5,440 "
-28003328,HC IMPL LEAD EXTENSION STRETCH COIL DBS," 6,205 "
-28003329,HC IMP GENERATOR NEUROSTIMULATOR PROGRAMMER PT," 7,608 "
-28003330,HC IMP GENERATOR NEUROSTIMULATOR NON RECHARGE," 76,200 "
-28003331,HC IMP NEUROSTIMULATOR RESTORE RECHARGABLE," 87,000 "
-28003332,HC IMP SCREW BONE SPINAL 4X14 MM," 2,700 "
-28003333,HC IMPL SCREW BONE POLYDIRECTIONAL 6.5X45MM," 7,863 "
-28003334,HC IMPL SCREW BONE POLYDIRECTIONAL 6.5X50MM," 7,863 "
-28003335,HC IMP CAGE SPINAL VERTEBRAL XL 10X18X55MM," 30,387 "
-28003336,HC IMP GENERATOR NEUROSTIMULATOR ACTIVA," 85,450 "
-28003337,HC IMP STENT GRAFT AAA ZENITH FLEX 10X88," 21,595 "
-28003338,HC IMP STENT GRAFT AAA ZENITH FLEX 12X39," 21,595 "
-28003339,HC IMP STENT GRAFT AAA ZENITH FLEX 16X39," 20,972 "
-28003340,HC IMP STENT GRAFT AAA ZENITH FLEX 22X56," 20,972 "
-28003341,HC IMP STENT GRAFT AAA ZENITH FLEX 22X90," 21,595 "
-28003342,HC  CATHETER SUCTION SINGLE LUMEN PORTEX 14F, 94 
-28003343,HC IMP GRAFT ENDOVASCULAR BD EXT 32X62MM," 34,912 "
-28003344,HC IMP STENT GRAFT AAA ZENITH FLEX 10X71," 20,972 "
-28003345,HC  SHEATH GUIDING 5FX70CM, 587 
-28003347,HC ELECTRODE VAPOR 90D," 2,510 "
-28003349,HC IMP STENT CORONARY ION RX DES 2.25X8," 12,750 "
-28003350,HC IMP ICD DUAL PROTECTA XT D314DRG," 85,050 "
-28003351,HC IMP STENT CORONARY ION RX DES 2.5X8," 14,250 "
-28003352,HC IMP STENT CORONARY ION RX DES 2.75X8," 12,750 "
-28003353,HC IMP STENT CORONARY ION RX DES 3X8," 15,375 "
-28003354,HC IMP STENT CORONARY ION RX DES 3.5X8," 15,375 "
-28003355,HC IMP STENT CORONARY ION RX DES 4X8," 15,375 "
-28003356,HC IMP STENT CORONARY ION RX DES 2.25X12," 14,250 "
-28003357,HC IMP STENT CORONARY ION RX DES 2.5X12," 15,375 "
-28003358,HC IMP STENT CORONARY ION RX DES 2.75X12," 14,250 "
-28003359,HC IMPL SCREW DHS LAG 90MM," 2,839 "
-28003360,HC IMP STENT CORONARY ION RX DES 3X12," 14,250 "
-28003361,HC IMP STENT CORONARY ION RX DES 3.5X12," 14,250 "
-28003362,HC IMP STENT CORONARY ION RX DES 4X12," 15,375 "
-28003363,HC IMP STENT CORONARY ION RX DES 2.25X16," 14,250 "
-28003364,HC IMP STENT CORONARY ION RX DES 2.5X16," 14,250 "
-28003365,HC IMP STENT CORONARY ION RX DES 2.75X16," 15,375 "
-28003366,HC IMP STENT CORONARY ION RX DES 3X16," 12,750 "
-28003367,HC IMP STENT CORONARY ION RX DES 3.5X16," 12,750 "
-28003368,HC IMP STENT CORONARY ION RX DES 4X16," 15,375 "
-28003369,HC IMP STENT CORONARY ION RX DES 2.25X20," 14,250 "
-28003370,HC IMP STENT CORONARY ION RX DES 2.5X20," 12,750 "
-28003371,HC IMP STENT CORONARY ION RX DES 2.75X20," 12,750 "
-28003372,HC IMP STENT CORONARY ION RX DES 3X20," 14,250 "
-28003373,HC IMP STENT CORONARY ION RX DES 3.5X20," 15,375 "
-28003374,HC IMP STENT CORONARY ION RX DES 4X20," 15,375 "
-28003375,HC IMP STENT CORONARY ION RX DES 2.25X24," 12,750 "
-28003376,HC IMP STENT CORONARY ION RX DES 2.5X24," 15,375 "
-28003377,HC IMP STENT CORONARY ION RX DES 2.75X24," 12,750 "
-28003378,HC IMP STENT CORONARY ION RX DES 3X24," 12,750 "
-28003379,HC IMP STENT CORONARY ION RX DES 3.5X24," 15,375 "
-28003380,HC IMP STENT CORONARY ION RX DES 4X24," 15,375 "
-28003381,HC IMP STENT CORONARY ION RX DES 2.25X28," 15,375 "
-28003382,HC IMP STENT CORONARY ION RX DES 2.5X28," 12,750 "
-28003383,HC IMP STENT CORONARY ION RX DES 2.75X28," 12,750 "
-28003384,HC IMP STENT CORONARY ION RX DES 3X28," 12,750 "
-28003385,HC IMP STENT CORONARY ION RX DES 3.5X28," 15,375 "
-28003386,HC IMP STENT CORONARY ION RX DES 4X28," 15,375 "
-28003387,HC IMP STENT CORONARY ION RX DES 2.28X32," 14,250 "
-28003388,HC IMP STENT CORONARY ION RX DES 2.5X32," 14,250 "
-28003389,HC IMP STENT CORONARY ION RX DES 2.75X32," 12,750 "
-28003390,HC IMP STENT CORONARY ION RX DES 3X32," 15,375 "
-28003391,HC IMP STENT CORONARY ION RX DES 3.5X32," 12,750 "
-28003392,HC IMP STENT CORONARY ION RX DES 4X32," 15,375 "
-28003393,HC IMP STENT CORONARY ION RX DES 2.75X38," 15,375 "
-28003394,HC IMP STENT CORONARY ION RX DES 3X38," 12,750 "
-28003395,HC IMP STENT CORONARY ION RX DES 3.5X38," 15,375 "
-28003396,HC IMP STENT CORONARY ION RX DES 4X38," 15,375 "
-28003397,HC  CATHETER ACUITY 8FX54CM," 8,458 "
-28003398,HC  CATHETER GUIDING VISTA BRITE LBT JL4 5F, 442 
-28003399,HC  CATHETER GUIDING VISTA BRITE TIP LBT 5F JR4, 442 
-28003400,HC  CATHETER GUIDING VISTA BRITE TIP LBT 5F XB3.5, 442 
-28003401,HC  CATHETER GUIDING VISTA BRITE TIP 5F MPA 1 LBT, 442 
-28003407,HC IMP FILLER BONE NANOSS 10CC," 17,850 "
-28003408,HC  CATHETER DILATATION NUCLEUS 10FX20MMX5CM," 7,055 "
-28003409,HC  CATHETER DILATATION PTA Z MED 22X4X100CM," 4,565 "
-28003411,HC  CATHETER CPS AIM SL," 2,508 "
-28003412,HC IMPL COIL NEURO HELICAL 3MX4CM," 5,211 "
-28003413,HC IMPL COIL NEURO DETACHABLE 10MX30CM," 5,058 "
-28003414,HC IMPL COIL NEURO HELICAL 6MX20CM," 5,058 "
-28003415,HC IMPL COIL PERIPHERAL CONCERTO 12MX30CM," 5,058 "
-28003416,HC  CATHETER DF CURVE 7.5FX115CM," 15,998 "
-28003417,HC IMP ICD SINGLE PROTECA D334VRG," 70,200 "
-28003418,HC IMP ANCHOR SUTURE JUGGERKNOT 1.4MM," 2,975 "
-28003419,HC IMP ANCHOR CORKSCREW BIOCOMPOSITE 6.6X15MM," 2,848 "
-28003421,HC IMP STENT GRAFT TAA ZENITH TX2 38X136MM," 42,751 "
-28003422,HC IMP ICD SINGLE PROTECTA XT VR," 75,600 "
-28003423,HC  CATHETER DRAINAGE ULTRATHANE 6.3F 15CM, 667 
-28003425,HC IMP STENT GRAFT INTUITRAK 25X70X100MM," 64,770 "
-28003427,HC IMP PORT INF INJECT TITANIUM 6.6F," 6,188 "
-28003428,HC PEN ISOLATOR 20MM," 18,750 "
-28003429,HC IMP STENT GRAFT AAA ZENITH FLEX 28X125," 54,977 "
-28003430,HC  CATH POWERPORT 8F," 2,975 "
-28003431,HC  CATH CHRONOFLEX," 2,975 "
-28003432,HC IMP STENT GRAFT AAA ZENITH FLEX 36X149," 54,977 "
-28003433,HC IMPL COIL NEURO HELICAL 4MMX8CM," 5,058 "
-28003434,HC IMPL COIL NEURO DETACHABLE 5MMX15CM," 5,058 "
-28003435,HC IMPL COIL NEURO DETACHABLE 8MMX30CM," 5,058 "
-28003436,HC IMPL COIL NEURO DETACHABLE 9MMX30CM," 5,058 "
-28003437,HC IMPL COIL PERIPHERAL CONCERTO 14MMX30CM," 5,058 "
-28003438,HC IMPL COIL PERIPHERAL CONCERTO 16MMX40CM," 5,058 "
-28003439,HC IMPL COIL PERIPHERAL CONCERTO 18MMX40CM," 5,058 "
-28003440,HC IMPL COIL PERIPHERAL CONCERTO 20MMX50CM," 5,058 "
-28003451,HC IMPL SCREW AND TOP HATS 40MM," 2,975 "
-28003452,HC IMPL SCREW AND TOP HATS 42MM," 2,975 "
-28003453,HC IMPL SCREW AND TOP HATS 44MM," 2,975 "
-28003454,HC IMPL SCREW AND TOP HATS 46MM," 2,975 "
-28003455,HC IMPL SCREW AND TOP HATS 48MM," 2,975 "
-28003456,HC  GUIDEWIRE SINGLE ENDED 1.6MM, 102 
-28003457,HC IMPL SCREW MID BLUE 3.8X6.0MM," 1,913 "
-28003458,HC IMPL SCREW MID BLUE 4.3X6.0MM," 1,913 "
-28003459,HC IMPL SCREW MID GREY 3.8X6.0MM," 1,913 "
-28003460,HC IMPL SCREW MID GREY 4.3X6.0MM," 1,913 "
-28003461,HC IMPL SCREW MID GREY 5.0X5.0MM," 1,913 "
-28003462,HC IMPL SCREW MID GREY 5.0X6.0MM," 1,913 "
-28003463,HC IMPL SCREW DISTAL GOLD 5.5X10MM," 2,678 "
-28003464,HC IMPL SCREW DISTAL GOLD 6.5X10MM," 2,678 "
-28003465,HC IMPL SCREW PROXIMAL BLUE 2.8," 1,913 "
-28003466,HC IMPL SCREW PROXIMAL GREY 3.3MM," 1,913 "
-28003467,HC IMPL SCREW PROXIMAL GREY 3.8MM," 1,913 "
-28003468,HC IMPL SCREW PROXIMAL GOLD 6.5X10MM," 2,678 "
-28003469,HC  GUIDEWIRE J-TIP 14IN 180CM, 986 
-28003470,HC IMP STENT GRAFT TAA ZENITH TX2 40X208MM," 72,564 "
-28003472,HC  GUIDEWIRE ZINGER MEDIUM, 986 
-28003473,HC IMP DEVICE EMBOLIZATION PIPELINE 2.50X10," 75,000 "
-28003474,HC IMP DEVICE EMBOLIZATION PIPELINE 2.50X12," 75,000 "
-28003475,HC IMP DEVICE EMBOLIZATION PIPELINE 2.50X14," 75,000 "
-28003476,HC IMP DEVICE EMBOLIZATION PIPELINE 2.50X16," 75,000 "
-28003477,HC IMP DEVICE EMBOLIZATION PIPELINE 2.50X18," 75,000 "
-28003478,HC IMP DEVICE EMBOLIZATION PIPELINE 2.50X20," 75,000 "
-28003479,HC IMP DEVICE EMBOLIZATION PIPELINE 2.75X10," 75,000 "
-28003480,HC IMP DEVICE EMBOLIZATION PIPELINE 2.75X12," 75,000 "
-28003481,HC IMP DEVICE EMBOLIZATION PIPELINE 2.75X14," 75,000 "
-28003482,HC IMP DEVICE EMBOLIZATION PIPELINE 2.75X16," 75,000 "
-28003483,HC IMP DEVICE EMBOLIZATION PIPELINE 2.75X18," 75,000 "
-28003484,HC IMP DEVICE EMBOLIZATION PIPELINE 2.75X20," 75,000 "
-28003485,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X10," 75,000 "
-28003486,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X12," 75,000 "
-28003487,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X14," 75,000 "
-28003488,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X16," 75,000 "
-28003489,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X18," 75,000 "
-28003490,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X20," 75,000 "
-28003491,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X25," 80,400 "
-28003492,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X30," 80,400 "
-28003493,HC IMP DEVICE EMBOLIZATION PIPELINE 3.00X35," 80,400 "
-28003494,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X10," 77,400 "
-28003495,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X12," 75,000 "
-28003496,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X14," 75,000 "
-28003497,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X16," 75,000 "
-28003498,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X18," 77,400 "
-28003499,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X20," 75,000 "
-28003500,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X25," 80,400 "
-28003501,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X30," 80,400 "
-28003502,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X10," 75,000 "
-28003503,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X12," 75,000 "
-28003504,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X14," 75,000 "
-28003505,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X16," 75,000 "
-28003506,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X18," 75,000 "
-28003507,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X20," 75,000 "
-28003508,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X25," 80,400 "
-28003509,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X30," 80,400 "
-28003510,HC IMP DEVICE EMBOLIZATION PIPELINE 3.50X35," 80,400 "
-28003511,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X10," 75,000 "
-28003512,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X12," 75,000 "
-28003513,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X14," 77,400 "
-28003514,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X18," 75,000 "
-28003515,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X25," 80,400 "
-28003516,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X30," 80,400 "
-28003517,HC IMP DEVICE EMBOLIZATION PIPELINE 3.75X35," 80,400 "
-28003518,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X10," 75,000 "
-28003519,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X12," 75,000 "
-28003520,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X14," 75,000 "
-28003521,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X16," 77,400 "
-28003522,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X18," 75,000 "
-28003523,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X20," 75,000 "
-28003524,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X25," 80,400 "
-28003525,HC IMP DEVICE EMBOLIZATION PIPELINE 4.00X30," 80,400 "
-28003526,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X10," 75,000 "
-28003527,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X12," 75,000 "
-28003528,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X14," 75,000 "
-28003529,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X25," 80,400 "
-28003530,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X30," 80,400 "
-28003531,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X35," 80,400 "
-28003532,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X10," 75,000 "
-28003533,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X12," 75,000 "
-28003534,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X14," 75,000 "
-28003535,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X16," 75,000 "
-28003536,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X20," 77,400 "
-28003537,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X25," 87,600 "
-28003538,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X30," 80,400 "
-28003539,HC IMP DEVICE EMBOLIZATION PIPELINE 4.50X35," 80,400 "
-28003540,HC IMP STENT VASCULAR ZILVER 6X40X125," 8,213 "
-28003541,HC IMP STENT VASCULAR ZILVER 6X60X125," 8,213 "
-28003542,HC IMP STENT VASCULAR ZILVER 6X80X125," 8,213 "
-28003543,HC IMP STENT VASCULAR ZILVER 7X40X125," 11,250 "
-28003544,HC IMP STENT VASCULAR ZILVER 7X60X125," 11,250 "
-28003545,HC IMP STENT VASCULAR ZILVER 7X80X125," 8,213 "
-28003546,HC IMP STENT VASCULAR ZILVER 8X40X125," 11,250 "
-28003547,HC IMP STENT VASCULAR ZILVER 8X60X125," 8,213 "
-28003548,HC IMP STENT VASCULAR ZILVER 8X80X125," 8,213 "
-28003549,HC IMP STENT ZILVER 6X100X125," 10,088 "
-28003550,HC IMP STENT ZILVER 6X120X125," 10,088 "
-28003551,HC IMP STENT ZILVER 6X140X125," 10,088 "
-28003552,HC IMP STENT ESOPHAGEAL POLYFLEX 16X20," 14,925 "
-28003553,HC IMP STENT ESOPHAGEAL POLYFLEX 18X23," 14,925 "
-28003554,HC NEEDLE ULTRASOUND ECHOTIP 19GX8CM," 2,652 "
-28003555,HC  INTROD 5F SET, 383 
-28003556,HC IMP STENT BILIARY OMNILINK18 7X58X135," 9,750 "
-28003558,HC  CATHETER PICC TURBO JECT 5.5FX7X50CM," 1,275 "
-28003559,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X10," 75,000 "
-28003560,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X12," 75,000 "
-28003561,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X14," 79,800 "
-28003562,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X16," 79,800 "
-28003563,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X18," 75,000 "
-28003564,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X20," 75,000 "
-28003565,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X25," 80,400 "
-28003566,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X30," 80,400 "
-28003567,HC IMP DEVICE EMBOLIZATION PIPELINE 4.75X35," 82,800 "
-28003568,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X10," 75,000 "
-28003569,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X12," 75,000 "
-28003570,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X14," 75,000 "
-28003571,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X16," 75,000 "
-28003572,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X18," 75,000 "
-28003573,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X25," 80,400 "
-28003574,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X30," 80,400 "
-28003575,HC IMP DEVICE EMBOLIZATION PIPELINE 5.00X35," 80,400 "
-28003576,HC IMPL PACEMAKER SYNCRA CRT-P," 46,800 "
-28003578,HC IMP LEAD PACING 88CM," 17,963 "
-28003579,HC BUR ATHERECTOMY ROTALINK PL 1.25MM," 8,963 "
-28003583,HC IMP MENISCAL REPAIR CURVED 7 IMPLANTS," 10,463 "
-28003584,HC DILATOR SYSTEM," 3,400 "
-28003585,HC IMP RADIAL HEAD 18MM," 18,984 "
-28003586,HC IMP RADIAL HEAD 21MM," 18,984 "
-28003587,HC IMP RADIAL HEAD 24MM," 18,984 "
-28003588,HC IMP ELBOW STEM 20MMX6.5MM," 18,984 "
-28003589,HC IMP ELBOW STEM 20MMX7.5MM," 18,984 "
-28003590,HC  GUIDEWIRE ARCHER 0.035INX200CM," 1,233 "
-28003591,HC  CATHETER BALLOON 3MMX20CM 75CM," 2,125 "
-28003592,HC  CATHETER BALLOON 4MMX20CM 75CM," 2,125 "
-28003593,HC  CATHETER BALLOON 5MMX20CM 75CM," 2,125 "
-28003594,HC  CATHETER BALLOON 6MMX20CM 75CM," 2,125 "
-28003595,HC  CATHETER BALLOON 7MMX20CM 75CM," 2,125 "
-28003596,HC  CATHETER BALLOON 8MMX20CM 75CM," 2,125 "
-28003597,HC  CATHETER BALLOON 9MMX20CM 75CM," 2,125 "
-28003598,HC  CATHETER BALLOON 10MMX20CM 75CM," 2,125 "
-28003599,HC  CATHETER BALLOON 12MMX20CM 75CM," 2,125 "
-28003600,HC IMP STENT GRAFT AAA ENDURANT 16X16X82," 31,325 "
-28003601,HC  CATHETER ELECTROPHYSIOLOGY 7FX110CM," 9,000 "
-28003602,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X12," 64,968 "
-28003603,HC  GUIDEWIRE MINI ACUTRAK 35X5.75, 94 
-28003604,HC IMP STENT GRAFT AAA ZENITH FLEX 30X125," 54,977 "
-28003606,HC IMP STENT GRAFT AAA ZENITH FLEX 26X111," 54,977 "
-28003608,HC IMPL FINGER INTERPHALANGEAL PROXIMAL SZ30," 6,979 "
-28003609,HC IMPL SCREW HEAD 2.0X24MM," 1,726 "
-28003611,HC  GUIDEWIRE LONG 18IN 150CM, 457 
-28003612,HC IMP STENT FORMULA418 6X20X80," 8,625 "
-28003614,HC  CATHETER STINGRAY," 8,625 "
-28003615,HC  GUIDEWIRE ANGIOGRAPHY STINGRAY 300CM," 2,125 "
-28003616,HC IMP STENT GRAFT AAA ENDURANT 12X13X166," 64,838 "
-28003617,HC  CATHETER BALLOON 8MX40CM 75CM," 2,125 "
-28003618,HC  CATHETER BALLOON 10MX40CM 75CM," 2,125 "
-28003619,HC IMP ACHILLON SUTURE SYSTEM," 7,625 "
-28003620,HC IMP STENT GRAFT AAA ENDURANT 25X13X145," 64,838 "
-28003621,HC  CATHETER ACHIEVE MAPPING 15MM," 7,438 "
-28003622,HC  CATHETER ACHIEVE MAPPING 20MM," 7,438 "
-28003624,HC IMP SHAFT BONE FIBULAR 6CM," 5,559 "
-28003626,HC  CATHETER BALLOON 6MX40CM 75CM," 2,125 "
-28003627,HC  CATHETER BALLOON 4MX40CM 75CM," 2,125 "
-28003628,HC IMP PROSTHESIS TRAPEZIUM SM," 14,963 "
-28003629,HC IMP STENT GRAFT AAA ENDURANT 28X13X166," 64,838 "
-28003630,HC  CATHETER ACUITY BREAKAWAY 6FX68CM," 1,156 "
-28003631,HC  GUIDEWIRE COMBOWIRE XT NO OFFSET," 8,625 "
-28003632,HC  CATH PERPH TURBOHAWK SX-C," 23,765 "
-28003633,HC  CATH PERPH SILVERHAWK SS-C," 23,765 "
-28003634,HC IMP CEMENT BONE CALLOS INJECT 3ML," 7,583 "
-28003635,HC  CATHETER BALLOON 6MX60CM 75CM," 2,125 "
-28003636,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 16X56," 21,595 "
-28003637,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 11X56," 21,595 "
-28003638,HC IMP STENT GRAFT AAA ENDURANT 36X16X166," 64,838 "
-28003639,HC IMPL SCREW TENODESIS 4MMX10MM," 5,134 "
-28003640,HC IMP STENT GRAFT AAA ENDURANT 16X20X124," 34,825 "
-28003641,HC IMPL PACEMAKER SINGLE ADAPTA ADSR03," 18,750 "
-28003642,HC  CATHETER NEURO MAX 6FX90CMX4CM," 2,763 "
-28003650,HC IMP GRAFT ENDOVASCULAR MAIN BODY EXT 26X39MM," 14,573 "
-28003651,HC IMP STENT GRAFT AAA ZENITH FLEX 36X73," 14,573 "
-28003652,HC IMP STENT GRAFT AAA ZENITH RENU 28X81," 36,660 "
-28003653,HC IMP GRAFT ANCILLARY 32X81 20F," 36,660 "
-28003654,HC IMP STENT GRAFT AAA ZENITH FLEX 28X58," 14,573 "
-28003655,HC IMP STENT GRAFT AAA ZENITH FLEX 32X125," 54,977 "
-28003656,HC IMP STENT GRAFT AAA ZENITH FLEX 32X140," 54,977 "
-28003657,HC IMP GRAFT ENDOVASCULAR 22X55," 14,573 "
-28003658,HC IMP STENT PERIPHERAL NITINOL SYSTEM 7X40X135CM," 16,500 "
-28003659,HC IMP VALVE HEART SAPIEN XT 23MM," 87,500 "
-28003660,HC IMP VALVE HEART SAPIEN XT 26MM," 87,500 "
-28003661,HC IMP LEAD NEUROSTIMULATOR 50X70CM KIT," 12,975 "
-28003663,HC BUR DRILL BIT CANNULATED ARTHREX 2.75MM," 2,975 "
-28003665,HC IMPL PLATE WEDGED PROFILE," 2,125 "
-28003666,HC IMPL SCREW 3.75X34 MM," 1,700 "
-28003667,HC  GUIDEWIRE 62X6IN, 340 
-28003668,HC  GUIDEWIRE 62X7IN, 340 
-28003671,HC IMP STENT GRAFT INTUITRAK 25X80," 67,770 "
-28003672,HC  CATHETER BALLOON 7MMX40CM 75CM," 2,125 "
-28003676,HC IMP ALLOGRAFT TIBIAL DISTAL," 10,223 "
-28003677,HC  CATHETER CONSTELLATION MAPPING 48MMX120CM," 17,456 "
-28003678,HC  CATHETER CONSTELLATION MAPPING 60MMX120CM," 19,950 "
-28003679,HC  CATHETER DILATATION TYSHAK 12MMX3CMX100," 4,004 "
-28003680,HC IMP STENT GRAFT AAA ZENITH RENU 28-37X88," 42,341 "
-28003682,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X40MM," 4,888 "
-28003684,HC IMPL SCREW 5X85MM," 1,799 "
-28003685,HC IMPL SCREW 5X80MM," 1,799 "
-28003686,HC IMPL SCREW 5X46MM," 1,799 "
-28003687,HC IMPL SCREW 5X16MM," 2,519 "
-28003688,HC IMPL SCREW 5X14MM," 2,519 "
-28003689,HC IMPL PLATE CONDYLAR 4.5MM 10HOLE," 15,968 "
-28003691,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX45MM," 2,125 "
-28003692,HC IMP CAP LOCKING MATRIX TI," 2,346 "
-28003693,HC IMPL SCREW MATRIX POLYAXIAL 6X40MM," 9,375 "
-28003695,HC IMPL SCREW STERNAL 3X18MM," 1,235 "
-28003696,HC IMPL SCREW STERNAL 3X16MM," 1,301 "
-28003697,HC IMPL SCREW STERNAL 3X14MM," 1,301 "
-28003698,HC IMPL SCREW STERNAL 3X12MM," 1,301 "
-28003699,HC IMPL SCREW STERNAL 3X10MM," 1,235 "
-28003700,HC IMPL PLATE STERNAL LOCKING 13HOLES," 7,189 "
-28003701,HC IMPL PLATE STERNAL LOCKING 10HOLES," 6,336 "
-28003702,HC IMPL SCREW 5X30MM," 1,734 "
-28003703,HC IMP NAIL TIBIAL PROXIMAL CANNULATED EX TI 9X315MM," 11,730 "
-28003704,HC IMP WASHER FLAT LP TITANIUM 14MM, 536 
-28003705,HC IMP LEAD PACING ATTAIN 78CM," 17,963 "
-28003706,HC IMP KNEE LINER NEUTRAL H CROSSLINKED 36MM," 5,950 "
-28003707,HC IMP SHOULDER SHELL GLENOID POLY LARGE," 10,800 "
-28003708,HC IMP SHOULDER STEM HUMERAL 14X110MM," 31,374 "
-28003709,HC IMP SHOULDER HEAD HUMERAL 52X19MM," 14,310 "
-28003710,HC IMPL SCREW 4.5X36MM," 1,148 "
-28003711,HC IMPL SCREW 4.5X42MM," 1,148 "
-28003712,HC IMP SHOULDER STEM HUMERAL 10X130MM," 46,040 "
-28003713,HC IMPL PLATE REVERSE," 17,888 "
-28003714,HC IMP SHOULDER INSERT REVERSE TRABECULAR," 6,579 "
-28003715,HC IMP SHOULDER SPHERE GLENOID 36MM," 11,543 "
-28003716,HC IMP SHOULDER LINER REVERSE 36MM," 9,788 "
-28003717,HC IMP KNEE BASEPLATE TIBIAL EVOLUTION 3SR," 8,500 "
-28003718,HC IMP KNEE FEMORAL EVOLUTION RIGHT SZ4," 15,000 "
-28003719,HC IMP KNEE INSERT EVOLUTION STANDARD LEFT SZ4 12MM," 5,100 "
-28003720,HC IMP KNEE BASEPLATE TIBIAL EVOLUTION 4SL," 8,500 "
-28003721,HC IMP KNEE FEMORAL EVOLUTION LEFT SZ5," 15,000 "
-28003723,HC IMP STENT GRAFT TAA TAG COMFORMABLE 26X10," 88,500 "
-28003724,HC IMP STENT GRAFT TAA TAG COMFORMABLE 34X10," 88,500 "
-28003725,HC IMP STENT GRAFT TAA TAG COMFORMABLE 34X15," 88,500 "
-28003726,HC IMP STENT GRAFT TAA TAG COMFORMABLE 34X20," 88,500 "
-28003727,HC IMP STENT GRAFT TAA TAG COMFORMABLE 37X15," 88,500 "
-28003728,HC IMP STENT GRAFT TAA TAG COMFORMABLE 45X15," 88,500 "
-28003729,HC IMP STENT GRAFT TAA TAG COMFORMABLE 26-21X10," 88,500 "
-28003730,HC  CATHETER BALLOON 8MMX20CM 135CM," 2,125 "
-28003731,HC  CATHETER BALLOON 7MMX20CM 135CM," 2,125 "
-28003732,HC IMP PHYSIOMESH OVAL 10X15CM," 5,722 "
-28003733,HC IMP KNEE INSERT EVOLUTION STANDARD LEFT SZ5 10MM," 5,100 "
-28003734,HC IMP KNEE INSERT EVOLUTION STANDARD RIGHT SZ5 10MM," 5,100 "
-28003735,HC IMP KNEE BASEPLATE TIBIAL EVOLUTION 5SR," 8,500 "
-28003736,HC IMP KNEE FEMORAL EVOLUTION RIGHT SZ6," 15,000 "
-28003737,HC IMP KNEE FEMORAL EVOLUTION," 15,000 "
-28003738,HC IMP BONE FEMUR PROX FROZEN LEFT NO HEAD," 18,293 "
-28003739,HC IMP BONE FEMORAL HEAD FROZEN 48-54MM," 9,660 "
-28003740,HC IMP KNEE BASEPLATE TIBIAL EVOLUTION 3SL," 8,500 "
-28003741,HC IMP KNEE FEMORAL EVOLUTION LEFT SZ4," 15,000 "
-28003742,HC IMP HIP LINER NEUTRAL CROSSLINKED 36X56MM," 5,950 "
-28003743,HC IMP HIP SHELL CONTINUUM 56MM," 12,300 "
-28003744,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 16X74," 21,595 "
-28003745,HC IMP CLIP ANEURYSM SLIGHTLY CRVD KEES SLIMLINE 7X9MM," 3,502 "
-28003746,HC IMP STENT GRAFT TAA ZENITH TX2 34X186MM," 42,751 "
-28003747,HC IMP CLIP ANEURYSM CURVED SUNDT 6MM," 3,553 "
-28003748,HC IMP GRAFT DURAL 2X3," 2,766 "
-28003749,HC IMP GRAFT DURAL 4X7," 3,682 "
-28003750,HC IMP GRAFT DURAL 7X10," 5,259 "
-28003751,HC IMP GRAFT DURAL 7X20," 6,662 "
-28003752,HC IMPL PACEMAKER SINGLE MINIX 8360," 33,150 "
-28003753,HC  CATHETER DILATATION PTA SYNERGY 5.3X7X2X135CM," 1,769 "
-28003754,HC IMP ANCHOR SUTURE BIOCOMPOSITE PUSHLOCK 4.5X28MM," 3,358 "
-28003761,HC SUTURE PASSER UCL KIT," 5,908 "
-28003763,HC IMP ANCHOR SUTURE SUTURETAKS 2.4X8.5," 2,593 "
-28003764,HC  CATHETER MICRO 3F 110CM," 16,500 "
-28003765,HC  CATHETER ELECTROPHYSIOLOGY 7FX20MM," 9,248 "
-28003766,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 16X90," 21,595 "
-28003767,HC  CATHETER DILATATION CORONARY OTW 1.20MMX06MM," 1,615 "
-28003768,HC  CATHETER DILATATION CORONARY OTW 1.20MMX12MM," 1,615 "
-28003769,HC  GUIDEWIRE ANGIOGRAPHY ASAHI EXTENSION 150CM," 3,485 "
-28003770,HC VALVE HEMOSTATIC GUARDIAN II," 4,038 "
-28003771,HC  GUIDEWIRE STANDARD JTIP LNG 1.5MM 180CM, 337 
-28003772,HC  GUIDEWIRE STANDARD JTIP LNG 1.5MM 260CM, 377 
-28003773,HC IMP VALVE HEART SAPIEN RF3 SYS 23MM," 81,250 "
-28003774,HC IMP VALVE HEART SAPIEN RF3 SYS 26MM," 81,250 "
-28003780,HC CUTTER ENDOSCOPIC 6MM," 2,763 "
-28003781,HC CUTTER ENDOSCOPIC 10MM," 2,763 "
-28003783,HC IMP STENT CORONARY ELEMENT RX DES 2.25X8," 14,250 "
-28003784,HC IMP STENT CORONARY ELEMENT RX DES 2.75X16," 12,750 "
-28003785,HC IMP STENT CORONARY ELEMENT RX DES 2.25X32," 14,250 "
-28003786,HC IMP STENT CORONARY ELEMENT RX DES 2.5X8," 14,250 "
-28003787,HC IMP STENT CORONARY ELEMENT RX DES 2.75X8," 15,375 "
-28003788,HC IMP STENT CORONARY ELEMENT RX DES 3X8," 12,750 "
-28003789,HC IMP STENT CORONARY ELEMENT RX DES 3.5X8," 14,250 "
-28003790,HC IMP STENT CORONARY ELEMENT RX DES 4X8," 15,375 "
-28003791,HC IMP STENT CORONARY ELEMENT RX DES 2.25X12," 12,750 "
-28003792,HC IMP STENT CORONARY ELEMENT RX DES 2.5X12," 12,750 "
-28003793,HC IMP STENT CORONARY ELEMENT RX DES 2.75X12," 14,250 "
-28003794,HC IMP STENT CORONARY ELEMENT RX DES 3.5X12," 14,250 "
-28003795,HC IMP STENT CORONARY ELEMENT RX DES 4X12," 14,250 "
-28003796,HC IMP STENT CORONARY ELEMENT RX DES 2.25X16," 14,250 "
-28003797,HC IMP STENT CORONARY ELEMENT RX DES 2.5X16," 14,250 "
-28003798,HC IMP STENT CORONARY ELEMENT RX DES 3.5X16," 14,250 "
-28003799,HC IMP STENT CORONARY ELEMENT RX DES 4X16," 14,250 "
-28003800,HC IMP STENT CORONARY ELEMENT RX DES 2.25X20," 14,250 "
-28003801,HC IMP STENT CORONARY ELEMENT RX DES 2.5X20," 14,250 "
-28003802,HC IMP STENT CORONARY ELEMENT RX DES 2.75X20," 14,250 "
-28003803,HC IMP STENT CORONARY ELEMENT RX DES 3X20," 14,250 "
-28003804,HC IMP STENT CORONARY ELEMENT RX DES 3.5X20," 14,250 "
-28003805,HC IMP STENT CORONARY ELEMENT RX DES 4X20," 14,250 "
-28003806,HC IMP STENT CORONARY ELEMENT RX DES 2.25X24," 14,250 "
-28003807,HC IMP STENT CORONARY ELEMENT RX DES 2.5X24," 12,750 "
-28003808,HC IMP STENT CORONARY ELEMENT RX DES 2.75X24," 15,375 "
-28003809,HC IMP STENT CORONARY ELEMENT RX DES 3X24," 14,250 "
-28003810,HC IMP STENT CORONARY ELEMENT RX DES 3.5X24," 15,375 "
-28003811,HC IMP STENT CORONARY ELEMENT RX DES 4X24," 15,375 "
-28003812,HC IMP STENT CORONARY ELEMENT RX DES 2.25X28," 14,250 "
-28003813,HC IMP STENT CORONARY ELEMENT RX DES 2.5X28," 14,250 "
-28003814,HC IMP STENT CORONARY ELEMENT RX DES 2.75X28," 14,250 "
-28003815,HC IMP STENT CORONARY ELEMENT RX DES 3X28," 14,250 "
-28003816,HC IMP STENT CORONARY ELEMENT RX DES 3.5X28," 14,250 "
-28003817,HC IMP STENT CORONARY ELEMENT RX DES 4X28," 14,250 "
-28003818,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 2.7X30," 2,738 "
-28003820,HC IMPL PACEMAKER SINGLE VITALTY 8427," 28,700 "
-28003824,HC IMP STENT CORONARY ELEMENT RX DES 3X12," 14,250 "
-28003825,HC IMP STENT CORONARY ELEMENT RX DES 3X16," 14,250 "
-28003826,HC IMPL SCREW SCHANZ 5X170MM, 947 
-28003827,HC IMP CLAMP ADJUSTABLE LARGE," 4,310 "
-28003828,HC IMP PTERIONAL MEDPOR RIGHT 44X43X6MM," 7,997 "
-28003829,HC IMP STENT GRAFT AAA EXCLUDER 23X12X14," 64,968 "
-28003830,HC IMP STENT GRAFT AAA EXCLUDER 28.5X12X18," 64,968 "
-28003831,HC IMP CLOSURE DEVICE VASCULAR EXOSEAL 5F," 1,700 "
-28003833,HC IMP ICD OTHER UNIFY SYSTEM 40Q," 84,000 "
-28003834,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 18F 2.7X45," 2,668 "
-28003835,HC NEEDLE BIOPSY BONE ACKERMAN 14GX11CM," 3,638 "
-28003836,HC CATHETER DRAINAGE PIGTAIL 4SP NEEDLE 5F SET, 520 
-28003837,HC  CATHETER ANGIOGR SOFT VU MPA 5F 80CM, 158 
-28003839,HC  INTROD MICRP TRANS SS 10CM SET, 204 
-28003842,HC SHEATH SHEATH GUIDING, 791 
-28003843,HC SET INTRODUCER 8FR SHEATH, 136 
-28003851,HC NEEDLE BIOPSY ULTRASOUND ECHOTIP 5.2X25," 2,962 "
-28003852,HC BALLOON EUS RADIAL SCOPE," 3,145 "
-28003853,HC BALLOON O RING KIT," 2,550 "
-28003854,HC  CATHETER INFUSION TRELLIS 6X10X80CM," 15,713 "
-28003857,HC  SHEATH LASER DIRECTIONAL 14FX40CM," 22,260 "
-28003858,HC  SHEATH LASER DIRECTIONAL 16FX40CM," 22,260 "
-28003859,HC BIOPSY BRONCHIAL BRUSH SET," 2,231 "
-28003860,HC IMP STENT CORONARY ELEMENT RX DES 2.5X32," 14,250 "
-28003861,HC IMP STENT CORONARY ELEMENT RX DES 2.5X38," 14,250 "
-28003862,HC IMP STENT CORONARY ELEMENT RX DES 2.75X32," 14,250 "
-28003863,HC IMP STENT CORONARY ELEMENT RX DES 2.75X38," 14,250 "
-28003864,HC IMP STENT CORONARY ELEMENT RX DES 3X32," 14,250 "
-28003865,HC IMP STENT CORONARY ELEMENT RX DES 3X38," 14,250 "
-28003866,HC IMP STENT CORONARY ELEMENT RX DES 3.5X32," 14,250 "
-28003867,HC IMP STENT CORONARY ELEMENT RX DES 3.5X38," 14,250 "
-28003868,HC IMP STENT CORONARY ELEMENT RX DES 4X32," 14,250 "
-28003869,HC IMP STENT CORONARY ELEMENT RX DES 4X38," 14,250 "
-28003870,HC  SHEATH GUIDING STRAIGHT 24FX.38INX65CM," 2,856 "
-28003871,HC  GUIDEWIRE ROSEN TEFLON COATED 35IN 45CM, 91 
-28003872,HC IMP STENT GRAFT AAA ENDURANT II 16X16X124," 34,475 "
-28003874,HC IMP STENT BILIARY WALLFLEX PC 10X40," 16,875 "
-28003875,HC IMP STENT BILIARY WALLFLEX PC 10X60," 16,875 "
-28003876,HC IMP STENT BILIARY WALLFLEX PC 10X80," 16,875 "
-28003877,HC  GUIDEWIRE RX 44-20MM 450CM," 2,805 "
-28003878,HC KNIFE ELECTROSURGICAL DUAL," 4,941 "
-28003879,HC  GUIDEWIRE AMPLATZ 32IN 260CM 3MM, 113 
-28003880,HC  CATHETER ABLATION HALO ULTRA 90," 9,870 "
-28003881,HC  GUIDEWIRE AMPLATZ CURVED 35IN 145CM, 186 
-28003882,HC  CATHETER ABLATION HALO 60," 12,278 "
-28003883,HC IMP ICD OTHER MODEL N141," 71,750 "
-28003884,HC IMPL COIL HYDROCOIL 35D SYS 6MMX20CM," 7,875 "
-28003885,HC IMPL COIL HYDROCOIL 35D SYS 3MMX10CM," 5,166 "
-28003886,HC IMPL COIL HYDROCOIL 35D SYS 4MMX20CM," 5,834 "
-28003887,HC BIOPSY LIVER 16G 45CM 7F SET," 2,991 "
-28003888,HC  GUIDEWIRE CURVED TFE CTD 21IN 50CM, 97 
-28003889,HC  GUIDEWIRE CURVED TIP 145CM 21IN 3MM, 111 
-28003890,HC  GUIDEWIRE CURVED TIP 50CM 25IN 3MM, 92 
-28003891,HC  GUIDEWIRE FIXED CORE 145CM 0.025IN 3MM, 111 
-28003892,HC  GUIDEWIRE FIXED CORE 145CM 0.032IN 3MM, 111 
-28003894,HC  GUIDEWIRE CURVED 50CM 0.035IN 3MM, 85 
-28003895,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X106," 55,218 "
-28003896,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X121," 55,218 "
-28003897,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X76," 55,218 "
-28003898,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X91," 55,218 "
-28003899,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X106," 55,218 "
-28003900,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X121," 55,218 "
-28003901,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X76," 55,218 "
-28003902,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X91," 55,218 "
-28003903,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X106," 55,218 "
-28003904,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X121," 55,218 "
-28003905,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X76," 55,218 "
-28003906,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X91," 55,218 "
-28003907,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X106," 55,218 "
-28003908,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X121," 55,218 "
-28003909,HC  GUIDEWIRE TEFLON COATED CURVED 35IN 145CM, 94 
-28003910,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X76," 55,218 "
-28003911,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X91," 55,218 "
-28003912,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X106," 55,218 "
-28003913,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X121," 55,218 "
-28003914,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X76," 55,218 "
-28003915,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X91," 55,218 "
-28003916,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X114," 55,218 "
-28003917,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X129," 55,218 "
-28003918,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X84," 55,218 "
-28003919,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X99," 55,218 "
-28003920,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X114," 55,218 "
-28003921,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X129," 55,218 "
-28003922,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X84," 55,218 "
-28003923,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X99," 55,218 "
-28003924,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X109," 55,218 "
-28003926,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X94," 55,218 "
-28003928,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X124," 55,218 "
-28003929,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X94," 55,218 "
-28003930,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X109," 55,218 "
-28003931,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X124," 55,218 "
-28003932,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 28X94," 55,218 "
-28003933,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X109," 55,218 "
-28003934,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X124," 55,218 "
-28003935,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 30X94," 55,218 "
-28003936,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X109," 55,218 "
-28003937,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X124," 55,218 "
-28003938,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 32X94," 55,218 "
-28003939,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X107," 55,218 "
-28003940,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X122," 55,218 "
-28003941,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 34X137," 55,218 "
-28003942,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X107," 55,218 "
-28003943,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X122," 55,218 "
-28003944,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 36X137," 55,218 "
-28003945,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X28X106," 24,465 "
-28003946,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X28X121," 24,465 "
-28003947,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X28X76," 24,465 "
-28003948,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X28X91," 24,465 "
-28003949,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X45X106," 24,465 "
-28003950,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X45X121," 24,465 "
-28003951,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X45X76," 24,465 "
-28003952,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X45X91," 24,465 "
-28003953,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X62X106," 24,465 "
-28003954,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X62X121," 24,465 "
-28003955,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X62X76," 24,465 "
-28003956,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 12X62X91," 24,465 "
-28003957,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X28X106," 24,465 "
-28003958,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X28X121," 24,465 "
-28003959,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X28X76," 24,465 "
-28003960,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X28X91," 24,465 "
-28003961,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X45X106," 24,465 "
-28003962,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X45X121," 24,465 "
-28003963,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X45X76," 24,465 "
-28003964,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X45X91," 24,465 "
-28003965,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X62X106," 24,465 "
-28003966,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X62X121," 24,465 "
-28003967,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X62X76," 24,465 "
-28003968,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 16X62X91," 24,465 "
-28003969,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X28X106," 24,465 "
-28003970,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X28X121," 24,465 "
-28003971,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X28X76," 24,465 "
-28003972,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X28X91," 24,465 "
-28003973,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X45X106," 24,465 "
-28003974,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X45X121," 24,465 "
-28003975,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X45X76," 24,465 "
-28003976,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X45X91," 24,465 "
-28003977,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X62X106," 24,465 "
-28003978,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X62X121," 24,465 "
-28003979,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X62X76," 24,465 "
-28003980,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 20X62X91," 24,465 "
-28003981,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X45X106," 24,465 "
-28003982,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X45X121," 24,465 "
-28003983,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X45X76," 24,465 "
-28003984,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X45X91," 24,465 "
-28003985,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X62X10," 24,465 "
-28003986,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X62X121," 24,465 "
-28003987,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X62X76," 24,465 "
-28003988,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 24X62X91," 24,465 "
-28003989,HC IMP STENT GRAFT ZENITH ALIGNMENT 3X18MM," 11,250 "
-28003990,HC IMP STENT GRAFT ZENITH ALIGNMENT 3X26MM," 11,250 "
-28003991,HC IMP STENT GRAFT ZENITH ALIGNMENT 4X18MM," 11,250 "
-28003992,HC IMP STENT GRAFT ZENITH ALIGNMENT 4X26MM," 11,250 "
-28003993,HC IMP STENT GRAFT ZENITH ALIGNMENT 5X18MM," 11,250 "
-28003994,HC IMP STENT GRAFT ZENITH ALIGNMENT 5X26MM," 11,250 "
-28003995,HC IMP STENT GRAFT ZENITH ALIGNMENT 6X18MM," 11,250 "
-28003996,HC IMP STENT GRAFT ZENITH ALIGNMENT 6X26MM," 11,250 "
-28003997,HC IMP STENT GRAFT ZENITH ALIGNMENT 7X18MM," 11,250 "
-28003998,HC IMP STENT GRAFT ZENITH ALIGNMENT 7X26MM," 11,250 "
-28003999,HC IMP STENT GRAFT ZENITH ALIGNMENT 8X18MM," 11,250 "
-28004000,HC IMP STENT GRAFT ZENITH ALIGNMENT 8X26MM," 11,250 "
-28004001,HC  GUIDEWIRE FIXED CORE STRAIGHT SS 21IN 145CM, 130 
-28004002,HC  INTRODUCER SHEATH VALVE PEEL AWAY 7F 13CM, 468 
-28004003,HC IMP ICD SINGLE PROTECA VR DF4," 70,200 "
-28004004,HC IMP STENT GRAFT AAA ENDURANT II 25X16X166," 64,838 "
-28004005,HC IMP STENT GRAFT AAA ENDURANT II 36X16X166," 64,838 "
-28004006,HC IMP STENT GRAFT AAA ENDURANT II 16X16X82," 31,325 "
-28004007,HC IMP STENT GRAFT AAA ENDURANT II 16X16X156," 41,600 "
-28004009,HC IMP ANCHOR SYSTEM GRAFT," 9,375 "
-28004010,HC IMP ANCHOR SUTURE BIOCOMPOSITE 6.25X19.1," 3,230 "
-28004011,HC IMP DEVICE FIX SPEEDLOCK KNOTLESS 3MM," 2,975 "
-28004012,HC IMP STENT FORMULA418 6X20X135," 8,075 "
-28004014,HC  SHEATH GUIDING HIGH FLEX 12FX45CM, 553 
-28004015,HC  GUIDEWIRE CEREBRAL PTFE 035IN 260CM, 140 
-28004016,HC  GUIDEWIRE STRAIGHT BENTSON TEFLON COATED 145CM, 130 
-28004017,HC  CATHETER GUIDING JR4D 8F, 451 
-28004018,HC CABLE ELECTROPHYSIOLOGY CARTO 34PIN 9FT," 3,230 "
-28004019,HC  INFUSION SUREFIRE HI FLOW SYSTEM," 20,650 "
-28004020,HC IMP STENT ZILVER 10X120X80," 10,088 "
-28004021,HC IMP PLUG VASCULAR AMPLATZER 4MM," 8,075 "
-28004022,HC IMP PLUG VASCULAR AMPLATZER 5MM," 8,075 "
-28004023,HC IMP PLUG VASCULAR AMPLATZER 6MM," 8,075 "
-28004024,HC IMP PLUG VASCULAR AMPLATZER 7MM," 8,075 "
-28004025,HC IMP PLUG VASCULAR AMPLATZER 8MM," 8,075 "
-28004026,HC  CATHETER GUIDING 8FX100CM JDKNS 4.0, 451 
-28004027,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 64CM," 34,125 "
-28004028,HC IMP STENT BILIARY OMNILINK35 7X59X80," 9,750 "
-28004029,HC IMP STENT BILIARY OMNILINK35 6X12X80," 9,750 "
-28004030,HC IMP STENT BILIARY OMNILINK35 6X16X80," 9,750 "
-28004031,HC IMP STENT BILIARY OMNILINK35 6X19X80," 9,750 "
-28004032,HC IMP STENT BILIARY OMNILINK35 6X29X80," 9,750 "
-28004033,HC IMP STENT BILIARY OMNILINK35 6X39X80," 9,750 "
-28004034,HC IMP STENT BILIARY OMNILINK35 6X59X80," 9,750 "
-28004035,HC IMP STENT BILIARY OMNILINK35 7X12X80," 9,750 "
-28004036,HC IMP STENT BILIARY OMNILINK35 7X16X80," 9,750 "
-28004037,HC IMP STENT BILIARY OMNILINK35 7X19X80," 9,750 "
-28004038,HC IMP STENT BILIARY OMNILINK35 7X29X80," 9,750 "
-28004039,HC IMP STENT BILIARY OMNILINK35 7X39X80," 9,750 "
-28004040,HC IMP STENT BILIARY OMNILINK35 8X19X80," 9,750 "
-28004041,HC IMP STENT BILIARY OMNILINK35 8X29X80," 9,750 "
-28004042,HC IMP STENT BILIARY OMNILINK35 8X39X80," 9,750 "
-28004043,HC IMP STENT BILIARY OMNILINK35 8X12X135," 9,750 "
-28004044,HC IMP STENT BILIARY OMNILINK35 9X19X80," 9,750 "
-28004045,HC IMP STENT BILIARY OMNILINK35 9X29X80," 9,750 "
-28004046,HC IMP STENT BILIARY OMNILINK35 9X39X80," 9,750 "
-28004047,HC IMP STENT BILIARY OMNILINK35 9X59X80," 9,750 "
-28004048,HC IMP STENT BILIARY OMNILINK35 10X19X80," 9,750 "
-28004049,HC IMP STENT BILIARY OMNILINK35 10X29X80," 9,750 "
-28004050,HC IMP STENT BILIARY OMNILINK35 10X39X80," 9,750 "
-28004051,HC IMP STENT BILIARY OMNILINK35 10X59X80," 9,750 "
-28004052,HC IMP STENT BILIARY OMNILINK35 6X12X135," 9,750 "
-28004053,HC IMP STENT BILIARY OMNILINK35 6X16X135," 9,750 "
-28004054,HC IMP STENT BILIARY OMNILINK35 6X19X135," 9,750 "
-28004055,HC IMP STENT BILIARY OMNILINK35 6X29X135," 9,750 "
-28004056,HC IMP STENT BILIARY OMNILINK35 6X39X135," 9,750 "
-28004057,HC IMP STENT BILIARY OMNILINK35 6X59X135," 9,750 "
-28004058,HC IMP STENT BILIARY OMNILINK35 7X12X135," 9,750 "
-28004059,HC IMP STENT BILIARY OMNILINK35 7X16X135," 9,750 "
-28004060,HC IMP STENT BILIARY OMNILINK35 7X19X135," 9,750 "
-28004061,HC IMP STENT BILIARY OMNILINK35 7X29X135," 9,750 "
-28004062,HC IMP STENT BILIARY OMNILINK35 7X39X135," 9,750 "
-28004063,HC IMP STENT BILIARY OMNILINK35 7X59X135," 9,750 "
-28004064,HC IMP STENT BILIARY OMNILINK35 8X19X135," 9,750 "
-28004065,HC IMP STENT BILIARY OMNILINK35 8X29X135," 9,750 "
-28004066,HC IMP STENT BILIARY OMNILINK35 8X39X135," 9,750 "
-28004067,HC IMP STENT BILIARY OMNILINK35 8X59X135," 9,750 "
-28004068,HC IMP STENT BILIARY OMNILINK35 9X19X135," 9,750 "
-28004069,HC IMP STENT BILIARY OMNILINK35 9X29X135," 9,750 "
-28004070,HC IMP STENT BILIARY OMNILINK35 9X39X135," 9,750 "
-28004071,HC IMP STENT BILIARY OMNILINK35 9X59X135," 9,750 "
-28004072,HC IMP STENT BILIARY OMNILINK35 10X19X135," 9,750 "
-28004073,HC IMP STENT BILIARY OMNILINK35 10X29X135," 9,750 "
-28004074,HC IMP STENT BILIARY OMNILINK35 10X39X135," 9,750 "
-28004075,HC IMP STENT BILIARY OMNILINK35 10X59X135," 9,750 "
-28004077,HC  INTROD MICROINT COAX KT 5F, 538 
-28004078,HC  CATHETER GUIDING ATTAIN SELSITE STEERAB," 6,800 "
-28004079,HC IMP STENT ESOPHAGEAL WALLFLEX 18X10," 16,875 "
-28004080,HC IMP STENT ESOPHAGEAL WALLFLEX 18X12," 16,875 "
-28004081,HC IMP STENT ESOPHAGEAL WALLFLEX 18X15," 16,875 "
-28004082,HC IMP STENT FORMULA418 6X12X80," 8,625 "
-28004083,HC IMP STENT FORMULA418 6X16X80," 8,625 "
-28004084,HC IMP STENT FORMULA418 6X24X80," 8,625 "
-28004085,HC IMP STENT FORMULA418 7X12X80," 8,625 "
-28004086,HC IMP STENT FORMULA418 7X16X80," 8,625 "
-28004087,HC IMP STENT FORMULA418 7X20X80," 8,625 "
-28004088,HC IMP STENT FORMULA418 7X24X80," 8,625 "
-28004089,HC IMP STENT FORMULA418 5X12X135," 8,625 "
-28004090,HC IMP STENT FORMULA418 5X16X135," 8,625 "
-28004091,HC IMP STENT FORMULA418 5X20X135," 8,625 "
-28004092,HC IMP STENT FORMULA418 6X12X135," 8,625 "
-28004093,HC IMP STENT FORMULA418 6X16X135," 8,625 "
-28004094,HC IMP STENT FORMULA418 6X24X135," 8,625 "
-28004095,HC IMP STENT FORMULA418 7X12X135," 8,625 "
-28004096,HC IMP STENT FORMULA418 7X16X135," 8,625 "
-28004097,HC IMP STENT FORMULA418 7X20X135," 8,625 "
-28004098,HC IMP STENT FORMULA418 7X24X135," 8,625 "
-28004099,HC IMP STENT FORMULA418 7X30X135," 8,075 "
-28004100,HC IMP ICD DUAL ELLIPSE DF-1 IS-1," 84,000 "
-28004101,HC  CATHETER MICRO TREVO PRO SYS," 45,468 "
-28004102,HC  CATHETER MICRO TREVO PRO 18," 5,058 "
-28004103,HC  CATHETER PENTARAY D CURVE 4-4-4 7FX115CM," 13,470 "
-28004105,HC IMP LEAD VENTRICULAR 62CM," 33,600 "
-28004106,HC  CATHETER GUIDING ANGIOPLASTICA 6FX22CM, 451 
-28004108,HC  INTRODUCER PERFORMA CHECK-FLO 20FX25CM," 3,519 "
-28004109,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 11X90," 21,595 "
-28004110,HC IMP GRAFT ANCILLARY CONVERTER 22MX113CM," 42,341 "
-28004111,HC IMP GRAFT ENDOVASCULAR AAA ZENITH FLEX SPIRAL 9X90," 21,595 "
-28004112,HC IMP STENT GRAFT AAA ENDURANT 28X16X124," 64,838 "
-28004113,HC  CATHETER D TYPE CURVE 6FX115CM," 4,038 "
-28004114,HC  CATHETER F TYPE CURVE 6FX115CM," 4,038 "
-28004115,HC IMP LEAD VENTRICULAR 55CM," 33,600 "
-28004116,HC IMP ANCHOR KNOTLESS KNOTILUS 25MM," 1,445 "
-28004117,HC IMP ANCHOR KNOTLESS KNOTILUS 3.5MM," 2,533 "
-28004118,HC  INTRODUCER RADIOPAQUE LG CHECK-FLO 10X80, 740 
-28004119,HC  INTRODUCER SHEATH 25CM 11F 2.5CM, 170 
-28004120,HC IMP ICD OTHER CRT-D IS-1/DF4/IS-1," 71,750 "
-28004121,HC IMP ICD OTHER UNIFY ASSURE SYSTEM," 77,000 "
-28004122,HC  CATHETER FLEX 10 FR," 9,000 "
-28004124,HC  GUIDEWIRE J FIXED CURVED 18IN 50CM, 130 
-28004125,HC  CATHETER MEDIUM CURVE 7FX110CM," 11,438 "
-28004126,HC IMPL PACEMAKER INVIVE IS-1," 64,194 "
-28004127,HC IMP ICD OTHER QUADRA ASSURA ICD," 84,000 "
-28004128,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 2.3X30," 2,668 "
-28004129,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F 2.3X45," 2,668 "
-28004131,HC IMP STENT GRAFT AAA ZENITH RENU 22X62," 34,912 "
-28004132,HC  LEAD LONG SYS 4F 130DEGREE," 1,700 "
-28004133,HC  CATHETER ENVOY DA 7FX180CM," 4,548 "
-28004134,HC IMP VALVE SAPIEN ASCENDRA 23MM KIT," 81,250 "
-28004135,HC IMP VALVE SAPIEN ASCENDRA 26MM KIT," 81,250 "
-28004136,HC  CATHETER 6F DA MPD 105CM," 4,552 "
-28004137,HC  CATHETER 6F DA MPD 95CM," 4,689 "
-28004139,HC  CATHETER 6F DA STR 95CM," 4,552 "
-28004140,HC  GUIDEWIRE SHEATH 0.035," 1,063 "
-28004141,HC IMP STENT GRAFT AAA ENDURANT II 28X16X166," 64,838 "
-28004142,HC IMP STENT GRAFT AAA ENDURANT II 16X28X156," 41,600 "
-28004143,HC IMP STENT GRAFT AAA ENDURANT II 16X28X124," 34,825 "
-28004144,HC IMP STENT GRAFT TAA VALIANT 24X30X150," 88,725 "
-28004145,HC IMP ICD DUAL ELLIPSE DF4," 84,000 "
-28004146,HC IMP CLIP ANEURYSM STRAIGHT KEES SLIMLINE 9MM," 3,307 "
-28004147,HC  SHEATH GUIDING PINNACLE DESTINATION 7F 65CM, 803 
-28004148,HC IMPL COIL NEURO TARGET 360 ULT 1MMX2CM," 12,690 "
-28004149,HC IMPL COIL NEURO TARGET 360 ULT 1MMX3CM," 12,690 "
-28004150,HC IMPL COIL NEURO TARGET 360 ULT 1.5MMX2CM," 12,488 "
-28004151,HC IMPL COIL NEURO TARGET 360 ULT 1.5MMX3CM," 12,690 "
-28004152,HC IMPL COIL NEURO TARGET 360 ULT 1.5MM 4CM," 12,690 "
-28004153,HC IMPL COIL NEURO TARGET HELICAL ULT 1MMX1CM," 12,690 "
-28004154,HC IMPL COIL NEURO TARGET HELICAL ULT 1.5MMX1CM," 12,690 "
-28004155,HC IMP STENT FORMULA418 8X30X135," 8,625 "
-28004156,HC  CATHETER DILATION ADVANCE 18LP 4X135X5X2," 2,338 "
-28004157,HC  INTROD MICRP TRANS 10CM SET, 221 
-28004158,HC  INTROD TRANSITION MICRO 5F SET, 230 
-28004159,HC  CATHETER GUIDING RBU 3.5 6F," 12,713 "
-28004160,HC  CATHETER GUIDING RBU 4 6F," 12,713 "
-28004161,HC  CATHETER GUIDING RBU 3.5 7F," 12,713 "
-28004162,HC  CATHETER GUIDING RBU 4 7F," 12,713 "
-28004163,HC IMP STENT PERIPHERAL SUPERA BMS 5X40X120," 12,713 "
-28004164,HC IMP STENT PERIPHERAL SUPERA BMS 5X80X120," 12,713 "
-28004165,HC IMP STENT PERIPHERAL SUPERA BMS 5X120X120," 12,713 "
-28004166,HC IMP STENT PERIPHERAL SUPERA BMS 6X40X120," 12,713 "
-28004167,HC IMP STENT PERIPHERAL SUPERA BMS 6X80X120," 12,713 "
-28004168,HC IMP STENT PERIPHERAL SUPERA BMS 6X120X120," 12,713 "
-28004169,HC IMP STENT PERIPHERAL SUPERA BMS 7X60X120," 12,713 "
-28004170,HC IMP STENT PERIPHERAL SUPERA BMS 7X120X120," 12,713 "
-28004171,HC IMP LEAD VENTRICULAR 72CM," 33,600 "
-28004172,HC IMPL LENS IOL POSTERIOR CHAMBER CRYSTALENS 13.0D," 8,075 "
-28004173,HC IMPL LENS IOL TECNIS ACRYLIC 20.5D," 1,275 "
-28004174,HC IMPL LENS IOL POSTERIOR CHAMBER CRYSTALENS 14.5D," 8,075 "
-28004175,HC IMPL LENS IOL POSTERIOR CHAMBER CRYSTALENS 22.5D," 8,075 "
-28004176,HC IMPL LENS IOL POSTERIOR CHAMBER CRYSTALENS 23.5D," 8,075 "
-28004177,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MA60MA 1.0D," 1,054 "
-28004178,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MA60MA 5.0D," 1,054 "
-28004179,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MA60MA 3.0D," 1,054 "
-28004180,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MA60MA 4.0D," 1,054 "
-28004181,HC IMPL LENS IOL ACRYSOF IQ TORIC 18.0D," 4,208 "
-28004182,HC IMPL LENS IOL ACRYSOF IQ TORIC 18.5D," 4,208 "
-28004183,HC  CATHETER BALLOON EMERGE PUSH RX 1.2X8," 2,117 "
-28004184,HC  CATHETER BALLOON EMERGE RX 1.2X8," 2,117 "
-28004185,HC  CATHETER BALLOON EMERGE PUSH RX 1.5X8," 2,117 "
-28004186,HC  CATHETER BALLOON EMERGE RX 1.5X8," 2,117 "
-28004187,HC  CATHETER BALLOON EMERGE RX 2X8," 2,117 "
-28004188,HC  CATHETER BALLOON EMERGE RX 2.25X8," 2,117 "
-28004189,HC  CATHETER BALLOON EMERGE RX 2.5X8," 2,117 "
-28004190,HC  CATHETER BALLOON EMERGE RX 2.75X8," 2,117 "
-28004191,HC  CATHETER BALLOON EMERGE RX 3X8," 2,117 "
-28004192,HC  CATHETER BALLOON EMERGE RX 3.25X8," 2,117 "
-28004193,HC  CATHETER BALLOON EMERGE RX 3.5X8," 2,117 "
-28004194,HC  CATHETER BALLOON EMERGE RX 3.75X8," 2,117 "
-28004195,HC  CATHETER BALLOON EMERGE RX 4X8," 2,117 "
-28004196,HC  CATHETER BALLOON EMERGE PUSH RX 1.2X12," 2,117 "
-28004197,HC  CATHETER BALLOON EMERGE RX 1.2X12," 2,117 "
-28004198,HC  CATHETER BALLOON EMERGE PUSH RX 1.5X12," 2,117 "
-28004199,HC  CATHETER BALLOON EMERGE RX 1.5X12," 2,117 "
-28004200,HC  CATHETER BALLOON EMERGE RX 2X12," 2,117 "
-28004201,HC  CATHETER BALLOON EMERGE RX 2.25X12," 2,117 "
-28004202,HC  CATHETER BALLOON EMERGE RX 2.5X12," 2,117 "
-28004203,HC  CATHETER BALLOON EMERGE RX 2.75X12," 2,117 "
-28004204,HC  CATHETER BALLOON EMERGE RX 3X12," 2,117 "
-28004205,HC  CATHETER BALLOON EMERGE RX 3.25X12," 2,117 "
-28004206,HC  CATHETER BALLOON EMERGE RX 3.5X12," 2,117 "
-28004207,HC  CATHETER BALLOON EMERGE RX 3.75X12," 2,117 "
-28004208,HC  CATHETER BALLOON EMERGE RX 4X12," 2,117 "
-28004209,HC  CATHETER BALLOON EMERGE PUSH RX 1.2X15," 2,117 "
-28004210,HC  CATHETER BALLOON EMERGE RX 1.2X15," 2,117 "
-28004211,HC  CATHETER BALLOON EMERGE PUSH RX 1.5X15," 2,117 "
-28004212,HC  CATHETER BALLOON EMERGE RX 1.5X15," 2,117 "
-28004213,HC  CATHETER BALLOON EMERGE RX 2X15," 2,117 "
-28004214,HC  CATHETER BALLOON EMERGE RX 2.25X15," 2,117 "
-28004215,HC  CATHETER BALLOON EMERGE RX 2.5X15," 2,117 "
-28004216,HC  CATHETER BALLOON EMERGE RX 2.75X15," 2,117 "
-28004217,HC  CATHETER BALLOON EMERGE RX 3X15," 2,117 "
-28004218,HC  CATHETER BALLOON EMERGE RX 3.25X15," 2,117 "
-28004219,HC  CATHETER BALLOON EMERGE RX 3.5X15," 2,117 "
-28004220,HC  CATHETER BALLOON EMERGE RX 3.75X15," 2,117 "
-28004221,HC  CATHETER BALLOON EMERGE RX 4X15," 2,117 "
-28004222,HC  CATHETER BALLOON EMERGE PUSH RX 1.2X20," 2,117 "
-28004223,HC  CATHETER BALLOON EMERGE RX 1.2X20," 2,117 "
-28004224,HC  CATHETER BALLOON EMERGE PUSH RX 1.5X20," 2,117 "
-28004225,HC  CATHETER BALLOON EMERGE RX 1.5X20," 2,117 "
-28004226,HC  CATHETER BALLOON EMERGE RX 2X20," 2,117 "
-28004227,HC  CATHETER BALLOON EMERGE RX 2.25X20," 2,117 "
-28004228,HC  CATHETER BALLOON EMERGE RX 2.5X20," 2,117 "
-28004229,HC  CATHETER BALLOON EMERGE RX 2.75X20," 2,117 "
-28004230,HC  CATHETER BALLOON EMERGE RX 3X20," 2,117 "
-28004231,HC  CATHETER BALLOON EMERGE RX 3.25X20," 2,117 "
-28004232,HC  CATHETER BALLOON EMERGE RX 3.5X20," 2,117 "
-28004233,HC  CATHETER BALLOON EMERGE RX 3.75X20," 2,117 "
-28004234,HC  CATHETER BALLOON EMERGE RX 4X20," 2,117 "
-28004235,HC  CATHETER BALLOON EMERGE RX 2X30," 2,117 "
-28004236,HC  CATHETER BALLOON EMERGE RX 2.25X30," 2,117 "
-28004237,HC  CATHETER BALLOON EMERGE RX 2.5X30," 2,117 "
-28004238,HC  CATHETER BALLOON EMERGE RX 2.75X30," 2,117 "
-28004239,HC  CATHETER BALLOON EMERGE RX 3X30," 2,117 "
-28004240,HC  CATHETER BALLOON EMERGE RX 3.25X30," 2,117 "
-28004241,HC  CATHETER BALLOON EMERGE RX 3.5X30," 2,117 "
-28004242,HC  CATHETER BALLOON EMERGE RX 3.75X30," 2,117 "
-28004243,HC  CATHETER BALLOON EMERGE RX 4X30," 2,117 "
-28004244,HC  CATHETER BALLOON EMERGE PUSH OTW 1.2X8," 2,117 "
-28004245,HC  CATHETER BALLOON EMERGE OTW 1.2X8," 2,117 "
-28004246,HC  CATHETER BALLOON EMERGE PUSH OTW 1.5X8," 2,117 "
-28004247,HC  CATHETER BALLOON EMERGE OTW 1.5X8," 2,117 "
-28004248,HC  CATHETER BALLOON EMERGE OTW 2X8," 2,117 "
-28004249,HC  CATHETER BALLOON EMERGE OTW 2.25X8," 2,117 "
-28004250,HC  CATHETER BALLOON EMERGE OTW 2.5X8," 2,117 "
-28004251,HC  CATHETER BALLOON EMERGE OTW 2.75X8," 2,117 "
-28004252,HC  CATHETER BALLOON EMERGE OTW 3X8," 2,117 "
-28004253,HC  CATHETER BALLOON EMERGE OTW 3.25X8," 2,117 "
-28004254,HC  CATHETER BALLOON EMERGE OTW 3.5X8," 2,117 "
-28004255,HC  CATHETER BALLOON EMERGE OTW 3.75X8," 2,117 "
-28004256,HC IMP CLIP ANEURYSM SIDEWARD ANGLE KEES SLIMLINE 9MM," 3,987 "
-28004257,HC  CATHETER BALLOON EMERGE OTW 4X8," 2,117 "
-28004258,HC  CATHETER BALLOON EMERGE PUSH OTW 1.2X12," 2,117 "
-28004259,HC  CATHETER BALLOON EMERGE OTW 1.2X12," 2,117 "
-28004260,HC  CATHETER BALLOON EMERGE PUSH OTW 1.5X12," 2,117 "
-28004261,HC  CATHETER BALLOON EMERGE OTW 1.5X12," 2,117 "
-28004262,HC  CATHETER BALLOON EMERGE OTW 2X12," 2,117 "
-28004263,HC  CATHETER BALLOON EMERGE OTW 2.25X12," 2,117 "
-28004264,HC  CATHETER BALLOON EMERGE OTW 2.5X12," 2,117 "
-28004265,HC  CATHETER BALLOON EMERGE OTW 2.75X12," 2,117 "
-28004266,HC  CATHETER BALLOON EMERGE OTW 3X12," 2,117 "
-28004267,HC  CATHETER BALLOON EMERGE OTW 3.25X12," 2,117 "
-28004268,HC  CATHETER BALLOON EMERGE OTW 3.5X12," 2,117 "
-28004269,HC  CATHETER BALLOON EMERGE OTW 3.75X12," 2,117 "
-28004270,HC  CATHETER BALLOON EMERGE OTW 4X12," 2,117 "
-28004271,HC  CATHETER BALLOON EMERGE PUSH OTW 1.2X15," 2,117 "
-28004272,HC  CATHETER BALLOON EMERGE OTW 1.2X15," 2,117 "
-28004273,HC  CATHETER BALLOON EMERGE PUSH OTW 1.5X15," 2,117 "
-28004274,HC  CATHETER BALLOON EMERGE OTW 1.5X15," 2,117 "
-28004275,HC  CATHETER BALLOON EMERGE OTW 2X15," 2,117 "
-28004276,HC  CATHETER BALLOON EMERGE OTW 2.25X15," 2,117 "
-28004277,HC  CATHETER BALLOON EMERGE OTW 2.5X15," 2,117 "
-28004278,HC  CATHETER BALLOON EMERGE OTW 2.75X15," 2,117 "
-28004279,HC  CATHETER BALLOON EMERGE OTW 3X15," 2,117 "
-28004280,HC  CATHETER BALLOON EMERGE OTW 3.25X15," 2,117 "
-28004281,HC  CATHETER BALLOON EMERGE OTW 3.5X15," 2,117 "
-28004282,HC  CATHETER BALLOON EMERGE OTW 3.75X15," 2,117 "
-28004283,HC  CATHETER BALLOON EMERGE OTW 4X15," 2,117 "
-28004284,HC  CATHETER BALLOON EMERGE PUSH OTW 1.2X20," 2,117 "
-28004285,HC  CATHETER BALLOON EMERGE OTW 1.2X20," 2,117 "
-28004286,HC  CATHETER BALLOON EMERGE PUSH OTW 1.5X20," 2,117 "
-28004287,HC  CATHETER BALLOON EMERGE OTW 1.5X20," 2,117 "
-28004288,HC  CATHETER BALLOON EMERGE OTW 2X20," 2,117 "
-28004289,HC  CATHETER BALLOON EMERGE OTW 2.25X20," 2,117 "
-28004290,HC  CATHETER BALLOON EMERGE OTW 2.5X20," 2,117 "
-28004291,HC  CATHETER BALLOON EMERGE OTW 2.75X20," 2,117 "
-28004292,HC  CATHETER BALLOON EMERGE OTW 3X20," 2,117 "
-28004293,HC  CATHETER BALLOON EMERGE OTW 3.25X20," 2,117 "
-28004294,HC  CATHETER BALLOON EMERGE OTW 3.5X20," 2,117 "
-28004295,HC  CATHETER BALLOON EMERGE OTW 3.75X20," 2,117 "
-28004296,HC  CATHETER BALLOON EMERGE OTW 4X20," 2,117 "
-28004297,HC IMP BREAST EXPANDER STYLE 133FV 600ML," 8,963 "
-28004298,HC  CATHETER BALLOON EMERGE OTW 2X30," 2,117 "
-28004299,HC  CATHETER BALLOON EMERGE OTW 2.25X30," 2,117 "
-28004300,HC  CATHETER BALLOON EMERGE OTW 2.5X30," 2,117 "
-28004301,HC  CATHETER BALLOON EMERGE OTW 2.75X30," 2,117 "
-28004302,HC  CATHETER BALLOON EMERGE OTW 3X30," 2,117 "
-28004303,HC  CATHETER BALLOON EMERGE OTW 3.25X30," 2,117 "
-28004304,HC  CATHETER BALLOON EMERGE OTW 3.5X30," 2,117 "
-28004305,HC  CATHETER BALLOON EMERGE OTW 3.75X30," 2,117 "
-28004306,HC  CATHETER BALLOON EMERGE OTW 4X30," 2,117 "
-28004307,HC  CATHETER BALLOON 5F 4MMX60MM," 2,890 "
-28004309,HC IMP BREAST EXPANDER STYLE 133FV 500ML," 8,963 "
-28004310,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 20X56," 21,595 "
-28004311,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 22X74," 21,595 "
-28004312,HC  CATHETER BALLOON PTA 5MMX2CM," 1,488 "
-28004313,HC  INTROD MICROP SIMPLICITY SET, 366 
-28004314,HC  CATHETER DRAINAGE 12FX20CM, 757 
-28004315,HC  CATHETER THERMOCOOL F CURVE," 15,000 "
-28004316,HC  SHEATH GUIDING HIGH FLEX 6FX55CM, 519 
-28004317,HC IMP STENT GRAFT AAA EXCLUDER 26X14X16," 64,968 "
-28004318,HC  CATHETER BALLOON PTA 9MMX4CM 80CM," 1,488 "
-28004319,HC  RETRIEVER STENT TREVO PROVUE," 45,468 "
-28004320,HC IMP OCCLUDER AMPLATZ ASD 6F 07MM," 35,100 "
-28004321,HC IMP OCCLUDER AMPLATZ ASD 7F 11MM," 35,100 "
-28004324,HC IMP STENT GRAFT AAA ENDURANT II 25X13X166," 64,838 "
-28004325,HC IMP STENT GRAFT AAA ENDURANT II 16X10X124," 34,825 "
-28004326,HC CATHETER VISIONS PV DIGITAL IVUS 35IN," 7,646 "
-28004328,HC IMP STENT GRAFT AAA ENDURANT II 16X20X156," 41,600 "
-28004329,HC IMP STENT GRAFT AAA ENDURANT 25X25X49," 29,225 "
-28004330,HC IMP STENT GRAFT AAA ENDURANT II 16X20X124," 34,825 "
-28004331,HC IMP STENT GRAFT AAA ENDURANT II 32X16X166," 64,838 "
-28004332,HC IMP STENT GRAFT AAA ENDURANT II 16X13X156," 41,600 "
-28004333,HC  CATHETER BALLOON PTA .035IN 5MMX6CM," 1,488 "
-28004334,HC  CATHETER GUIDING CONVEY 5F, 468 
-28004335,HC  GUIDEWIRE ASAHI SION 180CM," 1,275 "
-28004336,HC  GUIDEWIRE ASAHI SION 300CM," 1,275 "
-28004337,HC  GUIDEWIRE BMW ELITE 190CM, 697 
-28004338,HC  GUIDEWIRE BMW ELITE 300CM, 697 
-28004339,HC  CATHETER PRIORITY ONE ASPIRATION 6F," 3,315 "
-28004340,HC IMP PORT MINI DIGNITY CT SILI 5F," 2,831 "
-28004341,HC  CATHETER THERMOCOOL SF D-F CRV 8F," 18,900 "
-28004342,HC  CATHETER THERMOCOOL SF F-F CRV 8F," 18,900 "
-28004343,HC  CATHETER THERMOCOOL SF F-J CRV 8F," 18,900 "
-28004346,HC IMP STENT GRAFT ICAST 5X59X120," 18,750 "
-28004347,HC IMP ICD OTHER PROTECA MR DF4," 91,500 "
-28004348,HC  CATHETER MICRO SUREFIRE HI FLOW LEFT," 20,650 "
-28004349,HC IMP STENT ZILVER PTX DES 6X80X125," 13,463 "
-28004350,HC IMP STENT ZILVER PTX DES 7X80X125," 13,463 "
-28004351,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 12FX28," 2,125 "
-28004352,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 14FX28," 2,125 "
-28004353,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 16FX28," 2,125 "
-28004354,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 18FX28," 2,508 "
-28004355,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 20FX28," 2,508 "
-28004356,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 22FX28," 2,763 "
-28004357,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 24FX28," 2,763 "
-28004358,HC  INTRODUCER SHEATH DRYSEAL HYDRPHLC 26FX28," 2,763 "
-28004359,HC  CATHETER GUIDING EBU4D 8F, 451 
-28004360,HC  CATHETER PTA SHAFT 10MMX60MM 80CM," 1,488 "
-28004361,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 13X56," 21,595 "
-28004362,HC IMP ICD SINGLE FORTIFY ASSURA VR," 65,000 "
-28004363,HC  CATHETER BALLOON PTA 4MMX10CM," 2,040 "
-28004364,HC  GUIDEWIRE 35IN 300CM, 306 
-28004365,HC  GUIDEWIRE AMPLATZ 35IN 80CM," 20,993 "
-28004366,HC  SNARE GOOSENECK 15MM 6 F," 2,338 "
-28004367,HC  INTRODUCER PEEL AWAY 9FX13CM, 191 
-28004368,HC IMP PORT POWERPORT MRI DUO 9.5F KIT," 4,973 "
-28004369,HC  CATHETER BALLOON PTA 5MMX8CM PTA," 1,488 "
-28004370,HC  CATHETER GUIDING CONVEY 5F AL1, 468 
-28004371,HC IMP PORT POWERPORT CLEARVUE 8 FR," 2,975 "
-28004372,HC  CATHETER BALLOON PTA LP 5MMX14CM," 1,445 "
-28004373,HC  CATHETER BALLOON Z-MED22MMX4CM," 5,062 "
-28004374,HC IMP STENT GRAFT TIPS VIATORR 12X4X2," 27,300 "
-28004375,HC IMP STENT GRAFT TIPS VIATORR 12X6X2," 27,300 "
-28004376,HC IMP STENT GRAFT TIPS VIATORR 12X8X2," 27,300 "
-28004377,HC IMP STENT GRAFT AAA ENDURANT II 16X16X93," 32,550 "
-28004378,HC IMP STENT GRAFT AAA ENDURANT II 28X16X145," 64,838 "
-28004379,HC IMP STENT GRAFT AAA ENDURANT II 16X13X124," 34,825 "
-28004380,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.25X8," 13,125 "
-28004381,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.25X12," 13,125 "
-28004382,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.25X15," 13,125 "
-28004383,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.25X18," 13,125 "
-28004384,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.25X23," 13,125 "
-28004385,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.25X28," 13,125 "
-28004386,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X8," 13,125 "
-28004387,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X12," 13,125 "
-28004388,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X15," 13,125 "
-28004389,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X18," 13,125 "
-28004390,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X23," 13,125 "
-28004391,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X28," 13,125 "
-28004392,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X33," 13,688 "
-28004396,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X12," 13,125 "
-28004397,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X15," 13,125 "
-28004398,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X18," 13,125 "
-28004399,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X23," 13,125 "
-28004400,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X28," 13,125 "
-28004401,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X33," 13,688 "
-28004402,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X38," 13,688 "
-28004403,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X8," 13,125 "
-28004404,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X12," 13,125 "
-28004405,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X15," 13,125 "
-28004406,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X18," 13,125 "
-28004407,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X23," 13,125 "
-28004408,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X28," 13,125 "
-28004409,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X33," 13,688 "
-28004410,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3X38," 13,688 "
-28004411,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X8," 13,125 "
-28004412,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X12," 13,125 "
-28004413,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X15," 13,125 "
-28004414,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X18," 13,125 "
-28004415,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X23," 13,125 "
-28004416,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X28," 13,125 "
-28004417,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X33," 13,688 "
-28004418,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.25X38," 13,688 "
-28004419,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X8," 13,125 "
-28004420,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X12," 13,125 "
-28004421,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X15," 13,125 "
-28004422,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X18," 13,125 "
-28004423,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X23," 13,125 "
-28004424,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X28," 13,125 "
-28004425,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X33," 13,688 "
-28004426,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 3.5X38," 13,688 "
-28004427,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X8," 13,125 "
-28004428,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X12," 13,125 "
-28004429,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X15," 13,125 "
-28004430,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X18," 13,125 "
-28004431,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X23," 13,125 "
-28004432,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X28," 13,125 "
-28004433,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X33," 13,688 "
-28004434,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 4X38," 13,688 "
-28004435,HC  CATHETER GUIDING CONVEY 5F JL4, 468 
-28004436,HC  CATHETER DILATATION 4MMX6CM 5.3F," 1,679 "
-28004443,HC  CATHETER DILATATION PTA SYNERGY 5.3X5X2X75CM," 1,677 "
-28004444,HC  GUIDEWIRE FLEXIBLE TIP 70D 16IN," 2,465 "
-28004445,HC IMPL PLATE T BUTTRESS 4 HOLE 80MM," 2,924 "
-28004446,HC IMP ANCHOR SUTURE QUICKANCHOR PLUS MINI SZ2," 3,326 "
-28004447,HC FORCEP BIOPSY MYOCARDIAL 3F 60CM," 4,922 "
-28004449,HC IMPL COIL PERIPHERAL EMBOLIZATION 3.2LOOP 5MM 5CM, 666 
-28004450,HC IMPL COIL PERIPHERAL EMBOLIZATION .038IN 8MM 5CM, 231 
-28004451,HC  INTRODUCER CATHETER PEEL AWAY 11FX15.5CM, 324 
-28004452,HC  INTRODUCER PEEL AWAY 13FX5X30CM, 494 
-28004453,HC  GUIDEWIRE COPE MANDRIL 18X60CM, 272 
-28004455,HC  CATHETER DILATATION 3X80CM 6.3F, 425 
-28004456,HC  GUIDEWIRE PERIPHERAL 35IN 260CM 1.5MM, 147 
-28004457,HC  GUIDEWIRE CURVED TIP 145CM 18IN 3MM, 140 
-28004458,HC  GUIDEWIRE FIXED CORE STRAIGHT SS 18IN 145CM, 130 
-28004459,HC IMPL COIL HELICAL 100CM 20MM 0.018IN, 629 
-28004460,HC  CATHETER MALECOT 16F, 173 
-28004461,HC  CATHETER MALECOT 14F, 173 
-28004462,HC  CATHETER MALECOT 12F, 173 
-28004464,HC  RETRIEVER STENT SOEHENDRA 8.5F, 973 
-28004465,HC  GUIDEWIRE AMPLATZ SUPER STIFF," 1,108 "
-28004466,HC IMP SCLERAL TIRE STYLE 287, 146 
-28004469,HC IMP PIN STEINMAN 3/16X9IN, 202 
-28004470,HC IMP PIN RUSH 4.8X318MM, 706 
-28004471,HC IMP PIN RUSH 4.8X337MM, 706 
-28004472,HC IMP SHEETING SILASTIC NONREINFORCED .005X6X8 SILC, 544 
-28004473,HC IMP BAND FALOPE RING, 593 
-28004478,HC IMP WASHER SPIKED 13.5X6MM," 1,182 "
-28004479,HC  CATHETER GUIDING VISTA BRITE TIP 8F HS 55, 512 
-28004480,HC IMP PIN RUSH 4.8X279MM, 706 
-28004481,HC IMP SPACER TENDON HUNTER 4MMX24CM," 9,285 "
-28004482,HC IMP SPACER TENDON HUNTER 5MMX24CM," 10,028 "
-28004483,HC IMPL SCREW DHS LAG 55MM," 2,610 "
-28004484,HC IMPL SCREW DHS LAG 65MM," 3,125 "
-28004485,HC IMPL SCREW DHS LAG 105MM," 3,171 "
-28004486,HC IMPL SCREW DHS LAG 110MM," 3,125 "
-28004487,HC IMPL SCREW DHS LAG 115MM," 3,171 "
-28004488,HC IMPL PLATE 140DEG 4 HOLE," 4,743 "
-28004489,HC IMPL PLATE 150DEG 5 HOLE," 4,114 "
-28004490,HC IMP SCREW CANCELLOUS FULLY THREAD SS 55MM, 298 
-28004491,HC IMP SCREW CANCELLOUS FULLY THREAD SS 60MM, 327 
-28004492,HC IMP SCREW BONE FULLY THREAD 4.0X30MM, 168 
-28004493,HC IMPL SCREW CANNULATED LARGE 16MMX45," 2,355 "
-28004494,HC IMPL SCREW CANNULATED LARGE 16MMX50," 1,938 "
-28004495,HC IMPL SCREW CANNULATED LARGE 16MMX55," 2,505 "
-28004496,HC IMPL SCREW CANNULATED LARGE 16MMX60," 2,261 "
-28004497,HC IMPL SCREW CANNULATED LARGE 16MMX65," 2,505 "
-28004498,HC IMPL SCREW CANNULATED LARGE 16MMX70," 2,420 "
-28004499,HC IMPL SCREW CANNULATED LARGE 16MMX105," 2,505 "
-28004500,HC IMPL SCREW CANNULATED LARGE 16MMX110," 2,542 "
-28004501,HC IMPL SCREW CANNULATED LARGE 16MMX115," 2,420 "
-28004502,HC IMPL SCREW CANNULATED LARGE 16MMX120," 2,420 "
-28004503,HC IMPL SCREW CANNULATED LARGE 16MMX125," 2,420 "
-28004504,HC IMPL SCREW CANNULATED LARGE 16MMX130," 2,091 "
-28004505,HC IMPL SCREW CANNULATED LARGE 45MM," 2,091 "
-28004506,HC IMPL SCREW CANNULATED LARGE 145MM," 2,091 "
-28004507,HC IMPL SCREW CANNULATED LARGE 55MM," 2,420 "
-28004508,HC IMPL SCREW CANNULATED LARGE 60MM," 2,505 "
-28004509,HC IMPL SCREW CANNULATED LARGE 65MM," 2,505 "
-28004510,HC IMPL SCREW CANNULATED LARGE 70MM," 2,505 "
-28004511,HC IMPL SCREW CANNULATED LARGE 75MM," 2,505 "
-28004512,HC IMPL SCREW CANNULATED LARGE 100MM," 2,542 "
-28004513,HC IMPL SCREW CANNULATED LARGE 105MM," 2,420 "
-28004514,HC IMPL SCREW CANNULATED LARGE 115MM," 2,091 "
-28004515,HC IMPL SCREW CANNULATED LARGE 120MM," 2,091 "
-28004516,HC IMPL SCREW CANNULATED LARGE 125MM," 2,091 "
-28004517,HC IMPL SCREW CANNULATED LARGE 130MM," 2,091 "
-28004518,HC IMPL SCREW CANNULATED SHORT THREAD 10MM," 1,811 "
-28004519,HC IMPL SCREW CANNULATED SHORT THREAD 12MM," 1,811 "
-28004520,HC IMPL SCREW CANNULATED SHORT THREAD 14MM," 1,811 "
-28004521,HC IMPL SCREW CANNULATED SHORT THREAD 16MM," 1,811 "
-28004522,HC IMPL SCREW CANNULATED SHORT THREAD 18MM," 1,811 "
-28004523,HC IMPL SCREW CANNULATED SHORT THREAD 20MM," 1,837 "
-28004524,HC IMPL SCREW CANNULATED SHORT THREAD 22MM," 1,902 "
-28004525,HC IMPL SCREW CANNULATED SHORT THREAD 24MM," 1,717 "
-28004526,HC IMPL SCREW CANNULATED SHORT THREAD 26MM," 1,902 "
-28004527,HC IMPL SCREW CANNULATED SHORT THREAD 28MM," 1,837 "
-28004528,HC IMPL SCREW CANNULATED SHORT THREAD 30MM," 1,837 "
-28004529,HC IMPL SCREW CANNULATED SHORT THREAD 48MM," 1,837 "
-28004530,HC IMPL SCREW CANNULATED LONG THREAD 16MM," 1,590 "
-28004531,HC IMPL SCREW CANNULATED LONG THREAD 18MM," 1,590 "
-28004532,HC IMPL SCREW CANNULATED LONG THREAD 20MM," 1,590 "
-28004533,HC IMPL SCREW CANNULATED LONG THREAD 22MM," 1,837 "
-28004534,HC IMPL SCREW CANNULATED LONG THREAD 24MM," 1,590 "
-28004535,HC IMPL SCREW CANNULATED LONG THREAD 26MM," 1,837 "
-28004536,HC IMPL SCREW CANNULATED LONG THREAD 28MM," 1,590 "
-28004537,HC IMPL SCREW CANNULATED LONG THREAD 30MM," 1,837 "
-28004538,HC IMPL SCREW CANNULATED LONG THREAD 32MM," 1,802 "
-28004539,HC IMPL SCREW CANNULATED LONG THREAD 34MM," 1,837 "
-28004540,HC IMPL SCREW CANNULATED LONG THREAD 36MM," 1,837 "
-28004541,HC IMPL SCREW CANNULATED LONG THREAD 38MM," 1,837 "
-28004542,HC IMPL SCREW CANNULATED LONG THREAD 44MM," 1,902 "
-28004543,HC IMPL SCREW CANNULATED LONG THREAD 46MM," 1,902 "
-28004544,HC IMPL SCREW CANNULATED LONG THREAD 48MM," 1,902 "
-28004545,HC IMPL SCREW CANNULATED LONG THREAD 50MM," 1,902 "
-28004546,HC IMP STAPLE OSTEOTOMY OFFSET 5X26.5X23MM," 1,828 "
-28004547,HC IMP STAPLE OSTEOTOMY OFFSET 10X21.5X23MM," 1,573 "
-28004548,HC IMP STAPLE FIXATION ACL LONG SMALL," 1,182 "
-28004549,HC IMP STAPLE BLOUNT EPIPHYSEAL 10X22," 1,410 "
-28004550,HC IMP STAPLE BLOUNT EPIPHYSEAL 22X22," 1,410 "
-28004551,HC IMPL SCREW CANNULATED SHORT THREAD 44MM," 1,902 "
-28004552,HC IMPL COIL PERIPHERAL EMBOLIZATION 4.3LOOP 3MM 4CM, 462 
-28004553,HC IMPL COIL PERIPHERAL EMBOLIZATION COMPLEX 2X10X5MM, 558 
-28004554,HC IMPL COIL PERIPHERAL EMBOLIZATION COMPLEX 3X30X6MM, 493 
-28004555,HC IMP SHEETING SILASTIC REINFORCED .020X6X8 SILIC," 1,233 "
-28004556,HC IMP SHEETING SILASTIC REINFORCED 6X8X0.04 SILAS," 1,275 "
-28004558,HC IMP CLIP ANEURYSM CURVED SUNDT 4.5MM," 3,732 "
-28004559,HC LASER FIBER HOLMIUM 30D," 2,508 "
-28004561,HC IMP PENILE 12CMX11 13MM," 25,200 "
-28004562,HC  CUFF URINARY SPHINCTER 5.5CM," 20,524 "
-28004563,HC  CUFF URINARY SPHINCTER 6CM," 14,250 "
-28004564,HC  CUFF URINARY SPHINCTER 6.5CM," 14,250 "
-28004565,HC  CUFF URINARY SPHINCTER 7CM," 18,563 "
-28004566,HC  CUFF URINARY SPHINCTER 7.5CM," 14,250 "
-28004567,HC  CUFF URINARY SPHINCTER 8CM," 14,250 "
-28004568,HC  CUFF URINARY SPHINCTER 9CM," 14,250 "
-28004569,HC  CUFF URINARY SPHINCTER 10CM," 14,250 "
-28004570,HC  CUFF URINARY SPHINCTER 11CM," 14,250 "
-28004571,HC  BALLOON URINARY SPHINCTER 51-60CM," 11,738 "
-28004572,HC  BALLOON URINARY SPHINCTER 81-90CM," 12,870 "
-28004573,HC IMP SCREW BONE SELF TAPPING 4.0X35MM, 170 
-28004575,HC IMP EYE SPHERE ORBITAL MEDP 18MM," 4,420 "
-28004576,HC IMP EYE SPHERE HYDROXAPETITE 20MM," 7,800 "
-28004577,HC  GUIDEWIRE FIXED CORE 260CM 0.035IN 3MM, 164 
-28004580,HC IMP CLIP ANEURYSM STRAIGHT KEES SLIMLINE 7MM," 3,307 "
-28004581,HC IMP CLIP ANEURYSM FEN STRAIGHT KEES SLIMLINE 4X9MM," 3,086 "
-28004582,HC IMP CLIP ANEURYSM FEN STRAIGHT KEES SLIMLINE 4X12MM," 3,451 "
-28004586,HC IMP BLOCK CORNEAL TEFLON 18MM, 227 
-28004587,HC BASKET RETRIEVAL STONE 8.5X50CM," 3,481 "
-28004589,HC BASKET STONE RETRIEVAL 4X5CM," 2,170 "
-28004590,HC  PORT A CATH PAS PORT," 4,463 "
-28004591,HC IMP SHEETING SILASTIC NONREINFORCED .020X6X8 SILIC, 621 
-28004592,HC IMP EYE SPHERE ORBITAL SILICONE 18MM, 298 
-28004593,HC IMP EYE SPHERE ORBITAL SILICONE 16MM, 298 
-28004594,HC IMPL PACEMAKER DUAL ELITE II 7086," 33,950 "
-28004596,HC WRIST JACK FIXATION KIT," 10,763 "
-28004597,HC IMP BAERVELDT 250MM," 5,980 "
-28004598,HC IMP GLAUCOMA BAERVELDT 350MM," 5,980 "
-28004599,HC OXYGENATOR AFFINITY," 2,295 "
-28004600,HC IMPL PLATE T BUTTRESS RIGHT 7 HOLE 149," 6,163 "
-28004601,HC IMP SCREW CORTEX 3.5X30MM, 216 
-28004602,HC IMP SCREW CORTEX 3.5X34MM, 216 
-28004603,HC IMP SCREW CORTEX 3.5X 38MM, 216 
-28004604,HC IMP SCREW BONE FULLY THREAD 4.0X35MM, 172 
-28004606,HC  SNARE GOOSENECK 120CM 5MM 4F," 2,338 "
-28004607,HC IMP SILASTIC BALLS 12MM, 204 
-28004608,HC IMP SILASTIC BALLS 14MM, 204 
-28004610,HC IMP CONFORMER OCULAR MEDIUM 23MM, 298 
-28004615,HC IMPL PLATE MAXILLOFACIAL CHIN 6MM," 2,040 "
-28004616,HC IMPL PLATE MAXILLOFACIAL CHIN 8MM," 2,049 "
-28004617,HC IMPL PLATE MAXILLOFACIAL CHIN 10MM," 2,049 "
-28004618,HC IMP BUR HOLE COVER LARGE 1.2MM," 1,329 "
-28004619,HC IMP SCREW CORTEX TITANIUM 1.5MM 8MM, 366 
-28004620,HC IMP SCREW CORTEX TITANIUM 1.5MM 9MM, 366 
-28004621,HC IMP SCREW CORTEX TITANIUM 2MM 12MM, 334 
-28004622,HC IMP SCREW CORTEX TITANIUM 2MM 16MM, 334 
-28004623,HC IMPL SCREW CORTEX TITANIUM 2.7X12MM, 366 
-28004624,HC IMPL SCREW CORTEX TITANIUM 2.7X14MM, 366 
-28004625,HC IMPL SCREW CORTEX TITANIUM 2.7 20MM, 366 
-28004626,HC IMPL SCREW CORTEX TITANIUM 2.7X24MM, 297 
-28004627,HC IMP WASHER TITANIUM 7MM, 243 
-28004628,HC IMP BONE CORT FEMUR 16MM," 5,483 "
-28004629,HC  CATHETER SPINNAKER ELITE FLOW DIRECTED 166X15CM," 7,231 "
-28004630,HC IMPL PACEMAKER SINGLE SOLUS II," 29,400 "
-28004631,HC  CATHETER FOGARTY ARTERIAL EMBOLECTOMY 2F, 638 
-28004632,HC  CATHETER FOGARTY VEN THROMBECTOMY 8FX80CM, 707 
-28004633,HC  CATHETER FOGARTY VEN THROMBECTOMY 8FX10CM, 707 
-28004634,HC  CATHETER FOGARTY OCCLUSION 8-14F, 689 
-28004636,HC IMPL SCREW STEREOTX FIX 2 SHORT 2 LONG, 325 
-28004637,HC IMP VALVE CSF DELTA SMALL LEVEL 1.5," 10,280 "
-28004638,HC  CATHETER NEPHROSTOMY REG 25CM 10F, 655 
-28004639,HC IMP PATELLA WEDGE 18-18MM," 8,130 "
-28004642,HC  CATHETER ANGIOGR SOFT VU HEADHUN 1 5X100CM, 158 
-28004643,HC  GUIDEWIRE PERIPHERAL 35IN 75CM 6MM, 243 
-28004644,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 26MMX30CM," 4,182 "
-28004645,HC IMP GRAFT HEMASHIELD WOVEN STRAIGH 30MMX30CM," 4,182 "
-28004646,HC IMP GRAFT HEMASHIELD WOVEN STRAIGH 34MMX30CM," 4,182 "
-28004647,HC CATHETER ANGIOGRAPHY FLUSH 65CM 4F, 135 
-28004648,HC CATHETER ANGIOGRAPHY FLUSH 90CM 4F, 127 
-28004649,HC  CATHETER PTCA 2CM 1.5X20MM," 2,253 "
-28004650,HC  CATHETER BALLOON ACHALASIA 30," 6,707 "
-28004651,HC IMP GRAFT HEMASHIELD WOVEN STRAIGH 38MMX30CM," 4,182 "
-28004652,HC IMP GRAFT HEMASHIELD WOVEN ST 26X15," 3,086 "
-28004653,HC IMP GRAFT HEMASHIELD WOVEN ST 30X15," 3,086 "
-28004654,HC IMP GRAFT HEMASHIELD WOVEN ST 34X15," 3,086 "
-28004656,HC IMP EYE SPHERE ORBITAL MEDP 16MM," 4,420 "
-28004657,HC IMP EYE SPHERE HYDROXAPETITE 22MM," 4,420 "
-28004658,HC  INTRODUCER SHEATH HEMOSTASIS 12CM 7F, 196 
-28004659,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X40MM," 1,734 "
-28004660,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X50MM," 1,935 "
-28004661,HC IMP PIN RUSH 2.4X102MM, 492 
-28004662,HC IMP GRAFT HEMASHIELD KNIT BIFURCATED 14X7," 5,355 "
-28004663,HC IMP GRAFT HEMASHIELD KNITTED ST 22X30," 3,570 "
-28004664,HC IMP GRAFT HEMASHIELD KNITTED ST 24X30," 3,570 "
-28004665,HC IMP CABLE SLEEVE DALL MILES GRIP MEDIUM 2," 8,208 "
-28004666,HC IMP CABLE SLEEVE DM 1.6MM," 3,638 "
-28004667,HC IMP VALVE AORTIC MECHANICAL 17MM," 28,049 "
-28004669,HC  SNARE GOOSENECK MICROCATHETER 150X175X2 KIT," 4,888 "
-28004670,HC  SNARE GOOSENECK MICROCATHETER 150X175X4 KIT," 4,888 "
-28004671,HC IMPL PLATE MAXI SAGITAL LONG 4HOLE," 1,988 "
-28004672,HC  CATHETER AMPLATZ GUIDEWIRE 25IN 125CM, 206 
-28004673,HC  GUIDEWIRE ARTHERECTOMY FLOPPY 325X2.8CM," 1,360 "
-28004674,HC  GUIDEWIRE ARTHERECTOMY FLOPPY 325X2.2CM," 1,360 "
-28004675,HC  CATHETER WIREGUIDED CRE 10-12MM," 1,875 "
-28004676,HC  CATHETER WIREGUIDED CRE 12-15MM," 1,875 "
-28004677,HC  CATHETER WIREGUIDED CRE 15-18MM," 1,875 "
-28004679,HC IMP STENT LARYNGEAL CHILD, 808 
-28004680,HC IMP STENT LARYGEAL SMALL ADULT, 893 
-28004681,HC IMP STENT LARYNGEAL MEDIUM ADULT, 893 
-28004682,HC IMP STENT LARYNGEAL ADULT, 918 
-28004683,HC  CATHETER QUINTON MISSOURI LEFT 62.2CM," 1,938 "
-28004684,HC IMP STENT LARYNGEAL KEEL CLEAR 12MM, 680 
-28004685,HC IMP STENT LARYNGEAL KEEL CLEAR 16MM, 782 
-28004686,HC IMP LOOP WIRE HOUSE STAPEDECTOMY SZ 4.5, 315 
-28004687,HC IMP LOOP WIRE HOUSE STAPEDECTOMY SZ 4.75, 315 
-28004688,HC IMP EAR PISTON LOOP MCGE MOD 5.0, 340 
-28004689,HC IMP CABLE SLEEVE DM 2.0MM," 3,825 "
-28004690,HC IMPL PLATE MAXI 4HOLE LONG," 2,220 "
-28004691,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X20MM," 1,598 "
-28004692,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X22MM," 1,598 "
-28004693,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X24MM," 1,870 "
-28004694,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X26MM," 1,935 "
-28004695,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X28MM," 1,870 "
-28004696,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X30MM," 1,870 "
-28004697,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X32MM," 1,935 "
-28004698,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X34MM," 1,734 "
-28004699,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X36MM," 1,935 "
-28004700,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X38MM," 1,598 "
-28004701,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X42MM," 1,870 "
-28004702,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X44MM," 1,870 "
-28004703,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X46MM," 1,935 "
-28004704,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X48MM," 1,935 "
-28004705,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X52MM," 1,935 "
-28004706,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X54MM," 1,598 "
-28004707,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X56MM," 1,935 "
-28004708,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X60MM," 1,870 "
-28004709,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X64MM," 1,598 "
-28004710,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X68MM," 1,598 "
-28004711,HC IMPL SCREW CANNULATED PARTIALLY THREAD 4.5X72MM," 1,935 "
-28004712,HC IMPL PLATE CONDYLAR 60MM 5 HOLE," 3,842 "
-28004713,HC IMPL PLATE CONDYLAR 95DEG 80MM 9HOLE," 4,905 "
-28004714,HC IMPL PLATE CONDYLAR 95DEG 80MM 12HOLE," 5,706 "
-28004715,HC IMPL PLATE CONDYLAR 95DEG 70MM 12HOLE," 5,906 "
-28004716,HC IMPL PLATE CONDYLAR 95DEG 60MM 12HOLE," 5,706 "
-28004717,HC IMPL PLATE CONDYLAR 95DEG 60MM 7HOLE," 4,913 "
-28004718,HC IMPL PLATE CONDYLAR 95DEG 70MM 5HOLE," 4,029 "
-28004719,HC IMPL PLATE OSTEOTOMY ADL 90DEG 40X10MM," 4,872 "
-28004720,HC IMPL PLATE OSTEOTOMY ADL 90DEG 50X15MM," 4,140 "
-28004721,HC IMPL PLATE OSTEOTOMY 50MM 10MM," 4,378 "
-28004722,HC IMPL PLATE OSTEOTOMY 40MM 10MM," 4,670 "
-28004724,HC IMPL PLATE OSTEOTOMY 35MM," 4,114 "
-28004725,HC IMPL PLATE OSTEOTOMY DISPL 25MM 7MM," 3,519 "
-28004726,HC IMPL PLATE OSTEOTOMY DISPL 32MM 7MM," 3,519 "
-28004727,HC IMPL PLATE OSTEOTOMY DISPL 25MM 12MM," 4,208 "
-28004729,HC IMP ROD CARBON 11MMX400MM," 2,773 "
-28004730,HC IMP ROD CARBON 11MMX350MM," 2,521 "
-28004731,HC IMP ROD CARBON 11MMX300MM," 2,521 "
-28004732,HC IMP ROD CARBON 11MMX250MM," 2,521 "
-28004733,HC IMP ROD CARBON 11MMX200MM," 2,354 "
-28004734,HC IMPL SCREW SCHANZ 4.5X150MM, 754 
-28004735,HC IMPL SCREW SCHANZ 4.5X200MM, 918 
-28004736,HC IMPL SCREW CANNULATED 16MMX30," 2,091 "
-28004737,HC IMPL SCREW CANNULATED 16MMX35," 2,091 "
-28004738,HC IMPL PLATE L BUTTRESS LEFT 4 HOLE 86MM," 3,770 "
-28004739,HC IMPL PLATE L BUTTRESS LEFT 4 HOLE 86MM," 3,770 "
-28004740,HC IMP PIN RUSH 2.4X25MM, 473 
-28004741,HC IMP PIN RUSH 2.4X32MM, 315 
-28004742,HC IMP PIN RUSH 2.4X38MM, 306 
-28004743,HC IMP PIN RUSH 2.4X44MM, 315 
-28004744,HC IMP PIN RUSH 2.4X51MM, 315 
-28004745,HC IMP PIN RUSH 3.2X165MM, 706 
-28004746,HC IMP PIN RUSH 3.2X178MM, 774 
-28004747,HC IMP PIN RUSH 3.2X191MM, 510 
-28004748,HC IMP PIN RUSH 4.8X203MM, 895 
-28004749,HC IMP PIN RUSH 4.8X222MM, 647 
-28004750,HC IMP PIN RUSH 4.8X241MM, 706 
-28004751,HC IMP PIN RUSH 4.8X260MM, 672 
-28004752,HC IMP VEIN SAPHENOUS 76CMX4-5MM," 29,170 "
-28004753,HC SHUNT DENVER PERITONEAL HIGH FLOW," 5,313 "
-28004757,HC IMPL SCREW CANNULATED LARGE 80MM," 2,542 "
-28004758,HC IMPL SCREW CANNULATED LARGE 85MM," 2,505 "
-28004761,HC IMP VALVE AORTIC MECHANICAL HEMODYNAMIC PLUS 19MM," 28,889 "
-28004762,HC IMP VALVE AORTIC MECHANICAL HEMODYNAMIC PLUS 21MM," 28,889 "
-28004763,HC STAPLER INTRALUM CIRCULAR 29MM," 2,819 "
-28004765,HC  CATHETER DILATATION PTA BM 5X20X75CM," 2,059 "
-28004766,HC  CATHETER DILATATION PTA BM 7X40X75CM," 2,059 "
-28004767,HC  CATHETER DILATATION PTA BM 8X30X75CM," 2,059 "
-28004768,HC  RETRIEVER STENT SOEHENDRA 11.5F, 973 
-28004770,HC  CATHETER INTRAVASCULAR 0.014IN," 5,112 "
-28004771,HC IMP SHEETING SILASTIC NONREINFORCED .080X6X8 SILIC, 859 
-28004773,HC  SHEATH ARROW FLEX 8F 24CM SET, 284 
-28004774,HC  INTROD ARROW FLEX 6F SET, 137 
-28004776,HC SUTURE ETHIBOND D SPECIAL RB1," 2,484 "
-28004782,HC  CATHETER WIREGUIDED CRE 8-10MM," 1,875 "
-28004783,HC IMP STENT PANCREATIC 5F 3CM SET," 1,012 "
-28004784,HC IMP STENT PANCREATIC 5F 5CM SET," 1,012 "
-28004785,HC IMP BREAST ROUND SALINE SILTEX 200CC," 6,375 "
-28004786,HC IMP BREAST ROUND SALINE SILTEX 225CC," 6,375 "
-28004787,HC IMP BREAST ROUND SALINE SILTEX 250CC," 6,375 "
-28004788,HC IMP BREAST ROUND SALINE SILTEX 275CC," 6,375 "
-28004789,HC IMP BREAST ROUND SALINE SILTEX 300CC," 6,375 "
-28004790,HC IMP BREAST ROUND SALINE SILTEX 325CC," 3,825 "
-28004791,HC IMP BREAST ROUND SALINE SILTEX 375CC," 3,825 "
-28004792,HC IMP BREAST ROUND SALINE SILTEX 425CC," 3,825 "
-28004793,HC IMP BREAST ROUND SALINE SILTEX 475CC," 3,825 "
-28004794,HC IMP BREAST ROUND SILTEX BECKER 50/50 300CC," 15,000 "
-28004795,HC IMP BREAST ROUND SILTEX BECKER 50/50 400CC," 1,700 "
-28004796,HC IMP BREAST ROUND SILTEX BECKER 500CC," 15,000 "
-28004797,HC IMP BREAST SILICONE SPECTRUM 125-150CC," 8,075 "
-28004798,HC IMP BREAST SILICONE SPECTRUM 175-210CC," 7,225 "
-28004799,HC IMP BREAST SILICONE SPECTRUM 225-270CC," 8,288 "
-28004800,HC IMP BREAST SILICONE SPECTRUM 275-330CC," 8,075 "
-28004801,HC IMP BREAST SILICONE SPECTRUM 325-390CC," 8,075 "
-28004802,HC IMP BREAST SILICONE SPECTRUM 375-450CC," 8,075 "
-28004803,HC IMP BREAST SILICONE SPECTRUM 425-510CC," 8,075 "
-28004804,HC IMP BREAST SILICONE SPECTRUM 475-570CC," 8,075 "
-28004805,HC IMP BREAST ROUND SILTEX BECKER 25/75 200CC," 13,500 "
-28004806,HC IMP BREAST ROUND SILTEX BECKER 25/75 250CC," 13,500 "
-28004807,HC IMP BREAST ROUND SILTEX BECKER 25/75 300CC," 7,438 "
-28004808,HC IMP BREAST ROUND SILTEX BECKER 25/75 350CC," 13,500 "
-28004809,HC IMP BREAST ROUND SILTEX BECKER 25/75 400CC," 13,500 "
-28004810,HC IMP BREAST ROUND SILTEX BECKER 25/75 500CC," 13,500 "
-28004811,HC IMP BREAST ROUND SMOOTH RADOVAN 400ML," 3,783 "
-28004812,HC IMP BREAST ROUND SMOOTH RADOVAN 700ML," 3,783 "
-28004813,HC IMP BREAST ROUND SMOOTH RADOVAN 550ML," 3,783 "
-28004814,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 15.0D, 952 
-28004815,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 15.5D, 952 
-28004816,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 16.0D, 952 
-28004817,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 17.5D, 952 
-28004818,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 18.0D, 952 
-28004819,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 19.0D, 952 
-28004820,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 19.5D, 952 
-28004821,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 20.0D, 952 
-28004822,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 20.5D, 952 
-28004823,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 21.0D, 952 
-28004824,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 21.5D, 952 
-28004825,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 22.0D, 952 
-28004826,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 23.0D, 952 
-28004827,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 23.5D, 952 
-28004828,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 24.0D, 952 
-28004829,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 25.0D, 952 
-28004830,HC CATHETER CENTRAL VENOUS TRIPLE LUM TRAY 12X16," 1,261 "
-28004831,HC CATHETER HEMODIALYSIS TRIPLE LUMEN TRAY 13X12, 252 
-28004832,HC PENILE ACCESSORY KIT," 4,505 "
-28004833,HC IMP SYRINGE COLLAGEN IMPLANT," 2,975 "
-28004835,HC  CATHETER GUIDING VISTA BRITE TIP 90CM 78IN 7F, 512 
-28004836,HC IMPL PATCH HEMASHIELD DOUBLE VELOUR WOVEN 2X3," 1,165 "
-28004837,HC IMPL PATCH HEMASHIELD DOUBLE VELOUR WOVEN 2X6," 2,406 "
-28004838,HC IMPL PATCH HEMASHIELD DOUBLE VELOUR WOVEN KNIT 2X3," 1,109 "
-28004839,HC  CATHETER FOGARTY GRAFT THROMBECTOMY 5F," 2,763 "
-28004840,HC  CATHETER FOGARTY GRAFT THROMBECTOMY 6F," 2,763 "
-28004841,HC  CATHETER FOGARTY ADHERENT CLOT 4-6," 1,658 "
-28004842,HC  CATHETER FOGARTY ADHERENT CLOT 5-8," 1,658 "
-28004843,HC  CATHETER FOGARTY ADHERENT CLOT 6-10," 1,658 "
-28004844,HC IMP VALVE AORTIC MECHANICAL 31MM," 27,265 "
-28004845,HC IMP EAR PROSTHESIS PISTON SHORT 6X3.5MM," 1,190 "
-28004846,HC IMP EAR PROSTHESIS PISTON SHORT 6X4.0MM," 2,346 "
-28004847,HC IMP EAR PROSTHESIS PISTON SHORT 6X4.5MM," 1,058 "
-28004848,HC IMP EAR PROSTHESIS PISTON LONG 6X4.0MM," 1,058 "
-28004849,HC IMP EAR PROSTHESIS PISTON LONG 6X4.5MM," 1,216 "
-28004850,HC IMP EAR PROSTHESIS PISTON LONG 6X5.0MM," 1,190 "
-28004851,HC IMP EAR PROSTHESIS PISTON SHORT 8X3.5MM, 638 
-28004852,HC IMP EAR PROSTHESIS PISTON SHORT 8X4.0MM, 723 
-28004853,HC IMP EAR PROSTHESIS PISTON LONG 8X3.5MM," 1,190 "
-28004854,HC IMP EAR PROSTHESIS PISTON LONG 8X4.0MM," 1,190 "
-28004855,HC IMP EAR PROSTHESIS PISTON LONG 8X5.0MM," 1,190 "
-28004856,HC  GUIDEWIRE NEURO 45D 16IN 200CM," 2,465 "
-28004857,HC IMP EYE SPHERE ORBITAL SILICONE 12MM, 289 
-28004858,HC IMP VALVE AORTIC MECHANICAL 29MM," 29,491 "
-28004867,HC IMP ICD DUAL JEWEL SYSTEM," 78,750 "
-28004870,HC STAPLER INTRALUM 29MM," 2,390 "
-28004872,HC STAPLER INTRALUM 21MM," 2,468 "
-28004876,HC IMP EAR PROSTHESIS OSSICULAR PARTIAL 1MM," 1,190 "
-28004877,HC IMP EAR PROSTHESIS OSSICULAR TOTAL 0.6MM," 1,446 "
-28004878,HC IMP EAR PROSTHESIS OSSICULAR PARTIAL," 1,190 "
-28004879,HC IMP EAR PROSTHESIS PARTIAL SHEEHY," 1,340 "
-28004880,HC IMP EAR PROSTHESIS OSSICULAR PARTIAL 0.8X3X8MM," 1,381 "
-28004883,HC IMP EAR PROSTHESIS PARTIAL MUSHROOM 1.17X4.75MM," 2,145 "
-28004885,HC IMP EAR PROSTHESIS INCUS STRUT CARPAL SZ 11 3.75MM," 2,414 "
-28004886,HC IMPL PLATE MINI CURVED 7HOLE," 2,550 "
-28004887,HC IMP VALVE AORTIC CAVG 21MM," 32,871 "
-28004888,HC IMP VALVE AORTIC CAVG 23MM," 34,184 "
-28004889,HC IMP VALVE AORTIC CAVG 25MM," 36,576 "
-28004890,HC IMP VALVE AORTIC CAVG 27MM," 36,641 "
-28004891,HC IMP VALVE AORTIC CAVG 29MM," 34,184 "
-28004892,HC IMP VALVE AORTIC CAVG 31MM," 32,871 "
-28004893,HC IMP VALVE AORTIC CAVG 33MM," 33,712 "
-28004894,HC IMP HIP BALL 28MM + 5," 3,870 "
-28004895,HC IMPL SCREW HIP CANCELLOUS 6.5X50MM," 1,566 "
-28004896,HC CRANIAL ACCESS PREP 5.31MM KIT," 4,002 "
-28004897,HC  CATHETER DRAINAGE ALPRPS REGULAR 25CM 8F, 655 
-28004898,HC  CATHETER DRAINAGE ALPRPS REGULAR 20CM 10F, 655 
-28004899,HC  CATHETER NEPHROSTOMY 12F, 655 
-28004900,HC  CATHETER NEPHROSTOMY REG 25CM 14F, 655 
-28004901,HC  CATHETER DRAINAGE ALPRPS REGULAR 25CM 12F, 655 
-28004902,HC  CATHETER DRAINAGE ALPRPS REGULAR 25CM 14F, 655 
-28004903,HC  CATHETER DRAINAGE ALPRPS 25CM 10F, 655 
-28004904,HC  CATHETER DRAINAGE ALPRPS LOOP 25CM 8F, 655 
-28004905,HC  CATHETER DRAINAGE ALPRPS REG 14CM 25F, 655 
-28004906,HC IMP ANNULOPLASTY RING COSGROVE M26," 15,000 "
-28004907,HC IMP ANNULOPLASTY RING COSGROVE M28," 15,914 "
-28004908,HC IMP ANNULOPLASTY RING COSGROVE M30," 15,914 "
-28004909,HC IMP ANNULOPLASTY RING COSGROVE M32," 15,914 "
-28004910,HC IMP ANNULOPLASTY RING COSGROVE M34," 16,234 "
-28004911,HC IMP ANNULOPLASTY RING COSGROVE M36," 16,311 "
-28004912,HC IMP VALVE CSF DELTA REG LEVEL 1.5," 10,280 "
-28004913,HC IMP BONE FEMUR PROX FROZEN RIGHT NO HEAD," 23,982 "
-28004915,HC  CATH SHLD CONTMNTN 7-7.5X 80, 89 
-28004916,HC  SNARE GOOSENECK 120CM 25MM 6F," 2,338 "
-28004917,HC  CATHETER BALLOON WEDGE PRESSURE 110CM 5F, 286 
-28004918,HC  CATHETER BALLOON DUAL LUMEN 60CM 4F, 257 
-28004919,HC  CATHETER BALLOON ANGIOGRAPHY BERMAN 110CM 7F, 495 
-28004920,HC  CATHETER BALLOON ANGIOGRAPHY BERMAN 90CM 6F, 495 
-28004921,HC  CATHETER BALLOON ANGIOGRAPHY BERMAN 80CM 5F, 495 
-28004922,HC  CATHETER BALLOON ANGIOGRAPHY 50CM 5F, 467 
-28004923,HC  CATHETER BALLOON ANGIOGRAPHY BERMAN 60CM 6F, 467 
-28004924,HC  CATHETER BALLOON DUAL LUMEN 60CM 6F, 311 
-28004925,HC  CATHETER BALLOON DUAL LUMEN WDG 60CM 5F, 311 
-28004926,HC  CATHETER BALLOON WEDGE PRESSURE 110CM 6F, 311 
-28004927,HC  CATHETER PRESSURE WEDGE BALLOON 7F 110CM, 286 
-28004928,HC IMP PENILE 12CMX11.5 9.5MM," 25,200 "
-28004929,HC IMP PENILE 14CMX11.5 9.5MM," 25,200 "
-28004930,HC IMP PENILE 16CMX11.5 9.5MM," 25,200 "
-28004931,HC IMP PENILE 18CMX11.5 9.5MM," 25,200 "
-28004932,HC IMP BREAST SILICONE STYLE 168 270ML," 4,038 "
-28004933,HC IMP BREAST ROUND MODERATE PROFILE STY168 650-700CC," 4,463 "
-28004934,HC IMP BREAST ROUND MODERATE PROFILE STY168 700-750CC," 4,463 "
-28004935,HC IMP BREAST ROUND MODERATE PROFILE STY168 750-800CC," 4,463 "
-28004936,HC IMP BREAST SALINE STYLE 68MP 120ML," 2,550 "
-28004937,HC IMP BREAST SALINE STYLE 68MP 150ML," 2,508 "
-28004938,HC IMP BREAST SALINE STYLE 68MP 180ML," 2,508 "
-28004939,HC IMP BREAST SALINE STYLE 68MP 210ML," 2,508 "
-28004940,HC IMP BREAST SALINE STYLE 68MP 240ML," 2,508 "
-28004941,HC IMP BREAST SALINE STYLE 68MP 270ML," 2,508 "
-28004942,HC IMP BREAST SALINE STYLE 68MP 300ML," 2,508 "
-28004943,HC IMP BREAST SALINE STYLE 68MP 330ML," 2,550 "
-28004944,HC IMP BREAST SALINE STYLE 68MP 360ML," 2,550 "
-28004945,HC IMP BREAST SALINE STYLE 68MP 390ML," 2,550 "
-28004946,HC IMP BREAST SALINE STYLE 68MP 420ML," 2,550 "
-28004947,HC IMP BREAST SALINE STYLE 68MP 450ML," 2,550 "
-28004948,HC IMP BREAST SALINE STYLE 68MP 480ML," 2,550 "
-28004949,HC IMP BREAST SALINE STYLE 68MP 510ML," 4,038 "
-28004950,HC IMP BREAST SALINE STYLE 68MP 550ML," 2,550 "
-28004951,HC IMP BREAST SALINE STYLE 68MP 600ML," 2,550 "
-28004952,HC  CUFF URINARY SPHINCTER 4.0CM," 20,524 "
-28004953,HC  CATHETER DRAINAGE PIGTAIL LOCKING 30CM 6F, 592 
-28004954,HC  CATHETER GUIDING VISTA BRITE MPC 90CM .056IN 5F," 2,883 "
-28004955,HC  GUIDEWIRE STEERABLE 35IN 145CM, 893 
-28004956,HC  GUIDEWIRE STEERABLE 14IN 190X2CM," 1,856 "
-28004957,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 16.5D, 952 
-28004958,HC IMP ANCHOR SUTURE QUICKANCHOR PLUS SZ0," 3,267 "
-28004959,HC IMPL SCREW CANNULATED INTERFERENCE 7X20MM, 808 
-28004960,HC IMPL SCREW CANNULATED INTERFERENCE 9X20MM, 808 
-28004961,HC IMPL SCREW CANNULATED INTERFERENCE 8X20MM, 808 
-28004963,HC IMPL GUIDE PIN W/SUTURE EYE AR-1297, 578 
-28004964,HC IMP CLIP ANEURYSM MINI BAYONET KEES SLIMLINE 7X9MM," 4,794 "
-28004965,HC IMP CLIP ANEURYSM MINI RIGHT KEES SLIMLINE 7X5.5MM," 3,502 "
-28004966,HC IMP CLIP ANEURYSM MINI FWD KEES SLIMLINE 7X8.5MM," 4,794 "
-28004967,HC IMP CLIP ANEURYSM MINI SIDE KEES SLIMLINE 7X10MM," 3,604 "
-28004968,HC IMP CLIP ANEURYSM CURVED SUNDT 6.5MM," 3,876 "
-28004969,HC  GUIDEWIRE NITREX 25IN 260CM, 468 
-28004970,HC IMPL PLATE MINI RIGHT L 15MM, 978 
-28004971,HC IMPL PLATE MINI LEFT L 15MM," 1,025 "
-28004976,HC  CATHETER HICKMAN DUAL LUMEN 13.5F," 2,665 "
-28004977,HC  CATHETER PEDIATRIC RADIAL ARTERY 3F 20G 8CM, 334 
-28004978,HC IMP STENT GRAFT WALLSTENT 16X90," 13,301 "
-28004979,HC  GUIDEWIRE CORONARY 14IN 190CM, 697 
-28004980,HC  GUIDEWIRE CORONARY STRAIGHT TP 14IN 300CM, 798 
-28004981,HC IMP GRAFT GORTEX RINGED 6X40," 5,695 "
-28004983,HC IMP GRAFT GORTEX RINGED 8X40," 5,695 "
-28004984,HC IMP GRAFT GORTEX RINGED 10X80," 8,918 "
-28004988,HC  INTRODUCER SHEATH GUIDING TRNSEPTL 8F," 1,063 "
-28004989,HC  INTRODUCER SHEATH GUIDING MULLINGS  60CM 8F," 2,440 "
-28004990,HC  INTRODUCER GUIDING SWARTZ 8 FR," 1,063 "
-28004991,HC  INTRODUCER GUIDING SWARTZ 8FX60CM," 1,360 "
-28004992,HC  INTRODUCER GUIDING SWARTZ FAST CATH 8X60," 1,360 "
-28004993,HC  CATHETER DRAINAGE ALPRPS 25CM 12F, 655 
-28004994,HC  CATHETER DILATATION BILIARY SOEHENDRA 6F, 486 
-28004995,HC  CATHETER DILATATION BILIARY SOEHENDRA 7F, 663 
-28004996,HC  CATHETER DILATATION BILIARY SOEHENDRA 8.5F, 604 
-28004997,HC IMP STENT TRACHEAL ZSTENT 20X5," 6,375 "
-28004998,HC IMP STENT TRACHEAL ZSTENT 25X5," 7,225 "
-28004999,HC IMP STENT TRACHEAL ZSTENT 30X5," 6,375 "
-28005000,HC IMP STENT TRACHEAL ZSTENT 35X5," 6,375 "
-28005002,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 10.0D, 808 
-28005003,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 10.5D, 808 
-28005004,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 11.0D, 808 
-28005005,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 11.5D, 808 
-28005006,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 12.0D, 808 
-28005007,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 12.5D, 808 
-28005008,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 13.0D, 808 
-28005009,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 13.5D, 808 
-28005010,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 14.0D, 808 
-28005011,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 14.5D, 808 
-28005012,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 15.0D, 808 
-28005013,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 15.5D, 808 
-28005014,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 16.0D, 808 
-28005015,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 16.5D, 808 
-28005016,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 17.0D, 808 
-28005017,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 17.5D, 808 
-28005018,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 18.0D, 808 
-28005019,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 18.5D, 808 
-28005020,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 19.0D, 808 
-28005021,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 19.5D, 808 
-28005022,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 20.0D, 808 
-28005023,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 20.5D, 808 
-28005024,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 21.0D, 808 
-28005025,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 21.5D, 808 
-28005026,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 22.0D, 808 
-28005027,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 22.5D, 808 
-28005028,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 23.0D, 808 
-28005029,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 23.5D, 808 
-28005030,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 24.0D, 425 
-28005031,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 24.5D, 808 
-28005032,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 25.0D, 808 
-28005033,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 25.5D, 808 
-28005034,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 26.0D, 808 
-28005035,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 26.5D, 808 
-28005036,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 27.0D, 808 
-28005037,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 27.5D, 808 
-28005038,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 28.0D, 425 
-28005039,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 28.5D, 808 
-28005040,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 29.0D, 808 
-28005041,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 29.5D, 808 
-28005042,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA BICONVEX 30.0D, 808 
-28005043,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 5.0D, 808 
-28005044,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 5.5D, 808 
-28005045,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 6.0D, 808 
-28005046,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 6.5D, 808 
-28005047,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 7.0D, 808 
-28005048,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 7.5D, 808 
-28005049,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 8.0D, 850 
-28005050,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 8.5D, 850 
-28005051,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 9.0D, 935 
-28005052,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 9.5D, 850 
-28005053,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 10.0D, 808 
-28005054,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 10.5D, 850 
-28005055,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 11.0D, 850 
-28005056,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 11.5D, 850 
-28005057,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 12.0D, 808 
-28005058,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 12.5D, 723 
-28005059,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 13.0D, 808 
-28005060,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 13.5D, 808 
-28005061,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 14.0D, 808 
-28005062,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 14.5D, 850 
-28005063,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 15.0D, 808 
-28005064,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 15.5D, 723 
-28005065,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 16.0D, 808 
-28005066,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 16.5D, 808 
-28005067,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 17.0D, 935 
-28005068,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 17.5D, 808 
-28005069,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 18.5D, 808 
-28005070,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 19.0D, 808 
-28005071,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 19.5D, 808 
-28005072,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 20.0D, 723 
-28005073,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 20.5D, 808 
-28005074,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 21.0D, 808 
-28005075,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 21.5D, 808 
-28005076,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 22.0D, 808 
-28005077,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 22.5D, 850 
-28005078,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 23.0D, 935 
-28005079,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 23.5D, 850 
-28005080,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 24.0D, 850 
-28005081,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 24.5D, 850 
-28005082,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 25.0D, 850 
-28005083,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 25.5D, 935 
-28005084,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 26.0D, 850 
-28005085,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 26.5D, 850 
-28005086,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 27.0D, 850 
-28005087,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 27.5D, 850 
-28005088,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 28.0D, 850 
-28005089,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 28.5D, 850 
-28005090,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 29.0D, 850 
-28005091,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 29.5D, 850 
-28005092,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 30.0D, 850 
-28005093,HC  CATHETER DILATATION BILIARY SOEHENDRA 10F, 486 
-28005094,HC IMPL SCREW LOCKING CERVICAL SPINE LOCKING PLATE 5MM, 297 
-28005095,HC  CATHETER ANGIOGR SOFT VU COBRA .035X5FX90CM, 136 
-28005097,HC IMP STENT PANCREATIC 5F 7CM SET," 1,012 "
-28005098,HC IMP STENT PANCREATIC 7F 5CM SET, 972 
-28005099,HC IMP STENT PANCREATIC 7F 7CM SET," 1,012 "
-28005100,HC  DRAIN BILE T TUBE DEAVER STERILE 5F, 114 
-28005101,HC ELECTRODE MICRO ENDOSCOPIC 60CM," 2,874 "
-28005103,HC IMPL PACEMAKER SINGLE THERA 8940U," 33,800 "
-28005105,HC IMPL PACEMAKER SINGLE THERA 8942," 33,800 "
-28005106,HC  GUIDEWIRE DEFLECTING 145CM 35IN 10MM, 553 
-28005107,HC BIOPSY LIVER 19G 20MM 7F SET," 3,604 "
-28005113,HC IMPL SCREW CANNULATED INTERFERENCE 10X20MM, 808 
-28005114,HC IMPL SCREW CANNULATED INTERFERENCE 10X25MM, 808 
-28005115,HC  CATHETER DRAINAGE 10.2F 45CM 18G, 753 
-28005116,HC IMPL PACEMAKER DUAL THERA 7942," 40,950 "
-28005117,HC TUBE JEJUNAL 22F FEEDIN," 1,775 "
-28005120,HC  CATHETER VENTRICULAR RETURN DUAL DRAIN, 238 
-28005128,HC  CANNULA VENOUS FEMORAL PERCUTANEOUS 19F," 2,696 "
-28005129,HC  TUBE GASTROSTOMY MIC 30F, 451 
-28005131,HC IMPL SCREW CANNULATED INTERFERENCE 7X25MM, 808 
-28005132,HC IMPL SCREW CANNULATED INTERFERENCE 7X30MM, 808 
-28005133,HC IMPL SCREW CANNULATED INTERFERENCE 9X25MM, 808 
-28005134,HC IMPL SCREW CANNULATED INTERFERENCE 9X30MM, 808 
-28005135,HC  CATHETER DILATATION 9MM 4X80CM 6F," 1,377 "
-28005136,HC  CATHETER DILATATION 10MM 4X80CM 6F," 1,377 "
-28005137,HC IMP ANCHOR SUTURE QUICKANCHOR PLUS GII SZ2," 3,302 "
-28005138,HC  CATHETER DILATATION 10MM 4X110CM 6F," 1,377 "
-28005139,HC IMPL SCREW MINI M3X 2.0MMX14MM, 493 
-28005140,HC IMPL SCREW MINI M3X 2.4MMX8MM, 527 
-28005141,HC IMPL SCREW MINI M3X 2.4MMX10MM, 493 
-28005142,HC  GUIDEWIRE VASCULAR 18IN 180CM, 538 
-28005143,HC  CATHETER TENCKHOFF NEONATAL STRAIGHT 31CM, 443 
-28005144,HC IMP ANNULOPLASTY RING COSGROVE M38," 16,391 "
-28005145,HC IMP ENDOPROSTHESIS 20X80MM 10F," 13,301 "
-28005146,HC IMP STENT GRAFT TRACHEOBRONCHIAL 70X24MM," 13,301 "
-28005147,HC IMP STENT GRAFT TRACHEOBRONCHIAL 55X20MM," 8,712 "
-28005148,HC IMP STENT GRAFT TRACHEOBRONCHIAL 45X24MM," 8,712 "
-28005149,HC IMPL COIL PERIPHERAL EMBOLIZATION 7CM 6X 2MM, 767 
-28005150,HC IMPL COIL PERIPHERAL EMBOLIZATION 5-2MM 0.018IN, 767 
-28005151,HC IMPL COIL PERIPHERAL EMBOLIZATION 4-2MM 4CM 0.018IN, 767 
-28005152,HC IMPL COIL PERIPHERAL EMBOLIZATION 3-2MM 0.018IN, 767 
-28005153,HC IMP VALVE MITRAL 17MM," 28,049 "
-28005155,HC FORCEP ALLIGATOR 3FX115CM," 2,510 "
-28005156,HC IMP PLUG MESH MARLEX PERFLEX LARGE," 2,372 "
-28005157,HC IMPL GRAFT GORETEX THIN WALLED 8X40," 4,752 "
-28005160,HC IMP STENT URETERAL SINGLE J 8.5F 90CM," 2,967 "
-28005161,HC  CATHETER BALLOON INOUE 26MM SET," 16,500 "
-28005162,HC  CATHETER BALLOON INOUE 70CM 12F SET," 16,500 "
-28005163,HC IMP BONE FEMUR DISTAL LEFT," 20,370 "
-28005164,HC  INTRODUCER PEEL AWAY WITHOUT GUIDEWIRE 10FX30, 680 
-28005165,HC IMP STENT PANCREATIC 7F 3CM SET, 973 
-28005166,HC IMP ANCHOR SUTURE QUICKANCHOR PLUS SZ2," 3,035 "
-28005167,HC IMPL PACEMAKER DUAL TRILOGY," 35,750 "
-28005168,HC  CATHETER BALLOON ATRIOSEPTOSTOMY 5F," 2,025 "
-28005169,HC IMPL COIL PERIPHERAL EMBOLIZATION SPRNG 12CM 10MM, 743 
-28005171,HC  INTRODUCER SHEATH MULLINS 7FX.25X45CM, 802 
-28005172,HC  INTRODUCER PEEL AWAY 6FX15.5CMX0.038IN, 680 
-28005173,HC  GUIDEWIRE FLOPPY TIP 18IN 450CM, 956 
-28005174,HC IMP BREAST EXPANDER STYLE 133FV 400ML," 8,963 "
-28005176,HC IMP PIN RUSH 2.4X76MM, 315 
-28005177,HC IMP PIN RUSH 2.4X89MM, 473 
-28005178,HC IMP PIN RUSH 3.2X102MM, 736 
-28005179,HC IMP PIN RUSH 3.2X114MM, 557 
-28005180,HC IMP PIN RUSH 3.2X127MM, 510 
-28005181,HC IMP PIN RUSH 3.2X140MM, 535 
-28005182,HC IMP PIN RUSH 3.2X152MM, 774 
-28005184,HC IMP PIN RUSH 4.8X356MM, 473 
-28005187,HC IMP ICD JEWEL SINGLE SYS," 92,500 "
-28005188,HC IMP SCREW BONE 1.2X3MM, 320 
-28005189,HC IMP SCREW BONE 1.2X5MM, 343 
-28005190,HC IMPL PLATE DYNAMIC COMPRESSION PLATE ZYGO 22MM 4HOLE," 1,591 "
-28005192,HC IMPL PLATE BRIDGING STRAIGHT 3HOLE," 1,887 "
-28005193,HC IMP CLIP ANEURYSM STRAIGHT KEES SLIMLINE 5MM," 3,987 "
-28005194,HC IMP CLIP ANEURYSM BAYONET STRGHT KEES SLIMLINE 7MM," 3,987 "
-28005195,HC IMP CLIP ANEURYSM BAYONET STRGHT KEES SLIMLINE 9MM," 3,740 "
-28005196,HC IMP CLIP ANEURYSM FORWARD ANGLE KEES SLIMLINE 12MM," 3,562 "
-28005197,HC IMP CLIP ANEURYSM FORWARD ANGLE KEES SLIMLINE 15MM," 3,562 "
-28005198,HC IMP CLIP ANEURYSM SIDEWARD ANGLE KEES SLIMLINE 15MM," 2,791 "
-28005199,HC IMP CLIP ANEURYSM CURVED KEES SLIMLINE 12MM," 3,060 "
-28005200,HC IMP CLIP ANEURYSM FEN STRAIGHT KEES SLIMLINE 20MM," 2,957 "
-28005201,HC IMP CLIP ANEURYSM REINF KEES SLIMLINE 0 DEG 6X12MM," 2,685 "
-28005202,HC IMP CLIP ANEURYSM REINF KEES SLIMLINE 30 DEG 6X6MM," 2,685 "
-28005203,HC IMP CLIP ANEURYSM REINF KEES SLIMLINE 30 DEG 6X18MM," 4,624 "
-28005204,HC IMP CLIP ANEURYSM REINF KEES SLIMLINE 60 DEG 6X12MM," 2,737 "
-28005205,HC IMP CLIP ANEURYSM REINF KEES SLIMLINE 60 DEG 6X18MM," 2,947 "
-28005206,HC IMP CLIP ANEURYSM BAYONET CURVED KEES SLIMLINE 7MM," 3,213 "
-28005207,HC STAPLER POWERED LDS TITANIUM 15MM," 2,944 "
-28005208,HC  CATHETER GUIDING VISTA BRITE TIP 7F AL1, 442 
-28005209,HC  CATHETER GUIDING VISTA BRITE TIP 7F AL2, 442 
-28005210,HC  CATHETER GUIDING VISTA BRITE TIP 7F AR1, 442 
-28005215,HC  CATHETER DILATATION SYMMETRY 3X20X135CM," 3,113 "
-28005216,HC  CATHETER FOLEY COUNCIL 16F, 100 
-28005217,HC  CATHETER FOLEY COUNCIL 18F, 100 
-28005218,HC  CATHETER FOLEY COUNCIL 22F, 100 
-28005219,HC  CATHETER BALLOON OCCLUSION 100CM 8.5MM 5F," 2,067 "
-28005220,HC  CATHETER BALLOON OCCLUSION 65CM 8.5MM 5F," 1,712 "
-28005221,HC  INTRODUCER SHEATH BANANA PEEL AWAY 12F 15CM, 374 
-28005222,HC  RETRIEVER STENT SOEHENDRA 5F, 973 
-28005223,HC  INTRODUCER SHEATH BANANA 11F 30CM, 374 
-28005224,HC IMPL LENS STORZ COBURN 95UV 27.0D," 1,097 "
-28005225,HC  GUIDEWIRE ANGLED STD 035X260, 377 
-28005226,HC  GUIDEWIRE GLIDEWIRE STD STR 035X180, 337 
-28005227,HC  GUIDEWIRE PERIPHERAL KATZEN 35IN 145CM," 1,597 "
-28005228,HC  GUIDEWIRE ANGLED TIP 25IN 180CM, 91 
-28005229,HC IMP STENT NEPHROSTOMY PERCUFLEX 10FX24CM," 1,095 "
-28005230,HC IMP STENT NEPHROSTOMY PERCUFLEX 10FX20CM," 1,095 "
-28005231,HC  CATHETER WIREGUIDED CRE 6-8MM," 1,875 "
-28005233,HC IMP URINARY SPHINCTER ACCESSORY KIT," 5,950 "
-28005234,HC  CUFF URINARY SPHINCTER 4.5CM," 23,800 "
-28005235,HC  PUMP URINARY SPHINCTER CONTROL," 25,200 "
-28005236,HC  BALLOON URINARY SPHINCTER 61-70CM," 15,000 "
-28005237,HC IMPL LOOP WIRE HOUSE STAPEDECTOMY SZ 4, 252 
-28005238,HC IMPL LOOP WIRE HOUSE STAPEDECTOMY SZ 4.25, 315 
-28005239,HC  CATHETER PEZZAR 16F, 128 
-28005240,HC IMPL GRAFT GORETEX THIN WALLED 6X80," 8,500 "
-28005245,HC IMPL SCREW CANNULATED SHORT THREAD 38MM," 1,902 "
-28005246,HC IMPL SCREW CANNULATED SHORT THREAD 46MM," 1,902 "
-28005247,HC IMP VALVE PULMONARY HUMAN CRYO," 41,548 "
-28005250,HC IMP SUMP PERICARDIAL FLUTED 15IN, 101 
-28005253,HC IMP WIRE KIRSCHNER DIAMOND POINT 0.035X9IN, 303 
-28005254,HC IMP WIRE KIRSCHNER DIAMOND POINT 0.045X9IN, 303 
-28005255,HC IMP WIRE KIRSCHNER DIAMOND POINT 0.062X9IN, 303 
-28005256,HC IMP STENT BILIARY GENESIS UNMOUNTED 10-12X29," 10,695 "
-28005258,HC  TUBE JEJUNAL MIC 3-5ML 12F," 1,049 "
-28005259,HC  CATHETER FOGARTY VEN THROMBECTOMY 6FX80CM, 597 
-28005262,HC IMPL PACEMAKER SINGLE THERA 8960," 32,550 "
-28005263,HC BASKET RETRIEVAL STONE TRAPEZOID180X2.5X5.5CM," 3,052 "
-28005264,HC IMPL PACEMAKER DUAL THERA 7960," 34,450 "
-28005266,HC SLING SWATHE SM, 141 
-28005267,HC SLING SWATHE M, 141 
-28005268,HC SLING SWATHE LG, 141 
-28005269,HC SLING SWATHE XL, 141 
-28005271,HC  CATHETER BALLOON PTA XXL 16X4X120CM," 2,785 "
-28005272,HC  CATHETER BALLOON PTA XXL 16X4X75CM," 2,785 "
-28005273,HC  GUIDEWIRE PERIPHERAL 35IN 260CM 1MM, 400 
-28005277,HC CRANIOPLASTY KIT," 5,300 "
-28005278,HC  INTROD PEEL AWAY 10F SET, 485 
-28005279,HC  INTROD PEEL AWAY 11F SET, 308 
-28005280,HC IMPL PLATE FEMORAL SPLIT," 4,964 "
-28005281,HC  INTROD PEEL AWAY 12F SET, 371 
-28005282,HC IMP BONE FIBULA SHAFT FROZEN," 10,305 "
-28005284,HC IMP GRAFT HEMASHIELD WOVEN DOUBLE VELOUR 28MMX60," 5,474 "
-28005285,HC  CATH ANGIOGR BALL DILATATION PTA OTW XXL12X4X75," 2,554 "
-28005286,HC  CATH ANGIOGR BALL DILATATION PTA OTW XXL14X4X75," 2,554 "
-28005288,HC  GUIDEWIRE CVS INTERMEDIATE STRAIGHT 182CM," 1,491 "
-28005289,HC  CATHETER INFUSION MICROCATHETER TRANSIT 2.5 135," 3,488 "
-28005290,HC  CATHETER DILATATION 2MM 4X120CM 4F," 2,114 "
-28005291,HC IMP STENT BILIARY GENESIS UNMOUNTED 10-12X19," 8,211 "
-28005292,HC  INTRODUCER SHEATH 35MMX5.5CM 6F, 301 
-28005295,HC  CATHETER DRAINAGE 8.5F 38IN 40 CM, 667 
-28005296,HC  CATHETER DRAINAGE 10.2F 38IN 40 CM, 667 
-28005297,HC  CATHETER DRAINAGE ULTRATHANE 10.2F, 667 
-28005299,HC IMP GRAFT HEMASHIELD KNIT BIFURCATED 12MMX7," 5,355 "
-28005300,HC IMP GRAFT HEMASHIELD KNIT BIFURCATED 16MMX8," 5,355 "
-28005301,HC IMP GRAFT HEMASHIELD KNIT BIFURCATED 18MMX9," 5,355 "
-28005302,HC IMP GRAFT HEMASHIELD KNIT BIFURCATED 20MMX1," 5,355 "
-28005303,HC IMP GRAFT HEMASHIELD KNIT BIFURCATED 24MMX1," 5,355 "
-28005304,HC IMP GRAFT HEMASHIELD KNIT STR 14MMX1," 2,709 "
-28005305,HC IMP GRAFT HEMASHIELD KNIT STR 16MMX1," 2,709 "
-28005306,HC IMP GRAFT HEMASHIELD KNIT STR 18MMX1," 2,709 "
-28005307,HC IMP LEAD PACEMAKER CAPSUREFIX PASSIVE 58CM," 6,078 "
-28005308,HC  CATHETER PIGTAIL 5F 90CM, 122 
-28005309,HC IMPL PACEMAKER DUAL UNIPOLAR THERA 7961," 36,270 "
-28005310,HC ACUCISE SYSTEM 6F," 11,588 "
-28005311,HC IMP VALVE AORTIC MECHANICAL HEMODYNAMIC PLUS 23MM," 28,889 "
-28005312,HC IMP VALVE MITRAL 21MM," 28,084 "
-28005313,HC IMP VALVE MITRAL 19MM," 27,265 "
-28005314,HC CATHETER ANGIOGRAPHY FLUSH 65CM 5F, 246 
-28005315,HC IMPL SCREW CANNULATED 16 THREAD 7.3X80MM," 2,420 "
-28005316,HC IMPL SCREW CANNULATED 16 THREAD 7.3X85MM," 2,420 "
-28005317,HC IMPL SCREW CANNULATED 16 THREAD 7.3X90MM," 2,505 "
-28005318,HC BUR DRILL BIT CANNULATED 2.9MM," 4,239 "
-28005319,HC  CATHETER DRAINAGE 20F 25CM 18G, 867 
-28005320,HC IMPL SCREW BIOABSORBABLE 9X25MM, 638 
-28005321,HC IMPL SCREW CANNULATED INTERFERENCE FULLY THREAD 7X2, 808 
-28005322,HC IMPL SCREW CANNULATED INTERFERENCE FULLY THREAD 9X2, 808 
-28005323,HC  CATHETER FOLEY 20F 5ML 3W, 87 
-28005325,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 13.0D, 952 
-28005326,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 14.0D, 952 
-28005327,HC IMP VALVE AORTIC CRYO," 63,050 "
-28005331,HC  INTRODUCER KELLER TIMMERMANS 24FX85CMX.038IN," 1,628 "
-28005332,HC  TUBE TRANSGASTRIC JEJUNAL 7-10ML 16FX30CM," 1,775 "
-28005333,HC IMPL COIL EMBOLIZATION 3MM 3CM 0.038IN, 230 
-28005334,HC  INTRODUCER SHEATH GUIDING 60CM 8.5F," 2,440 "
-28005335,HC  SHEATH INTRODUCER PINACLE RADIO 8F 25CM, 251 
-28005336,HC  CATH EP ABLAT 110CM 2.5X4MM 7F," 8,063 "
-28005337,HC  CATHETER HICKMAN SINGLE LUMEN VITCF 9.6F," 1,385 "
-28005338,HC IMPL PLATE MAXI SHORT 4HOLE," 1,849 "
-28005339,HC  GUIDEWIRE FIXED CORE STRAIGHT SS 15IN 15CM, 137 
-28005340,HC IMPL PATCH HEMASHIELD ULTRA THIN 3X3IN, 893 
-28005341,HC IMPL PLATE DCP NARROW 4 HOLE 4.5X71MM," 1,114 "
-28005342,HC IMPL PLATE DCP BROAD 6 HOLE 4.5X103MM," 1,692 "
-28005343,HC IMPL PLATE L AND F 3 D 3X2," 1,866 "
-28005344,HC IMPL PLATE L AND F 3 D 4X2," 2,143 "
-28005346,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 12.5D, 952 
-28005347,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 13.5D, 952 
-28005349,HC IMPL COIL EMBOLIZATION EXTENDED 5CM 3MM S, 462 
-28005350,HC IMPL PLATE 1/3 TUBULAR OBLIQUE 3 HOLE 63MM," 1,998 "
-28005351,HC IMPL SCREW DHS LAG 120MM," 2,202 "
-28005352,HC IMPL SCREW DHS LAG 135MM," 2,610 "
-28005353,HC IMPL PLATE 145DEG 4 HOLE," 4,471 "
-28005354,HC IMPL PLATE SHORT 135DEG 4 HOLE," 4,743 "
-28005355,HC IMPL SCREW SCHANZ 5X200MM, 947 
-28005356,HC IMPL SCREW SCHANZ 6X160MM, 813 
-28005357,HC BUR DRILL BIT CANNUALTED 4.5MM," 4,214 "
-28005358,HC BUR DRILL BIT CANNULATED 3.2MM," 4,214 "
-28005360,HC IMP HIP HEAD FEMORAL 12/14 TAPER 28X+1.5MM," 5,936 "
-28005361,HC IMP HIP HEAD FEMORAL 12/14 TAPER 28MM +5MM," 5,936 "
-28005362,HC IMP VALVE CSF DELTA REG LEVEL 2," 8,245 "
-28005363,HC IMP HIP HEAD FEMORAL 12/14 TAPER 28X+8.5MM," 4,663 "
-28005364,HC IMP VALVE CSF DELTA REG LEVEL 1," 7,823 "
-28005365,HC IMP VALVE CSF DELTA SMALL LEVEL 1," 10,280 "
-28005366,HC IMP KNEE LINER ACETABULAR BIPOLAR 28ID 57MM," 2,758 "
-28005367,HC IMP VALVE CSF DELTA SMALL LEVEL 2," 9,976 "
-28005368,HC IMP CLIP ANEURYSM FEN ANGLED 45 DEG 20MM," 2,805 "
-28005369,HC IMP CLIP ANEURYSM FEN ANGLED 45 DEG 24MM," 3,451 "
-28005371,HC BAG SILO VENT WALL INFANT 7.5CM," 2,389 "
-28005372,HC BAG SILO VENT WALL INFANT 10CM," 2,525 "
-28005374,HC OIL SILICONE ADATOSIL 8.5ML," 3,613 "
-28005375,HC  GUIDEWIRE MEASRNG 35IN 180CM, 808 
-28005378,HC  CATHETER OMNIFLUSH 5F 65CM, 122 
-28005379,HC IMP SCREW CORTEX SELF TAPPING 4.5X28MM, 218 
-28005380,HC IMP SCREW CORTEX SELF TAPPING 4.5X30MM, 218 
-28005381,HC IMP SCREW CORTEX SELF TAPPING 4.5X32MM, 218 
-28005382,HC IMP SCREW CORTEX SELF TAPPING 4.5X34MM, 218 
-28005383,HC IMP SCREW CORTEX SELF TAPPING 4.5X36MM, 218 
-28005384,HC IMP SCREW CORTEX SELF TAPPING 4.5X38MM, 218 
-28005385,HC IMP SCREW CORTEX SELF TAPPING 4.5X40MM, 218 
-28005386,HC IMP SCREW CORTEX SELF TAPPING 4.5X42MM, 218 
-28005387,HC IMP SCREW CORTEX SELF TAPPING 4.5X44MM, 218 
-28005388,HC IMP SCREW CORTEX SELF TAPPING 4.5X46MM, 218 
-28005389,HC IMP SCREW CORTEX SELF TAPPING 4.5X48MM, 218 
-28005390,HC IMP SCREW CORTEX SELF TAPPING 4.5X50MM, 218 
-28005391,HC IMP SCREW CORTEX SELF TAPPING 4.5X52MM, 218 
-28005392,HC IMP SCREW CORTEX SELF TAPPING 4.5X54MM, 218 
-28005393,HC  INTRODUCER CHECK FLO 14FX30CM, 876 
-28005394,HC  SHEATH GUIDING CUSTOM 14FX80CM," 1,428 "
-28005396,HC IMPL PLATE CALCANEAL 60MM," 4,929 "
-28005397,HC IMPL PLATE CALCANEAL 70MM," 5,034 "
-28005398,HC IMPL PLATE CALCANEAL Y 87MM," 5,211 "
-28005401,HC NEUROSCOPE CLARUS SHT 2MM," 9,052 "
-28005402,HC IMP KNEE LINER ACETABULAR BIPOLAR METAL 28ID 50MM," 2,758 "
-28005403,HC  INTRODUCER SHEATH PEEL AWAY 12F, 99 
-28005404,HC NEUROPEN NEUROENDOSCOPE," 7,352 "
-28005405,HC  INTRODUCER NON VASC PEEL AWAY 20X13X0.038, 309 
-28005406,HC  INTRODUCER DRAINAGE PEEL AWAY LG 22X13X20, 309 
-28005407,HC IMP VALVE CSF REG HI PRESS," 5,297 "
-28005408,HC IMP VALVE NEURO PED LO PRESS," 7,047 "
-28005409,HC IMP VALVE NEURO PED M PRESS," 6,712 "
-28005410,HC IMP LEAD PACEMAKER CAPSUREFIX 45CM," 4,505 "
-28005411,HC  CATHETER VENTRICULAR PED RIGHT ANG 10F, 155 
-28005412,HC  CATHETER VENTRICULAR PED RIGHT ANG 13F, 155 
-28005413,HC  CATHETER GUIDING VISTA BRITE TIP 6F JL4, 442 
-28005414,HC  CATHETER ATRIOSEPTOSTOMY 13.5MM 6F," 2,349 "
-28005417,HC BARRIER ADHESION SEPRAFILM 5X6," 2,219 "
-28005418,HC IMP ANCHOR CORKSCREW 5.0MM," 1,530 "
-28005419,HC IMP PERISTRIP REINFORCEMENT 4.5CM," 5,738 "
-28005420,HC IMP VALVE CSF FLOW CONTROL LOW LOW SM," 4,775 "
-28005421,HC IMP VALVE CSF FLOW CONTROL LOW SM," 4,803 "
-28005424,HC IMP LEAD PACEMAKER CAPSUREFIX BIPOLAR 45CM," 6,078 "
-28005426,HC  INTRODUCER LEAD LDS ATTAIN, 553 
-28005427,HC  INTRODUCER VASCULAR BALKIN FLEXOR 8FX40CM, 527 
-28005428,HC  INTRODUCER VASCULAR BALKIN RAABE 7FX40CM, 527 
-28005429,HC  INTRODUCER VASCULAR BALKIN RAABE 6FX40CM, 527 
-28005430,HC  INTRODUCER VASCULAR 7FX30CM 0.038 GUIDEWIRE, 393 
-28005431,HC  INTRODUCER VASCULAR SUPER ARROW FLEX 7X80, 829 
-28005432,HC IMPL PUMP LVAD PTFE NOVACOR 6CM," 113,100 "
-28005433,HC IMP PIN STEINMAN 5/64X9IN, 125 
-28005434,HC LIGATOR SPEEDBAND SUPERVIEW KIT," 2,338 "
-28005435,HC  CATHETER GUIDING VISTA BRITE TIP 6F JL5, 442 
-28005436,HC  CATHETER GUIDING VISTA BRITE TIP 6F XB3.5, 455 
-28005437,HC  CATHETER GUIDING VISTA BRITE TIP 6F JR4 100CM, 447 
-28005438,HC  CATHETER GUIDING 100CM 1X70IN, 442 
-28005442,HC  CATHETER BALLOON PTCA 0.014IN 2X20MM," 7,379 "
-28005443,HC  CATHETER BALLOON PTCA OTW GW 2.5X20MM," 7,164 "
-28005444,HC  CATHETER BALLOON PTCA OTW GW 4X20MM," 2,117 "
-28005446,HC  CATHETER ANGIOGR SOFT VU BRAIDED 5F 80CM, 136 
-28005447,HC IMPL PUMP VAD THORATEC," 112,456 "
-28005448,HC  CATHETER FOGARTY ARTERIAL EMBOLECTOMY 4F, 412 
-28005449,HC  CANNULA ARTERIAL STR LONG 14MMX30CM," 25,900 "
-28005450,HC  CANNULA VAD VENTRICULAR BLUNT," 19,600 "
-28005451,HC  CATHETER FOGARTY ARTERIAL EMBOLECTOMY 7F, 412 
-28005453,HC IMP ANCHOR SUTURE COPE GASTROINTESTINAL SET, 743 
-28005458,HC BUR ATHERECTOMY 1.75MM," 7,608 "
-28005459,HC BUR ATHERECTOMY 2MM," 7,608 "
-28005460,HC BUR ATHERECTOMY 2.15MM," 7,608 "
-28005461,HC BUR ATHERECTOMY 2.25MM," 7,608 "
-28005462,HC BUR ATHERECTOMY ROTALINK 2.38MM," 7,608 "
-28005466,HC COIL PUSHER 195X50CM 0.021IN," 3,902 "
-28005467,HC IMPL COIL PERIPHERAL EMBOLIZATION 2-7MM 0.018IN," 1,488 "
-28005468,HC IMPL COIL PERIPHERAL EMBOLIZATION 5-8MM 0.018IN," 1,163 "
-28005469,HC IMPL COIL PERIPHERAL EMBOLIZATION 4-10MM 0.018IN," 1,488 "
-28005470,HC IMPL COIL PERIPHERAL EMBOLIZATION 5-10MM 0.018IN," 1,163 "
-28005473,HC IMP LEAD PACEMAKER CAPSUREFIX 52CM," 4,505 "
-28005474,HC IMPL PLATE RECONSTRUCTION CURVED 3.5X70MM 6HOLE," 3,877 "
-28005475,HC IMPL PLATE RECONSTRUCTION CURVED 3.5X94MM 8HOLE," 4,209 "
-28005477,HC  CATHETER DRAINAGE 12F 38IN 40 CM, 667 
-28005478,HC  CATHETER DRAINAGE 14F 38IN 40 CM, 667 
-28005479,HC FREEHAND STIMULATOR SYSTEM," 85,750 "
-28005484,HC IMP SCREW BONE TECHMEDICA 2.0X4MM, 140 
-28005485,HC IMP SCREW BONE TECHMEDICA 2.0X5MM, 140 
-28005486,HC IMP SCREW BONE TECHMEDICA 2.0X8MM, 140 
-28005487,HC  GUIDEWIRE FIXED CORE 35IN 260CM 1.5MM, 179 
-28005488,HC IMP SCREW BONE TECHMEDICA 2.0X12MM, 159 
-28005489,HC IMP LEAD PACEMAKER CAPSUREFIX 58CM 4068-58," 4,505 "
-28005490,HC BUR DRILL BIT CANNUALTED 3.5MM," 3,893 "
-28005492,HC DRILL DISK ULTRA DRIVE 7MM," 5,627 "
-28005493,HC DRILL DISK ULTRA DRIVE 9.5MM," 5,627 "
-28005494,HC DRILL DISK ULTRA DRIVE 13MM," 5,627 "
-28005495,HC  GUIDEWIRE CORONARY STRAIGHT TIP 190X6CM, 798 
-28005496,HC  GUIDEWIRE CORONARY STRAIGHT 14IN 3X300CM, 798 
-28005498,HC  CATHETER FOGARTY THRU LUMEN 5.5F, 717 
-28005499,HC IMP VALVE GLAUCOMA AHMED," 4,888 "
-28005500,HC  CATHETER BALLOON PTCA OTW GW 2X15MM," 7,949 "
-28005501,HC IMP GRAFT HEMASHIELD WOVEN DOUBLE VELOUR 30MMX60CM," 5,474 "
-28005502,HC  GUIDEWIRE PTCA 14IN 300X35CM, 765 
-28005503,HC IMP GRAFT HEMASHIELD WOVEN ST 10X30," 3,766 "
-28005504,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 12MMX30CM," 4,021 "
-28005505,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 14MMX30CM," 4,021 "
-28005506,HC  CATHETER PED ULTRAMER 6F, 90 
-28005507,HC IMP LEAD PACEMAKER STEROID BIPOLAR 53CM," 6,078 "
-28005508,HC IMP FELT PTFE CARDIOVASCULAR 6X6IN," 1,037 "
-28005509,HC  CATHETER DILATATION UROMAX II PLU 5.8FX75CM," 2,865 "
-28005510,HC IMP KNEE STEM NEXGEN 4MM," 3,714 "
-28005513,HC IMP PERICARDIUM TUTOPLAST 1.5X1.5CM," 2,253 "
-28005514,HC IMP WIRE KIRSCHNER THREAD TROCAR 7/64X9IN, 244 
-28005515,HC IMPL SCREW TITANIUM 3.5X28MM, 153 
-28005516,HC IMPL COIL PERIPHERAL EMBOLIZATION 0.035IN 5-10MM," 1,488 "
-28005517,HC IMPL COIL PERIPHERAL EMBOLIZATION 0.035IN 4-8MM," 1,488 "
-28005518,HC IMPL COIL PERIPHERAL EMBOLIZATION 0.035IN 3-7MM," 1,488 "
-28005519,HC IMPL COIL PERIPHERAL EMBOLIZATION TORNADO 3-6MM," 1,488 "
-28005520,HC IMPL COIL PERIPHERAL EMBOLIZATION 0.35IN 3CM 5MM," 1,488 "
-28005521,HC IMPL COIL PERIPHERAL EMBOLIZATION 10-5MM 0.035IN, 767 
-28005522,HC IMPL COIL PERIPHERAL EMBOLIZATION 8.2CM 8-5MM, 795 
-28005523,HC IMPL COIL PERIPHERAL EMBOLIZATION 0.035IN 8CM 7-3MM, 767 
-28005524,HC IMPL COIL PERIPHERAL EMBOLIZATION 5.8CM 6-3MM, 767 
-28005525,HC IMPL COIL PERIPHERAL EMBOLIZATION 4.1CM 5-3MM, 767 
-28005526,HC  CATHETER DRAINAGE ULTRATHANE 8.5F, 667 
-28005528,HC GLIDEWIRE ANG 150CMX.038IN, 476 
-28005529,HC IMP BREAST ROUND FULL HEIGHT STYLE 468 380-400ML," 4,888 "
-28005530,HC  SHEATH ARROW FLEX 7F 24CM SET, 284 
-28005531,HC IMP KNEE PATELLA NEXGEN 29MM," 2,390 "
-28005532,HC IMP KNEE PATELLA NEXGEN 32MM," 2,390 "
-28005533,HC IMP KNEE PATELLA NEXGEN 35MM," 2,390 "
-28005534,HC IMP KNEE PATELLA NEXGEN 38MM," 2,390 "
-28005535,HC IMP KNEE STEM NEXGEN 3MM," 3,714 "
-28005536,HC IMP KNEE STEM NEXGEN 5MM," 3,714 "
-28005537,HC IMP KNEE STEM NEXGEN 6MM," 3,714 "
-28005538,HC IMP KNEE STEM NEXGEN 7MM," 3,714 "
-28005539,HC IMP KNEE STEM NEXGEN 8MM," 3,714 "
-28005540,HC IMP KNEE NEXGEN LPS FEMORAL SIZE D RIGHT," 19,958 "
-28005541,HC IMP KNEE NEXGEN LPS FEMORAL SIZE E RIGHT," 19,958 "
-28005542,HC IMP KNEE NEXGEN LPS FEMORAL SIZE F RIGHT," 19,958 "
-28005543,HC IMP KNEE NEXGEN LPS FEMORAL SIZE G RIGHT," 19,958 "
-28005544,HC IMP KNEE NEXGEN LPS FEMORALSIZE D LEFT," 19,958 "
-28005545,HC IMP KNEE NEXGEN LPS FEMORAL SIZE E LEFT," 19,957 "
-28005546,HC IMP KNEE NEXGEN LPS FEMORAL SIZE F LEFT," 19,958 "
-28005547,HC IMP KNEE NEXGEN LPS FEMORAL SIZEG LEFT," 19,958 "
-28005550,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 17MM," 4,590 "
-28005551,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 10MM," 4,597 "
-28005552,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 12MM," 4,597 "
-28005553,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 14MM," 4,597 "
-28005554,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 20MM," 4,590 "
-28005555,HC IMP KNEE ARTICULAR SURFACE NEXGEN 5-6 10MM," 4,597 "
-28005556,HC IMP KNEE ARTICULAR SURFACE NEXGEN 5-6 12MM," 4,597 "
-28005557,HC IMP KNEE ARTICULAR SURFACE NEXGEN 5-6 14MM," 4,420 "
-28005558,HC IMP KNEE ARTICULAR SURFACE NEXGEN 5-6 17MM," 4,597 "
-28005559,HC IMP KNEE ARTICULAR SURFACE NEXGEN 7-10 10MM," 4,597 "
-28005560,HC IMP KNEE ARTICULAR SURFACE NEXGEN 7-10 12MM," 4,597 "
-28005561,HC IMPL PLATE DCP NARROW 9 HOLE 4.5X151MM," 1,497 "
-28005562,HC IMP PISTON FLOROPLASTIC PLAT 6X3.75MM," 1,324 "
-28005563,HC IMP PISTON FLOROPLASTIC PLAT 6X4MM," 1,324 "
-28005564,HC IMP EAR PROSTHESIS STAPES PLATINUM 0.6X4.5MM," 1,324 "
-28005565,HC IMP EAR PROSTHESIS STAPES PLATINUM 0.6X4.75MM," 1,233 "
-28005566,HC IMP EAR PROSTHESIS STAPES PLATINUM 0.6X5MM," 1,343 "
-28005567,HC IMP EAR PISTON LOOP MCGE MOD 3.75, 505 
-28005568,HC IMP EAR PISTON LOOP MCGE MOD 4.25, 505 
-28005569,HC IMP EAR PISTON LOOP MCGE MOD 4.5, 505 
-28005570,HC IMP EAR PISTON LOOP MCGE MOD 4.75, 505 
-28005571,HC IMP EAR PROSTHESIS OSSICULAR HYDROXYLAPATITE 0.6X8," 2,338 "
-28005572,HC IMP EAR PROSTHESIS OSSICULAR HYDROXYLAPATITE 1.17MM," 2,463 "
-28005573,HC IMP EAR PROSTHESIS OSSICULAR HYDROXYLAPATITE 0.8X7," 2,463 "
-28005574,HC IMP EAR PROSTHESIS CAUSSE MORETZ STRUT 1.17X5MM," 2,035 "
-28005575,HC IMP EAR PROSTHESIS MORETZ SHORT ANGLED 1.17X5MM," 2,035 "
-28005576,HC IMP EYE SPHERE SILICONE SZ12, 128 
-28005579,HC IMP HIP CENTRALIZER STEM CEMENTRALIZER 10.5MM, 468 
-28005580,HC  INTRODUCER SHEATH SUP 35IN 6X24, 285 
-28005582,HC IMP ROD SILICONE 2 MMX30 CM, 196 
-28005583,HC IMP EYE LID WEIGHT GOLD 1 GRAM," 2,168 "
-28005584,HC IMP EYE LID WEIGHT GOLD 1.2 GRAM," 2,355 "
-28005585,HC CUFF VENAFLOW FOOT 16IN," 2,678 "
-28005586,HC IMP WIRE KIRSCHNER TROCAR POINT 1.6MMX230MM, 247 
-28005587,HC IMP WIRE KIRSCHNER TROCAR POINT 0.045X9IN, 303 
-28005588,HC IMP GRAFT HEMASHIELD KNIT STR 10MMX1," 2,678 "
-28005589,HC  CATHETER MICRO PROWLER STR .0165IN 2.3FX150X50," 6,280 "
-28005591,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 17MM," 1,598 "
-28005592,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 18MM," 1,828 "
-28005593,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 19MM," 1,598 "
-28005594,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 20MM," 1,598 "
-28005595,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 21MM," 1,598 "
-28005596,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 38MM," 1,918 "
-28005597,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 40MM," 1,918 "
-28005598,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 14MM," 1,918 "
-28005599,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 15MM," 1,598 "
-28005600,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 16MM," 1,918 "
-28005601,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 17MM," 1,947 "
-28005602,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 32MM," 1,918 "
-28005603,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 34MM," 1,598 "
-28005604,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 36MM," 1,918 "
-28005605,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 38MM," 1,947 "
-28005606,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 40MM," 1,947 "
-28005607,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 23MM," 1,598 "
-28005608,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 21MM," 1,471 "
-28005609,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 20MM," 1,743 "
-28005610,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 19MM," 1,598 "
-28005611,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 24MM," 1,918 "
-28005612,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 27MM," 1,918 "
-28005613,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 29MM," 1,598 "
-28005614,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 30MM," 1,853 "
-28005616,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 18MM," 1,598 "
-28005617,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 22MM," 1,918 "
-28005618,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 25MM," 1,598 "
-28005619,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 26MM," 1,918 "
-28005620,HC IMPL SCREW 3.0 CANNULATED SHORT THREAD 28MM," 1,918 "
-28005621,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 14MM," 1,598 "
-28005622,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 15MM," 1,598 "
-28005623,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 16MM," 1,598 "
-28005624,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 22MM," 1,598 "
-28005625,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 23MM," 1,598 "
-28005626,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 24MM," 1,947 "
-28005627,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 25MM," 1,598 "
-28005628,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 26MM," 1,853 "
-28005629,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 27MM," 1,598 "
-28005630,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 29MM," 1,598 "
-28005631,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 30MM," 1,947 "
-28005632,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 32MM," 1,853 "
-28005633,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 34MM," 1,918 "
-28005634,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 36MM," 1,918 "
-28005635,HC IMPL SCREW CANNULATED LG 16MMX135," 2,091 "
-28005636,HC IMPL SCREW CANNULATED LG 16MMX140," 2,091 "
-28005637,HC IMPL SCREW CANNULATED LG 16MMX145," 2,091 "
-28005639,HC IMPL SCREW CANNULATED LARGE 135MM," 2,091 "
-28005640,HC IMPL SCREW CANNULATED LARGE 140MM," 2,091 "
-28005641,HC IMP WASHER CANNULATED THREAD 3MM," 1,105 "
-28005642,HC IMP WASHER 6.5 3.0 CANN SCREW, 236 
-28005644,HC IMP PERFLURON 5ML," 1,238 "
-28005645,HC PERFLURON 7ML," 2,231 "
-28005646,HC IMP PIN STEINMAN SHANK 3/32X9IN, 202 
-28005649,HC IMP GRAFT COLLAGEN BOVINE 7X40CM," 8,067 "
-28005650,HC IMP SCREW CORTEX TITANIUM 2.4X22MM, 599 
-28005651,HC IMP SCREW CORTEX TITANIUM 2.4X26MM, 599 
-28005654,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X12MM, 312 
-28005655,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X14MM, 312 
-28005656,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X16MM, 312 
-28005657,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X18MM, 312 
-28005658,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X20MM, 312 
-28005659,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X22MM, 312 
-28005660,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.7X24MM, 312 
-28005662,HC IMPL SCREW CORTEX TITANIUM 3.5X16MM, 132 
-28005663,HC IMPL SCREW CORTEX TITANIUM 3.5X18MM, 119 
-28005664,HC IMPL SCREW CORTEX TITANIUM 3.5X20MM, 119 
-28005665,HC IMPL SCREW CORTEX TITANIUM 3.5X22MM, 119 
-28005666,HC IMPL SCREW CORTEX TITANIUM 3.5X24MM, 119 
-28005667,HC IMPL SCREW CORTEX TITANIUM 3.5X26MM, 119 
-28005668,HC IMPL SCREW CORTEX TITANIUM 3.5X28MM, 119 
-28005669,HC IMPL PLATE TI W/F STD/BND 8HOLE," 8,663 "
-28005670,HC IMPL PLATE TI W/F SHT/BND 8HOLE," 8,663 "
-28005671,HC IMPL PLATE TI W/F STR 1 HOLE," 7,515 "
-28005672,HC IMP GRAFT HEMASHIELD WOVEN ST 28X15," 3,086 "
-28005673,HC IMP SCREW BONE CANCELLOUS 4.0X60MM," 1,318 "
-28005674,HC IMPL PLATE BONE RECONSTRUCTION 16HOLE," 2,550 "
-28005675,HC IMP BREAST SALINE STYLE 363LF 230CC," 6,460 "
-28005676,HC IMP BREAST SALINE STYLE 363LF 300-315CC," 6,460 "
-28005677,HC IMP BREAST SALINE STYLE 363LF 410-430CC," 6,460 "
-28005678,HC IMP BREAST SALINE STYLE 363LF 510-535CC," 6,460 "
-28005679,HC IMP BREAST STYLE 468 300ML," 4,888 "
-28005680,HC IMP BREAST STYLE 468 350ML," 4,675 "
-28005681,HC IMP APEX HOLE ELIMINATOR, 901 
-28005682,HC STAPLER INTRALUM CIRCULAR 25MM," 2,819 "
-28005683,HC ENDOCUTTER ARTICULATING VASC 35MM," 2,215 "
-28005684,HC ENDOCUTTER ARTICULATING 35MM," 2,215 "
-28005686,HC  INTRODUCER SHEATH 35MM 55CM 6F, 298 
-28005687,HC  INTRODUCER SHEATH 55CM 35CMM 7F, 298 
-28005689,HC  CATHETER GUIDING VISTA BRITETIP 7F HS 55, 512 
-28005690,HC IMP ANCHOR SUTURE SOFT TISSUE ROCEZ 3.5X10CM," 2,057 "
-28005691,HC IMP ANCHOR SUTURE SOFT TISSUE ROCEZ 2.8X18CM," 3,486 "
-28005692,HC IMP ANCHOR SUTURE SOFT TISSUE ROCXS 3.5X18CM," 3,486 "
-28005693,HC IMPL PACEMAKER DUAL KAPPA 400 DR KDR401B," 40,950 "
-28005694,HC  CANNULA VENOUS FEM 24F," 2,779 "
-28005695,HC  CATHETER DILATATION 12MM 4X80CM 7F," 1,377 "
-28005696,HC  CATHETER DILATATION 12MM 4X110CM 7F," 1,377 "
-28005697,HC  CATHETER MICRO PROWLER STR .015IN 2.3FX150X50CM," 6,280 "
-28005699,HC STAPLER INTRALUM CIRCULAR 21MM," 2,911 "
-28005701,HC  CATHETER ELECTROPHYSIOLOGY DIAGNOSTIC 110CM 5F," 1,063 "
-28005702,HC  CATHETER ELECTROPHYSIOLOGY DIAGNOSTIC 110CM 6F, 765 
-28005703,HC  CATH ABLAT C TYPE GRN 4MM 7F," 5,236 "
-28005704,HC  CATH ABLAT CELSIUS 4 7F 115CM," 5,236 "
-28005705,HC  CATH ABLAT CRVD E TYPE 4MM 7F," 5,525 "
-28005706,HC  CATH EP ABLAT STD 7F," 8,063 "
-28005707,HC  CATH EP ABLAT TEMP 7F 2.5MM," 6,919 "
-28005708,HC  CATH EP ABLAT SM 7F 2.5MM," 6,919 "
-28005709,HC  CATH EP ABLAT STD 7F," 6,919 "
-28005712,HC IMP VALVE AORTIC MASTERS EXPANDED CUFF 23MM," 30,674 "
-28005713,HC IMP VALVE AORTIC MASTERS EXPANDED CUFF 25MM," 29,491 "
-28005714,HC IMPL PACEMAKER DUAL KAPPA 400 DR KDR403B," 40,950 "
-28005715,HC IMP VALVE AORTIC MASTERS EXPANDED CUFF 27MM," 29,491 "
-28005716,HC IMP VALVE MITRAL MASTERS EXPANDED CUFF 29MM," 30,674 "
-28005717,HC FIBER LASER SLIMLINE 550MIC," 4,505 "
-28005718,HC  GUIDEWIRE PERIPHERAL 10IN 2X205CM," 3,156 "
-28005719,HC CABLE STERNAL SYSTEM," 3,162 "
-28005721,HC IMPL SCREW AUTODRIVE 2X4.0MM, 541 
-28005722,HC IMPL SCREW AUTODRIVE 2X6.0MM, 541 
-28005724,HC PROLASPSE ACCESSORY KIT," 2,856 "
-28005725,HC IMP VALVE PULMONARY CRYO," 60,125 "
-28005726,HC IMP PUMP SPINAL W/POUCH NO SUT 18ML," 62,563 "
-28005727,HC  CATHETER INTRASPINAL SYNCHROMEDIUM 15G," 4,378 "
-28005728,HC  INTRODUCER SHEATH CHECK FLOW 10F 30CM, 298 
-28005729,HC  CANNULA THORATEC VENT LNG LARGE.," 19,145 "
-28005730,HC  CANNULA VENTRICULAR EXTRA LONG STRAIGHT 29CM," 21,315 "
-28005731,HC  CANNULA ARTERIAL LONG 18MM," 27,615 "
-28005732,HC  CANNULA ARTERIAL SHORT 10X25CM," 8,438 "
-28005733,HC  CANNULA ARTERIAL ASSIST DEVICE LONG," 9,600 "
-28005734,HC  INTRODUCER SHEATH 35MM 35CM 6F, 298 
-28005735,HC  CATHETER DILATATION CRE 6-8MMX8X180," 1,660 "
-28005740,HC  CATH ESOPHAGEAL DILATATION FIXED WIRE 6F 18-20," 1,662 "
-28005741,HC  GUIDEWIRE JAGWIRE STRAIGHT 35IN 450CM, 980 
-28005742,HC  GUIDEWIRE JAGWIRE STRAIGHT 25IN 450CM, 978 
-28005743,HC IMPL COIL PERIPHERAL EMBOLIZATION COMPLEX 2X20X4MM, 558 
-28005744,HC IMP SCREW BIO INTEFERENCE 9X23MM," 1,964 "
-28005745,HC IMP SCREW BIO INTEFERENCE 7X23MM," 1,207 "
-28005746,HC IMP SCREW BIO INTEFERENCE 8X23MM," 1,913 "
-28005749,HC IMP BREAST SILTEX CONTOUR 450CC," 9,000 "
-28005750,HC IMP GRAFT GELSOFT 16X8," 6,503 "
-28005751,HC IMP GRAFT GELSOFT 14X7," 6,503 "
-28005753,HC  CATHETER TRANSJUGULAR LIVER ACCESS," 1,054 "
-28005754,HC  CATHETER ABLATION TEMP SENSING 4MM 7F," 11,025 "
-28005755,HC  SHEATH LASER SLS II 16F 50CM," 18,760 "
-28005756,HC  SHEATH LASER SLS II 14F 50CM," 18,760 "
-28005757,HC  SHEATH LASER SLS II 12F 50CM," 18,760 "
-28005759,HC  CATHETER ELECTROPHYSIOLOGY 10PLR DUO 95CM 7F," 8,398 "
-28005760,HC CATHETER ACCESS TRANSJUGLAR 12F SET," 4,939 "
-28005761,HC  CATHETER DRAINAGE PIGTAIL LOCKING 30CM 8F," 1,010 "
-28005762,HC  CATHETER DRAINAGE PIGTAIL LOCKING 30CM 10F, 592 
-28005763,HC  CATHETER DRAINAGE PIGTAIL LOCKING 30CM 12F, 162 
-28005764,HC  INTRODUCER SHEATH 35MM 23CM 6F, 298 
-28005765,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 10.0D, 952 
-28005766,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 11.0D, 952 
-28005768,HC  SNARE GOOSENECK 120CM 30MM 6F," 2,338 "
-28005769,HC  SNARE GOOSENECK 120CM 20MM 6F," 2,338 "
-28005770,HC  CATHETER DRAINAGE 12F 38IN 25CM, 667 
-28005771,HC IMPL SCREW WEDGE 8X25MM," 1,568 "
-28005772,HC IMP WIRE KIRSCHNER THREAD TROCAR 3/32X9IN, 244 
-28005773,HC REPOSE BONE TONGUE SUSPENSION KIT," 17,183 "
-28005774,HC  GUIDEWIRE PERIPHERAL 35IN 150X15CM, 127 
-28005775,HC  TUBE GASTROSTOMY 20F KIT, 502 
-28005777,HC STAT CATHETER HEMATURIA COUDE 22F 30ML, 250 
-28005778,HC STAT CATHETER HEMATURIA COUDE 24F 30ML, 250 
-28005779,HC IMP CEMENT BONE SIMPLEX 1/2 DOSE, 626 
-28005780,HC IMP VALVE AORTIC MASTERS EXPANDED CUFF 19MM," 32,823 "
-28005783,HC IMP PATCH PERICARDIAL 10CMX15CM," 3,375 "
-28005785,HC IMP VALVE BIOPROSTHESIS FREESTYLE 19MM," 35,750 "
-28005786,HC IMP VALVE BIOPROSTHESIS FREESTYLE 21MM," 35,750 "
-28005787,HC IMP VALVE BIOPROSTHESIS FREESTYLE 23MM," 35,750 "
-28005788,HC IMP VALVE BIOPROSTHESIS FREESTYLE 25MM," 35,750 "
-28005789,HC IMP VALVE BIOPROSTHESIS FREESTYLE 27MM," 35,750 "
-28005790,HC IMP GRAFT HEMASHIELD WOVEN ST 32X15," 3,086 "
-28005791,HC IMP GRAFT HEMASHIELD WOVEN STRAIGH 32MMX30CM," 4,182 "
-28005792,HC IMP PERISTRIP REINFORCEMENT DRY SZ4," 1,254 "
-28005793,HC  CATHETER DILATATION 5MM 2X80CM 4F," 2,114 "
-28005794,HC  CATHETER DILATATION 6MM 2X80CM 4F," 2,114 "
-28005795,HC CUTTER MOLLRING ENDART," 4,208 "
-28005796,HC IMPL SCREW TITANIUM 1.0X6MM, 655 
-28005797,HC IMPL SCREW TITANIUM 1.0X7MM, 599 
-28005798,HC IMPL SCREW TITANIUM 1.0X8MM, 706 
-28005799,HC IMPL SCREW TITANIUM 1.0X9MM, 689 
-28005800,HC IMPL SCREW TITANIUM 1.0X10MM, 672 
-28005801,HC IMPL SCREW TITANIUM 1.0X11MM, 672 
-28005802,HC IMPL SCREW TITANIUM 1.0X12MM, 672 
-28005803,HC IMPL SCREW TITANIUM 1.0X13MM, 672 
-28005804,HC IMPL SCREW TITANIUM 1.0X14MM, 672 
-28005805,HC IMPL SCREW TITANIUM 1.3X6MM, 704 
-28005806,HC IMPL SCREW TITANIUM 1.3X7MM, 704 
-28005807,HC IMPL SCREW TITANIUM 1.3X8MM, 704 
-28005808,HC IMPL SCREW TITANIUM 1.3X9MM, 704 
-28005809,HC IMPL SCREW TITANIUM 1.3X10MM, 729 
-28005810,HC IMPL SCREW TITANIUM 1.3X11MM, 740 
-28005811,HC IMPL SCREW TITANIUM 1.3X12MM, 729 
-28005812,HC IMPL SCREW TITANIUM 1.3X13MM, 689 
-28005813,HC IMPL SCREW TITANIUM 1.3X14MM, 704 
-28005814,HC IMPL SCREW TITANIUM 1.3X16MM, 729 
-28005815,HC IMPL SCREW TITANIUM 1.3X18MM, 704 
-28005816,HC IMPL SCREW TITANIUM 1.5X6MM, 453 
-28005817,HC IMPL SCREW TITANIUM 1.5X7MM, 350 
-28005818,HC IMPL SCREW TITANIUM 1.5X8MM, 453 
-28005819,HC IMPL SCREW TITANIUM 1.5X9MM, 453 
-28005820,HC IMPL SCREW TITANIUM 1.5X10MM, 350 
-28005821,HC IMPL SCREW TITANIUM 1.5X11MM, 453 
-28005822,HC IMPL SCREW TITANIUM 1.5X12MM, 453 
-28005823,HC IMPL SCREW TITANIUM 1.5X13MM, 453 
-28005824,HC IMPL SCREW TITANIUM 1.5X14MM, 453 
-28005825,HC IMPL SCREW TITANIUM 1.5X16MM, 453 
-28005826,HC IMPL SCREW TITANIUM 1.5X18MM, 453 
-28005827,HC IMPL SCREW TITANIUM 1.5X20MM, 453 
-28005828,HC IMPL SCREW TITANIUM 1.5X22MM, 438 
-28005829,HC IMPL SCREW TITANIUM 1.5X24MM, 453 
-28005830,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX6MM, 749 
-28005831,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX7MM, 642 
-28005832,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX8MM, 749 
-28005833,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX9MM, 706 
-28005834,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX10MM, 775 
-28005835,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX11MM, 749 
-28005836,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX12MM, 775 
-28005837,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX13MM, 749 
-28005838,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX14MM, 666 
-28005840,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX18MM, 666 
-28005841,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX20MM, 749 
-28005842,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX22MM, 749 
-28005843,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX24MM, 775 
-28005844,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX26MM, 775 
-28005845,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX28MM, 749 
-28005846,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX30MM, 749 
-28005847,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX32MM, 775 
-28005848,HC IMPL SCREW TITANIUM 2.0X6MM, 348 
-28005849,HC IMPL SCREW TITANIUM 2.0X7MM, 389 
-28005850,HC IMPL SCREW TITANIUM 2.0X8MM, 348 
-28005851,HC IMPL SCREW TITANIUM 2.0X9MM, 348 
-28005852,HC IMPL SCREW TITANIUM 2.0X10MM, 348 
-28005853,HC IMPL SCREW TITANIUM 2.0X11MM, 348 
-28005854,HC IMPL SCREW TITANIUM 2.0X12MM, 348 
-28005855,HC IMPL SCREW TITANIUM 2.0X13MM, 389 
-28005856,HC IMPL SCREW TITANIUM 2.0X14MM, 348 
-28005857,HC IMPL SCREW TITANIUM 2.0X16MM, 348 
-28005858,HC IMPL SCREW TITANIUM 2.0X18MM, 348 
-28005859,HC IMPL SCREW TITANIUM 2.0X20MM, 348 
-28005860,HC IMPL SCREW TITANIUM 2.0X22MM, 348 
-28005861,HC IMPL SCREW TITANIUM 2.0X24MM, 389 
-28005862,HC IMPL SCREW TITANIUM 2.0X26MM, 389 
-28005863,HC IMPL SCREW TITANIUM 2.0X28MM, 389 
-28005864,HC IMPL SCREW TITANIUM 2.0X30MM, 389 
-28005865,HC IMPL SCREW TITANIUM 2.0X32MM, 389 
-28005866,HC IMP PIN TITANIUM 1.8X18MM, 489 
-28005867,HC IMPL PLATE TITANIUM 1.3X6HOLE," 1,785 "
-28005868,HC IMPL PLATE TITANIUM 1.3X12HOLE," 2,142 "
-28005869,HC IMPL PLATE TITANIUM 1.3X15MM RIGHT," 2,049 "
-28005870,HC IMPL PLATE TITANIUM 1.3X15MM LEFT," 2,049 "
-28005871,HC IMPL PLATE TITANIUM 1.5X6HOLE," 1,785 "
-28005872,HC IMPL PLATE TITANIUM 1.5X12HOLE," 1,964 "
-28005873,HC IMP NUT SPINAL FLAT TITANIUM 11MM, 555 
-28005874,HC IMP ROD HARD 6.0MM TITANIUM 100MM," 2,360 "
-28005875,HC IMP ROD HARD 6.0MM TITANIUM 125MM," 2,360 "
-28005876,HC IMP ROD HARD 6.0MM TITANIUM 150MM," 2,125 "
-28005877,HC IMP ROD HARD 6.0MM TITANIUM 200MM," 2,125 "
-28005878,HC IMP ROD HARD 6.0MM TITANIUM 300MM," 2,125 "
-28005879,HC IMP ROD HARD 6.0MM TITANIUM 350MM," 2,565 "
-28005880,HC IMP ROD HARD 6.0MM TITANIUM 400MM," 2,125 "
-28005881,HC IMP ROD HARD 6.0MM TITANIUM 500MM," 2,788 "
-28005882,HC IMP ROD HARD TITANIUM 3.5X80MM," 2,125 "
-28005883,HC IMP ROD SOFT 6.0MM TITANIUM 50MM," 2,125 "
-28005884,HC IMP ROD SOFT 6.0MM TITANIUM 75MM," 2,117 "
-28005885,HC IMP ROD SOFT 6.0MM TITANIUM 100MM," 2,125 "
-28005886,HC IMP ROD SOFT 6.0MM TITANIUM 125MM," 2,125 "
-28005887,HC IMPL PLATE LCP NARROW 10 HOLE 4.5X18MM," 5,107 "
-28005888,HC IMP ROD SOFT 6.0MM TITANIUM 150MM," 2,125 "
-28005889,HC IMPL SCREW SCHANZ 5X60X175MM," 1,522 "
-28005890,HC  CLAMP TUBE TO TUBE SS," 7,112 "
-28005891,HC  CATHETER INFUSION UNIFUSE 135X50CM 5F," 1,942 "
-28005894,HC BUR GUARD MICROAIRE EXTRA LONG," 2,533 "
-28005895,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 10.0D," 1,075 "
-28005896,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 11.0D," 1,075 "
-28005897,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 12.0D," 1,075 "
-28005898,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 13.0D," 1,075 "
-28005899,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 14.0D," 1,075 "
-28005900,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 15.0D," 1,075 "
-28005901,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 16.0D," 1,075 "
-28005902,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 17.0D," 1,075 "
-28005903,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 17.5D," 1,075 "
-28005904,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 18.0D," 1,075 "
-28005905,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 18.5D," 1,075 "
-28005906,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 19.0D," 1,075 "
-28005907,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 19.5D," 1,075 "
-28005908,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 20.0D," 1,075 "
-28005909,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 20.5D," 1,075 "
-28005910,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 21.0D," 1,071 "
-28005911,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 21.5D," 1,075 "
-28005912,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 22.0D," 1,075 "
-28005913,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 22.5D," 1,075 "
-28005914,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 23.0D," 1,075 "
-28005915,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 23.5D," 1,075 "
-28005916,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 24.0D," 1,071 "
-28005917,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 24.5D," 1,075 "
-28005918,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 25.0D," 1,075 "
-28005919,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 25.5D," 1,075 "
-28005920,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 26.0D," 1,054 "
-28005921,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 26.5D," 1,075 "
-28005922,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 27.0D," 1,075 "
-28005923,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 27.5D," 1,075 "
-28005924,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 28.0D," 1,054 "
-28005925,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 29.0D," 1,075 "
-28005926,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 30.0D," 1,075 "
-28005928,HC IMPL PLATE ANTERIOR CERVICAL 23MM," 8,500 "
-28005929,HC IMPL PLATE ANTERIOR CERVICAL 25MM," 8,500 "
-28005933,HC IMPL PLATE ANTERIOR CERVICAL 55MM," 10,312 "
-28005934,HC IMPL PLATE ANTERIOR CERVICAL 57.5MM," 9,068 "
-28005935,HC IMPL PLATE ANTERIOR CERVICAL 70MM," 9,195 "
-28005936,HC IMPL SCREW CANCELLOUS VARIABLE ANG 4.0X13MM," 2,338 "
-28005937,HC IMPL SCREW CANCELLOUS VARIABLE ANG 4.0X14MM," 2,338 "
-28005938,HC IMPL SCREW CANCELLOUS VARIABLE ANG 4.0X15MM," 2,338 "
-28005939,HC IMPL SCREW CANCELLOUS VARIABLE ANG 4.0X16MM," 2,335 "
-28005941,HC  CATHETER DILATATION 10MM 2X110CM 6F," 1,377 "
-28005942,HC  CATHETER DILATATION 12MM 2X80CM 7F," 1,377 "
-28005943,HC  CATHETER DILATATION 10MM 2X80CM 6F," 1,377 "
-28005945,HC  CATHETER BALLOON PTA XXL18X4X75CM," 2,785 "
-28005947,HC  CATH ABLAT BLUE 4MM 92CM 6F," 5,525 "
-28005949,HC  CATHETER QUINTON LEFT 62.5CM," 1,607 "
-28005955,HC  INTRODUCER SHEATH 6FX10, 156 
-28005956,HC  INTRODUCER SHEATH 7FX10, 156 
-28005957,HC  CATHETER DILATATION PTA BM 10X40X75CM," 2,059 "
-28005958,HC  CATHETER FOGARTY OCCLUSION 8-22F, 689 
-28005959,HC  PORT A CATH PAS PORT II," 6,248 "
-28005960,HC PROBE VAGINAL PATHWAY 7.5X20CM," 2,380 "
-28005961,HC IMP TISSUE BONE CANC CUBED 30CC," 3,392 "
-28005962,HC IMP MESH PROLENE HERNIA EXT," 3,939 "
-28005963,HC IMP CLAMP EXTERNAL FIXATOR COMPRESSOR OPEN," 5,504 "
-28005964,HC IMPL SCREW SHAFT 3.5MM 16MM, 191 
-28005965,HC IMPL SCREW SHAFT 3.5MM 18MM, 191 
-28005966,HC IMPL SCREW SHAFT 3.5MM 20MM, 140 
-28005967,HC IMPL SCREW SHAFT 3.5MM 22MM, 184 
-28005968,HC IMPL SCREW SHAFT 3.5MM 24MM, 140 
-28005969,HC IMPL SCREW SHAFT 3.5MM 26MM, 184 
-28005970,HC IMPL SCREW SHAFT 3.5MM 28MM, 140 
-28005971,HC IMPL SCREW SHAFT 3.5MM 30MM, 140 
-28005972,HC IMPL SCREW SHAFT 3.5MM 32MM, 140 
-28005973,HC IMPL SCREW SHAFT 3.5MM 34MM, 140 
-28005974,HC IMPL SCREW SHAFT 3.5MM 36MM, 140 
-28005975,HC IMPL SCREW SHAFT 3.5MM 38MM, 184 
-28005976,HC  SHEATH CUSTOM 8.0FX9CM, 791 
-28005977,HC  INTROD CSTM PED 5FX9CM SET, 725 
-28005978,HC IMP VALVE MITRAL MASTERS EXPANDED CUFF 31MM," 30,674 "
-28005979,HC IMPL COIL PERIPHERAL EMBOLIZATION SPRNG, 459 
-28005980,HC IMPL COIL PERIPHERAL EMBOLIZATION 8CM 12MM 0.038IN," 1,173 "
-28005981,HC IMPL COIL PERIPHERAL EMBOLIZATION 8MM 10CM 0.035IN," 1,173 "
-28005982,HC  GUIDEWIRE CURVED TIP 260CM 21IN 3MM, 199 
-28005983,HC  GUIDEWIRE STEERABLE FLEXIBLE 18IN 295CM, 680 
-28005985,HC  INTRODUCER SHEATH 25CM 9F 2.5CM, 170 
-28005986,HC  CATHETER CARDIO RETRO PED 6F, 170 
-28005987,HC  CATHETER CARDIO RETRO PED 10F, 165 
-28005989,HC IMPL COIL PERIPHERAL EMBOLIZATION 2-3MM 0.018IN," 1,488 "
-28005990,HC IMPL COIL PERIPHERAL EMBOLIZATION 2-4MM 0.018IN," 1,488 "
-28005991,HC IMPL COIL PERIPHERAL EMBOLIZATION 2-5MM 0.018IN," 1,488 "
-28005992,HC IMPL COIL PERIPHERAL EMBOLIZATION 2-6MM 0.018IN," 1,488 "
-28005993,HC  SHEATH INTEGRA 7.5CM SET, 187 
-28005994,HC  INTROD PERC 7.5CM 5F SET, 167 
-28005995,HC  SHEATH INTEGRA 11CM 6F SET, 167 
-28005996,HC  SHEATH INTEGRA ADULT 11CM 7F SET, 167 
-28005997,HC IMP HIP HEAD FEMORAL EZE COCR +1 32MM," 4,767 "
-28005998,HC IMP HIP HEAD FEMORAL EZE COCR +9 32MM," 4,767 "
-28005999,HC  CATHETER DILATATION 2X70CM 7MM 4F," 4,004 "
-28006000,HC  CATHETER DILATATION 2X70CM 8MM 4F," 3,945 "
-28006001,HC  CATHETER DILATATION TYSHAK II 12MMX3CM," 4,004 "
-28006002,HC  CATHETER BALLOON IMPACT 20MMX4CM," 2,595 "
-28006003,HC  CATHETER BALLOON IMPACT 25MMX4CM," 2,595 "
-28006007,HC  GUIDEWIRE T-J CURVED FIXD 25IN 145CM, 100 
-28006008,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 16.5D," 1,075 "
-28006009,HC IMPL PLATE H 1.5MM TITANIUM RIGHT 18MM," 1,879 "
-28006010,HC IMPL PLATE H 1.5MM TITANIUM LEFT 18MM," 2,270 "
-28006011,HC IMPL PLATE Y TITANIUM 1.5MM," 1,972 "
-28006012,HC IMPL PLATE ST TITANIUM 2.0MM 35MM," 2,069 "
-28006013,HC IMPL PLATE ST TITANIUM 2.0MM 71MM," 2,480 "
-28006014,HC IMPL PLATE Y TITANIUM 2.0MM 48MM," 2,480 "
-28006015,HC IMPL PLATE ST TITANIUM 2.4MM 36MM," 2,270 "
-28006016,HC IMPL PLATE ST TITANIUM 2.4MM 72MM," 2,681 "
-28006017,HC IMPL PLATE Y TITANIUM 2.4MM 56MM," 2,380 "
-28006018,HC IMP MESH PROLENE HERNIA LG," 3,831 "
-28006019,HC IMP GRAFT GORTEX THIN WALLED 5X80," 6,868 "
-28006020,HC IMP PECTUS BAR SUPPORT 8IN," 5,916 "
-28006021,HC IMP PECTUS BAR SUPPORT 9IN," 7,616 "
-28006022,HC IMP PECTUS BAR SS 10IN," 7,616 "
-28006023,HC IMP PECTUS BAR SS 11IN," 7,616 "
-28006024,HC IMP PECTUS BAR SS 12IN," 7,616 "
-28006025,HC IMP PECTUS BAR SS 13IN," 7,616 "
-28006026,HC IMP PECTUS BAR SS 14IN," 7,875 "
-28006027,HC IMP PECTUS BAR SS 15IN," 7,875 "
-28006028,HC IMPL SEALANT FIB TIS FROZEN 4ML," 1,545 "
-28006029,HC IMPL SEALANT FIB TIS PRFL 10ML," 3,791 "
-28006038,HC  CATHETER ARTERIAL EMBOLECTOMY 80CM 4F, 412 
-28006039,HC FORCEP BIOPSY 50CM 5F 1.5MM," 2,715 "
-28006040,HC FORCEP BIOPSY 105CM 5F 1.5MM," 2,715 "
-28006041,HC  INTROD GUID RADIOPAQ 5FX75CM, 706 
-28006042,HC  INTRODUCER RADIOPAQUE CHECK-FLO 5FX45CM, 706 
-28006043,HC IMP PATCH SOFT TISSUE 10X15X2MM," 8,118 "
-28006045,HC  CATHETER GUIDING JL4 6F, 451 
-28006046,HC  CATHETER GUIDING AL1 6F, 451 
-28006047,HC  CATHETER GUIDING EBU4 6F, 451 
-28006048,HC  CATHETER GUIDING JR4 6F, 451 
-28006050,HC FUHRMAN PLEURAL PNEUMOCAR C-PP, 735 
-28006052,HC IMP VALVE MITRAL MASTERS EXPANDED CUFF 25MM," 30,674 "
-28006057,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 10.5D," 1,075 "
-28006058,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 11.5D," 1,075 "
-28006059,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 12.5D," 1,075 "
-28006060,HC  CATHETER CORONARY RX 3X18MM 2.6F," 1,615 "
-28006061,HC  CATHETER CORONARY RX 18X3.5MM 2.7F," 1,615 "
-28006062,HC  CATHETER CORONARY RX 8X2.5MM 2.6F," 1,615 "
-28006063,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 13.5D," 1,075 "
-28006064,HC  CATHETER CORONARY RX 8X3MM 2.6F," 1,615 "
-28006065,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 14.5D," 1,075 "
-28006066,HC  CATHETER CORONARY RX 8X3.5MM 2.7F," 1,615 "
-28006067,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 15.5D," 1,075 "
-28006068,HC  CATHETER CORONARY RX 8X4MM 2.6F," 1,615 "
-28006069,HC IMP LEAD PACEMAKER VENTRICULAR 58CM 8.6MM," 4,675 "
-28006070,HC IMPL SCREW SHANTZ 5X150MM, 813 
-28006071,HC  CATHETER CORONARY RX 18X4MM 2.8F," 1,615 "
-28006072,HC TUBE BLAKEMORE 12F," 3,156 "
-28006073,HC IMP SHEETING SILASTIC NONREINFORCED .010X6X8 SILIC," 1,190 "
-28006074,HC  GUIDEWIRE J WIRE 0.25INX50CM, 92 
-28006076,HC IMP MATRIX BONE FLEX DBM 2.5X5CM," 7,132 "
-28006077,HC IMP ANCHOR FASTEN THRD 4.0MM SZ2," 2,448 "
-28006078,HC  CATHETER GUIDING VISTA BRITE TIP 7F HS, 451 
-28006081,HC  GUIDEWIRE INTRAVASCULAR 14IN 205CM," 3,276 "
-28006090,HC IMPL SCREW CORTEX TITANIUM 3.5X38MM, 119 
-28006092,HC IMPL SCREW CORTEX TITANIUM 3.5X45MM, 119 
-28006093,HC IMPL SCREW CORTEX TITANIUM 3.5X50MM, 119 
-28006094,HC IMPL SCREW CORTEX TITANIUM 3.5X55MM, 200 
-28006095,HC IMPL SCREW TITANIUM 3.5X16MM, 153 
-28006096,HC IMPL SCREW TITANIUM 3.5X18MM, 153 
-28006097,HC IMPL SCREW TITANIUM 3.5X20MM, 153 
-28006098,HC IMPL SCREW TITANIUM 3.5X22MM, 153 
-28006099,HC IMPL SCREW TITANIUM 3.5X24MM, 153 
-28006100,HC IMPL SCREW TITANIUM 3.5X26MM, 153 
-28006101,HC IMPL SCREW TITANIUM 3.5X30MM, 153 
-28006102,HC IMPL SCREW TITANIUM 3.5X32MM, 153 
-28006103,HC IMPL SCREW TITANIUM 3.5X34MM, 153 
-28006104,HC IMPL SCREW TITANIUM 3.5X36MM, 153 
-28006105,HC IMPL SCREW TITANIUM 3.5X38MM, 153 
-28006106,HC  CATHETER INFUSION UNIFUSE 45X10CM 5F," 1,165 "
-28006107,HC  CATHETER INFUSION UNIFUSE 45X15CM 5F," 1,165 "
-28006108,HC  CATHETER INFUSION UNIFUSE 90X10CM 4F," 1,165 "
-28006109,HC  CATHETER INFUSION UNIFUSE 90X20CM 4F," 1,204 "
-28006110,HC  CATHETER INFUSION UNIFUSE 90X30CM 5F," 1,165 "
-28006111,HC  CATHETER INFUSION UNIFUSE 90X40CM 5F," 1,165 "
-28006112,HC  CATHETER INFUSION UNIFUSE 90X50CM 5F," 1,165 "
-28006113,HC  CATHETER INFUSION UNIFUSE 135X20CM 4F," 1,204 "
-28006114,HC  CATHETER INFUSION UNIFUSE 135X30CM 5F," 1,165 "
-28006115,HC  CATHETER INFUSION UNIFUSE 135X40CM 5F," 1,165 "
-28006116,HC IMP PIN STEINMAN DMD PT 3/32X9IN, 143 
-28006117,HC  CATHETER GUIDING 150X10CM 3F 2.5F," 5,473 "
-28006141,HC STABILIZER TISSUE OCTOPUS 4," 8,500 "
-28006142,HC IMP BONE TIBIAL PROXIMAL RIGHT," 23,429 "
-28006143,HC IMP BONE TIBIAL PROXIMAL LEFT," 25,774 "
-28006148,HC AUTOGRAFT TRANSFER SYSTEM 8MM," 4,242 "
-28006149,HC AUTOGRAFT TRANSFER SYSTEM 9MM," 4,242 "
-28006150,HC AUTOGRAFT TRANSFER SYSTEM 10MM," 4,242 "
-28006157,HC OMNIPAQUE 350 100ML BT, 66 
-28006162,HC WRIST EXT FIX RADIOLUC KIT," 10,125 "
-28006163,HC IMP ACHILLES TENDON W CALC," 13,388 "
-28006164,HC  GUIDEWIRE GI 35IN 500CM," 1,095 "
-28006168,HC  GUIDEWIRE HI TORQUE STEEL 18IN 190, 779 
-28006169,HC  GUIDEWIRE PERIPHERAL STEELCORE 18X300, 779 
-28006170,HC  CATHETER BALLOON ANGIOGRAPHY BERMAN 50CM 4F, 467 
-28006171,HC  CATHETER BALLOON PTCA RX 2X9MM," 2,128 "
-28006172,HC  CATHETER BALLOON PTCA RX 2.5X9MM," 7,535 "
-28006173,HC  CATHETER BALLOON PTCA RX 3X9MM," 7,535 "
-28006174,HC IMP HIP STEM VERSYS CEMENTED 12MM," 15,421 "
-28006175,HC IMP HIP STEM VERSYS CEMENTED 14MM," 19,551 "
-28006176,HC IMPL PACEMAKER DUAL KAPPA 700 SR KDR703," 40,950 "
-28006177,HC IMP HIP HEAD ENDOPROSTHESIS 43MM," 3,677 "
-28006178,HC IMP HIP HEAD ENDOPROSTHESIS 45MM," 3,677 "
-28006179,HC IMP HIP HEAD ENDOPROSTHESIS 46MM," 3,677 "
-28006180,HC IMP HIP HEAD ENDOPROSTHESIS 50MM," 3,677 "
-28006181,HC IMP HIP HEAD ENDOPROSTHESIS 55MM," 3,677 "
-28006182,HC IMP ADAPTER VERSYS ENDO HEAD +7MM," 1,021 "
-28006183,HC IMP HIP SHELL TRILOGY MULTI HOLE 46MM," 8,274 "
-28006184,HC IMP HIP SHELL TRILOGY MULTI HOLE 50MM," 8,274 "
-28006185,HC IMP HIP SHELL TRILOGY MULTI HOLE 52MM," 8,274 "
-28006186,HC IMP HIP SHELL TRILOGY MULTI HOLE 54MM," 8,274 "
-28006187,HC IMP HIP SHELL TRILOGY MULTI HOLE 56MM," 8,274 "
-28006188,HC IMP HIP SHELL TRILOGY MULTI HOLE 58MM," 8,274 "
-28006189,HC IMP HIP SHELL TRILOGY MULTI HOLE 60MM," 8,274 "
-28006190,HC IMP HIP SHELL TRILOGY MULTI HOLE 62MM," 8,274 "
-28006191,HC IMPL SCREW TRILOGY 6.5X15MM, 128 
-28006192,HC IMPL SCREW TRILOGY 6.5X20MM, 128 
-28006193,HC IMPL SCREW TRILOGY 6.5X25MM, 821 
-28006194,HC IMPL SCREW TRILOGY 6.5X30MM, 128 
-28006195,HC IMPL SCREW TRILOGY 6.5X35MM, 128 
-28006196,HC IMPL SCREW TRILOGY 6.5X40MM, 821 
-28006197,HC IMP HIP CENTRALIZER DISTAL VERSYS 9MM, 230 
-28006198,HC IMP HIP CENTRALIZER DISTAL VERSYS 10MM, 230 
-28006199,HC IMP HIP CENTRALIZER DISTAL VERSYS 11MM, 230 
-28006200,HC IMP HIP CENTRALIZER DISTAL VERSYS 12MM, 230 
-28006201,HC IMP HIP CENTRALIZER DISTAL VERSYS 13MM, 230 
-28006202,HC IMP HIP CENTRALIZER DISTAL VERSYS 14MM, 230 
-28006203,HC IMP HIP HEAD FEMORAL COCR 28X-3.5MM," 3,494 "
-28006204,HC IMP HIP HEAD FEMORAL COCR 28X0.0MM," 3,494 "
-28006205,HC IMP HIP HEAD FEMORAL COCR 28X+3.5MM," 3,494 "
-28006206,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X34MM," 1,598 "
-28006207,HC IMP HIP HEAD FEMORAL COCR 32X-3.5MM," 3,494 "
-28006208,HC IMP HIP HEAD FEMORAL COCR 32X0.0MM," 3,494 "
-28006209,HC IMP HIP HEAD FEMORAL COCR 32X+3.5MM," 3,494 "
-28006210,HC IMP HIP HEAD FEMORAL COCR +7 32MM," 3,494 "
-28006211,HC IMPL PACEMAKER SINGLE KAPPA 700 SR KSR701," 34,265 "
-28006212,HC IMP STENT BILIARY SMART OTW 6X40X80," 11,104 "
-28006214,HC IMP STENT BILIARY SMART OTW 7X80X120," 13,331 "
-28006215,HC IMP STENT BILIARY SMART OTW 4X80X80," 11,100 "
-28006216,HC IMP STENT BILIARY SMART OTW 6X80X120," 13,331 "
-28006218,HC IMP STENT BILIARY SMART OTW 10X40X80," 11,100 "
-28006219,HC IMP STENT BILIARY SMART OTW 10X60X80," 11,100 "
-28006220,HC IMP STENT BILIARY SMART OTW 10X80X80," 11,100 "
-28006221,HC IMP POWDER DEMINERALIZED CORTICAL 5ML," 1,464 "
-28006223,HC  INTRODUCER ENDOVASCULAR MID LENGTH 8FX23CM, 213 
-28006224,HC IMPL SCREW CERVICAL 12MMX4.35MM," 2,354 "
-28006225,HC IMPL SCREW CERVICAL 14MMX4.35MM," 2,559 "
-28006226,HC  SNARE GOOSENECK 10MM," 2,338 "
-28006227,HC IMPL PLATE TUBULAR 1/3 TITANIUM WCOLLAR, 642 
-28006228,HC  CATHETER GUIDING VISTA BRITE TIP 6F XBLAD 3.5, 442 
-28006229,HC  CATHETER GUIDING VISTA BRITE TIP 6F IM, 442 
-28006230,HC IMP LEAD PACEMAKER CAPSUREFIX NOVUS 52CM," 5,525 "
-28006231,HC  CATHETER SIZING 5F 70CM .35 20CM, 978 
-28006233,HC  GUIDEWIRE NITINOL BABYWIRE 12IN 50CM, 200 
-28006234,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 22MMX3CM," 4,021 "
-28006235,HC IMP ALLODERM 2CMX4CM," 3,519 "
-28006236,HC IMP ALLODERM 3CMX7CM," 4,964 "
-28006237,HC IMP ALLODERM 4CMX16CM," 13,088 "
-28006238,HC IMP CLAMP FIX STANDARD MINIPENNIG," 1,806 "
-28006239,HC IMP NUT COMPRESS DISTRACTION, 128 
-28006240,HC IMP BAR FIXATION STRAIGHT MINI SMALL," 2,593 "
-28006241,HC IMP CLIP ANEURYSM MINI STRAIGHT KEES SLIMLINE 7MM," 3,732 "
-28006242,HC IMPL PACEMAKER DUAL KAPPA 700 SR KDR701," 38,318 "
-28006243,HC IMP PUTTY BONE NORIAN FAST 5CC SET," 9,894 "
-28006244,HC IMP PUTTY BONE NORIAN CRS 10ML," 18,192 "
-28006245,HC IMP LEAD PACEMAKER CAPSUREFIX SP NOVUS 58CM," 5,525 "
-28006246,HC IMP VALVE AORTIC TOP HAT 19MM," 30,611 "
-28006247,HC IMP VALVE AORTIC TOP HAT 21MM," 32,473 "
-28006248,HC IMP VALVE AORTIC TOP HAT 23MM," 32,473 "
-28006249,HC IMP VALVE AORTIC TOP HAT 25MM," 33,208 "
-28006250,HC  INTRODUCER SHEATH HEMOSTASIS 12CM 12F, 204 
-28006251,HC IMPL PACEMAKER DUAL KAPPA 700 DR KDR721," 38,318 "
-28006252,HC IMP CAGE SPINAL INTERBODY FUSION 10X30MM," 18,750 "
-28006253,HC IMP CAGE SPINAL INTERBODY FUSION 12X16MM," 9,638 "
-28006254,HC IMPL GRAFT GORETEX THIN WALLED 6MMX40CM," 4,752 "
-28006255,HC IMP STIMULATOR NEUROSTIM ITREL II," 55,543 "
-28006256,HC IMP EXTENSION LEAD NEUROSTIMULATOR 25CM KIT," 4,208 "
-28006257,HC IMP EXTENSION LEAD NEUROSTIMULATOR 51CM KIT," 5,440 "
-28006258,HC IMP GENERATOR NERVE VAGUS NCP 100," 44,850 "
-28006259,HC IMP LEAD BIPOLAR VAGUS NERVE STIMULATOR MOD 20," 17,475 "
-28006260,HC IMP LEAD BIPOLAR VAGUS NERVE STIMULATOR MOD 30," 17,475 "
-28006262,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 20MMX30CM," 4,021 "
-28006265,HC  CATHETER PIGTAIL 5F 110CM, 188 
-28006266,HC IMPL SCREW SHANTZ 5X250MM, 791 
-28006267,HC CLAMP UNIVERSAL 6 POSITION," 7,874 "
-28006268,HC IMP PATCH MESH COMPOSIX 10X14IN," 13,545 "
-28006271,HC NEEDLE ELECTRODE LEVEEN 2X15CM," 9,055 "
-28006272,HC NEEDLE ELECTRODE LEVEEN 3X15CM," 10,500 "
-28006273,HC NEEDLE ELECTRODE LEVEEN 3.5X15CM," 11,470 "
-28006275,HC IMP BARRIER ADHESION INTERCEED 3X4," 2,412 "
-28006279,HC IMP LEAD PACEMAKER CAPSUREFIX 4568-45," 6,120 "
-28006280,HC  SNARE NEEDLE EYE 12FX94CMX20MM," 7,081 "
-28006282,HC IMPL PACEMAKER DUAL KAPPA 600 DR KDR403B," 33,600 "
-28006283,HC  INTRODUCER LEAD SAFESHEATH 7F 13CM, 570 
-28006284,HC  CATHETER DILATATION CRE 18-20MM," 1,870 "
-28006285,HC IMP STENT ULTRAFLEX 0.038IN 10X7CM," 10,980 "
-28006286,HC CANNULA PITTSBURGH CELL," 12,713 "
-28006287,HC  CATHETER ELECTROPHYSIOLOGY 4PLR FXDCRV 120CM 6F, 842 
-28006290,HC BLADE LANCET DIAMOND," 3,613 "
-28006292,HC IMPL PACEMAKER SINGLE KAPPA 700 SR KSR703," 33,800 "
-28006294,HC IMPL PATCH DACRON SAUVAGE 6X6IN," 1,411 "
-28006296,HC CATHETER TREROTOLA PTD 5FX65CM SET," 3,706 "
-28006297,HC IMP LEAD PACEMAKER VENTRICULAR UNIPLR 35CM," 4,675 "
-28006298,HC IMP FASCIA LATA SMALL," 2,933 "
-28006299,HC IMP STRIP FASCIA 20CMX25MM," 3,366 "
-28006300,HC  GUIDEWIRE FIXED CORE 260CM 0.032IN 3MM, 113 
-28006302,HC CABLE SLEEVE 2MM SET," 3,114 "
-28006303,HC IMP TENDON BONE HEMI QUAD," 16,253 "
-28006304,HC IMP TENDON BONE HEMI NO QUAD," 8,550 "
-28006305,HC IMP SPACER FROZEN 11MM NARROW," 25,676 "
-28006306,HC IMP SPACER FROZEN 13MM NARROW," 25,676 "
-28006307,HC IMP SPACER FROZEN 15MM NARROW," 25,676 "
-28006308,HC IMP SPACER FROZEN 17MM NARROW," 25,676 "
-28006309,HC IMP SPACER FROZEN 17MM BROAD," 26,950 "
-28006310,HC IMP SPACER FROZEN 11MM A-L NARROW," 25,676 "
-28006311,HC IMP SPACER FROZEN 13MM A-L NARROW," 25,676 "
-28006312,HC IMP SPACER FROZEN 15MM A-L NARROW," 26,950 "
-28006313,HC IMP SPACER FROZEN 17MM A-L NARROW," 25,676 "
-28006314,HC IMP SPACER FROZEN 21MM A-L BROAD," 25,095 "
-28006315,HC  TUBE GASTRO ENTERIC 3-5ML 16FX25.4CM," 1,353 "
-28006316,HC  CATHETER CENTRAL VENOUS DUAL LUMEN 12FX90 CM," 1,063 "
-28006317,HC  CATHETER DILATATION PTA POWERFLEX P3 4X2X80CM, 978 
-28006318,HC  CATHETER DILATATION PTA POWERFLEX P3 4X4X80CM, 978 
-28006319,HC  CATHETER DILATATION PTA PF 5X2X80," 1,517 "
-28006320,HC  CATHETER DILATATION PTA POWERFLEX P3 5X4X80CM," 1,517 "
-28006321,HC  CATHETER DILATATION PTA POWERFLEX P3 6X2X80CM," 1,377 "
-28006322,HC  CATHETER DILATATION PTA PF 6X4X80," 1,517 "
-28006323,HC  CATHETER DILATATION PTA POWERFLEX P3 7X2X80CM," 1,517 "
-28006324,HC  CATHETER DILATATION PTA PF 7X4X80," 1,517 "
-28006325,HC  CATHETER DILATATION PTA POWERFLEX P3 8X2X80CM," 1,517 "
-28006326,HC  CATHETER DILATATION POWERFLEX PTA 8X3X80," 1,518 "
-28006327,HC  CATHETER DILATATION PTA POWERFLEX P3 8X4X80CM," 1,517 "
-28006328,HC  CATHETER DILATATION 8X6X80CM 7F," 1,517 "
-28006329,HC  CATHETER DILATATION PTA POWERFLEX P3 9X2X80CM," 1,517 "
-28006330,HC  CATHETER DILATATION 10MM 2X80CM 7F," 1,517 "
-28006331,HC  CATHETER DILATATION PTA PF 10X4X80," 1,517 "
-28006332,HC  CATHETER DILATATION PTA POWERFLEX P3 10X2X80CM," 1,517 "
-28006333,HC  CATHETER DILATATION PTA POWERFLEX P3 12X4X80CM," 1,517 "
-28006334,HC  CATHETER DILATATION 4MM 2X110CM 5F," 1,517 "
-28006335,HC  CATHETER DILATATION 12MM 2X110CM 7F," 1,517 "
-28006336,HC IMP MESH VICRYL KNITTED 12X12IN," 9,889 "
-28006337,HC  CATHETER GUIDING VISTA BRITE TIP 6F RCB, 442 
-28006338,HC  CATHETER GUIDING VISTA BRITE TIP 6F MPA1 100CM, 535 
-28006339,HC  CATHETER GUIDING VISTA BRITE TIP 6F AL1, 442 
-28006340,HC IMP PECTUS BAR SUPPORT 7IN," 7,616 "
-28006341,HC IMP LEAD PACEMAKER PASSIVE VENTRICULAR BIPOLAR," 5,525 "
-28006342,HC IMP MEMBRANE PERICARDIAL PRECLUDE 6X12," 3,035 "
-28006343,HC IMP MESH MARLEX 10X14IN," 2,023 "
-28006344,HC IMP CLIP ANEURYSM SUNDT ANG TEMP ANG," 4,284 "
-28006347,HC  CATHETER DILATATION TYSHAK II 14MMX3CM," 4,120 "
-28006348,HC IMP CABLE CERCLAGE 1.8MX22," 2,645 "
-28006349,HC IMP CABLE CERCLAGE 1.8MX36," 3,040 "
-28006350,HC IMPL PLATE OSTEOTOMY 90MM 50MM X15MM," 4,378 "
-28006351,HC  CATHETER GUIDING VISTA BRITE TIP 6F JL3.5, 442 
-28006352,HC  CATHETER GUIDING VISTA BRITE TIP 6F AR1, 442 
-28006353,HC  CATHETER GUIDING VISTA BRITE TIP 6F AL2, 442 
-28006355,HC  CATHETER INFUSION UNIFUSE 90X5CM 5F," 1,165 "
-28006356,HC  CATHETER INFUSION UNIFUSE 135X5CM 5F," 1,165 "
-28006357,HC IMP SCREW CORTEX SELF TAPPING 4.5X76MM, 352 
-28006358,HC IMP SCREW CORTEX SELF TAPPING 4.5X80MM, 352 
-28006359,HC IMP SCREW CORTEX SELF TAPPING 4.5X14MM, 218 
-28006360,HC IMP SCREW CORTEX SELF TAPPING 4.5X16MM, 218 
-28006361,HC IMP SCREW CORTEX SELF TAPPING 4.5X18MM, 210 
-28006362,HC IMP SCREW CORTEX SELF TAPPING 4.5X20MM, 208 
-28006363,HC IMP SCREW CORTEX SELF TAPPING 4.5X22MM, 218 
-28006364,HC IMP SCREW CORTEX SELF TAPPING 4.5X24MM, 210 
-28006365,HC IMP SCREW CORTEX SELF TAPPING 4.5X26MM, 218 
-28006366,HC IMP SCREW CORTEX SELF TAPPING 4.5X70MM, 218 
-28006367,HC IMP SCREW CORTEX SELF TAPPING 4.5X56MM, 218 
-28006368,HC IMP SCREW CORTEX SELF TAPPING 4.5X58MM, 218 
-28006369,HC IMP SCREW CORTEX SELF TAPPING 4.5X60MM, 218 
-28006370,HC IMP SCREW CORTEX SELF TAPPING 4.5X64MM, 218 
-28006371,HC IMP SCREW CORTEX SELF TAPPING 4.5X68MM, 210 
-28006372,HC IMP HIP LINER ACETABULAR XLPE 10 DEG 44MM," 3,400 "
-28006373,HC IMP HIP LINER ACETABULAR XLPE 10 DEG 46MM," 3,400 "
-28006374,HC IMP LEAD PACEMAKER VENTRICULAR SUREFIX 52CM," 6,120 "
-28006375,HC IMP BONE TIBIA SHAFT FROZEN 7CM," 4,437 "
-28006376,HC IMP BONE TIBIA SHAFT FROZEN 10CM," 6,095 "
-28006377,HC IMP PORT MRI 9.6F 76CM KIT," 2,026 "
-28006378,HC  CATHETER DILATATION 5MM 2X80CM 5F," 2,241 "
-28006379,HC  CATHETER DILATATION 6MM 2X80CM 5F," 2,907 "
-28006380,HC  CATHETER GUIDING 55CM 0.1X70IN 6F, 512 
-28006381,HC BUR DRILL BIT CANN QCK COUP 2X150MM," 4,063 "
-28006382,HC IMPL SCREW 3.0 CANNULATED LONG THREAD 28MM," 1,918 "
-28006383,HC  INTRODUCER GUIDING SWARTZ .032 8X81," 1,700 "
-28006384,HC  CATHETER GUIDING HKY STK 6F MACH1, 468 
-28006386,HC  GUIDEWIRE AMPLATZ J SHAPE 35IN 260CM, 155 
-28006387,HC  INTRODUCER SHEATH 35MM 55MM 8F, 298 
-28006388,HC  CATHETER DILATATION 4X2X80," 2,114 "
-28006389,HC  GUIDEWIRE XSTIF STRAIGHT 35IN 145CM, 91 
-28006391,HC IMP STENT GRAFT AAA ANEURX 20X12X13.5," 41,714 "
-28006392,HC IMP STENT GRAFT AAA ANEURX 22X13X13.5," 41,714 "
-28006393,HC IMP STENT GRAFT AAA ANEURX 24X14X13.5," 41,714 "
-28006394,HC IMP STENT GRAFT AAA ANEURX 26X15X13.5," 41,714 "
-28006395,HC IMP STENT GRAFT AAA ANEURX 28X16X13.5," 41,714 "
-28006396,HC IMP STENT GRAFT AAA ANEURX 20X12X16.5," 43,095 "
-28006397,HC IMP STENT GRAFT AAA ANEURX 22X13X16.5," 43,095 "
-28006398,HC IMP STENT GRAFT AAA ANEURX 24X14X16.5," 43,095 "
-28006399,HC IMP STENT GRAFT AAA ANEURX 26X15X16.5," 43,095 "
-28006400,HC IMP STENT GRAFT AAA ANEURX 28X16X16.5," 43,095 "
-28006401,HC IMP STENT GRAFT AAA ANEURX 12X12X8.5," 17,053 "
-28006402,HC IMP STENT GRAFT AAA ANEURX 13X13X8.5," 17,053 "
-28006403,HC IMP STENT GRAFT AAA ANEURX 14X14X8.5," 17,053 "
-28006404,HC IMP STENT GRAFT AAA ANEURX 15X15X8.5," 17,053 "
-28006405,HC IMP STENT GRAFT AAA ANEURX 16X16X8.5," 17,053 "
-28006406,HC IMP STENT GRAFT AAA ANEURX 12X12X11.5," 18,009 "
-28006407,HC IMP STENT GRAFT AAA ANEURX 13X13X11.5," 18,009 "
-28006408,HC IMP STENT GRAFT AAA ANEURX 14X14X11.5," 18,009 "
-28006409,HC IMP STENT GRAFT AAA ANEURX 15X15X11.5," 18,009 "
-28006410,HC IMP STENT GRAFT AAA ANEURX 16X16X11.5," 18,009 "
-28006411,HC IMP STENT GRAFT AAA ANEURX 12X12X5.5," 14,184 "
-28006412,HC IMP STENT GRAFT AAA ANEURX 13X13X5.5," 14,184 "
-28006413,HC IMP STENT GRAFT AAA ANEURX 14X14X5.5," 14,184 "
-28006414,HC IMP STENT GRAFT AAA ANEURX 15X15X5.5," 14,184 "
-28006415,HC IMP STENT GRAFT AAA ANEURX 16X16X5.5," 14,184 "
-28006416,HC IMP STENT GRAFT AAA ANEURX 20X20X4," 14,184 "
-28006417,HC IMP STENT GRAFT AAA ANEURX 22X22X4," 14,184 "
-28006418,HC IMP STENT GRAFT AAA ANEURX 24X24X4," 14,184 "
-28006419,HC IMP STENT GRAFT AAA ANEURX 26X26X4," 14,184 "
-28006420,HC IMP STENT GRAFT AAA ANEURX 28X28X4," 14,184 "
-28006421,HC IMP STENT BILIARY HERCULINK RX 7X18X80," 10,313 "
-28006422,HC  GUIDEWIRE PERIPHERAL 14IN 5X190CM, 876 
-28006423,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 6.0D," 1,075 "
-28006424,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 7.0D," 1,075 "
-28006425,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 7.5D," 1,075 "
-28006426,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 8.0D," 1,075 "
-28006427,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 8.5D," 1,075 "
-28006428,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 9.5D," 1,075 "
-28006429,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X20MM," 1,964 "
-28006430,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X22MM," 1,964 "
-28006431,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X24MM," 1,598 "
-28006432,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X26MM," 1,870 "
-28006433,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X28MM," 1,870 "
-28006434,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X30MM," 1,964 "
-28006435,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X32MM," 1,870 "
-28006436,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X36MM," 1,598 "
-28006437,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X38MM," 1,828 "
-28006438,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X40MM," 1,598 "
-28006439,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X42MM," 1,935 "
-28006440,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X44MM," 1,828 "
-28006441,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X46MM," 1,935 "
-28006442,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X48MM," 1,935 "
-28006443,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X50MM," 1,935 "
-28006444,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X52MM," 1,935 "
-28006445,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X54MM," 1,935 "
-28006446,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X56MM," 1,935 "
-28006447,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X60MM," 1,598 "
-28006448,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X64MM," 1,935 "
-28006449,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X68MM," 1,870 "
-28006450,HC IMPL SCREW CANNULATED FULLY THREAD 4.5X72MM," 1,935 "
-28006452,HC IMPL PACEMAKER SINGLE V186," 90,000 "
-28006453,HC IMPL PACEMAKER DUAL DIAMOND II 820," 40,300 "
-28006454,HC  CATHETER MARKER 7.5CM 2.4F 1.9F," 5,713 "
-28006455,HC IMP SPHERE HYDROXAPETITE SPHERE 14MM," 4,420 "
-28006456,HC  CATHETER VENTRICULAR 20F, 179 
-28006459,HC COUNTERSINK CANNULATED 7.3MM," 3,808 "
-28006460,HC  CATHETER GUIDING VISTA BRITE TIP LCB 6F, 442 
-28006461,HC IMPL SCREW CORTEX TITANIUM 3.5X10MM, 119 
-28006462,HC IMPL PLATE LC DCP TITANIUM 3.5MM 6HOLES," 1,326 "
-28006463,HC IMPL PLATE LC DCP TITANIUM 3.5MM 7HOLES," 1,386 "
-28006464,HC IMPL PLATE LC DCP TITANIUM 3.5MM 8HOLES," 1,420 "
-28006465,HC IMPL PLATE LC DCP TITANIUM 3.5MM 9HOLES," 1,488 "
-28006466,HC IMPL PLATE LC DCP TITANIUM 3.5MM 10HOLE," 1,590 "
-28006467,HC IMPL PLATE T RIGHT ANGLE TITANIUM 8X50MM," 1,131 "
-28006468,HC IMPL PLATE T RIGHT ANGLE TITANIUM 8X67MM," 1,292 "
-28006469,HC IMPL PLATE T OBLIQUE TITANIUM SMALL 53MM," 1,938 "
-28006470,HC IMPL PLATE T OBLIQUE TITANIUM 8X63MM," 2,040 "
-28006471,HC IMPL PLATE T OBLIQUE TITANIUM 8X75MM," 2,142 "
-28006472,HC IMPL PLATE 1/3 TUBULAR W COLLAR 2, 506 
-28006473,HC IMPL PLATE 1/3 TUBULAR W COLLAR 3, 514 
-28006474,HC IMPL PLATE 1/3 TUBULAR W COLLAR 4, 642 
-28006475,HC IMPL PLATE 1/3 TUBULAR W COLLAR 6, 667 
-28006476,HC IMPL PLATE 1/3 TUBULAR W COLLAR 7, 667 
-28006477,HC IMPL PLATE 1/3 TUBULAR W COLLAR 8, 689 
-28006478,HC IMPL PLATE 1/3 TUBULAR W COLLAR 10, 689 
-28006479,HC  CATHETER ABLATION COLD CRV LG CHILLI," 8,250 "
-28006480,HC IMP STENT BILIARY SMART OTW 10X40X120," 11,100 "
-28006481,HC IMP STENT BILIARY SMART OTW 7X40X120," 11,104 "
-28006482,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 6.5D," 1,075 "
-28006483,HC  CATHETER DILATATION 2X90CM 10MM 5F," 4,120 "
-28006484,HC  CATHETER DILATATION 3X100CM 15MM 7F," 4,004 "
-28006485,HC  CATHETER DILATATION 4X100CM 20MM 8F," 4,120 "
-28006486,HC  CATHETER DILATATION 4X100CM 22MM 8F," 4,120 "
-28006487,HC  CATHETER DILATATION 4X100CM 23MM 9F," 3,460 "
-28006488,HC  CATHETER DILATATION 4X100CM 25MM 9F," 4,120 "
-28006489,HC  CATHETER DILATATION SHAFT 16X4CM 7F," 4,004 "
-28006490,HC  CATHETER DILATATION TYSHAK II 30MM 4CM," 5,059 "
-28006491,HC IMP SHUNT LUMBAR PERITONEAL CSF 87CM," 7,321 "
-28006492,HC IMP STENT BILIARY GENESIS UNMOUNTED 10X40," 9,000 "
-28006493,HC IMP STENT BILIARY GENESIS UNMOUNTED 10X50," 9,000 "
-28006494,HC IMP SHOULDER HEAD HUMERAL BI-ANGULAR 40X15MM," 3,870 "
-28006499,HC IMPL PLATE NARROW STRAIGHT 6HOLE," 2,237 "
-28006500,HC IMPL PLATE NARROW CURVED 4HOLE," 2,434 "
-28006501,HC IMPL PLATE NARROW CURVED 6HOLE," 2,618 "
-28006502,HC IMPL PLATE ANG DYNAMIC COMPRESSION PLATE 4X4," 3,543 "
-28006503,HC IMPL PLATE CURVED STRUT," 3,543 "
-28006504,HC IMPL PLATE STRAIGHT BROAD 4HOLE," 2,618 "
-28006505,HC IMPL PLATE STRAIGHT BROAD 8HOLE," 2,978 "
-28006506,HC IMPL PLATE CURVED BROAD 6HOLE," 3,176 "
-28006507,HC IMPL PLATE DYNAMIC COMPRESSION 7 HOLE," 2,822 "
-28006508,HC IMPL PLATE DYNAMIC COMPRESSION 8 HOLE," 2,978 "
-28006509,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX6MM, 666 
-28006510,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX12MM, 666 
-28006512,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX16MM, 666 
-28006514,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX34MM, 666 
-28006515,HC IMPL SCREW CORTICAL SELF TAPPING 2.4MMX40MM, 666 
-28006516,HC IMPL PLATE BONE COMPRESSION 4HOLE," 2,625 "
-28006517,HC IMPL PLATE BONE COMPRESSION 6HOLE," 2,829 "
-28006518,HC IMPL PLATE LC DYNAMIC COMPRESSION 4 HOLE," 3,720 "
-28006519,HC IMPL PLATE LC DYNAMIC COMPRESSION 5 HOLE," 4,100 "
-28006520,HC IMPL PLATE LC DYNAMIC COMPRESSION 6 HOLE," 4,454 "
-28006521,HC IMPL PLATE LC DYNAMIC COMPRESSION CRESCENT 4 HOLE," 4,100 "
-28006522,HC IMPL PLATE LC DYNAMIC COMPRESSION CRESCENT 6 HOLE," 4,454 "
-28006523,HC IMPL PLATE UNIV FRACTURE 8 HOLE," 4,100 "
-28006524,HC IMPL PLATE UNIV FRACTURE 10 HOLE," 4,379 "
-28006525,HC IMPL SCREW 2.4MM LOCKING 10MM," 1,125 "
-28006526,HC IMPL SCREW 2.4MM LOCKING 12MM," 1,125 "
-28006527,HC IMPL SCREW 2.4MM LOCKING 14MM," 1,125 "
-28006528,HC IMPL SCREW 3.0MM LOCKING 8MM," 1,306 "
-28006529,HC IMPL SCREW 3.0MM LOCKING 10MM," 1,306 "
-28006530,HC IMPL SCREW 3.0MM LOCKING 12MM," 1,306 "
-28006531,HC IMPL SCREW 3.0MM LOCKING 14MM," 6,528 "
-28006532,HC IMPL SCREW 3.0MM LOCKING 16MM," 1,306 "
-28006533,HC IMPL PLATE STRAIGHT 12 HOLE," 6,589 "
-28006534,HC IMPL PLATE STRAIGHT 20 HOLE," 8,052 "
-28006535,HC IMPL PLATE ANGLE LEFT 6X23 HOLE," 12,126 "
-28006536,HC IMPL PLATE DOUBLE ANGLE 4X20X4 HOLE," 14,034 "
-28006537,HC IMPL PLATE DOUBLE ANGLE 5X22X5 HOLE," 14,808 "
-28006538,HC IMP SCREW CORTEX 1X3MM, 626 
-28006539,HC IMP SCREW CORTEX 1X4MM, 598 
-28006540,HC IMP SCREW CORTEX 1X6MM, 598 
-28006541,HC IMPL SCREW EMERGENCY 1.2MMX3MM, 622 
-28006542,HC IMPL SCREW EMERGENCY 1.2MMX4MM, 622 
-28006543,HC IMPL SCREW EMERGENCY 1.2MMX6MM, 622 
-28006544,HC IMPL PLATE STRUT 6MMX30MM," 3,420 "
-28006545,HC IMPL PLATE STRUT 6MMX42MM," 3,842 "
-28006546,HC IMPL PLATE ADAPTION 34HOLE," 4,216 "
-28006547,HC IMPL PLATE ORBITAL FLOOR," 7,936 "
-28006548,HC IMPL PLATE Y 9 HOLE," 1,598 "
-28006549,HC IMPL PLATE Y 13 HOLE," 1,598 "
-28006550,HC IMPL PLATE Y DOUBLE," 1,693 "
-28006551,HC IMPL PLATE H 6 HOLE," 1,510 "
-28006552,HC IMPL PLATE H 11 HOLE," 1,693 "
-28006553,HC IMP SCREW CORTEX 1.3MMX3MM, 568 
-28006554,HC IMP SCREW CORTEX 1.3MMX4MM, 568 
-28006555,HC IMP SCREW CORTEX 1.3MMX5MM, 568 
-28006556,HC IMP SCREW CORTEX 1.3MMX6MM, 568 
-28006557,HC IMPL SCREW EMERGENCY 1.3MMX3MM, 592 
-28006558,HC IMPL SCREW EMERGENCY 1.3MMX4MM, 592 
-28006559,HC IMPL SCREW EMERGENCY 1.3MMX5MM, 592 
-28006560,HC IMPL SCREW EMERGENCY 1.3MMX6MM, 592 
-28006561,HC IMPL SCREW EMERGENCY 1.3MMX8MM, 592 
-28006562,HC IMPL PLATE ADAPTION 24 HOLE," 4,372 "
-28006563,HC IMPL PLATE MESH SILVER MALLABLE 3D," 9,168 "
-28006564,HC IMPL PLATE MESH GOLD RIGID 3D," 9,168 "
-28006565,HC IMPL PLATE DOUBLE Y 15MM," 1,632 "
-28006566,HC IMPL PLATE DOUBLE Y 18MM," 1,632 "
-28006567,HC IMPL PLATE CRANIAL TSHP 1.3MM 7HOLE," 1,646 "
-28006568,HC IMPL PLATE STRUT TITANIUM 18H 1.3X43X5MM," 3,448 "
-28006569,HC IMP BUR HOLE COVER 17MM," 1,829 "
-28006570,HC IMPL PLATE BOX 10MMX5MM," 1,476 "
-28006571,HC IMPL PLATE BOX 10MMX10MM," 1,476 "
-28006572,HC IMP MESH SCREEN 100X100," 8,167 "
-28006573,HC IMP SCREW CORTEX 1.5MMX4MM, 401 
-28006574,HC IMP SCREW CORTEX 1.5MMX5MM, 422 
-28006575,HC IMP SCREW CORTEX 1.5MMX6MM, 401 
-28006576,HC IMP SCREW CORTEX 1.5MMX8MM, 401 
-28006577,HC IMP SCREW CORTEX 1.5MMX10MM, 401 
-28006578,HC IMP SCREW CORTEX 1.5MMX12MM, 401 
-28006579,HC IMPL SCREW EMERGENCY 1.5MMX4MM, 428 
-28006580,HC IMPL SCREW EMERGENCY 1.5MMX6MM, 428 
-28006581,HC IMPL SCREW EMERGENCY 1.5MMX8MM, 428 
-28006582,HC IMPL SCREW EMERGENCY 1.5MMX10MM, 428 
-28006584,HC IMPL PLATE 1.5MM OBLIQUE L LEFT 5 HOLE," 1,761 "
-28006585,HC IMPL PLATE OBLIQUE L RIGHT 5 HOLE," 1,761 "
-28006586,HC IMPL PLATE OBLIQUE L LEFT 7 HOLE," 1,863 "
-28006587,HC IMPL PLATE OBLIQUE L RIGHT 7 HOLE," 1,863 "
-28006589,HC IMP SCREW CORTEX 2.0MMX4MM, 405 
-28006590,HC IMP SCREW CORTEX 2.0MMX6MM, 405 
-28006591,HC IMP SCREW CORTEX 2.0MMX8MM, 405 
-28006592,HC IMP SCREW CORTEX 2.0MMX10MM, 405 
-28006593,HC IMP SCREW CORTEX 2.0MMX12MM, 405 
-28006594,HC IMPL PLATE OBLIQUE LEFT 5 HOLE," 1,686 "
-28006595,HC IMPL PLATE OBLIQUE RIGHT 5 HOLE," 1,686 "
-28006596,HC IMPL PLATE OBLIQUE LEFT 7 HOLE," 1,680 "
-28006597,HC IMPL PLATE OBLIQUE RIGHT 7 HOLE," 1,680 "
-28006598,HC IMPL PLATE OBLIQUE LEFT 10 HOLE," 1,836 "
-28006599,HC IMPL PLATE OBLIQUE RIGHT 10 HOLE," 1,836 "
-28006602,HC IMPL PLATE Y 5 HOLE," 1,618 "
-28006603,HC IMPL PLATE Y 8 HOLE," 1,659 "
-28006605,HC IMP BONE GRAFT OPTEFORM 2ML," 2,763 "
-28006606,HC IMP BONE GRAFT OPTEFORM 8ML," 8,075 "
-28006607,HC  GUIDEWIRE DEFLECTING 25IN 145CM 5MM, 553 
-28006608,HC DEVICE LEAD LOCKING 43X0.66MM," 3,868 "
-28006609,HC DEVICE LEAD LOCKING 69X0.81MM," 3,551 "
-28006610,HC DEVICE LEAD LOCKING 33X0.41MM," 3,868 "
-28006611,HC  CATH ABLAT EP 35MM 4MM," 4,038 "
-28006612,HC  CATH ABLAT EP 45MM 4MM," 4,038 "
-28006617,HC PROBE ELECTROSURGICAL VULCAN MINI TAC S," 3,039 "
-28006618,HC PROBE ELECTROSURGICAL VULCAN TEMP CONROL TAC S," 2,794 "
-28006625,HC IMPL SCREW HIP 6.5MMX25MM," 1,522 "
-28006626,HC IMP KNEE STEM EXTENSION 20X120MM," 5,814 "
-28006627,HC  CATHETER ELECTROPHYSIOLOGY 10PLR FXDCRV 65CM 5F," 2,975 "
-28006628,HC FIBER LASER CYLINDER OPTIGUIDE CYLN 200 2.0CM," 7,608 "
-28006629,HC FIBER LASER CYLINDER OPTIGUIDE CYLN 200 1.0CM," 7,608 "
-28006630,HC FIBER LASER CYLINDER OPTIGUIDE CYLN 200 2.5CM," 7,608 "
-28006631,HC FIBER LASER CYLINDER OPTIGUIDE CYLN 200 5.0CM," 7,608 "
-28006633,HC  CATHETER DILATATION 3X100CM 18MM 8F," 4,004 "
-28006640,HC IMP KNEE ARTICULAR SURFACE NEXGEN 1-2 10MM," 5,814 "
-28006641,HC IMP KNEE ARTICULAR SURFACE NEXGEN 1-2 12MM," 4,597 "
-28006642,HC IMP KNEE ARTICULAR SURFACE NEXGEN 1-2 14MM," 4,597 "
-28006643,HC IMP KNEE STEM NEXGEN 1MM," 3,714 "
-28006644,HC IMP KNEE STEM NEXGEN 2MM," 3,714 "
-28006647,HC IMP VALVE MITRAL MASTERS EXPANDED CUFF 27MM," 30,674 "
-28006648,HC IMP LEAD STIMULATOR DEEP BRAIN 1.5MM WIDE," 20,860 "
-28006649,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 16MMX30CM," 4,021 "
-28006650,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 24MMX30CM," 4,222 "
-28006652,HC IMP GENERATOR NERVE VAGUS NCP 101," 63,395 "
-28006653,HC IMP ROD TRANSCONNECTOR 6X15MM," 8,576 "
-28006654,HC IMP ROD TRANSCONNECTOR 6X18MM," 8,576 "
-28006655,HC IMP ROD TRANSCONNECTOR 6X24MM," 8,576 "
-28006656,HC IMP ROD TRANSCONNECTOR 35-41MM 25.5MM," 8,500 "
-28006657,HC IMP ROD TRANSCONNECTOR 35-41MM 6MM," 9,262 "
-28006658,HC IMP ROD TRANSCONNECTOR 42-55MM 6MM," 9,262 "
-28006659,HC IMP ROD TRANSCONNECTOR 56-83MM 6MM," 9,262 "
-28006661,HC  CATHETER INFUSION MICRO 2.9F 145CM," 1,530 "
-28006662,HC IMP SCREW CORTEX 1.3MMX8MM, 568 
-28006663,HC IMP CONNECTOR SPINAL PARALLEL TITANIUM 6MM/6MM," 5,517 "
-28006664,HC IMP EYE SPHERE ORBITAL SILICONE 14MM, 272 
-28006665,HC IMP EYE SPHERE ORBITAL SILICONE 20MM, 289 
-28006666,HC IMP EYE SPHERE ORBITAL SILICONE 22MM, 289 
-28006667,HC IMPL MATRIX BONE GRAFTN CRUNCH 15ML," 11,685 "
-28006672,HC IMP LINK SPINELINK STANDARD 24MM," 2,125 "
-28006673,HC  DRAIN BILE T TUBE WHELAN MOSS STERILE 12F, 166 
-28006675,HC  CATHETER DELIVERY VENTRICULAR SEPTL DEFCT," 4,250 "
-28006676,HC STAPLER PROTACK 5MM," 4,287 "
-28006677,HC IMP VALVE CSF CONTOURED LO PRESS," 5,297 "
-28006678,HC IMP STENT BILIARY SMART OTW 14X80X80," 12,000 "
-28006682,HC  CATHETER GUIDING 50CM 88IN 8F, 553 
-28006683,HC  CATHETER GUIDING LIMA 50CM 7F, 553 
-28006684,HC  CATHETER DILATATION PTA 0.018IN 7X20X80CM," 2,338 "
-28006685,HC  CATHETER DILATATION 18IN 9X20X80CM," 2,338 "
-28006686,HC  CATHETER DILATATION 8X30MM 80CM," 2,338 "
-28006687,HC  CATHETER DILATATION 10X30MM 80CM," 2,338 "
-28006688,HC  CATHETER DILATATION 7X40MM 80CM," 2,338 "
-28006689,HC  CATHETER DILATATION PTA AGILTRAC 018 8X40X80CM," 2,338 "
-28006690,HC  CATHETER DILATATION 9X40MM 80CM," 2,338 "
-28006691,HC  CATHETER DILATATION 10X40MM 80CM," 2,338 "
-28006692,HC  CATHETER DILATATION PTA 7X20X135CM," 2,338 "
-28006693,HC  CATHETER DILATATION 18IN 8X20X135CM," 2,338 "
-28006694,HC  CATHETER GUIDING JL 3.5 6F, 451 
-28006695,HC IMP PIN STEINMAN THREAD 5/64X9IN, 230 
-28006696,HC IMP PIN STEINMAN THREAD 1/8X9IN, 244 
-28006697,HC IMP PIN STEINMAN THREAD 9/64X9IN, 244 
-28006698,HC IMP PIN STEINMAN THREAD 5/32X9IN, 230 
-28006699,HC IMP PIN STEINMAN THREAD 3/16X9IN, 272 
-28006701,HC IMP WIRE KIRSCHNER TROCAR POINT 0.035X9IN, 303 
-28006702,HC IMPL DOWEL BONE POSTERIOR 16MM," 14,250 "
-28006703,HC IMPL DOWEL BONE POSTERIOR 14MM," 14,250 "
-28006704,HC IMP MESH DUAL 10X15CMX1MM," 3,213 "
-28006705,HC IMP MESH DUAL 15X19CMX1MM," 6,435 "
-28006706,HC IMP MESH DUAL 20X30CMX1MM," 11,033 "
-28006707,HC IMP SCREW CORTEX 2.0MMX5MM, 405 
-28006708,HC CATHETER CORONARY ADVANTAGE JL 2.5 5F, 315 
-28006709,HC IMPL PACEMAKER DUAL DIAMOND II 820E," 40,950 "
-28006710,HC  CATHETER GUIDING EBU3.5 6F, 451 
-28006711,HC IMP PARTICLE EMBOLIZATION CONTOUR PVA," 1,105 "
-28006713,HC  CATHETER DILATATION BILIARY SOEHENDRA 5F, 486 
-28006714,HC IMP STENT BILIARY SMART OTW 6X60X80," 11,104 "
-28006716,HC IMP STENT BILIARY SMART OTW 8X60X80," 11,104 "
-28006717,HC IMP STENT BILIARY SMART OTW 8X80X120," 13,331 "
-28006718,HC IMP STENT BILIARY SMART OTW 10X60X120," 11,104 "
-28006719,HC IMP STENT BILIARY SMART OTW 10X80X120," 11,100 "
-28006720,HC  CATHETER DILATATION PTA AGILTRAC 018 6X20X80CM," 2,338 "
-28006721,HC IMP STENT BILIARY GENESIS UNMOUNTED 10X30," 9,000 "
-28006722,HC  INTRODUCER LEAD SAFESHEATH 9F 13CM, 502 
-28006723,HC  INTRODUCER LEAD SAFESHEATH 11F 13CM, 306 
-28006724,HC  INTRODUCER LEAD SAFESHEATH 8F 13CM, 649 
-28006725,HC  CATHETER GUIDING VISTA BRITE TIP 7F VTBL, 557 
-28006727,HC  CATH ABLAT EP CRVD 6FX92CM," 5,236 "
-28006728,HC  GUIDEWIRE STEERABLE 18IN 165X2CM," 1,530 "
-28006729,HC  CATHETER RECORDING 2PLR 5F 100CM," 2,882 "
-28006730,HC IMP STENT URETERAL SINGLE J 7X90," 2,967 "
-28006731,HC IMPL AGENT BULKING DURASPHERE 1ML," 1,913 "
-28006732,HC IMP LOOP WIRE HOUSE STAPEDECTOMY SZ 5.0, 549 
-28006734,HC AUTOTRANSFUSION ORTHOPAT KIT," 3,237 "
-28006735,HC  CATHETER GUIDING CORONARY LEFT CRV 7F 0.081IN, 451 
-28006736,HC  CATHETER ELECTROPHYSIOLOGY 10PLR LMN 65CM 7F," 3,400 "
-28006737,HC  CATHETER GUIDING 7F 100CM, 451 
-28006738,HC  CATHETER GUIDING CORONARY AL Z2 1 .081IN 7F, 451 
-28006739,HC IMP STENT WALLGRAFT 12X50X90," 13,777 "
-28006740,HC IMP STENT WALLGRAFT 14X50X90," 13,777 "
-28006741,HC IMP STENT WALLGRAFT 8X30X90," 13,777 "
-28006742,HC IMP STENT WALLGRAFT 8X50X90," 13,777 "
-28006743,HC IMP STENT WALLGRAFT 10X20X90," 14,625 "
-28006744,HC CLAMP OCCLUDING NOVARE ANG SM," 4,463 "
-28006750,HC IMPL PLATE RESORBABLE 3D MESH .8MM," 8,736 "
-28006752,HC IMPL SCREW RESORBABLE 1.5 4MM 2PKG, 595 
-28006753,HC IMPL SCREW RESORBABLE 1.5 4MM 10PKG, 595 
-28006754,HC IMPL SCREW RESORBABLE 1.5 6MM 4PKG, 595 
-28006755,HC IMPL PLATE RESORBABLE RIGHT 2.0 OBLIQUE," 1,020 "
-28006756,HC IMPL SCREW RESORBABLE 2.0 4MM 2PKG, 595 
-28006757,HC IMPL SCREW RESORBABLE 2.0 6MM 2PKG, 595 
-28006758,HC IMPL PLATE RESORBABLE ORBITAL 1.5 FLOOR," 5,712 "
-28006759,HC IMP BUR HOLE COVER EXTENDED ARMS," 1,829 "
-28006760,HC IMPL PLATE ST RESORBABLE 1.5 MM 4HOLE," 1,319 "
-28006761,HC IMPL PLATE ST RESORBABLE 1.5 MM 2HOLE, 884 
-28006762,HC  CATHETER SIZING 5F 100CM .35 20CM," 1,010 "
-28006763,HC  INTRODUCER FLEXOR CHECK-FLO 10FX38.5CM, 713 
-28006764,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5MMX1IN," 6,681 "
-28006765,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5MMX1.125IN," 11,018 "
-28006766,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5MMX1.25IN," 7,225 "
-28006767,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5MMX1.375IN," 11,243 "
-28006768,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5MMX1.5IN," 7,650 "
-28006769,HC IMPL PLATE LOW PROFILE ADJUSTABLE 5.5X1.55-1.75IN," 12,960 "
-28006770,HC IMPL PLATE LOW PROFILE ADJUSTABLE 5.5X1.75-2.15IN," 7,718 "
-28006771,HC IMPL ICD DUAL JEWEL AF 7250H," 77,000 "
-28006772,HC IMP LEAD VENTRICULAR ADX 65CM," 37,375 "
-28006773,HC  INTRODUCER SOFT TIP CONVOY 120D 8.5FX79.4CM," 2,117 "
-28006774,HC  GUIDEWIRE NEURO TRANSEND 14IN 182CM," 3,276 "
-28006775,HC  CATHETER GUIDING CORONARY FL3.5 Z2 7F, 451 
-28006776,HC  CATHETER GUIDING CORONARY MP HS1 0.081IN 7F, 451 
-28006778,HC IMP LEAD PACEMAKER CAPSUREFIX 65CM," 10,969 "
-28006779,HC IMP PARTICLE EMBOLIZATION EMBOSPHERE RED," 2,210 "
-28006780,HC  CATHETER GUIDING CORONARY CORONARY XBU 3.5 7F, 451 
-28006781,HC  CATHETER FAST 12CM 10F, 204 
-28006782,HC IMPL COIL NEURO DETACHABLE 3CM 2MM GDC18," 6,871 "
-28006783,HC IMPL COIL NEURO DETACHABLE 4CM 2MM GDC18," 9,553 "
-28006784,HC IMPL COIL NEURO DETACHABLE 5CM 2MM GDC18," 10,187 "
-28006785,HC IMPL COIL NEURO DETACHABLE 6CM 2MM GDC18," 10,505 "
-28006786,HC  INTRODUCER SHEATH HEMOSTASIS 12CM 11F, 204 
-28006787,HC IMP EMBOLIC LIQUID TRUFILL 10CC," 29,593 "
-28006788,HC  CATHETER DILATATION 14MM 4X80CM 8F," 2,432 "
-28006789,HC  CATHETER DILATATION 14MM 6X80CM 7F," 2,924 "
-28006790,HC  CATHETER DILATATION 16MM 4X80CM 10F," 2,432 "
-28006791,HC  CATHETER DILATATION 16MM 6X80CM 7F," 2,924 "
-28006792,HC  CATHETER DILATATION 18MM 6X80CM 7F," 2,432 "
-28006793,HC  CATHETER DILATATION 20MM 6X80CM 7F," 2,924 "
-28006794,HC  CATHETER GUIDING VISTA BRITE RDC1 7X55CM, 760 
-28006795,HC  CATHETER GUIDING VISTA BRITE RDC 7X55CM, 760 
-28006796,HC  CATHETER GUIDING VISTA BRITE CONTRA 7X55CM, 760 
-28006797,HC  CATHETER GUIDING VISTA BRITE HS IG 7X55CM, 760 
-28006798,HC  CATHETER GUIDING VISTA BRITE RDC1 8FX55CM, 760 
-28006799,HC  CATHETER GUIDING RENAL DOUBLE CURVE VISTA 8F 55, 760 
-28006800,HC  CATHETER GUIDING VISTA BRITE 8FX55CM, 760 
-28006801,HC  CATHETER GUIDING VISTA BRITE RDC 7X35CM, 298 
-28006803,HC  CATHETER GUIDING SH JR 0.071IN 6F, 451 
-28006804,HC IMP LEAD PACEMAKER CAPSUREFIX NOVUS SILICONE 45CM," 4,675 "
-28006805,HC IMP LEAD PACEMAKER CAPSUREFIX NOVUS SILICONE 52CM," 4,675 "
-28006808,HC IMP PATCH SOFT TISSUE 10X15CMX1MM," 4,701 "
-28006809,HC  PROBE TEMPERATURE MYOCARDIAL 18MM, 212 
-28006810,HC LASER FIBER ENDOSTAT 3MM," 3,783 "
-28006811,HC  INTRODUCER GUIDING FAST CATH 8.5X73," 1,063 "
-28006813,HC  INTRODUCER SHEATH 45CM 35CM 7F, 298 
-28006814,HC  INTRODUCER SHEATH 35MM 35CM 8F, 298 
-28006815,HC  INTRODUCER SHEATH 35MM 45CM 8F, 298 
-28006816,HC  GUIDEWIRE PERIPHERAL 14IN 5X300CM, 876 
-28006817,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 9.0D, 952 
-28006820,HC  CATHETER BALLOON ENDOS ULTRASOUND 30F, 177 
-28006821,HC  GUIDEWIRE PERIPHERAL 14IN 10X190CM, 765 
-28006824,HC OMNIPAQUE 350  50ML BOTTLE, 106 
-28006827,HC IMP GLAUCOMA BAERVELDT," 5,942 "
-28006829,HC IMP PUTTY BONE MATRIX DBX 10CC," 10,688 "
-28006830,HC  CATHETER MICRO PROWLER 150X45CM 2.8F," 6,280 "
-28006831,HC  INTRODUCER LEAD PACEMAKER 9 FR, 638 
-28006832,HC  CATHETER BALLOON PTA 5MM 2X50CM 6F," 1,972 "
-28006833,HC  CATHETER BALLOON PTA 8MM 4X50CM 6F," 1,972 "
-28006834,HC  CATHETER BALLOON PTA 8MM 4X75CM 6F," 1,972 "
-28006835,HC IMP LEAD PACEMAKER CAPSUREFIX 110CM," 6,120 "
-28006836,HC  L PORT A CATH EPIDURAL LOW PROFILE SYSTEM," 6,358 "
-28006837,HC LIGATOR SIX SHOOTER MB 11.5 MM," 2,442 "
-28006838,HC PROBE SHRINK TAC C II," 2,794 "
-28006839,HC IMP PUTTY BONE MATRIX DBX 1CC," 1,955 "
-28006840,HC  INTROD CK FLO XLRG 22X25 SET," 2,882 "
-28006841,HC IMPL PACEMAKER SINGLE KAPPA 700 SR KSR706," 35,000 "
-28006842,HC IMPL PACEMAKER DUAL KAPPA 600 DR KDR603," 33,600 "
-28006843,HC IMP PUMP PROGRAMABLE SYNCHROMED 18ML," 62,563 "
-28006844,HC  CATHETER INTRATHECAL MODEL 8709," 4,420 "
-28006845,HC IMP COMPACT ELECT PISCES QUAD 33MM," 9,375 "
-28006846,HC RECEIVER 51CM," 34,370 "
-28006847,HC IMP NEUROSTIMULATOR SYNERGY EZ," 79,740 "
-28006849,HC IMPL COIL PERIPHERAL EMBOLIZATION 12CM 8MM, 565 
-28006852,HC IMP ANCHOR SUTURE BIO PUSHLOCK 3.5X19.5MM," 2,890 "
-28006853,HC LASER FIBER COAX 6MM 12FT," 4,208 "
-28006854,HC PROBE ABLATION INTRAOPERATIVE," 17,250 "
-28006856,HC  CATHETER GUIDING CORONARY 0.081IN 7F 100CM, 451 
-28006857,HC  CATHETER GUIDING CORONARY LCB 7F 100CM, 451 
-28006858,HC  CATHETER GUIDING CORONARY AR Z2 1 .081IN 7F, 451 
-28006859,HC  CATHETER GUIDING DUAL LUMEN, 202 
-28006860,HC PACK PERFUSION LIVER CARMEDA," 4,803 "
-28006862,HC IMPL COIL NEURO DETACHABLE NURVASCLR 1CM 2MM," 6,898 "
-28006863,HC IMPL COIL NEURO DETACHABLE NURVASCLR 2CM 2MM," 7,793 "
-28006864,HC IMPL COIL NEURO DETACHABLE NURVASCLR 3CM 2MM," 7,834 "
-28006865,HC IMPL COIL NEURO DETACHABLE NURVASCLR 4CM 3MM," 7,442 "
-28006866,HC IMPL COIL NEURO DETACHABLE NURVASCLR 6CM 3MM," 7,524 "
-28006867,HC IMPL COIL NEURO DETACHABLE NURVASCLR 4CM 4MM," 7,695 "
-28006868,HC  CANNULA ATRIAL TIP BEVELED LONG 11CM," 19,600 "
-28006869,HC PERFUSION ACCESSORY THORATEC KIT," 2,550 "
-28006870,HC  GUIDEWIRE VASCULAR 14IN 300CM," 1,102 "
-28006871,HC CABLE EXTENSION CATH 10FT," 2,975 "
-28006873,HC  CATHETER BALLOON PTCA OTW GW 1.5X9MM," 7,949 "
-28006874,HC  CATHETER BALLOON PTCA OTW GW 2X9MM," 7,379 "
-28006875,HC  CATHETER BALLOON PTCA 0.014IN 2.5X9MM," 7,949 "
-28006877,HC IMP PARTICLE EMBOLIZATION EMBOSPHERE YELLOW, 484 
-28006878,HC IMP PARTICLE EMBOLIZATION EMBOSPHERE BLUE," 2,210 "
-28006879,HC IMP FILTER VENA CAVA JUGULAR DELIVERY 30X50MM," 8,075 "
-28006882,HC IMP LEAD INTERSTIMULATOR KIT," 13,275 "
-28006885,HC IMP NEUROSTIMULATOR INTERSTIM TEST KIT," 19,600 "
-28006886,HC IMP INTERSTIM KIT," 63,668 "
-28006887,HC IMP EXTENSION INTERSTIMULATOR 10CM," 4,080 "
-28006889,HC IMP STENT WALLGRAFT 9X30X90," 13,777 "
-28006890,HC IMP STENT BILIARY EXPRESS 6X17X75," 9,735 "
-28006891,HC IMP STENT BILIARY EXPRESS 7X40X75," 9,143 "
-28006892,HC IMP STENT BILIARY EXPRESS 8X40X75," 10,487 "
-28006897,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 13.0D," 1,139 "
-28006898,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 13.5D," 1,139 "
-28006899,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 14.0D," 1,139 "
-28006900,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 14.5D," 1,139 "
-28006901,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 15.0D," 1,139 "
-28006902,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 15.5D," 1,139 "
-28006903,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 16.0D," 1,139 "
-28006904,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 16.5D," 1,139 "
-28006905,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 17.0D," 1,139 "
-28006906,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 17.5D," 1,139 "
-28006907,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 18.0D," 1,139 "
-28006908,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 21.0D," 1,139 "
-28006909,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 21.5D," 1,139 "
-28006910,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 22.0D," 1,139 "
-28006911,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 22.5D," 1,139 "
-28006912,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 23.0D," 1,139 "
-28006913,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 23.5D," 1,139 "
-28006914,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 24.0D," 1,139 "
-28006915,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 24.5D," 1,139 "
-28006916,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 25.0D," 1,139 "
-28006917,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 25.5D," 1,139 "
-28006918,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 26.0D," 1,139 "
-28006919,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 26.5D," 1,139 "
-28006920,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 27.0D," 1,139 "
-28006921,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 27.5D," 1,139 "
-28006922,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 28.0D," 1,139 "
-28006923,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 28.5D," 1,139 "
-28006924,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 29.0D," 1,139 "
-28006925,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 11.0D," 1,139 "
-28006926,HC CATHETER NEPHROMAX," 2,788 "
-28006927,HC IMP SCREW BONE FULLY THREAD 30MM, 327 
-28006928,HC IMP WASHER SCREW LARGE TITANIUM 13MM, 268 
-28006929,HC IMP MATRIX COLLAGEN PELVICOL 6X8CM," 8,310 "
-28006930,HC IMP MATRIX COLLAGEN PELVICOL 2X7CM," 4,449 "
-28006931,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 18.5D," 1,139 "
-28006932,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 19.0D," 1,139 "
-28006933,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 19.5D," 1,139 "
-28006934,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 20.0D," 1,139 "
-28006935,HC IMP FILLER BONE OSTEOSET 20ML 4.8MM," 9,488 "
-28006936,HC IMP FILLER BONE OSTEOSET 10ML 4.8MM," 6,163 "
-28006937,HC  GUIDEWIRE JAGWIRE BILIARY 25IN 260CM, 850 
-28006939,HC  INTRODUCER VASCULAR 8FX38CM, 646 
-28006940,HC  INTRODUCER RADIOPAQUE PERFORMER 9FX75CM, 450 
-28006941,HC  INTRODUCER CHECK FLO 10FX75CM, 791 
-28006942,HC  INTRODUCER SHEATH MULLINS 12FX75CM, 791 
-28006943,HC  CATHETER GUIDING 3 71IN 6F, 451 
-28006944,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 20.5D," 1,139 "
-28006945,HC  GUIDEWIRE STEERABLE EXTENSION 14IN 145CM, 808 
-28006946,HC  GUIDEWIRE EXTENSION 14IN 145CM, 846 
-28006947,HC IMP BREAST SMOOTH GEL ROUND 300CC," 7,225 "
-28006948,HC IMP PORT VENOUS ACCESS PAS ELITE," 5,882 "
-28006949,HC  CATHETER ABLATION 7F 4X1X7X4X115CM," 11,025 "
-28006950,HC  INTRODUCER SHEATH 35MM 45CM 6F, 385 
-28006951,HC IMP STENT COLON WALLSTENT 20X60," 12,251 "
-28006952,HC IMP BUTTON BEV ACTIVENT 1.27MM, 131 
-28006953,HC IMP STENT BILIARY COTTON LEUNG CLSO 10-9, 445 
-28006954,HC  CATHETER BALLOON PTCA RX 2X15MM," 7,535 "
-28006955,HC  CATHETER BALLOON PTCA RX 2.5X15MM," 2,128 "
-28006956,HC  CATHETER BALLOON PTCA RX 3X15MM," 2,128 "
-28006957,HC  CATHETER BALLOON PTCA RX 3.25X15MM," 2,117 "
-28006958,HC  CATHETER BALLOON PTCA RX 3.5X15MM," 2,117 "
-28006959,HC  CATHETER BALLOON PTCA RX 2.75X15MM," 7,535 "
-28006960,HC  CATHETER BALLOON PTCA RX 2.75X9MM," 2,117 "
-28006961,HC COVER CRANIAL BASE AND CAP," 5,525 "
-28006962,HC IMP STENT WALLGRAFT 10X50X90," 13,777 "
-28006963,HC IMP STENT WALLGRAFT 10X70X90," 15,896 "
-28006964,HC IMP RING ANNULOPLASTY ANNULOFLO 30MM," 15,735 "
-28006965,HC IMP RING ANNULOPLASTY ANNULOFLO 32MM," 16,129 "
-28006966,HC IMP ALLODERM TISSUE CYMETRA," 2,210 "
-28006967,HC IMPL PACEMAKER DUAL PHOTON V230," 100,000 "
-28006970,HC SLING MALE STRAIGHT KIT," 19,670 "
-28006971,HC  GUIDEWIRE VASCULAR 8IN 10X200CM," 3,885 "
-28006974,HC IMPL PATELLA TENDON-HEMI QUAD," 17,640 "
-28006975,HC IMPL PATELLA TENDON HEMI NO QUAD," 8,550 "
-28006976,HC IMPL PATELLA TENDON BONE WHOLE," 29,106 "
-28006977,HC IMP ACHILLES TENDON 16CM-19.4CM," 7,553 "
-28006978,HC IMP ACHILLES TENDON LARGE 19.5CM," 15,375 "
-28006979,HC IMP TENDONOSIS SEMITENDINOSUS 25.9," 8,279 "
-28006980,HC IMP TENDONOSIS SEMITENDINOSUS 26," 10,763 "
-28006981,HC IMP ACHILLES TENDON CRYO," 20,125 "
-28006982,HC IMPL PATELLA TENDON WHOLE CRYO," 20,125 "
-28006983,HC IMPL PATELLA TENDON HEMI CRYO," 20,125 "
-28006984,HC  CATHETER CORONARY 18X2.5MM 2.6F," 1,615 "
-28006985,HC  CATHETER CORONARY RX 8X2.75MM 2.6F," 1,615 "
-28006986,HC  CATHETER CORONARY RX 8X3.25MM 2.7F," 1,615 "
-28006987,HC  CATHETER CORONARY RX 13X2.5MM 2.6F," 6,290 "
-28006988,HC  CATHETER CORONARY 13X2.75MM 2.6F," 1,615 "
-28006989,HC  CATHETER CORONARY RX 13X3MM 2.6F," 1,615 "
-28006990,HC  CATHETER CORONARY RX 13X3.25MM 2.7F," 1,615 "
-28006991,HC  CATHETER CORONARY RX 13X4MM 2.8F," 1,615 "
-28006992,HC  CATHETER CORONARY RX 13X4.5MM 2.8F," 6,290 "
-28006993,HC IMP TENDON QUADRICEPS CRYO," 20,125 "
-28006994,HC IMP TENDON TIBIAL CRYO," 20,125 "
-28006995,HC IMP CONDYLE HEMI MEDIAL RIGHT," 55,250 "
-28006996,HC IMP CONDYLE HEMI MEDIAL LEFT," 55,250 "
-28006997,HC IMP CONDYLE HEMI LATERAL RIGHT," 55,250 "
-28006998,HC IMP CONDYLE HEMI LATERAL LEFT," 55,250 "
-28006999,HC IMP CONDYLE HEMI PARTIAL RIGHT," 26,250 "
-28007000,HC IMP CONDYLE HEMI PARTIAL LEFT," 26,250 "
-28007001,HC IMP ACHILLES TENDON," 14,625 "
-28007002,HC IMP TENDONOSIS SEMITENDINOSUS," 14,625 "
-28007003,HC  CATHETER GUIDING AL 71IN 6F, 451 
-28007004,HC CATHETER STRETTA WITH ACCESSORY," 7,725 "
-28007005,HC STAPLER PREMIUM PLUS CEEA 28MM," 4,434 "
-28007013,HC IMP NAIL FEMORAL UNIT FLEX RIGHT 12X4," 6,979 "
-28007014,HC DRILL DISK ULTRA DRIVE 11MM," 5,627 "
-28007015,HC  CATHETER ULTRASOUND 90CM 90D 10F," 18,225 "
-28007016,HC  CATHETER BALLOON ABLATION THERMAL," 9,030 "
-28007017,HC IMP SHOULDER MODULAR GLENOID KEELED SMALL 4MM," 9,413 "
-28007018,HC IMP STENT BILIARY COTTON LEUNG CLSO 10-12, 445 
-28007019,HC IMP STENT BILIARY RX 10FX7, 757 
-28007020,HC IMP STENT BILIARY RX 10FX10, 757 
-28007022,HC  GUIDEWIRE STRAIGHT MODULAR SUPP 190X3CM, 798 
-28007023,HC  GUIDEWIRE STRAIGHT MEDIUM SUPP 300X3CM, 697 
-28007024,HC IMP ICD DUAL PHOTON IS-1 DF-1," 82,250 "
-28007025,HC  CATHETER FOLEY TEMP PROBE 10F, 140 
-28007026,HC IMP STENT BILIARY COTTON LEUNG CLSO 8.5-7, 445 
-28007027,HC IMP STENT BILIARY COTTON LEUNG CLSO 8.5-9, 445 
-28007028,HC IMP STENT BILIARY COTTON LEUNG CLSO 10-7, 445 
-28007029,HC IMP STENT BILIARY COTTON LEUNG CLSO 11.5-12, 445 
-28007030,HC IMPL SEALANT FIB TIS FROZEN 2ML, 922 
-28007031,HC IMP ANCHOR SUTURE TRANSVAGINAL PREC TW," 9,413 "
-28007032,HC DEVICE CAPIO," 3,350 "
-28007033,HC IMP STABILIZER PECTUS ELONGATED SS," 3,400 "
-28007034,HC IMP HEARTMATE VAD SYSTEM LEFT," 118,500 "
-28007035,HC HANDPIECE LIGASURE CUTTING 10MM," 3,659 "
-28007036,HC IMP PUMP INFUSION SYNCHROMED W/POUCH 18ML," 60,743 "
-28007037,HC IMP PUMP W/POUCH 10ML," 48,653 "
-28007038,HC IMP PUMP INFUSION SYNCHROMED 10ML," 62,563 "
-28007039,HC IMP PUMP INFUSION SYNCHROMED 18ML," 62,563 "
-28007040,HC IMP GRAFT HEMASHIELD KNITTED ST 8X15," 2,678 "
-28007041,HC IMPL PLATE MATTA CVD R108 4HOLE," 3,451 "
-28007042,HC IMPL PLATE MATTA CVD R108 5HOLE," 3,766 "
-28007043,HC IMPL PLATE MATTA CVD R108 6HOLE," 4,082 "
-28007044,HC IMPL PLATE MATTA CVD R108 10HOLE," 4,706 "
-28007045,HC IMPL PLATE MATTA CVD R108 12HOLE," 4,752 "
-28007046,HC IMPL PLATE MATTA CVD R108 14HOLE," 4,939 "
-28007047,HC IMPL PLATE MATTA CVD R88 5HOLE," 3,766 "
-28007048,HC IMPL PLATE MATTA CVD R88 6HOLE," 4,082 "
-28007049,HC IMPL PLATE MATTA CVD R88 8HOLE," 4,021 "
-28007050,HC IMPL PLATE MATTA CVD R88 10HOLE," 4,706 "
-28007051,HC IMPL PLATE MATTA CVD R88 12HOLE," 4,752 "
-28007052,HC IMPL PLATE MATTA CVD R88 14HOLE," 4,939 "
-28007053,HC IMPL PLATE MATTA PELVIC STRAIGHT 3HOLE," 2,951 "
-28007054,HC IMPL PLATE MATTA PELVIC STRAIGHT 4HOLE," 3,100 "
-28007055,HC IMPL PLATE MATTA PELVIC STRAIGHT 6HOLE," 4,191 "
-28007056,HC IMPL PLATE MATTA PELVIC STRAIGHT 8HOLE," 3,842 "
-28007057,HC IMPL PLATE MATTA PELVIC STRAIGHT 10HOLE," 4,114 "
-28007058,HC IMPL PLATE MATTA ACETABULAR 6HOLE," 2,933 "
-28007059,HC IMPL PLATE MATTA ACETABULAR 8HOLE," 3,570 "
-28007060,HC IMPL PLATE MATTA ACETABULAR 10 HOLE," 4,097 "
-28007061,HC IMPL PLATE MATTA SYMPHSIS 4HOLE," 7,353 "
-28007062,HC IMPL PLATE MATTA SYMPHYSIS 6HOLE," 7,943 "
-28007063,HC IMP SCREW CORTICAL MATTA 3.5X12MM, 286 
-28007064,HC IMP SCREW CORTICAL MATTA 3.5 X14MM, 286 
-28007065,HC IMP SCREW CORTICAL MATTA 3.5X16MM, 286 
-28007066,HC IMP SCREW CORTICAL MATTA 3.5X18MM, 286 
-28007067,HC IMP SCREW CORTICAL MATTA 3.5X20MM, 286 
-28007068,HC IMP SCREW CORTICAL MATTA 3.5X22MM, 286 
-28007069,HC IMP SCREW CORTICAL MATTA 3.5X24MM, 286 
-28007070,HC IMP SCREW CORTICAL MATTA 3.5X26MM, 286 
-28007071,HC IMP SCREW CORTICAL MATTA 3.5X28MM, 286 
-28007072,HC IMP SCREW CORTICAL MATTA 3.5X30MM, 286 
-28007073,HC IMP SCREW CORTICAL MATTA 3.5X32MM, 286 
-28007074,HC IMP SCREW CORTICAL MATTA 3.5X34MM, 286 
-28007075,HC IMP SCREW CORTICAL MATTA 3.5X36MM, 286 
-28007076,HC IMP SCREW CORTICAL MATTA 3.5X38MM, 286 
-28007077,HC IMP SCREW CORTICAL MATTA 3.5X40MM, 286 
-28007078,HC IMP SCREW CORTICAL MATTA 3.5X45MM, 286 
-28007079,HC IMP SCREW CORTICAL MATTA 3.5X50MM, 286 
-28007080,HC IMP SCREW CORTICAL MATTA 3.5X55MM, 286 
-28007081,HC IMP SCREW CORTICAL MATTA 3.5X60MM, 286 
-28007082,HC IMP SCREW CORTICAL MATTA 3.5X65MM, 286 
-28007083,HC IMP SCREW CORTICAL MATTA 3.5X70MM, 286 
-28007084,HC IMP SCREW CORTICAL MATTA 3.5X75MM, 286 
-28007085,HC IMP SCREW CORTICAL MATTA 3.5X80MM, 286 
-28007086,HC IMP SCREW CORTICAL MATTA 3.5X85MM, 286 
-28007087,HC IMP SCREW CORTICAL MATTA 3.5X90MM, 286 
-28007088,HC IMP SCREW CORTICAL MATTA 3.5X95MM, 286 
-28007089,HC IMP SCREW CORTICAL MATTA 3.5X100MM, 286 
-28007090,HC IMP SCREW CORTICAL MATTA 3.5X105MM, 286 
-28007091,HC IMP SCREW CORTICAL MATTA 3.5X110MM, 286 
-28007092,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X90MM, 255 
-28007093,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X95MM, 349 
-28007094,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X100MM, 408 
-28007095,HC IMPL SCREW MATTA THREAD 32 MM 6.5X70, 374 
-28007096,HC IMP SCREW CORTICAL MATTA 4.5X16MM, 264 
-28007097,HC IMP SCREW CORTICAL MATTA 4.5X18MM, 230 
-28007098,HC IMP SCREW CORTICAL MATTA 4.5X20MM, 250 
-28007099,HC IMP SCREW CORTICAL MATTA 4.5X 22MM, 230 
-28007100,HC IMP SCREW CORTICAL MATTA 4.5X24MM, 230 
-28007101,HC IMP SCREW CORTICAL MATTA 4.5X26MM, 230 
-28007102,HC IMP SCREW CORTICAL MATTA 4.5X28MM, 250 
-28007103,HC IMP SCREW CORTICAL MATTA 4.5X30MM, 250 
-28007104,HC IMP SCREW CORTICAL MATTA 4.5X32MM, 230 
-28007105,HC IMP SCREW CORTICAL MATTA 4.5X34MM, 230 
-28007106,HC IMP SCREW CORTICAL MATTA 4.5X36MM, 187 
-28007107,HC IMP SCREW CORTICAL MATTA 4.5X38MM, 204 
-28007108,HC IMP SCREW CORTICAL MATTA 4.5X40MM, 250 
-28007109,HC IMP SCREW CORTICAL MATTA 4.5X42MM, 250 
-28007110,HC IMP SCREW CORTICAL MATTA 4.5X46MM, 264 
-28007111,HC IMP SCREW CORTICAL MATTA 4.5X48MM, 230 
-28007112,HC IMP SCREW CORTICAL MATTA 4.5X50MM, 250 
-28007113,HC IMP SCREW CORTICAL MATTA 4.5X52MM, 170 
-28007114,HC IMP SCREW CORTICAL MATTA 4.5X54MM, 264 
-28007116,HC IMP SCREW CORTICAL MATTA 4.5X60MM, 250 
-28007118,HC IMP SCREW CORTICAL MATTA 4.5X70MM, 187 
-28007119,HC IMP SCREW CORTICAL MATTA 4.5X75MM, 187 
-28007120,HC IMP SCREW CORTICAL MATTA 4.5X90MM, 187 
-28007121,HC IMP SCREW CORTICAL MATTA 4.5X100MM, 339 
-28007122,HC IMP SCREW CORTICAL MATTA 4.5X110MM, 339 
-28007123,HC IMP WASHER CANNULATED 4.5 SCREW, 405 
-28007124,HC IMP BAND SCLERAL CIRCLING STYLE 240, 102 
-28007125,HC IMP WASHER CANNULATED SS 9.0 MM, 345 
-28007126,HC  CATHETER ANGIOGR SOFT-VU PIGTAIL 10 SH 5X65CM, 122 
-28007127,HC IMP STENT BILIARY COTTON LEUNG CLSO 11.5-9, 445 
-28007128,HC IMP STENT BILIARY COTTON LEUNG CLSO 11.5-7, 445 
-28007129,HC IMP STENT BILIARY COTTON LEUNG CLSO 8.5-5, 513 
-28007130,HC IMP STENT BILIARY COTTON LEUNG CLSO 7-15, 445 
-28007131,HC IMP STENT BILIARY COTTON LEUNG CLSO 7-9, 445 
-28007132,HC  GUIDEWIRE DOUBLE FLEX TIP WIRE, 247 
-28007133,HC CATHETER SPECTRUM CENTRAL VEN 4F 8CM 18X21 TRAY," 1,212 "
-28007134,HC IMPL PACEMAKER SINGLE PHOTON V184," 90,000 "
-28007136,HC  CATHETER IMAGING 9F 0.038IN 90CM," 7,225 "
-28007137,HC  CATHETER IMAGING 6F 0.018IN 135CM," 6,126 "
-28007139,HC IMPL LENS IOL TECNIS ASPHERIC 8.0D," 1,020 "
-28007140,HC IMPL LENS IOL TECNIS ASPHERIC 8.5D," 1,020 "
-28007141,HC IMPL LENS IOL TECNIS ASPHERIC 9.0D," 1,020 "
-28007142,HC IMPL LENS IOL TECNIS ASPHERIC 9.5D," 1,020 "
-28007143,HC IMPL LENS IOL TECNIS ASPHERIC 10.0D," 1,020 "
-28007144,HC IMPL LENS IOL TECNIS ASPHERIC 10.5D," 1,020 "
-28007145,HC IMPL LENS IOL TECNIS ASPHERIC 11.0D," 1,020 "
-28007146,HC IMPL LENS IOL TECNIS ASPHERIC 11.5D," 1,020 "
-28007147,HC IMPL LENS IOL TECNIS ASPHERIC 12.0D," 1,020 "
-28007148,HC IMPL LENS IOL TECNIS ASPHERIC 12.5D," 1,020 "
-28007149,HC IMPL LENS IOL TECNIS ASPHERIC 13.0D," 1,020 "
-28007150,HC IMPL LENS IOL TECNIS ASPHERIC 13. 5D," 1,020 "
-28007151,HC IMPL LENS IOL TECNIS ASPHERIC 14.0D," 1,020 "
-28007152,HC IMPL LENS IOL TECNIS ASPHERIC 14.5D," 1,020 "
-28007153,HC IMPL LENS IOL TECNIS ASPHERIC 15.0D," 1,020 "
-28007154,HC IMPL LENS IOL TECNIS ASPHERIC 15.5D," 1,020 "
-28007155,HC IMPL LENS IOL TECNIS ASPHERIC 16.0D," 1,020 "
-28007156,HC IMPL LENS IOL TECNIS ASPHERIC 16.5D," 1,020 "
-28007157,HC IMPL LENS IOL TECNIS ASPHERIC 17.0D," 1,020 "
-28007158,HC IMPL LENS IOL TECNIS ASPHERIC 17.5D," 1,020 "
-28007159,HC IMPL LENS IOL TECNIS ASPHERIC 18.0D," 1,020 "
-28007160,HC IMPL LENS IOL TECNIS ASPHERIC 18.5D," 1,020 "
-28007161,HC IMPL LENS IOL TECNIS ASPHERIC 19.0D," 1,020 "
-28007162,HC IMPL LENS IOL TECNIS ASPHERIC 19.5D," 1,020 "
-28007163,HC IMPL LENS IOL TECNIS ASPHERIC 20.0D," 1,020 "
-28007164,HC IMPL LENS IOL TECNIS ASPHERIC 20.5D," 1,020 "
-28007165,HC IMPL LENS IOL TECNIS ASPHERIC 21.0D," 1,020 "
-28007166,HC IMPL LENS IOL TECNIS ASPHERIC 21.5D," 1,020 "
-28007167,HC IMPL LENS IOL TECNIS ASPHERIC 22.0D," 1,020 "
-28007168,HC IMPL LENS IOL TECNIS ASPHERIC 22.5D," 1,020 "
-28007169,HC IMPL LENS IOL TECNIS ASPHERIC 23.0D," 1,020 "
-28007170,HC IMPL LENS IOL TECNIS ASPHERIC 23.5D," 1,020 "
-28007171,HC IMPL LENS IOL TECNIS ASPHERIC 24.0D," 1,020 "
-28007172,HC IMPL LENS IOL TECNIS ASPHERIC 24.5D," 1,020 "
-28007173,HC IMPL LENS IOL TECNIS ASPHERIC 25.0," 1,020 "
-28007174,HC IMPL LENS IOL TECNIS ASPHERIC 25.5D," 1,020 "
-28007175,HC IMPL LENS IOL TECNIS ASPHERIC 26.0D," 1,020 "
-28007176,HC IMPL LENS IOL TECNIS ASPHERIC 26.5D," 1,870 "
-28007177,HC IMPL LENS IOL TECNIS ASPHERIC 27.0D," 1,020 "
-28007178,HC IMPL LENS IOL TECNIS ASPHERIC 27.5D," 1,020 "
-28007179,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 6.0D," 1,139 "
-28007180,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 6.5D," 1,139 "
-28007181,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 8.5D," 1,139 "
-28007182,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 9.5D," 1,139 "
-28007183,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYSOF 10.0D," 1,139 "
-28007184,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 10.5D," 1,139 "
-28007185,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 29.5D," 1,139 "
-28007186,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 30.0D," 1,139 "
-28007187,HC IMP SHUNT INTRA CORONARY 3.0MM, 659 
-28007188,HC IMPL COIL EMBOLIZATION 035 10X14, 730 
-28007189,HC IMPL COIL EMBOLIZATION NESTER 0.035IN 14CMX12MM, 691 
-28007190,HC  CATHETER ELECTROPHYSIOLOGY ABLAT 55-75MM 110CM," 4,675 "
-28007191,HC IMP STENT ESOPHAGEAL ULTRAFLEX 18X15," 11,323 "
-28007192,HC IMP STENT ESOPHAGEAL ULTRAFLEX 18X12," 11,888 "
-28007193,HC  GUIDEWIRE FLEX T 145CM KIT, 595 
-28007194,HC IMP BREAST EXPANDER STYLE133MV 300ML," 8,963 "
-28007195,HC IMP BREAST EXPANDER STYLE133MV 400ML," 8,963 "
-28007196,HC IMP BREAST EXPANDER STYLE 133MV 500ML," 8,963 "
-28007197,HC IMP BREAST EXPANDER STYLE 133MV 600ML," 8,963 "
-28007199,HC IMP COLLAR SPINAL VARIABLE AXIS SCREW," 1,447 "
-28007200,HC IMPL SCREW POLYAXIAL 6.5X50MM," 8,125 "
-28007201,HC IMP LINK SPINELINK STANDARD 30MM," 2,125 "
-28007202,HC IMP STENT BILIARY SMART OTW 7X60X80," 11,104 "
-28007204,HC  CATHETER DILATATION 3MM 2X80CM 4F," 2,907 "
-28007205,HC  CATHETER DILATATION 4MM 2X80CM 4F," 2,241 "
-28007206,HC  CATHETER DILATATION 5MM 4X80CM 5F," 2,907 "
-28007207,HC  CATHETER DILATATION 6MM 4X80CM 5F," 2,907 "
-28007208,HC  CATHETER DILATATION 7MM 2X80CM 6F," 2,241 "
-28007209,HC  CATHETER DILATATION 7MM 4X80CM 6F," 2,907 "
-28007210,HC  CATHETER DILATATION 8MM 2X80CM 6F," 2,241 "
-28007211,HC  CATHETER DILATATION 8MM 4X80CM 6F," 2,241 "
-28007212,HC IMP STENT BILIARY GENESIS 18 RX 3X12X135," 9,591 "
-28007213,HC IMP STENT BILIARY GENESIS 18 RX 3X18X135," 9,591 "
-28007214,HC IMP STENT BILIARY GENESIS 18 RX 3X24X135," 9,000 "
-28007215,HC  CATHETER DILATATION 7MM 2X135CM 6F," 2,907 "
-28007216,HC  CATHETER DILATATION 7MM 4X135CM 6F," 2,805 "
-28007217,HC  CATHETER DILATATION 8MM 2X135CM 6F," 2,805 "
-28007218,HC  CATHETER DILATATION 8MM 4X135CM 6F," 2,805 "
-28007219,HC  CATHETER DILATATION 4MM 4X135CM 4F," 2,907 "
-28007220,HC  CATHETER DILATATION 5MM 4X135CM 5F," 2,907 "
-28007225,HC IMP LEAD ACTIVE FIXATION PACING 45CM," 4,675 "
-28007226,HC IMP PIN REDUCTION 5X180MM, 171 
-28007227,HC IMP PIN REDUCTION 5X150MM," 1,714 "
-28007228,HC  CATHETER DILATATION 5MM 6X120CM 5F," 2,668 "
-28007229,HC  CATHETER DILATATION 6MM 6X120CM 4F," 2,668 "
-28007230,HC IMP STENT BILIARY COTTON LEUNG CLSO 8.5-12, 445 
-28007231,HC IMP LEAD PACEMAKER VENTRICULAR 65CM 8.6F," 35,000 "
-28007232,HC IMP STENT BILIARY HERCULINK RX 5X18X135," 10,313 "
-28007233,HC IMP STENT BILIARY OMNILINK18 6X38X135," 9,750 "
-28007234,HC IMP STENT BILIARY OMNILINK 18 6X58X135," 9,750 "
-28007235,HC IMP STENT BILIARY OMNILINK18 7X18X135," 9,750 "
-28007236,HC  CATHETER GUIDING VISTA BRITE TIP XBLAD 7F 3.5, 442 
-28007237,HC IMP STENT BILIARY OMNILINK 18 8X58X135," 9,750 "
-28007238,HC IMP STENT BILIARY OMNILINK35 6X18X80," 9,375 "
-28007239,HC IMP STENT BILIARY OMNILINK35 6X28X80," 9,375 "
-28007240,HC IMP STENT BILIARY OMNILINK35 7X28X80," 9,750 "
-28007241,HC IMP STENT BILIARY OMNILINK35 7X38X80," 9,375 "
-28007242,HC IMP STENT BILIARY OMNILINK35 7X58X80," 9,750 "
-28007243,HC IMP STENT BILIARY OMNILINK35 8X28X80," 9,750 "
-28007244,HC IMP STENT BILIARY OMNILINK35 8X38X80," 9,750 "
-28007245,HC IMP STENT BILIARY OMNILINK35 11X58X80," 9,750 "
-28007246,HC IMP STENT BILIARY OMNILINK35 9X38X80," 9,750 "
-28007247,HC IMP STENT BILIARY OMNILINK35 9X58X80," 9,750 "
-28007248,HC IMP STENT BILIARY OMNILINK35 10X58X80," 9,750 "
-28007249,HC  CATHETER DILATATION PERPH 4X20MM 80CM," 2,338 "
-28007250,HC  CATHETER DILATATION 80CM 20X5MM," 3,825 "
-28007251,HC  CATHETER DILATATION 80CM 20X6MM," 2,676 "
-28007252,HC  CATHETER DILATATION 80CM 20X7MM," 2,338 "
-28007253,HC  CATHETER DILATATION 135CM 4X20MM," 2,676 "
-28007254,HC  CATHETER DILATATION 4.5X20MM 135CM," 2,338 "
-28007255,HC  CATHETER DILATATION 135CM 5X20MM," 2,676 "
-28007256,HC  CATHETER DILATATION RX 135CM 20X5.5MM," 2,338 "
-28007257,HC  CATHETER DILATATION VIATRAC 135CM 6X20MM," 2,338 "
-28007258,HC  CATHETER DILATATION RX 135CM 20X6.5MM," 2,338 "
-28007259,HC  CATHETER DILATATION RX 135CM 20X7MM," 2,338 "
-28007260,HC  CATHETER DILATATION PERPH 135CM 4X40MM," 3,825 "
-28007261,HC  CATHETER DILATATION PERPH 135CM 5X40MM," 2,338 "
-28007262,HC  CATHETER DILATATION PERPH 135CM 6X40MM," 3,825 "
-28007263,HC PACK PERFUSION SUPPORT PEDI," 3,740 "
-28007264,HC IMPL MATRIX BONE DBM 2.5X5CM," 11,805 "
-28007265,HC IMPL SCREW FIDUCIAL, 850 
-28007266,HC IMP GRAFT VASCULAR STRETCH 3MM," 3,851 "
-28007267,HC IMP GRAFT VASCULAR STRETCH 5MM," 2,890 "
-28007268,HC IMP GRAFT VASCULAR STRETCH 3.5MM," 3,851 "
-28007269,HC IMP ROD SOFT CURVED 65MM," 2,125 "
-28007270,HC IMP ROD SOFT CURVED 85MM," 2,125 "
-28007271,HC IMP BREAST SMOOTH GEL ROUND 125CC," 7,225 "
-28007272,HC IMP BREAST SMOOTH GEL ROUND 150CC," 7,225 "
-28007273,HC IMP BREAST SMOOTH GEL ROUND 175CC," 7,225 "
-28007274,HC IMP BREAST SMOOTH GEL ROUND 200CC," 7,225 "
-28007275,HC IMP BREAST SMOOTH GEL ROUND 225CC," 7,225 "
-28007276,HC IMP BREAST SMOOTH GEL ROUND 250CC," 7,225 "
-28007277,HC IMP BREAST SMOOTH GEL ROUND 275CC," 7,225 "
-28007278,HC IMP BREAST SMOOTH GEL ROUND 325CC," 7,225 "
-28007279,HC IMP BREAST SMOOTH GEL ROUND 350CC," 7,225 "
-28007280,HC IMP BREAST SMOOTH GEL ROUND 375CC," 7,225 "
-28007281,HC IMP BREAST SMOOTH GEL ROUND 400CC," 7,225 "
-28007282,HC IMP BREAST SMOOTH GEL ROUND 450CC," 7,438 "
-28007283,HC IMP BREAST SMOOTH GEL ROUND 500CC," 7,225 "
-28007284,HC IMP BREAST SMOOTH GEL ROUND 550CC," 7,225 "
-28007285,HC IMP BREAST SMOOTH GEL ROUND 600CC," 7,225 "
-28007286,HC IMP STENT COLON WALLSTENT 22X90," 11,813 "
-28007287,HC IMP STENT BILIARY COTTON LEUNG CLSO 7-5, 255 
-28007288,HC IMP STENT BILIARY COTTON LEUNG CLSO 7-7, 445 
-28007289,HC IMP STENT BILIARY COTTON LEUNG CLSO 8.5-15, 513 
-28007290,HC IMP LEAD PACING IS1 UNIPLR 78CM 4F," 11,250 "
-28007291,HC IMP STENT BILIARY OMNILINK35 7X38X135," 9,375 "
-28007292,HC IMP STENT BILIARY OMNILINK35 8X28X135," 9,750 "
-28007293,HC  GUIDEWIRE PERIPHERAL STRAIGHT TP 35IN 190CM, 808 
-28007294,HC  GUIDEWIRE PERIPHERAL STRAIGHT TP CORE 300X3, 779 
-28007295,HC  CATHETER DILATATION 80CM 6X40MM," 2,338 "
-28007296,HC BARRIER ADHESION SEPRAFILM," 5,593 "
-28007297,HC IMPL COIL NEURO MICRO SPHERICAL 2.5CM 2MM," 7,613 "
-28007298,HC IMPL COIL NEURO MICRO SPHERICAL 5.4CM 3MM," 7,611 "
-28007299,HC IMPL COIL NEURO MICRO SPHERICAL PT 7.5CM 4MM," 7,739 "
-28007300,HC IMPL COIL NEURO MICRO SPHERICAL PT 9.7CM 5MM," 7,814 "
-28007301,HC IMPL COIL NEURO EMBOLIZATION SPHRCL 6MM 11.9CM," 7,913 "
-28007302,HC IMPL COIL NEURO MICRO SPHERICAL 13.9CM 7MM," 7,988 "
-28007303,HC IMPL COIL NEURO MICRO SPHERICAL 11.9CM 9MM," 8,175 "
-28007304,HC IMPL COIL NEURO MICRO SPHERICAL 10MM 20.3CM," 8,250 "
-28007305,HC IMPL COIL NEURO MICRO SPHERICAL FRAMING 2MMX2.7CM," 7,990 "
-28007306,HC IMPL COIL NEURO MICRO SPHERICAL SYS 7.9CM 4MM," 7,763 "
-28007307,HC IMPL COIL NEURO MICRO SPHERICAL SYS 10CM 5MM," 7,814 "
-28007308,HC IMPL COIL NEURO MICRO SPHERICAL 12.2CM 6MM," 8,025 "
-28007309,HC IMPL COIL NEURO MICRO SPHERICAL 14.3CM 7MM," 7,988 "
-28007310,HC IMPL COIL NEURO MICRO SPHERICAL 13.6CM 8MM," 7,988 "
-28007311,HC IMPL COIL NEURO MICRO SPHERICAL SYSTEM 9MM," 8,100 "
-28007312,HC IMPL COIL NEURO ENDOVASCULAR SPHERICAL 10MM," 8,175 "
-28007313,HC IMPL COIL NEURO ENDOVASCLR SPHRCL 12MM 20," 8,325 "
-28007315,HC IMPL SCREW CANNULATED THREAD 7.3X16X45MM," 2,420 "
-28007316,HC IMPL SCREW CANNULATED THREAD 7.3X16X60MM," 2,542 "
-28007317,HC IMPL SCREW CANNULATED THREAD 7.3X16X65MM," 2,420 "
-28007318,HC IMPL SCREW CANNULATED THREAD 7.3X16X70MM," 2,542 "
-28007319,HC IMPL SCREW CANNULATED THREAD 7.3X16X75MM," 2,420 "
-28007320,HC IMPL SCREW CANNULATED THREAD 7.3X16X80MM," 2,505 "
-28007321,HC IMPL SCREW CANNULATED THREAD 7.3X16X85MM," 2,505 "
-28007322,HC IMPL SCREW CANNULATED THREAD 7.3X16X90MM," 2,505 "
-28007323,HC IMPL SCREW CANNULATED THREAD 7.3X16X95MM," 2,505 "
-28007324,HC IMPL SCREW CANNULATED THREAD 7.3X16X105MM," 2,420 "
-28007325,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X60MM," 2,542 "
-28007326,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X65MM," 2,420 "
-28007327,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X90MM," 2,542 "
-28007328,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X110MM," 2,372 "
-28007329,HC IMP NAIL TIBIAL TITANIUM 8X315MM," 8,798 "
-28007330,HC IMP NAIL TIBIAL CANNULATED 10X300MM," 8,918 "
-28007331,HC IMP NAIL TIBIAL CANNULATED 10X330MM," 9,705 "
-28007332,HC IMP NAIL TIBIAL CANNULATED 10X345MM," 10,512 "
-28007333,HC IMP NAIL TIBIAL CANNULATED 10X360MM," 9,705 "
-28007334,HC IMP NAIL TIBIAL CANNULATED 10X380MM," 9,705 "
-28007335,HC IMP NAIL TIBIAL CANNULATED 10X400MM," 9,705 "
-28007336,HC IMP NAIL TIBIAL CANNULATED 11X315MM," 9,705 "
-28007337,HC IMP NAIL TIBIAL CANNULATED 11X345MM," 9,705 "
-28007338,HC IMP NAIL TIBIAL CANNULATED 11X360MM," 8,918 "
-28007339,HC IMP NAIL TIBIAL CANNULATED TITANIUM 12X345MM," 8,918 "
-28007340,HC IMP NAIL TIBIAL CANNULATED TITANIUM 12X380MM," 8,918 "
-28007343,HC IMP NAIL FEMORAL CANNULATED 10MM 380MM," 10,058 "
-28007344,HC IMP NAIL FEMORAL CANNULATED 11MM 340MM," 9,240 "
-28007345,HC IMP NAIL FEMORAL CANNULATED 11MM 380MM," 10,058 "
-28007346,HC IMP NAIL FEMORAL CANNULATED 11MM 400MM," 9,240 "
-28007347,HC IMP NAIL FEMORAL CANNULATED 13MM 380MM," 10,058 "
-28007348,HC IMP ENDCAP EXTENSION TITANIUM 12MM 0MM," 1,692 "
-28007349,HC IMP ENDCAP EXTENSION FEMORAL NAIL 12MM 10MM," 1,724 "
-28007350,HC IMP SLEEVE LOCKING SPIRAL BLADE 120D," 3,145 "
-28007351,HC IMP SLEEVE LOCKING NAIL TITANIUM 130DEG," 2,729 "
-28007352,HC IMP SLEEVE LOCKING NAIL TITANIUM F/13-15," 2,805 "
-28007353,HC IMP SLEEVE LOCKING LIGHT BLU TITANIUM 100D," 3,188 "
-28007355,HC IMP CAP END EXTENSION STANDARD 15MM 10MM," 1,819 "
-28007356,HC BLADE SPIRAL TITANIUM 80MM," 5,840 "
-28007357,HC BLADE SPIRAL TITANIUM 85MM," 5,457 "
-28007358,HC BLADE SPIRAL TITANIUM 90MM," 5,457 "
-28007359,HC BLADE SPIRAL TITANIUM 95MM," 5,840 "
-28007360,HC IMP NAIL FEMORAL DISTAL 10MM 160MM," 11,970 "
-28007361,HC IMP NAIL FEMORAL DISTAL 10MM 280MM," 11,970 "
-28007362,HC IMP NAIL FEMORAL DISTAL 10MM 340MM," 11,970 "
-28007363,HC IMP NAIL FEMORAL DISTAL 10MM 360MM," 12,563 "
-28007364,HC IMP NAIL FEMORAL DISTAL 10MM 380MM," 12,563 "
-28007365,HC IMP NAIL FEMORAL DISTAL 11MM 160MM," 11,970 "
-28007366,HC IMP NAIL FEMORAL DISTAL 12MM 320MM," 11,970 "
-28007367,HC IMP NAIL FEMORAL DISTAL 13MM 360MM," 12,563 "
-28007368,HC IMP NAIL FEMORAL DISTAL 13MM 380MM," 11,970 "
-28007369,HC IMP ENDCAP FOR DISTAL FEMORAL NAIL," 1,700 "
-28007370,HC IMP NAIL HUMERAL SOLID 7.5MM 205MM," 13,110 "
-28007371,HC IMP NAIL HUMERAL SOLID 7.5MM 240MM," 12,608 "
-28007372,HC IMP NAIL HUMERAL SOLID 7.5MM 250MM," 12,608 "
-28007373,HC IMP NAIL HUMERAL SOLID 9.5MM 220MM," 12,608 "
-28007374,HC IMP NAIL HUMERAL SOLID 9.5MM 230MM," 12,608 "
-28007375,HC IMP NAIL HUMERAL SOLID 9.5MM 250MM," 12,608 "
-28007376,HC IMP NAIL HUMERAL SOLID 9.5MM 270MM," 13,110 "
-28007377,HC IMP ENDCAP HUMERAL SOLID NAIL 10MM," 1,981 "
-28007378,HC IMP NAIL ELASTIC 2.0MM TITANIUM 440MM," 2,091 "
-28007379,HC IMP NAIL ELASTIC 3.0MM TITANIUM 440MM," 2,958 "
-28007380,HC IMP NAIL ELASTIC 3.5MM TITANIUM 440MM," 3,083 "
-28007381,HC IMP NAIL ELASTIC 4.0MM TITANIUM 440MM," 3,197 "
-28007382,HC IMP BOLT LOCKING 3.9X26MM," 1,701 "
-28007383,HC IMP BOLT LOCKING 3.9X28MM," 1,643 "
-28007384,HC IMP BOLT LOCKING TITANIUM 3.9MM 30MM," 1,726 "
-28007385,HC IMP BOLT LOCKING 3.9X32MM," 1,607 "
-28007386,HC IMP BOLT LOCKING TITANIUM 3.9MM 34MM," 1,643 "
-28007387,HC IMP BOLT LOCKING 3.9X36MM," 1,643 "
-28007388,HC IMP BOLT LOCKING 3.9X38MM," 1,643 "
-28007389,HC IMP BOLT LOCKING TITANIUM 3.9MM 40MM," 1,701 "
-28007390,HC IMP BOLT LOCKING 3.9X44MM," 1,643 "
-28007391,HC IMP BOLT LOCKING TITANIUM 3.9MM 46MM," 1,607 "
-28007392,HC IMP BOLT LOCKING TITANIUM 3.9MM 50MM," 1,643 "
-28007393,HC IMP BOLT LOCKING TITANIUM 3.9MM 52MM," 1,437 "
-28007394,HC IMP BOLT LOCKING 3.9X54MM," 1,437 "
-28007395,HC IMP BOLT LOCKING TITANIUM 3.9MM 56MM," 1,437 "
-28007397,HC IMP BOLT LOCKING 4.9X26MM," 1,607 "
-28007398,HC IMP BOLT LOCKING 4.9X28MM," 1,607 "
-28007399,HC IMP BOLT LOCKING TITANIUM 4.9MM 30MM," 1,726 "
-28007400,HC IMP BOLT LOCKING 4.9X32MM," 1,643 "
-28007401,HC IMP BOLT LOCKING 4.9X34MM," 1,701 "
-28007402,HC IMP BOLT LOCKING 4.9X36MM," 1,701 "
-28007403,HC IMP BOLT LOCKING 4.9X38MM," 1,701 "
-28007404,HC IMP BOLT LOCKING 4.9X42MM," 1,701 "
-28007405,HC IMP BOLT LOCKING 4.9X44MM," 1,701 "
-28007406,HC IMP BOLT LOCKING 4.9X46MM," 1,701 "
-28007407,HC IMP BOLT LOCKING 4.9X48MM," 1,643 "
-28007408,HC IMP BOLT LOCKING 4.9X50MM," 1,701 "
-28007409,HC IMP BOLT LOCKING 4.9X52MM," 1,643 "
-28007410,HC IMP BOLT LOCKING 4.9X54MM," 1,643 "
-28007411,HC IMP GRAFT HEMASHIELD KNITTED 6X15," 2,678 "
-28007412,HC IMP BOLT LOCKING TITANIUM 4.9MM 56MM," 1,643 "
-28007413,HC IMP BOLT LOCKING 4.9X58MM," 1,643 "
-28007414,HC IMP BOLT LOCKING TITANIUM 4.9MM 60MM," 1,643 "
-28007415,HC IMP BOLT LOCKING TITANIUM 4.9MM 64MM," 1,530 "
-28007416,HC IMP BOLT LOCKING 4.9X68MM," 1,643 "
-28007417,HC IMP BOLT LOCKING 4.9X72MM," 1,607 "
-28007418,HC IMP BOLT LOCKING TITANIUM 4.9MM 95MM," 1,437 "
-28007419,HC IMP BOLT LOCKING TITANIUM 4.9MM 100MM," 1,437 "
-28007420,HC IMPL SCREW LOCKING 6X55MM," 1,683 "
-28007421,HC IMPL SCREW LOCKING 6X60MM," 1,683 "
-28007422,HC IMPL SCREW LOCKING 6X65MM," 1,683 "
-28007423,HC IMPL SCREW LOCKING 6X0MM," 1,683 "
-28007424,HC IMPL SCREW LOCKING 6X75MM," 1,683 "
-28007425,HC IMPL SCREW LOCKING 6X80MM," 1,683 "
-28007426,HC IMPL SCREW LOCKING 6X85MM," 1,581 "
-28007427,HC  GUIDEWIRE TROCAR THREAD PNT 3.2X300," 1,139 "
-28007429,HC TAMP BONE INFLATABLE KYPHX 15/3," 8,138 "
-28007430,HC TAMP BONE INFLATABLE KYPHX 20/3," 8,243 "
-28007432,HC IMP HIP CUP PINNACLE SECTOR 52MM," 9,540 "
-28007434,HC IMP HIP CUP PINNACLE SECTOR 58MM," 9,540 "
-28007435,HC IMP HIP CUP PINNACLE SECTOR 60MM," 9,540 "
-28007436,HC IMP HIP CUP PINNACLE MULTI HOLE CUP 58MM," 12,645 "
-28007437,HC IMP SCREW HIP PINNACLE 6.5X25MM, 901 
-28007438,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 6.5X30MM, 901 
-28007439,HC IMP SCREW HIP PINNACLE 6.5X35MM, 850 
-28007440,HC IMP SCREW HIP PINNACLE 6.5X40MM, 901 
-28007441,HC IMP SCREW HIP PINNACLE 6.5X50MM, 901 
-28007442,HC IMP STENT VAGINAL SMALL 12X4CM," 3,953 "
-28007443,HC  GUIDEWIRE JAGWIRE STRAIGHT 35INX260CM," 1,021 "
-28007444,HC IMP MESH MARLEX 3X6IN," 1,003 "
-28007446,HC  INTROD PEEL AWAY 14F SET, 308 
-28007447,HC IMP HIP LINER PINNACLE NEUTRAL 520X32ID," 5,100 "
-28007448,HC CATHETER BRAID ULTRA TORQUE SHAFT 4F 180CM, 255 
-28007449,HC IMP SPACER ACF 6MM," 8,228 "
-28007450,HC IMP SPACER ACF 7MM," 7,905 "
-28007451,HC  CATHETER DILATATION BILIARY 8.5X4X2," 1,947 "
-28007452,HC  CATHETER DILATATION BILIARY 5.8X4X4X180," 1,947 "
-28007453,HC IMP STENT BILIARY RX 7FX5, 757 
-28007454,HC IMP STENT BILIARY RX 7FX7, 757 
-28007456,HC IMP STENT BILIARY RX 8.5FX5, 757 
-28007457,HC IMP STENT BILIARY RX 8.5FX7, 757 
-28007458,HC IMP STENT BILIARY RX 8.5FX10, 757 
-28007459,HC  GUIDEWIRE JAGWIRE STRAIGHT 25IN 260CM, 850 
-28007460,HC  CATHETER SIZING AUROUS PIGTAIL 5F 70CM .35," 1,030 "
-28007461,HC IMP VALVE MITRAL MASTERS EXPANDED CUFF 33MM," 29,491 "
-28007462,HC  CATHETER MICRO EXCELSIOR PRESHAPED 10 150X6CM," 6,032 "
-28007463,HC  GUIDEWIRE JAGWIRE ANGLED 35INX200CM, 714 
-28007464,HC  CATHETER DILATATION RX 2CMX6MM," 1,947 "
-28007465,HC  CATHETER DILATATION RX 4CMX6MM," 1,947 "
-28007467,HC IMP STENT BILIARY RX 7FX12, 757 
-28007468,HC BASKET RETRIEVAL STONE TRAPEZOID 2X4CM," 2,202 "
-28007469,HC BASKET RETRIEVAL STONE TRAPEZOID 2.5X5CM," 2,202 "
-28007470,HC IMP GRAFT HEMASHIELD WOVEN 22X50CM," 9,893 "
-28007471,HC IMP GRAFT HEMASHIELD WOVEN 24X50CM," 9,893 "
-28007472,HC IMP GRAFT HEMASHIELD WOVEN 26X50CM," 9,893 "
-28007473,HC IMP VALVE MITRAL 15MM CUSTOM," 26,698 "
-28007474,HC IMP SLING VAGINAL SUTURE SYSTEM," 8,500 "
-28007475,HC  TUBE GASTROSTOMY LOW PROFILE 18F 3.5, 146 
-28007476,HC  CATH ANGIOGR BALL DILATATION PTV 4X2X100CM," 4,565 "
-28007480,HC  CATHETER FOGARTY THRU LUMEN 6F, 717 
-28007481,HC  CATHETER FOGARTY THRU LUMEN 7F, 717 
-28007482,HC IMP OCCLUDER AMPLATZ ASD 6F 6MM," 35,000 "
-28007483,HC IMP OCCLUDER AMPLATZ ASD 6F 8MM," 35,100 "
-28007484,HC IMP OCCLUDER AMPLATZ ASD 6F 9MM," 35,100 "
-28007485,HC IMP OCCLUDER AMPLATZ ASD 6F 10MM," 35,100 "
-28007486,HC IMP OCCLUDER AMPLATZ ASD 7F 12MM," 35,100 "
-28007487,HC IMP OCCLUDER AMPLATZ ASD 7F 14MM," 36,504 "
-28007488,HC IMP OCCLUDER AMPLATZ ASD 7F 16MM," 36,504 "
-28007489,HC IMP OCCLUDER AMPLATZ ASD 8F 18MM," 35,100 "
-28007490,HC IMP OCCLUDER AMPLATZ ASD 9F 20MM," 36,504 "
-28007491,HC IMP OCCLUDER AMPLATZ ASD 9F 22MM," 35,100 "
-28007492,HC IMP OCCLUDER AMPLATZ ASD 9F 24MM," 35,100 "
-28007493,HC IMP OCCLUDER AMPLATZ ASD 10F 26MM," 35,100 "
-28007494,HC IMP OCCLUDER AMPLATZ ASD 10F 28MM," 35,100 "
-28007495,HC IMP OCCLUDER AMPLATZ ASD 10F 30MM," 33,250 "
-28007496,HC IMP OCCLUDER AMPLATZ ASD 12F 32MM," 33,250 "
-28007497,HC IMP OCCLUDER AMPLATZ ASD 12F 34MM," 33,250 "
-28007498,HC IMP OCCLUDER AMPLATZ ASD 12F 36MM," 35,000 "
-28007499,HC IMP OCCLUDER AMPLATZ ASD 12F 38MM," 35,000 "
-28007500,HC  EXCHANGE AMPLATZER SYSTEM 9F 45D 80CM," 4,930 "
-28007501,HC CATHETER BALLOON AMPLATZER SIZING 7FX70CM," 2,346 "
-28007502,HC CATHETER BALLOON AMPLATZER SIZING 8FX70CM," 2,253 "
-28007503,HC  GUIDEWIRE INTRAVASCULAR 35IN 260CM, 383 
-28007508,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 31MM," 10,757 "
-28007509,HC IMP PIN FIXATION TEMPORARY 2.1X16MM," 1,517 "
-28007510,HC IMP SCREW BONE SELF TAPPING 4.0X14MM," 2,045 "
-28007511,HC IMP LEAD INTRAMUSCULAR ENTERRA," 16,500 "
-28007513,HC IMPL SCREW RESORABLE 1.5X3MM, 595 
-28007514,HC IMP GRAFT SURGISIS GOLD 13X22," 30,940 "
-28007517,HC IMP SCREW INTERFERENCE 7X20MM, 978 
-28007518,HC IMP VALVE AORTIC CONDUIT CRYO," 54,275 "
-28007519,HC IMP VALVE PULMONARY SYNERGRAFT CRYO 7-32MM," 87,000 "
-28007520,HC IMP PULMONARY TRUNK PATCH CRYO," 35,000 "
-28007521,HC IMP VALVE PULMONARY CRYOLIFE," 63,050 "
-28007522,HC IMP GRAFT THORAC AORTA ASCEND CRYO," 24,500 "
-28007523,HC IMP TISSUE THORCIC AORTA DESCENDING," 61,750 "
-28007524,HC IMP PULMONARY ARTERY CRYO," 35,000 "
-28007525,HC IMP PULMONARY ARTERY 30MM," 36,400 "
-28007526,HC IMPL SCREW POLYAXIAL 6.5X45MM," 7,863 "
-28007528,HC IMP LINK SPINELINK STANDARD 42MM," 2,125 "
-28007531,HC IMPL PLATE FEMUR 236MM 9HOLE," 13,904 "
-28007532,HC IMPL SCREW LOCKING SELF DRILL 5.0X18MM," 1,870 "
-28007533,HC IMPL SCREW LOCKING SELF DRILL 5.0X40MM," 1,760 "
-28007534,HC IMPL SCREW LOCKING SELF DRILL 5.0X50MM," 1,870 "
-28007535,HC IMPL SCREW LOCKING SELF DRILL 5.0X65MM," 1,870 "
-28007536,HC IMPL SCREW LOCKING SELF DRILL 5.0X75MM," 1,870 "
-28007537,HC IMP WIRE KIRSCHNER THREAD 2X280MM, 400 
-28007538,HC IMP CRYOPATCH PULM HEMI ARTERY SG," 38,350 "
-28007539,HC IMP GRAFT PULM MONO HEMI ARTERY CRYO," 35,000 "
-28007540,HC IMP VALVE PULMONARY SYNERGRAFT MONO CRYO," 19,250 "
-28007541,HC IMP GRAFT AORTO ILLIAC CRYO 13X5.6CM," 85,050 "
-28007542,HC IMP STENT COLON WALLSTENT 22X60," 11,813 "
-28007543,HC IMP SCREW INTERFERENCE 9X25MM, 978 
-28007545,HC IMPL SCREW BIOABSORBABLE 8X25MM," 1,063 "
-28007546,HC IMPL SCREW BIOABSORBALBE 9X20MM," 1,063 "
-28007548,HC IMPL PACEMAKER DUAL KAPPA 900 DR KDR921," 37,570 "
-28007549,HC IMP SCREW CORTEX SELF TAPPING 4.5X62MM, 210 
-28007550,HC IMP SCREW CORTEX SELF TAPPING 4.5X66MM, 210 
-28007551,HC IMP BREAST SILTEX CONTOUR MEDIUM HEIGHT 350CC," 9,938 "
-28007552,HC IMP BREAST SILTEX CONTOUR 275CC," 9,000 "
-28007553,HC IMP CORKSCREW BIOABSORBABLE 5MM," 2,465 "
-28007554,HC IMPL PLATE HTO 10MM 4HOLE," 8,203 "
-28007555,HC IMPL SCREW HTO 6.0X40MM," 1,309 "
-28007556,HC IMPL SCREW HTO 6.0X50MM," 1,224 "
-28007557,HC IMPL SCREW HTO 6.0X55MM," 1,309 "
-28007558,HC IMP STAPLE RELOAD BONE TITANIUM 16X25MM," 1,811 "
-28007559,HC IMP LEAD PACING IS1 UNIPOLAR 88CM 4F," 11,250 "
-28007560,HC IMP SCREW ACUTRAK MINI 2.0MM," 3,035 "
-28007561,HC  CATHETER DILATATION 135CM 15X4MM," 3,825 "
-28007562,HC  CATHETER DILATATION RX 135CM 15X6MM," 2,676 "
-28007565,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X24MM, 553 
-28007566,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X22MM, 553 
-28007567,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X16MM, 553 
-28007568,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X12MM, 553 
-28007569,HC IMPL PLATE RADIAL 5 HOLE," 4,463 "
-28007570,HC IMPL PLATE ULNAR 3 HOLE," 3,953 "
-28007571,HC IMPL PLATE RADIAL 3 HOLE," 4,463 "
-28007572,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X14MM, 553 
-28007573,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X18MM, 553 
-28007574,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X20MM, 553 
-28007575,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X26MM, 553 
-28007576,HC IMP SCREW ACUTRAK 15.0MM," 3,035 "
-28007577,HC IMPL SCREW HTO 6X38MM," 1,309 "
-28007578,HC IMPL SCREW HTO 6X45MM," 1,224 "
-28007579,HC IMPL SCREW HTO 6X34MM," 1,224 "
-28007580,HC IMPL PLATE HTO 12.5MM," 9,450 "
-28007581,HC IMP MENISCUS BONE LATERAL RIGHT," 34,300 "
-28007582,HC  CATHETER MICRO RENEGADE HIFLO 135CM 3F 2.8F," 3,284 "
-28007583,HC  CATHETER MICRO 10X105CM 2.8F 3F," 3,284 "
-28007584,HC IMP STENT ASPIRE COVERED 7MM 5CM," 13,125 "
-28007585,HC IMP STENT ASPIRE COVERED 8MM 5CM," 13,125 "
-28007586,HC IMP STENT ASPIRE COVERED 6MM 5CM," 13,125 "
-28007587,HC IMP STENT ASPIRE COVERED 8MM 10CM," 13,125 "
-28007588,HC IMP ENDOPROSTHESIS 13MM 10X75CM 12F," 17,850 "
-28007589,HC IMP GRAFT HEMASHIELD GOLD 28X50CM," 9,893 "
-28007590,HC IMPL SCREW AUTODRIVE 2X5.0MM, 541 
-28007591,HC IMP PATCH MESH 3D MAX LEFT MEDIUM," 1,998 "
-28007592,HC IMP PATCH MESH 3D MAX RIGHT MEDIUM," 1,998 "
-28007593,HC IMP SEALANT HEMOS FLOSEAL 5ML KIT," 1,359 "
-28007595,HC IMPL PLATE VUELOCK TWO LEVEL 36MM," 11,386 "
-28007596,HC IMP LINK SPINELINK REVERSE RIGHT 30MM," 2,125 "
-28007597,HC IMP LINK SPINELINK REVERSE RIGHT 24MM," 2,125 "
-28007598,HC IMP LINK SPINELINK REVERSE RIGHT 33MM," 2,125 "
-28007599,HC IMP LINK SPINELINK REVERSE LEFT 24MM," 2,125 "
-28007600,HC IMP LINK SPINELINK REVERSE RIGHT 27MM," 2,125 "
-28007601,HC IMP LINK SPINELINK STANDARD 48MM," 2,496 "
-28007602,HC IMP LINK SPINELINK REVERSE LEFT 30MM," 2,125 "
-28007603,HC IMP LEAD NEURO RESUME II KIT," 11,595 "
-28007604,HC IMP NEUROSTIMULATOR BATTERY PROGRAMMER," 70,860 "
-28007605,HC IMP STENT BILIARY COTTON LEUNG CLSO 10-15, 255 
-28007606,HC  CATHETER DILATATION BILIARY SOEHENDRA 11.5F, 486 
-28007607,HC  CATHETER DILATATION BILIARY 5.8X8X4X180," 1,947 "
-28007608,HC  CATHETER DILATATION RX 2CMX10MM," 1,947 "
-28007609,HC CATH BLLN DILAT RX 4CMX10MM MICR," 1,947 "
-28007610,HC IMP BREAST ROUND SMOOTH SALINE 350CC," 5,100 "
-28007611,HC IMP BREAST ROUND SMOOTH SALINE 325CC," 4,888 "
-28007612,HC IMP BREAST ROUND SMOOTH SALINE 250CC," 4,888 "
-28007613,HC IMP BREAST ROUND SMOOTH SALINE 275CC," 5,100 "
-28007614,HC IMP BREAST ROUND SMOOTH SALINE 300CC," 3,944 "
-28007615,HC IMPL SCREW HTO 6X42MM," 1,148 "
-28007616,HC IMPL SCREW HTO 6X36MM," 1,309 "
-28007617,HC IMPL SCREW HTO 6X30MM," 1,309 "
-28007618,HC CATHETER ANGIOGRAPHY PERFORMATION 265CM," 7,225 "
-28007619,HC IMP COLLAGEN MATRIX ACELLULAR 2X20," 5,100 "
-28007622,HC  CATHETER DILATATION PTA AGILTRAC 018 5X20X80CM," 2,338 "
-28007623,HC  CATHETER DILATATION 18IN 4X20X80CM," 2,338 "
-28007624,HC  CATHETER DILATATION 4X40MM 80CM," 2,338 "
-28007625,HC  CATHETER DILATATION 18IN 5X40X80CM," 2,338 "
-28007626,HC IMP SCREW CORTEX 1.5X6MM, 271 
-28007627,HC  CATHETER DILATATION 7X60MM 80CM," 2,338 "
-28007628,HC  CATHETER DILATATION 18IN 5X20X135CM," 2,338 "
-28007629,HC IMP SCREW CORTEX 1.5X7MM, 271 
-28007630,HC  CATHETER DILATATION 4X40MM 135CM," 2,338 "
-28007631,HC IMPL PACEMAKER SINGLE SIGNA SD203," 22,365 "
-28007632,HC IMP SCREW CORTICAL DCP 1.5X10MM, 149 
-28007633,HC IMPL SCREW SCHANZ 6X225MM," 1,248 "
-28007634,HC IMP STENT BILIARY GENESIS UNMOUNTED 10-12X25," 8,970 "
-28007635,HC IMP STENT BILIARY GENESIS UNMOUNTED 10-12X39," 10,771 "
-28007636,HC IMP STENT BILIARY GENESIS UNMOUNTED 10-12X59," 9,000 "
-28007637,HC IMPL PACEMAKER SINGLE KAPPA 900 SR KSR901," 20,300 "
-28007638,HC PROBE INTRACRAN BAYONET 1.5MM," 8,458 "
-28007639,HC FLOWPROBE INTRACRANIAL BAYONET STERILE DISP 2MM," 8,075 "
-28007640,HC IMPL SCREW CORTEX 2.0X8MM, 271 
-28007641,HC IMPL CONNECTOR SPINAL LOW PROFILE 51CM," 5,610 "
-28007642,HC IMPL PACEMAKER DUAL KAPPA 900 DR KDR901," 37,570 "
-28007645,HC IMPL SCREW CORTEX 2X14MM, 281 
-28007646,HC IMP NEUROSTIMULATOR GENERATOR SOLETRA KIT," 59,215 "
-28007647,HC IMPL SCREW CORTEX 2X20MM, 281 
-28007648,HC IMP STENT BILIARY COTTON LEUNG CLSO 10-5, 255 
-28007649,HC IMP STENT BILIARY C-FLEX 10FX5, 383 
-28007651,HC  INTRODUCER VASCULAR MULLINS RADIOPAQUE 6X48, 646 
-28007652,HC IMPL SCREW CORTEX 2X22MM, 281 
-28007653,HC  INTRODUCER RADIOPAQUE PERFORMER CHECK-FLO, 638 
-28007654,HC  INTRODUCER VASCULAR MULLINS CHECK-FLO 7X63, 646 
-28007655,HC IMP DURAGEN 1X1," 1,602 "
-28007656,HC IMP DURAGEN 1X3," 2,609 "
-28007657,HC IMP DURAGEN 2X2," 2,738 "
-28007658,HC IMP DURAGEN 3X3," 5,215 "
-28007659,HC IMP SCREW CORTEX 2.7X6MM, 287 
-28007660,HC IMP OCCLUDER AMPLATZ VSD 6X7MM," 42,250 "
-28007661,HC IMP OCCLUDER AMPLATZ VSD 8X7MM," 42,250 "
-28007662,HC IMP SCREW CORTEX 2.7X8MM, 287 
-28007663,HC IMP OCCLUDER AMPLATZ VSD 10X7MM," 42,250 "
-28007664,HC IMP OCCLUDER AMPLATZ VSD 12X7MM," 39,000 "
-28007665,HC IMP OCCLUDER AMPLATZ VSD 14X8MM," 39,000 "
-28007666,HC IMP OCCLUDER VSD MUSCLR 8F 7X16MM," 42,250 "
-28007669,HC IMP LINK SPINELINK REVERSE LEFT 21MM," 2,125 "
-28007670,HC IMP LINK SPINELINK STANDARD 33MM," 2,125 "
-28007671,HC IMPL SCREW VERTEBRAL SET, 485 
-28007672,HC IMP BREAST EXPANDER STYLE 133MV 250ML," 8,963 "
-28007673,HC IMP BREAST SALINE STYLE 363LF 200CC," 6,460 "
-28007674,HC IMP EXTENSION LEAD NEUROSTIMULATOR 66CM KIT," 4,208 "
-28007675,HC CONTROLLER ACCESS REVIEW," 5,058 "
-28007676,HC IMP BREAST ROUND SMOOTH SALINE 200CC," 5,100 "
-28007677,HC IMP BREAST ROUND SMOOTH SALINE 175CC," 5,100 "
-28007678,HC IMP BREAST ROUND SMOOTH SALINE 225CC," 5,100 "
-28007679,HC IMP SCREW CORTEX SS 2.7X16MM, 297 
-28007680,HC SACRAL COLPOPEXY KIT," 10,425 "
-28007682,HC IMP SCREW INTERFERENCE 8X20MM, 978 
-28007684,HC IMP BREAST ROUND SMOOTH SALINE 425CC," 5,100 "
-28007687,HC IMP SCREW CORTEX 3.5X10MM, 216 
-28007688,HC IMP LINK SPINELINK STANDARD 27MM," 2,125 "
-28007689,HC IMPL PLATE VUELOCK ONE LEVEL 16MM," 9,314 "
-28007690,HC IMPL PLATE VUELOCK ONE LEVEL 18MM," 9,314 "
-28007691,HC IMP SCREW CORTEX 3.5X12MM, 216 
-28007692,HC  INTRODUCER CHECK FLO 9FX38.5CM, 713 
-28007693,HC IMP SCREW CORTEX 3.5X14MM, 216 
-28007694,HC IMP SCREW CORTEX 3.5X16MM, 216 
-28007695,HC IMP SCREW CORTEX 3.5X18MM, 216 
-28007696,HC IMP SCREW CORTEX 3.5X20MM, 216 
-28007697,HC IMP SCREW CORTEX 3.5X22MM, 216 
-28007698,HC IMP SCREW CORTEX 3.5X24MM, 216 
-28007699,HC  CATHETER MICRO PROWLER 90D .021IN 2.8FX150X20CM," 6,424 "
-28007700,HC  CATHETER MICRO PROWLER J .021IN 2.8FX150X20CM," 6,680 "
-28007701,HC IMP SCREW CORTEX 3.5X26MM, 216 
-28007702,HC IMP SCREW CORTEX 3.5X28MM, 216 
-28007704,HC IMP SCREW CORTEX 3.5X32MM, 216 
-28007706,HC IMP SCREW CORTEX 3.5X36MM, 216 
-28007707,HC IMP SCREW CORTEX 3.5X40MM, 216 
-28007708,HC IMP SCREW CORTEX 3.5X45MM, 216 
-28007709,HC IMP STENT BILIARY C-FLEX 7FX5, 383 
-28007710,HC IMP STENT BILIARY C-FLEX 7FX10, 383 
-28007711,HC IMP STENT BILIARY PIGTAIL C FLEX 10F 10CM, 570 
-28007712,HC IMP SCREW CORTEX 3.5X50MM, 216 
-28007713,HC IMP SCREW CORTEX TITANIUM 4.5X14MM, 196 
-28007714,HC IMP SCREW CORTEX TITANIUM 4.5X16MM, 179 
-28007715,HC IMP SCREW CORTEX TITANIUM 4.5X18MM, 179 
-28007716,HC IMP SCREW CORTEX TITANIUM 4.5X20MM, 179 
-28007717,HC IMP SCREW CORTEX TITANIUM 4.5X22MM, 179 
-28007718,HC IMP SCREW CORTEX 3.5X55MM, 216 
-28007719,HC IMP SCREW CORTEX TITANIUM 4.5X24MM, 206 
-28007720,HC IMP SCREW CORTEX TITANIUM 4.5X26MM, 206 
-28007721,HC IMP SCREW CORTEX TITANIUM 4.5X28MM, 210 
-28007722,HC IMP SCREW CORTEX TITANIUM 4.5X30MM, 210 
-28007723,HC IMP SCREW CORTEX TITANIUM 4.5X32MM, 210 
-28007724,HC IMP SCREW CORTEX TITANIUM 4.5X34MM, 210 
-28007725,HC IMP SCREW CORTEX TITANIUM 4.5X36MM, 218 
-28007726,HC IMP SCREW CORTEX 3.5X60MM, 216 
-28007727,HC IMP SCREW CORTEX TITANIUM 4.5X40MM, 196 
-28007728,HC IMP SCREW CORTEX TITANIUM 4.5X42MM, 210 
-28007729,HC IMP SCREW CORTEX 3.5X65MM, 216 
-28007730,HC IMP SCREW CORTEX TITANIUM 4.5X44MM, 218 
-28007731,HC IMP SCREW CORTEX TITANIUM 4.5X46MM, 253 
-28007732,HC IMP SCREW CORTEX TITANIUM 4.5X48MM, 276 
-28007733,HC IMP SCREW CORTEX TITANIUM 4.5X50MM, 253 
-28007734,HC IMP SCREW CORTEX TITANIUM 4.5X52MM, 253 
-28007735,HC IMP SCREW CORTEX TITANIUM 4.5X54MM, 253 
-28007736,HC IMP SCREW CORTEX TITANIUM 4.5X56MM, 253 
-28007737,HC IMP SCREW CORTEX TITANIUM 4.5X58MM, 253 
-28007738,HC IMP SCREW CORTEX TITANIUM 4.5X60MM, 218 
-28007739,HC IMP SCREW CORTEX TITANIUM 4.5X62MM, 253 
-28007740,HC IMP SCREW CORTEX 3.5X70MM, 208 
-28007741,HC IMP SCREW CORTEX TITANIUM 4.5X64MM, 210 
-28007742,HC IMP SCREW CORTEX TITANIUM 4.5X66MM, 253 
-28007743,HC IMP SCREW CORTEX TITANIUM 4.5X68MM, 253 
-28007744,HC IMP SCREW CORTEX TITANIUM 4.5X70MM, 210 
-28007745,HC IMP SCREW BONE 16MM THREAD 30MM, 316 
-28007746,HC IMP SCREW BONE 16MM THREAD 35MM, 291 
-28007747,HC IMP SCREW CORTEX 3.5X75MM, 216 
-28007748,HC IMP SCREW CORTEX 3.5X80MM, 216 
-28007749,HC IMP SCREW CORTEX 3.5X85MM, 216 
-28007750,HC IMP SCREW BONE FULLY THREAD 25MM, 316 
-28007751,HC IMP SCREW BONE FULLY THREAD 35MM, 316 
-28007752,HC IMP SCREW CORTEX 3.5X90MM, 208 
-28007753,HC IMP SCREW CORTEX 3.5X95MM, 208 
-28007754,HC IMP STENT ULTRAFLEX COVERED 10X7CM 23X28," 11,304 "
-28007757,HC IMP SCREW BONE FULLY THREAD 4.0X10MM, 178 
-28007758,HC IMP SCREW BONE FULLY THREAD 4.0X12MM, 178 
-28007759,HC  SHEATH INTEGRA 7.5CM 4F SET, 247 
-28007760,HC IMP SCREW BONE FULLY THREAD 4.0X14MM, 178 
-28007761,HC IMP SCREW BONE FULLY THREAD 4.0X16MM, 178 
-28007762,HC IMP SCREW BONE FULLY THREAD 4.0X18MM, 178 
-28007763,HC IMP SCREW BONE FULLY THREAD 4.0X20MM, 178 
-28007764,HC IMP SCREW BONE FULLY THREAD 4.0X22MM, 172 
-28007765,HC IMP SCREW BONE FULLY THREAD 4.0X24MM, 172 
-28007766,HC IMP SCREW BONE FULLY THREAD 4.0X26MM, 172 
-28007767,HC IMP SCREW BONE FULLY THREAD 4.0X28MM, 172 
-28007768,HC  SHEATH ARROW FLEX 6F 45CM SET, 614 
-28007769,HC IMP SCREW BONE FULLY THREAD 4.0X32MM, 172 
-28007770,HC IMP SCREW BONE FULLY THREAD 4.0X36MM, 172 
-28007771,HC IMP SCREW BONE FULLY THREAD 4.0X40MM, 178 
-28007772,HC  CATHETER DILATATION 14MM 4X110CM 7F," 2,924 "
-28007773,HC  CATHETER DILATATION 16MM 4X110CM 7F," 2,924 "
-28007774,HC  CATHETER DILATATION 18MM 110X4CM 7F," 2,432 "
-28007775,HC  CATHETER DILATATION 20MM 4X110CM 7F," 2,924 "
-28007776,HC IMP BREAST SALINE STYLE 363LF 370-390CC," 6,460 "
-28007778,HC IMP SCREW BONE FULLY THREAD 4.0X45MM, 172 
-28007779,HC IMPL SCREW HTO 6.0X44MM," 1,148 "
-28007780,HC IMPL PLATE HTO RIGHT 12.5MM," 8,378 "
-28007781,HC IMPL PLATE HTO 7.5MM," 8,378 "
-28007782,HC IMPL SCREW HTO 6.0X32MM," 1,148 "
-28007783,HC IMP SCREW BONE FULLY THREAD 4.0X50MM, 172 
-28007784,HC IMP SCREW BONE FULLY THREAD 4.0X55MM, 172 
-28007785,HC IMP SCREW BONE FULLY THREAD 4.0X60MM, 172 
-28007786,HC IMP SCREW BONE SELF TAPPING 4.0X10MM, 170 
-28007787,HC IMP SCREW BONE SELF TAPPING 4.0X12MM, 164 
-28007788,HC IMP SCREW BONE SELF TAPPING 4.0X14MM, 170 
-28007789,HC IMP STENT COLON WALLSTENT 18X60," 12,938 "
-28007790,HC IMP STENT COLON WALLSTENT 18X90," 12,938 "
-28007791,HC  CATHETER ELECTROPHYSIOLOGY MAP 115CM 6X20MM 7F," 8,768 "
-28007792,HC IMP STENT BILIARY COTTON LEUNG CLSO 11.5-5, 445 
-28007793,HC IMP SCREW BONE SELF TAPPING 4.0X16MM, 130 
-28007794,HC  CATHETER DILATATION RX 2CMX8MM," 1,947 "
-28007795,HC IMP STENT BILIARY C-FLEX 10FX15, 332 
-28007796,HC IMP SCREW BONE SELF TAPPING 4.0X18MM, 130 
-28007797,HC IMP SCREW BONE SELF TAPPING 4.0X20MM, 170 
-28007798,HC  CATHETER ARTERIAL CENTRAL STRAIGHT 2.5F, 319 
-28007799,HC IMP SCREW BONE SELF TAPPING 4.0X22MM, 142 
-28007800,HC  INTRODUCER SHEATH 5F 25X45CM, 298 
-28007801,HC  CATHETER BALLOON HYPERFORM 3F 150CM," 8,753 "
-28007802,HC IMP KNEE LINER ACETABULAR 47MM," 1,839 "
-28007803,HC IMP KNEE LINER ACETABULAR 50MM," 1,839 "
-28007804,HC IMP KNEE LINER ACETABULAR 53MM," 1,839 "
-28007805,HC IMP KNEE LINER ACETABULAR 57MM," 1,839 "
-28007806,HC IMP SCREW BONE SELF TAPPING 4.0X24MM, 130 
-28007807,HC IMP SCREW BONE SELF TAPPING 4.0X26MM, 142 
-28007808,HC IMP SCREW BONE SELF TAPPING 4.0X28MM, 130 
-28007809,HC IMP SCREW BONE SELF TAPPING 4.0X30MM, 164 
-28007810,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.25X8," 7,650 "
-28007811,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.25X12," 7,650 "
-28007812,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.5X12," 7,650 "
-28007813,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.25X16," 7,650 "
-28007814,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.5X16," 7,650 "
-28007815,HC IMP SCREW BONE SELF TAPPING 4.0X40MM, 170 
-28007816,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.25X20," 7,650 "
-28007817,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.5X20," 7,650 "
-28007818,HC IMP SCREW BONE SELF TAPPING 4.0X45MM, 170 
-28007819,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.25X24," 7,650 "
-28007820,HC IMP STENT CORONARY EXPRESS2 RX BMS 2.5X24," 7,650 "
-28007821,HC IMP SCREW BONE SELF TAPPING 4.0X50MM, 164 
-28007822,HC IMP WASHER 4.5MM SCREW, 256 
-28007823,HC IMP LEAD STIMULATION SACRAL NERVE EXTENDED ANCHOR," 13,275 "
-28007824,HC IMP EXTENSION LEAD NEUROSTIMULATOR 10CM KIT," 4,208 "
-28007825,HC IMP GENERATOR NEUROSTIMULATOR," 8,458 "
-28007826,HC IMP STIMULATOR NEUROSTIM INTERSTIM," 61,880 "
-28007827,HC  LEAD INTRODUCER KIT," 2,380 "
-28007828,HC BLADE TRICUT ANGLED 4MM," 2,612 "
-28007829,HC IMP WASHER SMALL CANNULATED BONE SCREW, 243 
-28007831,HC  CATHETER DILATATION 6MMX4CM," 2,865 "
-28007832,HC IMP GRAFT RINGED STANDARD WALL 14," 7,695 "
-28007833,HC IMP GRAFT RINGED STANDARD WALL 12," 7,695 "
-28007834,HC IMP GRAFT RINGED STANDARD WALL 16," 7,695 "
-28007835,HC IMP PUTTY BONE MATRIX DBM DBX 5CC," 8,438 "
-28007836,HC IMP DISTRACTOR LOGIC LEFT 52MM," 20,046 "
-28007837,HC IMP DISTRACTOR LOGIC RIGHT 52MM," 20,046 "
-28007838,HC IMP DISTRACTOR LOGIC LEFT STRAIGHT," 17,700 "
-28007839,HC IMP DISTRACTOR LOGIC RIGHT STRAIGHT," 15,353 "
-28007840,HC IMP WIRE ACTIVATION MEDIUM," 4,997 "
-28007841,HC  CATHETER FOLEY 2W SILICONE 6F 1.5ML, 192 
-28007842,HC IMP WIRE ACTIVATION LONG," 5,610 "
-28007844,HC IMPL SCREW STANDARD CFX 1.6X12MM, 371 
-28007848,HC IMPL PACEMAKER SINGLE KAPPA 900 SR KSR906," 37,570 "
-28007849,HC IMP VALVE CSF STRATA II DELTA SM," 26,378 "
-28007851,HC  SNARE ENDOSCOPIC 12-20MM 120CM," 3,413 "
-28007852,HC  CATHETER BALLOON OCCLUSION HYPERGLIDE 4X15MM," 8,016 "
-28007853,HC IMP GRAFT SURGISIS GOLD 7X10," 7,040 "
-28007854,HC IMPL PLATE 1/3 TUBULAR 2 HOLE 25MM, 540 
-28007855,HC IMPL PLATE 1/3 TUBULAR 3 HOLE 37MM, 557 
-28007856,HC IMP STENT TRACHEAL ULTRAFLEX 18X40X2.5," 13,125 "
-28007857,HC IMPL PLATE 1/3 TUBULAR 4 HOLE 49MM, 645 
-28007858,HC IMPL PLATE 1/3 TUBULAR 5 HOLE 61MM, 645 
-28007859,HC IMP ROD CARBON FIBER 4X80MM, 389 
-28007860,HC IMP ROD CARBON FIBER 4X100MM, 389 
-28007861,HC IMPL PLATE 1/3 TUBULAR 6 HOLE 73MM, 670 
-28007862,HC IMPL SCREW SCHANZ 3X4X80MM, 720 
-28007863,HC IMPL PLATE 1/3 TUBULAR 7 HOEL 85MM, 670 
-28007864,HC IMPL SCREW SCHANZ 4X80MM, 720 
-28007865,HC IMPL SCREW SCHANZ 4X100MM, 746 
-28007866,HC IMPL SCREW SCHANZ 4X120MM, 746 
-28007867,HC IMPL PLATE 1/3 TUBULAR 8 HOLE 97MM, 695 
-28007868,HC IMPL PLATE 1/3 TUBULAR 10 HOLE 121MM, 695 
-28007870,HC IMPL PACEMAKER DUAL KAPPA 900 DR KDR903," 38,318 "
-28007871,HC IMPL PLATE 1/4 TUBULAR 41MM 5 HOLE, 863 
-28007873,HC IMPL PLATE 1/4 TUBULAR 57MM 7 HOLE, 791 
-28007874,HC IMPL PLATE 1/4 TUBULAR 65MM 8 HOLE, 996 
-28007875,HC IMP SCREW CORTEX TITANIUM 5MM, 524 
-28007876,HC IMP SCREW CORTEX TITANIUM 6MM, 524 
-28007877,HC IMP SCREW CORTEX TITANIUM 8MM, 500 
-28007878,HC IMPL PLATE TI SHORT RIGHT 1.5MM," 1,632 "
-28007879,HC IMPL PLATE TI LONG RIGHT 1.5MM," 1,632 "
-28007880,HC IMPL PLATE TI LONG 1.5MM LEFT," 1,632 "
-28007881,HC IMPL PLATE 3X3 HOLE 22MM LEFT," 1,714 "
-28007882,HC IMPL PLATE 3X3 HOLE 24MM LEFT," 1,802 "
-28007883,HC IMPL PLATE 3X3 HOLES 26MM RIGHT," 1,802 "
-28007884,HC IMPL PLATE 3X3 HOLES 26MM LEFT," 1,802 "
-28007885,HC IMPL PLATE CURVED 10 HOLES 38MM," 2,380 "
-28007886,HC IMPL PLATE CURVED 8HOLE," 2,380 "
-28007887,HC IMPL PLATE LOW PROFILE 2.0MM 6HOLE," 2,251 "
-28007888,HC IMPL PLATE TI CURVED SPLIT 6 HOLE," 2,251 "
-28007889,HC IMPL PLATE DYNAMIC COMPRESSION 6 HOLE 52MM," 1,592 "
-28007890,HC IMPL SCREW RESORBABLE TAP 1.5X4MM," 2,128 "
-28007891,HC IMPL SCREW RESORBABLE TAP 1.5X6MM," 2,128 "
-28007892,HC IMPL SCREW RESORBABLE TAP 2.0X4MM," 2,128 "
-28007893,HC IMPL SCREW RESORBABLE TAP 2.0X6MM," 2,128 "
-28007894,HC IMPL PLATE DYNAMIC COMPRESSION SS 9 HOLE 76MM," 1,692 "
-28007895,HC IMP VALVED CONDUITS LP 12MM," 35,000 "
-28007896,HC IMP VALVED CONDUITS LP 14MM," 28,700 "
-28007897,HC IMP VALVED CONDUITS LP 20MM," 28,700 "
-28007898,HC IMP VALVED CONDUITS LP 26MM," 35,000 "
-28007899,HC IMP SHEET ORBITAL MEDPOR 50X76X1.5," 3,230 "
-28007900,HC IMPL PLATE DYNAMIC COMPRESSION SS 12 HOLE 100MM," 2,414 "
-28007901,HC IMP SHEET BARRIER CHANNEL 40MMX52MMX2.3MM," 3,910 "
-28007902,HC IMP ORBITAL MEDPORE TITAN 38X50X1MM," 3,230 "
-28007903,HC IMP ORBITAL RIM INFERIOR LEFT," 1,785 "
-28007904,HC IMP ORBITAL RIM INFERIOR RIGHT," 2,125 "
-28007905,HC IMP ORBITAL RIM EXTENDED LEFT," 5,313 "
-28007906,HC IMP ORBITAL RIM EXTENDED RIGHT," 2,975 "
-28007907,HC IMP WEDGE ENOPHTAL LEFT REGULAR," 2,508 "
-28007908,HC IMP WEDGE ENOPHTAL RIGHT REGULAR," 2,890 "
-28007909,HC IMP SPACER EYELID LOWER LEFT POREX," 1,488 "
-28007910,HC IMP SPACER EYELID LOWER RIGHT," 1,488 "
-28007911,HC IMP EYE SPHERE ORBITAL POREX 16MM," 5,695 "
-28007912,HC IMP EYE SPHERE ORBITAL POREX 18MM," 5,695 "
-28007913,HC IMP EYE SPHERE ORBITAL POREX 20MM," 5,695 "
-28007914,HC IMP EYE SPHERE ORBITAL POREX 22MM," 5,695 "
-28007915,HC IMP STENT ASPIRE COVERED 6X5X50L," 14,625 "
-28007916,HC IMP STENT ASPIRE COVERED 7X5X50L," 14,625 "
-28007917,HC IMP STENT ASPIRE COVERED 8X5X50L," 14,625 "
-28007918,HC IMP STENT ASPIRE COVERED 9X5X50L," 14,625 "
-28007919,HC IMP STENT ASPIRE COVERED 10X5X50L," 14,625 "
-28007920,HC IMP STENT ASPIRE COVERED 12X5X50L," 14,625 "
-28007921,HC IMP STENT ASPIRE COVERED 6X5X100L," 14,625 "
-28007922,HC IMP STENT ASPIRE COVERED 7X5X100L," 14,625 "
-28007923,HC IMP STENT ASPIRE COVERED 8X5X100L," 14,625 "
-28007924,HC IMP STENT ASPIRE COVERED 6X10X50L," 15,375 "
-28007925,HC IMP STENT ASPIRE COVERED 7X10X50L," 15,375 "
-28007926,HC IMP STENT ASPIRE COVERED 6X10X100L," 15,375 "
-28007927,HC IMP STENT ASPIRE COVERED 7X10X100L," 15,375 "
-28007928,HC IMPL PLATE 3HL 37MM," 1,206 "
-28007929,HC  CATHETER ELECTROPHYSIOLOGY 10PLR FXDCRV 65CM 4F," 1,700 "
-28007930,HC IMPL PLATE 4HL 49MM," 1,240 "
-28007931,HC IMPL PLATE 5HL 61MM," 1,360 "
-28007932,HC IMPL PLATE 6HL 73MM," 1,441 "
-28007933,HC IMPL PLATE 7HL 85MM," 1,491 "
-28007934,HC IMPL PLATE 8HL 97MM," 1,566 "
-28007935,HC IMPL PLATE 9HL 109MM," 1,594 "
-28007936,HC IMPL PLATE 10HL 121MM," 1,700 "
-28007937,HC IMPL PLATE CLOVERLEAF 3HL 80MM," 2,423 "
-28007938,HC  GUIDEWIRE ANGIOGRAPHY MEIER 35X185CM, 517 
-28007939,HC IMPL PLATE CLOVERLEAF 4HL 104MM," 3,019 "
-28007940,HC IMPL PLATE 1/3 TUBULAR OBLIQUE 3 HOLE 39MM," 2,015 "
-28007941,HC IMPL PLATE 1/3 TUBULAR OBLIQUE 3 HOLE 67MM," 2,202 "
-28007942,HC IMPL SCREW POLYAXIAL 6.5X40MM," 7,188 "
-28007943,HC IMP NUT LOCK EXTENDED, 477 
-28007944,HC IMP LINK STANDARD 30MM W/6MM STEP," 2,125 "
-28007945,HC IMP LINK STANDARD 27MM W/9MM STEP," 2,125 "
-28007946,HC IMPL PACEMAKER DUAL PACESETTER 2022T," 43,550 "
-28007947,HC IMP BREAST SALINE STYLE 363LF 260-275CC," 6,460 "
-28007948,HC IMP BREAST EXPANDER STYLE 133LV 300ML," 8,963 "
-28007949,HC IMP BREAST EXPANDER STYLE 133LV 350ML," 8,963 "
-28007950,HC IMP BREAST EXPANDER STYLE 133LV 200ML," 8,963 "
-28007951,HC IMP BREAST EXPANDER STYLE 133FV 350ML," 8,963 "
-28007952,HC IMP BREAST SALINE STYLE 363LF 450-475CC," 6,460 "
-28007953,HC IMP BREAST SALINE STYLE 363LF 560-585CC," 6,460 "
-28007954,HC IMP BREAST SILTEX CONTOUR 350CC," 9,000 "
-28007955,HC IMP BREAST ROUND SILTEX GEL 200CC," 13,500 "
-28007956,HC IMP BREAST ROUND SILTEX GEL 250CC," 7,438 "
-28007957,HC IMP BREAST ROUND SILTEX GEL 225CC," 13,500 "
-28007958,HC IMP BREAST TISSUE EXPANDER 75ML," 4,208 "
-28007959,HC IMP BREAST TISSUE EXPANDER 150ML," 4,208 "
-28007960,HC IMP BREAST TISSUE EXPANDER 250ML," 4,208 "
-28007961,HC IMP BREAST SILTEX CONTOUR 550CC," 9,000 "
-28007962,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 3 HOLE 39MM, 995 
-28007963,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 3 HOLE 67MM," 1,258 "
-28007964,HC IMP WIRE KIRSCHNER GAMMA 3 3.2X450MM," 1,333 "
-28007965,HC  GUIDEWIRE ORTHO BALL TIP 3X1000," 2,035 "
-28007966,HC  GUIDEWIRE ORTHO BALL TIP 3X800," 1,797 "
-28007967,HC IMPL PLATE L OBLIQUE RIGHT 2.7MM 3 HOLE, 579 
-28007968,HC  GUIDEWIRE ANGLED TP NTNL 15D 180X5CM, 538 
-28007969,HC  GUIDEWIRE ANGLED TP NITRX 25IN 180CM, 354 
-28007970,HC IMPL SCREW VUELOCK 5X14MM," 2,045 "
-28007971,HC IMPL SCREW VUELOCK 5X16MM," 2,045 "
-28007972,HC IMPL PLATE VUELOCK ONE LEVEL 14MM," 9,314 "
-28007973,HC IMPL PLATE MINI STRAIGHT 3 HOLE, 522 
-28007974,HC IMPL PLATE VUELOCK TWO LEVEL 38MM," 11,386 "
-28007975,HC IMPL PLATE MINI STRAIGHT 4 HOLE, 595 
-28007976,HC IMPL PLATE VUELOCK THREE LEVEL 58MM," 13,974 "
-28007977,HC IMPL PLATE MINI STRAIGHT 5 HOLE, 637 
-28007978,HC IMPL PLATE VUELOCK FOUR LEVEL 60MM," 17,183 "
-28007979,HC URETERAL INFRAVISION KIT," 11,610 "
-28007980,HC DISSECT BLLN HERNIA RND," 2,719 "
-28007981,HC  GUIDEWIRE ROADRUNNER 18X480CM," 1,149 "
-28007982,HC IMP DERMAL INJECTABLE RADIESSE 1ML," 2,210 "
-28007983,HC  CATHETER DILATATION PTA AGILTRAC 035 5X20X80CM," 1,615 "
-28007984,HC  CATHETER DILATATION PTA AGILTRAC 035 6X20X80CM," 1,615 "
-28007985,HC  CATHETER DILATATION 80CM 20X7MM 7F," 1,615 "
-28007986,HC  CATHETER DILATATION .035IN 8X20X80CM," 1,615 "
-28007987,HC  CATHETER DILATATION 80CM 20X9MM 6F," 1,615 "
-28007988,HC  CATHETER DILATATION 80CM 20X10MM 6F," 1,615 "
-28007989,HC  CATHETER DILATATION 80CM 20X12MM 9F," 1,615 "
-28007990,HC  CATHETER DILATATION 14MM 20X80CM," 1,615 "
-28007991,HC  CATHETER DILATATION 40X5MM 80CM 7F," 1,615 "
-28007992,HC  CATHETER DILATATION 6X40MM 80CM 5F," 1,615 "
-28007993,HC  CATHETER DILATATION 80CM 7X40MM," 1,615 "
-28007994,HC  CATHETER DILATATION .035IN 8X40X80CM," 1,615 "
-28007995,HC  CATHETER DILATATION 80CM 9X40MM," 1,615 "
-28007996,HC  CATHETER DILATATION 80CM 40X10MM 6F," 1,615 "
-28007997,HC  CATHETER DILATATION 80CM 40X12MM 7F," 1,615 "
-28007998,HC  CATHETER DILATATION 80CM 40X14MM 7F," 1,615 "
-28007999,HC  CATHETER DILATATION 80CM 6X60MM," 1,615 "
-28008000,HC  CATHETER DILATATION 6X80MM 80CM 5F," 1,615 "
-28008001,HC  CATHETER DILATATION 8X80MM 80CM," 1,615 "
-28008002,HC  CATHETER DILATATION 135CM 20X5MM 5F," 1,615 "
-28008003,HC  CATHETER DILATATION 135CM 20X6MM 7F," 1,615 "
-28008004,HC  CATHETER DILATATION 135CM 20X8MM 6F," 1,615 "
-28008005,HC  CATHETER DILATATION 135CM 20X9MM 6F," 1,615 "
-28008006,HC  CATHETER DILATATION 135CM 20X10MM 6F," 1,615 "
-28008007,HC  CATHETER DILATATION 5X40MM 135CM," 1,615 "
-28008008,HC  CATHETER DILATATION 35IN 6X40X135CM," 1,615 "
-28008009,HC  CATHETER DILATATION 135CM 40X7MM 6F," 1,615 "
-28008010,HC  CATHETER DILATATION 135CM 40X8MM 6F," 1,615 "
-28008011,HC  CATHETER DILATATION 135CM 40X9MM 6F," 1,615 "
-28008012,HC  CATHETER DILATATION 135CM 40X10MM 6F," 1,615 "
-28008013,HC  CATHETER DILATATION 135CM 100X5MM 5F," 1,615 "
-28008014,HC  CATHETER DILATATION 6X100X135CM," 1,615 "
-28008015,HC  CATHETER DILATATION 135CM 100X7MM 6F," 1,615 "
-28008016,HC IMPL PACEMAKER DUAL KAPPA 900 DR KDR906," 37,570 "
-28008017,HC IMP MESH PROLENE 3X6MM, 970 
-28008018,HC IMP MESH PROLENE 12X12," 2,500 "
-28008019,HC IMP BREAST ROUND SMOOTH SALINE 475CC," 3,944 "
-28008020,HC IMPL SCREW BIOABSORBABLE 10X25MM," 1,063 "
-28008021,HC BLADE SKIMMER ANGLED TIP 3.5," 2,720 "
-28008022,HC IMP STENT BILIARY EXPRESS 7X60X135," 11,237 "
-28008023,HC IMP CABLE BLOOD PUMP JARVIK," 112,500 "
-28008024,HC IMP LEAD VENTRICULAR RIATA 65CM," 37,375 "
-28008025,HC IMP LEAD VENTRICULAR 75CM," 37,375 "
-28008026,HC IMP STENT NEUROFORM2 4X20," 33,605 "
-28008027,HC  GUIDEWIRE PERIPHERAL NEUROVASCULAR 14IN 300CM," 3,955 "
-28008028,HC  SNARE ENDOVASCULAR NEURONET 5MM," 13,500 "
-28008029,HC IMP ANNULOPLASTY RING TAILOR 25MM," 8,500 "
-28008030,HC IMP ANNULOPLASTY RING TAILOR 27MM," 7,725 "
-28008031,HC IMP ANNULOPLASTY RING TAILOR 29MM," 7,725 "
-28008032,HC IMP ANNULOPLASTY RING TAILOR 31MM," 7,725 "
-28008033,HC IMP ANNULOPLASTY RING TAILOR 33MM," 7,725 "
-28008034,HC IMP ANNULOPLASTY RING TAILOR 35MM," 8,500 "
-28008035,HC  CATHETER HEMODIALYSIS CHRONIC 22CM SET," 2,890 "
-28008036,HC IMP STENT BILIARY RX 8.5FX12, 757 
-28008037,HC IMP STENT BILIARY RX 8.5FX15, 757 
-28008038,HC IMP STENT BILIARY RX 10FX12, 757 
-28008039,HC CATHETER PLEURAL PLUERX," 6,679 "
-28008040,HC IMP GRAFT BOVINE 6X45CM," 7,943 "
-28008041,HC IMPL WASHER SFCW, 553 
-28008042,HC IMP WIRE KIRSCHNER 1.1MM, 128 
-28008044,HC IMPL PLATE RECONSTRUCTION 3.5X84MM 6 HOLE," 4,942 "
-28008045,HC IMPL PLATE RECONSTRUCTION 3.5X98MM 7 HOLE," 5,194 "
-28008046,HC IMPL PLATE RECONSTRUCTION 3.5X112MM 8 HOLE," 5,237 "
-28008047,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X10MM," 1,198 "
-28008048,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X12MM," 1,198 "
-28008049,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X14MM," 1,198 "
-28008050,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X16MM," 1,198 "
-28008051,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X18MM," 1,198 "
-28008052,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X20MM," 1,198 "
-28008053,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X22MM," 1,198 "
-28008054,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X24MM," 1,198 "
-28008055,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X26MM," 1,198 "
-28008056,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X28MM," 1,198 "
-28008057,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X30MM," 1,198 "
-28008058,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X34MM," 1,198 "
-28008059,HC IMPL PLATE BONE LCP 8 HOLE," 3,376 "
-28008060,HC IMPL PLATE BONE LCP 9 HOLE," 3,399 "
-28008061,HC IMP SCREW ACUTRAK MINI 24MM," 2,805 "
-28008062,HC IMP SCREW ACUTRAK MINII 20MM," 3,035 "
-28008065,HC IMP VENACURE LASER VARICOSE TREATMENT KIT," 4,354 "
-28008066,HC  CATHETER DURA FLOW 28CM," 3,534 "
-28008067,HC  CATHETER DURA FLOW 24CM," 3,534 "
-28008068,HC  CATHETER DURA FLOW 32CM," 3,534 "
-28008070,HC  CATHETER HEART LEFT 9.5MMX20F, 315 
-28008071,HC  CATHETER BALLOON HYPERFORM 2.5F 150CM," 8,753 "
-28008072,HC  CATHETER PICC PASV SINGLE LUMEN 4FX60CM," 1,360 "
-28008073,HC  GUIDEWIRE PICC DUAL 60CM 5F KIT," 1,743 "
-28008074,HC IMPL COIL NEURO MICRO HSR 1CM 2MM ULTIPAQ," 7,542 "
-28008075,HC IMPL COIL NEURO MICRO HSR 4CM 2MM ULTIPAQ," 7,183 "
-28008076,HC IMPL COIL NEURO MICRO HSR 6CM 2MM ULTIPAQ," 7,754 "
-28008077,HC IMPL COIL NEURO MICRO HSR 6CM 3MM ULTIPAQ," 7,778 "
-28008078,HC IMPL COIL NEURO MICRO HSR 8CM 3MM ULTIPAQ," 7,863 "
-28008079,HC IMPL COIL NEURO MICRO HSR 4CM 8MM ULTIPAQ," 7,839 "
-28008080,HC IMPL PLATE RESORBABLE 3D MESH THICK .8MM," 17,802 "
-28008081,HC IMPL PLATE RESORBABLE 3D MESH ST .8MM," 8,112 "
-28008082,HC IMP STENT GRAFT AAA EXCLUDER 14.5X14," 27,860 "
-28008083,HC  INTRODUCER SHEATH ENDOPROSTHESIS 18F 30CM," 1,105 "
-28008085,HC IMPL PLATE COMPRESSION 2.0MM 7HOLE," 1,632 "
-28008086,HC IMPL PLATE RECONSTRUCTION 2.7X245MM 6 HOLE," 2,440 "
-28008089,HC  GUIDEWIRE JAGWIRE ANGLED 35INX260CM," 1,020 "
-28008090,HC IMP PUTTY BONE NORIAN CRS 15ML," 26,645 "
-28008091,HC IMPL PLATE ORBITAL FLOOR 1.3 SMALL," 7,310 "
-28008092,HC IMPL SCREW HTO 60MM," 1,148 "
-28008093,HC IMPL PLATE HTO 10MM," 8,378 "
-28008094,HC STAPLER ENDOCUTTER LNG 45MM," 2,589 "
-28008095,HC IMP PLUG MESH MARLEX PERFLEX MEDIUM," 1,866 "
-28008097,HC  INTRODUCER SHEATH GUIDING SL1 63CM 8F," 2,440 "
-28008098,HC  CATHETER ELECTROPHYSIOLOGY 4PLR FXDCRV 120CM 4F," 1,700 "
-28008099,HC IMP STENT BILIARY RX 10FX5, 757 
-28008100,HC IMPL PLATE RECONSTRUCTION 3.5X58MM 5 HOLE," 3,052 "
-28008101,HC IMP PECTUS BAR 16IN," 7,875 "
-28008102,HC IMP PECTUS BAR 17IN," 7,616 "
-28008103,HC IMPL SCREW BIOABSORBABLE 7X20MM," 1,063 "
-28008104,HC IMPL SCREW BIOABSORBABLE 7X25MM," 1,063 "
-28008105,HC IMP SCREW INTERFERENCE 7X25MM, 978 
-28008106,HC IMP SCREW INTERFERENCE 8X25MM, 978 
-28008107,HC IMP SCREW INTERFERENCE 9X20MM, 978 
-28008108,HC IMPL PLATE RECONSTRUCTION 3.5X70MM 6 HOLE," 3,577 "
-28008109,HC IMP LEAD TINED KIT," 22,995 "
-28008111,HC IMPL PLATE RECONSTRUCTION 3.5X82MM 7 HOLE," 3,755 "
-28008112,HC IMP LEAD PACEMAKER CAPSUREFIX 40CM," 4,675 "
-28008113,HC IMP LEAD PACEMAKER MYOCARDIAL UNIPOLAR 35CM," 4,675 "
-28008114,HC IMP LEAD PACEMAKER VENTRCULAR UNIPOLAR 53CM," 4,675 "
-28008115,HC IMPL PLATE RECONSTRUCTION 3.5X245MM 8 HOLE," 3,655 "
-28008116,HC IMPL SCREW BIOABSORBABLE 10X30MM," 1,063 "
-28008117,HC IMPL SCREW BIOABSORBABLE 7X30MM," 1,063 "
-28008119,HC IMPL PLATE RECONSTRUCTION 3.5X106MM 9 HOLE," 3,749 "
-28008120,HC IMP STENT BILIARY COTTON LEUNG CLSO 11.5-15, 255 
-28008121,HC  INTRODUCER NON VASC PEEL AWAY 24X13X0.038, 309 
-28008122,HC IMPL PLATE RECONSTRUCTION 3.5X118MM 10 HOLE," 3,460 "
-28008123,HC IMP STENT PANCREATIC 5F 8CM, 973 
-28008125,HC IMPL SCREW THREAD 5.2X40MM," 8,135 "
-28008126,HC IMPL SCREW THREAD 7.0X45MM," 8,460 "
-28008127,HC IMP ROD HARD CURVED TITANIUM 45MM," 2,125 "
-28008128,HC IMP ROD SOFT CURVED TITANIUM 55MM," 2,125 "
-28008129,HC IMP WIRE SPINAL PEARL TIP 1.2M35CM," 4,420 "
-28008130,HC IMPL PLATE FIXED LOW PROFILE SS 5.5MMX0.75IN," 7,225 "
-28008131,HC IMPL PLATE FIXED LOW PROFILE SS 5.5MMX0.625IN," 9,840 "
-28008132,HC IMPL PLATE RECONSTRUCTION 3.5X214MM 18 HOLE," 5,100 "
-28008133,HC IMP VALVE PROGRAMMABLE SIPHONGUARD SHUNT," 20,748 "
-28008134,HC IMP SCREW FIXED TITANIUM 45MM," 5,525 "
-28008135,HC IMPL PLATE RECONSTRUCTION CURVED 3.5X118MM 10HOLE," 4,140 "
-28008136,HC IMPL PLATE RECONSTRUCTION CURVED 3.5X142MM 12HOLE," 3,953 "
-28008137,HC IMP STENT PANCREATIC 5F 2CM, 973 
-28008138,HC IMP STENT PANCREATIC 5F 4CM, 973 
-28008139,HC IMP CARTILAGE FRESH FEMORAL LEFT MEDIUM," 61,380 "
-28008140,HC IMP CARTILAGE FRESH FEMORAL LATERAL LEFT," 61,380 "
-28008141,HC IMP CARTILAGE FRESH FEMORAL RIGHT MEDIUM," 61,380 "
-28008142,HC IMP CARTILAGE FRESH FEMORAL LATERAL RIGHT," 61,380 "
-28008143,HC IMP CARTILAGE FRESH DISTAL FEMORAL LEFT," 93,600 "
-28008144,HC  CATHETER GUIDING VISTA BRITE TIP SH JL4 6F, 442 
-28008145,HC  CATHETER GUIDING VISTA BRITE TIP JL4.5 6F, 442 
-28008146,HC  CATHETER GUIDING VISTA BRITE TIP SH JR4 6F, 442 
-28008147,HC  CATHETER GUIDING VISTA BRITE TIP 6F XB3, 451 
-28008148,HC  GUIDEWIRE CORONARY STRAIGHT 14IN 30X300CM, 723 
-28008149,HC IMPL SCREW THREAD 6.2X30MM," 8,135 "
-28008150,HC IMPL SCREW THREAD 6.2X40MM," 8,460 "
-28008151,HC IMPL SCREW THREAD 6.2X45MM," 8,135 "
-28008152,HC IMPL SCREW THREAD 6.2X50MM," 8,135 "
-28008153,HC IMPL SCREW THREAD 7.0X30MM," 8,460 "
-28008154,HC IMPL SCREW THREAD 7.0X50MM," 8,460 "
-28008155,HC IMPL SCREW VARIABLE THREAD 8.0X45MM," 8,399 "
-28008156,HC IMPL SCREW THREAD 8.0X50MM," 8,135 "
-28008157,HC IMPL SCREW THREAD 8.0X65MM," 8,460 "
-28008158,HC CIRCUIT RESCUE PERFU ADL," 3,910 "
-28008159,HC  CANNULA VENTRICULAR LONG STRAIGHT 48X16X20CM," 18,655 "
-28008160,HC IMP BIOGLUE SYRINGE 5ML," 5,942 "
-28008161,HC IMPL PLATE RECONSTRUCTION TITANIUM 3.5X70MM 6HOLE," 3,519 "
-28008162,HC IMPL PLATE RECONSTRUCTION TITANIUM 3.5X82MM 7HOLE," 3,613 "
-28008163,HC  GUIDEWIRE SPADE 1.6X150MM, 322 
-28008164,HC IMPL PLATE RECONSTRUCTION TITANIUM 3.5X94MM 8HOLE," 3,834 "
-28008165,HC IMPL PLATE RECONSTRUCTION TITANIUM 3.5X106MM 9HOLE," 3,944 "
-28008166,HC IMPL PLATE RECONSTRUCTION TITANIUM 3.5X118MM 10HOLE," 4,012 "
-28008167,HC IMP CARTILAGE FRESH DISTAL FEMORAL RIGHT," 93,600 "
-28008168,HC IMP CARTILAGE FRESH HUM HEAD LEFT," 20,650 "
-28008169,HC IMP CARTILAGE FRESH TROCHLEA RIGHT," 45,013 "
-28008170,HC IMP CARTILAGE FRESH TIBIAL PLATE LEFT," 83,700 "
-28008171,HC IMP CARTILAGE FRESH TIBIAL MEDIUM LEFT," 45,338 "
-28008172,HC IMP CARTILAGE FRESH TIBIAL PLATE RIGHT," 83,700 "
-28008173,HC IMP CARTILAGE FRESH TIBIAL MEDIUM RIGHT," 45,338 "
-28008174,HC IMP CARTILAGE FRESH TIBIAL LATERAL LEFT," 45,338 "
-28008175,HC IMP CARTILAGE FRESH TIBIAL LATERAL RIGHT," 45,338 "
-28008176,HC IMP CARTILAGE FRESH TIBIAL DISTAL LEFT," 20,650 "
-28008177,HC IMP CARTILAGE FRESH TIBIAL DISTAL RIGHT," 20,650 "
-28008178,HC IMP CARTILAGE FRESH TALUS LEFT," 28,840 "
-28008179,HC IMP CARTILAGE FRESH TALUS RIGHT," 28,840 "
-28008180,HC IMP CARTILAGE FRESH ANKLE LEFT," 39,520 "
-28008181,HC IMP STENT GRAFT AAA EXCLUDER 23X14.5X16," 50,564 "
-28008183,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X16," 49,569 "
-28008184,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X18," 54,405 "
-28008185,HC IMP STENT GRAFT AAA EXCLUDER 28.5X12X18," 49,569 "
-28008186,HC IMP STENT GRAFT AAA EXCLUDER C3 28.5X14.5X16," 49,569 "
-28008187,HC IMP STENT GRAFT AAA EXCLUDER C3 28.5X14.5X18," 50,564 "
-28008188,HC IMP STENT GRAFT AAA EXCLUDER C3 23X3.3," 19,530 "
-28008189,HC IMP STENT GRAFT AAA EXCLUDER C3 26X3.3," 18,970 "
-28008190,HC IMP STENT GRAFT AAA EXCLUDER 10X7," 19,530 "
-28008191,HC IMP STENT GRAFT AAA EXCLUDER 12X7," 19,530 "
-28008192,HC IMP STENT GRAFT AAA EXCLUDER 14.5X7," 19,530 "
-28008193,HC IMP STENT GRAFT AAA EXCLUDER 12X10," 27,860 "
-28008194,HC IMP STENT GRAFT AAA EXCLUDER 12X12," 27,860 "
-28008195,HC IMP STENT GRAFT AAA EXCLUDER 12X14," 29,400 "
-28008196,HC IMP HIP LINER ACETABULAR TRILOGY 0D 32X54," 3,558 "
-28008197,HC IMP HIP LINER ACETABULAR TRILOGY 0D 32X56," 3,536 "
-28008198,HC IMP HIP LINER ACETABULAR TRILOGY 0D 32X58," 3,677 "
-28008199,HC IMP LINK SPINELINK STANDARD CROSSLNK 18MM," 2,125 "
-28008201,HC IMP LINK SPINELINK STANDARD 51MM," 2,496 "
-28008203,HC IMPL PLATE 34MM," 11,386 "
-28008205,HC IMP SPACER SPINAL WASHERS, 776 
-28008206,HC IMP LINK SPINELINK CROSSLNK GOLD 18X3MM," 2,672 "
-28008207,HC IMP LINK SPINELINK CROSSLNK GOLD 21X3MM," 2,672 "
-28008208,HC IMP LINK SPINELINK CROSSLNK GOLD 27X3MM," 2,672 "
-28008210,HC IMP HIP LINER ACETABULAR TRILOGY 10D 32X48," 3,558 "
-28008211,HC IMP HIP LINER ACETABULAR TRILOGY 10D 32X54," 3,558 "
-28008212,HC IMP HIP LINER ACETABULAR TRILOGY 10D 32X56," 3,558 "
-28008213,HC IMP HIP LINER ACETABULAR TRILOGY 10D 32X58," 3,558 "
-28008215,HC IMP NUT SPINAL LCKING SPINELINK TI, 626 
-28008217,HC IMP KNEE INSERT TIBIAL ROTAT PLAT SZ4 10MM," 8,250 "
-28008219,HC IMP KNEE INSERT TIBIAL SZ3 8MM," 5,814 "
-28008222,HC  CATHETER MICRO PROWLER STR .021IN 2.3FX150X20CM," 5,449 "
-28008223,HC  CATHETER BALLOON 4.5MM 0.014X0.018IN," 2,117 "
-28008224,HC  GUIDEWIRE CORONARY STRAIGHT 14IN 190CMX3MM, 697 
-28008225,HC IMPL COIL PERIPHERAL EMBOLIZATION 2MM 3CM 0.038IN," 1,360 "
-28008226,HC IMPL COIL PERIPHERAL EMBOLIZATION EXTND 6CM 5MM, 840 
-28008227,HC IMPL COIL PERIPHERAL EMBOLIZATION OCLSN SPRNG," 1,360 "
-28008228,HC IMPL COIL PERIPHERAL EMBOLIZATION EXTND 4LOOP 10CM, 282 
-28008229,HC IMPL COIL PERIPHERAL EMBOLIZATION 12MM 15CM," 1,009 "
-28008230,HC IMP VALVE CSF STRATA II DELTA PROGRAMMABLE REGULAR," 26,378 "
-28008231,HC  CATHETER FOLEY COUNCIL 14F 5ML, 126 
-28008232,HC  CATHETER COUDE 12F 5ML, 126 
-28008236,HC IMP STENT BILIARY EXPRESS 10X60X75," 11,238 "
-28008238,HC IMP ICD OTHER INSYNC III CRT D," 47,125 "
-28008239,HC IMPL PLATE PROXIMAL HUMERUS 3.5X90MM 3HOLE," 13,039 "
-28008240,HC IMPL PLATE PROXIMAL HUMERUS 3.5X114MM 5HOLE," 13,452 "
-28008241,HC  RETRIEVER STENT SOEHENDRA 10F, 973 
-28008242,HC IMPL COIL NEURO MICRO SPHERICAL 16MM 27.9CM," 8,550 "
-28008244,HC IMPL COIL NEURO MICRO HELICAL10CM 5MM," 6,695 "
-28008247,HC IMPL COIL NEURO MICRO HELICAL STR STR10 6MM 2," 6,483 "
-28008248,HC IMP STENT PANCREATIC ZIMMON 10CM 5F, 973 
-28008249,HC IMP STENT BILIARY OMNILINK35 7X18X135," 9,375 "
-28008250,HC IMP STENT BILIARY OMNILINK35 8X18X135," 9,375 "
-28008251,HC IMP STENT BILIARY OMNILINK35 10X28X135," 9,750 "
-28008252,HC IMPL SLING URETHRAL TVT MESH BLUE KIT," 8,512 "
-28008253,HC  INTROD INNERCOOL 10.7F, 888 
-28008254,HC  INTROD INNERCOOL 14F, 888 
-28008255,HC IMPL PLATE LCP 5 HOLE 3.5X72MM," 2,890 "
-28008256,HC IMPL PLATE LCP 6 HOLE 3.5X85MM," 2,978 "
-28008257,HC IMPL PLATE LCP 10 HOLE 3.5X137MM," 3,496 "
-28008258,HC IMPL PLATE LCP 12 HOLE 3.5X163MM," 3,987 "
-28008259,HC IMPL PLATE LCP 14 HOLE 3.5X189MM," 5,990 "
-28008260,HC IMPL PLATE RECONSTRUCTION 3.5X70MM 5 HOLE," 4,128 "
-28008261,HC IMPL PLATE RECONSTRUCTION 3.5X140MM 10 HOLE," 5,423 "
-28008262,HC IMPL PLATE RECONSTRUCTION 3.5X168MM 12 HOLE," 5,892 "
-28008263,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED SS 3.5X50MM," 2,788 "
-28008264,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED SS 3.5X67MM," 2,950 "
-28008265,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED SS 3.5X87MM," 4,978 "
-28008266,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 4 H SS 3.5X56MM," 3,116 "
-28008267,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED SS 3.5X78MM," 3,974 "
-28008268,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 3.5X52MM," 3,383 "
-28008269,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 3.5X63 MM," 3,077 "
-28008270,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 3.5X74MM," 3,691 "
-28008271,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 3.5X96MM," 3,477 "
-28008272,HC IMPL PLATE T LCP LEFT 3.5X52MM," 3,171 "
-28008273,HC IMPL PLATE T LCP LEFT 3.5X63MM," 3,077 "
-28008274,HC IMPL PLATE T LCP LEFT 3.5X74MM," 3,196 "
-28008275,HC IMPL PLATE T LCP LEFT 3.5X96MM," 3,477 "
-28008276,HC IMPL PLATE 1/3 TUBULAR 5 HOLE 57MM," 1,667 "
-28008277,HC IMPL PLATE 1/3 TUBULAR 6 HOLE 69MM," 1,667 "
-28008278,HC IMPL PLATE 1/3 TUBULAR 7 HOLE 81MM," 1,643 "
-28008279,HC IMPL PLATE 1/3 TUBULAR 8 HOLE 93MM," 1,784 "
-28008280,HC IMPL PLATE 1/3 TUBULAR 10 HOLE 117MM," 1,724 "
-28008281,HC IMPL PLATE 1/3 TUBULAR 12 HOLE 141MM," 1,851 "
-28008282,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X36MM," 1,198 "
-28008283,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X38MM," 1,198 "
-28008284,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X40MM," 1,198 "
-28008285,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X45MM," 1,198 "
-28008286,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X50MM," 1,198 "
-28008287,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X55MM," 1,198 "
-28008288,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X60MM, 892 
-28008289,HC  SHEATH VASCULAR BRITE TIP .35INX8FX90CM, 479 
-28008290,HC  INTRODUCER CHECK FLO 8FX70CM, 510 
-28008291,HC  CATHETER DILATATION ULTRATHIN SDS 6FX8MMX4X75CM," 1,679 "
-28008292,HC  CATHETER DILATATION ULTRATHIN SDS 5FX7MMX4X75CM," 1,679 "
-28008293,HC  CATHETER DILATATION ULTRATHIN SDS 5FX7MMX2X75CM," 1,679 "
-28008294,HC  CATHETER PERIPHERAL IMAGING IVUS 95CM 8F," 5,451 "
-28008295,HC  SHEATH GUIDING PINNACLE DESTINATION STR 6FX45CM, 697 
-28008296,HC  GUIDEWIRE MEIER 035 300CM, 549 
-28008297,HC  SHEATH GUIDING PINNACLE DESTINATION 6FX45CM, 765 
-28008298,HC  SHEATH GUIDING PINNACLE DESTINATION MP 6FX45CM, 697 
-28008299,HC  SHEATH GUIDING PINNACLE DESTINATION 7F 45CM, 697 
-28008301,HC IMP STENT BILIARY EXPRESS 7X20X75," 9,143 "
-28008302,HC IMP STENT BILIARY EXPRESS 6X20X75," 9,143 "
-28008303,HC IMP EAR PROSTHESIS MODIFIED PART 4.75MM," 1,292 "
-28008304,HC STAPLES INCUS CONNECTOR 1MM," 2,245 "
-28008305,HC IMP EAR PROSTHESIS CAUSSE PARTIAL SHOE 1.17X4.75MM," 2,511 "
-28008306,HC IMP EAR PROSTHESIS CAUSSE TOTAL SHOE 0.4X10MM," 2,802 "
-28008307,HC IMP EAR PROSTHESIS OSSICULAR ROUND HEAD TI 10ML," 3,213 "
-28008308,HC IMP EAR PROSTHESIS CAM HEAD TITANIUM 10ML," 2,941 "
-28008309,HC CSF DRAIN LUMBAR EXTERNAL CATHETER KIT," 4,206 "
-28008310,HC  GUIDEWIRE 3.2X400MM, 905 
-28008311,HC IMP STENT COLON WALLSTENT 8X80," 9,630 "
-28008312,HC IMP STENT VAGINAL MEDIUM 14X4.5CM," 3,953 "
-28008313,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X320MM 130D," 13,103 "
-28008314,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X340MM 130D," 13,103 "
-28008315,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X360MM 130D," 14,562 "
-28008316,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X380MM 130D," 14,562 "
-28008317,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X400MM 130D," 15,071 "
-28008318,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X420MM 130D," 13,628 "
-28008319,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X320MM 130D," 13,628 "
-28008320,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X340MM 130D," 14,562 "
-28008321,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X360MM 130D," 14,562 "
-28008322,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X380MM 130D," 15,071 "
-28008323,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X400MM 130D," 14,562 "
-28008324,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X420MM 130D," 13,628 "
-28008325,HC IMP NAIL TROCHANTERIC FIXATION RIGHT 11X440M 130D," 14,562 "
-28008326,HC IMP NAIL TROCHANTERIC FIXATION 10X170MM 130D," 11,449 "
-28008327,HC IMP NAIL TROCHANTERIC FIXATION 11X170MM 130D," 11,449 "
-28008328,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X80," 5,552 "
-28008329,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X85," 5,747 "
-28008330,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X90," 5,747 "
-28008331,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X95," 5,747 "
-28008332,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X100," 5,747 "
-28008333,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X105," 5,747 "
-28008334,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X110," 5,747 "
-28008335,HC IMP BLADE HELICAL TITANIUM GOLD NON STERILE 11X115," 5,747 "
-28008336,HC IMP BLADE HELICAL 11X80 456.309," 5,552 "
-28008337,HC IMP ENDCAP TROCHANTERIC FIXATION NAIL 5MM," 1,275 "
-28008338,HC IMP ENDCAP TROCHANTERIC FIXATION NAIL 10MM," 1,465 "
-28008339,HC IMP STENT GRAFT TIPS VIATORR 8X4X2," 27,300 "
-28008340,HC IMP STENT GRAFT TIPS VIATORR 10X4X2," 27,300 "
-28008341,HC IMP STENT GRAFT TIPS VIATORR 10X5X2," 27,300 "
-28008342,HC IMP STENT COLON WALLSTENT 8X40," 9,630 "
-28008343,HC IMP ENDOPROSTHESIS BILI 10MM 10X40CM," 18,130 "
-28008345,HC IMP STENT ASPIRE COVERED 10MM 5CM," 14,625 "
-28008346,HC IMP STENT ASPIRE COVERED 12MM 5CM," 14,625 "
-28008347,HC IMP STENT ASPIRE COVERED 6MM 10CM," 15,375 "
-28008348,HC IMP STENT ASPIRE COVERED 7MM 10CM," 15,375 "
-28008349,HC IMP VALVE AORTIC MOSAIC 19MM," 29,400 "
-28008350,HC IMP VALVE AORTIC MOSAIC 21MM," 29,400 "
-28008351,HC IMP VALVE AORTIC MOSAIC 23MM," 29,400 "
-28008352,HC IMP VALVE AORTIC MOSAIC 25MM," 29,400 "
-28008353,HC IMP VALVE AORTIC MOSAIC 27MM," 29,400 "
-28008354,HC IMP VALVE AORTIC MOSAIC 29MM," 29,400 "
-28008355,HC IMP VALVE MITRAL MOSAIC 25MM," 29,400 "
-28008356,HC IMP VALVE MITRAL MOSAIC 27MM," 29,400 "
-28008357,HC IMP VALVE MITRAL MOSAIC 29MM," 29,400 "
-28008358,HC IMP VALVE MITRAL MOSAIC 31MM," 29,400 "
-28008359,HC IMP VALVE MITRAL MOSAIC 33MM," 29,400 "
-28008362,HC PACK CUSA MICRO," 2,771 "
-28008363,HC  EXCHANGE AMPLATZER SYSTEM 6F 180D 80CM," 4,930 "
-28008364,HC IMP OCCLUDER AMPLATZ PDA 5X4X5MM," 12,000 "
-28008365,HC IMP OCCLUDER AMPLATZ PDA 6X4X7MM," 12,000 "
-28008366,HC IMP OCCLUDER AMPLATZ PDA 8X6X7MM," 12,000 "
-28008367,HC IMP OCCLUDER AMPLATZ PDA 10X8X8MM," 12,000 "
-28008368,HC IMP OCCLUDER AMPLATZ PDA 12X10X8MM," 12,000 "
-28008369,HC  CATHETER THROMBECTOMY 15MM 120CM 3MM," 9,413 "
-28008370,HC IMP DEVICE VASCULAR CLOSURE PERCLOSE 6F," 1,998 "
-28008373,HC  GUIDEWIRE EXTENSION LOCKING 35IN 115CM, 562 
-28008375,HC  CATHETER CHRONIC 82X76CM 6F," 2,338 "
-28008376,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.5X30," 2,738 "
-28008377,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 18F 3X45," 2,738 "
-28008378,HC  CATHETER DILATATION PTA CNQ 6X4X75," 2,129 "
-28008379,HC  CATHETER DILATATION PTA CNQ 7X4X75," 1,743 "
-28008383,HC IMP BREAST ROUND SMOOTH SALINE 525CC," 5,100 "
-28008384,HC IMP BREAST ROUND SMOOTH SALINE 575CC," 3,825 "
-28008385,HC IMP BREAST ROUND SMOOTH SALINE 625CC," 3,825 "
-28008386,HC IMP LEAD PACEMAKER BIPOLAR 60CM," 4,675 "
-28008387,HC IMP LEAD PACEMAKER MYOCARDIAL UNIPOLAR 25CM," 4,675 "
-28008388,HC  CATHETER DILATATION 3MM 4X150CM 4F," 2,114 "
-28008389,HC  CATHETER DILATATION 4MM 4X150CM 4F," 2,668 "
-28008390,HC  CATHETER DILATATION 3MM 10X150CM 5F," 2,668 "
-28008391,HC  CATHETER DILATATION 4MM 10X150CM 5F," 2,668 "
-28008392,HC  CATHETER DILATATION 5MM 10X120CM 5F," 2,668 "
-28008393,HC  CATHETER DILATATION 6MM 10X80CM 5F," 2,668 "
-28008394,HC  CATHETER BALLOON PTCA RX 2X12MM," 2,128 "
-28008395,HC  CATHETER BALLOON PTCA RX 2.5X12MM," 2,128 "
-28008396,HC  CATHETER BALLOON PTCA RX 3X12MM," 7,535 "
-28008397,HC  CATHETER BALLOON PTCA RX 3.25X12MM," 2,117 "
-28008398,HC  CATHETER BALLOON PTCA RX 3.5X12MM," 2,117 "
-28008399,HC  CATHETER BALLOON PTCA RX 4X12MM," 2,117 "
-28008400,HC  CATHETER DILATATION PTCA 2X15MM," 2,117 "
-28008401,HC  CATHETER DILATATION PTCA 2.25X15MM," 2,117 "
-28008402,HC  CATHETER DILATATION PTCA 2.5X15MM," 2,117 "
-28008403,HC  CATHETER DILATATION PTCA 2.75X15MM," 2,117 "
-28008404,HC  CATHETER DILATATION PTCA 3X15MM," 2,117 "
-28008405,HC  CATHETER DILATATION PTCA 3.25X15MM," 2,117 "
-28008406,HC  CATHETER DILATATION PTCA 3.5X15MM," 2,117 "
-28008407,HC  CATHETER DILATATION PTCA 3.75X15MM," 2,117 "
-28008408,HC  CATHETER DILATATION PTCA 4X15MM," 2,117 "
-28008410,HC  TUBE GASTROSTOMY LOW PROFILE 18F 2.7, 146 
-28008411,HC  TUBE GASTROSTOMY LOW PROFILE 18F 4.0, 838 
-28008412,HC  TUBE GASTROSTOMY LOW PROFILE 14F 2.3, 168 
-28008413,HC IMP STENT ESOPHAGEAL ULTRAFLEX COVRD 23X9," 12,188 "
-28008414,HC IMP STENT COLON WALLSTENT 8X60," 9,630 "
-28008415,HC IMP STENT BILIARY C-FLEX 7FX15, 332 
-28008416,HC IMP STENT PANCREATIC 12CM 5F SET, 748 
-28008417,HC IMP STENT PANCREATIC 12CM 5F SET, 973 
-28008418,HC IMP STENT PANCREATIC 7F 12CM SET, 973 
-28008419,HC IMP RETRIEVAL BASKET LITHOTRIPSY 30MM," 3,426 "
-28008420,HC IMP STENT BILIARY RX 10FX15, 757 
-28008421,HC IMP STENT BILIARY RX 11.5FX5, 757 
-28008422,HC IMP STENT BILIARY RX 11.5FX7, 935 
-28008423,HC IMP STENT BILIARY RX 11.5FX10, 935 
-28008424,HC IMP STENT BILIARY RX 11.5FX12, 757 
-28008425,HC IMP STENT BILIARY RX 11.5FX15, 935 
-28008426,HC STAPLER ENDO MULTIFIRE 30 2.5MM," 4,059 "
-28008427,HC SEALER DISSECTING TISSUELINK 3MM," 8,458 "
-28008428,HC IMPL PACEMAKER SINGLE UNIPOLAR RR," 44,168 "
-28008429,HC IMP LEAD ACTIVE FIXATION PACING 52CM," 4,675 "
-28008430,HC  CATH PERPH SLVRHWK 135X2.6CM," 21,665 "
-28008431,HC  CATHETER DILATATION PTA 6X135CM 6MM," 1,679 "
-28008432,HC  CATHETER GUIDING MPC 90CM 0.070IN 6F," 2,890 "
-28008433,HC FLOWPROBE ULTRASOUND INTRACRANIAL BAYONET 3MM," 8,075 "
-28008434,HC IMP GRAFT HEMASHIELD WOVEN 20X50CM," 9,893 "
-28008435,HC  CATHETER BALLOON PTCA RX 2.25X12MM," 2,117 "
-28008436,HC IMP ICD OTHER BIVENT INSYNC II," 95,000 "
-28008437,HC IMP HIP LINER PINNACLE MARATHON LIP 32IDX56OD," 5,406 "
-28008438,HC IMP HIP LINER PINNACLE MARATHON LIP 32IDX58OD," 5,100 "
-28008439,HC IMPL PLATE D Y MATRIXNEURO 6HX18MM," 1,098 "
-28008441,HC IMP BUR HOLE COVER LARGE," 1,085 "
-28008442,HC IMPL PLATE ADAPTION 20 HOLES," 1,628 "
-28008443,HC IMP MESH MATRIXNEURO TITANIUM 100X100MM," 15,906 "
-28008444,HC IMPL PLATE MESH 3D RIGID PINK," 15,906 "
-28008445,HC IMP SCREW EMERGENCY LOW PROFILE 4MM," 2,530 "
-28008446,HC IMPL PLATE CRANIAL STRAIGHT 4HOLE, 605 
-28008447,HC IMPL PLATE CRANIAL BOX 10X16MM," 1,725 "
-28008448,HC IMP BAND GASTRIC REALIZE ADJUSTABLE," 21,908 "
-28008449,HC IMP KNEE BASEPLATE TIBIAL NON POROUS SIZE 2.5," 17,100 "
-28008450,HC  GUIDEWIRE VASCULAR 14IN 175CM, 697 
-28008451,HC IMP ANCHOR BIO SUTURETAK 3.0MM," 2,083 "
-28008453,HC  GUIDEWIRE CORONARY STRAIGHT TP XSUP 190X2, 697 
-28008454,HC IMP KNEE STEM TIBIAL SIGMA CEMENTED 13X6," 7,813 "
-28008455,HC IMP KNEE INSERT TIBIAL PFC SIGMA 10MM," 13,604 "
-28008456,HC IMP STRIP HEALOS II SINGLE 5ML," 5,406 "
-28008457,HC IMP KNEE INSERT TIBIAL PFC SIGMA SZ3 12.5," 10,395 "
-28008458,HC IMP KNEE FEMORAL PFC SIGMA TC3 RIGHT SIZE 5," 35,108 "
-28008459,HC IMP KNEE INSERT TIBIAL TC3 SZ2 17.5MM," 10,530 "
-28008460,HC IMP KNEE INSERT TIBIAL TC3 SZ2.5 10MM," 13,779 "
-28008461,HC IMP KNEE INSERT TIBIAL TC3 SZ4 15MM," 13,779 "
-28008462,HC IMP SPACER AUGMENT FEMORAL DISTAL PFC SZ3 4MM," 6,018 "
-28008464,HC IMP SCREW FIXED 14MM," 2,848 "
-28008465,HC  CATH EP ABLAT HG 7F," 8,063 "
-28008466,HC  CATHETER DIAGNOSTIC 10PIN 115CM 7F," 11,685 "
-28008467,HC IMP STENT BILIARY HERCULINK RX 4X15X80," 9,750 "
-28008468,HC  CATHETER GUIDING VISTA BRITE 90CM .056IN 5F," 2,890 "
-28008469,HC  CATHETER GUIDING 100CM 7IN 6F," 2,805 "
-28008470,HC IMP STENT BILIARY SMART OTW 12X30X120," 11,779 "
-28008471,HC IMP STENT BILIARY SMART OTW 12X40X80," 12,000 "
-28008472,HC IMP KNEE STEM FEMORAL PFC 5DG 13X90MM," 13,125 "
-28008473,HC IMP KNEE STEM BOLT FEMORAL PFC SIGMA 0MM," 2,890 "
-28008474,HC IMP HIP SHELL TRILOGY CLUSTER HOLE 56MM," 8,274 "
-28008475,HC LASER FIBER GREEN DISP," 9,863 "
-28008476,HC IMP NUT USS TITANIUM 12PT 11MM, 425 
-28008478,HC IMPL SCREW DUAL OPENING 6.2MM," 6,375 "
-28008479,HC IMP SPACER TPLIF 7MM," 31,311 "
-28008480,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 14MM," 8,250 "
-28008481,HC IMP CAGE SPINAL SMALL VBR 16X24MM," 39,975 "
-28008482,HC IMP PIN CAP THREAD BLACK 5MM, 193 
-28008483,HC IMP SLEEVE CENTER COMPRESSION 6MM, 988 
-28008484,HC IMP EXTERNAL FIXATOR CUBE 3 HOLE RANCHO," 1,589 "
-28008485,HC IMP EXTERNAL FIXATOR CUBE 2 HOLE RANCHO," 1,422 "
-28008486,HC IMP EXTERNAL FIXATOR CUBE 1 HOLE RANCHO," 1,244 "
-28008487,HC IMP PIN HALF 35MM," 1,640 "
-28008488,HC IMP PIN HALF 30MM," 1,640 "
-28008489,HC IMP WIRE CORTICAL ILIZAROV SS 1.8X400MM," 1,872 "
-28008490,HC BAND ID KIT," 3,315 "
-28008491,HC IMP EXTERNAL FIXATOR STRUT TAYLOR MED," 9,188 "
-28008492,HC IMP EXTERNAL FIXATOR STRUT TAYLOR SHRT," 7,121 "
-28008493,HC IMP BREAST SILTEX CONTOUR MEDIUM HEIGHT 450CC," 9,938 "
-28008494,HC IMP ANCHOR SUTURE MENISCAL FAST FIX CVD," 2,165 "
-28008495,HC IMPL PLATE DISTAL LEFT," 5,908 "
-28008496,HC IMPL PLATE DISTAL RIGHT," 5,908 "
-28008497,HC IMP PEG SMOOTH 2.0X14MM, 865 
-28008498,HC IMP PEG SMOOTH 2.0X16MM, 865 
-28008499,HC IMP PEG SMOOTH 2.0X18MM, 865 
-28008500,HC IMP PEG SMOOTH 2.0X20MM, 918 
-28008502,HC IMP PEG SMOOTH 2.0X24MM, 865 
-28008504,HC IMP PEG SMOOTH 2.0X28MM, 680 
-28008505,HC IMP PEG THREAD 2.5X14MM FP-14, 865 
-28008509,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X22MM," 1,532 "
-28008513,HC IMP SCREW CORTICAL 3.5X10MM, 918 
-28008514,HC IMP SCREW CORTICAL 3.5X12MM, 865 
-28008515,HC IMP SCREW CORTICAL 3.5X14MM, 865 
-28008516,HC IMP SCREW CORTICAL 3.5X16MM, 865 
-28008517,HC IMP SCREW CORTICAL 3.5X18MM, 865 
-28008518,HC IMPL SCREW CANCELLOUS 2.5X16MM, 865 
-28008519,HC IMPL SCREW CANCELLOUS 2.5X18MM, 865 
-28008520,HC IMPL SCREW CANCELLOUS 2.5X20MM, 865 
-28008523,HC IMPL SCREW CANCELLOUS 2.5X26MM, 865 
-28008524,HC IMPL SCREW CANCELLOUS 2.5X28MM, 865 
-28008526,HC IMPL PLATE DISTAL XLONG TR," 5,908 "
-28008527,HC IMPL PLATE DISTAL XLONG LEFT," 5,908 "
-28008528,HC IMPL SCREW AUTO DRIVE 1.6X4MM, 385 
-28008529,HC IMPL PLATE L MIDFACE LEFT 13MM," 1,524 "
-28008530,HC IMPL PLATE MIDFACE STRAIGHT 4HOLE, 657 
-28008531,HC  CATHETER BALLOON EXTRACTION TRI-EX 8.5X12X15MM," 1,014 "
-28008532,HC  CATHETER BALLOON OCCLUSION BOUNCER 15X200, 956 
-28008533,HC IMP STENT COLON WALLSTENT 8X100," 10,500 "
-28008534,HC IMP STENT COLON WALLSTENT 10X100," 10,500 "
-28008535,HC IMP GRAFT ENDOVASCULAR 12X88MM 14F," 21,595 "
-28008537,HC  CATHETER ABLATION 7F 115CM 1-7-4X8MM," 13,530 "
-28008538,HC  CATHETER DILATATION PTA CNQ 8X4X75," 2,129 "
-28008539,HC  CATHETER DILATATION PTA CNQ 10X4X75," 2,129 "
-28008540,HC  CATHETER DILATATION PTA CNQ 12X4X75," 2,129 "
-28008541,HC IMP FILTER VENA CAVA FEMORAL RECOV SYS," 11,209 "
-28008542,HC IMP STENT BILIARY SMART OTW 12X40X120," 12,000 "
-28008543,HC IMP STENT BILIARY SMART OTW 12X60X80," 12,000 "
-28008544,HC IMP STENT BILIARY SMART OTW 12X60X120," 12,000 "
-28008545,HC IMP STENT BILIARY SMART OTW 12X80X80," 12,000 "
-28008546,HC IMP STENT BILIARY SMART OTW 12X80X120," 12,000 "
-28008547,HC IMP STENT BILIARY SMART OTW 14X30X120," 12,000 "
-28008548,HC IMP STENT BILIARY SMART OTW 14X40X80," 12,000 "
-28008549,HC IMP STENT BILIARY SMART OTW 14X40X120," 12,000 "
-28008550,HC IMP STENT BILIARY SMART OTW 14X60X80," 12,000 "
-28008551,HC IMP STENT BILIARY SMART OTW 14X60X120," 11,779 "
-28008552,HC IMP STENT BILIARY SMART OTW 14X80X120," 12,000 "
-28008553,HC IMP LEAD PISCES-Z QUAD 33CM KIT," 11,400 "
-28008554,HC IMP EXTENSION LEAD NEUROSTIMULATOR LP 25CM KIT," 5,440 "
-28008555,HC DEVICE ABLATION ENDOMETRIAL," 8,700 "
-28008556,HC IMPL PLATE MINI RIGHT 15MM," 1,343 "
-28008557,HC IMPL PLATE LEFT MINI 15MM," 1,470 "
-28008558,HC IMPL PLATE RIGHT MINI 18MM," 1,470 "
-28008559,HC IMPL PLATE LEFT MINI 18MM," 1,470 "
-28008561,HC IMP ANCHOR SUTURE BIO CORKSCREW 5.0X17.9MM, 442 
-28008562,HC IMP MESH HERNIA COMEPOSIX PE 4X6IN," 3,474 "
-28008564,HC IMPL PLATE MINI RIGHT ZED 18MM," 1,250 "
-28008566,HC IMPL PLATE MINI CURVED 9HOLE," 2,700 "
-28008567,HC IMPL PLATE MAXI 34MM 4HOLE," 2,746 "
-28008568,HC IMPL PLATE MAXI 37MM 4HOLE," 2,933 "
-28008569,HC IMPL PLATE MAXI 52MM 6HOLE," 3,655 "
-28008580,HC IMP BREAST SILTEX CONTOUR LOW HEIGHT 450CC," 9,938 "
-28008581,HC IMP BREAST SILTEX CONTOUR LOW HEIGHT 550CC," 9,938 "
-28008582,HC IMP BREAST SILTEX CONTOUR LOW HEIGHT 650CC," 9,938 "
-28008583,HC IMP BREAST SILTEX CONTOUR LOW HEIGHT 750CC," 9,713 "
-28008584,HC IMP BREAST SILTEX CONTOUR MEDIUM HEIGHT 275CC," 9,938 "
-28008585,HC IMP BREAST SILTEX CONTOUR MEDIUM HEIGHT 550CC," 9,938 "
-28008586,HC IMP BREAST SILTEX CONTOUR MEDIUM HEIGHT 650CC," 10,313 "
-28008587,HC IMP BREAST SILTEX CONTOUR MEDIUM HEIGHT 800CC," 10,313 "
-28008589,HC  CATHETER ELECTROPHYSIOLOGY WOVEN 6FX125CM, 859 
-28008591,HC CUSA NOSE CONE BLACK 36KHZ," 2,550 "
-28008592,HC ELECTRODE COAGULATING TISSUE TONGUE 2PRONG," 2,232 "
-28008593,HC  CATHETER GUIDING 8X100CM, 451 
-28008594,HC  CATHETER GUIDING 90IN 8F, 451 
-28008595,HC  CATHETER GUIDING 90IN 100CM 8F, 451 
-28008596,HC  CATHETER GUIDING JR4 100CM 8F LNCHR, 451 
-28008597,HC  CATHETER GUIDING JR 4 SH STD, 451 
-28008598,HC  CATHETER GUIDING LT BL, 451 
-28008599,HC IMPL SCREW MALLEOLAR 4.5X25MM, 310 
-28008600,HC IMPL SCREW MALLEOLAR 4.5X30MM, 310 
-28008601,HC IMPL SCREW MALLEOLAR 4.5X35MM, 314 
-28008602,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 10.0D," 1,075 "
-28008603,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 10.5D," 1,075 "
-28008604,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 11.0D," 1,075 "
-28008605,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 11.5D," 1,075 "
-28008606,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 12.0D," 1,075 "
-28008607,HC IMPL SCREW MALLEOLAR 4.5X40MM, 314 
-28008608,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 12.5D," 1,075 "
-28008609,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 13.0D," 1,075 "
-28008610,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 13.5D," 1,075 "
-28008611,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 14.0D," 1,054 "
-28008612,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 14.5D," 1,075 "
-28008613,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 15.0D," 1,075 "
-28008614,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 15.5D," 1,075 "
-28008615,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 16.0D," 1,075 "
-28008616,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 16.5D," 1,075 "
-28008617,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 17.0D," 1,075 "
-28008618,HC IMPL SCREW MALLEOLAR 4.5X45MM, 304 
-28008619,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 17.5D," 1,075 "
-28008620,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 18.0D," 1,075 "
-28008621,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 18.5D," 1,054 "
-28008622,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 19.0D," 1,054 "
-28008623,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 19.5D," 1,075 "
-28008624,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 20.0D," 1,075 "
-28008625,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 20.5D," 1,075 "
-28008626,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 21.0D," 1,054 "
-28008627,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 21.5D," 1,075 "
-28008628,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 22.0D," 1,075 "
-28008629,HC IMPL SCREW MALLEOLAR 4.5X50MM, 314 
-28008630,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 22.5D," 1,075 "
-28008631,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 23.0D," 1,054 "
-28008632,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 23.5D," 1,075 "
-28008633,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 24.0D," 1,054 "
-28008634,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 24.5D," 1,054 "
-28008635,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC CONVEX 25.0D," 1,075 "
-28008636,HC IMPL SCREW MALLEOLAR 4.5X55MM, 253 
-28008637,HC IMP PATCH MESH 3D MAX RIGHT LARGE," 2,125 "
-28008638,HC IMP PATCH MESH 3D MAX LEFT LARGE," 2,125 "
-28008639,HC IMP STENT ASPIRE COVERED 10X8X50L," 15,375 "
-28008641,HC IMP STENT ASPIRE COVERED 5X8X50L," 14,625 "
-28008642,HC IMP STENT ASPIRE COVERED 5X7X50L," 14,625 "
-28008643,HC IMP STENT ASPIRE COVERED 5X7X50L," 14,625 "
-28008644,HC IMPL SCREW MALLEOLAR 4.5X60MM, 253 
-28008645,HC IMP STENT ASPIRE COVERED 10X6X50L," 15,375 "
-28008646,HC IMP STENT ASPIRE COVERED 5X6X50L," 14,625 "
-28008647,HC IMPL SCREW MALLEOLAR 4.5X65MM, 253 
-28008648,HC IMPL SCREW SPINAL PEDICLE TITANIUM 3D STD," 3,400 "
-28008649,HC IMPL SCREW SPINAL PEDICLE TITANIUM 3D 3.5MM," 1,700 "
-28008650,HC IMPL SCREW MALLEOLAR 4.5X70MM, 304 
-28008651,HC IMP STENT ASPIRE COVERED 5X6X100L," 14,625 "
-28008652,HC IMP STENT ASPIRE COVERED 10X6X100L," 15,375 "
-28008653,HC IMP SCREW BONE THREAD 6.5X30MM 16MM, 316 
-28008654,HC IMP STENT ASPIRE COVERED 10X7X50L," 15,375 "
-28008655,HC IMP STENT ASPIRE COVERED 10X8X100L," 15,375 "
-28008656,HC IMP STENT ASPIRE COVERED 15X6X100L," 16,125 "
-28008657,HC IMP STENT ASPIRE COVERED 15X7X100L," 16,125 "
-28008658,HC IMP SCREW BONE THREAD 6.5X35MM 16MM, 316 
-28008659,HC IMP STENT ASPIRE COVERED 2.5X8X50L," 14,625 "
-28008660,HC IMP STENT ASPIRE COVERED 2.5X8X100L," 14,625 "
-28008661,HC IMP SCREW BONE THREAD 6.5X40MM 16MM, 319 
-28008662,HC IMP SCREW BONE THREAD 6.5X45MM 16MM, 316 
-28008663,HC IMP SCREW BONE THREAD 6.5X50MM 16MM, 298 
-28008664,HC IMP SCREW BONE THREAD 6.5X55MM 16MM, 283 
-28008665,HC  CATHETER ENDOTRACHEAL DOUBLE SWIVEL 12FX54CM, 128 
-28008666,HC IMP SCREW BONE THREAD 6.5X60MM 16MM, 283 
-28008667,HC  CATHETER DILATATION RX EXTC 9-12MM," 1,233 "
-28008668,HC  CATHETER DILATATION RX EXTC 12-15MM," 1,233 "
-28008669,HC  RECOVERY CONE," 7,703 "
-28008670,HC IMP SCREW BONE THREAD 6.5X65MM 16MM, 217 
-28008671,HC IMP SCREW BONE THREAD 6.5X70MM 16MM, 319 
-28008672,HC KYPHOPAK EXPRESS FIRST FRACTURE TRAY 15/3," 23,961 "
-28008673,HC KYPHOPAK EXPRESS ADDL FRACTURE TRAY 15/3," 17,460 "
-28008674,HC IMP SCREW BONE THREAD 6.5X75MM 16MM, 316 
-28008676,HC KYPHOPAK LUMBAR 20/3," 17,685 "
-28008677,HC  CATHETER BALLOON RELIANT 12F," 3,783 "
-28008678,HC IMPL BRACE THORACIC LUMBAR SACRAL," 8,970 "
-28008679,HC IMP SCREW BONE THREAD 6.5X80MM 16MM, 316 
-28008680,HC IMP SCREW BONE THREAD 6.5X85MM 16MM, 340 
-28008682,HC IMP CLOSURE VASCULAR DVC 6F PLUS," 2,100 "
-28008683,HC IMP CLOSURE VASCULAR DVC 8F PLUS," 2,100 "
-28008684,HC  CATHETER GUIDING VISTA BRITE TIP 6F XB4, 455 
-28008685,HC  CATHETER GUIDING VISTA BRITE TIP 6F JR3.5, 442 
-28008686,HC IMP SCREW BONE THREAD 6.5X90MM 16MM, 352 
-28008687,HC IMP ICD DUAL ATLAS V240," 82,250 "
-28008688,HC IMP SCREW BONE THREAD 6.5X95MM 16MM, 281 
-28008689,HC  CATHETER ABLATION 115CM 1-7-4MM 7F," 13,530 "
-28008690,HC IMP PATCH MESH COMPOSIX EX 7X9IN," 8,738 "
-28008691,HC IMP PATCH MESH COMPOSIX EX 8X10IN," 11,228 "
-28008692,HC IMP PATCH MESH COMPOSIX EX 10X13IN," 14,198 "
-28008693,HC IMP SCREW BONE THREAD 6.5X100X16MM, 281 
-28008694,HC IMP SCREW BONE THREAD 6.5X105MM 16MM, 281 
-28008695,HC IMP SCREW BONE THREAD 6.5X110MM 16MM, 281 
-28008696,HC  CATH CARD CRYOABLAT SHRT 1 7F," 10,875 "
-28008697,HC  CATH CARD CRYOABLAT MED 6MM 7F," 11,625 "
-28008698,HC  CATH CARD CRYOABLAT LONG 5 7F," 10,875 "
-28008699,HC IMP SCREW BONE THREAD 6.5X115MM 16MM, 349 
-28008702,HC IMP SCREW BONE THREAD 6.5X120MM 16MM, 349 
-28008703,HC IMP SCREW BONE THREAD 6.5X125MM 16MM, 349 
-28008704,HC IMP STENT GRAFT AAA EXCLUDER 16X9.5," 28,700 "
-28008705,HC IMP STENT GRAFT AAA EXCLUDER 16X11.5," 27,860 "
-28008706,HC IMP STENT GRAFT AAA EXCLUDER 16X13.5," 27,860 "
-28008707,HC IMP STENT GRAFT AAA EXCLUDER 18X9.5," 27,860 "
-28008708,HC IMP STENT GRAFT AAA EXCLUDER 18X11.5," 27,860 "
-28008709,HC IMP STENT GRAFT AAA EXCLUDER 18X13.5," 27,860 "
-28008710,HC IMP SCREW BONE THREAD 6.5X130MM 16MM, 349 
-28008711,HC IMP STENT GRAFT AAA EXCLUDER 20X9.5," 27,860 "
-28008712,HC IMP STENT GRAFT AAA EXCLUDER 20X11.5," 27,860 "
-28008713,HC IMP STENT GRAFT AAA EXCLUDER 20X13.5," 27,860 "
-28008714,HC  SHEATH INTRODUCER AAA 12X30CM, 723 
-28008715,HC  CATH ABLAT FTYP 7F 8X1X6X2MM," 6,996 "
-28008716,HC IMP SCREW BONE THREAD 6.5X135MM 16MM, 433 
-28008717,HC IMP SCREW BONE THREAD 6.5X140MM 16MM, 448 
-28008718,HC IMP SCREW BONE THREAD 6.5X145MM 16MM, 397 
-28008720,HC IMP LEAD BIPOLAR VAGUS NERVE," 40,902 "
-28008721,HC IMP GENERATOR NEUROSTIMULATOR DEMIP SINGLE PIN," 90,645 "
-28008722,HC IMP SCREW BONE THREAD 6.5X45MM 32MM, 327 
-28008723,HC IMP SCREW BONE THREAD 6.5X50MM 32MM, 327 
-28008724,HC IMP SCREW BONE THREAD 6.5X55MM 32MM, 316 
-28008725,HC  CANNULA DAVINCI 5MM," 7,650 "
-28008726,HC OBTURATOR DAVINCI SHARP 5MM," 5,100 "
-28008727,HC OBTURATOR DAVINCI DULL 5MM," 4,675 "
-28008729,HC IMP SCREW BONE THREAD 6.5X60MM 32MM, 327 
-28008730,HC CAUTERY DAVINCI 5MM," 28,980 "
-28008732,HC IMP SCREW BONE THREAD 6.5X65MM 32MM, 327 
-28008733,HC IMP STENT CORONARY VISION RX BMS 3X8," 7,299 "
-28008734,HC IMP STENT CORONARY VISION RX BMS 3.5X8," 6,375 "
-28008735,HC IMP SCREW BONE THREAD 6.5X70MM 32MM, 316 
-28008736,HC IMP STENT CORONARY VISION RX BMS 4X8," 7,299 "
-28008737,HC IMP STENT CORONARY VISION RX BMS 3X12," 7,299 "
-28008738,HC IMP STENT CORONARY VISION RX BMS 3.5X12," 6,375 "
-28008739,HC IMP STENT CORONARY VISION RX BMS 4X12," 6,375 "
-28008740,HC IMP STENT CORONARY VISION RX BMS 3X15," 6,375 "
-28008741,HC IMP STENT CORONARY VISION RX BMS 3.5X15," 7,299 "
-28008742,HC IMP STENT CORONARY VISION RX BMS 4X15," 6,375 "
-28008743,HC IMP SCREW BONE THREAD 6.5X75MM 32MM, 283 
-28008744,HC IMP STENT CORONARY VISION RX BMS 3X18," 6,375 "
-28008745,HC IMP STENT CORONARY VISION RX BMS 3.5X18," 6,375 "
-28008746,HC IMP STENT CORONARY VISION RX BMS 4X18," 6,673 "
-28008747,HC IMP SCREW BONE THREAD 6.5X80MM 32MM, 327 
-28008748,HC IMP STENT CORONARY VISION RX BMS 3X23," 7,299 "
-28008749,HC IMP STENT CORONARY VISION RX BMS 3.5X23," 8,250 "
-28008750,HC IMP STENT CORONARY VISION RX BMS 4X23," 8,075 "
-28008751,HC IMP STENT CORONARY VISION RX BMS 3X28," 6,375 "
-28008752,HC IMP STENT CORONARY VISION RX BMS 3.5X28," 6,673 "
-28008753,HC IMP STENT CORONARY VISION RX BMS 4X28," 6,375 "
-28008754,HC IMP SCREW BONE THREAD 6.5X85MM 32MM, 344 
-28008755,HC IMP ALLOGRAFT ACETABULUM LEFT," 13,680 "
-28008756,HC IMP ALLOGRAFT ACETABULUM RIGHT," 13,680 "
-28008757,HC IMP SCREW BONE THREAD 6.5X90MM 32MM, 340 
-28008758,HC IMP CEMENT BONE CALLOS INJECT 10ML," 18,550 "
-28008759,HC  CATHETER DILATATION PTA CNQ 6X2X75," 2,129 "
-28008760,HC IMP SCREW BONE THREAD 6.5X95MM 32MM, 306 
-28008761,HC  CATHETER DILATATION PTA CNQ 7X2X75," 2,129 "
-28008762,HC  CATHETER DILATATION PTA CNQ 8X2X75," 2,129 "
-28008763,HC  CATHETER DILATATION PTA CNQ 9X2X75," 1,743 "
-28008764,HC  CATHETER DILATATION PTA CNQ 10X2X75," 2,129 "
-28008765,HC IMP LEAD PACING ACTV FIXATION 58CM 7F," 4,675 "
-28008766,HC IMP SCREW BONE THREAD 6.5X100MM 32MM, 281 
-28008767,HC IMP SCREW BONE THREAD 6.5X110MM 32MM, 344 
-28008768,HC IMP BREAST EXPANDER STYLE 133MV 700ML," 8,963 "
-28008769,HC IMP BREAST EXPANDER STYLE 133LV 400ML," 8,963 "
-28008770,HC IMP BREAST EXPANDER STYLE133LV 500ML," 8,813 "
-28008771,HC IMP BREAST SALINE STYLE 363LF 330-350CC," 6,460 "
-28008772,HC  CATHETER CORONARY 135CM 2MM 9F," 11,438 "
-28008773,HC  GUIDEWIRE CORONARY PILOT 14IN 3X190CM, 798 
-28008774,HC  GUIDEWIRE HI TORQUE 190X3CM 150, 697 
-28008775,HC  GUIDEWIRE HI TORQUE 190X3CM 200, 697 
-28008776,HC  CATHETER EMBOLECTOMY OTW 5F 7F 14MM, 680 
-28008777,HC  CATHETER EMBOLECTOMY OTW 80CM 5X33F," 1,025 "
-28008778,HC  CATHETER EMBOLECTOMY 80CM 9MM 5F 6F," 1,025 "
-28008779,HC  INTRODUCER RAAB FLEXOR CHECK-FLO 7FX80CMX0.038, 510 
-28008780,HC  GUIDEWIRE HI TORQUE FLP 14IN 300CM, 697 
-28008781,HC  CATH CARD CRYOABLAT SHT 7F 49," 11,625 "
-28008783,HC  CATH CARD CRYOABLAT XLNG 5 7F," 11,625 "
-28008784,HC  CATH EP ABLAT STR 7F 10MM," 10,841 "
-28008785,HC  CATH EP ABLAT TEMP 7F 10MM," 10,875 "
-28008789,HC IMP LEAD PACEMAKER VENTRICULAR 58CM," 4,675 "
-28008790,HC  CATHETER DILATATION PERPH 80CM 4X20MM," 5,100 "
-28008791,HC  CATHETER DILATATION PERPH 120CM 4X40MM," 5,100 "
-28008792,HC  CATHETER DILATATION PERPH 120CM 4X60MM," 7,018 "
-28008793,HC  CATHETER DILATATION 120CM 5X20MM," 5,100 "
-28008794,HC  CATHETER DILATATION PERPH 5X60MM 6F," 5,100 "
-28008795,HC  CATHETER DILATATION 120CM 6X20MM," 5,100 "
-28008796,HC  CATHETER DILATATION 120CM 7X20MM," 5,100 "
-28008797,HC  CATHETER DILATATION PERPH 7X60MM 7F," 5,100 "
-28008798,HC INFLATION POLARCATH DILAT SYS," 9,332 "
-28008799,HC IMP STENT ASPIRE COVERED 8X5 LONG," 14,625 "
-28008800,HC IMP STENT ASPIRE COVERED 7F 6X3 SHT," 14,625 "
-28008801,HC IMP STENT ASPIRE COVERED 7F 6X3 LNG," 14,625 "
-28008802,HC IMP STENT ASPIRE COVERED 7F 9X5 SHT," 14,625 "
-28008803,HC IMP STENT ASPIRE COVERED 7F 9X5 LNG," 14,625 "
-28008804,HC  CATH ABLAT JTYP 7F 8X1X6X2MM," 6,996 "
-28008805,HC IMP SPACER CORTICAL 6MM LL," 7,608 "
-28008806,HC IMP SPACER CORTICAL 7MM LL," 7,608 "
-28008807,HC IMP SPACER CORTICAL 8MM LL," 7,608 "
-28008808,HC IMP SPACER CORTICLA 9MM LL," 7,608 "
-28008809,HC IMP SPACER CORTICAL 10MM LL," 7,608 "
-28008810,HC IMPL PLATE TENSION BAND SHORT," 2,455 "
-28008811,HC IMPL PLATE TENSION BAND LONG," 2,564 "
-28008812,HC IMP RAMP ANTERIOR ALIF 11MM," 20,825 "
-28008813,HC IMP RAMP ANTERIOR ALIF 13MM," 20,825 "
-28008814,HC IMP RAMP ANTERIOR ALIF 15MM," 20,825 "
-28008815,HC IMP RAMP ANTERIOR ALIF 17MM," 20,825 "
-28008816,HC IMP STENT COLON ULTRAFLEX 87MM," 13,313 "
-28008817,HC IMP STENT COLON C-FLEX 10FX16," 13,313 "
-28008818,HC IMP STENT ESOPHAGEAL POLYFLEX 16-20X90," 13,875 "
-28008820,HC  CATHETER RECORDING 97.5CM 10F," 1,466 "
-28008821,HC IMP STENT ABSOLUTE 5X20X80," 10,429 "
-28008822,HC IMP STENT ABSOLUTE 6X100X80," 10,313 "
-28008823,HC IMP STENT ABSOLUTE 7X60X80," 10,313 "
-28008824,HC IMP STENT ABSOLUTE 8X40X80," 10,313 "
-28008825,HC IMP STENT ABSOLUTE 8X60X80," 11,063 "
-28008826,HC IMP STENT ABSOLUTE 80X100X80," 10,313 "
-28008827,HC IMP STENT ABSOLUTE 9X80X80," 10,313 "
-28008828,HC IMP STENT ABSOLUTE 10X20X80," 9,938 "
-28008829,HC IMP STENT ABSOLUTE 10X30X80," 10,313 "
-28008830,HC IMP STENT ABSOLUTE 10X40X80," 10,688 "
-28008831,HC IMP STENT ABSOLUTE 10X60X80," 11,063 "
-28008832,HC IMP STENT ABSOLUTE 10X80X80," 11,625 "
-28008833,HC IMP STENT ABSOLUTE 10X100X80," 12,000 "
-28008834,HC IMP BREAST EXPANDER STYLE 133LV 150ML," 8,963 "
-28008835,HC  CATHETER THROMBECTOMY 3MM 50CM 15MM," 3,561 "
-28008836,HC  CATHETER CABG 135CM 2MM 6F 4F," 13,463 "
-28008837,HC  INTRODUCER SHEATH 5F 25X23CM, 343 
-28008838,HC  INTRODUCER SHEATH 35MM 23CM 7F, 298 
-28008839,HC NEEDLE ELECTRODE LEVEEN 3.5X25CM," 11,205 "
-28008840,HC  CATHETER DRAINAGE BILIARY MARKER 35CM 8F, 702 
-28008841,HC  CATHETER DRAINAGE BILIARY MARKER 35CM 10F," 1,140 "
-28008842,HC  CATHETER DRAINAGE BILIARY MARKER 35CM 12F, 702 
-28008843,HC  CATHETER DRAINAGE BILIARY MARKER 35CM 14F, 702 
-28008844,HC  CATHETER EMBOLIZATION PERIPHERAL 30X130CM," 3,071 "
-28008845,HC IMPL COIL EMBOLIZATION 2X4MM, 587 
-28008846,HC IMPL COIL EMBOLIZATION 3X6MM, 484 
-28008847,HC IMPL COIL EMBOLIZATION 3X7MM, 484 
-28008848,HC IMPL COIL EMBOLIZATION PERIPHERAL 3X3.3MM, 683 
-28008849,HC IMPL COIL EMBOLIZATION PERIPHERAL 3.7X4MM, 685 
-28008850,HC IMPL COIL EMBOLIZATION PERIPHERAL 5X5.5MM, 137 
-28008851,HC IMPL COIL EMBOLIZATION PERIPHERAL 6X6.7MM, 137 
-28008852,HC  INTRODUCER LEAD SAFESHEATH 7F 25CM, 544 
-28008853,HC  INTRODUCER SHEATH 25X31.5X80CM 9F," 1,148 "
-28008854,HC IMP ANCHOR BIO SUTURE MINI 2.4X7MM," 1,913 "
-28008855,HC  CATHETER PEZZAR 36F, 128 
-28008857,HC  CATHETER ELECTROPHYSIOLOGY DECPL CRV 6F 110 2X1," 4,314 "
-28008858,HC  CATHETER DIAGNOSTIC 5MM 120CM 6F S, 859 
-28008859,HC  CATHETER ELECTROPHYSIOLOGY WOVENFLEXIE 5FX110CM, 890 
-28008860,HC IMP LEAD PACING 52CM," 4,675 "
-28008861,HC IMP RESERVOIR OMMAYA FLAT SIDE 2.5CM," 2,791 "
-28008863,HC PROBE SURGIFROST 60MM," 22,050 "
-28008864,HC IMPL COIL NEURO MICRO HSR 3CM 2MM ULTIPAQ," 7,650 "
-28008865,HC  CATHETER INFUSION UNIFUSE 135X10CM 5F," 1,942 "
-28008866,HC IMP STENT NEUROFORM2 4.5X30," 33,605 "
-28008868,HC  INTRODUCER SHEATH HEMOSTASIS 30CM 18F," 1,233 "
-28008869,HC  INTRODUCER SHEATH HEMOSTASIS 25CM 22F," 2,125 "
-28008870,HC IMP LEAD TACARHYTH 58CM," 18,750 "
-28008871,HC IMPL COIL MICRO HELICAL SRE 6MM 20CM," 4,364 "
-28008872,HC IMPL COIL MICRO HELICAL SRE 7MM 30CM," 4,661 "
-28008873,HC IMPL COIL MICRO HELICAL SRE 9MM 30CM," 4,675 "
-28008874,HC IMPL COIL MICRO HELICAL SRE 10MM 30CM," 4,675 "
-28008875,HC IMPL COIL MICRO HELICAL SRE 12MM 30CM," 4,675 "
-28008876,HC IMPL COIL MICRO HELICAL SRE 14MM 18CM," 4,675 "
-28008877,HC IMPL COIL MICRO HELICAL SRE 16MM 30CM," 5,015 "
-28008878,HC IMPL COIL MICRO HELICAL SRE 18MM 30CM," 5,015 "
-28008879,HC IMPL COIL NEURO HELICAL HELOIPAQ 20X30CM," 5,185 "
-28008880,HC IMPL COIL NEURO SPH HELOIPAQ 20X30CM," 9,375 "
-28008881,HC  CATHETER GUIDING STEERABLE ATTAIN PREVAIL 0.016," 2,975 "
-28008882,HC IMP LEAD DEFIBRILLATOR FIXATION 70CM 9F 10.5F," 35,000 "
-28008883,HC  GUIDEWIRE CORONARY STRAIGHT TIP 180X11CM," 1,275 "
-28008884,HC  GUIDEWIRE CORONARY STRAIGHT TIP 80X20CM," 1,275 "
-28008885,HC  GUIDEWIRE CORONARY STRAIGHT TP 14IN 180CM," 1,275 "
-28008886,HC IMP SPACER TPLIF 9MM," 31,311 "
-28008887,HC IMP SPACER TPLIF 11MM," 31,311 "
-28008888,HC IMP SPACER TPLIF 13MM," 28,665 "
-28008889,HC IMP SPACER TPLIF 15MM," 28,665 "
-28008890,HC IMP GRAFT DISTAFLO SMALL CUFF 6X80CM," 17,325 "
-28008891,HC IMP LEAD CORONARY 35CM," 9,000 "
-28008892,HC IMP STENT BILIARY GENESIS 35 OTW 7X40X80CM," 9,626 "
-28008894,HC IMP STENT BILIARY GENESIS 35 OTW 7X29X80," 9,000 "
-28008895,HC IMP STENT BILIARY GENESIS 35 OTW 8X29X80," 9,281 "
-28008896,HC IMP STENT BILIARY GENESIS 35 OTW 8X39X80," 9,000 "
-28008897,HC  CATHETER BALLOON PTA 80CM 20X3MM," 1,615 "
-28008898,HC  CATHETER BALLOON PTA 80CM 20X4MM," 1,615 "
-28008899,HC  CATHETER BALLOON PTA 4X40X80MM," 1,615 "
-28008900,HC  CATHETER BALLOON PTA 80CM 20X5MM," 1,658 "
-28008901,HC  CATHETER BALLOON PTA 80CM 30X5MM," 1,658 "
-28008902,HC  CATHETER BALLOON PTA 5X40X80MM," 1,615 "
-28008903,HC  CATHETER BALLOON PTA 80CM 20X6MM," 1,615 "
-28008904,HC  CATHETER BALLOON PTA 80CM 30X6MM," 1,615 "
-28008905,HC  CATHETER BALLOON PTA 80CM 40X6MM," 1,615 "
-28008906,HC  CATHETER BALLOON PTA 80CM 20X7MM," 1,615 "
-28008907,HC  CATHETER BALLOON PTA 80CM 30X7MM," 1,615 "
-28008908,HC  CATHETER BALLOON PTA 80CM 40X7MM," 1,658 "
-28008909,HC  CATHETER BALLOON PTA 80CM 60X7MM," 1,615 "
-28008910,HC  CATHETER BALLOON PTA 80CM 20X8MM," 1,615 "
-28008911,HC  CATHETER BALLOON PTA 80CM 30X8MM," 1,615 "
-28008912,HC  CATHETER BALLOON PTA 80CM 40X8MM," 1,615 "
-28008913,HC  CATHETER BALLOON PTA 80CM 60X8MM," 1,615 "
-28008914,HC  CATHETER BALLOON PTA 6F 9X20X80CM," 1,913 "
-28008915,HC  CATHETER BALLOON PTA 10X20X80CM 7F," 1,615 "
-28008916,HC  CATHETER BALLOON PTA 10X40MM 80CM 7F," 1,615 "
-28008917,HC  CATHETER BALLOON PTA 80CM 20X12MM," 1,658 "
-28008918,HC  CATHETER BALLOON PTA 12X40X80CM 7F," 1,615 "
-28008919,HC  CATHETER BALLOON PTA 80CM 60X12MM," 1,615 "
-28008920,HC  CATHETER BALLOON PTA 135CM 20X5MM," 1,615 "
-28008921,HC  CATHETER BALLOON PTA 135CM 40X5MM," 1,615 "
-28008922,HC  CATHETER BALLOON PTA 135CM 60X5MM," 1,615 "
-28008923,HC  CATHETER BALLOON PTA 135CM 80X5MM," 1,615 "
-28008924,HC  CATHETER BALLOON PTA 135CM 20X6MM," 1,615 "
-28008925,HC  CATHETER BALLOON PTA 135CM 30X6MM," 1,615 "
-28008926,HC  CATHETER BALLOON PTA 135CM 80X6MM," 1,615 "
-28008927,HC  CATHETER BALLOON PTA 7X20X135MM 5F," 1,445 "
-28008928,HC  CATHETER BALLOON PTA 135CM 40X7MM," 1,615 "
-28008929,HC  CATHETER BALLOON PTA 135CM 40X8MM," 1,615 "
-28008930,HC  CATHETER BALLOON PTA 6F 9X20X135MM," 1,615 "
-28008931,HC  CATHETER BALLOON PTA 135CM 20X10MM," 1,445 "
-28008932,HC IMP STENT BILIARY SENTINOL 8X42X75," 9,000 "
-28008933,HC  CATHETER BIOCARDIA 124CM," 3,188 "
-28008934,HC  CATHETER BIOCARDIA 64CM," 7,438 "
-28008935,HC  CATHETER BIOCARDIA 90CM," 3,188 "
-28008936,HC IMP PISTON ROBINSON-LIPPY," 1,752 "
-28008937,HC IMP EAR PROSTHESIS OSSICULAR 4MM," 1,017 "
-28008938,HC IMP EAR PROSTHESIS OSSICULAR 6MM 4.25MM," 1,122 "
-28008939,HC IMP EAR PROSTHESIS OSSICULAR 0.6X4.5MM, 977 
-28008940,HC  CATHETER DILATATION CORONARY OTW 2X8MM," 1,488 "
-28008941,HC  CATHETER DILATATION CORONARY OTW 2.5X8MM," 1,488 "
-28008942,HC  CATHETER DILATATION CORONARY OTW 3X8MM," 1,488 "
-28008943,HC  CATHETER DILATATION CORONARY OTW 1.5X15MM," 1,488 "
-28008944,HC  CATHETER DILATATION CORONARY OTW 2X15MM," 1,488 "
-28008945,HC  CATHETER DILATATION CORONARY OTW 2.5X15MM," 1,488 "
-28008946,HC  CATHETER DILATATION CORONARY OTW 3X15MM," 1,488 "
-28008947,HC  CATHETER DILATATION OTW 143CM 15X3.5MM," 2,763 "
-28008948,HC  CATHETER DILATATION OTW 143CM 15X4MM," 1,615 "
-28008949,HC  CATHETER DILATATION CORONARY OTW 2X20MM," 1,615 "
-28008950,HC  CATHETER DILATATION CORONARY OTW 2.5X20MM," 1,488 "
-28008951,HC  CATHETER DILATATION CORONARY OTW 3X20MM," 1,488 "
-28008952,HC  CATHETER DILATATION OTW 143CM 20X3.5MM," 1,615 "
-28008953,HC  CATHETER DILATATION OTW 143CM 20X4MM," 1,615 "
-28008957,HC IMP SCREW ACUTRAK 20.0MM," 3,035 "
-28008958,HC IMP SCREW ACUTRAK 27.5MM," 2,617 "
-28008959,HC IMP SCREW ACUTRAK 30.0MM," 3,035 "
-28008960,HC IMP SCREW ACUTRAK MINI 10.0MM," 3,035 "
-28008961,HC IMP SCREW ACUTRAK MINI 12.0MM," 2,805 "
-28008962,HC IMP SCREW ACUTRAK MINI 14.0MM," 2,805 "
-28008963,HC IMP SCREW ACUTRAK MINI 18.0MM," 3,035 "
-28008964,HC IMP SCREW ACUTRAK 12.5MM," 2,617 "
-28008965,HC  TUBE JEJUNAL LOW PROFILE 5ML 18F 1.7CM," 1,326 "
-28008966,HC IMPL SCREW IMF 2X8MM, 870 
-28008967,HC IMPL SCREW IMF 2X12MM, 870 
-28008968,HC  CATH PERPH SLVRHWK 7F," 22,365 "
-28008969,HC IMPL COIL NEURO DETACHABLE 2MM 1.5CM," 10,838 "
-28008970,HC IMPL COIL NEURO DETACHABLE 2MM 2CM," 10,838 "
-28008971,HC IMPL COIL NEURO DETACHABLE 3MM 3CM," 10,838 "
-28008972,HC IMPL COIL NEURO DETACHABLE 3MM 4CM," 10,838 "
-28008973,HC IMPL COIL NEURO DETACHABLE 4MM 3CM," 11,378 "
-28008974,HC IMPL COIL NEURO DETACHABLE 4MM 7CM," 10,838 "
-28008975,HC IMPL COIL NEURO DETACHABLE 4MM 10CM," 10,838 "
-28008976,HC IMPL COIL NEURO DETACHABLE 5MM 5CM," 10,463 "
-28008977,HC IMPL COIL NEURO DETACHABLE 5MM 10CM," 10,463 "
-28008979,HC IMPL COIL NEURO DETACHABLE 6MM 15CM," 10,463 "
-28008980,HC IMPL COIL NEURO DETACHABLE 7MM 13CM," 10,463 "
-28008981,HC IMPL COIL NEURO DETACHABLE 7MM 21CM," 10,463 "
-28008982,HC IMPL COIL NEURO DETACHABLE 8MM 15CM," 10,463 "
-28008983,HC IMPL COIL NEURO DETACHABLE 8MM 24CM," 10,463 "
-28008984,HC IMPL COIL NEURO DETACHABLE 9MM 15CM," 10,463 "
-28008985,HC IMPL COIL NEURO DETACHABLE 9MM 25CM," 10,463 "
-28008986,HC IMPL COIL NEURO DETACHABLE 12MM 30CM," 10,463 "
-28008987,HC IMP LEAD DEFIBRILLATOR 59CM 9F," 35,000 "
-28008989,HC  CATHETER DRAINAGE 40CM 38IN 20F, 592 
-28008990,HC  CATHETER DILATATION 16MM 6X110CM 7F," 2,924 "
-28008991,HC  CATHETER DILATATION 18MM 6X110CM 7F," 2,924 "
-28008992,HC  CATHETER DRAINAGE LGBR GORDON CRVD GW 18F 40CM, 592 
-28008993,HC  CATHETER DILATATION 2X90CM 9MM 5F," 3,945 "
-28008994,HC  CATH CHROMIC TOTAL OCCLUSIONS 120CM 0.014IN 6F," 14,985 "
-28008995,HC IMP STENT PERIPHERAL XCEED 8X30X120," 10,313 "
-28008997,HC IMP BONE VITOSS MORSEL MACRO 10ML," 6,766 "
-28008998,HC IMP BONE VITOSS MORSEL MICRO 30ML," 12,158 "
-28008999,HC IMP CEMENT BONE KYPHON MIXER, 298 
-28009000,HC IMP CEMENT BONE KYPHON," 4,250 "
-28009003,HC IMP ANCHOR CORKSCREW 5X17.9MM," 2,380 "
-28009004,HC IMPL SCREW LOCKING 5X25MM," 1,743 "
-28009005,HC IMPL SCREW LOCKING 5X30MM," 1,862 "
-28009006,HC IMPL SCREW LOCKING 5X35MM," 1,624 "
-28009007,HC IMPL SCREW LOCKING 5X40MM," 1,743 "
-28009008,HC IMPL SCREW LOCKING 5X45MM," 1,955 "
-28009009,HC IMPL SCREW LOCKING 5X50MM," 1,862 "
-28009010,HC IMPL SCREW LOCKING 5X60MM," 1,862 "
-28009011,HC IMPL SCREW LOCKING 5X65MM," 1,927 "
-28009012,HC IMPL SCREW LOCKING 5X70MM," 1,927 "
-28009013,HC IMPL SCREW LOCKING 5X75MM," 1,927 "
-28009014,HC IMPL SCREW LOCKING 5X85MM," 1,927 "
-28009015,HC IMPL SCREW LOCKING 5X90MM," 1,927 "
-28009016,HC IMPL SCREW LOCKING 5X70MM," 1,428 "
-28009017,HC IMPL SCREW LOCKING 4.0X14MM," 1,165 "
-28009018,HC IMPL SCREW LOCKING 4.0X30MM," 1,250 "
-28009019,HC IMPL SCREW LOCKING 4.0X34MM," 1,250 "
-28009020,HC IMPL SCREW LOCKING 7.3X50MM," 2,848 "
-28009021,HC IMPL SCREW LOCKING 7.3X60MM," 2,752 "
-28009022,HC IMPL SCREW LOCKING 7.3X65MM," 2,752 "
-28009023,HC IMPL SCREW LOCKING 7.3X70MM," 2,752 "
-28009024,HC IMPL SCREW LOCKING 7.3X75MM," 2,848 "
-28009025,HC IMPL SCREW LOCKING 7.3X80MM," 2,752 "
-28009026,HC IMPL SCREW LOCKING 7.3X85MM," 2,848 "
-28009027,HC IMPL SCREW LOCKING 7.3X90MM," 2,752 "
-28009028,HC IMPL SCREW LOCKING 7.3X95MM," 2,703 "
-28009029,HC IMPL SCREW CONICAL 7.3 75MM," 2,304 "
-28009030,HC IMPL SCREW CONICAL 7.3 90MM," 2,151 "
-28009031,HC IMPL SCREW CONICAL PARTIALLY THREAD 7.3 70MM," 2,304 "
-28009032,HC IMPL SCREW CONICAL PARTIALLY THREAD 7.3 75MM," 1,862 "
-28009033,HC IMPL SCREW CONICAL PARTIALLY THREAD 7.3 85MM," 2,304 "
-28009034,HC IMPL SCREW CONICAL PARTIALLY THREAD 7.3 90MM," 2,538 "
-28009035,HC IMPL SCREW CONICAL PARTIALLY THREAD 7.3 95MM," 1,862 "
-28009036,HC IMPL PLATE LCP CONDYLAR 4.5MM 4.5MM 278MM 12HOLES," 12,648 "
-28009037,HC IMPL PLATE LCP CONDYLAR 4.5MM 170MM 6HOLE," 12,648 "
-28009038,HC IMPL PLATE LCP CONDYLAR RIGHT 4.5MM 206MM 8HOLE," 12,068 "
-28009039,HC IMPL PLATE LCP CONDYLAR LEFT 4.5MM 206MM 8HOLE," 12,068 "
-28009040,HC IMPL PLATE LCP CONDYLAR RIGHT 4.5MM 242MM 10HOLE," 13,362 "
-28009041,HC IMPL PLATE LCP CONDYLAR LEFT 4.5MM 242MM 10HOLE," 13,362 "
-28009042,HC IMPL PLATE LCP CONDYLAR RIGHT 4.5MM 278MM 12HOLE," 11,925 "
-28009043,HC IMPL PLATE LCP CONDYLAR LEFT 4.5MM 278MM 12HOLE," 13,640 "
-28009044,HC IMPL PLATE LCP CONDYLAR RIGHT 4.5MM 314MM 14HOLE," 14,399 "
-28009045,HC IMPL PLATE LCP CONDYLAR LEFT 4.5MM 314MM 14HOLE," 12,173 "
-28009046,HC IMPL PLATE PROX TIBIAL LEFT 4.5X118MM 6 HOLE," 11,826 "
-28009047,HC  INTRODUCER SHEATH 0.032IN 67CM 8F, 767 
-28009048,HC IMP LINK SPINELINK STANDARD 45MM," 2,125 "
-28009049,HC IMP LINK SPINELINK REVERSE RIGHT 36MM," 2,125 "
-28009050,HC IMP STENT BILIARY EXPRESS RX 5X15X90," 9,022 "
-28009051,HC IMP STENT BILIARY EXPRESS RX 6X14X90," 9,022 "
-28009052,HC IMP STENT BILIARY EXPRESS RX 6X18X90," 9,022 "
-28009053,HC IMP STENT BILIARY EXPRESS RX 7X15X150," 9,000 "
-28009054,HC IMP STENT BILIARY EXPRESS RX 7X19X150," 9,000 "
-28009055,HC RIGID BODY JACKET," 7,429 "
-28009056,HC  INTRODUCER 0.087X0.038IN 90CM 6F, 969 
-28009058,HC INTRODUCER STIFFENED CANNULA CATH SET 5F, 332 
-28009059,HC INTRODUCER CATH WIRE GUIDE NITNOL TIP 5F SET, 349 
-28009060,HC IMP STENT GRAFT AAA EXCLUDER 23X12X14," 50,564 "
-28009061,HC IMP LEAD PACEMAKER TRANSVENOUS 52CM," 4,675 "
-28009062,HC IMP MESH PROLENE 4X6IN," 3,506 "
-28009063,HC IMPL SCREW BIOABSORBABLE11X25MM," 1,063 "
-28009064,HC IMPL SCREW BIOABSORBABLE 11X30MM," 1,063 "
-28009065,HC IMP SLING MONARC SYSTEM," 8,500 "
-28009066,HC IMP BREAST ROUND HIGH 420 +80CC," 5,313 "
-28009067,HC IMP BREAST ROUND HIGH 380 +70CC," 5,313 "
-28009068,HC IMP BREAST ROUND HIGH 330 +70CC," 5,313 "
-28009070,HC IMPL SCREW EMERGENCY TITANIUM 2X4MM," 2,295 "
-28009071,HC IMPL SCREW EMERGENCY TITANIUM 2X5MM, 439 
-28009072,HC IMPL PLATE TI CURVED SAGITTAL SPLIT," 2,251 "
-28009073,HC IMPL SCREW RESCUE 2.4MMX5MM, 428 
-28009074,HC  INTRODUCER SHEATH 13X20X45CM 7F, 389 
-28009075,HC  INTRODUCER SHEATH 13X20X45CM 8F, 306 
-28009076,HC  INTRODUCER SHEATH 13X20X45CM 9F, 389 
-28009077,HC  INTRODUCER SHEATH 13X20X45CM 10F, 306 
-28009078,HC IMP GRAFT HEMASHIELD KNIT ST 8X40," 3,613 "
-28009080,HC IMP STENT PERIPHERAL XCEED 7X60X120," 11,213 "
-28009081,HC IMP STENT PERIPHERAL XCEED 7X80X120," 11,213 "
-28009082,HC IMP STENT PERIPHERAL XCEED 6X20X120," 10,313 "
-28009085,HC  CATHETER MICRO PROWLER STR .0165IN 2.3FX150X5CM," 6,871 "
-28009086,HC  CATHETER MICRO PROWLER 45D .0165IN 2.3FX150X50," 7,174 "
-28009087,HC  CATHETER MICRO PROWLER 90D .0165IN 2.3FX150X5CM," 7,015 "
-28009088,HC  CATHETER MICRO PROWLER J .0165IN 2.3FX150X5CM," 7,015 "
-28009089,HC IMP LEAD PACING TACARHYTH 65CM 6.6F," 35,000 "
-28009090,HC TAMP BONE INFLATABLE KYPHX 10MM," 8,243 "
-28009091,HC  INTROD OSTEO W TROCAR KYPHX," 8,513 "
-28009094,HC  GUIDEWIRE JAGWIRE STRAIGHT 35IN 260CM," 1,483 "
-28009095,HC  GUIDEWIRE JAGWIRE ANGLED 35IN 260CM," 1,483 "
-28009096,HC IMP STENT CAROTID ACCULINK RX 5X20X130," 17,213 "
-28009097,HC IMP STENT CAROTID ACCULINK RX 5X30X130," 15,375 "
-28009098,HC IMP STENT CAROTID ACCULINK RX 6X20X130," 17,213 "
-28009099,HC IMP STENT CAROTID ACCULINK RX 6X40X130," 17,213 "
-28009100,HC IMP STENT CAROTID ACCULINK RX 7X20X130," 17,603 "
-28009101,HC IMP STENT CAROTID ACCULINK RX 7X30X130," 17,213 "
-28009102,HC IMP STENT CAROTID ACCULINK RX 9X30X130," 17,213 "
-28009103,HC IMP STENT CAROTID ACCULINK RX 9X40X130," 15,375 "
-28009104,HC IMP STENT CAROTID ACCULINK RX 10X20X130," 17,213 "
-28009105,HC IMP STENT CAROTID ACCULINK RX 8X30X130," 15,375 "
-28009106,HC IMP STENT CAROTID ACCULINK RX 8X40X130," 15,375 "
-28009107,HC IMP STENT CAROTID ACCULINK RX 10X30X130," 15,375 "
-28009108,HC IMP STENT CAROTID ACCULINK 7-10X40," 17,603 "
-28009109,HC IMP PROTECTION EMBOLIC RX ACCUNET 5.5MM," 12,451 "
-28009110,HC IMP PROTECTION EMBOLIC ACCUNET SYS 6.5MM," 11,625 "
-28009111,HC IMP PROTECTION EMBOLIC RAPID EXCHAN ACCUNET 7.5X190," 10,875 "
-28009112,HC IMP PROTECTION EMBOLIC SYS 300CM 7.5MM 6F," 11,625 "
-28009113,HC PROBE RF LEVEEN SUPERSLIM 17GX2X15CM," 9,962 "
-28009114,HC PROBE RF LEVEEN SUPERSLIM 17GX2X25CM," 9,962 "
-28009115,HC PROBE RF LEVEEN SUPERSLIM 17GX3X15CM," 10,848 "
-28009116,HC PROBE RF LEVEEN SUPERSLIM 17GX3X25CM," 10,330 "
-28009117,HC NEEDLE ELECTRODE LEVEEN 2X12CM," 8,627 "
-28009118,HC  GUIDEWIRE JAGWIRE STRAIGHT 35IN 450CM," 1,483 "
-28009119,HC  GUIDEWIRE JAGWIRE 35IN 450CM," 1,483 "
-28009120,HC  CATHETER DILATATION RX EXTC 15-18MM," 1,233 "
-28009121,HC BASKET RETRIEVAL STONE TRAPEZOID 3X5X180CM," 2,202 "
-28009122,HC  SHEATH GUIDING PINNACLE DESTINATION 6F 45CM, 697 
-28009123,HC  SHEATH GUIDING PINNACLE DESTINATION 7F 45CC, 697 
-28009124,HC HANDPIECE LIGASURE CUTTING 5MM," 3,568 "
-28009126,HC  CATHETER ANGIOPLASTY BLLN CUT PTA 6X20MMX90CM," 8,196 "
-28009127,HC  CATHETER ANGIOPLASTY BLLN CUT PTA 7X20MMX90CM," 7,955 "
-28009128,HC  CATHETER ANGIOPLASTY BLLN CUT PTA 8X20MMX90CM," 6,419 "
-28009129,HC  CATHETER ANGIOPLASTY BLLN CUT PTC 5X20MMX135CM," 7,955 "
-28009130,HC  CATHETER ANGIOPLASTY BLLN CUT PTA 7X20MMX135CM," 6,406 "
-28009131,HC IMP ICD DUAL IC INTRINSIC 38CM," 84,000 "
-28009132,HC CATHETER 14.5F 13X37CM," 8,458 "
-28009133,HC IMPL COIL MICRO HELICAL SRE 1CM 2MM ULTIPAQ," 8,150 "
-28009134,HC IMPL COIL MICRO HELICAL SRE 2CM 2MM ULTIPAQ," 8,250 "
-28009135,HC IMPL COIL MICRO HELICAL SRE 4CM 2MM ULTIPAQ," 8,400 "
-28009136,HC IMPL COIL MICRO HELICAL SRE 2CM 3MM ULTIPAQ," 5,830 "
-28009137,HC IMPL COIL MICRO HELICAL SRE 4CM 3MM ULTIPAQ," 8,375 "
-28009138,HC IMPL COIL MICRO HELICAL SRE 6CM 3MM ULTIPAQ," 8,524 "
-28009139,HC IMPL COIL MICRO HELICAL SRE 8CM 3MM ULTIPAQ," 8,674 "
-28009140,HC IMPL COIL MICRO HELICAL SRE 8CM 4MM ULTIPAQ," 8,674 "
-28009141,HC IMPL COIL MICRO HELICAL SRE 5MM 10CM," 8,412 "
-28009142,HC IMPL COIL MICRO HELICAL SRE 15CM 6MM," 8,786 "
-28009143,HC IMPL COIL MICRO HELICAL SRE 20CM 7MM," 5,593 "
-28009144,HC IMPL COIL MICRO HELICAL SRE 30CM 7MM," 5,593 "
-28009145,HC IMPL COIL MICRO HELICAL SRE 30CM 10MM," 5,712 "
-28009146,HC IMPL COIL NEURO MICRO SPHERICAL 9.7CM 5MM," 6,780 "
-28009147,HC IMPL COIL NEURO MICRO SPHERICAL 11.9CM 6MM," 6,780 "
-28009148,HC IMPL COIL NEURO MICRO SPHERICAL 16.1CM 8MM," 12,226 "
-28009149,HC IMPL COIL NEURO MICRO SPHERICAL 18.4CM 9MM," 7,119 "
-28009150,HC  CATHETER SUPPORT QUICK CROSS 0.018IX135 6F," 1,420 "
-28009151,HC IMP LEAD ATTAIN BIPOLAR OTW 88CM 6F," 11,250 "
-28009154,HC  CATHETER DILATATION CORONARY RX 2.25X8MM," 1,488 "
-28009155,HC  CATHETER DILATATION CORONARY RX 2.5X8MM," 1,488 "
-28009156,HC  CATHETER DILATATION CORONARY RX 3X8MM," 1,615 "
-28009157,HC  CATHETER DILATATION CORONARY RX 3.5X8MM," 1,615 "
-28009158,HC FIBER LASER KTP ENDOSTAT 0.6X12," 3,103 "
-28009159,HC  CATHETER DILATATION CORONARY RX 4X8MM," 1,615 "
-28009160,HC  CATHETER DILATATION CORONARY RX 1.5X12MM," 1,488 "
-28009161,HC  CATHETER DILATATION CORONARY RX 2X12MM," 1,488 "
-28009162,HC  CATHETER DILATATION CORONARY RX 2.25X12MM," 1,488 "
-28009163,HC  CATHETER DILATATION CORONARY RX 2.5X12MM," 1,488 "
-28009164,HC  CATHETER DILATATION CORONARY RX 3X12MM," 1,488 "
-28009165,HC  CATHETER DILATATION CORONARY RX 3.5X12MM," 1,488 "
-28009166,HC  CATHETER DILATATION CORONARY RX 4X12MM," 1,488 "
-28009167,HC  CATHETER DILATATION CORONARY RX 1.5X15MM," 1,488 "
-28009168,HC  CATHETER DILATATION CORONARY RX 2X15MM," 1,488 "
-28009169,HC  CATHETER DILATATION CORONARY RX 2.25X15MM," 1,488 "
-28009170,HC  CATHETER DILATATION CORONARY RX 2.5X15MM," 1,488 "
-28009171,HC  CATHETER DILATATION CORONARY RX 3X15MM," 1,488 "
-28009172,HC  CATHETER DILATATION CORONARY RX 3.5X15MM," 1,615 "
-28009173,HC  CATHETER DILATATION CORONARY RX 4X15MM," 1,488 "
-28009174,HC  CATHETER DILATATION CORONARY RX 2X20MM," 1,488 "
-28009175,HC  CATHETER DILATATION CORONARY RX 2.5X20MM," 1,488 "
-28009176,HC  CATHETER DILATATION CORONARY RX 3X20MM," 1,488 "
-28009177,HC  CATHETER DILATATION CORONARY RX 3.5X20MM," 1,615 "
-28009178,HC  CATHETER DILATATION CORONARY RX 4X20MM," 1,615 "
-28009179,HC  CATHETER DILATATION CORONARY RX 2X30MM," 1,488 "
-28009180,HC  CATHETER DILATATION CORONARY RX 2.5X30MM," 1,488 "
-28009181,HC  CATHETER DILATATION CORONARY RX 3X30MM," 1,488 "
-28009182,HC  CATHETER DILATATION CORONARY RX 3.5X30MM," 1,488 "
-28009183,HC  CATHETER DILATATION CORONARY RX 4X30MM," 1,488 "
-28009185,HC IMP PROTECTION EMBOLIC FILTER SYS 190CM," 9,734 "
-28009186,HC  SHEATH RETRIEVAL 4F 1.3MM 0.052IN, 680 
-28009187,HC CATHETER ICP MONITOR KIT," 7,982 "
-28009188,HC  CATHETER BALLOON TRILOBE 108CM 20F," 4,973 "
-28009189,HC IMP STENT GRAFT ICAST 7X38X80," 18,750 "
-28009190,HC IMP STENT PANCREATIC ZEPDF-5F 6CM, 973 
-28009191,HC  GUIDEWIRE FRACTIONAL FLEXIBLE 175CM," 5,100 "
-28009192,HC IMP STAPES PROSTHESIS ROBERSON 0.6X0.9X4.0MM," 1,717 "
-28009193,HC IMP STAPES PROSTHESIS ROBERSON 0.6X0.9X4.5MM," 1,486 "
-28009194,HC IMP STAPES PROSTHESIS ROBERSON 0.6X1.0X4.0MM," 1,486 "
-28009195,HC IMP STAPES PROSTHESIS ROBERSON 0.6X1.0X4.5MM," 1,486 "
-28009196,HC IMP STAPES PROSTHESIS ROBERSON 0.6X0.9X4.25MM," 1,486 "
-28009197,HC IMPL COCHLEAR HIRES 90K," 87,090 "
-28009198,HC IMP BREAST ROUND HIGH 460 +90CC," 5,100 "
-28009199,HC  GUIDEWIRE INTERVENTIONAL 180CM 1.5MM, 91 
-28009200,HC  CATHETER SUPPORT QUICK CROSS .014X135," 1,420 "
-28009203,HC  CATHETER SUPPORT QUICK CROSS .035IN 150CM," 1,420 "
-28009204,HC IMP LEAD PACEMAKER VENTRICULAR 45CM," 4,675 "
-28009205,HC IMP LEAD PACING 58CM," 4,675 "
-28009206,HC IMP LEAD VENTRICULAR BIPOLAR 52CM," 4,675 "
-28009207,HC IMP PLUG VASCULAR AMPLATZER 4X7X100," 6,188 "
-28009208,HC IMP PLUG VASCULAR AMPLATZER 6X7X100," 5,950 "
-28009209,HC IMP PLUG VASCULAR AMPLATZER 8X7X100," 5,950 "
-28009210,HC IMP PLUG VASCULAR AMPLATZER 10X7X100," 5,950 "
-28009211,HC IMPL PLUG VASCULAR AMPLATZER 12X8MM," 6,188 "
-28009212,HC IMP PLUG VASCULAR AMPLATZER 14X8X100," 5,950 "
-28009213,HC IMP PLUG VASCULAR AMPLATZER 16X8X100," 3,825 "
-28009214,HC IMPL SCREW CAP ENDIUS," 2,142 "
-28009215,HC IMP WEDGE ILIAC CREST ACF 13-15MM," 7,948 "
-28009216,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 16.0D," 1,275 "
-28009217,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 16.5D," 1,275 "
-28009218,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 17.0D," 1,275 "
-28009219,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 17.5D," 1,275 "
-28009220,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 18.0D," 1,275 "
-28009221,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 18.5D," 1,275 "
-28009222,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 19.0D," 1,275 "
-28009223,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 19.5D," 1,275 "
-28009224,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 20.0D," 1,275 "
-28009225,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 20.5D," 1,275 "
-28009226,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 21.0D," 1,275 "
-28009227,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 21.5D," 1,275 "
-28009228,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 22.0D," 1,275 "
-28009229,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 22.5D," 1,275 "
-28009230,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 23.0D," 1,275 "
-28009231,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 23.5D," 1,275 "
-28009232,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 24.0D," 1,275 "
-28009233,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 24.5D," 1,275 "
-28009234,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 25.0D," 1,275 "
-28009235,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 25.5D," 1,275 "
-28009236,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 26.0D," 1,275 "
-28009237,HC IMP BONE FILLER CALLOS IMPACT 5ML," 10,013 "
-28009238,HC IMP BONE FILLER CALLOS IMPACT 10ML," 18,550 "
-28009239,HC IMP ANNULOPLASTY RING 4100 24MM," 18,277 "
-28009240,HC IMP ANNULOPLASTY RING 4100 26MM," 17,213 "
-28009241,HC IMP ANNULOPLASTY RING 4100-28MM," 18,262 "
-28009242,HC IMP ANNULOPLASTY RING 4100 30MM," 18,262 "
-28009243,HC IMP ANNULOPLASTY RING 4100 32MM," 18,262 "
-28009244,HC IMP ANNULOPLASTY RING 4100 34MM," 17,730 "
-28009246,HC IMP LEAD PACEMAKER VENTRICULAR 65CM," 4,675 "
-28009247,HC  CATH CRYOABLAT MAX 9F 90CM," 14,250 "
-28009248,HC IMP STENT CORONARY MINIVISION RX BMS 2X8," 6,375 "
-28009249,HC IMP STENT CORONARY MINIVISION RX BMS 2.25X8," 8,075 "
-28009250,HC IMP STENT CORONARY MINIVISION RX BMS 2.5X8," 6,375 "
-28009251,HC IMP STENT CORONARY MINIVISION RX BMS 2X12," 6,375 "
-28009252,HC IMP STENT CORONARY MINIVISION RX BMS 2.25X12," 8,075 "
-28009253,HC IMP STENT CORONARY MINIVISION RX BMS 2.5X12," 6,375 "
-28009254,HC IMP STENT CORONARY MINIVISION RX BMS 2X15," 8,625 "
-28009255,HC IMP STENT CORONARY MINIVISION RX BMS 2.25X15," 6,375 "
-28009256,HC IMP STENT CORONARY MINIVISION RX BMS 2.5X15," 7,299 "
-28009257,HC IMP STENT CORONARY MINIVISION RX BMS 2X18," 6,375 "
-28009258,HC IMP STENT CORONARY MINIVISION RX BMS 2.25X18," 6,375 "
-28009259,HC IMP STENT CORONARY MINIVISION RX BMS 2X23," 6,375 "
-28009260,HC IMP STENT CORONARY MINIVISION RX BMS 2.25X23," 6,375 "
-28009261,HC IMP STENT CORONARY MINIVISION RX BMS 2.5X23," 6,375 "
-28009262,HC IMP STENT CORONARY MINIVISION RX BMS 2.5X28," 6,375 "
-28009263,HC IMP STENT PANCREATIC ZIMMON 7F 2CM, 445 
-28009264,HC IMP STENT PANCREATIC ZIMMON 7F 4CM, 445 
-28009265,HC IMP STENT PANCREATIC ZIMMON 7F 6CM, 445 
-28009266,HC IMP STENT PANCREATIC ZIMMON 7F 8CM, 445 
-28009269,HC IMP BREAST SALINE STYLE 363LF 620-645CC," 6,460 "
-28009270,HC  CATHETER GUIDING ATTAIN MB2 7F 45CM," 1,700 "
-28009271,HC IMP FILTER VENA CAVA OPTEASE 55CM," 12,204 "
-28009272,HC IMP LEAD DEFIBRILLATOR FXTN 64CM 9F," 35,000 "
-28009274,HC IMP LEAD ATTAIN BIPOLAR OTW 78CM 6F," 11,250 "
-28009275,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 16MM," 8,250 "
-28009276,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 18MM," 8,250 "
-28009277,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 20MM," 8,250 "
-28009278,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 28MM," 9,750 "
-28009279,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 34MM," 9,750 "
-28009280,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 45MM," 11,250 "
-28009281,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 48MM," 11,250 "
-28009282,HC IMPL PLATE CERVICAL SPINE LOCKING PLATE 54MM," 11,250 "
-28009283,HC IMPL SCREW CERVICAL SPINE LOCKING PLATE 4.0X12MM," 2,015 "
-28009284,HC IMPL SCREW CERVICAL SPINE LOCKING PLATE 4.5 X12MM," 2,015 "
-28009285,HC IMPL SCREW CERVICAL SPINE LOCKING PLATE 4.5X14MM," 2,015 "
-28009286,HC  CATHETER DIAGNOSTIC ELECTRODE 2X8MM 6F," 6,035 "
-28009287,HC IMP PUMP INFUSION NERVE BLOCK 10ML," 1,681 "
-28009288,HC IMP KNEE FEMORAL COMPONENT ADV POST NP SZ3," 13,905 "
-28009289,HC IMP EAR PROSTHESIS BELL 2.25MM," 3,086 "
-28009290,HC IMP EAR PROSTHESIS BELL 2.5MM," 3,298 "
-28009291,HC IMP EAR PROSTHESIS BELL 2.75MM," 3,298 "
-28009292,HC IMP EAR PROSTHESIS AERIAL 3.0MM," 3,086 "
-28009293,HC IMP EAR PROSTHESIS AERIAL 3.25MM," 3,298 "
-28009294,HC IMP EAR PROSTHESIS AERIAL 3.5MM," 3,298 "
-28009295,HC IMP EAR PROSTHESIS AERIAL 3.75MM," 3,086 "
-28009296,HC IMP EAR PROSTHESIS AERIAL 4.0MM," 3,086 "
-28009297,HC IMP EAR PROSTHESIS AERIAL 4.25MM," 2,992 "
-28009298,HC IMP EAR PROSTHESIS AERIAL 4.5MM," 2,992 "
-28009299,HC IMP EAR PROSTHESIS AERIAL 4.75MM," 3,298 "
-28009300,HC  CATHETER IVUS EAGLE EYE PLATINUM 2.9F 150CM," 7,013 "
-28009301,HC IMP STENT GRAFT TIPS VIATORR 10X8X2," 27,300 "
-28009302,HC IMP STENT GRAFT TIPS VIATORR 10X7X2," 27,300 "
-28009303,HC IMP STENT GRAFT TIPS VIATORR 10X6X2," 27,300 "
-28009304,HC IMP STENT GRAFT TIPS VIATORR 8X8X2," 27,300 "
-28009305,HC IMP STENT GRAFT TIPS VIATORR 8X7X2," 27,300 "
-28009306,HC IMP STENT GRAFT TIPS VIATORR 8X6X2," 27,300 "
-28009307,HC IMP LEAD PACEMAKER VENTRICULAR 52CM," 4,675 "
-28009308,HC TUBE EMG REINFORCED 7MM," 2,357 "
-28009309,HC INSERT BIPOLAR COBRA," 18,000 "
-28009310,HC IMP PUMP PAIN ON-Q 400ML," 2,385 "
-28009311,HC IMP LEAD PACEMAKER TACARHYTH 75CM 8.6F," 35,000 "
-28009312,HC IMP LEAD PACEMAKER CARDIAC RHYTHM 58CM 8.6F," 35,000 "
-28009313,HC CATHETER BALLOON PHOTODYNAMIC ESOPHAGEAL 7-9CM," 10,875 "
-28009314,HC  CATHETER VENTRICULAR PED 18F, 167 
-28009315,HC IMP PATCH MESH PRO 3D LG," 3,707 "
-28009316,HC IMP PATCH MESH PRO 3D M," 1,840 "
-28009322,HC PERFUSION SPY ELITE SYSTEM KIT," 9,713 "
-28009323,HC IMP BONE FEMORAL CORTICAL STRUT 20CM," 7,853 "
-28009324,HC IMP BONE GRAFT SYNTHETIC MIIG 15ML," 22,225 "
-28009326,HC  CATHETER GUIDING SOFT TIP 7.5F," 1,615 "
-28009327,HC IMPL PACEMAKER FRONTIER II CRT P," 47,125 "
-28009328,HC IMPL COIL NEURO MICRO SPHERICAL 7.5CM 4MM," 11,777 "
-28009329,HC ADAPTER ASSEMBLY DAVINCI," 5,950 "
-28009330,HC IMP BREAST ROUND SALINE STYLE 68LP 325CC," 2,508 "
-28009331,HC IMP LEAD CORONARY VENOUS EASYTRAK 90CM," 15,000 "
-28009332,HC IMP STENT BILIARY PARAMOUNT 618X80," 8,160 "
-28009333,HC IMP ICD MAXIMO VR 7232E," 61,750 "
-28009334,HC IMP BREAST ROUND HIGH PROFILE COHESIVE 425ML," 7,650 "
-28009335,HC IMP BREAST ROUND HIGH PROFILE COHESIVE 450ML," 7,650 "
-28009336,HC IMP BREAST ROUND HIGH PROFILE COHESIVE 500ML," 7,650 "
-28009338,HC VASOVIEW VESSEL 7X12MM KIT," 7,523 "
-28009339,HC IMP STENT BILIARY PARAMOUNT 5X14X80," 8,160 "
-28009340,HC IMP STENT BILIARY PARAMOUNT 5X18X80," 8,160 "
-28009341,HC IMP STENT BILIARY PARAMOUNT 5X21X80," 8,160 "
-28009342,HC IMP STENT BILIARY PARAMOUNT 6X14X80," 8,160 "
-28009343,HC IMP STENT BILIARY PARAMOUNT 7X14X80," 8,160 "
-28009344,HC IMP STENT BILIARY PARAMOUNT 7X18X80," 8,160 "
-28009345,HC IMP STENT BILIARY PARAMOUNT 7X21X80," 9,375 "
-28009346,HC  INTROD TEARAWAY 3FX5CM, 315 
-28009352,HC KIT CUSTOM VASCULAR," 2,460 "
-28009355,HC HEART PEDIATRIC KIT," 2,844 "
-28009358,HC KNEE ARTHROSCOPY 245 KIT," 2,164 "
-28009362,HC ORTHO SPLIT, 850 
-28009363,HC KIT CUSTOM PELVIC RECONSTRUCTION," 3,026 "
-28009367,HC GASTRIC ENDOSCOPIC KIT," 4,707 "
-28009368,HC IMP STENT GRAFT ICAST 6X59X80," 18,750 "
-28009369,HC IMP STENT GRAFT ICAST 9X38X80," 18,750 "
-28009370,HC IMP MICROSPHERE EMBOLIC RADIOACTIVE 90 UNIT," 90,000 "
-28009371,HC IMPL PLATE MESH MASTOID SMALL," 8,038 "
-28009372,HC IMPL PLATE MESH MASTOID LARGE," 7,276 "
-28009375,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 10.5D," 1,275 "
-28009376,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 11.0D," 1,275 "
-28009377,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 11.5D," 1,275 "
-28009378,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 12.0D," 1,275 "
-28009379,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 12.5D," 1,275 "
-28009380,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 13.0D," 1,275 "
-28009381,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 13.5D," 1,275 "
-28009382,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 14.0D," 1,275 "
-28009383,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 14.5D," 1,275 "
-28009384,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 15.0D," 1,275 "
-28009385,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 15.5D," 1,275 "
-28009386,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 26.5D," 1,275 "
-28009387,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 27.0D," 1,275 "
-28009388,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 27.5D," 1,275 "
-28009389,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 28.0D," 1,275 "
-28009390,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 28.5D," 1,275 "
-28009391,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 29.0D," 1,267 "
-28009392,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 29.5D," 1,275 "
-28009393,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 30.0D," 1,275 "
-28009394,HC SHEAR COAGULATING CURVED HANDLE 5MMX36CM," 3,300 "
-28009395,HC  SHEATH GUIDING MULTI PURPOSE PREFACE 8F 62CM," 1,488 "
-28009396,HC  CATHETER ELECTROPHYSIOLOGY MAP ELECTRO 64 2X31," 18,375 "
-28009398,HC  CATHETER GUIDING ANGRIOGRAPHY 6X140CM," 5,100 "
-28009399,HC IMPL PLATE LCP NARROW 6 HOLE 4.5X116MM," 3,666 "
-28009400,HC IMPL PLATE LCP NARROW 7 HOLE 4.5X134MM," 4,454 "
-28009401,HC IMPL PLATE LCP NARROW 8 HOLE 4.5X152MM," 4,641 "
-28009402,HC IMPL PLATE LCP NARROW 9 HOLE 4.5X170MM," 4,702 "
-28009403,HC IMPL PLATE LCP NARROW 12 HOLE 4.5X22MM," 6,115 "
-28009404,HC IMPL PLATE LCP NARROW 14 HOLE 4.5X26MM," 6,265 "
-28009405,HC IMPL PLATE LCP NARROW 16 HOLE 4.5X29MM," 8,025 "
-28009406,HC IMPL PLATE LCP BROAD 8 HOLE 4.5X152MM," 5,006 "
-28009407,HC IMPL PLATE LCP BROAD 12 HOLE 4.5X224MM," 6,677 "
-28009408,HC IMPL PLATE LCP BROAD CURVED 16 HOLE 4.5X296MM," 8,278 "
-28009409,HC IMPL PLATE T 4.5X83MM 4HOLE," 5,872 "
-28009410,HC IMPL SCREW SHAFT 4.5X28MM, 130 
-28009411,HC IMPL SCREW SHAFT 4.5X30MM, 130 
-28009412,HC IMPL SCREW SHAFT 4.5X32MM, 130 
-28009413,HC IMPL SCREW SHAFT 4.5X34MM, 130 
-28009414,HC IMPL SCREW SHAFT 4.5X36MM, 147 
-28009415,HC IMPL SCREW SHAFT 4.5X38MM, 130 
-28009416,HC IMPL SCREW SHAFT 4.5X40MM, 147 
-28009417,HC IMPL SCREW SHAFT 4.5X42MM, 147 
-28009418,HC IMPL SCREW SHAFT 4.5 44MM, 147 
-28009419,HC IMPL SCREW SHAFT 4.5X46MM, 147 
-28009420,HC IMPL SCREW SHAFT 4.5X48MM, 147 
-28009421,HC IMPL SCREW SHAFT 4.5X50MM, 147 
-28009422,HC IMPL SCREW LOCKING 4.0X46MM," 1,382 "
-28009423,HC IMPL SCREW LOCKING 4.0X18MM," 1,335 "
-28009424,HC IMPL SCREW LOCKING 4.0X26MM," 1,335 "
-28009425,HC IMPL SCREW LOCKING 4.0X42MM," 1,309 "
-28009426,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X14MM," 1,407 "
-28009427,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X16MM," 1,407 "
-28009428,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X18MM," 1,407 "
-28009429,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X22MM," 1,360 "
-28009430,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X24MM," 1,360 "
-28009431,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X26MM," 1,407 "
-28009432,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X28MM," 1,360 "
-28009433,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X30MM," 1,407 "
-28009434,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X32MM," 1,407 "
-28009435,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X34MM," 1,407 "
-28009436,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X36MM," 1,407 "
-28009437,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X38MM," 1,407 "
-28009438,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X40MM," 1,407 "
-28009439,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X42MM," 1,407 "
-28009440,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X44MM," 1,407 "
-28009441,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X48MM," 1,407 "
-28009442,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X50MM," 1,407 "
-28009443,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X55MM," 1,407 "
-28009444,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X60MM," 1,407 "
-28009445,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X70MM," 1,407 "
-28009446,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X75MM," 1,407 "
-28009447,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X80MM," 1,407 "
-28009448,HC  CATHETER GUIDING VISTA BRITE TIP SH JR4 7F, 442 
-28009449,HC  CATHETER GUIDING VISTA BRITE TIP 7F, 442 
-28009451,HC IMP LEAD CORONARY PACING 90CM," 11,250 "
-28009452,HC  INTRODUCER SHEATH MULLINS 6FX44X52CM, 723 
-28009453,HC  CATHETER INFUSION PTRN 90X20CM 5F," 1,165 "
-28009454,HC IMP KNEE INSERT TIBIAL LEFT SIZE 3 10MM," 5,814 "
-28009455,HC IMP KNEE INSERT TIBIAL LEFT SIZE 4 17MM," 7,880 "
-28009456,HC IMP KNEE INSERT TIBIAL POST SZ3 1," 7,880 "
-28009457,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X8," 7,650 "
-28009458,HC IMP STENT CORONARY VERIFLEX RX BMS 3X8," 7,650 "
-28009459,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X8," 7,650 "
-28009460,HC IMP STENT CORONARY VERIFLEX RX BMS 4X8," 7,650 "
-28009461,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X12," 7,650 "
-28009462,HC IMP STENT CORONARY VERIFLEX RX BMS 3X12," 7,650 "
-28009463,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X12," 7,589 "
-28009464,HC IMP STENT CORONARY VERIFLEX RX BMS 4X12," 7,589 "
-28009465,HC IMP STENT CORONARY VERIFLEX RX BMS 4.5X12," 7,589 "
-28009466,HC IMP STENT CORONARY VERIFLEX RX BMS 5X12," 7,589 "
-28009467,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X16," 7,589 "
-28009468,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X16," 7,589 "
-28009469,HC IMP STENT CORONARY VERIFLEX RX BMS 4X16," 7,589 "
-28009470,HC IMP STENT CORONARY VERIFLEX RX BMS 5X16," 7,589 "
-28009471,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X20," 7,650 "
-28009472,HC IMP STENT CORONARY VERIFLEX RX BMS 3X20," 7,650 "
-28009473,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X20," 7,650 "
-28009474,HC IMP STENT CORONARY VERIFLEX RX BMS 4X20," 7,650 "
-28009475,HC IMP STENT CORONARY VERIFLEX RX BMS 4.5X20," 7,650 "
-28009476,HC IMP STENT CORONARY VERIFLEX RX BMS 5X20," 7,591 "
-28009477,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X24," 7,650 "
-28009478,HC IMP STENT CORONARY VERIFLEX RX BMS 3X24," 7,650 "
-28009479,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X24," 7,650 "
-28009480,HC IMP STENT CORONARY VERIFLEX RX BMS 4X24," 7,650 "
-28009481,HC IMP STENT CORONARY VERIFLEX RX BMS 4.5X24," 17,631 "
-28009482,HC IMP STENT CORONARY VERIFLEX RX BMS 5X24," 7,650 "
-28009483,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X28," 7,650 "
-28009484,HC IMP STENT CORONARY VERIFLEX RX BMS 3X28," 7,650 "
-28009485,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X28," 7,503 "
-28009486,HC IMP STENT CORONARY VERIFLEX RX BMS 4X28," 7,650 "
-28009487,HC IMP STENT CORONARY VERIFLEX RX BMS 4.5X28," 7,597 "
-28009488,HC IMP STENT CORONARY VERIFLEX RX BMS 5X28," 18,732 "
-28009489,HC IMP STENT CORONARY VERIFLEX RX BMS 2.75X32," 7,650 "
-28009490,HC IMP STENT CORONARY VERIFLEX RX BMS 3X32," 7,598 "
-28009491,HC IMP STENT CORONARY VERIFLEX RX BMS 3.5X32," 7,598 "
-28009492,HC IMP STENT CORONARY VERIFLEX RX BMS 4X32," 7,598 "
-28009493,HC IMP STENT CORONARY VERIFLEX RX BMS 4.5X32," 7,650 "
-28009494,HC IMP STENT CORONARY VERIFLEX RX BMS 5X32," 7,598 "
-28009495,HC IMP STENT GRAFT TAA TAG 34X15," 69,000 "
-28009496,HC IMP STENT GRAFT TAA TAG 34X20," 69,000 "
-28009497,HC IMP STENT GRAFT TAA TAG 37X15," 69,000 "
-28009498,HC  INTRODUCER SHEATH THORACIC 22F 30CM," 2,678 "
-28009499,HC  INTRODUCER VASCULAR TUOHY FLEXOR 6FX80CM, 969 
-28009500,HC  INTRODUCER SHEATH THORACIC 20F 30CM," 2,678 "
-28009501,HC  INTRODUCER SHEATH THORACIC 24F 30CM," 2,550 "
-28009502,HC IMP BONE VITOSS MORSEL MACRO 15ML," 7,673 "
-28009503,HC IMP BONE VITOSS MORSEL MACRO 30ML," 12,158 "
-28009504,HC IMP GRAFT MATRIX DURAGEN PLUS 4X5IN," 7,755 "
-28009505,HC IMP GRAFT DURAL MATRIX DURAGEN PLUS 5X7IN," 11,490 "
-28009506,HC  CATHETER GUIDING DEFLECTABLE UNIVERSAL 6X4.25F," 7,438 "
-28009507,HC IMP LASER VENACURE 45CM KIT," 4,973 "
-28009508,HC IMP LASER VENACURE 65CM KIT," 7,784 "
-28009511,HC IMP PIN STEINMAN 7/64X9, 143 
-28009512,HC IMPL PACEMAKER DUAL RR ENRHYTHM," 26,600 "
-28009513,HC IMPL SCREW CANNULATED 6.5 THREAD 16X30MM," 2,420 "
-28009514,HC IMPL SCREW CANNULATED 6.5 THREAD 16X35MM," 2,261 "
-28009515,HC IMPL SCREW CANNULATED 6.5 THREAD 16X40MM," 2,420 "
-28009516,HC IMPL SCREW CANNULATED 6.5 THREAD 16X45MM," 2,505 "
-28009517,HC IMPL SCREW CANNULATED 6.5 THREAD 16X50MM," 2,420 "
-28009518,HC IMPL SCREW CANNULATED 6.5 THREAD 16X55MM," 2,420 "
-28009519,HC IMPL SCREW CANNULATED 6.5 THREAD 16X60MM," 2,420 "
-28009520,HC IMPL SCREW CANNULATED 6.5 THREAD 16X65MM," 2,420 "
-28009521,HC IMPL SCREW CANNULATED 6.5 THREAD 16X70MM," 2,505 "
-28009522,HC IMPL SCREW CANNULATED 6.5 THREAD 16X75MM," 2,505 "
-28009523,HC IMPL SCREW CANNULATED 6.5 THREAD 16X80MM," 2,505 "
-28009524,HC IMPL SCREW CANNULATED 6.5 THREAD 16X85MM," 2,420 "
-28009525,HC IMPL SCREW CANNULATED 6.5 THREAD 16X90MM," 2,505 "
-28009526,HC IMPL SCREW CANNULATED 6.5 THREAD 16X95MM," 2,505 "
-28009527,HC IMPL SCREW CANNULATED 6.5 THREAD 16X100MM," 2,505 "
-28009528,HC IMPL SCREW CANNULATED 6.5 THREAD 16X105MM," 2,420 "
-28009529,HC IMPL SCREW CANNULATED 6.5 THREAD 16X110MM," 2,420 "
-28009530,HC IMPL SCREW CANNULATED 6.5 THREAD 16X115MM," 2,420 "
-28009531,HC IMPL SCREW CANNULATED 6.5 THREAD 16X120MM," 2,505 "
-28009532,HC IMPL SCREW CANNULATED 6.5 THREAD 16X125MM," 2,542 "
-28009533,HC IMPL SCREW CANNULATED 6.5 THREAD 16X130MM," 2,091 "
-28009534,HC IMPL SCREW CANNULATED 6.5 THREAD 16X135MM," 2,091 "
-28009535,HC IMPL SCREW CANNULATED 6.5 THREAD 16X140MM," 2,542 "
-28009536,HC IMPL SCREW CANNULATED 6.5 THREAD 16X145MM," 2,091 "
-28009537,HC IMPL SCREW CANNULATED 6.5 THREAD 16X150MM," 2,091 "
-28009538,HC IMPL SCREW CANNULATED 6.5 THREAD 32X45MM," 2,542 "
-28009539,HC IMPL SCREW CANNULATED 6.5 THREAD 32X50MM," 2,420 "
-28009540,HC IMPL SCREW CANNULATED 6.5 THREAD 32X55MM," 2,420 "
-28009541,HC IMPL SCREW CANNULATED 6.5 THREAD 32X60MM," 2,420 "
-28009542,HC IMPL SCREW CANNULATED 6.5 THREAD 32X65MM," 2,420 "
-28009543,HC IMPL SCREW CANNULATED 6.5 THREAD 32X70MM," 2,505 "
-28009544,HC IMPL SCREW CANNULATED 6.5 THREAD 32X75MM," 2,505 "
-28009545,HC IMPL SCREW CANNULATED 6.5 THREAD 32X80MM," 2,505 "
-28009546,HC IMPL SCREW CANNULATED 6.5 THREAD 32X85MM," 2,505 "
-28009547,HC IMPL SCREW CANNULATED 6.5 THREAD 32X90MM," 2,505 "
-28009548,HC IMPL SCREW CANNULATED 6.5 THREAD 32X95MM," 2,505 "
-28009549,HC IMPL SCREW CANNULATED 6.5 THREAD 32X100MM," 2,420 "
-28009550,HC IMPL SCREW CANNULATED 6.5 THREAD 32X105MM," 2,420 "
-28009551,HC IMPL SCREW CANNULATED 6.5 THREAD 32X110MM," 2,420 "
-28009552,HC IMPL SCREW CANNULATED 6.5 THREAD 32X115MM," 2,420 "
-28009553,HC IMPL SCREW CANNULATED 6.5 THREAD 32X12MM," 2,420 "
-28009554,HC IMPL SCREW CANNULATED 6.5 THREAD 32X125MM," 2,420 "
-28009555,HC IMPL SCREW CANNULATED 6.5 THREAD 32X130MM," 2,420 "
-28009556,HC IMPL SCREW CANNULATED 6.5 THREAD 32X135MM," 2,091 "
-28009557,HC IMPL SCREW CANNULATED 6.5 THREAD 32X140MM," 2,091 "
-28009558,HC IMPL SCREW CANNULATED 6.5 THREAD 32X145MM," 2,091 "
-28009559,HC IMPL SCREW CANNULATED 6.5 THREAD 32X150MM," 2,420 "
-28009560,HC BUR DRILL BIT QUICK COUPLING 7.3X300MM," 4,297 "
-28009562,HC  GUIDEWIRE INSERTION 2.8MM, 752 
-28009564,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 5MM," 9,420 "
-28009565,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 6MM," 9,420 "
-28009566,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 7MM," 9,420 "
-28009567,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 8MM," 9,420 "
-28009568,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 9MM," 8,888 "
-28009569,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 10MM," 9,420 "
-28009570,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 11MM," 9,420 "
-28009571,HC IMP SPACER CORTICAL CANCELLOUS PARALLEL 12MM," 9,990 "
-28009572,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 5MM," 9,990 "
-28009573,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 6MM," 9,990 "
-28009574,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 7MM," 9,990 "
-28009575,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 8MM," 9,990 "
-28009576,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 9MM," 9,990 "
-28009577,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 10MM," 9,420 "
-28009578,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 11MM," 9,420 "
-28009579,HC IMP SPACER CORTICAL CANCELLOUS LORDOTIC 7DD 12MM," 9,990 "
-28009580,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 5MM," 9,990 "
-28009581,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 6MM," 9,990 "
-28009582,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 7MM," 9,990 "
-28009583,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 8MM," 9,990 "
-28009584,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 9MM," 9,990 "
-28009585,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 10MM," 9,990 "
-28009586,HC IMP SPACER CORTICAL CANCELLOUS CONVEX 11MM," 9,990 "
-28009587,HC  DEVICE RETRIEVAL ALLIGATOR ARD 2MM," 18,750 "
-28009588,HC  DEVICE RETRIEVAL ALLIGATOR ARD 3MM," 18,480 "
-28009589,HC  DEVICE RETRIEVAL ALLIGATOR ARD 4MM," 18,750 "
-28009590,HC  DEVICE RETRIEVAL BODY ARD 5MM," 18,750 "
-28009591,HC COAGULATOR BIPOLAR INLINE 4CM," 10,797 "
-28009592,HC IMPL COCHLEAR HIRES 90K BACKUP," 86,100 "
-28009593,HC IMP EAR PROSTHESIS BELL 2.0MM," 3,086 "
-28009594,HC IMP EAR PROSTHESIS BELL 3.0MM," 3,298 "
-28009595,HC IMP EAR PROSTHESIS BELL 3.25MM," 3,086 "
-28009596,HC IMP EAR PROSTHESIS BELL 3.5MM," 3,086 "
-28009597,HC  CATHETER GUIDING CVS FL 3.5 6F, 468 
-28009598,HC  CATHETER GUIDING CVS FL 4 W/O SH 6F," 1,560 "
-28009599,HC  CATHETER GUIDING CVS FL 4.5 6F," 1,560 "
-28009600,HC  CATHETER GUIDING CVS FL 5 W/O SH 6F, 468 
-28009601,HC  CATHETER GUIDING CVS 0SH JL 4.5 6F, 468 
-28009602,HC  CATHETER GUIDING CVS AL .75 6F," 1,560 "
-28009603,HC  CATHETER GUIDING CVS AL 1 W/O SH 6F, 470 
-28009604,HC  CATHETER GUIDING CVS AL 1.5 6F, 470 
-28009605,HC  CATHETER GUIDING CVS AL 2 W/O SH 6F, 468 
-28009606,HC  CATHETER GUIDING CVS AL 2 W/ SH 6F, 468 
-28009607,HC  CATHETER GUIDING CVS 0SH CLS3 RUNWAY 6F, 470 
-28009608,HC  CATHETER GUIDING CVS CLS3.5 6F, 470 
-28009609,HC  CATHETER GUIDING CVS CLS4 6F, 468 
-28009610,HC  CATHETER GUIDING CVS JR4 0SH 6F," 1,560 "
-28009611,HC  CATHETER GUIDING CVS SHEATH JR 4 6F, 468 
-28009612,HC  CATHETER GUIDING CVS AR 1 W/O SH 6F, 468 
-28009613,HC  CATHETER GUIDING CVS AR 2 W/O SH 6F," 1,560 "
-28009614,HC  CATHETER GUIDING CVS MP1 W/O SH 6F, 470 
-28009615,HC  CATHETER GUIDING CVS IM W/O SH 6F, 470 
-28009616,HC  CATHETER GUIDING CVS IM 90CM 6F, 468 
-28009617,HC  CATHETER GUIDING CVS LCB W/O SH 6F, 470 
-28009618,HC  CATHETER GUIDING CVS 6F 0.070IN, 470 
-28009619,HC IMP SEALANT COSEAL LIQUID 4ML," 4,718 "
-28009620,HC  GUIDEWIRE PTCA CONFIANZAPRO 180CM," 1,275 "
-28009621,HC  GUIDEWIRE PTCA ASAHI 14IN 300CM," 1,105 "
-28009622,HC IMP ICD OTHER IC CRT SENTRY 40CC," 75,250 "
-28009623,HC FORCEP CUTTING 5MMX33CM," 3,953 "
-28009624,HC IMP NEUROSTIMULATOR GENERATOR DURAL CH KINETRA KIT," 88,750 "
-28009625,HC IMP RESERVOIR CSF BUR HOLE 1.5CM," 2,791 "
-28009626,HC  INTRODUCER SHEATH 5F 25X35CM, 300 
-28009627,HC IMP STENT GRAFT TAA TAG 31X10," 69,000 "
-28009628,HC IMP STENT GRAFT TAA TAG 26X10," 69,000 "
-28009629,HC IMP STENT GRAFT TAA TAG 28X10," 74,250 "
-28009630,HC IMP STENT GRAFT TAA TAG 28X15," 69,000 "
-28009631,HC IMP STENT GRAFT TAA TAG 31X15," 69,000 "
-28009632,HC IMP STENT GRAFT TAA TAG 34X10," 69,000 "
-28009633,HC IMP SHUNT LUMBAR PERITONEAL SYS 80CM," 5,061 "
-28009634,HC IMP STENT GRAFT TAA TAG 40X15," 69,000 "
-28009635,HC IMP STENT BILIARY VISIPRO 7X17X80," 7,958 "
-28009637,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X32MM," 1,198 "
-28009638,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X65MM," 1,368 "
-28009639,HC IMP SCREW BONE THREAD 6.5X150MM 16MM, 433 
-28009640,HC IMP BOLT LOCKING 4.9X40MM," 1,701 "
-28009641,HC IMPL PLATE TIBAL PROXIMAL RIGHT 4 HOLE 3.5X81MM," 10,215 "
-28009642,HC BUR DRILL BIT CALIB 3 FLUT QC 5.0X330MM," 2,610 "
-28009643,HC  CATHETER GUIDING VISTA BRITE TIP 6F IM 90CM, 442 
-28009645,HC  CATHETER PTA 12MM 75X4CM 6.5F," 2,980 "
-28009646,HC  CATHETER PTA 75X2CM 12MM 6.5F," 2,980 "
-28009647,HC  CATHETER PTA 14MM 75X2CM 6.5F," 2,980 "
-28009648,HC IMPL PLATE DCP BROAD 7 HOLE 4.5X119MM," 1,955 "
-28009649,HC  CATHETER PTA 14MM 75X4CM 6.5F," 2,980 "
-28009650,HC  CATHETER PTA 16MM 75X2CM 6.5F," 3,236 "
-28009651,HC  CATHETER PTA 16MM 75X4CM 6.5F," 3,236 "
-28009652,HC  CATHETER PTA 18MM 75X2CM 7F," 3,236 "
-28009653,HC  CATHETER PTA 20MM 75X2CM 7F," 3,491 "
-28009654,HC IMPL PLATE DCP BROAD 8 HOLE 4.5X135MM," 2,493 "
-28009655,HC  CATHETER PTA 120X4CM 14MM 6.5F," 2,980 "
-28009656,HC  CATHETER DILATATION PTA 16X4X120CM," 3,236 "
-28009657,HC  CATHETER DILATATION PTA CNQ 12X2X75," 2,129 "
-28009658,HC IMP EAR PROSTHESIS AERIAL 5.25MM," 3,298 "
-28009659,HC IMP EAR PROSTHESIS AERIAL 5.50MM," 3,298 "
-28009660,HC IMPL SCREW DHS LAG 85MM," 3,019 "
-28009661,HC IMP EAR PROSTHESIS AERIAL 6.0MM," 3,298 "
-28009662,HC IMP EAR PROSTHESIS AERIAL 6.50MM," 3,298 "
-28009663,HC IMP EAR PROSTHESIS AERIAL 7.0MM," 3,298 "
-28009664,HC IMPL SCREW DHS LAG 100MM," 3,125 "
-28009665,HC IMPL SCREW CANNULATED LARGE 110MM," 2,420 "
-28009666,HC  CATHETER PEZZAR 10F, 128 
-28009668,HC  GUIDEWIRE PERIPHERAL 200X55CM 10MM," 3,875 "
-28009669,HC  GUIDEWIRE PERIPHERAL SYNCHRO 1IN 300CM," 4,563 "
-28009670,HC IMPL SCREW PERIPROSTHETIC 5X8MM," 2,069 "
-28009671,HC IMPL SCREW PERIPROSTHETIC 5X10MM," 2,069 "
-28009672,HC IMPL SCREW PERIPROSTHETIC 5X12MM," 2,069 "
-28009673,HC IMPL PLATE LCP BROAD CURVED 18 HOLE 4.5X336MM," 9,127 "
-28009674,HC IMP CABLE CRIMP 1.7X750MM," 4,599 "
-28009675,HC IMP PIN THREAD CERCLAGE 4.5MM," 1,541 "
-28009676,HC  CATHETER INFUSION PERPH SYS 80CM 8F," 14,963 "
-28009677,HC  CATHETER THROMBOLYSIS SYSTEM 30X80CM," 14,963 "
-28009678,HC  CATHETER PTA 90CM 5X20MM 4F," 2,380 "
-28009679,HC  CATHETER GUIDING VISTA BRITE TIP 7F MPA 1, 451 
-28009680,HC IMP ICD DUAL VITALITY 30ML," 81,000 "
-28009681,HC  CATHETER GUIDING VISTA BRITE TIP 7F JL 3.5, 442 
-28009682,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X75MM," 1,259 "
-28009683,HC IMPL PLATE TIBAL PROXIMAL LEFT 10 HOLE 3.5X159MM," 11,370 "
-28009684,HC IMPL COIL NEURO HELICAL 30CM 14MM 360D," 14,086 "
-28009685,HC IMP PORT DUAL LUMEN 10F," 3,222 "
-28009686,HC IMPL COIL NEURO HELICAL SRE STD 30CM 10MM," 14,086 "
-28009687,HC IMPL COIL NEURO HELICAL SRE STD 30CM 9MM," 14,086 "
-28009688,HC IMPL COIL NEURO HELICAL SRE STD 20CM 8MM," 14,044 "
-28009689,HC IMPL COIL NEURO HELICAL SRE STD 20CM 7MM," 14,044 "
-28009690,HC IMPL COIL NEURO HELICAL SRE STD 15CM 6MM," 14,015 "
-28009691,HC IMPL COIL NEURO HELICAL SRE STD 10CM 5MM," 13,769 "
-28009692,HC IMPL COIL NEURO DETACHABLE 8CM 4MM 360D," 13,585 "
-28009693,HC IMPL COIL NEURO HELICAL SRE SOFT 30CM 8MM," 14,086 "
-28009694,HC IMPL COIL NEURO DETACHABLE 20CM 7MM 360D," 14,044 "
-28009695,HC IMPL COIL NEURO HELICAL SRE SOFT 15CM 7MM," 14,015 "
-28009696,HC IMPL COIL NEURO HELICAL 10CM 6MM 360D," 13,769 "
-28009697,HC IMPL COIL NEURO HELICAL SRE SOFT 10CM 5MM," 14,303 "
-28009698,HC IMPL COIL NEURO HELICAL SRE SOFT 8CM 4MM," 14,092 "
-28009699,HC IMPL COIL NEURO HELICAL SRE SOFT 6CM 3MM," 13,325 "
-28009700,HC IMPL COIL NEURO HELICAL 15CM 6MM ULTRASOFT," 18,620 "
-28009701,HC IMPL COIL NEURO DETACHABLE SRE 10CM 6MM 360D," 13,769 "
-28009702,HC IMPL COIL NEURO DETACHABLE SRE 15CM 5MM 360D," 14,015 "
-28009703,HC IMPL COIL NEURO HELICAL SRE 10CM 5MM 360D," 13,769 "
-28009704,HC IMPL COIL NEURO HELICAL 8CM 4MM 360D," 13,585 "
-28009705,HC IMPL COIL NEURO HELICAL 6CM 4MM ULTRASOFT," 13,325 "
-28009706,HC IMPL COIL NEURO HELICAL SRE 8CM 3MM 360D," 13,585 "
-28009707,HC IMPL COIL NEURO HELICAL SRE 6CM 3MM 360D," 13,325 "
-28009708,HC IMPL COIL NEURO DETACHABLE ANEUR 4CM 3X360D," 17,373 "
-28009709,HC IMPL COIL NEURO HELICAL SRE 6CM 2MM 360D," 13,325 "
-28009710,HC IMPL COIL NEURO HELICAL SRE 4CM 2MM 360D," 13,106 "
-28009711,HC IMP ENDURA SUBSTITUTE 5X6CM," 2,890 "
-28009712,HC IMP STENT ESOPHAGEAL POLYFLEX 18-20X13," 13,875 "
-28009713,HC  CATHETER ASPIRATION 0.080IN 145CM 7F," 4,038 "
-28009714,HC IMP ENDOPROSTHESIS TIPS 8MM 5X2CM," 26,376 "
-28009715,HC  GUIDEWIRE INFUSION NITINOL TP 18IN 80CM, 482 
-28009716,HC IMP BONE GRAFT INFUSE LARGE 18X26MM," 35,152 "
-28009717,HC IMP BONE GRAFT INFUSE LARGE 14X20MM," 24,157 "
-28009718,HC IMP GRAFT BONE INFUSE LARGE II 18X26MM," 35,152 "
-28009719,HC IMP ENDURA SUBSTITUTE 6X10CM," 5,610 "
-28009720,HC IMP ENDURA SUBSTITUTE 4X4CM," 2,091 "
-28009721,HC  INTROD PERFORMER," 1,252 "
-28009722,HC IMP SCREW ACUTRAK MINI 26.0MM," 2,805 "
-28009723,HC IMP GENERATOR NEUROSTIMULATOR PULSE DUAL PIN," 81,635 "
-28009724,HC IMP VALVE AORTIC MASTERS HP 23MM," 30,331 "
-28009725,HC IMPL COIL DETACHABLE ANEUR 2CM 2MM," 10,364 "
-28009726,HC IMP BONE GRAFT INFUSE MEDIUM 16X23MM," 34,251 "
-28009727,HC IMPL PLATE DVR A STD LEFT," 8,028 "
-28009728,HC IMPL PLATE DVR AN NARROW LEFT," 8,028 "
-28009729,HC IMPL PLATE DVR AN NARROW RIGHT," 8,028 "
-28009730,HC IMPL PLATE DVR A STD RIGHT," 8,028 "
-28009731,HC IMPL PLATE DVR AS SHORT LEFT," 8,028 "
-28009732,HC IMPL PLATE DVR AS SHORT RIGHT," 7,508 "
-28009733,HC IMPL PLATE DVR AW WIDE LEFT," 7,574 "
-28009734,HC IMPL PLATE DVR AW WIDE RIGHT," 7,574 "
-28009735,HC IMPL PLATE DVR AX EXTENDED LEFT," 7,574 "
-28009736,HC IMPL PLATE DVR AX ENTENDED RIGHT," 7,574 "
-28009737,HC IMPL PLATE DISTAL VOLAR RADIUS AXX EXTRA EXTENDED LT," 10,125 "
-28009738,HC IMPL PLATE DISTAL VOLAR RADIUS AXX EXTRA EXTENDED RT," 10,125 "
-28009739,HC IMP PIN CARBON FIBER 2.5X10 MM, 865 
-28009748,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X28MM," 1,445 "
-28009755,HC IMPL PLATE HOCKEY STICK RIGHT," 5,423 "
-28009756,HC IMPL SCREW CANCELLOUS 2.5X10MM, 865 
-28009757,HC IMPL SCREW CANCELLOUS 2.5X30MM, 865 
-28009758,HC IMP PEG THREAD 2.5X10MM, 865 
-28009759,HC IMPL PLATE FRAGMENT STRAIGHT HIGH STRENGTH 5.6X50MM," 5,423 "
-28009760,HC IMP PEG THREAD 2.5X12MM FP-12, 865 
-28009763,HC IMP GRAFT GORTEX REG TAPER 4-7X40MM," 4,488 "
-28009764,HC  CATHETER BALLOON ULTRASOUND CUFF, 148 
-28009765,HC IMPL SCREW VIPER 6X35," 9,188 "
-28009766,HC IMPL SCREW VIPER 6X40," 9,188 "
-28009767,HC IMPL SCREW VIPER 6X45," 9,188 "
-28009768,HC IMPL SCREW VIPER 6X50," 9,188 "
-28009770,HC IMP GRAFT FIBULA SHAFT 10CM," 5,058 "
-28009771,HC IMPL PLATE MINI RIGHT L 18MM," 1,338 "
-28009772,HC IMPL PLATE MINI LEFT L 18MM," 1,420 "
-28009774,HC ELECTRODE VELOCITY ENSITE 6PR," 7,838 "
-28009775,HC IMPL PLATE MINI STRAIGHT 24MM," 1,420 "
-28009776,HC IMP ICD DUAL IC PCTRL 35CC," 84,000 "
-28009777,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2X6MM," 9,142 "
-28009778,HC  CATHETER ANGIOPLASTY BLLN ANGIOPL 2.25X6MM," 6,815 "
-28009779,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2.5X6MM," 9,142 "
-28009780,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2.75X6," 6,815 "
-28009781,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3X6MM," 9,142 "
-28009782,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.25X6," 9,142 "
-28009783,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.5X6MM," 9,142 "
-28009784,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 4X6MM," 9,142 "
-28009785,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2.25X10," 6,815 "
-28009786,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA FLEX 2.5X10," 6,815 "
-28009787,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2.75X10," 6,812 "
-28009788,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3X10MM," 6,815 "
-28009789,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.25X10," 9,142 "
-28009790,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.5X10," 6,815 "
-28009791,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.75X10," 6,815 "
-28009792,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 4X10MM," 9,142 "
-28009793,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2X15MM," 6,815 "
-28009794,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 2.5X10," 6,815 "
-28009795,HC  CATHETER ANGIOPLASTY BLLN FLEXTOME 2.75X15MM," 6,815 "
-28009796,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3X15MM," 8,712 "
-28009797,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.25X15," 6,800 "
-28009798,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA MONO 3.5X15," 6,815 "
-28009799,HC  CATHETER ANGIOPLASTY BLLN CUT PTCA RX 3.75X15MM," 6,800 "
-28009800,HC IMPL PLATE MAXI SHORT 29MM 4HOLE," 2,220 "
-28009801,HC  GUIDEWIRE CORONARY STR TIP GRAND SLAM 180X3CM, 697 
-28009802,HC  GUIDEWIRE CORONARY STR TIP GRAND SLAM 300X3CM, 139 
-28009803,HC IMP SEALANT DURASEAL 5ML," 1,139 "
-28009804,HC IMP RESERVOIR CSF NEONATE 3CM," 2,695 "
-28009805,HC IMP SHUNT DELTA SMALL 0.5X23X1 KIT," 9,960 "
-28009806,HC IMP SHUNT DELTA SMALL 1.0X23X1 KIT," 13,426 "
-28009807,HC IMP SHUNT DELTA SMALL 1.5X23X12 KIT," 13,426 "
-28009808,HC IMP SHUNT DELTA 1.5X23X105 KIT," 10,733 "
-28009809,HC IMP SHUNT DELTA REGULAR 1.0X23 KIT," 9,945 "
-28009810,HC IMP SHUNT DELTA REGULAR 2.0X23 KIT," 10,643 "
-28009811,HC IMP VALVE STRATA NSC REG NO SIPHON," 24,230 "
-28009812,HC IMPL SCREW CRANIOFACIAL MICRO 1.2MMX6MM, 511 
-28009813,HC  CATH ABLAT 115CM 4MM 7F," 5,525 "
-28009814,HC IMPL SCREW CRANIOFACIAL MICRO 1.2MMX8MM, 511 
-28009815,HC IMPL SCREW CRANIOFACIAL MICRO 1.5MMX3MM, 595 
-28009817,HC IMPL SCREW MINI 2.0MMX4MM, 480 
-28009818,HC  CATHETER ULTRAFLOW DIRECT MICRO 3F 1.5F 42CM," 4,437 "
-28009819,HC  CATHETER NEURO GUIDING 25X165X170X170CM," 5,330 "
-28009820,HC  CATHETER NEURO PERPH 45D 150X155CM," 5,993 "
-28009821,HC  CATHETER NEURO PERPH 90D 150X155CM," 5,993 "
-28009822,HC IMP EMBOLIC LIQUID ONYX SYS 18," 18,060 "
-28009823,HC IMP EMBOLIC LIQUID ONYX SYS 34," 18,060 "
-28009824,HC IMP STENT BILIARY PROTEGE 10X40X80," 9,188 "
-28009825,HC IMPL SCREW MINI 2.0MMX12MM, 511 
-28009826,HC IMP STENT BILIARY PROTEGE 12X40X80," 10,140 "
-28009827,HC IMP STENT BILIARY PROTEGE 12X60X80," 9,188 "
-28009828,HC IMP STENT BILIARY PROTEGE 12X80X80," 10,140 "
-28009829,HC IMP STENT BILIARY PROTEGE 14X20X80," 9,188 "
-28009830,HC IMP STENT BILIARY PROTEGE 14X40X80," 9,188 "
-28009831,HC IMP STENT BILIARY PROTEGE 14X80X80," 10,140 "
-28009833,HC RESTRAINT CERVICAL 4 POST," 4,038 "
-28009834,HC IMP VALVE AORTIC PERICARDIAL MAGNA 19MM," 38,318 "
-28009835,HC IMP VALVE AORTIC PERICARDIAL MAGNA 21MM," 38,318 "
-28009836,HC IMP VALVE AORTIC PERICARDIAL MAGNA 23MM," 38,318 "
-28009837,HC IMPL SCREW MAXI SAFETY 2.7MMX8MM, 595 
-28009839,HC IMP ANCHOR SUTURE MENISCAL FAST FIX STR," 2,607 "
-28009841,HC IMP GRAFT VENAFLO STEPPED 20CM," 6,877 "
-28009842,HC IMP GRAFT VENAFLO STEPPED 25CM," 6,877 "
-28009843,HC IMP GRAFT VENAFLO STEPPED 30CM," 6,877 "
-28009844,HC IMP GRAFT VENAFLO STEPPED 40CM," 6,877 "
-28009845,HC IMP GRAFT CARBOFLO STEPPED 45CM," 5,194 "
-28009846,HC SHEAR COAGULATING CURVED 5X23CM," 3,121 "
-28009852,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 7.0D," 1,275 "
-28009853,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 7.5D," 1,275 "
-28009854,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 8.0D," 1,275 "
-28009855,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 8.5D," 1,275 "
-28009856,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 9.0D," 1,275 "
-28009857,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 9.5D," 1,275 "
-28009858,HC HEART STRING III 3.8MM," 4,535 "
-28009859,HC HEART STRING III 4.3MM," 4,535 "
-28009860,HC VACUUM STD BLDE ACROBAT SYS," 12,913 "
-28009861,HC IMP PORT XPORT ISP 6F KIT," 2,933 "
-28009862,HC BLADE SKIMMER CURVED 2.9X18CM," 3,124 "
-28009863,HC  CATHETER CORONARY GUIDING JL3.5 STD 0.070I, 451 
-28009864,HC  CATHETER CORONARY GUIDING JL 4 0.070IN 6F, 451 
-28009865,HC  CATHETER CORONARY GUIDING JL 5 0.070IN 6F, 451 
-28009866,HC  CATHETER CORONARY GUIDING BALANCED GRN 6F, 451 
-28009867,HC  CATHETER CORONARY GUIDING AL1 70IN 6F, 451 
-28009868,HC  CATHETER CORONARY GUIDING AL2 70IN 6F, 451 
-28009869,HC  CATHETER CORONARY GUIDING EBU3 0.070IN 6F, 451 
-28009870,HC  CATHETER CORONARY GUIDING EBU3.5 0.070IN 6, 451 
-28009871,HC  CATHETER CORONARY GUIDING EBU3.75 0.070IN, 451 
-28009872,HC  CATHETER CORONARY GUIDING EBU4 0.070IN 6F, 451 
-28009873,HC  CATHETER CORONARY GUIDING JR 4 0.070IN 6F, 451 
-28009874,HC  CATHETER CORONARY GUIDING JR4 STD, 451 
-28009875,HC  CATHETER CORONARY GUIDING AR1 0.070IN 6F, 451 
-28009876,HC  CATHETER GUIDING 3 100CM 6F SHERPA, 451 
-28009877,HC  CATHETER GUIDING BALLOON SHERPA NX MP1 6F, 451 
-28009878,HC  CATHETER GUIDING HKY STK I 100CM 6F, 451 
-28009879,HC  CATHETER GUIDING BALLOON SHERPA NX LCB 6F, 451 
-28009880,HC  CATHETER GUIDING BALLOON SHERPA NX RCB 6F, 451 
-28009881,HC  CATHETER GUIDING SHERPA NX BAL GDE 6F 0.070I 90, 451 
-28009882,HC BLADE SHAVER LARYNGEAL ANG SKIM 4MM," 2,959 "
-28009883,HC  GUIDEWIRE CORONARY STRAIGHT TIP 190X3CM, 798 
-28009884,HC IMP WASHER SS 13MM, 268 
-28009885,HC  CATHETER NEURO SINGLE LUMEN MICRO 150CM," 4,038 "
-28009886,HC  CATHETER GUIDING BALLOON CONCTRNC 8F," 7,438 "
-28009887,HC IMP STENT NEUROFORM3 2.5X15," 33,605 "
-28009888,HC IMP STENT NEUROFORM3 3X15," 33,605 "
-28009889,HC IMP STENT NEUROFORM3 3X20," 21,700 "
-28009890,HC IMP STENT NEUROFORM3 3.5X20," 33,605 "
-28009891,HC IMP STENT NEUROFORM3 4X20," 35,199 "
-28009892,HC IMP STENT NEUROFORM3 4.5X20," 33,605 "
-28009893,HC IMP STENT NEUROFORM3 3.5X30," 33,605 "
-28009894,HC IMP STENT NEUROFORM3 4X30," 33,605 "
-28009895,HC IMP STENT NEUROFORM3 4.5X30," 33,605 "
-28009896,HC  CATHETER BALLOON RETRIEVAL EXTRAC 9-12," 1,233 "
-28009897,HC  CATHETER BALLOON RETRIEVAL EXTRAC 12-15," 1,233 "
-28009898,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 4X12X80," 9,000 "
-28009899,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 5X15X80," 9,000 "
-28009900,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 6X15X80," 9,000 "
-28009901,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 5X18X80," 9,971 "
-28009902,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 6X18X80," 9,000 "
-28009903,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 7X18X80," 9,000 "
-28009904,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 6X24X80," 9,000 "
-28009905,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 7X24X80," 9,000 "
-28009906,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 4X12X135," 9,971 "
-28009907,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 5X15X135," 9,971 "
-28009908,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 6X15X135," 9,971 "
-28009909,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 5X18X135," 9,971 "
-28009910,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 6X18X135," 9,971 "
-28009911,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 7X18X135," 9,971 "
-28009912,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 6X24X135," 9,000 "
-28009913,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 7X24X135," 9,000 "
-28009914,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2.5X9MM," 8,458 "
-28009915,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2.75X9MM," 8,110 "
-28009916,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3X9MM," 8,110 "
-28009917,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2.5X15MM," 7,626 "
-28009918,HC IMP STENT NEURO WINGSPAN 3X15," 38,968 "
-28009919,HC IMP LEAD TACARHYTH 41CM," 18,750 "
-28009920,HC IMP LEAD CORONARY 52CM," 18,750 "
-28009921,HC IMP STENT GRAFT TRACH FLUENCY 8X40," 14,141 "
-28009922,HC IMP STENT GRAFT TRACH FLUENCY 8X60," 21,372 "
-28009923,HC IMP STENT GRAFT TRACH FLUENCY 9X40," 16,392 "
-28009924,HC IMP STENT GRAFT TRACH FLUENCY 10X60," 20,353 "
-28009925,HC IMPL SCREW CANNULATED SHORT THREAD 32MM," 1,837 "
-28009926,HC IMPL SCREW CANNULATED SHORT THREAD 34MM," 1,902 "
-28009927,HC IMPL SCREW CANNULATED SHORT THREAD 36MM," 1,902 "
-28009928,HC IMPL SCREW CANNULATED SHORT THREAD 40MM," 1,902 "
-28009929,HC IMP SHUNT INTRA CORONARY, 126 
-28009930,HC IMP SHUNT INTRA CORONARY 2.5X14MM, 126 
-28009931,HC IMP SHUNT INTRA CORONARY CLEARVIEW 14MM, 126 
-28009932,HC IMP SHUNT INTRA CORONARY 1.0MM, 126 
-28009933,HC IMP SHUNT INTRA CORONARY 2X14MM, 126 
-28009934,HC IMPL SCREW CANNULATED SHORT THREAD 42MM," 1,902 "
-28009936,HC  CATHETER GUIDING ATTAIN SFT TIP SYSTEM," 4,590 "
-28009937,HC BUR DRILL BIT CANNULATED 2.7MM," 4,289 "
-28009938,HC IMPL SCREW CANNULATED SHORT THREAD 50MM," 1,902 "
-28009939,HC IMP TISSUE SCLERA WHOLE," 4,123 "
-28009940,HC IMP KNEE LINER ACETABULAR BIPOLAR 28ID 55MM," 2,758 "
-28009941,HC  CATH ABLAT DCRV 3.5MM 7.5F," 15,998 "
-28009942,HC  CATHETER ABLATION 115CM 3.5X5.2X2MM," 15,998 "
-28009943,HC  CATHETER ABLATION J TYPE 3.5X5.2X2MM," 15,998 "
-28009944,HC  CATHETER ABLATION DCRV 3.5MM 7F," 8,333 "
-28009945,HC  CATHETER ABLATION 115CM 2-5-2X3.5MM," 8,333 "
-28009946,HC  CATH ABLAT JTYP 2-5.2X3.5MM," 8,333 "
-28009948,HC IMPL PLATE T 8HOLE 148MM," 3,213 "
-28009949,HC IMP CLIP ANEURYSM BAYONET STRGHT KEES SLIMLINE 20MM," 3,562 "
-28009950,HC IMP PUMP INFUSION NERVE BLOCK 400ML," 2,550 "
-28009951,HC RESTRAINT CERVICAL THORACIC," 6,647 "
-28009952,HC GASTRIC BYPASS KIT," 15,060 "
-28009953,HC IMPL DERMAMATRIX THIN 1X2," 1,020 "
-28009954,HC IMPL DERMAMATRIX THIN 2X4," 1,896 "
-28009955,HC IMPL DERMAMATRIX THICK 4X7," 7,786 "
-28009956,HC IMPL DERMAMATRIX THICK 4X12," 12,398 "
-28009957,HC IMPL DERMAMATRIX THICK 6X12," 18,525 "
-28009958,HC IMPL DERMAMATRIX THICK 4X16," 15,578 "
-28009959,HC SHEAR COAGULATING HARMONIC CVD 14CM," 3,427 "
-28009960,HC IMP CEMENT BONE PALECOS ANTB 40GRN," 2,567 "
-28009961,HC IMP CEMENT BONE PALECOS SINGLE 40GRN, 628 
-28009963,HC  INTRODUCER MICROPUNCTURE ECHOGENIC TIP 5FX40CM, 323 
-28009964,HC  CATHETER DILATATION PTCA 2X8MM," 2,117 "
-28009965,HC  CATHETER DILATATION PTCA 2.25X8MM," 2,117 "
-28009966,HC  CATHETER DILATATION PTCA 2.5X8MM," 2,117 "
-28009967,HC  CATHETER DILATATION PTCA 2.75X8MM," 2,117 "
-28009968,HC  CATHETER DILATATION PTCA 3X8MM," 2,117 "
-28009969,HC  CATHETER DILATATION PTCA 3.25X8MM," 2,117 "
-28009970,HC  CATHETER DILATATION PTCA 3.5X8MM," 2,117 "
-28009971,HC  CATHETER DILATATION PTCA 3.75X8MM," 2,117 "
-28009972,HC  CATHETER DILATATION PTCA 4X8MM," 2,117 "
-28009973,HC  CATHETER DILATATION PTCA 4.5X8MM," 2,117 "
-28009974,HC  CATHETER DILATATION PTCA 5X8MM," 2,117 "
-28009975,HC  CATHETER DILATATION PTCA 2X12MM," 2,117 "
-28009976,HC  CATHETER DILATATION PTCA 2.25X12MM," 2,117 "
-28009977,HC  CATHETER DILATATION PTCA 2.5X12MM," 2,117 "
-28009978,HC  CATHETER DILATATION PTCA 2.75X12MM," 2,117 "
-28009979,HC  CATHETER DILATATION PTCA 3X12MM," 2,117 "
-28009980,HC  CATHETER DILATATION PTCA 3.25X12MM," 2,117 "
-28009981,HC  CATHETER DILATATION PTCA 3.5X12MM," 2,117 "
-28009982,HC  CATHETER DILATATION PTCA 3.75X12MM," 2,117 "
-28009983,HC  CATHETER DILATATION PTCA 4X12MM," 2,117 "
-28009984,HC  CATHETER DILATATION PTCA 4.5X12MM," 2,117 "
-28009985,HC  CATHETER DILATATION PTCA 5X12MM," 2,117 "
-28009989,HC  INTRODUCER SHEATH 6F 45CM, 527 
-28009990,HC IMP LEAD DEFIBRILLATOR ENDOTAK SQ ARRAY XP," 18,750 "
-28009991,HC IMP CEMNET BONE RADIOPAQUE KIT," 1,919 "
-28009992,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.2X30," 2,738 "
-28009993,HC IMPL TISSUE ALLOGRAFT PERICARDIUM 4X8CM," 13,500 "
-28009994,HC IMP CLIP ANEURYSM STRAIGHT KEES SLIMLINE 20MM," 2,933 "
-28009995,HC  GUIDEWIRE STEERABLE COUGAR XT 14IN 190CM, 680 
-28009996,HC IMP CLIP ANEURYSM REINF KEES SLIMLINE 0 DEG 6X6MM," 3,091 "
-28009997,HC IMP CLIP ANEURYSM SUNDT 4X5MM," 5,525 "
-28009998,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 1.5X9MM," 8,458 "
-28009999,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2X9MM," 8,458 "
-28010000,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2.25X9MM," 8,110 "
-28010001,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3.25X9MM," 8,110 "
-28010002,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3.5X9MM," 8,110 "
-28010003,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3.75X9MM," 8,110 "
-28010004,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 4X9MM," 8,110 "
-28010005,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 1.5X15MM," 8,110 "
-28010006,HC IMP CLIP ANEURYSM SUNDT 4X3MM," 5,525 "
-28010007,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2X15MM," 8,110 "
-28010008,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2.25X15," 7,613 "
-28010009,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 2.75X15," 8,458 "
-28010010,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3X15MM," 7,950 "
-28010011,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3.25X15," 8,458 "
-28010012,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3.5X15MM," 7,950 "
-28010013,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 3.75X15," 8,458 "
-28010014,HC  CATHETER BALLOON PTA GATEWAY SMICMPLNT 4X15MM," 7,950 "
-28010015,HC IMP STENT NEURO WINGSPAN 2.5X15," 40,918 "
-28010016,HC IMP CLIP ANEURYSM FEN STRAIGHT KEES SLIMLINE 12MM," 3,239 "
-28010017,HC IMP STENT NEURO WINGSPAN 3X20," 38,968 "
-28010018,HC IMP STENT NEURO WINGSPAN 3.5X20," 38,968 "
-28010019,HC IMP STENT NEURO WINGSPAN 4X20," 38,968 "
-28010020,HC IMP STENT NEURO WINGSPAN 4.5X20," 38,968 "
-28010021,HC  CATHETER GUIDING VISTA BRITE TIP 6F AL .75, 506 
-28010022,HC IMP BONE CANCELLOUS CHIP 30ML," 3,094 "
-28010023,HC  CATHETER THROMBECTOMY 3MM 90CM 15MM," 9,413 "
-28010024,HC IMP STENT GRAFT BILIARY VIABIL 8X4," 17,605 "
-28010025,HC IMP STENT GRAFT BILIARY VIABIL 8X6," 18,130 "
-28010026,HC IMP STENT GRAFT BILIARY VIABIL 8X8," 18,130 "
-28010027,HC IMP STENT GRAFT BILIARY VIABIL 10X10X40," 18,130 "
-28010028,HC IMP ENDOPROSTHESIS BILI 8MM 6CM 40CM," 18,130 "
-28010029,HC IMP ENDOPROSTHESIS BILI 8MM 8X40CM," 18,130 "
-28010030,HC IMP ENDOPROSTHESIS BILI 8MM 10X40CM," 18,130 "
-28010031,HC IMP STENT GRAFT BILIARY VIABIL WITH HOLES 10X6X2," 18,130 "
-28010032,HC IMP ENDOPROSTHESIS BILI 10MM 8X40CM," 18,130 "
-28010033,HC  GUIDEWIRE STIFF SHAFT ANGLED TIP 180CM, 274 
-28010034,HC IMPL PLATE TEMPORAL PRECUT," 6,128 "
-28010035,HC GELPORT LAPAROSCOPIC SYS 120MM," 3,825 "
-28010036,HC WAND SUPERTURBO 70D," 2,134 "
-28010037,HC WAND SUPERTURBO 90D," 2,176 "
-28010038,HC IMP PLUG FISTULA ANAL," 8,126 "
-28010039,HC  LEAD CATH DELV TRNSVNS 59CM," 4,675 "
-28010040,HC  CATHETER GUIDING DEFLECTABLE 3830-59CM," 6,800 "
-28010041,HC  CATHETER ACCUVU OMNI 5FX.035X100X20, 978 
-28010042,HC  CATHETER MICRO EXCELSIOR PRESHAPED 90D 6CM 1.7F," 6,472 "
-28010043,HC IMP ANCHOR CORKSCREW FULLY THREAD 5.5MM, 485 
-28010044,HC IMPL COIL EMBOLIC HELICAL 15CM SZ14 8MM," 11,756 "
-28010045,HC IMPL COIL EMBOLIC HELICAL 15CM SZ14 10MM," 11,756 "
-28010046,HC IMPL COIL NEURO HELICAL FINISHING SZ14 10MM 20CM," 12,469 "
-28010047,HC IMPL COIL NEURO EMBOLIZATION 14MM 20CM," 14,179 "
-28010048,HC IMPL COIL NEURO EMBOLIZATION 16MM 20CM," 14,179 "
-28010049,HC IMPL COIL NEURO EMBOLIZATION 18MM 20CM," 14,179 "
-28010050,HC  GUIDEWIRE ANGLED 25IN 260CM, 98 
-28010051,HC IMPL COIL NEURO EMBOLIZATION SOFT 2MM 1CM," 7,833 "
-28010052,HC IMPL COIL NEURO EMBOLIZATION SOFT 2MM 2CM," 7,833 "
-28010053,HC IMPL COIL NEURO EMBOLIZATION SOFT 2MM 4CM," 7,671 "
-28010054,HC IMPL COIL NEURO EMBOLIZATION SOFT 2MM 6CM," 7,833 "
-28010055,HC IMPL COIL NEURO EMBOLIZATION SOFT 3MM 4CM," 7,833 "
-28010056,HC IMPL COIL NEURO EMBOLIZATION SOFT 3MM 6CM," 8,245 "
-28010057,HC IMPL COIL NEURO EMBOLIZATION SOFT 3MM 8CM," 7,671 "
-28010058,HC IMPL COIL NEURO EMBOLIZATION SOFT 4MM 4CM," 8,156 "
-28010059,HC IMPL COIL NEURO EMBOLIZATION SOFT 4MM 6CM," 8,156 "
-28010060,HC IMPL COIL NEURO EMBOLIZATION SOFT 4MM 8CM," 7,575 "
-28010061,HC IMPL COIL NEURO EMBOLIZATION SOFT 5MM 6CM," 8,156 "
-28010062,HC IMPL COIL NEURO EMBOLIZATION SOFT 5MM 8CM," 8,156 "
-28010063,HC IMPL COIL NEURO EMBOLIZATION SOFT 6MM 6CM," 8,156 "
-28010064,HC IMPL COIL NEURO EMBOLIC HELICAL SZ10 8CM 6MM," 7,575 "
-28010066,HC  GUIDEWIRE STEERABLE COUGAR 14IN 190CM, 136 
-28010067,HC  CATHETER ULTRASOUND ACUNAV 90CM 8F," 17,640 "
-28010068,HC  CATHETER GUIDING VISTA BRITE TIP 9F HS 98, 451 
-28010069,HC  CATH PERPH SLVRHWK 8F," 22,365 "
-28010070,HC  GUIDEWIRE ANGLED TIP GOLD 18IN 180CM, 457 
-28010071,HC  GUIDEWIRE ANGLED TIP 35IN 180CM, 337 
-28010072,HC  CATHETER FONTRUNNER XP CTO 6F 140CM," 7,875 "
-28010073,HC  CATH CHROMIC TOT OCCLU MICRO GDE 6F 132X126CM," 2,125 "
-28010074,HC IMP EAR PROSTHESIS PART KURZ 0.2X4.5," 3,086 "
-28010075,HC IMP EAR PROSTHESIS PARTIAL DUSSELDORF 0.2X4MM," 3,298 "
-28010076,HC  GUIDEWIRE PERIPHERAL 45D 18IN 3CM 2MM," 1,024 "
-28010077,HC  CATHETER GLIDECATH 5F AT 65CM, 413 
-28010078,HC  INTRODUCER SHEATH PINNACLE, 149 
-28010079,HC IMP SPACER PLIF 7MM," 18,452 "
-28010080,HC IMP SPACER PLIF 9MM," 18,452 "
-28010081,HC IMP SPACER PLIF 11MM," 18,098 "
-28010082,HC IMP SPACER PLIF13MM," 18,452 "
-28010083,HC IMP SPACER PLIF 15MM," 18,452 "
-28010084,HC IMP SPACER PLIF 17MM," 18,452 "
-28010089,HC IMP KNEE ARTICULAR SURFACE TIBIAL 74 8MM," 4,250 "
-28010090,HC IMP PUTTY BONE MATRIX DBM 10CC," 13,238 "
-28010091,HC IMP SPACER ACF LORDOTIC 5MM," 10,200 "
-28010092,HC IMP SPACER ACF LORDOTIC 6MM," 10,200 "
-28010093,HC IMP SPACER ACF LORDOTIC 7MM," 10,200 "
-28010094,HC IMP SPACER ACF LORDOTIC 8MM," 10,200 "
-28010095,HC IMP SPACER ACF LORDOTIC 9MM," 10,808 "
-28010096,HC IMP SPACER ACF LORDOTIC 10MM," 10,200 "
-28010097,HC IMP SPACER ACF LORDOTIC 11MM," 10,200 "
-28010098,HC IMP SPACER ACF LORDOTIC 12MM," 10,808 "
-28010099,HC IMP SPACER ACF CONVEX 5MM," 10,200 "
-28010100,HC IMP SPACER ACF CONVEX 6MM," 10,200 "
-28010101,HC IMP SPACER ACF CONVEX 7MM," 10,200 "
-28010102,HC IMP SPACER ACF CONVEX 8MM," 10,200 "
-28010103,HC IMP SPACER ACF CONVEX 9MM," 10,200 "
-28010104,HC IMP SPACER ACF CONVEX 10MM," 9,615 "
-28010105,HC IMP SPACER ACF CONVEX 11MM," 9,615 "
-28010106,HC IMP SPACER ACF CONVEX 12MM," 10,200 "
-28010107,HC IMP STENT CAROTID XACT 7X30," 18,713 "
-28010108,HC IMP STENT CAROTID XACT 9X30," 16,875 "
-28010109,HC IMP STENT CAROTID XACT 10X30," 18,338 "
-28010110,HC IMP STENT CAROTID XACT 8-6X30," 18,713 "
-28010111,HC IMP STENT CAROTID XACT 9-7X30," 18,713 "
-28010112,HC IMP STENT CAROTID XACT 10-8X30," 18,713 "
-28010113,HC IMP STENT CAROTID XACT 8-6X40," 18,713 "
-28010114,HC IMP STENT CAROTID XACT 9-7X40," 18,713 "
-28010115,HC IMP STENT CAROTID XACT 10-8X40," 18,713 "
-28010116,HC IMP PROTECTION EMBOLIC EMBOSHELD SYS 6MM," 12,713 "
-28010117,HC  CATHETER OCCLUSION EQUALIZER 11.5X65CM," 1,898 "
-28010118,HC  CATHETER DILATATION ULTRATHIN SDS 7FX10MMX2X75," 1,679 "
-28010119,HC  CATHETER DILATATION ULTRATHIN SDS 7FX10MMX4X75," 1,679 "
-28010120,HC  CATHETER DILATATION ULTRATHIN DIA 8FX12X2X75," 2,667 "
-28010121,HC  CATHETER DILATATION ULTRATHIN DIA 8FX12X4X75," 2,667 "
-28010122,HC  CATH ANGIOGR BALL DILATATION ULTRATHIN 8X2X7CM," 1,679 "
-28010123,HC  CATHETER DILATATION SYMMETRY 2.5X100X4X6," 3,113 "
-28010124,HC  CATHETER DILATATION SYMMETRY 2X100X4X6," 3,113 "
-28010125,HC  CATHETER DILATATION SYMMETRY 2X20X4X6," 3,113 "
-28010126,HC  CATHETER ANGIO BLLN CUTTING OTW 2.5X1.5X140CM," 6,800 "
-28010127,HC  CATHETER ANGIO BLLN CUTTING OTW 2X1.5X140CM," 6,800 "
-28010128,HC  CATHETER BALLOON CUTTING OTW PCB 3X1.5," 6,800 "
-28010129,HC  CATHETER BALLOON CUTTING OTW PCB 4X1.5," 6,800 "
-28010130,HC  CATHETER BALLOON PERIPHERAL OTW CUT 135CM," 6,406 "
-28010131,HC IMP STENT BILIARY SENTINOL 7X39X135," 9,000 "
-28010132,HC IMP STENT BILIARY SENTINOL 7X78X136," 9,000 "
-28010133,HC IMP STENT BILIARY EXPRESS 7X30X75," 9,143 "
-28010134,HC IMP STENT BILIARY EXPRESS 8X30X75," 9,143 "
-28010135,HC IMP SPACER X STOP 10MM," 27,650 "
-28010136,HC IMP SPACER X STOP 12MM," 27,650 "
-28010137,HC  CATHETER OCCLUSION EQUALIZER 27X65CM," 2,253 "
-28010138,HC  CATHETER BALLOON SIZING PTS 30.0MM 4.0CM 8F," 1,924 "
-28010139,HC  CATHETER BALLOON INTRDCR SHAFT GW 40MM," 1,924 "
-28010140,HC  CATHETER BALLOON SIZING 25.0MM 3.0CM 8F," 2,125 "
-28010142,HC IMP DEVICE EMBOLIC PROTECTION SPIDER 6X320CM," 9,975 "
-28010143,HC  GUIDEWIRE PERIPHERAL 35IN 80CM 1.5MM, 98 
-28010145,HC  CATHETER PTA 22MM 75X4CM 10F," 4,046 "
-28010146,HC  CATHETER PTA 24MM 75X4CM 10F," 4,208 "
-28010147,HC  CATHETER PTA 26MM 75X4CM 12F," 4,896 "
-28010148,HC  CATHETER PTA 26MM 75X2CM 12F," 4,896 "
-28010149,HC ELECTRODE NEEDLE CANNULA 9X18CM," 10,321 "
-28010150,HC IMP STENT NEPHROSTOMY PERCUFLEX 10F X 22CM, 823 
-28010151,HC IMP MATRIX BONE STRIP DBX 2.5X10CM," 11,588 "
-28010152,HC IMP BONE VITOSS CANCELLOUS PACK 10ML," 13,163 "
-28010153,HC IMP GUIDE NEURAGEN NERVE 2X2CM," 9,293 "
-28010154,HC IMP GUIDE NEURAGEN NERVE 3X2CM," 9,293 "
-28010155,HC IMP GUIDE NEURAGEN NERVE 4X2CM," 9,698 "
-28010156,HC IMP GUIDE NEURAGEN NERVE 5X2CM," 7,510 "
-28010157,HC IMP SPACER X STOPPK 8MM," 30,450 "
-28010158,HC IMPL SCREW DUAL OPENING TITANIUM 5.2X40MM," 6,375 "
-28010159,HC IMPL SCREW DUAL OPENING TITANIUM 5.2X30MM," 6,375 "
-28010160,HC IMP CEMENT BONE GHV SMARTSET," 2,844 "
-28010161,HC IMP CEMENT BONE HV SMARTSET 40GRN, 595 
-28010162,HC CATHETER POWER PICC BASIC TRAY 5F," 1,352 "
-28010164,HC PROBE ARTHROSCOPIC ANGLED MICRO TAC-S," 3,039 "
-28010177,HC  CATHETER ABLATION DTYPE TEMP 8MM 7F," 13,530 "
-28010178,HC CATHETER DUAL LUMEN INTRODUCER SET," 1,716 "
-28010179,HC IMP SPACER ODL ARCH ANGLED 4MM," 7,740 "
-28010180,HC IMP SPACER ODL ARCH ANGLED 6MM," 8,432 "
-28010181,HC IMP SPACER ODL ARCH ANGLED 8MM," 8,432 "
-28010182,HC IMP SPACER ODL ARCH ANGLED 10MM," 7,740 "
-28010183,HC IMP SPACER ODL ARCH ANGLED 12MM," 7,740 "
-28010184,HC IMP SPACER ODL ARCH PARALLEL 4MM," 7,740 "
-28010185,HC IMP SPACER ODL ARCH PARALLEL 6MM," 7,740 "
-28010186,HC IMP SPACER ODL ARCH PARALLEL 8MM," 7,740 "
-28010187,HC IMP SPACER ODL ARCH PARALLEL 10MM," 7,740 "
-28010188,HC IMP SPACER ODL ARCH PARALLEL 12MM," 7,740 "
-28010189,HC  CATHETER CENTRAL VENOUS MULTI LUMEN 7FX6IN, 202 
-28010190,HC IMPL PACEMAKER SINGLE RR SENSIA," 18,000 "
-28010196,HC IMPL PACEMAKER DUAL RR VERSA," 25,900 "
-28010197,HC FIBER LASER SLIMLINE 1000MIC," 7,875 "
-28010198,HC IMP ANCHOR SUTURE MICROFIX PLUS," 3,281 "
-28010200,HC IMPL PACEMAKER DUAL ADAPTA," 26,600 "
-28010201,HC IMPL PACEMAKER DUAL ADAPTA 12.1," 26,600 "
-28010202,HC IMPL PACEMAKER DUAL ADAPTA 14.2," 26,600 "
-28010203,HC IMPL PACEMAKER DUAL ADAPTA 13.1," 26,600 "
-28010204,HC IMPL PACEMAKER DUAL ADAPTA ADDRS1," 29,750 "
-28010205,HC IMPL PACEMAKER SINGLE ADAPTA," 18,750 "
-28010206,HC IMPL PACEMAKER SINGLE ADAPTA 11CC," 18,750 "
-28010207,HC CATHETER ANGIOGRAPHY FLUSH 65CM 3F, 136 
-28010208,HC IMPL PLATE NARROW 2MM 6HOLE," 2,625 "
-28010209,HC IMPL PLATE TENSION BAND 2MM 4HOLE," 3,019 "
-28010210,HC IMPL PLATE BROAD 2MM 4HOLE," 3,094 "
-28010211,HC IMPL PLATE BROAD CVD 2MM 4HOLE," 3,720 "
-28010212,HC IMPL PLATE PREBENT MINI LEFT 4HOLE," 2,985 "
-28010213,HC IMPL PLATE LOCKING LG 3X3 HOLES 2MM," 4,658 "
-28010214,HC IMPL PLATE MLP EXTRA 2MM 12HOLE," 5,590 "
-28010215,HC IMPL PLATE MLP EXTRA RIGHT 6X21 HOLE," 11,268 "
-28010216,HC IMPL PLATE DOUBLE ANGLE EXTRA 5X22X5," 13,764 "
-28010217,HC IMPL SCREW PLUSDRIVE RECESS 2X8MM, 873 
-28010218,HC IMPL SCREW PLUSDRIVE RECESS 2X12MM, 873 
-28010219,HC IMPL SCREW PLUSDRIVE RECESS 2X14MM," 4,366 "
-28010220,HC IMP DURAL SUBSTITUTE DUREPAIR 3X3," 5,640 "
-28010221,HC IMP DURAL SUBSTITUTE DUREPAIR 2X2," 3,097 "
-28010222,HC IMP DURAL SUBSTITUTE DUREPAIR 1X1," 1,249 "
-28010223,HC IMP DURAL SUBSTITUTE DUREPAIR 1X3," 2,690 "
-28010224,HC CATHETER SPYGLASS ACCESS AND DELIVERY," 6,460 "
-28010225,HC FORCEP SPYBITE 1.2X286CM," 4,208 "
-28010226,HC  PERICARDIOCENTESIS GUIDEWIRE TRAY," 1,462 "
-28010228,HC IMP ICD DUAL IC VIRTUOSO 36CM," 81,000 "
-28010229,HC  CATHETER DILATATION OTW 135CM 2.1F," 6,758 "
-28010230,HC  CATHETER SPECIALTY 2.6F 135CM," 6,758 "
-28010231,HC  GUIDEWIRE CORONARY STRAIGHT ASAHI 80X20CM," 1,275 "
-28010232,HC IMP LEAD CORONARY PACING 80CM," 11,250 "
-28010235,HC IMPL PLATE MANDIBLE LOCKING TITANIUM 2MM 20HOLE," 7,725 "
-28010236,HC  CATHETER DILATATION 4F 135CM 3X20MM," 3,412 "
-28010237,HC  CATHETER DILATATION 4F 135CM 3X40MM," 3,404 "
-28010238,HC  CATHETER DILATATION DILATATION 3X60MM," 3,404 "
-28010239,HC  CATHETER DILATATION DILATATION 4X15MM," 3,404 "
-28010240,HC  CATHETER DILATATION 4X20MM," 3,404 "
-28010241,HC  CATHETER DILATATION 4F 135CM 4X20MM," 3,404 "
-28010242,HC  CATHETER DILATATION LP 5X15MM," 3,404 "
-28010243,HC  CATHETER DILATATION LP 5X20MM," 3,412 "
-28010244,HC  CATHETER DILATATION 4F 135CM 5X20MM," 3,404 "
-28010245,HC  CATHETER DILATATION 4F 135CM 5X30MM," 3,404 "
-28010246,HC  CATHETER DILATATION LP 6X15MM," 3,404 "
-28010247,HC  CATHETER DILATATION LP 6X20MM," 3,404 "
-28010249,HC  CATHETER DILATATION 4F 135CM 6X30MM," 3,404 "
-28010250,HC  CATHETER DILATATION LP 7X15MM," 3,404 "
-28010251,HC  CATHETER DILATATION LP 7X20MM," 3,412 "
-28010252,HC  CATHETER DILATATION 7X20MM 135CM," 3,412 "
-28010253,HC  CATHETER DILATATION 80CM 4X20MM 3.8F," 4,922 "
-28010254,HC  CATHETER DILATATION 4X100MM," 3,411 "
-28010255,HC  CATHETER DILATATION 80CM 5X100MM 3.8F," 3,404 "
-28010256,HC  CATHETER DILATATION 5X40MM," 3,404 "
-28010257,HC  CATHETER DILATATION 6X40MM," 3,404 "
-28010258,HC  CATHETER DILATATION 135CM 6X100MM," 3,404 "
-28010259,HC  CATHETER DILATATION 7X40MM," 3,404 "
-28010260,HC  CATHETER DILATATION 7X100MM," 3,404 "
-28010261,HC  CATHETER DILATATION OTW 8X20MM," 3,412 "
-28010262,HC  CATHETER DILATATION 9X40MM," 3,404 "
-28010263,HC  CATHETER DILATATION 10X40MM," 3,404 "
-28010265,HC  CATHETER BALLOON 10F 32MM 100CM 34CC," 3,171 "
-28010266,HC IMP FLANGE ABUTMENT FIXTURE 4MM," 17,990 "
-28010268,HC IMPL COIL PERIPHERAL DETACHABLE SRE 7CM 4MM 360D," 10,688 "
-28010269,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 7X20CM," 12,690 "
-28010270,HC IMPL COIL PERIPHERAL DETACHABLE SRE 20CM 8MM 360D," 11,202 "
-28010271,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 9X30CM," 12,690 "
-28010272,HC IMPL COIL NEURO DETACHABLE NURVASCLR 3CM 2.5MM," 7,692 "
-28010273,HC IMPL COIL NEURO DETACHABLE NURVASCLR 8CM 4MM," 7,910 "
-28010274,HC IMPL COIL PERIPHERAL DETACHABLE 15CM 7MM 360D," 12,972 "
-28010275,HC IMPL COIL PERIPHERAL DETACHABLE 20CM 9MM 360D," 11,957 "
-28010276,HC IMPL COIL PERIPHERAL DETACHABLE 30CM 10MM 360D," 12,147 "
-28010277,HC IMPL COIL PERIPHERAL DETACHABLE 30CM 12MM 360D," 12,147 "
-28010278,HC IMPL COIL PERIPHERAL DETACHABLE 30CM 14MM 360D," 10,540 "
-28010279,HC IMPL COIL PERIPHERAL DETACHABLE 30CM 16MM 360D," 10,540 "
-28010280,HC IMPL COIL PERIPHERAL DETACHABLE 40CM 18MM 360D," 10,892 "
-28010281,HC IMPL COIL PERIPHERAL DETACHABLE 33CM 20MM 360D," 12,218 "
-28010282,HC IMPL COIL PERIPHERAL DETACHABLE 40CM 24MM 360D," 10,892 "
-28010283,HC IMPL COIL PERIPHERAL DETACHABLE SOFT 8CM 2MM," 5,201 "
-28010284,HC IMPL COIL PERIPHERAL DETACHABLE SOFT 8CM 3MM," 5,201 "
-28010285,HC IMPL COIL PERIPHERAL DETACHABLE 10CM 4MM GDC18," 5,289 "
-28010286,HC IMPL COIL PERIPHERAL DETACHABLE 12CM 5MM GDC18," 5,417 "
-28010287,HC IMP CABLE SPINAL ATLAS," 3,399 "
-28010290,HC IMP STENT BILIARY LUMINEXX 10X40X80," 15,258 "
-28010291,HC IMP STENT BILIARY WALLFLEX UNCOVERED 8X40," 12,375 "
-28010292,HC IMP STENT BILIARY WALLFLEX UNCOVERED 8X60," 12,375 "
-28010293,HC IMP STENT BILIARY WALLFLEX UNCOVERED 8X80," 12,375 "
-28010294,HC IMP STENT BILIARY WALLFLEX UNCOVERED 8X10," 12,750 "
-28010295,HC IMP STENT BILIARY WALLFLEX UNCOVERED 10X40," 12,375 "
-28010296,HC IMP STENT BILIARY WALLFLEX UNCOVERED 10X60," 12,375 "
-28010297,HC IMP STENT BILIARY WALLFLEX UNCOVERED 10X80," 12,375 "
-28010298,HC IMP STENT BILIARY WALLFLEX UNCOVERED 10X100," 12,750 "
-28010299,HC IMP CLIP ANEURYSM KEES SLIMLINE MICRO 3," 4,063 "
-28010300,HC IMP CLIP ANEURYSM KEES SLIMLINE MICRO 4," 3,341 "
-28010301,HC IMP CLIP ANEURYSM KEES SLIMLINE MICRO 5," 2,618 "
-28010302,HC IMP PIN STEINMAN 1/8X9, 167 
-28010303,HC IMP SHEETING SILASTIC NONREINFORCED .060X6X8 SILIC, 808 
-28010304,HC IMP SHEETING SILASTIC NONREINFORCED .040X3X4 SILIC, 493 
-28010305,HC IMP SHEETING SILASTIC NONREINFORCED .010X6X8 SILIC, 578 
-28010306,HC  TUBE GASTROSTOMY MIC 22F, 492 
-28010308,HC IMPL FINGER JOINT SWANSON GROMMET SIZE 4," 8,273 "
-28010309,HC IMPL PATCH CARDIOVASCULAR 4X2X9CM," 1,811 "
-28010310,HC IMPL PATCH CARDIOVASCULAR 6X5X7.5CM," 2,304 "
-28010319,HC  GUIDEWIRE EXTENSION DOC 14IN 145CM, 876 
-28010322,HC IMP MESH PARIETEX 6INX4IN," 4,013 "
-28010323,HC IMP MESH PARIETEX 8INX6IN," 6,430 "
-28010324,HC IMP MESH PARIETEX 14.5INX11IN," 14,017 "
-28010325,HC IMP MESH PARIETEX 10INX8IN," 8,634 "
-28010326,HC IMP MESH PARIETEX LEFT 6INX4IN," 1,614 "
-28010327,HC IMP MESH PARIETEX RIGHT 6INX4IN," 1,671 "
-28010328,HC IMP PIN ANT CERVICAL FUSION DISTRACTION CASPAR 12MM, 680 
-28010333,HC IMP DISSECTOR SPACEMAKER PLUS," 2,644 "
-28010334,HC  CANNULA PERFUSION VENTRICULAR MALLEABLE 32F," 8,175 "
-28010335,HC  CANNULA PERFUSION VENTRICULAR MALLEABLE 34F," 8,175 "
-28010336,HC  CANNULA SHORT APICAL SHT 42F," 8,175 "
-28010337,HC IMP ICD SINGLE IC VIRTUOSO VR 36," 65,000 "
-28010338,HC CATHETER IAB 8F 40CC," 6,758 "
-28010339,HC CATHETER IAB 8F 34CC," 6,758 "
-28010340,HC  CATHETER BALLOON INTRA AORTIC 34CC," 6,987 "
-28010341,HC  CATHETER BALLOON INTRA AORTIC 40CC," 6,987 "
-28010342,HC IMP LEAD NEUROSTIMULATOR COMPACT 45CM KIT," 13,500 "
-28010343,HC IMP KNEE BASEPLATE TIBIAL KEELED MBT TRAY SZ6," 11,250 "
-28010344,HC  CATHETER MICRO COAX 2.8F 17X70X130CM," 4,420 "
-28010345,HC  CATHETER TIP 2.7F 17X50X110CM," 2,720 "
-28010346,HC IMP STENT DUODENAL WALLFLEX 25-30X60," 15,713 "
-28010347,HC IMP STENT COLON WALLFLEX UNCOVERED 25-30X90," 17,963 "
-28010348,HC IMP STENT COLON WALLFLEX UNCOVERED 12X230CM," 17,963 "
-28010349,HC IMP STENT COLON WALLFLEX UNCOVERED 22-27X60," 17,963 "
-28010350,HC IMP STENT COLON WALLFLEX UNCOVERED 22-27X901," 17,963 "
-28010351,HC IMP STENT COLON WALLFLEX UNCOVERED 22-27X120," 17,963 "
-28010352,HC IMP STENT DUODENAL WALLFLEX 22-27X60," 17,963 "
-28010353,HC IMP STENT DUODENAL WALLFLEX 22-27X90," 17,963 "
-28010354,HC IMP STENT DUODENAL WALLFLEX 22-27X120," 17,963 "
-28010355,HC IMP TRANSSPHENOIDAL SELLAR LARGE 73MM," 5,296 "
-28010356,HC IMP HIP HEAD 2X36MM," 7,208 "
-28010357,HC IMP PENILE DURA II 10MM," 27,615 "
-28010358,HC IMP PENILE 10MM DIAM 1 CM UNIV," 3,613 "
-28010359,HC IMP PENILE 10MM DIAM 2 CM UNIV," 3,613 "
-28010360,HC IMP PENILE 10MM DIAM 3 CM PROX," 3,613 "
-28010361,HC IMP PENILE 10MM DIAM 4 CM PROX," 3,613 "
-28010362,HC IMP PENILE 10MM DIAM 5 CM PROX," 3,613 "
-28010363,HC IMP PENILE 10MM DIAM 6 CM PROX," 3,613 "
-28010364,HC IMP PENILE 10MM DIAM 7 CM PROX," 3,613 "
-28010365,HC IMPL SCREW CANNULATED LARGE 16X95MM," 2,505 "
-28010366,HC IMP PENILE 10MM DIAM 8 CM PROX," 3,613 "
-28010367,HC IMP PENILE 10MM DIAM 9 CM PROX," 3,613 "
-28010368,HC IMP PENILE 10MM DIAM 4 CM DIST," 3,613 "
-28010369,HC IMPL SCREW CANNULATED LARGE 16MMX100," 2,505 "
-28010370,HC IMP PENILE 10MM DIAM 5 CM DIST," 3,613 "
-28010371,HC IMP PENILE 10MM DIAM 6 CM DIST," 3,613 "
-28010372,HC IMP PENILE 10MM DIAM 7 CM DIST," 3,613 "
-28010373,HC IMP PENILE 10MM DIAM 8 CM DIST," 3,613 "
-28010374,HC IMP PENILE 10MM DIAM 9 CM DIST," 3,613 "
-28010375,HC IMP PENILE 12MM DIA," 27,615 "
-28010376,HC IMP PENILE 12MM DIAM 1 CM UNIV," 3,613 "
-28010377,HC IMP PENILE 12MM DIAM 2 CM UNIV," 3,613 "
-28010378,HC IMP PENILE 12MM DIAM 3 CM PROX," 3,613 "
-28010379,HC IMP PENILE 12MM DIAM 4 CM PROX," 3,613 "
-28010380,HC IMP PENILE 12MM DIAM 5 CM PROX," 3,613 "
-28010381,HC IMP PENILE 12MM DIAM 6 CM PROX," 3,613 "
-28010382,HC IMP PENILE 12MM DIAM 7 CM PROX," 3,613 "
-28010383,HC IMP PENILE 12MM DIAM 9 CM PROX," 3,613 "
-28010384,HC IMP PENILE 12MM DIAM 3 CM DIST," 3,613 "
-28010385,HC IMP PENILE 12MM DIAM 4 CM DIST," 3,613 "
-28010386,HC IMP PENILE 12MM DIAM 5 CM DIST," 3,613 "
-28010387,HC IMP PENILE 12MM DIAM 6 CM DIST," 3,613 "
-28010388,HC IMP PENILE 12MM DIAM 7 CM DIST," 3,613 "
-28010389,HC IMP PENILE 12MM DIAM 8 CM DIST," 3,613 "
-28010390,HC IMP PENILE 12MM DIAM 9 CM DIST," 3,613 "
-28010391,HC IMP PENILE LGX MS 12CM," 44,844 "
-28010392,HC IMP PENILE LGX MS 15CM," 44,844 "
-28010393,HC IMP PENILE LGX MS 18CM," 43,479 "
-28010394,HC IMP PENILE LGX MS 21CM," 44,844 "
-28010395,HC IMPL PUMP SQUEEZE PENILE," 25,200 "
-28010396,HC IMP PENILE RESERVOIR 65ML," 11,093 "
-28010397,HC IMP PENILE RESERVOIR 100ML," 12,975 "
-28010398,HC  CATHETER ABLATION TURBO ELITE OTW 0.9MM," 16,920 "
-28010399,HC  CATHETER ABLATION TURBO ELITE OTW 1.7MM," 18,750 "
-28010400,HC  CATHETER ABLATION TURBO ELITE OTW 2MM," 20,965 "
-28010401,HC  CATHETER ABLATION TURBO ELITE OTW 2.3MM," 18,750 "
-28010402,HC IMP HIP LINER ACETABULAR 36X52," 5,100 "
-28010403,HC  CATHETER ABLATION TURBO ELITE OTW 2.5MM," 15,000 "
-28010408,HC  CATHETER DILATATION POWERFLEX P3 OTW 5X10X80," 1,517 "
-28010409,HC  CATHETER DILATATION 6MM 8X80CM 6F," 1,517 "
-28010410,HC  CATHETER DILATATION 5MM 10X110CM 6F," 1,517 "
-28010411,HC  CATHETER DILATATION 5MM 2X135CM 5F," 1,517 "
-28010412,HC  CATHETER DILATATION POWERFLEX 6MM 2X135CM 5F," 1,517 "
-28010413,HC  CATHETER DILATATION POWERFLEX 7MM 2X135CM 6F," 1,517 "
-28010414,HC  CATHETER DILATATION POWERFLEX 8MM 2X135CM 6F," 1,517 "
-28010415,HC  CATHETER DILATATION 8MM 4X120CM 7F," 1,626 "
-28010416,HC  CATHETER DILATATION 22MM 110X4CM," 2,432 "
-28010417,HC  CATHETER DILATATION 25MM 110X4CM," 2,432 "
-28010418,HC  CATHETER DILATATION 4MM 2X150CM 3.5F," 2,668 "
-28010419,HC  CATHETER DILATATION 4MM 10X80CM 5F," 2,114 "
-28010420,HC  CATHETER DILATATION 5MM 10X80CM 5F," 2,668 "
-28010421,HC  CATHETER DILATATION 5MM 4X120CM 4F," 2,668 "
-28010422,HC IMP STENT BILIARY GENESIS 35 OTW 7X24X135," 8,591 "
-28010423,HC IMP STENT BILIARY GENESIS 35 OTW 7X26X135," 9,000 "
-28010424,HC IMP STENT BILIARY GENESIS 35 OTW 7X36X135," 9,000 "
-28010425,HC IMP STENT BILIARY GENESIS 35 OTW 9X29X80," 9,281 "
-28010426,HC IMP STENT BILIARY GENESIS 35 OTW 9X39X80," 9,000 "
-28010427,HC IMP STENT BILIARY GENESIS 35 OTW 10X40X80," 9,626 "
-28010428,HC IMP STENT BILIARY SMART OTW 6X100X80," 14,006 "
-28010429,HC IMP STENT BILIARY SMART OTW 7X4X80," 11,104 "
-28010430,HC IMP STENT BILIARY SMART OTW 8X100X80," 14,006 "
-28010431,HC IMP STENT BILIARY SMART OTW 9X60X80," 11,104 "
-28010432,HC IMP STENT CAROTID PRECISE 7X20X135," 16,875 "
-28010433,HC IMP STENT CAROTID PRECISE 7X30X135," 16,500 "
-28010434,HC IMP STENT CAROTID PRECISE 8X30X135," 16,500 "
-28010435,HC IMP STENT CAROTID PRECISE 8X40X135," 16,125 "
-28010436,HC  GUIDEWIRE EMBLI CAPTURE 14IN 5MM 3.3F," 13,125 "
-28010437,HC  GUIDEWIRE EMBLI CPTR 14IN 180CM 6MM," 14,625 "
-28010438,HC  CATHETER DILATATION 6MM 10X80CM 6F," 1,517 "
-28010439,HC  INTRODUCER SHEATH GUIDING 63CM 8.5F," 1,743 "
-28010440,HC IMP LEAD PACING FINELINE 58CM," 4,675 "
-28010441,HC IMP ICD OTHER IC CONCERTO 38CC," 71,750 "
-28010442,HC  CATHETER DILATATION 4MM 8X80CM 5F," 1,517 "
-28010445,HC PROBE LITHOTRIPSY BILIARY 1.9FX375CM," 3,315 "
-28010446,HC IMP PIN HIP ODYSSEY," 2,423 "
-28010449,HC  CATHETER DILATATION BILIARY QUANTUM 6MM," 1,847 "
-28010450,HC  CATHETER DILATATION BILIARY QUANTUM 9MM," 1,847 "
-28010451,HC  GUIDEWIRE COONS BENTSON 35IN 6CM, 113 
-28010452,HC IMPL COIL NEURO MICRO SPHERICAL 20.7CM 12MM," 12,600 "
-28010453,HC IMPL COIL NEURO MICRO SPHERICAL 26CM 15MM," 12,824 "
-28010454,HC IMPL COIL NEURO MICRO SPHERICAL 18MM 30CM," 13,088 "
-28010455,HC IMPL COIL NEURO MICRO SPHERICAL 11.5CM 4MM," 17,669 "
-28010456,HC IMPL COIL NEURO MICRO SPHERICAL 17CM 5MM," 17,834 "
-28010457,HC IMPL COIL NEURO MICRO SPHERICAL 26CM 6MM," 18,006 "
-28010458,HC IMPL COIL NEURO MICRO SPHERICAL 30CM 7MM," 18,170 "
-28010459,HC IMPL COIL NEURO MICRO SPHERICAL SRE 8MM 29CM," 18,342 "
-28010460,HC IMPL COIL NEURO MICRO SPHERICAL 30CM 8MM," 18,550 "
-28010461,HC IMPL COIL NEURO MICRO SPHERICAL 33CM 9MM," 18,865 "
-28010462,HC IMPL COIL NEURO MICRO SPHERICAL 34CM 10MM," 18,970 "
-28010463,HC IMPL COIL NEURO MICRO SPHERICAL 37CM 11MM," 19,285 "
-28010464,HC IMPL COIL NEURO MICRO SPHERICAL 40CM 12MM," 19,600 "
-28010465,HC IMPL COIL NEURO MICRO SPHERICAL 43CM 13MM," 19,915 "
-28010466,HC IMPL COIL NEURO MICRO SPHERICAL 47CM 14MM," 20,335 "
-28010467,HC IMPL COIL NEURO MICRO SPHERICAL 50CM 15MM," 20,650 "
-28010468,HC IMPL COIL NEURO MICRO SPHERICAL 47CM 16MM," 20,335 "
-28010469,HC IMPL COIL NEURO MICRO SPHERICAL 50CM 17MM," 20,650 "
-28010470,HC IMPL COIL NEURO MICRO SPHERICAL 46CM 18MM," 20,230 "
-28010471,HC IMPL COIL NEURO SPHERICAL SRE LOOP 50CM 19MM," 20,650 "
-28010472,HC IMPL COIL NEURO MICRO SPHERICAL 50CM 20MM," 20,650 "
-28010473,HC  CATHETER PTA 5F 80CM 4X40MM," 1,445 "
-28010474,HC  CATHETER PTA 5F135CM 4X40MM," 1,488 "
-28010475,HC  CATHETER PTA OTW 80CM 4X120MM 5F," 1,445 "
-28010476,HC  CATHETER PTA 5F135CM 4X120MM," 1,530 "
-28010477,HC  CATHETER PTA 5F 80CM 5X20MM," 1,488 "
-28010478,HC  CATHETER PTA OTW 135CM 5X20MM 5F," 1,445 "
-28010479,HC  CATHETER PTA 5F 80CM 5X40MM," 1,488 "
-28010480,HC  CATHETER PTA 5X80MM 80CM 5F," 1,445 "
-28010481,HC  CATHETER PTA 5F135CM 5X80MM," 1,445 "
-28010482,HC  CATHETER PTA OTW 80CM 5X120MM 5F," 1,445 "
-28010483,HC  CATHETER PTA 5F135CM 5X120MM," 1,530 "
-28010484,HC  CATHETER PTA 5F 80CM 6X20MM," 1,488 "
-28010488,HC IMP STENT BILIARY GENESIS 35 OTW 10X39X135," 9,000 "
-28010489,HC  CATHETER DILATATION 4MM 4X80CM 4F," 2,907 "
-28010490,HC  CATHETER PTA 5F 80CM 6X40MM," 1,488 "
-28010491,HC  CATHETER PTA 5F 80CM 6X80MM," 1,488 "
-28010493,HC  CATHETER PTA 5F135CM 6X120MM," 1,445 "
-28010494,HC  CATHETER PTA 5F 80CM 7X20MM," 1,488 "
-28010495,HC  CATHETER PTA 5F 80CM 7X40MM," 1,488 "
-28010496,HC  CATHETER PTA 6F 80CM 8X20MM," 1,488 "
-28010497,HC  CATHETER PTA 6F 80CM 8X40MM," 1,488 "
-28010498,HC  CATHETER PTA 6F 80CM 8X60MM," 1,445 "
-28010499,HC  CATHETER PTA 6F 80CM 9X20MM," 1,488 "
-28010500,HC  CATHETER PTA 6F 80CM 9X40MM," 1,488 "
-28010501,HC  CATHETER PTA OTW 80CM 9X60MM 6F," 1,445 "
-28010502,HC  CATHETER PTA OTW 80CM 10X20MM 6F," 1,488 "
-28010503,HC  CATHETER PTA 6F 80CM 10X40MM," 1,488 "
-28010504,HC  CATHETER PTA OTW 80CM 12X20MM 7F," 1,530 "
-28010505,HC  CATHETER PTA 7F 80CM 12X40MM," 1,445 "
-28010506,HC  CATHETER FOLEY HEMATURIA 18F, 184 
-28010507,HC  CATHETER FOLEY HEMATURIA 20F, 184 
-28010508,HC  CATHETER FOLEY HEMATURIA 22F, 184 
-28010509,HC  CATHETER FOLEY HEMATURIA 24F, 175 
-28010510,HC  CATHETER FOLEY SILICONE 3 WAY 22F, 93 
-28010513,HC IMP VALVE AORTIC REGENT 23MM," 30,331 "
-28010514,HC IMP CEMENT RESTRICTOR TITANIUM 24MM," 2,193 "
-28010515,HC IMP SHOULDER GLENOID KEELED AEQ SM," 10,635 "
-28010516,HC IMP SHOULDER GLENOID KEELED AEQ MEDIUM," 10,635 "
-28010517,HC IMP SHOULDER GLENOID KEELED TITANIUM LARGE," 16,448 "
-28010518,HC IMP SHOULDER GLENOID KEELED TITANIUM XLARGE," 10,635 "
-28010519,HC IMP SHOULDER HEAD HUMERAL AEQUALIS 41X15MM," 11,498 "
-28010520,HC IMP SHOULDER HEAD HUMERAL AEQUALIS 43X16MM," 11,498 "
-28010521,HC IMP SHOULDER HEAD HUMERAL AEQUALIS 46X17MM," 11,498 "
-28010522,HC IMP SHOULDER HEAD HUMERAL AEQUALIS 48X18MM," 17,355 "
-28010523,HC IMP SHOULDER HEAD HUMERAL AEQUALIS 50X19MM," 11,498 "
-28010524,HC IMP SHOULDER HEAD HUMERAL AEQUALIS 52X19MM," 11,498 "
-28010525,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 7M135D," 23,814 "
-28010526,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 9M125D," 23,814 "
-28010527,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 9M130D," 23,814 "
-28010528,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 11M125D," 33,378 "
-28010529,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 11M130D," 23,814 "
-28010530,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 11M135D," 23,814 "
-28010531,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 13M135D," 23,814 "
-28010532,HC  GUIDEWIRE PERIPHERAL 14IN 200CM 14MM," 3,962 "
-28010533,HC  GUIDEWIRE PERIPHERAL STANDARD 14IN 35X200CM," 4,293 "
-28010534,HC IMP BONE VITOSS CANCELLOUS PACK 2.5ML," 4,327 "
-28010535,HC IMP BONE VITOSS CANCELLOUS PACK 5ML," 8,373 "
-28010536,HC IMPL DERMAMATRIX THICK 8X16," 30,373 "
-28010537,HC IMPL PLATE RESORBABLE ORBITAL FLR 0.5 THK 1.5MM," 5,590 "
-28010538,HC IMP BLOCKER SPINAL ROD XIA," 1,216 "
-28010539,HC IMP ENDCAP XIA 18MM 0 DEG," 2,225 "
-28010540,HC IMP ENDCAP XIA 22MM 0 DEG," 2,425 "
-28010541,HC IMP ENDCAP XIA 22MM 3 DEG," 2,625 "
-28010542,HC IMP ENDCAP XIA 22MM 8 DEG," 2,748 "
-28010543,HC IMPL SCREW XIA MONOAXIAL 5.5X35MM," 7,863 "
-28010544,HC IMPL SCREW XIA MONOAXIAL 5.5X40MM," 7,863 "
-28010545,HC IMPL SCREW XIA MONOAXIAL 6.5X40MM," 6,596 "
-28010546,HC IMPL SCREW XIA MONOAXIAL 6.5X45MM," 6,596 "
-28010547,HC IMPL SCREW XIA POLYAXIAL 4.5X30MM," 7,851 "
-28010548,HC IMPL SCREW XIA POLYAXIAL 5.5X35MM," 7,851 "
-28010549,HC IMPL SCREW XIA POLYAXIAL 6.5X40MM," 7,851 "
-28010550,HC IMPL SCREW XIA POLYAXIAL 6.5X45MM," 7,851 "
-28010551,HC IMPL SCREW XIA POLYAXIAL 6.5X50MM," 7,851 "
-28010552,HC IMPL SCREW XIA POLYAXIAL 8.5X80MM," 8,318 "
-28010553,HC IMP ROD XIA RADIUS 40MM," 3,146 "
-28010554,HC IMP ROD XIA RADIUS 50MM," 2,703 "
-28010555,HC IMP ROD XIA RADIUS 60MM," 2,703 "
-28010556,HC IMP ROD XIA RADIUS 70MM," 2,703 "
-28010557,HC IMP ROD XIA RADIUS 90MM," 2,703 "
-28010558,HC IMP CONNECTOR SPINAL XIA TI," 2,763 "
-28010559,HC IMP ROD XIA DBL RADIUS 50MM," 2,828 "
-28010560,HC IMP ROD XIA 6X80," 3,080 "
-28010561,HC IMP HOOK SPINAL XIA LAMINA SMALL," 5,651 "
-28010562,HC IMP HOOK SPINAL XIA PEDICLE LARGE," 6,668 "
-28010563,HC IMP CAGE SPINAL FUSION 22X32MM," 46,510 "
-28010564,HC IMP CAGE SPINAL FUSION 18X32MM," 47,906 "
-28010565,HC  GUIDEWIRE ANGIOGRAPHY STR TIP COURGAR 190CM, 986 
-28010566,HC  CATHETER PTA 12MM 6CM 6.5F," 2,980 "
-28010567,HC  CATHETER PTA 14MM 75X6CM 8F," 2,980 "
-28010568,HC  CATH CARD CRYOABLAT MED 9F," 13,500 "
-28010569,HC IMP STENT BILIARY COTTON LEUNG 7-1, 445 
-28010570,HC  CATHETER DILATATION 4MM 2X142CM 4F," 2,893 "
-28010571,HC  CATHETER DILATATION 4.5MM 2X142CM 4F," 2,893 "
-28010572,HC  CATHETER DILATATION 4.5MM 4X142CM 4F," 2,893 "
-28010573,HC  CATHETER BALLOON PTA COAX RX 5X20MM," 2,090 "
-28010574,HC  CATHETER DILATATION 5MM 4X142CM 4F," 2,893 "
-28010575,HC  CATHETER DILATATION 5.5MM 2X142CM 4F," 2,893 "
-28010576,HC  CATHETER DILATATION 6MM 2X142CM 4F," 2,893 "
-28010577,HC  CATHETER DILATATION 7MM 2X142CM 4F," 2,893 "
-28010579,HC SEALER LAPAROSCOPIC ATLAS 10MMX37CM," 3,338 "
-28010580,HC  CATHETER DRAINAGE UREFLEX 20CM 14F," 1,070 "
-28010581,HC  CATHETER DRAINAGE GENERAL PURP 23CM 16F, 927 
-28010582,HC  GUIDEWIRE HI TORQUE WIGGLE 30CM 014X190, 697 
-28010584,HC  INTRODUCER VASCULAR MULLINS CHECK-FLO, 646 
-28010585,HC IMP PROTECTION EMBOLIC PROXIS 7F," 10,463 "
-28010586,HC  CATHETER INFUSION EMBOLIC PROTECTION SYSTEM 6F," 2,338 "
-28010587,HC IMPL PACEMAKER DUAL ZEPHYR XL DR," 29,750 "
-28010588,HC IMP STENT GRAFT VIABAHN .035 6X5X120," 17,100 "
-28010589,HC IMP STENT GRAFT VIABAHN .6MM 15CM," 22,295 "
-28010592,HC IMPL PLATE DOUBLE Y SHORT," 1,782 "
-28010593,HC IMP SCREW CORTEX 2.7X28MM, 322 
-28010594,HC IMP SCREW CORTEX 2.7X30MM, 322 
-28010595,HC IMP PIN RUSH 3/32X2 3/4IN H, 492 
-28010596,HC IMP ROD RUSH 3/32X3 1/4IN J, 492 
-28010597,HC IMP ROD RUSH 3/32INX3 3/4IN L, 306 
-28010598,HC IMPL PLATE TIBAL PROXIMAL LEFT 4 HOLE 3.5X81MM," 12,330 "
-28010599,HC IMPL PLATE PROX LCP 3.5X133MM 8HOLE," 11,805 "
-28010601,HC IMPL PLATE 1/3 TUBULAR 7 HOLE 85MM, 633 
-28010602,HC IMP BOLT LOCKING TITANIUM 3.9MM 22MM," 1,530 "
-28010603,HC IMP STAPLE FIXATION FRACTURE STEP 5/8X7/8," 1,403 "
-28010604,HC IMP SCREW CORTEX 1X7MM, 598 
-28010605,HC IMP SCREW CORTEX 1X8MM, 598 
-28010606,HC IMP SCREW CORTEX 1X9MM, 598 
-28010607,HC IMP SCREW CORTEX 1X11MM, 598 
-28010608,HC IMP SCREW CORTEX 1X12MM, 598 
-28010609,HC IMP SCREW CORTEX 1X13MM," 2,253 "
-28010610,HC IMP SCREW CORTEX 1X14MM, 598 
-28010611,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX10MM, 525 
-28010612,HC IMP CONDUIT INFLOW," 19,250 "
-28010613,HC IMP CONDUIT OUTFLOW," 16,500 "
-28010614,HC IMPL PACEMAKER SINGLE RR ZEPHYR 44MM," 20,300 "
-28010615,HC IMPL PACEMAKER DUAL ZEPHYR," 29,750 "
-28010616,HC IMPL PACEMAKER CONTAK RENEWAL TR," 47,125 "
-28010619,HC  GUIDEWIRE METRO DIRECT 35X480," 1,221 "
-28010620,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.2X15," 2,738 "
-28021433,HC STENT DBL J 4FR X 14CM," 1,105 "
-28021432,HC STENT DBL J 4FR X 12CM," 1,535 "
-28021431,HC STENT DBL J 4FR X 10CM," 1,615 "
-28021430,HC STENT DBL J 4FR X 8CM," 1,615 "
-28021422,HC BALLOON LOW PROFILE REPLACEMEN, 765 
-28021421,HC BALLOON LOW PROFILE REPLACEMEN, 765 
-28021410,HC FRAME POSTERIOR," 1,173 "
-28021409,HC IMMOBILIZER KNEE 18, 229 
-28021398,HC IMPL CLIP ANEURYSM 20-1686," 2,083 "
-28021390,HC BALLOON LOW PROFILE 20X4, 765 
-28021383,HC BUTTON LOW PROFILE 24FX3.4, 978 
-28021362,HC GUIDEWIRE TSF25 145CMX0.25IN, 113 
-28021344,HC SAFETY PEG WITH STER XYLOCAINE, 765 
-28021055,HC GLIDEWIRE ANG 150CMX.035IN, 374 
-28018510,HC SHEATH LG HUB BUR, 408 
-28021028,HC IMPL WIRE KIRSCHNER 2.0MM X 150MM, 85 
-28021027,HC IMPL WIRE KIRSCHNER 1.6 MM X 150MM, 85 
-28021026,HC IMPL WIRE KIRSCHNER 1.25MM X 150MM, 85 
-28020999,HC TUBE GASTROSTOMY 22F HOLD, 587 
-28020998,HC TUBE GASTROSTOMY 20F, 592 
-28012995,HC GLIDEWIRE STR 150CMX.038IN, 476 
-28020974,HC IMPL INTRO STENT OA 10, 595 
-28020970,HC DRAIN CHEST PNEUMONECT," 1,054 "
-28020930,HC GASTRO TUBE 16F," 1,275 "
-28020926,HC IMPL OSTEOTOMY GUIDE PIN 2.4MM, 315 
-28020919,HC  CATH PASSER W/ OBTURATOR, 833 
-28015293,HC PATCH DEFIB ANT PED, 213 
-28020902,HC TRAY LIVER BX AK00370 CS OF 6E, 388 
-28015292,HC PATCH DEFIB ANT, 323 
-28018752,HC SPLINT WRIST RIGHT LARGE, 141 
-28020884,HC CATH TENCKHOFF SNGL, 403 
-28020875,HC STENT PERCUFLEX PLUS 5 X 16," 1,349 "
-28020874,HC STNT URETL HYDROPLUS 20CM 8FR," 1,190 "
-28020857,HC TUBE GASTROSTOMY 20FR 0100-20, 254 
-28020849,HC BALLOON REPLACEMENT 16 FR, 595 
-28020844,HC CATH SHT TIP 14FR, 425 
-28020843,HC CATH SHT TIP 12FR, 425 
-28020807,HC BALLOON LOW PROFILE 20X3.5, 842 
-28012106,HC IMPL CATH EPISTAXIS NASAL, 833 
-28020800,HC GUIDEWIRE .035-150 ANG, 278 
-28020797,HC CATH HOHN 7FR CS OF 5  HOLD, 867 
-28020795,HC CATH URODYNMC TPNS  2LMN 9CM 12FR S, 978 
-28020788,HC CATH BLN ELNG SNGL," 1,029 "
-28020773,HC  GUIDEWIRE JAGWIRE .035 ST," 1,964 "
-28020768,HC DRAIN CHEST PLEUREVAC A4000, 366 
-28020767,HC IMPL CLIP ANEURYSM 20-1677," 1,343 "
-28020765,HC IMPL CLIP ANEURYSM 20-1735," 1,369 "
-28020740,HC INTRO PERITONEAL 31CM, 269 
-28020737,HC SHEATH PERITONEAL INTRO LONG 6, 289 
-28020736,HC SHEATH PERITONEAL INTRO SHORT, 309 
-28020718,HC SPLINT NASAL AQUAPLAST NSTER, 76 
-28020716,HC TUBE JEJUNOSTOMY FEEDING 16FR, 893 
-28020059,HC IMPL CLIP ANEURYSM 20-1671," 1,853 "
-28020058,HC IMPL CLIP ANEURYSM 20-1666, 349 
-28020057,HC IMPL CLIP ANEURYSM 20-1622," 1,335 "
-28020056,HC IMPL CLIP ANEURYSM 20-1621," 1,335 "
-28020055,HC IMPL CLIP ANEURYSM 20-1702," 1,360 "
-28020054,HC IMPL CLIP ANEURYSM 20-1701," 1,369 "
-28020053,HC IMPL CLIP ANEURYSM 20-1700," 1,828 "
-28020052,HC IMPL CLIP ANEURYSM 20-1684," 1,335 "
-28020051,HC IMPL CLIP ANEURYSM 20-1699," 1,564 "
-28020050,HC IMPL CLIP ANEURYSM 20-1698," 1,369 "
-28020049,HC IMPL CLIP ANEURYSM 20-1693," 2,066 "
-28020048,HC IMPL CLIP ANEURYSM 20-1736," 1,879 "
-28020047,HC IMPL CLIP ANEURYSM 20-1733," 4,437 "
-28020046,HC IMPL CLIP ANEURYSM 20-1637," 1,853 "
-28020045,HC IMPL CLIP ANEURYSM 20-1636," 1,853 "
-28020044,HC IMPL CLIP ANEURYSM 20-1635," 1,335 "
-28020043,HC IMPL CLIP ANEURYSM 20-1732," 1,879 "
-28020042,HC IMPL CLIP ANEURYSM 20-1731," 4,021 "
-28020041,HC IMPL CLIP ANEURYSM 20-1730," 2,814 "
-28020040,HC IMPL CLIP ANEURYSM 20-1729," 1,564 "
-28020039,HC IMPL  CLIP ANEURYSM 20-1728," 1,879 "
-28020038,HC IMPL CLIP ANEURYSM 20-1615," 1,343 "
-28020037,HC IMPL CLIP ANEURYSM 20-1614," 1,343 "
-28020036,HC IMPL CLIP ANEURYSM 20-1610," 2,049 "
-28020035,HC IMPL CLIP ANEURYSM 20-1649," 1,547 "
-28020034,HC IMPL CLIP ANEURYSM 20-1648," 1,539 "
-28020033,HC IMPL CLIP ANEURYSM 20-1681," 1,862 "
-28020032,HC IMPL CLIP ANEURYSM 20-1680," 1,547 "
-28020031,HC IMPL CLIP ANEURYSM 20-1661," 2,049 "
-28020026,HC CATH 2LUM 11.5FR X 12CM," 1,587 "
-28011864,HC SET CATH DUO FLOW 7FRX10CM, 655 
-28020020,HC REFILL FOR SYNCHROMED PUMP, 170 
-28020018,HC TUBE JEJUNAL 16F FEEDING   HLD," 1,775 "
-28020016,HC STENT ZAONTZ URETHRAL, 174 
-28020013,HC SHEATH ENDO 25DEG 4.0MM, 367 
-28020003,HC TUBE TRACH LONG TERM CANN #10, 808 
-28020002,HC TUBE TRACH LONG TERM CANN 8, 808 
-28020701,HC SLING ARM ELEVATOR, 121 
-28020700,HC IMPL PIN GUIDE 2.0MM, 179 
-28020698,HC CLMP PENILE ZIPSEE, 432 
-28020692,HC TUBE TRACH 9.0 MM CUFF ORAL RA, 20 
-28019552,HC TUBE TRACH 8.5 CUFF ORAL RAE, 281 
-28021335,HC IMPL STENT INTRODUCER SET OA 11.5FR, 595 
-28021332,HC IMMOB KNEE 19IN, 338 
-28021322,HC IMPL  INTRO STENT OA 8.5, 570 
-28021308,HC SPLINT LEG SKI BOX, 376 
-28021289,HC GUIDEWIRE .035 260CM 6CM TIP, 544 
-28018753,HC SPLINT WRIST LEFT SM, 141 
-28021284,HC GUIDEWIRE STR 260CM, 400 
-28021270,HC GUIDEWIRE 0.38 X 80CM COOK, 496 
-28021268,HC WIRE ORTHO C WIRE .028X5.0IN, 77 
-28021263,HC  CATH IG 8FR .088HSTICK 55CM, 757 
-28011729,HC CTH BLN PTA ADVANCE35 6X8X135," 1,488 "
-28021251,HC IMPL CLIP ANEURYSM 20-1620," 1,411 "
-28021249,HC GUIDEWIRE TFL COATED STR 145CM, 72 
-28021205,HC PAD ANTERIOR COMPRESSION," 1,488 "
-28021194,HC WIRE ORTHO C WIRE .045X5IN, 85 
-28021191,HC IMPL CLIP ANEURYSM 20 1692," 1,879 "
-28021190,HC IMPL CLIP ANEURYSM 20 1685," 1,505 "
-28021185,HC CATH VERSAFLEX Z ESOPHAGEAL PH, 893 
-28021182,HC CATH TRUCATH 25GA, 468 
-28021181,HC CATH TRUCATH 25GA, 468 
-28021173,HC WIRE ORTHO C WIRE .062X5IN, 85 
-28021171,HC WIRE ORTHO C WIRE .035X5IN, 88 
-28021167,HC  CATHETER ANGIOGRAPHY GUIDING AL2, 451 
-28021166,HC  CATHETER GDNG 90CM 0.1X0.088IN 8FR, 459 
-28021165,HC CATHETER 588-830T, 451 
-28021162,HC  CATHETER 12 IN, 485 
-28021147,HC CATH URETERAL 3FR X 70CM, 140 
-28021145,HC GWR BLUNT TIP .062 X 51CM, 655 
-28021133,HC GUIDWIRE RD BALL TP GDE 3MMX1000MM," 1,458 "
-28021127,HC SOLN OMNIPAQUE 300 10ML, 114 
-28021126,HC DRAIN CHEST SNGL BLD RECOV, 289 
-28021114,HC LEAD BIPOLAR PACING, 418 
-28021113,HC SHEATH TWOVU, 357 
-28014282,HC CATH PERIT DIALYSIS CSTM 8.5FR," 1,046 "
-28019815,HC AUTOGRAFT TRANSFER SYSTEM 6MM," 4,208 "
-28019816,HC IMP BREAST ROUND SILTEX BECKER TALL 450CC," 11,250 "
-28019817,HC IMP BREAST ROUND SILTEX BECKER TALL 550CC," 11,250 "
-28010621,HC IMPL SCREW CANNULATED THREAD 7.3X16X100MM," 2,420 "
-28010622,HC IMPL SCREW CANNULATED THREAD 7.3X16X110MM," 2,542 "
-28010623,HC IMPL SCREW CANNULATED THREAD 7.3X16X115MM," 2,542 "
-28010624,HC IMPL SCREW CANNULATED THREAD 7.3X16X120MM," 2,542 "
-28010625,HC IMPL SCREW CANNULATED THREAD 7.3X16X125MM," 2,420 "
-28010626,HC IMPL SCREW CANNULATED THREAD 7.3X16X130MM," 2,091 "
-28010627,HC IMPL SCREW CANNULATED THREAD 7.3X16X135MM," 2,091 "
-28010628,HC IMPL SCREW CANNULATED THREAD 7.3X16X140MM," 2,091 "
-28010629,HC IMPL SCREW CANNULATED THREAD 7.3X16X145MM," 2,091 "
-28010630,HC IMPL SCREW CANNULATED THREAD 7.3X16X150MM," 2,091 "
-28010631,HC IMPL SCREW CANNULATED THREAD 7.3X16X30MM," 2,091 "
-28010632,HC IMPL SCREW CANNULATED THREAD 7.3X16X35MM," 2,420 "
-28010633,HC IMPL SCREW CANNULATED THREAD 7.3X16X40MM," 2,542 "
-28010634,HC IMPL SCREW CANNULATED THREAD 7.3X16X50MM," 2,091 "
-28010635,HC IMPL SCREW CANNULATED THREAD 7.3X16X55MM," 2,420 "
-28010637,HC IMP ABUTMENT CONNECTION SCREW 9MM," 9,788 "
-28010639,HC IMP ADAPTER FEMORAL NEUTRAL BOLT," 2,402 "
-28010640,HC IMP ADAPTER FEMORAL NEUTRAL BOLT REV," 1,540 "
-28010641,HC IMP ADAPTER FEMORAL OFFSET PFC SIGMA 2MM," 1,734 "
-28010642,HC IMP ADAPTER FEMORAL OFFSET PFC SIGMA 2MM REV," 1,540 "
-28010643,HC IMP ADAPTER FEMORAL PFC SIGMA 5 DEGREE REV," 10,125 "
-28010644,HC IMP ADAPTER FEMORAL PFC SIGMA 5 DEGREE," 15,134 "
-28010645,HC IMP ADAPTER FEMORAL PFC SIGMA 5 DEGREE REV," 9,900 "
-28010649,HC ADAPTER TAPER OSS COMPRESS 5CM," 5,874 "
-28010650,HC ADAPTER TAPER OSS COMPRESS 5CM REV," 4,973 "
-28010656,HC IMP ADHESIVE DELIVERY SYSTEM SYRINGE 2ML," 4,157 "
-28010660,HC IMP ALLOGRAFT OSTEOCHONDRAL 11MM," 24,290 "
-28010661,HC IMP ALLOGRAFT OSTEOCHONDRAL 9MM," 22,890 "
-28010685,HC APPLICATOR MICROMYST," 5,806 "
-28010691,HC APPLIER CLIP DAVINCI MEDIUM LARGE," 5,950 "
-28010693,HC APPLIER CLIP MEDIUM LARGE SINGLE 5MM," 5,950 "
-28010695,HC APPLIER CLIP MINI SUNDT 6," 7,264 "
-28010720,HC IMP BAR LOCKING PRIMARY ASCENT MAXIM REV, 796 
-28010721,HC IMP BAR SPINAL TRANSVERSE 3.5MM," 3,298 "
-28010722,HC IMP BAR SPINAL TRANSVERSE 5MM 15MM," 2,210 "
-28010723,HC IMP BAR SPINAL TRANSVERSE 5MM 20MM," 2,033 "
-28010724,HC IMP BAR SPINAL TRANSVERSE 5MM 25MM," 2,033 "
-28010725,HC IMP BAR SPINAL TRANSVERSE 5MM 3.5MM," 3,191 "
-28010726,HC IMP BAR TRANSVERSE CLOSED TITANIUM 15MM," 5,593 "
-28010727,HC IMP BAR TRANSVERSE CLOSED TITANIUM 20MM," 5,593 "
-28010728,HC IMP BAR TRANSVERSE CLOSED TITANIUM 25MM," 5,593 "
-28010729,HC IMP BAR TRANSVERSE OPEN TITANIUM 15MM," 5,593 "
-28010730,HC IMP BAR TRANSVERSE OPEN TITANIUM 20MM," 5,593 "
-28010731,HC IMP BAR TRANSVERSE OPEN TITANIUM 25MM," 5,593 "
-28010733,HC BASKET RETRIEVAL POLYP TWISTER 25," 3,156 "
-28010734,HC BASKET RETRIEVAL STONE TRAPEZOID 3.2X1.5CM," 2,338 "
-28010747,HC BLADE ANGLE TIP SKIMMER 27.5CM," 3,121 "
-28010748,HC BLADE CURVED RAD 40 M4 XPS 4MMX11CM," 2,326 "
-28010831,HC IMP BOLT LOCKING 4.9X90MM," 1,530 "
-28010832,HC IMP BOLT LOCKING TITANIUM 3.9MM 42MM," 1,607 "
-28010833,HC IMP BOLT LOCKING TITANIUM 3.9MM 48MM," 1,607 "
-28010834,HC IMP BOLT LOCKING INTRAMEDULLARY TITANIUM 3.9X20MM," 1,437 "
-28010835,HC IMP BOLT SPINAL PEDICLE TITANIUM 5.5X45MM," 4,930 "
-28010836,HC IMP BONE CANCELLOUS CHIP 15ML," 1,762 "
-28010837,HC IMP BONE CANCELLOUS CRUSHED 15CC," 2,398 "
-28010838,HC IMP BONE CANCELLOUS CRUSHED 30CC," 4,199 "
-28010839,HC IMP BONE CANCELLOUS MILLED 15CC," 1,961 "
-28010840,HC IMP BONE CANCELLOUS MILLED 30CC," 3,359 "
-28010841,HC IMP BONE GRAFT HARVESTER OATS 6MM," 4,208 "
-28010843,HC IMP BONE MATRIX 5CC," 15,900 "
-28010845,HC IMP BONE SUBSTITUTE INJECTIBLE HYDROSET 15ML," 29,295 "
-28010846,HC IMP BONE VITOSS FOAM 25X100X4MM," 14,715 "
-28011079,HC BUR DRILL BIT CANNULATED 2.1MM," 4,071 "
-28011081,HC BUR DRILL BIT CANNULATED 3X4MM," 2,304 "
-28011125,HC BUR DRILL BIT JACOB CHUCK 3.2MMX200MM," 2,414 "
-28011135,HC BUR DRILL BIT MILLING HEX COUPLING STERILE," 3,682 "
-28011136,HC BUR DRILL BIT MILLING HEX COUPLING STERILE 2MM," 3,682 "
-28011159,HC BUR DRILL BIT QUICK COUPLING 2.0X14MM," 2,506 "
-28011160,HC BUR DRILL BIT QUICK COUPLING 2.0X16MM," 2,506 "
-28011183,HC BUR DRILL BIT SPINAL 2.4MM," 3,400 "
-28011207,HC BUR DRILL BIT VERTEX 2.9MM," 3,349 "
-28011251,HC IMP BUR HOLE COVER MALLEABLE 1.2X7MM," 1,612 "
-28011252,HC IMP BUR HOLE COVER 1.2MM 10MM," 1,439 "
-28011253,HC IMP BUR HOLE COVER 1.2MM 14MM," 1,439 "
-28011254,HC IMP BUR HOLE COVER 1.2MM 14MM 5 HOLE," 1,156 "
-28011255,HC IMP BUR HOLE COVER LARGE 2MM," 1,003 "
-28011256,HC IMP BUR HOLE COVER MALLEABLE 1.2X10MM," 1,612 "
-28011257,HC IMP BUR HOLE COVER MALLEABLE 1.2X14MM," 1,612 "
-28011258,HC IMP BUR HOLE COVER MALLEABLE 1.2X20MM," 1,612 "
-28011259,HC IMP BUR HOLE COVER UNIVERSAL 1.2X7MM," 1,439 "
-28011260,HC IMP BUR HOLE COVER W/BAR 10MM 2 HOLE," 1,131 "
-28011359,HC BUR TAPERED DIAMOND 70 DEGREE 4MM," 2,249 "
-28011365,HC IMP BUTTON GASTROSTOMY MINI ONE 12FX1.2CM, 765 
-28011366,HC IMP BUTTON GASTROSTOMY MINI ONE 14FX1.5CM, 823 
-28011367,HC IMP BUTTON GASTROSTOMY MINI ONE 14FX1.7CM, 765 
-28011375,HC CABLE ILLUMINATOR FIBEROPTIC CABLE 3.5IN 6FT," 2,975 "
-28011377,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 10X50MM," 28,000 "
-28011378,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 10X55MM," 28,000 "
-28011379,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 12X45MM," 40,300 "
-28011380,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 12X50MM," 28,000 "
-28011381,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 12X55MM," 28,000 "
-28011382,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 14X45MM," 40,300 "
-28011383,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 8X45MM," 28,000 "
-28011384,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 8X50MM," 28,000 "
-28011385,HC IMP CAGE SPINAL CLYDESDALE PEEK IB0 8X55MM," 28,000 "
-28011386,HC IMP CAGE SPINAL CLYDESDALE PEEK IB6 10X52MM," 40,300 "
-28011387,HC IMP CAGE SPINAL CLYDESDALE PEEK IB6 10X55MM," 28,000 "
-28011388,HC IMP CAGE SPINAL CLYDESDALE PEEK IB6 12X45MM," 28,000 "
-28011389,HC IMP CAGE SPINAL CLYDESDALE PEEK IB6 12X50MM," 28,000 "
-28011390,HC IMP CAGE SPINAL CLYDESDALE PEEK IB6 12X50MM," 28,000 "
-28011391,HC IMP CAGE PARALLEL COUGAR 18X8X50MM," 29,064 "
-28011392,HC IMP CAGE SPINAL COUGAR LARGE 10 DEGREE 12MM," 27,209 "
-28011393,HC IMP CAGE SPINAL COUGAR LARGE 10X14MM," 27,209 "
-28011394,HC IMP CAGE SPINAL COUGAR LARGE 15 DEGREE 14MM," 27,209 "
-28011395,HC IMP CAGE SPINAL COUGAR MEDIUM 10 DEGREE 11MM," 27,209 "
-28011396,HC IMP CAGE SPINAL COUGAR MEDIUM 10 DEGREE 12MM," 27,209 "
-28011397,HC IMP CAGE SPINAL COUGAR MEDIUM 10 DEGREE 14MM," 27,209 "
-28011398,HC IMP CAGE SPINAL COUGAR MEDIUM 15 DEGREE 11MM," 27,209 "
-28011399,HC IMP CAGE SPINAL COUGAR MEDIUM 15 DEGREE 14MM," 27,209 "
-28011400,HC IMP CAGE SPINAL COUGAR MEDIUM 5 DEGREE 10MM," 27,209 "
-28011401,HC IMP CAGE SPINAL COUGAR MEDIUM 5X12MM," 27,209 "
-28011402,HC IMP CAGE SPINAL COUGAR MEDIUM 5X14MM," 27,209 "
-28011403,HC IMP CAGE SPINAL COUGAR SMALL 10X12MM," 27,209 "
-28011404,HC IMP CAGE SPINAL COUGAR SMALL 5X10MM," 27,209 "
-28011405,HC IMP CAGE SPINAL COROENT LORDOTIC XLARGE 10X22X55MM," 37,156 "
-28011406,HC IMP CAGE SPINAL COROENT LORDOTIC XLARGE 12X18X55MM," 30,387 "
-28011407,HC IMP CAGE SPINAL INTERBODY FUSION TOP MEDIUM 7MM," 26,985 "
-28011408,HC IMP CAGE SPINAL INTERBODY FUSION TOP MEDIUM 9MM," 8,364 "
-28011409,HC IMP CAGE SPINAL LARGE 12X10X25MM," 28,000 "
-28011410,HC IMP CAGE SPINAL LUMBAR XLARGE 10X18X55MM," 30,387 "
-28011411,HC IMP CAGE SPINAL LUMBAR XLARGE 10X18X55MM," 30,387 "
-28011412,HC IMP CAGE SPINAL PLIF CFRP 9X11MM," 18,000 "
-28011413,HC IMP CAGE SPINAL CONTOURED SYNMESH 12X150MM," 47,254 "
-28011414,HC IMP CAGE SPINAL CONTOURED SYNMESH 12X88MM," 50,651 "
-28011415,HC IMP CAGE SPINAL CONTOURED SYNMESH 15X150MM," 47,254 "
-28011416,HC IMP CAGE SPINAL CONTOURED SYNMESH 15X88MM," 43,206 "
-28011417,HC IMP CAGE SPINAL CONTOURED SYNMESH 17X22X150MM," 58,096 "
-28011418,HC IMP CAGE SPINAL CONTOURED SYNMESH 17X88MM," 50,651 "
-28011419,HC IMP CAGE SPINAL CONTOURED SYNMESH 22X28X150MM," 58,096 "
-28011420,HC IMP CAGE SPINAL CONTOURED SYNMESH 26X33X150MM," 58,096 "
-28011421,HC IMP CAGE SPINAL CONTOURED SYNMESH 26X88MM," 50,651 "
-28011422,HC IMP CAGE SPINAL SYNMESH ROUND TITANIUM 10X4MM," 12,096 "
-28011423,HC IMP CAGE SPINAL VERTEBRAL SMALL 12X17MM," 48,133 "
-28011424,HC IMP CAGE SPINAL VERTEBRAL XL 10X18X50MM," 30,387 "
-28011425,HC IMP CAGE SPINAL XLARGE 8X18X50MM," 30,387 "
-28011426,HC IMP CAGE SPINAL SMALL VBR 16X16MM," 39,975 "
-28011427,HC IMP CAGE SPINAL SYNFIX-LR 12 DEGREE 26X32X12MM," 54,925 "
-28011428,HC IMP CAGE SPINAL SYNFIX-LR 12 DEGREE 26X32X13.5MM," 54,925 "
-28011429,HC IMP CAGE SPINAL SYNFIX-LR 8 DEGREE 26X32X12MM," 54,925 "
-28011430,HC IMP CAGE SPINAL SYNFIX-LR 8 DEGREE 26X32X13.5MM," 54,925 "
-28011431,HC  CANNULA DEVICE VAENTRICULAR ASSIST CUFF 42F," 8,175 "
-28011433,HC IMP CAP COMPRESSION DIAPHYSEAL SEGMENT, 423 
-28011434,HC IMP CAP COMPRESSION DIAPHYSEAL SEGMENT REV, 536 
-28011435,HC IMP CAP END NAIL ALLOY TITANIUM 3X4MM, 604 
-28011436,HC CAP LOCKING TITANIUM PANGEA," 3,519 "
-28011441,HC CATHETER DUAL LUMEN 7FX65CM SET," 1,445 "
-28011442,HC CATHETER 5MAX DISTAL DELIVERY 115CM," 10,425 "
-28011443,HC CATHETER 5MAX DISTAL DELIVERY 125CM," 10,425 "
-28011460,HC CATH ANGIOGRAPHY MICRO DELIVERY 45 DEG .025IN," 7,905 "
-28011461,HC CATHETER ANGIOGRAPHY MICRO VELOCITY 2.6FX164CM," 7,905 "
-28011583,HC CATH BACTISEAL 3X15X1.5," 4,803 "
-28011590,HC CATH BALLOON ENDOVIVE 18FX1.5CM, 842 
-28011591,HC CATH BALLOON ENDOVIVE 18FX2.5CM, 842 
-28011640,HC  CATHETER BALLOON AMPHIRION 3.5MMX80MM," 4,038 "
-28011681,HC CATHETER BALLOON ESOPHAGEAL 4.2X1MM," 61,750 "
-28011776,HC CATH BALLOON VALVULOPLASTY TYSHAK 22MMX4CM," 2,720 "
-28011781,HC CATHETER CELSIUS CONTROL 10.7F," 9,068 "
-28011782,HC CATHETER CELSIUS CONTROL 14F," 9,068 "
-28011783,HC CATHETER CIRCA S PROBE," 6,114 "
-28011790,HC CATHETER CTO FRONTRUNNER 3.1FX6FX90CM," 8,250 "
-28011791,HC CATHETER CENTRAL VENOUS 12CM 4F GW NDL 21T SET, 463 
-28011792,HC CATHETER CENTRAL VENOUS SGL LUMEN 2.5X2.5 SET, 273 
-28011793,HC CATHETER CENTRAL VENOUS SGL LUMEN TRAY 4X12 SET, 334 
-28011798,HC CATHETER DIAGNOSTIC EZ STEER J-J CURVED 7X115," 8,250 "
-28011799,HC CATHETER DIAGNOSTIC GUIDEWIRE 100CM 35IN 4F REV," 30,626 "
-28011815,HC CATHETER DILATATION ASCEND AQ 7FX10MM SET," 2,168 "
-28011866,HC CATHETER ENDOSCOPIC VENTRICULAR BACTISEAL," 5,865 "
-28011895,HC CATH ESOPHAGEAL MANOMETRY H2O PERFUSED 20CMX3CM," 2,975 "
-28011896,HC CATH ESOPHAGEAL MANOMETRY H2O PERFUSED 3X3.5," 2,720 "
-28011951,HC CATHETER GLIASITE RTS TRAY 2CM," 17,250 "
-28011952,HC CATHETER GLIASITE RTS TRAY 3CM," 17,250 "
-28011955,HC CATHETER GROSHONG DL TRAY RED 9.5F REP, 155 
-28011956,HC CATHETER GROSHONG DL TRAY WHT 9.5F REP, 155 
-28012047,HC CATHETER HEART PUMP IMPELLA CP SET," 87,500 "
-28012053,HC CATHETER ICP MONITOR CAMINO KIT," 7,709 "
-28012054,HC CATHETER ICP MONITOR TEMP KIT," 7,608 "
-28012062,HC CATHETER INFUSION RIC EXCHANGE 7FX5.08CM SET, 91 
-28012069,HC CATHETER VASCULAR INTRACRANIAL REFLEX 6FX105CM," 7,800 "
-28012076,HC CATHETER LEAD DELIVERY S5," 2,465 "
-28012078,HC CATHETER MAHURKAR 3LUM 12X20 TRAY," 1,261 "
-28012092,HC  CATHETER MICRO EXCELSIOR PRESHAPED 45D150X6CM," 6,472 "
-28012093,HC  CATHETER MICRO EXCELSIOR 90 DEGREE 150X6CM," 6,472 "
-28012094,HC  CATHETER MICRO EXCELSIOR PRESHAPED J 150X6CM," 6,485 "
-28012095,HC  CATHETER MICRO EXCELSIOR PRESHAPED S 150X6CM," 6,485 "
-28012096,HC  CATHETER MICRO EXCELSIOR STRAIGHT 150X6CM," 5,194 "
-28012098,HC  CATHETER MICRO EXCELSIOR X27 135CM," 7,591 "
-28012099,HC  CATHETER MICRO EXCELSIOR X27 150CM," 7,591 "
-28012100,HC  CATHETER MICRO EXCELSIOR X27 FLEX 150CM," 7,591 "
-28012101,HC  CATHETER MICRO EXCELSIOR X27 FLEX PRESHAPED 150," 7,591 "
-28012102,HC  CATHETER MICRO EXCELSIOR X27 PRESHAPED 150CM," 7,591 "
-28012103,HC CATHETER MULTITASK A 4F 035 100CM, 330 
-28012111,HC CATHETER PACING HEPARIN SWAN GANZ 7F," 2,722 "
-28012119,HC CATHETER PERITONEAL LUMBER SPETZLER 4.5INX80CM," 4,751 "
-28012120,HC CATHETER PERITONEAL SMALL LUMEN," 1,913 "
-28012128,HC CATHETER PH VERSAFLEX DUAL CHANNEL 21CMX4.7F," 5,525 "
-28012130,HC CATHETER PICC ARGYLE DUAL LUMEN 1.9FX30CM," 1,036 "
-28012134,HC CATHETER POWER PICC SINGLE LUMEN TRAY 5F," 1,403 "
-28012135,HC CATHETER POWER PICC TRIPLE LUMEN TRAY 5F," 1,547 "
-28012136,HC CATHETER PRESEP OXMTRY 8.5FX20CM KIT," 2,483 "
-28012169,HC CATHETER PUMP HEART ASSIST IMPELLA 5.0," 98,000 "
-28012175,HC CATHETER RIGHTSITE FIX SHAPE 30CM," 2,465 "
-28012177,HC CATHETER RESERVOIR CEREBRAL NEONATAL 3.5MMX4CM," 2,397 "
-28012179,HC  CATHETER SELECT NEURON H1 5FX130CM," 1,063 "
-28012195,HC CATHETER STRAIGHT 14FX24CM SET," 13,838 "
-28012199,HC CATHETER SUPRAPUBIC RUTNER 10F SET, 529 
-28012200,HC CATHETER SWAN GANZ 8FX110CM," 2,365 "
-28012202,HC CATHETER SWAN GANZ THERMODILUTION 3 LUMEN 8F, 236 
-28012204,HC CATHETER ABLATION THERMOCOUPLE J-TYPE 7X115X4," 11,025 "
-28012205,HC CATHETER THERMODILUTION 5 LUMEN 8F, 236 
-28012206,HC CATHETER THROMBECTOMY 3X140CM 15MM SET," 13,088 "
-28012207,HC CATHETER THROMBECTOMY 3X50CM 15MM SET," 5,950 "
-28012208,HC CATHETER THROMBECTOMY 4FX135CM SET," 13,838 "
-28012209,HC CATHETER THROMBECTOMY 6FX90CM SET," 12,150 "
-28012247,HC CATHETER URODYNMC ABDL W/O LUMEN 50CM, 186 
-28012248,HC CATHETER URODYNMC W/ LUMEN BLADDER 50CM, 303 
-28012249,HC CATHETER URODYNMC W/ LUMEN BLADDER RADIO OPAQUE, 292 
-28012274,HC CATHETER VENTRICULAR W RESERVOIRS 2CM," 2,465 "
-28012275,HC CATHETER VENTRICULAR CSF BACTISEAL," 3,494 "
-28012287,HC CATH VSL UMBCL SINGLE LUMEN 3.5F 15IN, 189 
-28012288,HC CATH VSL UMBCL SINGLE LUMEN 5F 15IN, 189 
-28012289,HC CATH VSL UMBCL DUAL LUMEN 15IN 3.5F, 476 
-28012290,HC CATH VSL UMBCL DUAL LUMEN 15IN 5F, 476 
-28012292,HC CATHETER TRAY DUAL LUMEN 4F," 2,222 "
-28012297,HC IMP CEMENT BONE ARTHREX QUICKSET 16CC KIT," 20,948 "
-28012298,HC IMP CEMENT BONE ARTHREX QUICKSET 5CC KIT," 11,550 "
-28012299,HC IMP CEMENT BONE ARTHREX QUICKSET 8CC KIT," 14,963 "
-28012300,HC IMP CEMENT BONE CALLOS INJECT 5ML," 9,263 "
-28012301,HC IMP CEMENT RALIZER 8.5MM, 468 
-28012302,HC IMP CEMENT RALIZER 9.25MM, 468 
-28012303,HC IMP CHIN PROJECTION 2 PIECE 5X33X56MM," 2,125 "
-28012304,HC IMP CLAMP EXTERNAL FIXATOR ASSEMBLY EXTRA SMALL 4CM," 8,407 "
-28012311,HC CLAMP ISOLATOR 5MMX6.5CM," 18,200 "
-28012312,HC CLAMP ISOLATOR LEFT," 28,000 "
-28012313,HC IMP CLAMP SPINAL TRANS CONNECTOR BODY TITANIUM," 2,703 "
-28012314,HC CLAMP SYNERGY ACCESS," 8,500 "
-28012315,HC IMP CLIP ANEURYSM ANGLED 5MM," 2,890 "
-28012316,HC IMP CLIP ANEURYSM BAYONET 6MM," 3,040 "
-28012317,HC IMP CLIP ANEURYSM BAYONET STRGHT KEES SLIMLINE 12MM," 2,791 "
-28012318,HC IMP CLIP ANEURYSM FEN STRAIGHT KEES SLIMLINE 6X9MM," 4,437 "
-28012319,HC IMP CLIP ANEURSYM SUGITA 9MM," 2,525 "
-28012320,HC IMP CLIP ANEURYSM SUGITA L SHAPED 10MM," 2,525 "
-28012321,HC IMP CLIP ANEURYSM SUGITA 12MM," 2,525 "
-28012322,HC IMP CLIP ANEURYSM SUGITA 15MM," 2,525 "
-28012323,HC IMP CLIP ANEURYSM SUGITA 18MM," 2,651 "
-28012324,HC IMP CLIP ANEURYSM SUGITA J SHAPED 7.5MM," 2,525 "
-28012325,HC IMP CLIP ANEURYSM SUGITA 8.7MM," 2,525 "
-28012326,HC IMP CLIP ANEURYSM SUGITA 9MM," 2,525 "
-28012327,HC IMP CLIP FILSHIE SYSTEM TITANIUM 0.5IN, 603 
-28012328,HC IMP CLIP INTERNAL AVALON FILSHIE, 644 
-28012330,HC IMP CLIP NEURO 1.5X1.7, 729 
-28012334,HC IMP CLIP TII MINI 15 DEG ANGLED 90," 9,038 "
-28012335,HC IMP CLIP TII MINI STRAIGHT 70MM AP," 9,038 "
-28012336,HC IMP CLIP TII MINI STRAIGHT 90MM AP," 9,038 "
-28012337,HC IMP CLIP TII STD 15 DEG ANGLED 90MM," 9,038 "
-28012338,HC IMP CLIP TII STD 30 DEG ANGLED 90MM," 9,038 "
-28012339,HC IMP CLIP TII STD STRAIGHT 70MM APP," 9,038 "
-28012340,HC IMP CLIP TII STD STRAIGHT 90MM APP," 9,038 "
-28012380,HC IMPL COIL DELTAMAXX 4X15," 14,963 "
-28012381,HC IMPL COIL DELTAMAXX 5X20," 27,930 "
-28012382,HC IMPL COIL EMBOLIZATION 3MM 14CM 0.018IN, 730 
-28012383,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 10MMX30CM," 11,985 "
-28012384,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 11MMX30CM," 12,000 "
-28012385,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 12MMX30CM," 12,000 "
-28012386,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 13MMX30CM," 10,258 "
-28012387,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 14MMX30CM," 10,293 "
-28012388,HC IMPL COIL EMBOLIZATION DETACHABLE 2MMX4CM," 12,750 "
-28012389,HC IMPL COIL EMBOLIZATION DETACHABLE 3MMX6CM," 12,750 "
-28012390,HC IMPL COIL EMBOLIZATION DETACHABLE 3MMX8CM," 11,985 "
-28012391,HC IMPL COIL EMBOLIZATION DETACHABLE 4MMX10CM," 11,985 "
-28012392,HC IMPL COIL EMBOLIZATION DETACHABLE 4MMX6CM," 11,985 "
-28012393,HC IMPL COIL EMBOLIZATION DETACHABLE 4MMX8CM," 11,985 "
-28012394,HC IMPL COIL EMBOLIZATION DETACHABLE 5MMX10CM," 11,985 "
-28012395,HC IMPL COIL EMBOLIZATION DETACHABLE 5MMX15CM," 11,985 "
-28012396,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 5MMX20CM," 13,125 "
-28012397,HC IMPL COIL EMBOLIZATION DETACHABLE 6MMX10CM," 11,985 "
-28012398,HC IMPL COIL EMBOLIZATION DETACHABLE 6MMX15CM," 12,000 "
-28012399,HC IMPL COIL EMBOLIZATION DETACHABLE SOFT 6MMX30CM," 11,985 "
-28012400,HC IMPL COIL EMBOLIZATION DETACHABLE 7MMX15CM," 11,985 "
-28012401,HC IMPL COIL EMBOLIZATION DETACHABLE 7MMX20CM," 11,985 "
-28012402,HC IMPL COIL EMBOLIZATION DETACHABLE 8MMX20CM," 11,985 "
-28012403,HC IMPL COIL EMBOLIZATION DETACHABLE 9MMX20CM," 11,985 "
-28012404,HC IMPL COIL EMBOLIZATION DETACHABLE 9MMX30CM," 12,000 "
-28012405,HC IMPL COIL EMBOLIZATION MICRO NESTER 14X14MM, 785 
-28012406,HC IMPL COIL EMBOLIZATION MICRO NESTER 16X14MM, 785 
-28012407,HC IMPL COIL EMBOLIZATION NEUROINTERVENTIONAL 3MMX6CM," 9,090 "
-28012408,HC IMPL COIL EMBOLIZATION NEUROINTERVENTIONAL 3MMX8CM," 9,090 "
-28012409,HC IMPL COIL EMBOLIZATION NEUROINTERVENTIONAL 3MMX12CM," 10,223 "
-28012410,HC IMPL COIL EMBOLIZATION OCCLUDING SPRING," 1,275 "
-28012411,HC IMPL COIL HYDROCOIL DETACHABLE 35 SYS 10MMX26CM," 7,183 "
-28012412,HC IMPL COIL HYDROCOIL DETACHABLE 35 SYS 14MMX34CM," 8,925 "
-28012413,HC IMPL COIL HYDROCOIL DETACHABLE 35 SYS 4MMX15CM," 8,330 "
-28012414,HC IMPL COIL HYDROCOIL 35D SYS 6MMX20CM," 5,834 "
-28012415,HC IMPL COIL HYDROCOIL DETACHABLE 35 SYS 8MMX20CM," 6,400 "
-28012416,HC IMPL COIL HYDROFILL 10MMX20CM," 13,875 "
-28012417,HC IMPL COIL HYDROFILL 10MMX30CM," 16,125 "
-28012418,HC IMPL COIL HYDROFILL 11MMX30CM," 16,125 "
-28012419,HC IMPL COIL HYDROFILL 12MMX30CM," 16,125 "
-28012420,HC IMPL COIL HYDROFILL 13MMX40CM," 16,125 "
-28012421,HC IMPL COIL HYDROFILL 14MMX40CM," 16,125 "
-28012422,HC IMPL COIL HYDROFILL 16MMX40CM," 16,125 "
-28012423,HC IMPL COIL HYDROFILL 18MMX50CM," 16,125 "
-28012424,HC IMPL COIL HYDROFILL 20MMX50CM," 16,125 "
-28012425,HC IMPL COIL HYDROFILL 22MMX50CM," 16,125 "
-28012426,HC IMPL COIL HYDROFILL 24MMX50CM," 16,125 "
-28012427,HC IMPL COIL HYDROFILL 2MMX2CM," 9,900 "
-28012428,HC IMPL COIL HYDROFILL 2MMX4CM," 9,900 "
-28012429,HC IMPL COIL HYDROFILL 2MMX6CM," 11,775 "
-28012430,HC IMPL COIL HYDROFILL 3MMX4CM," 9,900 "
-28012431,HC IMPL COIL HYDROFILL 3MMX6CM," 11,775 "
-28012432,HC IMPL COIL HYDROFILL 4MMX10CM," 13,875 "
-28012433,HC IMPL COIL HYDROFILL 4MMX8CM," 11,775 "
-28012434,HC IMPL COIL HYDROFILL 5MMX10CM," 13,875 "
-28012435,HC IMPL COIL HYDROFILL 5MMX15CM," 13,875 "
-28012436,HC IMPL COIL HYDROFILL 6MMX10CM," 13,875 "
-28012437,HC IMPL COIL HYDROFILL 6MMX15CM," 13,875 "
-28012438,HC IMPL COIL HYDROFILL 6MMX20CM," 13,875 "
-28012439,HC IMPL COIL HYDROFILL 7MMX15CM," 16,650 "
-28012440,HC IMPL COIL HYDROFILL 7MMX20CM," 13,875 "
-28012441,HC IMPL COIL HYDROFILL 8MMX20CM," 13,875 "
-28012442,HC IMPL COIL HYDROFILL 8MMX30CM," 16,125 "
-28012443,HC IMPL COIL HYDROFILL 9MMX20CM," 13,875 "
-28012444,HC IMPL COIL HYDROFILL 9MMX30CM," 16,125 "
-28012445,HC IMPL COIL HYDROFRAME 10 10MMX36CM," 17,385 "
-28012446,HC IMPL COIL HYDROFRAME 10 8MMX33CM," 17,171 "
-28012447,HC IMPL COIL HYDROFRAME 10 9MMX31CM," 17,925 "
-28012448,HC IMPL COIL HYDROFRAME 18 10MMX36CM," 19,635 "
-28012449,HC IMPL COIL HYDROFRAME 18 11MMX39CM," 19,845 "
-28012450,HC IMPL COIL HYDROFRAME 18 13MMX47CM," 20,405 "
-28012451,HC IMPL COIL HYDROFRAME 18 14MMX45CM," 20,265 "
-28012452,HC IMPL COIL HYDROFRAME 18 16MMX44CM," 20,195 "
-28012453,HC IMPL COIL HYDROFRAME 18 18MMX50CM," 20,615 "
-28012454,HC IMPL COIL HYDROFRAME 18 20MMX48CM," 20,475 "
-28012455,HC IMPL COIL HYDROFRAME 18 6MMX19CM," 18,270 "
-28012456,HC IMPL COIL HYDROFRAME 18 7MMX18CM," 17,656 "
-28012457,HC IMPL COIL HYDROFRAME 18 8MMX27CM," 18,830 "
-28012458,HC IMPL COIL HYDROFRAME 18 9MMX31CM," 19,250 "
-28012459,HC IMPL COIL MICRO HELICAL SRE CERECYTE 10 8MM 30CM," 7,969 "
-28012460,HC IMPL COIL NEURO CASHMERE 14 10MMX165CM," 8,910 "
-28012461,HC IMPL COIL NEURO CASHMERE 1411MMX18CM," 9,083 "
-28012462,HC IMPL COIL NEURO CASHMERE 14 12MMX19CM," 9,150 "
-28012463,HC IMPL COIL NEURO CASHMERE 14 2MMX2.5CM," 8,340 "
-28012464,HC IMPL COIL NEURO CASHMERE 14 9MMX14CM," 8,978 "
-28012465,HC IMPL COIL NEURO COMPLEX LOOP 10 17X8," 16,031 "
-28012466,HC IMPL COIL NEURO EMBOLIZATION STANDARD 10MM 30CM," 18,375 "
-28012467,HC IMPL COIL NEURO EMBOLIZATION 10MM 40CM," 18,750 "
-28012468,HC IMPL COIL NEURO EMBOLIZATION 11MM 35CM," 18,900 "
-28012469,HC IMPL COIL NEURO EMBOLIZATION 13MM 35CM," 18,900 "
-28012470,HC IMPL COIL NEURO EMBOLIZATION STANDARD 13MM 48CM," 18,795 "
-28012471,HC IMPL COIL NEURO EMBOLIZATION 14MM 50CM," 17,850 "
-28012472,HC IMPL COIL NEURO EMBOLIZATION 14MM 40CM," 19,250 "
-28012473,HC IMPL COIL NEURO EMBOLIZATION 15MM 45CM," 19,600 "
-28012474,HC IMPL COIL NEURO EMBOLIZATION 16MM 45CM," 19,600 "
-28012475,HC IMPL COIL NEURO EMBOLIZATION 18MM 45CM," 19,600 "
-28012476,HC IMPL COIL NEURO EMBOLIZATION 2.3CM 2X3MM," 2,868 "
-28012477,HC IMPL COIL NEURO EMBOLIZATION 2MM 2CM," 13,650 "
-28012478,HC IMPL COIL NEURO EMBOLIZATION 3MM 10CM," 18,375 "
-28012479,HC IMPL COIL NEURO EMBOLIZATION 3MM 2CM," 13,650 "
-28012480,HC IMPL COIL NEURO EMBOLIZATION 3MM 5CM," 16,013 "
-28012481,HC IMPL COIL NEURO EMBOLIZATION STANDARD 3MM 6CM," 16,688 "
-28012482,HC IMPL COIL NEURO EMBOLIZATION 4.1CM 2X4MM," 2,868 "
-28012483,HC IMPL COIL NEURO EMBOLIZATION 4MM 4CM," 15,225 "
-28012484,HC IMPL COIL NEURO EMBOLIZATION STANDARD 4MM 8CM," 17,550 "
-28012485,HC IMPL COIL NEURO EMBOLIZATION 5.8CM 2X5MM," 2,868 "
-28012486,HC IMPL COIL NEURO EMBOLIZATION 5MM 10CM," 16,875 "
-28012487,HC IMPL COIL NEURO EMBOLIZATION 6MM 19CM," 10,500 "
-28012488,HC IMPL COIL NEURO EMBOLIZATION 6MM 15CM," 17,250 "
-28012489,HC IMPL COIL NEURO EMBOLIZATION 6MM 10CM," 16,875 "
-28012490,HC IMPL COIL NEURO EMBOLIZATION 7CM," 16,950 "
-28012491,HC IMPL COIL NEURO EMBOLIZATION 7MM 10CM," 16,875 "
-28012492,HC IMPL COIL NEURO EMBOLIZATION 7MM 20CM," 17,625 "
-28012493,HC IMPL COIL NEURO EMBOLIZATION 8CM 2X6MM," 2,868 "
-28012494,HC IMPL COIL NEURO EMBOLIZATION 8MM 20CM," 17,625 "
-28012495,HC IMPL COIL NEURO EMBOLIZATION 8MM 30CM," 18,375 "
-28012496,HC IMPL COIL NEURO EMBOLIZATION 9MM 25CM," 18,000 "
-28012497,HC IMPL COIL NEURO EMBOLIZATION 9MM 35CM," 18,563 "
-28012498,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 1.5MMX2," 8,813 "
-28012499,HC IMPL COIL NEURO EMBOLIZATION HLCL HYDROSOFT 1.5MMX4," 9,750 "
-28012500,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 10X20," 2,868 "
-28012501,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 10X30," 2,868 "
-28012502,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 12X20," 2,868 "
-28012503,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK12X30," 2,868 "
-28012504,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 14X20," 2,868 "
-28012505,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 14X30," 2,868 "
-28012506,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 3X6," 2,868 "
-28012507,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 4X15," 3,476 "
-28012508,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 4X8," 2,868 "
-28012509,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 5X15," 2,868 "
-28012510,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 5X8," 2,868 "
-28012511,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 6X10," 2,868 "
-28012512,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 6X20," 2,868 "
-28012513,HC IMPL COIL NEURO EMBOLIZATION HLCL INTERLOCK 8X20," 2,868 "
-28012514,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 3MM 6CM," 18,075 "
-28012515,HC IMPL COIL NEURO EMBOLIZATION IMPLANTABLE 4MM 8CM," 18,225 "
-28012516,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 10X30CM," 12,690 "
-28012517,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 11X30CM," 12,690 "
-28012518,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 12X30CM," 12,690 "
-28012519,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 13X30CM," 12,690 "
-28012520,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 14X30CM," 13,500 "
-28012521,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 15X40CM," 13,500 "
-28012522,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 2X1CM," 8,460 "
-28012523,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 2X2CM," 8,460 "
-28012524,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 2X3CM," 12,690 "
-28012525,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 2X4CM," 12,690 "
-28012526,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 2X6CM," 8,460 "
-28012527,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 3X4CM," 12,690 "
-28012528,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE ULTRA 3X6CM," 12,690 "
-28012529,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE ULTRA 3X8CM," 12,345 "
-28012530,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE ULTRA 4X10CM," 11,985 "
-28012531,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 4X6CM," 12,690 "
-28012532,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE ULTRA 4X8CM," 12,690 "
-28012533,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 5X10CM," 12,690 "
-28012534,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 5X15CM," 11,280 "
-28012535,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 5X4CM," 12,690 "
-28012536,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 6X15CM," 13,500 "
-28012537,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 6MMX30CM," 12,750 "
-28012538,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 7X15CM," 13,500 "
-28012539,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 7MMX30CM," 12,690 "
-28012540,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 8X20CM," 12,690 "
-28012541,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 8MMX30CM," 12,488 "
-28012542,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 9X20CM," 12,488 "
-28012543,HC IMPL COIL NEURO EMBOLIZATION V-TRAK 3X6," 16,050 "
-28012544,HC IMPL COIL NEURO EMBOLIZATION V-TRAK 4X5," 15,975 "
-28012545,HC IMPL COIL NEURO EMBOLIZATION V-TRAK 4X8," 15,390 "
-28012546,HC IMPL COIL NEURO EMBOLIZATION V-TRAK 6X19," 16,174 "
-28012547,HC IMPL COIL NEURO EMBOLIZATION V-TRAK 7X28," 16,815 "
-28012548,HC IMPL COIL NEURO EMBOLIC HELICAL SZ10 30CM 10MM," 13,894 "
-28012549,HC IMPL COIL NEURO EMBOLIC HELICAL SZ10 30CM 9MM," 13,894 "
-28012550,HC IMPL COIL NEURO HELICAL 2MX4CM," 5,058 "
-28012551,HC IMPL COIL NEURO HELICAL 2MX6CM," 5,058 "
-28012552,HC IMPL COIL NEURO HELICAL 2MX8CM," 5,058 "
-28012553,HC IMPL COIL NEURO HELICAL STANDARD 20CM 8MM 360D," 12,690 "
-28012554,HC IMPL COIL NEURO HELICAL 360D 12XMM30CM," 14,354 "
-28012555,HC IMPL COIL NEURO MICRO SPHERICAL FRAMING 13MMX22.6CM," 8,325 "
-28012556,HC IMPL COIL NEURO MICRO SPHERICAL FRAMING 15MMX26CM," 8,363 "
-28012557,HC IMPL COIL NEURO MICRO SPHERICAL FRAMING 11MMX18.9CM," 8,250 "
-28012558,HC IMPL COIL NEURO MICRO SPHERICAL FRAMING 17MMX30CM," 8,363 "
-28012559,HC IMPL COIL NEURO MICRO SPHERICAL CSP 24.3CM 14MM," 10,556 "
-28012560,HC IMPL COIL NEURO MICRO 1.5MM 1.0CM," 10,463 "
-28012561,HC IMPL COIL NEURO MICRO 2.5MM 6.0CM," 10,463 "
-28012562,HC IMPL COIL NEURO MICRO 2MM 1.0CM," 10,463 "
-28012563,HC IMPL COIL NEURO MICRO 2MM 6.0CM," 10,463 "
-28012564,HC IMPL COIL NEURO MICRO 4MM 6.0CM," 10,463 "
-28012565,HC IMPL COIL NEURO MICRO 4MM 8.0CM," 10,463 "
-28012566,HC IMPL COIL NEURO ULTIPZQ HIL 10 2X3," 7,650 "
-28012567,HC IMPL COIL NEURO XL10 4MMX10CM," 9,420 "
-28012568,HC IMPL COIL NEURO XL18 10MMX28CM," 10,770 "
-28012569,HC IMPL COIL NEURO XL18 11MMX28CM," 10,770 "
-28012570,HC IMPL COIL NEURO XL18 12MMX30CM," 10,770 "
-28012571,HC IMPL COIL NEURO XL18 13MMX34CM," 10,770 "
-28012572,HC IMPL COIL NEURO XL18 14MMX36CM," 10,770 "
-28012573,HC IMPL COIL NEURO XL18 15MMX40CM," 10,770 "
-28012574,HC IMPL COIL NEURO XL18 16MMX40CM," 10,770 "
-28012575,HC IMPL COIL NEURO XL18 17MMX40CM," 10,770 "
-28012576,HC IMPL COIL NEURO XL18 18MMX40CM," 10,770 "
-28012577,HC IMPL COIL PERIPHERAL DETACHABLE SRE STD 15X5X360," 12,553 "
-28012578,HC IMPL COIL PERIPHERAL DETACHABLE 15CM 6MM 360D," 11,844 "
-28012579,HC IMPL COIL PERIPHERAL DETACHABLE SRE STD 15X6X360," 12,553 "
-28012580,HC IMPL COIL PERIPHERAL EMBOLIZATION 038 8X5, 319 
-28012581,HC IMPL COIL PERIPHERAL EMBOLIZATION 3MM 14CM 0.018IN, 723 
-28012582,HC IMPL COIL PERIPHERAL EMBOLIZATION 3MM 2CM 0.035IN, 723 
-28012583,HC IMPL COIL PERIPHERAL EMBOLIZATION 4MM 3CM 0.035IN, 723 
-28012584,HC IMPL COIL VERSASTILE RANGE FILL 10-15MMX30CM," 10,125 "
-28012585,HC IMPL COIL VERSASTILE RANGE FILL 15-20MMX40CM," 10,125 "
-28012586,HC IMPL COIL VERSASTILE RANGE FILL 15-20MMX60CM," 10,125 "
-28012587,HC IMPL COIL VERSASTILE RANGE FILL 3-6MMX10CM," 9,450 "
-28012588,HC IMPL COIL VERSASTILE RANGE FILL 3-6MMX15CM," 9,450 "
-28012589,HC IMPL COIL VERSASTILE RANGE FILL 3-6MMX6CM," 9,450 "
-28012590,HC IMPL COIL VERSASTILE RANGE FILL 6-10MMX20CM," 9,450 "
-28012591,HC IMPL COIL VERSASTILE RANGE FILL 6-10MMX30CM," 9,450 "
-28012603,HC IMP COLLAR SPINAL 6.0MM TITANIUM TRANS C," 1,819 "
-28012609,HC IMP CONNECTOR DOMINO SS LEGACY 5.5MM," 4,335 "
-28012610,HC IMP CONNECTOR DOMINO SS LEGACY 6.35MM," 9,750 "
-28012611,HC IMP CONNECTOR DOMINO TITANIUM LEGACY 5.5MM," 8,573 "
-28012613,HC IMP CONNECTOR PICC LINE SINGLE LUMEN 4F REP KIT, 170 
-28012614,HC IMP CONNECTOR ROD LATERAL CLOSED 5.5X10MM," 7,673 "
-28012615,HC IMP CONNECTOR ROD LATERAL CLOSED 5.5X30MM," 6,587 "
-28012616,HC IMP CONNECTOR ROD LATERAL CLOSED SS 5.5X20," 7,642 "
-28012617,HC IMP CONNECTOR ROD LATERAL CLOSED SS 6.35X30," 7,673 "
-28012618,HC IMP CONNECTOR ROD LATERAL CLOSED SS 6.35X60," 7,872 "
-28012619,HC IMP CONNECTOR ROD LATERAL CLOSED TITANIUM 6.35X10," 7,023 "
-28012620,HC IMP CONNECTOR ROD LATERAL CLOSED TITANIUM 5.5X20," 8,147 "
-28012621,HC IMP CONNECTOR ROD LATERAL CLOSED TITANIUM 5.5X30," 7,023 "
-28012622,HC IMP CONNECTOR ROD LATERAL CLOSED TITANIUM 5.5X60," 8,364 "
-28012623,HC IMP CONNECTOR ROD LATERAL CLOSED TITANIUM 6.5X10," 8,181 "
-28012624,HC IMP CONNECTOR ROD LATERAL CLOSED SS 5.5X6.35X6," 7,872 "
-28012625,HC IMP CONNECTOR ROD LATERAL OPEN," 11,129 "
-28012626,HC IMP CONNECTOR ROD LOW PROFILE 5X21MM," 9,990 "
-28012627,HC IMP CONNECTOR ROD LOW PROFILE 5X24.5MM-30.5MM," 9,990 "
-28012628,HC IMP CONNECTOR ROD LOW PROFILE 5X24MM," 9,990 "
-28012629,HC IMP CONNECTOR ROD LOW PROFILE 5X31.5X34MM," 10,778 "
-28012630,HC IMP CONNECTOR ROD LOW PROFILE 5X35MM-41MM," 10,778 "
-28012631,HC IMP CONNECTOR ROD LOW PROFILE 5X42X55MM," 10,778 "
-28012632,HC IMP CONNECTOR ROD LOW PROFILE TITANIUM 15MM," 9,990 "
-28012633,HC IMP CONNECTOR ROD LOW PROFILE TITANIUM 18MM," 9,990 "
-28012634,HC IMP CONNECTOR ROD LOW PROFILE TITANIUM 56MM-83MM," 10,778 "
-28012635,HC IMP CONNECTOR ROD TITANIUM SFX 5.5 A3," 10,440 "
-28012636,HC IMP CONNECTOR ROD TITANIUM SFX 5.5 A5," 10,440 "
-28012648,HC COUNTERSINK CANNULATED 3.5MM," 3,391 "
-28012654,HC IMP COVER SCREW CONICAL BAHA," 1,525 "
-28012655,HC IMP COVER SCREW INTERNAL HEX SLEEP," 1,539 "
-28012656,HC IMP COVER SCREW UNGRIP STD TI," 1,539 "
-28012657,HC CROWN HALO BREMER W/HIFIX PIN SMALL," 5,388 "
-28012670,HC DEVICE SUTURING ENDOSCOPIC COR-KNOT," 3,400 "
-28012671,HC IMPL DIALYSIS PERITONEAL W WARMING COIL SET, 813 
-28012675,HC IMP DILATOR STANDARD 038IN 10F 30CM, 111 
-28012676,HC IMP DILATOR STANDARD 038IN 22F 20CM, 105 
-28012680,HC DISSECTOR MARYLAND DAVINCI SINGLE," 32,200 "
-28012681,HC DISSECTOR MARYLAND DAVINCI SINGLE," 5,525 "
-28012707,HC IMPL DRAIN VALVE CHEST PNEUMOSTAT, 272 
-28012717,HC IMP DRESSING WOUND MATRIX BILAYER MESHED 2X2IN," 16,976 "
-28012718,HC IMP DRESSING WOUND MATRIX BILAYER MESHED 4X10IN," 42,315 "
-28012719,HC IMP DRESSING WOUND MATRIX BILAYER MESHED 4X5IN," 26,429 "
-28012720,HC IMP DRESSING WOUND MATRIX BILAYER MESHED 8X10IN," 36,725 "
-28012725,HC DRILL JUGGERKNOT KIT," 2,890 "
-28012727,HC DRILL UNIV GUIDE 2.0/2.4MM," 3,658 "
-28012729,HC DRIVER NEEDLE LARGE ENDOWRIST," 16,500 "
-28012734,HC DEVICE HABIB LAPROSCOPIC RESECTION X4," 28,028 "
-28012771,HC ELECTRODE CAUTERY LOOP SUPER-LOOP," 4,590 "
-28012772,HC ELECTRODE CAUTERY LOOP SUPER-SECT," 4,590 "
-28012797,HC ELECTRODE LOOP LARGE ACMI PK GENERATOR," 3,995 "
-28012805,HC ELECTRODE RADIOFREQUENCY 3 CABLE YEL DISP 15CM," 3,400 "
-28012816,HC ELECTRODE VAPOR COOL PULSE 90 W/O HAND CONTROLS," 2,550 "
-28012819,HC ELECTRODE VAPR PREMIER 90," 9,000 "
-28012824,HC IMP ENDCAP STARDRIVE TITANIUM T40 10X12MM," 1,926 "
-28012825,HC IMP ENDCAP PLN NAIL," 2,661 "
-28012826,HC IMP ENDCAP SPINELINK, 813 
-28012827,HC IMP ENDCAP SPINELINK II," 1,074 "
-28012836,HC IMP EXTERNAL FIXATOR BOLT RING MAC XS," 1,207 "
-28012837,HC IMP EXTERNAL FIXATOR HINGED DJD II 5MM," 25,256 "
-28012838,HC IMP EXTERNAL FIXATOR PROCALLUS," 26,509 "
-28012839,HC IMP EXTERNAL FIXATOR RING 6 TAB 180MM," 10,043 "
-28012840,HC IMP EXTERNAL FIXATION RING FOOT 155MM," 8,222 "
-28012841,HC IMP EXTERNAL FIXATION RING FOOT CIRCLR 155MM," 7,872 "
-28012842,HC EXTERNAL EAR BASE LEFT 30X60MM," 5,525 "
-28012843,HC EXTERNAL EAR RIM LEFT 37X62MM," 2,975 "
-28012846,HC IMP EYE LID WEIGHT GOLD CONTOUR 0.6G," 1,913 "
-28012847,HC IMP EYE LID WEIGHT GOLD CONTOUR 0.8G," 2,040 "
-28012848,HC IMP EYE LID WEIGHT GOLD 1.4X1MM," 2,423 "
-28012851,HC IMP EYEBOLT CROSSLINK TSRH 5MM CLS, 612 
-28012858,HC FIBER LASER BEAMPATH BP-ROBOTIC," 8,075 "
-28012860,HC IMP FILIFORM TIP STRAIGHT 3F, 348 
-28012861,HC IMP FILLER BONE NANOSS 20CC," 28,490 "
-28012862,HC IMP FILLER BONE NANOSS 5CC," 11,850 "
-28012863,HC IMP FILLER BONE VITOSS FOAM 1.2ML," 2,321 "
-28012864,HC IMP FILLER PRO OSTEON 10X10X40MM," 5,891 "
-28012865,HC IMP FILLER PRO OSTEON 10X20X50MM," 8,381 "
-28012868,HC IMP FIXATOR ORTHOFIX SMALL KIT," 7,523 "
-28012880,HC IMP GRAFT BONE BMP7 OP-1," 35,000 "
-28012891,HC IMP GRAFT ENDOPROTHESIS 23X12CM," 27,860 "
-28012910,HC IMP GRAFT FIB SHAFT LPCH," 5,058 "
-28012963,HC IMP GRAFT PERONEUS LONGS," 10,763 "
-28012964,HC IMP GRAFT POWERLINK LIMB EXTENSION 16X88," 18,515 "
-28012965,HC IMP GRAFT PULMONARY PATCH THICK," 16,463 "
-28013000,HC IMP GRAFT MESHED SKIN 5-11CMX12-29CM," 2,091 "
-28013001,HC IMP GRAFT STRIP CHRONOS 100X25X3MM," 10,496 "
-28013002,HC IMP GRAFT STRIP CHRONOS 50X25X3MM," 6,770 "
-28013003,HC IMP GRAFT TENDON TIBIALIS ANTERIOR 23CM," 14,625 "
-28013004,HC IMP GRANULES BONE PRO OSTEON 15ML," 4,208 "
-28013005,HC IMP GRANULES BONE PRO OSTEON 20ML," 3,315 "
-28013006,HC IMP GRANULES BONE PRO OSTEON 2ML," 2,161 "
-28013007,HC IMP GRANULES BONE PRO OSTEON 30ML," 5,627 "
-28013008,HC IMP GRANULES BONE PRO OSTEON 5ML," 4,603 "
-28013009,HC GRASPER CROCODILE SINGLE 5MM," 5,313 "
-28013010,HC GUIDE DRILL MATRIXMANDIBLE 1.5MM SHORT," 2,373 "
-28013011,HC GUIDE DRILL MATRIXMANDIBLE 1.8MM SHORT," 2,373 "
-28013012,HC GUIDE DRILL MATRIXMANDIBLE 2.4MM SHORT," 2,373 "
-28013112,HC HEARING AID OTICON CHILI," 21,000 "
-28013113,HC IMP HEARTMATE II CONDUIT INFLOW," 35,000 "
-28013114,HC IMP HEARTMATE II GRAFT OUTFLOW W/BEND 4IN," 33,600 "
-28013116,HC IMP HEMOSTATIC ABSORBABLE POWDER ARISTA 5G," 1,893 "
-28013146,HC IMP HIP BOLT TROCHANTER MALLORY HEAD OBLONG," 4,352 "
-28013149,HC IMP HIP CENTRALIZER DISTAL CEMENTED 11MM, 425 
-28013150,HC IMP HIP CENTRALIZER DISTAL CEMENTED 12MM, 425 
-28013151,HC IMP HIP CENTRALIZER DISTAL CEMENTED 13MM, 425 
-28013152,HC IMP HIP CENTRALIZER DISTAL VERSYS 10MM, 425 
-28013153,HC IMP HIP CENTRALIZER DISTAL VERSYS 14MM, 425 
-28013154,HC IMP HIP CENTRALIZER DISTAL VERSYS 16MM, 425 
-28013155,HC IMP HIP CENTRALIZER DISTAL VERSYS 18MM, 425 
-28013156,HC IMP HIP CENTRALIZER DISTAL VERSYS 19MM REV, 702 
-28013157,HC IMP HIP CENTRALIZER DISTAL VERSYS 9MM, 425 
-28013158,HC IMP HIP CENTRALIZER DISTAL VERSYS 9MM REV, 702 
-28013159,HC IMP HIP CENTRALIZER FEMORAL 9X125MM," 13,500 "
-28013160,HC IMP HIP CENTRALIZER STEM DISTAL 10MM, 496 
-28013161,HC IMP HIP CENTRALIZER STEM DISTAL 8.5MM, 496 
-28013162,HC IMP HIP CENTRALIZER STEM DISTAL 9.5MM, 468 
-28013163,HC IMP HIP CENTRALIZER STEM DISTAL SUMMIT 11MM, 496 
-28013164,HC IMP HIP CENTRALIZER STEM DISTAL SUMMIT 12MM, 496 
-28013165,HC IMP HIP CENTRALIZER STEM DISTAL SUMMIT 13MM, 496 
-28013166,HC IMP HIP CENTRALIZER STEM DISTAL SUMMIT 14MM, 496 
-28013167,HC IMP HIP CENTRALIZER STEM VERSYS 10MM REV, 702 
-28013215,HC IMP HIP HEAD ADAPTER ALLOY 7MM, 816 
-28013567,HC IMP HIP MODULAR BLOCK 10MM," 2,827 "
-28013576,HC IMPL SCREW ACETABULAR HGPII 4.5X20MM, 862 
-28013691,HC HIP SOFT TISSUE CLAW 40-50MM," 3,817 "
-28013692,HC IMP HIP SPACER CEMENT STAGE ONE," 17,438 "
-28013693,HC IMP HIP SPACER FEMORAL TAPER +0MM, 638 
-28013694,HC IMP HIP SPACER FEMORAL TAPER +5MM, 638 
-28013993,HC IMP HOMOGRAFT HA-IP 3.75MM," 2,508 "
-28013994,HC IMP HOMOGRAFT HIP MF 2.25MM," 2,508 "
-28013995,HC IMP HOMOGRAFT HIP MF 2.50MM," 2,508 "
-28013996,HC IMP HOMOGRAFT HIP MF 2.75MM," 1,658 "
-28013997,HC IMP HOMOGRAFT HIP MF 3.00MM," 2,508 "
-28013998,HC IMP HOMOGRAFT HIP MF 3.25MM," 1,658 "
-28013999,HC IMP HOMOGRAFT HIP MF 3.50MM," 2,508 "
-28014000,HC IMP HOMOGRAFT HIP MF 3.75MM," 1,658 "
-28014001,HC IMP HOMOGRAFT HIP MF 4.00MM," 2,508 "
-28014002,HC IMP HOMOGRAFT HIP MF 4.25MM," 1,658 "
-28014003,HC IMP HOMOGRAFT HIP MF 4.50MM," 2,508 "
-28014004,HC IMP HOMOGRAFT HIP MF 4.75MM," 1,658 "
-28014005,HC IMP HOMOGRAFT HIP MF 5.00MM," 2,508 "
-28014006,HC IMP HOMOGRAFT HIP MF 5.25MM," 2,508 "
-28014007,HC IMP HOMOGRAFT HIP MF 5.50MM," 2,508 "
-28014008,HC IMP HOMOGRAFT HIP MF 5.75MM," 2,508 "
-28014009,HC IMP HOMOGRAFT HIP MF 6.00MM," 2,508 "
-28014010,HC IMP HOMOGRAFT HIP MF 6.25MM," 2,508 "
-28014011,HC IMP HOMOGRAFT HIP MF 6.50MM," 2,508 "
-28014012,HC IMP HOMOGRAFT HIP MF 6.75MM," 2,508 "
-28014013,HC IMP HOMOGRAFT HIP MF 7.00MM," 2,508 "
-28014014,HC IMP HOMOGRAFT HIP MF 7.25MM," 1,658 "
-28014015,HC IMP HOMOGRAFT HIP MF 7.50MM," 2,508 "
-28014016,HC IMP HOMOGRAFT HIP MH 2.00MM," 2,508 "
-28014017,HC IMP HOMOGRAFT HIP MH 2.25MM," 2,508 "
-28014018,HC IMP HOMOGRAFT HIP MH 2.50MM," 2,508 "
-28014019,HC IMP HOMOGRAFT HIP MH 2.75MM," 1,688 "
-28014020,HC IMP HOMOGRAFT HIP MH 3.00MM," 2,508 "
-28014021,HC IMP HOMOGRAFT HIP MH 3.25MM," 2,508 "
-28014022,HC IMP HOMOGRAFT HIP MH 3.50MM," 2,508 "
-28014023,HC IMP HOMOGRAFT HIP MH 3.75MM," 2,508 "
-28014024,HC IMP HOMOGRAFT HIP MH 4.00MM," 2,508 "
-28014025,HC IMP HOMOGRAFT HIP MH 4.25MM," 3,188 "
-28014026,HC IMP HOMOGRAFT HIP MH 4.50MM," 2,508 "
-28014027,HC IMP HOMOGRAFT HIP MH 4.75MM," 2,508 "
-28014028,HC IMP HOMOGRAFT HIP MH 5.00MM," 2,508 "
-28014029,HC IMP HOMOGRAFT HIP MH 5.25MM," 2,508 "
-28014030,HC IMP HOMOGRAFT HIP MH 5.50MM," 2,508 "
-28014031,HC IMP HOMOGRAFT HIP MH 5.75MM," 2,508 "
-28014032,HC IMP HOMOGRAFT HIP MH 6.00MM," 2,508 "
-28014033,HC IMP HOMOGRAFT HIP MH 6.25MM," 2,508 "
-28014034,HC IMP HOMOGRAFT HIP MH 6.50MM," 2,508 "
-28014035,HC IMP HOMOGRAFT HIP MH 6.75MM," 2,508 "
-28014036,HC IMP HOMOGRAFT HIP MH 7.00MM," 2,508 "
-28014037,HC IMP HOMOGRAFT INCUS LEFT," 1,275 "
-28014038,HC IMP HOMOGRAFT INCUS RIGHT," 1,275 "
-28014039,HC IMP HOMOGRAFT MALLEUS LEFT," 1,275 "
-28014040,HC IMP HOMOGRAFT MALLEUS RIGHT," 1,275 "
-28014041,HC IMP HOMOGRAFT MIP-TF 2.00MM," 3,188 "
-28014042,HC IMP HOMOGRAFT MIP-TF 2.50MM," 3,188 "
-28014043,HC IMP HOMOGRAFT MIP-TF 3.00MM," 3,188 "
-28014044,HC IMP HOMOGRAFT MIP-TF 3.50MM," 3,188 "
-28014045,HC IMP HOMOGRAFT MIP-TF 4.00MM," 3,188 "
-28014046,HC IMP HOMOGRAFT MIP-TF 4.50MM," 3,188 "
-28014047,HC IMP HOMOGRAFT MIP-TF 5.00MM," 3,188 "
-28014048,HC IMP HOMOGRAFT MIP-TF 5.50MM," 3,188 "
-28014049,HC IMP HOMOGRAFT MIP-TF 6.00MM," 3,188 "
-28014050,HC IMP HOMOGRAFT MIP-TF 6.50MM," 3,145 "
-28014051,HC IMP HOMOGRAFT MIP-TF 7.00MM," 3,188 "
-28014052,HC IMP HOMOGRAFT MIP-TF 7.50MM," 3,188 "
-28014053,HC IMP HOMOGRAFT MIP-TF 8.00MM," 3,188 "
-28014054,HC IMP HOMOGRAFT MIP-TF 8.50MM," 2,295 "
-28014055,HC IMP HOMOGRAFT MIP-TH 2.00MM," 3,188 "
-28014056,HC IMP HOMOGRAFT MIP-TH 2.50MM," 3,188 "
-28014057,HC IMP HOMOGRAFT MIP-TH 3.00MM," 3,188 "
-28014058,HC IMP HOMOGRAFT MIP-TH 3.50MM," 3,188 "
-28014059,HC IMP HOMOGRAFT MIP-TH 4.00MM," 3,188 "
-28014060,HC IMP HOMOGRAFT MIP-TH 4.50MM," 3,188 "
-28014061,HC IMP HOMOGRAFT MIP-TH 5.00MM," 3,188 "
-28014062,HC IMP HOMOGRAFT MIP-TH 5.50MM," 3,188 "
-28014063,HC IMP HOMOGRAFT MIP-TH 6.00MM," 3,188 "
-28014064,HC IMP HOMOGRAFT MIP-TH 6.50MM," 3,188 "
-28014065,HC IMP HOMOGRAFT MIP-TH 7.00MM," 3,188 "
-28014066,HC IMP HOMOGRAFT MIP-TH 7.50MM," 3,188 "
-28014067,HC IMP HOMOGRAFT TYMP MEMBRANE LEFT," 4,335 "
-28014068,HC IMP HOMOGRAFT TYMP MEMBRANE RIGHT," 4,335 "
-28014069,HC IMP HOOK ANGLED BLADE LEFT TITANIUM 5.5MM," 7,891 "
-28014070,HC IMP HOOK ANGLED BLADE LUMBAR 5.5MM," 5,131 "
-28014071,HC IMP HOOK ANGLED BLADE LUMBAR SMALL 5.5MM," 6,554 "
-28014072,HC IMP HOOK ANGLED BLADE RIGHT SS 5.5MM," 6,455 "
-28014073,HC IMP HOOK ANGLED BLADE RIGHT TITANIUM 5.5MM," 7,891 "
-28014074,HC IMP HOOK ANGLED BLADE TITANIUM MEDIUM," 6,554 "
-28014075,HC IMP HOOK ANGLED LEFT 5.5X7.5MM," 5,063 "
-28014076,HC IMP HOOK ANGLED LEFT LARGE 5.5X9MM," 5,063 "
-28014077,HC IMP HOOK ANGLED LUMBAR BLADE SS SMALL 4.5MM," 5,131 "
-28014078,HC IMP HOOK ANGLED RIGHT LARGE 5.5X9MM," 6,134 "
-28014079,HC IMP HOOK ANGLED THORACIC BLADE SS LEFT," 6,455 "
-28014081,HC IMP HOOK EXTENDED BODY SS LARGE," 6,561 "
-28014082,HC IMP HOOK EXTENDED BODY SS MEDIUM 5.5MM," 6,561 "
-28014083,HC IMP HOOK EXTENDED BODY SS SMALL 5.5MM," 5,131 "
-28014084,HC IMP HOOK EXTENDED BODY TITANIUM LARGE 5.5MM," 7,891 "
-28014085,HC IMP HOOK EXTENDED BODY TITANIUM MEDIUM 5.5MM," 7,891 "
-28014086,HC IMP HOOK EXTENDED BODY TITANIUM SMALL 5.5MM," 6,554 "
-28014087,HC IMP HOOK LAMINA DUAL OPENING TI SMALL," 6,273 "
-28014088,HC IMP HOOK LAMINA DUAL-OPENING TALL BODY TI LARGE," 6,273 "
-28014089,HC IMP HOOK LAMINA DUAL-OPENING TALL BODY MEDIUM," 6,273 "
-28014090,HC IMP HOOK LAMINA DUAL-OPENING TALL BODY SMALL," 6,273 "
-28014091,HC IMP HOOK LAMINA DUAL-OPENING TI LARGE," 6,273 "
-28014092,HC IMP HOOK LAMINA EXTENDED BLADE LEFT LARGE," 4,981 "
-28014093,HC IMP HOOK LAMINA EXTENDED BLADE LEFT MEDIUM," 4,981 "
-28014094,HC IMP HOOK LAMINA EXTENDED BLADE RIGHT LARGE," 4,981 "
-28014095,HC IMP HOOK LAMINA EXTENDED BLADE RIGHT MEDIUM," 4,981 "
-28014096,HC IMP HOOK LAMINA EXTENDED TI SMALL 4.5MM," 7,105 "
-28014097,HC IMP HOOK LAMINA REVERE SS 6.35," 9,878 "
-28014098,HC IMP HOOK LAMINA THORACIC TI," 6,273 "
-28014099,HC IMP HOOK LAMINA WIDE BLADE TI 4.5MM," 7,105 "
-28014100,HC HOOK MONOPOLAR CAUTERY SINGLE," 5,525 "
-28014101,HC IMP HOOK NARROW BLADE RAMPED 5.5MM M8," 6,554 "
-28014102,HC IMP HOOK NARROW BLADE RAMPED SS MEDIUM," 5,231 "
-28014103,HC IMP HOOK NARROW BLADE RAMPED SS SMALL," 5,131 "
-28014104,HC IMP HOOK NARROW BLADE RAMPED TITANIUM SMALL," 6,554 "
-28014105,HC IMP HOOK NARROW BLADE SS 5.5X10MM," 7,809 "
-28014106,HC IMP HOOK NARROW BLADE SS LARGE 5.5MM," 6,455 "
-28014107,HC IMP HOOK NARROW BLADE SS MEDIUM 5.5MM," 3,876 "
-28014108,HC IMP HOOK NARROW BLADE SS SMALL 5.5MM," 5,131 "
-28014109,HC IMP HOOK NARROW BLADE TITANIUM LARGE," 7,891 "
-28014110,HC IMP HOOK NARROW BLADE TITANIUM MEDIUM," 6,682 "
-28014111,HC IMP HOOK NARROW BLADE TITANIUM MEDIUM 4.75MM," 5,390 "
-28014112,HC IMP HOOK NARROW BLADE TITANIUM SMALL 5.5MM," 6,682 "
-28014113,HC IMP HOOK NERVE BAYONET LEFT," 1,886 "
-28014114,HC IMP HOOK NERVE BAYONET MICRO LEFT," 4,368 "
-28014115,HC IMP HOOK NERVE BAYONET MICRO RIGHT," 4,116 "
-28014116,HC IMP HOOK NERVE BAYONET RIGHT," 1,997 "
-28014117,HC IMP HOOK NERVE BAYONET STRAIGHT," 1,997 "
-28014118,HC IMP HOOK OFFSET SS LEFT 5.5MM," 6,561 "
-28014119,HC IMP HOOK OFFSET SS RIGHT 5.5MM," 6,561 "
-28014120,HC IMP HOOK OFFSET TITANIUM LEFT 5.5MM," 7,891 "
-28014121,HC IMP HOOK OFFSET TITANIUM RIGHT 5.5MM," 7,891 "
-28014122,HC IMP HOOK PEDICLE EXPEDIUM SS 5.5MM," 6,911 "
-28014123,HC IMP HOOK PEDICLE EXTENDED SMALL 4.5MM," 7,105 "
-28014124,HC IMP HOOK PEDICLE MEDIUM 5.5X7.5MM," 5,063 "
-28014125,HC IMP HOOK PEDICLE SPINAL SYSTEM MEDIUM 5.5MM," 6,682 "
-28014126,HC IMP HOOK PEDICLE SS LARGE 5.5MM," 1,438 "
-28014127,HC IMP HOOK PEDICLE SS MEDIUM 5.5MM," 3,876 "
-28014128,HC IMP HOOK PEDICLE SS SMALL 5.5MM," 5,131 "
-28014129,HC IMP HOOK PEDICLE TITANIUM LARGE 5.5MM," 7,891 "
-28014130,HC IMP HOOK PEDICLE TITANIUM LARGE 5.5X9MM," 6,134 "
-28014131,HC IMP HOOK PEDICLE TITANIUM MEDIUM 4.5MM," 7,105 "
-28014132,HC IMP HOOK PEDICLE TITANIUM SMALL 5.5MM," 6,554 "
-28014133,HC IMP HOOK PEDICLE W/LIP SS SMALL," 6,688 "
-28014134,HC HOOK RETRACTION LAMINA 4.5MM," 10,658 "
-28014137,HC IMP HOOK SPINAL BLADE SYSTEM TITANIUM LARGE 5.5MM," 7,891 "
-28014138,HC IMP HOOK SPINAL BLADE SYSTEM TITANIUM MEDIUM 5.5MM," 4,951 "
-28014139,HC IMP HOOK SPINAL TRANSVERSE SIDE LEFT," 6,273 "
-28014140,HC IMP HOOK SPINAL WIDE BLADE TITANIUM SMALL M8," 6,554 "
-28014141,HC IMP HOOK SPINAL XIA LAMINA MEDIUM," 7,078 "
-28014142,HC IMP HOOK TRANSVERSE TITANIUM 3MM," 2,933 "
-28014143,HC IMP HOOK WIDE BLADE SS 5.5MM," 5,131 "
-28014144,HC IMP HOOK WIDE BLADE SS LARGE 5.5MM," 6,455 "
-28014145,HC IMP HOOK WIDE BLADE SS MEDIUM 5.5MM," 3,791 "
-28014146,HC IMP HOOK WIDE BLADE SS SMALL 5.5MM," 5,131 "
-28014147,HC IMP HOOK WIDE BLADE TITANIUM 5.5MM," 6,554 "
-28014148,HC IMP HOOK WIDE EXPEDIUM 8.0MM," 6,543 "
-28014149,HC HUMMINGBIRD SYNERGY VENT KIT," 4,027 "
-28014184,HC INTRODUCER 13F 13CM KIT," 5,950 "
-28014185,HC  INTROD ACC PL 3MM 13CM 9F SET, 716 
-28014194,HC  INTROD GUIDW PLUS 4FX40CM SET, 351 
-28014208,HC  INTROD OSTEO BEVEL KYPHX," 3,400 "
-28014209,HC  INTROD OSTEO KYPHX 3MM," 7,763 "
-28014210,HC  INTROD PEEL AWAY 16F SET, 518 
-28014211,HC  INTROD PEEL AWAY 8F SET, 587 
-28014257,HC  INTRODUCER SHEATH W/GUIDEWIRE 16F KIT, 748 
-28014272,HC IRRIGATOR SUCTION ENDOSCOPIC DA VINCI 5MM," 2,550 "
-28014273,HC IMP IUD MIRENA," 5,980 "
-28014283,HC KIT CUSTOM CRANIAL EXTRA LARGE," 75,632 "
-28014284,HC KIT CUSTOM CRANIAL LARGE," 77,021 "
-28014285,HC IMP KIT CUSTOM CRANIAL MEDIUM," 64,482 "
-28014286,HC IMP KIT CUSTOM CRANIAL PEEK," 70,500 "
-28014287,HC IMP KIT CUSTOM CRANIAL SMALL," 55,626 "
-28014303,HC KIT CUSTOM HIP OPC ARTHROSCOPY," 2,440 "
-28014307,HC KIT CUSTOM KNEE ARTHROSCOPY," 2,250 "
-28014347,HC IMP KNEE ADAPTER OSS SEGMENTAL STACKING," 1,037 "
-28014516,HC IMP KNEE BEARING TIBIAL ANT STAB COMPLETE 12X63 REV," 7,574 "
-28014517,HC IMP KNEE BEARING TIBIAL ANT STAB COMPLETE 12X79 REV," 7,574 "
-28014518,HC IMP KNEE BEARING TIBIAL COMPLETE 10X59MM," 5,814 "
-28014519,HC IMP KNEE BEARING TIBIAL POST VANGUARD 12X59MM," 5,814 "
-28014520,HC IMP KNEE BEARING TIBIAL PRIMARY 16X63X67MM REV," 6,441 "
-28014521,HC IMP KNEE BEARING TIBIAL POST STAB 10X63X67MM REV," 8,136 "
-28014522,HC IMP KNEE BEARING TIBIAL POST STAB 12X79X83MM," 5,814 "
-28014523,HC IMP KNEE BEARING TIBIAL POST STAB CONST 22X59MM REV," 10,719 "
-28014524,HC IMP KNEE BEARING TIBIAL POST STAB DIRC 10X79X83 REV," 7,217 "
-28014525,HC IMP KNEE BEARING TIBIAL POST STAB PL 10X71X75MM," 6,545 "
-28014526,HC IMP KNEE BEARING TIBIAL SUPER STABIL 12X71X75MM REV," 10,719 "
-28014527,HC IMP KNEE BUSHING FEMORAL POLY PE OSS," 2,950 "
-28014528,HC IMP KNEE BUSHING FEMORAL POLY PE OSS REV," 2,499 "
-28014529,HC IMP KNEE BUSHING TIBIAL PE OSS," 2,108 "
-28014530,HC IMP KNEE BUSHING TIBIAL PE OSS REV," 1,785 "
-28014666,HC IMP KNEE FEMORAL SPACER CEMENT STAGEONE 65MM," 15,563 "
-28014667,HC IMP KNEE FEMORAL SPACER CEMENT STAGEONE 70MM," 15,998 "
-28014668,HC IMP KNEE FEMORAL SPACER CEMENT STAGEONE 75MM," 15,563 "
-28014669,HC IMP KNEE FEMORAL SPACER CEMENT STAGEONE 80MM," 15,563 "
-28015012,HC KNIFE BAYONET LONG 188MM," 2,910 "
-28015020,HC KNIFE CORING HEARTMATE," 4,675 "
-28015022,HC KYPHOPAK EXPRESS FIRST FRACTURE TRAY 15/2," 25,165 "
-28015023,HC KYPHOPAK EXPRESS FIRST FRACTURE TRAY 20/3," 24,171 "
-28015024,HC IMP LEAD ACCESSORY KIT, 272 
-28015145,HC LENS VITRECTOMY FLAT MACHEMER," 2,550 "
-28015149,HC IMP LINK SPINELINK STANDARD 21MM, 477 
-28015170,HC LVAS NOVACOR KIT," 104,562 "
-28015171,HC IMP MALAR FACIAL EXTEN CONTRD LG LEFT 55X27X5," 2,125 "
-28015172,HC IMP MALAR FACIAL EXTEN CONTRD LG RIGHT 55X27X5," 2,125 "
-28015173,HC IMP MANDIBLE RAMUS INFERIOR RIDGE RIGHT 79X3," 3,145 "
-28015192,HC IMPL MESH MICRO UNIVERSAL TI 0.1X0.5X60X60MM," 2,814 "
-28015193,HC IMPL MESH MICRO UNIVERSAL TI 0.2X1.5X60X60MM," 2,814 "
-28015198,HC IMP MICROSPHERE EMBOLIC 2ML 100-300MH," 11,213 "
-28015199,HC IMP MICROSPHERE EMBOLIC EMBOZENE 2ML 100UM," 1,403 "
-28015200,HC IMP MICROSPHERE EMBOLIC EMBOZENE 2ML 250UM," 1,403 "
-28015201,HC IMP MICROSPHERE EMBOLIC 2ML 300-500MH," 11,213 "
-28015202,HC IMP MICROSPHERE EMBOLIC EMBOZENE 2ML 40U," 1,403 "
-28015204,HC MOLD BEAD RESORBABLE OSTEOSET 7.0MM," 2,933 "
-28015210,HC IMP NAIL ANKLE LOCKING 10X150MM," 20,640 "
-28015211,HC IMP NAIL ELASTIC 1.5X300MM," 2,315 "
-28015212,HC IMP NAIL ELASTIC 2.5X440MM," 2,655 "
-28015213,HC IMP NAIL FEMORAL INTRAMEDULLARY 12MM," 21,970 "
-28015214,HC IMP NAIL FEMORAL INTRAMEDULLARY 18MM," 25,357 "
-28015215,HC IMP NAIL FEMORAL LOCKING 6.5MM 28CM," 9,758 "
-28015216,HC IMP NAIL FEMORAL LOCKING 6.5MM 30CM," 9,173 "
-28015217,HC IMP NAIL FEMORAL LOCKING 6.5MM 34CM," 9,173 "
-28015218,HC IMP NAIL FEMORAL LOCKING 6.5MM 36CM," 9,173 "
-28015219,HC IMP NAIL FEMORAL LOCKING 6.5MM 42CM," 9,758 "
-28015220,HC IMP NAIL INTRAMEDULLARY SS 0.31IN 0.21IN," 4,675 "
-28015221,HC IMP NAIL LOCKING PEDI 6.5MMX32CM," 9,758 "
-28015222,HC IMP NAIL TIBIAL 9X340MM," 11,625 "
-28015227,HC NEEDLE ASPIRATION FLEX ULTRASOUND 19G," 4,157 "
-28015238,HC NEEDLE BIOPSY CRANIAL NAVIGUS," 4,735 "
-28015247,HC NEEDLE ELECTRODE MODULE NVM5," 7,853 "
-28015260,HC  NDL INTRODUCER EXCALIBER 5F, 213 
-28015276,HC NEEDLE ULTRASOUND QUICKCORE 5.2FX19G," 2,818 "
-28015281,HC IMP NUT DIAPHYSL SGMNT COMPRESS," 2,321 "
-28015286,HC IMP ORBITAL FLOOR MAX MEDPORE 41X42X1MM," 8,364 "
-28015287,HC IMP ORBITAL FLOOR MAX MEDPORE 41X42X1X1MM," 8,160 "
-28015288,HC IMP OVATION POLYMER FILL KIT," 5,738 "
-28015295,HC IMPL PLATE CRANIOFACIAL BOX RESORBABLE 2X2," 1,002 "
-28015296,HC IMPL PLATE OLCRANON LARGE 13 HOLE," 7,875 "
-28015297,HC IMPL PLATE OLCRANON SMALL 10 HOLE," 8,500 "
-28015298,HC IMP PARTICLE EMBOLIZATION PVOH 710-1000U, 785 
-28015301,HC IMP PATCH PERICARDIAL BOVINE 5X5," 1,615 "
-28015302,HC IMP PATCH PERICARDIAL BOVINE 6X8," 2,423 "
-28015303,HC IMP PATCH PULMONARY BRANCH," 32,200 "
-28015304,HC IMP PATCH PULMONARY THIN," 13,650 "
-28015305,HC IMP PATCH PULMONARY TRUNK," 32,200 "
-28015306,HC IMPL PATELLA TENDON HEMI," 7,565 "
-28015307,HC IMPL PATELLA TENDON WHOLE," 10,223 "
-28015316,HC IMP PEG DISTAL FEMORAL VANGUARD, 425 
-28015317,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X18MM," 1,532 "
-28015318,HC PEN ISOLATOR 19MM," 13,388 "
-28015319,HC PEN ISOLATOR LONG," 13,125 "
-28015331,HC IMP PERISTRIP REINFORCEMENT VERITAS PSD6," 1,585 "
-28015353,HC PIN HEMORRHAGE OCCLUDER," 3,236 "
-28015359,HC IMP PIN RUSH MEDULLARY 2.4X216MM, 306 
-28015360,HC IMP PIN RUSH MEDULLARY 6.4X241MM, 683 
-28015361,HC IMP PIN RUSH MEDULLARY 6.4X260MM, 683 
-28015362,HC IMP PIN RUSH MEDULLARY 6.4X279MM, 564 
-28015363,HC IMP PIN RUSH MEDULLARY 6.4X298MM, 683 
-28015364,HC IMP PIN RUSH MEDULLARY 6.4X318MM, 564 
-28015365,HC IMP PIN RUSH MEDULLARY 6.4X337MM, 710 
-28015366,HC IMP PIN RUSH MEDULLARY 6.4X356MM, 564 
-28015367,HC IMP PIN RUSH MEDULLARY 6.4X375MM, 564 
-28015368,HC IMP PIN RUSH MEDULLARY 6.4X394MM, 710 
-28015369,HC IMP PIN RUSH MEDULLARY 6.4X413MM, 564 
-28015370,HC PIN SKULL HALO BREMER HIFIX," 4,862 "
-28015392,HC IMPL PLATE CLAW 25MM 2 HOLE," 13,425 "
-28015393,HC IMPL PLATE CONDYLAR 45MM," 4,114 "
-28015394,HC IMPL PLATE SPINAL LATERAL XLP 10MM," 23,100 "
-28015395,HC IMPL PLATE SPINAL LATERAL XLP 12MM," 23,100 "
-28015396,HC IMPL PLATE SPINAL POLYAXIAL 35MM," 24,500 "
-28015397,HC IMPL PLATE SPINAL POLYAXIAL 45MM," 24,500 "
-28015398,HC IMPL PLATE VA LCP 2.4MM 6H HD 3H RIGHT," 8,271 "
-28015399,HC IMPL PLATE MATRIXMANDIBLE ORBITAL SMALL LEFT," 11,112 "
-28015400,HC IMPL PLATE 1/3 TUBULAR 8 HOLE, 395 
-28015401,HC IMPL PLATE 1/3 TUBULAR RIGHT ANGLED 4 HOLE 4X56MM," 1,029 "
-28015402,HC IMPL PLATE 1/4 TUBULAR SS W/COLLAR 3 HOLE, 737 
-28015403,HC IMPL PLATE 1/4 TUBULAR SS W/COLLAR 4 HOLE, 821 
-28015404,HC IMPL PLATE 1/4 TUBULAR SS W/COLLAR 6 HOLE, 938 
-28015405,HC IMPL PLATE 1/4 TUBULAR TI W/COLLAR 4 HOLE, 676 
-28015406,HC IMPL PLATE 1/4 TUBULAR TI W/COLLAR 5 HOLE, 710 
-28015407,HC IMPL PLATE 1/4 TUBULAR TI W/COLLAR 6 HOLE, 769 
-28015408,HC IMPL PLATE 18 HOLE STRAIGHT," 4,454 "
-28015409,HC IMPL PLATE 2X5MM," 14,250 "
-28015410,HC IMPL PLATE THREE LEVEL 45MM," 10,500 "
-28015411,HC IMPL PLATE THREE LEVEL 48MM," 10,500 "
-28015412,HC IMPL PLATE THREE LEVEL 51MM," 10,500 "
-28015413,HC IMPL PLATE THREE LEVEL 54MM," 10,500 "
-28015414,HC IMPL PLATE THREE LEVEL 57MM," 10,500 "
-28015415,HC IMPL PLATE THREE LEVEL 60MM," 10,500 "
-28015416,HC IMPL PLATE THREE LEVEL 63MM," 10,500 "
-28015417,HC IMPL PLATE THREE LEVEL 66MM," 10,500 "
-28015418,HC IMPL PLATE THREE LEVEL 69MM," 10,500 "
-28015419,HC IMPL PLATE 4 HOLE 2.7X41MM," 1,298 "
-28015420,HC IMPL PLATE 6 HOLES 2.7X59MM," 1,407 "
-28015421,HC IMPL PLATE 6 HOLE GRID," 4,378 "
-28015422,HC IMPL PLATE ADAPTION 20H 100MM," 3,203 "
-28015423,HC IMPL PLATE ADAPTION 20H 90MM," 2,462 "
-28015424,HC IMPL PLATE ADAPTION MATRIXMANDIBLE 12HOLE," 4,991 "
-28015425,HC IMPL PLATE LOW PROFILE ADJUSTABLE 5.5X2.15-2.95IN," 7,718 "
-28015426,HC IMPL PLATE ALVEOLAR COMPRESSION SHORT 4 HOLE," 1,727 "
-28015427,HC IMPL PLATE ANGLED UNIVERSAL 3X3 2.4MM," 6,834 "
-28015428,HC IMPL PLATE ANGLED UNIVERSAL 4X4 2.4MM," 7,378 "
-28015429,HC IMPL PLATE ANTERIOR CLAVICLE 150MM," 9,000 "
-28015430,HC IMPL PLATE ANTERIOR CERVICAL 19MM," 8,500 "
-28015431,HC IMPL PLATE ANTERIOR CERVICAL 21MM," 8,500 "
-28015432,HC IMPL PLATE ANTERIOR CERVICAL 35MM," 9,000 "
-28015433,HC IMPL PLATE ANTERIOR CERVICAL 37.5MM," 9,000 "
-28015434,HC IMPL PLATE ANTERIOR CERVICAL 40MM," 9,000 "
-28015435,HC IMPL PLATE ANTERIOR CERVICAL 42.5MM," 9,000 "
-28015436,HC IMPL PLATE ANTERIOR CERVICAL 45MM," 12,761 "
-28015437,HC IMPL PLATE ANTERIOR CERVICAL 47.5MM," 12,761 "
-28015438,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 3H LEFT," 8,118 "
-28015439,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 3H RIGHT," 7,565 "
-28015440,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 4H LEFT," 8,256 "
-28015441,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 5H RIGHT," 8,393 "
-28015442,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 6H LEFT," 8,160 "
-28015443,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 6H RIGHT," 7,905 "
-28015444,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 7H LEFT," 8,118 "
-28015445,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 7H RIGHT," 8,118 "
-28015446,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 8H RIGHT," 7,884 "
-28015447,HC IMPL PLATE ANTERIOR CLAVICLE LAT EXTN 9H LEFT," 8,241 "
-28015448,HC IMPL PLATE ANTERIOR LUMBAR TI 37MM," 32,432 "
-28015449,HC IMPL PLATE ANTERIOR LUMBAR TI 39MM," 32,432 "
-28015450,HC IMPL PLATE BONE MINI STRAIGHT 30 HOLE," 3,611 "
-28015451,HC IMPL PLATE BONE MINI CURVED 12 HOLE," 1,879 "
-28015452,HC IMPL PLATE BONE MINI DOUBLE Y SHPD W/BAR 6 HOLE," 1,532 "
-28015453,HC IMPL PLATE BONE MINI DOUBLE Y SHPD W/BAR LONG 6 HOLE," 1,532 "
-28015454,HC IMPL PLATE BONE MINI L SHAPED W/BAR LONG LEFT 5HOLE," 1,626 "
-28015455,HC IMPL PLATE BONE MINI L SHAPED W/BAR LONG RIGHT 4HOLE," 1,503 "
-28015456,HC IMPL PLATE BONE MINI L SHAPED W/BAR LONG RIGHT 5HOLE," 1,626 "
-28015457,HC IMPL PLATE BONE MINI L SHAPED W/BAR MEDIUM LEFT 4H," 1,561 "
-28015458,HC IMPL PLATE BONE MINI L SHAPED LARGE RIGHT," 1,879 "
-28015459,HC IMPL PLATE BONE MINI L SHAPED SHORT LEFT 4 HOLE," 1,423 "
-28015460,HC IMPL PLATE BONE MINI L SHAPED W/BAR LEFT 4 HOLE," 1,503 "
-28015461,HC IMPL PLATE BONE MINI L SHAPED W/BAR RIGHT 4 HOLE," 1,469 "
-28015462,HC IMPL PLATE BONE MINI L SHAPED FIX L W/O BAR RIGHT 2," 1,423 "
-28015463,HC IMPL PLATE BONE MIN LP L-PLATE 9H LEFT 7MM," 1,879 "
-28015464,HC IMPL PLATE BONE MIN LP T-PLATE 8H 75MM," 1,792 "
-28015465,HC IMPL PLATE BONE MINI STRAIGHT 16 HOLE," 3,490 "
-28015466,HC IMPL PLATE BONE MINI STRAIGHT 4 HOLE," 1,026 "
-28015467,HC IMPL PLATE BONE MINI STRAIGHT 6 HOLE," 1,792 "
-28015468,HC IMPL PLATE BONE MINI T SHAPED 90D SHORT," 1,185 "
-28015469,HC IMPL PLATE BONE MINI W/BAR LONG 4 HOLE," 1,199 "
-28015470,HC IMPL PLATE BONE MINI W/BAR MEDIUM 4 HOLE," 1,199 "
-28015471,HC IMPL PLATE BONE MINI Y SHAPED LARGE 8 HOLE," 1,792 "
-28015472,HC IMPL PLATE BONE MINI Y SHAPED W/BAR LONG 5 HOLE," 1,358 "
-28015473,HC IMPL PLATE BONE MINI Y SHAPED W/BAR SHORT 5 HOLE," 1,358 "
-28015474,HC IMPL PLATE BONE T SHAPED TI 2 X 8H 2.4MM 54MM," 2,547 "
-28015475,HC IMPL PLATE BONE T SHAPED TI 3 X 8H 2.4MM 54MM," 2,714 "
-28015476,HC IMPL PLATE BRIDGE C-TUBE LEFT 4 HOLE," 2,584 "
-28015477,HC IMPL PLATE BRIDGE C-TUBE RIGHT 4 HOLE," 2,584 "
-28015478,HC IMPL PLATE BRIDGING ANGLED LEFT 3-HOLE," 3,086 "
-28015479,HC IMPL PLATE BRIDGING STRAIGHT 4-HOLE," 2,244 "
-28015480,HC IMPL PLATE BROAD 4.5MM 152MM 8 HOLE," 5,083 "
-28015481,HC IMPL PLATE BROAD ADAPTION TI 2X193MM 30 HOLE," 3,312 "
-28015482,HC IMPL PLATE BROAD TI 4.5MM 10HOLE," 2,397 "
-28015483,HC IMPL PLATE BROAD TI 4.5MM 14HOLE," 2,992 "
-28015484,HC IMPL PLATE BROAD TI 4.5MM 8HOLE," 2,841 "
-28015485,HC IMPL PLATE CABLE GREATER TROCHANTER 4 HOLE," 20,279 "
-28015486,HC IMPL PLATE CHIN DOUBLE BEND 1.5X10MM 6 HOLE," 2,285 "
-28015487,HC IMPL PLATE CHIN DOUBLE BEND 1.5X4MM 6 HOLE," 2,285 "
-28015488,HC IMPL PLATE CHIN DOUBLE BEND 1.5X6MM 6 HOLE," 2,285 "
-28015489,HC IMPL PLATE CHIN DOUBLE BEND 1.5X8MM 6 HOLE," 2,285 "
-28015490,HC IMPL PLATE CHIN MICROPLUS W/BAR 2X10MM," 2,052 "
-28015491,HC IMPL PLATE CHIN MICROPLUS W/BAR 2X8MM," 1,965 "
-28015492,HC IMPL PLATE CHIN MICROPLUS W/BAR 4MM," 1,792 "
-28015493,HC IMPL PLATE CHIN MICROPLUS W/BAR 6MM," 1,879 "
-28015494,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE LEFT 10 HOLE," 7,557 "
-28015495,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE LEFT 8 HOLE," 6,333 "
-28015496,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE RIGHT 10 HOLE," 7,557 "
-28015497,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE RIGHT 6 HOLE," 7,557 "
-28015498,HC IMPL PLATE CLAVICLE LOW PROFILE LARGE RIGHT 8 HOLE," 6,333 "
-28015499,HC IMPL PLATE CLAVICLE LOW PROFILE MEDIUM RIGHT 8 HOLE," 7,557 "
-28015500,HC IMPL PLATE CLAVICLE LOW PROFILE SMALL RIGHT 8 HOLE," 6,333 "
-28015501,HC IMPL PLATE CLAVICLE LOW PROFILE STR RIGHT 8 HOLE," 7,642 "
-28015502,HC IMPL PLATE CLAVICLE LOW PROFILE STRAIGHT RIGHT 8HOLE," 8,177 "
-28015503,HC IMPL PLATE CLAVICLE SUPERIOR LEFT 120MM 7 HOLE," 7,684 "
-28015504,HC IMPL PLATE CLAVICLE SUPERIOR LEFT 130MM 8 HOLE," 7,884 "
-28015505,HC IMPL PLATE CLAVICLE SUPERIOR RIGHT 120MM 8 HOLE," 7,953 "
-28015506,HC IMPL PLATE CLAVICLE SUPERIOR RIGHT 130MM 8 HOLE," 7,884 "
-28015507,HC IMPL PLATE CLOVERLEAF THIN BLADE 104MM 4 HOLE," 2,865 "
-28015508,HC IMPL PLATE CLOVERLEAF THIN BLADE 88MM 8 HOLE," 2,720 "
-28015509,HC IMPL PLATE COMPRESSION COMMAND STR MEDIUM 4 HOLE," 1,906 "
-28015510,HC IMPL PLATE COMPRESSION COMMAND STR MEDIUM 6 HOLE," 2,692 "
-28015511,HC IMPL PLATE COMPRESSION COMMAND TI 2.3MM 4 HOLE," 2,251 "
-28015512,HC IMPL PLATE COMPRESSION COMMAND TI 6 HOLE," 3,580 "
-28015513,HC IMPL PLATE COMPRESSION COMMAND TI LONG 6 HOLE," 3,070 "
-28015514,HC IMPL PLATE COMPRESSION COMMAND TI SHORT 4 HOLE," 1,727 "
-28015515,HC IMPL PLATE COMPRESSION COMMAND TI SHORT 6 HOLE," 2,102 "
-28015516,HC IMPL PLATE COMPRESSION COMMAND TI STRAIGHT 14 HOLE," 3,918 "
-28015517,HC IMPL PLATE COMPRESSION COMMAND TI STRAIGHT 4 HOLE," 1,906 "
-28015518,HC IMPL PLATE COMPRESSION COMMAND TI STRAIGHT 8 HOLE," 2,567 "
-28015519,HC IMPL PLATE CONDYLAR COMPRESSION SS 95D 7 HOLE," 5,002 "
-28015520,HC IMPL PLATE CONDYLAR SS 95D 50X156MM 9 HOLE," 4,454 "
-28015521,HC IMPL PLATE CONDYLAR SS 95D 50X204MM 12 HOLE," 4,845 "
-28015522,HC IMPL PLATE CONDYLAR SS 95D 50X92MM 5 HOLE," 4,029 "
-28015523,HC IMPL PLATE CONDYLAR SS 95D 60X156MM 9 HOLE," 5,428 "
-28015524,HC IMPL PLATE CONDYLAR SS 95D 70X124MM 7 HOLE," 5,177 "
-28015525,HC IMPL PLATE CONDYLAR SS 95D 70X235MM 14 HOLE," 7,268 "
-28015526,HC IMPL PLATE CONDYLAR SS 95D 80X124MM 7 HOLE," 4,250 "
-28015527,HC IMPL PLATE CONDYLAR SS 95D 80X92MM 5 HOLE," 4,029 "
-28015528,HC IMPL PLATE CONDYLAR TI LEFT 1.5MM 36MM 7 HOLE," 2,431 "
-28015529,HC IMPL PLATE CONDYLAR TI LEFT 1.5MM 7 HOLE," 2,431 "
-28015530,HC IMPL PLATE CONDYLAR TI LEFT 2.4MM," 2,974 "
-28015531,HC IMPL PLATE CONDYLAR TI LEFT 2MM 7 HOLE," 2,431 "
-28015532,HC IMPL PLATE CONDYLAR TI RIGHT 1.5MM 36MM 7 HOLE," 2,652 "
-28015533,HC IMPL PLATE CONDYLAR TI RIGHT 1.5MM 7 HOLE," 2,431 "
-28015534,HC IMPL PLATE CONDYLAR TI RIGHT 2.4MM," 2,465 "
-28015535,HC IMPL PLATE CONDYLAR TI RIGHT 2.7MM 57MM 6 HOLE," 2,865 "
-28015536,HC IMPL PLATE CONDYLAR TI RIGHT 2MM 7 HOLE," 2,431 "
-28015537,HC IMPL PLATE CONDYLAR LEFT 7 HOLE," 2,711 "
-28015538,HC IMPL PLATE CONDYLAR RIGHT 7 HOLE," 2,711 "
-28015539,HC IMPL PLATE CONTOURABLE MESH MALLEABLE 1.5MM," 13,644 "
-28015540,HC IMPL PLATE CONTOURABLE MESH RIGID 1.5MM," 13,644 "
-28015541,HC IMPL PLATE CONTOURABLE MESH TI 1.5X30MM," 5,698 "
-28015542,HC IMPL PLATE CRANIOFACIAL ADAPTION TI 20 HOLE," 4,005 "
-28015543,HC IMPL PLATE CRANIOFACIAL CURVED RESORBABLE 8 HOLE," 1,037 "
-28015544,HC IMPL PLATE CRANIOFACIAL DOUBLE Y SHAPED RESORB 6HOLE," 1,118 "
-28015545,HC IMPL PLATE CRANIOFACIAL DOUBLE Y SHAPED RESORB 8HOLE," 1,150 "
-28015546,HC IMPL PLATE CRANIOFACIAL L SHPD RESORB LEFT 7 HOLE," 1,037 "
-28015547,HC IMPL PLATE CRANIOFACIAL L SHPD RESORB RIGHT 7 HOLE," 1,037 "
-28015548,HC IMPL PLATE CRANIOFACIAL L SHAPED TI LEFT 16MM," 1,394 "
-28015549,HC IMPL PLATE CRANIOFACIAL MESH 37X50MM," 2,916 "
-28015550,HC IMPL PLATE CRANIOFACIAL MESH RESORBABLE 1.4X55MM," 5,609 "
-28015551,HC IMPL PLATE CRANIOFACIAL MESH RESORBABLE 1.7X122MM," 12,372 "
-28015552,HC IMPL PLATE CRANIOFACIAL MESH RESORBABLE 1.7X55MM," 4,949 "
-28015553,HC IMPL PLATE CRANIOFACIAL MESH RESORBABLE 2.2X55MM," 5,288 "
-28015554,HC IMPL PLATE CRANIOFACIAL MESH TI 29X40MM 7X8 HOLE," 7,466 "
-28015555,HC IMPL PLATE CRANIOFACIAL OBLIQUE L SHPD LEFT 1.5MM 2H," 1,632 "
-28015556,HC IMPL PLATE CRANIOFACIAL RESORBABLE CURVED 8 HOLE," 1,044 "
-28015557,HC IMPL PLATE CRANIOFACIAL RESORBABLE LADDER 2X5," 1,806 "
-28015558,HC IMPL PLATE CRANIOFACIAL RESORBABLE RIGHT 9 HOLE," 1,083 "
-28015559,HC IMPL PLATE CRANIOFACIAL RESORBABLE STRAIGHT 14 HOLE," 1,245 "
-28015560,HC IMPL PLATE CRANIOFACIAL RESORBABLE STRAIGHT 21 HOLE," 1,601 "
-28015561,HC IMPL PLATE CRANIOFACIAL RESORBABLE STRAIGHT 4 HOLE, 593 
-28015562,HC IMPL PLATE CRANIOFACIAL RESORBABLE STRAIGHT 8 HOLE, 875 
-28015563,HC IMPL PLATE CRANIOFACIAL RESORBABLE Y SHAPED 12 HOLE," 1,206 "
-28015564,HC IMPL PLATE CRANIOFACIAL RESORBABLE Y SHAPED 9 HOLE," 1,044 "
-28015565,HC IMPL PLATE CRANIOFACIAL RESORBABLE STR 2.2MM 14 HOLE," 1,245 "
-28015566,HC IMPL PLATE CRANIOFACIAL Y SHAPED RESORB 6 HOLE," 1,083 "
-28015567,HC IMPL PLATE CRANIOFACIAL Y SHAPED TI STRAIGHT 7 HOLE," 1,394 "
-28015568,HC IMPL PLATE DOUBLE ANGLE 6X26X6 HOLE," 16,500 "
-28015569,HC IMPL PLATE DOUBLE Y SHAPED ULTRA LP W/BAR 8MM 6 HOLE," 3,485 "
-28015570,HC IMPL PLATE DYNAMIC COMPRESSION SPOON SS 4.5X100MM," 2,023 "
-28015571,HC IMPL PLATE DYNAMIC COMPRESSION SPOON SS 4.5X120MM," 2,176 "
-28015572,HC IMPL PLATE DYNAMIC COMPRESSION SS 3.5X122MM 10 HOLE," 1,386 "
-28015573,HC IMPL PLATE DYNAMIC COMPRESSION SS 3.5X74MM 6 HOLE," 1,190 "
-28015574,HC IMPL PLATE DYNAMIC COMPRESSION SS 3.5X86MM 7 HOLE," 1,233 "
-28015575,HC IMPL PLATE DYNAMIC COMPRESSION SS 3.5X98MM 8 HOLE," 1,301 "
-28015576,HC IMPL PLATE DYNAMIC COMPRESSION SS 8 HOLE 68MM," 1,624 "
-28015577,HC IMPL PLATE DISTAL CLAVICLE LEFT 2.3MM 13 HOLE," 7,557 "
-28015578,HC IMPL PLATE DISTAL CLAVICLE LEFT 2.3MM 16 HOLE," 7,013 "
-28015579,HC IMPL PLATE DISTAL CLAVICLE LEFT 3.5MM 12 HOLE," 6,333 "
-28015580,HC IMPL PLATE DISTAL CLAVICLE LEFT 3.5MM 9 HOLE," 7,557 "
-28015581,HC IMPL PLATE DISTAL CLAVICLE RIGHT 2.3MM 13 HOLE," 7,557 "
-28015582,HC IMPL PLATE DISTAL CLAVICLE RIGHT 2.3MM 16 HOLE," 7,013 "
-28015583,HC IMPL PLATE DISTAL CLAVICLE RIGHT 3.5MM 12 HOLE," 7,557 "
-28015584,HC IMPL PLATE DISTAL CLAVICLE RIGHT 3.5MM 9 HOLE," 6,333 "
-28015585,HC IMPL PLATE DORSAL DR STANDARD RIGHT," 8,075 "
-28015586,HC IMPL PLATE DORSAL DR STANDARD RIGHT EXTRA LONG," 8,108 "
-28015587,HC IMPL PLATE DORSAL ULNAR 3HOLE," 3,953 "
-28015588,HC IMPL PLATE DORSAL ULNAR 5HOLE," 3,953 "
-28015589,HC IMPL PLATE DORSAL ULNAR 7HOLE," 5,653 "
-28015590,HC IMPL PLATE DOUBLE T SHAPED MICROPLUS 1.2MM 14 HOLE," 1,029 "
-28015591,HC IMPL PLATE DOUBLE T SHAPED MICROPLUS 1.2MM 9 HOLE, 796 
-28015592,HC IMPL PLATE DOUBLE Y SHAPED MICROPLUS 1.2MM 6 HOLE, 796 
-28015593,HC IMPL PLATE DOUBLE Y SHAPED MICROPLUS 1.2MM 7 HOLE, 915 
-28015594,HC IMPL PLATE DOUBLE Y SHAPED MINI TI 1.7MM 6 HOLE," 1,080 "
-28015595,HC IMPL PLATE DOUBLE Y SHAPED MINI 1.7MM 7 HOLE, 891 
-28015596,HC IMPL PLATE DOUBLE Y SHAPED MINI TI W/BAR 1.7MM 6 H," 1,283 "
-28015597,HC IMPL PLATE DOUBLE Y SHAPED RESORBABLE 1MM 6 HOLE," 1,445 "
-28015598,HC IMPL PLATE DOUBLE Y SHAPED SHORT 2MM 6 HOLE, 871 
-28015599,HC IMPL PLATE DOUBLE Y SHAPED STANDARD 2MM 6 HOLE, 941 
-28015600,HC IMPL PLATE DOUBLE Y SHAPED TI 21MM 6 HOLE," 1,782 "
-28015601,HC IMPL PLATE FIXED 1 LEVEL 11MM," 8,500 "
-28015602,HC IMPL PLATE FIXED LOW PROFILE SS 5.5MMX0.375IN," 9,643 "
-28015603,HC IMPL PLATE FIXED LOW PROFILE SS 5.5MMX0.875IN," 9,840 "
-28015604,HC IMPL PLATE FIXED LOW PROFILE SS 5.5MMX1 IN," 9,840 "
-28015605,HC IMPL PLATE FIXED TI X10 CROSSLINK 25MM," 9,186 "
-28015606,HC IMPL PLATE FRACTURE 14 HOLE," 4,978 "
-28015607,HC IMPL PLATE FRACTURE 6 HOLE," 3,201 "
-28015608,HC IMPL PLATE FRACTURE COMPRESSION 2.3MM SCREW 4 HOLE," 2,254 "
-28015609,HC IMPL PLATE FRACTURE COMPRESSION 4 HOLE," 2,254 "
-28015610,HC IMPL PLATE FRACTURE COMPRESSION 6 HOLE," 3,287 "
-28015611,HC IMPL PLATE FRACTURE MANDIBLE COMPRESSION 6 HOLE," 3,287 "
-28015612,HC IMPL PLATE FRACTURE MAXILLOFACIAL C SHAPED TI 4 HOLE," 2,825 "
-28015613,HC IMPL PLATE FRACTURE MAXILLOFACIAL TI 140D 6 HOLE," 4,790 "
-28015614,HC IMPL PLATE FRACTURE MAXILLOFACIAL UNIV 1.5MM 4 HOLE," 2,637 "
-28015615,HC IMPL PLATE FRACTURE MAXILLOFACIAL UNIV W/BAR 6 HOLE," 3,475 "
-28015616,HC IMPL PLATE FRACTURE MINI 16 HOLE," 1,185 "
-28015617,HC IMPL PLATE FRACTURE MINI 6 HOLE, 845 
-28015618,HC IMPL PLATE FRACTURE MINI STRAIGHT CONDENSED 16 HOLE," 2,962 "
-28015619,HC IMPL PLATE FRACTURE MINI STRAIGHT W/BAR 6 HOLE," 1,698 "
-28015620,HC IMPL PLATE FRACTURE MINI STRAIGHT W/BAR LONG 4 HOLE, 997 
-28015621,HC IMPL PLATE FRACTURE ORTHOGNATHIC MICRO REINF BAR 4 H," 2,731 "
-28015622,HC IMPL PLATE FRACTURE PRIMARY 11 HOLE," 4,663 "
-28015623,HC IMPL PLATE FRACTURE RECTANGLE LOCKING 3D 3X2 HOLE," 2,348 "
-28015624,HC IMPL PLATE FRACTURE SQUARE 3D 4X2 HOLE," 2,637 "
-28015625,HC IMPL PLATE FRACTURE SQUARE 3D CRVD 6X2HOLE," 2,962 "
-28015626,HC IMPL PLATE H SHAPED MICRO TI 1.2MM," 1,431 "
-28015627,HC IMPL PLATE HIND MIDFACE 5 HOLE," 2,242 "
-28015628,HC IMPL PLATE HIND MIDFACE 8 HOLE," 2,315 "
-28015629,HC IMPL PLATE HOLE LEFT 3.5X6," 17,511 "
-28015630,HC IMPL PLATE HOLE RIGHT 3.5X6," 17,511 "
-28015631,HC IMPL PLATE L CRANIOMAXILFACIAL RESORBABLE 9 HOLE," 1,083 "
-28015632,HC IMPL PLATE L EXT LOCKING," 3,188 "
-28015633,HC IMPL PLATE L LEFT LONG," 1,557 "
-28015634,HC IMPL PLATE L LEFT SS 3 HOLE 2.7X32MM, 522 
-28015635,HC IMPL PLATE L SHAPED MEDIUM LEFT 2MM, 890 
-28015636,HC IMPL PLATE L SHAPED MEDIUM EX LONG TI RIGHT 2 HOLE, 890 
-28015637,HC IMPL PLATE L SHAPED MICRO TI LEFT 90D 1.2MM 8 HOLE, 857 
-28015638,HC IMPL PLATE L SHAPED MICRO TI RIGHT 16MM," 1,394 "
-28015639,HC IMPL PLATE L SHAPED MICROPLUS TI LEFT 90D 1.2 5 HOLE," 1,051 "
-28015640,HC IMPL PLATE L SHAPED MICROPLUS TI RIGHT 90D 8 HOLE, 741 
-28015641,HC IMPL PLATE L SHAPED MINI TI W/BAR RIGHT 90D 2 HOLE, 956 
-28015642,HC IMPL PLATE L SHAPED MINI TI LEFT 10MM 0.55MM 2 HOLE," 1,141 "
-28015643,HC IMPL PLATE L SHAPED MINI TI LEFT 0.55X12MM 2 HOLE," 1,006 "
-28015644,HC IMPL PLATE L SHAPED MINI TI W/BAR LEFT 90D 2 HOLE," 1,141 "
-28015645,HC IMPL PLATE L OBLIQUE LEFT 2.7MM 3 HOLE, 579 
-28015646,HC IMPL PLATE L SHAPED REGULAR TI RIGHT 2 HOLE, 890 
-28015647,HC IMPL PLATE L RIGHT LONG," 1,557 "
-28015648,HC IMPL PLATE L RIGHT SS 3 HOLE 2.7X32MM, 522 
-28015649,HC IMPL PLATE L SHAPED STANDARD TI LEFT 2 HOLE, 890 
-28015650,HC IMPL PLATE L SHAPED TI RIGHT 1.2MM 5 HOLE," 1,051 "
-28015651,HC IMPL PLATE L SHAPED TI W/BAR 90D 1.7MM 12MM 5 HOLE," 1,006 "
-28015652,HC IMPL PLATE LC-DYNAMIC COMPRESSION TI 2.4X31MM 4 HOLE," 3,175 "
-28015653,HC IMPL PLATE LC-DYNAMIC COMPRESSION TI 2.4X47MM 6 HOLE," 3,560 "
-28015654,HC IMPL PLATE LC-DYNAMIC COMPRESSION TI 2.4X63MM 8 HOLE," 3,041 "
-28015655,HC IMPL PLATE LC-DYNAMIC COMPRESSION TI 2X24MM 4 HOLE," 2,270 "
-28015656,HC IMPL PLATE LC-DYNAMIC COMPRESSION TI 2X36MM 6 HOLE," 2,681 "
-28015657,HC IMPL PLATE LC-DYNAMIC COMPRESSION TI 2X48MM 8 HOLE," 3,041 "
-28015658,HC IMPL PLATE LCP BROAD 11 HOLE 4.5X206MM," 6,517 "
-28015659,HC IMPL PLATE LCP BROAD 8 HOLE 4.5X134MM," 4,038 "
-28015660,HC IMPL PLATE LCP BROAD CURVED 13 HOLE 4.5X247MM," 7,098 "
-28015661,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 9H 155MM," 6,167 "
-28015662,HC IMPL PLATE LCP LATERAL DISTAL FIBUL LEFT 11H 181MM," 6,256 "
-28015663,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 9H 155MM," 6,383 "
-28015664,HC IMPL PLATE LCP LATERAL DISTAL FIBUL RIGHT 11H 181MM," 6,256 "
-28015665,HC IMPL PLATE LCP NARROW TI 4.5MM 296MM 16 HOLE," 7,616 "
-28015666,HC IMPL PLATE LCP NARROW 5 HOLE 4.5X98MM," 3,366 "
-28015667,HC IMPL PLATE LCP OBLIQUE RIGHT TITANIUM 3.5X52MM," 3,171 "
-28015668,HC IMPL PLATE LCP OBLIQUE RIGHT TITANIUM 3.5X63MM," 3,324 "
-28015669,HC IMPL PLATE LCP OBLIQUE RIGHT TITANIUM 3.5X74MM," 3,460 "
-28015670,HC IMPL PLATE LCP OBLIQUE RIGHT TITANIUM 3.5X96MM," 3,757 "
-28015671,HC IMPL PLATE LCP OBLIQUE LEFT TITANIUM 3.5X52MM," 2,525 "
-28015672,HC IMPL PLATE LCP OBLIQUE LEFT TITANIUM 3.5X63MM," 3,324 "
-28015673,HC IMPL PLATE LCP OBLIQUE LEFT TITANIUM 3.5X74MM," 2,754 "
-28015674,HC IMPL PLATE LCP OBLIQUE LEFT TITANIUM 3.5X96MM," 3,001 "
-28015675,HC IMPL PLATE LCP PROXIMAL HUMERUS 3.5MM 114MM 5 HOLE," 13,452 "
-28015676,HC IMPL PLATE LCP RIGHT ANGLE TITANIUM 3.5X50MM," 2,270 "
-28015677,HC IMPL PLATE LCP RIGHT ANGLE TITANIUM 3.5X56MM," 2,372 "
-28015678,HC IMPL PLATE LCP RIGHT ANGLE TITANIUM 3.5X67MM," 2,618 "
-28015679,HC IMPL PLATE LCP RIGHT ANGLE TITANIUM 3.5X78MM," 3,043 "
-28015680,HC IMPL PLATE LCP RIGHT ANGLE TITANIUM 3.5X87MM," 3,817 "
-28015681,HC IMPL PLATE LOCKING 2.0 L SHAPE LEFT 2/4," 3,511 "
-28015682,HC IMPL PLATE LOCKING 2.5 DR FRAME LEFT," 8,330 "
-28015683,HC IMPL PLATE LOCKING 2.5 DR FRAME RIGHT," 8,330 "
-28015684,HC IMPL PLATE LCP BROAD CURVED 15 HOLE 4.5X282MM," 7,569 "
-28015685,HC IMPL PLATE LCP BROAD CURVED 16 HOLE 4.5X300MM," 8,020 "
-28015686,HC IMPL PLATE LCP BROAD CURVED 17 HOLE 4.5X318MM," 8,584 "
-28015687,HC IMPL PLATE LCP BROAD CURVED 18 HOLE 4.5X332MM," 9,113 "
-28015688,HC IMPL PLATE LOCKING CLAVICLE J SHAPED LEFT 8 HOLE," 6,333 "
-28015689,HC IMPL PLATE LOCKING CLAVICLE J SHAPED LEFT 9 HOLE," 6,333 "
-28015690,HC IMPL PLATE LOCKING CLAVICLE J SHAPED RIGHT 8 HOLE," 6,333 "
-28015691,HC IMPL PLATE LOCKING CLAVICLE J SHAPED RIGHT 9 HOLE," 6,333 "
-28015692,HC IMPL PLATE LOCKING CLAVICLE LARGE LEFT 10 HOLE," 6,333 "
-28015693,HC IMPL PLATE LOCKING CLAVICLE LARGE LEFT 8 HOLE," 6,333 "
-28015694,HC IMPL PLATE LOCKING CLAVICLE LARGE RIGHT 10 HOLE," 6,333 "
-28015695,HC IMPL PLATE LOCKING CLAVICLE LARGE RIGHT 8 HOLE," 6,333 "
-28015696,HC IMPL PLATE LOCKING CLAVICLE MEDIUM LEFT 8 HOLE," 6,333 "
-28015697,HC IMPL PLATE LOCKING CLAVICLE MEDIUM RIGHT 8 HOLE," 6,333 "
-28015698,HC IMPL PLATE LOCKING CLAVICLE SMALL LEFT 6 HOLE," 6,333 "
-28015699,HC IMPL PLATE LOCKING CLAVICLE SMALL LEFT 8 HOLE, 309 
-28015700,HC IMPL PLATE LOCKING CLAVICLE SMALL RIGHT 6 HOLE," 6,333 "
-28015701,HC IMPL PLATE LOCKING CLAVICLE SMALL RIGHT 8 HOLE," 6,333 "
-28015702,HC IMPL PLATE LOCKING CLAVICLE STRAIGHT LEFT 8 HOLE," 6,333 "
-28015703,HC IMPL PLATE LOCKING CLAVICLE STRAIGHT RIGHT 8 HOLE," 6,333 "
-28015704,HC IMPL PLATE LOCKING EXTENSION," 3,528 "
-28015705,HC IMPL PLATE LOCKING GRID LEFT 2.0 2.3 3X2," 4,660 "
-28015706,HC IMPL PLATE LOCKING GRID RIGHT 2.0 2.3 3X2," 4,488 "
-28015707,HC IMPL PLATE LOCKING L SHAPED LEFT 2.0 2.3," 5,500 "
-28015708,HC IMPL PLATE LOCKING L SHAPED LONG LEFT 2.0 2.3," 5,814 "
-28015709,HC IMPL PLATE LOCKING L SHAPED LONG RIGHT 2.0 2.3," 5,814 "
-28015710,HC IMPL PLATE LOCKING L SHAPED RIGHT 2.0 2.3," 5,500 "
-28015711,HC IMPL PLATE LOCKING MANDIBULAR FRACTURE 2X6 CURVED," 5,708 "
-28015712,HC IMPL PLATE LOCKING NARROW MINI TI MALLEABLE 2 4HOLE," 2,550 "
-28015713,HC IMPL PLATE LOCKING NARROW MINI TI MALLEABLE 2 6HOLE," 2,625 "
-28015714,HC IMPL PLATE LOCKING NARROW MINI TI 2MM 4 HOLE," 2,414 "
-28015715,HC IMPL PLATE LOCKING RECONSTRUCTION ANGLED RT 23 HOLE," 12,126 "
-28015716,HC IMPL PLATE LOCKING T SHAPED 2.0 2.3," 5,500 "
-28015717,HC IMPL PLATE LOCKING T SHAPED LONG 2.0 2.3," 6,036 "
-28015718,HC IMPL PLATE LUMBAR 37MM," 32,890 "
-28015719,HC IMPL PLATE LUMBAR 39MM," 32,890 "
-28015720,HC IMPL PLATE LUMBAR 41MM," 32,890 "
-28015721,HC IMPL PLATE LUMBAR 43MM," 32,890 "
-28015722,HC IMPL PLATE LUMBAR 45MM," 32,890 "
-28015723,HC IMPL PLATE LUMBAR 47MM," 32,890 "
-28015724,HC IMPL PLATE LUMBAR 49MM," 32,890 "
-28015725,HC IMPL PLATE LUMBAR 51MM," 32,890 "
-28015726,HC IMPL PLATE LUMBAR 53MM," 32,890 "
-28015727,HC IMPL PLATE MANDIBLE LOCKING DOUBLE WIDE MALL 12 HOLE," 3,026 "
-28015728,HC IMPL PLATE MANDIBLE LOCKING DOUBLE WIDE MALL 9 HOLE," 2,720 "
-28015729,HC IMPL PLATE MANDIBLE ADAPTION 2MM 12 HOLE," 4,298 "
-28015730,HC IMPL PLATE MANDIBLE ADAPTION 2MM 20 HOLE," 4,733 "
-28015731,HC IMPL PLATE MANDIBLE ANGLED COMPRESSION 10 HOLE," 3,461 "
-28015732,HC IMPL PLATE MANDIBLE ANGLED COMPRESSION 8 HOLE," 3,720 "
-28015733,HC IMPL PLATE MANDIBLE LOCKING CRESENT 2MM 3 HOLE," 6,412 "
-28015734,HC IMPL PLATE MANDIBLE LOCKING CRESENT 2MM 5 HOLE," 3,101 "
-28015735,HC IMPL PLATE MANDIBLE LOCKING CRESENT 2MM 7 HOLE," 3,305 "
-28015736,HC IMPL PLATE MANDIBLE CURVED BROAD 4 HOLE," 2,978 "
-28015737,HC IMPL PLATE MANDIBLE DYNAMIC COMPR TI 2X30MM 4 HOLE," 2,237 "
-28015738,HC IMPL PLATE MANDIBLE DYNAMIC COMPR TI 2X30MM 5 HOLE," 2,434 "
-28015739,HC IMPL PLATE MANDIBLE DYNAMIC COMPR TI 2X30MM 6 HOLE," 2,618 "
-28015740,HC IMPL PLATE MANDIBLE RECON HEMI 6X17X6 HOLE," 11,175 "
-28015741,HC IMPL PLATE MANDIBLE RECON HEMI LEFT 6X17 HOLE," 8,229 "
-28015742,HC IMPL PLATE MANDIBLE RECON HEMI RIGHT 6X17 HOLE," 9,110 "
-28015743,HC IMPL PLATE MANDIBLE LOCKING INTERMEDIATE 2MM 6 HOLE," 4,073 "
-28015744,HC IMPL PLATE MANDIBLE LOCKING COMPRESSION 2MM 4 HOLE," 2,339 "
-28015745,HC IMPL PLATE MANDIBLE LOCKING COMPRESSION 2MM 5 HOLE," 2,543 "
-28015746,HC IMPL PLATE MANDIBLE LOCKING COMPRESSION 2MM 6 HOLE," 2,740 "
-28015747,HC IMPL PLATE MANDIBLE LOCKING COMPRESSION 2MM 8 HOLE," 3,101 "
-28015748,HC IMPL PLATE MANDIBLE LOCKING 2MM 12 HOLE," 4,882 "
-28015749,HC IMPL PLATE MANDIBLE LOCKING ANGLED LARGE 3 HOLE," 6,412 "
-28015750,HC IMPL PLATE MANDIBLE LOCKING ANGLED LARGE 4 HOLE," 6,943 "
-28015751,HC IMPL PLATE MANDIBLE LOCKING LEFT 2MM 6 HOLE," 11,268 "
-28015752,HC IMPL PLATE MANDIBLE LOCKING PREBENT RIGHT 4 HOLE," 2,829 "
-28015753,HC IMPL PLATE MANDIBLE LOCKING LARGE 2MM 6 HOLE," 4,658 "
-28015754,HC IMPL PLATE MANDIBLE MINI BOX MALLEABLE 2MM," 1,897 "
-28015755,HC IMPL PLATE MANDIBLE STRAIGHT BROAD TI 2X42 6 HOLE," 2,822 "
-28015756,HC IMPL PLATE MANDIBLE STRAIGHT NARROW TI 2X55 8 HOLE," 2,434 "
-28015757,HC IMPL PLATE MANDIBLE STRUT CURVED MALLEABLE 8 HOLE," 3,720 "
-28015758,HC IMPL PLATE MANDIBLE STRUT MINI 2MM 8 HOLE," 3,720 "
-28015759,HC IMPL PLATE MATRIXNEURO STRAIGHT TI 12MM 2 HOLE, 362 
-28015760,HC IMPL PLATE MAXILLOFACIAL CHIN 12MM," 1,376 "
-28015761,HC IMPL PLATE MAXILLOFACIAL CURVED 12 HOLE," 1,445 "
-28015762,HC IMPL PLATE MAXILLOFACIAL L SHAPED STANDARD LEFT 2MM, 890 
-28015763,HC IMPL PLATE MAXILLOFACIAL L SHAPED STANDARD RIGHT 2MM, 890 
-28015764,HC IMPL PLATE MAXILLOFACIAL LONG W/BAR 5 HOLE," 2,052 "
-28015765,HC IMPL PLATE MAXILLOFACIAL MAXI 32MM 4 HOLE," 1,777 "
-28015766,HC IMPL PLATE MAXILLOFACIAL MAXI 38MM 4 HOLE," 1,216 "
-28015767,HC IMPL PLATE MAXILLOFACIAL MAXI 48MM 6 HOLE," 2,057 "
-28015768,HC IMPL PLATE MAXILLOFACIAL MEDIUM W/BAR 5 HOLE," 2,003 "
-28015769,HC IMPL PLATE MAXILLOFACIAL MICRO STRAIGHT 5 HOLE," 1,302 "
-28015770,HC IMPL PLATE MAXILLOFACIAL PROFILE 3D 2 HOLE," 1,792 "
-28015771,HC IMPL PLATE MAXILLOFACIAL PROFILE 3D 2X0.6MM 2 HOLE," 1,611 "
-28015772,HC IMPL PLATE MAXILLOFACIAL PROFILE 3D SQUARE 2 HOLE," 1,611 "
-28015773,HC IMPL PLATE MAXILLOFACIAL RIGID COMMINUTED 2.4MM," 5,840 "
-28015774,HC IMPL PLATE MAXILLOFACIAL SHORT W/BAR 5 HOLE," 1,567 "
-28015775,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 0.55MM 16 HOLE," 1,423 "
-28015776,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 1.2MM 12 HOLE, 995 
-28015777,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 1.2MM 24 HOLE," 2,383 "
-28015778,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 1.2MM 6 HOLE, 551 
-28015779,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 2MM 16 HOLE, 117 
-28015780,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 2MM 6 HOLE, 85 
-28015781,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 2MM 8 HOLE, 91 
-28015782,HC IMPL PLATE MAXILLOFACIAL Y SHAPED EXTENDED," 2,015 "
-28015783,HC IMPL PLATE MAXILLARY L SHAPED 22X13MM 3 HOLE," 1,714 "
-28015784,HC IMPL PLATE MAXILLARY L SHAPED 24X13MM 3 HOLE," 1,802 "
-28015785,HC IMPL PLATE MAXILLOFACIAL T 22MM," 1,423 "
-28015786,HC IMPL PLATE METACARPAL COMPRESSION LEFT 2.0 2.3 2/4," 2,125 "
-28015787,HC IMPL PLATE METACARPAL COMPRESSION LEFT 2.0 2.3 2/8," 2,873 "
-28015788,HC IMPL PLATE METACARPAL COMPRESSION RIGHT 2.0 2.3 2/4," 2,125 "
-28015789,HC IMPL PLATE METACARPAL COMPRESSION RIGHT 2.0 2.3 2/8," 2,873 "
-28015790,HC IMPL PLATE MICRO ULTRA LOW PROFILE STRAIGHT 8 HOLE," 1,535 "
-28015791,HC IMPL PLATE MICROPLUS STRAIGHT 1.2MM 4 HOLE, 352 
-28015792,HC IMPL PLATE MICROPLUS STRAIGHT TI 1.2MM 8 HOLE, 849 
-28015793,HC IMPL PLATE MICROVASCULAR DOUBLE ANGLE 22 HOLE," 20,910 "
-28015794,HC IMPL PLATE MICROVASCULAR STRAIGHT 2.4MM 12 HOLE," 5,460 "
-28015795,HC IMPL PLATE MICROVASCULAR DOUBLE ANGLE 20X4 HOLE," 20,076 "
-28015796,HC IMPL PLATE MICROVASCULAR STRAIGHT 2.4X160MM 20 HOLE," 8,388 "
-28015797,HC IMPL PLATE MICROVASCULAR TI LEFT 6MM 2.4MM 18 HOLE," 11,496 "
-28015798,HC IMPL PLATE MICROVASCULAR TI RIGHT 6MM 2.4MM 18 HOLE," 11,496 "
-28015799,HC IMPL PLATE MIDFACE 3D MALLEABLE 1.7MM 2X2 HOLE," 1,224 "
-28015800,HC IMPL PLATE MIDFACE 3D MALLEABLE 1.7MM 6X2 HOLE," 3,220 "
-28015801,HC IMPL PLATE MIDFACE CURVED 1.7MM 10 HOLE," 1,980 "
-28015802,HC IMPL PLATE MIDFACE CURVED W/BAR 1.7MM 4 HOLE," 1,040 "
-28015803,HC IMPL PLATE MIDFACE DOUBLE Y SHAPED 7 HOLE," 1,510 "
-28015804,HC IMPL PLATE MIDFACE L SHAPED W/BAR LEFT 12MM 6 HOLE," 1,604 "
-28015805,HC IMPL PLATE MIDFACE L SHAPED W/BAR LEFT 2MM 5 HOLE," 1,423 "
-28015806,HC IMPL PLATE MIDFACE L SHAPED W/BAR LEFT 5MM 5 HOLE," 1,467 "
-28015807,HC IMPL PLATE MIDFACE L SHAPED W/BAR LEFT 8MM 6 HOLE," 1,607 "
-28015808,HC IMPL PLATE MIDFACE L SHAPED W/BAR RIGHT 12MM 6 HOLE," 1,604 "
-28015809,HC IMPL PLATE MIDFACE L SHAPED W/BAR RIGHT 2MM 5 HOLE," 1,423 "
-28015810,HC IMPL PLATE MIDFACE L SHAPED W/BAR RIGHT 5MM 5 HOLE," 1,467 "
-28015811,HC IMPL PLATE MIDFACE L SHAPED W/BAR RIGHT 8MM 6 HOLE," 1,561 "
-28015812,HC IMPL PLATE MIDFACE L SHAPED TI LEFT 1MM 4X5 HOLE," 1,591 "
-28015813,HC IMPL PLATE MIDFACE L SHAPED TI RIGHT 1MM 4X5 HOLE," 1,591 "
-28015814,HC IMPL PLATE MIDFACE LOCKING 2X2 HOLE," 1,980 "
-28015815,HC IMPL PLATE MIDFACE LOCKING 3D 6X2 HOLE," 4,458 "
-28015816,HC IMPL PLATE MIDFACE LOCKING DOUBLE Y SHAPED 7 HOLE," 2,218 "
-28015817,HC IMPL PLATE MIDFACE LOCKING L SHPD W/BAR 2MM 5 HOLE," 1,604 "
-28015818,HC IMPL PLATE MIDFACE LOCKING L SHPD W/BAR 8MM 6 HOLE," 1,792 "
-28015819,HC IMPL PLATE MIDFACE LOCKING W/BAR 4MM 5 HOLE," 1,741 "
-28015820,HC IMPL PLATE MIDFACE LOCKING W/BAR 8MM 5 HOLE," 1,741 "
-28015821,HC IMPL PLATE MIDFACE LOCKING Y SHAPED 8 HOLE," 2,030 "
-28015822,HC IMPL PLATE MIDFACE STANDARD 1.7MM 8 HOLE," 1,879 "
-28015823,HC IMPL PLATE MIDFACE STANDARD 100D 2MM 5 HOLE," 1,234 "
-28015824,HC IMPL PLATE MIDFACE STANDARD 3D 2X2 HOLE," 1,720 "
-28015825,HC IMPL PLATE MIDFACE STANDARD 3D 6X2 HOLE," 3,107 "
-28015826,HC IMPL PLATE MIDFACE STANDARD STRAIGHT 24 HOLE," 3,598 "
-28015827,HC IMPL PLATE MIDFACE STRAIGHT MALLEABLE 20 HOLE," 3,595 "
-28015828,HC IMPL PLATE MIDFACE STRAIGHT COND MALLEABLE 24 HOLE," 3,723 "
-28015829,HC IMPL PLATE MIDFACE STRAIGHT W/BAR 4 HOLE, 845 
-28015830,HC IMPL PLATE MIDFACE T SHAPED STANDARD 1.7MM 5 HOLE," 1,185 "
-28015831,HC IMPL PLATE MIN LEFT L 23MM," 1,025 "
-28015832,HC IMPL PLATE MINI 26MM," 1,470 "
-28015833,HC IMPL PLATE MINI CURVED 5 HOLE," 2,114 "
-28015834,HC IMPL PLATE MINI I 16MM, 748 
-28015835,HC IMPL PLATE MINI L SHAPED RIGID LEFT 23MM," 1,470 "
-28015836,HC IMPL PLATE MINI L SHAPED RIGID RIGHT 23MM," 1,470 "
-28015837,HC IMPL PLATE MINI LEFT ZED 15MM," 1,301 "
-28015838,HC IMPL PLATE MINI LEFT ZED 20MM," 1,250 "
-28015839,HC IMPL PLATE MINI REGULAR W/BAR 6MM 4 HOLE, 438 
-28015840,HC IMPL PLATE MINI RIGHT 26MM, 681 
-28015841,HC IMPL PLATE MINI RIGHT L 23MM," 1,423 "
-28015842,HC IMPL PLATE MINI RIGHT ZED 15MM," 1,301 "
-28015843,HC IMPL PLATE MINI STRAIGHT 16 HOLE," 2,190 "
-28015844,HC IMPL PLATE MINI STRAIGHT 4HOLE," 1,343 "
-28015845,HC IMPL PLATE MINI STRAIGHT 6 HOLE, 699 
-28015846,HC IMPL PLATE MINI STRAIGHT REGULAR TI 1.7MM 8 HOLE, 680 
-28015847,HC IMPL PLATE MINI STRAIGHT REGULAR TI 1.7MM 6 HOLE, 493 
-28015848,HC IMPL PLATE MINI STRAIGHT TI W/BAR 1.7MM 4 HOLE, 462 
-28015849,HC IMPL PLATE MOUNTAINEER HEAD/HEAD CROSS CONN 21MM," 7,898 "
-28015850,HC IMPL PLATE MOUNTAINEER HEAD/HEAD CROSS CONN 28MM," 7,898 "
-28015851,HC IMPL PLATE MOUNTAINEER HEAD/HEAD CROSS CONN 35MM," 7,898 "
-28015852,HC IMPL PLATE MOUNTAINEER HEAD/HEAD CROSS CONN 42MM," 7,898 "
-28015853,HC IMPL PLATE MATRIXMANDIBLE ADAPTION 20 HOLE," 5,182 "
-28015854,HC IMPL PLATE MATRIXMANDIBLE ANGLES 3X3 HOLE," 7,038 "
-28015855,HC IMPL PLATE MATRIXMANDIBLE ANGLED BROAD 3X3HOLE," 7,317 "
-28015856,HC IMPL PLATE MATRIXMANDIBLE ANGLED LEFT 2.5 7X23HOLE," 13,296 "
-28015857,HC IMPL PLATE MATRIXMANDIBLE ANGLED LEFT 3MM 7X23HOLE," 12,348 "
-28015858,HC IMPL PLATE MATRIXMANDIBLE ANGLED RIGHT 2.5 7X23HOLE," 13,296 "
-28015859,HC IMPL PLATE MATRIXMANDIBLE ANGLED RIGHT 7X23HOLE," 12,348 "
-28015860,HC IMPL PLATE MATRIXMANDIBLE CRESCENT 1.25MM 2X2 HOLE," 3,590 "
-28015861,HC IMPL PLATE MATRIXMANDIBLE CRESCENT 1.5MM 3X3 HOLE," 7,038 "
-28015862,HC IMPL PLATE MATRIXMANDIBLE CRESCENT 3X3 HOLE," 3,822 "
-28015863,HC IMPL PLATE MATRIXMANDIBLE CRESCENT BROAD 2X2HOLE," 4,957 "
-28015864,HC IMPL PLATE MATRIXMANDIBLE CRESCENT BROAD 3X3HOLE," 5,331 "
-28015865,HC IMPL PLATE MATRIXMANDIBLE DOUBLE ANGLED LARGE 6X24X6," 18,090 "
-28015866,HC IMPL PLATE MATRIXMANDIBLE DOUBLE ANGLED MED 5X22X5," 17,142 "
-28015867,HC IMPL PLATE MATRIXMANDIBLE DOUBLE ANGLED SMALL 4X20X4," 16,242 "
-28015868,HC IMPL PLATE MATRIXMANDIBLE DYNAMIC COMPR 1.25MM 2X2H," 2,700 "
-28015869,HC IMPL PLATE MATRIXMANDIBLE DYNAMIC COMPRESSION 2X2H," 4,556 "
-28015870,HC IMPL PLATE MATRIXMANDIBLE DYNAMIC COMP 1.25MM 3X3H," 3,913 "
-28015871,HC IMPL PLATE MATRIXMANDIBLE DYNAMIC COMPR ANGLED 3X3H," 3,788 "
-28015872,HC IMPL PLATE MATRIXMANDIBLE MINI TENSIONBAND 2 HOLE," 2,638 "
-28015873,HC IMPL PLATE MATRIXMANDIBLE MINI TENSIONBAND 3 HOLE," 3,040 "
-28015874,HC IMPL PLATE MATRIXMANDIBLE MINI TENSNBND BRD MALL 2X2," 3,482 "
-28015875,HC IMPL PLATE MATRIXMANDIBLE MINI TENSNBND BRD MALL 2X3," 3,427 "
-28015876,HC IMPL PLATE MATRIXMANDIBLE MINI TENSNBND BRD MALL 3X3," 3,563 "
-28015877,HC IMPL PLATE MATRIXMANDIBLE MINI TENSIONBAND MALL 2H," 2,788 "
-28015878,HC IMPL PLATE MATRIXMANDIBLE MINI TENSIONBAND MALL 3H," 2,876 "
-28015879,HC IMPL PLATE MATRIXMANDIBLE RECONSTRUCTION STR 12 HOLE," 8,493 "
-28015880,HC IMPL PLATE MATRIXMANDIBLE RECONSTRUCTION STR 20 HOLE," 11,568 "
-28015881,HC IMPL PLATE MATRIXMANDIBLE SQUARE MALLEABLE 4 HOLE," 2,067 "
-28015882,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 1.5MM 12 HOLE," 5,848 "
-28015883,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 1.5MM 20 HOLE," 8,072 "
-28015884,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 2.5MM 12 HOLE," 7,215 "
-28015885,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 2.5MM 20 HOLE," 8,826 "
-28015886,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 2MM 12 HOLE," 6,705 "
-28015887,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 2MM 20 HOLE," 8,459 "
-28015888,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 3X3 HOLE," 5,392 "
-28015889,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 1.5MM 6 HOLE," 5,392 "
-28015890,HC IMPL PLATE MATRIXMANDIBLE STRUT MALLEABLE 8 HOLE," 4,066 "
-28015891,HC IMPL PLATE MATRIXMANDIBLE STRUT MALLEABLE CURVED 8H," 4,066 "
-28015892,HC IMPL PLATE MATRIXMANDIBLE TI 1.25MM 12 HOLE," 5,073 "
-28015893,HC IMPL PLATE MATRIXMANDIBLE TI ANGLED LEFT 7X23 HOLE," 16,284 "
-28015894,HC IMPL PLATE MATRIXMANDIBLE TI ANGLED RIGHT 7X23 HOLE," 17,184 "
-28015895,HC IMPL PLATE MULTISPAN ADJUSTABLE 45-57," 12,287 "
-28015896,HC IMPL PLATE NEURO LOW PROFILE ADAPTION TI 20 HOLE," 1,550 "
-28015897,HC IMPL PLATE NEURO LOW PROFILE UNIVERSAL 2 HOLE," 1,098 "
-28015898,HC IMPL PLATE NEURO LOW PROFILE UNIVERSAL 4 HOLE, 918 
-28015899,HC IMPL PLATE NEURO LOW PROFILE UNIVERSAL BOX 1.5MM," 1,373 "
-28015900,HC IMPL PLATE NEURO LOW PROFILE UNIVERSAL BOX LARGE 2H," 2,059 "
-28015901,HC IMPL PLATE NEURO LOW PROFILE UNIV DBL Y SHPD W/BAR," 1,943 "
-28015902,HC IMPL PLATE NEURO LOW PROFILE UNIV BONE GAP TI LARGE," 2,514 "
-28015903,HC IMPL PLATE NEURO LOW PROFILE UNIV BONE GAP TI SMALL," 2,283 "
-28015904,HC IMPL PLATE NEURO LOW PROFILE UNIVERSAL STRAIGHT 16H," 2,196 "
-28015905,HC IMPL PLATE NEURO LOW PROFILE UNIV STRAIGHT 8 HOLE," 1,243 "
-28015906,HC IMPL PLATE NEURO LOW PROFILE UNIVERSAL W/TAB 1.5MM, 918 
-28015907,HC IMPL PLATE NEURO LOW PROFILE UNIV X SHAPED 4 HOLE," 1,915 "
-28015908,HC IMPL PLATE NEURO RIGID 1.5MM 2 HOLE, 792 
-28015909,HC IMPL PLATE OBLIQUE L SHAPED LEFT 2MM 2 HOLE, 510 
-28015910,HC IMPL PLATE OBLIQUE L SHAPED RIGHT 1.3MM 3 HOLE," 1,646 "
-28015911,HC IMPL PLATE OBLIQUE L SHAPED RIGHT 20X11MM 3 HOLE," 1,646 "
-28015912,HC IMPL PLATE OBLIQUE L SHAPED RIGHT 2MM 2 HOLE, 442 
-28015913,HC IMPL PLATE OBLIQUE L SHAPED TI LEFT 3X4 HOLE," 1,646 "
-28015914,HC IMPL PLATE OBLIQUE LOW PROFILE TI LEFT 3X3HOLE," 1,646 "
-28015915,HC IMPL PLATE OBLIQUE TI RIGHT 2.7MM, 493 
-28015916,HC IMPL PLATE OBLIQUE X SHAPED 2MM 4 HOLE," 1,510 "
-28015917,HC IMPL PLATE OBLIQUE L SHAPED TI MALLEABLE LEFT 2 HOLE," 1,924 "
-28015918,HC IMPL PLATE OBLIQUE L SHAPED TI MALLEABLE RIGHT 2HOLE," 1,924 "
-28015919,HC IMPL PLATE OCCIPITAL CERVICAL THORACIC 27MM," 22,764 "
-28015920,HC IMPL PLATE OLECRANON DISTAL SHORT RIGHT 9 HOLE," 8,500 "
-28015921,HC IMPL PLATE ORBITAL RESORBABLE 11X126X1MM," 3,825 "
-28015922,HC IMPL PLATE ORBITAL RESORBABLE 126X126MM," 16,500 "
-28015923,HC IMPL PLATE ORBITAL RESORBABLE MESH 51MM," 6,800 "
-28015924,HC IMPL PLATE ORBITAL FLOOR BASIC MEDIUM 0.4MM," 4,548 "
-28015925,HC IMPL PLATE ORBITAL FLOOR BASIC MEDIUM 1.2MM," 4,541 "
-28015926,HC IMPL PLATE ORBITAL FLOOR COMPLEX LARGE 1.2X0.3MM," 4,859 "
-28015927,HC IMPL PLATE ORBITAL FLOOR COMPLEX LARGE 0.4MM," 4,859 "
-28015928,HC IMPL PLATE ORBITAL FLOOR ISOLATED SMALL 0.3MM," 4,349 "
-28015929,HC IMPL PLATE ORBITAL FLOOR LARGE," 5,049 "
-28015930,HC IMPL PLATE ORBITAL FLOOR 1.2MM," 3,728 "
-28015931,HC IMPL PLATE ORBITAL MESH 0.3MM," 8,276 "
-28015932,HC IMPL PLATE ORBITAL MICROPLUS 1.2MM 8 HOLE, 998 
-28015933,HC IMPL PLATE ORBITAL MINI 6MM 0.55MM 7MM 4 HOLE, 726 
-28015934,HC IMPL PLATE ORBITAL MINI TITANIUM 1.7MM 10 HOLE, 961 
-28015935,HC IMPL PLATE ORBITAL MINI TITANIUM 1.7MM 8 HOLE," 1,148 "
-28015936,HC IMPL PLATE ORBITAL RIM CURVED W/BAR 1.7MM 6 HOLE," 1,698 "
-28015937,HC IMPL PLATE ORBITAL RIM TITANIUM 1.3MM 9 HOLE," 1,646 "
-28015938,HC IMPL PLATE ORBITAL RIM TITANIUM 1.5MM 12 HOLE," 2,224 "
-28015939,HC IMPL PLATE ORBITAL RIM TITANIUM 1.5MM 8 HOLE," 1,958 "
-28015940,HC IMPL PLATE ORBITAL RIM TITANIUM 1MM 11 HOLE," 1,863 "
-28015941,HC IMPL PLATE ORBITAL RIM TITANIUM 2MM 12 HOLE," 1,686 "
-28015942,HC IMPL PLATE ORTHOGNATHIC L SHPD RESORB CPS LT 7 HOLE," 1,649 "
-28015943,HC IMPL PLATE ORTHOGNATHIC L SHPD RESORB CPS RT 7 HOLE," 1,649 "
-28015944,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR LEFT 12MM 6H," 1,350 "
-28015945,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR LEFT 5MM 5H," 1,272 "
-28015946,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR LEFT 8MM 6H," 1,313 "
-28015947,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL LT 2MM 5H," 1,459 "
-28015948,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL LT 5MM 5H," 1,385 "
-28015949,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL LT 8MM 6H," 1,431 "
-28015950,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL LT 12MM 6H," 1,472 "
-28015951,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL RT 2MM 5H," 1,344 "
-28015952,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL RT 5MM 5H," 1,385 "
-28015953,HC IMPL PLATE ORTHOGNATHIC L SHPD W/BAR MALL RT 8MM 6H," 1,431 "
-28015954,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR RIGHT 12MM 6H," 1,350 "
-28015955,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR RIGHT 2MM 5H," 1,234 "
-28015956,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR RIGHT 5MM 5H," 1,272 "
-28015957,HC IMPL PLATE ORTHOGNATHIC L SHAPED W/BAR RIGHT 8MM 6H," 1,313 "
-28015958,HC IMPL PLATE ORTHOGNATHIC RESORB CPS 2MM 6 HOLE, 948 
-28015959,HC IMPL PLATE ORTHOGNATHIC Y SHPD W/BAR 4MM 5 HOLE," 1,272 "
-28015960,HC IMPL PLATE ORTHOGNATHIC Y SHPD W/BAR 8MM 5 HOLE," 1,344 "
-28015961,HC IMPL PLATE OSTEOTOMY CHILD 3 HOLE 60X15MM," 4,378 "
-28015962,HC IMPL PLATE OSTEOTOMY ADOLESCENT 90D 10MM 50MM," 4,670 "
-28015963,HC IMPL PLATE OSTEOTOMY ADOLESCENT 90D 15MM 40MM," 4,114 "
-28015964,HC IMPL PLATE OSTEOTOMY ADOLESCENT 90D 15MM 50MM," 4,670 "
-28015965,HC IMPL PLATE OSTEOTOMY CHILD 90DEGREE," 4,114 "
-28015966,HC IMPL PLATE OSTEOTOMY INFANT 90D 12MM 32MM," 3,315 "
-28015967,HC IMPL PLATE PEANUT ARCHED 12MM," 3,009 "
-28015968,HC IMPL PLATE PEANUT ARCHED 16MM," 3,009 "
-28015969,HC IMPL PLATE PEANUT STEPPED 12MM," 3,009 "
-28015970,HC IMPL PLATE PREBENT RIGID STEP LEFT 1.6X6MM," 2,630 "
-28015971,HC IMPL PLATE PREBENT RIGID STEP RIGHT 1.6X6MM," 2,630 "
-28015972,HC IMPL PLATE PROX TIBIAL LEFT 4.5X154MM 8 HOLE," 11,962 "
-28015973,HC IMPL PLATE PROX TIBIAL LEFT 4.5X190MM 10 HOLE," 10,568 "
-28015974,HC IMPL PLATE PROX TIBIAL LEFT 4.5X226MM 12 HOLE," 10,688 "
-28015975,HC IMPL PLATE QUAD GUIDED GROWTH SYSTEM 22MM," 3,595 "
-28015976,HC IMPL PLATE RECONSTRUCTION 2.7X128MM 16 HOLE," 3,740 "
-28015977,HC IMPL PLATE RECONSTRUCTION 3.5X142MM 12 HOLE," 4,427 "
-28015978,HC IMPL PLATE RECONSTRUCTION 3.5X166MM 14 HOLE," 3,528 "
-28015979,HC IMPL PLATE RECONSTRUCTION 3.5X190MM 16 HOLE," 4,463 "
-28015980,HC IMPL PLATE RECONSTRUCTION 4.5X109MM 7 HOLE," 3,494 "
-28015981,HC IMPL PLATE RECONSTRUCTION 4.5X125MM 8 HOLE," 3,681 "
-28015982,HC IMPL PLATE RECONSTRUCTION 4.5X141MM 9 HOLE," 3,944 "
-28015983,HC IMPL PLATE RECONSTRUCTION 4.5X157MM 10 HOLE," 3,001 "
-28015984,HC IMPL PLATE RECONSTRUCTION 4.5X189MM 12 HOLE," 4,480 "
-28015985,HC IMPL PLATE RECONSTRUCTION 4.5X61MM 4 HOLE," 2,899 "
-28015986,HC IMPL PLATE RECONSTRUCTION 4.5X77MM 5 HOLE," 3,145 "
-28015987,HC IMPL PLATE RECONSTRUCTION 4.5X93MM 6 HOLE," 3,341 "
-28015988,HC IMPL PLATE RECONSTRUCTION ANGLED RIGHT," 3,859 "
-28015989,HC IMPL PLATE RECONSTRUCTION CALCANEAL 2.7X64MM 8 HOLE," 3,502 "
-28015990,HC IMPL PLATE RECONSTRUCTION CALCANEAL 2.7X72MM 9 HOLE," 3,505 "
-28015991,HC IMPL PLATE RECONSTRUCTION CURVED 3.5X166MM 14HOLE," 4,437 "
-28015992,HC IMPL PLATE RESORBABLE ADAPTION 2MM 20 HOLE," 2,244 "
-28015993,HC IMPL PLATE RESORBABLE BABY 1.5MM 20 HOLE," 2,061 "
-28015994,HC IMPL PLATE RESORBABLE BABY 1.5MM 4 HOLE, 948 
-28015995,HC IMPL PLATE RESORBABLE C SHAPED BABY 1.5MM 7 HOLE," 1,649 "
-28015996,HC IMPL PLATE RESORBABLE CPS 1.5MM 7 HOLE," 1,649 "
-28015997,HC IMPL PLATE RESORBABLE C SHAPED 2MM 7 HOLE," 1,649 "
-28015998,HC IMPL PLATE RESORBABLE CPS 1.5MM 20 HOLE," 2,061 "
-28015999,HC IMPL PLATE RESORBABLE CPS 1.5MM 4 HOLE, 948 
-28016000,HC IMPL PLATE RESORBABLE CPS 1.5MM 6 HOLE, 948 
-28016001,HC IMPL PLATE RESORBABLE CPS 2.0MM 4 HOLE, 948 
-28016002,HC IMPL PLATE RESORBABLE CPS 2.5MM 4 HOLE, 948 
-28016003,HC IMPL PLATE RESORBABLE CPS 2.5MM 6 HOLE," 1,154 "
-28016004,HC IMPL PLATE RESORBABLE CPS 2.5MM 8 HOLE," 1,154 "
-28016005,HC IMPL PLATE RESORBABLE EXTENDED 2.5MM 4 HOLE, 948 
-28016006,HC IMPL PLATE RESORBABLE EXTENDED CPS 2.5MM 4 HOLE, 948 
-28016007,HC IMPL PLATE RESORBABLE EXTENDED CPS 2.5MM 6 HOLE," 1,154 "
-28016008,HC IMPL PLATE RESORBABLE EXTENDED CPS 2MM 4 HOLE, 948 
-28016009,HC IMPL PLATE RESORBABLE H SHAPED 2MM 8 HOLE," 2,576 "
-28016010,HC IMPL PLATE RESORBABLE L SHAPED 1.5MM," 1,649 "
-28016011,HC IMPL PLATE RESORBABLE L SHAPED 1.5MM LEFT 7 HOLE," 1,649 "
-28016012,HC IMPL PLATE RESORBABLE L SHAPED 1.5MM RIGHT 7 HOLE," 1,649 "
-28016013,HC IMPL PLATE RESORBABLE MESH ADULT 1.5MM 7 HOLE," 8,245 "
-28016014,HC IMPL PLATE RESORBABLE MESH BABY 1.5MM 7 HOLE," 8,245 "
-28016015,HC IMPL PLATE RESORBABLE MESH BABY 14X14 HOLE," 13,095 "
-28016016,HC IMPL PLATE RESORBABLE MESH CPS 1.5MM 14X14 HOLE," 13,095 "
-28016017,HC IMPL PLATE RESORBABLE MESH CPS 2.5MM 14X14 HOLE," 13,095 "
-28016018,HC IMPL PLATE RESORBABLE MESH CPS 2MM 14X14 HOLE," 13,095 "
-28016019,HC IMPL PLATE RESORBABLE MESH CPS 2.5MM 10 HOLE," 1,154 "
-28016020,HC IMPL PLATE RESORBABLE MESH CPS 2MM 7 HOLE," 8,245 "
-28016021,HC IMPL PLATE RESORBABLE MESH CPS 2.5MM 7X7 HOLE," 8,245 "
-28016022,HC IMPL PLATE RESORBABLE MESH PROFILE 0.6MM 51MM," 6,800 "
-28016023,HC IMPL PLATE RESORBABLE MESH STRAIGHT 22 HOLE," 2,040 "
-28016024,HC IMPL PLATE RESORBABLE STRAIGHT 2 HOLE," 1,496 "
-28016025,HC IMPL PLATE RESORBABLE STRAIGHT BABY 1.5MM 2 HOLE," 1,649 "
-28016026,HC IMPL PLATE RESORBABLE STRAIGHT 2MM 10 HOLE," 1,113 "
-28016027,HC IMPL PLATE RESORBABLE X SHAPED BABY 1.5MM 7 HOLE," 1,649 "
-28016028,HC IMPL PLATE RESORBABLE X SHAPED CPS 1.5MM 7 HOLE," 1,649 "
-28016029,HC IMPL PLATE RESORBABLE X SHAPED CPS 2MM," 1,649 "
-28016030,HC IMPL PLATE RETENTION THICK 1.5MM," 6,375 "
-28016031,HC IMPL PLATE ROD SPINAL LP SS 5.5X1.55-1.75IN," 11,253 "
-28016032,HC IMPL PLATE ROD SPINAL LP SS 10H 5.5X16MM," 9,186 "
-28016033,HC IMPL PLATE ROD SPINAL LP SS 5.5X2.15-2.95IN," 11,253 "
-28016034,HC IMPL PLATE SACRAL 37MM," 32,890 "
-28016035,HC IMPL PLATE SACRAL 39MM," 32,890 "
-28016036,HC IMPL PLATE SACRAL 41MM," 32,890 "
-28016037,HC IMPL PLATE SACRAL 43MM," 32,890 "
-28016038,HC IMPL PLATE SACRAL 45MM," 32,890 "
-28016039,HC IMPL PLATE SACRAL 47MM," 32,890 "
-28016040,HC IMPL PLATE SACRAL 49MM," 32,890 "
-28016041,HC IMPL PLATE SACRAL 51MM," 32,890 "
-28016042,HC IMPL PLATE SACRAL 53MM," 32,890 "
-28016043,HC IMPL PLATE SAGGITAL SPLIT STRAIGHT 2X30MM 4 HOLE," 2,502 "
-28016044,HC IMPL PLATE SAGGITAL SPLIT STRUT 2MM 8 HOLE," 2,713 "
-28016045,HC IMPL PLATE SAGITTAL SPLIT BILATERAL SHORT," 2,057 "
-28016046,HC IMPL PLATE SAGITTAL SPLIT MATRIXORTHGN TI GOLD 4HOLE," 2,706 "
-28016047,HC IMPL PLATE SEMI TUBULAR 10 HOLE, 680 
-28016048,HC IMPL PLATE SEMI TUBULAR 12 HOLE, 735 
-28016049,HC IMPL PLATE SEMI TUBULAR 8 HOLE, 638 
-28016050,HC IMPL PLATE SEMI TUBULAR 9 HOLE, 663 
-28016051,HC IMPL PLATE SEMI TUBULAR SS 29MM 2 HOLE, 446 
-28016052,HC IMPL PLATE SEMI TUBULAR SS 29MM 3 HOLE, 463 
-28016053,HC IMPL PLATE SMALL FRAG LOCKING HIGH FLEX LEFT 3F," 5,423 "
-28016054,HC IMPL PLATE SMALL FRAG LOCKING HS LEFT 45.3MM," 5,423 "
-28016055,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 12MM," 8,500 "
-28016056,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 14MM," 8,500 "
-28016057,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 16MM," 8,500 "
-28016058,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 18MM," 8,500 "
-28016059,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 20MM," 8,500 "
-28016060,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 22MM," 8,500 "
-28016061,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 24MM," 8,500 "
-28016062,HC IMPL PLATE SPINAL ONE LEVEL TITANIUM 26MM," 8,500 "
-28016063,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 28MM," 9,000 "
-28016064,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 30MM," 9,000 "
-28016065,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 32MM," 9,000 "
-28016066,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 34MM," 9,000 "
-28016067,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 36MM," 9,000 "
-28016068,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 38MM," 9,000 "
-28016069,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 40MM," 9,000 "
-28016070,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 42MM," 9,000 "
-28016071,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 44MM," 9,000 "
-28016072,HC IMPL PLATE SPINAL TWO LEVEL TITANIUM 46MM," 9,000 "
-28016073,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 24MM," 6,494 "
-28016074,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 28MM," 8,211 "
-28016075,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 32MM," 5,151 "
-28016076,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 34MM," 6,494 "
-28016077,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 36MM," 4,350 "
-28016078,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 38MM," 5,619 "
-28016079,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 40MM," 7,081 "
-28016080,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 42MM," 7,081 "
-28016081,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 44MM," 7,081 "
-28016082,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 46MM," 7,081 "
-28016083,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 48MM," 7,081 "
-28016084,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 50MM," 7,081 "
-28016085,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 52MM," 7,081 "
-28016086,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 54MM," 7,081 "
-28016087,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 56MM," 7,591 "
-28016088,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 58MM," 7,591 "
-28016089,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 60MM," 7,591 "
-28016090,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 62MM," 5,881 "
-28016091,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 64MM," 7,591 "
-28016092,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 66MM," 4,420 "
-28016093,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 68MM," 4,420 "
-28016094,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 70MM," 7,591 "
-28016095,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 74MM," 4,420 "
-28016096,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 78MM," 4,420 "
-28016097,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 82MM," 5,083 "
-28016098,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 86MM," 4,420 "
-28016099,HC IMPL PLATE SPINAL ANTERIOR CERVICAL TITANIUM 90MM," 4,420 "
-28016100,HC IMPL PLATE SPINAL ANTERIOR LUMBAR 1 L 35MM," 32,432 "
-28016101,HC IMPL PLATE SPINAL ANTERIOR LUMBAR 1 L 37MM," 31,034 "
-28016102,HC IMPL PLATE SPINAL ANTERIOR SACRAL 1 L 35MM," 24,500 "
-28016103,HC IMPL PLATE SPINAL ANTERIOR SACRAL 1 L 37MM," 24,500 "
-28016104,HC IMPL PLATE SPINAL ANTERIOR SACRAL 1 L 39MM," 24,500 "
-28016105,HC IMPL PLATE SPINAL ANTERIOR SACRAL 2 L 93MM," 33,250 "
-28016106,HC IMPL PLATE SPINAL LOCKING ONE LEVEL 22X31MM," 10,380 "
-28016107,HC IMPL PLATE SPINAL LOCKING ONE LEVEL 24X35MM," 10,380 "
-28016108,HC IMPL PLATE SPINAL LOCKING ONE LEVEL 26X35MM," 9,984 "
-28016109,HC IMPL PLATE SPINAL LOCKING TWO LEVEL 37X46MM," 9,750 "
-28016110,HC IMPL PLATE SPINAL LOCKING TWO LEVEL 40X49MM," 9,750 "
-28016111,HC IMPL PLATE SPINAL LOCKING TWO LEVEL 43X53MM," 9,750 "
-28016112,HC IMPL PLATE SPINAL LOCKING TWO LEVEL 46X55MM," 11,183 "
-28016113,HC IMPL PLATE SPINAL LOCKING THREE LEVEL 51X60MM," 12,218 "
-28016114,HC IMPL PLATE SPINAL LOCKING THREE LEVEL 57X66MM," 12,218 "
-28016115,HC IMPL PLATE SPINAL LOCKING THREE LEVEL 60X69MM," 12,218 "
-28016116,HC IMPL PLATE SPINAL LOCKING THREE LEVEL 63X72MM," 12,218 "
-28016117,HC IMPL PLATE SPINAL LOCKING THREE LEVEL 66X75MM," 12,218 "
-28016118,HC IMPL PLATE SPINAL LOCKING THREE LEVEL 69X78MM," 12,218 "
-28016119,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 100X109MM," 13,260 "
-28016120,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 60X69MM," 12,751 "
-28016121,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 64X73MM," 13,260 "
-28016122,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 68X77MM," 12,750 "
-28016123,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 72X81MM," 13,260 "
-28016124,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 76X85MM," 13,260 "
-28016125,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 80X89MM," 13,260 "
-28016126,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 84X93MM," 13,260 "
-28016127,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 88X97MM," 12,199 "
-28016128,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 92X101MM," 13,260 "
-28016129,HC IMPL PLATE SPINAL LOCKING FOUR LEVEL 96X105MM," 13,260 "
-28016130,HC IMPL PLATE SPINAL LOW PROFILE SS 5.5X1.375MM," 9,643 "
-28016131,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5X0.375IN," 6,681 "
-28016132,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5X0.75 IN," 6,681 "
-28016133,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5X0.875 IN," 11,018 "
-28016134,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5X28-30MM," 12,042 "
-28016135,HC IMPL PLATE SPINAL LOW PROFILE TITANIUM 5.5X36-39MM," 12,042 "
-28016136,HC IMPL PLATE SPINAL MINI ARCH 2X33MM," 6,624 "
-28016137,HC IMPL PLATE SPINAL MINI SINGLE BEND 2X27MM," 6,272 "
-28016138,HC IMPL PLATE SPINAL MINI SINGLE BEND 2X29MM," 6,272 "
-28016139,HC IMPL PLATE SPINAL MINI SINGLE BEND 2X33MM," 6,272 "
-28016140,HC IMPL PLATE SPINAL MINI DOUBLE BEND 6MM," 6,624 "
-28016141,HC IMPL PLATE SPINAL MINI DOUBLE BEND 8MM," 6,624 "
-28016142,HC IMPL PLATE SPINAL OCCIPITAL SMALL 31MM," 22,764 "
-28016143,HC IMPL PLATE SPINAL ORACLE TITANIUM 32MM," 24,500 "
-28016144,HC IMPL PLATE SPINAL SKYLINE ONE LEVEL 12MM," 8,415 "
-28016145,HC IMPL PLATE SPINAL SKYLINE ONE LEVEL 14MM," 8,415 "
-28016146,HC IMPL PLATE SPINAL SKYLINE ONE LEVEL 16MM," 9,811 "
-28016147,HC IMPL PLATE SPINAL SKYLINE ONE LEVEL 26MM," 8,415 "
-28016148,HC IMPL PLATE SPINAL SKYLINE ONE LEVEL 28MM," 8,415 "
-28016149,HC IMPL PLATE SPINAL SKYLINE ONE LEVEL 30MM," 8,415 "
-28016150,HC IMPL PLATE SPINAL SKYLINE TWO LEVEL 30MM," 9,000 "
-28016151,HC IMPL PLATE SPINAL SKYLINE TWO LEVEL 32MM," 9,000 "
-28016152,HC IMPL PLATE SPINAL SKYLINE TWO LEVEL 34MM," 9,000 "
-28016153,HC IMPL PLATE SPINAL SKYLINE TWO LEVEL 36MM," 9,000 "
-28016154,HC IMPL PLATE SPINAL SKYLINE THREE LEVEL 45MM," 10,500 "
-28016155,HC IMPL PLATE SPINAL SKYLINE THREE LEVEL 48MM," 10,500 "
-28016156,HC IMPL PLATE SPINAL SKYLINE THREE LEVEL 51MM," 10,500 "
-28016157,HC IMPL PLATE SPINAL SKYLINE THREE LEVEL 54MM," 10,500 "
-28016158,HC IMPL PLATE SPINAL SPIRE 35MM," 34,808 "
-28016159,HC IMPL PLATE SPINAL SPIRE 45MM," 34,808 "
-28016160,HC IMPL PLATE SPINAL VARIABLE ANGLE 12 HOLE," 5,304 "
-28016161,HC IMPL PLATE SQUARE MICROPLUS 3D 1.2MM 2X2 HOLE, 954 
-28016162,HC IMPL PLATE SQUARE MICROPLUS 3D 1.2MM 3X2 HOLE," 1,387 "
-28016163,HC IMPL PLATE SQUARE MICROPLUS 3D 1.2MM 4X2 HOLE," 1,686 "
-28016164,HC IMPL PLATE SQUARE MICROPLUS 3D 1.2MM 6X4 HOLE," 4,208 "
-28016165,HC IMPL PLATE SQUARE MINI 3D 1.7MM 2 HOLE, 762 
-28016166,HC IMPL PLATE STRAIGHT MICRO 18 HOLE," 1,828 "
-28016167,HC IMPL PLATE STRAIGHT STANDARD TITANIUM 2MM 4 HOLE, 94 
-28016168,HC IMPL PLATE STRAIGHT TITANIUM 2X100MM 20 HOLE," 2,015 "
-28016169,HC IMPL PLATE STRUT MATRIXMIDFACE TI 18 HOLE .7MM," 4,576 "
-28016170,HC IMPL PLATE SYMPHYSIS COMPRESSION TI 2.3MM 4 HOLE," 2,251 "
-28016172,HC IMPL PLATE T 6HOLE 4.5X115MM," 6,507 "
-28016173,HC IMPL PLATE T SHAPED MICROPLUS TI 1.2MM 90D 7 HOLE, 857 
-28016174,HC IMPL PLATE T SHAPED MINI TI W/BAR 1.7MM 2 HOLE, 964 
-28016175,HC IMPL PLATE T SHAPED SS 2.7MM 3 HOLE, 579 
-28016176,HC IMPL PLATE T SHAPED TITANIUM 1.5MM 3X9 HOLE," 1,760 "
-28016177,HC IMPL PLATE T SHAPED TITANIUM 1.5MM 4X9 HOLE," 1,904 "
-28016178,HC IMPL PLATE T SHAPED TITANIUM 18X14MM 5X5 HOLE," 1,598 "
-28016179,HC IMPL PLATE T SHAPED TITANIUM 2.0MM 48MM 2 HOLE," 2,270 "
-28016180,HC IMPL PLATE T SHAPED TITANIUM 2.0MM 53MM 3 HOLE," 2,480 "
-28016181,HC IMPL PLATE T SHAPED TITANIUM 2.7X32MM 3 HOLE, 493 
-28016182,HC IMPL PLATE T SHAPED TITANIUM 24X14MM 5X5 HOLE," 1,693 "
-28016183,HC IMPL PLATE T SHAPED TITANIUM 2X50MM 3X9 HOLE," 1,760 "
-28016184,HC IMPL PLATE T SHAPED TITANIUM 2X50MM 4X9 HOLE," 1,904 "
-28016185,HC IMPL PLATE TITANIUM T-PLATE 3 HOLE 1.3X8HOLE," 1,998 "
-28016186,HC IMPL PLATE TITANIUM HEAD 3 HOLE 1.5X8HOLE," 1,930 "
-28016187,HC IMPL PLATE TITANIUM HEAD 4 HOLE 2.0X7H LEFT," 2,542 "
-28016188,HC IMPL PLATE TIBIAL FLUTED ZIMALOY STEM SZ5," 9,960 "
-28016189,HC IMPL PLATE TIBIAL FLUTED ZIMALOY STEM SZ6," 9,960 "
-28016190,HC IMPL PLATE TIBIAL LPS FLEXIBLE PRECOAT FLUTED," 9,960 "
-28016191,HC IMPL PLATE TIBIAL LPS PRECOAT FLUTED SZ5," 9,960 "
-28016192,HC IMPL PLATE TIBIAL MEDIAL DISTAL LCP LEFT 6 HOLE," 11,498 "
-28016193,HC IMPL PLATE TIBIAL PEGGED PRECOAT SZ4 46X66MM," 7,038 "
-28016194,HC IMPL PLATE TIBIAL PRECOAT HI FLEX RECOAT UNI 29X50MM," 9,563 "
-28016195,HC IMPL PLATE TIBIAL PRECOAT UNI MR/LL 27X47MM," 9,563 "
-28016196,HC IMPL PLATE TIBIAL PRECOAT UNI LM/RL SZ1 23X41MM," 9,563 "
-28016197,HC IMPL PLATE TIBIAL PRECOAT UNI LM/RL SZ2 25X44MM," 9,563 "
-28016198,HC IMPL PLATE TIBIAL PRECOAT UNI LM/RL SZ3 27X47MM," 9,563 "
-28016199,HC IMPL PLATE TIBIAL PRECOAT UNI LM/RL SZ5 31X53MM," 9,563 "
-28016200,HC IMPL PLATE TIBIAL PRECOAT UNI RM/LL SZ2 25X44MM," 9,563 "
-28016201,HC IMPL PLATE TIBIAL PRECOAT UNI RM/LL SZ5 31X53MM," 9,563 "
-28016202,HC IMPL PLATE TIBIAL PRECOAT STEMMED 40X58MM," 7,710 "
-28016203,HC IMPL PLATE TIBIAL PRECOAT STEMMED SZ4 46X66MM," 7,710 "
-28016204,HC IMPL PLATE TIBIAL PRECOAT STEMMED SZ4 46X70MM REV," 26,370 "
-28016205,HC IMPL PLATE TIBIAL PRECOAT STEMMED SZ7 51X82MM," 7,710 "
-28016206,HC IMPL PLATE TIBIAL PRECOAT STEMMED SZ8 54X82MM," 7,710 "
-28016207,HC IMPL PLATE TIBAL PROXIMAL LEFT 6 HOLE 3.5X107MM," 11,123 "
-28016208,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT 12 H 3.5X197," 12,060 "
-28016209,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT 14 H 3.5X223," 12,195 "
-28016210,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT 16 H 3.5X249," 12,623 "
-28016211,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT L 4 H 3.5X93," 11,712 "
-28016212,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT 6 H 3.5X119," 11,655 "
-28016213,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT 8 H 3.5X145," 11,790 "
-28016214,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL RIGHT 8 H 3.5X145," 11,790 "
-28016216,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL RIGHT 12 H 3.5X197," 12,060 "
-28016217,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL RIGHT 14 H 3.5X223," 12,195 "
-28016218,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL RIGHT 16 H 3.5X249," 12,623 "
-28016219,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL RIGHT 4 H 3.5X93," 11,505 "
-28016220,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL RIGHT 6 H 3.5X119," 11,869 "
-28016221,HC IMPL PLATE TIBAL PROXIMAL RIGHT 6 HOLE 3.5X107MM," 10,343 "
-28016222,HC IMPL PLATE TIBAL PROXIMAL RIGHT 10 HOLE 3.5X159MM," 10,575 "
-28016223,HC IMPL PLATE TIBAL PROXIMAL RIGHT 12 HOLE 3.5X185MM," 10,703 "
-28016224,HC IMPL PLATE TIBAL PROXIMAL RIGHT 14 HOLE 3.5X211MM," 11,640 "
-28016225,HC IMPL PLATE TIBAL PROXIMAL RIGHT 16 HOLE 3.5X237MM," 12,060 "
-28016226,HC IMPL PLATE TIBAL PROXIMAL RIGHT 8 HOLE 3.5X133MM," 12,623 "
-28016227,HC IMPL PLATE TIBIAL ROTATING 41X56MM REV," 26,370 "
-28016228,HC IMPL PLATE TIBIAL ROTATING 41X62MM," 26,370 "
-28016229,HC IMPL PLATE TIBIAL ROTATING 46X67MM REV," 26,370 "
-28016230,HC IMPL PLATE TIBIAL STEMMED PRECOAT WEDGE 42X66MM REV," 14,071 "
-28016231,HC IMPL PLATE TIBIAL STEMMED PRECOAT 40X58MM REV," 11,987 "
-28016232,HC IMPL PLATE TIBIAL STEMMED PRECOAT 41X62MM REV," 11,987 "
-28016233,HC IMPL PLATE TIBIAL STEMMED PRECOAT 42X66MM REV," 11,987 "
-28016234,HC IMPL PLATE TIBIAL STEMMED PRECOAT 46X66MM REV," 11,987 "
-28016235,HC IMPL PLATE TIBIAL STEMMED PRECOAT 46X74MM REV," 11,987 "
-28016236,HC IMPL PLATE TIBIAL STEMMED PRECOAT 50X74MM REV," 11,987 "
-28016237,HC IMPL PLATE TIBIAL STEMMED PRECOAT 51X82MM REV," 11,987 "
-28016238,HC IMPL PLATE TIBIAL STEMMED PRECOAT 54X82MM REV," 11,987 "
-28016239,HC IMPL PLATE TIBIAL UNI LM/RL 44X27MM," 8,063 "
-28016240,HC IMPL PLATE TIBIAL UNI RM/LL 44X27MM," 8,063 "
-28016241,HC IMPL PLATE TRAUMA 10MM OD," 3,742 "
-28016242,HC IMPL PLATE UPPER FACE 3D MALLEABLE 10 HOLE," 7,832 "
-28016243,HC IMPL PLATE UPPER FACE 3D MALLEABLE 2X2 HOLE," 1,749 "
-28016244,HC IMPL PLATE UPPER FACE 3D MALLEABLE 6X2 HOLE," 2,816 "
-28016245,HC IMPL PLATE UPPER FACE 3D MALLEABLE 6X6 HOLE," 6,493 "
-28016246,HC IMPL PLATE UPPER FACE 3D MALLEABLE CRVD COND 10 HOLE," 1,647 "
-28016247,HC IMPL PLATE UPPER FACE 3D MALLEABLE DBL Y SHPD 7 HOLE," 1,459 "
-28016248,HC IMPL PLATE UPPER FACE 3D MALLEABLE STR COND 24HOLE," 3,952 "
-28016249,HC IMPL PLATE UPPER FACE MALLEABLE STRAIGHT 18 HOLE," 3,714 "
-28016250,HC IMPL PLATE UPPER FACE 3D 6X6 HOLE," 6,279 "
-28016251,HC IMPL PLATE UPPER FACE CURVED STANDARD 10 HOLE," 1,647 "
-28016252,HC IMPL PLATE UPPER FACE CURVED STANDARD 4 HOLE, 834 
-28016253,HC IMPL PLATE UPPER FACE DOUBLE Y SHPD STANDARD 7 HOLE," 1,212 "
-28016254,HC IMPL PLATE UPPER FACE L SHAPED STANDARD LEFT 8 HOLE," 1,272 "
-28016255,HC IMPL PLATE UPPER FACE L SHAPED STANDARD RIGHT 8 HOLE," 1,171 "
-28016256,HC IMPL PLATE UPPER FACE STANDARD 3D 10 HOLE," 8,484 "
-28016257,HC IMPL PLATE UPPER FACE STRAIGHT STANDARD 24 HOLE," 3,432 "
-28016258,HC IMPL PLATE UPPER FACE STRAIGHT STANDARD 8 HOLE," 1,879 "
-28016259,HC IMPL PLATE UPPER FACE T SHAPED STANDARD 7 HOLE," 1,196 "
-28016260,HC IMPL PLATE UPPER FACE TITANIUM 3D 2X2 HOLE," 1,464 "
-28016261,HC IMPL PLATE UPPER FACE TITANIUM 3D 6X2 HOLE," 2,643 "
-28016262,HC IMPL PLATE VOLAR DISTAL RADIUS LEFT 4X5 HOLE," 8,598 "
-28016263,HC IMPL PLATE VOLAR DISTAL RADIUS LONG STANDARD LEFT," 7,718 "
-28016264,HC IMPL PLATE VOLAR DISTAL RADIUS LONG STANDARD RIGHT," 7,718 "
-28016265,HC IMPL PLATE VOLAR DISTAL RADIUS NARROW LONG LEFT," 7,718 "
-28016266,HC IMPL PLATE VOLAR DISTAL RADIUS NARROW LONG RIGHT 12H," 7,718 "
-28016267,HC IMPL PLATE VOLAR DISTAL RADIUS NARROW SHORT RIGHT," 7,560 "
-28016268,HC IMPL PLATE VOLAR DISTAL RADIUS NARROW SHORT LEFT," 7,560 "
-28016269,HC IMPL PLATE VOLAR DISTAL RADIUS WIDE STANDARD LEFT," 7,560 "
-28016270,HC IMPL PLATE VOLAR DISTAL RADIUS WIDE STANDARD RIGHT," 7,560 "
-28016271,HC IMPL PLATE VECTRA ONE LEVEL 10MM," 11,625 "
-28016272,HC IMPL PLATE VECTRA ONE LEVEL 12MM," 11,625 "
-28016273,HC IMPL PLATE VECTRA ONE LEVEL 14MM," 11,625 "
-28016274,HC IMPL PLATE VECTRA ONE LEVEL 16MM," 11,625 "
-28016275,HC IMPL PLATE VECTRA ONE LEVEL 18MM," 11,625 "
-28016276,HC IMPL PLATE VECTRA ONE LEVEL 20MM," 11,625 "
-28016277,HC IMPL PLATE VECTRA ONE LEVEL 22MM," 11,625 "
-28016278,HC IMPL PLATE VECTRA ONE LEVEL 24MM," 11,625 "
-28016279,HC IMPL PLATE VECTRA ONE/TWO LEVEL 26MM," 12,717 "
-28016280,HC IMPL PLATE VECTRA ONE/TWO LEVEL 28MM," 12,717 "
-28016281,HC IMPL PLATE VECTRA ONE/TWO LEVEL 30MM," 12,717 "
-28016282,HC IMPL PLATE VECTRA ONE/TWO LEVEL 32MM," 12,717 "
-28016283,HC IMPL PLATE VECTRA ONE/TWO LEVEL 34MM," 12,717 "
-28016284,HC IMPL PLATE VECTRA ONE/TWO LEVEL 36MM," 12,717 "
-28016285,HC IMPL PLATE VECTRA ONE/TWO LEVEL 38MM," 12,717 "
-28016286,HC IMPL PLATE VECTRA ONE/TWO LEVEL 40MM," 12,717 "
-28016287,HC IMPL PLATE VECTRA FOUR LEVEL 60MM," 12,000 "
-28016288,HC IMPL PLATE VECTRA FOUR LEVEL 64MM," 12,000 "
-28016289,HC IMPL PLATE VECTRA FOUR LEVEL 68MM," 12,000 "
-28016290,HC IMPL PLATE VECTRA FOUR LEVEL 72MM," 12,000 "
-28016291,HC IMPL PLATE VECTRA FOUR LEVEL 76MM," 12,000 "
-28016292,HC IMPL PLATE VECTRA FOUR LEVEL 80MM," 12,000 "
-28016293,HC IMPL PLATE VECTRA FOUR LEVEL 84MM," 12,000 "
-28016294,HC IMPL PLATE VECTRA FOUR LEVEL 88MM," 12,000 "
-28016295,HC IMPL PLATE VECTRA FOUR LEVEL 92MM," 12,000 "
-28016296,HC IMPL PLATE VECTRA FOUR LEVEL 96MM," 12,000 "
-28016297,HC IMPL PLATE X SHAPED 15X15MM 4 HOLE," 1,510 "
-28016298,HC IMPL PLATE X SHAPED TITANIUM 1.5MM 4 HOLE," 1,782 "
-28016299,HC IMPL PLATE X SHAPED TITANIUM 1MM 5 HOLE," 1,428 "
-28016300,HC IMPL PLATE Y SHAPED FRAGMENT TI," 4,208 "
-28016301,HC IMPL PLATE Y SHAPED MAXILLOFACIAL LONG 2MM 5 HOLE, 141 
-28016302,HC IMPL PLATE Y SHAPED MAXILLOFACIAL REGULAR 2MM 5 HOL, 141 
-28016303,HC IMPL PLATE Y SHAPED MICROPLUS 1.2MM 6 HOLE, 915 
-28016304,HC IMPL PLATE Y SHAPED MINI TITANIUM W/BAR 1.7MM 5 HOLE, 796 
-28016305,HC IMPL PLATE Y SHAPED TITANIUM 1.3MM 1X3 HOLE," 1,510 "
-28016306,HC IMPL PLATE Y SHAPED TITANIUM 1.5MM 1 HOLE X 3 HOLE," 1,782 "
-28016307,HC IMPL PLATE ZYGOMATIC TITANIUM 2X27MM 5 HOLE, 208 
-28016308,HC IMPL PLATE ZYGOMATIC TITANIUM 2X32MM 6 HOLE," 1,775 "
-28016309,HC IMP PLUG BONE ANCHOR COMPRESS 20MM," 4,386 "
-28016310,HC IMP PLUG BONE ANCHOR COMPRESS 10MM," 6,545 "
-28016311,HC IMP PLUG BONE ANCHOR COMPRESS 12MM," 6,545 "
-28016312,HC IMP PLUG BONE ANCHOR COMPRESS 14MM," 6,545 "
-28016313,HC IMP PLUG BONE ANCHOR COMPRESS 18MM," 4,386 "
-28016314,HC IMP PLUG CEMENT RESTRICTOR 18.5MM, 114 
-28016318,HC IMP PLUG APICAL 24MM 0.37IN, 850 
-28016319,HC IMP PLUG SPINAL HELICAL DEROTATION FLANGE 5.5MM, 850 
-28016320,HC IMP PLUG SPINAL HELICAL FLANGE 5.5MM," 2,108 "
-28016321,HC IMP PLUG SPINAL REDUCTION SS LEGACY 5.5MM," 1,319 "
-28016322,HC IMP PLUG SPINAL REDUCTION TI LEGACY 5.5MM," 1,483 "
-28016323,HC IMP PLUG STEM POSTERIOR STABIL TAPER TIVANIUM REV," 5,055 "
-28016324,HC IMP PLUG STEM POSTERIOR STABILIZED TAPER TIVANIUM," 4,814 "
-28016327,HC IMP PORT INJECTION 8F," 6,188 "
-28016333,HC IMP POST FEMALE 2 HOLE," 1,066 "
-28016350,HC IMP PROTECTOR MENISCUS SUTURING SET, 808 
-28016352,HC IMP PULMONARY ARTERY HEMI LEFT," 23,065 "
-28016353,HC IMP PULMONARY ARTERY HEMI RIGHT," 23,065 "
-28016359,HC PUNCH LAMINECTOMY 40D UP 4X300MM," 21,000 "
-28016360,HC PUNCH SCLERAL GASS LEFT HAND," 2,254 "
-28016364,HC IMP PUTTY ALLOMATRIX," 2,338 "
-28016365,HC IMP PUTTY ALLOMATRIX 10ML," 14,288 "
-28016366,HC IMP PUTTY BONE ACTIFUSE 1.5ML," 3,028 "
-28016367,HC IMP PUTTY BONE ACTIFUSE 10ML," 17,100 "
-28016368,HC IMP PUTTY BONE ACTIFUSE 2.5ML," 5,854 "
-28016369,HC IMP PUTTY BONE ACTIFUSE 20ML," 20,615 "
-28016370,HC IMP PUTTY BONE ACTIFUSE 5ML," 9,263 "
-28016371,HC IMP PUTTY BONE CUSTOM," 9,413 "
-28016372,HC REAMER PLATE 4CF," 2,627 "
-28016382,HC RETRACTOR PIN STABILITY ENDORING 12CM," 3,953 "
-28016383,HC RETRACTOR PIN STABILITY ENDORING 13CM," 3,953 "
-28016388,HC IMP RING ANNULOPLASTY ANNULOFLO 26MM," 15,735 "
-28016389,HC IMP RING ANNULOPLASTY ANNULOFLO 28MM," 16,208 "
-28016390,HC IMP RING ANNULOPLASTY PHYSIO," 20,014 "
-28016391,HC IMP RING ANNULOPLASTY TRICUSPID 28MM," 16,391 "
-28016392,HC IMP RING ANNULOPLASTY TRICUSPID 30MM," 15,450 "
-28016393,HC IMP RING ANNULOPLASTY TRICUSPID 32MM," 16,391 "
-28016394,HC IMP RING ANNULOPLASTY TRICUSPID 36MM," 16,391 "
-28016395,HC IMP RING APICAL SEWING HEARTMATE," 5,313 "
-28016396,HC IMP RING HALO TRACTION PEDIATRIC 15X16IN," 5,389 "
-28016397,HC IMP RING HALO TRACTION PEDIATRIC 17X19.5IN," 5,388 "
-28016398,HC RING ROTATING XS 100MM," 14,498 "
-28016399,HC IMP ROD ALLOY TITANIUM CURVED 5.5X100MM," 3,374 "
-28016400,HC IMP ROD ALLOY TITANIUM CURVED 5.5X25MM," 3,374 "
-28016401,HC IMP ROD ALLOY TITANIUM CURVED 5.5X30MM," 3,374 "
-28016402,HC IMP ROD ALLOY TITANIUM CURVED 5.5X35MM," 3,374 "
-28016403,HC IMP ROD ALLOY TITANIUM CURVED 5.5X40MM," 3,374 "
-28016404,HC IMP ROD ALLOY TITANIUM CURVED 5.5X45MM," 3,374 "
-28016405,HC IMP ROD ALLOY TITANIUM CURVED 5.5X50MM," 3,374 "
-28016406,HC IMP ROD ALLOY TITANIUM CURVED 5.5X55MM," 3,374 "
-28016407,HC IMP ROD ALLOY TITANIUM CURVED 5.5X60MM," 3,374 "
-28016408,HC IMP ROD ALLOY TITANIUM CURVED 5.5X65MM," 3,374 "
-28016409,HC IMP ROD ALLOY TITANIUM CURVED 5.5X70MM," 3,374 "
-28016410,HC IMP ROD ALLOY TITANIUM CURVED 5.5X75MM," 3,374 "
-28016411,HC IMP ROD ALLOY TITANIUM CURVED 5.5X80MM," 3,374 "
-28016412,HC IMP ROD ALLOY TITANIUM CURVED 5.5X85MM," 3,374 "
-28016413,HC IMP ROD ALLOY TITANIUM CURVED 5.5X90MM," 3,374 "
-28016414,HC IMP ROD ALLOY TITANIUM CURVED 5.5X95MM," 3,374 "
-28016415,HC IMP ROD CARBON 11MMX100MM," 2,389 "
-28016416,HC IMP ROD CONNECTOR PARALLEL OPEN 3.5MM," 4,457 "
-28016417,HC IMP ROD CONNECTOR PARALLEL OPEN 5.0MM," 5,826 "
-28016418,HC IMP ROD CONNECTOR PARALLEL OPEN 6.0MM," 5,826 "
-28016419,HC IMP ROD HARD 6X250MM," 2,125 "
-28016420,HC IMP ROD HARD 6X450MM," 2,788 "
-28016421,HC IMP ROD HARD STRAIGHT 6X50MM," 1,194 "
-28016422,HC IMP ROD HARD STRAIGHT 6X75MM," 1,791 "
-28016423,HC IMP ROD OCCIPITAL PREBENT MOUNTAINEER 3.5X120MM," 2,984 "
-28016424,HC IMP ROD REAMING BALL TIP OFFSET 3X950MM," 1,003 "
-28016425,HC IMP ROD REAMING BALL TIP STRAIGHT 3X950MM," 1,003 "
-28016426,HC IMP ROD SOFT CURVED 6X75MM," 2,125 "
-28016427,HC IMP ROD SPINAL CCM PREBENT SOLERA 4.75X120MM," 3,787 "
-28016428,HC IMP ROD SPINAL CCM PREBENT SOLERA 4.75X40MM," 3,787 "
-28016429,HC IMP ROD SPINAL CCM PREBENT SOLERA 4.75X60MM," 2,125 "
-28016430,HC IMP ROD SPINAL CCM PREBENT SOLERA 4.75X70MM," 2,125 "
-28016431,HC IMP ROD SPINAL CCM PREBENT SOLERA 4.75X80MM," 3,787 "
-28016432,HC IMP ROD SPINAL CURVED SOFT TITANIUM 6.0MM 35MM," 2,125 "
-28016433,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX125MM," 2,125 "
-28016434,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX30MM," 2,125 "
-28016435,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX50MM," 2,125 "
-28016436,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX55MM," 2,125 "
-28016437,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX65MM," 2,125 "
-28016438,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX75MM," 2,125 "
-28016439,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX80MM," 2,125 "
-28016440,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX85MM," 2,125 "
-28016441,HC IMP ROD SPINAL DUAL DIAMETER TITANIUM 420MM," 7,293 "
-28016442,HC IMP ROD SPINAL EXPEDIUM SS 5.5X300MM," 3,635 "
-28016443,HC IMP ROD SPINAL EXPEDIUM SS 5.5X480MM," 6,870 "
-28016444,HC IMP ROD SPINAL EXPEDIUM SS 5.5X600MM," 7,550 "
-28016445,HC IMP ROD SPINAL EXPEDIUM PRELORDOSED TI 5X5X120MM," 4,743 "
-28016446,HC IMP ROD SPINAL EXPEDIUM PRELORDOSED TI 5X5X300MM," 4,743 "
-28016447,HC IMP ROD SPINAL EXPEDIUM PRELORDOSED TI 5X5X30MM," 3,137 "
-28016448,HC IMP ROD SPINAL EXPEDIUM PRELORDOSED TI 5X5X45MM," 3,137 "
-28016449,HC IMP ROD SPINAL EXPEDIUM PRELORDOSED TI 5X5X65MM," 3,137 "
-28016450,HC IMP ROD SPINAL HARD 5.5MMX200MM," 3,349 "
-28016451,HC IMP ROD SPINAL HARD 5.5MMX300MM," 3,349 "
-28016452,HC IMP ROD SPINAL HARD 5.5MMX400MM," 3,349 "
-28016453,HC IMP ROD SPINAL HARD 5.5MMX500MM," 3,349 "
-28016454,HC IMP ROD SPINAL HARD TITANIUM 5X150MM," 2,788 "
-28016455,HC IMP ROD SPINAL HARD TITANIUM 5X200MM," 2,788 "
-28016456,HC IMP ROD SPINAL HARD TITANIUM 5X300MM," 2,788 "
-28016457,HC IMP ROD SPINAL HARD TITANIUM 5X400MM," 2,788 "
-28016458,HC IMP ROD SPINAL HARD TITANIUM 5X500MM," 2,788 "
-28016459,HC IMP ROD SPINAL HEX END SS HORIZON 5.5X500MM," 2,125 "
-28016460,HC IMP ROD SPINAL HEX END SS LEGACY 5.5MM," 2,125 "
-28016461,HC IMP ROD SPINAL HEX END TITANIUM LEGACY 5.5X120MM," 3,251 "
-28016462,HC IMP ROD SPINAL HEX END TITANIUM HORIZON 4.5MM," 6,486 "
-28016463,HC IMP ROD SPINAL HEX END TITANIUM LEGACY 5.5X500MM," 3,098 "
-28016464,HC IMP ROD SPINAL MOUNTAINEER 3.5X120MM," 2,652 "
-28016465,HC IMP ROD SPINAL MOUNTAINEER 3.5X200MM," 2,984 "
-28016466,HC IMP ROD SPINAL MOUNTAINEER 3.5MMX25MM," 2,550 "
-28016467,HC IMP ROD SPINAL MOUNTAINEER 3.5MMX30MM," 2,550 "
-28016468,HC IMP ROD SPINAL MOUNTAINEER 3.5MMX45MM," 2,550 "
-28016469,HC IMP ROD SPINAL MOUNTAINEER 3.5MMX50MM," 2,550 "
-28016470,HC IMP ROD SPINAL MOUNTAINEER 3.5X60MM," 2,550 "
-28016471,HC IMP ROD SPINAL OCCIPITAL CERVICAL 3.5X240MM," 2,125 "
-28016472,HC IMP ROD SPINAL OCCIPITAL PRECURVED VERTEX 3.2X240MM," 2,125 "
-28016473,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X30MM," 5,275 "
-28016474,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X35MM," 3,400 "
-28016475,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X40MM," 3,400 "
-28016476,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X45MM," 3,400 "
-28016477,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X50MM," 3,400 "
-28016478,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X60MM," 3,400 "
-28016479,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X70MM," 3,400 "
-28016480,HC IMP ROD SPINAL PREBENT TITANIUM SEXTANT 5.5X75MM," 3,400 "
-28016481,HC IMP ROD SPINAL COBALT CHROME SOLERA 4.75X500MM," 6,732 "
-28016482,HC IMP ROD SPINAL STRAIGHT HORIZON 220MM," 5,100 "
-28016483,HC IMP ROD SPINAL STRAIGHT EXTRA HARD 5.5MMX100MM," 3,349 "
-28016484,HC IMP ROD SPINAL STRAIGHT EXTRA HARD 5.5MMX150MM," 3,349 "
-28016485,HC IMP ROD SPINAL STRAIGHT EXTRA HARD 5.5MMX190MM," 3,349 "
-28016486,HC IMP ROD SPINAL STRAIGHT EXTRA HARD 5.5MMX300MM," 3,349 "
-28016487,HC IMP ROD SPINAL STRAIGHT EXTRA HARD 5.5MMX400MM," 3,349 "
-28016488,HC IMP ROD SPINAL STRAIGHT EXTRA HARD 5.5MMX500MM," 3,349 "
-28016489,HC IMP ROD SPINAL TITANIUM 3.5X500MM," 2,125 "
-28016490,HC IMP ROD SPINAL TITANIUM 500MM," 2,125 "
-28016491,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X100MM," 2,814 "
-28016492,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X30MM," 2,125 "
-28016493,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X40MM," 2,125 "
-28016494,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X50MM," 2,760 "
-28016495,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X60MM," 2,760 "
-28016496,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X70MM," 2,125 "
-28016497,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X80MM," 2,814 "
-28016498,HC IMP ROD SPINAL PREBENT TITANIUM HORIZON 5.5X90MM," 2,760 "
-28016499,HC IMP ROD SPINAL CONTOURED TITANIUM HORIZON 6.35X40MM," 2,814 "
-28016500,HC IMP ROD SPINAL TITANIUM W/HEX 510MM," 2,125 "
-28016501,HC IMP ROD SPINAL VIPER II STRAIGHT 300MM," 6,656 "
-28016502,HC IMP ROD SPINAL VIPER II STRAIGHT 480MM," 7,242 "
-28016503,HC IMP ROD SPINAL VIPER II STRAIGHT 120MM," 5,228 "
-28016504,HC IMP ROD SPINAL VIPER II STRAIGHT 200MM," 5,814 "
-28016505,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X100MM," 5,228 "
-28016506,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X200MM," 5,228 "
-28016507,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X35MM," 4,888 "
-28016508,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X45MM," 4,888 "
-28016509,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X60MM," 4,888 "
-28016510,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X70MM," 4,888 "
-28016511,HC IMP ROD SPINAL VIPER LORDOSED TITANIUM 6.5X75MM," 4,888 "
-28016512,HC IMP ROD SPINAL EXTRA EXTRA HARD COBALT CHROME 510MM," 2,125 "
-28016513,HC IMP ROD SPINE SELECT CROSSLINK SMALL," 10,620 "
-28016514,HC IMP ROD TENDON HUNTER 4MM," 11,715 "
-28016515,HC IMP ROD THREADED 3.5," 4,854 "
-28016516,HC IMP ROD THREADED 5.5," 6,341 "
-28016517,HC IMP ROD TITANIUM 3.5X300MM," 2,125 "
-28016518,HC IMP ROD TITANIUM 6.0X500MM," 3,065 "
-28016519,HC IMP ROD TRANSCONNECTOR 6X21MM," 8,576 "
-28016520,HC IMP ROD TRANSCONNECTOR 6X31.5-34MM," 9,262 "
-28016526,HC  RSVR INTROD CSF 12X40MM," 2,589 "
-28016528,HC  RETRIEVER VASCULAR INDY OTW 100CM," 21,595 "
-28016530,HC IMPL SCREW 1.5 X 7MM, 493 
-28016531,HC IMPL SCREW 260/25MM," 1,292 "
-28016532,HC IMPL SCREW 260/35MM," 1,063 "
-28016533,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 10MM, 136 
-28016534,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 12MM, 136 
-28016535,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 16MM, 133 
-28016536,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 18MM, 133 
-28016537,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 20MM, 133 
-28016538,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 22MM, 136 
-28016539,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 24MM, 136 
-28016540,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 26MM, 136 
-28016541,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 28MM, 136 
-28016542,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 30MM, 150 
-28016543,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 35MM, 137 
-28016544,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 40MM, 144 
-28016545,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 45MM, 136 
-28016546,HC IMPL SCREW 4.0 TITANIUM FULLY THREADED 50MM, 149 
-28016547,HC IMPL SCREW 6.0X20MM," 5,083 "
-28016548,HC IMPL SCREW 6.0X22MM," 5,083 "
-28016549,HC IMPL SCREW 6.0X24MM," 5,083 "
-28016550,HC IMPL SCREW 6.0X26MM," 5,083 "
-28016551,HC IMPL SCREW 6.0X28MM," 5,083 "
-28016552,HC IMPL SCREW 6.0X30MM," 5,083 "
-28016553,HC IMPL SCREW 6.0X32MM," 5,083 "
-28016554,HC IMPL SCREW 6.0X34MM," 5,083 "
-28016555,HC IMPL SCREW 6.0X36MM," 5,083 "
-28016556,HC IMPL SCREW 6.5X20MM," 5,083 "
-28016557,HC IMPL SCREW 6.5X22MM," 5,083 "
-28016558,HC IMPL SCREW 6.5X24MM," 5,083 "
-28016559,HC IMPL SCREW 6.5X26MM," 5,083 "
-28016560,HC IMPL SCREW 6.5X28MM," 5,083 "
-28016561,HC IMPL SCREW 6.5X30MM," 5,083 "
-28016562,HC IMPL SCREW 6.5X32MM," 5,083 "
-28016563,HC IMPL SCREW 6.5X34MM," 5,083 "
-28016564,HC IMPL SCREW 6.5X36MM," 5,083 "
-28016565,HC IMPL SCREW ACETABULAR 6.5X15MM, 570 
-28016566,HC IMPL SCREW ACETABULAR 6.5X40MM, 570 
-28016567,HC IMPL SCREW ACETABULAR 6.5X50MM, 826 
-28016570,HC IMPL SCREW ANTIBACKOUT TITANIUM 25MM," 3,902 "
-28016571,HC IMPL SCREW AUTODRIVE 2X8.0MM, 541 
-28016572,HC IMPL SCREW BIO 7 X 25MM," 1,063 "
-28016573,HC IMPL SCREW BIO 8 X 25MM," 1,063 "
-28016575,HC IMPL SCREW BIO RETRO 9MMX20MM," 1,913 "
-28016576,HC IMPL SCREW BIO TENODESIS 5.5X15MM," 2,423 "
-28016577,HC IMPL SCREW BIO TENODESIS 7X23MM," 2,423 "
-28016578,HC IMPL SCREW BIO TENODESIS 8X23MM," 2,423 "
-28016579,HC IMPL SCREW BIO TENODESIS 9X23MM," 2,423 "
-28016580,HC IMPL SCREW BIOABSORBABLE 8X20MM," 1,063 "
-28016581,HC IMPL SCREW BIOABSORBABLE 8X30MM," 1,065 "
-28016582,HC IMPL SCREW BIOABSORBABLE 9X30MM," 1,063 "
-28016815,HC IMPL SCREW BONE SPINAL LEGACY 5.5X25MM," 9,000 "
-28016858,HC IMPL SCREW BREAKOFF SS 5.5MM SET, 850 
-28016859,HC IMPL SCREW BREAKOFF SS SET, 890 
-28016860,HC IMPL SCREW BREAKOFF TITANIUM 5.5MM SET, 850 
-28016861,HC IMPL SCREW BREAKOFF TITANIUM SET," 1,641 "
-28016863,HC IMPL SCREW CANC ACETABULAR LOW PROFILE 6.5X20MM, 570 
-28016864,HC IMPL SCREW CANC ACETABULAR LOW PROFILE 6.5X25MM, 570 
-28016865,HC IMPL SCREW CANC ACETABULAR LOW PROFILE 6.5X30MM, 570 
-28016866,HC IMPL SCREW CANC ACETABULAR LOW PROFILE 6.5X35MM, 570 
-28016867,HC IMPL SCREW CANCELLOUS CANNULATED 40X105MM," 1,913 "
-28016868,HC IMPL SCREW CANCELLOUS CANNULATED 40X95MM," 1,913 "
-28016869,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X100MM," 1,998 "
-28016870,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X105MM," 1,913 "
-28016871,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X110MM," 1,913 "
-28016872,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X115MM," 1,913 "
-28016873,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X120MM," 1,913 "
-28016874,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X25MM," 1,785 "
-28016875,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X30MM," 2,210 "
-28016876,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X35MM," 1,913 "
-28016877,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X40MM," 1,998 "
-28016878,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X45MM," 1,913 "
-28016879,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X50MM," 1,998 "
-28016880,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X55MM," 1,913 "
-28016881,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X60MM," 1,998 "
-28016882,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X65MM," 1,785 "
-28016883,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X70MM," 1,785 "
-28016884,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X75MM," 2,125 "
-28016885,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X80MM," 2,210 "
-28016886,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X85MM," 2,125 "
-28016887,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X90MM," 1,785 "
-28016888,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X22X95MM," 1,785 "
-28016889,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X100MM," 1,913 "
-28016890,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X110MM," 1,913 "
-28016891,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X115MM," 1,505 "
-28016892,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X120MM," 1,632 "
-28016893,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X40MM," 1,505 "
-28016894,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X45MM," 1,785 "
-28016895,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X50MM," 1,785 "
-28016896,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X55MM," 1,505 "
-28016897,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X60MM," 1,505 "
-28016898,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X65MM," 2,125 "
-28016899,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X70MM," 1,785 "
-28016900,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X75MM," 1,505 "
-28016901,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X80MM," 2,125 "
-28016902,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X85MM," 1,913 "
-28016903,HC IMPL SCREW CANCELLOUS CANNULATED 6.5X40X90MM," 1,913 "
-28016904,HC IMPL SCREW CANCELLOUS CANNULATED NON LOCKING 4X32MM, 986 
-28016905,HC IMPL SCREW CANCELLOUS FIXED ANGLED 4.0X14MM," 2,338 "
-28016906,HC IMPL SCREW CANCELLOUS FULLY THREAD 4X34MM NS, 289 
-28016910,HC IMPL SCREW CANCELLOUS FULLY THREAD SS 70MM, 259 
-28016911,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X25MM, 587 
-28016912,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X30MM, 587 
-28016913,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X35MM, 587 
-28016914,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X40MM, 587 
-28016915,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X45MM, 587 
-28016916,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X50MM, 587 
-28016917,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X55MM, 587 
-28016918,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X60MM, 587 
-28016919,HC IMPL SCREW CANCELLOUS LOW PROFILE 6.5X65MM, 587 
-28016920,HC IMPL SCREW CANCELLOUS MULTI AXIAL 3.5X12MM," 7,225 "
-28016921,HC IMPL SCREW CANCELLOUS MULTI AXIAL TITANIUM 3.5X14MM," 7,225 "
-28016922,HC IMPL SCREW CANCELLOUS MULTI AXIAL TITANIUM 4X14MM," 7,225 "
-28016923,HC IMPL SCREW CANCELLOUS MULTI AXIAL TITANIUM 4X16MM," 7,225 "
-28016925,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X105MM, 275 
-28016926,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X110MM, 349 
-28016927,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X115MM, 323 
-28016928,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X120MM, 275 
-28016929,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X50MM, 272 
-28016930,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X55MM, 231 
-28016931,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X60MM, 231 
-28016932,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X65MM, 196 
-28016933,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X70MM, 230 
-28016934,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X75MM, 231 
-28016935,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X80MM, 298 
-28016936,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X16X85MM, 231 
-28016937,HC IMPL SCREW CANCELLOUS THREAD SS 6.5X32X105MM, 275 
-28016938,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X20MM," 4,038 "
-28016939,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X22MM," 4,504 "
-28016940,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X24MM," 4,038 "
-28016941,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X28MM," 4,504 "
-28016942,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X30MM," 4,038 "
-28016943,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X33MM," 4,038 "
-28016944,HC IMPL SCREW CANCELLOUS TITANIUM 5.5X36MM," 4,038 "
-28016945,HC IMPL SCREW CANCELLOUS VARIABLE ANG 4.0X10MM," 2,338 "
-28016946,HC IMPL SCREW CANCELLOUS VARIABLE ANG 4.5X13MM," 2,338 "
-28016947,HC IMPL SCREW CANCELLOUS EXPANSION HEAD 4.35X16MM," 2,354 "
-28016948,HC IMPL SCREW CANCELLOUS EXPANSION HEAD 4.5X16MM," 2,015 "
-28016949,HC IMPL SCREW CANCELLOUS EXPANSION HEAD 4X14MM," 1,845 "
-28016950,HC IMPL SCREW CANCELLOUS EXPANSION HEAD 4X16MM," 1,775 "
-28016951,HC IMPL SCREW CANCELLOUS 2.5X12MM, 865 
-28016952,HC IMPL SCREW CANCELLOUS 2.5X14MM, 865 
-28016953,HC IMPL SCREW CANCELLOUS 4.0X55MM, 680 
-28016954,HC IMPL SCREW CANCELLOUS 6.5X20MM REV," 12,195 "
-28016955,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X15MM, 850 
-28016956,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X20MM, 901 
-28016957,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X25MM, 901 
-28016958,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X30MM, 901 
-28016959,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X35MM, 850 
-28016960,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X40MM, 850 
-28016961,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X45MM, 850 
-28016962,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X50MM, 901 
-28016963,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X55MM, 901 
-28016964,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X55MM REV, 938 
-28016965,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X60MM, 901 
-28016966,HC IMPL SCREW CANCELLOUS ACETABULAR 6.5X65MM, 901 
-28016967,HC IMPL SCREW CANNULATED 4.5MMX24MM," 1,938 "
-28016968,HC IMPL SCREW CANNULATED 6.5X100X16MM," 2,091 "
-28016969,HC IMPL SCREW CANNULATED 6.5X100X32MM," 2,091 "
-28016970,HC IMPL SCREW CANNULATED 6.5X105X16MM," 2,091 "
-28016971,HC IMPL SCREW CANNULATED 6.5X105X32MM," 2,091 "
-28016972,HC IMPL SCREW CANNULATED 6.5X110X16MM," 2,091 "
-28016973,HC IMPL SCREW CANNULATED 6.5X110X32MM," 2,091 "
-28016974,HC IMPL SCREW CANNULATED 6.5X115X16MM," 2,091 "
-28016975,HC IMPL SCREW CANNULATED 6.5X115X32MM," 2,091 "
-28016976,HC IMPL SCREW CANNULATED 6.5X120X16MM," 2,091 "
-28016977,HC IMPL SCREW CANNULATED 6.5X120X32MM," 2,091 "
-28016978,HC IMPL SCREW CANNULATED 6.5X125X16MM," 2,091 "
-28016979,HC IMPL SCREW CANNULATED 6.5X125X32MM," 2,091 "
-28016980,HC IMPL SCREW CANNULATED 6.5X130X16MM," 2,091 "
-28016981,HC IMPL SCREW CANNULATED 6.5X130X32MM," 2,091 "
-28016982,HC IMPL SCREW CANNULATED 6.5X135X16MM," 2,091 "
-28016983,HC IMPL SCREW CANNULATED 6.5X135X32MM," 2,091 "
-28016984,HC IMPL SCREW CANNULATED 6.5X140X32MM," 2,091 "
-28016985,HC IMPL SCREW CANNULATED 6.5X145X16MM," 2,091 "
-28016986,HC IMPL SCREW CANNULATED 6.5X145X32MM," 2,091 "
-28016987,HC IMPL SCREW CANNULATED 6.5X150X32MM," 2,091 "
-28016988,HC IMPL SCREW CANNULATED 6.5X30X16MM," 2,091 "
-28016989,HC IMPL SCREW CANNULATED 6.5X35X16MM," 2,091 "
-28016990,HC IMPL SCREW CANNULATED 6.5X40X16MM," 2,091 "
-28016991,HC IMPL SCREW CANNULATED 6.5X45X16MM," 2,091 "
-28016992,HC IMPL SCREW CANNULATED 6.5X45X32MM," 2,091 "
-28016993,HC IMPL SCREW CANNULATED 6.5X50X16MM," 2,091 "
-28016994,HC IMPL SCREW CANNULATED 6.5X50X32MM," 2,091 "
-28016995,HC IMPL SCREW CANNULATED 6.5X55X16MM," 2,091 "
-28016996,HC IMPL SCREW CANNULATED 6.5X55X32MM," 2,091 "
-28016997,HC IMPL SCREW CANNULATED 6.5X60X16MM," 2,091 "
-28016998,HC IMPL SCREW CANNULATED 6.5X60X32MM," 2,420 "
-28016999,HC IMPL SCREW CANNULATED 6.5X65X16MM," 2,091 "
-28017000,HC IMPL SCREW CANNULATED 6.5X65X32MM," 2,091 "
-28017001,HC IMPL SCREW CANNULATED 6.5X70X16MM," 2,091 "
-28017002,HC IMPL SCREW CANNULATED 6.5X70X32MM," 2,420 "
-28017003,HC IMPL SCREW CANNULATED 6.5X75X16MM," 2,091 "
-28017004,HC IMPL SCREW CANNULATED 6.5X75X32MM," 2,091 "
-28017005,HC IMPL SCREW CANNULATED 6.5X80X16MM," 2,091 "
-28017006,HC IMPL SCREW CANNULATED 6.5X80X32MM," 2,091 "
-28017007,HC IMPL SCREW CANNULATED 6.5X85X16MM," 2,091 "
-28017008,HC IMPL SCREW CANNULATED 6.5X85X32MM," 2,091 "
-28017009,HC IMPL SCREW CANNULATED 6.5X90X16MM," 2,091 "
-28017010,HC IMPL SCREW CANNULATED 6.5X90X32MM," 2,091 "
-28017011,HC IMPL SCREW CANNULATED 6.5X95X16MM," 2,091 "
-28017012,HC IMPL SCREW CANNULATED 6.5X95X32MM," 2,091 "
-28017013,HC IMPL SCREW CANNULATED 7.3X30X16MM," 1,938 "
-28017014,HC IMPL SCREW CANNULATED COMPRESSION 1 1.7X10MM," 2,508 "
-28017015,HC IMPL SCREW CANNULATED COMPRESSION 1 1.7X11MM," 2,508 "
-28017016,HC IMPL SCREW CANNULATED COMPRESSION 1 1.7X12MM," 2,508 "
-28017017,HC IMPL SCREW CANNULATED COMPRESSION 1 1.7X13MM," 2,508 "
-28017018,HC IMPL SCREW CANNULATED COMPRESSION 1 1.7X14MM," 2,508 "
-28017019,HC IMPL SCREW CANNULATED COMPRESSION 1 2.3X10MM," 2,338 "
-28017020,HC IMPL SCREW CANNULATED COMPRESSION 1 2.3X12MM," 2,338 "
-28017021,HC IMPL SCREW CANNULATED COMPRESSION 1 2.3X14MM," 2,338 "
-28017022,HC IMPL SCREW CANNULATED COMPRESSION 1 2.3X16MM," 2,338 "
-28017023,HC IMPL SCREW CANNULATED COMPRESSION 1 2.3X18MM," 2,338 "
-28017024,HC IMPL SCREW CANNULATED COMPRESSION1 3.5X14MM," 2,338 "
-28017025,HC IMPL SCREW CANNULATED COMPRESSION 1 3.5X16MM," 2,338 "
-28017026,HC IMPL SCREW CANNULATED COMPRESSION 1 3.5X18MM," 2,338 "
-28017027,HC IMPL SCREW CANNULATED COMPRESSION 1 YELLOW 3X14MM," 2,338 "
-28017028,HC IMPL SCREW CANNULATED COMPRESSION 1 YELLOW 3X16MM," 2,338 "
-28017029,HC IMPL SCREW CANNULATED COMPRESSION 1 YELLOW 3X18MM," 2,338 "
-28017030,HC IMPL SCREW CANNULATED COMPRESSION 2- 2.3X20MM," 2,338 "
-28017031,HC IMPL SCREW CANNULATED COMPRESSION 2- 2.3X22MM," 2,338 "
-28017032,HC IMPL SCREW CANNULATED COMPRESSION 2 2.3X23MM," 2,338 "
-28017033,HC IMPL SCREW CANNULATED COMPRESSION 2 2.3X24MM," 2,338 "
-28017034,HC IMPL SCREW CANNULATED COMPRESSION 2 2.3X25MM," 2,338 "
-28017035,HC IMPL SCREW CANNULATED COMPRESSION 2 2.3X26MM," 2,338 "
-28017036,HC IMPL SCREW CANNULATED COMPRESSION 2 2.3X28MM," 2,338 "
-28017037,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X20MM," 2,338 "
-28017038,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X21MM," 2,338 "
-28017039,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X22MM," 2,338 "
-28017040,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X23MM," 2,338 "
-28017041,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X25MM," 2,338 "
-28017042,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X25MM," 2,338 "
-28017043,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X26MM," 2,338 "
-28017044,HC IMPL SCREW CANNULATED COMPRESSION 2 3.5X28MM," 2,338 "
-28017045,HC IMPL SCREW CANNULATED COMPRESSION 2 YELLOW 3X20MM," 2,338 "
-28017046,HC IMPL SCREW CANNULATED COMPRESSION 2 YELLOW 3X21MM," 2,338 "
-28017047,HC IMPL SCREW CANNULATED COMPRESSION 2 YELLOW 3X23MM," 2,338 "
-28017048,HC IMPL SCREW CANNULATED COMPRESSION 2 YELLOW 3X24MM," 2,338 "
-28017049,HC IMPL SCREW CANNULATED COMPRESSION 2- YELLOW 3X25MM," 2,338 "
-28017050,HC IMPL SCREW CANNULATED COMPRESSION 2- YELLOW 3X26MM," 2,338 "
-28017051,HC IMPL SCREW CANNULATED COMPRESSION 2- YELLOW 3X28MM," 2,338 "
-28017052,HC IMPL SCREW CANNULATED COMPRESSION 3 3.5X30MM," 2,338 "
-28017053,HC IMPL SCREW CANNULATED COMPRESSION 3 3.5X32MM," 2,338 "
-28017054,HC IMPL SCREW CANNULATED COMPRESSION 3- 3.5X35MM," 2,338 "
-28017055,HC IMPL SCREW CANNULATED COMPRESSION 4 3.5X40MM," 2,338 "
-28017056,HC IMPL SCREW CANNULATED COMPRESSION 4 3.5X45MM," 2,338 "
-28017057,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X115MM," 2,091 "
-28017058,HC IMPL SCREW CANNULATED FULLY THREAD 6.5X140MM," 2,420 "
-28017059,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X100MM," 2,091 "
-28017060,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X105MM," 2,091 "
-28017061,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X110MM," 2,091 "
-28017062,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X115MM," 2,091 "
-28017063,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X125MM," 2,091 "
-28017064,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X130MM," 2,091 "
-28017065,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X20MM," 2,015 "
-28017066,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X25MM," 2,091 "
-28017067,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X30MM," 2,091 "
-28017068,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X35MM," 2,091 "
-28017069,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X40MM," 2,091 "
-28017070,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X45MM," 2,091 "
-28017071,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X50MM," 2,420 "
-28017072,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X55MM," 2,420 "
-28017073,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X60MM," 2,420 "
-28017074,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X65MM," 2,091 "
-28017075,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X70MM," 2,091 "
-28017076,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X75MM," 2,420 "
-28017077,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X80MM," 2,420 "
-28017078,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X85MM," 2,420 "
-28017079,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X90MM," 2,091 "
-28017080,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X60MM," 2,420 "
-28017081,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X65MM," 2,420 "
-28017082,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X70MM," 2,420 "
-28017083,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X75MM," 2,091 "
-28017084,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X80MM," 2,091 "
-28017085,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X85MM," 2,420 "
-28017086,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X90MM," 2,091 "
-28017087,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X95MM," 2,091 "
-28017088,HC IMPL SCREW CANNULATED LG 16MMX150," 2,091 "
-28017089,HC IMPL SCREW CANN LOCKING FULLY THREAD SS 6.5X90MM," 1,531 "
-28017090,HC IMPL SCREW CANNULATED LARGE 150MM," 2,420 "
-28017091,HC IMPL SCREW CANNULATED LONG THREAD 52MM," 1,590 "
-28017092,HC IMPL SCREW CANNULATED LONG THREAD 54MM," 1,590 "
-28017093,HC IMPL SCREW CANNULATED LONG THREAD 56MM," 1,590 "
-28017094,HC IMPL SCREW CANNULATED LONG THREAD 58MM," 1,837 "
-28017095,HC IMPL SCREW CANNULATED LONG THREAD 60MM," 1,717 "
-28017096,HC IMPL SCREW CANNULATED LONG THREAD 64MM," 1,590 "
-28017097,HC IMPL SCREW CANNULATED LONG THREAD 68MM," 1,590 "
-28017098,HC IMPL SCREW CANNULATED LONG THREAD 72MM," 1,837 "
-28017099,HC IMPL SCREW CANNULATED POLY AXIAL TITANIUM 7X35MM," 9,188 "
-28017100,HC IMPL SCREW CANNULATED POLY AXIAL TITANIUM 7X40MM," 9,188 "
-28017101,HC IMPL SCREW CANNULATED POLY AXIAL TITANIUM 7X50MM," 9,188 "
-28017102,HC IMPL SCREW CANNULATED POLYAXIAL TITANIUM 7X55MM," 9,188 "
-28017103,HC IMPL SCREW CANNULATED SS 7X100X16MM," 2,125 "
-28017104,HC IMPL SCREW CANNULATED SHORT THREAD 72MM," 1,590 "
-28017105,HC IMPL SCREW CANNULATED SHORT THREAD 52MM," 1,590 "
-28017106,HC IMPL SCREW CANNULATED SHORT THREAD 54MM," 1,717 "
-28017107,HC IMPL SCREW CANNULATED SHORT THREAD 56MM," 1,590 "
-28017108,HC IMPL SCREW CANNULATED SHORT THREAD 58MM," 1,590 "
-28017109,HC IMPL SCREW CANNULATED SHORT THREAD 60MM," 1,471 "
-28017110,HC IMPL SCREW CANNULATED SHORT THREAD 64MM," 1,590 "
-28017111,HC IMPL SCREW CANNULATED SHORT THREAD 68MM," 1,590 "
-28017112,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X 32X120MM," 2,091 "
-28017113,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X100MM," 2,372 "
-28017114,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X105MM," 2,261 "
-28017115,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X115MM," 2,420 "
-28017116,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X125MM," 2,261 "
-28017117,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X130MM," 2,091 "
-28017118,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X135MM," 2,420 "
-28017119,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X140MM," 2,091 "
-28017120,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X145MM," 2,420 "
-28017121,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X150MM," 2,542 "
-28017122,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X45MM," 2,420 "
-28017123,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X50MM," 2,542 "
-28017124,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X55MM," 2,420 "
-28017125,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X70MM," 2,542 "
-28017126,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X75MM," 2,420 "
-28017127,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X80MM," 2,542 "
-28017128,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X85MM," 2,420 "
-28017129,HC IMPL SCREW CANNULATED THREAD TITANIUM 7.3X32X95MM," 2,505 "
-28017130,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X8MM," 1,598 "
-28017131,HC IMPL SCREW CAP SOCKET HD 3/8-16X1, 85 
-28017132,HC IMPL SCREW CDH LEGACY MAS STAINLESS STEEL 5.5X40MM," 8,440 "
-28017133,HC IMPL SCREW CDH LEGACY MAS STAINLESS STEEL 5.5X45MM," 8,440 "
-28017134,HC IMPL SCREW CDH LEGACY MAS STAINLESS STEEL 6.5X40MM," 8,440 "
-28017135,HC IMPL SCREW CDH LEGACY MAS STAINLESS STEEL 6.5X45MM," 8,440 "
-28017136,HC IMPL SCREW CERVICAL SELF DRILLING FIXED ANGLE 4X12," 2,338 "
-28017137,HC IMPL SCREW CLICK CROSS DUAL 8.0X55MM," 5,100 "
-28017138,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X18," 1,052 "
-28017139,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X22," 1,052 "
-28017140,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X26," 1,052 "
-28017141,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X30," 1,052 "
-28017142,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X34," 1,052 "
-28017143,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X38," 1,052 "
-28017144,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X42," 1,052 "
-28017145,HC IMPL SCREW COMPRESSION LOCKING CAP 4.5X46," 1,052 "
-28017146,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X65MM," 1,315 "
-28017147,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X70MM," 1,315 "
-28017148,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X75MM," 1,335 "
-28017149,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X80MM," 1,271 "
-28017150,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X85MM," 1,315 "
-28017151,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X90MM," 1,241 "
-28017152,HC IMPL SCREW CONICAL PARTIALLY THREAD 3.5X95MM," 1,241 "
-28017222,HC IMPL SCREW CORTICAL SELF TAPPING MOD 2.4X8MM, 666 
-28017223,HC IMP SCREW CORTICAL SELF TAPPING TITANIUM 2.4MM 36MM, 666 
-28017224,HC IMP SCREW CORTICAL SELF TAPPING TITANIUM 2.4MM 38MM, 666 
-28017225,HC IMP SCREW CORTICAL SELF TAPPING TITANIUM 2.4MMX10MM, 666 
-28017226,HC IMP SCREW CORTICAL SELF TAPPING TITANIUM 2.4MMX32MM, 666 
-28017254,HC IMPL SCREW CORTEX 2X10MM, 130 
-28017255,HC IMPL SCREW CORTEX 2X12MM, 281 
-28017256,HC IMPL SCREW CORTEX 2X16MM, 281 
-28017257,HC IMPL SCREW CORTEX 2X18MM, 271 
-28017258,HC IMPL SCREW CORTEX 2X24MM, 281 
-28017259,HC IMPL SCREW CORTEX 2.0X6MM, 281 
-28017305,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.4X12MM, 295 
-28017306,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.4X14MM, 208 
-28017307,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.4X16MM, 295 
-28017308,HC IMPL SCREW CORTEX SELF TAPPING TIATANIUM 2.4X18MM, 295 
-28017309,HC IMPL SCREW CORTEX SELF TAPPING TITANIUM 2.4X8MM, 295 
-28017310,HC IMP SCREW CORTEX SELF TAPPING TITANIUM 2.7X26MM, 404 
-28017320,HC IMPL SCREW CORTEX TITANIUM 2.7X10MM, 366 
-28017321,HC IMPL SCREW CORTEX TITANIUM 2.7X16MM, 366 
-28017322,HC IMPL SCREW CORTEX TITANIUM 2.7X18MM, 366 
-28017323,HC IMPL SCREW CORTEX TITANIUM 2.7X22MM, 366 
-28017324,HC IMPL SCREW CORTEX TITANIUM 2.7X6MM, 366 
-28017325,HC IMPL SCREW CORTEX TITANIUM 2.7X8MM, 366 
-28017372,HC IMPL SCREW CORTEX SELF TAPPING 2.4X10MM, 193 
-28017373,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X10MM, 502 
-28017374,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X12MM, 519 
-28017375,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X14MM, 519 
-28017376,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X16MM, 519 
-28017377,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X18MM, 519 
-28017378,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X20MM, 519 
-28017379,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X24MM, 519 
-28017380,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X26MM, 502 
-28017381,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X28MM, 502 
-28017382,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X2MM, 519 
-28017383,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X30MM, 502 
-28017384,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X6MM, 438 
-28017385,HC IMPL SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.4X8MM, 502 
-28017397,HC IMPL SCREW COUNTERSINK 2.7MM," 1,457 "
-28017398,HC IMPL SCREW CRANIOFACIAL 2.4X18MM, 493 
-28017399,HC IMPL SCREW CRANIOFACIAL 2X17MM, 231 
-28017400,HC IMPL SCREW CRANIOFACIAL MICRO 1.2MMX2MM, 511 
-28017401,HC IMPL SCREW CRANIOFACIAL MICRO 1.2MMX3MM, 493 
-28017402,HC IMPL SCREW CRANIOFACIAL MICRO 1.2MMX4MM, 511 
-28017403,HC IMPL SCREW CRANIOFACIAL SLF-DRLNG 3MM, 632 
-28017408,HC IMPL SCREW CROSSPIN SELF TAPPING 1.9X3MM, 90 
-28017409,HC IMPL SCREW CROSSPIN SELF TAPPING 1.9X5MM, 448 
-28017414,HC IMPL SCREW DELTA SYS 1.7 X 4MM, 363 
-28017415,HC IMPL SCREW DELTA SYS 1.7 X 5MM, 363 
-28017416,HC IMPL SCREW DELTA SYS 1.7 X 6MM, 363 
-28017417,HC IMPL SCREW DELTA SYS PKG 01 2.2 X 4MM, 360 
-28017418,HC IMPL SCREW DELTA SYS PKG 01 2.2 X 6MM, 356 
-28017419,HC IMPL SCREW DF CROSS DRIVE TITANIUM 1.5X4MM, 493 
-28017420,HC IMPL SCREW DF CROSS DRIVE TITANIUM 1.5X5MM, 485 
-28017421,HC IMPL SCREW DHS LAG 125MM," 3,019 "
-28017422,HC IMPL SCREW DHS LAG 75MM," 3,019 "
-28017423,HC IMPL SCREW DHS LAG 80MM," 3,171 "
-28017424,HC IMPL SCREW DISTRACTION TITANIUM 12MM, 265 
-28017425,HC IMPL SCREW DISTRACTION TITANIUM 14MM, 265 
-28017426,HC IMPL SCREW DISTRACTION TITANIUM 16MM, 265 
-28017427,HC IMPL SCREW DRILL FREE HEX 5MM 1.2X7MM," 1,063 "
-28017428,HC IMP SCREW DS RESORB 2.2X4MM, 356 
-28017430,HC IMPL SCREW DUAL CORE SCHANZ 6.2X180MM," 3,547 "
-28017431,HC IMPL SCREW DUAL CORE SCHANZ 6.2X190MM," 3,547 "
-28017432,HC IMPL SCREW DUAL CORE SCHANZ 7X180MM," 3,547 "
-28017433,HC IMPL SCREW DUAL CORE SCHANZ 7X190MM," 3,547 "
-28017434,HC IMPL SCREW DUAL CORE SCHANZ 7X35X180MM," 2,440 "
-28017435,HC IMPL SCREW DUAL OPENING 5.2X25MM," 8,109 "
-28017436,HC IMPL SCREW DUAL OPENING TITANIUM 30X7.0MM THREAD," 8,109 "
-28017437,HC IMPL SCREW DUAL OPENING TITANIUM 7X40MM," 8,109 "
-28017438,HC IMP SCREW EMERGENCY LOCKING PLT 2X2.4X14MM, 484 
-28017439,HC IMP SCREW EMERGENCY LOCKING PLT 2X2.4X16MM, 484 
-28017440,HC IMP SCREW EMERGENCY LOCKING PLT 2X2.4X18MM, 484 
-28017441,HC IMP SCREW EMERGENCY PINK 1.9MM 9MM, 388 
-28017442,HC IMP SCREW EMERGENCY PINK 1.9X7MM, 388 
-28017443,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS 2.4X12MM, 484 
-28017444,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS 2.4X6MM, 484 
-28017445,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS 2.4X8MM, 484 
-28017446,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.4X12MM, 428 
-28017447,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.4X14MM, 428 
-28017448,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.4X16MM, 428 
-28017449,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.4X18MM, 428 
-28017450,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.4X8MM, 428 
-28017451,HC IMPL SCREW EMERGENCY SRPD MANDIBULAR 2.7X10MM, 729 
-28017452,HC IMPL SCREW EMERGENCY SRPD MANDIBULAR 2.7X12MM, 729 
-28017453,HC IMPL SCREW EMERGENCY SRPD MANDIBULAR 2.7X14MM, 729 
-28017454,HC IMPL SCREW EMERGENCY SRPD MANDIBULAR 2.7X16MM, 729 
-28017455,HC IMPL SCREW EMERGENCY SRPD MANDIBULAR 2.7X18MM, 729 
-28017456,HC IMPL SCREW EMERGENCY SRPD MANDIBULAR 2.7X8MM, 729 
-28017457,HC IMPL SCREW EMERGENCY CROSS DRIVE TITANIUM 1.8MM 7MM, 451 
-28017458,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1.8X4MM, 632 
-28017459,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1.8X6MM, 629 
-28017460,HC IMPL SCREW EMERGENCY 1.2MMX5MM, 622 
-28017461,HC IMPL SCREW EMERGENCY 1.2X4MM," 3,145 "
-28017462,HC IMPL SCREW EMERGENCY 2MMX5MM, 428 
-28017463,HC IMPL SCREW EMERGENCY TITANIUM 2X6MM," 2,295 "
-28017464,HC IMPL SCREW EMERGENCY TITANIUM 2X8MM," 2,193 "
-28017465,HC IMPL SCREW EMERGENCY 1.2X5MM, 629 
-28017466,HC IMPL SCREW EMERGENCY 1.2X7MM, 629 
-28017467,HC IMPL SCREW EXTENDED 5.5X55MM," 10,600 "
-28017468,HC IMPL SCREW EXTENDED 6.5X55MM," 10,600 "
-28017469,HC IMPL SCREW EXTENDED 7.5X30MM," 10,600 "
-28017470,HC IMPL SCREW EXTENDED POLYDIRECTIONAL 6.5X35MM," 7,863 "
-28017471,HC IMPL SCREW EXTENDED POLYDIRECTIONAL 6.5X40MM," 7,863 "
-28017472,HC IMPL SCREW EXTENDED POLYDIRECTIONAL 7.5X40MM," 7,863 "
-28017473,HC IMPL SCREW EXTENDED POLYDIRECTIONAL 7.5X45MM," 7,863 "
-28017474,HC IMPL SCREW EXTENDED POLYDIRECTIONAL 7.5X50MM," 7,863 "
-28017475,HC IMPL SCREW FIXED SELF RETAIN/DRILL 4.0MM X 14MM," 2,338 "
-28017476,HC IMPL SCREW FIXED SELF RETAIN/DRILL 4.0MM X 16MM," 2,338 "
-28017477,HC IMPL SCREW FIXED SELF RETAIN/DRILL 4.0MMX18MM," 2,338 "
-28017478,HC IMPL SCREW FIXED SELF RETAIN/DRILL 4.5MMX14MM," 2,338 "
-28017479,HC IMPL SCREW FIXED SELF RETAIN/DRILL 4.5MMX16MM," 2,338 "
-28017480,HC IMPL SCREW FIXED SELF RETAIN/DRILL 4.5MMX18MM," 2,338 "
-28017481,HC IMPL SCREW FIXED ANGLED SELF TAPPING 4.0X14MM," 2,338 "
-28017482,HC IMPL SCREW FIXED ANGLED SELF TAPPING 4.0X16MM," 2,338 "
-28017483,HC IMPL SCREW FIXED ANGLED SELF TAPPING 4.0X18MM," 2,338 "
-28017484,HC IMPL SCREW FIXED ANGLED SELF TAPPING 4.5X14MM," 2,338 "
-28017485,HC IMPL SCREW FIXED ANGLED SELF TAPPING 4.5X16MM," 2,338 "
-28017486,HC IMPL SCREW FIXED ANGLED SELF TAPPING 4.5X18MM," 2,338 "
-28017487,HC IMPL SCREW FEMORAL INTRAFIX 8-8.5X23MM," 4,828 "
-28017499,HC IMPL SCREW FIXED ANGLED 4.5X30MM," 6,163 "
-28017500,HC IMPL SCREW FIXED ANGLED 6X30MM," 6,600 "
-28017501,HC IMPL SCREW FIXED ANGLED 6X35MM," 6,600 "
-28017502,HC IMPL SCREW FIXED ANGLED 6X40MM," 6,600 "
-28017503,HC IMPL SCREW FIXED ANGLED 6X45MM," 6,600 "
-28017518,HC IMPL SCREW FIXED ANGLE SELF DRILL 4.5X17MM," 4,327 "
-28017519,HC IMPL SCREW FIXED ANGLE SELF DRILL 4X12MM," 2,338 "
-28017520,HC IMPL SCREW FIXED ANGLE SELF DRILL 4X14MM," 2,338 "
-28017521,HC IMPL SCREW FIXED ANGLE SELF DRILL 4X16MM," 2,338 "
-28017522,HC IMPL SCREW FIXED ANGLED SS 4.5X25MM," 6,474 "
-28017523,HC IMPL SCREW FIXED ANGLED SS 4.5X30MM," 6,474 "
-28017524,HC IMPL SCREW FIXED ANGLED SS 4.5X35MM," 6,474 "
-28017525,HC IMPL SCREW FIXED ANGLED SS 4.5X40MM," 6,474 "
-28017526,HC IMPL SCREW FIXED ANGLED SS 4.5X45MM," 6,474 "
-28017527,HC IMPL SCREW FIXED ANGLED SS 5.5X25MM," 6,600 "
-28017528,HC IMPL SCREW FIXED ANGLED SS 5.5X30MM," 6,163 "
-28017529,HC IMPL SCREW FIXED ANGLED SS 5.5X35MM," 6,600 "
-28017530,HC IMPL SCREW FIXED ANGLED SS 5.5X40MM," 6,474 "
-28017531,HC IMPL SCREW FIXED ANGLED SS 5.5X45MM," 6,474 "
-28017532,HC IMPL SCREW FIXED ANGLED SS 5.5X50MM," 6,474 "
-28017533,HC IMPL SCREW FIXED ANGLED SS 5X25MM," 6,474 "
-28017534,HC IMPL SCREW FIXED ANGLED SS 5X30MM," 6,474 "
-28017535,HC IMPL SCREW FIXED ANGLED SS 5X35MM," 6,474 "
-28017536,HC IMPL SCREW FIXED ANGLED SS 5X40MM," 6,474 "
-28017537,HC IMPL SCREW FIXED ANGLED SS 5X45MM," 6,474 "
-28017538,HC IMPL SCREW FIXED ANGLED SS 6.5X30MM," 6,474 "
-28017539,HC IMPL SCREW FIXED ANGLED SS 6.5X35MM," 6,474 "
-28017540,HC IMPL SCREW FIXED ANGLED SS 6.5X40MM," 6,474 "
-28017541,HC IMPL SCREW FIXED ANGLED SS 6.5X45MM," 6,474 "
-28017542,HC IMPL SCREW FIXED ANGLED SS 6.5X50MM," 6,474 "
-28017543,HC IMPL SCREW FIXED ANGLED SS 6X30MM," 8,273 "
-28017544,HC IMPL SCREW FIXED ANGLED SS 6X35MM," 6,163 "
-28017545,HC IMPL SCREW FIXED ANGLED SS 6X40MM," 6,163 "
-28017546,HC IMPL SCREW FIXED ANGLED SS 6X45MM," 8,273 "
-28017547,HC IMPL SCREW FIXED ANGLED SS 7.5X40MM," 6,474 "
-28017548,HC IMPL SCREW FIXED ANGLED SS 7.5X45MM," 6,474 "
-28017549,HC IMPL SCREW FIXED ANGLED SS 7.5X50MM," 6,474 "
-28017550,HC IMPL SCREW FIXED ANGLED SS 7.5X55MM," 6,474 "
-28017551,HC IMPL SCREW FIXED ANGLED TITANIUM 4.5X25MM," 7,111 "
-28017552,HC IMPL SCREW FIXED ANGLED TITANIUM 4.5X30MM," 7,111 "
-28017553,HC IMPL SCREW FIXED ANGLED TITANIUM 4.5X35MM," 7,111 "
-28017554,HC IMPL SCREW FIXED ANGLED TITANIUM 4.5X40MM," 7,111 "
-28017555,HC IMPL SCREW FIXED ANGLED TITANIUM 4.5X45MM," 7,111 "
-28017556,HC IMPL SCREW FIXED ANGLED TITANIUM 5.5X25MM," 6,163 "
-28017557,HC IMPL SCREW FIXED ANGLED TITANIUM 5.5X30MM," 6,163 "
-28017558,HC IMPL SCREW FIXED ANGLED TITANIUM 5.5X35MM," 6,163 "
-28017559,HC IMPL SCREW FIXED ANGLED TITANIUM 5.5X40MM," 6,163 "
-28017560,HC IMPL SCREW FIXED ANGLED TITANIUM 5.5X45MM," 6,163 "
-28017561,HC IMPL SCREW FIXED ANGLED TITANIUM 5.5X50MM," 7,111 "
-28017562,HC IMPL SCREW FIXED ANGLED TITANIUM 5X25MM," 7,111 "
-28017563,HC IMPL SCREW FIXED ANGLED TITANIUM 5X30MM," 7,250 "
-28017564,HC IMPL SCREW FIXED ANGLED TITANIUM 5X35MM," 7,250 "
-28017565,HC IMPL SCREW FIXED ANGLED TITANIUM 5X40MM," 7,250 "
-28017566,HC IMPL SCREW FIXED ANGLED TITANIUM 5X45MM," 7,111 "
-28017567,HC IMPL SCREW FIXED ANGLED TITANIUM 6.5X30MM," 7,111 "
-28017568,HC IMPL SCREW FIXED ANGLED TITANIUM 6.5X35MM," 7,111 "
-28017569,HC IMPL SCREW FIXED ANGLED TITANIUM 6.5X40MM," 6,163 "
-28017570,HC IMPL SCREW FIXED ANGLED TITANIUM 6.5X45MM," 7,250 "
-28017571,HC IMPL SCREW FIXED ANGLED TITANIUM 6.5X50MM," 7,111 "
-28017572,HC IMPL SCREW FIXED ANGLED TITANIUM 6X25MM," 7,250 "
-28017573,HC IMPL SCREW FIXED ANGLED TITANIUM 6X30MM," 6,163 "
-28017574,HC IMPL SCREW FIXED ANGLED TITANIUM 6X35MM," 6,163 "
-28017575,HC IMPL SCREW FIXED ANGLED TITANIUM 6X40MM," 6,163 "
-28017576,HC IMPL SCREW FIXED ANGLED TITANIUM 6X45MM," 7,250 "
-28017577,HC IMPL SCREW FIXED ANGLED TITANIUM 6X50MM," 8,279 "
-28017578,HC IMPL SCREW FIXED ANGLED TITANIUM 7.5X40MM," 7,111 "
-28017579,HC IMPL SCREW FIXED ANGLED TITANIUM 7.5X45MM," 7,111 "
-28017580,HC IMPL SCREW FIXED ANGLED TITANIUM 7.5X50MM," 8,279 "
-28017581,HC IMPL SCREW FIXED ANGLED TITANIUM 7.5X55MM," 7,111 "
-28017582,HC IMPL SCREW FIXED FULLY THREAD SYSTEM, 313 
-28017583,HC IMPL SCREW FIXED MXLRY MANDIBULAR 2X12MM, 683 
-28017584,HC IMPL SCREW FIXED MXLRY MANDIBULAR 2X8MM, 967 
-28017589,HC IMPL SCREW FRAG TIB LCP 14H 246MM," 11,205 "
-28017590,HC IMPL SCREW FULLY THREAD 3.5X65MM," 1,271 "
-28017591,HC IMPL SCREW FULLY THREAD 3.5X70MM," 1,315 "
-28017592,HC IMPL SCREW FULLY THREAD 3.5X75MM," 1,271 "
-28017593,HC IMPL SCREW FULLY THREAD 3.5X80MM," 1,315 "
-28017594,HC IMPL SCREW FULLY THREAD 3.5X85MM," 1,271 "
-28017595,HC IMPL SCREW FULLY THREAD 3.5X90MM," 1,271 "
-28017596,HC IMPL SCREW FULLY THREAD 3.5X95MM," 1,271 "
-28017597,HC IMPL SCREW GLENOID LOCKING 3.5X26MM, 782 
-28017598,HC IMPL SCREW GLENOID LOCKING 3.5X34MM, 782 
-28017599,HC IMPL SCREW GLENOID LOCKING 3.5X38MM, 782 
-28017600,HC IMPL SCREW GLENOID LOCKING 5X14MM, 782 
-28017601,HC IMPL SCREW GLENOID LOCKING 5X18MM, 782 
-28017602,HC IMPL SCREW GLENOID LOCKING 5X22MM, 782 
-28017603,HC IMPL SCREW GLENOID LOCKING 5X30MM, 782 
-28017604,HC IMPL SCREW GLENOID LOCKING 5X34MM, 782 
-28017605,HC IMPL SCREW GLENOID LOCKING 5X38MM, 782 
-28017606,HC IMPL SCREW GUARDSMAN TAPER TIP 7X30MM, 978 
-28017607,HC IMPL SCREW GUARDSMAN TAPER TIP 8X30MM, 978 
-28017608,HC IMPL SCREW GUARDSMAN TAPER TIP 9X30MM, 978 
-28017609,HC IMPL SCREW HEAD TO HEAD CROSS CONNECT INNER MNTR," 1,352 "
-28017610,HC IMPL SCREW HEAD QUAD-SPARING 33MM, 479 
-28017611,HC IMPL SCREW HEAD QUAD-SPARING HEX HEADED 48MM, 479 
-28017612,HC IMPL SCREW HEAD QUAD-SPARING 48MM REV, 479 
-28017613,HC IMPL SCREW HEADLESS QUAD-SPARING 48MM REV, 479 
-28017615,HC IMPL SCREW HEADLESS 2.5X24MM," 2,627 "
-28017616,HC IMPL SCREW HEADLESS 3.0X22MM," 2,720 "
-28017617,HC IMPL SCREW HEADLESS 4.3X36MM," 3,273 "
-28017618,HC IMPL SCREW HEADLESS 4.3X40MM," 3,273 "
-28017619,HC IMPL SCREW HEADLESS 4.3X46MM," 3,273 "
-28017620,HC IMPL SCREW HEADLESS 4417-4516," 8,003 "
-28017621,HC IMPL SCREW HEADLESS 4417-7016," 8,003 "
-28017622,HC IMPL SCREW HEADLESS LONG THREAD 3X18MM," 3,035 "
-28017623,HC IMPL SCREW HEADLESS LONG THREAD 3X20MM," 3,092 "
-28017624,HC IMPL SCREW HEADLESS LONG THREAD 3X22MM," 3,200 "
-28017625,HC IMPL SCREW HEADLESS LONG THREAD 3X24MM," 3,092 "
-28017626,HC IMPL SCREW HEADLESS LONG THREAD 3X26MM," 3,035 "
-28017627,HC IMPL SCREW HEADLESS LONG THREAD 3X28MM," 3,092 "
-28017628,HC IMPL SCREW HEADLESS LONG THREAD 3X30MM," 2,890 "
-28017629,HC IMPL SCREW HEADLESS LONG THREAD 3X32MM," 2,890 "
-28017630,HC IMPL SCREW HEADLESS LONG THREAD 3X34MM," 2,890 "
-28017631,HC IMPL SCREW HEADLESS LONG THREAD 3X36MM," 2,890 "
-28017632,HC IMPL SCREW HEADLESS LONG THREAD 3X38MM," 2,890 "
-28017633,HC IMPL SCREW HEADLESS LONG THREAD 3X40MM," 3,092 "
-28017634,HC IMPL SCREW HEADLESS SHORT THREAD 3X10MM," 2,975 "
-28017635,HC IMPL SCREW HEADLESS SHORT THREAD 3X12MM," 2,890 "
-28017636,HC IMPL SCREW HEADLESS SHORT THREAD 3X14MM," 2,890 "
-28017637,HC IMPL SCREW HEADLESS SHORT THREAD 3X16MM," 2,890 "
-28017638,HC IMPL SCREW HEADLESS SHORT THREAD 3X18MM," 2,890 "
-28017639,HC IMPL SCREW HEADLESS SHORT THREAD 3X19MM," 3,092 "
-28017640,HC IMPL SCREW HEADLESS SHORT THREAD 3X20MM," 2,890 "
-28017641,HC IMPL SCREW HEADLESS SHORT THREAD 3X21MM," 3,092 "
-28017642,HC IMPL SCREW HEADLESS SHORT THREAD 3X22MM," 3,200 "
-28017643,HC IMPL SCREW HEADLESS SHORT THREAD 3X23MM," 3,035 "
-28017644,HC IMPL SCREW HEADLESS SHORT THREAD 3X24MM," 3,092 "
-28017645,HC IMPL SCREW HEADLESS SHORT THREAD 3X25MM," 3,092 "
-28017646,HC IMPL SCREW HEADLESS SHORT THREAD 3X26MM," 2,890 "
-28017647,HC IMPL SCREW HEADLESS SHORT THREAD 3X27MM," 3,092 "
-28017648,HC IMPL SCREW HEADLESS SHORT THREAD 3X28MM," 3,092 "
-28017649,HC IMPL SCREW HEADLESS SHORT THREAD 3X29MM," 3,092 "
-28017650,HC IMPL SCREW HEADLESS SHORT THREAD 3X30MM," 3,092 "
-28017651,HC IMPL SCREW HEADLESS SHORT THREAD 3X32MM," 2,890 "
-28017652,HC IMPL SCREW HEADLESS SHORT THREAD 3X34MM," 2,890 "
-28017653,HC IMPL SCREW HEADLESS SHORT THREAD 3X36MM," 2,890 "
-28017654,HC IMPL SCREW HEADLESS SHORT THREAD 3X38MM," 2,890 "
-28017655,HC IMPL SCREW HEADLESS SHORT THREAD 3X40MM," 3,035 "
-28017656,HC IMPL SCREW HEX 1.2X9MM," 1,063 "
-28017657,HC IMPL SCREW HEX 1.5X5MM," 1,063 "
-28017658,HC IMPL SCREW HEX BREAKOFF TITANIUM 0.25-32MM," 1,110 "
-28017659,HC IMPL SCREW HIP 6.5MMX15MM," 1,377 "
-28017660,HC IMPL SCREW HIP 6.5MMX20MM," 1,377 "
-28017661,HC IMPL SCREW HIP 6.5MMX30MM," 1,377 "
-28017662,HC IMPL SCREW HIP 6.5MMX35MM," 1,522 "
-28017663,HC IMPL SCREW HIP 6.5MMX40MM," 1,224 "
-28017670,HC IMPL SCREW HUMERAL 18CM," 84,560 "
-28017671,HC IMPL SCREW ILIAC 6.5X50MM," 9,832 "
-28017672,HC IMP SCREW INTERFERENCE 7X20," 1,063 "
-28017673,HC IMPL SCREW INTRAFIX 7-9MM," 2,168 "
-28017674,HC IMPL SCREW INTRAFIX 8-10MM," 2,168 "
-28017675,HC IMPL SCREW LINEUM 3.5X16MM," 8,075 "
-28017676,HC IMPL SCREW LINEUM 3.5X22MM," 8,075 "
-28017677,HC IMPL SCREW LINEUM 3.5X24MM," 8,075 "
-28017678,HC IMPL SCREW LINEUM 4.0X20MM," 8,075 "
-28017679,HC IMPL SCREW LINEUM 4.35X24MM," 8,075 "
-28017680,HC IMPL SCREW LINEUM 4.35X28MM," 8,075 "
-28017681,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 3.5X22MM," 1,547 "
-28017682,HC IMPL SCREW LOCKING 3.5X24MM," 2,312 "
-28017683,HC IMPL SCREW LOCKING 3.5X28MM," 2,312 "
-28017684,HC IMPL SCREW LOCKING 3.5X30MM," 2,312 "
-28017685,HC IMPL SCREW LOCKING 3.5X34MM," 2,312 "
-28017686,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 3.5X40MM," 1,547 "
-28017687,HC IMPL SCREW LOCKING 3.5X60MM," 1,547 "
-28017688,HC IMPL SCREW LOCKING 4.0X120MM," 2,074 "
-28017689,HC IMPL SCREW LOCKING 4MM," 1,148 "
-28017690,HC IMPL SCREW LOCKING 4X38MM," 1,403 "
-28017691,HC IMPL SCREW LOCKING 4X58MM," 1,309 "
-28017692,HC IMPL SCREW LOCKING 4X62MM," 1,382 "
-28017693,HC IMPL SCREW LOCKING 5X52.5MM," 2,134 "
-28017694,HC IMPL SCREW LOCKING 6.0X50MM," 1,581 "
-28017695,HC IMPL SCREW LOCKING GLENOSPHERE 4.8X21MM," 1,052 "
-28017696,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X42MM," 1,651 "
-28017697,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X52MM," 1,651 "
-28017698,HC IMPL SCREW LOCKING LOW PROFILE TITANIUM 2MM, 301 
-28017699,HC IMPL SCREW LOCKING LOW PROFILE TITANIUM 3MM, 301 
-28017700,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.0X10MM, 959 
-28017701,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.0X6MM, 959 
-28017702,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.0X8MM, 959 
-28017703,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.4X10MM," 1,176 "
-28017704,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.4X12MM," 1,176 "
-28017705,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.4X14MM," 1,176 "
-28017706,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.4X16MM," 1,122 "
-28017707,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.4X18MM," 1,176 "
-28017708,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.4X8MM," 1,176 "
-28017709,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.9X10MM," 1,353 "
-28017710,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.9X12MM," 1,353 "
-28017711,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.9X14MM," 1,353 "
-28017712,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.9X16MM," 1,353 "
-28017713,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.9X18MM," 1,292 "
-28017714,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 2.9X8MM," 1,292 "
-28017715,HC IMPL SCREW LOCKING POLYAXIAL 3.5X16MM," 2,261 "
-28017716,HC IMPL SCREW LOCKING POLYAXIAL 3.5X18MM," 2,346 "
-28017717,HC IMPL SCREW LOCKING POLYAXIAL 3.5X20MM," 2,346 "
-28017718,HC IMPL SCREW LOCKING SCAN STAINLESS STEEL 6.5MMX95MM," 1,531 "
-28017719,HC IMPL SCREW LOCKING SELF TAPPING 2.7X14MM," 1,139 "
-28017720,HC IMPL SCREW LOCKING SELF TAPPING 2.7X16MM," 1,139 "
-28017721,HC IMPL SCREW LOCKING SELF TAPPING 2.7X18MM," 1,139 "
-28017722,HC IMPL SCREW LOCKING SELF TAPPING 4.0 X 26MM, 927 
-28017723,HC IMPL SCREW LOCKING SELF TAPPING 4.0X22MM," 1,080 "
-28017724,HC IMPL SCREW LOCKING SELF TAPPING 4.0X30MM," 1,080 "
-28017725,HC IMPL SCREW LOCKING SELF TAPPING 4.0X34MM, 927 
-28017726,HC IMPL SCREW LOCKING SELF TAPPING 4.0X38MM," 1,080 "
-28017727,HC IMPL SCREW LOCKING SELF TAPPING 4X14MM," 1,080 "
-28017728,HC IMPL SCREW LOCKING SELF TAPPING LCP 2.4X24MM," 1,046 "
-28017729,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X70MM," 1,368 "
-28017730,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X80MM," 1,368 "
-28017731,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X85MM," 1,368 "
-28017732,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X90MM," 1,368 "
-28017733,HC IMPL SCREW LOCKING SELF TAPPING LCP 3.5X95MM," 1,368 "
-28017734,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X46MM," 1,407 "
-28017735,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X65MM," 1,407 "
-28017736,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X85MM," 1,407 "
-28017737,HC IMPL SCREW LOCKING SELF TAPPING LCP 5X90MM," 1,407 "
-28017738,HC IMPL SCREW LOCKING SELF TAPPING PLUS DRIVE 2X10MM, 873 
-28017739,HC IMPL SCREW LOCKING SELF TAPPING PLUS DRIVE 2X16MM," 4,366 "
-28017740,HC IMPL SCREW LOCKING SELF TAPPING PLUS DRIVE 2X18MM," 4,366 "
-28017741,HC IMPL SCREW LOCKING SELF TAPPING PLUS DRIVE 2X6MM, 873 
-28017742,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X10MM, 995 
-28017743,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X12MM," 1,097 "
-28017744,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X14MM," 1,097 "
-28017745,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X16MM," 1,097 "
-28017746,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X18MM," 1,097 "
-28017747,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X20MM," 1,097 "
-28017748,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X22MM," 1,060 "
-28017749,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X24MM," 1,060 "
-28017750,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X26MM," 1,060 "
-28017751,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X28MM," 1,097 "
-28017752,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X30MM," 1,097 "
-28017753,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X6MM, 935 
-28017754,HC IMPL SCREW LOCKING SELF TAPPING STARDRIVE 2.4X8MM," 1,060 "
-28017755,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 2.9X10MM," 1,360 "
-28017756,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 2.9X12MM," 1,278 "
-28017757,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 2.9X14MM," 1,278 "
-28017758,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 2.9X8MM," 1,278 "
-28017759,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X10MM," 1,198 "
-28017760,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X12MM," 1,198 "
-28017761,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X14MM," 1,157 "
-28017762,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X16MM," 1,198 "
-28017763,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X18MM," 1,198 "
-28017764,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X20MM," 1,157 "
-28017765,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X22MM," 1,157 "
-28017766,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X24MM," 1,198 "
-28017767,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X26MM," 1,198 "
-28017768,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X28MM," 1,157 "
-28017769,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X30MM," 1,198 "
-28017770,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X32MM," 1,157 "
-28017771,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X34MM," 1,003 "
-28017772,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X35MM," 1,157 "
-28017773,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X36MM," 1,088 "
-28017774,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X38MM," 1,157 "
-28017775,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X40MM," 1,003 "
-28017776,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X45MM," 1,157 "
-28017777,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X50MM," 1,157 "
-28017778,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X55MM, 859 
-28017779,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 3.5X60MM," 1,003 "
-28017780,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X14MM," 1,360 "
-28017781,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X16MM," 1,407 "
-28017782,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X18MM," 1,407 "
-28017783,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X20MM," 1,407 "
-28017784,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X22MM," 1,360 "
-28017785,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X24MM," 1,407 "
-28017786,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X26MM," 1,407 "
-28017787,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X28MM," 1,407 "
-28017788,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X30MM," 1,407 "
-28017789,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X32MM," 1,360 "
-28017790,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X34MM," 1,407 "
-28017791,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X36MM," 1,407 "
-28017792,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X38MM," 1,428 "
-28017793,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X40MM," 1,407 "
-28017794,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X42MM," 1,407 "
-28017795,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X44MM," 1,407 "
-28017796,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X46MM," 1,407 "
-28017797,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X48MM," 1,360 "
-28017798,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X50MM," 1,360 "
-28017799,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X55MM," 1,407 "
-28017800,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X60MM," 1,407 "
-28017801,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X65MM," 1,407 "
-28017802,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X70MM," 1,407 "
-28017803,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X75MM," 1,407 "
-28017804,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X80MM," 1,407 "
-28017805,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X85MM," 1,407 "
-28017806,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 5X90MM," 1,407 "
-28017807,HC IMPL SCREW LOCKING STARDRIVE RECESS T8 2.7X10MM," 1,139 "
-28017808,HC IMPL SCREW LOCKING STARDRIVE RECESS T8 2.7X12MM," 1,139 "
-28017809,HC IMPL SCREW LOCKING STARDRIVE RECESS T8 2.7X8MM," 1,101 "
-28017810,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 5X40MM," 1,709 "
-28017811,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 5X70MM," 1,651 "
-28017812,HC IMPL SCREW LOW PROFILE EMERGENCY PURPLE 1.7X4MM," 1,353 "
-28017813,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.35X32MM," 7,988 "
-28017814,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.35X34MM," 7,988 "
-28017815,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.5X26MM," 7,988 "
-28017816,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.5X28MM," 7,988 "
-28017817,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.5X30MM," 7,988 "
-28017818,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.5X36MM," 7,988 "
-28017819,HC IMPL SCREW LONG SHANK MOUNTAINEER 3.5X38MM," 7,988 "
-28017820,HC IMPL SCREW LONG SHANK MOUNTAINEER 4X30MM," 8,055 "
-28017821,HC IMPL SCREW MAS COBALT CHROME 5.5X35MM," 7,863 "
-28017822,HC IMPL SCREW MAS COBALT CHROME 5.5X40MM," 7,863 "
-28017823,HC IMPL SCREW MAS COBALT CHROME 5.5X45MM," 7,863 "
-28017824,HC IMPL SCREW MAS COBALT CHROME 5X30MM," 7,863 "
-28017825,HC IMPL SCREW MAS COBALT CHROME 5X35MM," 7,863 "
-28017826,HC IMPL SCREW MAS COBALT CHROME 5X40MM," 7,863 "
-28017827,HC IMPL SCREW MAS COBALT CHROME 6.0X35MM," 8,920 "
-28017828,HC IMPL SCREW MAS COBALT CHROME 6.0X45MM," 7,863 "
-28017829,HC IMPL SCREW MAS COBALT CHROME 7.5X40MM," 8,920 "
-28017830,HC IMPL SCREW MAS COBALT CHROME 7.5X45MM," 8,920 "
-28017831,HC IMPL SCREW MATRIX POLYAXIAL 4X35MM," 9,375 "
-28017832,HC IMPL SCREW MATRIX POLYAXIAL 4X40MM," 9,375 "
-28017833,HC IMPL SCREW MATRIX POLYAXIAL 4X45MM," 9,375 "
-28017834,HC IMPL SCREW MATRIX POLYAXIAL 5X35MM," 9,375 "
-28017835,HC IMPL SCREW MATRIX POLYAXIAL 5X45MM," 9,375 "
-28017836,HC IMPL SCREW MATRIX POLYAXIAL 5X50MM," 9,375 "
-28017837,HC IMPL SCREW MATRIX POLYAXIAL 5X55MM," 9,375 "
-28017838,HC IMPL SCREW MATRIX POLYAXIAL 6X25MM," 9,375 "
-28017839,HC IMPL SCREW MATRIX POLYAXIAL 6X30MM," 9,375 "
-28017840,HC IMPL SCREW MATRIX POLYAXIAL 6X35MM," 9,375 "
-28017841,HC IMPL SCREW MATRIX POLYAXIAL 6X55MM," 9,375 "
-28017842,HC IMPL SCREW MATRIX POLYAXIAL 6X60MM," 9,375 "
-28017843,HC IMPL SCREW MATRIX POLYAXIAL 7X30MM," 9,375 "
-28017844,HC IMPL SCREW MATRIX POLYAXIAL 7X35MM," 9,375 "
-28017845,HC IMPL SCREW MATRIX POLYAXIAL 7X45MM," 9,375 "
-28017846,HC IMPL SCREW MATRIX POLYAXIAL 7X55MM," 9,375 "
-28017847,HC IMPL SCREW MATRIX POLYAXIAL 7X60MM," 9,375 "
-28017848,HC IMPL SCREW MATRIX SELF TAPPING TITANIUM 1.85MM, 612 
-28017849,HC IMPL SCREW MATRIX SELF TAPPING TITANIUM 2.1MM, 646 
-28017850,HC IMPL SCREW MATRYX 7.3X20MM," 2,270 "
-28017851,HC IMPL SCREW MATRYX 7.3X25MM," 2,270 "
-28017852,HC IMPL SCREW MATRYX 7.3X30MM," 2,270 "
-28017853,HC IMPL SCREW MATRYX 8X20MM," 2,270 "
-28017854,HC IMPL SCREW MATRYX 8X25MM," 2,270 "
-28017855,HC IMPL SCREW MATRYX 8X30MM," 2,270 "
-28017856,HC IMPL SCREW MATRYX 9X20MM," 2,270 "
-28017857,HC IMPL SCREW MATRYX 9X25MM," 2,270 "
-28017858,HC IMPL SCREW MATRYX 9X30MM," 2,270 "
-28017859,HC IMPL SCREW MATTA THREAD 32 MM 6.5X100, 408 
-28017860,HC IMPL SCREW MATTA THREAD 32 MM 6.5X85, 231 
-28017861,HC IMPL SCREW MAXI SAFETY 2.4MMX6MM, 493 
-28017862,HC IMPL SCREW MAXI TITANIUM 2.4MMX5MM, 391 
-28017863,HC IMPL SCREW MCN DIST MDFC TITANIUM 1.2MM, 850 
-28017864,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 3.5X12MM," 7,650 "
-28017865,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 3.5X14MM," 7,650 "
-28017866,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 3.5X26MM," 8,236 "
-28017867,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 3.5X30MM," 7,650 "
-28017868,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 4.0X24MM," 7,920 "
-28017869,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 4.0X26MM," 7,920 "
-28017870,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 4.35X20MM," 7,943 "
-28017871,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 4X20MM," 7,920 "
-28017872,HC IMPL SCREW MILAGRO ADVANCE 10X23MM," 3,417 "
-28017873,HC IMPL SCREW MILAGRO ADVANCE 10X30MM," 3,417 "
-28017874,HC IMPL SCREW MILAGRO ADV 10X35MM," 3,417 "
-28017875,HC IMPL SCREW MILAGRO ADV 11X35MM," 3,417 "
-28017876,HC IMPL SCREW MILAGRO ADV 12X30MM," 3,417 "
-28017877,HC IMPL SCREW MILAGRO ADV 12X35MM," 3,417 "
-28017878,HC IMPL SCREW MILAGRO ADV 7X23MM," 3,417 "
-28017879,HC IMPL SCREW MILAGRO ADV 7X30MM," 3,417 "
-28017880,HC IMPL SCREW MILAGRO ADV 8X23MM," 3,417 "
-28017881,HC IMPL SCREW MILAGRO ADV 8X30MM," 3,417 "
-28017882,HC IMPL SCREW MILAGRO ADVANCE 9X23MM," 3,417 "
-28017883,HC IMPL SCREW MILAGRO ADV 9X30MM," 3,417 "
-28017884,HC IMPL SCREW MILAGRO ADV 9X35MM," 3,417 "
-28017885,HC IMPL SCREW MINI 2.0MMX10MM, 511 
-28017886,HC IMPL SCREW MINI 2.0MMX5MM, 449 
-28017887,HC IMPL SCREW MINI 2.0MMX6MM, 511 
-28017888,HC IMPL SCREW MINI 2.0MMX8MM, 511 
-28017889,HC IMPL SCREW MULTI AXIAL 5.5X30MM," 7,863 "
-28017890,HC IMPL SCREW MULTI AXIAL 5.5X35MM," 7,863 "
-28017891,HC IMPL SCREW MULTI AXIAL 6.5X25MM," 8,115 "
-28017892,HC IMPL SCREW MULTI AXIAL CLOSED SS 5.5X50MM," 12,980 "
-28017893,HC IMPL SCREW MULTI AXIAL CLOSED SS 5.5X60MM," 12,980 "
-28017894,HC IMPL SCREW MULTI AXIAL CLOSED SS 6.5X50MM," 9,832 "
-28017895,HC IMPL SCREW MULTI AXIAL CLOSED SS 6.5X60MM," 12,980 "
-28017896,HC IMPL SCREW MULTI AXIAL CLOSED SS 6.5X70MM," 9,832 "
-28017897,HC IMPL SCREW MULTI AXIAL CLOSED SS 7.5X60MM," 12,980 "
-28017898,HC IMPL SCREW MULTI AXIAL CLOSED SS 7.5X70MM," 12,980 "
-28017899,HC IMPL SCREW MULTI AXIAL CLOSED SS 7.5X80MM," 12,980 "
-28017900,HC IMPL SCREW MULTI AXIAL CLOSED SS 8.5X70MM," 12,980 "
-28017901,HC IMPL SCREW MULTI AXIAL CLOSED SS 8.5X80MM," 9,832 "
-28017902,HC IMPL SCREW MULTI AXIAL CLOSED SS 8.5X90MM," 9,832 "
-28017903,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 5.5X60MM," 13,774 "
-28017904,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 6.5X50MM," 13,774 "
-28017905,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 6.5X60MM," 13,774 "
-28017906,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 6.5X70MM," 13,774 "
-28017907,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 7.5X60MM," 13,774 "
-28017908,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 7.5X70MM," 13,774 "
-28017909,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 7.5X80MM," 13,774 "
-28017910,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 8.5X70MM," 13,774 "
-28017911,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 8.5X80MM," 13,774 "
-28017912,HC IMPL SCREW MULTI AXIAL CLOSED TITANIUM 8.5X90MM," 10,433 "
-28017913,HC IMPL SCREW MULTI AXIAL SS 4.5X20MM," 8,115 "
-28017914,HC IMPL SCREW MULTI AXIAL SS 4.5X25MM," 8,115 "
-28017915,HC IMPL SCREW MULTI AXIAL SS 4.5X30MM," 8,115 "
-28017916,HC IMPL SCREW MULTI AXIAL SS 4.5X35MM," 8,115 "
-28017917,HC IMPL SCREW MULTI AXIAL SS 4.5X40MM," 8,115 "
-28017918,HC IMPL SCREW MULTI AXIAL SS 4.5X45MM," 8,115 "
-28017919,HC IMPL SCREW MULTI AXIAL SS 5.0X50MM," 8,115 "
-28017920,HC IMPL SCREW MULTI AXIAL SS 5.5X25MM," 8,115 "
-28017921,HC IMPL SCREW MULTI AXIAL SS 5.5X30MM," 8,115 "
-28017922,HC IMPL SCREW MULTI AXIAL SS 5.5X35MM," 8,273 "
-28017923,HC IMPL SCREW MULTI AXIAL SS 5.5X40MM," 6,163 "
-28017924,HC IMPL SCREW MULTI AXIAL SS 5.5X45MM," 6,163 "
-28017925,HC IMPL SCREW MULTI AXIAL SS 5.5X50MM," 8,115 "
-28017926,HC IMPL SCREW MULTI AXIAL SS 5.5X55MM," 8,115 "
-28017927,HC IMPL SCREW MULTI AXIAL SS 5X25MM," 6,163 "
-28017928,HC IMPL SCREW MULTI AXIAL SS 5X30MM," 8,115 "
-28017929,HC IMPL SCREW MULTI AXIAL SS 5X35MM," 8,115 "
-28017930,HC IMPL SCREW MULTI AXIAL SS 5X40MM," 8,115 "
-28017931,HC IMPL SCREW MULTI AXIAL SS 5X45MM," 8,115 "
-28017932,HC IMPL SCREW MULTI AXIAL SS 6.0X50MM," 8,273 "
-28017933,HC IMPL SCREW MULTI AXIAL SS 6.5X30MM," 6,163 "
-28017934,HC IMPL SCREW MULTI AXIAL SS 6.5X35MM," 6,163 "
-28017935,HC IMPL SCREW MULTI AXIAL SS 6.5X40MM," 6,163 "
-28017936,HC IMPL SCREW MULTI AXIAL SS 6.5X45MM," 8,115 "
-28017937,HC IMPL SCREW MULTI AXIAL SS 6.5X50MM," 8,115 "
-28017938,HC IMPL SCREW MULTI AXIAL SS 6.5X55MM," 8,115 "
-28017939,HC IMPL SCREW MULTI AXIAL SS 6.5X60MM," 8,115 "
-28017940,HC IMPL SCREW MULTI AXIAL SS 7.5X25MM," 8,115 "
-28017941,HC IMPL SCREW MULTI AXIAL SS 7.5X30MM," 8,115 "
-28017942,HC IMPL SCREW MULTI AXIAL SS 7.5X35MM," 8,115 "
-28017943,HC IMPL SCREW MULTI AXIAL SS 7.5X40MM," 8,115 "
-28017944,HC IMPL SCREW MULTI AXIAL SS 7.5X50MM," 8,115 "
-28017945,HC IMPL SCREW MULTI AXIAL SS 7.5X55MM," 8,115 "
-28017946,HC IMPL SCREW MULTI AXIAL SS 7.5X60MM," 8,115 "
-28017947,HC IMPL SCREW MULTI AXIAL VUE POINT 3.5X28MM," 7,225 "
-28017948,HC IMPL SCREW MULTI AXIAL VUE POINT 3.5X30MM," 7,225 "
-28017952,HC IMPL SCREW MODFY SELF DRILL 1.5X4MM, 101 
-28017953,HC IMPL SCREW MAXILLOFACIAL CRNL SELF TAPPING 1.9X5MM, 265 
-28017954,HC IMPL SCREW MAXILLOFACIAL LAG 2.4X16MM, 493 
-28017955,HC IMPL SCREW NON LOCKING 3.5X22MM, 265 
-28017956,HC IMPL SCREW NON LOCKING 3.5X60MM, 265 
-28017957,HC IMPL SCREW OCCIPITAL MOUNTAINEER 5.25X6MM," 2,780 "
-28017958,HC IMPL SCREW OCCIPITAL MOUNTAINEER 5.25X8MM," 2,780 "
-28017959,HC IMPL SCREW OCCIPITAL MOUNTAINEER 4.5X10MM," 2,601 "
-28017960,HC IMPL SCREW OCCIPITAL MOUNTAINEER 4.5X11MM," 2,601 "
-28017961,HC IMPL SCREW OCCIPITAL MOUNTAINEER 4.5X12MM," 2,601 "
-28017962,HC IMPL SCREW OCCIPITAL MOUNTAINEER 4.5X6MM," 2,601 "
-28017963,HC IMPL SCREW OCCIPITAL MOUNTAINEER 4.5X8MM," 2,601 "
-28017964,HC IMPL SCREW OCCIPITAL MOUNTAINEER CERVICAL," 1,148 "
-28017965,HC IMPL SCREW OMF DF GRN 1.5X4MM, 493 
-28017966,HC IMPL SCREW OMF DF GRN 1.5X5MM, 493 
-28017967,HC IMPL SCREW OPENING TITANIUM 4.2MM 25MM," 7,242 "
-28017968,HC IMPL SCREW OPENING TITANIUM 4.2MM 28MM," 7,242 "
-28017969,HC IMPL SCREW OPENING TITANIUM 4.2MM 30MM," 7,242 "
-28017970,HC IMPL SCREW OPENING TITANIUM 4.2MM 32MM," 7,242 "
-28017971,HC IMPL SCREW OPENING TITANIUM 4.2MM 35MM," 7,242 "
-28017972,HC IMPL SCREW OPENING TITANIUM 4.2MM 38MM," 7,242 "
-28017973,HC IMPL SCREW OPENING TITANIUM 4.2MM 40MM," 7,242 "
-28017974,HC IMPL SCREW OPENING TITANIUM 6.2MM 35MM," 6,375 "
-28017975,HC IMPL SCREW OPENING TITANIUM 6.2MM 40MM," 6,375 "
-28017976,HC IMPL SCREW OPENING TITANIUM 6.2MM 50MM," 6,375 "
-28017978,HC IMPL SCREW PEDICLE DUAL CORE 8X35MM," 8,109 "
-28017979,HC IMPL SCREW PEDICLE PANGLEDEA 5X25X5.0MM," 9,923 "
-28017980,HC IMPL SCREW PEDICLE PANGLEDEA 5X30X5.0MM," 9,923 "
-28017981,HC IMPL SCREW PEDICLE PANGLEDEA 5X35X5.0MM," 9,923 "
-28017982,HC IMPL SCREW PEDICLE PANGLEDEA 6X40X6.0MM," 9,923 "
-28017983,HC IMPL SCREW PEDICLE PANGLEDEA 6X45X6.0MM," 9,923 "
-28017984,HC IMPL SCREW PEDICLE PANGLEDEA 6X50X6.0MM," 9,923 "
-28017985,HC IMPL SCREW PEDICLE PANGLEDEA 6X55X6.0MM," 9,923 "
-28017994,HC IMPL SCREW PLATE SOLID 32MM," 1,533 "
-28017995,HC IMPL SCREW PLATE SYSTEM SMALL 3.5X45X18MM, 153 
-28017996,HC IMPL SCREW PLATE SYSTEM SMALL 3.5X50X18MM, 153 
-28017997,HC IMPL SCREW POLYAXIAL7.5X40MM," 6,657 "
-28017998,HC IMPL SCREW POLYAXIAL 7.5X55MM," 7,188 "
-28017999,HC IMPL SCREW POLYAXIAL EXPEDIUM SS 5.5X6X30MM," 8,641 "
-28018000,HC IMPL SCREW POLYAXIAL EXPEDIUM SS 5.5X6X35MM," 8,641 "
-28018001,HC IMPL SCREW POLYAXIAL EXPEDIUM SS 5.5X7X80MM," 8,641 "
-28018002,HC IMPL SCREW POLYAXIAL EXPEDIUM SS 5X5.5X30MM," 8,641 "
-28018003,HC IMPL SCREW POLYAXIAL EXPEDIUM SS 5X5.5X35MM," 8,641 "
-28018004,HC IMPL SCREW POLYAXIAL EXPEDIUM 6.0X45MM," 8,641 "
-28018005,HC IMPL SCREW POLYAXIAL EXPEDIUM EXP SS 7.0X65," 8,843 "
-28018006,HC IMPL SCREW POLYAXIAL EXPEDIUM 7.5X65MM," 8,641 "
-28018007,HC IMPL SCREW POLYAXIAL MIDFACE 1.7X10MM, 92 
-28018008,HC IMPL SCREW POLYAXIAL MIDFACE 1.7X12MM, 92 
-28018009,HC IMPL SCREW POLYDIRECTIONAL EBI 5.5X40MM," 7,677 "
-28018010,HC IMPL SCREW PROPEL 8X20, 978 
-28018011,HC IMPL SCREW PROPEL 8X25, 978 
-28018012,HC IMPL SCREW PROPEL 8X30, 978 
-28018013,HC IMPL SCREW PROPEL TAPER TIP 7X30MM, 978 
-28018014,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4.5X14MM," 2,338 "
-28018015,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4.5X16MM," 2,338 "
-28018016,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4X14MM," 2,338 "
-28018017,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4X16MM," 2,338 "
-28018018,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4X18MM," 2,338 "
-28018019,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4X12MM," 2,338 "
-28018020,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4X14MM," 2,338 "
-28018021,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 4.5X25MM," 10,196 "
-28018022,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 4.5X30MM," 10,196 "
-28018023,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 4.5X35MM," 10,196 "
-28018024,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 4.5X40MM," 10,196 "
-28018025,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 5.5X25MM," 10,196 "
-28018026,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 5.5X30MM," 10,196 "
-28018027,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 5.5X35MM," 10,394 "
-28018028,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 5.5X40MM," 10,394 "
-28018029,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 5.5X45MM," 10,196 "
-28018030,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 5.5X50MM," 10,196 "
-28018031,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 6.5X35MM," 10,196 "
-28018032,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 6.5X40MM," 10,196 "
-28018033,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 6.5X45MM," 10,196 "
-28018034,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 6.5X50MM," 10,196 "
-28018035,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 6.5X55MM," 10,196 "
-28018036,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 7.5X30MM," 10,196 "
-28018037,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 7.5X35MM," 10,196 "
-28018038,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 7.5X40MM," 10,196 "
-28018039,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 7.5X45MM," 10,196 "
-28018040,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 7.5X50MM," 10,196 "
-28018041,HC IMPL SCREW REDUCTION MULTI AXIAL TITANIUM 7.5X55MM," 10,196 "
-28018042,HC IMPL SCREW RESORBABLE 1.5X6MM, 537 
-28018043,HC IMPL SCREW RESORBABLE 1.7X3MM, 90 
-28018044,HC IMPL SCREW RESORBABLE 1.7X4MM, 90 
-28018045,HC IMPL SCREW RESORBABLE 1.7 5MM, 90 
-28018046,HC IMPL SCREW RESORBABLE 1.7X6MM," 1,436 "
-28018047,HC IMPL SCREW RESORBABLE SURGICAL 1.7X3MM, 363 
-28018048,HC IMPL SCREW RESORBABLE SURGICAL 1.7X4MM, 361 
-28018049,HC IMPL SCREW RESORBABLE SURGICAL 1.7X5MM, 361 
-28018050,HC IMPL SCREW SAG CD HORIZON 5.5X30MM," 8,273 "
-28018051,HC IMPL SCREW SAG CD HORIZON 5.5X35MM," 8,273 "
-28018052,HC IMPL SCREW SAG CD HORIZON 5.5X40MM," 8,273 "
-28018053,HC IMPL SCREW SAG CD HORIZON 5.5X45MM," 8,273 "
-28018054,HC IMPL SCREW SAG CD HORIZON 5.5X50MM," 8,273 "
-28018055,HC IMPL SCREW SAG CD HORIZON 5X30MM," 8,273 "
-28018056,HC IMPL SCREW SAG CD HORIZON 5X35MM," 8,273 "
-28018057,HC IMPL SCREW SAG CD HORIZON 5X40MM," 8,273 "
-28018058,HC IMPL SCREW SAG CD HORIZON 6.5X35MM," 9,448 "
-28018059,HC IMPL SCREW SCHANZ 4X60MM, 616 
-28018060,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1.5X4MM, 660 
-28018061,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1.5X5MM, 636 
-28018062,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1.5X6MM, 636 
-28018063,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 4MM," 3,179 "
-28018064,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 6MM," 3,298 "
-28018065,HC IMPL SCREW SELF DRILL AUTODRIVE 1.6X5MM, 518 
-28018066,HC IMPL SCREW SELF DRILL AUTODRIVE 1.6X6MM, 442 
-28018067,HC IMPL SCREW SELF DRILL LOW PROFILE 4MM, 446 
-28018068,HC IMPL SCREW SELF TAPPING 1.5X6MM," 1,955 "
-28018069,HC IMPL SCREW SELF TAPPING 2.4X12MM, 461 
-28018070,HC IMPL SCREW SELF TAPPING 2.4X14MM, 461 
-28018071,HC IMPL SCREW SELF TAPPING 2.4X16MM, 461 
-28018072,HC IMPL SCREW SELF TAPPING 2.4X18MM, 461 
-28018073,HC IMPL SCREW SELF TAPPING 2.4X20MM, 461 
-28018074,HC IMPL SCREW SELF TAPPING 2.4X22MM, 461 
-28018075,HC IMPL SCREW SELF TAPPING 2.4X24MM, 461 
-28018076,HC IMPL SCREW SELF TAPPING 2.4X26MM, 461 
-28018077,HC IMPL SCREW SELF TAPPING 2.4X28MM, 461 
-28018078,HC IMPL SCREW SELF TAPPING 2.4X30MM, 461 
-28018079,HC IMPL SCREW SELF TAPPING 2.4X32MM, 445 
-28018080,HC IMPL SCREW SELF TAPPING 2.4X34MM, 461 
-28018081,HC IMPL SCREW SELF TAPPING 2.4X36MM, 461 
-28018082,HC IMPL SCREW SELF TAPPING 2.4X38MM, 461 
-28018083,HC IMPL SCREW SELF TAPPING 2.4X8MM, 461 
-28018084,HC IMPL SCREW SINGLE INNIE SS SET," 2,312 "
-28018085,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X20MM," 11,040 "
-28018086,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X22MM," 11,040 "
-28018087,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X24MM," 11,040 "
-28018088,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X26MM," 11,040 "
-28018089,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X28MM," 11,040 "
-28018090,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X30MM," 11,040 "
-28018091,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X32MM," 11,040 "
-28018092,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X34MM," 11,040 "
-28018093,HC IMPL SCREW SHAFT POLYAXIAL TITANIUM 3.5X36MM," 11,040 "
-28018094,HC IMPL SCREW SHFT IMP FRGMNT 3.5X40MM, 153 
-28018095,HC IMPL SCREW SNAP OFF 2X14MM," 2,593 "
-28018096,HC IMPL SCREW SPINAL SELF DRILL SKYLINE 4X14MM," 2,338 "
-28018097,HC IMPL SCREW SPINAL SELF TAPPING SKYLINE 4X12MM," 2,338 "
-28018098,HC IMPL SCREW SPINAL SELF TAPPING SKYLINE 4X14MM," 2,338 "
-28018099,HC IMPL SCREW SOLERA SAS 5.5X30MM," 7,863 "
-28018100,HC IMPL SCREW SOLERA SAS 5.5X35MM," 7,863 "
-28018101,HC IMPL SCREW SOLERA SAS 5.5X40MM," 7,863 "
-28018102,HC IMPL SCREW SPINAL 3.5X26MM," 7,225 "
-28018103,HC IMPL SCREW SPINAL 4.5X25MM," 6,163 "
-28018104,HC IMPL SCREW SPINAL 4X20MM," 6,163 "
-28018105,HC IMPL SCREW SPINAL 4X25MM," 6,163 "
-28018106,HC IMPL SCREW SPINAL 4X30MM," 6,163 "
-28018107,HC IMPL SCREW SPINAL ANGLED 3.5X25MM," 6,163 "
-28018108,HC IMPL SCREW SPINAL ANTERIOR 5.5X26MM," 4,504 "
-28018109,HC IMPL SCREW SPINAL ANTERIOR 5.5X28MM," 4,309 "
-28018110,HC IMPL SCREW SPINAL BUTTRESS BREAKOFF M6XI, 850 
-28018111,HC IMPL SCREW SPINAL CANNULATED 6X45MM," 10,118 "
-28018112,HC IMPL SCREW SPINAL CANNULATED 6X50MM," 10,118 "
-28018113,HC IMPL SCREW SPINAL CANNULATED 7X45MM," 10,118 "
-28018114,HC IMPL SCREW SPINAL CENTR POST 6.5X40MM," 4,488 "
-28018115,HC IMPL SCREW SPINAL CONSTRAINED SKYLINE 14MM," 2,338 "
-28018116,HC IMPL SCREW SPINAL CONSTRAINED SKYLINE LARGE 12MM," 2,338 "
-28018117,HC IMPL SCREW SPINAL CONSTRAINED SKYLINE LARGE 14MM," 2,338 "
-28018118,HC IMPL SCREW SPINAL CONSTRAINED SKYLINE STAP 16MM," 2,338 "
-28018119,HC IMPL SCREW SPINAL CONSTRAINED SKYLINE STAP TI 4X12MM," 2,338 "
-28018120,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 7X45MM," 7,242 "
-28018121,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 30X7.0MM," 7,242 "
-28018122,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 35X7.0MM," 7,242 "
-28018123,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 50X7.0MM," 7,242 "
-28018124,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 50X8.0MM," 6,375 "
-28018125,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 55X7.0MM," 7,242 "
-28018126,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 6.2X30MM," 8,109 "
-28018127,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 60X7.0MM," 7,242 "
-28018128,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 7X35MM," 8,109 "
-28018129,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 7X45MM," 8,109 "
-28018130,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 7X50MM," 7,460 "
-28018131,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 7X55MM," 8,109 "
-28018132,HC IMPL SCREW SPINAL DUAL OPENING TITANIUM 8X40MM," 8,109 "
-28018133,HC IMPL SCREW SPINAL FIXED TITANIUM 4.5X25MM," 7,863 "
-28018134,HC IMPL SCREW SPINAL FIXED TITANIUM 4.5X30MM," 7,863 "
-28018135,HC IMPL SCREW SPINAL FIXED TITANIUM 5.0X25MM," 7,863 "
-28018136,HC IMPL SCREW SPINAL FIXED TITANIUM 5.0X30MM," 7,863 "
-28018137,HC IMPL SCREW SPINAL FIXED TITANIUM 5.0X35MM," 7,863 "
-28018138,HC IMPL SCREW SPINAL FIXED TITANIUM 5.5X30MM," 7,863 "
-28018139,HC IMPL SCREW SPINAL FIXED TITANIUM 5.5X35MM," 7,863 "
-28018140,HC IMPL SCREW SPINAL FIXED TITANIUM 5.5X40MM," 7,863 "
-28018141,HC IMPL SCREW SPINAL FIXED ANGLE TITANIUM 3.5X30MM," 6,163 "
-28018142,HC IMPL SCREW SPINAL LOCKING RECON 2.4X16MM," 1,125 "
-28018143,HC IMPL SCREW SPINAL LOCKING RECON 2.4X18MM," 1,125 "
-28018144,HC IMPL SCREW SPINAL LOCKING RECON 2.4X8MM," 1,125 "
-28018145,HC IMPL SCREW SPINAL LOCKING RECON PLT 3X18MM," 1,306 "
-28018146,HC IMPL SCREW SPINAL LOCKING X-LARGE DBR 11, 850 
-28018147,HC IMPL SCREW SPINAL MULTI AXIAL 3.5X14MM," 7,225 "
-28018148,HC IMPL SCREW SPINAL MULTI AXIAL 3.5X16MM," 7,225 "
-28018149,HC IMPL SCREW SPINAL MULTI AXIAL 3.5X22MM," 7,225 "
-28018150,HC IMPL SCREW SPINAL MULTI AXIAL 5.5X40MM," 10,125 "
-28018151,HC IMPL SCREW SPINAL MULTI AXIAL 5.5X45MM," 10,125 "
-28018152,HC IMPL SCREW SPINAL MULTI AXIAL 5.5X50MM," 10,125 "
-28018153,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X35MM," 10,125 "
-28018154,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X40MM," 10,125 "
-28018155,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X45MM," 10,125 "
-28018156,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X50MM," 10,125 "
-28018157,HC IMPL SCREW SPINAL MULTI AXIAL 7.5X45MM," 10,125 "
-28018158,HC IMPL SCREW SPINAL MULTI AXIAL CLOSED SS 5.5X40," 12,980 "
-28018159,HC IMPL SCREW SPINAL MULTI AXIAL CLOSED TITANIUM 5.5X40," 13,774 "
-28018160,HC IMPL SCREW SPINAL MULTI AXIAL CLOSED TITANIUM 5.5X50," 13,774 "
-28018161,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X20MM," 8,741 "
-28018162,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X25MM," 8,741 "
-28018163,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X30MM," 8,741 "
-28018164,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X35MM," 8,741 "
-28018165,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X40MM," 8,741 "
-28018166,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X45MM," 8,741 "
-28018167,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 4.75X50MM," 8,741 "
-28018168,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 5.5X40MM," 7,013 "
-28018169,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X35MM," 8,741 "
-28018170,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X40MM," 7,863 "
-28018171,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X45MM," 7,863 "
-28018172,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 6.5X50MM," 8,741 "
-28018173,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X40MM," 8,741 "
-28018174,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.0X30MM," 8,919 "
-28018175,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.5X20MM," 8,749 "
-28018176,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.5X25MM," 8,749 "
-28018177,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.5X30MM," 6,163 "
-28018178,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.5X35MM," 6,163 "
-28018179,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.5X40MM," 8,749 "
-28018180,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 4.5X45MM," 8,749 "
-28018181,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X 35MM," 6,163 "
-28018182,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X25MM," 8,749 "
-28018183,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X30MM," 6,163 "
-28018184,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X35MM," 8,279 "
-28018185,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X40MM," 6,163 "
-28018186,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X45MM," 6,163 "
-28018187,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X50MM," 6,163 "
-28018188,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5.5X55MM," 6,163 "
-28018189,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5X25MM," 8,919 "
-28018190,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5X30MM," 6,163 "
-28018191,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5X35MM," 8,749 "
-28018192,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5X40MM," 8,749 "
-28018193,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5X5.5X45MM," 8,749 "
-28018194,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 5X5.5X50MM," 8,749 "
-28018195,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X25MM," 8,749 "
-28018196,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X30MM," 8,919 "
-28018197,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X35MM," 8,749 "
-28018198,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X40MM," 6,163 "
-28018199,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X45MM," 6,163 "
-28018200,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X50MM," 6,163 "
-28018201,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X55MM," 6,163 "
-28018202,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6.5X60MM," 8,749 "
-28018203,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6X30MM," 8,919 "
-28018204,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6X35MM," 8,919 "
-28018205,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6MM 40MM," 8,919 "
-28018206,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6X45MM," 6,163 "
-28018207,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 6X50MM," 10,185 "
-28018208,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X25MM," 8,749 "
-28018209,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X30MM," 8,749 "
-28018210,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X35MM," 8,749 "
-28018211,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X40MM," 8,919 "
-28018212,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X55MM," 8,749 "
-28018213,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X60MM," 8,749 "
-28018214,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X45MM," 8,919 "
-28018215,HC IMPL SCREW SPINAL MULTI AXIAL TITANIUM 7.5X50MM," 8,919 "
-28018216,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X40MM," 7,863 "
-28018217,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X45MM," 7,863 "
-28018218,HC IMPL SCREW SPINAL MULTI AXIAL 6.5X50MM," 7,863 "
-28018219,HC IMPL SCREW SPINAL OPENING DUAL 7.0X40MM," 7,242 "
-28018220,HC IMPL SCREW SPINAL PEDICLE 6.2X40MM," 5,100 "
-28018221,HC IMPL SCREW SPINAL PEDICLE 6.2X45MM," 5,100 "
-28018222,HC IMPL SCREW SPINAL PEDICLE 6.2X50MM," 5,100 "
-28018223,HC IMPL SCREW SPINAL PEDICLE 7X40MM," 5,100 "
-28018224,HC IMPL SCREW SPINAL PEDICLE 7X45MM," 5,100 "
-28018225,HC IMPL SCREW SPINAL PEDICLE 7X50MM," 5,100 "
-28018226,HC IMPL SCREW SPINAL POLYAXIAL 4.35X30MM," 8,843 "
-28018227,HC IMPL SCREW SPINAL POLYAXIAL 4.35X35MM," 8,843 "
-28018228,HC IMPL SCREW SPINAL POLYAXIAL 4.35X40MM," 8,843 "
-28018229,HC IMPL SCREW SPINAL POLYAXIAL 5.5X40MM," 11,655 "
-28018230,HC IMPL SCREW SPINAL POLYAXIAL 5X35MM," 8,843 "
-28018231,HC IMPL SCREW SPINAL POLYAXIAL 5X40MM," 9,015 "
-28018232,HC IMPL SCREW SPINAL POLYAXIAL 6.0X35MM," 9,169 "
-28018233,HC IMPL SCREW SPINAL POLYAXIAL 6.5X45MM," 10,472 "
-28018234,HC IMPL SCREW SPINAL POLYAXIAL 6.5X55MM," 8,500 "
-28018235,HC IMPL SCREW SPINAL POLYAXIAL 6X40MM," 8,843 "
-28018236,HC IMPL SCREW SPINAL POLYAXIAL 6X45MM," 8,843 "
-28018237,HC IMPL SCREW SPINAL POLYAXIAL 6X50MM," 8,843 "
-28018238,HC IMPL SCREW SPINAL POLYAXIAL 7.0X65MM," 9,169 "
-28018239,HC IMPL SCREW SPINAL POLYAXIAL 7.5X45MM," 8,500 "
-28018240,HC IMPL SCREW SPINAL POLYAXIAL 7.5X50MM," 8,500 "
-28018241,HC IMPL SCREW SPINAL POLYDIRECTIONAL 6.5X40MM," 7,677 "
-28018242,HC IMPL SCREW SPINAL POLYDIRECTIONAL 6.5X45MM," 7,677 "
-28018243,HC IMPL SCREW SPINAL POLYDIRECTIONAL 7.5X45MM," 7,674 "
-28018244,HC IMPL SCREW SPINAL POLYDIRECTIONAL 7.5X50MM," 1,280 "
-28018245,HC IMPL SCREW SPINAL PARTIALLY THREAD 3.5X26MM," 7,225 "
-28018246,HC IMPL SCREW SPINAL PARTIALLY THREAD 3.5X30MM," 7,225 "
-28018247,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 4.5X25MM," 9,317 "
-28018248,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 4.5X30MM," 9,317 "
-28018249,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 4.5X35MM," 9,317 "
-28018250,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 4.5X40MM," 9,317 "
-28018251,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 5.5X25MM," 9,317 "
-28018252,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 5.5X30MM," 9,317 "
-28018253,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 5.5X35MM," 9,498 "
-28018254,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 5.5X40MM," 9,498 "
-28018255,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 5.5X45MM," 9,317 "
-28018256,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 5.5X50MM," 9,317 "
-28018257,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 6.5X30MM," 9,317 "
-28018258,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 6.5X35MM," 9,317 "
-28018259,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 6.5X40MM," 9,317 "
-28018260,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 6.5X45MM," 9,317 "
-28018261,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 6.5X50MM," 9,317 "
-28018262,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 6.5X55MM," 9,317 "
-28018263,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 7.5X35MM," 9,317 "
-28018264,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 7.5X40MM," 9,317 "
-28018265,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 7.5X45MM," 9,317 "
-28018266,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 7.5X50MM," 9,317 "
-28018267,HC IMPL SCREW SPINAL REDUCTION MULTI AXIAL SS 7.5X55MM," 9,317 "
-28018268,HC IMPL SCREW DUAL CORE SCHANZ 6X35X180MM," 2,440 "
-28018269,HC IMPL SCREW SPINAL SELF DRILL SKYLINE 4X12MM," 2,338 "
-28018270,HC IMPL SCREW SPINAL SELF DRILL SKYLINE 4X16MM," 2,338 "
-28018271,HC IMPL SCREW SPINAL SKYLINE LARGE-DIAM 4.5X12MM," 2,338 "
-28018272,HC IMPL SCREW SPINAL SKYLINE LARGE-DIAM 4X16MM," 2,338 "
-28018273,HC IMPL SCREW SPINAL SELF TAPPING SKYLINE 4X16MM," 2,338 "
-28018274,HC IMPL SCREW SPINAL STATURE SMALL 5X30MM," 7,242 "
-28018275,HC IMPL SCREW SPINAL STATURE SMALL 5X35MM," 7,242 "
-28018276,HC IMPL SCREW SPINAL STATURE SMALL 5X40MM," 7,242 "
-28018277,HC IMPL SCREW SPINAL STATURE SMALL 5X45MM," 7,242 "
-28018278,HC IMPL SCREW SPINAL STATURE SMALL 6X45MM," 7,242 "
-28018279,HC IMPL SCREW SPINAL STATURE SMALL 6X55MM," 7,242 "
-28018280,HC IMPL SCREW SPINAL STATURE SMALL 6X30MM," 7,242 "
-28018281,HC IMPL SCREW SPINAL STATURE SMALL 6X35MM," 7,242 "
-28018282,HC IMPL SCREW SPINAL STATURE SMALL 6X40MM," 7,242 "
-28018283,HC IMPL SCREW SPINAL STATURE SMALL 6X50MM," 7,242 "
-28018284,HC IMPL SCREW SPINAL STATURE SMALL 5X25MM," 7,242 "
-28018285,HC IMPL SCREW SPINAL TITANIUM 3.2X20MM, 625 
-28018286,HC IMPL SCREW SPINAL VARIABLE 4.5X15MM," 2,338 "
-28018287,HC IMPL SCREW SPINAL VARIABLE 4.5X17MM," 2,338 "
-28018288,HC IMPL SCREW SPINAL VARIABLE 4X12MM," 3,885 "
-28018289,HC IMPL SCREW SPINAL VARIABLE 4X14MM," 2,338 "
-28018290,HC IMPL SCREW SPINAL VARIABLE 4X15MM," 2,338 "
-28018291,HC IMPL SCREW SPINAL VARIABLE 4X16MM," 2,338 "
-28018292,HC IMPL SCREW SPINAL VARIABLE 4X17MM," 2,338 "
-28018293,HC IMPL SCREW SPINAL VERTEX 1X6MM SET," 1,394 "
-28018294,HC IMPL SCREW SPINE CERVICAL TITANIUM 3X14MM," 2,057 "
-28018295,HC IMPL SCREW SPINE CERVICAL TITANIUM 3X16MM," 2,057 "
-28018296,HC IMPL SCREW SELF TAPPING 2.4X40MM, 461 
-28018297,HC IMPL SCREW SELF TAPPING MTRXMNDBL TI 1/PKG 2.4X12MM, 700 
-28018298,HC IMPL SCREW SELF TAPPING MTRXMNDBL TI 1/PKG 2.4X14MM, 700 
-28018299,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.4X16MM, 734 
-28018300,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.4X18MM, 700 
-28018302,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.4X8MM, 734 
-28018303,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X10MM, 762 
-28018304,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X14MM, 762 
-28018305,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X16MM, 762 
-28018306,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM2.7X18MM, 762 
-28018307,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X6MM, 762 
-28018308,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X6MM, 762 
-28018309,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 2.7X8MM, 762 
-28018310,HC IMPL SCREW SURGICAL RESORBABLE EME 2.2X4MM, 90 
-28018311,HC IMPL SCREW SURGICAL RESORBABLE EME 2.2X6MM, 90 
-28018313,HC IMPL SCREW THREAD 5.2X30MM," 8,135 "
-28018314,HC IMPL SCREW THREAD 5.2X35MM," 8,135 "
-28018315,HC IMPL SCREW TIBIAL 2LEAD THREAD 5X30MM," 1,828 "
-28018316,HC IMPL SCREW TITANIUM 1.0X10MM, 553 
-28018339,HC IMPL SCREW UNI AXIAL 5.5X30MM," 12,870 "
-28018340,HC IMPL SCREW UNI AXIAL 5.5X35MM," 12,870 "
-28018341,HC IMPL SCREW UNI AXIAL 5.5X40MM," 12,870 "
-28018342,HC IMPL SCREW UNI AXIAL 6.5X25MM," 8,273 "
-28018343,HC IMPL SCREW UNI AXIAL 6.5X40MM," 12,870 "
-28018344,HC IMPL SCREW UNI AXIAL 6.5X45MM," 12,870 "
-28018345,HC IMPL SCREW UNIPLANER 5X35MM," 8,250 "
-28018346,HC IMPL SCREW UNIPLANER 6X35MM," 8,250 "
-28018347,HC IMPL SCREW UNIPLANER 6X40MM," 8,250 "
-28018348,HC IMPL SCREW UNIPLANAR EXTENDED 6X35MM," 8,775 "
-28018349,HC IMPL SCREW UNIPLANAR EXTENDED 6X40MM," 8,775 "
-28018350,HC IMPL SCREW UNIPLANAR EXTENDED 6X45MM," 8,775 "
-28018351,HC IMPL SCREW UNIPLANAR TITANIUM 5X25MM," 8,325 "
-28018352,HC IMPL SCREW UNIPLANAR TITANIUM 5X30MM," 8,325 "
-28018353,HC IMPL SCREW UNIPLANAR XIA 5.5X35MM," 12,848 "
-28018354,HC IMPL SCREW UNIPLANAR XIA 5.5X40MM," 12,848 "
-28018355,HC IMPL SCREW UNIPLANAR XIA 5X35MM," 12,848 "
-28018356,HC IMPL SCREW UNIPLANAR XIA 5X40MM," 12,848 "
-28018357,HC IMPL SCREW UNIPLANAR XIA 6.5X40MM," 12,848 "
-28018358,HC IMPL SCREW UNIPLANAR XIA 6.5X45MM," 12,848 "
-28018359,HC IMPL SCREW UNIPLANAR 6X45MM," 8,325 "
-28018360,HC IMPL SCREW UNIPLANAR 4.0X25MM," 7,863 "
-28018361,HC IMPL SCREW UNIPLANAR 4X30MM," 7,863 "
-28018362,HC IMPL SCREW UNIPLANAR 4.75X35MM," 7,863 "
-28018363,HC IMPL SCREW UNIPLANAR 4X35MM," 7,863 "
-28018364,HC IMPL SCREW UNIPLANAR 5.5X20MM," 7,863 "
-28018365,HC IMPL SCREW UNIPLANAR STAINLESS STEEL 5.5X25MM," 7,863 "
-28018366,HC IMPL SCREW UNIPLANAR STAINLESS STEEL 5.5X30MM," 7,438 "
-28018367,HC IMPL SCREW UNIPLANAR 5.5X40MM REV," 7,863 "
-28018368,HC IMPL SCREW UNIPLANAR 5.5X45MM," 7,013 "
-28018369,HC IMPL SCREW UNIPLANAR 6.5X35MM," 10,491 "
-28018370,HC IMPL SCREW UNIPLANAR 6.5X45MM," 7,013 "
-28018371,HC IMPL SCREW UNIPLANAR 6.5X45MM REV," 7,863 "
-28018372,HC IMPL SCREW UNIPLANAR 6.5X50MM," 7,863 "
-28018373,HC IMPL SCREW UNIPLANAR SS 5.5X30X5MM," 10,678 "
-28018374,HC IMPL SCREW UNIPLANAR SS 5.5X35X5MM," 10,678 "
-28018375,HC IMPL SCREW UNIPLANAR SS 5.5X40X5MM," 7,509 "
-28018376,HC IMPL SCREW UNIPLANAR SS 5.5X40X6MM," 10,678 "
-28018377,HC IMPL SCREW UNIPLANAR STAINLESS STEEL 6X35MM," 7,509 "
-28018378,HC IMPL SCREW UNIPLANAR TITANIUM 5X45MM," 8,325 "
-28018379,HC IMPL SCREW VARIABLE SELF RETAIN/DRILL 4X14MM," 2,338 "
-28018380,HC IMPL SCREW VARIABLE SELF RETAIN/DRILL 4X16MM," 2,338 "
-28018381,HC IMPL SCREW VARIABLE SELF RETAIN/DRILL 4X18MM," 2,338 "
-28018382,HC IMPL SCREW VARIABLE SELF RETAIN/DRILL 4.5X14MM," 2,338 "
-28018383,HC IMPL SCREW VARIABLE SELF RETAIN/DRILL 4.5X16MM," 2,338 "
-28018384,HC IMPL SCREW VARIABLE SELF RETAIN/DRILL 4.5X18MM," 2,338 "
-28018385,HC IMPL SCREW VARIABLE ANGLED SELF TAPPING 4X14MM," 2,338 "
-28018386,HC IMPL SCREW VARIABLE ANGLED SELF TAPPING 4X16MM," 2,338 "
-28018387,HC IMPL SCREW VARIABLE ANGLED SELF TAPPING 4X18MM," 2,338 "
-28018388,HC IMPL SCREW VARIABLE ANGLED SELF TAPPING 4.5X14MM," 2,338 "
-28018389,HC IMPL SCREW VARIABLE ANGLED SELF TAPPING 4.5X16MM," 2,338 "
-28018390,HC IMPL SCREW VARIABLE ANGLED SELF TAPPING 4.5X18MM," 2,338 "
-28018391,HC IMPL SCREW VARIABLE ANGLE SELF DRILLING 4X15MM," 2,338 "
-28018392,HC IMPL SCREW VARIABLE ANGLE SELF DRILLING 4X16MM," 2,338 "
-28018393,HC IMPL SCREW VARIABLE ANGLE 4X14MM," 2,848 "
-28018394,HC IMPL SCREW VARIABLE SELF DRILLING AVIATOR 4X12MM," 2,700 "
-28018395,HC IMPL SCREW VIPER 7X45," 9,188 "
-28018396,HC IMPL SCREW WEDGE UNIV 7MM 20MM, 967 
-28018397,HC IMPL SCREW XCALIBER 260/35MM," 1,292 "
-28018416,HC SEALER BIPOLAR MALLEABLE AQUAMANTYS," 4,803 "
-28018417,HC SEALER TISSUE ENSEAL 22CM," 5,950 "
-28018418,HC SEALER TISSUE ENSEAL 25CM," 3,090 "
-28018420,HC  SHEATH 4MM 24CM, 638 
-28018421,HC  SHEATH 4MM 35CM, 638 
-28018422,HC  SHEATH DISPOSABLE STERILE, 146 
-28018426,HC IMP SHEETING SILICONE 6IN," 1,557 "
-28018427,HC IMP SHAFT FEMORAL 10X200MM," 14,610 "
-28018428,HC IMP SHAFT GRAFT FIBULA SPINAL FUSION 10CM," 7,098 "
-28018481,HC IMP SHOULDER INTERSPACE KIT," 29,330 "
-28018521,HC SHEATH TIBIAL INTRAFIX 9X30MM," 2,253 "
-28018529,HC IMP SHUNT CORONARY AXIUS 1.75X17.5MM, 808 
-28018530,HC IMP SHUNT CORONARY AXIUS 2.25X18.5MM, 808 
-28018531,HC IMP SHUNT CORONARY AXIUS 2.5X19MM, 808 
-28018532,HC IMP SHUNT CORONARY AXIUS 2.75X19.5MM, 808 
-28018533,HC IMP SHUNT CORONARY AXIUS 2X18MM, 808 
-28018534,HC IMP SHUNT CORONARY AXIUS 3X20MM, 808 
-28018535,HC IMP SHUNT CORONARY INTRALUMINAL 1.5X18MM, 808 
-28018536,HC IMP SHUNT CSF SYSTEM OSV II 8F 15CM," 19,530 "
-28018537,HC IMP SHUNT CSF SYSTEM OSV II 8F 7CM," 19,530 "
-28018538,HC IMP SHUNT DOUBLE VALVE 15.5FX66CM," 12,068 "
-28018541,HC IMP SHUNT LUMBAR PERITONEAL STRATA," 26,463 "
-28018542,HC IMP SINUS ETHMOID PROPEL STEROID RELEASING," 5,908 "
-28018543,HC IMP SLEEVE CENTER COMPRESSION 13MM," 1,754 "
-28018544,HC IMP SLEEVE CENTER COMPRESSION 14MM," 2,627 "
-28018545,HC IMP SLEEVE CENTER COMPRESSION 15MM," 2,627 "
-28018546,HC IMP SLEEVE CENTER COMPRESSION 16MM," 1,754 "
-28018547,HC IMP SLEEVE CENTER COMPRESSION 17MM," 2,627 "
-28018549,HC IMP SLEEVE CENTER COMPRESSION 21MM," 1,754 "
-28018550,HC IMP SLEEVE CENTER COMPRESSION 22MM," 1,754 "
-28018551,HC IMP SLEEVE GUIDE PIN GRDTD 2.4MM," 1,207 "
-28018557,HC IMP SLEEVE SPINAL W/GROOVES USS," 1,411 "
-28018561,HC SNARE GOOSENECK 3 KIT," 2,338 "
-28018564,HC IMP SPACER SPINAL COROENT LORDOTIC XLARGE 8X22X50MM," 32,956 "
-28018566,HC IMP SPACER CEMENT DISTAL UNIVL 12MM, 425 
-28018567,HC IMP SPACER CONCORDE PARALLEL 9X10X23," 25,536 "
-28018568,HC IMP SPACER CONCORDE PARALLEL 9X9X23," 30,709 "
-28018569,HC IMP SPACER SHOULDER TEMPORARY W/ GENTAMICIN," 29,330 "
-28018570,HC IMP SPACER SPINAL 4 DEG 12X18X45MM," 33,894 "
-28018571,HC IMP SPACER SPINAL CAPSTONE 22X10MM," 28,805 "
-28018572,HC IMP SPACER SPINAL PEEK 14X11X5," 13,500 "
-28018573,HC IMP SPACER SPINAL CORNERSTONE PEEK 14X11X6MM," 8,250 "
-28018574,HC IMP SPACER SPINAL THORACOLUMBAR 9MM," 28,000 "
-28018575,HC IMP SPACER SPINAL TPAL 10X10X28MM," 30,800 "
-28018576,HC IMP SPACER SPINAL TPAL 10X12X32MM," 30,800 "
-28018577,HC IMP SPACER SPINAL TPAL 11X10X28MM," 30,800 "
-28018578,HC IMP SPACER SPINAL TPAL 11X12X32MM," 30,800 "
-28018579,HC IMP SPACER SPINAL TPAL 12X10X28MM," 30,800 "
-28018580,HC IMP SPACER SPINAL TPAL 12X12X32MM," 30,800 "
-28018581,HC IMP SPACER SPINAL TPAL 13X10X28MM," 30,800 "
-28018582,HC IMP SPACER SPINAL TPAL 13X12X32MM," 30,800 "
-28018583,HC IMP SPACER SPINAL TPAL 14X10X28MM," 30,800 "
-28018584,HC IMP SPACER SPINAL TPAL 14X12X32MM," 30,800 "
-28018585,HC IMP SPACER SPINAL TPAL 15X10X28MM," 30,800 "
-28018586,HC IMP SPACER SPINAL TPAL 15X12X32MM," 30,800 "
-28018587,HC IMP SPACER SPINAL TPAL 16X10X28MM," 30,800 "
-28018588,HC IMP SPACER SPINAL TPAL 16X12X32MM," 30,800 "
-28018589,HC IMP SPACER SPINAL TPAL 17X10X28MM," 30,800 "
-28018590,HC IMP SPACER SPINAL TPAL 17X12X32MM," 30,800 "
-28018591,HC IMP SPACER SPINAL TPAL 7X10X28MM," 30,800 "
-28018592,HC IMP SPACER SPINAL TPAL 7X12X32MM," 30,800 "
-28018593,HC IMP SPACER SPINAL TPAL 8X10X28MM," 30,800 "
-28018594,HC IMP SPACER SPNAL TPAL 8X12X32MM," 30,800 "
-28018595,HC IMP SPACER SPINAL TPAL 9X10X28MM," 30,800 "
-28018596,HC IMP SPACER SPINAL TPAL 9X12X32MM," 30,800 "
-28018597,HC IMP SPACER SPINAL VERTE-STACK 10X22MM," 28,805 "
-28018598,HC IMP SPACER SPINAL VERTE-STACK 10X26MM," 28,000 "
-28018599,HC IMP SPACER SPINAL VERTE-STACK 12X22MM," 28,805 "
-28018600,HC IMP SPACER SPINAL VERTE-STACK 12X26MM," 28,000 "
-28018601,HC IMP SPACER SPINAL VERTE-STACK 8X22MM," 24,500 "
-28018602,HC IMP SPACER SPINAL X-STOP 10MM," 30,450 "
-28018603,HC IMP SPACER TENDON HUNTER 6MMX24CM," 1,488 "
-28018604,HC IMP SPACER TIBIAL WEDGE 15MM," 8,550 "
-28018611,HC SPEECH PROCESSOR HEARING AID BEIGE BEIGE," 21,000 "
-28018612,HC SPEECH PROCESSOR HEARING AID BEIGE BROWN," 21,000 "
-28018613,HC SPEECH PROCESSOR HEARING AID BROWN BROWN," 21,000 "
-28018614,HC IMP SPACER ALGRFT CCACF CONVX 12MM," 9,990 "
-28018618,HC IMP SPINE CAGE BENGAL STANDARD 4MM 7D," 8,843 "
-28018619,HC IMP SPINE CAGE BENGAL STANDARD 5MM 7D," 8,843 "
-28018620,HC IMP SPINE END RING 12MM 0D," 1,627 "
-28018621,HC IMP SPINE END RING 12X2.5D," 1,627 "
-28018622,HC IMP SPINE END RING TITANIUM 15MM 2.5D, 102 
-28018623,HC IMP SPINE END RING CONVEX 10MM 0D," 2,346 "
-28018624,HC IMP SPINE END RING CONVEX 10MM 2.5D," 2,346 "
-28018625,HC IMP SPINE END RING CONVEX 12MM 0D," 2,346 "
-28018626,HC IMP SPINE END RING CONVEX 12MM 2.5D," 2,346 "
-28018627,HC IMP SPINE END RING CONVEX 15MM 0D," 2,346 "
-28018628,HC IMP SPINE END RING CONVEX 15MM 2.5D," 2,346 "
-28018629,HC IMP SPINE END RING ANTERIOR OR LATERAL 17X22MM 0D," 6,405 "
-28018630,HC IMP SPINE END RING ANTERIOR OR LATERAL 17X22MM 5D," 6,405 "
-28018631,HC IMP SPINE END RING ANTERIOR OR LATERAL 22X28MM 5D," 6,405 "
-28018632,HC IMP SPINE END RING ANTERIOR OR LATERAL 22X28MM 0D," 6,405 "
-28018633,HC IMP SPINE END RING ANTERIOR OR LATERAL 26X33MM 0D," 6,405 "
-28018634,HC IMP SPINE END RING ANTERIOR OR LATERAL 26X33MM 5D," 6,405 "
-28018635,HC IMP SPINE END RING OBLONG LATERAL 17X22MM 5D," 4,942 "
-28018636,HC IMP SPINE END RING OBLONG LATERAL 22X28MM 0D," 4,942 "
-28018637,HC IMP SPINE END RING OBLONG LATERAL 22X28MM 5D," 4,942 "
-28018638,HC IMP SPINE END RING OBLONG LATERAL 26X33MM 0D," 4,942 "
-28018639,HC IMP SPINE END RING OBLONG LATERAL 26X33MM 5D," 4,942 "
-28018640,HC IMP SPINE END RING OBLONG LATERAL 17X22MM 0D," 4,942 "
-28018641,HC IMP SPINE END RING STANDARD 27X22MM," 2,213 "
-28018642,HC IMP SPINE END RING STANDARD 22X28MM," 2,213 "
-28018643,HC IMP SPINE END RING STANDARD 26X33MM," 2,213 "
-28018644,HC IMP SPINE END RING TITANIUM 10MM 0D," 1,627 "
-28018645,HC IMP SPINE END RING TITANIUM 10MM 2.5D," 1,627 "
-28018646,HC IMP SPINE END RING TITANIUM 15MM 0D," 1,627 "
-28018647,HC IMP SPINE OBLONG TI SYNMESH 17X22X12MM," 21,465 "
-28018648,HC IMP SPINE OBLONG TI SYNMESH 17X22X32MM," 31,761 "
-28018649,HC IMP SPINE OBLONG TI SYNMESH 17X22X88MM," 43,913 "
-28018650,HC IMP SPINE OBLONG TI SYNMESH 17X22X8MM," 21,465 "
-28018651,HC IMP SPINE OBLONG TI SYNMESH 22X28X10MM," 21,465 "
-28018652,HC IMP SPINE OBLONG TI SYNMESH 22X28X12MM," 21,465 "
-28018653,HC IMP SPINE OBLONG TI SYNMESH 22X28X14MM," 21,465 "
-28018654,HC IMP SPINE OBLONG TI SYNMESH 22X28X28MM," 29,894 "
-28018655,HC IMP SPINE OBLONG TI SYNMESH 22X28X30MM," 31,846 "
-28018656,HC IMP SPINE OBLONG TI SYNMESH 22X28X32MM," 34,377 "
-28018657,HC IMP SPINE OBLONG TI SYNMESH 22X28X34MM," 34,377 "
-28018658,HC IMP SPINE OBLONG TI SYNMESH 22X28X36MM," 34,377 "
-28018659,HC IMP SPINE OBLONG TI SYNMESH 22X28X52MM," 37,710 "
-28018660,HC IMP SPINE OBLONG TI SYNMESH 22X28X64MM," 37,504 "
-28018661,HC IMP SPINE OBLONG TI SYNMESH 22X28X6MM," 21,465 "
-28018662,HC IMP SPINE OBLONG TI SYNMESH 22X28X88MM," 47,529 "
-28018663,HC IMP SPINE OBLONG TI SYNMESH 22X28X8MM," 21,465 "
-28018664,HC IMP SPINE OBLONG TI SYNMESH 26X33X10MM," 21,465 "
-28018665,HC IMP SPINE OBLONG TI SYNMESH 26X33X12MM," 21,465 "
-28018666,HC IMP SPINE OBLONG TI SYNMESH 26X33X14MM," 21,465 "
-28018667,HC IMP SPINE OBLONG TI SYNMESH 26X33X46MM," 35,826 "
-28018668,HC IMP SPINE OBLONG TI SYNMESH 26X33X50MM," 37,710 "
-28018669,HC IMP SPINE OBLONG TI SYNMESH 26X33X52MM," 37,710 "
-28018670,HC IMP SPINE OBLONG TI SYNMESH 26X33X64MM," 37,504 "
-28018671,HC IMP SPINE OBLONG TI SYNMESH 26X33X6MM," 21,465 "
-28018672,HC IMP SPINE OBLONG TI SYNMESH 26X33X88MM," 43,913 "
-28018673,HC IMP SPINE OBLONG TI SYNMESH 26X33X8MM," 21,465 "
-28018674,HC IMP SPINE ROUND SYNMESH 10MM 12MM," 12,358 "
-28018675,HC IMP SPINE ROUND SYNMESH 10MM 16MM," 12,358 "
-28018676,HC IMP SPINE ROUND SYNMESH 10MM 24MM," 16,112 "
-28018677,HC IMP SPINE ROUND SYNMESH 12MM 18MM," 12,358 "
-28018678,HC IMP SPINE ROUND SYNMESH 12MM 22MM," 16,112 "
-28018679,HC IMP SPINE ROUND SYNMESH 12MM 24MM," 14,886 "
-28018680,HC IMP SPINE ROUND SYNMESH 12X88MM," 39,918 "
-28018681,HC IMP SPINE ROUND SYNMESH 15MM 18MM," 12,358 "
-28018682,HC IMP SPINE ROUND SYNMESH 15MM 24MM," 14,886 "
-28018683,HC IMP SPINE ROUND SYNMESH 15X20MM," 16,112 "
-28018684,HC IMP SPINE ROUND TI SYNMESH 10X10MM," 12,096 "
-28018685,HC IMP SPINE ROUND TI SYNMESH 10X11MM," 12,358 "
-28018686,HC IMP SPINE ROUND TI SYNMESH 10X14MM," 12,358 "
-28018687,HC IMP SPINE ROUND TI SYNMESH 10X18MM," 12,358 "
-28018688,HC IMP SPINE ROUND TI SYNMESH 10X20MM," 16,112 "
-28018689,HC IMP SPINE ROUND TI SYNMESH 10X22MM," 16,112 "
-28018690,HC IMP SPINE ROUND TI SYNMESH 10X32MM," 23,474 "
-28018691,HC IMP SPINE ROUND TI SYNMESH 10X5MM," 12,096 "
-28018692,HC IMP SPINE ROUND TI SYNMESH 10X6MM," 12,096 "
-28018693,HC IMP SPINE ROUND TI SYNMESH 10X7MM," 12,096 "
-28018694,HC IMP SPINE ROUND TI SYNMESH 10X8MM," 12,096 "
-28018695,HC IMP SPINE ROUND TI SYNMESH 10X9MM," 12,096 "
-28018696,HC IMP SPINE ROUND TI SYNMESH 12X10MM," 11,175 "
-28018697,HC IMP SPINE ROUND TI SYNMESH 12X11MM," 12,358 "
-28018698,HC IMP SPINE ROUND TI SYNMESH 12X12MM," 12,358 "
-28018699,HC IMP SPINE ROUND TI SYNMESH 12X20MM," 16,112 "
-28018700,HC IMP SPINE ROUND TI SYNMESH 12X4MM," 11,175 "
-28018701,HC IMP SPINE ROUND TI SYNMESH 12X5MM," 11,175 "
-28018702,HC IMP SPINE ROUND TI SYNMESH 12X6MM," 11,175 "
-28018703,HC IMP SPINE ROUND TI SYNMESH 12X7MM," 12,096 "
-28018704,HC IMP SPINE ROUND TI SYNMESH 12X8MM," 12,096 "
-28018705,HC IMP SPINE ROUND TI SYNMESH 12X9MM," 12,096 "
-28018706,HC IMP SPINE ROUND TI SYNMESH 15X10MM," 12,096 "
-28018707,HC IMP SPINE ROUND TI SYNMESH 15X12MM," 12,358 "
-28018708,HC IMP SPINE ROUND TI SYNMESH 15X14MM," 11,418 "
-28018709,HC IMP SPINE ROUND TI SYNMESH 15X22MM," 14,886 "
-28018710,HC IMP SPINE ROUND TI SYNMESH 15X32MM," 21,688 "
-28018711,HC IMP SPINE ROUND TI SYNMESH 15X88MM," 31,535 "
-28018712,HC IMP SPINE ROUND TI SYNMESH 15X8MM," 11,175 "
-28018713,HC IMP SPINE ROUND TI SYNMESH 26X16MM," 12,358 "
-28018714,HC IMP SPINE SPACER SYNMESH 12MM 14MM," 12,358 "
-28018715,HC IMP SPINE SPACER SYNMESH TI 17MM 20MM 22MM," 29,894 "
-28018716,HC IMP SPINE SPACER SYNMESH TI 17MM 22MM 14MM," 21,465 "
-28018717,HC IMP SPINE SPACER SYNMESH TI 17MM 22MM 22MM," 29,894 "
-28018718,HC IMP SPINE SPACER SYNMESH TI 17MM 22MM 26MM," 29,894 "
-28018719,HC IMP SPINE SPACER SYNMESH TI 17MM 22MM 52MM," 37,710 "
-28018720,HC IMP SPINE SPACER SYNMESH TI 26MM 33MM 48MM," 35,826 "
-28018721,HC IMP SPINE TI SYNMESH 17X22X10MM," 21,465 "
-28018722,HC IMP SPINE TI SYNMESH 17X22X24MM," 29,894 "
-28018723,HC IMP SPINE TI SYNMESH 17X22X28MM," 29,894 "
-28018724,HC IMP SPINE TI SYNMESH 17X22X6MM," 21,465 "
-28018754,HC IMP SPLITTER UNIVERSAL, 298 
-28018789,HC STAPLE 20X20," 4,497 "
-28018790,HC IMP STAPLE BUTTRESS ANTERIOR BOWTI 20MM," 7,701 "
-28018809,HC IMP STAPLE ENDO RELOAD GIA 30MM 2.0," 1,573 "
-28018810,HC IMP STAPLE RELOAD ENDO GIA 30 2.5," 1,223 "
-28018811,HC IMP STAPLE RELOAD ENDO GIA 45MM 2.0," 1,647 "
-28018812,HC IMP STAPLE RELOAD ENDO GIA 45 2.5," 1,398 "
-28018813,HC IMP STAPLE RELOAD ENDO GIA 45 3.5," 1,398 "
-28018814,HC IMP STAPLE RELOAD ENDO GIA 45 4.8," 1,468 "
-28018817,HC IMP STAPLE RELOAD BONE CARTRIDGE BLACK 45MM," 1,909 "
-28018818,HC IMP STAPLE RELOAD BONE CARTRIDGE BLACK 60MM, 551 
-28018849,HC STAPLER CONTOUR CURVED BLUE," 3,824 "
-28018850,HC STAPLER DLU SURGASSIST 29MM," 3,974 "
-28018851,HC STAPLER ECHELON COMPACT 45MM," 2,958 "
-28018854,HC STAPLER ECHELON FLEX PWD 60X440 LNG," 3,413 "
-28018855,HC STAPLER ECHELON LONG 45MM," 2,958 "
-28018856,HC STAPLER ECHOLON STD 45MM," 2,958 "
-28018857,HC STAPLER EEA GREEN 25MM," 7,817 "
-28018858,HC STAPLER EEA SINGLE USE 28MM," 6,224 "
-28018901,HC IMP STENT GRAFT TALENT 32X16X170MM," 60,125 "
-28018905,HC STIMULATOR NERVE BIPOLAR," 3,953 "
-28018906,HC STIMULATOR NERVE PERIPHERAL DIGISTIM 2 PLUS," 5,092 "
-28019022,HC IMP STENT GRAFT 20X20X82MM," 33,950 "
-28019023,HC IMP STENT GRAFT 25X13X166MM," 64,838 "
-28019024,HC IMP STENT GRAFT 32X32X49MM," 32,025 "
-28019025,HC IMP STENT GRAFT 36X36X70MM," 50,213 "
-28019026,HC IMP STENT GRAFT AAA ANEURX 16X16X13.5," 18,445 "
-28019027,HC IMP STENT GRAFT AAA ANEURX 16X20X13.5," 21,718 "
-28019028,HC IMP STENT GRAFT AAA ANEURX 18X18X5.5," 14,822 "
-28019029,HC IMP STENT GRAFT AAA ANEURX 18X24X8.5," 19,338 "
-28019030,HC IMP STENT GRAFT AAA ANEURX 20X20X5.5," 14,822 "
-28019031,HC IMP STENT GRAFT AAA ENDURANT 10X10X82," 32,550 "
-28019032,HC IMP STENT GRAFT AAA ENDURANT 16X13X82," 33,950 "
-28019033,HC IMP STENT GRAFT AAA ENDURANT 16X16X124," 34,475 "
-28019034,HC IMP STENT GRAFT AAA ENDURANT 16X93MM," 32,550 "
-28019035,HC IMP STENT GRAFT AAA ENDURANT 25X13X166," 64,838 "
-28019036,HC IMP STENT GRAFT AAA ENDURANT 28X16X145MM," 64,838 "
-28019037,HC IMP STENT GRAFT AAA ENDURANT II 16X10X156," 41,600 "
-28019038,HC IMP STENT GRAFT AAA ENDURANT II 16X13X93," 34,475 "
-28019039,HC IMP STENT GRAFT AAA ENDURANT II 16X24X124," 34,825 "
-28019040,HC IMP STENT GRAFT AAA ENDURANT II 16X28X82," 33,950 "
-28019041,HC IMP STENT GRAFT AAA ENDURANT II 16X28X93," 34,475 "
-28019042,HC IMP STENT GRAFT AAA ENDURANT II 25X16X145," 64,838 "
-28019043,HC IMP STENT GRAFT AAA ENDURANT II 16X20X93," 34,475 "
-28019044,HC IMP STENT GRAFT AAA EXCLUDER 14.5X10," 27,860 "
-28019045,HC IMP STENT GRAFT AAA EXCLUDER 23X14.5X14," 56,030 "
-28019046,HC IMP STENT GRAFT AAA EXCLUDER 26X12X18," 50,564 "
-28019047,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X12," 56,030 "
-28019048,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X14," 54,405 "
-28019049,HC IMP STENT GRAFT AAA EXCLUDER 27X10," 27,860 "
-28019050,HC IMP STENT GRAFT AAA EXCLUDER 27X14," 27,860 "
-28019051,HC IMP STENT GRAFT AAA EXCLUDER 28.5X12X14," 56,030 "
-28019052,HC IMP STENT GRAFT AAA EXCLUDER 28.5X3.3," 19,530 "
-28019053,HC IMP STENT GRAFT AAA EXCLUDER 23X12X12," 64,968 "
-28019054,HC IMP STENT GRAFT AAA EXCLUDER 23X12X16," 64,968 "
-28019055,HC IMP STENT GRAFT AAA EXCLUDER 23X12X18," 64,968 "
-28019056,HC IMP STENT GRAFT AAA EXCLUDER 23X14X12," 64,968 "
-28019057,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X14," 64,968 "
-28019058,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X16," 64,968 "
-28019059,HC IMP STENT GRAFT AAA EXCLUDER 26X14.5X18," 64,968 "
-28019060,HC IMP STENT GRAFT AAA EXCLUDER 31X14X15," 64,968 "
-28019061,HC IMP STENT GRAFT AAA EXCLUDER 31X14X17," 64,968 "
-28019062,HC IMP STENT GRAFT AAA OVATION 16X120," 28,350 "
-28019063,HC IMP STENT GRAFT AAA OVATION 26X80," 57,915 "
-28019064,HC IMP STENT GRAFT AAA OVATION AUTOINJECTOR," 1,913 "
-28019093,HC IMP STENT GRAFT AORTC EXTENSION 28X28X75MM," 18,865 "
-28019094,HC IMP STENT GRAFT AORTC EXTENSION 28X75X95MM," 20,965 "
-28019095,HC IMP STENT GRAFT AORTC EXTENSION 28X95X20X115MM," 23,765 "
-28019096,HC IMP STENT GRAFT AORTC EXTENSION 34X100X100X21F," 28,665 "
-28019099,HC IMP STENT GRAFT CONTRALATERAL LEG 14.5X18CM," 64,968 "
-28019100,HC IMP STENT GRAFT CONTRALATERAL LEG 16X10CM," 28,700 "
-28019101,HC IMP STENT GRAFT CONTRALATERAL LEG 16X12CM," 27,055 "
-28019102,HC IMP STENT GRAFT CONTRALATERAL LEG 16X14CM," 27,860 "
-28019103,HC IMP STENT GRAFT CONTRALATERAL LEG 18X10CM," 24,647 "
-28019104,HC IMP STENT GRAFT CONTRALATERAL LEG 18X12CM," 24,647 "
-28019105,HC IMP STENT GRAFT CONTRALATERAL LEG 18X14CM," 27,860 "
-28019106,HC IMP STENT GRAFT CONTRALATERAL LEG 20X10CM," 27,860 "
-28019107,HC IMP STENT GRAFT CONTRALATERAL LEG 20X12CM," 27,860 "
-28019108,HC IMP STENT GRAFT CONTRALATERAL LEG 20X14CM," 27,860 "
-28019109,HC IMP STENT GRAFT CONTRALATERAL LEG 23X10CM," 27,860 "
-28019110,HC IMP STENT GRAFT CONTRALATERAL LEG 23X12CM," 27,860 "
-28019111,HC IMP STENT GRAFT CONTRALATERAL LEG 23X14CM," 28,700 "
-28019112,HC IMP STENT GRAFT CONTRALATERAL LEG 27X10CM," 28,700 "
-28019113,HC IMP STENT GRAFT CONTRALATERAL LEG 27X12CM," 28,700 "
-28019114,HC IMP STENT GRAFT CONTRALATERAL LEG 27X14CM," 28,700 "
-28019115,HC IMP STENT GRAFT AAA ENDURANT 13X13X82," 32,550 "
-28019116,HC IMP STENT GRAFT AAA EXCLUDER C3 28.5X14.5X14," 64,968 "
-28019117,HC IMP STENT GRAFT EXCLUDER 32X4.5CM," 19,530 "
-28019118,HC IMP STENT GRAFT EXCLUDER 36X4.5CM," 19,530 "
-28019127,HC IMP STENT GRAFT IPSILATERAL LEG 31X14.5X13CM," 53,073 "
-28019128,HC IMP STENT GRAFT IPSILATERAL LEG 31X14.5X15CM," 64,968 "
-28019129,HC IMP STENT GRAFT IPSILATERAL LEG 31X14.5X17CM," 64,968 "
-28019130,HC IMP STENT GRAFT IPSILATERAL LEG 35X14.5X14CM," 64,968 "
-28019131,HC IMP STENT GRAFT IPSILATERAL LEG 35X14.5X16CM," 64,968 "
-28019132,HC IMP STENT GRAFT TAA TAG COMFORMABLE 21X10," 88,500 "
-28019133,HC IMP STENT GRAFT TAA TAG COMFORMABLE 28X10," 88,500 "
-28019134,HC IMP STENT GRAFT TAA TAG COMFORMABLE 28X15," 88,500 "
-28019135,HC IMP STENT GRAFT TAA TAG COMFORMABLE 31X10," 88,500 "
-28019136,HC IMP STENT GRAFT TAA TAG COMFORMABLE 31X15," 88,500 "
-28019137,HC IMP STENT GRAFT TAA TAG COMFORMABLE 37X20," 88,500 "
-28019138,HC IMP STENT GRAFT TAA TAG COMFORMABLE 40X10," 88,500 "
-28019139,HC IMP STENT GRAFT TAA TAG COMFORMABLE 40X15," 88,500 "
-28019140,HC IMP STENT GRAFT TAA TAG COMFORMABLE 40X20," 88,500 "
-28019141,HC IMP STENT GRAFT TAA TAG COMFORMABLE 45X20," 88,500 "
-28019142,HC IMP STENT GRAFT TAA TAG COMFORMABLE37X10," 88,500 "
-28019143,HC IMP STENT GRAFT TAA TAG COMFORMABLE45X10," 88,500 "
-28019324,HC IMP STRIP BIO FIBER 13MMX23MM," 3,400 "
-28019325,HC IMP STRUT EAR SHEEHY 0.13X6MM, 642 
-28019326,HC IMP STRUT EXTERNAL FIXATOR SHORT," 7,838 "
-28019327,HC IMP STRUT SHOULDER STANDARD TAYLOR 116-178MM," 8,152 "
-28019328,HC SURGICEL HEMOSTATIC 10X15X1.5CM," 15,750 "
-28019329,HC SURGICEL HEMOSTATIC 10X20CMX1.5MM," 19,600 "
-28019330,HC SURGICEL HEMOSTATIC 15X20X1.5MM," 29,400 "
-28019331,HC SURGICEL HEMOSTATIC 20X30CMX1.5MM," 60,060 "
-28019472,HC IMP SYNMESH TITANIUM ROUND 12X32MM," 21,688 "
-28019480,HC SYSTEM DRAINAGE PERITONEAL ASEPT," 4,250 "
-28019481,HC IMP SYSTEM DETACHMENT CABLE CONNECTING, 400 
-28019482,HC IMP SYSTEM DETACHMENT COIL INZONE," 1,556 "
-28019484,HC SYSTEM SKIN SURE CLOSURE 50MM," 3,532 "
-28019485,HC IMP TESTICULAR SILICONE CARVING BLOCK 30," 7,225 "
-28019486,HC IMP TESTICULAR SILICONE CARVING BLOCK 40," 7,225 "
-28019487,HC IMP TESTICULAR SILICONE CARVING BLOCK 45," 7,225 "
-28019488,HC IMP TIBIAL BEARING IMPLANT 12MM," 5,678 "
-28019492,HC TISEEL FIBRIN SEALANT 10ML," 4,610 "
-28019493,HC IMP TISSUE TENDON GRACIALIS >20.0 FROZEN," 9,375 "
-28019494,HC IMP TISSUE CORNEAL," 19,338 "
-28019501,HC IMP TISSUE FILE SYS 3.8X2.5X5.7CM, 325 
-28019502,HC IMP TISSUE LENS ROSS 4.25X5IN, 149 
-28019522,HC IMP TISSUE TENDON SEMITENODINSUS FST," 11,423 "
-28019524,HC IMP TRISTAPLER CURVED TIP 60MM," 3,269 "
-28019525,HC TRISTAPLER VASCULAR ARTICULATING 30," 2,376 "
-28019554,HC  TUBE FEED ENTERAL CORFLO 10X43, 311 
-28019555,HC  TUBE FEED ENTERAL CORFLO 10X55, 320 
-28019556,HC  TUBE FEED ENTERAL CORFLO 8X91, 185 
-28019557,HC  TUBE FEEDING 14F 1.0 STOMA," 1,462 "
-28019621,HC  TUBE JONES ANGLED 3.5X14MM, 128 
-28019622,HC  TUBE KEOFEEDING 6F, 213 
-28019663,HC  TUBE TRACH PEDI LONG 52MM, 268 
-28019697,HC  TUBE VENT XOMED, 93 
-28019698,HC V.A.C. VIA STARTER KIT," 6,154 "
-28019699,HC IMP VALVE AORTC MECHANICAL PIVOT 16MM," 33,150 "
-28019700,HC IMP VALVE AORTC TISSUE HANCOCK 21MM," 22,400 "
-28019701,HC IMP VALVE AORTC TISSUE HANCOCK 23MM," 22,400 "
-28019702,HC IMP VALVE AORTC TISSUE HANCOCK 25MM," 22,400 "
-28019703,HC IMP VALVE AORTC TISSUE HANCOCK 27MM," 22,400 "
-28019704,HC IMP VALVE AORTC TISSUE HANCOCK 29MM," 22,400 "
-28019705,HC IMP VALVE AORTIC 25MM," 29,400 "
-28019706,HC IMP VALVE AORTIC 27MM," 29,400 "
-28019707,HC IMP VALVE AORTIC 29MM," 29,400 "
-28019708,HC IMP VALVE AORTIC HEART MEDIUM," 67,812 "
-28019709,HC IMP VALVE AORTIC HEART SMALL," 61,718 "
-28019710,HC IMP VALVE AORTIC W HOLDER MOSAIC 19MM," 29,400 "
-28019711,HC IMP VALVE AORTIC W HOLDER MOSAIC 21MM," 29,400 "
-28019712,HC IMP VALVE AORTIC W HOLDER MOSAIC 23MM," 29,400 "
-28019713,HC IMP VALVE AORTIC W HOLDER MOSAIC 25MM," 29,400 "
-28019714,HC IMP VALVE AORTIC W HOLDER MOSAIC 27MM," 29,400 "
-28019715,HC IMP VALVE AORTIC W HOLDER MOSAIC 29MM," 29,400 "
-28019716,HC IMP VALVE AORTIC MECHANICAL REGENT 19MM," 28,889 "
-28019717,HC IMP VALVE AORTIC MECHANICAL REGENT 21MM," 31,542 "
-28019718,HC IMP VALVE AORTIC MECHANICAL REGENT 25MM," 30,331 "
-28019719,HC IMP VALVE AORTIC PERICARDIAL MAGNA 25MM," 38,318 "
-28019720,HC IMP VALVE AORTIC PERICARDIAL MAGNA 27MM," 38,318 "
-28019721,HC IMP VALVE AORTIC PERICARDIAL MAGNA 29MM," 37,375 "
-28019722,HC IMP VALVE AORTIC PERIMOUNT 19MM," 30,858 "
-28019723,HC IMP VALVE AORTIC PERIMOUNT 21MM," 34,650 "
-28019724,HC IMP VALVE AORTIC PERIMOUNT 23MM," 34,650 "
-28019725,HC IMP VALVE AORTIC PERICARDIAL PERIMOUNT 25MM," 30,100 "
-28019726,HC IMP VALVE AORTIC PERIMOUNT 25MM," 34,650 "
-28019727,HC IMP VALVE AORTIC PERIMOUNT 27MM," 34,650 "
-28019728,HC IMP VALVE AORTIC PERIMOUNT 29MM," 34,650 "
-28019729,HC IMP VALVE AORTIC STENTED EPIC 19MM," 34,965 "
-28019730,HC IMP VALVE AORTIC TORONTO SPV 21MM," 35,874 "
-28019731,HC IMP VALVE AORTIC TORONTO SPV 23MM," 35,874 "
-28019732,HC IMP VALVE AORTIC TORONTO SPV 25MM," 35,874 "
-28019733,HC IMP VALVE AORTIC TORONTO SPV 27MM," 35,874 "
-28019734,HC IMP VALVE AORTIC TORONTO SPV 29MM," 35,874 "
-28019735,HC IMP VALVE BIOPROSTHESIS FREESTYLE 29MM," 35,750 "
-28019736,HC IMP VALVE CONDUIT PORCINE 11MM," 34,650 "
-28019737,HC IMP VALVE CONDUIT PORCINE 13MM," 33,800 "
-28019738,HC IMP VALVE CSF DELTA 5CM," 8,374 "
-28019739,HC IMP VALVE CSF STRATA II BURR HOLE," 26,378 "
-28019740,HC IMP VALVE CSF STRATA SMALL 23X13X7MM," 22,180 "
-28019741,HC IMP VALVE DELTA BURR HOLE W BIOGLIDE L1 16MM," 7,804 "
-28019742,HC IMP VALVE DELTA BURR HOLE L 0.5 12MM," 7,734 "
-28019743,HC IMP VALVE DELTA BURR HOLE L1 12MM," 7,734 "
-28019744,HC IMP VALVE DELTA BURR HOLE L1.5 12MM," 7,734 "
-28019745,HC IMP VALVE DELTA BURR HOLE L2 12MM," 7,734 "
-28019746,HC IMP VALVE HEART SAPIEN XT 29MM," 87,500 "
-28019747,HC IMP VALVE HEART AORTIC CONDUIT UNIVERSAL 11MM," 42,523 "
-28019748,HC IMP VALVE HEART AORTIC CONDUIT UNIVERSAL 17MM," 63,050 "
-28019749,HC IMP VALVE HEART MITRAL HANCOCK II 25MM," 22,400 "
-28019750,HC IMP VALVE HEART MITRAL HANCOCK II 27MM," 22,400 "
-28019751,HC IMP VALVE HEART MITRAL HANCOCK II 29MM," 22,400 "
-28019752,HC IMP VALVE HEART MITRAL HANCOCK II 31MM," 22,400 "
-28019753,HC IMP VALVE HEART MITRAL HANCOCK II 33MM," 22,400 "
-28019754,HC IMP VALVE MITRAL MAGNA EASE 25MM," 41,872 "
-28019755,HC IMP VALVE MITRAL MAGNA EASE 27MM," 41,872 "
-28019756,HC IMP VALVE MITRAL MAGNA EASE 29MM," 40,652 "
-28019757,HC IMP VALVE MITRAL MAGNA EASE 31MM," 40,652 "
-28019758,HC IMP VALVE MITRAL MAGNA EASE 33MM," 40,652 "
-28019759,HC IMP VALVE PROGRAMMABLE CERTAS," 2,848 "
-28019760,HC IMP VALVE TRIFECTA STENTED 19," 35,750 "
-28019761,HC IMP VALVED CONDUITS LP 16MM," 35,000 "
-28019762,HC IMP VALVED CONDUITS LP 18MM," 35,000 "
-28019763,HC IMP VALVED CONDUITS LP 22MM," 35,000 "
-28019764,HC IMP VALVE MITRAL MECHANICAL 35X26.1," 30,674 "
-28019765,HC IMP VALVE MITRAL MECHANICAL 37X26.1," 30,674 "
-28019766,HC IMP VALVE PULMONARY CARDIOGRAFT 16MM OR LESS," 61,718 "
-28019773,HC IMP WASHER CORTICAL LOW PROFILE SS DVR 3.5MM, 510 
-28019774,HC IMP WASHER DBL ROD SMALL LEFT," 7,769 "
-28019775,HC IMP WASHER DBL ROD SMALL RIGHT," 7,769 "
-28019776,HC IMP WASHER FLAT 17MM, 536 
-28019779,HC IMP WASHER SPIKED 20MM," 1,445 "
-28019780,HC IMP WASHER USS 5X16MM TITANIUM, 321 
-28019781,HC IMP WASHER USS 5X18MM TITANIUM, 321 
-28019782,HC IMP WASHER USS 6X16MM TITANIUM, 321 
-28019783,HC IMP WASHER USS 6X18MM TITANIUM, 309 
-28019784,HC IMP WASHER USS 7X16MM TITANIUM, 321 
-28019785,HC IMP WASHER USS 7X18MM TITANIUM, 309 
-28019786,HC IMP WASHER USS 8X16MM TITANIUM, 321 
-28019787,HC IMP WASHER USS 8X18MM TITANIUM, 309 
-28019788,HC IMP WEDGE TIB FULL SZ1.5 15MM," 9,315 "
-28019789,HC IMP WEDGE TIB ORTHO MODLR + 10MM SZ4 REV," 32,963 "
-28019792,HC IMP WIRE DOUBLE SIDED THREAD SS 1.6MM 3516, 433 
-28019793,HC WIRE DRILL TIP OLIVE 1.8X400MM," 2,963 "
-28012079,HC  CATHETER MALECOT 12F LF, 299 
-28012080,HC  CATHETER MALECOT 14F LF, 292 
-28012081,HC  CATHETER MALECOT 16F LF, 299 
-28012082,HC  CATHETER MALECOT 18F LF, 257 
-28018743,HC SPLINT SCOTCHCAST 3X35IN," 2,286 "
-28018745,HC SPLINT SCOTCHCAST 4X30IN," 2,335 "
-28018746,HC SPLINT SCOTCHCAST 5X30IN," 2,938 "
-28015207,HC  MARKER BREAST BIOPSY ULTRA TAG 10G, 680 
-28015208,HC  MARKER BREAST BIOPSY NEEDLE SYSTEM, 680 
-28015209,HC  MARKER TISSUE ULTRASOUND 17GA 3MM 10CM, 134 
-28012218,HC  CATHETER TROCAR THORACIC ATP PVC 24F, 85 
-28012219,HC  CATHETER TROCAR REPLACEMENT 8F, 201 
-28012220,HC  CATHETER TROCAR THORACIC 10F, 201 
-28012221,HC  CATHETER TROCAR THORACIC 12F, 201 
-28012222,HC  CATHETER TROCAR THORACIC 16F, 201 
-28012223,HC  CATHETER TROCAR THORACIC 20F, 201 
-28012224,HC  CATHETER TROCAR THORACIC 24F, 201 
-28012225,HC  CATHETER TROCAR THORACIC 28F, 201 
-28012226,HC  CATHETER TROCAR THORACIC 32FF, 201 
-28019623,HC  TUBE TRACHEOSTOMY CUFFLESS CFN 5X9.4X65MM, 359 
-28019624,HC  TUBE TRACHEOSTOMY CUFFLESS CFN 6.4X10.8X76MM, 359 
-28019625,HC  TUBE TRACHEOSTOMY CUFFLESS CFN 8.9X13.8X81MM, 359 
-28019626,HC  TUBE TRACHEOSTOMY CUFFLESS CFS 5X9.4X65MM, 338 
-28019627,HC  TUBE TRACHEOSTOMY CUFFLESS CFS 6.4X10.8X76MM, 338 
-28019628,HC  TUBE TRACHEOSTOMY CUFFLESS CFS 7.6X12.2X81MM, 338 
-28019642,HC  TUBE TRACHEOSTOMY CUFFLESS EXTENDED LENGTH 7MM, 527 
-28019653,HC  TUBE TRACHEOSTOMY CUFFLESS NEO 4.5X6.5X36MM, 268 
-28019654,HC  TUBE TRACHEOSTOMY CUFFLESS NEO 4X5.9X34MM, 268 
-28019655,HC  TUBE TRACHEOSTOMY CUFFLESS PDL 5X7.1X50MM, 268 
-28019666,HC  TUBE TRACEOSTOMY UNCUFFED 6MM, 527 
-28019629,HC  TUBE TRACHEOSTOMY CUFFED DISTAL 6X11X95MM, 527 
-28019630,HC  TUBE TRACHEOSTOMY CUFFED EXTENDED 6X11X95MM, 527 
-28019635,HC  TUBE TRACHEOSTOMY CUFFED PROX EXT, 527 
-28019643,HC  TUBE TRACHEOSTOMY CUFFED HYPERFLEX ADJ 110MM," 1,569 "
-28019644,HC  TUBE TRACHEOSTOMY CUFFED HYPERFLEX ADJ 140MM," 1,569 "
-28019645,HC  TUBE TRACHEOSTOMY CUFFED HYPERFLEX FIXED 100MM, 909 
-28019646,HC  TUBE TRACHEOSTOMY CUFFED HYPERFLEX FIXED 110MM, 909 
-28019647,HC  TUBE TRACHEOSTOMY CUFFED HYPERFLEX FIXED 120MM, 909 
-28019648,HC  TUBE TRACHEOSTOMY CUFFED LPC 5X9.4X65MM, 421 
-28019649,HC  TUBE TRACHEOSTOMY CUFFED LPC 6.4X10.8X76MM, 421 
-28019656,HC  TUBE TRACHEOSTOMY CUFFED PDL 6X8.3X54MM, 268 
-28012875,HC  GASTROSTOMY PERCUTANIOUS ENDOSCPC 20 20F SET, 574 
-28019559,HC  TUBE FEEDING GASTROSTOMY 6F 36IN, 115 
-28019560,HC  TUBE GASTROSTOMY 18F," 1,666 "
-28019591,HC  TUBE GASTROSTOMY REPLACEMENT 28F STRAIGHT, 425 
-28019592,HC  TUBE GASTROSTOMY REPLACEMENT ANGLE RIGHT 14F, 489 
-28019593,HC  TUBE GASTROSTOMY REPLACEMENT STRAIGHT 14F, 425 
-28019594,HC  TUBE GASTROSTOMY REPLACEMENT STRAIGHT 16F, 425 
-28019596,HC  TUBE JEJUNAL BENT TIP 12F," 1,148 "
-28019597,HC  TUBE JEJUNAL BENT TIP 8.5F," 1,148 "
-28019599,HC  TUBE JEJUNAL CORFLO 8FX36IN, 118 
-28019600,HC  TUBE JEJUNOSTOMY FLEXIFLO 10F, 509 
-28019618,HC  TUBE JEJUNAL MIC 7-10 ML 22F," 1,031 "
-28019619,HC  TUBE JEJUNAL PIGTAIL TIP 8.5F," 1,148 "
-28019620,HC  TUBE JEJUNAL PIGTAIL TIP 8.5F," 1,148 "
-28019693,HC  TUBE TRANSGASTRIC JEJUNAL 7-10ML 16FX45CM," 1,775 "
-28019694,HC  TUBE TRANSGASTRIC JEJUNAL 7-10ML 18FX30CM," 1,732 "
-28019695,HC  TUBE TRANSGASTRIC JEJUNAL 7-10ML 18FX22CM," 1,775 "
-28019696,HC  TUBE TRANSGASTRIC JEJUNAL 7-10ML 22FX45CM," 1,818 "
-28019558,HC  TUBE FEEDING FLOW 20 JEJUNAL 9F, 853 
-28019561,HC  TUBE GASTROSTOMY BUTTON MINI 14FX1.2CM, 765 
-28019562,HC  TUBE GASTROSTOMY BUTTON MINI 14FX2CM, 553 
-28019563,HC  TUBE GASTRIC JEJUNAL GJET 14FX1.2X30CM," 3,400 "
-28019564,HC  TUBE GASTRIC JEJUNAL GJET 14X2.5X45CM," 3,400 "
-28019565,HC  TUBE GASTRIC JEJUNAL GJET 14X3.5X45CM," 3,400 "
-28019571,HC  TUBE GASTROSTOMY LOW PROFILE 16F 1.0, 838 
-28019572,HC  TUBE GASTROSTOMY LOW PROFILE 16F 1.2, 838 
-28019573,HC  TUBE GASTROSTOMY LOW PROFILE 16F 1.5, 145 
-28019574,HC  TUBE GASTROSTOMY LOW PROFILE 16F 1.7, 838 
-28019575,HC  TUBE GASTROSTOMY LOW PROFILE 16F 2.5, 838 
-28019578,HC  TUBE GASTROSTOMY LOW PROFILE 18F 1.0, 168 
-28019583,HC  TUBE GASTROSTOMY LOW PROFILE 20F 4.0, 786 
-28019586,HC  TUBE GASTROSTOMY LOW PROFILE 14F 2.0, 168 
-28019602,HC  TUBE JEJUNAL LOW PROFILE 5ML14FX0.8CM," 1,353 "
-28019603,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX1.7CM," 1,353 "
-28019604,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX2.0CM," 1,353 "
-28019605,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX2.3CM," 1,353 "
-28019606,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX2.5CM," 1,353 "
-28019607,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX2.7CM," 1,353 "
-28019608,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX3.0CM," 1,353 "
-28019609,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX3.5CM," 1,353 "
-28019610,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX4.5CM," 1,353 "
-28019611,HC  TUBE JEJUNAL LOW PROFILE 5ML 18FX0.8CM," 1,353 "
-28019612,HC  TUBE JEJUNAL LOW PROFILE 5ML 18FX1.0CM," 1,353 "
-28019613,HC  TUBE JEJUNAL LOW PROFILE 5ML 18FX1.2CM," 1,353 "
-28019614,HC  TUBE JEJUNAL LOW PROFILE 5ML 18FX1.5CM," 1,353 "
-28019615,HC  TUBE JEJUNAL LOW PROFILE 5ML 18FX2.5CM," 1,353 "
-28019616,HC  TUBE JEJUNAL LOW PROFILE 5ML 18FX3.0CM," 1,353 "
-28019667,HC  TUBE TRANSGASTRIC 3-5ML 18FX2.3X30CM," 2,668 "
-28019668,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 1.5X30," 2,738 "
-28019669,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 2X30," 2,738 "
-28019670,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 16F 2.5X30," 2,668 "
-28019671,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 1.2X22," 2,632 "
-28019672,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 1.5X22," 2,668 "
-28019673,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.0X30," 2,668 "
-28019674,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.0X45," 2,668 "
-28019675,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.3X30," 2,668 "
-28019676,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 2.3X45," 2,738 "
-28019677,HC  TUBE TRANSG JEJNL LOW PROFILE 3-5 ML 18F 1.7X22," 2,668 "
-28019678,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F 1.7X45," 2,668 "
-28019679,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F 2.5X45," 2,668 "
-28019680,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F2.7X45," 2,668 "
-28019681,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F 3.0X45," 2,668 "
-28019682,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F 3.5X45," 2,668 "
-28019683,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 18F 1.5X45," 2,668 "
-28019684,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 18F 1.7X45," 2,668 "
-28019685,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 18F 2.5X45," 2,668 "
-28019686,HC  TUBE TRANSG JEJNL LP 7-10ML 2.7F 207X45CM," 2,668 "
-28019687,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 22F 1.5X45," 2,668 "
-28019688,HC  TUBE TRANSG JEJNL LP 7-10ML 22F 1.7X45CM," 2,668 "
-28019689,HC  TUBE TRANSG JEJNL LP 7-10ML 22F 2.0X45CM," 2,668 "
-28019690,HC  TUBE TRANSG JEJNL LP 7-10ML 22F 2.3X45CM," 2,668 "
-28019691,HC  TUBE TRANSG JEJNL LP 7-10ML 22F 2.5X45CM," 2,668 "
-28019692,HC  TUBE TRANSG JEJNL LOW PROFILE 7-10ML 16F 20X45," 2,668 "
-28011592,HC  CATH BALLOON NEURO," 2,789 "
-28010662,HC IMP ANCHOR BIO SUTURETAK 3.0X14.5MM," 2,763 "
-28010663,HC IMP ANCHOR CORKSCREW FT II 5.5MM," 1,955 "
-28010664,HC IMP ANCHOR CORKSCREW MICRO FT 2-0," 2,423 "
-28010665,HC IMP ANCHOR CORKSCREW MICRO FT 4-0, 485 
-28010666,HC IMP ANCHOR OTSC 165CM," 3,137 "
-28010667,HC IMP ANCHOR SHOULDER MAGNUM," 4,208 "
-28010668,HC IMP ANCHOR SURETAC II W/SPIKES," 2,031 "
-28010669,HC IMP ANCHOR SUTURE BIO PUSHLOCK 2.9X15.5MM, 646 
-28010670,HC IMP ANCHOR SUTURE BIOCOMPOSITE PUSHLCK 3.5X19.5, 646 
-28010671,HC IMP ANCHOR SUTURE FASTAK 2.4X7.5MM," 1,573 "
-28010672,HC IMP ANCHOR SUTURE FAST FIX CURVED 360," 4,038 "
-28010673,HC IMP ANCHOR SUTURE FAST FIX REV CURVED 360," 4,038 "
-28010674,HC IMP ANCHOR SUTURE FAST FIX STRAIGHT 360," 4,038 "
-28010675,HC IMP ANCHOR SUTURE ICONIX 1.4MM," 3,018 "
-28010676,HC IMP ANCHOR SUTURE ICONIX 2.3MM," 3,018 "
-28010677,HC IMP ANCHOR SUTURE OSTEORAPTOR 2.3MM," 3,256 "
-28010678,HC IMP ANCHOR SUTURE OSTEORAPTOR BLK 2.3MM," 3,256 "
-28010679,HC IMP ANCHOR SUTURE PUSHLOCK 2.9X15.5MM," 12,750 "
-28010680,HC IMP ANCHOR SUTURE SOFT TISSUE ROCEZ 3.5X18CM," 4,029 "
-28010826,HC IMP BLOCK PRO-OSTEON 50X15X10MM," 7,098 "
-28010827,HC IMP BLOCK PRO-OSTEON 50X15X6MM," 4,463 "
-28011368,HC IMP BUTTON SUTURE ENDO LOOP 55MM," 2,550 "
-28011369,HC IMP BUTTON SUTURE ENDO LOOP 60MM," 2,550 "
-28012637,HC IMP CONNECTOR SPINAL MONOBLOCK XIA 16MM," 7,225 "
-28012638,HC IMP CONNECTOR SPINAL PARALLEL TITANIUM 5MM/5MM," 5,355 "
-28012639,HC IMP CONNECTOR SPINAL PARALLEL TITANIUM 5MM/6MM," 5,865 "
-28012640,HC IMP CONNECTOR SPINAL XIA," 7,225 "
-28012642,HC IMP CONNECTOR CROSS POLARIS LARGE," 9,770 "
-28012643,HC IMP CONNECTOR CROSS POLARIS MEDIUM," 9,770 "
-28012644,HC IMP CONNECTOR CROSS POLARIS EXTRA SMALL," 9,770 "
-28012645,HC IMP CONNECTOR CROSS POLARIS EXTRA EXTRA SMALL," 9,770 "
-28012646,HC IMP CONNECTOR FIXED CROSS 24MM," 7,225 "
-28012829,HC IMP ENDOBUTTON CL ULTRA 15MM," 2,328 "
-28014811,HC IMP KNEE PATELLA OVAL UHMWPE 41MM REV," 4,871 "
-28015146,HC IMP LINK SPINELINK REVERSE LEFT 33MM," 2,125 "
-28015147,HC IMP LINK SPINELINK REVERSE LEFT 36MM," 2,125 "
-28015148,HC IMP LINK SPINELINK REVERSE LEFT 39MM," 2,125 "
-28015150,HC IMP LINK SPINELINK STANDARD 36MM," 2,125 "
-28015151,HC IMP LINK SPINELINK STANDARD 39MM," 2,125 "
-28015152,HC IMP LINK SPINELINK STANDARD 54MM," 2,125 "
-28015153,HC IMP LINK SPINELINK STANDARD 57MM," 2,125 "
-28015154,HC IMP LINK SPINELINK STANDARD 60MM," 2,125 "
-28015155,HC IMP LINK SPINELINK STANDARD 63MM," 2,125 "
-28015156,HC IMP LINK STANDARD 21MM W/6MM STEP," 2,125 "
-28015157,HC IMP LINK STANDARD 24MM W/6MM STEP," 2,125 "
-28015158,HC IMP LINK STANDARD 24MM W/9MM STEP," 2,125 "
-28015159,HC IMP LINK STANDARD 27MM W/6MM STEP," 2,125 "
-28015160,HC IMP LINK STANDARD 30MM W/3MM STEP," 2,125 "
-28015161,HC IMP LINK STANDARD 30MM W/9MM STEP," 2,125 "
-28015162,HC IMP LINK STANDARD 33MM W/3MM STEP," 2,125 "
-28015163,HC IMP LINK STANDARD 33MM W/6MM STEP," 2,125 "
-28015164,HC IMP LINK STANDARD 39MM W/6MM STEP," 2,125 "
-28015165,HC IMP LINK STANDARD 39MM W/9MM STEP," 2,125 "
-28015174,HC IMPL MATRIX BIOLOGICAL MASTERGFT 10CC," 8,441 "
-28015175,HC IMPL MATRIX BIOLOGICAL MASTERGFT 20CC," 9,000 "
-28015176,HC IMPL MATRIX BIOLOGICAL MASTERGFT 5CC," 4,038 "
-28015187,HC IMP MENDER MENISCAL II, 979 
-28015344,HC IMP PIN BONE SONIC 2.1X4MM 2PK," 1,258 "
-28015345,HC IMP PIN BONE SONIC 2.1X4MM 5PK," 2,933 "
-28015346,HC IMP PIN CENTERING SPARE 3.5X4MM, 978 
-28015347,HC IMP PIN DRILL PACK STEINMAN 1/8 IN TWIST BIT," 2,014 "
-28015348,HC IMP PIN FIXATION TEMPORARY SKYLINE THREAD, 689 
-28015349,HC IMP PIN GUIDING BHR LONG, 169 
-28015350,HC IMP PIN GUIDING BHR LONG REV, 169 
-28015351,HC IMP PIN GUIDING BHR SHORT, 169 
-28015352,HC IMP PIN GUIDING RESURFACE SHORT, 464 
-28015354,HC IMP PIN HIP FRACTURE SHORT, 464 
-28015355,HC IMP PIN LOCKING AUX COMPONENT POLYETHYLENE OSS," 1,564 "
-28015356,HC IMP PIN LOCKING AUX COMPONENT POLYETHYLENE OSS REV," 1,318 "
-28015357,HC IMP PIN ORTHOSORB 1.3X50MM," 1,284 "
-28015358,HC IMP PIN PLATE HOLDING STANDARD SS ATLANTIS," 1,131 "
-28015374,HC IMP PIN STEINMAN 2.0MM 22.9CM 22.2MM, 134 
-28015375,HC IMP PIN STEINMAN 2.4MM 22.9CM 22.2MM, 134 
-28015376,HC IMP PIN STEINMAN 2.8MM 22.9CM 22.2MM, 134 
-28015377,HC IMP PIN STEINMAN 3.6MM 22.9CM 22.2MM, 95 
-28015378,HC IMP PIN STEINMAN 4.0MM 22.9CM 22.2MM, 95 
-28015379,HC IMP PIN STEINMAN 4.8MM 22.9CM 22.2MM, 134 
-28015380,HC IMP PIN STEINMAN SS 0.14X9IN, 655 
-28015381,HC IMP PIN STEINMAN TROCAR POINT 9IN, 383 
-28015382,HC IMP PIN TRANSVERSE DIAPHYSEAL 24MM," 2,916 "
-28015383,HC IMP PIN TRANSVERSE DIAPHYSEAL 28MM," 2,916 "
-28015384,HC IMP PIN TRANSVERSE DIAPHYSEAL 32MM," 2,916 "
-28015385,HC IMP PIN TRANSVERSE DIAPHYSEAL 36MM," 2,916 "
-28015386,HC IMP PIN WILLIAMS FEMALE 3.2MM," 7,939 "
-28015387,HC IMP PIN WILLIAMS FEMALE 4MM," 7,939 "
-28015388,HC IMP PIN WILLIAMS MALE 3.2MM," 6,656 "
-28015389,HC IMP PIN WILLIAMS MALE 4MM," 7,939 "
-28016568,HC IMP SCREW ACUTRAK HEADLESS 17.5MM," 3,035 "
-28016569,HC IMP SCREW ACUTRAK MINI 16.0MM," 3,035 "
-28016574,HC IMP SCREW BIO INTEFERENCE 22MM," 2,975 "
-28016583,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X5MM, 874 
-28016584,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X10MM, 808 
-28016585,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X12MM, 808 
-28016586,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X14MM, 808 
-28016587,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X16MM, 808 
-28016588,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X18MM, 808 
-28016589,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X20MM, 808 
-28016590,HC IMP SCREW BONE CROSSPIN LOCKING EMERGENCY 2.7X8MM, 808 
-28016591,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.3X10MM, 513 
-28016592,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.3X14MM, 513 
-28016593,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.3X16MM, 697 
-28016594,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.3X6MM, 513 
-28016595,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.3X8MM, 513 
-28016596,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X10MM, 535 
-28016597,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X12MM, 535 
-28016598,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X14MM, 535 
-28016599,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X16MM, 535 
-28016600,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X18MM, 535 
-28016601,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X20MM, 731 
-28016602,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X5MM, 321 
-28016603,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.7X8MM, 535 
-28016604,HC IMP SCREW BONE CROSSPIN SELF TAPPING EMERG 2.3X12MM, 513 
-28016605,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X10, 816 
-28016606,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X12, 144 
-28016607,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X14, 163 
-28016608,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X16, 144 
-28016609,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X18, 721 
-28016610,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X20, 721 
-28016611,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X4, 163 
-28016612,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X6, 816 
-28016613,HC IMP SCREW BONE CROSSPIN STRAP MAXILLOFACIAL 2.3X8, 816 
-28016614,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX10MM, 144 
-28016615,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX12MM, 477 
-28016616,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX14MM, 95 
-28016617,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX16MM, 95 
-28016618,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX18MM, 95 
-28016619,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX20MM, 477 
-28016620,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX4MM, 95 
-28016621,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX8MM, 95 
-28016622,HC IMP SCREW BONE CROSSPIN SELF TAPPING TI 2.3MMX6MM, 95 
-28016623,HC IMP SCREW BONE DILATATION 2.2X10MM, 363 
-28016624,HC IMP SCREW BONE 2.6X10MM 7026010, 381 
-28016625,HC IMP SCREW BONE 2.6X5MM, 884 
-28016626,HC IMP SCREW BONE 2.6X6MM 7026006, 377 
-28016627,HC IMP SCREW BONE 2.6X7MM, 587 
-28016628,HC IMP SCREW BONE 2.6X8MM 7026008, 377 
-28016629,HC IMP SCREW BONE 2.7X12MM," 1,063 "
-28016630,HC IMP SCREW BONE 2.7X14MM," 1,063 "
-28016631,HC IMP SCREW BONE 2.7X16MM," 1,063 "
-28016632,HC IMP SCREW BONE 4.4X25MM," 7,130 "
-28016633,HC IMP SCREW BONE 4.5X30MM, 821 
-28016634,HC IMP SCREW BONE 4.5X35MM, 821 
-28016635,HC IMP SCREW BONE 4.5X35MM REV, 857 
-28016636,HC IMP SCREW BONE 4.5X40MM, 821 
-28016637,HC IMP SCREW BONE 4X30MM," 7,130 "
-28016638,HC IMP SCREW BONE 5X30MM," 7,130 "
-28016639,HC IMP SCREW BONE 5.5X30MM," 7,130 "
-28016640,HC IMP SCREW BONE 5.5X35MM," 7,130 "
-28016641,HC IMP SCREW BONE 5.5X40MM," 7,130 "
-28016642,HC IMP SCREW BONE 5.5X50MM REV, 857 
-28016643,HC IMP SCREW BONE 5X25MM," 7,130 "
-28016644,HC IMP SCREW BONE 6.5X15MM, 821 
-28016645,HC IMP SCREW BONE 6.5X15MM REV, 857 
-28016646,HC IMP SCREW BONE 6.5X20MM REV, 857 
-28016647,HC IMP SCREW BONE 6.5X25MM, 821 
-28016648,HC IMP SCREW BONE 6.5X25MM REV, 857 
-28016649,HC IMP SCREW BONE 6.5X30MM, 821 
-28016650,HC IMP SCREW BONE 6.5X30MM REV, 857 
-28016651,HC IMP SCREW BONE 6.5X35MM, 821 
-28016652,HC IMP SCREW BONE 6.5X35MM REV, 857 
-28016653,HC IMP SCREW BONE 6.5X40MM, 821 
-28016654,HC IMP SCREW BONE 6.5X40MM REV, 857 
-28016655,HC IMP SCREW BONE 6X5X6X3.2X40X130MM," 1,207 "
-28016656,HC IMP SCREW BONE 6X5X6X3.2X50X140MM," 2,414 "
-28016657,HC IMP SCREW BONE 6X5X6X4.8X30X110MM," 2,414 "
-28016658,HC IMP SCREW BONE ANTERIOR LEGACY 5.5X35MM," 9,000 "
-28016659,HC IMP SCREW BONE ANTERIOR LEGACY 6.5X30MM," 9,000 "
-28016660,HC IMP SCREW BONE ANTERIOR LEGACY 6.5X40MM," 9,000 "
-28016661,HC IMP SCREW BONE CANCELLOUS FULLY THREAD 4X32MM, 136 
-28016662,HC IMP SCREW BONE CANCELLOUS FULLY THREAD 4X38MM, 136 
-28016663,HC IMP SCREW BONE CANCELLOUS 6.5X20MM, 901 
-28016664,HC IMP SCREW BONE CANNULATED 2.4X12MM, 541 
-28016665,HC IMP SCREW BONE CANNULATED 2.4X14MM, 493 
-28016666,HC IMP SCREW BONE CANNULATED 24X4.5MM," 1,046 "
-28016667,HC IMP SCREW BONE CANNULATED 4.5X32MM," 1,046 "
-28016668,HC IMP SCREW BONE CANNULATED HERB WHIPE 14MM," 2,508 "
-28016669,HC IMP SCREW BONE CANNULATED HERB WHIPE 16MM," 3,120 "
-28016670,HC IMP SCREW BONE CANNULATED HERB WHIPE 18MM," 3,120 "
-28016671,HC IMP SCREW BONE CANNULATED HERB WHIPE 20MM," 3,475 "
-28016672,HC IMP SCREW BONE CANNULATED HERB WHIPE 22MM," 3,475 "
-28016673,HC IMP SCREW BONE CANNULATED HERB WHIPE 24MM," 3,106 "
-28016674,HC IMP SCREW BONE CANNULATED HERB WHIPE 26MM," 3,106 "
-28016675,HC IMP SCREW BONE CANNULATED HERB WHIPE 28MM," 3,475 "
-28016676,HC IMP SCREW BONE CANNULATED HERB WHIPE 30MM," 3,106 "
-28016677,HC IMP SCREW BONE CORTICAL LAG 3.2X16MM, 723 
-28016678,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X28MM, 553 
-28016679,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X30MM, 553 
-28016680,HC IMP SCREW BONE CORTICAL LOW PROFILE 2.3X32MM, 553 
-28016681,HC IMP SCREW BONE CORTICAL LOW PROFILE 3.2X10MM, 553 
-28016682,HC IMP SCREW BONE CORTICAL LOW PROFILE 3.2X12MM, 638 
-28016683,HC IMP SCREW BONE CORTICAL LOW PROFILE 3.2X14MM, 638 
-28016684,HC IMP SCREW BONE CORTICAL LOW PROFILE 3.2X16MM, 638 
-28016685,HC IMP SCREW BONE CORTICAL LOW PROFILE 3.2X18MM, 553 
-28016686,HC IMP SCREW BONE CORTICAL LOW PROFILE 3.2X20MM, 553 
-28016687,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X26MM, 119 
-28016688,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X28MM, 119 
-28016689,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X30MM, 119 
-28016690,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X32MM, 119 
-28016691,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X34MM, 119 
-28016692,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X36MM, 153 
-28016693,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X38MM, 153 
-28016694,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X40MM, 119 
-28016695,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X42MM, 119 
-28016696,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X44MM, 153 
-28016697,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X46MM, 119 
-28016698,HC IMP SCREW BONE CORTICAL SELF TAPPING 4.5X48MM, 153 
-28016699,HC IMP SCREW BONE CORTICAL SELF TAPPING 50X4.5MM, 119 
-28016700,HC IMP SCREW BONE CORTICAL SELF TAPPING 52X4.5MM, 119 
-28016701,HC IMP SCREW BONE CORTICAL SELF TAPPING 54X4.5MM, 119 
-28016702,HC IMP SCREW BONE CORTICAL SELF TAPPING 58X4.5MM, 119 
-28016703,HC IMP SCREW BONE CORTICAL SELF TAPPING 60X4.5MM, 119 
-28016704,HC IMP SCREW BONE CRANIAL LOW PROFILE 2.0X6MM, 145 
-28016705,HC IMP SCREW BONE CROSS DRIVE TITANIUM 1.8X4MM, 493 
-28016706,HC IMP SCREW BONE CROSS DRIVE TITANIUM 1.5X9MM, 451 
-28016707,HC IMP SCREW BONE CROSSPIN SELF DRILL 2X4MM, 556 
-28016708,HC IMP SCREW BONE CROSSPIN SELF DRILL 2X5MM, 556 
-28016709,HC IMP SCREW BONE CROSSPIN SELF DRILL 2X6MM, 556 
-28016710,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX12MM, 470 
-28016711,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX14MM, 470 
-28016712,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX16MM, 470 
-28016713,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX18MM, 470 
-28016714,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX20MM, 470 
-28016715,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX4MM, 470 
-28016716,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MM 5MM, 470 
-28016717,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX6MM, 470 
-28016718,HC IMP SCREW BONE CROSSPIN SELF TAPPING MP 2MMX8MM, 478 
-28016719,HC IMP SCREW BONE CENTER DRIVE MICRO TITANIUM 1X4MM, 612 
-28016720,HC IMP SCREW BONE CENTER DRIVE MICRO TITANIUM 1X5MM, 612 
-28016721,HC IMP SCREW BONE CENTER DRIVE MICRO TITANIUM 1X6MM, 612 
-28016722,HC IMP SCREW BONE CENTER DRIVE MICRO TITANIUM 1X7MM, 612 
-28016723,HC IMP SCREW BONE DF CROSS DRVIE 1.5X7MM, 493 
-28016724,HC IMP SCREW BONE DILATATION 2.2X8MM, 363 
-28016725,HC IMP SCREW BONE FIXED ANGLED 4X25MM," 7,130 "
-28016726,HC IMP SCREW BONE FIXED ANGLE LEGACY 6.5X35MM," 9,000 "
-28016727,HC IMP SCREW BONE FIXED ANGLE LEGACY 5.5X40MM," 9,000 "
-28016728,HC IMP SCREW BONE GUIDED 4.1X12MM," 3,230 "
-28016729,HC IMP SCREW BONE INION CPS 1.5X4MM," 2,267 "
-28016730,HC IMP SCREW BONE INION CPS 1.5X6MM," 2,267 "
-28016731,HC IMP SCREW BONE INION CPS 2.0X11MM, 231 
-28016732,HC IMP SCREW BONE INION CPS 2.0X13MM, 231 
-28016733,HC IMP SCREW BONE INION CPS 2.0X15MM, 231 
-28016734,HC IMP SCREW BONE INION CPS 2.0X20MM, 231 
-28016735,HC IMP SCREW BONE INION CPS 2.0X5MM," 1,179 "
-28016736,HC IMP SCREW BONE INION CPS 2.0X7MM," 1,179 "
-28016737,HC IMP SCREW BONE INION CPS 2.0X9MM, 231 
-28016738,HC IMP SCREW BONE INION CPS 2.5X12MM, 453 
-28016739,HC IMP SCREW BONE INION CPS 2.5X14MM, 731 
-28016740,HC IMP SCREW BONE INION CPS 2.5X16MM, 731 
-28016741,HC IMP SCREW BONE INION CPS 2.5X18MM, 453 
-28016742,HC IMP SCREW BONE INION CPS 2.5X23MM, 453 
-28016743,HC IMP SCREW BONE INION CPS 2.5X6MM, 91 
-28016744,HC IMP SCREW BONE INION CPS 2.5X8MM, 91 
-28016745,HC IMP SCREW BONE INION CPS 2.8X10MM, 731 
-28016746,HC IMP SCREW BONE INION CPS 2.8X12MM, 731 
-28016747,HC IMP SCREW BONE INION CPS 2.8X14MM, 731 
-28016748,HC IMP SCREW BONE INION CPS 2.8X16MM, 731 
-28016749,HC IMP SCREW BONE INION CPS 2.8X18MM, 731 
-28016750,HC IMP SCREW BONE INION CPS 2.8X23MM, 453 
-28016751,HC IMP SCREW BONE INION CPS 3.1X10MM, 453 
-28016752,HC IMP SCREW BONE INION CPS 3.1X12MM, 453 
-28016753,HC IMP SCREW BONE INION CPS 3.1X14MM, 453 
-28016754,HC IMP SCREW BONE INION CPS 3.1X16MM, 453 
-28016755,HC IMP SCREW BONE INION CPS 3.1X18MM, 453 
-28016756,HC IMP SCREW BONE LAG CROSSPIN 2MM 24MM, 477 
-28016758,HC IMP SCREW BONE LAG CROSSPIN 2MM 30MM, 646 
-28016759,HC IMP SCREW BONE LAG CROSSPIN 2MM 33MM, 646 
-28016760,HC IMP SCREW BONE LAG CROSSPIN 2.0X36MM, 646 
-28016761,HC IMP SCREW BONE LAG CROSSPIN 2X39MM, 646 
-28016762,HC IMP SCREW BONE LAG CROSSPIN 2MM 42MM, 646 
-28016763,HC IMP SCREW BONE LOCKING 1.7X4MM, 120 
-28016764,HC IMP SCREW BONE LOCKING 1.7X5MM, 107 
-28016765,HC IMP SCREW BONE LOCKING 1.7X6MM, 107 
-28016766,HC IMP SCREW BONE LOCKING 2.7MMX18MM," 1,335 "
-28016767,HC IMP SCREW BONE LOCKING SYNFIX 4X20MM," 3,188 "
-28016768,HC IMP SCREW BONE LOCKING SYNFIX 4X25MM," 3,188 "
-28016769,HC IMP SCREW BONE MICRO DRILL FREE 1.8X5MM, 493 
-28016770,HC IMP SCREW BONE MAXILLOFACIAL 1.5X4MM," 2,267 "
-28016771,HC IMP SCREW BONE PILOT TAPERED 4X24MM," 2,329 "
-28016772,HC IMP SCREW BONE PILOT TAPERED 4X28MM," 2,329 "
-28016773,HC IMP SCREW BONE PILOT TAPERED 4X34MM," 2,329 "
-28016774,HC IMP SCREW BONE PILOT TAPERED 4X36MM," 2,329 "
-28016775,HC IMP SCREW BONE PILOT TAPERED 4X38MM," 2,482 "
-28016776,HC IMP SCREW BONE PILOT TAPERED 4X40MM," 2,329 "
-28016777,HC IMP SCREW BONE PILOT TAPERED 4X42MM," 2,482 "
-28016778,HC IMP SCREW BONE PILOT TAPERED 4X44MM," 2,329 "
-28016779,HC IMP SCREW BONE PILOT TAPERED 4X46MM," 2,329 "
-28016780,HC IMP SCREW BONE PILOT TAPERED 4X48MM," 2,482 "
-28016781,HC IMP SCREW BONE PLATE 6.5X15MM, 836 
-28016782,HC IMP SCREW BONE PLATE 6.5X15MM REV," 1,173 "
-28016783,HC IMP SCREW BONE PLATE 6.5X20MM, 836 
-28016784,HC IMP SCREW BONE PLATE 6.5X20MM REV," 1,173 "
-28016785,HC IMP SCREW BONE PLATE 6.5X25MM, 836 
-28016786,HC IMP SCREW BONE PLATE 6.5X25MM REV," 1,173 "
-28016787,HC IMP SCREW BONE PLATE 6.5X30MM REV," 1,173 "
-28016788,HC IMP SCREW BONE PLATE 6.5X30MM, 836 
-28016789,HC IMP SCREW BONE PLATE 6.5X35MM, 836 
-28016790,HC IMP SCREW BONE PLATE 6.5X35MM REV, 836 
-28016791,HC IMP SCREW BONE PLATE 6.5X40MM, 836 
-28016792,HC IMP SCREW BONE PLATE 6.5X45MM REV, 836 
-28016793,HC IMP SCREW BONE PLATE 6.5X45MM, 836 
-28016794,HC IMP SCREW BONE PLATE 6.5X50MM, 836 
-28016795,HC IMP SCREW BONE PLATE 6.5X55MM, 836 
-28016796,HC IMP SCREW BONE PARTIALLY THREAD T7 2.7X16MM," 1,063 "
-28016797,HC IMP SCREW BONE PARTIALLY THREAD T7 2.7X18MM," 1,063 "
-28016798,HC IMP SCREW BONE PARTIALLY THREAD T7 2.7X20MM," 1,063 "
-28016799,HC IMP SCREW BONE PARTIALLY THREAD T7 2.7X22MM," 1,063 "
-28016800,HC IMP SCREW BONE PARTIALLY THREAD T7 2.7X24MM," 1,063 "
-28016801,HC IMP SCREW BONE PARTIALLY THREAD T7 2.7X26MM," 1,063 "
-28016802,HC IMP SCREW BONE SELF DRILLING TITANIUM 1.85X4MM, 646 
-28016803,HC IMP SCREW BONE SELF DRILLING TITANIUM 1.85X6MM, 646 
-28016804,HC IMP SCREW BONE SELF DRILLING 1.2X3MM, 112 
-28016805,HC IMP SCREW BONE SELF DRILLING 1.2X4MM, 112 
-28016806,HC IMP SCREW BONE SELF DRILLING 1.7X3MM, 92 
-28016807,HC IMP SCREW BONE SELF DRILLING 1.7X5MM, 92 
-28016808,HC IMP SCREW BONE SELF DRILLING 1.7X6MM, 92 
-28016809,HC IMP SCREW BONE SELF DRILLING NS 1.5X4MM, 535 
-28016810,HC IMP SCREW BONE SELF DRILLING NS 1.5X5MM, 535 
-28016811,HC IMP SCREW BONE SPINAL FAVORED 4.35X25MM," 7,943 "
-28016812,HC IMP SCREW BONE SPINAL FAVORED 4.35X30MM," 7,943 "
-28016813,HC IMP SCREW BONE SPINAL FAVORED 4.35X35MM," 7,943 "
-28016814,HC IMP SCREW BONE SPINAL LEGACY 5.5X30MM," 9,000 "
-28016816,HC IMPL SCREW SPINAL XIZ POLYAXIAL 3.5X35MM," 9,191 "
-28016817,HC IMP SCREW BONE SELF TAPPING 6.5X45MM, 750 
-28016818,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX10MM, 108 
-28016819,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX12MM, 541 
-28016820,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX3MM, 97 
-28016821,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX4MM, 97 
-28016822,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX5MM, 97 
-28016823,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MM 6MM, 97 
-28016824,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX7MM, 541 
-28016825,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.2MMX8MM, 541 
-28016826,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.4X3, 101 
-28016827,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.4X5, 565 
-28016828,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.4X7, 565 
-28016829,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.4X9, 445 
-28016830,HC IMP SCREW BONE SELF TAPPING 6.5X45MM REV, 787 
-28016831,HC IMP SCREW BONE SELF TAPPING 6.5X55MM REV, 787 
-28016832,HC IMP SCREW BONE SELF TAPPING TITANIUM 1.85X6MM, 612 
-28016833,HC IMP SCREW BONE STEM LOCKING AUXILAIRY COMPONENT REV," 1,127 "
-28016834,HC IMP SCREW BONE T7 2.7X20MM, 928 
-28016835,HC IMP SCREW BONE THREAD 6.5X115MM 32MM, 349 
-28016836,HC IMP SCREW BONE THREAD 6.5X120MM 32MM, 349 
-28016837,HC IMP SCREW BONE THREAD 6.5X125MM 32MM, 349 
-28016838,HC IMP SCREW BONE THREAD 6.5X130MM 32MM, 349 
-28016839,HC IMP SCREW BONE THREAD 6.5X135MM 32MM, 349 
-28016840,HC IMP SCREW BONE THREAD 6.5X140MM 32MM, 349 
-28016841,HC IMP SCREW BONE THREAD 6.5X145MM 32MM, 349 
-28016842,HC IMP SCREW BONE THREAD 6.5X25MM 32MM, 132 
-28016843,HC IMP SCREW BONE THREAD 6.5X30MM 32MM, 132 
-28016844,HC IMP SCREW BONE THREAD 6.5X35MM 32MM, 132 
-28016845,HC IMP SCREW BONE THREAD 6.5X40MM 32MM, 132 
-28016846,HC IMP SCREW BONE UNI AXIAL 5.5X25MM," 10,393 "
-28016847,HC IMP SCREW BONE UNI AXIAL 5.5X30MM," 8,919 "
-28016848,HC IMP SCREW BONE UNI AXIAL 5.5X35MM," 6,163 "
-28016849,HC IMP SCREW BONE UNI AXIAL 5.5X40MM, 616 
-28016850,HC IMP SCREW BONE UNI AXIAL 5.5X45MM," 6,163 "
-28016851,HC IMP SCREW BONE UNI AXIAL 5X30MM," 8,919 "
-28016852,HC IMP SCREW BONE UNI AXIAL 5X35MM," 8,919 "
-28016853,HC IMP SCREW BONE UNI AXIAL 5X40MM," 10,185 "
-28016854,HC IMP SCREW BONE UNI AXIAL 6.5X40MM," 7,758 "
-28016855,HC IMP SCREW BONE UNI AXIAL 6.5X45MM," 8,135 "
-28016856,HC IMP SCREW BONE UNI AXIAL 6X40MM," 6,163 "
-28016857,HC IMP SCREW BONE UNI AXIAL 6X45MM," 6,163 "
-28016862,HC IMP SCREW BUTTRESS FULLY THREAD 5X55MM," 1,828 "
-28016907,HC IMP SCREW CANCELLOUS FULLY THREAD SS 40MM, 316 
-28016908,HC IMP SCREW CANCELLOUS FULLY THREAD SS 45MM, 316 
-28016909,HC IMP SCREW CANCELLOUS FULLY THREAD SS 65MM, 298 
-28016924,HC IMPL SCREW CANCELLOUS THREAD 6.5X105X32MM, 340 
-28017153,HC IMP SCREW CORTICAL DCP 1.5X11MM, 149 
-28017154,HC IMP SCREW CORTICAL DCP 1.5X12MM, 130 
-28017155,HC IMP SCREW CORTICAL DCP 1.5X14MM, 281 
-28017156,HC IMP SCREW CORTICAL DCP 1.5X16MM, 271 
-28017157,HC IMP SCREW CORTICAL DCP 1.5X8MM, 271 
-28017158,HC IMP SCREW CORTICAL DCP 1.5X9MM, 271 
-28017159,HC IMP SCREW CORTICAL FULLY THREAD SOL 2.3X20MM," 3,179 "
-28017160,HC IMP SCREW CORTICAL LOCKING 3.5X10MM," 1,173 "
-28017161,HC IMP SCREW CORTICAL LOCKING 3.5X12MM," 1,173 "
-28017162,HC IMP SCREW CORTICAL LOCKING 3.5X14MM," 1,173 "
-28017163,HC IMP SCREW CORTICAL LOCKING 3.5X16MM," 1,173 "
-28017164,HC IMP SCREW CORTICAL LOCKING 3.5X18MM," 1,173 "
-28017165,HC IMP SCREW CORTICAL LOCKING 3.5X20MM," 1,105 "
-28017166,HC IMP SCREW CORTICAL LOCKING 3.5X22MM," 1,105 "
-28017167,HC IMP SCREW CORTICAL LOCKING 3.5X24MM," 1,105 "
-28017168,HC IMP SCREW CORTICAL LOCKING 3.5X26MM," 1,105 "
-28017169,HC IMP SCREW CORTICAL LOCKING 3.5X28MM," 1,105 "
-28017170,HC IMP SCREW CORTICAL LOCKING 3.5X32MM," 1,105 "
-28017171,HC IMP SCREW CORTICAL LOCKING 3.5X36MM," 1,105 "
-28017172,HC IMP SCREW CORTICAL LOCKING 3.5X46MM," 1,105 "
-28017173,HC IMP SCREW CORTICAL LOCKING 3.5X50MM," 1,105 "
-28017174,HC IMP SCREW CORTICAL LOW PROFILE 3.5X20MM, 468 
-28017175,HC IMP SCREW CORTICAL LOW PROFILE 3.5X22MM, 502 
-28017176,HC IMP SCREW CORTICAL LOW PROFILE 3.5X30MM, 468 
-28017177,HC IMP SCREW CORTICAL LOW PROFILE 3.5X42MM, 468 
-28017178,HC IMP SCREW CORTICAL LOW PROFILE 3.5X46MM, 468 
-28017179,HC IMP SCREW CORTICAL LOW PROFILE 4.5X3.5X30MM, 714 
-28017180,HC IMP SCREW CORTICAL LOW PROFILE 4.5X3.5X32MM, 714 
-28017181,HC IMP SCREW CORTICAL LOW PROFILE 4.5X58MM, 714 
-28017182,HC IMP SCREW CORTICAL LOW PROFILE4.5X60MM, 714 
-28017183,HC IMP SCREW CORTICAL LOW PROFILE 4.5X68MM, 714 
-28017184,HC IMP SCREW CORTICAL LOW PROFILE 4.5X72MM, 714 
-28017185,HC IMP SCREW CORTICAL LOW PROFILE 4.5X74MM, 714 
-28017186,HC IMP SCREW CORTICAL LOW PROFILE NLOCKING 3.5X16MM, 468 
-28017187,HC IMP SCREW CORTICAL LOW PROFILE NLOCKING 3.5X18MM, 502 
-28017188,HC IMP SCREW CORTICAL LOW PROFILE NLOCKING 3.5X26MM, 468 
-28017189,HC IMP SCREW CORTICAL LOW PROFILE NLOCKING 3.5X28MM, 468 
-28017190,HC IMP SCREW CORTICAL LOW PROFILE NLOCKING 3.5X34MM, 468 
-28017191,HC IMP SCREW CORTICAL NLOCKING SFS 3.5X18MM, 272 
-28017192,HC IMP SCREW CORTICAL NLOCKING SFS 3.5X22MM, 272 
-28017193,HC IMP SCREW CORTICAL NLOCKING SFS 3.5X26MM, 255 
-28017194,HC IMP SCREW CORTICAL NON LOCKING 2.7X28MM, 510 
-28017195,HC IMP SCREW CORTICAL NON LOCKING 2.7X30MM, 510 
-28017196,HC IMP SCREW CORTICAL NON LOCKING 2.7X32MM, 510 
-28017197,HC IMP SCREW CORTICAL NON LOCKING 2.7X34MM, 510 
-28017198,HC IMP SCREW CORTICAL NON LOCKING 2.7X36MM, 510 
-28017199,HC IMP SCREW CORTICAL NON LOCKING 2.7X38MM, 510 
-28017200,HC IMP SCREW CORTICAL NON LOCKING 2.7X40MM, 510 
-28017201,HC IMP SCREW CORTICAL NON LOCKING 2.7X45MM, 510 
-28017202,HC IMP SCREW CORTICAL NON LOCKING 2.7X50MM, 680 
-28017203,HC IMP SCREW CORTICAL NON LOCKING 2.7X55MM, 680 
-28017204,HC IMP SCREW CORTICAL NON LOCKING 2.7X60MM, 680 
-28017205,HC IMP SCREW CORTICAL NON LOCKING 2.7X65MM, 680 
-28017206,HC IMP SCREW CORTICAL NON LOCKING 3.5X25MM, 510 
-28017207,HC IMP SCREW CORTICAL NON LOCKING 3.5X28MM, 510 
-28017208,HC IMP SCREW CORTICAL NON LOCKING 3.5X30MM, 510 
-28017209,HC IMP SCREW CORTICAL NON LOCKING 3.5X34MM, 510 
-28017210,HC IMP SCREW CORTICAL NON LOCKING 3.5X36MM, 510 
-28017211,HC IMP SCREW CORTICAL NON LOCKING 3.5X38MM, 510 
-28017212,HC IMP SCREW CORTICAL NON LOCKING 3.5X40MM, 510 
-28017213,HC IMP SCREW CORTICAL NON LOCKING 3.5X45MM, 510 
-28017214,HC IMP SCREW CORTICAL NON LOCKING 3.5X50MM, 680 
-28017215,HC IMP SCREW CORTICAL NON LOCKING 3.5X55MM, 680 
-28017216,HC IMP SCREW CORTICAL NON LOCKING 3.5X60MM, 680 
-28017217,HC IMP SCREW CORTICAL NON LOCKING 3.5X65MM, 680 
-28017218,HC IMP SCREW CORTICAL SELF DRILLING 60/20, 893 
-28017219,HC IMP SCREW CORTICAL SELF DRILLING 60/25, 893 
-28017220,HC IMP SCREW CORTICAL SELF TAPPING FLUTED 2X14MM, 405 
-28017221,HC IMP SCREW CORTICAL SELF TAPPING FLUTED 2X16MM, 405 
-28017227,HC IMP SCREW CORTICAL SELF TAPPING TITANIUM 2X16MM, 458 
-28017228,HC IMP SCREW CORTICAL SELF TAPPING TITANIUM 2X18MM, 458 
-28017229,HC IMP SCREW CORTEX SELF TAPPING TITANIUM 2X4MM, 458 
-28017230,HC IMP SCREW CORTEX SELF TAPPING TITANIUM 2X6MM, 458 
-28017231,HC IMP SCREW CORTICAL TRMA SS 2.7X28MM, 128 
-28017232,HC IMP SCREW CORTICAL TRMA SS 2.7X30MM, 128 
-28017233,HC IMP SCREW CORTICAL CROSS DRIVE MICRO TI 1.5X7MM, 510 
-28017234,HC IMP SCREW CORTEX 1.3MMX7MM, 568 
-28017235,HC IMP SCREW CORTEX 1.5MMX7MM, 401 
-28017236,HC IMP SCREW CORTEX 1X5MM, 598 
-28017237,HC IMP SCREW CORTEX 2.0X10MM, 493 
-28017238,HC IMP SCREW CORTEX 2.0X12MM, 469 
-28017239,HC IMP SCREW CORTEX 2.0X14MM, 469 
-28017240,HC IMP SCREW CORTEX 2.0X16MM, 469 
-28017241,HC IMP SCREW CORTEX 2.0X18MM, 469 
-28017242,HC IMP SCREW CORTEX 2.0X4MM, 493 
-28017243,HC IMP SCREW CORTEX 2.0X5MM, 469 
-28017244,HC IMP SCREW CORTEX 2.0X6MM, 493 
-28017245,HC IMP SCREW CORTEX 2.0X8MM, 469 
-28017246,HC IMP SCREW CORTEX SS 2.7X10MM, 297 
-28017247,HC IMP SCREW CORTEX SS 2.7X12MM, 287 
-28017248,HC IMP SCREW CORTEX SS 2.7X14MM, 287 
-28017249,HC IMP SCREW CORTEX SS 2.7X18MM, 287 
-28017250,HC IMP SCREW CORTEX SS 2.7X20MM, 140 
-28017251,HC IMP SCREW CORTEX SS 2.7X22MM, 128 
-28017252,HC IMP SCREW CORTEX SS 2.7X24MM, 140 
-28017253,HC IMP SCREW CORTEX SS 2.7X26MM, 128 
-28017260,HC IMP SCREW CORTEX 3.5X100MM, 208 
-28017261,HC IMP SCREW CORTEX 3.5X105MM, 208 
-28017262,HC IMP SCREW CORTEX 3.5X110MM, 181 
-28017263,HC IMP SCREW CORTEX 3.5X42MM, 208 
-28017264,HC IMP SCREW CORTEX 3.5X44MM, 208 
-28017265,HC IMP SCREW CORTEX 3.5X46MM, 216 
-28017266,HC IMP SCREW CORTEX 3.5X48MM, 208 
-28017267,HC IMP SCREW CORTEX 4X24MM, 253 
-28017268,HC IMP SCREW CORTEX 4X26MM, 253 
-28017269,HC IMP SCREW CORTEX 4X28MM, 253 
-28017270,HC IMP SCREW CORTEX 4X30MM, 255 
-28017271,HC IMP SCREW CORTEX 4X32MM, 253 
-28017272,HC IMP SCREW CORTEX 4X34MM, 253 
-28017273,HC IMP SCREW CORTEX 4X36MM, 253 
-28017274,HC IMP SCREW CORTEX 4X38MM, 262 
-28017275,HC IMP SCREW CORTEX 4X40MM, 262 
-28017276,HC IMP SCREW CORTEX 4X42MM, 253 
-28017277,HC IMP SCREW CORTEX 4X44MM, 253 
-28017278,HC IMP SCREW CORTEX 4X46MM, 253 
-28017279,HC IMP SCREW CORTEX 4X50MM, 255 
-28017280,HC IMP SCREW CORTEX 4X52MM, 253 
-28017281,HC IMP SCREW CORTEX 4X54MM, 253 
-28017282,HC IMP SCREW CORTEX 4X56MM, 253 
-28017283,HC IMP SCREW CORTEX 4X58MM, 253 
-28017284,HC IMP SCREW CORTEX 4X60MM, 255 
-28017285,HC IMP SCREW CORTEX 4X65MM, 253 
-28017286,HC IMP SCREW CORTEX 4X70MM, 255 
-28017287,HC IMP SCREW CORTEX 4X75MM, 478 
-28017288,HC IMP SCREW CORTEX 4X80MM, 463 
-28017289,HC IMP SCREW CORTEX 4X85MM, 461 
-28017290,HC IMP SCREW CORTEX 4X90MM, 463 
-28017291,HC IMP SCREW CORTEX 4X95MM, 461 
-28017292,HC IMP SCREW CORTEX SELF DRILLING 2X5MM, 518 
-28017293,HC IMP SCREW CORTEX SELF DRILLING 2X6MM, 491 
-28017294,HC IMP SCREW CORTEX SELF DRILLING 2X8MM, 491 
-28017295,HC IMP SCREW CORTEX SELF TAPPING 1.3X4MM," 3,077 "
-28017296,HC IMP SCREW CORTEX SELF TAPPING 1.3X6MM," 3,077 "
-28017297,HC IMP SCREW CORTEX SELF TAPPING 4.5MM, 149 
-28017298,HC IMP SCREW CORTEX SELF TAPPING 4.5X100MM, 270 
-28017299,HC IMP SCREW CORTEX SELF TAPPING 4.5X105MM, 270 
-28017300,HC IMP SCREW CORTEX SELF TAPPING 4.5X110MM, 270 
-28017301,HC IMP SCREW CORTEX SELF TAPPING 4.5X72MM, 270 
-28017302,HC IMP SCREW CORTEX SELF TAPPING 4.5X85MM, 352 
-28017303,HC IMP SCREW CORTEX SELF TAPPING 4.5X90MM, 270 
-28017304,HC IMP SCREW CORTEX SELF TAPPING 4.5X95MM, 196 
-28017311,HC IMP SCREW CORTEX TITANIUM 1.5MM 10MM, 334 
-28017312,HC IMP SCREW CORTEX TITANIUM 1.5MM 11MM, 334 
-28017313,HC IMP SCREW CORTEX TITANIUM 1.5MM 12MM, 334 
-28017314,HC IMP SCREW CORTEX TITANIUM 1.5MM 14MM, 334 
-28017315,HC IMP SCREW CORTEX TITANIUM 1.5MM 16MM, 334 
-28017316,HC IMP SCREW CORTEX TITANIUM 1.5MM 18MM, 334 
-28017317,HC IMP SCREW CORTEX TITANIUM 1.5MM 20MM, 334 
-28017318,HC IMP SCREW CORTEX TITANIUM 1.5MM 6MM, 334 
-28017319,HC IMP SCREW CORTEX TITANIUM 1.5MM 7MM, 334 
-28017326,HC IMP SCREW CORTEX TITANIUM 2MM 10MM, 334 
-28017327,HC IMP SCREW CORTEX TITANIUM 2MM 14MM, 334 
-28017328,HC IMP SCREW CORTEX TITANIUM 2MM 18MM, 334 
-28017329,HC IMP SCREW CORTEX TITANIUM 2MM 20MM, 334 
-28017330,HC IMP SCREW CORTEX TITANIUM 2MM 22MM, 334 
-28017331,HC IMP SCREW CORTEX TITANIUM 2MM 24MM, 334 
-28017332,HC IMP SCREW CORTEX TITANIUM 2MM 8MM, 334 
-28017333,HC IMP SCREW CORTEX TITANIUM 4.0X18MM," 2,015 "
-28017334,HC IMP SCREW CORTEX TITANIUM 4.5X38MM, 206 
-28017335,HC IMP SCREW CORTEX TITANIUM 4MM, 524 
-28017336,HC IMP SCREW CORTEX TITANIUM 2MM 6MM, 281 
-28017337,HC IMP SCREW CORTICAL 1.5X16MM, 628 
-28017338,HC IMP SCREW CORTICAL 1.5X17MM, 638 
-28017339,HC IMP SCREW CORTICAL 1.5X18MM, 678 
-28017340,HC IMP SCREW CORTICAL 1.5X20MM, 628 
-28017341,HC IMP SCREW CORTICAL 4.0X50MM," 1,590 "
-28017342,HC IMP SCREW CORTICAL 4.0X40MM," 1,590 "
-28017343,HC IMP SCREW CORTICAL 4.5X16MM," 1,785 "
-28017344,HC IMP SCREW CORTICAL 4.5X40MM," 1,785 "
-28017345,HC IMP SCREW CORTICAL 3.5X10MM, 125 
-28017346,HC IMP SCREW CORTICAL LOCKING 3.5X20MM, 272 
-28017347,HC IMP SCREW CORTICAL LOCKING 2.7X10MM, 748 
-28017348,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X12MM, 748 
-28017349,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X14MM, 748 
-28017350,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X16MM, 723 
-28017351,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X18MM, 723 
-28017352,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X20MM, 748 
-28017353,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X22MM, 748 
-28017354,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X24MM, 723 
-28017355,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X26MM, 723 
-28017356,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X30MM, 723 
-28017357,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X32MM, 723 
-28017358,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X34MM, 723 
-28017359,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X36MM, 723 
-28017360,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X38MM, 723 
-28017361,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X40MM, 723 
-28017362,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X50MM, 723 
-28017363,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X55MM, 723 
-28017364,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X60MM, 723 
-28017365,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X65MM, 723 
-28017366,HC IMP SCREW CORTICAL LOCKING TITANIUM 2.7X8MM, 723 
-28017367,HC IMP SCREW CORTICAL MATTA 4.5X105MM, 217 
-28017368,HC IMP SCREW CORTICAL MATTA 4.5X115MM, 298 
-28017369,HC IMP SCREW CORTICAL MATTA 4.5X44MM, 170 
-28017370,HC IMP SCREW CORTICAL MATTA 4.5X80MM, 264 
-28017371,HC IMP SCREW CORTICAL MATTA 4.5X85MM, 264 
-28017386,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X10MM, 291 
-28017387,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X12MM, 352 
-28017388,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X14MM, 352 
-28017389,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X16MM, 352 
-28017390,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X18MM, 352 
-28017391,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X20MM, 352 
-28017392,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X22MM, 340 
-28017393,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X24MM, 352 
-28017394,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X26MM, 352 
-28017395,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X28MM, 340 
-28017396,HC IMP SCREW CORTEX SELF TAPPING T8 STARDRIVE 2.7X30MM, 310 
-28017404,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.75X4MM, 412 
-28017405,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.75X5MM, 412 
-28017406,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.7X3MM, 412 
-28017407,HC IMP SCREW BONE SELF TAPPING CROSSPIN 1.7X7MM, 367 
-28017410,HC IMP SCREW CORTEX PULSEDRIVE 2X10MM, 458 
-28017411,HC IMP SCREW CORTEX PULSEDRIVE 2X12MM, 458 
-28017412,HC IMP SCREW CORTEX PULSEDRIVE 2X14MM, 458 
-28017413,HC IMP SCREW CORTEX PULSEDRIVE 2X8MM, 458 
-28017429,HC IMP SCREW DS RESORB 2.2X6MM, 356 
-28017488,HC IMP SCREW FIXED 4.75X25MM," 7,013 "
-28017489,HC IMP SCREW FIXED 4.75X30MM," 7,013 "
-28017490,HC IMP SCREW FIXED STAINLESS 5.5X25MM," 7,863 "
-28017491,HC IMP SCREW FIXED 5.5X35MM," 7,013 "
-28017492,HC IMP SCREW FIXED 5.5X40MM," 7,863 "
-28017493,HC IMP SCREW FIXED 5.5X45MM," 7,863 "
-28017494,HC IMP SCREW FIXED 5.5X45MM," 7,013 "
-28017495,HC IMP SCREW FIXED STAINLESS 6.5X30MM," 7,863 "
-28017496,HC IMP SCREW FIXED STAINLESS 6.5X40MM," 7,863 "
-28017497,HC IMP SCREW FIXED STAINLESS 6.5X45MM," 7,438 "
-28017498,HC IMP SCREW FIXED 6.5X50MM," 7,943 "
-28017504,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5 X 24MM," 7,650 "
-28017505,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5 X22MM," 7,650 "
-28017506,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X10MM," 7,650 "
-28017507,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X14MM," 7,650 "
-28017508,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X16MM," 7,650 "
-28017509,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X20MM," 7,650 "
-28017510,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X22MM," 7,650 "
-28017511,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X24MM," 7,650 "
-28017512,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X26MM," 7,650 "
-28017513,HC IMP SCREW FIXED ANGLE MOUNTAINEER 4.0X12MM," 7,920 "
-28017514,HC IMP SCREW FIXED ANGLE MOUNTAINEER 4.0X14MM," 7,920 "
-28017515,HC IMP SCREW FIXED ANGLE MOUNTAINEER 4.0X20MM," 7,920 "
-28017516,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X12MM," 7,650 "
-28017517,HC IMP SCREW FIXED ANGLE MOUNTAINEER 3.5X18MM," 8,112 "
-28017585,HC IMP SCREW FIXED XCALIBER 260/20MM," 1,292 "
-28017586,HC IMP SCREW FIXED XCALIBER 260/30MM," 1,292 "
-28017587,HC IMP SCREW FIXED XCALIBER 260/40MM," 1,292 "
-28017588,HC IMP SCREW FIXED XCALIBER 260/70MM," 1,292 "
-28017666,HC IMP SCREW HIP PINNACLE 6.5X45MM, 901 
-28017667,HC IMP SCREW HIP PINNACLE 6.5X55MM, 901 
-28017668,HC IMP SCREW HIP PINNACLE 6.5X60MM, 901 
-28017669,HC IMP SCREW HIP PINNACLE 6.5X65MM, 901 
-28017949,HC IMP SCREW MULTI DIRECTIONAL 3.5X50MM," 1,615 "
-28017950,HC IMP SCREW MULTI DIRECTIONAL 3.5X52MM," 1,615 "
-28017951,HC IMP SCREW MULTI DIRECTIONAL 3.5X60MM," 1,615 "
-28017977,HC IMP SCREW OSTEOTITE CORTICAL 120/30," 1,343 "
-28017986,HC IMP SCREW PELVIC CORTEX 3.5X100MM, 251 
-28017987,HC IMP SCREW PELVIC CORTEX 3.5X115MM, 238 
-28017988,HC IMP SCREW PELVIC CORTEX 3.5X120MM, 270 
-28017989,HC IMP SCREW PELVIC CORTEX 3.5X125MM, 274 
-28017990,HC IMP SCREW PELVIC CORTEX 3.5X130MM, 817 
-28017991,HC IMP SCREW PELVIC CORTEX 3.5X135MM, 817 
-28017992,HC IMP SCREW PELVIC CORTEX 3.5X145MM, 850 
-28017993,HC IMP SCREW PELVIC CORTEX 3.5X150MM, 817 
-28018312,HC IMPL SCREW SURGICAL RESORBABLE EME 2.2X8MM, 90 
-28018317,HC IMP SCREW TWIN PITCH COMP 2.5MM 10MM," 2,066 "
-28018318,HC IMP SCREW TWIN PITCH COMP 2.5MM 11MM," 2,244 "
-28018319,HC IMP SCREW TWIN PITCH COMP SR 2MM 10MM," 2,244 "
-28018320,HC IMP SCREW TWIN PITCH COMP SR 2MM 12MM," 2,066 "
-28018321,HC IMP SCREW TWIN PITCH COMP SR 2MM 13MM," 2,066 "
-28018322,HC IMP SCREW TWIN PITCH COMP SR 2MM 14MM," 2,244 "
-28018323,HC IMP SCREW TWIN PITCH COMP SR 2MM 15MM," 2,066 "
-28018324,HC IMP SCREW TWIN PITCH COMP SR 2MM 16MM," 2,244 "
-28018325,HC IMP SCREW TWIN PITCH COMP SR 2MM 17MM," 3,273 "
-28018326,HC IMP SCREW TWIN PITCH COMP SR 2MM 18MM," 2,244 "
-28018327,HC IMP SCREW TWIN PITCH COMP SR 2MM 19MM," 3,094 "
-28018328,HC IMP SCREW TWIN PITCH COMP SR 2MM 20MM," 2,355 "
-28018329,HC IMP SCREW TWIN PITCH COMP SR 2MM 21MM," 2,244 "
-28018330,HC IMP SCREW TWIN PITCH COMP SR 2MM 22MM," 2,176 "
-28018331,HC IMP SCREW TWIN PITCH COMP SR 2MM 23MM," 2,066 "
-28018332,HC IMP SCREW TWIN PITCH COMP SR 2MM 24MM," 2,066 "
-28018333,HC IMP SCREW TWIN PITCH COMP SR 2MM 25MM," 2,066 "
-28018334,HC IMP SCREW TWIN PITCH COMP SR 2MM 28MM," 2,176 "
-28018335,HC IMP SCREW TWIN PITCH COMP SR 2MM 27MM," 2,066 "
-28018336,HC IMP SCREW TWIN PITCH COMP SR 2MM 28MM," 2,244 "
-28018337,HC IMP SCREW TWIN PITCH COMP SR 2MM 29MM," 2,066 "
-28018338,HC IMP SCREW TWIN PITCH COMP SR 2MM 30MM," 2,244 "
-28018398,HC IMP SCREW ACUTRAK 2 4.7X40MM," 3,494 "
-28018399,HC IMP SCREW ACUTRAK 2 4.7X45MM," 3,494 "
-28018400,HC IMP SCREW ACUTRAK 2 4.7X50MM," 3,494 "
-28018401,HC IMP SCREW ACUTRAK 2 7.5X70MM," 4,157 "
-28018402,HC IMP SCREW ACUTRAK 2 7.5X80MM," 4,157 "
-28018403,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X10MM, 548 
-28018404,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X12MM, 508 
-28018405,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X14MM, 266 
-28018406,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X16MM, 627 
-28018407,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X14MM, 477 
-28018408,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X18MM, 627 
-28018409,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X20MM, 552 
-28018410,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X4MM, 258 
-28018411,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X6MM, 125 
-28018412,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X7MM, 536 
-28018413,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X8MM, 390 
-28018414,HC IMP SCREW BONE CROSSPIN SELF TAPPING 2X5MM, 729 
-28018838,HC IMP STAPLE SPINAL CAUDAL TITANIUM 21MM," 13,326 "
-28018839,HC IMP STAPLE SPINAL CAUDAL TITANIUM 23MM," 13,326 "
-28018840,HC IMP STAPLE SPINAL CAUDAL TITANIUM25MM," 13,323 "
-28018841,HC IMP STAPLE SPINAL IMP SMALL TITANIUM 5.2MM," 3,978 "
-28018842,HC IMP STAPLE SPINAL ROSTRAL 21MM," 13,326 "
-28018843,HC IMP STAPLE SPINAL ROSTRAL TITANIUM 19MM," 13,326 "
-28018844,HC IMP STAPLE SPINAL ROSTRAL TITANIUM 23MM," 13,323 "
-28018845,HC IMP STAPLE SPINAL ROSTRAL TITANIUM 25MM," 13,323 "
-28018846,HC IMP STAPLE SPINAL STATR SYS TITANIUM 6.2MM," 3,978 "
-28018847,HC IMP STAPLE SPINAL STATR SYS TITANIUM 7MM," 3,978 "
-28019393,HC IMP SUTURE POLYMER INSYTE 3MM, 340 
-28019394,HC IMP SUTURE POLYMER INSYTE 6.5MM, 340 
-28019469,HC IMP SUTURETAK JOINT SMALL," 2,593 "
-28019470,HC IMP SUTURETAK MICRO 2.4X6.5," 2,593 "
-28019471,HC IMP SUTURETAK MICRO BIO-COMPOSITE," 2,593 "
-28019791,HC IMP WIRE DCKNG TRAXCESS 14IN 115CM," 1,275 "
-28019794,HC IMP WIRE KIRSCHNER .035 0.9X22.9, 132 
-28019795,HC IMP WIRE KIRSCHNER 0.9X150MM, 247 
-28019796,HC IMP WIRE KIRSCHNER 1.0X150MM, 247 
-28019797,HC IMP WIRE KIRSCHNER 1.6X150," 1,546 "
-28019798,HC IMP WIRE KIRSCHNER1END SS .63X22.9, 132 
-28019799,HC IMP WIRE KIRSCHNER 1MMX5IN, 126 
-28019800,HC IMP WIRE KIRSCHNER 5.0MM, 247 
-28019801,HC IMP WIRE KIRSCHNER BAYONET 1.8X370MM," 1,114 "
-28019802,HC IMP WIRE KIRSCHNER SS 2MM, 272 
-28019803,HC IMP WIRE KIRSCHNER SPADE POINT SS 2X350MM, 728 
-28019804,HC IMP WIRE KIRSCHNER SS 1.6MM, 213 
-28019805,HC IMP WIRE KIRSCHNER T2 SS 3X285MM," 1,279 "
-28019806,HC IMP WIRE KIRSCHNER THREAD 1X150MM, 963 
-28019808,HC IMP WIRE KIRSCHNER THREAD 1.6MMX8IN, 323 
-28019809,HC IMP WIRE KIRSCHNER TRCR PNT THRDD 2END SS, 132 
-28019810,HC IMP WIRE KIRSCHNER TROCAR THREAD PNT 1X150, 86 
-28019811,HC IMP WIRE KIRSCHNER W STOP QC 2.4MM," 1,525 "
-28012123,HC  CATH PERPH SLVRHWK 4.5 7.0MM," 22,365 "
-28012124,HC  CATH PERPH SLVRHWK 8F 110CM," 24,500 "
-28012125,HC  CATH PERPH SLVRHWK 8F 9CM," 23,765 "
-28012180,HC  CATH SILVERHAWK EXL 6F 2-3CM," 21,805 "
-28014153,HC IMP ICD DUAL IC LUMAX 540 VR-T," 61,750 "
-28014154,HC IMP ICD DUAL PROTECTA XT D314DRM," 85,050 "
-28014155,HC IMP ICD DUAL VIRTUOSO II DR," 93,000 "
-28014156,HC IMP ICD FORTIFY ASSURA DR 4," 84,000 "
-28014157,HC IMP ICD FORTIFY DR," 95,000 "
-28014152,HC IMP ICD CURRENT PLUS VR," 65,000 "
-28014158,HC IMP ICD FORTIFY VR," 65,000 "
-28014160,HC IMP ICD JEWEL CD," 81,000 "
-28014161,HC IMP ICD JEWEL PCD," 78,750 "
-28014167,HC IMP ICD PCD 7217B," 63,000 "
-28014169,HC IMPL ICD PROTECTA XT DF-4 CRT-D," 71,050 "
-28014170,HC IMP ICD SINGLE DR ICD-U-PR," 84,000 "
-28014171,HC IMP ICD SINGLE INCEPTA CRT-D," 85,470 "
-28011457,HC  CATHETER ANGIO BALLOON CUTTING OTW 3.5X1.5X140," 6,800 "
-28011463,HC  CATHETER ANGIOPLASTY BLLN CUT 6X3.5X150," 6,800 "
-28011464,HC  CATHETER ANGIOPLASTY BLLN CUT PTC 6X20MMX135CM," 6,406 "
-28011467,HC  CATHETER APEX OTW PUSH 1.5X20MM," 2,117 "
-28011468,HC  CATHETER ARMADA 14 2.5X80X150," 3,825 "
-28011469,HC  CATHETER ARMADA WRKHR 4.0X150X135," 2,125 "
-28011470,HC  CATHETER ARMADA WRKHR 4.0X200X135," 2,125 "
-28011471,HC  CATHETER ARMADA WRKHR 4.0X250X135," 2,125 "
-28011472,HC  CATHETER ARMADA WRKHR 5.0X150X135," 2,125 "
-28011473,HC  CATHETER ARMADA WRKHR 5.0X200X135," 2,125 "
-28011474,HC  CATHETER ARMADA WRKHR 5.0X250X135," 2,125 "
-28011475,HC  CATHETER ARMADA WRKHR 6.0X150X135," 2,125 "
-28011476,HC  CATHETER ARMADA WRKHR 6.0X200X135," 2,125 "
-28011477,HC  CATHETER ARMADA WRKHR 6.0X250X135," 2,125 "
-28011478,HC  CATHETER ARMADA WRKHR 7.0X150X135," 2,125 "
-28011479,HC  CATHETER ARMADA WRKHR 7.0X200X135," 2,125 "
-28011480,HC  CATHETER ARMADA WRKHRS 10X20X135," 1,615 "
-28011481,HC  CATHETER ARMADA WRKHRS 10X20X80," 1,573 "
-28011482,HC  CATHETER ARMADA WRKHRS 10X40X135," 1,615 "
-28011483,HC  CATHETER ARMADA WRKHRS 10X40X80," 1,615 "
-28011484,HC  CATHETER ARMADA WRKHRS 10X60X135," 1,615 "
-28011485,HC  CATHETER ARMADA WRKHRS 10X60X80," 1,615 "
-28011486,HC  CATHETER ARMADA WRKHRS 10X80X135," 1,615 "
-28011487,HC  CATHETER ARMADA WRKHRS 12X20X135," 1,615 "
-28011488,HC  CATHETER ARMADA WRKHRS 12X20X80," 1,615 "
-28011489,HC  CATHETER ARMADA WRKHRS 12X40X135," 1,615 "
-28011490,HC  CATHETER ARMADA WRKHRS 12X40X80," 1,615 "
-28011491,HC  CATHETER ARMADA WRKHRS 12X60X135," 1,615 "
-28011492,HC  CATHETER ARMADA WRKHRS 12X60X80," 1,615 "
-28011493,HC  CATHETER ARMADA WRKHRS 12X80X135," 1,615 "
-28011494,HC  CATHETER ARMADA WRKHRS 12X80X80," 1,615 "
-28011495,HC  CATHETER ARMADA WRKHRS 14X20X135," 1,615 "
-28011496,HC  CATHETER ARMADA WRKHRS 14X20X80," 1,530 "
-28011497,HC  CATHETER ARMADA WRKHRS 14X40X135," 1,615 "
-28011498,HC  CATHETER ARMADA WRKHRS 14X40X80," 1,615 "
-28011499,HC  CATHETER ARMADA WRKHRS 14X60X135," 1,615 "
-28011500,HC  CATHETER ARMADA WRKHRS 14X60X80," 1,615 "
-28011501,HC  CATHETER ARMADA WRKHRS 14X80X135," 1,615 "
-28011502,HC  CATHETER ARMADA WRKHRS 14X80X80," 1,615 "
-28011504,HC  CATHETER ARMADA WRKHRS 3X20X80," 1,615 "
-28011505,HC  CATHETER ARMADA WRKHRS 3X40X135," 1,615 "
-28011506,HC  CATHETER ARMADA WRKHRS 3X40X80," 1,615 "
-28011510,HC  CATHETER ARMADA WRKHRS 4X100X135," 1,615 "
-28011511,HC  CATHETER ARMADA WRKHRS 4X100X80," 1,615 "
-28011512,HC  CATHETER ARMADA WRKHRS 4X120X135," 1,615 "
-28011513,HC  CATHETER ARMADA WRKHRS 4X120X80," 1,615 "
-28011514,HC  CATHETER ARMADA WRKHRS 4X20X135," 1,615 "
-28011515,HC  CATHETER ARMADA WRKHRS 4X20X80," 1,615 "
-28011516,HC  CATHETER ARMADA WRKHRS 4X40X135," 1,615 "
-28011517,HC  CATHETER ARMADA WRKHRS 4X40X80," 1,615 "
-28011518,HC  CATHETER ARMADA WRKHRS 4X60X135," 1,615 "
-28011519,HC  CATHETER ARMADA WRKHRS 4X60X80," 1,615 "
-28011520,HC  CATHETER ARMADA WRKHRS 4X80X135," 1,615 "
-28011521,HC  CATHETER ARMADA WRKHRS 4X80X80," 1,615 "
-28011525,HC  CATHETER ARMADA WRKHRS 5X100X135," 1,615 "
-28011526,HC  CATHETER ARMADA WRKHRS 5X100X80," 1,615 "
-28011527,HC  CATHETER ARMADA WRKHRS 5X120X135," 1,615 "
-28011529,HC  CATHETER ARMADA WRKHRS 5X20X135," 1,615 "
-28011530,HC  CATHETER ARMADA WRKHRS 5X20X80," 1,530 "
-28011531,HC  CATHETER ARMADA WRKHRS 5X40X135," 1,530 "
-28011532,HC  CATHETER ARMADA WRKHRS 5X40X80," 1,615 "
-28011533,HC  CATHETER ARMADA WRKHRS 5X60X135," 1,615 "
-28011534,HC  CATHETER ARMADA WRKHRS 5X60X80," 1,530 "
-28011535,HC  CATHETER ARMADA WRKHRS 5X80X135," 1,615 "
-28011536,HC  CATHETER ARMADA WRKHRS 5X80X80," 1,615 "
-28011540,HC  CATHETER ARMADA WRKHRS 6X100X135," 1,615 "
-28011541,HC  CATHETER ARMADA WRKHRS 6X100X80," 1,615 "
-28011542,HC  CATHETER ARMADA WRKHRS 6X120X135," 1,615 "
-28011543,HC  CATHETER ARMADA WRKHRS 6X120X80," 1,615 "
-28011544,HC  CATHETER ARMADA WRKHRS 6X20X135," 1,615 "
-28011545,HC  CATHETER ARMADA WRKHRS 6X20X80," 1,615 "
-28011546,HC  CATHETER ARMADA WRKHRS 6X40X135," 1,615 "
-28011547,HC  CATHETER ARMADA WRKHRS 6X40X80," 1,615 "
-28011548,HC  CATHETER ARMADA WRKHRS 6X60X135," 1,615 "
-28011549,HC  CATHETER ARMADA WRKHRS 6X60X80," 1,530 "
-28011550,HC  CATHETER ARMADA WRKHRS 6X80X135," 1,615 "
-28011551,HC  CATHETER ARMADA WRKHRS 6X80X80," 1,615 "
-28011554,HC  CATHETER ARMADA WRKHRS 7X100X135," 1,615 "
-28011555,HC  CATHETER ARMADA WRKHRS 7X100X80," 1,615 "
-28011556,HC  CATHETER ARMADA WRKHRS 7X120X135," 1,615 "
-28011557,HC  CATHETER ARMADA WRKHRS 7X120X80," 1,615 "
-28011558,HC  CATHETER ARMADA WRKHRS 7X20X135," 1,615 "
-28011559,HC  CATHETER ARMADA WRKHRS 7X20X80," 1,615 "
-28011560,HC  CATHETER ARMADA WRKHRS 7X40X135," 1,615 "
-28011561,HC  CATHETER ARMADA WRKHRS 7X40X80," 1,615 "
-28011562,HC  CATHETER ARMADA WRKHRS 7X60X135," 1,615 "
-28011563,HC  CATHETER ARMADA WRKHRS 7X60X80," 1,615 "
-28011564,HC  CATHETER ARMADA WRKHRS 7X80X135," 1,615 "
-28011565,HC  CATHETER ARMADA WRKHRS 7X80X80," 1,615 "
-28011566,HC  CATHETER ARMADA WRKHRS 8X20X135," 1,615 "
-28011567,HC  CATHETER ARMADA WRKHRS 8X20X80," 1,615 "
-28011568,HC  CATHETER ARMADA WRKHRS 8X40X135," 1,615 "
-28011569,HC  CATHETER ARMADA WRKHRS 8X40X80," 1,615 "
-28011570,HC  CATHETER ARMADA WRKHRS 8X60X135," 1,615 "
-28011571,HC  CATHETER ARMADA WRKHRS 8X60X80," 1,615 "
-28011572,HC  CATHETER ARMADA WRKHRS 8X80X135," 1,615 "
-28011573,HC  CATHETER ARMADA WRKHRS 9X20X135," 1,615 "
-28011574,HC  CATHETER ARMADA WRKHRS 9X20X80," 1,615 "
-28011575,HC  CATHETER ARMADA WRKHRS 9X40X135," 1,615 "
-28011576,HC  CATHETER ARMADA WRKHRS 9X40X80," 1,615 "
-28011577,HC  CATHETER ARMADA WRKHRS 9X60X135," 1,615 "
-28011578,HC  CATHETER ARMADA WRKHRS 9X60X80," 1,615 "
-28011579,HC  CATHETER ARMADA WRKHRS 9X80X135," 1,615 "
-28011580,HC  CATHETER ARMADA WRKHRS 9X80X80," 1,615 "
-28011584,HC  CATHETER DILATATION PTA 10X4CMX60," 2,602 "
-28011585,HC  CATHETER DILATATION PTA 9FX10MM," 2,602 "
-28011586,HC  CATHETER DILATATION PTA 9FX12MM," 2,602 "
-28011587,HC  CATHETER DILATATION PTA 9FX18MM," 3,763 "
-28011593,HC  CATHETER BALLOON 16MMX34MM," 4,973 "
-28011594,HC  CATHETER BALLOON 4F 2.5MMX150MM," 3,915 "
-28011595,HC  CATHETER BALLOON 4F 3MMX150MM," 3,915 "
-28011596,HC  CATHETER BALLOON 4F 4MMX120MM," 3,915 "
-28011597,HC  CATHETER BALLOON 4MMX10CM 135CM," 2,125 "
-28011598,HC  CATHETER BALLOON 4MMX15CM 135CM," 2,125 "
-28011599,HC  CATHETER BALLOON 4MMX20CM 135CM," 2,125 "
-28011600,HC  CATHETER BALLOON 4MMX2CM 135CM," 2,125 "
-28011601,HC  CATHETER BALLOON 4MMX4CM 135CM," 2,125 "
-28011602,HC  CATHETER BALLOON 4MMX6CM 135CM," 2,125 "
-28011603,HC  CATHETER BALLOON 5MMX10CM 135CM," 2,125 "
-28011604,HC  CATHETER BALLOON 5MMX10CM 75CM," 2,125 "
-28011606,HC  CATHETER BALLOON 5MMX15CM 135CM," 2,125 "
-28011607,HC  CATHETER BALLOON 5MMX20CM 135CM," 2,125 "
-28011608,HC  CATHETER BALLOON 5MMX2CM 135CM," 2,125 "
-28011609,HC  CATHETER BALLOON 5MMX4CM 135CM," 2,125 "
-28011610,HC  CATHETER BALLOON 5MMX4CM 4CM," 2,125 "
-28011611,HC  CATHETER BALLOON 5MMX4CM 75CM," 2,125 "
-28011612,HC  CATHETER BALLOON 5MMX6CM 135CM," 2,125 "
-28011613,HC  CATHETER BALLOON 5MMX8CM 75CM," 2,125 "
-28011614,HC  CATHETER BALLOON 6MMX10CM 135CM," 2,125 "
-28011615,HC  CATHETER BALLOON 6MMX10CM 75CM," 2,125 "
-28011616,HC  CATHETER BALLOON 6MMX10MMX40CM," 6,800 "
-28011617,HC  CATHETER BALLOON 6MMX10MMX80CM," 6,800 "
-28011618,HC  CATHETER BALLOON 6MMX15CM 135CM," 2,125 "
-28011619,HC  CATHETER BALLOON 6MMX20CM 135CM," 2,125 "
-28011620,HC  CATHETER BALLOON 6MMX2CM 135CM," 2,125 "
-28011621,HC  CATHETER BALLOON 6MMX40CM 135CM," 2,125 "
-28011622,HC  CATHETER BALLOON 6MMX4CM 4CM," 2,125 "
-28011623,HC  CATHETER BALLOON 6MMX6CM 135CM," 2,125 "
-28011624,HC  CATHETER BALLOON 6MMX8CM 75CM," 2,125 "
-28011625,HC  CATHETER BALLOON 7MMX10CM 135CM," 2,125 "
-28011626,HC  CATHETER BALLOON 7MMX15CM 135CM," 2,125 "
-28011627,HC  CATHETER BALLOON 7MMX2CM 135CM," 2,125 "
-28011628,HC  CATHETER BALLOON 7MMX2CM 40CM," 2,125 "
-28011629,HC  CATHETER BALLOON 7MMX4CM 135CM," 2,125 "
-28011630,HC  CATHETER BALLOON 7MMX4CM 4CM," 2,125 "
-28011631,HC  CATHETER BALLOON 7MMX6CM 135CM," 2,125 "
-28011632,HC  CATHETER BALLOON 8MMX10MMX80CM," 6,800 "
-28011633,HC  CATHETER BALLOON 8MMX4CM 40CM," 2,125 "
-28011634,HC  CATHETER BALLOON 8MMX8CM 75CM," 2,125 "
-28011635,HC  CATHETER BALLOON 9MMX2CM 135CM," 2,125 "
-28011636,HC  CATHETER BALLOON 9MMX4CM 135CM," 2,125 "
-28011637,HC  CATHETER BALLOON 9MMX4CM 40CM," 2,125 "
-28011638,HC  CATHETER BALLOON 9MMX4CM 75CM," 2,125 "
-28011639,HC  CATHETER BALLOON ADMIRAL 5FX9MMX60MM," 2,890 "
-28011643,HC  CATHETER DILATATION 8FX100CM TYSHAKII," 4,120 "
-28011644,HC  CATHETER DILATATION 8MMX12CM 80CM," 2,040 "
-28011645,HC  CATHETER DILATATION BALLOON-IN-BALLOON 20X4CM," 6,375 "
-28011646,HC  CATHETER DILATATION BALLOON-IN-BALLOON 22X4CM," 6,375 "
-28011647,HC  CATHETER DILATATION BALLOON-IN-BALLOON 24X4CM," 6,375 "
-28011650,HC  CATHETER DILATATION OTW 1.5X20MM," 2,117 "
-28011651,HC  CATHETER DILATATION PTA 12X2X120," 2,975 "
-28011652,HC  CATHETER DILATATION PTA CNQ 2X5X75," 1,743 "
-28011653,HC  CATHETER DILATATION PTA 7X20MM, 650 
-28011654,HC  CATHETER DILATATION PTCA 2.25X20MM," 2,117 "
-28011655,HC  CATHETER DILATATION PTCA 2.25X6MM," 2,117 "
-28011656,HC  CATHETER DILATATION PTCA 2.5X20MM," 2,117 "
-28011657,HC  CATHETER DILATATION PTCA 2.75X20MM," 2,117 "
-28011658,HC  CATHETER DILATATION PTCA 2.75X6MM," 2,117 "
-28011659,HC  CATHETER DILATATION PTCA 2X20MM," 2,117 "
-28011660,HC  CATHETER DILATATION PTCA 3.25X20MM," 2,117 "
-28011661,HC  CATHETER DILATATION PTCA 3.25X6MM," 2,117 "
-28011662,HC  CATHETER DILATATION PTCA 3.5X20MM," 2,117 "
-28011663,HC  CATHETER DILATATION PTCA 3.5X6MM," 2,117 "
-28011664,HC  CATHETER DILATATION PTCA 3.75X20MM," 2,117 "
-28011665,HC  CATHETER DILATATION PTCA 3.75X6MM," 2,117 "
-28011666,HC  CATHETER DILATATION PTCA 3X20MM," 2,117 "
-28011667,HC  CATHETER DILATATION PTCA 3X6MM," 2,117 "
-28011668,HC  CATHETER DILATATION PTCA 4.5X20MM," 2,117 "
-28011669,HC  CATHETER DILATATION PTCA 4.5X6MM," 2,117 "
-28011670,HC  CATHETER DILATATION PTCA 4X20MM," 2,117 "
-28011671,HC  CATHETER DILATATION PTCA 4X6MM," 2,117 "
-28011672,HC  CATHETER DILATATION PTCA 5X20MM," 2,117 "
-28011673,HC  CATHETER DILATATION PTCA 5X6MM," 2,117 "
-28011674,HC  CATHETER DILATATION PTCA 6X2.5MM," 2,117 "
-28011675,HC  CATHETER DILATATION PTCA RX 2X6MM," 2,117 "
-28011680,HC  CATHETER DILATATION NC SPRINTER RX 15X3.25MM," 1,726 "
-28011695,HC  CATHETER DILATATION OTW SPRINTER 1.5X10MM," 1,726 "
-28011696,HC  CATHETER DILATATION OTW SPRINTER 1.5X12MM," 1,488 "
-28011697,HC  CATHETER DILATATION OTW SPRINTER 1.5X15MM," 1,488 "
-28011698,HC  CATHETER DILATATION OTW SPRINTER 2.5X10MM," 1,488 "
-28011699,HC  CATHETER DILATATION OTW SPRINTER 2.5X12MM," 1,488 "
-28011700,HC  CATHETER DILATATION OTW SPRINTER 2X10MM," 1,488 "
-28011701,HC  CATHETER DILATATION OTW SPRINTER 2X12MM," 1,726 "
-28011702,HC  CATHETER DILATATION OTW SPRINTER 2X15MM," 1,488 "
-28011704,HC  CATHETER BALLOON PTA 10X10X135," 1,488 "
-28011705,HC  CATHETER BALLOON PTA 10X3X135," 1,615 "
-28011706,HC  CATHETER BALLOON PTA 10X6X135," 1,615 "
-28011707,HC  CATHETER BALLOON PTA 12X40X135MM 7F," 1,615 "
-28011708,HC  CATHETER BALLOON PTA 135CM 80X7MM," 1,615 "
-28011709,HC  CATHETER BALLOON PTA 35 8X14X80," 1,488 "
-28011710,HC  CATHETER BALLOON PTA 4MM 10X135CM 5F," 1,275 "
-28011711,HC  CATHETER BALLOON PTA 4MM 15X135CM 5F," 1,275 "
-28011712,HC  CATHETER BALLOON PTA 4MM 4X135CM 5F," 1,275 "
-28011716,HC  CATHETER BALLOON PTA 5FX135X6X10," 1,445 "
-28011718,HC  CATHETER BALLOON PTA 5FX80X5X2," 2,975 "
-28011719,HC  CATHETER BALLOON PTA 5FX80X6X4," 2,040 "
-28011720,HC  CATHETER BALLOON PTA 6MM 15X135CM 5F," 1,275 "
-28011721,HC  CATHETER BALLOON PTA 6MM 4X135CM 5F," 1,275 "
-28011722,HC  CATHETER BALLOON PTA 6MMX14CM 80CM," 1,488 "
-28011723,HC  CATHETER BALLOON PTA 6MMX4CM 80CM," 2,040 "
-28011725,HC  CATHETER BALLOON PTA 6X14X135," 1,445 "
-28011726,HC  CATHETER BALLOON PTA 6X20X150," 2,338 "
-28011728,HC  CATHETER BALLOON PTA 6X40X150," 2,338 "
-28011730,HC  CATHETER BALLOON PTA 7X14X135," 2,040 "
-28011731,HC  CATHETER BALLOON PTA 7X2X135," 1,445 "
-28011732,HC  CATHETER BALLOON PTA 7X4X135," 1,615 "
-28011733,HC  CATHETER BALLOON PTA 7X6X135," 1,445 "
-28011734,HC  CATHETER BALLOON PTA 7FX80X10X14," 1,488 "
-28011735,HC  CATHETER BALLOON PTA 7FX80X12X4," 1,488 "
-28011736,HC  CATHETER BALLOON PTA 8MMX4CM 80CM," 2,040 "
-28011737,HC  CATHETER BALLOON PTA 8MMX6CM 135CM," 1,445 "
-28011738,HC  CATHETER BALLOON PTA 8MX10CM," 1,488 "
-28011739,HC  CATHETER BALLOON PTA 8X10X135," 1,445 "
-28011740,HC  CATHETER BALLOON PTA 8X2X135," 1,615 "
-28011741,HC  CATHETER BALLOON PTA 8X8X135," 1,615 "
-28011742,HC  CATHETER BALLOON PTA 9X3X135," 1,615 "
-28011743,HC  CATHETER BALLOON PTA 9X4X135," 1,615 "
-28011744,HC  CATHETER BALLOON PTA 9X6X135," 1,445 "
-28011745,HC  CATHETER BALLOON PTA 5FX135X6X4," 2,040 "
-28011746,HC  CATHETER BALLOON PTA 5FX135X6X6," 1,445 "
-28011747,HC  CATHETER BALLOON PTA LP 0.1MMX5," 1,488 "
-28011750,HC  CATHETER BALLOON PTA35 7X10X135," 2,040 "
-28011751,HC  CATHETER BALLOON PTCA 10X75CM 6MM 5F," 1,978 "
-28011752,HC  CATHETER BALLOON PTCA 2X135CM 6MM 5F," 3,113 "
-28011753,HC  CATH BALLOON RX OTW LEGEND 1.25X6MM," 1,726 "
-28011754,HC  CATH BALLOON RX OTW LEGEND 1.25X10MM," 1,488 "
-28011755,HC  CATH BALLOON RX OTW LEGEND 1.25X15MM," 1,488 "
-28011756,HC  CATH BALLOON RX OTW LEGEND 1.25X20MM," 1,488 "
-28011757,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.25X6MM," 1,488 "
-28011758,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.25X10MM," 1,488 "
-28011759,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.25X15MM," 1,488 "
-28011760,HC  CATH BALLOON RX RAPID EXCHANGE LEGEND 1.25X20MM," 1,726 "
-28011761,HC  CATHETER BALLOON SHAFT 10MMX20CM 135CM," 2,125 "
-28011762,HC  CATHETER BALLOON SHAFT 10MX4CM 40CM," 2,125 "
-28011763,HC  CATHETER BALLOON SHAFT 12MMX3CM 75CM," 2,125 "
-28011764,HC  CATHETER BALLOON SHAFT 37MMX4CM 135CM," 2,125 "
-28011765,HC  CATHETER BALLOON SHAFT 3MMX4CM 75CM," 2,125 "
-28011767,HC  CATHETER BALLOON STENT 100CM 10-50CM," 3,698 "
-28011768,HC  CATHETER BALLOON STENT 12F 10-50MM," 3,698 "
-28011769,HC  CATHETER BALLOON STENT 65CM 10-50CM," 3,698 "
-28011770,HC  CATHETER BALLOON SIZING .035 25MMX3CM," 1,785 "
-28011771,HC  CATHETER BALLOON THERMODILUTION 110CM 7F," 1,111 "
-28011772,HC  CATHETER BALLOON THERMODILUTION 4 80CM 5F," 1,134 "
-28011795,HC  CATHETER DOUBLE LUMEN 10CC 8FX12CM," 1,275 "
-28011796,HC  CATHETER DOUBLE LUMEN J TYPE 9FX12CM, 121 
-28011810,HC  CATHETER DILATATION 143CM 3X20MM," 3,404 "
-28011811,HC  CATHETER DILATATION 18MM 6X110CM 9F," 6,375 "
-28011812,HC  CATHETER DILATATION 2MM 10X150CM 5F," 2,668 "
-28011813,HC  CATHETER DILATATION 4X110CM 9F," 6,375 "
-28011817,HC  CATHETER DILATATION CORONARY OTW 2X6MM," 1,488 "
-28011818,HC  CATHETER DILATATION CORONARY OTW 3X12MM," 1,488 "
-28011819,HC  CATHETER DILATATION CORONARY RX 1.2X06MM," 1,615 "
-28011820,HC  CATHETER DILATATION CORONARY RX 1.2X12MM," 1,615 "
-28011821,HC  CATHETER DILATATION CORONARY RX 2.25X20MM," 1,488 "
-28011822,HC  CATHETER DILATATION CORONARY RX 4.5X12MM," 1,488 "
-28011823,HC  CATHETER DILATATION CORONARY RX 4.5X15MM," 1,615 "
-28011824,HC  CATHETER DILATATION CORONARY RX 5MM 12MM," 1,488 "
-28011825,HC  CATHETER DILATATION GAZELLE 2.5X2X135," 3,757 "
-28011826,HC  CATHETER DILATATION GAZELLE 3X90," 3,587 "
-28011827,HC  CATHETER DILATATION GAZELLE 5X90," 3,587 "
-28011828,HC  CATHETER DILATATION GAZELLE 6X90," 3,587 "
-28011829,HC  CATHETER DILATATION PTA 10X80CM 7MM 6F," 2,383 "
-28011830,HC  CATHETER DILATATION PTA 6X10X135CM 5F," 1,275 "
-28011831,HC  CATHETER DILATATION PTA GAZELLE," 3,757 "
-28011833,HC  CATHETER DILATATION SYMMETRY .018 6X4X1," 3,113 "
-28011834,HC  CATHETER DILATATION SYMMETRY 2.5X20X4X6," 3,113 "
-28011835,HC  CATHETER DILATATION SYMMETRY 2.5X20X4X6," 3,113 "
-28011836,HC  CATHETER DILATATION SYMMETRY 2.5X40X4X6," 3,113 "
-28011837,HC  CATHETER DILATATION SYMMETRY 2X40X4X6," 3,113 "
-28011838,HC  CATHETER DILATATION SYMMETRY 3X100X4X6," 3,113 "
-28011839,HC  CATHETER DILATATION SYMMETRY 3X20X4X6," 3,113 "
-28011840,HC  CATHETER DILATATION SYMMETRY 3X40X4X6," 3,113 "
-28011841,HC  CATHETER DILATATION SYMMETRY 4X100X5X7," 3,113 "
-28011842,HC  CATHETER DILATATION SYMMETRY 4X20X4X6CM," 3,113 "
-28011843,HC  CATHETER DILATATION SYMMETRY 4X40X4X6," 3,113 "
-28011844,HC  CATHETER DILATATION SYMMETRY 4X40X4X6CM," 3,113 "
-28011845,HC  CATHETER DILATATION SYMMETRY 5X20X4X6," 3,113 "
-28011846,HC  CATHETER DILATATION SYMMETRY 5X40X4X6," 3,113 "
-28011847,HC  CATHETER DILATATION SYMMETRY 6X40X5X7," 2,955 "
-28011848,HC  CATHETER DILATATION TYSHAK STD 20MMX4CM," 4,120 "
-28011849,HC  CATHETER DILATATION TYSHAK X 22MMX4CMX100CM," 4,120 "
-28011850,HC  CATHETER DILATATION VAN ANDEL 9FX80CM, 425 
-28011897,HC  CATHETER EVERCROSS 0.35 8MMX10MMX13MM," 3,187 "
-28012137,HC  CATHETER PTA 0.35 5MMX150MMX135CM," 1,964 "
-28012138,HC  CATHETER PTA 0.35 5MMX40MMX135CM," 1,488 "
-28012139,HC  CATHETER PTA 0.35 5MMX60MMX130CM," 1,488 "
-28012140,HC  CATHETER PTA 0.35 6MMX200MMX135CM," 1,964 "
-28012141,HC  CATHETER PTA 0.35 6MMX40MMX135CM," 1,488 "
-28012142,HC  CATHETER PTA 0.35 6MMX40MMX80CM," 1,488 "
-28012143,HC  CATHETER PTA 0.35 7MMX60MMX135CM," 1,488 "
-28012144,HC  CATHETER PTA 0.35 8MMX8MMX80CM," 1,488 "
-28012145,HC  CATHETER PTA 120X2CM 16MM 6.5F," 2,763 "
-28012146,HC  CATHETER PTA 120X2CM 18MM 6.5F," 2,763 "
-28012147,HC  CATHETER PTA 120X2CM 20MM 7F," 3,060 "
-28012148,HC  CATHETER PTA 5F 5MM 4X135CM," 1,275 "
-28012149,HC  CATHETER PTA 5F135CM 5X8MM," 1,275 "
-28012150,HC  CATHETER PTA 6.5FX120CM," 2,975 "
-28012151,HC  CATHETER PTA 7MM 4X135CM 6F," 1,275 "
-28012152,HC  CATHETER PTA 7MM 8X135CM 6F," 1,275 "
-28012153,HC  CATHETER DILATATION PTA VASCUTRAK 2X120X140," 7,608 "
-28012154,HC  CATHETER DILATATION PTCA OTW ANGIOSCULPT 3X10," 7,438 "
-28012155,HC  CATHETER DILATATION PTCA TREK NC RX 2.0X8MM," 1,488 "
-28012156,HC  CATHETER DILATATION PTCA TREK NC RX 2.25X12MM," 1,488 "
-28012157,HC  CATHETER DILATATION PTCA TREK NC RX 2.25X15MM," 1,615 "
-28012158,HC  CATHETER DILATATION PTCA TREK NC RX 2.25X20MM," 1,615 "
-28012159,HC  CATHETER DILATATION PTCA TREK NC RX 2.25X8MM," 1,488 "
-28012160,HC  CATHETER DILATATION PTCA TREK NC RX 2X12MM," 1,488 "
-28012161,HC  CATHETER DILATATION PTCA TREK NC RX 2X15MM," 1,488 "
-28012162,HC  CATHETER DILATATION PTCA TREK NC RX 2X20MM," 1,615 "
-28012163,HC  CATHETER DILATATION PTCA TREK NC RX 3.25X8MM," 1,488 "
-28012164,HC  CATHETER DILATATION PTCA TREK NC RX 3.75X8MM," 1,488 "
-28012165,HC  CATHETER DILATATION PTCA TREK NC RX 4.5X12MM," 1,488 "
-28012166,HC  CATHETER DILATATION PTCA TREK NC RX 4.5X15MM," 1,488 "
-28012167,HC  CATHETER DILATATION PTCA TREK NC RX 4.5X8MM," 1,488 "
-28012168,HC  CATHETER DILATATION PTCA TREK NC RX 5X15MM," 1,488 "
-28012183,HC  CATHETER SIZING 5F 100CM 10CM, 111 
-28012203,HC  CATHETER SYMMETRY BALLOON .018 5X4X1," 3,113 "
-28012228,HC  CATHETER DILATATION ULTRATHIN SDS 5.8X4X10X90CM," 1,679 "
-28012229,HC  CATHETER DILATATION ULTRATHIN SDS 5FX4MMX2X135," 1,679 "
-28012230,HC  CATHETER DILATATION ULTRATHIN SDS 5FX4MMX2X75CM," 1,679 "
-28012231,HC  CATHETER DILATATION ULTRATHIN SDS 5FX4MMX4X75CM," 1,679 "
-28012232,HC  CATHETER DILATATION ULTRATHIN SDS 5FX5MMX2X135," 1,679 "
-28012233,HC  CATHETER DILATATION ULTRATHIN SDS 5FX5MMX2X75CM," 1,679 "
-28012234,HC  CATHETER DILATATION ULTRATHIN SDS 5FX5MMX4X75CM," 1,679 "
-28012235,HC  CATHETER DILATATION ULTRATHIN SDS 5FX6MMX2X135," 1,679 "
-28012236,HC  CATHETER DILATATION ULTRATHIN SDS 5FX6MMX2X75CM," 1,679 "
-28012237,HC  CATHETER DILATATION ULTRATHIN SDS 5FX6MMX4X75CM," 1,679 "
-28012238,HC  CATHETER DILATATION ULTRATHIN SDS 5FX7MMX4X135," 1,679 "
-28012239,HC  CATHETER DILATATION ULTRATHIN SDS 6FX5MMX6X135," 1,679 "
-28012252,HC  CATHETER VENOGRAPHY BALLOON BIV CS, 510 
-28011641,HC  CATHETER BALLOON ANORECTAL 4.2X6MM," 63,000 "
-28011648,HC  CATHETER DILATATION CERVICAL 18FX40CM, 323 
-28011649,HC  CATHETER DILATATION ESOPH ACHALASIA 30," 6,707 "
-28011679,HC  CATHETER DILATATION PULM 5.5X18-20MM," 2,168 "
-28011682,HC  CATHETER BALLOON INSPIRA AIR 16X40MM," 5,908 "
-28011816,HC  CATHETER DILATATION COONS 20F 30CM, 374 
-28019476,HC  SYSTEM BALLOON SINUSPLASTY FRONTAL 6X16MM," 12,563 "
-28019477,HC  SYSTEM BALLOON SINUSPLASTY MAXILLARY 6X16MM," 12,563 "
-28012678,HC DISSECT BLLN SPACEMAKER PLUS," 1,322 "
-28011786,HC  CATHETER COUDE 12F 5ML 2W LF, 199 
-28011787,HC  CATHETER COUDE 14F 5ML 2W LF," 2,442 "
-28011788,HC  CATHETER COUDE 16F 5ML 2W LF, 199 
-28011789,HC  CATHETER COUDE 18F 5ML 2W LF, 203 
-28011851,HC  CATHETER DRAINAGE BILIARY RING DUCT 5F, 382 
-28011852,HC  CATHETER DRAINAGE CENTESIS ONE STEP 4X10CM, 187 
-28011853,HC  CATHETER DRAINAGE CHAIT 10.2FX28CM," 1,590 "
-28011854,HC  CATHETER DRAINAGE MINI PIGTAIL 6FX20CM, 740 
-28011855,HC  CATHETER DRAINAGE MINI PIGTAIL 8FX24CM, 740 
-28011856,HC  CATHETER DRAINAGE THAL-QUICK 24FX41CM, 672 
-28011857,HC  CATHETER DRAINAGE THAL-QUICK 28FX41CM, 672 
-28011858,HC  CATHETER DRAINAGE ULTRATHANE 18FX40CM, 867 
-28011859,HC  CATHETER DRAINAGE ULTRATHANE 6.3F 25CM, 667 
-28011860,HC  CATHETER DRAINAGE URI-CATH 3.5F, 101 
-28011861,HC  CATHETER DRAINAGE URI-CATH 5F, 101 
-28011915,HC  CATHETER FOLEY 2WAY 20FX30ML LF, 91 
-28011916,HC  CATHETER FOLEY 2WAY 24FX30ML LF, 91 
-28011917,HC  CATHETER FOLEY 3WAY 18F 5CC, 87 
-28011918,HC  CATHETER FOLEY 3W 22F, 87 
-28011919,HC  CATHETER FOLEY 3WY 30CC 22F, 225 
-28011920,HC  CATHETER FOLEY COUNCIL IC 14F 5CC, 162 
-28011921,HC  CATHETER FOLEY COUNCIL IC 18F 5CC, 162 
-28011922,HC  CATHETER FOLEY COUNCIL IC 20F 5CC, 162 
-28011923,HC  CATHETER FOLEY DOVER COUNCIL 16F, 244 
-28011924,HC  CATHETER FOLEY DOVER COUNCIL 18F, 244 
-28011925,HC  CATHETER FOLEY DOVER COUNCIL 20F, 244 
-28011926,HC  CATHETER FOLEY DOVER COUNCIL 22F, 244 
-28011927,HC  CATHETER FOLEY LUBRI-SIL 12F 5CC, 254 
-28011928,HC  CATHETER FOLEY LUBRI-SIL 16F 30CC, 116 
-28011929,HC  CATHETER FOLEY LUBRI-SIL 16F 5CC, 170 
-28011930,HC  CATHETER FOLEY LUBRI-SIL 18F 30CC, 116 
-28011931,HC  CATHETER FOLEY LUBRI-SIL 18F 5CC, 87 
-28011932,HC  CATHETER FOLEY LUBRI-SIL 20F 30CC, 116 
-28011933,HC  CATHETER FOLEY LUBRI-SIL 22F 22CC, 116 
-28011934,HC  CATHETER FOLEY LUBRI-SIL 24F 30CC, 116 
-28011935,HC  CATHETER FOLEY SIMPLASTIC 2WAY 24F, 185 
-28012683,HC  DRAIN BILE T TUBE WHELAN MOSS STERILE 14F, 154 
-28012684,HC  DRAIN BILE T TUBE WHELAN MOSS STERILE 16F, 169 
-28012685,HC  DRAIN BILE T TUBE WHELAN MOSS STERILE 18F, 154 
-28011867,HC  CATHETER ELECTROPHYSIOLOGY 10PLR FXDCRV 65CM 6F," 1,190 "
-28011868,HC  CATHETER ELECTROPHYSIOLOGY 10PLR STRBL 110CM 4F," 4,250 "
-28011870,HC  CATHETER ELECTROPHYSIOLOGY 4PLR STRBL 110CM 5F," 2,695 "
-28011871,HC  CATHETER ELECTROPHYSIOLOGY 6PLR STRBL 110CM 6F," 4,250 "
-28011876,HC  CATHETER ELECTROPHYSIOLOGY DUO10PLR 110CM 7F," 8,458 "
-28011877,HC  CATHETER ELECTROPHYSIOLOGY INQ 5FX100CM," 4,675 "
-28011878,HC  CATHETER ELECTROPHYSIOLOGY INQ AFOCUS 7FX15," 8,458 "
-28011879,HC  CATHETER ELECTROPHYSIOLOGY INQ AFOCUS 7FX20," 8,458 "
-28011880,HC  CATHETER ELECTROPHYSIOLOGY INQ AFOCUS II 7X20," 9,000 "
-28011885,HC  CATHETER ELECTROPHYSIOLOGY POLARIS 6FX2X8MM," 1,262 "
-28011888,HC  CATHETER ELECTROPHYSIOLOGY RESPONSE 6FX65CM," 3,400 "
-28011890,HC  CATHETER ELECTROPHYSIOLOGY SUPREME QUAD 5 6X120," 1,700 "
-28011891,HC  CATHETER ELECTROPHYSIOLOGY TORQR 5FX110CM, 344 
-28011892,HC  CATHETER EP WEBSTER QUAD 6F, 978 
-28011893,HC  CATHETER EP WEBSTER QUAD 92X5X12, 978 
-28011881,HC  CATHETER ELECTROPHYSIOLOGY LASSO 7RX20X6MM," 8,109 "
-28011882,HC  CATHETER ELECTROPHYSIOLOGY LASSO 7X4.5X15MM," 9,375 "
-28011886,HC  CATHETER ELECTROPHYSIOLOGY REFLEXION 10 P 7X99," 12,375 "
-28011887,HC  CATHETER ELECTROPHYSIOLOGY REFLEXION 20 P 7X99," 11,250 "
-28012083,HC  CATHETER MAPPING LASSO 115CM 7FX4.5X15MM," 8,109 "
-28012084,HC  CATHETER MAPPING LASSO 7FX20MM," 8,280 "
-28011874,HC  CATHETER ELECTROPHYSIOLOGY CONSTELLATION 3X38MM," 2,886 "
-28011875,HC  CATHETER ELECTROPHYSIOLOGY DECANAV 7FX115CM," 7,763 "
-28011883,HC  CATHETER ELECTROPHYSIOLOGY MAP 115CM 7X20X6MM," 7,875 "
-28011884,HC  CATHETER ELECTROPHYSIOLOGY PATHFINDER MAP 1.4F," 7,763 "
-28011899,HC  CATHETER EZ STEER F-F 7FX115CMX8MM," 10,125 "
-28011900,HC  CATHETER EZ STEER F-J 7FX115CMX4MM," 12,503 "
-28012075,HC  CATHETER LASSO 7F 22POLES," 12,420 "
-28012113,HC  CATHETER PENTARAY D CURVE 2-6-2 7FX115CM," 13,470 "
-28012114,HC  CATHETER PENTARAY F CURVE 2-6-2 7FX115CM," 13,470 "
-28012115,HC  CATHETER PENTARAY F CURVE 4-4-4 7FX115CM," 13,470 "
-28011444,HC  CATH ABLAT TCOOL B," 17,640 "
-28011445,HC  CATH ABLAT TCOOL D," 17,640 "
-28011446,HC  CATH ABLAT TCOOL F," 17,640 "
-28011447,HC  CATH ABLAT TCOOL J," 17,640 "
-28011797,HC  CATH DD CRVD 7.5FX115CM," 15,998 "
-28011869,HC  CATH EP 10PLR STRB MED 115X6F," 2,429 "
-28011872,HC  CATH EP ABLAT TEMP 7F 8MM," 10,367 "
-28011873,HC  CATH EP ABLAT XP 7X8MM," 10,367 "
-28011889,HC  CATH EP SAFIRE 7FX4MM," 6,970 "
-28011898,HC  CATH EZ STEER D-F 7X115X4," 15,120 "
-28011901,HC  CATH FF CRVD 7.5FX115CM," 15,998 "
-28012074,HC  CATH JJ CRVD 7.5FX115CM," 15,998 "
-28012049,HC  CATHETER HEMODIALYSIS DUAL LUMEN STR 8FX18CM," 1,530 "
-28012050,HC  CATHETER HEMODIALYSIS ASH SPLIT 14FX32X27CM," 3,137 "
-28011957,HC  CATHETER INF GROSHONG RED CONN DUAL LUMEN 5F, 139 
-28011958,HC  CATHETER INF GROSHONG WHITE CONN DUAL LUMEN 5F, 149 
-28012056,HC  CATHETER INFUSION MICRO VASCULAR 1.6FX156CM," 5,313 "
-28012057,HC  CATHETER INFUSION PER-Q-CATH SINGLE LUMEN 3F, 604 
-28012058,HC  CATHETER INFUSION PICC .032 1.9FX30CM, 470 
-28012059,HC  CATHETER INFUSION POWERLINE SINGLE LUMEN 5F," 2,040 "
-28012060,HC  CATHETER INFUSION POWER PICC SINGLE LUMEN 4F," 1,250 "
-28012061,HC  CATHETER INFUSION POWERHICKMAN DUAL LUMEN 9.5X5," 2,786 "
-28012063,HC  CATHETER INFUSION THERAPEUTIC .035 7X10MMX40CM," 6,800 "
-28012064,HC  CATHETER INFUSION THERAPEUTIC .035 8X10MMX40CM," 6,800 "
-28012065,HC  CATHETER INFUSION TRELLIS PLUS 6FX10CM," 11,213 "
-28012066,HC  CATHETER INFUSION TRELLIS PLUS 6FX20CM," 11,963 "
-28012067,HC  CATHETER INFUSION TRELLIS RES 6FX10CM," 11,213 "
-28012068,HC  CATHETER INFUSION TRELLIS RES 6FX20CM," 11,963 "
-28012122,HC  CATHETER PERIPHERAL POWERPICC 5F," 1,250 "
-28012131,HC  CATHETER PICC OTW TURBO JECT 5.5FX7X50CM," 1,339 "
-28011808,HC  CATHETER DIALYSIS EQUISTREAM 16FX23CM," 3,902 "
-28011809,HC  CATHETER DIALYSIS EQUISTREAM 16FX27CM," 3,902 "
-28011703,HC  CATHETER BALLOON PIONEER PLUS 6FX120CM," 22,365 "
-28012055,HC  CATHETER IMAGING DRAGONFLY 8.5FX63CM," 7,608 "
-28012071,HC  CATHETER IVUS LIPISCAN .014 3.2F," 7,225 "
-28012072,HC  CATHETER IVUS OPTICROSS 5F," 6,035 "
-28011911,HC  CATHETER FOGARTY IRRIGATION ARTERIAL 10FX23CM, 185 
-28012174,HC  CATHETER RETRIEVAL EXTRAC PRONTO V3," 2,508 "
-28012210,HC  CATHETER THROMBECTOMY SOLENT OMNI 6FX120CM," 12,900 "
-28012240,HC  CATHETER URETERAL AXXCESS 6F," 1,000 "
-28012244,HC  CATHETER URETERAL OPEN END 6F 70CM, 89 
-28012245,HC  CATHETER URETERAL OPEN END 7F 70CM, 89 
-28012246,HC  CATHETER URETERAL OPEN END 8F 70CM, 89 
-28011807,HC  CATHETER DIAGNOSTIC US SOUNDSTAR 3D 10F 90CM," 15,563 "
-28012250,HC  CATHETER ULTRASOUND ACUNAV 90CM 8F REP," 18,225 "
-28012293,HC  CATHETER ULTRASOUND 10F," 24,987 "
-28012341,HC IMP CLOSURE DEVICE GRIP 6F," 1,828 "
-28012342,HC IMP CLOSURE DEVICE VASCULAR EXOSEAL 6F," 1,700 "
-28012343,HC IMP CLOSURE VASCULAR DVC 6F VIP," 2,100 "
-28012344,HC IMP CLOSURE VASCULAR DVC 8F VIP," 2,100 "
-28012345,HC IMP CLOSURE VASCULAR DEVICE MYNX 5F," 1,700 "
-28012346,HC IMP CLOSURE V-LOC 0 GS21 6 GREEN, 155 
-28010638,HC IMP ACHILLES TENDON STRUT 12X20MM," 6,375 "
-28012994,HC IMP GRAFTON FLEX 5CMX50CM," 7,735 "
-28015177,HC IMPL MATRIX BONE 5X5CM," 9,120 "
-28015178,HC IMPL MATRIX BONE DBX MIX 10ML," 11,228 "
-28012889,HC IMP GRAFT DURAL MATRIX DURAGN SUT 1X3IN," 2,490 "
-28012890,HC IMP GRAFT DURAL MATRIX DURAGN SUT 4X5IN," 8,518 "
-28015179,HC IMP MATRIX COLLAGEN DUREPAIR 4X5IN," 8,276 "
-28016315,HC IMP PLUG FISTULA ANAL 4MM," 23,800 "
-28016316,HC IMP PLUG FISTULA ENTEROCUTANEOUS 4MM," 23,800 "
-28016317,HC IMP PLUG FISTULA ENTEROCUTANEOUS," 23,800 "
-28019503,HC IMP TISSUE MATRIX CONEXA 200 3X3CM," 15,338 "
-28016373,HC RECORDER CARDIAC EVENT REVEAL," 34,418 "
-28014261,HC  INTRODUCER STEERABLE ACCESS PRO 6FX45CM," 4,888 "
-28012876,HC IMP GENERATOR NEUROSTIMULATOR ACTIVA SC," 60,255 "
-28012879,HC IMP GRAFT BIFURCATED MICROVEL 10X6 CUS," 2,125 "
-28012923,HC IMPL GRAFT GORETEX STRETCH THIN WALL 15X4," 3,417 "
-28012924,HC IMPL GRAFT GORETEX STRETCH THIN WALL 3.5X10," 2,958 "
-28012925,HC IMPL GRAFT GORETEX STRETCH THIN WALL 8X80," 7,531 "
-28012961,HC IMP GRAFT HEMASHIELD WOVEN STRAIGHT 18MMX30CM," 4,021 "
-28012962,HC IMP GRAFT IMPRA CENTERFLEX 6X50," 6,069 "
-28012966,HC IMPL GRAFT STRAIGHT 24X60CM," 4,420 "
-28012967,HC IMP GRAFT VASCULAR 1 VELOUR 10MMX40CM," 3,964 "
-28012968,HC IMP GRAFT VASCULAR 1 VELOUR 6MMX40CM," 5,455 "
-28012969,HC IMP GRAFT VASCULAR 1 VELOUR 8MMX40CM," 3,814 "
-28012970,HC IMP GRAFT VASCULAR 10X5," 1,496 "
-28012971,HC IMP GRAFT VASCULAR 2 VELOUR 10MMX60CM," 3,761 "
-28012972,HC IMP GRAFT VASCULAR 2 VELOUR 12X6MMX40CM," 4,904 "
-28012973,HC IMP GRAFT VASCULAR 2 VELOUR 14X7MMX40CM," 4,667 "
-28012974,HC IMP GRAFT VASCULAR 2 VELOUR 16MMX50CM," 2,410 "
-28012975,HC IMP GRAFT VASCULAR 2 VELOUR 18MMX50CM," 2,410 "
-28012976,HC IMP GRAFT VASCULAR 2 VELOUR 20MMX50CM," 2,410 "
-28012977,HC IMP GRAFT VASCULAR 2 VELOUR 22MMX50CM," 2,410 "
-28012978,HC IMP GRAFT VASCULAR 2 VELOUR 24MMX50CM," 2,410 "
-28012979,HC IMP GRAFT VASCULAR 2 VELOUR 26MMX50CM," 2,410 "
-28012980,HC IMP GRAFT VASCULAR 2 VELOUR 28MMX50CM," 2,410 "
-28012981,HC IMP GRAFT VASCULAR 2 VELOUR 30MMX50CM," 10,387 "
-28012982,HC IMP GRAFT VASCULAR 2 VELOUR 6MMX40CM," 3,443 "
-28012983,HC IMP GRAFT VASCULAR 2 VELOUR 8MMX60CM," 3,761 "
-28012984,HC IMP GRAFT VASCULAR ACCESS 40X5CM 6MM," 5,084 "
-28012985,HC IMP GRAFT VASCULAR ACCESS STR 40CM," 4,102 "
-28012986,HC IMP GRAFT VASCULAR ACCESS STR 40CM 8MM," 4,102 "
-28012987,HC IMP GRAFT VASCULAR LINED 3X40," 3,179 "
-28012988,HC IMP GRAFT VASCULAR STANDARD WALL STRETCH 6X70," 5,891 "
-28012989,HC IMP GRAFT VASCULAR STANDARD WALL STRETCH 8X40," 3,834 "
-28012990,HC IMP GRAFT VASCULAR STRETCH TW 4X80," 6,656 "
-28012991,HC IMP GRAFT VASCULAR WOVEN 14X7 40CM," 3,485 "
-28012992,HC IMP GRAFT VASCULAR WOVEN 36MMX30CM," 3,985 "
-28012993,HC IMP GRAFT VENAFLO STEPPED 35CM," 6,112 "
-28015299,HC IMP PATCH CARDIOVASCULAR 0.6MM 10X15CM," 8,138 "
-28013014,HC  GUIDEWIRE 2.4MM, 850 
-28013015,HC  GUIDEWIRE .014," 1,530 "
-28013016,HC  GUIDEWIRE ACUTRAK 1.6MMX9.25, 128 
-28013017,HC  GUIDEWIRE ACUTRAK 2 2.4MMX9.25IN, 128 
-28013020,HC  GUIDEWIRE AMPLATZ STRAIGHT .035IN 300CM, 306 
-28013021,HC  GUIDEWIRE APPROACH CTO 014X300 18G," 1,148 "
-28013022,HC  GUIDEWIRE APPROACH CTO 014X300 25G," 1,148 "
-28013023,HC  GUIDEWIRE ATTAIN HYBRID OTW LEAD 98CM," 1,360 "
-28013024,HC  GUIDEWIRE ATTAIN HYBRID 4194 98CM," 1,700 "
-28013025,HC  GUIDEWIRE ATTAIN HYBRID OTW 108CM PURPLE," 1,700 "
-28013027,HC  GUIDEWIRE BALL TIPPED 2.7MM," 2,312 "
-28013028,HC  GUIDEWIRE BENTSON STRAIGHT FC 035X150, 115 
-28013030,HC  GUIDEWIRE CATH NITINOL 018IN 40CM, 204 
-28013031,HC  GUIDEWIRE CEREBRAL PTFE 035IN 145CM, 148 
-28013032,HC  GUIDEWIRE CEREBRAL PTFE 038IN 145CM, 109 
-28013033,HC  GUIDEWIRE COATED PTFE DUAL, 130 
-28013034,HC  GUIDEWIRE CVS 180X3CM 0.035IN, 99 
-28013036,HC  GUIDEWIRE DEFLECTING 100CM 25IN 5MM, 163 
-28013037,HC  GUIDEWIRE DOUBLE FLEXIBLE 018X30, 275 
-28013038,HC  GUIDEWIRE DOUBLE FLEXIBLE 021X40, 272 
-28013039,HC  GUIDEWIRE DOUBLE FLEXIBLE 025X50, 247 
-28013040,HC  GUIDEWIRE ENDOLOGIX 014IN," 1,488 "
-28013041,HC  GUIDEWIRE ENDOLOGIX 035IN," 1,360 "
-28013042,HC  GUIDEWIRE FLUTED 2.8X300MM, 939 
-28013043,HC  GUIDEWIRE GLIDEWIRE ANG STIFF 035X450, 850 
-28013044,HC  GUIDEWIRE HI TORQUE 0.014INX190CM," 1,190 "
-28013045,HC  GUIDEWIRE HI TORQUE 300X3CM 200, 139 
-28013046,HC  GUIDEWIRE HI TORQUE CONN .018X300," 1,063 "
-28013047,HC  GUIDEWIRE HI TORQUE FLEX .018X300," 1,063 "
-28013048,HC  GUIDEWIRE HI TORQUE STANDARD .035X300CM," 5,695 "
-28013049,HC  GUIDEWIRE HYDROPHILIC COATED ANGGLED 3CMX150CM, 332 
-28013050,HC  GUIDEWIRE HYDROPHILIC SUPP 14X200CM," 3,230 "
-28013051,HC  GUIDEWIRE IMPELLA 0.028 47MM 260CM," 4,250 "
-28013052,HC  GUIDEWIRE INQWIRE STRAIGHT TIP 035X150CM, 115 
-28013053,HC  GUIDEWIRE INTERVENTIONAL ULTRA STIFF STR 260X14, 485 
-28013054,HC  GUIDEWIRE INTERVENTIONAL ULTRA STIFF 260X14, 510 
-28013055,HC  GUIDEWIRE INTERVENTIONAL ULTRA STIFF 145CM 8CM, 186 
-28013056,HC  GUIDEWIRE INTERVENTIONAL ULTRA STIFF 180CM 8CM," 1,411 "
-28013057,HC  GUIDEWIRE INTERVENTIONAL ULTRA STIFF 80CM 8CM, 151 
-28013058,HC  GUIDEWIRE JAGWIRE STRAIGHT .035X180CM, 999 
-28013059,HC  GUIDEWIRE KINETIX STRAIGHT MOD 014X185CM, 808 
-28013060,HC  GUIDEWIRE KINETIX STRAIGHT MOD 014X300, 808 
-28013061,HC  GUIDEWIRE KINETIX STRAIGHT PLUS 014X185CM, 808 
-28013062,HC  GUIDEWIRE KINETIX STRAIGHT PLUS 014X300CM, 808 
-28013063,HC  GUIDEWIRE LEAD OVER THE WIRE HYBRID 108 BLUE," 1,700 "
-28013064,HC  GUIDEWIRE MDCL TECH STRAIGHT FLEXIBLE REP, 220 
-28013065,HC  GUIDEWIRE METRO DIRECT 035X480," 1,318 "
-28013066,HC  GUIDEWIRE NITREX .012 18CM, 213 
-28013068,HC  GUIDEWIRE NON THREAD SS METRX MEDIU, 646 
-28013070,HC  GUIDEWIRE NITINOL ANGIOGRAPHY ULTRA 300CM, 439 
-28013071,HC  GUIDEWIRE ORTHO 1.6MM BALLISTA, 655 
-28013072,HC  GUIDEWIRE PARALLEL ADJUSTABLE 2.8MM," 3,264 "
-28013073,HC  GUIDEWIRE PERIPHERAL 0.018IN 50CM 3MM, 97 
-28013075,HC  GUIDEWIRE PTCA CORONARY 0.014IN 300CM, 638 
-28013079,HC  GUIDEWIRE PTFE COATED STIFF 035X150, 119 
-28013080,HC  GUIDEWIRE PULMONARY 180CM 0.035IN DISP, 999 
-28013083,HC  GUIDEWIRE SMOOTH TIP 3X800MM," 2,176 "
-28013084,HC  GUIDEWIRE SSL WITH SPRING COIL 025X175CM," 1,275 "
-28013085,HC  GUIDEWIRE STEERABLE ANGLED TIP 014X200," 2,975 "
-28013086,HC  GUIDEWIRE STINGRAY HPC 185CM," 2,125 "
-28013087,HC  GUIDEWIRE STRAIGHT 0.35 X50CM, 100 
-28013088,HC  GUIDEWIRE STRAIGHT 014 IN X 182 CM," 1,190 "
-28013089,HC  GUIDEWIRE STRAIGHT MAXXWIRE 035X180," 1,063 "
-28013090,HC  GUIDEWIRE STRAIGHT TP NTNL 0D 80X2CM, 354 
-28013091,HC  GUIDEWIRE STRAIGHT TAPRED 145CM 0.032IN, 91 
-28013092,HC  GUIDEWIRE SURGICAL BENDED TIP 2.6MM 80CM, 816 
-28013093,HC  GUIDEWIRE SUREPASS 014 IN X 260 CM," 1,488 "
-28013094,HC  GUIDEWIRE SUREPASS 035 IN X 260 CM," 1,360 "
-28013095,HC  GUIDEWIRE SYMMETRY SMOOTH 2MM," 2,108 "
-28013099,HC  GUIDEWIRE T-J CURVED FIXD 35IN 145CM, 87 
-28013100,HC  GUIDEWIRE TORQUE WHOLEY .032-.038, 103 
-28013101,HC  GUIDEWIRE VASCULAR STEER 140X25CM 016IN," 2,088 "
-28013102,HC  GUIDEWIRE VASCULAR STEER 180X25CM 016IN," 2,457 "
-28013103,HC  GUIDEWIRE VIPER BLUNT TIP 2 1.45MM," 1,173 "
-28013104,HC  GUIDEWIRE VIPER SHARP TIP 2 1.45MM," 1,564 "
-28018560,HC IMP SLING SYSTEM URETHERAL VENTRAL MESH," 31,115 "
-28016356,HC IMP PUMP INFUSION NERVE BLOCK 5ML," 1,681 "
-28016358,HC IMP PUMP W/POUCH MESH SYNCHROMED, 723 
-28016384,HC  RETRIEVER CATH THROMBECTOMY 175X135CM," 18,675 "
-28016527,HC  RETRIEVER THROMBECTOMY MERCI 2.5X4.5MM," 23,100 "
-28010681,HC IMP ANKLE TALAR STAR RIGHT SMALL," 36,725 "
-28010706,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG LGLT," 16,430 "
-28010707,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG LGRT," 16,430 "
-28010708,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG MDLT," 16,430 "
-28010709,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG MDRT," 16,430 "
-28010710,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG SMLT," 16,430 "
-28010711,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG SMRT," 16,430 "
-28010712,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG XLGLT," 16,430 "
-28010713,HC IMP AUGMENT GLENOID EQUINOX POSTERIOR 8 DEG XLGRT," 16,430 "
-28012759,HC IMP ELBOW HEAD RADIAL CAPITELLUM SIZE 3," 16,583 "
-28012760,HC IMP ELBOW HUMERAL ASSEMBLY SMALL 4IN," 28,727 "
-28012761,HC IMP ELBOW HUMERAL ASSEMBLY EXTRA SMALL 4IN," 28,466 "
-28012762,HC IMP ELBOW MODULAR RADIAL HEAD 24MM," 14,138 "
-28012763,HC IMP ELBOW RADIO CAPITELLUM SMALL," 20,475 "
-28012764,HC IMP ELBOW STEM RADIAL HEAD 9.5MM," 16,815 "
-28012765,HC IMP ELBOW STEM RADIAL HEAD SZ1," 19,460 "
-28012766,HC IMP ELBOW ULNAR EXTRA SMALL RIGHT 3IN," 26,673 "
-28013107,HC IMP HIP HEAD FEMORAL 12/14 CERAMIC 28 +1.5MM," 10,335 "
-28013108,HC IMP HIP HEAD FEMORAL VERSYS 12/14 0.5X40MM REV," 6,206 "
-28013109,HC IMP HIP HEAD FEMORAL VERSYS 12/14 40X0MM REV," 6,206 "
-28013110,HC IMP HIP HEAD FEMORAL VERSYS 12/14 40X3.5MM REV," 6,206 "
-28013111,HC IMP HIP HEAD FEMORAL VERSYS 12/14 40X3.5MM REV," 6,206 "
-28013119,HC IMP HIP ACETABULAR ASR 46X100MM," 17,850 "
-28013120,HC IMP HIP ACETABULAR ASR 48X100MM," 17,850 "
-28013121,HC IMP HIP ACETABULAR ASR 58X100MM," 17,850 "
-28013122,HC IMP HIP ACETABULAR ASR 60X100MM," 17,850 "
-28013123,HC IMP HIP ACETABULAR FEMORAL MODULAR ASR SZ43," 6,800 "
-28013124,HC IMP HIP ACETABULAR FEMORAL MODULAR ASR SZ51," 6,800 "
-28013125,HC IMP HIP ACETABULAR FEMORAL MODULAR ASR SZ53," 6,800 "
-28013126,HC IMP HIP AUGMENT ACETABULAR 54X10MM REV," 13,797 "
-28013127,HC IMP HIP AUGMENT ACETABULAR 54X15MM REV," 13,797 "
-28013128,HC IMP HIP AUGMENT ACETABULAR 58X10MM," 15,390 "
-28013129,HC IMP HIP AUGMENT ACETABULAR 58X10MM REV," 13,797 "
-28013130,HC IMP HIP AUGMENT ACETABULAR 58X20MM REV," 13,797 "
-28013131,HC IMP HIP AUGMENT ACETABULAR 62X20MM REV," 13,797 "
-28013132,HC IMP HIP BLOCK REVISION BUILD-UP SZ 13/1," 2,827 "
-28013133,HC IMP HIP BODY CALCAR A 40X45 90X45MM 37.5MM REV," 21,265 "
-28013134,HC IMP HIP BODY CALCAR AA 36X35 75X35MM 33.5MM REV," 21,265 "
-28013135,HC IMP HIP BODY CALCAR AA 40X35 75X35MM 37.5MM REV," 21,058 "
-28013136,HC IMP HIP BODY CALCAR AA 40X35 80X35MM 37.5MM REV," 21,058 "
-28013137,HC IMP HIP BODY CALCAR C 40X45 90X45MM 37.5MM REV," 21,265 "
-28013138,HC IMP HIP BODY CONE B 40X55 100X55MM 37.5MM," 20,252 "
-28013139,HC IMP HIP BODY CONE B 46X35 80X35MM 43.5MM REV," 21,058 "
-28013140,HC IMP HIP BODY CONE C 40X35 80X35MM 37.5MM REV," 21,265 "
-28013141,HC IMP HIP BODY CONE C 40X55 100X55MM 37.5MM REV," 21,265 "
-28013142,HC IMP HIP BODY CONEC 40X45 90X45MM 37.5MM REV," 21,265 "
-28013143,HC IMP HIP BODY SPOUT PLASMA AA 36X3MM REV," 21,265 "
-28013144,HC IMP HIP BODY SPOUT PLASMA AA 40X3MM REV," 21,265 "
-28013145,HC IMP HIP BODY SPOUT PLASMA B 40X45MM REV," 21,265 "
-28013147,HC IMP HIP CAGE ACETABULAR REVISION FLANGE LONG," 15,188 "
-28013148,HC IMP HIP CAGE RECONSTRUCTION CEMENTED TI 58X54MM REV," 17,676 "
-28013168,HC IMP HIP CUP ACETABULAR ASR XL 56MM," 17,850 "
-28013169,HC IMP HIP CUP ACETABULAR BANTAM 40OD," 8,306 "
-28013170,HC IMP HIP CUP ACETABULAR BI POLAR 42MM," 4,250 "
-28013171,HC IMP HIP CUP ACETABULAR BI POLAR 42MM REV," 9,750 "
-28013172,HC IMP HIP CUP ACETABULAR BI POLAR 49MM," 8,063 "
-28013173,HC IMP HIP CUP ACETABULAR HEAD 52MM," 40,300 "
-28013174,HC IMP HIP CUP ACETABULAR HEAD 54MM," 40,300 "
-28013175,HC IMP HIP CUP ACETABULAR HEAD 56MM," 40,300 "
-28013176,HC IMP HIP CUP ACETABULAR HEAD 60MM," 40,300 "
-28013177,HC IMP HIP CUP ACETABULAR HEAD BHR 44X50MM," 28,788 "
-28013178,HC IMP HIP CUP ACETABULAR HEAD BHR 46X54MM," 28,788 "
-28013179,HC IMP HIP CUP ACETABULAR HEAD BHR 58X52MM," 28,788 "
-28013180,HC IMP HIP CUP ACETABULAR HEAD BHR 62X56MM," 28,788 "
-28013181,HC IMP HIP CUP ACETABULAR HEAD BHR 64MM," 40,300 "
-28013182,HC IMP HIP CUP ACETABULAR LNGVTY ELVTD 32X55MM," 9,000 "
-28013183,HC IMP HIP CUP ACETABULAR NEUTRAL CEMNTD 32X49 REV," 8,370 "
-28013184,HC IMP HIP CUP ACETABULAR PINNACLE 46MM REV," 12,137 "
-28013185,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 50MM," 19,811 "
-28013186,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 52MM," 19,811 "
-28013187,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 54MM," 19,811 "
-28013188,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 56MM," 19,811 "
-28013189,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 58MM," 19,811 "
-28013190,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 60MM," 18,690 "
-28013191,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 64MM," 18,690 "
-28013192,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON 66MM," 18,690 "
-28013193,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON MH 58MM," 17,100 "
-28013194,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON MH 68MM," 17,100 "
-28013195,HC IMP HIP CUP ACETABULAR PINNACLE GRIPTON MH 70MM," 17,100 "
-28013196,HC IMP HIP CUP ACETABULAR XLPE BANTAM 46MM," 9,000 "
-28013197,HC IMP HIP CUP ACETABULAR XLPE BANTAM 46MM REV," 5,574 "
-28013198,HC IMP HIP CUP ALL POLY POLYETHYLENE 32X42MM," 7,478 "
-28013199,HC IMP HIP CUP ALL POLY POLYETHYLENE 32X42MM REV," 7,055 "
-28013200,HC IMP HIP CUP ALL POLY PE 32X42MM REV," 5,574 "
-28013201,HC IMP HIP CUP BIPOLAR RINGLOC 28X50MM," 8,063 "
-28013202,HC IMP HIP CUP BIPOLAR UNIVERSAL 43X2," 4,250 "
-28013203,HC IMP HIP CUP PINNACLE MULTI HOLE CUP 56MM," 12,645 "
-28013204,HC IMP HIP CUP PINNACLE SECTOR 48MM," 9,270 "
-28013205,HC IMP HIP CUP PINNACLE SECTOR 50MM," 9,270 "
-28013206,HC IMP HIP CUP PINNACLE SECTOR 54MM," 9,270 "
-28013207,HC IMP HIP CUP PINNACLE SECTOR 56MM," 9,540 "
-28013208,HC IMP HIP CUP PINNACLE SECTOR 62MM," 9,000 "
-28013209,HC IMP HIP CUP PINNACLE SECTOR 64MM," 9,540 "
-28013210,HC IMP HIP HEAD FEMORAL BANTAM +5 28MM REV," 5,238 "
-28013211,HC IMP HIP FEMORAL STD COLLAR POROUS HA SZ14 REV," 33,235 "
-28013212,HC IMP HIP HEAD FEMORAL DELTA CERAMIC 36MM +1.5MM," 12,762 "
-28013213,HC IMP HIP HEAD 8.5X36MM," 6,800 "
-28013214,HC IMP HIP HEAD ADAPTER ALLOY 14MM REV, 857 
-28013216,HC IMP HIP HEAD FEMORAL +0MM 28MM," 6,800 "
-28013217,HC IMP HIP HEAD FEMORAL +1.5MM 36MM," 10,335 "
-28013218,HC IMP HIP HEAD FEMORAL 12/14 TAPER +10.5MM 36MM REV," 6,206 "
-28013219,HC IMP HIP HEAD FEMORAL VERSYS 12/14 +3.5 32MM," 3,188 "
-28013220,HC IMP HIP HEAD FEMORAL VERSYS 12/14 +3.5MM 36MM," 3,188 "
-28013221,HC IMP HIP HEAD FEMORAL VERSYS 12/14 +7MM 32MM," 3,188 "
-28013222,HC IMP HIP HEAD FEMORAL VERSYS 12/14 +7MM 36MM," 3,188 "
-28013223,HC IMP HIP HEAD FEMORAL VERSYS 12/14 0X28MM," 5,568 "
-28013224,HC IMP HIP HEAD FEMORAL VERSYS 12/14 0X32MM," 3,188 "
-28013225,HC IMP HIP HEAD FEMORAL VERSYS 12/14 0X36MM," 3,188 "
-28013226,HC IMP HIP HEAD FEMORAL VERSYS 12/14 22X0MM," 3,188 "
-28013227,HC IMP HIP HEAD FEMORAL VERSYS 12/14 22X-2MM," 3,188 "
-28013228,HC IMP HIP HEAD FEMORAL VERSYS 12/14 28X-3.5MM," 3,188 "
-28013229,HC IMP HIP HEAD FEMORAL VERSYS 12/14 28X7MM," 3,188 "
-28013230,HC IMP HIP HEAD FEMORAL VERSYS 12/14 3.5X28MM," 3,188 "
-28013231,HC IMP HIP HEAD FEMORAL VERSYS 12/14 -3.5X32MM," 3,188 "
-28013232,HC IMP HIP HEAD FEMORAL VERSYS 12/14 -3.5X36MM," 3,188 "
-28013233,HC IMP HIP HEAD FEMORAL VERSYS 12/14 32X10.5MM," 3,188 "
-28013234,HC IMP HIP HEAD FEMORAL VERSYS 12/14 3X22MM," 3,188 "
-28013235,HC IMP HIP HEAD FEMORAL VERSYS 12/14 40X0MM," 3,188 "
-28013236,HC IMP HIP HEAD FEMORAL VERSYS 12/14 40X3.5MM," 3,188 "
-28013237,HC IMP HIP HEAD FEMORAL 12/14 TAPER +3.5 36 REV," 6,206 "
-28013238,HC IMP HIP HEAD FEMORAL 12/14 TAPER +3.5X32 REV," 5,184 "
-28013239,HC IMP HIP HEAD FEMORAL 12/14 TAPER +7 32MM REV," 5,184 "
-28013240,HC IMP HIP HEAD FEMORAL 12/14 TAPER +7 36MM REV," 6,206 "
-28013241,HC IMP HIP HEAD FEMORAL 12/14 TAPER 0X28MM REV," 5,184 "
-28013242,HC IMP HIP HEAD FEMORAL 12/14 TAPER 0X32MM REV," 5,184 "
-28013243,HC IMP HIP HEAD FEMORAL 12/14 TAPER 0X36MM REV," 6,206 "
-28013244,HC IMP HIP HEAD FEMORAL 12/14 TAPER 26X-3.5MM REV," 5,184 "
-28013245,HC IMP HIP HEAD FEMORAL 12/14 TAPER 28X+8.5MM REV," 6,800 "
-28013246,HC IMP HIP HEAD FEMORAL 12/14 TAPER 28X-3.5MM REV," 5,184 "
-28013247,HC IMP HIP HEAD FEMORAL 12/14 TAPER 28X7MM REV," 5,184 "
-28013248,HC IMP HIP HEAD FEMORAL 12/14 TAPER -3.5 32 REV," 5,184 "
-28013249,HC IMP HIP HEAD FEMORAL 12/14 TAPER 3.5X28MM REV," 5,184 "
-28013250,HC IMP HIP HEAD FEMORAL 12/14 TAPER 14 -3.5X36MM REV," 6,206 "
-28013251,HC IMP HIP HEAD FEMORAL VERSYS 12/14 +10.5MM 36MM," 3,188 "
-28013252,HC IMP HIP HEAD FEMORAL 14/16 TAPER +0MM 36MM REV," 13,410 "
-28013253,HC IMP HIP HEAD FEMORAL 22.225X5MM," 6,800 "
-28013254,HC IMP HIP HEAD FEMORAL 26MM," 3,188 "
-28013255,HC IMP HIP HEAD FEMORAL 32MM +8.5MM," 9,750 "
-28013256,HC IMP HIP HEAD FEMORAL 36MM +12MM," 7,208 "
-28013257,HC IMP HIP HEAD FEMORAL 36MM +5MM," 16,376 "
-28013258,HC IMP HIP HEAD FEMORAL 36MM +8.5MM," 6,800 "
-28013259,HC IMP HIP HEAD FEMORAL 44MM +1.5MM," 7,208 "
-28013260,HC IMP HIP HEAD FEMORAL ACETABULAR 28X-3.5MM," 8,063 "
-28013261,HC IMP HIP HEAD FEMORAL ACETABULAR 32X0MM," 8,063 "
-28013262,HC IMP HIP HEAD FEMORAL ACETABULAR 32X-3.5MM," 8,063 "
-28013263,HC IMP HIP HEAD FEMORAL ACETABULAR 36MM -3.5MM," 8,063 "
-28013264,HC IMP HIP HEAD FEMORAL ACETABULAR 36+3.5MM," 8,063 "
-28013265,HC IMP HIP HEAD FEMORAL ACETABULAR 36X-3.5MM," 8,063 "
-28013266,HC IMP HIP HEAD FEMORAL BALL 28MM +5MM," 7,208 "
-28013267,HC IMP HIP HEAD FEMORAL BALL 36MM +0MM," 6,800 "
-28013268,HC IMP HIP HEAD FEMORAL BALL 36MM +11MM," 6,800 "
-28013269,HC IMP HIP HEAD FEMORAL BALL 36MM +5MM," 6,800 "
-28013270,HC IMP HIP HEAD FEMORAL BIPOLAR 28X44MM," 4,250 "
-28013271,HC IMP HIP HEAD FEMORAL BIPOLAR 28X45MM," 4,250 "
-28013272,HC IMP HIP HEAD FEMORAL BIPOLAR 28X46MM," 4,250 "
-28013273,HC IMP HIP HEAD FEMORAL BIPOLAR 28X48MM," 4,250 "
-28013274,HC IMP HIP HEAD FEMORAL BIPOLAR 28X49MM," 4,250 "
-28013275,HC IMP HIP HEAD FEMORAL BIPOLAR 28X53MM," 4,250 "
-28013276,HC IMP HIP HEAD FEMORAL BIPOLAR SELF CENTERING 28X55," 4,250 "
-28013277,HC IMP HIP HEAD FEMORAL C TAPER +0 40MM," 3,188 "
-28013278,HC IMP HIP HEAD FEMORAL C TAPER +0 40MM REV," 8,807 "
-28013279,HC IMP HIP HEAD FEMORAL C TAPER +10 36MM REV," 8,807 "
-28013280,HC IMP HIP HEAD FEMORAL C TAPER +2.5 32MM REV," 5,722 "
-28013281,HC IMP HIP HEAD FEMORAL C TAPER COCR +0 32MM REV," 5,253 "
-28013282,HC IMP HIP HEAD FEMORAL C TAPER COCR +5 32MM REV," 5,253 "
-28013283,HC IMP HIP HEAD FEMORAL CERAMIC +5 32MM," 10,335 "
-28013284,HC IMP HIP HEAD FEMORAL CERAMIC +5 32MM REV," 9,149 "
-28013285,HC IMP HIP HEAD FEMORAL CERAMIC 28MM +1.5MM," 10,335 "
-28013286,HC IMP HIP HEAD FEMORAL CERAMIC 28MM +5MM," 9,750 "
-28013287,HC IMP HIP HEAD FEMORAL CERAMIC 3 28MM," 5,738 "
-28013288,HC IMP HIP HEAD FEMORAL CERAMIC 3 32MM," 5,738 "
-28013289,HC IMP HIP HEAD FEMORAL CERAMIC 3 32MM REV," 9,023 "
-28013290,HC IMP HIP HEAD FEMORAL CERAMIC 32MM +1MM," 10,335 "
-28013291,HC IMP HIP HEAD FEMORAL CERAMIC 32MM +5MM REV," 5,105 "
-28013292,HC IMP HIP HEAD FEMORAL CERAMIC 32MM +9MM," 9,750 "
-28013293,HC IMP HIP HEAD FEMORAL CERAMIC 44MM +5MM," 6,800 "
-28013294,HC IMP HIP HEAD FEMORAL COCR +1 32MM REV, 938 
-28013295,HC IMP HIP HEAD FEMORAL COCR +13 32MM REV, 938 
-28013296,HC IMP HIP HEAD FEMORAL COCR +5 32MM REV, 938 
-28013297,HC IMP HIP HEAD FEMORAL COCR PCA +10 28MM REV," 5,518 "
-28013298,HC IMP HIP HEAD FEMORAL COCR PCA +10 32MM," 3,188 "
-28013299,HC IMP HIP HEAD FEMORAL COCR PCA +10 32MM REV," 5,518 "
-28013300,HC IMP HIP HEAD FEMORAL COCR PCA +2 22MM REV," 3,659 "
-28013301,HC IMP HIP HEAD FEMORAL COCR PCA +5 22MM REV," 3,659 "
-28013302,HC IMP HIP HEAD FEMORAL COCR PCA +5 32MM," 3,188 "
-28013303,HC IMP HIP HEAD FEMORAL COCR PCA +5 32MM REV," 5,518 "
-28013304,HC IMP HIP HEAD FEMORAL DELTA CERAMIC 28MM +5MM," 10,335 "
-28013305,HC IMP HIP HEAD FEMORAL DELTA CERAMIC 32MM +1MM," 10,335 "
-28013306,HC IMP HIP HEAD FEMORAL DELTA CERAMIC 32MM +5MM," 10,335 "
-28013307,HC IMP HIP HEAD FEMORAL DELTA CERAMIC 32MM +9MM," 9,750 "
-28013308,HC IMP HIP HEAD FEMORAL DELTA CERAMIC 36MM +5MM," 9,750 "
-28013309,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 42MM," 3,188 "
-28013310,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 44MM," 3,188 "
-28013311,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 45MM," 3,188 "
-28013312,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 46MM," 3,188 "
-28013313,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 47MM," 3,188 "
-28013314,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 48MM," 3,188 "
-28013315,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 50MM," 3,188 "
-28013316,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 51 MM," 3,188 "
-28013317,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 12/14 50MM REV," 3,283 "
-28013318,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 53MM," 3,188 "
-28013319,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 55MM," 3,188 "
-28013320,HC IMP HIP HEAD FEMORAL ENDOPROSTHESIS 57MM," 3,188 "
-28013321,HC IMP HIP HEAD FEMORAL EZE COCR +1 32MM REV," 6,800 "
-28013322,HC IMP HIP HEAD FEMORAL EZE COCR +1.5 40MM," 7,208 "
-28013323,HC IMP HIP HEAD FEMORAL EZE COCR +12 40MM," 7,208 "
-28013324,HC IMP HIP HEAD FEMORAL EZE COCR +13 32MM," 7,208 "
-28013325,HC IMP HIP HEAD FEMORAL EZE COCR +13 32MM REV," 5,105 "
-28013326,HC IMP HIP HEAD FEMORAL EZE COCR +15.5 40MM," 7,208 "
-28013327,HC IMP HIP HEAD FEMORAL EZE COCR +17 32MM," 6,800 "
-28013328,HC IMP HIP HEAD FEMORAL EZE COCR +5 40MM," 7,208 "
-28013329,HC IMP HIP HEAD FEMORAL EZE COCR+8.5 40MM," 7,208 "
-28013330,HC IMP HIP HEAD FEMORAL EZE COCR+8.5 40MM REV," 9,509 "
-28013331,HC IMP HIP HEAD FEMORAL EZE COCR +9 32MM REV," 6,800 "
-28013332,HC IMP HIP HEAD FEMORAL M2A MODULAR +3 32MM," 3,188 "
-28013333,HC IMP HIP HEAD FEMORAL M2A MODULAR -3 32MM," 3,188 "
-28013334,HC IMP HIP HEAD FEMORAL M2A MODULAR 32MM," 3,188 "
-28013335,HC IMP HIP HEAD FEMORAL M2A MODULAR -6 32MM," 3,188 "
-28013336,HC IMP HIP HEAD FEMORAL METASUL LDH 38FT 44MM REV," 9,266 "
-28013337,HC IMP HIP HEAD FEMORAL MODULAR CONTRAINED +9 36MM," 8,528 "
-28013338,HC IMP HIP HEAD FEMORAL MODULAR CONTRAINED -6 36MM," 8,528 "
-28013339,HC IMP HIP HEAD FEMORAL MOD COBALT CHROME +0 36MM," 4,250 "
-28013340,HC IMP HIP HEAD FEMORAL MOD COBALT CHROME +0 36MM," 4,250 "
-28013341,HC IMP HIP HEAD FEMORAL MODULAR COBALT CHROME +3 36MM," 3,188 "
-28013342,HC IMP HIP HEAD FEMORAL MODULAR COBALT CHROME +9 36MM," 3,188 "
-28013343,HC IMP HIP HEAD FEMORAL MODULAR HEMI 42MM REV," 16,538 "
-28013344,HC IMP HIP HEAD FEMORAL MOD T1 NO SKIRT -6 26MM REV," 4,213 "
-28013345,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAPER +12 32MM," 3,188 "
-28013346,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAP +3 32MM REV," 4,213 "
-28013347,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAPER +3 32MM," 4,250 "
-28013348,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAPER +6 32MM," 3,188 "
-28013349,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAPER -3 32MM," 4,250 "
-28013350,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAPER -6 32MM," 3,188 "
-28013351,HC IMP HIP HEAD FEMORAL MODULAR TYPE 1 TAPER STD 32MM," 4,250 "
-28013352,HC IMP HIP HEAD FEMORAL MODULAR 22.2MM," 9,563 "
-28013353,HC IMP HIP HEAD FEMORAL M-SHAPED +9 36MM," 6,800 "
-28013354,HC IMP HIP HEAD FEMORAL OFFSET +0 40MM REV," 8,807 "
-28013355,HC IMP HIP HEAD FEMORAL OFFSET +4 40MM REV," 8,807 "
-28013356,HC IMP HIP HEAD FEMORAL RESURFACING BHR 46MM," 23,100 "
-28013357,HC IMP HIP HEAD FEMORAL RESURFACING 50MM REV," 16,538 "
-28013358,HC IMP HIP HEAD FEMORAL RESURFACING 54MM REV," 16,538 "
-28013359,HC IMP HIP HEAD FEMORAL RESURFACING BHR 44MM," 3,188 "
-28013360,HC IMP HIP HEAD FEMORAL RESURFACING BHR 44MM REV," 21,420 "
-28013361,HC IMP HIP HEAD FEMORAL RESURFACING BHR 48MM, 563 
-28013362,HC IMP HIP HEAD FEMORAL RESURFACING BHR 50MM," 23,100 "
-28013363,HC IMP HIP HEAD FEMORAL RESURFACING BHR 52MM," 13,781 "
-28013364,HC IMP HIP HEAD FEMORAL RESURFACING BHR 54MM," 23,100 "
-28013365,HC IMP HIP HEAD FEMORAL RESURFACING BHR 56MM," 13,781 "
-28013366,HC IMP HIP HEAD FEMORAL RESURFACING BHR 58MM," 23,100 "
-28013367,HC IMP HIP HEAD FEMORAL RESURFACING BHR 58MM REV," 21,420 "
-28013368,HC IMP HIP HEAD FEMORAL SOL SYS BOW 8/13.5 L REV," 32,798 "
-28013369,HC IMP HIP HEAD FEMORALS-ROM 36MM +0MM," 6,800 "
-28013370,HC IMP HIP HEAD FEMORAL TAPER LONG 6 DEGREE 36MM REV," 6,512 "
-28013371,HC IMP HIP HEAD FEMORAL TAPER MEDIUM 6 DEGREE 36MM REV," 6,512 "
-28013372,HC IMP HIP HEAD FEMORAL TAPER MEDIUM 6 DEGREE 7X32 REV," 5,430 "
-28013373,HC IMP HIP HEAD FEMORAL TAPER MEDIUM PLUS 6 DEG 36 REV," 6,512 "
-28013374,HC IMP HIP HEAD FEMORAL TAPER MODULAR M2A -6MM," 3,188 "
-28013375,HC IMP HIP HEAD FEMORAL TAPER MORSE +5 32MM," 3,188 "
-28013376,HC IMP HIP HEAD FEMORAL TAPER MORSE +5 32MM REV," 5,518 "
-28013377,HC IMP HIP HEAD FEMORAL TAPER SHORT 6 DEGREE 36MM REV," 6,512 "
-28013378,HC IMP HIP HEAD FEMORAL TAPER SHORT PLUS 6 DEG 36 REV," 6,512 "
-28013379,HC IMP HIP HEAD FEMORAL TAPER SYS 44MM," 6,800 "
-28013380,HC IMP HIP HEAD FEMORAL ULTAMET 36MM +5MM," 7,208 "
-28013381,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 42MM," 6,163 "
-28013382,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 44MM," 4,777 "
-28013383,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 45MM," 4,777 "
-28013384,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 46MM," 6,163 "
-28013385,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 47MM," 6,163 "
-28013386,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 48MM," 6,163 "
-28013387,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 46MM," 6,163 "
-28013388,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 50MM," 6,163 "
-28013389,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 51MM," 4,777 "
-28013390,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 52MM," 6,163 "
-28013391,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 53MM," 4,777 "
-28013392,HC IMP HIP HEAD FEMORAL UNIPOLAR MODULAR 56MM," 6,163 "
-28013393,HC IMP HIP HEAD FEMORAL 28X1.5," 9,750 "
-28013394,HC IMP HIP HEAD FEMORAL 32X1.5," 9,750 "
-28013395,HC IMP HIP HEAD FEMORAL 32X5.0," 9,750 "
-28013396,HC IMP HIP HEAD FEMORAL 36X1.5," 9,750 "
-28013397,HC IMP HIP HEAD FEMORAL 36X5.0," 9,750 "
-28013398,HC IMP HIP HEAD FEMORAL 36X8.5," 9,750 "
-28013399,HC IMP HIP HEAD HIP BANTAM 28MM +5," 6,800 "
-28013400,HC IMP HIP HEAD MODULAR COBALT CHROME 32MM," 4,514 "
-28013401,HC IMP HIP HEAD PROSTHESIS TAP 4X107MM," 21,000 "
-28013402,HC IMP HIP HEAD STEM PROSTALAC LEFT 200 SIZE 23 REV," 17,051 "
-28013403,HC IMP HIP HEADS 12X36," 6,800 "
-28013404,HC IMP HIP INSERT ACETABULAR 0D 32X50X52MM REV," 8,216 "
-28013405,HC IMP HIP INSERT ACETABULAR 10D 32MM +4MM REV," 6,778 "
-28013406,HC IMP HIP INSERT ACETABULAR 10D 32X50X52MM REV," 8,216 "
-28013407,HC IMP HIP INSERT ACETABULAR 10D 32X58X60MM REV," 8,216 "
-28013408,HC IMP HIP INSERT ACETABULAR 32X62X64X66MM," 9,540 "
-28013409,HC IMP HIP INSERT ACETABULAR CERAMIC 28X48X50MM," 9,540 "
-28013410,HC IMP HIP INSERT ACETABULAR CERAMIC 32X52MM," 9,540 "
-28013411,HC IMP HIP INSERT ACETABULAR CERAMIC 32X54X56MM," 9,540 "
-28013412,HC IMP HIP INSERT ACETABULAR CERAMIC 32X54X56MM REV," 11,165 "
-28013413,HC IMP HIP INSERT ACETABULAR CERAMIC 32X58X60MM," 9,540 "
-28013414,HC IMP HIP INSERT ACETABULAR CONSTRAINED SZ F 28MM REV," 16,745 "
-28013415,HC IMP HIP INSERT ACETABULAR DURATION 15D 32MM P4 REV," 5,702 "
-28013416,HC IMP HIP INSERT ACETABULAR DURATION 15D 32MM P6 REV," 5,702 "
-28013417,HC IMP HIP INSERT ACETABULAR NEUTRAL 4PLUS 32X50MM REV," 7,988 "
-28013418,HC IMP HIP INSERT ACETABULAR NEUTRAL PE 28X50MM REV," 7,344 "
-28013419,HC IMP HIP INSERT RIM ELEVATED PE SIZE H 36MM REV," 10,242 "
-28013420,HC IMP HIP LINER ACETABULAR 10D 22.225X38MM," 5,100 "
-28013421,HC IMP HIP LINER ACETABULAR 10D 28X46MM +4 REV," 8,604 "
-28013422,HC IMP HIP LINER ACETABULAR 10D 32MM 25MM," 12,000 "
-28013423,HC IMP HIP LINER ACETABULAR 10D 32X50MM," 5,100 "
-28013424,HC IMP HIP LINER ACETABULAR 10D 36X54MM," 5,100 "
-28013425,HC IMP HIP LINER ACETABULAR 10D 36X56X58MM +4," 5,100 "
-28013426,HC IMP HIP LINER ACETABULAR 10D 36X58X70MM +4," 5,100 "
-28013427,HC IMP HIP LINER ACETABULAR 10D 36X60MM +4 REV," 10,076 "
-28013428,HC IMP HIP LINER ACETABULAR 10D 36X64," 5,950 "
-28013429,HC IMP HIP LINER ACETABULAR 10D 38X22X5.1MM," 5,950 "
-28013430,HC IMP HIP LINER ACETABULAR 10D 40X22X6.1MM," 5,950 "
-28013431,HC IMP HIP LINER ACETABULAR 10D 40X76X78X80MM REV," 10,820 "
-28013432,HC IMP HIP LINER ACETABULAR 10D 46X28X6.3MM REV," 7,513 "
-28013433,HC IMP HIP LINER ACETABULAR 10D 46X28X6.2MM," 5,950 "
-28013434,HC IMP HIP LINER ACETABULAR 10D 5.3X32X48MM," 5,950 "
-28013435,HC IMP HIP LINER ACETABULAR 10D 5.3X32X48MM REV," 7,513 "
-28013436,HC IMP HIP LINER ACETABULAR 10D 50X52X54MM REV," 7,513 "
-28013437,HC IMP HIP LINER ACETABULAR 10D 6.3X32X54MM," 5,950 "
-28013438,HC IMP HIP LINER ACETABULAR 10D 6.4X32X56MM," 5,950 "
-28013439,HC IMP HIP LINER ACETABULAR 10D 6.4X32X56MM REV," 7,513 "
-28013440,HC IMP HIP LINER ACETABULAR 10D 64X32X10.3MM REV," 7,513 "
-28013441,HC IMP HIP LINER ACETABULAR 10D 7.3X32X58MM," 5,950 "
-28013442,HC IMP HIP LINER ACETABULAR 10D 8.9X36X58MM," 5,950 "
-28013443,HC IMP HIP LINER ACETABULAR 10D 8.9X36X58MM REV," 9,389 "
-28013444,HC IMP HIP LINER ACETABULAR 10D 9.9X36X60MM," 5,950 "
-28013445,HC IMP HIP LINER ACETABULAR 10D 9.9X36X60MM REV," 9,389 "
-28013446,HC IMP HIP LINER ACETABULAR 20D 50X52X32X6.3MM REV," 7,513 "
-28013447,HC IMP HIP LINER ACETABULAR 20D 56X32X6.4MM REV," 7,513 "
-28013448,HC IMP HIP LINER ACETABULAR 20D 62X32X9.3MM REV," 7,513 "
-28013449,HC IMP HIP LINER ACETABULAR 23X6.0X5MM," 13,928 "
-28013450,HC IMP HIP LINER ACETABULAR 32X54MM REV," 8,924 "
-28013451,HC IMP HIP LINER ACETABULAR 32X56MM REV," 8,924 "
-28013452,HC IMP HIP LINER ACETABULAR 32X58MM REV," 8,924 "
-28013453,HC IMP HIP LINER ACETABULAR 36X54MM," 5,100 "
-28013454,HC IMP HIP LINER ACETABULAR 36X60X72MM +4MM REV," 10,076 "
-28013455,HC IMP HIP LINER ACETABULAR 50X52X54X28X7.2MM REV," 7,508 "
-28013456,HC IMP HIP LINER ACETABULAR 50X52X54X32X6.3MM REV," 8,059 "
-28013457,HC IMP HIP LINER ACETABULAR HI 6.8X36X50X52X54MM," 5,950 "
-28013458,HC IMP HIP LINER ACETABULAR 6.8X36X50X52X54MM REV," 9,389 "
-28013459,HC IMP HIP LINER ACETABULAR 72X36X15.9MM REV," 9,389 "
-28013460,HC IMP HIP LINER ACETABULAR BRNG 52X36MM," 9,000 "
-28013461,HC IMP HIP LINER ACETABULAR CONSTRAINED 32X54MM," 10,036 "
-28013462,HC IMP HIP LINER ACETABULAR CONSTRAINED 32X56MM REV," 10,036 "
-28013463,HC IMP HIP LINER ACETABULAR CONSTRAINED 32X58MM REV," 10,036 "
-28013464,HC IMP HIP LINER ACETABULAR CONSTRAINED 32X62MM REV," 10,036 "
-28013465,HC IMP HIP LINER ACETABULAR CONSTRAINED 60X36MM REV," 18,685 "
-28013466,HC IMP HIP LINER ACETABULAR CROSSLINKED 42X26X5.3 REV," 8,059 "
-28013467,HC IMP HIP LINER ACETABULAR CROSSLINKED 55X36X7.9 REV," 9,389 "
-28013468,HC IMP HIP LINER ACETABULAR CROSSLINKED 58X36X8.9 REV," 9,389 "
-28013469,HC IMP HIP LINER ACETABULAR CROSSLINKED 58X40X6.8 REV," 9,856 "
-28013470,HC IMP HIP LINER ACETABULAR CROSSLINKED 58X40X8.9 REV," 9,856 "
-28013471,HC IMP HIP LINER ACETABULAR CROSSLINKED 60X36X9.9 REV," 9,389 "
-28013472,HC IMP HIP LINER ACETABULAR CROSSLINKED 62X36X10.9 REV," 9,389 "
-28013473,HC IMP HIP LINER ACETABULAR CROSSLINKED 64X36X11.9 REV," 9,389 "
-28013474,HC IMP HIP LINER ACETABULAR CROSSLINKED 66X36X12.9 REV," 9,389 "
-28013475,HC IMP HIP LINER ACETABULAR CUP SYSTEM 28X50MM," 5,100 "
-28013476,HC IMP HIP LINER ACETABULAR DURALOC NEUTRAL 28X50 REV," 7,857 "
-28013477,HC IMP HIP LINER ACETABULAR DURALOC 32X60X72MM," 15,435 "
-28013478,HC IMP HIP LINER ACETABULAR ENHANCED APR 32X51 REV," 5,853 "
-28013479,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 10.9X66MM," 5,950 "
-28013480,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 23X36MM," 12,000 "
-28013481,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 36X56MM," 5,950 "
-28013482,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 36X58MM," 5,950 "
-28013483,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 36X60MM," 5,950 "
-28013484,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 36X62MM," 5,950 "
-28013485,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 40X22X6.1," 5,950 "
-28013486,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 40X62MM," 5,950 "
-28013487,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 44X28X5.2," 5,950 "
-28013488,HC IMP HIP LINER ACETABULAR HI CRSLNK PE 44X28X5.2 REV," 8,059 "
-28013489,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 46X28X6.3," 5,950 "
-28013490,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 48X28X6.2," 5,950 "
-28013491,HC IMP HIP LINER ACETABULAR HI CRSLNK PE 48X28X6.2 REV," 8,059 "
-28013492,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 48X32X5.3," 5,950 "
-28013493,HC IMP HIP LINER ACETABULAR HI CRSLNK PE 48X32X5.3 REV," 8,059 "
-28013494,HC IMP HIP LINER ACETABULAR HI 50X52X54X32X6.3MM," 5,950 "
-28013495,HC IMP HIP LINER ACETABULAR 50X52X54X28X7.2MM," 5,950 "
-28013496,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 56X32X6.4," 5,950 "
-28013497,HC IMP HIP LINER ACETABULAR HI CRSLNK PE 56X32X6.4 REV," 8,059 "
-28013498,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 58X32X7.3," 5,950 "
-28013499,HC IMP HIP LINER ACETABULAR HI 64X32X10.3MM REV," 5,950 "
-28013500,HC IMP HIP LINER ACETABULAR HI 64X36X11.9MM REV," 5,950 "
-28013501,HC IMP HIP LINER ACETABULAR HI CRSLNK PE 64X40X9.9 REV," 9,856 "
-28013502,HC IMP HIP LINER ACETABULAR H CRSLNK PE 66X36X12.9 REV," 9,389 "
-28013503,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 66X36X12.9," 5,950 "
-28013504,HC IMP HIP LINER ACETABULAR H CRSLNK PE 68X32X12.4 REV," 8,059 "
-28013505,HC IMP HIP LINER ACETABULAR H CRSLNK PE 68X36X13.9 REV," 9,389 "
-28013506,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 7.9X60MM," 5,950 "
-28013507,HC IMP HIP LINER ACET HI CROSSLINK PE 74X36X16.9 REV," 9,389 "
-28013508,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE 9.9X64MM," 5,950 "
-28013509,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE G 28," 5,950 "
-28013510,HC IMP HIP LINER ACETABULAR HI CROSSLINK PE G 32 REV," 9,956 "
-28013511,HC IMP HIP LINER ACETABULAR HI CROSSLINK OFFSET 32MM," 5,950 "
-28013512,HC IMP HIP LINER ACETABULAR INSERT PE TRIDENT 40MM REV," 9,734 "
-28013513,HC IMP HIP LINER ACETABULAR LIPPD 28X48MM," 5,406 "
-28013514,HC IMP HIP LINER ACETABULAR LIPPD 28X50MM," 5,406 "
-28013515,HC IMP HIP LINER ACETABULAR LIPPD 32X54MM," 5,406 "
-28013516,HC IMP HIP LINER ACETABULAR LIPPD 32X52MM," 5,406 "
-28013517,HC IMP HIP LINER ACETABULAR LARGE 10D 32X72MM," 5,100 "
-28013518,HC IMP HIP LINER ACETABULAR MARATHON 28X44MM," 5,100 "
-28013519,HC IMP HIP LINER ACETABULAR MARATHON 28X46MM," 5,100 "
-28013520,HC IMP HIP LINER ACETABULAR MEDIUM 10D 28MM +3MM," 5,100 "
-28013521,HC IMP HIP LINER ACETABULAR METAL 44X62MM," 9,000 "
-28013522,HC IMP HIP LINER ACETABULAR MULTIPOLAR 28MM," 4,633 "
-28013523,HC IMP HIP LINER ACETABULAR NEUTRAL 36X60MM," 5,100 "
-28013524,HC IMP HIP LINER ACETABULAR NEUTRAL 10D 36X56MM," 5,100 "
-28013525,HC IMP HIP LINER ACETABULAR NEUTRAL 32X56MM," 5,100 "
-28013526,HC IMP HIP LINER ACETABULAR NEUTRAL 36X54MM," 5,100 "
-28013527,HC IMP HIP LINER ACETABULAR NEUTRAL 36X54MM +4MM," 5,100 "
-28013528,HC IMP HIP LINER ACETABULAR NEUTRAL 36X54MM +4MM REV," 10,076 "
-28013529,HC IMP HIP LINER ACETABULAR NEUTRAL 36X56MM," 5,100 "
-28013530,HC IMP HIP LINER ACETABULAR NEUTRAL 36X58MM +4," 5,100 "
-28013531,HC IMP HIP LINER ACETABULAR NEUTRAL 36X58MM," 5,406 "
-28013532,HC IMP HIP LINER ACETABULAR NEUTRAL 36X60MM +4," 5,100 "
-28013533,HC IMP HIP LINER ACETABULAR NEUTRAL 36X62MM," 5,100 "
-28013534,HC IMP HIP LINER ACETABULAR NEUTRAL 36X64MM," 5,100 "
-28013535,HC IMP HIP LINER ACETABULAR NEUTRAL 36X68MM," 5,100 "
-28013536,HC IMP HIP LINER ACETABULAR NEUTRAL 36X70MM," 5,100 "
-28013537,HC IMP HIP LINER ACETABULAR NEUTRAL 40X60MM," 5,100 "
-28013538,HC IMP HIP LINER ACETABULAR NEUTRAL 4PLUS 32X48MM," 5,100 "
-28013539,HC IMP HIP LINER ACETABULAR NEUTRAL 4PLUS 32X50MM," 5,100 "
-28013540,HC IMP HIP LINER ACETABULAR NEUTRAL 4PLUS 32X50MM REV," 7,664 "
-28013541,HC IMP HIP LINER ACETABULAR NEUTRAL PE 28X50MM REV," 7,767 "
-28013542,HC IMP HIP LINER ACETABULAR NEUTRAL PINNACLE 36X52MM," 5,406 "
-28013543,HC IMP HIP LINER ACETABULAR NEUTRAL PINNACLE 40X56 +4," 5,406 "
-28013544,HC IMP HIP LINER ACETABULAR NEUTRAL PINNACLE 40X58 +4," 5,406 "
-28013545,HC IMP HIP LINER ACETABULAR NEUTRAL PINNACLE 40X60 +4," 5,406 "
-28013546,HC IMP HIP LINER ACETABULAR NEUTRAL PINNACLE 44X64 +4," 5,100 "
-28013547,HC IMP HIP LINER ACETABULAR NEUTRAL PINNACLE 44X66 +4," 5,100 "
-28013548,HC IMP HIP LINER ACETABULAR STND PE 28X52MM," 5,100 "
-28013549,HC IMP HIP LINER ACETABULAR SZ23 10D 32MM," 12,000 "
-28013550,HC IMP HIP LINER ACETABULAR SZ23 10D 32MM REV," 9,000 "
-28013551,HC IMP HIP LINER ACETABULAR SZ24 10D 32MM," 12,000 "
-28013552,HC IMP HIP LINER ACETABULAR SZE 20D 32X50-52MM REV," 9,536 "
-28013553,HC IMP HIP LINER ACETABULAR SZF 20D 36X54-56MM REV," 10,715 "
-28013554,HC IMP HIP LINER ACETABULAR XL RINGLOC SZ 23 32MM," 7,650 "
-28013555,HC IMP HIP LINER ACETABULAR XL RINGLOC SZ 24 32MM," 7,650 "
-28013556,HC IMP HIP LINER ACETABULAR XL RINGLOC SZ 25 32MM," 7,650 "
-28013557,HC IMP HIP LINER ACETABULAR XL RINGLOC SZ 26 32MM," 7,650 "
-28013558,HC IMP HIP LINER ACETABULAR XLPE SZF 36X56MM REV," 10,715 "
-28013559,HC IMP HIP LINER INSERT CUP 51X32MM REV," 8,259 "
-28013560,HC IMP HIP LINER INSERT CUP 53X32MM REV," 7,825 "
-28013561,HC IMP HIP LINER INSERT CUP 57X32MM REV," 8,259 "
-28013562,HC IMP HIP LINER PINNACLE MARATHON LIP 28IDX48OD," 5,100 "
-28013563,HC IMP HIP LINER PINNACLE MARATHON LIP 28IDX50OD," 5,100 "
-28013564,HC IMP HIP LINER PINNACLE MARATHON LIP 32IDX52OD," 5,100 "
-28013565,HC IMP HIP LINER PINNACLE MARATHON LIP 32IDX54OD," 5,100 "
-28013566,HC IMP HIP LINER SHELL LONGEVITY OD 32X54MM REV," 8,108 "
-28013568,HC IMP HIP RING LOCKING ACETABULAR DYNAMIC 50MM," 1,887 "
-28013569,HC IMP HIP RING LOCKING ACETABULAR DYNAMIC 50MM REV," 1,693 "
-28013570,HC IMP HIP RING LOCKING ACETABULAR DYNAMIC 54MM," 1,887 "
-28013571,HC IMP HIP RING LOCKING ACETABULAR DYNAMIC 72MM," 1,887 "
-28013572,HC IMP HIP RING LOCKING ACETABULAR DYNAMIC 72MM REV," 1,693 "
-28013573,HC IMP HIP RING LOCKING ACETABULAR REPLACEMENT 23 REV, 638 
-28013574,HC IMP HIP RING RECON ROOF CEMENTED 58MM REV," 9,993 "
-28013575,HC IMP HIP REVISION MODULAR REACH SZ2 REV," 28,027 "
-28013577,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE MODULAR 50MM," 12,300 "
-28013578,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE MODULAR 52MM," 12,300 "
-28013579,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE MOD 56MM REV," 13,136 "
-28013580,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE MODULAR 58MM," 12,300 "
-28013581,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE MOD 60MM REV," 13,136 "
-28013582,HC IMP HIP SHELL ACETABULAR CONVERGE MULTI HOLE 51 REV," 12,285 "
-28013583,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 50," 12,300 "
-28013584,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 50 REV," 16,509 "
-28013585,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 52," 12,300 "
-28013586,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 52 REV," 16,509 "
-28013587,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 54," 12,300 "
-28013588,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 54 REV," 16,509 "
-28013589,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 58," 12,300 "
-28013590,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 58 REV," 16,509 "
-28013591,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 62," 12,300 "
-28013592,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 62 REV," 16,509 "
-28013593,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 64," 12,300 "
-28013594,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 64 REV," 16,509 "
-28013595,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODLR RVSN 66," 12,300 "
-28013596,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 66 REV," 16,509 "
-28013597,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 72 REV," 16,509 "
-28013598,HC IMP HIP SHELL ACETABULAR OPTION DURLOC 56MM," 9,540 "
-28013599,HC IMP HIP SHELL ACETABULAR OPTION DURLOC 56MM REV," 10,418 "
-28013600,HC IMP HIP SHELL ACETABULAR OPTION DURLOC 58MM," 9,540 "
-28013601,HC IMP HIP SHELL ACETABULAR REVISION TITANIUM 56MM REV," 22,607 "
-28013602,HC IMP HIP SHELL ACETABULAR REVISION TITANIUM 58MM," 15,188 "
-28013603,HC IMP HIP SHELL ACETABULAR RINGLOC BIOPLAR +3 28 REV," 4,250 "
-28013604,HC IMP HIP SHELL ACETABULAR RINGLOC BIOPLAR +3 28MM," 4,250 "
-28013605,HC IMP HIP SHELL ACETABULAR RINGLOC BIOPLAR 28X44MM," 4,250 "
-28013606,HC IMP HIP SHELL ACETABULAR TRABECULAR 0D 28X50MM REV," 5,773 "
-28013607,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 48MM," 10,050 "
-28013608,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 50MM," 10,050 "
-28013609,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 52MM," 10,050 "
-28013610,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 56MM," 10,050 "
-28013611,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 56MM REV," 9,918 "
-28013612,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 60MM," 10,050 "
-28013613,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 62MM," 10,050 "
-28013614,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 64MM," 10,050 "
-28013615,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 66MM," 10,050 "
-28013616,HC IMP HIP SHELL ACETABULAR CLUSTER HOLE 52MM," 12,300 "
-28013617,HC IMP HIP SHELL ACETABULAR HA/TCP CLUSTER HOLE 50MM," 10,050 "
-28013618,HC IMP HIP SHELL ACETABULAR HA/TCP CLUSTER HOLE 52MM," 10,050 "
-28013619,HC IMP HIP SHELL ACETABULAR HA/TCP CLUSTER HOLE 56MM," 10,050 "
-28013620,HC IMP HIP SHELL ACETABULAR L HOLE 50X23MM," 8,330 "
-28013621,HC IMP HIP SHELL ACETABULAR L HOLE 52X23MM," 8,330 "
-28013622,HC IMP HIP SHELL ACETABULAR L HOLE 54X24MM," 15,188 "
-28013623,HC IMP HIP SHELL ACETABULAR L HOLE 56X24MM," 8,330 "
-28013624,HC IMP HIP SHELL ACETABULAR L HOLE 58X25MM," 8,330 "
-28013625,HC IMP HIP SHELL ACETABULAR L HOLE 60X25MM," 15,188 "
-28013626,HC IMP HIP SHELL ACETABULAR LEFT 56MM," 15,188 "
-28013627,HC IMP HIP SHELL ACETABULAR MULTI HOLE 44MM," 10,050 "
-28013628,HC IMP HIP SHELL ACETABULAR MULTI HOLE 44MM REV," 11,827 "
-28013629,HC IMP HIP SHELL ACETABULAR MULTI HOLE 46MM," 10,050 "
-28013630,HC IMP HIP SHELL ACETABULAR MULTI HOLE 48MM," 10,050 "
-28013631,HC IMP HIP SHELL ACETABULAR MULTI HOLE 48MM REV," 11,827 "
-28013632,HC IMP HIP SHELL ACETABULAR MULTI HOLE 50MM," 10,050 "
-28013633,HC IMP HIP SHELL ACETABULAR MULTI HOLE 52MM," 10,050 "
-28013634,HC IMP HIP SHELL ACETABULAR MULTI HOLE 54MM REV," 10,938 "
-28013635,HC IMP HIP SHELL ACETABULAR MULTI HOLE 56MM," 10,050 "
-28013636,HC IMP HIP SHELL ACETABULAR MULTI HOLE 56MM REV," 11,827 "
-28013637,HC IMP HIP SHELL ACETABULAR MULTI HOLE 62MM," 10,050 "
-28013638,HC IMP HIP SHELL ACETABULAR MULTI HOLE 62MM REV," 11,827 "
-28013639,HC IMP HIP SHELL ACETABULAR MULTI HOLE HATCP 54MM," 10,050 "
-28013640,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 38MM," 12,300 "
-28013641,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 40MM," 12,300 "
-28013642,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 42 REV," 16,509 "
-28013643,HC IMP HIP SHELL ACETABULAR MULTI HOLE MODULAR 44MM," 12,300 "
-28013644,HC IMP HIP SHELL ACETABULAR MULTI HOLE 48MM REV," 16,509 "
-28013645,HC IMP HIP SHELL ACETABULAR MULTI HOLE 56MM REV," 16,509 "
-28013646,HC IMP HIP SHELL ACETABULAR MULTI HOLE REVISION 56MM," 12,300 "
-28013647,HC IMP HIP SHELL ACETABULAR MULTI HOLE 60MM REV," 16,509 "
-28013648,HC IMP HIP SHELL ACETABULAR MULTI HOLE REVISION 60MM," 12,300 "
-28013649,HC IMP HIP SHELL ACETABULAR MULTI HOLE 68MM REV," 16,509 "
-28013650,HC IMP HIP SHELL ACETABULAR MULTI HOLE 74MM REV," 16,509 "
-28013651,HC IMP HIP SHELL ACETABULAR MULTI HOLE TRILOG 58MM REV," 11,827 "
-28013652,HC IMP HIP SHELL ACETABULAR MULTI HOLE TRILOG 60MM REV," 11,827 "
-28013653,HC IMP HIP SHELL ACETABULAR MULTI HOLE TRILOGY 60MM," 10,050 "
-28013654,HC IMP HIP SHELL ACETABULAR MULTI HOLE TRILOGY 64MM," 10,050 "
-28013655,HC IMP HIP SHELL ACETABULAR MULTIPOLAR BIPOLAR 44MM," 4,250 "
-28013656,HC IMP HIP SHELL ACETABULAR MULTIPOLAR BIPOLAR 46MM," 5,568 "
-28013657,HC IMP HIP SHELL ACETABULAR MULTIPOLAR BIPOLAR 48MM," 5,568 "
-28013658,HC IMP HIP SHELL ACETABULAR MULTIPOLAR BIPOLAR 53MM," 5,568 "
-28013659,HC IMP HIP SHELL ACETABULAR MULTIPOLAR BIPOLAR 45MM," 4,250 "
-28013660,HC IMP HIP SHELL ACETABULAR PINNACLE 28 48MM," 9,540 "
-28013661,HC IMP HIP SHELL ACETABULAR PINNACLE 28 50MM," 9,540 "
-28013662,HC IMP HIP SHELL ACETABULAR PINNACLE 32 52MM," 9,540 "
-28013663,HC IMP HIP SHELL ACETABULAR PINNACLE 32 54MM," 9,540 "
-28013664,HC IMP HIP SHELL ACETABULAR PINNACLE 32 54MM REV," 10,418 "
-28013665,HC IMP HIP SHELL ACETABULAR PINNACLE 32 60MM," 9,000 "
-28013666,HC IMP HIP SHELL ACETABULAR PINNACLE 32 62MM," 9,540 "
-28013667,HC IMP HIP SHELL ACETABULAR PINNACLE 32 64MM," 9,000 "
-28013668,HC IMP HIP SHELL ACETABULAR ORACE 23 50MM," 15,188 "
-28013669,HC IMP HIP SHELL ACETABULAR PINNACLE MULTI HOLE 62MM," 12,645 "
-28013670,HC IMP HIP SHELL ACETABULAR PINNACLE MULTI HOLE 64MM," 12,645 "
-28013671,HC IMP HIP HEAD FEMORAL TAPER MODULAR M2A STD 28MM," 4,250 "
-28013672,HC IMP HIP SHELL ACETABULAR RIGHT 60MM," 15,188 "
-28013673,HC IMP HIP SHELL ACETABULAR REVISION CUP 78MM REV," 18,687 "
-28013674,HC IMP HIP SHELL ACETABULAR SOLIDBACK 54MM," 15,188 "
-28013675,HC IMP HIP SHELL ACETABULAR STANDARD 3HOLE 56MM," 15,188 "
-28013676,HC IMP HIP SHELL ACETABULAR SZ22 48MM," 15,188 "
-28013677,HC IMP HIP SHELL ACETABULAR SZ22 50MM," 17,250 "
-28013678,HC IMP HIP SHELL ACETABULAR SZ23 52MM," 17,250 "
-28013679,HC IMP HIP SHELL ACETABULAR SZ23 54MM," 17,250 "
-28013680,HC IMP HIP SHELL ACETABULAR SZ24 56MM," 17,250 "
-28013681,HC IMP HIP SHELL ACETABULAR PLUS SZ24 58MM," 16,219 "
-28013682,HC IMP HIP SHELL ACETABULAR SZ25 60MM," 17,250 "
-28013683,HC IMP HIP SHELL ACETABULAR SZ26 66MM," 15,188 "
-28013684,HC IMP HIP SHELL ACETABULAR TM MOD CLUSTER HOLE 48MM," 12,300 "
-28013685,HC IMP HIP SHELL CONVERGE CLUSTER HOLE 53MM REV," 10,414 "
-28013686,HC IMP HIP SLEEVE MODULAR +0MM," 1,658 "
-28013687,HC IMP HIP SLEEVE MODULAR +4MM," 1,658 "
-28013688,HC IMP HIP SLEEVE MODULAR +4MM REV," 1,658 "
-28013689,HC IMP HIP SLEEVE MODULAR -4MM," 1,658 "
-28013690,HC IMP HIP SLEEVE MODULAR -4MM REV," 1,658 "
-28013695,HC IMP HIP STEM ADVOCATE 16X145X42X45X47," 13,500 "
-28013696,HC IMP HIP STEM ARTICULEZE 32MM +5," 6,800 "
-28013697,HC IMP HIP STEM BEADED 6IN 15X160MM," 28,043 "
-28013698,HC IMP HIP STEM BEADED FULL COAT 11X130MM," 25,200 "
-28013699,HC IMP HIP STEM BEADED FULL COAT 12X140MM," 25,200 "
-28013700,HC IMP HIP STEM BDED FLCT 12X140MM REV," 31,739 "
-28013701,HC IMP HIP STEM BODY CONE ZMR 100X55MM REV," 21,265 "
-28013702,HC IMP HIP STEM BODY CONE ZMR 75X35MM REV," 21,265 "
-28013703,HC IMP HIP STEM BODY CONE AA ZMR 36X35MM REV," 21,265 "
-28013704,HC IMP HIP STEM BODY CONE ZMR 90X45MM REV," 21,265 "
-28013705,HC IMP HIP STEM BODY SPT ZMR 100X55MM REV," 21,058 "
-28013706,HC IMP HIP STEM BODY SPOUT A ZMR 40X35MM REV," 21,141 "
-28013707,HC IMP HIP STEM BODY SPT ZMR 90X45MM REV," 21,058 "
-28013708,HC IMP HIP STEM BODY TAPERED XL ZMR 103X23MM REV," 20,828 "
-28013709,HC IMP HIP STEM BODY TAPERED XL STD ZMR 78X23MM REV," 20,828 "
-28013710,HC IMP HIP STEM BODY TAPERED XL ZMR 93X23MM REV," 20,828 "
-28013711,HC IMP HIP STEM CEMENTED 11X120X9MM," 13,500 "
-28013712,HC IMP HIP STEM CEMENTED 11X120X9MM REV," 18,390 "
-28013713,HC IMP HIP STEM CEMENTED 12X125X9MM REV," 18,390 "
-28013714,HC IMP HIP STEM CEMENTED EXTENDED 13X130X9.5MM," 13,500 "
-28013715,HC IMP HIP STEM CEMENTED 13X130X9.5MM REV," 18,390 "
-28013716,HC IMP HIP STEM CEMENTED 15X140X10.5MM," 13,500 "
-28013717,HC IMP HIP STEM CEMENTED 9X110X38X1MM," 13,500 "
-28013718,HC IMP HIP STEM CEMENTED 9X110X38X1MM REV," 19,911 "
-28013719,HC IMP HIP STEM CEMENTED EXTENDED 16X145MM," 13,500 "
-28013720,HC IMP HIP STEM CEMENTED EXTENDED 12X125X9MM," 13,500 "
-28013721,HC IMP HIP STEM CEMENTED STANDARD 13X130X9.5MM," 13,500 "
-28013722,HC IMP HIP STEM CEMENTED STANDARD 12X125X9MM," 13,500 "
-28013723,HC IMP HIP STEM CEMENTED STANDARD 14X135X10MM," 13,500 "
-28013724,HC IMP HIP STEM CEMENTED VERSYS 14X135X10MM," 13,500 "
-28013725,HC IMP HIP STEM COLLARED STD SZ16 170MM," 21,000 "
-28013726,HC IMP HIP STEM COLLARLESS LATERALISED SZ9 130MM," 21,000 "
-28013727,HC IMP HIP STEM COLLARLESS LARGE 12.5X11MM," 18,750 "
-28013728,HC IMP HIP STEM COLLARLESS STANDARD SZ10 140MM," 22,260 "
-28013729,HC IMP HIP STEM COLLARLESS STANDARD SZ11 145MM," 22,260 "
-28013730,HC IMP HIP STEM COLLARLESS STANDARD SZ12 150MM," 22,260 "
-28013731,HC IMP HIP STEM COLLARLESS STANDARD SZ13 155MM," 22,260 "
-28013732,HC IMP HIP STEM COLLARLESS STANDARD SZ14 160MM," 22,260 "
-28013733,HC IMP HIP STEM COLLARLESS STANDARD SZ15 165MM," 22,260 "
-28013734,HC IMP HIP STEM COLLARLESS STANDARD SZ16 170MM," 22,260 "
-28013735,HC IMP HIP STEM COLLARLESS STANDARD SZ18 180MM," 21,000 "
-28013736,HC IMP HIP STEM COLLARLESS STANDARD SZ20 190MM," 21,000 "
-28013737,HC IMP HIP STEM COLLARLESS STANDARD SZ8 125MM," 22,260 "
-28013738,HC IMP HIP STEM COLLARLESS STANDARD SZ9 130MM," 22,260 "
-28013739,HC IMP HIP STEM ENDURANCE CENT 11.0, 468 
-28013740,HC IMP HIP STEM EXTENDED 130X10.5X9MM SZ1 REV," 17,861 "
-28013741,HC IMP HIP STEM EXTENDED 14X160MM," 28,043 "
-28013742,HC IMP HIP STEM EXTENDED 15X140X10.5MM," 13,500 "
-28013743,HC IMP HIP STEM EXTENDED MID COAT OFFSET 13X140MM," 21,000 "
-28013744,HC IMP HIP STEM EXTENDED OFFSET 4X107MM," 21,000 "
-28013745,HC IMP HIP STEM EXTENDED POROUS 12X140MM," 21,000 "
-28013746,HC IMP HIP STEM EXTENDED POROUS 14X140MM," 21,000 "
-28013747,HC IMP HIP STEM EXTENDED POROUS 15X160MM," 21,000 "
-28013748,HC IMP HIP STEM EXTENDED STRAIGHT LG 12X140MM," 21,000 "
-28013749,HC IMP HIP STEM EXTENDED STR LG 13X140MM," 21,000 "
-28013750,HC IMP HIP STEM EXTENDED STRAIGHT LG 14X140MM," 21,000 "
-28013751,HC IMP HIP STEM EXTENDED STRAIGHT LG 15X160MM," 21,000 "
-28013752,HC IMP HIP STEM EXTENDED TAPER 12.5MM," 25,724 "
-28013753,HC IMP HIP STEM FEMORAL 15X190MM 7.5IN REV," 33,159 "
-28013754,HC IMP HIP STEM FEMORAL 16MM," 21,000 "
-28013755,HC IMP HIP STEM FEMORAL 18X33X45X155MM," 18,750 "
-28013756,HC IMP HIP STEM FEMORAL BOWD LEFT 8IN 15MM," 46,671 "
-28013757,HC IMP HIP STEM FEMORAL BOWD RIGHT 10IN 15MM," 37,089 "
-28013758,HC IMP HIP STEM FEMORAL BOWD RIGHT 8IN 15MM," 36,543 "
-28013759,HC IMP HIP STEM FEMORAL CEMENTED 11X120MM," 13,500 "
-28013760,HC IMP HIP STEM FEMORAL CEMENTED 12X125X10MM," 13,500 "
-28013761,HC IMP HIP STEM FEMORAL CEMENTED 14X135MM," 13,500 "
-28013762,HC IMP HIP STEM FEMORAL CEMENTED HI OFFSET SZ4," 9,338 "
-28013763,HC IMP HIP STEM FEMORAL CEMENTED HI OFFSET SZ5," 9,898 "
-28013764,HC IMP HIP STEM FEMORAL CEMENTED HI OFFSET SZ6," 9,898 "
-28013765,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ2," 9,898 "
-28013766,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ3," 9,898 "
-28013767,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ4," 9,898 "
-28013768,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ5," 9,898 "
-28013769,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ6," 9,898 "
-28013770,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ7," 9,898 "
-28013771,HC IMP HIP STEM FEMORAL CEMENTED OFFSET STD SZ8," 9,898 "
-28013772,HC IMP HIP STEM FEMORAL COLLARLESS HI OFFSET 10MM," 21,000 "
-28013773,HC IMP HIP STEM FEMORAL COLLARLESS HI OFFSET 16MM," 21,000 "
-28013774,HC IMP HIP STEM FEMORAL COLLARLESS HI OFFSET 18MM," 21,000 "
-28013775,HC IMP HIP STEM FEMORAL COLLARLESS CORAIL SZ11," 33,949 "
-28013776,HC IMP HIP STEM FEMORAL COLLARLESS CORAIL SZ12," 21,000 "
-28013777,HC IMP HIP STEM FEMORAL COLLARLESS CORAIL SZ13," 33,949 "
-28013778,HC IMP HIP STEM FEMORAL COLLARLESS CORAIL SZ14," 21,000 "
-28013779,HC IMP HIP STEM FEMORAL COLLARLESS CORAIL SZ15," 21,000 "
-28013780,HC IMP HIP STEM FEMORAL ENDURANCE SZ3 190MM," 24,276 "
-28013781,HC IMP HIP STEM FEMORAL HA 14MM," 21,000 "
-28013782,HC IMP HIP STEM FEMORAL HA 14MM REV," 29,917 "
-28013783,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 10.0X140MM," 21,000 "
-28013784,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 11.0X142MM," 17,250 "
-28013785,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 12.5X145MM," 21,000 "
-28013786,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 13.5X147MM," 17,250 "
-28013787,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 15.0X150MM," 21,000 "
-28013788,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 17.5X155MM," 21,000 "
-28013789,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 5X130MM," 21,000 "
-28013790,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 6X132MM," 21,000 "
-28013791,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 6X135MM," 21,000 "
-28013792,HC IMP HIP STEM FEMORAL LATERALIZED POROUS 9.0X137MM," 21,000 "
-28013793,HC IMP HIP STEM FEMORAL LEFT 10IN 13.5X45MM," 37,089 "
-28013794,HC IMP HIP STEM FEMORAL MID COAT LM 12X140MM," 21,000 "
-28013795,HC IMP HIP STEM FEMORAL MID COAT LM 13X140MM," 21,000 "
-28013796,HC IMP HIP STEM FEMORAL MID COAT LM 14X140MM," 21,000 "
-28013797,HC IMP HIP STEM FEMORAL MID COAT OFFSET 11X130MM," 21,000 "
-28013798,HC IMP HIP STEM FEMORAL MID COAT OFFSET 12X140MM," 21,000 "
-28013799,HC IMP HIP STEM FEMORAL MID COAT OFFSET 13X140MM," 21,000 "
-28013800,HC IMP HIP STEM FEMORAL MID COAT OFFSET 14X140MM," 21,000 "
-28013801,HC IMP HIP STEM FEMORAL OMNIFIT 127D 35X130MM," 13,500 "
-28013802,HC IMP HIP STEM FEMORAL POROUS COATED SZ13 155," 21,000 "
-28013803,HC IMP HIP STEM FEMORAL POROUS OFFSET STD SZ9," 21,000 "
-28013804,HC IMP HIP STEM FEMORAL RIGHT 10IN 13.5X45MM," 37,089 "
-28013805,HC IMP HIP STEM FEM STANDARD OFFSET ANTHOLOGY SZ6," 21,000 "
-28013806,HC IMP HIP STEM FEMORAL STR 8IN 15MM," 36,543 "
-28013807,HC IMP HIP STEM FEMORAL TAPERLOC 10X140MM," 21,000 "
-28013808,HC IMP HIP STEM FEMORAL TAPERLOC 12.5X145MM," 21,000 "
-28013809,HC IMP HIP STEM FEMORAL TAPERLOC 13.5X147MM," 21,000 "
-28013810,HC IMP HIP STEM FEMORAL TAPERLOC 5X130MM," 21,000 "
-28013811,HC IMP HIP STEM FEMORAL TAPERLOC 7.5X135MM," 17,250 "
-28013812,HC IMP HIP STEM FEMORAL TAPERLOC 9X137MM," 21,000 "
-28013813,HC IMP HIP STEM FEMORAL TAPERLOC STD 9X137MM," 21,000 "
-28013814,HC IMP HIP STEM FEMORAL UNIVERSAL FLUTED 115X12MM REV," 7,880 "
-28013815,HC IMP HIP STEM FULL COAT POROUS 13.5X200MM REV," 34,649 "
-28013816,HC IMP HIP STEM FULL COAT POROUS 15X200MM REV," 34,649 "
-28013817,HC IMP HIP STEM FULL COAT VERSYS STANDARD 16X160MM REV," 31,739 "
-28013818,HC IMP HIP STEM FULL COAT VERSYS 13X140MM REV," 31,739 "
-28013819,HC IMP HIP STEM FULL COAT STD BODY VERSYS 14X140MM REV," 31,739 "
-28013820,HC IMP HIP STEM FULL COAT STD BODY VERSYS 15X160MM REV," 31,739 "
-28013821,HC IMP HIP STEM FULL COAT VERSYS 16X160MM REV," 31,739 "
-28013822,HC IMP HIP STEM FULL COAT VERSYS 17X160MM REV," 31,739 "
-28013823,HC IMP HIP STEM FULL COAT STD BODY VERSYS 18X160MM REV," 31,739 "
-28013824,HC IMP HIP STEM FULL COAT VERSYS 19X160MM REV," 31,739 "
-28013825,HC IMP HIP STEM FULL COAT VERSYS 12X140MM," 25,200 "
-28013826,HC IMP HIP STEM FULL COAT VERSYS 13X140MM," 25,200 "
-28013827,HC IMP HIP STEM FULL COAT VERSYS 13X140MM," 25,200 "
-28013828,HC IMP HIP STEM FULL COAT STD BODY VERSYS 14X140MM," 25,200 "
-28013829,HC IMP HIP STEM FULL COAT VERSYS 15X160MM," 25,200 "
-28013830,HC IMP HIP STEM FULL COAT VERSYS 16X160MM," 25,200 "
-28013831,HC IMP HIP STEM FULL COAT VERSYS 17X160MM," 25,200 "
-28013832,HC IMP HIP STEM FULL COAT VERSYS 22X160MM," 34,427 "
-28013833,HC IMP HIP STEM FULL COAT VERSYS EXTENDED 15X160MM," 25,200 "
-28013834,HC IMP HIP STEM FULL COAT VERSYS METAPHYSIS 14X140MM," 25,200 "
-28013835,HC IMP HIP STEM FULL COAT VERSYS METAPHYSIS 15X160MM," 25,200 "
-28013836,HC IMP HIP STEM FULL COAT VERSYS METAPHYSIS 13X140MM," 25,200 "
-28013837,HC IMP HIP STEM FULL COAT VERSYS STANDARD 13X140MM," 25,200 "
-28013838,HC IMP HIP STEM FULL COAT VERSYS STANDARD 15X160MM," 25,200 "
-28013839,HC IMP HIP STEM MODULAR POROUS 12X115MM REV," 16,349 "
-28013840,HC IMP HIP STEM MODULAR POROUS 12X170MM REV," 18,046 "
-28013841,HC IMP HIP STEM MODULAR POROUS 13.5X115MM REV," 16,349 "
-28013842,HC IMP HIP STEM MODULAR POROUS 13.5X170MM REV," 18,046 "
-28013843,HC IMP HIP STEM MODULAR POROUS 13.5X220MM REV," 20,630 "
-28013844,HC IMP HIP STEM MODULAR POROUS 15X115MM REV," 16,349 "
-28013845,HC IMP HIP STEM MODULAR POROUS 15X170MM REV," 18,046 "
-28013846,HC IMP HIP STEM MODULAR POROUS 15X220MM REV," 20,630 "
-28013847,HC IMP HIP STEM MODULAR POROUS 16.5X115MM REV," 16,349 "
-28013848,HC IMP HIP STEM MODULAR POROUS 16.5X170MM REV," 18,046 "
-28013849,HC IMP HIP STEM MODULAR POROUS 18X170MM REV," 18,046 "
-28013850,HC IMP HIP STEM MODULAR POROUS 19.5X170MM REV," 18,046 "
-28013851,HC IMP HIP STEM MODULAR POROUS BOW 16.5X170 REV," 18,046 "
-28013852,HC IMP HIP STEM MODULAR POROUS REVISION 17X135MM REV," 14,969 "
-28013853,HC IMP HIP STEM MODULAR POROUS REVISION 18X170MM," 18,046 "
-28013854,HC IMP HIP STEM MODULAR POROUS REVISION 19X135MM REV," 14,969 "
-28013855,HC IMP HIP STEM MODULAR POROUS XL 17X185MM REV," 17,903 "
-28013856,HC IMP HIP STEM MODULAR POROUS XL 18X135MM REV," 15,673 "
-28013857,HC IMP HIP STEM MODULAR POROUS XL 19X185MM REV," 17,903 "
-28013858,HC IMP HIP STEM MODULAR POROUS XL 21X185MM REV," 17,903 "
-28013859,HC IMP HIP STEM MODULAR POROUS XL 24X185MM REV," 17,903 "
-28013860,HC IMP HIP STEM MODULAR TAPERED 15X185MM REV," 17,086 "
-28013861,HC IMP HIP STEM MODULAR TAPERED 16X185MM REV," 17,086 "
-28013862,HC IMP HIP STEM OFFSET 17X150X12MM," 13,500 "
-28013863,HC IMP HIP STEM OFFSET 17X150X12MM REV," 18,390 "
-28013864,HC IMP HIP STEM POROUS STRAIGHT VERSYS 12X160MM," 21,000 "
-28013865,HC IMP HIP STEM POROUS STRAIGHT VERSYS 13X160MM," 21,000 "
-28013866,HC IMP HIP STEM POROUS STRAIGHT VERSYS 14X160MM," 21,000 "
-28013867,HC IMP HIP STEM POROUS STRAIGHT VERSYS 15X160MM," 21,000 "
-28013868,HC IMP HIP STEM POROUS 8X120MM," 21,000 "
-28013869,HC IMP HIP STEM POROUS BOWED 22.5X170MM REV," 18,046 "
-28013870,HC IMP HIP STEM PRECISION NO 2X," 13,611 "
-28013871,HC IMP HIP STEM PROSTALAC TEMPORARY SIZE 1 105MM," 29,190 "
-28013872,HC IMP HIP STEM PROSTALAC TEMPORARY SIZE 3 150MM," 22,063 "
-28013873,HC IMP HIP STEM PROSTALAC TEMPORARY SIZE 3 200MM," 29,190 "
-28013874,HC IMP HIP STEM PROSTALAC TEMPORARY SIZE 3 240MM," 29,190 "
-28013875,HC IMP HIP STEM STD PROSTALAC SZ1 105MM," 29,190 "
-28013876,HC IMP HIP STEM STD PROSTALAC SZ1 105MM REV," 29,190 "
-28013877,HC IMP HIP STEM STEM SPOUT SZC TIVANIUM 80X35MM," 21,265 "
-28013878,HC IMP HIP STEM STEM SPOUT SZC TIVANIUM 80X35MM REV," 21,265 "
-28013879,HC IMP HIP STEM STANDARD TAPER 12.5MM," 25,724 "
-28013880,HC IMP HIP STEM SUMMIT CEMENTLESS 3," 18,750 "
-28013881,HC IMP HIP STEM SUMMIT CEMENTLESS 4," 18,750 "
-28013882,HC IMP HIP STEM SUMMIT CEMENTLESS 5," 18,750 "
-28013883,HC IMP HIP STEM SUMMIT CEMENTLESS 6," 18,750 "
-28013884,HC IMP HIP STEM SUMMIT CEMENTLESS 7," 18,750 "
-28013885,HC IMP HIP STEM SUMMIT CEMENTLESS 8," 18,750 "
-28013886,HC IMP HIP STEM SUMMIT CEMENTLESS 9," 18,750 "
-28013887,HC IMP HIP STEM SUMMIT STD CEMENTLESS 3," 18,750 "
-28013888,HC IMP HIP STEM SUMMIT STD CEMENTLESS 4," 18,750 "
-28013889,HC IMP HIP STEM SUMMIT STD CEMENTLESS 5," 18,750 "
-28013890,HC IMP HIP STEM SUMMIT STD CEMENTLESS 6," 18,750 "
-28013891,HC IMP HIP STEM SUMMIT STD CEMENTLESS 7," 18,750 "
-28013892,HC IMP HIP STEM SUMMIT STD CEMENTLESS 8," 18,750 "
-28013893,HC IMP HIP STEM SUMMIT STD CEMENTLESS 9," 18,750 "
-28013894,HC IMP HIP STEM TAPERED POROUS 10X119MM," 21,000 "
-28013895,HC IMP HIP STEM TAPERED POROUS 13.5X126MM," 21,000 "
-28013896,HC IMP HIP STEM TAPERED POROUS 15X129MM," 21,000 "
-28013897,HC IMP HIP STEM TAPERED POROUS 16.25X132MM," 21,000 "
-28013898,HC IMP HIP STEM TAPERED POROUS 12.5X124MM," 21,000 "
-28013899,HC IMP HIP STEM TAPERED POROUS EXTENDED 10X119MM," 21,000 "
-28013900,HC IMP HIP STEM TAPERED EXTENDED 11X120MM," 21,000 "
-28013901,HC IMP HIP STEM TAPERED POROUS EXTENDED 11X121MM," 21,000 "
-28013902,HC IMP HIP STEM TAPERED POROUS EXTENDED 12.5X124MM," 21,000 "
-28013903,HC IMP HIP STEM TAPERED EXTENDED 12X125MM," 21,000 "
-28013904,HC IMP HIP STEM TAPERED POROUS EXTENDED 13.5X126MM," 21,000 "
-28013905,HC IMP HIP STEM TAPERED EXTENDED 14X135MM," 21,000 "
-28013906,HC IMP HIP STEM TAPERED POROUS EXTENDED 15X129MM," 21,000 "
-28013907,HC IMP HIP STEM TAPERED POROUS EXTENDED 17.5X134MM," 21,000 "
-28013908,HC IMP HIP STEM TAPERED POROUS EXTENDED 6X111MM," 21,000 "
-28013909,HC IMP HIP STEM TAPERED POROUS EXTENDED 7.5X114MM," 21,000 "
-28013910,HC IMP HIP STEM TAPERED POROUS EXTENDED 9X117MM," 21,000 "
-28013911,HC IMP HIP STEM TAPERED POROUS MDL 9X117MM," 21,000 "
-28013912,HC IMP HIP STEM TAPERED EXTENDED STR 13X130MM," 21,000 "
-28013913,HC IMP HIP STEM TAPERED EXTENDED STRAIGHT 14X135MM," 21,000 "
-28013914,HC IMP HIP STEM TAPERED EXTENDED STR 15X140MM," 21,000 "
-28013915,HC IMP HIP STEM TAPERED HIGH OFFSET SZ1," 18,750 "
-28013916,HC IMP HIP STEM TAPERED HIGH OFFSET SZ10," 18,550 "
-28013917,HC IMP HIP STEM TAPERED HIGH OFFSET SZ3," 18,550 "
-28013918,HC IMP HIP STEM TAPERED HIGH OFFSET SZ4," 18,550 "
-28013919,HC IMP HIP STEM TAPERED HIGH OFFSET SZ5," 18,550 "
-28013920,HC IMP HIP STEM TAPERED HIGH OFFSET SZ6," 18,550 "
-28013921,HC IMP HIP STEM TAPERED HIGH OFFSET SZ7," 18,550 "
-28013922,HC IMP HIP STEM TAPERED HIGH OFFSET SZ8," 18,550 "
-28013923,HC IMP HIP STEM TAPERED HIGH OFFSET SZ9," 18,550 "
-28013924,HC IMP HIP STEM TAPERED POROUS MDL 7.5X114MM," 21,000 "
-28013925,HC IMP HIP STEM TAPERED POROUS MDL TAPER SZ7," 21,000 "
-28013926,HC IMP HIP STEM TAPERED POROUS MDL TAPER 10MM," 21,000 "
-28013927,HC IMP HIP STEM TAPERED POROUS MDL TAPER 11MM," 21,000 "
-28013928,HC IMP HIP STEM TAPERED POROUS MDL TAPER 12.5MM," 21,000 "
-28013929,HC IMP HIP STEM TAPERED POROUS MDL TAPER 9MM," 21,000 "
-28013930,HC IMP HIP STEM TAPERED POROUS MDL TAPER STR 10MM," 21,000 "
-28013931,HC IMP HIP STEM TAPERED POROUS MDL TAPER STR 11MM," 21,000 "
-28013932,HC IMP HIP STEM TAPERED POROUS MDL SZ6," 21,000 "
-28013933,HC IMP HIP STEM TAPERED MODULAR 16X235MM," 17,901 "
-28013934,HC IMP HIP STEM TAPERED METAPHYSIS 12X125MM," 21,000 "
-28013935,HC IMP HIP STEM TAPERED METAPHYSIS 15X140MM," 21,000 "
-28013936,HC IMP HIP STEM TAPERED OFFSET 10X115MM," 21,000 "
-28013937,HC IMP HIP STEM TAPERED OFFSET 11X120MM," 21,000 "
-28013938,HC IMP HIP STEM TAPERED OFFSET 12X125MM," 21,000 "
-28013939,HC IMP HIP STEM TAPERED STD OFFSET 13X130MM," 21,000 "
-28013940,HC IMP HIP STEM TAPERED OFFSET 14X135MM," 21,000 "
-28013941,HC IMP HIP STEM TAPERED OFFSET 14X135MM REV," 26,495 "
-28013942,HC IMP HIP STEM TAPERED STD OFFSET 15X140MM," 21,000 "
-28013943,HC IMP HIP STEM TAPERED OFFSET 16X145MM," 21,000 "
-28013944,HC IMP HIP STEM TAPERED POROUS 125D SZ14 REV," 18,357 "
-28013945,HC IMP HIP STEM TAPERED POROUS 125D SZ16 REV," 18,357 "
-28013946,HC IMP HIP STEM TAPERED POROUS 125D SZ17 REV," 18,357 "
-28013947,HC IMP HIP STEM TAPERED POROUS 135D SZ15," 17,483 "
-28013948,HC IMP HIP STEM TAPERED POROUS 135D SZ17," 17,483 "
-28013949,HC IMP HIP STEM TAPERED POROUS 135D SZ20," 17,483 "
-28013950,HC IMP HIP STEM TAPERED POROUS STANDARD 11X121MM," 21,000 "
-28013951,HC IMP HIP STEM TAPERED POROUS STANDARD 5X109MM," 21,000 "
-28013952,HC IMP HIP STEM TAPERED POROUS STANDARD 7.5X114MM," 21,000 "
-28013953,HC IMP HIP STEM TAPERED POROUS STANDARD 6X111MM," 21,000 "
-28013954,HC IMP HIP STEM TAPERED POROUS STANDARD 9X117MM," 21,000 "
-28013955,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ1," 18,550 "
-28013956,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ3," 18,550 "
-28013957,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ5," 18,550 "
-28013958,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ6," 18,550 "
-28013959,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ7," 18,550 "
-28013960,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ8," 18,550 "
-28013961,HC IMP HIP STEM TAPERED STANDARD OFFSET SZ9," 18,750 "
-28013962,HC IMP HIP STEM TAPERED STANDARD OFFSET LARGE 17X150MM," 21,000 "
-28013963,HC IMP HIP STEM TAPERED STANDARD OFFSET 14X135MM," 21,000 "
-28013964,HC IMP HIP STEM TAPERED STANDARD OFFSET 16X145MM," 21,000 "
-28013965,HC IMP HIP STEM TAPERED STANDARD OFFSET 17X150MM," 21,000 "
-28013966,HC IMP HIP STEM TAPERED POROUS STRAIGHT 17.5X134MM," 21,000 "
-28013967,HC IMP HIP STEM WAGNER 125D SZ13," 17,483 "
-28013968,HC IMP HIP STEM WAGNER 125D SZ14," 17,483 "
-28013969,HC IMP HIP STEM WAGNER 125D SZ15," 17,483 "
-28013970,HC IMP HIP STEM WAGNER 125D SZ17," 17,483 "
-28013971,HC IMP HIP STEM WAGNER 125D SZ18," 17,483 "
-28013972,HC IMP HIP STEM WAGNER CONE 16 135DD REV," 18,357 "
-28013973,HC IMP HIP STEM WAGNER CONE 18 135DD REV," 17,483 "
-28013974,HC IMP HUMERAL STEM DISTAL SOLAR 11 15X1135MM," 24,098 "
-28013975,HC IMP HUMERAL STEM DISTAL SOLAR 9 13X120MM," 24,098 "
-28013976,HC IMP HUMERAL STEM FRACTURE EQUINOXE RIGHT 10.5MM," 26,770 "
-28013977,HC IMP HUMERAL STEM FRACTURE EQUINOXE RIGHT 12.5MM," 26,770 "
-28013978,HC IMP HUMERAL STEM FRACTURE EQUINOXE RIGHT 6.5MM," 26,770 "
-28013979,HC IMP HUMERAL STEM FRACTURE EQUINOXE RIGHT 8.5MM," 26,770 "
-28013980,HC IMP HUMERAL STEM PLATFORM FRACTURE LEFT 10.5MM," 26,770 "
-28013981,HC IMP HUMERAL STEM PLATFORM FRACTURE LEFT 12.5MM," 26,770 "
-28013982,HC IMP HUMERAL STEM PLATFORM FRACTURE LEFT 6.5MM," 26,770 "
-28013983,HC IMP HUMERAL STEM PLATFORM FRACTURE LEFT 8.5MM," 26,770 "
-28013984,HC IMP HUMERAL STEM PRIMARY PRESS FIT 13MM," 16,457 "
-28013985,HC IMP HUMERAL STEM PRIMARY PRESS FIT 15MM," 16,457 "
-28013986,HC IMP HUMERAL STEM PRIMARY PRESS FIT 17MM," 16,457 "
-28013987,HC IMP HUMERAL STEM PRIMARY PRESS FIT 7MM," 16,457 "
-28013988,HC IMP HUMERAL STEM PRIMARY PRESS FIT 9MM," 16,457 "
-28013989,HC IMP HUMERAL STEM PROXIMAL MOSIAC 8.5X75MM," 20,685 "
-28013990,HC IMP HUMERAL STEM PRESSFIT ANATOMICAL 12X110MM," 33,117 "
-28013991,HC IMP HUMERAL STEM REVISION REVERSE 10X175MM," 25,200 "
-28013992,HC IMP HUMERAL STEM REVISION REVERSE 8X175MM," 25,200 "
-28014150,HC IMP HUMREAL SEGMENTAL MOSAIC 110MM," 49,797 "
-28014342,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ4 10 REV," 6,635 "
-28014343,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ5 20 REV," 6,860 "
-28014344,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ5 46 REV," 14,071 "
-28014345,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ6 10 REV," 6,635 "
-28014346,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ6 50 REV," 14,071 "
-28014348,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 1-2 10X58MM," 5,814 "
-28014349,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 1-2 10X58MM REV," 11,146 "
-28014350,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 1-2 12X58MM," 11,146 "
-28014351,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 1-2 12X58MM REV," 11,146 "
-28014352,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 1-2 14X40MM REV," 11,146 "
-28014353,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 3-4 10X42X66MM," 11,146 "
-28014354,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 3-4 12X66MM REV," 11,146 "
-28014355,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 3-4 14X42X66MM," 11,146 "
-28014356,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 3-4 17X66MM REV," 10,937 "
-28014357,HC IMP KNEE ARTICULAR SURFACE CR-FLEX 12X42X66MM REV," 7,877 "
-28014358,HC IMP KNEE ARTICULAR SURFACE CR-FLEX 12X46X74MM REV," 7,877 "
-28014359,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 10X66MM," 11,146 "
-28014360,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 10X66MM REV," 11,146 "
-28014361,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 12X66MM," 11,146 "
-28014362,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 12X66MM REV," 11,146 "
-28014363,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 14X66MM," 5,814 "
-28014364,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 14X66MM REV," 11,146 "
-28014365,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 17X42X66MM," 11,146 "
-28014366,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 3-4 20X66MM REV," 11,146 "
-28014367,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 5-6 10X46MM," 11,146 "
-28014368,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 5-6 10X46MM REV," 11,146 "
-28014369,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 5-6 12X46MM," 11,146 "
-28014370,HC IMP KNEE ARTICULAR SURFACE SIZE E-F 5-6 12X46MM REV," 11,146 "
-28014371,HC IMP KNEE ARTICULAR SURFACE FLEX CONGRUENT 13MM REV," 10,628 "
-28014372,HC IMP KNEE ARTICULAR SURFACE SIZE G 5-6 10X46X74MM," 11,146 "
-28014373,HC IMP KNEE ARTICULAR SURFACE SZ G 5-6 12X46X74MM REV," 11,146 "
-28014374,HC IMP KNEE ARTICULAR SURFACE SIZE G 5-6 14X46MM REV," 11,146 "
-28014375,HC IMP KNEE AFTICULAR SURFACE LPS 46X74MM," 5,814 "
-28014376,HC IMP KNEE ARTICULAR SURFACE LPS F5-7 12MM," 5,814 "
-28014377,HC IMP KNEE ARTICULAR SURFACE LPS G6-8 12MM," 5,814 "
-28014378,HC IMP KNEE ARTICULAR SURFACE MOBILE D 3-5 10MM," 5,814 "
-28014379,HC IMP KNEE ARTICULAR SURFACE MOBILE D 3-5 12MM," 5,814 "
-28014380,HC IMP KNEE ARTICULAR SURFACE MOBILE E 4-6 10MM," 5,814 "
-28014381,HC IMP KNEE ARTICULAR SURFACE MOBILE E 4-6 12MM," 5,814 "
-28014382,HC IMP KNEE ARTICULAR SURFACE MOBILE F 3-5 17MM," 5,814 "
-28014383,HC IMP KNEE ARTICULAR SURFACE MOBILE F 5-7 10MM," 5,814 "
-28014384,HC IMP KNEE ARTICULAR SURFACE MOBILE F 5-7 14MM," 5,814 "
-28014385,HC IMP KNEE ARTICULAR SURFACE MOBILE FLUTED SZ4," 9,960 "
-28014386,HC IMP KNEE ARTICULAR SURFACE MOBILE G 6-8 10MM," 5,814 "
-28014387,HC IMP KNEE ARTICULAR SURFACE ROTATE HIN 10X44X74 REV," 12,361 "
-28014388,HC IMP KNEE ARTICULAR SURFACE ROTATE HIN 12X40MM REV," 12,361 "
-28014389,HC IMP KNEE ARTICULAR SURFACE ROTATE HIN 12X40X58 REV," 12,361 "
-28014390,HC IMP KNEE ARTICULAR SURFACE ROTATE HIN 17X40X64 REV," 12,361 "
-28014391,HC IMP KNEE ARTICULAR SURFACE SEGMENTAL SIZE C 14MM," 12,750 "
-28014392,HC IMP KNEE ARTICULAR SURFACE SEGMENTAL SZ C 14MM REV," 12,750 "
-28014393,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 1 8MM," 4,250 "
-28014394,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 2 8MM," 4,250 "
-28014395,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 2 9MM," 4,250 "
-28014396,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 3 8MM," 4,250 "
-28014397,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 3 9MM," 4,250 "
-28014398,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 4 8MM," 4,250 "
-28014399,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 4 9MM," 4,250 "
-28014400,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ 5 8MM," 4,250 "
-28014401,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ2 10MM," 4,250 "
-28014402,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ3 10MM," 4,250 "
-28014403,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ3 11MM," 4,250 "
-28014404,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ4 10MM," 4,250 "
-28014405,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ4 11MM," 4,250 "
-28014406,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ4 12MM," 4,250 "
-28014407,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ5 10MM," 4,250 "
-28014408,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL SZ5 12MM," 4,250 "
-28014409,HC IMP KNEE ARTICULAR SURFACE ZIMALOY STEM SZ7," 9,960 "
-28014410,HC IMP KNEE ARTICULAR SURFACE ZIMALOY STEM SZ8," 9,960 "
-28014411,HC IMP KNEE AUGMENT DISTAL SIZE E 20MM REV," 6,860 "
-28014412,HC IMP KNEE AUGMENT FEMORAL CCK LEFT SZ2 REV," 30,660 "
-28014413,HC IMP KNEE AUGMENT FEMORAL CCK LEFT SZ3," 16,980 "
-28014414,HC IMP KNEE AUGMENT FEMORAL CCK LEFT SZ4 REV," 30,660 "
-28014415,HC IMP KNEE AUGMENT FEMORAL CCK RIGHT SZ3 REV," 30,660 "
-28014416,HC IMP KNEE AUGMENT FEMORAL CCK RIGHT SZ4 REV," 30,660 "
-28014417,HC IMP KNEE AUGMENT FEMORAL CONE LEFT SMALL 40MM REV," 24,552 "
-28014418,HC IMP KNEE AUGMENT FEMORAL CONE RIGHT SMALL 50MM," 26,019 "
-28014419,HC IMP KNEE AUGMENT FEMORAL DISTAL ADVANCE SZ1 10MM," 6,477 "
-28014420,HC IMP KNEE AUGMENT FEMORAL DISTAL ADVANCE SZ3 5MM," 6,477 "
-28014421,HC IMP KNEE AUGMENT FEMORAL DISTAL SZD 10MM REV," 6,635 "
-28014422,HC IMP KNEE AUGMENT FEMORAL DISTAL SZ3 10MM REV," 6,477 "
-28014423,HC IMP KNEE AUGMENT FEMORAL DISTAL SZ3 5MM REV," 6,477 "
-28014424,HC IMP KNEE AUGMENT FEMORAL DISTAL SZ4 10MM REV," 6,477 "
-28014425,HC IMP KNEE AUGMENT FEMORAL DISTAL SZ4 5MM REV," 6,477 "
-28014426,HC IMP KNEE AUGMENT FEMORAL DISTAL SIZE C 10MM," 8,019 "
-28014427,HC IMP KNEE AUGMENT FEMORAL DISTAL SZC 10MM REV," 6,635 "
-28014428,HC IMP KNEE AUGMENT FEMORAL DISTAL SZC 5MM REV," 6,635 "
-28014429,HC IMP KNEE AUGMENT FEMORAL DISTAL SZD 10MM REV," 6,635 "
-28014430,HC IMP KNEE AUGMENT FEMORAL DISTAL SZD 5MM REV," 6,635 "
-28014431,HC IMP KNEE AUGMENT FEMORAL DISTAL SZE 10MM REV," 6,635 "
-28014432,HC IMP KNEE AUGMENT FEMORAL DISTAL SIZE E 5MM," 8,019 "
-28014433,HC IMP KNEE AUGMENT FEMORAL DISTAL SZE 5MM REV," 6,635 "
-28014434,HC IMP KNEE AUGMENT FEMORAL DISTAL SZF 10MM REV," 6,635 "
-28014435,HC IMP KNEE AUGMENT FEMORAL DISTAL SZF 5MM," 6,635 "
-28014436,HC IMP KNEE AUGMENT FEMORAL DISTAL SZF 5MM REV," 6,635 "
-28014437,HC IMP KNEE AUGMENT FEMORAL DISTAL SZG 5MM REV," 6,635 "
-28014438,HC IMP KNEE AUGMENT FEMORAL POSTERIOR ADVANCE SZ1 5MM," 6,477 "
-28014439,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZ1 10MM REV," 6,477 "
-28014440,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZ3 5MM REV," 6,477 "
-28014441,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZ4 5MM REV," 6,477 "
-28014442,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZC 10MM REV," 6,635 "
-28014443,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZD 10MM REV," 6,635 "
-28014444,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZD 5MM REV," 6,635 "
-28014445,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZE 10MM REV," 6,635 "
-28014446,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SIZE E 5MM," 8,019 "
-28014447,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZE 5MM REV," 6,635 "
-28014448,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZF 10MM REV," 6,635 "
-28014449,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SIZE F 5MM," 6,635 "
-28014450,HC IMP KNEE AUGMENT FEMORAL POSTERIOR SZF 5MM REV," 6,635 "
-28014451,HC IMP KNEE AUGMENT FEMORAL POSTERIOR TIV SZG 5MM REV," 6,635 "
-28014452,HC IMP KNEE AUGMENT FEMORAL PRECOAT DIST SZC 15MM REV," 6,860 "
-28014453,HC IMP KNEE AUGMENT FEMORAL PRECOAT DIST SZC 20MM REV," 6,860 "
-28014454,HC IMP KNEE AUGMENT FEMORAL RIGHT SIGMA SZ4 4MM," 6,018 "
-28014455,HC IMP KNEE AUGMENT FEMORAL RIGHT SIGMA SZ5 4MM REV," 7,880 "
-28014456,HC IMP KNEE AUGMENT TIB FIBULAR SEGMEN MALE FEM 30 REV," 21,420 "
-28014457,HC IMP KNEE AUGMENT TIB FIBULAR SEGMEN MALE FEM 60 REV," 21,420 "
-28014458,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 SZ2 10MM REV," 6,635 "
-28014459,HC IMP KNEE AUGMENT TIBIAL PRE COAT 1/2 SZ2 5MM," 8,019 "
-28014460,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 SZ2 5MM REV," 6,635 "
-28014461,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ5 10 REV," 6,635 "
-28014462,HC IMP KNEE AUGMENT TIBIAL PRE COAT 1/2 SZ5 5MM," 8,019 "
-28014463,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ5 5MM REV," 6,635 "
-28014464,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ3 10 REV," 6,635 "
-28014465,HC IMP KNEE AUGMENT TIBIAL PRE COAT 1/2 BLOK SZ3 5MM," 8,019 "
-28014466,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ3 5 REV," 6,635 "
-28014467,HC IMP KNEE AUGMENT TIBIAL PRECOAT ROTNG 10MM REV," 7,919 "
-28014468,HC IMP KNEE AUGMENT TIBIAL PRECOAT TAPERED SZ1 5MM REV," 6,635 "
-28014469,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ4 5MM REV," 6,635 "
-28014470,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ6 5MM REV," 6,635 "
-28014471,HC IMP KNEE AUGMENT TIBIAL PRECOAT 1/2 BLK SZ7 5MM REV," 6,635 "
-28014472,HC IMP KNEE AUGMENT TIBIAL TAPER PRECOAT SZ4 6X66 REV," 14,071 "
-28014473,HC IMP KNEE AUGMENT TIBIAL TAPER PRECOAT SZ3 15MM REV," 6,860 "
-28014474,HC IMP KNEE AUGMENT TIBIAL TAPER PRECOAT SZ3 15MM REV," 6,860 "
-28014475,HC IMP KNEE AUGMENT TIB PRECOAT 1/2 BLK SZ5 20MM REV," 6,860 "
-28014476,HC IMP KNEE AUGMENT TIBAL BLOCK SZ2 10MM REV," 6,171 "
-28014477,HC IMP KNEE AXLE AUX OSS," 3,205 "
-28014478,HC IMP KNEE AXLE AUX OSS REV," 2,712 "
-28014479,HC IMP KNEE BASEPLATE FNND CRUCIATE TRAY 1PC 59MM," 7,038 "
-28014480,HC IMP KNEE BASEPLATE FNND CRUCIATE TRAY 1PC 67MM," 8,415 "
-28014481,HC IMP KNEE BASEPLATE FNND CRUCIATE TRAY 1PC 71MM," 8,415 "
-28014482,HC IMP KNEE BASEPLATE FNND CRUCIATE TRAY 1PC 79MM," 8,415 "
-28014483,HC IMP KNEE BASEPLATE FNND CRUCIATE TRAY 1PC 83MM," 7,038 "
-28014484,HC IMP KNEE BASEPLATE TIBIAL CEMENTED MODULAR SIZE 4," 9,540 "
-28014485,HC IMP KNEE BASEPLATE TIBIAL CEMENTED MODULAR SIZE 5," 9,000 "
-28014486,HC IMP KNEE BASEPLATE TIBIAL CO CR SZ4," 7,038 "
-28014487,HC IMP KNEE BASEPLATE TIBIAL LEGION SZ3 REV," 13,838 "
-28014488,HC IMP KNEE BASEPLATE TIBIAL MODULAR TRAY 1.5MM," 9,000 "
-28014489,HC IMP KNEE BASEPLATE TIBIAL NON MODULAR OSS LONG 63MM," 14,540 "
-28014490,HC IMP KNEE BASEPLATE TIBIAL NON MODULAR OSS LONG 67MM," 20,209 "
-28014491,HC IMP KNEE BASEPLATE TIBIAL NON MODULAR 75MMX10X160MM," 20,209 "
-28014492,HC IMP KNEE BASEPLATE TIBIAL NON POROUS ADV TI SZ2 REV," 12,263 "
-28014493,HC IMP KNEE BASEPLATE TIBIAL NON POROUS ADV TI SZ3 REV," 12,263 "
-28014494,HC IMP KNEE BASEPLATE TIBIAL NON POROUS ADVANCE TI SZ3," 7,038 "
-28014495,HC IMP KNEE BASEPLATE TIBIAL NON POROUS ADV TI SZ4 REV," 12,263 "
-28014496,HC IMP KNEE BASEPLATE TIBIAL NON POROUS ADVANCE TI SZ4," 7,038 "
-28014497,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ1," 7,038 "
-28014498,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ1+," 7,038 "
-28014499,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ2," 7,038 "
-28014500,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ2+," 7,038 "
-28014501,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ3," 7,038 "
-28014502,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ3+," 7,038 "
-28014503,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ4+," 7,038 "
-28014504,HC IMP KNEE BASEPLATE TIBIAL NON POROUS CO CR SZ5," 7,038 "
-28014505,HC IMP KNEE BASEPLATE TIBIAL NONPORS LT MDL RT SZ3 REV," 13,725 "
-28014506,HC IMP KNEE BASEPLATE TIBIAL NONPORS LT MDL RT SZ4 REV," 13,725 "
-28014507,HC IMP KNEE BASEPLATE TIBIAL NONPORS MEDIAL RIGHT SZ3," 7,038 "
-28014508,HC IMP KNEE BASEPLATE TIBIAL TS TRIATHLON SZ3 REV," 14,693 "
-28014509,HC IMP KNEE BASEPLATE TIBIAL UNI RIGHT MED LAT LT SZ5," 7,038 "
-28014510,HC IMP KNEE BLOCK AUGMENTATION FEMORAL DIST 60X10 REV," 6,222 "
-28014511,HC IMP KNEE BLOCK AUGMENTATION FEMORAL POST 10X60 REV," 6,222 "
-28014512,HC IMP KNEE BLOCK AUGMENTATION FEMORAL POST 60X10 REV," 6,222 "
-28014513,HC IMP KNEE BLOCK AUGMENTATION FEMORAL POST 60X15 REV," 6,222 "
-28014514,HC IMP KNEE BLOCK AUGMENTATION TIBIAL 16X83MM," 7,538 "
-28014515,HC IMP KNEE BLOCK AUGMENTATION TIBIAL 6X63MM REV," 5,829 "
-28014531,HC IMP KNEE COMPONENT DISTAL FEMUR SZC," 44,753 "
-28014532,HC IMP KNEE FEMORAL COMPONENT CEMENTED PATELLO JNT SZ1," 22,400 "
-28014533,HC IMP KNEE FEMORAL COMPONENT CEMENTED PS LT SZ2," 9,540 "
-28014534,HC IMP KNEE FEMORAL COMPONENT CRUCIATE LEFT SZ4 65X71," 9,000 "
-28014535,HC IMP KNEE FEMORAL COMPONENT CR PRE COAT RIGHT SZF," 16,980 "
-28014536,HC IMP KNEE FEMORAL COMPONENT PFC CR RIGHT SZ5 69X73," 9,000 "
-28014537,HC IMP KNEE FEMORAL COMPONENT CR FLEX PRE COAT LT SZC," 16,980 "
-28014538,HC IMP KNEE FEMORAL COMPONENT CR FLEX PRE COAT LT SZF," 16,980 "
-28014539,HC IMP KNEE FEMORAL COMPONENT DISTAL RIGHT SZC REV," 44,753 "
-28014540,HC IMP KNEE FEMORAL COMPONENT LPS FLEX LEFT SZD," 16,980 "
-28014541,HC IMP KNEE FEMORAL COMPONENT LPS FLEX LEFT SZF," 16,980 "
-28014542,HC IMP KNEE FEMORAL COMPONENT LPS FLEX LEFT SZF REV," 25,715 "
-28014543,HC IMP KNEE FEMORAL COMPONENT LPS FLEX RIGHT SZG," 16,980 "
-28014544,HC IMP KNEE FEMORAL COMPONENT LPS OPTN RIGHT SZH," 13,230 "
-28014545,HC IMP KNEE FEMORAL COMPONENT LATERAL 50X25MM," 12,938 "
-28014546,HC IMP KNEE FEMORAL COMPONENT LATERAL CEMENTED SZ4," 9,540 "
-28014547,HC IMP KNEE FEMORAL COMPONENT LATERAL SZD 25.9X24.1MM," 14,730 "
-28014548,HC IMP KNEE FEMORAL COMPONENT LATERAL SZ5 1.5X24MM," 14,438 "
-28014549,HC IMP KNEE FEMORAL COMPONENT LATERAL SZB 42.5X22MM," 14,438 "
-28014550,HC IMP KNEE FEMORAL COMPONENT LATERAL SZD 48X23MM," 14,438 "
-28014551,HC IMP KNEE FEMORAL COMPONENT LATERAL SZE 26.8X25MM," 14,730 "
-28014552,HC IMP KNEE FEMORAL COMPONENT LATERAL SZF 55.5X25MM," 14,438 "
-28014553,HC IMP KNEE FEMORAL COMPONENT NEXGEN RIGHT SZE," 13,230 "
-28014554,HC IMP KNEE FEMORAL COMPONENT NEXGEN RIGHT SZF," 13,230 "
-28014555,HC IMP KNEE FEMORAL COMPONENT NEXGEN LEFT SZE," 13,230 "
-28014556,HC IMP KNEE FEMORAL COMPONENT NEXGEN LEFT SZF," 13,230 "
-28014557,HC IMP KNEE FEM COMP NON POROUS RIGHT C/R SZ2.5 59X63," 18,648 "
-28014558,HC IMP KNEE FEM COMP NON POROUS LEFT C/R SZ3 61X66," 9,540 "
-28014559,HC IMP KNEE FEM COMP NON POROUS RIGHT C/R SZ4 65X71," 18,648 "
-28014560,HC IMP KNEE FEMORAL COMPONENT NON POROUS SZ5 69X73," 28,896 "
-28014561,HC IMP KNEE FEMORAL COMPONENT NON POROUS SZ5 69X73 REV," 26,246 "
-28014562,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV LT SZ1," 13,230 "
-28014563,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV LT SZ2," 13,230 "
-28014564,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV LT SZ3," 13,230 "
-28014565,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV LT SZ5," 13,230 "
-28014566,HC IMP KNEE FEMORAL COMPONENT NON POR ADV LT SZ4 REV," 18,312 "
-28014567,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV LT SZ4," 13,230 "
-28014568,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV RT SZ1," 13,230 "
-28014569,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV RT SZ2," 13,230 "
-28014570,HC IMP KNEE FEMORAL COMPONENT NPOR ADV STATURE RT SZ3," 13,230 "
-28014571,HC IMP KNEE FEMORAL COMPONENT NPOR ADV STATURE RT SZ4," 13,230 "
-28014572,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV RT SZ5," 13,230 "
-28014573,HC IMP KNEE FEMORAL COMPONENT NON POROUS SZ2 56X60," 28,896 "
-28014574,HC IMP KNEE FEMORAL COMPONENT PFC CR SZ5 69," 9,000 "
-28014575,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED LT 60," 13,230 "
-28014576,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED LT 65," 14,775 "
-28014577,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED LT 75," 14,775 "
-28014578,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED RT 60," 14,775 "
-28014579,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED RT 65," 14,775 "
-28014580,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED RT 70," 14,775 "
-28014581,HC IMP KNEE FEMORAL COMPONENT POSTERIOR CEMENTED RT 75," 14,775 "
-28014582,HC IMP KNEE FEMORAL COMPONENT POSTERIOR STAB 55MM," 13,230 "
-28014583,HC IMP KNEE FEMORAL COMPONENT POSTERIOR STAB 62.5MM," 13,230 "
-28014584,HC IMP KNEE FEMORAL COMPONENT POSTERIOR STAB RT 67.5MM," 14,775 "
-28014585,HC IMP KNEE FEMORAL COMPONENT POSTERIOR STAB 72.5MM," 14,775 "
-28014586,HC IMP KNEE FEMORAL COMPONENT PRECOAT CRFLX RT SZD MN," 14,730 "
-28014587,HC IMP KNEE FEMORAL COMPONENT PRECOAT LEFT SZB," 14,730 "
-28014588,HC IMP KNEE FEMORAL COMPONENT PRECOAT NEXGEN LEFT SZ3," 22,400 "
-28014589,HC IMP KNEE FEMORAL COMPONENT PRECOAT RT SZF MIN 28.8," 14,730 "
-28014590,HC IMP KNEE FEMORAL COMPONENT PRECOAT SZB 42.5X22MM," 14,438 "
-28014591,HC IMP KNEE FEMORAL COMPONENT PRECOAT SZB 42.5X22 REV," 17,426 "
-28014592,HC IMP KNEE FEMORAL COMPONENT PRECOAT SZC 45X22MM," 14,438 "
-28014593,HC IMP KNEE FEMORAL COMPONENT PRECOAT SZD 48X23MM," 14,438 "
-28014594,HC IMP KNEE FEMORAL COMPONENT PRECOAT SZE 51.5X24MM," 14,438 "
-28014595,HC IMP KNEE FEMORAL COMPONENT PRECOAT SZF 55.5X25MM," 14,438 "
-28014596,HC IMP KNEE FEMORAL COMPONENT PROLONG ZIMALOY LT SZG," 13,230 "
-28014597,HC IMP KNEE FEMORAL COMPONENT PROLONG ZIMALOY RT SZD," 13,230 "
-28014598,HC IMP KNEE FEMORAL COMPONENT PROLONG ZIMALOY RT SZG," 13,230 "
-28014599,HC IMP KNEE FEMORAL COMPONENT PROXIMAL FINN LT 7CM," 33,376 "
-28014600,HC IMP KNEE FEMORAL COMPONENT PROXIMAL FINN RT 7CM," 22,411 "
-28014601,HC IMP KNEE FEMORAL COMPONENT PROXIMAL FINN RT 7CM REV," 28,315 "
-28014602,HC IMP KNEE FEMORAL COMPONENT PS LEFT 2.5 59X63MM," 9,540 "
-28014603,HC IMP KNEE FEMORAL COMPONENT PS 2.5 59X63MM REV," 18,228 "
-28014604,HC IMP KNEE FEMORAL COMPONENT PS RIGHT SZ5," 16,980 "
-28014605,HC IMP KNEE FEMORAL COMPONENT POST ORTHO LEFT SZE," 16,980 "
-28014606,HC IMP KNEE FEMORAL COMPONENT POST ORTHO RIGHT SZF," 16,980 "
-28014607,HC IMP KNEE FEMORAL COMPONENT POST ORTHO RIGHT SZE REV," 25,715 "
-28014608,HC IMP KNEE FEMORAL COMPONENT SEGMENTAL LEFT 7CM," 38,864 "
-28014609,HC IMP KNEE FEMORAL CR LEFT SZH MACRO 76X85MM," 14,730 "
-28014610,HC IMP KNEE FEMORAL CR RIGHT SZH MACRO 76X85MM," 14,730 "
-28014611,HC IMP KNEE FEMORAL CR STD 70.5X81MM SZG," 14,730 "
-28014612,HC IMP KNEE FEMORAL LCCK LEFF SZG 70.5X76.5," 30,985 "
-28014613,HC IMP KNEE FEMORAL LCCK LEFT SZG 70.5X76.5 REV," 30,985 "
-28014614,HC IMP KNEE FEMORAL LCCK LEFT SZD 57.5X64MM," 30,985 "
-28014615,HC IMP KNEE FEMORAL LCCK LEFT SZD 57.5X64MM REV," 30,985 "
-28014616,HC IMP KNEE FEMORAL LCCK LEFT SZE 61.5X68MM," 30,985 "
-28014617,HC IMP KNEE FEMORAL LCCK LEFT SZE 61.5X68MM REV," 30,985 "
-28014618,HC IMP KNEE FEMORAL LCCK LEFT SZF 65.5X72MM," 30,985 "
-28014619,HC IMP KNEE FEMORAL LCCK LEFT SZF 65.5X72MM REV," 30,985 "
-28014620,HC IMP KNEE FEMORAL LCCK RIGHT SZC 53.5X60MM," 14,730 "
-28014621,HC IMP KNEE FEMORAL LCCK RIGHT SZC 53.5X60MM REV," 30,985 "
-28014622,HC IMP KNEE FEMORAL LCCK RIGHT SZD 57.5X64MM," 30,985 "
-28014623,HC IMP KNEE FEMORAL LCCK RIGHT SZD 57.5X64MM REV," 30,985 "
-28014624,HC IMP KNEE FEMORAL LCCK RIGHT SZE 61.5X68MM," 30,985 "
-28014625,HC IMP KNEE FEMORAL LCCK RIGHT SZE 61.5X68MM REV," 30,985 "
-28014626,HC IMP KNEE FEMORAL LCCK RIGHT SZF 65.5X72MM," 30,985 "
-28014627,HC IMP KNEE FEMORAL LCCK RIGHT SZF 65.5X72MM REV," 30,985 "
-28014628,HC IMP KNEE FEMORAL LCCK RIGHT SZG 70.5X76.5 REV," 30,985 "
-28014629,HC IMP KNEE FEMORAL LPS FLEX LEFT SZC," 16,980 "
-28014630,HC IMP KNEE FEMORAL LPS FLEX RIGHT SZC," 16,980 "
-28014631,HC IMP KNEE FEMORAL LPS FLEX TIVANIUM RIGHT SZE," 16,980 "
-28014632,HC IMP KNEE FEMORAL LPS OPTION LEFT SZC REV," 17,317 "
-28014633,HC IMP KNEE FEMORAL LPS OPTION LEFT SZD," 13,230 "
-28014634,HC IMP KNEE FEMORAL LPS OPTION LEFT SZE," 13,230 "
-28014635,HC IMP KNEE FEMORAL LPS OPTION RIGHT SZD," 13,230 "
-28014636,HC IMP KNEE FEMORAL LPS OPTION NEXGEN RIGHT SZG," 13,230 "
-28014637,HC IMP KNEE FEMORAL LPS OPTION NEXGEN LEFT SZF," 13,230 "
-28014638,HC IMP KNEE FEMORAL LPS OPTION NEXGEN LEFT SZF REV," 17,317 "
-28014639,HC IMP KNEE FEMORAL LPS OPTION NEXGEN LEFT SZG," 13,230 "
-28014640,HC IMP KNEE FEMORAL LPS OPTION NEXGEN LEFT SZG REV," 17,317 "
-28014641,HC IMP KNEE FEMORAL LPS OPTION NEXGEN LEFT SZH," 13,230 "
-28014642,HC IMP KNEE FEMORAL LPS OPTION NEXGEN RIGHT SZC," 13,230 "
-28014643,HC IMP KNEE FEMORAL LPS OPTION NEXGEN RIGHT SZE," 13,230 "
-28014644,HC IMP KNEE FEMORAL LPS OPTION NEXGEN RIGHT SZF," 13,230 "
-28014645,HC IMP KNEE FEMORAL LPS PROLONG RF SZG REV," 23,250 "
-28014646,HC IMP KNEE FEMORAL MOST BASIC PROXIMAL HINGED," 17,762 "
-28014647,HC IMP KNEE FEMORAL MOST BASIC PROXIMAL HINGED REV," 18,650 "
-28014648,HC IMP KNEE FEMORAL PFC SIGMA TC3 LEFT SIZE 2.5," 28,896 "
-28014649,HC IMP KNEE FEMORAL PFC SIGMA TC3 LEFT SIZE 5," 28,896 "
-28014650,HC IMP KNEE FEMORAL PFC SIGMA TC3 LEFT SIZE 4," 28,896 "
-28014651,HC IMP KNEE FEMORAL PFC SIGMA TC3 RIGHT SIZE 2.5," 35,108 "
-28014652,HC IMP KNEE FEMORAL PFC SIGMA TC3 RIGHT SIZE 3," 28,896 "
-28014653,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ2 56X60," 9,540 "
-28014654,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ3," 9,540 "
-28014655,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ3 61X66," 9,540 "
-28014656,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ4," 9,540 "
-28014657,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ4.5," 9,540 "
-28014658,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ4 65X66," 9,540 "
-28014659,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ5," 9,540 "
-28014660,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ5 69X73," 9,540 "
-28014661,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ6," 9,000 "
-28014662,HC IMP KNEE FEMORAL COMPONENT PS NON POROUS SZ6 74X78," 9,000 "
-28014663,HC IMP KNEE FEMORAL ROTATING HINGED NEXGEN LT SZE REV," 50,692 "
-28014664,HC IMP KNEE FEMORAL ROTATING HINGED NEXGEN RT SZC REV," 50,692 "
-28014665,HC IMP KNEE FEMORAL ROTATING HINGED NEXGEN RT SZD REV," 50,692 "
-28014670,HC IMP KNEE FEMORAL STEM COMPONENT 40MM," 3,840 "
-28014671,HC IMP KNEE FEMORAL UNICOMPARTMENTAL LEFT 47X24MM," 12,938 "
-28014672,HC IMP KNEE FEMORAL UNICOMPARTMENTAL PRCT LT MDL 45," 14,438 "
-28014673,HC IMP KNEE FEMORAL UNICOMPARTMENTAL REG PLUS 50X25," 12,938 "
-28014674,HC IMP KNEE FEMORAL COMPONENT CS TC3," 35,108 "
-28014675,HC IMP KNEE FEMUR SPINDLE STD COMPRESS 38MM 800LB," 21,798 "
-28014676,HC IMP KNEE FEMUR SPINDLE STD COMPRESS 44MM 800LB," 21,798 "
-28014677,HC IMP KNEE INSERT ACETABULAR 0D 22MM REV," 21,966 "
-28014678,HC IMP KNEE INSERT CCK STANDARD SIZE 2 14MM REV," 9,878 "
-28014679,HC IMP KNEE INSERT CCK STANDARD SIZE 3 10MM," 5,814 "
-28014680,HC IMP KNEE INSERT CCK STANDARD SIZE 3 10MM REV," 9,878 "
-28014681,HC IMP KNEE INSERT CCK STANDARD SIZE 3 12MM REV," 9,878 "
-28014682,HC IMP KNEE INSERT CCK STANDARD SIZE 3 14MM REV," 9,878 "
-28014683,HC IMP KNEE INSERT CCK STANDARD SIZE 3 17MM," 5,814 "
-28014684,HC IMP KNEE INSERT CCK STANDARD SIZE 3 17MM REV," 9,878 "
-28014685,HC IMP KNEE INSERT CCK STANDARD SIZE 4 12MM REV," 9,878 "
-28014686,HC IMP KNEE INSERT CCK STANDARD SIZE 4 14MM REV," 9,878 "
-28014687,HC IMP KNEE INSERT CRUCIATE DURACON MEDIUM 11MM REV," 8,204 "
-28014688,HC IMP KNEE INSERT CRUCIATE DURACON MEDIUM 9MM REV," 8,204 "
-28014689,HC IMP KNEE INSERT FIXED BEAR SIZE2 22.5MM REV," 5,452 "
-28014690,HC IMP KNEE INSERT SEGMENTAL POLY BOX SIZE C," 2,384 "
-28014691,HC IMP KNEE INSERT SEGMENTAL POLY BOX SIZE C REV," 2,384 "
-28014692,HC IMP KNEE INSERT TIBIAL ART TRIATHLON SZ 1 10 REV," 6,758 "
-28014693,HC IMP KNEE INSERT TIBIAL ART SIZE 3-4 11," 5,814 "
-28014694,HC IMP KNEE INSERT TIBIAL BEARING OSS 12MM," 7,999 "
-28014695,HC IMP KNEE INSERT TIBIAL BEARING OSS 12MM REV," 5,360 "
-28014696,HC IMP KNEE INSERT TIBIAL BEARING OSS 14MM," 7,999 "
-28014697,HC IMP KNEE INSERT TIBIAL BEARING OSS 14MM REV," 5,360 "
-28014698,HC IMP KNEE INSERT TIBIAL BEARING OSS 18MM," 7,999 "
-28014699,HC IMP KNEE INSERT TIBIAL FIXED BEARING SZ 2 22.5 REV," 9,450 "
-28014700,HC IMP KNEE INSERT TIBIAL FIXED BEARING SZ 4 10MM," 13,779 "
-28014701,HC IMP KNEE INSERT TIBIAL FIXED BEARING SZ 4 10MM REV," 11,187 "
-28014702,HC IMP KNEE INSERT TIBIAL FIXED BEARING SZ 4 12.5 REV," 9,549 "
-28014703,HC IMP KNEE INSERT TIBIAL FIXED BEARING SZ 4 12.5MM," 13,604 "
-28014704,HC IMP KNEE INSERT TIBIAL BEARING SZ 5 10MM," 10,395 "
-28014705,HC IMP KNEE INSERT TIBIAL CRUCIATE SIZE 5 9MM REV," 12,438 "
-28014706,HC IMP KNEE INSERT TIBIAL CURVED LOCK SIZE 2 12.5MM," 6,800 "
-28014707,HC IMP KNEE INSERT TIBIAL GVF CURVED SIZE 5 10MM," 6,800 "
-28014708,HC IMP KNEE INSERT TIBIAL STAB GVF SIZE 2 10MM," 7,208 "
-28014709,HC IMP KNEE INSERT TIBIAL GVF SIZE 2.5 10MM," 6,800 "
-28014710,HC IMP KNEE INSERT TIBIAL STAB GVF SIZE 3 10MM," 6,800 "
-28014711,HC IMP KNEE INSERT TIBIAL STAB GVF SIZE 3 12.5MM REV," 8,006 "
-28014712,HC IMP KNEE INSERT TIBIAL GVF SIZE 3 8MM," 7,208 "
-28014713,HC IMP KNEE INSERT TIBIAL GVF SIZE 4 10MM," 6,800 "
-28014714,HC IMP KNEE INSERT TIBIAL STAB GVF SIZE 4 17.5MM," 6,800 "
-28014715,HC IMP KNEE INSERT TIBIAL STAB GVF SIZE 5 10MM," 6,800 "
-28014716,HC IMP KNEE INSERT TIBIAL STAB GVF SIZE 5 12.5MM," 6,800 "
-28014717,HC IMP KNEE INSERT TIBIAL LEFT SIZE 1 12MM," 5,814 "
-28014718,HC IMP KNEE INSERT TIBIAL LEFT SIZE 2 12MM," 5,814 "
-28014719,HC IMP KNEE INSERT TIBIAL LEFT SIZE 3 12MM," 5,814 "
-28014720,HC IMP KNEE INSERT TIBIAL LEFT SIZE 3 14MM," 5,814 "
-28014721,HC IMP KNEE INSERT TIBIAL LEFT SIZE3 17MM," 5,814 "
-28014722,HC IMP KNEE INSERT TIBIAL MEDIAL LEFT SIZE 4 10MM REV," 8,730 "
-28014723,HC IMP KNEE INSERT TIBIAL LEFT SIZE 4 10MM," 5,814 "
-28014724,HC IMP KNEE INSERT TIBIAL LEFT SIZE 5 17MM REV," 8,730 "
-28014725,HC IMP KNEE INSERT TIBIAL LEFT SIZE 5 17MM," 5,814 "
-28014726,HC IMP KNEE INSERT TIBIAL LEFT SIZE 5 20MM," 5,814 "
-28014727,HC IMP KNEE INSERT TIBIAL LOCK SIZE 3 12.5MM," 6,800 "
-28014728,HC IMP KNEE INSERT TIBIAL GVF LOCK SIZE 3 8MM," 6,800 "
-28014729,HC IMP KNEE INSERT TIBIAL STABILIZED NEOINTM 10MM," 6,800 "
-28014730,HC IMP KNEE INSERT TIBIAL PFC GVF 15MM," 7,208 "
-28014731,HC IMP KNEE INSERT TIBIAL PFC RP SIZE 3 10MM," 8,250 "
-28014732,HC IMP KNEE INSERT TIBIAL PFC RP SIZE 2 12.5," 13,604 "
-28014733,HC IMP KNEE INSERT TIBIAL PFC RP SIZE 2 15MM," 8,250 "
-28014734,HC IMP KNEE INSERT TIBIAL PFC SIZE 2.5," 13,604 "
-28014735,HC IMP KNEE INSERT TIBIAL PFC SIZE 2.5 10MM," 11,019 "
-28014736,HC IMP KNEE INSERT TIBIAL PFC SIG SIZE 3 10MM," 10,530 "
-28014737,HC IMP KNEE INSERT TIBIAL PFC SIZE 3 10MM," 13,604 "
-28014738,HC IMP KNEE INSERT TIBIAL PFC SIZE 3 15MM," 13,604 "
-28014739,HC IMP KNEE INSERT TIBIAL PFC SIZE 4 10MM," 7,208 "
-28014740,HC IMP KNEE INSERT TIBIAL PFC SIZE 4 15MM," 13,604 "
-28014741,HC IMP KNEE INSERT TIBIAL PFC SIZE 4 8MM REV," 8,006 "
-28014742,HC IMP KNEE INSERT TIBIAL PFC SIZE 6 10MM," 8,250 "
-28014743,HC IMP KNEE INSERT TIBIAL POST SIZE 3 16MM REV," 12,438 "
-28014744,HC IMP KNEE INSERT TIBIAL POST SIZE 4 12MM REV," 7,875 "
-28014745,HC IMP KNEE INSERT TIBIAL POST TRIATHLON 9MX3 REV," 12,438 "
-28014746,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 1 12MM," 5,814 "
-28014747,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 1 14MM," 5,814 "
-28014748,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 2 10MM," 5,814 "
-28014749,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 2 10MM REV," 8,730 "
-28014750,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 2 12MM," 5,814 "
-28014751,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 2 14MM," 5,814 "
-28014752,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 3 10MM," 5,814 "
-28014753,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 3 12MM," 5,814 "
-28014754,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 3 14MM," 5,814 "
-28014755,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 4 10MM," 5,814 "
-28014756,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 4 12MM," 5,814 "
-28014757,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 5 12MM," 5,814 "
-28014758,HC IMP KNEE INSERT TIBIAL RIGHT SIZE 5 14MM," 5,814 "
-28014759,HC IMP KNEE INSERT TIBIAL ROTATING STD 10MM," 8,250 "
-28014760,HC IMP KNEE INSERT TIBIAL RP STD 12.5MM," 8,250 "
-28014761,HC IMP KNEE INSERT TIBIAL RP SIZE 2 12.5MM," 8,250 "
-28014762,HC IMP KNEE INSERT TIBIAL RP SIZE 2 17.5MM," 6,800 "
-28014763,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ2.5 10," 8,745 "
-28014764,HC IMP KNEE INSERT TIBIAL ROTAT PLAT SZ2.5 12.5MM," 8,745 "
-28014765,HC IMP KNEE INSERT TIBIAL ROTAT PLAT SZ2.5 12.5MM REV," 8,847 "
-28014766,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ2.5 15 REV," 8,847 "
-28014767,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ3 10MM," 8,745 "
-28014768,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ3 12.5MM," 8,745 "
-28014769,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ3 15MM," 8,745 "
-28014770,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ4 12.5MM," 8,745 "
-28014771,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ4 15MM," 8,745 "
-28014772,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ5 10MM," 8,745 "
-28014773,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ5 12.5," 10,943 "
-28014774,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB SZ5 15MM," 8,745 "
-28014775,HC IMP KNEE INSERT TIBIAL RP STD PL 17.5MM," 8,250 "
-28014776,HC IMP KNEE INSERT TIBIAL SIZE 1.5 12.5MM PLUS," 10,395 "
-28014777,HC IMP KNEE INSERT TIBIAL STABILIZED GVF SZ2.5 17.5MM," 6,800 "
-28014778,HC IMP KNEE INSERT TIBIAL TC3 PFC SIZE 5 10MM," 10,980 "
-28014779,HC IMP KNEE INSERT TIBIAL TKS CRV SIZE 5 10MM," 8,250 "
-28014780,HC IMP KNEE INSERT TIBIAL TKS SIZE 2 20MM," 10,530 "
-28014781,HC IMP KNEE INSERT TIBIAL TKS SIZE 2.5 8MM," 6,800 "
-28014782,HC IMP KNEE INSERT TIBIAL TKS SIZE 2.5 8MM REV," 8,354 "
-28014783,HC IMP KNEE INSERT TIBIAL TRIATHLON SIZE 4 9X3 REV," 13,122 "
-28014784,HC IMP KNEE INSERT TIBIAL ROTAT RIGHT SZ 2.5 12.5MM," 8,250 "
-28014785,HC IMP KNEE INSERT TRIDENT 10D SZE 36X3 REV," 9,734 "
-28014786,HC IMP KNEE INSERT TRIDENT 10D SIZE G 36X3 REV," 9,734 "
-28014787,HC IMP KNEE INSERT TIBIAL FXD BRNG SIZE 3 10MM," 7,208 "
-28014788,HC IMP KNEE LINER TIBIAL HI FLEX SIZE 7-8 21MM," 5,814 "
-28014789,HC IMP KNEE PATELLA 3PEG 37X10MM," 2,754 "
-28014790,HC IMP KNEE PATELLA 3PEG THIN 10X40MM," 2,754 "
-28014791,HC IMP KNEE PATELLA 3PEG THIN 6.2X28MM," 2,754 "
-28014792,HC IMP KNEE PATELLA 3PEG THIN 7.8X34MM," 3,570 "
-28014793,HC IMP KNEE PATELLA 3PEG THIN 8.5X34MM," 3,570 "
-28014794,HC IMP KNEE PATELLA 3PEG THIN 8X28MM," 3,570 "
-28014795,HC IMP KNEE PATELLA 3PEG THIN 8X31MM," 3,570 "
-28014796,HC IMP KNEE PATELLA 3PEG THIN 9.6X37MM," 2,754 "
-28014797,HC IMP KNEE PATELLA DOME 3PEG 32MM," 3,604 "
-28014798,HC IMP KNEE PATELLA DOME ADVANCE PE 3PEG 29MM," 2,754 "
-28014799,HC IMP KNEE PATELLA DOME ADVANCE PE 3PEG 32MM," 2,754 "
-28014800,HC IMP KNEE PATELLA DOME ADVANCE PE 3PEG 35MM," 2,754 "
-28014801,HC IMP KNEE PATELLA DOME ADVANCE PE 3PEG 38MM," 2,754 "
-28014802,HC IMP KNEE PATELLA DOME ADVANCE PE 3PEG 41MM," 2,754 "
-28014803,HC IMP KNEE PATELLA METAL BACKED 1 POST 28MM," 2,754 "
-28014804,HC IMP KNEE PATELLA METAL BACKED 32MM REV," 8,288 "
-28014805,HC IMP KNEE PATELLA MOLDED BAR STOCK 26X7.5MM," 2,754 "
-28014806,HC IMP KNEE PATELLA OVAL UHMWPE 35MM," 3,604 "
-28014807,HC IMP KNEE PATELLA OVAL UHMWPE 35MM REV," 4,871 "
-28014808,HC IMP KNEE PATELLA OVAL UHMWPE 38MM," 3,604 "
-28014809,HC IMP KNEE PATELLA OVAL UHMWPE 38MM REV," 4,871 "
-28014810,HC IMP KNEE PATELLA OVAL UHMWPE 41MM," 3,604 "
-28014812,HC IMP KNEE PATELLA STANDARD 29X8MM," 2,754 "
-28014813,HC IMP KNEE PATELLA STANDARD 29X8MM REV," 5,430 "
-28014814,HC IMP KNEE PATELLA STANDARD 32X8.5MM," 2,754 "
-28014815,HC IMP KNEE PATELLA STANDARD 32X8.5MM REV," 5,430 "
-28014816,HC IMP KNEE PATELLA STANDARD 38X9.5MM REV," 5,430 "
-28014817,HC IMP KNEE PATELLA STANDARD 41X10MM," 2,754 "
-28014818,HC IMP KNEE PATELLA STANDARD 41X10MM REV," 5,430 "
-28014819,HC IMP KNEE PATELLA STANDARD 7.5X26MM," 2,754 "
-28014820,HC IMP KNEE PATELLA STANDARD 9X35MM," 2,754 "
-28014821,HC IMP KNEE PATELLA STANDARD 9X35MM REV," 5,430 "
-28014822,HC IMP KNEE PATELLA SIZE 32," 3,400 "
-28014823,HC IMP KNEE PATELLA SIZE 35," 3,400 "
-28014824,HC IMP KNEE PATELLA SIZE 38," 3,400 "
-28014825,HC IMP KNEE PATELLA SIZE 41," 3,400 "
-28014826,HC IMP KNEE PATELLA TRI PEG ONLY 29X8MM REV," 5,100 "
-28014827,HC IMP KNEE PATELLA TRI PEG ONLY 32X8MM REV," 5,100 "
-28014828,HC IMP KNEE PATELLA TRI PEG ONLY 38X10MM REV," 5,100 "
-28014829,HC IMP KNEE SEGMENTAL COLLAR 17X19MM REV," 17,647 "
-28014830,HC IMP KNEE SEGMENTAL COLLAR 17X19X30MM REV," 15,938 "
-28014831,HC IMP KNEE SEGMENTAL COLLAR 9X16MM," 17,647 "
-28014832,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 11CM," 23,688 "
-28014833,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 13CM," 32,747 "
-28014834,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 17CM," 32,747 "
-28014835,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 3CM," 28,469 "
-28014836,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 3CM REV," 22,537 "
-28014837,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 5CM," 33,551 "
-28014838,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 5CM REV," 22,537 "
-28014839,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 7CM," 33,551 "
-28014840,HC IMP KNEE SEGMENT DIAPHYSEAL FEMORAL OSS 9CM," 33,551 "
-28014841,HC IMP KNEE SEGMENT DIAPHYSEAL MODULAR 220X11MM REV," 12,969 "
-28014842,HC IMP KNEE SEGMENT FEMORAL MOST 30MM REV," 11,331 "
-28014843,HC IMP KNEE SPINDLE DIAPHYSEAL SEGMENT LARGE 600LB," 16,587 "
-28014844,HC IMP KNEE SPINDLE DIAPHYSEAL SEGMENT LARGE 800LB," 23,051 "
-28014845,HC IMP KNEE SPINDLE DIAPHYSEAL SEGMENT SMALL 600LB," 16,587 "
-28014846,HC IMP KNEE SPINDLE DIAPHYSEAL SEGMENT SMALL 800LB," 16,583 "
-28014847,HC IMP KNEE STEM CEMENTED EXTENDED 16X145MM REV," 18,390 "
-28014848,HC IMP KNEE STEM CEMENTED KNEE 12X100MM REV," 6,849 "
-28014849,HC IMP KNEE STEM CEMENTED CALCAR REVISION 170X9MM," 27,889 "
-28014850,HC IMP KNEE STEM CEMENTED CALCAR REVISION 170X9MM REV," 27,889 "
-28014851,HC IMP KNEE STEM CEMENTED CALCAR REVISION 300X9MM," 26,561 "
-28014852,HC IMP KNEE STEM CEMENTED SUMMIT HI 4," 9,338 "
-28014853,HC IMP KNEE STEM CEMENTED SUMMIT HI 5," 9,338 "
-28014854,HC IMP KNEE STEM CEMENTED SUMMIT HI 6," 9,338 "
-28014855,HC IMP KNEE STEM CEMENTED SUMMIT STD2," 9,338 "
-28014856,HC IMP KNEE STEM CEMENTED SUMMIT STD4," 9,338 "
-28014857,HC IMP KNEE STEM CEMENTED SUMMIT STD5," 9,338 "
-28014858,HC IMP KNEE STEM CEMENTED XTNDR 10X65MM," 6,579 "
-28014859,HC IMP KNEE STEM CEMENTED XTNDR 12X100MM," 6,579 "
-28014860,HC IMP KNEE STEM CEMENTED XTNDR 15X30MM," 6,579 "
-28014861,HC IMP KNEE STEM DISTAL FEMORAL COLLAR 25MM REV," 15,938 "
-28014862,HC IMP KNEE STEM DISTAL FEMORAL COLLAR 35MM," 15,938 "
-28014863,HC IMP KNEE STEM DISTAL FEMORAL COLLAR 35MM REV," 15,938 "
-28014864,HC IMP KNEE STEM EXTENSION 11X100X145MM REV," 8,236 "
-28014865,HC IMP KNEE STEM EXTENSION 12X100X145MM REV," 8,236 "
-28014866,HC IMP KNEE STEM EXTENSION 13X100X145MM REV," 8,236 "
-28014867,HC IMP KNEE STEM EXTENSION 14X100X145MM REV," 8,236 "
-28014868,HC IMP KNEE STEM EXTENSION COMPONENT TIB 12X120MM REV," 8,033 "
-28014869,HC IMP KNEE STEM EXTENSION COMPONENT TIB 12X80MM REV," 8,033 "
-28014870,HC IMP KNEE STEM EXTENSION COMPONENT TIB 16X80MM REV," 8,033 "
-28014871,HC IMP KNEE STEM EXTENSION COMPONENT TIB 18X80MM REV," 8,033 "
-28014872,HC IMP KNEE STEM EXTENSION OFFSET 14X155X200MMREV," 8,236 "
-28014873,HC IMP KNEE STEM EXTENSION OFFSET 12X155X200MM," 7,844 "
-28014874,HC IMP KNEE STEM EXTENSION OFFSET 12X155X200MM REV," 8,236 "
-28014875,HC IMP KNEE STEM EXTENSION OFFSET 13X155X200MM REV," 8,236 "
-28014876,HC IMP KNEE STEM EXTENSION OFFSET 13X155X200MM," 7,844 "
-28014877,HC IMP KNEE STEM EXTENSION OFFSET 15X155X200MM REV," 8,236 "
-28014878,HC IMP KNEE STEM EXTENSION OFFSET 15X155X200MM," 7,844 "
-28014879,HC IMP KNEE STEM EXTENSION OFFSET 16X155X200 REV," 8,236 "
-28014880,HC IMP KNEE STEM EXTENSION OFFSET 18X155X200 REV," 8,236 "
-28014881,HC IMP KNEE STEM EXTENSION STRAIGHT 10X100X145MM," 6,803 "
-28014882,HC IMP KNEE STEM EXTENSION STRAIGHT 10X100X145MM REV," 7,144 "
-28014883,HC IMP KNEE STEM EXTENSION STRAIGHT 11X100X145MM," 6,803 "
-28014884,HC IMP KNEE STEM EXTENSION STRAIGHT 11X100X145MM REV," 7,144 "
-28014885,HC IMP KNEE STEM EXTENSION STRAIGHT 12X100X145MM REV," 7,144 "
-28014886,HC IMP KNEE STEM EXTENSION STRAIGHT 12X155X200MM REV," 7,144 "
-28014887,HC IMP KNEE STEM EXTENSION STRAIGHT 13X100X145MM," 6,803 "
-28014888,HC IMP KNEE STEM EXTENSION STRAIGHT 13X100X145MM REV," 7,144 "
-28014889,HC IMP KNEE STEM EXTENSION STRAIGHT 13X155X200MM REV," 7,144 "
-28014890,HC IMP KNEE STEM EXTENSION STRAIGHT 14X100X145MM," 6,803 "
-28014891,HC IMP KNEE STEM EXTENSION STRAIGHT 14X100X145MM REV," 7,144 "
-28014892,HC IMP KNEE STEM EXTENSION STRAIGHT 14X155X200MM," 7,144 "
-28014893,HC IMP KNEE STEM EXTENSION STRAIGHT 14X155X200MM REV," 7,144 "
-28014894,HC IMP KNEE STEM EXTENSION STRAIGHT 15X100X145MM," 6,803 "
-28014895,HC IMP KNEE STEM EXTENSION STRAIGHT 15X100X145MM REV," 7,144 "
-28014896,HC IMP KNEE STEM EXTENSION STRAIGHT 15X155X200MM," 6,803 "
-28014897,HC IMP KNEE STEM EXTENSION STRAIGHT 15X155X200MM REV," 7,144 "
-28014898,HC IMP KNEE STEM EXTENSION STRAIGHT 15X30X75MM," 6,803 "
-28014899,HC IMP KNEE STEM EXTENSION STRAIGHT 15X30X75MM REV," 7,144 "
-28014900,HC IMP KNEE STEM EXTENSION STRAIGHT 16X100X145MM REV," 7,144 "
-28014901,HC IMP KNEE STEM EXTENSION STRAIGHT 16X155X200MM REV," 7,144 "
-28014902,HC IMP KNEE STEM EXTENSION STRAIGHT 17X100X145MM REV," 7,144 "
-28014903,HC IMP KNEE STEM EXTENSION STRAIGHT 17X155X200MM REV," 7,144 "
-28014904,HC IMP KNEE STEM EXTENSION STRAIGHT 18X100X145MM REV," 7,144 "
-28014905,HC IMP KNEE STEM EXTENSION STRAIGHT 18X155X200MM REV," 7,144 "
-28014906,HC IMP KNEE STEM EXTENSION STRAIGHT 20X100X145MM REV," 7,144 "
-28014907,HC IMP KNEE STEM FEMORAL 12MM REV," 32,928 "
-28014908,HC IMP KNEE STEM FEMORAL 12X32MM 5PL," 7,208 "
-28014909,HC IMP KNEE STEM FEMORAL IM CEMENTED 15X150MM," 7,551 "
-28014910,HC IMP KNEE STEM FEMORAL EXTENSION 14X100MM REV," 9,675 "
-28014911,HC IMP KNEE STEM FEMORAL EXTENSION 16X100MM REV," 6,579 "
-28014912,HC IMP KNEE STEM FEM EXTENSION 18X100MM REV," 6,579 "
-28014913,HC IMP KNEE STEM FEMORAL IM BOWD 17X150MM REV," 7,551 "
-28014914,HC IMP KNEE STEM MODULAR CEMENTED EXTENSION 12X65 REV," 6,579 "
-28014915,HC IMP KNEE STEM MODULAR CEMENTED EXTENSION 15X30 REV," 6,579 "
-28014916,HC IMP KNEE STEM REVISION 13X120MM REV," 7,829 "
-28014917,HC IMP KNEE STEM SEGMENTAL CEMENT STR 12X130MM REV," 23,800 "
-28014918,HC IMP KNEE STEM SEGMENTAL CEMENT STR 13X130MM REV," 23,800 "
-28014919,HC IMP KNEE STEM SEGMENTAL CEMENT STR 16X130MM," 23,800 "
-28014920,HC IMP KNEE STEM SEGMENTAL CEMENT STR 17X190MM REV," 23,800 "
-28014921,HC IMP KNEE STEM SEGMENTAL CEMENT STR 18X130MM REV," 23,800 "
-28014922,HC IMP KNEE STEM SEGMENTAL STR 13X190MM," 23,800 "
-28014923,HC IMP KNEE STEM TIBIAL CEMENTED 13X60MM REV," 13,649 "
-28014924,HC IMP KNEE STEM TIBIAL CEMENTED TAPERED 13X90MM," 7,813 "
-28014925,HC IMP KNEE STEM TIBIAL CEMENTED TAPERED 13X90MM REV," 13,568 "
-28014926,HC IMP KNEE STEM TIBIAL CEMENTED 15X60MM REV," 20,744 "
-28014927,HC IMP KNEE STEM TIBIAL CRUCIATE RETAINING 12MM REV," 6,630 "
-28014928,HC IMP KNEE STEM TIBIAL SIGMA CEMENTED 13X30MM," 7,813 "
-28014929,HC IMP KNEE STEM TIBIAL SIGMA CEMENTED 13X6MM REV," 5,396 "
-28014930,HC IMP KNEE STEM TIBIAL SIGMA CEMENTED 2.5 59X63MM REV," 5,396 "
-28014931,HC IMP KNEE STEM TIBIAL TRAY 63MM REV," 13,280 "
-28014932,HC IMP KNEE STEM TIBIAL TRAY 67MM REV," 16,793 "
-28014933,HC IMP KNEE STEM TIBIAL TRAY 75MM REV," 13,280 "
-28014934,HC IMP KNEE STEM XTNDR PRSFIT 14X155MM REV," 5,508 "
-28014935,HC IMP KNEE ARTICULAR SURF CR-FLEX C-H 3-4 10X42X66MM," 5,814 "
-28014936,HC IMP KNEE ARTICULAR SURF CR-FLEX C-H 3-4 12X42X66MM," 5,814 "
-28014937,HC IMP KNEE ARTICULAR SURF CR-FLEX C-H 3-4 14X42X66MM," 5,814 "
-28014938,HC IMP KNEE ARTICULAR SURF LCCK C-D 1-2 17X40X58MM REV," 11,146 "
-28014939,HC IMP KNEE ARTICULAR SURF LCCK C-D 3-4 10X42X66MM REV," 11,146 "
-28014940,HC IMP KNEE ARTICULAR SURF LCCK C-D 3-4 14X42X66MM REV," 11,146 "
-28014941,HC IMP KNEE ARTICULAR SURF LCCK C-D 5-6 12X46X74MM REV," 11,146 "
-28014942,HC IMP KNEE ARTICULAR SURF LCCK E-F 3-4 17X42X66MM REV," 11,146 "
-28014943,HC IMP KNEE ARTICULAR SURF LCCK E-F 3-4 23X42X66MM REV," 11,146 "
-28014944,HC IMP KNEE ARTICULAR SURF LCCK E-F 5-6 14X46X74MM REV," 11,146 "
-28014945,HC IMP KNEE ARTICULAR SURF LCCK E-F 5-6 20X46X74MM REV," 11,146 "
-28014946,HC IMP KNEE ARTICULAR SURF LCCK G 5-6 10X46X74MM REV," 11,146 "
-28014947,HC IMP KNEE ARTICULAR SURF LPS FLX CD 1-2 12MM REV," 8,737 "
-28014948,HC IMP KNEE ARTICULAR SURF LPS FLX CD 3-4 14MM REV," 8,737 "
-28014949,HC IMP KNEE ARTICULAR SURF LPS FLX CD 3-4 17MM REV," 8,737 "
-28014950,HC IMP KNEE ARTICULAR SURF LPS FLX EF 3-4 10MM REV," 8,737 "
-28014951,HC IMP KNEE ARTICULAR SURF LPS FLX EF 3-4 12MM REV," 13,688 "
-28014952,HC IMP KNEE ARTICULAR SURF LPS FLX EF 3-4 17MM REV," 8,737 "
-28014953,HC IMP KNEE ARTICULAR SURF LPS FLX EF 5-6 10MM REV," 8,737 "
-28014954,HC IMP KNEE ARTICULAR SURF LPS FLX EF 5-6 12MM REV," 8,737 "
-28014955,HC IMP KNEE ARTICULAR SURF LPS FLX EF 5-6 14MM REV," 8,737 "
-28014956,HC IMP KNEE ARTICULAR SURF LPS FLX GH 5-6 10MM REV," 8,737 "
-28014957,HC IMP KNEE ARTICULAR SURF LPS FLX GH 5-6 12MM REV," 8,737 "
-28014958,HC IMP KNEE ARTICULAR SURF LPS FLX GH 7-10 10MM REV," 8,737 "
-28014959,HC IMP KNEE ARTICULAR SURF LPS FLX GH 7-10 12MM REV," 8,737 "
-28014960,HC IMP KNEE ARTICULAR SURF LPS FLX GH 7-10 23MM REV," 8,737 "
-28014961,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL 10X44X27," 4,250 "
-28014962,HC IMP KNEE ARTICULAR SURF UNICOMPARTMENTAL 8X44X27MM," 4,250 "
-28014963,HC IMP KNEE ARTICULAR SURF CR-FLEX C-H 10X51X82MM," 5,814 "
-28014964,HC IMP KNEE ARTICULAR SURF CR-FLEX C-H 12X51X82MM," 5,814 "
-28014965,HC IMP KNEE ARTICULAR SURF CR-FLEX C-H 14X40X58MM," 5,814 "
-28014966,HC IMP KNEE ARTICULAR SURFACE LPS FLX AB 1-2 10MM," 5,814 "
-28014967,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 1-2 10MM," 5,814 "
-28014968,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 1-2 12MM," 5,814 "
-28014969,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 1-2 14MM," 5,814 "
-28014970,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 3-4 10MM," 5,814 "
-28014971,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 3-4 12MM," 5,814 "
-28014972,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 3-4 14MM," 5,814 "
-28014973,HC IMP KNEE ARTICULAR SURFACE LPS FLX CD 3-4 17MM," 5,814 "
-28014974,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 3-4 10MM," 5,814 "
-28014975,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 3-4 12MM," 5,814 "
-28014976,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 3-4 14MM," 5,814 "
-28014977,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 3-4 17MM," 5,814 "
-28014978,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 5-6 10MM," 5,814 "
-28014979,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 5-6 12MM," 5,814 "
-28014980,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 5-6 14MM," 5,814 "
-28014981,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 7-10 10MM," 5,814 "
-28014982,HC IMP KNEE ARTICULAR SURFACE LPS FLX EF 7-10 12MM," 5,814 "
-28014983,HC IMP KNEE ARTICULAR SURFACE LPS FLX GH 5-6 10MM," 5,814 "
-28014984,HC IMP KNEE ARTICULAR SURFACE LPS FLX GH 5-6 12MM," 5,814 "
-28014985,HC IMP KNEE ARTICULAR SURFACE LPS FLX GH 5-6 14MM," 5,814 "
-28014986,HC IMP KNEE ARTICULAR SURFACE LPS FLX GH 7-10 10MM," 5,814 "
-28014987,HC IMP KNEE ARTICULAR SURFACE LPS FLX GH 7-10 12MM," 5,814 "
-28014988,HC IMP KNEE ARTICULAR SURFACE LPS FLX GH 7-10 14MM," 5,814 "
-28014989,HC IMP KNEE ARTICULAR SURF NEXGEN 46X74MM," 5,814 "
-28014990,HC IMP KNEE TIB KEEL CEMENTED MBT TRAY SIZE 2 REV, 770 
-28014991,HC IMP KNEE TIBIAL KEELED CEMENTED MBT TRAY SIZE 2," 11,925 "
-28014992,HC IMP KNEE TIBIAL KEELED CEMENTED MBT TRAY SIZE 2 REV," 15,786 "
-28014993,HC IMP KNEE TIBIAL KEELED CEMENTED MBT TRAY SIZE 3," 11,925 "
-28014994,HC IMP KNEE TIBIAL KEELED CEMENTED MBT TRAY SIZE 4," 11,925 "
-28014995,HC IMP KNEE TIBIAL KEELED CEMENTED MBT TRAY SIZE 5," 11,925 "
-28014996,HC IMP KNEE TIBIAL PRIMARY REGENEREX TRAY 67MM," 9,960 "
-28014997,HC IMP KNEE TIBIAL PRIMARY REGENEREX TRAY 71MM," 9,960 "
-28014998,HC IMP KNEE TIBIAL PRIMARY REGENEREX TRAY 75MM," 9,960 "
-28014999,HC IMP KNEE TIBIAL TRABCULAR NEXGEN 15X48MM," 19,144 "
-28015000,HC IMP KNEE TIBIAL TRABCULAR NEXGEN 15X52MM REV," 20,101 "
-28015001,HC IMP KNEE TIBIAL," 8,498 "
-28015002,HC IMP KNEE TIBIAL 10MM," 6,171 "
-28015003,HC IMP KNEE TIBIAL CRICIFORM 85X60MM REV," 9,378 "
-28015004,HC IMP KNEE TIBIAL INSERT STAB SZ6 12," 8,250 "
-28015005,HC IMP KNEE TIBIAL INSERT STAB SZ6 15," 8,250 "
-28015006,HC IMP KNEE TIBIAL INSERT TC3 SZ2 1," 10,530 "
-28015007,HC IMP KNEE TIBIAL INSERT TC3 SZ4 1," 10,530 "
-28015008,HC IMP KNEE TIBIAL TRAY SIZE 1.5," 11,925 "
-28015009,HC IMP KNEE TIBIAL KEELED TRAY SZ2.5," 11,925 "
-28015010,HC IMP KNEE YOKE REINFORCED OSS," 7,472 "
-28015011,HC IMP KNEE YOKE REINFORCED OSS REV," 6,333 "
-28017664,HC IMP SCREW HIP PINNACLE 6.5X15MM, 850 
-28017665,HC IMP SCREW HIP PINNACLE 6.5X20MM, 850 
-28018432,HC IMP SHOULDER GLENOID CAGE ALPHA SM," 16,430 "
-28018433,HC IMP SHOULDER GLENOID KEELED ALPHA LARGE," 10,888 "
-28018434,HC IMP SHOULDER GLENOID KEELED ALPHA MEDIUM," 10,888 "
-28018435,HC IMP SHOULDER GLENOID KEELED ALPHA SMALL," 10,888 "
-28018436,HC IMP SHOULDER GLENOID KEELED BETA LARGE," 10,888 "
-28018437,HC IMP SHOULDER GLENOID KEELED BETA MEDIUM," 10,888 "
-28018438,HC IMP SHOULDER GLENOID KEELED BETA SMALL," 10,888 "
-28018439,HC IMP SHOULDER GLENOID PEGGED ALPHA MEDIUM," 10,888 "
-28018440,HC IMP SHOULDER GLENOID PEGGED BETA LARGE," 10,888 "
-28018441,HC IMP SHOULDER GLENOID PEGGED BETA SMALL," 10,888 "
-28018442,HC IMP SHOULDER GLENOID PEGGED BETA XLARGE," 10,888 "
-28018443,HC IMP SHOULDER GLENOID PEGGED MEDIUM," 10,888 "
-28018444,HC IMP SHOULDER GLENOID PEGGED SMALL," 10,888 "
-28018445,HC IMP SHOULDER GLENOSPHERE OFFSET 38MM +4MM," 11,387 "
-28018446,HC IMP SHOULDER GLENOSPHERE OFFSET 42MM +4MM," 11,387 "
-28018447,HC IMP SHOULDER GLENOSPHERE REVERSE 42MM," 11,387 "
-28018448,HC IMP SHOULDER GLENOSPHERE REVERSE 46MM," 11,387 "
-28018449,HC IMP SHOULDER HEAD HUMERAL 6.5X30MM," 10,650 "
-28018450,HC IMP SHOULDER HEAD HUMERAL BI-ANGULAR 30X15CM," 16,965 "
-28018451,HC IMP SHOULDER HEAD HUMERAL GLENOID 36MM," 8,606 "
-28018452,HC IMP SHOULDER HEAD HUMERAL SHORT ALPHA EQUINOXE 38MM," 12,522 "
-28018453,HC IMP SHOULDER HEAD HUMERAL SHORT ALPHA EQUINOXE 41MM," 12,522 "
-28018454,HC IMP SHOULDER HEAD HUMERAL SHORT ALPHA EQUINOXE 44MM," 12,522 "
-28018455,HC IMP SHOULDER HEAD HUMERAL SHORT BETA EQUINOXE 47MM," 12,525 "
-28018456,HC IMP SHOULDER HEAD HUMERAL SHORT BETA EQUINOXE 50MM," 12,522 "
-28018457,HC IMP SHOULDER HEAD HUMERAL SHORT BETA EQUINOXE 53MM," 12,522 "
-28018458,HC IMP SHOULDER HEAD HUMERAL STANDARD 54MMX27MM," 8,550 "
-28018459,HC IMP SHOULDER HEAD HUMERAL SYSTEM 4X32MM," 8,606 "
-28018460,HC IMP SHOULDER HEAD HUMERAL TALL ALPHA EQUINOXE 38MM," 12,522 "
-28018461,HC IMP SHOULDER HEAD HUMERAL TALL ALPHA EQUINOXE 41MM," 12,522 "
-28018462,HC IMP SHOULDER HEAD HUMERAL TALL ALPHA EQUINOXE 44MM," 12,522 "
-28018463,HC IMP SHOULDER HEAD HUMERAL TALL BETA EQUINOXE 47MM," 12,522 "
-28018464,HC IMP SHOULDER HEAD HUMERAL TALL BETA EQUINOXE 50MM," 12,522 "
-28018465,HC IMP SHOULDER HEAD HUMERAL TALL BETA EQUINOXE 53MM," 12,522 "
-28018466,HC IMP SHOULDER HEAD HUMERAL W  SCREW 36MM," 8,100 "
-28018467,HC IMP SHOULDER HEAD HUMERAL W SCREW 40MM," 8,100 "
-28018468,HC IMP SHOULDER HEAD HUMERAL EXPAND BETA EQUINOXE 47MM," 12,522 "
-28018469,HC IMP SHOULDER HEAD HUMERAL EXPAND BETA EQUINOXE 50MM," 12,522 "
-28018470,HC IMP SHOULDER HEAD HUMERAL EXPAND BETA EQUINOXE 53MM," 12,522 "
-28018471,HC IMP SHOULDER HUMERAL PROXIMAL MOSAIC +20," 32,242 "
-28018472,HC IMP SHOULDER INSERT HUMERAL SOCKET 32MM," 7,480 "
-28018473,HC IMP SHOULDER INSERT HUMERAL SOCKET 36MM," 7,480 "
-28018474,HC IMP SHOULDER INSERT HUMERAL SOCKET 40MM," 7,480 "
-28018475,HC IMP SHOULDER INSERT HUMERAL SOCKET PRIMARY NEUTRAL," 12,900 "
-28018476,HC IMP SHOULDER INSERT HUMERAL SOCKET PRIMARY RSP 4MM," 12,900 "
-28018477,HC IMP SHOULDER INSERT HUMERAL SOCKET PRIMARY RSP 8MM," 12,900 "
-28018478,HC IMP SHOULDER INSERT HUMERAL SOCKET STND 32MM," 7,480 "
-28018479,HC IMP SHOULDER INSERT HUMERAL SOCKET STND 36MM," 7,480 "
-28018480,HC IMP SHOULDER INSERT HUMERAL SOCKET STND 40MM," 7,480 "
-28018482,HC IMP SHOULDER LINER HUMERAL REV CONST EQUINOXE +0 38," 7,362 "
-28018483,HC IMP SHOULDER LINER HUMERAL REV CONST EQUINOXE +0 46," 7,362 "
-28018484,HC IMP SHOULDER LINER HUMERAL REV CONST EQUINOXE +2.5," 7,362 "
-28018485,HC IMP SHOULDER LINER HUMERAL REV CONST EQUINOXE 42MM," 7,362 "
-28018486,HC IMP SHOULDER LINER HUMERAL REV CONST EQUNOX +2.5 46," 7,362 "
-28018487,HC IMP SHOULDER LINER HUMERAL REV CONST EQUINOXE +0 42," 7,362 "
-28018488,HC IMP SHOULDER LINER HUMERAL REVERSE EQUINOXE +0 38MM," 7,362 "
-28018489,HC IMP SHOULDER LINER HUMERAL REVERSE EQUINOXE +0 42MM," 7,362 "
-28018490,HC IMP SHOULDER LINER HUMERAL REVERSE EQUINOXE +0 46MM," 7,362 "
-28018491,HC IMP SHOULDER LINER HUMERAL REVERSE EQUINOXE +2.5 46," 7,362 "
-28018492,HC IMP SHOULDER LINER HUMERAL REVERSE EQUINOXE +2.5 38," 7,362 "
-28018493,HC IMP SHOULDER LINER HUMERAL REVERSE EQUINOXE +2.5 42," 7,362 "
-28018494,HC IMP SHOULDER SPHERE GLENOID 10DEG 25X36MM," 28,735 "
-28018495,HC IMP SHOULDER STEM ANATOMICAL 10.5X110MM," 33,117 "
-28018496,HC IMP SHOULDER STEM HUMERAL ADAPTER RSP 12MM," 6,630 "
-28018497,HC IMP SHOULDER STEM PRIMARY 8 10X116MM," 21,980 "
-28018498,HC IMP SHOULDER STEM PRIMARY REVERSE 10X116MM," 21,980 "
-28018499,HC IMP SHOULDER STEM PRIMARY REVERSE 12X124MM," 21,980 "
-28018500,HC IMP SHOULDER STEM PRIMARY REVERSE 6X101MM," 21,980 "
-28018501,HC IMP SHOULDER STEM PRIMARY REVERSE 7X105MM," 21,980 "
-28019489,HC IMP TIBIAL IMPLANT MEDIUM 32.5X35MM," 33,150 "
-28019490,HC IMP TIBIAL INSERT STANDARD DURACON 16MM," 4,118 "
-28015028,HC IMP LEAD DEFIBRILLATOR 11MM 52CM 6.8F," 37,375 "
-28015030,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 52CM," 33,600 "
-28015032,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 58CM," 37,375 "
-28015035,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 65MM," 35,000 "
-28015037,HC IMP LEAD DEFIBRILLATOR S-65," 35,000 "
-28015041,HC IMP LEAD LINOX SMART S60," 35,000 "
-28015042,HC IMP LEAD NEUROSTIMULATOR EXTENSION KIT 2X8X30CM," 33,800 "
-28015065,HC IMP LEAD STIMULATION SPECIFY 7.9X1.8MM," 18,000 "
-28015142,HC IMPL LENS IOL TECNIS ACRYLIC 24.5D," 7,608 "
-28015188,HC IMP MESH DYNAMIC 0.3X0.5X90X90MM," 8,417 "
-28015189,HC IMP MESH DYNAMIC 0.6X200X200MM," 41,786 "
-28015190,HC IMP MESH DYNAMIC 0.6X90X90MM," 9,114 "
-28015191,HC IMP MESH HERNIA SURGIMEND 10X15CM," 21,000 "
-28015194,HC IMP MESH PELVIC RESTORELLE," 8,373 "
-28015195,HC IMP MESH VENTRAL PATCH MEDIUM 6.4X6.4CM," 5,606 "
-28015196,HC IMP MESH VENTRAL PATCH SMALL 4.3X4.3CM," 2,343 "
-28015335,HC IMP PHYSIOMESH OVAL 15X15CM," 5,848 "
-28015336,HC IMP PHYSIOMESH OVAL 15X20CM," 9,170 "
-28015337,HC IMP PHYSIOMESH OVAL 15X35CM," 20,097 "
-28016351,HC IMP PTERIONAL MEDPOR LEFT 44X43X6MM," 7,997 "
-28015310,HC IMPL PACEMAKER DDDR INGENIO," 33,250 "
-28015311,HC IMPL PACEMAKER DR ADVISA," 42,218 "
-28015312,HC IMPL PACEMAKER DUAL ADAPTA SMALL," 26,600 "
-28015313,HC IMPL PACEMAKER DUAL TRIUMPH DR," 33,768 "
-28015314,HC IMPL PACEMAKER SINGLE EVIA SR-T," 18,750 "
-28015315,HC IMPL PACEMAKER SINGLE RATE MICRONY II," 20,300 "
-28015280,HC IMP NEUROSTIMULATOR PROGRAMMER MYSTIM," 7,608 "
-28016326,HC IMP PORT ENDO FLEXIBLE SILS 5MM, 691 
-28016328,HC IMP PORT MINI DIGNITY CT SILI 8F," 2,788 "
-28016332,HC IMP PORT VENOUS ACCESS SLIM 6F," 3,995 "
-28010847,HC IMP BREAST EXPANDER STYLE 133FV 300ML," 8,963 "
-28010848,HC IMP BREAST EXPANDER STYLE 133MX 300ML," 8,963 "
-28010849,HC IMP BREAST EXPANDER STYLE 133MX 560ML," 8,963 "
-28010850,HC IMP BREAST EXPANDER STYLE 133MX 700ML," 8,963 "
-28010851,HC IMP BREAST EXPANDER STYLE 133SV 300ML," 8,963 "
-28010852,HC IMP BREAST EXPANDER STYLE 133SX 250ML," 8,963 "
-28010853,HC IMP BREAST EXPANDER STYLE 133SX 300ML," 8,963 "
-28010854,HC IMP BREAST EXPANDER STYLE 133SX 400ML," 8,963 "
-28010855,HC IMP BREAST EXPANDER STYLE 133SX 500ML," 8,963 "
-28010942,HC IMP BREAST ROUND MODERATE PROFILE STY168 480-510CC," 4,463 "
-28010943,HC IMP BREAST ROUND MODERATE PROFILE STY168 510-540CC," 4,463 "
-28010944,HC IMP BREAST ROUND MODERATE PROFILE STY168 550-600CC," 4,590 "
-28010945,HC IMP BREAST ROUND MODERATE PROFILE STY168 600-650CC," 4,590 "
-28010946,HC IMP BREAST ROUND MODERATE PROFILE STY168 800-850CC," 4,590 "
-28010947,HC IMP BREAST ROUND SALINE STYLE 68HP 320CC," 2,508 "
-28010948,HC IMP BREAST ROUND SALINE STYLE 68HP 350CC," 2,508 "
-28010949,HC IMP BREAST ROUND SALINE STYLE 68HP 400CC," 2,508 "
-28010950,HC IMP BREAST ROUND SALINE STYLE 68LP 150CC," 2,508 "
-28010951,HC IMP BREAST ROUND SALINE STYLE 68LP 200CC," 2,508 "
-28010952,HC IMP BREAST ROUND SALINE STYLE 68LP 225CC," 2,508 "
-28010953,HC IMP BREAST ROUND SALINE STYLE 68LP 250CC," 2,508 "
-28010954,HC IMP BREAST ROUND SALINE STYLE 68LP 275CC," 2,508 "
-28010955,HC IMP BREAST ROUND SALINE STYLE 68LP 300CC," 2,508 "
-28010956,HC IMP BREAST ROUND SALINE STYLE 68LP 350CC," 2,508 "
-28010957,HC IMP BREAST ROUND SALINE STYLE 68LP 400CC," 2,508 "
-28010958,HC IMP BREAST ROUND SALINE FILLED SMOOTH 15.2, 344 
-28010959,HC IMP BREAST ROUND SILTEX BECKER 275CC," 11,250 "
-28010960,HC IMP BREAST ROUND SILTEX BECKER 350CC," 11,250 "
-28010961,HC IMP BREAST ROUND SILTEX BECKER MEDIUM 450CC," 11,250 "
-28010962,HC IMP BREAST ROUND SILTEX BECKER MEDIUM 550CC," 11,250 "
-28010963,HC IMP BREAST ROUND SILTEX BECKER 650CC," 11,250 "
-28010964,HC IMP BREAST ROUND MODERATE PROFILE SMOOTH SALINE 375," 3,825 "
-28010965,HC IMP BREAST SALINE STYLE 363LF 170CC," 6,460 "
-28010966,HC IMP BREAST SALINE STYLE 363LF 690-720CC," 6,460 "
-28010967,HC IMP BREAST SILICONE STYLE 168 120ML," 5,525 "
-28010968,HC IMP BREAST SILICONE STYLE 168 150ML," 4,463 "
-28010969,HC IMP BREAST SILICONE STYLE 168 180ML," 4,463 "
-28010970,HC IMP BREAST SILICONE STYLE 168 210ML," 5,525 "
-28010971,HC IMP BREAST SILICONE STYLE 168 240ML," 4,038 "
-28010972,HC IMP BREAST SILICONE STYLE 168 300ML," 4,038 "
-28010973,HC IMP BREAST SILICONE STYLE 168 330ML," 4,463 "
-28010974,HC IMP BREAST SILICONE STYLE 168 360ML," 4,463 "
-28010975,HC IMP BREAST SILICONE STYLE 168 390ML," 4,463 "
-28010976,HC IMP BREAST SILICONE STYLE 168 420ML," 4,463 "
-28010977,HC IMP BREAST SILICONE STYLE 168 450ML," 4,463 "
-28010978,HC IMP BREAST SILICONE STYLE 20 300ML," 6,758 "
-28010979,HC IMP BREAST SILICONE STYLE 20 325ML," 6,758 "
-28010980,HC IMP BREAST SMOOTH GEL ROUND 390CC," 7,650 "
-28010981,HC IMP BREAST STYLE 163 360ML," 6,800 "
-28010982,HC IMP BREAST STYLE 163 440ML," 6,800 "
-28010983,HC IMP BREAST STYLE 163 530ML," 6,800 "
-28010984,HC IMP BREAST STYLE 163 655ML," 6,800 "
-28010985,HC IMP BREAST STYLE 468 195ML," 5,525 "
-28010986,HC IMP BREAST STYLE 468 230ML," 5,525 "
-28010987,HC IMP BREAST STYLE 468 270ML," 4,888 "
-28010988,HC IMP BREAST STYLE 468 650ML," 5,525 "
-28012834,HC IMP EXPANDER TISSUE CURVED 2.5 X 9.25," 9,000 "
-28012835,HC IMP EXPANDER TISSUE RECTANGLE 5X10," 7,820 "
-28019497,HC IMP TISSUE EXPANDER CUI STD10X8X4," 4,208 "
-28019498,HC IMP TISSUE EXPANDER VERSAFIL 6X4X4," 4,463 "
-28019499,HC IMP TISSUE EXPANDER WO STABLE BASE VERSAFIL 8X5X5," 4,463 "
-28019500,HC IMP TISSUE EXPANDER VERSAFIL 8X6," 4,463 "
-28011748,HC  CATHETER BALLOON Z-MED20MMX4CM," 4,919 "
-28011749,HC  CATHETER BALLOON Z-MED22MMX3CM," 5,903 "
-28012659,HC  CUFF URINARY SPHINCTER INHIBIZONE 4.5CM," 28,700 "
-28012660,HC  CUFF URINARY SPHINCTER INHIBIZONE 4CM," 28,700 "
-28012661,HC  CUFF URINARY SPHINCTER INHIBIZONE SILIC 3," 30,800 "
-28015329,HC  PENILE PUMP CONTROL AMS 800," 29,400 "
-28015284,HC IMP OCCLUDER AMPLATZER 18X7MM," 39,000 "
-28015285,HC IMP OCCLUDER AMPLATZER 4X7MM," 39,000 "
-28012877,HC IMP GENERATOR NEUROSTIMULATOR DEEP BRAIN ACTIVA," 91,700 "
-28012878,HC IMP GENERATOR NEUROSTIMULATOR RESTORE," 77,500 "
-28018605,HC IMP SPACER US X STOP 10MM," 27,650 "
-28018606,HC IMP SPACER US X STOP 8MM," 27,650 "
-28018607,HC IMP SPACER US X STOP PEEK 10MM," 30,450 "
-28018608,HC IMP SPACER US X STOP PEEK 12MM," 30,450 "
-28018609,HC IMP SPACER US X STOP PEEK 14MM," 30,450 "
-28018610,HC IMP SPACER US X STOP PEEK 6MM," 30,450 "
-28011605,HC  CATHETER BALLOON 5MMX10MMX80CM," 6,800 "
-28012831,HC IMP ENDOPROSTHESIS 31X14.5MM 17CM," 54,405 "
-28012892,HC IMP GRAFT ENDOVASCULAR 14X55MM 14F," 14,153 "
-28012893,HC IMP GRAFT ENDOVASCULAR 14X71MM 14F," 20,048 "
-28012894,HC IMP GRAFT ENDOVASCULAR 16F 18X55MM," 14,573 "
-28012895,HC IMP GRAFT ENDOVASCULAR 16X55MM 14F," 14,573 "
-28012896,HC IMP GRAFT ENDOVASCULAR 18F 24X58," 14,573 "
-28012897,HC IMP GRAFT ENDOVASCULAR 18F 26X39," 14,573 "
-28012898,HC IMP GRAFT ENDOVASCULAR 18F 26X82," 54,977 "
-28012899,HC IMP GRAFT ENDOVASCULAR 20F 30X39," 14,573 "
-28012900,HC IMP GRAFT ENDOVASCULAR 20F 30X58," 14,153 "
-28012901,HC IMP GRAFT ENDOVASCULAR 20F 32X39," 14,573 "
-28012902,HC IMP GRAFT ENDOVASCULAR 24X12X80MM," 17,145 "
-28012903,HC IMP GRAFT ENDOVASCULAR 24X30MM," 14,145 "
-28012904,HC IMP GRAFT ENDOVASCULAR 25X60X40MM," 67,770 "
-28012905,HC IMP GRAFT ENDOVASCULAR THORACIC 28MM 10CM," 74,250 "
-28012906,HC IMP GRAFT ENDOVASCULAR 28X12X80MM," 17,145 "
-28012907,HC IMP GRAFT ENDOVASCULAR 28X28X75MM," 24,465 "
-28012908,HC IMP GRAFT ENDOVASCULAR 32X12X82MM," 17,145 "
-28012909,HC IMP GRAFT ENDOVASCULAR BD EXT 32X43MM," 34,912 "
-28018924,HC IMP STENT BILIARY RX 8.5MM," 1,182 "
-28018947,HC IMP STENT CORONARY CYPHER 2.25X13MM," 16,005 "
-28018948,HC IMP STENT CORONARY CYPHER 2.25X18MM," 16,005 "
-28018949,HC IMP STENT CORONARY CYPHER 2.25X23MM," 16,005 "
-28018950,HC IMP STENT CORONARY CYPHER 2.25X8MM," 16,005 "
-28018951,HC IMP STENT CORONARY MICRODRIVER MX2 2.5X12MM," 7,875 "
-28018952,HC IMP STENT CORONARY ENDEAVOR RX 2.5X12MM," 13,463 "
-28018953,HC IMP STENT CORONARY ENDEAVOR RX 2.5X24MM," 9,919 "
-28018958,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X12," 13,463 "
-28018959,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X14," 11,963 "
-28018960,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X18," 11,963 "
-28018961,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X22," 13,463 "
-28018962,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X26," 11,963 "
-28018963,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X30," 11,963 "
-28018964,HC IMP STENT CORONARY RESOLUTE RX DES 2.25X8," 13,463 "
-28018965,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X12," 13,463 "
-28018966,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X14," 11,963 "
-28018967,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X18," 11,963 "
-28018968,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X22," 11,963 "
-28018969,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X26," 11,963 "
-28018970,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X30," 11,963 "
-28018971,HC IMP STENT CORONARY RESOLUTE RX DES 2.75X8," 13,463 "
-28018972,HC IMP STENT CORONARY RESOLUTE RX DES 4X12," 11,213 "
-28018973,HC IMP STENT CORONARY RESOLUTE RX DES 4X15," 11,963 "
-28018974,HC IMP STENT CORONARY RESOLUTE RX DES 4X18," 11,963 "
-28018975,HC IMP STENT CORONARY RESOLUTE RX DES 4X22," 11,963 "
-28018976,HC IMP STENT CORONARY RESOLUTE RX DES 4X26," 13,463 "
-28018977,HC IMP STENT CORONARY RESOLUTE RX DES 4X30," 13,463 "
-28018978,HC IMP STENT CORONARY RESOLUTE RX DES 4X9," 13,463 "
-28018979,HC IMP STENT CORONARY RESOLUTE RX 3.5X34MM," 13,463 "
-28018980,HC IMP STENT CORONARY RESOLUTE RX 3.5X38MM," 13,463 "
-28018981,HC IMP STENT CORONARY RESOLUTE RX INTEGRITY 3X34MM," 13,463 "
-28018982,HC IMP STENT CORONARY RESOLUTE RX INTEGRITY 3X38MM," 11,963 "
-28018983,HC IMP STENT CORONARY RESOLUTE RX INTEGRITY 4X34MM," 13,463 "
-28018984,HC IMP STENT CORONARY RESOLUTE RX INTEGRITY 4X38MM," 11,963 "
-28019009,HC IMP STENT CORONARY TAXOL ELUTING LIBERTE 2.25X16," 15,563 "
-28019010,HC IMP STENT CORONARY TAXOL ELUTING 3.5X38MM," 20,650 "
-28019011,HC IMP STENT CORONARY TAXOL ELUTING 3X38MM," 20,650 "
-28019012,HC IMP STENT CORONARY TAXOL ELUTING 4X38MM," 20,650 "
-28019015,HC IMP STENT ESOPHAGEAL FULLY COVERED 22X120MM," 18,200 "
-28019016,HC IMP STENT ESOPHAGEAL FULLY COVERED 22X70MM," 16,875 "
-28019017,HC IMP STENT ESOPHAGEAL FULLY COVERED OTW 16X60MM," 18,200 "
-28019018,HC IMP STENT ESOPHAGEAL FULLY COVERED OTW 18X100MM," 18,200 "
-28019019,HC IMP STENT ESOPHAGEAL FULLY COVERED OTW 18X120MM," 18,200 "
-28019020,HC IMP STENT ESOPHAGEAL FULLY COVERED OTW 18X70MM," 16,875 "
-28019065,HC IMP STENT GRAFT AAA ZENITH 13X74," 21,595 "
-28019066,HC IMP STENT GRAFT AAA ZENITH 20X74," 21,595 "
-28019067,HC IMP STENT GRAFT AAA ZENITH 24X96," 54,977 "
-28019068,HC IMP STENT GRAFT AAA ZENITH 26X82," 53,378 "
-28019069,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 2X34X122," 55,218 "
-28019070,HC IMP STENT GRAFT AAA ZENITH FLEX 12X124," 20,972 "
-28019071,HC IMP STENT GRAFT AAA ZENITH FLEX 18X39," 20,972 "
-28019072,HC IMP STENT GRAFT AAA ZENITH FLEX 26X125," 53,378 "
-28019073,HC IMP STENT GRAFT AAA ZENITH FLEX 28X111," 53,378 "
-28019074,HC IMP STENT GRAFT AAA ZENITH FLEX 28X82," 54,977 "
-28019075,HC IMP STENT GRAFT AAA ZENITH FLEX 28X96," 54,977 "
-28019076,HC IMP STENT GRAFT AAA ZENITH FLEX 30X111," 54,977 "
-28019077,HC IMP STENT GRAFT AAA ZENITH FLEX 30X85," 54,977 "
-28019078,HC IMP STENT GRAFT AAA ZENITH FLEX 30X96," 54,977 "
-28019079,HC IMP STENT GRAFT AAA ZENITH FLEX 32X58," 14,573 "
-28019080,HC IMP STENT GRAFT AAA ZENITH FLEX 32X82," 54,977 "
-28019081,HC IMP STENT GRAFT AAA ZENITH FLEX 36X113," 53,378 "
-28019082,HC IMP STENT GRAFT AAA ZENITH FLEX 36X131," 54,977 "
-28019083,HC IMP STENT GRAFT AAA ZENITH FLEX 36X50," 14,573 "
-28019084,HC IMP STENT GRAFT AAA ZENITH FLEX 36X77," 36,660 "
-28019085,HC IMP STENT GRAFT AAA ZENITH FLEX 36X12X82MM," 17,145 "
-28019086,HC IMP STENT GRAFT AAA ZENITH FLEX 36X95MM," 54,977 "
-28019087,HC IMP STENT GRAFT AAA ZENITH FLEX 8X122," 21,595 "
-28019088,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 11X74," 21,595 "
-28019089,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 16X39," 21,595 "
-28019090,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 20X90," 21,595 "
-28019091,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 24X90," 21,595 "
-28019092,HC IMP STENT GRAFT AAA ZENITH RENU 18X113," 42,341 "
-28019097,HC IMP STENT GRAFT BILIARY VIABIL NO HOLES 10X6," 15,488 "
-28019098,HC IMP STENT GRAFT BILIARY VIABIL NO HOLES 10X8," 15,488 "
-28019119,HC IMP STENT GRAFT ICAST 5X38X120," 18,750 "
-28019120,HC IMP STENT GRAFT ICAST 7X16X120," 2,125 "
-28019144,HC IMP STENT GRAFT TAA TALENT 26X22X114," 72,000 "
-28019145,HC IMP STENT GRAFT TAA TALENT 26X26X10," 69,000 "
-28019146,HC IMP STENT GRAFT TAA TALENT 32X28X113," 28,000 "
-28019147,HC IMP STENT GRAFT TAA TALENT 32X32X115," 28,000 "
-28019148,HC IMP STENT GRAFT TAA TALENT 36X199," 86,375 "
-28019149,HC IMP STENT GRAFT TAA TALENT 36X32X112," 28,000 "
-28019150,HC IMP STENT GRAFT TAA TALENT 36X36X114," 28,000 "
-28019151,HC IMP STENT GRAFT TAA TALENT 40X112," 86,550 "
-28019152,HC IMP STENT GRAFT TAA TALENT 42X38X111," 28,000 "
-28019153,HC IMP STENT GRAFT TAA TALENT 44X44X54," 35,750 "
-28019154,HC IMP STENT GRAFT TAA TALENT 46X46X112," 28,000 "
-28019155,HC IMP STENT GRAFT TAA TALENT 46X46X52," 35,750 "
-28019156,HC IMP STENT GRAFT TAA VALIANT 40X36X150," 88,725 "
-28019157,HC IMP STENT GRAFT TAA ZENITH TX2 PROX 34X127," 68,400 "
-28019158,HC IMP STENT GRAFT TAA ZENITH TX2 PROX 34X77," 33,787 "
-28019159,HC IMP STENT GRAFT TAA ZENITH TX2 PROX 36X77," 34,300 "
-28019160,HC IMP STENT GRAFT TAA ZENITH TX2 PROX 42X135," 70,452 "
-28019161,HC IMP STENT GRAFT TAA ZENITH TX2 PROX 42X162," 70,452 "
-28019162,HC IMP STENT GRAFT ENDOVASCULAR TAA W PROFORM 28X120," 72,564 "
-28019163,HC IMP STENT GRAFT TAA ZENITH TX2 28X80MM," 33,787 "
-28019164,HC IMP STENT GRAFT TAA ZENITH TX2 30X80MM," 33,787 "
-28019165,HC IMP STENT GRAFT TAA ZENITH TX2 32X80MM," 33,787 "
-28019166,HC IMP STENT GRAFT TAA ZENITH TX2 34X127MM," 72,564 "
-28019167,HC IMP STENT GRAFT TAA ZENITH TX2 34X152MM," 70,452 "
-28019168,HC IMP STENT GRAFT TAA ZENITH TX2 34X202MM," 72,564 "
-28019169,HC IMP STENT GRAFT TAA ZENITH TX2 34X80MM," 33,787 "
-28019170,HC IMP STENT GRAFT TAA ZENITH TX2 36X157MM," 68,400 "
-28019171,HC IMP STENT GRAFT TAA ZENITH TX2 38X152MM," 72,564 "
-28019172,HC IMP STENT GRAFT TAA ZENITH TX2 38X202MM," 72,564 "
-28019173,HC IMP STENT GRAFT TAA ZENITH TX2 38X77MM," 33,787 "
-28019174,HC IMP STENT GRAFT TAA ZENITH TX2 42X208MM," 72,564 "
-28019175,HC IMP STENT GRAFT TAA ZENITH TX2 42X81MM," 33,787 "
-28019176,HC IMP STENT GRAFT TRACHEOBRONCHIAL VIABAHN 13X10," 19,621 "
-28019177,HC IMP STENT GRAFT VIABAHN .8MM 5CM," 17,100 "
-28019178,HC IMP STENT GRAFT VIABAHN 10MMX10CM," 21,665 "
-28019179,HC IMP STENT GRAFT VIABAHN .018 8X15X120," 25,865 "
-28019180,HC IMP STENT GRAFT VIABAHN 8MMX5CM," 18,865 "
-28019181,HC IMP STENT GRAFT SPIRAL ZENITH 13X90 14F," 21,595 "
-28019182,HC IMP STENT GRAFT ZENITH 30X140MM," 54,977 "
-28019183,HC IMP STENT GRAFT PROFORM ZENITH 38X186 22F," 42,751 "
-28019184,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 11X107," 21,595 "
-28019185,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL 11X122," 21,595 "
-28019186,HC IMP STENT GRAFT AAA ZENITH FLEX SPIRAL13X107," 21,595 "
-28019197,HC IMP STENT RX XIENCE 2.25MMX28MM," 13,688 "
-28019199,HC IMP STENT TRACHEOBRONCHIAL COVERED 14X30MM," 15,000 "
-28019200,HC IMP STENT TRACHEOBRONCHIAL FULLY COVERED OTW 10X20," 15,000 "
-28019201,HC IMP STENT TRACHEOBRONCHIAL FULLY COVERED OTW 10X30," 15,000 "
-28019202,HC IMP STENT TRACHEOBRONCHIAL FULLY COVERED OTW 10X40," 15,000 "
-28019203,HC IMP STENT TRACHEOBRONCHIAL FULLY COVERED OTW 12X20," 16,875 "
-28019204,HC IMP STENT TRACHEOBRONCHIAL FULLY COVERED OTW 16X40," 16,875 "
-28019205,HC IMP STENT TRACHEOBRONCHIAL FULLY COVERED OTW 18X40," 15,000 "
-28019206,HC IMP STENT TRACHEOBRONCHIAL OTW 12X30X62," 16,875 "
-28019207,HC IMP STENT TRACHEOBRACHIAL ICAST 7X59X80," 18,750 "
-28018907,HC IMP STENT BILIARY 1.67CM 8X29MM 5F," 6,694 "
-28018908,HC IMP STENT BILIARY 1.67CM 8X36MM 5F," 6,694 "
-28018909,HC IMP STENT BILIARY 1.97CM 10X37MM 5.9F," 6,694 "
-28018910,HC IMP STENT BILIARY 10X120MM 135CM 0.035," 14,993 "
-28018911,HC IMP STENT BILIARY CONFORMEXX 6X30MM," 15,207 "
-28018912,HC IMP STENT BILI E-LUMINEXX 135CM 14X120MM," 10,493 "
-28018913,HC IMP STENT BILIARY LUMINEXX 80CM 7X60MM," 11,243 "
-28018914,HC IMP STENT BILIARY LUMINEXX 80X14MM," 14,993 "
-28018915,HC IMP STENT BILIARY MONORAIL 90CM 5X19MM," 5,259 "
-28018916,HC IMP STENT BILIARY NITINOL 120CM 6X20MM," 6,694 "
-28018917,HC IMP STENT BILIARY NITINOL 130CM 6X120MM," 18,563 "
-28018918,HC IMP STENT BILIARY OMNILINK18 6X28X135," 9,750 "
-28018919,HC IMP STENT BILIARY OMNILINK35 10X58X135," 9,750 "
-28018920,HC IMP STENT BILIARY PARAMOUNT 7X17X80," 8,160 "
-28018921,HC IMP STENT BILIARY PLATFORM 18X135MM 0.018IN," 9,971 "
-28018922,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 4X24X80," 7,859 "
-28018923,HC IMP STENT BILIARY PROTEGE 8X40X120," 10,388 "
-28018925,HC IMP STENT BILIARY SENTINOL 5X40MMX135CM," 9,000 "
-28018926,HC IMP STENT BILIARY SENTINOL 5X60MMX135CM," 9,000 "
-28018927,HC IMP STENT BILIARY SENTINOL 5X80MMX135CM," 9,000 "
-28018928,HC IMP STENT BILIARY SENTINOL 6X40MMX135CM," 9,000 "
-28018929,HC IMP STENT BILIARY SENTINOL 6X42X135CM," 9,000 "
-28018930,HC IMP STENT BILIARY SENTINOL 6X59MMC135CM," 9,000 "
-28018931,HC IMP STENT BILIARY SENTINOL 6X60X135CM," 9,000 "
-28018932,HC IMP STENT BILIARY SENTINOL 7X59MMX135CM," 9,000 "
-28018933,HC IMP STENT BILIARY VISIPRO 7X17X80MM 6F," 8,625 "
-28018934,HC IMP STENT BILIARY VISIPRO 8X17X80MM," 7,958 "
-28018935,HC IMP STENT BILIARY VISPRO 8X57X80," 7,958 "
-28018936,HC IMP STENT BILIARY VISIPRO 9X37X80MM," 7,958 "
-28018937,HC IMP STENT BILIARY WALLFLEX PC 8X60," 17,843 "
-28018938,HC IMP STENT BILIARY SELF EXPANDING NITINOL 75CM 10X41," 4,494 "
-28018939,HC IMP STENT BILIARY EXPRESS 8X60X75," 11,213 "
-28018940,HC IMP STENT BILIARY EXPRESS 9X40X75," 8,856 "
-28018941,HC IMP STENT CAROTID MONORAIL 5.9F 10X24MM," 18,713 "
-28018942,HC IMP STENT CAROTID MONORAIL 5F 6X22MM," 18,713 "
-28018943,HC IMP STENT CAROTID MONORAIL 8F 8X21MM," 18,713 "
-28018944,HC IMP STENT COLON WALLSTENT RAPID EXCHANGE 10X40," 10,043 "
-28018945,HC IMP STENT COLON WALLSTENT RAPID EXCHANGE 10X60," 9,630 "
-28018946,HC IMP STENT COLON WALLSTENT RAPID EXCHANGE 10X80," 10,523 "
-28018954,HC IMP STENT CORONARY INTEGRITY RX BMS 2.25X22," 6,800 "
-28018955,HC IMP STENT CORONARY INTEGRITY RX BMS 2.25X26," 6,800 "
-28018956,HC IMP STENT CORONARY INTEGRITY RX BMS 2.5X26," 6,800 "
-28018957,HC IMP STENT CORONARY MINIVISION RX BMS 2.5X18," 7,299 "
-28018985,HC STNT COR RX BLLN XPND 3.5X9MM," 7,650 "
-28018986,HC IMP STENT CORONARY RX INTEGRITY 2.75X22MM," 6,800 "
-28018987,HC IMP STENT CORONARY RX INTEGRITY 2.75X26MM," 7,875 "
-28018988,HC IMP STENT CORONARY RX INTEGRITY 3.5X12MM," 6,800 "
-28018989,HC IMP STENT CORONARY RX INTEGRITY 3.5X15MM," 7,650 "
-28018990,HC IMP STENT CORONARY RX INTEGRITY 3.5X18MM," 6,800 "
-28018991,HC IMP STENT CORONARY RX INTEGRITY 3.5X22MM," 7,650 "
-28018992,HC IMP STENT CORONARY RX INTEGRITY 3.5X26MM," 7,650 "
-28018993,HC IMP STENT CORONARY RX INTEGRITY 3.5X30MM," 7,650 "
-28018994,HC IMP STENT CORONARY RX INTEGRITY 3.5X9MM," 6,800 "
-28018995,HC IMP STENT CORONARY RX INTEGRITY 3X12MM," 6,800 "
-28018996,HC IMP STENT CORONARY RX INTEGRITY 3X15MM," 6,800 "
-28018997,HC IMP STENT CORONARY RX INTEGRITY 3X18MM," 6,800 "
-28018998,HC IMP STENT CORONARY RX INTEGRITY 3X22MM," 6,800 "
-28018999,HC IMP STENT CORONARY RX INTEGRITY 3X26MM," 7,650 "
-28019000,HC IMP STENT CORONARY RX INTEGRITY 3X30MM," 6,800 "
-28019001,HC IMP STENT CORONARY RX INTEGRITY 3X9MM," 6,800 "
-28019002,HC IMP STENT CORONARY RX INTEGRITY 4X12MM," 7,650 "
-28019003,HC IMP STENT CORONARY RX INTEGRITY 4X15MM," 7,650 "
-28019004,HC IMP STENT CORONARY RX INTEGRITY 4X18MM," 7,650 "
-28019005,HC IMP STENT CORONARY RX INTEGRITY 4X22MM," 7,650 "
-28019006,HC IMP STENT CORONARY RX INTEGRITY 4X26MM," 7,650 "
-28019007,HC IMP STENT CORONARY RX INTEGRITY 4X30MM," 7,650 "
-28019008,HC IMP STENT CORONARY RX INTEGRITY 4X9MM," 7,650 "
-28019013,HC IMP STENT CORONARY VERIFLEX RX BMS 3X16," 7,620 "
-28019021,HC IMP STENT FORMULA418 5X20X80," 8,625 "
-28019121,HC IMP STENT GRAFT INTUITRK 16X40X140MM," 64,770 "
-28019122,HC IMP STENT GRAFT INTUITRAK 22X16X100MM," 64,770 "
-28019123,HC IMP STENT GRAFT INTUITRAK 25X16X120MM," 64,770 "
-28019124,HC IMP STENT GRAFT INTUITRAK 28X16X100MM," 64,770 "
-28019125,HC IMP STENT GRAFT INTUITRAK 28X16X120MM," 64,770 "
-28019126,HC IMP STENT GRAFT INTUITRAK 16X40X140MM," 64,770 "
-28019190,HC IMP STENT PERIPHERAL LIFESTENT 6X170X130," 20,650 "
-28019191,HC IMP STENT PERIPHERAL LIFESTENT 6X170X80," 20,650 "
-28019192,HC IMP STENT PERIPHERAL LIFESTENT 7X170X130," 20,650 "
-28019193,HC IMP STENT PERIPHERAL LIFESTENT 7X170X80," 20,650 "
-28019258,HC IMP STENT URETERAL UROLOME 2CMX14MM," 32,520 "
-28019259,HC IMP STENT VASCULAR NITINOL 135CM 8X30MM," 16,500 "
-28019260,HC IMP STENT VASCULAR ZILVER 10X4080," 8,213 "
-28019261,HC IMP STENT VASCULAR ZILVER 10X60X80," 8,213 "
-28019262,HC IMP STENT VASCULAR ZILVER 8X60X80," 11,250 "
-28019263,HC IMP STENT VASCULAR ZILVER 9X30X125," 11,250 "
-28019264,HC IMP STENT VASCULAR ZILVER 9X40X125," 11,250 "
-28019265,HC IMP STENT VASCULAR ZILVER 9X40X80," 11,250 "
-28019266,HC IMP STENT VASCULAR ZILVER 9X60X125," 8,213 "
-28019267,HC IMP STENT WALLSTENT 14X90X75," 12,569 "
-28019268,HC IMP STENT WALLSTENT 18X90," 13,301 "
-28019269,HC IMP STENT EXPANDING 10X100MM 135CM," 10,313 "
-28019270,HC IMP STENT EXPANDING 10X100MM 80CM," 10,313 "
-28019271,HC IMP STENT EXPANDING 10X20MM 80CM," 10,313 "
-28019272,HC IMP STENT EXPANDING 10X30MM 135CM," 10,313 "
-28019273,HC IMP STENT EXPANDING 10X30MM 80CM," 10,313 "
-28019274,HC IMP STENT EXPANDING 10X40MM 80CM," 10,313 "
-28019275,HC IMP STENT EXPANDING 10X60MM 80CM," 10,313 "
-28019276,HC IMP STENT EXPANDING 10X80MM 80CM," 10,313 "
-28019277,HC IMP STENT EXPANDING 6X100MM 135CM," 10,313 "
-28019278,HC IMP STENT EXPANDING 6X100MM 80CM," 10,313 "
-28019279,HC IMP STENT EXPANDING 6X20MM 135CM," 10,313 "
-28019280,HC IMP STENT EXPANDING 6X20MM 80CM," 10,313 "
-28019281,HC IMP STENT EXPANDING 6X30MM 135CM," 10,313 "
-28019282,HC IMP STENT EXPANDING 6X30MM 80CM," 10,313 "
-28019283,HC IMP STENT EXPANDING 6X40MM 135CM," 10,313 "
-28019284,HC IMP STENT EXPANDING 6X40MM 80CM," 10,313 "
-28019285,HC IMP STENT EXPANDING 6X60MM 80CM," 10,313 "
-28019286,HC IMP STENT EXPANDING 6X80MM 135CM," 10,313 "
-28019287,HC IMP STENT EXPANDING 6X80MM 80CM," 10,313 "
-28019288,HC IMP STENT EXPANDING 7X100MM 135CM," 10,313 "
-28019289,HC IMP STENT EXPANDING 7X100MM 80CM," 10,313 "
-28019290,HC IMP STENT EXPANDING 7X20MM 80CM," 10,313 "
-28019291,HC IMP STENT EXPANDING 7X30MM 80CM," 10,313 "
-28019292,HC IMP STENT EXPANDING 7X40MM 80CM," 10,313 "
-28019293,HC IMP STENT EXPANDING 7X60MM 80CM," 10,313 "
-28019294,HC IMP STENT EXPANDING 7X80MM 80CM," 10,313 "
-28019295,HC IMP STENT EXPANDING 8X100MM 135CM," 10,313 "
-28019296,HC IMP STENT EXPANDING 8X100MM 80CM," 10,313 "
-28019297,HC IMP STENT EXPANDING 8X20MM 80CM," 10,313 "
-28019298,HC IMP STENT EXPANDING 8X30MM 80CM," 10,313 "
-28019299,HC IMP STENT EXPANDING 8X40MM 80CM," 10,313 "
-28019300,HC IMP STENT EXPANDING 8X60MM 80CM," 10,313 "
-28019301,HC IMP STENT EXPANDING 8X80MM 80CM," 10,313 "
-28019302,HC IMP STENT EXPANDING 9X100MM 135CM," 10,313 "
-28019303,HC IMP STENT EXPANDING 9X100MM 80CM," 10,313 "
-28019304,HC IMP STENT EXPANDING 9X20MM 135CM," 10,313 "
-28019305,HC IMP STENT EXPANDING 9X20MM 80CM," 10,313 "
-28019306,HC IMP STENT EXPANDING 9X30MM 135CM," 10,313 "
-28019307,HC IMP STENT EXPANDING 9X30MM 80CM," 10,313 "
-28019308,HC IMP STENT EXPANDING 9X40MM 80CM," 10,313 "
-28019309,HC IMP STENT EXPANDING 9X60MM 80CM," 10,313 "
-28019310,HC IMP STENT EXPANDING 9X80MM 80CM," 10,313 "
-28019311,HC IMP STENT ZILVER 10X80X125," 11,250 "
-28019312,HC IMP STENT ZILVER VASCULAR 12X30X125," 11,250 "
-28019313,HC IMP STENT ZILVER VASCULAR 12X40X125," 8,213 "
-28019314,HC IMP STENT ZILVER VASCULAR 12X60X125," 8,213 "
-28019315,HC IMP STENT ZILVER VASCULAR 12X60X80," 11,250 "
-28019316,HC IMP STENT ZILVER VASCULAR 12X80X80," 11,250 "
-28019317,HC IMP STENT ZILVER VASCULAR 14X30X125," 11,250 "
-28019318,HC IMP STENT ZILVER VASCULAR 14X40X125," 8,213 "
-28019319,HC IMP STENT ZILVER VASCULAR 14X60X125," 8,213 "
-28019320,HC IMP STENT ZILVER635 12X80X125," 8,213 "
-28019321,HC IMP STENT ZILVER635 14X80X125," 8,213 "
-28019322,HC IMP STENT ZILVER635 8X120X80," 14,250 "
-28019323,HC IMP STENT ZILVER635 8X140X80," 10,088 "
-28019014,HC IMP STENT DOUBLE STRUT 12X56MM," 8,250 "
-28014275,HC IMP FILTER VENA CAVA JUGULAR DELV 30X48MM," 9,000 "
-28014276,HC IMP FILTER VENA CAVA JUGULAR DELV 30X50MM," 7,225 "
-28014151,HC IMP ICD COGNIS LV," 71,750 "
-28014159,HC IMP ICD INCEPTA SNG VR," 75,233 "
-28014162,HC IMP ICD LUMAX HF-T 540 66X13MM," 91,595 "
-28014163,HC IMP ICD MICRO JEWEL 7221B," 80,000 "
-28014164,HC IMP ICD OTHER QUADRA ASSURA DF4," 84,000 "
-28014165,HC IMP ICD OTHER SECURA VR DF4," 70,200 "
-28014166,HC IMP ICD OTHER VIVA QUAD C CRT-D," 71,050 "
-28014168,HC IMP ICD PROMOTE PLUS," 98,000 "
-28014172,HC IMP ICD UNIFY CRT," 98,000 "
-28015308,HC IMPL PACEMAKER CRT INSYNC III," 54,275 "
-28015045,HC IMP LEAD PACING EXTENSION BIPOLAR 45CM," 3,400 "
-28015064,HC IMPL LEAD SPINAL CORD STIMULATOR 5-6-5 KIT," 33,800 "
-28015278,HC IMP NEUROSTIMULATOR EXTENSION LEAD 1X8X60CM KIT," 5,440 "
-28015279,HC IMP NEUROSTIMULATOR EXTENSION LEAD 60CM KIT," 6,205 "
-28012665,HC IMP DEVICE REVASCULARIZATION SOLITAIRE2 4X15," 42,413 "
-28012666,HC IMP DEVICE REVASCULARIZATION SOLITAIRE2 4X20," 42,413 "
-28012667,HC IMP DEVICE REVASCULARIZATION SOLITAIRE2 6X20," 42,413 "
-28012668,HC IMP DEVICE REVASCULARIZATION SOLITAIRE2 6X30," 42,413 "
-28012732,HC IMP DEVICE EMBOLIZATION PIPELINE 3.25X35," 80,400 "
-28012733,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X16," 75,000 "
-28018897,HC IMP STENT FLOW RESTORATION SOLITAIRE 4X15MM," 42,413 "
-28018898,HC IMP STENT FLOW RESTORATION SOLITAIRE 4X20MM," 42,413 "
-28018899,HC IMP STENT FLOW RESTORATION SOLITAIRE 6X20MM," 42,413 "
-28018900,HC IMP STENT FLOW RESTORATION SOLITAIRE 6X30MM," 42,413 "
-28011449,HC  CATHETER ACUITY BREAKAWAY 6FX63CM," 1,658 "
-28011450,HC  CATHETER ACUITY BREAKAWAY 8FX54CM," 3,783 "
-28011582,HC  CATHETER ASAHI CORSAIR 2.6FX150CM," 8,458 "
-28011588,HC  CATH BALLOON INFUSION CLEARWAY 6X50X90CM," 8,250 "
-28011865,HC  CATHETER EMBOLIZATION PERIPHERAL 20X130CM," 2,861 "
-28011943,HC  CATHETER GUIDING HEARTRAIL III IL3.5 6FX100CM, 595 
-28011944,HC  CATHETER GUIDING HEARTRAIL III IL3.75 6FX100CM, 595 
-28011945,HC  CATHETER GUIDING HEARTRAIL III IL4.0 6FX100CM, 595 
-28011946,HC  CATHETER GUIDING HEARTRAIL III IL4.5 6FX100CM, 595 
-28011947,HC  CATHETER GUIDING HEARTRAIL III IR1.0 6FX100CM, 595 
-28011948,HC  CATHETER GUIDING HEARTRAIL III IR1.5 6FX100CM, 595 
-28011949,HC  CATHETER GUIDING HEARTRAIL III IR2.0 6FX100CM, 595 
-28011950,HC  CATHETER GUIDING HEARTRAIL III TIG4.0 6FX100CM, 595 
-28011953,HC  CATHETER GLIDECATH C1 4FX65CM, 85 
-28011954,HC  CATHETER GLIDECATH J TIP 4FX65CM, 91 
-28011959,HC  CATHETER GUIDING 7F .078 90CM AL.75," 1,277 "
-28011960,HC  CATHETER GUIDING 7FX90CM, 451 
-28011961,HC  CATHETER GUIDING 8F .088 90CM 2SH XB3.5," 1,169 "
-28011962,HC  CATHETER GUIDING 8F .088 90CM XB3.5," 1,169 "
-28011963,HC  CATHETER GUIDING 8FX0.090IN, 451 
-28011964,HC  CATHETER GUIDING 8FX90CMX7.5, 451 
-28011965,HC  CATHETER GUIDING ACUITY INNER 130 DEGREE," 1,156 "
-28011966,HC  CATHETER GUIDING ACUITY OUTER 6FX63CM," 2,644 "
-28011968,HC  CATHETER GUIDING ATTAIN AMPLATZ 50CM," 2,040 "
-28011969,HC  CATHETER GUIDING ATTAIN CSEH 50CM," 2,040 "
-28011970,HC  CATHETER GUIDING ATTAIN MB2 45CM," 2,040 "
-28011971,HC  CATHETER GUIDING ATTAIN MB2X 50CM," 1,632 "
-28011972,HC  CATHETER GUIDING ATTAIN MP 50CM," 1,700 "
-28011973,HC  CATHETER GUIDING ATTAIN MPX 50CM," 2,040 "
-28011974,HC  CATHETER GUIDING ATTAIN STRAIGHT 45CM," 1,632 "
-28011975,HC  CATHETER GUIDING ATTAIN STRAIGHT 50CM," 1,632 "
-28011976,HC  CATHETER GUIDING CHAPERON MP2 5F," 2,125 "
-28011977,HC  CATHETER GUIDING CHAPERON STR 5F," 2,550 "
-28011978,HC  CATHETER GUIDING CONVEY 5F AL.75, 468 
-28011979,HC  CATHETER GUIDING CONVEY 5F AL1.5, 468 
-28011980,HC  CATHETER GUIDING CONVEY 5F AL2, 468 
-28011981,HC  CATHETER GUIDING CONVEY 5F AR2, 468 
-28011982,HC  CATHETER GUIDING CONVEY 5F ART3, 468 
-28011983,HC  CATHETER GUIDING CONVEY 5F ART3.5, 468 
-28011984,HC  CATHETER GUIDING CONVEY 5F ART3.75, 468 
-28011985,HC  CATHETER GUIDING CONVEY 5F CLS3.25, 468 
-28011986,HC  CATHETER GUIDING CONVEY 5F HS, 468 
-28011987,HC  CATHETER GUIDING CONVEY 5F JL3.5, 468 
-28011988,HC  CATHETER GUIDING CONVEY 5F JL4.5, 468 
-28011989,HC  CATHETER GUIDING CONVEY 5F JL5, 468 
-28011990,HC  CATHETER GUIDING CONVEY 5F JR3, 468 
-28011991,HC  CATHETER GUIDING CONVEY 5F JR3.5, 468 
-28011992,HC  CATHETER GUIDING CONVEY 5F JR4, 468 
-28011993,HC  CATHETER GUIDING CONVEY 5F JR4.5, 468 
-28011994,HC  CATHETER GUIDING CONVEY 5F KIMNEY, 468 
-28011995,HC  CATHETER GUIDING CONVEY 5F LEFTBU3.25, 468 
-28011996,HC  CATHETER GUIDING CONVEY 5F LEFTBU3.5, 468 
-28011997,HC  CATHETER GUIDING CONVEY 5F LEFTBU4, 468 
-28011998,HC  CATHETER GUIDING CONVEY 5F LEFTBU4.25, 468 
-28011999,HC  CATHETER GUIDING CONVEY 5F LEFTBU4.5, 468 
-28012000,HC  CATHETER GUIDING CONVEY 5F MPA, 468 
-28012001,HC  CATHETER GUIDING CONVEY 5F RADIAL, 468 
-28012002,HC  CATHETER GUIDING CONVEY 5F RB, 468 
-28012003,HC  CATHETER GUIDING CONVEY 5F RBL3.5, 468 
-28012004,HC  CATHETER GUIDING CONVEY 5F RBL4, 468 
-28012005,HC  CATHETER GUIDING CONVEY 5F RBR3, 468 
-28012006,HC  CATHETER GUIDING CONVEY 5F RBR3.5, 468 
-28012007,HC  CATHETER GUIDING CONVEY 5F RBR4, 468 
-28012008,HC  CATHETER GUIDING CONVEY 5F TIG3, 468 
-28012009,HC  CATHETER GUIDING CONVEY 5F TIG3.5, 468 
-28012010,HC  CATHETER GUIDING CONVEY 5F TIG4, 468 
-28012011,HC  CATHETER GUIDING CONVEY 5F TIG4.5, 468 
-28012012,HC  CATHETER GUIDING CORONARY EBU 4.0 7F 100CM, 451 
-28012013,HC  CATHETER GUIDING CORONARY F4 Z2 7F, 451 
-28012014,HC  CATHETER GUIDING CORONARY GUIDELINER 6F," 3,383 "
-28012015,HC  CATHETER GUIDING CORONARY LEFT CRV 7F 0.081IN, 201 
-28012016,HC  CATHETER GUIDING CORONARY MB1 7F 100CM, 451 
-28012017,HC  CATHETER GUIDING CORONARY MPHK 7F, 451 
-28012018,HC  CATHETER GUIDING CORONARY MPK1 7F, 451 
-28012019,HC  CATHETER GUIDING CORONARY PK1W 7F, 451 
-28012020,HC  CATHETER GUIDING CORONARY RDCK 7F, 451 
-28012021,HC  CATHETER GUIDING CORONARY Z27S40K 7F, 451 
-28012022,HC  CATHETER GUIDING CVS AL 3 W/O SH 6F, 468 
-28012023,HC  CATHETER GUIDING DAC .038IN 3.9FX125CM," 8,458 "
-28012024,HC  CATHETER GUIDING DAC .038IN 3.9FX136CM," 8,458 "
-28012025,HC  CATHETER GUIDING DAC .057IN 5.2FX115CM," 8,458 "
-28012026,HC  CATHETER GUIDING DAC .057IN 5.2FX125CM," 8,458 "
-28012027,HC  CATHETER GUIDING HEADWAY17 45 DEGREE 1.7FX150CM," 6,205 "
-28012028,HC  CATHETER GUIDING HEADWAY17 90 DEGREE 1.7FX150CM," 6,205 "
-28012029,HC  CATHETER GUIDING HEADWAY17 STRAIGHT 1.7FX150CM," 6,035 "
-28012030,HC  CATHETER GUIDING HEADWAY21 STRAIGHT 2.0X150CM," 6,035 "
-28012031,HC  CATHETER GUIDING HEADWAY27 STRAIGHT 2.6X150CM," 7,480 "
-28012032,HC  CATHETER GUIDING LEFT HEART 4F ATTAIN," 1,700 "
-28012033,HC  CATHETER GUIDING MAGIC 155CM 2.7F 1.5FX15CM," 5,610 "
-28012034,HC  CATHETER GUIDING MAGIC 165CM 2.7F 1.5FX20CM," 5,610 "
-28012035,HC  CATHETER GUIDING MAGIC 165CM 2.7F 1.8FX20CM," 5,321 "
-28012036,HC  CATHETER GUIDING MERCI .038IN 9FX95CM," 5,738 "
-28012037,HC  CATHETER GUIDING MORPH 5.2F 124 110CM," 10,500 "
-28012038,HC  CATHETER GUIDING MORPH 6F 90 106CM," 4,888 "
-28012039,HC  CATHETER GUIDING MORPH 8FX104X90CM," 7,438 "
-28012040,HC  CATHETER GUIDING NEUROPATH HEADHUNTER 0.07X6X90," 2,259 "
-28012041,HC  CATHETER GUIDING NEUROPATH SIMMONS 0.07X6X90," 2,259 "
-28012042,HC  CATHETER GUIDING SIM2 .07INX6FX90CM," 2,805 "
-28012043,HC  CATHETER GUIDING TURBO TANDEM 7FX110CM," 23,800 "
-28012044,HC  CATHETER GUIDING VENAPORT MP 6FX4FX65CM," 1,063 "
-28012045,HC  CATHETER GUIDING XB MPC .07INX6FX90," 2,746 "
-28012046,HC  CATHETER GUIDING CONVEY 5F AR1, 468 
-28012085,HC  CATHETER MARKSMAN 135X10CM," 8,115 "
-28012086,HC  CATHETER MARKSMAN 150X10CM," 8,438 "
-28012088,HC  CATHETER MICRO 1 TIP 2.8FX150X155X50CM," 5,685 "
-28012089,HC  CATHETER MICRO 2 TIP 2.8FX150X155X50CM," 4,208 "
-28012090,HC  CATHETER MICRO PROWLER 45D .021IN 2.3FX150X50CM," 6,680 "
-28012091,HC  CATHETER MICRO PROWLER 90D .021IN 2.8FX150X5CM," 6,680 "
-28012097,HC  CATHETER MICRO PROWLER STR .021IN 2.8FX150X5CM," 6,528 "
-28012132,HC  CATHETER PIGTAIL 5F 70CM, 122 
-28012172,HC  CATHETER RENEGADE HI-FLO 150X10CM," 5,433 "
-28012173,HC  CATHETER REPERFUSION 132X136CM 5F 6F," 10,425 "
-28012178,HC  CATHETER RUNWAY KIMNY 6F, 468 
-28012188,HC  CATHETER ANGIOGR SOFT VU COBRA .038 5FX65CM, 244 
-28012189,HC  CATHETER ANGIOGR SOFT VU HOOK1 .035 5FX65CM, 92 
-28012190,HC  CATHETER ANGIOGR SOFT VU HOOK2 .035 5FX65CM, 158 
-28012197,HC  CATHETER SUPPORT CXI ANGLED TIP 4FX135CM," 1,403 "
-28012198,HC  CATHETER SUPPORT CXI ANGLED TIP 4FX90CM," 1,403 "
-28012251,HC  CATHETER VASCULAR TPRD THRDD DISTL T," 5,908 "
-28012277,HC  CATHETER VIANCE CROSSING 0.014X150CM," 17,213 "
-28012278,HC  CATHETER GUIDING VISTA BRITE AL1 .01X7FX100CM, 451 
-28012279,HC  CATHETER GUIDING VISTA BRITE AR1 7FX100CM, 220 
-28012280,HC  CATHETER GUIDING VISTA BRITE JL4 .01INX7FX100, 455 
-28012281,HC  CATHETER GUIDING VISTA BRITE JL5 .01INX7FX100CM, 451 
-28012282,HC  CATHETER GUIDING VISTA BRITE JR4 .078INX7X100CM, 451 
-28012283,HC  CATHETER GUIDING VISTA BRITE JR4 .078INX7FX90CM, 442 
-28012284,HC  CATHETER GUIDING VISTA BRITE TIP 6FX55CM 0.070," 1,037 "
-28012285,HC  CATHETER GUIDING VISTA BRITE XB3.5 7X100, 451 
-28012286,HC  CATHETER GUIDING VISTA BRITE XB3.5 SH 7X100, 451 
-28015040,HC  LEAD LEFT HEART DELIVERY SYSTEM 40X45CM," 5,100 "
-28018511,HC  SHEATH GUIDING 4FX55CM, 553 
-28018512,HC  SHEATH GUIDING 4FX55CM, 519 
-28018513,HC  SHEATH GUIDING 5FX55CM, 553 
-28018514,HC  SHEATH GUIDING CURVED 5FX55CM, 553 
-28018515,HC  SHEATH GUIDING PINNACLE DESTINATION 5FX 45CM, 765 
-28018516,HC  SHEATH GUIDING PINNACLE DESTINATION 5FX45CM, 765 
-28018517,HC  SHEATH GUIDING PINNACLE DESTINATION STR 6FX90CM, 782 
-28018518,HC  SHEATH GUIDING PINNACLE DESTINATION 7F 90CM, 782 
-28018519,HC  SHEATH GUIDING STRAIGHT 6FX55CM, 553 
-28018520,HC  SHEATH GUIDING TIB 7FX80CM, 969 
-28014197,HC  INTRODUCER LEAD SAFESHEATH 8F, 553 
-28014198,HC  INTRODUCER LEAD SAFESHEATH 10F 13CM, 468 
-28014199,HC  INTRODUCER LEAD SAFESHEATH 12.5FX13CM, 808 
-28014200,HC  INTRODUCER LEAD SAFESHEATH 6FX13CM, 723 
-28014201,HC  INTRODUCER LEAD SAFESHEATH 7FX13CM, 553 
-28014203,HC  INTRODUCER LEAD SAFESHEATH7FX25CM, 493 
-28014233,HC  INTRODUCER SHEATH PEEL AWAY, 597 
-28014235,HC  INTRODUCER SHEATH PEEL AWAY 15F, 99 
-28014236,HC  INTRODUCER SHEATH PEEL AWAY 18F, 129 
-28014237,HC  INTRODUCER SHEATH PEEL AWAY 7FX30 CM, 597 
-28014238,HC  INTRODUCER SHEATH PEEL AWAY 9FX30CM, 597 
-28014239,HC  INTRODUCER SHEATH PEEL AWAY OPTISEAL 7FX25CM, 621 
-28014240,HC  INTRODUCER SHEATH PEEL AWAY OPTISEAL 9FX13CM, 442 
-28014246,HC  INTRODUCER SHEATH VALVE PEEL AWAY 10F 13CM, 468 
-28014247,HC  INTRODUCER SHEATH VALVE PEEL AWAY 11F 13CM, 468 
-28014248,HC  INTRODUCER SHEATH VALVE PEEL AWAY 6F 13CM, 468 
-28014249,HC  INTRODUCER SHEATH VALVE PEEL AWAY 7F 25CM, 468 
-28014250,HC  INTRODUCER SHEATH VALVE PEEL AWAY 8F 13CM, 468 
-28014251,HC  INTRODUCER SHEATH VALVE PEEL AWAY 9F 13CM, 468 
-28014252,HC  INTRODUCER SHEATH VALVE PEEL AWAY 9F 25CM, 468 
-28014179,HC  INTRODUCER GUID CPS DIRECT 115DEG 47X50.7X7FX9," 5,908 "
-28014180,HC  INTRODUCER GUID CPS DIRECT 135DEG 47X50.7X7FX9," 5,908 "
-28014181,HC  INTRODUCER GUID CPS DIRECT STR 47X50.7X7X9," 5,908 "
-28014182,HC  INTRODUCER GUID CPS DIRECT WIDE 47X50.7X7X9," 5,908 "
-28014183,HC  INTRODUCER GUID SWARTZ 8.5FX63CM," 1,913 "
-28014192,HC  INTRODUCER GUIDING SWARTZ .038 8X81," 2,440 "
-28014193,HC  INTRODUCER GUIDING SWARTZ LAMP 8X63," 1,063 "
-28014231,HC  INTRODUCER SHEATH MULLINS 7FX4X52CM, 723 
-28018423,HC  SHEATH TRANSSEPTAL SLO," 1,828 "
-28018424,HC  SHEATH TRANSSEPTAL SRO," 1,828 "
-28014186,HC  INTRODUCER CATHETER AVANTI PLUS 4FX11CM, 180 
-28014188,HC  INTRODUCER CHECK FLO 10FX30CM, 597 
-28014190,HC  INTRODUCER FLO EXTRA LARGE 22FX25CM," 2,882 "
-28014191,HC  INTRODUCER GUIDING CURVED SR1 8FX60CM," 1,743 "
-28014195,HC  INTRODUCER HEMOSTASIS VALVE 3F," 1,658 "
-28014202,HC  INTRODUCER LEAD SAFESHEATH 8F," 3,103 "
-28014212,HC  INTRODUCER PICC AUTOGUARD INTROSYTE 26G, 350 
-28014213,HC  INTRODUCER ROD CATH VENTRI 15CM," 1,163 "
-28014214,HC  INTRODUCER SHEATH 0.035IN 45CM 6F, 306 
-28014215,HC  INTRODUCER SHEATH 25CM 10F 2.5CM, 174 
-28014216,HC  INTRODUCER SHEATH 5F 6CM 2.5CM, 184 
-28014217,HC  INTRODUCER SHEATH 5FX55CM, 527 
-28014218,HC  INTRODUCER SHEATH 6F 6CM 2.5CM, 184 
-28014219,HC  INTRODUCER SHEATH 6F 90CM 0.038IN, 653 
-28014220,HC  INTRODUCER SHEATH 7F 45CM, 527 
-28014221,HC  INTRODUCER SHEATH 7F 55CM, 850 
-28014222,HC  INTRODUCER SHEATH AFX 17FX45CM," 2,848 "
-28014223,HC  INTRODUCER SHEATH ANSEL 6F X 45CM, 444 
-28014224,HC  INTRODUCER SHEATH AVANTI 5F 11CM, 175 
-28014225,HC  INTRODUCER SHEATH AVANTI 5F 5.5CM, 213 
-28014228,HC  INTRODUCER SHEATH HEMOSTASIS 23CM 10F, 485 
-28014229,HC  INTRODUCER SHEATH HEMOSTASIS 8.5CM 5F, 213 
-28014230,HC  INTRODUCER SHEATH MINOP 19F, 373 
-28014232,HC  INTRODUCER SHEATH MULLINS 8FX59X67CM, 723 
-28014241,HC  INTRODUCER SHEATH PERCUTANEOUS 8.5F, 173 
-28014244,HC  INTRODUCER SHEATH PLNG 14CM 9.5F, 425 
-28014245,HC  INTRODUCER SHEATH SAFESHEATH CSG 9F 45CM," 2,550 "
-28014253,HC  INTRODUCER SHEATH VASCULAR ANSEL 45CM 7F, 444 
-28014254,HC  INTRODUCER SHEATH VASCULAR ANSEL 5FX90CM, 818 
-28014255,HC  INTRODUCER SHEATH VASCULAR AVANTI 11CM 6F, 243 
-28014256,HC  INTRODUCER SHEATH VASCULAR SUPER 25CM 11F," 2,762 "
-28014258,HC  INTRODUCER SHUNT PERITONEAL 31CM," 3,898 "
-28014259,HC  INTRODUCER SHUNT PERITONEAL 61CM," 3,898 "
-28014262,HC  INTRODUCER STENT Z TRACHEOBRONCHIAL," 1,700 "
-28014263,HC  INTRODUCER STRAIGHT ANSEL 5F X 45CM, 527 
-28014266,HC  INTRODUCER VASCULAR FLEXOR RAABE 7FX90CM, 510 
-28014268,HC  INTRODUCER VASCULAR TEAR AWAY 7FX13G, 587 
-28014269,HC  INTRODUCER SHEATH 9F, 228 
-28015038,HC  LEAD DELIVERY BREAKAWAY SYSTEM," 8,458 "
-28015029,HC IMPL LEAD DEFIBRILLATOR SINGLE COIL 64CM 9F," 35,000 "
-28015031,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 55CM," 33,600 "
-28015034,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 65CM," 37,375 "
-28015036,HC IMP LEAD DEFIBRILLATOR DF4 QUATTRO," 33,600 "
-28015059,HC IMP LEAD PACEMAKER VENTRICULAR TRIPOLAR SPRINT RT," 39,650 "
-28015033,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 58CM 9F," 9,000 "
-28015066,HC IMPL LEAD STIMULATOR UNIPOLAR TEST, 978 
-28015025,HC IMP LEAD ACTIVE FIXATION PACING 53CM," 4,675 "
-28015043,HC IMP LEAD PACING 52CM 7F," 4,675 "
-28015044,HC IMP LEAD PACING 58CM 7F," 4,675 "
-28015047,HC IMP LEAD PACING TENDRIL 46CM," 4,675 "
-28015048,HC IMP LEAD PACEMAKER BIPOLAR PASSIVE," 5,525 "
-28015049,HC IMP LEAD PACEMAKER CAPSURE 25CM," 4,675 "
-28015050,HC IMP LEAD PACEMAKER CAPSURE 35CM," 4,675 "
-28015051,HC IMP LEAD PACEMAKER CAPSUREFIX MRI 52CM," 8,458 "
-28015052,HC IMP LEAD PACEMAKER CAPSUREFIX MRI 58CM," 8,458 "
-28015053,HC IMP LEAD PACEMAKER DEXTRUS 45CM," 4,675 "
-28015054,HC IMP LEAD PACEMAKER SECTROX S-45," 5,525 "
-28015055,HC IMP LEAD PACEMAKER TENDRIL 52CM," 4,675 "
-28015056,HC IMP LEAD PACEMAKER TENDRIL 58CM," 4,675 "
-28015057,HC IMP LEAD PACEMAKER VENTRICULAR BIPOLAR 25CM," 4,675 "
-28015058,HC IMP LEAD PACEMAKER VENTRICULAR TENDRIL 58CM," 4,675 "
-28015060,HC IMP LEAD PACEMAKER VENTRICULAR UNIPLR 15CM," 4,675 "
-28015070,HC IMP LEAD VENTRICULAR BIPOLAR ST 10MM 46CM," 4,675 "
-28015026,HC IMP LEAD CORONARY VENOUS ACUITY," 11,250 "
-28015027,HC IMP LEAD CORONARY VENOUS EASYTRAK 80CM," 15,000 "
-28015039,HC IMP LEAD HEART QUICKFLEX 75X86CM 5F 5.6F," 15,000 "
-28015067,HC IMP LEAD VENTRICULAR ATTAIN 78CM 5F," 20,545 "
-28015068,HC IMP LEAD VENTRICULAR ATTAIN 88CM," 17,963 "
-28015069,HC IMP LEAD VENTRICULAR ATTAIN OTW LEFT 78CM," 17,963 "
-28015071,HC IMP LEAD VENTRICULAR COROX 75BP," 11,250 "
-28015072,HC IMP LEAD VENTRICULAR COROX 85BP," 11,250 "
-28015073,HC IMP LEAD VENTRICULAR LEFT 86CM," 21,000 "
-28019220,HC IMP STENT URETERAL EXTRA FLO 6FX26CM," 1,105 "
-28019221,HC IMP STENT URETERAL GUIDEWIRE 10CM 3.7F SET," 1,101 "
-28019222,HC IMP STENT URETERAL GUIDEWIRE 12CM 3.7F SET," 1,101 "
-28019223,HC IMP STENT URETERAL GUIDEWIRE 14CM 3.7F SET," 1,084 "
-28019247,HC IMP STENT URETERAL PERCUFLEX 8FX30CM," 1,403 "
-28019252,HC IMP STENT URETERAL PERCUFLEX PLUS 6FX10CM," 1,190 "
-28019254,HC IMP STENT URETERAL PERCUFLEX PLUS 7FX10CM," 1,190 "
-28015309,HC IMPL PACEMAKER CRT-P CONSULTA," 53,560 "
-28015325,HC IMP PENILE 10MM DIAM 3 CM DIST," 3,613 "
-28015326,HC IMP PENILE ACU-FORM 9.5X23," 20,650 "
-28015327,HC IMP PENILE CYLINDER ACU-FORM 27CM X 27MM," 20,650 "
-28015328,HC IMP PENILE MALLEABLE GENESIS 11.5," 28,700 "
-28015330,HC IMP PENILE SPECTRA AMS 16CMX12MM," 37,180 "
-28019188,HC IMP STENT NEPHROSTOMY AMPLATZ 10.2FX26," 1,087 "
-28019189,HC IMP STENT NEPHROSTOMY ULTRATHANE 10.2FX2, 866 
-28011685,HC  CATHETER BALLOON OCCLUSION ASCENT 4X10MM," 8,972 "
-28011686,HC  CATHETER BALLOON OCCLUSION ASCENT 4X15MM," 8,972 "
-28011687,HC  CATHETER BALLOON OCCLUSION ASCENT 4X17MM," 8,972 "
-28011688,HC  CATHETER BALLOON OCCLUSION ASCENT 6X9MM," 9,507 "
-28011689,HC  CATHETER BALLOON OCCLUSION HYPERFORM 3X15," 11,550 "
-28011690,HC  CATHETER BALLOON OCCLUSION HYPERFORM 3X7," 8,753 "
-28011691,HC  CATHETER BALLOON OCCLUSION HYPERGLIDE 3X10X150," 8,016 "
-28011692,HC  CATHETER BALLOON OCCLUSION HYPERGLIDE 5X20X150," 8,160 "
-28011693,HC  CATHETER BALLOON OCCLUSION SCEPTER 4X10," 9,000 "
-28011694,HC  CATHETER BALLOON OCCLUSION SCEPTER 4X15," 9,000 "
-28012108,HC  CATHETER OCCLUSION EQUALIZER 40X65," 2,040 "
-28012109,HC  CATHETER OCCLUSION SILICONE HSG 5FX30CM, 162 
-28012110,HC  CATHETER OCCLUSION SILICONE HSG 7FX30CM, 244 
-28015180,HC IMPL MATRIX TISSUE ACELLULAR SURGIMEND 6X16CM," 14,400 "
-28015333,HC IMP PERMACOL BIOLOGICAL 18X28 1.5," 50,450 "
-28012597,HC IMPL COLLAR CERVICAL LOW FOAM DENSITY, 90 
-28012598,HC IMPL COLLAR EXTRICATION ADJUSTABLE ADULT, 91 
-28012599,HC IMPL COLLAR EXTRICATION ADJUSTABLE PEDIATRIC, 91 
-28012594,HC IMPL COLLAR ADULT UNIVERSAL W/EXTRA PAD SET, 357 
-28012595,HC IMPL COLLAR CERVICAL ASPEN ADULT REGULAR SET, 247 
-28012596,HC IMPL COLLAR CERVICAL ASPEN PED PD1 SET, 247 
-28012600,HC IMPL COLLAR PEDIATRIC REGULAR PD5 SET, 247 
-28012601,HC IMPL COLLAR PEDIATRIC W/PAD TOT PD3 SET, 247 
-28012602,HC IMPL COLLAR PEDIATRIC W/EXTRA PAD PD2 SET, 247 
-28012604,HC IMPL COLLAR W/EXTRA PAD SHORT PD4 SET, 247 
-28014173,HC IMPL IMMOBILIZER KNEE QUICK WRAP W/TERRY 16IN, 157 
-28014174,HC IMPL IMMOBILIZER KNEE QUICK WRAP W/TERRY 24IN, 207 
-28018781,HC IMPL SPLINT WRIST LACER LEFT MEDIUM, 141 
-28018782,HC IMPL SPLINT WRIST RESTING PROGRESS NEUTRAL LEFT, 458 
-28018783,HC IMPL SPLINT WRIST RESTING PROGRESS NEUTRAL RIGHT, 458 
-28018784,HC IMPL SPLINT WRIST SPICA RIGHT 14.6CM 5.75 IN, 205 
-28018725,HC IMPL SPLINT FINGER PLASTALUME 58MM, 91 
-28018726,HC IMPL SPLINT FINGER EXTENSION 0.87X2IN, 106 
-28018727,HC IMPL SPLINT FINGER EXTENSION 2.5X0.875IN, 106 
-28018728,HC IMPL SPLINT FINGER EXTENSION 3X0.875IN, 106 
-28018759,HC IMPL SPLINT FINGER EXTENSTION 1.75-2.25IN, 110 
-28018760,HC IMPL SPLINT FINGER EXTENSION 2.5-2.75IN, 110 
-28018761,HC IMPL SPLINT FINGER EXTENSION 3.5X0.125X1IN, 106 
-28018762,HC IMPL SPLINT FINGER EXTENSION LARGE 2.75-3IN, 110 
-28018763,HC IMPL SPLINT FINGER EXTENSION SMALL 2.25-2.5IN, 110 
-28018764,HC IMPL SPLINT FINGER EXTENSION EXTRA LARGE 3-3.755IN, 110 
-28019767,HC IMPL VALVE TRACHEOSTOMY SPEAKING AQUA, 538 
-28019768,HC IMPL VALVE TRACH SPEAKING PURPLE 15X23MM, 836 
-28016346,HC IMP PROSTHESIS VOICE PROVOX 2 10MM," 1,998 "
-28016347,HC IMP PROSTHESIS VOICE PROVOX 2 4.5MM," 1,998 "
-28016348,HC IMP PROSTHESIS VOICE PROVOX 2 6MM," 1,998 "
-28016349,HC IMP PROSTHESIS VOICE PROVOX 2 8MM," 1,998 "
-28010856,HC IMP BREAST HIGH PROFILE STYLE 120 300CC," 6,758 "
-28010857,HC IMP BREAST HIGH PROFILE STYLE 120 340CC," 6,800 "
-28010858,HC IMP BREAST HIGH PROFILE STYLE 120 400CC," 6,800 "
-28010859,HC IMP BREAST HIGH PROFILE STYLE 120 440CC," 6,758 "
-28010860,HC IMP BREAST HIGH PROFILE STYLE 120 500CC," 6,758 "
-28010861,HC IMP BREAST HIGH PROFILE STYLE 120 550CC," 6,800 "
-28010862,HC IMP BREAST HIGH PROFILE STYLE 120 600CC," 6,800 "
-28010863,HC IMP BREAST HIGH PROFILE STYLE 120 650CC," 6,800 "
-28010864,HC IMP BREAST HIGH PROFILE STYLE 20 230CC," 6,800 "
-28010865,HC IMP BREAST HIGH PROFILE STYLE 20 260CC," 6,800 "
-28010866,HC IMP BREAST HIGH PROFILE STYLE 20 280CC," 6,800 "
-28010867,HC IMP BREAST HIGH PROFILE STYLE 20 350CC," 6,758 "
-28010868,HC IMP BREAST HIGH PROFILE STYLE 20 375CC," 6,758 "
-28010869,HC IMP BREAST HIGH PROFILE STYLE 20 400CC," 6,758 "
-28010870,HC IMP BREAST HIGH PROFILE STYLE 20 425CC," 6,758 "
-28010871,HC IMP BREAST HIGH PROFILE STYLE 20 450CC," 6,758 "
-28010872,HC IMP BREAST HIGH PROFILE STYLE 20 475CC," 6,960 "
-28010873,HC IMP BREAST HIGH PROFILE STYLE 20 550CC," 6,960 "
-28010874,HC IMP BREAST HIGH PROFILE STYLE 20 600CC," 6,960 "
-28010875,HC IMP BREAST HIGH PROFILE STYLE 20 650CC," 6,960 "
-28010876,HC IMP BREAST HIGH PROFILE STYLE 20 700CC," 6,758 "
-28010877,HC IMP BREAST HIGH PROFILE STYLE 20 750CC," 6,758 "
-28010878,HC IMP BREAST HIGH PROFILE STYLE 20 800CC," 6,758 "
-28010879,HC IMP BREAST MODERATE PROFILE STYLE 10 120CC," 6,758 "
-28010880,HC IMP BREAST MODERATE PROFILE STYLE 10 150CC," 6,800 "
-28010881,HC IMP BREAST MODERATE PROFILE STYLE 10 210CC," 6,960 "
-28010882,HC IMP BREAST MODERATE PROFILE STYLE 10 270CC," 6,758 "
-28010883,HC IMP BREAST MODERATE PROFILE STYLE 10 300CC," 6,758 "
-28010884,HC IMP BREAST MODERATE PROFILE STYLE 10 330CC," 6,800 "
-28010885,HC IMP BREAST MODERATE PROFILE STYLE 10 360CC," 6,960 "
-28010886,HC IMP BREAST MODERATE PROFILE STYLE 10 390CC," 6,800 "
-28010887,HC IMP BREAST MODERATE PROFILE STYLE 10 420CC," 6,800 "
-28010888,HC IMP BREAST MODERATE PROFILE STYLE 10 450CC," 6,960 "
-28010889,HC IMP BREAST MODERATE PROFILE STYLE 10 480CC," 6,960 "
-28010890,HC IMP BREAST MODERATE PROFILE STYLE 10 510CC," 6,758 "
-28010891,HC IMP BREAST MODERATE PROFILE STYLE 10 550CC," 6,758 "
-28010892,HC IMP BREAST MODERATE PROFILE STYLE 10 600CC," 6,758 "
-28010893,HC IMP BREAST MODERATE PROFILE STYLE 10 650CC," 6,800 "
-28010894,HC IMP BREAST MODERATE PROFILE STYLE 10 700CC," 6,800 "
-28010895,HC IMP BREAST MODERATE PROFILE STYLE 10 750CC," 6,800 "
-28010896,HC IMP BREAST MODERATE PROFILE STYLE 10 800CC," 6,758 "
-28010897,HC IMP BREAST MODERATE PROFILE STYLE 110 150CC," 6,800 "
-28010898,HC IMP BREAST MODERATE PROFILE STYLE 110 180CC," 6,800 "
-28010899,HC IMP BREAST MODERATE PROFILE STYLE 110 210CC," 6,800 "
-28010900,HC IMP BREAST MODERATE PROFILE STYLE 110 240CC," 6,800 "
-28010901,HC IMP BREAST MODERATE PROFILE STYLE 110 270CC," 6,800 "
-28010902,HC IMP BREAST MODERATE PROFILE STYLE 110 300CC," 6,800 "
-28010903,HC IMP BREAST MODERATE PROFILE STYLE 110 330CC," 6,800 "
-28010904,HC IMP BREAST MODERATE PROFILE STYLE 40 400CC," 6,800 "
-28010905,HC IMP BREAST MODERATE PROFILE STYLE 40 460CC," 6,800 "
-28010906,HC IMP BREAST MODERATE PROFILE STYLE 40 500CC," 6,800 "
-28010907,HC IMP BREAST MODERATE PROFILE STYLE 40 560CC," 6,800 "
-28010908,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 167CC," 6,800 "
-28010909,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 185CC," 6,800 "
-28010910,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 203CC," 6,758 "
-28010911,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 222CC," 6,758 "
-28010912,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 253CC," 6,758 "
-28010913,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 272CC," 6,758 "
-28010914,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 290CC," 6,758 "
-28010915,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 322CC," 6,758 "
-28010916,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 354CC," 6,758 "
-28010917,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 378CC," 6,800 "
-28010918,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 401CC," 6,800 "
-28010919,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 435CC," 6,758 "
-28010920,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 469CC," 6,758 "
-28010921,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 507CC," 6,758 "
-28010922,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 547CC," 6,758 "
-28010923,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 586CC," 6,800 "
-28010924,HC IMP BREAST MODERATE PROFILE PLUS STYLE 115 627CC," 6,800 "
-28010925,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 158CC," 6,800 "
-28010926,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 194CC," 6,800 "
-28010927,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 213CC," 6,800 "
-28010928,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 234CC," 6,758 "
-28010929,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 265CC," 6,800 "
-28010930,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 286CC," 6,800 "
-28010931,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 304CC," 6,758 "
-28010932,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 371CC," 6,758 "
-28010933,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 397CC," 6,960 "
-28010934,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 421CC," 6,758 "
-28010935,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 457CC," 6,758 "
-28010936,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 492CC," 6,758 "
-28010937,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 533CC," 6,800 "
-28010938,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 575CC," 6,758 "
-28010939,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 616CC," 6,758 "
-28010940,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 659CC," 6,800 "
-28010941,HC IMP BREAST MODERATE PROFILE PLUS STYLE 15 700CC," 6,758 "
-28010989,HC IMP BREAST EXTRA HIGH PROFILE STYLE 45 500CC," 6,800 "
-28010990,HC IMP BREAST EXTRA HIGH PROFILE STYLE 45 550CC," 6,800 "
-28010991,HC IMP BREAST EXTRA HIGH PROFILE STYLE 45 600CC," 6,800 "
-28010992,HC IMP BREAST EXTRA HIGH PROFILE STYLE 45 800CC," 6,800 "
-28019473,HC IMP SYRINGE IMPLANT COAPTITE 1ML," 2,627 "
-28012849,HC IMP EYE SPHERE CONICAL SST 18MM 4.2ML," 5,984 "
-28012850,HC IMP EYE SPHERE CONICAL SST 20MM 5.6ML," 5,695 "
-28018539,HC IMP SHUNT DRAINAGE GLAUCOMA 185MM," 5,653 "
-28018540,HC IMP SHUNT DRAINAGE GLAUCOMA 245MM," 5,653 "
-28012738,HC IMP EAR HYDROXAPETITE M/FOOTPLATE 5.5MM," 2,508 "
-28012739,HC IMP EAR HYDROXAPETITE M/FOOTPLATE 5.75MM," 2,508 "
-28012740,HC IMP EAR HYDROXAPETITE M/FOOTPLATE 6.0MM," 2,508 "
-28012741,HC IMP EAR HYDROXAPETITE M/FOOTPLATE 6.5MM," 2,508 "
-28012742,HC IMP EAR HYDROXAPETITE M/FOOTPLATE 7.0MM," 2,508 "
-28012743,HC IMP EAR HYDROXAPETITE M/FOOTPLATE 7.5MM," 2,508 "
-28012744,HC IMP EAR HYDROXAPETITE M/H STAPES 2.25MM," 2,508 "
-28012745,HC IMP EAR HYDROXAPETITE M/H STAPES 2.50MM," 2,508 "
-28012746,HC IMP EAR HYDROXAPETITE M/H STAPES 2.75MM," 2,508 "
-28012747,HC IMP EAR HYDROXAPETITE M/H STAPES 3.0MM," 2,508 "
-28012748,HC IMP EAR HYDROXAPETITE M/H STAPES 3.25MM," 2,508 "
-28012749,HC IMP EAR HYDROXAPETITE M/H STAPES 3.5MM," 2,508 "
-28012750,HC IMP EAR HYDROXAPETITE M/H STAPES 3.75MM," 2,508 "
-28012751,HC IMP EAR PROSTHESIS AERIAL 5.0MM," 3,086 "
-28012752,HC IMP EAR PROSTHESIS INCUS STRUT LONG," 3,060 "
-28012753,HC IMP EAR PROSTHESIS INCUS STRUT MEDIUM," 2,563 "
-28012754,HC IMP EAR PROSTHESIS OSSICULAR TITANIUM 1.50X3MM," 2,805 "
-28012755,HC IMP EAR PROSTHESIS OSSICULAR TITANIUM 1.00X3MM," 2,890 "
-28012756,HC IMP EAR PROSTHESIS PISTON DE LA CRUZ 0.6X4.25MM," 1,267 "
-28012757,HC IMP EAR PROSTHESIS PISTON FISCH 0.4X6MM, 630 
-28012758,HC IMP EAR PROSTHESIS OSSICULAR 4.25MM," 1,882 "
-28012874,HC IMP FOOTPLATE SHOE DORNHOFFER 1.4X1.09MM, 979 
-28015390,HC IMP PISTON FLOROPLASTIC PLAT," 1,233 "
-28015391,HC IMP PISTON FLOROPLASTIC PLAT 0.6X4.25," 1,324 "
-28012351,HC IMPL COCHLEAR B1300 BAHA 4MM," 10,313 "
-28012352,HC IMPL COCHLEAR CL 24MM," 71,700 "
-28012353,HC IMPL COCHLEAR CONCERT," 90,500 "
-28012354,HC IMPL COCHLEAR CONCERT FLEX 28," 98,000 "
-28012355,HC IMPL COCHLEAR CONCERT MEDIUM," 98,000 "
-28012356,HC IMPL COCHLEAR CONTOUR FREEDOM BACK UP," 79,332 "
-28012357,HC IMPL COCHLEAR CONTOUR FREEDOM ADVANCE," 79,332 "
-28012358,HC IMPL COCHLEAR EAR FLANGE BIA 210 3MM 5.5MM," 17,745 "
-28012359,HC IMPL COCHLEAR EAR FLANGE TITANIUM 3MM," 8,250 "
-28012360,HC IMPL COCHLEAR EAR FLANGE TITANIUM 4MM," 8,250 "
-28012361,HC IMPL COCHLEAR HEARING DEVICE SONATA," 90,500 "
-28012362,HC IMPL COCHLEAR NUCLEUS CONTOUR ADVANCE," 77,500 "
-28012363,HC IMPL COCHLEAR NUCLEUS CONTOUR SYSTEM 24," 75,250 "
-28012364,HC IMPL COCHLEAR NUCLEUS CONTROL ADVANCE," 80,233 "
-28012365,HC IMPL COCHLEAR NUCLEUS W/STR ELECTRODE MODEL C1422," 87,055 "
-28012378,HC IMPL COCHLEAR PROCESSOR NUCLEUS W/CONTROL ADVANCE," 80,233 "
-28012379,HC IMPL COCHLEAR W/ ADVANCE CONTOUR," 87,055 "
-28015185,HC IMP MCP DISTAL CARBON PYROLYTIC SZ 30," 17,513 "
-28015186,HC IMP MCP PROXIMAL CARBON PYROLYTIC SZ 30," 11,775 "
-28018565,HC IMP SPACER ARTELON CMC 1X1X1CM," 13,500 "
-28012866,HC IMPL FINGER JOINT SWANSON GROMMET SIZE 3," 8,118 "
-28012867,HC IMPL FINGER JOINT SWANSON GROMMET SIZE 5," 6,163 "
-28012881,HC IMP GRAFT BYPS MINI CUFF 6X80CM," 18,270 "
-28012882,HC IMP GRAFT BYPASS PERPH THINWALL TAPERED 5MM 80CM," 6,197 "
-28012883,HC IMP GRAFT BYPASS PERPH THINWALL TAPERED 7-4MM 70CM," 5,338 "
-28012884,HC IMP GRAFT BYPASS PERPH THINWALL TAPERED 8-5MM 70CM," 5,100 "
-28012885,HC IMP GRAFT CARBOFLO STR 6MMX40CM," 3,315 "
-28012886,HC IMP GRAFT DIALYSIS ACCESS RINGED 5X45," 4,845 "
-28012887,HC IMP GRAFT DIALYSIS ACCESS VECTRA 5MMX40CM," 8,500 "
-28012888,HC IMP GRAFT DIALYSIS ACCESS VECTRA 6MMX40CM," 8,500 "
-28012911,HC IMP GRAFT GORETEX BIFURCATED TW 16X8X40," 6,690 "
-28012912,HC IMP GRAFT GORETEX BIFURCATED TW 18X9X40," 6,690 "
-28012913,HC IMP GRAFT GORETEX STD 4X40," 3,179 "
-28012914,HC IMP GRAFT GORETEX STRETCH S 12X30," 4,658 "
-28012915,HC IMP GRAFT GORETEX STRETCH S 7X10," 1,352 "
-28012916,HC IMP GRAFT GORETEX STRETCH S 8X10," 1,352 "
-28012917,HC IMP GRAFT GORETEX STRETCH SA 14X30," 4,318 "
-28012918,HC IMP GRAFT GORETEX STRETCH SA 16X30," 4,658 "
-28012919,HC IMP GRAFT GORETEX STRETCH SA 18X30," 4,658 "
-28012920,HC IMP GRAFT GORETEX STRETCH SA 20X30," 4,658 "
-28012921,HC IMP GRAFT GORETEX STRETCH SA 22X30," 5,151 "
-28012922,HC IMP GRAFT GORETEX STRETCH SA 24X30," 4,896 "
-28012926,HC IMP GRAFT GORTEX RINGED 5X40," 6,239 "
-28012927,HC IMP GRAFT GORTEX RINGED REMOVABLE 6X70X70," 8,364 "
-28012928,HC IMP GRAFT GORTEX RINGED REMOVABLE 8X70X80," 8,918 "
-28012929,HC IMP GRAFT HEMASHIELD DOUBLE VELOUR WOVEN 12X60," 2,410 "
-28012930,HC IMP GRAFT HEMASHIELD DOUBLE VELOUR WOVEN 8X60," 2,410 "
-28012931,HC IMP GRAFT HEMASHIELD GOLD BIFURCATED 18X10X40," 5,533 "
-28012932,HC IMP GRAFT HEMASHIELD GOLD BIFURCATED 20X11X40," 5,533 "
-28012933,HC IMP GRAFT HEMASHIELD GOLD BIFURCATED 22X11X40," 5,533 "
-28012934,HC IMP GRAFT HEMASHIELD GOLD BIFUR 14X8X40," 5,533 "
-28012935,HC IMP GRAFT HEMASHIELD GOLD BIFUR 15X24," 2,915 "
-28012936,HC IMP GRAFT HEMASHIELD GOLD BIFUR 16X9X40," 5,533 "
-28012937,HC IMP GRAFT HEMASHIELD GOLD BIFUR 30X10," 3,699 "
-28012938,HC IMP GRAFT HEMASHIELD GOLD BIFUR 30X14," 3,875 "
-28012939,HC IMP GRAFT HEMASHIELD GOLD BIFUR 30X18," 3,309 "
-28012940,HC IMP GRAFT HEMASHIELD GOLD BIFUR 30X7," 3,699 "
-28012941,HC IMP GRAFT HEMASHIELD GOLD DOUBLE VELOUR 15X12," 2,811 "
-28012942,HC IMP GRAFT HEMASHIELD GOLD DOUBLE VELOUR 15X20," 2,915 "
-28012943,HC IMP GRAFT HEMASHIELD GOLD DOUBLE VELOUR 15X22," 2,915 "
-28012944,HC IMP GRAFT HEMASHIELD 6MM X 30CM," 3,545 "
-28012945,HC IMP GRAFT HEMASHIELD 8MM X 30CM," 3,954 "
-28012946,HC IMP GRAFT HEMASHIELD KNITTED ST 20X30," 3,570 "
-28012947,HC IMP GRAFT HEMASHIELD PLAT 26MM," 19,448 "
-28012948,HC IMP GRAFT HEMASHIELD PLAT 28MM," 16,868 "
-28012949,HC IMP GRAFT HEMASHIELD PLAT 30MM," 16,868 "
-28012950,HC IMP GRAFT HEMASHIELD PLAT 32MM," 16,868 "
-28012951,HC IMP GRAFT HEMASHIELD RENAL RIGHT 14X7," 3,485 "
-28012952,HC IMP GRAFT HEMASHIELD VANTAGE 10X80," 6,949 "
-28012953,HC IMP GRAFT HEMASHIELD VANTAGE 6X100," 8,321 "
-28012954,HC IMP GRAFT HEMASHIELD VANTAGE 6X60," 5,455 "
-28012955,HC IMP GRAFT HEMASHIELD VANTAGE 8X60," 5,455 "
-28012956,HC IMP GRAFT HEMASHIELD VANTAGE 8X80," 6,949 "
-28012957,HC IMP GRAFT HEMASHIELD WOVEN DOUBLE VELOUR 32MMX60CM," 5,474 "
-28012958,HC IMP GRAFT HEMASHIELD WOVEN DOUBLE VELOUR 34MMX60CM," 4,966 "
-28012959,HC IMP GRAFT HEMASHIELD WOVEN DOUBLE VELOUR 38MMX60CM," 5,474 "
-28012960,HC IMP GRAFT HEMASHIELD WOVEN STRAIGH 36MMX30CM," 3,844 "
-28012996,HC IMP GRAFT COOLEY 34MM," 2,168 "
-28012997,HC IMP GRAFT DEBAKEY STRAIGHT 6MMX60CM," 2,125 "
-28012998,HC IMP GRAFT GORETEX PROPATEN 6X10," 2,754 "
-28012999,HC IMP GRAFT GORETEX PROPATEN 6X40," 6,273 "
-28010636,HC IMPL ABUTMENT COCHLEAR SNAP 5.5MM," 11,138 "
-28012366,HC COCHLEAR PROCESSOR ACCESSORY KIT," 39,000 "
-28016345,HC PROCESSOR NUCLEUS SOUND," 69,300 "
-28012367,HC COCHLEAR PROCESSOR BONE BLONDE," 20,505 "
-28012368,HC COCHLEAR PROCESSOR BONE BROWN," 19,847 "
-28012369,HC COCHLEAR PROCESSOR BAHA CHAMPAGNE," 23,800 "
-28012370,HC COCHLEAR PROCESSOR ESPRIT 3G BEIGE," 39,000 "
-28012371,HC COCHLEAR PROCESSOR ESPRIT 3G BLACK," 39,000 "
-28012372,HC COCHLEAR PROCESSOR ESPRIT 3G BROWN," 39,000 "
-28012373,HC COCHLEAR PROCESSOR ESPRIT 3G SILVER," 39,000 "
-28012374,HC COCHLEAR PROCESSOR NUCLEUS BEIGE," 33,394 "
-28012375,HC COCHLEAR PROCESSOR NUCLEUS BLACK," 33,394 "
-28012376,HC COCHLEAR PROCESSOR NUCLEUS WHITE," 24,850 "
-28012377,HC COCHLEAR PROCESSOR SOFT BAHA BLACK," 23,800 "
-28015061,HC LEAD RVAD PNEUMATIC EXTENSION 7FT," 2,508 "
-28015062,HC LEAD RVAD PNEUMATIC EXTENSION NS 7FT," 2,423 "
-28016340,HC IMPL POWER MODULE HEARTMATE," 50,700 "
-28011376,HC IMPL CABLE POWER EMERG VE HEARTMATE REPLACEMENT," 1,658 "
-28014177,HC IMPL INTEGRA WOUND MATRIX 2X2," 10,500 "
-28014178,HC IMPL INTEGRA WOUND MATRIX 4X10," 26,593 "
-28010659,HC IMP ALLODERM REGEN TISSUE MATRIX THICK 2.03MM," 10,110 "
-28012592,HC IMP COLLAGEN BIOLOGIC 10X10CM 1MM," 10,500 "
-28012663,HC IMPL DERMAMATRIX THICK 6X16," 23,065 "
-28019495,HC IMPL TISSUE DERMAL ALLODERM 16X20CM," 66,330 "
-28019496,HC IMPL TISSUE DERMAL ALLODERM 6X16CM," 23,401 "
-28015181,HC IMP MATRIX TISSUE RECONSTRUCTIVE STRATTICE 10X16CM," 31,038 "
-28015182,HC IMP MATRIX TISSUE RECONSTRUCTIVE STRATTICE 16X20CM," 57,649 "
-28015183,HC IMP MATRIX TISSUE RECONSTRUCTIVE STRATTICE 20X20CM," 66,510 "
-28015184,HC IMP MATRIX TISSUE RECONSTRUCTIVE STRATTICE 6X16CM," 18,620 "
-28019504,HC IMPL TISSUE PORCINE XCM 10X16CM," 20,748 "
-28019505,HC IMPL TISSUE PORCINE XCM 10X20CM," 27,232 "
-28019506,HC IMPL TISSUE PORCINE XCM 12X20CM," 31,122 "
-28019507,HC IMPL TISSUE PORCINE XCM 16X20CM," 40,459 "
-28019508,HC IMPL TISSUE PORCINE XCM 16X25CM," 45,157 "
-28019509,HC IMPL TISSUE PORCINE XCM 20X20CM," 50,573 "
-28019510,HC IMPL TISSUE PORCINE XCM 20X25CM," 60,206 "
-28019511,HC IMPL TISSUE PORCINE XCM 20X30CM," 70,025 "
-28019512,HC IMPL TISSUE PORCINE XCM 2X4CM," 1,017 "
-28019513,HC IMPL TISSUE PORCINE XCM 4X16CM," 8,892 "
-28019514,HC IMPL TISSUE PORCINE XCM 4X7CM," 4,629 "
-28019515,HC IMPL TISSUE PORCINE XCM 5X16CM," 11,115 "
-28019516,HC IMPL TISSUE PORCINE XCM 6X12CM," 10,004 "
-28019517,HC IMPL TISSUE PORCINE XCM 6X16CM," 13,338 "
-28019518,HC IMPL TISSUE PORCINE XCM 6X20CM," 16,673 "
-28019519,HC IMPL TISSUE PORCINE XCM 8X12CM," 13,338 "
-28019520,HC IMPL TISSUE PORCINE XCM 8X16CM," 17,784 "
-28019521,HC IMPL TISSUE PORCINE XCM 8X20CM," 21,788 "
-28016757,HC IMP SCREW BONE LAG CROSSPIN 2MM 27MM, 477 
-28015074,HC IMPL LENS IOL ANT CHAMBER PMMA CONVEXOPLANO 18.0D, 808 
-28015075,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 12.0D, 952 
-28015076,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 14.5D, 952 
-28015077,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 17.0D, 952 
-28015078,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 18.5D, 952 
-28015079,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 22.5D, 952 
-28015080,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 24.5D, 952 
-28015081,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 26.0D, 952 
-28015082,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 26.5D, 952 
-28015083,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 27.0D, 952 
-28015084,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 27.5D, 952 
-28015085,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 28.0D, 952 
-28015086,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 28.5D, 952 
-28015087,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 29.0D, 952 
-28015088,HC IMP LENS IOL POSTERIOR CHAMBER CLARIFLEX 29.5D, 952 
-28015089,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 30.0D, 952 
-28015090,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 6.0D, 952 
-28015091,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 7.0D, 952 
-28015092,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 7.5D, 952 
-28015093,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 8.0D, 952 
-28015094,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 8.5D, 952 
-28015095,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 9.5D, 952 
-28015096,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 10.5D, 952 
-28015097,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 11.5D, 952 
-28015098,HC IMPL LENS IOL POSTERIOR CHAMBER CLARIFLEX 23.5D, 952 
-28015099,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MA60MA 0D," 1,054 "
-28015100,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 10.0D," 1,267 "
-28015101,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 10.5D," 1,267 "
-28015102,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 11.0D," 1,267 "
-28015103,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 11.5D," 1,267 "
-28015104,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 12.0D," 1,267 "
-28015105,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 12.5D," 1,267 "
-28015106,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 13.0D," 1,267 "
-28015107,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 13.5D," 1,267 "
-28015108,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 14.0D," 1,267 "
-28015109,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 14.5D," 1,267 "
-28015110,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 15.0D," 1,267 "
-28015111,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 15.5D," 1,267 "
-28015112,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 16.0D," 1,267 "
-28015113,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 16.5D," 1,267 "
-28015114,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 17.0D," 1,267 "
-28015115,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 17.5D," 1,267 "
-28015116,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 18.0D," 1,267 "
-28015117,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 18.5D," 1,267 "
-28015118,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 19.0D," 1,267 "
-28015119,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 19.5D," 1,267 "
-28015120,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 20.0D," 1,267 "
-28015121,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 20.5D," 1,267 "
-28015122,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 21.0D," 1,267 "
-28015123,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 21.5D," 1,267 "
-28015124,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 22.0D," 1,267 "
-28015125,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 22.5D," 1,267 "
-28015126,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 23.0D," 1,267 "
-28015127,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 23.5D," 1,267 "
-28015128,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 24.0D," 1,267 "
-28015129,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 24.5D," 1,267 "
-28015130,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 25.0D," 1,267 "
-28015131,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 25.5D," 1,267 "
-28015132,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 26.0D," 1,267 "
-28015133,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 26.5D," 1,267 "
-28015134,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 27.0D," 1,267 "
-28015135,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 27.5D," 1,267 "
-28015136,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 28.0D," 1,267 "
-28015137,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 7.0D," 1,139 "
-28015138,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 7.5D," 1,139 "
-28015139,HC IMPL LENS IOL POSTERIOR CHAMBER SA60AT 8.0D," 1,139 "
-28015140,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 9.0D," 1,139 "
-28015141,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 22.0D," 1,488 "
-28019814,HC IMP ANCHOR SUTURE BIOCOMPOSITE HIP PUSHLCK 3.5X19.5," 2,890 "
-28019818,HC IMP BREAST ROUND SMOOTH SALINE SPECTRUM 375CC," 7,013 "
-28019819,HC  CATHETER BALLOON 6X20X135," 1,530 "
-28019820,HC  CATHETER BALLOON SIZING 30.0MM 4.0CM 8F," 2,125 "
-28019821,HC  CATHETER BROVIAC SINGLE LUMEN VITACUFF 4.2F," 1,562 "
-28019822,HC  CATH CARD CRYOABLAT MED 4MM 7F," 10,875 "
-28019823,HC  CATHETER CORONARY DIAG OPTITORQUE SARAH 6F 4.0, 255 
-28019824,HC  CATHETER DILATATION MONORAIL 135CM 6X20MM," 3,404 "
-28019825,HC  CATH EP 10PLR STRB XLRG 115X6F," 2,440 "
-28019826,HC  CATHETER MALECOT 4 WING 20F, 173 
-28019827,HC IMP CLIP ANEURYSM SUGITA 45 ANDLE 10MM," 2,525 "
-28019828,HC IMP CLIP ANEURYSM SUGITA BENT 7.5MM," 2,525 "
-28019829,HC IMP CLIP ANEURYSM SUGITA L SHAPED CURVED 10MM," 2,525 "
-28019830,HC IMPL COIL HYDROCOIL DETACHABLE 18 SYS 10MMX26CM," 4,973 "
-28019831,HC IMPL COIL HYDROCOIL DETACHABLE 18 SYS 14MMX34CM," 5,249 "
-28019832,HC IMPL COIL HYDROCOIL DETACHABLE 18 SYS 8MMX20CM," 4,696 "
-28019833,HC IMPL COIL HYDROCOIL DETACHABLE 35 SYS 4MMX15CM," 5,531 "
-28019834,HC IMPL COIL HYDROCOIL DETACHABLE 35 SYS 8MMX20CM," 7,650 "
-28019835,HC IMPL COIL MICRO HELICAL SRE PLATINUM 18 8MM 30CM," 4,675 "
-28019836,HC IMPL COIL NEURO DETACHABLE 5MM 15CM," 10,463 "
-28019837,HC IMPL COIL NEURO DETACHABLE AXIUM 7MMX30CM," 6,435 "
-28019838,HC IMPL COIL NEURO EMBOLIZATION CURVE X-SOFT 3MM 6CM," 16,800 "
-28019839,HC IMPL COIL NEURO EMBOLIZATION CURVE X-SOFT 4MM 8CM," 18,375 "
-28019840,HC IMPL COIL NEURO EMBOLIZATION SOFT 12MM 35CM," 18,900 "
-28019841,HC IMPL COIL NEURO EMBOLIZATION SOFT  13MM 48CM," 19,810 "
-28019842,HC IMPL COIL NEURO EMBOLIZATION SOFT 10MM 30CM," 18,550 "
-28019843,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 3X6CM," 11,280 "
-28019844,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 3X8CM," 12,000 "
-28019845,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 4X10CM," 11,280 "
-28019846,HC IMPL COIL NEURO EMBOLIZATION DETACHABLE STD 4X8CM," 11,280 "
-28019847,HC IMPL COIL NEURO HELICAL SOFT 20CM 8MM 360D," 14,586 "
-28019848,HC IMPL COIL NEURO MICRO SPHERICAL PLATINUM 24.3CM 14MM," 8,400 "
-28019849,HC IMPL COIL NEURO XL10 5MMX10CM," 9,420 "
-28019850,HC IMPL COIL PERIPHERAL DETACHABLE SRE SOFT 15X5X360," 14,015 "
-28019851,HC IMPL COIL PERIPHERAL DETACHABLE SRE SOFT 15X6X360," 14,015 "
-28019852,HC IMPL COIL PERIPHERAL EMBOLIZATION 3MM 2CM 0.038IN, 723 
-28019853,HC IMP CONNECTOR ROD LATERAL CLOSED SS 6.35X20," 7,673 "
-28019854,HC IMP CONNECTOR ROD LATERAL CLOSED TI 6.35X20," 8,181 "
-28019855,HC IMP CONNECTOR ROD LATERAL CLOSED TI 6.35X30," 8,181 "
-28019856,HC IMP CONNECTOR ROD LATERAL CLOSED TI 6.35X60," 8,364 "
-28019857,HC IMP DEVICE EMBOLIZATION PIPELINE 4.25X18," 75,000 "
-28019858,HC  GUIDEWIRE ACUTRAK 2THREADED  2.4MMX9.25IN, 128 
-28019859,HC  GUIDEWIRE CORONARY STR TIP PROWATER 180X3CM, 139 
-28019860,HC  GUIDEWIRE CORONARY STR TIP PROWATER 300X3CM, 850 
-28019861,HC IMP HIP HEAD FEMORAL MOD COBALT CHROME +0 36MM REV," 4,213 "
-28019862,HC IMP HIP LINER ACETABULAR CONSTRAINED 32X56MM," 5,950 "
-28019863,HC IMP HIP LINER ACETABULAR STD 50X52X54X28X7.2MM," 5,950 "
-28019864,HC IMP HIP LINER ACETABULAR STD 50X52X54X28X7.2MM REV," 7,513 "
-28019865,HC IMP HIP LINER SHELL LONGEVITY OD 32X54MM," 5,950 "
-28019866,HC IMP HIP SHELL ACETABULAR LIMITED SZ24 58MM," 17,250 "
-28019867,HC IMP HIP SHELL ACETABULAR MULTI HOLE 54MM," 10,050 "
-28019868,HC IMP HIP STEM BODY CONE A ZMR 40X35MM REV," 21,265 "
-28019869,HC IMP HIP STEM BODY CONE AA ZMR 40X35MM REV," 21,265 "
-28019870,HC IMP HIP STEM BODY SPOUT A ZMR 46X35MM REV," 21,058 "
-28019871,HC IMP HIP STEM BODY SPOUT B ZMR 40X35MM REV," 21,265 "
-28019872,HC IMP HIP STEM BODY SPOUT D ZMR 46X35MM REV," 21,058 "
-28019873,HC IMP HIP STEM BODY SPOUT F ZMR 46X35MM REV," 21,058 "
-28019874,HC IMP HIP STEM BODY TAPERED XL EXT ZMR 78X23MM REV," 20,828 "
-28019875,HC IMP HIP STEM FULL COAT EXT LM BODY VERSYS 14X140MM," 25,200 "
-28019876,HC IMP HIP STEM FULL COAT EXT STD BODY VERSYS 14X140MM," 25,200 "
-28019877,HC IMP HIP STEM FULL COAT LM BODY VERSYS 14X140MM REV," 31,739 "
-28019878,HC IMP HIP STEM FULL COAT LM BODY VERSYS 15X160MM REV," 31,739 "
-28019879,HC IMP HIP STEM FULL COAT LM BODY VERSYS 18X160MM REV," 31,739 "
-28019880,HC IMP HIP STEM TAPERED EXT STD OFFSET 13X130MM," 21,000 "
-28019881,HC IMP HIP STEM TAPERED EXT STDOFFSET 15X140MM," 21,000 "
-28019882,HC  INTRODUCER SHEATH GUIDING SWARTZ 60CM 8F," 2,440 "
-28019883,HC  INTRODUCER SHEATH GUIDING FAST-CATH 60CM 8F," 2,440 "
-28019884,HC  INTRODUCER SHEATH GUIDING RAMP 60CM 8F," 1,318 "
-28019885,HC  INTRODUCER SHEATH GUIDING SL3 63CM 8F," 1,063 "
-28019886,HC  INTRODUCER SHEATH GUIDING SL4 63CM 8F," 1,530 "
-28019887,HC  INTRODUCER SHEATH STRBL 71CM 11.5F," 8,492 "
-28019888,HC IMP FILTER VENA CAVA FEMORAL DELIVERY 30X50MM," 8,075 "
-28019889,HC IMP KNEE ARTICULAR SURFACE SIZE C-D 3-4 17X66MM," 10,728 "
-28019890,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 STR 10MM," 4,597 "
-28019891,HC IMP KNEE ARTICULAR SURFACE NEXGEN 3-4 STR 12MM," 4,597 "
-28019892,HC IMP KNEE ARTICULAR SURFACE NEXGEN 5-6 STR10MM," 4,597 "
-28019893,HC IMP KNEE ARTICULAR SURFACE NEXGEN 5-6 STR 12MM," 4,597 "
-28019894,HC IMP KNEE AUGMENT FEMORAL DISTAL ONLY SZD 10MM REV," 6,635 "
-28019895,HC IMP KNEE AUGMENT FEMORAL DISTAL ONLY SZE 10MM REV," 6,635 "
-28019896,HC IMP KNEE AUGMENT FEMORAL DISTAL ONLY SZF 10MM REV," 6,635 "
-28019897,HC IMP KNEE AUGMENT FEMORAL PRECOAT  DIST SZD 15MM REV," 6,860 "
-28019898,HC IMP KNEE AUGMENT FEMORAL PRECOAT  DIST SZD 20MM REV," 6,860 "
-28019899,HC IMP KNEE AUGMENT FEMORAL PRECOAT  DIST SZE 15MM REV," 6,860 "
-28019900,HC IMP KNEE AUGMENT FEMORAL PRECOAT  DIST SZF 15MM REV," 6,860 "
-28019901,HC IMP KNEE FEM COMP NON POROUS LEFT TC3 SZ3 61X66," 28,896 "
-28019902,HC IMP KNEE FEM COMP NON POROUS LEFT TC3 SZ2.5 59X63," 28,896 "
-28019903,HC IMP KNEE FEM COMP NON POROUS LEFT TC3 SZ4 65X71," 28,896 "
-28019904,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV RT SZ3," 13,230 "
-28019905,HC IMP KNEE FEMORAL COMPONENT NON POROUS ADV RT SZ4," 13,230 "
-28019906,HC IMP KNEE FEMORAL COMPONENT PSRT  2.5 59X63MM," 9,540 "
-28019907,HC IMP KNEE FEMORAL COMPONENT POSTERIOR STAB LT 67.5MM," 14,775 "
-28019908,HC IMP KNEE INSERT TIBIAL ROTAT PLAT STAB TC3 SZ5 12.5," 13,140 "
-28019909,HC IMP KNEE STEM FEMORAL 12X32MM 5PL REV," 6,800 "
-28019910,HC IMP KNEE STEM TIBIAL CEMENTED TAPERED SZ4 13X90 REV," 20,882 "
-28019911,HC IMPL LEAD DEFIBRILLATOR DUAL COIL 64CM 9F," 35,000 "
-28019912,HC IMP LEAD PACEMAKER CAPSUREFIX 58CM 5068-58," 4,505 "
-28019913,HC IMP LEAD STIMULATOR DEEP BRAIN 0.5MM NARROW," 21,525 "
-28019914,HC IMP LEAD VENTRICULAR BIPOLAR ST OPTIM 10MM 46CM," 4,675 "
-28019915,HC IMP LEAD VENTRICULAR SPRINT QUATTRO 65CM," 35,000 "
-28019916,HC IMP LINK SPINELINK REVERSE CROSSLNK 18MM," 2,125 "
-28019917,HC IMP OCCLUDER AMPLATZ ASD 7F 13MM," 35,100 "
-28019918,HC IMP PEG SMOOTH 2.0X22MM, 865 
-28019919,HC IMP PEG SMOOTH 2.0X26MM, 865 
-28019920,HC IMP PEG SMOOTH 2.0X30MM, 680 
-28019921,HC IMP PEG THREAD 2.5X12MM TP-12, 865 
-28019922,HC IMP PEG THREAD 2.5X14MM TP-14, 816 
-28019923,HC IMP PEG THREAD 2.5X16MM TP-16, 865 
-28019924,HC IMP PEG THREAD 2.5X18MM FP-18, 865 
-28019925,HC IMP PEG THREAD 2.5X18MM TP-18, 918 
-28019926,HC IMP PEG THREAD 2.5X20MM FP-20, 816 
-28019927,HC IMP PEG THREAD 2.5X20MM TP-20, 918 
-28019928,HC IMP PEG THREAD 2.5X22MM FP-22, 865 
-28019929,HC IMP PEG THREAD 2.5X22MM TP-22, 918 
-28019930,HC IMP PEG THREAD 2.5X24MM FP-24, 865 
-28019931,HC IMP PEG THREAD 2.5X24MM TP-24, 918 
-28019932,HC IMP PEG FULLY THREAD LOCKING 2.5X26MM, 816 
-28019933,HC IMP PEG THREAD 2.5X26MM DVR, 595 
-28019934,HC IMP PEG THREAD 2.5X28MM FP-28, 816 
-28019935,HC IMP PEG THREAD 2.5X28MM TP-28, 816 
-28019936,HC IMP PEG THREAD 2.5X30MM FP-30, 865 
-28019937,HC IMP PEG THREAD 2.5X30MM TP-30, 865 
-28019938,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X26MM," 1,445 "
-28019939,HC IMP PEG THREAD MULTI DIRECTIONAL NS 2.5X16MM," 1,445 "
-28019940,HC IMP PEG THREAD TRX 20MM, 723 
-28019941,HC IMP PIN BUTTRESS 27MM," 2,125 "
-28019942,HC IMP PIN BUTTRESS 32MM," 2,125 "
-28019943,HC IMPL PLATE ANTERIOR CERVICAL VISION 21MM," 8,500 "
-28019944,HC IMPL PLATE ANTERIOR CERVICAL VISION 23MM," 11,807 "
-28019945,HC IMPL PLATE ANTERIOR CERVICAL VISION 37.5MM," 9,000 "
-28019946,HC IMPL PLATE ANTERIOR CERVICAL VISION 40MM," 9,000 "
-28019947,HC IMPL PLATE ANTERIOR CERVICAL VISION 42.5MM," 9,000 "
-28019948,HC IMPL PLATE CLAVICLE LOW PROFILE STR LEFT 8HOLE," 6,333 "
-28019949,HC IMPL PLATE LOCKING 2.0 L SHAPE RIGHT 2/4," 3,511 "
-28019950,HC IMPL PLATE MINI LEFT ZED RIGID 15MM," 1,343 "
-28019951,HC IMPL PLATE MINI LEFT ZED RIGID 20MM," 1,343 "
-28019952,HC IMPL PLATE MINI RIGHT ZED RIGID 15MM," 1,060 "
-28019953,HC IMPL PLATE MINI RIGHT ZED RIGID 18MM," 1,060 "
-28019954,HC IMPL PLATE MATRIXMANDIBLE DYNAMIC COMP 1.5MM 3X3HOLE," 4,821 "
-28019955,HC IMPL PLATE MATRIXMANDIBLE STRAIGHT 2.0MM 6 HOLE," 4,957 "
-28019956,HC IMPL PLATE OSTEOTOMY ADULT 3 HOLE 60X15MM," 4,872 "
-28019957,HC IMPL PLATE RESORBABLE 3D MESH THICKNESS 100MM .5MM," 18,096 "
-28019958,HC IMPL PLATE TITANIUM HEAD 4 HOLD 1.5X8HOLE," 1,879 "
-28019959,HC IMPL PLATE TITANIUM HEAD 4 HOLE 2.0X7H RIGHT," 2,652 "
-28019960,HC IMPL PLATE TITANIUM T-PLATE 4 HOLE 1.3X8HOLE," 2,049 "
-28019961,HC IMPL PLATE TITANIUM Y PLATE 1.3X8HOLE," 1,972 "
-28019962,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL LEFT 10 H 3.5X/171," 11,925 "
-28019963,HC IMP PUTTY BONE MATRIX DBM 5CC," 7,225 "
-28019964,HC  RETRIEVER STENT SOEHENDRA 7F, 973 
-28019965,HC IMPL SCREW 4.0 PARTIAL THREAD TITANIUM 14MM, 130 
-28019966,HC IMPL SCREW 4.0 TITANIUM PARTIALLY THREADED 10MM, 130 
-28019967,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 12MM, 130 
-28019968,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 16MM, 130 
-28019969,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 18MM, 130 
-28019970,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 20MM, 130 
-28019971,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 22MM, 130 
-28019972,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 24MM, 130 
-28019973,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 26MM, 130 
-28019974,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 28MM, 130 
-28019975,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 30MM, 159 
-28019976,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 35MM, 130 
-28019977,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 40MM, 149 
-28019978,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 45MM, 130 
-28019979,HC IMPL SCREW 4.0  TITANIUM PARTIALLY THREADED 50MM, 149 
-28019980,HC IMPL SCREW BIOABSORBABLE BIOSCREW 9X25MM," 1,065 "
-28019981,HC IMPL SCREW BIOABSORBABLE FEMORAL 8X20MM," 1,063 "
-28019982,HC IMPL SCREW BIOABSORBABLE FEMORAL 8X30MM," 1,063 "
-28019983,HC IMPL SCREW BIOABSORBABLE FEMORAL 9X30MM," 1,063 "
-28019984,HC IMPL SCREW BIOABSORBABLE GUARDSMAN 9X25MM," 1,063 "
-28019985,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X10MM, 627 
-28019986,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X12MM, 627 
-28019987,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X14MM, 142 
-28019988,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X20MM, 627 
-28019989,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X4MM, 125 
-28019990,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X7MM, 614 
-28019991,HC IMP SCREW BONE CROSSPIN LOCKING SELF TAP 2X8MM, 627 
-28019992,HC IMP SCREW BONE 2.6X10MM 7026210, 385 
-28019993,HC IMP SCREW BONE 2.6X6MM 7026206, 385 
-28019994,HC IMP SCREW BONE 2.6X8MM 7026208, 385 
-28019995,HC IMP SCREW BONE INION CPS 1.5X6MM," 2,267 "
-28019996,HC IMP SCREW BONE INION CPS 2.0X5MM," 2,267 "
-28019997,HC IMP SCREW BONE INION CPS 2.0X7MM," 2,267 "
-28019998,HC IMP SCREW BONE INION CPS 2.5X10MM, 731 
-28019999,HC IMPL SCREW CANNULATED FULLY THREAD 7.3X120MM," 2,091 "
-28020000,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X100MM," 2,091 "
-28020062,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 5.5X30MM," 8,820 "
-28020063,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 5.5X35MM," 8,820 "
-28020064,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 5.5X45MM," 8,820 "
-28020065,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 5.5X50MM," 8,820 "
-28020066,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 5.5X55MM," 8,820 "
-28020067,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 7.5X30MM," 8,820 "
-28020068,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 7.5X35MM," 8,820 "
-28020069,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 7.5X50MM," 8,820 "
-28020070,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS CANN 7.5X55MM," 8,820 "
-28020071,HC IMPL SCREW SPINAL MULTI AXIAL POLARIS 7.5X45MM," 8,741 "
-28020072,HC IMPL SCREW SPINAL POLYAXIAL CANNULATED 5X40MM," 9,188 "
-28020073,HC IMPL SCREW SPINAL POLYAXIAL CANNULATED 6.5X45MM," 8,500 "
-28020074,HC IMPL SCREW SELF TAPPING MTRXMNDBL TI 5/PKG 2.4X12MM, 734 
-28020075,HC IMPL SCREW SELF TAPPING MTRXMNDBL TI 5/PKG 2.4X14MM, 734 
-28020076,HC IMPL SCREW SELF TAPPING MTRXMNDBL TI 5/PKG 2.4X6MM, 734 
-28020077,HC IMPL SCREW UNIPLANER 4.75X25MM," 7,013 "
-28020078,HC IMPL SCREW UNIPLANER 5.5X25MM," 7,863 "
-28020079,HC IMPL SCREW UNIPLANER 5.5X30MM," 7,013 "
-28020080,HC  SHEATH GUIDING PINN DESTIN MULTI PURPOSE 7X45, 697 
-28020081,HC IMP SPACER X STOP 8MM," 27,650 "
-28020082,HC IMP STENT ASPIRE COVERED 5X7X100L," 15,375 "
-28020083,HC IMP STENT BILIARY GENESIS 35 OTW7X60X80CM," 9,000 "
-28020084,HC IMP STENT BILIARY RX 7FX15, 757 
-28020085,HC IMP STENT BILIARY SMART OTW 6X80X80," 13,331 "
-28020086,HC IMP STENT BILIARY SMART OTW 7X80X80," 13,331 "
-28020087,HC IMP STENT BILIARY SMART OTW 8X60X80," 11,104 "
-28020088,HC IMP STENT BILIARY SMART OTW 8X80X80," 13,331 "
-28020089,HC IMP STENT BILIARY WALLFLEX PC 8X80," 17,843 "
-28020090,HC IMP STENT CAROTID PRECISE PRO 7X20X135," 16,500 "
-28020091,HC IMP STENT CAROTID PRECISE PRO 7X30X135," 16,500 "
-28020092,HC IMP STENT CAROTID PRECISE PRO 8X40X135," 16,500 "
-28020093,HC IMP STENT CAROTID PRECISE PRO 9X40X135," 16,500 "
-28020094,HC IMP STENT COLON WALLSTENT COATED 10X40," 11,415 "
-28020095,HC IMP STENT COLON WALLSTENT COATED 10X60," 11,415 "
-28020096,HC IMP STENT COLON WALLSTENT COATED 10X80," 11,415 "
-28020097,HC IMP STENT CORONARY MICRO DRIVER RX DES  2.5X18," 7,875 "
-28020098,HC IMP STENT CORONARY ENDEAVOR RX DES 2.5X24," 11,963 "
-28020099,HC IMP STENT CORONARY TAXOL ELUTING EXPRESS2 2.25X16," 15,750 "
-28020100,HC IMP STENT CORONARY VERIFLEX RX BMS 4X16," 7,650 "
-28020101,HC IMP STENT FLEXI FEEMAN 3FX5CM, 374 
-28020102,HC IMP STENT GRAFT AAA EXCLUDER 14.5X12," 27,860 "
-28020103,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X109," 55,218 "
-28020104,HC IMP STENT GRAFT AAA ZENITH FENESTRATED 26X124," 55,218 "
-28020105,HC IMP STENT GRAFT BILIARY NO HOLES VIABIL 10X60X200," 15,488 "
-28020106,HC IMP STENT GRAFT BILIARY NO HOLES VIABIL 10X80X200," 15,488 "
-28020107,HC IMP STENT GRAFT TAA TALENT 42X42X113," 28,000 "
-28020108,HC IMP STENT GRAFT TAA TALENT 46X44X110," 28,000 "
-28020109,HC IMP STENT GRAFT TAA TALENT XCELERANT 46X46X112," 78,120 "
-28020110,HC IMP STENT GRAFT TAA ZENITH TX2 PROX EXTENSION 36X77," 34,300 "
-28020111,HC IMP STENT GRAFT TAA ZENITH TX2 TAPER 38X152MM," 72,564 "
-28020112,HC IMP TISSUE EXPANDER WITH STABLE VASE VERSAFIL 8X5X5," 4,463 "
-28020114,HC IMP VALVE AORTIC PERIMOUNT REDUCED SEWING RING 19MM," 30,100 "
-28020115,HC IMP VALVE AORTIC PERIMOUNT REDUCED SEWING RING 21MM," 30,100 "
-28020116,HC IMP VALVE AORTIC PERIMOUNT REDUCED SEWING RING 23MM," 30,100 "
-28020117,HC IMP WIRE KIRSCHNER DIAMOND POINT 1.6MMX230MM, 303 
-28020118,HC  INTROD MICRO ECHO 5F 10CM, 221 
-28020119,HC  CATHETER DILATATION 3X100CM 22MM 8F," 5,821 "
-28020120,HC IMP STENT GRAFT AAA ZENITH RENU ANCILLARY 39X77," 34,912 "
-28020121,HC IMP STENT GRAFT AAA ZENITH FLEX MAIN BODY EXT 36X50," 14,573 "
-28020122,HC IMP STENT GRAFT AAA OVATION 29X80," 57,915 "
-28020123,HC IMP STENT GRAFT AAA OVATION 10X120," 28,350 "
-28020124,HC BUR ATHERECTOMY ROTALINK PL 1.5MM," 8,963 "
-28020125,HC IMP MATRIX DERMAL PORCINE INTEXEN 2X7," 4,548 "
-28020126,HC IMP MATRIX DERMAL PORCINE INTEXEN 6X8," 8,025 "
-28020127,HC  CATHETER GUIDING AL4 6F, 94 
-28020128,HC IMPL PLATE STERNAL LOCKING TITANIUM 30HOLES," 18,517 "
-28020129,HC IMP FILLER BONE STIMULAN 10CC," 10,875 "
-28020130,HC IMP FILLER BONE STIMULAN 20CC," 18,375 "
-28020131,HC IMP STENT PANCREATIC FREEMAN 7F 5CM, 374 
-28020132,HC STAPLER EEA BLUE 25MM," 7,760 "
-28020133,HC IMP MEMBRANE PERICARDIAL PRECLUDE 15X20," 10,455 "
-28020134,HC  GUIDEWIRE VASCULAR AVIGO .014X205CM," 3,706 "
-28020135,HC IMP ANCHOR ASSEMBLY MALAR SHORT SPECTRUM," 13,905 "
-28020136,HC IMP ANCHOR SCR OMI 1.6X8MM, 920 
-28020137,HC IMP ANCHOR TACIT THRD 2.0MM, 871 
-28020138,HC IMP ARM REMOVABLE EXTENSION 30MM," 6,202 "
-28020139,HC IMP ARM REMOVABLE EXTENSION 60MM," 6,202 "
-28020140,HC IMP BAND POLARIS PARALLEL 3.5X5.5MM," 10,527 "
-28020141,HC IMPL BATTERY CLIP HEARTMATE II 14 VOLT," 8,250 "
-28020142,HC IMP BONE CANCELLOUS BLOCK 1.5CMX10X20MM," 7,412 "
-28020143,HC IMP BONE VITOSS CANCELLOUS PACK 10ML," 13,163 "
-28020144,HC IMP BONE VITOSS MORSEL STANDARD 10ML," 6,783 "
-28020145,HC IMP BOW HORIZONTAL LARGE SPECTRUM," 7,089 "
-28020146,HC IMP BOW HORIZONTAL SMALL SPECTRUM," 7,183 "
-28020147,HC BUR DRILL BIT 3X14MM," 2,643 "
-28020148,HC IMP BUR HOLE COVER SMALL 1.2MM, 714 
-28020149,HC IMP BUTTON GASTROSTOMY REPLACEMENT 18FX1.7CM," 1,666 "
-28020150,HC IMP CAGE SPINAL SYNFIX-LR 12 DEGREE 26X32X15MM," 54,925 "
-28020151,HC IMP CAGE SPINAL SYNFIX-LR 12 DEGREE 30X38X13.5MM," 54,925 "
-28020152,HC  CANNULA SHORT APICAL SHORT 32F," 8,175 "
-28020153,HC  CATH FOLEY 3W 5CCX24F, 87 
-28020154,HC  CATH FOLEY 3W 5CCX26F, 87 
-28020155,HC  CATH GUID CVS NO SIDE HOLE CLS3.5 6F, 468 
-28020156,HC CATH ICP MONITOR CAMINO 4F KIT," 4,522 "
-28020157,HC  CATHETER BALLOON PTCA TX 4X20MM," 2,798 "
-28020158,HC CATHETER BICAVAL ELITE 16FR," 17,625 "
-28020159,HC CATHETER BICAVAL ELITE 19FR," 17,625 "
-28020160,HC CATHETER BICAVAL ELITE 20FR," 17,625 "
-28020161,HC  CATHETER DILATATION CORONARY OTW 1.20MMX08MM," 1,615 "
-28020162,HC  CATHETER DILATATION CORONARY OTW 1.20MMX15MM," 1,615 "
-28020163,HC  CATHETER DILATATION CORONARY OTW 1.20MMX20MM," 1,615 "
-28020164,HC  CATHETER DILATATION LP 8X40MM 80CM," 3,404 "
-28020165,HC  CATHETER DILATATION PTA BM 6X40X120CM," 2,329 "
-28020166,HC  CATHETER DILATATION PTA BM 6X40X75CM," 2,059 "
-28020167,HC  CATHETER DILATATION ULTRATHIN SDS 5FX4MMX2X90CM," 1,679 "
-28020168,HC  CATHETER DILATATION ULTRATHIN SDS 5FX5MMX2X90CM," 1,679 "
-28020169,HC  CATHETER DILATATION ULTRATHIN SDS 5FX6MMX2X90CM," 1,679 "
-28020170,HC  CATHETER DILATATION ULTRATHIN SDS 5FX7MMX2X90CM," 1,679 "
-28020171,HC  CATHETER DILATATION ULTRATHIN SDS 6FX8MMX2X90CM," 1,679 "
-28020172,HC  CATHETER DILATATION ULTRATHIN SDS 7FX10MMX2X90," 1,679 "
-28020173,HC  CATHETER DILATATION ULTRATHIN SDS 7FX10MMX3X90," 1,679 "
-28020174,HC  CATHETER DILATATION ULTRATHIN SDS 7FX9MMX2X90CM," 1,679 "
-28020175,HC  CATHETER DILATATION ULTRATHIN SDS 7FX9MMX3X90CM," 1,679 "
-28020176,HC  CATH EP SAFIRE SM CURL 7FX4MM," 5,525 "
-28020177,HC  CATHETER FOLEY 3WY 30CC 24F, 225 
-28020178,HC  CATHETER GUIDING VIKING OPTIMA 8FX100CM, 468 
-28020179,HC  CATHETER HEMODIALYSIS DURAFLOW 15.5FX20CM, 694 
-28020180,HC  CATHETER HEMODIALYSIS DURAFLOW 15.5FX22CM, 892 
-28020182,HC  CATHETER INFUSION UNIFUSE 135X20CM 5F," 1,165 "
-28020184,HC  CATHETER POWERPICC DUAL LUMEN 5F, 728 
-28020185,HC  CATHETER POWERPICC TRIPLE LUMEN 6F, 799 
-28020186,HC CATHETER SWAN GANZ STANDARD 4 LUMEN 7FX110CM, 88 
-28020188,HC IMP CLIP ANEURYSM SUGITA L SHAPE 7.5MM," 2,295 "
-28020189,HC CLIP APPLIER SUGITA STRAIGHT LONG 20MM," 5,848 "
-28020190,HC CLIP APPLIER SUGITA STRAIGHT STANDARD 20MM," 5,848 "
-28020191,HC CLIP GUIDE M5," 4,692 "
-28020192,HC IMP CLIP STIMULATION INLINE ACTIVATOR," 2,593 "
-28020193,HC IMPL COCHLEAR PROCESSOR CONCERT FLEX 24," 98,000 "
-28020194,HC IMP CONN ILIOS OFFSET 105 DEGREE," 7,225 "
-28020195,HC IMP CONN ILIOS OFFSET 75 DEGREE," 7,225 "
-28020196,HC IMP CONN ILIOS OFFSET NEUTRAL," 7,225 "
-28020197,HC IMP CONN ILIOS OFFSET NEUTRAL LONG," 7,225 "
-28020198,HC IMP CONN LINK TRANSVERSE 34-42MM," 7,225 "
-28020199,HC IMP CONNECTOR CLOSED EXPEDIUM TI 40X5.5MM," 7,640 "
-28020200,HC IMP DISTRACTOR CURVILINEAR LEFT R40," 33,051 "
-28020201,HC IMP DISTRACTOR CURVILINEAR RIGHT R40," 33,051 "
-28020202,HC IMP DISTRACTOR LOGIC JR LEFT 52MM," 26,355 "
-28020203,HC IMP DISTRACTOR LOGIC JR RIGHT 52MM," 26,355 "
-28020204,HC IMP DISTRACTOR LOGIC JR SHORT LEFT 52MM," 26,951 "
-28020205,HC IMP DISTRACTOR LOGIC JR SHORT RIGHT 52MM," 26,951 "
-28020206,HC IMP EAR PISTON LOOP MCGE MOD 4.0, 565 
-28020207,HC IMP EAR PROSTHESIS STAPES PLATINUM 0.6X4.75," 1,324 "
-28020208,HC ELECTRODE CORTICAL GRID 4X16," 10,308 "
-28020209,HC ELECTRODE NANOKNIFE 19GX15CM," 14,963 "
-28020210,HC ELECTRODE NANOKNIFE 19GX25CM," 14,963 "
-28020211,HC IMP ENDCAP STARDRIVE T40 ADOLESCENT TI 0MM," 1,598 "
-28020212,HC IMP ENDCAP STARDRIVE T40 TITANIUM 0MM," 1,926 "
-28020213,HC IMP ENDCAP STARDRIVE T40 TITANIUM 5MM," 1,959 "
-28020214,HC IMP EXPANDER TISSUE CURVE 4X14CM 400CC," 9,000 "
-28020215,HC IMP EXPANDER TISSUE CURVE 4X18.5CM 475CC," 9,000 "
-28020216,HC IMP EXPANDER TISSUE CURVE 5X15.5CM 400CC," 9,000 "
-28020217,HC IMP EXPANDER TISSUE RECTANGLE 4X6CM 75CC," 7,820 "
-28020218,HC IMP EXPANDER TISSUE RECTANGLE 5X14CM 360CC," 9,000 "
-28020219,HC IMP EXPANDER TISSUE RECTANGLE 5X14CM 440CC," 9,000 "
-28020220,HC IMP EXTERNAL FIXATOR WRIST PENNING DYNAMIC," 12,000 "
-28020221,HC IMP EXTERNAL FIXATOR POST FEMALE 3 HOLE, 954 
-28020222,HC IMP EXTERNAL FIXATOR POST FEMALE 4 HOLE, 954 
-28020223,HC IMP EXTERNAL FIXATOR POST MALE 2 HOLE, 803 
-28020224,HC IMP EXTERNAL FIXATOR POST MALE 3 HOLE, 828 
-28020225,HC IMP EXTERNAL FIXATOR POST MALE 4 HOLE, 802 
-28020226,HC IMP FASCIA LATA MEDIUM 10X60MM," 4,293 "
-28020227,HC IMP FIBULAR SHAFT 100MM 14X18MM," 5,908 "
-28020228,HC IMP GRAFT FIB SHAFT LPCH," 5,058 "
-28020229,HC IMPL GRAFT GORETEX 1X2X3MM," 1,360 "
-28020230,HC IMP GRAFT STRIP CHRONOS 100X25X3MM," 18,320 "
-28020231,HC IMP GRAFT STRIP CHRONOS 47X18X3MM," 7,796 "
-28020232,HC  GUIDEWIRE PERILOC 2X150," 1,581 "
-28020233,HC  GUIDEWIRE HI TORQUE FLEX T .018X145CM, 638 
-28020234,HC  GUIDEWIRE HI TORQUE FLEX T .018X295CM, 638 
-28020235,HC  GUIDEWIRE AVALON ELITE 100 CM KIT," 6,163 "
-28020236,HC  GUIDEWIRE AVALON ELITE 210 CM KIT," 6,163 "
-28020237,HC  GUIDEWIRE SYMMETRY THREADED 3.2MM," 1,795 "
-28020238,HC  GUIDEWIRE TADII TAPERED .035X145CM, 833 
-28020239,HC  GUIDEWIRE THREAD 2X230MM, 372 
-28020240,HC IMP HIP HEAD FEMORAL MODULAR COBALT TYPE 1 +12 36MM," 4,250 "
-28020241,HC IMP HIP LINER BIPOLAR 44X45X46MM," 4,633 "
-28020242,HC IMP HIP LINER BIPOLAR 47X48X49MM," 4,633 "
-28020243,HC IMP HOOK DEFORMITY MEDIUM LEFT 7.5MM," 5,063 "
-28020244,HC IMP HOOK DEFORMITY MEDIUM RIGHT 7.5MM," 5,063 "
-28020245,HC IMP HOOK LAMINA MEDIUM TI 8 EACH," 6,996 "
-28020246,HC IMP HOOK LEFT ANGLED EXPEDIUM STAINLESS STEEL," 8,943 "
-28020247,HC IMP HOOK LEFT ANGLED EXPEDIUM TITANIUM," 8,943 "
-28020248,HC IMP HOOK LINEUM LEFT 4.5MM," 6,078 "
-28020249,HC IMP HOOK LINEUM LEFT 6.0MM," 6,078 "
-28020250,HC IMP HOOK LINEUM RIGHT 6.0MM," 6,078 "
-28020251,HC IMP HOOK NARROW BLADE SS 5.5X8MM," 6,543 "
-28020252,HC IMP HOOK NARROW OPEN IEXP 8MM," 6,911 "
-28020253,HC IMP HOOK OFFSET RIGHT EXPEDIUM," 8,505 "
-28020254,HC IMP HOOK POLARIS TI MEDIUM RIGHT 7.5MM," 5,063 "
-28020255,HC IMP HOOK RIGHT ANGLED EXPEDIUM STAINLESS STEEL," 8,943 "
-28020256,HC IMP HOOK RIGHT ANGLED EXPEDIUM TITANIUM," 8,943 "
-28020257,HC IMP HOOK SPINAL XIA LAMINA LARGE STANDARD," 6,828 "
-28020258,HC IMP HOOK SPINAL XIA LAMINA SMALL NARROW," 6,828 "
-28020259,HC IMP HOOK SPINAL XIA PEDICLE SMALL," 6,668 "
-28020260,HC IMP HOOK SPINAL XIA THORACIC SMALL RIGHT," 7,042 "
-28020261,HC IMP HOOK SPINAL XIA TRANSVERSE LEFT," 7,527 "
-28020262,HC IMP HOOK SPINAL XIA TRANSVERSE RIGHT," 6,668 "
-28020263,HC IMP HOOK TRANSVERSE PROCESS RIGHT TI 4 EACH," 6,996 "
-28020264,HC IMP HOOK WIDE BLADE EXPEDIUM," 6,911 "
-28020265,HC IMP HOOK WIDE BLADE EXPEDIUM 5MM," 6,911 "
-28020266,HC IMP HOOK WIDE BLADE EXPEDIUM 8MM," 7,185 "
-28020267,HC IMPL IMMOBILIZER KNEE TRICOT MEDIUM 19IN, 283 
-28020268,HC  INTROD COACCESS SET 15CM," 1,122 "
-28020269,HC  INTRODUCER LEAD SAFESHTH 8FR, 553 
-28020270,HC  INTRODUCER LEAD SAFESHTH 9.5FR, 553 
-28020271,HC  INTRODUCER SHEATH CPS DIRECT SL 47CM," 5,908 "
-28020272,HC  INTRODUCER SHEATH GUIDING SWARTZ 8FX60CM," 1,318 "
-28020273,HC  INTRODUCER SHEATH PEEL AWAY OPTISEAL 11FX13CM, 553 
-28020274,HC  INTRODUCER SHEATH PEEL AWAY OPTISEAL 8FX13CM, 553 
-28020275,HC INTRODUCER V-CATH SAFE T PEEL 3FR, 129 
-28020276,HC INTRODUCER V-CATH SAFE T PEEL 4FR, 164 
-28020277,HC IPAS II BEVELED TIP," 2,729 "
-28020278,HC IPAS II DIAMOND TIP," 2,729 "
-28020279,HC IMP IUD PARAGUARD," 5,083 "
-28020280,HC IMP KNEE BASEPLATE TIBIAL MODULAR OSS 83MM," 14,810 "
-28020281,HC IMP KNEE FEMUR SPINDLE STD COMPRESS 38MM 600LB," 21,798 "
-28020282,HC IMP KNEE INSERT TIBIAL BEARING OSS 17MM," 6,698 "
-28020283,HC IMP KNEE STEM POROUS STRAIGHT 13.5X90MM," 7,677 "
-28020284,HC IMP LEAD DEFIBRILLATOR CARDIOVERTER 59CM," 34,125 "
-28020285,HC LEAD LVAD EXTENSION TLCII 5FT," 2,338 "
-28020286,HC LEAD LVAD EXTENSION TLCII 7FT," 2,423 "
-28020287,HC IMP LEAD PACEMAKER DEXTRUS 60CM," 4,675 "
-28020288,HC IMPL LENS IOL COBURN 207UV 10.0, 757 
-28020289,HC IMPL LENS IOL COBURN 207UV 10.5, 757 
-28020290,HC IMPL LENS IOL COBURN 207UV 11.0, 757 
-28020291,HC IMPL LENS IOL COBURN 207UV 12.0, 757 
-28020292,HC IMPL LENS IOL COBURN 207UV 13.0, 757 
-28020293,HC IMPL LENS IOL COBURN 207UV 14.0, 757 
-28020294,HC IMPL LENS IOL COBURN 207UV 14.5, 757 
-28020295,HC IMPL LENS IOL COBURN 207UV 15.0, 757 
-28020296,HC IMPL LENS IOL COBURN 207UV 15.5, 757 
-28020297,HC IMPL LENS IOL COBURN 207UV 16.0, 757 
-28020298,HC IMPL LENS IOL COBURN 207UV 16.5, 757 
-28020299,HC IMPL LENS IOL COBURN 207UV 17.0, 757 
-28020300,HC IMPL LENS IOL COBURN 207UV 17.5, 757 
-28020301,HC IMPL LENS IOL COBURN 207UV 18.0, 757 
-28020302,HC IMPL LENS IOL COBURN 207UV 18.5, 757 
-28020303,HC IMPL LENS IOL COBURN 207UV 19.0, 757 
-28020304,HC IMPL LENS IOL COBURN 207UV 19.5, 757 
-28020305,HC IMPL LENS IOL COBURN 207UV 20.0, 757 
-28020306,HC IMPL LENS IOL COBURN 207UV 20.5, 757 
-28020307,HC IMPL LENS IOL COBURN 207UV 21.0, 757 
-28020308,HC IMPL LENS IOL COBURN 207UV 21.5, 757 
-28020309,HC IMPL LENS IOL COBURN 207UV 22.0, 757 
-28020310,HC IMPL LENS IOL COBURN 207UV 22.5, 757 
-28020311,HC IMPL LENS IOL COBURN 207UV 23.0, 757 
-28020312,HC IMPL LENS IOL COBURN 207UV 23.5, 757 
-28020313,HC IMPL LENS IOL COBURN 207UV 24.0, 757 
-28020314,HC IMPL LENS IOL COBURN 207UV 24.5, 757 
-28020315,HC IMPL LENS IOL COBURN 207UV 25.0, 757 
-28020316,HC IMPL LENS IOL COBURN 207UV 25.5, 757 
-28020317,HC IMPL LENS IOL COBURN 207UV 26.0, 757 
-28020318,HC IMPL LENS IOL COBURN 207UV 26.5, 757 
-28020319,HC IMPL LENS IOL COBURN 207UV 27.0, 757 
-28020320,HC IMPL LENS IOL COBURN 207UV 28.0, 757 
-28020321,HC IMPL LENS IOL COBURN 207UV 29.0, 757 
-28020322,HC IMPL LENS IOL COBURN 207UV 30.0, 757 
-28020323,HC IMP LOOP WIRE RIBBON PLATINUM 3.5MM, 935 
-28020324,HC IMP LOOP WIRE RIBBON PLATINUM 3.75MM, 935 
-28020325,HC IMP LOOP WIRE RIBBON PLATINUM 4.25MM, 935 
-28020326,HC IMP LOOP WIRE RIBBON PLATINUM 4.5MM, 935 
-28020327,HC IMP LOOP WIRE RIBBON PLATINUM 4.75MM, 935 
-28020328,HC IMP LOOP WIRE RIBBON PLATINUM 4MM, 935 
-28020329,HC IMP LOOP WIRE RIBBON PLATINUM 5.25MM, 935 
-28020330,HC IMP LOOP WIRE RIBBON PLATINUM 5.5MM, 935 
-28020331,HC IMP LOOP WIRE RIBBON PLATINUM 5.75MM, 935 
-28020332,HC IMP LOOP WIRE RIBBON PLATINUM 5MM, 935 
-28020333,HC IMP MARKER SOFT TISSUE CLIPLOC 18X150MM," 1,263 "
-28020334,HC IMP MATRIX BONE STRIP DBX 2.5X10CM," 11,025 "
-28020335,HC IMP MEMBRANE PERICARDIAL PRECLUDE 12X12," 6,316 "
-28020336,HC IMP MENISCAL FASTERNER 8MM, 893 
-28020337,HC IMP MESH MANDIBLE B TYPE 1.0MM," 5,032 "
-28020338,HC IMP MESH MANDIBLE B TYPE 1.3MM," 5,032 "
-28020339,HC IMP MESH MANDIBLE B TYPE 1.5MM," 5,032 "
-28020340,HC IMP MESH MANDIBLE C TYPE 1.3MM," 5,032 "
-28020341,HC IMP MESH MANDIBLE C TYPE 1.5MM," 5,032 "
-28020342,HC IMP MESH MINI 124X124MM," 4,114 "
-28020343,HC IMP MESH MINI RIGID 124X124MM," 4,114 "
-28020344,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM LEFT 8.2X280MM," 11,250 "
-28020345,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM LEFT 8.2X340MM," 11,813 "
-28020346,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM LEFT 9X340MM," 11,983 "
-28020347,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM RIGHT 8.2X340," 11,250 "
-28020348,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM RIGHT 9X320MM," 11,813 "
-28020349,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM RIGHT 9X340MM," 11,983 "
-28020350,HC IMP NAIL FEMORAL ADOLESCENT TITANIUM RIGHT 9X360MM," 11,813 "
-28020351,HC IMP NAIL FEMORAL GROSSE KEMPF RIGHT 14MMX44CM," 3,004 "
-28020352,HC IMP NAIL FEMORAL LATERAL ENTRY TI RIGHT 10X360," 13,319 "
-28020353,HC IMP NAIL FEMUR LEFT 7MMX36CM," 19,292 "
-28020354,HC IMP NAIL FEMUR LEFT 7MMX38CM," 19,292 "
-28020355,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X210MM," 16,005 "
-28020356,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X220MM," 16,005 "
-28020357,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X230MM," 16,005 "
-28020358,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X240MM," 16,005 "
-28020359,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X250MM," 16,005 "
-28020360,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X260MM," 16,005 "
-28020361,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X270MM," 16,005 "
-28020362,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X280MM," 16,005 "
-28020363,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 7X290MM," 16,005 "
-28020364,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X210MM," 16,005 "
-28020365,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X220MM," 16,005 "
-28020366,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X230MM," 16,005 "
-28020367,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X240MM," 16,005 "
-28020368,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X250MM," 16,005 "
-28020369,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X260MM," 16,005 "
-28020370,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X270MM," 16,005 "
-28020371,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X280MM," 16,005 "
-28020372,HC IMP NAIL HUMERAL CANNULATED EX TITANIUM 9X290MM," 16,005 "
-28020373,HC IMP NAIL R T RECONSTRUCTION 12MMX34CM," 8,153 "
-28020374,HC IMP NAIL TIBIAL GROSSE KEMPF 11MMX31.5CM," 3,004 "
-28020375,HC IMP NAIL TIBIAL GROSSE KEMPF 11MMX34.5CM," 3,004 "
-28020376,HC IMP NAIL TIBIAL PROXIMAL CANNULATED EX TI 12X375MM," 11,730 "
-28020377,HC IMP NAIL TIBIAL PROXIMAL CANNULATED EX TI 9X285MM," 11,941 "
-28020378,HC IMP NAIL TIBIAL PROXIMAL CANNULATED EX TI 9X300MM," 11,175 "
-28020379,HC IMP NAIL TIBIAL T2 STANDARD 8X240MM," 15,090 "
-28020380,HC NEEDLE BIOPSY BRAIN 1.8X150MM," 15,750 "
-28020381,HC NEEDLE ELECTRODE LEVEEN 2X15CM," 8,180 "
-28020382,HC NEEDLE ELECTRODE LEVEEN 3X12CM," 9,465 "
-28020383,HC NEEDLE ELECTRODE LEVEEN 3X15CM," 5,313 "
-28020384,HC NEEDLE ELECTRODE LEVEEN 5X15CM," 14,098 "
-28020385,HC NEEDLE ELECTRODE LEVEEN 5X25CM," 14,098 "
-28020386,HC NEEDLE ELECTRODE LEVEEN COACCESS 3.5X15CM," 12,112 "
-28020387,HC NEEDLE ELECTRODE LEVEEN COACCESS 3X15CM," 11,531 "
-28020388,HC NEEDLE ELECTRODE LEVEEN COACCESS 4X15CM," 14,250 "
-28020389,HC IMP NUT CAP LEFORTIII SEPCTRUM, 901 
-28020390,HC IMP PATELLA WEDGE 18-18MM," 7,948 "
-28020391,HC IMP PIN MALAR LEFORTIII SPECTRUM, 901 
-28020392,HC IMP PIN TRANSVERSE DIAPHYSEAL 40MM," 2,465 "
-28020393,HC IMP PIN TRANSVERSE DIAPHYSEAL 44MM," 2,916 "
-28020394,HC IMP PIN TRANSVERSE DIAPHYSEAL 56MM," 2,916 "
-28020395,HC IMP PISTON RICHARDS FLUOROPLASTIC 0.8X4.75, 893 
-28020396,HC IMPL PLATE ADAPTION 5 HOLE," 1,754 "
-28020397,HC IMPL PLATE ADAPTION 7 HOLE," 2,176 "
-28020398,HC IMPL PLATE ADAPTION MATRIXMIDFACE TI 20 HOLE .4MM," 5,080 "
-28020399,HC IMPL PLATE ADAPTION MATRIXMIDFACE TI 20 HOLE .5MM," 5,080 "
-28020400,HC IMPL PLATE ADAPTION MATRIXMIDFACE TI 20 HOLE .7MM," 5,080 "
-28020401,HC IMPL PLATE ADAPTION MATRIXMIDFACE TI 20H .8MM," 4,814 "
-28020402,HC IMPL PLATE AMBI BARREL 135D 3H 80MM," 4,318 "
-28020403,HC IMPL PLATE ANCHOR ASSEM LEFORTIII SPECTRUM 3MM," 6,307 "
-28020404,HC IMPL PLATE ANCHOR BRACKET TITANIUM 4 HOLE," 1,380 "
-28020405,HC IMPL PLATE ANCHOR BRACKET TITANIUM 45HOLE," 1,380 "
-28020406,HC IMPL PLATE ASSEMBLY MAXILLARY LEFT SPECTRUM," 12,645 "
-28020407,HC IMPL PLATE ASSEMBLY MAXILLARY RIGHT SPECTRUM," 12,645 "
-28020408,HC IMPL PLATE BOX MATRIXMIDFACE TI 4 HOLE 10X10X.4MM," 2,169 "
-28020409,HC IMPL PLATE BOX MATRIXMIDFACE TI 4 HOLE 10X10X.5MM," 2,169 "
-28020410,HC IMPL PLATE BOX MATRIXMIDFACE TI 4 HOLE 10X10X.7MM," 2,169 "
-28020411,HC IMPL PLATE BOX MATRIXMIDFACE TI 4 HOLE 5X10X.4MM," 2,135 "
-28020412,HC IMPL PLATE BOX MATRIXMIDFACE TI 4 HOLE 5X10X.5MM," 2,258 "
-28020413,HC IMPL PLATE BOX MATRIXMIDFACE TI 4 HOLE 5X10X.7MM," 2,135 "
-28020414,HC IMPL PLATE BSSO 2X10MM," 2,057 "
-28020415,HC IMPL PLATE BSSO LONG 6 HOLE," 2,134 "
-28020416,HC IMPL PLATE CLAVICLE ANTERIOR LATERAL 10H 2.7X101MM," 8,206 "
-28020417,HC IMPL PLATE CLAVICLE ANTERIOR LATERAL 11H 2.7X113MM," 8,280 "
-28020418,HC IMPL PLATE CLAVICLE ANTERIOR LATERAL 12H 2.7X124MM," 8,655 "
-28020419,HC IMPL PLATE CLAVICLE ANTERIOR LATERAL 7H 2.7X77MM," 8,281 "
-28020420,HC IMPL PLATE CLAVICLE ANTERIOR LATERAL 9H 2.7X89MM," 8,280 "
-28020421,HC IMPL PLATE CLAVICLE ANTERIOR MEDIAL 6H 3.5X79MM," 8,280 "
-28020422,HC IMPL PLATE CLAVICLE ANTERIOR MEDIAL 7H 3.5X91MM," 8,280 "
-28020423,HC IMPL PLATE CLAVICLE ANTERIOR MEDIAL 8H 3.5X102MM," 8,206 "
-28020425,HC IMPL PLATE COMPRESSION 4HOLE," 1,972 "
-28020426,HC IMPL PLATE COMPRESSION 5HOLE," 2,015 "
-28020427,HC IMPL PLATE COMPRESSION 7HOLE," 2,635 "
-28020428,HC IMPL PLATE COMPRESSION STRAIGHT 4 HOLE, 425 
-28020429,HC IMPL PLATE COMPRESSION STRAIGHT 4H 2MM, 422 
-28020430,HC IMPL PLATE COMPRESSION STRAIGHT 5 HOLE, 459 
-28020431,HC IMPL PLATE COMPRESSION STRAIGHT 5H 2MM, 459 
-28020432,HC IMPL PLATE COMPRESSION STRAIGHT 6 HOLE, 502 
-28020433,HC IMPL PLATE COMPRESSION STRAIGHT 6H 2MM, 502 
-28020434,HC IMPL PLATE CONDYLAR 4.5MM 6HOLE," 13,919 "
-28020435,HC IMPL PLATE CURVED 6 HOLE," 2,227 "
-28020436,HC IMPL PLATE DOUBLE ROW BSSO 2X8MM," 2,746 "
-28020437,HC IMPL PLATE DOUBLE ROW STRAIGHT 48 HOLE," 1,828 "
-28020438,HC IMPL PLATE DOUBLE ROW STRAIGHT RIGID 48 HOLE," 1,828 "
-28020439,HC IMPL PLATE DOUBLE Y MATRIXMIDFACE TI 6 HOLE .4MM," 2,169 "
-28020440,HC IMPL PLATE DOUBLE Y MATRIXMIDFACE TI 6 HOLE .5MM," 2,169 "
-28020441,HC IMPL PLATE DOUBLE Y MATRIXMIDFACE TI 6 HOLE .7MM," 2,169 "
-28020442,HC IMPL PLATE DOUBLE Y MATRIXNEURO 6 HOLE 21MM," 1,952 "
-28020443,HC IMPL PLATE FUSION MEDIUM RIGHT 0D 2.4 2.7MM," 7,856 "
-28020444,HC IMPL PLATE FUSION SMALL LEFT 5D 2.4 2.7MM," 8,250 "
-28020445,HC IMPL PLATE FUSION SMALL RIGHT 5D 2.4 2.7MM," 8,250 "
-28020446,HC IMPL PLATE H MATRIXMIDFACE TITANIUM 11 HOLE .5MM," 2,169 "
-28020447,HC IMPL PLATE H MATRIXMIDFACE TITANIUM 11 HOLE .7MM," 2,169 "
-28020448,HC IMPL PLATE H MATRIXMIDFACE TITANIUM 11 HOLE .8MM," 2,169 "
-28020449,HC IMPL PLATE HIP LCP PEDIATRIC 100 DEGREE 3.5X75MM," 4,250 "
-28020450,HC IMPL PLATE HIP LCP PEDIATRIC 100 DEGREE 5X90MM," 4,420 "
-28020451,HC IMPL PLATE HIP LCP PEDIATRIC 110 DEGREE 3.5X75MM," 4,250 "
-28020452,HC IMPL PLATE HIP LCP PEDIATRIC 110 DEGREE 5X90MM," 5,050 "
-28020453,HC IMPL PLATE HIP LCP PEDIATRIC 120 DEGREE 3.5X75MM," 4,250 "
-28020454,HC IMPL PLATE HIP LCP PEDIATRIC 120 DEGREE 5X95MM," 5,050 "
-28020455,HC IMPL PLATE HIP LCP PEDIATRIC 150 DEGREE 3.5X60MM," 4,856 "
-28020456,HC IMPL PLATE HIP LCP PEDIATRIC 150 DEGREE 5X75MM," 4,973 "
-28020457,HC IMPL PLATE HUMERUS POSTLATERAL 2.7X3.5X4X111MM," 10,109 "
-28020458,HC IMPL PLATE HUMERUS POSTLATERAL 2.7X3.5X7X127MM," 9,805 "
-28020459,HC IMPL PLATE ILIZAROV SHORT CONNECTION 10 HOLE 115MM," 1,574 "
-28020460,HC IMPL PLATE ILIZAROV SHORT CONNECTION 2 HOLE 35MM," 1,392 "
-28020461,HC IMPL PLATE L 2X2X2MM," 1,437 "
-28020462,HC IMPL PLATE L ALLOY TITANIUM LEFT RIGHT 22MM," 1,301 "
-28020463,HC IMPL PLATE L COMPRESSION LEFT 2X4 HOLE  2MM, 638 
-28020464,HC IMPL PLATE L COMPRESSION OBLIQUE LEFT 2X4 HOLE 2MM, 638 
-28020465,HC IMPL PLATE L COMPRESSION OBLIQUE RIGHT 2X4 HOLE 2MM, 638 
-28020466,HC IMPL PLATE L COMPRESSION RIGHT 2X4 HOLE, 638 
-28020467,HC IMPL PLATE L COMPRESSION RIGHT 2X4 HOLE 2MM, 638 
-28020468,HC IMPL PLATE L EXTENDED CFX LEFT 6X4 HOLE 2MM," 2,015 "
-28020469,HC IMPL PLATE L EXTENDED CFX RIGHT 6X4 HOLE 2MM," 2,204 "
-28020470,HC IMPL PLATE L EXTENDED THICK LEFT 1X17MM," 1,250 "
-28020471,HC IMPL PLATE L EXTENDED THICK LEFT 1X19MM," 1,250 "
-28020472,HC IMPL PLATE L EXTENDED THICK RIGHT 1X17MM," 1,250 "
-28020473,HC IMPL PLATE L EXTENDED THICK RIGHT 1X19MM," 1,250 "
-28020474,HC IMPL PLATE L MATRIX LONG TI 90DEGREE 2X2 HOLE .7MM," 1,890 "
-28020475,HC IMPL PLATE L MATRIX MEDIUM TI 90DEGREE 2X2 HOLE .5MM," 1,890 "
-28020476,HC IMPL PLATE L MATRIX MEDIUM TI 90DEGREE 2X2 HOLE .7MM," 1,890 "
-28020477,HC IMPL PLATE L MICRO LEFT 13MM, 808 
-28020478,HC IMPL PLATE L MICRO RIGHT 13MM," 1,275 "
-28020479,HC IMPL PLATE L MIDFACE RIGHT 13MM," 1,096 "
-28020480,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT 3X4 HOLE .7MM," 2,346 "
-28020481,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT 4X6 HOLE .7MM," 2,428 "
-28020482,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT 2X3 HOLE .7MM," 2,292 "
-28020483,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT 3X4 HOLE .7MM," 2,346 "
-28020484,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT 4X6 HOLE .7MM," 2,428 "
-28020485,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT 3X4 HOLE .8MM," 2,346 "
-28020486,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT 4X6 HOLE .5MM," 2,298 "
-28020487,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT 4X6 HOLE .8MM," 2,428 "
-28020488,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE LT TI 3X4H .5MM," 2,346 "
-28020489,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT 3X4 HOLE .8MM," 2,346 "
-28020490,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT 4X6 HOLE .5MM," 2,428 "
-28020491,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT 4X6 HOLE .8MM," 2,428 "
-28020492,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT TI 2X3H .5MM," 2,169 "
-28020493,HC IMPL PLATE L OBLIQUE MATRIXMIDFACE RT TI 3X4H .5MM," 2,346 "
-28020494,HC IMPL PLATE L RIGID LEFT 2X2 HOLE 1.6X10MM," 1,392 "
-28020495,HC IMPL PLATE L RIGID LEFT 2X2 HOLE 1.6X12MM," 1,570 "
-28020496,HC IMPL PLATE L RIGID LEFT 2X2 HOLE 1.6X6MM," 1,392 "
-28020497,HC IMPL PLATE L RIGID LEFT 2X2 HOLE 1.6X8MM," 1,570 "
-28020498,HC IMPL PLATE L RIGID RIGHT 2X2 HOLE 1.6X10MM," 1,570 "
-28020499,HC IMPL PLATE L RIGID RIGHT 2X2 HOLE 1.6X12MM," 1,570 "
-28020500,HC IMPL PLATE L RIGID RIGHT 2X2 HOLE 1.6X6MM," 1,392 "
-28020501,HC IMPL PLATE L RIGID RIGHT 2X2 HOLE 1.6X8MM," 1,570 "
-28020502,HC IMPL PLATE LOCKING COMMINUTION 2MM," 4,966 "
-28020503,HC IMPL PLATE LOCKING FRACTURE 4 HOLE 2.4MM," 3,164 "
-28020504,HC IMPL PLATE LOCKING FRACTURE MEDIUM 4 HOLE 2MM," 3,071 "
-28020505,HC IMPL PLATE LOCKING FRACTURE SHORT 4 HOLE 2MM," 2,963 "
-28020506,HC IMPL PLATE LOCKING MANDIBULAR FRACTURE 2X4 CURVED," 4,610 "
-28020507,HC IMPL PLATE LOCKING MANDIBULAR FRACTURE 2X8 CURVED," 5,538 "
-28020508,HC IMPL PLATE MATRIXNEURO 14X14MM," 1,952 "
-28020509,HC IMPL PLATE MATRIXRIB INTRAMEDULLARY SPLINTS LRG 5MM," 3,652 "
-28020510,HC IMPL PLATE MATRIXRIB INTRAMEDULLARY SPLINTS MED 4MM," 3,652 "
-28020511,HC IMPL PLATE MATRIXRIB LEFT RIB 3 15HOLE," 8,934 "
-28020512,HC IMPL PLATE MATRIXRIB LEFT RIB 4/5 16HOLE," 8,934 "
-28020513,HC IMPL PLATE MATRIXRIB LEFT RIB 6/7 17HOLE," 8,934 "
-28020514,HC IMPL PLATE MATRIXRIB LEFT RIB 8/9 18HOLE," 8,934 "
-28020515,HC IMPL PLATE MATRIXRIB RIGHT RIB 6/7 17HOLE," 9,564 "
-28020516,HC IMPL PLATE MATRIXRIB RIGHT RIB 8/9 18HOLE," 8,934 "
-28020517,HC IMPL PLATE MAXI RECONSTRUCTION LEFT," 3,859 "
-28020518,HC IMPL PLATE MAXI RECONSTRUCTION STRAIGHT," 3,239 "
-28020519,HC IMPL PLATE MAXILLOFACIAL 10 HOLE 1.2MM," 1,570 "
-28020520,HC IMPL PLATE MAXILLOFACIAL 28MM 4 HOLE," 1,777 "
-28020521,HC IMPL PLATE MAXILLOFACIAL 40MM 6 HOLE," 1,870 "
-28020522,HC IMPL PLATE MAXILLOFACIAL CHIN 2MM," 2,042 "
-28020523,HC IMPL PLATE MAXILLOFACIAL CHIN 3MM," 2,042 "
-28020524,HC IMPL PLATE MAXILLOFACIAL EDC LONG," 1,870 "
-28020525,HC IMPL PLATE MAXILLOFACIAL MAXI 33MM 4 HOLE," 1,870 "
-28020526,HC IMPL PLATE MAXILLOFACIAL MAXI STRAIGHT 15 HOLE," 3,154 "
-28020527,HC IMPL PLATE MAXILLOFACIAL RIGID STRAIGHT 4HX29MM," 1,343 "
-28020528,HC IMPL PLATE MAXILLOFACIAL RIGID STRAIGHT 4HX30MM," 1,199 "
-28020529,HC IMPL PLATE MAXILLOFACIAL SHORT 2.4MM," 2,057 "
-28020530,HC IMPL PLATE MAXILLOFACIAL STRAIGHT 18 HOLE," 2,916 "
-28020531,HC IMPL PLATE MESH 127X127MM," 5,882 "
-28020532,HC IMPL PLATE MESH 127X90MM," 4,301 "
-28020533,HC IMPL PLATE MESH 150X150MM," 6,979 "
-28020534,HC IMPL PLATE MESH 150X300MM," 10,373 "
-28020535,HC IMPL PLATE MESH 74X100MM," 5,168 "
-28020536,HC IMPL PLATE MESH CURVED 18 ROW," 5,542 "
-28020537,HC IMPL PLATE MICRO STRAIGHT 4 HOLE 1.2MM," 1,233 "
-28020538,HC IMPL PLATE MICRO STRAIGHT 6 HOLE 1.2MM," 1,326 "
-28020539,HC IMPL PLATE MICRO STRAIGHT 8 HOLE 1.2MM," 1,233 "
-28020540,HC IMPL PLATE MINI LEFT L 90 DEGREE 15MM," 1,250 "
-28020541,HC IMPL PLATE MINI LEFT L 90 DEGREE 20MM," 1,250 "
-28020542,HC IMPL PLATE MINI RIGHT L 90 DEGREE 15MM," 1,250 "
-28020543,HC IMPL PLATE MINI RIGHT L 90 DEGREE 20MM," 1,250 "
-28020544,HC IMPL PLATE MINI SQUARE 4HOLE 2MM, 867 
-28020545,HC IMPL PLATE MINI STRAIGHT 1.0MM 16HOLE," 2,635 "
-28020546,HC IMPL PLATE MINI STRAIGHT 1.0MM 4HOLE," 1,299 "
-28020547,HC IMPL PLATE MINI STRAIGHT 1.0MM 8HOLE," 1,488 "
-28020548,HC IMPL PLATE MINI STRAIGHT 10 HOLE," 1,439 "
-28020549,HC IMPL PLATE O PEDI 12MM," 3,953 "
-28020550,HC IMPL PLATE O PEDI 16MM," 3,953 "
-28020551,HC IMPL PLATE O PEDI 20MM," 3,953 "
-28020552,HC IMPL PLATE OBA ANCHOR SCREW SELF DRILLING TI 4MM, 622 
-28020553,HC IMPL PLATE OBA ANCHOR SCREW SELF DRILLING TI 6MM, 622 
-28020554,HC IMPL PLATE ORBITAL MATRIXMIDFACE TI 12 HOLE .4MM," 2,797 "
-28020555,HC IMPL PLATE ORBITAL MATRIXMIDFACE TI 12 HOLE .7MM," 2,802 "
-28020556,HC IMPL PLATE ORBITAL PREFORMED LARGE RIGHT," 10,896 "
-28020557,HC IMPL PLATE ORBITAL PREFORMED SMALL RIGHT," 11,112 "
-28020558,HC IMPL PLATE OSTEOTOMY ADL 120DEG 65MM," 5,277 "
-28020559,HC IMPL PLATE OSTEOTOMY TODDLER 90D 4X26MM," 4,913 "
-28020560,HC IMPL PLATE OSTEOTOMY TODDLER 90D 4X32MM," 5,075 "
-28020561,HC IMPL PLATE PALATAL," 1,199 "
-28020562,HC IMPL PLATE PEDILOC 100 DEGREE 6X45X3HOLE," 8,075 "
-28020563,HC IMPL PLATE PLEO LEFT 6 HOLE 4.5MM," 20,748 "
-28020564,HC IMPL PLATE PLEO RIGHT 6 HOLE 4.5MM," 20,748 "
-28020565,HC IMPL PLATE POSITIONING TRIANGLAR 100X60X20 DEGREE, 395 
-28020566,HC IMPL PLATE POSITIONING TRIANGLAR 80X70X30 DEGREE, 342 
-28020567,HC IMPL PLATE POSITIONING TRIANGLAR 90X50X40 DEGREE, 261 
-28020568,HC IMPL PLATE PREBENT RIGID STEP LEFT 10MM," 2,630 "
-28020569,HC IMPL PLATE PREBENT RIGID STEP LEFT 12MM," 2,491 "
-28020570,HC IMPL PLATE PREBENT RIGID STEP LEFT 4MM," 2,491 "
-28020571,HC IMPL PLATE PREBENT RIGID STEP LEFT 6MM," 2,491 "
-28020572,HC IMPL PLATE PREBENT RIGID STEP LEFT 8MM," 2,491 "
-28020573,HC IMPL PLATE PREBENT RIGID STEP RIGHT 1.6X2MM," 2,491 "
-28020574,HC IMPL PLATE PREBENT RIGID STEP RIGHT 1.6X4MM," 2,491 "
-28020575,HC IMPL PLATE PREBENT RIGID STEP RIGHT 10MM," 2,630 "
-28020576,HC IMPL PLATE PREBENT RIGID STEP RIGHT 12MM," 2,491 "
-28020577,HC IMPL PLATE PREBENT RIGID STEP RIGHT 8MM," 2,491 "
-28020578,HC IMPL PLATE QUAD MICRO LARGE 2X4," 2,117 "
-28020579,HC IMPL PLATE QUAD MICRO MEDIUM 2X4," 1,479 "
-28020580,HC IMPL PLATE QUAD MICRO SMALL 2X4," 1,532 "
-28020581,HC IMPL PLATE REPLICATOR EQUINOXE 0MM," 9,558 "
-28020582,HC IMPL PLATE REPLICATOR EQUINOXE 1.5MM," 6,814 "
-28020583,HC IMPL PLATE REPLICATOR EQUINOXE 4.5MM," 6,814 "
-28020584,HC IMPL PLATE RIGID CURVED 10 HOLE 1.6MM," 1,725 "
-28020585,HC IMPL PLATE RIGID STRAIGHT 10 HOLE 1.6MM," 1,725 "
-28020586,HC IMPL PLATE RIGID STRAIGHT 18 HOLE 1.6MM," 1,949 "
-28020587,HC IMPL PLATE SAGITTAL SPLIT MTRXORTHGN CRVD TI 6H 6MM," 2,890 "
-28020588,HC IMPL PLATE SAGITTAL SPLIT MTRXORTHGN STR TI 4H 8MM," 2,706 "
-28020589,HC IMPL PLATE SEMI COMPRESS MAXI 32MM, 816 
-28020590,HC IMPL PLATE SEMI COMPRESS MAXI 44MM, 867 
-28020591,HC IMPL PLATE SQUARE 4 HOLE 1.2MM," 1,530 "
-28020592,HC IMPL PLATE STRUT 2X3 HOLE," 3,386 "
-28020593,HC IMPL PLATE STRUT 2X4 HOLE," 4,821 "
-28020594,HC IMPL PLATE STRUT MATRIXMIDFACE TI 18 HOLE .5MM," 4,835 "
-28020595,HC IMPL PLATE T COMPRESSION 2X1X4X2MM, 719 
-28020596,HC IMPL PLATE T COMPRESSION 2X4 HOLE, 638 
-28020597,HC IMPL PLATE T COMPRESSION 2X4 HOLE 2.4X22MM, 638 
-28020598,HC IMPL PLATE T COMPRESSION 3X4H 2MM, 723 
-28020599,HC IMPL PLATE T COMPRESSION OBLIQ LEFT 2X4H 2MM, 638 
-28020600,HC IMPL PLATE T COMPRESSION OBLIQ RIGHT 2X4H 2MM, 638 
-28020601,HC IMPL PLATE T COMPRESSION OBLIQUE LEFT 2X4 HOLE, 638 
-28020602,HC IMPL PLATE T COMPRESSION OBLIQUE RIGHT 2X4 HOLE, 638 
-28020603,HC IMPL PLATE T MATRIXMIDFACE TITANIUM 3X4 HOLE .5MM," 2,040 "
-28020604,HC IMPL PLATE T MATRIXMIDFACE TITANIUM 3X4 HOLE .7MM," 1,931 "
-28020605,HC IMPL PLATE T MATRIXMIDFACE TITANIUM 3X4 HOLE .8MM," 1,931 "
-28020606,HC IMPL PLATE T MICRO 19MM, 680 
-28020607,HC IMPL PLATE THICK STRAIGHT 12HOLEX1MM," 1,764 "
-28020608,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 10H 3.5X154MM," 11,933 "
-28020609,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 12H 3.5X180MM," 12,083 "
-28020610,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 12H 3.5X206MM," 12,218 "
-28020611,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 12H 3.5X232MM," 12,653 "
-28020612,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 4H 3.5X76MM," 11,741 "
-28020613,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 6H 3.5X102MM," 11,883 "
-28020614,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND LT 8H 3.5X128MM," 12,440 "
-28020615,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 10H 3.5X154MM," 12,573 "
-28020616,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 12H 3.5X180MM," 12,083 "
-28020617,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 12H 3.5X206MM," 12,218 "
-28020618,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 12H 3.5X232MM," 12,653 "
-28020619,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 4H 3.5X76MM," 12,152 "
-28020620,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 6H 3.5X102MM," 11,670 "
-28020621,HC IMPL PLATE TIBIAL PROXIMAL LOW BEND RT 8H 3.5X128MM," 12,440 "
-28020622,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL 2 HOLE 3.5X79MM," 7,556 "
-28020623,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL 4 HOLE 3.5X105MM," 8,500 "
-28020624,HC IMPL PLATE TIBIAL PROXIMAL MEDIAL 6 HOLE 3.5X131MM," 7,713 "
-28020625,HC IMPL PLATE U 7 HOLE," 1,726 "
-28020626,HC IMPL PLATE U 8 HOLE," 1,726 "
-28020627,HC IMPL PLATE VECTRA FOUR LEVEL 100MM," 12,000 "
-28020628,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X6X2," 7,684 "
-28020629,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X6X3," 7,991 "
-28020630,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X6X4," 8,175 "
-28020631,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X6X5," 8,475 "
-28020632,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X7X2," 7,575 "
-28020633,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X7X3," 7,875 "
-28020634,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA LT 2.4X7X4," 8,175 "
-28020635,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X6X2," 7,684 "
-28020636,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X6X4," 8,175 "
-28020637,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X6X5," 8,899 "
-28020638,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X7X2," 7,575 "
-28020639,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X7X3," 8,271 "
-28020640,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X7X4," 8,175 "
-28020641,HC IMPL PLATE VOLAR DISTAL RADIUS 2COLMN VA RT 2.4X7X5," 8,475 "
-28020642,HC IMPL PLATE X MATRIXMIDFACE TITANIUM 18 HOLE .4MM," 4,835 "
-28020643,HC IMPL PLATE X MATRIXMIDFACE TITANIUM 4 HOLE .4MM," 2,258 "
-28020644,HC IMPL PLATE X MATRIXMIDFACE TITANIUM 4 HOLE .5MM," 2,135 "
-28020645,HC IMPL PLATE X MATRIXMIDFACE TITANIUM 4 HOLE .7MM," 2,135 "
-28020646,HC IMPL PLATE X MATRIXNEURO," 1,952 "
-28020647,HC IMPL PLATE X MICRO RIGID 1.2MM," 1,386 "
-28020648,HC IMPL PLATE Y 11 HOLE," 1,581 "
-28020649,HC IMPL PLATE Y COMPRESSION 3X4H 2MM, 723 
-28020650,HC IMPL PLATE Y DOUBLE MICRO 6 HOLE 1.2MM," 1,386 "
-28020651,HC IMPL PLATE Y EXTENDED 11X1.6MM," 1,470 "
-28020652,HC IMPL PLATE Y MATRIXMIDFACE TITANIUM 1X3 HOLE .4MM," 2,135 "
-28020653,HC IMPL PLATE Y MATRIXMIDFACE TITANIUM 1X3 HOLE .5MM," 2,135 "
-28020654,HC IMPL PLATE Y MATRIXMIDFACE TITANIUM 1X3 HOLE .7MM," 2,258 "
-28020655,HC IMPL PLATE Y MATRIXNEURO BLUE 5 HOLE," 1,952 "
-28020656,HC IMPL PLATE Y RIGID," 1,307 "
-28020657,HC IMPL PLATE BSSO LONG 8 HOLE," 2,346 "
-28020658,HC IMPL PLATE BSSO MEDIUM 8 HOLE," 2,253 "
-28020659,HC IMPL PLATE BSSO SHORT 6 HOLE," 2,019 "
-28020660,HC IMPL PLATE BSSO SHORT 8 HOLE," 2,253 "
-28020661,HC IMP PLUG BONE ANCHOR COMPRESS 16MM," 6,545 "
-28020662,HC IMP PLUG HELICAL FLANGE DEROTAT SS," 2,108 "
-28020663,HC IMP PLUG HELICAL FLANGE STD SS," 2,108 "
-28020664,HC IMP PLUG LINEUM HELICAL FLANGE, 958 
-28020665,HC IMP PORT POWERPORT ISP 6FR," 4,222 "
-28020666,HC IMP PORT VENOUS ACCESS SLIM 6F," 4,005 "
-28020667,HC IMP POWDER DEMINERALIZED CORTICAL 3ML," 1,006 "
-28020668,HC IMP POWDER DEMINERALIZED CORTICAL 5ML," 1,464 "
-28020669,HC IMPL PROCESSOR SOUND PONTO RIGHT DIAMOND BLACK," 22,610 "
-28020670,HC PUMP VENTRICLE MALL 42F KIT," 100,000 "
-28020671,HC PUMP VENTRICLE MALLEABLE 32F KIT," 100,000 "
-28020672,HC PUMP VENTRICLE SHORT 32F KIT," 100,000 "
-28020673,HC PUMP VENTRICLE SHORT 42F KIT," 100,000 "
-28020674,HC IMP PUTTY BONE MATRIX DBX 1CC," 1,641 "
-28020675,HC  RETRIEVAL POLYP 3X6CM, 697 
-28020676,HC IMP ROD CARBON 11MMX100MM," 1,539 "
-28020677,HC IMP ROD CARBON 11MMX150MM," 2,274 "
-28020678,HC IMP ROD CARBON FIBER 4X120MM, 389 
-28020679,HC IMP ROD CARBON FIBER 4X200MM, 530 
-28020680,HC IMP ROD DEFORMITY HARD 5.5X510MM," 2,125 "
-28020681,HC IMP ROD DEFORMITY MEDIUM 5.5X510MM," 2,125 "
-28020682,HC IMP ROD HEX EXPEDIUM SS 5.5X480MM," 3,892 "
-28020683,HC IMP ROD HEXAGONAL END 500MM 2 EACH," 4,259 "
-28020684,HC IMP ROD LINEUM 3.5X180MM," 2,125 "
-28020685,HC IMP ROD LINEUM TRANSITIONAL 3.5X5.5MM," 2,125 "
-28020686,HC IMP ROD LNG ACT LEFORTIII SEPCTRUM," 7,208 "
-28020687,HC IMP ROD RIGID TRIUS 6X120MM," 2,125 "
-28020688,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX35MM," 2,315 "
-28020689,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX40MM," 2,125 "
-28020690,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX60MM," 2,125 "
-28020691,HC IMP ROD SPINAL CURVED TITANIUM 5.5MMX70MM," 2,125 "
-28021435,HC IMP HOOK SPINAL PEDICLE SMALL 4.5M," 7,105 "
-28021436,HC IMP HOOK SPINAL PEDICLE SS FRONTAL," 5,712 "
-28021437,HC IMP HOOK SPINAL PEDICLE SS LEFT," 4,981 "
-28021438,HC IMP HOOK SPINAL PEDICLE SS RIGHT," 4,981 "
-28021439,HC IMP HOOK SPINAL PEDICLE TI FRONAL," 6,579 "
-28021440,HC IMP HOOK SPINAL PEDICLE TI SIDE," 5,857 "
-28021441,HC IMP HOOK SPINAL RECTANGULAR LEFT 11M," 4,981 "
-28021442,HC IMP HOOK SPINAL RECTANGULAR LEFT 9.5M," 4,981 "
-28021443,HC IMP HOOK SPINAL RECTANGULAR RIGHT 11M," 4,981 "
-28021444,HC IMP HOOK SPINAL RECTANGULAR RIGHT 9.5M," 4,981 "
-28021445,HC IMP HOOK SPINAL TALL BODY FRONTAL LARGE," 7,030 "
-28021446,HC IMP HOOK SPINAL TALL BODY FRONTAL MEDIUM," 7,030 "
-28021447,HC IMP HOOK SPINAL TALL BODY FRONTAL SMALL," 7,030 "
-28021448,HC IMP HOOK SPINAL TRANSVERSE FRONTAL LEFT," 7,030 "
-28021449,HC IMP HOOK SPINAL TRANSVERSE FRONTAL RIGHT," 7,030 "
-28021450,HC IMP HOOK SPINAL TRANSVERSE LEFT," 4,981 "
-28021451,HC IMP HOOK SPINAL TRANSVERSE RIGHT," 4,981 "
-28021452,HC IMP HOOK SPINAL TRANSVERSE SIDE LEFT," 5,857 "
-28021453,HC IMP HOOK SPINAL TRANSVERSE SIDE RIGHT," 6,037 "
-28021454,HC IMP HOOK SPINAL TRANSVERSE SS FRONTAL LEFT," 5,712 "
-28021455,HC IMP HOOK SPINAL TRANSVERSE SS FRONTAL RIGHT," 5,712 "
-28021456,HC IMP HOOK SPINAL TRANSVERSE TI FRONTAL LEFT," 6,579 "
-28021457,HC IMP HOOK SPINAL TRANSVERSE TI FRONTAL RIGHT," 6,579 "
-28021458,HC IMP HOOK SPINAL TRANSVERSE TI SIDE RIGHT," 5,857 "
-28021459,HC IMP HOOK SPINAL XIA LAMINA LEFT," 7,247 "
-28021460,HC IMP HOOK SPINAL XIA LAMINA LARGE," 7,247 "
-28021461,HC IMP HOOK SPINAL XIA LAMINA LARGE LEFT," 6,555 "
-28021462,HC IMP HOOK SPINAL XIA LAMINA LARGE RIGHT," 6,555 "
-28021463,HC IMP HOOK SPINAL XIA LAMINA NARROW SMALL," 9,686 "
-28021464,HC IMP HOOK SPINAL XIA LAMINA RIGHT," 7,247 "
-28021465,HC IMP HOOK SPINAL XIA LAMINA SMALL," 7,247 "
-28021466,HC IMP HOOK SPINAL XIA LAMINA TP LEFT," 7,247 "
-28021467,HC IMP HOOK SPINAL XIA LAMINA TP SMALL," 8,072 "
-28021468,HC IMP HOOK SPINAL XIA LAMINA TRANSVERSE RIGHT," 8,401 "
-28021469,HC IMP ICD DUAL CHAMBER  PROTECTA DF-4," 80,280 "
-28021470,HC IMP ICD DUAL EVERA XT DF-4," 88,050 "
-28021471,HC IMP ICD EVERA XT VR DF-4," 78,000 "
-28021472,HC IMP ICD INCEPTA DUAL RATE RESPONSIVE DF4," 86,970 "
-28021473,HC IMP ICD VIVA XT CRT-D DF-1," 74,025 "
-28021474,HC IMP ICD VIVA XT CRT-D DF-4," 74,025 "
-28021475,HC ILLUMINATION RADIANCE METRX SYSTEM," 3,954 "
-28021476,HC ILLUMINATION RADIANCE SYSTEM 18M," 3,567 "
-28021477,HC IMPL IMMOBILIZER KNEE TRI PANEL 16 IN, 90 
-28021478,HC IMPL IMMOBILIZER KNEE TRI PANEL 19 IN, 92 
-28021479,HC IMPL IMMOBILIZER KNEE TRI PANEL 24 IN, 96 
-28021480,HC IMPL IMMOBILIZER KNEE 3 PANEL 12, 107 
-28021481,HC IMPL IMMOBILIZER KNEE 3 PANEL 14, 123 
-28021482,HC IMPL IMMOBILIZER KNEE 3 PANEL 20, 141 
-28021483,HC IMP INSERTER SLNG TRANSVAGINAL IN FAST," 6,877 "
-28021484,HC  INTRODUCER 6.0F 5CM," 1,184 "
-28021485,HC  INTROD BREAST BIOPSY 12G, 808 
-28021486,HC  INTROD BREAST BIOPSY 9G," 7,790 "
-28021487,HC INTRODUCER CATH SPITTABLE W/NEEDLE, 332 
-28021488,HC  INTROD INTUBATION 14F 3MMX65CM, 859 
-28021489,HC IMP IUD MIRENA," 5,976 "
-28021490,HC  IVC RETRIEVER GUNTHER SET," 1,828 "
-28021491,HC IMP KIT TRACHEOSTOMY 8.0MM," 2,295 "
-28021492,HC IMP KNEE FEM ORALBUSHING DURATION HINGE," 3,009 "
-28021493,HC IMP KNEE INSERT TIBIAL RIGHT SZ3," 7,880 "
-28021494,HC IMP KNEE INSERT TIBIAL  XSMALL/SMALL 13MM," 9,960 "
-28021495,HC IMP KNEE MODULAR AXLE ROTATING HINGE," 8,142 "
-28021496,HC IMP KNEE SLEEVE TIBIAL MODULAR DUARTION," 5,185 "
-28021497,HC IMP KNEE TIBIAL ROTATING HINGE X SMALL/SMALL," 21,745 "
-28021498,HC IMP LEAD CORONARY VENOUS ACUITY 80CM," 11,250 "
-28021499,HC IMP LEAD CORONARY VENOUS ACUITY 90CM," 11,250 "
-28021500,HC IMP LEAD DEFIBRILLATOR LINOX 60/16," 35,000 "
-28021501,HC IMP LEAD NEUROSTIMULATOR 12 LEAD KIT," 6,630 "
-28021502,HC IMPL LENS IOL POSTERIOR CHAMBER PMMA MACRYSOF 29.5D," 1,075 "
-28021503,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 31.0D," 1,139 "
-28021504,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 32.0D," 1,139 "
-28021505,HC IMPL LENS STORZ COBURN 95UV 14.0D," 1,097 "
-28021506,HC IMPL LENS STORZ COBURN 95UV 14.5D," 1,097 "
-28021507,HC IMPL LENS STORZ COBURN 95UV 15.0D," 1,097 "
-28021508,HC IMPL LENS STORZ COBURN 95UV 15.5D," 1,097 "
-28021509,HC IMPL LENS STORZ COBURN 95UV 16.0D," 1,097 "
-28021510,HC IMPL LENS STORZ COBURN 95UV 16.5D," 1,097 "
-28021511,HC IMPL LENS STORZ COBURN 95UV 17.0D," 1,097 "
-28021512,HC IMPL LENS STORZ COBURN 95UV 17.5D," 1,097 "
-28021513,HC IMPL LENS STORZ COBURN 95UV 18.0D," 1,097 "
-28021514,HC IMPL LENS STORZ COBURN 95UV 18.5D," 1,097 "
-28021515,HC IMPL LENS STORZ COBURN 95UV 19.0D," 1,097 "
-28021516,HC IMPL LENS STORZ COBURN 95UV 19.5D," 1,097 "
-28021517,HC IMPL LENS STORZ COBURN 95UV 20.0D," 1,097 "
-28021518,HC IMPL LENS STORZ COBURN 95UV 20.5D," 1,097 "
-28021519,HC IMPL LENS STORZ COBURN 95UV 21.5D," 1,097 "
-28021520,HC IMPL LENS STORZ COBURN 95UV 22.0D," 1,097 "
-28021521,HC IMPL LENS STORZ COBURN 95UV 22.5D," 1,097 "
-28021522,HC IMPL LENS STORZ COBURN 95UV 23.0D," 1,097 "
-28021523,HC IMPL LENS STORZ COBURN 95UV 23.5D," 1,097 "
-28021524,HC IMPL LENS STORZ COBURN 95UV 24.0D," 1,097 "
-28021525,HC IMPL LENS STORZ COBURN 95UV 24.5D," 1,097 "
-28021526,HC IMPL LENS STORZ COBURN 95UV 25.0D," 1,097 "
-28021527,HC IMPL LENS STORZ COBURN 95UV 25.5D," 1,097 "
-28021528,HC IMPL LENS STORZ COBURN 95UV 26.0D," 1,097 "
-28021529,HC IMPL LENS STORZ COBURN 95UV 26.5D," 1,097 "
-28021530,HC  MARKER TISSUE BREAST RIBBON 17X10, 670 
-28021531,HC IMPL MATRIX SHEETING EXTRA STRENGTH 5X5," 6,375 "
-28021532,HC IMP MESH HERNIA ABSORBABLE DEXON 7X9IN, 967 
-28021533,HC IMP MESH RAPID RESORBABLE 1.2MM," 13,632 "
-28021534,HC IMP MESH RAPID RESORBABLE 1.5MM," 65,000 "
-28021535,HC METADIAPHYSEAL KIT," 29,232 "
-28021536,HC  MARKER BREAST BIOPSY STARCHMARK 10G," 8,500 "
-28021537,HC IMP NAIL BIOTRAK HELICAL 20MM," 3,120 "
-28021538,HC IMP NAIL BIOTRAK HELICAL 30MM," 3,120 "
-28021539,HC IMP NAIL HUMERAL SOLID 10MM 340M," 9,240 "
-28021540,HC IMP NAIL HUMERAL SOLID 10MM 360M," 10,058 "
-28021541,HC IMP NAIL FEMORAL CANNULATED 10MM 400MM," 10,862 "
-28021542,HC IMP NAIL FEMORAL CANNULATED 11MM 360MM," 10,058 "
-28021543,HC IMP NAIL FEMORAL CANNULATED 11MM 420MM," 10,058 "
-28021544,HC IMP NAIL FEMORAL CANNULATED 11MM 440MM," 9,240 "
-28021545,HC IMP NAIL FEMORAL CANNULATED 12MM 360MM," 9,240 "
-28021546,HC IMP NAIL FEMORAL CANNULATED 13MM 400MM," 10,058 "
-28021547,HC IMP NAIL FEMORAL CANNULATED 14MM 400MM," 9,240 "
-28021548,HC IMP NAIL FEMORAL DISTAL 10MM 300MM," 11,970 "
-28021549,HC IMP NAIL FEMORAL DISTAL 10MM 320MM," 11,970 "
-28021550,HC IMP NAIL FEMORAL DISTAL 9MM 180MM," 12,563 "
-28021551,HC IMP NAIL FEMORAL DISTAL 9MM 300MM," 11,970 "
-28021552,HC IMP NAIL HUMERAL SOLID 7.5MM 260MM," 14,183 "
-28021553,HC IMP NAIL HUMERAL SOLID 9.5MM 280M," 12,608 "
-28021554,HC IMP NAIL HUMERAL SOLID 9MM 340M," 8,445 "
-28021555,HC IMP NAIL HUMERAL SOLID 9MM 380M," 7,988 "
-28021556,HC IMP NAIL INTRAMEDULLARY TRIGEN 10X34CM," 13,864 "
-28021557,HC IMP NAIL TIBIAL CANNULATED 10X315MM," 8,918 "
-28021558,HC IMP NAIL TIBIAL CANNULATED 11X330MM," 8,918 "
-28021559,HC IMP NAIL TIBIAL CANNULATED 12X300MM," 8,918 "
-28021560,HC IMP NAIL TIBIAL CANNULATED 12X330MM," 8,918 "
-28021561,HC IMP NAIL TIBIAL CANNULATED TI 12X360MM," 8,918 "
-28021562,HC IMP NAIL TROCHANTERIC FIXATION LEFT 11X440M 130D," 14,562 "
-28021563,HC IMP NAIL TROCHANTERIC FIXATION 12X170M 130D," 11,062 "
-28021564,HC NEEDL FLEX EUS FNA 19G," 4,157 "
-28021565,HC IMP NUT DIST MIDFACE HEX TI 7MM, 809 
-28021566,HC IMP NUT LOCKING LAETRAL PLATE, 850 
-28021567,HC OBTURATOR SOFT PASS FIRM," 2,219 "
-28021568,HC OCCLUDER SEPTAL 18MM," 39,423 "
-28021569,HC ORTHOVISC SYRINGE 2ML 30MG," 2,975 "
-28021570,HC OXYGENATOR OPEN HEART PEDIACTRIC," 14,175 "
-28021571,HC PACK PIN ODYSSEY," 2,423 "
-28021572,HC IMP PEEK CRANIAL CUSTOM," 41,600 "
-28021573,HC PERFORATOR CODMAN 4MM," 15,375 "
-28021574,HC IMP PIN BONE HALF TRULOK 6MM," 1,343 "
-28021575,HC IMP PIN HALF THREAD HYDROXYAPATITE 6X20X200MM," 1,440 "
-28021576,HC IMP PIN HALF THREAD HYDROXYAPATITE 6X30X150MM," 1,133 "
-28021577,HC IMP PIN HALF THREAD HYDROXYAPATITE 6X30X200MM," 1,440 "
-28021578,HC IMP PIN HALF THREAD HYDROXYAPATITE 6X45X200MM," 1,286 "
-28021579,HC IMP PIN HALF THREAD HYDROXYAPATITE 6X50X200MM," 1,748 "
-28021580,HC IMP PIN HALF THREAD HYDROXYAPATITE 6X60X200MM," 1,440 "
-28021581,HC IMP PIN BONE REPOSITION UNIV 3.2M," 3,128 "
-28021582,HC IMP PIN GUIDING BIRMINGHAM LONG, 169 
-28021583,HC IMP PIN GUIDING BIRMINGHAM SHORT, 168 
-28021584,HC PIN SKULL MAYFIELD RADIOLUCENT," 4,939 "
-28021585,HC IMPL PLATE ANCHOR 4H," 3,188 "
-28021586,HC IMPL PLATE SPINAL ANTERIOR CERVICAL 1LEVEL 22MM," 8,500 "
-28021587,HC IMPL PLATE SPINAL ANTERIOR CERVICAL 1LEVEL 26MM," 8,500 "
-28021588,HC IMPL PLATE SPINAL ANTERIOR CERVICAL 2LEVEL 46MM," 9,000 "
-28021589,HC IMPL PLATE SPINAL LAMINAR LARGE 11MM," 8,033 "
-28021590,HC IMPL PLATE SPINAL LAMINAR LARGE 8MM," 8,033 "
-28021591,HC IMPL PLATE SPINAL LAMINAR LARGE 9.5MM," 8,033 "
-28021592,HC IMPL PLATE SPINAL LUMBAR 55MM," 24,500 "
-28021593,HC IMPL PLATE SPINAL TRADITIONAL 8MM," 8,033 "
-28021594,HC IMPL PLATE SPINAL TRADITIONAL 9.5MM," 8,033 "
-28021595,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 1 LEVEL 12M," 9,742 "
-28021596,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 1 LEVEL 14M," 9,742 "
-28021597,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 1 LEVEL 18M," 9,742 "
-28021598,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 2 LEVEL 26M," 10,476 "
-28021599,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 2 LEVEL 28M," 10,476 "
-28021600,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 3 LEVEL 30M," 10,476 "
-28021601,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 3 LEVEL 37M," 10,476 "
-28021602,HC IMPL PLATE ANTEIROR CERVICAL AVIATOR 3 LEVEL 48M," 11,014 "
-28021603,HC IMPL PLATE CURVED 2.0MM 6H," 2,196 "
-28021604,HC IMPL PLATE FEMUR 276MM 11HOLE," 12,953 "
-28021605,HC IMPL PLATE FUSION METATARSAL SMALL LEFT 0 DEGREE," 16,223 "
-28021606,HC IMPL PLATE MANDIBLE STRAIGHT BLUE 16 HOLE," 1,185 "
-28021607,HC IMPL PLATE MANDIBLE STRAIGHT BLUE 6 HOLE, 845 
-28021608,HC IMPL PLATE MATTA ACETABULAR 16 HOLE," 4,063 "
-28021609,HC IMPL PLATE MATTA CVD R108 4HOLES," 3,324 "
-28021610,HC IMPL PLATE MATTA CVD R108 8HOLES," 4,310 "
-28021611,HC IMPL PLATE MIDFACE MALLEABLE RIGHT 12M 6 HOLE," 1,472 "
-28021612,HC IMPL PLATE QUAD GUIDED GROWTH SYSTEM 12MM," 2,635 "
-28021613,HC IMPL PLATE QUAD GUIDED GROWTH SYSTEM 16MM," 2,635 "
-28021614,HC IMPL PLATE RESORBABLE 1.5 STRUT 2X36M," 7,584 "
-28021615,HC IMPL PLATE RESORBABLE 2.0 STRUT 2X10M," 3,658 "
-28021616,HC IMPL PLATE RESORBABLE 2.0 STRUT 2X18M," 4,536 "
-28021617,HC IMPL PLATE RESORBABLE DELTA 1.4X55X55MM," 5,609 "
-28021618,HC IMPL PLATE RESORBABLE DELTA 1X55X55MM," 4,970 "
-28021619,HC IMPL PLATE RESORBABLE DELTA 2X5MM," 1,799 "
-28021620,HC IMPL PLATE RESORBABLE ORBITAL 1.5 FLOOR 4 HOLE," 1,897 "
-28021621,HC IMPL PLATE SPINAL ANTERIOR CERVICAL 20MM," 8,500 "
-28021622,HC IMPL PLATE SPINAL ANTERIOR CERVICAL 30MM," 5,151 "
-28021623,HC IMPL PLATE SPINAL XLP 6MM 2H," 23,100 "
-28021624,HC IMPL PLATE SPINAL XLP 8MM 2H," 23,100 "
-28021625,HC IMP PMMA CRANIAL CUSTUM LARGE," 76,662 "
-28021626,HC IMPL POWER PACK VAD KIT," 34,300 "
-28021627,HC PROBE ABLATION CERTUS LK15CM," 12,420 "
-28021628,HC PROBE ABLATION CERTUS LK20CM," 12,420 "
-28021629,HC PROBE ABLATION CERTUS LN15CM," 12,420 "
-28021630,HC PROBE ABLATION CERTUS LN20CM," 12,420 "
-28021631,HC PROBE BREAST ENCOR 12G," 8,085 "
-28021632,HC PROBE ENDOSCOPIC 77X365CM," 25,200 "
-28021633,HC PROBE NEONATAL OXYGEN 4FR, 366 
-28021634,HC IMP PUMP PAIN PROGRAMMABLE SYNCHROMED II 20," 61,800 "
-28021635,HC IMP PUMP PAIN PROGRAMMABLE SYNCHROMED II 40," 61,800 "
-28021636,HC IMP PUTTY CRANIOS REINFORCED FAST SET 10CC," 18,155 "
-28021637,HC IMP PUTTY CRANIOS REINFORCED FAST SET 5CC," 9,894 "
-28021638,HC RESERVOIR DOME BURR HLE 1.5CM," 2,620 "
-28021639,HC RESERVOIR DOME BURR HLE CONVERTIBLE 1.5CM," 2,620 "
-28021640,HC RESERVOIR SPINAL CEREBRAL ON/OFF FLUSHING," 8,288 "
-28021641,HC RESERVOIR SPETZLER LARGE," 2,203 "
-28021642,HC IMP RING EXTERNAL FIXATOR 2/3 150MM," 7,703 "
-28021643,HC IMP RING EXTERNAL FIXATOR 2/3 175MM," 7,703 "
-28021644,HC IMP RING EXTERNAL FIXATOR 2/3 180MM," 8,337 "
-28021645,HC IMP RING EXTERNAL FIXATOR 2/3 205MM," 8,264 "
-28021646,HC IMP RING EXTERNAL FIXATOR 5/8 160MM," 6,120 "
-28021647,HC IMP RING EXTERNAL FIXATOR 130MM," 7,872 "
-28021648,HC IMP RING EXTERNAL FIXATOR TAYLOR FULL 155MM," 7,610 "
-28021649,HC IMP RING EXTERNAL FIXATOR TAYLOR FULL 205MM," 7,741 "
-28021650,HC IMP RING EXTERNAL FIXATOR TAYLOR HALF 205MM," 8,222 "
-28021651,HC IMP RING EXTERNAL FIXATOR TAYLOR LONG 180MM," 8,066 "
-28021652,HC IMP RING EXTERNAL FIXATOR TAYLOR SHORT 155MM," 7,336 "
-28021653,HC IMP RING USS HALF TI 6MM," 1,655 "
-28021654,HC IMP ROD SPINAL 3.5X120MM," 2,125 "
-28021655,HC IMP ROD SPINAL 3.5X240MM," 3,035 "
-28021656,HC IMP ROD SPINAL 3.5X60MM," 2,125 "
-28021657,HC IMP ROD SPINAL 5.5X300MM," 2,125 "
-28021658,HC IMP ROD SPINAL 5.5X500MM," 2,125 "
-28021659,HC IMP ROD SPINAL ARMADA 5.5X500MM," 6,418 "
-28021660,HC IMP ROD SPINAL DUAL BALL SPHERX 17.5MM," 5,950 "
-28021661,HC IMP ROD SPINAL DUAL BALL SPHERX 20MM," 5,950 "
-28021662,HC IMP ROD SPINAL DUAL BALL SPHERX 22.5MM," 5,950 "
-28021663,HC IMP ROD SPINAL DUAL BALL SPHERX 25MM," 5,950 "
-28021664,HC IMP ROD SPINAL DUAL BALL SPHERX 27.5MM," 5,950 "
-28021665,HC IMP ROD SPINAL DUAL BALL SPHERX 30MM," 5,950 "
-28021666,HC IMP ROD SPINAL DUAL BALL SPHERX 35MM," 5,950 "
-28021667,HC IMP ROD SPINAL HEX 6.35X500MM," 3,319 "
-28021668,HC IMP ROD SPINAL MULTI AXIAL HEX 40MM," 4,888 "
-28021669,HC IMP ROD SPINAL MULTI AXIAL HEX 50MM," 4,888 "
-28021670,HC IMP ROD SPINAL OASYS TI 3.5X50MM," 2,125 "
-28021671,HC IMP ROD SPINAL PREBENT 65MM," 2,125 "
-28021672,HC IMP ROD SPINAL PREBENT SPHERX 35MM," 2,125 "
-28021673,HC IMP ROD SPINAL PREBENT SPHERX 40MM," 2,125 "
-28021674,HC IMP ROD SPINAL RADIUS MANTIS 6.0X40MM," 3,849 "
-28021675,HC IMP ROD SPINAL RADIUS MANTIS 6.0X45MM," 3,849 "
-28021676,HC IMP ROD SPINAL RADIUS 5.5X400MM," 2,999 "
-28021677,HC IMP ROD SPINAL RADIUS 5.5X600MM," 2,999 "
-28021678,HC IMP ROD SPINAL STIFF XIA 5.5X480MM," 2,450 "
-28021679,HC IMP ROD SPINAL STIFF XIA 5.5X600MM," 2,520 "
-28021680,HC IMP ROD SPINAL TI 3.5X240MM," 2,849 "
-28021681,HC IMP ROD SPINAL TITANIUM 450MM," 4,157 "
-28021682,HC IMP ROD SPINAL VITALLIUM 5.5X600MM," 8,118 "
-28021683,HC IMP ROD SPINAL XIA TI 4.5X200MM," 4,646 "
-28021684,HC IMP ROD SPINAL XIA TI 4.5X600MM," 7,579 "
-28021685,HC IMP ROD SPINAL XIA UNION TI LARGE 6X40MM," 2,125 "
-28021686,HC IMP ROD SPINAL XIA UNION TI LARGE 6X50MM," 2,125 "
-28021687,HC IMP ROD SPINAL XIA3 TI 6X40MM," 3,374 "
-28021688,HC IMP ROD SPINAL XIA3 TI 6X45MM," 3,374 "
-28021689,HC IMP ROD SPINAL XIA3 TI 6X50MM," 3,374 "
-28021690,HC IMP ROD SPINAL XIA3 TI 6X70MM," 3,374 "
-28021691,HC IMP ROD SYNAPSE TI SET," 32,528 "
-28021692,HC IMP ROD THREADED 4.5," 5,984 "
-28021693,HC IMP ROD VERTICAL GEAR RED SYSTEM," 17,850 "
-28021694,HC IMP ROD XIA DBL RADIUS 80MM," 3,417 "
-28021695,HC IMP ROD XIA3 VITALLIUM 600MM," 4,893 "
-28021696,HC IMPL SCREW CANNULATED INTERFERENCE 6X20MM," 1,913 "
-28021697,HC IMPL SCREW BONE CANCELLOUS SELF TAPPING 12M," 1,488 "
-28021698,HC IMPL SCREW BONE CANCELLOUS SELF TAPPING 15M," 1,258 "
-28021699,HC IMP SCREW BONE LOCKING 2.7X10MM," 2,346 "
-28021700,HC IMP SCREW BONE LOCKING 2.7X12MM," 2,346 "
-28021701,HC IMP SCREW BONE LOCKING 2.7X16MM," 2,346 "
-28021702,HC IMP SCREW BONE NON LOCKING 2.7X14MM," 1,216 "
-28021703,HC IMP SCREW BONE NON LOCKING 2.7X16MM," 1,216 "
-28021704,HC IMP SCREW BONE SPINAL MONOAXIAL 7.5X40MM," 8,401 "
-28021705,HC IMP SCREW BONE T7 2.3X30MM," 1,063 "
-28021706,HC IMP SCREW BONE VARIABLE ANGLED 4X10MM," 1,700 "
-28021707,HC IMP SCREW BONE VARIABLE ANGLED 4X12MM," 1,700 "
-28021708,HC IMPL SCREW CANN LOCKING FULLY THREAD SS 6.5X85MM," 1,531 "
-28021709,HC IMPL SCREW CANNULATED SHNK 5.5X30MM," 13,880 "
-28021710,HC IMPL SCREW CANNULATED SHNK 6.5X40MM," 4,454 "
-28021711,HC IMPL SCREW CAPTURED TRIGEN 5.OX25MM," 1,344 "
-28021712,HC IMPL SCREW CAPTURED TRIGEN 5.OX30MM," 1,344 "
-28021713,HC IMPL SCREW CAPTURED TRIGEN 5.OX35MM," 1,517 "
-28021714,HC IMPL SCREW CAPTURED TRIGEN 5.OX40MM," 1,344 "
-28021715,HC IMPL SCREW CAPTURED TRIGEN 5.OX45MM," 1,344 "
-28021716,HC IMPL SCREW CORTEX LOCKING 3.5X60MM, 805 
-28021717,HC IMPL SCREW CORTX RESORABLE RAPID 1.5X3 2/PK," 5,950 "
-28021718,HC IMPL SCREW CORTX RESORABLE RAPID 1.5X4 4/PK, 595 
-28021719,HC IMPL SCREW CORTX RESORABLE RAPID 1.5X8MM 2/PK," 1,190 "
-28021720,HC IMPL SCREW CORTX RESORABLE RAPID 1.5X8MM 4/PK," 2,380 "
-28021721,HC IMPL SCREW CORTX RESORABLE RAPID 2.0X6 4/PK," 2,380 "
-28021722,HC IMPL SCREW DUAL OPENING TI 4.2X30MM," 6,375 "
-28021723,HC IMPL SCREW DUAL OPENING TI 5.2X30MM," 6,375 "
-28021724,HC IMPL SCREW DUAL OPENING TI 5.2X45MM," 6,375 "
-28021725,HC IMPL SCREW DUAL OPENING TI 5.2X50MM," 7,460 "
-28021726,HC IMPL SCREW DUAL OPENING TI 5.2X55MM," 8,109 "
-28021727,HC IMPL SCREW DUAL OPENING TI 6.2X55MM," 7,460 "
-28021728,HC IMPL SCREW DUAL OPENING TI 6.2X60MM," 8,109 "
-28021729,HC IMPL SCREW DUAL OPENING TI 6.2X65MM," 8,109 "
-28021730,HC IMPL SCREW DUAL OPENING TI 7X60MM," 8,109 "
-28021731,HC IMPL SCREW DUAL OPENING TI 7X65MM," 8,109 "
-28021732,HC IMPL SCREW DUAL OPENING TI 8X45MM," 8,109 "
-28021733,HC IMPL SCREW DUAL OPENING TI 8X50MM," 8,109 "
-28021734,HC IMPL SCREW DUAL OPENING TI 8X55MM," 8,109 "
-28021735,HC IMPL SCREW DUAL OPENING TI 8X60MM," 8,109 "
-28021736,HC IMPL SCREW DUAL OPENING TI 8X65MM," 8,109 "
-28021737,HC IMPL SCREW DUAL OPENING TI 8X70MM," 8,109 "
-28021738,HC IMPL SCREW DUAL OPENING TI 8X75MM," 8,109 "
-28021739,HC IMPL SCREW DUAL OPENING TI 8X80MM," 8,109 "
-28021740,HC IMPL SCR FACET 4.5X40MM," 7,605 "
-28021741,HC IMPL SCR FACET 5.2X40MM," 7,605 "
-28021742,HC IMPL SCR FACET KWIRE, 731 
-28021743,HC IMP SCREW FIXED TI 5.5 5.0X30MM," 8,408 "
-28021744,HC IMP SCREW FIXED TI 5.5 5.0X40MM," 8,408 "
-28021745,HC IMP SCREW FIXED TI 5.5 5.5X30MM," 8,408 "
-28021746,HC IMP SSCREWCR FIXED TI 5.5 5.5X35MM," 8,408 "
-28021747,HC IMP SCREW FIXED TI 5.5 5.5X40MM," 8,408 "
-28021748,HC IMP SCREW FIXED TI 5.5 5.5X45MM," 8,408 "
-28021749,HC IMP SCREW FIXED TI 5.5 6.5X40MM," 8,408 "
-28021750,HC IMP SCREW FIXED TI 5.5 6.5X45MM," 13,823 "
-28021751,HC IMPL SCREW HEAD RING SHORT, 652 
-28021752,HC IMPL SCREW LOCKING SCAN STAINLESS STEEL 6.5MMX100MM," 1,531 "
-28021753,HC IMPL SCREW LOCKING SCAN STAINLESS STEEL 6.5MMX105MM," 1,531 "
-28021754,HC IMPL SCREW MIS HEADLESS 48MM, 479 
-28021755,HC IMP SCREW OSTEOTITE CORTICAL 130/50, 956 
-28021756,HC IMPL SCREW PLATE GUIDED GROWTH 24MM," 1,394 "
-28021757,HC IMPL SCREW PLATE GUIDED GROWTH 32MM," 1,394 "
-28021758,HC IMPL SCREW RESORBABLE 2.0 6MM 10PKG," 5,950 "
-28021759,HC IMP SCR SPINAL BONE SET," 1,700 "
-28021760,HC IMPL SCREW SPINAL CANNULATED 5.5X45MM," 4,454 "
-28021761,HC IMPL SCREW SPINAL CANNULATED 6.5X45MM," 4,454 "
-28021762,HC IMPL SCREW SPINAL CANNULATED 6.5X50MM," 4,454 "
-28021763,HC IMPL SCREW SPINAL CANNULATED 7.5X45MM," 4,454 "
-28021764,HC IMPL SCREW SPINAL CANNULATED 7.5X50MM," 4,454 "
-28021765,HC IMPL SCREW SPINAL FIXED SELF DRILLING VARIABLE 4X11M," 2,338 "
-28021766,HC IMPL SCREW SPINAL FIXED SELF DRILLING VARIABLE 4X15M," 2,338 "
-28021767,HC IMPL SCREW ILIAC SL CLOSED 8.5X90MM," 9,600 "
-28021768,HC IMPL SCREW ILIAC SL CLOSED 9.5X90MM," 9,600 "
-28021769,HC IMPL SCREW SPINAL LOCKING LLIAC TI, 850 
-28021770,HC IMPL SCREW SPINAL LOCKING PRECEPT, 850 
-28021771,HC IMPL SCREW SPINAL LOCKING ROD 5.5MM," 1,768 "
-28021772,HC IMPL SCREW SPINAL LOCKING ROD 6.25MM," 1,768 "
-28021773,HC IMPL SCREW SPINAL LCK TULIP TI ARMADA, 850 
-28021774,HC IMPL SCREW SPINAL MANTIS PA 6.5X35MM," 10,472 "
-28021775,HC IMPL SCREW SPINAL MANTIS PA 6.5X40MM," 10,472 "
-28021776,HC IMPL SCREW SPINAL MANTIS PA 6.5X50MM," 10,472 "
-28021777,HC IMPL SCREW SPINAL MULTI AXIAL 3.2X16MM," 7,225 "
-28021778,HC IMPL SCREW SPINAL MULTI AXIAL 3.2X18MM," 7,225 "
-28021779,HC IMPL SCREW SPINAL MULTI AXIAL 3.5X12MM," 7,225 "
-28021780,HC IMPL SCREW SPINAL MULTI AXIAL 3.5X34MM," 7,225 "
-28021781,HC IMPL SCREW SPINAL MULTI AXIAL 4X20MM," 7,225 "
-28021782,HC IMPL SCREW SPINAL MULTI AXIAL 4X28MM," 7,225 "
-28021783,HC IMPL SCREW SPINAL MULTI AXIAL FAVOURED ANG 3.5X12," 7,225 "
-28021784,HC IMPL SCREW SPINAL MULTI AXIAL FAVOURED ANG 3.5X28," 7,225 "
-28021785,HC IMPL SCREW SPINAL MULTI AXIAL LATERAL 2.6X6MM," 1,488 "
-28021786,HC IMPL SCREW SPINAL MULTI AXIAL LATERAL 2.6X7MM," 1,488 "
-28021787,HC IMPL SCREW SPINAL MULTI AXIAL LATERAL 2.6X8MM," 1,488 "
-28021788,HC IMPL SCREW SPINAL MULTI AXIAL LATERAL 3X7MM," 1,488 "
-28021789,HC IMPL SCREW SPINAL PEDICLE LOCKING 6.5 X 40MM," 15,735 "
-28021790,HC IMPL SCREW SPINAL PEDICLE LOCKING 6.5 X 45MM," 15,735 "
-28021791,HC IMPL SCREW SPINAL POLYAXIAL 3.5X12MM," 8,075 "
-28021792,HC IMPL SCREW SPINAL POLYAXIAL 3.5X14MM," 8,075 "
-28021793,HC IMPL SCREW SPINAL POLYAXIAL PRECEPT 5.5X45MM," 8,438 "
-28021794,HC IMPL SCREW SPINAL POLYAXIAL PRECEPT 6.5X50MM," 8,438 "
-28021795,HC IMPL SCREW SPINAL POLYAXIAL RED 5.5X40MM," 7,013 "
-28021796,HC IMPL SCREW SPINAL POLYAXIAL RED 5.5X45MM," 7,013 "
-28021797,HC IMPL SCREW SPINAL POLYAXIAL RED 5.5X50MM," 7,013 "
-28021798,HC IMPL SCREW SPINAL POLYAXIAL RED 6.5X45MM," 7,013 "
-28021799,HC IMPL SCREW SPINAL POLYAXIAL TI 5.0X25MM," 8,500 "
-28021800,HC IMPL SCREW SPINAL POLYAXIAL TI 5.0X35MM," 13,823 "
-28021801,HC IMPL SCREW SPINAL POLYAXIAL TI 5.0X40MM," 8,500 "
-28021802,HC IMPL SCREW SPINAL POLYAXIAL TI 5.5X30MM," 13,823 "
-28021803,HC IMPL SCREW SPINAL POLYAXIAL TI 5.5X50MM," 8,500 "
-28021804,HC IMPL SCREW SPINAL POLYAXIAL TI 6.5X30MM," 8,500 "
-28021805,HC IMPL SCREW SPINAL POLYAXIAL TI 6.5X35MM," 8,500 "
-28021806,HC IMPL SCREW SPINAL POLYAXIAL TI 6.5X40MM," 8,500 "
-28021807,HC IMPL SCREW SPINAL POLYAXIAL TI 6.5X45MM," 8,500 "
-28021808,HC IMPL SCREW SPINAL POLYAXIAL TI 6.5X50MM," 8,500 "
-28021809,HC IMPL SCREW SPINAL POLYAXIAL TI 7.5X40MM," 8,500 "
-28021810,HC IMPL SCREW SPINAL POLYAXIAL TI 7.5X45MM," 8,500 "
-28021811,HC IMPL SCREW SPINAL PARTIAL THREAD FAVOURED ANG 3.5X28," 7,225 "
-28021812,HC IMPL SCREW SPINAL PARTIAL THREAD FAVOURED ANG 3.5X30," 7,225 "
-28021813,HC IMPL SCREW SPINAL REDUCTION 5.5X40MM," 8,948 "
-28021814,HC IMPL SCREW SPINAL REDUCTION 5.5X45MM," 8,948 "
-28021815,HC IMPL SCREW SPINAL REDUCTION 6.5X40MM," 8,948 "
-28021816,HC IMPL SCREW SPINAL REDUCTION 6.5X45MM," 8,948 "
-28021817,HC IMPL SCREW SPINAL REDUCTION 6.5X50MM," 8,948 "
-28021818,HC IMPL SCREW SPINAL REDUCTION 6.5X55MM," 8,948 "
-28021819,HC IMPL SCREW SPINAL SELF DRILLING VARIABLE 4X11MM," 2,338 "
-28021820,HC IMPL SCREW SPINAL SELF DRILLING VARIABLE 4X16MM," 2,338 "
-28021821,HC IMPL SCREW SPINAL FIXED ANGLED SOLERA 4.75 4.5X35," 7,863 "
-28021822,HC IMPL SCREW SPINAL SELF TAPPING VARIABLE 4X15MM," 2,338 "
-28021823,HC IMPL SCREW SPINAL SELF TAPPING VARIABLE 4X17MM," 2,338 "
-28021824,HC IMPL SCREW SPINAL TULIP ROD 5.5," 6,035 "
-28021825,HC IMPL SCREW SPINAL TULIP ROD 6.25MM," 6,035 "
-28021826,HC IMPL SCREW SPINAL UNIPLANAR 5.0X35MM," 14,835 "
-28021827,HC IMPL SCREW SPINAL UNIPLANAR 5.0X40MM," 14,835 "
-28021828,HC IMPL SCREW SPINAL UNIPLANAR 6.0X30MM," 14,835 "
-28021829,HC IMPL SCREW SPINAL UNIPLANAR 6.0X35MM," 8,903 "
-28021830,HC IMPL SCREW SPINAL UNIPLANAR 6.0X40MM," 14,835 "
-28021831,HC IMPL SCREW SPINAL UNIPLANAR 6.0X45MM," 14,835 "
-28021832,HC IMPL SCREW XIA MULTI AXIAL 4.5X30MM," 7,631 "
-28021833,HC IMPL SCREW XIA MULTI AXIAL 4.5X35MM," 7,631 "
-28021834,HC IMPL SCREW SPINAL XIA MONOAXIAL 5.5X30MM," 7,532 "
-28021835,HC IMPL SCREW SPINAL XIA MONOAXIAL 5.5X35MM," 7,532 "
-28021836,HC IMPL SCREW SPINAL XIA MONOAXIAL 5.5X40MM," 7,532 "
-28021837,HC IMPL SCREW SPINAL XIA MONOAXIAL 6.5X30MM," 7,532 "
-28021838,HC IMPL SCREW SPINAL XIA MONOAXIAL 6.5X35MM," 7,532 "
-28021839,HC IMPL SCREW SPINAL XIA MONOAXIAL 6.5X40MM," 7,532 "
-28021840,HC IMPL SCREW SPINAL XIA MONOAXIAL SCOLI 5.0X25MM," 7,108 "
-28021841,HC IMPL SCREW SPINAL XIA MONOAXIAL SCOLI 5.0X30MM," 7,532 "
-28021842,HC IMPL SCREW SPINAL XIA MONOAXIAL SCOLI 5.0X35MM," 7,532 "
-28021843,HC IMPL SCREW SPINAL XIA MONOAXIAL TI 5.0X25MM," 8,072 "
-28021844,HC IMPL SCREW SPINAL XIA MONOAXIAL TI 5.0X30MM," 8,072 "
-28021845,HC IMPL SCREW SPINAL XIA MONOAXIAL TI 5.0X35MM," 8,072 "
-28021846,HC IMPL SCREW SPINAL XIA MONOAXIAL TI 5.5X30MM," 7,749 "
-28021847,HC IMPL SCREW SPINAL XIA MONOAXIAL TI 5.5X35MM," 8,033 "
-28021848,HC IMPL SCREW SPINAL XIA MONOAXIAL TI 5.5X40MM," 8,065 "
-28021849,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 5.5X45MM," 8,033 "
-28021850,HC IMPL SCREW XIA POLYAXIAL 4.5X25MM," 9,040 "
-28021851,HC IMPL SCREW XIA POLYAXIAL 4.5X35MM," 9,040 "
-28021852,HC IMPL SCREW SPINAL XIA POLYAXIAL 5.5X30MM," 7,837 "
-28021853,HC IMPL SCREW SPINAL XIA POLYAXIAL 5.5X35MM," 7,837 "
-28021854,HC IMPL SCREW SPINAL XIA POLYAXIAL 5.5X40MM," 7,838 "
-28021855,HC IMPL SCREW XIA POLYAXIAL 5.5X35MM," 9,040 "
-28021856,HC IMPL SCREW SPINAL XIA POLYAXIAL 6.5X30MM," 7,838 "
-28021857,HC IMPL SCREW SPINAL XIA POLYAXIAL 6.5X35MM," 7,837 "
-28021858,HC IMPL SCREW SPINAL XIA POLYAXIAL 6.5X40MM," 7,837 "
-28021859,HC IMPL SCREW SPINAL XIA POLYAXIAL SS 5.0X30MM," 7,838 "
-28021860,HC IMPL SCREW SPINAL XIA POLYAXIAL TI 8.5X80MM REV," 10,081 "
-28021861,HC IMPL SCREW SPINAL XIA POLYAXIAL TI 8.5X90MM REV," 10,081 "
-28021862,HC IMPL SCREW SPINAL XIA POLYAXIAL TI 9.5X100MM REV," 10,278 "
-28021863,HC IMPL SCREW SPINAL XIA REDUCTION PA 5.5X30MM," 9,982 "
-28021864,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6.5X35MM," 8,033 "
-28021865,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6.5X40MM," 8,033 "
-28021866,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6.5X45MM," 8,033 "
-28021867,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6.5X50MM," 7,749 "
-28021868,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6X35MM," 8,395 "
-28021869,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6X40MM," 8,401 "
-28021870,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6X45MM," 8,395 "
-28021871,HC IMPL SCREW SPINAL XIA3 MONOAXIAL TI 6X50MM," 8,395 "
-28021872,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 4.0X30MM," 9,191 "
-28021873,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 4.0X35MM," 9,191 "
-28021874,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 5.5X35MM," 8,895 "
-28021875,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 5.5X50MM," 8,895 "
-28021876,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 5X30MM," 10,041 "
-28021877,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 5X40MM," 9,191 "
-28021878,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 5X45MM," 9,191 "
-28021879,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6.5X35MM," 8,895 "
-28021880,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6.5X50MM," 8,895 "
-28021881,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6X55MM," 8,895 "
-28021882,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6X60MM," 11,380 "
-28021883,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6X35MM," 10,332 "
-28021884,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6X40MM," 9,488 "
-28021885,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 6X50MM," 9,488 "
-28021886,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 7.5X40MM," 7,863 "
-28021887,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 7.5X45MM," 7,863 "
-28021888,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 7.5X50MM," 7,863 "
-28021889,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 8.5X80MM," 12,613 "
-28021890,HC IMPL SCREW SPINAL XIA3 POLYAXIAL TI 8.5X90MM," 12,613 "
-28021891,HC IMPL SCREW SPINAL XLRF 4.5X25," 6,120 "
-28021892,HC IMPL SCREW THREAD 8.0X55MM," 8,460 "
-28021893,HC IMPL SCREW THREAD 8.0X60MM," 8,460 "
-28021894,HC IMPL SCREW THREAD 9.0X90MM," 8,135 "
-28021895,HC IMPL SCREW DRIVE LEFT 15CM," 77,808 "
-28021896,HC IMPL SCREW LAMINAR 2.6X5MM," 2,083 "
-28021897,HC IMPL SCREW LAMINAR 3X5MM," 2,083 "
-28021898,HC  RATORS DEVICE OCCLUSION," 27,300 "
-28021899,HC  SHEATH 10FRX65CM SET," 3,169 "
-28021900,HC  SHEATH ACCESS 14FR 35CM, 808 
-28021901,HC  SHEATH ACCESS 14FR 45CM, 808 
-28021902,HC IMP SHOULDER HEAD GLENOID AEQUALIS 50X16," 11,498 "
-28021903,HC IMP SHOULDER HEAD GLENOID AEQUALIS 52X23," 17,355 "
-28021904,HC IMP SHOULDER SHELL GLENOID AEQUALIS X LARGE," 10,125 "
-28021905,HC IMP SHOULDER SHELL GLENOID AEQUALIS MEDIUM," 10,125 "
-28021906,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 7M125D," 22,680 "
-28021907,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 7M130D," 22,680 "
-28021908,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 9M135D," 23,814 "
-28021909,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 11M140D," 23,814 "
-28021910,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 13M125D," 23,814 "
-28021911,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 13M130D," 23,814 "
-28021912,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 15M125D," 22,680 "
-28021913,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 15M130D," 22,680 "
-28021914,HC IMP SHOULDER STEM AEQUALIS HUMERAL PRESSFIT 15M135D," 22,680 "
-28021915,HC IMP SHUNT EXPRESS GLAUCOMA PRELOAD," 8,458 "
-28021916,HC IMP SLING VAGINAL 2X12CM," 7,863 "
-28021917,HC IMP SLING VAGINAL 2X7CM," 5,313 "
-28021918,HC IMP SPACER TRULOK  15MM, 884 
-28021919,HC IMP SPACER TRULOK  30MM, 969 
-28021920,HC IMP SPACER VERTEBRAL AR ANTERIOR 13MM," 28,000 "
-28021921,HC IMP SPACER VERTEBRAL CR  LORDOTIC 6MM," 8,250 "
-28021922,HC IMP SPACER VERTEBRAL CR PARALLEL 6MM," 8,250 "
-28021923,HC SPHINCTEROTOME DASH 6F 480CM," 2,053 "
-28021924,HC IMP SPHINCTEROTOME DREAMTOME 44X30," 2,805 "
-28021925,HC SPHINCTEROTOME JAGTOME 3.9X450," 2,805 "
-28021926,HC SPHINCTEROTOME OMNI 5.5F," 2,053 "
-28021927,HC SPHINCTEROTOME OMNI TOME PRE LOADED," 4,369 "
-28021928,HC SPHINCTEROTOME OMNI TOME FUSION 7F," 4,352 "
-28021929,HC IMPL SPLINT FOOTDROP NIGHT INFANT LEFT, 645 
-28021930,HC IMPL SPLINT FOOTDROP NIGHT INFANT RIGHT, 645 
-28021931,HC IMPL SPLINT FOOTDROP NIGHT JUNIOR LEFT, 645 
-28021932,HC IMPL SPLINT FOOTDROP NIGHT JUNIOR RIGHT, 645 
-28021933,HC IMPL SPLINT FOOTHOLD SECURE, 459 
-28021934,HC STAPLE RING VALTRAC BAR 28X1.5MM," 3,085 "
-28021935,HC IMP STAPLE BLOUNT EPIPHYSEAL 27MM," 1,539 "
-28021936,HC STAPLE CAUDAL 19MM," 13,326 "
-28021937,HC STAPLE INSERTER," 9,488 "
-28021938,HC IMP STAPLE RELOAD CARTRIDGE BLUE 45M, 978 
-28021939,HC IMP STAPLE TIBIAL STEPPED HIGH 5MM, 557 
-28021940,HC STAPLER ENDOSCOPIC LINEAR GREEN 45M," 2,467 "
-28021941,HC IMP STENT PIGTAIL DOUBLE 7X4CM, 485 
-28021942,HC IMP STENT PIGTAIL DOUBLE 7X7CM, 485 
-28021944,HC IMP STENT BILIARY ASSURANT 9X40X80," 8,250 "
-28021945,HC IMP STENT BILIARY COTTON LEUNG CLSO 5-5, 587 
-28021946,HC IMP STENT BILIARY COTTON LEUNG CLSO 5-7, 587 
-28021947,HC IMP STENT BILIARY COTTON LEUNG CLSO 7-5, 445 
-28021948,HC IMP STENT BILIARY COTTON LEUNG CLSO 7-7, 445 
-28021949,HC IMP STENT BILIARY LIFESTENT 10X30X130," 11,250 "
-28021950,HC IMP STENT BILIARY LIFESTENT 7X100X130," 16,313 "
-28021951,HC IMP STENT BILIARY LUMINEXX 6X60X135," 13,088 "
-28021952,HC IMP STENT BILIARY SELF EXPANDING 6X40X120," 8,250 "
-28021953,HC IMP STENT BILIARY SELF EXPANDING 7X40X120," 8,250 "
-28021954,HC IMP STENT BILIARY OASIS 8.5-5, 973 
-28021955,HC IMP STENT BILIARY OASIS 8.5-7, 973 
-28021956,HC IMP STENT GEENEN PANCREATIC 5FR 3CM, 485 
-28021957,HC IMP STENT GEENEN PANCREATIC 5FR 5CM, 485 
-28021958,HC IMP STENT GRAFT AAA EXCLUDER 23X12X16," 49,569 "
-28021959,HC IMP STENT GRAFT AAA EXCLUDER 23X15.5X16CM," 64,968 "
-28021960,HC IMP STENT GRAFT AAA EXCLUDER 26X12X16," 50,564 "
-28021961,HC IMP STENT GRAFT AAA EXCLUDER 28.5X12X16," 49,569 "
-28021962,HC IMP STENT GRAFT AAA OVATION 18X48," 25,200 "
-28021963,HC IMP STENT GRAFT AAA OVATION 23X80," 57,915 "
-28021964,HC IMP STENT GRAFT BILIARY VIABIL 10X10X200," 15,488 "
-28021965,HC IMP STENT GRAFT INTUITRAK 20X70," 69,480 "
-28021966,HC IMP STENT GRAFT TAA VALIANT 42X42X150," 92,475 "
-28021967,HC IMP STENT GRAFT TAA VALIANT 38X34X150," 88,725 "
-28021968,HC IMP STENT GRAFT TAA VALIANT 38X38X150," 88,725 "
-28021969,HC IMP STENT GRAFT TRACHEOBRONCHIAL VIABAHN 11X10X110," 19,621 "
-28021970,HC IMP STENT PANCREATIC ZIMMON 3F 4CM, 544 
-28021971,HC IMP STENT PANCREATIC ZIMMON 5F 3CM, 612 
-28021972,HC IMP STENT PANCREATIC ZIMMON 5F 5CM, 485 
-28021973,HC STENT TUBE LACRIMAL INTUBATION, 676 
-28021974,HC IMP STENT VASCULAR ZILVER 6X20X125," 8,213 "
-28021975,HC IMP STRUT STD TAYLOR SPATIAL LONG," 7,336 "
-28021976,HC IMP STRUT STD TAYLOR SPATIAL MEDIUM," 7,349 "
-28021977,HC IMP STRUT STD TAYLOR SPATIAL SHORT," 7,744 "
-28021978,HC IMP SYNMESH ROUND TI 15X16MM," 4,211 "
-28021979,HC SYRINGE STELLANT SINGLE SPIKE KIT," 3,078 "
-28021980,HC IMP TACK FIXATION TEMPORARY," 1,522 "
-28021981,HC IMP TESTICULAR IMPLANT ADULT REGULAR," 1,488 "
-28021982,HC IMP TESTICULAR IMPLANT CHILD," 1,488 "
-28021983,HC IMP TESTICULAR IMPLANT YOUTH," 1,488 "
-28021984,HC IMP TESTICULAR PROSTHESIS LARGE," 12,375 "
-28021985,HC IMP TESTICULAR PROSTHESIS MEDIUM," 12,375 "
-28021986,HC IMP TESTICULAR SALINE X SMALL 2.2CM," 13,125 "
-28021987,HC TIP CUSA MICRO PLUS EXCEL 36KHZ," 4,888 "
-28021988,HC IMP TISSUE ALLOGRAFT FEMUR LEFT," 33,618 "
-28021989,HC IMP TISSUE ALLOGRAFT FEMUR RIGHT," 33,618 "
-28021990,HC IMP TISSUE ALLOGRAFT SKIN MESH 22X2CM," 3,035 "
-28021991,HC IMP TISSUE BONE FEMORAL SHAFT FROZEN 10CM," 8,070 "
-28021992,HC IMP TISSUE BONE FIBULA SHAFT 18CM," 7,939 "
-28021993,HC IMP TISSUE MATRIX CONEXA 100 2X4CM," 18,515 "
-28021994,HC IMP TISSUE MATRIX CONEXA 100 4X4CM," 18,515 "
-28021995,HC IMP TISSUE MATRIX CONEXA 100 5X10CM," 18,515 "
-28021996,HC IMP TISSUE MATRIX CONEXA 100 6X6CM," 18,515 "
-28021997,HC IMP TISSUE MATRIX CONEXA 200 5X10CM," 18,515 "
-28021998,HC IMP TISSUE MATRIX CONEXA 200 5X5CM," 18,515 "
-28021999,HC IMP TISSUE SKIN NON MESHED 1/2SQ CM," 3,545 "
-28022000,HC IMP TOE GREAT SWANSON TI SIZE 0," 5,823 "
-28022001,HC IMP TOE GREAT SWANSON TI SIZE 1," 1,870 "
-28022002,HC IMP TOE GREAT SWANSON TI SIZE 2," 1,870 "
-28022003,HC IMP TOE GREAT SWANSON TI SIZE 3," 1,870 "
-28022004,HC IMP TOE GREAT SWANSON TI SIZE 4," 1,870 "
-28022005,HC IMP TOE HINGE FLEX SIZE 1," 1,998 "
-28022006,HC IMP TOE HINGE FLEX SIZE 2," 1,998 "
-28022007,HC IMP TOE HINGE FLEX SIZE 3," 1,998 "
-28022008,HC IMP TOE HINGE FLEX SIZE 4," 2,593 "
-28022009,HC IMP TOE HINGE FLEX SIZE 5," 1,998 "
-28022010,HC IMP TOE HINGE FLEX SIZE 6," 2,593 "
-28022011,HC IMP TOE HINGE FLEX SIZE 7," 2,593 "
-28022012,HC TRACH 7.0 PERCUTANEOUS KIT," 2,246 "
-28022013,HC TRACH 9.0 PERCUTANEOUS KIT," 2,246 "
-28022014,HC TUBE BERLIN BLUE," 4,250 "
-28022015,HC TUBE BERLIN RED," 4,250 "
-28022016,HC TUBE ENDOTRACH NIM EMG 6MM," 2,357 "
-28022017,HC TUBE ENDOTRACH NIM EMG 7MM," 2,357 "
-28022018,HC  TUBE GASTROSTOMY BUTTON 4X0.8CM, 765 
-28022019,HC  TUBE LACRIMAL CANAL MONOKA, 496 
-28022020,HC  TUBE LARYTUBE 10.5ID 13.5D 55L, 995 
-28022021,HC  TUBE LARYTUBE 12.0ID 15D 55L, 995 
-28022022,HC  TUBE LARYTUBE 9.5ID 12.OD 55L, 995 
-28022023,HC  TUBE TRACH CUFFED BIVONA 6MM, 845 
-28022024,HC  TUBE TRACH MONTGOMERY SAFE T 12MM," 1,445 "
-28022025,HC  TUBE TRACH MOORE 10MM," 1,318 "
-28022026,HC  TUBE TRACH MOORE 8MM," 1,318 "
-28022027,HC  TUBE TRACH MONTGOMERY SAFE T TUBE 11MM," 1,445 "
-28022028,HC  TUBE TRACH EXTRA LENGTH 6.0MM, 527 
-28022029,HC  TUBE TRACH EXTRA LENGTH 7.0MM, 527 
-28022030,HC IMP VALVE AORTIC 15MM," 49,465 "
-28022031,HC IMP VALVE AORTIC MTF CRYO 8MM," 47,418 "
-28022032,HC IMP VALVE AORTIC PROSTHETIC ON-X 19M," 28,000 "
-28022033,HC IMP VALVE AORTIC PROSTHETIC ON-X 21M," 28,000 "
-28022034,HC IMP VALVE AORTIC PROSTHETIC ON-X 23M," 28,000 "
-28022035,HC IMP VALVE AORTIC PROSTHETIC ON-X 25M," 28,000 "
-28022036,HC IMP VALVE AORTIC PROS ON-X 27/29M," 28,000 "
-28022037,HC IMP VALVE AORTIC PROS W/EXT HLD 19," 28,000 "
-28022038,HC IMP VALVE AORTIC PROS W/EXT HLD 23," 28,000 "
-28022039,HC IMP VALVE AORTIC TRIFECTA STENT 21MM," 31,465 "
-28022040,HC IMP VALVE AORTIC TRIFECTA STENT 23MM," 31,465 "
-28022041,HC IMP VALVE AORTIC TRIFECTA STENT 25MM," 31,465 "
-28022042,HC IMP VALVE AORTIC TRIFECTA STENT 27MM," 31,465 "
-28022043,HC IMP VALVE AORTIC TRIFECTA STENT 29MM," 31,465 "
-28022044,HC IMP VALVE LUMBAR LOW FLOW OSVII," 20,806 "
-28022045,HC IMP VALVE LUMBAR LOW FLOW W/CHAM OSVII," 20,806 "
-28022046,HC IMP VALVE LUMBAR LOW PROFILE OSVII," 23,219 "
-28022047,HC IMP VALVE LUMBAR SYSTEM OSVII," 20,806 "
-28022048,HC IMP VALVE LUMBAR W/ANTECH SYSTEM," 20,806 "
-28022049,HC IMP VALVE MITRAL 15MM," 49,465 "
-28022050,HC IMP VALVE MITRAL PROS ON-X 25/3M," 28,000 "
-28022051,HC IMP VALVE MITRAL PROS ON-X 25M," 28,000 "
-28022052,HC IMP VALVE MITRAL PROS ON-X 27/29M," 28,000 "
-28022053,HC IMP VALVE MITRAL PROS ON-X 31/33M," 28,000 "
-28022054,HC IMP VALVE NEURO MULTIPURPOSE ANTI SIPHON," 8,654 "
-28022055,HC IMP VALVE NEURO ULTRA VS NEON HIGH," 4,651 "
-28022056,HC IMP VALVE NEURO ULTRA VS NEON LOW," 4,651 "
-28022057,HC IMP VALVE NEURO ULTRA VS NEON MEDIUM," 4,651 "
-28022058,HC IMP VEIN SAPHENOUS 21-30MM," 38,103 "
-28022059,HC VEST BREMER  PETITE 160CM 26-3," 8,083 "
-28022060,HC VEST BREMER CHILD 22-28," 7,773 "
-28022061,HC VEST BREMER LARGE 36-42," 7,548 "
-28022062,HC VEST BREMER TODDLER 20-24," 7,773 "
-28022063,HC IMP WIRE KIRSCHNER BEVEL TIP SPHERX DBR II, 536 
-28022064,HC IMP WIRE KIRSCHNER THREADED PRECEPT SS, 680 
-28022065,HC IMP WIRE MANDRIN MALLEABLE TIP 4FX44CM, 229 
-28022066,HC IMP ALLOGRAFT FEMORAL BIOCOMPARTMENT RIGHT," 86,625 "
-28022067,HC APPLIER HELIX-FX ENDOANCHOR," 33,250 "
-28022068,HC APPLIER HELIX-FX ENDOANCHOR 18FX114CM," 38,480 "
-28022069,HC  CATHETER BALLOON OCCULSION 7X7MM," 11,213 "
-28022070,HC  CATHETER BALLOON PTA 10X4X135," 2,338 "
-28022071,HC  CATHETER BALLOON PTA 10X8X135," 1,488 "
-28022072,HC  CATHETER BALLOON PTA 12X10X135," 1,488 "
-28022073,HC  CATHETER BALLOON PTA 12X4X135," 2,338 "
-28022074,HC  CATHETER BALLOON PTA 3X2X135," 1,488 "
-28022075,HC  CATHETER BALLOON PTA 3X2X80," 1,488 "
-28022076,HC  CATHETER BALLOON PTA 3X4X135," 1,488 "
-28022077,HC  CATHETER BALLOON PTA 3X8X80," 1,488 "
-28022078,HC  CATHETER BALLOON PTA 7X8X135," 2,338 "
-28022080,HC  CATHETER DIALYSIS EQUISTREAM 16FX31CM," 3,100 "
-28022081,HC  CATHETER DILATATION Z-MED 16X6X100MM," 4,697 "
-28022082,HC  CATHETER DILATION ADVANCE 18LP 4X135X3X10," 2,338 "
-28022083,HC  CATHETER DILATION ADVANCE 18LP 4X135X3X2," 2,338 "
-28022084,HC  CATHETER DILATION ADVANCE 18LP 4X135X3X4," 2,338 "
-28022085,HC  CATHETER DILATION ADVANCE 18LP 4X135X3X6," 2,338 "
-28022086,HC  CATHETER DILATION ADVANCE 18LP 4X135X4X2," 2,338 "
-28022087,HC  CATHETER DILATION ADVANCE 18LP 4X135X4X6," 2,338 "
-28022088,HC  CATHETER DILATION ADVANCE 18LP 4X135X5X4," 2,338 "
-28022089,HC  CATHETER DILATION ADVANCE 18LP 4X135X5X8," 2,338 "
-28022090,HC  CATHETER DILATION ADVANCE 18LP 4X135X6X2," 2,338 "
-28022091,HC  CATHETER DILATION ADVANCE 18LP 4X135X6X4," 2,338 "
-28022092,HC  CATHETER DILATION ADVANCE 18LP 4X135X7X10," 2,338 "
-28022093,HC  CATHETER DILATION ADVANCE 18LP 4X135X7X4," 2,338 "
-28022094,HC  CATHETER DILATION ADVANCE 18LP 4X135X7X6," 2,338 "
-28022095,HC  CATHETER DILATION ADVANCE 18LP 4X135X7X8," 2,338 "
-28022096,HC  CATHETER DILATION ADVANCE 18LP 4X135X8X6," 2,338 "
-28022099,HC  CATHETER SELECT NEURON SIM 6FX125CM," 1,063 "
-28022100,HC  CATHETER SELECT NEURON SIM-V 6X125," 1,063 "
-28022101,HC IMP ELBOW MODULAR RADIAL HEAD 6.5MM," 17,815 "
-28022102,HC IMP GFT CONDYLE PARTIAL MEDIAL LEFT," 46,638 "
-28022103,HC GUIDE HELIX-FX 16FX62X28CM," 13,208 "
-28022104,HC GUIDE HELIX-FX 18FX90X22CM," 16,418 "
-28022105,HC GUIDE HELIX-FX 18FX90X32CM," 16,418 "
-28022106,HC  GUIDEWIRE APPROACH CTO 014X300 12G," 1,148 "
-28022107,HC  GUIDEWIRE ROADRUNNER .35X60CM, 561 
-28022108,HC  INTROD MICKEY GASTRO 16F," 1,587 "
-28022109,HC  INTRODUCER SHEATH SENTRANT 16FX28CM," 1,700 "
-28022110,HC IMP LEAD NEUROSTIMULATOR 50X16CM KIT," 27,300 "
-28022111,HC IMP MATRIX HEMOSTATIC SURGIFLO 8ML," 6,633 "
-28022112,HC IMP SCREW CORTICAL LOW PROFILE 4.5X38MM, 714 
-28022113,HC IMP SCREW CORTICAL LOW PROFILE 4.5X42MM, 714 
-28022114,HC IMP SCREW CORTICAL LOW PROFILE 4.5X44MM, 714 
-28022115,HC IMP SCREW CORTICAL LOW PROFILE 4.5X46MM, 714 
-28022116,HC IMP SCREW CORTICAL LOW PROFILE 4.5X48MM, 714 
-28022117,HC IMP SCREW CORTICAL LOW PROFILE 4.5X50MM, 714 
-28022118,HC IMP SCREW CORTICAL LOW PROFILE 4.5X56MM, 714 
-28022119,HC IMP SCREW CORTICAL LOW PROFILE 4.5X78MM, 714 
-28022120,HC IMP SCREW CORTICAL LOW PROFILE4.5X62MM, 714 
-28022121,HC IMP SCREW CORTICAL LOW PROFILE4.5X64MM, 714 
-28022122,HC IMP SCREW CORTICAL LOW PROFILE4.5X66MM, 714 
-28022123,HC IMP SCREW INTERFERENCE 8X20MM, 978 
-28022124,HC  RATOR FLEX 064X0.054IN," 20,930 "
-28022125,HC IMP SPHINCTEROTOME JAGTOME 3.9X260," 2,805 "
-28022126,HC SPLITTER ADAPTOR HIGH VOLTAGE KIT," 3,655 "
-28022127,HC IMP STENT GRAFT AAA 18F 25X14X102," 64,838 "
-28022128,HC IMP STENT GRAFT AAA ENDURANT 16X20X82MM," 34,475 "
-28022129,HC IMP STENT GRAFT AAA ENDURANT II 28X13X145," 64,838 "
-28022130,HC IMP VALVE HEART  SAPIEN ASCENDRA 3 23MM KIT," 81,250 "
-28022131,HC IMP VALVE HEART  SAPIEN ASCENDRA 3 26MM KIT," 81,250 "
-28022132,HC IMPL SCREW BIO RETRO 10MMX20MM," 1,913 "
-28022133,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4.5X12MM," 3,535 "
-28022134,HC IMP ICD DUAL EVERA XT DEVICE," 88,050 "
-28022135,HC IMP ANCHOR SUTURE BIORAPTOR 2.9MM," 2,985 "
-28022136,HC IMP CONFORMER OCULAR SMALL 20MM, 298 
-28022137,HC IMPL PACEMAKER DUAL ADAPTA ADDR01P," 29,750 "
-28022138,HC IMP ANCHOR SUTURE FIBERWIRE FIX KIT," 1,488 "
-28022140,HC IMP STENT URETERAL DOUBLE J ACMI 7FX12CM," 2,125 "
-28022143,HC PLATELET CONCEN MIN BIOCUE KIT," 12,000 "
-28022144,HC GENERATOR NEUROSTIMULATOR ACTIVA KIT," 87,950 "
-28022145,HC IMP ANCHOR SUTURE FIBERWIRE 2 3X14MM, 544 
-28022146,HC IMP ANCHOR SUTURE FIBERWIRE SZ 2 3MM, 519 
-28022147,HC  INTRODUCER MAX BARER 9FX10CM, 530 
-28022148,HC  CATHETER FOLEY 3WAY 20FX30ML, 225 
-28022149,HC IMP LEAD DEFIBRILLATOR 11MM 21X58CM," 37,375 "
-28022150,HC  RETRIEVAL FOREIGN BODY 4X5.5CM, 850 
-28022151,HC  CATHETER DIALYSIS PERITONEAL 11X11, 129 
-28022152,HC IMP STENT ZILVER VASCULAR 14X60X80," 11,250 "
-28022153,HC IMP BAR SPINAL TRANSVERSE 6MM 3.5MM," 3,355 "
-28022154,HC IMP ROD HARD TITANIUM 3.5X120MM," 2,125 "
-28022155,HC IMP PERICARDIOCENTESIS 6F KIT, 848 
-28022156,HC IMP SYRINGE DELV SYS ALIQUO 4IN," 3,400 "
-28022157,HC CATHETER REVISION SPINAL," 2,550 "
-28022158,HC IMP PIN FLEXIBLE PASSING 13.5X2.4MM, 919 
-28022159,HC SUTURE PASSER CRV LEFT 45D," 2,188 "
-28022160,HC SUTURE PASSER CRV RIGHT 45D," 2,188 "
-28022161,HC IMP CARTRIDGE CORTOSS 5CC," 6,800 "
-28022162,HC IMPL SCREW FIXED ANGLED 6.0X25MM," 6,600 "
-28022163,HC IMP NAIL ADOLSECENT TAN 8.5X30," 15,728 "
-28022164,HC IMP SYNMESH VBR TITANIUM ROUND 12X88MM," 31,535 "
-28022165,HC IMP SCREW INTERFERENCE HEX CAP 4.5X35MM," 3,808 "
-28022166,HC IMP MESH MANDIBLE C TYPE 1.0MM," 5,032 "
-28022167,HC IMP CLOVERLEAF MANDIBLE B TYPE 1.0," 5,032 "
-28022168,HC IMP ARM REMOVABLE EXTENSION 40MM," 6,202 "
-28022169,HC IMP SCREW CORTEX SELF TAPPING 1.0X4MM, 615 
-28022170,HC IMP SCREW CORTEX SELF TAPPING 1.0X6MM, 615 
-28022171,HC  CATHETER PEZZAR 12F, 128 
-28022172,HC IMP WIRE KIRSCHNER VARIAX STD 1.1X160MM, 250 
-28022173,HC IMP PEG THREAD 2.5X16MM FP-16, 865 
-28022174,HC IMPL PLATE CONDYLAR 2.4MM," 5,713 "
-28022175,HC IMP STENT GRAFT ICAST 7X22X80," 16,875 "
-28022176,HC NEEDLE PROCORE ECHO HD 19G," 2,962 "
-28022177,HC NEEDLE PROCORE ECHO HD 22G," 2,962 "
-28022178,HC  CATHETER MALECOT 34F, 173 
-28022179,HC IMP LEAD NEUROSTIMULATOR 75CM KIT," 13,500 "
-28022180,HC IMP PIN CLAVICLE ROCKWOOD 2.5MM," 10,448 "
-28022181,HC ELECTRODE VAPOR HOOK 90D 3.5MM," 2,363 "
-28022182,HC IMP ANCHOR BIO SUTURE KNOTLESS," 3,375 "
-28022183,HC IMP ANCHOR SCREW TAG 3.7MM, 831 
-28022184,HC IMP STENT GRAFT AAA EXCLUDER 23X14X14," 64,968 "
-28022185,HC BONE GRAFT HARVESTER 6MM," 4,420 "
-28022186,HC BONE GRAFT HARVESTER 8MM," 2,508 "
-28022187,HC BONE GRAFT HARVESTER 10MM," 4,420 "
-28022188,HC NERVE PROTECTOR AXOGUARD 10X40," 10,875 "
-28022189,HC NERVE PROTECTOR AXOGUARD 7X40," 11,625 "
-28022190,HC AUTOGRAFT SMALL JOINT TRANSFER 8MM SET," 4,208 "
-28022191,HC IMPL SCREW AND TOP HATS 36MM," 2,975 "
-28022192,HC IMPL SCREW AND TOP HATS 38MM," 2,975 "
-28022193,HC  TUBE GASTROSTOMY MIC 16F, 244 
-28022194,HC IMP ELBOW STEM ULNAR," 28,028 "
-28022195,HC IMP WRIST HEAD ULNAR SZ4," 11,550 "
-28022196,HC CATHETER INTRATHECAL 2 PC," 5,610 "
-28022197,HC  CATHETER BALLOON 4F 2MMX120MM," 3,915 "
-28022198,HC  TUBE GASTROSTOMY MIC 7-10ML 20F, 492 
-28022199,HC  TUBE GASTROSTOMY MIC 12F, 244 
-28022200,HC  CATHETER DISTAL INTRACRANIAL 5F 115CM," 10,650 "
-28022201,HC  CATHETER DISTAL INTRACRANIAL 5F 125CM," 10,650 "
-28022202,HC  CATHETER DISTAL INTRACRANIAL 6F 0.07," 7,800 "
-28022203,HC  CATHETER ARMADA 14 2.0X80X150," 3,825 "
-28022204,HC  CATHETER ARMADA 14 3.0X120X150," 3,825 "
-28022205,HC  TUBE GASTROSTOMY MIC 24F, 492 
-28022206,HC INTRODUCER VASCULAR CATH 5F SET, 204 
-28022207,HC  INTROD PL AWY 7X15.5 SET, 808 
-28022208,HC  INTROD PL AWY 9X15.5X0.038 SET, 324 
-28022209,HC  INTRODUCER NON VASC PEEL AWAY WO GDE 18F, 324 
-28022210,HC IMP STENT GRAFT AAA EXCLUDER 28.5X12X16," 64,968 "
-28022211,HC  CATHETER BALLOON GUIDING MERCI 7FX95CM," 7,438 "
-28022213,HC ACCESS LIVER TRANSJUGULAR 14G SET," 3,445 "
-28022214,HC IMP ICD SINGLE PROTECA XT VR DF4," 75,600 "
-28022215,HC IMP LEAD PACING QUATTRO SECURE 72CM," 35,000 "
-28022216,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.5X38," 13,688 "
-28022217,HC IMP STENT CORONARY XIENCE EXPEDITION RX DES 2.75X8," 13,125 "
-28022218,HC IMP PIN STEINMAN 9/64X9IN, 167 
-28022219,HC IMP EXTERNAL FIXATOR HINGE TRANSLATION," 1,768 "
-28022220,HC IMP GRAFT TIBIAL STRUT 150-200MM," 6,783 "
-28022221,HC IMP SILASTIC BALLS 16MM, 587 
-28022222,HC IMP SILASTIC BALLS 18MM, 587 
-28022223,HC IMP SILASTIC BALLS 22MM, 587 
-28022224,HC IMPL PLATE MAXILLOFACIAL CHIN 4MM," 2,042 "
-28022226,HC FORCEP BIOPSY JAW 7F 2.2X50CM," 2,206 "
-28022227,HC  INTRODUCER SHEATH PLNG 14CM 9F, 425 
-28022228,HC IMP EAR PROSTHESIS CAUSSE TOTAL SHOE 0.4X2.7X10MM," 2,547 "
-28022229,HC IMP EAR PROSTHESIS PARTIAL MORETZ 1.17X4.75MM," 2,593 "
-28022230,HC IMP PIN GRILL GUIDE TIP 2.4MM, 272 
-28022231,HC  CATHETER BROVIAC SINGLE LUMEN 6.6F," 1,051 "
-28022232,HC IMP STENT URETERAL DOUBLE J ACMI 8.5FX16," 1,233 "
-28022233,HC  TUBE TRANSGASTRIC JEJUNAL 7-10ML 18FX45CM," 1,775 "
-28022234,HC GRASPER SUTURE IDEAL 15 DEGREE," 2,301 "
-28022235,HC GRASPER SUTURE IDEAL 45 DEGREE," 2,355 "
-28022236,HC IMP STENT URETERAL ACMI DOUBLE-J 6FX16," 1,862 "
-28022237,HC  CATHETER HEMODIALYSIS STRAIGHT 8FX24CM," 1,530 "
-28022238,HC  CATHETER NEPHROSTOMY 8F, 655 
-28022239,HC IMPL PLATE T 4H 84MM," 2,193 "
-28022240,HC  CATHETER BILIARY PROBE FOGARTY 6FX23CM, 652 
-28022241,HC IMPL PACEMAKER DUAL THERA 7962," 36,270 "
-28022242,HC IMP STENT PANCREATIC 7F 9CM SET, 791 
-28022243,HC IMP STENT PANCREATIC 5F 9CM SET," 1,012 "
-28022244,HC  INTRODUCER SHEATH 35MM 5.5CM 7F, 230 
-28022245,HC CATHETER CENTRAL VENOUS NEEDLE 20G 21G 3F SET, 273 
-28022247,HC  CATHETER BROVIAC SINGLE LUMEN 2.7F, 812 
-28022248,HC  INTRODUCER CHECK FLO LONG 12F 30CM, 561 
-28022249,HC IMPL PACEMAKER DUAL THERA 7968," 36,855 "
-28022250,HC BUR ATHERECTOMY 1.25MM," 7,608 "
-28022251,HC BUR ATHERECTOMY 1.5MM," 7,608 "
-28022252,HC DISSECT BLLN ENDO HERNIA," 2,719 "
-28022253,HC IMP BLOCK SILICONE 7X4X1.5CM," 1,216 "
-28022254,HC  CATHETER FOGARTY THRU LUMEN 3F, 717 
-28022255,HC IMP ROD FRONTALIS EYE SUSPENSION SET, 338 
-28022256,HC  GUIDEWIRE THREAD 1.1X150MM, 285 
-28022257,HC  GUIDEWIRE NON THREAD 1.1X150MM, 268 
-28022258,HC IMP VALVE AORTIC MASTERS EXPANDED CUFF 21MM," 30,674 "
-28022259,HC  CATH ESOPHAGEAL DILATATION FIXED WIRE 6F 8-10," 2,788 "
-28022260,HC  CATHETER DILATATION CRE 10-12X8X180CM," 1,658 "
-28022261,HC  CATH ESOPHAGEAL DILATATION FIXED WIRE 6F 12-15," 1,662 "
-28022262,HC  CATHETER DILATATION CRE 15-16.5-18X8CM," 1,662 "
-28022263,HC  GUIDEWIRE THREAD 150MM 5MM, 160 
-28022264,HC CATHETER CENTRAL VENOUS DOUBLE LUM PED 7X65," 1,716 "
-28022266,HC  CATHETER HEMODIALYSIS PRE-CURVED 12.5FX28CM," 1,870 "
-28022267,HC  CATHETER HEMODIALYSIS PRE-CURVED 12.5FX32CM," 1,870 "
-28022268,HC  CATHETER HEMODIALYSIS DUAL LUMEN 12.5FX32CM," 1,870 "
-28022269,HC  INTROD CK FLO LRG 16X35 SET, 876 
-28022270,HC  CATHETER QUINTON RIGHT 62.5CM," 1,522 "
-28022271,HC  CATHETER QUINTON INFANT LEFT 38.9CM, 988 
-28022272,HC  CATHETER QUINTON PEDIATRIC LEFT 43CM," 1,557 "
-28022274,HC IMPL SCREW CANNULATED 5MM," 1,819 "
-28022275,HC  TUBE GASTROSTOMY LOW PROFILE 18F 3.0, 731 
-28022276,HC  TUBE GASTROSTOMY LOW PROFILE 18F 2.3, 731 
-28022277,HC  TUBE GASTROSTOMY LOW PROFILE 14F 1.7, 769 
-28022279,HC  CATHETER EMBOLECTOMY FOGARTY ARTERIAL 6F, 412 
-28022281,HC IMPL SCREW CORTEX TITANIUM 3.5X14MM, 132 
-28022282,HC IMPL SCREW CORTEX TITANIUM 3.5X30MM, 119 
-28022283,HC IMPL SCREW CORTEX TITANIUM 3.5X32MM, 119 
-28022284,HC IMPL SCREW CORTEX TITANIUM 3.5X34MM, 119 
-28022285,HC IMPL SCREW CORTEX TITANIUM 3.5X36MM, 119 
-28022286,HC  CATHETER INTRA 19GX24IN, 231 
-28022287,HC AUTOGRAFT TRANSFER SYSTEM 5MM," 4,242 "
-28022288,HC AUTOGRAFT TRANSFER SYSTEM  6 & 7MM," 4,242 "
-28022289,HC AUTOGRAFT TRANSFER SYSTEM 7MM," 4,242 "
-28022291,HC CLIP APPLIER 5MM," 3,320 "
-28022292,HC IMPL SCREW 4.0 FULL THREAD TITANIUM 14MM, 136 
-28022293,HC IMPL SCREW 4.0 TITANIUM 55MM, 136 
-28022294,HC IMPL SCREW 4.0 TITANIUM 60MM, 136 
-28022295,HC  CATHETER EMBOLECTOMY FOGARTY ARTERIAL 5F, 412 
-28022296,HC  GUIDEWIRE LUNDERQUIST EXTRA STIFF 35IN 260CM, 825 
-28022297,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.0X6MM, 415 
-28022298,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL 2.0X10MM, 422 
-28022299,HC IMPL PLATE NARROW STRAIGHT 4 HOLE," 2,060 "
-28022300,HC IMP LEAD PACEMAKER VENTRCULAR UNIPOLAR 35CM," 6,120 "
-28022301,HC IMP PORT INF OPEN END 6.6F KIT," 1,887 "
-28022302,HC  CATHETER TENCKHOFF STRAIGHT PEDIATRICS 37CM, 477 
-28022305,HC IMPL PLATE BONE SPIDER OFFST TIMX 16MM, 553 
-28022306,HC CATHETER JEJUNOSTOMY BALLOON 16F," 3,078 "
-28022307,HC IMP KNEE STEM EXTENSION STRAIGHT 15X75MM," 7,686 "
-28022308,HC IMPL PLATE RESORBABLE 1.5 ADAPT 20HOLE," 2,128 "
-28022309,HC IMPL PLATE RESORBABLE 1.5 STRUT 2X10MM," 3,482 "
-28022310,HC IMPL PLATE RESORBABLE 1.5 STRUT 2X18MM," 4,311 "
-28022311,HC IMPL PLATE RESORBABLE 3D MESH THICKNESS 50MM .5MM," 8,400 "
-28022312,HC IMP LEAD PACEMAKER CAPSUREFIX NOVUS SILICONE 58CM," 4,675 "
-28022313,HC IMP SCREW CORTICAL 3.5MM, 289 
-28022314,HC IMPL PACEMAKER DUAL KAPPA 700 DR KDR706," 40,950 "
-28022315,HC IMP LEAD EXTENSION PERCUTANEOUS," 1,318 "
-28022316,HC IMPL NEUROSTIMULATOR LEAD EZ KIT, 383 
-28022317,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 11.5D," 1,139 "
-28022318,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 12.0D," 1,139 "
-28022319,HC IMPL LENS IOL POSTERIOR CHAMBER ACRYLIC 12.5D," 1,139 "
-28022320,HC IMP KNEE INSERT LOCKING BAR MODULAR, 859 
-28022321,HC IMP HIP HEAD MODULAR COBALT CHROME 28MM," 4,250 "
-28022322,HC IMP STENT BILIARY EXPRESS 10X25X75," 15,051 "
-28022323,HC IMPL SCREW BUTTRESS THREAD TITANIUM 5X50MM," 1,828 "
-28022324,HC IMP ENDCAP TI FOR SOLID TIBIAL NAILS," 1,214 "
-28022325,HC IMP ENDCAP TI CANNULATED TIBIAL NAILS," 1,275 "
-28022326,HC  CATHETER DUAL LUMEN 12FX90CM," 1,253 "
-28022327,HC IMP OCCLUDER AMPLATZ ASD 6F 4MM," 33,250 "
-28022328,HC IMPL SCREW CERVICAL SPINE LOCKING PLATE 4.0X16MM," 2,015 "
-28022329,HC IMPL SCREW CERVICAL SPINE LOCKING PLATE 4.0X14MM," 2,015 "
-28022330,HC  TUBE VENT MORETZ 1.27MM, 96 
-28022331,HC IMP ANCHOR FASTAK SPEAR, 680 
-28022332,HC IMP CAP SPINAL TYPHOON MONARCH TI," 2,426 "
-28022333,HC  CATHETER THROMBECTOMY VSL SHAFT 5F 7F," 12,713 "
-28022334,HC  RETRIEVAL FOREIGN BODY 3X6CM, 757 
-28022335,HC IMP STENT BILIARY EXPRESS 8X20X75," 9,735 "
-28022336,HC  GUIDEWIRE HI TORQUE WHOL 35IN 145CM, 666 
-28022337,HC  CATHETER DILATATION PTA XT 75X6X2," 1,958 "
-28022338,HC  CATHETER BALLOON PTA DILATATION XT 8X4X75," 1,882 "
-28022339,HC  CATHETER DILATATION PTA XT 75X10X4," 1,958 "
-28022340,HC  TUBE GASTROSTOMY LOW PROFILE 18F 1.7, 838 
-28022341,HC IMPL SCREW CANCELLOUS 2.5X22MM, 865 
-28022342,HC IMPL SCREW CANCELLOUS 2.5X24MM, 918 
-28022343,HC  GUIDEWIRE DRILL TIP 2.5X200MM, 641 
-28022344,HC IMPL SCREW LOCKING 5.0X80MM," 1,927 "
-28022345,HC IMPL PLATE TIBIAL 4HOLE," 10,208 "
-28022346,HC IMPL GRAFT VEIN FEMORAL CRYOVEIN 60-69CM," 19,075 "
-28022347,HC IMP LINK SPINELINK CROSSLNK BLUE 21X3MM," 2,672 "
-28022348,HC IMPL SCREW LOCKING 5.0X55MM," 1,927 "
-28022349,HC IMP HIP LINER ACETABULAR CUP 56X36MM," 5,406 "
-28022350,HC IMP HIP HEAD 1.5X36MM," 7,208 "
-28022351,HC IMP BREAST SILTEX CONTOUR LOW HEIGHT 250CC," 9,938 "
-28022352,HC IMP BREAST SILTEX CONTOUR LOW HEIGHT 350CC," 9,938 "
-28022353,HC  GUIDEWIRE THREAD SINGLE 54X7IN, 94 
-28022354,HC IMP CLOSURE PROGLIDER 6F," 1,946 "
-28022355,HC  CATHETER QUINTON PRESTERNAL 112.8CM," 4,509 "
-28022356,HC IMP STENT PERIPHERAL XCEED 7X30X120," 10,313 "
-28022357,HC IMP LAMINA OTOLOGIC EPIDISC 8MM, 638 
-28022358,HC  TUBE VENT T GOODE 12MM, 220 
-28022359,HC  CATHETER DILATATION CORONARY RX 2X8MM," 1,488 "
-28022360,HC  CATHETER SUPPORT QUICK CROSS .035IN 90CM," 1,420 "
-28022361,HC  CATHETER SUPPORT QUICK CROSS .035IN 135CM," 1,420 "
-28022362,HC  INTRODUCER GASTROSTMY FAST," 1,409 "
-28022363,HC IMP STENT GRAFT ICAST 7X38X120," 18,750 "
-28022364,HC  GUIDEWIRE STRAIGHT TIP IQ MARKER 185CM, 281 
-28022365,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 10.0D," 1,275 "
-28022367,HC  INTRODUCER PEEL AWAY 16FX15.5CMX0.038IN, 324 
-28022368,HC IMP WIRE KIRSCHNER TITANIUM 0.062X6IN, 198 
-28022369,HC IMPL PLATE HOCKEY STICK LEFT," 4,463 "
-28022370,HC IMP PEG SMOOTH 2.0X10MM, 865 
-28022371,HC IMP PEG SMOOTH 2.0X12MM, 865 
-28022372,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 6.0D," 1,275 "
-28022373,HC IMPL LENS IOL POSTERIOR CHAMBER ASPHERIC 6.5D," 1,275 "
-28022374,HC IMP BIOGLUE SYRINGE 10ML," 6,866 "
-28022375,HC IMP CEMENT RESTRICTOR INSERTER 18S, 944 
-28022376,HC IMP CEMENT RESTRICTOR INSERTER 25S, 944 
-28022377,HC  LEAD CATH DELV TRNSVNS 69CM," 4,675 "
-28022378,HC IMP BASKET LITHO ROTATING 4.2X195CM," 3,400 "
-28022379,HC IMP STENT BILIARY PALMAZ BLUE OTW .018 5X14X135," 9,000 "
-28022380,HC IMP STENT BILIARY PALMAZ BLUE OTW .014 6X15X135," 9,000 "
-28022381,HC IMP STENT BILIARY PALMAZ BLUE OTW .014 5X18X135," 10,178 "
-28022382,HC IMP STENT BILIARY PALMAZ BLUE OTW .014 6X18X135," 10,178 "
-28022383,HC  SHEATH GUIDING INCLD DILTR WIRE CMP 6FX45CM, 765 
-28022384,HC  CATHETER HICKMAN DUAL LUMEN 12F," 2,586 "
-28022385,HC  ADAP INTERFACE CONN CATH 0.5FT, 714 
-28022386,HC IMP RESERVOIR OMMAYA FLAT 2.5CM," 2,791 "
-28022387,HC IMP PIN ACF DISTRACTION CASPAR14MM, 340 
-28022388,HC TRANSSPHENOIDAL KIT," 3,496 "
-28022389,HC SHEAR INSERT DAVINCI HARMONIC," 3,655 "
-28022390,HC  CATHETER FOGARTY IRRIGATION ARTERIAL 4FX80CM, 201 
-28022391,HC IMPL PLATE RESORBABLE 3D MESH 50X50X0.5MM," 7,680 "
-28022392,HC  CATHETER FOLEY SILICONE 3 WAY 24F, 95 
-28022393,HC  INTRODUCER FOLEY SUPRA LAWRENCE 16F, 173 
-28022394,HC  GUIDEWIRE TIGERWIRE 38IN, 187 
-28022395,HC BLADE TRICUT ST M4 4.0X15CM," 2,326 "
-28022396,HC IMP BONE OSTENE 2.5G, 492 
-28022397,HC BUR CHOANAL ATRESIA HIGH SPEED 4MM," 2,244 "
-28022398,HC BUR DRILL BIT 3 FLUTED CALIB 4.2X330MM," 2,178 "
-28022399,HC BUR DRILL BIT CALIB 3 FLUT QC 4X270MM," 2,245 "
-28022400,HC BUR DRILL BIT QUICK COUPLING 3.2X230MM," 2,153 "
-28022401,HC  CATHETER ACCUVU OMNI 0.35 5FX70CM, 978 
-28022403,HC  CATHETER ARMADA WRKHRS 3X20X135," 1,615 "
-28022404,HC  CATHETER ARMADA WRKHRS 4.0X150X80," 2,125 "
-28022405,HC  CATHETER ARMADA WRKHRS 4.0X200X80," 2,125 "
-28022406,HC  CATHETER ARMADA WRKHRS 4.0X250X80," 2,125 "
-28022407,HC  CATHETER ARMADA WRKHRS 5.0X150X80," 2,125 "
-28022408,HC  CATHETER ARMADA WRKHRS 5.0X200X80," 2,125 "
-28022409,HC  CATHETER ARMADA WRKHRS 5.0X250X80," 2,125 "
-28022410,HC  CATHETER ARMADA WRKHRS 5X120X80," 1,615 "
-28022411,HC  CATHETER ARMADA WRKHRS 6.0X150X80," 2,125 "
-28022412,HC  CATHETER ARMADA WRKHRS 6.0X200X80," 2,125 "
-28022413,HC  CATHETER ARMADA WRKHRS 6.0X250X80," 2,125 "
-28022414,HC  CATHETER ARMADA WRKHRS 7.0X150X80," 2,125 "
-28022415,HC  CATHETER ARMADA WRKHRS 7.0X200X80," 2,125 "
-28022416,HC  CATHETER DILATATION PULM 12X13.5X15MM," 2,168 "
-28022417,HC  CATHETER DILATATION PULM 3CM 10-12MM," 2,168 "
-28022418,HC  CATHETER DILATATION PULM 3X8-10X75CM," 2,168 "
-28022419,HC CATHETER BALLOON KARLAN LAP CHOLE SET, 132 
-28022420,HC  CATHETER BALLOON PTA 4X2X135," 1,615 "
-28022421,HC  CATHETER BALLOON PTA 4X4X135," 1,615 "
-28022422,HC  CATHETER BALLOON PTA 4X8X135," 1,615 "
-28022423,HC  CATHETER BALLOON PTA 5X4X135," 1,615 "
-28022424,HC  CATHETER BALLOON PTA 6X12X135," 1,615 "
-28022425,HC  CATHETER BALLOON PTA 6X2X135," 1,445 "
-28022426,HC  CATHETER BALLOON PTA 6X8X135," 1,488 "
-28022428,HC  CATHETER BALLOON URETERAL DUAL LUMEN 10FX50CM, 352 
-28022429,HC  CATHETER BALLOON URETERAL 5FX65CM, 989 
-28022430,HC  CATHETER BALLOON URETERAL 7FX65CM," 1,211 "
-28022431,HC  CATHETER BROVIAC SINGLE LUMEN 4.2F," 1,215 "
-28022432,HC CATHETER CENTRAL VENOUS DOUBLE LUM PED 9X90," 1,445 "
-28022433,HC  CATHETER DILATATION BIOGLIDE 90CM," 1,346 "
-28022434,HC  CATHETER DILATATION RENAL AMPLATZ 8FX84CM, 188 
-28022437,HC  CATHETER FOGARTY IRRIGATION ARTERIAL 6FX80CM, 201 
-28022438,HC  CATHETER FOGARTY ARTERIAL EMBOLECTOMY 6F, 412 
-28022439,HC  CATHETER FOGARTY THRU LUMEN 4F, 717 
-28022440,HC  CATHETER GUIDING ATTAIN 7FX57.5CM," 1,700 "
-28022441,HC  CATHETER HEMODIALYSIS DUAL LUMEN 12.5FX28CM, 374 
-28022442,HC  CATHETER HICKMAN SINGLE LUMEN 9.6F," 1,031 "
-28022443,HC  CATHETER HICKMAN TRIPLE LUMEN 12.5F," 2,062 "
-28022446,HC  CATHETER PERMCATH DUAL LUMEN 45CM, 358 
-28022449,HC CATHETER SUPRAPUBIC STAMEY 10FX23CM SET, 431 
-28022450,HC CATHETER SUPRAPUBIC STAMEY 12FX23CM SET, 431 
-28022451,HC CATHETER SUPRAPUBIC STAMEY 14FX23CM SET, 431 
-28022452,HC CATHETER SUPRAPUBIC STAMEY 14FX23CM SET, 431 
-28022453,HC CATHETER SWAN GANZ PACEPORT 7.5F, 605 
-28022454,HC  CATHETER DILATATION ULTRATHIN SDS 6FX8MMX4X90CM," 1,679 "
-28022455,HC  CATHETER URETERAL FORTE 12-14F 28CM, 765 
-28022456,HC  CATHETER URETERAL OPEN END 4F 70CM, 289 
-28022457,HC  CATHETER URETERAL OPEN END 5F 70CM, 87 
-28022458,HC  CATHETER VENTRICULAR 20CM," 1,752 "
-28022462,HC  CATHETER VENTRICULAR SALMON 90CM," 1,128 "
-28022467,HC  CATHETER VENTRICULAR TRAUMA 35CM," 1,655 "
-28022468,HC IMP CONFORMER OCULAR LARGE 26MM, 298 
-28022470,HC IMP COUPLER ANASTOMOTIC MICRO GEM 1.5MM," 2,118 "
-28022471,HC IMP COUPLER ANASTOMOTIC MICRO GEM 2.0MM," 2,196 "
-28022472,HC IMP COUPLER ANASTOMOTIC MICRO GEM 2.5MM," 2,196 "
-28022473,HC IMP COUPLER ANASTOMOTIC MICRO GEM 3.0MM," 2,196 "
-28022474,HC IMP DILATOR RENAL AMPLATZ 10F 37CM, 506 
-28022475,HC DRAINE W ANTI REFLUX VALVE," 2,953 "
-28022476,HC IMP DEVICE SUTURING ENDOSTITCH 10MM," 2,197 "
-28022477,HC IMP DEVICE SUTURING ENDOSTITCH SURGIDAC 0, 423 
-28022478,HC ENDOCUTTER ARTICULATING 45MM," 2,308 "
-28022479,HC IMP EXTRACTOR STONE HELICAL 4.5F," 1,110 "
-28022480,HC IMP EXTRACTOR STONE NCIRCLE 3F 115CM," 1,719 "
-28022481,HC  GUIDEWIRE AMPLATZ 145CMX.038IN, 273 
-28022482,HC  GUIDEWIRE AMPLATZ EX STIFF, 273 
-28022483,HC  GUIDEWIRE BALL TIP 3.2MMX55CM, 901 
-28022484,HC  GUIDEWIRE BULLET TIP 2.4MM X100CM," 1,411 "
-28022485,HC  GUIDEWIRE DOUBLE END 025X150, 108 
-28022486,HC  GUIDEWIRE NITREX STIFF .035IN 260CM, 161 
-28022487,HC  GUIDEWIRE NON THREAD 2.0X230MM, 362 
-28022488,HC  GUIDEWIRE PTFE COATED STD 025X150, 114 
-28022489,HC  GUIDEWIRE PTFE COATED STD 035X150, 114 
-28022490,HC  GUIDEWIRE PTFE COATED STIFF 038X150, 122 
-28022491,HC  GUIDEWIRE SINGLE END 018X45, 118 
-28022492,HC  GUIDEWIRE SINUS RELIEVA STANDARD .035," 1,318 "
-28022493,HC  GUIDEWIRE THREAD 1.25X150MM, 285 
-28022494,HC  GUIDEWIRE THREAD 2.8X300MM, 436 
-28022495,HC  GUIDEWIRE THREAD DHS 2.5X230MM, 465 
-28022496,HC  GUIDEWIRE W TIP 2.5MM," 1,097 "
-28022497,HC  INTRODUCER CHECK FLO LARGE 18X30X42, 876 
-28022498,HC  INTROD MICROP 7F 11CM SET, 234 
-28022499,HC  INTROD MICROP 7F 7CM SET, 245 
-28022500,HC  INTRODUCER SHEATH CATHETER GUIDING 8F 60CM," 1,063 "
-28022501,HC  INTRODUCER SHEATH CATHETER GUIDING SR 8F 60CM," 1,743 "
-28022502,HC  INTRODUCER SHEATH PEEL AWAY 14F, 612 
-28022503,HC  INTRODUCER SHEATH PINNACLE 7F 25CM, 131 
-28022504,HC  INTRODUCER SHEATH PINNACLE R/O 8FX4CM, 408 
-28022505,HC INTRODUCER TRACHEOSTOMY CIAGLIA KIT," 2,366 "
-28022506,HC  INTROD VASC PERC 6.5F SET, 808 
-28022507,HC IMP INTUBATION LACRIMAL W/ SUTURES CRAWFORD SET, 723 
-28022509,HC KIT CUSTOM THORACOTOMY," 2,252 "
-28022510,HC IMP MONOKA COLLARETTE MEDIUM 3MM, 198 
-28022511,HC IMP PIGMENT CHROMA 0, 170 
-28022512,HC IMP PIGMENT CHROMA 4, 230 
-28022513,HC IMP PIGMENT CHROMA 6, 213 
-28022514,HC IMP PIGMENT CHROMA DEEP RED, 213 
-28022515,HC IMP PIGMENT LASHLINER BROWN, 247 
-28022516,HC IMP PORT SINGLE LUMEN OPEN ENDED 8F," 1,981 "
-28022517,HC IMP PORT VENOUS ACCESS PERITONEAL 14.3F," 1,817 "
-28022518,HC  RETRIEVER STENT NSNARE 4.5F, 277 
-28022519,HC IMP SCLERAL SPONGE STYLE 5.5X7.5MM, 87 
-28022520,HC IMPL SCREW HEAD RING MRI LONG 48MM, 918 
-28022521,HC IMPL SCREW SELF TAPPING MTRXMNDBL TI 1/PKG 2.4X6MM, 700 
-28022522,HC IMP SHEETING MEPIFORM SEL 4X7IN, 91 
-28022523,HC  SHEATH URETHERAL ACCESS 12/14F 38CM," 1,233 "
-28022524,HC  SHEATH URETHERAL ACCESS 12/14F 54CM," 1,120 "
-28022525,HC IMP SHUNT CAROTID VASCULAR 10FX13CM, 92 
-28022526,HC IMP SHUNT CAROTID VASCULAR 12FX13CM, 92 
-28022527,HC IMP SHUNT CAROTID VASCULAR STRAIGHT 14FX13CM, 104 
-28022528,HC IMP SHUNT CAROTID VASCULAR STRAIGHT 16FX13CM, 92 
-28022529,HC IMP SPHERE MICRO MEROCEL 5MM X 1.25CM, 119 
-28022530,HC IMPL SPLINT WRIST LACER LEFT LARGE, 141 
-28022531,HC IMPL SPLINT WRIST LACER RIGHT MEDIUM, 141 
-28022532,HC IMPL SPLINT WRIST LACER RIGHT SMALL, 141 
-28022533,HC IMP STAPLE ENDO RELOAD CONTOUR LINEAR BLUE 40MM," 2,151 "
-28022534,HC IMP STAPLE ENDO RELOAD CONTOUR LINEAR GREEN 40MM, 398 
-28022535,HC IMP STAPLE ENDO RELOAD ARTICULATING CUTTER ETS 35MM, 954 
-28022536,HC IMP STAPLE ENDO RELOAD ECHELON BLUE 45MM," 1,172 "
-28022537,HC IMP STAPLE ENDO RELOAD ECHELON BLUE 60MM," 1,279 "
-28022538,HC IMP STAPLE ENDO RELOAD ECHELON GOLD 45MM," 1,172 "
-28022539,HC IMP STAPLE ENDO RELOAD ECHELON GREEN 45MM," 1,172 "
-28022540,HC IMP STAPLE ENDO RELOAD ECHELON GREEN 60MM," 1,279 "
-28022541,HC IMP STAPLE ENDO RELOAD ECHELON GRAY 45MM," 1,172 "
-28022542,HC IMP STAPLE ENDO RELOAD ECHELON WHITE 45MM," 1,172 "
-28022543,HC IMP STAPLE ENDO RELOAD ECHELON WHITE 60MM," 1,279 "
-28022544,HC IMP STAPLE ENDO RELOAD STANDARD ETS 3.5MM 1.5X45MM," 1,172 "
-28022545,HC IMP STAPLE ENDO RELOAD ETS GREEN 45MM," 1,172 "
-28022546,HC IMP STAPLE ENDO RELOAD ETS WHITE 35MM, 954 
-28022547,HC IMP STAPLE ENDO RELOAD ETS WHITE 45MM," 1,172 "
-28022548,HC IMP STAPLE ENDO RELOAD MESENTARY 2.0MM .85MM 45MM," 1,172 "
-28022549,HC IMP STAPLE ENDO RELOAD MULTIFIRE 30 2.5," 1,899 "
-28022550,HC IMP STAPLE RELOAD LINEAR CUTTER BLUE 55MM, 444 
-28022551,HC IMP STAPLE RELOAD LINEAR CUTTER BLUE 75MM, 659 
-28022552,HC IMP STAPLE RELOAD LINEAR CUTTER GREEN 75MM," 1,082 "
-28022553,HC IMP STAPLE RELOAD LINEAR CUTTER THICK 75MM, 672 
-28022554,HC IMP STAPLE RELOAD LINEAR CUTTER WHITE 55MM, 490 
-28022555,HC IMP STAPLE RELOAD LINEAR CUTTER 55MM, 562 
-28022556,HC IMP STAPLE RELOAD LINEAR CUTTER 75MM, 778 
-28022557,HC IMP STAPLE RELOAD LINEAR CUTTER 100MM, 993 
-28022558,HC IMP STAPLE RELOAD LINEAR TL 30MM, 385 
-28022559,HC IMP STAPLE RELOAD LINEAR TL 60MM, 415 
-28022560,HC IMP STAPLE RELOAD LINEAR TL 90MM, 467 
-28022561,HC IMP STAPLE RELOAD LINEAR TLH 90MM, 484 
-28022562,HC IMP STAPLE RELOAD LINEAR TX BLUE 30MM, 376 
-28022563,HC IMP STAPLE RELOAD LINEAR TX BLUE 60MM, 405 
-28022564,HC IMP STAPLE RELOAD LINEAR TX GREEN 30MM, 376 
-28022565,HC IMP STAPLE RELOAD LINEAR TX WHITE 30MM, 387 
-28022566,HC IMP STAPLE UNIVERSAL RELOAD MULTIFIRE 4MM, 778 
-28022567,HC IMPL STENT SINUSPACER TURBINATE, 102 
-28022568,HC IMP STENT NEPHROSTOMY AMPLATZ 10.2FX22," 1,055 "
-28022569,HC IMP STENT RETROMAX PLUS 14FCX26CM," 2,040 "
-28022570,HC IMP STENT RETROMAX PLUS 14FX24CM," 2,040 "
-28022571,HC IMP STENT RETROMAX PLUS 14FX28CM," 2,040 "
-28022572,HC IMP STENT URETERAL DOUBLE J 4.6FX22CM," 1,105 "
-28022573,HC IMP STENT URETERAL DOUBLE J 4.6FX24CM," 1,105 "
-28022574,HC IMP STENT URETERAL DOUBLE J GUIDEWIRE 7FX16CM," 1,862 "
-28022575,HC IMP STENT URETERAL DOUBLE J GUIDEWIRE 7FX24CM," 1,862 "
-28022576,HC IMP STENT URETERAL DOUBLE PIGTAIL 6FX21-32CM, 944 
-28022577,HC IMP STENT URETERAL DOUBLE PIGTAIL 7FX16CM," 1,078 "
-28022578,HC IMP STENT URETERAL DOUBLE PIGTAIL 7FX18CM," 1,097 "
-28022579,HC IMP STENT URETERAL DOUBLE PIGTAIL 7FX21-32CM, 937 
-28022580,HC IMP STENT URETHRAL DOUBLE PIGTAIL PED 3FX10CM, 937 
-28022581,HC IMP STENT URETERAL DOUBLE PIGTAIL PED 3FX12CM, 857 
-28022582,HC IMP STENT URETERAL DOUBLE PIGTAIL PED 3FX12CM, 857 
-28022583,HC IMP STENT URETERAL DOUBLE-J GUIDEWIRE 8.5FX24CM," 1,233 "
-28022584,HC IMP STENT URETERAL PERCUFLEX 4.8FX16CM," 1,318 "
-28022585,HC IMP STENT URETERAL PERCUFLEX 4.8FX20CM," 1,403 "
-28022586,HC IMP STENT URETERAL PERCUFLEX 4.8FX22CM," 1,403 "
-28022587,HC IMP STENT URETERAL PERCUFLEX 4.8FX24CM," 1,403 "
-28022588,HC IMP STENT URETERAL PERCUFLEX 4.8FX26CM," 1,403 "
-28022589,HC IMP STENT URETERAL PERCUFLEX 4.8FX28CM," 1,403 "
-28022590,HC IMP STENT URETERAL PERCUFLEX 4.8FX30CM," 1,403 "
-28022591,HC IMP STENT URETERAL PERCUFLEX 6FX26CM," 1,318 "
-28022592,HC IMP STENT URETERAL PERCUFLEX 6FX20CM," 1,403 "
-28022593,HC IMP STENT URETERAL PERCUFLEX 6FX22CM," 1,403 "
-28022594,HC IMP STENT URETERAL PERCUFLEX 6FX24CM," 1,403 "
-28022595,HC IMP STENT URETERAL PERCUFLEX 6FX28CM," 1,403 "
-28022596,HC IMP STENT URETERAL PERCUFLEX 6FX30CM," 1,403 "
-28022597,HC IMP STENT URETERAL PERCUFLEX 7FX20CM," 1,403 "
-28022598,HC IMP STENT URETERAL PERCUFLEX 7FX22CM," 1,403 "
-28022599,HC IMP STENT URETERAL PERCUFLEX 7FX24CM," 1,403 "
-28022600,HC IMP STENT URETERAL PERCUFLEX 7FX26CM," 1,403 "
-28022601,HC IMP STENT URETERAL PERCUFLEX 7FX28CM," 1,403 "
-28022602,HC IMP STENT URETERAL PERCUFLEX 7FX30CM," 1,403 "
-28022603,HC IMP STENT URETERAL PERCUFLEX 8FX20CM," 1,403 "
-28022604,HC IMP STENT URETERAL PERCUFLEX 8FX22CM," 1,403 "
-28022605,HC IMP STENT URETERAL PERCUFLEX 8FX24CM," 1,403 "
-28022606,HC IMP STENT URETERAL PERCUFLEX 8FX26CM," 1,403 "
-28022607,HC IMP STENT URETERAL PERCUFLEX PLUS 4.8FX10CM," 1,318 "
-28022608,HC IMP STENT URETERAL PERCUFLEX PLUS 4.8FX12CM," 1,318 "
-28022609,HC IMP STENT URETERAL PERCUFLEX PLUS 4.8FX14CM," 1,235 "
-28022610,HC IMP STENT URETERAL PERCUFLEX PLUS 4.8FX18CM," 1,318 "
-28022611,HC IMP STENT URETERAL PERCULFEX PLUS 6FX20CM," 1,658 "
-28022612,HC IMP STENT URETERAL SILHOUETTE 4.6FCX22CM," 1,105 "
-28022613,HC IMP STENT URETERAL SILHOUETTE 4.6FX26CM," 1,105 "
-28022614,HC IMP STENT URETERAL SILHOUETTE 6FX24CM," 1,105 "
-28022615,HC IMP SYSTEM CLOSURE CARTER THOMASON 15MM, 842 
-28022616,HC  TUBE GASTROSTOMY LOW PROFILE 14F 0.8, 731 
-28022617,HC  TUBE GASTROSTOMY LOW PROFILE 14F 1.0, 838 
-28022618,HC  TUBE GASTROSTOMY LOW PROFILE 14F 1.2, 447 
-28022619,HC  TUBE GASTROSTOMY LOW PROFILE 14F 1.5, 731 
-28022620,HC  TUBE GASTROSTOMY LOW PROFILE 14F 2.0, 731 
-28022621,HC  TUBE GASTROSTOMY LOW PROFILE 16F 8.0, 156 
-28022622,HC  TUBE GASTROSTOMY LOW PROFILE 18F 0.8, 731 
-28022623,HC  TUBE GASTROSTOMY LOW PROFILE 18F 1.2, 168 
-28022624,HC  TUBE GASTROSTOMY LOW PROFILE 18F 1.5, 731 
-28022625,HC  TUBE GASTROSTOMY LOW PROFILE 18F 2.0, 731 
-28022626,HC  TUBE GASTROSTOMY LOW PROFILE 18F 2.5, 731 
-28022627,HC  TUBE GASTROSTOMY LOW PROFILE 24F 1.5, 731 
-28022628,HC  TUBE GASTROSTOMY LOW PROFILE 24F 2.3, 731 
-28022629,HC  TUBE GASTROSTOMY MIC 14F, 277 
-28022630,HC  TUBE GASTROSTOMY MIC 18F, 244 
-28022631,HC  TUBE GASTROSTOMY MIC KEY 16F 4.5, 821 
-28022632,HC  TUBE GASTROSTOMY MOSS 18F," 2,083 "
-28022633,HC  TUBE JEJUNAL CORFLO 6FX36IN, 118 
-28022634,HC  TUBE JEJUNAL LOW PROFILE 5ML 14FX1.5CM," 1,392 "
-28022635,HC  TUBE JEJUNOSTOMY MIC 14F," 1,445 "
-28022636,HC  TUBE TRACHEOSTOMY CUFFED PEDIATRIC 4.5X42MM, 357 
-28022637,HC  TUBE TRACHEOSTOMY CUFFED PEDIATRIC 5.5X48MM, 357 
-28022638,HC  TUBE TRACHEOSTOMY CUFFED PEDIATRIC 5X44MM, 357 
-28022639,HC  TUBE TRACHEOSTOMY CUFFED PEDIATRIC 4PDC, 357 
-28022640,HC  TUBE TRACH CUSTOM CUFF 50 SZ10," 1,233 "
-28022641,HC  TUBE TRACH CUSTOM CUFF SZ8," 1,360 "
-28022642,HC  TUBE TRACHEOSTOMY CUFFED DCT 5.0X9.4X62MM, 312 
-28022643,HC  TUBE TRACHEOSTOMY CUFFED DCT 6.4X10.8X74MM, 312 
-28022644,HC  TUBE TRACHEOSTOMY CUFFED DCT 7.6X12.2X79MM, 312 
-28022645,HC  TUBE TRACHEOSTOMY CUFFED DCT 8.9X13.8X79MM, 312 
-28022646,HC  TUBE TRACHEOSTOMY CUFFLESS MOORE 115X11X6.5MM," 1,318 "
-28022647,HC  TUBE TRACHEOSTOMY CUFFLESS NEO 3.5X5.2X32MM, 268 
-28022648,HC  TUBE TRACHEOSTOMY CUFFLESS NEO 3X4.5X30MM, 268 
-28022649,HC  TUBE TRACHEOSTOMY CUFFLESS PED 3.0X4.5X39MM, 268 
-28022650,HC  TUBE TRACHEOSTOMY CUFFLESS PED 3.5X5.2X40MM, 268 
-28022651,HC  TUBE TRACHEOSTOMY CUFFLESS PED 4.0X5.9X41MM, 268 
-28022652,HC  TUBE TRACHEOSTOMY CUFFLESS PED 4.5X6.5X42MM, 268 
-28022653,HC  TUBE TRACHEOSTOMY CUFFLESS PED 5.0X7.1X44MM, 268 
-28022654,HC  TUBE TRACHEOSTOMY CUFFLESS PED 5.5X7.7X46MM, 268 
-28022655,HC  TUBE TRACHEOSTOMY CUFFED PLC 5.5X7.7X52MM, 357 
-28022656,HC  TUBE TRACHEOSTOMY CUFFED PLC 5X7.1X50MM, 357 
-28022657,HC IMP WIRE ARCH BAR, 311 
-28022658,HC IMP WIRE KIRSCHNER THREAD 2X15X100MM, 202 
-28022659,HC  WIRE THREAD 70/15 1.6MM, 202 
-28022660,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X105MM," 2,091 "
-28022661,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X110MM," 2,091 "
-28022662,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X120MM," 2,091 "
-28022663,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X125MM," 2,091 "
-28022664,HC IMPL SCREW CANNULATED FULLY THREAD SS 6.5X130MM," 2,091 "
-28022665,HC IMPL SCREW CORTICAL SELF TAPPING TI 2.4MMX14MM, 775 
-28022666,HC IMPL SCREW CORTICAL SELF TAPPING TI 2.4MMX16MM, 749 
-28022667,HC IMPL SCREW CORTICAL SELF TAPPING TI 2.4MMX18MM, 775 
-28022668,HC IMP SCREW CORTEX SELF TAPPING 2.7X10MM, 322 
-28022669,HC IMP SCREW CORTEX SELF TAPPING 2.7X12MM, 322 
-28022670,HC IMP SCREW CORTEX SELF TAPPING 2.7X14MM, 322 
-28022671,HC IMP SCREW CORTEX SELF TAPPING 2.7X16MM, 322 
-28022672,HC IMP SCREW CORTEX SELF TAPPING 2.7X18MM, 322 
-28022673,HC IMP SCREW CORTEX SELF TAPPING 2.7X20MM, 322 
-28022674,HC IMP SCREW CORTEX SELF TAPPING 2.7X22MM, 322 
-28022675,HC IMP SCREW CORTEX SELF TAPPING 2.7X24MM, 322 
-28022676,HC IMP SCREW CORTEX SELF TAPPING 2.7X26MM, 322 
-28022677,HC IMP SCREW CORTEX TITANIUM 1.5MMX5MM, 401 
-28022678,HC IMP SCREW CORTEX TITANIUM 1.5MMX6MM, 401 
-28022679,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 2.7X10MM, 442 
-28022680,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X10MM, 216 
-28022681,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X12MM, 208 
-28022682,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X14MM, 208 
-28022683,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X16MM, 208 
-28022684,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X18MM, 216 
-28022685,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X20MM, 230 
-28022686,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X22MM, 216 
-28022687,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X24MM, 208 
-28022688,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X26MM, 216 
-28022689,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X28MM, 216 
-28022690,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X30MM, 200 
-28022691,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X32MM, 208 
-28022692,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X34MM, 208 
-28022693,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X36MM, 208 
-28022694,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X38MM, 208 
-28022695,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X40MM, 208 
-28022696,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X45MM, 208 
-28022697,HC IMPL SCREW CORTEX TITANIUM SELF TAP HEX 3.5X50MM, 200 
-28022698,HC IMP SCREW CORTICAL 3.5X12MM, 125 
-28022699,HC IMP SCREW CORTICAL 3.5X14MM, 125 
-28022700,HC IMP SCREW CORTICAL 3.5X16MM, 125 
-28022701,HC IMP SCREW CORTICAL 3.5X18MM, 125 
-28022702,HC IMP SCREW CORTICAL LOCKING 2.7X28MM, 723 
-28022703,HC IMP SCREW CORTICAL LOCKING 3.5X22MM, 723 
-28022704,HC IMP SCREW CORTICAL LOCKING 3.5X24MM, 120 
-28022705,HC IMP SCREW CORTICAL LOCKING 3.5X26MM, 120 
-28022706,HC IMPL SCREW DELTA SYS PKG 02 2.2 X 4MM, 363 
-28022707,HC IMPL SCREW DELTA SYS PKG 02 2.2 X 6MM, 363 
-28022708,HC IMP SCREW FIXED TITANIUM 5.5X25MM," 7,013 "
-28022709,HC IMP SCREW FIXED TITANIUM 6.5X30MM," 8,401 "
-28022710,HC IMP SCREW FIXED TITANIUM 6.5X40MM," 7,013 "
-28022711,HC IMP SCREW FIXED TITANIUM 6.5X45MM," 7,013 "
-28022712,HC IMPL SCREW HEAD QUAD-SPARING MIS HEADLESS 48MM, 479 
-28022713,HC IMPL SCREW LOCKING TI 5X50MM," 1,886 "
-28022714,HC IMPL SCREW MILAGRO ADV 11X30MM," 3,417 "
-28022715,HC IMPL SCREW MILAGRO INTERFERENCE 10X23MM," 3,383 "
-28022716,HC IMPL SCREW MILAGRO INTERFERENCE 10X30MM," 3,341 "
-28022717,HC IMPL SCREW MILAGRO INTERFERENCE 9X23MM," 3,315 "
-28022718,HC IMPL SCREW SPINAL CANNULATED POLARIS 5.5X40MM," 8,820 "
-28022719,HC IMPL SCREW SPINAL CANNULATED POLARIS 7.5X40MM," 8,820 "
-28022720,HC IMPL SCREW SPINAL FIDUCIAL, 425 
-28022721,HC IMP ROD SPINAL EXPEDIUM PRELORDOSED TI 5X5X480MM," 4,405 "
-28022722,HC IMP ROD SPINAL EXPEDIUM SS 5.5X600MM," 5,049 "
-28022723,HC IMP ROD SPINAL LOW PROFILE VITALLIUM 6MM," 8,075 "
-28022724,HC IMP ROD SPINAL MIS CURVED 5.5X40MM," 2,550 "
-28022725,HC IMP ROD SPINAL MIS CURVED 5.5X45MM," 2,550 "
-28022726,HC IMP ROD SPINAL MIS CURVED 5.5X50MM," 2,550 "
-28022727,HC IMP ROD SPINAL MIS CURVED 5.5X80MM," 2,550 "
-28022728,HC IMP ROD SPINAL VIPER II STRAIGHT 400MM," 7,242 "
-28022729,HC IMP ROD THORACIC LUMBAR SOLERA 4.75CMX45MM," 7,863 "
-28022730,HC IMP ROD THORACIC LUMBAR SOLERA 4.75CMX50MM," 3,787 "
-28022731,HC IMP ROD THORACIC LUMBAR STR TITANIUM 5.5X500," 4,808 "
-28022732,HC IMP ROD THREADED 350MM, 464 
-28022733,HC SAW PIEZOELECTRIC 105.7X4X0.6MM," 3,400 "
-28022734,HC SAW PIEZOELECTRIC ROUND 38.9X5X0.6MM," 2,720 "
-28022735,HC IMPL SCREW CORTEX SELF TAPPING RECESS 2X4MM, 434 
-28022736,HC IMPL SCREW CORTEX SELF TAPPING RECESS 2X6MM, 457 
-28022737,HC IMPL SCREW CORTEX SELF TAPPING RECESS 2X8MM, 457 
-28022738,HC IMPL SCR IMF SELF DRILLING EACH 2X12MM, 823 
-28022739,HC IMPL SCR IMF SELF DRILLING EACH 2X8MM, 823 
-28022740,HC IMPL SCREW LOCKING VISIDISK BLUE 2MM," 1,122 "
-28022741,HC IMPL SCREW LOCKING VISIDISK GREEN 2MM," 1,122 "
-28022742,HC IMPL SCREW PERIPROSTHETIC LOCK T25 TI 5X10MM," 2,125 "
-28022743,HC IMPL SCR RECON STARDRIVE T25 TITANIUM 5X55MM," 1,709 "
-28022744,HC IMPL SCR RECON STARDRIVE T25 TITANIUM 5X70MM," 1,959 "
-28022745,HC IMPL SCR RECON STARDRIVE T25 TITANIUM 5X75MM," 1,709 "
-28022746,HC IMPL SCR RECON STARDRIVE T25 TITANIUM 5X80MM," 1,862 "
-28022747,HC IMPL SCR RECON STARDRIVE T25 TITANIUM 5X85MM," 1,959 "
-28022748,HC IMPL SCREW SAFETY 2.3X4MM, 619 
-28022749,HC IMPL SCREW SAFETY 2.3X6MM, 619 
-28022750,HC IMPL SCREW VISIDISK AUTO DRIVE 1.2MM," 1,131 "
-28022751,HC IMPL SCREW VISIDISK AUTO DRIVE 1.6MM," 1,131 "
-28022752,HC IMPL SCREW VISIDISK AUTO DRIVE 2.0MM," 1,122 "
-28022753,HC IMPL SCREW VISIDISK BLUE 2.0MM," 1,122 "
-28022754,HC IMPL SCREW VISIDISK GREEN 2.4MM," 1,122 "
-28022755,HC IMPL SCREW VISIDISK WHITE 1.6MM," 1,122 "
-28022756,HC IMPL SCREW VISIDISK YELLOW 1.2MM," 1,122 "
-28022757,HC IMPL SCREW 5X26MM," 1,716 "
-28022758,HC IMPL SCREW 5X70MM," 1,716 "
-28022759,HC IMPL SCREW AUTO DRIVE 1.2MMX3MM, 488 
-28022760,HC IMP SCREW BONE ACETABULAR CANCELLOUS SS 6.5X25MM," 1,065 "
-28022761,HC IMP SCREW BONE ACETABULAR CANCELLOUS SS 6.5X30MM," 1,065 "
-28022762,HC IMP SCREW BONE DOUBLE THREAD PILOT 4X52MM," 2,482 "
-28022763,HC IMP SCREW BONE LOCKING 2.0X10MM, 944 
-28022764,HC IMP SCREW BONE LOCKING 2.0X12MM, 859 
-28022765,HC IMP SCREW BONE LOCKING 2.0X14MM, 859 
-28022766,HC IMP SCREW BONE LOCKING 2.0X16MM, 859 
-28022767,HC IMP SCREW BONE LOCKING 2.0X18MM, 859 
-28022768,HC IMP SCREW BONE LOCKING SYNFIX 4X15MM," 3,188 "
-28022769,HC IMP SCREW BONE LOCKING SYNFIX FINE TIP 4X20MM," 3,188 "
-28022770,HC IMP SCREW BONE LOCKING SYNFIX FINE TIP 4X25MM," 3,188 "
-28022771,HC IMP SCREW BONE LOCKING SYNFIX FINE TIP 4X30MM," 3,188 "
-28022772,HC IMPL SCREW CANNULATED LOW PROFILE 4.5MMX16MM," 1,938 "
-28022773,HC IMPL SCREW CANNULATED LOW PROFILE 4.5MMX24MM," 1,938 "
-28022774,HC IMPL SCREW CANNULATED LOW PROFILE 4.5MMX28MM," 1,938 "
-28022775,HC IMPL SCREW CANNULATED LOW PROFILE 4.5X20MM," 1,938 "
-28022776,HC IMPL SCREW CONICAL 5X60MM," 1,607 "
-28022777,HC IMPL SCREW CONICAL 5X65MM," 1,233 "
-28022778,HC IMPL SCREW CONICAL 7.6 60MM," 2,151 "
-28022779,HC IMP SCREW CORTEX EACH 1.3MMX3MM, 568 
-28022780,HC IMP SCREW CORTEX EACH 1.3MMX4MM, 568 
-28022781,HC IMP SCREW CORTEX EACH 1.3MMX5MM, 568 
-28022782,HC IMP SCREW CORTEX EACH 1.3MMX6MM, 568 
-28022783,HC IMP SCREW CORTEX EACH 1.3MMX8MM, 568 
-28022784,HC IMP SCREW CORTEX EACH 1.5MMX10MM, 401 
-28022785,HC IMP SCREW CORTEX EACH 1.5MMX12MM, 401 
-28022786,HC IMP SCREW CORTEX EACH 1.5MMX4MM, 401 
-28022787,HC IMP SCREW CORTEX EACH 1.5MMX8MM, 401 
-28022788,HC IMP SCREW CORTEX EACH 1X3MM, 626 
-28022789,HC IMP SCREW CORTEX EACH 1X4MM, 626 
-28022790,HC IMP SCREW CORTEX EACH 1X5MM, 626 
-28022791,HC IMP SCREW CORTEX EACH 1X6MM, 598 
-28022792,HC IMP SCREW CORTEX EACH 2.0MMX10MM, 405 
-28022793,HC IMP SCREW CORTEX EACH 2.0MMX12MM, 405 
-28022794,HC IMP SCREW CORTEX EACH 2.0MMX4MM, 405 
-28022795,HC IMP SCREW CORTEX EACH 2.0MMX5MM, 405 
-28022796,HC IMP SCREW CORTEX EACH 2.0MMX6MM, 405 
-28022797,HC IMP SCREW CORTEX EACH 2.0MMX8MM, 405 
-28022798,HC IMP SCREW CORTEX SELF DRILLING RECESS 2X4MM, 469 
-28022799,HC IMP SCREW CORTEX SELF DRILLING RECESS 2X6MM, 469 
-28022800,HC IMP SCREW CORTEX SELF TAPPING EACH 1.3X6MM, 575 
-28022801,HC IMP SCREW CORTEX SELF TAPPING TI EACH 1.5MM 4MM, 415 
-28022802,HC IMP SCREW CORTEX SELF TAPPING TI EACH 1.5MM 6MM, 415 
-28022803,HC IMP SCREW CORTICAL 4.5X44MM," 1,785 "
-28022804,HC IMP SCREW CORTICAL LOCKING PEDILOC 3.5X14MM," 1,445 "
-28022805,HC IMP SCREW CORTICAL LOCKING PEDILOC 3.5X16MM," 1,445 "
-28022806,HC IMP SCREW CORTICAL LOCKING PEDILOC 3.5X35MM," 1,445 "
-28022807,HC IMP SCREW CORTICAL LOCKING PEDILOC 3.5X40MM," 1,445 "
-28022808,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 3.5X26MM," 1,547 "
-28022809,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 3.5X28MM," 1,547 "
-28022810,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 3.5X36MM," 1,547 "
-28022811,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 3.5X42MM," 1,547 "
-28022812,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X14MM," 1,945 "
-28022813,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X22MM," 1,945 "
-28022814,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X26MM," 1,945 "
-28022815,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X28MM," 1,945 "
-28022816,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X32MM," 1,945 "
-28022817,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X40MM," 1,945 "
-28022818,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X44MM," 1,945 "
-28022819,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X46MM," 1,945 "
-28022820,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X48MM," 1,945 "
-28022821,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X50MM," 1,945 "
-28022822,HC IMP SCREW CORTICAL LOCKING PEDILOC FTHRD 4.5X52MM," 1,945 "
-28022823,HC IMP SCREW CORTICAL PENNING 70X20MM, 808 
-28022824,HC IMP SCREW CORTICAL PENNING 80X35MM, 680 
-28022825,HC IMP SCREW CORTICAL SELF DRILLING 80X25MM, 803 
-28022826,HC IMP SCREW CORTICAL SELF TAPPING EACH 1.3X6MM, 592 
-28022827,HC IMPL SCREW CORTICAL SELF TAPPING RECESS TI 2.4X34MM, 642 
-28022828,HC IMPL SCREW CORTICAL SELF TAPPING RECESS TI 2.4X36MM, 642 
-28022829,HC IMPL SCREW CORTICAL SELF TAPPING RECESS TI 2.4X38MM, 400 
-28022830,HC IMPL SCREW CORTICAL SELF TAPPING RECESS TI 2.4X40MM, 383 
-28022831,HC IMPL SCREW CRANIOFACIAL 2.4X22MM, 493 
-28022832,HC IMPL SCREW CRANIOFACIAL SAFETY 1.5X6MM, 536 
-28022833,HC IMPL SCREW CRANIOFACIAL STANDARD 1.2X10MM, 493 
-28022834,HC IMPL SCREW CRANIOFACIAL STANDARD 1.2X12MM, 493 
-28022835,HC IMPL SCREW CRANIOFACIAL STANDARD 1.2X14MM, 493 
-28022836,HC IMPL SCREW EMERGENCY EACH 1.2MMX3MM, 653 
-28022837,HC IMPL SCREW EMERGENCY EACH 1.2MMX4MM, 622 
-28022838,HC IMPL SCREW EMERGENCY EACH 1.2MMX5MM, 622 
-28022839,HC IMPL SCREW EMERGENCY EACH 1.2MMX6MM, 622 
-28022840,HC IMPL SCREW EMERGENCY EACH 1.3MMX3MM, 592 
-28022841,HC IMPL SCREW EMERGENCY EACH 1.3MMX4MM, 118 
-28022842,HC IMPL SCREW EMERGENCY EACH 1.3MMX5MM, 592 
-28022843,HC IMPL SCREW EMERGENCY EACH 1.3MMX6MM, 118 
-28022844,HC IMPL SCREW EMERGENCY EACH 1.3MMX8MM, 592 
-28022845,HC IMPL SCREW EMERGENCY EACH 1.5MMX10MM, 428 
-28022846,HC IMPL SCREW EMERGENCY EACH 1.5MMX4MM, 626 
-28022847,HC IMPL SCREW EMERGENCY EACH 1.5MMX6MM, 449 
-28022848,HC IMPL SCREW EMERGENCY EACH 1.5MMX8MM, 428 
-28022849,HC IMPL SCREW EMERGENCY EACH 1.7MMX4MM, 598 
-28022850,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS EACH 2.4X10MM, 484 
-28022851,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS EACH 2.4X12MM, 484 
-28022852,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS EACH 2.4X6MM, 484 
-28022853,HC IMPL SCREW EMERGENCY PLUSDRIVE RECS EACH 2.4X8MM, 484 
-28022854,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.0X10MM, 428 
-28022855,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.0X6MM, 428 
-28022856,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.4X12MM, 428 
-28022857,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.4X14MM, 428 
-28022858,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.4X16MM, 428 
-28022859,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.4X18MM, 428 
-28022860,HC IMPL SCREW EMERGENCY SRPD CRANIOFACIAL EACH 2.4X8MM, 449 
-28022861,HC IMPL SCREW FRACTURE HPS 2.4X10MM, 425 
-28022862,HC IMPL SCREW FRACTURE HPS 2.4X12MM, 442 
-28022863,HC IMPL SCREW FRACTURE HPS 2.4X14MM, 442 
-28022864,HC IMPL SCREW FRACTURE HPS 2.4X16MM, 442 
-28022865,HC IMPL SCREW FRACTURE HPS 2.4X18MM, 442 
-28022866,HC IMPL SCREW FRACTURE HPS 2.4X20MM, 468 
-28022867,HC IMPL SCREW FRACTURE HPS 2.4X22MM, 451 
-28022868,HC IMPL SCREW FRACTURE HPS 2X12MM, 434 
-28022869,HC IMPL SCREW FRACTURE HPS 2X14MM, 434 
-28022870,HC IMPL SCREW LAG STANDARD 3.94X100MM," 2,763 "
-28022871,HC IMPL SCREW LOCKING CANCELLOUS STARDRIVE 5.5X45MM," 4,038 "
-28022872,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X10MM," 4,794 "
-28022873,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X12MM," 4,794 "
-28022874,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X16MM," 4,794 "
-28022875,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X18MM," 4,794 "
-28022876,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X6MM," 4,794 "
-28022877,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X8MM," 4,794 "
-28022878,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.4X10MM," 1,176 "
-28022879,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.4X12MM," 5,882 "
-28022880,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.4X14MM," 5,882 "
-28022881,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.4X16MM," 5,882 "
-28022882,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.4X18MM," 5,882 "
-28022883,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.4X8MM," 5,882 "
-28022884,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.9X10MM," 6,766 "
-28022885,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.9X12MM," 6,766 "
-28022886,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.9X14MM," 6,766 "
-28022887,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.9X16MM," 6,766 "
-28022888,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.9X18MM," 6,766 "
-28022889,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.9X8MM," 6,766 "
-28022890,HC IMPL SCREW LOCKING MFX 2.4X10MM, 859 
-28022891,HC IMPL SCREW LOCKING MFX 2.4X12MM, 944 
-28022892,HC IMPL SCREW LOCKING MFX 2.4X14MM, 859 
-28022893,HC IMPL SCREW LOCKING MFX 2.4X16MM, 884 
-28022894,HC IMPL SCREW LOCKING MFX 2.4X18MM, 884 
-28022895,HC IMPL SCREW LOCKING MFX 2.4X6MM, 859 
-28022896,HC IMPL SCREW LOCKING MFX 2.4X8MM, 859 
-28022897,HC IMPL SCREW LOCKING SELF TAPPING TITANIUM 2.9X6MM," 1,360 "
-28022898,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X24MM," 2,056 "
-28022899,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X28MM," 2,056 "
-28022900,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X30MM," 2,056 "
-28022901,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X32MM," 2,056 "
-28022902,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X34MM," 1,709 "
-28022903,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X38MM," 2,056 "
-28022904,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X40MM," 2,056 "
-28022905,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X44MM," 1,651 "
-28022906,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X46MM," 1,802 "
-28022907,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X48MM," 1,802 "
-28022908,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X50MM," 1,651 "
-28022909,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 4X60MM," 1,802 "
-28022910,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 5X34MM," 1,709 "
-28022911,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 5X48MM," 1,709 "
-28022912,HC IMPL SCREW LOCKING STARDRIVE T25 TITANIUM 5X56MM," 1,709 "
-28022913,HC IMPL SCREW MATRIX POLYAXIAL 5X40MM," 9,375 "
-28022914,HC IMPL SCREW MATRIX POLYAXIAL 6X45MM," 9,375 "
-28022915,HC IMPL SCREW MATRIX POLYAXIAL 6X50MM," 9,375 "
-28022916,HC IMPL SCREW MATRIX POLYAXIAL 7X40MM," 9,375 "
-28022917,HC IMPL SCREW MATRIX POLYAXIAL 7X50MM," 9,375 "
-28022918,HC IMPL SCREW MATRIXNEURO EMERGENCY TI 1.8X3MM, 537 
-28022919,HC IMPL SCREW MATRIXNEURO EMERGENCY TI 1.8X4MM, 537 
-28022920,HC IMPL SCREW MATRIXNEURO SELF DRILLING TI 1.5X3MM, 489 
-28022921,HC IMPL SCREW MAXI SAFETY 2.7MMX12MM, 536 
-28022922,HC IMPL SCREW MAXI SAFETY 2.7MMX16MM, 536 
-28022923,HC IMPL SCREW MAXI SAFETY 2.7MMX20MM, 536 
-28022924,HC IMPL SCREW MAXI TITANIUM 2.4MMX4MM, 493 
-28022925,HC IMPL SCREW MAXIOMANDIBULAR FIX AUTO DRIVE 2.0X11MM, 975 
-28022926,HC IMPL SCREW MAXIOMANDIBULAR FIX AUTO DRIVE 2.0X14MM, 859 
-28022927,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 3.5X16MM," 8,113 "
-28022928,HC IMPL SCREW MEDIAL LATERAL MOUNTAINEER 3.5X18MM," 8,113 "
-28022929,HC IMPL SCREW MINI M3 LAG 3X12MM, 451 
-28022930,HC IMPL SCREW MINI M3 LAG 3X14MM, 468 
-28022931,HC IMPL SCREW MINI M3 LAG 3X16MM, 451 
-28022932,HC IMPL SCREW MINI M3 LAG 3X18MM, 451 
-28022933,HC IMPL SCREW MINI M3 LAG 3X20MM, 451 
-28022934,HC IMPL SCREW MINI M3 LAG 3X28MM, 468 
-28022935,HC IMPL SCREW MINI M3 LAG 3X30MM, 468 
-28022936,HC IMPL SCREW MINI M3 LAG 3X32MM, 468 
-28022937,HC IMPL SCREW MINI M3 LAG 3X34MM, 468 
-28022938,HC IMPL SCREW MINI M3 LAG 3X36MM, 468 
-28022939,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 5X25MM," 7,770 "
-28022940,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 5X30MM," 7,770 "
-28022941,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 5X35MM," 7,770 "
-28022942,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 5X40MM," 7,770 "
-28022943,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 6X40MM," 7,770 "
-28022944,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 6X45MM," 7,770 "
-28022945,HC IMPL SCREW MONOAXIAL PANGEA TITANIUM 6X50MM," 7,770 "
-28022946,HC IMPL SCREW MULTI AXIAL 5.5X25MM," 7,863 "
-28022947,HC IMPL SCREW PEDICLE MATRIX POLYAXIAL 6X45MM," 10,875 "
-28022948,HC IMPL SCREW PEDICLE MATRIX POLYAXIAL 6X50MM," 10,875 "
-28022949,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4.5X18MM," 3,535 "
-28022950,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4.5X20MM," 2,338 "
-28022951,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4X12MM," 3,003 "
-28022952,HC IMPL SCREW QUICK LOCKING SELF DRILLING TI 4X20MM," 2,338 "
-28022953,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4.5X12MM," 2,886 "
-28022954,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4.5X14MM," 2,886 "
-28022955,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4.5X16MM," 2,886 "
-28022956,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4.5X18MM," 2,886 "
-28022957,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4.5X20MM," 2,886 "
-28022958,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4X16MM," 2,260 "
-28022959,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4X18MM," 2,260 "
-28022960,HC IMPL SCREW QUICK LOCKING SELF TAPPING TI 4X20MM," 2,260 "
-28022961,HC IMPL SCREW RESORBABLE TAP 2.0X8MM," 2,128 "
-28022962,HC IMPL SCREW RESORBABLE TAP MINI QUICK 1.5X4MM," 2,128 "
-28022963,HC IMPL SCREW RESORBABLE TAP MINI QUICK 2X8MM," 2,128 "
-28022964,HC IMPL SCREW RETAINER 3.5X12MM," 2,199 "
-28022965,HC IMPL SCREW RETAINER 3.5X14MM," 2,199 "
-28022966,HC IMPL SCREW RETAINER 3.5X16MM," 2,199 "
-28022967,HC IMPL SCREW SELF DRILL AUTODRIVE 1.6X8MM, 442 
-28022968,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1 PKG 8MM, 660 
-28022969,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 1.8X5MM, 632 
-28022970,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 5 PKG 8MM," 3,162 "
-28022971,HC IMPL SCREW SELF DRILLING MATRIXMIDFACE TI 5MM, 660 
-28022972,HC IMPL SCREW SELF TAPPING MATRIXMIDFACE TI 12MM," 3,196 "
-28022973,HC IMPL SCREW SELF TAPPING MATRIXMIDFACE TI 10MM," 3,060 "
-28022974,HC IMPL SCREW LOCKING MATRIX MANDIBULAR TI 5P 2.0X5MM," 4,794 "
-28022975,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.4X10," 3,672 "
-28022976,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.4X16," 3,672 "
-28022977,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.4X18," 3,672 "
-28022978,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.4X8," 3,672 "
-28022979,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.7X10," 4,012 "
-28022980,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.7X12," 4,012 "
-28022981,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.7X14," 4,012 "
-28022982,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.7X16," 4,012 "
-28022983,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.7X18," 4,012 "
-28022984,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2.7X8," 4,012 "
-28022985,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X12MM," 2,550 "
-28022986,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X14MM," 2,550 "
-28022987,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X16MM," 2,550 "
-28022988,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X18MM," 2,550 "
-28022989,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X5MM," 2,550 "
-28022990,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X6MM, 510 
-28022991,HC IMPL SCREW SELF TAPPING MTRXMNDBL TITANIUM 5P 2X8MM," 2,550 "
-28022992,HC IMPL SCREW SELF TAPPING T8 2.7X20MM, 446 
-28022993,HC IMPL SCREW SELF TAPPING T8 2.7X22MM, 446 
-28022994,HC IMPL SCREW SELF TAPPING T8 2.7X24MM, 446 
-28022995,HC IMPL SCREW SELF TAPPING T8 2.7X26MM, 446 
-28022996,HC IMPL SCREW SELF TAPPING T8 2.7X28MM, 446 
-28022997,HC IMPL SCREW SET SEPCTRUM, 434 
-28022998,HC IMPL SCREW STANDARD CFX 1.6X10MM, 511 
-28022999,HC IMPL SCREW STANDARD CFX 1.6X5MM, 518 
-28023000,HC IMPL SCREW STANDARD CFX 1.6X8MM, 511 
-28023001,HC IMPL SCREW TITANIUM 2.0X34MM, 480 
-28023002,HC IMPL SCREW TITANIUM 2.0X36MM, 480 
-28023003,HC IMPL SCREW TITANIUM 2.0X38MM, 480 
-28023004,HC IMPL SCREW UNIPLANER 4.75X30MM," 7,863 "
-28023005,HC IMPL SCREW UNIPLANER SS 5.5X45X6MM," 7,509 "
-28023006,HC IMPL SCREW UNIPLANER SS 5.5X50X6MM," 7,509 "
-28023007,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X10MM," 1,190 "
-28023008,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X12MM," 1,284 "
-28023009,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X14MM," 1,284 "
-28023010,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X16MM," 1,265 "
-28023011,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X18MM," 1,265 "
-28023012,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X20MM," 1,265 "
-28023013,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X22MM," 1,222 "
-28023014,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X24MM," 1,190 "
-28023015,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X26MM," 1,190 "
-28023016,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X28MM," 1,190 "
-28023017,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X30MM," 1,190 "
-28023018,HC IMPL SCREW VARIABLE ANGLE LOCKING 2.4X8MM," 1,190 "
-28023019,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X10MM," 1,335 "
-28023020,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X12MM," 1,271 "
-28023021,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X14MM," 1,271 "
-28023022,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X16MM," 1,271 "
-28023023,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X18MM," 1,271 "
-28023024,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X20MM," 1,271 "
-28023025,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X22MM," 1,315 "
-28023026,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X24MM," 1,315 "
-28023027,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X26MM," 1,315 "
-28023028,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X28MM," 1,271 "
-28023029,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X30MM," 1,335 "
-28023030,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X32MM," 1,335 "
-28023031,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X34MM," 1,335 "
-28023032,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X36MM," 1,335 "
-28023033,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X38MM," 1,335 "
-28023034,HC IMPL SCREW VARIABLE ANGLE LOCKING T8 2.7X40MM," 1,335 "
-28023035,HC IMPL SCREW WEDGE BIOSTEON 7X23MM," 1,785 "
-28023036,HC IMPL SCREW XIA MONOAXIAL 5.5X45MM," 7,969 "
-28023037,HC IMPL SCREW XIA MONOAXIAL 6.5X50MM," 6,596 "
-28023038,HC IMPL SCREW XIA MONOAXIAL 7.5X50MM," 7,969 "
-28023039,HC IMPL SCREW XIA POLYAXIAL 4.5X25MM," 8,370 "
-28023040,HC IMPL SCREW XIA POLYAXIAL 4.5X35MM," 7,851 "
-28023041,HC IMPL SCREW XIA POLYAXIAL 4.5X40MM," 8,370 "
-28023042,HC IMPL SCREW XIA POLYAXIAL 4X20MM," 8,370 "
-28023043,HC IMPL SCREW XIA POLYAXIAL 4X25MM," 7,851 "
-28023044,HC IMPL SCREW XIA POLYAXIAL 4X35MM," 8,370 "
-28023045,HC IMPL SCREW XIA POLYAXIAL 7.5X40MM," 8,370 "
-28023046,HC IMPL SCREW XIA POLYAXIAL 7.5X45MM," 7,851 "
-28023047,HC IMPL SCREW XIA POLYAXIAL 7.5X50MM," 8,789 "
-28023048,HC IMPL SCREW XIA POLYAXIAL 7.5X60MM," 7,851 "
-28023049,HC IMPL SCREW XIA POLYAXIAL 7.5X80MM," 9,371 "
-28023050,HC IMPL SCREW XIA POLYAXIAL 8.5X45MM," 8,868 "
-28023051,HC IMPL SCREW XIA POLYAXIAL 8.5X50MM," 8,868 "
-28023052,HC IMPL SCREW XIA POLYAXIAL 8.5X60MM," 8,318 "
-28023053,HC IMPL SCREW XIA POLYAXIAL 8.5X90MM," 8,318 "
-28023054,HC IMPL SCREW XIA POLYAXIAL 9.5X100MM," 8,868 "
-28023055,HC IMPL SCREW XIA POLYAXIAL 9.5X80MM," 8,318 "
-28023056,HC IMPL SCREW XIA POLYAXIAL 9.5X90MM," 8,868 "
-28023057,HC IMPL SCREW XIA POLYAXIAL REDUCTION 5.5X30MM," 9,981 "
-28023058,HC IMPL SCREW XIA POLYAXIAL REDUCTION 5.5X35MM," 9,981 "
-28023059,HC IMPL SCREW XIA POLYAXIAL REDUCTION 5.5X40MM," 8,250 "
-28023060,HC IMPL SCREW XIA POLYAXIAL REDUCTION 6.5X45MM," 8,250 "
-28023061,HC IMP SHEET SILICON .13MMX5X5CM," 1,445 "
-28023062,HC IMP SHIM BONE HOOP INTERBODY FUSION," 6,248 "
-28023063,HC IMP SHOULDER ADAPTER HUMERAL REV EQUINOX +10MM," 14,772 "
-28023064,HC IMP SHOULDER ADAPTER HUMERAL REV EQUINOX +15MM," 14,772 "
-28023065,HC IMP SHOULDER ADAPTER HUMERAL REV EQUINOX +5MM," 14,772 "
-28023066,HC IMP SLEEVE CENTER COMPRESSION 18MM," 2,219 "
-28023067,HC SLEEVE DRILL DOUBLE 4X2.9MM," 2,618 "
-28023068,HC IMP SPACER ALIF ANTERIOR 11MM," 29,694 "
-28023069,HC IMP SPACER ALIF ANTERIOR 9MM," 29,694 "
-28023070,HC IMP SPACER FROZEN 11MM BROAD," 25,676 "
-28023071,HC IMP SPACER FROZEN 13MM BROAD," 26,950 "
-28023072,HC IMP SPACER FROZEN 15MM BROAD," 25,676 "
-28023073,HC IMP SPACER ORACLE 0 DEGREE 11X20X45MM," 38,350 "
-28023074,HC IMP SPACER ORACLE 8 DEGREE 11X20X50MM," 38,350 "
-28023075,HC IMP SPACER ORACLE 8 DEGREE 13X20X50MM," 40,268 "
-28023076,HC IMP SPACER ORACLE 8 DEGREE 15X20X50MM," 38,350 "
-28023077,HC IMP SPACER ORACLE 8 DEGREE 17X20X50MM," 38,350 "
-28023078,HC IMP SPACER SPINAL OPAL 10X24X11MM," 28,000 "
-28023079,HC IMP SPACER SPINAL OPAL 10X32X8MM," 28,000 "
-28023080,HC IMP SPACER TENDON HUNTER 3MMX24CM," 1,488 "
-28023081,HC IMP SPACER ZERO P CONVEX STANDARD 9MM," 24,640 "
-28023082,HC IMP SPACER ZERO P LORDOTIC STANDARD 6MM," 24,640 "
-28023083,HC IMP SPACER ZERO P LORDOTIC STANDARD 7MM," 25,760 "
-28023084,HC IMP SPACER ZERO P PARALLEL STANDARD 7MM," 24,640 "
-28023085,HC IMP SPHERE OCULAR SYNPOR 16MM," 3,536 "
-28023086,HC IMP SPHERE OCULAR SYNPOR 18MM," 3,536 "
-28023087,HC IMP SPHERE OCULAR SYNPOR 19MM," 3,536 "
-28023088,HC IMP SPHERE OCULAR SYNPOR 20MM," 3,536 "
-28023089,HC IMP SPHERE OCULAR SYNPOR 21MM," 3,536 "
-28023090,HC IMP SPHERE OCULAR SYNPOR 22MM," 3,536 "
-28023091,HC IMP SPINAL PRODISC-C LARGE 5MM," 35,787 "
-28023092,HC IMP SPINAL PRODISC-C LARGE DEEP 5MM," 35,787 "
-28023093,HC IMP SPINAL PRODISC-C MEDIUM 5MM," 35,787 "
-28023094,HC IMP SPINAL PRODISC-C MEDIUM DEEP 5MM," 4,298 "
-28023095,HC IMPL SPLINT TRACTION LEG UNIVERSAL, 444 
-28023096,HC IMP STAPES PROSTHESIS ROBERSON 0.6X1.0X4.25MM," 1,717 "
-28023097,HC IMP STAPLE FIXATION ACL LONG SMALL," 1,204 "
-28023098,HC IMP STAPLE FIXATION SPIKE MEDIUM 11.11MM," 1,204 "
-28023099,HC IMP STAPLE OSTEOTOMY OFFSET 14X17.5X23MM," 1,573 "
-28023100,HC STAPLER MULTIFIRE GIA 60 3.8," 2,472 "
-28023101,HC IMP STENT BILIARY EXPRESS 9X60X75," 9,143 "
-28023102,HC IMP STENT BILIARY OMNILINK 18 7X38X80," 9,750 "
-28023103,HC IMP STENT BILIARY OMNILINK35 6X38X135," 9,750 "
-28023104,HC IMP STENT BILIARY OMNILINK35 6X58X135," 9,750 "
-28023105,HC IMP STENT BILIARY OMNILINK35 8X58X135," 9,750 "
-28023106,HC IMP STENT BILIARY OMNILINK35 9X58X135," 9,750 "
-28023107,HC IMP STENT CORONARY MULTI LINK ZETA RX 2.75X8MM," 7,560 "
-28023108,HC IMP STENT URETERAL AMPLATZ 10FX20CM," 1,105 "
-28023109,HC IMP STENT URETERAL AMPLATZ 10FX24CM," 1,105 "
-28023110,HC IMP STENT URETERAL AMPLATZ 10FX28CM," 1,105 "
-28023111,HC IMP STENT WALLSTENT BILIARY 8FX12X90MM," 12,569 "
-28023113,HC STRIP CORTICAL EPILEPSY 1X6," 3,679 "
-28023114,HC STRIP CORTICAL EPILEPSY 1X8," 4,879 "
-28023115,HC STRIP CORTICAL EPILEPSY 2X6," 5,250 "
-28023116,HC STRIP CORTICAL EPILEPSY 4X5," 6,562 "
-28023117,HC IMP STRIP HEALOS II SGL 10ML," 11,198 "
-28023118,HC IMP STRIP MINI  LOW PROFILE 68 HOLE," 1,913 "
-28023119,HC IMP STRIP MINI 34 HOLE," 1,261 "
-28023120,HC IMP STRIP MINI 68 HOLE," 1,870 "
-28023121,HC IMP STRIP MINI RIGID 34 HOLE," 1,343 "
-28023122,HC IMP STRIP MINI RIGID 68 HOLE," 1,849 "
-28023123,HC IMP SYRINGE DEFLUX PREFILLED 1ML," 14,246 "
-28023124,HC IMP TISSUE BONE CUBE CONFORM 11MM," 6,290 "
-28023125,HC IMP TISSUE BONE CUBE CONFORM 13MM," 6,120 "
-28023126,HC IMP TISSUE BONE CUBE CONFORM 15MM," 6,120 "
-28023127,HC IMP TISSUE BONE CUBE CONFORM 17MM," 6,120 "
-28023128,HC IMP TISSUE BONE CUBE CONFORM 9MM," 4,505 "
-28023129,HC TROCAR 3.2MM," 3,314 "
-28023130,HC TUBE BLAKEMORE MEDIUM 16F," 2,342 "
-28023131,HC  TUBE ESOPHAGEAL 185X83MM," 1,488 "
-28023132,HC  TUBE FEDING CORFLO KIT, 570 
-28023133,HC  TUBE FEED ENTERAL CORFLO 8X43, 476 
-28023134,HC  TUBE FEED ENTERAL CORFLO 8X55," 1,700 "
-28023135,HC  TUBE GASTRO ENTERIC 3-5ML 18FX25.4CM," 1,105 "
-28023136,HC  TUBE GASTROSTOMY LOW PROFILE 12F 0.8, 808 
-28023137,HC  TUBE GASTROSTOMY LOW PROFILE 12F 1.2, 838 
-28023138,HC  TUBE GASTROSTOMY LOW PROFILE 14F 2.7, 808 
-28023139,HC  TUBE GASTROSTOMY LOW PROFILE 14F 3.0, 808 
-28023140,HC  TUBE GASTROSTOMY LOW PROFILE 16F 2.0, 838 
-28023141,HC  TUBE GASTROSTOMY LOW PROFILE 18F 5.0, 821 
-28023142,HC  TUBE GASTROSTOMY LOW PROFILE 24F 2.5, 835 
-28023143,HC IMP VALVE AORTIC CONFORM-X 25MM," 28,000 "
-28023144,HC IMP VALVE AORTIC CONFORM-X W EXTERNAL HOLDER 21MM," 28,000 "
-28023145,HC IMP VALVE AORTIC MASTERS EXPANDED CUFF 19MM," 28,084 "
-28023146,HC IMP VALVE AORTIC MECHANICAL 31MM," 28,084 "
-28023147,HC IMP VALVE AORTIC MECHANICAL STANDARD 17MM," 28,889 "
-28023148,HC IMP WASHER CONICAL COUPLE," 7,872 "
-28023149,HC IMP WIRE ACTIVATION 72MM," 4,777 "
-28023150,HC IMP WIRE ACTIVATION 82MM," 4,871 "
-28023151,HC IMP WIRE ACTIVATION 92MM," 5,353 "
-28023152,HC IMP WIRE CORTICAL ILIZAROV BAY PT 1.5X300MM, 460 
-28023153,HC IMP WIRE CORTICAL ILIZAROV BAY PT 1.5X370MM, 465 
-28023154,HC IMP WIRE KIRSCHNER BLUNT TIP 1.6X480MM, 641 
-28023155,HC IMP STENT NEPHROSTOMY ULTRATHANE 10.2X26, 866 
-28023156,HC  CATHETER ARMADA WRKHRS 8X60X60," 1,615 "
-28023157,HC IMP LINK SPINELINK REVERSE LEFT 27MM," 2,125 "
-28023158,HC IMP LINK SPINELINK REVERSE RIGHT 21MM," 2,125 "
-28023159,HC IMP ANCHOR SUTURE JUGGERKNOT STRAIGHT DISP 1.5 KIT," 2,601 "
-28023160,HC  CATHETER ARMADA WRKHRS 10X80X80," 1,615 "
-28023161,HC CATH BACTISEAL 3X15X1.9," 5,270 "
-28023162,HC CATH BACTISEAL VENTTRICULAR AND PERITIONEAL," 6,163 "
-28023163,HC IMPL COIL NEURO HELICAL SR 360D 12XMM30CM," 14,621 "
-28023164,HC IMPL SCREW CANNULATED SHORT THREAD 3.0X8MM," 1,598 "
-28023165,HC IMP STENT GRAFT AAA EXCLUDER ORIGINAL 26X12X18," 64,968 "
-28023166,HC STAPLER INTRALUM CURVED 23MM," 2,819 "
-28023167,HC STAPLER INTRALUM CURVED 25MM," 2,390 "
-28023168,HC STAPLER INTRALUM CURVED33MM," 2,390 "
-28023169,HC IMP ICD EVIA DR-T W/HMM," 71,750 "
-28023170,HC IMP ABUTMENT BONE ANCHORED 9MM," 18,750 "
-28023171,HC IMP ALLOGRAFT  MEMBRANE AMBIOTIC," 5,058 "
-28023172,HC IMP ANCHOR SUTURE OSTEORAPTOR 2.3M," 2,992 "
-28023173,HC APPLIER CLIP ENDO HEMOLOK AUTO 5MM," 3,317 "
-28023174,HC APPLIER CLIP ENDO HEMOLOK MEDIUM LARGE," 10,500 "
-28023175,HC IMP APPLIER MINI Y TEMP REG STRAIGHT 3M," 6,460 "
-28023176,HC IMP BAR TRANSVERSE TI 6.0MM 15MM," 2,268 "
-28023177,HC IMP BAR TRANSVERSE TI 6.0MM 20MM," 2,355 "
-28023178,HC IMP BAR TRANSVERSE TI 6.0MM 25MM," 2,355 "
-28023179,HC IMP BLOCKER SPINAL ROD XIA 4.5MM," 1,005 "
-28023180,HC IMP BLOCKER SPINAL XIA TI 4.5MM," 1,100 "
-28023181,HC IMP BOLT FIXATION HALF PIN," 1,182 "
-28023182,HC IMP BOLT SPINAL XLP 5.5X50MM," 2,338 "
-28023183,HC IMP BOLT SPINAL XLP 5.5X55MM," 2,338 "
-28023184,HC IMP BOLT SPINAL XLP 5.5X60MM," 2,338 "
-28023185,HC IMP BONE ACETABULUM FROZEN LEFT," 17,941 "
-28023186,HC IMP BONE ACETABULUM FROZEN RIGHT," 17,941 "
-28023187,HC IMP BONE BLOCK LPCHOR, 432 
-28023188,HC IMP BONE GRAFT CYLINDER CHRONOS SIZE 6," 3,557 "
-28023189,HC IMP BONE GRAFT CYLINDER CHRONOS SIZE 7," 3,630 "
-28023190,HC IMP BONE GRAFT CYLINDER CHRONOS SIZE 9," 3,557 "
-28023191,HC IMP BONE GRAFT OSTEOSET MIIG 15ML," 13,988 "
-28023192,HC IMP BONE GRAFT OSTEOSET MIIG 5ML," 7,225 "
-28023193,HC IMP BONE MATRIX 10CC," 26,180 "
-28023194,HC IMP BONE MATRIX 1CC," 3,664 "
-28023195,HC BUTTON TRACH 11X27," 1,830 "
-28023196,HC BUTTON TRACH 11X40," 1,830 "
-28023197,HC CABLE DIAGNOSTIC EP QUICK CONNECT 20POLE," 2,125 "
-28023198,HC CABLE INTERFACE 34HYP LEMO 10FT," 3,400 "
-28023199,HC IMP CAGE LARGE OBLIQUE 5D 10X10X25," 28,000 "
-28023200,HC IMP CAGE LARGE OBLIQUE 5D 10X10X35," 28,000 "
-28023201,HC IMP CAGE LARGE OBLIQUE 5D 8X10X25," 28,000 "
-28023202,HC IMP CAGE SPINAL XIA FUSION 18X20," 42,142 "
-28023203,HC IMP CAGE SPINAL XIA FUSION 22X37," 48,059 "
-28023204,HC IMP CAGE SPINAL XIA FUSION 22X45," 44,815 "
-28023205,HC IMP CAP HEALING 26MM, 315 
-28023206,HC IMP CARTILAGE FRESH ANKLET RIGHT," 39,520 "
-28023207,HC CATHETER 5MAX DISTAL DELIVERY 132CM," 14,925 "
-28023208,HC  CATH ABLAT CARDIAC CRV D TYPE," 17,640 "
-28023209,HC  CATH ABLAT CARDIAC CRV F TYPE," 17,640 "
-28023210,HC  CATH ABLAT CARDIAC CRV J TYPE," 17,640 "
-28023211,HC  CATHETER DILATION ADVANCE 18LP 4X135X8X4," 2,338 "
-28023212,HC CATHETER ANGIOGRAPHY BEACON TORCON 5X65CM, 119 
-28023213,HC  CATHETER ANGIOPLASTY PTCA TITAL 3.5X9MM," 2,614 "
-28023214,HC  CATHETER ARMADA 1.5X120X150," 3,825 "
-28023215,HC  CATHETER ARMADA 1.5X40X150," 3,400 "
-28023216,HC  CATHETER ARMADA 2.0X200X150," 3,825 "
-28023217,HC  CATHETER ARMADA 2.0X40X150," 3,400 "
-28023218,HC  CATHETER ARMADA 2.0X80X150," 3,400 "
-28023219,HC  CATHETER ARMADA 2.5X200X150," 3,825 "
-28023220,HC  CATHETER ARMADA 2.5X40X150," 3,400 "
-28023221,HC  CATHETER ARMADA 2.5X80X150," 3,400 "
-28023222,HC  CATHETER ARMADA 3.0X200X150," 3,825 "
-28023223,HC  CATHETER ARMADA 3.0X40X150," 3,400 "
-28023224,HC  CATHETER ARMADA 3.0X80X150," 3,400 "
-28023225,HC  CATHETER ARMADA 4.0X200X150," 3,825 "
-28023226,HC  CATHETER BILIARY DILATATION BALL 4X3X180," 1,468 "
-28023227,HC  CATHETER BILIARY DILATATION BALL 6X3X180," 1,459 "
-28023228,HC  CATHETER BILIARY DILATION FUSION 10X3MM," 1,649 "
-28023229,HC  CATHETER BILIARY DILATION FUSION 8X3MM," 1,649 "
-28023230,HC  CATHETER BALLOON DILATATION 10X4CM 80CM," 1,488 "
-28023231,HC CATHETER BALLOON DILAT SPRINTER OTW 1.5X6," 1,488 "
-28023232,HC CATHETER BALLOON DILAT SPRINTER OTW 2X6," 1,726 "
-28023233,HC  CATHETER BALLOON DILATION SYS 8.5X24," 5,568 "
-28023234,HC CATHETER BALLOON ENDOVIVE 20F 3.0CM, 842 
-28023235,HC  CATHETER BALLOON INSPIRA AIR 12X40MM," 5,908 "
-28023236,HC  CATHETER BALLOON INSPIRA AIR 5X24MM," 5,568 "
-28023237,HC  CATHETER BALLOON LACRIMAL DUCT 2MM," 2,601 "
-28023238,HC  CATHETER BALLOON OCCLU SCEPTOR 4X20," 9,000 "
-28023239,HC CATHETER BALLOON WIRE GUID HERCULES 20M," 1,989 "
-28023240,HC  CATHETER CENTRAL VENOUS DOUBLE LUMEN 6FR," 2,083 "
-28023241,HC CATH CHORIONIC VILLUS SAMPLING 5.7F, 281 
-28023242,HC CATHETER DIAG ISMUS CARTO 7F," 10,260 "
-28023243,HC  CATHETER DIALYSIS PALINDROME 19CM," 4,420 "
-28023244,HC  CATHETER DIALYSIS PALINDROME 23CM," 4,420 "
-28023245,HC  CATHETER DIALYSIS PALINDROME 28CM, 884 
-28023246,HC CATHETER DILATION PTA ATLAS 7FR," 5,498 "
-28023247,HC  CATHETER DRAINAGE 12F 38IN 60CM," 1,162 "
-28023248,HC  CATHETER DRAINAGE 8FX20CM, 740 
-28023249,HC CATHETER DUAL LUMEN IMPREGNATED 5X12CM, 649 
-28023250,HC CATHETER DUAL LUMEN JTIP 4X12CM, 607 
-28023251,HC CATHETER DUAL LUMEN JTIP 4X8CM, 607 
-28023252,HC CATHETER DUAL LUMEN JTIP 5X12CM, 779 
-28023256,HC  CATHETER GUIDING VISTA BRITE TIP XBRCA 6F, 519 
-28023257,HC  CATHETER GUIDING FL5 W/SHEATH 8X100, 451 
-28023258,HC CATHETER IV DUAL LUMEN 16X1.75, 85 
-28023260,HC CATHETER LASER TURBOTANDEM 8X20CM," 20,965 "
-28023261,HC CATHETHER PASSER 38CM," 1,105 "
-28023262,HC  CATHETER PENTARAY F CURVE 5-5-5 5FX115CM," 1,496 "
-28023263,HC CATHETER QUICK CROSS EXTREME.035X65," 1,675 "
-28023264,HC CATHETER SINGLE LUMEN 3FX8CM SET, 542 
-28023265,HC CATHETER SINGLE LUMEN 5FX15CM SET, 300 
-28023266,HC CATHETER SINGLE LUMEN GROSHONG 4F KIT, 547 
-28023267,HC CATHETER SINUS GUIDE FRONTAL F70," 2,720 "
-28023268,HC CATHETER SINUS GUIDE MAXIL M110," 2,720 "
-28023269,HC CATHETER SINUS GUIDE SPHENOID S-0," 2,720 "
-28023270,HC  CATHETER SMALL BOWEL CUSTOMIZED," 3,570 "
-28023271,HC  CATH SPINCATH INTRADISCAL X LONG," 7,913 "
-28023274,HC CATHETER TRIPLE LUMEN IMPREGNATED 5X8CM, 731 
-28023275,HC CATHETER TRIPLE LUMEN IMPREGNATED 7X15CM, 731 
-28023276,HC  CATHETER TRIPLE LUMEN JTIP 5X8CM, 622 
-28023277,HC CATHETER TRIPLE LUM JTIP 5.0F 12CM, 799 
-28023278,HC  CATH ULTRASOUND IVUS PIONEER," 10,500 "
-28023281,HC IMP CEMENT HYDROSET HYDROXYPATITE INJECTABL 15CC," 23,436 "
-28023282,HC IMP CLAMP COUPLING BALL JOINT HYBRID," 4,463 "
-28023283,HC CLAMP ENDO ISOLATOR SYNERGY," 7,813 "
-28023284,HC IMP CLAMP UNIVERSAL 5.5MM," 8,126 "
-28023285,HC CLIP APPLIER ANGLED LARGE, 510 
-28023286,HC CLIP APPLIER SHORT," 8,458 "
-28023287,HC IMP CLIP MINI Y STRAIGHT NARROW JAY 3M," 2,763 "
-28023288,HC IMP CLIP MINI Y STRAIGHT NARROW JAY 5M," 2,763 "
-28023289,HC IMP CLIP RESOLUTION 2.8X235," 1,318 "
-28023290,HC IMP COCHLEAR BIONICS CLARION," 87,325 "
-28023291,HC IMP COCHLEAR PONTO 4MM," 8,186 "
-28023292,HC IMPL COIL DETACHABLE 5X15CM," 5,211 "
-28023293,HC IMPL COIL EMBOLIZATION 3CMX2MM," 1,131 "
-28023294,HC IMP CONNECTOR SPINAL CLOSED OFFSET 5.5X30," 7,225 "
-28023295,HC IMP CONNECTOR SPINAL FIXED CROSS 5.5X25MM," 10,373 "
-28023296,HC IMP CONNECTOR SPINAL OPEN OFFSET 5.5X30," 7,225 "
-28023297,HC IMP CONNECTOR CROSS FIXED 20M," 7,225 "
-28023298,HC IMP CONNECTOR CROSS FIXED 24M," 7,225 "
-28023299,HC IMP CONNECTOR CROSS POLARIS XXS," 7,225 "
-28023300,HC IMP CONNECTOR CROSSLINKED 4.75X28MM," 11,126 "
-28023301,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 17MM," 7,225 "
-28023302,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 25MM," 8,409 "
-28023303,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 26MM," 8,333 "
-28023304,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 38-31MM," 8,598 "
-28023305,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 29-30MM," 8,409 "
-28023306,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 31-34MM," 8,409 "
-28023307,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 35-37MM," 8,409 "
-28023308,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 35-45MM," 8,598 "
-28023309,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 38-43MM," 8,323 "
-28023310,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 42-51MM," 8,323 "
-28023311,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 43-54MM," 8,598 "
-28023312,HC IMP CONNECTOR CROSSLINKED MULTI AXIAL XIA 70-99MM," 8,598 "
-28023313,HC IMP CONNECTOR FIXED CROSS 27.5MM," 10,373 "
-28023314,HC IMP CONNECTOR FIXED CROSS 32.5MM," 10,373 "
-28023315,HC IMP CONNECTOR FIXED CROSS 35MM," 7,225 "
-28023316,HC IMP CONNECTOR FIXED CROSS 37.5MM," 10,373 "
-28023317,HC IMP CONNECTOR PARALLEL TI 3.5/5.0," 6,205 "
-28023318,HC IMP CONNECTOR PARALLEL TI 3.5/6.0," 6,698 "
-28023319,HC IMP CONNECTOR ROD XIA3  ANG ILIOS," 7,225 "
-28023320,HC IMP CONNECTOR XIA EXTENDED LARGE 4.5," 10,041 "
-28023321,HC IMP CORNEAL MEMBRANE PROKERA 16MM," 8,067 "
-28023322,HC IMP CROSS BAR STANDARD 24INCH," 1,870 "
-28023323,HC CROWN HALO BREMER W/HIFIX LARGE," 5,075 "
-28023324,HC CRYO UNIT SPRAY TIP 300ML," 6,222 "
-28023325,HC CRYO UNIT SPRAY TIP 500ML," 6,222 "
-28023326,HC CRYOPROBE 1.7M 15CM," 9,096 "
-28023327,HC CRYOPROBE 2.4M 23CM," 9,096 "
-28023328,HC IMP CRYOPROBE IR 1.7MM," 9,096 "
-28023329,HC IMP CRYOPROBE IR 2.4MM," 9,096 "
-28023330,HC  DEVICE GRASPIN RAPTOR 2.4X230CM, 791 
-28023331,HC IMP DEVICE INTRAUTERINE PARAGARD," 1,017 "
-28023332,HC  DEVICE RETRIVIAL ROTH NET STANDARD, 697 
-28023333,HC  DILATOR FUSION QUATTRO 12-20M," 1,853 "
-28023334,HC  DILATOR FUSION QUATTRO 8.5-15M," 1,853 "
-28023335,HC DILATOR NIM STIMULATED SET," 6,375 "
-28023336,HC DISTRACTOR MIDFACE ALLOY TI 40MM," 26,785 "
-28023337,HC IMP DISTRACTOR MICRO ZURICH 20MM," 30,205 "
-28023338,HC IMP EAR PROSTHESIS ALTO TOTAL ADJUSTABLE 3X0.8MM," 2,530 "
-28023339,HC IMP EAR PROSTHESIS PISTON LONG 8X4.5M," 1,216 "
-28023340,HC IMP EAR PROSTHESIS PISTON MALLEUS 8X6.00," 1,012 "
-28023341,HC IMP EAR PROSTHESIS PISTON MALLEUS 8X6.25," 1,012 "
-28023342,HC IMP EAR PROSTHESIS PISTON MALLEUS 8X6.50, 884 
-28023343,HC ELECTRODE GRID PLATINUM 16 CONTACT 2X8MM," 5,568 "
-28023344,HC IMP ENDCAP SPINAL XIA 3DEG 18MM," 2,312 "
-28023345,HC IMP ENDCAP SPINAL XIA 8DEG 18MM," 2,548 "
-28023346,HC IMP ENDCAP SPINAL XIA EXT 18MM," 1,931 "
-28023347,HC IMP ENDCAP SPINAL XIA EXT 22MM," 1,806 "
-28023348,HC IMP ESOPHAGEAL ESOPHYX 2 KIT," 22,750 "
-28023349,HC IMP ESOPHAGEAL ESOPHYX 2 LINK," 23,800 "
-28023350,HC IMP ESOPHAGEAL ESOPHYX 3 LINK," 23,800 "
-28023351,HC IMP EXTERNAL FIXATOR COMPRESSION DISTRACTION LONG," 3,519 "
-28023352,HC IMP EXTERNAL FIXATOR COMPRESSION DISTRACTION STD," 3,519 "
-28023353,HC IMP EXTERNAL FIXATOR POST 1 HOLE," 1,343 "
-28023354,HC IMP EXTERNAL FIXATOR POST 2 HOLE," 1,479 "
-28023355,HC IMP EXTERNAL FIXATOR POST 3 HOLE," 1,530 "
-28023356,HC IMP EXTERNAL FIXATOR POST 4 HOLE," 1,573 "
-28023357,HC IMP EXTERNAL FIXATOR POST 5 HOLE," 1,615 "
-28023358,HC IMP EYELID GOLD CONTOUR 1.8 GRAM," 3,060 "
-28023359,HC FIBER LASER BEAM PATH," 7,650 "
-28023360,HC FIBER LASER BEAMPATH 130CM," 4,675 "
-28023361,HC IMP FILLER BONE OSTEOSET PELLET 5ML," 4,420 "
-28023362,HC IMP FILTER VENA CAVA JUGLR DENALI," 15,750 "
-28023363,HC IMP FINGER JOINT SIZE 2," 6,163 "
-28023364,HC IMPL FINGER JOINT SWANSON GROM SZ6," 4,080 "
-28023365,HC IMPL FINGER JOINT SWANSON GROM SZ7," 5,908 "
-28023366,HC IMPL FINGER JOINT SWANSON GROM SZ8," 1,870 "
-28023367,HC IMPL FINGER JOINT SWANSON GROM SZ9," 1,870 "
-28023368,HC IMP FOOT PLATE ALLOY TI TAN," 3,645 "
-28023369,HC IMP FOOT PLATE POSTERIOR MONOAXIAL TI TAN," 5,794 "
-28023370,HC  GUIDEWIRE FUSION LOOP TIP," 1,607 "
-28023371,HC IMP GRAFT GELSOFT 14X8," 4,322 "
-28023372,HC IMP GRAFT GELSOFT 6X60," 4,322 "
-28023373,HC IMP GRAFT GELSOFT 8X60," 4,324 "
-28023374,HC IMP GRAFT GORTEX RINGED 5X15," 2,644 "
-28023375,HC IMP GRAFT GORTEX RINGED 5X70," 7,829 "
-28023376,HC IMP GRAFT GORTEX THIN WALLED 3.5X15," 3,392 "
-28023377,HC IMP GRAFT GORTEX THIN WALLED 3X40," 3,553 "
-28023378,HC IMP GRAFT MEMBRANE AMNIOTIC FROZEN," 6,959 "
-28023379,HC IMP GFT VASCULAR PROPATEN STANDARD 6M 40X50CM," 13,305 "
-28023380,HC IMP GRAFT PROPATEN STANDARD 6X80CM," 17,724 "
-28023381,HC IMP GRAFT PROPATEN STANDARD 7X40CM," 9,480 "
-28023382,HC IMP GRAFT PROPATEN STANDARD 8X40CM," 13,305 "
-28023383,HC IMP GRAFT PROPATEN STANDARD 8X80CM," 19,663 "
-28023384,HC IMP GRAFT RINGED STANDARD WALL 20," 7,553 "
-28023385,HC IMPL GRAFT RINGED STANDARD WALL 30," 8,381 "
-28023386,HC IMP GRAFT STENT AAA ENDURANT 20X20X82," 33,950 "
-28023387,HC IMP GRAFT STENT AAA ENDURANT 23X13X166," 64,838 "
-28023388,HC IMP GRAFT STENT AAA ZENITH SPIRAL 13X122," 21,595 "
-28023389,HC IMP GRAFT STENT AORTIC EXT SUPRARENAL 25X75X95," 25,795 "
-28023390,HC IMP GRAFT VASCULAR PROPATEN 3.5X10," 7,514 "
-28023391,HC IMP GRAFT VASCULARPROPATEN 3X10," 7,514 "
-28023392,HC IMP GRAFT VASCULAR PROPATEN 4X10," 7,514 "
-28023393,HC IMP GRAFT VASCULAR PROPATEN 5X10," 7,514 "
-28023394,HC IMP GRAFT VASCULAR PROPATEN 6X15," 7,958 "
-28023395,HC IMP GRAFT VASCULAR STANDARD 6X40," 2,950 "
-28023396,HC IMP GRAFT VASCULAR STRETCH 10X40MM," 4,505 "
-28023397,HC IMP GRAFT VASCULAR STRETCH 6X10MM," 1,352 "
-28023398,HC IMP GRAFT VASCULAR STRETCH 7X40MM," 6,690 "
-28023399,HC IMP GRAFT VASCULAR STRETCH RINGED 6X60," 9,090 "
-28023400,HC IMP GRAFT VASCULAR STRETCH RINGED 8X30," 6,239 "
-28023401,HC IMP GRAFT VASCULAR THORACOABDOM GELWEAV 26X60," 18,593 "
-28023402,HC IMP GRAFT VASCULAR THORACOABDOM GELWEAV 28X60," 19,495 "
-28023403,HC IMP GRAFT VASCULAR THORACOABDOM GELWEAV 30X60," 18,571 "
-28023404,HC IMP GRAFT AMNIOTIC MEMBRANE," 7,353 "
-28023405,HC  GUIDEWIRE NTHRD 1.25MM, 257 
-28023406,HC  GUIDEWIRE ANGIO NEUROVASCUALR .014X205CM," 3,740 "
-28023407,HC  GUIDEWIRE DELTA .035IN 260CM," 4,123 "
-28023408,HC  GUIDEWIRE DREAMWIRE ANG .035X260, 850 
-28023409,HC  GUIDEWIRE DREAMWIRE ANG .035X450, 850 
-28023410,HC  GUIDEWIRE DREAMWIRE STR .035X260, 850 
-28023411,HC  GUIDEWIRE DREAMWIRE STR .035X450, 850 
-28023412,HC  GUIDEWIRE JAGWIRE STIFF ANGLED 25IN 260CM, 850 
-28023413,HC  GUIDEWIRE METRO DIRECT 25X480," 1,462 "
-28023414,HC  GUIDEWIRE METRO DIRECT 035X650," 1,994 "
-28023415,HC  GUIDEWIRE PRIMEWIRE STRAIGHT 185CM," 5,100 "
-28023416,HC  GUIDEWIRE STR 0.21X50CM, 444 
-28023417,HC  GUIDEWIRE REUTER CURVE TIP .035X145CM, 553 
-28023418,HC HANDSET LIGASURE VESSEL STANDARD," 6,324 "
-28023419,HC IMP HEART ARTIFICIAL TOTAL," 187,050 "
-28023420,HC IMP HIP CUP ACET BIRMINGHAM 52MM," 35,506 "
-28023421,HC IMP HIP CUP ACET BIRMINGHAM 56MM," 40,885 "
-28023422,HC IMP HIP CUP ACET BIRMINGHAM 60MM," 35,506 "
-28023423,HC IMP HIP CUP ACET BIRMINGHAM 64MM," 40,885 "
-28023424,HC IMP HIP HEAD ACET BIRMINGHAM 46MM," 28,263 "
-28023425,HC IMP HIP HEAD ACET BIRMINGHAM 50MM," 30,345 "
-28023426,HC IMP HIP HEAD ACET BIRMINGHAM 54MM," 28,263 "
-28023427,HC IMP HIP HEAD ACET BIRMINGHAM 58MM," 30,345 "
-28023428,HC IMP HIP HEAD FEMORAL  BIRMINGHAM 44MM," 27,062 "
-28023429,HC IMP HIP HEAD FEMORAL LOW FRICTION V40 36M," 12,150 "
-28023430,HC IMP HIP HEAD UNIPOLAR UNITRAX 60MM," 1,662 "
-28023431,HC IMP HIP RINGLOC BIPOLAR 22X37MM," 17,580 "
-28023432,HC HOOK PEDICLE SMALL," 7,038 "
-28023433,HC IMP HOOK SPINAL FRONTAL ANGLED," 7,030 "
-28023434,HC IMP HOOK SPINAL LAMINA DUAL OPENING TI FRONTAL LRG," 6,579 "
-28023435,HC IMP HOOK SPINAL LAMINA DUAL OPENING TI FRONTAL MED," 6,579 "
-28023436,HC IMP HOOK SPINAL LAMINA DUAL OPENING TI FRONTAL SM," 6,579 "
-28023437,HC IMP HOOK SPINAL LAMINA DUAL OPENING TI SIDE LARGE," 5,857 "
-28023438,HC IMP HOOK SPINAL LAMINA DUAL OPENING TI SIDE MEDIUM," 5,857 "
-28023439,HC IMP HOOK SPINAL LAMINA DUAL OPENING TI SIDE SMALL," 5,857 "
-28023440,HC IMP HOOK SPINAL LAMINA DUAL OPENING LARGE," 7,030 "
-28023441,HC IMP HOOK SPINAL LAMINA DUAL OPENING MEDIUM," 7,030 "
-28023442,HC IMP HOOK SPINAL LAMINA DUAL OPENING SMALL," 7,030 "
-28023443,HC IMP HOOK SPINAL LAMINA FRONTAL LARGE," 5,712 "
-28023444,HC IMP HOOK SPINAL LAMINA FRONTAL MEDIUM," 5,712 "
-28023445,HC IMP HOOK SPINAL LAMINA FRONTAL SMALL," 5,712 "
-28023446,HC IMP HOOK SPINAL LAMINA LEFT LARGE," 4,981 "
-28023447,HC IMP HOOK SPINAL LAMINA LEFT MEDIUM," 4,981 "
-28023448,HC IMP HOOK SPINAL LAMINA LEFT SMALL," 4,981 "
-28023449,HC IMP HOOK SPINAL LAMINA RIGHT LARGE," 4,981 "
-28023450,HC IMP HOOK SPINAL LAMINA RIGHT MEDIUM," 4,981 "
-28023451,HC IMP HOOK SPINAL LAMINA RIGHT SMALL," 4,981 "
-28023452,HC IMP HOOK SPINAL LAMINA THORACIC FRONTAL," 7,030 "
-28023453,HC IMP HOOK SPINAL LAMINA THORACIC LEFT," 4,981 "
-28023454,HC IMP HOOK SPINAL LAMINA THORACIC RIGHT," 4,981 "
-28023455,HC IMP HOOK SPINAL LAMINA TI THORACIC FRONTAL," 6,579 "
-28023456,HC IMP HOOK SPINAL LAMINA TI THORACIC SIDE," 5,857 "
-28023457,HC IMP HOOK SPINAL LAMINAR 6M," 10,658 "
-28023458,HC IMP HOOK SPINAL LAMINAR NARROW LARGE," 8,203 "
-28023459,HC IMP HOOK SPINAL PEDICLE FRONTAL," 7,030 "
-28023460,HC IMPL ENDPLATE XRL 26 X 30MM," 12,750 "
-28023461,HC  INTRODCR PERCUTANEOUS SET 6F, 248 
-28023462,HC  CATH-DIALYSIS ST 9F X 12CM, 604 
-28023463,HC  CATH CENTESIS ONE-STEP 5FX10, 187 
-28023464,HC  CATH CENTESIS ONE-STEP 5X15, 187 
-28023466,HC  CATHETER ATHERECT 0.035 X 2.3M," 20,965 "
-28023467,HC IMPL AMPLATZER OCCLUD  SEPTAL 17 MM," 39,423 "
-28023468,HC IMPL GFT PROX ZENITH TX2 20F 36X202," 72,564 "
-28023469,HC IMPL GRAFT EXCLUDER 28.5X12," 64,968 "
-28023470,HC IMPL STENT GRFT OVATION PRM 13X14X10X100X150," 28,350 "
-28023471,HC IMPL STENT GRFT OVATION PRM 13X14X16X100X150," 28,350 "
-28023472,HC IMPL STENT PREMIER MR 2.25X8," 14,250 "
-28023473,HC  CATH TRAILBLAZER 5X135," 7,013 "
-28023474,HC IMPL GW HITORQUE CMMD ES 14X300," 1,275 "
-28023475,HC IMPL STENT P PREMIER MR 2.25X12," 14,250 "
-28023476,HC SHEATH GLIDSHEATH SLENDER 6X10," 2,338 "
-28023477,HC IMPL JUGULAR FILTER ALN," 7,875 "
-28023478,HC IMPL STENT P PREMIER MR 2.25X16," 14,250 "
-28023479,HC IMPL STENT P PREMIER MR 2.25X20," 14,250 "
-28023481,HC IMPL STENT P PREMIER MR 2.25X24," 14,250 "
-28023482,HC IMPL STENT P PREMIER MR 2.25X28," 14,250 "
-28023483,HC IMPL STENT P PREMIER MR 2.25X32," 14,250 "
-28023484,HC IMPL STENT P PREMIER M 2.50X8," 14,250 "
-28023485,HC IMPL STENT P PREMIER M 2.50X12," 14,250 "
-28023486,HC IMPL STENT P PREMIER M 2.50X16," 14,250 "
-28023487,HC IMPL STENT P PREMIER M 2.50X20," 14,250 "
-28023488,HC IMPL STENT P PREMIER M 2.50X24," 14,250 "
-28023489,HC IMPL STENT P PREMIER M 2.50X28," 14,250 "
-28023490,HC IMPL STENT P PREMIER M 2.50X32," 14,250 "
-28023491,HC IMPL STENT P PREMIER M 2.50X38," 14,250 "
-28023492,HC IMPL STENT P PREMIER M 2.75X8," 14,250 "
-28023493,HC IMPL STENT P PREMIER M 2.75X12," 14,250 "
-28023494,HC IMPL STENT P PREMIER M 2.75X16," 14,250 "
-28023495,HC IMPL STENT P PREMIER M 2.75X20," 14,250 "
-28023496,HC IMPL STENT P PREMIER M 2.75X24," 14,250 "
-28023497,HC IMPL STENT P PREMIER M 2.75X28," 14,250 "
-28023498,HC IMPL STENT P PREMIER M 2.75X32," 14,250 "
-28023499,HC IMPL STENT P PREMIER M 2.75X38," 14,250 "
-28023500,HC IMPL STENT P PREMIER M 3.00X8," 14,250 "
-28023501,HC IMPL STENT P PREMIER M 3.00X12," 14,250 "
-28023502,HC IMPL STENT P PREMIER M 3.00X16," 14,250 "
-28023503,HC IMPL STENT P PREMIER M 3.00X20," 14,250 "
-28023504,HC IMPL STENT P PREMIER M 3.00X24," 14,250 "
-28023505,HC IMPL STENT P PREMIER M 3.00X28," 14,250 "
-28023506,HC IMPL STENT P PREMIER M 3.00X32," 14,250 "
-28023507,HC IMPL STENT P PREMIER M 3.00X38," 14,250 "
-28023508,HC IMPL STENT P PREMIER M 3.50X8," 14,250 "
-28023509,HC IMPL STENT P PREMIER M 3.50X12," 14,250 "
-28023510,HC IMPL STENT P PREMIER M 3.50X16," 14,250 "
-28023511,HC IMPL STENT P PREMIER M 3.50X20," 14,250 "
-28023512,HC IMPL STENT P PREMIER M 3.50X24," 14,250 "
-28023513,HC IMPL STENT P PREMIER M 3.50X28," 14,250 "
-28023514,HC IMPL STENT P PREMIER M 3.50X32," 14,250 "
-28023515,HC IMPL STENT P PREMIER M 3.50X38," 14,250 "
-28023516,HC IMPL STENT P PREMIER M 4.00X8," 14,250 "
-28023517,HC IMPL STENT P PREMIER M 4.00X12," 14,250 "
-28023518,HC IMPL STENT P PREMIER M 4.00X16," 14,250 "
-28023519,HC IMPL STENT P PREMIER M 4.00X20," 14,250 "
-28023520,HC IMPL STENT P PREMIER M 4.00X24," 14,250 "
-28023521,HC IMPL STENT P PREMIER M 4.00X28," 14,250 "
-28023522,HC IMPL STENT P PREMIER M 4.00X32," 14,250 "
-28023523,HC IMPL STENT P PREMIER M 4.00X38," 14,250 "
-28023524,HC  GUIDE CATH GUIDZILLA EXT 6F," 4,250 "
-28023526,HC IMPL GW HI-TORQE COMMAND 14X190," 1,275 "
-28023527,HC IMPL GW HI-TORQE COMMAND 14X300," 1,275 "
-28023528,HC IMPL GW HI-TORQE ES CMMD 14X190," 1,275 "
-28023529,HC  CATH ATTAIN SLCT II DLVRY SYT," 1,700 "
-28023530,HC IMPL DEFIB ELLIPSE VR ICD," 65,000 "
-28023531,HC  CATH US SOUNDSTAR 3D," 8,303 "
-28023532,HC  CATH US ACUNAV 10F," 6,197 "
-28023533,HC  CATH US ACUNAV 8F," 6,197 "
-28023534,HC  BLLN CATH MUSTANG OTW 10X40X13," 2,125 "
-28023535,HC  BLLN CATH MUSTANG OTW12X40C135," 2,125 "
-28023536,HC  BLLN CATH MUSTANG OTW 12X80X75," 2,125 "
-28023537,HC  BLLN CATH MUSTANG OTW9X80X135," 2,125 "
-28023538,HC  BLLN CATH MUSTANG OTW8X40X135," 2,125 "
-28023539,HC  BLLN CATH MUSTANG OTW10X80X135," 2,125 "
-28023540,HC  BLLN CATH MUSTANG OTW7X80X75," 2,125 "
-28023541,HC  BLLN CATH MUSTANG OTW8X60X75," 2,125 "
-28023542,HC  BLLN CATH MUSTANG OTW10X60X75," 2,125 "
-28023543,HC  BLLN CATH MUSTANG OTW9X60X75," 2,125 "
-28023544,HC  BLLN CATH MUSTANG OTW9X80X75," 2,125 "
-28023545,HC  BLLN CATH MUSTANG OTW10X80X75," 2,125 "
-28023546,HC IMPL BUTTON TIGHTROPE BTB L1," 3,188 "
-28023547,HC GDE SHTH SAFTSHEATH CSG 9X50," 3,273 "
-28023548,HC  BLLN CATH MUSTANG OTW 12X40," 2,125 "
-28023549,HC ROD BENDING DIGITTIZER ARRAY," 18,865 "
-28023550,HC IMPL HUMERAL STEM 9MM X 146MM," 25,725 "
-28023551,HC IMPL HUMERAL HEAD 52MM X 20MM," 25,725 "
-28023552,HC IMPL SCREW PRECEPT 7.5 X 40MM," 10,125 "
-28023553,HC IMPL SCREW PRECEPT 7.5 X 35MM," 10,125 "
-28023554,HC IMPL ROD PRECEPT 50MM," 7,353 "
-28023555,HC IMPL SCREW PRECEPT 7.5 X 45MM," 8,438 "
-28023556,HC IMPL SCREW SHANK PRECEPT 7.5 X 45MM," 8,438 "
-28023557,HC IMPL ROD PRECEPT 55MM," 7,786 "
-28023558,HC IMPL MINIPLATE DOUBLE BEND 2.0MM," 6,624 "
-28023559,HC IMPL PLATE ANGLED 4.5MM 90' 10HOLE," 9,986 "
-28023560,HC IMPL CAGE STRYKER 120X 80MM," 34,715 "
-28023561,HC IMPL ENDCAP 0\' STRYKER SPINE," 2,225 "
-28023562,HC IMPL SNARE ONESNARE ENDO SYS 120X10," 2,465 "
-28023563,HC GUIDEWIRE WINN HGH TORQ 14X190," 1,488 "
-28023564,"HC IMPL TIBIAL BASE KEELED ""EVOLUTION"""," 8,500 "
-28023565,HC IMPL KNEE INSERT EVOLUTION 14MM," 5,100 "
-28023566,HC IMPL KNEE INSERT EVOLUTION 12MM," 5,100 "
-28023567,HC IMPL EMBLZTN COIL RETRACTA 14X18," 5,185 "
-28023568,HC IMPL EMBLZTN COIL RETRACTA 14X10," 5,185 "
-28023569,HC IMPL INTR SHTH FLXR TUOHYBORST 5X90, 969 
-28023570,HC IMPL GRFT ENDURANT AAA 23X16X166," 64,838 "
-28023571,HC  CATH ATTAIN COMMAND 42CM," 1,632 "
-28023572,HC  CATH CELSIUS DS D-XR 8MM," 6,732 "
-28023573,HC  CATH EZ STEER F-J CURVE 7X115," 6,707 "
-28023574,HC  CATH EZ STEER 7X11," 14,112 "
-28023575,HC  SHTH MOBICATH BI GDE SML CURV," 6,375 "
-28023576,HC  CATH ABLAT NAVISTAR 4MM J CURV," 7,560 "
-28023578,HC  CATH ANGIO VAN SCHIE 8X80, 417 
-28023579,HC IMPL COIL EMBO RETRACTA 14X20X35," 5,185 "
-28023580,HC IMPL COIL EMBO RETRACTA 14X12X35," 5,185 "
-28023581,HC IMPL CAGE X-CORE 18X20 -25MM," 62,070 "
-28023582,HC IMPL ENDCAP X-CORE," 17,675 "
-28023583,HC IMPL SCREW LOCKING X-CORE ENDCAP," 4,208 "
-28023584,HC IMPL STNT GRFT OVATION PRME15X34X80," 57,915 "
-28023585,HC IMPL STNT GRFT OVATION PRME 13X14X10," 28,350 "
-28023586,HC IMPL INTERSPACE KNEE HIGH REL ANTIBIOT," 26,740 "
-28023587,HC IMPL SCREW CERVICAL AEGIS 4.0 X 20," 7,225 "
-28023588,HC IMPL PLATE LATERAL 8MM," 3,742 "
-28023589,HC IMPL PLATE LATERAL MEDTRONIC 10MM," 3,742 "
-28023590,HC IMPL KNEE INSERT CRVD SIGMA 2.5," 7,208 "
-28023591,HC IMPL SCREW CANN S&N 5.7MM X 85MM," 1,726 "
-28023592,HC IMPL SCREW CANN S&N 5.7 X 80MM," 1,726 "
-28023593,HC DRILL BIT CANN 4.5MM S&N," 3,100 "
-28023594,HC IMPL RING CARBON STRYKER 180MM," 11,340 "
-28023595,HC IMPL SHTH ACCSS SYST SOLOPATH 20X35," 4,505 "
-28023596,HC IMPL RING CARBON STRYKER 2/3 180MM," 10,710 "
-28023597,HC IMPL ENDPLATE XRL 26MMX30MM/10 DEG," 12,750 "
-28023598,HC IMPL STENT NAVIFLEX DELIVERY SYSTEM 7FR, 672 
-28023599,HC DISPOS DISTAL ATTACHMNT 11.35," 2,252 "
-28023600,HC DISPOSBL DISTAL ATTACHMT 12.4M," 2,252 "
-28023601,HC BALLOON ORACLE LATEX FREE EUS, 145 
-28023603,HC NANO KNIFE 230CM," 5,738 "
-28023604,HC EXACTO COLD SNARE," 1,020 "
-28023605,HC MISTIGIER SPRAY CATHETER, 408 
-28023607,HC IMPL JAGTOME SHORT RX 39," 2,542 "
-28023608,HC TWIN GRASPER OVER THE SCOPE," 4,412 "
-28023609,HC INTRO SHEATH L22," 3,825 "
-28023612,HC  OCCLUSION CATH L38," 11,213 "
-28023613,HC IMPL PISTON TIT EAR 0.6X3.50MM," 1,488 "
-28023614,HC IMPL PISTON TIT EAR  0.6X4.50MM," 1,488 "
-28023615,HC IMPL SLING FEMALE TVT-O," 9,421 "
-28023616,HC IMPL SLING FEMALE ADVANTAGE FIT," 9,188 "
-28023617,HC IMPL COIL MICROPLEX VFC 1X3X3," 17,640 "
-28023618,HC IMPL COIL MICROPLEX HYPERSOFT 1X2," 11,250 "
-28023619,HC IMPL COIL MICROPLEX HYPERSOFT 1X2," 11,250 "
-28023620,HC IMPL COIL MICROPLEX HYPERSOFT 2X2," 21,000 "
-28023621,HC IMPL COIL MICROPLEX HYPERSOFT 2X3," 21,000 "
-28023622,HC IMPL COIL MICROPLEX HYPERSOFT 2X4," 21,000 "
-28023623,HC IMPL COIL MICROPLEX HYPERSOFT3X4," 21,000 "
-28023624,HC IMPL COIL MICROPLEX HYPERSOFT 3X6," 21,000 "
-28023625,HC IMPL COIL MICROPLEX HYPERSOFT 4X6," 21,000 "
-28023626,HC IMPL COIL MICROPLEX HYPERSOFT 4X8," 21,000 "
-28023627,HC IMPL COIL MICROPLEX HYPERSOFT 5X10," 21,000 "
-28023628,HC IMPL COIL MICROPLEX HYPERSOFT 5X15," 21,000 "
-28023629,HC  BLLN CATH THERMODILUTION 7X110, 494 
-28023630,HC IMPL VALVE EDWARDS SAPIEN (3) 29MM," 87,500 "
-28023631,HC IMPL VALVE SAPIEN (3) 23MM," 87,500 "
-28023632,HC IMPL VALVE SAPIEN (3) 26MM," 87,500 "
-28023633,HC  CATH CRSS SET TRIFORCE 4X65," 2,975 "
-28023634,HC  CATH CRSS SET TRIFORCE 4.0X65," 2,975 "
-28023635,HC  CATH CRSS SET TRIFORCE 4.0X65," 2,975 "
-28023636,HC  CATH CRSS SET TRIFORCE 4.0X65," 2,975 "
-28023637,HC  CATH CRSS SET TRIFORCE 4.0X100," 2,975 "
-28023638,HC  CTH SET TRIFORCE 4F CXI 5F FLX," 2,975 "
-28023639,HC  CTH SET TRIFORCE," 2,975 "
-28023640,HC IMPL GW HI TORQUE CONNECT 0.18X300," 8,288 "
-28023642,HC IMPL BREAST EXPAND  350 ML 11 X 11," 11,588 "
-28023643,HC IMPL BREAST EXPAND FULL HEIGHT 450 ML," 8,963 "
-28023644,HC IMPL BREAST EXPAND 450 ML W/TABS," 11,588 "
-28023645,HC IMPL BREAST EXPAND 550 ML W/TABS," 11,588 "
-28023646,HC IMPL BREAST EXPAND 650 ML W/TABS," 11,588 "
-28023647,HC IMPL BREAST EXPAND 800 ML W/TABS," 11,588 "
-28023648,HC IMPL BREAST EXPAND 400 ML W/TABS," 11,588 "
-28023649,HC IMPL BREAST EXPAND 500 ML W/TABS," 11,588 "
-28023650,HC IMPL BREAST EXPAND 700 ML W/TABS," 11,588 "
-28023651,HC IMPL BREAST EXPAND PROJECTION 700," 11,588 "
-28023652,HC IMPL BREAST EXPAND PROJECTION 850," 11,588 "
-28023653,HC IMPL BREAST ALLERG  NATRELLE 176 ML," 6,961 "
-28023654,HC IMPL BREAST ALLERG NATRELLE  460 ML," 6,961 "
-28023655,HC IMPL BREAST ALLERG SALINE FILLED 550 ML," 2,583 "
-28023656,HC IMPL BREAST ALLERG SALINE FILLED 650 ML," 2,583 "
-28023657,HC IMPL BREAST ALLERG NATRELLE 420 ML," 2,583 "
-28023658,HC IMPL BREAST ALLERG NATRELLE 255 ML," 10,125 "
-28023659,HC IMPL BREAST ALLERG NATRELLE 375 ML," 10,125 "
-28023660,HC IMPL BREAST ALLERG SILICONE 410FF," 10,125 "
-28023661,HC IMPL BREAST ALLERG NATRELLE 595 ML," 10,125 "
-28023662,HC IMPL BREAST ALLERG NATRELLE 740 ML," 10,125 "
-28023663,HC IMPL BREAST ALLERG NATRELLE 235 ML," 10,125 "
-28023664,HC IMPL BREAST ALLERG NATRELLE 310 ML," 10,125 "
-28023665,HC IMPL BREAST ALLERG NATRELLE 395 ML," 10,125 "
-28023666,HC IMPL BREAST ALLERG NATRELLE 500 ML," 10,125 "
-28023667,HC IMPL BREAST ALLERG TEXT STYLE 410MF," 12,375 "
-28023668,HC IMPL BREAST ALLERG NATRELLE 295 ML," 12,375 "
-28023669,HC IMPL BREAST ALLERG NATRELLE 375 ML," 10,125 "
-28023670,HC IMPL BREAST ALLERG  NATRELLE 640 ML," 10,125 "
-28023671,HC IMPL BREAST ALLERG  NATRELLE 215 ML," 10,125 "
-28023672,HC IMPL BREAST ALLERG  NATRELLE 400 ML," 10,125 "
-28023872,HC IMPL PLATE TIBIA DISTAL 12 HOLE," 14,863 "
-28023873,HC IMPL LEAD  VECTRIS 90CM KIT," 15,750 "
-28023874,HC IMPL LEAD VECTRIS  60CM KIT," 15,750 "
-28023875,HC IMPL LEAD  COMPACT VECTRIS 90CM KIT," 15,750 "
-28023876,HC IMPL STIMULATOR RESTORE ADVACED," 87,000 "
-28023877,HC IMPL STIMULATOR IMPL RSTORE ULTRA," 77,500 "
-28023878,HC IMPL STIMULATOR IMPL RESTORE SENSOR," 88,375 "
-28023879,HC IMPL RECHARGER FOR STIMULATOR," 11,250 "
-28023880,HC IMPL ANCHOR INJEX BI-WING," 2,338 "
-28023881,HC IMPL RING SYMBLEPHARON SMALL, 425 
-28023882,HC IMPL RING SYMBLEPHARON MEDIUM, 425 
-28023883,HC IMPL JONES TUBE FROSTED 4X17, 765 
-28023884,HC IMPL JONES TUBE  FROSTED 4X18, 765 
-28023885,HC IMPL JONES TUBE FROSTED 4X17, 765 
-28023886,HC IMPL ORBITAL FLOOR & WALL 41X42X0.6," 8,364 "
-28023887,HC IMPL TITAN BARRIER 76X50X1," 11,183 "
-28023888,HC IMPL TITAN DBL BARRIER 76X50X0.6," 11,183 "
-28023889,HC IMPL TITAN MEDPOR MTM 76X50X1.5," 9,803 "
-28023891,HC IMPL FLEXBLOCK IMPL MED RT 93X74X20," 9,748 "
-28023892,HC IMPL FLEXBLCK MED LFT 93X74X20," 9,748 "
-28023893,HC IMPL FLEXBLOCK IMPL SM LFT 78X61X18," 9,750 "
-28023894,HC IMPL FLEXBLOCK IMPL SM RT  78X61X18," 9,750 "
-28023899,HC  CATH MERIT MAESTRO 2.8X110," 2,763 "
-28023901,HC  CATH ANGIO VAN SCHIE 8X120, 417 
-28023902,HC  BLLN CATH THERMODILUTION 5X80," 1,133 "
-28023903,HC IMPL STENT ZILVER 8X20X125," 8,213 "
-28023904,HC IMPL DVC EKOSONIC MACH 4 106X12," 18,865 "
-28023905,HC IMPL GW WHOLEY 35X145, 667 
-28023906,HC  MICRCATH DIREXION TORQUE J SHP," 5,449 "
-28023907,HC  CATH ARMADA 14 1.5X20X150," 2,508 "
-28023908,HC  CATH ARMADA 14 1.5X80X150," 2,508 "
-28023909,HC  CATH ARMADA 14 1.5X80X150," 2,508 "
-28023910,HC  CATH ARMADA 14 2X60X150," 2,508 "
-28023911,HC  CATH ARMADA 14 2.5X20S150," 2,508 "
-28023912,HC  CATH ARMADA 14 2.5X60X150," 2,508 "
-28023913,HC  CATH ARMADA 14 3.0X20X150," 2,508 "
-28023914,HC  CATH ARMADA 14 3.0X60X150," 2,508 "
-28023915,HC  CATH ARMADA 14 4.0X20X150," 2,508 "
-28023916,HC  CATH ARMADA 14 4.0X40X150," 2,508 "
-28023917,HC  CATH ARMADA 14 4.0X60X150," 2,508 "
-28023918,HC  CATH ARMADA 14 4.0X80X150," 2,508 "
-28023919,HC IMPL SCREW LOCKING 2.7 X 46MM," 1,315 "
-28023920,HC IMPL SCREW LOCKING 2.7 X 52MM," 1,315 "
-28023921,HC IMPL SCREW LOCKING 2.7 X 54MM," 1,315 "
-28023923,HC IMPL NAIL FEMORAL 13MM X 380MM," 14,110 "
-28023924,HC IMPL SCREW LOCKING 4.0 04.005.470," 1,709 "
-28023925,HC IMPL PLATE CLAVICLE 3.5MM LCP 6 HOL," 7,754 "
-28023926,HC IMPL SCREW POLYAXIAL 4.0X30MM," 9,375 "
-28023927,HC IMPL SCREW POLYAXIAL 4.0 X 20MM," 9,375 "
-28023928,HC IMPL ROD TAPERED 500MM 04.633.190," 11,363 "
-28023929,HC IMPL PLATE CLAVICLE LCP 3.5MM 6HOLE," 8,123 "
-28023931,HC IMPL PLATE LCP 2.7MM 10HOLE," 4,013 "
-28023932,HC IMPL PLATE LC-DCP 2.0MM 6 HOLE," 2,396 "
-28023933,HC IMPL CAGE CENTRAL BODY XRL 22MM-27M," 46,150 "
-28023934,HC IMPL ENDPLATE SCREW XRL MEDIUM," 2,550 "
-28023935,HC IMPL SCREW 4.35 X 30MM," 8,843 "
-28023936,HC CONNECTOR CROSS A5," 10,440 "
-28023937,HC IMPL PLATE CERVICAL AEGIS 6513-1757," 10,500 "
-28023938,HC IMPL SCREW VARIABLE 4.0 X 14," 2,338 "
-28023939,HC IMPL  CAGE ALIF PEEK AEGIS 3742-2914," 33,600 "
-28023940,HC IMPL PLATE OLECRANON VA-LCP 2 HOLE," 7,935 "
-28023941,HC IMPL PLATE DISTAL HUMERUS VA-LCP," 9,803 "
-28023942,HC IMPL PLATE DISTAL HUMERUS VA-LCP," 9,803 "
-28023943,HC IMPL SCREW LOCKING 2.7MM X 50MM," 1,315 "
-28023944,HC IMPL CAGE XRL MEDIUM 24MM-29MM," 57,850 "
-28023945,HC IMPL INSERTER TIP 03.820.140S," 1,516 "
-28023946,HC IMPL ACIS HEIGHT 9MM," 14,738 "
-28023947,HC IMPL SCREW METAPHYSEAL 2.7X 16MM, 446 
-28023948,HC IMPL SCREW METAPHYSEAL 2.7 X 32MM, 446 
-28023949,HC IMPL SCREW METAPHYSEAL 2.7MM X 36MM, 446 
-28023950,HC IMPL SCREW METAPHYSEAL 2.7MM X 40MM, 446 
-28023951,HC IMPL SCREW CORTEX ST 3.5MM X 14MM, 293 
-28023952,HC IMPL SCREW CORTEX ST 3.5MM X 24MM, 293 
-28023953,HC IMPL SCREW CORTEX ST 3.5MM X 26MM, 293 
-28023954,HC IMPL SCREW CORTEX ST 3.5MM X 40MM, 293 
-28023955,HC IMPL PIN FIXATION TEMP 03.820.136, 900 
-28023956,HC  CATH ARMADA 14 4.0X120X150," 2,508 "
-28023957,HC  CATH ARMANDA 14XT 1.2X12X145," 2,508 "
-28023958,HC  CATH ARMANDA 14XT 1.2X20X145," 2,508 "
-28023959,HC  CATH ARMANDA 14XT 1.5X12X145," 2,508 "
-28023960,HC  CATH ARMANDA 14XT 1.5X20X145," 2,508 "
-28023961,HC  CATH ARMANDA 14XT 2.0X12X145," 2,508 "
-28023962,HC  CATH ARMANDA 14XT 2.0X20X145," 2,508 "
-28023963,HC  CATH MICRO SUREFIRE INFUS SYST," 20,650 "
-28023673,HC BALLN URIN SPHINC AMS 800," 16,125 "
-28023674,HC IMPL URIN SLING MALE," 35,000 "
-28023675,HC IMPL URIN RESRV MALE," 14,018 "
-28023676,HC IMPL SCREW TENODESIS 4.75 X 15," 2,678 "
-28023677,HC IMPL SCREW TENODESIS 5.5X15," 2,678 "
-28023678,HC IMPL SCREW QUICK 3.5X16, 298 
-28023679,HC IMPL SCREW QUICK 2X12," 1,870 "
-28023680,HC IMPL SCREW QUICK  2 X 14," 1,870 "
-28023682,HC  GUIDEWIRE ARTHRO 0.062IN, 60 
-28023684,HC  GUIDEWIRE ENT ATOS, 536 
-28023685,HC IMPL LEAD 70CM KIT," 19,915 "
-28023686,HC IMPL LEAD  INFINION 50CM KIT," 30,380 "
-28023687,HC GUIDE WIRE  .018, 162 
-28023688,HC GUIDE WIRE  .015, 9 
-28023690,HC IMPL SCREW EXTREMITY MED," 5,525 "
-28023691,HC IMPL SCREW EXTREMITY MED," 5,525 "
-28023692,HC IMPL SCREW EXTREMITY MED," 5,525 "
-28023693,HC IMPL SCREW MEDART, 628 
-28023694,HC IMPL SCREW MEDART, 628 
-28023695,HC IMPL SCREW MEDART, 628 
-28023696,HC IMPL SCREW MEDART," 1,010 "
-28023697,HC IMPL SCREW MEDART 2.5," 1,010 "
-28023698,HC IMPL SCREW MEDART 2.5X18," 1,010 "
-28023699,HC IMPL BREAST EXPAND MENT 25," 11,250 "
-28023700,HC IMPL BREAST EXPAND MENT 35," 11,250 "
-28023701,HC IMPL BREAST EXPAND MENT 650," 11,250 "
-28023702,HC IMPL BREAST EXPAND MENT 750," 11,250 "
-28023703,HC IMPL BREAST EXPAND MENT," 11,925 "
-28023704,HC IMPL BREAST EXPAND MENT," 11,925 "
-28023705,HC IMPL BREAST EXPAND MENT," 11,925 "
-28023706,HC IMPL BREAST EXPAND MENT," 11,925 "
-28023707,HC IMPL BREAST EXPAND MENT," 11,925 "
-28023708,HC IMPL BREAST EXPAND MENT," 11,925 "
-28023709,HC IMPL BREAST SIENTR 575," 7,608 "
-28023710,HC IMPL BREAST SIENTR 330," 7,608 "
-28023711,HC IMPL BREAST SIENTR 370," 7,608 "
-28023712,HC IMPL BREAST SIENTR 400," 7,608 "
-28023713,HC IMPL BREAST SIENTR 450," 7,608 "
-28023714,HC IMPL BREAST SIENTR 500," 7,608 "
-28023715,HC IMPL BREAST SIENTR 550," 7,608 "
-28023716,HC IMPL BREAST SIENTR 350," 7,608 "
-28023717,HC IMPL BREAST SIENTR 450," 7,608 "
-28023718,HC IMPL BREAST EXPAN 390," 13,958 "
-28023719,HC IMPL BREAST EXPAN 470," 13,958 "
-28023720,HC IMPL BREAST EXPAN 570," 13,958 "
-28023721,HC IMPL BREAST EXPAN 780," 13,958 "
-28023723,HC IMPL SCREW S&N 3.5 X 26 MM, 161 
-28023724,HC IMPL SCREW S&N 3.5 X 22 MM, 161 
-28023725,HC IMPL SCREW S&N  3.5X30 MM, 161 
-28023726,HC IMPL SCREW S&N  3.5X32 MM, 161 
-28023727,HC IMPL SCREW S&N  3.5X34 MM, 161 
-28023728,HC IMPL SCREW S&N 3.5 X 38 MM, 161 
-28023729,HC IMPL SCREW S&N 3.5 X 42 MM, 161 
-28023730,HC IMPL SCREW S&N 3.5 X 22 MM, 827 
-28023731,HC IMPL SCREW S&N 3.5 X 22 MM, 827 
-28023732,HC IMPL SCREW S&N 3.5 X 30MM, 827 
-28023733,HC IMPL SCREW S&N 3.5 X 32MM, 827 
-28023734,HC IMPL SCREW S&N 3.5 X 34MM, 827 
-28023735,HC IMPL SCREW S&N 3.5 X 40 MM, 827 
-28023736,HC IMPL SCREW STRYK," 2,278 "
-28023737,HC IMPL SCREW STRYK 3.5 X 12MM," 2,278 "
-28023738,HC IMPL SCREW STRYK 3.5 X 14MM," 2,278 "
-28023739,HC IMPL SCREW STRYK 3.5 X 16MM," 2,355 "
-28023740,HC IMPL SCREW STRYK 3.5 X 10MM," 1,564 "
-28023741,HC IMPL SCREW STRYK 3.5 X 14MM," 1,564 "
-28023742,HC IMPL SCREW STRYK 3.5 X 18MM," 1,615 "
-28023743,HC IMPL SCREW STRYK 3.5 X 20MM," 2,355 "
-28023744,HC IMPL SCREW STRYK 3.5 X 24MM," 1,615 "
-28023746,HC IMPL SCREW WRIGHT 7 X 50MM," 8,160 "
-28023747,HC IMPL PLATE WRIGHT," 15,750 "
-28023748,HC IMPL PLATE WRIGHT 3DI," 15,750 "
-28023749,HC IMPL SCREW WRIGHT 2.7 X 10MM," 2,261 "
-28023750,HC IMPL SCREW WRIGHT 2.7 X 12MM," 2,261 "
-28023751,HC IMPL SCREW WRIGHT 2.7 X 16MM," 2,261 "
-28023752,HC IMPL SCREW WRIGHT 2.7 X 18MM," 2,261 "
-28023753,HC IMPL SCREW WRIGHT 2.7 X 10MM," 1,182 "
-28023754,HC IMPL SCREW WRIGHT 2.7 X 12MM," 1,182 "
-28023755,HC IMPL SCREW WRIGHT 2.7 X 14MM," 1,182 "
-28023756,HC IMPL SCREW WRIGHT 2.7 X 18MM," 1,182 "
-28023757,HC IMPL SCREW WRIGHT 2.7 X 20MM," 1,182 "
-28023758,HC IMPL SCREW WRIGHT 2.7 X 26 MM," 1,182 "
-28023764,HC IMPL PLATE WRIGHT," 18,830 "
-28023765,HC IMPL SCREW WRIGHT, 765 
-28023766,HC IMPL SCREW WRIGHT, 961 
-28023768,HC IMPL SCREW SYNTHES, 524 
-28023769,HC IMPL SCREW SYNTHES 1.5 X 6 MM, 524 
-28023770,HC IMPL SCREW SYNTHES 2.0 X 5MM, 524 
-28023771,HC IMPL SCREW SYNTHES 2 X 4MM, 493 
-28023772,HC IMPL SCREW SYNTHES 2 X 6MM, 493 
-28023773,HC IMPL SCREW SYNTHES 2 X 8 MM, 493 
-28023774,HC DRILL BIT STRYKER 2 X 110MM," 2,261 "
-28023775,HC DRILL BIT STRYKER 2.5 X 110MM," 2,193 "
-28023778,HC IMPL PLATE MAXILO 1.5 X 5MM," 2,924 "
-28023779,HC IMPL PLATE SYNTHES 6MM," 2,196 "
-28023780,HC IMPL PLATE SYNTHES," 2,366 "
-28023781,HC IMPL PLATE SYNTHES 2MM," 2,366 "
-28023782,HC IMPL PLATE SYNTHES 8MM," 2,366 "
-28023784,HC IMPL SCREW SYNTHES 1.5 X 3MM, 660 
-28023785,HC IMPL PLATE SYNTHES 1.8 X 35MM," 4,898 "
-28023786,HC IMPL SCREW ZIMMER 3.5 X 16MM, 354 
-28023787,HC IMPL SCREW ZIMMER 3.5 X 20 MM, 354 
-28023788,HC IMPL PLATE ZIMMER 80MM," 6,755 "
-28023789,HC IMPL SCREW ZIMMER 3.5 X 14MM," 1,048 "
-28023790,HC DRILL BIT ZIMMER 2 X 175MM," 2,550 "
-28023793,HC IMPL SCREW ZIMMER 2.7 X 18MM," 1,133 "
-28023794,HC IMPL SCREW ZIMMER 4 X 18MM, 179 
-28023837,HC COCHLEAR PROCESSOR," 52,000 "
-28023838,HC COCHLEAR PROCESSOR," 24,850 "
-28023839,HC COCHLEAR PROCESSOR," 24,850 "
-28023840,HC COCHLEAR PROCESSOR," 24,850 "
-28023841,HC COCHLEAR PROCESSOR," 24,850 "
-28023842,HC COCHLEAR PROCESSOR," 20,937 "
-28023843,HC COCHLEAR PROCESSOR," 24,850 "
-28023844,HC COCHLEAR PROCESSOR," 20,937 "
-28023845,HC IMPL BREAST MENTOR," 7,140 "
-28023846,HC IMPL BREAST MENTOR," 7,438 "
-28023847,HC IMPL BREAST MENTOR," 7,438 "
-28023848,HC IMPL BREAST MENTOR," 7,438 "
-28023849,HC IMPL COCHLEAR ADV BION," 81,857 "
-28023851,HC IMPL ANCHOR PUSHLOCK 4.5 X 18.5MM," 3,230 "
-28023853,HC IMPL PLATE ARTHRO," 5,525 "
-28023854,HC IMPL ARTHRO TAK, 255 
-28023855,HC IMPL TISS SURGIMEND 10 X 15MM," 25,200 "
-28023856,HC IMPL TISS SURGIMEND 20 X 20MM," 62,400 "
-28023857,HC IMPL EYELID PLATINUM 1.2M," 3,528 "
-28023858,HC IMPL COCHLEAR 07670, 9 
-28023859,HC IMPL COCHLEAR 07671," 98,000 "
-28023860,HC IMPL COCHLEAR BACKUP," 83,750 "
-28023861,HC IMPL COCHLEAR BACKUP," 79,050 "
-28023862,HC IMPL MESH RESTORELLE L," 7,183 "
-28023863,HC IMPL TESTICULAR TEROSA 2.5 X45MM," 13,125 "
-28023864,HC IMPL NIPPLE 1 X 1.5," 4,208 "
-28023865,HC  CATH INTRASPINAL PERCUTANEOUS, 723 
-28023866,HC  CATH INTRATHECAL ASCENDA," 7,838 "
-28023867,HC IMPL PLATE METATARSOPHAL MTP STRYKER," 14,925 "
-28023868,HC IMPL PLATE BONE COMP METATARSAL SRYKER," 14,925 "
-28023869,HC IMPL VOICE PROVOX2 8MM," 2,032 "
-28023870,HC IMPL VOICE PROVOX2 10MM," 2,032 "
-28023871,HC IMPL LEAD ANCHOR CINCH, 638 
-28023964,HC  TUBE LARYN ATOS 9 X 55 MM, 918 
-28023965,HC PULMONARY REHAB KIT," 2,193 "
-28023967,HC TENSION BAND TISSUE FIXATE SCS," 9,000 "
-28023968,HC CABLE EXT 61 CM," 5,100 "
-28023969,HC ANCHOR LEAD CLICK," 3,358 "
-28023970,HC REMOTE CONTROL SPECTRA," 13,050 "
-28023972,HC SALIVARY STONE EXTRACTOR 1.5," 2,763 "
-28023978,HC REAMER EXTREMITY 8 X 9.5 MM," 2,380 "
-28023979,HC X-POST EXTEMITY MED 8.0 X 25," 13,500 "
-28023980,HC IMPL AMNIOTIC MEMBRANE AMBIO 5," 5,738 "
-28023981,HC IMPL STONE RETRACTOR 0.4 MM," 3,400 "
-28023982,HC IMPL STONE EXTRACTION BASKET," 3,278 "
-28023983,HC ALLODERM 8 X 16 THICK," 29,547 "
-28023984,HC ALLODERM 6 X 16 THICK," 22,169 "
-28023985,HC ALLODERM CONTOUR LARGE," 17,773 "
-28023986,HC ALLODERM CONTOUR SMALL," 17,773 "
-28023987,HC IMPL PLATE WRIST FUSION," 14,295 "
-28023989,HC IMPL PUMP SYNCHROMED PROGRAMMER," 6,758 "
-28023990,HC IMPL CHARGING SYSTEM SENSOR RESTORE," 11,250 "
-28023991,HC IMPL NEUROSTIM GEN PRIME ADVANCED," 78,000 "
-28023992,HC IMPL ANCHOR INJEX BUMPY," 2,338 "
-28023994,HC IMPL ROD THREADED 200 MM, 325 
-28023996,HC IMPL NUT EXTERNAL FIXATOR 10 X 6 X 5 MM, 29 
-28023997,HC IMPL SCREW SET S&N, 76 
-28023999,HC IMPL HA PIN S&N 6 X 200 MM," 1,440 "
-28024000,HC IMPL PLATE S&N 3," 10,350 "
-28024001,HC IMPL PLATE ANKLE FUSION S&N," 10,575 "
-28024002,HC IMPL ROD THREADED S& N, 464 
-28024003,HC IMPL GENERATOR EON," 89,975 "
-28024004,HC IMPL SPHERE 23 MM," 4,641 "
-28024005,HC IMPL CLAMP EXT FIXATOR ROD  3.5 X 11," 6,086 "
-28024006,HC IMPL WIRE BOLT STRYKER, 821 
-28024007,HC IMPL NUT CONNECTING SHORT M8 X 6 MM, 173 
-28024008,HC IMPL NUT EXTERNAL FIXATOR M6 X 6 MM, 173 
-28024009,HC IMPL PIN FIXATOR HALF 6 X 150," 1,819 "
-28024010,HC  GUIDEWIRE KWIRE 1.7 X 70 MM, 451 
-28024011,HC IMPL PIN FIXATION KWIRE  1.2X65 MM," 1,564 "
-28024012,HC REAMER HEAD STRYKER," 6,248 "
-28024014,HC ROD CARBON FIBER 4 X 60, 402 
-28024015,HC SPLINT ORTHOGNATHIC OCCLUSAL PATIENT SPECIFIC," 18,550 "
-28024016,HC IMPL TACK BONE PLATE, 901 
-28024017,HC IMPL WIRE K KIRSCHNER 2.5X150 MM, 230 
-28024018,HC IMPL TEMP PIN WRIGHT 2.0, 748 
-28024019,HC IMPL PIN TEMP FIX, 935 
-28024020,HC IMPL SHUNT EXPRESS GLAUCOMA P-50," 8,588 "
-28024021,HC IMPL MULTIPIECE ACRYSOF MENISCUS 6-13," 1,267 "
-28024022,HC IMPL IOL TORIC SN6AT3 + 26.0," 4,208 "
-28024023,HC IMPL IOL SN6AT3 32.0," 4,208 "
-28024024,HC IMPL IOL SN6AT6-27.5," 4,208 "
-28024025,HC IMPL IOL SN6AT6 9.5," 4,208 "
-28024026,HC IMPL IOL LENS SN6AT7 14.0," 4,208 "
-28024027,HC IMPL IOL SN6AT7-28.5," 4,208 "
-28024028,HC IMPL IOL SENSAR + 4.5, 978 
-28024029,HC IMPL IOL ZCB00 19.5," 1,275 "
-28024030,HC IMPL IOL ZCB00 21.0," 1,275 "
-28024031,HC IMPL IOL ZCB00 23.5," 1,275 "
-28024032,HC IMPL IOL ZMB00 19.0," 7,608 "
-28024033,HC IMPL IOL ZMB00 20.5," 7,608 "
-28024035,HC IMPL TUTOPLAST WHOLE SCLERA," 6,758 "
-28024036,HC IMPL  AMNIOTIC GRAFT," 7,530 "
-28024037,HC IMPL TUTOPLAST WHOLE," 6,758 "
-28024038,HC IMPL AHMED VALVE 13 X 16," 5,058 "
-28024040,HC IMPL SCREW CORTEX ST 3.5MM X 12MM, 293 
-28024041,HC IMPL PLATE TIBIA VA-LCP 8 HOLE," 12,958 "
-28024042,HC IMPL NAIL FEMORAL CANN 420MM," 14,110 "
-28024043,HC IMPL NAIL FEMORAL CANN 380MM," 14,110 "
-28024044,HC IMPL SCREW LOCKING 7.3MM X 110MM," 2,915 "
-28024045,HC IMPL PLATE FEMUR LCP 4.5MM 6 HOLE," 12,395 "
-28024046,HC IMPL NAIL FEMORAL CANN 11 X 400MM," 14,110 "
-28024047,HC IMPL NAIL TIBIAL CANN 10 X 375MM," 12,359 "
-28024048,HC IMPL GENERATOR PULSE SQ-RX," 97,500 "
-28024049,HC IMPL SYNMESH VBR 10MM X 4MM 495.341," 8,940 "
-28024050,HC IMPL ROD AEGIS 3.5MM X 200MM," 2,125 "
-28024051,HC IMPL SPACER ORACLE 50MM X 22MM," 36,400 "
-28024052,HC IMPL PRODISC - C XL 5MM," 35,787 "
-28024053,HC IMPL NUT RETAINER 03.820.110, 666 
-28024054,HC IMPL ACIS HEIGHT 7MM," 14,738 "
-28024056,HC DUAL KNIFE LOWER LENGTH," 4,941 "
-28024057,HC IMPL STNT GRF OVATION PRME 13X14X12," 28,350 "
-28024058,HC IMPL  STNT GRF OVATION PRME 13X14X10," 28,350 "
-28024060,HC ELECTRODE Q-TRAK," 28,000 "
-28024061,HC INSERTION TL Q-GUIDE ELECTRODE," 8,500 "
-28024062,HC IMPL SCREW METAPHYSEAL 2.7MM X 30MM, 446 
-28024063,HC IMPL MALAR EXT CNTURD SM RT 45X24X3," 3,247 "
-28024064,HC IMPL BREAST FM-410205," 10,125 "
-28024065,HC IMPL BREAST FF-410185," 10,125 "
-28024066,HC IMPL BREAST FF-410220," 10,125 "
-28024067,HC IMPL BREAST FF-410335," 10,125 "
-28024068,HC IMPL BREAST FF-410535," 10,125 "
-28024069,HC IMPL BREAST MM-410160," 10,125 "
-28024070,HC IMPL BREAST MM-410185," 10,125 "
-28024071,HC IMPL BREAST MF-410140," 10,125 "
-28024072,HC IMPL BREAST MF-410195," 10,125 "
-28024083,HC IMPL PLATE PDS FLEX .15X50X40MM," 2,542 "
-28024084,HC IMPL PLATE PDS FLEX ABSORB.02X40X50," 2,542 "
-28024085,HC IMPL PLATE PDS FLEX ABSORB 0.5X40X50," 2,542 "
-28024086,HC NDL TIT WIRE BARBED 500MM," 2,848 "
-28024087,HC BLADE TRICUT ANG TIP 27.5CM," 3,534 "
-28024088,HC BLADE TRACHEAL STR 37CM 4MM," 3,221 "
-28024089,HC IMPL ENDURAGEN COLLAGEN 1X4CMX1MM," 2,933 "
-28024090,HC IMPL ESSURE CONTRACEPTIVE COILS," 12,000 "
-28024091,HC SET HTA PROCEDURE PROCERVA," 10,500 "
-28024095,HC IMPL PISTON BIG EASY STR 4.0," 1,930 "
-28024096,HC IMPL PISTON BIG EASY STR 4.5," 1,930 "
-28024097,HC IMPL PISTON BIG EASY LFT OFFSET 4.5," 1,930 "
-28024098,HC IMPL PISTON BIG EZ LEFT OFFSET 5.0," 1,930 "
-28024099,HC IMPL PISTON BIG EZ RIGHT OFFSET 4.5," 1,930 "
-28024100,HC IMPL PISTON BIG EZ RIGHT OFFST 4.75," 1,930 "
-28024101,HC IMPL PISTON BIG EZ RIGHT OFFST 5.0," 1,930 "
-28024102,HC IMPL MESH PARIETEX PROGRIP 15X15CM," 3,050 "
-28024103,HC IMPL PATCH SFT TISS GORE-TEX 5X10CM," 1,853 "
-28024106,HC IMPL EXPANDER BREAST W/TAB 300CC," 11,585 "
-28024107,HC IMPL BREAST FM-410605," 10,125 "
-28024108,HC IMPL BREAST FM-410670," 10,125 "
-28024109,HC IMPL BREAST MENTOR 354-1058," 9,938 "
-28024110,HC IMPL BREAST MENTOR 354-1108," 9,938 "
-28024111,HC IMPL BREAST MENTOR 354-1158," 9,938 "
-28024112,HC IMPL BREAST MENTOR 354-1208," 9,938 "
-28024113,HC IMPL GDE SHTH SAFTSHEATH CSG 9X50," 3,273 "
-28024114,HC IMPL STNT GRFT ZENITH RENU 24X59," 42,341 "
-28024115,HC CANNULA HIP 5.5MM," 3,332 "
-28024116,HC DRILL TIBIAL CORE 10MM," 4,896 "
-28024117,HC DRILL TIBIAL CORE 11MM," 4,896 "
-28024118,HC DRILL TIBIAL CORE 12MM," 4,896 "
-28024119,HC ELECTRODE NEUROTHERM FRE-10," 5,389 "
-28024120,HC GRASPER TISSUE GRAFTMASTER III," 5,194 "
-28024121,HC IMPL DRILL PIN  3.2MM," 8,313 "
-28024122,HC IMPL ENDOBUTTON SMITH NEPH 15MM," 2,328 "
-28024123,HC IMPL ENDOBUTTON ULTRA 1.2MM," 3,358 "
-28024124,HC IMPL KWIRE 1 X 110MM, 136 
-28024125,HC IMPL KWIRE THREAD 2.5 X 150, 289 
-28024126,HC IMPL KWIRE WRIGHT 2 X 228MM, 221 
-28024127,HC IMPL PEG BONE CORT TITANI 2.3 X 16MM, 663 
-28024128,HC IMPL PIN TEMP WRIGHT 1.1MM, 706 
-28024129,HC IMPL PIN TRIMIT 1.5MM," 1,615 "
-28024130,HC IMPL PLATE CORNER FUSION," 7,914 "
-28024131,HC IMPL PLATE MTP 3 DI," 12,165 "
-28024132,HC IMPL PLATE TACK, 306 
-28024133,HC IMPL PLATE ULNA 2 VDR NARROW," 7,820 "
-28024134,HC IMPL PLATE ULNA DORSAL ST," 6,588 "
-28024135,HC IMPL PLATE ULNA LEFT 2VDR," 7,820 "
-28024136,HC IMPL PLATE ULNA MIDSHAFT 6 HOLE," 5,908 "
-28024137,HC IMPL PLATE ULNA VDU RIGHT," 6,588 "
-28024138,HC IMPL PLATE WRIST FUSION LCP," 13,875 "
-28024139,HC IMPL PLEURISTIK," 2,550 "
-28024140,HC IMPL SCREW  CORTEX 1.5 X 13MM, 714 
-28024141,HC IMPL SCREW 1.5 X 09 MM, 628 
-28024142,HC IMPL SCREW 2.0X12MM," 1,010 "
-28024143,HC IMPL SCREW 2.5 X 16MM, 628 
-28024144,HC IMPL SCREW 2.5 X 16MM," 1,010 "
-28024145,HC IMPL SCREW 2.5 X 18MM, 628 
-28024146,HC IMPL SCREW 2.5 X 20MM," 1,010 "
-28024147,HC IMPL SCREW 2.5 X 22MM, 628 
-28024148,HC IMPL SCREW 2.7 X 20 MM," 1,139 "
-28024149,HC IMPL SCREW 2.7 X 58 MM," 1,315 "
-28024150,HC IMPL SCREW 3 X 18MM, 723 
-28024151,HC IMPL SCREW 3.5 X 18MM, 298 
-28024152,HC IMPL SCREW 3.5 X 14 MM, 298 
-28024153,HC IMPL SCREW 3.5 X 16 MM, 241 
-28024154,HC IMPL SCREW 3.5 X 18 MM, 241 
-28024155,HC IMPL SCREW 6.25MM," 2,678 "
-28024156,HC IMPL SCREW ACUTRAK 2 - 20.0MM," 3,451 "
-28024157,HC IMPL SCREW ACUTRAK 2 - 22.0MM," 3,451 "
-28024158,HC IMPL SCREW ACUTRAK 2 - 24.0MM," 3,451 "
-28024159,HC IMPL SCREW ACUTRAK 2 - 26.0MM," 3,451 "
-28024160,HC IMPL SCREW CORTEX 2.0 X 10MM, 341 
-28024161,HC IMPL SCREW HEX 3.5MM, 578 
-28024162,HC IMPL SCREW HEX LOCK 3.0 X 12MM, 901 
-28024163,HC IMPL SCREW HEX LOCK 3.2 X 10MM, 850 
-28024164,HC IMPL SCREW HEX LOCK 3.2 X 12MM, 638 
-28024165,HC IMPL SCREW HEX LOCK 3.5 X 14MM, 901 
-28024166,HC IMPL SCREW HEX LOCK 3.5 X 16MM, 901 
-28024167,HC IMPL SCREW HEX NONLOCK 3.0 X 16MM, 578 
-28024168,HC IMPL SCREW HEX NONLOCK 3.5 X 14MM, 578 
-28024169,HC IMPL SCREW HEX NONLOCK 3.5 X 18MM, 578 
-28024170,HC IMPL SCREW HEXALOBE 3.5 X 10MM, 901 
-28024171,HC IMPL SCREW HEXALOBE 3.5MM, 578 
-28024172,HC IMPL SCREW JONES 5.5 X 50MM," 8,213 "
-28024173,HC IMPL SCREW LOCK  2.7 X 14MM," 1,760 "
-28024174,HC IMPL SCREW LOCK  3. 5 X 14MM," 1,760 "
-28024175,HC IMPL SCREW NONLOCK 2.7 X 16MM, 910 
-28024176,HC IMPL SCREW WRIGHT 3.7 X 38MM," 9,735 "
-28024177,HC IMPL SKIN MESHED," 1,836 "
-28024178,HC IMPL STEM EVOLVE WRIGHT 6.5MM," 17,815 "
-28024179,HC IMPL WIRE 1.6MM, 419 
-28024180,HC PLUNGER REAMER TIBIAL 2.4MM," 4,114 "
-28024181,HC RONGEUR RAPTOR 2.25IN," 10,822 "
-28024197,HC SUTURE PASSER MULTIFIRE FASTPASS," 27,965 "
-28024199,HC  CATH INTRATHECAL ASCENDA 86CM," 7,838 "
-28024200,HC HNDPIECE MYOSURE CLASSIC," 9,713 "
-28024201,HC HNDPIECE MYOSURE XL," 10,088 "
-28024202,HC IMPL ANCHOR SWIFT-LOCK ST. JUDE, 255 
-28024203,HC IMPL ANCHOR TITAN," 1,275 "
-28024204,HC IMPL BREAST MENTOR 354-0908," 9,938 "
-28024205,HC IMPL BREAST MENTOR 354-0958," 9,938 "
-28024206,HC IMPL BREAST MENTOR 354-1258," 9,938 "
-28024207,HC IMPL BREAST MENTOR 354-1358," 9,938 "
-28024208,HC IMPL BREAST MENTOR 354-1408," 9,938 "
-28024209,HC IMPL BREAST MENTOR 354-1458," 9,938 "
-28024210,HC IMPL BREAST MENTOR 354-1508," 9,938 "
-28024211,HC IMPL BREAST MENTOR 354-1608," 9,938 "
-28024212,HC IMPL BREAST MENTOR 354-1708," 9,938 "
-28024213,HC IMPL PEG THREADED TRX 30MM, 723 
-28024214,HC IMPL PEG THREADED TRX 32MM, 723 
-28024215,HC IMPL PIN FIX. TEMP LRG WRIGHT1.4, 706 
-28024216,HC IMPL PIN FIXATION TEMP WRIGHT 1.1MM, 706 
-28024217,HC IMPL PLATE BOW WRIGHT 0.MM," 10,980 "
-28024218,HC IMPL PLATE CLAW WRIGHT 2H 20MM," 11,550 "
-28024219,HC IMPL PLATE LATERAL FUSION SM WRIGHT," 13,830 "
-28024220,HC IMPL PLT  ADVANCE RT 2.0MP X 5MM," 4,871 "
-28024221,HC IMPL PLT ADVANCE LFT 2.0MP X 7MM," 5,109 "
-28024222,HC IMPL PLT ADVANCE RT 2.0MP X 7MM," 5,109 "
-28024223,HC IMPL PLT MANDBL SLIDING MALABL," 2,478 "
-28024224,HC IMPL SCREW CORT 3.2X.08MM, 638 
-28024225,HC IMPL SCREW CROSS PIN 2.0X4," 1,233 "
-28024226,HC IMPL SCREW LCKNG HEX 3.2 X 16MM, 723 
-28024227,HC IMPL SCREW LCKNG HEX 3.2X14MM, 723 
-28024228,HC IMPL SCREW LCKNG HEX 3.2X18MM, 723 
-28024229,HC IMPL SCREW LCKNG HEX 3.2X20MM, 723 
-28024230,HC IMPL SCREW LCKNG WRIGHT 3.5X28," 1,760 "
-28024231,HC IMPL SCREW LCKNG WRIGHT 3.5X30," 1,760 "
-28024232,HC IMPL SCREW WRIGHT 7X75X16MM," 7,183 "
-28024233,HC IMPL SCRW LCKING WRIGHT 2.7X20," 1,760 "
-28024234,HC IMPL SCRW LCKING WRIGHT 2.7X26," 1,760 "
-28024235,HC IMPL SCRW LCKNG WRIGHT 4.5X45," 1,760 "
-28024236,HC IMPL SCRW LCKNG WRIGHT 4.5X55," 1,760 "
-28024237,HC IMPL SCRW LCKNG WRIGHT 5.5X40," 1,760 "
-28024238,HC IMPL SCRW NO-LCK WRIGHT 2.7X28, 910 
-28024239,HC IMPL SCRW NO-LKNG WRIGT 5.5X40, 723 
-28024240,HC IMPL SCRW PERILOC T15 2.7X22MM, 161 
-28024241,HC IMPL SCRW PERILOC T20 3.5X35MM, 827 
-28024242,HC IMPL SCRW PERILOC T20 3.5X50MM, 827 
-28024243,HC IMPL SCRW PERILOC T20 4X50MM, 212 
-28024244,HC IMPL SCRW WRIGHT 7X100X16MM," 7,149 "
-28024245,HC IMPL SPHERE EYE 16MM," 5,440 "
-28024246,HC IMPL SPHERE EYE DIA 18MM," 5,440 "
-28024247,HC IMPL SPHERE EYE EGG SHAPE DIA 16MM," 5,440 "
-28024248,HC IMPL PROSTHS VOICE 12.5MM," 2,032 "
-28024249,HC SIZER BREAST MENTOR RSZ-1208," 3,315 "
-28024250,HC SIZER BREAST MENTOR RSZ-1258," 3,315 "
-28024251,HC SIZER BREAST MENTOR RSZ-1308," 3,315 "
-28024252,HC SIZER BREAST MENTOR RSZ-1358," 3,315 "
-28024253,HC SIZER BREAST MENTOR RSZ-1408," 3,315 "
-28024254,HC SIZER BREAST MENTOR RSZ-1458," 3,315 "
-28024255,HC SIZER BREAST MENTOR RSZ-1508," 3,315 "
-28024256,HC SIZER BREAST MENTOR RSZ-1608," 3,315 "
-28024257,HC SIZER BREAST MENTOR RSZ-1708," 3,315 "
-28024258,HC SIZER MENTOR RSZ-0908," 3,315 "
-28024259,HC SIZER MENTOR RSZ-0958," 3,315 "
-28024260,HC SIZER MENTOR RSZ-1008," 3,315 "
-28024261,HC SIZER MENTOR RSZ-1058," 3,315 "
-28024262,HC SIZER MENTOR RSZ-1108," 3,315 "
-28024271,HC VSIZER MENTOR RSZ-1158," 3,315 "
-28024272,HC IMPL BREAST MENTOR 354-1308," 9,938 "
-28024273,HC IMPL PLT ADVANCE LFT  2.0MP X 5MM," 4,871 "
-28024274,HC  CATH BALLON ATLAS GOLD OTW 12X2," 2,933 "
-28024275,HC  GUIDEWIRE ZINGER STRAIGHT 14X180, 986 
-28024285,HC IMPL INTRO SHTH CHECK FLO 8X38X30, 318 
-28024286,HC  INTRODUCER MICRO 4FX10CM SET, 349 
-28024287,HC  MICROCATH DIREXION 2.4X130," 5,449 "
-28024289,HC IMPL APPLBAUM INCUD-STAPIAL JNT 2.0," 3,080 "
-28024290,HC IMPL APPLBAUM INCUD-STAPIAL JNT 2.5," 3,080 "
-28024291,HC IMPL SLING VAG UPHOLD," 16,125 "
-28024293,HC IMPL MESH PARITEX COMPOSITE 30X20CM," 11,804 "
-28024294,HC IMPL BREAST MENTOR 354-1008," 9,938 "
-28024295,HC IMPL PISTON BIG EZ LEFT OFFSET 4.75," 1,930 "
-28024297,HC  CTH DIAG US ACUSON REPROC 8X90," 6,197 "
-28024298,HC IMPL ACCESS SET NEFF STNLSS STL GW, 592 
-28024300,HC MESH SURIGMEND PRS 7 X 17CM," 19,159 "
-28024301,HC IMPL BREAST MF-410165," 10,125 "
-28024302,HC IMPL PLATE TITANIUM 36MM," 7,628 "
-28024303,HC IMPL PLATE CLAVICLE 3.5MM LCP," 8,530 "
-28024304,HC IMPL PLATE BROAD LCP 4.5MM 14 HOLE," 7,707 "
-28024305,HC IMPL PLATE RIM RADIAL HEAD 2.4MM," 6,199 "
-28024306,HC IMPL PLATE RIM RADIAL HEAD 2.4MM," 6,082 "
-28024307,HC IMPL PRODISC - C IMPLANT XL 6MM," 35,787 "
-28024308,HC IMPL PLATE DISTAL TIBIA 3.5MM," 12,750 "
-28024309,HC IMPL PLATE DISTAL HUMERUS 2.7/3.5," 9,805 "
-28024310,HC IMPL SCREW LOCKING 2.7MM X 60MM," 1,315 "
-28024311,HC IMPL SCREW LOCKING 2.7MM X 44MM," 1,315 "
-28024312,HC IMPL SCREW METAPHYSEAL 2.7MM X38MM, 446 
-28024313,HC IMPL SCREW METAPHYSEAL 2.7MM X 44MM, 446 
-28024314,HC IMPL PLATE DISTAL FEMUR LCP 9 HOLE," 14,155 "
-28024315,HC IMPL NAIL TIBIAL CANN 10MM X 315MM," 12,359 "
-28024316,HC IMPL PLATE TIBIA 4.5MM 14 HOLE," 12,795 "
-28024317,HC IMPL PLATE BROAD 4.5MM X 70M 9 HOLE," 5,696 "
-28024318,HC IMPL SCREW CORTEX ST 2.7MM X 40MM, 352 
-28024319,HC IMPL SCREW CORTEX ST 2.7MM X 42MM, 352 
-28024320,HC IMPL SCREW CORTEX ST 2.7MM X 40MM, 352 
-28024321,HC IMPL PLATE LCP 2.7MM X 58MM 6HOLE," 4,013 "
-28024322,HC IMPL PLATE - L LCP 2.7MM," 3,811 "
-28024323,HC IMPL ROD CURVED 5.5MM X 200MM," 2,563 "
-28024325,HC  CTH DIAG US ACUSON REPROC10X90," 6,197 "
-28024326,HC IMPL COIL DTCHMNT HANDLE RUBY," 2,125 "
-28024327,HC  MICROCATH DLVR PX SLIM 150 X90," 7,905 "
-28024328,HC IMPL COIL RUBY 10X35X0.020," 11,925 "
-28024329,HC IMPL COIL RUBY 9X30X0.20," 11,925 "
-28024330,HC IMPL COIL RUBY 8X40X.020," 11,925 "
-28024331,HC IMPL SCREW ACUMED 2 22MM," 3,451 "
-28024332,HC IMPL SCREW BIOMET 3.8 X 26 MM, 969 
-28024333,HC IMPL SCREW BIOMET 3.8 X 30 MM, 969 
-28024334,HC IMPL SCREW BIOMET 3.8 X 22 MM, 969 
-28024335,HC IMPL SCREW BIOMET 3.8 X 24 MM, 969 
-28024336,HC IMPL SCREW BIOMET 3.8 X 26 MM, 969 
-28024337,HC CATH BALLOON DILATOR 15-18MM," 2,168 "
-28024338,HC IMPL SCREW 1.2X3MM, 499 
-28024339,HC IMPL SCREW 1.2X4MM, 558 
-28024340,HC IMPL SCREW 1.7X3MM, 425 
-28024341,HC IMPL SCREW 1.7X4MM, 425 
-28024342,HC IMPL SCREW 1.7X5MM, 425 
-28024343,HC IMPL SCREW SELF DRILL 1.7X3MM, 476 
-28024344,HC IMPL SCREW SELF DRILL 1.7X4MM, 476 
-28024345,HC IMPL SCREW SELF DRILL 1.7X5MM, 476 
-28024346,HC IMPL SCREW SELF DRILL 1.7X6MM, 476 
-28024347,HC IMPL SCREW EMERGENCY 1.9X3MM, 462 
-28024348,HC IMPL SCREW 8MM MMF, 952 
-28024349,HC IMPL SCREW 2.0X4MM, 407 
-28024350,HC IMPL SCREW 2.0X6MM, 407 
-28024351,HC IMPL SCREW 2.0X10MM, 407 
-28024352,HC IMPL SCREW TITANIUM GOLD  2.0X12MM, 402 
-28024353,HC IMPL SCREW 6MM," 1,190 "
-28024354,HC IMPL SCREW 2.3X4MM, 497 
-28024355,HC IMPL SCREW 2.3X6MM, 491 
-28024356,HC IMPL PLATE STRYK CRANI 18 X 20MM," 5,708 "
-28024357,HC IMPL PLATE BONE  SMARTLOCK HYBR," 2,763 "
-28024358,HC IMPL PLATE BONE ORBITAL STRYK CRVD 1.7MM," 1,749 "
-28024359,HC IMPL PLATE BONE LADDER 6 X 2 MM," 2,950 "
-28024360,HC IMPL PLATE X BONE UPERFACE STRYK 7 HOLE," 1,362 "
-28024361,HC IMPL PLATE BONE CRVD ORBIT 24 HOLE," 3,706 "
-28024362,HC IMPL PLATE T 5 HOLE 1.7MM," 1,220 "
-28024363,HC IMPL PLATE LEFT TITA SM GSP 5MM," 1,570 "
-28024364,HC IMPL PLATE SM GOLD 5MM," 1,570 "
-28024365,HC IMPL PLATE BONE BSSO STRYK 6MM," 2,039 "
-28024366,HC IMPL PLATE BSSO STRYK GOLD 6MM," 2,091 "
-28024367,HC IMPL PLATE BONE BSSO STRYK  4 HOLE," 1,877 "
-28024368,HC IMPL PLATE BSSO STRYK TITANIUM 4 HOLE," 2,910 "
-28024369,HC IMPL PLATE BONE MALLEABLE 6 HOLE," 7,404 "
-28024370,HC IMPL PLATE SILVER L 92-55063," 3,553 "
-28024371,HC IMPL PLATE SILVER L 92-55465," 2,210 "
-28024372,HC IMPL PLATE  SILVER BONE 4 HOLE," 1,831 "
-28024373,HC IMPL PLATE SILVER L  PREBENT 4 HOLE," 1,831 "
-28024374,HC IMPL PLATE SILVER L 92-55767," 2,277 "
-28024375,HC IMPL PLATE PELVIC 8HOLE, 696 
-28024376,HC IMPL SCREW SELF DRILL 2 X 8 MM," 1,190 "
-28024377,HC IMPL SCREW 2.3X12MM, 491 
-28024378,HC IMPL PLATE BLUE 4 HOLE INLAY," 1,749 "
-28024379,HC IMPL PLATE BSSO 6HOLE  STRAIGHT," 2,091 "
-28024380,HC BIT DRILL FLEX 35MM," 4,973 "
-28024381,HC GASTRO INTRO FLEXIFLO 18FR KIT," 1,587 "
-28024382,HC CUSTOM CRANIOTOMY KIT," 5,027 "
-28024389,HC IMPL ROD SPINE 60MM," 2,125 "
-28024390,HC IMPL ROD SPINE 100MM," 2,125 "
-28024391,HC IMPL ROD SPINE 300MM," 2,125 "
-28024392,HC IMPL ROD SPINE 400MM," 2,125 "
-28024393,HC IMPL ROD AEGIS 100MM," 2,125 "
-28024394,HC IMPL SCREW EXTREMITY 5 X 50 MM," 5,525 "
-28024395,HC IMPL ROD GLOBUS 120 MM," 2,125 "
-28024396,HC IMPL SCREW GLOBUS 3.6MM," 2,474 "
-28024397,HC IMPL SPACER GLOBUS 14MM," 28,875 "
-28024398,HC IMPL SCREW 5.0 X 35MM," 8,843 "
-28024399,HC IMPL SCREW 5.0 X 40MM," 8,843 "
-28024400,HC IMPL SCREW 6.0 X 35MM," 8,843 "
-28024401,HC IMPL SCREW 7.0 X 45MM," 8,843 "
-28024402,HC IMPL SCREW 7.0 X 50MM," 8,843 "
-28024403,HC IMPL SCREW 9.0 X 90MM," 15,900 "
-28024404,HC IMPL ROD 300 MM," 4,743 "
-28024405,HC IMPL ROD 35 MM," 3,137 "
-28024406,HC IMPL ROD 40 MM," 3,137 "
-28024407,HC IMPL PLATE 12MM," 7,829 "
-28024408,HC IMPL SCREW 4.25MM," 2,040 "
-28024409,HC IMPL SCREW 5.0MM X 30MM," 11,385 "
-28024410,HC IMPL SCREW 5.0MM X 35MM," 11,385 "
-28024411,HC IMPL SCREW 5.0MM X 40MM," 11,385 "
-28024412,HC IMPL PLATE BONE CERV 12MM," 8,415 "
-28024413,HC IMPL PLATE 30MM," 9,000 "
-28024414,HC IMPL PLATE 48MM," 10,500 "
-28024415,HC IMPL PLATE 54MM," 10,500 "
-28024416,HC IMPL SCREW 14MM," 2,338 "
-28024417,HC IMPL PLATE 23MM," 7,875 "
-28024418,HC IMPL PLATE BONE ANTERIOR CERVICAL 25 MM," 7,875 "
-28024419,HC IMPL SHANK NUVASIVE 5.0MM," 7,744 "
-28024420,HC IMPL SCREW 5.5 X 40MM," 8,948 "
-28024421,HC IMPL SCREW 7.5 X 45MM," 8,948 "
-28024422,HC IMPL SCREW 7.5 X 50MM," 8,948 "
-28024423,HC IMPL SCREW 7.5 X 70MM," 8,805 "
-28024424,HC IMPL SCREW 8.5 X 80MM," 8,805 "
-28024425,HC IMPL SCREW 4.5 X 30MM," 8,500 "
-28024426,HC IMPL SCREW 6.5 X 40MM," 8,438 "
-28024427,HC IMPL SCREW 6.5 X 45MM," 8,438 "
-28024428,HC IMPL SCREW 5.5 X 50MM," 10,125 "
-28024429,HC IMPL SCREW NUVAS 6.5 X 45MM," 10,125 "
-28024430,HC IMPL SCREW 6.5 X 50MM," 10,125 "
-28024431,HC IMPL SCREW NUVAS 7.5 X 50MM," 10,125 "
-28024432,HC IMPL SCREW 7.5 X 55MM," 8,948 "
-28024433,HC IMPL ROD NUVASIVE 30MM," 7,353 "
-28024434,HC IMPL ROD NUVASIVE 35MM," 7,353 "
-28024435,HC IMPL ROD NUVASIVE 40MM," 7,353 "
-28024436,HC IMPL ROD NUVASIVE 60MM," 7,786 "
-28024437,HC IMPL ROD NUVASIVE 65MM," 7,786 "
-28024438,HC IMPL ROD NUVASIVE 70MM," 7,786 "
-28024439,HC BONE SCALPEL BLUNT 10MM," 3,783 "
-28024440,HC BONE SCALPEL BLUNT 20MM," 3,783 "
-28024441,HC BONE SCALPEL UNILATERAL 30MM," 3,783 "
-28024442,HC IMPL BREAST SIENTRA 480CC," 8,458 "
-28024443,HC IMPL BREAST SIENTRA 400CC," 8,458 "
-28024444,HC IMPL TISSUE EXPANDER 830CC," 8,963 "
-28024445,HC IMPL TISSUE EXPANDER 840CC," 8,963 "
-28024446,HC IMPL TISSUE EXPANDER 310CC," 10,463 "
-28024447,HC IMPL TISSUE EXPANDER 620CC," 10,463 "
-28024448,HC IMPL SOCKET 40MM, 388 
-28024449,HC IMPL PATCH WITTMAN 20 X 40 MM," 11,925 "
-28024451,HC IMPL WASHER 4MM, 393 
-28024452,HC IMPL RING PARTIAL 180MM," 10,080 "
-28024453,HC IMPL WIRE OLIVE 1.8MM, 857 
-28024454,HC IMPL ROD STRYKER 600MM," 9,119 "
-28024455,HC IMPL ROD STRYKER 80MM," 3,374 "
-28024456,HC IMPL ROD STRYKER 90MM," 3,374 "
-28024457,HC IMPL ROD STRYKER 480MM," 4,003 "
-28024458,HC IMPL ROD STRYKER 40MM," 2,125 "
-28024459,HC IMPL ROD STRYKER 60MM," 2,125 "
-28024460,HC IMPL ROD STRYKER 70MM," 2,125 "
-28024461,HC IMPL ROD STRYKER  3.5X80MM," 1,948 "
-28024462,HC IMPL ROD STRYKER 120MM," 2,125 "
-28024463,HC IMPL ROD STRYKER 240MM," 2,125 "
-28024464,HC IMPL PLATE STRYKER 45MM," 11,014 "
-28024465,HC IMPL SCREW STRYKER  4.5 X 25MM," 7,863 "
-28024466,HC IMPL SCREW STRYKER 4.5 X 30MM," 8,895 "
-28024467,HC IMPL SCREW STRYKER 4.5 X 35MM," 7,863 "
-28024468,HC IMPL SCREW STRYKER 5.5 X 25MM," 7,863 "
-28024469,HC IMPL SCREW STRYKER 6.5 X 30MM," 7,863 "
-28024470,HC IMPL SCREW STRYKER 7.0 X 45MM," 7,863 "
-28024471,HC IMPL SCREW STRYKER 7.0 X 50MM," 7,863 "
-28024472,HC IMPL SCREW CORT 2.4 X 10 MM, 850 
-28024473,HC IMPL SCREW CORT 2.4 X 3.5 MM, 850 
-28024474,HC IMPL SCREW CORT 2.4 X 3.5 X 65 MM, 850 
-28024475,HC IMPL SCREW CORT 2.4 X 3.5 X 48 MM, 850 
-28024476,HC IMPL SCREW CORT 2.4 X 3.5 X 54 MM, 850 
-28024477,HC IMPL SCREW CORT 2.4 X 3.5 X 58 MM, 582 
-28024478,HC IMPL SCREW CORT 2.4 X 32 MM, 519 
-28024479,HC IMPL SCREW CORT 2.0 X 20 MM, 295 
-28024480,HC IMPL SCREW CORT 2.0 X 22 MM, 306 
-28024481,HC IMPL SCREW CORT 2.0 X 24 MM, 295 
-28024482,HC IMPL SCREW CORT 2.0 X 28 MM, 176 
-28024483,HC IMPL SCREW CORT 2.0 X 30 MM, 850 
-28024484,HC IMPL SCREW CORT 2.0 X 10 MM," 1,097 "
-28024485,HC IMPL SCREW CORT 2.0 X 16 MM, 850 
-28024486,HC IMPL SCREW CORT 2.0 X 18 MM, 850 
-28024487,HC IMPL SCREW CORT 2.7 X 28 MM," 1,097 "
-28024488,HC IMPL SCREW LOCK 2.7 X 22 MM, 850 
-28024489,HC IMPL SCREW LOCK 2.7 MM," 1,139 "
-28024490,HC IMPL SCREW LOCK 2.7 X 32 MM," 1,139 "
-28024491,HC IMPL SCREW LOCK 2.7 X 36 MM," 1,139 "
-28024492,HC IMPL SCREW LOCK 2.7 X 40 MM," 1,139 "
-28024493,HC IMPL SCREW LOCK 2.7 X 46 MM, 322 
-28024494,HC IMPL SCREW LOCK 2.7 X 48 MM, 322 
-28024495,HC IMPL SCREW 7.3 X 140MM," 2,505 "
-28024496,HC IMPL SCREW 7.3 X 145MM," 2,505 "
-28024497,HC IMPL SCREW 7.3 X 150MM," 2,505 "
-28024498,HC IMPL SCREW 7.3 X 180MM," 2,505 "
-28024499,HC IMPL PLATE 4.5MM 12 HOLE, 850 
-28024500,HC IMPL PLATE 4.5MM 14 HOLE," 7,732 "
-28024501,HC IMPL PLATE T-PLATE  2.0MM, 850 
-28024502,HC IMPL PLATE 4 HOLE, 850 
-28024503,HC IMPL PLATE 2.0MM 6 HOLE," 2,023 "
-28024504,HC IMPL PLATE CONDYLAR 7 HOLE, 850 
-28024505,HC IMPL PLATE 2.0 LCP 12 HOLE, 850 
-28024506,HC IMPL PLATE 2.7MM LCP 8 HOLE, 850 
-28024507,HC IMPL PLATE CONDYLAR 8 HOLE," 2,244 "
-28024508,HC IMPL SCREW LAG 70MM," 3,125 "
-28024509,HC IMPL ROD CARBON FIBER 500 MM, 850 
-28024510,HC IMPL SPIRAL BLADE 50 MM," 5,579 "
-28024511,HC IMPL SCREW CANN 5.0," 1,692 "
-28024512,HC IMPL SCREW 5.0 X 75 MM," 1,716 "
-28024513,HC GUIDE WIRE 2.0 X 240 MM, 325 
-28024514,HC GUIDE WIRE 3.2 X 290 MM, 905 
-28024516,HC IMPL SCREW 4.0X26 MM, 850 
-28024517,HC IMPL SCREW 4.0X28 MM," 1,709 "
-28024518,HC IMPL SCREW 4.0X30 MM," 1,709 "
-28024519,HC IMPL SCREW 4.0X32 MM," 1,709 "
-28024520,HC IMPL SCREW 4.0X36 MM," 1,709 "
-28024521,HC IMPL SCREW 4.0X38 MM," 1,709 "
-28024522,HC IMPL SCREW 4.0X40 MM," 1,709 "
-28024523,HC IMPL SCREW 4.0X46 MM," 1,709 "
-28024524,HC IMPL SCREW 5.0X30 MM, 850 
-28024525,HC IMPL SCREW 5.0X36 MM," 1,709 "
-28024526,HC IMPL SCREW 5.0X38 MM," 1,709 "
-28024527,HC IMPL SCREW 5.0X42 MM," 1,709 "
-28024528,HC IMPL SCREW 5.0X44 MM," 1,709 "
-28024529,HC IMPL SCREW 5.0X46 MM," 1,709 "
-28024530,HC IMPL SCREW 5.0X52 MM," 1,709 "
-28024531,HC IMPL SCREW 5.0X54 MM, 850 
-28024532,HC IMPL SCREW 5.0X58 MM," 1,709 "
-28024533,HC IMPL SCREW 5.0X64 MM, 850 
-28024534,HC IMPL SCREW CORTEX 2.0 X 20MM, 850 
-28024535,HC IMPL SCREW CORTEX 2.0 X 30MM, 850 
-28024536,HC IMPL SCREW CORTEX 2.0 X 40MM, 341 
-28024537,HC IMPL SCREW CORTEX 2.0 X 50MM, 341 
-28024538,HC IMPL SURGIMEND 20X30," 86,400 "
-28024539,HC BLADE TRUNCATED 50MM," 5,950 "
-28024540,HC BLADE FULL 50MM," 5,950 "
-28024541,HC BLADE TRUNCATED 54MM," 7,140 "
-28024542,HC BLADE FULL 54MM," 5,950 "
-28024543,HC IMPL HEAD FEMORAL TAPER 54MM, 850 
-28024544,HC IMPL SCREW ZIMMER 4.5 X 30 MM," 1,020 "
-28024545,HC IMPL SCREW ZIMMER 4.5 X 48 MM," 1,148 "
-28024546,HC IMPL SCREW ZIMMER  7.0 X 85 MM," 2,199 "
-28024547,HC IMPL SCREW ZIMMER  7.0 X 125 MM," 2,200 "
-28024548,HC IMPL SCREW PERIARTICULAR ZIMMER 3.5 X 20 MM, 354 
-28024549,HC IMPL SCREW ZIMMER 3.5 X 22 MM, 354 
-28024550,HC IMPL SCREW ZIMMER 3.5 X 24 MM, 354 
-28024551,HC IMPL SCREW ZIMMER 3.5 X 48 MM, 354 
-28024552,HC IMPL TIBIAL LATERAL 3.5MM," 11,985 "
-28024553,HC IMPL SCREW  ZIMMER 5.0 X 37.5 MM," 2,023 "
-28024554,HC IMPL SCREW ZIMMER  5.0 X 57.5 MM," 2,023 "
-28024555,HC IMPL SCREW ZIMMER 5.0 X 60 MM," 2,023 "
-28024556,HC IMPL SCREW ZIMMER 6.0 X 70 MM," 2,095 "
-28024557,HC IMPL SCREW ZIMMER 6.0 X 80 MM," 2,095 "
-28024558,HC IMPL SCREW ZIMMER 6.0 X 90 MM," 2,095 "
-28024559,HC IMPL SCREW ZIMMER 6.0 X 95 MM," 2,095 "
-28024560,HC IMPL SCREW ZIMMER 6.0 X 110 MM," 2,095 "
-28024561,HC IMPL SCREW ZIMMER 3.5 X 100 MM, 249 
-28024562,HC IMPL SCREW ZIMMER 3.5 MM, 204 
-28024563,HC IMPL SCREW ZIMMER 3.5  X 28 MM, 175 
-28024564,HC IMPL SCREW ZIMMER 3.5 X 30 MM, 204 
-28024565,HC IMPL SCREW ZIMMER 3.5 X 36 MM, 204 
-28024566,HC IMPL SCREW ZIMMER 3.5  X 38 MM, 175 
-28024567,HC IMPL SCREW ZIMMER 3.5 X 40 MM, 204 
-28024568,HC IMPL SCREW ZIMMER 3.5 X 45 MM, 204 
-28024569,HC IMPL SCREW ZIMMER 3.5 X 50 MM, 204 
-28024570,HC IMPL SCREW ZIMMER 3.5 X 55 MM, 204 
-28024571,HC IMPL SCREW ZIMMER 3.5 X 60 MM, 249 
-28024572,HC IMPL SCREW ZIMMER 3.5 X 65 MM, 249 
-28024573,HC IMPL SCREW ZIMMER 3.5 X 70 MM, 249 
-28024574,HC IMPL SCREW ZIMMER 3.5 X 75 MM, 249 
-28024575,HC IMPL SCREW ZIMMER 3.5 X 80 MM, 249 
-28024576,HC IMPL SCREW ZIMMER 3.5 X 85 MM, 249 
-28024577,HC IMPL SCREW ZIMMER 4.5 X 100 MM, 261 
-28024579,HC IMPL SCREW BONE ZIMMER 6.5 X 60 MM, 821 
-28024580,HC IMPL ROD  S&N 250MM, 372 
-28024581,HC IMPL BOLT S&N 16MM, 35 
-28024582,HC IMPL NUT  S&N 10MM, 41 
-28024583,HC TIP RETRIEVAL GLOBUS 10X 30," 3,604 "
-28024584,HC IMPL SCREW INTEGRA 1.6MM, 434 
-28024585,HC DRILL BIT DEPUY 3.6MM," 6,035 "
-28024586,HC SCALPEL BONE 10MM," 3,783 "
-28024587,HC SCALPEL BONE BLUNT LONG 10MM," 3,783 "
-28024588,HC SCALPEL BONE UNILATERAL 20MM," 3,783 "
-28024589,HC TAP STRYKER  5.5MM," 2,201 "
-28024590,HC IMPL SPIRAL BLADE 46MM, 850 
-28024595,HC DRAGOO KNEE ARTHROSCOPY," 3,205 "
-28024596,HC IMPL K WIRE TELEFLEX .054 X 1.4 MM, 205 
-28024597,HC VBEAM LASER," 8,500 "
-28024598,HC SPLITTER NEUROTRANSMITER KIT," 14,025 "
-28024599,HC IMPL GRAFT ART CRYOL HERO 1002," 8,330 "
-28024600,HC IMPL SCREW GLOBUS," 7,225 "
-28024601,HC IMPL CABLE KINAMED," 3,698 "
-28024602,HC IMPL ALLODERM MEDIUM CONTOUR," 30,471 "
-28024603,HC IMPL NEUROSTIM RESTORE SENSOR," 85,875 "
-28024605,HC IMPL NEUROSTIM  LEAD," 13,500 "
-28024608,HC IMPL ALLOGRAFT SEMI - T," 10,763 "
-28024609,HC IMPL ALLOGRAF MENISCUS MEDIAL," 38,675 "
-28024610,HC IMPL ALLOGRAFT FEMORAL HEAD," 10,658 "
-28024611,HC IMPL ALLOGRAFT FIBULAR STRUT," 9,090 "
-28024612,HC IMPL PLATE BOLT DECADE," 10,320 "
-28024613,HC IMPL END CAP NUVASIVE," 11,970 "
-28024614,HC IMPL ENDCAP X-CORE," 11,970 "
-28024615,HC MAXCESS 4 MODULE NUVASIVE," 17,551 "
-28024619,HC IMPL INSERT POLY SBI," 14,603 "
-28024620,HC IMPL TALAR COMPONENT SBI XSMALL RIGHT," 39,722 "
-28024621,HC IMPL TIBIAL COMPONENT SBI STAR LARGE 33 X 40 MM," 35,854 "
-28024622,HC IMPL WASHER CONICAL, 273 
-28024624,HC IMPL HALF PIN 200MM," 1,440 "
-28024625,HC IMPL NAIL RECON STRYKER," 17,532 "
-28024626,HC IMPL SET SCREW STRYKER," 1,868 "
-28024627,HC IMPL SCREW LOCKING STRYKER," 1,886 "
-28024628,HC IMPL SCREW LAG STRYKER," 2,445 "
-28024629,HC IMPL CONNECTOR ROD TO ROD," 6,087 "
-28024630,HC IMPL ROD CONNECTING STRYKER," 3,225 "
-28024631,HC IMPL WIRE BOLT MED, 821 
-28024632,HC IMPL SCREW LAG STRYKER," 5,635 "
-28024633,HC IMPL SCREW COMPRESSION," 1,063 "
-28024634,HC GUIDE WIRE TEAR DROP," 1,998 "
-28024635,HC IMPL INSERTER TIP ZIMMER, 678 
-28024636,HC IMPL KNEE FEMORAL COMPONENT," 13,230 "
-28024637,HC IMPL END CAP 15MM," 2,027 "
-28024639,HC IMPL PLATE MANDIBLE RECON," 43,128 "
-28024640,HC IMPL NAIL FEMORAL STRYKER," 15,461 "
-28024641,HC IMPL TISS EXPAND 600 ML W/TABS," 11,588 "
-28024642,HC IMPL PLATE RECON ZIMMER," 3,251 "
-28024643,HC IMPL PLATE CLAVICLE HOOK 3.5MM," 7,707 "
-28024644,HC IMPL NAIL TIBIAL CANN 10MM X 330MM," 12,359 "
-28024645,HC IMPL SCREW LOCKING 5.0 X 36MM," 1,742 "
-28024646,HC IMPL PLATE PROXIMAL OLECRANON 2H," 7,935 "
-28024647,HC IMPL SCREW METAPHYSEAL 2.7MMX34MM, 446 
-28024648,HC IMPL SCREW METAPHYSEAL 2.7MMX52MM, 446 
-28024649,HC IMPL SCREW METAPHYSEAL 2.7MMX54MM, 446 
-28024650,HC IMPL SCREW METAPHYSEAL 2.7MMX58MM, 446 
-28024651,HC IMPL PLATE PROX FEMUR HOOK 4.5MM," 14,760 "
-28024652,HC IMPL SCREW CONICAL CANN 7.3MMX80MM," 2,538 "
-28024653,HC IMPL PLATE CONDYLAR CVD 4.5MM LCP," 15,212 "
-28024654,HC IMPL NAIL TROCH CANN 10MM X 380MM," 15,071 "
-28024655,HC IMPL ACIS 10MM," 14,738 "
-28024656,HC IMPL NAIL TIBIAL CANN 8MM X 270MM," 12,359 "
-28024657,HC IMPL SCREW CORTEX ST 2.4MM X 40MM, 519 
-28024658,HC IMPL PLATE DISTAL HUMERUS 3.5MM 3H," 7,894 "
-28024659,HC IMPL PLATE DISTAL HUMERUS 2.7 X 3.5," 9,803 "
-28024660,HC  BLLN CATH SPRINTER RX 1.5X10," 1,488 "
-28024661,HC  BLLN CTH NC SPRINTER RX 5X15," 1,488 "
-28024662,HC  BLLN CTH NC SPRINTER RX 4X6," 1,488 "
-28024663,HC  BLLN CTH NC SPRINTER RX 3.75 X 15," 1,488 "
-28024664,HC  BLLN CTH NC SPRINTER RX 3.75 X 12," 1,488 "
-28024665,HC  BLLN CTH NC SPRINTER RX 3.75 X 9," 1,488 "
-28024666,HC IMPL SCREW 130\' SET," 1,611 "
-28024667,HC IMPL XRL MEDIUM 28MM-36MM," 57,850 "
-28024668,HC IMPL SYNFIX-LR 8 DEG 30," 55,200 "
-28024669,HC IMPL VERTEBRAL SPACER ANTERIOR 21MM," 7,565 "
-28024670,HC IMPL K-WIRE STRYKER 1.6MMX150MM, 451 
-28024671,HC IMPL SCREW NON-LOK 3X16MM T8 STRYKR," 1,615 "
-28024672,HC IMPL SCREW NON-LOK 3.5X22MM STRYKR," 1,615 "
-28024673,HC IMPL SCREW NON-LOK 3X12MM T8 STRYKR," 1,615 "
-28024674,HC VSP ORTHGNATHIC BUNDLE," 20,300 "
-28024675,HC HNDPCE MYOSURE <4CM FIBROIDS," 9,713 "
-28024676,HC HNDPCE MYOSURE XL >4CM FIBROID," 10,088 "
-28024679,HC IMPL PLATE DORSLATRL MIDSHFT RAD 6H," 5,908 "
-28024680,HC IMPL SCRW LOKNG HEXLOBE 3.5X8 ACUMD, 901 
-28024682,HC IMPL SCREW 2.5X26MM T10 STRYKR T10, 750 
-28024683,HC IMPL SCREW STRYKER  3.5X24MM T10, 750 
-28024684,HC IMPL SCREW STRYKER 3.5X40MM T10, 750 
-28024685,HC IMPL SCREW STRYKER 3.5X38MM T10, 750 
-28024686,HC IMPL PLATE FIB STR VARIAX 8H 108MM," 4,457 "
-28024687,HC IMPL PLATE FIB STR VARIAX  6H 84MM," 4,457 "
-28024688,HC IMPL SCREW LKNG STRYKER 3.5X32MM T10," 1,684 "
-28024689,HC IMPL SCREW STRYKER 3.5X42MM T10, 750 
-28024690,HC IMPL SCREW STRYKER 3.5X34MM T10, 750 
-28024691,HC IMPL SCREW LKNG STRYKR 3.5X36MM T10," 1,684 "
-28024692,HC IMPL SCREW LKNG STRYKR 3.5X50MM T10," 1,684 "
-28024693,HC IMPL SCREW LKNG STRYKR 3.5X42MM T10," 1,684 "
-28024694,HC IMPL SCREW STRYKR 3.5X36MM T10, 750 
-28024695,HC IMPL SCREW STRYKR 3.5X50MM T10, 750 
-28024696,HC IMPL SCREW HEADLESS WRIGHT 7X80X16," 7,149 "
-28024697,HC IMPL SCREW HEADLESS WRIGHT 7X85X16," 7,149 "
-28024698,HC IMPL SCREW HEADLESS WRIGHT 7X65X16," 7,149 "
-28024699,HC IMPL END CAP WRIGHT," 1,998 "
-28024701,HC IMPL KWIRE WRIGHT 3.0," 1,777 "
-28024702,HC IMPL SCREW VALOR 5MM WRIGHT," 2,015 "
-28024703,HC IMPL SCREW VALOR 5X20 WRIGHT," 2,015 "
-28024704,HC IMPL SCREW VALOR 5X75 WRIGHT," 2,015 "
-28024705,HC IMPL SCREW VALOR 5X30 WRIGHT," 2,015 "
-28024706,HC  BLLN CATH DILAT SAVVY 3X10," 2,114 "
-28024707,HC IMPL STNT SMART CONTROL 14X30X80," 2,506 "
-28024708,HC  BLLN CTH MUSTANG OTW 5X20X40," 2,125 "
-28024709,HC  BLLN CTH MUSTANG OTW 6X20X40," 2,125 "
-28024710,HC  BLLN CTH MUSTANG OTW 8X20X40," 2,125 "
-28024711,HC  BLLN CTH MUSTANG OTW 10X20X40," 2,125 "
-28024712,HC  BLLN CTH MUSTANG OTW 10X30X75," 2,125 "
-28024713,HC  BLLN CTH DILAT DORADO 5X20," 2,763 "
-28024714,HC IMPL CEMENT RESTRICT TPA-13, 723 
-28024715,HC IMPL CEMENT RESTRICT TPA-14, 723 
-28024716,HC IMPL CAP LOCK GLOBUS, 850 
-28024717,HC IMPL SCREW GLOBUS," 2,695 "
-28024718,HC GUIDE WIRE RETRIEVE," 2,253 "
-28024719,HC IMPL PLATE SQUARE, 621 
-28024720,HC IMPL ILIAC CREST," 7,710 "
-28024721,HC IMPL TRINITY ELITE LARGE," 34,808 "
-28024722,HC IMPL CORTICAL POWDER 10CC," 1,989 "
-28024723,HC IMPL TULIP MODULAR NUVASIVE," 9,630 "
-28024724,HC IMPL SET SCREW NUVASIVE," 1,488 "
-28024725,HC IMPL TIBIAL COMPONENT," 35,854 "
-28024726,HC IMPL SCREW LAG STRYKER," 5,635 "
-28024727,HC IMPL NAIL TROCH STRYKER," 12,180 "
-28024728,HC ADAPTOR APEX," 3,213 "
-28024729,HC ADAPTOR APEX LONG," 3,284 "
-28024730,HC IMPL HUMERAL COMPONENT STRYKER," 24,098 "
-28024731,HC IMPL HEART TOTAL ARTIF SYNCARD," 187,050 "
-28024732,HC IMPL BUTTON CERCLAGE," 1,541 "
-28024733,HC IMPL FIXATOR WRIST SYNTHES," 12,137 "
-28024735,HC IMPL SYNFIX VBR - LR," 54,925 "
-28024736,HC GRAFT FILTER," 3,007 "
-28024737,HC IMPL CRANIAL PEEK CUSTOM," 76,134 "
-28024739,HC IMPL RADIAL HEAD EVOLVE," 16,058 "
-28024740,HC IMPL TIBIAL BLOCK ZIMMER," 6,635 "
-28024741,HC IMPL STEM EXTENSION ZIMMER," 7,144 "
-28024742,HC IMPL FEMORAL STEM ZIMMER," 21,000 "
-28024743,HC IMPL PIN THREADED ZIMMER," 1,105 "
-28024744,HC IMPL BASEPLATE ZIMMER," 17,888 "
-28024745,HC IMPL HIP LINER ZIMMER," 9,788 "
-28024746,HC IMPL AUGMENT ACETABULAR," 13,797 "
-28024747,HC IMPL BAHA ABUTMENT  4MM W/6MM," 22,365 "
-28024748,HC IMPL BAHA ABUTMENT  4MM W/8MM," 22,365 "
-28024749,HC IMPL BAHA ABUTMENT  4MM W/10MM," 22,365 "
-28024750,HC IMPL BAHA ABUTMENT  4MM W/12MM," 22,365 "
-28024751,HC IMPL BAHA ABUTMENT 6MM," 15,375 "
-28024752,HC IMPL BAHA ABUTMENT 8MM," 15,375 "
-28024753,HC IMPL BAHA ABUTMENT 10MM," 15,375 "
-28024754,HC IMPL BAHA ABUTMENT 12MM," 15,375 "
-28024755,HC IMPL BAHA 3MM B1300," 11,625 "
-28024758,HC IMPL CAP HEALING W/PLUG BAHA 30MM, 145 
-28024759,HC IMPL PROCESSOR BAHA SLATE GREY," 24,500 "
-28024760,HC IMPL PROCESSOR BAHA GLACIER WHITE," 24,500 "
-28024761,HC PROCESSOR BP110 BAHA CHAM BLND," 24,500 "
-28024762,HC PROCESSOR BP110 BAHA SOFT BLK," 24,500 "
-28024763,HC PROCESSOR BP110 BAHA SLATE GRY," 24,500 "
-28024764,HC PROCESSOR BP110 BAHA CHSNT BRN," 24,500 "
-28024765,HC PROCESSOR BP110 BAHA GLACR WHT," 24,500 "
-28024766,HC PROCESSOR BP110 BAHA PIANO BLK," 24,500 "
-28024767,HC PROCESSOR CRDELL II BAHA BEIGE," 23,100 "
-28024768,HC PROCESSOR CRDELL II BAHA BLK," 23,100 "
-28024769,HC PROCESSOR CRDELL II BAHA GRAY," 23,100 "
-28024770,HC  BAHA RENTAL DRILL KIT," 8,250 "
-28024772,HC IMPL ANCOR CLK  WRNCH BSTN SCI 7.6HX," 8,250 "
-28024774,HC IMPL SCRW  MP INCREASE PROFIL 2X5MM," 1,258 "
-28024775,HC IMPL SCRW TI EMRGNCY SYNTHS 2.4X5MM, 449 
-28024776,HC IMPL SCREW TI MATRX SLF-TAP 2.1X6MM, 673 
-28024778,HC RREAMER WRIGHT 5.0MM," 3,307 "
-28024779,HC IMPL SCRW TNODSIS G-FRCE WRI 5X15MM," 3,970 "
-28024780,HC IMPL SCRW CANN 1/3 THRD 4X40MM," 1,785 "
-28024781,HC IMPL SCRW PERI SLF-TAP ZIM 3.5X50MM, 354 
-28024782,HC IMPL SCRW PERI SLF-TAP ZIM  3.5X80MM, 354 
-28024783,HC IMPL SCRW PERI SLF-TAP ZIM 3.5X85MM, 354 
-28024785,HC WASHER SM O.D. ZIMMER 7MM, 204 
-28024786,HC IMPL PLATE FIB STR 5H VARI-AX 72MM," 4,457 "
-28024787,HC IMPL SCREW BONE T10 STRYKR  3.5X44MM, 750 
-28024788,HC IMPL SCREW BONE T10 STRYKR 3.5X16MM, 750 
-28024789,HC IMPL SCREW BONE T10 STRYKR  3.5X18MM, 750 
-28024790,HC IMPL SCREW BONE T10 STRYKR  3.5X20MM, 750 
-28024792,HC  CATH EP SUPREME 4X65," 1,700 "
-28024793,HC IMPL COIL EMBLZTN HYDROSOFT 10 2.5X4," 8,775 "
-28024795,HC IMPL PLUG VASC MIRCO MVP 5.3X12X180," 9,375 "
-28024796,HC  CATH GDE ANGIOGR 6X15 ALT VBT," 1,285 "
-28024797,HC  CATH GDE VBT JR4 6X125X0.070," 1,285 "
-28024798,HC  CATH GDE XB4.0 0.617 ID 6X125," 1,285 "
-28024799,HC IMPL CAGE ALIF PEEK 24X32X8X14," 33,600 "
-28024800,HC IMPL CAGE TLIF PEEK AEGIS  26X0X11," 25,200 "
-28024801,HC IMPL CAGE CERVICAL PEEK 14X17X8," 8,250 "
-28024802,HC IMPL CAGE TLIF PEEK AEGIS 26X9," 25,200 "
-28024803,HC IMPL ROD PRE-BEND AEGIS 90MM," 2,125 "
-28024804,HC IMPL CAGE ALIF PEEK AEGI 29X38X8X14," 33,600 "
-28024805,HC IMPL ROD PRE-BEND AEGIS 80MM," 33,600 "
-28024806,HC IMPL ROD PRE-BEND  AEGIS 70MM," 2,125 "
-28024807,HC IMPL TIBIAL TRAY MOD  DEPUY SZ 2," 20,882 "
-28024808,HC IMPL LOCK RING DURALOC DEPUY 56MM," 2,423 "
-28024809,HC IMPL TIBIAL TRAY SZ2 CEM REVISION," 31,613 "
-28024810,HC IMPL CUP ACETABULAR W/CRIPTION 48MM," 21,225 "
-28024811,HC  GUIDEWIRE BLUNT 2-1.45MM," 1,955 "
-28024812,HC IMPL ROD SPINAL VIPER 2 6.35 X 80 MM," 4,888 "
-28024813,HC IMPL SCREW SPINE 5.0 X 45MM," 8,843 "
-28024814,HC IMPL SCREW ML 4.35 X 20MM," 7,943 "
-28024815,HC IMPL SCREW PRECEPT 6.5 X 45MM," 8,438 "
-28024816,HC IMPL SCREW POLYAXIAL 5.5MM X 50MM," 9,844 "
-28024817,HC IMPL SPACER ORACLE 45 X 22 X 11MM," 36,582 "
-28024818,HC IMPL SPACER ORACLE 50 X 22 X 13MM," 36,582 "
-28024819,HC IMPL ACIS STD 11MM," 14,738 "
-28024820,HC IMPL PLATE DISTAL FEMUR LCP 9H," 13,785 "
-28024822,HC IMPL SPACER VERTEBRAL ANTERIOR 11MM," 28,140 "
-28024823,HC IMPL PLATE DISTAL TIBIA 11 HOLE," 13,009 "
-28024824,HC IMPL NAIL FEMORAL 13MM X 340MM," 14,125 "
-28024825,HC IMPL END CAP FOR FEMORAL NAIL," 1,575 "
-28024826,HC IMPL SPIRAL BLADE FOR FEMORAL NAIL NAIL," 5,303 "
-28024827,HC IMPL PLATE DISTAL RADIUS LCP," 13,704 "
-28024828,HC IMPL PLATE PROXIMAL HUMERUS 3.5MM," 15,034 "
-28024829,HC IMPL PLATE PROX HUMERUS LONG 3.5MM," 14,487 "
-28024831,HC IMPL STNT GRFT ZENITH FLX AAA 30X58," 14,573 "
-28024835,HC  MICROCTH DUO HEADDWAY 2.1-1.6," 5,313 "
-28024836,HC IMPL PEG CORT ACUMED, 663 
-28024837,HC IMPL SPINE LINK AEGIS," 7,225 "
-28024838,HC IMPL SCREW BIOMET, 570 
-28024839,HC IMPL PLATE TIBIA BIOMET," 8,415 "
-28024840,HC IMPL STEM HUMERL BIOMET," 10,335 "
-28024841,HC IMPL PLUG ANCHOR BIOMET," 7,191 "
-28024842,HC IMPL COMP SEG BIOMET," 17,738 "
-28024843,HC IMPL GENERATOR SPECTRA," 91,543 "
-28024844,HC IMPL  INFINION LEAD KIT," 30,380 "
-28024846,HC IMPL NEUROSTIM LEAD MRI SAFE," 15,750 "
-28024847,HC IMPL WASHER BLUE, 393 
-28024848,HC IMPL WIRE OLIVE STRYKER, 857 
-28024849,HC IMPL GLENOID COMPONENT," 12,049 "
-28024850,HC IMPL POLY INSERT STRYKER," 7,542 "
-28024851,HC CLAW MICRO SUPER LONG," 7,757 "
-28024852,HC IMPL CROSS LINK STRYKER," 8,598 "
-28024853,HC IMPL OFFSET CONNECTOR," 7,225 "
-28024854,HC IMPL BONE PUTTY 3CC," 6,827 "
-28024855,HC IMPL GLENOID BASEPLATE," 9,375 "
-28024856,HC IMPL GLENOSPHERE," 31,500 "
-28024863,HC CABLE EXTENSION NEUROSTIM," 5,100 "
-28024864,HC IMPL SURGICAL ADHESIVE BOS -SCI, 595 
-28024865,HC PATIENT CHARGING KIT BOS - SCI," 24,920 "
-28024866,HC SIMULATOR LOCATOR," 5,636 "
-28024868,HC PLATFORM KIT BILATERAL FHC," 23,625 "
-28024870,HC GUIDE WIRE ZIMMER," 1,998 "
-28024871,HC DRILL & PIN GUIDE KIT," 8,075 "
-28024873,HC GUIDE WIRE SYNTHES, 754 
-28024875,HC  CATH ATTIN GUIDE LFT 4FR," 1,700 "
-28024876,HC  CATH DILATATION 150CM 6MMX12CM," 2,338 "
-28024877,HC  CATH DILATATION 150CM 6MMX20CM," 2,338 "
-28024878,HC  CATH DILATATION 150CM 5MMX12CM," 2,338 "
-28024879,HC  CATH DILATATION 150CM 7MMX12CM," 2,338 "
-28024880,HC  CATH DILATATION 150CM 3MMX12CM," 2,338 "
-28024881,HC  CATH DILATATION 150CM 3MMX15CM," 2,338 "
-28024882,HC  CATH DILATATION 150CM 3MMX20CM," 2,338 "
-28024883,HC  CATH DILATATION 150CM 4MMX15CM," 2,338 "
-28024884,HC  CATH DILATATION 150CM 4MMX20CM," 2,338 "
-28024885,HC  CATH DILATATION 150CM 5MMX20CM," 2,338 "
-28024886,HC CATH ANGPHC TIP 5FR 0.038IN, 145 
-28024887,HC CATH ANGPHC TIP 5FR 0.038IN, 731 
-28024891,HC CATH ANGPHC TIP 5FR 0.038IN, 595 
-28024893,HC CATH ANGPHC TIP 5FR 0.038IN, 731 
-28024896,HC IMPL PLATE CERVICAL AEGIS L 23," 8,500 "
-28024897,HC IMPL SCREW CERVICAL POSTERIOR 4.0," 7,225 "
-28024898,HC IMPL PLATE CERVICAL AEGIS L 41," 9,000 "
-28024899,HC IMPL PLATE CERCVICAL AEGIS 3 L 54," 10,500 "
-28024900,HC IMPL WIRE BEADED WRIGHT," 1,777 "
-28024901,HC IMPL NAIL VALOR MEDIUM LFT WRIGHT," 20,858 "
-28024903,HC IMPL ANCHOR PEEK W/NDL MITEK 5.5MM," 3,749 "
-28024904,HC IMPL PLATE LAPIDUS WRIGHT 1MM," 12,165 "
-28024905,HC IMPL SCREW LOCKING WRIGHT 3.5X22," 1,760 "
-28024906,HC IMPL SCREW LOCKING WRIGHT 3.5X24," 1,760 "
-28024907,HC IMPL SCREW NON-LOKING WRIGHT 3.5X20, 910 
-28024908,HC IMPL SCREW NON-LCKING WRIGHT 3.5X24, 910 
-28024909,HC IMPL SCREW NON-LCKING WRIGHT 3.5X38, 910 
-28024910,HC IMPL PLATE POW LCKNG TI ARTHREX 3MM," 7,608 "
-28024911,HC IMPL SCRW LOW PROF LKNG ARTHEX 3X14, 723 
-28024912,HC IMPL SCRW LOW PROF LCKNG ARTHX 3X16, 723 
-28024913,HC IMPL SCRW LOW PROF LCKNG ARTHX 3X20, 723 
-28024914,HC IMPL SCRW LOW PROF LCKNG ARTHX  3X22, 723 
-28024915,HC IMPL SCRW LOW PROF CORT ARTHRX 3X22, 935 
-28024916,HC IMPL SCREW LKNG STRYKR 3.5X38MM T10," 1,684 "
-28024917,HC IMPL OCCLSN DEVICE AMPLATZ PLUG 3MM," 6,800 "
-28024918,HC IMPL OCCLSN DEVICE AMPLATZ PLUG 4MM," 6,800 "
-28024919,HC  BLLN CTH ATLAS GOLD OTW 16X2," 3,188 "
-28024920,HC  BLLN CTH ATLAS GOLD OTW 16X4," 3,188 "
-28024921,HC  BLLN CTH ATLAS GOLD OTW 18X2," 3,188 "
-28024922,HC IMPL DIFIB SNGL CHMBR EVERA XT DF-1," 78,000 "
-28024923,HC IMPL PLATE ANT CLAVICLE 2.7/3.5 10H," 8,598 "
-28024924,HC REAMER HEAD  IRR/ASP 14.5MM," 3,141 "
-28024926,HC IMPL SCRW LKING 3.5X26MM T10 STRYKR," 1,675 "
-28024927,HC IMPL SCRW LKING T10 STRYKR  3.5X30MM," 1,684 "
-28024928,HC IMPL SCRW LKING T10 STRYKR  3.5X34MM," 1,684 "
-28024929,HC IMPL PLATE MTP STRYKR V1 LONG RT 8H," 14,588 "
-28024930,HC IMPL SCRW NON-LOK T8 STRYK  3.5X36MM," 1,615 "
-28024932,HC IMPL SCRW NON-LOK T8 STRYK 3.5X12MM," 1,615 "
-28024933,HC IMPL VITOSS STRYKER 1.2CC BA2X," 6,613 "
-28024934,HC IMPL SCRW CORTX PERI-LOK  3.5X36MM, 161 
-28024935,HC IMPL SCRW CORTX PERI-LOK  3.5X24MM, 161 
-28024936,HC IMPL SCRW CORTX PERI-LOK  3.5X20MM, 827 
-28024937,HC IMPL SCRW LOKNG PERI-LOK  3.5X24MM, 827 
-28024938,HC IMPL SCRW LOKNG PERI-LOK 3.5X16MM, 827 
-28024939,HC IMPL SCRW LOKNG PERI-LOK  3.5X14MM, 827 
-28024940,HC IMPL PIN PF S&N  2.7X18MM, 916 
-28024941,HC IMPL SCREW NON-LOK T8 STRYKR  3X16MM," 1,615 "
-28024942,HC IMPL GLIDESHTH  SHORTANGE .021 6FX10, 675 
-28024943,HC RETAINER SCREW 3.5MM X 18MM," 2,210 "
-28024944,HC IMPL PLATE NARROW LCP 4.5MMX152 8H," 4,641 "
-28024945,HC IMPL PLATE PROX TIBIA 4.5MM X 116MM," 12,240 "
-28024946,HC IMPL TRANSCONNECTOR SNAP-ON 47MM-62," 12,015 "
-28024947,HC SCREWDRIVER SHAFT 2.0MM," 3,896 "
-28024948,HC IMPL PLATE SUP CLAVICLE 3.5MMX105MM," 8,123 "
-28024949,HC IMPL KNEE FEMORAL COMPONENT NEXGEN," 13,230 "
-28024950,HC IMPL KNEE ARTICULAR SURFACE NEXGEN CH 5-6 12 MM," 5,823 "
-28024951,HC IMPL KNEE ARTICULAR SURFACE NEXGEN CH 14 MM," 5,823 "
-28024952,HC IMPL SCREW NON-LOK T8 STRYKR  3X14MM," 1,615 "
-28024953,HC IMPL FILTER DENALI VENA FEMORAL," 15,750 "
-28024954,HC IMPL STNT GFT AAA OVATION 14X14," 28,350 "
-28024955,HC IMPL EMBLZTN COIL RETRACTA 7X6X35," 4,760 "
-28024956,HC IMPL EMBLZTN COIL RETRACTA 7X12X35," 4,760 "
-28024957,HC IMPL EMBLZTN COIL RETRACTA 7X10X35," 4,760 "
-28024958,HC IMPL EMBLZTN COIL RETRACTA 7X14X35," 4,760 "
-28024959,HC IMPL EMBLZTN COIL RETRACTA 7X16X35," 4,760 "
-28024960,HC IMPL EMBLZTN COIL RETRACTA 7X18X35," 4,760 "
-28024961,HC IMPL EMBLZTN COIL RETRACTA 7X20X35," 4,760 "
-28024962,HC IMPL EMBLZTN COIL RETRACTA 14X14X35," 4,760 "
-28024963,HC IMPL EMBLZTN COIL RETRACTA 14X16X35," 4,760 "
-28024964,HC IMPL EMBLZTN COIL RETRACTA 14X18X35," 4,760 "
-28024965,HC IMPL PRODISC IMPLANT LG DEEP 6MM," 35,966 "
-28024966,HC IMPL PROCESSOR BAHA PIANO BLACK," 24,500 "
-28024968,HC  CTH ENDOVAS EKOSONIC 50CM," 18,865 "
-28024972,HC  INTRDCR SHTH CATH 11CM X 7FR, 73 
-28024973,HC  INTRDCR SHTH CATH 11CM X 9FR, 67 
-28024974,HC IMPL INTRDCR SHTH HMSTS 30CM X 12FR, 340 
-28024984,HC IMPL LEAD DURATA ICD DIFIB 65CM," 37,375 "
-28024986,HC MDIA CNTRST BO 300MG 100ML, 59 
-28024987,HC MDIA CNTRST IOHXL 300MGL/ML, 164 
-28024988,HC MDIA CNTRST PLYMR 300MG 100ML, 567 
-28024989,HC MDIA CNTRST VIAL 320MG 50ML, 157 
-28024995,HC OMNIPAQUE  BOTTLE Y352 300 50ML, 38 
-28024996,HC ONCOZENE  MICROSPHERES 100UM," 11,250 "
-28024997,HC  IVUS CATHETER OPTI 5FR," 6,035 "
-28024999,HC  CATH PTA BLN DILTTN 4MM X 10CM," 2,338 "
-28025000,HC  CATH PTA BLN DILTTN 5MM X 10CM," 2,338 "
-28025001,HC  CATH PTA BLN DILTTN 6MM X 10CM," 2,338 "
-28025009,HC IMPL ENDPRTHS VIABIL 10X6," 18,130 "
-28025014,HC GUIDE XB3.5 125CM," 1,284 "
-28025015,HC IMPL GW PERPH 6X16X145CM 0.035IN, 244 
-28025016,HC IMPL GW VASCLR STR 190CM 0.014IN," 4,930 "
-28025018,HC IMPL SHTH INTRDCR 7FRX13CM, 442 
-28025019,HC IMPL THERASPHERE RADIOACTIVE PART," 90,000 "
-28025020,HC IMPL THERASPHERE RADIOACTIVE PART 7GBQ," 90,000 "
-28025021,HC IMPL PRTCL RDACTV RADIOPHARMACEUTIC," 90,000 "
-28025026,HC HOOK KNIFE LONG OLYMPUS," 4,941 "
-28025027,HC IMPL EMBLZTN COIL RETRACTA 7X8X35," 4,760 "
-28025029,HC IMPL STNT GFT ZENITH RENU 28X37X125," 42,341 "
-28025032,HC IMPL STNT GRFT ZENITH RENU 28X59," 42,341 "
-28025033,"HC  CTH ATTAIN SLCT DELVRY 63,130"," 1,700 "
-28025035,HC IMPL BLN TRUE DILATATION 20X4.5X10F," 6,783 "
-28025036,HC  CATH SOFIA DISTAL ACCESS 5X125," 9,375 "
-28025039,HC  CTH ATTAIN SUREVLVE OUTR GD 45," 1,632 "
-28025040,HC IMPL CABLE MEDTRONIC 12FT, 731 
-28025041,HC IMPL STNT VALIANT DELV SY 44X40X150," 88,725 "
-28025042,HC IMPL STNT VALIANT DELV SY 40X40X150," 88,725 "
-28025043,HC IMPL SNT GRFT ZENITH FLX AAA 24X56," 21,595 "
-28025044,HC VISUALASE NEURO LASER T10 KIT," 76,980 "
-28025045,HC VISUALASE NEURO LASER T03 KIT," 87,483 "
-28025047,HC IMPL CBLE REVERSE MED DETACH SET, 842 
-28025049,HC  CATH ABLTN SAFIRE 7X5," 5,525 "
-28025051,HC  CTH ENDOVAS EKOSONIC 30CM," 17,963 "
-28025052,HC  CATH ATTAIN SUREVLVE OUTER GD," 1,632 "
-28025053,HC IMPL SHTH PLWY OPTISEAL 9X25, 442 
-28025063,HC IMPL STNT GFT ZENITH FLX AAA 22X96," 54,977 "
-28025064,HC IMPL STNT ZILVER PTX 6X40," 13,463 "
-28025065,HC IMPL STNT ZILVER PTX 7X40," 13,463 "
-28025066,HC CANNULA 34/38 OVAL 2 STAGE 3/8," 2,293 "
-28025067,HC CANNULA 12G PLEGIA NEEDLE 31CM," 2,331 "
-28025070,HC CANNULA 20FR X 15," 2,139 "
-28025071,HC IMPL STNT GRF ZENITH TX2 34X157," 72,564 "
-28025072,HC CANNULA WITH LUER  6.0 18FR," 4,250 "
-28025073,HC IMPL CANNULA CARDIO AO ROOT W/VENT," 1,281 "
-28025074,HC IMPL CANNULA RETROGRADE EDWARDS," 8,916 "
-28025075,HC IMPL CANNULA CARDIO UNEVEN," 1,281 "
-28025076,HC IMPL CANNULA CARDIO AO ROOT + 3 VES," 2,433 "
-28025077,HC IMPL CANNULA 2-STAGE EDWARDS," 3,579 "
-28025078,HC IMPL CANNULA CARDIO EDWARDS, 888 
-28025079,HC CANNULA VENT  SORIN 20FR," 1,530 "
-28025080,HC IMPL CANNULA HEARTPORT VENOUS 22," 3,613 "
-28025081,HC IMPL CANNULA HEARTPORT VENOUS 25," 3,613 "
-28025082,HC IMPL CANNULA EDWARDS ARTERIAL 16FR," 2,083 "
-28025083,HC IMPL CANNULA EDWARDS ARTERIAL 18FR," 2,083 "
-28025084,HC IMPL CANNULA EDWARDS ARTERIAL 20FR," 2,083 "
-28025085,HC IMPL CANNULA EDWARDS ARTERIAL 22FR," 2,083 "
-28025087,HC  BLLN CATH RAPIDCROSS 2.5X210," 3,919 "
-28025088,HC  BLLN CATH RAPIDCROSS 4X120," 3,919 "
-28025089,HC  BLLN CATH ADVANCE 18LP 8X12," 2,338 "
-28025090,HC  BLLN CATH ADVANCE 18LP 8X15," 2,338 "
-28025091,HC GUIDW ROADRUNNER 0.35X180, 9 
-28025092,HC IMPL STNT GFT GORE 23X14X10," 45,500 "
-28025093,HC IMPL STNT WLLFLXCOLONIC 30X25X9X135," 17,963 "
-28025094,HC IMPL STNF GFT ENDO ZENITH FLX 28X58," 14,573 "
-28025095,HC IMPL STNT GFT GORE 16X4X7," 24,500 "
-28025096,HC IMPL FLTR VENA CAVA OPTION 6F," 8,625 "
-28025097,HC GUIDW PRIMEWIRE 185CM," 5,908 "
-28025098,HC IMPL CAGE X-CORE 2 16X16-22MM," 62,070 "
-28025099,HC IMPL ENDCAP X-CORE 2 16MM," 13,763 "
-28025100,HC IMPL CAGE AVS UNILIF 13X25X0X11," 38,587 "
-28025101,HC IMPL CAGE UNILIF 11X25X88X0'," 38,587 "
-28025138,HC IMPL SCREW XIA POLYAXIAL 7.0 X 55MM," 7,863 "
-28025139,HC IMPL SCREW SPINE 7.0 X 45MM," 8,843 "
-28025140,HC IMPL SCREW LOCKING 2.0 X 6MM," 1,097 "
-28025141,HC IMPL SCREW LOCKING 2.0 X 8MM," 1,716 "
-28025142,HC IMPL PLATE 2.0 Y-PLATE 247.350," 4,197 "
-28025143,HC IMPL SPACER SPINAL SYNFIX 30X38X12X8\'," 55,200 "
-28025144,HC IMPL PLATE HUMERUS 3.5MM X 90MM," 13,039 "
-28025145,HC IMPL NAIL TROCH CANN 12MM X 380MM," 15,071 "
-28025146,HC IMPL PLATE LCP 2.0 X 31MM 4HOLE," 2,371 "
-28025147,HC IMPL SCREW CORTEX 2.0 X 24MM, 341 
-28025148,HC IMPL SPIRAL BLADE FOR HUMERAL NAIL," 5,576 "
-28025149,HC IMPL K-WIRE 2.5MM W/TROCAR POINT," 1,056 "
-28025150,HC IMPL ROD HARD 6.0MM X 150MM," 2,136 "
-28025151,HC IMPL NAIL TIBIAL CANN 10MM X 345MM," 12,359 "
-28025152,HC IMPL SCREW LOCKING 5.0 X 40MM," 1,927 "
-28025153,HC IMPLSCREW LOCKING 5.0 X 50MM," 1,927 "
-28025154,HC IMPL SCREW LOCKING 5.0 X 65MM," 1,927 "
-28025155,HC IMPL SCREW POLYAXIAL 5.5MM X 45MM," 9,844 "
-28025156,HC IMPL NAIL TIBIAL CANN 10MM X 300MM," 12,359 "
-28025157,HC IMPL SCREW LOCKING 5.0MM X 45MM," 1,927 "
-28025158,HC IMPL SCREW LOCKING 5.0MM X 70MM," 1,927 "
-28025161,HC IMPL CLIP SYSTEM 12/6GC OVESCO," 4,582 "
-28025162,HC  BLLN CTH ATLAS GOLD OTW 12X6," 2,933 "
-28025163,HC  BLLN CTH ATLAS GOLD OTW 14X4," 2,933 "
-28025164,HC BLLN CTH ATLAS GOLD OTW 12X4," 2,933 "
-28025165,HC IMPL DIFIB QUADRA ASSURA CRDIAC DEV," 84,000 "
-28025166,HC  CTH BLLN DILAT BIB 7X1.5," 6,375 "
-28025167,HC IMPL GRFT OVATN PRM 13X14X14X100," 28,350 "
-28025168,HC IMPL STNT BILI PROTEGE GPS 14X60X80," 10,140 "
-28025169,HC  GLIDECATH TAPER 5X100X40, 413 
-28025170,HC  GLIDECATH TAPER 5X100X38, 413 
-28025171,HC  GLIDECATH ANGLE 4X65X38, 413 
-28025172,HC  GLIDECATH SIM2 5X100X38, 413 
-28025173,HC  GLIDECATH COBRA2 4X65X38, 413 
-28025175,HC CATH DRAIN PERICARDIOCENTEIS 8.3 X40," 1,462 "
-28025176,HC CONTRAST VISIPAQUE 320MG/100ML, 313 
-28025177,HC CONTRAST MAGNEVIST 50ML," 2,100 "
-28025178,HC IMPL PORT VORTEX 6.6F," 7,800 "
-28025179,HC  BLLN CTH DILAT STERLING 8X20," 3,215 "
-28025180,HC CONTRST OMNIPAQUE 350MG/200ML, 171 
-28025181,HC IMPL ROD TEMPLATE DEPUY/SNTHES," 1,700 "
-28025190,"HC IMPL WIRE GUIDE 0.62 X 6""", 94 
-28025191,HC IMPL PEG CORICIAL 2.3 X 20MM, 663 
-28025192,HC IMPL SCREW HEADLESS 4.3 X 6.0," 2,695 "
-28025193,HC IMPL SCREW HEADLESS 4.3 X 46MM," 2,695 "
-28025194,HC IMPL SCREW TRILOCK 2.0X9MM," 1,010 "
-28025195,HC IMPL NAIL FEMORAL 11MM X 360MM," 14,110 "
-28025196,HC IMPL SCREW LOCKING 5.0 X 72MM," 1,709 "
-28025197,HC IMPL ORACLE SPACER 55MMX22MMX15MM," 36,582 "
-28025199,HC IMPL SCREW CORTEX ST 2.0MM X 4MM, 436 
-28025200,HC IMPL NAIL FEMORAL 10MM X 340MM," 14,110 "
-28025201,HC IMPL SCREW CORTEX ST 3.5MM X 28MM, 293 
-28025202,HC IMPL SCREW CORTEX ST 3.5MM X 30MM, 293 
-28025203,HC IMPL SCREW CORTEX ST 3.5MM X 32MM, 293 
-28025204,HC IMPL SCREW CORTEX ST 3.5MM X 42MM, 293 
-28025205,HC IMPL SCREW LOCKING VAR ANGLE 3.5MM," 1,466 "
-28025206,HC IMPL SCREW LOCKING VAR ANGLE 3.5MM," 1,466 "
-28025207,HC IMPL PLATE DISTAL HUMERUS 3.5MM," 14,769 "
-28025208,HC IMPL SCREW REDUCTION POLY 4.5 X 30," 7,013 "
-28025209,HC IMPL SCREW POLY AXIAL 6.5 X 45MM," 7,013 "
-28025210,HC IMPL SCREW POLY REDUCTION 7.5 X 45," 7,013 "
-28025211,HC IMPL SCREW POLY REDUCTION 7.5 X 55," 7,013 "
-28025212,HC IMPL SCREW STRYKER 4.0 X 18MM," 8,075 "
-28025215,HC IMPL PLATE FEMUR HOOK 4.5MM LCP," 14,825 "
-28025217,HC IMPL SCREW METAPHYSEAL 2.7MM X 50MM, 446 
-28025218,HC IMPL ROD CURVED 5.5MM X 100MM," 2,562 "
-28025220,HC IMPL SCREW BONE CORTICAL 2 X 10 MM," 1,010 "
-28025221,HC IMPL PLATE DISTAL TIBIA 2.7/3.5MM," 13,931 "
-28025224,HC IMPL PLATE DISTAL TIBIA 3.5MM LCP," 13,327 "
-28025227,HC IMPL SCREW 2.0X11MM," 1,010 "
-28025228,HC IMPL SCREW 2.0X11MM, 628 
-28025229,HC IMPL SCREW 2.0X12MM, 628 
-28025262,HC IMPL CATH DIALYS STR 11.5F 20CM, 604 
-28025263,HC  CATH ECMO 2LMN 13FR," 3,613 "
-28025271,HC COUPLER 4MM," 2,303 "
-28025275,HC DRSNG PRRSR INCLD," 3,035 "
-28025277,HC ECHELON PWRD FLEX 60 X28CM," 3,409 "
-28025279,HC ERBE APPLICATOR W/SUCTION," 2,516 "
-28025280,HC FIBR LSR SURGCL FOR LUMENIS BA," 2,746 "
-28025287,HC IMPL ENDO GIA XL," 2,746 "
-28025288,HC IMPL INTRDCR LPROSCPC 2MM, 723 
-28025289,HC JLY ELCTRD W/ EARPHN TAB SNSR," 3,409 "
-28025298,HC KNIFE TRIANGLE TIP," 4,939 "
-28025304,HC PLATFORMS GELPOINT ADVANCED," 4,038 "
-28025308,HC RELOAD ENDO 60," 2,967 "
-28025317,HC IMPL CATH HMODIAL 2LMN 15CM, 604 
-28025324,HC  SPLNT BCKL RSTNG LT TAB 3.75IN, 459 
-28025325,HC  SPLNT BCKL RSTNG RT TAB 3.75IN, 519 
-28025326,HC SPLNT RSTNG NTRL LT MED, 485 
-28025327,HC SPLNT RSTNG NTRL RT MED, 468 
-28025328,HC IMPL STAPLR ENDOSCPC 2.5X45-3.5MM," 1,284 "
-28025333,HC SYS MGMT INCSN THRPY UNIT WTR," 4,208 "
-28025338,HC IMPL TRAY PEG PONSKY 20FR, 680 
-28025342,HC  TUBE FDNG ENTRL 10FR X 43IN, 476 
-28025343,HC  TUBE FDNG ENTRL 10FR X 55IN, 485 
-28025344,HC  TUBE SALIVARY BYP 8MM," 1,556 "
-28025384,HC  NG FEEDING TUBE 8FRX43, 309 
-28025385,HC  SNGL LUM CATH 6.6FR," 1,411 "
-28025386,HC IMPL ENTEROSTOMY SUTURE SET, 850 
-28025387,HC IMPL DOUBLE FLEXIBLE GDEWIRE 0.21X40CM, 298 
-28025388,HC IMPL NEUROSTIMULATOR GASTRIC ENTERA," 54,925 "
-28025389,HC IMPL SCREW MAS 8.5 X 40MM," 6,163 "
-28025390,HC IMPL SCREW MEDTRONIC 4.5 X 40MM," 7,863 "
-28025391,HC IMPL OFFSET LONG MEDTRONIC," 7,655 "
-28025392,HC IMPL ROD JJ 3.5MM X 80MM," 2,550 "
-28025393,HC IMPL SCREW CORTICAL FIX 4.0 X 30MM," 11,385 "
-28025394,HC IMPL SCREW 2.0X13MM," 1,010 "
-28025395,HC  BLLN CATH ATTAIN 6X10X80, 850 
-28025396,HC  CATH ATTAIN DLVRY SYS 65X130," 1,700 "
-28025118,HC  GUIDEWIRE WIRE 021, 298 
-28025121,HC  CATHETER 20 GA, 757 
-28025116,HC  DUAL LUMEN CATHETER 14.5 F," 1,020 "
-28025124,HC IMPL END CAPS, 561 
-28025126,HC  GUIDEWIRE 45CM X  0.018 IN, 145 
-28025129,HC  GUIDEWIRE 120 CM X 0.038 IN, 187 
-28025134,HC  MICROINTRODUCER 5F, 978 
-28025132,HC  CATHETER 7 FR X 120 CM," 2,890 "
-28025355,HC SHEATH 7 FR X  2," 3,519 "
-28025356,HC  CATHETER," 2,890 "
-28025357,HC  GUIDEWIRE 0.18," 2,712 "
-28025358,HC INTRODUCER SHEATH 9F X 38.5, 536 
-28025359,HC INTRODUCER SHEATH 22F X 13CM, 748 
-28025360,HC INTRODUCER SHEATH 10F X 38.5CM, 655 
-28025361,HC  GUIDEWIRE 0.018, 128 
-28025362,HC INTRODUCER SHEATH 10 F X 13 CM, 111 
-28025363,HC INTRODUCER SHEATH 24 F X 13 CM, 196 
-28025364,HC INTRODUCER SHEATH 18 F X 13 CM, 128 
-28025365,HC INTRODUCER SHEATH PEEL AWAY, 340 
-28025366,HC  GUIDEWIRE 0.018, 255 
-28025367,HC  INTRODUCER SHETAH PEEL AWAY, 340 
-28025368,HC  GUIDEWIRE 0.018, 255 
-28025371,HC  J-TIP GUIDEWIRE," 1,020 "
-28025372,HC INTRODUCER SHEATH, 672 
-28025373,HC  GUIDEWIRE 0.32, 374 
-28025374,HC  CATHETER, 177 
-28025375,HC  CATHETER 5.5 FR, 298 
-28025376,HC  GUIDEWIRE 0.018, 247 
-28025379,HC SHEATH 7FR," 1,836 "
-28025380,HC  GUIDEWIRE 0.35, 850 
-28025399,HC IMPL LEAD INTRAMUSCULAR ENTERRA," 16,500 "
-28025400,HC  CATH ABLATION BARRX RFA ENDO," 11,250 "
-28025404,HC  CATH GDNG ATTAIN SUREVLV 45CM," 1,632 "
-28025405,HC IMPL PLATE VOLR BEARING LT 9HL-7PG," 12,188 "
-28025406,HC IMPL PLATE VOLR BEARING RT 11HL-7PG," 14,963 "
-28025407,HC IMPL PLATE VOLR BEARING RT 3HL-7PG," 7,763 "
-28025408,HC IMPL PLATE VOLR BEARING RT 5HL-7PG," 8,513 "
-28025409,HC IMPL PLATE VOLR BEARING RT 7HL-7PG," 9,338 "
-28025410,HC IMPL PLATE VOLR BEARING RT 9HL-7PG," 12,188 "
-28025411,HC IMPL PLATE WIRE FORM 3HL," 3,485 "
-28025412,HC IMPL PLATE WIRE FORM 5HL," 3,570 "
-28025413,HC IMPL SCREW CORT HEX 3.2X11MM, 706 
-28025414,HC IMPL SCREW CORT HEX 3.2X13MM, 706 
-28025415,HC IMPL SCREW CORT HEX 3.2X15MM, 706 
-28025416,HC IMPL PLATE TUBULR LKNG 6HL," 2,040 "
-28025417,HC IMPL SCREW CORT LOW-PROFIL 3.5X12MM, 278 
-28025418,HC IMPL SCREW CORT LOW-PROFIL 3.5X12MM, 298 
-28025419,HC IMPL SCREW CORT LOW-PROFIL 3.5X24MM, 298 
-28025420,HC IMPL SCREW CANCLUS LO-PROFIL 4X14MM, 298 
-28025421,HC IMPL SCREW CANCLUS LO-PROFIL 4X16MM, 298 
-28025422,HC IMPL SCRW LO-PROFL SHRT THRED 4X50M, 298 
-28025423,HC IMPL SCRW LOW-PROFL SRT THRED 4X46M, 298 
-28025424,HC IMPL TALUS ANKLE COMPONENT LFT XS," 39,722 "
-28025425,HC IMPL TALUS COMPONENT ANKLE LFT MED," 39,722 "
-28025427,HC IMPL PLATE HTO LFT 4HL 11.25MM," 9,450 "
-28025428,HC IMPL STEM TIB TOP ANKLE INBONE 14MM," 5,279 "
-28025429,HC IMPL TIBIAL TRAY ANKLE RT 3LONG," 13,133 "
-28025430,HC IMPL TALAR DOME ANKLE INBONE SIZE 2," 3,119 "
-28025431,HC IMPL TIBL STEM MID ANKL INBONE 14MM," 5,279 "
-28025432,HC IMPL TIB STEM BASE ANKL INBONE 16MM," 5,746 "
-28025433,HC IMPL TALAR STEM ANKLE INBONE 10MM," 9,555 "
-28025434,HC IMPL POLY INSRT ANKL INBONE SIZE 2+," 13,463 "
-28025435,HC IMPL PIN STEINMAN 2.4, 230 
-28025436,HC IMPL SNARE OVAL CAPTIFLEX SMALL," 1,105 "
-28025437,HC IMPL SNARE HEX CAPTIVATOR SMALL," 1,275 "
-28025438,HC IMPL CAGE COROENT 8X10X30MM 5'," 28,000 "
-28025439,HC IMPL SCREW HELIX ST 4.0 X 13MM," 2,338 "
-28025440,HC IMPL PLATE VOLR LFT FIX ANGL3HL-7PG," 5,525 "
-28025441,HC IMPL PLATE VOLR LFT FIX ANGL5HL-7PG," 6,375 "
-28025442,HC IMPL PLATE VOLR RT FIX ANGL5HL-7PG," 6,375 "
-28025443,HC IMPL PLATE RADIAL LFT 10PEG," 9,300 "
-28025444,HC IMPL PLATE RADIAL LFT 7PEG," 7,905 "
-28025445,HC IMPL PLATE RADIAL RT 10PEG," 9,300 "
-28025446,HC IMPL PLATE RADIAL RT 7PEG," 7,905 "
-28025447,HC IMPL PLATE VOLR BEARING LT 11HL-7PG," 14,963 "
-28025448,HC IMPL PLATE VOLR BEARING LT 5HL-7PG," 8,513 "
-28025450,HC IMPL STNT GFT ZENITH FLX AAA 14X30," 14,145 "
-28025451,HC IMPL STNT GFT ZENITH FLX AAA 16X30," 14,145 "
-28025452,HC IMPL DELV SYST RETROFLEX3 26MM," 81,250 "
-28025453,HC IMPL COIL TRGET XL 360 STD 7X20," 16,463 "
-28025454,HC IMPL COIL TRGET XL 360 STD 6X20," 16,463 "
-28025455,HC IMPL COIL TRGET XL 360 STD 8X30," 16,463 "
-28025456,HC IMPL COIL TRGET XL 360 STD 14X50," 16,463 "
-28025457,HC IMPL COIL TRGET XL 360 STD 24X50," 16,463 "
-28025458,HC CABLE ELECTROPHYSIOLOGY OCTAPOLAR," 2,210 "
-28025459,HC IMPL ENPOWER CONTROL CABLE," 1,547 "
-28025461,HC IMPL K-WIRE OLIVE 1.6X100MM, 680 
-28025462,HC IMPL PUMP PENILE 15CMX12MM," 53,898 "
-28025465,HC IMPL POLY INSERT ANKLE 8MM," 14,603 "
-28025466,HC IMPL TALUS COMPONENT ANKLE LFT SM," 39,722 "
-28025468,HC  TUBE FEEDING LOW PROFILE 18X6," 1,320 "
-28025469,HC IMPL NDL ACCUSTICK 21GA, 83 
-28025470,HC  BLLN CATH RAPIDCROSS 2.5X150," 3,919 "
-28025471,HC  BLLN CATH RAPIDCROSS 2X150," 3,919 "
-28025472,HC GUIDW NITREX 0.14X300, 935 
-28025473,HC GDE SHTH SAFT SHEATH CSG 9 X 50," 3,273 "
-28025474,HC IMPL CANNULA ANGIOVAC W CIRCUIT," 78,000 "
-28025488,HC STRAIGHT FIXCO GUIDW 0.25X145CM, 113 
-28025489,HC IMPL CTH PERFORMA DIAG 5FRX100CM, 808 
-28025490,HC NAVI-STAR INTERFACE CABLE 5FT," 2,100 "
-28025491,HC IMPL CTH DIAG IMPLS 5F, 218 
-28025492,HC IMPL CTH DIAG IMPLS 6F, 74 
-28025495,HC CONTRAST ISOVUE 300 100ML SING DS, 95 
-28025622,HC CONTRAST ISOVUE 300 200ML  SING DS, 191 
-28025623,HC GUIDW AMPLTZ SUPER STF 180X0.035, 368 
-28025624,HC IMPL CTH DIAG SPER TORQUE PLS 6X100, 70 
-28025625,HC  CTH DIAG SITESEER 5FX100, 185 
-28025627,HC  CTH SPPRT CROSSCATH 0.035X135," 1,063 "
-28025628,HC  CTH ANGIO BEACON TP  KMP 5X40X.038, 129 
-28035496,HC  CATH GDN LAUNCHER 8X10X0.090, 451 
-28035497,HC  CTH ABLAT TURBO ELITE 0.09X150," 20,965 "
-28035511,HC SCREW DRIVER SYNTHES 387.282," 3,290 "
-28035512,HC IMPL EMBLZTN COIL RUBY 3X5CM," 8,415 "
-28035513,HC IMPL PLATE DISTAL HUMERUS 3.5MM," 7,890 "
-28035514,HC IMPL EMBOLIC PROCT DVICE .014INX180," 13,125 "
-28035515,HC IMPL PLATE MED-DIST HUMERUS 3.5MM," 8,759 "
-28035516,HC IMPL ENDPROTHE VASC 5X25X120CM," 39,390 "
-28035519,HC IMPL SCREW ILIAC 8.5 X 90MM," 8,805 "
-28035522,HC BLADE FASCIAL SPLITTER," 8,500 "
-28035523,HC IMPL ROD LORDOTIC PRECEPT 100MM," 8,033 "
-28035524,HC IMPL LINER OFFSET CONTINUUM 7MM," 8,793 "
-28035525,HC  CTH SMRTTOUCH THERMOCOOL 8F," 20,888 "
-28035526,HC  CTH SMRTTOUCH THERMCOOL 8F," 20,888 "
-28035527,HC  CTH SMRTTOUCH THERMCOOL 8F," 20,888 "
-28035528,HC  CTH SMRTTOUCH THERMCOOL 8F," 20,888 "
-28035529,HC  CTH SMRTTOUCH THERMCOOL 8F," 20,888 "
-28035530,HC  CTH SMRTTOUCH THERMCOOL 8F," 19,880 "
-28035531,HC  CTH SMRTTOUCH THERMCOOL 8F," 19,880 "
-28035532,HC  CTH SMRTTOUCH THERMCOOL 8F," 19,880 "
-28035533,HC IMPL PLUG VASC MIRCO MVP 5.3X12X180," 14,625 "
-28035534,HC IMPL INSERT KNEE EVOLUTION," 5,100 "
-28035535,HC IMPL INSERT TIBIAL BEARING STRYKER," 7,824 "
-28035536,HC IMPL SCREW 6.4 X 105," 2,531 "
-28035537,HC IMPL CANCELLOUS GRANULES 30CC," 3,647 "
-28035538,HC IMPL SCREW 6.4 X 110," 2,531 "
-28035539,HC IMPL INSERT TIBIAL ATTUNE SZ 7," 7,875 "
-28035540,HC IMPL TIBIAL BASE ATTUNE SZ 6," 10,500 "
-28035541,HC IMPL PATELLA ATTUNE 41MM," 4,463 "
-28035542,HC IMPL FEMORAL COMPONENT ATTUNE SZ 7," 15,300 "
-28035543,HC IMPL SCREW EXPERIAN 5.0 X 30MM," 8,843 "
-28035544,HC IMPL NAIL FEMORAL 13MM-11.5MMX76," 42,250 "
-28035545,HC IMPL ROD 3.5-5.5 X 420MM," 7,293 "
-28035546,HC IMPL FEMORAL COMPONENT," 15,000 "
-28035547,HC IMPL INSERT KNEE EVOLUTION," 5,100 "
-28035548,HC IMPL SCREW METAPHYSEAL 2.7MM 48MM, 446 
-28035549,HC IMPL PLATE OLECRANON 2.7MM 116MM," 8,922 "
-28035550,HC IMPL PLATE 3.5MM HUMERUS 8H," 12,684 "
-28035551,HC IMPL WRIGHT KNEE INSERT#EIS6-S10L," 5,100 "
-28035552,HC IMPL HIP LINER 36X62 8751-015-36," 5,950 "
-28035553,HC IMPL CAGE TLIF 12X26 3610-2612," 25,200 "
-28035554,HC IMPL BONE CHIPS 30 CC 750753," 3,426 "
-28035555,HC IMPL CAGE SPINAL 10X27X6," 43,401 "
-28035556,HC IMPL FEMORAL COMPONENT EVOLUTION," 15,000 "
-28035557,HC IMPL FEMUR CEMENTED PERSONA SZ 12," 16,980 "
-28035558,HC IMPL TIBIA CEMENTED PERSONA," 7,710 "
-28035559,HC IMPL ARTICULAR SURFACE PERSONA," 5,814 "
-28035560,HC IMPL TIBIA CEMENTED PERSONA SIZE F," 7,710 "
-28035561,HC IMPL FEMUR CEMENTED PERSONA SZ 6," 16,980 "
-28035562,HC IMPL ARTICULAR SURFACE PERSONA," 16,980 "
-28035563,HC IMPL STNT GRFT OVATION PRME 15X14X22," 28,350 "
-28035564,HC IMPL KNEE ART. SURF. 5962-024-14," 5,950 "
-28035565,HC IMPL CAGE AEGIS PEEK #3718-2414," 33,600 "
-28035566,HC IMPL CAGE AEGIS PEEK 3744-2916," 33,600 "
-28035567,HC IMPL SCREW CORTEX 2.0MM 14MM, 341 
-28035568,HC IMPL SCREW CORTEX 2.0MM 22MM, 341 
-28035569,HC IMPL SCREW LOCKING 2.0MM 12MM," 1,097 "
-28035570,HC IMPL SCREW LOCKING 2.0MM 14MM," 1,097 "
-28035571,HC IMPL T-PLATE 2.0MM 3 HOLES HEAD," 4,197 "
-28035575,HC  CTH CXI SUPPORT 2.3X90," 1,403 "
-28035576,HC IMPL SCREW LIZFRANK 3.7MM," 8,279 "
-28035577,HC IMPL IGNITE MIX POWER 4CC," 11,220 "
-28035578,HC GUIDW APPROACH HYDRO 0.014X190," 1,203 "
-28035579,HC GUIDW APPROACH HYDRO 300X0.014," 1,203 "
-28035580,HC IMPL PLATE-T 4 HOLE 30MM," 14,085 "
-28035581,"HC  BLLN CTH ADVANCE 14LP 2X20,4F"," 3,400 "
-28035582,HC IMPL SCREW LOCKING3.5X24MM," 1,760 "
-28035583,HC IMPL SCREW LOCKING 3.5X26MM," 1,760 "
-28035584,HC IMPL KWIRE 1.6, 221 
-28035585,HC IMPL PLATE 4 HOLE," 4,361 "
-28035586,HC IMPL SCREW NON LOCKING 2.4X10, 723 
-28035587,HC IMPL SCREW NON LOCKING 2.4 X 12, 723 
-28035588,HC IMPL SCREW NON LOCKING 2.4X14MM, 723 
-28035589,HC IMPL SCREW NON LOCKING 2.0X12MM," 1,760 "
-28035591,HC IMPL SCREW HEADED 3.0X18MM," 1,539 "
-28035593,HC IMPL KWIRE 1.1MMX150MM, 221 
-28035594,HC IMPL SCREW 7.0MM MVC," 7,149 "
-28035595,HC IMPL SCREW JONES 5.5X40MM," 8,213 "
-28035596,HC IMPL KWIRE 2.0X228M, 221 
-28035598,HC IMPL ROD TENDON HUNTER 5MMX24.5CM," 8,790 "
-28035599,HC IMPL SCREW BONE CORTEX 1.5X10MM, 628 
-28035600,HC IMPL LINER HIP 8851-012-36REV," 21,952 "
-28035601,HC IMPL SHELL HIP 8757-056-02REV," 18,064 "
-28035602,HC IMPL SCREW 3.5X11M LOCKING, 901 
-28035603,HC IMPL SCREW ANGLE 2.3X18MM," 1,071 "
-28035604,HC IMPL SCREW ANGLED 2.3X20MM," 1,071 "
-28035605,HC IMPL SCREW ANGLED 2.3X22MM," 1,071 "
-28026107,HC IMPL COIL TRGET XL 360 SOFT 8X30," 15,356 "
-28026108,HC  TUBE JEJUNAL 18F 3.5," 2,661 "
-28026110,HC  GLIDEECATH   4FR X 100CM," 2,108 "
-28026112,HC IMPL NDL BPSY ASPRTN 21GA 150MM, 213 
-28026113,HC  GUIDEWIRE ANGIOGRAPHY 3 MM J SHAPE, 47 
-28026116,HC IMPL BLN 22X4.5 TRUE DILITATION," 6,783 "
-28026117,HC  CATH NDL 10CM 19GA 5FR, 145 
-28026118,HC NDL 18GAX71CM ADULT LRG CURVE," 4,208 "
-28026119,HC BAG DRNG GRAVITY 600MLX20," 2,295 "
-28026121,HC IMPL CTH BEACON TIP TRCN NB ADV 5F VANSCHIE3, 221 
-28026122,HC IMPL CTH BEACON TIP TRCN NB ADV 5F VANSCHIE4, 221 
-28026123,HC IMPL CTH DIAG INFINITY TL 6X100, 76 
-28026124,HC PLASMABLD 4.0 PEAK 4MM SPTULA TIP," 2,210 "
-28026125,HC SHEATH SLNDR GLIDESHEATH 6X10, 672 
-28026126,HC  BLLN CTH ATLAS GOLD OTW 14X6," 2,890 "
-28026127,HC IMPL MARKER RADIATION GOLD SEED," 1,947 "
-28026135,HC IMPL CTH DIAG SUPER TORQUE PLS 6X100, 68 
-28026139,HC IMPL CTH DIAG UL REPROCSS 10X90," 11,550 "
-28035509,HC IMPL CRANIAL PT SPECIFIC, 108 
-28035510,HC DRILL BIT W/STOP 3.0MM," 2,652 "
-28035517,HC IMPL SPACER OPAL 10MM X 32MM 13MM," 28,140 "
-28035518,HC IMPL CONNECTOR OPEN OFFSET 20MM," 7,225 "
-28035520,HC IMPL TAP VUEPOINT 3.0MM," 4,080 "
-28035521,HC DRILL BIT VUEPOINT II," 3,230 "
-28035607,HC IMPL CAGE 12MM X 27MM," 43,401 "
-28035611,HC IMPL SCREW SHLDR 01.04223.033," 1,148 "
-28035613,HC IMPL INSERT CERAMIC 58MM 36MM," 27,671 "
-28035664,HC IMPL STNT MICRO BY-PASS L45," 28,700 "
-28035608,HC DRILL 2.5MM 47-4309-061-01," 2,975 "
-28035609,HC DRILL 6MM 47-4309-061-15," 2,975 "
-28035610,HC IMPL STEM SHLDR#4349-008-13," 46,040 "
-28035612,HC IMPL SCREW 6.5 X 50 PS13-6550," 7,225 "
-28035614,HC IMPL FIBULA SHAFT 4CM," 6,239 "
-28035615,HC IMPL SCREW POLYAXIAL 6.5 X 55MM," 8,500 "
-28035616,HC IMPL PLATE ANCHORING M LDR SPINE," 22,750 "
-28035617,HC IMPL MEDIAN ROI-A 27X30MM," 24,500 "
-28035618,HC IMPL NAIL TIBIAL 13MM X 345MM," 12,353 "
-28035619,HC IMPL NAIL TIBIAL 13MM X 375MM," 12,359 "
-28035620,HC IMP SCREW LOCKING 5.0 X 52MM," 1,709 "
-28035621,HC IMPL ARTICULAR SURFACE PERSONA 16MM," 5,814 "
-28035622,HC IMPL FEMORAL HEAD CERAMIC BIOLOX," 9,900 "
-28035623,HC IMPL ADAPTER SLEEVE C-TAPER V-40," 1,986 "
-28035624,HC IMPL TISSUE EXPANDER VERSAFIL 225CC," 4,463 "
-28035625,HC IMP TISSUE EXPANDER VERSAFIL 350CC," 4,463 "
-28035626,HC IMPL REVERSE MEDICAL DETACHMENT BX," 6,758 "
-28035628,HC  BLLN CTH ATLAS GOLD OTW 12X2," 2,890 "
-28035629,HC  BLLN CTH ATLAS GOLD OTW 14X2," 2,890 "
-28035630,HC  BLLN CTH ATLAS GOLD OTW 16X2," 2,890 "
-28035631,HC  BLLN CTH ATLAS GOLD OTW 16X4," 3,145 "
-28035632,HC  BLLN CTH ATLAS GOLD OTW 18X2," 3,145 "
-28035633,HC BLLN CTH ATLAS GOLD OTW 20X2," 5,100 "
-28035634,HC  BLLN CTH ATLAS GOLD OTW 22X4," 5,100 "
-28035636,HC  BLLN CTH ATLAS GOLD OTW 24X4," 5,100 "
-28035637,HC  BLLN CTH ATLAS GOLD OTW 26X2," 5,100 "
-28035638,HC  BLLN CTH ATLAS GOLD OTW 26X4," 5,100 "
-28035641,HC  BLLN CTH ATLAS GOLD OTW 14X2," 2,890 "
-28035644,HC  BLLN CTH ATLAS GOLD OTW 14X4," 5,100 "
-28035646,HC IMPL STNT ZILVER PTX DES 6X60," 11,213 "
-28035647,HC IMPL STNT ZILVER PTX DES 7X60," 11,213 "
-28035648,HC IMPL STNT ZILVER PTX DES 6X100," 13,463 "
-28035649,HC NANOPASS REACH 45 R," 2,465 "
-28035650,HC IMPL STNT ZILVER PTX DES 7X100," 13,463 "
-28035651,HC NANOPASS CLASSIC CRESENT," 2,168 "
-28035653,HC  BLLN CTH ATLAS GOLD OTW 20X2," 5,100 "
-28035654,HC IMPL INSERTER NANOTACK," 2,975 "
-28035656,HC  BLLN CTH ATLAS GOLD OTW 22X2," 5,313 "
-28035658,HC  BLLN CTH ATLAS GOLD OTW 24X2," 5,313 "
-28035661,HC CARTRIDGE INJECTOR II," 2,210 "
-28035662,HC SUTURE ANCHOR NANOTACK FLEX," 3,358 "
-28035665,HC IMPL ANCHOR INSERTER CINCHLOCK," 3,698 "
-28035666,HC IMPL CORNEAL PRC/PRSRV/TRNST TIS," 28,700 "
-28035669,HC IMPL NIT PDA OCCLUDER FLEX 4X4," 12,728 "
-28035676,HC CRESCENT NANOPASS REACH," 2,168 "
-28035659,HC  BLLN CTH ATLAS GOLD OTW 26X2," 5,313 "
-28035668,HC PASS CAPSULE," 2,635 "
-28035670,HC IMPL NIT PDA OCCLUDER FLEX 5X4," 12,731 "
-28035671,HC IMPL NIT PDA OCCLUDER FLEX 6X5," 12,731 "
-28035672,HC IMPL NIT PDA OCCLUDER FLEX 7X6," 12,731 "
-28035673,HC IMPL NIT RING MALYUGIN SYSTEM 6.25MM," 1,063 "
-28035674,HC IMPL NIT PDA OCCLUDER MED 9X6," 12,731 "
-28035675,HC IMPL RING MALYUGIN SYSTEM 7.0MM," 1,063 "
-28026157,HC IMPL BUR HOLE COVER SMALL 12MM," 1,085 "
-28026161,HC IMPL PLT MNFRGMNT T SHFT 2H 2MM, 442 
-28026163,HC CERVICAL DISC  CRVCL DISC STANFORD HOSP L," 2,185 "
-28026165,HC IMPL CEMENT BONE TOBRAMYCIN FULL," 3,341 "
-28026169,HC IMPL SCREW TI MTRXMNDBL 2.0MM 10MM, 510 
-28026170,HC IMPL HEAD FEM THS CO-CR 32MM +9MM," 7,208 "
-28026172,HC  FREE FLAP," 3,825 "
-28026176,HC  DR NEZHAT S VIDEO LAPAROSC," 7,404 "
-28026177,HC IMPL ELMNTR H APEX PINNACLE, 901 
-28026191,HC IMPL WASHER WSHR WRST VIROFORM, 748 
-28026192,HC IMPL SCREW NON-LOK T8 3.5X16MM," 1,615 "
-28026193,HC IMPL COAPTITE INJECTABLE," 3,145 "
-28026196,HC IMPL LEAD RELIANCE GORE DF4," 34,125 "
-28026198,HC TUNNELING TOOL FOR DEFIB," 4,250 "
-28026199,HC DISP PRSSUR TRANDUCER LOGICAL, 46 
-28026202,HC CONTRAST SNGL DS ISOVUE-300 50ML, 50 
-28026203,HC IMPL CTH DIAG IMPULSE 5F PIG 110CM, 75 
-28035704,HC IMPL SCREW TAPERED 6-8X30MM INTRAFI," 2,389 "
-28035789,HC IMPL LINER ACETABULAR RINGLOC SZ22," 12,000 "
-28035796,HC IMPL SCREW PEDICLE REVERE 8.5 X 45," 7,013 "
-28035842,HC IMPL PLATE PROX FEMUR HOOK 4.5," 16,387 "
-28035685,HC IMPL SCREW LO PROFILE TITAN 3X20MM, 935 
-28035686,HC IMPL SCREW TITANIUM QWIK-FIX 2X11MM," 1,870 "
-28035708,HC COCLR PROCESR SNG CREME," 57,850 "
-28035719,HC COCLR PROCESR DBL CREME," 68,400 "
-28035721,HC COCLR PROCESR DBL BABY PNK," 68,400 "
-28035725,HC IMPL PLATE TIBIA DISTAL 7HOL 3.5MM," 12,750 "
-28035729,HC IMPL ACIS 5MM," 14,738 "
-28035733,HC IMPL FEMORAL HEAD BIOLOX," 8,063 "
-28035740,HC IMPL PLATE CALCANEAL LEFT 2.4MM," 8,475 "
-28035742,HC IMPL SCREW CORTEX 2.7MM X 32MM, 352 
-28035747,HC IMPL SCREW LOCKING 2.0 X 28MM," 1,097 "
-28035749,HC IMPL SCREW LOCKING 2.0 X 30MM," 1,097 "
-28035768,HC IMPL SCREW LAG 4X45MM," 5,857 "
-28035771,HC IMPL FEMORAL HEAD 00-8770-032-04," 3,188 "
-28035772,HC IMPL L-PLATE RIGHT X-SHORT," 1,469 "
-28035773,HC IMPL CAGE ALIF PEEK 3728-2910," 33,600 "
-28035774,HC IMPL SCREW 7.4 40 PS14-7540," 7,013 "
-28035775,HC IMPL ROD STRAIGHT 200 3611," 2,125 "
-28035777,HC IMPL SCREW LOCKING 3X18MM," 2,355 "
-28035778,HC IMPL SCREW LOCKING 3X12MM," 2,355 "
-28035792,HC IMPL SCREW PEDICLE REVERE 5.5 X 45," 7,013 "
-28035793,HC IMPL SCREW PEDICLE REVERE 5.0 X 45," 7,013 "
-28035794,HC IMPL SCREW PEDICLE REVERE 6.5 X 45," 7,013 "
-28035795,HC IMPL SCREW PEDICLE REVERE 8.5 X 40," 7,013 "
-28035798,HC IMPL ROD STRAIGHT 5.5MM X 400MM," 2,125 "
-28035801,HC IMPL SCREW ILIAC 8.5 X 60MM," 8,805 "
-28035809,HC IMPL CLIP ANEURYSM 6.5 CURVED 5.5," 2,678 "
-28035810,HC IMPL CAGE PEEK AEGIS 3610-2610," 25,200 "
-28035811,HC IMPL POSTERIOR CHAMBER BICONVEX IOL," 1,275 "
-28035812,HC IMPL POSTERIOR CHAMBER BICONVEX IOL," 1,275 "
-28035813,HC IMPL POSTERIOR CHAMBER BICONVEX IOL," 1,275 "
-28035814,HC IMPL POSTERIOR CHAMBER BICONVEX IOL," 1,275 "
-28035816,HC IMPL LINER ZMR 8758-009-28REV," 18,405 "
-28035817,HC IMPL LINER ZMR 8752-008-32 REV," 8,531 "
-28035818,HC IMPL STEM OSS BIOMET 150369," 11,250 "
-28035819,HC IMPL PLATE ANKL ANT.FUSHN 3HOL," 11,400 "
-28035820,HC IMPL FEMUR PERSONA CEMENTED SZ9," 16,980 "
-28035823,HC IMPL SCREW PERI-LOK CRTX 3.5X40MM, 166 
-28035825,HC IMPL SCREW PERI-LOK CRTX 3.5X48MM, 166 
-28035827,HC IMPL SCREW MAS 7.5 X 80MM," 7,863 "
-28035828,HC IMPL SCREW PERI-LOK CANC 4X34MM, 219 
-28035829,HC IMPL SCREW PERI-LOK CANC 4X40MM, 219 
-28035830,HC IMPL SCREW MAS 6.5 X 45MM," 7,863 "
-28035831,HC IMPL SCREW MAS 6.5 X 480MM," 7,863 "
-28035832,HC IMPL SCREW MAS 5.5 X 35MM," 7,863 "
-28035833,HC IMPL SCREW MAS 5.5 X 40MM," 7,863 "
-28035834,HC IMPL SCREW MAS 5.5 X 45MM," 7,863 "
-28035835,HC IMPL SET SCREW MEDTRONIC, 850 
-28035836,HC IMPL ROD SS 5.5 X 500MM," 6,435 "
-28035837,HC IMPL PLATE AVIATOR 39MM," 11,014 "
-28035838,HC IMPL SCREW STRYKER 4.35 X 14MM," 2,832 "
-28035839,HC DRILL BIT 14MM," 3,017 "
-28035841,HC IMPL SOL STEM 8/12 1572-02-121," 46,671 "
-28026351,HC  BLLN CTH ATLAS GOLD OTW 12X4," 2,890 "
-28026353,HC NANOPASS REACH 45 L," 2,465 "
-28035681,HC IMPL WEIGHT EYELID PLATINUM 1.4GM," 3,825 "
-28035689,HC IMPL COCHLEAR CONCERT FLEX28," 98,000 "
-28035690,HC IMPL COCHLEAR CONCERT FLEX24," 98,000 "
-28035692,HC IMPL COCHLEAR CONCERT COMPRESD," 98,000 "
-28035693,HC IMPL COCHLEAR CONCERT MEDIUM," 98,000 "
-28035694,HC IMPL COCHLEAR SONATA COMPRESD," 90,500 "
-28035695,HC IMPL COCHLEAR SONATA MEDIUM," 90,500 "
-28035696,HC IMPL COCHLEAR PULSAR STD," 90,500 "
-28035697,HC IMPL COCHLEAR PULSAR COMPRESD," 90,500 "
-28035698,HC IMPL COCHLEAR PULSAR SPLIT," 90,500 "
-28035699,HC PROCESSR COCHLER SNGL ANTH," 57,850 "
-28035700,HC PROCESSR COCHLER SNGL BEIG," 57,850 "
-28035701,HC PROCESSR COCHLER SNGL GRAY," 57,850 "
-28035702,HC PROCESR COCHLR SNGL PAC BL," 57,850 "
-28035703,HC COCLR PROCESR SNGL SEN BR," 57,850 "
-28035705,HC COCLR PROCESR SNGL BORD RD," 57,850 "
-28035706,HC COCLR PROCESR SNGL EBNY," 57,850 "
-28035707,HC COCLR PROCESR SNGL WHITE," 57,850 "
-28035709,HC COCLR PROCESR SNG BABY BLU KIT," 57,850 "
-28035710,HC COCLR PROCESR SNG BABY PNK," 57,850 "
-28035711,HC COCLR PROCESR SNGL GREEN," 57,850 "
-28035712,HC COCLR PROCESR SNGL ORNG," 57,850 "
-28035713,HC COCLR PROCESR DBL ANTHRCIT," 68,400 "
-28035715,HC COCLR PROCESR DBL SIEN BRN KIT," 68,400 "
-28035716,HC COCLR PROCESR DBL BORDU RD," 68,400 "
-28035717,HC COCLR PROCESR DBL EBONY," 68,400 "
-28035718,HC COCLR PROCESR DBL WHITE KIT," 68,400 "
-28035720,HC COCLR PROCESR DBL BABY BLU," 68,400 "
-28035722,HC COCLR PROCESR DBL GREEN," 68,400 "
-28035723,HC COCLR PROCESR DBL ORNG," 68,400 "
-28035724,HC IMPL SLF TPNG STARDRIVE 2.0X20MM, 153 
-28035726,HC IMPL WASHER SLEEVE 15.5X13.0MM, 211 
-28035727,HC IMPL 2.0MM 7 HOLE LCP PLATE," 2,371 "
-28035728,HC IMPL SLF TPNG STARDRIVE  2.0X20MM," 1,097 "
-28035730,HC HYBRIDKNIFE T TYPE ERBE," 17,115 "
-28035731,HC IMPL COMPONENT MODULAR HEAD 28MM," 4,250 "
-28035732,HC HYBRID I TYPE ERBE," 17,115 "
-28035734,HC IMPL SHELL TRABECULAR METAL 80MM," 12,300 "
-28035735,HC IMPL LINER NEUTRAL CONTINUUM 40MM," 5,950 "
-28035736,HC PROCESSOR RONDO ANTHRACITE," 57,850 "
-28035737,HC PROCESSOR RONDO NORD GRAY," 57,850 "
-28035738,HC PROCESSOR RONDO CREME," 57,850 "
-28035739,HC PROCESSOR RONDO CREME," 57,850 "
-28035741,HC PROCSOR RONDO DUAL ANTHACT," 68,400 "
-28035743,HC PROCSOR RONDO DUAL NOR GRA," 68,400 "
-28035744,HC PROCSOR RONDO DUAL EBONY," 68,400 "
-28035745,HC IMPL SCREW LOCKING 2.0 X 26MM," 1,097 "
-28035746,HC PROCSOR RONDO DUAL CREME," 68,400 "
-28035748,HC PROCSOR COMBO RONDO ANTHRA," 68,400 "
-28035750,HC PROCSOR COMBO RONDO N GRAY," 68,400 "
-28035751,HC PROCSOR COMBO RONDO EBONY," 68,400 "
-28035752,HC PROCSOR COMBO RONDO CREME," 68,400 "
-28035753,HC PROCSOR COMBO OPUS ANTRACI," 68,400 "
-28035754,HC PROCSOR COMBO OPUS BEIGE," 6,840 "
-28035755,HC PROCSOR COMBO OPUS N-GRAY," 68,400 "
-28035756,HC PROCSOR COMBO OPUS PAC BLU," 68,400 "
-28035757,HC PROCSOR COMBO OPUS S-BRN," 68,400 "
-28035758,HC PROCSOR COMBO OPUS BORD-RD," 68,400 "
-28035759,HC PROCSOR COMBO OPUS EBONY," 68,400 "
-28035760,HC PROCSOR COMBO OPUS WHITE," 68,400 "
-28035761,HC PROCSOR COMBO OPUS CREME," 68,400 "
-28035762,HC PROCSOR COMBO OPUS BBY BLU," 68,400 "
-28035763,HC PROCSOR COMBO OPUS BBY PNK," 68,400 "
-28035764,HC PROCSOR COMBO OPUS GREEN," 68,400 "
-28035765,HC PROCSOR COMBO OPUS ORNGE," 68,400 "
-28035766,HC IMPL X-POST 6.6MMX25MM," 9,938 "
-28035767,HC IMPL X-POST 6.6MMX20MM," 9,938 "
-28035770,HC GUIDE WIRE 1.6MM, 111 
-28035776,HC IMPL LINK TRANSVERSE 50/70 4206," 7,225 "
-28035779,HC K-WIRE 1.2X100MM, 425 
-28035780,HC PROCESSOR COCHLEAR MAISE," 41,763 "
-28035781,HC PROCESSOR COCHLEAR CP910 MOCHA," 41,763 "
-28035782,HC PROCESSOR COCHLEAR CP910 SMOKE," 41,763 "
-28035783,HC PROCESSOR COCHLEAR CP910 CARBN," 41,762 "
-28035784,HC PROCESSOR COCHLEAR CP920 MAISE," 57,850 "
-28035785,HC PROCESSOR COCHLEAR CP920 MOCHA," 41,763 "
-28035786,HC PROCESSOR COCHLEAR CP920 SMOKE," 41,763 "
-28035787,HC PROCESSOR COCHLEAR CP920 CARBN," 41,763 "
-28035788,HC IMPL CONNECTOR S BY S 5.5MM," 12,300 "
-28035790,HC IMPL ADATER TAPER BIOLOX," 8,500 "
-28035791,HC IMPL HEAD CERAMIC BIOLOX 32MM," 8,500 "
-28035797,HC IMPL CAP LOCKING REVERE, 850 
-28035799,HC IMPL CROSS CONNECTOR REVERE 5.5MM," 7,514 "
-28035800,HC IMPL CROSS CONNECTOR REVERE 5.5MM," 7,514 "
-28035802,HC IMPL ROD PRE-BENT 85MM," 2,125 "
-28035803,HC IMPL SCREW POLYAXIAL 5.5 X 50MM," 8,438 "
-28035804,HC IMPL SCREW POLYAXIAL 5.5 X 40MM," 8,438 "
-28035805,HC IMPL ROD LORDOTIC PRE BENT 150MM," 8,033 "
-28035806,HC IMPL STENT OVATION PRIME ILIAC LEG," 28,350 "
-28035807,HC IMPL FEMUR PERSONA SIZE 7," 16,980 "
-28035808,HC IMPL ARTICULAR SURFACE 11MM H," 5,814 "
-28035843,HC IMPL FAST-FIX TFCC," 4,888 "
-28035844,HC IMPL LEAD TRIAL 50CM," 13,613 "
-28035845,HC INTRODUCER SHEATH  BOS SCIENTIFIC, 765 
-28035846,HC IMPL INTRAOSSEOUS FIXATION," 12,150 "
-28035847,HC IMPL INTRAOSSEOUS 2.2MM," 12,150 "
-28035848,HC IMPL MED INTRAOSSEOUS," 12,150 "
-28035849,HC IMPL INTRAOSSEOUS 2.4MM," 12,150 "
-28035850,HC IMPL INTRAOS MED 2.2MM," 12,150 "
-28035851,HC IMPL INTRAOSSEOUS LARGE," 12,150 "
-28035852,HC IMPL 2.9MM INTRAOSSEOUS," 12,150 "
-28035853,HC IMPL 2.9MM INTRAOSS MED," 12,150 "
-28035855,HC IMPL AMINOGRAFT 1.5 X 1.0 CM," 4,760 "
-28035857,HC IMPL AMINOGRAFT 2.5 X 2.0 CM," 6,545 "
-28035859,HC IMPL AMINOGRAFT 2.0 X 1.5 CM," 5,695 "
-28035864,HC IMPL WIRE GUIDE 1.1MM, 162 
-28035865,HC IMPL WIRE GUIDE 1.4MM, 162 
-28035866,HC IMPL WIRE GUIDE 1.6MM, 162 
-28035867,HC COAGRASPER OLYMPUS LONG," 2,151 "
-28035868,HC IMPL LNR ACTBLR 26X56 122036556REV," 14,261 "
-28035869,HC IMPL PENILE 14X20," 42,686 "
-28035870,HC IMPL LINK SPINAL BIOMET 75318," 2,127 "
-28035871,HC IMPL LINK SPNAL BIOMET 18 X 9 75209," 2,127 "
-28035872,HC IMPL PLATE CERVICAL L35 AEGIS," 8,500 "
-28025648,HC IMPL BUTTON CERCLAGE," 1,539 "
-28025657,HC CATH ANGPHC TIP 5FR 0.038IN, 119 
-28025670,HC IMPL CLIP SUGITA 07 940 18," 2,651 "
-28025673,HC IMPL CLIP TII 10.5 STR 10MM," 2,678 "
-28025675,HC IMPL CLIP TII 9.5 BENT 8MM," 2,678 "
-28025681,HC IMPL CLIP TII 11.5 STRAIGHT 12MM," 2,678 "
-28025686,HC IMPL CLIP TII 13.5 STRAIGHT 15MM," 2,678 "
-28025699,HC IMPL CLIP TII 12.0 FEN 5.0 STRAIGHT," 2,678 "
-28025718,HC IMPL CLIP TII TEMP 9.0 STRAIGHT 7MM," 2,678 "
-28025719,HC IMPL CLIP TII TEMP 10.5 STRAIGHT 10," 2,678 "
-28025722,HC IMPL CLIP TII TEMP 11.0 SLIGHT CURV," 2,678 "
-28025732,HC IMPL CLIP TII 10.0 SLIGHTLY CURVED," 2,678 "
-28025735,HC IMPL CLIP TII MINI 5.5 STRAIGHT 4MM," 2,678 "
-28025736,HC IMPL CLIP TII MINI 7.0 STRAIGHT 6MM," 2,678 "
-28025737,HC IMPL CLIP TII MINI 6.5 CURVED 5MM," 2,678 "
-28025741,HC IMPL CLIP TII MINI 6.0 CURVED 5MM," 2,678 "
-28025744,HC IMPL CLIP TII MINI 5.5 STRAIGHT SLI," 2,550 "
-28025746,HC IMPL CLIP TII MINI 7.0 CURVED SLIM," 2,678 "
-28025748,HC IMPL CLIP TII MINI 6.5 CURVED SLIM," 2,678 "
-28025749,HC IMPL CLIP TII MINI 8.0 STRAIGHT 8MM," 2,678 "
-28025750,HC IMPL CLIP TII MINI 7.0 CURVED 6.5MM," 2,678 "
-28025752,HC IMPL CLIP TII MINI 8.5 SIDEWARD BEN," 2,678 "
-28025792,HC IMPL HEAD FEM 12/14 TPRD 28X+1.5MM," 7,208 "
-28025793,HC IMPL HEAD FEM 12/14 TPRD 28MM +5MM," 7,208 "
-28025795,HC IMPL HEAD FEM THS CO-CR 32MM +1MM," 7,208 "
-28025800,HC IMPL INSRT TIB RP STBLZD 4 10MM," 8,745 "
-28025801,HC FORCEPS SPELZER MALIS 1.0MMTIP," 3,305 "
-28025823,HC INSLTD STR FORCEP JEWELER," 2,941 "
-28025829,HC IMPL CLIP ANEUR YSRGL STR 5MM," 2,933 "
-28025830,HC IMPL CLIP ANEUR YSRGL STR 7MM," 2,933 "
-28025831,HC COAGULATOR TISSUE PLASMAJET," 4,208 "
-28025863,HC CUTTER FLIP 7MM," 2,763 "
-28025864,HC CUTTER FLIP 8MM," 2,763 "
-28025865,HC CUTTER FLIP 9MM," 2,763 "
-28025871,HC IMPL BISEPS DISTAL," 7,225 "
-28025872,HC IMPL CAP BLOCKING 48551000, 969 
-28025892,HC PACK STRYKER DISP. BLADES," 3,528 "
-28025896,HC MILD DIS. INSTRUMENTS," 17,850 "
-28025902,HC  BLLN CATH MUSTANG OTW12X40C135," 2,125 "
-28025903,HC  BLLN CATH MUSTANG OTW8X80X135," 2,125 "
-28025913,HC IMPL GLIDEWIRE HYDRPHLC 260X3CM 0.035IN, 424 
-28026017,HC IMPL EXPRESS BIL STENT 9X37X75," 11,883 "
-28026065,HC APPLICATOR HYDROJET," 2,508 "
-28026084,HC IMPL MICROCLIP GEME, 88 
-28026085,HC IMPL CLIP ANEURYSM 20-1803 2MMBLADE," 4,063 "
-28026086,HC IMPL CLIP ANEURYSM 20-1805 4MMBLADE," 3,927 "
-28026087,HC IMPL CLIP SUGITA 07-940-79," 2,651 "
-28026279,HC SHUNT EDWARDS BARBARO," 7,361 "
-28026288,HC PACK VITRECT CONSTELLATION 23G," 4,573 "
-28026383,HC  CATH GDNG LEAD STR 45CM 7FR," 1,700 "
-28026384,HC  CATH GDNG STR 7FR 50CM ATTAIN," 1,700 "
-28026387,HC IMPL CUSTOM PERICARDIOCENTESIS TRAY, 719 
-28026388,HC IMPL TRANSJGLR ACCESS SET," 5,167 "
-28026392,HC  CATH DIALYSIS 14.5FR X 19CM ST," 2,890 "
-28026394,HC  CATH EQUISTREAM 14.5FR X 35CM," 3,902 "
-28026397,HC IMPL CTH CONNECTOR CBLE BMC 10F," 1,275 "
-28026398,HC IMPL ANGIOGR CANITER FLTR TUB PENUMBRA SYS," 3,145 "
-28026401,HC GUIDW COUGAR 0.014X190, 986 
-28026402,HC IMPL STNT GRFT JOSTENT OTW 3.0X19," 17,963 "
-28026403,HC IMPL STNT GRFT JOSTENT OTW 3.5X19," 17,963 "
-28026404,HC IMPL STNT GRFT JOSTENT OTW 4.0X19," 17,963 "
-28026405,HC IMPL CTH DIAG IMPULSE 5F AR1 SNGLE, 75 
-28026406,HC IMPL CTH DIAG IMPULSE 5F FL5, 75 
-28026407,HC IMPL STNT BILI RX HERCULINK ELITE 5X12," 10,125 "
-28026408,HC IMPL STNT BLILI RX HERCULINK ELITE 4X15," 10,125 "
-28026409,HC IMPL STNT BILI RX HERCULINK ELITE 5X15," 10,125 "
-28026410,HC IMPL STNT BILI RX HERCULINK ELITE 6X15," 10,125 "
-28026411,HC IMPL STNT BILI RX HERCULINK ELITE 7X15," 10,125 "
-28026412,HC IMPL STNT BILI RX HERCULINK ELITE 5X18," 10,125 "
-28026413,HC IMPL STNT BILI RX HERCULINK ELITE 5X12," 10,125 "
-28026414,HC IMPL STNT BILI RX HERCULINK ELITE 4X15," 10,125 "
-28026415,HC IMPL STNT BILI RX HERCULINK ELITE 6X15," 10,125 "
-28026416,HC IMPL STENT TTS THRU THE SCP ESPGL18X6," 10,450 "
-28026417,HC IMPL STENT TTS THRU THE SCP ESPGL18X8," 10,450 "
-28026418,HC IMPL STENT TTS THRU THE SCP ESPGL 18X10," 10,450 "
-28026419,HC IMPL STENT TTS THRU THE SCP ESPGL18X12," 10,450 "
-28026420,HC IMPL STENT TTS THRU THE SCP ESPGL18X14," 10,450 "
-28026421,HC IMPL STENT TTS THRU THE SCP ESPGL18X15," 10,450 "
-28026422,HC IMPL STENT TTS THRU THE SCP ESPGL18X6," 10,450 "
-28026423,HC IMPL STENT TTS THRU THE SCP ESPGL18X8," 10,450 "
-28026424,HC IMPL STENT TTS THRU THE SCP ESPGL1810," 10,450 "
-28026425,HC IMPL STENT TTS THRU THE SCP ESPGL18X12," 10,450 "
-28026426,HC IMPL STENT TTS THRU THE SCP ESPGL18X14," 10,450 "
-28026427,HC IMPL STENT TTS THRU THE SCP ESPGL18X15," 10,450 "
-28026435,HC IMPL STNT ZENITH FLX AAA 24X82," 54,977 "
-28026436,HC IMPL INTRO SHEA PINNACLE 7F, 77 
-28026440,HC  CTH  ABLT LASER TURBOR ELITE 2.5X112," 20,965 "
-28026442,HC IMPL ICD SNGL CHMB DF4 CNNCTR ELLIPSE VR36," 65,000 "
-28026443,HC  CTH EQUISTREAM W/INTROD 14.5X31," 4,650 "
-28026445,HC GUIDW GLIDEWIRE .035X180, 400 
-28026446,HC GUIDW GLIDEWIRE .035X150, 281 
-28026447,HC GUIDW GLIDEWIRE  FLX 0.035X180X3, 400 
-28035680,HC HTA CONSOLE USE FEE," 2,125 "
-28025672,HC IMPL CLIP TII 8.5 STR 7MM," 2,678 "
-28025806,HC D RX OMNIPAQUE VILE 240MG, 25 
-28026386,HC IMPL AVATEX BONE CEMENT 13GA," 2,627 "
-28026395,HC IMPL INTRO FLEXOR TUOHY-BORST 6X90," 1,063 "
-28026430,HC IMPL STENT BILIARY RX PLASTIC 7FRX12CM, 757 
-28035873,HC GUIDW GLIDEWIRE 0.035X260," 1,530 "
-28035874,HC IMPL NDLE BIOP SUPERCORE 20GAX15, 446 
-28035875,HC IMPL FLTR VENA CAVA JUG DEL7F 30X45," 8,075 "
-28035876,HC HANDPIECE HIP VAC 50," 3,825 "
-28035877,HC IMPL INTRO PEEL-AWAY 7F, 697 
-28035878,HC IMPL STEM RADIAL STRAIGHT 8MM," 15,525 "
-28035879,HC IMPL RADIAL HEAD COCR 20MM," 15,525 "
-28035880,HC IMPL PLATE CERVICAL AEGIS L39," 9,000 "
-28035881,HC IMPL SCREW FIXED AEGIS 3.5 X 16," 2,338 "
-28035882,HC IMPL PLATE 2 LEVEL STRYKER 34MM," 10,470 "
-28035883,HC IMPL PLATE 2 LEVEL STRYKER 32MM," 10,470 "
-28035884,HC IMPL NAIL TROCH CANN 10MM X 320MM," 15,068 "
-28035885,HC IMPL SCREW FA 4.0 X 16MM," 7,920 "
-28035886,HC IMPL PLATE OC 37MM JJ," 22,764 "
-28035887,HC IMPL PLATE PROXL TIBIA 3.5MM 185MM," 12,735 "
-28035888,HC IMPL PLATE FUSION MEDIAL COLMN SML," 14,835 "
-28035889,HC IMPL SCREW LOKNG 3.5X2.0," 1,760 "
-28035890,HC IMPL SCREW LOKNG 3.5X22," 1,760 "
-28035891,HC IMPL SCREW LOKNG 3.5X30," 1,760 "
-28035892,HC IMPL SCREW LOKNG 3.5X34," 1,760 "
-28035893,HC IMPL SCREW LOKNG 3.5X40," 1,760 "
-28035894,HC IMPL SCREW NON-LOKNG 3.5X24, 910 
-28035895,HC IMPL SCREW LINSFRNC SOLID 4.5X48," 8,279 "
-28035896,HC IMPL SCREW LINSFRNC SOLID 4.5X50," 8,279 "
-28035898,HC IMPL SCREW VALOR WRIGHT 5X45MM," 2,015 "
-28035899,HC IMPL SCREW VALOR WRIGHT 5X40MM," 2,015 "
-28035900,HC IMPL SCREW VALOR WRIGHT 5X90MM," 2,015 "
-28035901,HC IMPL SCREW VALOR WRIGHT 5X85MM," 2,015 "
-28035902,HC IMPL NAIL VALOR MED RT 10X200MM," 19,467 "
-28035903,HC IMPL NAIL END CAP VALOR WRIGHT," 1,998 "
-28035904,HC IMPL SCREW VALOR NAIL WRIGHT 5X35MM," 2,015 "
-28035905,HC IMPL DRIL BIT LONG WRIGHT 4.3MM," 2,015 "
-28035906,HC IMPL LEAD SNS MEDTRONIC 28CM," 22,995 "
-28035907,HC IMPL PENILE 12 X 20 720074-03," 42,686 "
-28035908,HC GUIDW UNIGLIDE GLDWR 035X260, 213 
-28035910,HC IMPL LINER ACET BIOMET EP-105894," 12,000 "
-28035911,HC IMPL LINER ACET BIOMET EP-105895," 12,000 "
-28035913,HC IMPL TIBIA STEMMED PERSONA," 7,710 "
-28035914,HC IMPL FEMUR CEMENTED PERSONA," 16,980 "
-28035916,HC IMPL FEMORAL HEAD CERAMIC C-TAPER," 21,848 "
-28035918,HC IMPL HEAD FEMORAL," 13,815 "
-28035919,HC IMPL SCREW OC 4.5X 16," 2,601 "
-28035920,HC IMPL PLATE OC 37MM," 22,764 "
-28035921,HC IMPL WASHER OC," 6,477 "
-28035922,HC IMPL ROD PREBENT 3.5X 120," 2,984 "
-28035923,HC IMPL SCREW 4.0X 18MM," 7,920 "
-28035924,HC IMPL PLATE CONDYLAR CURVED 4.5MM," 15,518 "
-28035925,HC IMPL SCREW MATRIX POLY 5.5X 25," 9,840 "
-28035926,HC IMPL FEMORAL HEAD 35MM 0MM," 16,118 "
-28035928,HC IMPL CAGE 11X22MMX6DEG," 34,566 "
-28035929,HC IMPL SCREW VUEPOINT II 4.OX 18MM," 8,798 "
-28035930,HC IMPL SCREW VUEPOINT II 4.0X 20MM," 8,798 "
-28035931,HC IMPL SCREW VUEPOINT II 4.5X 26MM," 9,323 "
-28035932,HC IMPL SCREW VUEPOINT II 4.5X 28MM," 9,323 "
-28035933,HC IMPL SCREW VUEPOINT II 4.5X 30MM," 9,323 "
-28035934,HC IMPL SCREW VUEPOINT II 4.5X 24MM," 9,323 "
-28035935,HC IMPL SCREW PRECEPT SHANK 7.5X 40MM," 8,271 "
-28035936,HC IMPL SCREW REDUCTION 6.5X 40MM," 10,125 "
-28035937,HC IMPL ROD PREBENT LORDOTIC 95MM," 7,786 "
-28035938,HC IMPL ROD PREBENT LORDOTIC 90MM," 7,786 "
-28035939,HC IMPL CAGE COROENT XL 14X22X55MM," 48,191 "
-28035940,HC IMPL SCREW REDUCTION 7.5X 45MM," 10,125 "
-28035941,HC IMPL SCREW SPNL 7X45 1867-31-745," 11,385 "
-28035942,HC IMPL SCREW ZMR 7X75 1147-075-71," 2,199 "
-28035948,HC IMPL SCREW STRKR 4X14 48552414," 8,075 "
-28035949,HC IMPL ROD SPINE 25MM 48552025," 2,125 "
-28035950,HC  BLLN CTH EVERCROSS 5X200X135," 1,964 "
-28035951,HC IMPL TRUNION ECLIPSE 53MM," 13,781 "
-28035952,HC IMPL SCREW HOLLOW ECLIPSE MED," 13,781 "
-28035953,HC IMPL HUMERAL HEAD ECLIPSE 53/22," 13,125 "
-28035954,HC IMPL NAIL FEMORAL 12MMX 380MM," 14,110 "
-28035955,HC IMPL BLADE SPIRAL FOR NAILS 75MM," 5,304 "
-28035956,HC IMPL NAIL FEMORAL 10MMX 380MM," 14,110 "
-28035957,HC IMPL SCREW LOCKING 5.0X 80MM," 1,709 "
-28035958,HC IMPL NAIL TIBIAL 11X 345MM," 12,353 "
-28035959,HC IMPL NAIL TIBIAL 10X 360MM," 12,353 "
-28035961,HC IMPL SCREW LAG 10.5X 105MM," 6,027 "
-28035962,HC IMPL NAIL LONG 10X 380MM," 20,671 "
-28035963,HC IMPL NAIL LONG 10X 340MM KIT," 20,673 "
-28035965,HC IMPL TIBIAL REVISION TRAY," 31,613 "
-28035966,HC IMPL SLEEVE FEMORAL UNIVERSAL," 19,929 "
-28035967,HC IMPL  SLEEVE MBI 37MM TRAY," 18,617 "
-28035968,HC IMPL SCREW EXP 7.0X 55MM," 8,843 "
-28035969,HC IMPL SCREW EXP 7.5X 55MM," 8,843 "
-28035970,HC IMPL SCREW EXP 7.5X 45MM," 8,843 "
-28035971,HC IMPL FEMORAL HEAD 32MM," 7,806 "
-28035972,HC IMPL LINER 36MM," 10,481 "
-28035973,HC IMPL SHELL 62MM," 14,966 "
-28035974,HC IMPL LINER CONTINUUM 36MM," 17,640 "
-28035975,HC IMPL PLATE TIBIA 4.5X 14H 262MM," 13,163 "
-28035976,HC IMPL SCREW LEVERAGE 2.6X 5MM," 1,488 "
-28035977,HC IMPL SCREW GSB 6.5X 50MM," 8,500 "
-28035978,HC IMPL SCREW GSB 6.5X 45MM," 8,500 "
-28035979,HC IMPL SCREW LOCK GSB CADDY 5.5MM, 850 
-28035980,HC IMPL ROD LORDOTIC GSB 5.5X 40MM," 2,125 "
-28035981,HC IMPL CAGE COROENT XL WIDE 8X22X55MM," 32,956 "
-28035982,HC IMPL SCREW LOCKING 3.5X 20MM, 901 
-28035983,HC IMPL SCREW EXP 7.0X 40MM," 8,843 "
-28035984,HC IMPL SCREW CORTICAL 5.0X 45MM," 11,385 "
-28035985,HC IMPL SCREW CORTICAL 6.0X 40MM," 11,385 "
-28035986,HC TEM PLATE ROD," 1,700 "
-28035987,HC IMPL CAGE 15X27MMX 6 DEG," 43,401 "
-28035990,HC IMPL SCREW NON LOCKING 1.5X11, 628 
-28035991,HC IMPL SCREW CANNULATED 3.75X32MM," 1,700 "
-28035992,HC IMPL SCREW CANNULATED 3.75X34MM," 1,700 "
-28035993,HC DRILL GUIDE PARALLEL2.75MM," 3,613 "
-28035994,HC IMPL PLATE BOW 3MM," 10,980 "
-28035995,HC IMPL SCREW NON LOCKING 3.5X14MM, 910 
-28035996,HC IMPL SCREW NON LOCLING 3.5X22MM, 910 
-28035997,HC IMPL SCREW NON LOCKING 3.5X14M," 1,760 "
-28035998,HC IMPL SCREW LOCKING 3.5X20MM," 1,760 "
-28035999,HC IMPL SCREW SNAP OFF 2.0X12MM," 2,057 "
-28036001,HC PIN FIXATION 1.4X5MM, 706 
-28036002,HC IMPL SCREW NON LOCLING 3.0X14MM, 578 
-28036003,HC IMPL PLATE CURVED 4.5MM 20H 372MM," 10,851 "
-28036004,HC IMPL CROSSLINK STRYR SPINE 48551071," 7,225 "
-28036006,HC IMPL PLATE DISTAL FIBULA LOCKING," 5,525 "
-28036007,HC IMPL SCREW ZMR 7.0 X 80 1147-080-70," 2,193 "
-28036008,HC IMPL PLATE HUMERUS 2.7/3.5 7H 147MM," 9,803 "
-28036009,HC IMPL SCREW LOCKING 4.0X 18MM," 1,709 "
-28036011,HC IMPL PLATE RECON ZMR 3.5X82 7 HOLE," 3,251 "
-28036012,HC IMPL SCREW LOCKING 4.0X 20MM," 1,709 "
-28036013,HC IMPL SCREW LP LOCKING SS 2.7X12MM, 893 
-28036014,HC DRILL BIT 4.5X 135MM," 3,267 "
-28036015,HC IMPL SCREW LP LOCKING SS 2.7X16MM, 893 
-28036016,HC IMPL SCREW LP LOCKING SS 2.7X18MM, 893 
-28036017,HC IMPL STIM VAGAL NRVE SNGL PIN 105," 79,233 "
-28036018,HC IMPL SCREW CANCELLOUS THREAD 3X20MM, 298 
-28036019,HC IMPL SCREW LP LOCKING SS 3.5X14MM, 935 
-28036020,HC IMPL LINER CONTINUUM 36MM," 5,950 "
-28026498,HC IMPL SEALANT PLEURAL AIR LEAK PROGEL," 7,225 "
-28026499,HC  ELECTRODE EMG NEEDLE," 7,845 "
-28026502,HC FORCEPS BIOPSY ENDOSCOPIC OVAL CUP 3," 2,805 "
-28026507,HC SPECIAL ELECT COAG TISSUE PALATE," 2,329 "
-28026522,HC OVESCO CLIP SYSTEM 165 CM," 4,242 "
-28026525,HC IMPL THERASPHERE 3GBQ RADIOACTV PARTICLES," 90,000 "
-28026526,HC IMPL BILI STNT ZILVER 14X80X80," 8,213 "
-28026527,HC IMPL STNT ILIAC ZILVER SLF EXPNDING 6X30X125," 8,213 "
-28026528,HC GUIDW FLX ANGLD TIP 0.035X80X3, 349 
-28026529,HC IMPL CTH DIAG IMPULSE 5F FR3.5, 77 
-28026530,HC IMPL CTH DIAG IMPULSE 5F PIG 145 110CM, 77 
-28026532,HC IMPL CTH DIALYS EQUISTREAM 14.5 X27 KIT," 2,720 "
-28026533,HC IMPL ADPTR SEAL EXTNDD WING SAFESHEATH 9F, 374 
-28026534,HC IMPL TUB BUFFALO FILTER 7/8X10FT, 68 
-28026535,HC  BLLN CTH THERMODUILUTION 7X110X4," 1,301 "
-28026536,HC  BLLN CTH DILAT STERLING OTW 9X20X80," 3,409 "
-28026537,HC IMPL CTH EKOSONIC MACH 4 ENDO DEVICE 40X106X0.035," 18,865 "
-28026538,HC IMPL FDNG MIC-KEY TRNSGSTRIC JEJUNAL 18X1.7X30," 2,661 "
-28026539,HC IMPL YTTRIUM-90 L108," 90,000 "
-28026540,HC IMPL STNT OVATION PRIM ABDOMINL AAA," 5,738 "
-28026541,HC  GUIDEWIRE ANGIOGRAPHY 2 CM STRAIGHT," 3,868 "
-28026542,HC  TUBE GJ 14FR 1.0CMX22CM," 3,400 "
-28026545,HC IML BILIARY STENTZLIVER 635 8MMXCM," 10,928 "
-28026546,HC IMPL BILIARY STENTZLIVER 634 8MMX8CM," 10,928 "
-28026551,HC IMPL CLIP SYSTEM OTSC 12/6P-165," 4,072 "
-28026552,HC IMPL NEEDLE EXPECT FNA 19G SLIMLINE," 1,785 "
-28026553,HC TUBING ASPIRATION REPERFUSION CATH PUMP PNMR," 3,145 "
-28026554,HC IMPL CTH DIALY VAXCEL PLUS 14.5X9," 4,208 "
-28026556,HC  INTROD W HAWKINS NDL APRIMA ACCSS, 135 
-28036021,HC IMPL SCREW SHANK 6.5X50 8896550," 8,271 "
-28036022,HC IMPL PLATE SPNL 21MM 7200021," 7,875 "
-28036023,HC IMPL POST CHAMBER BICONVEX IOL 7.0," 1,275 "
-28036024,HC IMPL POST CHAMBER BICONVEX IOL 6.5," 1,275 "
-28036025,HC IMPL POST CHAMBER BICONVEX IOL 7.5," 1,275 "
-28036026,HC IMPL POST CHAMBER BICONVEX IOL 8.0," 1,275 "
-28036027,HC IMPL POST CHAMBER BICONVEX IOL 8.5," 1,275 "
-28036028,HC IMPL POST CHAMBER BICONVEX IOL 9.0," 1,275 "
-28036029,HC IMPL PLATE PER. PROX LCP 3 HOLE 3.5," 13,230 "
-28036030,HC IMPL POST CHAMBER BICONVEX IOL 9.5," 1,275 "
-28036031,HC IMPL POST CHAMBER BICONVEX IOL 10.0," 1,275 "
-28036032,HC IMPL POST CHAMBER BICONVEX IOL 10.5," 1,275 "
-28036033,HC IMPL POST CHAMBER BICONVEX IOL 11.0," 1,275 "
-28036034,HC IMPL POST CHAMBER BICONVEX IOL 11.5," 1,275 "
-28036035,HC IMPL POST CHAMBER BICONVEX IOL 12.0," 1,275 "
-28036036,HC IMPL POST CHAMBER BICONVEX IOL 12.5," 1,275 "
-28036037,HC IMPL POST CHAMBER BICONVEX IOL 13.0," 1,275 "
-28036038,HC IMPL POST CHAMBER BICONVEX IOL 13.5," 1,275 "
-28036039,HC IMPL POST CHAMBER BICONVEX IOL 14.0," 1,275 "
-28036040,HC IMPL POST CHAMBER BICONVEX IOL 14.5," 1,275 "
-28036041,HC IMPL POST CHAMBER BICONVEX IOL 15.0," 1,275 "
-28036042,HC IMPL POST CHAMBER BICONVEX IOL 15.5," 1,275 "
-28036043,HC IMPL CHAMBER POST BICONVEX IOL 16.0," 1,275 "
-28036044,HC IMPL CHAMBER POST BICONVEX IOL 16.5," 1,275 "
-28036045,HC IMPL CHAMBER POST BICONVEX IOL 17.0," 1,275 "
-28036046,HC IMPL CHAMBER POST BICONVEX IOL 17.5," 1,275 "
-28036047,HC IMPL CHAMBER POST BICONVEX IOL 18.0," 1,275 "
-28036048,HC IMPL CHAMBER POST BICONVEX IOL 18.5," 1,275 "
-28036049,HC IMPL CHAMBER POST BICONVEX IOL 22.0," 1,275 "
-28036050,HC IMPL CHAMBER POST BICONVEX IOL 22.5," 1,275 "
-28036051,HC IMPL CHAMBER POST BICONVEX IOL 23.0," 1,275 "
-28036052,HC IMPL CHAMBER POST BICONVEX IOL 24.0," 1,275 "
-28036053,HC IMPL CHAMBER POST BICONVEX IOL 24.5," 1,275 "
-28036054,HC IMPL CHAMBER POST BICONVEX IOL 25.0," 1,275 "
-28036055,HC IMPL HUMERAL HEAD 44X15 1130-44-600," 18,466 "
-28026345,HC ADAPTER JET SWIVEL," 1,080 "
-28026344,HC CATHETER LASER JET 40 CM," 1,913 "
-28035909,HC IMPL ART SURFACE ZIMMR 12 MM SZ E," 5,814 "
-28035912,HC IMPL PUTTY ALLOMATRIX INJECTABLE 5C," 6,044 "
-28035915,HC IMP LARTICULAR SURFACE PERSONA," 5,814 "
-28035917,HC IMPL GTR W/CABLES," 15,720 "
-28035927,HC IMPL LINK 36X 3," 2,125 "
-28035964,HC REAMER SHAFT MOD TRINKLE," 5,224 "
-28025638,HC CUTTER SLT INSTRU BOX 8MM," 5,330 "
-28025645,HC IMPL GUIDE FLEXIBLE GD FLXIBL," 2,933 "
-28025658,HC EXTENDER TIP 120MM XTNDR TIP," 5,627 "
-28025663,HC IMPL LINK 21MM STANDARD FXTN STD 21MM," 2,125 "
-28025756,HC  INTRO CATH CPLV5.025 CATH IV, 935 
-28025768,HC FIBER BEAMPATH NEURO-L," 11,250 "
-28025790,HC IMPL HIP FEMORAL HEAD 28MM +5," 7,208 "
-28025799,HC IMPL CMPNT FEM SGMNTL RT 7CM," 38,864 "
-28025812,HC BLADE CURVED HARMONIC SYNERGY," 2,168 "
-28025815,HC ENDO GIA ARTICULATING 60 VASCU," 3,103 "
-28025822,HC ACTIVATION INSTRUMENT 1.7MM," 2,431 "
-28025836,HC IMPL SLING URETHRAL ANTERIOR," 17,325 "
-28025842,HC GUIDE WIRE VIPER 2 SHARP," 1,955 "
-28025844,HC IMPL INSERT EVOLUTION," 5,100 "
-28025862,HC IMPL LEAD 60CM LEAD 60CM," 11,213 "
-28025883,HC BLADE MTRXMNDBL THORAX," 4,327 "
-28025888,HC IMPL BAND MTRXMNDBL PRE-BNT TENSION," 2,984 "
-28025889,HC IMPL BAND MTRXMNDBL PRE-BNT TENSION," 2,984 "
-28025890,HC IMPL SQUARE TRQ DEFINE SCR DRV EQUINOXE," 4,012 "
-28025898,HC IMPL BUTTON BONE DOG," 2,975 "
-28025900,HC BIT DRILL 4.3MM X 200MM," 3,383 "
-28025911,HC COLL BONE MARROW SYS COLCTN," 2,227 "
-28025953,HC FORCEPS GRASPING 3.0FR X 90CM," 2,250 "
-28025970,HC DRILL SLEEVE DBL 3.5MM/2.5MM," 2,992 "
-28026003,HC HANDPIECE SELECTOR MICRO 24," 6,027 "
-28026006,HC IMPL MORCELLATOR TISS MORCELLEX," 6,273 "
-28026024,HC APPLICATOR FALOPE RING," 15,300 "
-28026041,HC CHAIR BACK LOW PROFILE," 5,236 "
-28026046,HC PACK LNG TABLE W/PUMP HEAD," 4,250 "
-28026051,HC LASER RENTAL," 7,225 "
-28026082,HC PACK CSTM CARDIOPLEGIA ADL 4/P," 5,100 "
-28026289,HC PLATFORM GELPOINT MINI," 4,038 "
-28026356,HC PROGRAMMER PATIENT 97740," 8,458 "
-28026358,HC PIN THREADED DEPUY," 2,304 "
-28026370,HC IMPL HIP LINER CONTINUUM," 5,950 "
-28026371,HC IMPL HIP SHELL CONTINUUM," 13,940 "
-28026372,HC IMPL HIP LINER PINNACLE," 5,406 "
-28026377,HC IMPL FEMUR PERSONA," 16,980 "
-28026378,HC IMP PATELLA PERSONA," 2,754 "
-28026380,HC IMPL SCREW FEMALE PERSONA 25MM, 808 
-28026381,HC IMPL SCREW FEMALE PERSONA 48MM, 808 
-28026494,HC IMPL HIP SHELL CONTINUUM," 18,060 "
-28026495,HC PIN THREADED DEPUY," 2,414 "
-28026496,HC IMPL PLATE SYNTHES 447.036," 2,363 "
-28026564,HC IMPL SCREW THREADED 50MM, 315 
-28026565,HC IMPL PUTTY BONE DBX 5CC," 7,863 "
-28026566,HC IMPL HIP SHELL PINNACLE 54MM," 9,540 "
-28026567,HC IMPL STIMULATOR VAGUS NERVE," 72,020 "
-28026568,HC IMPL SCREW SET SCREW AEGIS, 850 
-28026569,HC FIBER LASER HOLMIUM," 3,587 "
-28026570,HC IMPL PIN PLATE HOLDING," 1,488 "
-28026572,HC IMPL SET SCREW INNER," 1,811 "
-28026573,HC K SHUNT PLACEMENT," 5,483 "
-28026574,HC IMPL SCREW TORQUE RVRS," 3,638 "
-28026577,HC IMPL K-WIRE BEVEL TIP PRECEPT," 1,275 "
-28026578,HC IMPL SCREW SHANK 6.5 X 45MM," 8,271 "
-28026579,HC IMPL CAP LOCKING MATRIX," 1,700 "
-28026580,HC IMPL SCREW VARIABLE 3.5 X 14MM," 2,338 "
-28026581,HC IMPL CAGE CERVICAL 14X17X7," 8,250 "
-28026582,HC IMPL STENT AERO TRACH 16X60," 16,875 "
-28026583,HC IMPL VALVE SYSTEM IBV 7MM," 19,250 "
-28026584,HC IMPL CAGE ALIF PEEK," 33,600 "
-28026585,HC IMPL CAGE ALIF PEEK AEGIS," 33,600 "
-28026586,HC IMPL CAGE CERVICAL PEEK," 8,250 "
-28026587,HC IMPL SCREW SET 6107-7037, 850 
-28026588,HC IMPL SCREW CERVICAL 3.5MM," 7,225 "
-28026589,HC IMPL SCREW CERVICAL 3.5MM," 7,225 "
-28026594,HC IMPL LINER ACET ALTRX," 5,406 "
-28026595,HC IMPL LINER ACET ALTRX 36MM," 5,406 "
-28026596,HC PROGRAMMER PATIENT 37651," 11,250 "
-28026597,HC IMPL SCREW MEDT 3120314," 2,338 "
-28026598,HC DRILL BIT MEDT 7080510," 2,584 "
-28026599,HC IMPL CAGE UNILIF AVS," 31,661 "
-28026601,HC IMPL CAGE UNILIF 48395100," 38,587 "
-28026602,HC IMPL SCREW STRYKER 48552314," 8,075 "
-28026603,HC IMPL SCREW STRYKER 48552418," 8,075 "
-28026604,HC IMPL END CAP 04.001.000," 1,454 "
-28026605,HC IMPL END CAP 04.004.002," 2,027 "
-28026606,HC IMPL SCREW SELF DRILLING 5MM, 510 
-28026607,HC IMPL ACIS SPACER 7MM," 14,738 "
-28026608,HC IMPL ACIS SPACER 8MM," 14,738 "
-28026609,HC IMPL ACIS SPACER 9MM," 14,738 "
-28026610,HC IMPL NAIL FIXATION 456.357S," 15,068 "
-28026611,HC IMPL TIBIA ETPK-N4SR," 8,500 "
-28026612,HC IMPL PIN PACK WRIGHT MED," 2,423 "
-28026613,HC IMPL FEMORAL HEAD METASUL," 3,188 "
-28026614,HC IMPL FEMORAL HEAD METASUL," 3,188 "
-28026615,HC IMPL HIP LINER CONTINUUM," 5,950 "
-28026616,HC IMPL FEMORAL HEAD METASUL," 3,188 "
-28026617,HC IMPL HIP LINER CONTINUUM," 5,950 "
-28026618,HC IMPL FEMUR ZIMMER," 16,980 "
-28026619,HC IMPL FEMUR ZIMMER 4250," 16,980 "
-28026620,HC IMPL ARTICULAR SURFACE ZIMMER," 5,814 "
-28026621,HC IMPL ARTICULAR SURFACE ZIMMER," 5,814 "
-28026622,HC IMPL TIBIA 42532006701," 7,710 "
-28026623,HC IMPL TIBIA 42532006702," 7,710 "
-28026624,HC IMPL TIBIA 42532007102," 7,710 "
-28026625,HC IMPL TIBIA 42532007902," 7,710 "
-28026626,HC IMPL PATELLA 42540000029," 2,754 "
-28026627,HC IMPL PATELLA 42540000035," 2,754 "
-28026628,HC IMPL PATELLA 42540000038," 2,754 "
-28026629,HC IMPL RING ANNULOPLASTY 26MM," 12,750 "
-28026631,HC IMPL RING ANNULOPLASTY 30MM," 12,750 "
-28026632,HC IMPL RING ANNULOPLASTY 34MM," 12,750 "
-28026633,HC IMPL RING ANNULOPLASTY 36MM," 12,750 "
-28026634,HC IMPL RING ANNULOPLASTY 38MM," 12,750 "
-28026635,HC IMPL RING ANNULOPLASTY 40MM," 12,750 "
-28026636,HC IMPL VALVE MITRAL 27MM," 35,750 "
-28026637,HC IMPL INTRO SHEA AVANTI 8X11, 69 
-28026638,HC IMPL CTH DIAG SUPER TORQUE 6X110, 69 
-28026640,HC IMPL SHTH NVASCLR 30CM 12FR, 374 
-28026641,HC GUIDW ENTEER STNDRD 0.014X300," 3,358 "
-28026643,HC  CATH DIAGNSTC 100CM 5FR, 111 
-28026644,HC IMPL SCREW LG CANNULATED 16MM X 50," 2,355 "
-28026645,HC IMPL SCR DOME CANC 6.5X35MM, 901 
-28026649,HC IMPL SHTH BRITE TIP INTERVENTIONAL 5X55, 336 
-28026650,HC IMPL CTH DIAG PERFORMA INTERNL MAMMARY  6X100, 72 
-28026651,HC IMPL ELECTRODE TZ MULTI FUNCTION RADIOLUCENT W CONNECT ZOLL, 213 
-28026653,"HC IMPL  EMBLZTN PRTICLES EMBOZENE 700UM, 21ML SYRINGE"," 1,403 "
-28026654,HC  CTH LIVEWIRE DUO-DECA LARGE C REPROCESSED 7X95," 2,797 "
-28026655,HC  CTH LIVEWIRE DUO-DECA LARGE C REPROCESSED 6X115," 1,233 "
-28026562,HC IMPL PLATE PROXIMAL 8 H 2.7X5.5MM," 10,260 "
-28026674,HC BALLOON OCCLUSION 4X11X150," 8,250 "
-28026675,HC IMPL SCREW VARIABLE 3.5 X 16MM," 2,338 "
-28026676,HC IMPL PLATE CERVICAL AEGIS," 9,000 "
-28026677,HC IMPL SCREW 6.5 X 40 AEGIS," 7,013 "
-28026678,HC IMPL SCREW 6.5 X 50 AEGIS," 7,013 "
-28026679,HC IMPL SCREW 14-510645R," 8,250 "
-28026680,HC IMPL SCREW  14-510650R," 8,250 "
-28026681,HC IMPL LINER ACET ALTRX," 5,406 "
-28026682,HC IMPL LINER ACET ALTRX," 5,406 "
-28026683,HC IMPL CAGE UNILIF 48395090," 38,587 "
-28026684,HC IMPL SCREW STRYKER 48552318," 8,075 "
-28026685,HC TAP STRYKER 48560314," 2,703 "
-28026686,HC IMPL RING ANNULOPLASTY 28MM," 12,750 "
-28026687,HC IMPL RING ANNULOPLASTY 32MM," 12,750 "
-28026688,HC  CTH DELIV ATTAIN SELECT 2 SUREVLVE 65X130 CURV TP," 1,700 "
-28036056,HC IMPL SCREW HEADED DARTFIRE 3X2.0MM," 1,539 "
-28036057,HC IMPL AMINOGRAFT 5.0 X 5.0," 9,263 "
-28036058,HC TUNLING TOOL LONG BOS SCI 35CM," 2,550 "
-28036059,HC IMPL TIGHTROPE MINI CMC  1.1," 4,463 "
-28036060,HC IMPL LINER CONTINUUM 36MM," 5,950 "
-28036061,HC IMPL PLATE FEMUR DISTAL 13H 316MM," 14,325 "
-28036062,HC IMPL PLATE CONDYLAR 4.5MM 16H 350MM," 14,685 "
-28036063,HC IMPL INSERT POLY P4 32MM," 7,538 "
-28036064,HC  CTH VITESSE RX EXCIMER 6X135," 16,163 "
-28036066,HC GUIDW GLDWRE ADVNT 0.035X180," 1,360 "
-28036067,HC IMPL SCREW NON-LOKING T8 3X26MM," 1,615 "
-28036070,HC IMPL FEMUR PERSONA SIZE 7," 16,980 "
-28036071,HC IMPL ARTICULAR SURFACE PERSONA 12MM," 5,814 "
-28036072,HC IMPL PIN KNEE 45MM SMITH NEPHEW, 78 
-28036073,HC IMPL BASEPLATE TIBIA  SMITH NEPHEW," 7,515 "
-28036074,HC IMPL INSERT ARTICULAT 11MM 71453278," 8,205 "
-28036075,HC IMPL PATELLA SMITH NEPHEW RESURFACE," 3,596 "
-28036076,HC IMPL FEMORAL COMP SZ 4 SMITH NEPHEW," 15,525 "
-28036077,HC INST PAYNER ANGLE 5450 800 304," 7,793 "
-28036078,HC INST PAYNER STD STR LONG," 7,359 "
-28036079,HC IMPL SCREW POLYAXIAL 7.5 X 50MM," 8,500 "
-28036080,HC IMPL SCREW POLYAXIAL 8.5 X 45MM," 8,500 "
-28036081,HC IMPL SCREW POLYAXIAL 8.5 X 55MM," 13,823 "
-28036082,HC IMPL SCREW ILIAC SL 7.5 X 80MM," 8,805 "
-28036083,HC IMPL HOOK LAMINAR TRANSVERSE 8MM," 8,805 "
-28036084,HC IMPL ROD LORDOTIC PRECEPT 45MM," 7,353 "
-28036085,HC IMPL PLATE STRYKER 42MM," 11,014 "
-28036086,HC IMPL ENDCAP STRYKER SPINE 5'," 2,219 "
-28036087,HC IMPL INTERBODY FUSION DEVICE 15X27," 43,401 "
-28036088,HC IMPL ENDPLATE XRL MEDIUM 0'," 12,750 "
-28036089,HC IMPL XRL MED CENTRAL BODY 29MM-39MM," 47,450 "
-28036090,HC IMPL SPACER ORACLE 55MM X 22MM," 36,582 "
-28026692,HC ANGIOJET PUMP SET," 2,125 "
-28026693,HC  SHTH PEEL AWAY INTROD 12FR X 15.5CM, 324 
-28026694,HC IMPL SHTH STEERABLE AGILIS 8.5 X 71," 2,440 "
-28026696,HC IMPL DURATA DEFIB LEADS 65CM," 37,375 "
-28026697,HC IMPL ULTRA COIL 4MMX15CM," 12,413 "
-28026698,HC IMPL ULTRA COIL 5MMX15CM," 13,230 "
-28026699,HC IMPL ULTRA COIL 2MMX6CM," 12,488 "
-28026700,HC IMPL COIL TARGET 6MMX20CM," 12,488 "
-28026701,HC IMPL COIL TARGET 360 SOFT 6 X 20MM," 11,813 "
-28026702,HC IMPL COIL HYDROSOFT 8MM X 20CM," 13,181 "
-28026703,HC IMPL COIL HYDROSOFT 7MM X 20CM," 12,488 "
-28026705,HC IMPL STNT DRUG ELUT 2.75 X38 MM," 22,125 "
-28026708,HC IMPL VASC RETRIEVER 6FR X 90 CM," 5,398 "
-28026709,HC IMPL COIL HYDROFRAME 2 X 2CM," 15,750 "
-28026710,HC IMPL COIL TARGET 7 X 20CM," 16,463 "
-28026711,HC IMPL COIL TARGET 10 X 40CM," 16,463 "
-28026712,HC IMPL COIL TARGET 12 X 45CM," 16,463 "
-28026713,HC IMPL COIL TARGET 16 X 50," 16,463 "
-28026714,HC IMPL COIL TARGET HELICAL 8 X 30 CM," 16,463 "
-28026715,HC IMPL COIL TARGET 9 X 30 CM," 16,463 "
-28026716,HC IMPL COIL TARGET 22 X 50 CM," 16,463 "
-28026717,HC IMPL COIL TARGET XL 6 X 20 CM," 16,463 "
-28026718,HC  GSTRSTMY CATH 1LMN 14FR," 1,471 "
-28026720,HC IMPL LEADS ICD 59CM," 35,000 "
-28036091,HC IMPL CONNECTOR DR ICD DF4 ELLIPSE," 84,000 "
-28036092,HC GUIDW SAFET-T-J FXD 0.035X125, 264 
-28036093,HC IMPL CTH VENS 2 LUMEN HNDS OFF 7X30, 174 
-28036094,HC IMPL T-PLATE LCP 2.4MM 3H/7H," 6,979 "
-28036095,HC IMPL ENDOPROSTHESE BILRY VH808200," 15,488 "
-28036096,HC IMPL ENDOPROSTHESIS BILRY VH810200," 15,488 "
-28036097,HC IMPL SNT GRFT ENDURANT 16X24X93," 34,475 "
-28036098,HC  CTH BLLN ATLAS GOLD OTW 14X6," 2,890 "
-28036099,HC  CATH BLLN ATLAS GOLD OTW 12X6," 2,890 "
-28036102,HC IMPL INSERT TIBIAL LPS XXSMALL 12MM," 17,815 "
-28036103,HC TAP OC 4.5MM," 3,315 "
-28036104,HC TAP 3.0 ACROMED," 3,825 "
-28036107,HC IMPL CONNECTOR OFFSET ANGLED 20MM," 5,993 "
-28036108,HC IMPL LINER CONTINUUM TRILOGY 36MM," 5,950 "
-28036109,HC IMPL LINER OFFSET CONTINUUM 7MM," 10,481 "
-28036110,HC IMPL CAP TIBIAL BASE STEM ADVANCE," 6,579 "
-28036111,HC IMPL PATELLA NEXGEN SZ 26 REV," 5,423 "
-28036112,HC IMPL ARTICULAR SURFACE LEGACY 23MM," 11,146 "
-28036113,HC IMPL SCREW MULTI-AXIAL 3.5 X 16MM," 8,798 "
-28036114,HC IMPL ROD 120MM NUVASIVE," 2,652 "
-28036115,HC IMPL COMPONET SEGMENT KNEE DEPUY," 15,225 "
-28036116,HC IMPL FEMORAL COMP LEFT SM DEPUY," 66,717 "
-28036117,HC IMPL FEMORAL STEM DEPUY LPS 11X200," 34,052 "
-28036118,HC IMPL LINER HIP ZMR 8752-015-36REV," 10,481 "
-28036119,HC IMPL PLATE SUPRAPECTINEAL," 18,000 "
-28036120,HC LIGHT PIPE," 3,366 "
-28036121,HC BIT DRILL SCALED 2.5," 2,975 "
-28036122,HC SUCTION TIP," 3,613 "
-28036123,HC GUIDE WIRE 3.2, 918 
-28036124,HC IMPL PLATE HUMERUS SYNTH 6 H 163MM," 14,155 "
-28036125,HC IMPL PLATE HUMERUS SYNTH 6 H 199MM," 14,700 "
-28026726,HC IMPL RING ANNULOPLASTY 34MM," 15,450 "
-28026727,HC IMPL TIBIAL TRAY CEMENTED," 9,540 "
-28026728,HC IMPL VITOSS FOAM 5CC," 8,373 "
-28026731,HC IMPL SILICONE SHEET 5 X 5CM, 155 
-28026732,HC IMPL SILICONE SHEET 5 X .25MM, 155 
-28026733,HC IMPL BONE CEMENT/VOID FILLER," 2,753 "
-28026734,HC IMPL SHELL PINNACLE 48MM," 9,540 "
-28026735,HC GRASPER LAP ALGTR GBC25," 2,125 "
-28026737,HC IMPL PROGRAMMER PT INTERSTIM," 8,925 "
-28026738,HC IMPL HINGE MALE EX-FIX, 798 
-28026742,HC IMPL GEL RADIESSE INJ," 4,888 "
-28026745,HC IMPL CAGE COROENT 12X18X50," 30,387 "
-28026746,HC DILATOR XLIF," 6,222 "
-28026748,HC IMPL CLIP LAA HANDLE 40MM," 8,500 "
-28026749,HC IMPL STERNAL ZIPFIX, 612 
-28026750,HC IMPL WASHER SPACING EX-FIX, 64 
-28026752,HC DRILL REVERSE SHOULDER," 2,788 "
-28026753,HC IMPL BOLT 20MM EX-FIX, 35 
-28026754,HC IMPL WASHER SLOTTED EX-FIX, 139 
-28026758,HC IMPL PIN HA 40MM X 200MM," 1,437 "
-28026761,HC MORCELLATOR LINA CORDLESS," 5,780 "
-28026762,HC IMPL PLATE CERVICAL L21," 8,500 "
-28026763,HC IMPL CAGE ALIF PEEK," 33,600 "
-28026764,HC IMPL CAGE PEEK ALIF," 33,600 "
-28026766,HC RETRACTION SYSTEM," 2,746 "
-28026767,HC IMPL SLING DESARA," 11,178 "
-28026768,HC IMPL LINER PINNACLE," 5,406 "
-28026769,HC IMPL GLENOID 314-13-14," 16,425 "
-28026770,HC INSTRUMENTS STERILE," 8,500 "
-28026771,HC CLAW ENT SUPER LONG," 7,793 "
-28026773,HC TUBING IRRIGATION STRYKER," 2,910 "
-28026774,HC TAP STRYKER SPINE," 2,200 "
-28026775,HC IMPL SCREW 48552416," 8,075 "
-28026776,HC IMPL PIN TEMP FIXATION," 1,522 "
-28026777,HC IMPL PLATE 222.255S," 14,153 "
-28026778,HC IMPL LINER ZIMMER 8751-009-32," 5,950 "
-28026779,HC IMPL SHELL ZIMMER 8757-050-01," 12,300 "
-28026780,HC IMPL SHELL REV 8757-050-02," 18,060 "
-28026781,HC IMPL SHELL REV 8757-052-02," 18,060 "
-28026782,HC IMPL SHELL REV 8757-058-02," 18,060 "
-28026783,HC IMPL SHELL REV 8757-060-02," 18,060 "
-28026784,HC IMPL SHELL REV 8757-062-02," 18,060 "
-28026785,HC IMPL FEMORAL HEAD," 3,188 "
-28026786,HC IMPL PLATE RECON 1179-05-08," 3,341 "
-28026787,HC IMPL TIBIA 42-5320-071-01," 8,738 "
-28026788,HC IMPL TIBIA 42-5320-083-01," 8,738 "
-28026789,HC IMPL TIBIA 42-5320-083-02," 8,738 "
-28026790,HC IMPL PATELLA 42-5400-000-32," 2,754 "
-28026791,HC IMPL PATELLA 42-5400-000-26," 2,754 "
-28026792,HC IMPL EXTENSION STEM," 8,235 "
-28026793,HC IMPL PISTON EAR .6X4.75MM," 1,488 "
-28026794,HC IMPL SLING T-SLING," 7,863 "
-28026795,HC IMPL GRAFT ACUSEAL," 9,600 "
-28026796,HC IMPL COIL TARGET 9 X 30CM," 15,353 "
-28026797,HC IMPL COIL TARGET  5 X 15CM," 11,963 "
-28026798,HC IMPL COIL TARGET  7 X 20CM," 11,963 "
-28026799,HC IMPL COIL TARGET 18 X 50CM," 11,963 "
-28026800,HC IMPL COIL HYDROFRAME 2.5 X 4CM," 15,900 "
-28026801,HC IMPL TRUFILL SYRINGE," 1,870 "
-28026804,HC  CATH ANGIOPLASTY BALLADVANCE 6 X 8CM," 2,338 "
-28026805,HC IMPL COIL TARGET 5 X 20CM," 11,280 "
-28026806,HC IMPL COIL TARGET 4 X 15CM," 11,280 "
-28026807,HC CONTROLLER HEARMATE POCKET," 35,750 "
-28026808,HC IMPL HEAD FEM BHR 52MM," 28,259 "
-28026811,HC IMPL PASR SUT SWANSON 155X36MM LONG, 460 
-28026814,HC IMPL CUP ACETABULAR PINNACLE SECTOR II 48," 9,540 "
-28026815,HC IMPL KNOTLESS TIGHTROPE SS L30," 7,225 "
-28026817,HC SPECIAL HANDLE SCALPEL HEMOSTATIX," 2,544 "
-28026818,HC IMPL PISTON TIT EAR 0.6X4.75MM," 1,488 "
-28026822,HC IMPL SLEEVE VIPER INNER SCREW," 1,836 "
-28026825,HC EQUIP SUPPLY LASER RENTAL," 8,075 "
-28026826,HC  CATHETER ELECTROPHYSIOLOGY 10PLR STRBL115CM 6F," 1,234 "
-28036126,"HC  CTH GUID Z2 6F,EBU 3.5 CURVE", 451 
-28036127,HC IMPL PLATE TRI LOCK 5/4 HOLE 1.2/1.," 3,417 "
-28036128,HC IMPL STAPLE 9MM," 1,539 "
-28036129,HC IMPL HEAD HUMERAL LEFT MTF," 34,398 "
-28036130,HC IMPL FEMUR CEMENTED PERSONA L SZ10," 16,980 "
-28036131,HC IMPL ARTICULAR SURFACE PERSONA SZ10," 5,814 "
-28036132,HC IMPL SURE SCAN TRIAL," 7,650 "
-28036133,HC IMPL SCREW MUC 7.0 WRIGHT 45X16MM," 7,149 "
-28036135,HC IMPL SCREW LISFRANC 3.7X46MM," 8,279 "
-28036137,HC IMPL GUIDE WIRE W/TROCAR TIP .045, 128 
-28036138,HC REAMER PHALANGEAL 14MM," 3,825 "
-28036139,HC IMPL PLATE MTP CNTURD LOW PROFIL," 6,800 "
-28036140,HC IMPL SCREW TI CORT LO PROFIL 3X16MM, 935 
-28036141,HC IMPL SCREW TI CORT LOW PROFL3X18MM, 935 
-28036206,HC IMPL PLATE NUVAS HELIX 36MM," 9,000 "
-28036207,HC IMPL CAGE MEDTRONIC SOFAMO11X22X12," 34,566 "
-28036208,HC IMPL CAGE MEDTRONIC SOFMOR 14X27X12," 43,401 "
-28036209,HC IMPL LINER ZMR TRILOGY REV 32 X 60," 9,389 "
-28036210,HC IMPL HEAD FEMORAL ZMR 36MM REV," 13,695 "
-28036212,HC WIRE SUPPORTRCK FNSHG WR 90CM," 1,190 "
-28036213,HC IMPL CTH ACUITY BRKAWAY GUID 8X49," 3,783 "
-28036214,HC IMPL CTH ACUITY BRKAWAY GUID 6X68," 1,658 "
-28036215,HC IMPL ICD CRT D QUADRA ASSURA DF4 CONNCTR," 84,000 "
-28026829,HC IMPL SCREW CORTICAL 1.5 X 12MM, 628 
-28026834,HC IMPL COIL TARGET 8 X 30CM," 11,813 "
-28026835,HC IMPL COIL TARGET 3 X 10CM," 12,338 "
-28026836,HC IMPL COIL HYPERSOFT 1.5 X 1CM," 7,421 "
-28026837,HC IMPL COIL HYPERSOFT 1.5 X 4CM," 8,245 "
-28026838,HC IMPL COIL HYPERSOFT 1.5 X 3CM," 7,421 "
-28026839,HC IMPL COIL TARGET 4 X 8CM," 8,460 "
-28026840,HC IMPL COIL TARGET 3 X 4CM," 8,460 "
-28026841,HC IMPL COIL TARGET 3 X 6CM," 8,460 "
-28026842,HC IMPL COIL DELTAPLUSH 3 X 4CM," 7,965 "
-28026843,HC IMPL COIL DELTAPLUSH 4 X 8CM," 7,965 "
-28026844,HC IMPL STNT HERCULINK ELITE 4 X 18MM," 9,750 "
-28026845,HC GUID SHEA FLEXOR 7 X 70CM, 604 
-28026846,HC IMPL OSTE-SITE BONE BIOP 11 X 10CM, 366 
-28026847,HC BLLN CATH TRANSFORM 4 X 7MM," 10,538 "
-28026849,HC GUID SHEA FLEXOR 6 X 70CM, 604 
-28026850,HC IMPL COIL HYPERSOFT 1.5 X 2CM," 7,421 "
-28026851,HC IMPL COIL TARGET 4 X 15CM," 8,438 "
-28026852,HC IMPL COIL TARGET 4 X 6CM," 8,460 "
-28026853,HC IMPL AVAMAX BONE BIOP 13GA X 12CM, 893 
-28026855,HC IMPL COIL TARGET 7 X 30CM," 11,813 "
-28026856,HC DECANAV CATH 7 X 115CM," 7,763 "
-28026862,HC NEURON MAC DELV CATH 6 X 80CM," 4,208 "
-28026871,HC AMPLATZ GUIDW STRAIGHT 0.35 X 80CM, 162 
-28026880,HC OCCL BLLN CTH TRANSFORM 3X5," 10,538 "
-28026883,HC TERUMO TORQUE DEVICE GUIDW 014 X .038IN, 68 
-28026890,HC CATH EXTEN GOOK GUID," 1,700 "
-28026900,HC CATH OCCL BLN TRNSFRM SC 3X5," 10,538 "
-28036143,HC IMPL CARTRIDGE MEDCL GRD CO2, 255 
-28036144,HC IMPL ALISTRAP X-WIDE 5X30, 969 
-28036145,HC PASSER SUTURE NOVASTITCH DIS," 7,650 "
-28036147,HC CTH BLLN ADVNCE 14LP 2X4X4X170," 2,763 "
-28036148,HC CTH BLLN ADVNCE 14LP 2X8X4X170," 2,763 "
-28036149,HC IMPL STEM HIP FEMORAL SIZE 16," 21,000 "
-28036150,HC CTH BLLN ADVNCE14LP 2X12X4X170," 2,763 "
-28036151,HC CTH BLLN ADVNCE14LP 2X16X4X170," 2,763 "
-28036152,HC CTH BLLN ADVNCE14LP 2.5X4X4F," 2,763 "
-28036153,HC CTH BLLN ADVANCE14LP 2.5X8X4F," 2,763 "
-28036154,HC CTH BLLN ADVANCE14LP 2.5X12X4F," 2,763 "
-28036155,HC CTH BLLN ADVANCE14LP 2.5X16X4F," 2,763 "
-28036156,HC CTH BLLN ADVANCE14LP 2.5X20X4F," 2,763 "
-28036157,HC CTH BLLN ADVANCE14LP 3X4X14F," 2,763 "
-28036158,HC CTH BLLN ADVANCE14LP 3X8X14F," 2,763 "
-28036159,HC CTH BLLN ADVANCE14LP 3X12X14F," 2,763 "
-28036160,HC CTH BLLN ADVANCE14LP 3X16X14F," 2,763 "
-28036161,HC CTH BLLN ADVANCE14LP 3X20X14F," 2,763 "
-28036162,HC CTH BLLN ADVANCE14LP 4X4X4F," 2,763 "
-28036163,HC IMPL NAIL FIXATION 10MM X 340MM," 15,068 "
-28036164,HC CTH BLLN ADVANCE14LP 4X8X4F," 2,763 "
-28036165,HC CTH BLLN ADVANCE14LP 4X12X4F," 2,763 "
-28036166,HC CTH BLLN ADVANCE14LP 4X16X4F," 2,763 "
-28036167,HC CTH BLLN ADVANCE14LP 4X20X4F," 2,763 "
-28036168,HC IMPL INTROD PEELABLE OPTISEAL 8X25," 3,103 "
-28036169,HC IMPL SCREW NON-BIAS 3.5 X 18MM," 8,075 "
-28036170,HC IMPL PLATE 51MM AVIATOR STRYKER," 11,014 "
-28036171,HC IMPL SCREW 4.35 X 12MM STRYKER," 2,831 "
-28036172,HC IMPL NAIL TIBIAL CANN 11MM X 390MM," 12,359 "
-28036173,HC IMPL SCREW LOCKING 5.0 X 26MM," 1,709 "
-28036174,HC IMPL PLATE CLAVICLE HOOK 3.5 LCP," 7,707 "
-28036175,HC IMPL PLATE PROX FEMUR HOOK 4.5 LCP," 16,572 "
-28036176,HC IMPL PIN STEINMAN 5.0MM X 200MM," 1,241 "
-28036177,HC IMPL SCREW SHANZ SELF DRILL 4.0," 1,391 "
-28036178,HC IMPL SCREW CANN 7.0 X 70MM," 2,199 "
-28036179,HC IMPL WASHER FOR CANN SCREW 5.5-7.5, 357 
-28036180,HC IMPL GUIDE PIN TROCAR TIP THREADED," 1,063 "
-28036181,"HC IMPL FEMORAL BODY REV ""ZMR"" SZ C"," 21,266 "
-28036182,HC IMPL HEAD FEMORAL BIOLOX CERAMIC," 12,165 "
-28036183,HC IMPL PLATE TRIAL ULNAR RT X-SM ZIMR," 4,250 "
-28036184,HC IMPL SCREW CANN TRIMED 3X30MM," 3,358 "
-28036185,HC IMPL SCREW CANN TRIMED 3X22MM," 3,358 "
-28036186,HC IMPL PLATE BOOMERANG TRIMED 1.6MM," 10,125 "
-28036187,HC IMPL SCREW NON-LOK TRIMED 07-27620," 1,105 "
-28036188,HC IMPL SCREW NON-LOK TRIMED 2.7X18MM," 1,105 "
-28036189,HC IMPL SCREW NON-LOK TRIMED 2.7X16MM," 1,105 "
-28036191,HC IMPL COUNTERSINK TRIMED 3.0," 1,020 "
-28036192,HC IMPL REAMER PHALANGEAL ARTHREX 16MM," 3,825 "
-28036193,HC IMPL ARTICULAR SURFACE SIZE 3 14MM," 4,250 "
-28036194,HC BLADE EXPLANT SIZE 46 10," 7,875 "
-28036195,HC BLADE EXPLANT SIZE 46 20," 7,875 "
-28036196,HC IMPL SCREW 4.0 X 20MM," 8,075 "
-28036197,HC IMPL SCREW 4.X X 20MM," 8,075 "
-28036198,HC IMPL SCREW ILIAC BOLT 8.5 X 80MM," 8,805 "
-28036199,HC IMPL PLATE DECADE XLIF SZ8 4HOLE," 35,815 "
-28036200,HC IMPL BOLT DECADE LG 6.5 X 50," 10,320 "
-28036201,HC IMPL BOLT DECADE SMALL 5.5 X 40," 10,320 "
-28036202,HC IMPL CAGE COROENT PARALLEL 8X26X50," 48,841 "
-28036203,HC IMPL LEAD NEUROSTIM 1X8 MEDTRONIC," 15,750 "
-28036204,HC IMPL ARTICULAR SURFACE LPS-FLEX 10M," 8,738 "
-28026904,HC CATH DIAG CARTO  10FT," 3,400 "
-28026905,HC CARTO HYPERTRONICS CABLE 10FT," 3,400 "
-28026906,HC CARTO DIAG CABLE 10FT," 3,400 "
-28026907,HC CATH  2.7 X 135CM," 6,375 "
-28026908,HC MICROCATHETER ECHELON  1.7M X 150CM," 5,967 "
-28026909,HC INTROD CHECK-FLO 11 X 75CM, 791 
-28026910,HC IMPL ZILVER DELV STNT 10 X 80MM," 8,213 "
-28026911,HC CATH  ABLATION SPACING 7 X 115CM," 8,250 "
-28026913,HC MICROCATHETER HEADWAY  150CM," 6,205 "
-28026914,HC CATH CENTRAL VENOUS DUAL LUM 5FR, 243 
-28026915,HC CARTO DIAG CABLE 10FT," 3,400 "
-28026925,HC GUIDW 0.035IN X 260CM, 424 
-28026926,HC IMPL LEAD DURATA DEFIB 65CM," 35,750 "
-28026927,HC IMPL STNT GRFT EXCLUDER AAA DELIV SYST 28.5X14.5X18," 64,968 "
-28026928,HC IMPL STNT GRFT EXCLUDER AAA DELIV SYST 28.5X14.5X12," 64,968 "
-28026930,HC IMPL PIN TEMP FIX SPINAL," 1,292 "
-28026931,HC DURASEAL EXACT SPINE," 6,010 "
-28026932,HC IMPL K-WIRE SMOOTH STEINMAN, 332 
-28026933,HC IMPL K-WIRE STRYKER, 221 
-28026935,HC RESTRICTOR CEMENT," 2,890 "
-28026936,HC IMPL HEAD FEMORAL TAPER METASUL 32 X 3.5 MM," 3,188 "
-28026937,HC IMPL VENTRICULAR ASSIST DEVICE," 116,769 "
-28026938,HC IMPL CAGE WIDE XL 50MM," 32,956 "
-28026939,HC IMPL CAGE WIDE XL 55MM," 32,956 "
-28026940,HC IMPL CAGE WIDE XL 60MM," 32,956 "
-28026942,HC IMPL HIP LINER ACETABULAR PINNACLE NEUTRAL 36," 5,406 "
-28026943,HC IMPL HIP SHELL ACETABULAR CLUSTER CNT GG 48," 12,300 "
-28026944,HC IMPL HIP SHELL ACETABULAR CLUSTER CONTINUUM SZ LL 58 MM," 12,300 "
-28026945,HC IMPL HIP SHELL ACETABULAR CLUSTER CONTINUUM SZ MM 60 MM," 12,300 "
-28026946,HC SUPER CERCLAGE 1MM," 3,868 "
-28026947,HC IMPL SHELL ACETABULAR CONTINUUM SZ II 52," 18,067 "
-28026948,HC IMPL ACETAB LINER CONT VIV-E KK 36," 21,952 "
-28026949,HC IMPL CLIP ANEURYSM 7.0," 2,550 "
-28026950,HC IMPL CLIP ANEURYSM 12.5," 2,678 "
-28026951,HC IMPL CLIP ANEURYSM 13.0," 2,550 "
-28026952,HC IMPL CLIP ANEURYSM 11.5," 2,550 "
-28026953,HC IMPL CLIP ANEURYSM 9.5," 2,550 "
-28026954,HC IMPL CLIP ANEURYSM 7.0," 2,550 "
-28026955,HC IMPL CLIP ANEURYSM BENT," 2,550 "
-28026956,HC IMPL CLIP ANEURYSM 9MM," 2,933 "
-28026958,HC ENSEAL TISSUE ENSEAL G2 5 MM X 35 CM," 4,480 "
-28026962,HC IMPL VIABIL BILIARY STNT 10 X 8CM," 18,130 "
-28026963,HC  CATH TRELLIS INFUSION 6 X 120CM," 15,713 "
-28026964,HC  FEEDING TUBE 16 X 1.2CM," 2,661 "
-28026965,HC  MICKEY FEDDING TUBE 18 X 2.0CM," 1,360 "
-28026966,HC CATH BILIARY DRAIN  24FR, 706 
-28026967,HC CATH BILIARY DRAIN  20FR, 850 
-28026968,HC IMPL PARTICLES EMBO BEAD 70-150UM," 18,865 "
-28026969,HC INTRODUCER CHECK-FLO  PERFORMER, 952 
-28036244,HC IMPL PLATE AVIATOR STRYKER 40MM," 10,476 "
-28036245,HC IMPL ROD STRAIGHT AEGIS 160MM," 2,125 "
-28036246,HC IMPL PLATE FUSION VA-LCP 2.4/2.7MM," 8,131 "
-28036247,HC IMPL FEMUR PERSONA CEMENTED SIZE 10," 16,980 "
-28036248,HC IMPL ARTICULAR SURFACE PERSONA 10MM," 5,814 "
-28036249,HC IMPL LINER CONTINUUM 36MM," 10,485 "
-28036250,HC IMPL SCREW CORTEX SYNTHES 2.0 X 34, 341 
-28036251,HC IMPL SCREW CORTEX SYNTHES 2.0 X 38, 341 
-28036252,HC IMPL PLATE TIBIA SYNTHES 3.5 X 12H," 12,758 "
-28036253,HC IMPL STEM SOL DEPUY 10/15 LRG," 47,369 "
-28036255,HC IMPL HEAD HUMERAL SHDLR ZMR 45-16," 14,310 "
-28036256,HC IMPL STEM HUMERAL ZMR SHDLR SZ7," 24,759 "
-28036257,HC IMPL ENDOPROSTHESIS BILRY STENT 10X40," 15,488 "
-28036258,HC IMPL SCREW LOCKING 2.7X14MM, 901 
-28036259,HC IMPL PLATE OLECTANON STND 5 HOLE RI," 6,413 "
-28036260,HC IMPL SCREW LOCKING 2.7 X 24MM, 901 
-28036261,HC IMPL SCREW LOCKING 2.7 X 16MM, 901 
-28036262,HC IMPL SCREW LOCKING 2.7 X 30MM, 901 
-28036263,HC IMPL SCREW NON LOCKING 3.5 X 32, 578 
-28036264,HC IMPL SCREW NON LOCKING 3.5 X 22MM, 578 
-28036265,HC IMPL SCREW NON LOCKING 3.5 X 36MM, 901 
-28036266,HC IMPL SCREW NON LOCKING 3.5 X 34MM, 901 
-28036267,HC IMPL SCREW LOCKING 3.0 X 55MM, 901 
-28036268,HC CATH SUPREME EP HEXAPOLAR 4X120, 765 
-28036270,HC IMPL EXPANDR BRST ALRGAN 133MX-11-T," 8,963 "
-28036271,HC IMPL EXPANDR BRST ALRGAN 133MX-12-T," 8,963 "
-28036272,HC IMPL EXPANDR BRST ALRGAN 133MX-13-T," 8,963 "
-28036273,HC CATH BLLN VENOGRAM, 935 
-28036277,HC IMPL SCREW LISFRANC WRIGHT 3.7X44MM," 8,279 "
-28036278,HC IMPL BREAST IMPLANT SIENTRA 300CC," 7,608 "
-28036279,HC IMPL BREAST IMPLANT SIENTRA 320CC," 7,608 "
-28036280,HC IMPL BREAST IMPLANT SIENTRA 350CC," 7,608 "
-28036281,HC IMPL HUMERUS MONOBLOC DELTA XTEND," 44,928 "
-28036282,HC IMPL SPACER HUMERAL DELTA XTEND 9MM," 16,418 "
-28036283,HC IMPL CUP HUMERAL STD DELTA XTEND," 12,535 "
-28036284,HC IMPL METAGLENE CEMENTLESS DELTA," 16,133 "
-28036285,HC IMPL GLENOSHERE DELTA XTEND  42MM," 21,483 "
-28036286,HC IMPL SCREW LOCKING DELTA 4.5 X 36," 2,976 "
-28036287,HC IMPL SCREW LOCKING DELTA 4.5 X 42," 2,976 "
-28036288,HC IMPL SCREW LOCKING DELTA 4.5 X 48," 2,796 "
-28036289,HC GUIDE PIN METAGLENE 2.5MM, 842 
-28036291,HC IMPL CUP ACETABULAR DYSPLA 58MM," 36,075 "
-28036293,HC IMPL SCREW 40MM BHR 74500040, 587 
-28036296,HC IMPL SCREW 24 MM BHR 74500024, 587 
-28036297,HC IMPL SCREW 32MM BHR 74500032, 587 
-28036301,HC IMPL ROD HUNTER TENDON 2MMX24.5CM," 8,790 "
-28036302,HC IMPL ROD HUNTER TENDON 3MM X 24.5CM," 8,790 "
-28036303,HC IMPL ROD HUNTER TENDON 4MM X 24.5CM," 8,790 "
-28036304,HC IMPL ROD TENDON HUNTER 6MM X 24.5CM," 8,790 "
-28036305,HC IMPL PLATE CLAW II 4HOLE WRIGHT30MM," 14,085 "
-28036306,HC IMPL SCREW HEADLESS WRIGHT 4.3X55MM," 2,593 "
-28036307,HC IMPL SCREW HEADLES 7.0 WRIGHT 55X16," 7,149 "
-28036308,HC IMPL SCREW LOCKING WRIGHT 3.5X36MM," 1,777 "
-28036309,HC IMPL SCREW LOCKING WRIGHT 3.5X38MM," 1,777 "
-28026972,HC HANDLE SHAW KNIFE ELECTROSURGICAL," 2,546 "
-28036311,HC IMPL SCREW CANCLUS LO-PROFIL 3X32MM," 1,105 "
-28036313,HC IMPL SCREW DARTFIRE WRIGHT 3X18MM," 1,539 "
-28036314,HC IMPL SCREW DARTFIRE WRIGHT 3X20MM," 1,539 "
-28036315,HC IMPL RING ACETABULAR LOCKING DURALOC 58/70MM," 2,423 "
-28036316,HC IMPL LINER DURALOC MARATHON 36X58MM," 14,258 "
-28036317,HC IMPL HEAD FEMORAL M-SPEC METAL ON M," 10,215 "
-28036318,HC IMPL LEAD PCMKR BPLR OPTISENSE 52CM," 4,675 "
-28036319,HC HEAD REAMER 12.5MM," 3,145 "
-28036320,HC IMPL HEAD EVOLVE 20MM," 12,045 "
-28036323,HC BLADE SPIRAL TI FOR NAIL 52MM," 5,579 "
-28036324,HC IMPL SCREW LOCKING 4.0 X 30MM," 1,742 "
-28036325,HC IMPL SCREW LOCKING 4.0MM X 28MM," 1,742 "
-28036326,HC IMPL PLATE OLECRANON 2.7/3.5MM 2H," 9,336 "
-28036327,HC IMPL SCREW LOCKING 2.7MM X 48MM," 1,315 "
-28036329,HC IMPL ROD 100MM," 3,375 "
-28036330,HC IMPL STENT ADVANIX DBLE PIGTAIL 7X3, 442 
-28036331,HC IMPL STENT ADVANIX DBLE PIGTAIL 7X5, 442 
-28036332,HC IMPL SCREW PERI 3.5X26MM W/2.7 HEAD, 354 
-28036333,HC IMPL SCREW PERI 3.5X28MM W/2.7 HEAD, 354 
-28036334,HC IMPL SCREW PERI 3.5X45MM W/2.7 HEAD, 354 
-28036335,HC IMPL PLATE ULNA PROXIMAL DORSAL 2H," 7,421 "
-28036336,HC IMPL PLATE LATERAL PROXIMAL 3.5 4H," 7,616 "
-28036337,HC IMPL SCREW PERI 3.5X54MM W/2.7 HEA, 354 
-28036338,HC IMPL SCREW PERI 3.5X65MM W/2.7 HEAD, 354 
-28036339,HC IMPL SCREW PERI 3.5X70MM W/2.7 HEAD, 354 
-28036340,HC IMPL SCREW PERI 3.5X75MM W/2.7 HEAD, 354 
-28026984,HC BONE FIXATN-GUIDEWIRE KIRSCHNER 1.6X65MM, 85 
-28036216,HC IMPL STNT GRFT ENDURANT 2 28X13X166," 64,981 "
-28036217,HC IMPL STNT GRFT ENDURANT 2 28X13X166," 33,950 "
-28036218,HC IMPL INSERT TIBIAL ADVANCE SZ5," 5,100 "
-28036219,HC IMPL INSERT KNEE REVISION CCK SZ5," 9,878 "
-28036220,HC IMPL STNT GRFT ENDURANT 2 16X10X82," 33,950 "
-28036221,HC IMPL FEMORAL COMPONENT REV ADVANCE," 33,950 "
-28036222,HC IMPL BASE TIBIAL MODULAR ADVANCE," 12,263 "
-28036223,HC IMPL STEM EXTENSION ADVANCE 15 X 10," 9,675 "
-28036224,HC INTRO SHTH FLXR CHECK-FLO 6X45, 527 
-28036225,HC IMPL LINER CONTINUUM 32MM SZGG," 5,950 "
-28036226,HC IMPL STEM FEMORAL VERSYS SZ16," 29,446 "
-28036227,HC IMPL HEAD FEMORAL BIOLOX CERAMIC," 12,167 "
-28036228,HC IMPL ACF CORTICO-CANN LORDOTIC 7MM," 9,990 "
-28036229,HC IMPL PIN HELIX TEMP FIXATION," 1,522 "
-28036230,HC IMPL HEAD FEMORAL BILOX CERAMIC," 16,772 "
-28036231,HC IMPL PLATE SUPRAPECTINEAL PELVIS," 18,000 "
-28036232,HC IMPL HALF-PIN BLUNT STTRYKER EX-FIX," 1,916 "
-28036233,HC IMPL HALF-PIN BLUNT STTRYKER EX-FIX," 1,916 "
-28036234,HC IMPL WIRE BOLT LONG STRYKER EX-FIX, 816 
-28036235,HC IMPL PIN BOLT APEX STRYKER EX-FIX," 1,709 "
-28036236,HC IMPL STRUT SHORT TELE STRYKER EXFIX," 11,498 "
-28036237,HC IMPL ROD THREADED 6MMX100MM, 571 
-28036238,HC IMPL WASHER RED STRYKER EX-FIX 7MM, 393 
-28036239,HC IMPL RING FULL STRYKER EX-FIX 155MM," 11,340 "
-28036240,HC IMPL RING OPEN STRYKER EX-FIX 155MM," 10,710 "
-28036241,HC IMPL RING FOOT LONG 155MM," 17,250 "
-28036242,HC IMPL ARCH FOOT STRYKER EX-FIX 155MM," 17,250 "
-28036243,HC IMPL NAIL TROCH 12 X 380MM," 15,071 "
-28036342,HC IMPL POST MED. 30DG 4.0X20MM," 9,938 "
-28036343,HC IMPL SCREW LAG CANN. 3.0X18MM," 3,604 "
-28036344,HC GUIDEWIRE 9MM, 111 
-28036345,HC IMPL SCREW CANN. 2.7MM," 1,505 "
-28036346,HC RASP PROXIMAL 10MM," 2,346 "
-28036347,HC RASP DISTAL 8MM," 2,346 "
-28036348,HC REAMER POST 4.6," 2,525 "
-28036349,HC IMPL SCREW CORTICL ARTHREX 2.3X18MM, 553 
-28036350,HC REAMER METARSAL ARTHREX 14MM," 3,825 "
-28036351,HC REAMER METARSAL ARTHREX 16MM," 3,825 "
-28036352,HC REAMER PHALANGEAL ARTHREX 20MM," 3,825 "
-28036353,HC IMPL PLATE MTP SHT LO-PROFL CNTURD," 6,800 "
-28036354,HC IMPL SCREW CNCELUS LO-PRO 3X24MM," 1,105 "
-28036355,HC REAMER METARSAL ARTHREX 20MM," 3,825 "
-28036357,HC GUIDEWIRE TROCAR DOUBLE 0.035, 128 
-28036358,HC IMPL SCREW BONE MICRO ACUTRAK 28.0M," 3,451 "
-28036359,HC IMPL INSERT EVOLUTION MP SIZE 4," 5,100 "
-28036361,HC IMPL SCREW LOCK 5 X 95MM SYNTH," 1,927 "
-28036362,HC SHEATH LRG BIO-INTRAFIX," 2,652 "
-28036363,HC IMPL TIBIAL BASE BRNG DEPUY SZ7," 10,500 "
-28036364,HC IMPL FEMORAL COMPONET SZ 7 DEPUY," 15,300 "
-28036365,HC IMPL INSERT TIBIAL DEPUY SZ7," 7,875 "
-28036366,HC IMPL ANGIO SHLD 14.5X16.5, 196 
-28036367,HC IMPL LINER ACET ZMR 36X62," 5,950 "
-28036368,HC IMPL CAGE 12X27MM X 6 DEG," 43,401 "
-28036369,HC IMPL SCREW SPNL POLYAXAL 7.5X35MM," 8,843 "
-28036370,HC IMPL SCREW SPNL POLYAXAL 7.5X40," 8,843 "
-28036371,HC IMPL SCREW SPINAL POLYAXAL 7.5X50MM," 8,843 "
-28036372,HC IMPL ROD SPINAL STR DEPUY 480MM," 7,242 "
-28036373,HC IMPL PLATE FIXATION 4/6 HOLE," 2,064 "
-28036374,HC IMPL SCREW BONE 1.5MM 1.5 X 14MM, 628 
-28036375,HC IMPL SCREW BONE 1.5MM X 08, 628 
-28036376,HC IMPL SCREW BONE 1.2 X 8MM, 628 
-28036377,HC IMPL SCREW BONE 1.5 X 7MM, 628 
-28026990,HC INTRO SHTH PINNACLE 11X10X0.035, 74 
-28036101,HC CTH THRMDLTIN SWAN-GANZ 7X110, 463 
-28036379,HC IMPL STEM IM OSS INTERLOK 13 X 90MM," 10,335 "
-28036380,HC IMPL HEAD MODULAR FREEDOM 36MM," 9,638 "
-28036381,HC MATRIX MONO-LAYR INTEGRA 4X5," 25,690 "
-28036382,HC IMPL PLATE STANDARD 6 HOLE," 9,014 "
-28036385,HC BLADE SCREWDRIVER SLF RET T10," 3,136 "
-28036387,HC IMPL SCREW SLF TAPPING 2.7X10MM, 928 
-28036388,HC IMPL SCREW BONE SLF TAPPING 2.7X12M, 928 
-28036389,HC IMPL SCREW BONE SLF TAPPING 2.7X14M, 928 
-28036390,HC IMPL SCREW BONE SLF TAPPING 2.7X8MM, 928 
-28036391,HC IMPL SCREW BONE SLF TAPPING 2.7X16M, 928 
-28036392,HC IMPL SCREW BONE SLF TAPPING 2.7X18M, 928 
-28036393,HC IMPL SCREW BONE SLF TAPPING 2.7X20M, 928 
-28036394,HC IMPL SCREW BONE SLF TAPPING 2.7X22M, 928 
-28036395,HC IMPL SCREW BONE SLF TAPPING 2.7X24, 928 
-28036396,HC IMPL SCREW BONE SLF TAPPING 2.7X26M, 928 
-28036397,HC IMPL SCREW BONE SLF TAPPING 2.7X28M, 928 
-28036398,HC IMPL SCREW BONE SLF TAPPING 2.7X30M, 928 
-28036399,HC IMPL SCREW BONE SLF TAPPING 2.7X32M, 928 
-28036400,HC IMPL SCREW BONE SLF TAPPING 2.7X34M, 928 
-28036401,HC IMPL SCREW BONE 22.0MM," 3,451 "
-28036402,HC IMPL SCREW BONE 24.0MM," 3,451 "
-28036403,HC IMPL SCREW BONE 26.0MM," 3,451 "
-28036404,HC IMPL SCREW BONE 30.0MM," 3,451 "
-28036405,HC CTH DELV ATTAIN SLCT2 65X130," 1,700 "
-28036406,HC CTH DELIV ATTAIN SLCT2 65X130," 1,700 "
-28036410,HC IMPL BREAST ROUND HP 800CC," 7,863 "
-28036411,HC VENOUS OUTFLOW COMPONENT," 16,800 "
-28036412,HC IMPL DISC CERVICAL MOBI-C 15 X 17 X 5 MM," 39,000 "
-28036413,HC IMPL PLATE PROX TIBIAL 6H ZMR RT," 12,030 "
-28036414,HC IMPL INSERT HIP CERAMIC CERAMAX," 16,781 "
-28036415,HC IMPL SCREW LOCK 3.5 X32 ZMR," 1,272 "
-28036416,HC IMPL SCREW LOCK 3.5X 58 ZMR," 1,272 "
-28036417,HC IMPL SCREW LOCK 3.5 X 70 ZMR," 1,272 "
-28036418,HC IMPL SCREW LOCK 3.5 X 75 ZMR," 1,272 "
-28036419,HC IMPL SCREW SLF TAP 3.5 X 30 ZMR, 236 
-28036420,HC IMPL ROD COCR 6.0 X 500MM MEDTRONIC," 7,393 "
-28036421,HC IMPL SCREW SLF TAP 3.5 X 36 ZMR, 236 
-28036422,HC IMPL SCREW MAS 7.5 X 50MM," 7,863 "
-28036423,HC IMPL SCREW SLF TAP 3.5 X 90, 236 
-28036425,HC IMPL SCREW MAS 7.5 X 45MM," 7,863 "
-28036427,HC IMPL SCREW MAS 6.5 X 50MM," 7,863 "
-28036428,HC IMPL SCREW MAS 7.5 X 40MM," 7,863 "
-28036429,HC IMPL HALF PIN BLUNT STRYKER EX FIX," 1,916 "
-28036430,HC IMPL HALF PIN BLUNT STRYKER 25MM," 1,916 "
-28036431,HC IMPL ROD VECTRAN 250MM EX-FIX," 3,468 "
-28036432,HC IMPL SCREW SLF TAP CORT 3.5 X40 ZMR, 236 
-28036433,HC IMPL SCREW SLF TAP 3.5 X 75 CORT ZM, 236 
-28036434,HC IMPL WIRE DIAMOND POINT 1.8MM EXFIX, 428 
-28036435,HC IMPL SCREW SLFTAP 3.5 X 80 CORT ZMR, 236 
-28036436,HC IMPL SCREW CANNULATED 7.0 X 32/80MM," 2,244 "
-28036438,HC IMPL GUIDE PIN 3.2MM X 2IN," 1,530 "
-28036439,HC IMPL SCREW CANNULATED 7.0 X 105MM," 1,530 "
-28036441,HC IMPL ROD ADAPTOR MEDIUM 44MM EX-FIX, 893 
-28036442,HC IMPL SCREW CORT 4.5X 66MM, 179 
-28036443,HC IMPL SCREW POLY STRYKR SPINE 4.5X20," 8,075 "
-28036444,HC IMPL ROCKER SHOE STRYKER EX-FIX," 15,000 "
-28036446,HC IMPL ACCUFILL SUBCHONDROPLASTY," 18,713 "
-28036447,HC IMPL LINER CONTINUUM 32MM," 5,950 "
-28036448,HC IMPL STEM AVENIR MULLER STANDARD," 21,700 "
-28036449,HC IMPL LINER CONTINUUM GG 32 X 48," 5,950 "
-28036450,HC IMPL STEM FEMORAL SIZE 9," 21,000 "
-28036451,HC IMPL BLADE HELICAL 11.0 X 75MM," 6,165 "
-28036452,HC IMPL NAIL FIXATION 12MM X 360MM," 15,071 "
-28036453,HC CTH GUID CPS AIM ACTE CRVE 59C," 6,503 "
-28036454,HC IMPL BREAST HP 200CC," 2,583 "
-28036455,HC IMPL BREAST HP 240CC," 2,583 "
-28036456,HC IMPL BREAST HP 280CC," 2,583 "
-28026993,HC IMPL CLIP ANEURYSM 10MM," 2,678 "
-28026997,HC IMPL SCREW SET SS STD," 1,530 "
-28027000,HC PROCESSOR COCH  OPUS 2," 96,900 "
-28027001,HC IMPL HIP SHELL 56MM REV," 18,064 "
-28027002,HC IMPL PLATE HELIX NUVASIVE," 9,000 "
-28027003,HC IMPL SCREW 4.0 NUVASIVE," 2,338 "
-28027004,HC TUBE ASSEMBLY RIA 520 MM," 4,013 "
-28027005,HC IMPL FEMUR WRIGHT MED," 15,000 "
-28027006,HC IMPL STEM HUMERAL," 31,374 "
-28027007,HC IMPL STEM HUMERAL," 31,374 "
-28027008,HC IMPL HEAD HUMERAL 420," 14,310 "
-28027009,HC IMPL HEAD HUMERAL 480," 13,200 "
-28027010,HC IMPL HEAD HUMERAL 500," 14,310 "
-28027011,HC IMPL GLENOID 01.04214.370," 10,800 "
-28027012,HC IMPL ARTICULAR SURFACE," 5,814 "
-28027013,HC IMPL ARTICULAR SURFACE ZIM," 5,814 "
-28027014,HC IMPL ARTICULAR SURFACE ZIM," 5,814 "
-28027015,HC IMPL TIBIA ZIMMER," 7,710 "
-28027018,HC IMPL SCREW 4828-020-02," 1,133 "
-28027023,HC REPLACEMENT DEVICE AT NO COST, 1.0 
-28027025,HC BASKET STONE EXTRACTION NITINOL12X2.2X4.5X24," 3,485 "
-28027026,HC IMPL STENT BILIARY LUER LOCK CONNECTOR 22, 706 
-28027027,HC FDNG TUBE PONSKY 20F," 18,585 "
-28027028,HC IMPL PLUG PIN UNIPOLOR PORT IS-1, 850 
-28027029,HC CTH GUID ATTAIN COMMAND 45CM," 1,632 "
-28036100,HC CTH BLLN LUMEN SWAN-GANZ 7X110, 323 
-28036458,HC IMPL ENDOBUTTON CL-BTB 30MM," 2,075 "
-28036459,HC IMPL BREAST SIENTRA 440 MP," 7,608 "
-28036460,HC IMPL BREAST SIENTRA 510MP," 7,608 "
-28036466,HC IMPL SCREW BONE CORTEX 1.5X5MM, 628 
-28036467,HC IMPL SCREW BONE CORTEX 1.6MM, 628 
-28036468,HC IMPL SCREW CORTEX BONE 1.5X15MM, 628 
-28036469,HC IMPL SCREW CORTEX BONE 1.5X04MM, 628 
-28036470,HC IMPL SCREW CORTEX BONE 1.5X19MM, 628 
-28036471,HC IMPL SCREW CORTEX BONE 1.5X21MM, 628 
-28036472,HC IMPL SCREW CORTEX BONE 1.5X22MM, 628 
-28036473,HC IMPL SCREW CORTEX BONE 1.5X23MM, 628 
-28036474,HC IMPL SCREW CORTEX BONE 1.5X24MM, 628 
-28036483,HC INTRO SHTH PERCUTANEOUS 4F, 191 
-28036484,HC IMPL SPACER SYNCAGE EVOL 12 X 14," 40,300 "
-28036486,HC IMPL PLATLET CONCENTRATE 30ML," 3,409 "
-28036487,HC IMPL MAMMARY IMPL ALLERGAN 68-650," 2,583 "
-28036488,HC IMPL MAMMARY IMPL ALLERGAN 68-700," 2,583 "
-28036489,HC IMPL MAMMARY IMPL ALLERGAN 68-750," 2,583 "
-28036490,HC IMPL MAMMARY IMPL ALLERGAN 68-800," 2,583 "
-28036491,HC IMPL MAMMARY IMPL ALLERGAN 68HP-500," 2,583 "
-28036495,HC IMPL SPACER-MED SYNCAGE 15MM," 40,300 "
-28036496,HC IMPL SPACER-MED SYNCAGE 13.5MM," 40,300 "
-28036497,HC IMPL TRINITY ELITE MEDIUM," 21,210 "
-28036498,HC IMPL LINER HIP CONTINUUM REV 32MM," 8,531 "
-28036499,HC IMPL STAXX LORDOTIC EXPANDBLE," 37,700 "
-28036500,HC IMPL SCREW PRECEPT 6.5 X 50MM," 8,438 "
-28036501,HC IMPL FEMORAL HEAD PCA STRYKER 32MM, 781 
-28036503,HC CTH BLLN Z-MED 18X4X100 4ATM," 4,815 "
-28036504,HC IMPL SCREW 7.5 X 55 STRYKER SPINE," 14,424 "
-28036505,HC IMPL SCREW 6.5 X 50 STRYKER SPINE," 14,424 "
-28036506,HC IMPL ARTICULAR SURFACE NEXGEN," 12,361 "
-28036507,HC IMPL TIBIAL COMPONENT NEXGEN SIZE 3," 26,342 "
-28027032,HC CTH SHTH GUID CPS AIM 59CM," 2,213 "
-28027033,HC COIL DETCHBLE TARGET 360 6X20," 12,488 "
-28036502,HC IMPL RETRVER TREVO PROVUE 4X20 SYS," 47,418 "
-28036509,HC IMPL SCREW STRYKER SPINE 4.35X14MM," 3,456 "
-28036510,HC IMPL HEAD FEMORAL COCR STELKAS 36MM," 8,007 "
-28036511,HC IMPL FEMORAL COMPONENT REACH REV," 49,667 "
-28036512,HC IMPL MODULAR HEAD BIOMET REV 36MM," 4,250 "
-28036513,HC IMPL SCREW ST BIOMET 6.5X70MM, 570 
-28036514,HC IMPL SCREW LOCKING ACET 6.5 X 25MM," 2,865 "
-28036515,HC IMPL SCREW LOCKING ACET 6.5 X 20MM," 2,865 "
-28036516,HC IMPL SCREW LOCKING ACET 6.5 X 70MM," 2,865 "
-28036517,HC IMPL SCREW LOCKING ACET 6.5 X 30MM," 2,865 "
-28036518,HC IMPL SCREW LOCKING ACET 6.5 X 15MM," 2,865 "
-28036519,HC IMPL ACETABULAR COMPONENT GIBSON," 75,120 "
-28036520,HC IMPL OBTURATOR HOOK GIBSON W/SCREW," 25,200 "
-28036521,HC IMPL PLATE T 4HOLE LO PROFL 2.4MM," 5,908 "
-28036522,HC IMPL SCREW LOKNG LO-PROFL 2.4X12MM," 1,063 "
-28036523,HC IMPL SCREW LOKNG TIT CORTEX 2.4X16M," 1,063 "
-28036524,HC IMPL SCREW LOKNG CORTEX 2.4X16M," 1,063 "
-28036525,HC IMPL K-WIRE GLENOID EQUINOXE 2.0MM, 850 
-28036526,HC IMPL STEM HIP TAPERED REVISION," 26,495 "
-28036527,HC IMPL HEAD FEMORAL CERAMIC BIOLX REV," 7,344 "
-28036528,HC IMPL RING LOCKING HIP 56MM REV," 2,423 "
-28036529,HC IMPL SHELL ACET REVISION ZMR 64MM," 18,690 "
-28036530,HC IMPL CENTRALIZER DISTAL 17MM ZMMR, 425 
-28036531,HC IMPL LINER SHELL REV ZMMR 40MM," 10,823 "
-28036532,HC IMPL HEAD FEMORAL CERAMIC ZMMR 40MM," 8,063 "
-28036533,HC IMPL INSERT TIBIAL SIZE 3 17.5MM," 8,745 "
-28036534,HC IMPL PLATE FEMUR LCP 6H 205MM," 14,229 "
-28036535,HC IMPL SCREW LOCKING 7.3MM 20MM," 2,848 "
-28036536,HC IMPL HEAD FEMORAL CONSERVE 46MM," 35,750 "
-28036537,HC IMPL SLEEVE NECK CONSERVE SHORT," 8,625 "
-28036538,HC IMPL GLENOID CAGE EQUINOXE MED," 16,427 "
-28036539,HC IMPL SCREW MAS 5.5X40MM," 9,097 "
-28036540,HC IMPL SCREW MAS 6.5X40MM," 9,097 "
-28036541,HC IMPL SCREW MAS 6.5X45MM," 9,097 "
-28036542,HC IMPL SCREW MAS 6.5X50MM," 9,097 "
-28036543,HC IMPL SCREW MAS 7.5X75MM," 9,097 "
-28036544,HC IMPL SCREW BONE SPINAL LOCKING CAP," 1,224 "
-28036545,HC IMPL FEMUR KNEE STD LT SZ 10 ZMMR," 16,980 "
-28036546,HC IMPL ROD PRE-BENT ARM15T 70MM," 2,125 "
-28036547,HC IMPL ROD PRE-BENT ARM15T 80MM," 2,125 "
-28036548,HC IMPL CAGE COROENT XL-H 4X22X50MM," 37,310 "
-28036549,HC IMPL SCREW POLYAXIAL ARM15T 5X45MM," 8,500 "
-28036550,HC IMPL SCREW POLYAXIAL ARM15T 6X45MM," 8,500 "
-28036551,HC IMPL CAGE COROENT XL-H 2X22X50MM," 37,310 "
-28036552,HC IMPL CAGE COROENT XL-H 30'," 37,310 "
-28036553,HC IMPL SCREW LOCKING STRYKER 5 X 60," 1,999 "
-28036554,HC IMPL SCREW LOCKING STRYKER 5 X 30," 1,999 "
-28036555,HC IMPL END CAP SCN STRYKER," 2,077 "
-28036556,HC IMPL NAIL ANKLE ARTHRODESIS 12X200," 19,399 "
-28036558,HC CTH SHTH GUID CPS AIM 65CM," 2,763 "
-28036559,HC IMPL ROD RIGID 6.0X150 AEGIS 3610," 2,125 "
-28036560,HC IMPL TIGHTROPE ACL RT," 2,338 "
-28036561,HC IMPL NAIL TIBIAL SYNTHES 9MMX330," 12,359 "
-28036562,HC IMPL SCREW BONE ACUTRAK 34.0MM," 3,451 "
-28036563,HC CTH BLLN ARROW 6X110, 425 
-28036564,HC IMPL GRFT LG EXC TRK C3 X28.5X14X16," 64,968 "
-28036565,HC IMPL SCREW XLF FIXED 5.5 X 25MM," 6,120 "
-28036566,HC IMPL SCREW XLF VARIABLE 5.5 X 25MM," 6,120 "
-28036567,HC IMPL FEMORAL COMPONENT NEXGEN E," 13,230 "
-28036568,HC IMPL FEMUR RT ZMMR PERSONA SZ 8," 16,980 "
-28036569,HC IMPL SCREW CORTEX 2.0 X 12MM, 321 
-28036570,HC IMPL SCREW CORTEX 2.0 X 16MM, 321 
-28036571,HC IMPL SCREW CORTEX 2.0X 18MM, 321 
-28036572,HC IMPL SCREW CORTEX 2.0 X 26MM, 321 
-28027034,HC INTRO SHTH SSV SPLT AWY 11X13, 604 
-28027035,HC IMPL STNT ICAST PTFE CVRD 8X59X120," 18,750 "
-28027036,HC BLLN OCCLSN TRANSFORM 3X10," 10,540 "
-28027037,HC COIL DETCHBLE TARGET 360 4X15," 12,521 "
-28027038,HC COIL DETCHBLE TARGET 360 3X8," 8,460 "
-28027039,HC COIL DETCHBLE TARGET 360 3X10," 8,460 "
-28027040,HC COIL DETCHBLE TARGET 360 2X8," 8,460 "
-28027041,HC IMPL STNT BILIARY RX HERCULINK ELITE 5X18X135," 9,750 "
-28027042,HC IMPL STNT BILIARY RX HERCULINK ELITE 6X18X135," 9,750 "
-28027043,HC IMPL STNT GRFT TAG CONFORMABLE 31X26X10," 88,500 "
-28027044,HC BLLN OCCLSN TRANSFORM 7X15," 10,540 "
-28027045,HC CTH BLLN OCCLSN TRANSFORM 4X110," 10,540 "
-28027046,HC NDL YUEH CENTESIS 5X29 19GA, 144 
-28027047,HC BASKET STONE EXT NITINOL WITTICH 12X24," 3,481 "
-28027048,HC FDNG TB MIC-KEY 16X1.2X22.3-5ML," 2,508 "
-28027049,HC IMPL SCREW REDUCTION 7.5 X 50," 14,424 "
-28027050,HC TRAY TIBIAL CEMENTED," 9,540 "
-28027051,HC IMPL VALVE STRATA  44420," 27,596 "
-28027052,HC REAMER ARTHREX," 3,825 "
-28027056,HC IMPL SCREW STRYKER 48552312," 8,075 "
-28027057,HC IMPL LINER 8851-009-32," 5,950 "
-28027058,HC IMPL HIP SHELL PINNACLE 50M," 9,540 "
-28027059,HC IMPL LINER HIP ALTREX," 5,406 "
-28027060,HC IMPL HIP HEAD FEMORAL," 8,063 "
-28027061,HC IMPL HIP HEAD FEMORAL REV," 10,043 "
-28027062,HC NEXFRAME BILATERAL," 13,313 "
-28027065,HC IMPL VALVE AORTIC EPIC 23MM," 34,450 "
-28027066,HC IMPL VALVE AORTIC EPIC 25MM," 34,450 "
-28027067,HC IMPL VALVE MITRAL EPIC 31MM," 34,450 "
-28027068,HC IMPL VALVE AORTIC EPIC 29MM," 34,450 "
-28027069,HC IMPL VALVE MITRAL EPIC 29MM," 34,450 "
-28027070,HC IMPL VALVE MITRAL EPIC 33MM," 34,450 "
-28027071,HC IMPL VALVE AORTIC EPIC 21MM," 34,450 "
-28027072,HC IMPL TIBIA 42-5320-064-02," 7,710 "
-28027073,HC IMPL SURFACE ARTICULAR," 5,814 "
-28027075,HC IMPL SURFACE ARTIC 42-5214-005-12," 5,814 "
-28027076,HC IMPL SURFACE ARTIC 42-5214-008-11," 5,814 "
-28027077,HC IMPL TIBIA 42-5320-064-01," 7,710 "
-28027078,HC IMPL SCREW SPINAL STRYKER," 8,075 "
-28027079,HC IMPL SCREW 30-0257, 578 
-28027080,HC IMPL SCREW 30-0259, 578 
-28027081,HC IMPL GLENOID ARTHREX," 11,813 "
-28027082,HC EXTENDER TIP 60MM," 5,627 "
-28036573,HC IMPL SCREW CENTRAL ARTHREX 6.5X15," 1,445 "
-28036574,HC IMPL BASEPLATE GLENOID ARTHRX SMALL," 12,863 "
-28036575,HC IMPL SCREW LOCKING ARTHREX 42MM," 1,020 "
-28036576,HC IMPL SCREW CENTRAL GLENOID 6.5X20MM," 1,445 "
-28036577,HC IMPL SCREW LOCKING 36MM ARTHREX," 1,020 "
-28036578,HC IMPL GLENOSPHERE ARTHREX SMALL 36," 15,750 "
-28036579,HC IMPL INSERT HUMERAL ARTHREX SMALL," 7,613 "
-28036580,HC IMPL SPACER HUMERAL ARTHREX 36," 4,165 "
-28036581,HC IMPL CUP HUMERAL ARTHREX 36," 13,388 "
-28036582,HC IMPL STEM REVERSE ARTHREX SZ 7," 18,638 "
-28036583,HC IMPL GLENOID BASEPLATE ARTHREX MED," 12,863 "
-28036584,HC IMPL GLENOSPHERE REV ARTHREX 39 +4," 15,750 "
-28036585,HC IMPL STEM REV SHLDR ARTHREX SZ. 9," 18,638 "
-28036586,HC IMPL CUP REV COAT LT ARTHREX 39+2," 13,013 "
-28036587,HC IMPL INSERT HUMERAL SHLDR MED 39 +3," 7,613 "
-28036588,HC IMPL PM1210 SYSTEM," 24,150 "
-28036589,HC IMPL SCREW SHLDL ARTHREX 4.5X46," 1,020 "
-28036590,HC IMPL SCREW SHLDR ARTHREX 4.5X48," 1,020 "
-28036591,HC IMPL SCREW CANN ZMMR TRAUMA 7.0X170," 2,244 "
-28036592,HC IMPL SCREW CANN ZMMR TRAUMA 7.0X175," 2,244 "
-28036593,HC IMPL SCREW CANN ZMMR TRAUMA 7.0X180," 2,244 "
-28036594,HC IMPL CAGE SPNL INTERBODY 14X22X12," 34,566 "
-28036598,HC IMPL PLATE STERNAL SYNTHES  8 HOLE," 6,841 "
-28036599,HC IMPL PLATE STERNAL SYNTHES 12 H," 9,036 "
-28036600,HC IMPL STEM HUMERAL ARTHREX 7MM," 25,725 "
-28036601,HC IMPL HEAD HUMERAL ARTHREX 46X18," 13,125 "
-28036602,HC IMPL SCREW MAS 8.5 X 40MM," 9,097 "
-28036603,HC IMPL SCREW MAS 8.5 X 45MM," 9,097 "
-28036604,HC IMPL SCREW MAS 7.5 X 35MM," 9,097 "
-28036605,HC IMPL SCREW MAS 7.5 X 40MM," 9,097 "
-28036606,HC IMPL SCREW MAS 7.5 X 45MM," 9,097 "
-28036607,HC IMPL SCREW MAS 6.5 X 35MM," 9,097 "
-28036609,HC IMPL SCREW MEDTRONIC SET," 1,232 "
-28027084,HC FEEDING TB MIC-KEY 18X4X5," 1,353 "
-28027085,HC FLOURO RADIOGRAPHIC SET J-RTAS-100," 1,517 "
-28036595,HC IMPL EXTENSION STEM  ZMMR 10X190," 23,800 "
-28036596,HC IMPL FEMORAL COMP DIST ZMMR SZ C LT," 44,753 "
-28036597,HC IMPL ART SURFACE ZMMR SZ C 12MM," 12,750 "
-28036608,HC IMPL ROD PRE-BENT 70MM," 2,814 "
-28036610,HC IMPL POLY INSERT ANKLE 7MM 400-141," 14,603 "
-28036612,HC IMPL SCREW NON LCKING 3.0X1.8MM, 578 
-28036646,HC IMPL PLATE DISTAL RADIUS L 3 HOLE," 5,706 "
-28036647,HC IMPL PEG THREADED LCK 2.3X18MM, 631 
-28036648,HC IMPL PEG THREADED LCK 2.3X20MM, 631 
-28036649,HC IMPL PEG NON LCK 2.7X18MM, 631 
-28036650,HC IMPL K-WIRE STD TIP 1.5X127MM, 179 
-28036654,HC IMPL PEG THREADED NON LCK 2.7MMX24M, 631 
-28036655,HC IMPL SCREW CORT NON LGKING 3.5X12MM, 631 
-28036656,HC IMPL SCREW CORT NON LGKING 3.5X13MM, 631 
-28036657,HC IMPL SCREW CORT NON LGKING 3.5X14MM, 631 
-28036658,HC IMPL SCREW CANNULATED 6.5 X 70MM," 2,176 "
-28036659,HC IMPL SCREW CANNULATED 6.5 X 75MM," 2,176 "
-28036660,HC IMPL SCREW CANNULATED 6.5 X 80MM," 2,176 "
-28036661,HC IMPL SCREW CORTICAL 2.0 X 6MM, 191 
-28036663,HC IMPL PLATE RECON CVD ZMMR 6 H," 3,347 "
-28036664,HC IMPL PLATE DIST RADIUS SYNTHES 5 H," 13,386 "
-28036665,HC IMPL PLATE SYNTHES 2.4MM 8 H 68MM," 3,803 "
-28036666,HC IMPL SCREW SYNTHES  2.7 X 24 202.224," 1,139 "
-28036667,HC IMPL SCREW SYNTHES  2.7 X 26 202.226," 1,139 "
-28036668,HC CONNECTOR CROSS VARI 50-60," 7,225 "
-28036669,HC CLOSUREZIP SURGCL SKN 8, 723 
-28036670,HC IMPL LINER CONTINUUM  SZ LL 36MM," 14,112 "
-28036671,HC IMPL NECK MODULAR 12/14 +0," 7,938 "
-28036672,HC IMPL HEAD FEMORAL 36MM 12/14 ZMMR," 18,064 "
-28036673,HC IMPL SCREW TIT CORTEX 2.0 X 8MM, 472 
-28036707,HC IMPL PEG THREADED NON LOK 2.7X22MM, 631 
-28036708,HC IMPL PEG THREADED NON LOK 2.7X26MM, 631 
-28036709,HC IMPL PEG THREADED NON LOK 2.7X28MM, 631 
-28036734,HC MODEL ANATOMICAL MAXILA-ORBIT," 19,815 "
-28036735,HC IMPL GRAFT FLEXHD 4 X 12," 12,773 "
-28036736,HC IMPL GRAFT FLEXHD 6 X 12," 17,815 "
-28036737,HC IMPL ENDOBUTTON BTB 15MM," 3,086 "
-28036739,HC IMPL ENDOBUTTON BTB 25MM," 2,075 "
-28036740,HC IMPL ENDOBUTTON BTB 35MM," 2,075 "
-28036741,HC CTH VENTRCULAR DVC IMPELLA 2.5," 15,000 "
-28036742,HC IMPL LINER ZMMR CONTINUM32MM SZ LL," 18,685 "
-28036744,HC IMPL ENDOBUTTON BTB 45MM," 2,075 "
-28036745,HC IMPL ENDOBUTTON BTB 45MM," 2,075 "
-28036746,HC IMPL ENDOBUTTON BTB 45MM," 2,075 "
-28036747,HC IMPL ENDOBUTTON BTB 55MM," 2,075 "
-28036748,HC IMPL ENDOBUTTON BTB 60MM," 2,075 "
-28036750,HC IMPL FEMORAL KNEE RT ZMMR SZ F," 13,230 "
-28036751,HC IMPL HEAD FEMORAL CERAMIC 12/14 40," 17,226 "
-28036752,HC IMPL SCREW CORTICAL 2.7MM X 28MM, 204 
-28036753,HC IMPL SCREW CORTICAL 2.7MM X 30MM, 204 
-28036754,HC IMPL SCREW CORTICAL 2.7MM X 32MM, 204 
-28036755,HC IMPL SCREW CORTICAL 2.7MM X 36MM, 204 
-28036756,HC IMPL WASHER TITANIUM 3.75MM, 425 
-28027091,HC IMPL ICD FORTIFY ASSURA VR-40 DF4," 65,000 "
-28035682,HC VOLUMA X2  1CC," 6,664 "
-28036613,HC IMPL ICD FORTIFY ASSURA VR-40 COATD," 65,000 "
-28036618,HC OTTHOGNATIC PLAN-SPLINT," 16,808 "
-28036619,HC IMPL PLSE GNERATOR CAMERON IS SQ-RX," 97,500 "
-28036621,HC IMPL LEAD Q-TRAK 45CM," 28,000 "
-28036622,HC IMPL CAGE UNILIF STRYKER 7X25X0X11," 38,587 "
-28036623,HC IMPL ROD STRYKER SPINE 30MM," 3,144 "
-28036624,HC IMPL SCREW STRYKER SPINE 7.5 X 30MM," 7,863 "
-28036625,HC IMPL SCREW STRYKER SPINE 7.5 X 35MM," 7,863 "
-28036626,HC IMPL NAIL FEMORAL GT LEFT 12MMX36CM," 16,065 "
-28036627,HC IMPL SCREW CORTICAL FOR FEM NAIL," 2,066 "
-28036628,HC GUIDE WIRE SMOOTH BULLET TIP," 1,445 "
-28036629,HC IMPL SHELL HIP CONTINUUM SZ00," 12,300 "
-28036630,HC IMPL LINER HIP CONTINUUM SZ 00," 5,950 "
-28036631,HC IMPL SHANK MAS PLIF 5.0 X 40MM," 7,744 "
-28036632,HC IMPL SCREW MAS PLIF 5.0 X 35MM," 15,143 "
-28036633,HC IMPL ROD MAS PLIF PRE-BENT 30MM," 2,125 "
-28036634,HC IMPL CAGE COROENT LARGE MP 11X9X23," 33,593 "
-28036635,HC IMPL PLATE HELIX R 24MM 1-LEVEL," 8,500 "
-28036636,HC IMPL SCREW HELIX VARIABLE 4 X 17MM," 2,338 "
-28036637,HC IMPL SCREW LOCKING 5.0MM X 68MM," 1,709 "
-28036638,HC IMPL INTERBODY FUSION DEVICE 10X22," 34,566 "
-28036639,HC IMPL LINER HIP ZMMR SZ LL 36MM," 10,478 "
-28036640,HC IMPL SCREW BIOTRAK 20MM," 3,910 "
-28036641,HC  K-WIRE 0.045 X 6 IN, 94 
-28036643,HC WIRE GUIDE ST .035 X 6IN, 94 
-28036644,HC IMPL FEMUR ZMMR STD RT PERSONA SZ 4," 16,980 "
-28036645,HC IMPL ART SURFACE FIXED BRNG ZMMR," 5,814 "
-28036757,HC IMPL FEMORAL HEAD BIOLOX REV," 17,226 "
-28036758,HC IMPL STEM TIBIAL CEMENTED 15MMX60MM," 7,813 "
-28036759,HC IMPL PLATE 2-LEVEL TENSION BAND ANT," 33,416 "
-28036760,HC IMPL SYNCAGE SPACER LARGE 15MM," 40,300 "
-28036761,HC IMPL PLATE HUMERAL 3.5MM 18HOLE," 7,556 "
-28036762,HC IMPL STEM HUMERAL INTERCALARY 100M," 18,181 "
-28036763,HC IMPL SCREW MRS ASSEMBLY STRYKER," 3,800 "
-28036764,HC IMPL CONNECTOR LATERAL OFFSET 20MM," 10,763 "
-28036765,HC BIT DRILL CANN 4.5MM ZIMMER," 6,545 "
-28036766,HC IMPL PLATE XLIF DECADE 2HOLE SZ10," 33,449 "
-28036767,HC IMPL BOLT XLIF DECADE 5.5 X 50MM LG," 10,320 "
-28036768,HC IMPL INSERT KNEE EVOLUTION 5 RIGHT," 5,100 "
-28036769,HC IMPL COMPONENT FEMORAL EVOLUTION 5L," 15,000 "
-28036770,HC IMPL INSERT KNEE EVOLUTION 4+ LEFT," 5,100 "
-28036771,HC IMPL STEM EXTENSION CANAL FILLING," 9,675 "
-28036772,HC IMPL TIBIAL BASE OFFSET W/PLUGS SZ5," 13,725 "
-28036773,HC IMPL AUGMENT FEMORAL ADVANCE SZ5," 6,477 "
-28036774,HC IMPL STEM EXTENSION CEMENTED 16MM," 9,675 "
-28036775,HC IMPL AUGMENT FEMORAL ADVANCE SZ 5," 6,477 "
-28036776,HC IMPL FEMUR REVISION ADVANCE SZ5 LT," 30,660 "
-28036777,HC IMPL GLENOSPHERE ARTHREX SMALL 36," 15,750 "
-28036778,HC IMPL SCREW ARTHREX 4.5 X 48MM," 1,020 "
-28036779,HC GUIDE WIRE 2.8MM ARTHREX," 2,805 "
-28036780,HC IMPL STEM REVERS ATHREX SIZE8," 18,638 "
-28036781,HC IMPL SCREW CERV POSTERIOR 4.0X14MM," 7,225 "
-28036782,HC IMPL SCREW CORTICAL 3.5 X 22MM, 204 
-28036783,HC IMPL SCREW CORTICAL 3.5 X 26MM, 204 
-28036784,HC IMPL SCREW CORTICAL 3.5 X 32MM, 204 
-28036785,HC IMPL SCREW 3.5 X 56MM W/2.7MM HEAD, 354 
-28036786,HC IMPL SCREW 3.5 X 40MM W/2.7MM HEAD, 354 
-28036787,HC IMPL LINER CONTINUUM 36MM," 5,950 "
-28027055,HC PROCESSOR SYSTEM TWO," 9,690 "
-28027094,HC CTH GUID DELIV ACUMENT C315 30CM," 2,125 "
-28027096,HC IMPL PUTTY DBX 20ML," 24,255 "
-28027097,HC IMPL HINGE MALE EX-FIX, 798 
-28027100,HC IMPL SCREW LOCKING 5.0X75MM," 1,999 "
-28027101,HC IMPL K-WIRE 1806-3030S," 2,547 "
-28027102,HC IMPL HEAD FEMORAL REV 36MM," 16,373 "
-28027103,HC IMPL SCREW HEADLESS 7.0," 7,149 "
-28027104,HC IMPL CAGE ALIF 3744-2914," 33,600 "
-28027105,HC IMPL SCREW 3.5MM 6112-3518," 7,225 "
-28027106,HC IMPL BREAST 655CC," 10,125 "
-28027107,HC IMPL BREAST 350CC," 10,125 "
-28027108,HC IMPL BREAST 255CC," 10,125 "
-28027111,HC IMPL HEAD FEMORAL CERAMIC," 8,500 "
-28027112,HC IMPL CAGE COROENT 6210050," 48,841 "
-28027113,HC IMPL TULIP HEAD 7070000," 8,310 "
-28027114,HC IMPL SCREW LOCKING 7070003," 2,457 "
-28027115,HC IMPL ROD 60MM  8976106," 2,652 "
-28027117,HC IMPL SCREW 1896-5037S," 1,999 "
-28027118,HC IMPL SCREW 1896-4052S," 1,999 "
-28027119,HC IMPL CAGE UNILIF 48355084," 21,199 "
-28027120,HC IMPL GUIDE PIN FLEXIBLE PATIENT SPECIFIC," 8,075 "
-28027122,HC CTH GUID ATTAIN COMMAND SUREVALVE 50CM," 1,632 "
-28027123,HC INTRO SHTH CLASSIC SHTH 9X25, 553 
-28036614,HC NAIDA PROCESSOR SNGL CI-5245," 66,592 "
-28036615,HC NEPTUNE PROCESSOR SNGL CI-5240," 66,592 "
-28036616,HC NEPTUNE PROCESSOR DUAL CI-1900," 66,592 "
-28036617,HC NAIDA PROCESSOR DUAL CI-9315," 66,592 "
-28036620,HC IMPL INSERTION Q-TRAK," 8,500 "
-28036662,HC IMPL FLEXHD STRUCTURAL 1CM X 2CM," 1,156 "
-28036788,HC IMPL ARTICULAR SURFACE LPS-FLEX 10M," 8,760 "
-28036789,HC IMPL PLATE 1/4 TUBLR 6-HOL 47MM, 797 
-28036790,HC IMPL SCREW MATRIXRIB 2.9MM X 6MM, 850 
-28036791,HC CTH DILAT MINI TREK OTW 2.0X12," 1,488 "
-28036792,HC  CLOSURE SURGCL SKN 16, 723 
-28036794,HC IMPL MOBI-C STD ARTIF. CERVICALDISC," 39,000 "
-28036795,HC CATH SPINAL REV ASCENDA," 4,675 "
-28036796,HC CATH SPINAL REV W/ 38.1 CATH," 2,550 "
-28036798,HC IMPL HEAD FEMORAL DEPUY 32MM 14/16," 6,664 "
-28036799,HC IMPL FEMUR PERSONA RIGHT SIZE 6," 16,980 "
-28036800,HC IMPL ARTICULAR SURFACE PERSONA 13MM," 5,130 "
-28036801,HC IMPL END CAP X-CORE NUVASIVE 26MM," 13,763 "
-28036802,HC IMPL ENDCAP X-CORE NUVASIVE 26 X 8," 13,763 "
-28036803,HC IMPL EXPANDABLE V.B.R. 18MM 28-41," 62,070 "
-28036804,HC IMPL PLATE SPNL DEPUY 36MM," 9,000 "
-28036805,HC IMPL SCREW SPNL 16MM 1868-52-016," 2,338 "
-28036806,HC IMPL SCREW SPNL 16MM 1868-62-016," 2,338 "
-28036807,HC IMPL PINS TEMP FIXATION SPNL STR, 689 
-28036808,HC IMPL FEMUR PERSONA RIGHT SIZE 3," 16,980 "
-28036809,HC IMPL HEAD FEM MODLR+6 32MM," 4,250 "
-28036810,HC IMPL OCCLSN DVC SYST INTERLOCK 2X4," 4,941 "
-28036811,HC CTH GUID CHAPERON 6F MP2 OUTER," 2,125 "
-28036812,HC IMPL SCREW CERVICAL AEGIS 3.5X12," 7,225 "
-28036813,HC IMPL SCREW CERVICAL 3.5X14," 7,225 "
-28036814,HC IMPL LINER CONTINUUM 40MM," 5,950 "
-28036815,HC IMPL HEAD FEMORAL CERAMIC BIOLOX," 8,063 "
-28036816,HC IMPL SCREW ARM15T 7.5 X 55MM," 8,948 "
-28036817,HC IMPL SCREW POLYAXIAL 5.0 X 50MM," 15,000 "
-28036818,HC IMPL SCREW GSB POLYAXIAL 6.5 X 35MM," 8,500 "
-28036819,HC IMPL SCREW GSB POLYAXIAL 6.5 X 40MM," 8,500 "
-28036820,HC IMPL SCREW GSB POLYAXIAL 6.5 X 55MM," 8,500 "
-28036821,HC IMPL SCREW GSB POLYAXIAL 5.5 X 45MM," 8,500 "
-28036822,HC IMPL SCREW GSB LOCK OPEN TULIP 5.5, 850 
-28036823,HC IMPL SCREW GSB POLYAXIAL 7.5 X 80MM," 8,903 "
-28036824,HC IMPL ROD GSB COCR 5.5 X 500MM," 2,125 "
-28036825,HC IMPL RING LOCKING FOR 62MM SHELL, 961 
-28036827,HC IMPL INSERT TIBIAL FIX BEARING SZ5," 10,395 "
-28036828,HC IMPL PLATE MATRIX/MAND RIGHT 2.5MM," 36,693 "
-28036830,HC PLATE MANDIBULAR FRACTURE PT SPECIFIC MAND," 27,447 "
-28036831,HC IMPL PLATE MATRIX/MAND LEFT 2.5MM," 36,693 "
-28036832,HC IMPL MANDIBLE PLANNED W/FIBULA GFT. CLR," 13,208 "
-28036833,HC IMPL TISSUE EXPANDER SCALP 220ML," 4,463 "
-28036834,HC IMPL TISSUE EXPANDER SCALP 105ML," 4,463 "
-28036835,HC IMPL NAIL FEMORAL 11X420MM," 14,110 "
-28036850,HC IMPL LEAD 8-CNTCT BOS SCI 50CM," 19,915 "
-28036851,HC CTH GLIDECATH 4X100, 413 
-28036852,HC CTH BLLN PTA EVERCROSS 7X40," 1,488 "
-28036853,HC IMPL PLATE LATERAL TT FUSION LARGE," 13,830 "
-28036854,HC IMPL SCREW LOCKING WRIGHT 5.5X40," 1,760 "
-28036855,HC IMPL SCREW LOCKING WRIGHT 5.5X50," 1,760 "
-28036856,HC IMPL SCREW NON-LKNG WRIGHT 5.5X40, 723 
-28036857,HC IMPL SCREW NON-LKNG WRIGHT 5.5X50, 723 
-28036858,HC IMPL SCREW NON-LKNG WRIGHT 5.5X55, 723 
-28036859,HC IMPL SCREW HEADED WRIGHT 6.5X120X16," 3,536 "
-28036861,HC IMPL LINER ACET PINNACLE 36MMX66MM," 5,406 "
-28036862,HC IMPL STEM HUMERAL UNIVERS 6 X 115MM," 25,725 "
-28036863,HC IMPL HEAD HUMERAL UNIVERS 44/17," 13,125 "
-28036864,HC IMPL SHELL ACETABULAR 56MM," 12,300 "
-28036865,HC IMPL ROD PREBENT 65MM," 3,188 "
-28036866,HC IMPL SCREW CORTICAL 6.0 X 45MM," 11,385 "
-28036867,HC IMPL SCREW CORTICAL 7.0 X 40MM," 11,385 "
-28036868,HC IMPL PUMP PENILE AM MED 72404252," 53,931 "
-28036869,HC IMPL NAIL VALOR SMALL LFT 10X150MM," 19,467 "
-28036892,HC IMPL FEMUR PERSONA LEFT SIZE 5," 16,980 "
-28036893,HC IMPL LINER 58MM CONTINIUM ZMMR," 5,950 "
-28036895,HC IMPL SCREW VARI ANGLE 4.0 X16," 2,338 "
-28036836,HC IMPL MENISCUS MEDIAL LEFT," 33,250 "
-28036837,HC IMPL FEMORAL COMP LT ZMMR SZ G," 13,230 "
-28036838,HC IMPL ART SURFACE ZMMR 5976-40-12," 5,814 "
-28036839,HC IMPL ART SURFACE ZMMR 5976-40-10," 5,814 "
-28036840,HC IMPL ROD PREBENT 5.5 X 35MM," 3,137 "
-28036843,HC IMPL ICD ILESTO 7 DR-T," 65,000 "
-28036844,HC IMPL LEAD LINOX SMART S 65CM," 35,000 "
-28036845,HC IMPL PLATE TITANIUM LOCKING2 X 12MM," 7,608 "
-28036847,HC IMPL LINER ACETABULAR SIZE G 36MM," 10,715 "
-28036848,HC IMPL HEAD HUMERAL ARTHREX 50/19," 13,125 "
-28036849,HC IMPL STEM HUMERAL 8MM AR-9100-08P," 25,725 "
-28036873,HC IMPL SCREW LOCKING 5.0 X 34MM," 1,742 "
-28036875,HC IMPL SYNCAGE EVOLUTION SMALL 15MM," 40,300 "
-28036876,HC IMPL INSERT TIBIAL ROTATING PLATFM," 8,745 "
-28036877,HC IMPL STAXX LORDOTIC EXPAND 22X9X7X8," 37,700 "
-28036878,HC IMPL SYNCAGE EVOLUTION MED 15MM," 40,300 "
-28027133,HC IMPL PLATE RESORB 1.5MM," 5,746 "
-28036846,HC IMPL CLAMP IN LINE GLOBUS 182.820," 10,313 "
-28036931,HC IMPL STEM HIP ZMMR 784301226," 21,000 "
-28036932,HC IMPL BARRIER ORBITL FLOOR 38X50X1.6," 4,692 "
-28036934,HC IMPL SCREW PERIARTICULR ZIMR 4X60MM, 306 
-28036935,HC IMPL SCREW PERIARTICULR 3.50X70MM, 306 
-28036936,HC IMPL SCREW CORTICAL FIX 7.0 X 50MM," 11,385 "
-28036937,HC IMPL HEAD RADIAL EVOLVE 22MM," 12,045 "
-28036938,HC IMPL FEMUR PERSONA LEFT SIZE 6," 16,980 "
-28036939,HC IMPL ARTICULAR SURFACE PERSONA LEFT," 5,814 "
-28036940,HC IMPL LEAD EXTENSION 20 CM," 6,205 "
-28036941,HC IMPL CAGE SPNL NUVAS 6912250," 32,956 "
-28036942,HC IMPL SCREW SPNL 55MM 8825555," 11,385 "
-28036943,HC IMPL LINER ZMMR TRILOGY REV 32X62," 10,640 "
-28036944,HC IMPL MICRSPHR QUADRASPHERE 30-60UM," 10,688 "
-28036946,HC IMPL HEAD RIGHT 26.0MM," 17,940 "
-28036947,HC IMPL STEM 10.0 X 4.0MM," 10,920 "
-28036948,HC IMPL CAGE COROENT XL 10X18X45MM," 30,387 "
-28036949,HC IMPL CAGE COROENT XL-XW 10X26X55MM," 48,841 "
-28036950,HC IMPL CAGE COROENT LO 12X10X35MM," 28,000 "
-28036951,HC IMPL ROD ARM15T PREBENT 50MM," 2,125 "
-28036952,HC IMPL PLATE XLIF DECADE 4HOLE SZ14," 35,815 "
-28036953,HC IMPL BOLT FOR DECADE PLATE 5.5X55MM," 10,320 "
-28036954,HC IMPL BOLT FOR DECADE PLATE 5.0X45MM," 10,320 "
-28036955,HC IMPL HEAD ENDO FEMORAL TAPER 12/14," 3,188 "
-28036956,HC IMPL STEM FEMORAL VERSYS SIZE 17," 21,000 "
-28036957,HC IMPL BODY FEMORAL REV SIZE A," 21,058 "
-28036958,HC IMPL SPACER ORACLE 55X22X11MM," 36,582 "
-28027124,HC IMPL LEAD PACEMAKER QUARTET 75CM," 21,000 "
-28027125,HC IMPL COIL TARGET XL DETCHABLE_� 6 X 20 CM," 15,356 "
-28027128,HC CTH CENTRAL VENOUS DUAL-LUMEN ARROW 4 FR X 13, 415 
-28027129,HC VALVE DRAIN CHEST HEIMLICH 1 WAY, 229 
-28027130,HC SHAFT REAMER 450MM," 3,477 "
-28036880,HC IMPL PLATE CONCTION SHRT  4-H 55MM, 734 
-28036881,HC IMPL ROD TELOSCPING LRG 150MM," 8,883 "
-28036883,HC IMPL BOLT CANNULA FOR WIRE FIXATION, 280 
-28036884,HC IMPL CLAMP EXTERNAL FIXATOR PIN ADJUSTABLE LOCKABLE," 2,313 "
-28036885,HC IMPL EXTERNAL FIXATOR RING HALF 180MM," 7,872 "
-28036886,HC IMPL HING EXTERNAL FIXATOR SMALL BONE  FEMALE, 798 
-28036889,HC IMPL FIXATOR TOOL LEADWIRE PACING ELOX, 400 
-28036896,HC IMPL NAIL TIBIAL STRYKER 1822-0928S," 14,370 "
-28036897,HC IMPL SCREW SYNTHES 2.7MM X 34, 352 
-28036898,HC IMPL INSERT TRIDENT POLY 32MM," 9,900 "
-28036899,HC IMPL HEAD FEMORAL CERAMIC 32MM," 9,900 "
-28036902,HC INTROD MICROP PEDAL ACC 4F SET, 349 
-28036903,HC CTH SUPPORT CXI ANGLE 2.3X150," 1,403 "
-28036904,HC IMPL COIL NEURO DTCHBLE GDC 18 5X20," 5,058 "
-28036905,HC IMPL SCREW STRYKER SPINE 6.5 X 40MM," 9,188 "
-28036906,HC IMPL SCREW STRYKER SPINE 6.5 X 50MM," 9,188 "
-28036907,HC IMPL ROD STRYKER SPINE 35MM," 4,250 "
-28036909,HC IMPL NAIL TIBIAL CANN 11MM X 315MM," 12,359 "
-28036910,HC IMPL SCREW LOCKING 5.0 X 76MM," 1,709 "
-28036911,HC IMPL SCREW LOCKING 5.0 X 74MM," 1,709 "
-28036912,HC IMPL SCREW LOCKING 5.0 X 28MM," 1,709 "
-28036913,HC IMPL SHELL CONTINIUM ZMMR 68MM," 18,064 "
-28036914,HC IMPL LINER CONTINIUM ZMMR 40MM," 17,640 "
-28036915,HC IMPL BODY HIP  ZMMR 9923-83-45," 20,828 "
-28036916,HC IMPL HEAD FEMORAL ZMMR 40 12/14," 12,167 "
-28036918,HC IMPL ANNULOPLASTY RING M24," 20,014 "
-28036919,HC IMPL ANULOPLASTY RING 5200 M26," 20,014 "
-28036920,HC IMPL ANNULOPLASTY RING 5200 M28," 20,014 "
-28036921,HC IMPL ANNULOPLASTY RING 5200 M30," 20,014 "
-28036922,HC IMPL ANNULOPLASTY RING 5200 M32," 20,014 "
-28036923,HC IMPL ANNULOPLASTY RING 5200 M34," 20,014 "
-28036924,HC IMPL ANNULOPLASTY RING 5200 M36," 20,014 "
-28036925,HC IMPL ANNULOPLASTY RING 5200 M38," 20,014 "
-28036926,HC IMPL ANNULOPLASTY RING 5200 M40," 20,014 "
-28036927,HC IMPL BASEPLATE TIBIA LT 71420166," 8,142 "
-28036928,HC IMPL KNEE FEMORAL 71421214 SZ4," 16,940 "
-28036929,HC IMPL PIN SPEED NON-RIM 65MM, 117 
-28036930,HC IMPL INSERT ARTIC 71420818 SZ11 3-4," 6,178 "
-28027127,HC IMPL PLUG CONN UNIPOLAR PIN ADAPT, 425 
-28036874,HC REAMER HEAD STERILE 16.5MM," 3,141 "
-28036959,HC IMPL STEM RADIAL EVOLVE SZ 6.5MM," 14,318 "
-28036961,HC IMPL CUP ACETABULAR ALL POLY ZCA," 8,370 "
-28036962,HC IMPL CAGE RECONSTRUCTION ZCA 54MM," 17,676 "
-28036963,HC IMPL WEIGHT PLTINUM EYLID THIN 1.0," 3,358 "
-28036964,HC IMPL PLATE POW LOKING TITANIUM 4MM," 7,608 "
-28036965,HC IMPL SHEET MEDPR ULT-THN 50X76X.85," 4,083 "
-28036966,HC IMPL SHEET MEDPR ULT-THN 76X127X.85," 7,924 "
-28036967,HC IMPL SHEET MEDPR ULT-THN 127X178X.85," 14,333 "
-28036968,HC IMPL WEDGE ENOTLMS LG LFT 28X40X7.5," 4,205 "
-28036969,HC IMPL WEDGE ENOTLMS LG RT 28X40X7.5," 4,205 "
-28036971,HC IMPL PLATE PKTLOK NEUTRL LFT ORTHLX," 13,500 "
-28036972,HC IMPL SCREW NON-LKNG ORTOHLX 3.5X10," 1,856 "
-28036973,HC IMPL SCREW NON-LKNG ORTOHLX 3.5X16," 1,856 "
-28036974,HC IMPL SCREW MAXLOK PRT THRD 4X325," 1,856 "
-28036975,HC IMPL SCREW LOKING ORTHOHLX 3.5X12MM," 1,856 "
-28036976,HC IMPL SCREW LOKING ORTHOHLX 3.5X14MM," 1,856 "
-28036977,HC IMPL SCREW LOKING ORTHOHLX 3.5X16MM," 1,856 "
-28036979,HC GUIDE WIRE MINI MXLOK 1.4MM, 204 
-28036980,HC IMPL SCREW HEADLESS WRIGHT 4.3X50," 2,593 "
-28036982,HC FULRMAN PLEURAL DRAIN SET, 894 
-28036984,HC IMPL SCREW BONE MINI ACUTRAK 18.0MM," 3,451 "
-28036985,HC IMPL PLATE NARROW LNG ACU-LOC 2 VDR," 7,820 "
-28036987,HC IMPL DEFIB ICD SNGL CHMBER VR-36," 70,000 "
-28036988,HC IMPL SCREW INTRAFIX PEEK TEMP 7-9MM," 2,295 "
-28036989,HC IMPL ROD PRECEPT PREBENT LORD 110MM," 8,033 "
-28036990,HC IMPL BASE TIBIAL LONG OSS," 20,209 "
-28036991,HC CATH BLLN PTA ADVNC 35LP," 1,488 "
-28036992,HC IMPL STEM FEMORAL 18X185 REV," 17,085 "
-28036993,HC IMPL BODY FEMORAL 40MM REVISION," 21,265 "
-28036994,HC IMPL PARS SUT ARTHREX ANCHOR," 4,463 "
-28036995,HC WIRE 3.0 WRIGHT 415115000425," 1,777 "
-28036997,HC IMPL LINER CONTINUUM REVISION 36X56," 10,481 "
-28027135,HC CTH GUID LD DELIV ACUMENC315 40CM," 2,465 "
-28036998,HC CATH BLLN ADVANC 35LP 4X14X135," 1,488 "
-28036999,HC IMPL SCREW CANN ASNIS III 5.0X80," 2,029 "
-28037000,HC IMPL SYNCAGE SPACER EVOL 10.5MM," 40,300 "
-28037001,HC IMPL SCREW CANN ASNIS III 4X40MM," 1,850 "
-28037002,HC IMPL SCREW CANN ASNIS III 4X36MM," 1,850 "
-28037003,HC WIRE THRDED ASNIS III 2X150MM," 1,065 "
-28037004,HC WIRE THRDED ASNS III 1.4X150MM," 1,071 "
-28037007,HC COUNTRSNK CANN AN SNIS III 4.0," 3,386 "
-28037008,HC COUNTRSNK CANN AN SNIS III 5.0," 2,202 "
-28037010,HC IMPL WASHER ASNIS III SSTRYKER 4.0, 309 
-28037011,HC CAM ASSEMBLY FOR BONE SAW," 7,800 "
-28037012,HC SAW BONE INTRAMEDULLARY BIOMET," 6,970 "
-28037013,HC IMPL ROD PREBENT 30MM," 3,188 "
-28037014,HC CATH GUID ACUITY BRKAWAY 8X49," 3,783 "
-28037015,HC IMPL INSERT HIP CERAMIC NEUTRAL 56," 27,671 "
-28037017,HC IMPL SCREW HEX NON LCK 3.5X12MM, 578 
-28037018,HC IMPL STNT GRFT EXLUDER LLIAC 16X12," 24,500 "
-28037019,HC IMPL STNT GRFT EXLUDER LLIAC 23X12," 45,500 "
-28037020,HC IMPL PLATE SUPERIOR AUGMENT GLENOID," 17,111 "
-28037021,HC IMPL LINER CONTINUUM 32MM," 5,950 "
-28037022,HC IMPL REPLACEMENT PIN BUSHING," 9,503 "
-28037023,HC IMPL FEMORAL COMPONENT UNI SIZE G," 14,813 "
-28037024,HC IMPL INSERT CERAMIC 54X36MM," 27,671 "
-28037027,HC IMPL SCREW MINI BIOTRAK ACUMED 16MM," 3,910 "
-28037028,HC CATH BLLN ADVANC 35LP 10X2X135," 1,488 "
-28037029,HC IMPL STNT SILVER 635 LLIAC 8X30X125," 8,213 "
-28037030,HC IMPL SCREW GSB POLYAXIAL 5.5X40MM," 7,500 "
-28037031,HC IMPL SCREW GSB POLYAXIAL 5.5X35MM," 7,500 "
-28037032,HC IMPL SCREW GSB POLYAXIAL 4.5X30MM," 7,500 "
-28037033,HC IMPL SCREW GSB POLYAXIAL 4.5X35MM," 7,500 "
-28037034,HC IMPL SCREW GSB POLYAXIAL 5.5X30MM," 7,500 "
-28037035,HC IMPL SCREW GSB POLYAXIAL 7.5X60MM," 8,903 "
-28037036,HC IMPL SCREW GSB POLYAXIAL 7.5X70MM," 8,903 "
-28037037,HC GUIDWRE SAFESEPT  0.031X150," 1,683 "
-28027138,HC IMPL IUD PARAGARD," 5,083 "
-28027142,HC IMPL CAGE XIA," 42,343 "
-28027143,HC IMPL CLIP ANEURYSM #4," 4,063 "
-28027147,HC IMPL EXT FIX SOCKET THREADED 60MM, 546 
-28027148,HC SHAFT REAMER 450MM," 3,225 "
-28027149,HC IMPL SCREW CORTICAL 3.5X24MM, 204 
-28027150,HC IMPL SCREW CORTICAL 3.5X38MM, 204 
-28027151,HC IMPL TISSUE TENDON ACHILLESBRIDGE SPEED 4.75," 12,450 "
-28027152,HC IMPL ROD 40MM AEGIS," 2,125 "
-28027153,HC IMPL CAGE ALIF PEEK," 33,600 "
-28027154,HC IMPL SCREW 7.5X50MM," 7,013 "
-28027155,HC IMPLANT BREAST 425CC," 10,125 "
-28027157,HC IMPL GLENOID ARTHREX," 11,813 "
-28027158,HC IMPL HEAD HUMERAL ARTHREX," 13,125 "
-28027159,HC IMPL LINER LIPPED 28MM," 5,406 "
-28027161,HC IMPL LINER ZIMMER," 5,950 "
-28027162,HC IMPL ARTICULAR SURFACE," 5,814 "
-28036960,HC GUIDE THREADED SYNTHES," 2,546 "
-28037005,HC TWIST CANNULATED ASNIS 2.7 ASNIS III 3.5MM," 3,225 "
-28037006,HC TWIST CANNULATED ASNIS 2.7 III 2.7MM," 3,290 "
-28037025,HC IMPL BARRIER RECTANGLE 50X76X1.0MM," 4,083 "
-28037026,HC IMPL BARRIER RECTANGLE 50X76X1.6MM," 4,083 "
-28037038,HC IMPL LINER CONTINUUM 36MM," 5,950 "
-28037046,HC IMPL SCREW HEX 6.4 X 100," 2,531 "
-28037063,HC IMPL SCREW ELLIPSE 5.0X 60MM," 1,751 "
-28037066,HC IMPL ARTICULAR SURFACE NEXGEN SZ E," 5,814 "
-28027087,HC IMPL STENT BIL VISI-PRO SYS 5X17X80," 7,958 "
-28027088,HC IMPL STENT BIL VISI-PRO SYS 5X27X80," 7,958 "
-28027089,HC IMPL STENT BIL VISI-PRO SYS 5X37X80," 7,958 "
-28027090,HC IMPL CTH GUID ATTAIN COMMAND SUREVALVE 45CM," 1,632 "
-28036674,HC CTH BLLN PTA FOX SV 4X80X150," 3,188 "
-28036675,HC IMPL SCREW CORTICIAL LCK 3.5MMX14MM, 631 
-28036678,HC IMPL PLATE DISTAL RADIUS 3 HOLE R," 5,706 "
-28036679,HC IMPL PLATE DISTAL RADIUS 4 HOLE R," 5,706 "
-28036680,HC IMPL PLATE DISTAL RADIUS 4 HOLE L," 5,706 "
-28036681,HC IMPL PLATE DISTAL RADIUS 3 HOLE R," 5,706 "
-28036682,HC IMPL PLATE DISTAL RADIUS 3 HOLE L," 5,706 "
-28036683,HC IMPL PLATE DISTAL RADIUS 4 HOLE R," 5,706 "
-28036684,HC IMPL PLATE DISTAL RADIUS 4 HOLE L," 5,706 "
-28036685,HC IMPL PLATE DISTAL RADIUS 7 HOLE R," 5,706 "
-28036686,HC IMPL PLATE DISTAL RADIUS L 7 HOLE," 5,706 "
-28036687,HC IMPL PLATE DISTAL RADIUS R 4 HOLE," 5,706 "
-28036688,HC IMPL PLATE DISTAL RADIUS L 4 HOLE," 5,706 "
-28036689,HC IMPL PEG THREADED LOCKING 2.03X10MM, 631 
-28036690,HC IMPL PEG THREADED LOCKING 2.3X12MM, 631 
-28036691,HC IMPL PEG THREADED LOCKING 2.3X14MM, 631 
-28036692,HC IMPL PEG THREADED LOCKING 2.3X16MM, 631 
-28036693,HC IMPL PEG THREADED LOCKING 2.3X19MM, 631 
-28036694,HC IMPL PEG THREADED LOCKING 2.3X21MM, 631 
-28036695,HC IMPL PEG THREADED LOCKING 2.3X22MM, 631 
-28036696,HC IMPL PEG THREADED LOCKING 2.3X23MM, 631 
-28036697,HC IMPL PEG THREADED LOCKING 2.3X24MM, 631 
-28036698,HC IMPL PEG THREADED LOCKING 2.3X26MM, 631 
-28036699,HC IMPL PEG THREADED LOCKING 2.3X28MM, 631 
-28036700,HC IMPL PEG THREADED LOCKING 2.3X30MM, 631 
-28036701,HC IMPL PEG THREADED LOCKING 2.3X32MM, 631 
-28036702,HC IMPL PEG THREADED NON LOK 2.7X10MM, 631 
-28036703,HC IMPL PEG THREADED NON LOK 2.7X12MM, 631 
-28036704,HC IMPL PEG THREADED NON LOK 2.7X14MM, 631 
-28036705,HC IMPL PEG THREADED NON LOK 2.7X16MM, 631 
-28036706,HC IMPL PEG THREADED NON LOK 2.7X20MM, 631 
-28036710,HC IMPL PEG THREADED NON LOK 2.7X30MM, 631 
-28036711,HC IMPL PEG THREADED NON LOK 2.7X32MM, 631 
-28036712,HC IMPL SCREW CORTICAL LOCKING 3.5X8MM, 631 
-28036713,HC IMPL SCREW CORTICAL LOCKING 3.5X9MM, 631 
-28036714,HC IMPL SCREW CORTICAL LOCKING 3.5X10M, 631 
-28036715,HC IMPL SCREW CORTICAL LOCKING 3.5X11M, 631 
-28036716,HC IMPL SCREW CORTICAL LOCKING 3.5X12M, 631 
-28036717,HC CUTTER LOOP OLYMPUS," 4,055 "
-28036718,HC IMPL SCREW CORTICAL LOCKING 3.5X13M, 631 
-28036719,HC IMPL SCREW CORTICAL LOCKING 3.5X15M, 631 
-28036720,HC SNARE OVAL DEVICE 10MM," 1,020 "
-28036721,HC IMPL SCREW CORTICAL LOCKING 3.5X16M, 631 
-28036722,HC IMPL SCREW CORTICAL LOCKING 3.5X18M, 631 
-28036723,HC IMPL SCREW CORT NON LCKING 3.5X8MM, 631 
-28036724,HC IMPL SCREW CORT NON LCKING 3.5X9MM, 631 
-28036725,HC IMPL SCREW CORT NON LCKING 3.5X10MM, 631 
-28036726,HC IMPL SCREW CORT NON LCKING 3.5X11MM, 631 
-28036727,HC IMPL SCREW CORT NON LCKING 3.5X15MM, 631 
-28036728,HC IMPL SCREW CORT NON LCKING 3.5X16MM, 631 
-28036729,HC IMPL SCREW CORT NON LCKING 3.5X18MM, 631 
-28036730,HC ATCMNT DISP DISTAL 15.0," 2,253 "
-28036731,HC ATCHMNT DISP DISTAL15.7MM," 2,253 "
-28036732,HC IMPL NAIL TIBIAL STANDARD 10X315MM," 14,375 "
-28036733,HC IMPL SCREW LOCKING 5.0X57, 200 
-28027175,HC BIT 3 FLUTE 3.2MM," 2,178 "
-28027176,HC HANDPIECE HYDRODEBRIDER," 2,703 "
-28027177,HC SPETZLER-MALIS 8X.5MM," 3,492 "
-28027178,HC SPETZLER-MALIS 8X1.0MM," 3,492 "
-28036676,HC PROCESSING SET BLOOD BIOLOGICS," 2,508 "
-28036797,HC STIMULATOR BONE GROWTH EXOGEN 4000," 18,900 "
-28036829,HC RAT TOOTH RAPTOR MINI," 4,144 "
-28036872,HC MORCELATR TRUCLR RECIP 4.0," 7,472 "
-28037039,HC IMPL TRAY TIBIAL MOD SZ 1.5," 20,882 "
-28037040,HC IMPL WEDGE TIBIAL MOD SZ 1.5," 11,183 "
-28037041,HC IMPL WEDGE TIBIAL MOD 1.5 15MM," 11,187 "
-28037042,HC IMPL INSERT TIBIAL SIGMA SZ 1.5 15M," 7,208 "
-28037043,HC IMPL NAIL FIXATION 12X420MM," 15,071 "
-28037044,HC CTH BLLN ADVANCE35LP 5X20X135," 1,488 "
-28037045,HC IMPL NAIL FEMORAL PT SPECIFIC 80CM," 42,250 "
-28037047,HC GUIDE PIN THREADED 3.2 X 343MM," 1,066 "
-28037048,HC IMPL FASCIA LATA MEDIUM," 6,163 "
-28037049,HC IMPL STAPLE ELAS SYMETRCL ES-10X13," 8,458 "
-28037050,HC IMPL STAPLE ELAS SYMETRCL ES-15X12," 8,963 "
-28037051,HC K-WIRE TRIMED 1.6X65MM, 213 
-28037053,HC IMPL PLATE TIT MAXLRY LFT 4X8MM THK," 3,325 "
-28037054,HC IMPL PLATE TIT MAXLRY RT 4X8MM THK," 3,325 "
-28037055,HC IMPL SCRW TIT MATRX SLF-TAP 1.85X14, 639 
-28037056,HC IMPL SCRW TIT MATRX SLF-TAP 1.85X16, 639 
-28037057,HC IMPL SCRW TIT MATRX SLF-TAP 1.85X12, 639 
-28037058,HC SPLINT ORTHGNATHIC PT SPECIFIC," 7,625 "
-28037059,HC IMPL ACTUATOR PRECICE ELLIPSE W/EXT," 69,216 "
-28037060,HC BIT DR ELLIPSE 5.0X152MM," 3,502 "
-28037062,HC IMPL FEMUR ANTEGRADE ELLIPSE 330MM," 20,188 "
-28037064,HC IMPL SCREW ELLIPSE 5.0X 45MM," 1,751 "
-28037065,HC IMPL SCREW ELLIPSE 5.0X 50MM," 1,751 "
-28037067,HC IMPL BODY FEMORAL REVISION 46MM," 21,058 "
-28037068,HC IMPL EXPANDER BRST ALRGN 133MV-11-T," 11,588 "
-28037069,HC IMPL K-WIRE PERI-LOK S AND N 71161020, 226 
-28037071,HC FIBER LASER MOXY GREENLIGHT," 9,863 "
-28037073,HC IMPL ICD DEFIB UNITY CRT-D DF1 CONN," 77,000 "
-28037074,HC IMPL LINER CONTINUUM 32 X 52," 5,950 "
-28037075,HC IMPL CAGE TLIF PEEK 32X0X11 AEGIS," 25,200 "
-28037076,HC IMPL PLATE TIBIA PROX LAT 260MM," 14,428 "
-28037077,HC IMPL NAIL FEMORAL CANN 360MM," 14,110 "
-28037081,HC IMPL BEAT BSM BONE SUBSTITUTE 5CC," 10,313 "
-28037082,HC IMPL BEAT BSM BONE SUBSTITUTE 10CC," 15,120 "
-28037083,HC IMPL GAMMA-BSM BONE SUBSTITUTE 5CC," 10,313 "
-28037084,HC IMPL TISSUE GRAFT  AM MEMBRANE FROZ 2.5X2.0," 7,132 "
-28027179,HC FIBER LASER MOXY GREENLIGHT," 9,863 "
-28037070,HC LASER GREENLIGHT XPS LGX0123," 7,225 "
-28037087,HC IMPL IMPL BRST SIENTRA 20645-170LP," 8,458 "
-28037088,HC IMPL IMPL BRST SIENTRA 20645-220LP," 8,458 "
-28037089,HC IMPL IMPL BRST SIENTRA 20645-270LP," 8,458 "
-28037090,HC IMPL IMPL BRST SIENTRA 20645-320LP," 8,458 "
-28037091,HC IMPL IMPL BRST SIENTRA 20645-410LP," 8,458 "
-28037096,HC IMPL CONDYLE HEMI LATERAL FRESH L," 65,700 "
-28037097,HC INTRO SHTH SYS PEELAWAY 6X13, 281 
-28037098,HC IMPL SCREW STRYKER 4.5 X 30MM," 14,424 "
-28037099,HC IMPL SCREW STRYKER 4.5 X 35MM," 14,424 "
-28037100,HC IMPL ROD VITALLIUM 350MM," 7,975 "
-28037101,HC IMPL CROSSLINK STRYKER SPINE," 7,225 "
-28037102,HC IMPL SCREW STRYKER 4.5 X 24MM," 8,075 "
-28037103,HC IMPL PLATE LOCKING TRAPEZ 1.5/ 4X2," 2,506 "
-28037104,HC IMPL SCREW LOCKING 1.5 X 08MM," 1,062 "
-28037105,HC IMPL SCREW LOCKING 1.5 X 09MM," 1,062 "
-28037106,HC IMPL SCREW LOCKING 1.5 X 10MM," 1,062 "
-28037107,HC IMPL SCREW LOCKING 1.5 X 11MM," 1,062 "
-28037108,HC IMPL PLATE CERVICAL L 19 AEGIS," 8,500 "
-28037109,HC IMPL CAGE UNILIF 12X30X0X11," 29,954 "
-28037110,HC IMPL NAIL FEMORAL 11MM X 340MM," 14,110 "
-28037111,HC IMPL STAPLE MEMORY ELASTIC 15X12MM," 10,158 "
-28037112,HC IMPL STAPLE MEMORY ELASTIC 13X16MM," 10,158 "
-28037113,HC K-WIRE 2.0X 60MM, 213 
-28037114,HC K-WIRE 1.6 X 65MM, 213 
-28037115,HC IMPL PLATE MAXILRY TIT PRE-BNT 3MM," 2,924 "
-28037116,HC IMPL PLATE MAXILRY TIT RT-BNT 3MM," 2,924 "
-28037117,HC IMPL SCREW VALOR NAIL WRIGHT 5X65MM," 2,015 "
-28037118,HC IMPL SCREW VALOR NAIL WRIGHT 5X25MM," 2,015 "
-28037119,HC IMPL VALOR NAIL LRG-RT  10X250MM," 19,467 "
-28037120,HC IMPL MATRIX SNGL LAYR INTEGRA 4X5IN," 25,690 "
-28037121,HC IMPL BREST SIENTRA 20645-700MP," 8,458 "
-28037122,HC IMPL SCREW TENODESIS WRIGHT 6X20MM," 3,970 "
-28037123,HC WIREGUIDED 180CM 10-12," 1,658 "
-28037124,HC WIREGUIDED 180CM 12-15," 1,658 "
-28037125,HC WIREGUIDED 180CM 15-18," 1,658 "
-28037126,HC IMPL PLATE TIBIA SYNTHES 238.710," 12,551 "
-28037134,HC IMPL SCREW CERV POSTERIOR 4.0X22MM," 7,225 "
-28037135,HC IMPL PLATE LCP 2.4MM X 84MM 10 HOLE," 3,803 "
-28037136,HC IMPL PLATE ADAPTATION 2.7MM LCP," 4,423 "
-28037139,HC IMPL PLATE MTP LOW PROFILE RIGHT," 6,800 "
-28037141,HC IMPL SCREW LOW PROFILE LCK 3.0X24MM, 723 
-28037144,HC IMPL SPACER SYNCAGE SMALL 13.5MM," 40,300 "
-28037146,HC IMPL FEMORAL COMPONENT OPTETRAK 2.5," 16,181 "
-28037148,HC IMPL INSERT TIBIAL OPTETRAK SZ 2.5," 6,165 "
-28037150,HC IMPL PLATE PROX FEMUR NCB 285MM," 13,662 "
-28037153,HC IMPL SCREW CORTICAL NCB 5.0X36MM," 1,840 "
-28037154,HC IMPL SCREW CORTICAL NCB 4.0X30MM," 1,234 "
-28037155,HC IMPL SCREW CORTICAL NCB 4.0X32MM," 1,234 "
-28037156,HC IMPL SCREW CORTICAL NCB 4.0X42MM," 1,234 "
-28037157,HC IMPL SCREW UNICORTICAL NCB 5.0X14MM," 1,683 "
-28037158,HC IMPL SCREW EXP 6.35 X 40MM," 8,843 "
-28037159,HC IMPL SCREW EXP 6.35 X 45MM," 8,843 "
-28037160,HC IMPL SCREW EXP 7.0 X 40MM," 8,843 "
-28037161,HC IMPL SCREW EXP 7.0 X 45MM," 8,843 "
-28037166,HC IMPL PLATE ANCHORING ROI-C  LDR," 12,000 "
-28037128,HC IMPL STEM EVOLVE 8.5MM +2," 14,318 "
-28037129,HC IMPL HEAD EVOLVE 24MM," 12,045 "
-28037130,HC IMPL PORT XCELA DUAL LUMEN 9.6X0.9," 3,825 "
-28037131,HC IMPL PORT BIOFLO 8F," 3,188 "
-28037132,HC IMPL PLATE GRID / TRILOCK 1.2/1.5/," 4,660 "
-28037133,HC IMPL DIAPHYSEAL SEGMENT OSS 4CM," 33,551 "
-28037137,HC IMPL PLATE TIBIA DIST POSTERIOR 3.5," 10,984 "
-28037138,HC IMPL EXPANDR BREST W/TABS 354-9300," 11,925 "
-28037140,HC GUIDEWIRE TROCAR TIP .045X5.91, 60 
-28037142,HC IMPL SCREW INTRA-FIX 8-10MM," 2,295 "
-28037143,HC CONTROL HAND COOL PULSE VPR S90," 2,550 "
-28037145,HC IMPL TRAY TIBIAL OPTETRAK SZ 2.5F," 8,524 "
-28037147,HC IMPL PATELLA OPTETRAK 29MM," 3,236 "
-28037149,HC IMPL CABLE BUTTON FOR NCB," 2,083 "
-28037151,HC IMPL SCREW CORTICAL NCB 5.0X32MM," 1,840 "
-28037152,HC IMPL SCREW CORTICAL NCB 5.0X34MM," 1,840 "
-28037162,HC IMPL ROD STRAIGHT 300MM," 8,843 "
-28037163,HC IMPL SET SCREW EXP 635 TI DI," 2,397 "
-28037164,HC IMPL SET SCREW EXP 635 TI SI," 1,811 "
-28037165,HC IMPL ROD PREBENT 75MM," 3,188 "
-28037167,HC IMPL IMPLANT LORDOTIC ROI-C 7X14X15," 12,000 "
-28037174,HC IMPL CAGE PEEK TLIF 26X0X7," 25,200 "
-28037178,HC IMPL ACETABULAR CUP RING-LOC BI-POL," 8,063 "
-28037181,HC IMPL BRST MENTOR 354-2157MC," 7,650 "
-28037182,HC IMPL BRST MENTOR 354-2357MC," 7,650 "
-28037183,HC IMPL BRST MENTOR 354-2557MC," 7,650 "
-28037184,HC IMPL BRST MENTOR 354-2757MC," 7,650 "
-28037185,HC IMPL BRST MENTOR 354-3007MC," 7,650 "
-28037188,HC IMPL BRST MENTOR 354-3407MC," 7,650 "
-28037189,HC IMPL ANCHOR PLUG COMPRESS 12MM," 7,191 "
-28037196,HC IMPL ANCHOR PLUG COMPRESS 16MM," 7,191 "
-28037200,HC IMPL BRST MENTOR 354-8007MC," 7,650 "
-28037206,HC IMPL BRST MENTOR 354-7501," 7,863 "
-28037208,HC IMPL SCREW CANN 7.0X150 FULL THREAD," 2,244 "
-28037209,HC IMPL SCREW CANN 7.0X145 FULL THREAD," 2,244 "
-28037210,HC IMPL SPACER SYNCAGE EVOL 13.2MM," 40,300 "
-28037211,HC IMPL SPACER SYNCAGE EVOL 13.5MM," 40,300 "
-28037213,HC IMPL NAIL TIBIAL 360MM 04.034.252S," 12,359 "
-28037215,HC IMPL BUTTON TIGHTROPE," 1,216 "
-28037170,HC CATH GUID SUREFIRE 5X65," 2,125 "
-28037171,HC CATH GUID SUREFIRE COBRA1 5X65," 2,125 "
-28037172,HC IMPL PECTORAL IMPL LFT SPCTRM DSIGN," 5,100 "
-28037173,HC IMPL PECTORAL IMPL RT SPCTRM DSIGN," 5,100 "
-28037176,HC IMPL SCREW ILIAC ARM 8.5 X 70MM," 8,805 "
-28037177,HC IMPL STEM HIP OSS INTERLOCK," 11,985 "
-28037186,HC IMPL BRST MENTOR 354-3207MC," 7,650 "
-28037199,HC IMPL BRST MENTOR 354-7507MC," 7,650 "
-28037201,HC IMPL TIBIAL COMPRESS INTERCALARY," 64,025 "
-28037202,HC IMPL BRST MENTOR 354-8001," 7,863 "
-28037216,HC IMPL TIGHTROPE NO BUTTON," 1,564 "
-28027208,HC IMPL BLOCKER STRYKER," 2,189 "
-28027209,HC INSERTER TIP ARTIC SURFACE, 678 
-28027210,HC IMPL ARTICULAR SURFACE," 5,814 "
-28027211,HC IMPL PLATE 2.4MM 10 HOLE," 2,438 "
-28027212,HC IMPL SHELL ZIMMER," 12,300 "
-28027213,HC IMPL PLATE 249.924," 2,840 "
-28027214,HC IMPL CAGE UNILIF 12X30X40," 36,447 "
-28027215,HC IMPL SCREW 5.5 X 40MM," 14,424 "
-28027216,HC IMPL CAGE COROENT 6921050," 32,596 "
-28027217,HC IMPL PATELLA 71966225," 4,027 "
-28027221,HC IMPL BREAST 245CC," 10,125 "
-28027222,HC IMPL LINER 1221-36-062," 5,406 "
-28027223,HC IMPL AUGMENT BLOCK," 7,669 "
-28027224,HC IMPL PLATE DIST HUMERUS 3.5MM," 9,638 "
-28027225,HC IMPL LINER HIP 36MM," 10,481 "
-28027227,HC IMPL SCREW 48556330," 8,075 "
-28027228,HC IMPL PLATE TIBIAL 3.5MM," 7,669 "
-28027229,HC IMPL SCREW LOCK 3.5 X 56MM," 1,272 "
-28037190,HC SIZER BRST MENTOR RSZ-7215MC, 272 
-28037191,HC SIZER BRST MENTOR RSZ-7235MC," 2,508 "
-28037192,HC SIZER BRST MENTOR RSZ-7255MC," 2,508 "
-28037194,HC SIZER BRST MENTOR RSZ-7275MC," 2,508 "
-28037195,HC SIZER BRST MENTOR RSZ-7300MC," 2,508 "
-28037197,HC SIZER BRST MENTOR RSZ-7320MC," 2,508 "
-28037198,HC SIZER BRST MENTOR RSZ-7340MC," 2,508 "
-28037203,HC SIZER BRST MENTOR RSZ-7750MC," 2,508 "
-28037204,HC SIZER BRST MENTOR RSZ-7800MC," 2,508 "
-28037205,HC SIZER BRST MENTOR RSZ-8001," 2,508 "
-28037207,HC SIZER BRST MENTOR RSZ-7501," 2,508 "
-28037218,HC IMPL SCREW CORT LOW PROFILE 3.5X22M, 298 
-28037219,HC IMPL SCREW CORT LOW PROFILE 3.5X45M, 298 
-28037220,HC IMPL SCREW CORT LOW PROFILE 4.0X40M, 298 
-28037221,HC IMPL PLATE LCKING 3RD TUNULAR 4H SS," 1,913 "
-28037222,HC IMPL SCREW CORT LOW PROFILE 3.5X24M, 298 
-28037223,HC IMPL SCREW  LOW PROFILE SS 4.0X18MM, 298 
-28037224,HC IMPL ART SURFACE 14MM RT ZMMR," 5,814 "
-28037225,HC IMPL EXPANDER BRST 20646-200RB," 8,458 "
-28037226,HC IMPL EXPANDER BRST 20646-220RB," 8,458 "
-28037227,HC IMPL EXPANDER BRST 20646-235RB," 8,458 "
-28037228,HC IMPL EXPANDER BRST 20646-255RB," 8,458 "
-28037229,HC IMPL EXPANDER BRST 20646-270RB," 8,458 "
-28037230,HC IMPL EXPANDER BRST 20646-320RB," 8,458 "
-28037231,HC IMPL EXPANDER BRST 20646-370RB," 8,458 "
-28037232,HC IMPL EXPANDER BRST 20646-425RB," 8,458 "
-28037234,HC IMPL SCREW LOKNG STRYKR 3X16MM," 2,355 "
-28037236,HC CATH GUIDE SINUS FLEX GC070CRF," 3,774 "
-28037237,HC CATH BALLOON SINUS ULTIRRA," 7,997 "
-28037238,HC IMPL PLATE OSTEOT CALCNEAL 27-03610," 10,125 "
-28037239,HC IMPL SCREW LOKNG 07-35130 30MM," 1,105 "
-28037240,HC IMPL SCREW LOKNG 07-35132 32MM," 1,105 "
-28037241,HC IMPL SCREW LOKNG 07-35134 34MM," 1,105 "
-28037242,HC K-WIRE MEDPAC WIRE-1.1, 85 
-28037244,HC IMPL COMPONENT FEMORAL EVO 4 LEFT," 15,000 "
-28037245,HC IMPL INSERT KNEE EVOL 4 LEFT," 5,100 "
-28037246,HC IMPL PLATE HELIX 22MM 1-LEVEL," 8,500 "
-28037247,HC IMPL FLTER VENA CAVA CRUX 9X67X.035," 14,250 "
-28037248,HC IMPL SCREW VUEPOINT 3.5X26MM," 8,798 "
-28037249,HC IMPL SCREW VUEPOINT 3.5X28MM," 8,798 "
-28037251,HC IMPL SCREW VP PART THREAD 3.5X34MM," 9,323 "
-28037252,HC IMPL SCREW VP PART THREAD 3.5X36MM," 9,323 "
-28037255,HC IMPL STNT GRFT ZENITH FLX AAA 32X11," 54,977 "
-28037256,HC IMPL BLOCK FEM POST 10MM 5990-37-02," 6,635 "
-28037257,HC IMPL BLOCK DIST FEM 10 M 5990-37-20," 6,635 "
-28037258,HC IMPL PLATE FX OSTEOMED 6HOLE," 3,728 "
-28037260,HC IMPL SCRW SCHANZ TIT THRD LNGTH 8MM," 2,781 "
-28037261,HC IMPL SCRW SCHANZ TIT THRD LNTH 10MM," 2,781 "
-28037262,HC IMPL SCRW SCHANZ TIT THRD LNTH 20MM," 2,781 "
-28037264,HC EXT FIXATOR MANDIBLE ADJ," 3,522 "
-28037265,HC IMPL PLATE LOW PROFILE MTP CONTURED," 6,800 "
-28037266,HC IMPL SCREW LCKING L/P 3.0 X 12MM, 723 
-28037267,HC IMPL SCREW CANCELLOUS L/P 3.0X36MM," 1,105 "
-28037268,HC GUIDEWIRE W/TROCAR TIP .045, 170 
-28037269,HC IMPL STNT GRFT OVATION PRIME 15X14," 28,350 "
-28037270,HC IMPL STNT GRFT OVATION PRIME 14X14," 28,350 "
-28037271,HC IMPL BASEPLATE TIB SZ 5 RT S&N," 7,625 "
-28037272,HC IMPL INSERT ART 11 MM  SZ 5-6 S&N," 7,901 "
-28037273,HC IMPL FEMORAL KNEE SZ 5 RT S&N," 15,767 "
-28037274,HC IMPL BASEPLATE TIB SZ 5 LT S&N," 7,728 "
-28037275,HC IMPL INSERT ART 13 MM SZ 5-6 S&N," 8,407 "
-28037276,HC IMPL FEMORAL KNEE SZ 5 LT S&N," 16,043 "
-28037277,HC IMPL SCREW 1.2 X 10MM, 628 
-28037179,HC ACL DISPSBLES MITEK 232300 KIT," 4,208 "
-28037180,HC VERSITOMIC REAMR RENTAL SYS," 2,975 "
-28037187,HC IMPL SPINDLE ANTI ROTATION COMPRESS," 21,798 "
-28037254,HC IMPL ADAPTER INTRO CATH CHECK FLOW, 85 
-28037279,HC IMPL NAIL FEMORAL SYNTHES 13X 420," 14,110 "
-28037280,HC IMPL SCREW LOCK SYNTHES 5.0 X 62MM," 1,708 "
-28037281,HC IMPL STNT GRFT ENDO AAA AFX 28X60," 69,480 "
-28037282,HC IMPL EMBO COILS VFC-AV 3X15," 11,250 "
-28037283,HC IMPL HEMI CONDYLE LATERIAL R FRESH," 65,700 "
-28037284,HC IMPL SNARE CATH ENSNARE 27-45X120," 3,740 "
-28037285,HC IMPL PATELLA 32MM EXACTECH200-02-32," 3,235 "
-28037286,HC IMPL TRAY TIBIAL EXACECH SZ3F 2T," 8,524 "
-28037287,HC IMPL FEMORAL KNEE LT EXACTECH SZ3," 16,182 "
-28037288,HC IMPL INSERT TIBIAL EXACTE SZ 3 11MM," 6,165 "
-28037290,HC IMPL LINER ZMMR 36 X 58 7MM," 5,950 "
-28037291,HC IMPL NAIL RECON STRYKER 9.0X420MM," 17,532 "
-28037292,HC IMPL END CAP STRYKER +15MM," 1,999 "
-28037293,HC IMPL CRANIAL PT SPECIFIC," 51,877 "
-28037294,HC IMPL SCREW LOKNG WRIGHT 4.5X32," 1,552 "
-28037295,HC IMPL SCREW LOKNG WRIGHT 4.5X60," 1,760 "
-28037296,HC IMPL SCREW NON LOKNG WRIGHT 4.5X34, 723 
-28037297,HC IMPL SCRW HEADED G WRIGHT 6.5X55X16," 3,536 "
-28037298,HC IMPL SCRW HEADED G WRIGHT 6.5X75X32," 3,536 "
-28037299,HC IMPL PLATE RADIAL HEAD NECK 2.4MM," 5,227 "
-28037301,HC IMPL CATH BLLN ADVANCE 35LP 5X20," 1,487 "
-28037302,HC IMPL CATH BLLN ADVANCE 35LP 6X20," 1,488 "
-28037303,HC IMPL CATH BLLN ADVANCE 35LP 8X20," 1,488 "
-28037304,HC IMPL CATH BLLN ADVANCE 35LP 8X20," 1,488 "
-28037305,HC IMPL NAIL TIBIAL CANN 11MM X 330MM," 12,359 "
-28037306,HC IMPL CATH BLLN ADVANCE 35LP 10X14," 1,488 "
-28037307,HC IMPL CATH BLLN ADVANCE 35LP 12X14," 1,488 "
-28037308,HC IMPL CATH BLLN ADVANCE 35LP 12X14," 1,488 "
-28037309,HC IMPL CATH BLLN ADVANCE 35LP 3X4," 1,488 "
-28037310,HC IMPL CATH BLLN ADVANCE 35LP 4X4," 1,488 "
-28037312,HC IMPL CATH SUPPRT CXI 2.3X150 ANG2," 1,238 "
-28037313,HC IMPL MESH MATRIXNEURO 200X200X.06MM," 56,482 "
-28037391,HC IMPL ICD FORTIFY ASSURA DR 40 DF4," 84,000 "
-28037393,HC BLADE EXPLANT SIZE 60 20," 7,140 "
-28036871,HC TRUCLEAR HYSER W ITH SVC KIT," 2,992 "
-28037315,HC IMPL NAIL VALOR SMALL RT 10X150MM," 19,467 "
-28037316,HC IMPL IPG STIMULATR ST JUDE 3789ANS," 70,539 "
-28037317,HC PROGRAMMER PT ST JUDE 3852ANS," 10,463 "
-28037319,HC IMPL ROD DEPUY 5.5 X 45MM," 3,188 "
-28037320,HC IMPL SCREW CORTICAL FIX 6.0 X 30MM," 11,385 "
-28037321,HC IMPL SCREW LOW PROFILE CAN 4.0 X 22, 298 
-28037322,HC IMPL LINER ACET BIOMET 36X24," 12,000 "
-28037323,HC IMPL STEM FEMORAL 14X148 51-100140," 17,250 "
-28037324,HC IMPL HEAD CERAMIC BIOMET 36MM -3," 17,250 "
-28037325,HC IMPL AUGMENT POST KNEE 5MMX62.5," 8,339 "
-28037326,HC IMPL STEM BIOMET KNEE 12MMX 40," 9,225 "
-28037327,HC IMPL FEMORAL STEM  RT BIOMET 62.5," 38,058 "
-28037328,HC IMPL TIBIAL BEARING BIOMET10X71/75," 14,355 "
-28037329,HC IMPL FEMORAL COMPONENT," 15,000 "
-28037330,HC IMPL INSERT EVOLUTION," 5,100 "
-28037331,HC IMPL SCREW PERI-LOK S&N 3.5X18MM, 850 
-28037332,HC IMPL SCREW PERI-LOK S&N 3.5X28MM, 852 
-28037333,HC IMPL SCREW PERI-LOK S&N 3.5X36MM, 850 
-28037334,HC IMPL SCREW LOKING HEXALOBE 3.5X55MM," 1,088 "
-28037335,HC IMPL SCREW NONLKNG HEXLOBE 3.5X55MM, 757 
-28037336,HC IMPL SCREW LOKNG HEXALOBE 3X50MM," 1,088 "
-28037337,HC IMPL SCREW NON LKNG HEXLOBE 3X12MM, 578 
-28037338,HC IMPL SCREW NON LKNG HEXLOBE 3X14MM, 578 
-28037339,HC IMPL SCREW NON LKNG HEXLOBE 3X16MM, 578 
-28037340,HC IMPL SCREW NON LKNG HEXLOBE 3X18MM, 578 
-28037341,HC IMPL SCREW NON LKNG HEXLOBE 3X20MM, 578 
-28037342,HC IMPL SCREW NON LKNG HEXLOBE 3X22MM, 578 
-28037343,HC IMPL SCREW NON LKNG HEXLOBE 3X26MM, 578 
-28037344,HC IMPL SCREW NON LKNG HEXLOBE 3X28MM, 578 
-28037348,HC IMPL PLATE LATERAL RT LOKNG 10HOLE," 7,310 "
-28037349,HC IMPL PLATE MEDIAL LOKNG 8HOLE," 7,310 "
-28037350,HC IMPL PLATE MEDIAL SHRT 9HOLE," 7,310 "
-28037353,HC IMPL SKIN MESHED 175 SQ. CM," 3,120 "
-28037356,HC IMPL COMPONET PATELLO FEM KNEE SZ 2," 22,400 "
-28037358,HC IMPL BLADE HUMERAL NAIL SYNTHE 44MM," 5,576 "
-28037359,HC IMPL SCREW SYNTHES 4.0 X 24," 1,713 "
-28037360,HC IMPL SCREW  PART THREADED 3.75X34," 1,700 "
-28037361,HC IMPL SCREW  PART THREADED 3.75X36," 1,700 "
-28037362,HC IMPL SCREW CONICAL CANN 7.3X85MM," 2,541 "
-28037363,HC IMPL STENT ESOPH ENDOMAXX 23X120," 18,200 "
-28037364,HC IMPL STEM FEMORAL TRI-LOCK SZ1 97MM," 20,776 "
-28037365,HC IMPL PLATE CLAVICLE 2MM 247.351," 4,197 "
-28037366,HC IMPL PLATE CLAVICLE SYNTHES 249.669," 6,987 "
-28037367,HC IMPL PLATE 2.7 LCP 7 H 249.684," 6,222 "
-28037368,HC IMPL EMBO COILS RUBY 12X60X0.020," 12,576 "
-28037369,HC IMPL CAGE COUGAR LS 21X12X50  15'," 44,980 "
-28037370,HC IMPL PT SPEC CRANI IMPL SD420.427," 71,545 "
-28037371,HC IMPL CATHCART BALL HIP 1363-41-000," 4,777 "
-28037372,HC IMPL SCREW LOKING STRYKER PLSL3518," 2,354 "
-28037373,HC IMPL SCREW THREADED 5.5 X 30MM," 8,478 "
-28037374,HC IMPL SCREW THREADED 5.5 X 35MM," 8,458 "
-28037375,HC IMPL CAP LOCKING THREADED GLOBUS, 850 
-28037376,HC IMPL ROD ELLIPSE 350MM GLOBUS," 2,125 "
-28037377,HC IMPL PLATE L LT 2H/4H 249.702," 3,811 "
-28037378,HC IMPL PLATE T SYNTH 2H/3H 249.685," 6,987 "
-28027253,HC CATH ANGIO GUID CORN ACUITY BRK AWY 8X49," 3,783 "
-28027259,HC EMBO COIL TARGET XL 360 10X40," 16,463 "
-28027260,HC CASSETTE IMPELLA PURGE," 8,500 "
-28037289,HC BIT MILLING 2.0 03.820.161S," 3,700 "
-28037318,HC CHARGING ST JUDE 3730ANS SYS," 10,125 "
-28037379,HC IMPL SCREW VALOR WRIGHT 5X60MM," 2,015 "
-28037380,HC IMPL SCREW VALOR WRIGHT 5X100MMMM," 2,015 "
-28037383,HC IMPL SCREW HEADED DARTFIRE 3.5X22MM," 1,649 "
-28037384,HC IMPL HAMMERTOE VO PRO-TOE  LRG 10DG," 4,386 "
-28037387,HC IMPL CAGE COUGAR LS 21X12X55," 40,855 "
-28037389,HC IMPL STIMULATOR BONE IMPLANTABLE," 41,490 "
-28037390,HC IMPL STNT GRFT ENDURNT II 32X32X49," 32,025 "
-33141938,ACETAMINOPHEN 120 MG SUPP, 1.1 
-33141946,ACETAMINOPHEN 160 MG/5 ML (5 ML) SOLN 5 ML CUP, 1.0 
-33141961,ACETAMINOPHEN 325 MG SUPP, 2 
-33141987,ACETAMINOPHEN 500 MG TABS, 0.9 
-33141995,ACETAMINOPHEN 650 MG SUPP, 2 
-33142001,ACETAZOLAMIDE 125 MG TABS, 9 
-33142019,ACETAZOLAMIDE 250 MG TABS, 11 
-33142027,ACETAZOLAMIDE 500 MG CPSR, 51 
-33142035,ACETAZOLAMIDE 500 MG SOLR, 184 
-33142035,ACETAZOLAMIDE 500 MG SOLR 1 EACH VIAL, 225 
-33142050,"ACETIC ACID 0.25 % SOLN 1,000 ML BOTTLE", 51 
-33142100,ACETYLCHOLINE 1:100 (20 MG/2 ML) KIT 1 EACH KIT, 166 
-33142118,ACETYLCYSTEINE 100 MG/ML (10 %) SOLN 4 ML VIAL, 14 
-33142118,ACETYLCYSTEINE 100 MG/ML (10 %) SOLN, 14 
-33142142,ACETYLCYSTEINE 20 % (200 MG/ML) SOLN, 16 
-33142142,ACETYLCYSTEINE 200 MG/ML (20 %) SOLN, 16 
-33142142,ACETYLCYSTEINE 200 MG/ML (20 %) SOLN 30 ML VIAL, 18 
-33142142,ACETYLCYSTEINE 20 % (200 MG/ML) SOLN 30 ML VIAL, 18 
-33142233,ACYCLOVIR 200 MG CAPS, 7 
-33142266,ADENOSINE 3 MG/ML SOLN, 102 
-33142266,ADENOSINE 3 MG/ML SOLN 2 ML VIAL, 102 
-33142365,ALBUTEROL 2 MG TABS, 6 
-33142373,ALBUTEROL 2 MG/5 ML SYRP, 7 
-33142399,ALBUTEROL 5 MG/ML NEBU, 41 
-33142399,ALBUTEROL 5 MG/ML NEBU 20 ML BOTTLE, 78 
-33142480,ALFENTANIL 500 MCG/ML SOLN 2 ML AMPUL, 32 
-33142480,ALFENTANIL 500 MCG/ML SOLN, 55 
-33142498,ALFENTANIL 500 MCG/ML SOLN 5 ML AMPUL, 87 
-33142506,ALLOPURINOL 100 MG TABS, 4 
-33142514,ALLOPURINOL 300 MG TABS, 6 
-33142522,ALPRAZOLAM 0.25 MG TABS, 5 
-33142530,ALPRAZOLAM 0.5 MG TABS, 5 
-33142548,ALPRAZOLAM 1 MG TABS, 4 
-33142555,ALPROSTADIL 500 MCG/ML SOLN, 0.9 
-33142639,ALUMINUM HYDROXIDE 600 MG/5 ML SUSP, 2 
-33142639,ALUMINUM HYDROXIDE 600 MG/5 ML SUSP 355 ML BOTTLE, 6 
-33142639,ALUMINUM HYDROXIDE 320 MG/5 ML SUSP, 7 
-33142639,ALUMINUM HYDROXIDE 320 MG/5 ML SUSP 30 ML CUP, 7 
-33142662,AMANTADINE 100 MG CAPS, 24 
-33142662,AMANTADINE HCL 100 MG CAPS, 25 
-33142688,AMIKACIN 500 MG/2 ML SOLN, 17 
-33142738,AMINOCAPROIC ACID 500 MG TABS, 19 
-33142746,AMINOCAPROIC ACID 250 MG/ML SOLN 20 ML VIAL, 36 
-33142746,AMINOCAPROIC ACID 250 MG/ML SOLN, 37 
-33142803,AMINOPHYLLINE 250 MG/10 ML SOLN 10 ML VIAL, 53 
-33142829,AMIODARONE 200 MG TABS, 6 
-33142837,AMITRIPTYLINE 10 MG TABS, 3 
-33142845,AMITRIPTYLINE 25 MG TABS, 4 
-33142852,AMITRIPTYLINE 50 MG TABS, 5 
-33142860,AMITRIPTYLINE 75 MG TABS, 5 
-33142878,AMITRIPTYLINE 100 MG TABS, 6 
-33142894,AMITRIPTYLINE 150 MG TABS, 6 
-33142944,AMMONIA 0.33 ML/AMP SOLN, 2 
-33143009,AMOXICILLIN 250 MG CAPS, 6 
-33143025,AMOXICILLIN 500 MG CAPS, 5 
-33143058,AMPHOTERICIN B 5 MG/ML SOLR 1 EACH VIAL, 117 
-33143058,AMPHOTERICIN B 5 MG/ML SOLR, 122 
-33143074,AMPICILLIN 125 MG SOLR, 97 
-33143074,AMPICILLIN 125 MG SOLR 1 EACH VIAL, 139 
-33143090,AMPICILLIN 1 GRAM SOLR, 73 
-33143108,AMPICILLIN 1 GRAM SOLR 1 EACH VIAL, 45 
-33143108,AMPICILLIN 1 GRAM SOLR, 49 
-33143116,AMPICILLIN 250 MG CAPS, 3 
-33143124,AMPICILLIN 250 MG SOLR, 41 
-33143124,AMPICILLIN 250 MG SOLR 1 EACH VIAL, 63 
-33143132,AMPICILLIN 250 MG/5 ML SUSR 200 ML BOTTLE, 101 
-33143140,AMPICILLIN 2 GRAM SOLR, 61 
-33143165,AMPICILLIN 500 MG CAPS, 6 
-33143173,AMPICILLIN 500 MG SOLR 1 EACH VIAL, 46 
-33143173,AMPICILLIN 500 MG SOLR, 57 
-33143389,MINERAL OIL-HYDROPHIL PETROLATUM OINT 454 G JAR, 8 
-33143397,ARGININE 10% 10% SOLP 300 ML BAG, 49 
-33143397,ARGININE 10% 10% SOLP, 49 
-33143439,ASCORBIC ACID 250 MG TABS, 1.5 
-33143454,ASCORBIC ACID 500 MG TABS, 1.5 
-33143504,ASPIRIN 300 MG SUPP, 2 
-33143520,ASPIRIN 325 MG TBEC, 1.4 
-33143538,ASPIRIN 600 MG SUPP, 3 
-33143546,ASPIRIN 81 MG CHEW, 1.3 
-33143561,ATENOLOL 50 MG TABS, 5 
-33143579,ATENOLOL 100 MG TABS, 6 
-33143595,ATROPINE 0.4 MG/ML SOLN, 0.9 
-33143595,ATROPINE 0.4 MG/ML SOLN 1 ML VIAL, 1.1 
-33143652,ATROPINE SULFATE (PF) 1 % DROP 2 ML DROP BTL, 41 
-33143660,ATROPINE 1 % DROP 5 ML DROP BTL, 96 
-33143686,ATROPINE 1 % OINT 3.5 G TUBE, 56 
-33143702,ANTIPYRINE-BENZOCAINE 5.4-1.4 % DROP 10 ML DROP BTL, 50 
-33143751,AZATHIOPRINE 50 MG TABS, 5 
-33143769,AZTREONAM 1 GRAM SOLR, 288 
-33143777,AZTREONAM 2 GRAM SOLR, 660 
-33143785,BACLOFEN 10 MG TABS, 5 
-33143793,"BACITRACIN 50,000 UNIT SOLR", 128 
-33143793,"BACITRACIN 50,000 UNIT SOLR 1 EACH VIAL", 130 
-33143843,BCG 50 MG SUSR 1 EACH VIAL," 1,418 "
-33143876,BECLOMETHASONE 40 MCG/ACTUATION AERO 7.3 G AER W/ADAP, 745 
-33143876,BECLOMETHASONE 40 MCG/ACTUATION AERO, 887 
-33143876,BECLOMETHASONE 40 MCG/ACTUATION AERO 8.7 G AER W/ADAP," 1,061 "
-33143884,BEER 1 EACH CAN, 2 
-33143884,BEER, 2 
-33143975,BENZONATATE 100 MG CAPS, 9 
-33144015,BENZOYL PEROXIDE 10 % CLSR 237 G BOTTLE, 6 
-33144031,BENZTROPINE 0.5 MG TABS, 5 
-33144049,BENZTROPINE 1 MG TABS, 6 
-33144056,BENZTROPINE 2 MG TABS, 6 
-33144064,POVIDONE-IODINE 10 % OINT 1 G TUBE, 2 
-33144072,POVIDONE-IODINE 10 % OINT 30 G TUBE, 3 
-33144114,BETAMETHASONE 6 MG/ML SUSP 5 ML VIAL, 47 
-33144171,BETAMETHASONE VALERATE 0.1 % CREA 15 G TUBE, 102 
-33144205,BETAMETHASONE VALERATE 0.1 % OINT 15 G TUBE, 157 
-33144221,BETAXOLOL 0.25 % DRPS 5 ML DROP BTL, 24 
-33144262,BETHANECHOL 10 MG TABS, 8 
-33144270,BETHANECHOL 25 MG TABS, 9 
-33144288,BETHANECHOL 50 MG TABS, 6 
-33144320,BISACODYL 10 MG SUPP, 2 
-33144338,BISACODYL 5 MG TBEC, 0.8 
-33144379,LIP PROTECTANT 0.6-0.5-1.1-0.5 % OINT 6 G TUBE, 3 
-33144429,BOTULINUM TOXIN TYPE A 100 UNIT SOLR, 55 
-33144429,BOTULINUM TOXIN TYPE A 100 UNIT SOLR 100 UNITS VIAL, 56 
-33144460,BROMOCRIPTINE 2.5 MG TABS, 12 
-33144528,BALANCED SALT SOLN, 14 
-33144528,BALANCED SALT SOLN 30 ML BOTTLE, 16 
-33144536,BUMETANIDE 1 MG TABS, 5 
-33144544,BUMETANIDE 0.25 MG/ML SOLN, 26 
-33144551,BUMETANIDE 0.5 MG TABS, 4 
-33144569,BUMETANIDE 0.25 MG/ML SOLN 10 ML VIAL, 52 
-33144569,BUMETANIDE 0.25 MG/ML SOLN, 66 
-33144577,BUPIVACAINE (PF) 0.25% SOLN 20 ML AMPUL, 50 
-33144577,BUPIVACAINE (PF) 0.25% SOLN, 71 
-33144585,BUPIVACAINE (PF) 0.25% SOLN 30 ML AMPUL, 71 
-33144593,BUPIVACAINE 0.25% SOLN 50 ML VIAL, 48 
-33144593,BUPIVACAINE 0.25% SOLN, 52 
-33144601,BUPIVACAINE (PF) 0.5% SOLN 20 ML AMPUL, 49 
-33144619,BUPIVACAINE (PF) 0.5% SOLN, 43 
-33144619,BUPIVACAINE (PF) 0.5% SOLN 30 ML AMPUL, 59 
-33144627,BUPIVACAINE 0.5% SOLN 50 ML VIAL, 47 
-33144627,BUPIVACAINE 0.5% SOLN, 51 
-33144650,BUPIVACAINE-DEXTROSE (PF) 7.5 MG/ML (0.75 %) SOLN 2 ML AMPUL, 48 
-33144668,"BUPIVACAINE-EPINEPHRINE (PF) 0.25%-1:200,000 SOLN 10 ML VIAL", 31 
-33144668,"BUPIVACAINE-EPINEPHRINE (PF) 0.25%-1:200,000 SOLN", 47 
-33144676,"BUPIVACAINE-EPINEPHRINE 0.25%-1:200,000 SOLN 50 ML VIAL", 59 
-33144676,"BUPIVACAINE-EPINEPHRINE 0.25%-1:200,000 SOLN", 72 
-33144684,"BUPIVACAINE-EPINEPHRINE (PF) 0.5%-1:200,000 SOLN 10 ML VIAL", 39 
-33144684,"BUPIVACAINE-EPINEPHRINE (PF) 0.5%-1:200,000 SOLN", 50 
-33144692,"BUPIVACAINE-EPINEPHRINE 0.5%-1:200,000 SOLN 50 ML VIAL", 52 
-33144692,"BUPIVACAINE-EPINEPHRINE 0.5%-1:200,000 SOLN", 75 
-33144734,CAFFEINE AND SODIUM BENZOATE 250 MG/ML (125 MG/ML CAFFEINE) SOLN, 62 
-33144734,CAFFEINE AND SODIUM BENZOATE 250 MG/ML (125 MG/ML CAFFEINE) SOLN 2 ML VIAL, 91 
-33144742,CALAMINE LOTN 120 ML BOTTLE, 2 
-33144767,CALCITRIOL 0.25 MCG CAPS, 9 
-33144775,CALCITRIOL 0.5 MCG CAPS, 13 
-33144858,CALCIUM CARBONATE-VITAMIN D3 250-125 MG TABS, 1.4 
-33144874,CALCIUM CHLORIDE 100 MG/ML (10 %) SYRG 10 ML SYRINGE, 101 
-33144874,CALCIUM CHLORIDE 100 MG/ML (10 %) SYRG, 113 
-33144882,CALCIUM CHLORIDE 100 MG/ML (10 %) SOLN 10 ML VIAL, 49 
-33144882,CALCIUM CHLORIDE 100 MG/ML (10 %) SOLN, 54 
-33144916,CALCIUM GLUCONATE 100 MG/ML (10%) SOLN, 11 
-33144940,CALCIUM GLUCONATE 45 MG (500 MG) TABS, 2 
-33145103,CAPTOPRIL 12.5 MG TABS, 5 
-33145111,CAPTOPRIL 25 MG TABS, 5 
-33145129,CAPTOPRIL 50 MG TABS, 5 
-33145137,CAPTOPRIL 100 MG TABS, 6 
-33145186,CARBACHOL 0.01 % SOLN 1.5 ML VIAL, 169 
-33145194,CARBAMAZEPINE 100 MG CHEW, 4 
-33145210,CARBAMAZEPINE 200 MG TABS, 5 
-33145228,CARBAMIDE PEROXIDE 6.5 % DROP 15 ML DROP BTL, 3 
-33145236,CARBAMIDE PEROXIDE 10 % SOLN 15 ML BOTTLE, 6 
-33145285,CARBOPLATIN 10 MG/ML SOLN, 42 
-33145301,CARBOXYMETHYLCELLULOSE SODIUM 1 % DPGE, 2 
-33145327,CARMUSTINE 100 MG SOLR," 12,760 "
-33145400,CEFAZOLIN 1 GRAM SOLR, 30 
-33145418,CEFAZOLIN 1 GRAM SOLR, 19 
-33145426,CEFAZOLIN 500 MG SOLR, 33 
-33145434,CEFOTAXIME 1 GRAM SOLR, 71 
-33145434,CEFOTAXIME 1 GRAM SOLR 1 EACH VIAL, 71 
-33145442,CEFOTAXIME 1 GRAM SOLR, 44 
-33145459,CEFOTAXIME 2 GRAM SOLR, 47 
-33145475,CEFOTETAN 1 GRAM SOLR, 64 
-33145475,CEFOTETAN 1 GRAM SOLR 1 EACH VIAL, 66 
-33145483,CEFOTETAN 2 GRAM SOLR, 56 
-33145517,CEFTAZIDIME 1 GRAM SOLR, 43 
-33145525,CEFTAZIDIME 1 GRAM SOLR, 67 
-33145525,CEFTAZIDIME 1 GRAM SOLR 1 EACH VIAL, 72 
-33145533,CEFTAZIDIME 2 GRAM SOLR, 34 
-33145541,CEFTAZIDIME 2 GRAM SOLR, 71 
-33145558,CEFTRIAXONE 1 GRAM SOLR 1 EACH VIAL, 19 
-33145558,CEFTRIAXONE 1 GRAM SOLR, 20 
-33145566,CEFTRIAXONE 1 GRAM SOLR, 18 
-33145566,CEFTRIAXONE 1 GRAM SOLR 1 EACH VIAL, 19 
-33145574,CEFTRIAXONE 250 MG SOLR, 29 
-33145574,CEFTRIAXONE 250 MG SOLR 1 EACH VIAL, 55 
-33145582,CEFTRIAXONE 2 GRAM SOLR, 14 
-33145582,CEFTRIAXONE 2 GRAM SOLR 1 EACH VIAL, 15 
-33145590,CEFTRIAXONE 2 GRAM SOLR, 15 
-33145590,CEFTRIAXONE 2 GRAM SOLR 1 EACH VIAL, 15 
-33145616,CEFUROXIME 1.5 GRAM SOLR, 78 
-33145632,CEFUROXIME 750 MG SOLR, 50 
-33145673,BENZOCAINE-MENTHOL-CETYLPYRIDINIUM 15-3.6 MG LOZG, 2 
-33145699,CEPHALEXIN 250 MG CAPS, 4 
-33145715,CEPHALEXIN 500 MG CAPS, 6 
-33145731,CETAPHIL CLSR 473 ML BOTTLE, 10 
-33145772,CHERRY SYRUP SYRP, 9 
-33145863,CHLORAMPHENICOL 1 GRAM SOLR 1 EACH VIAL, 337 
-33145939,CHLORDIAZEPOXIDE 5 MG CAPS, 7 
-33145947,CHLORDIAZEPOXIDE 10 MG CAPS, 6 
-33145954,CHLORDIAZEPOXIDE 25 MG CAPS, 6 
-33146028,CHLOROTHIAZIDE 250 MG/5 ML SUSP, 20 
-33146036,CHLOROTHIAZIDE 500 MG SOLR," 1,373 "
-33146036,CHLOROTHIAZIDE 500 MG SOLR 1 EACH VIAL," 1,422 "
-33146044,CHLORPHENIRAMINE 4 MG TABS, 4 
-33146085,CHLORPROMAZINE 10 MG TABS, 7 
-33146093,CHLORPROMAZINE 25 MG/ML SOLN 1 ML AMPUL, 93 
-33146093,CHLORPROMAZINE 25 MG/ML SOLN, 147 
-33146119,CHLORPROMAZINE 25 MG TABS, 11 
-33146168,CHLORPROMAZINE 100 MG TABS, 2 
-33146176,CHLORPROMAZINE 200 MG TABS, 0.9 
-33146259,CHOLESTYRAMINE 4 GRAM PWPK, 13 
-33146317,CICLOPIROX 0.77 % CREA 15 G TUBE, 108 
-33146416,CIPROFLOXACIN 250 MG TABS, 8 
-33146424,CIPROFLOXACIN 500 MG TABS, 7 
-33146432,CIPROFLOXACIN 750 MG TABS, 10 
-33146481,CLINDAMYCIN 75 MG/5 ML SOLR, 135 
-33146499,CLINDAMYCIN 150 MG CAPS, 9 
-33146507,CLINDAMYCIN 300 MG CAPS, 16 
-33146515,CLINDAMYCIN 150 MG/ML SOLN 2 ML VIAL, 49 
-33146515,CLINDAMYCIN 150 MG/ML SOLN, 61 
-33146556,CLINDAMYCIN 150 MG/ML SOLN, 85 
-33146556,CLINDAMYCIN 150 MG/ML SOLN 6 ML VIAL, 87 
-33146572,CLINDAMYCIN 1 % SOLN 30 ML BOTTLE, 59 
-33146655,CLOBETASOL 0.05 % CREA 15 G TUBE, 108 
-33146671,CLOBETASOL 0.05 % OINT 15 G TUBE, 120 
-33146713,CLOMIPRAMINE 25 MG CAPS, 7 
-33146721,CLOMIPRAMINE 50 MG CAPS, 65 
-33146739,CLOMIPRAMINE 75 MG CAPS, 75 
-33146747,CLONAZEPAM 0.5 MG TABS, 5 
-33146754,CLONAZEPAM 1 MG TABS, 5 
-33146762,CLONAZEPAM 2 MG TABS, 6 
-33146770,CLONIDINE 0.1 MG TABS, 4 
-33146788,CLONIDINE 0.2 MG TABS, 5 
-33146796,CLONIDINE 0.3 MG TABS, 5 
-33146820,CLONIDINE 0.3 MG/24 HR PTWK, 513 
-33146861,CLOTRIMAZOLE 10 MG TROC, 14 
-33146887,CLOTRIMAZOLE 1 % CREA 30 G TUBE, 2 
-33146887,CLOTRIMAZOLE 1 % CREA 28.35 G TUBE, 3 
-33146895,CLOTRIMAZOLE 1 % SOLN 30 ML BOTTLE, 54 
-33146903,CLOTRIMAZOLE 1 % CREA 45 G TUBE/KIT, 5 
-33147158,CODEINE 15 MG TABS, 6 
-33147174,CODEINE 30 MG TABS, 8 
-33147190,CODEINE 30 MG/ML SYRG 2 ML SYRINGE, 31 
-33147232,ACETAMINOPHEN-CODEINE 300-30 MG TABS, 5 
-33147257,ACETAMINOPHEN-CODEINE 300 MG-30 MG /12.5 ML SOLN 12.5 ML BLIST PACK, 14 
-33147307,COLCHICINE 0.6 MG TABS, 49 
-33147356,COLISTIN 37.5 MG/ML SOLR, 237 
-33147380,"PEG-ELECTROLYTE 236-22.74-6.74 GRAM SOLR 4,000 ML BOTTLE", 89 
-33147539,"NEOMYCIN-BACITRACIN-POLY-HC 3.5-400-10,000 MG-UNIT/G-1% OINT 3.5 G TUBE", 24 
-33147554,NEOMYCIN-POLYMYXIN-HYDROCORTISONE SOLN 10 ML BOTTLE, 156 
-33147562,"NEOMYCIN-POLYMYXIN-HC 3.5-10,000-1 MG-UNIT/ML-% DRPS 10 ML DROP BTL", 125 
-33147570,COSYNTROPIN 0.25 MG SOLR, 668 
-33147570,COSYNTROPIN 0.25 MG SOLR 1 EACH VIAL, 846 
-33147604,CROMOLYN 20 MG/2 ML NEBU 2 ML BLIST PACK, 9 
-33147604,CROMOLYN 20 MG/2 ML NEBU 2 ML VIAL, 10 
-33147612,CROMOLYN 5.2 MG/SPRAY (4 %) SPRY 26 ML AER W/ADAP, 6 
-33147612,CROMOLYN 5.2 MG/SPRAY (4 %) SPRY 26 ML SQUEEZ BTL, 6 
-33147661,CYCLOBENZAPRINE 10 MG TABS, 5 
-33147679,CYCLOPENTOLATE 1 % DROP 2 ML DROP BTL, 121 
-33147794,CYCLOPHOSPHAMIDE 25 MG TABS, 18 
-33147810,CYCLOPHOSPHAMIDE 500 MG SOLR, 320 
-33147828,CYCLOPHOSPHAMIDE 50 MG TABS, 11 
-33147844,CYCLOSPORINE 25 MG CAPS, 26 
-33147851,CYCLOSPORINE 100 MG CAPS, 90 
-33147877,CYCLOSPORINE 250 MG/5 ML SOLN, 160 
-33147885,CYPROHEPTADINE 2 MG/5 ML SYRP, 12 
-33147885,CYPROHEPTADINE 2 MG/5 ML SYRP 60 ML CUP, 34 
-33147893,CYPROHEPTADINE 4 MG TABS, 9 
-33147901,CYTARABINE 100 MG/ML SOLR, 38 
-33147943,DEXTROAMPHETAMINE 5 MG TABS, 18 
-33147950,DEXTROAMPHETAMINE 15 MG CPSR, 51 
-33147984,D10W SOLN, 49 
-33148008,D10W SOLP, 49 
-33148008,"D10W SOLP 1,000 ML BAG", 50 
-33148024,D50W SYRG 50 ML SYRINGE, 99 
-33148032,D50W SOLP 50 ML VIAL, 37 
-33148032,D50W SOLP, 45 
-33148065,"D5-1/4 NS SOLP 1,000 ML FLEX CONT", 43 
-33148065,D5-1/4 NS SOLP, 52 
-33148081,"D5-1/4 NS + KCL 20 MEQ/L 20 MEQ/L SOLP 1,000 ML FLEX CONT", 51 
-33148115,D5-1/2 NS SOLP, 51 
-33148123,"D5-1/2 NS SOLP 1,000 ML BAG", 49 
-33148123,D5-1/2 NS SOLP, 51 
-33148131,"D5-1/2 NS + KCL 10 MEQ/L 10 MEQ/L SOLP 1,000 ML BAG", 51 
-33148149,"D5-1/2 NS + KCL 20 MEQ/L 20 MEQ/L SOLP 1,000 ML BAG", 51 
-33148164,"D5-1/2 NS + KCL 40 MEQ/L 40 MEQ/L SOLP 1,000 ML BAG", 51 
-33148172,"D5-LR SOLP 1,000 ML BAG", 49 
-33148172,D5-LR SOLP, 51 
-33148180,D5-NS SOLP, 51 
-33148198,D5W SOLP, 51 
-33148198,D5W SOLP 50 ML BAG, 52 
-33148198,D5W SOLN, 53 
-33148206,D5W SOLP, 48 
-33148206,D5W SOLP 100 ML BAG, 51 
-33148214,D5W SOLP, 47 
-33148214,D5W SOLP 250 ML FLEX CONT, 54 
-33148222,D5W SOLP, 51 
-33148222,D5W SOLP 500 ML BAG, 52 
-33148230,"D5W SOLP 1,000 ML BAG", 48 
-33148230,D5W SOLP, 51 
-33148255,DEXTROSE SOLP, 66 
-33148271,DACARBAZINE 200 MG SOLR, 13 
-33148297,SODIUM HYPOCHLORITE 0.5 % SOLN 473 ML BOTTLE, 90 
-33148305,SODIUM HYPOCHLORITE 0.25 % SOLN, 52 
-33148305,SODIUM HYPOCHLORITE 0.25 % SOLN 473 ML BOTTLE, 89 
-33148347,DANTROLENE 25 MG CAPS, 12 
-33148354,DANTROLENE 50 MG CAPS, 16 
-33148362,DANTROLENE 100 MG CAPS, 18 
-33148404,DAPSONE 100 MG TABS, 16 
-33148412,DAUNORUBICIN 5 MG/ML SOLR, 27 
-33148420,DEFEROXAMINE 500 MG SOLR, 74 
-33148420,DEFEROXAMINE 500 MG SOLR 1 EACH VIAL, 88 
-33148461,DEMECLOCYCLINE 150 MG TABS, 19 
-33148487,DEMECLOCYCLINE 300 MG TABS, 38 
-33148495,DESIPRAMINE 10 MG TABS, 10 
-33148503,DESIPRAMINE 25 MG TABS, 13 
-33148511,DESIPRAMINE 50 MG TABS, 21 
-33148529,DESIPRAMINE 75 MG TABS, 27 
-33148537,DESIPRAMINE 100 MG TABS, 31 
-33148545,DESIPRAMINE 150 MG TABS, 47 
-33148552,LIVER OIL-ZINC OXIDE 40 % OINT 30 G TUBE, 4 
-33148594,DESONIDE 0.05 % CREA 15 G TUBE, 137 
-33148610,DESONIDE 0.05 % OINT 15 G TUBE, 112 
-33148651,DEXAMETHASONE 1 MG/ML DROP 30 ML DROP BTL, 1.4 
-33148669,DEXAMETHASONE 0.5 MG/5 ML ELIX 237 ML BOTTLE, 4 
-33148669,DEXAMETHASONE 0.5 MG/5 ML ELIX, 7 
-33148701,DEXAMETHASONE 0.5 MG TABS, 0.8 
-33148719,DEXAMETHASONE 0.75 MG TABS, 1.2 
-33148727,DEXAMETHASONE 1.5 MG TABS, 0.6 
-33148735,DEXAMETHASONE 10 MG/ML SOLN, 3 
-33148735,DEXAMETHASONE 10 MG/ML SOLN 10 ML VIAL, 4 
-33148743,DEXAMETHASONE 2 MG TABS, 1.2 
-33148750,DEXAMETHASONE 4 MG TABS, 3 
-33148768,DEXAMETHASONE 4 MG/ML SOLN 1 ML VIAL, 9 
-33148768,DEXAMETHASONE 4 MG/ML SOLN, 24 
-33148925,DIAZEPAM 2 MG TABS, 5 
-33148933,DIAZEPAM 5 MG TABS, 5 
-33148958,DIAZEPAM 5 MG/ML SYRG, 50 
-33148958,DIAZEPAM 5 MG/ML SYRG 2 ML SYRINGE, 64 
-33148966,DIAZEPAM 10 MG TABS, 5 
-33149006,DIBUCAINE 1 % OINT 28.35 G TUBE, 6 
-33149022,DICLOXACILLIN 250 MG CAPS, 7 
-33149030,DICLOXACILLIN 500 MG CAPS, 8 
-33149055,DICYCLOMINE 10 MG CAPS, 5 
-33149063,DICYCLOMINE 10 MG/5 ML SYRP, 9 
-33149063,DICYCLOMINE 10 MG/5 ML SOLN, 11 
-33149204,DIGOXIN 0.125 MG TABS, 12 
-33149212,DIGOXIN 0.25 MG TABS, 19 
-33149238,DIGOXIN 0.25 MG/ML SOLN 2 ML AMPUL, 43 
-33149238,DIGOXIN 0.25 MG/ML SOLN, 48 
-33149261,DILTIAZEM 30 MG TABS, 4 
-33149279,DILTIAZEM 60 MG CP12, 10 
-33149287,DILTIAZEM 60 MG TABS, 6 
-33149295,DILTIAZEM 90 MG CP12, 10 
-33149303,DILTIAZEM 90 MG TABS, 6 
-33149311,DILTIAZEM 120 MG CP12, 13 
-33149329,DILTIAZEM 120 MG TABS, 6 
-33149337,DILTIAZEM 5 MG/ML SOLN 5 ML VIAL, 47 
-33149337,DILTIAZEM 5 MG/ML SOLN, 49 
-33149436,DIPHENHYDRAMINE 25 MG CAPS, 1.4 
-33149444,DIPHENHYDRAMINE 50 MG CAPS, 5 
-33149451,DIPHENHYDRAMINE 50 MG/ML SOLN 1 ML VIAL, 45 
-33149451,DIPHENHYDRAMINE 50 MG/ML SOLN, 47 
-33149469,DIPHENOXYLATE-ATROPINE 2.5-0.025 MG/5 ML LIQD 5 ML CUP, 9 
-33149477,DIPHENOXYLATE-ATROPINE 2.5-0.025 MG TABS, 5 
-33149527,DIPYRIDAMOLE 75 MG TABS, 9 
-33149535,DISOPYRAMIDE 100 MG CAPS, 9 
-33149543,DISOPYRAMIDE 100 MG CPSR, 24 
-33149550,DISOPYRAMIDE 150 MG CAPS, 8 
-33149568,DISOPYRAMIDE 150 MG CPSR, 28 
-33149576,DISULFIRAM 250 MG TABS, 48 
-33149592,DIVALPROEX 250 MG TBEC, 7 
-33149600,DIVALPROEX 500 MG TBEC, 7 
-33149626,DOBUTAMINE 250 MG/20 ML (12.5 MG/ML) SOLN, 42 
-33149642,DOCUSATE 100 MG CAPS, 1.4 
-33149659,DOCUSATE 250 MG CAPS, 5 
-33149717,PHENOBARBITAL-BELLADONNA ALKALOIDS 16.2 MG/5 ML ELIX 5 ML CUP, 5 
-33149717,PHENOBARBITAL-BELLADONNA ALKALOIDS 16.2 MG/5 ML ELIX, 5 
-33149725,PHENOBARBITAL-BELLADONNA ALKALOIDS 16.2-0.1037 -0.0194 MG TABS, 42 
-33149733,DOPAMINE 200 MG/5 ML (40 MG/ML) SOLN, 3 
-33149741,"DOPAMINE 400 MG IN D5W 250 ML 400 MG/250 ML (1,600 MCG/ML) SOLN 250 ML BAG", 14 
-33149766,"DOPAMINE 800 MG IN D5W 250 ML 800 MG/250 ML (3,200 MCG/ML) SOLN 250 ML BAG", 9 
-33149782,DOXAZOSIN 1 MG TABS, 5 
-33149790,DOXAZOSIN 2 MG TABS, 4 
-33149808,DOXAZOSIN 4 MG TABS, 5 
-33149816,DOXAZOSIN 8 MG TABS, 6 
-33149824,DOXEPIN 10 MG CAPS, 4 
-33149832,DOXEPIN 25 MG CAPS, 5 
-33149840,DOXEPIN 50 MG CAPS, 5 
-33149865,DOXEPIN 100 MG CAPS, 7 
-33149873,DOXORUBICIN 10 MG/5 ML SOLN, 101 
-33149907,DOXYCYCLINE HYCLATE 50 MG CAPS, 3 
-33149915,DOXYCYCLINE HYCLATE 100 MG CAPS, 19 
-33149923,DOXYCYCLINE 100 MG SOLR 1 EACH VIAL, 113 
-33149923,DOXYCYCLINE 100 MG SOLR, 151 
-33149956,DRONABINOL 5 MG CAPS, 105 
-33150020,ARTIFICIAL TEARS WITH LANOLIN OINT 3.5 G TUBE, 5 
-33150178,SELEGILINE 5 MG TABS, 21 
-33150202,ENALAPRIL 5 MG TABS, 4 
-33150210,ENALAPRIL 10 MG TABS, 4 
-33150228,ENALAPRIL 2.5 MG TABS, 4 
-33150236,ENALAPRILAT 1.25 MG/ML INJ, 97 
-33150244,ENALAPRIL 20 MG TABS, 5 
-33150319,EPHEDRINE 50 MG/ML SOLN, 39 
-33150319,EPHEDRINE 50 MG/ML SOLN 1 ML AMPUL, 41 
-33150319,EPHEDRINE 50 MG/ML SOLN 1 ML VIAL, 58 
-33150384,EPINEPHRINE (FOR MIXING ET SOLN) 10 ML SYRINGE, 6 
-33150384,EPINEPHRINE 0.1 MG/ML SYRG 10 ML SYRINGE, 18 
-33150384,EPINEPHRINE 0.1 MG/ML SYRG, 42 
-33150392,EPINEPHRINE 1 MG/ML SOLN 30 ML VIAL, 0.5 
-33150392,EPINEPHRINE 1 MG/ML SOLN, 1.4 
-33150400,RACEPINEPHRINE 2.25% NEBU, 3 
-33150400,RACEPINEPHRINE 2.25% NEBU 1 EACH VIAL, 3 
-33150467,"EPOETIN 2,000 UNIT/ML SOLN 1 ML VIAL", 107 
-33150467,"EPOETIN 2,000 UNIT/ML SOLN", 195 
-33150475,"EPOETIN 3,000 UNIT/ML SOLN 1 ML VIAL", 92 
-33150475,"EPOETIN 3,000 UNIT/ML SOLN", 188 
-33150483,"EPOETIN 4,000 UNIT/ML SOLN 1 ML VIAL", 115 
-33150491,"EPOETIN 10,000 UNIT/ML SOLN 1 ML VIAL", 81 
-33150517,"ERGOCALCIFEROL 50,000 UNIT CAPS", 8 
-33150608,ERYTHROMYCIN 250 MG TBEC, 21 
-33150632,"ERYTHROMYCIN 1,000 MG SOLR", 72 
-33150681,ERYTHROMYCIN 500 MG SOLR, 155 
-33150699,ERYTHROMYCIN 500 MG SOLR, 130 
-33150707,ERYTHROMYCIN 5 MG/GRAM (0.5 %) OINT 1 G TUBE, 83 
-33150715,ERYTHROMYCIN 5 MG/GRAM (0.5 %) OINT 3.5 G TUBE, 116 
-33150723,ESMOLOL 100 MG/10 ML SOLN 10 ML VIAL, 50 
-33150723,ESMOLOL 100 MG/10 ML SOLN, 56 
-33150749,ESTRADIOL 0.05 MG/24 HR PTSW 8 EACH BOX, 73 
-33150756,ESTRADIOL 0.1 MG/24 HR PTSW, 79 
-33150798,ESTROGENS CONJUGATED 0.3 MG TABS, 28 
-33150806,ESTROGENS CONJUGATED 0.625 MG TABS, 29 
-33150814,ESTROGENS CONJUGATED 0.9 MG TABS, 25 
-33150822,ESTROGENS CONJUGATED 1.25 MG TABS, 27 
-33150848,ESTROGENS CONJUGATED 25 MG SOLR," 1,220 "
-33150863,ESTROPIPATE 0.75 MG TABS, 3 
-33150871,ESTROPIPATE 1.5 MG TABS, 4 
-33150897,ETHACRYNIC ACID 25 MG TABS, 56 
-33150913,ETHACRYNIC ACID 50 MG SOLR," 4,815 "
-33150921,ETHAMBUTOL 100 MG TABS, 5 
-33150939,ETHAMBUTOL 400 MG TABS, 15 
-33151093,ETOMIDATE 2 MG/ML SOLN 20 ML VIAL, 90 
-33151101,ETOPOSIDE 20 MG/ML SOLN, 10 
-33151143,WHITE PETROLATUM-MINERAL OIL CREA 454 G JAR, 5 
-33151150,FAMOTIDINE 20 MG TABS, 6 
-33151168,FAMOTIDINE 40 MG TABS, 7 
-33151176,FAMOTIDINE 20 MG/2 ML SOLN, 32 
-33151176,FAMOTIDINE 20 MG/2 ML SOLN 2 ML VIAL, 46 
-33151184,FAMOTIDINE 10 MG/ML SOLN 4 ML VIAL, 47 
-33151184,FAMOTIDINE 10 MG/ML SOLN, 51 
-33151192,FAMOTIDINE 40 MG/5 ML SUSP, 39 
-33151267,FENTANYL 25 MCG/HR PT72, 45 
-33151275,FENTANYL 50 MCG/HR PT72, 78 
-33151283,FENTANYL 75 MCG/HR PT72, 124 
-33151291,FENTANYL 50 MCG/ML, 30 
-33151291,FENTANYL 50 MCG/ML 2 ML AMPUL, 36 
-33151291,FENTANYL 50 MCG/ML 2 ML SYRINGE, 44 
-33151291,FENTANYL 50 MCG/ML SOLN 2 ML VIAL, 46 
-33151291,FENTANYL 50 MCG/ML 2 ML VIAL, 48 
-33151291,FENTANYL 50 MCG/ML SOLN 2 ML AMPUL, 49 
-33151309,FENTANYL 50 MCG/ML 5 ML AMPUL, 15 
-33151309,FENTANYL 50 MCG/ML 5 ML VIAL, 21 
-33151309,FENTANYL 50 MCG/ML SOLN 5 ML AMPUL, 27 
-33151309,FENTANYL 50 MCG/ML SOLN 5 ML VIAL, 27 
-33151309,FENTANYL 50 MCG/ML, 28 
-33151382,FERROUS SULFATE 325 MG (65 MG IRON) TABS, 2 
-33151416,FLECAINIDE 100 MG TABS, 7 
-33151424,SODIUM PHOSPHATES 19-7 GRAM/118 ML ENEM 133 ML BOTTLE, 1.1 
-33151432,SODIUM PHOSPHATES 9.5-3.5 GRAM/59 ML ENEM 66 ML SQUEEZ BTL, 3 
-33151440,MINERAL OIL ENEM 133 ML SQUEEZ BTL, 2 
-33151440,MINERAL OIL ENEM, 2 
-33151465,FLUCYTOSINE 250 MG CAPS, 205 
-33151473,FLUCYTOSINE 500 MG CAPS, 503 
-33151481,FLUDROCORTISONE 0.1 MG TABS, 11 
-33151606,FLUOCINONIDE 0.05 % CREA 15 G TUBE, 40 
-33151630,FLUOCINONIDE 0.05 % GEL 15 G JAR, 147 
-33151630,FLUOCINONIDE 0.05 % GEL 15 G TUBE, 171 
-33151663,FLUOCINONIDE 0.05 % OINT 15 G TUBE, 41 
-33151697,FLUORESCEIN 500 MG/5 ML (10 %) SOLN 5 ML AMPUL, 58 
-33151739,FLUORESCEIN 1 MG STRP, 5 
-33151788,FLUOROURACIL 500 MG/10 ML SOLN, 65 
-33151846,FLUOXETINE 20 MG CAPS, 4 
-33151895,FLUPHENAZINE 10 MG TABS, 7 
-33151903,FLUPHENAZINE 1 MG TABS, 3 
-33151945,FLUPHENAZINE 5 MG TABS, 7 
-33152075,FOLIC ACID 1 MG TABS, 6 
-33152091,FOLIC ACID 5 MG/ML SOLN, 45 
-33152091,FOLIC ACID 5 MG/ML SOLN 10 ML VIAL, 46 
-33152125,FUROSEMIDE 10 MG/ML SOLN 60 ML BOTTLE, 57 
-33152133,FUROSEMIDE 20 MG TABS, 5 
-33152141,FUROSEMIDE 10 MG/ML SOLN 2 ML VIAL, 36 
-33152141,FUROSEMIDE 10 MG/ML SOLN, 39 
-33152158,FUROSEMIDE 40 MG TABS, 5 
-33152166,FUROSEMIDE 10 MG/ML SOLN, 16 
-33152166,FUROSEMIDE 10 MG/ML SOLN 4 ML VIAL, 18 
-33152174,FUROSEMIDE 80 MG TABS, 6 
-33152182,FUROSEMIDE 10 MG/ML SOLN, 11 
-33152182,FUROSEMIDE 10 MG/ML SOLN 10 ML VIAL, 11 
-33152216,ALUMINUM HYDROXIDE-MAGNESIUM TRISILICATE 80-14.2 MG CHEW, 0.8 
-33152224,ALUMINUM HYDROXIDE-MAGNESIUM CARBONATE 95-358 MG/15 ML SUSP 355 ML BOTTLE, 2 
-33152257,GEMFIBROZIL 600 MG TABS, 7 
-33152273,GENTAMICIN 0.1 % CREA 15 G TUBE, 49 
-33152281,GENTAMICIN 0.1 % OINT 15 G TUBE, 64 
-33152299,GENTAMICIN (PF) 10 MG/ML SOLN, 75 
-33152307,GENTAMICIN 40 MG/ML SOLN, 26 
-33152307,GENTAMICIN 40 MG/ML SOLN 2 ML VIAL, 39 
-33152315,GENTAMICIN 100 MG/10 ML SOLN, 27 
-33152323,GENTAMICIN 0.3 % (3 MG/G) OINT 3.5 G TUBE, 141 
-33152331,GENTAMICIN 0.3 % DROP 5 ML DROP BTL, 47 
-33152356,GENTIAN VIOLET 1 % SOLN 30 ML BOTTLE, 15 
-33152380,GLIPIZIDE 5 MG TABS, 5 
-33152398,GLIPIZIDE 10 MG TABS, 5 
-33152430,GLYBURIDE 1.25 MG TABS, 6 
-33152448,GLYBURIDE 2.5 MG TABS, 7 
-33152455,GLYBURIDE 5 MG TABS, 7 
-33152489,GLYCERIN (ADULT) 3 GRAM SUPP, 2 
-33152497,GLYCERIN (CHILD) 1.5 GRAM SUPP, 2 
-33152505,GLYCOPYRROLATE 0.2 MG/ML SOLN, 28 
-33152505,GLYCOPYRROLATE 0.2 MG/ML SOLN 1 ML VIAL, 31 
-33152513,GLYCOPYRROLATE 1 MG TABS, 13 
-33152521,GLYCOPYRROLATE 0.2 MG/ML SOLN 5 ML VIAL, 46 
-33152521,GLYCOPYRROLATE 0.2 MG/ML SOLN, 47 
-33152604,HYDROXYPROPYL METHYLCELLULOSE 2.5 % DROP 15 ML DROP BTL, 10 
-33152695,HALOPERIDOL 0.5 MG TABS, 4 
-33152703,HALOPERIDOL 1 MG TABS, 5 
-33152711,HALOPERIDOL 2 MG TABS, 5 
-33152729,HALOPERIDOL 5 MG TABS, 6 
-33152737,HALOPERIDOL 10 MG TABS, 12 
-33152745,HALOPERIDOL 2 MG/ML CONC 120 ML BOTTLE, 43 
-33152778,HALOPERIDOL 5 MG/ML SOLN, 35 
-33152778,HALOPERIDOL 5 MG/ML SOLN 1 ML VIAL, 49 
-33152802,TRIAMTERENE-HYDROCHLOROTHIAZIDE 37.5-25 MG CAPS, 6 
-33152828,BENZOCAINE-BENZETHONIUM 20-0.1 % OINT 30 G TUBE, 5 
-33152828,BENZOCAINE 20 % OINT 28 G TUBE, 5 
-33152836,HYDROCORTISONE 2.5 % CREA 28.35 G TUBE, 44 
-33152836,HYDROCORTISONE 2.5 % CREA 30 G TUBE, 129 
-33152844,PRAMOXINE-MINERAL OIL-ZINC 1-12.5 % OINT 28.3 G TUBE, 5 
-33152844,PHENYLEPH-MIN OIL-PETROLATUM 0.25-14-74.9 % OINT 28 G TUBE, 7 
-33152844,PHENYLEPH-SHARK LIV OIL-MO-PET OINT 28 G TUBE, 7 
-33152851,HYDROCORTISONE 25 MG SUPP, 10 
-33152927,"HEPARIN 5,000 UNIT/ML SOLN", 5 
-33152927,"HEPARIN 5,000 UNIT/ML SOLN 1 ML VIAL", 10 
-33152943,"HEPARIN 10,000 UNIT/ML SOLN", 8 
-33152943,"HEPARIN 10,000 UNIT/ML SOLN 1 ML VIAL", 11 
-33152943,HEPARIN 5000 UNIT/0.5 ML SOLN, 14 
-33152950,HEPARIN IV BOLUS (DVT/PE PROTOCOL) 1 ML VIAL, 9 
-33152950,HEPARIN IV BOLUS (VASCULAR SURGERY LOW BLEEDING RISK) 1 ML VIAL, 11 
-33152950,HEPARIN IV REBOLUS 2000 UNITS (VASCULAR SURGERY LOW BLEEDING RISK) 1 ML VIAL, 12 
-33152950,"HEPARIN 1,000 UNIT/ML SOLN 1 ML VIAL", 13 
-33152950,HEPARIN IV BOLUS (ACS PROTOCOL) 1 ML VIAL, 13 
-33152950,"HEPARIN 1,000 UNIT/ML SOLN", 21 
-33152950,"HEPARIN IV REBOLUS (A-FIB PROTOCOL, HAL < 0.1) 1 ML VIAL", 24 
-33152950,"HEPARIN IV BOLUS (A-FIB PROTOCOL, A-FIB & INR < 2) 1 ML VIAL", 24 
-33152950,"HEPARIN IV REBOLUS (DVT/PE PROTOCOL, HAL < 0.3) 1 ML VIAL", 25 
-33152950,HEPARIN IV REBOLUS 1000 UNITS(VASCULAR SURGERY LOW BLEEDING RISK) 1 ML VIAL, 45 
-33152950,"HEPARIN IV REBOLUS (A-FIB PROTOCOL, HAL 0.1 - 0.19) 1 ML VIAL", 45 
-33152968,"HEPARIN 1,000 UNIT/ML SOLN 30 ML VIAL", 4 
-33152968,"HEPARIN 1,000 UNIT/ML SOLN", 4 
-33152976,"HEPARIN 20,000 UNIT/ML SOLN 1 ML VIAL", 4 
-33152984,HEPARIN IV INFUSION (NEURO POST IR PROCEDURE PROTOCOL) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (INTERVENTIONAL RADIOLOGY) 250 ML BAG, 3 
-33152984,"HEPARIN IV INFUSION (MHV PROTOCOL, INR IS NOT THERAPEUTIC) 250 ML BAG", 3 
-33152984,HEPARIN IV INFUSION (HIGH BLEEDING RISK PROTOCOL) 250 ML BAG, 3 
-33152984,"HEPARIN IV INFUSION (MHV PROTOCOL, INR IS THERAPEUTIC) 250 ML BAG", 3 
-33152984,HEPARIN IV INFUSION (DVT/PE PROTOCOL-HIGH BLEEDING RISK/POST PROCEDURE) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (DVT/PE PROTOCOL) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (EP PROTOCOL) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (MD TO SPECIFY PARAMETERS) 250 ML BAG, 3 
-33152984,"HEPARIN IV INFUSION (A-FIB PROTOCOL, A-FIB & INR > 2) 250 ML BAG", 3 
-33152984,HEPARIN IV INFUSION (NEURO POST STROKE PROTOCOL) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (ACS PROTOCOL) 250 ML BAG, 3 
-33152984,"HEPARIN IV INFUSION (A-FIB PROTOCOL, A-FIB & INR < 2) 250 ML BAG", 3 
-33152984,HEPARIN IV INFUSION (VASCULAR SURGERY LOW BLEEDING RISK) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (VASCULAR SURGERY HIGH BLEEDING RISK) 250 ML BAG, 3 
-33152984,HEPARIN IV INFUSION (NEURO CEREBRAL SINUS THROMBOSIS) 250 ML BAG, 3 
-33152984,"HEPARIN 25,000 UNITS IN D5W 250ML 100 UNITS/ML SOLP 250 ML BAG", 3 
-33153081,HETASTARCH IN NS 6 % SOLN 500 ML BAG, 149 
-33153230,HYDRALAZINE 10 MG TABS, 6 
-33153248,HYDRALAZINE 25 MG TABS, 6 
-33153255,HYDRALAZINE 50 MG TABS, 6 
-33153263,HYDRALAZINE 100 MG TABS, 6 
-33153271,HYDRALAZINE 20 MG/ML SOLN, 49 
-33153271,HYDRALAZINE 20 MG/ML SOLN 1 ML VIAL, 54 
-33153297,HYDROCHLOROTHIAZIDE 25 MG TABS, 5 
-33153305,HYDROCHLOROTHIAZIDE 50 MG TABS, 5 
-33153313,HYDROCODONE-ACETAMINOPHEN 5-500 MG TABS, 4 
-33153347,HYDROCORTISONE 5 MG TABS, 6 
-33153354,HYDROCORTISONE 10 MG TABS, 10 
-33153362,HYDROCORTISONE 20 MG TABS, 9 
-33153370,HYDROCORTISONE 2.5 % LOTN 59 ML BOTTLE, 108 
-33153388,HYDROCORTISONE 0.5 % CREA 28.35 G TUBE, 4 
-33153396,HYDROCORTISONE 0.5 % OINT 28.35 G TUBE, 4 
-33153404,HYDROCORTISONE 1 % CREA 28.35 G TUBE, 26 
-33153420,HYDROCORTISONE 1 % OINT 28.35 G TUBE, 6 
-33153446,HYDROCORTISONE 100 MG/60 ML ENEM 60 ML SQUEEZ BTL, 110 
-33153453,HYDROCORTISONE 2.5 % CREA 30 G TUBE, 39 
-33153503,HYDROCORTISONE 100 MG/2 ML SOLR, 49 
-33153503,HYDROCORTISONE SOD SUCCINATE 100 MG SOLR 1 EACH VIAL, 60 
-33153503,HYDROCORTISONE (PF) 100 MG/2 ML SOLR, 68 
-33153503,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML SOLR, 69 
-33153503,HYDROCORTISONE 100 MG/2 ML SOLR 1 EACH VIAL, 76 
-33153503,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML SOLR 1 EACH VIAL, 78 
-33153503,HYDROCORTISONE (PF) 100 MG/2 ML SOLR 1 EACH VIAL, 78 
-33153529,HYDROGEN PEROXIDE 3 % SOLN 480 ML BOTTLE, 76 
-33153537,HYDROMORPHONE 2 MG/ML SOLN, 43 
-33153537,HYDROMORPHONE, 44 
-33153537,HYDROMORPHONE 2 MG AMPUL, 44 
-33153537,HYDROMORPHONE 2 MG/ML SOLN 1 ML AMPUL, 45 
-33153537,HYDROMORPHONE 2 MG/ML SOLN 2 MG AMPUL, 46 
-33153537,HYDROMORPHONE 2 MG/ML SOLN 1 ML VIAL, 48 
-33153545,HYDROMORPHONE 4 MG/ML SOLN 4 MG AMPUL, 45 
-33153545,HYDROMORPHONE 4 MG/ML SOLN 1 ML AMPUL, 53 
-33153552,HYDROMORPHONE 10 MG/ML SOLN, 25 
-33153560,HYDROMORPHONE 10 MG/ML SOLN, 14 
-33153560,HYDROMORPHONE 10 MG/ML SOLN 5 ML AMPUL, 14 
-33153586,HYDROMORPHONE 2 MG/ML SYRG 1 ML SYRINGE, 45 
-33153586,HYDROMORPHONE 2 MG/ML SYRG 2 MG SYRINGE, 45 
-33153586,HYDROMORPHONE 2 MG/ML SYRG, 48 
-33153594,HYDROMORPHONE 2 MG TABS, 6 
-33153602,HYDROMORPHONE 2 MG/ML SOLN, 42 
-33153610,HYDROMORPHONE 3 MG SUPP, 110 
-33153636,HYDROMORPHONE 4 MG TABS, 5 
-33153685,HYDROXYCHLOROQUINE 200 MG TABS, 7 
-33153719,HYDROXYZINE 10 MG TABS, 6 
-33153727,HYDROXYZINE 25 MG TABS, 6 
-33153735,HYDROXYZINE 50 MG TABS, 7 
-33153750,HYDROXYZINE 50 MG/ML SOLN 2 ML VIAL, 49 
-33153768,HYDROXYZINE 25 MG CAPS, 4 
-33153776,HYDROXYZINE 50 MG CAPS, 6 
-33153784,HYDROXYZINE 100 MG CAPS, 4 
-33153800,IBUPROFEN 200 MG TABS, 0.9 
-33153826,IBUPROFEN 400 MG TABS, 5 
-33153834,IBUPROFEN 600 MG TABS, 6 
-33153842,IBUPROFEN 800 MG TABS, 6 
-33153933,IMIPRAMINE 10 MG TABS, 6 
-33153958,IMIPRAMINE 25 MG TABS, 7 
-33153966,IMIPRAMINE 50 MG TABS, 6 
-33153974,IMIPRAMINE 75 MG CAPS, 47 
-33153982,IMIPRAMINE 100 MG CAPS, 39 
-33154097,INDIGOTINDISULFONATE 8 MG/ML SOLN, 97 
-33154097,INDIGOTINDISULFONATE 8 MG/ML SOLN 5 ML AMPUL, 109 
-33154113,INDOMETHACIN 25 MG CAPS, 6 
-33154121,INDOMETHACIN 25 MG/5 ML SUSP, 89 
-33154139,INDOMETHACIN 50 MG CAPS, 7 
-33154154,INDOMETHACIN 75 MG CPSR, 30 
-33154337,INSULIN REGULAR 100 UNIT/ML SOLN 10 ML VIAL, 1.3 
-33154410,INTERFERON ALFA-2B 5 MILLION UNIT/0.2 ML PNKT 1 EACH SYRINGE, 189 
-33154428,INTERFERON ALFA-2B 10 MILLION UNIT (1 ML) SOLR, 179 
-33154428,INTERFERON ALFA-2B 10 MILLION UNIT (1 ML) SOLR 1 EACH VIAL, 194 
-33154451,POTASSIUM IODIDE-IODINE 100-50 MG/ML SOLN, 8 
-33154550,IRON DEXTRAN 50 MG/ML SOLN, 119 
-33154550,IRON DEXTRAN 100 MG/2 ML (50 MG/ML) SOLN, 128 
-33154550,IRON DEXTRAN 100 MG/2 ML (50 MG/ML) SOLN 2 ML VIAL, 162 
-33154626,ISONIAZID 100 MG TABS, 2 
-33154642,ISONIAZID 300 MG TABS, 7 
-33154659,ISONIAZID 50 MG/5 ML SOLN, 32 
-33154790,HYDROXYPROPYL METHYLCELLULOSE 0.5 % DROP 15 ML DROP BTL, 22 
-33154816,ISOSORBIDE DINITRATE 5 MG TABS, 11 
-33154824,ISOSORBIDE DINITRATE 10 MG TABS, 12 
-33154840,ISOSORBIDE DINITRATE 20 MG TABS, 8 
-33154857,ISOSORBIDE DINITRATE 30 MG TABS, 6 
-33154865,ISOSORBIDE DINITRATE 40 MG TABS, 27 
-33154998,KETAMINE 50 MG/ML SOLN 10 ML VIAL, 44 
-33154998,KETAMINE 50 MG/ML SOLN, 62 
-33155003,KETAMINE 100 MG/ML SOLN, 49 
-33155003,KETAMINE 100 MG/ML SOLN 5 ML VIAL, 53 
-33155029,KETOCONAZOLE 2 % CREA 15 G TUBE, 55 
-33155045,KETOCONAZOLE 2 % SHAM 120 ML BOTTLE, 184 
-33155060,KETOROLAC 15 MG/ML SOLN 1 ML VIAL, 45 
-33155086,KETOROLAC 60 MG/2 ML SOLN 2 ML VIAL, 11 
-33155102,LABETALOL 100 MG TABS, 6 
-33155110,LABETALOL 200 MG TABS, 5 
-33155128,LABETALOL 300 MG TABS, 7 
-33155144,LACTIC ACID 12 % LOTN 240 G BOTTLE, 40 
-33155151,HYDROXYPROPYL CELLULOSE 5 MG INST, 40 
-33155185,LACTULOSE 10 GRAM/15 ML SOLN 473 ML BOTTLE, 2 
-33155185,LACTULOSE 10 GRAM/15 ML SOLN, 3 
-33155193,LANOLIN ANHYDROUS OINT 30 G TUBE, 3 
-33155219,LEECHES MEDICINAL 1 EACH PACKAGE, 163 
-33155227,LEUCOVORIN 5 MG TABS, 15 
-33155235,LEUCOVORIN 25 MG TABS, 82 
-33155276,LEUCOVORIN 10 MG/ML SOLR, 59 
-33155276,LEUCOVORIN 10 MG/ML SOLR 1 EACH VIAL, 66 
-33155342,LEVOBUNOLOL 0.25 % DROP 5 ML DROP BTL, 46 
-33155359,LEVOBUNOLOL 0.5 % DROP 5 ML DROP BTL, 184 
-33155409,LEVOTHYROXINE 25 MCG TABS, 4 
-33155417,LEVOTHYROXINE 50 MCG TABS, 5 
-33155425,LEVOTHYROXINE 75 MCG TABS, 6 
-33155433,LEVOTHYROXINE 112 MCG TABS, 7 
-33155441,LEVOTHYROXINE 125 MCG TABS, 7 
-33155458,LEVOTHYROXINE 150 MCG TABS, 7 
-33155466,LEVOTHYROXINE 175 MCG TABS, 7 
-33155474,LEVOTHYROXINE 100 MCG TABS, 6 
-33155482,LEVOTHYROXINE 200 MCG TABS, 7 
-33155490,LEVOTHYROXINE 300 MCG TABS, 7 
-33155508,LEVOTHYROXINE 200 MCG SOLR, 301 
-33155508,LEVOTHYROXINE 200 MCG SOLR 1 EACH VIAL, 410 
-33155516,LEVOTHYROXINE 500 MCG SOLR, 86 
-33155516,LEVOTHYROXINE 500 MCG SOLR 1 EACH VIAL, 182 
-33155524,LIDOCAINE 0.5% 0.5% SOLN 50 ML VIAL, 51 
-33155532,"LIDOCAINE-EPINEPHRINE 0.5%-1:200,000 SOLN", 43 
-33155532,"LIDOCAINE-EPINEPHRINE 0.5%-1:200,000 SOLN 50 ML VIAL", 46 
-33155540,LIDOCAINE (PF) 1% 1% SOLN, 5 
-33155540,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (SHC OR PNB) 2 ML AMPUL, 12 
-33155540,LIDOCAINE (PF) 1% 1% SOLN 2 ML AMPUL, 16 
-33155540,LIDOCAINE (PF) 1% 1% SOLN 2 ML VIAL, 25 
-33155557,LIDOCAINE (PF) 1% 1% SOLN, 12 
-33155557,LIDOCAINE (PF) 1% 1% SOLN 5 ML AMPUL, 33 
-33155557,LIDOCAINE (PF) 1% 1% SOLN 5 ML VIAL, 39 
-33155557,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (SHC OR PNB) 5 ML AMPUL, 39 
-33155565,LIDOCAINE (PF) 1% 1% SOLN 30 ML AMPUL, 88 
-33155573,"LIDOCAINE 1,000MG/100ML", 18 
-33155573,LIDOCAINE 1% SOLN, 45 
-33155573,"LIDOCAINE 1,000MG/100ML 20 ML VIAL", 48 
-33155573,LIDOCAINE 1% 1% SOLN, 48 
-33155573,LIDOCAINE 1% SOLN 20 ML VIAL, 50 
-33155573,LIDOCAINE 1% 1% SOLN 20 ML VIAL, 51 
-33155573,"LIDOCAINE 1,000MG/100ML 100 ML BAG", 92 
-33155573,LIDOCAINE 1% INFUSION, 92 
-33155573,LIDOCAINE 1% INFUSION 250 ML BAG, 169 
-33155573,LIDOCAINE 1% NERVE BLOCK INFUSION 250 ML BAG, 169 
-33155581,LIDOCAINE 1% SOLN 50 ML VIAL, 49 
-33155581,LIDOCAINE 1% SOLN, 53 
-33155581,LIDOCAINE 1% 1% SOLN 50 ML VIAL, 55 
-33155581,LIDOCAINE 1% 1% SOLN, 56 
-33155599,"LIDOCAINE-EPINEPHRINE 1%-1:100,000 SOLN 20 ML VIAL", 46 
-33155599,"LIDOCAINE-EPINEPHRINE 1%-1:100,000 SOLN", 48 
-33155607,"LIDOCAINE-EPINEPHRINE 1%-1:100,000 SOLN", 54 
-33155607,"LIDOCAINE-EPINEPHRINE 1%-1:100,000 SOLN 50 ML VIAL", 54 
-33155615,"LIDOCAINE-EPINEPHRINE 1%-1:200,000 SOLN 30 ML AMPUL", 68 
-33155623,LIDOCAINE (PF) 1.5% 1.5% SOLN 20 ML AMPUL, 55 
-33155698,LIDOCAINE 2% SOLN 2 ML AMPUL, 4 
-33155698,LIDOCAINE (PF) 2% SOLN, 6 
-33155698,LIDOCAINE (PF) 2% SOLN 2 ML AMPUL, 9 
-33155698,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN (SHC OR SUB-Q) 2 ML AMPUL, 12 
-33155698,LIDOCAINE (PF) 2% SOLN 2 ML VIAL, 12 
-33155706,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN (SHC OR EPIDURAL), 2 
-33155706,LIDOCAINE (PF) 2% SOLN, 2 
-33155706,LIDOCAINE 2% SOLN 10 ML AMPUL, 3 
-33155706,LIDOCAINE (PF) 2% SOLN 10 ML AMPUL, 7 
-33155730,LIDOCAINE 2% SOLN, 44 
-33155730,LIDOCAINE 2% SOLN 20 ML VIAL, 51 
-33155748,LIDOCAINE 2% SOLN 50 ML VIAL, 49 
-33155748,LIDOCAINE 2% SOLN, 59 
-33155755,LIDOCAINE 2% VISCOUS 2 % SOLN, 5 
-33155755,LIDOCAINE 2% VISCOUS 2 % SOLN 20 ML CUP, 8 
-33155755,LIDOCAINE 2% VISCOUS 2 % SOLN 20 ML BLIST PACK, 13 
-33155763,LIDOCAINE 2% VISCOUS 2 % SOLN 100 ML BOTTLE, 42 
-33155789,"LIDOCAINE-EPINEPHRINE 2%-1:200,000 SOLN 20 ML VIAL", 103 
-33155797,"LIDOCAINE-EPINEPHRINE 2%-1:100,000 SOLN", 44 
-33155797,"LIDOCAINE-EPINEPHRINE 2%-1:100,000 SOLN 50 ML VIAL", 44 
-33155805,"LIDOCAINE-EPINEPHRINE 2%-1:200,000 SOLN 20 ML AMPUL", 56 
-33155847,LIDOCAINE 2 GM IN D5W 250 ML 8 MG/ML SOLP 250 ML BAG, 80 
-33155847,LIDOCAINE 2 GM IN D5W 250 ML 8 MG/ML SOLP 250 ML FLEX CONT, 83 
-33155862,LIDOCAINE (PF) 4% SOLN 5 ML AMPUL, 53 
-33155862,LIDOCAINE (PF) 4% SOLN, 65 
-33155870,LIDOCAINE 4 % SOLN 50 ML BOTTLE, 53 
-33155979,LIOTHYRONINE 5 MCG TABS, 10 
-33155987,LIOTHYRONINE 25 MCG TABS, 14 
-33155995,LIOTHYRONINE 50 MCG TABS, 17 
-33156001,LITHIUM 300 MG TABS, 5 
-33156019,LITHIUM 300 MG TBSR, 5 
-33156027,LITHIUM 450 MG TBSR, 8 
-33156084,LOPERAMIDE 2 MG CAPS, 5 
-33156092,LORAZEPAM 0.5 MG TABS, 5 
-33156100,LORAZEPAM 1 MG TABS, 9 
-33156118,LORAZEPAM 2 MG/ML SYRG 1 ML SYRINGE, 55 
-33156126,LORAZEPAM 2 MG TABS, 7 
-33156142,LORAZEPAM 4 MG/ML SYRG 1 ML SYRINGE, 23 
-33156282,METHYLPREDNISOLONE 4 MG TABS, 10 
-33156290,METHYLPREDNISOLONE 16 MG TABS, 7 
-33156308,METHYLPREDNISOLONE ACETATE 40 MG/ML SUSP 1 ML VIAL, 82 
-33156308,METHYLPREDNISOLONE ACETATE 40 MG/ML SUSP, 83 
-33156316,METHYLPREDNISOLONE ACETATE 80 MG/ML SUSP, 91 
-33156316,METHYLPREDNISOLONE ACETATE 80 MG/ML SUSP 1 ML VIAL, 114 
-33156324,METHYLPREDNISOLONE(PF) 125 MG/2 ML SOLR 1 EACH VIAL, 54 
-33156324,METHYLPREDNISOLONE(PF) 125 MG/2 ML SOLR, 58 
-33156332,"METHYLPREDNISOLONE SOD SUC(PF) 1,000 MG/8 ML SOLR", 28 
-33156340,METHYLPREDNISOLONE 40 MG/ML SOLR 1 EACH VIAL, 41 
-33156340,METHYLPREDNISOLONE 40 MG/ML SOLR, 46 
-33156357,METHYLPREDNISOLONE 500 MG/4 ML SOLR, 46 
-33156357,METHYLPREDNISOLONE SOD SUC(PF) 500 MG/4 ML SOLR, 48 
-33156357,METHYLPREDNISOLONE SOD SUC(PF) 500 MG/4 ML SOLR 1 EACH VIAL, 50 
-33156431,MAGNESIUM CITRATE SOLN, 2 
-33156431,MAGNESIUM CITRATE SOLN 300 ML BOTTLE, 2 
-33156456,MAGNESIUM SULFATE 4 MEQ/ML (50 %) SOLN, 15 
-33156456,MAGNESIUM SULFATE 4 MEQ/ML (50 %) SOLN 2 ML VIAL, 15 
-33156464,MAGNESIUM SULFATE 4 MEQ/ML (50 %) SOLN, 5 
-33156480,MANNITOL 20% SOLP 500 ML FLEX CONT, 155 
-33156498,MANNITOL 25% SOLN 50 ML VIAL, 31 
-33156498,MANNITOL 25% SOLN, 36 
-33156530,NEOMYCIN-POLYMYXIN-DEXAMETHASONE OINT 3.5 G TUBE, 164 
-33156597,MECLIZINE 12.5 MG TABS, 9 
-33156605,MECLIZINE 25 MG TABS, 2 
-33156621,MEDROXYPROGESTERONE 10 MG TABS, 5 
-33156639,MEDROXYPROGESTERONE 2.5 MG TABS, 3 
-33156654,MEDROXYPROGESTERONE 5 MG TABS, 6 
-33156662,MEGESTROL 20 MG TABS, 8 
-33156670,MEGESTROL 40 MG TABS, 6 
-33156704,MEPERIDINE 100 MG/ML SOLN 1 ML AMPUL, 44 
-33156704,MEPERIDINE 100 MG/ML SOLN, 46 
-33156712,MEPERIDINE 100 MG SYRINGE, 46 
-33156712,MEPERIDINE 100 MG/ML SYRG 100 MG SYRINGE, 49 
-33156746,MEPERIDINE 25 MG/0.5 ML SOLN, 44 
-33156746,MEPERIDINE 25 MG/0.5 ML SOLN 0.5 ML AMPUL, 45 
-33156753,MEPERIDINE 25 MG/ML SYRG 25 MG SYRINGE, 53 
-33156753,MEPERIDINE 25 MG SYRINGE, 57 
-33156753,MEPERIDINE 25 MG/ML SYRG, 104 
-33156779,MEPERIDINE 50 MG/ML SOLN, 46 
-33156779,MEPERIDINE 50 MG/ML SOLN 1 ML AMPUL, 47 
-33156787,MEPERIDINE 50 MG/ML SYRG 50 MG SYRINGE, 53 
-33156837,MEPERIDINE 75 MG/ML SYRG 75 MG SYRINGE, 58 
-33156886,MEPIVACAINE 10 MG/ML (1 %) SOLN, 23 
-33156886,MEPIVACAINE 10 MG/ML (1 %) SOLN 30 ML VIAL, 26 
-33156894,MEPIVACAINE 2% SOLN 50 ML VIAL, 17 
-33156936,MERCAPTOPURINE 50 MG TABS, 12 
-33157124,METHADONE 5 MG TABS, 7 
-33157140,METHADONE 10 MG TABS, 6 
-33157165,METHADONE 10 MG/ML SOLN, 49 
-33157165,METHADONE 10 MG/ML SOLN 1 ML SYRINGE, 64 
-33157165,METHADONE 10 MG/ML SYRG 1 ML SYRINGE, 148 
-33157165,METHADONE 10 MG/ML SYRG, 199 
-33157231,METHIMAZOLE 5 MG TABS, 6 
-33157249,METHIMAZOLE 10 MG TABS, 8 
-33157264,METHOCARBAMOL 500 MG TABS, 5 
-33157272,METHOCARBAMOL 750 MG TABS, 5 
-33157280,METHOHEXITAL 500 MG SOLR 1 EACH VIAL, 96 
-33157280,METHOHEXITAL 500 MG SOLR, 272 
-33157298,METHOTREXATE 2.5 MG TABS, 9 
-33157314,METHOTREXATE 25 MG/ML SOLN, 58 
-33157314,METHOTREXATE (PF) 25 MG/ML SOLN, 61 
-33157413,METHYLDOPA 250 MG TABS, 7 
-33157447,METHYLDOPA 500 MG TABS, 8 
-33157454,METHYLENE BLUE 1% 1 % SOLN, 4 
-33157454,METHYLENE BLUE 1% 1 % SOLN 1 ML VIAL, 5 
-33157462,METHYLENE BLUE 1% 1 % SOLN, 0.5 
-33157462,METHYLENE BLUE 1% 1 % SOLN 10 ML VIAL, 0.9 
-33157470,METHYLERGONOVINE 0.2 MG/ML (1 ML) SOLN 1 ML VIAL, 79 
-33157470,METHYLERGONOVINE 0.2 MG/ML (1 ML) SOLN 1 ML AMPUL, 111 
-33157488,METHYLERGONOVINE 0.2 MG TABS, 107 
-33157496,METHYLPHENIDATE 5 MG TABS, 3 
-33157504,METHYLPHENIDATE 10 MG TABS, 12 
-33157512,METHYLPHENIDATE 20 MG TABS, 17 
-33157520,METHYLPHENIDATE 20 MG TBSR, 12 
-33157546,METOCLOPRAMIDE HCL 10 MG TABS, 6 
-33157553,METOCLOPRAMIDE HCL 5 MG/ML SOLN, 27 
-33157553,METOCLOPRAMIDE HCL 5 MG/ML SOLN 2 ML VIAL, 46 
-33157561,METOCLOPRAMIDE HCL 5 MG/ML SOLN 10 ML VIAL, 12 
-33157595,METOLAZONE 2.5 MG TABS, 8 
-33157603,METOLAZONE 5 MG TABS, 12 
-33157611,METOLAZONE 10 MG TABS, 15 
-33157629,METOPROLOL 50 MG TABS, 4 
-33157637,METOPROLOL 100 MG TABS, 6 
-33157645,METOPROLOL 5 MG/5 ML SOLN, 39 
-33157645,METOPROLOL 5 MG/5 ML SOLN 5 ML VIAL, 49 
-33157652,METRONIDAZOLE 250 MG TABS, 7 
-33157660,METRONIDAZOLE 500 MG/100 ML PGBK, 31 
-33157660,METRONIDAZOLE 500 MG/100 ML PGBK 100 ML BAG, 56 
-33157678,METRONIDAZOLE 500 MG TABS, 12 
-33157694,MEXILETINE 150 MG CAPS, 7 
-33157702,MEXILETINE 200 MG CAPS, 13 
-33157710,MEXILETINE 250 MG CAPS, 9 
-33157728,MAGNESIUM OXIDE-AMINO ACID CHELATE 133 MG TABS, 0.9 
-33157736,MICONAZOLE 100 MG SUPP, 2 
-33157751,MICONAZOLE 2 % CREA 30 G TUBE, 6 
-33157793,MICONAZOLE 200 MG SUPP, 154 
-33157819,MICONAZOLE 2 % CREA 45 G TUBE/KIT, 6 
-33157827,MIDAZOLAM 1 MG/ML SOLN, 24 
-33157827,MIDAZOLAM 1 MG/ML SOLN 2 ML VIAL, 24 
-33157843,MIDAZOLAM 5 MG/ML SOLN, 2 
-33157843,MIDAZOLAM 5 MG/ML SOLN 10 ML VIAL, 7 
-33157876,MAGNESIUM HYDROXIDE 400 MG/5 ML SUSP 30 ML CUP, 2 
-33157876,MAGNESIUM HYDROXIDE 400 MG/5 ML SUSP 30 ML BLIST PACK, 2 
-33157892,MINERAL OIL OIL 30 ML BLIST PACK, 2 
-33157900,MINERAL OIL OIL, 2 
-33157926,MINOCYCLINE 50 MG CAPS, 6 
-33157934,MINOCYCLINE 100 MG CAPS, 8 
-33157959,MINOXIDIL 2.5 MG TABS, 8 
-33157967,MINOXIDIL 10 MG TABS, 10 
-33158106,MORPHINE 10 MG/ML SOLN 1 ML AMPUL, 46 
-33158114,MORPHINE 10 MG/5 ML SOLN 5 ML CUP, 9 
-33158130,MORPHINE CRTG 10 MG SYRINGE, 46 
-33158130,MORPHINE SYRG 10 MG SYRINGE, 48 
-33158130,MORPHINE SYRG, 48 
-33158171,MORPHINE 15 MG TBSR, 8 
-33158197,MORPHINE 30 MG/30 ML (1 MG/ML) SPCA 30 ML SYRINGE, 42 
-33158205,MORPHINE 10 MG/5 ML SOLN 10 ML BLIST PACK, 12 
-33158213,MORPHINE CRTG 2 MG SYRINGE, 51 
-33158213,MORPHINE 2 MG/ML SYRG 2 MG SYRINGE, 56 
-33158213,MORPHINE 2 MG SYRINGE, 61 
-33158239,MORPHINE 30 MG TBSR, 8 
-33158247,MORPHINE CRTG 4 MG SYRINGE, 51 
-33158247,MORPHINE SYRG 4 MG SYRINGE, 54 
-33158254,MORPHINE 5 MG SUPP, 46 
-33158270,MORPHINE PF 1 MG/ML SOLN 2 ML AMPUL, 49 
-33158270,MORPHINE PF 1 MG/ML SOLN, 80 
-33158288,MORPHINE PF 0.5 MG/ML SOLN, 44 
-33158288,MORPHINE PF 0.5 MG/ML SOLN 10 ML AMPUL, 59 
-33158338,"MULTIVITAMINS ADULT 3,300 UNIT- 150 MCG/10 ML SOLN", 82 
-33158338,"MVI, ADULT NO.4 WITH VIT K 3,300 UNIT- 150 MCG/10 ML SOLN", 112 
-33158379,THERAPEUTIC MULTIVITAMIN TABS, 2 
-33158429,PRENATAL MULTIVITAMIN TABS, 2 
-33158429,PRENATAL VIT-IRON FUMARATE-FA 28-0.8 MG TABS, 2 
-33158437,MULTIVITAMIN 27-0.4 MG TABS, 2 
-33158437,THERAPEUTIC MULTIVITAMIN & MINERALS TABS, 2 
-33158437,MULTIVITAMIN CMB NO.21-IRON-FA 18-400 MG-MCG TABS, 2 
-33158494,NADOLOL 40 MG TABS, 13 
-33158502,NADOLOL 80 MG TABS, 30 
-33158544,NAFCILLIN 1 GRAM SOLR, 160 
-33158551,NAFCILLIN 1 GRAM SOLR, 110 
-33158577,NAFCILLIN 2 GRAM SOLR, 271 
-33158585,NAFCILLIN 2 GRAM SOLR, 234 
-33158601,NALBUPHINE 10 MG/ML SOLN 1 ML AMPUL, 49 
-33158619,NALBUPHINE 20 MG/ML SOLN 1 ML AMPUL, 45 
-33158619,NALBUPHINE 20 MG/ML SOLN, 46 
-33158668,NALOXONE 0.4 MG/ML SOLN 1 ML VIAL, 95 
-33158668,NALOXONE 0.4 MG/ML SOLN, 129 
-33158684,NAPHAZOLINE 0.1 % DROP 15 ML DROP BTL, 56 
-33158692,NAPROXEN 125 MG/5 ML SUSP, 12 
-33158700,NAPROXEN 250 MG TABS, 4 
-33158718,NAPROXEN 375 MG TABS, 6 
-33158726,NAPROXEN 500 MG TABS, 6 
-33158791,NEOMYCIN 25 MG/ML SOLN, 23 
-33158809,NEOMYCIN 500 MG TABS, 18 
-33158858,NEOMYCIN-POLYMYXIN-GRAMICIDIN 1.75-10K-0.025 MG-UNIT-MG/ML DROP 10 ML DROP BTL, 106 
-33158866,NEOSTIGMINE 0.5 MG/ML SOLN, 10 
-33158866,NEOSTIGMINE 0.5 MG/ML SOLN 10 ML VIAL, 11 
-33158940,NIACIN 500 MG TABS, 1.2 
-33158957,NICOTINE 2 MG GUM, 2 
-33158965,NIFEDIPINE 10 MG CAPS, 12 
-33158973,NIFEDIPINE 20 MG CAPS, 18 
-33158981,NIFEDIPINE 30 MG TR24, 13 
-33158999,NIFEDIPINE 60 MG TR24, 25 
-33159005,NIFEDIPINE 90 MG TR24, 45 
-33159013,NIMODIPINE 30 MG CAPS, 68 
-33159047,NITROFURANTOIN 50 MG CAPS, 19 
-33159054,NITROFURANTOIN 100 MG CAPS, 28 
-33159062,NITROFURANTOIN 25 MG/5 ML SUSP, 100 
-33159120,NITROGLYCERIN 0.2 MG/HR PT24, 11 
-33159138,NITROGLYCERIN 0.3 MG SUBL, 6 
-33159146,NITROGLYCERIN 0.3 MG/HR PT24, 43 
-33159153,NITROGLYCERIN 0.4 MG SUBL, 8 
-33159161,NITROGLYCERIN 0.4 MG/HR PT24, 35 
-33159187,NITROGLYCERIN 100 MG IN D5W 250 ML 100 MG/250 ML (0.4 MG/ML) SOLN 250 ML BOTTLE, 90 
-33159195,NITROGLYCERIN 2 % OINT 30 G TUBE, 112 
-33159211,NITROGLYCERIN 2.5 MG CPSR, 8 
-33159229,NITROGLYCERIN 50 MG/10 ML (5 MG/ML) SOLN 10 ML VIAL, 73 
-33159237,NITROGLYCERIN 50 MG IN D5W 250 ML 200 MCG/ML SOLN 250 ML BOTTLE, 78 
-33159245,NITROGLYCERIN 6.5 MG CPSR, 8 
-33159302,NOREPINEPHRINE 1 MG/ML SOLN, 66 
-33159302,NOREPINEPHRINE 1 MG/ML SOLN 4 ML AMPUL, 94 
-33159310,NORETHINDRONE 5 MG TABS, 15 
-33159328,NORTRIPTYLINE 10 MG CAPS, 5 
-33159344,NORTRIPTYLINE 25 MG CAPS, 6 
-33159351,NORTRIPTYLINE 50 MG CAPS, 6 
-33159369,NORTRIPTYLINE 75 MG CAPS, 7 
-33159393,"NYSTATIN 100,000 UNIT/G CREA 15 G TUBE", 32 
-33159401,"NYSTATIN 100,000 UNIT/G OINT 15 G TUBE", 136 
-33159419,"NYSTATIN 100,000 UNIT/G POWD 15 G JAR", 190 
-33159419,"NYSTATIN 100,000 UNIT/G POWD 15 G SQUEEZ BTL", 194 
-33159427,"NYSTATIN 500,000 UNIT TABS", 8 
-33159435,"NYSTATIN 100,000 UNIT/ML SUSP", 36 
-33159484,"NYSTATIN-TRIAMCINOLONE 100,000-0.1 UNIT/G-% OINT 15 G TUBE", 14 
-33159542,OCTREOTIDE 50 MCG/ML SOLN, 12 
-33159542,OCTREOTIDE 50 MCG/ML SOLN 1 ML AMPUL, 19 
-33159542,OCTREOTIDE 50 MCG/ML SOLN 1 ML VIAL, 27 
-33159559,OCTREOTIDE 100 MCG/ML SOLN 1 ML VIAL, 18 
-33159559,OCTREOTIDE 100 MCG/ML SOLN 1 ML AMPUL, 28 
-33159567,OCTREOTIDE 500 MCG/ML SOLN 1 ML VIAL, 12 
-33159567,OCTREOTIDE 500 MCG/ML SOLN, 13 
-33159583,ONDANSETRON 2 MG/ML SOLN, 4 
-33159583,ONDANSETRON 2 MG/ML SOLN 20 ML VIAL, 12 
-33159617,BELLADONNA-OPIUM 16.2-60 MG SUPP, 241 
-33159633,BENZOCAINE 20 % PSTE 11.9 G TUBE, 5 
-33159740,OXYBUTYNIN 5 MG TABS, 7 
-33159765,OXYCHLOROSENE POWD 2 G JAR, 49 
-33159765,OXYCHLOROSENE POWD, 49 
-33159781,OXYCODONE-ACETAMINOPHEN 5-325 MG TABS, 4 
-33159807,OXYMETAZOLINE 0.05 % SPRA 15 ML SQUEEZ BTL, 2 
-33159807,OXYMETAZOLINE 0.05 % SPRA, 2 
-33159898,OXYTOCIN 10 UNIT/ML SOLN, 23 
-33159898,OXYTOCIN 10 UNIT/ML SOLN 1 ML VIAL, 26 
-33159989,PAPAVERINE 30 MG/ML SOLN 2 ML VIAL, 53 
-33159989,PAPAVERINE 30 MG/ML SOLN, 65 
-33160094,"PENICILLIN G BENZATHINE 1,200,000 UNIT/2 ML SYRG 2 ML SYRINGE", 46 
-33160102,"PENICILLIN G BENZATHINE 2,400,000 UNIT/4 ML SYRG 4 ML SYRINGE", 51 
-33160128,"PENICILLIN G BENZATHINE-PENICILLIN G PROCAINE 1,200,000 UNIT/ 2 ML(900K/300K) SYRG 2 ML SYRINGE", 43 
-33160151,PENICILLIN G POTASSIUM 5 MILLION UNIT SOLR, 9 
-33160169,PENICILLIN G POTASSIUM 20 MILLION UNIT SOLR, 5 
-33160201,PENICILLIN G SODIUM 5 MILLION UNIT SOLR, 237 
-33160227,PENICILLIN V POTASSIUM 250 MG TABS, 6 
-33160235,PENICILLIN VK 250 MG/5 ML SOLR 200 ML BOTTLE, 60 
-33160243,PENICILLIN V POTASSIUM 500 MG TABS, 6 
-33160326,PENTAMIDINE 75 MG/ML SOLR, 419 
-33160326,PENTAMIDINE 300 MG SOLR, 598 
-33160326,PENTAMIDINE 300 MG SOLR 1 EACH VIAL, 785 
-33160441,PENTOXIFYLLINE 400 MG TBSR, 7 
-33160466,BISMUTH 262 MG/15 ML SUSP, 2 
-33160466,BISMUTH 262 MG/15 ML SUSP 120 ML BOTTLE, 4 
-33160508,PERPHENAZINE 2 MG TABS, 13 
-33160516,PERPHENAZINE 4 MG TABS, 15 
-33160524,PERPHENAZINE 8 MG TABS, 18 
-33160532,PERPHENAZINE 16 MG TABS, 10 
-33160599,WHITE PETROLATUM OINT 3.5 G TUBE, 10 
-33160599,WHITE PETROLATUM 56.8-42.5 % OINT 3.5 G TUBE, 10 
-33160615,PHENAZOPYRIDINE 100 MG TABS, 7 
-33160623,PHENAZOPYRIDINE 200 MG TABS, 7 
-33160631,PHENELZINE 15 MG TABS, 11 
-33160649,PHENOBARBITAL 15 MG TABS, 3 
-33160656,PHENOBARBITAL 30 MG TABS, 5 
-33160664,PHENOBARBITAL 60 MG TABS, 4 
-33160672,PHENOBARBITAL 65 MG/ML SOLN 1 ML VIAL, 60 
-33160672,PHENOBARBITAL 65 MG/ML SOLN, 189 
-33160680,PHENOBARBITAL 100 MG TABS, 7 
-33160698,PHENOBARBITAL 130 MG/ML SOLN 1 ML VIAL, 63 
-33160714,PHENOBARBITAL 20 MG/5 ML ELIX 5 ML BLIST PACK, 5 
-33160748,PHENOXYBENZAMINE 10 MG CAPS, 85 
-33160755,PHENTOLAMINE 5 MG SOLR 1 EACH VIAL, 225 
-33160771,PHENYLEPHRINE 10 % DROP 5 ML DROP BTL, 60 
-33160789,PHENYLEPHRINE 10 MG/ML SOLN, 34 
-33160789,PHENYLEPHRINE 10 MG/ML SOLN 1 ML VIAL, 42 
-33160805,PHENYLEPHRINE 1 % DROP 15 ML DROP BTL, 3 
-33160813,PHENYLEPHRINE 0.25% SPRY 15 ML SQUEEZ BTL, 5 
-33160821,PHENYLEPHRINE 0.5 % DROP 15 ML DROP BTL, 8 
-33160847,PHENYLEPHRINE 1% SPRY 15 ML SQUEEZ BTL, 5 
-33160888,PHENYTOIN 100 MG CAPS, 5 
-33160896,PHENYTOIN 50 MG/ML SOLN, 11 
-33160896,PHENYTOIN 50 MG/ML SOLN 2 ML VIAL, 21 
-33160912,PHENYTOIN 50 MG/ML SOLN, 4 
-33160912,PHENYTOIN 50 MG/ML SOLN 5 ML VIAL, 10 
-33160912,PHENYTOIN 50 MG/ML SOLN 2 ML VIAL, 24 
-33160920,PHENYTOIN 50 MG CHEW, 9 
-33160953,PHYSOSTIGMINE 1 MG/ML SOLN 2 ML AMPUL, 67 
-33160961,PHYTONADIONE 1 MG/0.5 ML SOLN, 45 
-33160961,PHYTONADIONE 1 MG/0.5 ML SOLN 0.5 ML AMPUL, 52 
-33160979,PHYTONADIONE 10 MG/ML SOLN, 13 
-33160979,PHYTONADIONE 10 MG/ML SOLN 1 ML AMPUL, 15 
-33160979,PHYTONADIONE 10 MG/ML SOLN 1 ML AMPULE, 17 
-33160995,PHYTONADIONE 5 MG TABS, 112 
-33161035,PILOCARPINE 1 % DROP 2 ML DROP BTL, 40 
-33161175,PINDOLOL 5 MG TABS, 13 
-33161183,PINDOLOL 10 MG TABS, 14 
-33161308,"POLYMYXIN B 500,000 UNIT SOLR", 130 
-33161308,"POLYMYXIN B 500,000 UNIT SOLR 1 EACH VIAL", 164 
-33161316,"BACITRACIN-POLYMYXIN B 500-10,000 UNIT/G OINT 28.35 G TUBE", 8 
-33161332,"BACITRACIN-POLYMYXIN B 500-10,000 UNIT/G OINT 3.5 G TUBE", 94 
-33161340,"TRIMETHOPRIM-POLYMYXIN B 0.1 - 10,000 %-UNIT/ML DROP 10 ML DROP BTL", 49 
-33161357,POTASSIUM ACETATE 2 MEQ/ML SOLN, 36 
-33161373,POTASSIUM CHLORIDE 8 MEQ TBSR, 6 
-33161373,POTASSIUM CHLORIDE 8 MEQ TBSR 1 EACH BLIST PACK, 7 
-33161415,POTASSIUM CHLORIDE 10 % LIQD 15 ML CUP, 8 
-33161423,POTASSIUM CHLORIDE 20 MEQ PACK, 10 
-33161449,POTASSIUM CHLORIDE 2 MEQ/ML SOLP, 4 
-33161449,POTASSIUM CHLORIDE 2 MEQ/ML SOLP 10 ML VIAL, 5 
-33161464,POTASSIUM CHLORIDE 10 % LIQD 30 ML CUP, 10 
-33161472,POTASSIUM CHLORIDE 2 MEQ/ML SOLP, 2 
-33161514,POTASSIUM IODIDE 1 GRAM/ML SOLN 30 ML BOTTLE, 47 
-33161522,POTASSIUM PHOSPHATE 3 MILLIMOLE/ML SOLP, 45 
-33161555,PRAMOXINE 1 % LOTN 118 ML BOTTLE, 15 
-33161597,PRAZIQUANTEL 600 MG TABS, 154 
-33161605,PRAZOSIN 1 MG CAPS, 5 
-33161613,PRAZOSIN 2 MG CAPS, 7 
-33161621,PRAZOSIN 5 MG CAPS, 7 
-33161688,PREDNISOLONE ACETATE 1 % DRPS, 71 
-33161688,PREDNISOLONE ACETATE 1 % DRPS 5 ML DROP BTL, 74 
-33161704,PREDNISOLONE 5 MG TABS, 5 
-33161738,PREDNISONE 1 MG TABS, 8 
-33161746,PREDNISONE 5 MG TABS, 5 
-33161753,PREDNISONE 10 MG TABS, 3 
-33161761,PREDNISONE 20 MG TABS, 1.1 
-33161779,PREDNISONE 50 MG TABS, 0.6 
-33161787,PREDNISONE 5 MG/5 ML SOLN, 9 
-33161811,PRIMAQUINE 26.3 MG TABS, 17 
-33161829,IMIPENEM-CILASTATIN 250 MG SOLR, 99 
-33161837,IMIPENEM-CILASTATIN 250 MG SOLR, 66 
-33161845,IMIPENEM-CILASTATIN 500 MG SOLR, 76 
-33161852,IMIPENEM-CILASTATIN 500 MG SOLR 1 EACH VIAL, 62 
-33161878,PRIMIDONE 250 MG TABS, 10 
-33161894,PROBENECID 500 MG TABS, 8 
-33161951,PROCAINAMIDE 100 MG/ML SOLN, 168 
-33162058,PROCHLORPERAZINE 5 MG TABS, 4 
-33162066,PROCHLORPERAZINE 10 MG/2 ML (5 MG/ML) SOLN 2 ML VIAL, 80 
-33162082,PROCHLORPERAZINE 10 MG TABS, 6 
-33162108,PROCHLORPERAZINE 25 MG SUPP, 11 
-33162181,PROMETHAZINE 12.5 MG SUPP, 11 
-33162199,PROMETHAZINE 12.5 MG TABS, 9 
-33162207,PROMETHAZINE 25 MG/ML SOLN 1 ML VIAL, 45 
-33162207,PROMETHAZINE 25 MG/ML SOLN 1 ML AMPUL, 45 
-33162215,PROMETHAZINE 25 MG SUPP, 22 
-33162223,PROMETHAZINE 25 MG TABS, 7 
-33162256,PROMETHAZINE 50 MG TABS, 4 
-33162264,PROMETHAZINE 6.25 MG/5 ML SYRP 118 ML BOTTLE, 7 
-33162264,PROMETHAZINE 6.25 MG/5 ML SYRP, 11 
-33162280,PROPAFENONE 150 MG TABS, 9 
-33162298,PROPAFENONE 300 MG TABS, 13 
-33162314,PROPANTHELINE 15 MG TABS, 9 
-33162322,PROPARACAINE 0.5 % DROP 15 ML DROP BTL, 50 
-33162330,PROPOFOL 10 MG/ML, 38 
-33162330,PROPOFOL 10 MG/ML EMUL, 41 
-33162330,PROPOFOL 10 MG/ML 20 ML VIAL, 46 
-33162330,PROPOFOL 10 MG/ML EMUL 20 ML VIAL, 57 
-33162348,PROPOFOL 10 MG/ML 50 ML VIAL, 63 
-33162348,PROPOFOL 10 MG/ML EMUL 50 ML VIAL, 72 
-33162348,PROPOFOL 10 MG/ML, 80 
-33162348,PROPOFOL (PF) 500 MG/50 ML (10 MG/ML) EMUL, 81 
-33162389,PROPRANOLOL 1 MG/ML SOLN 1 ML VIAL, 76 
-33162397,PROPRANOLOL 10 MG TABS, 5 
-33162405,PROPRANOLOL 20 MG TABS, 5 
-33162421,PROPRANOLOL 40 MG TABS, 5 
-33162439,PROPRANOLOL 80 MG CS24, 10 
-33162447,PROPRANOLOL 80 MG TABS, 4 
-33162454,PROPRANOLOL 120 MG CS24, 11 
-33162462,PROPRANOLOL 160 MG CS24, 13 
-33162470,PROPYLTHIOURACIL 50 MG TABS, 7 
-33162488,PROTAMINE 10 MG/ML SOLN 5 ML VIAL, 19 
-33162488,PROTAMINE 10 MG/ML SOLN, 21 
-33162520,PSEUDOEPHEDRINE 30 MG TABS, 5 
-33162538,PSEUDOEPHEDRINE 60 MG TABS, 3 
-33162553,PSEUDOEPHEDRINE 30 MG/5 ML LIQD 118 ML BOTTLE, 3 
-33162579,PYRAZINAMIDE 500 MG TABS, 11 
-33162587,PYRIDOSTIGMINE 60 MG TABS, 10 
-33162603,PYRIDOSTIGMINE 180 MG TBSR, 47 
-33162611,PYRIDOSTIGMINE 5 MG/ML SOLN 2 ML AMPUL, 86 
-33162611,PYRIDOSTIGMINE 5 MG/ML SOLN, 97 
-33162629,PYRIDOXINE 100 MG/ML SOLN 1 ML VIAL, 103 
-33162637,PYRIDOXINE 25 MG TABS, 4 
-33162645,PYRIDOXINE 50 MG TABS, 5 
-33162652,PYRIMETHAMINE 25 MG TABS, 106 
-33162686,QUINIDINE GLUCONATE 324 MG TBSR, 13 
-33162751,QUININE 324 MG CAPS, 71 
-33162785,RABIES VIRUS VACCINE 2.5 UNIT SOLR 1 EACH KIT," 2,093 "
-33162785,RABIES VIRUS VACCINE 2.5 UNIT KIT," 2,199 "
-33162892,RIFAMPIN 300 MG CAPS, 16 
-33162900,RIFAMPIN 600 MG SOLR, 244 
-33162900,RIFAMPIN 600 MG SOLR 1 EACH VIAL, 364 
-33163023,CAMPHOR-MENTHOL 0.5-0.5 % LOTN 222 ML BOTTLE, 5 
-33163056,SCOPOLAMINE 0.4 MG/ML SOLN 1 ML VIAL, 103 
-33163098,SCOPOLAMINE 1.5 MG/72 HR PT72, 131 
-33163171,SELENIUM SULFIDE 2.5 % SUSP 118 ML BOTTLE, 48 
-33163189,SELENIUM 40 MCG/ML SOLN, 43 
-33163197,SENNA TABS, 3 
-33163205,SENNOSIDES 8.8 MG/5 ML SYRP, 2 
-33163239,SILVER NITRATE 75-25 % STCK, 6 
-33163247,SILVER SULFADIAZINE 1 % CREA 50 G JAR, 88 
-33163247,SILVER SULFADIAZINE 1 % CREA 50 G TUBE, 132 
-33163270,SIMETHICONE 40 MG/0.6 ML DRPS 30 ML DROP BTL, 18 
-33163288,SIMETHICONE 80 MG CHEW, 3 
-33163296,CARBIDOPA-LEVODOPA 10-100 MG TABS, 7 
-33163304,CARBIDOPA-LEVODOPA 25-100 MG TABS, 5 
-33163312,CARBIDOPA-LEVODOPA 25-250 MG TABS, 6 
-33163338,TRIMETHOPRIM-SULFAMETHOXAZOLE 80-400 MG TABS, 5 
-33163346,TRIMETHOPRIM-SMZ 400-80 MG/5 ML SOLN, 44 
-33163353,TRIMETHOPRIM-SULFAMETHOXAZOLE 160-800 MG TABS, 6 
-33163429,SODIUM ACETATE 2 MEQ/ML SOLN, 42 
-33163437,SODIUM BICARBONATE 0.5 MEQ/ML SYRG 10 ML SYRINGE, 53 
-33163445,SODIUM BICARBONATE 10 MEQ/10 ML (8.4 %) SYRG 10 ML SYRINGE, 117 
-33163460,SODIUM BICARBONATE 1 MEQ/ML SYRG, 46 
-33163460,SODIUM BICARBONATE 1 MEQ/ML SYRG 50 ML SYRINGE, 78 
-33163478,SODIUM BICARBONATE 1 MEQ/ML SOLP, 16 
-33163478,SODIUM BICARBONATE 1 MEQ/ML SOLN, 31 
-33163478,SODIUM BICARBONATE 1 MEQ/ML SOLN 50 ML VIAL, 43 
-33163478,SODIUM BICARBONATE 50 ML VIAL, 48 
-33163486,SODIUM BICARBONATE 650 MG TABS, 1.0 
-33163502,"1/2 NS 0.45 % SOLP 1,000 ML BAG", 45 
-33163502,1/2 NS 0.45 % SOLP, 51 
-33163510,NS 0.9 % SOLP, 40 
-33163510,NS 0.9 % SOLP 50 ML BAG, 53 
-33163528,NS 0.9 % SOLP, 49 
-33163528,NS 0.9 % SOLP 100 ML BAG, 52 
-33163536,NS SOLN, 48 
-33163536,NS SOLN 250 ML BAG, 51 
-33163536,NS 0.9 % SOLP, 54 
-33163536,NS 0.9 % SOLP 250 ML FLEX CONT, 54 
-33163544,NS FOR IRRIGATION 0.9 % SOLN 500 ML BOTTLE, 15 
-33163544,NS 0.9 % SOLN, 15 
-33163544,NS FOR IRRIGATION 0.9 % SOLN, 15 
-33163551,NS BOLUS, 49 
-33163551,NS 0.9 % SOLP, 51 
-33163551,NS 0.9 % SOLP 500 ML BAG, 51 
-33163551,NS BOLUS 500 ML BAG, 52 
-33163569,NS FOR IRRIGATION 0.9 % SOLN, 13 
-33163569,"NS FOR IRRIGATION 0.9 % SOLN 1,000 ML BOTTLE", 15 
-33163577,NS 0.9 % SOLP, 47 
-33163577,"NS 0.9 % SOLP 1,000 ML BAG", 51 
-33163577,"NS BOLUS 1,000 ML BAG", 52 
-33163585,NS 0.9 % SOLN 10 ML VIAL, 10 
-33163585,NS 0.9 % SOLN, 15 
-33163593,"NS FOR IRRIGATION 0.9 % SOLN 3,000 ML BAG", 17 
-33163593,NS FOR IRRIGATION 0.9 % SOLN, 19 
-33163619,SODIUM CHLORIDE 1 G TBSO, 1.0 
-33163619,SODIUM CHLORIDE 1 GRAM TABS, 10 
-33163627,SODIUM CHLORIDE 2 % DROP 15 ML DROP BTL, 15 
-33163643,SODIUM CHLORIDE 3% 3% SOLP, 49 
-33163643,SODIUM CHLORIDE 3% 3 % SOLP, 49 
-33163643,SODIUM CHLORIDE 3% 3 % SOLP 500 ML BAG, 57 
-33163650,SODIUM CHLORIDE 4 MEQ/ML SOLP 30 ML VIAL, 30 
-33163650,SODIUM CHLORIDE 4 MEQ/ML SOLP, 39 
-33163684,SODIUM CHLORIDE 5 % OINT 3.5 G TUBE, 9 
-33163692,SODIUM CHLORIDE 5 % DROP 15 ML DROP BTL, 12 
-33163700,SODIUM CHLORIDE 0.65 % SPRA 45 ML SQUEEZ BTL, 1.3 
-33163700,SODIUM CHLORIDE 0.65 % SPRA 44 ML SQUEEZ BTL, 2 
-33163726,SODIUM PHOSPHATE 3 MILLIMOLE/ML SOLN, 50 
-33163767,SODIUM THIOSULFATE 12.5 GRAM/50 ML (250 MG/ML) SOLN, 175 
-33163817,SPIRONOLACTONE 25 MG TABS, 7 
-33163825,SPIRONOLACTONE 100 MG TABS, 12 
-33163858,SPIRONOLACTONE-HYDROCHLOROTHIAZIDE 25-25 MG TABS, 7 
-33163924,SUCCINYLCHOLINE 20 MG/ML SOLN 10 ML VIAL, 10 
-33163924,SUCCINYLCHOLINE 20 MG/ML SOLN, 10 
-33163932,SUCCINYLCHOLINE 100 MG/ML SOLN 10 ML VIAL, 5 
-33163932,SUCCINYLCHOLINE 100 MG/ML SOLN, 11 
-33163940,SUCRALFATE 1 GRAM TABS, 8 
-33163957,SUFENTANIL 50 MCG/ML SOLN, 39 
-33163957,SUFENTANIL 50 MCG/ML SOLN 1 ML AMPUL, 48 
-33163965,SUFENTANIL 50 MCG/ML SOLN 5 ML AMPUL, 126 
-33164021,SULFASALAZINE 500 MG TBEC, 8 
-33164039,SULFASALAZINE 500 MG TABS, 4 
-33164088,SULINDAC 150 MG TABS, 6 
-33164096,SULINDAC 200 MG TABS, 7 
-33164187,TAMOXIFEN 10 MG TABS, 4 
-33164195,TERBUTALINE 1 MG/ML SOLN, 50 
-33164195,TERBUTALINE 1 MG/ML SOLN 1 ML VIAL, 51 
-33164211,TERBUTALINE 5 MG TABS, 8 
-33164369,TETRACAINE (PF) 0.5 % DROP 2 ML DROP BTL, 50 
-33164377,TETRACAINE 0.5 % DROP 15 ML DROP BTL, 87 
-33164393,TETRACAINE 1% SOLN 2 ML AMPUL, 55 
-33164419,TETRACAINE (PF) 20 MG SOLR 1 EACH AMPUL, 135 
-33164468,TETRACYCLINE 500 MG CAPS, 100 
-33164492,TETRAHYDROZOLINE 0.05 % DROP 15 ML DROP BTL, 4 
-33164542,THEOPHYLLINE 100 MG TB12, 6 
-33164591,THEOPHYLLINE 200 MG TB12, 6 
-33164617,THEOPHYLLINE 300 MG TB12, 5 
-33164625,THEOPHYLLINE 80 MG/15 ML ELIX, 30 
-33164658,THIAMINE 100 MG TABS, 1.3 
-33164674,THIAMINE 100 MG/ML SOLN, 56 
-33164674,THIAMINE 100 MG/ML SOLN 2 ML VIAL, 67 
-33164872,THIOTHIXENE 2 MG CAPS, 6 
-33164880,THIOTHIXENE 5 MG CAPS, 7 
-33164963,"THROMBIN (BOVINE) 5,000 UNIT SOLR 1 EACH VIAL", 612 
-33164963,"THROMBIN (BOVINE) 5,000 UNIT SOLR", 644 
-33165002,THYROID (PORK) 15 MG TABS, 2 
-33165010,THYROID (PORK) 30 MG TABS, 6 
-33165028,THYROID (PORK) 60 MG TABS, 6 
-33165036,THYROID (PORK) 120 MG TABS, 7 
-33165044,THYROID (PORK) 180 MG TABS, 8 
-33165119,TIMOLOL 0.25 % DROP 5 ML DROP BTL, 24 
-33165135,TIMOLOL 0.5 % DROP 5 ML DROP BTL, 58 
-33165218,TOBRAMYCIN 0.3 % DROP 5 ML DROP BTL, 21 
-33165259,TOBRAMYCIN 40 MG/ML SOLN, 18 
-33165259,TOBRAMYCIN 40 MG/ML SOLN 2 ML VIAL, 23 
-33165366,TOLNAFTATE 1 % CREA 15 G TUBE, 3 
-33165382,TOLNAFTATE 1 % SOLN 10 ML BOTTLE, 3 
-33165457,TRACE ELEMENTS 0.8-0.2-0.16 MG SOLN, 57 
-33165465,TRANYLCYPROMINE 10 MG TABS, 14 
-33165473,TRAZODONE 50 MG TABS, 5 
-33165481,TRAZODONE 100 MG TABS, 5 
-33165499,TRAZODONE 150 MG TABS, 4 
-33165549,TRETINOIN 0.05 % CREA 20 G TUBE, 195 
-33165606,TRIAMCINOLONE ACETONIDE 10 MG/ML SUSP 5 ML VIAL, 43 
-33165606,TRIAMCINOLONE ACETONIDE 10 MG/ML SUSP, 53 
-33165614,TRIAMCINOLONE ACETONIDE 40 MG/ML SUSP 5 ML VIAL, 26 
-33165614,TRIAMCINOLONE ACETONIDE 40 MG/ML SUSP, 29 
-33165697,TRIAMCINOLONE ACETONIDE 0.025 % CREA 15 G TUBE, 26 
-33165713,TRIAMCINOLONE ACETONIDE 0.025 % OINT 15 G TUBE, 42 
-33165747,TRIAMCINOLONE ACETONIDE 0.1 % CREA 15 G TUBE, 34 
-33165788,TRIAMCINOLONE ACETONIDE 0.1 % LOTN 60 ML BOTTLE, 174 
-33165796,TRIAMCINOLONE ACETONIDE 0.1 % OINT 15 G TUBE, 34 
-33165853,TRIAMTERENE 50 MG CAPS, 18 
-33165861,TRIAMTERENE 100 MG CAPS, 28 
-33165879,TRIAZOLAM 0.125 MG TABS, 6 
-33165887,TRIAZOLAM 0.25 MG TABS, 6 
-33165911,TRIFLUOPERAZINE 2 MG TABS, 9 
-33165929,TRIFLUOPERAZINE 5 MG TABS, 10 
-33165937,TRIFLUOPERAZINE 10 MG TABS, 9 
-33165978,MEDIUM CHAIN TRIGLYCERIDES 7.7 KCAL/ML OIL, 2 
-33165978,MEDIUM CHAIN TRIGLYCERIDES 7.7 KCAL/ML OIL 960 ML BOTTLE, 52 
-33165986,TRIHEXYPHENIDYL 2 MG TABS, 5 
-33166000,TRIHEXYPHENIDYL 5 MG TABS, 6 
-33166091,TRIMETHOPRIM 100 MG TABS, 7 
-33166125,NEOMYCIN-BACITRACIN-POLYMYXIN PACK, 1.5 
-33166125,NEOMYCIN-BACITRACIN-POLYMYXIN PACK 1 EACH BLIST PACK, 2 
-33166133,"NEOMYCIN-BACITRACIN-POLYMYXIN 3.5-400-5,000 MG-UNIT-UNIT/G OINT", 3 
-33166133,"NEOMYCIN-BACITRACIN-POLYMYXIN 3.5-400-5,000 MG-UNIT-UNIT/G OINT 15 G TUBE", 5 
-33166158,"NEOMYCIN-BACITRACIN-POLYMYXIN 3.5-400-10,000 MG-UNIT-UNIT/G OINT 3.5 G TUBE", 20 
-33166216,TROPICAMIDE 1 % DROP 3 ML DROP BTL, 75 
-33166224,TROPICAMIDE 1 % DROP 15 ML DROP BTL, 65 
-33166257,TUBERCULIN 5 TUB. UNIT /0.1 ML SOLN 1 ML VIAL, 74 
-33166323,UREA 10 % CREA 90 G TUBE, 9 
-33166323,UREA 10 % CREA 90 G JAR, 12 
-33166356,UREA 20 % CREA 85 G JAR, 15 
-33166406,URSODIOL 300 MG CAPS, 21 
-33166422,VALPROIC ACID 250 MG CAPS, 4 
-33166448,VANCOMYCIN 125 MG CAPS, 269 
-33166455,"VANCOMYCIN 1,000 MG SOLR", 40 
-33166463,"VANCOMYCIN 1,000 MG SOLR", 31 
-33166463,"VANCOMYCIN 1,000 MG SOLR 1 EACH VIAL", 34 
-33166471,VANCOMYCIN 250 MG CAPS, 497 
-33166489,VANCOMYCIN 500 MG SOLR, 51 
-33166489,VANCOMYCIN 500 MG SOLR 1 EACH VIAL, 80 
-33166521,VASOPRESSIN 20 UNIT/ML SOLN 1 ML VIAL, 47 
-33166521,VASOPRESSIN 20 UNIT/ML SOLN, 53 
-33166554,VERAPAMIL 80 MG TABS, 6 
-33166562,VERAPAMIL 120 MG TABS, 5 
-33166570,VERAPAMIL 240 MG TBSR, 7 
-33166588,VERAPAMIL 2.5 MG/ML SOLN 2 ML VIAL, 56 
-33166646,VINBLASTINE 1 MG/ML SOLN, 30 
-33166687,"VITAMIN A 25,000 UNIT CAPS", 5 
-33166745,VITAMIN B COMPLEX WITH VITAMIN C TABS, 5 
-33166752,"CYANOCOBALAMIN 1,000 MCG/ML SOLN 1 ML VIAL", 53 
-33166778,VITAMIN E 400 UNIT CAPS, 2 
-33166786,VITAMIN E 50 UNIT/ML DROP, 14 
-33166786,VITAMIN E 50 UNIT/ML DROP 12 ML DROP BTL, 20 
-33166836,VODKA 480 ML BOTTLE, 2 
-33166844,WARFARIN 1 MG TABS, 5 
-33166851,WARFARIN 2.5 MG TABS, 5 
-33166869,WARFARIN 2 MG TABS, 5 
-33166885,WARFARIN 7.5 MG TABS, 5 
-33166893,WARFARIN 10 MG TABS, 21 
-33166919,STERILE WATER SOLN, 2 
-33166919,STERILE WATER SOLN 10 ML VIAL, 2 
-33166927,STERILE WATER SOLN, 23 
-33166935,STERILE WATER SOLN, 10 
-33166943,STERILE WATER SOLN, 6 
-33166950,STERILE WATER SOLP, 42 
-33166968,STERILE WATER FOR IRRIGATION SOLN, 13 
-33166968,"STERILE WATER FOR IRRIGATION SOLN 1,000 ML BOTTLE", 13 
-33167040,ZIDOVUDINE 100 MG CAPS, 22 
-33167057,ZINC CHLORIDE 1 MG/ML SOLN, 56 
-33167065,ZINC OXIDE 20 % OINT 28.35 G TUBE, 4 
-33167115,ZINC SULFATE 220 (50) MG CAPS, 5 
-33167164,SODIUM HYPOCHLORITE 0.125 % SOLN 473 ML BOTTLE, 90 
-33167172,PHENYTOIN 30 MG CAPS, 8 
-33167248,BUPIVACAINE (PF) 0.25% SOLN 10 ML VIAL, 43 
-33167248,BUPIVACAINE (PF) 0.25% SOLN, 44 
-33167289,POTASSIUM CHLORIDE 20 MEQ TBTQ 1 EACH BLIST PACK, 7 
-33167289,POTASSIUM CHLORIDE 20 MEQ TBTQ, 8 
-33167289,POTASSIUM CHLORIDE 20 MEQ TBTQ 100 EACH BLIST PACK, 9 
-33167289,POTASSIUM CHLORIDE 20 MEQ TBTQ 20 MEQ BLIST PACK, 9 
-33167289,POTASSIUM CHLORIDE, 11 
-33167305,DIVALPROEX 125 MG TBEC, 6 
-33167313,FLUOCINONIDE-EMOLLIENT 0.05 % CREA 15 G TUBE, 57 
-33167347,GENTAMICIN 60 MG/6 ML SOLN, 52 
-33167354,GENTAMICIN 80 MG/8 ML SOLN 8 ML VIAL, 31 
-33167354,GENTAMICIN 80 MG/8 ML SOLN, 49 
-33167453,METHYLPREDNISOLONE 4 MG DSPK 21 EACH DOSE-PACK, 7 
-33167479,BENZOYL PEROXIDE 10 % GLAL 60 G JAR, 17 
-33167503,SULFADIAZINE 500 MG TABS, 29 
-33167537,NICOTINE 7 MG/24 HR PT24, 27 
-33167545,NICOTINE 14 MG/24 HR PT24, 24 
-33167552,NICOTINE 21 MG/24 HR PT24, 23 
-33167560,BETHANECHOL 5 MG TABS, 6 
-33167578,DILTIAZEM 180 MG CP24, 8 
-33167586,DILTIAZEM 240 MG CP24, 9 
-33167594,DILTIAZEM 300 MG CP24, 13 
-33167636,FLUMAZENIL 0.1 MG/ML SOLN 5 ML VIAL, 104 
-33167636,FLUMAZENIL 0.1 MG/ML SOLN, 139 
-33167651,PAMIDRONATE 30 MG SOLR, 435 
-33167735,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) TABS", 2 
-33167784,B COMPLEX-VITAMIN C-FOLIC ACID 0.8 MG TABS, 2 
-33167800,"HEPARIN 2 UNITS/ML IN NS 500 ML 1,000 UNIT/500 ML SOLP 500 ML BAG", 67 
-33167818,VERAPAMIL 180 MG TBSR, 7 
-33167834,NITROGLYCERIN 0.6 MG/HR PT24, 12 
-33167842,ALBUTEROL 2.5 MG /3 ML (0.083 %) NEBU 3 ML BLIST PACK, 2 
-33167842,ALBUTEROL 2.5 MG /3 ML (0.083 %) NEBU 3 ML VIAL, 2 
-33167867,CARBIDOPA-LEVODOPA 50-200 MG TBSR, 11 
-33167883,DEXAMETHASONE 1 MG TABS, 2 
-33167891,DIPYRIDAMOLE 50 MG TABS, 6 
-33167917,ERYTHROMYCIN 500 MG TBEC, 19 
-33167933,SODIUM BICARBONATE 325 MG TABS, 1.2 
-33167966,LORAZEPAM 2 MG/ML CONC, 9 
-33167966,LORAZEPAM 2 MG/ML CONC 1 ML SYRINGE, 11 
-33167974,SERTRALINE 50 MG TABS, 5 
-33167982,SERTRALINE 100 MG TABS, 6 
-33168048,NORETHINDRONE-ETHINYL ESTRADIOL 1-35 MG-MCG TABS 28 EACH BLIST PACK, 16 
-33168063,FLUDARABINE 50 MG SOLR," 1,458 "
-33168097,CIPROFLOXACIN 400 MG/40 ML SOLN, 46 
-33168105,DOCUSATE 50 MG/5 ML LIQD 473 ML BOTTLE, 1.5 
-33168105,DOCUSATE 50 MG/5 ML LIQD 10 ML CUP, 2 
-33168105,DOCUSATE 50 MG/5 ML LIQD 25 ML CUP, 7 
-33168105,DOCUSATE 50 MG/5 ML LIQD 25 ML BLIST PACK, 10 
-33168147,HYDROCORTISONE 1 % LOTN 118 ML BOTTLE, 66 
-33168154,NADOLOL 20 MG TABS, 17 
-33168220,PROPRANOLOL 60 MG CS24, 9 
-33168246,D5W 5 % PGBK 250 ML BAG, 45 
-33168246,D5W 5 % PGBK, 59 
-33168279,D5W 5 % PGBK, 55 
-33168287,D5W 5 % PGBK, 56 
-33168295,NS 0.9 % PGBK, 56 
-33168303,NS 0.9 % PGBK, 56 
-33168311,NS 0.9 % PGBK, 56 
-33168329,ATENOLOL 25 MG TABS, 5 
-33168345,PE-SHARK LIVER OIL 0.25-3 % SUPP, 2 
-33168352,TEMAZEPAM 15 MG CAPS, 5 
-33168360,TEMAZEPAM 30 MG CAPS, 6 
-33168378,METOCLOPRAMIDE HCL 5 MG TABS, 4 
-33168436,ACYCLOVIR 800 MG TABS, 8 
-33168444,DILTIAZEM 120 MG CP24, 7 
-33168451,MORPHINE 100 MG TBSR, 19 
-33168550,AMOXICILLIN-CLAVULANATE 250-125 MG TABS, 37 
-33168568,AMOXICILLIN-CLAVULANATE 500-125 MG TABS, 14 
-33168576,BUSPIRONE 5 MG TABS, 4 
-33168584,BUSPIRONE 10 MG TABS, 4 
-33168592,CEFUROXIME 250 MG TABS, 8 
-33168600,CEFUROXIME 500 MG TABS, 12 
-33168642,POTASSIUM CHLORIDE 10 MEQ TBTQ 1 EACH BLIST PACK, 5 
-33168642,POTASSIUM CHLORIDE 10 MEQ TBTQ, 6 
-33168642,POTASSIUM CHLORIDE 10 MEQ TBTQ 100 EACH BLIST PACK, 8 
-33168642,POTASSIUM CHLORIDE, 8 
-33168642,POTASSIUM CHLORIDE 10 MEQ TBTQ 10 MEQ BLIST PACK, 9 
-33168733,WHITE PETROLATUM GEL 28.35 G TUBE, 2 
-33168758,MAGNESIUM SULFATE 20 GRAM/500 ML SOLP 500 ML FLEX CONT, 2 
-33168790,BUPROPION 75 MG TABS, 9 
-33168808,BUPROPION 100 MG TABS, 7 
-33168816,CLOZAPINE 25 MG TABS, 7 
-33168824,CLOZAPINE 100 MG TABS, 13 
-33168832,PARENTERAL ELECTROLYTES 25-20-5-5-30-30 MEQ/20 ML SOLN, 66 
-33168915,TRIAMTERENE-HYDROCHLOROTHIAZIDE 37.5-25 MG TABS, 6 
-33168964,D5W SOLP 100 ML BAG, 51 
-33168964,D5W SOLP, 51 
-33168964,D5W SOLN 100 ML BAG, 99 
-33168964,D5W SOLN, 118 
-33169012,TOBRAMYCIN POWDER 1.2 G SOLR 1 EACH VIAL, 47 
-33169012,TOBRAMYCIN POWDER 1.2 G SOLR, 47 
-33169038,LR SOLP, 46 
-33169038,"LR SOLP 1,000 ML BAG", 51 
-33169061,MEPIVACAINE 15 MG/ML (1.5 %) SOLN 30 ML VIAL, 29 
-33169095,APRACLONIDINE 1 % DPET 24 EACH BLIST PACK, 22 
-33169095,APRACLONIDINE 1 % DPET, 23 
-33169129,PACLITAXEL 6 MG/ML CONC, 169 
-33169236,OXYCODONE 5 MG TABS, 3 
-33169277,SOTALOL 80 MG TABS, 18 
-33169285,SOTALOL 160 MG TABS, 16 
-33169376,ONDANSETRON 8 MG TABS, 9 
-33169426,INSULIN NPH-REGULAR 70/30 100 UNIT/ML (70-30) SUSP 10 ML VIAL, 0.5 
-33169533,CIPROFLOXACIN 0.3 % DROP 5 ML DROP BTL, 31 
-33169590,FLUCONAZOLE 400 MG/200 ML PGBK 200 ML FLEX CONT, 48 
-33169590,FLUCONAZOLE 200 MG/100 ML PGBK 100 ML BAG, 122 
-33169640,FLUCONAZOLE 400 MG/200 ML PGBK 200 ML BAG, 56 
-33169681,D20W 20 % SOLP 500 ML BAG, 26 
-33169681,D20W 20 % SOLP, 32 
-33169699,KETOROLAC 10 MG TABS, 6 
-33169731,PAROXETINE 20 MG TABS, 4 
-33169749,PAROXETINE 30 MG TABS, 12 
-33169780,ENOXAPARIN 30 MG/0.3 ML SYRG 0.3 ML SYRINGE, 30 
-33169830,ALDESLEUKIN 18 MIL UNITS/ML SOLR," 10,516 "
-33169889,AMPICILLIN-SULBACTAM 1.5 GRAM SOLR, 43 
-33169897,AMPICILLIN-SULBACTAM 1.5 GRAM SOLR, 43 
-33169939,ITRACONAZOLE 100 MG CAPS, 67 
-33169988,VERAPAMIL 120 MG TBSR, 8 
-33170036,NS 0.9 % SOLP, 19 
-33170044,ALBUTEROL 4 MG TABS, 6 
-33170176,LIDOCAINE-PRILOCAINE 2.5-2.5 % CREA 30 G TUBE, 134 
-33170184,DIPHENHYDRAMINE-ZINC 2-0.1 % CREA 28.4 G TUBE, 3 
-33170242,PROCARBAZINE 50 MG CAPS, 543 
-33170283,DIGOXIN 0.05 MG/ML SOLN 2.5 ML CUP, 14 
-33170291,DIPHENHYDRAMINE 12.5 MG/5 ML ELIX 10 ML BLIST PACK, 2 
-33170291,DIPHENHYDRAMINE 12.5 MG/5 ML ELIX, 2 
-33170309,GUAIFENESIN 100 MG/5 ML LIQD 5 ML CUP, 1.2 
-33170317,METOCLOPRAMIDE HCL 5 MG/5 ML SOLN 10 ML BLIST PACK, 8 
-33170317,METOCLOPRAMIDE HCL 5 MG/5 ML SOLN 10 ML CUP, 10 
-33170325,PHENYTOIN 100 MG/4 ML SYRG, 13 
-33170325,PHENYTOIN 100 MG/4 ML SUSP, 16 
-33170325,PHENYTOIN 100 MG/4 ML SYRG 4 ML SYRINGE, 16 
-33170325,PHENYTOIN 100 MG/4 ML SUSP 4 ML CUP, 17 
-33170341,SORBITOL 70 % SOLN 30 ML BLIST PACK, 2 
-33170374,MEDROXYPROGESTERONE (CONTRACEPTIVE) 150 MG/ML SUSP 1 ML VIAL, 27 
-33170382,DRONABINOL 2.5 MG CAPS, 51 
-33170408,PROPOFOL 10 MG/ML EMUL 100 ML VIAL, 133 
-33170465,LIDOCAINE 2 % JELP 20 ML VIAL/KIT, 59 
-33170481,FENTANYL 100 MCG/HR PT72, 169 
-33170531,D5-NS SOLP 500 ML BAG, 42 
-33170556,LR BOLUS 500 ML BAG, 51 
-33170556,LR SOLP, 52 
-33170572,GUAIFENESIN-DEXTROMETHORPHAN 100-10 MG/5 ML SYRP 10 ML CUP, 2 
-33170572,GUAIFENESIN-DEXTROMETHORPHAN 100-10 MG/5 ML SYRP, 2 
-33170572,GUAIFENESIN-DEXTROMETHORPHAN 100-10 MG/5 ML SYRP 10 ML BOTTLE, 2 
-33170572,GUAIFENESIN-DEXTROMETHORPHAN 100-10 MG/5 ML SYRP 10 ML BLIST PACK, 2 
-33170606,ACETAMINOPHEN 325 MG/10.15 ML SOLN 10.15 ML BLIST PACK, 1.3 
-33170622,DIGOXIN 0.05 MG/ML SOLN 5 ML CUP, 25 
-33170648,FERROUS SULFATE 300 MG (60 MG IRON)/5 ML LIQD 5 ML BLIST PACK, 2 
-33170655,POTASSIUM CHLORIDE 10 MEQ IN 100 ML PGBK 1 EACH BAG, 13 
-33170655,POTASSIUM CHLORIDE 1 EACH BAG, 44 
-33170663,SODIUM CITRATE-CITRIC ACID 500-334 MG/5 ML SOLN 30 ML BLIST PACK, 9 
-33170721,PSYLLIUM HUSK 6 GRAM PACK, 2 
-33170721,PSYLLIUM PACK, 2 
-33170721,PSYLLIUM HUSK 3.4 GRAM PWPK, 2 
-33170754,LACTULOSE 20 GRAM/30 ML SOLN, 11 
-33170754,LACTULOSE 20 GRAM/30 ML SOLN 30 ML CUP, 11 
-33170770,"DOBUTAMINE 500 MG IN D5W 250 ML 2,000 MCG/ML SOLP 250 ML BAG", 63 
-33170804,QUINAPRIL 5 MG TABS, 6 
-33170812,QUINAPRIL 10 MG TABS, 25 
-33170820,QUINAPRIL 20 MG TABS, 17 
-33170838,QUINAPRIL 40 MG TABS, 11 
-33170887,LORATADINE 10 MG TABS, 7 
-33170895,NABUMETONE 500 MG TABS, 10 
-33170903,NABUMETONE 750 MG TABS, 7 
-33170929,PSEUDOEPHEDRINE 120 MG TBSR, 2 
-33170937,OXYCODONE 5 MG/5 ML SOLN, 13 
-33170937,OXYCODONE 5 MG/5 ML SOLN 5 ML CUP, 32 
-33170986,LEVOTHYROXINE 88 MCG TABS, 6 
-33171117,ACETAMINOPHEN-CODEINE 120-12 MG/5 ML ELIX, 6 
-33171125,AMLODIPINE 5 MG TABS, 5 
-33171133,AMLODIPINE 10 MG TABS, 9 
-33171208,ACETAMINOPHEN 650 MG/20.3 ML SOLN 20.3 ML BLIST PACK, 2 
-33171224,ALUM-MAG(OH)-SIMETHICONE 400-400-40 MG/5 ML SUSP 30 ML CUP, 2 
-33171240,NALOXONE 0.4 MG/ML SOLN, 119 
-33171240,NALOXONE 0.4 MG/ML SOLN 10 ML VIAL, 121 
-33171257,GABAPENTIN 100 MG CAPS, 7 
-33171265,GABAPENTIN 300 MG CAPS, 5 
-33171273,GABAPENTIN 400 MG CAPS, 9 
-33171281,RISPERIDONE 1 MG TABS, 11 
-33171299,RISPERIDONE 2 MG TABS, 10 
-33171307,RISPERIDONE 3 MG TABS, 13 
-33171315,RISPERIDONE 4 MG TABS, 12 
-33171323,ZOLPIDEM 5 MG TABS, 5 
-33171331,ZOLPIDEM 10 MG TABS, 6 
-33171380,"DOBUTAMINE 1000 MG IN D5W 250 ML 4,000 MCG/ML SOLP 250 ML BAG", 41 
-33171414,AMLODIPINE 2.5 MG TABS, 6 
-33171430,ROCURONIUM 10 MG/ML SOLN, 54 
-33171430,ROCURONIUM 10 MG/ML SOLN 5 ML VIAL, 59 
-33171448,AZITHROMYCIN 250 MG TABS, 25 
-33171455,ENALAPRIL-HYDROCHLOROTHIAZIDE 10-25 MG TABS, 6 
-33171463,FLUOXETINE 10 MG CAPS, 5 
-33171471,VENLAFAXINE 25 MG TABS, 11 
-33171489,CLARITHROMYCIN 250 MG TABS, 37 
-33171513,TRIAMTERENE-HYDROCHLOROTHIAZIDE 75-50 MG TABS, 6 
-33171570,NICARDIPINE 20 MG CAPS, 7 
-33171596,PRAVASTATIN 10 MG TABS, 5 
-33171604,PRAVASTATIN 20 MG TABS, 6 
-33171612,ESTRADIOL 1 MG TABS, 4 
-33171620,MISOPROSTOL 100 MCG TABS, 21 
-33171638,MISOPROSTOL 200 MCG TABS, 28 
-33171646,FINASTERIDE 5 MG TABS, 15 
-33171679,GUAIFENESIN 600 MG TBSR, 8 
-33171687,CLARITHROMYCIN 500 MG TABS, 40 
-33171745,ALUM-MAG(OH)-SIMETHICONE 200-200-20 MG/5 ML SUSP, 0.9 
-33171745,ALUM-MAG(OH)-SIMETHICONE 200-200-20 MG/5 ML SUSP 30 ML CUP, 2 
-33171760,ISOSORBIDE MONONITRATE 60 MG TB24, 8 
-33171778,VENLAFAXINE 37.5 MG TABS, 9 
-33171802,BUPRENORPHINE 0.3 MG/ML SOLN 1 ML VIAL, 23 
-33171810,TACROLIMUS 1 MG CAPS, 31 
-33171828,TACROLIMUS 5 MG CAPS, 29 
-33171844,CARISOPRODOL 350 MG TABS, 7 
-33171851,MILRINONE 1 MG/ML SOLN, 33 
-33171851,MILRINONE 1 MG/ML SOLN 10 ML VIAL, 48 
-33171869,MILRINONE 1 MG/ML SOLN, 35 
-33171877,PIPERACILLIN-TAZOBACTAM 3.375 GRAM SOLR 1 EACH VIAL, 29 
-33171877,PIPERACILLIN-TAZOBACTAM 3.375 GRAM SOLR, 31 
-33171885,PIPERACILLIN-TAZOBACTAM 2.25 GRAM SOLR 1 EACH VIAL, 36 
-33171885,PIPERACILLIN-TAZOBACTAM 2.25 GRAM SOLR, 38 
-33171893,PIPERACILLIN-TAZOBACTAM 4.5 GRAM SOLR 1 EACH VIAL, 25 
-33171893,PIPERACILLIN-TAZOBACTAM 4.5 GRAM SOLR, 28 
-33171901,LIDOCAINE 2 % GEL 5 ML TUBE, 40 
-33171901,LIDOCAINE 2 % GEL, 55 
-33171919,ATROPINE 0.1 MG/ML SYRG 10 ML SYRINGE, 1.5 
-33171919,ATROPINE 0.1 MG/ML SYRG, 2 
-33171984,FELODIPINE 5 MG TB24, 12 
-33172057,GLIPIZIDE 5 MG TR24, 7 
-33172065,ACETAMINOPHEN-CAFFEINE-BUTALBITAL 325-40-50 MG TABS, 5 
-33172107,SIMVASTATIN 5 MG TABS, 6 
-33172115,SIMVASTATIN 10 MG TABS, 6 
-33172156,HAEMOPHILUS B CONJUGATE VACCINE 10 MCG/0.5 ML SOLR 1 EACH VIAL, 297 
-33172164,MESALAMINE 250 MG CPSR, 16 
-33172271,ONDANSETRON 4 MG/2 ML SOLN, 4 
-33172271,ONDANSETRON 4 MG/2 ML SOLN 2 ML VIAL, 10 
-33172339,PAROXETINE 10 MG TABS, 12 
-33172453,METOPROLOL 50 MG TB24, 15 
-33172479,CARBAMAZEPINE 200 MG/10 ML SUSP 10 ML CUP, 14 
-33172479,CARBAMAZEPINE 200 MG/10 ML SUSP, 29 
-33172586,D5W BOLUS, 41 
-33172586,"D5W BOLUS 1,000 ML BAG", 52 
-33172594,"NS 0.9 % SOLP 1,000 ML BAG", 51 
-33172628,HYDROXYUREA 500 MG CAPS, 7 
-33172636,PREDNISONE 2.5 MG TABS, 3 
-33172727,ISOSORBIDE MONONITRATE 20 MG TABS, 5 
-33172768,ACTIVATED CHARCOAL 25 GRAM/120 ML SUSP 120 ML TUBE, 15 
-33172768,ACTIVATED CHARCOAL 25 GRAM/120 ML SUSP 120 ML BOTTLE, 15 
-33172776,ACTIVATED CHARCOAL-SORBITOL 25 GRAM/120 ML SUSP 120 ML BOTTLE, 14 
-33172867,DAPSONE 25 MG TABS, 13 
-33172891,CLINDAMYCIN 600 MG/50 ML PGBK 50 ML BOTTLE, 56 
-33172891,CLINDAMYCIN 600 MG/50 ML PGBK 50 ML BAG, 78 
-33172909,CLINDAMYCIN 900 MG/50 ML PGBK 50 ML BOTTLE, 41 
-33172909,CLINDAMYCIN 900 MG/50 ML PGBK 50 ML BAG, 173 
-33172917,GLIPIZIDE 10 MG TR24, 8 
-33172974,VENLAFAXINE 50 MG TABS, 8 
-33172982,VENLAFAXINE 75 MG TABS, 9 
-33172990,VENLAFAXINE 100 MG TABS, 15 
-33173006,CLINDAMYCIN 150 MG/ML SOLN, 54 
-33173006,CLINDAMYCIN 150 MG/ML SOLN 4 ML VIAL, 61 
-33173006,CLINDAMYCIN PHOSPHATE 600 MG/4 ML SOLN 4 ML VIAL, 72 
-33173055,ASCORBIC ACID 500 MG/ML SOLN, 47 
-33173055,ASCORBIC ACID 500 MG/ML SOLN 2 ML AMPUL, 49 
-33173162,AMPICILLIN-SULBACTAM 3 GRAM SOLR, 23 
-33173444,ASPIRIN 81 MG TBEC, 1.5 
-33173477,LITHIUM 150 MG CAPS, 3 
-33173519,WARFARIN 5 MG TABS, 5 
-33173535,HYDROMORPHONE SYRG 30 ML SYRINGE, 11 
-33173543,FLUCONAZOLE 150 MG TABS, 14 
-33173550,INDAPAMIDE 2.5 MG TABS, 6 
-33173576,FAMCICLOVIR 500 MG TABS, 27 
-33173600,BUPIVACAINE (PF) 0.75% SOLN, 42 
-33173618,NORETHINDRONE-ETHINYL ESTRADIOL 0.5/0.75/1 MG- 35 MCG TABS 28 EACH BLIST PACK, 249 
-33173634,HYDROCODONE-ACETAMINOPHEN 7.5-500 MG TABS, 5 
-33173667,CODEINE-GUAIFENESIN 10-100 MG/5ML LIQD 10 ML CUP, 11 
-33173667,CODEINE-GUAIFENESIN 20-200 MG/10 ML LIQD 10 ML CUP, 11 
-33173675,VINCRISTINE (CNS LYMPHOMA PROTOCOL) 2 MG/2 ML SOLN, 78 
-33173675,VINCRISTINE 2 MG/2 ML SOLN, 103 
-33173709,CALCIUM ACETATE 667 MG CAPS, 8 
-33173733,VINORELBINE 10 MG/ML SOLN, 191 
-33173741,VINORELBINE 50 MG/5 ML SOLN, 182 
-33173774,GUAIFENESIN 100 MG/5 ML LIQD 10 ML CUP, 2 
-33173782,CHLOROTHIAZIDE 250 MG TABS, 6 
-33173873,LORATADINE-PSEUDOEPHEDRINE 5-120 MG TB12, 2 
-33173923,TRAMADOL 50 MG TABS, 5 
-33173964,NAPROXEN SODIUM 550 MG TABS, 7 
-33173972,MYCOPHENOLATE 250 MG CAPS, 10 
-33173980,LOSARTAN 25 MG TABS, 9 
-33173998,TERAZOSIN 5 MG CAPS, 5 
-33174012,CHOLESTYRAMINE LIGHT 4 GRAM PWPK, 13 
-33174061,GRANISETRON 1 MG TABS, 34 
-33174087,"EPOETIN 20,000 UNIT/ML SOLN 1 ML VIAL", 96 
-33174087,"EPOETIN 20,000 UNIT/ML SOLN", 100 
-33174095,CARBIDOPA-LEVODOPA 25-100 MG TBSR, 7 
-33174103,CIPROFLOXACIN 400 MG/200 ML PGBK 200 ML BAG, 31 
-33174111,CIPROFLOXACIN 200 MG/100 ML PGBK 100 ML BAG, 58 
-33174137,LAMOTRIGINE 25 MG TABS, 7 
-33174145,LAMOTRIGINE 100 MG TABS, 6 
-33174160,ASPIRIN-CAFFEINE-BUTALBITAL 325-40-50 MG CAPS, 6 
-33174194,METFORMIN 500 MG TABS, 5 
-33174244,BUMETANIDE 2 MG TABS, 6 
-33174277,TORSEMIDE 20 MG TABS, 4 
-33174301,RIFAMPIN 150 MG CAPS, 13 
-33174335,LOSARTAN 50 MG TABS, 10 
-33174343,TRIAMCINOLONE ACETONIDE 0.5 % CREA 15 G TUBE, 74 
-33174368,CYCLOSPORINE MODIFIED 25 MG CAPS, 15 
-33174376,CYCLOSPORINE MODIFIED 100 MG CAPS, 56 
-33174384,BUTORPHANOL 1 MG/ACTUATION SPRY 2.5 ML SQUEEZ BTL, 107 
-33174392,BACLOFEN 20 MG TABS, 7 
-33174400,AMIODARONE 50 MG/ML SOLN, 4 
-33174400,AMIODARONE 50 MG/ML SOLN 3 ML AMPUL, 5 
-33174400,AMIODARONE 50 MG/ML SOLN 3 ML VIAL, 11 
-33174442,SORBITOL-FREE POLYSTYRENE SODIUM 15 GRAM/60 ML SUSP 60 ML BLIST PACK, 65 
-33174517,METFORMIN 850 MG TABS, 6 
-33174541,FUROSEMIDE 40 MG/5 ML SOLN, 17 
-33174608,MAGNESIUM GLUCONATE 27 MG (500 MG) TABS, 1.1 
-33174608,MAGNESIUM GLUCONATE 500 MG TABS, 2 
-33174632,MANNITOL 20% SOLP, 155 
-33174723,LAMIVUDINE 150 MG TABS, 56 
-33174731,"GLYCERIN-DIMETHICONE-PETRO, WHITE CREA 453 G JAR", 11 
-33174749,LANSOPRAZOLE 15 MG CPDR, 26 
-33174756,LANSOPRAZOLE 30 MG CPDR, 28 
-33174772,BACITRACIN-ZINC 500 UNIT/GRAM OINT 28.35 G TUBE, 5 
-33174822,HYOSCYAMINE 0.125 MG TABS, 7 
-33174830,HYOSCYAMINE 0.125 MG SUBL, 9 
-33174863,IBUPROFEN 100 MG/5 ML SUSP, 5 
-33174863,IBUPROFEN 100 MG/5 ML SUSP 5 ML CUP, 6 
-33174897,BICALUTAMIDE 50 MG TABS, 18 
-33174905,ALENDRONATE 10 MG TABS, 7 
-33174921,NITROFURANTOIN 100 MG CAPS, 25 
-33174954,CYANOCOBALAMIN 100 MCG TABS, 2 
-33174962,DIVALPROEX 125 MG CPSP, 12 
-33175050,BISMUTH SUBSALICYLATE CHEW, 1.4 
-33175092,PRAMOXINE 1 % FOAM 15 G CAN, 38 
-33175191,"BACITRACIN-POLYMYXIN B 500-10,000 UNIT/G POWD 10 G JAR", 9 
-33175274,L.E.T. SOLN 5 ML BOTTLE, 101 
-33175282,TRETINOIN 10 MG CAPS, 213 
-33175381,NAPHAZOLINE-PHENIRAMINE 0.025-0.3 % DROP 15 ML DROP BTL, 9 
-33175399,DEXTROMETHORPHAN-GUAIFENESIN 30-600 MG TB12, 2 
-33175464,ACARBOSE 50 MG TABS, 11 
-33175472,ACARBOSE 100 MG TABS, 12 
-33175522,OFLOXACIN 0.3 % DROP, 50 
-33175563,CYANOCOBALAMIN 500 MCG TABS, 0.9 
-33175589,LOSARTAN-HYDROCHLOROTHIAZIDE 50-12.5 MG TABS, 9 
-33175597,AMANTADINE 50 MG/5 ML SYRP 10 ML BLIST PACK, 16 
-33175597,AMANTADINE HCL 50 MG/5 ML SYRP 10 ML BLIST PACK, 19 
-33175647,NAPROXEN SODIUM 275 MG TABS, 6 
-33175654,MORPHINE 60 MG TBSR, 11 
-33175670,ISOSORBIDE MONONITRATE 30 MG TB24, 8 
-33175704,IPRATROPIUM 0.03 % SPRY 30 ML CANISTER, 44 
-33175712,IPRATROPIUM 0.06 % SPRY 15 ML CANISTER, 46 
-33175746,MINERAL OIL-UREA-POLYETHYLENE GLYCOL CREA 113 G JAR, 7 
-33175811,FENTANYL 200 MCG LPOP, 146 
-33175837,FENTANYL 400 MCG LPOP, 187 
-33175886,MAGNESIUM CHLORIDE 64 MG TBSR, 0.9 
-33175886,MAGNESIUM CHLORIDE 64 MG TBEC, 1.4 
-33175902,THEOPHYLLINE 300 MG CP24, 23 
-33175928,VALPROIC ACID 250 MG/5 ML (5 ML) SOLN, 7 
-33175928,VALPROIC ACID 250 MG/5 ML (5 ML) SOLN 5 ML CUP, 9 
-33175944,KETOROLAC 0.5 % DROP 5 ML DROP BTL, 56 
-33176025,LACTOBACILLUS ACIDOPHILUS 1 BILLION-250 CELL-MG TABS, 2 
-33176074,SPIRONOLACTONE 50 MG TABS, 8 
-33176090,FLECAINIDE 50 MG TABS, 6 
-33176108,PREDNISOLONE 5 MG/5 ML SOLN, 19 
-33176140,CLINDAMYCIN 300 MG/50 ML PGBK 50 ML BAG, 127 
-33176157,ACETAMINOPHEN-ISOMETHEPTENE-DICHLORALPHENAZONE 325-65-100 MG CAPS, 22 
-33176165,ACTIVATED CHARCOAL 50 GRAM/240 ML SUSP 240 ML TUBE, 16 
-33176173,ACTIVATED CHARCOAL-SORBITOL 50 GRAM/240 ML SUSP 240 ML TUBE, 15 
-33176173,ACTIVATED CHARCOAL-SORBITOL 50 GRAM/240 ML SUSP 240 ML BOTTLE, 20 
-33176272,MAGNESIUM 84 MG TBSR, 1.3 
-33176272,MAGNESIUM L-LACTATE 84 MG TBSR, 2 
-33176280,HALOPERIDOL 2 MG/ML CONC 5 ML BLIST PACK, 10 
-33176280,HALOPERIDOL 2 MG/ML CONC, 11 
-33176306,MICONAZOLE 2 % POWD 71 G JAR, 3 
-33176355,TOPOTECAN 4 MG SOLR, 51 
-33176363,TERAZOSIN 10 MG CAPS, 5 
-33176454,DOCETAXEL 20 MG/2 ML (FINAL) SOLN, 81 
-33176462,DOCETAXEL 80 MG/8 ML (FINAL) SOLN, 82 
-33176553,ESTRADIOL 0.05 MG/24 HR PTWK, 211 
-33176561,ESTRADIOL 0.1 MG/24 HR PTWK, 212 
-33176579,CALCIUM CARBONATE-MAGNESIUM HYDROXIDE 700-300 MG CHEW, 3 
-33176595,TERAZOSIN 1 MG CAPS, 6 
-33176603,TERAZOSIN 2 MG CAPS, 6 
-33176629,CISATRACURIUM 2 MG/ML SOLN, 120 
-33176629,CISATRACURIUM 2 MG/ML SOLN 10 ML VIAL, 137 
-33176645,WARFARIN 5 MG SOLR 6 EACH VIAL, 224 
-33176678,IRINOTECAN 100 MG/5 ML SOLN, 67 
-33176678,IRINOTECAN 100 MG/5 ML SOLN 5 ML VIAL, 68 
-33176686,AMOXICILLIN-CLAVULANATE 875-125 MG TABS, 12 
-33176702,THEOPHYLLINE 100 MG CP24, 12 
-33176710,GEMCITABINE 200 MG SOLR, 134 
-33176728,GEMCITABINE 1 GRAM SOLR, 72 
-33176769,MUPIROCIN 2 % OINT, 111 
-33176777,DICYCLOMINE 20 MG TABS, 6 
-33176801,DOCUSATE 50 MG/5 ML LIQD 10 ML BLIST PACK, 2 
-33176819,CARBAMAZEPINE 100 MG TB12, 9 
-33176827,CARBAMAZEPINE 400 MG TB12, 30 
-33176868,OLANZAPINE 10 MG TABS, 18 
-33176876,OLANZAPINE 5 MG TABS, 11 
-33176892,EPOPROSTENOL (GLYCINE) 0.5 MG SOLR, 253 
-33176975,PILOCARPINE 5 MG TABS, 7 
-33176983,ZAFIRLUKAST 20 MG TABS, 16 
-33177023,MESNA 100 MG/ML SOLN, 96 
-33177031,FENTANYL 50 MCG/ML 20 ML AMPUL, 5 
-33177031,FENTANYL 50 MCG/ML, 5 
-33177072,FOSPHENYTOIN 100 MG PE/2 ML SOLN, 17 
-33177072,FOSPHENYTOIN 100 MG PE/2 ML SOLN 2 ML VIAL, 30 
-33177080,FOSPHENYTOIN 500 MG PE/10 ML SOLN, 6 
-33177098,LATANOPROST 0.005 % DROP 2.5 ML DROP BTL, 109 
-33177106,MESALAMINE 4 GRAM/60 ML ENEM 60 ML SQUEEZ BTL, 86 
-33177122,CHLOROTHIAZIDE 500 MG TABS, 7 
-33177130,ESTRADIOL CYPIONATE 5 MG/ML OIL 5 ML VIAL, 85 
-33177148,ANASTROZOLE 1 MG TABS, 15 
-33177163,TORSEMIDE 10 MG TABS, 6 
-33177171,DESMOPRESSIN 0.2 MG TABS, 26 
-33177189,DESMOPRESSIN 0.1 MG TABS, 14 
-33177395,TERBINAFINE 250 MG TABS, 7 
-33177403,LEVOTHYROXINE 137 MCG TABS, 7 
-33177411,LAMIVUDINE 10 MG/ML SOLN, 68 
-33177452,BUPROPION 150 MG TBSR, 9 
-33177460,BUPROPION 100 MG TBSR, 8 
-33177478,FELODIPINE 2.5 MG TB24, 9 
-33177494,DIAZEPAM 5 MG/ML SOLN, 43 
-33177593,WARFARIN 3 MG TABS, 5 
-33177601,WARFARIN 6 MG TABS, 5 
-33177635,REMIFENTANIL 2 MG SOLR 1 EACH VIAL, 405 
-33177635,REMIFENTANIL 2 MG SOLR, 624 
-33177643,REMIFENTANIL 1 MG SOLR, 86 
-33177643,REMIFENTANIL 1 MG SOLR 1 EACH VIAL, 411 
-33177684,MILRINONE IN 5 % DEXTROSE 20 MG/100 ML PGBK 100 ML BAG, 39 
-33177700,PHENYLEPHRINE 2.5 % DROP 2 ML DROP BTL, 27 
-33177734,CISPLATIN 1 MG/ML SOLN, 28 
-33177742,INSULIN LISPRO 100 UNIT/ML SOLN 10 ML VIAL, 1.1 
-33177767,ATORVASTATIN 20 MG TABS, 23 
-33177775,ATORVASTATIN 10 MG TABS, 13 
-33177783,SODIUM FLUORIDE 0.02 % FLUORIDE (0.044 %) SOLN 473 ML BOTTLE, 10 
-33177817,DONEPEZIL 10 MG TABS, 17 
-33177825,DONEPEZIL 5 MG TABS, 12 
-33177932,NEVIRAPINE 200 MG TABS, 5 
-33178096,OLANZAPINE 2.5 MG TABS, 9 
-33178104,ENOXAPARIN 40 MG/0.4 ML SYRG 0.4 ML SYRINGE, 27 
-33178104,ENOXAPARIN 40 MG/0.4 ML SYRG, 29 
-33178112,SELEGILINE 5 MG CAPS, 17 
-33178120,CARVEDILOL 12.5 MG TABS, 6 
-33178286,MITOMYCIN 1 ML SYRINGE, 162 
-33178294,CALCIUM CARBONATE-VITAMIN D2 250-125 MG TABS, 0.8 
-33178294,CALCIUM CITRATE 200 MG (950 MG) TABS, 2 
-33178294,TIZANIDINE 2 MG TABS, 4 
-33178294,BALSALAZIDE 750 MG CAPS, 7 
-33178302,OMEPRAZOLE 20 MG CPDR, 2 
-33178302,QUETIAPINE TABS, 5 
-33178302,TIZANIDINE 4 MG TABS, 6 
-33178302,CILOSTAZOL 100 MG TABS, 8 
-33178302,CILOSTAZOL 100 MG TABS 100 EACH BLIST PACK, 8 
-33178302,HYDROMORPHONE 1 MG/ML LIQD, 23 
-33178310,SACCHAROMYCES BOULARDII 250 MG CAPS, 2 
-33178310,SACCHAROMYCES BOULARDII 250 MG PACK, 2 
-33178310,ZINC SULFATE 220 MG TABS, 5 
-33178310,MULTIVITAMINS-MINERALS-LUTEIN TABS, 6 
-33178310,CITRULLINE POWD, 6 
-33178310,BISOPROLOL 5 MG TABS, 8 
-33178310,CHLORTHALIDONE 25 MG TABS, 9 
-33178310,LACTOBACILLUS ACIDOPHILUS CAPS, 10 
-33178310,NORETHINDRONE (CONTRACEPTIVE) 0.35 MG TABS 28 EACH BLIST PACK, 95 
-33178328,ZONISAMIDE 25 MG CAPS, 4 
-33178328,GUANFACINE 1 MG TABS, 6 
-33178328,LETROZOLE 2.5 MG TABS, 11 
-33178328,CEFDINIR 125 MG/5 ML SUSR, 30 
-33178328,OXCARBAZEPINE 300 MG/5 ML SUSP 250 ML BOTTLE," 1,967 "
-33178336,FENOFIBRATE NANOCRYSTALLIZED 48 MG TABS, 11 
-33178336,SALSALATE 500 MG TABS, 12 
-33178336,ALPRAZOLAM 0.25 MG TBDL, 14 
-33178336,ISRADIPINE 2.5 MG CAPS, 15 
-33178336,FENOFIBRATE 160 MG TABS, 16 
-33178336,ETHOSUXIMIDE 250 MG CAPS, 23 
-33178344,OMEGA-3 ACID ETHYL ESTERS 1 GRAM CAPS, 2 
-33178344,TIZANIDINE 2 MG CAPS, 4 
-33178344,PROGESTERONE MICRONIZED 100 MG CAPS, 12 
-33178344,DROSPIRENONE-ETHINYL ESTRADIOL 3-20 MG-MCG TABS, 24 
-33178344,DIPHENHYDRAMINE-ZINC 2-0.1 % CREA 28 G TUBE, 26 
-33178344,NEBIVOLOL 10 MG TABS, 27 
-33178344,ETIDRONATE 200 MG TABS, 29 
-33178344,BUPRENORPHINE-NALOXONE 2-0.5 MG SUBL, 48 
-33178344,GENTAMICIN IN SALINE (ISO-OSM) 100 MG/50 ML PGBK, 58 
-33178344,GENTAMICIN IN SALINE (ISO-OSM) 100 MG/50 ML PGBK 50 ML BAG, 58 
-33178351,SALIVA STIMULANT AGENTS COMB.2 LIQD, 2 
-33178351,SALIVA STIMULANT AGENTS COMB.2 LIQD 44.3 ML BOTTLE, 4 
-33178351,NALTREXONE 50 MG TABS, 14 
-33178351,LOVASTATIN 20 MG TABS, 17 
-33178351,NEBIVOLOL 5 MG TABS, 27 
-33178351,PROGESTERONE MICRONIZED 200 MG CAPS, 28 
-33178351,TIZANIDINE 4 MG CAPS, 28 
-33178351,MILNACIPRAN 100 MG TABS, 34 
-33178351,NIFEDIPINE 90 MG TBSR, 34 
-33178351,CANDESARTAN 16 MG TABS, 35 
-33178351,CEFPODOXIME 100 MG TABS, 40 
-33178351,RANOLAZINE 500 MG TB12, 42 
-33178369,TELMISARTAN 80 MG TABS, 27 
-33178369,LUBIPROSTONE 8 MCG CAPS, 33 
-33178369,CEFPODOXIME 200 MG TABS, 35 
-33178369,DUTASTERIDE 0.5 MG CAPS, 36 
-33178369,VILAZODONE 40 MG TABS, 39 
-33178369,VILAZODONE 20 MG TABS, 43 
-33178369,APIXABAN 2.5 MG TABS, 44 
-33178369,DUTASTERIDE 0.5 MG CAPS 90 EACH BOTTLE, 45 
-33178369,APIXABAN 5 MG TABS, 45 
-33178369,LUBIPROSTONE 24 MCG CAPS, 45 
-33178369,TELMISARTAN 40 MG TABS, 46 
-33178369,VILAZODONE 10 MG TABS, 46 
-33178369,FENOFIBRATE NANOCRYSTALLIZED 145 MG TABS, 48 
-33178369,DESVENLAFAXINE 50 MG TB24, 53 
-33178369,DARIFENACIN 7.5 MG TB24, 53 
-33178369,DESVENLAFAXINE 100 MG TB24, 54 
-33178369,BUPRENORPHINE-NALOXONE 8-2 MG SUBL, 91 
-33178377,ESOMEPRAZOLE 40 MG CPDR, 41 
-33178377,PENTOSAN POLYSULFATE SODIUM 100 MG CAPS, 42 
-33178377,SOLIFENACIN 10 MG TABS, 47 
-33178377,MANGANESE CHLORIDE 0.1 MG/ML SOLN, 47 
-33178377,GLUC OXID-LACTOPEROXID-MURAMID MWSH 474 ML BOTTLE, 47 
-33178377,MESALAMINE 1.2 G TBEC, 49 
-33178377,CUPRIC CHLORIDE 0.4 MG/ML SOLN, 50 
-33178377,ORLISTAT 120 MG CAPS, 51 
-33178377,DILTIAZEM HCL 240 MG TB24, 51 
-33178377,COPPER 0.4 MG/ML SOLN, 52 
-33178377,MIRTAZAPINE 45 MG TABS, 56 
-33178377,VALSARTAN-HYDROCHLOROTHIAZIDE 320-25 MG TABS, 57 
-33178377,FEBUXOSTAT 40 MG TABS, 60 
-33178377,FEBUXOSTAT 80 MG TABS, 62 
-33178377,SITAGLIPTIN 50 MG TABS, 77 
-33178385,"LANTHANUM 1,000 MG CHEW", 7 
-33178385,PRAVASTATIN 80 MG TABS, 56 
-33178385,FENOFIBRATE NANOCRYSTALLIZED 145 MG TABS, 61 
-33178385,"RANOLAZINE 1,000 MG TB12", 68 
-33178385,SITAGLIPTIN 100 MG TABS, 73 
-33178385,LANTHANUM 500 MG CHEW, 74 
-33178393,EVEROLIMUS 0.25 MG TABS, 72 
-33178393,SITAGLIPTIN 25 MG TABS, 79 
-33178393,RIVASTIGMINE 9.5 MG/24 HOUR PT24, 90 
-33178393,RIVASTIGMINE 4.6 MG/24 HOUR PT24, 92 
-33178401,LINACLOTIDE 145 MCG CAPS 30 EACH BOTTLE, 81 
-33178401,ASENAPINE 5 MG SUBL, 99 
-33178419,OLANZAPINE-FLUOXETINE 3-25 MG CAPS, 99 
-33178419,MORPHINE 150 MG/30 ML SYRG 30 ML SYRINGE, 147 
-33178427,ILOPERIDONE 2 MG TABS, 60 
-33178427,ALFUZOSIN 10 MG TB24, 76 
-33178427,EXEMESTANE 25 MG TABS, 110 
-33178427,RASAGILINE 1 MG TABS, 113 
-33178427,DEXTROMETHORPHAN-QUINIDINE 20-10 MG CAPS, 115 
-33178427,LAMOTRIGINE 100 MG TR24, 120 
-33178427,EVEROLIMUS 0.5 MG TABS, 131 
-33178427,CLOBAZAM 20 MG TABS, 133 
-33178427,OLANZAPINE-FLUOXETINE 6-25 MG CAPS, 134 
-33178427,FLUOROMETHOLONE 0.1 % DRPS 10 ML DROP BTL, 141 
-33178427,OLANZAPINE-FLUOXETINE 6-50 MG CAPS, 142 
-33178427,RALTEGRAVIR 400 MG TABS, 178 
-33178427,EVEROLIMUS 0.75 MG TABS, 194 
-33178435,RILUZOLE 50 MG TABS, 97 
-33178435,MARAVIROC 300 MG TABS, 164 
-33178435,OLANZAPINE-FLUOXETINE 12-50 MG CAPS, 172 
-33178435,ROTIGOTINE 4 MG/24 HOUR PT24, 173 
-33178435,SILVER GLSR 44 ML TUBE, 221 
-33178443,DORZOLAMIDE-TIMOLOL 2-0.5 % DROP 10 ML DROP BTL, 11 
-33178443,BEXAROTENE 75 MG CAPS, 140 
-33178443,POLIOVIRUS VACCINE 40-8-32 UNIT/0.5 ML SUSP, 314 
-33178450,METHYLNALTREXONE 8 MG/0.4 ML SYRG, 6 
-33178450,METHYLNALTREXONE 8 MG/0.4 ML SYRG 0.4 ML SYRINGE, 6 
-33178450,DABRAFENIB 50 MG CAPS, 59 
-33178450,PAZOPANIB 200 MG TABS, 180 
-33178468,METHACHOLINE 100 MG SOLR, 7 
-33178468,MEMANTINE 10 MG/5 ML SOLN, 46 
-33178468,EFAVIRENZ-EMTRICITABINE-TENOFOVIR 600-200-300 MG TABS, 582 
-33178476,ESTRADIOL 0.075 MG/24 HR PTSW, 189 
-33178559,DIAZOXIDE (DIABETIC USE) 50 MG/ML SUSP, 113 
-33178559,MIFEPRISTONE 300 MG TABS," 2,022 "
-33178559,BUDESONIDE-FORMOTEROL 160-4.5 MCG/ACTUATION HFAA 10.2 G AER W/ADAP," 2,528 "
-33178559,EVEROLIMUS 5 MG TABS," 2,528 "
-33178641,TOCILIZUMAB 200 MG/10 ML (20 MG/ML) SOLN, 36 
-33178658,PALIPERIDONE PALMITATE 117 MG/0.75 ML SYRG 0.75 ML SYRINGE, 67 
-33178666,NALTREXONE 380 MG SSRR, 30 
-33178666,BELATACEPT 250 MG SOLR, 42 
-33178666,ERIBULIN 1 MG/2 ML (0.5 MG/ML) SOLN, 972 
-33178666,VALRUBICIN 40 MG/ML SOLN," 10,250 "
-33178674,PALIPERIDONE PALMITATE 156 MG/ML (1 ML) SYRG, 63 
-33178682,DENOSUMAB 120 MG/1.7 ML (70 MG/ML) SOLN 1.7 ML VIAL, 139 
-33178682,CARFILZOMIB 60 MG SOLR, 186 
-33178682,VELAGLUCERASE ALFA 400 UNIT SOLR," 3,528 "
-33178682,ARIPIPRAZOLE 400 MG SSRR 1 EACH VIAL," 14,867 "
-33178732,PERTUZUMAB 420 MG/14 ML (30 MG/ML) SOLN, 141 
-33178732,SODIUM PHENYLBUTYRATE POWD, 160 
-33178765,DESMOPRESSIN 4 MCG/ML SOLN, 18 
-33178765,DESMOPRESSIN 4 MCG/ML SOLN 1 ML VIAL, 37 
-33178765,DESMOPRESSIN 4 MCG/ML SOLN 1 ML AMPUL, 70 
-33178807,TOPIRAMATE 100 MG TABS, 7 
-33178856,VALPROATE 500 MG/5 ML (100 MG/ML) SOLN, 52 
-33178872,CLARITHROMYCIN 250 MG/5 ML SUSR, 45 
-33178880,DEXTROSE 15 GM/TUBE GEL 45 G TUBE, 4 
-33178906,PROPAFENONE 225 MG TABS, 7 
-33178930,MYCOPHENOLATE 500 MG TABS, 8 
-33178948,TOPIRAMATE 25 MG TABS, 4 
-33178955,TOPIRAMATE 200 MG TABS, 10 
-33178997,AMPHOTERICIN B LIPID COMPLEX 5 MG/ML SUSP 20 ML VIAL, 92 
-33178997,AMPHOTERICIN B LIPID COMPLEX 5 MG/ML SUSP, 97 
-33179011,IFOSFAMIDE 1 GRAM SOLR, 277 
-33179060,LEVOFLOXACIN 500 MG/100 ML PGBK 100 ML BAG, 87 
-33179086,LEVOFLOXACIN 500 MG TABS, 13 
-33179102,OXYCODONE 10 MG TB12, 22 
-33179110,OXYCODONE 20 MG TB12, 39 
-33179128,OXYCODONE 40 MG TB12, 64 
-33179136,POTASSIUM CHLORIDE 10 MEQ IN 50 ML PGBK 1 EACH BAG, 13 
-33179227,ATROPINE 1 MG/ML SOLN 1 ML AMPUL, 0.7 
-33179227,ATROPINE 1 MG/ML SOLN 1 ML VIAL, 0.7 
-33179235,POVIDONE-IODINE 5 % SOLN, 81 
-33179235,POVIDONE-IODINE 5 % SOLN 30 ML BOTTLE, 98 
-33179276,MAGNESIUM SULFATE 8 MEQ/25 ML, 17 
-33179276,MAGNESIUM SULFATE 8 MEQ/25 ML 25 ML BAG, 31 
-33179284,MAGNESIUM SULFATE 16 MEQ/25 ML 25 ML BAG, 16 
-33179318,NITROGLYCERIN 2 % OINT 1 G PACKET, 15 
-33179318,NITROGLYCERIN 2 % OINT 1 G BLIST PACK, 67 
-33179326,VECURONIUM 10 MG SOLR, 44 
-33179326,VECURONIUM 10 MG SOLR 1 EACH VIAL, 52 
-33179342,LORAZEPAM 2 MG/ML SOLN, 8 
-33179342,LORAZEPAM 2 MG/ML SOLN 1 ML VIAL, 43 
-33179359,ETOMIDATE 2 MG/ML SOLN 10 ML VIAL, 101 
-33179359,ETOMIDATE 2 MG/ML SOLN 10 ML AMPUL, 140 
-33179359,ETOMIDATE 2 MG/ML SOLN, 155 
-33179367,DILTIAZEM 5 MG/ML SOLN 10 ML VIAL, 44 
-33179383,ACYCLOVIR 50 MG/ML SOLN, 0.9 
-33179409,CARBAMAZEPINE 200 MG TB12, 17 
-33179417,POTASSIUM CHLORIDE 20 MEQ IN 100 ML PGBK, 6 
-33179417,POTASSIUM CHLORIDE 20 MEQ IN 100 ML PGBK 1 EACH BAG, 6 
-33179466,ROPIVACAINE 5 MG/ML (0.5 %) SOLN, 1.2 
-33179466,ROPIVACAINE 5 MG/ML (0.5 %) SOLN 30 ML VIAL, 1.4 
-33179516,COLISTIN 37.5 MG/ML SOLR, 144 
-33179540,"SORBITOL 3 % SOLN 3,000 ML BOTTLE", 79 
-33179557,CARVEDILOL 6.25 MG TABS, 6 
-33179565,CARVEDILOL 25 MG TABS, 6 
-33179573,CARVEDILOL 3.125 MG TABS, 5 
-33179581,ALTEPLASE {TPA} 100 MG SOLR, 528 
-33179581,ALTEPLASE {TPA} SOLR 1 EACH VIAL," 1,176 "
-33179607,CLOPIDOGREL 75 MG TABS, 14 
-33179631,ENOXAPARIN 80 MG/0.8 ML SYRG 0.8 ML SYRINGE, 23 
-33179649,ENOXAPARIN 100 MG/ML SYRG 1 ML SYRINGE, 22 
-33179649,ENOXAPARIN 100 MG/ML SYRG, 23 
-33179656,ENOXAPARIN 60 MG/0.6 ML SYRG 0.6 ML SYRINGE, 25 
-33179672,METHADONE 5 MG/5 ML SOLN 5 ML CUP, 4 
-33179680,METHADONE 10 MG/5 ML SOLN 5 ML CUP, 3 
-33179680,METHADONE 10 MG/5 ML SOLN, 5 
-33179698,METHADONE 10 MG/ML CONC 1 ML SYRINGE, 8 
-33179698,METHADONE 10 MG/ML CONC, 18 
-33179730,OPIUM TINCTURE 10 MG/ML (MORPHINE) TINC 1 ML SYRINGE, 5 
-33179771,GLYCERIN (BULK) 100 % LIQD, 5 
-33179789,CALCIUM GLUBIONATE 115 MG/5 ML (1.8 GRAM/5 ML) SYRP, 2 
-33179789,CALCIUM GLUBIONATE 115 MG/5 ML (1.8 GRAM/5 ML) SYRP 30 ML SYRINGE, 2 
-33179797,TRIMETHOPRIM-SULFAMETHOXAZOLE 40-200 MG/5 ML SUSP, 16 
-33179813,PYRIDOSTIGMINE 60 MG/5 ML SYRP, 17 
-33179821,ACYCLOVIR 200 MG/5 ML SUSP, 28 
-33179847,NORTRIPTYLINE 10 MG/5 ML SOLN, 20 
-33179888,FELBAMATE 600 MG/5 ML SUSP, 148 
-33179896,PHENYTOIN 125 MG/5 ML SUSP, 8 
-33179904,MEGESTROL 400 MG/10 ML (40 MG/ML) SUSP, 30 
-33179953,PREDNISONE 5 MG/5 ML SOLN 5 ML BLIST PACK, 11 
-33179979,SUCRALFATE 100 MG/ML SUSP 10 ML CUP, 41 
-33179979,SUCRALFATE 100 MG/ML SUSP, 57 
-33179987,LITHIUM 8 MEQ/5 ML (5 ML) SOLN 5 ML CUP, 14 
-33179987,LITHIUM 8 MEQ/5 ML (5 ML) SOLN, 17 
-33180001,DIAZEPAM 5 MG/5 ML (5 ML) SOLN 5 ML CUP, 41 
-33180001,DIAZEPAM 5 MG/5 ML (5 ML) SOLN, 43 
-33180027,"CALCIUM CARBONATE 500 MG/5 ML (1,250 MG/5 ML) SUSP 5 ML CUP", 1.3 
-33180126,TETANUS IMMUNE GLOBULIN 250 UNIT SYRG 1 EACH SYRINGE," 2,826 "
-33180134,TORSEMIDE 5 MG TABS, 6 
-33180142,TORSEMIDE 100 MG TABS, 8 
-33180183,ATORVASTATIN 40 MG TABS, 21 
-33180191,OLANZAPINE 7.5 MG TABS, 20 
-33180209,TIAGABINE 12 MG TABS, 69 
-33180241,ROPIVACAINE 2 MG/ML SOLN, 2 
-33180241,ROPIVACAINE 2 MG/ML SOLN 20 ML AMPUL, 2 
-33180258,ROPIVACAINE 2 MG/ML SOLN, 1.0 
-33180258,ROPIVACAINE 2 MG/ML SOLN 100 ML BOTTLE, 1.0 
-33180266,ROPIVACAINE 2 MG/ML SOLN, 0.9 
-33180282,ROPIVACAINE (PF) 7.5 MG/ML SOLN, 0.8 
-33180308,ROPIVACAINE (PF) 10 MG/ML SOLN 20 ML AMPUL, 0.8 
-33180308,ROPIVACAINE (PF) 10 MG/ML SOLN, 0.8 
-33180316,"CLONIDINE 1,000 MCG/10 ML (100 MCG/ML) SOLN 10 ML VIAL", 66 
-33180316,"CLONIDINE 1,000 MCG/10 ML (100 MCG/ML) SOLN", 72 
-33180324,QUETIAPINE 25 MG TABS, 7 
-33180340,ANAGRELIDE 0.5 MG CAPS, 5 
-33180357,FOMEPIZOLE 1 GRAM/ML SOLN, 158 
-33180506,BENAZEPRIL 5 MG TABS, 4 
-33180514,BENAZEPRIL 10 MG TABS, 4 
-33180522,BENAZEPRIL 20 MG TABS, 5 
-33180530,BENAZEPRIL 40 MG TABS, 5 
-33180571,DOLASETRON 12.5 MG/0.625 ML SOLN, 103 
-33180589,LISINOPRIL 2.5 MG TABS, 4 
-33180597,LISINOPRIL 5 MG TABS, 5 
-33180605,LISINOPRIL 10 MG TABS, 5 
-33180613,LISINOPRIL 20 MG TABS, 5 
-33180621,LISINOPRIL 40 MG TABS, 6 
-33180639,MIRTAZAPINE 15 MG TABS, 6 
-33180647,MIRTAZAPINE 30 MG TABS, 7 
-33180662,TERBINAFINE 1 % CREA 24 G TUBE, 108 
-33180670,QUETIAPINE 100 MG TABS, 12 
-33180696,PHENYLEPHRINE 2.5 % DROP 3 ML DROP BTL, 119 
-33180761,VENLAFAXINE 150 MG CP24, 28 
-33180779,VENLAFAXINE 37.5 MG CP24, 26 
-33180787,VENLAFAXINE 75 MG CP24, 26 
-33180795,CHOLECALCIFEROL (VITAMIN D3) 400 UNIT TABS, 1.2 
-33180845,TESTOSTERONE ENANTHATE 200 MG/ML OIL 5 ML VIAL, 94 
-33180845,TESTOSTERONE ENANTHATE 200 MG/ML OIL, 100 
-33180878,CITALOPRAM 20 MG TABS, 5 
-33180886,LEFLUNOMIDE 10 MG TABS, 7 
-33180894,LEFLUNOMIDE 20 MG TABS, 15 
-33180977,MIDAZOLAM 2 MG/ML SYRP 1 ML SYRINGE, 5 
-33181066,ETANERCEPT 25 MG (1 ML) KIT," 2,161 "
-33181108,LAMIVUDINE-ZIDOVUDINE 150-300 MG TABS, 120 
-33181132,GLIMEPIRIDE 2 MG TABS, 6 
-33181157,MEROPENEM 1 GRAM SOLR, 73 
-33181157,MEROPENEM 1 GRAM SOLR 1 EACH VIAL, 73 
-33181165,EFAVIRENZ 200 MG CAPS, 70 
-33181207,FOSINOPRIL 10 MG TABS, 3 
-33181231,TOLTERODINE 1 MG TABS, 30 
-33181256,HYDROCORTISONE-PRAMOXINE 1-1 % FOAM 10 G CAN, 63 
-33181280,FLUCONAZOLE 100 MG TABS, 9 
-33181322,CALCITRIOL 1 MCG/ML SOLN 1 ML VIAL, 6 
-33181322,CALCITRIOL 1 MCG/ML SOLN 1 ML AMPUL, 7 
-33181348,BLEOMYCIN 2 UNITS/ML SOLR, 207 
-33181371,IPRATROPIUM 0.02 % SOLN 2.5 ML BLIST PACK, 7 
-33181371,IPRATROPIUM 0.02 % SOLN 2.5 ML VIAL, 7 
-33181405,MELPHALAN 50 MG SOLR," 11,352 "
-33181413,CLONIDINE 0.1 MG/24 HR PTWK, 201 
-33181421,LABETALOL 5 MG/ML SOLN 20 ML VIAL, 41 
-33181439,CALCITONIN [SALMON] 200 UNIT/ACTUATION SPRY 3.7 ML SQUEEZ BTL, 12 
-33181447,FLUCONAZOLE 200 MG TABS, 14 
-33181512,GRANISETRON 1 MG/ML (1 ML) SOLN, 9 
-33181512,GRANISETRON 1 MG/ML (1 ML) SOLN 1 ML VIAL, 11 
-33181538,CYCLOPHOSPHAMIDE 1 GRAM SOLR, 232 
-33181546,CLONIDINE 0.2 MG/24 HR PTWK, 316 
-33181553,DIGOXIN IMMUNE FAB 40 MG SOLR," 10,084 "
-33181561,DESMOPRESSIN 4 MCG/ML SOLN, 68 
-33181579,MITOMYCIN 0.5 MG/ML SOLR, 469 
-33181595,LIDOCAINE 100 MG/5 ML (2 %) SYRG 5 ML SYRINGE, 5 
-33181603,BLEOMYCIN, 182 
-33181686,IRINOTECAN 40 MG/2 ML SOLN, 90 
-33181702,TAMSULOSIN 0.4 MG CP24, 10 
-33181728,MYCOPHENOLATE 200 MG/ML SUSR, 9 
-33181769,MONTELUKAST 10 MG TABS, 11 
-33181777,AMPHOTERICIN B LIPOSOMAL 50 MG SUSR, 106 
-33181777,AMPHOTERICIN B LIPOSOMAL 50 MG SUSR 1 EACH VIAL, 116 
-33181801,ANTITHYMOCYTE GLOBULIN RABBIT (THYMOGLOBULIN) 25 MG SOLR," 3,682 "
-33181801,ANTITHYMOCYTE GLOBULIN RABBIT IN NS 250 ML 25 MG BAG," 3,729 "
-33181819,ISOSULFAN BLUE 1 % SOLN 5 ML VIAL, 128 
-33181827,NITROGLYCERIN 0.1 MG/HR PT24, 10 
-33181835,RISPERIDONE 1 MG/ML SOLN, 100 
-33181876,DILTIAZEM 300 MG CPSR, 15 
-33181884,DILTIAZEM 360 MG CPSR, 17 
-33181892,DILTIAZEM 180 MG CPSR, 10 
-33181900,DILTIAZEM 240 MG CPSR, 25 
-33181926,PIOGLITAZONE 15 MG TABS, 45 
-33181934,PIOGLITAZONE 30 MG TABS, 52 
-33181942,PIOGLITAZONE 45 MG TABS, 47 
-33181959,TACROLIMUS 0.5 MG CAPS, 20 
-33181975,GLIMEPIRIDE 1 MG TABS, 4 
-33181983,GLIMEPIRIDE 4 MG TABS, 6 
-33181991,DILTIAZEM 120 MG CPSR, 8 
-33182023,SIROLIMUS 1 MG/ML SOLN 60 ML BOTTLE, 90 
-33182023,SIROLIMUS 1 MG/ML SOLN, 92 
-33182031,QUINUPRISTIN-DALFOPRISTIN 500 MG SOLR," 1,433 "
-33182098,LEVORPHANOL 2 MG TABS, 19 
-33182130,CLOBETASOL 0.05 % SOLN 25 ML BOTTLE, 72 
-33182155,FAMCICLOVIR 250 MG TABS, 76 
-33182163,SIMVASTATIN 20 MG TABS, 5 
-33182171,TOLTERODINE 2 MG TABS, 39 
-33182189,HYDROCODONE-ACETAMINOPHEN 10-500 MG TABS, 7 
-33182197,"EPOETIN 40,000 UNIT/ML SOLN", 94 
-33182197,"EPOETIN 40,000 UNIT/ML SOLN 1 ML VIAL", 98 
-33182205,LEUPROLIDE 11.25 MG SYKT 1 EACH KIT," 7,201 "
-33182254,ENALAPRILAT 1.25 MG/ML INJ, 55 
-33182262,TICARCILLIN-CLAVULANATE 3.1 G SOLR, 146 
-33182270,CEFOXITIN 1 GRAM SOLR 1 EACH VIAL, 66 
-33182270,CEFOXITIN 1 GRAM SOLR, 69 
-33182296,CALCIUM CARBONATE-VITAMIN D 500-200 MG TABS, 1.4 
-33182312,BUSULFAN 60 MG/10 ML SOLN, 163 
-33182320,CEFEPIME 2 GRAM SOLR, 103 
-33182338,OCTREOTIDE 30 MG KIT 1 EACH KIT," 1,265 "
-33182353,OCTREOTIDE 20 MG KIT 1 EACH KIT," 1,269 "
-33182361,BENZOCAINE 20% SPRA 59.2 ML CAN, 24 
-33182379,LAMIVUDINE 100 MG TABS, 118 
-33182387,CEFEPIME 1 GRAM SOLR, 115 
-33182445,"CLONIDINE 5,000 MCG/10 ML SOLN 10 ML VIAL", 52 
-33182445,"CLONIDINE 5,000 MCG/10 ML SOLN", 52 
-33182452,GABAPENTIN 600 MG TABS, 6 
-33182460,URSODIOL 250 MG TABS, 32 
-33182502,MIDODRINE 5 MG TABS, 4 
-33182510,MIDODRINE 2.5 MG TABS, 9 
-33182528,LINEZOLID 600 MG TABS, 985 
-33182551,LINEZOLID 200 MG/100 ML SOLP 100 ML BAG, 99 
-33182577,"HEPARIN, PORCINE (PF) 100 UNIT/ML SYRG 5 ML SYRINGE", 0.9 
-33182577,"HEPARIN, PORCINE (PF) 100 UNIT/ML SYRG", 1.5 
-33182585,GABAPENTIN 800 MG TABS, 14 
-33182619,ASPIRIN-DIPYRIDAMOLE 25-200 MG CM12, 45 
-33182973,MORPHINE 20 MG/ML SOLN 1 ML SYRINGE, 5 
-33182981,"NS + KCL 20 MEQ/L 20 MEQ/L SOLP 1,000 ML BAG", 51 
-33182999,"NS + KCL 40 MEQ/L 40 MEQ/L SOLP 1,000 ML VIAL", 51 
-33183005,"D5-NS + KCL 20 MEQ/L 20 MEQ/L SOLP 1,000 ML BAG", 63 
-33183013,"D5-NS + KCL 40 MEQ/L 40 MEQ/L SOLP 1,000 ML BAG", 61 
-33183153,WARFARIN 4 MG TABS, 5 
-33183161,INDOCYANINE GREEN 25 MG INJ, 85 
-33183179,FILGRASTIM 480 MCG/0.8 ML SYRG 0.8 ML SYRINGE, 18 
-33183179,FILGRASTIM 480 MCG/0.8 ML SYRG, 19 
-33183187,FILGRASTIM 300 MCG/0.5 ML SYRG 0.5 ML SYRINGE, 16 
-33183237,CETAPHIL LOTN 473 ML BOTTLE, 89 
-33183245,ZALEPLON 10 MG CAPS, 8 
-33183252,ZALEPLON 5 MG CAPS, 24 
-33183278,DOFETILIDE 500 MCG CAPS, 47 
-33183286,DOFETILIDE 250 MCG CAPS, 46 
-33183294,DOFETILIDE 125 MCG CAPS, 46 
-33183344,SIMPLE SYRUP SYRP, 9 
-33183369,OLANZAPINE 5 MG TBDL, 52 
-33183377,OLANZAPINE 10 MG TBDL, 94 
-33183476,OTHER DRUG, 40 
-33183476,OTHER DRUG 1 EACH PACKAGE, 41 
-33183559,LEVETIRACETAM 250 MG TABS, 6 
-33183567,LEVETIRACETAM 500 MG TABS, 7 
-33183575,LEVETIRACETAM 750 MG TABS, 11 
-33183583,OXCARBAZEPINE 150 MG TABS, 9 
-33183591,OXCARBAZEPINE 300 MG TABS, 9 
-33183609,OXCARBAZEPINE 600 MG TABS, 14 
-33183617,ZONISAMIDE 100 MG CAPS, 8 
-33183625,PRAMIPEXOLE 0.125 MG TABS, 8 
-33183633,PRAMIPEXOLE 0.5 MG TABS, 20 
-33183641,PRAMIPEXOLE 1 MG TABS, 5 
-33183658,ROPINIROLE 0.25 MG TABS, 11 
-33183666,ROPINIROLE 1 MG TABS, 9 
-33183674,ROPINIROLE 5 MG TABS, 6 
-33183708,ENTACAPONE 200 MG TABS, 21 
-33183716,CYCLOSPORINE MODIFIED 25 MG CAPS, 14 
-33183724,CYCLOSPORINE MODIFIED 100 MG CAPS, 50 
-33184094,FERRIC GLUCONATE COMPLEX 62.5 MG/5 ML SOLN, 31 
-33184094,FERRIC GLUCONATE COMPLEX 62.5 MG/5 ML SOLN 5 ML VIAL, 65 
-33184102,CASPOFUNGIN 50 MG SOLR, 113 
-33184110,CASPOFUNGIN 70 MG SOLR, 83 
-33184128,GLYBURIDE-METFORMIN 2.5-500 MG TABS, 6 
-33184144,GLYBURIDE-METFORMIN 5-500 MG TABS, 6 
-33184169,SODIUM PHOSPHATES 7.2-2.7 GRAM/15 ML LIQD 45 ML BOTTLE, 3 
-33184185,FENTANYL 1500 MCG/30 ML 50 MCG/ML, 9 
-33184193,FENTANYL 1500 MCG/30 ML 50 MCG/ML 30 ML VIAL, 9 
-33184193,FENTANYL 1500 MCG/30 ML 50 MCG/ML, 10 
-33184219,HYDROMORPHONE IN NS 30 MG/30 ML (1 MG/ML) SPCA, 14 
-33184219,HYDROMORPHONE 1 MG/ML 30 ML SYRINGE, 32 
-33184219,HYDROMORPHONE IN NS 30 MG/30 ML (1 MG/ML) SPCA 30 ML SYRINGE, 38 
-33184219,HYDROMORPHONE 1 MG/ML, 42 
-33184250,FENTANYL 50MCG/ML SOLN 100 ML BAG, 4 
-33184367,ZIPRASIDONE 60 MG CAPS, 99 
-33184375,ZIPRASIDONE 80 MG CAPS, 66 
-33184383,ZIPRASIDONE 20 MG CAPS, 60 
-33184391,ZIPRASIDONE 40 MG CAPS, 57 
-33184466,ABACAVIR 300 MG TABS, 93 
-33184474,ABACAVIR 20 MG/ML SOLN, 91 
-33184664,AMILORIDE 5 MG TABS, 12 
-33184730,"HEPARIN 1,000 UNIT/ML SOLN", 6 
-33184730,"HEPARIN 1,000 UNIT/ML SOLN 10 ML VIAL", 7 
-33184755,EPIRUBICIN 2 MG/ML SOLN, 46 
-33184755,EPIRUBICIN 2 MG/ML SOLN 25 ML VIAL, 63 
-33184805,INSULIN GLARGINE 100 UNIT/ML SOLN, 0.9 
-33184805,INSULIN GLARGINE 100 UNIT/ML SOLN 10 ML VIAL, 0.9 
-33184813,IMATINIB 100 MG TABS, 545 
-33184854,TEMAZEPAM 7.5 MG CAPS, 60 
-33184888,NITRIC OXIDE 800 PPM GAS 1 EACH CANISTER, 548 
-33184912,RAMIPRIL 1.25 MG CAPS, 18 
-33184920,RAMIPRIL 2.5 MG CAPS, 6 
-33184938,RAMIPRIL 5 MG CAPS, 6 
-33184946,RAMIPRIL 10 MG CAPS, 22 
-33184961,SIROLIMUS 1 MG TABS, 120 
-33184995,FACTOR VIII AND VWF 1 UNIT KIT 1 UNITS VIAL, 4 
-33185000,FACTOR VIII [RECOMBINANT] 1 UNIT SOLR 1 UNITS VIAL, 4 
-33185000,FACTOR VIII (RECOMBINANT) 1 UNIT SOLR 1 UNITS VIAL, 5 
-33185042,"FACTOR VIII (RECOMBINANT) 2,000 (+/-) UNIT SOLR 1 INT'L UNITS VIAL", 5 
-33185042,FACTOR VIII (RECOMBINANT) 1 UNIT SOLR 1 EACH VIAL, 5 
-33185042,FACTOR VIII [RECOMBINANT] 1 UNIT SOLR 1 UNITS VIAL, 5 
-33185042,FACTOR VIII (RECOMBINANT) 1 UNIT SOLR 1 UNITS VIAL, 6 
-33185042,FACTOR VIII (RECOMBINANT) 1 UNIT SOLR, 6 
-33185075,FACTOR IX 500 UNIT KIT, 6 
-33185075,FACTOR IX 500 UNIT KIT 1 UNITS VIAL, 7 
-33185091,ANTI-INHIBITOR COAGULANT COMPLEX 1 UNIT SOLR 1 UNITS VIAL, 8 
-33185091,"ANTI-INHIBITOR COAGULANT COMPLEX 651-1,200 UNIT SOLR 1 EACH VIAL", 8 
-33185190,ANTITHROMBIN III [HUMAN] 1 UNIT SOLR 1 EACH VIAL, 33 
-33185208,HYDROCODONE-ACETAMINOPHEN 7.5-500 MG/15 ML(15 ML) SOLN 15 ML CUP, 40 
-33185257,PRIMIDONE 50 MG TABS, 9 
-33185273,FACTOR IX COMPLEX 1 UNIT SOLR 1 UNITS VIAL, 4 
-33185307,DIVALPROEX 500 MG TB24, 10 
-33185323,NORGESTREL-ETHINYL ESTRADIOL 0.3-30 MG-MCG TABS 28 EACH BLIST PACK, 479 
-33185331,NORGESTREL-ETHINYL ESTRADIOL 0.5-50 MG-MCG TABS 28 EACH BLIST PACK, 11 
-33185349,NORGESTIMATE-ETHINYL ESTRADIOL 0.18/0.215/0.25 MG-35 MCG (28) TABS 28 EACH BLIST PACK, 23 
-33185356,LEVONORGESTREL-ETHINYL ESTRADIOL 0.1-20 MG-MCG TABS 28 EACH BLIST PACK, 5 
-33185372,GALANTAMINE 4 MG TABS, 20 
-33185380,GALANTAMINE 8 MG TABS, 22 
-33185414,D5W SOLP, 24 
-33185521,ALENDRONATE 70 MG TABS, 20 
-33185539,ALENDRONATE 35 MG TABS, 16 
-33185547,STREPTOMYCIN 1 GRAM SOLR, 123 
-33185554,CELL SAVER SOLUTION, 3 
-33185554,"CELL SAVER SOLUTION 1,000 ML BAG", 4 
-33185562,ALTEPLASE 2 MG SOLR 1 EACH VIAL, 443 
-33185562,ALTEPLASE 2 MG SOLR, 467 
-33185570,"NEOMYCIN-POLYMYXIN-DEXAMETHASONE 3.5-10,000-0.1 MG/ML-UNIT/ML-% DRPS 5 ML DROP BTL", 102 
-33185588,DIPHENHYDRAMINE-ZINC ACETATE 1% CREA 28.3 G TUBE, 5 
-33185646,D10-1/2 NS SOLP, 73 
-33185646,"D10-1/2 NS SOLP 1,000 ML BAG", 75 
-33185661,D5W SOLP 250 ML BAG, 59 
-33185661,D5W SOLP, 59 
-33185679,NATEGLINIDE 60 MG TABS, 25 
-33185687,NATEGLINIDE 120 MG TABS, 14 
-33185695,PANTOPRAZOLE 40 MG SOLR, 75 
-33185695,PANTOPRAZOLE 40 MG SOLR 1 EACH VIAL, 77 
-33185711,"BOTULINUM TOXIN TYPE B 5,000 UNIT/ML SOLN 1 ML VIAL", 104 
-33185729,ALBENDAZOLE 200 MG TABS, 370 
-33185737,FLUVOXAMINE 100 MG TABS, 7 
-33185745,FLUVOXAMINE 25 MG TABS, 10 
-33185752,FLUVOXAMINE 50 MG TABS, 7 
-33185810,TENOFOVIR DISOPROXIL FUMARATE 300 MG TABS, 264 
-33185828,MIRTAZAPINE 15 MG TBDL, 12 
-33185869,PERINDOPRIL 8 MG TABS, 11 
-33185877,IRBESARTAN 75 MG TABS, 6 
-33185885,IRBESARTAN 150 MG TABS, 9 
-33185893,IRBESARTAN 300 MG TABS, 21 
-33185901,VALSARTAN 80 MG TABS, 36 
-33185919,VALSARTAN 160 MG TABS, 37 
-33185950,DESENSITIZATION DRUG, 64 
-33185984,STERILE WATER FOR IRRIGATION SOLN, 9 
-33185984,STERILE WATER FOR IRRIGATION SOLN 500 ML BOTTLE, 16 
-33186008,BIVALIRUDIN 250 MG SOLR, 30 
-33186032,RIBAVIRIN 200 MG CAPS, 8 
-33186057,OLANZAPINE 15 MG TABS, 32 
-33186065,GI COCKTAIL SUSP 50 ML BOTTLE, 5 
-33186073,ALBUTEROL-IPRATROPIUM 2.5-0.5 MG/3 ML NEBU 3 ML BLIST PACK, 8 
-33186073,ALBUTEROL-IPRATROPIUM 2.5-0.5 MG/3 ML NEBU 3 ML VIAL, 9 
-33186081,RIFABUTIN 150 MG CAPS, 134 
-33186099,MOXIFLOXACIN 400 MG/250 ML PGBK 250 ML BAG, 32 
-33186107,MOXIFLOXACIN 400 MG TABS, 143 
-33186115,ERTAPENEM 1 GRAM SOLR 1 EACH VIAL, 352 
-33186115,ERTAPENEM 1 GRAM SOLR, 363 
-33186123,FONDAPARINUX 2.5 MG/0.5 ML SYRG 0.5 ML SYRINGE, 34 
-33186164,CITALOPRAM 10 MG/5 ML SOLN, 34 
-33186172,CITALOPRAM 10 MG TABS, 4 
-33186180,CITALOPRAM 40 MG TABS, 6 
-33186222,BOSENTAN 62.5 MG TABS," 1,424 "
-33186230,BOSENTAN 125 MG TABS," 1,413 "
-33186248,PIPERACILLIN-TAZOBACTAM 2.25 GRAM/50 ML PGBK 50 ML BAG, 59 
-33186248,PIPERACILLIN-TAZOBACTAM 2.25 GRAM/50 ML PGBK, 60 
-33186255,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/50 ML PGBK 50 ML BAG, 33 
-33186255,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML PGBK 50 ML BAG, 45 
-33186255,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML PGBK, 45 
-33186263,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML PGBK 100 ML BAG, 39 
-33186263,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML PGBK, 39 
-33186354,SILDENAFIL 25 MG TABS, 227 
-33186362,SILDENAFIL 50 MG TABS, 233 
-33186396,NALOXONE 1 MG/ML SYRG 2 ML SYRINGE, 101 
-33186487,GLATIRAMER 20 MG SYKT 1 EACH KIT," 1,379 "
-33186495,MESNA 400 MG TABS, 732 
-33186503,VORICONAZOLE 200 MG SOLN, 45 
-33186511,VORICONAZOLE 200 MG TABS, 298 
-33186529,VORICONAZOLE 50 MG TABS, 81 
-33186537,BUDESONIDE 3 MG CECX, 119 
-33186552,KETAMINE 10 MG/ML SOLN, 65 
-33186552,KETAMINE 10 MG/ML SOLN 20 ML VIAL, 77 
-33186578,DEXRAZOXANE 250 MG SOLR, 696 
-33186586,DEXRAZOXANE 500 MG SOLR," 1,104 "
-33186669,POTASSIUM CHLORIDE 40 MEQ IN 100 ML PGBK 100 ML BAG, 3 
-33186677,POTASSIUM CHLORIDE 20 MEQ IN 50 ML PGBK 1 EACH BAG, 6 
-33186743,ZIPRASIDONE 20 MG SOLR 1 EACH VIAL, 120 
-33186776,OXALIPLATIN 50 MG/10 ML (5 MG/ML) SOLN, 121 
-33186792,QUETIAPINE 300 MG TABS, 27 
-33186800,RISPERIDONE 0.5 MG TABS, 6 
-33186818,ESCITALOPRAM 10 MG TABS, 33 
-33186818,ESCITALOPRAM OXALATE 10 MG TABS, 38 
-33186826,ESCITALOPRAM OXALATE 20 MG TABS, 29 
-33186826,ESCITALOPRAM 20 MG TABS, 31 
-33186842,CEFEPIME 1 GRAM SOLR, 36 
-33186842,CEFEPIME 1 GRAM SOLR 1 EACH VIAL, 41 
-33186859,CEFEPIME 2 GRAM SOLR, 28 
-33186859,CEFEPIME 2 GRAM SOLR 1 EACH VIAL, 30 
-33186867,ARIPIPRAZOLE 10 MG TABS, 193 
-33186875,ARIPIPRAZOLE 15 MG TABS, 182 
-33186883,ARIPIPRAZOLE 20 MG TABS, 275 
-33186891,ARIPIPRAZOLE 30 MG TABS, 271 
-33186917,ADENOSINE (DIAGNOSTIC) 3 MG/ML SOLN 20 ML VIAL, 62 
-33186941,DIVALPROEX 250 MG TB24, 17 
-33186958,ENOXAPARIN 120 MG/0.8 ML SYRG 0.8 ML SYRINGE, 22 
-33186966,ENOXAPARIN 150 MG/ML SYRG 1 ML SYRINGE, 21 
-33186982,DEXAMETHASONE 4 MG/ML SOLN, 1.4 
-33186982,DEXAMETHASONE 4 MG/ML SOLN 5 ML VIAL, 8 
-33187055,CAPECITABINE 150 MG TABS, 86 
-33187063,CAPECITABINE 500 MG TABS, 296 
-33187147,EZETIMIBE 10 MG TABS, 46 
-33187154,ADALIMUMAB 40 MG/0.8 ML KIT 2 EACH KIT," 2,568 "
-33187212,ATOMOXETINE 40 MG CAPS, 71 
-33187253,ALBUTEROL 2.5 MG/0.5 ML NEBU 1 EACH VIAL, 2 
-33187253,ALBUTEROL 2.5 MG/0.5 ML NEBU, 3 
-33187261,OFLOXACIN 0.3 % DROP 5 ML DROP BTL, 29 
-33187279,CYCLOSPORINE 0.05 % DPET, 7 
-33187287,ETOMIDATE 2 MG/ML SYRG, 147 
-33187303,FEXOFENADINE 180 MG TABS, 13 
-33187451,CARBOXYMETHYLCELLULOSE 0.5 % DROP 15 ML DROP BTL, 72 
-33187469,POLYVINYL ALCOHOL-POVIDON(PF) 1.4-0.6 % DPET 30 EACH BLIST PACK, 6 
-33187477,ESCITALOPRAM 5 MG/5 ML SOLN, 100 
-33187477,ESCITALOPRAM OXALATE 5 MG/5 ML SOLN, 183 
-33187485,MEROPENEM 500 MG SOLR, 70 
-33187501,MONSEL'S PASTE 1 EACH, 16 
-33187568,"BUPIVACAINE-EPINEPHRINE (PF) 0.25%-1:200,000 SOLN 30 ML VIAL", 70 
-33187576,RISPERIDONE 0.5 MG TBDL, 19 
-33187584,RISPERIDONE 1 MG TBDL, 77 
-33187592,"ALUMINUM-CALCIUM 952-1,347 MG PWPK", 2 
-33187667,BECLOMETHASONE 80 MCG/ACTUATION AERO 7.3 G AER W/ADAP, 987 
-33187667,BECLOMETHASONE 80 MCG/ACTUATION AERO 8.7 G AER W/ADAP," 1,428 "
-33187691,POLYVINYL ALCOHOL 1.4 % DROP, 9 
-33187691,POLYVINYL ALCOHOL 1.4 % DROP 15 ML DROP BTL, 22 
-33187709,SOTALOL 120 MG TABS, 24 
-33187717,BUPROPION 150 MG TB24, 38 
-33187725,BUPROPION 300 MG TB24, 30 
-33187741,PERMETHRIN 1 % LIQD 59 ML BOTTLE, 8 
-33187758,ESTRADIOL 0.075 MG/24 HR PTWK, 216 
-33187782,MORPHINE 100 MG/100 ML (1 MG/ML) SOLN 100 ML BAG, 128 
-33187782,MORPHINE 100 MG/100 ML (1 MG/ML) SOLN, 129 
-33187790,MICONAZOLE 2 % CREA 92 G BOTTLE, 9 
-33187808,DARBEPOETIN 100 MCG/0.5 ML SYRG 0.5 ML SYRINGE, 61 
-33187816,DARBEPOETIN 200 MCG/0.4 ML SYRG 0.4 ML SYRINGE, 35 
-33187824,DARBEPOETIN 60 MCG/0.3 ML SYRG 0.3 ML SYRINGE, 61 
-33187832,CYTARABINE 100 MG/ML SOLN, 57 
-33187873,LEVALBUTEROL 1.25 MG/3 ML NEBU 3 ML BLIST PACK, 22 
-33187873,LEVALBUTEROL 1.25 MG/3 ML NEBU 3 ML VIAL, 23 
-33187899,CALCIUM CHLORIDE 13.6 MEQ/25 ML 25 ML BAG, 94 
-33187949,MAGNESIUM SULFATE IN D5W 8 MEQ/100 ML PGBK 100 ML BAG, 38 
-33187964,DILTIAZEM 100 MG SOLR, 98 
-33187972,PANTOPRAZOLE 40 MG TBEC, 8 
-33187998,METOPROLOL 25 MG TB24, 15 
-33188004,OLANZAPINE 20 MG TBDL, 127 
-33188012,OLANZAPINE 15 MG TBDL, 187 
-33188020,ELLIOTTS B SOLUTION 73-19-8-3 MG/10 ML SOLN, 47 
-33188038,MORPHINE PF 0.5 MG/ML SOLN, 79 
-33188038,MORPHINE PF 0.5 MG/ML SOLN 2 ML AMPUL, 87 
-33188046,CIPROFLOXACIN 500 MG TM24, 64 
-33188079,SERTRALINE 20 MG/ML CONC 60 ML BOTTLE, 77 
-33188087,METOPROLOL 25 MG TABS, 5 
-33188095,RISPERIDONE MICROSPHERES 25 MG/2 ML SYRG 1 EACH SYRINGE, 54 
-33188111,RISPERIDONE MICROSPHERES 50 MG/2 ML SYRG 1 EACH SYRINGE, 53 
-33188129,EPLERENONE 25 MG TABS, 33 
-33188137,EPLERENONE 50 MG TABS, 23 
-33188178,SINCALIDE 5 MCG SOLR 1 EACH VIAL, 194 
-33188236,SODIUM CHLORIDE 0.9% NEBU, 2 
-33188236,SODIUM CHLORIDE 0.9% NEBU 5 ML BLIST PACK, 2 
-33188236,SODIUM CHLORIDE 0.9% NEBU 5 ML VIAL, 2 
-33188244,SODIUM CHLORIDE 3% NEBU 15 ML VIAL, 2 
-33188244,SODIUM CHLORIDE 3% NEBU 15 ML VIAL/KIT, 2 
-33188251,LEVONORGESTREL 0.75 MG TABS 2 EACH BLIST PACK, 107 
-33188269,DIPHENHYDRAMINE 25 MG/50 ML SOLN, 82 
-33188269,DIPHENHYDRAMINE 25 MG/50 ML SOLN 50 ML BAG, 83 
-33188277,DIPHENHYDRAMINE 50 MG/50 ML SOLN, 82 
-33188277,DIPHENHYDRAMINE 50 MG/50 ML SOLN 50 ML BAG, 83 
-33188285,DEXAMETHASONE 10 MG/50 ML SOLN, 85 
-33188285,DEXAMETHASONE 10 MG/50 ML SOLN 50 ML BAG, 87 
-33188293,DEXAMETHASONE 20 MG/50 ML SOLN 50 ML BAG, 81 
-33188293,DEXAMETHASONE 20 MG/50 ML SOLN, 83 
-33188343,NAFCILLIN 1 GRAM/50 ML PGBK 50 ML BAG, 66 
-33188350,NAFCILLIN 2 GRAM/100 ML PGBK 100 ML BAG, 76 
-33188376,CEFAZOLIN 1 GRAM/50 ML PGBK, 48 
-33188376,CEFAZOLIN 1 GRAM/50 ML PGBK 50 ML BAG, 50 
-33188384,VANCOMYCIN 500 MG/100 ML PGBK 100 ML BAG, 98 
-33188392,VANCOMYCIN 1 GRAM/200 ML PGBK, 60 
-33188392,VANCOMYCIN 1 GRAM/200 ML PGBK 200 ML BAG, 60 
-33188400,CEFTRIAXONE 1 GRAM/50 ML PGBK, 36 
-33188400,CEFTRIAXONE 1 GRAM/50 ML PGBK 50 ML BAG, 130 
-33188418,CEFTRIAXONE 2 GRAM/50 ML PGBK 50 ML BAG, 105 
-33188459,PENICILLIN G POTASSIUM 2 MILLION UNIT/50 ML PGBK 50 ML BAG, 15 
-33188467,PENICILLIN G POTASSIUM 3 MILLION UNIT/50 ML PGBK 50 ML BAG, 13 
-33188483,ALLOPURINOL 500 MG SOLR," 2,681 "
-33188491,OLANZAPINE 10 MG SOLR 1 EACH VIAL, 254 
-33188491,OLANZAPINE 10 MG SOLR, 294 
-33188525,CEFTAZIDIME 1 GRAM/50 ML PGBK 50 ML BAG, 92 
-33188533,CEFTAZIDIME 2 GRAM/50 ML PGBK 50 ML BAG, 78 
-33188608,SEVELAMER 400 MG TABS, 17 
-33188616,LIDOCAINE 5% PTMD, 75 
-33188624,ACETYLCYSTEINE 20 % (200 MG/ML) SOLN, 29 
-33188640,MAGNESIUM SULFATE 16 MEQ/100 ML, 62 
-33188657,CARBOXYMETHYLCELLULOSE 0.5 % DPET, 2 
-33188749,FLUTICASONE-SALMETEROL 100-50 MCG/DOSE DSDV 14 EACH BLIST PACK," 1,029 "
-33188749,FLUTICASONE-SALMETEROL 100-50 MCG/DOSE DSDV 60 EACH BLIST PACK," 2,044 "
-33188756,FLUTICASONE-SALMETEROL 250-50 MCG/DOSE DSDV 14 EACH BLIST PACK, 80 
-33188772,MEMANTINE 5 MG TABS, 39 
-33188780,MEMANTINE 10 MG TABS, 39 
-33188798,TIOTROPIUM 18 MCG CPDV 5 EACH BLIST PACK, 147 
-33188822,HYDROCODONE-ACETAMINOPHEN 10-325 MG TABS, 7 
-33188913,ONDANSETRON 4 MG TABS, 9 
-33188921,ONDANSETRON 4 MG TBDL, 6 
-33188939,ONDANSETRON 8 MG TBDL, 12 
-33188954,EZETIMIBE-SIMVASTATIN 10-20 MG TABS, 54 
-33188962,EZETIMIBE-SIMVASTATIN 10-10 MG TABS, 53 
-33188970,EZETIMIBE-SIMVASTATIN 10-40 MG TABS, 53 
-33188988,EZETIMIBE-SIMVASTATIN 10-80 MG TABS, 53 
-33188996,"LIDOCAINE-EPINEPHRINE 1.5%-1:200,000 SOLN 5 ML AMPUL", 53 
-33188996,LIDOCAINE-EPINEPHRINE 5 ML AMPUL, 55 
-33189002,KETOROLAC 30 MG/ML (1 ML) SOLN, 23 
-33189002,KETOROLAC 30 MG/ML (1 ML) SOLN 1 ML VIAL, 24 
-33189028,QUETIAPINE 200 MG TABS, 20 
-33189036,HEPARIN FLUSH 10 UNITS/ML SOLN 10 ML VIAL, 8 
-33189044,LEVOFLOXACIN 750 MG/150 ML PGBK 150 ML BAG, 29 
-33189051,DAPTOMYCIN 500 MG SOLR, 6 
-33189051,DAPTOMYCIN SOLR, 6 
-33189077,APREPITANT 80 MG CAPS, 60 
-33189085,APREPITANT 125 MG CAPS, 60 
-33189101,AZACITIDINE 25 MG/ML SOLR, 53 
-33189119,DARBEPOETIN 300 MCG/0.6 ML SYRG 0.6 ML SYRINGE, 35 
-33189119,DARBEPOETIN 300 MCG/0.6 ML SYRG, 36 
-33189127,KETAMINE 10 MG/ML 1 ML SYRINGE, 22 
-33189135,DULOXETINE 20 MG CPDR, 57 
-33189143,DULOXETINE 30 MG CPDR, 58 
-33189150,DULOXETINE 60 MG CPDR, 58 
-33189168,ACYCLOVIR 400 MG TABS, 9 
-33189267,HYALURONIDASE 200 UNIT/ML SOLN, 3 
-33189267,HYALURONIDASE 200 UNIT/ML SOLN 1.2 ML VIAL, 3 
-33189325,PROPAFENONE 325 MG CP12, 63 
-33189333,PROPAFENONE 425 MG CP12, 80 
-33189341,PROPAFENONE 225 MG CP12, 59 
-33189366,ERLOTINIB 100 MG TABS," 1,641 "
-33189374,ERLOTINIB 150 MG TABS," 1,850 "
-33189382,NATALIZUMAB 300 MG/15 ML SOLN, 157 
-33189432,CLOZAPINE 25 MG TBDL, 30 
-33189440,CLOZAPINE 100 MG TBDL, 75 
-33189457,ARIPIPRAZOLE 5 MG TABS, 169 
-33189473,TOLTERODINE 2 MG CP24, 61 
-33189481,TOLTERODINE 4 MG CP24, 63 
-33189507,PACLITAXEL PROTEIN-BOUND 5 MG/ML SOLR, 96 
-33189515,FAMOTIDINE 10 MG TABS, 3 
-33189572,PREDNISOLONE 15 MG/5 ML SOLN, 9 
-33189572,PREDNISOLONE 15 MG/5 ML SOLN 20 ML BOTTLE, 9 
-33189580,TRIMETHOBENZAMIDE 300 MG CAPS, 9 
-33189598,"HEPARIN (PF) 1,000 UNIT/ML SOLN", 31 
-33189598,"HEPARIN (PF) 1,000 UNIT/ML SOLN 2 ML VIAL", 39 
-33189622,CARBIDOPA-LEVODOPA 10-100 MG TBDL, 30 
-33189630,CARBIDOPA-LEVODOPA 25-100 MG TBDL, 13 
-33189655,ATAZANAVIR 150 MG CAPS, 176 
-33189689,IVERMECTIN 3 MG TABS, 47 
-33189697,RIFAXIMIN 200 MG TABS, 124 
-33189705,INSULIN ASPART 100 UNIT/ML INPN, 1.0 
-33189739,MICAFUNGIN 50 MG SOLR, 8 
-33189739,MICAFUNGIN 50 MG SOLR 1 EACH VIAL, 8 
-33189747,ACAMPROSATE 333 MG TBEC, 12 
-33189754,ENTECAVIR 0.05 MG/ML SOLN, 145 
-33189762,ENTECAVIR 0.5 MG TABS, 314 
-33189770,ENTECAVIR 1 MG TABS, 289 
-33189788,TEMOZOLOMIDE 5 MG CAPS, 101 
-33189796,TEMOZOLOMIDE 20 MG CAPS, 100 
-33189804,TEMOZOLOMIDE 100 MG CAPS, 111 
-33189812,TEMOZOLOMIDE 250 MG CAPS, 107 
-33189820,DARBEPOETIN 40 MCG/0.4 ML SYRG 0.4 ML SYRINGE, 52 
-33189838,DARBEPOETIN 25 MCG/0.42 ML SYRG 0.42 ML SYRINGE, 54 
-33189846,DARBEPOETIN 150 MCG/0.3 ML SYRG 0.3 ML SYRINGE, 59 
-33189853,DARBEPOETIN 500 MCG/ML SYRG 1 ML SYRINGE, 35 
-33189861,NS FLUSH 0.9 % SYRG, 9 
-33189861,NS FLUSH 0.9 % SYRG 10 ML SYRINGE, 20 
-33189879,MORPHINE 150 MG/30 ML SYRG, 47 
-33189879,MORPHINE 150 MG/30 ML SYRG 30 ML SYRINGE, 63 
-33189903,ZICONOTIDE 25 MCG/ML SOLN, 65 
-33189903,ZICONOTIDE 25 MCG/ML SOLN 20 ML VIAL, 66 
-33189911,FENTANYL 12 MCG/HR PT72, 132 
-33189929,EMTRICITABINE-TENOFOVIR 200-300 MG TABS, 394 
-33189937,EMTRICITABINE 200 MG CAPS, 154 
-33189945,LEUPROLIDE 30 MG SYKT 1 EACH KIT," 1,741 "
-33189952,PAROXETINE 10 MG/5 ML SUSP, 78 
-33190018,MODAFINIL 100 MG TABS, 175 
-33190059,TIGECYCLINE 50 MG SOLR, 13 
-33190067,OSELTAMIVIR 75 MG CAPS, 107 
-33190083,RIZATRIPTAN 5 MG TABS, 73 
-33190091,RIZATRIPTAN 10 MG TABS, 106 
-33190109,RIZATRIPTAN 5 MG TBDL, 273 
-33190117,RIZATRIPTAN 10 MG TBDL, 268 
-33190133,PREGABALIN 25 MG CAPS, 33 
-33190141,PREGABALIN 50 MG CAPS, 38 
-33190158,PREGABALIN 75 MG CAPS, 38 
-33190166,PREGABALIN 100 MG CAPS, 38 
-33190174,PREGABALIN 150 MG CAPS, 38 
-33190182,PREGABALIN 200 MG CAPS, 36 
-33190216,ROSUVASTATIN 5 MG TABS, 54 
-33190224,ROSUVASTATIN 10 MG TABS, 52 
-33190232,ROSUVASTATIN 20 MG TABS, 55 
-33190240,ROSUVASTATIN 40 MG TABS, 55 
-33190307,LOPINAVIR-RITONAVIR 200-50 MG TABS, 64 
-33190331,SILDENAFIL 20 MG TABS, 15 
-33190349,SODIUM CHLORIDE 10 % NEBU 15 ML VIAL, 2 
-33190349,SODIUM CHLORIDE 10 % NEBU 15 ML VIAL/KIT, 2 
-33190372,METHYLPREDNISOLONE 2 GRAM SOLR, 25 
-33190380,GRANISETRON 100 MCG/ML SOLN, 25 
-33190380,GRANISETRON 100 MCG/ML SOLN 1 ML VIAL, 56 
-33190406,OXYBUTYNIN 5 MG TR24, 27 
-33190414,OXYBUTYNIN 10 MG TR24, 29 
-33190422,OXYBUTYNIN 15 MG TR24, 16 
-33190430,LEVOFLOXACIN 750 MG TABS, 11 
-33190448,IRON SUCROSE 100 MG/5 ML IRON SOLN, 4 
-33190448,IRON SUCROSE 100 MG/5 ML IRON SOLN 5 ML VIAL, 4 
-33190455,DEXTROSE 15 GM/TUBE GEL 15 G TUBE, 4 
-33190463,CEFOXITIN 2 GRAM SOLR, 55 
-33190463,CEFOXITIN 2 GRAM SOLR 1 EACH VIAL, 59 
-33190489,"NYSTATIN 100,000 UNIT/ML SUSP 5 ML CUP", 18 
-33190489,"NYSTATIN 100,000 UNIT/ML SUSP", 18 
-33190489,"NYSTATIN 100,000 UNIT/ML SUSP 5 ML BLIST PACK", 18 
-33190497,"MESALAMINE 1,000 MG SUPP", 203 
-33190505,BUPRENORPHINE 2 MG SUBL, 43 
-33190513,BUPRENORPHINE 8 MG SUBL, 70 
-33190547,SELEGILINE 6 MG/24 HR PT24, 209 
-33190596,TRIAMCINOLONE ACETONIDE 40 MG/ML SUSP 1 ML VIAL, 34 
-33190596,TRIAMCINOLONE ACETONIDE 40 MG/ML SUSP, 35 
-33190687,FONDAPARINUX 5 MG/0.4 ML SYRG 0.4 ML SYRINGE, 41 
-33190695,FONDAPARINUX 10 MG/0.8 ML SYRG 0.8 ML SYRINGE, 29 
-33190695,FONDAPARINUX 7.5 MG/0.6 ML SYRG 0.6 ML SYRINGE, 32 
-33190703,FONDAPARINUX 10 MG/0.8 ML SYRG 0.8 ML SYRINGE, 23 
-33190737,URSODIOL 500 MG TABS, 32 
-33190745,EPOPROSTENOL (GLYCINE) 1.5 MG SOLR, 212 
-33190752,LEVETIRACETAM 500 MG/5 ML SOLN, 8 
-33190760,CALCIUM GLUCONATE 60 MG (648 MG) TABS, 2 
-33190778,BUPIVACAINE (PF) 0.75% SOLN, 29 
-33190778,BUPIVACAINE (PF) 0.75% SOLN 10 ML VIAL, 45 
-33190802,NITAZOXANIDE 500 MG TABS, 231 
-33190828,LIDOCAINE-TETRACAINE 70-70 MG PTMH, 133 
-33190844,"BOTULINUM TOXIN TYPE B 10,000 UNIT/2 ML SOLN 2 ML VIAL", 104 
-33190885,"ZOSTER VACCINE 19,400 UNIT SUSR 1 EACH VIAL"," 1,672 "
-33190901,SIROLIMUS 2 MG TABS, 120 
-33190919,PROPRANOLOL 40 MG/5 ML SOLN, 13 
-33190927,ANTICOAGULANT CITRATE DEXTROSE SOLN 750 ML FLEX CONT, 45 
-33190927,ANTICOAGULANT CITRATE DEXTROSE SOLN, 145 
-33190935,TRIAMCINOLONE ACETONIDE 1 ML VIAL, 39 
-33190943,LEVOCARNITINE 200 MG/ML SOLN 5 ML VIAL, 74 
-33190976,COLESEVELAM 625 MG TABS, 15 
-33190984,BUDESONIDE 0.5 MG/2 ML NBSP 2 ML VIAL, 39 
-33190984,BUDESONIDE 0.5 MG/2 ML NBSP, 39 
-33190984,BUDESONIDE 0.5 MG/2 ML NBSP 2 ML AMPUL/KIT, 51 
-33190992,SERTRALINE 25 MG TABS, 5 
-33191305,DASATINIB 50 MG TABS," 1,394 "
-33191313,DASATINIB 70 MG TABS," 1,387 "
-33191339,LEVOCARNITINE 330 MG TABS, 7 
-33191362,ARIPIPRAZOLE 9.75 MG/1.3 ML SOLN 1.3 ML VIAL, 6 
-33191370,CORTICORELIN 100 MCG SOLR 1 EACH VIAL, 53 
-33191552,ALGLUCOSIDASE ALFA 50 MG SOLR," 2,038 "
-33191578,PALIPERIDONE 3 MG TR24, 181 
-33191586,PALIPERIDONE 6 MG TR24, 187 
-33191594,PALIPERIDONE 9 MG TR24, 268 
-33191610,BEVACIZUMAB 7.5 MG/0.3 ML SOLN 0.3 ML VIAL, 25 
-33191628,ERTAPENEM 1 GRAM SOLR, 373 
-33191636,HYDROCODONE-ACETAMINOPHEN 5-325 MG TABS, 5 
-33191644,ONDANSETRON 32 MG/50 ML PGBK, 7 
-33191651,NIACIN 500 MG TB24, 33 
-33191669,NIACIN 750 MG TB24, 39 
-33191677,"NIACIN 1,000 MG TB24", 56 
-33191685,CLONAZEPAM 0.125 MG TBDL, 9 
-33191693,CLONAZEPAM 0.25 MG TBDL, 12 
-33191727,HYDROMORPHONE 1 MG/ML SYRG, 55 
-33191727,HYDROMORPHONE 1 MG/ML SYRG 1 ML SYRINGE, 56 
-33191727,HYDROMORPHONE 1 MG/ML SYRG 1 MG SYRINGE, 57 
-33191727,HYDROMORPHONE 1 MG SYRINGE, 58 
-33191750,ATOVAQUONE-PROGUANIL 250-100 MG TABS, 75 
-33191768,ARIPIPRAZOLE 10 MG TBDL, 235 
-33191776,ARIPIPRAZOLE 15 MG TBDL, 231 
-33191784,CINACALCET 30 MG TABS, 148 
-33191792,CINACALCET 60 MG TABS, 292 
-33191800,CINACALCET 90 MG TABS, 442 
-33191859,BUDESONIDE 180 MCG/INHALATION AEPB 1 EACH CANISTER," 1,517 "
-33191875,"PENICILLIN G BENZATHINE-PENICILLIN G PROCAINE 1,200,000 UNIT/ 2 ML(600K/600K) SYRG 2 ML SYRINGE", 38 
-33191883,MEGESTROL 400 MG/10 ML (10 ML) SUSP, 39 
-33191883,MEGESTROL 400 MG/10 ML (10 ML) SUSP 10 ML CUP, 39 
-33191891,MEGESTROL 800 MG/20 ML (20 ML) SUSP 20 ML CUP, 65 
-33191891,MEGESTROL 800 MG/20 ML (20 ML) SUSP, 72 
-33191917,FLUOXETINE 20 MG/5 ML SOLN 5 ML CUP, 45 
-33191917,FLUOXETINE 20 MG/5 ML SOLN, 53 
-33191925,OXYBUTYNIN 5 MG/5 ML SYRP 5 ML BLIST PACK, 20 
-33191933,VALSARTAN 40 MG TABS, 29 
-33191974,DEXAMETHASONE 10 MG/ML SOLN, 6 
-33191974,DEXAMETHASONE 10 MG/ML SOLN 1 ML VIAL, 7 
-33191982,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) SOLN 1 ML VIAL", 5 
-33191982,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) SOLN", 8 
-33191982,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) SOLN 1 ML AMPUL", 10 
-33191982,EPINEPHRINE 1-TO-, 15 
-33191982,"EPINEPHRINE (PF) 1 MG/ML (1:1,000) (1ML) SOLN", 58 
-33192014,CARVEDILOL 10 MG CM24, 49 
-33192022,CARVEDILOL 20 MG CM24, 49 
-33192030,CARVEDILOL 40 MG CM24, 50 
-33192048,CARVEDILOL 80 MG CM24, 54 
-33192055,INSULIN DETEMIR 100 UNIT/ML SOLN, 6 
-33192055,INSULIN DETEMIR 100 UNIT/ML SOLN 10 ML VIAL, 18 
-33192071,CLOPIDOGREL 300 MG TABS, 217 
-33192113,CEFAZOLIN 50 MG/ML, 45 
-33192121,TOBRAMYCIN 15 MG/ML 10 ML DROP BTL, 29 
-33192139,VANCOMYCIN 25 MG/ML 10 ML DROP BTL, 112 
-33192139,VANCOMYCIN 50 MG/ML 10 ML DROP BTL, 112 
-33192154,AZATHIOPRINE 50 MG/ML SUSP, 5 
-33192154,AZATHIOPRINE 50 MG/ML SUSP 1 ML CUP, 7 
-33192188,IMMUNE GLOBULIN 10 GRAM SOLR, 440 
-33192196,QUETIAPINE 200 MG TB24, 115 
-33192204,QUETIAPINE 400 MG TB24, 173 
-33192212,QUETIAPINE 300 MG TB24, 149 
-33192436,D5W BOLUS, 41 
-33192436,D5W SOLP, 57 
-33192436,D5W SOLP 500 ML BAG, 57 
-33192444,LABETALOL 5 MG/ML, 60 
-33192444,LABETALOL 5 MG/ML 100 ML VIAL, 115 
-33192527,HEPARIN 500 UNITS/ML 500 UNIT/ML SYRG 10 ML SYRINGE, 72 
-33192535,"HEPARIN 5,000 UNIT/0.5 ML SOLN", 14 
-33192535,"HEPARIN 5,000 UNIT/0.5 ML SOLN 0.5 ML SYRINGE", 15 
-33192543,"1/2 NS + KCL 20 MEQ/L 20 MEQ/L SOLP 1,000 ML BAG", 62 
-33192568,DOXORUBICIN 50 MG SOLR, 116 
-33192568,DOXORUBICIN 50 MG SOLR 1 EACH VIAL, 128 
-33192618,MAGNESIUM SULFATE GRAN 454 G BOX, 2 
-33192626,LIDOCAINE 4 % CREA 5 G TUBE, 88 
-33192659,LEVOFLOXACIN 250 MG/50 ML PGBK 50 ML BAG, 108 
-33192667,GLIPIZIDE 2.5 MG TR24, 10 
-33192675,CLONAZEPAM 0.5 MG TBDL, 14 
-33192683,CLONAZEPAM 1 MG TBDL, 9 
-33192709,AMBRISENTAN 5 MG TABS," 2,477 "
-33192717,AMBRISENTAN 10 MG TABS," 2,577 "
-33192741,MITOMYCIN 1 ML SYRINGE, 139 
-33192766,FLUCONAZOLE 100 MG/50 ML PGBK 50 ML GLASS CONT, 104 
-33192774,FLUCONAZOLE PGBK, 88 
-33192782,PANTOPRAZOLE 20 MG TBEC, 8 
-33192790,MAGNESIUM SULFATE IN WATER 2 GRAM/50 ML (4 %) PGBK 50 ML FLEX CONT, 27 
-33192808,LIDOCAINE 1% SOLN, 4 
-33192808,LIDOCAINE 1% 1% SOLN, 4 
-33192808,LIDOCAINE 1% 1% SOLN 10 ML VIAL, 5 
-33192808,LIDOCAINE 1% SOLN 10 ML VIAL, 5 
-33192816,LIDOCAINE (PF) 1% 1% SOLN, 8 
-33192816,LIDOCAINE (PF) 1% 1% SOLN 2 ML VIAL, 12 
-33192840,CYCLOBENZAPRINE 5 MG TABS, 6 
-33192857,PHENYLEPHRINE IN NS 100 MCG/ML 0.5 MG/5 ML (100 MCG/ML) SYRG, 64 
-33192857,PHENYLEPHRINE IN NS 100 MCG/ML 0.5 MG/5 ML (100 MCG/ML) SYRG 10 ML SYRINGE, 143 
-33192865,"LIDOCAINE-EPINEPHRINE 2%-1:100,000 SOLN", 48 
-33192865,"LIDOCAINE-EPINEPHRINE 2%-1:100,000 SOLN 20 ML VIAL", 59 
-33192873,CALCIUM CARBONATE CHEW, 1.0 
-33192881,EPINEPHRINE HCL-0.9% NACL (PF) 100 MCG/10 ML (10 MCG/ML) SYRG 10 ML SYRINGE, 64 
-33192899,PHENYLEPHRINE 100 ML BAG, 126 
-33192907,PHENYLEPHRINE 250 ML BAG, 31 
-33192915,NITROGLYCERIN 25 MG IN D5W 250 ML 25 MG/250 ML (0.1 MG/ML) SOLN 250 ML BOTTLE, 84 
-33192923,SODIUM BICARBONATE 1 MEQ/ML SOLP, 131 
-33192980,SEVELAMER CARBONATE 800 MG TABS, 27 
-33192998,POTASSIUM PHOSPHATE-SODIUM PHOSPHATE 280-160-250 MG PWPK, 6 
-33193004,"ERGOCALCIFEROL 8,000 UNIT/ML DROP", 9 
-33193012,DORIPENEM 500 MG SUSR, 9 
-33193020,ZILEUTON 600 MG TM12, 49 
-33193038,BIMATOPROST 0.03 % DROP, 19 
-33193061,ARTIFICIAL SALIVA SPRA 60 ML SQUEEZ BTL, 3 
-33193087,BUMETANIDE 0.25 MG/ML SOLN 100 ML BAG, 191 
-33193095,FUROSEMIDE 1000 MG/100 ML SOLN 100 ML BAG, 5 
-33193103,ARFORMOTEROL 15 MCG/2 ML NEBU 2 ML VIAL, 77 
-33193111,POLYETHYLENE GLYCOL 17 GRAM PWPK, 2 
-33193129,"FACTOR VIIA 1 MG (1,000 MCG) SOLR", 8 
-33193137,"FACTOR VIIA 2 MG (2,000 MCG) SOLR", 8 
-33193145,"FACTOR VIIA 5 MG (5,000 MCG) SOLR", 8 
-33193152,MYCOPHENOLATE 180 MG TBEC, 42 
-33193160,MYCOPHENOLATE 360 MG TBEC, 39 
-33193178,TACROLIMUS 1 MG/ML, 45 
-33193236,ROMIPLOSTIM 250 MCG SOLR, 522 
-33193244,ROMIPLOSTIM 500 MCG SOLR, 495 
-33193251,TRIMETHOPRIM-SULFAMETHOXAZOLE 40-200 MG/5 ML SUSP, 21 
-33193251,TRIMETHOPRIM-SULFAMETHOXAZOLE 40-200 MG/5 ML SUSP 20 ML CUP, 30 
-33193269,SF SORBITOL(ORASWEET) LIQD, 2 
-33193277,BENDAMUSTINE 100 MG SOLR, 183 
-33193301,CYCLOSPORINE 100 MG/ML SOLN, 36 
-33193319,CYCLOSPORINE 100 MG/ML SOLN, 37 
-33193327,MYCOPHENOLATE 200 MG/ML SUSP, 28 
-33193335,RIBAVIRIN 40 MG/ML SOLN, 21 
-33193426,DEFERASIROX 500 MG TBDP, 749 
-33193434,CELECOXIB 100 MG CAPS, 34 
-33193442,CELECOXIB 200 MG CAPS, 53 
-33193459,RALOXIFENE 60 MG TABS, 49 
-33193467,RISPERIDONE 2 MG TBDL, 29 
-33193475,ZANAMIVIR 5 MG/ACTUATION DSDV 20 EACH BOX, 59 
-33193517,ROPIVACAINE 0.2% ANESTHETIC PUMP 400 ML BAG, 0.9 
-33193517,ROPIVACAINE 0.2% ANESTHETIC PUMP, 1.0 
-33193517,ROPIVACAINE 0.2% 400 ML BAG, 1.1 
-33193517,ROPIVACAINE 400 ML BAG, 1.1 
-33193517,ROPIVACAINE 0.2%, 1.2 
-33193525,LANSOPRAZOLE 15 MG TBLD, 54 
-33193533,LANSOPRAZOLE 30 MG TBLD, 65 
-33193541,ESTRADIOL 0.5 MG TABS, 6 
-33193558,RISPERIDONE MICROSPHERES 12.5 MG/2 ML SYRG 1 EACH SYRINGE, 53 
-33193608,TIROFIBAN IN NS 5 MG/100 ML (50 MCG/ML) SOLN 100 ML BAG, 120 
-33193616,MODAFINIL 200 MG TABS, 205 
-33193624,METHYLPHENIDATE 10 MG/9 HR PT24, 60 
-33193640,METHYLPHENIDATE 15 MG/9 HR PT24, 60 
-33193665,HEPARIN 10 UNITS/ML 10 UNIT/ML SYRG 5 ML SYRINGE, 7 
-33193681,RIVASTIGMINE 1.5 MG CAPS, 33 
-33193699,RIVASTIGMINE 3 MG CAPS, 27 
-33193707,RIVASTIGMINE 4.5 MG CAPS, 21 
-33193723,RIVASTIGMINE 2 MG/ML SOLN, 35 
-33193731,LEVETIRACETAM 500 MG/5 ML (5 ML) SOLN, 30 
-33193731,LEVETIRACETAM 500 MG/5 ML (5 ML) SOLN 5 ML CUP, 61 
-33193756,"LR SOLN 3,000 ML BOTTLE", 34 
-33193756,LR SOLN, 62 
-33193764,EPINEPHRINE IN LACTATED RINGERS 1 EACH BOTTLE, 61 
-33193772,DIPHENHYDRAMINE 12.5 MG/5 ML LIQD, 0.5 
-33193772,DIPHENHYDRAMINE 12.5 MG/5 ML LIQD 10 ML CUP, 2 
-33193780,FERROUS SULFATE 220 MG (44 MG IRON)/5 ML SOLN 6.818 ML CUP, 2 
-33193822,RIBAVIRIN 40 MG/ML SOLN 5 ML CUP, 95 
-33193822,RIBAVIRIN 40 MG/ML SOLN, 96 
-33193830,RISPERIDONE 0.25 MG TABS, 6 
-33193848,LEVOFLOXACIN 250 MG TABS, 15 
-33193855,RAMELTEON 8 MG TABS, 61 
-33193905,"AMYLASE-LIPASE-PROTEASE 6,000-19,000 -30,000 UNIT CPDR 100 EACH BOTTLE", 14 
-33193913,"AMYLASE-LIPASE-PROTEASE 12,000-38,000 -60,000 UNIT CPDR 100 EACH BOTTLE", 20 
-33193921,"AMYLASE-LIPASE-PROTEASE 24,000-76,000 -120,000 UNIT CPDR 250 EACH BOTTLE", 34 
-33193921,"AMYLASE-LIPASE-PROTEASE 24,000-76,000 -120,000 UNIT CPDR", 34 
-33193921,"AMYLASE-LIPASE-PROTEASE 24,000-76,000 -120,000 UNIT CPDR 100 EACH BOTTLE", 36 
-33193947,PRASUGREL 5 MG TABS, 73 
-33193954,PRASUGREL 10 MG TABS, 68 
-33193962,DRONEDARONE 400 MG TABS, 45 
-33193970,FERROUS SULFATE 142 MG (45 MG IRON) TBSR, 2 
-33194069,OLMESARTAN 5 MG TABS, 28 
-33194077,OLMESARTAN 20 MG TABS, 36 
-33194085,OLMESARTAN 40 MG TABS, 48 
-33194093,SODIUM CHLORIDE 7 % NEBU 4 ML VIAL, 2 
-33194101,TACROLIMUS 0.5 MG CAPS, 19 
-33194143,ARTEMETHER-LUMEFANTRINE 20-120 MG TABS, 32 
-33194168,TADALAFIL 20 MG TABS, 241 
-33194168,TADALAFIL 20 MG TABS 60 EACH BOTTLE, 250 
-33194184,IOHEXOL 300 MG/ML SOLN, 14 
-33194184,IOHEXOL 300 MG/ML SOLN 10 ML VIAL, 14 
-33194192,LACOSAMIDE 200 MG/20 ML SOLN 20 ML VIAL, 1.3 
-33194192,LACOSAMIDE 200 MG/20 ML SOLN, 1.4 
-33194200,LACOSAMIDE 50 MG TABS, 62 
-33194218,LACOSAMIDE 100 MG TABS, 82 
-33194226,LACOSAMIDE 150 MG TABS, 82 
-33194234,LACOSAMIDE 200 MG TABS, 88 
-33194267,CHLORHEXEDINE GLUCONATE 1 EACH PACKAGE, 2 
-33194275,FERUMOXYTOL 510 MG/17 ML (30 MG/ML) SOLN 17 ML VIAL, 8 
-33194283,HEMIN 313 MG SOLR, 128 
-33194291,"MENINGOCOCCAL VAC A,C,Y,W135 (PF) 50 MCG SOLR 1 EACH VIAL"," 1,196 "
-33194325,INTERFERON ALFA-2B 50 MILLION UNIT (1 ML) SOLR, 179 
-33194341,ARIPIPRAZOLE 2 MG TABS, 177 
-33194358,BUPIVACAINE (PF) 0.5% SOLN, 55 
-33194358,BUPIVACAINE (PF) 0.5% SOLN 10 ML VIAL, 82 
-33194366,SODIUM HYPOCHLORITE 0.0125 % SOLN 473 ML BOTTLE, 89 
-33194374,KETOROLAC (PF) 0.45 % DPET, 55 
-33194408,SENNA LEAF EXTRACT 176 MG/5 ML SYRP 15 ML CUP, 10 
-33194424,ITRACONAZOLE 10 MG/ML SOLN, 9 
-33194424,ITRACONAZOLE 10 MG/ML SOLN 10 ML CUP, 143 
-33194440,SORAFENIB 200 MG TABS, 680 
-33194515,INSULIN ASPART 100 UNIT/ML SOLN 10 ML VIAL, 1.0 
-33194580,PHENYLEPHRINE IN NS 100 MCG/ML 0.5 MG/5 ML (100 MCG/ML) SYRG 5 ML SYRINGE, 60 
-33194598,HYDROMORPHONE (DILAUDID) EPIDURAL INJECTION, 43 
-33194606,TAPENTADOL 75 MG TABS, 18 
-33194614,"LIPASE-PROTEASE-AMYLASE 5,000-17,000 -27,000 UNIT CPDR 100 EACH BOTTLE", 14 
-33194622,"LIPASE-PROTEASE-AMYLASE 10,000-34,000 -55,000 UNIT CPDR 100 EACH BOTTLE", 22 
-33194630,"LIPASE-PROTEASE-AMYLASE 15,000-51,000 -82,000 UNIT CPDR 100 EACH BOTTLE", 29 
-33194648,"LIPASE-PROTEASE-AMYLASE 20,000-68,000 -109,000 UNIT CPDR 100 EACH BOTTLE", 37 
-33194804,ZOLPIDEM 12.5 MG TBMP, 39 
-33194812,ZOLPIDEM 6.25 MG TBMP, 36 
-33194820,POTASSIUM CITRATE 10 MEQ TBSR, 11 
-33194838,"LIPASE-PROTEASE-AMYLASE 4,200-10,000 -17,500 UNIT CPDR 100 EACH BOTTLE", 12 
-33194846,"LIPASE-PROTEASE-AMYLASE 10,500-25,000 -43,750 UNIT CPDR 100 EACH BOTTLE", 18 
-33194853,"LIPASE-PROTEASE-AMYLASE 16,800-40,000 -70,000 UNIT CPDR 100 EACH BOTTLE", 28 
-33194861,"LIPASE-PROTEASE-AMYLASE 21,000-37,000 -61,000 UNIT CPDR 100 EACH BOTTLE", 34 
-33194887,DENOSUMAB 60 MG/ML SYRG 1 ML SYRINGE, 139 
-33194937,HYALURONIDASE HUMAN RECOMBINANT 150 UNIT/ML SOLN, 6 
-33194937,HYALURONIDASE HUMAN RECOMBINANT 150 UNIT/ML SOLN 1 ML VIAL, 6 
-33194978,RIFAXIMIN 550 MG TABS, 203 
-33194986,ANIDULAFUNGIN 100 MG SOLR, 11 
-33195009,ALBUMIN 5% 5 % SOLP, 51 
-33195017,FOLIC ACID 400 MCG TABS, 1.2 
-33195025,CUSTOM DRUG 1 EACH PACKAGE, 7 
-33195025,CUSTOM CHEMO - SIMPLE DRUG 1 EACH, 18 
-33195025,CUSTOM DRUG, 40 
-33195025,CUSTOM CHEMO - SIMPLE DRUG, 55 
-33195041,CABAZITAXEL 10 MG/ML (FINAL) SOLN," 1,387 "
-33195058,TOLVAPTAN 15 MG TABS," 2,619 "
-33195066,TOLVAPTAN 30 MG TABS," 2,747 "
-33195090,NILOTINIB 200 MG CAPS, 545 
-33195140,INSULIN REGULAR 100 UNIT/ML SOLN 3 ML VIAL, 1.2 
-33195157,INSULIN NPH 100 UNIT/ML SUSP, 0.5 
-33195157,INSULIN NPH 100 UNIT/ML SUSP 3 ML VIAL, 0.7 
-33195165,INSULIN LISPRO 100 UNIT/ML SOLN, 0.7 
-33195165,INSULIN LISPRO 100 UNIT/ML SOLN 3 ML VIAL, 1.3 
-33195207,SIROLIMUS 0.5 MG TABS, 68 
-33195215,OCTREOTIDE 10 MG KIT 1 EACH KIT," 1,907 "
-33195249,"CHOLECALCIFEROL (VITAMIN D3) 1,000 UNIT TABS", 0.8 
-33195280,CHLORPHENIRAMINE 12 MG TBSR, 2 
-33195306,PALIPERIDONE 1.5 MG TR24, 192 
-33195314,DABIGATRAN 75 MG CAPS, 39 
-33195322,DABIGATRAN 150 MG CAPS, 40 
-33195348,ESZOPICLONE 1 MG TABS, 68 
-33195355,ESZOPICLONE 2 MG TABS, 63 
-33195363,ESZOPICLONE 3 MG TABS, 75 
-33195371,MICONAZOLE 2 % OINT 57 G TUBE, 8 
-33195405,LIDOCAINE 4 % SOLN 20 ML BOTTLE, 27 
-33195447,LOPERAMIDE 1 MG/5 ML LIQD, 2 
-33195462,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) SOLN 100 ML VIAL", 2 
-33195462,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) SOLN", 2 
-33195462,ACETAMINOPHEN 10 MG/ML SOLN, 109 
-33195488,BLUE FOOD COLORING (DEEP BLUE SHADE) LIQD 2 ML SYRINGE, 6 
-33195488,BLUE FOOD COLORING (DEEP BLUE SHADE) LIQD, 7 
-33195520,NEOSTIGMINE 1 MG/ML SOLN, 7 
-33195538,DICLOFENAC EPOLAMINE 1.3 % PT12, 61 
-33195561,PALIPERIDONE PALMITATE 234 MG/1.5 ML SYRG 1.5 ML SYRINGE, 72 
-33195603,"HEPARIN 5,000 UNIT/0.5 ML SOLN 0.5 ML VIAL", 11 
-33195603,"HEPARIN 5,000 UNIT/0.5 ML SOLN", 11 
-33195611,NITROGLYCERIN 100 MCG IN D5W 30 ML 0.1 MG/ML 30 ML VIAL, 47 
-33195611,NITROGLYCERIN 25 MG IN D5W 250 ML 25 MG/250 ML (0.1 MG/ML) SOLN 30 ML VIAL, 47 
-33195629,ACETYLCYSTEINE 200 MG/ML (20 %) SOLN, 199 
-33195637,ACETYLCYSTEINE 10% (100 MG/ML) SOLN 6 ML SYRINGE, 14 
-33195678,GLYCOPYRROLATE 0.2 MG/ML SOLN 2 ML VIAL, 46 
-33195686,ATROPINE 1 % DROP 15 ML DROP BTL, 171 
-33195694,ARTIFICIAL SALIVA SPRA 240 ML SQUEEZ BTL, 10 
-33195702,SODIUM CITRATE-CITRIC ACID 500-334 MG/5 ML SOLN 15 ML BLIST PACK, 10 
-33195751,EPOPROSTENOL 1.5 MG SOLR, 170 
-33195777,TRIAMCINOLONE ACETONIDE 0.1 % CREA, 58 
-33195777,TRIAMCINOLONE ACETONIDE 0.1 % CREA 30 G TUBE, 66 
-33195785,C1 ESTERASE INHIBITOR 500 UNIT (10 ML) SOLR, 329 
-33195793,LIDOCAINE-PRILOCAINE 2.5-2.5 % CREA 5 G TUBE, 68 
-33195801,SULFACETAMIDE 10 % DROP 5 ML DROP BTL, 184 
-33195819,BACITRACIN-ZINC 500 UNIT/GRAM OINT 15 G TUBE, 4 
-33195827,METHYL SALICYLATE-MENTHOL 15-10 % CREA 57 G TUBE, 3 
-33195827,METHYL SALICYLATE-MENTHOL 15-10 % CREA 57 G JAR, 3 
-33195835,MULTIVITAMINS 100-10 MCG-MG CAPS, 2 
-33195850,SUFENTANIL 50 MCG/ML SOLN 1 ML AMPUL, 26 
-33195868,FENTANYL 50 MCG/ML SOLN 2 ML AMPUL, 6 
-33195876,STERILE TALC 5 GRAM SUSR, 991 
-33195884,OXYCODONE 15 MG TABS, 9 
-33195892,OXYCODONE 30 MG TABS, 9 
-33195900,FAMOTIDINE 20 MG/50 ML PGBK 50 ML BAG, 70 
-33195900,FAMOTIDINE 20 MG/50 ML PGBK, 70 
-33195959,FLUTICASONE-SALMETEROL 45-21 MCG/ACTUATION HFAA 8 G CANISTER," 1,332 "
-33195967,FLUTICASONE-SALMETEROL 115-21 MCG/ACTUATION HFAA 8 G CANISTER," 1,383 "
-33195975,FLUTICASONE-SALMETEROL 230-21 MCG/ACTUATION HFAA 8 G CANISTER," 1,938 "
-33196015,FIDAXOMICIN 200 MG TABS," 1,065 "
-33196023,BELIMUMAB 120 MG SOLR, 388 
-33196031,BELIMUMAB 400 MG SOLR, 368 
-33196056,CEFEPIME 1 GRAM/50 ML PGBK 50 ML BAG, 162 
-33196064,DEGARELIX 80 MG SOLR, 28 
-33196072,DEGARELIX 120 MG SOLR, 52 
-33196080,CEFEPIME 2 GRAM/100 ML PGBK 100 ML BAG, 120 
-33196114,BENZOCAINE-MENTHOL SUGAR FREE LOZG, 2 
-33196114,BENZOCAINE-MENTHOL-CETYLPYRID 15-4 MG LOZG, 2 
-33196130,RITONAVIR 100 MG TABS 30 EACH BOTTLE, 91 
-33196148,ABACAVIR-LAMIVUDINE 600-300 MG TABS, 332 
-33196155,ETRAVIRINE 200 MG TABS, 139 
-33196197,FENTANYL 1500 MCG/30 ML 50 MCG/ML 30 ML SYRINGE, 39 
-33196197,"FENTANYL CITRATE (PF) 1,500 MCG/30 ML (50 MCG/ML) SPCA", 40 
-33196197,"FENTANYL CITRATE (PF) 1,500 MCG/30 ML (50 MCG/ML) SPCA 30 ML SYRINGE", 40 
-33196239,VALACYCLOVIR 1 G TABS, 36 
-33196247,VALACYCLOVIR 500 MG TABS, 17 
-33196254,LEVOTHYROXINE 100 MCG SOLR, 308 
-33196254,LEVOTHYROXINE 100 MCG SOLR 1 EACH VIAL, 322 
-33196270,NS FOR IRRIGATION 0.9 % SOLN, 22 
-33196270,NS FOR IRRIGATION 0.9 % SOLN 250 ML BOTTLE, 24 
-33196288,LIDOCAINE 2% SOLN, 51 
-33196288,LIDOCAINE 2% SOLN 10 ML VIAL, 56 
-33196304,TROPICAMIDE 1 % DROP 2 ML DROP BTL, 62 
-33196338,ARGATROBAN IV INFUSION 125 MG/125 ML (1 MG/ML) SOLN 125 ML VIAL, 192 
-33196346,TESTOSTERONE 2 MG/24 HOUR PT24, 51 
-33196353,TESTOSTERONE 4 MG/24 HR PT24, 98 
-33196387,HYDROMORPHONE 1 MG/ML SOLN 1 ML AMPUL, 45 
-33196387,HYDROMORPHONE 1 MG/ML SOLN, 53 
-33196395,FACTOR VIII 500 (+/-) UNIT SOLR, 8 
-33196395,"FACTOR VIII 1,000 (+/-) UNIT SOLR", 11 
-33196403,BUPIVACAINE (PF) 0.125 % SOLN, 151 
-33196403,BUPIVACAINE (PF) 0.125 % SOLN 250 ML FLEX CONT, 173 
-33196445,METOPROLOL 100 MG TB24, 18 
-33196452,CALCIUM GLUCONATE 4.6 MEQ/50 ML SOLN, 63 
-33196452,CALCIUM GLUCONATE 4.6 MEQ/50 ML SOLN 50 ML BAG, 96 
-33196460,MAGNESIUM SULFATE PGBK, 61 
-33196460,MAGNESIUM SULFATE 50 ML BAG, 62 
-33196460,MAGNESIUM SULFATE PGBK 50 ML BAG, 84 
-33196478,MAGNESIUM SULFATE 50 ML BAG, 67 
-33196478,MAGNESIUM SULFATE PGBK, 79 
-33196478,MAGNESIUM SULFATE PGBK 50 ML BAG, 86 
-33196486,CALCIUM CHLORIDE 13.6 MEQ/50 ML 50 ML BAG, 99 
-33196494,CALCIUM GLUCONATE 4.6 MEQ/50 ML SOLN 50 ML FLEX CONT, 73 
-33196502,MAGNESIUM SULFATE PGBK 50 ML FLEX CONT, 83 
-33196502,MAGNESIUM SULFATE PGBK, 85 
-33196510,MAGNESIUM SULFATE PGBK 50 ML FLEX CONT, 92 
-33196510,MAGNESIUM SULFATE PGBK, 94 
-33196528,CALCIUM CHLORIDE 13.6 MEQ/50 ML 50 ML FLEX CONT, 101 
-33196544,LIDOCAINE 2% SOLN, 33 
-33196544,LIDOCAINE 2% SOLN 2 ML VIAL, 50 
-33196569,CAFFEINE 200 MG TABS, 1.3 
-33196619,LIDOCAINE 1% EPIDURAL INFUSION, 27 
-33196619,LIDOCAINE (PF) 1% 1% SOLN 30 ML VIAL, 36 
-33196619,LIDOCAINE (PF) 1% 1% SOLN, 41 
-33196635,AZITHROMYCIN 500 MG SOLR, 99 
-33196684,VANCOMYCIN 750 MG SOLR, 37 
-33196734,DARUNAVIR 600 MG TABS, 177 
-33196767,NS 0.9 % SOLN, 24 
-33196767,NS 0.9 % SOLN 20 ML VIAL, 34 
-33196783,MIDAZOLAM 5 MG/ML SOLN, 42 
-33196817,ALBUTEROL 90 MCG/ACTUATION HFAA 8 G CANISTER, 163 
-33196825,OCTREOTIDE ACETATE 500 MCG/ML (1 ML) SYRG, 11 
-33196825,OCTREOTIDE ACETATE 500 MCG/ML (1 ML) SYRG 1 ML SYRINGE, 12 
-33196833,OCTREOTIDE 100 MCG/ML (1 ML) SYRG 1 ML SYRINGE, 15 
-33196858,FOSAPREPITANT 150 MG SOLR, 17 
-33196866,POTASSIUM IODIDE-IODINE 100-50 MG/ML SOLN 14 ML BOTTLE, 192 
-33196882,MEPERIDINE (PF) 25 MG/ML SOLN, 44 
-33196908,"LIPASE-PROTEASE-AMYLASE 25,000-85,000- 136,000 UNIT CPDR", 49 
-33196916,RIVAROXABAN 10 MG TABS, 67 
-33196924,RIVAROXABAN 15 MG TABS, 74 
-33196932,RIVAROXABAN 20 MG TABS, 74 
-33196981,NITROPRUSSIDE BOLUS 20 MCG/ML IN NS 1 ML SYRINGE, 28 
-33196981,BUPIVACAINE (PF) 0.125% SOLN, 34 
-33196981,BUPIVACAINE (PF) 0.125% SOLN 10 ML SYRINGE, 46 
-33197005,INSULIN REGULAR IN NS INFUSION, 63 
-33197005,INSULIN REGULAR IN NS INFUSION 100 ML BAG, 63 
-33197039,HYDROMORPHONE 10 MG IN NS 100 ML 100 ML BAG, 78 
-33197039,PHENYLEPHRINE IN NS INFUSION 250 ML BAG, 85 
-33197054,"ROPIVACAINE 0.5% W/EPINEPHRINE 1:400,000", 71 
-33197054,VASOPRESSIN IN NS INFUSION, 77 
-33197054,"ROPIVACAINE 0.2% W/EPINEPHRINE 1:400,000", 87 
-33197054,VASOPRESSIN IN NS INFUSION 100 ML BAG, 105 
-33197054,ALFENTANIL 2500 MCG/10 ML NS IV INFUSION 10 ML SYRINGE, 106 
-33197054,NOREPINEPHRINE IN NS INFUSION 250 ML BAG, 106 
-33197054,OCTREOTIDE IN NS INFUSION 100 ML BAG, 107 
-33197054,NOREPINEPHRINE IN NS INFUSION, 108 
-33197070,ROPIVACAINE 2.5 MG/ML SOLN, 91 
-33197070,"ROPIVACAINE 0.25% W/EPINEPHRINE 1:400,000", 93 
-33197492,MELATONIN 1 MG TABS, 0.8 
-33197500,MELATONIN 3 MG TABS, 1.0 
-33197526,DOCUSATE SODIUM 60 MG/15 ML SYRP 25 ML CUP, 2 
-33197542,"EPOETIN ALFA 20,000 UNIT/2 ML SOLN 2 ML VIAL", 146 
-33197567,VANCOMYCIN 750 MG SOLR, 36 
-33197575,DIPHENHYDRAMINE 12.5 MG/5 ML ELIX, 2 
-33197575,DIPHENHYDRAMINE 12.5 MG/5 ML ELIX 5 ML BLIST PACK, 2 
-33197583,DOXEPIN 3 MG TABS, 51 
-33197591,DOXEPIN 6 MG TABS, 62 
-33197609,INDOMETHACIN 50 MG SUPP, 98 
-33197633,RUFINAMIDE 200 MG TABS, 45 
-33197641,RUFINAMIDE 400 MG TABS, 88 
-33197666,LEVETIRACETAM 500 MG TB24, 6 
-33197674,LEVETIRACETAM 750 MG TB24, 7 
-33197682,CLOBETASOL 0.05 % OINT 30 G TUBE, 84 
-33197732,BUMETANIDE 0.25 MG/ML SOLN, 39 
-33197732,BUMETANIDE 0.25 MG/ML SOLN 4 ML VIAL, 47 
-33197740,TRIAMCINOLONE ACETONIDE 0.1 % OINT 453.6 G JAR, 187 
-33197799,ESCITALOPRAM 5 MG TABS, 5 
-33197799,ESCITALOPRAM OXALATE 5 MG TABS, 6 
-33197823,SODIUM BICARBONATE 4.2 % SOLN, 45 
-33197831,BRIMONIDINE 0.2 % DROP 5 ML DROP BTL, 28 
-33197849,TICAGRELOR 90 MG TABS, 43 
-33197864,LEVETIRACETAM 500 MG/100 ML PGBK 100 ML BAG, 4 
-33197872,"LEVETIRACETAM 1,000 MG/100 ML PGBK 100 ML BAG", 3 
-33197880,"LEVETIRACETAM 1,500 MG/100 ML PGBK 100 ML BAG", 3 
-33197955,VANCOMYCIN 1 MG/0.1 ML INJ 1 ML, 10 
-33197963,CEFTAZIDIME 2.25 MG/0.1 ML INJ 1 ML, 32 
-33198011,FOSFOMYCIN TROMETHAMINE 3 GRAM PACK, 467 
-33198029,DIATRIZOATE MEGLUMINE & SODIUM 66-10 % SOLN 120 ML BOTTLE, 129 
-33198060,AMIODARONE 50 MG/ML SOLN, 4 
-33198078,MIDAZOLAM 5MG/ML SOLN 2 ML VIAL, 8 
-33198086,PRAMIPEXOLE 0.25 MG TABS, 5 
-33198102,"MULTIVITAMINS ADULT 3,300 UNIT- 150 MCG/10 ML SOLN", 76 
-33198110,GABAPENTIN 250 MG/5 ML SOLN, 4 
-33198128,AMINOCAPROIC ACID 25 % SOLN, 11 
-33198136,CIPROFLOXACIN 500 MG/5 ML SUMC, 12 
-33198144,GUAIFENESIN-DEXTROMETHORPHAN 100-10 MG/5 ML SYRP, 1.2 
-33198144,GUAIFENESIN-DEXTROMETHORPHAN 100-10 MG/5 ML SYRP 5 ML BLIST PACK, 1.2 
-33198151,AMOXICILLIN-CLAVULANATE POTASSIUM 250-62.5 MG/5 ML SUSR, 6 
-33198177,FLUCONAZOLE 40 MG/ML SUSR, 33 
-33198185,AMOXICILLIN 250 MG/5 ML SUSR, 17 
-33198193,AMOXICILLIN 400 MG/5 ML SUSR, 1.0 
-33198201,ERYTHROMYCIN ETHYLSUCCINATE 200 MG/5 ML SUSR, 186 
-33198219,AZITHROMYCIN 200 MG/5 ML SUSR, 79 
-33198227,CEPHALEXIN 250 MG/5 ML SUSR, 1.1 
-33198235,AMOXICILLIN-CLAVULANATE POTASSIUM 400-57 MG/5 ML SUSR, 2 
-33198250,PHENYLEPHRINE 10 MG/ML SOLN 10 ML VIAL, 12 
-33198284,"HEPARIN (PORCINE) IN NACL (PF) 30,000 UNIT/1,000 ML SOLP 1,000 ML BAG", 12 
-33198292,LIDOCAINE (PF) 3.5% 3.5 % GEL 1 ML TUBE, 39 
-33198300,"VITAMIN A 10,000 UNIT CAPS", 5 
-33198318,"LIDOCAINE-EPINEPHRINE 1%-1:100,000 SOLN 30 ML VIAL", 62 
-33198318,"LIDOCAINE-EPINEPHRINE 1%-1:100,000 SOLN", 66 
-33198334,ZINC SULFATE 5 MG/ML SOLN, 48 
-33198359,SORBITOL-FREE POLYSTYRENE SODIUM 15 GRAM/60 ML SUSP 60 ML BOTTLE, 77 
-33198367,METHOTREXATE 25 MG/ML SOLN, 11 
-33198367,METHOTREXATE (PF) 25 MG/ML SOLN, 12 
-33198375,HYDROXYZINE 10 MG/5 ML SYRP, 110 
-33198383,DOCETAXEL 80 MG/8 ML (10 MG/ML) SOLN, 57 
-33198391,MESALAMINE 400 MG CPDR, 23 
-33198417,TRIAMCINOLONE ACETONIDE 0.1 % OINT 80 G TUBE, 113 
-33198441,LEVOCARNITINE (WITH SUCROSE) 100 MG/ML SOLN, 2 
-33198466,GRANISETRON (PF) 1 MG/ML (1 ML) SOLN 1 ML VIAL, 8 
-33198466,GRANISETRON (PF) 1 MG/ML (1 ML) SOLN, 8 
-33198508,IOHEXOL 240 MG IODINE/ML SOLN 10 ML VIAL, 15 
-33198516,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL CPSP, 9 
-33198524,"LIPASE-PROTEASE-AMYLASE 16,000-57,500- 60,500 UNIT CPDR 100 EACH BOTTLE", 28 
-33198524,"LIPASE-PROTEASE-AMYLASE 16,000-57,500- 60,500 UNIT CPDR", 34 
-33198532,"LIPASE-PROTEASE-AMYLASE 20,700-41,400 UNIT CPDR 100 EACH BOTTLE", 38 
-33198540,"LIPASE-PROTEASE-AMYLASE 20,880-78,300- 78,300 UNIT TABS 100 EACH BOTTLE", 47 
-33198557,"LIPASE-PROTEASE-AMYLASE 10,440-39,150- 39,150 UNIT TABS 100 EACH BOTTLE", 24 
-33198599,"AMYLASE-LIPASE-PROTEASE 36,000-114,000- 180,000 UNIT CPDR 100 EACH BOTTLE", 69 
-33198607,"HEPARIN 10,000 UNIT/ML SOLN", 2 
-33198623,QUETIAPINE 400 MG TABS, 21 
-33198656,NALBUPHINE 10 MG/ML SOLN 10 ML VIAL, 46 
-33198672,MULTIVITAMIN WITH IRON-MINERAL LIQD, 7 
-33198672,MULTIVITAMIN WITH IRON-MINERAL LIQD 15 ML CUP, 7 
-33198680,BENZOCAINE-MENTHOL 15-3.6 MG LOZG, 2 
-33198730,SODIUM GLYCEROPHOSPHATE 1 MMOL/ML SOLN, 133 
-33198763,INFLUENZA VIRUS VACCINE 45 MCG/0.5 ML SYRG 0.5 ML SYRINGE, 151 
-33198763,INFLUENZA VIRUS VACCINE 15 MCG/0.5 ML SYRG 0.5 ML SYRINGE, 151 
-33198763,INFLUENZA VIRUS VACCINE 45 MCG (15 MCG X 3)/0.5 ML SYRG 0.5 ML SYRINGE, 157 
-33198771,HUMAN PROTHROMBIN COMPLEX(PCC) 500 UNIT (FII 380-800 UNIT) KIT, 7 
-33198771,HUMAN PROTHROMBIN COMPLEX(PCC) 500 UNIT (FII 380-800 UNIT) KIT 500 UNITS KIT, 7 
-33198771,HUMAN PROTHROMBIN COMPLEX(PCC) 500 UNIT (FII 380-800 UNIT) KIT 1 UNITS KIT, 7 
-33198797,CHLOROPROCAINE 30 MG/ML (3 %) SOLN 20 ML VIAL, 100 
-33198813,CETIRIZINE 5 MG TABS, 1.4 
-33198821,CEFTAROLINE 600 MG SOLR, 8 
-33198839,DOCETAXEL 80 MG/4 ML (20 MG/ML) SOLN, 51 
-33198854,CETIRIZINE 10 MG TABS, 2 
-33198862,CETIRIZINE 1 MG/ML SOLN, 0.8 
-33198896,TACROLIMUS 1 MG CP24, 42 
-33198904,PSYLLIUM HUSK 6 GRAM PACK, 8 
-33198946,LURASIDONE 20 MG TABS, 179 
-33198953,LURASIDONE 40 MG TABS, 172 
-33198987,SEVELAMER CARBONATE 0.8 GRAM PWPK, 97 
-33198995,SEVELAMER CARBONATE 2.4 GRAM PWPK, 92 
-33199027,QUETIAPINE 50 MG TABS, 10 
-33199035,QUETIAPINE 50 MG TB24, 67 
-33199043,ZILEUTON 600 MG TABS, 188 
-33199050,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG TABS, 7 
-33199050,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG TABS 1 EACH BLIST PACK, 39 
-33199068,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML SOLN 15 ML CUP, 38 
-33199068,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML SOLN, 48 
-33199076,MIDAZOLAM 1 MG/ML SOLN, 19 
-33199076,MIDAZOLAM 1 MG/ML SOLN 2 ML VIAL, 22 
-33199076,MIDAZOLAM (PF) 1 MG/ML SOLN 2 ML VIAL, 25 
-33199084,MIDAZOLAM 1 MG/ML SOLN 5 ML VIAL, 21 
-33199100,MIDAZOLAM 5 MG/ML SOLN 2 ML VIAL, 6 
-33199100,MIDAZOLAM (PF) 5 MG/ML SOLN 2 ML VIAL, 7 
-33199100,MIDAZOLAM 5MG/ML SOLN 2 ML VIAL, 7 
-33199100,MIDAZOLAM 5 MG/ML SOLN, 16 
-33199134,NALBUPHINE 20 MG/ML SOLN 10 ML VIAL, 46 
-33199134,NALBUPHINE 20 MG/ML SOLN, 46 
-33199175,BENZOCAINE 20 % SPRY, 97 
-33199175,BENZOCAINE 20 % SPRY 1 EACH PF APPLI, 97 
-33199183,ACYCLOVIR 50 MG/ML SOLN 20 ML VIAL, 0.6 
-33199183,ACYCLOVIR 50 MG/ML SOLN, 0.7 
-33199225,PHENOBARBITAL 16.2 MG TABS, 3 
-33199233,PHENOBARBITAL 32.4 MG TABS, 4 
-33199241,TRACE ELEMENTS CONCENTRATE 0.01-1-.5-.06-5 MG/ML SOLN, 63 
-33199266,TRIAMCINOLONE ACETONIDE 0.1 % CREA 454 G JAR, 194 
-33199308,SALIVA SUBSTITUTION COMBO NO.8 MWSH, 7 
-33199316,DOXORUBICIN 50 MG/25 ML SOLN, 49 
-33199324,CYTARABINE (PF) 100 MG/5 ML (20 MG/ML) SOLN, 108 
-33199332,AMINO ACID 15 % SOLP, 160 
-33199365,K PHOS DI & MONO-SOD PHOS MONO 250 MG TABS, 9 
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/driverExtractData.py b/scripts/LabTestAnalysis/machine_learning/dataExtraction/driverExtractData.py
deleted file mode 100644
index b9c8b6a5..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/driverExtractData.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
-Example shell script background call to run script to start up several data conversion processes in series
-nohup python scripts/CDSS/driverExtractData.py &> log/log.driverExtractData &
-"""
-
-import subprocess;
-from medinfo.common.Util import stdOpen, log, ProgressDots;
-
-LOG_FILE_TEMPLATE = "log/extractData.%s";
-
-baseArgv = ["python","scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData.py"];
-
-# Which lab tests to evaluate prediction for. Call these from command-line
-#do one lab
-LAB_PROC_CODES = \
-[
-    "LABFER",
-    #"LABSPLAC",
-    #"LABNTBNP",
-    #"LABTSH",
-    #"LABLIPS",
-    #"LABLAC",
-    #"LABLACWB",
-    #"LABTNI",
-    #"LABABG",
-    #"LABPHOS"
-    ]
-"""
-"LABPTT",
-#"LABMETC",
-"LABMGN",
-"LABPT",
-"LABCAI",
-"LABMB",
-"LABCRP",
-"LABB12",
-"LABESRP",
-"LABFOL",
-"LABPROCT",
-"LABLDH",
-"LABA1C",
-"LABANAS",
-"LABDDIM",
-"LABDDIML",
-"LABHIVWBL",
-#"LABCBCO",
-#"LABCBCD",
-#"LABMETB",
-];
-"""
-
-# do one week
-DATE_RANGES = \
-[("2013-01-01","2013-01-02")]
-"""
-[   ("2013-01-01","2013-03-01"),
-    ("2013-03-01","2013-05-01"),
-    ("2013-05-01","2013-07-01"),
-    ("2013-07-01","2013-09-01"),
-    ("2013-09-01","2013-11-01"),
-    ("2013-11-01","2014-01-01"),
-]
-"""
-
-specificArgvList = \
-    [
-    ];
-
-for labProcCode in LAB_PROC_CODES:
-    for dateRangePair in DATE_RANGES:
-        specificArgv = list();
-        specificArgv.append(labProcCode);
-        specificArgv.extend(dateRangePair);
-        specificArgvList.append(specificArgv);
-
-prog = ProgressDots(10,1,"Processes",total=len(specificArgvList));
-for specificArgv in specificArgvList:
-    key = "%.3d.%s" % (prog.getCounts(), str.join("_",specificArgv) );
-    argv = list(baseArgv)
-    argv.extend(specificArgv);
-    log.info( LOG_FILE_TEMPLATE % key );
-    logFile = stdOpen(LOG_FILE_TEMPLATE % key,"w")
-
-    subprocess.call(argv, stderr=logFile);
-    prog.update();
-prog.printStatus();
diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData.py b/scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData.py
deleted file mode 100644
index 8a040f58..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# Extract lab orders with marked results that can distinguish between normal vs. abnormal
-# Extract out the preceding clinical information to see how well those results can be predicted
-
-import csv
-import sys, os;
-import time;
-from cStringIO import StringIO;
-from datetime import datetime, timedelta;
-from medinfo.common.Util import stdOpen, log, ProgressDots;
-from medinfo.db import DBUtil;
-from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList, columnFromModelList;
-from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
-
-from medinfo.dataconversion.DataExtractor import DataExtractor;
-
-# Specify subset of patient IDs to evaluate if just testing small scale
-#SAMPLE_PATIENT_IDS = ("-12432455207729","-12434586418575","-12436949623244","-12428492282572","-12441340721352","-12439806620263","-12420916146030","-12431524026341","-12435846176833","-12425000988930","-12424829659809","-12438987905370","-12427531834455","-12425738548110");
-#SAMPLE_PATIENT_IDS = ("-12432455207729","-12434586418575","-12436949623244");
-SAMPLE_PATIENT_IDS = None
-"""
-SAMPLE_PATIENT_IDS = []
-with open('scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_100.csv', 'rb') as f:
-    reader = csv.DictReader(f)
-    for line in reader:
-        SAMPLE_PATIENT_IDS.append(line['pat_id'])
-"""
-
-# Specify start and end date for lab order events to look for (if want to work with smaller sample size to start with)
-#START_DATE = datetime(2013,1,1);
-#END_DATE = datetime(2014,1,1);
-
-
-ADMIT_DX_CATEGORY_ID = 2;
-
-# Which lab tests to evaluate prediction for. Call these from command-line (driverExtractData.py)
-#LAB_PROC_CODES
-
-FLOWSHEET_NAMES = \
-(
-    "BP_High_Systolic",
-    "BP_Low_Diastolic",
-    "FiO2",
-    "Glasgow Coma Scale Score",
-    "Pulse",
-    "Resp",
-    "Temp",
-    "Urine",
-);
-
-RESULT_BASE_NAMES = \
-(   # SAPS = f(BUN, Na, K, HCO3, Bili, WBC, PO2)
-    # APACHE = f(pH, Na, K, Cr, Hct, WBC, PO2)
-    'WBC','HCT','PLT',
-    'NA','K','CO2','BUN','CR',
-    'TBIL','ALB','CA',
-    'LAC',
-    'ESR','CRP',
-    'TNI',
-    'PHA','PO2A','PCO2A',
-    'PHV','PO2V','PCO2V',
-);
-
-LAB_PRE_TIME_DELTAS = [timedelta(-1),timedelta(-3),timedelta(-7),timedelta(-30),timedelta(-90)]; # Look at lab results from the previous days
-LAB_POST_TIME_DELTA = timedelta(+0); # Don't look into the future, otherwise cheating the prediction
-
-
-
-
-
-def main(argv):
-    timer = time.time();
-
-    labProcCode = argv[1];
-    startDate = DBUtil.parseDateValue(argv[2]);
-    endDate = DBUtil.parseDateValue(argv[3]);
-    patientEpisodeFilename = None;
-    if len(argv) > 4:
-        patientEpisodeFilename = argv[4];
-
-
-    fileLabel = "%s.%s.%s" % (labProcCode, startDate.strftime("%Y-%m-%d"), endDate.strftime("%Y-%m-%d"));
-    
-    extractor = DataExtractor();
-    extractor.dataCache = dict();   # Don't repeat sub-queries for clinical item ID lookups
-
-    log.info("Extract data for %s" % labProcCode);
-
-    patientEpisodes = None;
-    if patientEpisodeFilename is None:
-        patientEpisodes = queryPatientEpisodes(stdOpen("patientEpisodes.%s.tab" % fileLabel,"w"), labProcCode, startDate, endDate, extractor);    # Maybe just do this first time, then comment out and load from file with line below
-    else:
-        patientEpisodes = extractor.parsePatientEpisodeFile(stdOpen(patientEpisodeFilename), list()); # Read from prior file if main query already done to avoid expensive query
-    #patientIds = set(columnFromModelList(patientEpisodes, "patient_id"));
-
-    #outFile = sys.stdout;
-    outFile = stdOpen("labFeatureMatrix.%s.tab.gz" % fileLabel,"w");
-    formatter = TextResultsFormatter(outFile);
-
-    lastPatientId = None;
-    colNames = None;
-    patientEpisodeByIndexTime = None;
-
-    prog = ProgressDots(total=len(patientEpisodes));
-    for patientEpisode in patientEpisodes:
-        patientId = patientEpisode["patient_id"];
-
-        if lastPatientId is not None and lastPatientId != patientId:
-            # New patient ID so start querying for patient specific data and
-            #   populating patient episode data
-            tempColNames = populatePatientData(patientEpisodeByIndexTime, extractor);
-            if colNames is None:    
-                # First row, print header row
-                colNames = tempColNames;
-                formatter.formatTuple(colNames);
-            # Print out patient (episode) data (one row per episode)
-            formatter.formatResultDicts(patientEpisodeByIndexTime.values(), colNames);
-
-        if lastPatientId is None or lastPatientId != patientId:
-            # Prepare to aggregate patient episode record per patient 
-            patientEpisodeByIndexTime = dict();
-
-        patientEpisodeByIndexTime[patientEpisode["order_time"]] = patientEpisode;
-        lastPatientId = patientId;
-        prog.update();
-        outFile.flush();
-    # Last Iteration
-    populatePatientData(patientEpisodeByIndexTime, extractor);
-    formatter.formatResultDicts(patientEpisodeByIndexTime.values(), colNames);
-    prog.printStatus();
-
-    outFile.close();
-
-    timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
-
-def queryPatientEpisodes(outputFile, labProcCode, startDate, endDate, extractor):
-    """ -- Example query elements
-    select cast(pat_id as bigint), pat_enc_csn_id, sop.order_proc_id, proc_code, abnormal_yn, count(result_in_range_yn), count(case result_in_range_yn when 'Y' then 1 else null end), count(ord_num_value)
-    from stride_order_proc as sop inner join stride_order_results sor on sop.order_proc_id = sor.order_proc_id
-    where proc_code = 'LABMETB'
-    and pat_id = '2570758452935'
-    group by pat_id, pat_enc_csn_id, sop.order_proc_id, proc_code, abnormal_yn
-
-    Query out lab procs vs. results when available, and count up cases where results are all 'Y' for result_in_range_yn
-
-    """
-
-    conn = DBUtil.connection();
-    cursor = conn.cursor();
-    try:
-        # First pass query to get the list of patients with lab result times
-        cohortQuery = SQLQuery();
-        cohortQuery.addSelect("cast(pat_id as bigint)");
-        cohortQuery.addSelect("pat_enc_csn_id");
-        cohortQuery.addSelect("sop.order_proc_id");
-        cohortQuery.addSelect("proc_code");
-        cohortQuery.addSelect("order_time");
-        cohortQuery.addSelect("abnormal_yn");
-        cohortQuery.addSelect("count(case result_in_range_yn when 'Y' then 1 else null end)");
-        cohortQuery.addSelect("count(ord_num_value)");
-        cohortQuery.addFrom("stride_order_proc as sop");
-        cohortQuery.addFrom("stride_order_results as sor");
-        cohortQuery.addWhere("sop.order_proc_id = sor.order_proc_id");
-        cohortQuery.addWhereEqual("proc_code", labProcCode);
-        if SAMPLE_PATIENT_IDS is not None:
-            cohortQuery.addWhereIn("pat_id", SAMPLE_PATIENT_IDS);
-        if startDate is not None:
-            cohortQuery.addWhereOp("order_time",">=", startDate);
-        if endDate is not None:
-            cohortQuery.addWhereOp("order_time","<", endDate);
-        cohortQuery.addGroupBy("pat_id, pat_enc_csn_id, sop.order_proc_id, proc_code, order_time, abnormal_yn");
-        cursor.execute(str(cohortQuery), cohortQuery.params);
-
-        # This design requires full list of patient episodes to be in memory for now. Revise later to allow streaming process (maybe with intermediary patient episode temp file).
-        patientEpisodes = list();
-
-        # Collect Build basic patient ID and datetimes
-        row = cursor.fetchone();
-        while row is not None:
-            (patientId, encounterId, orderProcId, procCode, orderTime, abnormalYN, normalResultCount, totalResultCount) = row;
-            patientEpisode = \
-                RowItemModel \
-                (   {   "patient_id": patientId, 
-                        "encounter_id": encounterId,
-                        "order_proc_id": orderProcId,
-                        "proc_code": procCode,
-                        "order_time": orderTime,
-                        "abnormal": int(abnormalYN == 'Y'), # Convert to binary 1 vs 0
-                        "result_normal_count": normalResultCount,
-                        "result_total_count": totalResultCount,
-                        "all_result_normal": int(normalResultCount == totalResultCount), # Convert to binary 1 vs 0
-                    }
-                );
-            patientEpisodes.append(patientEpisode);
-
-            row = cursor.fetchone();
-
-        # Drop results as tab-delimited text output
-        formatter = TextResultsFormatter(outputFile);
-        formatter.formatResultDicts(patientEpisodes, addHeaderRow=True);
-
-        return patientEpisodes;
-    finally:
-        cursor.close();
-        conn.close();
-
-def populatePatientData(patientEpisodeByIndexTime, extractor):
-    """Query for specific data relating to the patient designated by the patientEpisodes
-    given and populate those episodes with relative data / features.
-    Return list of output column names to expect based on populated results.
-    """
-    # Look for the patient ID based on the contents of the patientEpisodes
-    patientId = None;
-    labProcCode = None;
-    for patientEpisode in patientEpisodeByIndexTime.itervalues():
-        patientId = patientEpisode["patient_id"];
-        labProcCode = patientEpisode["proc_code"];
-        break;
-
-    tempColNames = \
-        ["patient_id", "encounter_id", "order_proc_id", "proc_code", "order_time", 
-            "abnormal", "result_normal_count", "result_total_count", "all_result_normal"];
-
-    if patientId is not None:
-        # Basic Demographics (Age + Gender)
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("Birth",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "Birth", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("Male",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "Male", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("Female",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "Female", daysBins=[]) );
-
-        # Race
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceWhiteNonHispanicLatino",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceWhiteNonHispanicLatino", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceAsian",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceAsian", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceWhiteHispanicLatino",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceWhiteHispanicLatino", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceHispanicLatino",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceHispanicLatino", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceUnknown",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceUnknown", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceOther",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceOther", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceBlack",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceBlack", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RacePacificIslander",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RacePacificIslander", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceNativeAmerican",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceNativeAmerican", daysBins=[]) );
-
-        # Look for most recent any Admit Dx to mark time since admission date
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByCategory((ADMIT_DX_CATEGORY_ID,), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "AdmitDxDate", daysBins=[]) );
-
-        # Assign time features to reflect time of year (seasonality) and time of day (daily cycle) patterns 
-        tempColNames.extend(extractor.addTimeCycleFeatures_singlePatient(patientEpisodeByIndexTime, "order_time", "month"));
-        tempColNames.extend(extractor.addTimeCycleFeatures_singlePatient(patientEpisodeByIndexTime, "order_time", "hour"));
-
-        # Extract out lists of ICD9 prefixes per disease category
-        icd9prefixesByDisease = dict();
-        for row in extractor.loadMapData("CharlsonComorbidity-ICD9CM"):
-            (disease, icd9prefix) = (row["charlson"],row["icd9cm"]);
-            if disease not in icd9prefixesByDisease:
-                icd9prefixesByDisease[disease] = list();
-            icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
-        for disease, icd9prefixes in icd9prefixesByDisease.iteritems():
-            disease = disease.translate(None," ()-/");   # Strip off punctuation
-            eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(icd9prefixes, [patientId], operator="~*")) );
-            tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, disease) );
-
-        # Extract out lists of treatment team names per care category
-        teamNameByCategory = dict();
-        for row in extractor.loadMapData("TreatmentTeamGroups"):
-            (category, teamName) = (row["team_category"],row["treatment_team"]);
-            if category not in teamNameByCategory:
-                teamNameByCategory[category] = list();
-            teamNameByCategory[category].append(teamName);
-        for category, teamNames in teamNameByCategory.iteritems():
-            eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(teamNames, [patientId], col="description")) );
-            tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, category) );
-
-        # Key feature should be stats on how often the given lab proc_code has been ordered recently
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName((labProcCode,), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, labProcCode) );
-
-        # Additional key feature is results of previous orders for the same lab test
-        resultBaseNames = set(RESULT_BASE_NAMES);   # Start with common lab results to include regardless
-        for row in extractor.loadMapData("LabResultMap"):
-            if row["proc_code"] == labProcCode and row["base_name"] not in resultBaseNames:
-                resultBaseNames.add(row["base_name"]);
-        labResultTable = extractor.queryLabResults(resultBaseNames, [patientId]);
-        labsByBaseName = extractor.parseLabResultsData_singlePatient(modelListFromTable(labResultTable));
-        for preTimeDelta in LAB_PRE_TIME_DELTAS:
-            tempColNames.extend( extractor.addLabFeatures_singlePatient(patientEpisodeByIndexTime, labsByBaseName, resultBaseNames, preTimeDelta, LAB_POST_TIME_DELTA) );
-
-    return tempColNames;
-
-if __name__ == "__main__":
-    main(sys.argv);
diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData_forLocalRuns.py b/scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData_forLocalRuns.py
deleted file mode 100644
index 67888a8f..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/extractData_forLocalRuns.py
+++ /dev/null
@@ -1,327 +0,0 @@
-# Extract lab orders with marked results that can distinguish between normal vs. abnormal
-# Extract out the preceding clinical information to see how well those results can be predicted
-
-import csv
-import sys, os;
-import time;
-from cStringIO import StringIO;
-from datetime import datetime, timedelta;
-from medinfo.common.Util import stdOpen, log, ProgressDots;
-from medinfo.db import DBUtil;
-from medinfo.db.Model import SQLQuery, RowItemModel, modelListFromTable, modelDictFromList, columnFromModelList;
-from medinfo.db.ResultsFormatter import TextResultsFormatter, TabDictReader;
-
-from medinfo.dataconversion.DataExtractor import DataExtractor;
-
-# Specify subset of patient IDs to evaluate if just testing small scale
-#SAMPLE_PATIENT_IDS = ("-12432455207729","-12434586418575","-12436949623244","-12428492282572","-12441340721352","-12439806620263","-12420916146030","-12431524026341","-12435846176833","-12425000988930","-12424829659809","-12438987905370","-12427531834455","-12425738548110");
-#SAMPLE_PATIENT_IDS = ("-12432455207729","-12434586418575","-12436949623244");
-SAMPLE_PATIENT_IDS = None
-"""
-SAMPLE_PATIENT_IDS = []
-with open('scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_100.csv', 'rb') as f:
-    reader = csv.DictReader(f)
-    for line in reader:
-        SAMPLE_PATIENT_IDS.append(line['pat_id'])
-"""
-
-# Specify start and end date for lab order events to look for (if want to work with smaller sample size to start with)
-#START_DATE = datetime(2013,1,1);
-#END_DATE = datetime(2014,1,1);
-
-
-ADMIT_DX_CATEGORY_ID = 2;
-
-# Which lab tests to evaluate prediction for. Call these from command-line (driverExtractData.py)
-#LAB_PROC_CODES
-
-FLOWSHEET_NAMES = \
-(
-    "BP_High_Systolic",
-    "BP_Low_Diastolic",
-    "FiO2",
-    "Glasgow Coma Scale Score",
-    "Pulse",
-    "Resp",
-    "Temp",
-    "Urine",
-);
-
-RESULT_BASE_NAMES = \
-(   # SAPS = f(BUN, Na, K, HCO3, Bili, WBC, PO2)
-    # APACHE = f(pH, Na, K, Cr, Hct, WBC, PO2)
-    'WBC','HCT','PLT',
-    'NA','K','CO2','BUN','CR',
-    'TBIL','ALB','CA',
-    'LAC',
-    'ESR','CRP',
-    'TNI',
-    'PHA','PO2A','PCO2A',
-    'PHV','PO2V','PCO2V',
-);
-
-LAB_PRE_TIME_DELTAS = [timedelta(-1),timedelta(-3),timedelta(-7),timedelta(-30),timedelta(-90)]; # Look at lab results from the previous days
-LAB_POST_TIME_DELTA = timedelta(+0); # Don't look into the future, otherwise cheating the prediction
-
-
-
-
-
-def main(argv):
-    timer = time.time();
-    labProcCode = argv[1];
-    startDate = DBUtil.parseDateValue(argv[2]);
-    endDate = DBUtil.parseDateValue(argv[3]);
-    patientEpisodeFilename = None;
-    if len(argv) > 4:
-        patientEpisodeFilename = argv[4];
-
-
-    fileLabel = "%s.%s.%s" % (labProcCode, startDate.strftime("%Y-%m-%d"), endDate.strftime("%Y-%m-%d"));
-
-    extractor = DataExtractor("tempOutputFileName");
-    extractor.dataCache = dict();   # Don't repeat sub-queries for clinical item ID lookups
-
-    log.info("Extract data for %s" % labProcCode);
-
-    patientEpisodes = [{\
-    "patient_id": "100",
-    "encounter_id": "1",
-    "order_proc_id": "1",
-    "proc_code": "777",
-    "order_time": "2013-07-04 00:00:00",
-    "abnormal": 1,
-    "result_normal_count": 0,
-    "result_total_count": 1,
-    "all_result_normal": 0
-    }]
-
-    """
-    None;
-    if patientEpisodeFilename is None:
-        patientEpisodes = queryPatientEpisodes(stdOpen("patientEpisodes.%s.tab" % fileLabel,"w"), labProcCode, startDate, endDate, extractor);    # Maybe just do this first time, then comment out and load from file with line below
-    else:
-        patientEpisodes = extractor.parsePatientEpisodeFile(stdOpen(patientEpisodeFilename), list()); # Read from prior file if main query already done to avoid expensive query
-    #patientIds = set(columnFromModelList(patientEpisodes, "patient_id"));
-    """
-
-    #outFile = sys.stdout;
-    outFile = stdOpen("labFeatureMatrix.newDataExtractor.%s.tab.gz" % fileLabel,"w");
-    formatter = TextResultsFormatter(outFile);
-
-    lastPatientId = None;
-    colNames = None;
-    patientEpisodeByIndexTime = None;
-
-    prog = ProgressDots(total=len(patientEpisodes));
-    for patientEpisode in patientEpisodes:
-        patientId = patientEpisode["patient_id"];
-
-        if lastPatientId is not None and lastPatientId != patientId:
-            # New patient ID so start querying for patient specific data and
-            #   populating patient episode data
-            tempColNames = populatePatientData(patientEpisodeByIndexTime, extractor);
-            #if colNames is None:
-                # First row, print header row
-            #    colNames = tempColNames;
-            #    formatter.formatTuple(colNames);
-            # Print out patient (episode) data (one row per episode)
-            #formatter.formatResultDicts(patientEpisodeByIndexTime.values(), colNames);
-
-        if lastPatientId is None or lastPatientId != patientId:
-            # Prepare to aggregate patient episode record per patient
-            patientEpisodeByIndexTime = dict();
-
-        patientEpisodeByIndexTime[patientEpisode["order_time"]] = patientEpisode;
-        lastPatientId = patientId;
-        prog.update();
-        outFile.flush();
-    # Last Iteration
-    populatePatientData(patientEpisodeByIndexTime, extractor);
-    #formatter.formatResultDicts(patientEpisodeByIndexTime.values(), colNames);
-    prog.printStatus();
-
-    print "\nfeatures: "
-    print extractor.features
-    print extractor.features["timeCycle"].timeAttributes
-
-    # Added for new DataExtractor to work
-    extractor.writeMatrixFile(formatter)
-
-    outFile.close();
-
-    timer = time.time() - timer;
-    print >> sys.stderr, "%.3f seconds to complete" % timer;
-
-def queryPatientEpisodes(outputFile, labProcCode, startDate, endDate, extractor):
-    """ -- Example query elements
-    select cast(pat_id as bigint), pat_enc_csn_id, sop.order_proc_id, proc_code, abnormal_yn, count(result_in_range_yn), count(case result_in_range_yn when 'Y' then 1 else null end), count(ord_num_value)
-    from stride_order_proc as sop inner join stride_order_results sor on sop.order_proc_id = sor.order_proc_id
-    where proc_code = 'LABMETB'
-    and pat_id = '2570758452935'
-    group by pat_id, pat_enc_csn_id, sop.order_proc_id, proc_code, abnormal_yn
-
-    Query out lab procs vs. results when available, and count up cases where results are all 'Y' for result_in_range_yn
-
-    """
-
-    conn = DBUtil.connection();
-    cursor = conn.cursor();
-    try:
-        # First pass query to get the list of patients with lab result times
-        cohortQuery = SQLQuery();
-        cohortQuery.addSelect("cast(pat_id as bigint)");
-        cohortQuery.addSelect("pat_enc_csn_id");
-        cohortQuery.addSelect("sop.order_proc_id");
-        cohortQuery.addSelect("proc_code");
-        cohortQuery.addSelect("order_time");
-        cohortQuery.addSelect("abnormal_yn");
-        cohortQuery.addSelect("count(case result_in_range_yn when 'Y' then 1 else null end)");
-        cohortQuery.addSelect("count(ord_num_value)");
-        cohortQuery.addFrom("stride_order_proc as sop");
-        cohortQuery.addFrom("stride_order_results as sor");
-        cohortQuery.addWhere("sop.order_proc_id = sor.order_proc_id");
-        cohortQuery.addWhereEqual("proc_code", labProcCode);
-        if SAMPLE_PATIENT_IDS is not None:
-            cohortQuery.addWhereIn("pat_id", SAMPLE_PATIENT_IDS);
-        if startDate is not None:
-            cohortQuery.addWhereOp("order_time",">=", startDate);
-        if endDate is not None:
-            cohortQuery.addWhereOp("order_time","<", endDate);
-        cohortQuery.addGroupBy("pat_id, pat_enc_csn_id, sop.order_proc_id, proc_code, order_time, abnormal_yn");
-
-        cohortQueryString =  DBUtil.parameterizeQueryString(cohortQuery)
-
-        cursor.execute(str(cohortQuery), cohortQuery.params);
-
-        # This design requires full list of patient episodes to be in memory for now. Revise later to allow streaming process (maybe with intermediary patient episode temp file).
-        patientEpisodes = list();
-
-        # Collect Build basic patient ID and datetimes
-        row = cursor.fetchone();
-        while row is not None:
-            (patientId, encounterId, orderProcId, procCode, orderTime, abnormalYN, normalResultCount, totalResultCount) = row;
-            patientEpisode = \
-                RowItemModel \
-                (   {   "patient_id": patientId,
-                        "encounter_id": encounterId,
-                        "order_proc_id": orderProcId,
-                        "proc_code": procCode,
-                        "order_time": orderTime,
-                        "abnormal": int(abnormalYN == 'Y'), # Convert to binary 1 vs 0
-                        "result_normal_count": normalResultCount,
-                        "result_total_count": totalResultCount,
-                        "all_result_normal": int(normalResultCount == totalResultCount), # Convert to binary 1 vs 0
-                    }
-                );
-            patientEpisodes.append(patientEpisode);
-
-            row = cursor.fetchone();
-
-        # Drop results as tab-delimited text output
-        formatter = TextResultsFormatter(outputFile);
-        formatter.formatResultDicts(patientEpisodes, addHeaderRow=True);
-
-        return patientEpisodes;
-    finally:
-        cursor.close();
-        conn.close();
-
-def populatePatientData(patientEpisodeByIndexTime, extractor):
-    """Query for specific data relating to the patient designated by the patientEpisodes
-    given and populate those episodes with relative data / features.
-    Return list of output column names to expect based on populated results.
-    """
-    # Look for the patient ID based on the contents of the patientEpisodes
-    patientId = None;
-    labProcCode = None;
-    for patientEpisode in patientEpisodeByIndexTime.itervalues():
-        patientId = patientEpisode["patient_id"];
-        labProcCode = patientEpisode["proc_code"];
-        break;
-
-    tempColNames = \
-        ["patient_id", "encounter_id", "order_proc_id", "proc_code", "order_time",
-            "abnormal", "result_normal_count", "result_total_count", "all_result_normal"];
-
-    if patientId is not None:
-        # Basic Demographics (Age + Gender)
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("Birth",), [patientId], prefix="Birth", daysBins=[])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "Birth", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("Male",), [patientId], prefix="Male", daysBins=[])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "Male", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("Female",), [patientId], prefix="Female",daysBins=[])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "Female", daysBins=[]) );
-
-        """
-        # Race
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceWhiteNonHispanicLatino",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceWhiteNonHispanicLatino", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceAsian",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceAsian", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceWhiteHispanicLatino",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceWhiteHispanicLatino", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceHispanicLatino",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceHispanicLatino", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceUnknown",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceUnknown", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceOther",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceOther", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceBlack",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceBlack", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RacePacificIslander",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RacePacificIslander", daysBins=[]) );
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(("RaceNativeAmerican",), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "RaceNativeAmerican", daysBins=[]) );
-
-        # Extract out lists of ICD9 prefixes per disease category
-        icd9prefixesByDisease = dict();
-        for row in extractor.loadMapData("CharlsonComorbidity-ICD9CM"):
-            (disease, icd9prefix) = (row["charlson"],row["icd9cm"]);
-            if disease not in icd9prefixesByDisease:
-                icd9prefixesByDisease[disease] = list();
-            icd9prefixesByDisease[disease].append("^ICD9."+icd9prefix);
-        for disease, icd9prefixes in icd9prefixesByDisease.iteritems():
-            disease = disease.translate(None," ()-/");   # Strip off punctuation
-            eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(icd9prefixes, [patientId], operator="~*")) );
-            tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, disease) );
-
-        # Extract out lists of treatment team names per care category
-        teamNameByCategory = dict();
-        for row in extractor.loadMapData("TreatmentTeamGroups"):
-            (category, teamName) = (row["team_category"],row["treatment_team"]);
-            if category not in teamNameByCategory:
-                teamNameByCategory[category] = list();
-            teamNameByCategory[category].append(teamName);
-        for category, teamNames in teamNameByCategory.iteritems():
-            eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName(teamNames, [patientId], col="description")) );
-            tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, category) );
-
-        # Key feature should be stats on how often the given lab proc_code has been ordered recently
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByName((labProcCode,), [patientId])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, labProcCode) );
-        """
-
-        """
-        # Look for most recent any Admit Dx to mark time since admission date
-        eventTimes = extractor.parseClinicalItemData_singlePatient( modelListFromTable(extractor.queryClinicalItemsByCategory((ADMIT_DX_CATEGORY_ID,), [patientId],prefix="AdmitDxDate",daysBins=[])) );
-        tempColNames.extend( extractor.addClinicalItemFeatures_singlePatient(eventTimes, patientEpisodeByIndexTime, "AdmitDxDate", daysBins=[]) );
-
-        # Assign time features to reflect time of year (seasonality) and time of day (daily cycle) patterns
-        tempColNames.extend(extractor.addTimeCycleFeatures_singlePatient(patientEpisodeByIndexTime, "order_time", "month"));
-        tempColNames.extend(extractor.addTimeCycleFeatures_singlePatient(patientEpisodeByIndexTime, "order_time", "hour"));
-
-        # Additional key feature is results of previous orders for the same lab test
-        resultBaseNames = set(RESULT_BASE_NAMES);   # Start with common lab results to include regardless
-        for row in extractor.loadMapData("LabResultMap"):
-            if row["proc_code"] == labProcCode and row["base_name"] not in resultBaseNames:
-                resultBaseNames.add(row["base_name"]);
-        labResultTable = extractor.queryLabResults(resultBaseNames, [patientId]);
-        labsByBaseName = extractor.parseLabResultsData_singlePatient(modelListFromTable(labResultTable));
-        for preTimeDelta in LAB_PRE_TIME_DELTAS:
-            tempColNames.extend( extractor.addLabFeatures_singlePatient(patientEpisodeByIndexTime, labsByBaseName, resultBaseNames, preTimeDelta, LAB_POST_TIME_DELTA) );
-        """
-    return tempColNames;
-
-if __name__ == "__main__":
-    main(sys.argv);
diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/preprocessed_zero_sampleData.csv b/scripts/LabTestAnalysis/machine_learning/dataExtraction/preprocessed_zero_sampleData.csv
deleted file mode 100644
index 1228ba7b..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/preprocessed_zero_sampleData.csv
+++ /dev/null
@@ -1,19 +0,0 @@
-Birth.preTimeDays,Male.pre,Female.pre,WBC.count,WBC.countInRange,HCT.count,HCT.countInRange,PLT.count,PLT.countInRange,NA.count,NA.countInRange,K.count,K.countInRange,CO2.count,CO2.countInRange,BUN.count,BUN.countInRange,CR.count,CR.countInRange,TBIL.count,TBIL.countInRange,ALB.count,ALB.countInRange,LAC.count,LAC.countInRange,ESR.count,ESR.countInRange,CRP.count,CRP.countInRange,TNI.count,TNI.countInRange,PHA.count,PHA.countInRange,PO2A.count,PO2A.countInRange,PCO2A.count,PCO2A.countInRange,PHV.count,PHV.countInRange,PO2V.count,PO2V.countInRange,PCO2V.count,PCO2V.countInRange,y,WBC.normalizedDelta,HCT.normalizedDelta,PLT.normalizedDelta,NA.normalizedDelta,K.normalizedDelta,CO2.normalizedDelta,BUN.normalizedDelta,CR.normalizedDelta,TBIL.normalizedDelta,ALB.normalizedDelta,LAC.normalizedDelta,ESR.normalizedDelta,CRP.normalizedDelta,TNI.normalizedDelta,PHA.normalizedDelta,PO2A.normalizedDelta,PCO2A.normalizedDelta,PHV.normalizedDelta,PO2V.normalizedDelta,PCO2V.normalizedDelta
--20601.85903,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
--20066.49097,1,0,3,2,3,0,3,3,2,0,2,0,2,2,2,2,2,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0.38855421675,0.148972602714,-0.229895931904,0.03861003861,0.125,-0.0444444444444,-0.666666666667,-0.363636363636,0.0,0.0,0.0,0.0,0.0,2.07142857143,0.0,0.0,0.0,0.0,0.0,0.0
--20776.46458,1,0,2,2,2,0,2,2,2,1,2,2,2,2,2,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11320754717,-0.14364640884,-0.155009451796,0.00743494423792,0.0,0.11320754717,-0.0740740740741,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
--20556.43611,1,0,2,2,2,0,2,1,2,2,2,0,2,2,2,2,3,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.117647058824,-0.18487394958,-0.370927318296,0.0505415162455,0.0298507462687,0.166666666667,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
--27620.44583,1,0,2,0,2,2,2,2,2,2,2,2,2,2,2,2,2,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-0.0683760683761,-0.021978021978,-0.310112359551,-0.00732600732601,-0.0444444444444,-0.32,-0.285714285714,-0.2,0.4,-0.0869565217391,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_100.csv b/scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_100.csv
deleted file mode 100644
index 1811d013..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_100.csv
+++ /dev/null
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diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_1000.csv b/scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_1000.csv
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index a979f463..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/random_patient_ids_1000.csv
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diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/sampleData.csv b/scripts/LabTestAnalysis/machine_learning/dataExtraction/sampleData.csv
deleted file mode 100644
index 1f5ba369..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/sampleData.csv
+++ /dev/null
@@ -1,19 +0,0 @@
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--1.24413E+13,464816,66355,5/27/12 20:37,,,FALSE,FALSE,-20601.85903,,TRUE,FALSE,-20601.85903,,TRUE,FALSE,,,FALSE,FALSE,1,0,13,13,13,13,0,13,13,13,-0.555555556,-0.555555556,-0.555555556,1,0,22.5,22.5,22.5,22.5,0,22.5,22.5,22.5,-0.555555556,-0.555555556,-0.555555556,1,1,194,194,194,194,0,194,194,194,-0.555555556,-0.555555556,-0.555555556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24413E+13,460081,66513,12/9/10 11:47,,,FALSE,FALSE,-20066.49097,,TRUE,FALSE,-20066.49097,,TRUE,FALSE,,,FALSE,FALSE,3,2,9.5,14,9.7,11.06666667,2.07578633,9.7,14,14,-0.577083333,-0.094444444,-0.094444444,3,0,18.1,21.6,18.7,19.46666667,1.528252452,18.7,21.6,21.6,-0.577083333,-0.094444444,-0.094444444,3,3,301,382,374,352.3333333,36.4447832,382,301,301,-0.577083333,-0.094444444,-0.094444444,2,0,127,132,129.5,129.5,2.5,127,132,132,-0.581944444,-0.085416667,-0.085416667,2,0,3,3.4,3.2,3.2,0.2,3,3.4,3.4,-0.581944444,-0.085416667,-0.085416667,2,2,22,23,22.5,22.5,0.5,23,22,22,-0.581944444,-0.085416667,-0.085416667,2,2,8,16,12,12,4,16,8,8,-0.581944444,-0.085416667,-0.085416667,2,0,0.9,1.3,1.1,1.1,0.2,1.3,0.9,0.9,-0.581944444,-0.085416667,-0.085416667,0,0,,,,,,,,,,,,1,0,2.7,2.7,2.7,2.7,0,2.7,2.7,2.7,-0.010416667,-0.010416667,-0.010416667,1,0,4.02,4.02,4.02,4.02,0,4.02,4.02,4.02,-0.607638889,-0.607638889,-0.607638889,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,4,0,0.16,4.8,2,2.24,2.047876949,0.16,4.8,4.8,-0.595833333,-0.084722222,-0.084722222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,1,7.39,7.39,7.39,7.39,0,7.39,7.39,7.39,-0.607638889,-0.607638889,-0.607638889,1,1,44,44,44,44,0,44,44,44,-0.607638889,-0.607638889,-0.607638889,1,0,39.9,39.9,39.9,39.9,0,39.9,39.9,39.9,-0.607638889,-0.607638889,-0.607638889
--1.24413E+13,383489,66513,11/18/12 11:09,,,FALSE,FALSE,-20776.46458,,TRUE,FALSE,-20776.46458,,TRUE,FALSE,,,FALSE,FALSE,2,2,7.5,8.4,7.95,7.95,0.45,7.5,8.4,8.4,-0.623611111,-0.266666667,-0.266666667,2,0,33.6,38.8,36.2,36.2,2.6,38.8,33.6,33.6,-0.623611111,-0.266666667,-0.266666667,2,2,244,285,264.5,264.5,20.5,285,244,244,-0.623611111,-0.266666667,-0.266666667,2,1,134,135,134.5,134.5,0.5,134,135,135,-0.784722222,-0.249305556,-0.249305556,2,2,3.8,3.8,3.8,3.8,0,3.8,3.8,3.8,-0.784722222,-0.249305556,-0.249305556,2,2,25,28,26.5,26.5,1.5,25,28,28,-0.784722222,-0.249305556,-0.249305556,2,2,13,14,13.5,13.5,0.5,14,13,13,-0.784722222,-0.249305556,-0.249305556,2,0,1,1,1,1,0,1,1,1,-0.784722222,-0.249305556,-0.249305556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,-0.784722222,-0.784722222,-0.784722222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24413E+13,398744,66355,4/12/12 10:28,,,FALSE,FALSE,-20556.43611,,TRUE,FALSE,-20556.43611,,TRUE,FALSE,,,FALSE,FALSE,2,2,8.8,9.9,9.35,9.35,0.55,9.9,8.8,8.8,-0.529166667,-0.274305556,-0.274305556,2,0,27,32.5,29.75,29.75,2.75,32.5,27,27,-0.529166667,-0.274305556,-0.274305556,2,1,325,473,399,399,74,473,325,325,-0.529166667,-0.274305556,-0.274305556,2,2,135,142,138.5,138.5,3.5,135,142,142,-0.533333333,-0.252083333,-0.252083333,2,0,3.3,3.4,3.35,3.35,0.05,3.3,3.4,3.4,-0.533333333,-0.252083333,-0.252083333,2,2,22,26,24,24,2,22,26,26,-0.533333333,-0.252083333,-0.252083333,2,2,12,12,12,12,0,12,12,12,-0.533333333,-0.252083333,-0.252083333,3,0,0.9,0.9,0.9,0.9,0,0.9,0.9,0.9,-0.533333333,-0.252083333,-0.252083333,1,0,0.7,0.7,0.7,0.7,0,0.7,0.7,0.7,-0.533333333,-0.533333333,-0.533333333,1,1,4.3,4.3,4.3,4.3,0,4.3,4.3,4.3,-0.533333333,-0.533333333,-0.533333333,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,-0.550694444,-0.550694444,-0.550694444,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24398E+13,416592,66513,8/15/13 10:42,,,FALSE,FALSE,-27620.44583,,TRUE,FALSE,-27620.44583,,TRUE,FALSE,,,FALSE,FALSE,2,0,11.3,12.1,11.7,11.7,0.4,12.1,11.3,11.3,-0.545138889,-0.142361111,-0.142361111,2,2,40.5,41.4,40.95,40.95,0.45,41.4,40.5,40.5,-0.545138889,-0.142361111,-0.142361111,2,2,188,257,222.5,222.5,34.5,257,188,188,-0.545138889,-0.142361111,-0.142361111,2,2,136,137,136.5,136.5,0.5,137,136,136,-0.533333333,-0.088194444,-0.088194444,2,2,4.4,4.6,4.5,4.5,0.1,4.6,4.4,4.4,-0.533333333,-0.088194444,-0.088194444,2,2,21,29,25,25,4,29,21,21,-0.533333333,-0.088194444,-0.088194444,2,2,12,16,14,14,2,16,12,12,-0.533333333,-0.088194444,-0.088194444,2,0,0.9,1.1,1,1,0.1,1.1,0.9,0.9,-0.533333333,-0.088194444,-0.088194444,2,0,0.2,0.3,0.25,0.25,0.05,0.2,0.3,0.3,-0.533333333,-0.088194444,-0.088194444,2,1,3.3,3.6,3.45,3.45,0.15,3.6,3.3,3.3,-0.533333333,-0.088194444,-0.088194444,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.2439E+13,457112,66355,2/4/13 22:47,,,FALSE,FALSE,-23776.94931,,TRUE,FALSE,,,FALSE,FALSE,-23776.94931,,TRUE,FALSE,2,2,9,9.4,9.2,9.2,0.2,9,9.4,9.4,-0.397916667,-0.13125,-0.13125,2,0,33.4,33.5,33.45,33.45,0.05,33.4,33.5,33.5,-0.397916667,-0.13125,-0.13125,2,2,279,322,300.5,300.5,21.5,322,279,279,-0.397916667,-0.13125,-0.13125,2,1,134,136,135,135,1,136,134,134,-0.388888889,-0.105555556,-0.105555556,2,1,3.3,3.5,3.4,3.4,0.1,3.3,3.5,3.5,-0.388888889,-0.105555556,-0.105555556,2,2,26,26,26,26,0,26,26,26,-0.388888889,-0.105555556,-0.105555556,2,0,84,87,85.5,85.5,1.5,84,87,87,-0.388888889,-0.105555556,-0.105555556,2,0,9.5,10.2,9.85,9.85,0.35,9.5,10.2,10.2,-0.388888889,-0.105555556,-0.105555556,0,0,,,,,,,,,,,,2,0,2.9,3.1,3,3,0.1,3.1,2.9,2.9,-0.388888889,-0.105555556,-0.105555556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24369E+13,431798,66355,5/5/13 10:56,,,FALSE,FALSE,-26422.45556,,TRUE,FALSE,,,FALSE,FALSE,-26422.45556,,TRUE,FALSE,2,0,1.7,1.9,1.8,1.8,0.1,1.7,1.9,1.9,-0.679166667,-0.144444444,-0.144444444,2,0,24.1,25.5,24.8,24.8,0.7,25.5,24.1,24.1,-0.679166667,-0.144444444,-0.144444444,2,0,40,43,41.5,41.5,1.5,43,40,40,-0.679166667,-0.144444444,-0.144444444,2,1,143,146,144.5,144.5,1.5,143,146,146,-0.901388889,-0.176388889,-0.176388889,2,2,4,4.4,4.2,4.2,0.2,4,4.4,4.4,-0.901388889,-0.176388889,-0.176388889,2,2,27,29,28,28,1,27,29,29,-0.901388889,-0.176388889,-0.176388889,2,2,17,19,18,18,1,17,19,19,-0.901388889,-0.176388889,-0.176388889,2,0,0.7,0.7,0.7,0.7,0,0.7,0.7,0.7,-0.901388889,-0.176388889,-0.176388889,0,0,,,,,,,,,,,,2,1,3.1,3.8,3.45,3.45,0.35,3.8,3.1,3.1,-0.901388889,-0.176388889,-0.176388889,1,0,0.7,0.7,0.7,0.7,0,0.7,0.7,0.7,-0.939583333,-0.939583333,-0.939583333,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,1,7.37,7.37,7.37,7.37,0,7.37,7.37,7.37,-0.939583333,-0.939583333,-0.939583333,1,0,24,24,24,24,0,24,24,24,-0.939583333,-0.939583333,-0.939583333,1,0,39.1,39.1,39.1,39.1,0,39.1,39.1,39.1,-0.939583333,-0.939583333,-0.939583333
--1.24369E+13,431798,66355,5/5/13 9:57,,,FALSE,FALSE,-26422.41458,,TRUE,FALSE,,,FALSE,FALSE,-26422.41458,,TRUE,FALSE,2,0,1.7,1.9,1.8,1.8,0.1,1.7,1.9,1.9,-0.638194444,-0.103472222,-0.103472222,2,0,24.1,25.5,24.8,24.8,0.7,25.5,24.1,24.1,-0.638194444,-0.103472222,-0.103472222,2,0,40,43,41.5,41.5,1.5,43,40,40,-0.638194444,-0.103472222,-0.103472222,2,1,143,146,144.5,144.5,1.5,143,146,146,-0.860416667,-0.135416667,-0.135416667,2,2,4,4.4,4.2,4.2,0.2,4,4.4,4.4,-0.860416667,-0.135416667,-0.135416667,2,2,27,29,28,28,1,27,29,29,-0.860416667,-0.135416667,-0.135416667,2,2,17,19,18,18,1,17,19,19,-0.860416667,-0.135416667,-0.135416667,2,0,0.7,0.7,0.7,0.7,0,0.7,0.7,0.7,-0.860416667,-0.135416667,-0.135416667,0,0,,,,,,,,,,,,2,1,3.1,3.8,3.45,3.45,0.35,3.8,3.1,3.1,-0.860416667,-0.135416667,-0.135416667,1,0,0.7,0.7,0.7,0.7,0,0.7,0.7,0.7,-0.898611111,-0.898611111,-0.898611111,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,1,7.37,7.37,7.37,7.37,0,7.37,7.37,7.37,-0.898611111,-0.898611111,-0.898611111,1,0,24,24,24,24,0,24,24,24,-0.898611111,-0.898611111,-0.898611111,1,0,39.1,39.1,39.1,39.1,0,39.1,39.1,39.1,-0.898611111,-0.898611111,-0.898611111
--1.24358E+13,413730,66355,10/11/13 12:38,,,FALSE,FALSE,-12337.52639,,TRUE,FALSE,,,FALSE,FALSE,-12337.52639,,TRUE,FALSE,2,2,4.6,5.5,5.05,5.05,0.45,5.5,4.6,4.6,-0.742361111,-0.221527778,-0.221527778,2,1,31.8,36.2,34,34,2.2,36.2,31.8,31.8,-0.742361111,-0.221527778,-0.221527778,2,2,187,217,202,202,15,217,187,187,-0.742361111,-0.221527778,-0.221527778,2,2,138,139,138.5,138.5,0.5,138,139,139,-0.731944444,-0.186111111,-0.186111111,2,2,3.9,4.2,4.05,4.05,0.15,3.9,4.2,4.2,-0.731944444,-0.186111111,-0.186111111,2,2,26,27,26.5,26.5,0.5,27,26,26,-0.731944444,-0.186111111,-0.186111111,2,2,5,7,6,6,1,7,5,5,-0.731944444,-0.186111111,-0.186111111,2,0,1,1.2,1.1,1.1,0.1,1.2,1,1,-0.731944444,-0.186111111,-0.186111111,2,0,0.2,0.3,0.25,0.25,0.05,0.2,0.3,0.3,-0.731944444,-0.186111111,-0.186111111,2,1,3.2,3.7,3.45,3.45,0.25,3.7,3.2,3.2,-0.731944444,-0.186111111,-0.186111111,0,0,,,,,,,,,,,,1,1,9,9,9,9,0,9,9,9,-0.669444444,-0.669444444,-0.669444444,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24346E+13,407358,66355,11/21/10 22:24,,475.0666667,FALSE,TRUE,-27353.93333,,TRUE,FALSE,-27353.93333,,TRUE,FALSE,,,FALSE,FALSE,1,0,16.8,16.8,16.8,16.8,0,16.8,16.8,16.8,-0.171527778,-0.171527778,-0.171527778,1,1,40.8,40.8,40.8,40.8,0,40.8,40.8,40.8,-0.171527778,-0.171527778,-0.171527778,1,1,371,371,371,371,0,371,371,371,-0.171527778,-0.171527778,-0.171527778,1,0,124,124,124,124,0,124,124,124,-0.154861111,-0.154861111,-0.154861111,1,0,3.3,3.3,3.3,3.3,0,3.3,3.3,3.3,-0.154861111,-0.154861111,-0.154861111,1,1,27,27,27,27,0,27,27,27,-0.154861111,-0.154861111,-0.154861111,1,1,12,12,12,12,0,12,12,12,-0.154861111,-0.154861111,-0.154861111,1,0,0.9,0.9,0.9,0.9,0,0.9,0.9,0.9,-0.154861111,-0.154861111,-0.154861111,1,0,0.5,0.5,0.5,0.5,0,0.5,0.5,0.5,-0.154861111,-0.154861111,-0.154861111,1,0,3.4,3.4,3.4,3.4,0,3.4,3.4,3.4,-0.154861111,-0.154861111,-0.154861111,1,0,1.13,1.13,1.13,1.13,0,1.13,1.13,1.13,-0.179861111,-0.179861111,-0.179861111,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,-0.165972222,-0.165972222,-0.165972222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,7.46,7.46,7.46,7.46,0,7.46,7.46,7.46,-0.179861111,-0.179861111,-0.179861111,1,0,21,21,21,21,0,21,21,21,-0.179861111,-0.179861111,-0.179861111,1,1,42.2,42.2,42.2,42.2,0,42.2,42.2,42.2,-0.179861111,-0.179861111,-0.179861111
--1.24325E+13,454507,66355,6/27/10 10:31,,,FALSE,FALSE,-18440.43819,,TRUE,FALSE,-18440.43819,,TRUE,FALSE,,,FALSE,FALSE,3,3,6.2,9.4,6.7,7.433333333,1.405544576,9.4,6.2,6.2,-0.906944444,-0.293055556,-0.293055556,4,3,39.8,51,43.65,44.525,4.306608294,45.7,39.8,39.8,-0.906944444,-0.293055556,-0.293055556,3,3,181,226,207,204.6666667,18.44511378,226,181,181,-0.906944444,-0.293055556,-0.293055556,7,4,130,139,136,135.1428571,3.522522287,131,139,139,-0.872916667,-0.004166667,-0.004166667,7,7,3.5,5.1,4.1,4.242857143,0.531459293,5.1,3.9,3.9,-0.872916667,-0.004166667,-0.004166667,6,3,14,23,19,18.66666667,3.036811193,14,23,23,-0.872916667,-0.004166667,-0.004166667,6,6,9,14,11,11.33333333,1.699673171,14,9,9,-0.872916667,-0.004166667,-0.004166667,6,0,0.7,1,0.9,0.883333333,0.121335165,1,0.7,0.7,-0.872916667,-0.004166667,-0.004166667,2,0,0.6,0.8,0.7,0.7,0.1,0.8,0.6,0.6,-0.872916667,-0.271527778,-0.271527778,2,0,2.7,3.2,2.95,2.95,0.25,3.2,2.7,2.7,-0.872916667,-0.271527778,-0.271527778,5,0,1.1,1.5,1.29,1.278,0.159799875,1.29,1.1,1.1,-0.840277778,-0.038194444,-0.038194444,1,0,28,28,28,28,0,28,28,28,-0.849305556,-0.849305556,-0.849305556,1,0,5.7,5.7,5.7,5.7,0,5.7,5.7,5.7,-0.883333333,-0.883333333,-0.883333333,1,0,0,0,0,0,0,0,0,0,-0.870138889,-0.870138889,-0.870138889,2,0,7.32,7.33,7.325,7.325,0.005,7.33,7.32,7.32,-0.579861111,-0.302777778,-0.302777778,2,1,76,98,87,87,11,76,98,98,-0.579861111,-0.302777778,-0.302777778,2,2,37.4,41,39.2,39.2,1.8,37.4,41,41,-0.579861111,-0.302777778,-0.302777778,1,1,7.34,7.34,7.34,7.34,0,7.34,7.34,7.34,-0.840277778,-0.840277778,-0.840277778,1,0,86,86,86,86,0,86,86,86,-0.840277778,-0.840277778,-0.840277778,1,0,25.2,25.2,25.2,25.2,0,25.2,25.2,25.2,-0.840277778,-0.840277778,-0.840277778
--1.24325E+13,454507,66355,6/27/10 16:17,,,FALSE,FALSE,-18440.67847,,TRUE,FALSE,-18440.67847,,TRUE,FALSE,,,FALSE,FALSE,2,2,6.2,6.7,6.45,6.45,0.25,6.7,6.2,6.2,-0.813888889,-0.533333333,-0.533333333,2,1,39.8,41.6,40.7,40.7,0.9,41.6,39.8,39.8,-0.813888889,-0.533333333,-0.533333333,2,2,181,207,194,194,13,207,181,181,-0.813888889,-0.533333333,-0.533333333,6,5,133,139,138,137.1666667,2.114762923,133,138,138,-0.997222222,-0.013194444,-0.013194444,6,6,3.5,4.3,4,3.966666667,0.249443826,4.3,4.1,4.1,-0.997222222,-0.013194444,-0.013194444,6,4,16,23,20.5,19.83333333,2.266911751,16,21,21,-0.997222222,-0.013194444,-0.013194444,6,6,8,13,10.5,10.33333333,1.598610508,13,8,8,-0.997222222,-0.013194444,-0.013194444,6,0,0.7,1,0.8,0.833333333,0.124721913,1,0.7,0.7,-0.997222222,-0.013194444,-0.013194444,1,0,0.6,0.6,0.6,0.6,0,0.6,0.6,0.6,-0.511805556,-0.511805556,-0.511805556,1,0,2.7,2.7,2.7,2.7,0,2.7,2.7,2.7,-0.511805556,-0.511805556,-0.511805556,4,0,1.1,1.5,1.25,1.275,0.178535711,1.5,1.1,1.1,-0.803472222,-0.278472222,-0.278472222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,2,0,7.32,7.33,7.325,7.325,0.005,7.33,7.32,7.32,-0.820138889,-0.543055556,-0.543055556,2,1,76,98,87,87,11,76,98,98,-0.820138889,-0.543055556,-0.543055556,2,2,37.4,41,39.2,39.2,1.8,37.4,41,41,-0.820138889,-0.543055556,-0.543055556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24315E+13,425565,66355,2/12/13 11:29,,,FALSE,FALSE,-8808.478472,,TRUE,FALSE,-8808.478472,,TRUE,FALSE,,,FALSE,FALSE,1,0,12.1,12.1,12.1,12.1,0,12.1,12.1,12.1,-0.88125,-0.88125,-0.88125,1,0,32.6,32.6,32.6,32.6,0,32.6,32.6,32.6,-0.88125,-0.88125,-0.88125,1,1,397,397,397,397,0,397,397,397,-0.88125,-0.88125,-0.88125,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24285E+13,360335,66350,6/18/10 11:29,,7.521527778,FALSE,TRUE,-27562.47847,,TRUE,FALSE,-27562.47847,,TRUE,FALSE,,,FALSE,FALSE,1,0,13.8,13.8,13.8,13.8,0,13.8,13.8,13.8,-0.905555556,-0.905555556,-0.905555556,1,0,37.8,37.8,37.8,37.8,0,37.8,37.8,37.8,-0.905555556,-0.905555556,-0.905555556,2,2,232,293,262.5,262.5,30.5,293,232,232,-0.905555556,-0.236805556,-0.236805556,3,0,149,150,150,149.6666667,0.471404521,150,149,149,-0.875694444,-0.243055556,-0.243055556,3,3,3.7,4.1,3.9,3.9,0.163299316,4.1,3.7,3.7,-0.875694444,-0.243055556,-0.243055556,3,3,20,22,21,21,0.816496581,21,20,20,-0.875694444,-0.243055556,-0.243055556,3,0,41,41,41,41,0,41,41,41,-0.875694444,-0.243055556,-0.243055556,4,0,1.6,1.7,1.65,1.65,0.05,1.6,1.6,1.6,-0.910416667,-0.243055556,-0.243055556,1,0,1,1,1,1,0,1,1,1,-0.875694444,-0.875694444,-0.875694444,1,0,2.2,2.2,2.2,2.2,0,2.2,2.2,2.2,-0.875694444,-0.875694444,-0.875694444,4,0,2.6,3.88,3.535,3.3875,0.478193214,3.88,2.6,2.6,-0.905555556,-0.527083333,-0.527083333,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0.12,0.12,0.12,0.12,0,0.12,0.12,0.12,-0.884722222,-0.884722222,-0.884722222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,2,2,7.38,7.4,7.39,7.39,0.01,7.38,7.4,7.4,-0.905555556,-0.870138889,-0.870138889,2,0,17,28,22.5,22.5,5.5,17,28,28,-0.905555556,-0.870138889,-0.870138889,2,0,36.9,38.8,37.85,37.85,0.95,38.8,36.9,36.9,-0.905555556,-0.870138889,-0.870138889
--1.24275E+13,434544,66355,3/27/13 14:41,,,FALSE,FALSE,-27479.61181,,TRUE,FALSE,-27479.61181,,TRUE,FALSE,,,FALSE,FALSE,2,2,6.8,7.2,7,7,0.2,6.8,7.2,7.2,-0.802777778,-0.29375,-0.29375,2,2,45.6,46.4,46,46,0.4,45.6,46.4,46.4,-0.802777778,-0.29375,-0.29375,2,2,203,206,204.5,204.5,1.5,203,206,206,-0.802777778,-0.29375,-0.29375,2,2,136,138,137,137,1,138,136,136,-0.788194444,-0.270833333,-0.270833333,2,1,3.4,3.6,3.5,3.5,0.1,3.6,3.4,3.4,-0.788194444,-0.270833333,-0.270833333,2,2,26,27,26.5,26.5,0.5,27,26,26,-0.788194444,-0.270833333,-0.270833333,2,2,13,14,13.5,13.5,0.5,14,13,13,-0.788194444,-0.270833333,-0.270833333,3,0,1,1.1,1.1,1.066666667,0.047140452,1,1.1,1.1,-0.80625,-0.270833333,-0.270833333,2,0,0.6,1,0.8,0.8,0.2,0.6,1,1,-0.788194444,-0.270833333,-0.270833333,2,2,3.8,3.9,3.85,3.85,0.05,3.8,3.9,3.9,-0.788194444,-0.270833333,-0.270833333,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0.1,0.1,0.1,0.1,0,0.1,0.1,0.1,-0.486805556,-0.486805556,-0.486805556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24257E+13,399441,66355,4/3/11 9:57,,,FALSE,FALSE,-19815.41458,,TRUE,FALSE,,,FALSE,FALSE,-19815.41458,,TRUE,FALSE,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.2425E+13,361859,66355,3/13/11 14:10,,,FALSE,FALSE,-16142.59028,,TRUE,FALSE,-16142.59028,,TRUE,FALSE,,,FALSE,FALSE,1,1,5.3,5.3,5.3,5.3,0,5.3,5.3,5.3,-0.214583333,-0.214583333,-0.214583333,1,1,43.9,43.9,43.9,43.9,0,43.9,43.9,43.9,-0.214583333,-0.214583333,-0.214583333,1,1,224,224,224,224,0,224,224,224,-0.214583333,-0.214583333,-0.214583333,1,1,143,143,143,143,0,143,143,143,-0.175,-0.175,-0.175,1,1,4.1,4.1,4.1,4.1,0,4.1,4.1,4.1,-0.175,-0.175,-0.175,1,0,33,33,33,33,0,33,33,33,-0.175,-0.175,-0.175,1,1,14,14,14,14,0,14,14,14,-0.175,-0.175,-0.175,1,0,1.1,1.1,1.1,1.1,0,1.1,1.1,1.1,-0.175,-0.175,-0.175,1,0,0.5,0.5,0.5,0.5,0,0.5,0.5,0.5,-0.175,-0.175,-0.175,1,1,4.5,4.5,4.5,4.5,0,4.5,4.5,4.5,-0.175,-0.175,-0.175,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24248E+13,434276,66355,9/16/13 10:55,,9.545138889,FALSE,TRUE,-32766.45486,,TRUE,FALSE,,,FALSE,FALSE,-32766.45486,,TRUE,FALSE,2,0,14.2,14.6,14.4,14.4,0.2,14.6,14.2,14.2,-0.561805556,-0.211111111,-0.211111111,3,0,27,32.4,27.2,28.86666667,2.499777768,32.4,27,27,-0.561805556,-0.031944444,-0.031944444,2,1,148,158,153,153,5,158,148,148,-0.561805556,-0.211111111,-0.211111111,3,2,144,146,145,145,0.816496581,146,145,145,-0.568055556,-0.007638889,-0.007638889,3,2,3.4,4.1,3.6,3.7,0.294392029,4.1,3.6,3.6,-0.568055556,-0.007638889,-0.007638889,2,1,30,34,32,32,2,34,30,30,-0.568055556,-0.007638889,-0.007638889,2,0,88,102,95,95,7,102,88,88,-0.568055556,-0.007638889,-0.007638889,2,0,3,3.7,3.35,3.35,0.35,3.7,3,3,-0.568055556,-0.007638889,-0.007638889,1,0,0.4,0.4,0.4,0.4,0,0.4,0.4,0.4,-0.568055556,-0.568055556,-0.568055556,1,0,1.8,1.8,1.8,1.8,0,1.8,1.8,1.8,-0.568055556,-0.568055556,-0.568055556,2,0,2.67,3.06,2.865,2.865,0.195,2.67,3.06,3.06,-0.531944444,-0.488194444,-0.488194444,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0.05,0.05,0.05,0.05,0,0.05,0.05,0.05,-0.582638889,-0.582638889,-0.582638889,2,2,7.43,7.44,7.435,7.435,0.005,7.43,7.44,7.44,-0.281944444,-0.029166667,-0.029166667,2,1,101,107,104,104,3,101,107,107,-0.281944444,-0.029166667,-0.029166667,2,2,42.3,43.5,42.9,42.9,0.6,43.5,42.3,42.3,-0.281944444,-0.029166667,-0.029166667,3,2,7.38,7.45,7.41,7.413333333,0.028674418,7.41,7.38,7.38,-0.531944444,-0.031944444,-0.031944444,3,0,28,32.7,30,30.23333333,1.925847577,28,32.7,32.7,-0.531944444,-0.031944444,-0.031944444,3,2,47.9,53.8,49,50.23333333,2.561683474,53.8,47.9,47.9,-0.531944444,-0.031944444,-0.031944444
\ No newline at end of file
diff --git a/scripts/LabTestAnalysis/machine_learning/dataExtraction/testNewExtractorOutput.py b/scripts/LabTestAnalysis/machine_learning/dataExtraction/testNewExtractorOutput.py
deleted file mode 100644
index 4b2f0c70..00000000
--- a/scripts/LabTestAnalysis/machine_learning/dataExtraction/testNewExtractorOutput.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import sys, os, gzip
-from cStringIO import StringIO
-from datetime import datetime, timedelta;
-import unittest
-
-from medinfo.dataconversion.test.Const import RUNNER_VERBOSITY;
-
-from medinfo.db.test.Util import DBTestCase;
-
-from medinfo.db import DBUtil
-
-class TestOutput(DBTestCase):
-    def setUp(self):
-        """Prepare state for test cases"""
-        DBTestCase.setUp(self);
-
-    def test_newExtractorOutput(self):
-        firstFile = gzip.open("labFeatureMatrix.LABFER.2013-01-01.2013-01-02.tab.gz",'r').read(12000)
-        secondFile = gzip.open("labFeatureMatrix.newDataExtractor.LABFER.2013-01-01.2013-01-02.tab.gz",'r').read(12000)
-        self.assertEqualFile(StringIO(firstFile), StringIO(secondFile), whitespace=False)
-
-def suite():
-    """Returns the suite of tests to run for this test class / module.
-    Use unittest.makeSuite methods which simply extracts all of the
-    methods for the given class whose name starts with "test"
-    """
-    suite = unittest.TestSuite();
-    suite.addTest(TestOutput("test_newExtractorOutput"))
-    return suite
-
-if __name__ == "__main__":
-    unittest.TextTestRunner(verbosity=RUNNER_VERBOSITY).run(suite())
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABFER_outputs.csv b/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABFER_outputs.csv
deleted file mode 100644
index 6a709109..00000000
--- a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABFER_outputs.csv
+++ /dev/null
@@ -1,1044 +0,0 @@
-patient_id,encounter_id,order_proc_id,proc_code,order_time,abnormal,result_normal_count,result_total_count,all_result_normal,predictedTest.decision_tree_max_depth=5,predictedTest.decision_tree_max_depth=20,predictedTest.decision_tree_max_depth=50,"predictedTest.random_forest_n_estimators=5,_max_depth=5","predictedTest.random_forest_n_estimators=10,_max_depth=5","predictedTest.random_forest_n_estimators=30,_max_depth=5","predictedTest.random_forest_n_estimators=5,_max_depth=10","predictedTest.random_forest_n_estimators=10,_max_depth=10","predictedTest.random_forest_n_estimators=30,_max_depth=10","predictedTest.random_forest_n_estimators=5,_max_depth=15","predictedTest.random_forest_n_estimators=10,_max_depth=15","predictedTest.random_forest_n_estimators=30,_max_depth=15","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=10,_learning_rate=1","predictedTest.ada_boost_n_estimators=50,_learning_rate=1","predictedTest.ada_boost_n_estimators=100,_learning_rate=1",predictedTest.logistic_regression_c=0.1,predictedTest.logistic_regression_c=1.0,predictedTest.logistic_regression_c=10.0,predictedTest.gaussian_naive_bayes
--7835708959878,416406,31117650,LABFER,2012-12-01 15:00:00,1,0,1,0,0.477840451249,1.0,1.0,0.407650675424,0.335003089853,0.282392959134,0.256193197197,0.327797688271,0.31045749543,0.394431372549,0.106197948844,0.27767105716,0.474085517325,0.462338008602,0.434939683951,0.421077804787,0.307870273711,0.396649808231,0.0262317995372,0.325683625914,0.409067614517,0.0128809904849,0.0026857598097,0.000392874508319,3.04257553257e-70
-1685240828676,417768,37952391,LABFER,2013-09-20 13:40:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.263221027416,0.22403416511,0.235508273885,0.362510654057,0.306306765159,0.213812580416,0.214285714286,0.246408986489,0.229675213847,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.449928926468,0.471825433733,0.468417916605,0.495707119441,0.496623015492,0.208934171349,0.204478043464,0.210114864476,1.0
-11168445619411,445083,18608139,LABFER,2011-07-13 22:01:00,1,0,1,0,0.477840451249,1.0,1.0,0.559618669265,0.518108295477,0.461267686232,0.619112582692,0.667841743657,0.530744944891,0.504079254079,0.5,0.491375090219,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.500687571354,0.509322489469,0.499488366221,0.500082567289,0.630608631588,0.642794466642,0.661948875008,1.0
--11268442679132,402905,40266029,LABFER,2013-12-09 13:26:00,1,0,1,0,0.218911917098,0.0,1.0,0.19656860272,0.294786830183,0.254214699707,0.051051740721,0.237116132521,0.200528298491,0.2,0.183127183091,0.146059800291,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.449119226635,0.470709243907,0.468417916605,0.501022789406,0.499517809899,0.215041715737,0.216927734439,0.20947529908,1.0
-11053450943591,399131,29962371,LABFER,2012-10-26 17:44:00,1,0,1,0,0.634686346863,1.0,1.0,0.544348835095,0.541837143126,0.560264911899,0.596918473394,0.763623050789,0.712889472894,0.316346153846,0.470212765957,0.642094125199,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.51167658993,0.511237309215,0.521811420026,0.502531475048,0.502264374419,0.490069849037,0.478135859392,0.47451883134,1.0
-4941954307114,417625,33473124,LABFER,2013-03-22 03:44:00,0,1,1,1,0.634686346863,0.0,0.0,0.620755809273,0.57925354694,0.513259003922,0.620511724345,0.566795623485,0.60949556429,0.917657456773,0.303270280731,0.502098723232,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492806548043,0.497525739331,0.521811420026,0.49952426088,0.498408480028,0.544899124528,0.596508496498,0.635626341313,1.0
--7724945494737,377260,15796079,LABFER,2011-03-13 08:01:00,1,0,1,0,0.477840451249,0.0,0.0,0.459705424403,0.458952557221,0.373169871489,0.294689390742,0.461586256163,0.421310178264,0.609535428729,0.474879807692,0.348377858497,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.47684399649,0.486937485854,0.486583208859,0.494639216511,0.4966157915,0.339808237272,0.344968288551,0.34991906113,1.0
-11805916653237,432316,32786289,LABFER,2013-02-18 17:18:00,0,1,1,1,0.634686346863,0.936046511628,0.0,0.58802033268,0.636078899691,0.60254144382,0.675460395811,0.581630280499,0.636874075851,0.392836801752,0.876299902966,0.591819444958,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516068956906,0.521811420026,0.505047560758,0.503342371875,0.775968803155,0.802506199367,0.839570868252,1.0
--7743025940003,404059,8853171,LABFER,2010-05-18 11:31:00,1,0,1,0,0.218911917098,0.0,1.0,0.319734926228,0.293559334274,0.320974187345,0.352386592048,0.135067368254,0.31851051866,0.254545454545,0.301770200099,0.213439037843,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.456299087725,0.479793289318,0.479322944411,0.498537257255,0.499800507584,0.388770978523,0.396675039691,0.434679043778,1.0
--4460036218843,363203,20760769,LABFER,2011-09-27 05:34:00,1,0,1,0,0.644444444444,1.0,1.0,0.390889926679,0.535160725647,0.471189958334,0.805889884763,0.558762087361,0.464408708052,0.545454545455,0.452741532977,0.407861050986,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.481612861808,0.487331037035,0.485779235087,0.50038683504,0.498706298838,0.371074280919,0.357244891982,0.349067129278,1.0
-1690471064196,446426,9363118,LABFER,2010-06-16 06:11:00,1,0,1,0,0.218911917098,1.0,0.0,0.344430485551,0.381481871819,0.411531278342,0.3017429403,0.41604332868,0.392258130426,0.491132505545,0.430902258531,0.364703558832,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474148767005,0.491258138317,0.479322944411,0.499696792432,0.500009628879,0.432565414429,0.428277118926,0.424176669164,1.0
--2320465826260,417103,28025575,LABFER,2012-08-02 08:27:00,1,0,1,0,0.218911917098,0.0,0.0,0.217658516895,0.129771083826,0.165137906222,0.0889495793663,0.115625353178,0.115554233633,0.0387402933563,0.251824336621,0.0779470028074,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.416458052859,0.450634168368,0.435412626422,0.490297859209,0.495362890772,0.0873913938512,0.0938763832013,0.0908846591343,7.98188542295e-35
--9190714365122,374866,26400855,LABFER,2012-05-27 22:00:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.581596336541,0.587053019826,0.455671843747,0.581335040516,0.639220611283,0.478993706729,0.615265693127,0.50933664683,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504667153246,0.506101880706,0.499081689524,0.501293513586,0.501502369976,0.713920833298,0.704551038704,0.70033510811,1.0
-7974873387622,382525,17220772,LABFER,2011-05-20 22:01:00,1,0,1,0,0.218911917098,0.0,1.0,0.217658516895,0.236773818403,0.289580649798,0.0696054304917,0.248731345269,0.218540770433,0.0158014671453,0.0875993310414,0.152377086931,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.451427981118,0.476994122515,0.469108659157,0.49699729239,0.497547774359,0.579232389769,0.596513788131,0.590085002842,1.0
-6928149359872,435623,30000166,LABFER,2012-10-29 03:59:00,1,0,1,0,0.477840451249,1.0,1.0,0.492049174403,0.501601638815,0.482870920861,0.521869813536,0.386129496079,0.522064191195,0.582008547009,0.367925904266,0.374026775656,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.482245062741,0.488018172382,0.465615864198,0.492308451448,0.495764042752,0.463760353659,0.48240747523,0.485633158078,1.0
-10541535516763,421151,15460359,LABFER,2011-03-13 19:13:00,1,0,1,0,0.634686346863,0.963636363636,0.0,0.500103829763,0.480996780217,0.584227644163,0.510653404681,0.438978541521,0.539316617311,0.419317499317,0.615714285714,0.472466973075,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.504407661943,0.510888218981,0.496170514259,0.498996196763,0.480899690053,0.471359185136,0.472772481678,1.89156034296e-06
--11520987914388,420004,2639283,LABFER,2009-08-21 11:56:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.548646984401,0.527865050751,0.559649462221,0.6775199307,0.565048046829,0.625479370325,0.488177339901,0.73155700093,0.726103735377,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.511336770549,0.521811420026,0.504688360832,0.503589908526,0.76504728033,0.750024373187,0.706194348114,1.0
--1197415439662,446950,8581917,LABFER,2010-04-24 23:48:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.474588198752,0.483730285787,0.551485565242,0.594142405169,0.679014431445,0.674303915431,0.325427350427,0.707045725135,0.69823391059,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506821767668,0.510888218981,0.502614056345,0.500998228709,0.667120567508,0.690144660962,0.716619055419,1.0
--5886116934041,375585,22521848,LABFER,2011-12-09 06:26:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.468492119135,0.475540295041,0.398209236255,0.445913049396,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.502109074936,0.499665591139,0.41108613309,0.412640595435,0.425706276392,1.0
--3154461675990,407318,10397629,LABFER,2010-07-21 17:50:00,1,0,1,0,0.11914893617,0.0,1.0,0.250759872305,0.251953704793,0.25693223371,0.154411921085,0.246354912031,0.162275753994,0.00909090909091,0.212902867746,0.109504003919,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.443409049792,0.469409955475,0.479322944411,0.490107372245,0.494085464807,0.332145520036,0.307706538769,0.299956036981,1.0
--8300845543805,421177,3089018,LABFER,2009-09-04 08:21:00,0,1,1,1,0.634686346863,1.0,1.0,0.533877242246,0.482622477435,0.505395613944,0.597012603901,0.592605076785,0.594573990284,0.784588937585,0.47576707605,0.64411828159,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.500000168261,0.510888218981,0.504695831952,0.502552185512,0.633969219625,0.626943298541,0.617391719663,1.0
--10978684156981,377481,23306370,LABFER,2012-01-16 01:20:00,1,0,1,0,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.475382041615,0.475540295041,0.482697340597,0.442183349163,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.501267743893,0.499855376782,0.414850854904,0.418541642971,0.428562853607,1.0
-10492350357343,448118,15260834,LABFER,2011-03-08 23:21:00,1,0,1,0,0.108695652174,0.0,0.0,0.431896973734,0.521060228832,0.355207284771,0.255149984695,0.280434471753,0.369843708249,0.4,0.292307692308,0.329276315789,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.483533150281,0.488076794279,0.482332208905,0.500223745533,0.499704411129,0.412500182301,0.456141474061,0.45579306903,5.73117943963e-07
-6688166977946,427852,22279812,LABFER,2011-12-18 05:19:00,0,1,1,1,0.218911917098,0.04,0.0,0.305718403862,0.468166933557,0.348529740643,0.250278581247,0.193981480152,0.340122341905,0.453100780168,0.268088004908,0.357318128936,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.482243918516,0.498383705846,0.491813578127,0.503968384542,0.500584471087,0.542396885567,0.535557124489,0.529929160449,1.0
--2959118438824,451981,10163954,LABFER,2010-07-08 07:50:00,1,0,1,0,0.644444444444,1.0,0.0,0.457472461988,0.454184346457,0.470067305944,0.479069131583,0.461685483163,0.559467308639,0.35177045177,0.45375,0.534332330125,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.484516913089,0.491315142412,0.47486128235,0.49424482054,0.496813516272,0.33141183433,0.315131867892,0.302651182999,1.0
--12168559908246,394099,35553270,LABFER,2013-06-01 22:00:00,1,0,1,0,0.644444444444,1.0,0.0,0.649044688653,0.701797554215,0.615714838867,0.68090965114,0.694606614653,0.800208725774,0.703207131858,0.804329539791,0.725322236902,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.502212575359,0.508859709972,0.498277106792,0.510196312068,0.505729531276,0.874438034056,0.877306741938,0.876782952279,1.0
--4546170330502,379166,18434640,LABFER,2011-06-26 23:38:00,1,0,1,0,0.634686346863,0.0,0.0,0.440790284236,0.440963628654,0.507652945094,0.383855760428,0.426699908111,0.411881670138,0.32282104963,0.600481029071,0.404457212602,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.480513480135,0.482764326053,0.478077273881,0.494717523704,0.499391357049,0.303674629364,0.293949290762,0.287322812128,0.000547636228621
--1633755772623,444665,6252653,LABFER,2010-01-17 13:30:00,1,0,1,0,0.634686346863,0.0,0.0,0.446303234401,0.51724324751,0.551529082952,0.588725701933,0.511567838713,0.600186717343,0.761103913913,0.434650537634,0.505532386661,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.504373995421,0.510888218981,0.50164406936,0.499041380473,0.414241808299,0.358802190313,0.325318076553,1.0
--677105905322,397700,12807827,LABFER,2010-10-30 14:05:00,0,1,1,1,0.477840451249,0.0,0.0,0.542041106266,0.482914865294,0.52666611206,0.407213516539,0.796904639504,0.562815343588,0.680341880342,0.382608695652,0.59120160663,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496262107531,0.504034296413,0.49751533833,0.504170315124,0.504555156328,0.794816298639,0.791432064483,0.788848477638,1.0
-3255416560472,434907,30324144,LABFER,2012-11-14 22:01:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.649044688653,0.701797554215,0.631343261387,0.753922019494,0.76201138155,0.833135191874,0.747891698546,0.890724097614,0.864310046838,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.512254802287,0.510888218981,0.50681415354,0.502230436325,0.721043593348,0.715445736545,0.715938492631,1.0
-4662062643677,355015,37149806,LABFER,2013-08-30 13:40:00,1,0,1,0,0.477840451249,1.0,1.0,0.30329632107,0.288165751028,0.345818246227,0.196368795022,0.132953269872,0.263220789623,0.208571428571,0.560899503722,0.212829665739,0.474085517325,0.462338008602,0.434939683951,0.421077804787,0.471673858599,0.483058550818,0.486583208859,0.49484243563,0.497221923758,0.542107831178,0.550956925179,0.520197779141,0.999983847438
-11182728194808,457420,22602136,LABFER,2011-12-31 11:10:00,0,1,1,1,0.477840451249,0.0,0.0,0.531329789885,0.508460805314,0.496241321931,0.266590980269,0.559905105562,0.481076813155,0.359761904762,0.284722222222,0.533207735259,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.492733512891,0.498388244928,0.494768929304,0.495989543488,0.234688702749,0.223657621904,0.219643514463,1.0
-4976738531131,401921,3758824,LABFER,2009-10-26 00:53:00,1,0,1,0,0.224899598394,0.0,0.0,0.446757590453,0.363726644406,0.288263145585,0.272174832732,0.206615087224,0.203689076363,0.7,0.0429163226309,0.190386848943,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.307838887047,0.394466155862,0.0239301828368,0.319183450625,0.405813650926,0.0066769904406,0.000774129352244,5.2424134928e-05,0.0
-5141239008810,450057,25307666,LABFER,2012-04-23 22:00:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.102530774301,0.22932216673,0.220585107623,0.258193839973,0.212316717833,0.124334274179,0.219865113427,0.237309185929,0.151788904209,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.450848492119,0.47341866238,0.479322944411,0.491799914856,0.494199534352,0.598232536879,0.725341830804,0.835381510914,1.0
--12316912960703,422871,26361774,LABFER,2012-05-11 21:03:00,0,1,1,1,0.477840451249,1.0,0.0,0.481016750293,0.568942657826,0.51994803002,0.353014278826,0.550152970531,0.540385584089,0.521060037523,0.301587388794,0.55657310978,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.504082121618,0.507821918131,0.509322489469,0.506581930814,0.506203337671,0.691465270875,0.694733160627,0.701097291859,1.0
-9178952130041,415139,2210853,LABFER,2009-07-31 22:00:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.506931948752,0.424492822049,0.534797129764,0.566449633708,0.614471619664,0.505060196214,0.505870020964,0.435901515373,0.604209266084,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.505634685246,0.510888218981,0.501373527683,0.500493358748,0.631731146047,0.652156969444,0.658533279389,1.0
--11538191126321,450620,9501815,LABFER,2010-06-16 14:13:00,0,1,1,1,0.477840451249,0.0,0.0,0.672593204301,0.607676109271,0.562107076774,0.593772341091,0.355115409241,0.689091416339,0.720779220779,0.396628056628,0.547058648185,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.509368748739,0.50875181752,0.509322489469,0.49458970863,0.50074978604,0.360634504572,0.38975754337,0.370530884095,1.0841427635e-44
--7336229178310,402745,21225357,LABFER,2011-10-20 22:01:00,1,0,1,0,0.11914893617,1.0,0.0,0.176891159364,0.138912414208,0.20105494849,0.428771586836,0.430876877363,0.281230969966,0.225926246245,0.234452129999,0.221030430756,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.404399752566,0.441163473676,0.439239570503,0.484866848976,0.489592705884,0.290390369309,0.405954680752,0.509856482335,2.62912216295e-10
--2508514123864,360674,5090842,LABFER,2009-12-02 18:47:00,1,0,1,0,0.634686346863,0.0,0.0,0.533877242246,0.530238193527,0.568125478012,0.674536047981,0.684437247728,0.613785111922,0.795797720798,0.70078550735,0.756871162116,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.502800542937,0.501010626982,0.568156559534,0.566945107101,0.563974621442,1.0
-3968321469147,365917,36184012,LABFER,2013-07-17 13:34:00,1,0,1,0,0.477840451249,0.0,0.0,0.259456227755,0.396936343563,0.337360891775,0.28018697684,0.52599918058,0.322073339638,0.411764705882,0.225892857143,0.289814844289,0.474085517325,0.42795946424,0.407916505749,0.3911391348,0.462530425324,0.475052712908,0.498388244928,0.501041162734,0.504267054811,0.0526715903075,0.0283904020955,0.0240998434626,2.43191129998e-19
--2184061698707,461520,15436111,LABFER,2011-02-25 09:02:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.533877242246,0.449046602742,0.415059010498,0.445412115464,0.586225776799,0.512711687704,0.467953667954,0.453325120033,0.580579206279,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.500612149014,0.501596994047,0.510015679179,0.502285038856,0.501215389045,0.696413087771,0.700251252987,0.694474575073,1.0
-9555564681896,437877,36128151,LABFER,2013-07-14 12:23:00,1,0,1,0,0.218911917098,0.118881118881,1.0,0.253082501533,0.228021929917,0.175748886005,0.128965372877,0.189676678481,0.168848682578,0.205128205128,0.0235812512102,0.142963526152,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.444674695169,0.471453410138,0.479322944411,0.50464843988,0.502009863179,0.366955020826,0.369474979089,0.370171647504,0.999999999965
-3362675564234,404628,6608992,LABFER,2010-02-23 11:31:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.533877242246,0.481733638786,0.49876534003,0.537509673032,0.553413004212,0.514738584757,0.558377573088,0.511863070955,0.522851005958,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.500000168261,0.510888218981,0.502625248681,0.500533387664,0.622182289987,0.642411711971,0.65518491837,1.0
--8215876967831,359425,34273221,LABFER,2013-04-08 07:14:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.474488296765,0.476758014365,0.66857907521,0.317742205441,0.509780302552,0.708333333333,0.439987337117,0.400080653831,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.500404847726,0.500116927329,0.509322489469,0.498519066466,0.500592552366,0.4193253225,0.441864680828,0.446006651016,2.02455860489e-17
-1376199981577,459726,15630770,LABFER,2011-03-17 18:00:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.448897920763,0.439576802793,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.502225951552,0.501274213052,0.628929450174,0.639539084664,0.651878702497,1.0
--5909287901099,363635,23961968,LABFER,2012-02-04 22:02:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.548646984401,0.502991073871,0.541417453923,0.714704756705,0.407038677901,0.605619572003,0.5442043222,0.850918572587,0.573392594831,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.500642516742,0.510888218981,0.509721689116,0.506406371675,0.350780587675,0.342192453767,0.337076463772,1.0
--6101377426699,372805,22253532,LABFER,2011-11-27 08:09:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.102530774301,0.145673464133,0.156275323305,0.280648850868,0.220999859993,0.189812259553,0.00930032937419,0.139630343059,0.0673572058954,0.222416167301,0.257917683272,0.309542440243,0.319720123692,0.401111860913,0.442164928738,0.435303344675,0.480643056047,0.489922450008,0.0270430813404,0.0282108976494,0.0297887966044,1.0
-5150679919198,401611,26572439,LABFER,2012-06-17 09:05:00,0,1,1,1,0.218911917098,0.0,0.0,0.134908213579,0.278948051742,0.258200205942,0.283279143174,0.0954456319598,0.296118920629,0.41662591687,0.149219675398,0.128709904994,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.476904337581,0.467548574593,0.497365927265,0.499418770302,0.272648580161,0.283122846214,0.309200669338,1.0
-7804886422910,380931,40208883,LABFER,2013-12-30 08:46:00,1,0,1,0,0.215827338129,1.0,0.0,0.224875204934,0.226062390121,0.29286896973,0.294861009621,0.257867114302,0.301330910814,0.24603868472,0.220934139198,0.258626707418,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.482284021546,0.492312718019,0.487808173527,0.496837746176,0.49585948877,0.170398126189,0.158619004201,0.140784668689,0.999999999253
--10702119107770,421116,33276166,LABFER,2013-02-22 20:28:00,0,1,1,1,0.477840451249,0.0,0.0,0.437345252708,0.561193250371,0.490252862426,0.643654896727,0.507543742137,0.433325646556,0.539329268293,0.257589285714,0.515416254565,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.501526711629,0.504848393901,0.509322489469,0.499978081767,0.49905778266,0.618042680427,0.603495960598,0.598331788597,1.0
--673220402922,451446,31932868,LABFER,2012-12-30 16:11:00,0,1,1,1,0.634686346863,0.936046511628,0.0,0.565903439671,0.676802456176,0.624204878408,0.753922019494,0.687321944332,0.727940989473,0.85726611841,0.755499759543,0.754950489243,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.514070626838,0.521811420026,0.50693101027,0.504049020069,0.633010130959,0.64668442526,0.661968331494,1.0
--5090318064023,438658,38280359,LABFER,2013-09-24 00:22:00,1,0,1,0,0.477840451249,0.0,0.0,0.347817305907,0.297351523533,0.477055909496,0.0993188467129,0.489956874318,0.374534133303,0.6,0.621875,0.239538342633,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.477256803225,0.491908879684,0.509322489469,0.505862460096,0.502990975207,0.824738619173,0.829242805132,0.83081477625,1.0
--11663414152015,451751,29374639,LABFER,2012-09-14 08:05:00,1,0,1,0,0.11914893617,0.0,0.0,0.186503308411,0.137645262066,0.168150435821,0.136285857858,0.0874937354052,0.204548002126,0.405579399142,0.116862939426,0.190424782811,0.222416167301,0.257917683272,0.28911874352,0.287666847741,0.400233004534,0.438763235733,0.434621628464,0.480078312126,0.491824478376,0.126162768222,0.130884033195,0.15909821532,1.0
--4525104382114,364498,24335761,LABFER,2012-02-29 07:43:00,1,0,1,0,0.215827338129,0.0,0.0,0.274179054238,0.255829518956,0.239651405212,0.290198624905,0.0974093450735,0.238850834598,0.0315222482436,0.132328458519,0.16739105943,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.457667423268,0.47253001741,0.487808173527,0.497129843673,0.499340941067,0.253305077148,0.187455586726,0.172733865528,1.0
-9336915269753,459061,25393085,LABFER,2012-04-23 11:33:00,1,0,1,0,0.224899598394,0.0,0.0,0.316200637724,0.369410027837,0.408774099814,0.204850334691,0.428540712802,0.387076928822,0.187777777778,0.299714173213,0.243407637839,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.463153083969,0.480484868074,0.487344472973,0.496379223872,0.49961785341,0.348700329931,0.347303174796,0.349833994038,1.0
-11244481972622,393293,16943483,LABFER,2011-05-16 22:01:00,0,1,1,1,0.218911917098,1.0,0.0,0.207470535213,0.375516272137,0.268161582949,0.370042500248,0.269898010544,0.404636275849,0.151624901835,0.481269675634,0.45829118615,0.222416167301,0.300974526888,0.332627590244,0.327263965497,0.43647780479,0.463775654622,0.434621628464,0.480889523849,0.490217482927,0.0720213060422,0.07205507846,0.0781681273276,1.79609544359e-54
-1856158262683,450674,20290834,LABFER,2011-09-30 20:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.116643614157,0.122481331933,0.126928713421,0.0593645146742,0.19259017897,0.088203761158,0.2,0.2460524181,0.117082778855,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.38673527958,0.419126099427,0.414021157508,0.467826066667,0.48243126862,0.00114392573933,0.000757136295669,0.000685492777667,1.43585251637e-113
--25132739657,397383,39282164,LABFER,2013-10-31 11:23:00,0,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.161995323369,0.167771435495,0.26891864559,0.158798630488,0.112158273528,0.221380866287,0.180032732653,0.157620139991,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.439029999055,0.462902219471,0.447373500781,0.491310578261,0.495333276073,0.0075572190414,0.00847900109443,0.0145924105991,1.44644680521e-165
-11148618293075,417975,38669518,LABFER,2013-10-18 17:03:00,0,1,1,1,0.477840451249,0.0,0.0,0.567381247998,0.551109823632,0.530438893058,0.64180288798,0.485678848755,0.576492210426,0.801606765328,0.572167736352,0.610118829432,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.503651172485,0.507443402613,0.509322489469,0.502757661697,0.498313145075,0.677246751314,0.647949599723,0.600213275769,7.84753454161e-15
--11387440160559,382638,14227192,LABFER,2010-12-27 11:03:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.533877242246,0.533978262144,0.565855628,0.721584368947,0.671788476784,0.671065585053,0.482012625009,0.622237507312,0.527464425745,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.503257207089,0.499309750992,0.605200481739,0.608636769215,0.621388521,1.0
-8788729772386,422957,15487871,LABFER,2011-03-06 08:15:00,1,0,1,0,0.477840451249,0.0,1.0,0.499050123376,0.359772849702,0.369560537757,0.420269457929,0.246430571758,0.296262264233,0.2912130623,0.275613025494,0.407274229236,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.483583457838,0.4884578337,0.509322489469,0.497629499042,0.497754732205,0.0986217285977,0.0565627984693,0.045189213413,1.0
-2358677295284,407168,2656752,LABFER,2009-08-31 11:39:00,1,0,1,0,0.218911917098,1.0,0.0,0.274519185441,0.353755503194,0.313167352762,0.213485984369,0.314897651346,0.316802624064,0.25262591687,0.225117392789,0.457032261521,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.480765645093,0.468417916605,0.496978171687,0.498709868663,0.532471301037,0.581238414514,0.615876352091,1.0
--7012062011546,447265,40884150,LABFER,2014-01-11 07:42:00,1,0,1,0,0.11914893617,0.0,1.0,0.102530774301,0.163327708052,0.122227754134,0.0555157725844,0.16928984013,0.273011816588,0.4,0.105949312715,0.0838509537442,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.427961182359,0.441141514045,0.455878037868,0.480199568122,0.488097555907,0.00150613062103,0.000756924963247,0.000587011248952,0.0
--701928586422,377001,38040404,LABFER,2013-09-12 06:35:00,0,1,1,1,0.215827338129,0.0,0.0,0.447748924527,0.24684294306,0.252081914647,0.192363636364,0.16005942493,0.212657993641,0.210869565217,0.415303239354,0.307648804259,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.468331498747,0.482600123299,0.476015925077,0.489615395029,0.497989446167,0.314327270608,0.293600252078,0.300761986223,2.48256132457e-26
--1255044051801,405080,22106478,LABFER,2011-11-24 19:23:00,1,0,1,0,0.403846153846,0.0,1.0,0.287916586738,0.226349167291,0.248043610219,0.283109429352,0.315557079839,0.306662223597,0.2,0.221195144725,0.26801931231,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.44731594079,0.469877611504,0.440099782885,0.486673011301,0.495649023483,0.177558078599,0.170635105016,0.166195867812,1.0
-311756715486,399882,2916776,LABFER,2009-09-24 07:20:00,0,1,1,1,0.634686346863,0.0,1.0,0.576935863781,0.589438551854,0.594280715606,0.633051943068,0.747853313172,0.669674188788,0.737041123953,0.807181752736,0.606449781078,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.508276666201,0.510888218981,0.504497680873,0.507517711962,0.670955505092,0.728955343061,0.812460831927,1.34697112583e-83
-10301913063458,406074,16544234,LABFER,2011-04-18 06:43:00,0,1,1,1,0.215827338129,0.0,1.0,0.356307568294,0.215661770156,0.277450462155,0.695537933483,0.173525576846,0.410995220918,0.241272332674,0.314261572688,0.303466669794,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.480162499875,0.489076095311,0.487808173527,0.498988698288,0.499449948968,0.447398807467,0.458875783228,0.461210005245,1.0
--6294217792255,427997,37209795,LABFER,2013-08-05 01:13:00,1,0,1,0,0.477840451249,1.0,1.0,0.474588198752,0.487691164698,0.424688749636,0.248061035411,0.567636303215,0.4990615627,0.631457960644,0.719203727039,0.544314253181,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.490367394558,0.493099396504,0.498388244928,0.501366453556,0.502257457272,0.326640302987,0.325133137141,0.325400636926,1.0
--45842118031,379069,8152711,LABFER,2010-04-04 12:55:00,1,0,1,0,0.477840451249,0.0,0.0,0.474471611404,0.37892170654,0.417298762412,0.22562305296,0.435649484177,0.442926816018,0.4363056398,0.286593444194,0.482285700202,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.478587271054,0.490720249901,0.49751533833,0.498740562823,0.499131350865,0.479269910747,0.471688544434,0.467476926682,1.0
--3944526935728,376678,11467968,LABFER,2010-09-09 22:04:00,0,1,1,1,0.224899598394,0.0,0.0,0.333515093174,0.399120609377,0.459822008598,0.344620180929,0.581347401448,0.378869658632,0.323580246914,0.552348759385,0.532929512681,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.450192508381,0.47352588179,0.485779235087,0.493962226052,0.495536064227,0.295763258589,0.293783846488,0.299711994345,1.0
-1093889067554,456334,7516391,LABFER,2010-03-25 07:30:00,1,0,1,0,0.477840451249,0.0,1.0,0.303049461918,0.385323616474,0.325362854593,0.279159159159,0.535955196041,0.423235288263,0.218947368421,0.362471888804,0.48469043134,0.474085517325,0.42795946424,0.422661747809,0.412729468214,0.453095090053,0.474937628007,0.486583208859,0.495701104068,0.499486531361,0.194549321713,0.189700899012,0.165710253457,1.0
-6002374311059,454642,21990923,LABFER,2011-11-30 20:11:00,1,0,1,0,0.218911917098,0.04,0.0,0.217658516895,0.252444358509,0.265619563116,0.62950072763,0.133830208904,0.499459661539,0.217829169559,0.250782600714,0.188762022042,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45918551083,0.478866424017,0.480886833765,0.490805942768,0.495410948434,0.203126932138,0.20432962902,0.1879139843,1.0
-7105580975824,451725,28749029,LABFER,2012-09-09 07:34:00,1,0,1,0,0.11914893617,0.0,1.0,0.217255558392,0.209515501979,0.273863036557,0.241770991771,0.265622449132,0.198599916877,0.0,0.26273006135,0.289775366455,0.222416167301,0.300974526888,0.346810092122,0.344413982292,0.437881407083,0.454414980696,0.457893296213,0.468757740267,0.483292800032,0.0206919455458,0.0235855117337,0.0297119059044,9.88048094538e-61
--6001974771904,439928,23909940,LABFER,2012-02-08 09:42:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.193476921927,0.277080324974,0.319822482401,0.116230288496,0.194621273649,0.199093668509,0.0197209302326,0.20537037037,0.407990214925,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.417976959374,0.45156148576,0.445050483537,0.480775060207,0.490750474004,0.0800426848446,0.0705509951236,0.0651927634033,1.0
-3995139694573,420284,11186759,LABFER,2010-09-13 22:06:00,1,0,1,0,0.218911917098,0.04,0.0,0.217870637328,0.173530071301,0.223410346545,0.190632535754,0.0992340436493,0.153844033199,0.4125,0.0193137801234,0.1015397635,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.43291622945,0.458834678862,0.457893296213,0.484579045239,0.493008851868,0.0901985094508,0.092976222969,0.0951514161037,1.0
-8347529368553,383477,28000432,LABFER,2012-08-11 10:19:00,1,0,1,0,0.477840451249,1.0,1.0,0.341770130848,0.348194405419,0.380716839807,0.494878837571,0.285114246877,0.410724635823,0.507210567211,0.514857979314,0.290809374935,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.472127280163,0.48153013879,0.476514134316,0.492120312134,0.4972303675,0.264828837663,0.303724470024,0.330035209447,8.69353252177e-09
-7336383827903,456752,3357017,LABFER,2009-10-13 08:39:00,1,0,1,0,0.224899598394,0.0,0.0,0.395815886417,0.368937275156,0.379617421213,0.495915514124,0.374096893706,0.359912386974,0.311111111111,0.461526365218,0.320658631185,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.474771937583,0.485889325674,0.47486128235,0.49296542538,0.496086161789,0.192357740536,0.179119030273,0.167471462494,1.0
--3817374868761,365395,6882067,LABFER,2010-02-16 18:59:00,1,0,1,0,0.224899598394,0.0833333333333,0.0,0.384943664603,0.568641921722,0.55656392709,0.471182182686,0.610166592685,0.517933099862,0.251428571429,0.715234320679,0.670467655638,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.470572808352,0.483323628298,0.47486128235,0.49079166782,0.495646700332,0.350373137435,0.338775566141,0.335516345577,1.0
-91262297866,400039,999056,LABFER,2009-04-18 00:18:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.587927599884,0.528643763791,0.497975844066,0.474319862395,0.60810437286,0.530012466037,0.752710975655,0.59720943861,0.514691407791,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498303287924,0.50465411228,0.49751533833,0.503330648277,0.501896096074,0.637153340476,0.636996587821,0.636465941747,1.0
--9469058267868,368271,21530207,LABFER,2011-10-28 22:01:00,0,1,1,1,0.11914893617,0.0,0.0,0.136453329329,0.139375392378,0.171686278292,0.229876090857,0.11851005531,0.175053134567,0.016448964359,0.0318778258205,0.0959157856909,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.442962357675,0.466765032346,0.470329931948,0.489933424146,0.495769545966,0.199002330651,0.205493602843,0.212408237511,1.0
--7373928066203,419678,26624797,LABFER,2012-05-25 09:14:00,0,1,1,1,0.477840451249,0.0,0.0,0.429847549775,0.441373285523,0.492658351609,0.40929322848,0.565259016806,0.375523480453,0.339912280702,0.387583298647,0.414291152263,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.501559919088,0.495919289432,0.498388244928,0.495285244487,0.498342234857,0.282282027612,0.271205429321,0.287342772813,1.0
--10248752096172,366569,21930636,LABFER,2011-11-12 08:55:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.0719500639275,0.137276707039,0.164762219002,0.0533307868602,0.230495148585,0.188030720277,0.6,0.226962529453,0.250523940003,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.425257549607,0.443105462773,0.479322944411,0.479821319435,0.488460890967,0.0796091512508,0.0521276874798,0.0383064020184,1.61423495585e-54
-4276889449603,354643,39721580,LABFER,2013-11-29 09:05:00,1,0,1,0,0.485294117647,0.0,0.0,0.20174023566,0.309480137348,0.348744680759,0.394068965517,0.367508895831,0.379237755308,0.410869565217,0.5,0.437987656398,0.474085517325,0.481003341925,0.466907858521,0.451523128836,0.478771175582,0.484924498027,0.510015679179,0.501141971662,0.499956497935,0.512665566445,0.543932581614,0.579629373943,0.999999999994
-9003413041985,411989,32449767,LABFER,2013-01-31 23:32:00,0,1,1,1,1.0,1.0,1.0,0.324893391347,0.505580311166,0.332103578664,0.697632320625,0.22698420994,0.383039197596,0.4,0.2,0.365455724728,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.475142451678,0.48721908753,0.480767972341,0.49725300016,0.498768337834,0.711253245468,0.717733458806,0.710832760825,1.0
--9586396071333,361021,960300,LABFER,2009-03-28 23:21:00,1,0,1,0,0.218911917098,0.04,0.0,0.189222751251,0.22234318527,0.220761519297,0.247472604149,0.127331505579,0.145663388874,0.0488098249164,0.110420102352,0.213350118007,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.455135202202,0.477381704054,0.479322944411,0.494668116882,0.49654470591,0.306292878647,0.284915070172,0.267097587045,1.0
--7014427305696,452234,28242129,LABFER,2012-08-08 06:50:00,1,0,1,0,0.218911917098,0.04,0.0,0.198332683533,0.168388248029,0.172032490045,0.0384639719934,0.0946513967764,0.119667985866,0.0,0.132790859727,0.166717734973,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.422304977076,0.454081780117,0.447057203458,0.486057954617,0.494028561184,0.0928524735018,0.0819789087785,0.0676553768999,1.0
-2584546952687,427192,21651205,LABFER,2011-11-29 10:03:00,0,1,1,1,0.477840451249,0.0,0.0,0.614217569783,0.496543220977,0.577107153485,0.530369452207,0.595734321885,0.504297050732,0.581927710843,0.453250382177,0.459538800937,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.500404847726,0.5002509798,0.509322489469,0.492776511454,0.495863734189,0.211275887552,0.0324910594992,0.00423670131848,3.1622245905e-48
-2052930246643,466044,20248259,LABFER,2011-09-19 22:00:00,0,1,1,1,0.634686346863,1.0,1.0,0.501533492246,0.641608034745,0.473300823714,0.249089861751,0.494173260401,0.625789836759,0.548484848485,0.4475,0.521375383875,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.541817489794,0.534035018372,0.576567253252,0.512988619801,0.505973993631,0.893324786803,0.923998044352,0.948804961669,1.0
--8916365302850,437911,33515619,LABFER,2013-03-10 06:09:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.102530774301,0.163986059785,0.147164770191,0.116020553506,0.258680481911,0.119586264458,0.401342281879,0.151640973509,0.147169475146,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.408113404384,0.438149170428,0.468307222203,0.473794515284,0.485232604387,0.0195300055961,0.0151639745099,0.0132461658796,0.000217863138864
--6562668615325,448721,32255091,LABFER,2013-01-10 19:33:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.310624993947,0.37578943184,0.357300494429,0.312554721909,0.243728704325,0.30562573766,0.354545454545,0.342983450418,0.351687290906,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.471900669637,0.485994680414,0.480886833765,0.496592197611,0.498360197745,0.366879311443,0.357818518843,0.350015217965,1.0
--6078411462916,386508,28336400,LABFER,2012-08-11 12:49:00,0,0,1,0,0.477840451249,0.0,0.0,0.463279172992,0.422739907898,0.459150799109,0.394838572642,0.393986802078,0.455189075775,0.623432893743,0.364110441245,0.451313368208,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.488715322977,0.495260527594,0.486583208859,0.501221684315,0.500258792343,0.408958909884,0.405900022353,0.446825878008,1.0
-845151543860,359114,6757474,LABFER,2010-02-27 14:52:00,1,0,1,0,0.218911917098,0.04,1.0,0.215580522621,0.379742975977,0.336912750689,0.480761093241,0.388454406618,0.3316907571,0.216799292661,0.228794198913,0.39387246877,0.222416167301,0.300974526888,0.368633683804,0.379422352055,0.468300557717,0.487635575886,0.468307222203,0.494182375809,0.497337508008,0.244620682039,0.242385742902,0.233235742822,1.0
-9581402605864,408382,4497964,LABFER,2009-11-25 12:28:00,1,0,1,0,0.218911917098,0.04,0.0,0.286614068614,0.285158137056,0.276292589912,0.108294278009,0.101349460321,0.151326752561,0.032183908046,0.227353555457,0.251787031531,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.468655546047,0.486754529742,0.480015246909,0.499344803434,0.498938420314,0.228777292331,0.220781818098,0.213471056047,1.0
-753325172862,359445,39299714,LABFER,2013-11-25 15:40:00,0,1,1,1,0.634686346863,1.0,1.0,0.321228719889,0.496267766133,0.467390124661,0.515742847591,0.561949281641,0.505603460487,0.4,0.444881846356,0.548613308674,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.475128327394,0.48693583485,0.510888218981,0.502445802783,0.500394045535,0.491136903427,0.495501831522,0.4951572399,1.0
--11556434765210,464039,5140833,LABFER,2009-12-03 11:51:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.649044688653,0.5567890222,0.567693286733,0.70939748479,0.752111146014,0.583965988056,0.715667636759,0.941063297144,0.540706178153,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506189633847,0.510888218981,0.4994811759,0.498911724047,0.581755570469,0.562681176509,0.56379151677,1.0
-8335133296767,355663,3401128,LABFER,2009-10-14 10:09:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.120519087416,0.197467164944,0.0126623376623,0.278784232247,0.17309889772,0.0142857142857,0.142024088986,0.184373172229,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.288393259693,0.391071810708,0.0243514919689,0.324466211042,0.408960243298,1.30736715142e-06,8.56160197071e-20,4.42570567796e-60,0.0
-9672381659826,418825,33635377,LABFER,2013-03-20 09:07:00,0,1,1,1,0.224899598394,0.0,0.0,0.353572469889,0.461826484018,0.526780053607,0.488366966562,0.487971444507,0.517584701222,0.587080979284,0.297820310525,0.510978810453,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.466627413008,0.480498488012,0.487344472973,0.492743889351,0.496045616397,0.245089678007,0.215278737778,0.198086401389,1.0
--6588101852601,358022,11461957,LABFER,2010-09-04 19:32:00,0,1,1,1,0.634686346863,1.0,1.0,0.55519212329,0.500337481779,0.544776773347,0.691149686926,0.673873817319,0.666200524206,0.78074704383,0.542954895515,0.609488393916,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.500642516742,0.510888218981,0.499044321973,0.498486098051,0.407966681807,0.358800036224,0.339412017589,1.0
--2293636088696,387152,7270923,LABFER,2010-03-07 02:06:00,0,1,1,1,0.477840451249,1.0,0.0,0.454813004587,0.415426080779,0.347763151326,0.31303030303,0.310562168316,0.247649376615,0.336060037523,0.4,0.379051808328,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.4891144531,0.497152542594,0.49751533833,0.500231382851,0.498355729751,0.658508898073,0.617875323877,0.588803234408,1.0
-4797169323223,359799,20431213,LABFER,2011-10-01 22:00:00,1,0,1,0,0.8,1.0,1.0,0.261195043165,0.2109928785,0.32748528994,0.465416065416,0.287147645569,0.249807423832,0.406,0.12739524388,0.317070778984,0.222416167301,0.257917683272,0.300217184164,0.287666847741,0.425431725799,0.453755399162,0.456676473752,0.48779414651,0.40925761075,0.077964454593,0.0611129912617,0.0411716070346,8.69629812793e-255
--11339201037837,358849,15294659,LABFER,2011-02-09 22:00:00,1,0,1,0,0.634686346863,0.589887640449,0.0,0.559358300782,0.626910875303,0.601025836054,0.759216655437,0.703325442924,0.68942896714,0.478993706729,0.733524275282,0.58158903956,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.50420191315,0.498388244928,0.499407776901,0.498662617428,0.333760504462,0.297842011167,0.301709254508,1.0
-9775777075894,403365,12272923,LABFER,2010-10-24 14:38:00,1,0,1,0,0.0512820512821,0.0,0.0,0.234515525043,0.165285868002,0.161261629355,0.125751621079,0.129547263974,0.0389613934738,0.0,0.108733974359,0.144700412983,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.43904943342,0.454231268025,0.448488745234,0.478302124707,0.489955857762,0.0288487691243,0.0313955670917,0.0334067317838,1.33801506429e-214
--11071158683702,402797,38091391,LABFER,2013-09-19 07:33:00,0,1,1,1,0.11914893617,0.0644257703081,1.0,0.161439026276,0.17045653941,0.220616235187,0.0960037082019,0.165991876843,0.172449125223,0.205579399142,0.148444163005,0.304787705673,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.41365639905,0.45026982631,0.433417205192,0.488689368175,0.493622208002,0.187995112895,0.158762322669,0.100571785385,1.0
-11295459357880,456166,714451,LABFER,2009-03-22 14:18:00,0,1,1,1,0.634686346863,0.857142857143,1.0,0.533877242246,0.529349354878,0.583224605072,0.607805894294,0.561880927444,0.572992561081,0.597723915814,0.652562248321,0.727415571631,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.500612149014,0.507541649759,0.510015679179,0.504323110165,0.501841980621,0.826293984694,0.831718493227,0.842317895033,1.0
--9800379641701,422147,20214746,LABFER,2011-09-10 08:37:00,1,0,1,0,0.477840451249,0.0,0.0,0.316874258849,0.290099405081,0.318570038508,0.320302033857,0.26468388502,0.29018934927,0.313580246914,0.382380952381,0.440888443376,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.4891144531,0.496341482785,0.49751533833,0.499513644274,0.499472571928,0.373384021176,0.350106831728,0.323616471268,1.0
--12084916836089,397368,32637795,LABFER,2013-01-30 11:40:00,1,0,1,0,0.477840451249,0.0,0.0,0.395815886417,0.374155109677,0.37529341193,0.344858520626,0.463654234154,0.347577899567,0.422725392887,0.47503481926,0.317405895651,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.491659503043,0.498388244928,0.498915146156,0.499279792521,0.349963700583,0.375615266517,0.378783976625,1.0
--1475209838078,469526,1445135,LABFER,2009-05-03 02:05:00,0,1,1,1,0.634686346863,0.0,0.0,0.45576466934,0.414918743865,0.443175979845,0.21393575625,0.260536672851,0.328754596262,0.2,0.18275862069,0.262435416423,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.498046745792,0.510888218981,0.502653361824,0.500285419262,0.480807339785,0.46461406399,0.458291311403,1.0
-5374019938279,400244,31608746,LABFER,2013-01-03 20:24:00,0,1,1,1,0.634686346863,0.0,0.0,0.51469596705,0.466626883387,0.56001120267,0.608275222706,0.610437067735,0.549767334963,0.763957159925,0.575495584388,0.464289638658,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.50890649436,0.521811420026,0.501357534133,0.499635302286,0.508066898701,0.508664714415,0.5149977951,1.0
--7000341820176,413003,10153647,LABFER,2010-07-14 17:05:00,1,0,1,0,0.477840451249,0.928571428571,1.0,0.414413840453,0.433575643491,0.366059093765,0.188689054735,0.400665879959,0.294184233137,0.155072463768,0.0454545454545,0.242685825594,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.493978375973,0.495596358147,0.498388244928,0.497256998153,0.498319681597,0.39203182366,0.374472155775,0.365448925833,1.0
--11857367409704,435048,12138859,LABFER,2010-10-06 20:46:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.533877242246,0.510283719873,0.58007243824,0.655873363395,0.684480325881,0.606171462626,0.80552610562,0.589054972391,0.697238454134,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506529365886,0.510888218981,0.501089329159,0.501920550465,0.464226575878,0.461051554644,0.450379714463,1.0
-9740799711765,378895,17902759,LABFER,2011-06-16 23:37:00,0,1,1,1,0.218911917098,0.0,1.0,0.263923135753,0.159928846613,0.211658140523,0.248924631976,0.0857939716892,0.192780500428,0.4,0.129915050242,0.236932911744,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.418692224203,0.449405881866,0.428495991973,0.484063072119,0.493067255182,0.19067296027,0.226337121559,0.229106880901,0.00135852373088
--8732791666285,372840,22350052,LABFER,2011-12-01 06:06:00,0,1,1,1,0.634686346863,1.0,1.0,0.427620614462,0.301141298389,0.351059600766,0.149517262686,0.343681128835,0.420496065689,0.327890222985,0.473761578735,0.371147178848,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.484675350432,0.490165034146,0.486583208859,0.500373668077,0.501680385916,0.0626901654366,0.0246028565014,0.0809408194934,1.36320014604e-194
-5097040350444,422662,12104465,LABFER,2010-10-20 20:00:00,0,1,1,1,0.224899598394,1.0,1.0,0.401557123503,0.587901515704,0.484675789897,0.559311790542,0.44859598246,0.423700991492,0.629768561665,0.54948011468,0.463628063475,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.495249723954,0.49861224018,0.485779235087,0.490640888438,0.497362736088,0.0660541956922,0.00755457740939,0.222928568625,0.0
--10153679438974,370471,12864763,LABFER,2010-10-26 10:16:00,1,0,1,0,0.218911917098,0.0,0.0,0.217658516895,0.1878711326,0.24407050909,0.269459326905,0.161069550235,0.174839736493,0.0263662511985,0.0907574093983,0.129654321282,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.456892908049,0.478015409687,0.468417916605,0.49524520343,0.496928513678,0.446345768487,0.471603894117,0.495148270649,1.0
-11493206228269,431432,39155614,LABFER,2013-11-17 13:22:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.452785099418,0.475540295041,0.459430758433,0.464069631169,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.492082175513,0.495591647591,0.090697776923,0.0647387213934,0.0533151031679,0.0
--11899697947985,429960,36733351,LABFER,2013-07-26 12:35:00,1,0,1,0,0.11914893617,0.0,0.0,0.136319701156,0.189191526126,0.187259222267,0.184620856093,0.363411799165,0.196447796211,0.00692168102083,0.116308776242,0.360608532546,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.446333236588,0.465501450354,0.468417916605,0.489986409876,0.494593248928,0.0935729234641,0.0861790382809,0.0835246783369,1.0
-6069168843405,422388,24678926,LABFER,2012-03-29 06:15:00,0,1,1,1,0.11914893617,0.0,0.0,0.234750691137,0.301117753436,0.224621293682,0.0881698588727,0.206599873259,0.262684831233,0.0897910835954,0.222263216738,0.179456528613,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.459973449412,0.483871629651,0.467548574593,0.498348353338,0.497697382733,0.514129945649,0.530510942083,0.537953740915,1.0
-9035696655110,371465,16892606,LABFER,2011-05-09 23:07:00,1,0,1,0,0.218911917098,0.04,0.0,0.129617171645,0.254468349448,0.29113160741,0.252894974572,0.220700224026,0.320744622262,0.251587602694,0.240959063637,0.270072800889,0.222416167301,0.300974526888,0.360138656365,0.364971187629,0.449065605901,0.470740139787,0.445050483537,0.494135700147,0.496288535246,0.149227653211,0.137982844424,0.129953543411,1.0
-7127944087869,407262,6239514,LABFER,2010-02-12 00:22:00,1,0,1,0,0.477840451249,1.0,0.0,0.525866698689,0.369124494205,0.385473055498,0.221521830097,0.448827064175,0.386259716293,0.0409675704413,0.178923479684,0.397716590961,0.474085517325,0.481003341925,0.4728983646,0.458461829056,0.492689061713,0.493012265105,0.498388244928,0.501949660702,0.501341512517,0.260983370677,0.251931029312,0.257524078418,1.0
--5602980712309,467479,8014269,LABFER,2010-04-08 13:49:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.533877242246,0.505095156781,0.561321514891,0.612130975011,0.562971284546,0.588115626162,0.603238167723,0.724556089984,0.460847494481,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.504959600069,0.498388244928,0.500486697106,0.499020946414,0.731347222819,0.76922916014,0.782789088215,1.0
-4923798477792,393660,18795638,LABFER,2011-08-01 12:25:00,1,0,1,0,0.218911917098,0.04,0.0,0.153288879666,0.228279632649,0.221364140635,0.0656328417594,0.0926574956806,0.209158417521,0.0287064537832,0.257065702391,0.262559850969,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.473934703128,0.467548574593,0.497566398771,0.498544263801,0.636311731884,0.638277740735,0.633961851251,1.0
--8888010857169,382476,14025248,LABFER,2010-12-31 16:37:00,1,0,1,0,0.477840451249,1.0,1.0,0.509521716225,0.489490188655,0.469413089383,0.651338837423,0.498810662036,0.460056256651,0.580940680941,0.275449877498,0.411366894526,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.48677520081,0.493285931298,0.498388244928,0.494806820258,0.497008717819,0.532715697452,0.521134574183,0.521074565327,1.0
-7109301759845,385777,33563204,LABFER,2013-03-19 22:00:00,0,1,1,1,0.218911917098,1.0,0.0,0.216914526778,0.285158137056,0.269064024603,0.26031917315,0.115591199841,0.223580793725,0.00372093023256,0.220091615415,0.267188116022,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.451427981118,0.475508440654,0.469108659157,0.500548740444,0.501819587914,0.244703043624,0.233404759202,0.224851533695,1.0
-4612852474065,361014,39009050,LABFER,2013-10-29 07:06:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.161439026276,0.137276707039,0.134325058712,0.0359051313863,0.242907251634,0.107111003652,0.0259262462446,0.0509681795104,0.113290417066,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.42999566908,0.454803621419,0.450904755627,0.480341721427,0.488497429961,0.0487702190115,0.0582887518428,0.057853647733,3.54735787459e-30
-8692542740154,417685,26869324,LABFER,2012-06-27 17:29:00,0,1,1,1,0.640625,0.0,1.0,0.261649028597,0.415375837119,0.384433203929,0.212784606967,0.47518308468,0.51346547702,0.436341483063,0.401550802139,0.493513027975,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.462432391046,0.482114191028,0.459448963611,0.500318885162,0.499941033181,0.559139202431,0.585886555967,0.593301846648,1.0
-7044119503973,461229,40674083,LABFER,2014-01-11 06:30:00,0,1,1,1,0.224899598394,0.0,0.0,0.542108744912,0.505582145867,0.542914123456,0.610481498548,0.606934764191,0.540638626061,0.399797979798,0.473591395981,0.668441735705,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.487703429456,0.492661531277,0.47486128235,0.496128365766,0.498345637142,0.316673353908,0.288218887471,0.284165331573,1.0
-3431996421775,441924,11268157,LABFER,2010-09-18 22:32:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.533877242246,0.478844771042,0.541453406268,0.530227375079,0.613698572429,0.605984959668,0.689076117072,0.692621291596,0.660855771193,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506823555481,0.510888218981,0.504497680873,0.503660415969,0.63336980721,0.638293294682,0.636052470312,1.0
--1033212919639,393650,18454027,LABFER,2011-06-27 10:14:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.520338053783,0.482648862917,0.490084767052,0.5134631586,0.570859994688,0.512734177572,0.528232636928,0.346222396654,0.36301337525,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.495724935038,0.497763239962,0.509322489469,0.500103959308,0.500853016199,0.43026193777,0.412026003425,0.393049059066,1.0
-2357746389094,431560,33207733,LABFER,2013-03-06 22:00:00,0,1,1,1,0.477840451249,1.0,0.0,0.426337728212,0.61735093775,0.561116104162,0.567149407248,0.821684968255,0.614673101964,0.78273381295,0.633834838809,0.547582964158,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.533759542027,0.534035539705,0.576567253252,0.522714164412,0.512046549379,0.987076576681,0.994166594372,0.995920866879,1.0
-8154228029319,359100,4850024,LABFER,2009-12-10 09:22:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.48595854843,0.480602232878,0.432603412434,0.392944597158,0.589767927388,0.586294711155,0.660698399674,0.602201035399,0.413601353483,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.488145971349,0.498914394846,0.500307457597,0.502625048701,0.500367523072,0.672471089388,0.684101874002,0.697747328606,1.0
--466849143906,386631,38920406,LABFER,2013-10-25 03:58:00,1,0,1,0,0.218911917098,0.0,0.0,0.242565280973,0.292760387808,0.25409205882,0.312861715749,0.268607077207,0.222196314017,0.4,0.240040321218,0.345515822378,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.43267517745,0.45982459034,0.433417205192,0.48539946311,0.493735958198,0.209014936134,0.193609288768,0.180179394056,1.0
--12441736567611,465301,23731466,LABFER,2012-01-27 22:00:00,1,0,1,0,0.640625,0.0,0.0,0.24256619665,0.374995360019,0.424025478581,0.483608987293,0.514119122456,0.48151244218,0.923192892718,0.587526157999,0.594740502742,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.490800332608,0.480886833765,0.500210543162,0.500538907117,0.623047128004,0.651653856451,0.676640945213,1.0
--2774173654794,405055,3380969,LABFER,2009-09-20 15:41:00,1,0,1,0,0.477840451249,0.0,0.0,0.372127861404,0.543744154818,0.481885519937,0.571383303163,0.495810022883,0.498235532503,0.489572649573,0.49945018548,0.524009031616,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.5023354493,0.504761120843,0.498388244928,0.49371112441,0.49654664124,0.413447298607,0.435604016936,0.446184687827,1.0
-11076693943127,379754,11743793,LABFER,2010-09-30 07:41:00,1,0,1,0,0.218911917098,1.0,0.0,0.287916586738,0.225977255692,0.25341670322,0.251901989951,0.456930364491,0.17513308287,0.4,0.153282722847,0.147830812729,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.449693387108,0.474521504929,0.457893296213,0.495474362987,0.496679589181,0.238014473481,0.23995384578,0.225936304797,1.0
-8584080867320,391335,9617600,LABFER,2010-06-30 01:41:00,1,0,1,0,0.218911917098,0.0,1.0,0.377644144781,0.325650128751,0.333688659294,0.335735652412,0.381733969041,0.265199771889,0.397693693694,0.321704961864,0.304259299688,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.457489392485,0.476268740008,0.468417916605,0.495789134119,0.498305334309,0.212990419062,0.207416771472,0.207185377811,1.0
--9296170330256,386659,12796162,LABFER,2010-11-06 08:56:00,1,0,1,0,0.634686346863,1.0,1.0,0.533877242246,0.555567089982,0.581350682704,0.668040219614,0.532385140595,0.606860527601,0.722604203477,0.861995603628,0.680109144464,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.503257207089,0.502204007288,0.705218142478,0.699122060295,0.687028197317,1.0
-10174026966698,447571,31888906,LABFER,2013-01-18 19:00:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.531167040876,0.550938182717,0.575052595944,0.773309071835,0.700984224317,0.558376284606,0.63095897344,0.466108956811,0.637986392872,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.509840081172,0.521811420026,0.502116845047,0.50177453409,0.591090734153,0.596336339787,0.596618189375,1.0
-9463777210180,472517,12962975,LABFER,2010-11-19 14:26:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.0970693890468,0.207257095228,0.209167585535,0.379761214453,0.217799390421,0.131937474289,0.0176599360544,0.0345454298497,0.230660394784,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.450859407546,0.468844045743,0.468417916605,0.495289085337,0.497505536653,0.0740254804553,0.0335552507883,0.0144493616889,2.35025531393e-50
-10727499499815,424819,37912888,LABFER,2013-09-28 11:20:00,1,0,1,0,0.11914893617,0.0644257703081,1.0,0.161439026276,0.104366721429,0.148473213183,0.210461035253,0.179609250892,0.0765506737324,0.0272685281238,0.048855503454,0.116279753261,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.412737066938,0.439908204828,0.440099782885,0.468122985211,0.483735972022,0.00330590562369,0.00313127084866,0.00317715889502,5.17156314546e-71
-9040043422344,436572,12934252,LABFER,2010-11-23 22:04:00,1,0,1,0,0.215827338129,0.0,0.0,0.359039413049,0.175825138479,0.204062924429,0.163183699531,0.0672179425133,0.090628363942,0.00857142857143,0.0126616513639,0.12587831886,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.312150624683,0.39570785678,0.0264286100674,0.315277718045,0.401722527738,0.00362681058382,0.000533683429889,9.42135611249e-05,1.01355161707e-95
--9373835818071,469604,3191654,LABFER,2009-09-15 17:01:00,1,0,1,0,0.11914893617,0.0,0.0,0.136319701156,0.10529541079,0.144229861397,0.181166150671,0.228364020947,0.165593987406,0.160767834867,0.312212266083,0.0208436079546,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.270479805104,0.36820454196,0.0243514919689,0.317392616909,0.402725776661,0.000471626117918,1.76958417646e-06,2.48020305108e-12,0.0
-1680972562595,367679,20320263,LABFER,2011-09-24 08:14:00,0,1,1,1,0.224899598394,1.0,1.0,0.487994303783,0.521497531351,0.492408391735,0.440677782568,0.546757625608,0.596357309253,0.334887334887,0.712820512821,0.623650199153,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.479868709149,0.487038664405,0.47486128235,0.493004473841,0.496112106104,0.291545283538,0.278453590475,0.256752667345,1.0
-2927975355158,391306,8610282,LABFER,2010-05-17 01:34:00,0,1,1,1,0.634686346863,1.0,1.0,0.620755809273,0.655213628337,0.627663982127,0.695920786484,0.733460163523,0.750036723864,0.633387686388,0.770568538176,0.853991790202,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516068956906,0.521811420026,0.508627827746,0.504837187175,0.729603209157,0.730307664829,0.729538922288,1.0
-1000942960595,449718,39582924,LABFER,2013-11-23 17:37:00,0,1,1,1,0.224899598394,0.0833333333333,0.0,0.318745351019,0.387664942178,0.407882877283,0.22308290762,0.178018528936,0.258798041702,0.340860215054,0.302400980888,0.263717764469,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.457969323011,0.474606313453,0.485779235087,0.491794746959,0.493330136611,0.172549288618,0.168594590137,0.171524938516,1.0
--6870572417591,468068,30691313,LABFER,2012-11-11 13:00:00,0,1,1,1,0.8,0.0,1.0,0.0922096575285,0.202084276517,0.214199938483,0.0896725599068,0.175897443214,0.269597431231,0.205579399142,0.127311257406,0.127236352812,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.444852110567,0.472252604689,0.467548574593,0.495202853051,0.497634184953,0.432785751026,0.610013139454,0.740528952722,1.13510465822e-17
-586051580124,461402,28904004,LABFER,2012-09-19 22:44:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.548646984401,0.550693765754,0.594626297974,0.561141172159,0.670784491138,0.596581682852,0.43826055309,0.597317730174,0.602889140517,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.502945257778,0.501697938087,0.510888218981,0.49993488209,0.49963082754,0.384979163732,0.382644353053,0.378838366743,1.0
-12046267225220,379725,26561874,LABFER,2012-06-07 21:00:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.572637714475,0.658274620214,0.647501399618,0.753922019494,0.726453419688,0.697752578934,0.847932442228,0.640910630846,0.780376471583,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.514209385101,0.521811420026,0.50934883247,0.507804942598,0.556619014963,0.547340315451,0.542767111997,1.0
-1335143260022,449360,3740413,LABFER,2009-10-21 14:22:00,1,0,1,0,0.218911917098,0.04,0.0,0.263116921288,0.238198849698,0.331421619097,0.3425625567,0.156771418514,0.212275844766,0.032183908046,0.304411764706,0.319699984728,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.44166722547,0.467257690647,0.428495991973,0.49004816813,0.494879927187,0.0509783759858,0.0488901028899,0.0530137473771,5.52318544913e-253
--10906468120936,452628,2658837,LABFER,2009-08-19 08:06:00,1,0,1,0,0.11914893617,0.0,0.0,0.116643614157,0.163080041812,0.205772207845,0.0719312359788,0.123584786379,0.136704512745,0.422950819672,0.132233658847,0.240848168019,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.418157019722,0.454343287172,0.446194229381,0.490863745524,0.493278980524,0.0522845190592,0.0400775091721,0.036460230583,9.14414908965e-28
--2574969374672,429694,16523589,LABFER,2011-04-07 22:32:00,0,1,1,1,0.640625,1.0,1.0,0.27991831586,0.374643776012,0.373257035256,0.278120569441,0.420679158717,0.345021850786,0.213774014979,0.342654382004,0.33172020218,0.222416167301,0.300974526888,0.368633683804,0.379422352055,0.461930916336,0.481196192051,0.457432635845,0.495128105773,0.496785523552,0.346557298018,0.339705679505,0.33791624725,1.0
--10831611285501,460058,6696940,LABFER,2010-02-06 01:27:00,0,1,1,1,0.477840451249,1.0,1.0,0.398368156716,0.38603486657,0.33906202305,0.47774822695,0.424339175512,0.368186968065,0.410909090909,0.436388888889,0.535813602307,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.476392575204,0.485629347711,0.49751533833,0.500319033143,0.498664171871,0.430362848572,0.623200090254,0.879691038624,0.0
-9764585240014,428613,281794,LABFER,2009-02-18 20:02:00,1,0,1,0,0.0833333333333,0.0,0.0,0.222471608773,0.183489246257,0.239244428463,0.113904086233,0.154613673065,0.180091989001,0.238183908046,0.178918712211,0.243017952068,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.446294504222,0.474401231975,0.470329931948,0.489006334872,0.491737976446,0.263816266487,0.289034747967,0.298222387189,1.0
--2727895496270,364592,2951592,LABFER,2009-08-27 22:00:00,0,1,1,1,0.477840451249,0.0,0.0,0.520338053783,0.568458143836,0.493849959865,0.555775377987,0.798787468505,0.489144235722,0.4,0.562875675676,0.534765420339,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.534019631359,0.535318881445,0.576567253252,0.514268849533,0.506849623936,0.999997902401,0.999999698975,0.999999350533,1.0
-12222812249723,419967,4691575,LABFER,2009-12-06 23:34:00,1,0,1,0,0.0512820512821,0.0,0.0,0.151260504202,0.368624557053,0.38159040347,0.416363636364,0.275194794494,0.376326968382,0.286,0.302702702703,0.369523809524,0.474085517325,0.481003341925,0.46241236511,0.470629423827,0.285928276604,0.380812689662,0.0210064023427,0.306986840649,0.398270895845,0.00988711181525,0.000230763297508,3.47549095688e-08,0.0
--11156665972792,401471,34504191,LABFER,2013-04-17 11:33:00,1,0,1,0,0.0512820512821,0.0,0.0,0.217306868292,0.29373972451,0.229562608881,0.172037504784,0.151313060768,0.240775451443,0.006,0.221456509943,0.208080428891,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.468684911553,0.479837581326,0.476886910056,0.492995588829,0.495538366578,0.372971335853,0.331275104779,0.311688287899,1.0
--1362789067297,376416,3608282,LABFER,2009-10-08 05:54:00,1,0,1,0,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.461747281314,0.475540295041,0.46526723589,0.41950754243,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.500027197585,0.498579391907,0.316684274622,0.315176324367,0.319924790336,1.0
-6564217338440,466692,3139661,LABFER,2009-09-26 17:43:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.33497593669,0.31302091554,0.429604103397,0.486931915655,0.390192071242,0.509059453512,0.740242165242,0.516567062872,0.38952947448,0.474085517325,0.462338008602,0.452687451931,0.458429662267,0.486836566081,0.498027091751,0.500307457597,0.49692786288,0.498150259656,0.722469862151,0.746916159075,0.767521464892,1.0
--6044003793463,451285,40473416,LABFER,2013-12-21 22:17:00,0,1,1,1,0.477840451249,1.0,1.0,0.466706373376,0.3659198277,0.44089757509,0.378061035411,0.386220001887,0.405379641245,0.467922270918,0.500159961103,0.381775655962,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498658105002,0.498531290539,0.498388244928,0.499126682177,0.497846206563,0.657180220056,0.632611271311,0.62548295261,1.0
--1791894702562,458206,37216541,LABFER,2013-08-16 07:22:00,1,0,1,0,0.634686346863,0.0,0.0,0.355588853993,0.345312013488,0.430801607925,0.510011201614,0.292142928785,0.330859267493,0.566489066489,0.529781288657,0.324352001243,0.474085517325,0.462338008602,0.443974869853,0.436224459079,0.487493755442,0.493412261981,0.510888218981,0.500410547828,0.500071789216,0.304162854674,0.30802208321,0.305822246544,1.0
-2055640568417,377655,33708076,LABFER,2013-03-23 11:26:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.388478357514,0.373916550952,0.361541863994,0.402863231306,0.364642973523,0.355806332556,0.212765957447,0.247381049846,0.359984726618,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.489945636617,0.480886833765,0.501250612934,0.49914690603,0.689504148172,0.69911023335,0.699409732423,1.0
--5368015416270,412237,15172760,LABFER,2011-02-03 10:11:00,0,1,1,1,0.634686346863,0.0,0.0,0.320765758815,0.433721985626,0.459336120885,0.690839160839,0.453648215894,0.348897170359,0.595555555556,0.443283156884,0.470325788244,0.474085517325,0.481003341925,0.482049390786,0.473852543851,0.496767154225,0.500287516151,0.510015679179,0.503097346517,0.511460958373,0.895153866089,0.893832523183,0.845000610813,1.03972181326e-113
--7602532771083,433169,25490803,LABFER,2012-04-07 21:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.120214812321,0.119763869219,0.211852038892,0.230713655797,0.153171393533,0.118665341162,0.00557939914163,0.221349571812,0.0267570684478,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.418810057383,0.45248760347,0.457893296213,0.491809773031,0.495918710651,0.278634688251,0.271137000767,0.25756030628,1.50559730652e-58
--677203509108,400599,29520148,LABFER,2012-09-30 08:16:00,0,1,1,1,0.218911917098,0.322580645161,0.0,0.242248288799,0.249015726764,0.229346810576,0.0686423372175,0.155775164412,0.140207980882,0.603720930233,0.477272727273,0.432956304771,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.448318984081,0.471209201471,0.479322944411,0.494144187881,0.497651786051,0.179762675474,0.188738036961,0.186971236439,1.0
-4497204794123,469732,29629635,LABFER,2012-10-18 22:00:00,0,1,1,1,0.11914893617,0.0,0.0,0.186134585598,0.157899429055,0.181042026721,0.0778298585195,0.269494591382,0.143232604677,0.029872500693,0.230273609529,0.202037507273,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.437778272358,0.460915385548,0.457893296213,0.496402810258,0.496505099842,0.528808066165,0.477155295266,0.46339707295,1.0
--971737188014,463675,27004012,LABFER,2012-06-06 10:14:00,0,1,1,1,0.634686346863,1.0,1.0,0.533877242246,0.535612616328,0.576426026536,0.691901207047,0.683978253443,0.631013115609,0.686188449185,0.584910316825,0.753972631496,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.507418107448,0.5042325398,0.81545682989,0.810575702393,0.808030319191,1.0
--11595325848118,358290,26728443,LABFER,2012-06-05 22:00:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.544348835095,0.42149974367,0.536509067357,0.440084241342,0.455209706028,0.622744198815,0.559002809658,0.570058892815,0.636224438814,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.507084429992,0.510900919135,0.521811420026,0.505198872643,0.500849272297,0.868354733204,0.868351334761,0.869145905027,1.0
-4359214956381,413329,29940759,LABFER,2012-11-02 08:09:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.649044688653,0.633279522175,0.637183200179,0.753922019494,0.758126842579,0.798927274004,0.806935251885,0.854076995315,0.679276616071,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.511499285023,0.510888218981,0.503771242595,0.500490745162,0.54332991436,0.538734228925,0.542529370313,1.0
--6992673949191,365669,13488546,LABFER,2010-12-02 09:37:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.516303234401,0.443446304244,0.4677461775,0.580679195932,0.522865855326,0.455325073563,0.481026221468,0.546933586958,0.441447543941,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.496852838484,0.510888218981,0.498648721985,0.498498329622,0.406672753789,0.375961842709,0.343515904829,1.0
-1899543860214,415289,18372900,LABFER,2011-07-07 02:38:00,1,0,1,0,0.477840451249,0.0,0.0,0.477279432247,0.474475579008,0.388400287708,0.513026428955,0.439979860068,0.476242771299,0.668728432108,0.486926067688,0.418071996155,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.47684399649,0.487107904603,0.486583208859,0.496522789174,0.498562106771,0.360492180917,0.362847437937,0.356522820519,1.0
--8372047641121,432720,26480105,LABFER,2012-05-29 21:00:00,1,0,1,0,0.634686346863,1.0,1.0,0.534361701635,0.446440348094,0.498320655451,0.500808925453,0.288777956822,0.43145976663,0.448484848485,0.526623376623,0.485992863643,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507463985322,0.510888218981,0.502817291954,0.503438100763,0.0730331251048,0.0439810953766,0.037219147257,0.998897074128
--5956681525778,381452,13064252,LABFER,2010-11-19 10:25:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.533877242246,0.405977140536,0.45304124412,0.607621748236,0.410301836985,0.3955994083,0.338263802264,0.353074977972,0.462613122254,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.484675350432,0.493203771383,0.486583208859,0.497966534958,0.497467473105,0.305056938876,0.310314618697,0.305112150204,1.0
-8888039123818,382091,20283883,LABFER,2011-09-29 01:26:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.469760288475,0.475540295041,0.459430758433,0.451115163778,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.499654240296,0.499048624742,0.399644595687,0.405327164704,0.415467385808,1.0
--9689759111504,471879,40583794,LABFER,2013-12-29 13:02:00,0,1,1,1,0.634686346863,0.0,0.0,0.485583056694,0.449955649093,0.464991501534,0.493140724073,0.616494430245,0.540644076471,0.344144144144,0.495804833561,0.430202096604,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504667153246,0.504401610157,0.499081689524,0.500470091109,0.496744484773,0.383893861963,0.345436815329,0.333306602341,0.999911660686
--2764299973308,396492,40118388,LABFER,2013-12-02 09:47:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.533877242246,0.359139801712,0.434557547688,0.461798873184,0.358217338879,0.37249083304,0.248164215397,0.339938031632,0.392891077686,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.494056232464,0.499081689524,0.496636680379,0.498041914972,0.428992197169,0.400550785848,0.427927895431,1.0
-6337850472537,411198,9305237,LABFER,2010-06-16 21:55:00,1,0,1,0,0.634686346863,0.936046511628,1.0,0.448174239801,0.518972294676,0.470416585663,0.682654324448,0.508299257812,0.427468752327,0.45057797002,0.444011712747,0.275663547922,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50154966602,0.504903969834,0.49751533833,0.497851604441,0.498942423257,0.491610116182,0.483986090873,0.45941035674,1.0
--3818522234701,463817,6264972,LABFER,2010-01-18 09:22:00,0,1,1,1,0.477840451249,1.0,1.0,0.46497764875,0.424689670526,0.363594762426,0.52473254214,0.538140390431,0.345601174138,0.259393939394,0.4,0.465446807352,0.474085517325,0.481003341925,0.466572373462,0.45525281484,0.4909728627,0.494741285675,0.509322489469,0.505397756748,0.503085671832,0.42531674416,0.455098253756,0.454448245899,1.0
--7680446618672,428822,29202004,LABFER,2012-09-10 15:28:00,1,0,1,0,0.634686346863,0.0,0.0,0.533877242246,0.505095156781,0.563249798745,0.509664704698,0.631442820036,0.602183209182,0.638255338483,0.542610172556,0.607124383934,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.504534949725,0.510888218981,0.49821413017,0.498906520225,0.51339471609,0.50784307315,0.504685138333,1.0
--11874292073263,394628,31548555,LABFER,2012-12-10 20:17:00,1,0,1,0,1.0,1.0,1.0,0.342045120087,0.386643504967,0.342639012358,0.224849798089,0.184954880063,0.421356365825,0.6,0.502189781022,0.242686254834,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.466709016897,0.483952508036,0.459330712414,0.489696185764,0.493737546203,0.435453133959,0.421744732817,0.405573663933,1.0
--12420916146030,448131,27211540,LABFER,2012-06-15 06:17:00,0,1,1,1,0.218911917098,0.04,0.0,0.24625386448,0.369769054197,0.308089933127,0.310796317473,0.112100738148,0.252247199864,0.0488098249164,0.224337741333,0.295897129912,0.222416167301,0.300974526888,0.352037739686,0.347339870876,0.448048236428,0.465533970314,0.445050483537,0.488622260757,0.491889215963,0.12435554475,0.11295548452,0.107271070576,1.0
-10947305099870,434052,25428652,LABFER,2012-05-01 09:14:00,1,0,1,0,0.218911917098,1.0,1.0,0.217658516895,0.210665563995,0.201916756777,0.085325257813,0.181008724076,0.15780557086,0.0,0.364611060988,0.245741000021,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.42004573672,0.456539535755,0.445050483537,0.489192104834,0.494346563342,0.185199915859,0.169446871121,0.170105019556,1.0
--5052146363224,471169,22848173,LABFER,2011-12-25 07:49:00,1,0,1,0,0.634686346863,0.0,0.0,0.395276864462,0.397937403578,0.431414857162,0.483544693008,0.369286972458,0.283156466567,0.636,0.34487879847,0.416554604437,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.491060258428,0.490703778628,0.510888218981,0.497540456057,0.501408629499,0.49519296956,0.516220528463,0.548003972193,1.0
-3168294816086,371365,14378487,LABFER,2011-01-21 23:10:00,1,0,1,0,0.634686346863,0.0,0.0,0.523129521253,0.604841909786,0.621257104768,0.610417847861,0.621263488719,0.467259918018,0.526314808257,0.362532003267,0.44124927728,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.492390178983,0.491035708979,0.478077273881,0.494717523704,0.496506403378,0.46605064576,0.473310812466,0.481192931144,1.0
--8629512847010,458759,13580066,LABFER,2010-12-12 00:05:00,1,0,1,0,0.224899598394,0.0,1.0,0.350184312635,0.30838372424,0.308360557644,0.120781125458,0.279600776037,0.269326381927,0.344242424242,0.30736029223,0.245198011283,0.474085517325,0.462338008602,0.434324112449,0.424233526155,0.457614546527,0.472069140268,0.47486128235,0.493207676808,0.492921180302,0.0624529470936,0.0653310779626,0.059259898122,1.44148508508e-31
--3554813650316,419527,28992132,LABFER,2012-08-30 18:46:00,1,0,1,0,0.477840451249,0.0,0.0,0.466706373376,0.445388068757,0.433177357826,0.548033740336,0.582548715132,0.43020150576,0.323717948718,0.173039508433,0.227509322036,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.497512164297,0.498388244928,0.493340922886,0.496367026381,0.385921524415,0.385561819068,0.34913272431,1.0
-9425714102533,393258,12605459,LABFER,2010-11-06 22:22:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.445137151139,0.385583963163,0.375987014032,0.431324902208,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.501142091061,0.509322489469,0.502214249235,0.500183014848,0.514099521281,0.521679725365,0.530819126316,1.0
-2264898079820,421075,35067787,LABFER,2013-05-31 22:00:00,1,0,1,0,0.218911917098,0.0,0.0,0.18386959004,0.259268897728,0.235910058021,0.420122049956,0.273776064163,0.192239098112,0.412080536913,0.226473044014,0.239089817211,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.460243263807,0.479770025285,0.468417916605,0.491004570154,0.494520666925,0.319084542231,0.329445987605,0.320586577145,1.0
--3706589236383,442432,38566212,LABFER,2013-09-30 07:05:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.152696564885,0.147034350337,0.318364783156,0.0880413258843,0.145056206096,0.00557939914163,0.145224333347,0.0356980966617,0.222416167301,0.257917683272,0.300217184164,0.287666847741,0.441100040989,0.46528855236,0.479322944411,0.493148744695,0.49582418419,0.133066819295,0.102069326278,0.0893173651097,1.0
-12155425913650,427117,21971512,LABFER,2011-11-30 22:00:00,1,0,1,0,0.477840451249,0.0,0.0,0.466706373376,0.480763261527,0.432701190161,0.333966832513,0.47173115955,0.344120712685,0.629292929293,0.578257474353,0.511900247198,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.490367394558,0.492094852511,0.498388244928,0.497916905237,0.49852650645,0.377894072449,0.32492933076,0.278081151203,1.0
-10495965899490,382001,32432688,LABFER,2013-02-01 09:45:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.319734926228,0.213988064248,0.232103341826,0.128217410012,0.183921611554,0.190202267235,0.42888073881,0.25292068563,0.149884495129,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.426895318832,0.458574533103,0.435412626422,0.492085723217,0.495898944432,0.409211202971,0.406250055922,0.393986342143,1.0
--5153299984559,363656,31564114,LABFER,2012-12-09 16:41:00,1,0,1,0,0.644444444444,0.0,0.0,0.481016750293,0.395458757163,0.384412263585,0.136820751409,0.456862319585,0.383948352359,0.587314582753,0.326364898733,0.358818166528,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.460235812405,0.475599516181,0.463099032304,0.49014790516,0.49368394625,0.0517028122932,0.0515555551031,0.0514640078009,1.0
--6152353689065,424000,17026589,LABFER,2011-04-27 11:59:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.127650099421,0.173240054469,0.119138684308,0.271323743276,0.0942454412235,0.0989079875195,0.016448964359,0.127478312125,0.105834251027,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.418844608563,0.44547774934,0.457893296213,0.487431302169,0.494444936122,0.0172805412271,0.00696521023339,0.00579474714678,5.99694541051e-89
--5833665444677,451989,1850110,LABFER,2009-06-17 11:36:00,1,0,1,0,0.218911917098,0.0,0.0,0.193209028834,0.243528415618,0.286832554203,0.405356919541,0.134839436369,0.270481575657,0.0316046369738,0.306224684255,0.183200467307,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.46253836053,0.483840016109,0.480886833765,0.503620064681,0.500905460669,0.487479733205,0.520597704636,0.517379492822,1.0
--4059579498054,371752,10290259,LABFER,2010-07-16 17:57:00,1,0,1,0,0.224899598394,0.0,0.0,0.419770783473,0.386357329039,0.444252219808,0.395157558507,0.289816914462,0.419370908716,0.351000625391,0.331626603732,0.540559888898,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.460262749686,0.481006236461,0.498277106792,0.496637622905,0.498480023473,0.432268045037,0.425672408544,0.419628382643,1.0
-955236575116,453617,28030707,LABFER,2012-08-16 02:19:00,1,0,1,0,0.11914893617,0.0644257703081,1.0,0.206946439824,0.144575242544,0.18581353887,0.0355125992539,0.188974363853,0.0951087226642,0.0106426112534,0.135616393503,0.081827765698,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.420680798567,0.450857848645,0.456676473752,0.48369732725,0.490857654009,0.0653717656214,0.0575240139192,0.0535775944349,0.974187086237
--7382324277667,471561,27131697,LABFER,2012-06-26 08:36:00,1,0,1,0,0.215827338129,1.0,1.0,0.325854108957,0.34983019555,0.390945776866,0.187991287301,0.100598377128,0.391093276667,0.406,0.211900525753,0.350760816976,0.474085517325,0.481003341925,0.45330786382,0.45525281484,0.468173550251,0.483320136336,0.464318343184,0.491530606202,0.495431871449,0.61324633342,0.609470766156,0.612870400056,1.0
-4853526238279,450881,37624916,LABFER,2013-09-12 10:53:00,1,0,1,0,0.218911917098,0.04,0.0,0.225106237129,0.284226673708,0.227590928887,0.140736493981,0.105100877579,0.117956664103,0.2,0.251938566234,0.138725499342,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443666016897,0.46552588375,0.467548574593,0.494192954878,0.496679476288,0.155380575345,0.178016398555,0.211825956021,2.694650714e-174
-10303755747985,453822,39683156,LABFER,2013-11-25 22:00:00,1,0,1,0,0.224899598394,0.0,0.0,0.499050123376,0.370322964481,0.37566111392,0.511043652923,0.374160996066,0.311475787168,0.724691358025,0.154128344481,0.296008363047,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.470131958544,0.478063924265,0.47486128235,0.492322391678,0.492113491999,0.11058584779,0.106720588381,0.112379806667,1.0
-3799514765502,375534,30865245,LABFER,2012-12-12 05:40:00,1,0,1,0,0.218911917098,0.04,0.0,0.167751973159,0.206869081985,0.161736856454,0.192917526984,0.066279621854,0.0651689181429,0.0166259168704,0.149956304654,0.146737029825,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.401028216429,0.442623446315,0.41816275361,0.484067158713,0.491657163042,0.167647157846,0.168181429541,0.1680605469,0.0127955612758
-2031425784613,437665,31136555,LABFER,2012-12-16 23:23:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.452785099418,0.475540295041,0.459430758433,0.456285587609,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.501841298042,0.499996536184,0.501579243681,0.507113239973,0.517591254798,1.0
--8337960603995,461831,18544172,LABFER,2011-06-27 07:39:00,0,1,1,1,0.477840451249,1.0,1.0,0.466706373376,0.422137603776,0.418280455898,0.400131971302,0.493338554433,0.356658450762,0.531746031746,0.649656784175,0.515439899905,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.499180404938,0.509322489469,0.50305276695,0.501999418611,0.64919052322,0.672476439519,0.773327780889,1.0
--9810097028805,471596,31204052,LABFER,2012-12-05 08:50:00,1,0,1,0,0.218911917098,1.0,1.0,0.158750264921,0.274602100082,0.224853642162,0.427480916031,0.529495413633,0.310150027004,0.405128205128,0.00538983585859,0.194756145742,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.444852110567,0.471633331884,0.467548574593,0.489248282753,0.497865507575,0.000220161863898,4.23993081906e-06,5.15757998481e-07,2.66006009391e-09
--6194536975208,417522,8707627,LABFER,2010-05-05 22:04:00,1,0,1,0,0.218911917098,0.0,0.0,0.213136948964,0.350481912113,0.325684775195,0.101639553429,0.187672505001,0.172044328435,0.61662591687,0.157887617021,0.111797576308,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.472069313013,0.467548574593,0.490478853302,0.49374100685,0.111836445214,0.0864995764287,0.06865760042,1.0
-373455415119,417897,7446920,LABFER,2010-04-04 00:50:00,1,0,1,0,0.224899598394,1.0,1.0,0.267803646983,0.416335688231,0.398275507308,0.373603525299,0.412119457156,0.2906568092,0.01,0.283408026058,0.407924451764,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.463394733611,0.472105748212,0.47486128235,0.487070394919,0.489464078313,0.0619661144636,0.0432891234698,0.036772194893,1.0
-11413042773043,450172,38965025,LABFER,2013-11-01 23:36:00,1,0,1,0,0.0512820512821,0.0,0.0,0.353441448672,0.278885574515,0.327582410055,0.406666666667,0.418956665963,0.242868013687,0.233340430062,0.189582135327,0.30072859745,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.488559329404,0.490515127586,0.489373727303,0.496304070592,0.498233575785,0.731880691175,0.77649866098,0.806252503036,5.52606510136e-09
-6831078889514,369067,7712141,LABFER,2010-04-11 07:06:00,1,0,1,0,0.640625,1.0,1.0,0.310624993947,0.241516437201,0.286420326998,0.307224789388,0.287254876752,0.215284374913,0.408233050788,0.134549487928,0.275598038336,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.456668542835,0.477499579438,0.459448963611,0.493823096035,0.49331262969,0.116682097943,0.110666303814,0.116209976886,0.999999921008
--12081577111892,426951,7303403,LABFER,2010-03-11 22:01:00,0,1,1,1,0.11914893617,0.0,1.0,0.22464030696,0.340640456615,0.281544062511,0.34273522575,0.283240248399,0.222742148187,0.0664459173847,0.0611324329949,0.33473472491,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.480001313039,0.468417916605,0.497590754628,0.499305652802,0.295819731941,0.298502565309,0.302393008789,1.0
-8955012005803,451398,35120214,LABFER,2013-05-31 20:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.152329093994,0.268733399557,0.192871373447,0.440044804092,0.145522830104,0.0828555210258,0.2,0.0210971142596,0.111370099698,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.432179002388,0.457517802576,0.446194229381,0.485830748532,0.490599975523,0.191810922999,0.199248863938,0.0694930434802,5.07557160294e-13
--8990811057094,404341,37424137,LABFER,2013-08-22 15:19:00,0,1,1,1,0.634686346863,0.558823529412,1.0,0.537614560291,0.522193648952,0.451574993675,0.589727041796,0.517610173033,0.545762238413,0.847932442228,0.526699319976,0.500492370145,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492781804106,0.499223223032,0.499081689524,0.502207423962,0.50087484611,0.696157977772,0.67280433098,0.651469757655,1.0
--2647866425292,452437,18846866,LABFER,2011-07-09 18:29:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.448897920763,0.435158538859,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500144076394,0.499758377326,0.567032591318,0.576149082567,0.584527268611,1.0
--775676370853,412710,26481719,LABFER,2012-05-27 09:26:00,1,0,1,0,0.218911917098,1.0,1.0,0.286537421168,0.374435277094,0.310344570031,0.293454190797,0.388332628275,0.356388784252,0.395555555556,0.613134548701,0.291447729958,0.222416167301,0.272513145711,0.341251976233,0.336701589837,0.464263057266,0.482622499722,0.468417916605,0.502168001689,0.499612931416,0.680563512193,0.707902430945,0.719148172994,1.0
--2778240292674,415579,40510644,LABFER,2013-12-28 22:43:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.452785099418,0.601346746654,0.421331143587,0.463749990284,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.495364640787,0.498388244928,0.498413696538,0.497817906828,0.250823994948,0.25107309457,0.258839789414,1.0
--8347960146681,424352,14858823,LABFER,2011-01-28 11:16:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.48810841934,0.359139801712,0.372443320747,0.501404166744,0.451700825227,0.387887179251,0.4,0.354337237001,0.250518902589,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.488728073257,0.494384232209,0.510015679179,0.497357615999,0.500086548078,0.576903100037,0.601118450661,0.607297538441,1.0
--599608630070,419959,1747688,LABFER,2009-06-10 07:00:00,1,0,1,0,0.477840451249,0.0,0.0,0.429847549775,0.400087082997,0.501168247814,0.469595741155,0.695030812762,0.53966122437,0.588571428571,0.53035000144,0.585694924551,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.499435403377,0.500656076653,0.498388244928,0.498873192798,0.499127786284,0.702108269275,0.710726485241,0.724592652986,1.0
--6751756075182,407833,4873547,LABFER,2009-11-18 11:15:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.27422751268,0.364608915397,0.295118187675,0.50744024813,0.258661387083,0.309122089311,0.286945962356,0.569229484321,0.415772346104,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.464275748893,0.484011987994,0.491813578127,0.499886624867,0.500175579518,0.351845883443,0.315653696562,0.30929087962,1.0
--4312123672104,436722,34636773,LABFER,2013-04-24 22:29:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.161439026276,0.177683011175,0.207369035862,0.165483703929,0.10782862429,0.150841086417,0.00692168102083,0.0471443822925,0.142376567534,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.449068067092,0.472929898745,0.468417916605,0.499296029342,0.498742106936,0.45852632036,0.462562313304,0.432815703141,1.0
--11251914371192,358355,5850090,LABFER,2010-01-06 12:22:00,1,0,1,0,0.218911917098,0.04,0.0,0.138727103927,0.251773800803,0.230048943126,0.0505579454357,0.318145545943,0.163951627205,0.219403694648,0.167358323476,0.142567562727,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.418430622301,0.458287505901,0.455878037868,0.491548008098,0.494406598096,0.160197282084,0.14615472808,0.134828861107,1.0
-5165754439281,472738,15240016,LABFER,2011-02-20 17:13:00,1,0,1,0,1.0,1.0,1.0,0.319734926228,0.386733569437,0.319447002796,0.16302357363,0.242671156304,0.306631951437,0.718773510329,0.424689922481,0.453355873299,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.442962357675,0.472914171648,0.470329931948,0.505041446615,0.502115369103,0.045577967577,0.0454903008856,0.0436803783126,9.2362764297e-240
--4552257838494,420452,9508021,LABFER,2010-06-04 01:27:00,0,1,1,1,0.634686346863,1.0,1.0,0.544348835095,0.62381009648,0.596115991736,0.721153167272,0.719591473498,0.676517809491,0.738449449974,0.810143711681,0.772744442044,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.509930661182,0.50852647603,0.510888218981,0.503398304438,0.501390364754,0.53126090122,0.523240581856,0.523143852612,1.0
-1604301774092,406984,15645652,LABFER,2011-03-23 08:10:00,1,0,1,0,0.477840451249,0.890410958904,1.0,0.477279432247,0.459584947537,0.411190940963,0.489675456354,0.455370912936,0.46863429288,0.468675213675,0.594661614438,0.459811899937,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.486397697734,0.496963797603,0.509322489469,0.500428276549,0.501150471029,0.540650920917,0.542884444033,0.56218548704,1.0
-6868196632666,464501,31911777,LABFER,2013-01-18 17:41:00,0,1,1,1,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.485256921134,0.492559499028,0.480732002755,0.465296775039,0.475540295041,0.440074370836,0.456571269658,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.501142091061,0.509322489469,0.501757575529,0.500565130251,0.570349458489,0.57521392972,0.581223026187,1.0
--10296818530718,419924,34637070,LABFER,2013-04-18 11:27:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.57925354694,0.557127249605,0.674126303155,0.666091968875,0.715532687014,0.872378402499,0.684130110298,0.715051025796,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.500601476731,0.505477145948,0.510888218981,0.508054427674,0.503778602174,0.619298588733,0.614596703774,0.609416038117,1.0
--4221539885191,425761,1047331,LABFER,2009-04-04 06:28:00,1,0,1,0,0.224899598394,0.0,0.0,0.35948609176,0.387664942178,0.43438883416,0.284483801808,0.296170454163,0.349552406846,0.139203354298,0.232360381092,0.360654421544,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.462594061406,0.480479334217,0.485779235087,0.492251311631,0.495811782672,0.274137569134,0.271050214141,0.276950585102,1.0
-2740034896216,413442,28957699,LABFER,2012-09-17 14:52:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.263221027416,0.147737394736,0.184378984022,0.099405345686,0.11722324484,0.0800872074155,0.333333333333,0.0310739766689,0.030215414324,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.413976039927,0.444836934213,0.435412626422,0.489043554995,0.493519352484,0.0876674537216,0.112158438956,0.122764540174,1.0
-6043785269477,404082,9610109,LABFER,2010-06-28 23:26:00,0,1,1,1,0.218911917098,0.118881118881,1.0,0.253082501533,0.37248549439,0.270518343356,0.13408265765,0.314095490898,0.136658301021,0.216486887679,0.0363366175147,0.292311326042,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.451427981118,0.474542249284,0.469108659157,0.493550634397,0.497927575641,0.188663853532,0.183460755415,0.182017597546,1.0
--9672485727363,406136,1642763,LABFER,2009-06-03 04:31:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.616216479264,0.605322047464,0.59727403933,0.753922019494,0.723502801596,0.713924369194,0.664872830622,0.689115982216,0.683115908566,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.512092299405,0.521811420026,0.504973462937,0.502642814464,0.7748959906,0.780057547189,0.779812340054,1.0
-2998715685802,419939,5032831,LABFER,2009-12-19 18:49:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.533877242246,0.530238193527,0.56656021261,0.678446723679,0.676995900665,0.602531664642,0.511209907178,0.538960158531,0.600705331846,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.511450306683,0.521811420026,0.503747019177,0.500875335329,0.572040915898,0.561444646911,0.55449186634,1.0
-2268658033890,390566,35589750,LABFER,2013-06-12 13:53:00,1,0,1,0,0.634686346863,0.0,0.0,0.395276864462,0.384114409848,0.389155539627,0.592447278596,0.272994430051,0.338800881615,0.4,0.320977695952,0.528391468262,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.497454356717,0.521811420026,0.491683596582,0.493225736255,0.325688981757,0.279933840203,0.362257338508,1.92122978233e-11
-10191631025536,466727,19143952,LABFER,2011-08-14 01:36:00,0,1,1,1,0.634686346863,0.0,0.0,0.620755809273,0.679863448053,0.619828605458,0.721153167272,0.6487031944,0.719424078154,0.40754985755,0.720524965296,0.800020339206,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.510644691324,0.510888218981,0.500157938487,0.501511895256,0.693316532778,0.685938523916,0.681464719305,1.0
--775676370853,412710,26582135,LABFER,2012-06-03 09:07:00,1,0,1,0,0.218911917098,0.04,1.0,0.217658516895,0.285825049213,0.27255014727,0.201441069092,0.342136767341,0.154753810411,0.215801467145,0.176583084698,0.089967511487,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.456892908049,0.478015409687,0.468417916605,0.494030481869,0.497408414681,0.493244243008,0.567135965719,0.598368428719,1.0
-6385525757986,469491,15580484,LABFER,2011-03-09 17:52:00,1,0,1,0,0.644444444444,1.0,1.0,0.318745351019,0.387664942178,0.407882877283,0.284483801808,0.250856290384,0.300915963063,0.125252525253,0.340427100426,0.292365985395,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.456235436268,0.472571758867,0.47486128235,0.488368493999,0.493879900277,0.0939895493117,0.0908375409769,0.0931128860443,1.0
-11171820846761,461485,25148720,LABFER,2012-04-13 08:13:00,0,1,1,1,0.477840451249,1.0,1.0,0.585928690403,0.57925354694,0.494588503225,0.535700418099,0.699875570901,0.645018529613,0.693444031643,0.558684919677,0.505577547987,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.498292615748,0.502581047312,0.498388244928,0.503211518361,0.501356889804,0.623001208893,0.624248938873,0.631867505336,1.0
-11325904025896,430329,4801890,LABFER,2009-12-08 22:13:00,0,1,1,1,0.215827338129,1.0,0.0,0.455764018867,0.327369788705,0.350587689885,0.453521126761,0.124261294261,0.367283394198,0.738765957447,0.3,0.480335411,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.490305293382,0.497876992982,0.500307457597,0.497245758972,0.495362295173,0.293305117118,0.266887582953,0.247131235972,0.999999898877
--3685442047736,416931,3988299,LABFER,2009-10-16 01:41:00,1,0,1,0,0.634686346863,1.0,1.0,0.464775030315,0.555557873777,0.509551936777,0.591355904485,0.438264066902,0.508479602077,0.438893280632,0.318191907981,0.446921453538,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.510177189495,0.50228249795,0.498388244928,0.501547464258,0.500555324949,0.413147642326,0.408412284877,0.405471624468,1.0
--5096513136336,402174,32826226,LABFER,2013-02-04 22:01:00,1,0,1,0,0.218911917098,1.0,0.0,0.310624993947,0.326516976522,0.345564675534,0.333981281253,0.591090196559,0.389242324925,0.180086580087,0.332055118559,0.355815896393,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.471900669637,0.4876045133,0.480886833765,0.501517881746,0.499243562197,0.472763371016,0.488875392738,0.491544962229,1.0
--1725252277005,457147,38421160,LABFER,2013-09-23 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,1.0,0.161015260479,0.224625180347,0.211151908831,0.114690935308,0.0825656558228,0.145839200343,0.219865113427,0.158298666689,0.18346006898,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.466333583038,0.481515623524,0.479322944411,0.494388453908,0.496722602853,0.340292266383,0.378454797949,0.397195992613,1.0
--8062863243266,411265,38490074,LABFER,2013-10-07 07:41:00,1,0,1,0,0.634686346863,1.0,1.0,0.548646984401,0.41557077893,0.441035531328,0.747723073534,0.526744710174,0.508553252787,0.55357670578,0.559070647801,0.449051133705,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.493924907348,0.498388244928,0.491973744997,0.496379883366,0.441548544448,0.395252363793,0.332926941038,1.0
-614597905890,422817,32922242,LABFER,2013-02-20 09:21:00,1,0,1,0,0.666666666667,1.0,0.0,0.217658516895,0.25601344064,0.274778185956,0.223828455358,0.385278913041,0.247753646901,0.212765957447,0.242479859379,0.162479918271,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.44674798276,0.465815468837,0.459077856466,0.478791425599,0.489379799439,0.0589010652233,0.0615370395145,0.0616308273149,1.39995138773e-45
-1076189865429,355198,29955999,LABFER,2012-10-26 11:23:00,1,0,1,0,0.218911917098,0.0,0.0,0.190196216845,0.210502301341,0.206417917061,0.0450000212889,0.132032010041,0.166795932318,0.21662591687,0.233979143507,0.135631334366,0.222416167301,0.300974526888,0.352037739686,0.347339870876,0.440325667699,0.466420300566,0.433417205192,0.487396790136,0.493265739423,0.0323598817427,0.0230917596361,0.0235078222285,1.0
--9626975680759,458970,34473522,LABFER,2013-04-18 07:35:00,1,0,1,0,0.403846153846,1.0,1.0,0.338885544749,0.233245778264,0.195193828867,0.0615671129363,0.227181129846,0.169595434097,0.0325662042875,0.123020520036,0.173317187875,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.436059331024,0.46075379986,0.447057203458,0.493610229706,0.49715036508,0.0804693135956,0.0683934697317,0.0574526824789,4.97615665105e-22
-8073198014790,397193,30840968,LABFER,2012-12-04 08:54:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.22130346657,0.170923243644,0.403490355434,0.094836949552,0.0993752760528,0.00557939914163,0.119363852168,0.128142144967,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.394508909226,0.432316617561,0.429697111247,0.490984965378,0.495181663803,2.0885080761e-27,1.35758984282e-69,1.31713266823e-113,1.0
-11922225181812,464711,39825106,LABFER,2013-12-05 11:28:00,0,1,1,1,0.477840451249,1.0,1.0,0.300884932298,0.248324215718,0.36149165433,0.0839648044905,0.306651897055,0.418735079449,0.508333333333,0.211111111111,0.414600611377,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.471628466119,0.483364588115,0.465118925402,0.494860001694,0.497712150693,0.353832732708,0.40075392155,0.464039680909,1.0
--636106992483,434770,26864136,LABFER,2012-06-14 11:11:00,0,1,1,1,0.218911917098,1.0,0.0,0.254072562911,0.286574167541,0.312693742017,0.231308105737,0.184082456266,0.231958511496,0.426366251198,0.232221396451,0.35154322425,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.476451996497,0.493761840306,0.491813578127,0.502495143717,0.501934744617,0.752436401466,0.760197888814,0.778536363281,1.0
-1881665211572,442824,8197900,LABFER,2010-04-28 21:56:00,1,0,1,0,0.634686346863,0.589887640449,0.0,0.576935863781,0.533620428745,0.563542056687,0.691149686926,0.619692675234,0.551358674536,0.703667146497,0.698711847304,0.485503623549,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.512136192474,0.521811420026,0.507057356937,0.502739578939,0.719745832706,0.722148535343,0.718766239844,1.0
-9800373106113,428599,33788933,LABFER,2013-03-26 22:13:00,0,1,1,1,0.477840451249,1.0,1.0,0.531329789885,0.461048397489,0.416529513583,0.524321917417,0.427085046539,0.435018287738,0.438333333333,0.729361942405,0.442455498878,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.490461309822,0.498388447278,0.49751533833,0.501822869574,0.50300147699,0.540524659109,0.545957434357,0.53711426938,1.0
-9639321187160,428393,34934234,LABFER,2013-05-22 18:12:00,1,0,1,0,0.218911917098,0.0,0.0,0.31004347577,0.311260576601,0.262741706635,0.407457279298,0.209026058369,0.160442771758,0.439333333333,0.230650679834,0.245777102829,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.436059331024,0.46028419217,0.447057203458,0.484525341685,0.491567400197,0.0324612300773,0.017712596137,0.0133773184956,8.0756346219e-08
--2637292066654,441239,5153181,LABFER,2009-12-01 07:39:00,1,0,1,0,0.218911917098,0.04,0.0,0.209102349328,0.334909083552,0.269411113551,0.140841871048,0.226897685229,0.268731718401,0.0449498654928,0.130036151961,0.159399864744,0.222416167301,0.300974526888,0.360138656365,0.364971187629,0.45820375046,0.471417048544,0.455878037868,0.480842375531,0.490665252878,0.226465881197,0.231264389341,0.25175181056,1.0
-10880453084442,444673,35655841,LABFER,2013-06-19 14:10:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.501533492246,0.40369682743,0.462866680514,0.615002302677,0.592088873861,0.434146437863,0.378312447786,0.325568758878,0.284667120933,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.500158925017,0.521811420026,0.500835784041,0.500664318232,0.536677647299,0.531171653498,0.526570747161,1.0
-3982032228386,421061,8940752,LABFER,2010-05-28 12:46:00,1,0,1,0,0.218911917098,0.0,0.0,0.18386959004,0.139567365193,0.132827275602,0.160507697947,0.108868596824,0.0774714049492,0.0134228187919,0.0563018301538,0.134372436232,0.222416167301,0.257917683272,0.28911874352,0.287666847741,0.418736833581,0.447747200977,0.434621628464,0.474702595971,0.48499773285,0.0116860083852,0.00562467920636,0.0036961883353,8.31608921336e-22
--7091433037012,434196,6795209,LABFER,2010-02-18 10:00:00,1,0,1,0,0.403846153846,0.0,0.0,0.361608330302,0.126753807772,0.168430217555,0.229765848087,0.148331051529,0.082214523997,0.00372093023256,0.0058934847256,0.107403518063,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.395592314565,0.431765551158,0.407560018026,0.482176699697,0.490994021276,0.0387703887305,0.0340649930904,0.0300738860221,1.3839710221e-15
-10257315274643,455095,18585745,LABFER,2011-07-12 14:49:00,1,0,1,0,0.477840451249,1.0,1.0,0.477279432247,0.415679001331,0.411235536251,0.596014530391,0.393131369992,0.435780775544,0.191615255383,0.259593841911,0.465619596655,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.491659503043,0.498388244928,0.504018942262,0.501791097325,0.370793462942,0.370166971992,0.377447665157,1.0
--9084737061884,356845,18151603,LABFER,2011-06-08 22:01:00,1,0,1,0,0.477840451249,0.0,0.0,0.513819865532,0.57370097967,0.515259459223,0.531035171719,0.588560081614,0.575065452334,0.548333333333,0.853281512928,0.652569321169,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.492793793917,0.494202212163,0.498388244928,0.497057698292,0.498749633124,0.563526096439,0.551950225177,0.53140770532,1.0
-2498892056012,420892,14058203,LABFER,2011-01-15 07:40:00,1,0,1,0,0.644444444444,0.0,0.0,0.405001041317,0.386119146866,0.423868392294,0.222487029647,0.443595768978,0.375262753305,0.358265582656,0.263405448402,0.475598346223,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.455384966113,0.475907374722,0.475553026373,0.492546088886,0.496043978712,0.207757472879,0.205810724309,0.218182839202,1.0
-11594463263209,459456,23776653,LABFER,2012-02-18 17:26:00,1,0,1,0,0.2,0.0,1.0,0.344430485551,0.38024101925,0.335248588512,0.313028206758,0.246569441814,0.263813522014,0.552941176471,0.660572751645,0.322646824636,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45813290055,0.472744722666,0.468417916605,0.493246617678,0.496531896118,0.453776099239,0.467459617776,0.468506265371,1.0
--6849020836703,410971,17190687,LABFER,2011-04-30 08:59:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.335672123141,0.227622071381,0.21525953977,0.0814047731942,0.24858529434,0.268557977935,0.405579399142,0.308387094144,0.259900641504,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.413108644492,0.449582125866,0.41816275361,0.487497478684,0.491315790197,0.13028293375,0.118560189765,0.101220625361,1.08029234566e-08
-6137592242325,449622,24645656,LABFER,2012-03-19 16:46:00,1,0,1,0,0.215827338129,0.0,0.0,0.446807977497,0.277665815337,0.358892955867,0.481950239936,0.366290950985,0.403150852405,0.187927710843,0.158492882919,0.404706243374,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.488145971349,0.500403435308,0.500307457597,0.498871642052,0.499288694567,0.534220538845,0.359276646663,0.278682776459,1.0
-142790115887,468001,1741664,LABFER,2009-06-29 22:00:00,0,1,1,1,0.11914893617,0.0,0.0,0.220807786413,0.22070609961,0.212713718973,0.155039180143,0.213071152568,0.214018355243,0.2,0.0870433738978,0.14341902663,0.222416167301,0.257917683272,0.300217184164,0.287666847741,0.429092837454,0.459127988303,0.456676473752,0.489483547872,0.494655714015,0.113880926218,0.0934742322188,0.0774309081586,1.0
--11164542335917,454049,9372284,LABFER,2010-05-30 18:09:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.55519212329,0.528643763791,0.559429488448,0.618171843747,0.627913373678,0.551223251909,0.782721826756,0.721585904844,0.68178145459,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498865405233,0.502878261035,0.499081689524,0.501946731861,0.501268897281,0.667163390972,0.665517677837,0.656600676434,1.0
--5536747982133,368125,9004042,LABFER,2010-05-21 13:49:00,1,0,1,0,0.307692307692,0.0,0.0,0.318849869661,0.232531310423,0.205631446437,0.424362022102,0.122091644739,0.148252293198,0.2,0.0107579157317,0.086608576328,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.456920549273,0.467708393231,0.458002940771,0.482712320951,0.488630722447,0.00865652403224,0.00455304352485,0.00213102661401,0.0
-4305102595745,356981,1534239,LABFER,2009-05-29 11:02:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.150432541012,0.116219715117,0.175404872374,0.0421796411903,0.118490638782,0.0758998232236,0.0,0.152265195496,0.024605259356,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.268934414952,0.370999336909,0.0222810961269,0.32179092049,0.406439756011,0.00589155182554,0.00125391975242,0.000143246593709,1.77746966902e-51
-912509930855,422435,22543205,LABFER,2011-12-26 07:52:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.533877242246,0.405977140536,0.458102285193,0.601422183449,0.614622097685,0.403995335018,0.581217745751,0.541778100386,0.438611097516,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.496090160743,0.499081689524,0.494463685127,0.497407389106,0.33038056651,0.339526753489,0.419575749159,1.0
--7634880437308,448519,34746782,LABFER,2013-04-26 04:59:00,0,1,1,1,0.634686346863,0.0,0.0,0.501533492246,0.493316205678,0.481834595862,0.512752495695,0.362922013255,0.439032380289,0.338095238095,0.195539238529,0.327646889034,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506123149708,0.501433642923,0.509322489469,0.500004732351,0.500174700941,0.677828945623,0.667842299249,0.659602420154,1.0
--10388108592137,373758,12078957,LABFER,2010-09-29 22:07:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.19656860272,0.226349167291,0.234991501283,0.206925036064,0.126827987322,0.328632446196,0.174892301648,0.111015162725,0.153591151183,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.413054653549,0.446790743098,0.428495991973,0.483985701444,0.490743625069,0.0402265850188,0.0372282243179,0.0348713429161,1.11361778397e-17
-6582455115219,357123,37496644,LABFER,2013-09-01 15:01:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.458309750428,0.492559499028,0.39621073519,0.459675021899,0.378559162965,0.413025395549,0.392166713374,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.5079211516,0.503647181609,0.626071887124,0.631925621535,0.632826832267,1.0
--394175897700,452871,23272975,LABFER,2012-01-20 09:51:00,1,0,1,0,0.634686346863,0.0,0.0,0.48810841934,0.439215627483,0.485052304357,0.457139445902,0.409646489671,0.488941791975,0.491981132075,0.34778725407,0.459445641829,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.484990388345,0.490250245618,0.510015679179,0.483992684253,0.493319662558,0.0707910749605,0.0587017752745,0.0584898279022,4.0159388088e-86
-9040043422344,436572,12318105,LABFER,2010-10-26 22:06:00,1,0,1,0,0.218911917098,1.0,0.0,0.242354076217,0.199138413705,0.241331635982,0.325693716067,0.180884672333,0.15814608428,0.194738000243,0.0539234316457,0.118969890067,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.460556613929,0.478951555981,0.491813578127,0.499932305213,0.498964992458,0.571279642952,0.728411616946,0.834941407198,3.1299803823e-24
-6928149359872,363449,38062800,LABFER,2013-09-27 07:35:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.136319701156,0.155674473893,0.1770819738,0.22309880615,0.17788251735,0.158490631435,0.00692168102083,0.428395487311,0.233842710947,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.451427981118,0.47113624478,0.469108659157,0.494118417816,0.497836392773,0.147827151113,0.141401010962,0.121334692641,4.84214084426e-13
-11211108636149,367820,24505084,LABFER,2012-03-13 14:02:00,0,1,1,1,0.224899598394,0.0,0.0,0.533877242246,0.529866719993,0.506895644196,0.635216879678,0.71924813961,0.432494908714,0.401606765328,0.538323081848,0.467715945282,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.494664160821,0.497452482365,0.487344472973,0.484015075074,0.491006768893,0.182019858031,0.162913415828,0.155882349588,1.0
--8473289944094,400982,23418430,LABFER,2012-01-11 20:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.0970693890468,0.11476524794,0.151613731689,0.0162972882491,0.247369727378,0.181760451198,0.281746532156,0.104637928582,0.131751981004,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.424212991038,0.456048330738,0.458581907474,0.480644516307,0.488588607218,0.0227101780866,0.0239006417013,0.0260476609787,1.14440312035e-68
--2872563512313,429544,3102648,LABFER,2009-09-07 22:01:00,1,0,1,0,0.477840451249,0.890410958904,1.0,0.492049174403,0.4854239264,0.458037356979,0.505789316193,0.533269445908,0.492246458429,0.535341880342,0.820038374088,0.605967898604,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.486397697734,0.496713484397,0.509322489469,0.50268493022,0.50102881567,0.582453152346,0.592847718977,0.598555158553,1.0
-6284869114916,397461,39235970,LABFER,2013-11-13 20:26:00,0,1,1,1,0.477840451249,1.0,1.0,0.509521716225,0.596372033093,0.50288870317,0.690888743704,0.64905031614,0.731603387559,0.577473195568,0.76085832012,0.675305002157,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508184324419,0.506642248171,0.509322489469,0.502788039248,0.501825750935,0.801076615001,0.783720444832,0.767307881778,1.0
--7511187880734,460668,7307587,LABFER,2010-02-28 19:42:00,0,1,1,1,0.477840451249,1.0,1.0,0.459705424403,0.488548111845,0.413399679492,0.549348495519,0.49284531291,0.386957165909,0.55342377261,0.282070006978,0.548044798731,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.501363728197,0.509322489469,0.499444036989,0.498405940195,0.453675669588,0.468217147242,0.473509960959,1.0
--4880322923756,422806,32839800,LABFER,2013-02-02 21:00:00,1,0,1,0,0.634686346863,0.0,0.0,0.349519288705,0.313630901046,0.394169405484,0.456595744681,0.333397277611,0.324376992695,0.42,0.67375,0.181698565653,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.483245406495,0.486763149549,0.499081689524,0.497062541625,0.497235330399,0.526077350664,0.604728426997,0.695293352898,0.999999999997
--10104834091776,364769,34267065,LABFER,2013-04-08 10:39:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.622099395159,0.54850404295,0.579520885357,0.612142405169,0.685356011656,0.546490402478,0.766217633867,0.823366631441,0.647158711138,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506276942852,0.510888218981,0.502786532,0.502214225927,0.70529692884,0.718738128165,0.729113046561,1.0
--899646289249,465678,37773651,LABFER,2013-09-09 23:46:00,0,1,1,1,0.224899598394,0.0,0.0,0.405001041317,0.479222491032,0.511135343693,0.337187347081,0.510707695899,0.367392369387,0.491428571429,0.65293476667,0.388070254627,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.476735705711,0.490496131252,0.498277106792,0.506003473658,0.501194935537,0.552617110263,0.54397334125,0.537447245197,1.0
--4988257654579,428996,28182003,LABFER,2012-08-09 21:43:00,0,1,1,1,0.477840451249,0.0,0.0,0.393993978212,0.50056982581,0.448185083143,0.525638485611,0.468143220946,0.617519883069,0.680341880342,0.823985771888,0.563159974728,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.499326199082,0.498388244928,0.503034552259,0.502243450336,0.824820193804,0.853350727442,0.86231740331,1.0
-1285598157302,399469,9757447,LABFER,2010-07-04 23:36:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.544592113781,0.599099947423,0.616730285712,0.708229248033,0.758912864602,0.749073848627,0.612858368383,0.78921138099,0.684452649857,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.546643749141,0.54996788899,0.588726696487,0.518735478222,0.510561562175,0.989452803056,0.994867207211,0.99544831728,1.0
-8422410846601,371344,13020192,LABFER,2010-11-26 22:07:00,1,0,1,0,0.477840451249,1.0,1.0,0.470416740784,0.512086438318,0.490446260504,0.567362164043,0.566255454121,0.570260205348,0.170260671228,0.378257091013,0.642371974517,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498658105002,0.501718500798,0.498388244928,0.496607930162,0.497944771588,0.520017200199,0.518769009214,0.520234271532,1.0
-8740981064850,443040,15587000,LABFER,2011-03-09 10:52:00,1,0,1,0,0.222222222222,0.0,0.0,0.173797046728,0.309531353642,0.255414930008,0.208771929825,0.201597182577,0.198547451346,0.222950819672,0.275694585689,0.2712732101,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.46830863629,0.479382558563,0.487808173527,0.498721428829,0.498390591496,0.00689900716271,0.00640125398119,0.0122993447282,1.0
-6827050246237,419745,35501482,LABFER,2013-06-24 15:10:00,1,0,1,0,0.0512820512821,0.0,0.0,0.10958576394,0.322874290587,0.291566472053,0.0363760683761,0.26883604159,0.145925395277,0.406,0.280392156863,0.240203370876,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.467749911894,0.479221114686,0.476886910056,0.492491915086,0.495014423371,0.533180696422,0.485970891886,0.466965790251,1.0
--6141011663023,360071,19810824,LABFER,2011-08-13 20:46:00,1,0,1,0,0.108695652174,0.0,0.0,0.346664082476,0.323618664047,0.282523924221,0.308644183409,0.402111812165,0.340577883379,0.2,0.676442687747,0.237596749226,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.475142451678,0.488031471896,0.480767972341,0.499172063723,0.499784695109,0.356921492845,0.370370757673,0.384662606821,6.01389072439e-90
-8990872272323,467665,18131605,LABFER,2011-06-22 22:01:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.616216479264,0.605322047464,0.628590942008,0.753922019494,0.77602712872,0.731387574514,0.806935251885,0.809402534666,0.766886585766,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507171594053,0.510888218981,0.503257207089,0.501319813472,0.533088865748,0.531996333505,0.521570937463,1.0
-9883709042055,369978,1403376,LABFER,2009-05-21 10:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.206946439824,0.166160429382,0.201795501591,0.0413853989813,0.101419940593,0.157231070809,0.203720930233,0.047079948215,0.102541276974,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.281716224011,0.386830978875,0.0244174802257,0.326163840267,0.410397984721,0.00614719411509,0.000469310791449,2.20304715297e-05,0.0
--1797813912837,381038,32889208,LABFER,2013-02-11 07:39:00,0,1,1,1,0.403846153846,1.0,0.0,0.242354076217,0.205029119937,0.229737893659,0.560024281922,0.221476889878,0.264009876925,0.224293101551,0.0629807970046,0.233757400121,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.445514408169,0.469980994109,0.457893296213,0.501250680926,0.502715093838,0.676386850557,0.64191625248,0.60095408861,1.0
-6068241177574,373265,17068740,LABFER,2011-05-12 08:21:00,1,0,1,0,0.477840451249,0.0,1.0,0.477279432247,0.394090173493,0.417910209805,0.421236637789,0.399686201578,0.42381714609,0.476944998154,0.446302389596,0.461983927873,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.491659503043,0.498388244928,0.498444457155,0.49953342518,0.346084915013,0.356888068824,0.383059199597,1.0
-1324935632735,360148,15181099,LABFER,2011-03-05 18:25:00,1,0,1,0,0.218911917098,0.0,0.0,0.229377003589,0.272641899806,0.243995913376,0.24146130031,0.185255036179,0.186983278551,0.00372093023256,0.306328550364,0.167358350206,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.439800003643,0.466313176899,0.456676473752,0.48778425493,0.494407539398,0.192075568327,0.20043762669,0.214289805667,1.0
--8658785747069,441059,15831938,LABFER,2011-03-04 22:01:00,1,0,1,0,0.477840451249,1.0,1.0,0.542108744912,0.496548118214,0.501994710533,0.673849084916,0.673682692298,0.542017210504,0.285897435897,0.616837394755,0.442160569435,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.501882363083,0.509322489469,0.501313744391,0.501455075683,0.417405395388,0.425091090338,0.433334861175,1.0
--11931624918520,399117,32376160,LABFER,2013-01-19 11:32:00,0,1,1,1,0.485294117647,1.0,1.0,0.301708115339,0.397494029481,0.321338348764,0.183233798686,0.119084759057,0.433393874681,0.518948717949,0.0627931743652,0.228775410385,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.526318306818,0.529677771086,0.555427770922,0.515203175481,0.507238485729,0.947190482788,0.977641948702,0.982368954967,1.0
-10273323783718,435737,30856448,LABFER,2012-12-02 21:49:00,0,1,1,1,0.224899598394,0.0833333333333,0.0,0.54703971705,0.454508421779,0.49775500829,0.734942552046,0.497614131497,0.584780920667,0.520427350427,0.694740164107,0.437033899693,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.49175731024,0.497248184483,0.498277106792,0.499268581191,0.497791452324,0.253967016629,0.232214301827,0.216371302699,1.0
--6222416382704,461146,34576757,LABFER,2013-04-28 08:25:00,0,1,1,1,0.218911917098,1.0,1.0,0.249823626119,0.218530536993,0.258124972937,0.401312399789,0.100980458496,0.221016557939,0.272131147541,0.379242150097,0.286388899315,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.448322735854,0.470638683622,0.445050483537,0.488038387149,0.496722717567,0.435967662626,0.56409633796,0.592066677499,7.39855101075e-70
-6748983815607,428682,37824128,LABFER,2013-09-18 15:26:00,0,1,1,1,0.11914893617,0.0,0.0,0.161439026276,0.212010336815,0.195802342503,0.0896725599068,0.136044078409,0.178580626316,0.205579399142,0.0497322549648,0.18612530043,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.441715562207,0.45832099916,0.456676473752,0.496044141459,0.498569630438,0.0401447325961,0.0185594705292,0.0115786037163,1.52719090092e-38
--7867780793158,407655,29742201,LABFER,2012-10-07 22:00:00,0,1,1,1,0.224899598394,0.0,0.0,0.620755809273,0.549045569675,0.485230971921,0.557876042629,0.617792481612,0.632504426855,0.935948008816,0.720381511079,0.645895602666,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.490327810328,0.500380285503,0.498277106792,0.500162798565,0.501532160073,0.479494786893,0.491151687771,0.509519789283,1.0
--126611153973,392816,18988670,LABFER,2011-07-21 18:24:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.203388698719,0.225093815826,0.131392794838,0.184509336447,0.204693264214,0.206921681021,0.127927016475,0.109246471742,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.456299087725,0.477496812221,0.479322944411,0.494987229963,0.497527370211,0.274119493194,0.255690606894,0.233486469706,1.0
--3327342782469,471102,33223159,LABFER,2013-02-24 21:28:00,0,1,1,1,0.634686346863,0.963636363636,0.0,0.533877242246,0.445575498869,0.470706290688,0.599722611873,0.231884848946,0.375422974732,0.401681181371,0.316187543888,0.341594195705,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.500636423591,0.499814176955,0.498388244928,0.500879211714,0.502045019539,0.646262957253,0.685798904162,0.711283502689,1.0
-3087590705227,358964,16492789,LABFER,2011-04-25 17:13:00,1,0,1,0,0.477840451249,0.0,0.0,0.444935682247,0.393249421343,0.453072913977,0.37274207564,0.478414028482,0.43738905315,0.383011583012,0.724274075097,0.363489786612,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.493717125832,0.493080771581,0.498388244928,0.492420008704,0.497150930382,0.640172663137,0.641680420301,0.637464062731,1.0
-11676961766389,371713,27940922,LABFER,2012-08-04 21:00:00,1,0,1,0,0.218911917098,1.0,0.0,0.254111095134,0.358676292563,0.280484029358,0.305859999886,0.210868283765,0.293680274194,0.301523021858,0.516,0.324517082125,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.480846708379,0.468417916605,0.495707119441,0.497907485276,0.326753265307,0.327975376111,0.323826092271,1.0
--11675076940634,393827,14763255,LABFER,2011-01-21 17:18:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.459675021899,0.475540295041,0.396583429884,0.434665766465,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500985415957,0.500179047716,0.49081296108,0.501082377896,0.512583216958,1.0
--2792865507497,403673,33858652,LABFER,2013-03-19 08:27:00,0,1,1,1,0.485294117647,0.0,0.0,0.290698498106,0.384179060402,0.388909080054,0.629521126761,0.251799326276,0.320054705152,0.215230769231,0.295876963351,0.188142694553,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.469489775956,0.47853946977,0.487808173527,0.50277417743,0.500909589615,0.483790579952,0.520965547553,0.530075490683,1.0
-3925958711253,406742,10073844,LABFER,2010-07-21 09:57:00,1,0,1,0,0.477840451249,1.0,1.0,0.51469596705,0.374893430588,0.381736882691,0.257449698189,0.367470778643,0.395708595754,0.116904761905,0.325523528669,0.428777419526,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.49782378844,0.509322489469,0.498309110254,0.499019869919,0.469494129012,0.399551308608,0.378054236143,1.0
-3764617508426,354970,31781889,LABFER,2013-01-15 00:48:00,1,0,1,0,0.218911917098,0.04,0.0,0.149155125441,0.375906547796,0.344392155278,0.294080546477,0.234553978933,0.350979351879,0.407514831905,0.101514316838,0.267184687924,0.222416167301,0.300974526888,0.368633683804,0.379422352055,0.466001659478,0.485464388496,0.455878037868,0.494496701231,0.497111010326,0.36520073689,0.361905537351,0.363473221795,1.0
--10960991472663,422155,40341023,LABFER,2013-12-17 12:53:00,1,0,1,0,0.477840451249,1.0,1.0,0.593898552532,0.55924136496,0.504265556313,0.519328271375,0.558613836927,0.604329649651,0.610251941248,0.662264822962,0.475031530967,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.510177189495,0.509458902848,0.498388244928,0.503924472007,0.501076318479,0.526807952763,0.521723934214,0.525952195213,1.0
-865298744929,454433,4053301,LABFER,2009-10-28 16:54:00,1,0,1,0,0.634686346863,0.257142857143,0.0,0.279438749583,0.393351410725,0.376983441025,0.537264446446,0.449374762189,0.365490123124,0.707124870674,0.331765174698,0.197619581358,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.481227552222,0.491383207677,0.487808173527,0.497427937027,0.49709059691,0.438798096543,0.534717713592,0.5675478235,1.0
-7835235072385,436433,9964809,LABFER,2010-07-12 11:00:00,0,1,1,1,0.477840451249,1.0,1.0,0.499050123376,0.367522552082,0.408338160781,0.373178961163,0.265060199584,0.441961250063,0.422529913067,0.674581901008,0.568880413718,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.48677520081,0.491515723378,0.498388244928,0.497021397428,0.501984551223,0.384999878816,0.394903137741,0.425239939327,0.999709833163
-3069894957340,370919,15542429,LABFER,2011-03-19 00:51:00,1,0,1,0,0.634686346863,1.0,1.0,0.394369320503,0.306162416239,0.32706619802,0.394506915012,0.291283572261,0.262742955793,0.2,0.473638008199,0.318194814546,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.477881294971,0.480580412685,0.499081689524,0.491709847755,0.495185963615,0.0998788580113,0.070065568428,0.0474501085073,0.999999999999
--11524004872904,456192,3333185,LABFER,2009-09-19 08:40:00,0,1,1,1,0.634686346863,1.0,1.0,0.581475193791,0.46133957098,0.492013619478,0.525864855115,0.677152074525,0.613293143395,0.763151690621,0.522347487629,0.502379807979,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.499456248841,0.498388244928,0.50070113105,0.5008739475,0.321703247589,0.27889064828,0.252364185656,0.999999974864
--1975960733700,385227,8222391,LABFER,2010-04-18 17:08:00,0,1,1,1,0.477840451249,0.0,0.0,0.513819865532,0.51045466703,0.462382086701,0.54851921426,0.464823820092,0.561516223318,0.652537655534,0.63167181423,0.56088538633,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.488520879056,0.498713723176,0.509322489469,0.499404980892,0.501767298449,0.546416833259,0.561557941334,0.575725580296,1.0
--5827990066754,420614,34126038,LABFER,2013-04-08 06:12:00,1,0,1,0,0.218911917098,0.0625,1.0,0.289154215854,0.179365838613,0.243455980351,0.225488719515,0.202518913901,0.240609324099,0.577358490566,0.137905903755,0.270071151417,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.433770504237,0.46064011045,0.457893296213,0.490055206333,0.494713483437,0.266131848136,0.272268433799,0.272253858638,1.0
-11532192165772,455096,17632766,LABFER,2011-06-13 13:19:00,0,1,1,1,0.11914893617,0.0,0.0,0.0970693890468,0.158993048563,0.158641768907,0.0826591831773,0.161801606574,0.110615080898,0.405579399142,0.132354965324,0.190982759306,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443116406313,0.468582228697,0.467548574593,0.499275424689,0.497277392083,0.0865746269056,0.00525806118164,0.000325780635306,0.0
--5532272450355,376954,16959452,LABFER,2011-04-28 14:42:00,1,0,1,0,0.485294117647,1.0,0.0,0.203426999413,0.246976120813,0.265566545847,0.259592564298,0.210080762987,0.187543689648,0.250223546945,0.126382905408,0.159377290058,0.474085517325,0.481003341925,0.46241236511,0.470629423827,0.444042855727,0.467122643934,0.452592169942,0.487961787591,0.494249257428,0.0975613418959,0.0886686684094,0.0935540396334,1.0
-12149544283747,464554,17858658,LABFER,2011-06-12 03:09:00,1,0,1,0,0.215827338129,0.0,0.0,0.324198999858,0.331539628797,0.246724499577,0.530172543326,0.19029027719,0.335796896651,0.0163695610504,0.2,0.270556599833,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.46668550741,0.479706553452,0.459330712414,0.489533408322,0.494922139727,0.319329364793,0.302842605658,0.296908869856,1.0
--4988257654579,449017,32659491,LABFER,2013-02-08 00:58:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.533877242246,0.485140683127,0.529177951991,0.543391967284,0.578424193647,0.568593489696,0.52535959975,0.627898571624,0.593816193063,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.505334734443,0.510888218981,0.504121806681,0.501024881611,0.785305153926,0.824205761471,0.841187704127,1.0
-8214287770760,401515,35114079,LABFER,2013-05-29 09:52:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.161439026276,0.186365374305,0.17228139254,0.105870629918,0.138307969804,0.13783196684,0.222205316012,0.235417641173,0.150871026937,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443666016897,0.467747715499,0.467548574593,0.495124652674,0.495579264909,0.275503256082,0.327382311335,0.342357434764,1.0
-8037726650806,434127,8655884,LABFER,2010-05-20 13:08:00,0,1,1,1,0.477840451249,1.0,1.0,0.499050123376,0.467012818976,0.472352963435,0.498711138123,0.472861192164,0.52052095801,0.529518787609,0.385647926232,0.443972846983,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496853591771,0.498388244928,0.498413696538,0.498427602255,0.31968821324,0.308558475291,0.300811492283,1.0
--12324610511888,433713,11981019,LABFER,2010-09-22 15:00:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.459675021899,0.475540295041,0.421331143587,0.450889413469,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500600757052,0.499986717778,0.469186249185,0.469135243002,0.474691566227,1.0
--6991905212746,407839,34656113,LABFER,2013-04-21 22:00:00,1,0,1,0,0.215827338129,0.0,0.0,0.277167737857,0.321381027194,0.340099180166,0.101886335342,0.19987776716,0.260707618024,0.222950819672,0.213399503722,0.239933762682,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.48799751652,0.497593486268,0.487808173527,0.49864041968,0.496860423255,0.384630464148,0.393079642062,0.407186609452,1.0
-10740417380908,363611,23031741,LABFER,2012-01-21 07:29:00,1,0,1,0,0.634686346863,0.963636363636,1.0,0.533877242246,0.397713262396,0.440803093808,0.623094655498,0.273766860721,0.402279647231,0.469230769231,0.555062646231,0.41804081411,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.49198349286,0.495746281777,0.510888218981,0.499119411923,0.499381925617,0.386024518651,0.395191131582,0.382969000212,1.0
--565196898546,384647,13093121,LABFER,2010-11-21 09:25:00,0,1,1,1,0.403846153846,1.0,0.0,0.344430485551,0.392155353562,0.400742584619,0.568928801524,0.422257671347,0.51765274868,0.380487804878,0.285303817661,0.424759691555,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.476241893939,0.490681611771,0.468417916605,0.498480064316,0.498342286015,0.572158910121,0.567472979765,0.556561225965,1.0
-8770557202180,386405,2359632,LABFER,2009-08-07 11:42:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.469760288475,0.475540295041,0.459430758433,0.473412586011,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.501841298042,0.500606991992,0.600415995389,0.605596717928,0.609595125966,1.0
--5658195408453,467209,18368931,LABFER,2011-06-24 20:42:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.548646984401,0.550938182717,0.597587402474,0.678446723679,0.669214107281,0.636173834835,0.733349621346,0.663171792947,0.686900278371,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.504213502192,0.503362529306,0.653757178955,0.655717624238,0.654715583741,1.0
--10046402440744,394152,21290480,LABFER,2011-10-25 13:42:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.388478357514,0.314249721823,0.371059086346,0.294846388576,0.36252829422,0.295864131853,0.0818181818182,0.173840589437,0.331236539916,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.485266019479,0.480886833765,0.498578643544,0.499545804134,0.541111528806,0.569913808744,0.5711147577,1.0
-3732756649118,453942,22382966,LABFER,2011-12-28 09:58:00,0,1,1,1,0.485294117647,0.0,0.0,0.298101953515,0.436921370158,0.273338264789,0.419228927935,0.470698873469,0.291392082802,0.0,0.505130957492,0.429003719233,0.474085517325,0.462338008602,0.421863261099,0.421077804787,0.441543541797,0.463960278875,0.452592169942,0.489006008637,0.496209363569,0.143137519654,0.129929651546,0.143864818515,1.0
--2569007331600,407469,13209751,LABFER,2010-11-18 10:09:00,1,0,1,0,0.0512820512821,0.0,0.0,0.23307868602,0.317118044446,0.346214190724,0.13479020979,0.437620900979,0.327717484655,0.4,0.507517150781,0.478933223511,0.474085517325,0.481003341925,0.45330786382,0.45525281484,0.459694872601,0.470301479278,0.45345738618,0.490145521513,0.495010375321,0.0475676263775,0.0292649175091,0.0295574367373,1.0
--10831611285501,452358,26043221,LABFER,2012-04-28 21:00:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.48810841934,0.430917311477,0.454462140313,0.65076418738,0.477033279732,0.423415175984,0.550965391702,0.555720049966,0.470146013614,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.50382689559,0.521811420026,0.506628698516,0.502743284581,0.636304558929,0.717135807225,0.748305530407,1.0
--8398878290804,373936,25249423,LABFER,2012-04-04 07:16:00,1,0,1,0,0.634686346863,0.0,0.0,0.33497593669,0.310463068837,0.448022345397,0.475954276161,0.618997793887,0.487894679052,0.203703703704,0.626354844697,0.538888649849,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.491919685985,0.50137487188,0.487808173527,0.501293992177,0.501481814958,0.758491939296,0.766851035739,0.77302176724,1.0
--7685078489523,442458,28635670,LABFER,2012-08-26 10:29:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.477279432247,0.421048343586,0.497574545957,0.613494676233,0.518232946568,0.500964423579,0.718052214355,0.532690650622,0.499757972159,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.498138940068,0.498388244928,0.49650286015,0.499062497971,0.530621323331,0.535947830391,0.56235403108,1.0
--9660233670843,450204,35273072,LABFER,2013-05-18 03:26:00,0,1,1,1,0.477840451249,0.0,0.0,0.437345252708,0.428945682546,0.442819135242,0.269771109883,0.581901965865,0.411379199083,0.326829268293,0.3,0.46534556125,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.501526711629,0.504035575772,0.509322489469,0.501257691428,0.501368030029,0.478560488876,0.478896878725,0.478920235084,1.0
--10746048523338,429193,19934341,LABFER,2011-08-26 16:51:00,0,0,1,0,0.218911917098,0.118881118881,0.0,0.263221027416,0.227204105658,0.248646862675,0.0453448275862,0.169164013423,0.248402692978,0.203603603604,0.466875,0.140901539095,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.447112051635,0.471565105419,0.479322944411,0.492956897725,0.495363339567,0.100928609583,0.0714861141877,0.067197983477,1.0
--2172336906197,455079,1677721,LABFER,2009-06-04 08:53:00,0,1,1,1,0.477840451249,1.0,1.0,0.261359470336,0.339067091586,0.384951789197,0.232309624629,0.395058433704,0.420180071558,0.188675213675,0.224293713679,0.291704981078,0.474085517325,0.462338008602,0.44361642082,0.443117998191,0.474955756352,0.486716979852,0.476886910056,0.495993936256,0.497218457433,0.574374872966,0.606983500742,0.630357615762,1.0
-6318658483532,454007,21733770,LABFER,2011-11-28 01:58:00,1,0,1,0,0.224899598394,0.0,0.0,0.26139103103,0.306985187449,0.391675906047,0.240725658466,0.431227402995,0.307915187088,0.677049005379,0.397363414716,0.399331176626,0.474085517325,0.481003341925,0.477538705036,0.493054316564,0.478890271932,0.487719337206,0.465008328635,0.489242497066,0.49424097335,0.36579895066,0.354713817337,0.349102345539,1.0
-10359469260990,453836,36186874,LABFER,2013-07-23 12:19:00,1,0,1,0,0.218911917098,0.04,0.0,0.217870637328,0.387531577896,0.293591083628,0.33785241681,0.429459971232,0.314826699521,0.6,0.40239672849,0.205423068045,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.457205909306,0.472820395361,0.491813578127,0.485894279429,0.4912786365,0.0584311601655,0.0400602126471,0.0343502131201,6.39234163255e-39
-4560718992221,424882,40980385,LABFER,2014-02-16 11:58:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.188306679826,0.196004220471,0.0594658567239,0.098608451618,0.111928877622,0.0285302188138,0.0627822218327,0.101817301705,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.425786577849,0.453752818679,0.457893296213,0.4875921811,0.492748448156,0.119248272782,0.127648135726,0.121596785058,1.0
--12000305235915,459231,2182166,LABFER,2009-07-09 23:56:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.533877242246,0.486362546052,0.488359463056,0.572418035098,0.596183953366,0.52920375226,0.817408482503,0.638173090477,0.653854396867,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.494601248614,0.499081689524,0.502136138776,0.50089391761,0.497250559218,0.498141974614,0.496301537228,1.0
-1933492252410,432834,1899398,LABFER,2009-07-06 23:45:00,1,0,1,0,0.11914893617,1.0,0.0,0.150432541012,0.182959392499,0.175704290078,0.0630008783106,0.149618165644,0.0643631494756,0.41662591687,0.250455507778,0.0922155364558,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.413054653549,0.441990324029,0.456676473752,0.484768066198,0.492647424686,0.0652415986583,0.0457100575476,0.0227067616636,2.00628328338e-126
--1165273467866,427577,3656335,LABFER,2009-10-01 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.122053767164,0.137241582941,0.0495888455882,0.143316256874,0.0371857009413,0.0235475978912,0.208855503454,0.0544234369186,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.424292813345,0.455301768051,0.428495991973,0.481918290817,0.491666201703,0.000914119181932,0.00010805552231,1.26536363558e-05,0.0
-5237701080801,387552,36086817,LABFER,2013-07-03 22:01:00,1,0,1,0,0.11914893617,1.0,0.0,0.205196760615,0.119902920724,0.19659131039,0.226075164665,0.253896253691,0.114454564259,0.401898188093,0.513399503722,0.286981009344,0.222416167301,0.300974526888,0.332627590244,0.327263965497,0.420051593587,0.448056437716,0.392201516402,0.480121990845,0.487402766628,0.0130635362096,0.0116200214437,0.0119605053971,1.73438760329e-32
-6982196613410,441532,27690830,LABFER,2012-07-22 21:00:00,1,0,1,0,0.11914893617,1.0,1.0,0.331221297585,0.179926588699,0.195159814765,0.480852280852,0.212747878176,0.422623471224,0.220141700405,0.330145629329,0.310775419106,0.222416167301,0.300974526888,0.346810092122,0.344413982292,0.439048251238,0.462751838188,0.457893296213,0.487030796116,0.493238911422,0.262703618617,0.3508003425,0.43813033193,1.0
-10661204451242,379245,9563427,LABFER,2010-06-29 20:47:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.533877242246,0.462786888413,0.476465275425,0.431530170306,0.543100507832,0.54476879362,0.614717142713,0.610603373348,0.497363361993,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.498813902017,0.498388244928,0.497120211358,0.499468619269,0.624914575724,0.638625315783,0.655786868942,1.0
--9688882858203,387844,25699276,LABFER,2012-04-16 09:12:00,1,0,1,0,0.11914893617,1.0,0.0,0.151771834245,0.169718803407,0.1674325485,0.202614379085,0.166927246101,0.0860352722147,0.209300329374,0.270244999267,0.0928212910494,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443116406313,0.468323373533,0.467548574593,0.489049184415,0.494127164143,0.0579758029588,0.0578150445181,0.0640621449082,1.0
-1956566956999,392574,27235718,LABFER,2012-07-12 01:51:00,0,1,1,1,0.477840451249,1.0,1.0,0.423233676679,0.525898722266,0.484310133105,0.45256135636,0.533149024911,0.484060041347,0.748765652952,0.419755963698,0.521974980228,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.500139081134,0.498388244928,0.499463289437,0.500427336438,0.563804151967,0.559659961472,0.554866367751,1.0
-10059152269957,472785,20678316,LABFER,2011-10-08 11:11:00,1,0,1,0,0.477840451249,0.0,0.0,0.499050123376,0.456776041389,0.453877808812,0.346258581205,0.557518318182,0.598473550816,0.55196458218,0.626135868704,0.452909845753,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.490838952681,0.49533534402,0.486583208859,0.497185504123,0.499547965145,0.417925593712,0.388777148356,0.311153197379,1.0
--5253776906253,406865,21466658,LABFER,2011-10-23 12:19:00,0,1,1,1,0.644444444444,0.0,0.0,0.311580081738,0.370482234055,0.382586806631,0.37123946683,0.471157088235,0.313939278187,0.4,0.508728070175,0.462038995227,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.464305404491,0.481630182721,0.486472150176,0.4962267138,0.498547618325,0.317994564727,0.1153049132,0.00554742004648,1.46472521888e-98
-3761834569263,404196,34408647,LABFER,2013-05-05 12:26:00,1,0,1,0,0.218911917098,0.04,0.0,0.223028242855,0.329613234755,0.306843736956,0.332968564969,0.206557613052,0.281011161497,0.432183908046,0.134901573653,0.193079571569,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.460642277899,0.477880789332,0.456676473752,0.493735373982,0.497386398211,0.265044605947,0.265420836316,0.26364856901,1.0
--1602644894765,400441,25807417,LABFER,2012-04-19 17:30:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.465296775039,0.475540295041,0.52186869617,0.491544428916,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.499570516748,0.499006763101,0.450770417456,0.456929191893,0.466821717313,1.0
-8755283798453,457357,5429695,LABFER,2009-12-31 13:18:00,1,0,1,0,0.0512820512821,0.0,0.0,0.217306868292,0.271300918377,0.323043234081,0.0446666666667,0.44489136955,0.236097201507,0.210081632653,0.397415039048,0.407198755186,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.488559329404,0.493564563253,0.489373727303,0.497034865675,0.496104146295,0.184674422387,0.176455129633,0.172831912972,1.0
--11180297435013,389365,21257425,LABFER,2011-10-23 09:28:00,1,0,1,0,0.218911917098,0.04,0.0,0.174417414943,0.191923694437,0.217637641525,0.238151925729,0.0860857120161,0.128688955552,0.415789473684,0.213227485515,0.29017002373,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.46691605258,0.479041232326,0.469108659157,0.491783378034,0.495517806335,0.419282087171,0.45131514196,0.4288795155,2.47593998275e-49
--5496357027779,432052,1408619,LABFER,2009-05-13 11:42:00,1,0,1,0,0.218911917098,0.04,0.0,0.198332683533,0.33539018336,0.251036955964,0.280971274216,0.193375092192,0.106094619089,0.0166259168704,0.0140859287395,0.115155011852,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.473171334499,0.467548574593,0.494119671079,0.49682081273,0.278226744003,0.275138061318,0.274735605255,1.0
-8689343546022,445589,7877816,LABFER,2010-04-14 09:17:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.186134585598,0.325106590172,0.329532783036,0.195765612964,0.220073631253,0.407447508768,0.166009852217,0.266997879801,0.418186216717,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.468655546047,0.486754529742,0.480015246909,0.498706706715,0.501968533635,0.549882229234,0.553727257511,0.550475782558,1.0
--4264948954612,466619,8472612,LABFER,2010-05-01 22:02:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.176495409217,0.246456166253,0.181209533617,0.0382915573391,0.129765688357,0.135466486163,0.0166259168704,0.0658958693752,0.134737142009,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.429572098935,0.458368386551,0.433417205192,0.485588083303,0.492563721261,0.130344858234,0.11357414652,0.0754602267057,1.0
-3197823768337,439773,30037143,LABFER,2012-10-25 12:04:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.261733597594,0.208736929282,0.131003708202,0.0926546754509,0.105648757497,0.00557939914163,0.0524060883619,0.0292677594211,0.222416167301,0.257917683272,0.315537389262,0.319720123692,0.434611425352,0.461871565965,0.447742978088,0.49080408236,0.49578755209,0.124083478202,0.11858297795,0.109191586691,1.0
--1201022030340,403094,20438648,LABFER,2011-09-19 11:40:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.467012818976,0.491011036995,0.492559499028,0.480732002755,0.490510119384,0.624508013796,0.627522671939,0.499749131818,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500296646393,0.509322489469,0.502214249235,0.502818203976,0.460938084628,0.428556511168,0.424132854682,1.0
-9854513507683,365499,2570611,LABFER,2009-08-26 22:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.200928083955,0.216579507327,0.209547369564,0.243786294523,0.152543297154,0.215627996165,0.0560089660187,0.204877377281,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.422490716393,0.453775125813,0.457893296213,0.483085776215,0.490494680303,0.102768892872,0.0999913792092,0.10037494424,1.0
--12168682940419,362134,23279250,LABFER,2012-01-17 07:19:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.469760288475,0.475540295041,0.459430758433,0.442567329204,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.499654240296,0.499048624742,0.430295669009,0.431891969333,0.438244671233,1.0
--1088416416037,420941,1240496,LABFER,2009-04-24 18:28:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.477279432247,0.480042173679,0.503640597069,0.344847914799,0.517468617616,0.553892883491,0.61108974359,0.468119047619,0.568048155588,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.502467254368,0.509322489469,0.499363027392,0.499293761661,0.630935597539,0.634632764285,0.636473599496,1.0
-8955012005803,451398,36351374,LABFER,2013-07-23 22:00:00,1,0,1,0,0.215827338129,1.0,1.0,0.331207700846,0.281011373024,0.264836138691,0.258771929825,0.198498771391,0.230232752791,0.0574285714286,0.338525766348,0.295566306712,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.457667423268,0.47253001741,0.487808173527,0.493833373102,0.495214837203,0.277670062503,0.27625318525,0.259941383481,0.999999999994
-8788729772386,378936,19562371,LABFER,2011-08-22 07:12:00,1,0,1,0,0.218911917098,1.0,0.0,0.259628394585,0.252729459357,0.261485446309,0.216596568048,0.361138735464,0.195375170522,0.0287064537832,0.244570166805,0.358521467221,0.222416167301,0.300974526888,0.352585588095,0.347339870876,0.456678647094,0.477099306229,0.457893296213,0.488483938617,0.493574446155,0.121272025631,0.0720583284139,0.0583513161806,0.999999989474
--7849397766258,412441,1720704,LABFER,2009-06-12 10:07:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.589238504989,0.698500452766,0.59486964687,0.812983213097,0.816998408826,0.78530476733,0.678993706729,0.796560923639,0.807708532551,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516111043981,0.521811420026,0.508357690052,0.504162786768,0.920948075726,0.924159615878,0.921324112874,1.0
-2243727926022,421154,38683504,LABFER,2013-10-21 14:15:00,0,1,1,1,0.634686346863,1.0,1.0,0.533877242246,0.477728741111,0.46026732074,0.622363065361,0.528533770471,0.545665992904,0.609090909091,0.321144349388,0.67096482475,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.496355147116,0.510888218981,0.505179628931,0.502371479908,0.671589281643,0.676488958956,0.696415562369,1.0
-1534073467683,435756,10617403,LABFER,2010-08-12 22:05:00,1,0,1,0,0.224899598394,0.0,0.0,0.318745351019,0.387664942178,0.425752620792,0.284483801808,0.263803131564,0.318756243061,0.213396902685,0.232360381092,0.320935272599,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.460857552498,0.477772168432,0.47486128235,0.491305050746,0.494873770151,0.270218398945,0.269061126269,0.274224869029,1.0
--5088324056551,411572,15468760,LABFER,2011-02-22 17:48:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.459675021899,0.475540295041,0.448897920763,0.434891516774,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.497957021643,0.498674905286,0.395258021998,0.397329853419,0.406699561272,1.0
-11708508439050,397072,39898521,LABFER,2013-12-18 23:19:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.549131443791,0.375124883664,0.460281067769,0.530505227393,0.562403415682,0.471233368565,0.759739804706,0.543511165555,0.60823427644,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.502712928226,0.521811420026,0.501718822026,0.4999352974,0.538099477985,0.564354439649,0.570830713397,1.0
--4444923444012,420427,40936443,LABFER,2014-01-25 13:01:00,1,0,1,0,0.108695652174,0.0,0.0,0.321675418037,0.183329870975,0.272131892218,0.00235294117647,0.405264803192,0.131895974419,0.2,0.218749770368,0.187692084893,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.461951357288,0.471095598133,0.458113298112,0.481582965595,0.489980799543,0.0036622255576,0.00125471643264,0.000467178595581,2.64831484954e-106
-10289516542942,436437,8595626,LABFER,2010-05-23 04:28:00,0,1,1,1,0.218911917098,0.322580645161,0.0,0.29148322557,0.379045575939,0.323910225885,0.358886005535,0.357018575002,0.432236054446,0.246430721675,0.373424602184,0.366300650132,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.459950641992,0.484782513784,0.479322944411,0.499756886301,0.500216827395,0.603150884576,0.618643485417,0.619881556405,1.0
-6409957952257,390083,1037764,LABFER,2009-04-14 10:15:00,1,0,1,0,0.477840451249,1.0,1.0,0.492049174403,0.447519441543,0.475600697181,0.692781781789,0.442369761439,0.44398603428,0.694107715737,0.668729593233,0.553654126099,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.492504737447,0.498388244928,0.500328174118,0.499915198955,0.476201581125,0.511978991148,0.548980158643,1.0
-11921616845495,360061,1387216,LABFER,2009-05-20 16:07:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.169970494807,0.161160352748,0.194144570248,0.0555375466439,0.240480141439,0.0879586389579,0.0105263157895,0.0618705718026,0.0245161972626,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.449141943645,0.469872974931,0.456676473752,0.487375745286,0.491563601071,0.0656314777821,0.0497771428982,0.0408554046191,1.0
--822681520685,397361,38051936,LABFER,2013-09-08 11:04:00,0,1,1,1,0.224899598394,0.0,0.0,0.368342212044,0.490836126287,0.484851316623,0.369358000531,0.600043383408,0.556553469461,0.523076923077,0.57601150188,0.763138486826,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.470352826786,0.482106113555,0.487344472973,0.496765899783,0.499638706434,0.219813188646,0.212643718278,0.205038755886,1.0
-3765845597437,392254,39263498,LABFER,2013-11-24 15:58:00,1,0,1,0,0.108695652174,0.0,0.0,0.238069239283,0.331094772984,0.289451352893,0.0280985530986,0.21254401349,0.159910820349,0.039527027027,0.000833333333333,0.221693572567,0.474085517325,0.481003341925,0.4728983646,0.458461829056,0.475292518187,0.487701103613,0.469859735916,0.495328282083,0.497092319634,0.385252504663,0.426188866018,0.414118669374,1.0
-1040721005494,401169,36001472,LABFER,2013-07-02 18:00:00,1,0,1,0,0.11914893617,1.0,0.0,0.20361544781,0.214872664657,0.204705827625,0.0862874711415,0.209545033367,0.192559425901,0.152941176471,0.392136014196,0.154339768929,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.426724752057,0.459819126184,0.444188271345,0.49440068586,0.497621012417,0.0688649950585,0.0620194483128,0.060485660746,1.0
-11594463263209,459456,23906308,LABFER,2012-02-23 21:00:00,1,0,1,0,0.403846153846,0.0,0.0,0.201574600811,0.234455501744,0.219642986862,0.0315177447772,0.211265327639,0.133306464184,0.21245339299,0.179502559946,0.10674726274,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.288703470659,0.383229775051,0.0233336038528,0.314064423918,0.403907042605,0.0206604289126,9.33969830795e-05,1.6662622457e-11,0.0
--3137862444354,454625,39476997,LABFER,2013-11-13 20:27:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.525342565207,0.452785099418,0.475540295041,0.434314587429,0.440040182948,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500144076394,0.499622814845,0.728531745055,0.734709503836,0.740407220385,1.0
--8198073689213,434477,38365940,LABFER,2013-09-20 08:38:00,1,0,1,0,0.477840451249,0.0,0.0,0.520338053783,0.47335368728,0.501050644557,0.45298349782,0.680271489288,0.414938505774,0.590697674419,0.631707252556,0.490243193422,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.482245062741,0.481081821157,0.465615864198,0.486912758443,0.493261921797,0.150613363286,0.157644217689,0.156361030432,1.69984111946e-37
--6952713257715,367979,39436647,LABFER,2013-11-12 14:17:00,0,1,1,1,0.640625,1.0,1.0,0.319734926228,0.367813009401,0.404309990617,0.365932399221,0.481334684689,0.531777911039,0.777358490566,0.596757891616,0.48867328761,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.46253836053,0.4828724814,0.480886833765,0.500091076228,0.499131479928,0.393827668208,0.381581135932,0.365905690378,1.0
--6889482433625,378841,34696775,LABFER,2013-04-26 22:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.183245254545,0.266517028374,0.0933906152039,0.109996289724,0.202084832143,0.169384073985,0.302641222999,0.115736137051,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.445021699579,0.461257128149,0.448236909634,0.487489918168,0.492476180593,0.0287557740255,0.027188089644,0.0252436309283,2.02077045838e-98
-7822253989843,391302,10806372,LABFER,2010-08-17 09:47:00,1,0,1,0,0.222222222222,0.0,0.0,0.267436853909,0.325342043109,0.422161031153,0.26878448349,0.0838482117134,0.408506711237,0.227272727273,0.301041666667,0.340130306852,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.469489775956,0.482570453216,0.487808173527,0.500551954262,0.50043483401,0.738541092492,0.739196434955,0.741401427368,1.0
--853873670381,417703,5562733,LABFER,2009-12-21 18:11:00,0,1,1,1,0.634686346863,0.0,0.0,0.533877242246,0.505095156781,0.534901448055,0.679258200633,0.436013135323,0.431935411369,0.301858942086,0.391141575605,0.371786282677,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504129987835,0.502416058009,0.510888218981,0.497929934147,0.498714052187,0.400445508215,0.390153772249,0.380991520668,1.0
--4333655032198,397295,40419321,LABFER,2013-12-20 10:55:00,0,1,1,1,0.11914893617,0.0,0.0,0.12705890817,0.16646719339,0.224680955497,0.0881339565943,0.0747826111899,0.170038339452,0.00557939914163,0.144939122962,0.155156032166,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.432006348531,0.459621911075,0.469108659157,0.489172441998,0.492590885664,0.0487927183937,0.0453141401767,0.0485773534744,1.0
-1785014468197,437361,12262071,LABFER,2010-10-21 14:05:00,1,0,1,0,0.11914893617,0.0,0.0,0.127226333624,0.192986503909,0.165655988836,0.110107638587,0.0947104080964,0.197168100667,0.00557939914163,0.216442721828,0.0965987908217,0.222416167301,0.257917683272,0.294388116307,0.287666847741,0.419649923753,0.443841094613,0.455878037868,0.478352149866,0.489204801001,0.0181071221176,0.00960076367848,0.00624218455853,0.999719779408
-11862110381778,454715,12440822,LABFER,2010-10-28 10:25:00,1,0,1,0,0.477840451249,0.0,0.0,0.363472136417,0.490121309485,0.432903183066,0.334231702174,0.378118751124,0.441601573352,0.384230769231,0.320438545968,0.537556909527,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.499326199082,0.498388244928,0.499230696545,0.500231513272,0.410973468589,0.393163212096,0.369034139819,1.0
-403322788460,402649,16610209,LABFER,2011-04-30 18:54:00,0,1,1,1,0.634686346863,1.0,1.0,0.58802033268,0.676802456176,0.643509654932,0.721153167272,0.748268548315,0.736129034731,0.88995411981,0.788098146415,0.827626932646,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.512254802287,0.510888218981,0.502860634899,0.502211204098,0.751684868513,0.750113021717,0.749910952736,1.0
-1088213171655,377426,26772892,LABFER,2012-06-21 21:00:00,0,1,1,1,0.272727272727,1.0,1.0,0.419502579753,0.389945338213,0.392201431934,0.185287504784,0.51996410354,0.359249581913,0.67,0.404546072302,0.568875932978,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.526878837863,0.532955235504,0.556974350325,0.509583420595,0.504280526946,0.975194043401,0.985081549281,0.987239714459,1.0
-3976627338507,396115,32014113,LABFER,2013-01-23 11:49:00,0,1,1,1,0.477840451249,0.0,0.0,0.477279432247,0.525898722266,0.485275965963,0.487609397749,0.509124578082,0.487761890446,0.594534883721,0.568573671498,0.636980607545,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.500139081134,0.498388244928,0.500756912341,0.502124381867,0.649436130547,0.640201664432,0.625329210563,1.0
-2311692231393,450921,40998153,LABFER,2014-04-22 12:41:00,0,1,1,1,0.477840451249,0.80487804878,1.0,0.499050123376,0.467012818976,0.466186867735,0.492559499028,0.52993249476,0.481274462239,0.424508013796,0.67543787856,0.450105176913,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.499107965408,0.509322489469,0.500865126552,0.499861332365,0.347589818649,0.410510783292,0.427860431761,0.597537842253
--3881913494700,425287,32174444,LABFER,2013-01-07 08:44:00,1,0,1,0,0.218911917098,1.0,0.0,0.308900713495,0.192969810018,0.284627492518,0.15993050687,0.245181110709,0.285065703327,0.243652943897,0.117546191554,0.314974366511,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.423950482562,0.455692936736,0.440099782885,0.489995331366,0.495922665708,0.0478906404648,0.043904399185,0.0398551041316,9.72364676647e-192
-10462992299883,395827,34543908,LABFER,2013-05-10 11:42:00,0,1,1,1,0.485294117647,1.0,1.0,0.187919874614,0.302935378494,0.220255675998,0.629633173844,0.347169306256,0.264967616567,0.281342281879,0.370270154455,0.275828171984,0.474085517325,0.462338008602,0.420052373951,0.402453806688,0.451319993507,0.466089110192,0.487808173527,0.507266680821,0.503346046157,0.452562662257,0.707051841559,0.786149413926,6.25185868003e-25
--9404459197633,432263,876516,LABFER,2009-03-29 08:42:00,1,0,1,0,0.218911917098,1.0,0.0,0.308900713495,0.300927341255,0.322813734221,0.233889785899,0.28930399171,0.287973049993,0.43074795726,0.138925761124,0.319278529692,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.451222644192,0.476646721162,0.468417916605,0.491865190645,0.49579885051,0.375410455719,0.437355073333,0.448791552197,1.0
-10599558602476,415413,18749084,LABFER,2011-07-28 07:13:00,0,1,1,1,0.0833333333333,0.0,0.0,0.377684598532,0.366572156832,0.348739015033,0.515662353521,0.538531583176,0.35600639471,0.151724137931,0.287576562874,0.278795581305,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.464275748893,0.484593903263,0.491813578127,0.496109628118,0.499305703435,0.308347358072,0.310012330707,0.36216563973,1.0
-3885107242120,396095,36642281,LABFER,2013-08-04 09:33:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.548646984401,0.575579695042,0.593839225833,0.39211751928,0.630154355245,0.627749897561,0.947891698546,0.735563572422,0.758131018134,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.500612149014,0.506615358126,0.510015679179,0.505044284677,0.50445008651,0.83681290062,0.833163554647,0.782722701363,1.0
--1165273467866,427577,3694029,LABFER,2009-10-02 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.11256629298,0.131886030166,0.0495888455882,0.0527424863823,0.0444591068255,0.0235475978912,0.261342550086,0.0552698585282,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.424292813345,0.455301768051,0.428495991973,0.481918290817,0.492216435063,0.000941051884649,0.000106016041724,1.36204648064e-05,0.0
--5071623079586,428652,30258724,LABFER,2012-10-30 02:14:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.48810841934,0.481733638786,0.467110874031,0.704372068212,0.505616066232,0.580663919919,0.641673259154,0.547917619801,0.383093308703,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.480939504332,0.488448305726,0.486583208859,0.499270159979,0.500143446268,0.667586186984,0.661730800084,0.637140663554,1.0
--2266854966496,369679,34796006,LABFER,2013-05-05 07:25:00,1,0,1,0,0.640625,0.0,0.0,0.18386959004,0.169581107688,0.205857776491,0.498988268972,0.184888423882,0.0669921247385,0.222950819672,0.226943918897,0.228798123215,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.440877379687,0.461824367961,0.459448963611,0.491521804413,0.495022165152,0.0805879223582,0.0449518688371,0.021552387131,9.31874668116e-12
-6197912753432,443796,7961563,LABFER,2010-04-21 15:05:00,0,1,1,1,0.218911917098,1.0,0.0,0.344430485551,0.326049093902,0.357881942719,0.392456033177,0.36789241712,0.35319090653,0.2,0.405674811894,0.370735089265,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.462001412088,0.477076050162,0.435903692135,0.489062403139,0.496087909973,0.408466012447,0.404693460944,0.391804608107,1.0
--1165273467866,427577,3616337,LABFER,2009-09-29 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.127424511725,0.153966092294,0.0222063791582,0.158425741282,0.0676869948675,0.0272685281238,0.048855503454,0.0555438959657,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.4206584218,0.447042730558,0.440099782885,0.478233154356,0.486845655446,0.00143043678871,0.000252978385648,2.87869082661e-05,0.0
--8261140243379,357751,4222341,LABFER,2009-11-01 18:45:00,1,0,1,0,0.218911917098,0.04,0.0,0.129727423314,0.316287386668,0.27452703749,0.143577515512,0.199857259985,0.343455583609,0.403720930233,0.299797214007,0.265522119786,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.43270058457,0.462197985515,0.433417205192,0.49081261419,0.49698069628,0.132764307989,0.133550292055,0.13052965116,1.0
--12138758789842,438693,33745322,LABFER,2013-03-22 22:00:00,1,0,1,0,0.218911917098,0.0,0.0,0.217658516895,0.139567365193,0.155556371802,0.0862874711415,0.122124020657,0.0914908772297,0.0409190184217,0.0433324409203,0.168380261416,0.222416167301,0.257917683272,0.28911874352,0.287666847741,0.380056594387,0.424068955631,0.42305314025,0.474961134447,0.486151638639,0.00449259346729,0.00429628484481,0.00429032468484,3.33057865238e-25
--9382561257211,424598,40108048,LABFER,2013-12-07 05:21:00,0,1,1,1,0.634686346863,0.936046511628,0.0,0.649044688653,0.615825727222,0.604982073292,0.523345206331,0.701822721221,0.643941590772,0.63826055309,0.359673662538,0.513165982006,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50641368303,0.50906341311,0.510015679179,0.509804745,0.505558053033,0.665771641252,0.68615742052,0.712294824147,1.0
--308836743658,423890,33821611,LABFER,2013-03-22 07:07:00,1,0,1,0,0.477840451249,1.0,1.0,0.408076858646,0.315053215166,0.372799532168,0.342113442113,0.353962264662,0.381913939916,0.555238095238,0.329475097209,0.321591871592,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.48677520081,0.49332061807,0.498388244928,0.496433209205,0.497604631061,0.392611615941,0.401017090325,0.419962308768,1.0
--9264332387974,365904,10617901,LABFER,2010-07-31 21:46:00,1,0,1,0,0.634686346863,0.0,0.0,0.433362620885,0.509721945977,0.559551108923,0.289915713663,0.450941934498,0.440733409287,0.116981132075,0.307177382581,0.363679031925,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.512254802287,0.510888218981,0.505757323096,0.417599067602,0.236322495703,0.0577678166689,0.0437208554005,0.0
--5411762938685,424675,34139537,LABFER,2013-03-25 17:55:00,0,1,1,1,0.477840451249,1.0,1.0,0.513819865532,0.550938182717,0.513677571374,0.572774302154,0.502203920358,0.547159134999,0.281060606061,0.528653069989,0.453269487555,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.502252173557,0.497402430411,0.498388244928,0.506158270186,0.504333170723,0.51641462868,0.500724900275,0.485103677634,1.0
--5602980712309,415976,8616780,LABFER,2010-05-05 22:02:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.0809854729629,0.122764457154,0.164254489492,0.0385335761591,0.195756832355,0.160765977532,0.605579399142,0.016639548405,0.146297888277,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.285640882042,0.376340795537,0.0233336038528,0.32159792792,0.405191824538,0.0049913759311,0.000440876235499,1.45366651098e-06,0.0
--5581554616514,381306,25142491,LABFER,2012-04-02 13:59:00,0,1,1,1,0.634686346863,0.963636363636,0.0,0.581475193791,0.626910875303,0.629619207361,0.675460395811,0.784325812084,0.771402945848,0.860349989001,0.882858068635,0.79627570006,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.51294677631,0.521811420026,0.507057356937,0.505371757569,0.874106341801,0.87869213013,0.880739514063,1.0
-1825213722560,359313,27847616,LABFER,2012-08-01 08:08:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.117155441992,0.112862368008,0.276215436215,0.153551729772,0.0609392826176,0.0235475978912,0.117655911608,0.0866108396808,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.38860693681,0.423947225914,0.407560018026,0.472550952189,0.484441157484,0.00631207313813,0.00458194256377,0.00497954912511,1.01086851772e-31
-9895578020247,389075,13388158,LABFER,2010-12-11 13:59:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.217658516895,0.191575153475,0.191974109393,0.420131384179,0.210452010621,0.13007972051,0.21662591687,0.0336004033742,0.106941069368,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443116406313,0.467820330916,0.467548574593,0.488130617161,0.494654455866,0.0306602774372,0.0211571757555,0.0160158631223,5.92389834353e-24
-5341557096498,398282,17062669,LABFER,2011-05-10 12:23:00,0,1,1,1,0.634686346863,1.0,0.0,0.554529900297,0.433118984121,0.443213631127,0.456349933762,0.432069043931,0.465953081147,0.217306537307,0.363622233486,0.389482969948,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.497796935067,0.510888218981,0.501682749215,0.500725410703,0.30182614975,0.292151799093,0.291389615955,1.0
--5673363942769,402526,14903067,LABFER,2011-02-03 22:07:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.537743342583,0.407030947624,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.495624509445,0.498388244928,0.499654240296,0.499523517311,0.380276230636,0.380718924127,0.388432255913,1.0
-8351851310452,451390,671218,LABFER,2009-03-14 22:00:00,1,0,1,0,0.477840451249,1.0,1.0,0.546648074921,0.442296095988,0.425085245033,0.602474148827,0.307606899743,0.37849954134,0.296511470985,0.27589424066,0.61472805968,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.486397697734,0.494963780943,0.509322489469,0.501274688504,0.501201877868,0.346487515568,0.311931623262,0.321535278081,1.0
--8662610342378,420090,10965188,LABFER,2010-08-12 09:48:00,1,0,1,0,0.634686346863,0.0,0.0,0.472528611403,0.350857974227,0.423026808719,0.693426791989,0.322827431233,0.4140388409,0.2,0.441288453324,0.340576514834,0.474085517325,0.462338008602,0.450253087727,0.43940753645,0.485024628169,0.492292907776,0.509322489469,0.500927081835,0.501017763887,0.482862550515,0.453507390112,0.4593791325,1.0
-11796818880322,369518,30271701,LABFER,2012-11-07 11:39:00,1,0,1,0,0.224899598394,1.0,1.0,0.321228719889,0.374771161089,0.418399107365,0.305773513159,0.260844919914,0.317324574482,0.369230769231,0.177786337956,0.304761904762,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.470352826786,0.474831362892,0.487344472973,0.485115976164,0.492364028754,0.210884726917,0.199936749825,0.197381570545,1.0
--5122609797244,383708,1395196,LABFER,2009-05-02 16:18:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.459675021899,0.475540295041,0.38200009655,0.45278068062,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.498798353422,0.4992727247,0.435414956,0.443430159364,0.454991154569,1.0
-4695011277635,366655,20119219,LABFER,2011-09-15 00:51:00,1,0,1,0,0.477840451249,0.0,0.0,0.423233676679,0.347252834668,0.350526896085,0.392996005926,0.395281402281,0.449143118499,0.435604395604,0.184940462015,0.367064591253,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.478587271054,0.49007813799,0.49751533833,0.49744695866,0.499519518974,0.358001925568,0.412733846765,0.438357553262,1.0
-9964880576845,405833,22988968,LABFER,2012-01-21 11:56:00,1,0,1,0,0.218911917098,0.04,0.0,0.190884963299,0.184168744676,0.180937764597,0.0254273067756,0.0845234371967,0.154841443073,0.0120805369128,0.139602159476,0.0577339410592,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.449119226635,0.469395988048,0.468417916605,0.490003128439,0.495140757409,0.0602529035051,0.0495302407896,0.0458694346402,1.0
--5176106119781,448716,32088819,LABFER,2013-01-13 17:35:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.459675021899,0.437710383017,0.410798305917,0.41321672531,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.495364640787,0.498388244928,0.49671650845,0.497579753,0.266339678047,0.266376402881,0.271570879593,1.0
--1710421503735,470921,39986461,LABFER,2013-12-01 20:22:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.481824055375,0.445295109401,0.525342565207,0.46408306405,0.534680079987,0.434314587429,0.530808756902,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.504099187192,0.501125520618,0.432661295134,0.437588852473,0.448398397087,1.0
--500499911960,378764,29095871,LABFER,2012-09-08 11:21:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.497910406048,0.538163897979,0.636018383918,0.487924758029,0.606163751729,0.772997557998,0.781010023609,0.62831527464,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.50559355846,0.498388244928,0.500756912341,0.499596832206,0.632903221681,0.652553908835,0.656702608359,1.0
--10433407360148,446415,16240493,LABFER,2011-04-02 10:17:00,0,1,1,1,0.218911917098,1.0,0.0,0.308900713495,0.23579845561,0.204004678274,0.387678236144,0.137334256967,0.303509221839,0.203603603604,0.252138794431,0.308444054216,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.436619298011,0.467750672866,0.467548574593,0.497614337622,0.497792606842,0.144049569562,0.130881122449,0.138441322667,1.0
-8465592975982,393694,19145103,LABFER,2011-08-16 01:47:00,1,0,1,0,0.634686346863,0.936046511628,1.0,0.459051398955,0.628153233735,0.60927477939,0.779936175309,0.664101751943,0.716532075592,0.648307427512,0.745837044639,0.645706743089,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.492234157122,0.501778778358,0.509322489469,0.499817608114,0.500539723006,0.339185816918,0.327225428977,0.323204067019,1.0
-620375150821,464104,32241244,LABFER,2013-01-25 21:00:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.61630921206,0.645927246478,0.604333223778,0.814004324021,0.758040249061,0.772886530429,0.694726166329,0.816038611304,0.823026827218,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.510644691324,0.510888218981,0.504072200649,0.502472589029,0.808692441432,0.806595391853,0.802387010548,1.0
--1736366164678,414323,31617635,LABFER,2012-12-21 08:34:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.533877242246,0.524214820054,0.550800298674,0.761372745073,0.663548869084,0.70220648334,0.718775827018,0.77814227971,0.657560474043,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.510093136871,0.521811420026,0.505004220795,0.503219507827,0.869901916634,0.901415228741,0.915117832807,1.0
--10957375199115,359864,24275072,LABFER,2012-02-18 16:28:00,0,1,1,1,0.634686346863,1.0,1.0,0.516303234401,0.530983709062,0.572615268437,0.465488098154,0.655925004509,0.546795284559,0.826284054935,0.412395396707,0.579193118725,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506666070176,0.510888218981,0.49992979157,0.499656085793,0.382248889002,0.373125830011,0.365270391295,1.0
--2935887162321,392234,30056267,LABFER,2012-10-24 12:44:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.513752226886,0.565536387625,0.524671286022,0.64177942786,0.591447408849,0.56104479394,0.608360215054,0.539014076524,0.710718004982,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.5023354493,0.504761120843,0.498388244928,0.503941196232,0.502136709451,0.951496874036,0.955697382739,0.956063970012,1.0
-3095359807283,437114,17141554,LABFER,2011-05-13 22:01:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.374266676256,0.479157950376,0.474566891973,0.568910024381,0.383780758627,0.508826298202,0.391525423729,0.767007756857,0.58983284864,0.474085517325,0.481003341925,0.482049390786,0.473852543851,0.504587154895,0.50792100211,0.521811420026,0.507260556877,0.505114469839,0.840676419743,0.836679701327,0.800406569613,1.0
--4546170330502,379166,18417842,LABFER,2011-06-26 23:43:00,1,0,1,0,0.634686346863,0.0,0.0,0.440790284236,0.440963628654,0.507652945094,0.383855760428,0.426699908111,0.411881670138,0.32282104963,0.600481029071,0.420319281568,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.480513480135,0.482764326053,0.478077273881,0.494717523704,0.498658895938,0.302477887541,0.290619085529,0.283185737011,2.79356890881e-07
-8432274060487,373722,19272757,LABFER,2011-08-12 07:43:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.701797554215,0.632081793907,0.721153167272,0.67382801965,0.778039127686,0.696755518955,0.531943715886,0.638327885818,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.513109265186,0.510888218981,0.508226862638,0.504449886828,0.693690751309,0.732436825166,0.75079622548,1.0
--4977362177871,408108,14030550,LABFER,2010-12-16 22:07:00,0,1,1,1,0.224899598394,0.0,0.0,0.396631091424,0.493018701702,0.449572125615,0.358247342244,0.626810719924,0.422575840922,0.140967741935,0.309440073309,0.467411125329,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.46805272257,0.474246832268,0.47486128235,0.488632728371,0.494159843901,0.147160204138,0.1363447936,0.135000964622,1.0
--11552578329126,457394,7194019,LABFER,2010-03-05 20:14:00,1,0,1,0,0.477840451249,1.0,1.0,0.477279432247,0.481795412497,0.502733231754,0.912965005721,0.4681603313,0.593702308227,0.507840685837,0.577963525836,0.459775695545,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.500856316434,0.509322489469,0.497821910773,0.501339600925,0.615745218725,0.591568457128,0.567645915165,1.0
-10727499499815,413658,36735102,LABFER,2013-08-05 13:58:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.124368409038,0.241932794331,0.210001165878,0.213435919126,0.262729501149,0.16953453754,0.00930032937419,0.115781150906,0.161238168995,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.452460068214,0.471991874164,0.468417916605,0.487624536514,0.492170566843,0.0704745794356,0.0399532459721,0.0282732140782,1.0
--6804160262547,369053,22717288,LABFER,2011-12-23 22:00:00,0,1,1,1,0.215827338129,1.0,0.0,0.293693020999,0.401245565442,0.332652284087,0.459505177797,0.387536405515,0.475664086485,0.608571428571,0.462426849987,0.239282410393,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.508944406249,0.522958491291,0.556112592579,0.508795339637,0.502737846085,0.848679858589,0.899175531838,0.905878606271,1.0
-8527119283510,391587,30481136,LABFER,2012-11-18 21:00:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.18386959004,0.123946998808,0.155863927016,0.0731862664731,0.092851019337,0.0763634480787,0.0339084861667,0.0460547410747,0.0525370133692,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.423597206221,0.454304234349,0.435412626422,0.486699263634,0.492340940909,0.0447905428795,0.0379592003692,0.0376568515692,1.0
--1051598035966,404217,39909141,LABFER,2013-11-21 12:43:00,1,0,1,0,0.215827338129,0.0,0.0,0.193987010987,0.255829518956,0.216028813082,0.547212121212,0.249157586772,0.218287306784,0.0181868131868,0.0217177130385,0.193024594826,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.457667423268,0.464584354842,0.487808173527,0.491443032092,0.494671077271,0.0747112479501,0.051294338469,0.0392748453125,1.0
--7654191813513,418155,30355044,LABFER,2012-10-28 20:29:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.396214972583,0.439019461844,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.49671650845,0.497579753,0.318682597339,0.321492872421,0.330797432232,1.0
-4870444244709,459452,36595923,LABFER,2013-07-29 00:05:00,1,0,1,0,0.644444444444,1.0,1.0,0.521701690908,0.443368977364,0.460789426475,0.520329835082,0.433648147275,0.422808842101,0.108333333333,0.49804791087,0.428939635031,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.490231812356,0.49187415177,0.47486128235,0.493808347953,0.496019204713,0.273819592182,0.258089270687,0.248578771092,1.0
--8361211224910,464405,38435849,LABFER,2013-09-23 11:19:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.649044688653,0.658274620214,0.63056231411,0.784418473394,0.751785012802,0.752299188473,0.894732555587,0.795633616644,0.767630442449,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.515313757607,0.521811420026,0.50719843186,0.503758250727,0.797056770649,0.807394554307,0.822047184132,1.0
--10227853364563,358703,17184766,LABFER,2011-05-08 23:13:00,0,1,1,1,0.218911917098,0.04,0.0,0.208760705046,0.367941835797,0.389836300064,0.219593072153,0.368869993038,0.380064113474,0.545957923025,0.366829064992,0.388361753519,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.476241893939,0.490469023,0.468417916605,0.497337270755,0.497889386791,0.696431165028,0.715158588386,0.721536174154,1.0
--1165273467866,427577,3710530,LABFER,2009-10-03 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.119164533449,0.131185514945,0.0495888455882,0.154494504608,0.0542440576059,0.223547597891,0.111204671277,0.0571707923857,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.430310572307,0.456874403014,0.428495991973,0.481918290817,0.49116924884,0.00116378897987,0.000139517536311,1.41664270978e-05,8.574179018e-303
--8916051562307,403340,39515151,LABFER,2013-11-15 08:53:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.284824905945,0.327637576162,0.312700442422,0.142006634418,0.462546789834,0.286180870323,0.227027027027,0.280286492375,0.266509623998,0.222416167301,0.300974526888,0.360449324411,0.361490321039,0.438631227182,0.466206956905,0.457432635845,0.492759853903,0.496079745096,0.189629890578,0.178855573743,0.17816349829,1.0
-4218367181249,377696,12860151,LABFER,2010-11-12 09:10:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161015260479,0.288958896469,0.265724936566,0.117353800626,0.281539099167,0.303505889687,0.277710546683,0.305762006233,0.328209339037,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.473902804534,0.487829304574,0.491813578127,0.496594999539,0.497373380613,0.372799311032,0.365184720012,0.354402070385,1.0
--1165273467866,389676,3151353,LABFER,2009-09-08 22:00:00,1,0,1,0,0.8,1.0,0.0,0.206478753858,0.119239485986,0.224013948357,0.240737838511,0.0832482693061,0.171019106492,0.022205316012,0.148855503454,0.211347235442,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.41632997063,0.448707930659,0.428495991973,0.48609313917,0.491407247586,0.00389624434069,0.00125986831052,0.00107671907785,2.78757587955e-146
--3843630598428,437522,30869188,LABFER,2012-11-14 09:13:00,1,0,1,0,0.477840451249,0.890410958904,1.0,0.477279432247,0.4653627967,0.439195118038,0.575121131285,0.39037646214,0.502510806348,0.405058651026,0.341105962597,0.472894005319,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.48677520081,0.494896662055,0.498388244928,0.499087626153,0.498799874025,0.332572671878,0.35333123724,0.36733027308,1.0
-910770688616,469522,114478,LABFER,2009-01-29 13:43:00,1,0,1,0,0.218911917098,0.322580645161,1.0,0.270785923208,0.280310064009,0.264661879314,0.26866345034,0.209593274828,0.23563179482,0.248809824916,0.190244908744,0.200956617438,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.452460068214,0.476065134513,0.468417916605,0.49750974686,0.498743817796,0.283583441091,0.282839333715,0.293490545336,1.0
--1302576055483,407248,13616947,LABFER,2010-12-02 13:40:00,1,0,1,0,0.11914893617,0.0,0.0,0.127226333624,0.186591618008,0.152857256352,0.204779270763,0.189384027468,0.132149292107,0.421207395307,0.119500885527,0.190532392608,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.445939693079,0.461051791286,0.452456496526,0.489831217473,0.491617796383,0.0308252696712,0.0330447221564,0.0329187019887,1.15522726356e-106
--4444923444012,420427,40922263,LABFER,2014-01-24 14:30:00,1,0,1,0,0.108695652174,0.0,0.0,0.270733714001,0.199524866174,0.257144871744,0.00235294117647,0.22544590998,0.139114424605,0.2,0.410372807018,0.271714197397,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.461951357288,0.471095598133,0.458113298112,0.479702069215,0.489972630628,0.0892002016946,0.0729291421866,0.0684565731509,4.90955388594e-19
-7058006713655,401890,3598361,LABFER,2009-10-16 23:45:00,1,0,1,0,0.640625,0.0,0.0,0.452903160968,0.352248449021,0.374006879938,0.285825258747,0.455124114044,0.344113204651,0.443692919103,0.313522842127,0.171902614291,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.471414756684,0.4850315338,0.480886833765,0.496752667005,0.497595687098,0.202989024066,0.202734715641,0.207723156718,1.0
-10084103943774,393120,19449590,LABFER,2011-08-27 10:05:00,1,0,1,0,0.11914893617,0.0,0.0,0.308547134641,0.320150602472,0.241274924652,0.244582936843,0.355826337342,0.161012990321,0.205579399142,0.286858974359,0.164916235394,0.222416167301,0.272513145711,0.341251976233,0.336701589837,0.453072185278,0.475094659448,0.440099782885,0.493381361531,0.496488681061,0.42311370839,0.363829890592,0.286449102977,0.974199193075
-2783695720838,422383,31726406,LABFER,2013-01-08 07:32:00,1,0,1,0,0.11914893617,0.0644257703081,1.0,0.161439026276,0.214619194099,0.167061916589,0.14682083861,0.102577491161,0.139112331235,0.0251034992026,0.399779639665,0.256400646103,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.471456777372,0.467548574593,0.489412083357,0.497596674579,0.0831747197566,0.0352471278131,0.0201972902014,7.87112789747e-123
--12441340721352,460081,13718494,LABFER,2010-12-09 01:20:00,1,0,1,0,0.477840451249,1.0,1.0,0.341770130848,0.458280583809,0.42220625468,0.406542056075,0.494263082098,0.560065699816,0.6,0.40716625078,0.493722727276,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.486397697734,0.496321477818,0.509322489469,0.503724959479,0.50414236734,0.683690512506,0.713840645228,0.722646710143,1.0
-5575027708191,398852,6996904,LABFER,2010-03-18 16:54:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.61630921206,0.711454389156,0.619713878358,0.709374694391,0.754124035047,0.687133355226,0.878428921131,0.938969722116,0.717486787131,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.512254802287,0.510888218981,0.501650872008,0.502281039608,0.868128550815,0.870423056692,0.868664264699,1.0
--7965650355755,384064,28298102,LABFER,2012-08-05 21:47:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.459430758433,0.452864811026,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.499654240296,0.499048624742,0.419887256135,0.424805241418,0.432606425409,1.0
--3900265555083,364612,26504686,LABFER,2012-05-20 13:56:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.38575867162,0.495033654394,0.465188143242,0.458054131557,0.441200952839,0.428535160049,0.206910039113,0.479984854004,0.616801586715,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.474650690767,0.492083312968,0.509322489469,0.497894842367,0.500024709085,0.424049096126,0.44645427225,0.476947838425,1.0
--8966227303039,371649,24731951,LABFER,2012-03-05 04:04:00,0,1,1,1,0.477840451249,0.0,0.0,0.531329789885,0.576998081119,0.51936702028,0.653528669518,0.562220358177,0.565846688221,0.714383116883,0.554470216875,0.74569559547,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.500781429336,0.498388244928,0.50237040153,0.502532925064,0.688575938361,0.682027708674,0.6755304592,1.0
-12244564225910,384290,3049844,LABFER,2009-09-21 12:04:00,1,0,1,0,0.218911917098,0.04,0.0,0.38357176743,0.396966320085,0.341482869644,0.157073573806,0.294062583313,0.239591406753,0.102777777778,0.40678486942,0.222211159417,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.472407011961,0.487059196694,0.468417916605,0.497337270755,0.498609872433,0.3212936969,0.310522706822,0.297482998878,1.0
--5909874727797,418962,23410784,LABFER,2012-01-22 14:20:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161015260479,0.168200628442,0.179461654578,0.203969554164,0.179329905491,0.177310146834,0.160767834867,0.126386590783,0.0931803457702,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.288393259693,0.386379196516,0.0243514919689,0.321047933662,0.405719594847,0.000477915945531,3.17466625868e-09,3.19366537296e-25,0.0
-566997240912,394066,10225962,LABFER,2010-07-30 12:22:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.581475193791,0.398529564833,0.46798787044,0.657783726071,0.517553213967,0.547698616413,0.473714285714,0.470839202044,0.450394445697,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.488728073257,0.499060624244,0.510015679179,0.50141337733,0.501894898599,0.417674967849,0.404494702071,0.380977337259,1.0
--6629359073799,438391,3988415,LABFER,2009-10-17 09:01:00,1,0,1,0,0.477840451249,1.0,1.0,0.386697426851,0.374947102881,0.368742705057,0.251230550969,0.398132156548,0.492463589157,0.524959250204,0.429597765986,0.370546216458,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.501534260393,0.509322489469,0.50302495185,0.501816035216,0.644865719146,0.689559121667,0.709351699083,1.0
-8551903818503,416465,31126441,LABFER,2012-12-16 01:50:00,1,0,1,0,0.0512820512821,0.0,0.0,0.319029099243,0.389991312037,0.420672463296,0.134423300277,0.193053091836,0.175218415782,0.274379391101,0.29722826087,0.428401749512,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.484212470522,0.487000762088,0.476886910056,0.482333486611,0.491805452383,0.272195082444,0.333748090026,0.327574078042,1.0
--4630647190538,390603,36724822,LABFER,2013-07-18 22:00:00,0,1,1,1,0.0512820512821,0.0,0.0,0.217306868292,0.265751866394,0.231844976233,0.0431355661882,0.0981467304643,0.142479727576,0.231522248244,0.123244415825,0.203350422772,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.458792851234,0.471795426205,0.465118925402,0.493632544366,0.49609887981,0.117803207027,0.113080935213,0.114909803905,1.0
--12049316323065,462708,28591120,LABFER,2012-08-20 12:00:00,1,0,1,0,0.634686346863,0.0,0.0,0.482352593314,0.406069595942,0.456074910326,0.436210683848,0.479252514085,0.514165043111,0.200725146199,0.238941581098,0.394037979354,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497788367418,0.498388244928,0.50239963635,0.500929533357,0.473042051158,0.489240493933,0.513176308677,1.0
-2584546952687,427192,21627542,LABFER,2011-11-28 13:21:00,0,1,1,1,0.477840451249,1.0,1.0,0.614217569783,0.461676615641,0.586570066005,0.717304900202,0.620985161705,0.65214520148,0.539398975211,0.740825588894,0.646799218258,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.500404847726,0.505582883006,0.509322489469,0.499900717704,0.500158458283,0.404712765113,0.0768152491819,0.00918400907332,0.000230512779779
-4053563986043,384610,30226026,LABFER,2012-11-13 05:05:00,1,0,1,0,0.218911917098,0.04,1.0,0.198332683533,0.208803149249,0.210800231107,0.298239655836,0.335637557438,0.106145895088,0.41662591687,0.112122836139,0.0695723362245,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.414749604288,0.44947345237,0.439239570503,0.488169366016,0.49371508407,0.112444873466,0.136959235508,0.131213442259,0.154900940851
-11236368843548,393703,19298780,LABFER,2011-08-18 19:42:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.410798305917,0.444963248287,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.498903529131,0.499790147967,0.543818635247,0.549328832175,0.553784953024,1.0
--7416929917920,401105,36224889,LABFER,2013-06-30 22:00:00,1,0,1,0,0.0512820512821,0.0,0.0,0.329046176303,0.325040521552,0.329666224594,0.45428030303,0.205511351762,0.24858619192,0.219313192239,0.486879432624,0.386639149787,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.478441664077,0.484235593483,0.465118925402,0.500217452521,0.499147034156,0.22730335947,0.219846764305,0.242276035019,1.0
-7001254444794,381322,21394652,LABFER,2011-11-14 11:30:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.462870365995,0.437710383017,0.532940012841,0.461088139032,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.495364640787,0.498388244928,0.499921511108,0.498571806804,0.361833621601,0.359316652802,0.362710908433,1.0
--6429004288891,366817,20823320,LABFER,2011-09-29 13:11:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.533877242246,0.446728290152,0.47202423642,0.544989111393,0.502177168846,0.528499393325,0.264201200999,0.394967825421,0.347280653337,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.499154722446,0.510888218981,0.502923271284,0.501718129686,0.296346924198,0.257583145841,0.264420545146,1.0
--35417108270,356910,38389387,LABFER,2013-09-23 07:20:00,1,0,1,0,0.477840451249,0.0,0.0,0.446757590453,0.388964415551,0.410683410786,0.400873931959,0.35954756116,0.48056399201,0.438363726134,0.410182026028,0.292793459528,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.486397697734,0.496321477818,0.509322489469,0.502515219074,0.502675809706,0.447857892165,0.451878102656,0.462574402593,1.0
-8986480427125,355882,27142202,LABFER,2012-07-01 21:00:00,0,1,1,1,0.224899598394,0.0,0.0,0.520338053783,0.42453631572,0.418808995081,0.34123946683,0.600625808752,0.479013437053,0.354535428729,0.39353839433,0.395218614321,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.47845188813,0.491986367095,0.473990644778,0.497113275478,0.499262272203,0.652960791567,0.673322462315,0.680132390051,1.0
--4343000145198,420107,12339863,LABFER,2010-10-05 20:08:00,1,0,1,0,0.477840451249,0.0,1.0,0.520338053783,0.520310019034,0.487623835215,0.602584873124,0.609087454107,0.489244137561,0.660541125541,0.590860329639,0.465012385012,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.502987672803,0.509322489469,0.500857065632,0.501804336764,0.671743701028,0.693231652658,0.721975901866,1.0
--1990377971250,396938,18127467,LABFER,2011-06-18 12:53:00,1,0,1,0,0.634686346863,0.257142857143,0.0,0.44510175132,0.543555037558,0.545173870838,0.604638859069,0.606011495342,0.615718780959,0.670519130458,0.629367727165,0.636448677999,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.503868714851,0.50512205253,0.510888218981,0.504121806681,0.501320668716,0.293855402719,0.284036881389,0.269815868686,1.0
-8953478728578,437252,36334052,LABFER,2013-07-13 09:19:00,1,0,1,0,0.11914893617,0.0,0.0,0.136319701156,0.161470291173,0.192902338553,0.214603174603,0.0928835148461,0.171039790055,0.0452619388242,0.100472110508,0.0844503721204,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.421291331272,0.444659743298,0.423540723627,0.478481632551,0.488477309536,0.0442229860163,0.0457419469029,0.0747762153861,2.81178320365e-119
-1190135354001,436098,4114521,LABFER,2009-10-31 23:33:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.465296775039,0.475540295041,0.459430758433,0.466495838723,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.501267743893,0.499855376782,0.446886345087,0.453884257602,0.462818663349,1.0
-5106025860564,472394,4046737,LABFER,2009-10-31 10:00:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.544592113781,0.669985249356,0.622139400505,0.680870072454,0.799129578856,0.664142706117,0.8,0.795368916797,0.67091821606,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.544911646819,0.540308983681,0.578096153735,0.509078449328,0.504502829747,0.999423532407,0.999812146036,0.999707475732,1.0
--1302576055483,428302,14090721,LABFER,2010-12-27 09:27:00,1,0,1,0,0.11914893617,0.0,0.0,0.102530774301,0.158917201382,0.113447635471,0.0556910316066,0.290158808032,0.0899271110969,0.00557939914163,0.00743417392514,0.139014728252,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.409281301117,0.438893419031,0.431233186325,0.477226170039,0.488104909413,0.000297758405126,0.000435427120229,0.000952365537317,0.0
-9949947915576,362848,13059792,LABFER,2010-11-29 23:32:00,1,0,1,0,0.634686346863,1.0,1.0,0.52045379723,0.410266449167,0.490434952205,0.485485653114,0.739524890792,0.572731904364,0.538449449974,0.428513675589,0.394092969648,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.51167658993,0.51152961154,0.521811420026,0.507839151031,0.503112228476,0.33176187333,0.28039181239,0.268333475074,1.0
--3445462326418,424798,40502005,LABFER,2013-12-26 23:21:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.58265981714,0.544851795328,0.58990483679,0.609068715384,0.648794490718,0.462930711907,0.532123234196,0.696002295209,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.502348205791,0.506950097456,0.521811420026,0.50779265138,0.502972489192,0.817558153137,0.930531057419,0.954591513362,1.0
--7648096421717,434588,38263001,LABFER,2013-09-25 11:59:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.308900713495,0.196093909795,0.208756381863,0.380639232568,0.134151179754,0.120197828045,0.209720930233,0.173158225721,0.180669961116,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.431252454438,0.460048793485,0.447057203458,0.487869855737,0.486483478125,0.05736055738,0.0526198502722,0.0474581901625,1.0
--729736097296,363248,27740810,LABFER,2012-07-12 21:00:00,1,0,1,0,0.218911917098,1.0,0.0,0.249801796451,0.349539404719,0.313201213416,0.189893120487,0.250380038842,0.191473903925,0.0449498654928,0.253034129307,0.281325299204,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.453113519631,0.471144130842,0.456676473752,0.491825457847,0.494682022409,0.203006770107,0.191244351112,0.185102258209,1.0
-5212716573247,362094,21287276,LABFER,2011-11-13 12:19:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.368342212044,0.491175374501,0.503441094591,0.488466383769,0.623988662742,0.50396209174,0.21047279215,0.753193155131,0.552765059003,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.475128327394,0.49000038263,0.510888218981,0.499357798621,0.499976972339,0.409305858037,0.425146768247,0.450353587079,1.0
--8216777451623,371778,5038340,LABFER,2009-12-03 09:46:00,1,0,1,0,0.634686346863,0.936046511628,1.0,0.581475193791,0.660734789102,0.606578749199,0.670069205121,0.708696414091,0.690465730306,0.79224716295,0.827465316639,0.743961298053,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506666070176,0.510888218981,0.504751149583,0.502097379614,0.561712639872,0.55771478643,0.555604179985,1.0
-6023661698694,382615,20382447,LABFER,2011-09-27 06:43:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.475382041615,0.475540295041,0.498702013494,0.457926246492,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.501267743893,0.499855376782,0.495195959339,0.500131834424,0.506837577383,1.0
--3580404086288,359932,144005,LABFER,2009-01-19 22:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.169970494807,0.203586177027,0.205004171079,0.284497592958,0.104309073333,0.0989451000769,0.0,0.333395431912,0.058486870328,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.435038724745,0.450386639433,0.424326690307,0.474990545161,0.484630360922,0.0330631680545,0.0258328484718,0.0224239837309,8.38764287481e-25
--5252889897511,445137,32386313,LABFER,2013-01-23 08:15:00,1,0,1,0,0.477840451249,1.0,0.0,0.446757590453,0.401841398655,0.433364418192,0.238636363636,0.251445998034,0.379189344616,0.160909090909,0.541093347908,0.41158449441,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.491147300333,0.498388244928,0.495667509745,0.496838856933,0.120832416289,0.095315286913,0.0873016660133,1.0
-7638477886346,374695,37362733,LABFER,2013-08-31 08:33:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.136319701156,0.201058391674,0.227393438848,0.250220933493,0.250877010493,0.274926816895,0.360767834867,0.22997043423,0.260639179984,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.450797296788,0.473449537209,0.479322944411,0.496001809874,0.496456253032,0.355137283979,0.322559007707,0.271435016442,1.0
--11775283379790,375631,21671526,LABFER,2011-11-01 21:44:00,1,0,1,0,0.0,0.0,0.0,0.253082501533,0.366002356262,0.331129010509,0.166119205952,0.337416512668,0.264554040172,0.4,0.34339439237,0.2606065648,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.460921105668,0.483222448346,0.491813578127,0.49884618293,0.502432650099,0.464815250385,0.462216901069,0.458268003248,1.0
--5602980712309,467479,8191852,LABFER,2010-04-17 20:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.172935433799,0.253167468651,0.197205448875,0.210866105888,0.159695409273,0.121995888318,0.00692168102083,0.0511779851619,0.0906041093012,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.480044621299,0.468417916605,0.490483729657,0.494078086493,0.00251831570891,0.000128473236734,7.78760961501e-06,0.0
--8012874952853,399281,12222824,LABFER,2010-10-11 00:04:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.513819865532,0.488601646815,0.503650832971,0.512129961758,0.514181435074,0.577696387291,0.674985172175,0.56105215325,0.51856302713,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.498813902017,0.498388244928,0.501910910311,0.501563352078,0.455798582389,0.466474835332,0.471351964296,1.0
--4987801056914,464539,17265877,LABFER,2011-05-12 13:35:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.548646984401,0.605322047464,0.609818338079,0.738197044874,0.684974578401,0.632028454034,0.638340874375,0.685164253063,0.781037578136,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.50623581732,0.498388244928,0.500756912341,0.502660218728,0.695647234175,0.706023166391,0.706733760497,1.0
-3274478969077,450399,8102044,LABFER,2010-04-22 22:43:00,1,0,1,0,0.224899598394,1.0,0.0,0.333515093174,0.402257545132,0.444621537593,0.445448697537,0.341510510209,0.312102466578,0.53,0.346232690705,0.298719285765,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.454221149053,0.475755902609,0.473990644778,0.493473182276,0.497944624186,0.350377579268,0.357768685152,0.348831528086,1.0
--4708351539376,433139,36693005,LABFER,2013-07-24 13:02:00,0,1,1,1,0.11914893617,1.0,1.0,0.288610725776,0.280949920418,0.279657623136,0.489594341081,0.278836564929,0.311637864217,0.106976744186,0.474039803008,0.333746603286,0.222416167301,0.300974526888,0.361002970038,0.361490321039,0.464472851919,0.48347952473,0.457893296213,0.497377472472,0.49787256329,0.366833657382,0.29868128869,0.249365377766,1.0
-8100824226645,412385,3827964,LABFER,2009-10-23 12:16:00,1,0,1,0,0.634686346863,0.257142857143,0.0,0.471148063781,0.39366230727,0.44164935118,0.441587461652,0.380152403168,0.558296794625,0.48019413041,0.379589289092,0.476554211718,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.504414561691,0.498388244928,0.501224522152,0.499325681301,0.644783742477,0.676768183913,0.679424693663,1.0
-2728605788525,397491,19729016,LABFER,2011-09-07 22:00:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.283130161464,0.317455442223,0.391565711079,0.474305054882,0.428728577482,0.323336160688,0.580031110247,0.436512872994,0.309923317344,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.492481671026,0.497598790267,0.489373727303,0.497743917659,0.499874884958,0.695137644042,0.710656422075,0.739271326998,1.0
--1165273467866,427577,3763853,LABFER,2009-10-06 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.109003491047,0.149828679459,0.0495888455882,0.0639207341164,0.0571057637369,0.0237612173234,0.151545369311,0.0241239438668,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.430310572307,0.456874403014,0.456676473752,0.487625947527,0.493252978336,0.000838459938076,9.99823727833e-05,9.73152030392e-06,0.0
--1560487432354,466534,40556293,LABFER,2013-12-29 18:21:00,0,1,1,1,0.634686346863,1.0,1.0,0.601182807484,0.563868094904,0.574057508393,0.658671467391,0.609365796893,0.599744043287,0.851619818574,0.659411871011,0.601127457026,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50641368303,0.50812977416,0.510015679179,0.501526537763,0.500383571463,0.504475643961,0.488835542048,0.480488526366,1.0
--8093690887686,358533,38057835,LABFER,2013-09-15 16:56:00,0,1,1,1,0.224899598394,0.0,0.0,0.321228719889,0.447849317461,0.476600686421,0.3631325955,0.547875289861,0.361978197152,0.367142857143,0.394479278959,0.399045059398,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.476735705711,0.490317343717,0.498277106792,0.49129911562,0.496840166929,0.34616792938,0.310284360876,0.287535490056,1.0
--3882353026601,361747,25317314,LABFER,2012-04-03 22:01:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.560949625609,0.57925354694,0.529420190421,0.58990483679,0.668271376299,0.649007728801,0.712879020996,0.656429717394,0.712404914811,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.502348205791,0.507626113484,0.521811420026,0.503631819919,0.502062356413,0.623379304295,0.63013001287,0.634034889838,1.0
-660260741253,429982,28829331,LABFER,2012-09-09 21:15:00,0,1,1,1,0.108695652174,0.0,0.0,0.16470208224,0.359163409979,0.324752537362,0.301928571429,0.1381526542,0.23231829109,0.288888888889,0.270833333333,0.114330152707,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.463895970494,0.466071861502,0.469859735916,0.494390500289,0.492291988338,0.163102680776,0.173391272695,0.23429968771,2.17057234236e-54
-2051652492732,384501,33194637,LABFER,2013-03-12 22:01:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.328018391347,0.520197754571,0.433774806329,0.354132555502,0.549366491912,0.478776855102,0.421739130435,0.421495327103,0.392847538616,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.49281434983,0.498513566534,0.509322489469,0.498602937974,0.498724279489,0.611292632584,0.627221178156,0.649010032861,1.0
--6625226320024,408143,17597591,LABFER,2011-05-22 14:09:00,1,0,1,0,0.218911917098,0.04,0.0,0.187289926954,0.348375401751,0.320281468181,0.0835856533009,0.279150865484,0.344929258537,0.6,0.414849428868,0.322793453776,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.456504813081,0.482259941408,0.480015246909,0.486782018151,0.494259449991,0.0721403919987,0.0702768903783,0.0682795175057,1.0
-590496075017,459059,22762777,LABFER,2012-01-06 19:53:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.448897920763,0.434891516774,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.497957021643,0.498200016386,0.408339713257,0.412531074008,0.420301986875,1.0
--11229496529666,376171,35807265,LABFER,2013-06-19 20:21:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.548646984401,0.46057262526,0.502041836444,0.517683273116,0.502848299101,0.517205350996,0.570816599732,0.536401378708,0.591058926535,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.49198349286,0.498300215525,0.510888218981,0.500928041773,0.501125879697,0.527679180339,0.537870944033,0.547005140184,1.0
--10294322859304,360709,21375394,LABFER,2011-10-23 22:01:00,1,0,1,0,0.634686346863,0.813953488372,1.0,0.548646984401,0.550938182717,0.594272485203,0.753922019494,0.630168703598,0.688936353869,0.878422278157,0.76731293173,0.660864581508,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506666070176,0.510888218981,0.503338213278,0.501393627779,0.467296809979,0.465038750689,0.477756450605,1.0
--6798952146923,385426,40626571,LABFER,2013-12-25 11:15:00,0,1,1,1,0.634686346863,0.257142857143,0.0,0.581475193791,0.509013979185,0.522430208582,0.62437643366,0.637642010104,0.641385566287,0.769690334689,0.785248436193,0.490212191852,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.504666180039,0.510888218981,0.504199856605,0.501022817776,0.37391325599,0.3534837481,0.356307204886,1.0
--10735095088400,421978,35089733,LABFER,2013-05-13 13:50:00,1,0,1,0,0.218911917098,0.0,0.0,0.217658516895,0.195674543506,0.16974420226,0.150280193703,0.209008676276,0.176345800061,0.203720930233,0.206311508088,0.0955478663561,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.439549841254,0.467475513875,0.44573566134,0.490675196981,0.495012724019,0.172875093574,0.169488604815,0.163769611954,1.0
--10823465862620,462252,33093023,LABFER,2013-02-15 14:11:00,1,0,1,0,0.218911917098,0.322580645161,0.0,0.319734926228,0.35416443227,0.259444314266,0.347267589376,0.173658054892,0.170458665215,0.353846153846,0.149248264868,0.144336500867,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.457205909306,0.477328386202,0.491813578127,0.489170911022,0.493571041394,0.292928377296,0.246026941609,0.200927406636,1.0
-7151079386216,383063,18104378,LABFER,2011-06-26 01:18:00,1,0,1,0,0.224899598394,0.0,0.0,0.427578883872,0.286697108179,0.354126325608,0.136595744681,0.414386951383,0.250053574991,0.444571428571,0.559164069923,0.44201714176,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.464084887767,0.481391574386,0.473990644778,0.489666679664,0.494580352558,0.139791657554,0.126739367045,0.137158050661,1.0
-11968061499015,398908,40005156,LABFER,2013-12-15 22:00:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.587927599884,0.535810095278,0.584209981435,0.714704756705,0.795950791335,0.682350719351,0.8,0.734223623092,0.781276836585,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.499797070486,0.510888218981,0.505602996812,0.502633558162,0.503474971052,0.495442649295,0.492930668462,1.0
-10589016249732,465474,33642273,LABFER,2013-03-23 11:34:00,1,0,1,0,0.403846153846,1.0,0.0,0.344430485551,0.179998942183,0.289791455663,0.188931208758,0.423593052609,0.374385402224,0.039527027027,0.508213640168,0.348269486599,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.446998479078,0.477191653434,0.463313802904,0.4962949764,0.499005583605,0.483853787859,0.48810096499,0.484240522939,1.0
-12000859836885,402194,25195013,LABFER,2012-04-10 21:00:00,1,0,1,0,0.477840451249,0.890410958904,0.0,0.44810841934,0.405977140536,0.423883547021,0.450869791751,0.330143043492,0.39220829508,0.504412546518,0.38080296092,0.419181804399,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.499741993571,0.509322489469,0.499925554971,0.499654031562,0.568916759584,0.565985062184,0.569272006226,1.0
-9872984251245,468791,37250038,LABFER,2013-09-02 19:45:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.525342565207,0.446040261597,0.475540295041,0.511397920763,0.478739900665,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500144076394,0.499102664089,0.43876816772,0.441467938195,0.447673166717,1.0
--7608662508449,372261,6374107,LABFER,2010-02-03 22:01:00,1,0,1,0,0.218911917098,0.0,0.0,0.217658516895,0.172391985158,0.189325787852,0.150467255204,0.105573833944,0.121661559101,0.21662591687,0.14402239264,0.164534997443,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44139296035,0.466011202773,0.456676473752,0.490294943432,0.495630070524,0.160410375676,0.130666550576,0.110626258823,1.0
--8967516783628,434925,36006768,LABFER,2013-06-15 03:42:00,1,0,1,0,0.218911917098,0.0,0.0,0.331964458702,0.264695522438,0.277676453624,0.128217410012,0.141225614897,0.20942446788,0.2,0.445241013072,0.32931653667,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.454029056696,0.472019819897,0.456676473752,0.486758013704,0.493678531308,0.0786667156438,0.0733173090951,0.0785730557734,1.0
-755875392586,412099,31621017,LABFER,2013-01-09 09:27:00,0,1,1,1,0.215827338129,0.0,1.0,0.326471587161,0.274202439588,0.251088894407,0.277817132019,0.194590113187,0.370299092892,0.156284153005,0.2216316575,0.27325833674,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.467762615615,0.481837346949,0.500307457597,0.495214001502,0.498284241791,0.241446203023,0.256265597404,0.262660033131,1.0
-5513809783371,465104,9340441,LABFER,2010-06-23 00:13:00,0,1,1,1,0.477840451249,0.0,0.0,0.514304324921,0.380155351103,0.362031640804,0.165970632146,0.42130264708,0.43475552847,0.224848484848,0.154782873066,0.402419896849,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.480708268839,0.491798592395,0.49751533833,0.5005822218,0.498945275718,0.644118285475,0.687887360361,0.722881096466,1.0
-11960628357057,469051,1005814,LABFER,2009-04-07 18:19:00,0,1,1,1,0.224899598394,0.0,0.0,0.499050123376,0.423999789597,0.41995087613,0.433914542413,0.293581612635,0.357413155179,0.475540295041,0.321296410702,0.339944316531,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.477576760061,0.488967040643,0.485779235087,0.494194154084,0.496783330049,0.268743463269,0.263625434414,0.26949472111,1.0
-2055640568417,377655,33681604,LABFER,2013-03-23 08:39:00,0,0,1,0,0.218911917098,0.118881118881,0.0,0.388478357514,0.38230051405,0.365212684484,0.387380076184,0.364642973523,0.486076306498,0.212765957447,0.34194879177,0.453535909003,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.489945636617,0.480886833765,0.501250612934,0.499879369231,0.688643414505,0.700353060291,0.702092346465,1.0
-10675626237394,436693,22233853,LABFER,2011-12-09 02:18:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.506931948752,0.511052682693,0.520560002188,0.646803659395,0.630432353635,0.60101193089,0.640937894268,0.964536082474,0.614642314006,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498865405233,0.501130605681,0.499081689524,0.500895601791,0.500020629377,0.667646042615,0.688567686795,0.708705617635,1.0
--4470906837342,451171,7900805,LABFER,2010-03-27 13:28:00,1,0,1,0,0.634686346863,0.0,0.0,0.517646820288,0.468037890949,0.526297165572,0.504110896779,0.497337952458,0.556120409033,0.370464646465,0.510681144999,0.471681161293,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506358860585,0.510888218981,0.501373527683,0.49972224137,0.566516093695,0.593769884907,0.59970899874,1.0
--1921480960222,387964,12224356,LABFER,2010-10-01 07:41:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.186134585598,0.155877223798,0.152485759463,0.180398517748,0.100511460896,0.16398551241,0.0142857142857,0.146117860355,0.0986587567131,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.424079205818,0.452275330322,0.447057203458,0.488954911214,0.494368802288,0.304725070984,0.291149235654,0.256749338562,1.0
-5782366219571,463412,9273000,LABFER,2010-06-10 14:09:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.533877242246,0.507951373891,0.536406338956,0.692638859069,0.691921260365,0.610647305305,0.568428530833,0.640118947729,0.700906251056,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506666070176,0.510888218981,0.508705615363,0.504634601788,0.557059249529,0.562935286397,0.570720493848,1.0
-11236769104825,468550,32820013,LABFER,2013-02-24 08:00:00,0,1,1,1,0.218911917098,0.04,0.0,0.223028242855,0.220242021117,0.270967812591,0.389178195544,0.313864937511,0.199931496799,0.23578751165,0.61682745098,0.237092429759,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.455277964982,0.472775381719,0.467548574593,0.488052490426,0.494838569065,0.373772179557,0.354124487615,0.349139320498,1.0
--565111747638,444808,26813943,LABFER,2012-06-06 15:44:00,1,0,1,0,0.224899598394,0.0833333333333,0.0,0.568342212044,0.421631582356,0.485564557662,0.471287405455,0.495688496091,0.439090651682,0.595,0.419149251537,0.44513282544,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.463153083969,0.481938043502,0.487344472973,0.48087532671,0.489658053957,0.250031071657,0.265976151844,0.281998984019,1.0
-6786187984092,388069,1454927,LABFER,2009-06-02 08:00:00,1,0,1,0,0.218911917098,0.0,0.0,0.275395462917,0.334805888634,0.250915760639,0.35558709281,0.226676252161,0.275092688585,0.21662591687,0.238390712297,0.268061187016,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.462612198377,0.477590797821,0.479322944411,0.493199345506,0.49828339293,0.312471997909,0.289596173633,0.293428587694,1.0
--11820940462826,460571,11774636,LABFER,2010-09-14 20:11:00,1,0,1,0,0.215827338129,0.0,0.0,0.313221478238,0.356713055832,0.290303945753,0.0524621212121,0.403052535972,0.258199099565,0.0115555555556,0.117202749158,0.226805427953,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.469489775956,0.48039992059,0.487808173527,0.492753775848,0.497307787895,0.322079925131,0.294333420423,0.265509953465,1.0
-5514886648304,388523,21038027,LABFER,2011-10-23 08:21:00,1,0,1,0,0.218911917098,0.322580645161,1.0,0.344430485551,0.336511048478,0.351810197924,0.414062302523,0.366111914417,0.408284635486,0.407692307692,0.547938293484,0.349894782284,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.472161141741,0.48621781883,0.480886833765,0.496966842383,0.4993919322,0.555671602648,0.56807533874,0.568199592582,2.19527207554e-13
--9119232796603,359374,35922216,LABFER,2013-06-13 22:00:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.533877242246,0.539324547462,0.568934278701,0.722105399564,0.631166237669,0.537200400289,0.785630498534,0.540656569581,0.670800765851,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.502493771769,0.498388244928,0.499972538307,0.500767154009,0.673267487221,0.683313236697,0.688736068824,1.0
--7590137460989,461177,39098904,LABFER,2013-10-20 13:53:00,1,0,1,0,0.218911917098,0.04,0.0,0.24256619665,0.366572156832,0.385762410137,0.165778956789,0.255214386633,0.398621499876,0.232183908046,0.418264291732,0.450691300774,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.476451996497,0.486821477215,0.491813578127,0.496348095964,0.496857973286,0.304385267591,0.307303204337,0.33121214861,2.66721930773e-11
--10070564387676,446493,3309734,LABFER,2009-09-13 20:14:00,1,0,1,0,0.477840451249,0.0,0.0,0.55319321381,0.56281270889,0.517022477239,0.44689225351,0.517862716199,0.641496708417,0.683874061064,0.582396421514,0.604475469354,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.498292615748,0.502581047312,0.498388244928,0.50054399898,0.500879896055,0.545638503459,0.542044923169,0.540685596378,1.0
-5581226095569,409479,36685749,LABFER,2013-07-24 22:43:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.284824905945,0.293770895787,0.313916693617,0.344812335661,0.36645295876,0.25055242511,0.225,0.210084033613,0.184383079051,0.222416167301,0.300974526888,0.368633683804,0.379422352055,0.445989966376,0.464465110826,0.468307222203,0.492165899233,0.49357604897,0.0132570776457,0.00799569102965,0.00458777847746,3.13178175135e-46
--1031074046444,471020,22714006,LABFER,2011-12-13 21:47:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.533877242246,0.530238193527,0.575594515726,0.63150574495,0.692177877217,0.610781054027,0.729045592042,0.633736551925,0.558352280321,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.502786532,0.503173369864,0.619910719584,0.632996737431,0.636648692652,1.0
--4810215850040,405322,35597805,LABFER,2013-06-03 09:21:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.15951723988,0.163677291439,0.137326250525,0.181201472469,0.224385273847,0.241866533662,0.339613666571,0.135099536351,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.431252454438,0.458570852457,0.447057203458,0.491087991218,0.495266114694,0.165522477978,0.164294499639,0.152746528596,1.0
-4792749935546,424599,20383321,LABFER,2011-10-01 21:19:00,0,1,1,1,0.634686346863,1.0,1.0,0.576935863781,0.675072640009,0.57915169911,0.815712483805,0.677866715662,0.682253168003,0.836136563872,0.845949991575,0.673427456535,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.538905064617,0.538377555144,0.565854186426,0.510920970208,0.506114454145,0.976671268444,0.989340248787,0.994415976133,1.0
--6044003793463,451285,40560504,LABFER,2013-12-21 17:45:00,0,0,1,0,0.477840451249,1.0,1.0,0.466706373376,0.385755416722,0.460042312232,0.560292526548,0.449635743919,0.420800401776,0.452537655534,0.459926042974,0.456821792091,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498658105002,0.498531290539,0.498388244928,0.497429473969,0.49773005788,0.63892362449,0.614802861185,0.607232892027,1.0
-10338235197647,457270,36724014,LABFER,2013-08-15 10:16:00,1,0,1,0,0.634686346863,1.0,1.0,0.533877242246,0.51872151262,0.562952970174,0.74458147026,0.754853897155,0.672817423887,0.447214854111,0.723033824254,0.658179098386,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506878695491,0.510888218981,0.50708645768,0.504799097764,0.789851701394,0.818650989409,0.814836687568,1.0
-10161974915346,378612,4617639,LABFER,2009-11-22 22:04:00,1,0,1,0,0.218911917098,1.0,0.0,0.383923135753,0.252586549033,0.229055692729,0.237995913802,0.103958496434,0.189477816148,0.027027027027,0.21732107235,0.332663438994,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.410381246603,0.447350160716,0.429697111247,0.484118333402,0.491187253126,0.0779753572651,0.0732603884249,0.0715714699166,0.999999986376
-1052638176975,430698,33433914,LABFER,2013-03-23 10:03:00,1,0,1,0,0.863636363636,1.0,1.0,0.302372416431,0.444978919494,0.328463285647,0.550277676288,0.385728451564,0.290234569782,0.0332727272727,0.195798319328,0.347525044284,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.477310545595,0.484007040975,0.487808173527,0.496271158044,0.498113589676,0.103965810691,0.129528810961,0.160818173304,1.92326438328e-08
--8484004126004,468755,13771888,LABFER,2010-12-18 05:29:00,1,0,1,0,0.477840451249,0.0,0.0,0.466706373376,0.467012818976,0.466186867735,0.617559710697,0.434140740616,0.449049417537,0.385583963163,0.514499903115,0.386603349325,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.48989582322,0.493301101497,0.108538001954,0.123923969221,0.147842246595,0.999999999986
-638844893127,365394,4486808,LABFER,2009-11-24 00:47:00,1,0,1,0,0.640625,0.0,0.0,0.127684347349,0.296491131182,0.313062555861,0.474910668155,0.406000961224,0.231913004854,0.175,0.428570885869,0.330115894648,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.442876726842,0.468546695843,0.44573566134,0.490367404798,0.494847299436,0.125206888543,0.147102526111,0.176417195267,1.0
-11063007066342,439306,22735339,LABFER,2012-01-03 17:49:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.459675021899,0.475540295041,0.448897920763,0.439085655968,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500144076394,0.499758377326,0.501544687299,0.50790986693,0.515732304821,1.0
--8183097545634,470971,39011546,LABFER,2013-10-28 10:56:00,1,0,1,0,0.218911917098,0.04,0.0,0.215474735203,0.332378947658,0.235754773912,0.281140391157,0.20053262451,0.219637737816,0.21662591687,0.2179259863,0.201426982891,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.448060499903,0.472179359435,0.479322944411,0.495963368902,0.498839770684,0.138514888596,0.128195758115,0.12671258082,1.0
--8996911702299,361230,35220555,LABFER,2013-05-16 16:45:00,0,1,1,1,0.634686346863,0.0,1.0,0.449349575001,0.483730285787,0.535318624851,0.616293675213,0.543414084451,0.564890275363,0.69826055309,0.545427018098,0.587634955776,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504691900155,0.505514956461,0.521811420026,0.499953050554,0.499656905336,0.580723655328,0.554110465629,0.534267328898,1.0
-7986178328366,373702,22854466,LABFER,2012-01-17 07:57:00,1,0,1,0,0.218911917098,0.0,0.0,0.120795373724,0.247370043168,0.199559310888,0.0356935579706,0.143788922624,0.10471748286,0.439682539683,0.277983544234,0.140511792538,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.434393925404,0.455313246556,0.455878037868,0.477515432601,0.489654786632,0.122183965013,0.140964098309,0.150857925765,1.0
--4128972631479,384289,3017444,LABFER,2009-09-07 05:14:00,1,0,1,0,0.11914893617,1.0,1.0,0.31167376806,0.308799283031,0.200277652282,0.0896725599068,0.378907074745,0.174552669319,0.00557939914163,0.279357035112,0.28556748962,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.43570987586,0.461864016991,0.456676473752,0.489483547872,0.491254963461,0.134813698389,0.116777276056,0.0882750307675,0.998482843456
-7278042224592,447537,25801397,LABFER,2012-05-10 20:12:00,0,1,1,1,0.634686346863,1.0,1.0,0.618467041143,0.612764735345,0.577493222633,0.618171843747,0.654600314063,0.518001495748,0.103703703704,0.685116906475,0.622526043757,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50641368303,0.510673741281,0.510015679179,0.504569676609,0.502446754438,0.568495763266,0.532075437695,0.499955123072,8.66583257189e-15
-4927221004558,444657,3302579,LABFER,2009-10-09 14:22:00,1,0,1,0,0.477840451249,0.890410958904,0.0,0.513819865532,0.552506563077,0.536517737814,0.577363537928,0.523224868654,0.588631756624,0.533630958674,0.379885803448,0.488784459502,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.504625051617,0.498388244928,0.498387591091,0.498197489779,0.410464900954,0.408160008214,0.405334576996,1.0
-1966348183964,364315,15114891,LABFER,2011-02-26 19:46:00,1,0,1,0,0.477840451249,1.0,1.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.4794165906,0.475540295041,0.491046681093,0.439085655968,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500144076394,0.499758377326,0.541715813659,0.548356724967,0.556116281232,1.0
-6892266854915,390711,36518239,LABFER,2013-07-23 01:22:00,1,0,1,0,0.477840451249,0.0,0.0,0.437345252708,0.536307321082,0.481574847226,0.398769796653,0.546045269587,0.445861453863,0.421739130435,0.5,0.605676044173,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.501526711629,0.505703311665,0.509322489469,0.505341558795,0.503061889126,0.732208568636,0.728336980356,0.725048321596,1.0
-6815347588833,403766,19384889,LABFER,2011-08-11 10:50:00,1,0,1,0,0.224899598394,0.0,0.0,0.368342212044,0.415426329149,0.381730781192,0.255695752389,0.403029056402,0.330404546473,0.229758179232,0.43167181423,0.323152947208,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.450215235311,0.473378781091,0.473990644778,0.47940181676,0.491144098085,0.131328746632,0.134655604929,0.144459885322,0.999999999824
-1460540767480,455698,18153299,LABFER,2011-06-29 11:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.145355110192,0.174509101248,0.203108372409,0.0917943835839,0.0996451395643,0.144169159114,0.294336463553,0.231063699021,0.120595457372,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.471185506814,0.467548574593,0.493346855309,0.496784656769,0.174828156284,0.17007233337,0.172050549915,1.0
-2438768908013,431130,13144052,LABFER,2010-12-10 03:06:00,1,0,1,0,0.634686346863,0.0,1.0,0.533877242246,0.533978262144,0.557086431266,0.597568973315,0.689563016678,0.552226105058,0.328703703704,0.797204909808,0.486537838992,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506878695491,0.510888218981,0.501711344291,0.50128609608,0.628192039249,0.622681561144,0.616761760598,1.0
-2204489255948,417486,2721388,LABFER,2009-09-10 22:00:00,0,1,1,1,0.477840451249,1.0,1.0,0.563811797395,0.654726170602,0.584517424283,0.666812023259,0.719377288755,0.760397240279,0.334887334887,0.815266730265,0.707414342402,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.546268765047,0.543936577469,0.565854186426,0.519463375604,0.508218947684,0.986969701881,0.994142049924,0.996274941901,1.0
-8767229256687,429995,23637082,LABFER,2012-02-17 23:50:00,0,1,1,1,0.477840451249,0.0,0.0,0.459705424403,0.450457349284,0.40357524002,0.5791450942,0.304330596664,0.317429740749,0.501666666667,0.421161314936,0.342717065359,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.489320633705,0.498388244928,0.496318160735,0.497366676306,0.437400534952,0.43467480117,0.431256554535,1.0
--6655285524721,448680,37938719,LABFER,2013-09-17 05:14:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.426713070503,0.405658999809,0.522978783044,0.659423137272,0.589187538237,0.602221106392,0.357471264368,0.718791716489,0.673458876944,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.503048881621,0.498388244928,0.501875041416,0.501273724085,0.65391972556,0.652705352085,0.651104984368,1.0
-2323111489834,468793,24339458,LABFER,2012-03-12 07:44:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.191248437633,0.167086670482,0.144434429982,0.31179017323,0.0986853967105,0.00692168102083,0.0425250901564,0.0756071847107,0.222416167301,0.257917683272,0.296715374131,0.300505581722,0.422240015787,0.450502402247,0.434621628464,0.480499804736,0.489225364117,0.0163282305424,0.0134778319899,0.0120748821538,2.78313187464e-12
-3307593658129,464207,660686,LABFER,2009-03-10 08:05:00,1,0,1,0,0.218911917098,0.04,1.0,0.24256619665,0.392155353562,0.398976653426,0.200561883995,0.337420324628,0.411200286711,0.314657689048,0.24602871368,0.303749025021,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.4785460381,0.491697030862,0.480886833765,0.486956482038,0.492155130355,0.154018573465,0.145601419262,0.142161040831,1.0
-8900131118897,420252,22104746,LABFER,2011-12-07 12:47:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.130251922482,0.226131141253,0.455424760834,0.0749094779838,0.118557477322,0.205579399142,0.110367638343,0.0913448510531,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.453084829707,0.476443224081,0.491813578127,0.498745942427,0.497048992909,0.1966067378,0.193705556159,0.183547738999,1.0
--888561490285,421519,15448501,LABFER,2011-02-18 07:52:00,0,1,1,1,0.11914893617,0.0,0.0,0.118540167139,0.167877906066,0.222265117306,0.185834977119,0.0795034790808,0.175744491571,0.0848484848485,0.216094335249,0.116563220123,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.439555329544,0.461192718404,0.447057203458,0.48438289403,0.492262169378,0.113441194544,0.0964565122062,0.0873633736042,1.0
-6654901161905,397824,32489788,LABFER,2013-02-02 09:33:00,0,1,1,1,1.0,1.0,1.0,0.242354076217,0.205932962555,0.25698058545,0.406090149036,0.243046280143,0.32784922503,0.422950819672,0.326290612274,0.342297022825,0.222416167301,0.300974526888,0.352917138707,0.341023888151,0.456787189552,0.474502688618,0.480886833765,0.50844928585,0.502909824515,0.124882070259,0.100924720959,0.0782512302233,1.51510488394e-75
--4190226634673,355911,27782197,LABFER,2012-07-24 18:23:00,0,1,1,1,0.218911917098,0.0,1.0,0.174759657759,0.175334345624,0.201807091075,0.180773912198,0.146064041972,0.217858756997,0.0156153846154,0.0392491646823,0.123110344384,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.445514408169,0.468319449438,0.457893296213,0.493925227391,0.49594129477,0.575104487195,0.565220914064,0.578670027492,1.0
--971737188014,420417,24246506,LABFER,2012-02-14 10:22:00,0,1,1,1,0.477840451249,0.0,0.0,0.513819865532,0.529991632931,0.539166177536,0.571475347581,0.595419692837,0.576991702902,0.810249918071,0.875129010197,0.682614945966,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.505302026623,0.498388244928,0.504840980685,0.501887486509,0.867660246057,0.907687244726,0.933925685411,1.0
-1375194462885,458912,15625251,LABFER,2011-03-13 20:31:00,1,0,1,0,0.218911917098,0.04,0.0,0.223028242855,0.208689179617,0.225843285367,0.358798130475,0.109718028859,0.171241099407,0.0166259168704,0.0198368838723,0.113433896326,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.441437356105,0.465350301542,0.450904755627,0.489072542575,0.494221997473,0.142721145873,0.137900604799,0.139393341135,0.999583604633
--9842885824841,434071,18824710,LABFER,2011-07-09 16:53:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.138642371302,0.130212503155,0.07405771026,0.0462131773447,0.157378204601,0.00692168102083,0.22556767162,0.150418290028,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.434241280113,0.457245622532,0.469108659157,0.484818133499,0.491900520874,0.00762474776505,0.00775602939394,0.00854137911964,2.92951436882e-80
--4970671891536,444313,33913965,LABFER,2013-03-21 14:22:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.598638916264,0.680208726377,0.647753316459,0.614004324021,0.804300145966,0.6668078273,0.665740694681,0.751573846387,0.738256319928,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.512254802287,0.510888218981,0.506441261743,0.499372514347,0.836637258783,0.832148026155,0.8288160647,1.0
-6513206943923,438713,34620242,LABFER,2013-05-02 13:51:00,1,0,1,0,0.224899598394,0.0,1.0,0.504005613731,0.523670604332,0.506241680963,0.398476318203,0.268819676018,0.527054419239,0.607070707071,0.66591757223,0.335204287484,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.479868709149,0.483908673736,0.47486128235,0.496030636803,0.496063400806,0.0883282495896,0.0515154286499,0.0405248909895,1.0
-6975277055007,383059,37618424,LABFER,2013-09-19 01:58:00,0,1,1,1,0.218911917098,0.0,0.0,0.319734926228,0.337061594317,0.310960912723,0.268143652018,0.369794447128,0.354672083247,0.543506493506,0.39745537551,0.39925016896,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.460243263807,0.480192518381,0.468417916605,0.502116750182,0.503972424947,0.47452709772,0.467993046855,0.48668984939,5.00990657192e-07
--7062910564565,372272,16219055,LABFER,2011-03-28 15:06:00,1,0,1,0,0.477840451249,0.80487804878,1.0,0.499050123376,0.484145904664,0.525382105526,0.555304597067,0.521508733981,0.511930503938,0.678907160923,0.583604228567,0.504032648346,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.501314674,0.498388244928,0.501936679906,0.50044692747,0.51304093742,0.503868852718,0.500239687103,1.0
--6424426912452,419771,23586510,LABFER,2012-01-27 21:00:00,0,1,1,1,0.477840451249,1.0,1.0,0.492971822066,0.365072112507,0.412149373491,0.259631887986,0.463985053434,0.282461979964,0.419501039501,0.0931473949394,0.373291266141,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.495724935038,0.499210382663,0.509322489469,0.500103959308,0.500162684998,0.449831705632,0.417135343756,0.391856636753,1.0
--8688466809279,398634,24742433,LABFER,2012-03-04 07:59:00,0,1,1,1,0.634686346863,1.0,1.0,0.45576466934,0.539889801712,0.525376188197,0.699351932626,0.610313999992,0.5569896197,0.766666666667,0.508295135039,0.621455870077,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.508638417199,0.521811420026,0.504973462937,0.50381455396,0.785735441172,0.776308792993,0.777871477841,1.0
--10274571917167,382229,36958819,LABFER,2013-07-23 01:41:00,1,0,1,0,0.218911917098,0.0,1.0,0.18386959004,0.191610122319,0.256071121798,0.43253584306,0.140512527449,0.244199076722,0.126366251198,0.103248170863,0.137904344741,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.45456622138,0.47801407141,0.468417916605,0.495656874396,0.49387986526,0.227902488389,0.229376487177,0.232534207425,1.0
--12104338109023,468744,28864822,LABFER,2012-08-22 21:10:00,1,0,1,0,0.634686346863,0.813953488372,1.0,0.433362620885,0.676802456176,0.607406183843,0.689047694208,0.632365949516,0.67295658471,0.68995411981,0.616134253938,0.687484172186,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.512484867535,0.509376473095,0.510888218981,0.504730235639,0.503741543708,0.584889893934,0.571285231528,0.559925415191,1.0
-3858192093620,417761,17679529,LABFER,2011-06-09 10:29:00,1,0,1,0,0.224899598394,1.0,1.0,0.44810841934,0.396697603298,0.4515862933,0.557027057071,0.525572450595,0.464177490383,0.269696969697,0.599644201242,0.519084540906,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.479891626589,0.48828496683,0.463099032304,0.495502497534,0.497292874867,0.603522170886,0.609852647342,0.596977444167,1.0
--5629196644346,410219,5311616,LABFER,2009-12-11 22:02:00,1,0,1,0,0.477840451249,0.0,0.0,0.378469961203,0.411003622059,0.383001481062,0.27027526526,0.276557881896,0.352488786541,0.2,0.609361233679,0.340018945444,0.474085517325,0.462338008602,0.443974869853,0.436224459079,0.470775083819,0.477541758939,0.498388244928,0.496111979535,0.494961760141,0.136246132496,0.0701617494193,0.0455112111572,1.0022944094e-61
-713129565782,414165,24073305,LABFER,2012-02-26 21:00:00,1,0,1,0,0.634686346863,0.589887640449,0.0,0.533877242246,0.482741837664,0.522814030932,0.63717189406,0.530132573155,0.577996310828,0.935729745929,0.408269270697,0.568671473953,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.499031561184,0.510888218981,0.506052620822,0.502281255884,0.347715924987,0.344876755569,0.338462836927,1.0
--6619421277074,359139,224326,LABFER,2009-01-28 13:59:00,1,0,1,0,0.477840451249,1.0,0.0,0.499050123376,0.454189654649,0.536178622669,0.17586306099,0.444525140869,0.537150440585,0.188675213675,0.531608236328,0.371330370304,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.499170473612,0.498388244928,0.502250937136,0.50238191867,0.399026926371,0.419648616607,0.444780917827,1.0
--5991595651138,424064,24523127,LABFER,2012-03-06 21:00:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.18386959004,0.156845028322,0.174025239963,0.232697946757,0.0878857242704,0.132987734006,0.0242931015513,0.143888611993,0.0582632731723,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.430163397165,0.459310803899,0.457893296213,0.491245736175,0.496522826561,0.255911312828,0.287339519207,0.297804864894,1.0
-8173748987728,423161,25384229,LABFER,2012-04-26 22:01:00,1,0,1,0,0.108695652174,0.0,0.0,0.356502536907,0.335193258955,0.416217813133,0.114685314685,0.202392355558,0.228908575578,0.1971519907,0.1,0.237858187135,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.469941423006,0.481482437794,0.470550722573,0.495263736019,0.497239853635,0.49386686524,0.491187871384,0.488290893744,1.0
-8788729772386,422957,13737283,LABFER,2010-12-23 07:35:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.21543947462,0.282989354618,0.330436404844,0.613502938971,0.33499009228,0.347215841389,0.210869565217,0.342805543831,0.425547746793,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.441644977043,0.464506160226,0.457893296213,0.489792462376,0.493974527967,0.465029542855,0.495006093615,0.48636405046,0.999999999963
--6033799294436,433726,5923151,LABFER,2010-01-12 22:01:00,1,0,1,0,0.224899598394,0.0,0.0,0.446757590453,0.418484941849,0.382486821609,0.363353948761,0.311365450396,0.348621773299,0.022181472426,0.474139585904,0.375348871039,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.468015568593,0.479332847366,0.47486128235,0.481217151818,0.489826310332,0.0867628989684,0.0809061698085,0.0801372054043,1.0
--11448138242164,429711,19578110,LABFER,2011-08-14 09:53:00,0,1,1,1,0.218911917098,0.0,0.0,0.217658516895,0.241301081399,0.212335563262,0.192391260461,0.25370921847,0.161575531147,0.0,0.100826451281,0.266550609598,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.41879838834,0.456024310498,0.445050483537,0.487952204488,0.495580475753,0.210615750456,0.191180283522,0.181268519057,1.0
--832066932554,385357,32993222,LABFER,2013-02-09 19:05:00,1,0,1,0,0.634686346863,0.813953488372,1.0,0.548646984401,0.604976769141,0.565479610372,0.721153167272,0.664616806763,0.550953243988,0.695336547388,0.683366294299,0.677522558951,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504129987835,0.504547342522,0.510888218981,0.499757310902,0.500099453793,0.477051834938,0.469291519359,0.466840010219,1.0
-229178108252,447172,35637787,LABFER,2013-06-24 22:00:00,0,1,1,1,0.634686346863,0.0,0.0,0.483848905771,0.412595346447,0.463944116165,0.526570948233,0.444010565796,0.398845483337,0.22985645933,0.52853291345,0.516865301152,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.493699758222,0.510888218981,0.497472341821,0.497808645349,0.333965342152,0.356557019812,0.386980983638,0.999999953203
--11063953830192,437720,30407745,LABFER,2012-11-08 06:35:00,1,0,1,0,0.403846153846,1.0,0.0,0.382933416713,0.371949442785,0.317236747104,0.467742240641,0.599562957867,0.39526852755,0.188484030589,0.246251301717,0.333223299983,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.472407011961,0.487914920182,0.468417916605,0.497913456647,0.497528000126,0.635539206162,0.652102837579,0.658374372395,1.0
-11377004793789,427157,21134827,LABFER,2011-11-06 20:30:00,1,0,1,0,0.108695652174,0.0,0.0,0.439320123041,0.293112800572,0.343596801737,0.317038255862,0.287551541402,0.256972240032,0.2,0.25,0.158303886228,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.487599589398,0.495004738323,0.499081689524,0.499652339894,0.498759943437,0.454734978852,0.470248178822,0.486341877061,1.0
--4281038450567,468247,37333962,LABFER,2013-08-15 19:28:00,0,1,1,1,0.634686346863,0.0,1.0,0.433362620885,0.633279522175,0.546775486422,0.544936524336,0.50323728595,0.641771607895,0.865740694681,0.618843284638,0.445450348977,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.500927001874,0.502622681296,0.499081689524,0.489878155807,0.498625854537,0.0884139896258,0.0673859144479,0.0595059263352,0.0
-2165141267176,400628,24461197,LABFER,2012-03-17 07:26:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.187916166534,0.138992892525,0.0619595195462,0.0850294738551,0.11705918481,0.0156610317947,0.237945612543,0.0892746186168,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.424768133404,0.454006409941,0.458581907474,0.490651726708,0.493344112149,0.0247749423629,0.0216203429061,0.0188495582526,1.21949041306e-08
--486485651813,427142,22153428,LABFER,2011-11-28 17:29:00,1,0,1,0,0.477840451249,0.890410958904,1.0,0.438172517219,0.494285810236,0.385129994812,0.453575113526,0.674716109338,0.455691542816,0.30172197084,0.566970932294,0.506598094233,0.474085517325,0.462338008602,0.434939683951,0.421077804787,0.477354652268,0.485919612248,0.498388244928,0.496187915811,0.497472438415,0.300100985929,0.274269038762,0.246109403821,1.0
-10394835404104,425758,32057504,LABFER,2013-01-24 11:26:00,1,0,1,0,0.0833333333333,0.0,0.0,0.18386959004,0.189184959422,0.185115884254,0.09938963086,0.265868405963,0.176789350686,0.412080536913,0.230489108271,0.18645228068,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.451427981118,0.472969966448,0.469108659157,0.49052840833,0.493585648613,0.494494366503,0.498419316741,0.476084228485,1.0
--10265251627183,461600,8520615,LABFER,2010-04-22 14:15:00,0,1,1,1,0.218911917098,0.0,0.0,0.257806209306,0.308828457845,0.372681115969,0.292871821715,0.189429579017,0.245477662633,0.636,0.266117150752,0.4341279613,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.481225955398,0.489772595863,0.479322944411,0.501207730628,0.501479508343,0.454862983629,0.385667556317,0.305692799678,1.0
--7619542909496,359108,945542,LABFER,2009-03-26 15:02:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.253082501533,0.237933680988,0.244875088628,0.400534037379,0.176196546561,0.277260402256,0.413336314848,0.318836054914,0.305076602061,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.440441608476,0.468556874402,0.470329931948,0.495187251827,0.496790390596,0.352417051691,0.339594794273,0.318080901221,1.0
--2105846856753,364308,4180181,LABFER,2009-11-01 10:00:00,1,0,1,0,0.0512820512821,0.0,0.0,0.358842999735,0.292098029421,0.333114193838,0.0694568764569,0.393196513273,0.390064162444,0.0109090909091,0.0802693499919,0.194109836164,0.474085517325,0.481003341925,0.466572373462,0.45525281484,0.488796036513,0.490590282459,0.498388244928,0.501564581503,0.498196958587,0.509606020266,0.624704750978,0.496384827314,7.36663740257e-15
--10143561941882,408403,10055751,LABFER,2010-07-02 15:13:00,0,1,1,1,0.224899598394,0.0,0.0,0.347109573155,0.478545638757,0.404294056789,0.26962352316,0.469875456314,0.470830833245,0.414481945468,0.369354298802,0.434314615484,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.458527525275,0.476993017021,0.487344472973,0.491355038829,0.495373617695,0.240792990047,0.244204242726,0.250823732176,1.0
-6796737012655,382551,17887849,LABFER,2011-06-25 01:38:00,1,0,1,0,0.218911917098,0.04,0.0,0.319985941671,0.403969844542,0.32642953305,0.342397303384,0.156290808131,0.317906621885,0.21662591687,0.327733358999,0.309292836014,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.462587587773,0.480070949413,0.456676473752,0.495759973408,0.498265872618,0.34911041733,0.384507646904,0.399292860655,1.0
-4726952785480,355044,39012007,LABFER,2013-11-06 07:28:00,0,1,1,1,0.224899598394,0.0,0.0,0.283130161464,0.274705443348,0.372250349701,0.2442138458,0.348873251911,0.292960423501,0.491524733035,0.437837270801,0.468312676638,0.474085517325,0.462338008602,0.4308339202,0.436224459079,0.466315728877,0.480718296491,0.45345738618,0.485820617653,0.492646923608,0.513585326987,0.511284114212,0.500063577528,1.0
--12142666402990,454584,19785528,LABFER,2011-08-13 10:15:00,1,0,1,0,0.634686346863,1.0,1.0,0.161875211489,0.315660781957,0.373092208807,0.65772113943,0.602785453766,0.3828451491,0.2,0.387199557242,0.444218374218,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.489345405201,0.492461098103,0.510015679179,0.496332906931,0.499715647742,0.138904435938,0.145702019454,0.203174126559,1.38908587039e-43
--8506171543677,419887,4252207,LABFER,2009-10-22 16:37:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.459675021899,0.475540295041,0.410798305917,0.439019461844,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.49671650845,0.498231786629,0.438942284991,0.44114474874,0.44418040542,1.0
-5182683684009,387413,27961113,LABFER,2012-08-09 14:07:00,0,1,1,1,0.218911917098,0.0,0.0,0.217658516895,0.220454750947,0.197683695606,0.107152321696,0.134855545986,0.186278386589,0.21796819875,0.0575785598108,0.0990960646372,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.417215349808,0.454773754259,0.455878037868,0.480627096837,0.491560426642,0.0922007393323,0.0582718480798,0.0352000268143,1.0
--4546926499538,434525,38537194,LABFER,2013-10-10 21:08:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.162849150083,0.184420074615,0.119444198326,0.137813748824,0.123345279715,0.105579399142,0.151423905586,0.065008274843,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.441873976273,0.467747466891,0.468307222203,0.491410559274,0.494176429693,0.147852416399,0.173349686878,0.190851129529,1.0
-3373452184798,386340,20935917,LABFER,2011-10-30 07:57:00,1,0,1,0,1.0,1.0,0.0,0.199949434594,0.290824339878,0.283591041859,0.244153846154,0.225550623647,0.0905879925155,0.352566204288,0.26999812522,0.343509679874,0.474085517325,0.462338008602,0.4308339202,0.436224459079,0.453355126862,0.469695998696,0.465877080084,0.494646453629,0.49896772349,0.128490876692,0.104164344074,0.104668355865,1.0
-9715272077159,416034,36773957,LABFER,2013-08-07 10:03:00,0,1,1,1,0.477840451249,0.0,0.0,0.477279432247,0.462359601562,0.455194958875,0.444428397689,0.549996950718,0.585192373372,0.188675213675,0.409277203704,0.567382591959,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.488715322977,0.494626563434,0.486583208859,0.503107945509,0.502966561184,0.414801364227,0.441703956712,0.492219123036,1.0
-5704023633027,442439,39033506,LABFER,2013-11-02 08:58:00,0,1,1,1,0.108695652174,0.0,0.0,0.280347877764,0.398735527757,0.338988664365,0.413333333333,0.618600910306,0.433235013861,0.00571428571429,0.1,0.292277047952,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.475848094796,0.48211589768,0.458113298112,0.488643338054,0.494439552365,0.65616982433,0.659989156161,0.656685640562,0.999995501517
--1784466296831,457849,34750636,LABFER,2013-04-20 22:00:00,1,0,1,0,0.477840451249,0.0,0.0,0.581389360394,0.558349380689,0.497842134588,0.60189609732,0.558241937449,0.555438375764,0.615397576599,0.408884776329,0.575183349242,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506123149708,0.506102513837,0.509322489469,0.499358702175,0.498633229396,0.461938690697,0.439086586861,0.459743508625,1.0
-267716237453,457811,38846105,LABFER,2013-11-03 22:00:00,0,1,1,1,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.452785099418,0.475540295041,0.443426085536,0.452091448415,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.499654240296,0.498913062772,0.380010750152,0.380781220678,0.388754018747,1.0
--10171284107337,362702,27298073,LABFER,2012-06-27 12:03:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.602208366868,0.554506245136,0.583421912539,0.62437643366,0.600727695692,0.666246240696,0.702619876502,0.70410485689,0.621044322058,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.507549062038,0.521811420026,0.502604276006,0.502157008168,0.536213248536,0.452740715976,0.380450049109,1.0
--9495259926623,411544,13103391,LABFER,2010-11-06 03:07:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.513819865532,0.575579695042,0.553275617264,0.668252081193,0.672798365308,0.665867559969,0.523505397469,0.726128823052,0.693427160075,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506123149708,0.508441616835,0.509322489469,0.505945558105,0.502551776501,0.598594926018,0.604292109316,0.619393030348,1.0
--5482010121311,366236,36681184,LABFER,2013-07-12 01:48:00,1,0,1,0,0.11914893617,1.0,0.0,0.2829902739,0.2143054614,0.198271597242,0.215733739944,0.0842790088617,0.258923469094,0.22262591687,0.239681277916,0.184005246641,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.439922733055,0.459198338279,0.447057203458,0.49300908409,0.494812719196,0.218742980994,0.28323151003,0.358934827468,1.6980519798e-189
-6800410411591,442442,39862831,LABFER,2013-12-11 14:33:00,0,1,1,1,0.863636363636,1.0,1.0,0.518182027506,0.367212545555,0.36171084075,0.442016806723,0.354720041863,0.462004893036,0.6,0.4,0.434088457389,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.469489775956,0.48039992059,0.487808173527,0.498112759778,0.500057227763,0.285001394161,0.309254473904,0.484407199898,5.20421715077e-119
-9964880576845,444926,15453617,LABFER,2011-03-18 22:00:00,1,0,1,0,0.218911917098,0.04,0.0,0.190884963299,0.288325418378,0.217379538034,0.244855953519,0.193736883774,0.236164222388,0.213422818792,0.254030295329,0.15294897383,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.453182885212,0.470328382594,0.468417916605,0.484718478666,0.494083586897,0.0467294197618,0.0277207239729,0.0221045337701,0.999999998466
-8688774144482,356853,18316568,LABFER,2011-06-30 14:59:00,0,1,1,1,0.11914893617,0.0,1.0,0.250187320225,0.355198220416,0.338079306058,0.48031211447,0.354088164867,0.347855407295,0.474532627866,0.0285839728903,0.392273527665,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.472280578128,0.487141201359,0.479322944411,0.494189658398,0.496093616274,0.719089027011,0.71192822095,0.676037528502,1.0
--3495155321587,439008,32035776,LABFER,2013-01-11 00:55:00,0,1,1,1,1.0,1.0,1.0,0.426338624079,0.508573114442,0.445776704541,0.76,0.652680088652,0.455058960937,0.526829268293,0.313399503722,0.610970017637,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.489998403715,0.499004821585,0.509322489469,0.50484956058,0.505740769682,0.0926293522524,0.094955735046,0.104671995865,2.24514372198e-24
-5928710715241,384643,29325719,LABFER,2012-10-04 08:47:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.649044688653,0.680208726377,0.636688695014,0.753922019494,0.787425425094,0.717191040503,0.721279421015,0.822481571316,0.790626293529,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.514070626838,0.521811420026,0.510040364074,0.506481229971,0.863813006101,0.883445355123,0.89644383325,1.0
-5313581481254,387246,9216116,LABFER,2010-06-12 22:02:00,1,0,1,0,0.477840451249,1.0,0.0,0.542108744912,0.400164158676,0.421140534759,0.576500927796,0.463260941551,0.524482431176,0.234239130435,0.308104255319,0.281549497081,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.483844470326,0.491745513667,0.509322489469,0.502141931676,0.501719520037,0.529280218565,0.537289558722,0.547673673324,1.0
-4149605344778,447384,6777605,LABFER,2010-03-02 20:09:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.287916586738,0.326924120965,0.335472263288,0.35614040279,0.320280446313,0.322714855533,0.0444036946482,0.156498298545,0.241091508465,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.4701630983,0.486702138764,0.479322944411,0.497356414056,0.499454229539,0.473377719708,0.474511945541,0.47860251311,1.0
--11547115588151,444557,36741368,LABFER,2013-07-22 03:07:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.533877242246,0.530238193527,0.587869970004,0.756104675432,0.71002696621,0.630060265906,0.535729745929,0.738770499357,0.667013629211,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.50602382487,0.510888218981,0.505115742287,0.502809207904,0.513800371456,0.504421970732,0.501381432111,1.0
-4004459245779,421834,5923774,LABFER,2010-01-20 00:29:00,0,1,1,1,0.215827338129,1.0,1.0,0.357757118244,0.243679046545,0.355694602697,0.495537933483,0.281048211713,0.47637055777,0.2168475901,0.4,0.475159774436,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.474707216613,0.488034491711,0.487808173527,0.496394376859,0.499375530285,0.628149745292,0.671171386538,0.66658974674,7.80573489237e-31
--225955427232,433129,40758604,LABFER,2014-01-10 02:15:00,1,0,1,0,0.218911917098,0.04,1.0,0.198332683533,0.269819844789,0.218517905616,0.120157546613,0.222439364476,0.175648086862,0.61662591687,0.116554670479,0.174779046437,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.438335566603,0.462673805391,0.435903692135,0.487615024652,0.493395103475,0.154946288647,0.153332450728,0.154573165832,1.0
--10094936829919,447807,6017468,LABFER,2010-01-07 05:52:00,1,0,1,0,0.11914893617,1.0,0.0,0.220530887025,0.155441334418,0.226338925634,0.280044804092,0.440919719596,0.389415352021,0.41662591687,0.192866488721,0.457462925089,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.435710850743,0.463263150954,0.467548574593,0.50661057864,0.506919643175,0.0380727900916,0.0322334003714,0.0357880839959,2.8224372651e-36
--6468807098354,465783,37062894,LABFER,2013-08-08 08:00:00,0,1,1,1,0.477840451249,0.0,0.0,0.454639415829,0.408290557246,0.389982393089,0.488543424586,0.552420882295,0.409935081878,0.330341880342,0.8,0.471276300766,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.494758634974,0.498388244928,0.504228986083,0.501447025253,0.466028607181,0.470505920818,0.47525009233,1.0
--3665734325704,463664,16261334,LABFER,2011-03-26 11:21:00,1,0,1,0,0.218911917098,0.0,0.0,0.390133296648,0.326719762311,0.240692683456,0.159585691262,0.463656724192,0.234728553809,0.416,0.387272727273,0.324624183007,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.460896114125,0.480509279534,0.479322944411,0.492111847273,0.49243747762,0.431251686462,0.42923981194,0.419416456006,1.0
-246515198407,363154,4534758,LABFER,2009-11-26 11:12:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.225206324435,0.161079657176,0.0852468676154,0.0796501084838,0.110256737932,0.0259262462446,0.0481991899133,0.0697044162545,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44139296035,0.465300729222,0.456676473752,0.488429184811,0.492007476769,0.119814722218,0.111253231403,0.101026675696,1.0
-6093015320832,408480,14761877,LABFER,2011-02-11 08:13:00,1,0,1,0,0.11914893617,1.0,1.0,0.369455872264,0.185918249086,0.213086459746,0.282647674906,0.151719541837,0.267241264017,0.220229520474,0.211154598826,0.453832252266,0.222416167301,0.300974526888,0.352037739686,0.347339870876,0.423878809842,0.455642791039,0.416188111159,0.48363590062,0.4926281679,0.201671321348,0.20350375193,0.239771163779,1.7771721483e-23
--8370826379820,441897,12072594,LABFER,2010-09-25 18:53:00,1,0,1,0,0.640625,1.0,1.0,0.306788418873,0.546504729796,0.463243412673,0.489417438807,0.34898219018,0.508865993065,0.427027027027,0.481543587132,0.52542760678,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.480499598825,0.494566678583,0.480886833765,0.501497197016,0.500812719475,0.532473356425,0.527501358176,0.520370070861,1.0
--7541746288466,433942,19421972,LABFER,2011-08-09 16:52:00,1,0,1,0,0.477840451249,0.0,0.0,0.499534582765,0.478727985046,0.419401743602,0.516267144071,0.412334277092,0.466104385375,0.72202020202,0.562195121951,0.386967219527,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.490838952681,0.494003610013,0.486583208859,0.487921981199,0.49427273944,0.392053354459,0.532500589593,0.625103328169,5.74940776069e-54
--8157245263008,471599,21771880,LABFER,2011-11-07 14:41:00,0,1,1,1,0.477840451249,1.0,1.0,0.481476115532,0.417931006283,0.419821435289,0.489569143447,0.527950013373,0.439890432059,0.18252688172,0.183407058761,0.304546177139,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496308944338,0.498388244928,0.497761899765,0.500249216639,0.362774110861,0.352729976573,0.32684599131,1.0
--7977998396354,378591,2607043,LABFER,2009-08-20 13:38:00,0,1,1,1,0.634686346863,1.0,1.0,0.54703971705,0.506423775427,0.514677223146,0.55657662134,0.604279527706,0.531708530182,0.601741132457,0.749737172134,0.530697178863,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.500612149014,0.506052883796,0.510015679179,0.501198944546,0.500830229051,0.716604640384,0.69135391399,0.666483337051,1.0
-2659026259146,453270,39741972,LABFER,2013-12-07 22:01:00,1,0,1,0,0.634686346863,1.0,0.0,0.516303234401,0.401682402618,0.316832709243,0.105161290323,0.357494206764,0.370306637745,0.390697674419,0.375011812715,0.160055989332,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.313173027524,0.40244596413,0.0275398758591,0.337074737244,0.414016201756,0.0320609848421,0.00452050553122,8.66604304571e-05,2.34048957807e-251
-11412071682130,440870,31299332,LABFER,2012-12-20 10:54:00,0,1,1,1,0.477840451249,1.0,1.0,0.43267325297,0.445831412432,0.38916556057,0.57918849857,0.362773597375,0.356779198213,0.221342281879,0.397816362279,0.651599601494,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.48967540575,0.492066549625,0.498388244928,0.498509657044,0.498210918901,0.429090590185,0.2893441484,0.38892833687,6.40132826168e-53
--8270560758951,362151,23085589,LABFER,2012-01-07 02:32:00,1,0,1,0,0.640625,1.0,1.0,0.260129346623,0.350555953323,0.387248072196,0.484139784946,0.400381808114,0.490316464494,0.551724137931,0.415,0.398553462859,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.444396412645,0.471802352645,0.468307222203,0.493273617578,0.498609997793,0.196904471875,0.184053648963,0.176349140255,1.0
-5217754848017,364818,17941658,LABFER,2011-06-12 22:01:00,1,0,1,0,0.634686346863,0.0,0.0,0.609764073171,0.571940297182,0.587108377802,0.727616300374,0.620004027269,0.600525023594,0.778289368505,0.490594743807,0.545264199385,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.508069136084,0.521811420026,0.489782736899,0.496212395062,0.196016211836,0.141771808243,0.122625668883,5.78214043305e-40
--2152583132292,455102,17739432,LABFER,2011-05-31 13:40:00,1,0,1,0,0.0512820512821,0.0,0.0,0.290533616874,0.251195394175,0.28273190354,0.444153846154,0.481821884717,0.198225251551,0.609615384615,0.411879703503,0.312114078411,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.474138948058,0.483003973534,0.489373727303,0.496268003465,0.496391660441,0.346245613389,0.351510067823,0.363173826029,1.0
--10592999886142,373595,28993070,LABFER,2012-08-30 06:51:00,1,0,1,0,0.218911917098,0.0,0.0,0.253082501533,0.303536647776,0.307820552361,0.259117383165,0.255806531727,0.251310293676,0.0231246248808,0.22849433296,0.159470988531,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44139296035,0.468036480664,0.456676473752,0.492853135541,0.496934729322,0.407454117521,0.387147981342,0.363388638136,1.0
-5572027939696,449820,32393879,LABFER,2013-02-06 00:52:00,1,0,1,0,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.456490587056,0.475540295041,0.443426085536,0.41221452703,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.503923115715,0.501647931253,0.564339951333,0.576037128195,0.595789188145,1.0
--8711201844582,369087,571121,LABFER,2009-02-24 18:57:00,1,0,1,0,0.634686346863,1.0,1.0,0.372657291317,0.514603335298,0.546116231989,0.412250214593,0.576914883078,0.465893160536,0.403695014663,0.541117021277,0.604592544153,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.486996701684,0.494567979634,0.510888218981,0.494490190133,0.496312043596,0.437916177851,0.439631505306,0.435202475532,1.0
-11458658847657,469885,22479299,LABFER,2011-12-25 20:00:00,0,1,1,1,0.11914893617,1.0,1.0,0.11299293323,0.30651026994,0.31593531132,0.365561747506,0.335460321768,0.236208313077,0.2,0.328887718032,0.381504053397,0.222416167301,0.272513145711,0.341251976233,0.336701589837,0.44074978794,0.455193626965,0.424326690307,0.488464277822,0.499258882691,0.125171686935,0.23973924805,0.293985515543,2.25671795762e-269
-9883709042055,369978,1392161,LABFER,2009-05-23 10:00:00,1,0,1,0,0.218911917098,1.0,0.0,0.226189985427,0.234781313968,0.226751270186,0.0954616453156,0.127868564572,0.136649447774,0.0120805369128,0.114099850607,0.138809093114,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.450871225008,0.475163172198,0.467548574593,0.497372983133,0.498804783144,0.00372074356272,0.000166807360912,8.35745965273e-06,0.0
--4588728985658,369710,34059431,LABFER,2013-03-27 01:52:00,1,0,1,0,0.477840451249,1.0,1.0,0.539968424689,0.385364648681,0.443098520024,0.444547047593,0.359956323675,0.243039696676,0.438365047305,0.696666666667,0.261861327349,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.493612939599,0.495442952891,0.498388244928,0.499070629026,0.500223675043,0.360319736747,0.343895058617,0.332541794612,1.0
-5598469042957,418963,26817981,LABFER,2012-06-25 02:05:00,1,0,1,0,0.403846153846,0.0,0.0,0.223028242855,0.299416451749,0.244059935104,0.215124576111,0.228715683779,0.228871159376,0.42912591687,0.344663593593,0.458104443062,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.441317914092,0.469276711182,0.439239570503,0.489083520037,0.494091340795,0.320460678385,0.324468087562,0.306528197696,1.0
--8700362763611,369481,34943223,LABFER,2013-05-02 22:00:00,0,1,1,1,0.403846153846,0.0,1.0,0.187201678044,0.324933378914,0.279009307376,0.297174692049,0.263170425491,0.228213116056,0.0324153905546,0.234332978155,0.242211052112,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.41365639905,0.450785402787,0.433417205192,0.487285479318,0.495286345673,0.145621599548,0.152752668334,0.157977843526,1.0
--9037343101421,376236,29148768,LABFER,2012-09-02 07:13:00,0,1,1,1,0.0512820512821,0.0,0.0,0.345876938244,0.312848214755,0.218368799103,0.232820512821,0.215578276143,0.171138683485,0.0411376328589,0.309560872915,0.25432163187,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.476688259317,0.48673049395,0.489373727303,0.497713050813,0.499167717178,0.396299087364,0.385694667487,0.357964051216,1.0
--9915566801244,447839,22280730,LABFER,2011-11-27 11:30:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.462870365995,0.475540295041,0.421331143587,0.455455602472,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.498413696538,0.497817906828,0.315293047231,0.307141605918,0.311045323299,1.0
-11816096356568,366619,18728399,LABFER,2011-07-22 20:17:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.396214972583,0.440769109093,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.49671650845,0.498231786629,0.444676265017,0.448573643687,0.453894539729,1.0
-5853342565231,393908,1809380,LABFER,2009-07-05 08:25:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.487404823217,0.451377999447,0.489176690556,0.679065544386,0.685103177594,0.523559473644,0.0217391304348,0.493524674411,0.430858233353,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50549835671,0.504745485042,0.498388244928,0.50456759854,0.502535118137,0.545805865646,0.534371461655,0.526796480029,1.0
-4605484457981,471880,39362056,LABFER,2013-11-25 07:58:00,1,0,1,0,0.218911917098,1.0,1.0,0.484733099031,0.367215182622,0.356013605419,0.187705596181,0.297892854129,0.498602016958,0.2,0.496940587665,0.421161446484,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.457704978352,0.476163014527,0.468417916605,0.496548409616,0.49863421869,0.540523771296,0.546109785062,0.408170283815,1.0
-5745484616939,412751,34324312,LABFER,2013-04-28 13:12:00,0,1,1,1,0.634686346863,1.0,1.0,0.482981877205,0.499237810822,0.497675566364,0.390679574112,0.337171873826,0.518272456376,0.563636363636,0.674655926987,0.4703351101,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498865405233,0.49890390991,0.499081689524,0.497883114975,0.497032847637,0.200749587044,0.203801818011,0.216868110737,1.0
-2247697389047,416769,6490302,LABFER,2010-02-17 13:31:00,1,0,1,0,0.218911917098,1.0,0.0,0.193476921927,0.197879607212,0.198491419388,0.132210253889,0.227040972861,0.171074227048,0.00372093023256,0.131081043008,0.320546141018,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.412372608129,0.450147446305,0.457432635845,0.489447150346,0.495639084904,0.0831577217304,0.0831405448201,0.086657344408,1.0
--4312123672104,391397,39792282,LABFER,2013-11-22 05:33:00,0,1,1,1,0.403846153846,1.0,1.0,0.319734926228,0.320980464135,0.338818537057,0.207537473366,0.385217439694,0.319393794699,0.143506493506,0.146836466463,0.260265207839,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45918551083,0.480142460254,0.480886833765,0.497114239212,0.497470689178,0.322823258789,0.307777687993,0.298295182176,1.0
--11619665279648,386477,29056116,LABFER,2012-09-06 16:45:00,1,0,1,0,0.11914893617,0.0,0.0,0.152844377409,0.257236602771,0.261791200835,0.191660319872,0.125963166097,0.185761986607,0.14050531616,0.234413494379,0.200484331258,0.222416167301,0.316965826625,0.388320660906,0.379452859344,0.46583815302,0.480279294072,0.456676473752,0.489669958886,0.495611516616,0.0716668209425,0.0529141953066,0.0490843391561,1.0
--5915644922512,402503,36828,LABFER,2009-01-07 21:09:00,0,1,1,1,0.215827338129,1.0,1.0,0.380230467572,0.287871021799,0.26256329458,0.218719751665,0.174141814102,0.439021625106,0.0701739842027,0.240285500604,0.2495572818,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.46830863629,0.483867105013,0.487808173527,0.49941027331,0.50042046586,0.338256938909,0.327027106128,0.317291965935,1.0
--1456310625309,379658,22844620,LABFER,2011-12-20 13:30:00,0,0,1,0,0.218911917098,0.118881118881,0.0,0.21406348055,0.252978382391,0.266428826417,0.105424239194,0.272150744598,0.190866852385,0.00512820512821,0.367843109426,0.249092398551,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.46197944144,0.4789138087,0.479322944411,0.491746227973,0.496219193632,0.344327520595,0.345227292215,0.335534538574,1.0
-8387858185250,356495,4224441,LABFER,2009-11-07 09:58:00,0,1,1,1,0.634686346863,1.0,1.0,0.447345385384,0.380049830635,0.47011624905,0.384752398583,0.259683597961,0.341257322112,0.312824799721,0.347983870968,0.401376457273,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.480185418392,0.490928282473,0.476015925077,0.496653359551,0.504689611242,0.000810903455117,4.89270484243e-07,7.21050261493e-10,0.0
--6630496952977,392858,31078739,LABFER,2012-12-04 09:33:00,1,0,1,0,0.477840451249,1.0,1.0,0.333997908679,0.449088483554,0.41278265021,0.567921541746,0.558361071855,0.430152164372,0.405128205128,0.434115117339,0.450762358831,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.478601596599,0.488920389678,0.509322489469,0.496291114767,0.498783357539,0.342358033118,0.310416105913,0.283631648262,1.0
-3646022339458,419092,14091171,LABFER,2011-01-07 22:08:00,1,0,1,0,0.108695652174,0.0,0.0,0.522231942086,0.37981125432,0.422623008418,0.231351623653,0.315443013965,0.428416186843,0.468996695812,0.318947781704,0.389773152972,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.497840211889,0.501454539527,0.509322489469,0.497309464016,0.498895187932,0.282221963055,0.264317766507,0.243037470819,1.0
--12262621362173,451423,40908386,LABFER,2014-01-07 07:36:00,1,0,1,0,0.11914893617,0.0,0.0,0.220515265531,0.188474322039,0.199650110861,0.0658258258258,0.146355816809,0.254443977371,0.218079399142,0.123679523417,0.106236174571,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.271856764879,0.370506221412,0.0222810961269,0.321524572614,0.407890251995,0.00854021101561,0.00104827671412,1.09813542761e-05,0.0
-3975214047163,462165,2785649,LABFER,2009-09-13 13:57:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.230182457562,0.212279125225,0.211121126727,0.1836243242,0.101101706377,0.170275455635,0.0538099529858,0.0574380719803,0.143795902833,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.480002557845,0.468417916605,0.49372769819,0.495302198476,0.260748165598,0.261026654248,0.256443263984,1.0
--6859362869561,373423,10698526,LABFER,2010-08-03 18:35:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.427100191817,0.463676629337,0.524646618146,0.557136931802,0.861301986712,0.494218087249,0.749918607915,0.802300733382,0.554106986145,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498865405233,0.50436722402,0.499081689524,0.499401614674,0.500844896837,0.758201423816,0.775564603927,0.770019051592,1.0
-11538822942601,424624,23138223,LABFER,2012-01-26 07:29:00,1,0,1,0,0.634686346863,0.0,0.0,0.679491397117,0.653148331774,0.625322083289,0.748946633365,0.840584984181,0.717438443643,0.878993706729,0.938701761544,0.735355161487,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.511923080603,0.512419698582,0.509322489469,0.503728186553,0.499544132289,0.853625323563,0.848672293361,0.84418872046,1.0
-5171911761120,400215,11883919,LABFER,2010-10-03 11:11:00,0,1,1,1,0.11914893617,0.0,0.0,0.236259919501,0.343514666175,0.278821829398,0.0885013286653,0.505394718211,0.280090857102,0.218181818182,0.0810153597485,0.286189105403,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.463894776738,0.476632894489,0.479322944411,0.493547564898,0.498599739663,0.202759704684,0.119260316435,0.0939433140265,0.999758405744
--131827416186,380433,39069246,LABFER,2013-10-22 04:14:00,0,1,1,1,0.477840451249,1.0,0.0,0.492049174403,0.565384445864,0.502955620208,0.179742345034,0.366278422963,0.557349887455,0.4,0.462137862138,0.555922894154,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.496576147727,0.498388244928,0.496258848771,0.497344958802,0.271621063057,0.260089591294,0.247687840705,1.0
-7382363634015,411447,26115220,LABFER,2012-05-31 03:31:00,1,0,1,0,0.477840451249,0.0,0.0,0.352332735066,0.345882543625,0.365875140807,0.289501061494,0.408181091749,0.3623558464,0.447868217054,0.397596567765,0.242083433556,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.481982092318,0.491814032436,0.476015925077,0.494733058543,0.497657127316,0.454227503626,0.442022691743,0.43277754183,1.0
--1475209838078,469526,1413804,LABFER,2009-05-02 21:17:00,0,1,1,1,0.634686346863,0.0,1.0,0.45576466934,0.414918743865,0.443175979845,0.208667247387,0.289211136875,0.387729291739,0.2,0.1,0.308041589746,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.498046745792,0.510888218981,0.50065351711,0.499474170968,0.470295600387,0.456903163663,0.450173550939,1.0
-5359672103840,394983,9792921,LABFER,2010-07-03 06:14:00,1,0,1,0,0.218911917098,0.0,0.0,0.310624993947,0.424994103454,0.383468584078,0.732145631707,0.392703893006,0.457093389832,0.375,0.475406504065,0.434503501838,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.47272139061,0.488624425744,0.491813578127,0.501274847139,0.501005189293,0.7281871309,0.991544750429,0.999973486533,1.0
--7868553274685,454125,39398724,LABFER,2013-11-03 04:38:00,0,1,1,1,0.477840451249,0.0,0.0,0.546648074921,0.453190210509,0.450732311004,0.307338360564,0.428691754286,0.378599737929,0.444444444444,0.356056181913,0.446960549508,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.488520879056,0.498008632401,0.509322489469,0.499605277909,0.502601267122,0.616265599712,0.616589404988,0.587799016487,0.000318258131453
-1357293835951,373691,39162698,LABFER,2013-11-18 23:43:00,1,0,1,0,0.477840451249,0.0,0.0,0.429847549775,0.446030735944,0.481598579695,0.345161076814,0.341987699144,0.561933561101,0.179761904762,0.414194875663,0.524478934159,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.499435403377,0.497556097114,0.498388244928,0.500367230248,0.499264349535,0.296463768291,0.264433536797,0.232844148343,1.0
-423148726859,385430,4093,LABFER,2008-12-30 14:04:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.452785099418,0.475540295041,0.421331143587,0.459649741666,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500287079848,0.509322489469,0.500600757052,0.499376261995,0.508362010619,0.508489716633,0.51184540804,1.0
--9402290176695,426371,38494858,LABFER,2013-10-01 22:01:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.649044688653,0.56355235228,0.60681136602,0.814004324021,0.724795102609,0.742452944276,0.613723038778,0.843942070768,0.767919949463,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.511336770549,0.521811420026,0.509697959871,0.506036530847,0.819354278444,0.820478129063,0.822742360594,1.0
--6420242974348,360403,19582730,LABFER,2011-08-09 05:31:00,0,1,1,1,0.477840451249,0.0,0.0,0.420948797619,0.467012818976,0.461871190589,0.445295109401,0.480732002755,0.428207263043,0.589464345674,0.47563897069,0.436772578334,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.498321573272,0.509322489469,0.503923115715,0.500146582506,0.565262101369,0.616107250579,0.631845828933,7.01480273929e-203
-4338875195749,451463,35315839,LABFER,2013-06-03 09:26:00,1,0,1,0,0.485294117647,1.0,1.0,0.257307528428,0.282982046622,0.21969588961,0.316750054695,0.128083335075,0.273156537401,0.206,0.401041666667,0.310743509654,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.462210962125,0.466272772602,0.443414226478,0.489836256371,0.493343494614,0.0577403800458,0.0679969161517,0.151609875581,2.01300225778e-142
-8214419897852,357387,356486,LABFER,2009-02-24 15:03:00,0,1,1,1,0.477840451249,1.0,1.0,0.500900680886,0.62867143694,0.589877891311,0.71903508923,0.672372562065,0.618810542409,0.765762564073,0.825640852566,0.69132346483,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.504459869012,0.505745461407,0.498388244928,0.502781709848,0.501724232888,0.50348491758,0.504547169701,0.523149303022,1.0
--11699102797175,414151,38128789,LABFER,2013-09-19 11:25:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.136319701156,0.128045440421,0.160631534601,0.125145429558,0.213949548124,0.157694865616,0.0129039329778,0.0562908334527,0.0298920278406,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.425559997105,0.455480757061,0.429242356681,0.485896735024,0.489295480953,0.0316731402045,0.0297236238801,0.0291892367836,1.0
-4593073745432,440154,24127315,LABFER,2012-03-01 16:50:00,1,0,1,0,0.108695652174,0.0,0.0,0.384196261711,0.405649544879,0.338496179611,0.562068965517,0.227233704293,0.282188855847,0.4,0.5,0.328838579291,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.479483579555,0.486304459975,0.493260588842,0.490493736608,0.494951867194,0.289224637841,0.258172357748,0.247742117349,0.999999999999
--10166868294024,378060,12236696,LABFER,2010-10-07 05:46:00,1,0,1,0,0.224899598394,0.0833333333333,0.0,0.318745351019,0.387664942178,0.425752620792,0.284483801808,0.263803131564,0.312528721339,0.213396902685,0.199511351099,0.322816759029,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.462594061406,0.481583079284,0.485779235087,0.49349160786,0.495776345511,0.275691660407,0.274625637883,0.278628596586,1.0
-7336383827903,446099,17947415,LABFER,2011-06-28 00:26:00,1,0,1,0,0.218911917098,1.0,0.0,0.158265057722,0.330397223259,0.275736554457,0.147900682193,0.156223624453,0.244152923267,0.0036036036036,0.328872880654,0.243416246711,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.442464496276,0.467946879943,0.445050483537,0.492425019353,0.49663141885,0.15556035766,0.147699655479,0.142215169168,1.0
--9581584663129,399931,10120852,LABFER,2010-07-05 02:10:00,0,1,1,1,0.477840451249,0.0,0.0,0.404493004171,0.313206739211,0.409246731673,0.669617396981,0.423117616517,0.25728383458,0.403057081443,0.178373986123,0.274132669681,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.481231189353,0.488385548985,0.498388244928,0.497254179507,0.49850062908,0.402371454942,0.402769293192,0.407938907763,1.0
-8719345042589,438180,2326308,LABFER,2009-08-13 12:32:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.474011377083,0.475540295041,0.421331143587,0.480652449072,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.498413696538,0.498428354257,0.460414463073,0.45964177792,0.459761195722,1.0
-178531720664,362814,6999601,LABFER,2010-03-10 08:13:00,1,0,1,0,0.218911917098,0.04,0.0,0.157708377535,0.245103533987,0.222553142948,0.132346590494,0.143786820543,0.172783344549,0.006,0.116230376852,0.247332403861,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.436059331024,0.459143675577,0.447057203458,0.490634689991,0.493840409232,0.103207907732,0.0861039433629,0.075818429887,1.0
-10432684125278,453981,21578992,LABFER,2011-11-23 05:43:00,1,0,1,0,0.477840451249,0.0,0.0,0.473515957464,0.432665877039,0.446810502818,0.26231884058,0.368535353666,0.265669575944,0.6,0.3375,0.498372122233,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.491960488854,0.495632291993,0.486583208859,0.4958298094,0.497070354528,0.524780452591,0.536941459447,0.533227871209,1.0
-7719498890060,459964,8498022,LABFER,2010-05-19 11:39:00,1,0,1,0,0.218911917098,0.04,0.0,0.244644190924,0.228941425739,0.245535067218,0.25083354486,0.166437029692,0.139404428366,0.153846153846,0.114387302462,0.272886956549,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443042723741,0.471287983528,0.479322944411,0.497322416797,0.49900241248,0.327031973965,0.345510197241,0.366248611254,1.0
--1165273467866,427577,3504960,LABFER,2009-09-24 22:01:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.122053767164,0.143856923847,0.0222063791582,0.118205660539,0.100579589456,0.0252927325858,0.208855503454,0.0184358222912,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.4206584218,0.443753775985,0.440099782885,0.475941906302,0.488014483656,0.000846198360286,0.000205879338817,4.08339525404e-05,0.0
-2485065678045,455214,2426629,LABFER,2009-08-21 16:11:00,0,1,1,1,0.644444444444,0.0,0.0,0.477279432247,0.502131513254,0.453626731995,0.617612372801,0.371857054155,0.476388431725,0.647868217054,0.358108225108,0.455000804609,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.484516913089,0.491315142412,0.47486128235,0.490548593093,0.494421377103,0.258212779106,0.241410900029,0.233510055974,1.0
-9161548553055,461135,6789155,LABFER,2010-03-09 22:02:00,1,0,1,0,0.640625,0.0,0.0,0.18386959004,0.168885954894,0.196921220731,0.257136109733,0.11054620272,0.139751026336,0.406,0.184079633161,0.114804269731,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.438578144343,0.468987000102,0.458581907474,0.493679295126,0.496458441272,0.134346342793,0.108891800796,0.100217198957,1.0
-575663415841,425489,20264517,LABFER,2011-09-19 09:39:00,0,1,1,1,0.634686346863,1.0,1.0,0.501533492246,0.475016782654,0.536273571214,0.574289768318,0.386329552021,0.502167134598,0.500174825175,0.538208311411,0.669356566999,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.510637820992,0.521811420026,0.501562550274,0.502501871466,0.61614959439,0.601100335511,0.573897179145,1.0
--1019329743822,427368,616232,LABFER,2009-02-19 00:55:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.533877242246,0.482584839659,0.560945784842,0.609219781105,0.545659811631,0.524329381574,0.46702537183,0.655154179022,0.557325906243,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.504497680873,0.502024521507,0.564547621956,0.575800219834,0.576471795856,1.0
-9260301634208,438126,13445237,LABFER,2010-12-09 05:44:00,0,1,1,1,0.477840451249,1.0,1.0,0.51819773356,0.482432219938,0.435793537966,0.450103634436,0.689475295579,0.514774108463,0.821739130435,0.778861788618,0.538760920623,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.48863104954,0.491424418108,0.498388244928,0.498565469486,0.50216411965,0.490472605467,0.514981902381,0.532245102468,1.0
-10330745248757,466364,2260550,LABFER,2009-07-31 13:02:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.488601646815,0.496067032417,0.503030842167,0.475232302446,0.493758335486,0.71825662765,0.500275848381,0.595936059414,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497958897962,0.498388244928,0.50029741321,0.501786674127,0.540493797941,0.537389537977,0.525280111103,1.0
-11555610158960,385654,31666596,LABFER,2013-01-08 14:26:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.191610273142,0.234801539168,0.329120067565,0.0810570099612,0.139064774841,0.0911333654746,0.117074096944,0.13389372239,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.460243263807,0.479348455902,0.468417916605,0.492201784553,0.495751755119,0.196321638134,0.190429871582,0.15811033309,1.0
--9546168144646,418411,17092028,LABFER,2011-04-30 17:35:00,1,0,1,0,0.11914893617,0.0,0.0,0.186134585598,0.217427468917,0.184770497687,0.144699014933,0.0799357411774,0.227333035854,0.214670308233,0.142117091362,0.133272057866,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.462635024715,0.474890156936,0.467548574593,0.485343544089,0.491332450386,0.303680257861,0.225158015224,0.164543067851,1.0
-7828287387989,432863,29746911,LABFER,2012-10-17 20:12:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.481733638786,0.460026720037,0.507534758807,0.486164987302,0.466114067613,0.855,0.304276703639,0.52953576477,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.490367394558,0.493874744136,0.498388244928,0.497957021643,0.498729619742,0.433938976406,0.448077853679,0.464044915353,1.0
--625310413282,427342,11997027,LABFER,2010-10-02 18:40:00,1,0,1,0,0.218911917098,0.04,0.0,0.110189469519,0.260523618396,0.221323964175,0.0873480772021,0.132312828714,0.134886135957,0.220346847103,0.046426042355,0.0847904574373,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.412079016297,0.451638439789,0.444188271345,0.478068213917,0.489961274004,0.0607537046978,0.0489052859413,0.0414193220571,1.0
--4152483471907,391726,17716897,LABFER,2011-05-30 10:00:00,1,0,1,0,0.634686346863,0.0,0.0,0.501533492246,0.401146718838,0.488461196878,0.607649524841,0.537246887566,0.506109493551,0.370666666667,0.534863303592,0.37983485994,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.491716856028,0.496885285091,0.499081689524,0.495145840519,0.497211380046,0.489540607056,0.55706144393,0.581706300006,1.0
--9958235718858,392976,2445033,LABFER,2009-07-31 14:02:00,1,0,1,0,0.477840451249,1.0,1.0,0.309461340012,0.293354445113,0.337058360685,0.14131719371,0.169561077431,0.361180731015,0.308333333333,0.428560376665,0.324963042951,0.474085517325,0.481003341925,0.457790218795,0.436256322883,0.478564251423,0.486090220601,0.509322489469,0.501567726972,0.500816461773,0.110525026274,0.0855856581303,0.0777007274856,1.0
--3964852530414,447427,38624162,LABFER,2013-09-30 22:01:00,1,0,1,0,0.218911917098,0.04,1.0,0.208760705046,0.385443326554,0.355939512839,0.281564903139,0.489950667113,0.350202754793,0.432582582583,0.353359690979,0.359469426022,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.476241893939,0.4888587557,0.468417916605,0.500703732652,0.500269513316,0.301009555798,0.285483860367,0.287290580201,1.0
--997273156572,392908,11010650,LABFER,2010-08-11 18:59:00,0,1,1,1,0.634686346863,0.558823529412,1.0,0.533877242246,0.452506139315,0.462331689682,0.658317340418,0.452992532931,0.464965763361,0.537082414828,0.33577149955,0.44323334631,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.502971922152,0.521811420026,0.503318301851,0.502005202309,0.64350938857,0.664997280217,0.664486479654,1.0
--7278308922206,434556,32801470,LABFER,2013-01-29 22:40:00,1,0,1,0,0.634686346863,0.0,0.0,0.361969460629,0.491894885281,0.402698122125,0.485025252525,0.504755820432,0.419059688527,0.2,0.585106382979,0.457853498364,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.486434976657,0.492474641911,0.509322489469,0.49560859234,0.497433510729,0.683775507255,0.677237893984,0.66557966144,1.0
-6131598314781,471099,37073542,LABFER,2013-08-26 17:01:00,1,0,1,0,0.403846153846,0.0,0.0,0.323704978926,0.214681169681,0.273647265232,0.269872027338,0.312546914549,0.33342096891,0.394714714715,0.257491463935,0.470894566081,0.222416167301,0.272513145711,0.341251976233,0.336701589837,0.453072185278,0.474293941368,0.468417916605,0.495872948623,0.497560145055,0.194894821745,0.202687572285,0.209645922484,1.0
-8976647495136,447467,6256805,LABFER,2010-02-09 02:57:00,0,1,1,1,0.477840451249,0.0,0.0,0.492049174403,0.510375700479,0.483798198012,0.335488185628,0.529663154985,0.495031752683,0.45342377261,0.464639119368,0.556735038734,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.502345342325,0.509322489469,0.504062026962,0.502661952177,0.675900255553,0.665715498418,0.660351077556,1.0
--1484973424091,434108,24735715,LABFER,2012-03-07 22:02:00,0,1,1,1,0.634686346863,0.0,0.0,0.570350036884,0.552538430661,0.52952090309,0.704181763223,0.60450594949,0.624167328787,0.605806210656,0.667522426597,0.701332088542,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.500039355146,0.506397956524,0.509322489469,0.502372074774,0.501401990117,0.700811663303,0.709459588674,0.71502961901,1.0
-5106025860564,472394,3987310,LABFER,2009-10-29 17:52:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.501533492246,0.555567089982,0.585747674903,0.673419346874,0.764424137431,0.692215087184,0.680742783739,0.86954476672,0.737431884134,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.505211045188,0.510888218981,0.495022019145,0.497677027572,0.549762562473,0.521339478399,0.504422287557,1.0
--12365560028369,390122,15998671,LABFER,2011-03-24 00:40:00,1,0,1,0,0.634686346863,0.857142857143,1.0,0.516303234401,0.459586256115,0.485932425903,0.330815690762,0.546808670002,0.534280292252,0.43826055309,0.49863232181,0.560958322824,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.494563209833,0.497261913927,0.49751533833,0.499913536285,0.500662417721,0.476450960799,0.440254209379,0.415329577699,1.0
--9240079924843,430722,28292783,LABFER,2012-08-06 20:30:00,0,1,1,1,0.477840451249,0.0,1.0,0.499050123376,0.471641726242,0.44562087837,0.479585931153,0.437581196961,0.489374746392,0.664042597133,0.296038823644,0.489278175034,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.484675350432,0.493415151058,0.486583208859,0.495968404575,0.500037619761,0.695444492584,0.867996051328,0.912541318253,1.0
--7471020170028,458784,6006915,LABFER,2010-01-09 10:28:00,1,0,1,0,0.11914893617,0.0,0.0,0.0719500639275,0.273892629133,0.159911948583,0.0503896103896,0.149495413633,0.104223373748,0.2,0.113772325387,0.0756808774307,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.401038806532,0.437189638978,0.407560018026,0.471792430172,0.485054498058,0.0151784393876,0.00796826163197,0.00547219813485,1.74278326931e-127
--348622889316,356320,29380884,LABFER,2012-09-25 11:44:00,0,1,1,1,0.634686346863,1.0,1.0,0.602400979793,0.564769799478,0.566306336001,0.529561227281,0.695216024028,0.66983125608,0.734638686498,0.801559002458,0.620190090964,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.511415439559,0.511306417148,0.521811420026,0.505585190572,0.502951534039,0.652238877592,0.647417854795,0.642029680636,1.0
--7150160778147,433794,16063296,LABFER,2011-03-22 14:12:00,1,0,1,0,0.477840451249,0.0,0.0,0.614217569783,0.439187598558,0.461771659585,0.377987324904,0.410642616021,0.445853878559,0.495119617225,0.64552739718,0.441358454931,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.488520879056,0.49491004513,0.509322489469,0.501003282472,0.503954601812,0.683844144598,0.794109962417,0.868079744006,7.09647220205e-15
-2357746389094,415925,15728210,LABFER,2011-03-17 12:34:00,0,1,1,1,0.477840451249,0.0,0.0,0.56174182056,0.456032303367,0.426364522783,0.368278545982,0.349629993181,0.483385907291,0.25524691358,0.511275481173,0.415522254517,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.492196859707,0.500098148441,0.509322489469,0.499963470958,0.500258371522,0.584744265628,0.63425726684,0.657851323105,1.0
-7347925553175,465232,14371891,LABFER,2011-01-24 12:59:00,0,1,1,1,0.634686346863,0.0,0.0,0.501533492246,0.500345679109,0.535424147527,0.638328909589,0.640804999318,0.565465992908,0.434604105572,0.617871151597,0.523967199522,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.510637820992,0.521811420026,0.495244738476,0.49899811025,0.47747480758,0.477967339541,0.463753463708,1.0
--8748459762383,471132,37298182,LABFER,2013-08-05 10:53:00,0,1,1,1,0.634686346863,0.0,0.0,0.533877242246,0.531102593695,0.530113413922,0.657145285882,0.418370826444,0.506068141548,0.2,0.504945804677,0.438006174015,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506666070176,0.510888218981,0.503906053241,0.413106742494,0.578942192015,0.438328845811,0.231289087695,3.33440384986e-68
-2667427878890,399128,35105638,LABFER,2013-05-30 15:09:00,1,0,1,0,0.0512820512821,0.0,0.0,0.266610717596,0.255829518956,0.239194861321,0.0848121396148,0.0903715180361,0.284987493623,0.0145714285714,0.327695864538,0.151619235863,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.468684911553,0.481802268985,0.476886910056,0.499844785568,0.498436303634,0.133842575633,0.116409253578,0.111520996058,1.0
-8041689058343,386421,7252953,LABFER,2010-03-20 01:26:00,1,0,1,0,0.11914893617,0.0,0.0,0.111250002263,0.16892082396,0.204793404199,0.0378262652679,0.130980242032,0.231076518256,0.205579399142,0.51273006135,0.308948036309,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.44827647721,0.4664861628,0.469108659157,0.4852436726,0.49070272125,0.192354185751,0.210699862137,0.221925188012,3.86891864723e-06
--11183802708021,364080,17710943,LABFER,2011-05-28 10:19:00,1,0,1,0,0.634686346863,0.0,0.0,0.516303234401,0.483740049042,0.474389958078,0.388710683848,0.413026619007,0.450521689181,0.385023310023,0.254310626682,0.407204802616,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.499187286512,0.510888218981,0.500741550233,0.499578380354,0.640482332469,0.638060422111,0.640239889705,1.0
--939424887682,420517,26216457,LABFER,2012-05-19 14:10:00,0,1,1,1,0.634686346863,0.257142857143,0.0,0.533877242246,0.449515249953,0.489737197051,0.545740631513,0.515740994922,0.464005621837,0.752192969716,0.538608774239,0.453832623306,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.498821055027,0.510888218981,0.499198757412,0.499295775876,0.206156237543,0.213077366987,0.230942945286,1.0
--9158440870476,416203,9718213,LABFER,2010-06-16 02:38:00,1,0,1,0,0.477840451249,0.0,0.0,0.499050123376,0.467012818976,0.491011036995,0.445295109401,0.480732002755,0.475382041615,0.475540295041,0.454343065251,0.477319737164,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.501142091061,0.509322489469,0.502214249235,0.504202644033,0.616012823617,0.621793011696,0.630477734781,1.0
-5171911761120,438880,7328455,LABFER,2010-03-22 22:02:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.524531181912,0.52582544295,0.488598334444,0.468161441192,0.526039296252,0.602301727278,0.698523698524,0.64693737746,0.562155312238,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.502176600294,0.509322489469,0.501784432156,0.500578558715,0.527545697575,0.536324905855,0.553173547216,1.0
--7677202835877,418912,6835406,LABFER,2010-02-20 00:37:00,0,1,1,1,0.634686346863,0.558823529412,1.0,0.544592113781,0.533978262144,0.555145683492,0.673976119939,0.498616780919,0.517487755771,0.321982099978,0.700532575756,0.495354868165,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.495125271879,0.498798598706,0.499081689524,0.501759299804,0.502394010174,0.797195159584,0.849225351893,0.859566853942,1.0
-9590603753936,429152,1801910,LABFER,2009-06-29 12:32:00,1,0,1,0,0.634686346863,1.0,0.0,0.533877242246,0.555567089982,0.569314829769,0.564882324877,0.598201013858,0.580521123351,0.68315426115,0.318520355924,0.549861845816,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.50602382487,0.510888218981,0.501721804911,0.501764220756,0.58614846907,0.593553003503,0.604047084831,1.0
--10785125494377,412704,34097469,LABFER,2013-03-30 07:12:00,1,0,1,0,0.215827338129,1.0,1.0,0.235269926678,0.274202439588,0.246552761141,0.0831013362283,0.198834924349,0.183134254851,0.208119815668,0.214548183703,0.220993518026,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.457690213422,0.475255864158,0.476015925077,0.491498442929,0.494833583373,0.207160234388,0.194202511502,0.185010984783,1.0
-9960042155246,360618,1664596,LABFER,2009-06-23 18:00:00,1,0,1,0,0.218911917098,1.0,0.0,0.377644144781,0.22617449371,0.293709133335,0.186906191855,0.36814848057,0.253299287582,0.415583011583,0.353647421988,0.31940892406,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.459224020143,0.480078682984,0.479322944411,0.494956472153,0.497297437526,0.397380046266,0.396289326147,0.393199068406,1.0
-4512024041155,380580,18262790,LABFER,2011-06-28 00:40:00,0,1,1,1,0.644444444444,1.0,1.0,0.393993978212,0.428748646806,0.32544472974,0.650570707678,0.368415497314,0.470341436184,0.426867335563,0.361029411765,0.40612244898,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.483708979864,0.490711651019,0.485779235087,0.50029808738,0.501391277474,0.65421170782,0.64769150971,0.640381335846,1.0
-2734347483011,425644,26215659,LABFER,2012-05-23 10:39:00,0,1,1,1,0.477840451249,0.0,0.0,0.474471611404,0.507888597033,0.483956944888,0.605556300883,0.376551866729,0.377561223456,0.127280701754,0.545493562946,0.307998070355,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.492793793917,0.49048192415,0.498388244928,0.492524478666,0.49706622454,0.314653255131,0.305169966856,0.316872580236,1.0
--3727441076854,380683,12222304,LABFER,2010-10-10 09:30:00,1,0,1,0,0.0512820512821,0.0,0.0,0.268762925745,0.330364930018,0.265477459779,0.26804020979,0.159154683995,0.174806232033,0.0181868131868,0.032328458519,0.183989573339,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.466569299793,0.475858242399,0.476886910056,0.487468696085,0.494519347724,0.228640821075,0.16446391969,0.131228649381,1.0
--9642033386617,438182,1378725,LABFER,2009-05-04 08:27:00,1,0,1,0,0.218911917098,0.0,0.0,0.299790781213,0.251570691695,0.272375769457,0.107644009885,0.30493940756,0.130539409187,0.4,0.302132295767,0.351027966473,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.452224293153,0.476635443841,0.457893296213,0.491778122936,0.49484557985,0.378577366778,0.347243361986,0.326701643557,1.0
-6658860286141,368323,7051778,LABFER,2010-03-18 06:44:00,1,0,1,0,0.11914893617,0.0,0.0,0.152345658743,0.281647856674,0.169018328486,0.049941017235,0.1134160514,0.159299686752,0.0292369121562,0.141385089286,0.180993602341,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.452401735305,0.468672102181,0.457893296213,0.49014424523,0.499064028134,0.702597007287,0.552056821093,0.37031633059,2.84764112273e-30
--4646147808214,471136,39861987,LABFER,2013-11-27 06:30:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.12232021475,0.13367501268,0.0800448040923,0.244996703882,0.161025160547,0.016448964359,0.314060952367,0.199160001373,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.412826791043,0.441296274261,0.435412626422,0.485000480202,0.491629909915,0.0561272777189,0.0524758242059,0.0385478970822,4.18974604178e-20
--8107455795900,379147,18549619,LABFER,2011-06-30 11:56:00,1,0,1,0,0.11914893617,0.0,0.0,0.160877127274,0.194121549947,0.218100491678,0.0734284139465,0.0876252550786,0.159840702748,0.214285714286,0.139126552786,0.12369118925,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.466356433647,0.480877219134,0.467548574593,0.494793522063,0.498516335508,0.318834244685,0.337178277423,0.379256501583,1.0
-4766933480407,370442,31778929,LABFER,2013-01-10 22:01:00,1,0,1,0,0.224899598394,0.0,0.0,0.501533492246,0.426166012677,0.456173612629,0.501277399964,0.359615094883,0.483791151935,0.624031007752,0.466388888889,0.576328509387,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.496410765939,0.497357552664,0.498277106792,0.493678103366,0.496158451371,0.317054235679,0.286975458381,0.263903179901,1.0
-8607563731269,409706,26298965,LABFER,2012-05-27 08:06:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.19656860272,0.191261136018,0.211085343761,0.242823874418,0.111338220062,0.251873263383,0.419403694648,0.324923400996,0.279284688377,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.436734171503,0.459202413758,0.446687834329,0.486011035625,0.493621253554,0.057596617198,0.0499021997046,0.0465111154851,5.07928024182e-46
-7804886422910,380931,40681437,LABFER,2014-01-22 06:34:00,1,0,1,0,0.215827338129,0.0,0.0,0.255397046728,0.29373972451,0.195785092299,0.0420168067227,0.125566408607,0.173859720884,0.006,0.032328458519,0.0572896140445,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.457667423268,0.47253001741,0.487808173527,0.495716887206,0.496686209036,0.0938826398628,0.0455513350818,0.0258870622861,1.0
-3925958711253,434790,11835582,LABFER,2010-10-05 11:26:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.159566842218,0.158587708372,0.0674070331945,0.111877190448,0.10602679655,0.0342858529248,0.161304877216,0.0810753073522,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.454191591449,0.475425024339,0.479322944411,0.494832753806,0.496198055245,0.133004165597,0.128773808492,0.114726281654,1.0
--6751756075182,462096,8509720,LABFER,2010-04-22 09:54:00,1,0,1,0,0.634686346863,0.0,0.0,0.501533492246,0.371793091498,0.483104354706,0.290185570309,0.473769951469,0.37742529981,0.199797979798,0.357638435419,0.365304035705,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.503306621977,0.504644746018,0.509322489469,0.502340095523,0.500634246744,0.378435175581,0.350701937144,0.336472550768,1.0
-11381348521920,374609,6902361,LABFER,2010-02-28 22:01:00,1,0,1,0,0.477840451249,0.0,0.0,0.292358469279,0.485067765202,0.508982205759,0.303230210978,0.372799597093,0.550347376043,0.241904761905,0.416622807018,0.436534718345,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.483960834859,0.486302584731,0.498388244928,0.495779603327,0.496811238848,0.148220283463,0.106881992012,0.114164857375,1.52299795859e-28
-7592649544365,432583,5117437,LABFER,2009-12-24 17:15:00,1,0,1,0,0.108695652174,0.0,0.0,0.324158786907,0.253207211448,0.311260354995,0.119550021468,0.0644576162251,0.17689340158,0.116981132075,0.310294093407,0.197811643264,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.479483579555,0.489236761252,0.493260588842,0.498485734074,0.498872877635,0.283504356817,0.294485942234,0.30350793194,0.999995018264
-5168701597706,402908,28705114,LABFER,2012-09-11 18:10:00,1,0,1,0,0.224899598394,0.0,0.0,0.317864496039,0.293546178964,0.32318488024,0.136685234175,0.329513691585,0.241856687845,0.0941854636591,0.247369024138,0.323203367491,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.447405267753,0.461352562449,0.485779235087,0.489345286318,0.492144402279,0.0220798589957,0.0178199515449,0.0152303049337,1.0
-7511426550921,374677,8593852,LABFER,2010-05-18 12:32:00,1,0,1,0,0.224899598394,1.0,1.0,0.462403434127,0.348920079119,0.351594773923,0.285478140335,0.420619916673,0.221610540791,0.277575757576,0.539421534937,0.256322669797,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.475068189165,0.478707217758,0.485779235087,0.489291639596,0.489206791989,0.0575163724754,0.0466937624807,0.0495426640844,4.70759458127e-28
-311756715486,384430,14974999,LABFER,2011-02-23 07:44:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.230182457562,0.146613756339,0.149026458733,0.323793457323,0.0997329279512,0.0850532381657,0.228530218814,0.130454502601,0.149545996415,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.436059331024,0.458303990785,0.447057203458,0.492506578451,0.496566669403,0.455054446325,0.322735869097,0.285480237693,1.0
-4607937664691,456719,8961701,LABFER,2010-05-28 22:04:00,0,1,1,1,0.644444444444,1.0,1.0,0.35948609176,0.406910762546,0.422273206512,0.284483801808,0.237816068998,0.308404628008,0.306415770609,0.415883810339,0.357051598898,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.457969323011,0.47748201083,0.485779235087,0.49349160786,0.49643201979,0.286782446344,0.284103597122,0.290436772651,1.0
--5200373072753,357171,16398970,LABFER,2011-04-10 20:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.171896484661,0.162576479631,0.150808486203,0.0617045693432,0.107469186829,0.109207942336,0.00506321211175,0.0577773572586,0.0843940518638,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.276223983669,0.378874755359,0.0212807218132,0.314671072229,0.403703060807,0.0103954091642,0.00183810648261,0.000241012211382,0.999999997497
--7054229469617,417153,31543306,LABFER,2012-12-28 11:51:00,0,1,1,1,0.477840451249,0.80487804878,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.477998443764,0.640270797287,0.483415107354,0.431021213332,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.493330774955,0.498388244928,0.495824102993,0.496523106358,0.312093361036,0.311579132624,0.316843120132,1.0
-11276284061077,374339,11038049,LABFER,2010-08-29 01:00:00,0,1,1,1,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.481778805449,0.445295109401,0.448114312942,0.461895295862,0.385583963163,0.516736227644,0.464107502755,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.500027197585,0.498977533238,0.338295359434,0.344801960372,0.354791496335,1.0
-11576363125282,356506,34613721,LABFER,2013-05-02 08:22:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.649044688653,0.676802456176,0.615172019245,0.753922019494,0.760894408653,0.803825684662,0.838484840344,0.724647873058,0.808927822677,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516068956906,0.521811420026,0.508627827746,0.504470248593,0.781301970342,0.782478318372,0.78214740303,1.0
-9040043422344,436572,13207503,LABFER,2010-12-03 22:06:00,1,0,1,0,0.640625,0.0,0.0,0.18386959004,0.149702786577,0.189158117828,0.236240040118,0.0971865299511,0.157483884139,0.220285714286,0.31207165109,0.17738288758,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.281890090747,0.38453413055,0.0235091949214,0.318956013795,0.406173432177,0.0263905195154,0.00886166439424,0.00243311605812,2.62431574569e-34
--11170354810144,443812,29715699,LABFER,2012-10-03 06:56:00,1,0,1,0,0.477840451249,0.0,0.0,0.513752226886,0.44886705332,0.429353773875,0.495398113483,0.519370089244,0.472025164597,0.609535428729,0.145443156234,0.300928589593,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496262107531,0.503179327945,0.49751533833,0.503766622313,0.502588973611,0.724402474541,0.726327381371,0.723698645498,1.0
-3750844510223,440037,39302321,LABFER,2013-11-28 18:56:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.300695284671,0.290248132895,0.241736424042,0.166903233482,0.167388029783,0.168384181174,0.422950819672,0.218418592362,0.119395351069,0.222416167301,0.316965826625,0.376618066329,0.372886561768,0.455554056908,0.472212784389,0.447057203458,0.491415872275,0.493968737629,0.147619270174,0.138199681543,0.133793179592,8.86328943974e-34
--5602980712309,415976,8644595,LABFER,2010-05-07 22:02:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.104192002513,0.122764457154,0.160004113417,0.173163180431,0.173921619771,0.0928964557831,0.205579399142,0.120009227308,0.180526163559,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.285640882042,0.380462419826,0.0233336038528,0.32159792792,0.411882425632,3.06752030375e-05,2.27631352034e-09,4.08534773783e-16,0.0
-11747835974701,467933,36038768,LABFER,2013-07-13 07:57:00,0,1,1,1,0.634686346863,0.0,0.0,0.449349575001,0.367947156055,0.517153166603,0.591470168179,0.539364685709,0.530956346662,0.385481481481,0.485631725353,0.481929091429,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50587655719,0.506909032331,0.521811420026,0.505149244814,0.502549896308,0.67202797583,0.66979886687,0.672419574619,1.0
-1681845506049,368934,10026511,LABFER,2010-07-13 20:00:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.429847549775,0.283304866434,0.410193463033,0.450633224398,0.414810244696,0.479058123057,0.580041580042,0.412226218934,0.379228160708,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.490822558056,0.499081689524,0.494232589071,0.497064479797,0.238807980464,0.210275846953,0.264177506095,1.0
-5984923112651,462607,30631484,LABFER,2012-11-27 10:44:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.679863448053,0.575688406178,0.649373552947,0.756168453109,0.80300308158,0.710425261369,0.647216116297,0.824868955667,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.515313757607,0.521811420026,0.507465645248,0.505102326479,0.756040566765,0.759921624922,0.765260381236,1.0
--9296170330256,386659,12882823,LABFER,2010-11-07 22:02:00,1,0,1,0,0.634686346863,0.589887640449,0.0,0.533877242246,0.700493022427,0.615893748129,0.654224709695,0.678394615522,0.648158250473,0.721185454276,0.555404920731,0.608722366782,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.503257207089,0.50239334976,0.198188922014,0.065759523677,0.0504050259956,9.93472413607e-27
-11365835767224,420420,1310879,LABFER,2009-05-15 22:00:00,0,1,1,1,0.477840451249,0.216216216216,1.0,0.574936954301,0.557184726529,0.512736611032,0.542508703818,0.563065745425,0.60985385424,0.47365087834,0.633789756147,0.606319994539,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.503130613698,0.498388244928,0.497663419795,0.498350520408,0.469722224162,0.476618214412,0.48145415576,1.0
--4522046565653,417597,36832142,LABFER,2013-07-30 22:00:00,0,1,1,1,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.480108017232,0.522153577551,0.466758430429,0.476857483287,0.401346746654,0.476337220905,0.452055255067,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.499181744224,0.509322489469,0.502484458922,0.500928577725,0.451875616014,0.51377058096,0.576917279228,1.0
--2826027585302,414590,34545831,LABFER,2013-04-18 06:46:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.131708677776,0.116555635419,0.0222063791582,0.0928467468441,0.0637852156683,0.405579399142,0.166768486567,0.175858380292,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.265225786606,0.364443435968,0.0212807218132,0.319972791689,0.404792806863,9.25223036138e-05,1.39310559787e-11,8.37431377366e-33,0.0
-7122989722173,462479,16818241,LABFER,2011-05-11 01:12:00,1,0,1,0,0.11914893617,0.0,0.0,0.152329093994,0.279174217113,0.208867805239,0.295102819895,0.188490347157,0.253338048846,0.198485139022,0.249206685608,0.253599858809,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.485128837165,0.480886833765,0.49094065758,0.496378425662,0.159262411981,0.150334006659,0.127323633209,1.00030691373e-31
--9730294514907,413454,20477234,LABFER,2011-09-17 14:29:00,0,1,1,1,0.477840451249,1.0,1.0,0.499050123376,0.488601646815,0.475709478454,0.547360057582,0.451973559973,0.550731306453,0.530780849974,0.640197579573,0.549104183438,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.497731139469,0.498087074755,0.460489217759,0.45105250534,0.44640238786,1.0
--95441778689,448028,30649409,LABFER,2012-11-18 22:01:00,1,0,1,0,0.477840451249,0.216216216216,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.452785099418,0.401346746654,0.429210399261,0.434610796145,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.499181744224,0.509322489469,0.500600757052,0.499376261995,0.412317467094,0.415001177847,0.423092084887,1.0
--1591770688167,450372,24046405,LABFER,2012-02-12 21:00:00,1,0,1,0,0.218911917098,0.0,0.0,0.352595006605,0.252756311951,0.287139857019,0.0386688729938,0.339599676307,0.236420247958,0.294312619967,0.353650010317,0.146495095711,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.288473199836,0.387478575037,0.0222810961269,0.320139343341,0.412274382767,0.00408123770992,5.65160685657e-08,1.20077642334e-22,0.0
--9690940986940,403615,39778886,LABFER,2013-11-17 22:00:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.609764073171,0.583387941302,0.62728456594,0.54393714987,0.839555475493,0.739328618068,0.806935251885,0.654118821102,0.778970841048,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498865405233,0.50275635004,0.499081689524,0.500976611219,0.500203857975,0.62321656125,0.608672129691,0.610646105157,1.0
-5688028859736,390192,7260669,LABFER,2010-03-21 11:23:00,1,0,1,0,0.634686346863,0.0,0.0,0.440790284236,0.405977140536,0.45373316011,0.467474168129,0.359087954197,0.453174745566,0.0551102919079,0.333927269366,0.33553224806,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497113472072,0.498388244928,0.497674612195,0.499230590689,0.138423128549,0.133889382372,0.139118605504,1.09557373595e-10
--11339201037837,358849,15097365,LABFER,2011-02-02 22:08:00,1,0,1,0,0.634686346863,0.962025316456,0.0,0.533877242246,0.481733638786,0.513158957629,0.543391967284,0.63584269348,0.525908858104,0.276112508966,0.53200142415,0.486286976924,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.497665696345,0.510888218981,0.500741550233,0.498859816811,0.0924785834757,0.0489953168947,0.0312542013212,1.0
--4133714078207,471616,1176647,LABFER,2009-04-09 04:46:00,0,1,1,1,0.477840451249,0.0,0.0,0.437345252708,0.515350224435,0.501816400888,0.391666666667,0.376170906497,0.493715479326,0.702900696864,0.354012862138,0.48544596997,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.504082121618,0.505478281654,0.509322489469,0.503085043494,0.501773290279,0.684177138081,0.675096257963,0.666022999498,1.0
-5290211758072,379195,9791264,LABFER,2010-07-08 14:40:00,1,0,1,0,0.11914893617,1.0,0.0,0.145355110192,0.193821535573,0.21924831464,0.245891350304,0.100628857568,0.175709757454,0.0106426112534,0.219596984079,0.12852111232,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.448014679498,0.464891625628,0.480886833765,0.49277952464,0.497057741299,0.104494552982,0.0851476177183,0.0825854353879,1.0
-640355601190,472419,13121598,LABFER,2010-11-29 09:48:00,0,1,1,1,0.477840451249,0.0,1.0,0.489816211988,0.459353616993,0.485661352913,0.420256670044,0.542296469387,0.599587266593,0.43670003712,0.488711028233,0.435828953593,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.502345342325,0.509322489469,0.50302495185,0.50117346509,0.713824245907,0.793765814329,0.84165047143,1.0
-4526225693386,436657,36026985,LABFER,2013-07-09 00:34:00,1,0,1,0,0.218911917098,0.0625,1.0,0.344430485551,0.255127521417,0.286741105312,0.264902580222,0.194526368485,0.217588691479,0.410698920628,0.440030816853,0.212883399443,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.454377948534,0.473855999213,0.447057203458,0.49442188963,0.496912932295,0.186162883295,0.177765659687,0.177063638082,1.0
--8733638599984,390750,35849549,LABFER,2013-06-10 00:24:00,0,1,1,1,0.218911917098,1.0,0.0,0.308900713495,0.317076683797,0.297937783456,0.145977570161,0.315330212158,0.361581832334,0.354545454545,0.353448969331,0.380907810576,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.450848492119,0.475617594829,0.479322944411,0.502303943397,0.501367923945,0.386458140635,0.379199471611,0.377052851935,1.0
-8732790713402,454476,15317927,LABFER,2011-03-10 09:30:00,1,0,1,0,0.11914893617,0.0644257703081,1.0,0.377644144781,0.215377793398,0.241520615292,0.205099095305,0.164984753975,0.177257201739,0.0166259168704,0.386865331093,0.242021214004,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.442604056506,0.467203500688,0.468417916605,0.495707119441,0.494869883622,0.217803132312,0.20373982443,0.212760445338,1.0
-2053057190018,467298,26577169,LABFER,2012-06-07 10:17:00,1,0,1,0,0.403846153846,1.0,0.0,0.253082501533,0.298698173077,0.306600676239,0.159402369213,0.384660105622,0.261201401857,0.00372093023256,0.189777100902,0.150884846974,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45918551083,0.481751076704,0.480886833765,0.492494437462,0.494815743188,0.228149576622,0.21285155017,0.209282015505,1.0
--5088324056551,411572,15513200,LABFER,2011-02-25 16:00:00,0,1,1,1,0.477840451249,0.0,0.0,0.542108744912,0.694836635224,0.571528964655,0.686861145018,0.798192305691,0.619097571074,0.597297297297,0.434343818874,0.42871978022,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.532280565316,0.530069174096,0.565854186426,0.507320736293,0.504486291537,0.801953032103,0.821074657088,0.87336933147,1.0
--2096103130190,434447,37234083,LABFER,2013-08-05 22:00:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.48810841934,0.486362546052,0.497555059689,0.57891707624,0.548405624424,0.468681663815,0.610471168562,0.482865005757,0.446591614387,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486420635382,0.497866047879,0.49751533833,0.499479656135,0.499329788396,0.460423354253,0.416828448759,0.407725872848,1.0
--10514882122477,462153,783252,LABFER,2009-03-05 06:39:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.253329733524,0.398355322305,0.314739418438,0.120620459469,0.205824129169,0.28645197977,0.0,0.30078125,0.248678608136,0.222416167301,0.272513145711,0.341251976233,0.336701589837,0.45278267124,0.46863886213,0.456676473752,0.491013780295,0.496692107074,0.0328343039763,0.0192673480825,0.0157816651416,1.0
-1610742081287,416361,20531363,LABFER,2011-10-07 09:20:00,1,0,1,0,0.224899598394,0.0,0.0,0.353572469889,0.444993100351,0.508087113491,0.47019149342,0.537781405195,0.367025234461,0.393636363636,0.474266354389,0.478183866943,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.470352826786,0.481722909722,0.487344472973,0.493372133832,0.496851997918,0.407879948713,0.397061098389,0.383521928016,1.0
-8917810808979,440254,24539788,LABFER,2012-03-13 03:32:00,1,0,1,0,0.218911917098,1.0,0.0,0.19656860272,0.213988064248,0.258648795767,0.319423499763,0.312822311691,0.502901683692,0.403720930233,0.5225,0.409208304814,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.423597206221,0.454051967888,0.435412626422,0.485393834381,0.491269345049,0.0425684358668,0.0406249762694,0.040201201529,0.926905947547
-9924874556201,433073,12054606,LABFER,2010-10-16 11:13:00,0,1,1,1,0.477840451249,0.0,0.0,0.513819865532,0.506997427181,0.502257321393,0.562973914472,0.584474584832,0.567313988699,0.598926187021,0.702845967787,0.696079280699,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.48677520081,0.496596997162,0.498388244928,0.503418686231,0.503815131016,0.646422464069,0.652828945892,0.660244018653,1.0
-2268658033890,390566,34568899,LABFER,2013-04-30 11:48:00,1,0,1,0,0.218911917098,1.0,0.0,0.192539191776,0.246648384899,0.228067001032,0.277772828651,0.0911888667663,0.179899077705,0.6,0.160571037063,0.218749714863,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.46197944144,0.479285999238,0.479322944411,0.491718280733,0.496549657475,0.443146914677,0.421043766669,0.39385953621,1.0
--10914024279123,413525,27971329,LABFER,2012-07-31 22:00:00,0,1,1,1,0.218911917098,0.04,1.0,0.304450625996,0.304553084551,0.295967556703,0.690738122172,0.193582863912,0.21121515557,0.4,0.353612051988,0.263665116398,0.222416167301,0.300974526888,0.361027016565,0.358510710191,0.446856314649,0.466388961377,0.456676473752,0.492404173874,0.498697043235,0.35426569638,0.292520043027,0.264326536336,1.4556525104e-82
-6732329404181,407407,18766608,LABFER,2011-07-25 16:21:00,1,0,1,0,0.634686346863,0.963636363636,1.0,0.533877242246,0.555567089982,0.598699631434,0.496084702581,0.654617769454,0.622313999115,0.7442043222,0.569957117074,0.491115182072,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.504918621284,0.510888218981,0.501813505646,0.501632921627,0.318285246727,0.333066446666,0.333339617417,1.0
-9088701186569,399893,22254368,LABFER,2011-12-18 07:34:00,1,0,1,0,0.307692307692,1.0,1.0,0.477279432247,0.431462549628,0.403107354196,0.200068933466,0.411286318565,0.461291733755,0.776032281245,0.564720862093,0.365481887265,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.472653292188,0.483806943476,0.47486128235,0.494944778254,0.496643487889,0.283852821097,0.281084931591,0.255595010004,1.0
--1244462792321,431159,19288795,LABFER,2011-08-05 10:56:00,1,0,1,0,0.0512820512821,0.0,0.0,0.395903149302,0.308381404598,0.240028064755,0.359216991963,0.478817753889,0.219416762498,0.0108695652174,0.408051849987,0.165590447036,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.476688259317,0.481234717577,0.489373727303,0.493987940698,0.497970697799,0.573510232889,0.603344411648,0.604285855211,0.999999999985
--8916365302850,437911,33455493,LABFER,2013-03-09 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.102530774301,0.163986059785,0.147164770191,0.116020553506,0.162506377209,0.124310652698,0.205063212112,0.156290833453,0.0518821010785,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.408113404384,0.438149170428,0.468307222203,0.476333816517,0.486504406088,0.0155563293389,0.0118728720092,0.00998897842686,1.5497526472e-09
--1165273467866,389676,3201529,LABFER,2009-09-11 22:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.119164533449,0.120086740934,0.0355125992539,0.072092492893,0.0448790163128,0.229529849848,0.0991572153078,0.0213089843928,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.255250350974,0.358159779407,0.0199232408921,0.312947713121,0.402613883815,0.000426074904833,3.24528151163e-05,1.49544981389e-06,0.0
-3680990583169,423619,1911,LABFER,2009-01-10 04:15:00,0,1,1,1,0.477840451249,0.0,0.0,0.492049174403,0.442723407409,0.461412195986,0.492504020838,0.443102427384,0.471369310318,0.778076923077,0.327158591527,0.385095624455,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.491016100662,0.498388244928,0.498884385385,0.498748139377,0.481353878547,0.524606738087,0.545291749972,1.0
--3489393081457,385420,33395520,LABFER,2013-03-10 19:37:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.27568987111,0.179240213465,0.245662625114,0.309346299846,0.0939965964777,0.187870655321,0.159425552988,0.378609057537,0.137507054032,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.455509960897,0.473315021662,0.468417916605,0.490877891841,0.492313348149,0.19291424232,0.217846955434,0.245316701012,0.999999999959
--5283798258395,399006,38449224,LABFER,2013-09-27 13:21:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.186134585598,0.24593044179,0.269486901727,0.398669684596,0.293213824879,0.220755328341,0.0212073953065,0.339638354284,0.227253626499,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.466356433647,0.485999753428,0.467548574593,0.498848670436,0.498972798396,0.655781035748,0.647884810718,0.637995218604,1.0
--3771476430217,367163,12482828,LABFER,2010-10-17 19:17:00,1,0,1,0,0.477840451249,0.0,0.0,0.487994303783,0.436524573629,0.431068128837,0.427178400169,0.2922174625,0.365302274579,0.308333333333,0.252816461443,0.421712526753,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.491057317999,0.498388244928,0.486241011759,0.493452513355,0.358944259788,0.353318714174,0.346989784438,1.0
--11931624918520,399117,32403917,LABFER,2013-01-18 15:51:00,0,1,1,1,0.485294117647,1.0,1.0,0.301708115339,0.340165213593,0.31364206457,0.183233798686,0.215315843486,0.191648808792,0.404381342062,0.162793174365,0.203021981365,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.490305293382,0.495079677479,0.500307457597,0.495894606639,0.498707624858,0.219439971542,0.179818017499,0.149582113873,1.0
--8717100126641,466481,1370956,LABFER,2009-05-13 22:00:00,1,0,1,0,0.403846153846,0.0,1.0,0.217080053145,0.221499749261,0.290726568816,0.224719101124,0.249773149828,0.241365907324,0.0600539186429,0.370527973305,0.330505834195,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.491442478811,0.480886833765,0.49894768196,0.49748725522,0.744157550781,0.798528397822,0.799822601011,1.0
--6420812291240,425361,33870182,LABFER,2013-03-20 22:00:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.576935863781,0.530983709062,0.58633351554,0.691149686926,0.688390439736,0.630913132016,0.870519130458,0.592942698434,0.601098393844,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.505600710614,0.510888218981,0.501437420594,0.500185641764,0.603071599702,0.611036682142,0.619540870213,1.0
--9425810430093,359368,38735539,LABFER,2013-10-05 16:13:00,0,1,1,1,0.224899598394,0.0,0.0,0.533877242246,0.527170973705,0.541734373019,0.67659647871,0.462129924133,0.522164238011,0.739529649047,0.614138646387,0.583807949762,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.490011146582,0.49470248415,0.487344472973,0.493807444267,0.498281786362,0.557669475711,0.547599177593,0.534355465907,1.0
-7347925553175,458169,20255831,LABFER,2011-09-23 03:24:00,0,1,1,1,0.218911917098,1.0,1.0,0.24256619665,0.368284332843,0.394884246345,0.233755192281,0.501408931431,0.59551006758,0.6,0.371159744633,0.697026750334,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.480289822515,0.495684143866,0.491813578127,0.492265476181,0.497451207822,0.123882003571,0.133573989696,0.153883027101,0.999999999991
--5307396613316,407588,4678418,LABFER,2009-11-19 01:52:00,0,1,1,1,0.218911917098,0.04,0.0,0.390145964711,0.381718168269,0.279843628813,0.105005963271,0.0977552224387,0.262631938427,0.251587602694,0.437617698312,0.276096639927,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.475418681165,0.467548574593,0.497162691744,0.498374130674,0.817531794232,0.817362400533,0.791351083606,1.19638370253e-85
-9345182783160,372466,11497073,LABFER,2010-09-21 06:41:00,1,0,1,0,0.108695652174,0.0,0.0,0.259839930411,0.242712644736,0.320540619802,0.161609667123,0.196669367249,0.335563312703,0.316981132075,0.432558139535,0.33010644664,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.482971659126,0.492629416659,0.480767972341,0.497372088732,0.495022790922,0.456340185723,0.467836658655,0.460846356321,2.11145245537e-18
--6694013181823,459544,26434793,LABFER,2012-05-12 21:00:00,1,0,1,0,0.108695652174,0.0,0.0,0.435021973734,0.421662463052,0.312259992977,0.433267195767,0.388625980876,0.303666614791,0.0,0.3,0.27909860859,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.48122715372,0.485169549919,0.469859735916,0.492531871473,0.495784570932,0.269659687654,0.26136826345,0.254846135384,2.51526382856e-82
-10467696007654,384207,35166844,LABFER,2013-05-31 03:55:00,1,0,1,0,0.224899598394,0.0,0.0,0.513752226886,0.305670165751,0.306271585644,0.319477310525,0.277929994105,0.281438747635,0.324959250204,0.399215686275,0.255656707869,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.468655304588,0.481405156728,0.473990644778,0.497537420471,0.494619702536,0.213486597085,0.184626126397,0.161529634763,1.0
--10404394549404,392712,35124328,LABFER,2013-05-11 09:47:00,0,1,1,1,0.640625,1.0,1.0,0.182960617045,0.309620979685,0.343949718582,0.169429955932,0.342134335165,0.271515105848,0.375,0.274604379109,0.293470024169,0.222416167301,0.300974526888,0.360449324411,0.361490321039,0.440352390355,0.470973877754,0.468307222203,0.49243461422,0.49648657615,0.385791629269,0.375838985365,0.364935954377,1.0
-5725535249310,405990,34929497,LABFER,2013-05-29 16:26:00,0,1,1,1,0.477840451249,0.0,0.0,0.477279432247,0.416463234518,0.403926207874,0.650315087077,0.427641805733,0.402069066447,0.615341880342,0.417703009343,0.351518432962,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.490170953469,0.498388244928,0.494922015395,0.4973246299,0.280585141274,0.286603753327,0.306622920356,1.0
-2801448779804,457362,199713,LABFER,2009-02-09 09:51:00,1,0,1,0,0.634686346863,0.857142857143,0.0,0.533877242246,0.535612616328,0.579469263321,0.710128283565,0.601414431633,0.63021037567,0.492989417989,0.755052234857,0.614375600316,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.508489270752,0.510888218981,0.50628345844,0.504014651859,0.87442850217,0.880907018435,0.885223318226,1.0
--8344727412574,427633,39597688,LABFER,2013-11-08 22:01:00,0,1,1,1,0.224899598394,0.0,0.0,0.368342212044,0.488994276188,0.459489339295,0.395274375545,0.538181284718,0.509932296644,0.0,0.603436745078,0.5257237014,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.463153083969,0.480452353608,0.487344472973,0.493065863168,0.497550383222,0.301569736329,0.323743236159,0.34815659327,1.0
-9541950466981,472355,21676662,LABFER,2011-11-18 23:59:00,1,0,1,0,0.634686346863,0.0,0.0,0.544348835095,0.362957846291,0.559820060172,0.313496606288,0.410006542586,0.385483256822,0.563636363636,0.386666666667,0.365256531799,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.505337955284,0.507084937018,0.510888218981,0.50177143745,0.500159165034,0.65243304749,0.644533754541,0.63519892783,1.0
-5226637825133,363473,25874694,LABFER,2012-05-19 14:20:00,1,0,1,0,0.0833333333333,0.0,0.0,0.344430485551,0.217848498327,0.286719515657,0.198039943161,0.261334469446,0.360721348774,0.0,0.190706264581,0.3846407052,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.451354036644,0.4734710125,0.480886833765,0.494670933287,0.497242252336,0.288344460691,0.27379005489,0.259032989497,1.0
--6751756075182,462181,4100981,LABFER,2009-10-15 07:00:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.27422751268,0.207753050757,0.233634139876,0.223531037236,0.178577579222,0.199769827863,0.0291005291005,0.316643662285,0.240927787426,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.443769899275,0.46946567303,0.470329931948,0.495583177042,0.497952775197,0.460551207034,0.430997510822,0.414721839845,1.0
--10831611285501,460058,7883761,LABFER,2010-03-26 11:29:00,0,1,1,1,0.485294117647,0.0,0.0,0.273561576034,0.31084799549,0.318237673636,0.495537933483,0.150756691067,0.294408386976,0.21495119787,0.321776467073,0.398979794365,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.469489775956,0.479392969629,0.487808173527,0.494651940923,0.496918835243,0.358831589332,0.338375195055,0.340407924983,1.0
--3059243415786,391490,30533674,LABFER,2012-11-03 20:00:00,1,0,1,0,0.11914893617,0.0,0.0,0.105600857578,0.135660761525,0.148276572395,0.0387136557972,0.0595469532343,0.117427198642,0.205579399142,0.0133991347695,0.268225348914,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.404446373229,0.436953935863,0.41816275361,0.469920761788,0.483178490328,0.00121841212405,0.00100448958224,0.00143067856689,0.0
--10310174067697,369685,22992249,LABFER,2012-01-07 02:02:00,1,0,1,0,0.477840451249,0.0,0.0,0.461527332608,0.477951555469,0.484558354941,0.506441622435,0.352491136986,0.489669468942,0.739534883721,0.48142320709,0.434541895438,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.500139081134,0.498388244928,0.500113739913,0.499545204408,0.370952221768,0.354056394617,0.344224649313,1.0
-7348566041560,431956,37339817,LABFER,2013-08-31 05:20:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.533877242246,0.481733638786,0.512814115289,0.543391967284,0.567191524792,0.577446062617,0.790086848635,0.492387241374,0.602416002778,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.498511130406,0.510888218981,0.500741550233,0.500121887258,0.539982398221,0.558679643126,0.576058033104,1.0
--6208433761232,419034,22275908,LABFER,2011-12-09 18:30:00,0,1,1,1,0.640625,0.0,0.0,0.24256619665,0.458706467323,0.437062236301,0.363174159022,0.41669599348,0.579856407189,0.632183908046,0.450199736313,0.522184943325,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.480289822515,0.495684143866,0.491813578127,0.496380282801,0.497826093586,0.594973066527,0.603327145074,0.617707343605,1.0
--5015691110498,383051,13705977,LABFER,2010-12-16 13:06:00,1,0,1,0,0.218911917098,1.0,0.0,0.215580522621,0.194208529874,0.19840031696,0.0373698905077,0.311972454141,0.158642855729,0.41662591687,0.125355848268,0.175223638494,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.444722726535,0.462646592756,0.456676473752,0.486198468197,0.492810047529,0.00778846241188,0.00318796806084,0.00155849165782,5.55741240872e-90
-4644637567578,453604,30613409,LABFER,2012-11-24 21:00:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.423233676679,0.442723407409,0.432568166097,0.322347821134,0.531265021666,0.385343428604,0.8,0.552303740987,0.419571373402,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.489911166813,0.498388244928,0.50028622057,0.49877046933,0.237929466213,0.232734813712,0.233704221993,1.0
-10323767325948,414508,11603556,LABFER,2010-10-01 23:36:00,1,0,1,0,0.634686346863,0.558823529412,1.0,0.533877242246,0.384079972652,0.408372491703,0.480847579865,0.498444342169,0.444664727176,0.313580246914,0.40564247827,0.415407825301,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.4923983771,0.499081689524,0.499053835412,0.498823153591,0.317894049519,0.347134706139,0.381478379644,1.0
-64805889515,380721,24294764,LABFER,2012-03-09 22:00:00,0,1,1,1,0.634686346863,0.0,0.0,0.620755809273,0.609019757396,0.507542649396,0.525113334995,0.697913488508,0.530126101795,0.776282200046,0.610510001823,0.708475068671,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.500674623837,0.498388244928,0.49842072325,0.500078544939,0.458029348221,0.40655423428,0.355208651381,1.0
-7719498890060,456579,9026743,LABFER,2010-06-09 08:16:00,1,0,1,0,0.485294117647,0.0,1.0,0.240727769941,0.275460813446,0.288530288834,0.505350140056,0.144632473078,0.33732591298,0.204081632653,0.206055760403,0.344826757747,0.474085517325,0.409727498043,0.371473277954,0.358608678028,0.429854502233,0.452034655021,0.487808173527,0.484458121265,0.490324450772,0.0290347502614,0.0201562135702,0.0124805810926,5.84004162057e-195
--10236934958900,410228,16954780,LABFER,2011-04-29 20:00:00,1,0,1,0,0.218911917098,0.0,0.0,0.192539191776,0.163934810798,0.14637425174,0.0382202447164,0.0988511143062,0.0922822235903,0.4,0.248054951159,0.262782645034,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.448060499903,0.462146899175,0.479322944411,0.484406612977,0.49237502931,0.0209611560064,0.0199053196759,0.0155017671642,2.83515184264e-72
--5892214039458,451772,23480160,LABFER,2012-01-26 06:43:00,0,1,1,1,0.477840451249,0.216216216216,0.0,0.481476115532,0.482444215043,0.455120706403,0.550605248652,0.599277603759,0.541168076555,0.65119047619,0.631484112439,0.456709304246,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.501605582987,0.509322489469,0.497946292451,0.499573698102,0.648600381036,0.682499277989,0.672865274267,1.0
-804953605172,411254,38176092,LABFER,2013-09-29 16:50:00,0,1,1,1,0.863636363636,0.0,1.0,0.342379392099,0.257404824416,0.353820985655,0.608771929825,0.372831856799,0.429506272047,0.386869565217,0.19798810035,0.364436728975,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.48249106583,0.492659001234,0.488501233368,0.501359668537,0.500312316858,0.617330175142,0.583940781535,0.563286143238,1.0
--3047934856304,412051,29304448,LABFER,2012-09-16 21:00:00,1,0,1,0,0.224899598394,1.0,1.0,0.240025869205,0.432995420289,0.427313959366,0.520265558935,0.581171331761,0.45327391192,0.419090909091,0.533320663077,0.533361210353,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.461986254849,0.476540101898,0.485779235087,0.492225212208,0.496355391193,0.317675876716,0.305013263734,0.295946952439,1.0
-11137847244820,383986,799636,LABFER,2009-03-23 22:01:00,1,0,1,0,0.477840451249,0.890410958904,1.0,0.542041106266,0.482042782503,0.508915172774,0.292031506385,0.440344864715,0.519937004878,0.263741089828,0.509765516388,0.465882430141,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.5023354493,0.503150265944,0.498388244928,0.502781709848,0.499556719604,0.34166426706,0.333122094578,0.338943630836,1.0
-8240633378511,410777,20591868,LABFER,2011-10-17 21:12:00,0,1,1,1,0.634686346863,0.0,0.0,0.533877242246,0.498799244697,0.567648337522,0.599551095569,0.723695571777,0.608842971894,0.629562289562,0.522353132094,0.559806537261,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506529365886,0.510888218981,0.503257207089,0.502409493389,0.585570317292,0.572125974406,0.557876340524,1.0
-3919653107168,439942,27432319,LABFER,2012-07-16 10:06:00,1,0,1,0,0.634686346863,0.257142857143,0.0,0.474588198752,0.365956651684,0.436941954585,0.597906471704,0.461650021011,0.352720422049,0.777777777778,0.499518805431,0.442958652065,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492244717565,0.496270283048,0.510888218981,0.502867539122,0.500221024384,0.467611314937,0.47574834237,0.504571821415,1.0
--5838470829876,401561,27787938,LABFER,2012-07-21 23:26:00,0,1,1,1,0.477840451249,1.0,0.0,0.513819865532,0.550938182717,0.536865485986,0.53243188595,0.642431570084,0.609012289516,0.596114827202,0.684437101101,0.513696461026,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.502252173557,0.503853322028,0.498388244928,0.503783376791,0.501708175859,0.683789392719,0.711364213483,0.719528801542,1.0
-218210994548,403342,3782566,LABFER,2009-10-19 20:24:00,0,1,1,1,0.640625,0.0,0.0,0.319734926228,0.381247143264,0.310174054283,0.250159234185,0.46629187527,0.357480596747,0.420385517593,0.616225791752,0.356651284874,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.446998479078,0.476338486831,0.491813578127,0.499538531324,0.500107649863,0.543191453244,0.546174291266,0.544916481409,1.0
-9741911333166,405819,32306151,LABFER,2013-02-10 15:15:00,0,1,1,1,0.477840451249,0.0,1.0,0.499050123376,0.502991073871,0.446043387764,0.538427001709,0.581858965143,0.550367303133,0.541357753808,0.511117111039,0.482884193025,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496479469097,0.498388244928,0.500486697106,0.500499832615,0.495092451042,0.500795900228,0.526658226651,1.0
-4947757686000,401528,20570560,LABFER,2011-10-11 20:00:00,1,0,1,0,0.0512820512821,0.0,0.0,0.199949434594,0.306513004427,0.263028835541,0.323739837398,0.287829762971,0.17308766197,0.370666666667,0.147983870968,0.136776047577,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.44100830211,0.452372916369,0.444961078688,0.482800196599,0.490621551565,0.324000973618,0.466713680454,0.435407278589,0.0
--2622786645390,410070,4477744,LABFER,2009-11-02 12:21:00,0,1,1,1,0.477840451249,0.80487804878,0.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.472743207742,0.514464345674,0.565380893204,0.48772421521,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.500296646393,0.509322489469,0.503454757755,0.500803288677,0.576466505129,0.582813439261,0.589057195587,1.0
-2842751338736,411920,30821050,LABFER,2012-12-04 09:57:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.208565149362,0.198305399079,0.193757740191,0.180645784947,0.225127768396,0.220229661852,0.232566204288,0.157553210957,0.211251075094,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.440615757377,0.464093778541,0.446194229381,0.486781741225,0.493018122793,0.212639714528,0.208099438035,0.211141588619,1.0
-9262439303145,460606,25492176,LABFER,2012-04-30 01:45:00,1,0,1,0,0.477840451249,1.0,1.0,0.513819865532,0.467012818976,0.500377943309,0.475634151159,0.471158396166,0.500726241184,0.603712335996,0.413993807999,0.451933827183,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.501776114664,0.509322489469,0.503724959479,0.502459581775,0.693680306218,0.704682349592,0.714153659562,1.0
-4829986583531,407989,13513821,LABFER,2010-12-19 22:11:00,1,0,1,0,0.218911917098,0.0,0.0,0.377096617894,0.240379500694,0.288333694718,0.419263498112,0.191934618849,0.344565870936,0.2,0.136221382209,0.241682013838,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.446822254617,0.466893244821,0.456676473752,0.488938976782,0.492799486812,0.00278797951457,0.000479241154557,0.000197067004127,3.95849928458e-68
-3833302706426,444546,27662790,LABFER,2012-07-31 04:53:00,0,1,1,1,0.218911917098,0.04,1.0,0.182960617045,0.28571135993,0.279026368091,0.298438505115,0.188566765413,0.229713464676,0.4,0.38458670741,0.119903291903,0.222416167301,0.300974526888,0.368633683804,0.379422352055,0.435013269676,0.463664394339,0.43999024152,0.486562105843,0.493495748933,0.0800270605391,0.0745944126756,0.0720732436482,2.71813320173e-56
-5443609608996,380242,12887784,LABFER,2010-11-15 20:00:00,1,0,1,0,0.11914893617,1.0,1.0,0.180548261425,0.277300306726,0.292794099991,0.0450448040923,0.429674155068,0.302846964786,0.0198651134273,0.311165048544,0.441818857933,0.222416167301,0.300974526888,0.360449324411,0.361490321039,0.450704276227,0.473134517204,0.444188271345,0.479228174772,0.492776133459,0.0366335288673,0.0244357302661,0.0238470897179,4.13008662838e-139
--9781850737149,393249,11964455,LABFER,2010-09-19 14:34:00,1,0,1,0,0.634686346863,0.0,0.0,0.37576466934,0.50708277931,0.439946229516,0.114222332817,0.631281322085,0.528068921298,0.509952380952,0.454469216386,0.500151766102,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.509027488261,0.521811420026,0.503814013684,0.501982640161,0.682872858266,0.669535851759,0.654956385339,1.0
-798518854058,448946,31246411,LABFER,2012-12-23 21:00:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.576935863781,0.602656677206,0.605812253602,0.634418473394,0.757054011793,0.69189018878,0.618859440482,0.545085867414,0.606161895499,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506787962974,0.510888218981,0.50031800643,0.498622679925,0.555628619008,0.553419704174,0.563789019343,1.0
-2643292234194,361208,37325382,LABFER,2013-08-23 17:05:00,1,0,1,0,0.224899598394,0.0,0.0,0.312797091448,0.418216885094,0.408310918771,0.359506691586,0.25724576864,0.333962195258,0.427027027027,0.440046811781,0.286419436578,0.474085517325,0.481003341925,0.465948949345,0.458461829056,0.461054163277,0.477445596706,0.47486128235,0.489081121382,0.493161024974,0.26545347499,0.28809324445,0.297354855895,1.0
-4786708685252,387906,20549439,LABFER,2011-09-30 12:31:00,1,0,1,0,0.477840451249,1.0,1.0,0.426337728212,0.347252834668,0.292391129724,0.223973474583,0.324948108964,0.267704155263,0.153494623656,0.0701490406027,0.286222009243,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.473113094923,0.480112894342,0.486583208859,0.490732994558,0.495666811356,0.0950959828531,0.078700747505,0.0712743311511,1.0
-919688520768,459703,3962483,LABFER,2009-10-27 14:41:00,0,1,1,1,0.477840451249,1.0,1.0,0.479585799842,0.431687937339,0.505554865291,0.556941602728,0.513202370345,0.562617568702,0.810909090909,0.379770431589,0.442747567318,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496664611424,0.49867485995,0.509322489469,0.500642188642,0.505302258806,0.847834670973,0.805997035945,0.752301330555,3.56154925626e-53
--1283686689111,373209,29470666,LABFER,2012-09-23 12:03:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.680208726377,0.640031278209,0.721153167272,0.754935274893,0.71346255181,0.7442043222,0.876647969332,0.6806547737,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516068956906,0.521811420026,0.504681345012,0.502581185084,0.861797642386,0.865183703575,0.868928996479,1.0
--2228394833429,381901,31618437,LABFER,2012-12-22 15:38:00,1,0,1,0,0.634686346863,1.0,1.0,0.516303234401,0.506316906189,0.512361352334,0.428545178622,0.588310933503,0.543009330019,0.60180878553,0.672594669742,0.509464260408,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.509792727469,0.521811420026,0.505673004591,0.502890364875,0.851843500975,0.866103433635,0.874371322348,1.0
-5217754848017,401287,6482552,LABFER,2010-02-10 07:59:00,1,0,1,0,0.634686346863,0.0,0.0,0.548646984401,0.483449284727,0.491953838103,0.494410323208,0.593749209051,0.462580731966,0.393548387097,0.473230769231,0.387335591336,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492806548043,0.500346245159,0.521811420026,0.493221244171,0.495067967911,0.238104119551,0.20877556075,0.193343513047,0.99999964995
-12058571515400,443836,30609549,LABFER,2012-11-22 20:33:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.471310933109,0.475540295041,0.539964972583,0.493599212345,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.498224288606,0.498198089311,0.577171682691,0.584560265005,0.587086058214,1.0
--3436496937957,363000,15758011,LABFER,2011-03-10 02:21:00,1,0,1,0,0.640625,1.0,1.0,0.319734926228,0.374323162334,0.365973454891,0.493535903143,0.541577638646,0.518105890487,0.551724137931,0.622243589744,0.487495361317,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45918551083,0.481751076704,0.480886833765,0.499328146195,0.500693310543,0.379314241436,0.376959865269,0.369110952163,1.0
--1308013923516,449664,25440670,LABFER,2012-04-06 04:15:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.533877242246,0.505095156781,0.545561542196,0.592143627415,0.570055505401,0.53655106931,0.59394934334,0.638403974311,0.460360167238,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506123149708,0.507921322258,0.509322489469,0.501060255426,0.500617269435,0.498957207122,0.496305945073,0.501914821846,1.0
-3971714587867,397518,39448588,LABFER,2013-11-27 03:46:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.48956052997,0.630797659152,0.794924180196,0.804872478967,0.515751144364,0.31660692951,0.562403561687,0.600920575205,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.502673723485,0.504095793821,0.510015679179,0.506113688628,0.50532336635,0.268882832426,0.211390268271,0.149649545572,9.69713270162e-06
--12229305080263,368098,33299530,LABFER,2013-02-26 07:37:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.375922813286,0.721130650222,0.631357596969,0.644836824333,0.681306666448,0.694669676298,0.52153637248,0.857205695043,0.876882341467,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516068956906,0.521811420026,0.508627827746,0.505084242381,0.880832534522,0.899140205519,0.914594524668,1.0
--4785529268369,436605,37553182,LABFER,2013-08-30 01:05:00,1,0,1,0,0.218911917098,1.0,0.0,0.189333002919,0.313860961727,0.23001123205,0.0651779892255,0.221401538585,0.150456873334,0.020346847103,0.00764254510329,0.0600058044032,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44139296035,0.466432442915,0.456676473752,0.492762658666,0.495677418547,0.138998804403,0.109999858367,0.0883592901974,1.0
-1208112273720,421163,7349656,LABFER,2010-03-25 10:09:00,0,1,1,1,0.477840451249,1.0,1.0,0.499050123376,0.486362546052,0.477542334808,0.472253711631,0.586732689071,0.49459225681,0.594534883721,0.674723244638,0.599173241425,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.490367394558,0.496218565214,0.498388244928,0.500497889738,0.501126993649,0.582596776268,0.593558822657,0.625906506927,1.0
--3110193524961,455558,10583231,LABFER,2010-08-03 13:10:00,1,0,1,0,0.634686346863,0.0,0.0,0.533877242246,0.52668398462,0.581998171,0.756346176143,0.53090722525,0.574271682596,0.631643356643,0.571497501637,0.490700415508,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.50614572168,0.510888218981,0.50115934523,0.500668169206,0.6419745085,0.628846715296,0.631272974594,1.0
--6070778006409,470282,40030968,LABFER,2013-12-02 07:35:00,1,0,1,0,0.11914893617,0.0,0.0,0.161439026276,0.171046588432,0.193247872448,0.0279822029822,0.134071923713,0.0879587154127,0.214285714286,0.0368324088669,0.100487722779,0.222416167301,0.257917683272,0.28911874352,0.287666847741,0.419953344331,0.450887523414,0.434621628464,0.485180541621,0.491936719986,0.100283656701,0.0596105685594,0.0292504036227,1.0
--8820908770266,373187,30032815,LABFER,2012-10-15 23:07:00,0,1,1,1,0.11914893617,0.0,0.0,0.127650099421,0.120190880529,0.167295984831,0.310401493931,0.0895233673419,0.130188132002,0.0,0.321117155493,0.153350419445,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.425231088394,0.45569159053,0.457893296213,0.498113998481,0.500262602689,0.529786453378,0.537712250579,0.527429193617,1.0
--3818522234701,452625,4860549,LABFER,2009-11-20 12:24:00,0,1,1,1,0.485294117647,1.0,1.0,0.32862379531,0.312985128738,0.332589072727,0.398016806723,0.56636602146,0.457564891888,0.2,0.435389950364,0.345334467391,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.484081235251,0.491463026196,0.487808173527,0.498504722877,0.498571791728,0.546566907089,0.590864108504,0.619108206699,1.0
-10346569129318,463307,15903169,LABFER,2011-03-25 12:14:00,1,0,1,0,0.218911917098,0.0,0.0,0.235875902924,0.358676292563,0.28160758739,0.0331417624521,0.223348729474,0.192468167234,0.00873180873181,0.229519654752,0.30954371455,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.444267808867,0.470899480055,0.479322944411,0.491958268019,0.495540525821,0.0846469498952,0.0707341850977,0.0573922284505,1.0
-10092257287544,369148,5272693,LABFER,2009-12-27 09:45:00,0,1,1,1,0.218911917098,0.0,1.0,0.235534258643,0.300106393062,0.301935131874,0.0862770576395,0.234005287478,0.294902140532,0.22095959596,0.424377440673,0.357780664462,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.472407011961,0.488547351807,0.468417916605,0.495927145111,0.497270745297,0.381153956091,0.359305633213,0.375895282671,1.0
--2453680620415,370980,935223,LABFER,2009-03-18 22:00:00,1,0,1,0,0.108695652174,0.0,0.0,0.366760697002,0.382557807308,0.250755687708,0.444324045408,0.066253270539,0.264980533037,0.2,0.13490046214,0.307527576326,0.474085517325,0.42795946424,0.431636709476,0.427797731022,0.472669730349,0.48553982909,0.480767972341,0.490682916422,0.494842936632,0.319898598555,0.355694976039,0.333981145416,1.0
-119255658361,360004,4379364,LABFER,2009-11-19 12:16:00,0,1,1,1,0.634686346863,1.0,1.0,0.290229639138,0.360433223531,0.426563066578,0.199717630854,0.353379669099,0.389884365609,0.158024691358,0.340766865807,0.32470836969,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.46733420259,0.481844240127,0.499081689524,0.493902939946,0.496826446711,0.267706875983,0.272886597371,0.269704983344,1.0
--3153550504108,460906,26328108,LABFER,2012-05-11 21:00:00,1,0,1,0,0.477840451249,0.0,0.0,0.444935682247,0.471948232171,0.452165072926,0.426530451736,0.484438511199,0.480114205041,0.25119047619,0.397487593918,0.379897883526,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498658105002,0.499376733258,0.498388244928,0.495305167947,0.496515670237,0.13370013821,0.068821054698,0.0325413737747,1.0
--7250544431695,364479,34421679,LABFER,2013-04-09 16:24:00,1,0,1,0,0.634686346863,0.936046511628,1.0,0.58802033268,0.632601104796,0.629581489427,0.675460395811,0.666076112011,0.741532906289,0.84952818846,0.711121530959,0.700624050479,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.516068956906,0.521811420026,0.50569065951,0.50252581114,0.621247059895,0.597355063772,0.560182627649,1.0
--6515529093648,385045,29785330,LABFER,2012-10-12 08:36:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.151689592834,0.165164838924,0.0915129012126,0.412991238586,0.0796019148117,0.225155279503,0.127610540965,0.172089727192,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.422513119545,0.449131653822,0.446194229381,0.491333008893,0.495361856076,0.226453052903,0.225775772285,0.23012242093,0.999999999997
-9351927145206,442844,156153,LABFER,2009-01-29 08:11:00,1,0,1,0,0.11914893617,0.0644257703081,1.0,0.228254853826,0.163873847733,0.191646533934,0.258816183816,0.234055298982,0.103268068024,0.206,0.0314113081368,0.159753970608,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.424079205818,0.453080931261,0.447057203458,0.487606266122,0.493637251095,0.0819256189926,0.0548284600286,0.042178928726,1.0
--3396791248051,409851,19660830,LABFER,2011-08-20 19:13:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.318745351019,0.430677971557,0.457596984573,0.295864368796,0.365168919056,0.374926809395,0.280063569351,0.306361164421,0.347598117977,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.474567628026,0.488790477332,0.509322489469,0.495263237203,0.497317912579,0.437616555727,0.442700320137,0.448665399326,1.0
-5371232433255,423374,19520382,LABFER,2011-08-23 03:30:00,1,0,1,0,0.218911917098,0.04,0.0,0.217870637328,0.258807569979,0.200964292866,0.0460606273495,0.0937304470503,0.114794615197,0.020229520474,0.100249571767,0.0249018621563,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.442199912115,0.46789588639,0.456676473752,0.49257605246,0.495326553342,0.0656631375383,0.0444129717394,0.0403511490692,1.0
--7776091268135,411949,8435732,LABFER,2010-04-25 06:29:00,1,0,1,0,0.11914893617,0.0,0.0,0.186134585598,0.236010090703,0.257594521651,0.0346666949608,0.124253682829,0.189850966599,0.0504357312171,0.302370104045,0.139420635707,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.46379057026,0.481740377818,0.479322944411,0.500646958708,0.499225471698,0.52595777912,0.509307782776,0.510484081855,1.0
-3789446410847,366268,40108503,LABFER,2013-12-29 18:21:00,1,0,1,0,0.224899598394,1.0,1.0,0.427620614462,0.314857033762,0.373165978907,0.564187149964,0.201876546367,0.214544762783,0.201342281879,0.137295321637,0.359808073056,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.476148408268,0.48437064408,0.498277106792,0.491738306513,0.494418115054,0.165341318021,0.172872635312,0.194446455402,1.0
-4993835830390,370869,30335745,LABFER,2012-11-11 08:26:00,1,0,1,0,0.218911917098,0.0,1.0,0.394413708614,0.319059669562,0.292785010014,0.194556777965,0.212394873253,0.237985654108,0.2,0.0417486776264,0.275404347799,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.456823449009,0.475457940044,0.456676473752,0.49522923035,0.496987151615,0.111360899852,0.116029854048,0.134789477568,1.0
--5602980712309,467479,8256225,LABFER,2010-04-19 20:00:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.128887561836,0.23387458818,0.196868733471,0.0671274489007,0.075508360934,0.10871268849,0.405579399142,0.047846165821,0.112174648361,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.478931078745,0.468417916605,0.490467455033,0.494839446682,0.00169188748615,6.14930775967e-05,3.80062018404e-06,0.0
--1160516607606,388685,11241504,LABFER,2010-08-28 12:07:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.548646984401,0.527548014146,0.511660093672,0.667776113971,0.598312793984,0.517150722777,0.61889973999,0.608534902759,0.633551271406,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494799779018,0.500642516742,0.510888218981,0.500741550233,0.500152365962,0.704046596026,0.726503262513,0.74612452428,1.0
-1547541305525,439545,9096137,LABFER,2010-06-06 22:03:00,1,0,1,0,0.0512820512821,0.0,0.0,0.317648341506,0.249732787571,0.263498785358,0.284141414141,0.178709744492,0.121024645719,0.006,0.214261572688,0.159737174663,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.448185597647,0.466655601101,0.465118925402,0.488167008413,0.493850168244,0.0714853354646,0.080605485426,0.0831353935603,0.999999999998
-4368073088195,435011,24585359,LABFER,2012-03-17 03:18:00,1,0,1,0,0.218911917098,0.04,0.0,0.163422663249,0.321369047531,0.287600308449,0.170472231927,0.112475486582,0.204682606238,0.0515876026942,0.22594359046,0.163519621326,0.222416167301,0.300974526888,0.352037739686,0.347339870876,0.454388106361,0.477577675745,0.455878037868,0.494814863347,0.497728511373,0.46895680866,0.461243675327,0.451432143246,1.0
--11063953830192,394727,23563033,LABFER,2012-01-26 16:10:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.46966498342,0.492559499028,0.480732002755,0.465296775039,0.475540295041,0.450069560977,0.424124815249,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.498329973828,0.49951342617,0.468207238466,0.47275794486,0.477490692484,1.0
-3514952433181,371149,28484518,LABFER,2012-09-05 02:15:00,1,0,1,0,0.218911917098,0.118881118881,0.0,0.346759495267,0.377893257563,0.319041204182,0.523874290421,0.334160664399,0.298678527953,0.21662591687,0.35692953027,0.353310844752,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.472113420946,0.486977771635,0.479322944411,0.497838040218,0.498659318508,0.380829529997,0.364655774188,0.353439952527,1.0
--11214648348233,361949,28316340,LABFER,2012-08-07 20:01:00,1,0,1,0,0.477840451249,0.0,0.0,0.468072471497,0.576998081119,0.514471857903,0.49555872665,0.548024623767,0.545298828313,0.506428571429,0.488250959079,0.714851760112,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.500781429336,0.498388244928,0.502086206927,0.500734298943,0.679138460764,0.676211036193,0.668800316376,1.0
--9012400119634,361886,3166592,LABFER,2009-09-17 14:44:00,0,1,1,1,0.224899598394,0.0,0.0,0.542041106266,0.538647193082,0.558520883281,0.525448915339,0.6626356723,0.557520868798,0.701915708812,0.683654613803,0.722535098377,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.486262165864,0.497384834142,0.485779235087,0.499474894015,0.499893472501,0.859747185959,0.882032799336,0.895865481001,1.0
--10031150471951,423606,15561204,LABFER,2011-02-20 11:54:00,0,1,1,1,0.307692307692,0.0,0.0,0.446757590453,0.456102202637,0.40903531873,0.449858659714,0.427203619857,0.434575010324,0.443220668221,0.411651701718,0.482675791959,0.474085517325,0.462338008602,0.443023918171,0.443117998191,0.472653292188,0.483685157445,0.47486128235,0.497541337913,0.49899666584,0.434836549017,0.436168078049,0.437581647033,1.0
--5892214039458,372002,18570667,LABFER,2011-07-06 13:28:00,0,1,1,1,0.634686346863,1.0,1.0,0.529465709206,0.483062257432,0.500872268128,0.704830209925,0.614428803819,0.5162729353,0.692989417989,0.792225184345,0.541441692642,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.503004485145,0.521811420026,0.506213856063,0.501773996159,0.700323677249,0.699084156961,0.694497563451,1.0
-6537503229467,414609,38380972,LABFER,2013-10-15 08:18:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.533877242246,0.437150155609,0.519023023439,0.63150574495,0.525063619578,0.558026752756,0.405945165945,0.789929996958,0.470489507507,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504129987835,0.503905039667,0.510888218981,0.50237550333,0.501787817832,0.575749889948,0.568056413504,0.566677319202,1.0
--3060824383561,379927,14363458,LABFER,2011-01-08 10:37:00,1,0,1,0,0.108695652174,0.0,0.0,0.356502536907,0.391188773102,0.383486887643,0.206418963721,0.462387444828,0.231547009898,0.120584735679,0.2,0.194595709571,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.479483579555,0.488713693963,0.493260588842,0.496690293376,0.500249790246,0.729293115386,0.745054401474,0.74255711156,1.0
-7704280971184,410948,16303016,LABFER,2011-04-11 22:34:00,0,1,1,1,0.477840451249,0.0,0.0,0.563811797395,0.675082437936,0.573394341139,0.778938649308,0.771146188161,0.769287112984,0.788571428571,0.926289264866,0.849942995132,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.509799565295,0.511435809582,0.509322489469,0.503004036785,0.501658055977,0.816020649547,0.821130219531,0.826108255276,1.0
-5818957801330,426891,9586085,LABFER,2010-06-24 22:03:00,1,0,1,0,0.477840451249,1.0,1.0,0.521634052262,0.454952199879,0.472503647406,0.771551766741,0.361341980376,0.404889836471,0.317264957265,0.413399503722,0.369118771803,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.5023354493,0.501861827942,0.498388244928,0.498682234333,0.496263639253,0.519129351499,0.485333775945,0.424683561621,1.0
--6804160262547,369053,22720561,LABFER,2011-12-23 08:07:00,0,1,1,1,0.215827338129,1.0,0.0,0.301708115339,0.254920142937,0.244586110635,0.492750054695,0.436363228077,0.320469372073,0.0411376328589,0.0830636819444,0.287496555316,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.470631760874,0.486637708105,0.488501233368,0.49522624141,0.49785706417,0.226865206534,0.217295886571,0.239515143262,1.0
-6581395788795,408433,14730934,LABFER,2011-02-09 22:53:00,1,0,1,0,0.218911917098,0.0625,0.0,0.217943120379,0.356229018175,0.313922919712,0.17753997957,0.190856992647,0.254678919208,0.226566990019,0.172489614504,0.270636678919,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.481488145849,0.468417916605,0.495707119441,0.497074291832,0.274163504194,0.263433987588,0.255987160802,1.0
--2184432237379,365031,36608518,LABFER,2013-07-19 23:05:00,1,0,1,0,0.224899598394,0.0,0.0,0.321228719889,0.421438126379,0.337027979885,0.25752629927,0.28333556996,0.323429176487,0.4,0.515789473684,0.269363411503,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.472093785124,0.474150277039,0.498277106792,0.483577759728,0.490254717796,0.0204688071672,0.0169983751611,0.0152935705629,2.8913999249e-34
-8221086208115,432776,12160339,LABFER,2010-10-15 15:53:00,0,1,1,1,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.491011036995,0.492559499028,0.480732002755,0.465296775039,0.475540295041,0.55483584249,0.460278598845,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.501267743893,0.500330270304,0.472193163072,0.476900946847,0.482819703599,1.0
--7628306285668,396416,17090113,LABFER,2011-05-06 03:03:00,1,0,1,0,0.477840451249,0.0,0.0,0.370585628572,0.375636681074,0.356071702496,0.612877046325,0.44811274761,0.33489306261,0.588675213675,0.381186618568,0.347623126987,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.487645881698,0.494435406215,0.49751533833,0.500663337949,0.499565962079,0.480690650725,0.483445569467,0.479458400248,1.0
--1635146232844,371670,22616136,LABFER,2011-12-11 17:11:00,0,1,1,1,0.218911917098,1.0,0.0,0.289258653571,0.399339127429,0.316502303842,0.335764142441,0.294743928416,0.25550880411,0.41662591687,0.115174904792,0.295829593594,0.222416167301,0.300974526888,0.346810092122,0.344413982292,0.459260040644,0.480241533627,0.457893296213,0.491266433554,0.496203389695,0.313627234156,0.303956191233,0.302491437971,1.0
--5602980712309,415976,8415567,LABFER,2010-04-30 06:27:00,1,0,1,0,0.403846153846,1.0,1.0,0.333596272817,0.319659768194,0.28468994056,0.231041244084,0.200689663039,0.203608831582,0.203603603604,0.506666666667,0.305132849867,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.460447372629,0.469355149937,0.468417916605,0.488409101915,0.407325442901,0.000108918756146,4.27267264305e-05,7.17383632979e-05,0.0
--8183097545634,470971,39037490,LABFER,2013-10-28 22:01:00,1,0,1,0,0.11914893617,0.0,0.0,0.180801534388,0.234924583647,0.227591124895,0.255314565332,0.160010368242,0.168840274821,0.41662591687,0.138803066028,0.155264841419,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.448060499903,0.472179359435,0.479322944411,0.495963368902,0.497577715126,0.0216167724143,0.00869761176863,0.00332208299374,1.0
--7699213379569,463924,10255005,LABFER,2010-07-20 12:26:00,1,0,1,0,0.477840451249,0.0,0.0,0.492049174403,0.37892170654,0.412332983821,0.450322427659,0.368259933097,0.340997254382,0.308575424279,0.392399739287,0.397040018124,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.47684399649,0.486394201001,0.486583208859,0.496553548619,0.497497519377,0.472546383981,0.506382287629,0.52693800752,1.0
--11709020450674,387475,33376822,LABFER,2013-03-07 22:40:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.521930758433,0.495087868126,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.499654240296,0.499048624742,0.399229653545,0.403380059492,0.413744599076,1.0
--632048012389,411929,38661883,LABFER,2013-10-10 06:38:00,1,0,1,0,0.634686346863,1.0,1.0,0.549131443791,0.420178522434,0.471200454577,0.78397200789,0.399546226302,0.485027668774,0.420032175032,0.437134486727,0.458043633463,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504129987835,0.501756025153,0.510888218981,0.493383208866,0.497464241471,0.481839232155,0.551769711227,0.578847812043,1.0
--1603415898839,359361,36329172,LABFER,2013-07-05 22:01:00,0,1,1,1,0.477840451249,0.0,0.0,0.483211695087,0.562944672302,0.397738800413,0.0948385726423,0.576110564033,0.240214167308,0.234239130435,0.0795673076923,0.24260776391,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.487907015237,0.497813650982,0.49751533833,0.499152574465,0.500282688772,0.769247924637,0.999993263473,1.0,0.0
--2727895496270,364592,2982583,LABFER,2009-08-28 22:00:00,0,1,1,1,0.477840451249,1.0,1.0,0.520338053783,0.568458143836,0.529052672529,0.712280701754,0.798787468505,0.547487872533,0.2,0.522914285714,0.615720486033,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.534019631359,0.535318881445,0.576567253252,0.514268849533,0.509140237705,0.999998210694,0.999999761127,0.999999494894,1.0
-2659026259146,453270,39691806,LABFER,2013-12-05 15:08:00,1,0,1,0,0.634686346863,1.0,1.0,0.380528121896,0.476052764652,0.401037106433,0.892156862745,0.454939243524,0.350583600708,0.4,0.516622807018,0.47351823912,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.495417348791,0.498745375136,0.510888218981,0.505816664634,0.500523015809,0.342906796967,0.353299485024,0.375489424664,0.99999998589
-3822876955556,438035,40843782,LABFER,2014-01-23 11:25:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.205136910333,0.338457396905,0.348152531688,0.33318811741,0.423571767367,0.274380085059,0.316702576112,0.0914266258633,0.340528228183,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474148767005,0.488077409727,0.479322944411,0.495602539932,0.497148870339,0.398241251826,0.367322069509,0.338957640704,1.0
-8056025148836,398162,23342754,LABFER,2012-01-26 07:17:00,1,0,1,0,0.218911917098,0.04,0.0,0.167751973159,0.221359372491,0.189569639919,0.0641366304612,0.0779510138189,0.140993959866,0.4,0.226942157771,0.138058180956,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.440595473863,0.465031806096,0.467548574593,0.494762764498,0.497671981609,0.0949149939665,0.100134484421,0.0959203672129,1.0
-11052549744451,462427,1006546,LABFER,2009-04-10 16:32:00,0,1,1,1,0.477840451249,1.0,0.0,0.444584821761,0.337973712285,0.369525309536,0.235964912281,0.431576666498,0.32215290149,0.0383650473052,0.402117030381,0.259573937549,0.474085517325,0.481003341925,0.4728983646,0.458461829056,0.486396117117,0.490161704934,0.498388244928,0.498852157503,0.407770692146,0.465415337532,0.709591969942,0.751469759934,3.97347380744e-263
--10324393330922,363679,31704585,LABFER,2012-12-23 09:11:00,0,1,1,1,0.634686346863,1.0,1.0,0.501533492246,0.491135544244,0.529777075214,0.675460395811,0.630262317537,0.580764236487,0.321266968326,0.549970447376,0.466586819447,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.507637510306,0.521811420026,0.500625053118,0.50086259732,0.64470064768,0.671989789367,0.674179123678,1.0
--1923437674636,421940,40576552,LABFER,2013-12-21 21:36:00,1,0,1,0,0.218911917098,1.0,0.0,0.189222751251,0.287307497964,0.239456619568,0.190156751143,0.212954963037,0.242271752893,0.371171371416,0.171208519255,0.332106886417,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.456823449009,0.475457940044,0.456676473752,0.493324403187,0.496190471105,0.746931555989,0.706977968251,0.682694218242,1.0
--11619665279648,465878,38794055,LABFER,2013-10-11 17:27:00,1,0,1,0,0.0512820512821,0.0,0.0,0.217306868292,0.314726617102,0.310689942805,0.24303030303,0.297247414765,0.17584320323,0.213993213993,0.407191994997,0.336002667323,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.478195499789,0.484945296619,0.477578921321,0.488514974633,0.491404561619,0.17527910643,0.17061006238,0.170435146326,1.0
-10057112891919,402114,408797,LABFER,2009-02-28 00:39:00,0,1,1,1,0.477840451249,1.0,1.0,0.465634132088,0.500751561997,0.427315526016,0.474833377766,0.551588714135,0.459202469254,0.209230769231,0.523529411765,0.497532719823,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496932945658,0.501446300859,0.509322489469,0.501844525252,0.498082323817,0.548392763466,0.547620725629,0.550764153195,1.0
-6385998169296,420396,29880752,LABFER,2012-10-21 12:18:00,1,0,1,0,0.218911917098,1.0,0.0,0.337702208632,0.21384452574,0.29593748728,0.172264542092,0.285595386164,0.26149253488,0.356508159102,0.488226590573,0.327985998742,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.443658970903,0.46409742988,0.440099782885,0.488755343128,0.494240662114,0.235870228184,0.235339436281,0.233144951404,1.0
--970166046593,364917,39504267,LABFER,2013-11-16 03:12:00,0,1,1,1,0.108695652174,0.0,0.0,0.431491201346,0.339275375692,0.362591754068,0.362132555502,0.227354648167,0.362594251377,0.212903225806,0.3,0.362024853801,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.48527819542,0.49467036621,0.493260588842,0.497872548371,0.495903314291,0.406233198295,0.414935688841,0.431798241277,0.975902233328
--6730851364517,457089,20677541,LABFER,2011-09-28 22:00:00,1,0,1,0,0.477840451249,0.80487804878,0.0,0.461527332608,0.424424177073,0.439190548315,0.450869791751,0.557146587355,0.496124775151,0.507211063084,0.482153356562,0.535394812253,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.490008390098,0.498388244928,0.497166830291,0.498447088946,0.308479850688,0.32279438662,0.321707977384,1.0
--4988257654579,434096,8890849,LABFER,2010-05-19 12:00:00,0,1,1,1,0.477840451249,1.0,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.586979474626,0.560601729485,0.585583963163,0.522857115794,0.548135901649,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.50116205804,0.499432718282,0.478647744597,0.50613972159,0.517828908617,1.0
-3293719017821,407191,2489284,LABFER,2009-08-26 22:49:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.196112227091,0.155479471567,0.157823074713,0.201766737239,0.229875463745,0.114837611393,0.423950450707,0.0985136615447,0.129900062832,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.437994953251,0.464261290746,0.468417916605,0.495390740557,0.496789438961,0.0872720463274,0.0819710335252,0.0807058014379,1.0
--2205859866321,431932,30914254,LABFER,2012-11-28 21:59:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.54703971705,0.437675696385,0.424432294215,0.355232906388,0.460883762727,0.440545498988,0.725427350427,0.558457376772,0.573264738421,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.483606387735,0.494034967567,0.49751533833,0.499950348265,0.499474575921,0.45154797816,0.457662704582,0.470747752667,1.0
--2228394833429,365963,34599568,LABFER,2013-04-24 14:10:00,0,1,1,1,0.634686346863,0.0,0.0,0.544592113781,0.443898995484,0.569117619018,0.618997380244,0.494682598043,0.60700867772,0.714278429373,0.414622167491,0.441048875675,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.496871904267,0.502403149066,0.510015679179,0.49990724146,0.500751074238,0.800581707843,0.812996439495,0.815809440764,1.0
--7238512143635,414714,1670746,LABFER,2009-06-07 12:09:00,0,1,1,1,0.218911917098,0.118881118881,0.0,0.388478357514,0.288990005879,0.297215153517,0.362851750172,0.394656100269,0.302409897141,0.0617976911455,0.252198463109,0.25954514977,0.222416167301,0.300974526888,0.355165233618,0.358510710191,0.459928613059,0.479121050288,0.457893296213,0.492350565219,0.498380050054,0.0518906788162,0.0416256034189,0.032344291778,3.49790033766e-141
--753492052287,404160,36726627,LABFER,2013-07-14 23:06:00,1,0,1,0,0.218911917098,0.04,1.0,0.198332683533,0.287307497964,0.266793077527,0.424814156604,0.112332883893,0.299379142988,0.171171371416,0.256049070861,0.321421696081,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443116406313,0.470234348442,0.467548574593,0.489825508305,0.493948818642,0.165747011531,0.152762443739,0.143391532863,1.0
-8704645859869,454752,12274647,LABFER,2010-11-02 22:05:00,0,1,1,1,0.634686346863,0.963636363636,1.0,0.501533492246,0.646353093582,0.635597721251,0.613666317164,0.791459678138,0.743858734093,0.8,0.785439199603,0.750330034271,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.544911646819,0.542426020301,0.578096153735,0.51428798435,0.506610896309,0.999992654711,0.99999857664,0.999996106308,1.0
-4638452181698,463450,28442781,LABFER,2012-08-22 19:52:00,0,1,1,1,0.634686346863,1.0,1.0,0.426315922362,0.581596336541,0.450770743799,0.0213888888889,0.56833761663,0.608137773363,0.786746987952,0.664095689201,0.464459233819,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.484980000493,0.493791139221,0.499081689524,0.494072410315,0.497891695053,0.283158048003,0.264254534461,0.24801831412,1.0
--10474547996882,399787,37444307,LABFER,2013-08-24 18:05:00,1,0,1,0,0.224899598394,0.0833333333333,0.0,0.318745351019,0.387664942178,0.398650645737,0.22308290762,0.178018528936,0.24180714105,0.163082437276,0.181738409296,0.294703498493,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.457969323011,0.474606313453,0.485779235087,0.491794746959,0.493208150801,0.0450370790958,0.0446844529891,0.0458610324356,1.0
-544448376824,406180,13043387,LABFER,2010-11-28 16:39:00,0,1,1,1,0.218911917098,0.0,1.0,0.308262510795,0.367941835797,0.352216254838,0.214915608576,0.321413354558,0.35482853904,0.0,0.0837018135931,0.254376711363,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.456504813081,0.48290151052,0.480015246909,0.501450277822,0.49982387122,0.411290355135,0.411421727006,0.40914641276,1.0
-11561181796926,459917,1707092,LABFER,2009-06-25 11:00:00,0,1,1,1,0.477840451249,0.0,0.0,0.524531181912,0.481439009045,0.518232859016,0.518659612819,0.65103399742,0.604896601063,0.238888888889,0.628856640118,0.545472878779,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.503664684567,0.509322489469,0.502943945024,0.500975130981,0.405125754347,0.398111685453,0.425388606831,1.0
-4534830179633,371912,38533126,LABFER,2013-10-10 21:37:00,0,1,1,1,0.215827338129,1.0,0.0,0.277167737857,0.326941545789,0.287956803847,0.463611009621,0.572497550812,0.315352887497,0.218765957447,0.267089371981,0.327705650491,0.474085517325,0.462338008602,0.43768883651,0.436224459079,0.474008153735,0.485756053835,0.487808173527,0.49282702347,0.497104139124,0.329347038219,0.328902139702,0.325781019854,1.0
-3784916175571,431038,36774274,LABFER,2013-08-03 20:53:00,0,1,1,1,0.2,0.0,0.0,0.436953469275,0.538974793705,0.310941627238,0.263601523343,0.199876502552,0.285274456546,0.203720930233,0.61273006135,0.616459103056,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45918551083,0.478477422135,0.480886833765,0.501552149769,0.498168726819,0.132543015247,0.108325384743,0.0945954070602,1.0
--4748180368484,402576,162818,LABFER,2009-01-20 19:16:00,1,0,1,0,0.477840451249,0.0,0.0,0.429180027454,0.379961426582,0.375451088689,0.258478898924,0.291425533142,0.329594002277,0.127280701754,0.343682069917,0.306193702016,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.47684399649,0.485629289802,0.486583208859,0.495741837778,0.497378140658,0.219177432611,0.177146769277,0.159152800039,1.0
-8801264450808,383648,38252662,LABFER,2013-10-07 05:24:00,1,0,1,0,0.11914893617,0.0,0.0,0.167356826366,0.122777777655,0.170527208398,0.301348514499,0.136170825005,0.135589188457,0.205579399142,0.119936784267,0.143801760508,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.418396456234,0.447922340186,0.428495991973,0.477522860708,0.488536382252,0.019918081311,0.0211446601004,0.0213777423188,2.27013499755e-186
-3858192093620,417761,17776718,LABFER,2011-06-09 05:32:00,1,0,1,0,0.224899598394,1.0,1.0,0.44810841934,0.396697603298,0.4515862933,0.557027057071,0.319882948064,0.439428501277,0.233333333333,0.620222979377,0.456945252725,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.479891626589,0.48828496683,0.463099032304,0.495502497534,0.498554924864,0.550650244752,0.560024887619,0.558357046605,1.0
-3902222442193,436601,37633675,LABFER,2013-09-02 20:23:00,1,0,1,0,0.218911917098,0.04,0.0,0.223028242855,0.547519908007,0.387931053728,0.815730337079,0.315409952139,0.486949597936,0.248809824916,0.463470483584,0.448523009223,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.464595172236,0.480846708379,0.468417916605,0.493240339149,0.497053633482,0.0718212177618,0.0374859775586,0.0348607717868,0.999999984619
--2184905977949,417895,39176327,LABFER,2013-10-22 13:01:00,1,0,1,0,0.477840451249,0.478947368421,1.0,0.499050123376,0.467012818976,0.475419099281,0.492559499028,0.480732002755,0.485488424594,0.475540295041,0.498702013494,0.501091361926,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497324883062,0.498388244928,0.501267743893,0.500330270304,0.508521670848,0.514886294585,0.529061520218,1.0
-6930719812745,366061,3921181,LABFER,2009-10-30 22:01:00,1,0,1,0,0.477840451249,1.0,1.0,0.334026194777,0.440240525255,0.428359577509,0.331525406018,0.389554730205,0.452441111595,0.246666666667,0.52701416957,0.340590709072,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.465013056841,0.481656280312,0.498388244928,0.496473366515,0.498760039193,0.451668243363,0.478275181084,0.511098099779,1.0
--1986926071013,394607,31649197,LABFER,2012-12-22 21:29:00,1,0,1,0,0.640625,1.0,1.0,0.319734926228,0.247836775856,0.311043949154,0.285206747712,0.237526365933,0.344991504997,0.753358490566,0.449462649753,0.433583539544,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.443769899275,0.470682543749,0.470329931948,0.503695518802,0.500443961859,0.107222329731,0.105900643368,0.0975748964222,1.3905448772e-88
--2768725984227,371124,32853792,LABFER,2013-02-14 18:59:00,0,1,1,1,0.218911917098,0.0,1.0,0.319734926228,0.261459792349,0.259237684854,0.111531737941,0.210200883199,0.205628475942,0.234571428571,0.306566878199,0.185636242613,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.436282841992,0.467740594441,0.457893296213,0.493696333723,0.496055106217,0.303522934138,0.312533981809,0.319928930534,1.0
--7975594393211,370959,23425377,LABFER,2012-01-14 10:42:00,1,0,1,0,0.634686346863,1.0,1.0,0.576935863781,0.604976769141,0.624089404898,0.753922019494,0.768529954769,0.699705802314,0.678993706729,0.78228999493,0.802933731112,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506415789803,0.510888218981,0.503711152072,0.502106874701,0.602317256079,0.594754603818,0.588076510747,1.0
--3285392676909,466850,8071523,LABFER,2010-04-02 22:03:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.27568987111,0.185714119872,0.276568592648,0.242679833054,0.158831964064,0.16530751983,0.0777105466826,0.267550198251,0.170414845937,0.222416167301,0.300974526888,0.358428460958,0.347339870876,0.458932889066,0.479455322811,0.456676473752,0.492509093306,0.495322707625,0.309378469615,0.332678343485,0.361458143463,1.0
--7216811881398,377685,12220442,LABFER,2010-10-12 22:54:00,0,1,1,1,0.634686346863,0.962025316456,1.0,0.390534411496,0.385395450286,0.484988275547,0.429196124148,0.53760725705,0.35385404476,0.538392988132,0.45295616124,0.597432358352,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.501283015799,0.505254722887,0.521811420026,0.503641542586,0.501976818776,0.702799244372,0.695154266994,0.684906262526,1.0
-2987344857977,365323,1444589,LABFER,2009-05-27 15:39:00,0,1,1,1,0.477840451249,1.0,1.0,0.520338053783,0.51119351686,0.464042525585,0.349518086961,0.467036187559,0.466266978137,0.0217391304348,0.37059880465,0.660062356831,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.495724935038,0.500226362241,0.509322489469,0.503395585071,0.502496003987,0.710521336782,0.717736351233,0.71939218163,1.0
-7035513106389,422731,14397183,LABFER,2011-01-24 12:56:00,0,1,1,1,0.218911917098,0.0,1.0,0.263221027416,0.261690899084,0.281732476732,0.400921239007,0.225856995897,0.335413411232,0.233040132872,0.404148679881,0.303085298811,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.454191591449,0.480166041999,0.479322944411,0.502242415587,0.50199609887,0.332515081758,0.721267811331,0.933964346714,8.93899581588e-87
-11381348521920,374609,6840010,LABFER,2010-02-28 09:20:00,1,0,1,0,0.477840451249,0.0,0.0,0.344651002203,0.392844034403,0.369828392541,0.27573185695,0.206435175706,0.349497352136,0.316904761905,0.228560376665,0.320727051075,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.483960834859,0.487235845811,0.498388244928,0.495779603327,0.498070446331,0.549792694367,0.606544073404,0.640887119361,1.0
--7283308468819,386259,14493654,LABFER,2011-01-18 07:39:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.128674251414,0.121294060762,0.0629332111825,0.18602068239,0.143933950685,0.0299010989011,0.029630742985,0.0816369863894,0.222416167301,0.257917683272,0.294887078646,0.287666847741,0.414877545914,0.445052300734,0.446194229381,0.482113183331,0.489822089257,0.0219137218051,0.0162070847763,0.0129061602163,1.59443583399e-42
-652409353732,407708,26438352,LABFER,2012-05-30 21:00:00,0,1,1,1,0.218911917098,1.0,0.0,0.215580522621,0.221499749261,0.245016525611,0.595598537104,0.183657537497,0.240591504285,0.412080536913,0.209292501753,0.360320595412,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.472407011961,0.487060442659,0.468417916605,0.496127534909,0.497233376911,0.393527132289,0.389026090142,0.368969841436,1.0
--1302576055483,428302,14716240,LABFER,2011-01-19 12:52:00,1,0,1,0,0.11914893617,0.0,0.0,0.0719500639275,0.195676720775,0.107537136908,0.203883495146,0.167785770066,0.070354045838,0.2,0.104637928582,0.303233052367,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.410325743965,0.439464520016,0.428495991973,0.478612562281,0.492287083791,2.53933633727e-05,5.88043470479e-05,6.49173345714e-05,0.0
--5909874727797,375715,24486266,LABFER,2012-03-03 13:49:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.136319701156,0.184205530402,0.184120014531,0.114831012589,0.103927410971,0.0991906834615,0.0157708163816,0.153037155869,0.0602168715614,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.450848492119,0.473704304848,0.479322944411,0.49085326269,0.495941575896,0.028696320961,0.0129306999964,0.00831827369389,4.38869668643e-19
-5552682304035,382374,20967174,LABFER,2011-10-21 11:41:00,0,1,1,1,0.634686346863,1.0,1.0,0.544592113781,0.373073090841,0.403180668553,0.543245614035,0.549844748739,0.40419507828,0.493484848485,0.290363017032,0.544181296807,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.499536272713,0.494317346373,0.510888218981,0.502853832399,0.501683409486,0.577519329008,0.574485203783,0.572373607844,1.0
-3562723156754,454480,7899583,LABFER,2010-04-13 11:24:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.494986986367,0.646743215386,0.614103644537,0.743304437316,0.731780102279,0.644343034184,0.806935251885,0.788740034953,0.757521885204,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.514459502571,0.521811420026,0.507841459291,0.504253373696,0.834777036298,0.835573739164,0.829494379217,1.0
-11732308942167,399890,40401883,LABFER,2013-12-31 22:00:00,1,0,1,0,0.863636363636,1.0,1.0,0.380093153523,0.438052853111,0.403314486987,0.32,0.484282837294,0.432672659324,0.610869565217,0.5,0.517301587302,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.493797414006,0.499962345342,0.487808173527,0.498954002142,0.49957759804,0.502780722713,0.502515812733,0.521762192165,1.0
-11669616308250,376622,18963139,LABFER,2011-08-04 06:05:00,1,0,1,0,0.218911917098,0.0,1.0,0.577439115891,0.280780248637,0.330423575046,0.288304472506,0.524417491156,0.248667944937,0.392173305381,0.247392972551,0.282615909296,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.458122788469,0.482370192843,0.491813578127,0.49078540681,0.501461170724,0.373896080898,0.36547417559,0.35363729689,1.0
-3472639956135,464135,33995898,LABFER,2013-04-09 08:47:00,1,0,1,0,0.634686346863,0.257142857143,0.0,0.472781021268,0.514951264489,0.538440235963,0.636670094057,0.630960142381,0.592983379091,0.653075367905,0.485464291937,0.640832190833,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.497795377955,0.505841996613,0.510015679179,0.507807093661,0.503406202101,0.696719613477,0.712804363038,0.728757141896,1.0
-9134878891258,433767,3189053,LABFER,2009-10-01 05:00:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.436589244846,0.529453504009,0.393073476753,0.582200265083,0.642085344637,0.425989757086,0.480341880342,0.632558139535,0.432109410128,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.492196859707,0.496713607857,0.509322489469,0.499595041833,0.497886890774,0.209218145983,0.159324569251,0.128460865264,1.0
-3330478823059,467275,37046265,LABFER,2013-08-15 09:56:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.136319701156,0.212479494718,0.215184001089,0.12005355047,0.12086357493,0.151790894089,0.205579399142,0.0371810571134,0.126491667085,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.442470007786,0.462593395015,0.479322944411,0.48211307264,0.491090311751,0.182732914744,0.156634030386,0.150673564113,1.0
--5602980712309,415976,8884395,LABFER,2010-05-16 22:02:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.136261742659,0.182379706954,0.192496962008,0.0409852216749,0.0640460793314,0.132476181197,0.0305793991416,0.205029342723,0.0189027695541,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.442199912115,0.459629924474,0.456676473752,0.481301502143,0.490150572704,0.0504477379236,0.0626031981934,0.0505489600164,1.82981958543e-241
-11917044943391,426066,31190797,LABFER,2012-12-28 23:41:00,1,0,1,0,0.640625,1.0,1.0,0.217658516895,0.207263255887,0.241411354747,0.46744024813,0.44776692208,0.47554595809,0.6,0.732214845703,0.712342173214,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.460921105668,0.480351735371,0.491813578127,0.499353833999,0.50165403886,0.525495295151,0.520983964585,0.503943603826,0.999672871566
--234451816886,360202,7431424,LABFER,2010-03-14 19:02:00,1,0,1,0,0.477840451249,0.0,0.0,0.520365004421,0.485616661968,0.481149227738,0.547360057582,0.537103971944,0.597831668453,0.397698945856,0.642535854128,0.489582802291,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.494237724757,0.502418452109,0.509322489469,0.500144076394,0.500233270863,0.650050219236,0.650429745782,0.650036535435,1.0
--3804031993595,453882,22677612,LABFER,2011-12-15 20:45:00,1,0,1,0,0.477840451249,0.0,0.0,0.499050123376,0.467012818976,0.46966498342,0.445295109401,0.480732002755,0.439515397999,0.385583963163,0.418309914532,0.401299102385,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.497957021643,0.497589571177,0.321365548604,0.323285068565,0.334826086086,1.0
-10994557063805,431256,2307076,LABFER,2009-08-10 22:01:00,1,0,1,0,0.224899598394,0.0,0.0,0.333515093174,0.428499590385,0.47094385738,0.530759751075,0.385120279383,0.430418663356,0.44133640553,0.567392323586,0.553102942019,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.462594061406,0.484566052579,0.485779235087,0.495748022111,0.498866275881,0.509579604559,0.521425072164,0.522340896773,1.0
-2481574044596,469521,80658,LABFER,2009-01-18 22:00:00,1,0,1,0,0.403846153846,1.0,0.0,0.217870637328,0.340624396249,0.304225499298,0.132364949098,0.579944541362,0.215105411939,0.0194036946482,0.307299070861,0.201276677469,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.45813290055,0.478576792522,0.468417916605,0.497710218935,0.497600977763,0.398543344934,0.437412163788,0.451302780489,1.0
-1873084860411,456806,12624708,LABFER,2010-11-14 10:28:00,0,1,1,1,0.477840451249,1.0,1.0,0.504557326375,0.634235458981,0.584376488638,0.58826712624,0.620747717364,0.693068075773,0.305630630631,0.59036165738,0.614785373339,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.510177189495,0.511069179715,0.498388244928,0.501220722646,0.501522579237,0.856684838451,0.863014002289,0.859833765271,0.999999933579
-2503716969317,466122,980201,LABFER,2009-04-08 20:05:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.533877242246,0.550938182717,0.569314829769,0.740540219614,0.599377498895,0.568903016058,0.886914349301,0.600658138573,0.622697476117,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.500916849167,0.501239308017,0.640755416998,0.640576135069,0.634277164769,1.0
--10636362010007,368284,27244808,LABFER,2012-06-25 01:31:00,1,0,1,0,0.477840451249,0.25,0.0,0.446757590453,0.410499979262,0.380040171214,0.322382884248,0.360519144765,0.363027942167,0.399152798591,0.53997031397,0.400080381696,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.491870863226,0.498388244928,0.501727239413,0.500614734704,0.333291726085,0.330409813761,0.335393604003,1.0
-11296276407164,427213,22554550,LABFER,2011-12-31 10:10:00,1,0,1,0,0.0833333333333,0.0,0.0,0.278323064298,0.252652086316,0.210116880179,0.441943068777,0.308705484231,0.154689210863,0.224181818182,0.113646075289,0.207493311155,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.456668542835,0.478352925976,0.459448963611,0.492813965162,0.497022069246,0.441425418259,0.449071167111,0.437245531237,1.0
-8555186968185,417514,8578273,LABFER,2010-05-19 12:40:00,0,1,1,1,0.477840451249,0.0,0.0,0.428240722603,0.454138851489,0.455634187674,0.45072809839,0.529329638721,0.460778054041,0.481741972052,0.506922730172,0.54110241453,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498658105002,0.504231748772,0.498388244928,0.50237040153,0.502142481566,0.728995578701,0.73839709034,0.753881947044,1.0
-2642615512767,356047,19055606,LABFER,2011-08-11 22:00:00,1,0,1,0,0.634686346863,1.0,1.0,0.548646984401,0.503322466625,0.512359732088,0.595976518797,0.56129522216,0.591113296556,0.795204947591,0.649959231367,0.601968064578,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497958897962,0.498388244928,0.500124932555,0.499844056862,0.535024824861,0.543654339572,0.550588226939,1.0
--9373835818071,469604,3139100,LABFER,2009-09-13 01:59:00,1,0,1,0,0.218911917098,0.04,0.0,0.244012369612,0.263031319695,0.248958556697,0.0668864318864,0.204937876073,0.241832262772,0.208391608392,0.121509554025,0.164808194814,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.432235015347,0.457055879399,0.479322944411,0.489305627032,0.494976652568,0.188687099764,0.179784364859,0.168742240563,1.0
--5745701847978,385223,9211771,LABFER,2010-05-27 15:04:00,1,0,1,0,0.218911917098,0.04,0.0,0.18386959004,0.219034141832,0.216661113349,0.114517428422,0.11823167225,0.215352090879,0.2,0.379539203782,0.121414403003,0.222416167301,0.257917683272,0.307979090574,0.300505581722,0.455279407924,0.477059118736,0.480886833765,0.491609288994,0.495325346755,0.144053277461,0.128826509009,0.134086616892,1.0
--10045743370245,443209,9811664,LABFER,2010-06-25 21:42:00,1,0,1,0,0.224899598394,0.0,0.0,0.235459896983,0.413546080068,0.382607189799,0.191449547093,0.296562323667,0.264213013862,0.21,0.060736919303,0.234778906766,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.457969323011,0.470320555867,0.485779235087,0.491568923104,0.493095228801,0.0669822826084,0.0611650795458,0.0596875272295,1.0
-661887476393,454484,8295615,LABFER,2010-05-04 23:56:00,1,0,1,0,0.477840451249,0.478947368421,0.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.459675021899,0.475540295041,0.421331143587,0.469026085281,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.498413696538,0.499080395278,0.430333500584,0.434293657303,0.441970379981,1.0
--1633755772623,421745,15734175,LABFER,2011-02-26 04:18:00,1,0,1,0,0.634686346863,1.0,1.0,0.548646984401,0.56747523334,0.603005506285,0.721153167272,0.586156464757,0.686983558259,0.618859440482,0.8382950196,0.751382255644,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.509131420057,0.510888218981,0.50630914151,0.503320772895,0.719833623862,0.731903823467,0.740686229365,1.0
-8551903818503,426047,1682333,LABFER,2009-06-16 02:07:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.429847549775,0.481156862625,0.429858413374,0.480723862651,0.520142040806,0.394407195894,0.516259631354,0.541789934251,0.508487068637,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.48824468662,0.491201205751,0.510888218981,0.502602217798,0.500516935873,0.426629239837,0.428120253641,0.400670418146,1.0
-800011025014,470061,24755023,LABFER,2012-03-25 08:55:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.426713070503,0.480483232706,0.506874220105,0.626790129113,0.591923849379,0.4753678452,0.536538461538,0.635254372446,0.474356875463,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506823555481,0.510888218981,0.502110584084,0.498717070297,0.392019416621,0.416357969998,0.428655010203,1.0
--7063889764453,464853,13720804,LABFER,2010-12-12 13:25:00,1,0,1,0,0.0512820512821,0.0,0.0,0.10958576394,0.255829518956,0.219915806814,0.126666666667,0.307847732798,0.145427679684,0.372950819672,0.232328458519,0.214028636327,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.458042514205,0.473349476253,0.476886910056,0.496397631128,0.494911334376,0.0724855905684,0.0469862745758,0.0267669843117,8.68226384795e-59
-1504597830818,405011,1900913,LABFER,2009-06-28 07:48:00,0,1,1,1,0.477840451249,1.0,1.0,0.540554350101,0.503896010765,0.475232265165,0.489824756987,0.554659293,0.574616919591,0.410909090909,0.6,0.616580960807,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.492793793917,0.492202434862,0.498388244928,0.500246475541,0.500455770371,0.480200039723,0.549280867363,0.564678511619,1.0
--1892729371730,466224,26901601,LABFER,2012-06-16 00:06:00,1,0,1,0,0.477840451249,1.0,1.0,0.499050123376,0.467012818976,0.475419099281,0.445295109401,0.480732002755,0.469760288475,0.401346746654,0.429210399261,0.423994956807,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.495364640787,0.498388244928,0.500495580913,0.49946929421,0.329821343676,0.330986955171,0.343016822474,1.0
--10993153564261,395867,6639530,LABFER,2010-02-11 06:49:00,1,0,1,0,0.640625,0.0,0.0,0.260129346623,0.32190070088,0.347364522192,0.641771005591,0.352157891128,0.335673963376,0.2,0.442295332002,0.288528953221,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.461734248686,0.483927206547,0.491813578127,0.499902370968,0.499398422042,0.197196838475,0.173347061772,0.186129689051,1.0
--8152343256578,416247,13095568,LABFER,2010-11-19 22:06:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.533877242246,0.481733638786,0.481636020636,0.623347579865,0.578424193647,0.527780845487,0.63095897344,0.576829213406,0.482043750558,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.492990540915,0.499081689524,0.502389623978,0.499880599258,0.370885823347,0.356779783293,0.346717084997,1.0
-3920254012402,395426,33321486,LABFER,2013-03-08 14:51:00,1,0,1,0,0.224899598394,1.0,1.0,0.318745351019,0.419914281113,0.479559027656,0.640660708403,0.534616553869,0.466742806047,0.2,0.579565161847,0.461293499552,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.460272883128,0.478762583332,0.463099032304,0.491740463862,0.494838245483,0.0382759465493,0.0474442412836,0.053660740906,0.99999999894
--2165987689534,420329,19792015,LABFER,2011-08-14 22:00:00,1,0,1,0,0.477840451249,1.0,1.0,0.333475049363,0.431913275004,0.395161943498,0.560194922289,0.413356017559,0.361359334774,0.144333333333,0.5,0.2531266469,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.497525161012,0.492414034792,0.509322489469,0.486349185198,0.493341991313,0.13599542496,0.101840285728,0.0725415702635,1.0
-5268765954184,409336,38476296,LABFER,2013-10-17 10:51:00,1,0,1,0,0.477840451249,0.0,0.0,0.492049174403,0.480591651291,0.492100692879,0.486861145018,0.43533846239,0.586168138271,0.40263255469,0.456734725774,0.526414374726,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.499170473612,0.498388244928,0.496503920449,0.5002418493,0.717153656888,0.741472504124,0.736302479812,1.0
--158832986655,360523,7674210,LABFER,2010-03-12 21:31:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.127650099421,0.154948461168,0.141241337293,0.110304044571,0.166373834246,0.0892524932661,0.00692168102083,0.134524888258,0.163261776073,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.438328061553,0.461417990128,0.451591612068,0.485414319862,0.489751962963,0.0716483632243,0.044981415673,0.0260012419443,1.11196329272e-19
--11174674927354,439697,9224334,LABFER,2010-05-25 17:16:00,1,0,1,0,0.224899598394,0.0,0.0,0.55519212329,0.500337481779,0.493790078621,0.511043652923,0.600698779997,0.510727816926,0.892639738842,0.605598449262,0.685080043253,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.478137779442,0.487296112735,0.487344472973,0.49799321378,0.497227031369,0.0629762466377,0.0565122527035,0.0515217475781,0.957767898126
--1431211888954,368641,40790513,LABFER,2014-01-02 08:57:00,1,0,1,0,0.11914893617,0.0,0.0,0.127650099421,0.154188079306,0.178069475315,0.0800448040923,0.195121665363,0.177385937,0.0115793991416,0.232223454056,0.139087473271,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.442446354135,0.467102789445,0.458581907474,0.486456537406,0.489537443586,0.0140257870219,0.0039234257403,0.00134436566594,6.08590789683e-116
--1356469135464,458105,26788133,LABFER,2012-06-06 09:43:00,1,0,1,0,0.218911917098,0.0,0.0,0.253082501533,0.280999424717,0.31704031627,0.196308067214,0.251134712012,0.314016228707,0.250061149499,0.30544242873,0.390655067819,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.442962357675,0.472914171648,0.470329931948,0.49495514103,0.497217815551,0.391488220311,0.38474261007,0.372596194412,1.0
--8996911702299,361230,35248388,LABFER,2013-05-15 14:35:00,0,1,1,1,0.634686346863,0.857142857143,1.0,0.529465709206,0.547215024148,0.54680318702,0.583524822991,0.678463594695,0.627853840341,0.776462643682,0.591430207519,0.590912478026,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.504691900155,0.505514956461,0.521811420026,0.503362446549,0.501361625259,0.745528230654,0.737096307071,0.727829441623,1.0
--2214902060362,414948,31459756,LABFER,2012-12-09 17:13:00,1,0,1,0,0.477840451249,0.0,0.0,0.481016750293,0.464902646179,0.460904489787,0.498879881324,0.396880817703,0.4241879129,0.376387821632,0.512694805195,0.55665397993,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.490450641411,0.495515522682,0.498388244928,0.499257120564,0.497872241876,0.336838091511,0.326827880764,0.320372510828,1.0
--11990067789230,432826,14470614,LABFER,2011-01-06 14:17:00,1,0,1,0,0.215827338129,0.0,0.0,0.273561576034,0.247550185427,0.283867058795,0.471537933483,0.311964184934,0.24823505379,0.410081632653,0.326654197871,0.30344533634,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.46830863629,0.483022585452,0.487808173527,0.493460426143,0.49523326683,0.778790129539,0.757342559097,0.271943274192,1.0
-10198054406755,440806,25687177,LABFER,2012-05-02 10:32:00,1,0,1,0,0.224899598394,0.0,0.0,0.395276864462,0.354600721963,0.328255687525,0.222673958198,0.421589655132,0.265390617607,0.357142857143,0.357674230051,0.336324722762,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.478137779442,0.486318326548,0.487344472973,0.496235229286,0.498349257436,0.326785099297,0.2928930694,0.227972332838,1.0
-6838795682124,410767,20577434,LABFER,2011-10-11 22:01:00,0,1,1,1,0.634686346863,0.589887640449,1.0,0.533877242246,0.440853645386,0.512814115289,0.467085237215,0.584921250099,0.556669061138,0.716981132075,0.554932432232,0.47947174606,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492806548043,0.498857204949,0.521811420026,0.505675799436,0.503053931152,0.639182307367,0.658214037888,0.620621099352,1.0
--3818522234701,430665,22810914,LABFER,2011-12-23 08:44:00,0,1,1,1,0.218911917098,1.0,1.0,0.234370936262,0.281121893888,0.195925032675,0.30064845176,0.291674276316,0.399325528923,0.406,0.133191810974,0.195341131818,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.447796991293,0.469590301524,0.470329931948,0.492976452455,0.49431274787,0.105486393242,0.0832740656642,0.0656371512408,1.0
-9367070654818,371056,18382677,LABFER,2011-07-03 21:53:00,0,1,1,1,0.640625,0.0,0.0,0.24256619665,0.4584857707,0.406168167181,0.471326675891,0.454069233156,0.523903036696,0.375,0.265601905776,0.342678979612,0.222416167301,0.300974526888,0.375151250499,0.379422352055,0.480289822515,0.494578912726,0.491813578127,0.498900382674,0.499190493081,0.387030186645,0.392380581166,0.399008080946,1.0
--3206744493850,379188,18728909,LABFER,2011-07-05 09:28:00,1,0,1,0,0.477840451249,1.0,1.0,0.489816211988,0.504080869222,0.488240785824,0.687267799953,0.620088769282,0.635687607818,0.495341880342,0.429202555384,0.553317311457,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.496533636304,0.501636437298,0.498388244928,0.503610905536,0.502586346547,0.618151637926,0.644726560046,0.669111320056,1.0
-7398182369984,440605,20294276,LABFER,2011-09-23 22:14:00,1,0,1,0,0.640625,0.0,0.0,0.333070421208,0.292139689568,0.298295117018,0.242396350525,0.118330172972,0.217336410321,0.413774014979,0.0885650466341,0.155058929884,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.455472484123,0.473134372868,0.480886833765,0.496433290165,0.491347288051,0.158645587505,0.15980405843,0.177396303918,1.0
--4652847750591,368174,20561273,LABFER,2011-09-13 23:54:00,1,0,1,0,0.215827338129,0.0,1.0,0.460802783654,0.230740499401,0.319553945658,0.586789069344,0.324137686327,0.358340640253,0.423635522664,0.242766296973,0.327832498813,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.472218509698,0.488287402552,0.487808173527,0.493737835823,0.498891944758,0.480155354431,0.481768224225,0.484363223206,1.0
-7097340179860,464386,38343567,LABFER,2013-10-06 22:00:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.533877242246,0.43255532938,0.473866198816,0.758475035745,0.456672941006,0.51608163368,0.308581926341,0.657534044902,0.56969468152,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.500294232896,0.521811420026,0.501012864121,0.501151952461,0.424474919381,0.417748798361,0.41486424312,1.0
--939143942370,409486,35413499,LABFER,2013-05-31 20:45:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.58802033268,0.679863448053,0.647908193709,0.721153167272,0.735217868681,0.718694173216,0.708026408966,0.649692880637,0.765708071101,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.514230365131,0.515313757607,0.521811420026,0.506892935247,0.505386284993,0.844272423427,0.845397119915,0.845583716446,1.0
--379877927910,469701,34902260,LABFER,2013-05-08 13:38:00,0,1,1,1,0.11914893617,0.0,0.0,0.13780329251,0.19424524979,0.200732311131,0.216215436215,0.231823996273,0.152280465112,0.205579399142,0.0381802595915,0.235840725759,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.431583422128,0.461211605516,0.455878037868,0.490653280369,0.494325177768,0.174826774005,0.162096655595,0.15374865311,1.0
--10232823590382,469781,37785814,LABFER,2013-08-26 17:47:00,1,0,1,0,0.218911917098,0.04,1.0,0.390104409931,0.31731385083,0.30456666746,0.2008993529,0.225078582894,0.256713205604,0.0166259168704,0.234686790371,0.280807989395,0.222416167301,0.300974526888,0.366593236531,0.364971187629,0.462378671313,0.481828313578,0.467548574593,0.495719026334,0.497620548735,0.145784522463,0.136545244989,0.133904966759,1.0
-8700233732528,426937,1258441,LABFER,2009-05-12 22:55:00,1,0,1,0,0.224899598394,0.0,0.0,0.35948609176,0.387664942178,0.425752620792,0.284483801808,0.263803131564,0.321224913131,0.539203354298,0.208275919864,0.310089595556,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.462594061406,0.480479334217,0.485779235087,0.492251311631,0.495811782672,0.249881556723,0.250301612547,0.257590317646,1.0
-614597905890,459906,34891354,LABFER,2013-05-12 18:47:00,0,1,1,1,0.218911917098,0.118881118881,1.0,0.253082501533,0.274450127088,0.281721840826,0.219529529163,0.226950220528,0.162860703314,0.216486887679,0.266806722689,0.238978310904,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.453158823532,0.476123489696,0.480015246909,0.482502094736,0.490096126157,0.149402984694,0.217901534052,0.344087595779,3.0471107489e-15
--12359254427540,418812,6793215,LABFER,2010-02-19 23:56:00,0,1,1,1,0.218911917098,1.0,1.0,0.319734926228,0.281246794161,0.304384062161,0.36024292429,0.549309818319,0.418804509044,0.768350054801,0.899923400996,0.74615930422,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.443116406313,0.466528068621,0.467548574593,0.491452461526,0.504070616763,0.995651384166,0.999999963668,1.0,1.0
--5806790298493,450181,29405770,LABFER,2012-09-16 11:55:00,1,0,1,0,0.218911917098,0.118881118881,1.0,0.269339750246,0.277282688806,0.288641665265,0.458424230924,0.367336703081,0.331649360111,0.279564927148,0.298271769154,0.122279851787,0.222416167301,0.300974526888,0.347115499812,0.341023888151,0.462301767903,0.486371090327,0.470329931948,0.497893582389,0.498113047271,0.665230862178,0.706253003833,0.732662064095,1.0
--11619458100488,445376,33646433,LABFER,2013-03-11 15:21:00,0,1,1,1,0.224899598394,0.0,0.0,0.579349575001,0.304733876484,0.342204423191,0.134358370615,0.553339303527,0.490950204908,0.4,0.210204081633,0.310219468553,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.47733063351,0.484429478732,0.498277106792,0.507983710717,0.505037603026,0.506377771415,0.506775435815,0.503850709038,1.0
--6039021983984,395612,38101983,LABFER,2013-09-26 07:42:00,0,1,1,1,0.634686346863,0.813953488372,1.0,0.576935863781,0.534093483058,0.558131750715,0.708229248033,0.653801368875,0.690613323179,0.574259236678,0.677538895676,0.75093777063,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.504625051617,0.498388244928,0.511706243258,0.506634687446,0.679628592924,0.685871480623,0.692746502057,1.0
-7744133901444,385690,25609021,LABFER,2012-04-27 12:11:00,0,1,1,1,0.477840451249,0.0,0.0,0.535522918015,0.44087625205,0.45733483709,0.597171590036,0.454121420412,0.509735169118,0.358725392887,0.552858221108,0.503570028942,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.493146953951,0.498388244928,0.500328174118,0.500557379219,0.4540839612,0.456389658827,0.473256872875,1.0
-7973704716624,359534,23365,LABFER,2009-01-17 21:35:00,1,0,1,0,0.634686346863,0.0,0.0,0.455560026391,0.527548014146,0.542610408659,0.585873363395,0.635628227616,0.521803971729,0.90589275089,0.708362415066,0.460428537209,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496546392464,0.504459634341,0.521811420026,0.508766643066,0.504599588537,0.661032609832,0.689683217616,0.708673502616,1.0
-9880075000415,379308,31164577,LABFER,2012-12-25 09:58:00,0,1,1,1,0.0512820512821,0.0,0.0,0.10958576394,0.300138484726,0.281639155237,0.384820512821,0.122949045564,0.252918428932,0.172950819672,0.217944958268,0.31761383924,0.474085517325,0.481003341925,0.460231218464,0.45525281484,0.467749911894,0.479008821399,0.476886910056,0.496656641643,0.49845250567,0.192471636772,0.116746158723,0.0954601157915,0.999999999998
--4855781632877,454589,23631969,LABFER,2012-01-21 15:22:00,1,0,1,0,0.477840451249,0.0,0.0,0.44810841934,0.482622477435,0.471528082921,0.586853659884,0.440119757857,0.624638754118,0.468728432108,0.525932511988,0.762375607488,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492113602712,0.499180716192,0.509322489469,0.499187728344,0.50111667675,0.610937466646,0.619632418348,0.616765795083,1.0
-1411612041151,408472,10977845,LABFER,2010-09-07 22:30:00,0,1,1,1,0.224899598394,0.0,0.0,0.318745351019,0.387664942178,0.413636993144,0.284483801808,0.178018528936,0.278200588092,0.213396902685,0.172844684432,0.289483425696,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.457969323011,0.47748201083,0.485779235087,0.49349160786,0.49643201979,0.30321680007,0.303876965734,0.309703021177,1.0
-1166228739097,451722,25654626,LABFER,2012-05-06 16:33:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.58802033268,0.679863448053,0.591783082288,0.760827308533,0.725907526201,0.718781188799,0.811872562813,0.736359547304,0.729734924257,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.552388666774,0.550414006691,0.588726696487,0.519329482753,0.511565018043,0.983424992332,0.991788020059,0.990739386026,1.0
-8637027637256,424865,36104326,LABFER,2013-07-10 09:28:00,0,1,1,1,0.634686346863,0.589887640449,0.0,0.609764073171,0.612148696321,0.602679431744,0.652693032418,0.647013535851,0.636498750963,0.405714285714,0.583381846678,0.558916829879,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498865405233,0.503611328331,0.499081689524,0.505780548911,0.505446645418,0.524536048826,0.536794476928,0.566383256057,1.0
--10577435183011,445467,25994613,LABFER,2012-05-07 09:52:00,0,1,1,1,0.477840451249,0.0,0.0,0.510107641637,0.408665131066,0.43960492846,0.544318622361,0.335240229247,0.496464251133,0.220898289594,0.50391750735,0.569733180347,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.497849365132,0.502821738976,0.509322489469,0.497604662339,0.500130136486,0.505684745014,0.47689398106,0.461202121814,1.0
-2194609846994,358508,38433902,LABFER,2013-10-09 08:00:00,0,1,1,1,0.11914893617,0.0644257703081,1.0,0.136319701156,0.187907918326,0.218691062105,0.309663940081,0.230087742282,0.2416170544,0.00557939914163,0.161444539192,0.146475586168,0.222416167301,0.257917683272,0.31501909616,0.319720123692,0.428343905648,0.457593143521,0.468307222203,0.493085280632,0.494688339558,0.0247672768179,0.0216641742356,0.0185624569755,9.45978600929e-45
--10571315434736,446732,24545047,LABFER,2012-03-07 14:25:00,0,1,1,1,0.477840451249,0.0,0.0,0.477279432247,0.390350104876,0.381949699243,0.3960258341,0.508357589438,0.358219818818,0.358924796134,0.580155385805,0.305537482057,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.489660054017,0.498388244928,0.497031457304,0.498297308372,0.195062539221,0.175435491629,0.152657492105,1.0
-4153721938545,373682,34988798,LABFER,2013-05-18 01:58:00,1,0,1,0,0.477840451249,1.0,1.0,0.585928690403,0.541223881051,0.496419448955,0.547360057582,0.497600635329,0.604789328068,0.769518787609,0.472590973025,0.538715606141,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.500039355146,0.506147672711,0.509322489469,0.504157983774,0.501289730477,0.58799295294,0.593354685166,0.598961381905,1.0
--3476542647560,464902,18915751,LABFER,2011-07-14 20:11:00,0,1,1,1,0.477840451249,1.0,1.0,0.395815886417,0.456675950398,0.44542797418,0.711117476246,0.574172906834,0.412960301997,0.790090439276,0.665401572112,0.432831529556,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.503956234083,0.509322489469,0.502487277584,0.500097300052,0.721891633618,0.732346228596,0.740113822388,1.0
-12015892215618,419127,2530840,LABFER,2009-08-18 06:58:00,1,0,1,0,0.218911917098,0.0,0.0,0.321210621982,0.288225979013,0.322894526615,0.292785741599,0.235752903764,0.202200934085,0.228706453783,0.260256369943,0.304550062942,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.452460068214,0.475001453622,0.468417916605,0.495656874396,0.497742910046,0.528571335284,0.516958814544,0.517390692546,3.87899211589e-18
-12165622639426,376971,24028482,LABFER,2012-02-21 13:27:00,1,0,1,0,0.403846153846,0.0,1.0,0.314189985427,0.224366093841,0.255391670287,0.255218881247,0.21387760544,0.207057696102,0.227870010597,0.164236352816,0.193471722205,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.458627186009,0.482680406486,0.479322944411,0.499045633747,0.49960452245,0.436656992339,0.432079122297,0.409516558528,1.0
--8266418478181,383690,7133140,LABFER,2010-03-04 10:22:00,1,0,1,0,0.477840451249,1.0,1.0,0.420138847845,0.454261035703,0.45885271845,0.351244438307,0.531243711347,0.357276605664,0.44,0.578954552657,0.511114282115,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.489138199669,0.493410338259,0.509322489469,0.500037734317,0.500534531806,0.440146436052,0.456499433064,0.447955950361,1.0
-5878393726924,449183,14258768,LABFER,2011-01-27 16:47:00,1,0,1,0,0.218911917098,1.0,1.0,0.253082501533,0.269043118913,0.346853344063,0.462489959839,0.325834546143,0.456107673469,0.4125,0.232643097643,0.199046760395,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.428322506568,0.45754481844,0.440099782885,0.482767748206,0.493219093228,0.129141360782,0.102823818743,0.0820682386619,1.0
--4910774116159,392853,1609444,LABFER,2009-05-19 02:25:00,1,0,1,0,0.108695652174,0.0,0.0,0.321675418037,0.362984706125,0.343473538914,0.311904761905,0.316414024579,0.18193263614,0.4,0.203225806452,0.131707991112,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.475059380699,0.483117575923,0.480767972341,0.495682857201,0.496983972725,0.279546833766,0.275063439264,0.28219401313,1.0
-10561549539700,441926,2861560,LABFER,2009-09-10 03:51:00,1,0,1,0,0.224899598394,0.0,0.0,0.307670538946,0.246100206273,0.281695534367,0.233931888545,0.180915018141,0.20859938465,0.122088452088,0.163094036341,0.251190665864,0.474085517325,0.481003341925,0.46241236511,0.470629423827,0.469230059645,0.482646078772,0.465877080084,0.488906541999,0.494143818346,0.187911202253,0.173600463175,0.169696754728,1.0
--139632156971,468274,39926923,LABFER,2013-11-24 01:33:00,1,0,1,0,0.477840451249,1.0,1.0,0.483230385091,0.504544515923,0.456789860564,0.426897786425,0.45887333441,0.450889916489,0.137326007326,0.184841018138,0.328563318256,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.5023354493,0.502339196245,0.498388244928,0.492391318187,0.494331010116,0.281122379781,0.271410506318,0.268543238481,1.0
-1685971533363,433926,18652192,LABFER,2011-07-15 17:56:00,0,1,1,1,0.477840451249,0.0,0.0,0.499050123376,0.471641726242,0.480580956758,0.44201361309,0.502581607169,0.510420813463,0.603712335996,0.429938053632,0.375856314315,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.496469903024,0.498388244928,0.499840732308,0.500602994028,0.475714850533,0.476245753092,0.473927338461,1.0
-818249236493,449301,33657062,LABFER,2013-04-02 12:21:00,1,0,1,0,0.108695652174,0.0,0.0,0.444546749009,0.563398285611,0.327987230858,0.444078947368,0.327954605304,0.339739342714,0.0,0.38665754362,0.437698412698,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.477179052112,0.482816475226,0.480767972341,0.481711147443,0.492636795251,0.273986131743,0.283442040844,0.327323783698,0.991671063024
--3459464103407,418449,35475042,LABFER,2013-05-27 08:24:00,1,0,1,0,0.224899598394,0.0,0.0,0.516303234401,0.479156221665,0.484465500707,0.519556225116,0.525747694935,0.484843315143,0.7442043222,0.458524042114,0.495077476559,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.49175731024,0.49729031393,0.498277106792,0.499581425506,0.497944112493,0.439637724252,0.434937617861,0.434431372432,1.0
-11928681237666,421911,27221621,LABFER,2012-07-04 13:51:00,1,0,1,0,0.634686346863,0.962025316456,0.0,0.48810841934,0.384079972652,0.412846722599,0.49338088795,0.528026542477,0.390231455598,0.426086956522,0.462776301625,0.418321016553,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.486982359552,0.4937559168,0.499081689524,0.504689200013,0.502241049643,0.5059158006,0.476479374433,0.462136510557,1.0
--940068537149,445597,9082186,LABFER,2010-05-18 22:03:00,0,1,1,1,0.477840451249,0.0,0.0,0.543377594455,0.418283719132,0.407730966034,0.0895104895105,0.446831352975,0.393668341668,0.0802424242424,0.387196969697,0.316740246933,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.498280333584,0.499363582257,0.509322489469,0.495477089744,0.497346645857,0.354040330306,0.217601279402,0.0593464880538,2.13554086704e-52
-9068914877775,408683,39286627,LABFER,2013-11-21 10:04:00,1,0,1,0,0.634686346863,1.0,0.0,0.501533492246,0.264286757287,0.371985863461,0.353360566449,0.346056362289,0.337794905586,0.0189473684211,0.272128544999,0.225690939077,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.481585190845,0.491034407604,0.510015679179,0.495780534601,0.497662260562,0.249018789087,0.220884497051,0.215890782046,1.0
--3115060916873,425384,13291646,LABFER,2010-11-21 19:50:00,1,0,1,0,0.477840451249,1.0,1.0,0.373619415366,0.431737599592,0.402346293731,0.421353310459,0.497868693835,0.532421470459,0.480341880342,0.4872,0.49349293191,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.490846869521,0.498388244928,0.491798305475,0.495018200669,0.154595438431,0.241441931163,0.343057710251,0.164681597077
--8693057170669,414507,25123544,LABFER,2012-03-23 00:50:00,0,1,1,1,0.477840451249,1.0,1.0,0.378551637611,0.456771488668,0.396329140888,0.322119255088,0.559425614987,0.54979710439,0.653787878788,0.735022354694,0.642705883084,0.474085517325,0.481003341925,0.475349298049,0.477606062854,0.488569504781,0.497410073599,0.487808173527,0.495210938566,0.496629804132,0.430195972514,0.448941350392,0.466071385622,1.0
-11517660237937,442499,40400314,LABFER,2013-12-26 09:58:00,1,0,1,0,0.634686346863,0.936046511628,1.0,0.649044688653,0.626893474384,0.513870100831,0.412770997073,0.619195409124,0.722507709604,0.825007541042,0.663718572587,0.645251465817,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.496861232386,0.49808501426,0.510888218981,0.503020499939,0.500899842547,0.57930834045,0.516734663881,0.463837063615,1.0
-7442520151426,449101,35665793,LABFER,2013-06-16 23:04:00,1,0,1,0,0.218911917098,0.0,0.0,0.136963023115,0.249952409959,0.193368344004,0.0630760244006,0.233105428198,0.145672102244,0.0231246248808,0.166047084489,0.144461822837,0.222416167301,0.257917683272,0.30715851491,0.306420082621,0.412079016297,0.453234817785,0.444188271345,0.486050096968,0.493259609716,0.186543811507,0.187004669737,0.188414913756,1.0
-693169096628,422922,32335479,LABFER,2013-01-30 23:49:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.587927599884,0.605322047464,0.611068255051,0.721153167272,0.720062556034,0.608596633834,0.607431823731,0.744630659631,0.645167245746,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507171594053,0.510888218981,0.501373527683,0.501467646812,0.696400484539,0.696528540598,0.69654875093,1.0
--7287869021231,394682,32374424,LABFER,2013-01-14 21:00:00,0,1,1,1,0.634686346863,1.0,1.0,0.421029297402,0.425016525376,0.426842215484,0.465072781513,0.485169189936,0.547713823931,0.349918607915,0.586665791986,0.510952662448,0.474085517325,0.481003341925,0.482049390786,0.473852543851,0.492712011429,0.498914492824,0.486583208859,0.499808292222,0.499703376893,0.667421294599,0.682581004206,0.706898560047,1.0
--6401704853120,363837,9457286,LABFER,2010-06-10 09:30:00,1,0,1,0,0.477840451249,0.0,0.0,0.260531358658,0.311749024587,0.300673623433,0.315702772405,0.105656562886,0.213749384208,0.508333333333,0.389563581919,0.271715770902,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.448510140943,0.45978890464,0.432647319578,0.482276408471,0.490930821968,0.0681467910843,0.0638574589523,0.0576961544973,7.0858278552e-198
--7619542909496,467200,17887844,LABFER,2011-06-02 01:05:00,1,0,1,0,0.215827338129,0.0,0.0,0.298101953515,0.27826472265,0.258426058682,0.35549082373,0.174073861599,0.30998245187,0.218765957447,0.0737101256393,0.293671467509,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.46830863629,0.481446186471,0.487808173527,0.498345529943,0.498678283531,0.718625638907,0.695368621417,0.696082199886,1.0
--9119832060977,373035,5814882,LABFER,2010-01-11 22:02:00,1,0,1,0,0.218911917098,0.118881118881,1.0,0.19656860272,0.179719406794,0.177867390597,0.0554616453156,0.271004906848,0.182550656068,0.0166259168704,0.265646381824,0.155426165124,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.426000409246,0.459632495281,0.467548574593,0.491997137043,0.496240914139,0.12599169775,0.116744648769,0.109250537247,1.0
-222848509476,409783,1224946,LABFER,2009-05-02 07:28:00,1,0,1,0,0.634686346863,0.0,0.0,0.396023305849,0.463583048007,0.469819478754,0.678946087608,0.588695584401,0.476283456862,0.483660130719,0.615847882969,0.466604601225,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.492491288134,0.497495920009,0.498388244928,0.501622065034,0.500562146997,0.452344000144,0.481543738338,0.534879000172,1.0
--12344760910556,444459,19431862,LABFER,2011-08-01 07:08:00,1,0,1,0,0.11914893617,0.0644257703081,0.0,0.161439026276,0.163859093819,0.198988378916,0.0555789473684,0.157383364536,0.190440211759,0.0176599360544,0.217375195026,0.0948221475632,0.222416167301,0.257917683272,0.307979090574,0.300505581722,0.281035832642,0.379501817435,0.0222810961269,0.322095586992,0.406528080595,0.00354048909315,5.02673483615e-05,8.0456840424e-10,0.0
-9617258126799,432655,13251727,LABFER,2010-12-15 08:48:00,1,0,1,0,0.0512820512821,0.0,0.0,0.10958576394,0.255829518956,0.214326038196,0.0308205128205,0.0814093450735,0.0751146891768,0.0241868131868,0.0314663895535,0.0870784654539,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.455933718081,0.46704194897,0.476886910056,0.496024695216,0.496612702515,0.093253948743,0.112119704273,0.131079534618,1.0
--2645720131795,460473,31069701,LABFER,2012-12-02 21:12:00,1,0,1,0,0.644444444444,0.0,0.0,0.513819865532,0.585022295486,0.527760211156,0.41895153863,0.498475302861,0.540276972264,0.299296788483,0.332207930452,0.499875212106,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.485580428027,0.490709016016,0.47486128235,0.49412868902,0.496987595835,0.160008778877,0.15078092202,0.149575650096,1.0
-6676814137656,459422,33770229,LABFER,2013-04-04 07:08:00,1,0,1,0,0.477840451249,1.0,1.0,0.35934969074,0.368205911662,0.300494225284,0.41493874989,0.271733858391,0.493769026769,0.355238095238,0.339941885831,0.305402008107,0.474085517325,0.462338008602,0.428682948289,0.421077804787,0.470477501323,0.481327163366,0.476886910056,0.493702530024,0.495416264197,0.426713048171,0.442571945879,0.447159169378,1.0
-7480758703184,464201,38559977,LABFER,2013-10-18 13:41:00,0,1,1,1,0.11914893617,0.0,0.0,0.330844794737,0.421175184826,0.340200013819,0.211404197954,0.325269468435,0.298311772752,0.220362045698,0.253301645296,0.296863141182,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.474709418598,0.488132455482,0.480886833765,0.498977055107,0.499929816085,0.589321523559,0.621228835722,0.652239400141,0.999998183322
-3912576670763,370036,27618287,LABFER,2012-07-28 21:14:00,0,1,1,1,0.644444444444,1.0,1.0,0.533877242246,0.458855268424,0.461956635143,0.493331557375,0.644075829824,0.544741440755,0.596835571836,0.517865048834,0.508840500544,0.474085517325,0.462338008602,0.452092678313,0.458429662267,0.478137779442,0.488680272386,0.487344472973,0.499328668674,0.499360309316,0.371033351604,0.377646679899,0.381565262559,1.0
-4951281456763,361986,33757066,LABFER,2013-04-06 14:34:00,1,0,1,0,0.108695652174,0.0,0.0,0.451927240371,0.331946291144,0.322530100167,0.355628046683,0.282289827585,0.282547801068,0.2,0.204770431589,0.158503139449,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.469358502304,0.482398297881,0.480767972341,0.494591628803,0.49740643754,0.123492643294,0.137485352264,0.144796940984,0.0
-8857244177692,469647,18987432,LABFER,2011-08-06 11:00:00,1,0,1,0,0.634686346863,0.963636363636,1.0,0.501533492246,0.50598399543,0.574151542378,0.456896993471,0.572141559868,0.534914522838,0.800972553969,0.641597953751,0.671769465393,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.50614572168,0.510888218981,0.500490487003,0.500820778944,0.81282054626,0.894763920291,0.919948631294,1.0
-10579133289864,373760,11763246,LABFER,2010-10-03 15:47:00,1,0,1,0,0.634686346863,0.0,1.0,0.576935863781,0.530983709062,0.57432596217,0.691946073629,0.584436381254,0.573649345641,0.505416223507,0.400736181799,0.666994729327,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.50559355846,0.498388244928,0.500113739913,0.499748059933,0.584973843622,0.59304041946,0.591821078819,1.0
-4071680098954,407292,11165854,LABFER,2010-09-09 17:47:00,1,0,1,0,0.11914893617,0.0,0.0,0.220438795868,0.273388723844,0.24168404406,0.0645394816231,0.350463897705,0.240021671262,0.182205316012,0.324681434819,0.105569686532,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.470974963966,0.467548574593,0.494306138155,0.495570916851,0.237613629661,0.208092189436,0.196906096161,1.0
-12373115029776,364734,24300163,LABFER,2012-03-07 07:37:00,1,0,1,0,0.11914893617,0.0,0.0,0.1198521528,0.167736286537,0.146603729489,0.0231347084288,0.0693231014007,0.136830775779,0.206921681021,0.0331646114619,0.167996106174,0.222416167301,0.300974526888,0.338850216148,0.327263965497,0.414553704021,0.434758354204,0.419012532048,0.473205037363,0.486377042038,0.0833869288058,0.0720949831688,0.0616636102058,3.49986483874e-87
--1308013923516,418227,8684385,LABFER,2010-05-01 09:48:00,0,1,1,1,0.218911917098,0.04,0.0,0.24256619665,0.345253303515,0.32217216,0.23044505268,0.321884654388,0.41892489597,0.375,0.26318940936,0.257824462192,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.476451996497,0.493761840306,0.491813578127,0.500784102287,0.500247756753,0.524937028605,0.542328474784,0.563432180724,1.0
--5058418707136,470907,39776511,LABFER,2013-11-28 07:20:00,0,1,1,1,0.634686346863,1.0,1.0,0.533877242246,0.400675349525,0.46361823094,0.524872800129,0.435587888551,0.452908552742,0.204457478006,0.274954507858,0.450200909015,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.488728073257,0.497449688133,0.510015679179,0.504576580482,0.503188318908,0.480431831501,0.466725807515,0.460889541545,1.0
-5141239008810,450057,25180294,LABFER,2012-04-15 10:36:00,0,1,1,1,0.11914893617,0.0,1.0,0.102530774301,0.21334091465,0.24945506854,0.42245233185,0.52089439715,0.17196910138,0.419865113427,0.318097643098,0.359236399894,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.450848492119,0.474604132404,0.479322944411,0.501542279529,0.503469028676,0.625854834012,0.791556065428,0.895699688925,0.999999999933
-8776761759821,450880,39245722,LABFER,2013-11-21 00:07:00,0,1,1,1,0.634686346863,0.936046511628,1.0,0.620755809273,0.604248644979,0.540239784161,0.649377535028,0.668271376299,0.69323059374,0.797099774979,0.680646212552,0.761817428528,0.474085517325,0.462338008602,0.459013056367,0.458429662267,0.500601476731,0.504622220197,0.510888218981,0.504365556335,0.501828853525,0.642054597444,0.654634143671,0.663835074947,1.0
-9384638203752,446943,149377,LABFER,2009-02-02 09:56:00,1,0,1,0,0.634686346863,0.962025316456,1.0,0.344094069594,0.396785606119,0.397652348973,0.536534184431,0.384730672454,0.435558974324,0.608571428571,0.561352769796,0.475575998911,0.474085517325,0.481003341925,0.475711464817,0.470629423827,0.488678809545,0.493764344039,0.510888218981,0.504322779922,0.502681672922,0.657007403258,0.661356231251,0.651735562495,1.0
--7303411586864,360422,20467755,LABFER,2011-09-19 16:33:00,1,0,1,0,0.11914893617,0.0,0.0,0.186134585598,0.241877879952,0.289241436449,0.121772938971,0.170048881245,0.185736864599,0.0585501592444,0.0695549349515,0.223593294326,0.222416167301,0.300974526888,0.366906279777,0.361490321039,0.46691605258,0.484225375883,0.469108659157,0.493409759697,0.495450672449,0.216438108017,0.198786576957,0.174381538399,1.0
--10647957682243,393232,8904584,LABFER,2010-05-22 04:38:00,1,0,1,0,0.634686346863,0.0,0.0,0.679131443791,0.602221268641,0.48464833006,0.687231872503,0.396981182725,0.406223531863,0.2,0.496318175998,0.533805450268,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.498080937413,0.496724830087,0.498388244928,0.494096221575,0.496031952778,0.440487577517,0.520850212664,0.572464040121,1.0
--8387576447734,361211,33017109,LABFER,2013-02-16 13:51:00,0,1,1,1,0.224899598394,1.0,1.0,0.375131935434,0.383172333434,0.545367106667,0.436415922424,0.651138825299,0.502666859871,0.4,0.670810592278,0.501274535563,0.474085517325,0.481003341925,0.483904540447,0.493054316564,0.473925784413,0.486619219842,0.498277106792,0.495464145966,0.496196235085,0.451651110769,0.468404056798,0.4833463319,1.0
-3881224372209,405366,37682347,LABFER,2013-09-07 21:00:00,1,0,1,0,0.477840451249,0.0,0.0,0.498594313292,0.498472101325,0.424128164646,0.381677100451,0.336582923465,0.429411297616,0.0466666666667,0.262475745598,0.478162962408,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.490845623139,0.498388244928,0.494704785794,0.500660865258,0.295134850019,0.307324247095,0.321150990946,1.0
--144128612707,426248,9046462,LABFER,2010-05-26 12:01:00,1,0,1,0,0.634686346863,0.0,0.0,0.516303234401,0.548280129986,0.552561816602,0.696675931252,0.436589040684,0.576469491923,0.796376547031,0.505500091883,0.557431448527,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.509791481146,0.521811420026,0.501264520878,0.500132573191,0.43148791956,0.413820243471,0.406250811247,1.0
-12131382539340,469677,25217524,LABFER,2012-04-18 21:00:00,1,0,1,0,0.11914893617,1.0,0.0,0.197015809781,0.178049913741,0.195785167695,0.214092643126,0.185964263954,0.176789642923,0.0162393162393,0.246127042262,0.416477528894,0.222416167301,0.257917683272,0.321070789852,0.319720123692,0.456528904104,0.470917549552,0.468417916605,0.493405606295,0.494862266416,0.279574682045,0.272251204236,0.300271768795,4.34329966304e-08
--7913823415891,362639,33741651,LABFER,2013-03-22 19:42:00,0,1,1,1,0.634686346863,1.0,0.0,0.533877242246,0.533978262144,0.577650656495,0.700809071835,0.605439703648,0.53702440135,0.935317460317,0.543964055274,0.602168078448,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.508431524405,0.511450306683,0.521811420026,0.506667543668,0.505136496816,0.755427818458,0.788083580417,0.804162541975,1.0
--9499309655698,375755,7084242,LABFER,2010-03-06 20:12:00,1,0,1,0,0.307692307692,0.0,0.0,0.393993978212,0.292428209621,0.368315970416,0.175432978237,0.210282679919,0.336950709076,0.0217391304348,0.411201079622,0.422275423897,0.474085517325,0.481003341925,0.474750576929,0.477606062854,0.469580098235,0.479592362257,0.463099032304,0.488360400672,0.496390686301,0.222205639793,0.213907766095,0.206851262474,1.0
-8833580929998,387212,11557836,LABFER,2010-09-29 02:00:00,1,0,1,0,0.108695652174,0.0,0.0,0.215316181078,0.258287861241,0.263768853971,0.126068356196,0.128299507995,0.239148315396,0.4,0.216082949309,0.200993107769,0.474085517325,0.481003341925,0.481712857157,0.477606062854,0.479129551821,0.488980578066,0.469859735916,0.495125059002,0.496518419825,0.647147448643,0.682390373029,0.6923559835,1.0
-5115182510147,411756,29581988,LABFER,2012-10-08 16:01:00,1,0,1,0,0.634686346863,0.589887640449,1.0,0.533877242246,0.505095156781,0.552278419206,0.587920501851,0.588002022459,0.527317287856,0.524572649573,0.390578176652,0.551802776682,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.50437659802,0.502999377205,0.498388244928,0.497175990952,0.498295486203,0.464829837455,0.46609673365,0.46613879774,1.0
-3751386591037,374073,5604028,LABFER,2010-01-16 22:55:00,1,0,1,0,0.218911917098,0.04,1.0,0.189333002919,0.255704033643,0.243774519412,0.155250686679,0.170673488276,0.193312269381,0.374774975019,0.14586807659,0.209421582474,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.446591530575,0.468215881464,0.468417916605,0.488760032807,0.493709172121,0.0670656134054,0.0629237103545,0.0620100700159,1.0
-6612828905751,429289,7682998,LABFER,2010-04-07 16:47:00,1,0,1,0,0.0512820512821,0.0,0.0,0.252639202125,0.27271280141,0.299473256201,0.189081382386,0.151508204453,0.160142456457,0.210081632653,0.131539174176,0.130364753323,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.486251930516,0.490250080411,0.476886910056,0.489714440864,0.491370128186,0.0811095168998,0.0738690867015,0.0709977349783,0.30525765109
-8759794999210,366242,37128511,LABFER,2013-08-26 11:33:00,0,1,1,1,0.11914893617,0.0644257703081,0.0,0.161439026276,0.185335005321,0.202084037712,0.105870629918,0.160270005553,0.147360545804,0.0342858529248,0.0536263993009,0.0992731312446,0.222416167301,0.257917683272,0.313128232519,0.306420082621,0.44644878292,0.474453750742,0.467548574593,0.494590123543,0.498025082791,0.521954217215,0.517348889739,0.526584728641,1.0
--10229282645159,431529,37758347,LABFER,2013-09-03 07:55:00,0,1,1,1,0.477840451249,0.890410958904,1.0,0.477279432247,0.442924124272,0.424089284289,0.538427001709,0.426481865282,0.403703904405,0.324959250204,0.555229626626,0.417711407678,0.474085517325,0.462338008602,0.449919469731,0.443117998191,0.484652417419,0.489405762419,0.498388244928,0.500367230248,0.499880012206,0.387757723481,0.380375543188,0.384066476417,1.0
--6159908717664,441578,131971,LABFER,2009-01-12 22:00:00,1,0,1,0,0.634686346863,0.963636363636,1.0,0.533877242246,0.550938182717,0.608351099677,0.700809071835,0.595149183218,0.7079897139,0.833947911944,0.625631132685,0.822769471572,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.507634480075,0.510888218981,0.503257207089,0.501879894784,0.685496993054,0.683992762787,0.682304452261,1.0
-11021797718032,383866,15304464,LABFER,2011-03-05 09:34:00,1,0,1,0,0.218911917098,1.0,0.0,0.319734926228,0.10450367713,0.173877743242,0.24795917454,0.188432353526,0.179956521383,0.4,0.322780370291,0.120492078747,0.222416167301,0.257917683272,0.302570763256,0.300505581722,0.389300273648,0.427297750022,0.407560018026,0.477697506697,0.491730509855,0.00237674131567,0.000704686694286,0.000276086870542,0.0
--2223074485343,388458,9332318,LABFER,2010-05-24 22:03:00,0,1,1,1,0.0512820512821,1.0,1.0,0.240064377123,0.347535412467,0.368047465843,0.192037504784,0.250350095419,0.271297714881,0.431522248244,0.290814393939,0.266677585315,0.474085517325,0.481003341925,0.475711464817,0.470629423827,0.488678809545,0.491342834538,0.510888218981,0.504576250241,0.504410913722,0.697225059792,0.67739940614,0.663853039558,1.0
-1840140489573,389521,6355592,LABFER,2010-02-08 17:54:00,0,1,1,1,0.634686346863,0.0,0.0,0.548646984401,0.604976769141,0.617934117846,0.721153167272,0.66571549916,0.683812381466,0.749961448664,0.854330314804,0.734369342592,0.474085517325,0.481003341925,0.490875605989,0.493054316564,0.506685179305,0.506666070176,0.510888218981,0.499757310902,0.500099453793,0.646932338224,0.677868083857,0.684312831836,1.0
-4021520780524,462997,7840013,LABFER,2010-04-19 11:31:00,1,0,1,0,0.215827338129,0.0,0.0,0.325854108957,0.294847991994,0.341198994755,0.232459697166,0.0991017754176,0.246552390365,0.226908249937,0.365389269867,0.22222913654,0.474085517325,0.481003341925,0.469355390578,0.470629423827,0.490305293382,0.497847277292,0.500307457597,0.502754235089,0.497446442249,0.224573996983,0.221149733878,0.224520392913,0.103619297899
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABNTBNP_outputs.csv b/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABNTBNP_outputs.csv
deleted file mode 100644
index 0454d640..00000000
--- a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABNTBNP_outputs.csv
+++ /dev/null
@@ -1,405 +0,0 @@
-patient_id,encounter_id,order_proc_id,proc_code,order_time,abnormal,result_normal_count,result_total_count,all_result_normal,predictedTest.decision_tree_max_depth=5,predictedTest.decision_tree_max_depth=20,predictedTest.decision_tree_max_depth=50,"predictedTest.random_forest_n_estimators=5,_max_depth=5","predictedTest.random_forest_n_estimators=10,_max_depth=5","predictedTest.random_forest_n_estimators=30,_max_depth=5","predictedTest.random_forest_n_estimators=5,_max_depth=10","predictedTest.random_forest_n_estimators=10,_max_depth=10","predictedTest.random_forest_n_estimators=30,_max_depth=10","predictedTest.random_forest_n_estimators=5,_max_depth=15","predictedTest.random_forest_n_estimators=10,_max_depth=15","predictedTest.random_forest_n_estimators=30,_max_depth=15","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=10,_learning_rate=1","predictedTest.ada_boost_n_estimators=50,_learning_rate=1","predictedTest.ada_boost_n_estimators=100,_learning_rate=1",predictedTest.logistic_regression_c=0.1,predictedTest.logistic_regression_c=1.0,predictedTest.logistic_regression_c=10.0,predictedTest.gaussian_naive_bayes
--3419241154305,401171,36510879,LABNTBNP,2013-07-21 02:31:00,1,0,1,0,0.0558659217877,1.0,0.0,0.0634376082831,0.173542421084,0.170391051806,0.0423212405128,0.0587062151326,0.30091546535,0.637331334333,0.1,0.146172296727,0.251407126031,0.269768376947,0.267651334214,0.243957939393,0.39926635519,0.361956462469,0.447527027637,0.476445493984,0.476529015767,0.0100167323148,0.00747377781995,0.00617536925235,1.0
-8584413457569,356291,37987611,LABNTBNP,2013-09-20 00:34:00,1,0,1,0,0.31045751634,0.0,0.0,0.0487768095802,0.114842644852,0.0901772784003,0.0,0.0610526315789,0.294150013374,0.230434782609,0.1,0.105689626224,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.400912550081,0.447464403839,0.383928030409,0.47380713863,0.484482131135,0.0195892424375,0.0135355641911,0.0198326093133,7.92646579648e-89
-8735812646998,413679,36130192,LABNTBNP,2013-07-09 17:54:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.20365558659,0.179017527596,0.239123009241,0.207926151512,0.164590617053,0.177314279046,0.206455082498,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.475741682351,0.483487122071,0.491761991438,0.496121819689,0.209182883214,0.188987413349,0.154711083414,1.0
--233718175813,408709,39335242,LABNTBNP,2013-11-06 12:32:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.183760730966,0.207729196148,0.356051020419,0.191340750936,0.179486588213,0.187833390765,0.200663353777,0.21008672915,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.45277187498,0.47318368422,0.458685459748,0.487729973739,0.491848352279,0.0597987334879,0.0106498674027,0.00362912031254,1.0
-11805916653237,432316,32751773,LABNTBNP,2013-02-17 08:35:00,0,0,1,0,0.31045751634,0.0,0.0,0.283042469553,0.230126371149,0.262696888033,0.191615853659,0.36339869281,0.30284068394,0.2,0.102222222222,0.3,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.4453228589,0.478047615469,0.467943180287,0.485995338956,0.491773693044,0.165701665973,0.0913377804973,0.0513327311699,1.0
-2018337512985,387549,33745279,LABNTBNP,2013-04-02 19:15:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.10985196025,0.118013652366,0.177430088081,0.151249587938,0.102997996057,0.026591114396,0.123980698908,0.0468450401256,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.36792864013,0.429210683792,0.397083262336,0.478155165213,0.490770854081,0.0569823687972,0.0253174362278,0.018834006329,1.0
-8566628769345,433053,40155943,LABNTBNP,2013-12-16 16:57:00,0,0,1,0,0.31045751634,1.0,1.0,0.229622756363,0.225518668009,0.205797088662,0.179017527596,0.22994958314,0.279579428849,0.134155834444,0.410710469802,0.373335718961,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.484037464912,0.483487122071,0.504158856695,0.502320578612,0.822207725249,0.937868211319,0.965182306682,1.0
--8438904158351,376819,32240330,LABNTBNP,2013-01-07 04:20:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0903578501031,0.0622041530368,0.140242834641,0.0422183069049,0.116203864994,0.0311813911698,0.00975609756098,0.102739726027,0.11011273511,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.3652963849,0.424408748092,0.438918049659,0.477970320689,0.487519343618,0.00897318640724,0.000627217842117,5.5460149632e-05,0.0
--10075078764322,455374,35063766,LABNTBNP,2013-05-17 01:21:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.211067108177,0.207343706393,0.218717687085,0.152837395459,0.174681865226,0.150499502657,0.194490381889,0.215265485636,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444366844526,0.473821521044,0.483487122071,0.500770999501,0.499221286039,0.187185198705,0.125203773159,0.144864679473,1.0
--8295404763738,360057,40475012,LABNTBNP,2013-12-17 17:48:00,0,0,1,0,0.31045751634,0.0,1.0,0.356634037481,0.512648154751,0.346339971059,0.39647696477,0.308945467645,0.467154218821,0.251655629139,0.127604166667,0.502625514403,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.47269166567,0.498915579731,0.506105378625,0.5062190855,0.509132647191,0.626176547439,0.744040259901,0.795111180087,1.0
--6459562669297,459660,40451511,LABNTBNP,2013-12-21 15:06:00,1,0,1,0,0.0609756097561,0.0,0.0,0.332810506667,0.507842794068,0.349048594149,0.785365853659,0.268249187585,0.533718555893,0.430434782609,0.4,0.391666666667,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.423017800039,0.462725443584,0.480728589251,0.494506979898,0.494953180747,0.38354027343,0.411297792461,0.479556952911,1.0
-11004154899725,356325,36123927,LABNTBNP,2013-07-16 20:02:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.282180931998,0.226333517777,0.200266618257,0.243823609813,0.248830309402,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.27177518976,0.264098204165,0.271083585071,1.0
--3097626446944,362452,33133839,LABNTBNP,2013-02-25 19:54:00,0,0,1,0,0.31045751634,0.0,0.0,0.226985810607,0.348672085461,0.40179323572,0.473777777778,0.571274080747,0.621811061099,0.6,0.459801136364,0.38858362005,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.476307941189,0.503663067775,0.523720933872,0.506608301023,0.503257792239,0.5287814666,0.488438212634,0.447296443615,1.0
--1021583407893,457818,34511556,LABNTBNP,2013-04-19 08:14:00,1,0,1,0,0.0609756097561,0.133333333333,0.0,0.207107243707,0.180039864334,0.174667891533,0.179017527596,0.185541626169,0.230130139754,0.251321248364,0.172791994459,0.185276089713,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.464079925799,0.44074121791,0.493674380064,0.496759619501,0.409398309146,0.435097170356,0.453286971143,1.0
--11658967186366,471993,40580595,LABNTBNP,2013-12-17 19:17:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.183760730966,0.202478593557,0.223881244804,0.173586889487,0.174841103906,0.154834519492,0.155693686971,0.108271614455,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.446522876179,0.471351296427,0.458685459748,0.479723756677,0.490098728959,0.0323317365204,0.0074698342666,0.00295016241305,1.0
--5096513136336,402174,32820849,LABNTBNP,2013-02-04 14:38:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0475030683584,0.0720756415466,0.6,0.00401759373491,0.0404731650933,0.0,0.0,0.0334239130435,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.265017163991,0.359303478605,0.328483650786,0.440886972581,0.463470434051,0.000641951871232,5.00666566826e-05,1.26405819741e-05,1.0
--9674966773920,419687,34445392,LABNTBNP,2013-04-21 19:56:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.167232510742,0.115656580228,0.162643618697,0.123278069111,0.0927018982091,0.0840030303262,0.083448793224,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.486748297647,0.494434246541,0.161965143888,0.121669914787,0.107939421866,1.0
--2290499705707,386461,40432989,LABNTBNP,2013-12-21 22:38:00,0,0,1,0,0.588235294118,1.0,1.0,0.229622756363,0.183760730966,0.224462276995,0.395491194939,0.272551989223,0.31735672304,0.457637375396,0.367884667506,0.336399573038,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.486950168077,0.483487122071,0.500023249044,0.503698010691,0.859040019177,0.906602800729,0.931215669509,1.0
--5802978909260,390571,40119758,LABNTBNP,2013-11-30 08:01:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0847899887239,0.0438456504787,0.0621045671057,0.0,0.0,0.00971824624844,0.0,0.0,0.0666666666667,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.32540255485,0.319325248359,0.366580749957,0.302494066886,0.399295551609,0.000225808112515,3.96637442356e-08,7.30824121362e-13,0.0
--12024829258479,414087,34294709,LABNTBNP,2013-03-31 14:14:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.204673118149,0.208313057101,0.184194393664,0.243538825447,0.197849590724,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.475414910236,0.483487122071,0.490451947389,0.496286881397,0.146080057347,0.0859257903595,0.057563442433,1.0
-11867979601272,429965,32845199,LABNTBNP,2013-02-23 07:15:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0170234262889,0.0788962743824,0.0580474409573,0.410447761194,0.125431705329,0.123992928013,0.0,0.1,0.106592164964,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.329992545912,0.412283573371,0.379494852102,0.462609370367,0.475043582531,0.0628622451465,0.15592688653,0.775211048828,0.0
-1144305610969,413471,32689102,LABNTBNP,2013-02-18 11:10:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.122123126918,0.164684941958,0.0716468002766,0.0824765859357,0.0518846449692,0.0491465690497,0.0102843915344,0.0744516630094,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.380594696426,0.35046836962,0.438918049659,0.298665083093,0.395113712019,0.137840582254,0.0443193758952,0.0184413051138,1.0
-12017957787705,469815,34712562,LABNTBNP,2013-05-15 13:41:00,1,0,1,0,0.0410509031199,0.0,0.0,0.095352932715,0.160035505268,0.140461524606,0.0804402212952,0.0767324416184,0.0693641955763,0.2,0.0118352856094,0.0512028220314,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.349241973359,0.334106717461,0.401701530627,0.315542134253,0.404324755309,0.155705999341,0.0660541138711,0.0366888151053,1.0
-2893245492888,423486,31620632,LABNTBNP,2013-01-06 22:54:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.209538032433,0.239096891604,0.106676446618,0.131271497548,0.195731062044,0.2,0.253125,0.280799407504,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.4453228589,0.478761475076,0.467943180287,0.493034391039,0.503786179787,0.576350143097,0.344117340654,0.034364593929,0.0
-5710728617671,372053,32629102,LABNTBNP,2013-02-18 09:30:00,1,0,1,0,0.0609756097561,0.0,0.0,0.193446766253,0.153493852016,0.157999575525,0.0548399864958,0.00217391304348,0.111548096667,0.0879519254354,0.0270517144463,0.0794622763386,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.394684635464,0.441184068557,0.45819231438,0.484601424846,0.490031892563,0.0228987130352,0.00586096734773,0.00249108247726,1.0
-6483764538915,373619,35123260,LABNTBNP,2013-05-30 06:25:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0774441871754,0.105482865145,0.0399017896115,0.0698555902177,0.10113437275,0.0540797690586,0.104610951009,0.0844931136974,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.37482617241,0.340646813392,0.438918049659,0.305997925611,0.397079033651,0.0197720785453,0.000992854708178,0.000177168683801,3.57897878654e-105
--4020873796635,464172,32603757,LABNTBNP,2013-01-27 14:37:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0859854327186,0.204845541271,0.121019823765,0.224871531346,0.129411440395,0.17219571497,0.230434782609,0.0,0.133980582524,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.375127993215,0.437574444241,0.42745356546,0.478589441342,0.485492143602,0.140603603817,0.140500478506,0.138771989935,1.0
--3703380309242,400563,36925849,LABNTBNP,2013-07-26 14:29:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.16075004528,0.0811706098004,0.0988824645265,0.1060723445,0.211394043797,0.0116637018792,0.0378723133752,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.475679625103,0.488073590508,0.179023604473,0.121529114704,0.105439746923,1.0
--1383136231378,464644,39758477,LABNTBNP,2013-11-30 11:40:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.179741783774,0.168366348798,0.14851839246,0.141500382558,0.168547841454,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.489371523919,0.493787532995,0.21108889238,0.192857650141,0.180270548691,1.0
-12280880104736,432102,31802849,LABNTBNP,2013-01-12 12:06:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.155702101039,0.0716468002766,0.112758062613,0.101938473487,0.221568462402,0.0158303685459,0.0664349334224,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.477929505638,0.492686618165,0.135658213221,0.0656127237644,0.0388835951251,1.0
-5143282453079,369575,37542890,LABNTBNP,2013-09-07 17:47:00,1,0,1,0,0.0609756097561,0.133333333333,1.0,0.207107243707,0.14763209126,0.157933925721,0.179017527596,0.137684977915,0.130042357347,0.164292820746,0.0702342403379,0.106136375284,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.404799381823,0.365287713773,0.44074121791,0.315808966226,0.403683399378,0.0177302431655,0.00515217724853,0.00250593980055,1.0
--4176006569129,444544,36106104,LABNTBNP,2013-06-23 11:49:00,0,0,1,0,0.795454545455,1.0,1.0,0.429372329793,0.326664237603,0.4468457921,0.8,0.540528909796,0.360397460513,0.230434782609,0.4,0.484722222222,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.462302686335,0.485355772482,0.506105378625,0.504050568398,0.499659033918,0.357722957152,0.308276402638,0.342924126163,1.0
--1820665322600,372110,32699981,LABNTBNP,2013-02-03 11:52:00,1,0,1,0,0.31045751634,1.0,1.0,0.229622756363,0.180039864334,0.175339315262,0.179017527596,0.157157174146,0.20873956055,0.14851839246,0.166136182164,0.155725286021,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.443503955655,0.468515142697,0.483487122071,0.492706040822,0.495455138812,0.125744374421,0.0705450950788,0.0480468814464,1.0
--7151978809607,398999,37788537,LABNTBNP,2013-09-02 11:21:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.157157174146,0.147563525156,0.14851839246,0.123318564376,0.111580382339,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.441604158573,0.44074121791,0.484106935849,0.491153965116,0.091283033272,0.0455825017824,0.0294385856369,1.0
--10658560862122,468097,39418369,LABNTBNP,2013-11-04 01:04:00,1,0,1,0,0.0609756097561,1.0,0.0,0.214294097375,0.263287722405,0.271355056231,0.444009779951,0.340399268301,0.369401346593,0.107564720048,0.5,0.342117117117,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.398411571037,0.453749848998,0.44074121791,0.494043830207,0.493939964873,0.529417532656,0.453826073789,0.379499528908,1.0
--3706589236383,442432,38375942,LABNTBNP,2013-09-22 09:41:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0316594536853,0.0231198849225,0.0299093841734,0.4,0.1,0.107030023672,0.0,0.1,0.1,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.324243977238,0.403597825301,0.344119970463,0.463910243835,0.487940280779,0.000363840288569,1.65611733414e-05,1.97982370697e-05,0.0
-42151086705,435112,37989176,LABNTBNP,2013-09-21 19:20:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.166120094542,0.165755782388,0.164590617053,0.126259740847,0.157328333078,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.487118037209,0.492660372115,0.115604824832,0.0922646995717,0.0836008882214,1.0
--5936057739855,358462,40562318,LABNTBNP,2013-12-18 13:57:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.122123126918,0.155316611284,0.0716468002766,0.0700480145071,0.0536682909066,0.0588209876543,0.0124583045779,0.0250698649276,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.375764373727,0.424643615676,0.438918049659,0.465541387192,0.482991022831,0.0330493605642,0.00688105896039,0.0028802086168,1.0
-323175527377,371731,36529420,LABNTBNP,2013-07-21 01:42:00,0,0,1,0,0.795454545455,1.0,1.0,0.345257875682,0.473481473531,0.585204344827,0.785365853659,0.635018555876,0.54360323647,0.8,0.7,0.631707317073,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.48489338362,0.512591199866,0.532428519263,0.509323260596,0.51131849707,0.852212816459,0.878024243591,0.903615864249,1.0
-9103641719109,385459,32888380,LABNTBNP,2013-02-22 04:32:00,0,0,1,0,0.31045751634,0.0,0.0,0.210174216404,0.312535480775,0.348381117666,0.288286165493,0.504579955149,0.514836357246,0.6,0.736363636364,0.353631556603,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.463068794258,0.48943495259,0.49884868863,0.498225555851,0.499360263733,0.791540230218,0.846120220519,0.859594589151,1.0
--1852775084286,405329,37653630,LABNTBNP,2013-08-21 16:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0902859037327,0.0444136304929,0.0947462386872,0.2,0.0121739130435,0.00637215605797,0.00975609756098,0.0,9.05797101449e-05,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.315330093237,0.303426550943,0.390154911708,0.287181415212,0.384969620271,0.0033155293435,4.1574859071e-05,5.99911476905e-07,1.0
-7681240090675,425754,32913024,LABNTBNP,2013-02-19 18:56:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0360820605039,0.0753571963186,0.0849129074346,0.0671927718591,0.229577784813,0.103220425609,0.6,0.2,0.233846153846,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.316492891412,0.392913881375,0.363032757469,0.468750050051,0.472120232664,0.0118497983022,0.00304257329276,0.00158021602389,1.0
-3817417773922,434502,38272932,LABNTBNP,2013-10-02 20:56:00,1,0,1,0,0.0410509031199,0.0,0.0,0.17397362237,0.117657881319,0.179155182066,0.173911569563,0.0637027066942,0.0849871505,0.0314691631765,0.0652846492132,0.0413529098903,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.393901459306,0.444171644497,0.438918049659,0.486748297647,0.485079805173,0.000834875094399,2.47135213778e-05,1.56229716576e-06,0.0
-5058982857080,360647,38668846,LABNTBNP,2013-10-23 08:19:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0278647174067,0.0284816977777,0.0,0.0,0.00614337036941,0.0,0.0,9.05797101449e-05,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.256778923889,0.278279220282,0.319674119693,0.285244441094,0.382044997201,0.00212817107583,5.32181404847e-05,1.24148715695e-06,5.91567982707e-57
-8337851635737,365069,35773864,LABNTBNP,2013-06-28 09:49:00,0,0,1,0,0.0558659217877,0.0,0.0,0.192002855312,0.0704629945283,0.0680949439721,0.0,0.0687709637046,0.03205382898,0.0,0.2,0.0666666666667,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.412824312971,0.463247634371,0.446552764445,0.495187793993,0.497896188575,0.182226320961,0.164364749762,0.0978241377771,1.0
--939143942370,409486,35403146,LABNTBNP,2013-05-31 15:39:00,1,0,1,0,0.0410509031199,1.0,1.0,0.092533686654,0.136574686751,0.16075004528,0.0811706098004,0.105035222659,0.111395133994,0.265432098765,0.17287401934,0.0708798809383,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.481712941559,0.491914515274,0.113520240505,0.050211790946,0.0360327884832,1.0
--6141210308695,453362,39880778,LABNTBNP,2013-11-26 02:29:00,1,0,1,0,0.0558659217877,1.0,1.0,0.010133975969,0.154674713556,0.104481359194,0.295296229803,0.0296638874514,0.145244204549,0.0304347826087,0.222902494331,0.208333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.41225987988,0.367223516302,0.465023397382,0.481874393171,0.48733316494,0.244511238956,0.3238825166,0.579146525576,1.0
-4954158974038,457611,35022597,LABNTBNP,2013-05-26 16:51:00,1,0,1,0,0.0609756097561,0.0,0.0,0.142265149854,0.110633825986,0.0974446560167,0.0993060097536,0.0815999412272,0.0936784329711,0.0304347826087,0.17306534056,0.157393379245,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.378375733998,0.353333265725,0.413971967219,0.470201603335,0.483436032341,0.0974321294653,0.112911013838,0.25772010339,1.0
-4742338024788,358017,35299676,LABNTBNP,2013-06-02 19:34:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0126008194703,0.0946229178351,0.0517550557969,0.24,0.107721297439,0.137497167904,0.0304347826087,0.115557667934,0.0416666666667,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.354284520856,0.419432334811,0.42257438035,0.478886183271,0.488335427598,0.0484807260716,0.00296243955987,8.77060712381e-05,1.0
--1784466296831,457849,34268809,LABNTBNP,2013-03-30 08:25:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.269013464421,0.17288664388,0.256193456558,0.204556962025,0.591210196778,0.261764848439,0.366666666667,0.206018518519,0.188552188552,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.39988911393,0.438858623906,0.438918049659,0.468317485366,0.481339074165,0.2450030627,0.200645584559,0.209704337778,1.0
-11487506342692,470917,40107366,LABNTBNP,2013-12-21 15:31:00,1,0,1,0,0.0410509031199,0.0,0.0,0.10612413807,0.0804427002283,0.174317489608,0.224871531346,0.0730009893579,0.134625731675,0.2,0.118718381113,0.233333333333,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.372423342075,0.431477082875,0.387575357436,0.472921151333,0.482564062314,0.0529659581671,0.00110130133262,3.96060668233e-05,2.10289603773e-53
--11839996325209,471986,35068279,LABNTBNP,2013-05-11 13:05:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0954004004174,0.121570059114,0.115656580228,0.102444590622,0.0960385359366,0.228708663083,0.0307115482357,0.0729408944016,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.345337370356,0.422428880936,0.397083262336,0.482452579556,0.48376855822,0.106149060818,0.0872742756741,0.0837010099744,1.0
-4853590189977,390676,35382848,LABNTBNP,2013-06-12 17:52:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.149884876695,0.0811706098004,0.105035222659,0.104623026836,0.0622671156004,0.0153166079086,0.0333063473992,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.440020385839,0.438918049659,0.481712941559,0.483716884453,0.132909504523,0.0967669394947,0.0816288960135,1.0
-11455326540662,365020,37109798,LABNTBNP,2013-08-19 12:46:00,1,0,1,0,0.0609756097561,0.0,0.0,0.213145950875,0.264421430599,0.238165396323,0.312126398776,0.343834136833,0.350783383445,0.6,0.418718381113,0.521770817613,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.419692344878,0.456383450003,0.480728589251,0.492307649826,0.490668492903,0.178393642271,0.16360916648,0.155061192259,1.0
--11528148715515,406972,32583511,LABNTBNP,2013-01-26 14:22:00,1,0,1,0,0.31045751634,1.0,1.0,0.191597128394,0.23206692307,0.253889886669,0.413296655677,0.175529511441,0.251190487418,0.6,0.255082017477,0.223600549952,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.392461254344,0.506105378625,0.319499605241,0.414196189925,0.632195509957,0.543762038935,0.779389861965,1.0
-6459174831286,434715,33095434,LABNTBNP,2013-03-03 11:22:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.110432361279,0.107468158941,0.083783100909,0.133772250363,0.115539204461,0.0111449864499,0.0432933927498,0.0969496670044,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.352455895194,0.41239025776,0.397083262336,0.475148140018,0.400362888257,0.0163840387838,0.00251797283099,0.000686216348595,1.0
--11226257681096,383248,32144802,LABNTBNP,2013-01-10 19:53:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.229194447999,0.230471037246,0.200266618257,0.262072066603,0.31088977719,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.2354639629,0.23415609081,0.241573735578,1.0
--4976022744033,437744,39192318,LABNTBNP,2013-11-05 22:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0285080671916,0.05742538059,0.0402170123379,0.0352163589326,0.0941193264427,0.175739368233,0.0,0.1,0.0397358336398,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.27661115279,0.379779287166,0.290344238991,0.457279708763,0.475515188558,0.0814874590727,0.0650282430823,0.0929333789772,1.0
-363030338837,457354,38492810,LABNTBNP,2013-10-15 19:40:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.212623006725,0.18630508775,0.200266618257,0.193128316723,0.231529555352,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.120365749669,0.0941563241903,0.0975501757052,1.0
--416072497518,379993,34756295,LABNTBNP,2013-04-20 03:09:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0544797637138,0.052137323908,0.112596496646,0.4,0.00110497237569,0.116687060707,0.230434782609,0.0,0.133333333333,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.299339316521,0.381451996705,0.302467059805,0.450878523957,0.472476723872,0.00190406376299,0.000802172106223,0.000900384754416,7.98744202741e-119
--1280692316105,447178,40463514,LABNTBNP,2013-12-18 18:10:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.0731396698344,0.105226826116,0.0397733209579,0.0379547915165,0.0587324634787,0.026591114396,0.111671163851,0.0547168327956,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.329568422198,0.392900496932,0.397083262336,0.473128544853,0.395864158961,0.000533098747163,2.07746229188e-05,4.70751382344e-06,1.0
-1590904233426,385913,37311487,LABNTBNP,2013-08-25 10:09:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.110432361279,0.107468158941,0.083783100909,0.100869846232,0.0887469457536,0.0540797690586,0.076069058149,0.0405681232858,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.36792864013,0.343908541972,0.397083262336,0.310087016721,0.402088045809,0.00868869030692,0.0010790208152,0.000549243718001,1.0
--6705230907332,374754,36535105,LABNTBNP,2013-07-16 09:33:00,1,0,1,0,0.795454545455,1.0,1.0,0.432286743868,0.423185408838,0.360529712821,0.410447761194,0.330792805691,0.327101054547,0.2,0.2,0.235087719298,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.44687849967,0.485030480605,0.482502944195,0.491952767757,0.487372306392,0.0128560224329,0.00126448152458,0.000157033291401,4.03459812777e-130
-9670575472984,383032,40263357,LABNTBNP,2013-12-26 16:28:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.167232510742,0.115656580228,0.176265307928,0.125876791432,0.0645926969958,0.0954316017548,0.0898613437404,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.486748297647,0.493614062999,0.108841479248,0.053202864188,0.0356546035536,1.0
--5505260556425,392049,32306951,LABNTBNP,2013-01-15 01:18:00,1,0,1,0,0.0609756097561,0.0,0.0,0.169872990463,0.206629263703,0.194044736793,0.324871531346,0.168570118228,0.111889137873,0.230434782609,0.4234375,0.126524649873,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.406804435389,0.456924754359,0.438508753845,0.485908230457,0.491910288213,0.10694166369,0.0357656444242,0.0162083381049,1.0
--6442525469649,395655,38178844,LABNTBNP,2013-09-27 20:47:00,1,0,1,0,0.0558659217877,0.0,0.0,0.372002855312,0.135938479996,0.0784892698107,0.210447761194,0.0260461488463,0.114082196472,0.2,0.1,0.166666666667,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.413048292952,0.376468092207,0.49133546642,0.324668040901,0.406147132851,0.0105742408293,0.00207248211007,0.00387277374688,0.0
--9127380806328,369596,39308839,LABNTBNP,2013-11-03 07:37:00,0,0,1,0,0.31045751634,0.0,0.0,0.222596721047,0.161738059633,0.306192292597,0.393788198013,0.453550389775,0.214750824529,0.2,0.255082017477,0.149298348826,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.462890247936,0.489373366449,0.496385077757,0.497523025473,0.495375552317,0.225272327046,0.160194701512,0.0914760917503,1.0
--7345305415736,402063,34711064,LABNTBNP,2013-04-25 18:28:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0954004004174,0.11258021837,0.0716468002766,0.117891262858,0.0711641984034,0.0570258970047,0.0308555219091,0.0475045935939,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.360671671957,0.340365318772,0.397083262336,0.30185040653,0.396621872889,0.0191635661468,0.00283193212273,0.00105116670461,1.0
-6092985698002,403639,39221002,LABNTBNP,2013-11-12 11:30:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.179741783774,0.157839653271,0.188131187072,0.141500382558,0.165517538424,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.489371523919,0.493787532995,0.183917261807,0.16821576142,0.165811817118,1.0
-5581226095569,409479,36590048,LABNTBNP,2013-07-24 20:18:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.282180931998,0.232632983292,0.347067965059,0.243823609813,0.239091530904,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476977524668,0.483487122071,0.494558594892,0.497520329328,0.345057462993,0.371709207376,0.394790634803,1.0
-9350656171226,379397,32229614,LABNTBNP,2013-02-03 08:48:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.0910958830763,0.111531186904,0.0716468002766,0.0744137180276,0.0702068596924,0.0231449864499,0.017418832782,0.019614980351,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.335171843987,0.400524321419,0.397083262336,0.466564051472,0.481167306434,0.027849303879,0.00684878215465,0.00337346994421,1.0
--10534010569777,458021,35648325,LABNTBNP,2013-06-12 17:46:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.150320275888,0.0811706098004,0.145035222659,0.144999501667,0.106711560045,0.0176160828316,0.0727665730328,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.441241534169,0.438918049659,0.481712941559,0.491094451375,0.16461065037,0.0983540601667,0.0861143225215,1.0
--4446413194005,448814,39553813,LABNTBNP,2013-11-14 17:47:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.10886053704,0.180470643989,0.214901789612,0.246789879983,0.322749613198,0.6,0.30947206212,0.405050813826,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.346154032315,0.408687909959,0.397083262336,0.473541402273,0.487672433943,0.0221081646229,0.00553784994932,0.00323522192955,1.0
--620118683302,373633,33512585,LABNTBNP,2013-03-09 12:42:00,0,0,1,0,0.31045751634,0.433333333333,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.245472939528,0.245597113005,0.223807188277,0.280250006618,0.236508060058,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.497973877033,0.500048352841,0.371518147196,0.404150327063,0.410109659433,1.0
--337210973718,443988,33686760,LABNTBNP,2013-03-21 00:51:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0918957470081,0.112965708125,0.115656580228,0.165137946156,0.0863053400327,0.00138888888889,0.154400199295,0.0361971749599,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.357158025166,0.42346211605,0.397083262336,0.480406026422,0.487897276374,0.0513461667882,0.0284360903553,0.0214864200221,1.0
-11811089126379,459385,33785399,LABNTBNP,2013-04-05 00:55:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0126008194703,0.0450118505476,0.0433815493129,0.0248715313464,0.00612285689281,0.0693355353302,0.0,0.0,0.0429963798385,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.350476285907,0.415759825832,0.396126982757,0.475007494441,0.396126722006,0.0821402491457,0.0823275619934,0.125697158529,1.0
--4488041384501,448884,31992979,LABNTBNP,2013-01-06 06:21:00,1,0,1,0,0.31045751634,0.0,0.0,0.124858122722,0.085031486804,0.137954300394,0.0125,0.1625,0.118985589898,0.0,0.135593220339,0.0817788041862,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.385528410168,0.342952926654,0.398445517677,0.470301518862,0.399227463565,0.139138147072,0.0128443097224,6.27904031428e-05,0.0
-12114530727150,365113,38469203,LABNTBNP,2013-10-23 01:01:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.141723525296,0.0716468002766,0.103592100039,0.0823404381695,0.0231449864499,0.0237594124922,0.0620893847563,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.440020385839,0.438918049659,0.475138539787,0.485578495847,0.0827830605423,0.053308636221,0.062058778361,1.0
--7397786760371,359511,37073170,LABNTBNP,2013-08-14 22:33:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.113353363978,0.150204551855,0.0768103579949,0.0796932072266,0.103643671582,0.0622671156004,0.0123114679342,0.0441861951898,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.481712941559,0.402234185363,0.00570739244521,0.000448354270785,0.000167871565819,1.0
--3735541678897,426930,35372520,LABNTBNP,2013-05-30 16:37:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.10985196025,0.118013652366,0.115656580228,0.117625191054,0.0846516907416,0.026591114396,0.0490900812882,0.0861542741931,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.352455895194,0.417135381356,0.397083262336,0.478155165213,0.487794705582,0.0537480877439,0.020937203402,0.00998653328347,1.0
-3817417773922,456928,39315493,LABNTBNP,2013-11-13 08:26:00,1,0,1,0,0.0410509031199,0.0,0.0,0.093123451807,0.0997646754587,0.0775042205383,0.2,0.0031914893617,0.0756402869976,0.2,0.1,0.133333333333,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.297174857919,0.376999876392,0.313466664911,0.44959918759,0.454243252887,0.000430591799406,4.94517637545e-06,8.66703561416e-11,0.0
-1612274121959,367490,38936669,LABNTBNP,2013-11-03 11:22:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0316594536853,0.072266575711,0.0822245488624,0.0248715313464,0.1,0.0687155336813,0.0,0.1,0.133333333333,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.311487738485,0.37328779293,0.359901633933,0.470239165241,0.385453241065,0.000428834455564,9.78948908513e-06,8.03293282275e-09,5.6045487474e-73
-9681149753835,471907,31907291,LABNTBNP,2013-01-20 23:43:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.173504387656,0.229991822993,0.160653823645,0.250288370074,0.173791891282,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476650675468,0.483487122071,0.490451947389,0.495466636465,0.152108174071,0.0906953505578,0.058721112207,1.0
-7290763368190,401855,37600975,LABNTBNP,2013-09-17 16:12:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.140879204092,0.151253583321,0.120820137946,0.106767382113,0.0768895567778,0.0472697994437,0.0560185185185,0.0601944139411,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.393901459306,0.440894498626,0.438918049659,0.482794106707,0.489271440878,0.0351549862053,0.0186670193723,0.0138267417832,1.0
--9641902215941,458373,38383481,LABNTBNP,2013-09-30 11:01:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0314033320974,0.0409348189694,0.0514268009875,0.0248715313464,0.000990099009901,0.0138533227655,0.02,0.1,0.0385538250906,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.301327174485,0.378725787735,0.356347377371,0.457559991313,0.474474851245,0.00118465931742,8.7857582131e-06,7.69540374624e-08,0.999999999742
-3209080089319,377549,38190000,LABNTBNP,2013-10-04 15:06:00,1,0,1,0,0.0410509031199,1.0,1.0,0.189340698579,0.190486464318,0.19356400613,0.20625,0.118181818182,0.159123648864,0.2,0.202222222222,0.183278686081,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.345507522769,0.412353620926,0.377322896768,0.478237493969,0.490399730658,0.146734805838,0.0889332087414,0.0826705077344,1.0
-10823173736163,450939,34208945,LABNTBNP,2013-04-18 14:00:00,1,0,1,0,0.0609756097561,0.0,0.0,0.193446766253,0.221818409822,0.18155936418,0.0988497664469,0.165873153729,0.113813171103,0.251655629139,0.123621399177,0.0933249911038,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.389649946709,0.440497757174,0.403481276778,0.484119666884,0.489892075776,0.0934726871436,0.12584489202,0.320786274542,5.57529674506e-65
-5267235350973,411941,35203743,LABNTBNP,2013-06-08 14:32:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0999523784272,0.0509844367064,0.0623389995963,0.0443734793187,0.00217391304348,0.0565399359936,0.00689655172414,0.018718381113,0.00342670200826,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.375800423188,0.422144179546,0.413971967219,0.465776084758,0.480644491492,0.00172365493383,1.29475488953e-05,1.7656108662e-07,1.0
-3408735121842,419276,39514905,LABNTBNP,2013-11-23 22:33:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0717338658391,0.0527429300714,0.0156545620995,0.00612285689281,0.0886194669675,0.0304347826087,0.1,9.05797101449e-05,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.279847560884,0.29921220512,0.313394351739,0.292749170792,0.38927066304,0.000240487236848,4.9962703277e-09,1.85943066218e-12,0.0
--6830843000923,456887,39064444,LABNTBNP,2013-10-20 17:10:00,1,0,1,0,0.0410509031199,1.0,1.0,0.120504043878,0.0537388835591,0.152093566236,0.524871531346,0.422795115332,0.174946740864,0.4,0.3,0.166666666667,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.320744228348,0.405402886176,0.371529239756,0.459275799672,0.47154467995,0.000347509958964,5.61448752517e-07,5.57813704202e-10,0.0
--9912736989863,366945,34044888,LABNTBNP,2013-03-28 22:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.274000201822,0.120103458989,0.188962016153,0.0611111111111,0.29956966031,0.136356489672,0.2,0.206018518519,0.216581044127,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.386941978889,0.439939911274,0.431782510658,0.48214115339,0.495908822994,0.255903001965,0.53947641408,0.945021374021,1.0
-693997109308,441001,33391015,LABNTBNP,2013-03-23 22:00:00,1,0,1,0,0.31045751634,0.0,0.0,0.228133957107,0.282164130497,0.265505469503,0.247629601864,0.257271159211,0.29311226902,0.262805642633,0.165181737589,0.254326920585,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.461950777072,0.484008907134,0.481224446787,0.489310282657,0.499302920652,0.733238120414,0.810371727987,0.795243493466,1.0
--1657639543414,411919,38040572,LABNTBNP,2013-09-13 21:27:00,0,0,1,0,0.31045751634,1.0,1.0,0.163713409598,0.210357267349,0.184612620042,0.431121531346,0.254851983129,0.306138505444,0.230434782609,0.2,0.437417280292,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.442838213214,0.478982466894,0.496326160234,0.49830432834,0.49657308753,0.284168742342,0.284882161347,0.279471331116,1.0
-4777671823833,374682,32857716,LABNTBNP,2013-02-23 09:45:00,1,0,1,0,0.0609756097561,0.0,0.0,0.113764951508,0.210926338968,0.1654493928,0.154458247583,0.19556589272,0.0959719476324,0.0863438735178,0.1,0.177518126592,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.375023352765,0.433174054786,0.42745356546,0.479300464767,0.485472757543,0.109919919233,0.0469685553389,0.0159827075385,0.0
--6370355883380,364923,36871242,LABNTBNP,2013-07-29 10:48:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0337782054019,0.0633762449931,0.0442508788572,0.0752163589326,0.00720908309662,0.0452223884694,0.0,0.207224334601,0.1,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.300216955735,0.376139271453,0.363032757469,0.453666183965,0.474993326402,5.28457747009e-07,1.56450620758e-09,1.18423362649e-11,0.0
-10808497567956,402909,35682830,LABNTBNP,2013-06-18 11:54:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.170118375954,0.0811706098004,0.120401945046,0.112740691921,0.0491465690497,0.0116637018792,0.0780790726594,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.440191061629,0.438918049659,0.479433750708,0.489953541481,0.294921656556,0.199840733524,0.17006970584,1.0
-1313431926915,468324,34447177,LABNTBNP,2013-04-27 11:57:00,1,0,1,0,0.31045751634,1.0,1.0,0.207107243707,0.180039864334,0.175339315262,0.179017527596,0.185032417811,0.19797135607,0.14851839246,0.164469515497,0.145846098403,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.443503955655,0.468515142697,0.483487122071,0.492706040822,0.496275383432,0.168182882249,0.105443383428,0.0778560417257,1.0
-9000338576334,392230,39608971,LABNTBNP,2013-12-07 21:43:00,0,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.20947528063,0.179017527596,0.218951758592,0.21338775962,0.164590617053,0.20117791541,0.2089362479,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.480292477196,0.483487122071,0.495719233721,0.498100692667,0.152550415733,0.129777889147,0.122577539001,1.0
-7555875603325,390405,37794310,LABNTBNP,2013-09-23 15:47:00,0,0,1,0,0.0410509031199,0.0,0.0,0.194594453166,0.0750502895842,0.0523108535569,0.0157574957073,0.106122856893,0.0988008223932,0.0,0.2,0.200297619048,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.308783004923,0.399497009497,0.387756747482,0.462151464542,0.482471076942,0.0222648357705,0.0312783720913,0.0351074841462,4.18762255139e-58
--3651862964002,379720,32254516,LABNTBNP,2013-01-21 00:41:00,0,0,1,0,0.0609756097561,1.0,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.217976773995,0.182315069327,0.328212005039,0.120564580995,0.303695015312,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.489371523919,0.493787532995,0.356727080878,0.376631513597,0.40467799339,1.0
-10327775996613,434446,38133594,LABNTBNP,2013-09-30 08:06:00,0,0,1,0,0.31045751634,0.88,1.0,0.963636363636,0.585433018569,0.653563254748,0.531547977964,0.767128737772,0.586393433621,1.0,0.792363636364,0.70727787504,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.49301382691,0.483487122071,0.504833263139,0.502657800782,0.417436950388,0.60951196369,0.638282370684,1.0
--9229048718412,427832,32503015,LABNTBNP,2013-01-20 12:34:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.202461740772,0.167680477741,0.120642805605,0.166800121399,0.161158252764,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.492167697523,0.495185929123,0.228485297877,0.225766283897,0.220940412951,1.0
--7442045233216,434269,34117416,LABNTBNP,2013-03-29 23:17:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.286048334421,0.212777140388,0.218717687085,0.220273950257,0.234397876963,0.200266618257,0.222475873465,0.281059270923,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476977524668,0.483487122071,0.491761991438,0.492529636099,0.15672480847,0.138983293409,0.123398889988,1.0
--3092286256550,428265,34644581,LABNTBNP,2013-04-24 11:01:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.0948167497074,0.149143113137,0.0588209400055,0.0699128472357,0.0878713906844,0.0138888888889,0.0101375548907,0.0919608125993,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.37781388225,0.344784903512,0.41456763533,0.299989163272,0.396653380875,0.0208538997302,0.00184974204132,0.00067451079287,1.0
-4311957026349,449287,32271413,LABNTBNP,2013-02-06 11:17:00,1,0,1,0,0.0609756097561,0.0,0.0,0.207107243707,0.149313572301,0.153368872525,0.0758832592698,0.224131522222,0.0664841049965,0.137320817609,0.0836058472731,0.108267064313,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.390218537429,0.4379517354,0.44074121791,0.476364006635,0.48550001644,0.0951781208755,0.153525622013,0.569015496617,1.0
--3975140545782,396636,34153879,LABNTBNP,2013-04-04 06:33:00,1,0,1,0,0.31045751634,0.0,0.0,0.371884407088,0.193322913579,0.293199210118,0.124009779951,0.172429108311,0.336333507693,0.2,0.221399176955,0.309833327215,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.4453228589,0.473347644343,0.467943180287,0.490985534145,0.496553753074,0.702509038656,0.651993301289,0.0601163954883,1.0
--388073990007,448121,32774095,LABNTBNP,2013-02-04 17:22:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.223690527196,0.218717687085,0.249948747005,0.238178237401,0.303069474161,0.318559152067,0.251157036478,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.486950168077,0.483487122071,0.505982130394,0.502031418178,0.466898720116,0.514296524437,0.51062496892,1.0
--7562267421903,459392,32810448,LABNTBNP,2013-01-29 15:35:00,1,0,1,0,0.0558659217877,0.0,0.0,0.162346045427,0.0814364453544,0.11424448541,0.0318734793187,0.00217391304348,0.13803132218,0.00689655172414,0.0472898096844,0.0670033670034,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.389512495337,0.348536644818,0.408895025947,0.275545897645,0.367568664382,5.14468913338e-05,4.12966713514e-12,4.42993896513e-29,0.0
--1036050138478,456939,39412086,LABNTBNP,2013-10-30 20:42:00,1,0,1,0,0.0410509031199,0.0,0.0,0.176249731211,0.0982912654564,0.142786442057,0.115759513836,0.190236750487,0.148378942989,0.0146341463415,0.0561436891039,0.168953541296,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.399521993396,0.447072490204,0.461313482722,0.488240635145,0.493221900345,0.0242575206213,0.00667550768553,0.00375778088897,1.0
-3968321469147,365917,36139343,LABNTBNP,2013-07-15 03:35:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.264633593141,0.201655234913,0.142834427816,0.259152289117,0.224971023266,0.172151559138,0.361290190036,0.117003012352,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444366844526,0.471141138747,0.483487122071,0.494061512272,0.492486974761,0.0465580949365,0.0165118370385,0.00923937745186,1.0
-5390204664568,439401,32076782,LABNTBNP,2013-01-23 21:00:00,1,0,1,0,0.230769230769,0.0,0.0,0.177510657885,0.249200118056,0.348234958085,0.411111111111,0.0368232737627,0.1299617026,0.02,0.130945626478,0.278310411079,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.445008406069,0.469041040994,0.458685459748,0.472984722909,0.485280778091,0.278716096987,0.244711876847,0.0727975502933,1.0
--855100128389,447213,39148009,LABNTBNP,2013-10-30 08:16:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0487707620282,0.026009006242,0.152066342725,0.0,0.224969028376,0.0984853984474,0.0,0.118718381113,0.0700933686749,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.368454881201,0.429392279508,0.431180325257,0.47934737199,0.485109307724,0.00314731896256,1.16991421326e-05,1.32862497726e-08,0.0
-4419501789727,370422,36713207,LABNTBNP,2013-08-09 05:04:00,1,0,1,0,0.31045751634,0.0,0.0,0.571303463881,0.502102919704,0.307687435347,0.306676446618,0.34914319819,0.300304830784,0.22319284802,0.318890137501,0.475161926007,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.47269166567,0.489646282138,0.506105378625,0.49571916887,0.499074164527,0.391146601658,0.533565260039,0.723642457742,1.0
-6605086518492,442521,39755041,LABNTBNP,2013-12-12 15:14:00,1,0,1,0,0.0558659217877,0.0,0.0,0.141920322268,0.0966838455013,0.122959206916,0.0,0.13430070241,0.0543217559891,0.0454320987654,0.238095238095,0.0670033670034,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.423199218657,0.461164308432,0.473784509176,0.485154880911,0.486490279765,0.0688812375121,0.0494774583687,0.000752140986005,5.19829430467e-24
--5468017353517,355022,38465961,LABNTBNP,2013-09-23 11:28:00,1,0,1,0,0.31045751634,0.0,0.0,0.216510814457,0.209538032433,0.216965726139,0.0988497664469,0.114337009102,0.356493570965,0.291846509309,0.113077963487,0.268718319176,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.438668710164,0.468752009969,0.445492065717,0.490333763593,0.499929716752,0.275996441444,0.261247575786,0.25743733763,1.0
--490458961944,372013,32877682,LABNTBNP,2013-02-08 06:25:00,0,0,1,0,0.0609756097561,1.0,0.0,0.763636363636,0.502094652994,0.545503400942,0.291847818474,0.555517616802,0.627629545972,0.700322135297,0.703333333333,0.546830108725,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.470119754683,0.44074121791,0.499261111682,0.498732810244,0.387981351649,0.567563771184,0.646123409116,1.0
-2125014139139,461193,40013322,LABNTBNP,2013-12-12 15:51:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.0910958830763,0.111531186904,0.0716468002766,0.0805664761605,0.0764056770709,0.029756097561,0.017418832782,0.019501188941,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.335171843987,0.400524321419,0.397083262336,0.47312675875,0.485656607299,0.0387057401475,0.0115060377883,0.00767008980896,1.0
--373315232843,461188,39774422,LABNTBNP,2013-11-14 20:31:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0989703282089,0.108271096962,0.0837132665999,0.23018126586,0.224941176471,0.124993797747,0.2,0.1,0.100647249191,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.373965926025,0.435047919398,0.442970325166,0.48901892006,0.493853764111,0.181890991769,0.0896473897769,0.0524848933553,1.0
-12141260882221,423498,36416912,LABNTBNP,2013-07-28 20:11:00,1,0,1,0,0.0410509031199,0.0,0.0,0.062794338099,0.0687977334585,0.0507188886007,0.0301812658596,0.00501788451712,0.064966525604,0.0,0.2,0.0015401063338,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.299050393149,0.303921437732,0.326487850811,0.287724761967,0.389849433521,0.000120928708859,6.46692740945e-13,5.63890575122e-19,0.0
-2490900378960,465672,40052632,LABNTBNP,2013-12-18 08:07:00,1,0,1,0,0.0558659217877,0.0,0.0,0.410263184607,0.1269125485,0.14210040745,0.0,0.1,0.20058913512,0.0,0.1,0.0708161865569,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.381783804429,0.433373748785,0.400741521527,0.468324077089,0.486474975398,0.00242402605374,4.2918895142e-05,4.35754175919e-08,0.0
--9612158090243,369245,33907542,LABNTBNP,2013-03-28 00:10:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.092533686654,0.136574686751,0.16075004528,0.0716468002766,0.112552415265,0.157312373763,0.0774320987654,0.208092273308,0.082974334539,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.441241534169,0.438918049659,0.475138539787,0.488622394473,0.16029561812,0.107655193765,0.0899482727538,1.0
-7600997144581,414535,35960279,LABNTBNP,2013-06-30 19:02:00,1,0,1,0,0.0410509031199,0.0,0.0,0.158289990508,0.0842678533084,0.21859275,0.0,0.219919786096,0.239251500165,0.2,0.2,0.102777777778,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.380201895928,0.437443794604,0.45410852887,0.481667170641,0.486126750157,0.0362691621254,0.00320227530378,0.000793273071866,1.0
--2269748196,448755,36610758,LABNTBNP,2013-07-19 21:36:00,1,0,1,0,0.0410509031199,0.0,0.0,0.240347844327,0.204317140321,0.226089910796,0.219971570718,0.459057775275,0.285747770019,0.4,0.4,0.210072787381,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.375940222859,0.444168029543,0.462289529788,0.493613437875,0.498950745111,0.277408571169,0.226521127273,0.252719180074,1.0
--11999311363290,467944,36001130,LABNTBNP,2013-06-16 05:48:00,1,0,1,0,0.31045751634,1.0,1.0,0.368653548635,0.376533208362,0.458203945492,0.365586978501,0.458985786135,0.405733791304,0.851655629139,0.471956856703,0.499364998198,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.486925945952,0.506105378625,0.499676947547,0.501185221356,0.71212014408,0.844910262509,0.886125709522,1.0
-11103183175582,370439,32812217,LABNTBNP,2013-02-28 00:12:00,0,0,1,0,0.0558659217877,0.0,0.0,0.193101938667,0.201917517916,0.188752049015,0.131179489072,0.266457493461,0.192122642284,0.237645967303,0.416955036741,0.177598872666,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.442098032028,0.473910666621,0.483487122071,0.500770999501,0.500626618698,0.324426496465,0.378930419894,0.443985521461,1.0
-8603424077666,456957,37875577,LABNTBNP,2013-09-15 13:58:00,1,0,1,0,0.0609756097561,0.0,0.0,0.305739438535,0.25388405688,0.220153183265,0.0440097799511,0.275534111883,0.309117321527,0.117320817609,0.25031404321,0.0959009687553,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.411094470029,0.458221835424,0.44074121791,0.48205179173,0.48783017631,0.250514648723,0.304498081237,0.277007169043,1.0
-9011494969349,470565,37974143,LABNTBNP,2013-09-26 09:57:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0999523784272,0.0915856140102,0.100436617363,0.0373715313464,0.0887597920303,0.077386878337,0.0,0.220432098765,0.0771624135749,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.386559925009,0.42909302696,0.413971967219,0.469290692917,0.47348047184,0.00908191839744,8.15453865851e-07,2.2727380331e-13,0.0
-295546331498,362519,33028442,LABNTBNP,2013-03-03 20:20:00,1,0,1,0,0.0609756097561,0.0,0.0,0.228345821513,0.291958758992,0.244155769155,0.272360224039,0.25926961627,0.300235634963,0.2614117267,0.0473472222222,0.220477196028,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.39806697444,0.449696269279,0.425439591302,0.484004252788,0.491715205714,0.229869451342,0.239330481656,0.302985110098,1.0
-9555564681896,467776,35376119,LABNTBNP,2013-06-09 10:21:00,0,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0857943348643,0.0538828970438,0.010447761194,0.401104972376,0.119899673718,0.0,0.2,0.133333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.393911763646,0.350890579802,0.396602225611,0.475557931433,0.477499171405,0.0712211852144,0.0204184808375,0.00524659400821,1.0
-10889151697106,376843,32453631,LABNTBNP,2013-02-01 21:43:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.220273950257,0.2380945914,0.200266618257,0.262072066603,0.273956972728,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.261989374299,0.262374842209,0.262216007676,1.0
--2927821313749,383044,36424920,LABNTBNP,2013-07-12 23:14:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.292160446947,0.20129636586,0.235221724879,0.36987356039,0.331821556397,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.480292477196,0.483487122071,0.495719233721,0.503354914759,0.136962705658,0.106749245153,0.101261548478,1.0
--9467849065671,414123,35632346,LABNTBNP,2013-06-05 22:00:00,1,0,1,0,0.31045751634,0.0,0.0,0.206048980573,0.262953982823,0.270032723568,0.131547977964,0.259483184828,0.219673287803,0.218181818182,0.571748684143,0.248889086491,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.492349891809,0.506105378625,0.504548037103,0.50064163971,0.720281317265,0.819128160386,0.811876394119,1.0
-5205352239644,470949,35420518,LABNTBNP,2013-06-20 06:35:00,0,0,1,0,0.31045751634,0.88,1.0,0.963636363636,0.327709441333,0.535207770984,0.331547977964,0.647433089227,0.531422203955,0.8,0.536363636364,0.658152297587,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.503078940323,0.483487122071,0.509136516502,0.503063744432,0.826561690279,0.955643814613,0.981034219834,1.0
-2843293070822,427640,38477966,LABNTBNP,2013-10-14 10:22:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.16075004528,0.0716468002766,0.0988824645265,0.0925619643295,0.0491465690497,0.0138376149227,0.0409532610861,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.441241534169,0.438918049659,0.475138539787,0.487802549667,0.184865968952,0.12318553538,0.101353864998,1.0
-624461117595,471994,39616097,LABNTBNP,2013-12-06 18:38:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.31751845922,0.247348873862,0.298078357825,0.2,0.411428571429,0.277871583889,0.651655629139,0.0472898096844,0.209761192169,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.422736562517,0.460235035062,0.430935124214,0.482108920683,0.483386748479,0.0234130440307,0.000723763211598,6.83056635059e-07,0.0
-6857454272671,414248,31483019,LABNTBNP,2013-01-05 01:21:00,1,0,1,0,0.0558659217877,0.0,0.0,0.210133975969,0.0620236389632,0.176178971704,0.0,0.203658536585,0.144223454936,0.2,0.1,0.233333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.398244367932,0.443273890698,0.400741521527,0.470210070698,0.479033213744,0.112151982324,0.0456717151147,0.0542644138467,1.0
--11142643970418,370253,35338359,LABNTBNP,2013-05-29 20:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0924617402835,0.0577994531268,0.140491069836,0.0318734793187,0.2,0.108540108735,0.366666666667,0.3,0.0666666666667,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.332208836904,0.323017216214,0.366325328606,0.300802177393,0.393878665324,4.53719642264e-05,2.22378212476e-09,3.19026801963e-13,0.0
--1397059881501,437905,33404260,LABNTBNP,2013-02-26 14:46:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0956977603012,0.165997882711,0.177450933324,0.11120255181,0.046287320302,0.0975966964086,0.209756097561,0.0101410012674,0.0730379211676,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.377627555557,0.435016017555,0.418937921262,0.480246137197,0.487289432765,0.0185365577893,0.00219162982099,0.000627398104951,1.0
-2490900378960,465672,40038490,LABNTBNP,2013-12-15 11:44:00,0,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.223453856374,0.218717687085,0.272429091433,0.24676863238,0.184194393664,0.259151881511,0.236104046521,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476650675468,0.483487122071,0.492706040822,0.496593933334,0.0709509129189,0.0193435627303,0.00840973295584,1.0
--10409672249859,460193,33553056,LABNTBNP,2013-03-15 14:06:00,0,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.167232510742,0.115656580228,0.18015385282,0.125812421496,0.077255770263,0.122107546713,0.0982190715141,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.406352621066,0.45181123778,0.438918049659,0.486748297647,0.494434246541,0.118770718578,0.0706231738357,0.055861391361,1.0
--6266264291787,428067,38281234,LABNTBNP,2013-09-22 00:53:00,0,0,1,0,0.266666666667,1.0,1.0,0.401461623688,0.360473874417,0.352740258342,0.4,0.230440965611,0.294951204276,0.2,0.4,0.366666666667,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.480902066342,0.503200395682,0.506105378625,0.508562940842,0.50569784686,0.127701302313,0.0436892006798,0.0203716685096,0.0
-4151454974779,463431,32915447,LABNTBNP,2013-03-01 11:53:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.10985196025,0.118013652366,0.115656580228,0.106291334333,0.0811642044609,0.0111449864499,0.0489245702169,0.0569564260934,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.352455895194,0.414513981755,0.397083262336,0.475148140018,0.487488987124,0.0233022471159,0.00562539640651,0.00267929956532,1.0
--11420000351793,364817,35170549,LABNTBNP,2013-05-22 13:20:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.179741783774,0.166518731023,0.14851839246,0.151415506015,0.176451855687,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.487118037209,0.493480515271,0.222483663363,0.214099178476,0.213497623632,1.0
--6061019770509,455460,36625157,LABNTBNP,2013-07-10 11:20:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0233591322441,0.0477695907336,0.0,0.0,0.00832226839525,0.0,0.05,0.0666666666667,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.394090017396,0.366934197891,0.437934201048,0.325528270896,0.406213895614,0.0885255687758,0.0349782199659,0.120315693881,0.0
--7042801553491,405102,33613766,LABNTBNP,2013-03-13 23:32:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.162375488797,0.115656580228,0.143696250276,0.125748563821,0.0927018982091,0.0993876457108,0.0890890334869,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.406352621066,0.45181123778,0.438918049659,0.486748297647,0.494434246541,0.0556533308295,0.024415561769,0.0177731831643,1.0
-7354141580872,378898,35912472,LABNTBNP,2013-07-01 23:55:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.282180931998,0.219619710147,0.223431601422,0.213493547369,0.293985571292,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.493445529663,0.496963733379,0.292799677815,0.315422833724,0.336275504169,1.0
-7383661638904,387581,36272288,LABNTBNP,2013-07-16 20:12:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0475584108675,0.0812230389853,0.0512657631507,0.2,0.0289587702055,0.0543043185391,0.0,0.0,0.0333333333333,0.251407126031,0.269768376947,0.267651334214,0.243957939393,0.390997263512,0.440883386433,0.446552764445,0.479631556268,0.47644014786,0.00830143512008,0.00326758879252,0.0018854037131,0.999741622837
--7676110522786,362428,33697903,LABNTBNP,2013-03-18 11:35:00,1,0,1,0,0.0558659217877,0.0,0.0,0.151164429028,0.0786512498986,0.159726195844,0.38,0.102173913043,0.147855154834,0.0,0.137815442561,0.144584260686,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.371712451759,0.440421316047,0.372533972062,0.475660036574,0.493583551747,0.67007136511,0.852933088418,0.871140264589,0.0
-234409265286,427255,38630699,LABNTBNP,2013-10-20 10:41:00,1,0,1,0,0.0609756097561,0.133333333333,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.17603808007,0.193855403604,0.14851839246,0.133167049225,0.180653960167,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.489371523919,0.493787532995,0.248416996618,0.234529539385,0.221838015453,1.0
-5805367376201,390637,35951730,LABNTBNP,2013-07-06 06:36:00,1,0,1,0,0.0410509031199,0.0,0.0,0.093123451807,0.046848368557,0.0485456956805,0.224871531346,0.000990099009901,0.0158160465895,0.0,0.2,0.0,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.320347684032,0.313221487398,0.351553195404,0.299754698132,0.385957379243,0.0025786245123,5.73217911179e-06,7.81091743656e-09,0.0
-6714361305074,465825,37486229,LABNTBNP,2013-09-03 15:33:00,1,0,1,0,0.0609756097561,0.0,0.0,0.408712968077,0.268715647992,0.234434392143,0.447838038523,0.275442225486,0.239626128917,0.609756097561,0.523621399177,0.255491511232,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.356641043029,0.425006979035,0.404186029425,0.479631522215,0.487878408114,0.178356148332,0.171859223582,0.196276349509,1.0
--972766427778,456998,37471074,LABNTBNP,2013-08-24 01:21:00,1,0,1,0,0.31045751634,0.433333333333,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.326692625389,0.262109847914,0.183431601422,0.239090616966,0.283787326094,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.500770999501,0.498400989961,0.467114146492,0.51251418107,0.532819019797,1.0
-2628575390394,458990,38716239,LABNTBNP,2013-10-27 13:39:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.287963802798,0.296362740722,0.259506809425,0.457999502657,0.450795052426,0.286659238982,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.651083498233,0.740754384941,0.79801592997,1.0
-1512556554324,458386,39449505,LABNTBNP,2013-11-17 04:48:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0957984104417,0.116344715515,0.115656580228,0.152049440799,0.114589059348,0.19019088017,0.026069058149,0.0610026088142,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.354531679775,0.418239984258,0.394915781697,0.479953638367,0.486517273086,0.0303201188066,0.0101370191994,0.0109650363762,1.0
--8165394403036,408059,38063874,LABNTBNP,2013-09-10 09:36:00,1,0,1,0,0.31045751634,0.0,0.0,0.154867364174,0.168499217293,0.154448692107,0.0793290724915,0.282067646435,0.284282415667,0.4,0.00638888888889,0.205047469784,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.458918686724,0.491010517726,0.522737743022,0.509432769432,0.508238966628,0.284817039666,0.244955405585,0.28419583491,1.0
-390956814621,366376,36496601,LABNTBNP,2013-07-31 04:40:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.10985196025,0.127578205753,0.115656580228,0.150808061488,0.120709702961,0.0134705678452,0.0389245702169,0.0633978499817,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.352455895194,0.422421971454,0.397083262336,0.484704880515,0.491453138039,0.114816200525,0.0532159989656,0.0364477016331,1.0
-5059548951245,395540,35396394,LABNTBNP,2013-06-17 16:15:00,1,0,1,0,0.31045751634,1.0,1.0,0.162346045427,0.109042487317,0.234928553571,0.4125,0.16884057971,0.133766881271,0.0516556291391,0.118718381113,0.204486220227,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.441700125285,0.377565517061,0.449047857141,0.303003693378,0.394031224798,0.22931282037,0.0859430761823,0.191609273005,1.33397724377e-321
--11547246950654,435095,37743834,LABNTBNP,2013-08-26 17:24:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.106116241538,0.085997291714,0.0157574957073,0.281489754027,0.128782925523,0.2,0.1,0.0333333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.417343859909,0.459203915395,0.447527027637,0.471928883284,0.48345577029,0.159852062685,0.24003410011,0.28475394786,1.0
--1876686131353,466532,40380593,LABNTBNP,2013-12-17 10:02:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0438599625228,0.0444154272364,0.0352163589326,0.00500769274481,0.0133275498661,0.0,0.0,0.0407407407407,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.276832626748,0.363890324499,0.296361491704,0.44618931468,0.465485907032,0.0405147293323,0.00544794368953,0.00082579306031,1.0
-3971714587867,397518,39448286,LABNTBNP,2013-11-26 23:42:00,1,0,1,0,0.31045751634,0.0,0.0,0.199243344973,0.251998163594,0.257291697298,0.209596758452,0.142323852575,0.208850120278,0.230434782609,0.428148205674,0.179949661216,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.482043496901,0.506105378625,0.489127060669,0.492244722414,0.0657359975027,0.0204333685608,0.00383808963336,1.09232199862e-90
-3952467942613,395522,38703146,LABNTBNP,2013-10-22 20:10:00,1,0,1,0,0.0410509031199,0.0,0.0,0.254633558613,0.266613905545,0.361096601159,0.537890295359,0.45064814209,0.510552822677,0.4,0.306018518519,0.206694560669,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.41167874005,0.459467258451,0.456355863602,0.493069523075,0.503205581775,0.0469363879818,0.0259350059385,0.0256689318607,1.0
-620375150821,464104,32474187,LABNTBNP,2013-02-06 08:42:00,1,0,1,0,0.31045751634,0.0,0.0,0.215170904184,0.210279822917,0.258138896273,0.174707907134,0.117546319802,0.199323730781,0.251655629139,0.242395833372,0.326404843472,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.45277187498,0.483355348912,0.458685459748,0.49583397754,0.497133442161,0.40792955146,0.773699260645,0.977178287573,1.0
-5546615221822,440074,37578345,LABNTBNP,2013-09-16 20:20:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.197647439951,0.179017527596,0.239123009241,0.207926151512,0.308681526144,0.265584695071,0.251804671049,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.480292477196,0.483487122071,0.495719233721,0.498100692667,0.125618429796,0.0739591477346,0.0440134488637,1.0
-6960123696112,458922,31941784,LABNTBNP,2013-01-12 09:17:00,1,0,1,0,1.0,0.0,0.0,0.31751845922,0.132354771534,0.166657399445,0.0318734793187,0.17631460167,0.126777714127,0.182805642633,0.304166666667,0.13302788118,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.450811202606,0.471822573704,0.483487122071,0.492195842533,0.492246705634,0.200750396833,0.290648576179,0.173601091398,1.0
-9975296081188,449052,39966228,LABNTBNP,2013-12-12 07:14:00,1,0,1,0,0.0558659217877,0.0,0.0,0.273998737163,0.190395452557,0.195071794991,0.8,0.16347826087,0.191177105995,0.4,0.312337662338,0.30727451989,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.403437784423,0.436609101329,0.479233396994,0.485300816799,0.498298160773,0.0742397407877,0.0118551087828,9.81329122265e-09,0.0
-5276977629937,394400,40427208,LABNTBNP,2013-12-27 17:45:00,1,0,1,0,0.0609756097561,0.0,1.0,0.0456477999914,0.158078234844,0.115904087766,0.315,0.107721297439,0.134516576851,0.0304347826087,0.107224334601,0.0506811176458,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.377114808753,0.441569108892,0.43112252177,0.479320717456,0.485953325006,0.0136606256363,0.00480192697932,0.00202455716194,2.36991183978e-93
-3944636773519,447018,32919913,LABNTBNP,2013-03-03 06:13:00,1,0,1,0,0.0609756097561,0.0,0.0,0.137167049118,0.149415105447,0.128402790659,0.231121531346,0.0303554488021,0.245473689254,0.0,0.3,0.0047967690812,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.387259056257,0.444677870681,0.42745356546,0.486448057068,0.491994029667,0.180953520461,0.106931084711,0.0325770440989,1.0
--11699102797175,414151,38700864,LABNTBNP,2013-10-12 15:23:00,0,0,1,0,1.0,0.0,0.0,0.214294097375,0.162356967684,0.195632233763,0.0125,0.123933649289,0.331428290537,0.406896551724,0.2,0.369958847737,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.450811202606,0.468694867082,0.483487122071,0.488901514921,0.500955031557,0.247958659266,0.549437293474,0.911419850738,1.0
--11709920257528,398946,33357989,LABNTBNP,2013-02-26 17:34:00,1,0,1,0,0.0609756097561,0.133333333333,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.166120094542,0.181424627588,0.164590617053,0.159077358635,0.180902259131,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.487118037209,0.492660372115,0.153135717061,0.134491614786,0.126428067106,1.0
-8201408769361,394421,40007489,LABNTBNP,2013-12-10 11:37:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.15680274135,0.115656580228,0.189012508016,0.111727071256,0.077255770263,0.122107546713,0.0866752015107,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.366377521855,0.438918049659,0.3129486063,0.403182854966,0.025232025665,0.00252401162248,0.000869690167811,1.0
-1913672088780,380295,31660741,LABNTBNP,2013-01-12 17:58:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.113353363978,0.155702101039,0.0467752689303,0.123355609187,0.105685457912,0.0747671156004,0.0101375548907,0.136505574976,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.481712941559,0.490994873322,0.0417818354961,0.0133111404831,0.00699556458891,1.0
--11011593479536,376109,34280821,LABNTBNP,2013-04-15 11:19:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0954004004174,0.112766673511,0.0399017896115,0.207174024468,0.107451956473,0.034756097561,0.0125297300539,0.0544367409875,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.355948897304,0.334559656139,0.397083262336,0.306125839589,0.397934027975,0.0399549606048,0.0042219340458,0.000942491047277,1.0
--1731065808188,402861,35535283,LABNTBNP,2013-05-26 16:46:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.254066890137,0.232632983292,0.160653823645,0.289958329022,0.265208944962,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498920986106,0.205306024841,0.191467765617,0.188320695557,1.0
--637381280105,461179,38541702,LABNTBNP,2013-10-02 12:48:00,1,0,1,0,0.795454545455,1.0,1.0,0.350992226143,0.406700409765,0.335734215372,0.595813614853,0.411701713286,0.443052326277,0.630434782609,0.118209876543,0.573981477359,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.462302686335,0.488844260393,0.506105378625,0.506872056191,0.504347738057,0.276415860222,0.284073046941,0.314248185316,1.0
-2095916522909,428340,35323815,LABNTBNP,2013-06-11 05:55:00,1,0,1,0,0.31045751634,1.0,1.0,0.162346045427,0.0933528437623,0.272617779544,0.112538198013,0.507891196023,0.167954707669,0.651655629139,0.1,0.238830301339,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.401959556297,0.446325893194,0.428457282785,0.489776319788,0.483117422481,0.494059586129,0.748049721707,0.991404694411,1.0
-10554255747879,395147,31621243,LABNTBNP,2013-01-03 19:57:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.226015097716,0.217822283332,0.200266618257,0.233500638032,0.235212130153,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476650675468,0.483487122071,0.490451947389,0.495466636465,0.0337254975337,0.00527887984185,0.00118727805231,1.0
--11142643970418,370253,35263652,LABNTBNP,2013-05-26 20:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0919216942062,0.210372826018,0.176458257933,0.0116998191682,0.167166945069,0.144567744364,0.4,0.129456018519,0.0754174090119,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.381897647424,0.346222308313,0.438918049659,0.306707912451,0.399574503312,0.000870188816581,2.08536159523e-06,1.61524816879e-08,2.21349512483e-149
--8391965098317,362024,39503673,LABNTBNP,2013-11-02 00:23:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.021258935443,0.0532707676662,0.0,0.0,0.0038403471394,0.0,0.1,0.0666666666667,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.367603105814,0.261593368075,0.400741521527,0.463015599693,0.388844733309,0.118646651513,0.172744096405,0.232748531316,0.0
-10171505963010,390629,40235516,LABNTBNP,2013-12-17 00:11:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.15680274135,0.115656580228,0.224153688227,0.136921208442,0.198192462769,0.284697474771,0.129386070191,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.491797800395,0.496139727935,0.221077923385,0.169598971364,0.180252307588,1.0
--7036546450055,419750,32728936,LABNTBNP,2013-02-03 15:54:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0700257989107,0.0639121563105,0.0248715313464,0.0227951153324,0.0530636920613,0.02,0.0,0.0361111111111,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.37035047343,0.423761064242,0.391122828251,0.451818031357,0.472343780555,0.000316685399684,1.51989740101e-07,1.95179668281e-10,0.0
--922147370161,451079,35205537,LABNTBNP,2013-05-26 00:59:00,0,0,1,0,0.266666666667,1.0,1.0,0.222205128316,0.19514642296,0.325175197713,0.010447761194,0.399683374645,0.433742542756,0.2,0.4,0.3,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.475723104842,0.503911694882,0.523720933872,0.514698986653,0.507301740636,0.696376372069,0.760685395494,0.80615081276,1.0
--3350718262205,468187,38995512,LABNTBNP,2013-10-16 20:10:00,1,0,1,0,0.0558659217877,0.0,0.0,0.40896380038,0.195229088463,0.236619924382,0.27588325927,0.360954504199,0.190879451923,0.2,0.420432098765,0.298024526999,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.445678074808,0.476446316908,0.501114185087,0.496815472812,0.502796359825,0.157423185754,0.0238022445858,0.00503915754417,1.0
-7097340179860,464385,37026767,LABNTBNP,2013-08-13 23:17:00,1,0,1,0,0.31045751634,0.0,0.0,0.122161422829,0.116440988984,0.0896545171853,0.0,0.124941176471,0.131604505388,0.2,0.2,0.186904761905,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.394947651202,0.355358043047,0.405545668177,0.457902677442,0.394201554078,1.28569468143e-06,4.09394639586e-12,2.0588276709e-18,0.0
-5965293119413,425669,31864039,LABNTBNP,2013-01-13 09:33:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.142223488702,0.0838297447899,0.210447761194,0.00291262135922,0.0374786429402,0.0,0.1,0.0333333333333,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.267401048835,0.358637503885,0.296361491704,0.450633501473,0.469959483798,0.00371082531723,0.000423778325789,0.000108350466906,7.10683474468e-23
--1364337528694,367482,32137647,LABNTBNP,2013-01-11 11:18:00,0,0,1,0,0.31045751634,0.433333333333,0.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.249361484419,0.259661684548,0.223807188277,0.303214600959,0.300905002312,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.497973877033,0.499228052555,0.229448630792,0.216184663986,0.203664246478,1.0
--11362205751267,447294,37155569,LABNTBNP,2013-08-14 11:08:00,1,0,1,0,0.31045751634,0.0,0.0,0.248148852532,0.319517974564,0.282582411453,0.365586978501,0.153550389775,0.347110174425,0.6,0.418718381113,0.358465725859,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.459464846289,0.486073331836,0.481224446787,0.501173181962,0.497088723342,0.430379037402,0.380047373681,0.278441015943,1.0
-8986205958209,453795,39600818,LABNTBNP,2013-11-22 11:11:00,1,0,1,0,0.0609756097561,0.0,0.0,0.083133984791,0.0799725914658,0.109591101783,0.0,0.0662037037037,0.101819413742,0.0,0.00222222222222,0.0477195353752,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.374567149102,0.341261449435,0.420566508411,0.311968861753,0.39775594757,0.0326660614231,0.0010371949406,6.33334387428e-07,1.9948211313e-185
--9660233670843,426539,37944702,LABNTBNP,2013-09-07 13:52:00,0,0,1,0,0.795454545455,1.0,1.0,0.229622756363,0.280158189085,0.390270822714,0.837406015038,0.269368919672,0.30372611157,0.830434782609,0.459638000324,0.528655477193,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.469367675271,0.492363976299,0.483487122071,0.500092838632,0.499400288877,0.176925146856,0.134860920577,0.124570719437,1.0
--9032157529281,400245,35094450,LABNTBNP,2013-05-20 12:54:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.165588304502,0.173713818528,0.210136216298,0.123042076864,0.11839500619,0.0968627633204,0.170832638786,0.166074928232,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.388729689551,0.436162108968,0.44074121791,0.482001096144,0.487291721769,0.143263418284,0.0762563493646,0.0475857017129,1.0
--10444516886309,385859,39962999,LABNTBNP,2013-12-02 00:33:00,1,0,1,0,0.0609756097561,0.0,0.0,0.157276823695,0.127646898696,0.130858797565,0.0758832592698,0.0291354090324,0.111808499397,0.180434782609,0.235240156669,0.108333333333,0.251407126031,0.177898107878,0.194705230325,0.172133499626,0.382310746403,0.443253409925,0.405464658646,0.48195454985,0.494401268865,0.247870605484,0.254476069582,0.194666268887,1.0
--6003627341871,453784,39247863,LABNTBNP,2013-10-27 11:55:00,1,0,1,0,0.31045751634,1.0,1.0,0.228133957107,0.275561136753,0.268902621105,0.26980952381,0.533777755289,0.415371633012,0.251655629139,0.459801136364,0.368332503879,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.459288491397,0.402715984557,0.501114185087,0.328429995928,0.407560409488,0.357107488075,0.215700330007,0.160883071464,1.0
-11860443552972,464735,34570182,LABNTBNP,2013-05-11 13:44:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.191722628322,0.304727370754,0.218248446146,0.0567450106651,0.0852711102711,0.105735860664,0.206896551724,0.282883030023,0.290975463686,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.448775759901,0.477281761321,0.478764723972,0.501366574654,0.498245352584,0.838583429141,0.998550966999,0.999996330996,1.26057160103e-280
--1017118515932,464191,34668053,LABNTBNP,2013-04-20 16:11:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.092533686654,0.136574686751,0.155893023334,0.0716468002766,0.0912902184377,0.131983580857,0.0622671156004,0.00739782886335,0.0379325445148,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.440020385839,0.438918049659,0.481712941559,0.491914515274,0.0940914300127,0.0580957608452,0.0472939011181,1.0
-12018686408782,373483,33041851,LABNTBNP,2013-03-09 16:10:00,1,0,1,0,0.0609756097561,1.0,1.0,0.207107243707,0.162083651092,0.163367359717,0.179017527596,0.146055187501,0.132005890833,0.354834519492,0.200926568437,0.141237663233,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.396833958182,0.441556995345,0.416364715411,0.476885317349,0.489976868655,0.0089911334206,0.000585217097187,8.53136332569e-05,1.0
-8092406203971,378171,38818879,LABNTBNP,2013-10-23 14:45:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.223871059609,0.218717687085,0.299940402471,0.257894419048,0.223509391969,0.361397305958,0.229441159518,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476977524668,0.483487122071,0.491761991438,0.497790463182,0.137419578544,0.0953325787329,0.0922809755446,1.0
-275950817892,392802,38433094,LABNTBNP,2013-10-11 12:31:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.167232510742,0.115656580228,0.202178911494,0.119535265465,0.0491465690497,0.0954316017548,0.146153712549,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.489001704715,0.494741278062,0.342362657939,0.287112609691,0.265764478343,1.0
-2971856059248,430047,36900351,LABNTBNP,2013-08-14 18:40:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.211067108177,0.211626015694,0.174707907134,0.17491388613,0.165129381302,0.148610989118,0.183992857826,0.165401887736,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444366844526,0.383084132882,0.483487122071,0.312496260533,0.402929663858,0.0462610292865,0.0113189858536,0.00426674259092,1.0
-7347342102467,397798,35283454,LABNTBNP,2013-06-01 13:37:00,1,0,1,0,0.31045751634,0.0,0.0,0.207107243707,0.180039864334,0.169331168622,0.356051020419,0.168491030867,0.195785290433,0.187833390765,0.137887725374,0.211380803642,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.443503955655,0.473385800694,0.483487122071,0.495719233721,0.489580939899,0.366914282573,0.480173359198,0.563658534722,1.0
-5152237286846,453841,38874937,LABNTBNP,2013-11-07 04:24:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.0731396698344,0.122530261512,0.0716468002766,0.062908640167,0.0910677543777,0.11114498645,0.00872998738087,0.00881468011476,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.341168419051,0.335612265849,0.373508746625,0.309510639617,0.399008365993,0.0788502658489,0.0239833762417,0.0192450648041,1.0
-9035815106859,447150,39651585,LABNTBNP,2013-12-05 22:16:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0924617402835,0.07820346508,0.133606743019,0.0318734793187,0.0261075623326,0.231677304998,0.0,0.101379310345,0.0,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.328999413441,0.327673835337,0.384245146847,0.461206405347,0.47282500455,0.0334816285898,0.00764854837433,0.0015744372222,1.0
--11036712561255,390678,37802969,LABNTBNP,2013-09-05 14:13:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.202297345236,0.212586218093,0.218717687085,0.259681891345,0.24569922504,0.223133805115,0.250631416441,0.236134360199,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.498516267647,0.498298359644,0.461299609757,0.568855711035,0.582351097376,1.0
--883782563992,397953,34678373,LABNTBNP,2013-04-24 06:37:00,0,0,1,0,0.31045751634,0.0,0.0,0.472892051851,0.100839950497,0.242969026645,0.506676446618,0.15235822872,0.283805780402,0.00689655172414,0.6,0.176508027976,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.468233029437,0.493997167155,0.481224446787,0.504141063398,0.414933477017,0.303619399435,0.185218758837,0.0535771846837,1.8247123302e-54
--5784847684834,444009,34937278,LABNTBNP,2013-05-11 09:12:00,0,0,1,0,0.31045751634,0.433333333333,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.296362740722,0.243378265619,0.347067965059,0.317386900451,0.264397985723,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.500770999501,0.495365383133,0.194604983726,0.173090113102,0.155212726301,1.0
-7590133403752,425553,32842017,LABNTBNP,2013-02-24 20:37:00,1,0,1,0,0.0609756097561,1.0,1.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.166120094542,0.179651577943,0.164590617053,0.168636182164,0.186109551838,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.487118037209,0.492660372115,0.191297031248,0.178931099936,0.17382885477,1.0
-596297469749,364491,35656449,LABNTBNP,2013-06-15 11:01:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.147653628047,0.147563525156,0.14851839246,0.141500382558,0.113334768304,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.44376471116,0.44074121791,0.484106935849,0.491153965116,0.143097326247,0.0817334211163,0.0549552279042,1.0
--10531753668216,356278,34451118,LABNTBNP,2013-04-15 16:12:00,0,0,1,0,0.588235294118,0.0,1.0,0.271722628322,0.225518668009,0.217634162334,0.380491194939,0.280558677247,0.256557553169,0.160653823645,0.358109924767,0.282412593934,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.499641265851,0.386194532109,0.405892841823,0.419622969026,1.0
--7312974063511,457532,32261727,LABNTBNP,2013-01-15 17:13:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.271722628322,0.225518668009,0.217634162334,0.218717687085,0.240445200906,0.2380945914,0.160653823645,0.253499995689,0.272020312849,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.49584353535,0.346450060752,0.361103896219,0.356578434286,1.0
--10694831550554,382910,38604793,LABNTBNP,2013-10-04 12:03:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.15474189864,0.0716468002766,0.108869517721,0.0954093038645,0.0491465690497,0.0229930669586,0.04630314966,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.475138539787,0.487802549667,0.0735051606537,0.0395284867841,0.0314330276029,1.0
-8917478790142,442539,39432484,LABNTBNP,2013-12-02 00:07:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.174234997998,0.179017527596,0.189900417792,0.220951110947,0.18895274578,0.2428166121,0.192538579744,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.489371523919,0.404819176017,0.121242566752,0.0782391743779,0.0684797722304,1.0
-3822876955556,469992,37088738,LABNTBNP,2013-08-17 21:50:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0989703282089,0.0850497741888,0.0744900155141,0.0301812658596,0.105882352941,0.145711210833,0.2,0.2,0.105689626224,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.369785474622,0.43146107712,0.41856280654,0.474539093975,0.487383065167,0.11290003472,0.0376139201522,0.0186068332972,1.0
--11480243723702,445320,39469057,LABNTBNP,2013-11-09 07:59:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0145027621518,0.0256239406064,0.0237877487371,0.2,0.000990099009901,0.0135762052125,0.0,0.1,0.0334239130435,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.336526338433,0.398422168003,0.377643782014,0.467070085249,0.480097658712,0.0295332101753,0.00803249844199,0.0459375627415,0.0
-3817417773922,456928,38980471,LABNTBNP,2013-10-31 20:22:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.0991212670485,0.13539207934,0.173911569563,0.0637027066942,0.0655679935398,0.0314691631765,0.0634259259259,0.0261614973278,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.383648330217,0.348439736897,0.41456763533,0.306176206984,0.392803568379,0.000198729107793,1.72495684635e-06,8.15485485002e-08,0.0
-7976381572082,468225,37332534,LABNTBNP,2013-08-26 23:25:00,0,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.249948747005,0.232632983292,0.183818806809,0.213493547369,0.261608346239,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.498516267647,0.499499250783,0.541168132862,0.611784821394,0.645207834787,1.0
--10569442774829,411908,38249070,LABNTBNP,2013-09-14 22:00:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.169490967935,0.179893848719,0.179017527596,0.180171353328,0.156839711126,0.147755600218,0.127607347323,0.0974014253968,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.437367997177,0.470283499111,0.506105378625,0.500244042602,0.499224273892,0.167066520566,0.178830343725,0.235360111444,1.0
--10391941824994,439380,35980191,LABNTBNP,2013-06-24 15:00:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.207204392942,0.218717687085,0.254066890137,0.232632983292,0.273997639158,0.271709872232,0.267931246132,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.207784723163,0.144730897315,0.147145954839,1.0
-6059888247155,370635,35745142,LABNTBNP,2013-06-21 01:01:00,1,0,1,0,0.0609756097561,0.133333333333,0.0,0.207107243707,0.180039864334,0.168415303958,0.356051020419,0.21957521454,0.206193883455,0.14851839246,0.134484216602,0.177226883203,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.489371523919,0.491562379833,0.180693749156,0.093674731947,0.0539721658914,1.0
-3408735121842,419276,39570377,LABNTBNP,2013-11-23 18:53:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0285080671916,0.0896900790811,0.0675492885841,0.0405260934458,0.0199439951042,0.0779910261262,0.0,0.0,0.00518496783896,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.291949871508,0.386830334625,0.335204756793,0.46484413602,0.480067489639,0.00324946628473,0.00116316920153,0.000809257985577,4.1442137973e-66
--3462636195310,415614,35055420,LABNTBNP,2013-05-04 17:11:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0126008194703,0.0455118096485,0.107413602358,0.2,0.0031914893617,0.0411759340205,0.2,0.0,0.101518733434,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.30261054894,0.300298162598,0.301949386747,0.455537701474,0.389543301333,0.0717421705874,0.000362844981961,1.5834227728e-08,0.0
-8556985766793,375676,38073220,LABNTBNP,2013-10-01 12:05:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.113572826881,0.166055347991,0.102830719957,0.139599710916,0.12213147051,0.0593209876543,0.0867427563536,0.12075763354,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.406352621066,0.457192581706,0.438918049659,0.493304450541,0.496893184094,0.110987399159,0.0506433086879,0.0353371852476,1.0
--1012430374887,387348,34909195,LABNTBNP,2013-05-07 21:44:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0145027621518,0.0936354991431,0.0449094319979,0.0103448275862,0.00321023553359,0.00884148334846,0.0,0.1,0.102559715513,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.317051427036,0.314617074685,0.333453626408,0.293484885709,0.30830207757,7.79129665142e-06,2.10075113244e-09,1.90538502729e-11,0.0
--7216811881398,448220,36191855,LABNTBNP,2013-06-28 21:30:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.132109441152,0.151829995372,0.083783100909,0.120062204534,0.129903732766,0.0769012619419,0.0962665658774,0.076323425884,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.486748297647,0.492413400489,0.203608534551,0.247886378853,0.233158181489,1.0
--10019377599463,419656,36503973,LABNTBNP,2013-07-17 22:03:00,1,0,1,0,0.0609756097561,1.0,0.0,0.271126317445,0.0733644419809,0.062125170908,0.0,0.106616325063,0.182596695461,0.0,0.1,0.0666666666667,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.346217898925,0.407869249868,0.42257438035,0.480718783017,0.490681737831,0.00566019666341,0.000187919905871,1.01866716966e-05,1.0
--328786632540,429730,32472085,LABNTBNP,2013-01-20 01:06:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.189900417792,0.169058426617,0.188131187072,0.148011997243,0.144679564353,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.489371523919,0.491562379833,0.144097751441,0.119278468269,0.110221531522,1.0
--11161387153505,381125,40333446,LABNTBNP,2013-12-19 23:35:00,1,0,1,0,0.31045751634,1.0,1.0,0.229010763916,0.230320054012,0.232281269168,0.0918478184745,0.294148629653,0.197392339228,0.0601908801697,0.225043049774,0.225320000644,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.428631444971,0.457195347249,0.462975547494,0.478518527179,0.487666911602,0.152575292171,0.0833753010297,0.0507035754529,1.0
--1431211888954,368641,40510271,LABNTBNP,2013-12-20 09:13:00,1,0,1,0,0.31045751634,1.0,0.0,0.0678354437953,0.301914872789,0.179562029213,0.4,0.126046148846,0.278215479525,0.2,0.2,0.2,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.38851898648,0.446959826118,0.383928030409,0.483189950841,0.492097309524,0.00143252338827,6.05009464141e-05,2.13150084415e-06,0.0
-6169373447274,433128,39323717,LABNTBNP,2013-11-22 23:25:00,1,0,1,0,0.0609756097561,0.0,0.0,0.657286743868,0.403685983839,0.353136632188,0.0301093643199,0.437048639075,0.372211000691,0.209756097561,0.222820827552,0.337862845997,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.415339859996,0.468391964978,0.482204910959,0.494427728698,0.504314970948,0.432683272732,0.584304478658,0.446092132675,1.0
-8003153653559,370809,38831503,LABNTBNP,2013-10-28 22:24:00,0,0,1,0,0.0609756097561,0.0,0.0,0.0456477999914,0.189219534347,0.142316011536,0.0423212405128,0.030516412771,0.0603553847473,0.0301369863014,0.0155576679341,0.0733686067019,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.335612465824,0.419627315926,0.384541117439,0.482656145188,0.492016534324,0.263035342108,0.273561837981,0.193954105549,1.0
-2450593227305,387361,35896740,LABNTBNP,2013-07-01 17:24:00,0,0,1,0,0.0609756097561,0.0,0.0,0.0972717190049,0.100317699841,0.212526475293,0.355296229803,0.212882264589,0.237744107942,0.0,0.22604040404,0.375220458554,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.370290556706,0.428630936156,0.45891911057,0.48525116041,0.49186297666,0.379386609161,0.381825519852,0.0773455279836,0.999888475397
-1268493257345,447243,36731478,LABNTBNP,2013-07-30 19:11:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0145027621518,0.0327043037273,0.0545478899661,0.2,0.226046148846,0.063636969776,0.0,0.1,0.0,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.328384540799,0.396176347636,0.367496390764,0.449223292068,0.47042197104,0.00214490727506,0.000149702640161,3.79186713883e-05,1.0
--9605734797594,424729,39238909,LABNTBNP,2013-10-28 17:52:00,0,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.17889512822,0.164745681378,0.164590617053,0.126259740847,0.15691196286,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.487118037209,0.490818860881,0.103829797453,0.0797877719259,0.0711049750295,1.0
-620375150821,464104,32382481,LABNTBNP,2013-02-01 21:00:00,1,0,1,0,0.31045751634,1.0,1.0,0.142058297938,0.139394393645,0.15000583341,0.174707907134,0.0686762270398,0.241928320977,0.251655629139,0.28808423192,0.156393995779,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.435357437205,0.476099694881,0.440361375919,0.489877540088,0.496652830975,0.306838116016,0.701096994466,0.98728034467,1.0
-980328712500,454373,39788979,LABNTBNP,2013-12-04 08:13:00,1,0,1,0,0.31045751634,1.0,0.0,0.122161422829,0.0971464433374,0.164679761537,0.0248715313464,0.129548090601,0.17532705492,0.0,0.1,0.107407407407,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.400006120843,0.439742978136,0.412874083951,0.464637553306,0.472055675392,0.0236496612507,0.00185485674633,3.17025636656e-05,1.0
-3812203386597,419399,34716200,LABNTBNP,2013-05-10 20:36:00,1,0,1,0,0.31045751634,0.0,0.0,0.0487768095802,0.17827586365,0.116685702131,0.23531929254,0.0289179722252,0.079496730669,0.0304347826087,0.0,0.106701940035,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.394919694312,0.45095966311,0.417467093875,0.477324561105,0.487423803593,0.165434682017,0.137257836696,0.108812521947,1.0
-206867300033,360909,37937133,LABNTBNP,2013-09-17 10:24:00,1,0,1,0,0.0609756097561,0.133333333333,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.143949924343,0.173052579962,0.14851839246,0.133167049225,0.133892970682,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.453836262693,0.44074121791,0.484106935849,0.491153965116,0.152898483055,0.0864055151761,0.0553565685729,1.0
-7466900561344,366004,36279219,LABNTBNP,2013-07-09 15:07:00,1,0,1,0,0.0410509031199,0.0,0.0,0.194594453166,0.0854183510621,0.104416245887,0.405309734513,0.0743879641053,0.113108318779,0.218181818182,0.207224334601,0.104398960364,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.32689567294,0.419831643236,0.373067540312,0.480221413956,0.402698048238,0.132510347497,0.103606727908,0.113399612689,1.0
--5564803709195,381085,32992841,LABNTBNP,2013-02-10 13:52:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0472769889174,0.0506922527496,0.0248715313464,0.00833333333333,0.125052545092,0.2,0.2,0.1,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.412141491628,0.360897904048,0.437934201048,0.278014807113,0.381474084797,5.4216921935e-08,2.09487226683e-20,6.20632228691e-33,0.0
-7681240090675,465808,37357679,LABNTBNP,2013-08-27 12:48:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0918957470081,0.112965708125,0.115656580228,0.117625191054,0.0758822524751,0.0111449864499,0.026069058149,0.0483266800248,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.352455895194,0.417135381356,0.397083262336,0.480406026422,0.399357900702,0.0055829781097,0.00067165621828,0.000287127893885,1.0
--9382561257211,424598,40137783,LABNTBNP,2013-12-06 07:34:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.170118375954,0.0716468002766,0.111311035955,0.11053487115,0.0491465690497,0.0138376149227,0.065718209122,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.446599548135,0.438918049659,0.479433750708,0.489953541481,0.440930542079,0.413841556057,0.408805703086,1.0
--3863113421599,408067,39344652,LABNTBNP,2013-10-30 09:34:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0251841195768,0.0308324006567,0.0,0.0,0.0399282277002,0.0,0.0,0.00414951989026,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.237770478445,0.258275387451,0.265512527821,0.268883275022,0.369500276069,0.000158877669393,5.12988143549e-09,8.66015163653e-14,0.0
-1609054775487,389097,36234157,LABNTBNP,2013-07-24 14:20:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0426795322215,0.0654132890044,0.224871531346,0.0031914893617,0.094349486217,0.0,0.0,0.0391135392675,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.295498230527,0.310422255783,0.354090281151,0.297849847567,0.384801213174,4.84040397261e-07,2.22154915935e-16,1.64056357435e-22,0.0
--11142643970418,370253,35374986,LABNTBNP,2013-05-30 20:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0924617402835,0.0666229825386,0.159678762748,0.231873479319,0.2,0.0647017248963,0.2,0.00137931034483,0.0681818181818,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.377598349305,0.343948397092,0.438918049659,0.306868865197,0.396353216243,7.36157783592e-05,2.54621758635e-08,1.02695150367e-09,0.0
-4481122938031,383031,31666739,LABNTBNP,2013-01-13 21:08:00,1,0,1,0,0.0410509031199,1.0,1.0,0.092533686654,0.18265279333,0.164684941958,0.0716468002766,0.172629344069,0.0992618830095,0.0645926969958,0.0845899470899,0.0634542290898,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.380594696426,0.35046836962,0.438918049659,0.30852348053,0.40230350083,0.025294248066,0.00114670745708,0.000130679435195,3.75900830962e-57
-11315402498791,385393,38875314,LABNTBNP,2013-10-31 16:10:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.263570436237,0.231795125884,0.160653823645,0.271709872232,0.255340833795,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.227765931535,0.203896312421,0.190536842807,1.0
-2046873245049,356922,38424839,LABNTBNP,2013-10-14 22:14:00,1,0,1,0,0.266666666667,0.0,0.0,0.285172855314,0.222307235401,0.151106061839,0.405309734513,0.175,0.241668277925,0.430434782609,0.5,0.200647249191,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.412793285604,0.463437764316,0.448250902585,0.484342536931,0.489355533335,0.0358497593765,0.0141734227928,0.00860728199851,1.0
-2572643851659,370098,32229598,LABNTBNP,2013-02-04 08:26:00,0,0,1,0,0.31045751634,1.0,1.0,0.229622756363,0.26954755327,0.215526210204,0.138549925937,0.343565961823,0.313738228637,0.507564720048,0.301847045238,0.160953953055,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.450545891332,0.475316760739,0.501114185087,0.497542616961,0.496187089893,0.449126897519,0.468938749629,0.456738025008,1.0
-4547820182887,437664,33216553,LABNTBNP,2013-03-08 12:12:00,1,0,1,0,0.0410509031199,0.0,0.0,0.17397362237,0.177970295449,0.171419677156,0.177430088081,0.316475091134,0.178969577425,0.0236449864499,0.465284649213,0.205373816128,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.393901459306,0.449536613459,0.438918049659,0.496768069481,0.500165713456,0.0415131524077,0.0104767388436,0.00724903893178,1.0
-6274040833440,420418,35122843,LABNTBNP,2013-05-29 17:50:00,0,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.211067108177,0.212200728338,0.356051020419,0.17491388613,0.168195932499,0.154834519492,0.24335368549,0.165304937169,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444366844526,0.475056895236,0.483487122071,0.495719233721,0.498920986106,0.326681415131,0.243239743651,0.202604210339,1.0
--8387576447734,361211,33116980,LABNTBNP,2013-02-24 07:23:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0313146260991,0.0302004748001,0.075092665629,0.0,0.2,0.109173274824,0.0,0.2,0.0666666666667,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.402585193861,0.443883203103,0.420655632557,0.477406065616,0.481348922335,0.00822830366675,0.00144210801362,0.0015169256054,1.0
-8970554213668,369569,38281924,LABNTBNP,2013-10-04 12:10:00,0,0,1,0,0.31045751634,0.433333333333,1.0,0.229622756363,0.203841588136,0.188036009019,0.179017527596,0.20097925654,0.217653878085,0.14851839246,0.174251361738,0.22286650349,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.443503955655,0.473385800694,0.483487122071,0.497973877033,0.498089194233,0.240710015595,0.228201827969,0.215726703621,1.0
--4729712743326,455849,38723018,LABNTBNP,2013-10-03 09:08:00,1,0,1,0,0.31045751634,0.0,0.0,0.507757875682,0.249525788095,0.306427016281,0.223611111111,0.262524425929,0.313113553114,0.2,0.45,0.17037037037,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.425179460852,0.450841957289,0.406706563032,0.465166995481,0.482624230565,0.053794964169,0.01843237603,0.0105509320571,0.0
-8601238945172,405751,38315360,LABNTBNP,2013-10-06 09:56:00,1,0,1,0,0.31045751634,0.0,0.0,0.217839316873,0.194793994627,0.245911067508,0.367171726278,0.276423560937,0.204053081099,0.2,0.425794196042,0.372481080307,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.450950094047,0.479086567289,0.532428519263,0.499760785998,0.503163416104,0.229010769089,0.212855577517,0.167749455672,1.0
--3229795734163,450913,40170859,LABNTBNP,2013-12-11 18:16:00,1,0,1,0,0.31045751634,0.0,0.0,0.49751638902,0.233709753848,0.27454918879,0.278048780488,0.142815249267,0.283531273065,0.0,0.3,0.347625514403,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444484603731,0.393576683701,0.48701349549,0.320583219916,0.400120379561,0.340119000475,0.0653074851106,0.0176107490413,0.999999998566
--7776091268135,398786,35277635,LABNTBNP,2013-05-20 10:51:00,0,0,1,0,0.0410509031199,1.0,1.0,0.203391607554,0.209451351827,0.180945960978,0.172076835669,0.229482063857,0.153982531774,0.6,0.556018518519,0.295068104243,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.395987138868,0.44708541992,0.41456763533,0.493103240226,0.49573936421,0.593895424675,0.498760973855,0.502235207084,1.0
--4741467816456,409430,35739178,LABNTBNP,2013-06-08 07:07:00,1,0,1,0,0.31045751634,1.0,1.0,0.191597128394,0.269617344597,0.279341849316,0.0620244858335,0.296413524316,0.24535125287,0.4,0.137815442561,0.449847597315,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.410771776521,0.455516629214,0.437598107519,0.484863565197,0.495738948879,0.24987397834,0.168065201427,0.0117978522546,1.0
-5058982857080,360647,38401228,LABNTBNP,2013-10-15 11:04:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0569693000215,0.119388187202,0.0698806600966,0.0248715313464,0.153429432137,0.068777657683,0.0,0.0,0.1,0.251407126031,0.177898107878,0.194705230325,0.172133499626,0.352083904922,0.330695982723,0.371876380278,0.309159182691,0.389236404485,0.0983890410571,0.00862897816475,0.00419282530747,3.43833528895e-45
-7804886422910,380931,40186998,LABNTBNP,2013-12-29 15:53:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.223453856374,0.218717687085,0.254066890137,0.2380945914,0.234384844545,0.271709872232,0.261114753733,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.305872650726,0.310564493718,0.306060666105,1.0
--5225087125457,439888,35104975,LABNTBNP,2013-05-18 17:52:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109732838523,0.0838570965728,0.166236223208,0.0170569620253,0.12884057971,0.18804818751,0.0,0.4,0.335241301908,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.384904508041,0.441524212172,0.41456763533,0.483753309266,0.493791982142,0.802969600243,0.993418935329,0.999994595036,0.0
-283125696064,410489,31790490,LABNTBNP,2013-01-10 11:32:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.0959808014462,0.108958736249,0.0839115695626,0.0999188058988,0.0722183335822,0.0111449864499,0.067350677036,0.0391615419408,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.380762061559,0.437347283128,0.438918049659,0.489001704715,0.406953517569,0.00162964457369,9.8843780082e-05,2.62283943211e-05,1.0
--4987673030454,412643,33844935,LABNTBNP,2013-03-21 22:18:00,1,0,1,0,0.31045751634,1.0,1.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.249948747005,0.2380945914,0.156318806809,0.231742004159,0.278542106207,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.498516267647,0.500319551472,0.326484081538,0.354798128347,0.377865789365,1.0
--12259431767593,431752,39618229,LABNTBNP,2013-11-13 13:19:00,1,0,1,0,0.0410509031199,0.0,0.0,0.134733830903,0.11700551036,0.154269052988,0.235216358933,0.0766724674023,0.0752134398194,0.0,0.0056451833607,0.0875946906578,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.384220563996,0.438222594509,0.438918049659,0.477183261327,0.4873891495,0.128523930777,0.0923561665161,0.0642132610842,1.0
-2766881279042,429993,36120511,LABNTBNP,2013-07-06 10:41:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0269550822441,0.0686111382094,0.2,0.2,0.0769406419064,0.0,0.1,0.0,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.395715014447,0.351175534792,0.413598133989,0.446516475777,0.468245324238,0.0464290683261,0.0329505102679,0.00511197764208,6.36989388878e-231
--7536584978738,468158,39230348,LABNTBNP,2013-11-04 08:03:00,0,0,1,0,0.31045751634,0.0,0.0,0.63610861236,0.307868776551,0.383763695281,0.489159891599,0.46199856984,0.282279239671,0.27,0.469253707286,0.345548774237,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.47269166567,0.49547132933,0.506105378625,0.510199865033,0.503706021124,0.427785879242,0.225871670272,0.0525749398236,3.90888486482e-19
--5564803709195,381085,33364428,LABNTBNP,2013-02-24 03:35:00,1,0,1,0,0.31045751634,0.0,0.0,0.0999584259792,0.304194700287,0.0906213461579,0.0248715313464,0.0121052631579,0.22326075874,0.0516556291391,0.128723404255,0.168574635241,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.405014524477,0.346192740801,0.440361375919,0.304197749125,0.394385373227,0.00334825266816,2.14448118772e-07,7.2887932019e-11,0.0
--3719298338362,414081,36767474,LABNTBNP,2013-07-19 10:55:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.262925545333,0.233397252798,0.184194393664,0.268952706319,0.209786792043,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.475414910236,0.483487122071,0.492706040822,0.496593933334,0.185824619073,0.113467851122,0.0774548724896,1.0
--4728516030125,385340,39257849,LABNTBNP,2013-10-25 20:00:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0637824358693,0.147562717033,0.118969779093,0.0922115178421,0.106774193548,0.0796780614554,0.0614117267,0.2,0.0754721821388,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.380204393833,0.433676383664,0.413971967219,0.476393658592,0.486172647143,0.0262419100741,0.00223756362254,0.000794242054574,0.999999993263
--2080218697898,448998,36341312,LABNTBNP,2013-07-07 15:14:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.113572826881,0.151829995372,0.083783100909,0.19434817298,0.0818278206068,0.1,0.139068146284,0.117905946077,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.400910463118,0.446914205044,0.41456763533,0.473560514156,0.487011956158,0.0531100601702,0.0205825808286,0.0131163319697,1.0
-4405659959242,406346,34271454,LABNTBNP,2013-04-24 21:43:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.271722628322,0.225518668009,0.217841470592,0.193846155739,0.196812353523,0.194442279958,0.213414301748,0.364482413458,0.145773568665,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.482067704348,0.483487122071,0.488906373635,0.491661911989,0.136305507406,0.0653058209293,0.0496504087694,1.0
--5615673817976,380136,38136528,LABNTBNP,2013-09-19 20:49:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.226624062695,0.258246258905,0.200266618257,0.243823609813,0.254256214641,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.258196754243,0.279417175899,0.289555331916,1.0
--5192376130357,471745,34525595,LABNTBNP,2013-04-22 09:21:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.0910958830763,0.122011604955,0.24677526893,0.0847370262982,0.107604334862,0.0134705678452,0.00668470579792,0.038142779638,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.379771002359,0.438547127031,0.438918049659,0.479433750708,0.489635108103,0.369524072287,0.34327065555,0.347536057815,1.0
-9492338133118,397734,31951408,LABNTBNP,2013-01-26 08:52:00,1,0,1,0,0.0410509031199,0.0,0.0,0.109358237755,0.0774441871754,0.105378346345,0.083783100909,0.0734270187891,0.0787785400929,0.0111449864499,0.162350677036,0.018271906727,0.0569331265889,0.0827159126116,0.139222915466,0.131547122571,0.355948897304,0.42286796737,0.397083262336,0.480406026422,0.398712666325,0.000981982999022,8.73371360292e-05,3.1082834305e-05,1.0
--2843713726506,361085,32224887,LABNTBNP,2013-01-14 15:44:00,1,0,1,0,0.31045751634,0.0,0.0,0.178441139964,0.207764052883,0.214903794073,0.150754790616,0.276986246455,0.263014830701,0.232090411748,0.183865990835,0.260426870675,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.484897982958,0.506105378625,0.497422233521,0.498952225844,0.437605081401,0.514018757972,0.505638780024,1.0
--148699056088,449032,33423434,LABNTBNP,2013-03-03 11:40:00,1,0,1,0,0.31045751634,0.0,0.0,0.271722628322,0.225518668009,0.215988592548,0.218717687085,0.22542783513,0.260371245982,0.223807188277,0.352205162862,0.234487949102,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.472103041348,0.483487122071,0.488749539366,0.491336846981,0.160215867813,0.0944963798876,0.0671659881091,1.0
-11774459620238,433165,36152776,LABNTBNP,2013-07-19 15:16:00,1,0,1,0,0.0609756097561,0.0,0.0,0.142265149854,0.175609769769,0.152401197084,0.135942246982,0.153767741167,0.186457113145,0.0,0.030651355049,0.0723819683717,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.407623772357,0.45193437248,0.463152887311,0.487535260538,0.484200871641,0.22277207993,0.218096958969,0.209066505047,1.0
--7702970983950,409778,39234941,LABNTBNP,2013-10-24 16:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.0803651898748,0.160621913995,0.0397733209579,0.0780835726521,0.0376447179678,0.116835016835,0.0161656519533,0.00143049155146,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.371318327391,0.344963499161,0.41456763533,0.29924502189,0.395444431143,0.0129318683961,0.00163979243882,0.00048173859918,1.0
-10768645818560,419681,35332191,LABNTBNP,2013-06-05 06:53:00,0,0,1,0,0.31045751634,0.0,0.0,0.6,0.26474989374,0.469015523534,0.211111111111,0.3625,0.40806403545,0.2,0.1,0.537482853224,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.431713396517,0.466813975277,0.485536256596,0.490253258013,0.491799771084,0.0267440439479,0.00111381311452,6.26703372951e-08,7.42806488491e-210
-2824835806388,367949,35731798,LABNTBNP,2013-06-19 15:24:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.225518668009,0.180515512612,0.356051020419,0.21048280264,0.208715440959,0.198708843341,0.174251361738,0.175297659546,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.443503955655,0.468841410864,0.483487122071,0.491761991438,0.493141289077,0.224370230561,0.190306985777,0.198082394095,1.0
-1544801690863,431749,37861869,LABNTBNP,2013-09-28 07:56:00,1,0,1,0,0.0410509031199,0.0,0.0,0.106052191699,0.0868384019492,0.112488263515,0.0248715313464,0.00217391304348,0.0549006887963,0.245432098765,0.301379310345,0.150793650794,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.354779349476,0.425466775566,0.413843085856,0.485149459162,0.485863163115,0.0376768564348,0.00454626111773,0.000376585453439,2.72051179577e-21
-2537661816480,384197,33021679,LABNTBNP,2013-03-04 12:27:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.179147252524,0.340791035449,0.189992229286,0.175232633658,0.14851839246,0.173290506015,0.229722531531,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.489371523919,0.495327931516,0.191844994133,0.169909035862,0.163937446563,1.0
--10971316949232,457449,32832864,LABNTBNP,2013-02-11 22:09:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0678423374468,0.0670079522312,0.101998314482,0.0364304413441,0.0482666078938,0.0783441488568,0.00975609756098,0.0125297300539,0.0890745892369,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.311909222471,0.307825936355,0.361130860539,0.46551437057,0.393816498975,0.0151470769671,0.00593219518869,0.00467155159536,1.86959067065e-272
-684073278220,465967,33716562,LABNTBNP,2013-04-02 08:13:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.0700181739976,0.0910958830763,0.118455198208,0.0716468002766,0.0723084548697,0.0798002309705,0.0231449864499,0.00668470579792,0.0276296204882,0.0569331265889,0.133408726627,0.196453517751,0.190329930786,0.374944805507,0.426845546254,0.438918049659,0.475138539787,0.48748417813,0.0598455540055,0.0235131361852,0.0163351659546,1.0
--9556876538000,402570,38366164,LABNTBNP,2013-09-21 14:29:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.391722628322,0.180039864334,0.15872896535,0.354145996249,0.285541626169,0.165328254833,0.301787313555,0.114985231043,0.153331715947,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.450139689074,0.44074121791,0.49555512639,0.400691630201,0.078122487753,0.0472771413435,0.0407916932642,1.42997937838e-90
-4213071687369,418294,36896440,LABNTBNP,2013-08-13 01:36:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0288826679603,0.123499712243,0.0930567635804,0.0,0.0,0.0695070948496,0.0,0.0,0.0666666666667,0.0569331265889,0.129774155422,0.158389455943,0.148324352893,0.257330168966,0.274233404457,0.279893021337,0.434127007204,0.46984858962,7.04672675562e-05,1.27309149546e-08,2.23106207324e-15,0.0
-5390204664568,439401,31980307,LABNTBNP,2013-01-22 22:22:00,0,0,1,0,0.31045751634,1.0,1.0,0.216185905158,0.32749562018,0.27813250909,0.517787557729,0.412644904736,0.257327244802,0.455909090909,0.13873783427,0.456373627266,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.465972116394,0.490832955535,0.483487122071,0.491846936517,0.497062388827,0.344541729863,0.350489625849,0.286710489791,1.0
--3662715795508,364994,37019404,LABNTBNP,2013-08-07 04:09:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.140763322897,0.256299078454,0.112552415265,0.0811307159204,0.0134705678452,0.102073754789,0.0592532345743,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.440020385839,0.438918049659,0.478595085158,0.489533645397,0.0927831625751,0.0565836290913,0.0529793734706,1.0
-2867281800534,435091,39140990,LABNTBNP,2013-11-08 12:10:00,0,0,1,0,0.588235294118,0.0,0.0,0.229622756363,0.286048334421,0.213145033771,0.296020295468,0.281456339112,0.293497362181,0.37185376283,0.35291132199,0.347735120604,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.486950168077,0.483487122071,0.502277970103,0.501283176651,0.429507485837,0.228400844731,0.152384856761,1.0
-6515305398395,364580,32041621,LABNTBNP,2013-01-25 10:02:00,1,0,1,0,0.31045751634,0.0,0.0,0.176797897606,0.0864434057559,0.125814102318,0.237371531346,0.102173913043,0.202783961155,0.00689655172414,0.05,0.0817061487895,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.404718004169,0.442709467825,0.411279023807,0.478372245,0.488622176015,0.000983558263476,6.10752122855e-06,1.01459700441e-06,3.12324990313e-126
--7702970983950,396342,38732416,LABNTBNP,2013-10-04 21:20:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.0803651898748,0.154139448533,0.0588209400055,0.0780835726521,0.0489836645441,0.0747671156004,0.00875824454592,0.100604656274,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.374659837799,0.343251782651,0.438918049659,0.299192684907,0.395414596329,0.0494743620302,0.0136297595783,0.00554254871764,1.0
-10677083575176,381337,35347663,LABNTBNP,2013-06-08 10:05:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.155283781064,0.150216099117,0.188131187072,0.133167049225,0.175181922828,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.489371523919,0.493787532995,0.032423442755,0.00554970179503,0.000760063239148,0.0
--7316392675813,461163,38897497,LABNTBNP,2013-10-19 04:57:00,1,0,1,0,0.0609756097561,1.0,1.0,0.41823501973,0.148947766473,0.216499905973,0.244373479319,0.446711543115,0.196694268893,0.409756097561,0.4,0.249158249158,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.399410379566,0.448215203175,0.466878395429,0.486694312688,0.484988393749,0.024684643255,0.000307461233824,4.66654499887e-06,0.0
--10110890094159,424827,39333982,LABNTBNP,2013-10-26 11:17:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0313146260991,0.0497874471859,0.103266029589,0.0,0.0,0.0463356683798,0.2,0.1,0.0333333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.402585193861,0.350861331531,0.420655632557,0.445871800274,0.469674683594,0.00026089462872,9.36384037993e-08,1.97824717373e-11,2.05041320907e-301
--2602974885354,398567,38511867,LABNTBNP,2013-10-02 19:51:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.246060202114,0.241164653196,0.19996882195,0.245988111978,0.286621263284,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.501976475203,0.227525757356,0.189173483038,0.181530670966,1.0
--2167953395536,438022,39571587,LABNTBNP,2013-11-15 18:39:00,0,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.102637310084,0.174308653113,0.105311752642,0.235193000711,0.160508909727,0.2,0.16855938091,0.186368330847,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.406352621066,0.454469236954,0.438918049659,0.492380946257,0.49618218989,0.193338596687,0.148341576779,0.178658697492,1.0
--7504139965457,458067,32547530,LABNTBNP,2013-02-06 23:17:00,1,0,1,0,0.0609756097561,0.0,0.0,0.331358318952,0.252435258265,0.219851234875,0.0774240483222,0.161745186643,0.239749242651,0.216296296296,0.3,0.366666666667,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.419692344878,0.463763196931,0.480728589251,0.486497811642,0.4895824469,0.0579198719885,0.0316687743302,0.0256456019894,1.0
--8996911702299,361230,35237643,LABNTBNP,2013-05-15 06:59:00,1,0,1,0,0.31045751634,0.433333333333,0.0,0.229622756363,0.225518668009,0.212777140388,0.218717687085,0.286859194623,0.294562972143,0.378375089511,0.327117033254,0.326427054679,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.480292477196,0.483487122071,0.497973877033,0.500048352841,0.296455197689,0.29846693387,0.337765775818,1.0
-5154725986650,381424,37067533,LABNTBNP,2013-08-22 11:46:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.187410805453,0.206783019741,0.243954022847,0.188200696109,0.174858273478,0.246343873518,0.195203628982,0.188953972662,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444366844526,0.471141138747,0.483487122071,0.506128905777,0.503305677189,0.415157380362,0.313662251448,0.248423351386,1.0
-11780768230481,429866,31870199,LABNTBNP,2013-01-10 17:31:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.271722628322,0.225518668009,0.217841470592,0.293846155739,0.246974274616,0.224821560447,0.213414301748,0.294690746792,0.211335630776,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.486950168077,0.483487122071,0.494173159118,0.49347537971,0.237551394994,0.166429234726,0.0955979751158,1.0
-9369713386529,369520,38011426,LABNTBNP,2013-09-20 13:19:00,1,0,1,0,0.0410509031199,0.0,0.0,0.051023579848,0.058005461535,0.0485287776718,0.0373715313464,0.000990099009901,0.076971379014,0.0,0.0,0.0697482593861,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.352365303543,0.337632886836,0.375516322768,0.319113945811,0.40418194631,0.00550799473944,5.00607936661e-05,2.70665734774e-06,1.24615530972e-150
--4450900061028,437958,40111852,LABNTBNP,2013-12-09 22:00:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0316424949516,0.0812907962033,0.0670197292802,0.0552962298025,0.201104972376,0.0728333139462,0.0,0.1,0.0,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.404259279759,0.446858207379,0.446552764445,0.481125366209,0.482966643104,0.0190062328263,0.00157254445113,0.000104739645444,1.0
--5626510699508,436649,38113841,LABNTBNP,2013-09-18 17:08:00,1,0,1,0,0.31045751634,0.0,0.0,0.429372329793,0.324292535889,0.22673173924,0.386844207767,0.138435307683,0.214069095303,0.507564720048,0.355082017477,0.179704271946,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.425486067534,0.458943614819,0.457944119134,0.490592919823,0.488059546281,0.259264440002,0.346461102732,0.348962747887,0.997824028057
-3540622955713,383632,37668680,LABNTBNP,2013-09-12 15:16:00,1,0,1,0,0.31045751634,0.0,0.0,0.122161422829,0.0969859650726,0.127003676266,0.00530973451327,0.0210526315789,0.110669760048,0.0,0.0,0.110920617798,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.420524224196,0.465459209812,0.432171865309,0.485393487831,0.491529945915,0.235343321192,0.233057073543,0.180235160468,1.0
--7019574618537,414105,36155789,LABNTBNP,2013-06-24 23:30:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.16075004528,0.0811706098004,0.0988824645265,0.111666168333,0.0622671156004,0.0454930669586,0.0497053177658,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.441241534169,0.438918049659,0.481712941559,0.491094451375,0.329500993475,0.282798005281,0.270676475026,1.0
-1173998086216,447845,37013357,LABNTBNP,2013-08-17 20:58:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0126008194703,0.0692697507752,0.0755090076826,0.0301812658596,0.1109127868,0.0914843460037,0.0304347826087,0.0,0.075,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.353955634854,0.419917794022,0.390532528261,0.46899442348,0.481125524175,0.120300324051,0.0557727499964,0.0314981772132,1.0
--772099392318,408766,39447016,LABNTBNP,2013-11-04 16:27:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.201862365286,0.212216453737,0.218717687085,0.24017639684,0.22778269582,0.19996882195,0.22760580954,0.226503713071,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.24449230674,0.282951073795,0.277454527835,1.0
-11574063609057,397545,38544428,LABNTBNP,2013-10-12 09:36:00,1,0,1,0,0.31045751634,0.0,0.0,0.271722628322,0.14351864322,0.161979416598,0.0318734793187,0.102173913043,0.194484455582,0.0516556291391,0.0550820174766,0.179844780055,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.452366031862,0.475906531961,0.473784509176,0.487732211725,0.48825112027,0.0191282108369,0.000481324400946,1.67330095492e-06,7.39050364492e-186
-9785495679440,448423,31858364,LABNTBNP,2013-01-17 04:30:00,0,0,1,0,0.31045751634,0.433333333333,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.249361484419,0.267571456022,0.223807188277,0.279976828508,0.31516327141,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.497973877033,0.499228052555,0.344647555818,0.349002946337,0.340873603471,1.0
--8576387523146,399040,39620951,LABNTBNP,2013-11-14 16:43:00,1,0,1,0,0.31045751634,0.245283018868,1.0,0.190282692606,0.183106288112,0.195304218883,0.174707907134,0.168946311571,0.206900458267,0.162620650954,0.190764449179,0.136617285168,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.43826559462,0.46439703209,0.483487122071,0.490679796307,0.495580592364,0.234065916213,0.164722846709,0.149010403826,1.0
-6645493692299,416937,33238702,LABNTBNP,2013-03-13 17:15:00,1,0,1,0,0.230769230769,1.0,1.0,0.339664205114,0.18456973058,0.282294445305,0.2,0.365562529253,0.35039378626,0.2,0.32288504778,0.299383867162,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.441248337131,0.478640986472,0.483487122071,0.502006570878,0.499990293481,0.537303584921,0.80626598019,0.552556509245,1.35041665472e-58
-818658955660,468619,39418362,LABNTBNP,2013-11-26 00:24:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.16075004528,0.0716468002766,0.112552415265,0.155596687488,0.0654320987654,0.210266186351,0.0845616361263,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.441241534169,0.438918049659,0.483965018464,0.492221505496,0.141856910928,0.0883036416163,0.0688132684726,1.0
-1913672088780,356306,38849263,LABNTBNP,2013-11-03 14:02:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.113353363978,0.150204551855,0.166938826649,0.0796932072266,0.102731871414,0.0593209876543,0.0123114679342,0.0258943052828,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.472889896951,0.486576377303,0.0262283639199,0.00573079723202,0.00273241497246,1.0
--328786632540,429730,32441331,LABNTBNP,2013-01-20 18:35:00,1,0,1,0,0.0609756097561,0.0,0.0,0.368148852532,0.301267911737,0.22587344011,0.455774485833,0.0461117500012,0.340067654897,0.0,0.4,0.262880658436,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.389649946709,0.355378389499,0.425439591302,0.471484667277,0.407781478875,0.168415748405,0.117750608304,0.000647972186404,0.0
--7151978809607,398999,38451254,LABNTBNP,2013-10-01 00:08:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0440094701006,0.0273719390063,0.0248715313464,0.0,0.00367838713231,0.0,0.0,0.00414951989026,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.264526030058,0.209204550784,0.276029071056,0.154564917645,0.289994972102,1.30924228233e-09,6.83214310566e-21,3.03257703672e-28,0.0
-6196573796920,398947,35419624,LABNTBNP,2013-06-12 07:32:00,1,0,1,0,0.0410509031199,0.0,0.0,0.12969791386,0.14866217829,0.355975241531,0.6,0.441805455467,0.291018030395,0.0,0.229615457294,0.231619656385,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.426750084832,0.468625917541,0.456355863602,0.484885896489,0.489268417187,0.166722747532,0.204357041824,0.235766126429,1.0
-5932151281573,402893,37338496,LABNTBNP,2013-08-29 14:16:00,1,0,1,0,1.0,1.0,0.0,0.963636363636,0.475968526868,0.575584649873,0.444009779951,0.432538899688,0.637178849983,0.890566037736,0.628571428571,0.629900204246,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.442098032028,0.485383614682,0.483487122071,0.505610082198,0.503046240097,0.208483871311,0.329857144359,0.432360848129,1.0
--453934241489,410161,32209529,LABNTBNP,2013-01-20 11:45:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.179741783774,0.168366348798,0.14851839246,0.161415506015,0.161991586098,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.489371523919,0.404819176017,0.144421644872,0.120525169647,0.107499808268,1.0
-1341615895726,464306,33063158,LABNTBNP,2013-03-14 17:42:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.0700181739976,0.0910958830763,0.111531186904,0.0716468002766,0.07706287589,0.0754569588556,0.0289166957913,0.0168502168693,0.023228560256,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.339764375095,0.408889870588,0.397083262336,0.47312675875,0.486476270988,0.097794233253,0.0613846183711,0.0544144915861,1.0
-9369713386529,369520,37948964,LABNTBNP,2013-09-17 18:49:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.155893023334,0.271646800277,0.0821993093467,0.108333393989,0.2622671156,0.00739782886335,0.0770593729799,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.43363233127,0.438918049659,0.487666450254,0.492872678145,0.0131491459609,0.00202695066681,0.000567576294961,1.0
--9235283482346,443044,31977358,LABNTBNP,2013-01-03 13:07:00,1,0,1,0,0.31045751634,1.0,1.0,0.369623736622,0.150256345447,0.230659892445,0.494540145985,0.213742472153,0.314435484297,0.616296296296,0.3,0.473777631386,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.437744542416,0.456686954207,0.501114185087,0.477323890027,0.49116428718,0.250341627775,0.0284994125783,0.000334998421908,9.87469838694e-277
-6342010574001,438929,34377970,LABNTBNP,2013-04-21 07:18:00,0,0,1,0,0.31045751634,0.0,0.0,0.439032691446,0.379810153983,0.419465911442,0.517787557729,0.309829493088,0.437528764628,0.406896551724,0.436363636364,0.434144801353,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.468233029437,0.482982487421,0.458685459748,0.494657897993,0.497716423062,0.341326004122,0.556086521651,0.637389344913,1.0
-1291722776260,369532,39223823,LABNTBNP,2013-11-11 16:22:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.210113665928,0.218717687085,0.266841923815,0.2380945914,0.200266618257,0.271709872232,0.234738168161,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476977524668,0.483487122071,0.491761991438,0.494279975399,0.127410814569,0.100725670164,0.0910121626709,1.0
-363626077792,371922,38213521,LABNTBNP,2013-10-04 07:24:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0531994163988,0.0568574145655,0.0598663686687,0.256745010665,0.000990099009901,0.109383905673,0.2,0.0,0.00414951989026,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.359431025404,0.341199607937,0.396714355929,0.311579289945,0.39906662774,0.01344597884,0.00156509898981,0.00445845731912,0.0
--10692952279966,428737,35950898,LABNTBNP,2013-06-11 07:32:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0339062239531,0.0782664122634,0.0390461344159,0.0157574957073,0.0193392677916,0.0132806985607,0.4,0.0,0.0667572463768,0.0569331265889,0.0803343830243,0.106636970922,0.100898152284,0.26802686118,0.353790706107,0.298420618607,0.436763717865,0.466771623383,0.00515080873266,0.00105126833573,0.000384703133631,1.0
--11142643970418,370253,35355965,LABNTBNP,2013-05-31 20:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0924617402835,0.0666229825386,0.153530699392,0.0818734793187,0.0114285714286,0.0753082890423,0.0,0.00137931034483,0.0666666666667,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.373233505904,0.341851212736,0.412177921065,0.307892078421,0.398347981623,0.000198831213199,1.32417083915e-07,3.10997649567e-09,0.0
-11043441824874,361188,38190221,LABNTBNP,2013-10-07 22:00:00,0,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.161224364103,0.115656580228,0.162643618697,0.126231655609,0.0927018982091,0.0840030303262,0.124803319201,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.486748297647,0.493614062999,0.141225214642,0.0984680060871,0.0837547231518,1.0
--10569442774829,411908,38216212,LABNTBNP,2013-09-15 19:37:00,1,0,1,0,0.0558659217877,0.0,0.0,0.229622756363,0.148561960961,0.173224054679,0.156051020419,0.0455631949723,0.156379386024,0.107564720048,0.248912622294,0.16142197994,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.42421413349,0.466527439724,0.501114185087,0.496214175172,0.496945015045,0.16125130425,0.161892943576,0.078685153492,1.0
--3897184476412,384581,36971696,LABNTBNP,2013-07-29 07:11:00,1,0,1,0,0.0410509031199,0.0,0.0,0.164956657174,0.117198902615,0.275277439875,0.415004723219,0.321452733259,0.293564566108,0.4,0.500694444444,0.497248099976,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411136336985,0.455303436602,0.461313482722,0.480994164389,0.492543524356,0.262317689117,0.199265690728,0.178153463464,1.0
-5374019938279,400244,31566538,LABNTBNP,2013-01-03 16:33:00,1,0,1,0,0.31045751634,0.0,0.0,0.229622756363,0.211067108177,0.198222152595,0.179017527596,0.167000769725,0.170652800228,0.164590617053,0.200707991087,0.135241013661,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.444366844526,0.473821521044,0.483487122071,0.495719233721,0.494067274887,0.107089659996,0.0861531856527,0.0775591622478,1.0
-315494922515,361054,37840543,LABNTBNP,2013-09-15 07:59:00,1,0,1,0,0.0609756097561,0.0,0.0,0.064188593653,0.0534820184277,0.121469552857,0.2,0.00217391304348,0.0582762705525,0.2,0.0108888888889,0.0786096764528,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.379326739821,0.20352246165,0.403481276778,0.299558519607,0.311567482885,0.000253320021828,2.21374246639e-09,7.61475383092e-18,0.0
--1629241041207,400609,32178603,LABNTBNP,2013-01-13 11:29:00,0,0,1,0,0.31045751634,1.0,1.0,0.154867364174,0.364758825853,0.238812499692,0.00530973451327,0.114392704169,0.282470310602,0.2,0.439759887006,0.429872579297,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.459940006945,0.486195230803,0.506105378625,0.493692644522,0.495548745472,0.450742407351,0.234032431564,0.0643977598694,1.0
--3633826710510,443957,33784115,LABNTBNP,2013-03-13 13:11:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.249361484419,0.2380945914,0.223807188277,0.305971766872,0.268260550466,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498920986106,0.286625807218,0.289806717459,0.291035315694,1.0
-135854874945,384611,32865925,LABNTBNP,2013-02-16 07:42:00,1,0,1,0,0.31045751634,1.0,1.0,0.439032691446,0.425763047335,0.486755255717,0.262263748205,0.280508472316,0.38111436503,0.806896551724,0.636363636364,0.322050321954,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.456186315394,0.485999878561,0.458685459748,0.497479547997,0.499879011454,0.378258780312,0.263365396933,0.132671098162,1.0
--228622613739,426268,34205972,LABNTBNP,2013-04-01 17:44:00,0,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.193995338207,0.166518731023,0.164590617053,0.144508197637,0.172576503667,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.487118037209,0.493480515271,0.132729496677,0.113381955782,0.10932856699,1.0
--4533809802835,453868,40335267,LABNTBNP,2013-12-20 01:21:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.155893023334,0.0716468002766,0.112552415265,0.103241540703,0.0602576801608,0.017490520952,0.0707597629471,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.43363233127,0.438918049659,0.477929505638,0.489200376185,0.102161121087,0.053370595327,0.0405985535519,1.0
--5290845299915,408086,40167570,LABNTBNP,2013-12-05 10:41:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.174234997998,0.179017527596,0.1555587336,0.156401463623,0.187833390765,0.110444879126,0.101982008723,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.441604158573,0.44074121791,0.484106935849,0.491153965116,0.0649302700177,0.0232453465402,0.0117767520755,1.0
--1273486490412,457419,34971496,LABNTBNP,2013-05-03 23:53:00,0,0,1,0,0.0609756097561,1.0,1.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.18884005154,0.164657557929,0.160255600218,0.168117288777,0.239915790824,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.48991368847,0.494878891933,0.346827539332,0.385286621035,0.398456836651,1.0
--2701082086849,471749,34706214,LABNTBNP,2013-05-04 19:51:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131801804041,0.178891712637,0.127927743569,0.0318734793187,0.126107562333,0.0512034431306,0.0,0.118718381113,0.145648871049,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.385081275506,0.347307492396,0.456355863602,0.464166305555,0.479194790467,0.111917669116,0.160634799259,0.12360099805,1.0
-3137907813374,396176,37708440,LABNTBNP,2013-09-20 11:28:00,1,0,1,0,0.0558659217877,0.0,0.0,0.31751845922,0.242935215061,0.223796150943,0.27588325927,0.254818400172,0.382378437771,0.451655629139,0.604610951009,0.302857142857,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.440714237177,0.475292173837,0.501114185087,0.502216208479,0.493154956799,0.301799628892,0.175712636686,0.0984551998662,1.0
--9764128998478,375473,36695514,LABNTBNP,2013-07-15 22:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0282445447275,0.022951093954,0.0,0.1,0.00281152525546,0.0,0.0,0.0,0.0569331265889,0.0803343830243,0.110719720878,0.100898152284,0.288764315357,0.232273439484,0.316219495366,0.282466949998,0.377559258828,0.00145681075929,2.44055588226e-06,3.9369515873e-09,7.44989120931e-234
-11171325410258,404189,35171693,LABNTBNP,2013-05-25 15:40:00,1,0,1,0,0.31045751634,1.0,1.0,0.229010763916,0.178430842253,0.201852968406,0.134169058987,0.0799306469881,0.0927759418269,0.271655629139,0.0234375,0.165546576287,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.446958275057,0.446620846181,0.483487122071,0.45836799326,0.473435972246,0.00491771038503,5.67486595637e-05,3.50146964119e-07,0.0
--7523160221557,378755,37428153,LABNTBNP,2013-08-21 17:03:00,1,0,1,0,0.0558659217877,0.0,0.0,0.332735512848,0.113340149902,0.0528851102676,0.0,0.0,0.169136973118,0.0,0.0,0.107898894155,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.344151780111,0.417258056413,0.339403547921,0.469850964378,0.477123754964,0.27170051805,0.685563370673,0.772483309249,0.0
-9531523145406,404155,32498866,LABNTBNP,2013-02-15 22:13:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0956977603012,0.0784079664469,0.146306361891,0.0734382733228,0.144393773326,0.0641569175896,0.230434782609,0.206833173231,0.141783703811,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.344847674922,0.330918234662,0.39904911672,0.301924490803,0.390704646916,0.0627444016881,0.00589303089482,0.00109906478441,1.0
--9686907248055,354996,40514511,LABNTBNP,2013-12-24 22:00:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0145027621518,0.0422770163095,0.0277032285453,0.0,0.0,0.0531127216902,0.2,0.0,0.1,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.284477236033,0.377770891258,0.29432711083,0.436369021912,0.470351201184,0.000578782743774,7.20462410628e-07,8.86499872055e-11,0.0
-3134453830100,467534,32326665,LABNTBNP,2013-02-01 13:55:00,1,0,1,0,0.0609756097561,0.133333333333,1.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.178003738062,0.15801068835,0.147755600218,0.122678774346,0.178061515724,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.48991368847,0.491584936581,0.191104731291,0.150033573619,0.135559010226,1.0
-4151369558026,421979,35818545,LABNTBNP,2013-06-26 02:16:00,0,0,1,0,0.795454545455,1.0,1.0,0.182159739769,0.342295918282,0.320319209022,0.210447761194,0.0802323056882,0.504940097889,0.2,0.328723404255,0.291623263889,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.415673873933,0.467581060352,0.412874083951,0.517077816113,0.50959848236,0.959773630929,0.982217299499,0.962249217557,0.0
-294862441158,395604,40219846,LABNTBNP,2013-12-23 02:48:00,1,0,1,0,0.0410509031199,0.0,0.0,0.014847589738,0.0452142316064,0.0809116257331,0.15,0.0203293668016,0.0131491822114,0.0,0.1,0.0333333333333,0.0569331265889,0.0803343830243,0.110719720878,0.100898152284,0.283278887981,0.374916819374,0.298420618607,0.450452536004,0.466098680562,0.0172220388881,0.00591751133117,0.00375419316894,1.0
-959229114165,442523,40073169,LABNTBNP,2013-12-15 19:18:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.174234997998,0.179017527596,0.166120094542,0.165069911332,0.12497782244,0.126259740847,0.146734103861,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.487118037209,0.492660372115,0.106551845649,0.0827427133375,0.0745191365784,1.0
--8021339916837,409530,39041081,LABNTBNP,2013-10-25 07:27:00,0,0,1,0,1.0,1.0,0.0,0.406306306306,0.462211695796,0.514662805324,0.468881311297,0.331650282831,0.433992199902,0.406896551724,0.306906392694,0.48799247091,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.437688440818,0.483148693147,0.458685459748,0.499855400129,0.501274447127,0.240832925718,0.389013772531,0.485833680058,1.0
-384546538066,398660,35201727,LABNTBNP,2013-05-26 13:54:00,1,0,1,0,0.31045751634,1.0,0.0,0.199243344973,0.179914301116,0.255757659754,0.2,0.16899566508,0.203444071258,0.0304347826087,0.137815442561,0.262358096901,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.441378162431,0.473379124925,0.467943180287,0.486755481103,0.498608309197,0.0382837736923,0.0120395870664,0.00388667288824,1.03763650239e-240
-11752298748640,432940,35554792,LABNTBNP,2013-06-17 12:31:00,1,0,1,0,0.0609756097561,0.133333333333,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.178143343228,0.183769566694,0.14851839246,0.210444879126,0.183087783587,0.251407126031,0.182597675,0.239018093086,0.219146847908,0.417357816077,0.458686018563,0.44074121791,0.489371523919,0.493787532995,0.201877764128,0.189380444079,0.182843685239,1.0
--10019377599463,419656,36780238,LABNTBNP,2013-07-24 06:50:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0126008194703,0.0255999090443,0.0315789814646,0.2,0.0,0.00199501755489,0.0,0.1,0.0,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.298926283208,0.379712905977,0.350993785088,0.4549895431,0.470335602317,0.000427257555894,1.35753681366e-06,2.27526335816e-08,3.58931459751e-53
-8545308710488,409379,35051773,LABNTBNP,2013-05-26 06:59:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.165588304502,0.162407157318,0.135007747645,0.126076168658,0.112639191083,0.136475557933,0.0890144569677,0.123645571687,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.388729689551,0.438316935985,0.44074121791,0.482001096144,0.490920208895,0.159012532287,0.0931318422046,0.0686526846079,1.0
-2893245492888,390391,33740503,LABNTBNP,2013-04-05 21:58:00,0,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.183760730966,0.233533195942,0.287963802798,0.220600079414,0.23089851159,0.48842140618,0.276707755289,0.305201730106,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.45277187498,0.484714827427,0.458685459748,0.495045397656,0.50245981973,0.486247184998,0.42837943955,0.417057656087,1.0
-910133246415,405789,38565401,LABNTBNP,2013-10-29 07:35:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.146771469536,0.0716468002766,0.0760465512138,0.0818379256067,0.0134705678452,0.00599026135347,0.0327526129825,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.43363233127,0.438918049659,0.475138539787,0.487802549667,0.0257020177038,0.00554373510756,0.00311466721586,1.0
-7486853818842,432996,40066181,LABNTBNP,2013-12-20 15:14:00,1,0,1,0,0.795454545455,1.0,1.0,0.594188721906,0.510039053734,0.543486392819,0.567590618337,0.444855956862,0.535929951691,0.8,0.6,0.470833333333,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.488516526343,0.51758606841,0.549940106573,0.516596217465,0.512956308641,0.787498257747,0.795904477355,0.767004371541,1.0
-12058571515400,462746,31935088,LABNTBNP,2013-01-17 18:25:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0145027621518,0.0499216246005,0.0314841711403,0.0,0.0,0.124216871999,0.0,0.0,0.0333333333333,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.348404797416,0.341037574738,0.354544701365,0.309320735964,0.393770758162,0.00406803748839,4.40631627777e-06,1.15748492329e-08,1.97496104218e-137
-10013473578802,408770,38423885,LABNTBNP,2013-10-08 16:35:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.122123126918,0.150459589339,0.0716468002766,0.0533648593274,0.0513160896488,0.0645926969958,0.0196210029906,0.0299962161543,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.375764373727,0.424643615676,0.438918049659,0.474350708576,0.39869206856,0.000653634211711,2.19207105297e-05,5.17033126441e-06,1.0
-11449098927514,365914,32124244,LABNTBNP,2013-01-28 02:12:00,0,0,1,0,0.31045751634,0.0,0.0,0.271722628322,0.225518668009,0.234422475762,0.333846155739,0.242623688922,0.287905791852,0.326610044181,0.241789255776,0.398582342578,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.486950168077,0.483487122071,0.502277970103,0.497998531681,0.598926919981,0.653338949646,0.668778985397,1.0
-9067364961754,398003,33025557,LABNTBNP,2013-03-06 10:15:00,1,0,1,0,0.31045751634,0.88,1.0,0.306616245344,0.293148703363,0.223686222575,0.123721297793,0.305184549109,0.236016884817,0.4614117267,0.342871977968,0.303065561355,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.454016676067,0.481363174411,0.483487122071,0.495740888597,0.500164407801,0.320095756068,0.354442649246,0.366520574531,1.0
--3539559875411,418382,33840561,LABNTBNP,2013-03-22 15:04:00,0,0,1,0,0.31045751634,1.0,1.0,0.229622756363,0.302306475747,0.210113665928,0.218717687085,0.263570436237,0.273535983561,0.347067965059,0.444740970249,0.33030465106,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.476977524668,0.483487122071,0.49802236456,0.498230588309,0.182892130742,0.191717305984,0.157316159508,1.0
--4779616817364,395496,40616704,LABNTBNP,2013-12-31 10:01:00,0,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.174234997998,0.179017527596,0.198758037068,0.184347144408,0.120642805605,0.148551664609,0.146539590578,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.448595240371,0.44074121791,0.492167697523,0.495185929123,0.318270867153,0.333201037224,0.340344867011,1.0
-1636342335492,428664,31706747,LABNTBNP,2013-01-10 13:11:00,1,0,1,0,0.31045751634,0.0,0.0,0.331358318952,0.27324379338,0.301921819424,0.22937563361,0.192013690195,0.367504862914,0.251655629139,0.138680555556,0.120816186557,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.440359834637,0.468060655956,0.485536256596,0.491059194245,0.48967673706,0.0656358415815,0.0512323274226,0.0414867382693,0.000311774226741
-5670990602902,387380,36412224,LABNTBNP,2013-07-23 11:52:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0700181739976,0.0766443232437,0.106097752908,0.0811706098004,0.0517231630995,0.0471197190617,0.0231449864499,0.112529730054,0.0209046604603,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.328225460693,0.396765359673,0.397083262336,0.459228201036,0.478683915074,0.115527143935,0.0457150677973,0.0258993299558,1.0
--11854985732873,416087,34110124,LABNTBNP,2013-03-29 11:41:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0704948999593,0.0469745973208,0.0980804613127,0.0,0.137965772474,0.0370106057724,0.0,0.00866666666667,0.03367003367,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.335056584482,0.41319492524,0.393840041695,0.485357040974,0.483313512199,0.206461892135,0.501790132018,0.701746468125,1.0
--6222416382704,359203,32964456,LABNTBNP,2013-02-17 11:48:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.113353363978,0.165070431713,0.0716468002766,0.129631422483,0.110289773111,0.0593209876543,0.0137089834622,0.0514924018871,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.440191061629,0.438918049659,0.479433750708,0.399983594518,0.0524310788875,0.0148824803658,0.0072497068594,1.0
--5225087125457,472004,33623132,LABNTBNP,2013-03-17 10:56:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.155330763925,0.15680274135,0.115656580228,0.17155990221,0.117079573502,0.077255770263,0.0840030303262,0.123262513813,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.411317059619,0.458250414754,0.438918049659,0.489001704715,0.49556150164,0.252520147974,0.1521550414,0.138577223851,1.0
--3774770739899,449106,35130573,LABNTBNP,2013-05-13 22:00:00,0,0,1,0,0.31045751634,1.0,1.0,0.195323993775,0.185896234287,0.304695166695,0.224871531346,0.214316285765,0.292005964535,0.2,0.5,0.337540106334,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.453199975204,0.480796887498,0.481224446787,0.492147328014,0.494656254842,0.697197776457,0.756601516312,0.848181404196,1.0
--3092286256550,355174,38999016,LABNTBNP,2013-10-25 11:26:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.113572826881,0.166055347991,0.102830719957,0.139599710916,0.0999390549597,0.0593209876543,0.0848840330663,0.123423831462,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.400275665502,0.449298538059,0.438918049659,0.482282986207,0.48950688248,0.0255892463395,0.00436829709814,0.00171882333644,1.0
--2699078241653,372268,32214360,LABNTBNP,2013-01-14 22:41:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.132109441152,0.157327544556,0.115656580228,0.156249656697,0.115235983355,0.0769012619419,0.091227364927,0.0788731755913,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.406352621066,0.450584426356,0.438918049659,0.486748297647,0.493614062999,0.138934882087,0.130549337719,0.125766995501,1.0
-7024426849679,403557,36866623,LABNTBNP,2013-08-18 20:07:00,0,0,1,0,0.31045751634,0.245283018868,0.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.240445200906,0.232632983292,0.200266618257,0.243823609813,0.26729547228,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.293597359979,0.294256867876,0.294548421783,1.0
--7257407593172,409360,32379572,LABNTBNP,2013-01-24 08:25:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0316594536853,0.0391005204984,0.0371571268468,0.0,0.1,0.0694298193955,0.0,0.0,0.0666666666667,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.314407352321,0.383088690101,0.33510035506,0.458581568237,0.474836839568,0.000883143542859,6.92387158108e-05,7.81724706815e-06,0.0
-9293648864965,375486,35465980,LABNTBNP,2013-06-10 15:32:00,0,0,1,0,0.31045751634,0.245283018868,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.250178345246,0.217105057607,0.273997639158,0.245988111978,0.232750213865,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.495719233721,0.498100692667,0.292010923381,0.302584716234,0.304741601587,1.0
-8122668230080,418776,33071594,LABNTBNP,2013-02-26 09:15:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0265752549233,0.0453578340528,0.0,0.2,0.110596862229,0.0,0.00952380952381,0.0333333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.394090017396,0.452028959064,0.420655632557,0.518190455496,0.501484474908,0.326337644242,0.0369787775349,5.3743594611e-06,0.0
--9221371334986,397811,34725195,LABNTBNP,2013-04-24 21:53:00,1,0,1,0,0.31045751634,0.0,0.0,0.0742146744676,0.0993612136795,0.0846425146927,0.0301812658596,0.0619653468602,0.0941929763523,0.0,0.0,0.016833157175,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.434495579458,0.470062711561,0.445287558946,0.480153918202,0.486872432658,0.143757999026,0.051024324456,0.0106007405514,1.0
-6515305398395,364580,31933994,LABNTBNP,2013-01-21 10:03:00,1,0,1,0,0.31045751634,0.0,0.0,0.14432690653,0.106132790816,0.147559859933,0.287538198013,0.241886128056,0.163275964435,0.00689655172414,0.05,0.0333333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.394486305771,0.435094027822,0.39824709699,0.47236068087,0.485611053319,0.00285464856685,4.03273519321e-05,2.47403063554e-06,7.46236034792e-28
--2652256442528,434455,36133111,LABNTBNP,2013-06-29 10:39:00,0,0,1,0,0.31045751634,0.433333333333,1.0,0.229622756363,0.225518668009,0.217634162334,0.218717687085,0.286859194623,0.262109847914,0.223807188277,0.299138443661,0.298289484521,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.481529283024,0.483487122071,0.497973877033,0.499228052555,0.317881604568,0.322158916585,0.327619072199,1.0
-10013473578802,457910,34305083,LABNTBNP,2013-04-19 16:09:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.092533686654,0.122123126918,0.150459589339,0.0716468002766,0.085291681731,0.0869448194015,0.0645926969958,0.0102843915344,0.0337925972575,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.375764373727,0.424643615676,0.438918049659,0.474350708576,0.399478954357,0.00822829231073,0.000891987400429,0.000329567133067,1.0
--11069915052509,423878,34300450,LABNTBNP,2013-04-03 12:23:00,1,0,1,0,0.31045751634,0.0,0.0,0.214294097375,0.283178152934,0.339753559851,0.282994370381,0.194831024741,0.212752372574,0.0516556291391,0.136458333333,0.324593276392,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.468233029437,0.485398259485,0.481224446787,0.49971052196,0.498837684984,0.426641645254,0.189235455729,0.157908545463,1.0
-1263685297083,454352,36959159,LABNTBNP,2013-08-18 15:40:00,1,0,1,0,0.0558659217877,0.0,0.0,0.14027707991,0.12622240959,0.162249058079,0.211873479319,0.0261075623326,0.161916764377,0.0628056426332,0.1,0.170295548073,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.442098032028,0.46396503547,0.483487122071,0.501056952409,0.496604708896,0.281004095971,0.113195710702,0.00122832644084,4.91810555623e-94
-1849744711647,425219,31830260,LABNTBNP,2013-01-19 22:34:00,1,0,1,0,0.31045751634,1.0,1.0,0.298520614948,0.30577328706,0.264770063948,0.196930091785,0.373872470363,0.204975540255,0.180434782609,0.353109565038,0.438529699758,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.467521489386,0.492323105397,0.523720933872,0.502735743212,0.501359819365,0.573979996232,0.576586387902,0.585271491157,1.0
-3033026204483,365872,35443905,LABNTBNP,2013-06-07 05:04:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0721300320626,0.232872840459,0.0844047236761,0.0248715313464,0.137285034508,0.265555088047,0.2,0.2,0.272104461389,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.3856151186,0.354368538621,0.41856280654,0.307761052156,0.398634699368,0.0637788450846,0.0034313532978,0.000379148588653,1.0
--12259431767593,431752,39846528,LABNTBNP,2013-11-24 01:08:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0924617402835,0.223840206327,0.25186015053,0.211111111111,0.218272727273,0.188138536134,0.445432098765,0.00137931034483,0.166666666667,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.387626908332,0.455015583219,0.412177921065,0.497343636323,0.499645914605,0.439214162346,0.779135515974,0.96922552156,1.0
--8039357668286,364469,32492887,LABNTBNP,2013-01-19 10:14:00,1,0,1,0,0.0410509031199,0.0,0.0,0.051023579848,0.048621280873,0.0600042923438,0.00530973451327,0.00698732531687,0.0859759860566,0.0,0.0,0.0343625577321,0.0569331265889,0.13929039803,0.167172352555,0.166414373441,0.347985111506,0.332041947954,0.384038858041,0.300498806136,0.399262481184,0.000380169572278,3.40860559174e-06,3.25545372061e-07,0.0
-9206343546236,419680,34648927,LABNTBNP,2013-05-13 17:10:00,0,0,1,0,0.31045751634,0.0,0.0,0.273998737163,0.236551931671,0.209414697904,0.11120255181,0.136682864439,0.190822736841,0.0373313343328,0.283771293866,0.0794672536056,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.487577503756,0.506105378625,0.503082032339,0.5038349755,0.513527968887,0.607456260664,0.651445677076,1.0
--8721587150765,367261,35784131,LABNTBNP,2013-06-03 23:07:00,1,0,1,0,0.31045751634,0.245283018868,0.0,0.238801500325,0.209538032433,0.217634162334,0.138549925937,0.105485245692,0.206302261465,0.127522510513,0.0804364249569,0.256248087179,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.438668710164,0.469985972614,0.445492065717,0.48808008122,0.49428027962,0.256459430465,0.251162962417,0.288996104575,1.0
-7035513106389,402054,35401734,LABNTBNP,2013-06-07 12:25:00,1,0,1,0,0.0410509031199,0.0,0.0,0.134633558613,0.122123126918,0.132400040061,0.0588209400055,0.047562734124,0.101947550588,0.0236449864499,0.0160395224372,0.0516960164019,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.37756438408,0.347879623391,0.438918049659,0.302931223883,0.396976564213,0.0627488693002,0.00866631819302,0.00258217564887,1.0
--233718175813,408709,39504940,LABNTBNP,2013-11-13 06:49:00,1,0,1,0,0.0410509031199,0.0,0.0,0.0924617402835,0.102306828453,0.160446725396,0.010447761194,0.202173913043,0.0999144370459,0.2,0.00137931034483,0.10009057971,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.370528329931,0.432796442101,0.431782510658,0.477962101656,0.490746490978,0.00617805050649,0.000363809872999,0.000462781189809,2.01759038782e-51
--1973193476081,456913,40073798,LABNTBNP,2013-12-10 10:51:00,1,0,1,0,0.0558659217877,0.0,0.0,0.273386744715,0.210672602203,0.184950536121,0.0863310204639,0.165169657583,0.340496228033,0.137999502657,0.4,0.181149323927,0.251407126031,0.276079873498,0.321929148058,0.303398863363,0.43204269393,0.380448907679,0.501114185087,0.489982737384,0.490927867772,0.239668247294,0.285525624502,0.296420438069,1.0
-4943509008810,416402,32578246,LABNTBNP,2013-02-14 09:07:00,1,0,1,0,0.0609756097561,0.0,0.0,0.137167049118,0.162522856944,0.129544537089,0.0540880385234,0.0531505641345,0.129921635859,0.2,0.2,0.115248083381,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.386283690611,0.433821073262,0.417965496341,0.480857546205,0.484030140399,0.121329649677,0.0552168648599,0.0486441443902,1.0
-12002769758248,366874,33143579,LABNTBNP,2013-03-07 22:00:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0637824358693,0.0588471872798,0.20170633414,0.456745010665,0.0264536519178,0.0616634499253,0.2614117267,0.0,0.035241301908,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.364678142263,0.426775841872,0.405464658646,0.4650342334,0.484444810628,0.0208671858601,0.00134463312213,4.84937500947e-05,2.91563578375e-117
--3434457588535,387523,40453945,LABNTBNP,2013-12-20 12:45:00,0,0,1,0,0.31045751634,0.0,0.0,0.192937038667,0.267823871025,0.256092244074,0.184725227106,0.192825452314,0.439519033659,0.282090411748,0.137815442561,0.290088879871,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.4453228589,0.475371881438,0.467943180287,0.494857653909,0.502192159455,0.269367298369,0.229116351094,0.226099101933,1.0
-1232884553712,445352,33634371,LABNTBNP,2013-03-27 12:47:00,0,0,1,0,0.31045751634,1.0,1.0,0.271722628322,0.225518668009,0.223509088954,0.293846155739,0.246006902975,0.235568381856,0.188131187072,0.278357087858,0.340556398895,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.480292477196,0.483487122071,0.497973877033,0.498892398257,0.324388087744,0.345934471207,0.373653757472,1.0
-8205797237512,425610,34980197,LABNTBNP,2013-05-21 17:16:00,1,0,1,0,0.31045751634,0.0,0.0,0.271722628322,0.225518668009,0.218833505251,0.218717687085,0.24862655534,0.249402313259,0.303069474161,0.248884778362,0.225623641971,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.457209869318,0.485712482527,0.483487122071,0.500023249044,0.498815802677,0.39829724051,0.441660132331,0.45378981757,1.0
-2609229728967,424949,36565548,LABNTBNP,2013-07-31 20:52:00,0,0,1,0,0.31045751634,1.0,1.0,0.250424961372,0.251998163594,0.270975942503,0.504871531346,0.501462058826,0.371508002526,0.430434782609,0.459030720339,0.46220384353,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.46391282872,0.486925945952,0.506105378625,0.506927658275,0.507370675366,0.514599267598,0.517106869537,0.482789234533,1.0
--9674966773920,469801,35404257,LABNTBNP,2013-05-28 14:50:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.092533686654,0.136574686751,0.16075004528,0.0716468002766,0.103327252968,0.105604455896,0.125487654321,0.0188264002919,0.122407789191,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.388009244192,0.434849518153,0.438918049659,0.481712941559,0.491914515274,0.0459628683588,0.0131237106666,0.00728168218094,1.0
--5293434884275,355107,39177377,LABNTBNP,2013-11-02 12:29:00,1,0,1,0,0.0410509031199,0.0,0.0,0.140073741101,0.0838397056238,0.11595922576,0.0717497338844,0.0554949688515,0.0828994246629,0.0304347826087,0.127914345438,0.0782582583842,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.346794610156,0.412426303331,0.418937921262,0.471721031649,0.48495220794,0.040653128967,0.00925780708142,0.00352788272052,1.0
-4348307435458,382794,40005662,LABNTBNP,2013-12-11 11:50:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0849467521382,0.0581854175108,0.0775082620642,0.0,0.102173913043,0.109326480119,0.2,0.0,0.23367003367,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.405438688648,0.355552685001,0.394633419985,0.298204272023,0.397715425791,0.00231765880082,8.69952801276e-05,2.37518794573e-06,0.0
--276437708523,445318,32153967,LABNTBNP,2013-01-07 21:14:00,1,0,1,0,0.31045751634,0.0,0.0,0.283042469553,0.369392937773,0.358636732581,0.616487385005,0.501619732478,0.236515291644,0.2,0.259801136364,0.591666666667,0.251407126031,0.292715888221,0.336158370005,0.333007034571,0.467521489386,0.494352319318,0.506105378625,0.333264911975,0.418758199239,0.763770002995,0.748708226307,0.695571723799,1.0
-3540622955713,383632,37878141,LABNTBNP,2013-09-14 10:13:00,1,0,1,0,0.31045751634,1.0,1.0,0.0931452901315,0.0400256402922,0.106227831367,0.0,0.0306343168045,0.028398146706,0.0,0.1,0.111904761905,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.405731406932,0.364225803806,0.405545668177,0.308520149149,0.395465087775,0.0102941445382,0.000433357638065,1.57755428154e-05,1.66035646489e-154
-3583039670811,431634,32171015,LABNTBNP,2013-01-29 12:17:00,1,0,1,0,0.0609756097561,0.0,0.0,0.0892203007567,0.15790395073,0.125635867559,0.0797115178421,0.0581712852911,0.132436910432,0.0679519254354,0.104610951009,0.17977376569,0.251407126031,0.177898107878,0.188180888388,0.172133499626,0.390706014733,0.436265752932,0.444796981924,0.487163029498,0.39552912115,0.0552406619013,0.00946816645735,0.00144697862398,0.0
-11818559089141,466031,34670980,LABNTBNP,2013-05-07 11:44:00,1,0,1,0,0.0410509031199,0.0,0.0,0.131873750411,0.140879204092,0.151253583321,0.083783100909,0.303448231489,0.157572574374,0.1725,0.129792226825,0.0358276239402,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.393901459306,0.445395150204,0.438918049659,0.497756246398,0.496373721375,0.103822716947,0.046061240715,0.0259538282836,1.0
--8991572755545,435258,34427300,LABNTBNP,2013-04-11 12:16:00,1,0,1,0,0.0410509031199,0.0357142857143,0.0,0.014847589738,0.0558033533581,0.0590514912709,0.264871531346,0.13352487257,0.0252381300693,0.0,0.1,0.0400599269759,0.0569331265889,0.0803343830243,0.110719720878,0.100898152284,0.32164160812,0.398635554107,0.348746646374,0.456280019748,0.475218611776,0.1510324729,0.113703070082,0.103556703883,1.0
-12093104646123,357929,38121822,LABNTBNP,2013-10-04 10:07:00,1,0,1,0,0.0410509031199,0.0,0.0,0.169527175634,0.110998632653,0.274834277396,0.0150047232193,0.309418633433,0.305456341728,0.2,0.204610951009,0.200909090909,0.0569331265889,0.0827159126116,0.134247788615,0.131547122571,0.357647753809,0.429818121273,0.397083262336,0.46810930504,0.486110610302,0.125981483888,0.063492897731,0.0476772614204,1.0
--1399527982358,405413,33518150,LABNTBNP,2013-03-08 13:33:00,1,0,1,0,0.0609756097561,0.00952380952381,0.0,0.207107243707,0.180039864334,0.168415303958,0.179017527596,0.179741783774,0.164745681378,0.188131187072,0.202748839348,0.201800118535,0.251407126031,0.182597675,0.231435620399,0.219146847908,0.401125565353,0.446430045076,0.44074121791,0.487118037209,0.404523270514,0.100673486917,0.0712727215434,0.0592702057384,1.0
-1342858770578,423864,32496645,LABNTBNP,2013-02-06 23:23:00,1,0,1,0,0.0558659217877,0.0,0.0,0.0122559918841,0.0324865849725,0.0426658424007,0.0,0.103703703704,0.0375380529424,0.0,0.109523809524,0.0333333333333,0.251407126031,0.286174417984,0.280473824529,0.270013832711,0.355415424771,0.413757953045,0.374460723726,0.448930287103,0.46958392071,0.0001221388893,7.6743704994e-09,1.17360076108e-12,0.0
--3170215522748,450594,39430605,LABNTBNP,2013-11-08 23:30:00,1,0,1,0,0.0410509031199,0.0,0.0,0.092533686654,0.136574686751,0.150320275888,0.0716468002766,0.0988824645265,0.113304785974,0.10903714144,0.0197899958751,0.081942202999,0.0569331265889,0.143147648419,0.206828403738,0.212266209002,0.392897899668,0.441241534169,0.438918049659,0.481712941559,0.491094451375,0.279234052207,0.193064939175,0.178326654631,1.0
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABSPLAC_outputs.csv b/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABSPLAC_outputs.csv
deleted file mode 100644
index 4bd2dcec..00000000
--- a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABSPLAC_outputs.csv
+++ /dev/null
@@ -1,895 +0,0 @@
-patient_id,encounter_id,order_proc_id,proc_code,order_time,abnormal,result_normal_count,result_total_count,all_result_normal,predictedTest.decision_tree_max_depth=5,predictedTest.decision_tree_max_depth=20,predictedTest.decision_tree_max_depth=50,"predictedTest.random_forest_n_estimators=5,_max_depth=5","predictedTest.random_forest_n_estimators=10,_max_depth=5","predictedTest.random_forest_n_estimators=30,_max_depth=5","predictedTest.random_forest_n_estimators=5,_max_depth=10","predictedTest.random_forest_n_estimators=10,_max_depth=10","predictedTest.random_forest_n_estimators=30,_max_depth=10","predictedTest.random_forest_n_estimators=5,_max_depth=15","predictedTest.random_forest_n_estimators=10,_max_depth=15","predictedTest.random_forest_n_estimators=30,_max_depth=15","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=10,_learning_rate=1","predictedTest.ada_boost_n_estimators=50,_learning_rate=1","predictedTest.ada_boost_n_estimators=100,_learning_rate=1",predictedTest.logistic_regression_c=0.1,predictedTest.logistic_regression_c=1.0,predictedTest.logistic_regression_c=10.0,predictedTest.gaussian_naive_bayes
--11305475266615,372304,30118394,LABSPLAC,2012-10-20 21:00:00,1,0,1,0,1.0,0.0,1.0,0.362619780726,0.394311725668,0.363518913375,0.391525423729,0.2,0.297444831591,0.6,0.6,0.433333333333,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.480899551351,0.470808669169,0.448319649051,0.490776466143,0.490907950091,0.000623350778253,0.000384227219549,0.000335283286248,0.0
-4875363218674,449039,35363102,LABSPLAC,2013-06-03 01:16:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.88336980542,0.898656802806,0.92121072577,0.969945465672,0.866298709887,0.907560435712,0.987758094272,0.678427736011,0.901773899677,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.607987695524,0.551651243066,0.547402931986,0.504689398778,0.503091831709,0.933324917617,0.963308653922,0.97398851893,1.26700002354e-06
--4791277985463,450158,33139542,LABSPLAC,2013-02-21 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.956160864127,0.961561662247,0.870637653467,0.789102649652,0.981749243381,0.896229606718,0.6,0.989821007177,0.869284172981,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.641888105329,0.575371661816,0.577760550212,0.51391115192,0.511908476905,0.999738961051,0.999999925779,1.0,1.0
-10800651293913,463974,32501107,LABSPLAC,2013-02-13 17:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.957856431394,0.98570109238,0.975640843332,0.977761919475,0.98646775749,0.987038803979,0.984742854845,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.621750110895,0.654219968234,0.53359805375,0.524219287279,0.978700108741,0.998938399046,0.999999994249,1.0
-10291686566372,358471,32891918,LABSPLAC,2013-02-20 22:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959965031251,0.984716678926,0.977318365183,0.977764899,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.647608634064,0.697318231141,0.540651598205,0.527881085747,0.995642976331,0.998014599458,0.99868227435,8.37320190641e-06
-976138950707,457005,39351611,LABSPLAC,2013-11-17 18:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.977841699497,0.98646775749,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.710010777854,0.672961290111,0.698643541923,0.606094586525,0.993259814569,0.997952045734,0.999262730877,0.711306337658
-615396159447,462017,32429614,LABSPLAC,2013-02-05 14:03:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.880483890663,0.914881338524,0.869583821538,0.986144974997,0.765907049244,0.75888085627,0.989768319288,0.780664056206,0.880626814905,0.917871594643,0.848148017695,0.796825491016,0.797475809346,0.610653097902,0.558985755536,0.577760550212,0.527457287237,0.516008765165,0.971358386941,0.993603525113,0.997726056297,5.24063670201e-20
--1093815690578,376183,34029713,LABSPLAC,2013-04-02 17:50:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.88336980542,0.92121932275,0.937227796791,0.888282666818,0.960317048813,0.914156333105,0.971475566391,0.983035579148,0.97159213486,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.614819270977,0.555589515509,0.547402931986,0.506767120902,0.506464605722,0.986912457787,0.993715933897,0.995605729668,0.999990391765
--9278860741847,459407,38864163,LABSPLAC,2013-10-19 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.977308557687,0.978093446978,0.985303412661,0.984071570538,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.514899037017,0.508836828984,0.98368322796,0.991438203206,0.993433916851,1.0
-8337851635737,462709,34002258,LABSPLAC,2013-04-12 05:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.985303412661,0.982846065158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.651308567774,0.587091912176,0.592186262416,0.522233672389,0.511342209431,0.985880563473,0.992461706044,0.993885491627,5.47004612757e-08
--2266184951722,414185,39265295,LABSPLAC,2013-10-29 07:58:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.965125374887,0.832006953618,0.941454338035,0.962348176915,0.975389791015,0.911239179426,0.580100663464,0.79119765758,0.951704494272,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.612519600305,0.56609233552,0.547402931986,0.520074401917,0.508655579883,0.990628379431,0.998731574748,0.999799966611,1.0
--11391010719086,368130,39811330,LABSPLAC,2013-11-19 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.958765450075,0.984716678926,0.977318365183,0.979459278244,0.98646775749,0.987872137312,0.9857887643,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.614960149711,0.647214985244,0.531559642596,0.517885757843,0.96903099278,0.977928035097,0.973204218517,1.0
-3756072761788,406335,39665811,LABSPLAC,2013-12-03 04:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.982923999201,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.508675372968,0.910901585871,0.911814672272,0.911181966952,6.66281073381e-05
-3545183683043,467949,31298686,LABSPLAC,2012-12-17 11:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.907060346697,0.584057380189,0.977318365183,0.982064806626,0.987424952203,0.884767732794,0.98743747907,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646773416972,0.589110557207,0.62492509043,0.520988388365,0.5147344966,0.973273963479,0.988959679258,0.994783263031,2.2592571426e-22
-729492854779,431760,35554732,LABSPLAC,2013-06-20 10:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.8637355681,0.900614623777,0.932205261184,0.914081984385,0.873632998979,0.937718881414,0.780382842717,0.981858337394,0.951603236513,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.687851271872,0.597929000879,0.604438079785,0.517582137978,0.510838814372,0.92185120714,0.961678251261,0.984542647414,7.01562574464e-15
--10856513659214,397319,39668894,LABSPLAC,2013-11-11 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.712475545141,0.672961290111,0.70439513419,0.608009479945,0.992943813155,0.996890363452,0.997971637556,0.99983296239
--10735095088400,421978,35020034,LABSPLAC,2013-05-10 12:50:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.957210773623,0.808006787832,0.899072803918,0.964854850158,0.93312841564,0.757372369059,0.980788482439,0.88312847354,0.886233989038,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.618210178068,0.559032414148,0.522421043247,0.509476707763,0.50571120599,0.988439163705,0.998384947114,0.999367124087,1.0
--9937798449183,453771,32892336,LABSPLAC,2013-01-30 20:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.972864414573,0.977318365183,0.975352206385,0.978093446978,0.986182202112,0.951386141673,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.768113956163,0.739204244971,0.705047283599,0.537302562945,0.521646779294,0.968017679254,0.98108948067,0.986332247148,0.999999768937
-8081880032031,404200,36230784,LABSPLAC,2013-07-22 06:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.728167826648,0.768472943017,0.721637858562,0.8,0.857678062678,0.770407345721,0.8,0.798444790047,0.866666666667,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.557332660252,0.51910636796,0.520271360095,0.501258511066,0.500639359443,0.968794257391,0.991116861868,0.989053863802,0.0
-5143282453079,409747,33854796,LABSPLAC,2013-04-03 17:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.90839971117,0.964668359471,0.954998922616,0.988057911891,0.950750116286,0.968893862694,0.981394008776,0.873601379185,0.943579097148,0.917871594643,0.848148017695,0.800194633637,0.829616127665,0.620799321517,0.583088296542,0.63874696642,0.522793768891,0.512458435762,0.996826991745,0.99950617682,0.999862272927,8.63568721751e-44
--7693165717903,394715,30075474,LABSPLAC,2012-10-11 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.980117654779,0.967935630982,0.97791129386,0.98646775749,0.986464168561,0.98304148388,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.619989672949,0.640657052947,0.523540149853,0.520181850447,0.982924939278,0.99172157836,0.994028354684,9.77535234298e-07
--2201739919731,460021,37592255,LABSPLAC,2013-09-01 00:51:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982200257785,0.977318365183,0.978111020545,0.98646775749,0.985303412661,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.532248141381,0.519354177585,0.993834972675,0.998480078332,0.999285136022,0.998859463139
--152589643420,363538,29035103,LABSPLAC,2012-09-01 20:00:00,1,0,1,0,0.666666666667,0.0,0.0,0.0625334994488,0.274409506299,0.166503212694,0.392,0.190322580645,0.201298701299,0.2,0.3,0.1,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.479274604199,0.469345478641,0.433671722659,0.481512111862,0.488058852184,9.19133311324e-05,4.6818475991e-06,1.25883581753e-06,0.0
-7954136990788,424775,40256915,LABSPLAC,2013-12-23 08:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959728144476,0.970535048751,0.977318365183,0.940759981698,0.992648824013,0.885063431718,0.920985607145,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710614043217,0.694687909542,0.631528916061,0.691387708963,0.60353908279,0.994134246215,0.999528078088,0.999959610691,9.02881322002e-29
-1971637302343,379282,34135208,LABSPLAC,2013-04-15 03:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.978061574738,0.98646775749,0.983613477461,0.984226386823,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.597646532268,0.63874696642,0.556378939773,0.53781973711,0.988516355827,0.996570414762,0.998480551009,7.52003731438e-17
-2852925776464,452388,36881141,LABSPLAC,2013-08-13 05:05:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.955882500902,0.953295953669,0.977360559416,0.972221392779,0.973290734134,0.983642630155,0.984701216704,0.984035549474,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.575273322481,0.592186262416,0.519666364277,0.507768722069,0.982141951416,0.993837350608,0.997181495458,1.0
--1297489149383,428373,37450557,LABSPLAC,2013-08-21 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.972061352238,0.972417939447,0.98646775749,0.979265089637,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.524245760132,0.513392848241,0.955692186616,0.976160049015,0.987451990442,0.610908801647
-8636559223052,434865,31846938,LABSPLAC,2013-01-20 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95365513447,0.961043261284,0.928806119669,0.97863298675,0.977190772153,0.987099640836,0.987134443821,0.984900690096,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.663183471248,0.577760550212,0.531086296387,0.519211335372,0.997176025394,0.999717978594,0.99992057535,1.0
-8250204777255,380442,40788790,LABSPLAC,2014-01-18 12:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.94582387653,0.952834855259,0.902714733116,0.987749127463,0.881739451297,0.980410371333,0.992930349628,0.991097624591,0.926111186092,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716377712051,0.708006104075,0.633531218038,0.698569597404,0.605212126943,0.966610428695,0.994338494042,0.999495176575,1.0
--6268423893322,398742,38557298,LABSPLAC,2013-10-01 23:49:00,0,0,1,0,0.968845448992,0.666666666667,1.0,0.972100498572,0.866977039288,0.916812442383,0.586316876793,0.977318365183,0.7831344772,0.793537407862,0.791406522067,0.854569611883,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.519942049303,0.497338425529,0.481655711126,0.367644807446,0.471350740814,2.9771150041e-149
--7173767459680,431751,38985128,LABSPLAC,2013-10-25 15:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.954259400481,0.982200257785,0.97420073123,0.969734559624,0.98646775749,0.985430719167,0.98236707612,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.563140917508,0.562079676942,0.521913613766,0.51307207571,0.970905825516,0.977724397318,0.975630859736,2.0739900672e-10
--4629695421662,449720,37342085,LABSPLAC,2013-08-10 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961107767351,0.985452715467,0.977318365183,0.977147493438,0.988274209103,0.981622343712,0.983465777702,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.694716473014,0.692495046507,0.604438079785,0.689620485255,0.601903495344,0.906681858672,0.919798468005,0.926845263596,6.10685814108e-06
--6690260694882,429414,33220236,LABSPLAC,2013-02-28 09:10:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.959578136089,0.977655342441,0.977318365183,0.944085184098,0.98646775749,0.985408556066,0.98330198022,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.658764215596,0.577760550212,0.515010914639,0.50575061205,0.924901526843,0.928480049507,0.956699397063,1.0
--3796590953893,358390,37124702,LABSPLAC,2013-08-08 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.904679381968,0.924665185746,0.936084216242,0.958598273575,0.922138990212,0.984998208883,0.98061435094,0.913981441122,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.617745531753,0.559992791104,0.562079676942,0.51861757678,0.510997124633,0.961134627742,0.981714937416,0.985346908205,1.97691862774e-11
-6967509780721,369509,35555613,LABSPLAC,2013-06-14 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959790066252,0.977360559416,0.975128692964,0.977271516277,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.610985163112,0.618554100768,0.543554425321,0.524334467438,0.99552353023,0.999349000197,0.99985375857,0.999999681488
-5088743604188,433024,40802479,LABSPLAC,2014-01-23 16:04:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.776573128745,0.967510407878,0.960238636306,0.984716678926,0.977318365183,0.979580502576,0.98646775749,0.989703168472,0.955542546622,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.699268597902,0.672961290111,0.716749764442,0.614217950649,0.980894315809,0.988056265737,0.990296707944,4.16619174414e-17
--3594684123306,439638,31054779,LABSPLAC,2012-11-25 05:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954015686722,0.979876980557,0.975128692964,0.978203003675,0.98646775749,0.983594039937,0.98383309011,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57063118209,0.592186262416,0.521038726393,0.513585364524,0.951968365832,0.977000698013,0.984738836734,0.999999999996
-11383712365983,457714,36132972,LABSPLAC,2013-07-18 06:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.913278482895,0.917307686347,0.928289086473,0.993573239286,0.972942028848,0.959186352937,0.984675118088,0.991695874574,0.926799728714,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.597663543086,0.542626727823,0.547167372423,0.521029885148,0.513117662855,0.990729627466,0.997336741349,0.999985804395,0.0
--4142855947751,402829,36498382,LABSPLAC,2013-07-12 01:12:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.956353453397,0.979876980557,0.975128692964,0.977169668841,0.98646775749,0.983594039937,0.983681740074,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57063118209,0.592186262416,0.522539774168,0.517153896107,0.99407960703,0.999542044426,0.999885112218,1.0
--11714197021507,367003,30902022,LABSPLAC,2012-11-24 14:57:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.932348437859,0.886526951597,0.86236199985,0.817366868016,0.846985579022,0.812747910258,0.987693244344,0.591060894241,0.856599412927,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.624610705575,0.56910946068,0.59558486573,0.511821417544,0.50927228007,0.741847306054,0.84595859458,0.926348204327,5.70955315421e-31
--1195461952069,409714,33485707,LABSPLAC,2013-03-09 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962145712577,0.980220534083,0.978689802581,0.977406380427,0.980918574313,0.987134443821,0.984999521822,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635681240063,0.591060075766,0.609812874259,0.524143274842,0.517668274845,0.97899916787,0.989377421699,0.988156617888,1.6242282875e-11
--10170906945643,460514,35254637,LABSPLAC,2013-05-16 04:43:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.977318365183,0.978773186895,0.983642630155,0.987872137312,0.983786212814,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.677872703012,0.599799859157,0.619588095117,0.697609014637,0.604644720159,0.973882836105,0.984885175498,0.988921887111,0.999999987714
--6391895482844,380427,29242970,LABSPLAC,2012-09-10 20:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.956160864127,0.898797162113,0.898752152153,0.791081541112,0.977332869696,0.981525235768,0.789898989899,0.965056015466,0.991000099075,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.627377601287,0.565330424239,0.547402931986,0.515996461729,0.512863478088,0.850148477406,0.798370468702,0.796620139261,2.57217617481e-52
--12214517115415,371272,30636626,LABSPLAC,2012-11-08 19:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.957413168264,0.984716678926,0.977318365183,0.975867012959,0.983642630155,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.68538231588,0.616897531253,0.625799145584,0.530691288824,0.523745498611,0.979290956611,0.986017137763,0.989420655474,0.999973590665
-258062802391,387668,39501109,LABSPLAC,2013-11-27 14:02:00,0,0,1,0,0.931034482759,1.0,1.0,0.56,0.671135094287,0.653670454294,0.353846153846,0.796644295302,0.408998621921,0.0,0.8,0.532402538069,0.917871594643,0.875764265788,0.771128307788,0.787522896327,0.578085625163,0.533041416667,0.491348257276,0.495789326272,0.497067069518,0.00417485218706,0.000188538611595,0.0012375381544,0.0
--3796590953893,358390,37128125,LABSPLAC,2013-08-11 10:02:00,0,0,1,0,0.862745098039,1.0,1.0,0.851240631475,0.768689422528,0.821450442593,0.981794871795,0.843361297161,0.819301607819,0.996699438202,0.88537804155,0.692608031481,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.592472474988,0.536085618661,0.577528596615,0.507648363832,0.506538454445,0.948553692612,0.990310859866,0.995114264254,1.1248737583e-98
-9988875425969,375641,32052933,LABSPLAC,2013-01-16 22:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.982200257785,0.977318365183,0.977494456012,0.98646775749,0.985303412661,0.984420964457,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.593911534832,0.577760550212,0.531061488358,0.521332979703,0.987596545333,0.990816131738,0.996094387008,2.36923571741e-11
-5733211750851,464743,39521438,LABSPLAC,2013-11-22 08:17:00,0,0,1,0,0.968845448992,1.0,1.0,0.669373839356,0.859113133313,0.871402149303,0.935285523332,0.933057529149,0.89562266677,0.640584415584,0.86559898293,0.730857980188,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.596777602932,0.543408240638,0.550955970579,0.500037682066,0.495532566344,0.656447503688,0.680769376495,0.756415622779,2.21537889069e-23
--2473959494956,369969,38844532,LABSPLAC,2013-10-11 13:25:00,0,0,1,0,0.777777777778,1.0,1.0,0.867072156065,0.858964451777,0.711675962413,0.633163729254,0.588058292282,0.695402842663,0.9852065354,0.194047619048,0.565511371066,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.582140178791,0.536902910418,0.522421043247,0.508735500829,0.500502749182,0.326293361776,0.29565616001,0.271677271256,3.20025658246e-82
-2630052607461,359308,31666662,LABSPLAC,2013-01-03 18:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958787251301,0.985160561543,0.978897537185,0.979091663498,0.990842372401,0.985930550632,0.98653471139,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.591357356531,0.592186262416,0.530421248927,0.523972450994,0.997972809707,0.999189387511,0.999545795938,3.29437826257e-29
-6211620991328,424859,40899328,LABSPLAC,2014-01-31 10:00:00,0,0,1,0,0.777777777778,1.0,1.0,0.702190672003,0.733332221821,0.676097647182,0.780555555556,0.382469159193,0.73717712525,0.6,0.479104477612,0.696164546259,0.917871594643,0.79514940545,0.728887498451,0.738632887801,0.542337221297,0.516550542804,0.520271360095,0.510037083048,0.503113517741,0.39746728608,0.539141248245,0.693538841337,1.03839830582e-34
-787780975858,458300,36844543,LABSPLAC,2013-08-03 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.954015686722,0.979876980557,0.975128692964,0.975891247199,0.983642630155,0.985303412661,0.985592527309,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.639166986604,0.577042924978,0.592186262416,0.53463618078,0.521879757932,0.990564319397,0.995978543444,0.996438452047,1.32537749212e-19
--10266415524084,465953,31650636,LABSPLAC,2012-12-26 23:51:00,1,0,1,0,0.866666666667,0.96138996139,1.0,0.849680557734,0.764577801474,0.895452684752,0.923111270057,0.766999539612,0.730763674792,0.792981966083,0.868404298218,0.846323784674,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.595371530633,0.545806296814,0.518602050471,0.492401777369,0.499035889825,0.983346841991,0.999872638877,0.999999999411,1.0
--3489393081457,385420,33388877,LABSPLAC,2013-03-09 16:36:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.849680557734,0.898425659254,0.922661341428,0.893756337453,0.960317048813,0.934336174267,0.98452571852,0.983536184355,0.947367328932,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.672522478069,0.619013457348,0.625799145584,0.55543792204,0.533642686942,0.999184641107,0.999730233836,0.999903520268,1.0
--12154002820135,399050,38939732,LABSPLAC,2013-10-18 20:29:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.946674134622,0.956424368306,0.977360559416,0.971851018469,0.972184171964,0.988274209103,0.985430719167,0.983550383714,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.611389714965,0.55668440302,0.562079676942,0.511287961677,0.504205630586,0.97236822302,0.990017552125,0.994382768708,0.0412753194136
-4991205085339,419295,37586867,LABSPLAC,2013-09-15 17:50:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960006807017,0.972864414573,0.977318365183,0.970935215504,0.978093446978,0.987872137312,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.71569092075,0.647214985244,0.548889690491,0.526771710748,0.996838050185,0.999868484304,0.999999999904,1.0
-8636559223052,434865,31907213,LABSPLAC,2013-01-21 21:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.952763005662,0.962324530351,0.987139453002,0.981681945999,0.913623509277,0.986025587724,0.990358441439,0.7401781796,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.660175291696,0.577760550212,0.533533852792,0.522644891347,0.999675874996,0.99999912379,0.999999972389,6.8974168516e-30
-10595837778227,362616,30482877,LABSPLAC,2012-11-15 17:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.950548437246,0.960864443511,0.926289698528,0.977318365183,0.976256117224,0.980918574313,0.983613477461,0.984511340686,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.698346072149,0.622825373292,0.654219968234,0.527806734103,0.525101967745,0.976224638136,0.985276919037,0.985964616257,2.02105027975e-07
--2701082086849,471749,34483884,LABSPLAC,2013-04-24 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.931886593608,0.944925009098,0.940195729491,0.977360559416,0.968953762795,0.93479753431,0.989768319288,0.984490671888,0.951896649524,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.691299008891,0.618554100768,0.695534727345,0.602536373809,0.949435799239,0.9745711789,0.988907578445,0.999999619415
-10678434761668,454392,38709926,LABSPLAC,2013-10-23 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.982200257785,0.979585367702,0.978530874702,0.98646775749,0.989703168472,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713921163545,0.626063872608,0.645245582584,0.531953416798,0.518982996163,0.983878683577,0.99231897987,0.995553282009,0.000394022201238
-1393280600360,410679,35428921,LABSPLAC,2013-06-11 22:46:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.936496794188,0.870133434287,0.903375628039,0.825694548527,0.722629741317,0.939489144645,0.990518371679,0.981419100816,0.907226035256,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.598009620858,0.577151043871,0.555441868644,0.537414547383,0.523583343399,0.997628577034,0.999611366601,0.999856692336,0.999999998479
--8745284681128,415058,36695916,LABSPLAC,2013-07-22 09:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.903725769945,0.966986025501,0.946938467571,0.813103795587,0.871210292258,0.858211527794,0.998193548387,0.98541055824,0.952049163101,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.607035111563,0.554082480785,0.522183830833,0.507168706154,0.510006832288,0.871740477413,0.856964844332,0.842941950558,5.09365507111e-13
--1416063890367,438613,36407099,LABSPLAC,2013-06-28 16:20:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.984221177907,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.66153897206,0.577760550212,0.516974920737,0.506731836252,0.88640941226,0.875905842458,0.895690802958,1.0
-6298057914978,385648,36712621,LABSPLAC,2013-07-26 16:13:00,0,0,1,0,0.968845448992,1.0,1.0,0.868122065561,0.873015453161,0.826242319062,0.966146546532,0.756030450565,0.920111547517,1.0,0.677421961153,0.820991765823,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.596165782909,0.549581140231,0.5656089288,0.520378054073,0.511157232257,0.791132009607,0.902635786188,0.975103906252,0.999990057152
-10433058834428,401485,40309552,LABSPLAC,2013-12-31 01:46:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960085909416,0.980613017097,0.975640843332,0.97572337487,0.980918574313,0.985644567703,0.982071232571,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723487769495,0.623171982121,0.633531218038,0.535835655186,0.522459988764,0.998102438321,0.999886334715,0.999997710385,1.0
-9578862385436,468100,37356823,LABSPLAC,2013-09-04 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.952104997461,0.979876980557,0.970564203852,0.971467234334,0.983642630155,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.66702051501,0.592514525106,0.619588095117,0.530497962751,0.516945614296,0.938321852255,0.950573179691,0.954521033327,0.172476202725
-11128568023975,368777,39614985,LABSPLAC,2013-11-30 03:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.929704385122,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.889618862203,0.984767639927,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.702047090632,0.647214985244,0.529322505038,0.515899499433,0.988714263963,0.997419374723,0.999042225202,1.40846653247e-87
--2471898027856,461149,32756531,LABSPLAC,2013-01-30 02:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.984716678926,0.977318365183,0.979348404652,0.990842372401,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.632904201204,0.640657052947,0.540063093532,0.533983975063,0.995695220935,0.999487436073,0.999961016411,0.000171274720789
-12119380103560,365570,34331463,LABSPLAC,2013-04-25 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.916539227749,0.917370536748,0.91524833856,0.970534475054,0.957709662051,0.956085287736,0.980382842717,0.987900948316,0.982460117133,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.663601646025,0.587021411623,0.604438079785,0.527053921977,0.511992644965,0.985366234266,0.99177240819,0.993856480243,2.31303732671e-10
-11877788399950,461158,39584543,LABSPLAC,2013-12-02 09:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.957496563287,0.975138921301,0.972194889706,0.976283693783,0.978093446978,0.983613477461,0.984747636583,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.692414987569,0.601682266895,0.633531218038,0.531233602783,0.513838055164,0.794541897796,0.922766519449,0.979886283932,0.332320834663
--12052713498986,392693,38164589,LABSPLAC,2013-09-16 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.94582387653,0.85898395062,0.911817302136,0.960073670187,0.870904982307,0.829622664449,0.976800101666,0.9941897021,0.890796275673,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.68718647825,0.683180404093,0.631528916061,0.692128507878,0.604683790957,0.956620935185,0.977280694411,0.988489427125,2.2713991224e-56
--1138650520285,432830,32310906,LABSPLAC,2013-01-14 05:25:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.952763005662,0.960002514393,0.977360559416,0.977318365183,0.962846162738,0.988274209103,0.88542799359,0.981138461107,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.647045907356,0.697318231141,0.555736215486,0.537797040063,0.967867040717,0.981995759348,0.986777888352,0.995102839357
--4967834617680,364668,30986498,LABSPLAC,2012-11-19 14:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.953273832983,0.944033630707,0.792588825832,0.981348622255,0.847554558915,0.989049732999,0.79477101343,0.986185475013,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.631504660353,0.554167093105,0.549300938986,0.500406548107,0.496726303296,0.878254409268,0.981442972028,0.999978909594,1.0
--1276519412650,355042,36494218,LABSPLAC,2013-07-08 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958675316662,0.987674353582,0.986256651349,0.980266699257,0.98646775749,0.985303412661,0.985186809953,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.605641449471,0.63874696642,0.536297230864,0.527885161988,0.992142992844,0.985410624743,0.987613616885,1.0
-11004154899725,402508,36461865,LABSPLAC,2013-07-29 19:18:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.977318365183,0.979494705729,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.706702362941,0.632296302145,0.690773668204,0.540773299954,0.524186926841,0.992519633291,0.996720046968,0.997600845369,6.12863933703e-05
--11584622773005,405361,36099084,LABSPLAC,2013-06-20 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.901449019404,0.9241217929,0.975115087552,0.936251440351,0.924011881087,0.981875769026,0.978047125124,0.987815926787,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.609521848461,0.553903485855,0.522421043247,0.50818683795,0.507825173265,0.917820236329,0.945473143034,0.966006335668,7.01273553137e-15
--11699102797175,405894,36571937,LABSPLAC,2013-07-15 18:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.72615303655,0.88906437237,0.919883355688,0.95275741491,0.964463803734,0.923813426808,0.768251592489,0.892150973773,0.883252727292,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.638880217228,0.584037165964,0.575657631795,0.533522377097,0.516234671226,0.932617105209,0.987979556809,0.99930818558,1.4531363561e-26
--12148386735493,445279,29520125,LABSPLAC,2012-09-29 07:48:00,1,0,1,0,0.047619047619,0.0,0.0,0.920212724291,0.852475382878,0.653344702768,0.531318681319,0.840795613717,0.779123131061,0.2,0.384609692934,0.427950848693,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.582390113745,0.535173332137,0.533416685648,0.508364142836,0.501652007513,0.929691073192,0.920097574324,0.897657579879,0.000315920556975
--3987531456312,436603,37252711,LABSPLAC,2013-08-11 08:23:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.830242480543,0.851585296403,0.931717203894,0.985257429289,0.966506375579,0.893477063577,0.972425486755,0.888293805825,0.882648961371,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.610761174936,0.553248573974,0.547402931986,0.514214723341,0.502823004272,0.740966291053,0.500011279598,0.152116484865,1.146627829e-93
-1693654719095,362390,33786197,LABSPLAC,2013-03-29 15:25:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959804728644,0.977360559416,0.975640843332,0.978203003675,0.98646775749,0.985303412661,0.983636525472,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57063118209,0.577760550212,0.519370191346,0.510909572039,0.942195745185,0.956237128404,0.962009015771,0.000110163937344
-1612274121959,367490,39126737,LABSPLAC,2013-11-10 21:00:00,1,0,1,0,0.333333333333,0.0,0.0,0.475752409294,0.690360361746,0.740656094003,0.29375,0.48567251462,0.471279054156,0.78474025974,0.179104477612,0.491666666667,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.550575229004,0.522914215556,0.50644065596,0.503336049816,0.503203352987,0.798101057486,0.806675106482,0.784245557908,1.0
-8549577581306,362509,32096437,LABSPLAC,2013-01-26 21:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.925528326489,0.988057911891,0.975640843332,0.943002013136,0.978093446978,0.987251998297,0.916366030006,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.573298528927,0.577760550212,0.528372835035,0.520847159236,0.992124075666,0.997020474559,0.998760448204,2.05021033602e-47
-3299307349362,413529,32909480,LABSPLAC,2013-02-18 07:56:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.958520573394,0.977655342441,0.977318365183,0.970202587569,0.992648824013,0.98711792879,0.948920355914,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.640657052947,0.543111873914,0.528230446587,0.999433736511,0.999977052617,0.999994537672,8.35592115709e-05
-6967509780721,464210,38618360,LABSPLAC,2013-10-16 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.9794623621,0.98646775749,0.989703168472,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.627572613136,0.647214985244,0.555307693308,0.528825011393,0.989560886372,0.995733665684,0.997855038442,0.999999984745
-11039659045707,387598,34535912,LABSPLAC,2013-05-06 04:32:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.98646775749,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.680811796701,0.610492309641,0.619588095117,0.695633741518,0.601762323769,0.986330809769,0.992855570877,0.99564702943,0.999998098241
-7094992191229,380951,38904328,LABSPLAC,2013-11-08 22:59:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.97997861967,0.977318365183,0.952904079103,0.987424952203,0.983906434837,0.984773224933,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.572982539305,0.577760550212,0.521978984372,0.514160522869,0.961409536009,0.993884437342,0.999549753587,3.71765768624e-05
-2131484895996,435361,31386631,LABSPLAC,2012-12-29 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954456474071,0.984716678926,0.977220015334,0.979596411355,0.983642630155,0.987134443821,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.685191164165,0.605143947865,0.654219968234,0.537393831214,0.520019303928,0.99218855292,0.999166879858,0.999997480646,1.0
-10617775669075,378275,32137686,LABSPLAC,2013-01-20 11:59:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.88336980542,0.917435382831,0.935062828966,0.968312836939,0.948230875492,0.952733090237,0.98641628396,0.975125801167,0.98337955762,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.664831137058,0.594783068557,0.611782965058,0.528607084893,0.512335416424,0.984318906877,0.997131421886,0.999999823375,8.77705728498e-22
--6558132719840,470969,36437613,LABSPLAC,2013-07-06 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.943581798475,0.979876980557,0.856993082359,0.955788567381,0.984998208883,0.984701216704,0.949807030839,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.621805857838,0.563498447427,0.5656089288,0.523210984244,0.513272605497,0.968813262251,0.989697231657,0.998936746269,0.0177858395795
-8636559223052,434865,31882134,LABSPLAC,2013-01-20 05:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.95365513447,0.961043261284,0.982495040811,0.97863298675,0.976138862407,0.987099640836,0.987050137552,0.98472001845,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.654366452658,0.677256737292,0.577760550212,0.539754131972,0.527688520729,0.998233739922,0.999769438059,0.999905126394,1.0
-9924702471080,383509,31613338,LABSPLAC,2013-01-02 10:16:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.903734307621,0.987687489104,0.984878261428,0.970372379219,0.981394008776,0.989703168472,0.985510093613,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.658310883615,0.577760550212,0.515844121009,0.505714535587,0.995845894714,0.994906377277,0.985324260143,1.0
--7476774851820,370727,40026297,LABSPLAC,2013-12-02 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.932266052935,0.92769094168,0.960073670187,0.860775867368,0.952299761256,0.984998208883,0.978263793541,0.986023851907,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.605103432373,0.55828031182,0.50644065596,0.501623433625,0.504656737552,0.967503312086,0.984724112675,0.988912755828,1.45988214634e-09
--4077862333684,387904,30125919,LABSPLAC,2012-10-24 00:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.52443483851,0.517235016499,0.942365193635,0.945308156968,0.955966627376,0.995940750993
-8268514990716,398513,37706200,LABSPLAC,2013-09-20 23:02:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.96801938444,0.95913881356,0.984716678926,0.977318365183,0.96749296746,0.98646775749,0.983594039937,0.982959978372,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.592186262416,0.528684826146,0.521819421144,0.990797808756,0.999466672842,0.999992814517,1.0
--10791717457184,396820,35433084,LABSPLAC,2013-06-01 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.984716678926,0.977318365183,0.977271516277,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.614960149711,0.647214985244,0.535634149876,0.518554132831,0.99025789021,0.998468441424,0.999427964864,0.989977481496
--2331041472405,392358,37455339,LABSPLAC,2013-08-22 20:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.977318365183,0.973703893969,0.98646775749,0.981904104736,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.525882793093,0.515076269368,0.956776350366,0.965351403725,0.969544509084,0.989084519039
-3961922817294,443979,36768192,LABSPLAC,2013-08-13 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978090202411,0.983642630155,0.987872137312,0.98586906409,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.703897983739,0.622799067574,0.647214985244,0.536158526818,0.522521298719,0.9882968446,0.997222507853,0.99889547076,0.000652984328795
--6878776383975,464815,33541610,LABSPLAC,2013-03-07 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.962962962963,0.885614377635,0.773874307367,0.92729924352,0.92579950245,0.972104871787,0.926545521382,0.971069908027,0.975616688784,0.953481013288,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.617674152132,0.566770622478,0.547402931986,0.513358110655,0.509305830879,0.976668549119,0.990128301939,0.993058784985,2.24667920584e-17
-7112688009010,457988,36400161,LABSPLAC,2013-07-17 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.957401856632,0.977655342441,0.977318365183,0.976950019274,0.988274209103,0.975670903005,0.98305865663,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.668754054713,0.589156276859,0.577760550212,0.524584993223,0.51255689616,0.986118582197,0.993112687414,0.995003170894,0.000403010238527
--9921855862556,466081,33338595,LABSPLAC,2013-02-23 18:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.940536919431,0.972864414573,0.972194889706,0.976529145532,0.978093446978,0.982673430182,0.985305810673,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.590378593721,0.63874696642,0.530087386351,0.517530311008,0.999358250982,0.99997764589,0.999994502757,2.01856229995e-34
--2701082086849,471749,34490099,LABSPLAC,2013-04-23 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953553978616,0.962953894985,0.88503830197,0.975971510691,0.986943191953,0.987872137312,0.953367335946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.732288665718,0.72390416952,0.690773668204,0.536638834083,0.522064827509,0.994629987199,0.998609214725,0.999290550412,0.0013143973764
--4110095730246,447378,36175331,LABSPLAC,2013-06-28 09:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.95852854746,0.977655342441,0.977318365183,0.975352206385,0.978093446978,0.983613477461,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.516880962562,0.512159461766,0.990618143751,0.998072937963,0.997322069789,1.0
--8265401423260,427652,38224225,LABSPLAC,2013-09-16 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.939960891662,0.952281814493,0.969409513399,0.971851018469,0.975111527869,0.98646775749,0.986925696137,0.983629292582,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.696876422419,0.645245582584,0.695177302741,0.603303095825,0.975631739594,0.986690214591,0.992266194647,0.0342596953838
--3912329213703,437924,38523504,LABSPLAC,2013-10-04 03:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.977271516277,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713921163545,0.621314793305,0.618554100768,0.520928961808,0.514411717525,0.955134390352,0.950920864079,0.930245306367,0.999998698966
-4905714596026,441701,37631498,LABSPLAC,2013-09-13 17:49:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.946857598804,0.924757078584,0.941008500329,0.966732680317,0.913164385601,0.90974025974,0.86658838517,0.951397768286,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.558739651253,0.537223027254,0.507421172689,0.506676406624,0.974183777403,0.984022850893,0.983798928634,1.0
--8118296954482,403532,36657845,LABSPLAC,2013-07-24 02:00:00,1,0,1,0,0.968845448992,0.0,1.0,0.752669089252,0.899900999136,0.857130863435,0.734995526577,0.93377409924,0.784444348206,0.797727272727,0.795282926829,0.754489131012,0.917871594643,0.875764265788,0.793065314363,0.817336130992,0.572953755467,0.524664347122,0.520271360095,0.501684169499,0.500739283069,0.521846991581,0.358532931516,0.243451933663,5.94635060439e-33
-3054183943259,450562,40373959,LABSPLAC,2014-01-06 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.977179533147,0.98646775749,0.978898911989,0.982051229228,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.514688651593,0.509789015332,0.958328462308,0.976985041834,0.990549059505,0.48164093615
-3000592249976,442229,33261592,LABSPLAC,2013-03-10 19:39:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.973157933662,0.906021709044,0.955085089244,0.981204947537,0.975640843332,0.973930067642,0.789629787831,0.987038803979,0.984747636583,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.620041315552,0.654219968234,0.533773701973,0.523473533141,0.996872152235,0.999514808866,0.999929799659,1.13567310524e-05
-10056790894811,355029,30284628,LABSPLAC,2012-11-13 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.961043261284,0.977655342441,0.977318365183,0.975017242245,0.981875769026,0.982075403677,0.983261002917,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.636556513411,0.565592912803,0.577760550212,0.517044243471,0.50888678099,0.940399887155,0.974776826879,0.98732874564,1.38157173687e-05
--3048017322281,399790,30668180,LABSPLAC,2012-11-08 12:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.959382502513,0.975380835714,0.977318365183,0.976493042342,0.981875769026,0.983264240108,0.98568359549,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650451236481,0.596447200408,0.62492509043,0.527362340578,0.517764837376,0.892393897975,0.929252982483,0.952226684828,0.000815585729363
--5258348095994,457342,31607475,LABSPLAC,2012-12-15 21:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.956160864127,0.952763005662,0.926420814044,0.980613017097,0.873411413368,0.900697730911,0.992673385592,0.68993846099,0.947977019229,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635251802724,0.649893578103,0.577760550212,0.522106507274,0.515768562922,0.891742916092,0.924428810699,0.894634564066,1.0
--4629695421662,449720,37406642,LABSPLAC,2013-08-11 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.961107767351,0.987674353582,0.977318365183,0.978212867981,0.98646775749,0.987999443818,0.982978309355,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.61578367723,0.645245582584,0.695933622796,0.60539624689,0.930588104178,0.944772929584,0.949951221357,0.999999838186
-824529534144,440902,39213387,LABSPLAC,2013-11-15 20:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.957396352461,0.97997861967,0.977318365183,0.976527832622,0.98646775749,0.987391292594,0.982071232571,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723487769495,0.620947220952,0.633531218038,0.537824710719,0.518282048166,0.972060039486,0.969938754033,0.974322502637,1.74165589632e-07
--12148386735493,445279,29559874,LABSPLAC,2012-09-30 17:00:00,0,0,1,0,0.931034482759,1.0,1.0,0.830676314436,0.750262124281,0.802580402935,0.934382566586,0.960413277251,0.90458779943,1.0,1.0,0.959931207004,0.917871594643,0.825369867954,0.719762276307,0.708319280651,0.540423698416,0.509430264604,0.533416685648,0.506220891804,0.588783147042,0.899059355452,0.905172758784,0.916789862602,3.19343728048e-188
--11578123844433,405352,37781898,LABSPLAC,2013-09-10 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.510151213197,0.948029649719,0.961648760364,0.970882589032,0.0433424417936
--5567453440449,354964,37101571,LABSPLAC,2013-08-14 14:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.903725769945,0.963776230663,0.955966723432,0.977704112943,0.970356704795,0.965916782245,0.981816316964,0.975981318334,0.980421538434,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.613648838278,0.56164227797,0.547402931986,0.516184614505,0.510027366013,0.968054678311,0.979136412778,0.979878959904,1.15704359259e-09
-8838847181046,371163,36219550,LABSPLAC,2013-07-13 03:00:00,0,0,1,0,0.968845448992,1.0,0.0,0.972100498572,0.963776230663,0.927739766664,0.979876980557,0.977318365183,0.971406878849,0.786943191953,0.888013233271,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.625843002933,0.647214985244,0.715087770235,0.612905255121,0.98343718848,0.993389761662,0.99668324869,0.415360730242
-9533989433729,439012,29954066,LABSPLAC,2012-10-23 09:38:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.976409530142,0.978093446978,0.983594039937,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.581707478071,0.592186262416,0.521212783341,0.510741827209,0.96428563854,0.948741297549,0.915582123675,1.0
-1521161542906,420491,33926506,LABSPLAC,2013-04-10 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.956994502276,0.984716678926,0.977318365183,0.976238181443,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.644182588507,0.684630899643,0.540707815613,0.533317229589,0.999145843437,0.999944676097,0.999989571145,0.012789413818
--3489393081457,385420,33419417,LABSPLAC,2013-03-09 16:58:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.849680557734,0.898425659254,0.922661341428,0.893756337453,0.960317048813,0.934336174267,0.98452571852,0.983536184355,0.947367328932,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.672522478069,0.619013457348,0.625799145584,0.55543792204,0.533642686942,0.999187468892,0.999730780195,0.999903712394,1.0
--11145272056246,461156,33170946,LABSPLAC,2013-02-23 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.947741943847,0.949585737755,0.979783333492,0.979313254486,0.973585528963,0.988274209103,0.984356971486,0.970531032275,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.556937067206,0.562079676942,0.511375695891,0.507985959281,0.867832688763,0.918718865293,0.938677236332,2.37260066194e-07
-8528455099626,399847,40095885,LABSPLAC,2013-12-22 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954880852375,0.979876980557,0.975640843332,0.977585901932,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.520355887768,0.511644961025,0.959729360128,0.98257714726,0.995257512853,2.51274231057e-10
-8206387573905,433562,33273088,LABSPLAC,2013-03-05 10:01:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.961979822707,0.977655342441,0.977318365183,0.978527790846,0.990518371679,0.987314303318,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.640657052947,0.530519610265,0.524903867924,0.982940996889,0.9924199551,0.996150596615,2.26246924528e-05
--139632156971,438609,40161979,LABSPLAC,2013-12-08 21:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.96072858502,0.945844619431,0.982200257785,0.964515960506,0.937695197683,0.981911601203,0.881838561572,0.986898629184,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635049047227,0.564167557266,0.577760550212,0.531434548101,0.5121503762,0.934287253535,0.952617307153,0.963079237564,4.01550332959e-17
--7724816663221,358071,34122140,LABSPLAC,2013-04-06 00:29:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.957909078276,0.953087183878,0.977704112943,0.968953762795,0.972151793014,0.978093446978,0.980707280299,0.948719536594,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.607757473961,0.555713037984,0.547402931986,0.506444322492,0.503656693021,0.87500378321,0.903490806374,0.883700259777,2.40580216798e-08
-1521161542906,420491,33917816,LABSPLAC,2013-04-10 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.956994502276,0.984716678926,0.977318365183,0.976704725335,0.98646775749,0.987134443821,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.644182588507,0.697318231141,0.543657845785,0.53626483795,0.999017199212,0.999933861603,0.999986729753,0.759377196071
--5531495494637,428643,32526668,LABSPLAC,2013-01-19 22:01:00,0,0,1,0,0.666666666667,1.0,1.0,0.731881107263,0.578955257385,0.618378116346,0.551515151515,0.863991546477,0.733366676526,0.995625385089,0.598253275109,0.910497452739,0.917871594643,0.761791632626,0.698074758989,0.679476974952,0.520660734091,0.511932211793,0.533416685648,0.510235313838,0.506297016546,0.606119837663,0.51674808103,0.505466031896,3.75317851336e-43
--1400966364152,471096,39475524,LABSPLAC,2013-11-09 08:18:00,1,0,1,0,0.968845448992,0.0,0.0,0.816087231352,0.866619765769,0.897747322178,0.39045425108,0.866262013223,0.448097864299,0.588542277491,0.0958333333333,0.295263759816,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.607987695524,0.545051739969,0.547402931986,0.49987608979,0.498653927885,0.644381476749,0.62925123711,0.637496108757,1.130308913e-18
--4757345149909,468062,36422780,LABSPLAC,2013-07-16 11:47:00,0,0,1,0,0.777777777778,1.0,1.0,0.903725769945,0.86185727792,0.885599766836,0.792588825832,0.904138946578,0.863103102726,0.6,0.862855277839,0.626584361877,0.917871594643,0.812436958387,0.802045242215,0.793974432341,0.633219843813,0.568883159758,0.577760550212,0.514899037017,0.505863910956,0.913670280797,0.926128145481,0.890581434185,1.30834187531e-69
--4629695421662,396125,39026359,LABSPLAC,2013-10-18 15:16:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.9570458202,0.984716678926,0.975640843332,0.978944023937,0.98646775749,0.987872137312,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.710010777854,0.672961290111,0.700045713854,0.606377917673,0.979816593674,0.969780412517,0.956483478257,2.93019774762e-08
-6361496304962,437883,36543842,LABSPLAC,2013-07-21 22:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.956767817709,0.982495040811,0.975128692964,0.976238181443,0.98646775749,0.983594039937,0.982959978372,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.564624648235,0.547402931986,0.513667663807,0.508486338044,0.821964995396,0.832400606963,0.875291592835,0.999987556658
--11557752330043,471937,39610861,LABSPLAC,2013-11-11 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.961117893588,0.982834655213,0.972363168837,0.974124591299,0.983642630155,0.988124024747,0.980883554251,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.705614795139,0.615316315082,0.645245582584,0.718244901833,0.617407707062,0.983144994223,0.989702651286,0.993281934845,0.399492608881
-3156804258432,461264,37217603,LABSPLAC,2013-08-23 07:16:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.963787514442,0.988274209103,0.989686653441,0.916225764243,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.712475545141,0.672961290111,0.714491340966,0.614326768481,0.967651133165,0.973600364474,0.979857391455,0.999999999899
-8134242030319,447724,31538462,LABSPLAC,2013-01-08 00:14:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963033703961,0.977655342441,0.977318365183,0.975352206385,0.978093446978,0.981904104736,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.514899037017,0.507783958966,0.898891213683,0.943861037195,0.96465097108,9.35569820197e-08
-399257514207,380119,29984871,LABSPLAC,2012-11-06 09:52:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.980220534083,0.977318365183,0.976780698626,0.98646775749,0.985303412661,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.58738711519,0.577760550212,0.528760834058,0.515589980947,0.98110028768,0.992310882606,0.995559052698,0.00221487474341
--3821876237156,424830,32920213,LABSPLAC,2013-02-13 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958769742699,0.984716678926,0.977318365183,0.97773985206,0.983642630155,0.983613477461,0.984105188904,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.575611816541,0.592186262416,0.53875253288,0.525036604368,0.999533038943,0.999999668565,0.999999999132,1.0
-1459883316791,376069,34601003,LABSPLAC,2013-05-05 18:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.82164930366,0.857150115159,0.877556692032,0.76641025641,0.688481605528,0.943756056958,0.785524374988,0.990558347456,0.693625515241,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.626506409634,0.555069237912,0.577760550212,0.509035102269,0.507154407086,0.952836793338,0.984652737286,0.990153436778,4.627364294e-36
-7480777760494,405193,34448018,LABSPLAC,2013-05-09 14:38:00,0,0,1,0,0.862745098039,1.0,1.0,0.972100498572,0.963776230663,0.961905190403,0.979275099936,0.967935630982,0.900102400193,0.98137455629,0.788412754197,0.916691266896,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635775971153,0.565825759956,0.549300938986,0.50246102423,0.500485248345,0.967560213119,0.99029579142,0.998389245052,1.0
--5567453440449,354964,36805690,LABSPLAC,2013-07-30 12:34:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.978773186895,0.983642630155,0.982424071764,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.577760550212,0.521978984372,0.514493260637,0.961444932742,0.979872572722,0.988840175858,0.00590169394731
--11337429808024,451040,37235420,LABSPLAC,2013-08-16 17:00:00,0,0,1,0,0.777777777778,1.0,1.0,0.868122065561,0.800526068298,0.829740626054,0.896905066219,0.956260381911,0.891406919181,0.995368421053,0.983597120998,0.991888451983,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.587528585647,0.54752132658,0.537223027254,0.51008084281,0.505395731567,0.777432507909,0.700280172077,0.667262138654,1.0
--2701082086849,471749,34423595,LABSPLAC,2013-04-22 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.928854915808,0.795005177037,0.979580248183,0.943359036419,0.986943191953,0.986182202112,0.986372423752,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.729635336924,0.723761697108,0.690773668204,0.706827572663,0.610337145048,0.990155948239,0.99645206723,0.997855424717,0.235613575451
-4045940637253,424762,39074547,LABSPLAC,2013-11-06 01:00:00,0,0,1,0,0.968845448992,0.0,0.0,0.59515533747,0.840951742742,0.844638108394,0.698636636637,0.826980753105,0.887428588602,0.787105038704,0.991896685716,0.68091119899,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.566663925581,0.531149262585,0.53508067757,0.511093719969,0.505672196664,0.853697724942,0.925300810883,0.956001778077,2.47354950127e-23
-6664403853289,418703,39903383,LABSPLAC,2013-12-12 22:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959582428714,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.983594039937,0.984873422747,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.528140533929,0.513401597195,0.93917588582,0.921425033622,0.904721301422,0.999999999979
--806313880347,377589,29145671,LABSPLAC,2012-09-06 08:05:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.955563879202,0.977655342441,0.973509511272,0.977308557687,0.978093446978,0.983594039937,0.983580766711,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.658764215596,0.577760550212,0.521552362195,0.510130277357,0.999014599881,0.999999660823,0.999999999999,1.0
-6457886233483,397011,32527118,LABSPLAC,2013-02-13 08:02:00,1,0,1,0,0.968845448992,0.980986142443,0.0,0.932348437859,0.918027931343,0.892378618841,0.73239468411,0.895401833515,0.738471393476,0.582760858103,0.786225053154,0.699817508966,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.610304205402,0.54648893369,0.547402931986,0.499196455344,0.498322539318,0.802503470017,0.792858098879,0.772513969692,1.0
--5531495494637,428643,32666179,LABSPLAC,2013-01-25 22:00:00,1,0,1,0,0.0684931506849,0.0,0.0,0.333307202432,0.217518519525,0.344143722077,0.143243243243,0.1,0.101298701299,0.0,0.0,0.0666666666667,0.29944547465,0.555906810973,0.600558873622,0.607671556364,0.515083829897,0.491686715471,0.462285342001,0.48654369868,0.490774413408,0.053418332204,0.0270025518981,0.0151998546438,8.11791263487e-98
-8085227688894,447048,33447857,LABSPLAC,2013-03-26 05:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.965125374887,0.963776230663,0.959965031251,0.972864414573,0.977318365183,0.977533452843,0.984998208883,0.985303412661,0.983497905945,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.641984784403,0.577566729298,0.592186262416,0.512116155284,0.515136556231,0.982176018899,0.998840348487,0.999955818604,1.0
--7970765028213,461334,36243284,LABSPLAC,2013-07-03 20:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.935941233992,0.86921876998,0.903382180925,0.923111270057,0.964136937844,0.908926051327,0.852768242232,0.987265175951,0.89966660619,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.62435561131,0.564562147213,0.547402931986,0.511917981418,0.507572984689,0.807767756228,0.792841490004,0.863133559263,5.76896239381e-36
--11883639975600,462754,28616245,LABSPLAC,2012-08-26 22:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.972100498572,0.963776230663,0.93212618582,0.976812787598,0.881884218277,0.971272347296,0.792648824013,0.986666029307,0.954024013295,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.687181939605,0.607022514588,0.613601563346,0.53487544961,0.515334784525,0.997906634387,0.999946186275,0.999992093534,6.74398513099e-38
--8745284681128,415058,36847885,LABSPLAC,2013-07-22 01:00:00,1,0,1,0,0.862745098039,0.0,0.0,0.903725769945,0.940120353859,0.945809954013,0.784162622193,0.963517984565,0.905171113034,0.398193548387,0.88541055824,0.789937603636,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.606612874092,0.552566107384,0.522183830833,0.501375211864,0.502049225324,0.90314595045,0.936170346103,0.942614502441,1.15522766462e-21
-88250944256,467839,38173929,LABSPLAC,2013-10-03 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953153757604,0.98570109238,0.977318365183,0.945078220916,0.98646775749,0.987038803979,0.96674371206,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.618024817725,0.659751167278,0.526225682277,0.516661414033,0.922477904474,0.957387652619,0.972382079295,3.25829125859e-06
-1427323147403,375650,34566786,LABSPLAC,2013-05-07 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.975380835714,0.97863298675,0.976283693783,0.978093446978,0.986182202112,0.984538469918,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700707122236,0.625261809239,0.677938710863,0.549321549007,0.526784515908,0.986621460964,0.994866496102,0.996965239306,0.000826312729672
-3175022992398,435053,40820819,LABSPLAC,2014-01-26 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.978212867981,0.98646775749,0.987334502903,0.985096440524,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.707225676086,0.659751167278,0.700125935248,0.60530738953,0.991607759,0.997080751409,0.999058381503,1.79737724845e-06
-4880505499918,426503,31990958,LABSPLAC,2013-01-26 13:39:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.978212867981,0.98646775749,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.627076317771,0.640657052947,0.532465006583,0.5238877594,0.973698101012,0.983772919723,0.988800507583,0.391004177386
--8925058645127,427795,36637234,LABSPLAC,2013-07-17 07:55:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.943613475634,0.952774727159,0.977655342441,0.973451171113,0.966394779379,0.965556557028,0.984106493919,0.98356379241,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.609944551568,0.552905571259,0.575657631795,0.512438989327,0.507595501836,0.860447758448,0.894718751301,0.873814845546,6.44804707767e-09
--9058316045048,399129,32309714,LABSPLAC,2013-01-16 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.975138921301,0.977318365183,0.971630968145,0.981875769026,0.885653159728,0.980690642088,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.667968205906,0.589640629431,0.611782965058,0.530239397592,0.518361253153,0.928958175271,0.935707732764,0.945107020487,7.4758729e-05
-8206387573905,433562,33254630,LABSPLAC,2013-03-06 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.944184678065,0.942888666866,0.961979822707,0.979876980557,0.96753224086,0.973658627713,0.990518371679,0.988013909789,0.986850849555,0.917871594643,0.887213629677,0.849757775956,0.831495259098,0.648188729235,0.586216857752,0.592186262416,0.52272692018,0.522005667442,0.982384962797,0.987139277127,0.990392331676,6.65090553117e-09
--400756422449,398876,36523612,LABSPLAC,2013-07-18 01:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978111020545,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.593911534832,0.592186262416,0.51617145831,0.510568643862,0.982308917242,0.992557511408,0.995368911709,0.999628843857
--11062057294114,468812,29445783,LABSPLAC,2012-09-27 04:25:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960085909416,0.975380835714,0.975640843332,0.975352206385,0.978093446978,0.983594039937,0.982846065158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.52000462878,0.511223339217,0.952902222737,0.981166896603,0.992224929181,0.998973163831
--4716787243805,406354,36380061,LABSPLAC,2013-06-29 13:19:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.876749226838,0.820655302515,0.920241355292,0.960564234415,0.954642581789,0.946192975101,0.98452571852,0.988955111152,0.981249653253,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.603296075066,0.552796849343,0.562079676942,0.528291938771,0.515414237163,0.984855619308,0.995552894931,0.996343202179,0.999999999926
--11145272056246,461156,33093017,LABSPLAC,2013-02-21 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961979822707,0.977360559416,0.977318365183,0.942248415211,0.98646775749,0.884170796655,0.960788002954,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.570378414122,0.547402931986,0.513115277352,0.509802713582,0.964841177143,0.979324841814,0.982188030993,0.00147469419124
--3912329213703,437924,39445994,LABSPLAC,2013-11-08 20:38:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977360559416,0.977318365183,0.978773186895,0.983642630155,0.987134443821,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.517452988522,0.508906875881,0.952328901046,0.957482872313,0.955878300688,0.0316896603093
--3796590953893,358390,37062073,LABSPLAC,2013-08-08 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.936684278733,0.982834655213,0.973451171113,0.96503254442,0.984998208883,0.979026218496,0.980326756898,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.617745531753,0.559992791104,0.562079676942,0.514137973161,0.510260498736,0.905409240143,0.942354987488,0.932216901217,7.03986496824e-13
-3268683812264,448867,35252121,LABSPLAC,2013-06-05 04:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.972061352238,0.972417939447,0.98646775749,0.982985777753,0.982904970683,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.619588095117,0.532347747663,0.522017578275,0.981743916517,0.994480889017,0.997182201427,0.00065265106866
-4035289185680,449684,34181567,LABSPLAC,2013-04-25 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.987134443821,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.654219968234,0.531019017637,0.523431543381,0.964573586606,0.970141765591,0.970710386904,0.982010477404
-6971998382998,458687,30260569,LABSPLAC,2012-11-06 05:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.976808364662,0.983642630155,0.985303412661,0.983707303946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.51765672096,0.512508437859,0.844707095375,0.853384662837,0.844253755142,1.0
--7214175442709,385309,35132526,LABSPLAC,2013-05-16 14:11:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.962187415028,0.94582804405,0.954259400481,0.977360559416,0.977427543539,0.97584402302,0.979598827762,0.985430719167,0.983550383714,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.692765294387,0.605386463616,0.604438079785,0.522182488212,0.513527222646,0.984948696928,0.996838667248,0.998176517787,0.990115576788
--8387553780356,385327,40434456,LABSPLAC,2013-12-24 12:34:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.987424952203,0.984644128328,0.985076014426,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723312699125,0.708225238,0.659751167278,0.719480452079,0.620077643996,0.987214726614,0.994039121364,0.996010252831,0.000577081099482
--850101470532,462849,30861492,LABSPLAC,2012-11-24 23:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.977179533147,0.989768319288,0.982673430182,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.520199031915,0.509509012329,0.976495671197,0.980681594363,0.979430707257,1.0
-7112688009010,457988,36435385,LABSPLAC,2013-07-17 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.977841699497,0.983642630155,0.985303412661,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.672318961834,0.594147112595,0.577760550212,0.519694395246,0.509645060186,0.978173641669,0.9872539851,0.992158146801,0.421007783988
-11458953193626,424997,30527508,LABSPLAC,2012-11-23 02:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961979822707,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.985303412661,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.515581021764,0.509103634966,0.684514069585,0.557854925493,0.505140989911,0.998723448604
--2990625788346,369251,30953208,LABSPLAC,2012-11-19 17:53:00,1,0,1,0,0.866666666667,0.0,0.0,0.849680557734,0.898425659254,0.899322243972,0.710050936997,0.865023373233,0.80273095884,0.754321636887,0.544378908061,0.719205599959,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611826982836,0.553873383548,0.562079676942,0.521916197096,0.507962321311,0.839322936609,0.848915550819,0.846660315401,5.9648583569e-42
--1453633253690,414100,36238853,LABSPLAC,2013-06-29 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.984151449195,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.707225676086,0.633531218038,0.697697214256,0.604256542211,0.99324977659,0.998226193232,0.999334382108,0.999991009898
--5258348095994,457342,31473247,LABSPLAC,2012-12-11 05:58:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.97863298675,0.975352206385,0.978093446978,0.985444508621,0.978948845657,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.632550871699,0.672740028299,0.609812874259,0.528504832233,0.514654013158,0.983587777741,0.988337522565,0.987372921591,1.0
--12434922099285,413988,31573073,LABSPLAC,2012-12-23 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.945452944774,0.977318365183,0.976654862268,0.980918574313,0.983613477461,0.984119986278,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650048755741,0.58334806456,0.592186262416,0.522915561444,0.514037670899,0.966258036873,0.969058141189,0.96532615991,1.0
-10934898406526,389088,33730744,LABSPLAC,2013-04-05 02:51:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.943613475634,0.948997005964,0.975138921301,0.975288733505,0.945713239861,0.98095440649,0.987134443821,0.983701207754,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.678127648362,0.61388981154,0.611782965058,0.528049641114,0.517144004272,0.984533959925,0.994501195421,0.996278846722,8.68704866731e-22
-3092518292801,396072,30465263,LABSPLAC,2012-11-11 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.508774735878,0.971210840829,0.955627541782,0.946458391986,1.0
-798518854058,472177,32685597,LABSPLAC,2013-02-21 11:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982495040811,0.977318365183,0.97572337487,0.98646775749,0.983594039937,0.982566364801,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.577760550212,0.514688651593,0.507529061975,0.951919005602,0.9722918841,0.982180408601,0.999477201655
-729492854779,431760,35527825,LABSPLAC,2013-06-19 05:09:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.916963131802,0.894095645944,0.935062828966,0.857790325082,0.931922675841,0.861888316539,0.976301210064,0.986253745178,0.950671236172,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.603719546921,0.549590792841,0.562079676942,0.503500947555,0.500580337223,0.792209012511,0.711074218485,0.601506088465,0.992296271688
--12441340721352,433080,33576206,LABSPLAC,2013-03-10 00:06:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.959740222578,0.984716678926,0.952931498134,0.976390461339,0.98646775749,0.887909489478,0.953389560559,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.588757577463,0.577760550212,0.535536927301,0.518160881475,0.997394188346,0.999728896272,0.99993258462,0.0086239799465
--2752357834044,365947,36449439,LABSPLAC,2013-07-10 06:13:00,0,0,1,0,0.931034482759,1.0,1.0,0.716332086144,0.422967999379,0.772252240544,0.889378399378,0.877073958358,0.699256929191,0.6,0.7,0.698082706893,0.917871594643,0.856423141525,0.767777245663,0.774350060903,0.547240532845,0.506886974123,0.493501371374,0.500074677949,0.510721759065,0.707017450961,0.907004262919,0.983309096042,2.34273272792e-41
--6966091301136,402353,32993232,LABSPLAC,2013-02-13 19:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.509172835271,0.950731155289,0.954956136465,0.959427664878,0.00192545317276
--11249260642593,361236,37897635,LABSPLAC,2013-09-13 00:42:00,0,0,1,0,0.0,0.0,0.0,0.448614854747,0.563675891343,0.718113759907,0.4,0.865116016955,0.702219171506,0.6,0.7976,0.664286530432,0.917871594643,0.79514940545,0.728887498451,0.738632887801,0.532088563932,0.516344978065,0.477521091272,0.50669790642,0.503397923375,0.0567938959347,0.0692950276056,0.0904660756277,9.75086297181e-62
-4084444723392,459019,31447929,LABSPLAC,2012-12-26 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.923030945155,0.922732865637,0.946129726346,0.980963033124,0.977220015334,0.974005912525,0.975856096198,0.983938101501,0.98372294154,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.661752635738,0.594883949229,0.611782965058,0.522220827539,0.518378777392,0.911330789143,0.916965601366,0.926314950065,5.73277328327e-20
-8713249871765,432892,35047580,LABSPLAC,2013-05-24 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.975458769288,0.98646775749,0.987872137312,0.986633948842,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.625579995656,0.633531218038,0.699438147625,0.605515822554,0.991058673149,0.999251356903,0.999848944488,0.999635600197
-9988875425969,375641,32007905,LABSPLAC,2013-01-17 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.97997861967,0.977318365183,0.977494456012,0.98646775749,0.985303412661,0.984420964457,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.704456231596,0.633391563294,0.640657052947,0.542154740323,0.53182265981,0.988377923253,0.996330600412,0.999158575316,5.64843866188e-07
--3677929803434,461222,37905148,LABSPLAC,2013-09-04 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977704112943,0.972061352238,0.971522100084,0.971224517251,0.978395793211,0.95107195833,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.569164215047,0.592186262416,0.516413644941,0.512920224901,0.969491844213,0.983325302757,0.985496220702,1.0
--1195461952069,409714,33596544,LABSPLAC,2013-03-09 22:11:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962145712577,0.980220534083,0.978689802581,0.977406380427,0.980918574313,0.987134443821,0.984999521822,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635681240063,0.591060075766,0.609812874259,0.524143274842,0.51517551028,0.97953421624,0.989608287143,0.988483517611,1.51583210015e-11
-10800651293913,463974,32541906,LABSPLAC,2013-02-13 15:53:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.957856431394,0.98570109238,0.975640843332,0.977761919475,0.98646775749,0.987038803979,0.984742854845,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.621750110895,0.654219968234,0.53359805375,0.52274618027,0.975901575601,0.998749949623,0.999999993054,1.0
-1521161542906,420491,33962086,LABSPLAC,2013-04-12 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.952763005662,0.957856431394,0.984716678926,0.977318365183,0.969271100171,0.988274209103,0.88711792879,0.949258444265,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.644182588507,0.697318231141,0.560499114742,0.541935519967,0.998165042897,0.999782377334,0.999938324296,3.33885520566e-10
--2957128422855,390474,36202506,LABSPLAC,2013-06-28 21:58:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.512879954088,0.507704449329,0.950446810994,0.958959980003,0.968717357024,0.00348537844063
--4315814030046,461965,31178213,LABSPLAC,2012-11-24 18:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.884847122624,0.845909956783,0.930134599195,0.942927703065,0.875241089142,0.847755591216,0.95,0.990786142312,0.981358979285,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.546485082623,0.547402931986,0.510868173725,0.504692560364,0.830739204465,0.92196265517,0.969518573593,9.97773272873e-66
-10012292271878,450967,40680627,LABSPLAC,2014-01-12 23:00:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960085909416,0.975380835714,0.972194889706,0.976283693783,0.982468061889,0.985303412661,0.983947654971,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.581707478071,0.592186262416,0.525034786443,0.5127402594,0.998687728658,0.999995083319,0.999997858647,1.0
--11153902347398,453357,31940025,LABSPLAC,2013-01-06 10:01:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.804773355968,0.635898124914,0.723207132357,0.394525904203,0.483841578188,0.687736511899,0.8,0.687088016488,0.527104144094,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.612641189099,0.546671238923,0.549300938986,0.502667486497,0.504830633732,0.816653158224,0.874517731167,0.901633006662,5.33128150973e-54
-3673537304856,419299,39762525,LABSPLAC,2013-12-10 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.989703168472,0.981553325322,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.630426294291,0.647214985244,0.519012950979,0.517061689616,0.974904403473,0.978074290341,0.985600760397,0.164127355676
--2199288203036,419675,30109028,LABSPLAC,2012-10-18 01:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.972100498572,0.963776230663,0.961984115331,0.973457199789,0.977318365183,0.974643654653,0.977592234243,0.986179987295,0.981764647823,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635775971153,0.568269817705,0.549300938986,0.513708605118,0.511130366782,0.933856086915,0.986410718853,0.996722982479,3.1000939258e-07
-11707426741543,461219,34364932,LABSPLAC,2013-04-27 14:00:00,1,0,1,0,0.0487804878049,0.0,0.0,0.367525252525,0.275600135561,0.462662840264,0.0666666666667,0.208620689655,0.237692819292,0.2,0.2,0.2,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.524071605338,0.490768925815,0.40010684085,0.479979040549,0.487767384266,0.096527142081,0.108449172355,0.107383022539,3.71522654769e-113
--7151978809607,398999,38501145,LABSPLAC,2013-09-29 20:16:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.959578136089,0.975380835714,0.976955464898,0.976283693783,0.978093446978,0.983594039937,0.983657227786,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.521834122845,0.509249959816,0.684279687929,0.64614881058,0.639707009114,1.0
-10431524076881,459639,32783305,LABSPLAC,2013-02-13 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.958942799526,0.972864414573,0.971878825418,0.974962528091,0.775599132161,0.983613477461,0.983657227786,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.665355187727,0.597329835382,0.625799145584,0.531071219203,0.520367251949,0.981796380715,0.988787868023,0.995372927933,9.38485879722e-37
-8445007828929,394422,39009125,LABSPLAC,2013-10-29 10:17:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.97863298675,0.974287756108,0.980918574313,0.985408556066,0.98330198022,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.577258936451,0.577760550212,0.519940607582,0.513014798152,0.944611760973,0.974354513296,0.98544016627,1.0
--9921855862556,466081,33399453,LABSPLAC,2013-02-23 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.940536919431,0.972864414573,0.972194889706,0.976529145532,0.981394008776,0.982673430182,0.985305810673,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701639442999,0.634205738156,0.697318231141,0.543246954849,0.528780781327,0.999722997914,0.99999265274,0.999998604662,8.28681799932e-13
-9560210766082,461728,30551110,LABSPLAC,2012-11-16 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.976790145238,0.983642630155,0.985303412661,0.983413643,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.520264636801,0.51020381777,0.98561676629,0.991783530854,0.994573736185,0.999999248074
--8916365302850,432999,36787351,LABSPLAC,2013-07-27 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.958514016604,0.958905124619,0.982200257785,0.970724244393,0.976786128527,0.979598827762,0.988013233271,0.984841231394,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.700757643386,0.672961290111,0.704307974951,0.605266487176,0.944377126911,0.949599137623,0.963578930298,1.66466358881e-08
-5703202565387,414788,39941559,LABSPLAC,2013-12-17 19:25:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.86921876998,0.921659992142,0.960564234415,0.876550477118,0.943901090441,0.988243190639,0.988955111152,0.979177997377,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.551321058719,0.562079676942,0.52736445293,0.516511399411,0.979966339129,0.994303091358,0.997692322038,1.0
-11383712365983,457714,36198318,LABSPLAC,2013-07-18 10:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.913278482895,0.917307686347,0.925984526577,0.986906572619,0.975412817553,0.937448164499,0.98137455629,0.989949149683,0.925883236104,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.603736822065,0.55079414222,0.547167372423,0.521029885148,0.512011080912,0.993170905391,0.998176892541,0.999990930319,0.0
-4880505499918,426503,31999639,LABSPLAC,2013-01-27 10:36:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.978679411873,0.98646775749,0.987134443821,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691360310274,0.618468879795,0.640657052947,0.543401587387,0.529338318972,0.951676453553,0.973568488632,0.987153638369,0.000162959945238
-5377755854189,380329,39451659,LABSPLAC,2013-11-30 04:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.708312606006,0.647214985244,0.700775545912,0.606210579041,0.966910816835,0.981439172052,0.989491566872,0.999999972837
--8845434657442,457568,36336776,LABSPLAC,2013-07-11 11:39:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.927981355372,0.979876980557,0.977318365183,0.978805710289,0.980417361578,0.979093353994,0.98493671246,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.675284454762,0.597037756125,0.549300938986,0.516402647065,0.513568537253,0.941679359057,0.980882744951,0.993432130367,2.11541441851e-27
-787780975858,458300,36804602,LABSPLAC,2013-08-04 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.948894024704,0.984716678926,0.973451171113,0.975867012959,0.983642630155,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.632411354882,0.567688274555,0.592186262416,0.517757246599,0.511135314055,0.91598305231,0.926271749447,0.918162495102,0.00229670828625
--8137867561648,450149,33426119,LABSPLAC,2013-03-05 07:24:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.87440529466,0.899158284443,0.914591402994,0.953735365864,0.940687882607,0.927797000591,0.796282527881,0.892968490235,0.950811917059,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.664314724226,0.598120937596,0.611782965058,0.522273113492,0.50771896722,7.74028130919e-08,2.79289733771e-10,1.67770449618e-08,0.0
-4035289185680,449684,34181458,LABSPLAC,2013-04-25 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.985303412661,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.654219968234,0.528582395985,0.521049988947,0.981936741057,0.986706119849,0.986538017552,0.0662086625279
-729492854779,431760,35617859,LABSPLAC,2013-06-21 22:51:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.950221689037,0.946674134622,0.957484083173,0.977360559416,0.974040690688,0.969627596905,0.988274209103,0.984356971486,0.982211005062,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.703151838621,0.611344232719,0.618554100768,0.527685863254,0.517590299873,0.948739631724,0.964400615627,0.978496324424,0.0238285678002
--2222995990448,384175,36823572,LABSPLAC,2013-07-20 19:19:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961043261284,0.979876980557,0.977318365183,0.93795471757,0.990842372401,0.987050137552,0.972661415306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.519662910936,0.511066206601,0.985933842424,0.99421593774,0.995069500129,1.89071314875e-07
--5021671497571,366206,36659615,LABSPLAC,2013-07-22 02:53:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.956739446279,0.962167482218,0.977360559416,0.977318365183,0.980636217391,0.995949385811,0.984363365382,0.983365656414,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576316879836,0.592186262416,0.534019512421,0.51825500999,0.988038504238,0.997537097861,0.999674821391,1.0
-8998183775627,425928,38823790,LABSPLAC,2013-11-04 09:36:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963033703961,0.982495040811,0.973375973804,0.971522100084,0.978093446978,0.982985777753,0.98089947584,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.521374997719,0.511054506922,0.907366553978,0.883049390224,0.894306535151,1.0
--11083550882475,371167,33407944,LABSPLAC,2013-02-26 11:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.967587545152,0.924947434053,0.951848287844,0.979876980557,0.975128692964,0.9747298067,0.9642009783,0.982075403677,0.985392083789,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.610761174936,0.554547091948,0.547402931986,0.513049609862,0.509129526162,0.912414875417,0.936917184513,0.937407526232,2.8512123217e-11
--2996225922061,394674,37201301,LABSPLAC,2013-08-03 19:39:00,0,0,1,0,0.0487804878049,0.0,0.0,0.49974025974,0.733013339464,0.601636868113,0.580555555556,0.918043588225,0.548583432507,0.4,0.4,0.533333333333,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.538355082506,0.512081561566,0.53508067757,0.504986482485,0.504800989819,0.865123343888,0.918943428445,0.158843365678,2.00415366426e-221
--10595676551042,444021,33561549,LABSPLAC,2013-03-14 05:11:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.983594039937,0.98383309011,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.577760550212,0.514484841315,0.511788953686,0.991034469838,0.998975953708,0.999500823943,0.987853482614
--10710521817283,398981,30033693,LABSPLAC,2012-10-20 03:40:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.947309125999,0.972864414573,0.881787783009,0.944809391474,0.780417361578,0.886204879878,0.955200933172,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.631999854287,0.56469942842,0.563965162064,0.514695063102,0.50580764433,0.876000660004,0.934567614999,0.955792672756,2.92433544159e-11
--6391895482844,380427,29297669,LABSPLAC,2012-09-12 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.952763005662,0.941794265386,0.979613849002,0.979425545332,0.976844536151,0.982760858103,0.990056015466,0.95980477455,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.649425818474,0.585171411996,0.577760550212,0.515373306018,0.515700976424,0.987909783018,0.998386117137,0.99912404285,1.75505622797e-128
--4646147808214,471136,39875776,LABSPLAC,2013-11-26 23:01:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.961763510975,0.961496816165,0.95674926574,0.972864414573,0.975640843332,0.946497759722,0.784998208883,0.888738720237,0.917034117958,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.680622759135,0.577760550212,0.55748514263,0.530441612692,0.948345066617,0.963998917623,0.983692566994,1.0
-1612274121959,367490,39101161,LABSPLAC,2013-11-08 18:31:00,1,0,1,0,0.777777777778,0.0,0.0,0.497460453593,0.684493209359,0.767191101427,0.524318996416,0.773552653017,0.714950338407,0.841883116883,0.779104477612,0.765079161318,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.538355082506,0.51536015356,0.50644065596,0.501047530165,0.501472817468,0.762277258646,0.822009719342,0.831617989751,1.79133297055e-22
-3761834569263,409384,34720530,LABSPLAC,2013-05-18 13:55:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.829488389295,0.887362312439,0.965764428129,0.946357429381,0.950713933264,0.993818933476,0.887396377937,0.992194160736,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.603808412055,0.548045659369,0.522421043247,0.515498054344,0.509788112853,0.99184460183,0.999843978619,0.999888910969,1.71467015984e-202
-8838847181046,371163,36179795,LABSPLAC,2013-07-12 21:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.922974141078,0.977655342441,0.977318365183,0.972101568594,0.983642630155,0.977208976789,0.983625864839,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.672318961834,0.594147112595,0.577760550212,0.535015594105,0.517386695345,0.968204163973,0.985424177031,0.991771268804,8.37602830922e-05
--5531495494637,428643,32514042,LABSPLAC,2013-01-20 10:01:00,0,0,1,0,0.181818181818,1.0,1.0,0.704999977497,0.753127644637,0.740841844739,0.751515151515,0.861908213144,0.644231351755,0.995625385089,0.898253275109,0.782375478927,0.917871594643,0.827009027697,0.738690060496,0.7404070265,0.531432555922,0.514184159528,0.518602050471,0.507007700974,0.505423295665,0.386392726478,0.313537041119,0.33684715964,7.04991247831e-57
--1138650520285,387438,31357388,LABSPLAC,2012-12-02 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.98646775749,0.985303412661,0.985097511557,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.651653214899,0.697318231141,0.566287757004,0.544627249741,0.995415390178,0.999033487257,0.99958011928,1.0
--12311840067239,367256,31400548,LABSPLAC,2012-12-10 22:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.956642221289,0.979876980557,0.977318365183,0.977253296853,0.98646775749,0.887050137552,0.950080309666,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.591046839993,0.592186262416,0.53967172487,0.518813073694,0.993519259359,0.998201122424,0.998893640071,0.987532434451
-11020449699171,400266,36566067,LABSPLAC,2013-07-24 23:03:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.875085922367,0.920534872738,0.857790325082,0.94374371668,0.919980287156,0.968243190639,0.987935940165,0.956932520274,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.68841883343,0.684973545686,0.631528916061,0.6962767547,0.601774369245,0.989877689977,0.988552772879,0.980383032765,7.90907115807e-23
-6298057914978,397209,38693155,LABSPLAC,2013-10-19 12:08:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.983594039937,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.51765672096,0.507826740435,0.953155744058,0.955779912952,0.959630046326,1.0
-3175022992398,435053,40746857,LABSPLAC,2014-01-25 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.977179533147,0.98646775749,0.987334502903,0.981592087059,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.711397807195,0.633531218038,0.697205583255,0.602588992527,0.985110175889,0.993931340978,0.997903202651,0.0388476277674
--2957128422855,390474,36204402,LABSPLAC,2013-06-29 15:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.712475545141,0.647214985244,0.712351365752,0.613503930479,0.996370839034,0.998427576779,0.998759739904,0.999749613654
--2642904773247,419346,39208894,LABSPLAC,2013-10-24 15:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960085909416,0.977655342441,0.972194889706,0.975352206385,0.978093446978,0.983613477461,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.517498726959,0.50780866791,0.957935913523,0.972745430134,0.9830712335,7.12788648439e-07
--5158261747269,396788,40600632,LABSPLAC,2013-12-29 14:10:00,0,0,1,0,0.777777777778,0.0,0.0,0.783516978546,0.601697360692,0.674749955488,0.236555555556,0.1125,0.634453888417,0.4,0.429104477612,0.449450765006,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.566410705203,0.526011764436,0.522421043247,0.506620025052,0.500518813679,0.777156258869,0.850165080446,0.841635309781,1.5532577705e-40
-11782588765590,404360,33178767,LABSPLAC,2013-03-08 15:52:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.975380835714,0.97863298675,0.975478042744,0.980918574313,0.983594039937,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.593548659541,0.62492509043,0.524729209911,0.513561527307,0.988648176634,0.998864042682,0.999561370351,1.0
--7970765028213,461334,36419295,LABSPLAC,2013-07-10 20:00:00,1,0,1,0,0.866666666667,0.96138996139,1.0,0.935941233992,0.898425659254,0.923711160205,0.95695058108,0.813656479844,0.915293611075,0.788607351173,0.88981619636,0.95576228137,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.577153119386,0.534633972252,0.520271360095,0.501249424807,0.500674155011,0.84143986453,0.858878511163,0.880098987198,8.04200309543e-39
-1459883316791,376069,34576064,LABSPLAC,2013-05-05 22:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.82164930366,0.857150115159,0.890838743314,0.961508919682,0.871388327136,0.973592884571,0.785524374988,0.987541106076,0.925574241243,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.626506409634,0.557526495957,0.592186262416,0.51636824216,0.510424160638,0.961186233363,0.988147799616,0.992746210707,1.89785331907e-33
--5802978909260,392922,37125507,LABSPLAC,2013-08-02 21:40:00,0,0,1,0,0.862745098039,1.0,1.0,0.776573128745,0.963776230663,0.963033703961,0.77855540303,0.880801062635,0.967944894043,0.781966849154,0.986666029307,0.919551194085,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635775971153,0.656568053358,0.563965162064,0.521348585221,0.509999109472,0.988322230656,0.999745814627,0.99999999998,0.0
--3045716472323,405662,38935139,LABSPLAC,2013-10-22 05:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.982442076471,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.707225676086,0.633531218038,0.695136689227,0.598596820535,0.957344844914,0.963791541869,0.973160728769,6.5634334351e-07
--8916365302850,448667,37531323,LABSPLAC,2013-08-29 08:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.957856431394,0.977360559416,0.977318365183,0.976048208002,0.987424952203,0.983906434837,0.951572115978,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.576041733199,0.577760550212,0.52644282392,0.515227212528,0.979012477556,0.997893547259,0.999504760728,0.999999995115
-7449569794204,405817,37357270,LABSPLAC,2013-09-05 12:19:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.955882500902,0.958207226017,0.972864414573,0.977478405725,0.975688671081,0.987424952203,0.977716141785,0.984369048126,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.57150567583,0.577760550212,0.519416990464,0.51049524035,0.8781741048,0.905949015794,0.94685900977,1.19044535641e-12
--6882506627973,427919,30926497,LABSPLAC,2012-11-24 01:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955738553131,0.984716678926,0.977318365183,0.978203003675,0.98646775749,0.985303412661,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.578540830217,0.592186262416,0.527927474021,0.516310095361,0.961325273962,0.980215288873,0.990797460893,0.999999933333
-9958092196787,436618,37602723,LABSPLAC,2013-09-06 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.972061352238,0.972417939447,0.98646775749,0.979265089637,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.524245760132,0.51231542432,0.98436616493,0.996995606192,0.999063533999,1.0
-11299780083957,365957,30719896,LABSPLAC,2012-11-24 23:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.939912426257,0.883318023876,0.97833851037,0.914343443525,0.945417659818,0.990518371679,0.783250643105,0.922506822443,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.618146216367,0.558564845753,0.563965162064,0.50979779694,0.511954506912,0.887486479508,0.904641611402,0.77420766887,1.0
--5721383451998,447391,35743685,LABSPLAC,2013-06-02 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.950116846668,0.960073670187,0.974091735498,0.973832808533,0.98646775749,0.987219018264,0.985323433739,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.692585080182,0.618554100768,0.691101745889,0.597656375651,0.968395642335,0.981445133553,0.988194871487,0.99999999971
--2638059047916,424736,34812324,LABSPLAC,2013-04-26 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954977685081,0.984716678926,0.97088515102,0.969070918285,0.98646775749,0.982985777753,0.982803736411,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.624973493513,0.566448094223,0.5656089288,0.51735283693,0.509437028969,0.980959423478,0.99783468339,0.999957364843,1.0
-4035289185680,449684,34642260,LABSPLAC,2013-05-13 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.962167482218,0.977360559416,0.977907845851,0.979091193615,0.983642630155,0.987134443821,0.950965803864,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.672318961834,0.594147112595,0.619588095117,0.530819857729,0.518962939487,0.981678823323,0.985831801247,0.982976465111,1.91244929168e-08
--4646147808214,385860,33927512,LABSPLAC,2013-03-24 21:21:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.963776230663,0.950603275599,0.975380835714,0.969772973451,0.977801387575,0.989372823794,0.889854154721,0.9838032137,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.641984784403,0.67225975886,0.62492509043,0.527169595517,0.516256371906,0.995918915478,0.999021780894,0.999577769278,1.0
--11391010719086,368130,39814486,LABSPLAC,2013-11-18 23:55:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.958765450075,0.984716678926,0.977318365183,0.979459278244,0.98646775749,0.983594039937,0.985033308331,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.522827363179,0.513510896897,0.94490217165,0.949058220703,0.925377606416,1.0
--3048017322281,399790,30666980,LABSPLAC,2012-11-09 08:32:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.959382502513,0.977655342441,0.977318365183,0.976461002633,0.981875769026,0.986804643992,0.984731634083,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701639442999,0.63583186803,0.684630899643,0.53902022306,0.527839652393,0.901479888317,0.950080894864,0.969783485613,1.58116579076e-06
--4546926499538,434525,38711500,LABSPLAC,2013-10-14 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.911568230443,0.913324594227,0.960564234415,0.946499347729,0.943594939793,0.985003151066,0.890731124096,0.982668206672,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.548223384432,0.562079676942,0.518774962135,0.507435654766,0.949349234448,0.981389853982,0.995445893338,8.49896110187e-10
--10080190349180,371879,38967662,LABSPLAC,2013-10-15 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.946674134622,0.950820571481,0.984716678926,0.975128692964,0.971731754793,0.991574770901,0.984363365382,0.983961641581,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.61578367723,0.618554100768,0.689001691459,0.598992620316,0.998769890397,0.999980987869,0.999999611766,1.0
-10331165643100,371187,35852692,LABSPLAC,2013-07-03 21:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958942799526,0.982834655213,0.975128692964,0.977169668841,0.979598827762,0.983594039937,0.983681740074,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57063118209,0.592186262416,0.520199031915,0.516011699208,0.990790180462,0.99900557453,0.999585497456,1.0
--10559560539328,470441,35543366,LABSPLAC,2013-05-31 14:29:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.955882500902,0.961061333855,0.975138921301,0.958305142752,0.975979067813,0.983642630155,0.987018851612,0.984900690096,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.689155028425,0.595875684892,0.633531218038,0.531132248555,0.515332832302,0.991961419526,0.998412137285,0.999654790659,0.000169231177904
-3156804258432,461264,37243907,LABSPLAC,2013-08-23 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.956180654547,0.977674353582,0.964229233141,0.976877362397,0.98646775749,0.983430167721,0.983892169658,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.711151941719,0.70344568285,0.659751167278,0.712479715185,0.609492755484,0.961434439745,0.968474578849,0.974219216078,5.86143090756e-08
--4344509565427,439030,29751521,LABSPLAC,2012-10-11 03:21:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.88336980542,0.902209599173,0.873022500383,0.957154721286,0.85694859915,0.859019838035,0.787758094272,0.589986355367,0.815783815901,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611619166997,0.552624339372,0.547402931986,0.506767120902,0.502528468888,0.930602830926,0.947238751867,0.94921031199,1.36513079677e-30
--7249115173462,358402,35242667,LABSPLAC,2013-05-19 05:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.964334349221,0.977704112943,0.951624809912,0.974287756108,0.980918574313,0.986182202112,0.95287723682,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.683827531489,0.624039004089,0.63874696642,0.531824108382,0.519682121583,0.97313501855,0.977549713517,0.980138602299,5.61104252665e-41
-10678434761668,454392,38765195,LABSPLAC,2013-10-22 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.962187415028,0.94582804405,0.961969696469,0.982200257785,0.975128692964,0.977103381444,0.979598827762,0.989703168472,0.985239529306,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.61578367723,0.645245582584,0.531220194164,0.516686765244,0.979026684135,0.983872085284,0.986551351113,0.732301833423
--2548822971980,424302,31839882,LABSPLAC,2012-12-25 20:08:00,0,0,1,0,0.862745098039,1.0,1.0,0.903816418169,0.922424585968,0.931341081878,0.973935860411,0.971027917007,0.975750552137,0.987693244344,0.976534495684,0.901871532361,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.620137554589,0.562416606302,0.563965162064,0.524390240658,0.517322942773,0.944082677016,0.983406581065,0.991323522723,2.99726821708e-34
-10651616431569,371633,35005305,LABSPLAC,2013-05-17 10:02:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.795705367732,0.863103826707,0.889632233169,0.70642343223,0.855577839635,0.921554491113,0.794009800608,0.979143631661,0.979331704581,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.592786880884,0.550276436726,0.575657631795,0.521124501451,0.511431386076,0.858505755314,0.898969441502,0.923434432655,5.06959075628e-15
-7793746763801,395638,40461451,LABSPLAC,2014-01-07 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954015686722,0.977655342441,0.977318365183,0.976104294298,0.987424952203,0.985937525079,0.985076014426,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.686721664326,0.597980376281,0.633531218038,0.526030163675,0.517180461011,0.999160740591,0.999998840383,1.0,1.0
--11557752330043,471937,39298499,LABSPLAC,2013-10-29 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977655342441,0.977318365183,0.976283693783,0.983642630155,0.985644567703,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.705826886905,0.659751167278,0.701914464199,0.607187756863,0.97139016036,0.9828780383,0.992766679335,3.49745094857e-05
--5580891486963,413552,33519360,LABSPLAC,2013-03-13 08:00:00,0,0,1,0,0.862745098039,0.0,0.0,0.788704017614,0.856973875001,0.600131556285,0.6,0.672291715377,0.465517749704,0.6,0.498284561049,0.362624045366,0.917871594643,0.848148017695,0.800194633637,0.829616127665,0.60730033175,0.53968979692,0.549300938986,0.496409825007,0.49952844241,0.424600157657,0.389147535899,0.302575796796,2.50814508434e-213
-11707426741543,461219,34427430,LABSPLAC,2013-04-27 18:00:00,1,0,1,0,0.0487804878049,0.0,0.0,0.263546757902,0.211928513066,0.451886854823,0.2,0.208620689655,0.221135438027,0.2,0.0947368421053,0.0666666666667,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.520045239599,0.477558798049,0.40010684085,0.475815369583,0.490407680346,0.0743958077366,0.0929466286978,0.0820996458469,1.8890685619e-117
-1945144906651,424834,36876773,LABSPLAC,2013-08-14 14:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.874421278159,0.94214893922,0.925381354858,0.941077067689,0.978429681616,0.948090160057,1.0,0.992752289198,0.927630388838,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.698384723397,0.612465790246,0.621934350519,0.532314192594,0.518532292437,0.994962753822,0.999520234965,0.999852606156,4.6874575784e-81
-850624215139,435008,36442754,LABSPLAC,2013-07-19 16:59:00,1,0,1,0,0.777777777778,1.0,1.0,0.648888888889,0.76648359719,0.55344738584,0.780555555556,0.143650793651,0.531204594017,0.6,0.6,0.533333333333,0.917871594643,0.784472143326,0.676521413876,0.665895856135,0.499921571925,0.484074699018,0.462719312221,0.484158177147,0.492050180543,0.000935648031013,8.19299688578e-05,4.267045502e-05,2.11917227937e-310
--11083550882475,371167,33321822,LABSPLAC,2013-02-22 09:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.931886593608,0.932959197132,0.952897876474,0.985452715467,0.977318365183,0.974975500061,0.988274209103,0.983757920137,0.984306273163,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.610761174936,0.554547091948,0.547402931986,0.510809560293,0.509273140499,0.898593824132,0.900555623908,0.913267233735,9.86359589214e-07
-3299307349362,413529,32907296,LABSPLAC,2013-02-19 03:21:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.950548437246,0.93141125473,0.979876980557,0.980367324433,0.980539640137,0.990842372401,0.987134443821,0.985484340623,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.575283007562,0.592186262416,0.537373479437,0.520036562247,0.995741359216,0.999661292065,0.999925372026,0.999999911961
--3433758616708,380298,32248065,LABSPLAC,2013-01-19 06:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.979610899271,0.98646775749,0.987134443821,0.983707303946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.654219968234,0.553679701008,0.528610438101,0.984969233034,0.988130842293,0.988174288657,1.0
-8502619079963,457000,38310705,LABSPLAC,2013-10-09 12:14:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.987424952203,0.984973928157,0.985076014426,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723312699125,0.705243197551,0.659751167278,0.699724093037,0.604594171401,0.963527632158,0.966331499918,0.965218218701,5.53454587316e-08
-1662092320947,385394,40886557,LABSPLAC,2014-01-22 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.955905888591,0.977704112943,0.971851018469,0.967184278523,0.980918574313,0.987999443818,0.983192466749,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.616970307779,0.565941433209,0.59010421833,0.513213788021,0.506460516986,0.901479836216,0.909895336756,0.907991517241,0.539494490797
--11659089678845,357096,38698804,LABSPLAC,2013-10-09 06:20:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.977236536041,0.98646775749,0.989686653441,0.982493790975,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.712475545141,0.672961290111,0.704256552057,0.605119133899,0.986421516467,0.995631824325,0.998353001874,1.0
-10651616431569,371633,35005201,LABSPLAC,2013-05-17 06:00:00,0,0,1,0,0.866666666667,0.7,0.0,0.693187583002,0.799706916909,0.842530787843,0.904000658154,0.833165108284,0.827011602123,0.0,0.492804033744,0.489307181191,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.596676282811,0.555383144032,0.59010421833,0.517271920288,0.510175130263,0.907209832809,0.927480837406,0.940485554179,0.00025548604184
--5770789664233,379255,33055294,LABSPLAC,2013-02-12 15:51:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.973157933662,0.968918307822,0.954879080661,0.98570109238,0.972194889706,0.970595070963,0.989162792951,0.987359759435,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.572096361533,0.577760550212,0.511941862533,0.509653196885,0.907361799004,0.954246409987,0.968145655862,8.73513120891e-15
-11807485464320,392182,35191662,LABSPLAC,2013-05-31 18:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.956180654547,0.980220534083,0.972071396748,0.976882546062,0.980918574313,0.987872137312,0.98506433729,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713522872047,0.701360490697,0.647214985244,0.529008129026,0.517166644814,0.988714895091,0.994397822933,0.996165898895,0.436855246384
-11128568023975,368777,39608958,LABSPLAC,2013-11-30 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.961979822707,0.972864414573,0.977318365183,0.976283693783,0.978093446978,0.987872137312,0.952869846503,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.702047090632,0.647214985244,0.542372231375,0.525027847925,0.99850606883,0.999387475816,0.999607644038,1.0
--152589643420,363538,29176288,LABSPLAC,2012-09-05 12:00:00,1,0,1,0,0.0684931506849,1.0,0.0,0.547111863949,0.262908019656,0.374091615957,0.6,0.5,0.267654339795,0.4,0.4,0.366666666667,0.29944547465,0.555906810973,0.600558873622,0.607671556364,0.517046076558,0.495646170293,0.462285342001,0.494179429294,0.495127088321,0.438245955692,0.729438827336,0.902177406802,6.77379505378e-280
--11581086659975,467845,37441402,LABSPLAC,2013-08-22 22:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.906975362434,0.924639154384,0.902959354899,0.97141943927,0.970483139291,0.921588677817,0.982381408066,0.890128193148,0.978028198734,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.591751252797,0.546430649786,0.533416685648,0.507139066467,0.505606587469,0.900758505447,0.885759582433,0.867352824414,4.03089966296e-09
-4829678465080,403076,35346149,LABSPLAC,2013-06-08 10:31:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955821948154,0.977655342441,0.972194889706,0.9731711505,0.978093446978,0.985340764827,0.981704871063,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.520905030931,0.510985913105,0.953422584418,0.973868598256,0.985498999796,0.0620519978284
--12154002820135,399050,39143980,LABSPLAC,2013-10-26 01:07:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.829298694113,0.851057244275,0.877063211535,0.899504513311,0.948415378515,0.947673369642,0.996282527881,0.993980504332,0.983743934237,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.589737646226,0.54106971596,0.547402931986,0.513473089376,0.511225348842,0.942369439071,0.953707466545,0.926104359825,1.29279791511e-43
--3060577107622,464981,30365651,LABSPLAC,2012-10-30 10:01:00,1,0,1,0,0.047619047619,0.0,0.0,0.804773355968,0.457720643701,0.531001579399,0.4,0.177358490566,0.398011587958,0.2,0.299300393529,0.331120486787,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.59619747248,0.53440204571,0.549300938986,0.506753137106,0.501451265809,0.27826681207,0.0767626796419,0.0343343868701,2.81467691959e-65
-533096379802,363741,33796322,LABSPLAC,2013-04-06 23:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977360559416,0.977318365183,0.976900347793,0.983642630155,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.522179398159,0.51395501681,0.988683978381,0.996586510421,0.997944488193,0.997311603214
-10338235197647,411833,36183942,LABSPLAC,2013-07-21 10:37:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.979876980557,0.972061352238,0.972417939447,0.98646775749,0.982985777753,0.984747636583,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.523821391623,0.512169300675,0.971655322792,0.993961259396,0.998120928939,0.629810492684
-11347513609595,354759,33523772,LABSPLAC,2013-03-30 20:49:00,0,0,1,0,0.862745098039,1.0,1.0,0.956160864127,0.855218027092,0.917920057018,0.988671902558,0.878217927824,0.838538738443,0.785524374988,0.888842908505,0.759146472588,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63426712758,0.573191981994,0.63874696642,0.523838799175,0.510733351703,0.996182674913,0.998787370325,0.999694151936,7.77711154832e-39
--1646686166325,412804,33581102,LABSPLAC,2013-03-10 20:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.980117654779,0.967935630982,0.968195219048,0.987424952203,0.987253955653,0.958610336517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.584239789362,0.577760550212,0.534109521035,0.517436226217,0.979653178275,0.984665799229,0.985886389525,0.999999999988
-990546695934,405654,35927415,LABSPLAC,2013-07-05 04:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.977169668841,0.98646775749,0.986162764587,0.984714332242,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.633531218038,0.528420312121,0.517309300049,0.9314793264,0.950954737239,0.95849244743,0.853116969717
--8265401423260,427652,38317697,LABSPLAC,2013-09-21 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.95569450986,0.947608429441,0.969409513399,0.971851018469,0.975465301201,0.98646775749,0.989830474978,0.983629292582,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.696876422419,0.618554100768,0.695534727345,0.601790228307,0.953683845228,0.954907018363,0.961448581878,0.0120467858035
--11153902347398,453357,31939913,LABSPLAC,2013-01-06 02:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.963776230663,0.951727496532,0.977601233639,0.962313316174,0.976707222142,0.993155145841,0.885441119307,0.972381403783,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635251802724,0.571567594877,0.577760550212,0.504780374811,0.503663562543,0.877598987174,0.876308684033,0.874414125027,2.05153154337e-16
--6760626762581,459017,39677924,LABSPLAC,2013-11-19 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960812176402,0.982200257785,0.977478405725,0.976077641296,0.979598827762,0.985303412661,0.983181808019,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576556820611,0.592186262416,0.520264636801,0.511870844372,0.960153020167,0.945370061956,0.922035857896,0.99999990334
--5531495494637,428643,32587358,LABSPLAC,2013-01-22 14:02:00,0,0,1,0,0.862745098039,0.0,1.0,0.748233756434,0.772741425567,0.642919893445,0.751515151515,0.669086526622,0.706714404873,0.795625385089,0.6,0.643264862764,0.917871594643,0.742665317764,0.687147495072,0.679476974952,0.520660734091,0.509189091869,0.518602050471,0.518964833328,0.507880490025,0.560678977976,0.609776787349,0.638880167429,1.0131948268e-58
-3818618981737,391980,29861979,LABSPLAC,2012-10-31 22:01:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.909457146261,0.963776230663,0.929892044633,0.98570109238,0.955873591764,0.931086173414,0.989768319288,0.989468658571,0.901894860393,0.917871594643,0.827009027697,0.787467118762,0.829616127665,0.590702563494,0.539260758464,0.562079676942,0.516768456153,0.507871404995,0.974224976371,0.992946743332,0.997326847923,1.0
--11654716383698,371150,36337779,LABSPLAC,2013-07-09 17:42:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.961969696469,0.921450000159,0.975128692964,0.975849211228,0.979598827762,0.985303412661,0.950075381028,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.620926443027,0.559841789477,0.562079676942,0.50402039492,0.502748168437,0.856288313565,0.837037629088,0.820724230081,0.18645039319
-11486941629073,448687,37965912,LABSPLAC,2013-09-20 09:59:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.776573128745,0.963776230663,0.95998932212,0.975380835714,0.882115684202,0.973140518514,0.785293189224,0.984221177907,0.983657227786,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.581707478071,0.577760550212,0.525334019529,0.51577026038,0.959007258736,0.964966562207,0.915464689289,9.34518844037e-73
-614597905890,421688,30831306,LABSPLAC,2012-11-28 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.984716678926,0.977318365183,0.978679411873,0.98646775749,0.987134443821,0.983592786875,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.63759271501,0.654219968234,0.544241146626,0.529711396977,0.996995739502,0.999433680707,0.999830534245,1.0
-10283814989718,370828,30054622,LABSPLAC,2012-11-02 18:26:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.977318365183,0.977179533147,0.990842372401,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.528140533929,0.513586311935,0.977200028196,0.978608785608,0.977677722485,0.244469419947
--6481342916998,410155,40050632,LABSPLAC,2013-12-04 09:01:00,0,0,1,0,0.862745098039,1.0,1.0,0.819853520965,0.799892166751,0.865439657737,0.942307692308,0.977586070037,0.852302518685,0.8,0.995151727461,0.797025577452,0.917871594643,0.875764265788,0.771128307788,0.787522896327,0.540769824103,0.512359906292,0.491348257276,0.496882677229,0.511636036631,0.953363933145,0.988477549051,0.996859965107,5.64052535349e-180
-6457886233483,397011,32507291,LABSPLAC,2013-02-13 16:08:00,1,0,1,0,1.0,1.0,1.0,0.83826042576,0.835918677816,0.739362444981,0.789042325344,0.776114091573,0.753633999404,0.4,0.8,0.723950452704,0.29944547465,0.571725460405,0.657222982014,0.675364081911,0.530021611473,0.50000186486,0.477085565744,0.492925263252,0.499507085219,0.909955988522,0.942697276861,0.94327637294,1.0
-9068914877775,408683,38667387,LABSPLAC,2013-11-02 03:00:00,1,0,1,0,0.968845448992,0.666666666667,0.0,0.956160864127,0.801997970738,0.822959792835,0.394114249818,0.880377926284,0.540304811671,0.395368421053,0.49591991196,0.560134346488,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635251802724,0.560636174445,0.563965162064,0.50979779694,0.493107566994,0.277380143341,0.186905914512,0.110260044649,9.13940121834e-243
--5258348095994,457342,31580976,LABSPLAC,2012-12-15 09:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.972100498572,0.952763005662,0.953362277452,0.974296366457,0.977907845851,0.937071306347,0.970687472338,0.990311576595,0.912169676033,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635775971153,0.656568053358,0.577760550212,0.521809275586,0.516594499394,0.977934842598,0.990248831289,0.993751760324,1.0
-10804490185166,369494,38474459,LABSPLAC,2013-10-21 19:52:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955821948154,0.982299754633,0.975640843332,0.979144355379,0.983642630155,0.981904104736,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.547247122072,0.529362504055,0.991145677643,0.996971402933,0.998876974774,1.0
-3000592249976,442229,33173597,LABSPLAC,2013-03-10 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.973157933662,0.906021709044,0.955085089244,0.981204947537,0.975640843332,0.973930067642,0.98878053093,0.987038803979,0.984747636583,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691360310274,0.607026940055,0.640657052947,0.530814919006,0.515466301473,0.99283960898,0.998736445441,0.999800714495,1.07387077384e-08
-1826034452697,355056,36698202,LABSPLAC,2013-08-05 19:12:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.960021399219,0.979876980557,0.977318365183,0.978684977496,0.98646775749,0.98071469904,0.984868641009,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.512502356975,0.516911414511,0.959239786377,0.988828032978,0.995586627962,0.00012420076233
-2569156209298,392334,36965745,LABSPLAC,2013-08-11 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977360559416,0.977318365183,0.977169668841,0.989768319288,0.984992796415,0.983734804623,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723312699125,0.625184887262,0.659751167278,0.536314319877,0.523681112464,0.977217586075,0.966044459627,0.958408870438,1.0
--11145272056246,461156,33138938,LABSPLAC,2013-02-21 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.976696370216,0.98646775749,0.983413755704,0.885977745564,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.509344080831,0.910773451711,0.931032902895,0.947641564964,4.9290980523e-07
-1170014865359,464876,38387694,LABSPLAC,2013-10-20 22:00:00,1,0,1,0,0.777777777778,0.0,0.0,0.562005908138,0.776702587637,0.7285187953,0.562290715373,0.346528939248,0.372405877457,0.0428571428571,0.2,0.125764411028,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.550575229004,0.524419189356,0.50644065596,0.502753605935,0.500405786991,0.545335363213,0.476181236237,0.404800378522,4.10167057632e-22
--2329920307637,370397,35650460,LABSPLAC,2013-06-07 08:38:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960423249404,0.977655342441,0.977318365183,0.944045198496,0.980918574313,0.983594039937,0.982846065158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650048755741,0.593702564311,0.62492509043,0.527581496833,0.518418298848,0.998893043407,0.999997477786,0.999999999998,1.0
-2766881279042,429993,35793923,LABSPLAC,2013-06-21 04:18:00,0,0,1,0,0.777777777778,0.0,0.0,0.608219941762,0.532705488667,0.579158058615,0.322222222222,0.238888888889,0.305750079383,0.197727272727,0.214285714286,0.197192982456,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.554348707647,0.529208749377,0.50644065596,0.509151887262,0.502793038969,0.375444751199,0.23246983963,0.0797015812209,6.67690714304e-42
--8185245868328,463033,30749151,LABSPLAC,2012-11-12 10:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.946674134622,0.955562439188,0.980613017097,0.873944829953,0.964920480294,0.980556022475,0.982202710183,0.981012744207,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.603903994402,0.547090610268,0.547402931986,0.507357216023,0.502781063664,0.857300484948,0.695326397085,0.667348467339,2.21279863111e-07
-11663710697071,405748,32989718,LABSPLAC,2013-03-08 01:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.816864613228,0.748299788754,0.659906720499,0.2,0.389608294931,0.694482544718,0.2,0.598185483871,0.433141762452,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.602800265203,0.529964475818,0.549300938986,0.501609852842,0.511416405437,0.99226634869,0.99562478663,0.663309771185,4.61418422723e-193
--8387553780356,385327,40528443,LABSPLAC,2013-12-27 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954683066626,0.977360559416,0.968886682,0.979120003158,0.976773700428,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713884482281,0.69950470701,0.672961290111,0.704471256179,0.60801438099,0.976957536219,0.98797119245,0.991309861207,1.10135620517e-05
--6760626762581,459017,39595222,LABSPLAC,2013-11-19 01:23:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.893165488532,0.963029411336,0.982200257785,0.879559082213,0.977006142924,0.781146640539,0.982075403677,0.852331458445,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.570544998621,0.577760550212,0.517297651039,0.51427322757,0.956244234758,0.970246510907,0.96718338066,0.000141556110432
-7034207198327,354612,31620226,LABSPLAC,2013-01-05 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961969696469,0.984716678926,0.975128692964,0.978212867981,0.979598827762,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57558169174,0.592186262416,0.515416062274,0.509153930034,0.968778444554,0.968179714318,0.956191053426,0.999999965533
-8372526490187,356810,33429140,LABSPLAC,2013-03-22 17:43:00,0,0,1,0,0.866666666667,0.0,0.0,0.852930150223,0.788214956054,0.741749289847,0.382795698925,0.573604212669,0.578612054869,0.592324823292,0.59023117531,0.521622841508,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.572122712314,0.528341898498,0.533416685648,0.492261284416,0.494921117189,0.802309893871,0.854087175755,0.884582301031,4.31082746569e-34
-6478226497391,413446,30992073,LABSPLAC,2012-12-16 00:12:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.931782120994,0.982834655213,0.991604461173,0.979431041483,0.795949385811,0.986162764587,0.988575100593,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.568941545615,0.592186262416,0.53049039448,0.512501470408,0.997111236211,0.999792360087,0.999958580838,4.25781684389e-58
-11761736605021,391624,35546453,LABSPLAC,2013-06-21 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.944666019174,0.977704112943,0.96693787676,0.971091379078,0.978093446978,0.976626733199,0.947908786655,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.568274369199,0.577760550212,0.519156321241,0.514388576846,0.984254450533,0.999387118146,0.999977576835,1.0
--7970765028213,461334,36567260,LABSPLAC,2013-07-15 20:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.664469787731,0.761268650492,0.777299994814,0.95471043771,0.818767711532,0.818172449142,0.59190791297,0.897709495929,0.789624828202,0.917871594643,0.848330419257,0.746770706044,0.7551686106,0.555209957926,0.521141973821,0.491585873363,0.502300183973,0.501199539514,0.963392545697,0.98424826209,0.984529454039,1.55757242634e-68
-11807485464320,392182,35329135,LABSPLAC,2013-06-05 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.958942799526,0.977360559416,0.975128692964,0.974439519702,0.976773700428,0.983613477461,0.98355595391,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.689886132135,0.684976306089,0.618554100768,0.516248384152,0.509497686761,0.952580295796,0.958875556216,0.959923197416,0.0025758568072
--6889482433625,378841,34794632,LABSPLAC,2013-04-28 23:18:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.958716206971,0.984716678926,0.979617215758,0.978799771969,0.988017245066,0.884221177907,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.580036078719,0.592186262416,0.523598780612,0.513336971372,0.995972471364,0.99919994312,0.999863163659,0.890483739834
--2184432237379,458285,31641615,LABSPLAC,2012-12-28 02:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.958755168848,0.953143743612,0.982200257785,0.968377449784,0.975149807375,0.98419257645,0.984356971486,0.98253827153,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.665355187727,0.597329835382,0.611782965058,0.528973182306,0.524004152084,0.981641987901,0.988445182926,0.992348678828,7.36385288716e-11
--9376156477059,458864,33933513,LABSPLAC,2013-03-27 19:08:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955921599266,0.975380835714,0.973509511272,0.975852972777,0.978093446978,0.983594039937,0.984396452169,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.581707478071,0.577760550212,0.51261220554,0.509218292887,0.950216314548,0.941777826629,0.944344017805,4.77534206119e-06
-9127253960494,441622,37101106,LABSPLAC,2013-08-29 10:37:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.936800695669,0.92095980408,0.972061352238,0.97225243381,0.978093446978,0.981276405029,0.853716001679,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.573605901971,0.577760550212,0.5253710514,0.514404767813,0.910006066079,0.908497326011,0.929014215629,0.00210522190944
--11243051944067,375316,36915127,LABSPLAC,2013-08-03 03:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.883187943224,0.936578152087,0.939454652684,0.960073670187,0.962607337316,0.932911954652,0.989629787831,0.891872628929,0.953358634466,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.604752472931,0.551183570479,0.547402931986,0.528870953573,0.601953305996,0.985135397154,0.996162081896,0.998233893213,1.0
-4471106695961,442524,35683041,LABSPLAC,2013-06-19 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.984716678926,0.977318365183,0.977169668841,0.98646775749,0.985348868778,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.631096589479,0.672961290111,0.541215076873,0.523679915457,0.987929186341,0.994609734315,0.99689235195,0.996568001689
--2462456080026,440772,36555717,LABSPLAC,2013-07-10 19:55:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.953667932486,0.932608527835,0.585257429289,0.78503830197,0.949257063934,0.98646775749,0.885936616858,0.986985143404,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.56923602145,0.577760550212,0.537475544903,0.527014553693,0.959093422346,0.941865902041,0.870403406055,1.0
-4909842099311,387133,32442580,LABSPLAC,2013-02-13 19:49:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977360559416,0.977318365183,0.978111020545,0.98646775749,0.985303412661,0.985097511557,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.697550687122,0.62006043397,0.654219968234,0.536561058866,0.529109785295,0.996838071997,0.999847500464,0.999955256441,0.999999998396
-2852925776464,452388,36683255,LABSPLAC,2013-08-07 23:38:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958004582024,0.982440932008,0.962678618037,0.972644644531,0.987424952203,0.976232608995,0.981603619749,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640080003066,0.57256122193,0.577760550212,0.517186528197,0.510833579811,0.922807013222,0.955810870168,0.969009807683,1.35765078285e-07
-7954136990788,424775,40242658,LABSPLAC,2013-12-23 20:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.970535048751,0.977318365183,0.975069799073,0.988274209103,0.987334502903,0.98686311885,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713921163545,0.618221992801,0.604438079785,0.523743662059,0.516031496447,0.99420970828,0.99915288012,0.999792387293,4.56437194319e-21
--4041715080261,445800,40491805,LABSPLAC,2013-12-26 08:59:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.979144355379,0.98646775749,0.983594039937,0.98383309011,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.581678506444,0.577760550212,0.52156367211,0.516008331732,0.973549521976,0.988627605247,0.993999711778,0.99121715003
--11145272056246,461156,33142598,LABSPLAC,2013-02-22 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.961979822707,0.982834655213,0.977318365183,0.975561546568,0.989768319288,0.887134443821,0.984428143623,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.570378414122,0.562079676942,0.516083922033,0.514715199664,0.89987992116,0.936860711825,0.951599341801,0.0892151375289
--2756415722158,380340,40797528,LABSPLAC,2013-12-27 08:19:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.982495040811,0.977318365183,0.943058321292,0.980918574313,0.981904104736,0.982433081304,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.578540830217,0.577760550212,0.511834534439,0.506232107911,0.869533039651,0.888362997306,0.89103285292,0.000140210010866
--8254627011851,384402,40320763,LABSPLAC,2013-12-10 00:47:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958782870709,0.783178208739,0.972194889706,0.97534234208,0.978093446978,0.987134443821,0.982694715122,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.573724847753,0.592186262416,0.514663890104,0.510585685179,0.997818737168,0.999861221415,0.999960152914,1.0
-7549557727358,363382,34095202,LABSPLAC,2013-04-24 08:53:00,0,0,1,0,0.862745098039,0.0,0.0,0.972100498572,0.943957101745,0.864116554709,0.976812787598,0.981760366843,0.899088217417,0.987693244344,0.985048562607,0.820648637591,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.605952811085,0.552659662464,0.518602050471,0.505557678716,0.504321886129,0.929017350536,0.979766828214,0.991397842868,3.49885959373e-08
-11473721804680,397502,32010372,LABSPLAC,2013-01-21 17:48:00,0,0,1,0,0.733333333333,0.0,0.0,0.510952249492,0.606330440894,0.587668740652,0.988567749073,0.5,0.395775914719,0.4,0.5,0.4,0.29944547465,0.571725460405,0.627646648295,0.632789619551,0.523587921642,0.495395062272,0.478997794639,0.503065156543,0.501856154882,0.271038379964,0.299047885735,0.306012978497,1.76489312678e-225
-6928149359872,363449,38468565,LABSPLAC,2013-10-12 21:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.868122065561,0.757403702904,0.872075147954,0.961650401689,0.945458251022,0.920693668992,0.8,0.988134071429,0.883741126599,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.599409819896,0.558174289383,0.5656089288,0.515234478366,0.506679250784,0.898809650597,0.964684779234,0.98805148956,1.69949793909e-19
--3955832540437,363279,38170946,LABSPLAC,2013-09-22 00:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.977360559416,0.977318365183,0.974959855856,0.98646775749,0.985329431254,0.985137668109,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.574368388984,0.592186262416,0.523022723621,0.515300861818,0.985775274884,0.996187152221,0.99885056977,0.98898229293
-7379200574799,422337,32051553,LABSPLAC,2013-01-28 23:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.984716678926,0.977318365183,0.979477853156,0.989162792951,0.987314303318,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.594972234924,0.592186262416,0.522230931091,0.516546394042,0.995180944319,0.998865975363,0.999805995914,1.0
--2990625788346,369251,30924313,LABSPLAC,2012-11-19 18:01:00,1,0,1,0,0.866666666667,0.0,0.0,0.849680557734,0.898425659254,0.899322243972,0.710050936997,0.963741321951,0.80273095884,0.754321636887,0.544378908061,0.719205599959,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611826982836,0.553873383548,0.562079676942,0.521916197096,0.509999510019,0.874095634806,0.887697061813,0.890427145783,3.62089933248e-42
--3875628435899,398795,39293047,LABSPLAC,2013-10-29 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.954818368367,0.949949089616,0.970383830386,0.973349426845,0.98646775749,0.982985777753,0.949744919285,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.572313453689,0.619588095117,0.524166527883,0.517572804611,0.918398462343,0.928474858234,0.895056575124,0.999659608886
--7970765028213,461334,36191006,LABSPLAC,2013-06-29 20:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.84785289225,0.75523680674,0.843608169448,0.748632154882,0.823710066139,0.765532886558,0.996282527881,0.896613758887,0.873748037477,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.566663925581,0.531149262585,0.520271360095,0.510326778856,0.506777152938,0.952350678216,0.936556461392,0.905724201844,3.41633507818e-60
-8144260352233,451258,36669375,LABSPLAC,2013-08-06 19:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.838815736423,0.884326939939,0.884093421739,0.623973501064,0.942540641837,0.925946081113,0.989629787831,0.885508855353,0.916812006853,0.917871594643,0.875764265788,0.793065314363,0.817336130992,0.598975136613,0.554496208088,0.491585873363,0.500407140603,0.502307443975,0.921115732772,0.969906280211,0.98855515007,2.47611625598e-24
--4344509565427,439030,29733523,LABSPLAC,2012-10-11 13:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.902209599173,0.889288385036,0.939966670524,0.955133673569,0.88010883844,0.992324823292,0.797082357959,0.850264144227,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.615445383989,0.560180025713,0.562079676942,0.514101800494,0.50486298349,0.924418769367,0.934992036352,0.932434068158,7.43629862488e-30
--4967834617680,379837,33881295,LABSPLAC,2013-03-17 18:48:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977704112943,0.977318365183,0.975352206385,0.978093446978,0.983594039937,0.983786886847,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.575611816541,0.577760550212,0.51563755862,0.507454524234,0.998550217031,0.999922744936,0.999999777076,1.0
--9073047989046,468038,29602359,LABSPLAC,2012-09-30 02:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.972864414573,0.977318365183,0.975941194359,0.979598827762,0.985303412661,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.704456231596,0.628680351334,0.640657052947,0.532996399218,0.52546952647,0.996068377937,0.999798482547,0.999994149912,0.991685346961
-1647082873019,364844,34092799,LABSPLAC,2013-04-23 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.88336980542,0.917435382831,0.936309307939,0.950535776795,0.963653179381,0.912536125229,0.957629358465,0.981123498993,0.983664170639,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.599427653886,0.554110138002,0.547402931986,0.520157236057,0.509698718402,0.968300363336,0.987497051157,0.991762168301,1.65846277666e-10
-615396159447,462017,32421905,LABSPLAC,2013-02-05 08:10:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.977655342441,0.972061352238,0.973451274282,0.98646775749,0.974871904357,0.983100667776,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.531854047523,0.518265342476,0.960129443399,0.985950045953,0.994072352014,0.999510099517
--2990625788346,369251,30924780,LABSPLAC,2012-11-20 02:01:00,1,0,1,0,0.866666666667,0.0,0.0,0.933401487966,0.90029814738,0.83952917704,0.714443951712,0.771229843458,0.66600830095,0.761339670804,0.644378908061,0.480284905833,0.917871594643,0.749922511746,0.699435392749,0.685435184135,0.551987954269,0.520473110112,0.562079676942,0.524356269557,0.511446108516,0.585100026546,0.632057271522,0.739445052779,2.74865172338e-59
--4208564499228,449810,40167531,LABSPLAC,2013-12-04 09:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.975138921301,0.977318365183,0.974415013823,0.987424952203,0.982627889865,0.984719198392,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.571696255372,0.577760550212,0.523292237757,0.514707579267,0.846898663094,0.856659886844,0.825484612877,2.89174357333e-05
-11219222855025,408695,36724615,LABSPLAC,2013-07-29 11:18:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.898656802806,0.914031065753,0.962402684209,0.862276530712,0.929156947097,0.785455893564,0.788233621303,0.836366178846,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.553229972817,0.547402931986,0.507284026919,0.5029991518,0.889184683172,0.93209590567,0.960155532121,4.91671291134e-09
--1669382686083,385339,38783938,LABSPLAC,2013-10-17 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958765450075,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.989686653441,0.982493790975,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.633360371236,0.672961290111,0.53455211237,0.518647925195,0.976658253406,0.990474925606,0.996160016804,0.999999999997
-2369717193141,370555,33205409,LABSPLAC,2013-03-08 02:01:00,0,0,1,0,0.931034482759,1.0,1.0,0.554671064841,0.24276852823,0.212040827388,0.2,0.2,0.2371880412,0.0,0.3,0.198717948718,0.917871594643,0.811825685744,0.728661381615,0.7404070265,0.526225676204,0.499487337339,0.518602050471,0.495468474768,0.494244261472,0.162339231945,0.256238711884,0.439271675034,0.0
-2072074949622,446820,30935680,LABSPLAC,2012-12-04 23:29:00,1,0,1,0,0.862745098039,1.0,1.0,0.972100498572,0.963776230663,0.958104434386,0.97202185973,0.977318365183,0.973900721373,0.973512599673,0.986038891336,0.981059655241,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.624333737255,0.562027807957,0.562079676942,0.513065364466,0.506818660349,0.854143796561,0.856104692246,0.888917792859,5.37782445916e-07
--6514870753501,470878,39893103,LABSPLAC,2013-11-23 17:00:00,0,0,1,0,0.968845448992,0.0,0.0,0.868122065561,0.896091419115,0.879719596703,0.949538359397,0.831584977382,0.872385828199,0.995625385089,0.974222296846,0.974318743446,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.600151653256,0.560464372814,0.522421043247,0.525499189982,0.511060039413,0.9997611074,0.999993035399,0.999999216533,1.0
-6418247562596,466037,35466659,LABSPLAC,2013-06-06 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.860560234166,0.896091419115,0.881757601825,0.938145448449,0.954987955357,0.916085747826,0.991475566391,0.977486743253,0.954610669985,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.605905888043,0.579015938582,0.599036717496,0.551287016165,0.529309662126,0.969958520884,0.985125150485,0.990889292405,5.33244003426e-22
-8955012005803,451398,35235304,LABSPLAC,2013-06-06 09:58:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960947838534,0.97997861967,0.972194889706,0.974286831842,0.981875769026,0.982627889865,0.983333533505,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.621957209829,0.557889178484,0.547402931986,0.506444322492,0.506204056713,0.995887969383,0.999992238472,0.999999999999,1.0
-12352077353765,451009,29826108,LABSPLAC,2012-10-18 09:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.963029411336,0.982200257785,0.960147537185,0.977179533147,0.98646775749,0.983613477461,0.984742671558,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691360310274,0.614152955988,0.654219968234,0.533871395515,0.518913017444,0.96342464356,0.983117420207,0.988300338636,1.0
-1543025640681,452637,39190971,LABSPLAC,2013-11-15 18:30:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.953153757604,0.979876980557,0.975128692964,0.977271516277,0.98646775749,0.987872137312,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.620320555701,0.672961290111,0.716597686257,0.618715937797,0.998088862847,0.999660639208,0.999885925257,0.999999999992
--8039357668286,364469,32538644,LABSPLAC,2013-01-19 19:57:00,1,0,1,0,0.777777777778,1.0,1.0,0.899764463757,0.690993769398,0.665504343309,0.573998178506,0.768383620414,0.598040658707,0.6,0.5,0.491354842237,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.557294124834,0.62359311362,0.537223027254,0.526833312394,0.516099083748,0.999770630656,0.999999829265,1.0,1.0
-6992008753358,447022,39364138,LABSPLAC,2013-11-26 03:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.898425659254,0.90925663057,0.948261270068,0.918466953878,0.944937557077,0.981738421445,0.989188200296,0.979751817378,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.657152803742,0.592511144096,0.618554100768,0.536194740509,0.517844555145,0.96415548168,0.98775163787,0.994882447131,7.60228990883e-16
-6185840573073,380927,32725829,LABSPLAC,2013-02-10 10:00:00,0,0,1,0,0.931034482759,1.0,1.0,0.688625439689,0.542725636536,0.63847181021,0.241525423729,0.7,0.523288778857,0.8,0.3,0.73267827843,0.917871594643,0.839676183446,0.729997989811,0.708319280651,0.527504956147,0.5096697249,0.518602050471,0.509450742492,0.502124706062,0.809359695036,0.8600809445,0.925249270805,3.83809726675e-85
--2184432237379,458285,31509392,LABSPLAC,2012-12-19 07:14:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963033703961,0.982495040811,0.975640843332,0.978629101072,0.98646775749,0.985837030591,0.951392035607,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.577760550212,0.51895628863,0.510679791899,0.944530160674,0.96504832179,0.964485417984,0.99925155139
--10708945553031,376106,32060406,LABSPLAC,2013-01-10 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.963029411336,0.977655342441,0.977318365183,0.979144355379,0.98646775749,0.987761581791,0.9807893132,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.587428960738,0.577760550212,0.514249705085,0.51196089488,0.962678997117,0.992887472068,0.998703609025,1.11317376221e-13
--4629695421662,449720,37409852,LABSPLAC,2013-08-10 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961107767351,0.987674353582,0.977318365183,0.978212867981,0.98646775749,0.985120102921,0.983629292582,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.61578367723,0.604438079785,0.519255492949,0.512080341903,0.909366843951,0.92634373728,0.934048436946,2.93848251419e-05
-10336523374691,367367,30252111,LABSPLAC,2012-11-04 20:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961547470338,0.977704112943,0.977478405725,0.975886531068,0.978093446978,0.983594039937,0.979788445499,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.57618994938,0.592186262416,0.515945445347,0.508008397383,0.957513825384,0.977608188012,0.981813404149,4.62565593433e-23
-11022992441918,386947,35128430,LABSPLAC,2013-06-05 13:19:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982834655213,0.972061352238,0.973451274282,0.98646775749,0.981295842553,0.982853562492,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.573599266539,0.577760550212,0.526166759057,0.513290440588,0.978143843,0.990905054526,0.996727668253,1.0
--2701082086849,471749,34509070,LABSPLAC,2013-04-23 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.931886593608,0.954861432633,0.917364010356,0.799501321879,0.980006611366,0.941612424519,0.989768319288,0.985120102921,0.953835808051,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.704240798761,0.709755319607,0.65042570795,0.552028353611,0.527896017076,0.991746420818,0.997343590311,0.99849378391,2.46030405289e-22
--10708945553031,376106,32100508,LABSPLAC,2013-01-09 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.963029411336,0.979876980557,0.977318365183,0.979144355379,0.98646775749,0.887761581791,0.9807893132,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.590086824944,0.577760550212,0.519278926096,0.509934519434,0.961701724363,0.969865328363,0.975808307797,1.17850174195e-08
-6992008753358,447022,39253481,LABSPLAC,2013-11-21 15:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.832275694052,0.898425659254,0.920153172187,0.956261270068,0.954757219791,0.948099992969,0.981738421445,0.991664931762,0.917466051685,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.69139527484,0.628634510772,0.676054975499,0.581972175108,0.538912861875,0.992629528291,0.998901409239,0.999753313185,1.92772345141e-71
--10170906945643,460514,35224319,LABSPLAC,2013-05-14 19:45:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960002514393,0.979876980557,0.977318365183,0.977831835191,0.983642630155,0.987872137312,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.649574383177,0.580830048909,0.619588095117,0.545301399189,0.526988152861,0.967343660632,0.979405978946,0.98709379605,0.193743329759
--11402859392751,445240,33202589,LABSPLAC,2013-02-14 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.905248614955,0.914619038294,0.928411857925,0.960534475054,0.950634312013,0.950649048678,0.954321636887,0.987265175951,0.983985845603,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.603719546921,0.552711299766,0.547402931986,0.514794376677,0.504663821063,0.986885018814,0.997462936332,0.999025983399,1.0
-1521161542906,420491,33962651,LABSPLAC,2013-04-12 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.957856431394,0.984716678926,0.977318365183,0.976238181443,0.98646775749,0.987134443821,0.984686532701,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.697550687122,0.63150367411,0.684630899643,0.561989667946,0.535631083056,0.995842778002,0.999320082485,0.999769890249,1.4421329455e-11
--152589643420,363538,29182776,LABSPLAC,2012-09-09 14:00:00,1,0,1,0,0.0684931506849,0.0,0.0,0.205083422644,0.261905679795,0.299218916533,0.2,0.1,0.199637393689,0.4,0.1,0.133333333333,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.481071033299,0.466269864983,0.448319649051,0.485919948226,0.493135224165,0.00375429427412,0.000678243242743,2.43295400285e-05,1.60186840251e-271
--7876546784290,395444,31522864,LABSPLAC,2012-12-19 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.93827539095,0.98646775749,0.983329449435,0.973045982977,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.62109640782,0.640657052947,0.547422333633,0.529578046821,0.972774307266,0.977252089985,0.983508870815,0.906905533961
-4035289185680,449684,33975102,LABSPLAC,2013-04-14 00:46:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.944925009098,0.95317264243,0.975380835714,0.975128692964,0.969094510805,0.978093446978,0.983594039937,0.983079282793,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.628856322369,0.563231882233,0.592186262416,0.520105118723,0.514557631067,0.979325201498,0.988602689779,0.990414185711,0.999960477617
--3592907552014,454317,38825510,LABSPLAC,2013-10-08 17:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960807883777,0.977704112943,0.977478405725,0.974621483019,0.98646775749,0.987872137312,0.985567774833,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.732288665718,0.652177259653,0.690773668204,0.543603866465,0.529201049366,0.984580658439,0.994663930831,0.997817938248,0.0582497215423
--6760626762581,459017,39625024,LABSPLAC,2013-11-19 03:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982200257785,0.977318365183,0.969685764574,0.988274209103,0.976943339407,0.98081256823,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.69722322618,0.602852049924,0.633531218038,0.526039388685,0.517671352752,0.982480777059,0.98982060441,0.991713340367,0.977098144279
--9196576225980,355214,40209757,LABSPLAC,2013-12-05 05:36:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.980220534083,0.972061352238,0.970716449044,0.980918574313,0.981295842553,0.983532111931,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.524868836404,0.517368214656,0.981405269502,0.994428862149,0.997370376535,1.0
-4766933480407,370442,31340228,LABSPLAC,2012-12-22 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.956343532967,0.977655342441,0.972194889706,0.973760967676,0.981875769026,0.983822128568,0.984987382327,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.692067975228,0.60806867041,0.640657052947,0.527431780393,0.517922537275,0.995257863229,0.998933495787,0.99945710334,1.0
-2292062431231,448932,31697406,LABSPLAC,2013-01-14 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.980367324433,0.944896014989,0.984791976488,0.985646369366,0.9839800856,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691360310274,0.607675574136,0.627592053327,0.527369737447,0.515841545883,0.987105544057,0.99309777617,0.995040802759,2.63838040095e-05
-10838793330686,386713,39594488,LABSPLAC,2013-12-07 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.962920516388,0.979876980557,0.972061352238,0.975305778147,0.98646775749,0.982985777753,0.984932809326,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.600798384232,0.63874696642,0.534323195985,0.521600846481,0.96592753424,0.987597321162,0.992914066355,1.43577308916e-05
--2696842722055,403525,37904704,LABSPLAC,2013-09-05 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.960332157566,0.975380835714,0.975640843332,0.976996370207,0.980788482439,0.983613477461,0.984932809326,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635681240063,0.586132555539,0.59558486573,0.521433743201,0.515954776661,0.995779437149,0.99882098245,0.999518348608,0.557684837325
--7198383329371,448771,35071473,LABSPLAC,2013-05-10 09:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.915890012503,0.970234481557,0.97791129386,0.98646775749,0.890177775263,0.982082167858,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.65263936527,0.58132810252,0.605485933567,0.51606001213,0.514490470663,0.916039422353,0.913223361699,0.897957692196,2.87685075066e-10
--152589643420,363538,29188864,LABSPLAC,2012-09-08 13:39:00,0,0,1,0,0.866666666667,0.0,0.0,0.609494847045,0.448133951735,0.555019372696,0.15,0.17037037037,0.455663453825,0.4,0.3,0.332461387383,0.917871594643,0.825369867954,0.744994528012,0.745656574421,0.55691996579,0.518267684488,0.562079676942,0.501920403501,0.498850972117,0.804789238083,0.890609347928,0.919741960399,4.59153306228e-156
--10791717457184,396760,36583174,LABSPLAC,2013-07-21 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977360559416,0.977318365183,0.979144355379,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.708312606006,0.672961290111,0.718136408907,0.616569333303,0.981394678745,0.987043117892,0.992423127252,0.954288479736
-6366597921927,455955,35199669,LABSPLAC,2013-06-02 19:57:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.513444651765,0.939388316604,0.9539594998,0.967241144768,0.00138787532077
-8144260352233,455972,37624089,LABSPLAC,2013-09-13 10:40:00,1,0,1,0,0.0487804878049,0.0,0.0,0.562005908138,0.750157147931,0.710449120226,0.49,0.520247617364,0.61245469757,0.639139670738,0.484723197472,0.488301950217,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.516234777728,0.499266939717,0.491585873363,0.505076913642,0.500936269924,0.236485239466,0.371244299838,0.549431222149,8.96920955006e-52
-826016632666,392845,36878675,LABSPLAC,2013-08-15 17:24:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963033703961,0.984716678926,0.980367324433,0.978323123344,0.977592234243,0.982105965482,0.984954964962,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.580036078719,0.563965162064,0.516771000509,0.512360831741,0.989363314471,0.996864251624,0.998917178141,0.895836581607
--4716787243805,398725,38045584,LABSPLAC,2013-09-07 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.955905888591,0.983184671239,0.970564203852,0.94271924026,0.980556022475,0.985641826154,0.984773224933,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.610761174936,0.562788786265,0.547402931986,0.523109822698,0.516830066719,0.996870067188,0.999745905341,0.99996108843,1.0
-4694280720379,359477,38296769,LABSPLAC,2013-10-12 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.975138921301,0.977318365183,0.978111020545,0.98646775749,0.983594039937,0.983681740074,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.517297651039,0.509980830139,0.938831958241,0.957303252749,0.956278543864,0.0138589601504
-11020449699171,400266,37311286,LABSPLAC,2013-08-26 00:33:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982200257785,0.977318365183,0.978530874702,0.98646775749,0.987134443821,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.575329669228,0.562079676942,0.511922857285,0.507860001725,0.991907433398,0.999200913327,0.999734868926,1.0
-10678434761668,454392,37757196,LABSPLAC,2013-09-16 14:01:00,1,0,1,0,0.0487804878049,0.0,0.0,0.524506096479,0.349451623242,0.496507934548,0.4,0.277665245203,0.131537460769,0.2,0.2,0.266666666667,0.917871594643,0.79514940545,0.728887498451,0.738632887801,0.518831041794,0.472959816386,0.40010684085,0.487849772396,0.489209884987,0.00857195664118,0.000449948848304,9.24759721023e-05,2.90627845832e-209
--2214147591815,376134,34022589,LABSPLAC,2013-03-29 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960002514393,0.977360559416,0.977318365183,0.971178919509,0.98646775749,0.985444508621,0.981005283744,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.654219968234,0.530901589864,0.52941364802,0.993057071291,0.998284882697,0.99933647261,1.0
-8671263156868,354835,30748113,LABSPLAC,2012-11-25 02:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.959893619444,0.975380835714,0.975640843332,0.976738374468,0.978424259496,0.985303412661,0.984071570538,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691261548938,0.632773844605,0.670656510296,0.541017172847,0.523618101343,0.969821576588,0.985355049571,0.991594551221,5.42880065492e-05
-4880505499918,426503,31951430,LABSPLAC,2013-01-26 10:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.978212867981,0.98646775749,0.983613477461,0.98486618162,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.627076317771,0.640657052947,0.532465006583,0.527337700789,0.965588329824,0.977889752052,0.984752939961,0.243647188023
-7094992191229,431477,35515500,LABSPLAC,2013-06-20 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.977655342441,0.977318365183,0.976524148455,0.987424952203,0.985937525079,0.981393740929,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710614043217,0.615199081488,0.631528916061,0.705751977044,0.611397026652,0.980293690443,0.990379928148,0.987022868583,0.947751549863
-9908982610231,388164,33356637,LABSPLAC,2013-03-16 17:55:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962874243812,0.980220534083,0.981576697658,0.976626220536,0.983973442673,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.590388560673,0.592186262416,0.531913547272,0.52266399904,0.999626519872,0.999997445514,0.999999988389,1.0
--3568405392249,394447,30409620,LABSPLAC,2012-10-31 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977847812902,0.98646775749,0.983613477461,0.984742671558,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.522382976975,0.509869057915,0.938299108775,0.965056417805,0.979773312847,0.0088108179731
--11654716383698,375070,32648783,LABSPLAC,2013-02-05 07:16:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978111020545,0.98646775749,0.983594039937,0.984572314493,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.515416062274,0.512143799453,0.982271697631,0.994214675042,0.997330663695,0.999952019482
--6346348821454,415622,36294826,LABSPLAC,2013-06-29 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.950269262227,0.977360559416,0.974261068968,0.979144355379,0.98646775749,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.627421357016,0.647214985244,0.538607005304,0.522664800707,0.988205229758,0.993713542409,0.996508947389,4.70904954166e-13
--10708945553031,376106,32078971,LABSPLAC,2013-01-09 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.97773985206,0.983642630155,0.987761581791,0.980461067673,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.69871875053,0.619507508507,0.640657052947,0.530498155747,0.517522543614,0.965561610433,0.941976194024,0.882177858798,0.00148889117362
-8791163855128,461128,35179005,LABSPLAC,2013-06-02 20:54:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.950116846668,0.982200257785,0.968793722253,0.970040129359,0.98646775749,0.985430719167,0.983664522297,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.559644112287,0.562079676942,0.520090021268,0.511948706866,0.909804513061,0.936254639949,0.96675813133,0.997873124812
--4579799591032,430735,30581828,LABSPLAC,2012-11-07 07:17:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.950827697427,0.984716678926,0.977318365183,0.913326552564,0.98646775749,0.985303412661,0.918520913289,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.594378100426,0.592186262416,0.529929972786,0.520247723374,0.987446485951,0.992626693034,0.994837289407,0.999999059472
--5977848492839,468476,37740113,LABSPLAC,2013-09-01 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.978558063786,0.952820746846,0.942480521772,0.978688526396,0.93571580348,0.888447593319,0.784219136111,0.984721169071,0.979657343294,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.628898237909,0.562050770313,0.562079676942,0.504688142059,0.503571486703,0.914753007606,0.981304987561,0.995393073404,1.0
--3804768647407,437483,31458984,LABSPLAC,2012-12-05 18:24:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.984716678926,0.977318365183,0.978203003675,0.98646775749,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.521725673263,0.516908582275,0.966230870547,0.980848982561,0.983858351276,0.999999999999
--965534810739,386557,32743198,LABSPLAC,2013-02-08 14:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.963776230663,0.943222283982,0.975138921301,0.878110923765,0.981637040716,0.992673385592,0.889854154721,0.989585321683,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.697061715884,0.63309660643,0.684630899643,0.544077172343,0.533676755159,0.997956093833,0.999734214484,0.999335855652,1.0
--4175901866511,468873,40249303,LABSPLAC,2013-12-03 04:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953950581385,0.982834655213,0.972061352238,0.972417939447,0.98646775749,0.976581277082,0.980667823666,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.522382976975,0.516061885767,0.981344686138,0.991302032102,0.99446042393,0.999999999997
--7249115173462,358402,35153309,LABSPLAC,2013-05-19 13:09:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.963476712727,0.977704112943,0.949947288061,0.973917385738,0.980918574313,0.983613477461,0.984780093028,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.675376677527,0.615675108809,0.62492509043,0.52886387351,0.520477645546,0.96907762464,0.980263660188,0.976872041394,6.24639953491e-44
--3987531456312,436603,37335140,LABSPLAC,2013-08-11 15:28:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.830242480543,0.87059501998,0.93538309102,0.980613017097,0.966506375579,0.958359973283,0.971069908027,0.984991870981,0.94825125747,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.689320037135,0.600440893445,0.604438079785,0.524618410766,0.50811769735,0.851152470022,0.739780684845,0.465604615521,3.5033951344e-101
-7726346151901,369678,35067554,LABSPLAC,2013-06-02 14:32:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.932959197132,0.956796716527,0.972864414573,0.975128692964,0.973761219908,0.978093446978,0.983594039937,0.951361026547,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.607757473961,0.552204054748,0.547402931986,0.516598680611,0.50977505204,0.982713255026,0.995161102766,0.998098993931,2.94854338366e-11
--10110890094159,424827,39371268,LABSPLAC,2013-10-30 22:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.868122065561,0.846204515136,0.887633284955,0.961650401689,0.904947916123,0.923729023579,0.990822510823,0.87963505665,0.913157131909,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.555627388005,0.5656089288,0.509276132783,0.507362340421,0.992769696031,0.999875163765,0.99999997942,1.0
-6664403853289,418703,39877259,LABSPLAC,2013-12-13 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959582428714,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.987872137312,0.985628878715,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.647214985244,0.544002191663,0.521364798511,0.957878983301,0.959231628138,0.961327956313,1.0
--10170906945643,402872,36172838,LABSPLAC,2013-06-24 02:31:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.965125374887,0.963776230663,0.935545765002,0.979876980557,0.972061352238,0.909314157332,0.98646775749,0.781429549621,0.951088392654,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.648566355838,0.596715601243,0.63874696642,0.528763219785,0.518350667607,0.977151726222,0.993500031409,0.998237365462,9.29998050026e-33
--10110890094159,424827,39463477,LABSPLAC,2013-11-01 04:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.887975756893,0.863682164636,0.961650401689,0.817102154265,0.858629848692,0.990822510823,0.952913689162,0.852573491485,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.550625843053,0.5656089288,0.504543191466,0.505852388547,0.973896965652,0.999448808093,0.999999901276,1.0
-3042085896727,454095,29729577,LABSPLAC,2012-10-17 21:07:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.956858160689,0.975380835714,0.975640843332,0.865468026386,0.978093446978,0.877445727971,0.916255830364,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.502039897846,0.501212320541,0.826904394508,0.794943670135,0.731073293555,9.15439806658e-21
-798518854058,472177,32759921,LABSPLAC,2013-02-22 10:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982495040811,0.977318365183,0.975561546568,0.98646775749,0.985303412661,0.982440578638,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.577760550212,0.514688651593,0.511216560923,0.971381269545,0.989292443607,0.993254910875,0.657159482849
-9687136981407,440955,33283636,LABSPLAC,2013-03-19 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.953143743612,0.960073670187,0.968793722253,0.973873193623,0.980918574313,0.984356971486,0.98325067327,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.668352614469,0.600213063454,0.625799145584,0.527472682807,0.518600790159,0.989710224008,0.994579542466,0.995975563217,5.36591151161e-05
-9958092196787,436618,37581998,LABSPLAC,2013-09-06 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.972061352238,0.972417939447,0.98646775749,0.979265089637,0.983600606183,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.605485933567,0.525725967811,0.515494844699,0.991131638831,0.998525541992,0.999534143477,1.0
--8868984137427,472131,39816752,LABSPLAC,2013-12-01 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.982495040811,0.972061352238,0.972417939447,0.98646775749,0.982985777753,0.980097561038,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.588371353963,0.547402931986,0.541297566105,0.523872667612,0.993883205414,0.998984281386,0.999752348021,0.999992467803
-3756072761788,406335,39587055,LABSPLAC,2013-12-04 05:43:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.97773985206,0.983642630155,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.625843002933,0.672961290111,0.699171942188,0.606838407486,0.988361901368,0.99404688525,0.995172385746,0.243890399273
--5165875951838,418276,37824613,LABSPLAC,2013-08-29 00:22:00,1,0,1,0,0.0487804878049,0.0,0.0,0.388521059653,0.346420981382,0.465884183886,0.2,0.28986013986,0.157300701979,0.0,0.0,0.1,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.520263621174,0.484383106429,0.414450730905,0.48417397431,0.491107260544,0.0771421570049,0.0389913946278,0.0281309859801,1.61253881835e-127
--752925244831,446664,31507975,LABSPLAC,2012-12-04 18:02:00,0,0,1,0,0.733333333333,1.0,0.0,0.748195651251,0.597201731439,0.60115137331,0.588567749073,0.780049615877,0.664103580532,0.6,0.7,0.799520383693,0.29944547465,0.571725460405,0.627646648295,0.632789619551,0.527393488693,0.499929072054,0.478997794639,0.489846702343,0.497468397424,0.521059115289,0.484626404252,0.504240663111,2.24672635591e-79
--5015765678607,444869,33734595,LABSPLAC,2013-03-07 23:58:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.980220534083,0.977318365183,0.975352206385,0.978093446978,0.983613477461,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.632550871699,0.572580118939,0.562079676942,0.513447754048,0.508027841785,0.984311635751,0.99246390511,0.995645571282,0.000572952700722
-1945144906651,424834,36803409,LABSPLAC,2013-08-13 20:00:00,0,0,1,0,0.0487804878049,0.0,0.0,0.564256442995,0.318264212653,0.572326857505,0.6,0.644507543165,0.590227384138,0.6,0.2,0.416666666667,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.566410705203,0.505981660209,0.430057657512,0.503040986738,0.49925551967,0.291494908739,0.331562250098,0.241706440321,1.16213736871e-140
--2184432237379,447335,34760547,LABSPLAC,2013-05-05 06:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.954234493151,0.982495040811,0.977318365183,0.971091369008,0.971222842451,0.984658980107,0.948878263164,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.674338949495,0.594831133076,0.605485933567,0.692885369249,0.605184247615,0.984691972595,0.991253065536,0.990576670294,1.80343552474e-10
-4157568944261,414067,38031497,LABSPLAC,2013-09-26 08:38:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.829298694113,0.805600477676,0.847030738052,0.911807477658,0.938865098878,0.829488709225,0.797727272727,0.690973002584,0.703615590839,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.566663925581,0.524075131049,0.53508067757,0.497309208967,0.502907991066,0.612669926876,0.419856145986,0.377158453552,3.23907486509e-06
--9155114531407,433252,34309410,LABSPLAC,2013-04-03 07:32:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.963776230663,0.951727496532,0.980220534083,0.97203997597,0.977672383577,0.984998208883,0.88810742983,0.985439668482,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.641984784403,0.59433020888,0.62492509043,0.517499179696,0.510155299157,0.958549522817,0.970291982637,0.973852607395,1.02006568958e-11
--11416017146675,397070,37643974,LABSPLAC,2013-09-04 02:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.951533085983,0.946509160315,0.984716678926,0.965076625301,0.970405196007,0.987823336218,0.980225656431,0.984704364421,0.917871594643,0.869389119206,0.833958930408,0.831654292592,0.631923927166,0.575051630987,0.592186262416,0.522235749164,0.515571246411,0.974614152386,0.993688741264,0.99734169003,2.83874463498e-07
--8593973632324,457975,36181617,LABSPLAC,2013-07-04 06:00:00,0,0,1,0,0.968845448992,0.0,0.0,0.868122065561,0.801157025312,0.904556559976,0.856094846134,0.952442977958,0.871500435975,0.793095238095,0.885776903309,0.878190699883,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.611258624181,0.560402244602,0.5656089288,0.512742336627,0.505461427754,0.931328599568,0.953272017376,0.959261773939,3.37047331944e-06
--5531495494637,428643,32677478,LABSPLAC,2013-01-27 07:10:00,1,0,1,0,0.0684931506849,1.0,1.0,0.47274848203,0.119323596359,0.378714857962,0.393243243243,0.380987312765,0.34813522307,0.2,0.3,0.433333333333,0.29944547465,0.555906810973,0.600558873622,0.607671556364,0.519113329669,0.497908563539,0.477085565744,0.485492644285,0.493799896438,0.310684818425,0.360327971006,0.436176050345,8.31715306047e-93
--9974892928005,453796,39955152,LABSPLAC,2013-12-02 15:52:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.944318082216,0.978093446978,0.98071469904,0.983402129331,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.5152218537,0.506867574007,0.954980363847,0.956792094487,0.952035648201,0.00776834095637
--1477215260228,395388,32946729,LABSPLAC,2013-02-13 00:32:00,1,0,1,0,0.047619047619,0.0,0.0,0.604898887237,0.427261730684,0.584717451745,0.751515151515,0.492307692308,0.476786205485,0.2,0.396421052632,0.428571428571,0.917871594643,0.887213629677,0.818174840383,0.81548792015,0.56520624614,0.515652554868,0.518602050471,0.512146795537,0.513734345199,0.982115976804,0.998317203435,0.998438271602,2.4744250367e-205
-1521161542906,420491,33953011,LABSPLAC,2013-04-13 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.960002514393,0.984716678926,0.977318365183,0.978203003675,0.98646775749,0.985303412661,0.984686532701,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.624505673932,0.654219968234,0.536445473642,0.530888172122,0.993136745818,0.998878535964,0.999540353749,1.25595643901e-06
-1786579798642,440893,36521358,LABSPLAC,2013-07-21 09:41:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.945425968809,0.977655342441,0.977088174864,0.912809191537,0.980556022475,0.984106493919,0.95287785164,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.719272481548,0.705870675298,0.659751167278,0.711094396647,0.618292949583,0.982579880013,0.989222153605,0.992904638823,1.01966578063e-21
--5531495494637,428643,32503970,LABSPLAC,2013-01-20 02:01:00,1,0,1,0,0.666666666667,1.0,1.0,0.731881107263,0.506227984658,0.538756544224,0.551515151515,0.463991546477,0.422098611258,0.795625385089,0.598253275109,0.643830786072,0.917871594643,0.761791632626,0.698074758989,0.679476974952,0.520660734091,0.511932211793,0.533416685648,0.510235313838,0.505189881509,0.487556251083,0.367299425586,0.34048185882,3.63155451837e-49
-4084444723392,459019,32004891,LABSPLAC,2013-01-24 17:39:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.957856431394,0.977655342441,0.979411040818,0.974276967536,0.981875769026,0.984973612833,0.984004942832,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.510674778941,0.506156326528,0.837679837031,0.805761321416,0.727733161159,2.24246732388e-12
-3817417773922,456928,39276925,LABSPLAC,2013-11-10 13:11:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960085909416,0.980220534083,0.977220015334,0.973716569718,0.978093446978,0.985303412661,0.980032250628,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.665802761791,0.577760550212,0.563649532646,0.536884578381,0.999976153151,0.999999954251,1.0,1.0
-8250204777255,380442,40706442,LABSPLAC,2014-01-17 20:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.94582387653,0.952834855259,0.922448102704,0.987749127463,0.781760218867,0.941416062496,0.992930349628,0.995969349482,0.959444519426,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716377712051,0.708006104075,0.633531218038,0.527329946902,0.514388235172,0.979606776217,0.998315315171,0.999917839535,1.0
-8144260352233,455972,37634615,LABSPLAC,2013-09-13 09:23:00,1,0,1,0,0.0487804878049,0.0,1.0,0.562005908138,0.750157147931,0.710457085181,0.49,0.520247617364,0.570788030904,0.639139670738,0.484723197472,0.454968616883,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.516234777728,0.499266939717,0.491585873363,0.505076913642,0.498898423804,0.255726041417,0.3934063474,0.562976920809,1.07997715071e-50
--3875628435899,398795,39292721,LABSPLAC,2013-10-29 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.946568856364,0.949949089616,0.9686954788,0.965350990598,0.979598827762,0.981295842553,0.949859752487,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.613355665723,0.55900201412,0.5656089288,0.515066678652,0.512210779002,0.807691194508,0.795928012757,0.802331288012,0.626839403132
--8916365302850,432999,36776645,LABSPLAC,2013-07-30 22:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.948577593069,0.957856431394,0.980613017097,0.979745408244,0.954860217388,0.98646775749,0.989165534063,0.985243122647,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.627239405721,0.633531218038,0.537585950901,0.524349985248,0.948321308891,0.96452148696,0.973573214096,6.41990411517e-19
--10851305204247,437612,30382692,LABSPLAC,2012-10-31 06:21:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.765516597787,0.832441634772,0.868793896639,0.991081541112,0.859537996644,0.916676663348,0.792930349628,0.892391593432,0.815556999255,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.616342510982,0.573826902343,0.59558486573,0.521787157395,0.514231382542,0.958699098484,0.973027059855,0.978095357695,1.75293210959e-71
--7035520190185,448918,38821025,LABSPLAC,2013-10-13 14:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.972061352238,0.973441409976,0.98646775749,0.982985777753,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.529443203429,0.512702066265,0.997450998442,0.999985767453,0.999999879265,1.0
-976138950707,457005,39443444,LABSPLAC,2013-11-18 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.978629101072,0.98646775749,0.985644567703,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.710010777854,0.659751167278,0.698068353832,0.607222428482,0.979276875287,0.993053215425,0.997571055549,0.000617575158254
--4277117453453,465816,37457963,LABSPLAC,2013-08-15 03:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.953273832983,0.938230881456,0.965228401713,0.978711259617,0.936187568644,0.984998208883,0.991969551232,0.98954144763,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.669733192265,0.584088676816,0.605485933567,0.689924928586,0.601981296307,0.974884270262,0.989786494789,0.993006272828,1.62202183972e-24
--8118296954482,403532,37059272,LABSPLAC,2013-08-09 11:30:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.843155900456,0.877175249199,0.915224411489,0.95695058108,0.946572985431,0.921424059916,0.971403050097,0.982134929766,0.949948220535,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.603086654933,0.560073775078,0.547402931986,0.517484719277,0.508254282556,0.961552752324,0.97409430243,0.98705741627,4.14934174565e-38
-6685252621122,396839,40618809,LABSPLAC,2014-01-06 06:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.903725769945,0.951533085983,0.879336318622,0.977704112943,0.90454419309,0.926430911861,0.991546206204,0.977799165588,0.906587229472,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.585043268568,0.548225288801,0.50644065596,0.498019763388,0.500707912703,0.819199301203,0.859637713327,0.879260926093,5.87085893279e-06
--3047934856304,412051,29264628,LABSPLAC,2012-09-15 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979494705729,0.98646775749,0.983613477461,0.984518466575,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.524528537721,0.513534471432,0.986013523069,0.994461849332,0.995925642297,0.96739849558
-8103443958318,385299,38397451,LABSPLAC,2013-10-18 10:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.961984115331,0.972864414573,0.974040690688,0.967214058983,0.984998208883,0.985276556396,0.983139751511,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.619439146028,0.557844422077,0.562079676942,0.516607791021,0.506558893982,0.848507025267,0.884420876643,0.900554974504,5.0967859328e-06
-10843816996132,454360,40702615,LABSPLAC,2014-01-12 16:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.917435382831,0.929936938586,0.970534475054,0.957777981438,0.958089496029,0.987251772445,0.989465322488,0.951123503045,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.691305522813,0.689149840071,0.645245582584,0.709830590726,0.611744457944,0.994497894293,0.99847521389,0.999477944411,0.999845440746
--9278860741847,459407,38948582,LABSPLAC,2013-10-19 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963776230663,0.963029411336,0.975380835714,0.972061352238,0.972546963988,0.982468061889,0.982985777753,0.983269237274,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.522115761588,0.511701509552,0.98312094714,0.990799300232,0.988513532351,1.0
-8094239333802,464818,36373914,LABSPLAC,2013-07-24 13:48:00,0,0,1,0,0.968845448992,0.980986142443,0.962962962963,0.972100498572,0.961561662247,0.963029411336,0.979876980557,0.972061352238,0.972361693828,0.983642630155,0.981295842553,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.577760550212,0.518900509339,0.507413947402,0.815206399533,0.8234704916,0.850517281088,0.99999977151
--6661775681320,433049,40870029,LABSPLAC,2014-01-09 19:10:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.961812827904,0.98646775749,0.987996718241,0.982537509104,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.647214985244,0.531629309649,0.518090732378,0.97699103829,0.98735426683,0.98978395257,0.99999999995
-2118845947749,453839,33952589,LABSPLAC,2013-04-08 04:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.963776230663,0.951727496532,0.987915027804,0.929450392806,0.976677106004,0.993155145841,0.988897216922,0.948147559651,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64507987816,0.589937443605,0.62492509043,0.519628093162,0.51269963153,0.978403601963,0.984694915637,0.98824181048,5.52755548273e-18
-3389746800850,456978,37830060,LABSPLAC,2013-09-15 13:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.961117893588,0.982834655213,0.977318365183,0.978046129421,0.990842372401,0.985303412661,0.950061370103,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.526840466735,0.510326930912,0.973845354957,0.978405764302,0.963165807596,1.23068631414e-14
-5649945837622,384042,32473523,LABSPLAC,2013-02-08 19:57:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963033703961,0.982440932008,0.967935630982,0.950343825577,0.791799567113,0.884779066367,0.980375397678,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.569256891961,0.577760550212,0.51293484959,0.507020381344,0.909081637975,0.599652297863,0.493024413072,1.0
-6967509780721,369509,35693981,LABSPLAC,2013-06-17 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.963014748944,0.977360559416,0.977318365183,0.97680544013,0.987424952203,0.984644128328,0.919771008004,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710614043217,0.696354369918,0.631528916061,0.697076459113,0.603558251775,0.967434311361,0.973157700893,0.982557405128,6.10619117851e-09
-10651616431569,371633,34676627,LABSPLAC,2013-05-04 10:01:00,1,0,1,0,0.0487804878049,0.0,0.0,0.37391681616,0.570340795864,0.518068370652,0.414285714286,0.463014763015,0.402918179998,0.4,0.5,0.69962962963,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.538105648755,0.495979185018,0.427947471506,0.484506975548,0.493423108435,0.189312499039,0.17639394233,0.138257762388,1.38152646357e-196
--7843193723686,459400,36242704,LABSPLAC,2013-07-02 01:51:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.507715919012,0.855636567131,0.818790715654,0.814813360632,0.00302195057368
-9068914877775,408683,38761115,LABSPLAC,2013-11-03 15:00:00,1,0,1,0,0.0487804878049,0.0,0.0,0.30877152372,0.224417671858,0.290641021673,0.0,0.215789473684,0.362272449163,0.4,0.3,0.266666666667,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.514827344911,0.48851922743,0.462719312221,0.491007017967,0.477801964948,0.222067804831,0.913435374525,0.999979149554,0.0
--3351942076256,465509,29773343,LABSPLAC,2012-10-07 05:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.722363261926,0.867377271306,0.740616332255,0.889864192075,0.75275297183,0.908590661863,0.995625385089,0.837765719173,0.987511582007,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.566616086149,0.528322721261,0.518602050471,0.498719185096,0.503151369415,0.692957497139,0.829595625092,0.929366343156,6.04034632529e-19
--4959864243809,365026,35869759,LABSPLAC,2013-06-20 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.978203003675,0.98646775749,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.628648993443,0.672961290111,0.700915074258,0.606251674254,0.977183733881,0.986209710716,0.987679399564,1.06934349345e-09
-10651616431569,371633,34989326,LABSPLAC,2013-05-18 02:01:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.829298694113,0.863103826707,0.868652520931,0.904000658154,0.842319991285,0.95056126207,0.794009800608,0.888421982176,0.719900476783,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.573710718469,0.543671251956,0.53508067757,0.513170491406,0.507507384017,0.892813931298,0.927826300434,0.944013152448,1.060508717e-08
--9058316045048,412032,34099450,LABSPLAC,2013-03-30 20:21:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.505659409272,0.936306627952,0.929726162367,0.931433990952,0.0231172861368
-4157568944261,414067,37934779,LABSPLAC,2013-09-23 10:43:00,0,0,1,0,0.333333333333,0.0,0.0,0.700324643294,0.491704597347,0.594294730822,0.839976689977,0.485808270677,0.634366365125,0.4,0.5,0.631895656533,0.917871594643,0.79514940545,0.727302647247,0.728038413078,0.534356457091,0.503676598352,0.493501371374,0.47944411889,0.489146336119,0.128495287291,0.112777107567,0.119469234519,3.46327781115e-48
-6967509780721,369509,35536227,LABSPLAC,2013-06-11 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.960864443511,0.980220534083,0.977318365183,0.974782023166,0.980918574313,0.986182202112,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710793935857,0.620571746018,0.645245582584,0.53858218472,0.521628726952,0.981903401957,0.983533682123,0.983625094626,0.999992280177
--3629411009402,461403,37731188,LABSPLAC,2013-08-30 00:32:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.878869862286,0.883744568363,0.95275741491,0.946572985431,0.937721355693,0.987826280317,0.691808223289,0.975013574273,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.606942910715,0.559371692797,0.562079676942,0.512155415482,0.508322923344,0.82033214133,0.728438002666,0.571913664535,7.80051942997e-25
-3268683812264,448867,35331092,LABSPLAC,2013-06-04 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.977655342441,0.972061352238,0.972417939447,0.98646775749,0.982985777753,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.605485933567,0.529387893872,0.521610897438,0.982197930948,0.994228503413,0.99691139741,1.50668773344e-05
--6030777711989,376983,31371069,LABSPLAC,2012-12-10 22:15:00,0,0,1,0,0.862745098039,1.0,1.0,0.592984216339,0.942243714886,0.773180732587,0.984414874445,0.888555501924,0.862107744386,0.589898989899,0.986607499683,0.733439205767,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.616027223327,0.556061749022,0.518602050471,0.508879686002,0.506341143424,0.988974518812,0.999007872488,0.999058113518,7.3769977284e-112
--1923437674636,421940,40494479,LABSPLAC,2013-12-22 02:58:00,0,0,1,0,0.968845448992,0.0,0.0,0.972100498572,0.963776230663,0.963029411336,0.972864414573,0.878897537185,0.944702721414,0.981875769026,0.884236234666,0.863457239683,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.639672195901,0.65186731143,0.577760550212,0.523617274687,0.514978319405,0.868371152052,0.772568803728,0.72513462577,1.0
-3653811908756,356653,39072436,LABSPLAC,2013-11-05 05:00:00,1,0,1,0,0.777777777778,1.0,1.0,0.694628089657,0.841488614759,0.700820828887,0.193442622951,0.516613454495,0.627846221356,0.4,0.579104477612,0.595467990007,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.565127285891,0.527496446474,0.520271360095,0.502453868801,0.50098917386,0.466478701383,0.444190782325,0.300924908922,6.67841245261e-74
--636106992483,446876,29023427,LABSPLAC,2012-09-11 00:29:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.94582387653,0.956739446279,0.917031370355,0.77833851037,0.852079315684,0.974856380615,0.794004402741,0.886398057106,0.890813531208,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635251802724,0.564787345631,0.549300938986,0.524258267291,0.512072551324,0.999910027291,0.999999997681,1.0,1.0
--3441212726840,366200,36079872,LABSPLAC,2013-06-22 03:08:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.86943520041,0.963776230663,0.951727496532,0.980220534083,0.969752205882,0.978451795732,0.984998208883,0.982819109737,0.952540626986,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.641984784403,0.590919759793,0.62492509043,0.525051519036,0.515013409941,0.975169317598,0.992972491517,0.994954750275,0.999993170236
-6967509780721,369509,35600373,LABSPLAC,2013-06-11 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.960864443511,0.982495040811,0.977318365183,0.972958855546,0.981875769026,0.984106493919,0.984613415572,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.68054169363,0.59697305425,0.604438079785,0.52965612306,0.515026480297,0.961274258498,0.960723213686,0.957347099711,0.879243429405
-3175022992398,435053,40765131,LABSPLAC,2014-01-30 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961118722075,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.989554653771,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.633360371236,0.672961290111,0.708587298224,0.612421423712,0.997004146106,0.998952536122,0.999544416866,0.896074597948
-9924702471080,383509,31565662,LABSPLAC,2013-01-02 10:28:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.903734307621,0.987687489104,0.984878261428,0.970372379219,0.981394008776,0.989703168472,0.985510093613,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.658310883615,0.577760550212,0.519937493226,0.507763481852,0.996210001229,0.995181064524,0.98566589766,1.0
--8745284681128,415058,36729465,LABSPLAC,2013-07-21 15:17:00,1,0,1,0,0.862745098039,1.0,0.0,0.956160864127,0.963776230663,0.951727496532,0.788899441258,0.960390239251,0.967675343491,0.985749171201,0.990589633396,0.919580380655,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.633856501672,0.564375536484,0.549300938986,0.50246102423,0.505852804873,0.886064815701,0.847678584071,0.798009165436,4.47449950401e-14
--4411364501279,471004,39120545,LABSPLAC,2013-10-18 10:41:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.932959197132,0.945865790823,0.979876980557,0.977318365183,0.962612761054,0.983642630155,0.885408556066,0.98297936463,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.51770211576,0.508640802297,0.938027187773,0.963428367205,0.978600796442,5.10945706987e-08
--2045620018573,403484,39487149,LABSPLAC,2013-11-07 07:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.952820198699,0.923476225221,0.973509511272,0.94218267951,0.980788482439,0.885624368118,0.950221843107,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.632550871699,0.58348614165,0.59558486573,0.525079246706,0.513746197159,0.857252195549,0.85803886558,0.959103998997,1.0
-8969995254588,363461,34435389,LABSPLAC,2013-05-03 05:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.97863298675,0.943739375718,0.780788482439,0.983594039937,0.983006244559,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.573724847753,0.592186262416,0.524839556702,0.512647295915,0.979770898704,0.999075721529,0.999999999261,1.0
-12119380103560,365570,34201444,LABSPLAC,2013-04-25 18:40:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.916539227749,0.917370536748,0.91524833856,0.970534475054,0.85995037908,0.931893512156,0.980382842717,0.993814339874,0.982443804989,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.606942910715,0.552039536669,0.547402931986,0.51113251456,0.504420050413,0.973285554766,0.981312681605,0.981595031141,2.48872154677e-12
-11734122517498,451346,35346414,LABSPLAC,2013-06-12 23:05:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959898249905,0.977655342441,0.972194889706,0.976336693259,0.984998208883,0.987193468674,0.98439781162,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.694553060432,0.60418413449,0.633531218038,0.533302538101,0.522257140523,0.996931715073,0.99985307058,0.999999967598,1.0
--6346348821454,415622,36404752,LABSPLAC,2013-06-30 06:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.951318850856,0.977360559416,0.974261068968,0.978830189789,0.98646775749,0.98711792879,0.981612548843,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.718751646595,0.620325257975,0.647214985244,0.533732442806,0.521230654692,0.977252446301,0.988836606777,0.994150171081,4.64177659151e-10
--12311840067239,367256,31591523,LABSPLAC,2012-12-19 18:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959814854881,0.979876980557,0.977318365183,0.977169668841,0.98646775749,0.985303412661,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.634846708241,0.654219968234,0.545972411198,0.533093379857,0.985018611564,0.990877910311,0.991389013358,0.973670101198
-1653376319670,409492,33041968,LABSPLAC,2013-02-27 20:24:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963033703961,0.975380835714,0.97863298675,0.976001286471,0.98646775749,0.983594039937,0.985417335293,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650048755741,0.593277704254,0.63874696642,0.524556386763,0.513117252382,0.946019443574,0.942394295717,0.940042096802,0.0118233477574
-5450721457455,434583,34029291,LABSPLAC,2013-04-19 01:56:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.951051963038,0.979876980557,0.977318365183,0.977169668841,0.98646775749,0.979024763839,0.983681740074,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.514688651593,0.511242801561,0.961920045704,0.981367357174,0.984383838078,0.00520095102866
-10651616431569,371633,34693399,LABSPLAC,2013-05-03 14:01:00,1,0,1,0,0.0487804878049,0.0,0.0,0.316773959017,0.276264212653,0.409744529505,0.214285714286,0.186785714286,0.092036411864,0.2,0.2,0.1,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.547202564529,0.501174335152,0.427947471506,0.484413000417,0.492889283338,0.0270300995478,0.0116170324123,0.00497078188937,2.3633674741e-264
--1735061177961,356996,39148659,LABSPLAC,2013-10-28 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.976808364662,0.983642630155,0.987872137312,0.985628878715,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.697781234279,0.612114545005,0.647214985244,0.546583258269,0.525521492505,0.981863630563,0.995701358287,0.999493331636,6.72994573528e-13
--11719387809236,387538,30874544,LABSPLAC,2012-11-25 08:39:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.96085236541,0.977360559416,0.977318365183,0.978111020545,0.98646775749,0.985408556066,0.981738335007,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.521874736345,0.512102477824,0.976485410101,0.982561869013,0.992201289768,1.0
-7449569794204,405817,37568823,LABSPLAC,2013-09-14 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.953667932486,0.952972711619,0.977360559416,0.958305142752,0.978052327834,0.98646775749,0.989587576263,0.985966414768,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.707064501754,0.647214985244,0.532178508897,0.518079914633,0.961435010725,0.979697489266,0.988178712343,4.78238823272e-11
--8412542675284,405851,40516294,LABSPLAC,2013-12-29 04:54:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.953216930863,0.979876980557,0.977318365183,0.978764715964,0.983642630155,0.985340764827,0.985048846911,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.5202554019,0.510008139762,0.838431092378,0.810112161761,0.831999800795,1.0
-9208692225854,470424,30896198,LABSPLAC,2012-12-15 10:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.599537328205,0.62492509043,0.526801686777,0.515727661634,0.952100655749,0.969300391532,0.972778232864,6.66978259619e-05
-266670710059,440046,39315164,LABSPLAC,2013-11-21 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.975380835714,0.977318365183,0.975713510564,0.980918574313,0.987872137312,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.697407898433,0.605835287006,0.647214985244,0.552661780237,0.529420962933,0.966041916933,0.966343261096,0.957275216916,0.999998653998
-8636559223052,434865,31942784,LABSPLAC,2013-01-21 01:07:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.95365513447,0.961043261284,0.987139453002,0.97863298675,0.97520737501,0.982725025926,0.988527410279,0.986678197901,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.654366452658,0.674852641907,0.577760550212,0.52983280672,0.524661344877,0.999363820261,0.999983175812,0.999998435246,1.0
-11020449699171,400266,36609439,LABSPLAC,2013-07-25 14:00:00,0,0,1,0,0.0,0.0,0.0,0.951646362197,0.84363531452,0.820161678686,0.772384490062,0.855974508127,0.717427330232,0.78,0.593703784353,0.554789712193,0.917871594643,0.802436882439,0.760601053561,0.741549654775,0.648455558071,0.576428473882,0.604438079785,0.522973224591,0.512517740966,0.960153310436,0.954753371433,0.912062794194,3.24109261558e-48
--10232823590382,469781,37803050,LABSPLAC,2013-08-29 17:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.956160864127,0.963776230663,0.951727496532,0.982834655213,0.97203997597,0.957511736395,0.992673385592,0.990676154481,0.989559591898,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.719468391225,0.620203883358,0.659751167278,0.533339606466,0.518867358423,0.993629017587,0.999512803489,0.999991381735,1.0
--6591869526763,439881,38657721,LABSPLAC,2013-10-04 09:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.940046888795,0.984716678926,0.879462085629,0.962925087467,0.98646775749,0.981904104736,0.952617078598,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.642272866588,0.598391253752,0.63874696642,0.525189343694,0.511815987989,0.916496786944,0.89528741856,0.89835667431,1.45807457395e-06
--6346348821454,415622,36226197,LABSPLAC,2013-06-23 06:33:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.977655342441,0.977318365183,0.978212867981,0.98646775749,0.985644567703,0.983786212814,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.708312606006,0.659751167278,0.698867912843,0.608104833895,0.990069903488,0.99483858319,0.996902325499,0.912648379332
-8081880032031,404200,36126330,LABSPLAC,2013-07-20 06:00:00,1,0,1,0,0.333333333333,0.0,0.0,0.54481996594,0.634638687061,0.556760600754,0.275,0.274450549451,0.274315719685,0.0,0.1,0.199415204678,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.534342185612,0.501725193287,0.491585873363,0.497050975675,0.494133758641,0.343270712979,0.279726916919,0.183651767337,8.8793834922e-191
--10735095088400,421978,34908112,LABSPLAC,2013-05-07 01:00:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.953273832983,0.886262022474,0.782847790735,0.888388678972,0.913722669486,0.984998208883,0.98810742983,0.822482637961,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.638780763526,0.57102029967,0.577760550212,0.516202741123,0.507384358089,0.99147382608,0.996398853608,0.996815565725,6.52440127129e-19
--6467071522215,442457,36705306,LABSPLAC,2013-07-18 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.953273832983,0.927804388085,0.982847790735,0.981760218867,0.975599278747,0.988298770681,0.990676154481,0.9572411938,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.641984784403,0.574808100286,0.577760550212,0.522054019283,0.515438016727,0.9794747018,0.997583457243,0.999991755783,1.0
-6251107030444,420535,34353340,LABSPLAC,2013-05-02 04:36:00,0,0,1,0,0.862745098039,0.0,1.0,0.701577619072,0.883288943413,0.768273856923,0.585503863806,0.387222980536,0.720647704808,0.396699438202,0.487124766441,0.394698247337,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.598930223373,0.537881244886,0.549300938986,0.493604235769,0.497021962029,0.890919006029,0.902411578364,0.879686448578,2.26479450872e-21
-11128568023975,368777,39694318,LABSPLAC,2013-12-02 03:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.961979822707,0.972864414573,0.979617215758,0.976598616649,0.982468061889,0.987872137312,0.985814051458,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.705826886905,0.672961290111,0.70784430856,0.609156457577,0.985092331504,0.988048807734,0.987516305385,2.47432757714e-26
-5606958164431,460124,33232281,LABSPLAC,2013-03-17 00:47:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.979170940453,0.98646775749,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.627076317771,0.654219968234,0.542015754114,0.522359573778,0.994332576387,0.998201593666,0.999367712706,1.0
--3875628435899,398795,38943229,LABSPLAC,2013-10-17 14:17:00,1,0,1,0,0.333333333333,0.0,0.0,0.774008853821,0.682075015788,0.747196749092,0.472389991372,0.464107639337,0.601855077786,0.2,0.3976,0.414926575745,0.917871594643,0.79514940545,0.728887498451,0.738632887801,0.549681671882,0.52761170764,0.491585873363,0.498997053393,0.500874131661,0.557740545142,0.529670405917,0.528901684582,1.76579736259e-29
--1134663265184,426221,37291144,LABSPLAC,2013-08-16 18:49:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.956022861584,0.975380835714,0.975640843332,0.97534234208,0.978093446978,0.984472829387,0.983575957253,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.694553060432,0.602965784541,0.647214985244,0.529211421507,0.523193064974,0.966297826744,0.984497716307,0.990610081185,0.00635948698962
--8265401423260,427652,38224119,LABSPLAC,2013-09-16 13:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.939960891662,0.952281814493,0.969409513399,0.971851018469,0.975111527869,0.98646775749,0.98404635524,0.983629292582,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.698128093976,0.696876422419,0.631528916061,0.694512978394,0.604248126762,0.961921293908,0.976542889688,0.984871713995,0.00182418028176
--11391010719086,368130,39850326,LABSPLAC,2013-11-19 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.956600482249,0.982495040811,0.977318365183,0.977452551997,0.987424952203,0.983810794995,0.986203702059,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723312699125,0.706209284713,0.659751167278,0.539897543166,0.524665125917,0.964733336707,0.971660955884,0.970562599553,1.0
-3341778019677,454286,37808613,LABSPLAC,2013-09-25 07:38:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961714294528,0.977655342441,0.977318365183,0.977179533147,0.98646775749,0.985329431254,0.984714332242,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.706297006034,0.633531218038,0.515752124377,0.511096115525,0.955182936447,0.962406737973,0.974218177931,1.73959186587e-11
-3492183699512,439105,39758846,LABSPLAC,2013-12-05 14:51:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.508675372968,0.947605042987,0.95568176233,0.961505928966,0.0032555045453
--11608121799563,381305,31626168,LABSPLAC,2012-12-19 01:48:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.952533745724,0.977655342441,0.977318365183,0.978127741313,0.98646775749,0.983594039937,0.98505859549,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.642272866588,0.580398198684,0.577760550212,0.520255531309,0.512912039875,0.98430420499,0.996099599358,0.997216916111,0.117827972416
--1138650520285,432830,32329229,LABSPLAC,2013-01-14 05:43:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.952763005662,0.959407473092,0.977360559416,0.977318365183,0.979144355379,0.98646775749,0.985303412661,0.98323321621,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.635806967815,0.654219968234,0.550500239017,0.537021730876,0.986627952681,0.995597457507,0.997665146639,0.999999998152
-6967509780721,369509,35623849,LABSPLAC,2013-06-12 23:54:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.959804728644,0.980220534083,0.975128692964,0.974782023166,0.974049644585,0.986182202112,0.98506433729,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713522872047,0.622050907121,0.672961290111,0.707563586213,0.612190499364,0.982063889835,0.983317878605,0.98423233102,0.999999999976
-2268658033890,390566,34663720,LABSPLAC,2013-05-05 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.952214587867,0.983178208739,0.977318365183,0.962317824072,0.987099640836,0.97892441574,0.981501662461,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.580309934026,0.619588095117,0.533364392655,0.519060500583,0.996716922402,0.999914179171,0.999989476847,4.46063633427e-25
--6870572417591,468068,30674515,LABSPLAC,2012-11-13 18:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.85102285373,0.925515643495,0.965060379257,0.868714657826,0.935766615457,0.98726554086,0.889774711538,0.983780528518,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.606942910715,0.555676362949,0.562079676942,0.511287961677,0.511259283592,0.946110844208,0.977269289357,0.993589364848,5.31321993331e-21
-3688341149934,449015,35840421,LABSPLAC,2013-07-04 13:14:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.952454613344,0.979876980557,0.975128692964,0.974810688186,0.979598827762,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.559644112287,0.562079676942,0.520346997742,0.50835284204,0.962706373917,0.984988558677,0.992750537548,0.961923744852
-1393280600360,410679,35395247,LABSPLAC,2013-06-12 09:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960090202041,0.887139453002,0.877939693907,0.975360195805,0.980918574313,0.987360006654,0.985693786715,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.687419328916,0.625303372222,0.676054975499,0.561174335182,0.536103103515,0.997778507244,0.999608690254,0.999837164028,0.999997357625
-6967509780721,369509,35742052,LABSPLAC,2013-06-17 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.931886593608,0.950792161485,0.955917989163,0.975138921301,0.972000985461,0.975740065586,0.987424952203,0.884658980107,0.951651024846,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700377339648,0.689918047772,0.631528916061,0.706403482295,0.610476314891,0.968479679684,0.978989625802,0.982646026707,7.45246668582e-12
-3658913560377,371607,35504294,LABSPLAC,2013-06-17 06:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.978111020545,0.98646775749,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.647214985244,0.538434356587,0.522845432184,0.976943993284,0.979383177434,0.991383441564,1.0
--4716787243805,376917,37611028,LABSPLAC,2013-08-22 09:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.903725769945,0.963776230663,0.953816417999,0.92095980408,0.97363437929,0.972094139051,0.992463354749,0.981287968522,0.952755350151,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.632645626689,0.576191635151,0.575657631795,0.530797352475,0.518267437665,0.993530710312,0.99837242652,0.999530754156,1.0
-8838847181046,371163,36168097,LABSPLAC,2013-07-12 01:12:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.984151449195,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.707225676086,0.633531218038,0.697697214256,0.604256542211,0.986854873454,0.994200915431,0.997230240915,0.00254420367687
--3912329213703,437924,38492502,LABSPLAC,2013-10-03 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977655342441,0.977318365183,0.975448348657,0.987424952203,0.982075403677,0.984593412228,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.705379449224,0.610872190233,0.604438079785,0.522182488212,0.51309230216,0.941913945972,0.93860004565,0.916330058758,0.0102956395758
-1557101785253,360916,33869741,LABSPLAC,2013-04-03 13:30:00,0,0,1,0,0.862745098039,1.0,1.0,0.975350091061,0.924639154384,0.849246613351,0.980602527078,0.882166178325,0.965196584677,0.987693244344,0.986064395086,0.91688786754,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.61334098335,0.561482448901,0.549300938986,0.509115422197,0.503988807196,0.893297484662,0.917070457077,0.908851174083,2.25981011211e-11
-3658913560377,371607,35521164,LABSPLAC,2013-06-14 00:23:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.943338261405,0.987424952203,0.887253955653,0.980932091011,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.575599058332,0.577760550212,0.51895628863,0.51315077246,0.98336643172,0.99272993306,0.995648969902,0.999993423271
--5580891486963,415892,32394124,LABSPLAC,2013-01-24 17:30:00,1,0,1,0,0.866666666667,0.96138996139,1.0,0.848679051399,0.881570634977,0.849872305072,0.969321603128,0.859033763231,0.933538630086,0.795625385089,0.983852737319,0.987571925649,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.579146663614,0.544935918285,0.518602050471,0.514459179357,0.509957034072,0.981325688095,0.983724315702,0.987074160754,2.91771283456e-40
--1618817871499,389584,33451355,LABSPLAC,2013-02-27 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.925404403495,0.963776230663,0.92638180095,0.782834655213,0.973613163101,0.916528698445,0.989768319288,0.985444508621,0.987744187169,0.917871594643,0.870683716963,0.842355148267,0.859190007503,0.628176308336,0.559584340306,0.592186262416,0.516580844467,0.510151081849,0.773503190667,0.786605740669,0.801387401719,8.86963796395e-19
--11092918254097,471326,36059159,LABSPLAC,2013-06-28 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.927302698505,0.979876980557,0.879617215758,0.978212867981,0.989768319288,0.888499275282,0.977813142509,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.529186690865,0.513022055742,0.984156841268,0.993682558425,0.994684849534,2.32232583441e-20
--7970765028213,461334,36180147,LABSPLAC,2013-07-02 08:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.895727329028,0.8816049002,0.909910774675,0.951476485283,0.946572985431,0.945203102808,0.79190791297,0.981039192724,0.884475534692,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.670738222096,0.584037612861,0.604438079785,0.532647033964,0.521802932139,0.944981580601,0.960838746081,0.962587746048,7.3829588911e-30
--4936234061087,437582,30917486,LABSPLAC,2012-11-13 14:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.955882500902,0.962972851602,0.982200257785,0.958305142752,0.977120840436,0.98646775749,0.908358653627,0.984221682602,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.62091323396,0.640657052947,0.533573706626,0.520538483913,0.956299747813,0.983440518336,0.991928263335,0.682458268104
-8250204777255,380442,40606710,LABSPLAC,2014-01-08 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.954573237105,0.92095980408,0.972221392779,0.967773711517,0.98878053093,0.925425078625,0.980275864667,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643278935289,0.589792805534,0.577760550212,0.541700121199,0.524460766027,0.997714148716,0.999958186426,0.999999043361,1.0
-10364272536667,409518,34654082,LABSPLAC,2013-05-09 22:30:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.955318725429,0.979876980557,0.975128692964,0.978327885575,0.983642630155,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713884482281,0.69950470701,0.647214985244,0.699801014351,0.603311250839,0.984672734974,0.991301602422,0.993371846763,0.0131382618868
-2766881279042,429993,35845257,LABSPLAC,2013-06-23 01:06:00,0,0,1,0,0.777777777778,1.0,1.0,0.558362329494,0.598667507201,0.700327419327,0.792652329749,0.625687777529,0.732422926657,0.596282527881,0.414285714286,0.429893976133,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.542145840327,0.521663183721,0.53508067757,0.504814854475,0.501833427868,0.648765665883,0.64205645109,0.688497531109,1.34216352673e-36
-7112688009010,457988,36359991,LABSPLAC,2013-07-16 01:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.978773186895,0.983642630155,0.983594039937,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.522917105734,0.512421616303,0.989373057793,0.993852881174,0.995782412322,1.0
-7449569794204,405817,37535723,LABSPLAC,2013-09-15 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.955882500902,0.954022300248,0.975138921301,0.958305142752,0.97698695329,0.987424952203,0.98582193287,0.985982712593,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723312699125,0.702767576034,0.633531218038,0.531398404158,0.516214113693,0.955290368336,0.979046173672,0.988155809652,7.31235812068e-11
-7094992191229,448670,33375757,LABSPLAC,2013-03-21 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.979876980557,0.977318365183,0.978203003675,0.98646775749,0.983613477461,0.98355595391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.654219968234,0.533088472384,0.526409632425,0.982146628492,0.988515617308,0.989786507059,0.127414657001
-5485320279442,436587,32807383,LABSPLAC,2013-02-14 23:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.855454523104,0.933151311218,0.95275741491,0.939670187354,0.877069805363,0.934189215461,0.890494700787,0.981305961713,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.67106602942,0.610052505989,0.625799145584,0.529838561798,0.529154668552,0.963987285354,0.94934701937,0.942666033783,1.0
-8740981064850,451405,30929079,LABSPLAC,2012-11-30 12:18:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.768389249095,0.971049701988,0.916546985187,0.888567749073,0.973463491657,0.923088180311,0.985176330823,0.894976257163,0.981031291218,0.917871594643,0.827009027697,0.787467118762,0.829616127665,0.602516995392,0.569057269141,0.577760550212,0.541476832825,0.515476183621,0.994263724139,0.999600294611,0.999999824844,1.0
--7722010707798,438920,30331922,LABSPLAC,2012-11-01 10:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.880483890663,0.945103235282,0.934487650392,0.978099225058,0.946087467441,0.948298558353,0.785524374988,0.890830517107,0.951369593834,0.917871594643,0.834448628119,0.788388808379,0.797475809346,0.603585918743,0.550919835751,0.549300938986,0.523135764977,0.509818038951,0.642256754657,0.319144548372,0.203029697811,3.21752623936e-41
-2852925776464,452388,36669746,LABSPLAC,2013-08-08 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961118722075,0.982200257785,0.977318365183,0.971305031413,0.983642630155,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.677872703012,0.604647066821,0.619588095117,0.695370062011,0.602840616476,0.953165907149,0.969363809344,0.976308854072,1.87090254611e-12
--11416017146675,397070,37693152,LABSPLAC,2013-09-03 09:30:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982495040811,0.972061352238,0.971526259225,0.987424952203,0.982637109666,0.983916865128,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.577925991044,0.577760550212,0.524143758089,0.515261337874,0.9641217788,0.989653698963,0.994964918853,8.34525519259e-07
--11899697947985,429960,35940380,LABSPLAC,2013-06-24 04:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953216930863,0.975380835714,0.977318365183,0.976882546062,0.980918574313,0.987872137312,0.984413354063,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723487769495,0.704551379379,0.672961290111,0.698952790921,0.605878554284,0.991568105053,0.997096284627,0.99905544251,0.93178405204
--1431211888954,368641,40822857,LABSPLAC,2014-01-06 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.967163641099,0.910064853417,0.915593879364,0.975138921301,0.970383830386,0.93891921968,0.989372823794,0.884313083932,0.985296094205,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.563247205523,0.605485933567,0.52900395673,0.518298937413,0.981759079392,0.999960696246,1.0,1.0
--1490774025753,386490,31678231,LABSPLAC,2012-12-22 06:58:00,0,0,1,0,0.968845448992,1.0,1.0,0.972100498572,0.963776230663,0.950827697427,0.975380835714,0.979585367702,0.943780649391,0.781394008776,0.887993795747,0.948793186543,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.721555526951,0.638046932655,0.690773668204,0.541621542198,0.527446635104,0.991424660953,0.997069144999,0.997513687789,0.999999978442
-10651616431569,371633,34645683,LABSPLAC,2013-05-02 18:00:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.955318725429,0.975380835714,0.975128692964,0.976841944131,0.980918574313,0.985303412661,0.983380761894,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.631999854287,0.566181943572,0.592186262416,0.518311738527,0.509438595755,0.961100616554,0.975491445614,0.976914199216,0.00366225831195
--4990516022400,392255,30454956,LABSPLAC,2012-10-29 03:37:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.982200257785,0.977318365183,0.976028952507,0.983642630155,0.985430719167,0.980523897713,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.68538231588,0.619549321376,0.625799145584,0.538418264209,0.526192332414,0.988332553662,0.993061550357,0.994391805731,1.00528894008e-14
--10072357801385,447417,39347228,LABSPLAC,2013-11-03 02:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.976409530142,0.983642630155,0.983594039937,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.520264636801,0.510469204711,0.988743287049,0.99566038635,0.996644675812,1.0
-1543025640681,452637,39168967,LABSPLAC,2013-11-13 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958942799526,0.979876980557,0.975128692964,0.977271516277,0.979598827762,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.62155747671,0.672961290111,0.550572356163,0.530026890637,0.996384689113,0.999068082239,0.99961668231,1.0
-6366597921927,455955,35209170,LABSPLAC,2013-06-02 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.51304670357,0.96358640986,0.978818971217,0.987500166274,0.10220653333
--7175872476981,469698,32469521,LABSPLAC,2013-01-29 22:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.963029411336,0.96456981503,0.971569901983,0.972042531565,0.987424952203,0.983949435074,0.981142617351,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.676043714586,0.60160005771,0.611782965058,0.528607084893,0.522807410229,0.998037874008,0.999538276677,0.999812719107,0.999884336261
--152589643420,363538,29261510,LABSPLAC,2012-09-11 12:00:00,1,0,1,0,0.0684931506849,0.0,0.0,0.365390642306,0.274650576879,0.21156703139,0.0,0.2,0.298734177215,0.0,0.2,0.166666666667,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.47546693564,0.464372421513,0.433671722659,0.469550239073,0.480326659411,0.00778336006946,0.000375685322506,2.13045009955e-06,1.21402791977e-281
-10708436270837,358089,30631557,LABSPLAC,2012-11-21 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.980220534083,0.975640843332,0.976133673143,0.978093446978,0.985444508621,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691450053481,0.624463023628,0.684630899643,0.53683130257,0.526424706492,0.964604274611,0.985408115354,0.992886287166,6.87588641495e-15
-11537544808996,419794,30821308,LABSPLAC,2012-11-28 05:48:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.957484083173,0.982834655213,0.974040690688,0.971890784945,0.98646775749,0.985430719167,0.983283784871,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.671961176749,0.598653828329,0.625799145584,0.527309841618,0.515802491004,0.91972653616,0.933543067749,0.93299840052,0.851096309234
-1348737323727,452589,29610084,LABSPLAC,2012-10-21 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.956540600432,0.950978775787,0.977360559416,0.968793722253,0.973546289321,0.98419257645,0.886907991895,0.982084532728,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.661009427592,0.591361845176,0.625799145584,0.536838415645,0.520934163771,0.954062186868,0.972551132143,0.969704790977,0.0311171524479
--9174472743387,420507,33563692,LABSPLAC,2013-03-04 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.952594199427,0.977655342441,0.975640843332,0.975352206385,0.978093446978,0.985303412661,0.982694715122,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657904768363,0.595000926901,0.577760550212,0.522728380989,0.517029201531,0.986401821558,0.995926019228,0.998643952153,0.0494796651273
-12133147578434,360716,32494662,LABSPLAC,2013-02-05 14:27:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963014748944,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.517288412015,0.51276652507,0.991760079063,0.999128000692,0.999684896203,0.999999989111
--8387553780356,385327,40566224,LABSPLAC,2013-12-28 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955742781493,0.977360559416,0.972194889706,0.978332601583,0.983642630155,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.705826886905,0.647214985244,0.710547152447,0.615145796913,0.986684739949,0.995198059932,0.997387085919,2.54139875968e-07
-4629984484475,390657,35576535,LABSPLAC,2013-06-20 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.984716678926,0.977318365183,0.976238181443,0.98646775749,0.986182202112,0.983734804623,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.614960149711,0.647214985244,0.538847972573,0.519860362747,0.990076648862,0.997727371622,0.999004114013,0.999999294143
-9586384124802,435392,29949450,LABSPLAC,2012-10-22 19:08:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.866619765769,0.937213134398,0.962838741142,0.972053826977,0.933487056725,0.98726554086,0.893595722576,0.94041021666,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.618633619363,0.561841447848,0.547402931986,0.506334422547,0.502729284547,0.805538705886,0.739861411218,0.6631565525,2.76545589199e-13
-10678434761668,454392,38439785,LABSPLAC,2013-10-09 15:30:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.922488307163,0.957484083173,0.983178208739,0.968783677743,0.959543487738,0.980918574313,0.981423149059,0.983764541108,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.622614573695,0.562629933549,0.547402931986,0.513520742453,0.504269858073,0.850150254104,0.839435047919,0.819577372388,0.000294130607264
-364234842724,375520,29951429,LABSPLAC,2012-10-26 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960952131159,0.975380835714,0.977484565984,0.965401093956,0.978093446978,0.985303412661,0.983035267884,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.658764215596,0.592186262416,0.526496000358,0.511363007501,0.949981043718,0.966217126466,0.973332477861,1.0
-10804490185166,369494,38624148,LABSPLAC,2013-10-28 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955318725429,0.979876980557,0.975128692964,0.977338366247,0.98646775749,0.987872137312,0.98173517584,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713884482281,0.69950470701,0.633531218038,0.527952013956,0.517617309211,0.993240745598,0.997312266275,0.998381007804,2.39982118676e-15
-850624215139,381046,35080286,LABSPLAC,2013-05-24 06:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.933401487966,0.881723858485,0.88248898424,0.952269634871,0.918158674138,0.955490485758,0.785455893564,0.989006626332,0.989268409792,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.623158167318,0.567244711262,0.561845641879,0.693257464772,0.601116422922,0.879841131305,0.903130732452,0.918782608049,1.13885182903e-122
-11004154899725,356325,36203134,LABSPLAC,2013-07-19 22:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.528542250322,0.518831756447,0.992712410399,0.997242143438,0.998307164678,0.999996438841
--10851305204247,437612,30612300,LABSPLAC,2012-11-08 17:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.94594326631,0.925684649638,0.982200257785,0.977478405725,0.867497187445,0.788274209103,0.956643214676,0.910581623112,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691360310274,0.617044189706,0.654219968234,0.547336692698,0.524346212641,0.99288605755,0.999076242657,0.999733923817,7.38996889612e-09
-4035289185680,372848,37660582,LABSPLAC,2013-09-14 22:33:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.932266052935,0.93790507318,0.883894102717,0.95419299227,0.963391560895,0.98646775749,0.974796763529,0.984785745463,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.624973493513,0.565157064848,0.522421043247,0.513069684032,0.505761884007,0.898903847277,0.919665851529,0.945406265101,0.666311379211
-2525062310013,440931,36734463,LABSPLAC,2013-08-11 08:46:00,1,0,1,0,0.866666666667,0.96138996139,1.0,0.828444754959,0.87791229967,0.892436707681,0.75695058108,0.764002536615,0.78647044557,0.779078226806,0.789153022034,0.854047574974,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.596183664026,0.54068517091,0.547402931986,0.503437901321,0.499174322515,0.652650955781,0.792328877648,0.917468442607,5.13528718209e-47
--7843193723686,405855,34358530,LABSPLAC,2013-04-12 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.936496794188,0.797978211567,0.871423045351,0.680699592209,0.868803118826,0.855713696892,0.990518371679,0.883755611511,0.800072047276,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.606436834342,0.55367220428,0.537223027254,0.515779251368,0.512514521333,0.897398519626,0.954825438524,0.984047131656,2.02566179855e-06
-10934898406526,389088,33706221,LABSPLAC,2013-04-04 20:18:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.943606180573,0.977360559416,0.975288733505,0.975693347178,0.98095440649,0.985303412661,0.98113710519,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.6214776349,0.56928166844,0.562079676942,0.513099653624,0.507551733375,0.9900836496,0.996674310699,0.99776780578,2.56697398059e-15
-11202579318437,416464,32609522,LABSPLAC,2013-02-13 09:05:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959649180419,0.982938923428,0.977318365183,0.976585249551,0.98646775749,0.985303412661,0.985238531499,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.596237985174,0.577760550212,0.514688651593,0.51490608043,0.980014014126,0.985537750249,0.991212372076,4.62556316202e-08
--4180725302355,405843,38146094,LABSPLAC,2013-09-21 09:36:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.950458074541,0.951488058331,0.967249513643,0.962468284268,0.96194065742,0.981911601203,0.985980525316,0.917845424737,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.660755939287,0.57805336952,0.604438079785,0.513932056958,0.507655361134,0.826156535074,0.872147782502,0.902454462568,2.42872371727e-32
--10367188704482,405838,40792688,LABSPLAC,2014-01-09 06:13:00,0,0,1,0,0.968845448992,1.0,0.0,0.972100498572,0.963776230663,0.955742781493,0.980220534083,0.975640843332,0.97534234208,0.781394008776,0.789703168472,0.98372730729,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.702047090632,0.647214985244,0.690349827254,0.597956265005,0.958278917838,0.952551767107,0.958265268924,0.999937243408
--11641672978460,434976,37311020,LABSPLAC,2013-08-10 23:43:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.953220121912,0.960073670187,0.975355312255,0.968927322327,0.98646775749,0.889229702496,0.98347617214,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.681533647081,0.599472300412,0.604438079785,0.524763483809,0.513400439015,0.975145601433,0.992355361852,0.998642367232,1.0
--9688427533830,416001,30529883,LABSPLAC,2012-11-06 02:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.963029411336,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.985303412661,0.984686532701,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.645099421411,0.697318231141,0.545647842883,0.533303954701,0.9870962762,0.995867986946,0.998709133781,1.10363392749e-11
--7776091268135,390397,31204757,LABSPLAC,2012-12-03 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.980613017097,0.972061352238,0.972417939447,0.98646775749,0.984944115419,0.984431625846,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.694509209122,0.649280580868,0.657400685263,0.560456109539,0.535387073855,0.996881464642,0.999632498485,0.999883943213,1.0
--303735670891,460663,32591526,LABSPLAC,2013-01-27 10:03:00,1,0,1,0,0.866666666667,0.96138996139,1.0,0.84576719858,0.670230718469,0.79277864191,0.633525423729,0.747201886917,0.736481913304,0.775625385089,0.59244709222,0.722764972917,0.917871594643,0.817611457938,0.739935848699,0.745656574421,0.544964567987,0.520981792897,0.518602050471,0.511349939618,0.501983829963,0.812384251737,0.873532896396,0.914928252969,2.13010591556e-30
-2766881279042,429993,35871648,LABSPLAC,2013-06-23 15:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.718027805948,0.80653139666,0.773698072432,0.889054498651,0.796109730032,0.745771681501,0.594009800608,0.710899473173,0.660277574016,0.917871594643,0.848330419257,0.746770706044,0.7551686106,0.554446646053,0.531126181928,0.53508067757,0.51046892929,0.505272023578,0.892620674069,0.941991865221,0.952201499449,3.6297874132e-21
--8118296954482,403532,37091062,LABSPLAC,2013-08-11 02:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.843155900456,0.874243682403,0.90747058227,0.958231510707,0.946564648465,0.943516014772,0.788607351173,0.989096207111,0.986081131859,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.606942910715,0.563865231122,0.575657631795,0.525497761807,0.515021296423,0.978346537002,0.997648845009,0.999587729823,9.21815327245e-48
-9958092196787,436618,37656915,LABSPLAC,2013-09-08 10:02:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.843155900456,0.874243682403,0.908801000418,0.967483153334,0.955727110253,0.877559489332,0.975777665008,0.971311749115,0.949094510078,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.599852516255,0.545759366576,0.575657631795,0.52415529413,0.511777269884,0.989644265608,0.998933611003,0.999782717461,1.0
-11020449699171,400266,36922557,LABSPLAC,2013-08-06 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.950221689037,0.946674134622,0.948464863137,0.982200257785,0.974040690688,0.970575650966,0.988274209103,0.988756727297,0.985428240354,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.681533647081,0.604320858234,0.645245582584,0.531124825949,0.519043261926,0.973938956465,0.987220706856,0.992889009938,8.64751212417e-07
-2127718306338,378217,33801401,LABSPLAC,2013-03-29 22:37:00,1,0,1,0,0.047619047619,0.0,0.0,0.668635278428,0.413854515854,0.477092535562,0.188461538462,0.285101061658,0.412433643982,0.2,0.0988235294118,0.131153036754,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.561262118063,0.515514493803,0.518602050471,0.487001771781,0.489633791621,0.11692175792,0.0724560655451,0.0385308985425,2.51300724411e-88
--6391895482844,380427,29187206,LABSPLAC,2012-09-10 14:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.956160864127,0.963776230663,0.939617651566,0.790097127658,0.977332869696,0.947160920339,0.984791976488,0.966887046626,0.954437872663,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.627377601287,0.570949913868,0.518602050471,0.508381659568,0.505397669707,0.948095813736,0.977131382978,0.963305048857,6.57184434277e-43
--3009275983802,431782,33181187,LABSPLAC,2013-02-18 07:52:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960269402211,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.983613477461,0.983707303946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.677848328646,0.60187300633,0.625799145584,0.526683926181,0.518060259121,0.983018648682,0.992001804969,0.994494562469,0.999999999864
-2569156209298,392334,37005342,LABSPLAC,2013-08-11 05:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977360559416,0.977318365183,0.977169668841,0.989768319288,0.984363365382,0.982281488132,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.628648993443,0.647214985244,0.529349255827,0.52317525225,0.969875978069,0.949664526065,0.918055927764,1.0
--7173767459680,431751,38819446,LABSPLAC,2013-10-17 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.941607747384,0.938592413819,0.916233962608,0.987674353582,0.96255171129,0.967807916203,0.98419257645,0.986402288806,0.983210653705,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.564624648235,0.547402931986,0.519643054435,0.512860641559,0.93025609049,0.913339485608,0.878220391388,2.85953874051e-05
--7876546784290,395444,31529646,LABSPLAC,2012-12-20 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.944718680783,0.98646775749,0.983329449435,0.981715395756,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.62109640782,0.640657052947,0.547422333633,0.525025379851,0.968951108403,0.975021919348,0.981812802689,0.785759435582
--11872123153812,421077,38027301,LABSPLAC,2013-09-16 15:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.960002514393,0.979876980557,0.977318365183,0.977484591706,0.98646775749,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.647214985244,0.535384373474,0.522577782473,0.96065922879,0.968838727414,0.975986812072,0.845901727978
-5421152441382,374504,30813915,LABSPLAC,2012-12-03 14:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.921315329179,0.89637032705,0.942704089619,0.972413714579,0.966207294456,0.956624174431,0.9642009783,0.983612915724,0.9850436709,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.617674152132,0.595704386027,0.59558486573,0.563743827116,0.53641875593,0.998312609246,0.999609151909,0.999723809174,1.31260915368e-24
--7249115173462,358402,35190930,LABSPLAC,2013-05-18 21:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.964334349221,0.977704112943,0.951624809912,0.974287756108,0.980918574313,0.983613477461,0.984780093028,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.678327738103,0.618510871947,0.63874696642,0.531824108382,0.520758494829,0.972330581033,0.975473118294,0.977393979211,5.42269613403e-42
--1236259541802,442515,36390407,LABSPLAC,2013-07-05 08:35:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.957676722346,0.975138921301,0.972354930247,0.973019739507,0.981875769026,0.974658980107,0.950138666629,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.71752127279,0.696433999197,0.633531218038,0.526249382726,0.512265128854,0.940360042616,0.968885808507,0.978275677507,0.0333443342303
-3330706164607,395535,39582601,LABSPLAC,2013-12-08 13:14:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962972851602,0.977704112943,0.978793027291,0.97386550346,0.987424952203,0.982075403677,0.985047443919,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.57150567583,0.577760550212,0.519739604889,0.508498506985,0.965176335676,0.980524354368,0.987321721261,0.022405112025
--6450422733669,461111,29218632,LABSPLAC,2012-09-18 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982495040811,0.975640843332,0.977179533147,0.98646775749,0.985303412661,0.984599582777,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.627076317771,0.640657052947,0.538213806589,0.531747296377,0.987294596326,0.993308811235,0.994763376954,1.23289236672e-05
-4008105135928,429999,34129963,LABSPLAC,2013-04-17 03:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.916963131802,0.820323469473,0.898316100897,0.766038330211,0.938446120297,0.863375209112,0.787251772445,0.889700751077,0.755878358828,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.601978812564,0.550605007752,0.562079676942,0.512025224118,0.508437286929,0.985003658636,0.995469548996,0.997189415413,2.86274145084e-19
-8549577581306,362509,32033919,LABSPLAC,2013-01-26 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959827910226,0.980220534083,0.949013259253,0.943979926721,0.978093446978,0.989618862203,0.986701690097,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.573298528927,0.577760550212,0.524810016142,0.513761081199,0.95591226557,0.928603062242,0.89502027806,6.27950421241e-40
--4175901866511,468873,40231618,LABSPLAC,2013-12-02 17:13:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958716206971,0.982834655213,0.977318365183,0.977179533147,0.98646775749,0.983594039937,0.982177015391,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.519942049303,0.514321585347,0.985211378446,0.990912089955,0.992263624568,0.999999871764
-1495277037574,430101,38513150,LABSPLAC,2013-10-16 00:38:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.984221177907,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.510796430775,0.911009085689,0.895626564353,0.906113196213,1.0
-4035289185680,449684,34593607,LABSPLAC,2013-05-09 05:46:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.963416345957,0.983642630155,0.987996718241,0.981886525876,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.625843002933,0.672961290111,0.705214294702,0.60715131039,0.982850912043,0.992719624623,0.99594210975,0.999999997708
-1612274121959,367490,39045355,LABSPLAC,2013-11-07 06:35:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.826136846831,0.813271886792,0.8938915639,0.955781051968,0.809102993003,0.80126305795,0.985548155178,0.987477459408,0.884764667399,0.917871594643,0.875764265788,0.793065314363,0.817336130992,0.584984011247,0.538978878107,0.50644065596,0.506979495846,0.506213018516,0.606581550155,0.627123003102,0.698962528149,5.92943487639e-07
--11899697947985,429960,35979376,LABSPLAC,2013-06-24 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953216930863,0.975380835714,0.977318365183,0.976882546062,0.980918574313,0.987872137312,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723487769495,0.704551379379,0.672961290111,0.527783676695,0.518383406528,0.988471455845,0.994029384247,0.997119507806,0.00301650805123
-9958092196787,436618,37626290,LABSPLAC,2013-09-06 04:44:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.981582724545,0.983707303946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.515905214709,0.508140092341,0.988268317752,0.997645635523,0.999189479885,1.0
-2829300800463,391631,35612967,LABSPLAC,2013-06-28 06:23:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960864443511,0.975380835714,0.977318365183,0.976872681757,0.98646775749,0.987872137312,0.98516306823,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.705699630576,0.672961290111,0.690572423539,0.600256806903,0.967774484808,0.977324957359,0.98100685101,0.997882333069
--400756422449,398876,36368934,LABSPLAC,2013-07-18 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.978111020545,0.98646775749,0.987334502903,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.732288665718,0.721652670929,0.633531218038,0.70047391813,0.604841000455,0.988322323052,0.995063321052,0.997961066847,0.0126508017533
-9115251165042,360577,36611092,LABSPLAC,2013-07-26 00:52:00,0,0,1,0,0.75,1.0,1.0,0.728909465021,0.699824182969,0.619241056357,0.81989010989,0.69625,0.742956953776,0.8,0.7976,0.864679754872,0.917871594643,0.79514940545,0.727302647247,0.728038413078,0.534309672059,0.519215832302,0.493501371374,0.508410348288,0.504679702053,0.764346919367,0.918671507974,0.981684192388,1.813426591e-145
--5436527692591,403148,33589365,LABSPLAC,2013-02-28 20:46:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.960147537185,0.977179533147,0.98646775749,0.97987335182,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.505100349428,0.97687282162,0.982543730293,0.985608038843,0.0144938523118
--4180725302355,405843,38202724,LABSPLAC,2013-09-21 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.950458074541,0.951488058331,0.960073670187,0.971851018469,0.96299256125,0.97867922545,0.989746168709,0.918985278695,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.692033965082,0.602021339643,0.618554100768,0.525670085578,0.513680753183,0.857070656182,0.905261363435,0.937104064566,7.27978723487e-34
-6733802491780,447024,34580291,LABSPLAC,2013-05-03 14:58:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.943613475634,0.952774727159,0.979876980557,0.979179497233,0.976038321023,0.976773700428,0.981904104736,0.984484073337,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.6214776349,0.567974303659,0.562079676942,0.512459711914,0.504295065113,0.821935358126,0.832205665935,0.797748671975,0.999996714233
-6127212328467,461691,30597570,LABSPLAC,2012-11-22 04:56:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958716206971,0.984716678926,0.977318365183,0.976808364662,0.983642630155,0.983613477461,0.982904970683,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.629311172511,0.572394833369,0.547402931986,0.512349545977,0.506361206047,0.988775347931,0.995548088287,0.997396720353,0.0396555654929
-9115251165042,360577,36629994,LABSPLAC,2013-07-26 05:00:00,0,0,1,0,0.862745098039,0.0,0.0,0.919066619694,0.728641638528,0.62208736979,0.988461538462,0.697619047619,0.778894601907,0.6,0.9,0.7585623232,0.917871594643,0.875764265788,0.791748440161,0.809107397077,0.569941979369,0.533776427126,0.522183830833,0.517610873915,0.509043361787,0.888122924431,0.804802629709,0.437794397493,1.67740659372e-173
-11022992441918,386947,35390965,LABSPLAC,2013-06-15 17:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.972061352238,0.970865872995,0.98646775749,0.981295842553,0.985718313472,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.573383807476,0.592186262416,0.52616819912,0.51130180458,0.969273194947,0.987028961267,0.993937651597,1.19949597946e-13
--3009275983802,431782,33173959,LABSPLAC,2013-02-20 05:44:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961107767351,0.979876980557,0.975128692964,0.976266766694,0.972192853122,0.987336304566,0.982523374397,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.680748510127,0.602120170347,0.627592053327,0.523153504846,0.514305296836,0.899762368163,0.872884546331,0.852658932561,1.0
--4948092669621,456855,36624133,LABSPLAC,2013-07-17 23:15:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.873981390607,0.892674589777,0.872854871122,0.903614557767,0.865563273581,0.836076240639,0.792981966083,0.894625634347,0.874865285327,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.604998004548,0.561189811795,0.59558486573,0.513142919513,0.510914804061,0.982608784427,0.996270512932,0.998783746665,6.9968643642e-32
--3489863177,421242,37462292,LABSPLAC,2013-08-20 09:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.955160932465,0.970535048751,0.877220015334,0.974562929925,0.983642630155,0.985303412661,0.984790194045,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650451236481,0.584410855921,0.577760550212,0.549555467327,0.528997545925,0.986813863667,0.99243465185,0.993910524417,1.0
-1945144906651,424834,36933864,LABSPLAC,2013-08-16 20:01:00,0,0,1,0,0.968845448992,0.0,0.0,0.937839241455,0.869014610862,0.914755804827,0.786637096667,0.874186994832,0.918056915963,1.0,0.895069362369,0.989495952314,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.717935901842,0.621346403379,0.672961290111,0.707494724484,0.615399108057,0.997488257788,0.995118179973,0.996557700871,2.70070980733e-65
-5733211750851,464743,39510315,LABSPLAC,2013-11-22 07:40:00,0,0,1,0,0.968845448992,1.0,1.0,0.669373839356,0.859113133313,0.871402149303,0.935285523332,0.855008191552,0.828956000103,0.440584415584,0.86559898293,0.730857980188,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.596777602932,0.543408240638,0.550955970579,0.500037682066,0.495532566344,0.62108649303,0.667928804913,0.759682065675,1.04406264611e-20
--834398520529,445276,29706541,LABSPLAC,2012-10-12 02:01:00,1,0,1,0,0.0,0.0,0.0,0.610564782157,0.898077898032,0.756330582529,0.208666666667,0.645798655498,0.353743676525,0.586914171572,0.2,0.354641763355,0.29944547465,0.596656681247,0.668691501605,0.675364081911,0.540780936148,0.512050266678,0.477085565744,0.485672446742,0.491815398233,0.547960479271,0.412223567107,0.343232186405,3.79299924989e-11
--6391895482844,380427,29301607,LABSPLAC,2012-09-11 20:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.952763005662,0.941794265386,0.979613849002,0.978711259617,0.977360536011,0.982760858103,0.966589546038,0.957958143329,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.649425818474,0.585171411996,0.577760550212,0.517521272172,0.519718477771,0.984618164885,0.996925219029,0.998525553923,4.96712891409e-115
-2405696602645,407598,31757830,LABSPLAC,2013-01-07 14:02:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.90839971117,0.963776230663,0.90031960478,0.88274363725,0.955873591764,0.909526976467,0.989768319288,0.775918452356,0.977530163784,0.917871594643,0.848148017695,0.800194633637,0.829616127665,0.61710058329,0.558453202722,0.592186262416,0.522455129975,0.511954688504,0.965021850832,0.992123330021,0.996404786071,1.0
-10431524076881,459639,32780634,LABSPLAC,2013-02-13 09:06:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.94582804405,0.958942799526,0.975138921301,0.971878825418,0.965636986302,0.763026410034,0.982075403677,0.983892352841,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.603903994402,0.547090610268,0.547402931986,0.508732358989,0.503762737498,0.942232279263,0.944631340047,0.949843243526,1.59986488195e-08
-3907559420442,440290,33907207,LABSPLAC,2013-04-09 10:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.90839971117,0.963776230663,0.932522372328,0.98657380133,0.955873591764,0.964464424823,0.985176330823,0.875918452356,0.910314262123,0.917871594643,0.848148017695,0.800194633637,0.829616127665,0.613907530362,0.55678906786,0.577760550212,0.514899037017,0.508384165279,0.949917917449,0.978221066798,0.984936415262,2.26119504771e-15
-4392918398860,426286,38495793,LABSPLAC,2013-10-21 15:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.959935940667,0.977360559416,0.97420073123,0.972573849101,0.98646775749,0.987999443818,0.984759871373,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.681533647081,0.598022779612,0.604438079785,0.517274211094,0.510139734548,0.981534314639,0.990973823333,0.993738328848,0.0751213357773
-11020449699171,400266,36832138,LABSPLAC,2013-08-07 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.921973510064,0.942780398407,0.951251388364,0.982200257785,0.975128692964,0.973837692409,0.981405279375,0.984033741343,0.984739238554,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.661525216183,0.586396354662,0.618554100768,0.527685863254,0.515224173538,0.975528431364,0.991962252721,0.99632612148,3.31021478571e-09
-2194609846994,358508,37987037,LABSPLAC,2013-09-20 11:10:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.932348437859,0.963711384581,0.930976071943,0.977360559416,0.954890524239,0.975042570505,0.98646775749,0.984701216704,0.981036104697,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.570378414122,0.547402931986,0.519096610636,0.515083599415,0.981730994768,0.997416853576,0.999180990722,6.60950731039e-53
--10958967531904,461808,32344180,LABSPLAC,2013-01-24 17:26:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.956858160689,0.975380835714,0.972194889706,0.975852972777,0.978093446978,0.985444508621,0.979599828884,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.654219968234,0.530762602756,0.510595537576,0.992523692644,0.995692106086,0.997452716065,0.000995903940099
--2927821313749,383044,36510962,LABSPLAC,2013-07-17 08:15:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.628077162603,0.896029526692,0.836902083502,0.510858585859,0.810341896372,0.645564036582,0.796282527881,0.512730504332,0.727245892852,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.566449064417,0.529018384222,0.491585873363,0.497376023844,0.502123396393,0.509056636587,0.734211427943,0.787405865039,5.63092114245e-44
-11473721804680,397502,32046312,LABSPLAC,2013-01-22 21:00:00,0,0,1,0,0.931034482759,1.0,1.0,0.845884020787,0.7210338113,0.902432137038,0.940082900588,0.889772727273,0.958573498457,0.8,0.9,0.965193044503,0.917871594643,0.834448628119,0.766102260428,0.765351691623,0.570437521302,0.524711404634,0.563965162064,0.525174778324,0.511154187327,0.838789080565,0.90372241843,0.950313423676,6.62986894205e-209
--11145272056246,461156,33157014,LABSPLAC,2013-02-20 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.976229826324,0.98646775749,0.981582724545,0.919311078897,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.50838081358,0.940184673301,0.953811282351,0.965418980632,9.61885487333e-06
--11714197021507,367003,30904404,LABSPLAC,2012-11-25 00:03:00,0,0,1,0,0.0,0.0,0.0,0.138275933869,0.40311295686,0.35364483336,0.2,0.390322580645,0.333333333333,0.2,0.4,0.433333333333,0.29944547465,0.652792299821,0.708532238434,0.683493341442,0.523263074914,0.484313509991,0.482515388558,0.473514949836,0.483090310195,0.000162000366588,4.0104697439e-06,3.82987127683e-07,1.23157940444e-276
--2486809971770,449282,32296306,LABSPLAC,2013-01-14 15:35:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.876749226838,0.895028632762,0.877239179968,0.975628553856,0.83672752914,0.927827157182,0.992981966083,0.791413280282,0.984103556112,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.606942910715,0.560666546261,0.562079676942,0.513778941829,0.507244625951,0.961831656185,0.962452731642,0.974762862221,8.75764097701e-59
-5143282453079,409747,33841362,LABSPLAC,2013-04-03 12:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.964668359471,0.955921599266,0.980220534083,0.972194889706,0.941011753057,0.978093446978,0.981904104736,0.984558490041,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.596733657246,0.62492509043,0.519827988513,0.509763316335,0.993460354607,0.996856828094,0.998127430894,1.53157703018e-12
-6992008753358,448295,36449117,LABSPLAC,2013-08-01 01:12:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.898425659254,0.912387792001,0.95275741491,0.961077190899,0.939099450551,0.988607351173,0.991521436429,0.971310641349,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.618633619363,0.561649845514,0.562079676942,0.519107941616,0.511125437889,0.900844537902,0.883187631646,0.863335843167,0.0368990663802
--10710521817283,459410,32701706,LABSPLAC,2013-02-06 02:07:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.975849211228,0.980918574313,0.983594039937,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.573599266539,0.592186262416,0.521733662027,0.510151399962,0.968221915071,0.971776974094,0.969795054104,0.999993849989
-2983523972880,444720,29656159,LABSPLAC,2012-10-19 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.980220534083,0.977318365183,0.976164134093,0.98646775749,0.985303412661,0.984183095158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.573599266539,0.577760550212,0.515547715339,0.506688673003,0.870508850746,0.878896408628,0.899817099049,8.85154114833e-06
-8953478728578,437252,36133234,LABSPLAC,2013-07-05 13:49:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.801352268953,0.888071406625,0.947773490029,0.931355878193,0.942612932955,0.977620956615,0.892325731946,0.941602023999,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.607043248444,0.568191311508,0.59558486573,0.517408944142,0.509813942585,0.967937575131,0.991114197982,0.995433773582,1.45914247832e-18
--5531495494637,428643,32613419,LABSPLAC,2013-01-25 02:01:00,1,0,1,0,0.0684931506849,0.0,0.0,0.366900528814,0.135800869087,0.273531315813,0.143243243243,0.094701986755,0.0,0.2,0.0,0.0,0.29944547465,0.555906810973,0.600558873622,0.607671556364,0.519113329669,0.496850400374,0.477085565744,0.494559590381,0.495814975555,0.0568985560875,0.0445013887337,0.0358881784547,1.04631534997e-102
-1441889786044,408735,34683497,LABSPLAC,2013-05-15 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961979822707,0.977360559416,0.977318365183,0.978212867981,0.98646775749,0.986182202112,0.985628878715,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.633531218038,0.534854770966,0.519649733325,0.957599678038,0.988798010818,0.995137899418,4.1893244132e-05
--9923810162108,427712,39606417,LABSPLAC,2013-11-10 03:45:00,0,0,1,0,0.862745098039,1.0,1.0,0.971312316063,0.853989176147,0.845665360898,0.976893535067,0.686018551341,0.904056436856,0.593818933476,0.892925211361,0.758688408438,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.609664203086,0.555234613012,0.563965162064,0.523939192795,0.510798087023,0.810357688464,0.797069200011,0.823755345999,1.27903954067e-49
--10367188704482,405838,40708437,LABSPLAC,2014-01-07 04:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.957920941591,0.975380835714,0.975640843332,0.97534234208,0.978093446978,0.987872137312,0.98372730729,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.702047090632,0.647214985244,0.692303290098,0.600015873614,0.953175231778,0.926387040445,0.886256648454,2.50457712294e-07
-10804490185166,369494,38795087,LABSPLAC,2013-11-03 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.958514016604,0.956994502276,0.977655342441,0.975640843332,0.96833494863,0.987424952203,0.984658980107,0.949562234678,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720604350551,0.701519470746,0.659751167278,0.698092395741,0.60886185383,0.978249090084,0.979458746722,0.971135911261,3.07311820848e-18
--10110890094159,424827,39436869,LABSPLAC,2013-10-31 22:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.966584143873,0.886875936666,0.961650401689,0.902707199093,0.901013432395,0.990822510823,0.953853736441,0.91864503546,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.550625843053,0.5656089288,0.509276132783,0.509253819038,0.984849649548,0.999658574729,0.999999926749,1.0
-9958092196787,436618,37644786,LABSPLAC,2013-09-07 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.957469420781,0.977360559416,0.974040690688,0.97151961646,0.983642630155,0.987261750327,0.983550383714,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.623030309201,0.564139103403,0.59010421833,0.524836274648,0.515526174452,0.992588909427,0.99850365831,0.999413505783,1.0
-6471129852062,449133,35412826,LABSPLAC,2013-06-14 17:01:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.7606334943,0.867510407878,0.922814052702,0.977843148052,0.971696050374,0.879398921574,0.793155145841,0.881979088265,0.80570731973,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64507987816,0.588239093894,0.62492509043,0.514622303882,0.508680408418,0.959660778014,0.945501005154,0.932330119511,2.83963343709e-34
-4875363218674,399000,35802100,LABSPLAC,2013-06-23 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.956424368306,0.975138921301,0.971851018469,0.973002213861,0.980556022475,0.982202710183,0.983386868834,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614482894179,0.573233375963,0.59558486573,0.521771492619,0.513081176056,0.966491787106,0.982065781479,0.990412531487,1.3861843015e-08
-1397324513837,419726,31339303,LABSPLAC,2012-12-27 20:26:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961979822707,0.979876980557,0.977318365183,0.978679411873,0.98646775749,0.987134443821,0.982853562492,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.657810096871,0.590086824944,0.592186262416,0.519637890785,0.51103682888,0.993963013798,0.998070152514,0.998890015622,1.0
--1709013739655,454136,35691056,LABSPLAC,2013-06-12 05:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.9570458202,0.975380835714,0.972194889706,0.972701808088,0.978093446978,0.987872137312,0.984380902061,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.687419328916,0.604840108006,0.618554100768,0.532448831591,0.519520417179,0.98973708163,0.99646299469,0.99835933131,0.0176119783699
--6426223764696,471788,38318038,LABSPLAC,2013-09-25 16:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.980117654779,0.967935630982,0.941650241716,0.981875769026,0.883822128568,0.967103166497,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.621957209829,0.555432328519,0.547402931986,0.50059089319,0.501494169367,0.96023480041,0.960548068025,0.952906851388,0.00280338948048
-11949841524685,467889,35912345,LABSPLAC,2013-07-06 17:00:00,0,0,1,0,0.931034482759,1.0,1.0,0.859554216133,0.728060348665,0.756386850998,0.882211538462,0.947999040544,0.82673416096,1.0,0.8976,0.863747145705,0.917871594643,0.868594437999,0.748823611729,0.759366267781,0.5397508178,0.517026221965,0.506203008646,0.50018913093,0.499398773095,0.744044402527,0.754220802196,0.796830302864,6.31238259081e-49
-9046333051324,438915,30830636,LABSPLAC,2012-12-05 21:56:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.946171670161,0.933812322216,0.982200257785,0.969772973451,0.968876090417,0.994892986589,0.881419611945,0.985490797057,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.612260194273,0.55833208907,0.518602050471,0.505347133992,0.509094235616,0.970442548136,0.98440007541,0.98745784466,3.48510193594e-18
--10110890094159,424827,39382337,LABSPLAC,2013-10-29 10:01:00,0,0,1,0,0.968845448992,0.0,0.0,0.540992063912,0.727679451111,0.73684806456,0.559495222495,0.501164972933,0.595158769296,0.2,0.396613758887,0.338022032512,0.917871594643,0.848330419257,0.746770706044,0.7551686106,0.562832183202,0.525106881021,0.491585873363,0.494548831329,0.501731455132,0.634149271014,0.562382725054,0.459156145028,4.18233635793e-52
-7064184007833,415546,31013257,LABSPLAC,2012-12-11 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.978111020545,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.62526920698,0.654219968234,0.545149321481,0.532462837225,0.990485548716,0.996449880054,0.998077559315,1.0
--9412383105912,385375,33511091,LABSPLAC,2013-03-06 04:27:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.979876980557,0.977318365183,0.978111020545,0.990842372401,0.985303412661,0.982202579264,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.523286663342,0.505518255605,0.998188450758,0.999990889421,0.999999999908,1.0
--11578123844433,405352,37843818,LABSPLAC,2013-09-10 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.947560696441,0.984957353149,0.962678618037,0.970463588646,0.987424952203,0.977979333885,0.978439508601,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640080003066,0.57256122193,0.577760550212,0.517186528197,0.511013757637,0.97938142048,0.992061143348,0.995065325628,0.0118047603909
--11699102797175,405894,36557683,LABSPLAC,2013-07-16 06:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.639399789797,0.899000795905,0.919883355688,0.755180188987,0.972132875169,0.861436458734,0.968251592489,0.887446672698,0.914171341451,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.687851271872,0.610439856182,0.631528916061,0.712183720171,0.608698928435,0.917353679769,0.982037203936,0.998884729136,1.06359348767e-36
--8250624183794,452124,31803869,LABSPLAC,2013-01-04 23:45:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953029271352,0.975380835714,0.977318365183,0.975818750277,0.982468061889,0.983594039937,0.98388143277,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.524321027621,0.514609883101,0.995596774483,0.998495671673,0.998666853519,8.22824881662e-12
--7970765028213,461334,36270717,LABSPLAC,2013-07-05 08:00:00,0,0,1,0,0.222222222222,1.0,0.0,0.809973859325,0.893615097141,0.885184826289,0.922386060371,0.783120499207,0.858670657993,1.0,0.682750092441,0.790160123182,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.590931260206,0.537138224499,0.547402931986,0.508351270202,0.502037062961,0.98881887202,0.999636364598,0.997359580293,1.15714741967e-224
-1211252491153,397344,37740067,LABSPLAC,2013-09-09 01:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.96072858502,0.954912929474,0.972864414573,0.972194889706,0.968676402829,0.978093446978,0.987872137312,0.985784276378,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.611680688776,0.647214985244,0.536838593651,0.519909625069,0.994879606336,0.999846294047,0.999999992667,1.0
--2088388302702,421199,32714912,LABSPLAC,2013-01-27 09:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962171774843,0.979876980557,0.97863298675,0.975373544015,0.98646775749,0.985444508621,0.984747636583,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.654219968234,0.539240204062,0.524796772796,0.948798635889,0.950311444489,0.949175861315,0.999999955458
-10804490185166,369494,38736148,LABSPLAC,2013-11-02 06:20:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.950792161485,0.956994502276,0.977655342441,0.977318365183,0.942829345859,0.987424952203,0.884658980107,0.985218649364,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.694367409419,0.602362087065,0.633531218038,0.528365289874,0.52289129087,0.978986686893,0.983987238232,0.980059898107,2.60793068903e-17
-916300381238,392895,39592108,LABSPLAC,2013-11-28 22:27:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.943768134324,0.975380835714,0.973451171113,0.97534234208,0.978093446978,0.987872137312,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.684000860403,0.598709663547,0.672961290111,0.546840797349,0.523815339145,0.984882266237,0.993094663904,0.99694532339,1.0
--303735670891,460663,32671804,LABSPLAC,2013-01-27 20:52:00,1,0,1,0,0.0,0.0,0.0,0.400816268111,0.319360807238,0.411574626162,0.192,0.389772727273,0.244444444444,0.6,0.2,0.332853717026,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.488081083,0.477368850327,0.448319649051,0.489681121499,0.490041759005,0.127434670057,0.117119673469,0.067077993835,8.43091581702e-128
-718070713902,362515,37633696,LABSPLAC,2013-09-15 21:46:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.963029411336,0.979876980557,0.977318365183,0.977123287528,0.986337665616,0.987314303318,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.521328174824,0.514354303997,0.905437747665,0.891045290805,0.906464971588,1.0
-3818618981737,391980,29929494,LABSPLAC,2012-10-29 01:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.90839971117,0.963776230663,0.954974536524,0.980320863174,0.955873591764,0.908569629588,0.989768319288,0.978487177007,0.937559289866,0.917871594643,0.848148017695,0.800194633637,0.829616127665,0.62070030156,0.564213065087,0.592186262416,0.517757246599,0.508440123891,0.852767179976,0.920995750519,0.945098943786,1.04266636745e-08
--850101470532,462849,30959911,LABSPLAC,2012-11-26 02:01:00,0,0,1,0,0.733333333333,0.0,1.0,0.901961213162,0.960368160964,0.84091137562,0.988733801086,0.97058120011,0.757789847947,0.992463354749,0.992709278429,0.827532776193,0.29944547465,0.675681365788,0.744293461711,0.722764711653,0.592343686689,0.546762939906,0.507919851378,0.503042910651,0.500694542894,0.748231654218,0.708479053856,0.717238786548,5.57565953072e-30
-249520416257,376187,35271937,LABSPLAC,2013-05-29 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.984716678926,0.977318365183,0.9737958771,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.727295677823,0.726710603152,0.690773668204,0.538818765468,0.519721602515,0.990578404845,0.996207014575,0.998056065454,1.0
--8845434657442,457568,36287046,LABSPLAC,2013-07-13 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.961117893588,0.979876980557,0.977318365183,0.97842594341,0.980417361578,0.983815338206,0.953019265064,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.628648993443,0.620360663031,0.694841556124,0.601496815136,0.964754810746,0.981069845734,0.986477225204,9.13340418963e-19
--4546926499538,439851,36344772,LABSPLAC,2013-07-09 19:10:00,0,0,1,0,0.0487804878049,0.0,0.0,0.43913343966,0.532144770247,0.6606296347,0.172727272727,0.364130972771,0.389461083402,0.583295514894,0.599264705882,0.3,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.558899522507,0.534688978998,0.50644065596,0.505888528973,0.502317108057,0.44390266584,0.394636846888,0.299566750489,3.43231027711e-87
--632048012389,431979,31010591,LABSPLAC,2012-11-26 16:37:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.977595766237,0.788274209103,0.886078804333,0.951666188489,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.63759271501,0.654219968234,0.547193008178,0.533401350832,0.992956778006,0.998062161552,0.999794874606,1.0
-1170014865359,464876,39438682,LABSPLAC,2013-11-30 13:25:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.88336980542,0.892558506867,0.910406991656,0.935470525682,0.943515689216,0.942444513586,0.988607351173,0.987935940165,0.91692561197,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.639082053926,0.581193715979,0.575657631795,0.531782166456,0.515116538033,0.900267700822,0.865658014915,0.833377130776,5.75760950741e-31
-6211620991328,424859,40889223,LABSPLAC,2014-01-31 06:00:00,1,0,1,0,0.777777777778,0.0,0.0,0.702190672003,0.771236983726,0.658345681434,0.580555555556,0.557847947072,0.670832904621,0.4,0.319104477612,0.396098358353,0.917871594643,0.79514940545,0.728887498451,0.738632887801,0.542337221297,0.515090293643,0.520271360095,0.498255586681,0.496739641135,0.218540693532,0.257483442583,0.399314022229,5.33762186367e-50
-7272359611563,439998,31120795,LABSPLAC,2012-12-18 13:00:00,1,0,1,0,0.0684931506849,0.0,0.0,0.0993172119488,0.325106472079,0.227755447463,0.0,0.1,0.233333333333,0.2,0.1,0.199233716475,0.29944547465,0.571725460405,0.627646648295,0.632789619551,0.518154427083,0.487381745835,0.464190909068,0.483021339991,0.492717300545,0.0438246621359,0.0300207678604,0.0236081704016,3.45202702183e-97
--5258348095994,457342,31519318,LABSPLAC,2012-12-16 03:00:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.965125374887,0.952763005662,0.960196711689,0.977655342441,0.981681945999,0.944362770234,0.981394008776,0.987761581791,0.980196222566,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635251802724,0.649893578103,0.577760550212,0.513539805366,0.511488921453,0.876238772267,0.91141959614,0.86671260751,1.0
-2905422553771,441621,34130994,LABSPLAC,2013-04-20 22:20:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.5097531433,0.966845176545,0.976604544498,0.983563668159,0.0404057789645
--11718423220126,452229,31603946,LABSPLAC,2012-12-11 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960864443511,0.984716678926,0.977318365183,0.978203003675,0.98646775749,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.62109640782,0.654219968234,0.538275974663,0.51998483321,0.982504461132,0.991178627213,0.992812507975,0.0061431584069
-7449569794204,405817,37358169,LABSPLAC,2013-09-06 01:00:00,0,0,1,0,0.968845448992,0.0,0.0,0.903725769945,0.938963869042,0.932033698637,0.935551271494,0.973493819332,0.931773418251,0.985657408285,0.981825041193,0.947798633038,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.637029866259,0.571318403676,0.592186262416,0.524988787651,0.514145877515,0.957123105933,0.982897291282,0.991707273141,3.50710916113e-05
-3175022992398,435053,40863814,LABSPLAC,2014-01-30 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961118722075,0.984716678926,0.977318365183,0.978269870876,0.98646775749,0.989686653441,0.949160457642,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.633360371236,0.672961290111,0.702615183322,0.608611302737,0.997326368585,0.999206417465,0.999706591989,0.997695976906
-88250944256,362525,35958339,LABSPLAC,2013-07-04 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.876749226838,0.908150784741,0.93057682575,0.903512878115,0.948699064459,0.947543605335,0.788607351173,0.987794844206,0.987263665151,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.691305522813,0.685308973123,0.618554100768,0.698978333191,0.60556545741,0.98347993577,0.98857495748,0.990492472029,0.000169843901776
-8584413457569,472045,39722963,LABSPLAC,2013-11-28 06:55:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.961979822707,0.975380835714,0.977318365183,0.975352206385,0.984998208883,0.985303412661,0.983296671744,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.568841800947,0.619588095117,0.526312926947,0.515653809867,0.896914148985,0.869762259027,0.886941734107,1.0
--10856513659214,397319,39680291,LABSPLAC,2013-11-11 06:19:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.979144355379,0.98646775749,0.986162764587,0.983805241333,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.712475545141,0.647214985244,0.69959245033,0.605270288077,0.991083740655,0.996499603413,0.997883890127,0.981599270708
--8250624183794,452124,31888490,LABSPLAC,2013-01-05 02:23:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953029271352,0.975380835714,0.977318365183,0.975818750277,0.982468061889,0.985303412661,0.98388143277,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.654219968234,0.537501185913,0.526263209297,0.997832043177,0.999447188575,0.999598930234,3.12134672479e-13
--11337429808024,451040,37394621,LABSPLAC,2013-08-16 13:52:00,1,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.954456474071,0.975380835714,0.877220015334,0.97600504802,0.978093446978,0.983594039937,0.788507034016,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.578510640512,0.577760550212,0.514043891176,0.505901990829,0.892135417236,0.830144731172,0.787883349964,1.0
--8118296954482,403532,36797930,LABSPLAC,2013-07-31 15:36:00,1,0,1,0,0.777777777778,1.0,1.0,0.569319219593,0.804805899334,0.794649230331,0.705555555556,0.911897700792,0.876143932901,0.976941154968,0.792966989635,0.854378815973,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.542145840327,0.530989280054,0.50644065596,0.521501689304,0.513115898645,0.944080532671,0.978572049149,0.988648092713,3.69517182348e-19
-7514361231910,392171,37064430,LABSPLAC,2013-08-13 15:11:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959804728644,0.979876980557,0.977318365183,0.977271516277,0.979598827762,0.987872137312,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.702021294663,0.633531218038,0.694963747273,0.601263881105,0.974696019513,0.976667119083,0.977251121079,1.11502412732e-06
--2201739919731,460021,37605715,LABSPLAC,2013-09-01 04:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.982200257785,0.977318365183,0.978111020545,0.98646775749,0.985303412661,0.982904970683,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.532248141381,0.518277567909,0.992045550822,0.997436238407,0.998680372909,0.999926506103
-11807485464320,392182,35422426,LABSPLAC,2013-06-12 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960895571425,0.972864414573,0.975800883873,0.975419350033,0.983642630155,0.987872137312,0.983654330458,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.708012514523,0.694180907142,0.604438079785,0.517180256766,0.510105255553,0.973019018263,0.987672171394,0.993488826794,1.56370174392e-10
-10041568164274,362713,40654287,LABSPLAC,2014-01-09 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978111020545,0.98646775749,0.987872137312,0.984588546078,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.711397807195,0.647214985244,0.697922018153,0.605243150801,0.996200062906,0.999327127014,0.999768552763,1.0
-787045135963,422546,35921435,LABSPLAC,2013-07-04 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.881274213601,0.933187173692,0.862934068062,0.815837155648,0.954620733577,0.951600262157,0.975856096198,0.779762564074,0.855429512109,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.617585769497,0.568793720064,0.522421043247,0.52566238913,0.516735830945,0.99243705037,0.999055546904,0.999682955257,1.0
--2701082086849,471749,34470467,LABSPLAC,2013-04-22 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953553978616,0.77833851037,0.88503830197,0.975971510691,0.986943191953,0.987872137312,0.953367335946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.732288665718,0.72390416952,0.690773668204,0.710038908786,0.614030640354,0.995679275155,0.998884968973,0.999422341769,7.91562545605e-07
-4218306340706,458041,34400276,LABSPLAC,2013-04-21 10:26:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961905190403,0.982495040811,0.977318365183,0.976570595476,0.98646775749,0.988881168712,0.982051229228,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.50828489447,0.502291402668,0.982687340834,0.99337150259,0.994448208412,7.76404872293e-05
-11020449699171,400266,37349582,LABSPLAC,2013-08-26 10:19:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982200257785,0.977318365183,0.978530874702,0.987424952203,0.986475159488,0.985654467065,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.69722322618,0.610057279233,0.633531218038,0.527695413664,0.516958190153,0.955017963684,0.969538878841,0.979834492854,1.29036354974e-08
--5580891486963,413552,33624634,LABSPLAC,2013-03-15 02:00:00,1,0,1,0,0.733333333333,0.0,1.0,0.343196361104,0.520316671119,0.529413840676,0.395098663272,0.174929078014,0.492153158322,0.4,0.282463801425,0.353302676563,0.29944547465,0.571725460405,0.657222982014,0.675364081911,0.557763282321,0.511094702958,0.478997794639,0.480920895657,0.489645355282,0.319561840372,0.176989525111,0.0812262753991,1.33394471144e-169
-2829300800463,391631,35612516,LABSPLAC,2013-06-27 20:21:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960864443511,0.975380835714,0.977318365183,0.976872681757,0.98646775749,0.985303412661,0.984932809326,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.574949418439,0.592186262416,0.508075270509,0.504290047219,0.948795503559,0.961219224999,0.961123488061,0.140354400341
--4546926499538,458195,30241901,LABSPLAC,2012-11-04 18:07:00,0,0,1,0,0.862745098039,1.0,1.0,0.80804068435,0.765700881405,0.781385346889,0.794525904203,0.877148426622,0.807933895065,0.985455893564,0.89642224395,0.762037908176,0.917871594643,0.825369867954,0.719762276307,0.708319280651,0.537553332856,0.514303200747,0.533416685648,0.522293164745,0.519615734884,0.919104602689,0.963721994402,0.977303114337,4.28927438951e-108
--5056241930934,387589,34919232,LABSPLAC,2013-05-05 16:50:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963014748944,0.984716678926,0.979617215758,0.978212867981,0.990842372401,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.582956680649,0.592186262416,0.522324754451,0.514008590586,0.99035894166,0.996428336463,0.997920919746,1.27191980147e-19
--11167356309352,431499,38164685,LABSPLAC,2013-09-18 09:35:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.979876980557,0.977318365183,0.977831835191,0.987424952203,0.982075403677,0.984194772294,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640080003066,0.570043604834,0.577760550212,0.525800435783,0.516738755547,0.941092322224,0.959565852761,0.962267226263,1.65071450836e-07
--4497027042019,471968,40560299,LABSPLAC,2013-12-25 08:16:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.978111020545,0.98646775749,0.983594039937,0.983532111931,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.573599266539,0.592186262416,0.520131027002,0.510248985637,0.967114641702,0.982239629059,0.98561577265,0.999999999997
--9937798449183,453771,32833943,LABSPLAC,2013-01-30 18:33:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.935462936763,0.972864414573,0.977318365183,0.975352206385,0.780788482439,0.986182202112,0.951386141673,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.768113956163,0.739204244971,0.705047283599,0.537302562945,0.525025885174,0.995634403893,0.998289829871,0.998505990962,1.0
--568174095654,354850,34104432,LABSPLAC,2013-03-31 21:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.933454704065,0.928943677004,0.987687489104,0.978711259617,0.956085709353,0.990518371679,0.980811911499,0.955080500786,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.614956442423,0.550166318372,0.549300938986,0.505193678539,0.500708053852,0.95609054344,0.9839389866,0.987818732523,2.12823358858e-06
-3673537304856,467340,38492100,LABSPLAC,2013-10-17 22:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.956180654547,0.972834655213,0.972071396748,0.976877362397,0.98646775749,0.987999443818,0.983892169658,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.702021294663,0.647214985244,0.697392372634,0.604981888916,0.990735845558,0.996579132178,0.997985364416,0.607146243156
-10291686566372,358471,32886804,LABSPLAC,2013-02-20 18:03:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.956160864127,0.963776230663,0.951727496532,0.984716678926,0.871696050374,0.946527926237,0.987823336218,0.987407823359,0.954321169661,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.648566355838,0.601779785104,0.62492509043,0.524519150864,0.515144678327,0.990562873552,0.994130965538,0.994756641015,2.16564725597e-05
-10651616431569,371633,34650049,LABSPLAC,2013-05-02 17:24:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.955318725429,0.975380835714,0.975128692964,0.976841944131,0.980918574313,0.983594039937,0.983380761894,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.631999854287,0.566181943572,0.592186262416,0.518311738527,0.509438595755,0.955974822362,0.97275083159,0.974692306442,0.00131576875022
-11729522955909,385454,37653429,LABSPLAC,2013-09-09 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961107767351,0.982495040811,0.975128692964,0.977707812351,0.987424952203,0.984106493919,0.984273681162,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710577109817,0.61863064688,0.633531218038,0.528336864015,0.515400116553,0.948401449964,0.964424723148,0.973721302859,0.00848975675877
-11022992441918,386947,35181156,LABSPLAC,2013-06-06 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.976882546062,0.98646775749,0.983613477461,0.982853562492,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.572313453689,0.619588095117,0.525575706317,0.515108618133,0.987852457873,0.995334553924,0.997656428659,0.99999938329
--6222025060253,449808,36834528,LABSPLAC,2013-07-20 10:20:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.950792161485,0.957856431394,0.982200257785,0.977318365183,0.975747379769,0.983642630155,0.983594039937,0.984868641009,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.535244081356,0.520160694468,0.914338493052,0.919248601202,0.939072009576,0.000355848265623
--11840799707174,413360,31169747,LABSPLAC,2012-12-03 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.906021709044,0.963029411336,0.979876980557,0.977318365183,0.978111020545,0.98646775749,0.985303412661,0.982904970683,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.654219968234,0.526643264546,0.520627938052,0.984260040789,0.991140106122,0.994088475418,1.0
-399257514207,380119,29969662,LABSPLAC,2012-11-06 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.956160864127,0.951059264567,0.908037729944,0.970068100082,0.860425716009,0.982753441586,0.993818933476,0.98889269924,0.922765142596,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.696972889555,0.612509623983,0.640657052947,0.528331742944,0.525458881755,0.97391671278,0.988838385638,0.992377984853,4.08742669056e-19
--1437955700742,370770,31132595,LABSPLAC,2012-12-03 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.943260181545,0.982200257785,0.975128692964,0.977274908554,0.976773700428,0.985303412661,0.984742671558,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.688245866259,0.612349068589,0.654219968234,0.537603919214,0.525374617876,0.990574138643,0.994803641737,0.996723543019,3.38300130367e-23
--7214175442709,385309,34899293,LABSPLAC,2013-05-06 03:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960085909416,0.977704112943,0.975640843332,0.97572337487,0.978093446978,0.987872137312,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.708312606006,0.647214985244,0.692123839223,0.604788054976,0.994300955607,0.999269795825,0.999669312476,0.999978748433
-4035289185680,449684,34600714,LABSPLAC,2013-05-09 03:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978773186895,0.983642630155,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.625843002933,0.672961290111,0.705214294702,0.608179456494,0.983227426649,0.993641061095,0.996666270248,0.99999992393
--10110890094159,424827,39346278,LABSPLAC,2013-10-27 10:01:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.886448688078,0.844372972084,0.866789635075,0.574038330211,0.862357450012,0.833030461815,0.977670557207,0.789354323257,0.750769593675,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611826982836,0.552464139177,0.547402931986,0.513573963886,0.506988595386,0.441141473152,0.306101246271,0.323468485887,3.55517443328e-34
--5580891486963,413552,33617742,LABSPLAC,2013-03-14 14:00:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.538723730931,0.760183669839,0.59448835486,0.4,0.471880118765,0.384485668414,0.6,0.478917331315,0.329010595109,0.917871594643,0.834448628119,0.788388808379,0.797475809346,0.603795248512,0.537490093226,0.563965162064,0.494622105043,0.499321393289,0.396852602585,0.254772888442,0.0873290085385,7.4675826211e-168
--10110890094159,424827,39399051,LABSPLAC,2013-11-01 22:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.887975756893,0.8952439207,0.919497560241,0.855606427769,0.864286434793,0.973203918888,0.952913689162,0.882916735731,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.550625843053,0.5656089288,0.506988174547,0.507490841135,0.938663060228,0.990437724931,0.998054353155,5.85507762252e-42
--11812180265597,454298,38550150,LABSPLAC,2013-09-28 18:12:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.933401487966,0.775182384303,0.920383027387,0.947773490029,0.972053826977,0.946863084262,0.947430643397,0.79204991074,0.979983810719,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.618732847441,0.660486685205,0.609812874259,0.524704236596,0.513493841185,0.993403578757,0.999017498725,0.999766733137,1.0
-11764047290206,470019,34746451,LABSPLAC,2013-05-21 21:37:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.958583133096,0.975380835714,0.972194889706,0.976162462119,0.984998208883,0.983613477461,0.984006560496,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650048755741,0.573610638517,0.619588095117,0.519710482799,0.513228273313,0.980519319999,0.983819842945,0.987610510496,1.14076553423e-07
--11584622773005,405361,36138734,LABSPLAC,2013-06-20 21:56:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.901449019404,0.9241217929,0.975115087552,0.936251440351,0.924011881087,0.981875769026,0.978047125124,0.988214566721,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.609521848461,0.553903485855,0.522421043247,0.512283740283,0.509873849841,0.912943359408,0.944308299323,0.965898815919,1.89083907889e-14
-3673537304856,419299,39842699,LABSPLAC,2013-12-10 14:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.989703168472,0.985814051458,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.709884587509,0.633531218038,0.688417279551,0.602118285636,0.974027473508,0.97681587512,0.984959981122,9.04202739225e-07
--10110890094159,424827,39361478,LABSPLAC,2013-10-27 16:00:00,1,0,1,0,0.968845448992,1.0,1.0,0.698024805156,0.757313496311,0.727639202014,0.574038330211,0.665598983194,0.6227483802,0.592214663643,0.488208245493,0.649303846709,0.917871594643,0.861909385821,0.797509386323,0.786236058484,0.590406212604,0.539043379197,0.562079676942,0.516542446919,0.508473832433,0.54952287845,0.509727238464,0.690837625855,3.7793793732e-42
-10203366501284,364684,32800148,LABSPLAC,2013-02-17 22:03:00,0,0,1,0,0.968845448992,0.980986142443,0.962962962963,0.981142135723,0.828882308161,0.824534388162,0.988567749073,0.983653735886,0.978194447252,0.787988224685,0.883871437611,0.923976931646,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.627178182307,0.570190312363,0.549300938986,0.513932468129,0.511554443755,0.961202131957,0.972366457242,0.977375778575,1.0
-10041568164274,362713,40673062,LABSPLAC,2014-01-08 17:23:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984957353149,0.967935630982,0.97559635083,0.987424952203,0.98585321881,0.981135348232,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723312699125,0.705072200557,0.633531218038,0.691698111248,0.60328332032,0.995205948852,0.999149176023,0.99969771908,1.0
--7970765028213,461334,36399275,LABSPLAC,2013-07-09 20:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.898425659254,0.912243207861,0.95695058108,0.946572985431,0.943067605737,0.981738421445,0.924839464893,0.976415797773,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.548223384432,0.547402931986,0.508989675506,0.505943058135,0.887204486103,0.950083037746,0.970961667755,2.41389896698e-38
--4716787243805,406354,36353341,LABSPLAC,2013-06-28 22:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.876749226838,0.889372204717,0.920241355292,0.960564234415,0.954642581789,0.946192975101,0.98452571852,0.988955111152,0.980834715493,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.603296075066,0.552796849343,0.562079676942,0.522325237318,0.514516049702,0.990058166999,0.997587387497,0.998413188836,1.0
--9320996611060,399817,33595744,LABSPLAC,2013-03-12 20:15:00,0,0,1,0,0.968845448992,0.666666666667,1.0,0.972100498572,0.864762470872,0.916816735008,0.786512162971,0.975640843332,0.880418723071,0.58516309735,0.889724005607,0.851275528985,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.591041604305,0.577760550212,0.524245476229,0.502049757567,0.885007186305,0.93153613923,0.966808764625,2.7991653876e-205
--8073722494891,422045,40446572,LABSPLAC,2013-12-16 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.973375973804,0.970590612686,0.978093446978,0.982985777753,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.625743900882,0.562978094351,0.562079676942,0.516277354933,0.506389192127,0.809324462765,0.567534449288,0.448763755367,1.0
-4037696471372,447028,38430055,LABSPLAC,2013-10-21 03:19:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.910007784073,0.933690065444,0.931516399984,0.977360559416,0.871815524511,0.913887958719,0.985779894286,0.787711554185,0.950947184335,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.555946404359,0.547402931986,0.515654287974,0.509915013615,0.9957327133,0.999398943233,0.999914310183,7.12840089388e-29
-12440866954862,460768,29771144,LABSPLAC,2012-10-26 16:49:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978111020545,0.98646775749,0.983594039937,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.520264636801,0.509084042528,0.925614311692,0.930574930048,0.928218577155,0.0783573943793
-9908982610231,388164,33461238,LABSPLAC,2013-03-16 21:29:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962874243812,0.979139453002,0.981576697658,0.976626220536,0.983973442673,0.985303412661,0.984742671558,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691360310274,0.621389963092,0.654219968234,0.545065865337,0.528864935293,0.999677607418,0.999998181406,0.999999993373,1.0
-6967509780721,361263,37046957,LABSPLAC,2013-08-14 00:36:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.982442076471,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.608907125667,0.633531218038,0.528735935957,0.515496665149,0.970648503139,0.979101384682,0.988040045602,0.969684951025
--8073722494891,422045,40483899,LABSPLAC,2013-12-16 18:28:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.973375973804,0.970590612686,0.978093446978,0.981276405029,0.981992323703,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.625743900882,0.562978094351,0.562079676942,0.516277354933,0.50867889907,0.893684061003,0.88070423972,0.882385027741,1.0
-3907559420442,440290,34227043,LABSPLAC,2013-04-24 22:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.972100498572,0.963776230663,0.953846974237,0.97202185973,0.879277847083,0.97236012434,0.977592234243,0.986038891336,0.981886461538,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691348845041,0.612911499189,0.627592053327,0.533827114491,0.524267605905,0.98364772984,0.994224530016,0.997359237438,0.000830582738543
--152589643420,363538,29088608,LABSPLAC,2012-09-03 12:00:00,1,0,1,0,0.0684931506849,0.0,0.0,0.364377723971,0.128732702483,0.182700876459,0.392,0.3,0.167965367965,0.2,0.2,0.233333333333,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.479274604199,0.471194627368,0.433671722659,0.491494036406,0.502748090622,0.0152489695979,0.00431807684431,0.00395605982208,0.0
--11830271770917,428057,40332390,LABSPLAC,2013-12-16 13:40:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.516974920737,0.508675372968,0.893321333266,0.886402430619,0.877279638364,0.000787457742468
--5848373338376,443537,39909612,LABSPLAC,2013-11-23 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.955389411963,0.972864414573,0.972194889706,0.977298693381,0.976773700428,0.983613477461,0.984932809326,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.698593432958,0.624005423446,0.690773668204,0.549567577142,0.539085251739,0.989580442918,0.998481969556,0.999638491537,0.999999999508
-2766881279042,429993,35840635,LABSPLAC,2013-06-22 21:00:00,0,0,1,0,0.777777777778,1.0,1.0,0.558362329494,0.549961624849,0.7177081988,0.886402329749,0.625687777529,0.765756259991,0.796282527881,0.414285714286,0.629893976133,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.542145840327,0.521663183721,0.53508067757,0.504814854475,0.501833427868,0.672595543626,0.655599736471,0.696702568291,3.10965032367e-37
-10678434761668,454392,38368781,LABSPLAC,2013-10-10 18:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.955319115348,0.97833851037,0.974040690688,0.963973057749,0.98646775749,0.985430719167,0.98313739986,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.651923394766,0.581830407838,0.59010421833,0.520569951316,0.511690931533,0.87494766504,0.82210746209,0.79144916318,4.80257219465e-06
-7034207198327,354612,31670393,LABSPLAC,2013-01-05 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.982495040811,0.977318365183,0.977137629132,0.988274209103,0.987004703075,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.773195994132,0.748797528464,0.69254108076,0.71003487914,0.609867198152,0.986766800584,0.993346728603,0.995422743962,0.998269036592
--10414553693787,442544,39566310,LABSPLAC,2013-11-15 05:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.903725769945,0.963776230663,0.960256792956,0.978733764295,0.97363437929,0.927966617076,0.958342743,0.88410975361,0.916085141511,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.70573329382,0.612894317487,0.645245582584,0.696111828062,0.607425996101,0.972612108357,0.97698911141,0.973484663081,0.000331224835075
--7268118793014,402034,39955183,LABSPLAC,2013-12-01 17:13:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.931886593608,0.955707523205,0.92838352053,0.977360559416,0.951450535987,0.975829537404,0.988274209103,0.983113084259,0.983934951385,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.614590518188,0.559644112287,0.562079676942,0.522084275317,0.515800850907,0.995379349452,0.999055492821,0.99949174835,3.26640414987e-07
-1647082873019,364844,34231942,LABSPLAC,2013-04-23 06:37:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.916963131802,0.917435382831,0.936309307939,0.948261270068,0.957699878004,0.943792347335,0.97483365954,0.989498890332,0.98039229034,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.556247867948,0.562079676942,0.518958412727,0.508716009547,0.967934701039,0.989358741332,0.993633349943,0.00433998589205
-2369717193141,370555,33235979,LABSPLAC,2013-03-07 20:00:00,0,0,1,0,1.0,0.0,1.0,0.277373203211,0.159242451132,0.0911974850879,0.0,0.1,0.133333333333,0.2,0.3,0.0666666666667,0.29944547465,0.571725460405,0.599007250207,0.601097617532,0.483495716415,0.466824512947,0.433671722659,0.489668967501,0.490749709407,0.0398113011831,0.0265885307488,0.0289057830344,0.0
--10559560539328,470441,35503457,LABSPLAC,2013-05-31 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.955882500902,0.961061333855,0.975138921301,0.958305142752,0.976571660405,0.983642630155,0.987018851612,0.984900690096,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.689155028425,0.595875684892,0.633531218038,0.531132248555,0.516438995698,0.991362582914,0.998281958413,0.999626309713,0.000124728082745
--8577307749831,398854,39248534,LABSPLAC,2013-10-31 14:01:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.876749226838,0.8990774847,0.893656351365,0.948261270068,0.955806859806,0.937967706877,0.788607351173,0.990784641711,0.947970627569,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.600062511188,0.546728630403,0.547402931986,0.513998695512,0.509119369661,0.889508150103,0.925141669428,0.95840533212,1.23782991766e-31
-3825867214697,471544,33866725,LABSPLAC,2013-04-06 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977601233639,0.967935630982,0.975131407324,0.987424952203,0.889084986813,0.947867592393,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.619989672949,0.640657052947,0.525519304112,0.522024177805,0.976558671386,0.98336543493,0.986600808961,3.42447688692e-09
-8636559223052,434865,31905533,LABSPLAC,2013-01-20 13:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.95365513447,0.961043261284,0.928806119669,0.97863298675,0.976138862407,0.982725025926,0.987050137552,0.985920236671,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.654366452658,0.674852641907,0.577760550212,0.533039061828,0.524247135518,0.999318280819,0.999941799102,0.999982297656,1.0
--8250624183794,452124,31795172,LABSPLAC,2013-01-05 06:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953029271352,0.975380835714,0.977318365183,0.975818750277,0.982468061889,0.985303412661,0.983230449542,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.654219968234,0.537501185913,0.524950010819,0.997628150861,0.999323601253,0.999508651328,1.60606898945e-10
-3907559420442,440290,34741227,LABSPLAC,2013-05-14 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.962187415028,0.946674134622,0.952973093059,0.977704112943,0.969871680018,0.963639911926,0.978643393273,0.982332658706,0.982689144925,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.607757473961,0.557202159875,0.562079676942,0.518193292682,0.509393197591,0.954272086547,0.969628744589,0.979437482171,0.000434251781904
-1543025640681,452637,39259507,LABSPLAC,2013-11-14 10:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959804728644,0.977655342441,0.977318365183,0.977137629132,0.987424952203,0.984106493919,0.985076014426,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713343992298,0.697682022519,0.633531218038,0.547215104307,0.528281664668,0.995691533929,0.998967685823,0.999595850302,1.0
--5948137137711,380122,30909495,LABSPLAC,2012-11-15 14:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.958947092151,0.975380835714,0.683905978334,0.97139298781,0.970723304516,0.980935997309,0.987138819559,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.667732403808,0.593832784722,0.583965252868,0.53685407599,0.520612722043,0.965948945099,0.990557417859,0.997557839071,4.98806114302e-31
--7724945494737,361149,36519898,LABSPLAC,2013-07-20 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960087808594,0.972864414573,0.949947288061,0.967278138886,0.978093446978,0.987872137312,0.984828897103,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726614106765,0.655656880985,0.677938710863,0.557932786871,0.534772657595,0.995887778287,0.999226284619,0.999881546653,7.66228339811e-18
-6967509780721,369509,35754107,LABSPLAC,2013-06-18 10:29:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.950792161485,0.962152819825,0.975138921301,0.977318365183,0.976497858664,0.987424952203,0.984106493919,0.951903368139,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710614043217,0.697464394669,0.604438079785,0.688568305629,0.599690966596,0.976262824981,0.979097473485,0.979385993604,5.32991516769e-12
--7198383329371,448771,35091000,LABSPLAC,2013-05-09 21:29:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.932266052935,0.94707296698,0.979876980557,0.95419299227,0.966841220397,0.98646775749,0.978955139943,0.985105902988,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.624973493513,0.565157064848,0.522421043247,0.510043532133,0.506285767574,0.914137795888,0.909597249942,0.914224032801,8.07040651836e-06
--8700362763611,369481,34878076,LABSPLAC,2013-05-01 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.985303412661,0.984295178293,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.589022168301,0.577760550212,0.514688651593,0.513425694281,0.991069041297,0.996631118026,0.997827485589,0.999961599115
--6346348821454,415622,36414950,LABSPLAC,2013-06-28 18:16:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.950269262227,0.977360559416,0.974261068968,0.979144355379,0.98646775749,0.987872137312,0.984437196042,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.627421357016,0.647214985244,0.531454808047,0.521513186218,0.990632388341,0.996027465005,0.998034390614,1.1567718238e-09
-3361055613069,450458,32129543,LABSPLAC,2013-02-01 05:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.946895853274,0.953153757604,0.96456981503,0.972071396748,0.977757702356,0.98646775749,0.987261750327,0.983227646014,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.677616156732,0.607759901891,0.625799145584,0.531567504128,0.528934049276,0.98313519429,0.994820232978,0.998118944425,0.999999995679
--2871994338828,459988,31643306,LABSPLAC,2012-12-16 03:15:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961714294528,0.98570109238,0.977318365183,0.979352905953,0.780417361578,0.985348868778,0.952235069848,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.575652604564,0.563965162064,0.506794356345,0.504695369525,0.924174878456,0.959386411368,0.976273427631,0.999718528779
--4629695421662,396125,39017739,LABSPLAC,2013-10-16 09:13:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.986182202112,0.985814051458,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713921163545,0.623106151367,0.631528916061,0.691244091322,0.59949055986,0.993356631556,0.998072511749,0.998635053929,0.656193389386
--1195461952069,409714,33562203,LABSPLAC,2013-03-09 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.980220534083,0.975640843332,0.975248567058,0.980918574313,0.984255102924,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.68780702815,0.634033319722,0.657400685263,0.534368105559,0.524782272846,0.990434181907,0.995848500983,0.995889410078,3.11996702997e-10
--10110890094159,424827,39445171,LABSPLAC,2013-11-01 10:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.887975756893,0.868447790221,0.919497560241,0.809409846573,0.840682198358,0.990822510823,0.980213712221,0.885228112634,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.550625843053,0.550955970579,0.501572124671,0.504564843298,0.975422439111,0.999279503392,0.999999871504,1.0
--7451573610671,397091,37609223,LABSPLAC,2013-08-20 22:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.931886593608,0.944925009098,0.949273802377,0.982200257785,0.972414213647,0.97436780438,0.983642630155,0.987999443818,0.984691675089,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.617388440113,0.652982135776,0.59010421833,0.527591855386,0.517431285604,0.985416753973,0.998900013043,0.999995053097,1.0
--7151978809607,398999,38432918,LABSPLAC,2013-09-30 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959578136089,0.975380835714,0.97863298675,0.976283693783,0.978093446978,0.986182202112,0.983887486691,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.621586483647,0.659751167278,0.69041392498,0.600700888686,0.900645723538,0.945062437516,0.975546801678,1.0
--3875923952083,385075,30018470,LABSPLAC,2012-10-21 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962145712577,0.984716678926,0.980367324433,0.980235263085,0.986943191953,0.987134443821,0.952685663774,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.577293466998,0.592186262416,0.529864019946,0.511701309255,0.782829956242,0.805378686264,0.777447009481,1.0
--7722010707798,438920,30689990,LABSPLAC,2012-11-13 03:19:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.963029411336,0.980220534083,0.878897537185,0.975352206385,0.978093446978,0.985303412661,0.950902939435,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.575611816541,0.592186262416,0.516669186807,0.508705517233,0.90412312172,0.911385868798,0.918488385395,4.13760002199e-11
--2990625788346,369251,30934751,LABSPLAC,2012-11-19 22:01:00,1,0,1,0,0.866666666667,0.0,0.0,0.849680557734,0.898425659254,0.899322243972,0.708969855916,0.963741321951,0.798248645306,0.754321636887,0.444378908061,0.632607609046,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611826982836,0.553873383548,0.562079676942,0.521916197096,0.508229090649,0.843975238947,0.878944487447,0.902292123472,3.11003803649e-43
--12214517115415,371272,30563749,LABSPLAC,2012-11-08 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.984716678926,0.977318365183,0.976238181443,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.5740451995,0.562079676942,0.516388236565,0.511716872589,0.985283413619,0.992093470742,0.994353256112,0.999999596547
-8584413457569,472045,39676280,LABSPLAC,2013-11-28 04:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.961979822707,0.975380835714,0.977318365183,0.975365677099,0.984998208883,0.985303412661,0.982572217904,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.568841800947,0.605485933567,0.523349076962,0.511315827921,0.897525144994,0.876546959717,0.895423342853,1.0
--10851305204247,437612,30598750,LABSPLAC,2012-11-09 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.94594326631,0.925684649638,0.982200257785,0.977478405725,0.910931181371,0.988274209103,0.888193092768,0.887257259814,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.625664315102,0.640657052947,0.535734890587,0.523742294674,0.984123504978,0.997677171824,0.999312166589,9.17448662431e-08
--944003129237,405834,38293794,LABSPLAC,2013-09-28 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.953153757604,0.984716678926,0.972071396748,0.971922641717,0.98646775749,0.989686653441,0.981998052916,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.702021294663,0.647214985244,0.536742695345,0.522089884014,0.923208381112,0.883589214996,0.87363319517,1.0
-2268658033890,390566,34726667,LABSPLAC,2013-05-06 14:13:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959683662844,0.97833851037,0.977478405725,0.941640650762,0.987099640836,0.977208976789,0.948369877605,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.569182540884,0.619588095117,0.52413058196,0.514650932044,0.995279658101,0.999895828897,0.999988255034,4.80488784166e-28
-8671263156868,354835,30735310,LABSPLAC,2012-11-25 02:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.959893619444,0.975380835714,0.975640843332,0.976738374468,0.978424259496,0.985303412661,0.984071570538,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691261548938,0.632773844605,0.670656510296,0.541017172847,0.523618101343,0.969822995468,0.985327368786,0.991573448728,6.9972723065e-05
--11699102797175,405894,36622299,LABSPLAC,2013-07-17 06:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.777247147918,0.780664780605,0.893945627246,0.951476485283,0.836457624323,0.908985985708,0.785455893564,0.88053698775,0.853261237878,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.682388962285,0.605713129628,0.604438079785,0.533331535589,0.519203220644,0.872712889173,0.942839419382,0.986842097134,3.10226614748e-33
--5810413885474,468125,34169697,LABSPLAC,2013-04-10 10:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.957907749318,0.977655342441,0.972194889706,0.976319516669,0.978093446978,0.985444508621,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695496220774,0.615223227127,0.640657052947,0.527801615386,0.518169851764,0.984507316327,0.994903681669,0.995722372873,1.0
--10391435965578,403463,40619633,LABSPLAC,2013-12-24 10:36:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.980117654779,0.967935630982,0.97559635083,0.987424952203,0.883822128568,0.958685478649,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.570544998621,0.577760550212,0.51293484959,0.507666994211,0.987813216306,0.995374923698,0.996523670699,0.999994043293
-11020449699171,400266,36907250,LABSPLAC,2013-08-06 18:49:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.950221689037,0.946674134622,0.932221128801,0.982200257785,0.974040690688,0.970632653861,0.988274209103,0.988002518775,0.98184497802,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.626193854547,0.572048471873,0.562079676942,0.511922857285,0.51178828605,0.970807562254,0.986411118318,0.992920176786,6.15929327203e-12
--10089851098950,387737,30601270,LABSPLAC,2012-11-10 07:44:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.985303412661,0.982846065158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.651308567774,0.584054878876,0.592186262416,0.515581021764,0.508989105849,0.963589293051,0.983703961369,0.990053924303,0.999669926434
-3054183943259,450562,40251032,LABSPLAC,2013-12-29 09:32:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982495040811,0.977318365183,0.977179533147,0.98646775749,0.987334502903,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.628648993443,0.659751167278,0.541175906283,0.521733182168,0.982597203804,0.992343703295,0.996600762504,0.00358051064324
--4142855947751,402829,36717992,LABSPLAC,2013-07-19 23:35:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.976104294298,0.987424952203,0.982075403677,0.98339243903,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.572982539305,0.577760550212,0.514688651593,0.516283857916,0.975639531712,0.987458568361,0.989162062855,0.670221189537
-6185840573073,380927,32691324,LABSPLAC,2013-02-10 14:00:00,0,0,1,0,0.931034482759,1.0,1.0,0.611958773022,0.584075847507,0.622838845256,0.241525423729,0.494778481013,0.583223658792,0.6,0.4,0.699344945097,0.917871594643,0.839676183446,0.729997989811,0.708319280651,0.527504956147,0.511130805018,0.518602050471,0.509450742492,0.50447634958,0.852861464892,0.886438790262,0.933703355267,5.81995859224e-84
--9717283225948,465421,31763411,LABSPLAC,2012-12-25 03:00:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.952338179662,0.979139453002,0.973509511272,0.977168269226,0.978093446978,0.983613477461,0.974782893235,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.593548659541,0.597429412756,0.52840145094,0.515530625791,0.951642542743,0.979849503979,0.990071862085,4.68790980388e-13
--9937798449183,453771,32771190,LABSPLAC,2013-01-27 14:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962145712577,0.975380835714,0.980367324433,0.980242653053,0.980918574313,0.984255102924,0.982826457411,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650048755741,0.580611443294,0.577760550212,0.523198375074,0.517096726624,0.957047485163,0.979836718385,0.989861755464,8.31172547544e-06
--1646686166325,412804,33560926,LABSPLAC,2013-03-10 20:42:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.980117654779,0.967935630982,0.973334541513,0.987424952203,0.987253955653,0.981200925726,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.584239789362,0.577760550212,0.534109521035,0.517436226217,0.982675250575,0.98666350621,0.987321976263,0.999999999989
-3673537304856,419299,39840468,LABSPLAC,2013-12-10 08:16:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.962167482218,0.982495040811,0.977318365183,0.978212867981,0.98646775749,0.985837030591,0.984281826099,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.579886795248,0.577760550212,0.509101771481,0.508103611293,0.887380449797,0.856824402971,0.872351862669,0.000263917562757
-10708436270837,358089,30608207,LABSPLAC,2012-11-21 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.980220534083,0.975640843332,0.976133673143,0.978093446978,0.985444508621,0.983497048385,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691450053481,0.624463023628,0.697318231141,0.539785039322,0.528417081058,0.978749250973,0.992812227016,0.99675788861,1.50071877401e-05
--6751626207849,395552,39330534,LABSPLAC,2013-11-03 00:12:00,0,0,1,0,0.777777777778,1.0,1.0,0.841107356521,0.851294628844,0.779235914934,0.869017094017,0.943803728696,0.816137957855,0.839139670738,0.996456665506,0.929657955109,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.542472204497,0.525388489677,0.50644065596,0.507379011635,0.507121955696,0.996033129807,0.999884451665,0.999987423973,0.999759828343
--3322934571260,437651,33028824,LABSPLAC,2013-02-10 02:04:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.898628036372,0.933820826474,0.925403845808,0.966038330211,0.946332201221,0.956423214116,0.977620956615,0.988084776338,0.98438583717,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.658154609465,0.606320915983,0.625799145584,0.542586781997,0.526410843062,0.975482357356,0.974678418369,0.964300201391,1.68718036757e-10
--12154002820135,399050,38933571,LABSPLAC,2013-10-19 14:40:00,1,0,1,0,0.968845448992,0.980986142443,1.0,0.950221689037,0.946674134622,0.94740514827,0.975138921301,0.971851018469,0.96579147654,0.987424952203,0.875358570194,0.983034996188,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.607548876934,0.548458623329,0.547402931986,0.50293254128,0.500888863352,0.965409856551,0.976317089617,0.976395726258,6.54988107778e-07
--510227318657,441740,38316784,LABSPLAC,2013-10-01 00:11:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.955827536098,0.979876980557,0.972061352238,0.973349426845,0.98646775749,0.975013000316,0.978137771389,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.599332509219,0.63874696642,0.542060984666,0.52132366464,0.94383135971,0.964445796206,0.949333539312,1.22215241966e-06
-11807485464320,392182,35229902,LABSPLAC,2013-06-02 18:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.953153757604,0.960073670187,0.971851018469,0.975941194359,0.98646775749,0.983740783967,0.98274805045,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.692765294387,0.604130351232,0.618554100768,0.522964162473,0.511790795429,0.949627014009,0.949166063367,0.930485774386,0.000320325143526
--700088088040,450917,39896647,LABSPLAC,2013-11-27 14:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.863511864981,0.906477115528,0.835879702853,0.937316996178,0.928622297617,0.846812519219,0.8,0.887396307581,0.889959273263,0.917871594643,0.875764265788,0.793065314363,0.817336130992,0.58472848506,0.54689613899,0.520271360095,0.505661447317,0.503551940928,0.956392224879,0.990422610787,0.998986795902,2.53996985228e-62
-4271979773780,449742,39498194,LABSPLAC,2013-11-22 15:06:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961979822707,0.975380835714,0.977318365183,0.975352206385,0.978093446978,0.986182202112,0.984262004026,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.720780473067,0.702047090632,0.633531218038,0.691422645602,0.601495306472,0.978456294314,0.989646881345,0.9927136083,2.05807788829e-07
-1612274121959,382676,38506661,LABSPLAC,2013-10-16 00:23:00,0,0,1,0,0.968845448992,0.666666666667,0.0,0.972100498572,0.963776230663,0.947379196519,0.972864414573,0.97863298675,0.911539970865,0.982468061889,0.88711792879,0.951152453453,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.625743900882,0.562978094351,0.547402931986,0.504957551546,0.503444614414,0.754068916062,0.730250888053,0.72321151729,4.26743041297e-125
-4035289185680,449684,34418463,LABSPLAC,2013-05-08 21:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978773186895,0.983642630155,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.524776081758,0.514242794619,0.972698682209,0.987300781844,0.991694350537,0.999976033919
-6992008753358,448295,36516298,LABSPLAC,2013-08-01 06:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.902073406853,0.876835917989,0.880631445008,0.950517271541,0.84484365314,0.911921329098,0.988607351173,0.994072456837,0.937492915227,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.596071937134,0.54996926696,0.50644065596,0.512178279098,0.505617049175,0.820563539322,0.787251670407,0.776953191876,0.0048009638262
-6055921025956,386507,38739821,LABSPLAC,2013-10-23 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.960147537185,0.977179533147,0.990842372401,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.712475545141,0.647214985244,0.699972397781,0.602102186946,0.991703406052,0.997212463474,0.998501301906,1.61758538602e-06
--510227318657,441740,38322531,LABSPLAC,2013-10-01 01:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.955827536098,0.979876980557,0.972061352238,0.973349426845,0.98646775749,0.976722373041,0.978011985225,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.653512640103,0.599332509219,0.63874696642,0.539887765613,0.520231596292,0.946699249542,0.964240070715,0.947309543013,1.87337682327e-06
-11227943880580,407634,31906131,LABSPLAC,2013-01-12 23:22:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.644182588507,0.697318231141,0.540651598205,0.527881085747,0.992843210027,0.997355499881,0.998437790454,0.124534171193
-9958092196787,436618,37636359,LABSPLAC,2013-09-08 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.843155900456,0.908150784741,0.917537579173,0.962838741142,0.957163891862,0.906892139955,0.971403050097,0.974029309939,0.9133557408,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.599852516255,0.547598902162,0.547402931986,0.515817675447,0.511424288625,0.992867960309,0.999578349595,0.999945324617,1.0
--4344509565427,439030,29753869,LABSPLAC,2012-10-11 22:09:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.902209599173,0.889288385036,0.939966670524,0.955133673569,0.884639582777,0.992324823292,0.697082357959,0.88359747756,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.615445383989,0.560180025713,0.562079676942,0.514101800494,0.506904405106,0.937000560243,0.950973822316,0.956926501427,2.86939115324e-24
--4274652916938,404485,34699731,LABSPLAC,2013-04-23 14:04:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.955882500902,0.961643072402,0.981377681576,0.958305142752,0.956458473697,0.98646775749,0.742849101879,0.85507360944,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.574178112829,0.577760550212,0.508981892839,0.506085887161,0.979862752686,0.990472343026,0.994286901052,1.2771618694e-11
-10651349291612,358394,33176165,LABSPLAC,2013-03-13 01:27:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977646077039,0.98646775749,0.987134443821,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.582184330462,0.562079676942,0.531458894014,0.516077570977,0.985554817385,0.992160242655,0.99480059031,1.0
-11416666456609,466584,40923664,LABSPLAC,2014-01-24 16:24:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.957907749318,0.977360559416,0.975640843332,0.97773985206,0.983642630155,0.981904104736,0.983368698065,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.518070703457,0.512336084471,0.934174149858,0.957735068285,0.968656899287,0.999999845102
--8157245263008,457184,39335492,LABSPLAC,2013-11-01 20:24:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.849501321879,0.972061352238,0.939500839204,0.98646775749,0.93629408164,0.980562262889,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.63245310359,0.570378414122,0.562079676942,0.522056475141,0.517890990698,0.838115738281,0.933906318934,0.974164983165,0.999999979306
--3594684123306,439638,31100726,LABSPLAC,2012-11-25 18:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.979876980557,0.977318365183,0.978669547567,0.979598827762,0.987134443821,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.701551384239,0.622332340109,0.654219968234,0.538542995367,0.52700568529,0.980602422802,0.992543726704,0.995127343329,0.0812791458377
-9132026889488,394732,35277508,LABSPLAC,2013-06-03 23:48:00,0,0,1,0,0.862745098039,1.0,1.0,0.961763510975,0.932201206853,0.927561579317,0.97453828087,0.954214922095,0.955197683286,0.97906178285,0.977316755757,0.989936375076,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.61678901212,0.571465845291,0.537223027254,0.527999077802,0.517595879907,0.9839571287,0.993822017475,0.997574060947,2.82969080735e-06
--11261638676772,357495,35899315,LABSPLAC,2013-06-19 14:00:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.977500628695,0.948577593069,0.959159470101,0.745452944774,0.930231904179,0.961211474057,0.782468061889,0.976581277082,0.944341252145,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.628898237909,0.569604655077,0.562079676942,0.516211711818,0.508700620673,0.979550776788,0.985487250059,0.982794182197,1.0
-10394835404104,425758,31985903,LABSPLAC,2013-01-21 21:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.762524891672,0.845301402668,0.919557087179,0.7,0.865907049244,0.894492998464,0.784089044237,0.984210526316,0.919197287807,0.917871594643,0.761791632626,0.747959608509,0.745333370633,0.581830928211,0.540263628972,0.577760550212,0.517786550298,0.511055506717,0.872466696158,0.946070191588,0.980306218014,5.47970302057e-30
-6967509780721,361263,37048600,LABSPLAC,2013-08-14 20:44:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.978212867981,0.98646775749,0.989703168472,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.61015786181,0.647214985244,0.553781344479,0.533224218708,0.988188383187,0.993294769757,0.996840896934,0.99999996522
-6329463026309,364755,39979152,LABSPLAC,2013-12-16 06:00:00,0,0,1,0,0.968845448992,0.0,0.0,0.903725769945,0.963776230663,0.897196921278,0.983229909137,0.959936885814,0.741198957347,0.983384681012,0.883824673724,0.773663876505,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.620857253326,0.571918145937,0.5656089288,0.514652533162,0.505551817782,0.972441007737,0.971886365055,0.973989789411,0.997136087559
--11005058594856,358553,30748124,LABSPLAC,2012-11-13 02:51:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.930359593955,0.975138921301,0.87112739903,0.977628731826,0.983642630155,0.981904104736,0.983681740074,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.617745531753,0.559992791104,0.547402931986,0.520202170673,0.511605801947,0.985515820229,0.995483037522,0.997523089196,6.22500740836e-06
-6685252621122,396839,40611045,LABSPLAC,2014-01-05 18:37:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.930051484519,0.936629081397,0.972864414573,0.95867060421,0.929317731736,0.98646775749,0.978395793211,0.91668234386,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.606557605331,0.554271501918,0.50644065596,0.50624041413,0.504403366383,0.889825641542,0.923777732629,0.938641465475,0.999996654808
-6992008753358,447022,39208881,LABSPLAC,2013-11-22 15:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.88336980542,0.898425659254,0.920153172187,0.956261270068,0.96152114552,0.945067841749,0.981738421445,0.991664931762,0.95152076022,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.685135780342,0.609540883872,0.631528916061,0.543145930695,0.52729416222,0.979378962365,0.994239934329,0.997592173123,5.16436502068e-37
--2184432237379,458285,31551393,LABSPLAC,2012-12-22 06:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.952763005662,0.935462936763,0.984716678926,0.975640843332,0.977841699497,0.986337665616,0.985303412661,0.95104340063,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.69871875053,0.624265546398,0.654219968234,0.537542593517,0.524620743839,0.94966639837,0.963111462576,0.974026861513,5.10851167454e-13
--10669372069199,356725,34904641,LABSPLAC,2013-04-28 04:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.977500628695,0.948030903252,0.925030037261,0.975138921301,0.92655349761,0.882388236954,0.785455893564,0.829972531531,0.95472671414,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.670320326958,0.586678365712,0.631528916061,0.531503009684,0.522015356085,0.982243687859,0.989822156786,0.995304616092,2.69920751298e-94
-6967509780721,369509,35583894,LABSPLAC,2013-06-12 02:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.960864443511,0.980220534083,0.977318365183,0.974782023166,0.980918574313,0.986182202112,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.710793935857,0.620571746018,0.645245582584,0.53858218472,0.521628726952,0.980316259226,0.983761400146,0.984838703965,0.999999993261
-9042817128947,449708,30975911,LABSPLAC,2012-12-05 20:17:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.956994502276,0.845005177037,0.977318365183,0.974692707306,0.978093446978,0.983735135896,0.980085591772,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.512857949854,0.505437063327,0.67301187047,0.553005001705,0.49696157142,1.13929518894e-19
--10080190349180,371879,38932255,LABSPLAC,2013-10-13 02:55:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.955318725429,0.984716678926,0.977369409993,0.97616148788,0.98646775749,0.983594039937,0.985136299823,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57558169174,0.592186262416,0.521264789201,0.507781287728,0.889992567765,0.876495912619,0.905632735664,0.0135777707682
--8961793627567,464761,38881111,LABSPLAC,2013-10-08 14:23:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.860185135063,0.840130571961,0.913830395832,0.840035966063,0.898337258711,0.987693244344,0.780111232161,0.85549822825,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.618210178068,0.559032414148,0.537223027254,0.511193598852,0.507703099129,0.802885655571,0.850786109158,0.87321472208,6.24445963273e-29
--8669643970780,421747,33594213,LABSPLAC,2013-03-07 02:36:00,1,0,1,0,0.931034482759,1.0,1.0,0.804773355968,0.516891095093,0.623081270335,0.988461538462,0.659438910146,0.50661357818,0.6,0.392633726862,0.56445382012,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.608001365738,0.54402752058,0.563965162064,0.538117762647,0.517460965745,0.890236730099,0.937005230939,0.946829782724,3.97166356297e-132
-4035289185680,449684,34562097,LABSPLAC,2013-05-09 06:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.984716678926,0.977318365183,0.963416345957,0.983642630155,0.987996718241,0.949160457642,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.705826886905,0.672961290111,0.699202398464,0.603717325776,0.986452459704,0.99454807863,0.997071407796,0.999999998201
-2077859111509,394489,30953717,LABSPLAC,2012-12-05 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.976400120992,0.98646775749,0.983613477461,0.985592527309,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.642572785822,0.697318231141,0.537205090869,0.529066378555,0.993977395377,0.999567938008,0.999932848625,0.000114701582464
-11101459884079,461118,34184504,LABSPLAC,2013-04-25 08:55:00,0,0,1,0,0.777777777778,1.0,1.0,0.822560595597,0.784717043439,0.779275194844,0.987080979284,0.857727723211,0.674230856273,0.995368421053,0.7,0.798082706893,0.917871594643,0.733764670306,0.64904590656,0.681141672382,0.523095805698,0.502587138495,0.493501371374,0.496750325927,0.491731066809,0.327699772002,0.189899316145,0.136144835332,3.76120699292e-51
-3389108712186,362008,30969209,LABSPLAC,2012-12-11 15:59:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.961561662247,0.963033703961,0.979876980557,0.975640843332,0.977595766237,0.98646775749,0.981904104736,0.984256262226,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.532250089529,0.51556421962,0.818457087441,0.855932738359,0.887147777991,1.45676491132e-20
--3322934571260,437651,33053561,LABSPLAC,2013-02-10 19:05:00,0,0,1,0,0.866666666667,0.0,0.0,0.721369233576,0.889912454518,0.890960232368,0.924200492374,0.947142825574,0.766265883965,0.592617805549,0.991429483924,0.98874948184,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.658154609465,0.596814951733,0.611782965058,0.536485444125,0.523701759943,0.7672182015,0.855662314661,0.89289304605,8.80603717874e-29
-7094992191229,434579,34824680,LABSPLAC,2013-05-23 08:52:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977655342441,0.975640843332,0.975490813621,0.987424952203,0.986804643992,0.983707303946,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.681648793611,0.606414614536,0.605485933567,0.523266415447,0.514863775736,0.971372015935,0.969426321074,0.966787619784,5.87194313024e-10
--4176006569129,444544,36166185,LABSPLAC,2013-06-26 01:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960002514393,0.979876980557,0.979277847083,0.977302966658,0.98646775749,0.987872137312,0.98672493895,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.700618905719,0.607235034907,0.672961290111,0.558947210827,0.530893519647,0.989788436149,0.99833113439,0.999804833853,1.0
--2990625788346,369251,30927888,LABSPLAC,2012-11-20 06:00:00,0,0,1,0,1.0,1.0,1.0,0.607114433778,0.480682814315,0.581628226756,0.714443951712,0.3,0.503017241379,0.561339670804,0.499264705882,0.4,0.29944547465,0.555906810973,0.600558873622,0.607671556364,0.527597268424,0.50077014686,0.477085565744,0.507308610634,0.501836747997,0.271541493815,0.236114770295,0.263131898268,7.22574651339e-82
-10800651293913,463974,32559614,LABSPLAC,2013-02-12 22:13:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.973157933662,0.963776230663,0.959549326703,0.981204947537,0.975640843332,0.97708123233,0.98646775749,0.985348868778,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578706734423,0.592186262416,0.518911931289,0.508565918873,0.960065441792,0.997123972174,0.999999987548,1.0
-2569156209298,392334,36949573,LABSPLAC,2013-08-07 16:30:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977360559416,0.977318365183,0.977169668841,0.979598827762,0.981904104736,0.982979348655,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.592186262416,0.524511644367,0.515381184291,0.942164145426,0.949125643096,0.958064654314,0.999999999963
--1800311827834,467755,35502375,LABSPLAC,2013-05-28 11:04:00,1,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977841699497,0.983642630155,0.985303412661,0.982853562492,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.519637890785,0.515120216502,0.964964705073,0.979462668477,0.984827024129,0.999996598722
-10949660143184,428283,34363986,LABSPLAC,2013-05-03 10:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.977500628695,0.96072858502,0.95290482443,0.977655342441,0.977318365183,0.972869539685,0.789629787831,0.984106493919,0.984997983243,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.691118912211,0.598010021597,0.633531218038,0.533364250519,0.522333718576,0.850625971548,0.879557892139,0.908760873755,1.20878098363e-22
-11020449699171,400266,36446341,LABSPLAC,2013-07-19 18:57:00,0,0,2,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958942799526,0.979876980557,0.977318365183,0.977271516277,0.98646775749,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.702021294663,0.647214985244,0.528476212986,0.512643520298,0.983639649133,0.987594060915,0.989500110578,0.0219605756012
--11165275032556,355138,36582576,LABSPLAC,2013-07-09 18:19:00,0,0,1,0,0.866666666667,0.0,1.0,0.861491874747,0.811617338804,0.834993091095,0.797590361446,0.778464097586,0.831424226874,0.4,0.827288769166,0.901842225624,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.680420171738,0.600503854814,0.604438079785,0.516156004224,0.513058471903,0.96081372671,0.945439022413,0.939483792668,4.10657465333e-81
--4546926499538,434525,38665949,LABSPLAC,2013-10-14 10:05:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.88336980542,0.911568230443,0.913324594227,0.962838741142,0.860221188078,0.930097372308,0.980853332368,0.882079911551,0.978000187288,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.596183664026,0.543102094628,0.547402931986,0.513741496226,0.50385456512,0.851637018016,0.909322400164,0.962623561498,2.83207747269e-06
-10678434761668,387430,39996311,LABSPLAC,2013-12-11 20:27:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.962167482218,0.984716678926,0.977318365183,0.955961941915,0.983642630155,0.985425071096,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.580036078719,0.592186262416,0.528648914615,0.512163199239,0.937674084745,0.928955688746,0.949854374844,0.163673375616
--5294685623184,470444,40327755,LABSPLAC,2013-12-11 19:56:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.922263107256,0.983178208739,0.968917215211,0.975852972777,0.978093446978,0.961155345846,0.983570387057,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.702434613387,0.696902471482,0.645245582584,0.697556414664,0.605017656681,0.988762316525,0.997542862049,0.999087075403,6.36374522039e-06
-11004154899725,402508,36412162,LABSPLAC,2013-07-30 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.957627682718,0.979876980557,0.978897537185,0.978119075294,0.980556022475,0.925601066127,0.952719773666,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.692963760122,0.618084502995,0.677938710863,0.535360379967,0.519080045902,0.997830795835,0.99980105188,0.999934583554,1.70526262475e-13
--7145504119369,376973,35640266,LABSPLAC,2013-06-05 16:46:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.977704112943,0.97863298675,0.97572337487,0.980918574313,0.983594039937,0.982694715122,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.578540830217,0.592186262416,0.515838165029,0.512502566135,0.976917560611,0.989609063104,0.990188072984,0.999999999987
--9581161330290,448924,39923639,LABSPLAC,2013-12-04 12:06:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.975138921301,0.977318365183,0.977045646001,0.987424952203,0.982075403677,0.984709907867,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.572982539305,0.577760550212,0.517297651039,0.508633272957,0.916381189305,0.955617091537,0.972828753932,9.99585986464e-05
--7970765028213,461334,36126372,LABSPLAC,2013-06-30 08:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.895727329028,0.889141061165,0.914216701696,0.951476485283,0.95557762819,0.914691741174,0.988607351173,0.975832686675,0.951598422172,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.602264415693,0.548926043019,0.575657631795,0.529374344089,0.517257251959,0.992211264242,0.998451551727,0.999625732169,2.89983530229e-39
--2696842722055,403525,37821987,LABSPLAC,2013-09-05 08:15:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.961561662247,0.957267777481,0.977655342441,0.978689802581,0.944876698475,0.980788482439,0.985624368118,0.981509061538,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635681240063,0.58187600457,0.59558486573,0.517341285726,0.516791869658,0.987539641648,0.993733585048,0.996796247426,0.38180418904
-729492854779,431760,35420851,LABSPLAC,2013-06-19 22:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.937542799446,0.894095645944,0.939526477666,0.914081984385,0.969864154758,0.957948206744,0.976301210064,0.9870876682,0.984983479804,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.68841883343,0.601268858908,0.604438079785,0.519618445329,0.508689310271,0.913919227219,0.862801468148,0.800547302919,1.85969481812e-05
-2118845947749,453839,33979673,LABSPLAC,2013-04-09 02:48:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.982200257785,0.977318365183,0.978212867981,0.98646775749,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.707623800636,0.642781301012,0.697318231141,0.54476791591,0.53238126145,0.990893030068,0.995985231748,0.99846147741,0.679136437483
-11707426741543,461219,34415905,LABSPLAC,2013-04-27 20:56:00,1,0,1,0,0.0487804878049,0.0,0.0,0.263546757902,0.211928513066,0.468130589159,0.2,0.208620689655,0.25446877136,0.2,0.1,0.0,0.917871594643,0.784472143326,0.70404613871,0.706412800544,0.520045239599,0.481379790696,0.40010684085,0.475815369583,0.486879309061,0.0940749260598,0.121160408862,0.113923625033,1.5531070836e-113
--399432323579,472456,36662995,LABSPLAC,2013-07-14 00:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.937542799446,0.866619765769,0.943339309252,0.766038330211,0.973368448544,0.925474366269,0.981716357683,0.884902415186,0.948128306137,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611826982836,0.55570681002,0.562079676942,0.512025224118,0.506288951642,0.948181138078,0.990160059222,0.999587030324,1.0
--2314873582618,390743,29723712,LABSPLAC,2012-10-01 14:00:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.94582387653,0.963711384581,0.899747236893,0.791766249623,0.960425716009,0.918381084408,0.994004402741,0.889854154721,0.926770206599,0.917871594643,0.887213629677,0.849757775956,0.831495259098,0.636582245397,0.573102888966,0.577760550212,0.505128913801,0.508911290613,0.999807800213,0.999999932781,1.0,2.51220750439e-38
--4496378794802,367951,37759890,LABSPLAC,2013-09-08 02:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.950221689037,0.94582804405,0.957484083173,0.972864414573,0.974040690688,0.964666087039,0.978093446978,0.985276556396,0.981964691086,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.619439146028,0.559674502918,0.547402931986,0.511038494804,0.508736623551,0.949305790205,0.963339482339,0.966226904296,0.0013144584732
--5567453440449,354964,36796965,LABSPLAC,2013-07-31 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959578136089,0.979876980557,0.977318365183,0.978773186895,0.983642630155,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.577760550212,0.517758636609,0.512531590036,0.95136840102,0.964123741166,0.965432703625,0.000112473242688
--4757345149909,468062,36814954,LABSPLAC,2013-07-26 10:50:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.955680297486,0.977655342441,0.960147537185,0.975352206385,0.978093446978,0.985303412661,0.983886397795,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.514594666261,0.506027719323,0.938934822469,0.953897802516,0.964299814366,6.80799146545e-07
--12424829659809,434276,38223105,LABSPLAC,2013-09-26 00:34:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.980220534083,0.97863298675,0.975352206385,0.978093446978,0.983594039937,0.983886397795,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.664578157689,0.592186262416,0.52671768531,0.513506618538,0.982244216813,0.99397397094,0.996067974322,1.0
--10110890094159,424827,39435139,LABSPLAC,2013-11-01 16:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.888836044996,0.928383014365,0.872621793988,0.919497560241,0.702425119636,0.864546818329,0.797727272727,0.758248003712,0.852677643765,0.917871594643,0.856423141525,0.789947954552,0.80555524981,0.592486092064,0.542588814267,0.537223027254,0.498803839014,0.502396083573,0.892549297966,0.978479379279,0.996339190085,7.53354940865e-51
-1693654719095,362390,33746341,LABSPLAC,2013-03-29 15:17:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959804728644,0.977360559416,0.975640843332,0.978203003675,0.98646775749,0.983594039937,0.983636525472,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.635542226995,0.57063118209,0.577760550212,0.519370191346,0.508872523339,0.925605080616,0.940622904583,0.9463774299,0.00021712691432
-4655476872183,415688,28371580,LABSPLAC,2012-08-30 05:54:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.959965031251,0.980220534083,0.97863298675,0.975937572239,0.978093446978,0.983594039937,0.982846065158,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646973279766,0.598420889118,0.63874696642,0.525410112763,0.514115790167,0.952135407845,0.933360523659,0.895298934569,1.14085106585e-07
-10727499499815,424819,38191612,LABSPLAC,2013-10-04 04:00:00,0,0,1,0,0.866666666667,0.0,0.0,0.876749226838,0.87903766469,0.877458556462,0.950517271541,0.903254820017,0.798607019679,0.745714285714,0.870578446348,0.803455729774,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.57819292042,0.539427782852,0.53508067757,0.504102642242,0.501478295939,0.820032101829,0.815417132809,0.818037033578,4.61502875924e-12
--12154002820135,399050,38804106,LABSPLAC,2013-10-13 10:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.956180654547,0.97997861967,0.970564203852,0.977948602381,0.987424952203,0.984106493919,0.985076014426,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.713343992298,0.616686250229,0.659751167278,0.533591786691,0.516713911593,0.975545426698,0.988935164746,0.993296202242,0.981401904313
--2080218697898,361115,37939098,LABSPLAC,2013-09-08 17:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.925013496432,0.850450360172,0.855655145096,0.970604126499,0.96072305971,0.910687151211,0.8,0.869765855547,0.833273229365,0.917871594643,0.856423141525,0.769201514504,0.783676487125,0.570731031419,0.537900360405,0.53508067757,0.501555799905,0.504630740804,0.965888542752,0.989069264056,0.992516193335,1.84008608883e-70
--2016763991795,398868,34823115,LABSPLAC,2013-05-06 08:17:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.915890012503,0.967935630982,0.975730237975,0.98646775749,0.888210893452,0.979766637753,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.575599058332,0.577760550212,0.510324590991,0.507931831612,0.841137822145,0.836529440937,0.841527580397,0.491341953503
--4546926499538,439851,36331931,LABSPLAC,2013-07-09 16:32:00,1,0,1,0,0.866666666667,0.96138996139,1.0,0.843155900456,0.877175249199,0.930497337815,0.762402684209,0.951317764183,0.913399618501,0.781738421445,0.891866973058,0.874888117976,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.691305522813,0.688980617923,0.604438079785,0.520411492114,0.506929443087,0.931180262244,0.964761457416,0.990814832917,2.00065188125e-10
-10203366501284,364684,32800682,LABSPLAC,2013-02-18 10:04:00,0,0,1,0,0.862745098039,1.0,1.0,0.981142135723,0.96570831873,0.896625610882,0.981901082406,0.979800884943,0.980655265794,0.98343639776,0.987754330287,0.987740575057,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646228537175,0.58517526391,0.577760550212,0.530053838307,0.510930755937,0.965100008061,0.97302302836,0.990323343656,1.0
-6143284030311,444711,29127434,LABSPLAC,2012-09-24 01:18:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961547470338,0.979876980557,0.977318365183,0.979170940453,0.98646775749,0.983594039937,0.951696662687,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.592186262416,0.525786396834,0.510522507542,0.996009218037,0.999999352855,1.0,1.0
-7814360810179,430096,40036105,LABSPLAC,2013-12-13 17:00:00,0,0,1,0,0.968845448992,1.0,1.0,0.812899484974,0.966986025501,0.929845140108,0.985025393182,0.860218845863,0.835117434709,0.988463659148,0.862575310283,0.823182259221,0.917871594643,0.875764265788,0.812190395799,0.836520745027,0.608047796941,0.561822851451,0.550955970579,0.520657158036,0.512208929957,0.988214659274,0.990560025404,0.979145989436,8.22325284313e-10
-11364568466836,452032,34582762,LABSPLAC,2013-05-04 00:40:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.888490701267,0.854079122753,0.875453283267,0.95275741491,0.933780911344,0.851843018942,0.98,0.995158475521,0.8206924722,0.917871594643,0.848330419257,0.746770706044,0.7551686106,0.557777221848,0.530578249804,0.53508067757,0.511187739263,0.504978548948,0.951368460923,0.943914577962,0.936872785518,6.84647458778e-10
-1945144906651,424834,36836597,LABSPLAC,2013-08-16 02:01:00,0,0,1,0,0.968845448992,1.0,1.0,0.937839241455,0.969014610862,0.943625700363,0.986637096667,0.92921212046,0.891344610351,1.0,0.9950858774,0.925531305849,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.717935901842,0.621346403379,0.659751167278,0.707119238329,0.616644902059,0.999674582387,0.999961880769,0.999985214349,7.34391704682e-100
--152589643420,363538,29209912,LABSPLAC,2012-09-10 20:34:00,1,0,1,0,0.0684931506849,0.0,0.0,0.205390642306,0.152235799248,0.197419886318,0.2,0.297619047619,0.26088290086,0.2,0.1,0.133333333333,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.471444088375,0.464359508568,0.433671722659,0.469105496968,0.481700489594,0.0100413929349,0.000586996052603,1.79307815814e-05,3.1201525555e-228
--11513524632474,401140,39393474,LABSPLAC,2013-11-03 08:09:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.882939114969,0.922488307163,0.957488375798,0.971832287475,0.974040690688,0.939724744666,0.983642630155,0.985276556396,0.983654721435,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.623030309201,0.569107943189,0.547402931986,0.517132197134,0.51126121562,0.888588907682,0.857742253257,0.847240941775,0.979772929737
-6992008753358,447022,39298972,LABSPLAC,2013-11-24 15:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.88336980542,0.898425659254,0.918071599385,0.956261270068,0.940107165518,0.94338716948,0.977656788792,0.987246613615,0.948705252043,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.679648082945,0.603934872675,0.604438079785,0.541136812016,0.517603155141,0.968983972437,0.988530776445,0.993752484426,2.34424682047e-27
-8081880032031,404200,36155048,LABSPLAC,2013-07-22 18:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.691587740068,0.803298424244,0.777587519403,0.6,0.760863137097,0.85309355287,0.8,0.798444790047,0.798888888889,0.917871594643,0.868594437999,0.772267124532,0.79208720763,0.557332660252,0.51910636796,0.520271360095,0.508964827734,0.50707617091,0.967656391544,0.97944395591,0.992667859727,0.0
--3983259133669,428443,32429032,LABSPLAC,2013-01-23 22:41:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.928806119669,0.977318365183,0.97572337487,0.98646775749,0.983594039937,0.982694715122,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643383185783,0.578540830217,0.592186262416,0.520308769448,0.515138353616,0.982905458613,0.984414440602,0.990957884797,0.999998490122
-1595034879606,457599,40124938,LABSPLAC,2013-12-18 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.984151449195,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.726530546146,0.707225676086,0.633531218038,0.525717144382,0.51412982319,0.977620540277,0.985952511711,0.991537438151,0.000694272316798
-6574868228456,405672,39868588,LABSPLAC,2013-12-12 19:13:00,0,0,1,0,0.777777777778,1.0,1.0,0.732351917978,0.885747056235,0.783480861061,0.573888888889,0.762863454495,0.854698652487,0.8,0.8,0.958331103386,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.5717015374,0.534454461228,0.536986650894,0.509822838237,0.507571390551,0.388347569964,0.372143962156,0.379667927479,6.80914616902e-128
-1612274121959,367490,38904918,LABSPLAC,2013-11-03 21:00:00,1,0,1,0,0.968845448992,1.0,1.0,0.653482351403,0.817966940239,0.784862564604,0.383081081081,0.748938472949,0.875007669751,0.781022787621,0.575718236499,0.82575975431,0.917871594643,0.848330419257,0.746770706044,0.7551686106,0.550673324518,0.519707716485,0.53508067757,0.507091171737,0.501653846893,0.785264144395,0.81641183303,0.83149770113,4.53027512074e-22
--1385061346033,415939,32595964,LABSPLAC,2013-02-05 09:00:00,0,0,1,0,0.968845448992,0.980986142443,0.0,0.972100498572,0.963776230663,0.963029411336,0.987178991264,0.960147537185,0.971887119332,0.987424952203,0.987542337483,0.984725059275,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.773195994132,0.74692031131,0.69254108076,0.700682391866,0.60730483024,0.99500891619,0.996642729163,0.99737351019,0.999660058358
--11899697947985,429960,35952992,LABSPLAC,2013-06-24 16:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.953216930863,0.975380835714,0.977318365183,0.976882546062,0.980918574313,0.987872137312,0.984378290517,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723487769495,0.704551379379,0.647214985244,0.522058518528,0.514439803597,0.983598720762,0.99065904454,0.994974406735,2.38156279761e-06
--2959799475216,386692,39141949,LABSPLAC,2013-10-22 21:00:00,0,0,1,0,0.862745098039,0.0,0.0,0.975350091061,0.912577960334,0.859996252137,0.973935860411,0.976053042635,0.914937870979,0.794892986589,0.889473937675,0.873176048745,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.698343684581,0.625087322421,0.646997874261,0.545928635078,0.599324032925,0.953081723006,0.973707760211,0.990456797755,8.36589498912e-193
--7064358688213,386485,36063053,LABSPLAC,2013-06-22 09:57:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963776230663,0.955563879202,0.975380835714,0.972194889706,0.944762625929,0.978093446978,0.983594039937,0.984455954922,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.577760550212,0.5236599502,0.507349148979,0.917440985941,0.923075184274,0.907240083186,1.0
--12154002820135,399050,38774001,LABSPLAC,2013-10-13 22:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.95569450986,0.956180654547,0.982200257785,0.970564203852,0.979013976924,0.98646775749,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.711462421556,0.616015308273,0.672961290111,0.536804359752,0.519488179659,0.981556895521,0.992192844877,0.995818888903,0.999999955526
-11663710697071,405748,33006162,LABSPLAC,2013-03-07 21:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.786121933833,0.828373331599,0.836893274606,0.991081541112,0.854361758544,0.681255020647,0.392067859255,0.69580336094,0.658053362953,0.917871594643,0.887213629677,0.835460747683,0.834827418611,0.608165071446,0.553224415171,0.549300938986,0.513175304698,0.511024042925,0.939403555408,0.378622112241,0.000614369458519,7.15131752834e-135
--2471898027856,461149,32624793,LABSPLAC,2013-01-29 14:02:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.961043261284,0.977655342441,0.977318365183,0.977525237032,0.991799567113,0.982075403677,0.985332636611,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.704274036912,0.628091636827,0.640657052947,0.540063093532,0.534012985006,0.997280491477,0.999701824451,0.999970434222,0.29317526557
-533096379802,363741,33820822,LABSPLAC,2013-04-07 00:06:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.977360559416,0.977318365183,0.976900347793,0.983642630155,0.985303412661,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.575099170822,0.592186262416,0.522179398159,0.512992146529,0.990859839582,0.997386515333,0.99833707653,0.993450803481
-9296422589150,399004,39781805,LABSPLAC,2013-12-15 15:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.984716678926,0.977318365183,0.977179533147,0.98646775749,0.983613477461,0.984358287174,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.578029128891,0.577760550212,0.516974920737,0.509638619762,0.980991503197,0.992365487352,0.995500698364,0.999998960105
-8372526490187,356810,33511986,LABSPLAC,2013-03-24 15:02:00,1,0,1,0,1.0,0.0,1.0,0.723543540838,0.690609557247,0.504768432361,0.336585365854,0.4,0.600693170018,0.595625385089,0.599264705882,0.533333333333,0.29944547465,0.555906810973,0.569297402764,0.561977279376,0.480899551351,0.476014834815,0.448319649051,0.488388797263,0.491697429963,0.0951399720815,0.0303702962266,0.018300655184,1.49754987629e-64
-4207723773454,391908,30594020,LABSPLAC,2012-12-01 00:00:00,1,0,1,0,0.0684931506849,0.0,0.0,0.207251406007,0.396574145271,0.434415610849,0.2,0.2,0.201298701299,0.4,0.4,0.233333333333,0.29944547465,0.555906810973,0.600558873622,0.607671556364,0.509862833168,0.489059725371,0.462285342001,0.493953429444,0.497550945113,0.142012499782,0.438031766838,0.824414258198,1.44062490829e-247
-3907559420442,440290,34461503,LABSPLAC,2013-05-03 02:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.956098817925,0.975380835714,0.972061352238,0.97058074838,0.978093446978,0.974891341881,0.980420369794,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.57066139617,0.592186262416,0.522702771214,0.512994743757,0.960104346991,0.987336576656,0.994911392164,0.999986668313
--5977848492839,468476,37679611,LABSPLAC,2013-09-01 02:01:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.978558063786,0.952820746846,0.942480521772,0.978688526396,0.972354930247,0.969114944672,0.984219136111,0.987038803979,0.983010346723,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.628898237909,0.562050770313,0.562079676942,0.500432713937,0.505924102929,0.945983667053,0.987212146241,0.996804869684,1.0
-3230237039454,359104,30027513,LABSPLAC,2012-11-09 13:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962171774843,0.973492723406,0.977318365183,0.97400442453,0.981875769026,0.885930550632,0.949893230505,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.650048755741,0.593277704254,0.62492509043,0.522693676989,0.516227044144,0.972190283811,0.987508822203,0.992780238463,2.7726512539e-07
--944003129237,405834,38366208,LABSPLAC,2013-09-28 15:54:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.957909078276,0.953153757604,0.984716678926,0.972071396748,0.974902675859,0.98646775749,0.987872137312,0.984700073118,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.702021294663,0.647214985244,0.536742695345,0.523466229723,0.911254734268,0.863396480972,0.841946395891,1.0
-1427323147403,375650,34500855,LABSPLAC,2013-05-07 17:00:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.962167482218,0.975380835714,0.97863298675,0.976283693783,0.978093446978,0.983613477461,0.983657227786,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.695370147191,0.619740701003,0.690773668204,0.549677534694,0.526549302812,0.986426833304,0.995033960483,0.997289366752,5.79636156232e-08
-11807485464320,392182,35467916,LABSPLAC,2013-06-12 04:44:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.962187415028,0.94582804405,0.953640948902,0.977360559416,0.973451171113,0.969949647033,0.976773700428,0.987872137312,0.984413354063,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.692033965082,0.686131433978,0.618554100768,0.518211669259,0.512256620994,0.982479941544,0.992455118207,0.995876678044,0.999999943837
-7790554637268,427497,31309591,LABSPLAC,2012-12-17 20:29:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.977318365183,0.977179533147,0.98646775749,0.97987335182,0.983574982428,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.646877206823,0.576746817375,0.577760550212,0.52547891745,0.512933137477,0.956010242568,0.967704775995,0.972038135664,0.999999999982
--10708945553031,376106,32087523,LABSPLAC,2013-01-11 05:00:00,0,0,1,0,0.666666666667,1.0,1.0,0.875900597662,0.626471732464,0.579611618047,0.881890810193,0.765187373346,0.822076520691,0.8,0.8,0.699520383693,0.917871594643,0.876525485249,0.82528370626,0.803437219235,0.601499604793,0.539483140395,0.549300938986,0.499274188863,0.504532103609,0.504250114245,0.851576758846,0.960523843041,3.72810918214e-78
--11584622773005,405361,36169355,LABSPLAC,2013-06-21 02:01:00,1,0,1,0,0.777777777778,1.0,1.0,0.838554308366,0.728435540847,0.771033772413,0.633650793651,0.764987170254,0.840412230945,0.98474025974,0.792118226601,0.729497879592,0.917871594643,0.79514940545,0.752089818082,0.763693622672,0.557603393582,0.530490491508,0.522421043247,0.503546142419,0.504397733333,0.796597644654,0.87139210425,0.918668871783,6.56198618098e-44
-7293391827672,436630,33538805,LABSPLAC,2013-03-22 04:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.935581196483,0.789196038797,0.817548466116,0.591081541112,0.878461776802,0.741747159233,0.597304964539,0.598253275109,0.695889908489,0.917871594643,0.887213629677,0.836558268551,0.842163729228,0.603946124435,0.653684332333,0.62290375535,0.542577620486,0.525000560734,0.991971441125,0.997913502184,0.991142121278,1.0
--6853921804013,427870,34280756,LABSPLAC,2013-04-03 09:10:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.961763510975,0.963711384581,0.959382502513,0.977655342441,0.977318365183,0.9376933545,0.987424952203,0.972627889865,0.984367448501,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.643182411451,0.572982539305,0.577760550212,0.514688651593,0.513961379818,0.990405597145,0.999528650484,0.999990620577,1.0
--8505238895287,443918,39678425,LABSPLAC,2013-11-18 16:50:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.916963131802,0.898656802806,0.937227796791,0.889260192611,0.955060035867,0.933390771142,0.97483365954,0.987808955132,0.981137629019,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.611826982836,0.642098349932,0.562079676942,0.521899295528,0.513055795761,0.965979778599,0.993884329297,0.998147402932,1.0
--7970765028213,461334,36205244,LABSPLAC,2013-07-02 20:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.935941233992,0.875085922367,0.923386434009,0.908969855916,0.961896220814,0.950307393961,0.995625385089,0.987265175951,0.912498242978,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.674292175484,0.589047256442,0.604438079785,0.527411416496,0.517128909217,0.962768276406,0.988503891821,0.995045194881,1.88687437134e-36
-6992008753358,447022,39250168,LABSPLAC,2013-11-21 21:00:00,0,0,1,0,0.866666666667,0.96138996139,1.0,0.916963131802,0.898425659254,0.920153172187,0.956261270068,0.954757219791,0.945887235194,0.981738421445,0.991664931762,0.984854093553,0.917871594643,0.880614485829,0.819134469452,0.819589121672,0.685135780342,0.609540883872,0.645245582584,0.716219431547,0.611871758753,0.993824604108,0.99913591829,0.999818010186,6.88735068702e-36
-11020449699171,400266,36458870,LABSPLAC,2013-07-19 13:33:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.958942799526,0.979876980557,0.977318365183,0.977271516277,0.98646775749,0.986182202112,0.985239529306,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.716632355266,0.702021294663,0.633531218038,0.52551369587,0.51062921765,0.972595694464,0.978433585903,0.981205009456,0.000718250709366
-6967509780721,369509,35618952,LABSPLAC,2013-06-11 05:54:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.963776230663,0.963029411336,0.980220534083,0.977318365183,0.968699290703,0.980918574313,0.983594039937,0.980144044956,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.628898237909,0.570892505702,0.562079676942,0.512486499597,0.506515186625,0.981719952557,0.992035310742,0.995402029405,1.0
-7449569794204,405817,37364409,LABSPLAC,2013-09-05 21:00:00,0,0,1,0,0.968845448992,0.0,0.0,0.903725769945,0.938963869042,0.940848667452,0.935551271494,0.978750832278,0.93653501195,0.785657408285,0.984142676101,0.978101663341,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.637029866259,0.571318403676,0.592186262416,0.522549005452,0.5118927055,0.959198998716,0.979800335761,0.987987047059,4.71657463257e-08
-6478226497391,413446,30987067,LABSPLAC,2012-12-16 00:42:00,0,0,1,0,0.968845448992,0.980986142443,1.0,0.972100498572,0.961561662247,0.931782120994,0.982834655213,0.991604461173,0.979431041483,0.795949385811,0.987872137312,0.988575100593,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.640281546749,0.568941545615,0.592186262416,0.53049039448,0.512501470408,0.997329610637,0.999800929931,0.999959210718,1.4441082557e-57
--3875923952083,385075,30034836,LABSPLAC,2012-10-21 01:16:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.960864443511,0.984716678926,0.977318365183,0.975867012959,0.983642630155,0.983594039937,0.984484073337,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.64378913179,0.580036078719,0.577760550212,0.526902416528,0.513216653134,0.760430044574,0.582087714644,0.421030457016,1.0
--3351942076256,465509,29804614,LABSPLAC,2012-10-07 13:00:00,0,0,1,0,0.862745098039,1.0,1.0,0.766547713322,0.845036783912,0.834738449355,0.744992397204,0.86425332333,0.870780773156,0.968251592489,0.790706105298,0.886544024309,0.917871594643,0.880614485829,0.799261528439,0.790049149109,0.56264927948,0.523172052522,0.518602050471,0.500657662151,0.504998567966,0.942816471467,0.982174460647,0.993300012559,0.294317017703
--10232823590382,469781,37808811,LABSPLAC,2013-08-29 13:01:00,0,0,1,0,0.968845448992,0.980986142443,0.998770995494,0.972100498572,0.963776230663,0.963029411336,0.979876980557,0.97863298675,0.976808364662,0.983642630155,0.987872137312,0.985399708706,0.917871594643,0.887213629677,0.852411745725,0.859190007503,0.723841669642,0.625843002933,0.659751167278,0.704477686168,0.608109097522,0.99447415298,0.999680580315,0.999995249456,1.0
diff --git a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_outputs.csv b/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_outputs.csv
deleted file mode 100644
index 42a0326e..00000000
--- a/scripts/LabTestAnalysis/machine_learning/pipelineResults/LABTSH_outputs.csv
+++ /dev/null
@@ -1,561 +0,0 @@
-patient_id,encounter_id,order_proc_id,proc_code,order_time,abnormal,result_normal_count,result_total_count,all_result_normal,predictedTest.decision_tree_max_depth=5,predictedTest.decision_tree_max_depth=20,predictedTest.decision_tree_max_depth=50,"predictedTest.random_forest_n_estimators=5,_max_depth=5","predictedTest.random_forest_n_estimators=10,_max_depth=5","predictedTest.random_forest_n_estimators=30,_max_depth=5","predictedTest.random_forest_n_estimators=5,_max_depth=10","predictedTest.random_forest_n_estimators=10,_max_depth=10","predictedTest.random_forest_n_estimators=30,_max_depth=10","predictedTest.random_forest_n_estimators=5,_max_depth=15","predictedTest.random_forest_n_estimators=10,_max_depth=15","predictedTest.random_forest_n_estimators=30,_max_depth=15","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.01","predictedTest.ada_boost_n_estimators=10,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=50,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=100,_learning_rate=0.1","predictedTest.ada_boost_n_estimators=10,_learning_rate=1","predictedTest.ada_boost_n_estimators=50,_learning_rate=1","predictedTest.ada_boost_n_estimators=100,_learning_rate=1",predictedTest.logistic_regression_c=0.1,predictedTest.logistic_regression_c=1.0,predictedTest.logistic_regression_c=10.0,predictedTest.gaussian_naive_bayes
-7045970058431,357853,37524060,LABTSH,2013-09-14 23:34:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.828775674753,0.750565556595,0.523684462621,0.815850760077,0.733279477366,0.88847086611,0.729423026322,0.652985530098,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.558162539088,0.524915499138,0.529989690422,0.506016892982,0.506368330352,0.654854844856,0.613095038006,0.62581282412,2.90213526486e-09
-4727943432191,367384,35419138,LABTSH,2013-06-19 10:05:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.822395603613,0.875615909806,0.83328513052,0.840509324116,0.870249847627,0.860714762034,0.85957853731,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.580980392552,0.543165118099,0.550443778739,0.509815716348,0.505005090357,0.873238134798,0.879683789208,0.881812438184,0.999999089106
-2444294273757,403540,39136564,LABTSH,2013-11-17 09:43:00,0,1,1,1,0.721247563353,1.0,1.0,0.683391008597,0.623165905869,0.676462995446,0.783211383203,0.549119079676,0.670700695575,0.390476190476,0.681557929035,0.656811316646,0.650240561102,0.645760345789,0.618892348407,0.625324561677,0.535004818317,0.518368851061,0.503326547684,0.500977082743,0.500683820072,0.559226799312,0.523464362949,0.500811749115,3.1592861023e-67
--11118831884974,361004,36351760,LABTSH,2013-06-29 05:14:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.815021099676,0.904234091624,0.842281623314,0.835384501217,0.706715506716,0.858375182726,0.79155167948,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.536577986542,0.541464288286,0.508029525445,0.501248862371,0.817840614684,0.791145453245,0.779403311155,0.976426994388
-2199567772757,356776,35754979,LABTSH,2013-06-21 07:08:00,1,0,1,0,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.814692278417,0.849096934101,0.83328513052,0.80612153111,0.814491455076,0.833890425989,0.789976537618,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.537190238198,0.550443778739,0.50822215822,0.503611108466,0.806156458396,0.798742284608,0.790846637207,0.999999490081
--9763520756697,419643,36840342,LABTSH,2013-07-25 14:14:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.754056846419,0.762311275537,0.750633549277,0.710803375107,0.814672345527,0.784757392488,0.893925593052,0.90537598357,0.851201932736,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.560102845502,0.531890116023,0.519994167151,0.50385412632,0.502023859013,0.817305735844,0.830748870876,0.832431333513,0.996249222659
--11517002895440,392805,32939876,LABTSH,2013-02-15 16:57:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.838556404444,0.826082973142,0.821313082068,0.850256732793,0.859728478062,0.939396323754,0.887228651677,0.840928989599,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.541059348089,0.539002574476,0.509971905459,0.505083207552,0.856060291409,0.862493069713,0.86547032706,0.999999743207
--11494610321364,453294,39376339,LABTSH,2013-11-14 23:06:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.816754850347,0.803834021207,0.79351078908,0.787169587748,0.844414682338,0.859585975303,0.857596464929,0.825684964928,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.505698681961,0.504600335306,0.752004325508,0.741315334182,0.733784005786,0.999991363222
--11667488050425,462356,33590180,LABTSH,2013-03-06 18:01:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.839781687711,0.844919765442,0.862091731563,0.860714762034,0.861891402152,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.506385331862,0.816473513767,0.815076068825,0.810424321714,0.99999999124
-9583488225640,425591,34513213,LABTSH,2013-05-03 16:53:00,0,1,1,1,0.8125,0.830703012912,1.0,0.791796970184,0.794913049081,0.784778959088,0.881448025556,0.827541368435,0.630216694451,0.380753138075,0.70906200318,0.800467223881,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553027154197,0.521197640596,0.542385285598,0.504415339702,0.501966548587,0.666899943162,0.719545647139,0.751863007213,4.29256344115e-36
--5627113435440,361167,38970354,LABTSH,2013-10-26 14:32:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.817902216522,0.81820730512,0.836741409294,0.866395892252,0.860714762034,0.846236663052,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.578591973145,0.544841251108,0.539002574476,0.509734924238,0.504964682872,0.878619073596,0.88620406178,0.887087104417,0.999984390893
--1801167858340,398706,39605613,LABTSH,2013-11-17 05:07:00,0,1,1,1,0.8125,1.0,1.0,0.740933465313,0.7829698439,0.770788997442,0.703393369177,0.848527911945,0.805662703733,0.740622389307,0.706964716739,0.696646076318,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.559937652909,0.5282663565,0.533381282937,0.508135182425,0.501694719971,0.754251787559,0.739812271348,0.730694939664,1.0
-6415568061406,396545,35908737,LABTSH,2013-07-08 14:24:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.814829286971,0.823928435048,0.81820730512,0.827352646985,0.866395892252,0.773069816517,0.844365976153,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.815645550863,0.815786017425,0.814343484294,0.999991824393
--4817102150194,467886,33572254,LABTSH,2013-03-14 13:58:00,0,1,1,1,0.8125,0.830703012912,1.0,0.798068202586,0.803570690542,0.793436699078,0.817902216522,0.844930946844,0.79794760701,0.856950863756,0.899621264838,0.845101568277,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.535952541697,0.539002574476,0.509127727315,0.50535649154,0.86537038084,0.881855105231,0.885350932256,3.30133184031e-07
-10246931630978,396155,33252185,LABTSH,2013-03-06 21:35:00,0,1,1,1,0.8125,0.929515418502,1.0,0.851595475314,0.78461823183,0.793696311254,0.845221506092,0.847682408261,0.768166480228,0.94659206313,0.785473557032,0.694835620609,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533801293511,0.529989690422,0.509794477839,0.504152490016,0.73706701478,0.742796043362,0.72601535381,5.65968541499e-33
-9042817128947,397253,31675341,LABTSH,2013-01-05 09:57:00,1,0,1,0,1.0,1.0,1.0,0.7297753114,0.768299609888,0.758308094857,0.392457729469,0.704647017605,0.820614333021,0.980753138075,0.786251361251,0.734020240753,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.528604858898,0.514537252964,0.537411938773,0.505044332763,0.504599067512,0.84872031025,0.887948256858,0.910908484379,6.20955427612e-35
--9608457576465,432846,31948148,LABTSH,2013-01-09 11:00:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.804250545359,0.813623175848,0.885642301022,0.862110187232,0.877610456366,0.951170871285,0.835835531587,0.898578731548,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.571658585874,0.539057409788,0.550443778739,0.513961449671,0.509082418751,0.927566543714,0.939661920815,0.943393984204,8.16624941561e-05
--2837660267586,371162,36180444,LABTSH,2013-07-05 23:25:00,0,1,1,1,0.441860465116,0.0,0.0,0.606827696546,0.681811064621,0.649568953261,0.724698766587,0.602705167304,0.701967491156,0.96940509915,0.68291188693,0.645191255793,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.536809022044,0.515285202069,0.499929566985,0.501313444121,0.505402465997,0.843386828572,0.94551580349,0.978948763618,8.02992719849e-19
-945483570853,401643,36344516,LABTSH,2013-07-20 18:03:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.571697709373,0.711115539199,0.611773371227,0.72386794886,0.515705614091,0.652252580308,0.761484737485,0.912060690182,0.677723031302,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.547632530167,0.523595586688,0.520508298955,0.503797671746,0.503998696444,0.606926912055,0.568700596578,0.543570014897,1.00945002202e-06
--5729203180041,365979,37693020,LABTSH,2013-08-28 01:07:00,1,0,1,0,0.8125,0.929515418502,1.0,0.770415075572,0.685006934628,0.782388232263,0.857638652196,0.75392583005,0.711074730825,0.717236104029,0.783216646437,0.69963597926,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553903394042,0.526629032409,0.518997554745,0.505153178578,0.5002274523,0.745145399888,0.756327321932,0.780324577577,0.999970864375
--8615597440228,427771,39600430,LABTSH,2013-11-21 17:28:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.839263409856,0.870249847627,0.865027757664,0.854384873284,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.591878617801,0.550713873318,0.550443778739,0.514327147124,0.507261793209,0.908584774065,0.917793821655,0.920222708553,1.0
--6294217792255,427997,37256138,LABTSH,2013-08-05 01:13:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.754056846419,0.72567060002,0.770803896234,0.849140428127,0.784249867639,0.791163126086,0.714846031384,0.78093250613,0.913151080815,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.524495449261,0.510946844091,0.502082615504,0.500394543483,0.850868658302,0.860227497716,0.871264263541,0.0928148322119
-5033837341685,392861,34159223,LABTSH,2013-04-14 22:54:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.828502029753,0.816027016645,0.825096032806,0.851742437117,0.841518525084,0.86499755982,0.855335290013,0.856839684102,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.568536728729,0.53447145286,0.529989690422,0.505890236805,0.50273065222,0.804230532445,0.792756768189,0.787175168089,0.999999425871
-2758450705235,362393,39670650,LABTSH,2013-11-28 10:45:00,0,1,1,1,0.0,0.0,0.0,0.362121212121,0.600029381404,0.442479890604,0.333044323932,0.365063935867,0.51302961894,0.2,0.3,0.160627171794,0.72832805097,0.423674177729,0.502549388931,0.0339872295197,0.358441182672,0.429008791322,0.473228744772,0.326715449049,0.409187119883,0.297827483375,0.230648943712,0.240575356281,0.0
-9108926565292,384585,31832306,LABTSH,2013-01-17 22:01:00,1,0,1,0,0.721247563353,0.9375,0.0,0.754056846419,0.692123087115,0.731894023466,0.838896254678,0.713251166064,0.748162649349,0.638788445576,0.806963304333,0.738445366346,0.650240561102,0.674575323822,0.65176201513,0.656811041531,0.554069047837,0.526665927278,0.510946844091,0.503502008513,0.501849633912,0.804073427791,0.812916850451,0.823405539978,0.583681024657
-5872600648398,356812,31608502,LABTSH,2013-01-10 11:06:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.819303310065,0.823928435048,0.835894293867,0.829086764769,0.866395892252,0.867706631953,0.840012916614,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.821660708558,0.824415790609,0.822070405552,0.999999295004
-6527048352187,459431,36644963,LABTSH,2013-08-05 22:01:00,0,1,1,1,0.8125,0.830703012912,1.0,0.75871181605,0.822512579971,0.798690852971,0.853372301992,0.765256822871,0.835282316769,0.94659206313,0.883462594802,0.809477560731,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.561858477317,0.532507772568,0.529989690422,0.511252941763,0.504825548897,0.861722843367,0.869587569579,0.870277726957,9.40964613696e-14
--6164473252703,384065,36109710,LABTSH,2013-06-16 15:43:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.841923585944,0.870249847627,0.860714762034,0.849049123765,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.883018365096,0.891413749226,0.894908652266,0.999999253304
-3687799428341,395546,35444750,LABTSH,2013-06-08 16:09:00,1,0,1,0,0.826086956522,1.0,1.0,0.680630194684,0.642539150097,0.608929898712,0.834298780488,0.792394993607,0.710198662726,0.8,0.6217964124,0.639991085049,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.535929453518,0.517728718828,0.508984840768,0.506467983538,0.507497188046,0.979190635663,0.987038439503,0.98132636869,1.0
--10104834091776,364769,34208987,LABTSH,2013-04-07 15:15:00,0,1,1,1,0.8125,0.830703012912,1.0,0.6098891034,0.816754850347,0.820462278332,0.784233672676,0.805169267082,0.770322316122,0.827158075229,0.681816874534,0.694683184512,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.567869079446,0.534955074766,0.539002574476,0.507509612204,0.503099818079,0.623118809712,0.58523796899,0.570914817465,0.994493949132
--8333723084087,380164,39190100,LABTSH,2013-10-29 08:59:00,0,1,1,1,0.826086956522,0.0,0.0,0.713047475335,0.639763987208,0.621837736053,0.579816513761,0.462320271192,0.583215660964,0.780753138075,0.699871061265,0.6630430949,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.528334347367,0.512158653885,0.508984840768,0.500223377682,0.494540454713,0.355580574823,0.326196167825,0.223986890417,3.06528747473e-29
-10927925692044,428075,37576538,LABTSH,2013-09-16 11:35:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.794196170668,0.804395165551,0.87482137317,0.821565161722,0.799595300127,0.75607538076,0.893851202658,0.868974610692,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534472960217,0.539002574476,0.507509612204,0.506859373142,0.861443651494,0.889252170921,0.913129898265,0.998349078598
-3803639300701,395440,35867405,LABTSH,2013-07-04 11:04:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.82024462838,0.852546616866,0.873022467033,0.828269610157,0.67459792258,0.871549315382,0.848271900347,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542693038507,0.539002574476,0.512985549635,0.506279329754,0.897332101586,0.910591141581,0.911778439795,0.999998658028
--10710654928077,364583,33670708,LABTSH,2013-03-06 09:27:00,1,0,1,0,0.721247563353,1.0,1.0,0.72933605904,0.806551679099,0.720583396262,0.684765061103,0.618701201201,0.755730335678,0.733333333333,0.57856051994,0.707923197975,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.542402301799,0.522329750325,0.519994167151,0.502279179672,0.502113760792,0.787503602537,0.798969413372,0.54846495614,7.92738472682e-34
--3057483661229,362394,38379280,LABTSH,2013-09-21 13:32:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.815341072015,0.680983491987,0.75284761082,0.819253393969,0.866395892252,0.856185435066,0.877279470587,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.58308642011,0.544714542575,0.550443778739,0.512040626979,0.505366040926,0.859617146967,0.876629531648,0.899224416795,4.18562430652e-12
-11589451297058,462638,38122596,LABTSH,2013-09-26 07:12:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.807125961774,0.875615909806,0.745527855342,0.821290831363,0.823284558525,0.745195291538,0.762552787645,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.587104968037,0.546456158762,0.550443778739,0.512734237713,0.50586794815,0.777986329856,0.78117453869,0.780000457166,0.999992577892
--6387661803153,414034,37145819,LABTSH,2013-08-10 02:34:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.804302480147,0.850368445551,0.824026815733,0.810620707053,0.893062883648,0.833890425989,0.811996242908,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.587104968037,0.545214530361,0.541464288286,0.51092400526,0.504962430518,0.802435612553,0.81152999403,0.825391642221,0.999996309328
--10469432088235,415648,40614363,LABTSH,2013-12-19 22:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.773756332125,0.770385122859,0.842546010431,0.83833449895,0.832996949809,0.757940102055,0.917019358998,0.793919648757,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.503503869105,0.504743171184,0.599837827111,0.580058887031,0.623359452019,1.44065459861e-16
--8295404763738,360057,40463494,LABTSH,2013-12-17 17:48:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.808570525117,0.816298039907,0.664207055117,0.861673597677,0.82703651709,0.845410862607,0.9355400678,0.869327055096,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.5352325001,0.539002574476,0.505473675694,0.500955937113,0.811904428017,0.818082847501,0.82003377361,0.999999898682
-12041356412687,392022,32360406,LABTSH,2013-02-13 19:09:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.81711025756,0.875615909806,0.824026815733,0.82413615618,0.870249847627,0.838635804054,0.832994004205,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.537179687977,0.541464288286,0.508005045332,0.504099541569,0.875801058393,0.882498761448,0.882449536193,0.999999966766
-3000554941680,406748,31684554,LABTSH,2013-01-09 16:01:00,0,1,1,1,0.8125,1.0,1.0,0.528021595263,0.802101392403,0.809413272937,0.911703864997,0.908230658845,0.741070259432,0.8,0.955297141876,0.740426773386,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.573746615789,0.526958938002,0.550443778739,0.502488113049,0.499590231818,0.851310112769,0.851043882928,0.852508893198,0.000111436722071
-5703427173056,363961,31954062,LABTSH,2013-01-12 07:59:00,1,0,1,0,0.826086956522,0.0,0.0,0.721085230876,0.564062373332,0.702227358095,0.693379578922,0.709161255411,0.692506078365,0.422222222222,0.578716216216,0.595821092763,0.650240561102,0.616385848561,0.586995107235,0.58466802601,0.522432764463,0.505083419269,0.508984840768,0.49681534215,0.497027678903,0.644304142849,0.624670666499,0.658750142894,4.631654468e-25
--11325704552530,360203,34661619,LABTSH,2013-04-30 22:00:00,0,1,1,1,0.8125,0.929515418502,1.0,0.770415075572,0.807865524905,0.80424177511,0.874190376334,0.759857295178,0.815306052473,0.94659206313,0.726630871639,0.798964262188,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.558772420396,0.531788669873,0.533381282937,0.509631109297,0.506625494139,0.758122536459,0.769268778627,0.774502606318,2.90257383432e-06
-7255012352830,466087,32880728,LABTSH,2013-03-04 17:43:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.838556404444,0.8036886274,0.913488180819,0.778809950048,0.867005951852,0.95046176162,0.802963260458,0.870501239008,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.575552566025,0.540744931201,0.550443778739,0.513735683007,0.50534325922,0.810314384967,0.840236250636,0.849569685286,0.999637211275
-5500278447050,456872,37385212,LABTSH,2013-09-05 11:38:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.744814115466,0.765192888312,0.748232803967,0.878687369084,0.812532377244,0.759245697815,0.926183657568,0.810916767629,0.841090988612,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.551855436635,0.524984114833,0.519994167151,0.505313114,0.501852445919,0.789878969309,0.805618188673,0.805963988076,1.27181179829e-07
--7082168804198,447258,38472378,LABTSH,2013-10-02 08:31:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.754056846419,0.688181432524,0.746564705781,0.564106598017,0.660864319689,0.746868650864,0.776671505422,0.804691299736,0.764679255652,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.537543863043,0.521009006078,0.519994167151,0.501890371827,0.502028726863,0.612974028692,0.591178697262,0.554058028974,2.06561907187e-12
--3187652466316,379281,34115914,LABTSH,2013-03-29 22:00:00,1,0,1,0,0.162790697674,0.0,0.0,0.733014542928,0.62554745961,0.67659051773,0.500145304341,0.80513373303,0.669937524554,0.189196919692,0.580902703375,0.547369385717,0.650240561102,0.646281427091,0.606729254736,0.617481144548,0.52731280561,0.510239228915,0.508470469967,0.498973660606,0.493789726852,0.124108965622,0.109234378599,0.128525550674,5.47535079929e-88
--8180380855073,402558,33192678,LABTSH,2013-02-27 22:05:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.814301587645,0.821235090499,0.87780129669,0.842240859777,0.861035874544,0.677133239926,0.899939872253,0.869839425482,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.539868075057,0.555751254867,0.50943036114,0.506989678826,0.876581270882,0.892892352856,0.91130153838,0.999967195226
--10267170717575,356817,36746288,LABTSH,2013-07-22 17:06:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.81820730512,0.827352646985,0.827158075229,0.849653514241,0.835518589914,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.776976171192,0.782179216271,0.782760134823,0.999998442957
-12059308994351,437954,38271984,LABTSH,2013-10-18 08:06:00,0,1,1,1,0.162790697674,0.0,0.0,0.646135835216,0.52771628034,0.569632724537,0.822568294336,0.707244258014,0.379828640201,0.580753138075,0.6,0.466070577015,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.511926316286,0.501692836383,0.519994167151,0.505388214454,0.504904334303,0.298227250939,0.468007912062,0.688254969972,9.68060062772e-118
--9224146652469,364857,39478665,LABTSH,2013-11-13 11:09:00,1,0,1,0,0.8125,0.929515418502,1.0,0.657842790675,0.807398125672,0.799088860179,0.903444456854,0.839093634718,0.749895531073,0.469555385032,0.878091143455,0.617758554075,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.566055085768,0.529815256058,0.539002574476,0.508992997691,0.505546786344,0.788614093108,0.704951200035,0.69944204518,0.00554551927721
-9990754901025,397405,33929975,LABTSH,2013-04-06 16:50:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.893513136256,0.83328513052,0.836561187191,0.870249847627,0.865027757664,0.841932539382,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.878128365505,0.887400066572,0.89293085019,0.999999193318
--1060765060728,378883,36336813,LABTSH,2013-07-05 13:46:00,1,0,1,0,0.8125,0.95,1.0,0.689650724158,0.634163400216,0.736712895198,0.850128133267,0.72623675297,0.579444169712,0.2,0.66025029127,0.542001855725,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.442660830869,0.553302965396,0.340073132122,0.422695922439,0.593826429282,0.512197379611,0.513535712198,3.4679622596e-80
--11704622740074,434898,33209198,LABTSH,2013-02-19 23:29:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.814093691364,0.791890189916,0.808948990333,0.828158999598,0.827158075229,0.79729730628,0.839116820202,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.569567829113,0.53525406267,0.541464288286,0.506546243982,0.504272206397,0.695126205592,0.668468919201,0.64520940804,0.996586881246
--3692587657375,392657,32536756,LABTSH,2013-01-20 23:36:00,0,1,1,1,0.8125,1.0,1.0,0.768902643052,0.808175151721,0.79760925727,0.862118262485,0.76430778862,0.836343399567,0.879626443461,0.748253116107,0.783064732648,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.564397420605,0.532391896447,0.530502782545,0.507419943856,0.504546108786,0.758607260418,0.776614729615,0.79268810297,0.285429209128
--6367443007785,396130,35308762,LABTSH,2013-05-20 21:21:00,1,0,1,0,0.8125,0.929515418502,1.0,0.824768585728,0.806735645701,0.795926797839,0.821868170238,0.863122506539,0.83846181976,0.803235588078,0.896353829244,0.875176470208,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.566442290797,0.53361928446,0.529989690422,0.50718843832,0.504328224259,0.842245486995,0.856502831406,0.860249108184,0.9999999939
--5712478594626,467345,40316052,LABTSH,2013-12-15 07:32:00,1,0,1,0,0.8125,0.0,0.0,0.832248027839,0.653546638535,0.66549849889,0.37716499861,0.743113285683,0.609655104671,0.730169837987,0.841849787861,0.47228459887,0.782447187593,0.684152284793,0.63641322029,0.632404586216,0.533837351471,0.515356524643,0.539002574476,0.497526524621,0.497420947898,0.538668485862,0.416704172183,0.274465732246,8.53088353302e-26
--3729642280288,458015,34151695,LABTSH,2013-04-08 12:29:00,1,0,1,0,0.441860465116,0.0,0.0,0.444275037856,0.550547570312,0.52672843612,0.533841463415,0.336991484989,0.540712288292,0.380753138075,0.255555555556,0.521524447052,0.650240561102,0.645760345789,0.616171287353,0.625324561677,0.520066471496,0.503469441855,0.480783037393,0.493493111181,0.499421466462,0.436693622312,0.256456513762,0.112453073349,6.37095719594e-198
--11862231502258,397986,35845501,LABTSH,2013-06-15 10:28:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.631819172821,0.718169332973,0.716062311468,0.765425709824,0.853680452675,0.744568859562,0.875918367347,0.818337711933,0.727835944501,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.559079162879,0.531083522704,0.519994167151,0.50519853897,0.503328488144,0.560154112415,0.494539556474,0.569223637427,1.50229891137e-09
-1450284101655,394662,33405422,LABTSH,2013-03-15 22:16:00,0,1,1,1,0.8125,0.929515418502,1.0,0.798068202586,0.802868547455,0.795039639128,0.494238848899,0.761512038909,0.800524754746,0.985539515537,0.900572039093,0.80704890596,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.561858477317,0.529013638023,0.529989690422,0.510976670965,0.506546575496,0.731212234264,0.753250669482,0.741589675001,8.25040002973e-17
-2589879678099,387635,40465169,LABTSH,2013-12-31 08:14:00,0,1,1,1,0.721247563353,1.0,0.0,0.708283321699,0.696240337439,0.622724507412,0.412143068785,0.499516317016,0.681031872,0.780753138075,0.586576576577,0.757700478928,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.548908665684,0.527846176275,0.531497075666,0.523202175948,0.511727713945,0.903974344836,0.841750315196,0.798703766298,4.5337802243e-55
-3857623843100,431600,33381979,LABTSH,2013-03-21 00:35:00,0,1,1,1,0.8125,0.929515418502,1.0,0.802901935771,0.753264146401,0.767582008848,0.833764458855,0.776208682151,0.77970325913,0.561484737485,0.948545111446,0.669651344012,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.547097978183,0.522746103535,0.529989690422,0.50721896871,0.505411332525,0.818782773883,0.847437129201,0.852623765745,0.947167452633
-3178411787569,469617,33956816,LABTSH,2013-04-07 12:43:00,0,1,1,1,0.8125,0.830703012912,0.0,0.798068202586,0.804564684575,0.818220312292,0.823928435048,0.88472590395,0.836203003703,0.929424872288,0.866204888302,0.833424362774,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.543897825222,0.539002574476,0.510634214712,0.50523916663,0.843682022926,0.848084070388,0.855749324603,0.999942547673
--8974704973088,411850,37530566,LABTSH,2013-08-20 12:51:00,0,1,1,1,0.721247563353,0.0,0.0,0.812144666724,0.759077442649,0.753069512427,0.818078184345,0.857824479832,0.661711730009,0.714637851925,0.731666240586,0.665616882228,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.545764921354,0.522483563309,0.508984840768,0.49929594231,0.499918447139,0.731844572404,0.862840927514,0.933369807653,1.60145415881e-29
-11006179893866,396648,32056391,LABTSH,2013-01-24 15:16:00,0,1,1,1,0.721247563353,0.0,1.0,0.385730676954,0.587206200436,0.474393952374,0.178787878788,0.532467243669,0.553124994449,0.388524590164,0.396666666667,0.556410871783,0.650240561102,0.645760345789,0.616171287353,0.625324561677,0.524625212052,0.501522114766,0.480783037393,0.497665123178,0.496298161717,0.270956624855,0.272264262242,0.218297966884,0.0
-11678025190950,429946,33925618,LABTSH,2013-04-08 09:10:00,1,0,1,0,0.721247563353,1.0,1.0,0.744814115466,0.713396346107,0.760317517617,0.733644740207,0.792722505982,0.802913285521,0.843537414966,0.905243552546,0.829239257307,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.546971965246,0.522812912298,0.519994167151,0.50227529551,0.50194612456,0.868332400759,0.906889605774,0.922842497554,1.14039329479e-08
--11617309300418,430679,32403468,LABTSH,2013-01-26 22:01:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.812170875503,0.823905192794,0.824026815733,0.814750553666,0.684042951075,0.838635804054,0.811079269981,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.585958478961,0.546445641426,0.541464288286,0.512517206114,0.506356337301,0.86694905006,0.875376021311,0.877594837302,0.999994269703
-11517660237937,442499,40445274,LABTSH,2013-12-26 09:58:00,0,1,1,1,0.8125,1.0,1.0,0.768586592328,0.806085424776,0.790876925691,0.929845466278,0.806536664024,0.779677274434,0.917899204244,0.751983878766,0.712687163025,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.566821521737,0.535965467326,0.550443778739,0.50822215822,0.50462353965,0.851089416311,0.874885535894,0.882196200539,0.319915154577
--8254627011851,384402,39824168,LABTSH,2013-11-21 14:53:00,0,1,1,1,0.721247563353,0.0,1.0,0.626664130056,0.519030435103,0.675226996178,0.640016724754,0.61491888224,0.622034724225,0.56940509915,0.573154581837,0.608057478739,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.531288137814,0.514708096448,0.537411938773,0.504601031811,0.498247233912,0.862569255917,0.83950121661,0.723434000063,2.36714955023e-71
--3667992324999,374711,37284633,LABTSH,2013-08-15 22:00:00,0,1,1,1,0.721247563353,0.0,0.0,0.718120299745,0.627026600317,0.719254752365,0.852436973933,0.640179604998,0.686308375561,0.6,0.760005001384,0.807239619612,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.531445328071,0.515786184282,0.510946844091,0.501673204511,0.500710202167,0.613803251132,0.602012456916,0.626073225967,4.96671150321e-15
--5712478594626,467345,39112744,LABTSH,2013-11-04 00:26:00,1,0,1,0,0.441860465116,0.0,1.0,0.497798207931,0.554110101369,0.586641437105,0.350480048005,0.854972871381,0.538114886167,0.8,0.884773022049,0.524235596342,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.53714046763,0.516807693567,0.528395166143,0.505265528542,0.499339169408,0.485292014575,0.589262587252,0.725793953675,5.55623697413e-53
--7507599855153,427951,37358629,LABTSH,2013-08-22 11:15:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.7789909644,0.786322758023,0.776970098217,0.850838831189,0.812757979668,0.74659206313,0.886948294608,0.888302978382,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.533839380957,0.539002574476,0.513288900145,0.507798803347,0.863897116082,0.885009463971,0.891331243751,0.668090636922
-8979475802252,419534,34698444,LABTSH,2013-05-14 02:16:00,0,1,1,1,0.8125,0.929515418502,1.0,0.857842790675,0.818450987467,0.81414558831,0.79351078908,0.842240859777,0.851420015253,0.742786154263,0.801678758942,0.886186825862,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.531338887638,0.529989690422,0.508026323355,0.505226000517,0.881145537273,0.896923756535,0.907177683158,0.999999998802
-7049502016497,357156,37450961,LABTSH,2013-08-30 08:19:00,0,1,1,1,0.8125,0.0,0.0,0.768586592328,0.76454281372,0.770911070076,0.725317296746,0.889161855636,0.648364516792,0.780753138075,0.400998578536,0.791647772757,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.564674641394,0.534878620338,0.550443778739,0.512321982612,0.502993693355,0.977649782095,0.990297652598,0.996218629508,1.0
-4499521166583,361070,38615375,LABTSH,2013-10-20 15:43:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.802968299142,0.784892007676,0.896552354263,0.841389466512,0.750887438066,0.880774191558,0.806013127902,0.83298869772,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.564184515396,0.529846113024,0.539002574476,0.504735427489,0.500559160891,0.610604583589,0.613126179351,0.594780654362,1.93388743209e-07
-11811077195591,464918,38469198,LABTSH,2013-10-16 00:49:00,1,0,1,0,0.8125,0.830703012912,0.0,0.824768585728,0.68324423945,0.785911325289,0.646256905031,0.651546359005,0.683376465921,0.737450887946,0.594978522526,0.694508436061,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.560616605054,0.529595717339,0.529989690422,0.506330489964,0.501871073777,0.367399406018,0.25487981134,0.223088037833,1.56415434124e-12
-1540174283202,427239,33421708,LABTSH,2013-03-11 22:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.703312647031,0.790360910062,0.79873066372,0.833954826264,0.772056696345,0.831922055571,0.906855987096,0.912080745161,0.810048278319,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.535284737427,0.544333308272,0.512252508457,0.507788250123,0.903025639995,0.913401082326,0.940136197291,0.946520062971
-10725570253163,401639,36654257,LABTSH,2013-08-06 09:25:00,0,1,1,1,0.8125,0.929515418502,1.0,0.769615715606,0.784108415059,0.77972090126,0.657535540674,0.756651376889,0.738479641646,0.738602018842,0.654105985833,0.74510366733,0.782447187593,0.736457474868,0.690173147414,0.700503272381,0.54397746385,0.523756764208,0.510868018904,0.500851958756,0.502483210801,0.73243272945,0.761139093492,0.781517908671,9.40942874848e-11
-10983289517057,452029,34415742,LABTSH,2013-04-21 12:51:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.816758182752,0.822395603613,0.885601622933,0.83328513052,0.840374418102,0.870249847627,0.865027757664,0.859262710486,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.591878617801,0.550713873318,0.550443778739,0.514327147124,0.507261793209,0.880166606135,0.885511690681,0.887309827561,0.999999775243
-105994753501,381023,36938634,LABTSH,2013-08-21 11:09:00,0,1,1,1,0.721247563353,0.0,0.0,0.777606862309,0.584670072136,0.601923099858,0.55428640425,0.764173384544,0.703009375075,0.4,0.875055153711,0.678769528456,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.542071411865,0.524930081603,0.508984840768,0.513397341893,0.508900548113,0.74107814,0.808611400704,0.82138076334,1.93780061431e-72
-11081014001232,429952,38107763,LABTSH,2013-09-30 09:09:00,1,0,1,0,0.721247563353,1.0,1.0,0.443736771142,0.502760044625,0.569690172828,0.642713779098,0.403072649281,0.613064395098,0.6,0.538602407861,0.469226452084,0.650240561102,0.616385848561,0.583946568793,0.599932757913,0.519213748617,0.505909608869,0.500565340065,0.498390007821,0.498700795229,0.570386785061,0.695151422723,0.732020012564,6.33380619886e-29
--2543529172303,426228,39131127,LABTSH,2013-10-20 13:26:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.693295281736,0.785437250626,0.8568253813,0.741537368744,0.71458175093,0.871184341489,0.802606823175,0.763351081415,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.564248668882,0.532208151462,0.539002574476,0.504896552242,0.503059584031,0.566078285738,0.443496447436,0.389067165423,9.59466275141e-10
-4334181144134,371667,35359501,LABTSH,2013-06-06 15:14:00,0,1,1,1,0.721247563353,1.0,0.0,0.858677034032,0.773464427721,0.72568674265,0.61711095947,0.886791971443,0.792293127571,0.926183657568,0.842941869344,0.652972435,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.56878112347,0.622133271282,0.534374830545,0.690863653201,0.598509586139,0.983358109112,0.986457581412,0.985080346817,1.0
-3306865424901,397378,32703558,LABTSH,2013-02-14 20:14:00,0,1,1,1,0.8125,1.0,0.0,0.815525854775,0.688018134671,0.729691887163,0.748652181429,0.889047743055,0.660916791824,0.703830061152,0.739804139543,0.701634638,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561754663375,0.528735597289,0.529989690422,0.507651431401,0.503258249289,0.733439173639,0.738672982098,0.758318077526,1.30010646724e-06
--6184983103585,422167,39969939,LABTSH,2013-12-05 20:22:00,0,1,1,1,0.8125,0.929515418502,1.0,0.818518488529,0.736137467099,0.774574576346,0.794068224427,0.790846375961,0.790615122748,0.903235588078,0.930383545395,0.758295023048,0.782447187593,0.718940845638,0.681153310325,0.688610573577,0.561198733154,0.529532061105,0.529989690422,0.519189800897,0.508933928788,0.550816673891,0.546658391638,0.525627645726,8.9198457258e-130
--5621587824404,414282,35786424,LABTSH,2013-06-14 15:55:00,1,0,1,0,0.8125,0.929515418502,1.0,0.807734174804,0.797047853092,0.787771562874,0.742684696008,0.761941703066,0.805990209296,0.930867167025,0.479686061776,0.775556731201,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.567456503706,0.534213972366,0.52803351545,0.507255755379,0.50210385337,0.704945918581,0.648691879191,0.6478483982,2.03667095628e-14
-7003397542489,457973,35377914,LABTSH,2013-06-09 03:52:00,0,1,1,1,0.721247563353,0.0,0.0,0.754056846419,0.736066428567,0.703041401067,0.838896254678,0.78886617335,0.718100075971,0.349555385032,0.780637090752,0.730051467151,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.553801089662,0.528029828487,0.510946844091,0.50643720736,0.50187644552,0.518638021358,0.459434360335,0.414137311051,1.00127721857e-05
--1937743823833,440844,32238971,LABTSH,2013-01-12 04:54:00,0,1,1,1,0.8125,1.0,1.0,0.768586592328,0.806703808061,0.806308111318,0.851746616866,0.874241267565,0.784028135651,0.81940509915,0.802971454776,0.768648049134,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.570612271331,0.535252400712,0.54736048121,0.513075593655,0.504723362941,0.776716804143,0.750218100621,0.712777453663,1.32354150223e-14
-7137163335931,426195,37131675,LABTSH,2013-08-26 01:23:00,0,1,1,1,1.0,1.0,1.0,0.696216380724,0.576071417602,0.68165109976,0.640342745002,0.500778221581,0.615834245179,0.772742519535,0.518883823249,0.516084536779,0.72832805097,0.60529425083,0.593351778139,0.576391218294,0.535565932776,0.518967651255,0.464208200565,0.494181658199,0.496590472142,0.370898239124,0.267767049902,0.234249788839,1.14632962484e-124
-4402328075291,355727,32650292,LABTSH,2013-02-19 09:27:00,1,0,1,0,0.441860465116,0.0,0.0,0.602863491061,0.589376421107,0.601341370676,0.490928009189,0.624867357288,0.592065990453,0.580753138075,0.605952380952,0.609341297616,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.527620555967,0.512238686945,0.519994167151,0.504018312815,0.502983339915,0.544412790654,0.51233976067,0.514105969853,2.19830626489e-63
-2487475468723,424755,38181190,LABTSH,2013-09-29 12:34:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.811736993423,0.823928435048,0.74348806798,0.823135154491,0.866395892252,0.82864538416,0.841346693286,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.809920003516,0.821188312094,0.82319930457,0.999984133275
--4218770068289,459390,38056158,LABTSH,2013-09-20 16:58:00,0,1,1,1,0.721247563353,1.0,0.0,0.61893898485,0.707065662644,0.736226518916,0.543619683069,0.648761423761,0.792267349782,0.537147034634,0.843485575631,0.885053151024,0.650240561102,0.674575323822,0.643891502397,0.656811041531,0.552921784639,0.531818363648,0.503455589626,0.511667378705,0.513785796764,0.987389244669,0.990073019964,0.993291244093,1.0
--12218841015176,384041,32442299,LABTSH,2013-01-25 06:41:00,0,1,1,1,0.8125,0.929515418502,1.0,0.803489280519,0.749610823323,0.764403117453,0.743668302193,0.682128773135,0.695590900031,0.771428571429,0.919596035172,0.622037262343,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.548145996548,0.523135499171,0.52803351545,0.50502081884,0.503577182837,0.619646561666,0.66204382052,0.705772183339,1.0
-7045970058431,356258,35615629,LABTSH,2013-06-26 22:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.731884926464,0.73240944675,0.744739484092,0.850204257915,0.871416718021,0.739646347408,0.758974358974,0.650877552284,0.615808722055,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.558162539088,0.525550427734,0.529989690422,0.50783315286,0.504826146754,0.694922959078,0.682327865387,0.682699023856,9.57285597334e-05
--7624882133250,360752,33797686,LABTSH,2013-03-26 16:23:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.747988700826,0.703257579368,0.909954977905,0.765871329492,0.700269184173,0.968253968254,0.679768077601,0.73221265929,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561577826724,0.532840023446,0.550443778739,0.503858744055,0.502498543789,0.771427891337,0.782624110159,0.685874892554,2.04704586057e-31
--7970765028213,461334,35697874,LABTSH,2013-06-09 09:10:00,1,0,1,0,0.0,0.0,0.0,0.648716976497,0.58346046375,0.63669905165,0.8,0.278023159636,0.653727997188,0.4,0.594444444444,0.408803629321,0.650240561102,0.616385848561,0.575111269937,0.58466802601,0.507760283816,0.501314455193,0.489033096195,0.49348625078,0.497086585049,0.747874791516,0.926898721254,0.978113206975,4.75107879536e-194
-3022030492944,388471,31726969,LABTSH,2013-01-10 01:31:00,0,1,1,1,0.8125,1.0,1.0,0.707394785461,0.717721965066,0.716048969262,0.2,0.820874078781,0.542000718774,0.6,0.372872263303,0.571431789293,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.549965925702,0.521611374957,0.533381282937,0.508753616985,0.503552967287,0.566347484733,0.52696401652,0.530914448679,8.97925570683e-26
-1647082873019,426386,38484582,LABTSH,2013-10-22 12:31:00,0,1,1,1,0.721247563353,0.0,0.0,0.51486327315,0.705058488629,0.573411238161,0.453629148629,0.376123606124,0.663837156107,0.493796616336,0.673814945766,0.360549132948,0.650240561102,0.616385848561,0.595389407525,0.599932757913,0.530314970038,0.513099748643,0.520791757866,0.50776190069,0.505937200127,0.48703410611,0.626504489537,0.726214538435,0.0
-3389746800850,456978,37930459,LABTSH,2013-09-18 22:11:00,1,0,1,0,0.721247563353,0.9375,1.0,0.708283321699,0.556495173527,0.663107717661,0.621268310356,0.694413097101,0.670698289923,0.4,0.85518779593,0.645330526947,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.550221444918,0.527460430077,0.522467456931,0.513489759785,0.506684708358,0.750016859577,0.682332381806,0.54757932388,2.17536952912e-18
-4612852474065,361014,38418215,LABTSH,2013-10-07 08:09:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.694475643864,0.752013615528,0.724515052167,0.776924749544,0.779506133528,0.70499395196,0.737659067404,0.89007020649,0.859271463164,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.555236814687,0.527607031989,0.519994167151,0.499237626807,0.501328260104,0.723533257374,0.689659126414,0.670507313287,1.08387089084e-06
--10389843463153,425962,34156242,LABTSH,2013-04-08 09:01:00,1,0,1,0,0.8125,0.830703012912,1.0,0.810381377881,0.626247464625,0.717702978782,0.878485340823,0.691176688787,0.764898790744,0.768253968254,0.823371974251,0.798925482594,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556892449246,0.52688404268,0.539002574476,0.521031389862,0.509988397847,0.772408831685,0.799900149543,0.80916664338,4.42303725788e-10
--6966091301136,402353,33017554,LABTSH,2013-02-14 14:43:00,1,0,1,0,0.8125,0.830703012912,1.0,0.681445997073,0.779518514444,0.779410567389,0.697720128079,0.871935486713,0.835811995856,0.9469341713,0.874784853442,0.800320303081,0.782447187593,0.718940845638,0.679071337105,0.688610573577,0.558992343903,0.530647281248,0.553302965396,0.508039808175,0.503130863711,0.853887165674,0.871568926838,0.887755841962,0.787917010819
-11414324479105,458311,36790780,LABTSH,2013-08-01 00:55:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.783433454954,0.81599088755,0.840342745002,0.845983161098,0.780722263112,0.792658144066,0.725714571844,0.792818640644,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563691317678,0.532450738547,0.529989690422,0.507948723077,0.503913854287,0.915561663869,0.932810490033,0.938522533841,0.999999969783
-8971189417888,466560,39489470,LABTSH,2013-11-28 04:34:00,0,1,1,1,0.8125,1.0,1.0,0.653524203736,0.780822075504,0.801571406579,0.888033055911,0.829333160911,0.68934824101,0.740035526828,0.851105057784,0.807111758231,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.561643513551,0.530293232501,0.530502782545,0.505826146766,0.501230703608,0.755610993719,0.758118675456,0.757764662016,0.161588973963
--880032373772,397812,35170118,LABTSH,2013-05-11 01:48:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.816758182752,0.788100598243,0.822581173515,0.824026815733,0.816015292643,0.870249847627,0.81845043008,0.776312612914,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.566821521737,0.53408603646,0.541464288286,0.506411299845,0.502705520742,0.806397834943,0.803099921415,0.801779253724,0.999999992399
--8631004244098,364688,33556815,LABTSH,2013-03-12 23:50:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.606826329002,0.763652172108,0.646256905031,0.695100901046,0.640336834056,0.543390468079,0.498800739799,0.725488877605,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.562819129234,0.530237217534,0.529989690422,0.505311238451,0.501787062754,0.479877749915,0.301537894096,0.200102272528,1.0
-7525615450158,403590,39633225,LABTSH,2013-12-06 19:22:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.81711025756,0.855191528695,0.808948990333,0.833413676791,0.827158075229,0.834322808424,0.840181802937,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.567234053907,0.534935624541,0.529989690422,0.505698681961,0.502946199006,0.793192210887,0.796522821727,0.798485783676,0.998002321839
--12436949623244,431798,35011384,LABTSH,2013-05-04 20:18:00,0,1,1,1,0.721247563353,0.9375,1.0,0.689497459446,0.760585525351,0.786438587433,0.847235978568,0.781389955993,0.87284454314,0.77041773321,0.922938469447,0.805723823358,0.650240561102,0.674575323822,0.65176201513,0.656811041531,0.560874378418,0.5406525836,0.528395166143,0.513802198285,0.507694571045,0.808181193935,0.812679578423,0.807153407138,0.0233067938195
-11224238259840,393358,31594549,LABTSH,2013-01-04 00:48:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.830340241574,0.806442370395,0.799526418472,0.856432999515,0.824940136684,0.902806664425,0.822036090882,0.787132325949,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.566821521737,0.535927402583,0.541464288286,0.510332342439,0.505974896093,0.783810503755,0.800427676947,0.806084594104,0.999999350832
-2756881865711,451428,37609078,LABTSH,2013-09-19 17:54:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.827352646985,0.827158075229,0.849653514241,0.835518589914,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.624425545501,0.539002574476,0.507509612204,0.592884075561,0.829140996705,0.864337009898,0.879561715157,1.0
-11922574895990,431535,34248781,LABTSH,2013-04-20 02:54:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.816866398916,0.902910072345,0.853927439637,0.830867836976,0.932045482541,0.86537779865,0.790171842304,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.536577986542,0.541464288286,0.506570726178,0.503480774508,0.785161195653,0.788974268877,0.805053696896,0.999997188863
--1560487432354,466534,40548812,LABTSH,2013-12-29 18:19:00,0,1,1,1,0.0,0.0,0.0,0.468563253709,0.637557877494,0.657655333103,0.820452499219,0.438189755002,0.547809110883,0.415599662392,0.556446156685,0.556245594556,0.72832805097,0.60529425083,0.599103452668,0.576391218294,0.53799955849,0.518228043128,0.462256031289,0.490990719357,0.496170499967,0.684941767061,0.651712931284,0.618307319621,2.06102111244e-80
--309001537711,419471,34650757,LABTSH,2013-04-15 14:04:00,1,0,1,0,0.721247563353,1.0,1.0,0.74873835789,0.771291586384,0.721618141162,0.655712577196,0.720343114673,0.730158401757,0.324287652646,0.739094521976,0.771864300265,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.551855436635,0.519386801464,0.519994167151,0.502395930936,0.49901004134,0.819841998222,0.788883516714,0.716490348237,4.33745999184e-13
-10195985629831,391611,31668010,LABTSH,2013-01-04 22:01:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.793398042835,0.816027016645,0.772217718036,0.808948990333,0.820788184089,0.846641864968,0.821624979936,0.837347416756,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.503503869105,0.501848723545,0.742921145714,0.736498523154,0.725832476194,0.999569743089
--8981174268377,424879,35920374,LABTSH,2013-06-14 06:02:00,0,1,1,1,0.8125,1.0,1.0,0.805421138254,0.830340241574,0.824535386579,0.823905192794,0.875975966127,0.833558986372,0.956624629762,0.871163787428,0.839391612169,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.574395751152,0.540074626319,0.550443778739,0.511434984093,0.506061803111,0.781475538224,0.780574495569,0.781485530545,0.999966718789
-2337531336791,457555,37555626,LABTSH,2013-09-10 12:01:00,0,1,1,1,0.8125,1.0,0.0,0.824768585728,0.822512550322,0.773837660407,0.721498766587,0.694188449175,0.767945214805,0.61853743979,0.74251078441,0.695232416429,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.579200267403,0.54400338224,0.550443778739,0.519699144455,0.512479955387,0.942396968646,0.957395991645,0.959155585864,8.46202733119e-08
--9320463457823,403500,40467184,LABTSH,2013-12-17 22:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.862647373607,0.715021862918,0.758330729385,0.760422961758,0.643542921282,0.79944512189,0.727228327228,0.906581976376,0.758052921958,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.555755868598,0.528205403435,0.518997554745,0.504073342306,0.504754623961,0.851044736155,0.873894783787,0.883293615659,9.31664731696e-19
-7923389466416,367626,33508958,LABTSH,2013-03-20 20:21:00,0,0,1,0,0.8125,0.929515418502,0.0,0.824768585728,0.816754850347,0.821334339867,0.845221506092,0.854335772207,0.861342575332,0.850865486342,0.891768217896,0.87250284196,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.507316970896,0.503755444756,0.840438801366,0.858894264665,0.872301695228,0.999939671859
-2239942036183,471343,39923436,LABTSH,2013-12-13 05:20:00,1,0,1,0,0.8125,0.921052631579,1.0,0.805421138254,0.806703808061,0.801210186599,0.817833840453,0.826635979079,0.821584788195,0.806333339012,0.748232688827,0.816210079747,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572701760878,0.536544936879,0.529989690422,0.506330489964,0.502665110406,0.777664721022,0.782882799338,0.781589117672,0.999999908419
-1019063232016,385499,32010154,LABTSH,2013-01-24 07:01:00,1,0,1,0,0.8125,0.0,0.0,0.778720755112,0.792817505169,0.793611738703,0.749652022272,0.867038915203,0.816741600482,0.943115124153,0.909289509848,0.786914184627,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.563021933408,0.531309393534,0.539002574476,0.505354561262,0.502201191857,0.780569522217,0.809292731898,0.80559650803,0.4602942486
-5691187654673,402730,35130071,LABTSH,2013-05-25 02:21:00,1,0,1,0,0.8125,1.0,1.0,0.661116308468,0.794652384586,0.778179579987,0.79351078908,0.676810612132,0.758543090518,0.94659206313,0.784655398645,0.700672168606,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.530129585731,0.529989690422,0.507356635459,0.504891111557,0.666459139252,0.655834447844,0.663108842218,0.528791629569
--11695338349011,411997,33286254,LABTSH,2013-03-01 12:27:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.79601725837,0.61984292775,0.76301617751,0.665640366024,0.610436300175,0.729557375063,0.6,0.669333778834,0.675798838422,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.545074633723,0.522500396409,0.519994167151,0.50386931211,0.499374264785,0.937414612921,0.946693073245,0.960845923009,3.22636202044e-42
--8513067850412,437933,40573551,LABTSH,2013-12-21 13:33:00,0,1,1,1,0.721247563353,0.0,1.0,0.794137003889,0.811822222863,0.782520678545,0.798286196656,0.768886950336,0.842871131456,0.76606727286,0.819188809036,0.762448426808,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.560102845502,0.531890116023,0.519994167151,0.504751919888,0.504268006629,0.767451127211,0.780035259146,0.79079707711,0.254554705961
-1040372137182,406341,33006030,LABTSH,2013-03-06 16:57:00,0,1,1,1,0.8125,1.0,1.0,0.800047798349,0.803424021983,0.7527936226,0.849548269759,0.786026906127,0.808203772122,0.914846031384,0.807626882815,0.80187712567,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.556319119767,0.507313543612,0.503607247056,0.878868445635,0.88176042213,0.883144120453,0.000333091558586
-10744907153475,376580,40255578,LABTSH,2013-12-20 12:54:00,1,0,1,0,0.721247563353,0.0,1.0,0.611659291363,0.749558087352,0.659906147461,0.459915558023,0.690416242385,0.730233388088,0.937147034634,0.783653112532,0.80222173452,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.548859929099,0.52534306241,0.508984840768,0.504941144842,0.504413665088,0.644124205751,0.566059511748,0.561185454489,0.646656728023
-2758450705235,362393,39672401,LABTSH,2013-11-28 00:00:00,0,1,1,1,0.0,0.0,0.0,0.329047007174,0.500029381404,0.435482061052,0.333044323932,0.365063935867,0.490028734585,0.195555555556,0.291538461538,0.183194273309,0.72832805097,0.423674177729,0.502549388931,0.0339872295197,0.353917584011,0.424740506188,0.464208200565,0.323726238768,0.410318690133,0.1738429013,0.10944703878,0.101657640548,0.0
--5411352817163,445142,38661467,LABTSH,2013-10-03 20:35:00,0,1,1,1,0.307692307692,0.0,0.0,0.6098891034,0.5843255652,0.736176388571,0.784233672676,0.661153282598,0.672340314548,0.520025308209,0.748433051005,0.620336410225,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.565032186376,0.533613775287,0.529989690422,0.507924242837,0.503307174288,0.5254259346,0.491007582746,0.494079258339,1.52086497992e-06
-5531153367443,356168,34823146,LABTSH,2013-05-12 09:43:00,1,0,1,0,0.721247563353,1.0,1.0,0.754056846419,0.752013615528,0.753223092578,0.838896254678,0.809634887911,0.756292136774,0.746570310405,0.645121912597,0.832153974201,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.525744439655,0.519994167151,0.502100658952,0.501393942997,0.735132577091,0.731742214951,0.727672720411,0.994458879655
-6844142438076,385152,32374051,LABTSH,2013-02-10 08:23:00,0,1,1,1,0.8125,0.830703012912,0.0,0.768586592328,0.794196170668,0.807574856349,0.776970098217,0.824367200271,0.810693812244,0.877342381173,0.799433579537,0.856952168152,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.532494058268,0.539002574476,0.510488291183,0.506669966137,0.764899412196,0.779014212228,0.797769618802,0.942034874619
-7626186539750,382227,37576737,LABTSH,2013-09-10 17:04:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.805169267082,0.830012823073,0.827158075229,0.860714762034,0.842635174297,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.782437122803,0.785809412678,0.785227036457,0.999991975799
-3733550309411,467249,37362885,LABTSH,2013-08-24 13:53:00,0,1,1,1,0.441860465116,1.0,1.0,0.754056846419,0.724425784169,0.716477779995,0.915239475519,0.846682486127,0.726137699664,0.737895995218,0.631277219339,0.787939244165,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.55104617631,0.526390528819,0.519994167151,0.503537108535,0.503700833982,0.621224467359,0.613890345567,0.643301305672,4.96741777952e-06
--3692945496491,371891,35981581,LABTSH,2013-06-12 15:27:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.839263409856,0.870249847627,0.865027757664,0.841932539382,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.868895562017,0.879154679213,0.883521519265,0.999999085213
--2674935501860,397904,36042286,LABTSH,2013-06-26 03:44:00,1,0,1,0,0.8125,0.929515418502,1.0,0.857842790675,0.818450987467,0.811479327304,0.79351078908,0.842240859777,0.81296042271,0.942786154263,0.818371257921,0.871063202493,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.531338887638,0.529989690422,0.506408097456,0.503572958307,0.810856512062,0.830913091291,0.847579636875,0.999994549247
-11553484817152,413729,34132460,LABTSH,2013-04-12 16:13:00,0,1,1,1,0.721247563353,1.0,1.0,0.744814115466,0.806706205262,0.732967055622,0.85993046676,0.719578955548,0.856203195418,0.750158237225,0.740352974449,0.818817538652,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.560568768578,0.533900207042,0.519994167151,0.524712961266,0.511476998288,0.871401847787,0.89101964015,0.872423600537,6.99595609432e-08
--191086728848,403412,36520863,LABTSH,2013-07-16 21:07:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.796150030441,0.80482295584,0.823928435048,0.835672632056,0.844595391341,0.858904106975,0.868832353954,0.829142998733,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.505698681961,0.503641725694,0.807520824397,0.808870904007,0.812393048775,0.999995263299
-6451677327103,443885,35784064,LABTSH,2013-07-04 19:09:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.795447887354,0.818359812867,0.845221506092,0.854335772207,0.867612936568,0.782611518088,0.899822992573,0.853030734868,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.507316970896,0.505550597421,0.862801377653,0.887112820489,0.908410255346,0.999844751428
--2216606101273,364718,33747998,LABTSH,2013-03-17 17:56:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.830012823073,0.831946524466,0.860714762034,0.842635174297,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.814223031912,0.81483611566,0.808974483818,0.999995561884
-4778974442533,420573,33816856,LABTSH,2013-04-02 00:22:00,0,1,1,1,0.8125,0.921052631579,1.0,0.743078728902,0.806703808061,0.801210186599,0.817833840453,0.73366744586,0.774573675617,0.800695748931,0.729821048115,0.71264885375,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.531356291374,0.529989690422,0.504104758378,0.50155214183,0.629136110026,0.619774614762,0.621499712562,0.99999805687
--1319516213867,371823,35586092,LABTSH,2013-06-01 13:22:00,0,1,1,1,0.721247563353,1.0,1.0,0.672346775024,0.744892589343,0.7029092623,0.690386559474,0.640461686181,0.761467571125,0.960972704715,0.845804537184,0.740412019604,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.535640567717,0.519764643646,0.519994167151,0.505679216988,0.502629963469,0.633329434776,0.603504523918,0.612408048725,1.43258616699e-13
--4821278189206,384181,35817334,LABTSH,2013-06-13 09:26:00,1,0,1,0,0.8125,0.0,0.0,0.768586592328,0.723693293396,0.816731821666,0.655746616866,0.876749835171,0.787719848621,0.676834992547,0.842842039629,0.787221156507,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.570612271331,0.624045862113,0.539002574476,0.681367208651,0.589952171488,0.812053315925,0.799177336001,0.774330475331,0.322639860128
-9115251165042,360577,36602888,LABTSH,2013-07-25 03:58:00,1,0,1,0,0.8125,1.0,1.0,0.840673647054,0.782267700813,0.768106884543,0.656281275956,0.698411120309,0.773279189371,0.960972704715,0.909311284318,0.767948617795,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553903394042,0.528690599603,0.533381282937,0.508037530153,0.505231604749,0.816605606292,0.842232576172,0.848614934618,2.90546291896e-05
--5993432599067,402193,33680354,LABTSH,2013-03-10 19:28:00,0,1,1,1,0.8125,0.0,0.0,0.824768585728,0.81345117192,0.820797627089,0.865142881674,0.854335772207,0.848055088855,0.947071931111,0.8007437163,0.923429115522,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.539019380528,0.541464288286,0.508904451836,0.503420532987,0.94404414167,0.95020343881,0.956527869995,0.999999999996
--9657895640881,465316,33360921,LABTSH,2013-03-11 20:14:00,1,0,1,0,0.721247563353,1.0,1.0,0.794137003889,0.793512363708,0.776904156955,0.727847826467,0.86323383253,0.697065902058,0.433345566394,0.876101725566,0.584694768293,0.650240561102,0.674575323822,0.65176201513,0.656811041531,0.554069047837,0.52759645542,0.510946844091,0.501147526479,0.500495213176,0.869944947729,0.890019319928,0.889632389216,0.888596373986
--8111318059758,409233,34832108,LABTSH,2013-04-27 01:55:00,0,1,1,1,0.8125,0.0,0.0,0.824768585728,0.816758182752,0.804302480147,0.866448753272,0.824026815733,0.798954040386,0.843787521329,0.845195291538,0.778861342372,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.535944784742,0.541464288286,0.506411299845,0.502705520742,0.769056513104,0.768736729888,0.769470407958,0.999999992181
--2969018209459,446862,32460192,LABTSH,2013-01-28 21:00:00,1,0,1,0,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.818823353128,0.803480811683,0.749307578593,0.817553784332,0.684042951075,0.807788974018,0.831960269413,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.571658585874,0.537179687977,0.541464288286,0.508005045332,0.504099541569,0.842618332322,0.850369661247,0.851795467947,0.999988462993
--9007595869425,437962,35308594,LABTSH,2013-05-28 16:48:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.81820730512,0.833413676791,0.827158075229,0.860714762034,0.846645945272,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.884619479389,0.891632407246,0.895124087857,0.999999571951
--11686115729662,443068,38390614,LABTSH,2013-09-20 14:05:00,1,0,1,0,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.835894293867,0.840304687715,0.866395892252,0.849653514241,0.861284128908,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.580771991934,0.544397069062,0.539002574476,0.509734924238,0.504964682872,0.909965796214,0.923628201581,0.928687715052,0.999999967082
--4458991386842,399071,38179956,LABTSH,2013-09-17 00:11:00,1,0,1,0,0.721247563353,0.0,0.0,0.627211208836,0.78180549726,0.767547174014,0.717999848358,0.730105021811,0.687283583851,0.76940509915,0.738800154948,0.780720812904,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.547076457607,0.524627244804,0.525351144162,0.506426246538,0.502656554299,0.708870688369,0.754273302949,0.843528321708,1.0
-8924042370294,392882,38522835,LABTSH,2013-10-16 21:11:00,1,0,1,0,0.8125,0.731707317073,0.0,0.824768585728,0.826809225038,0.820946294004,0.875615909806,0.824026815733,0.841923585944,0.870249847627,0.834322808424,0.84258498143,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.537179687977,0.541464288286,0.508005045332,0.504099541569,0.871753858406,0.877102893122,0.877597800436,0.999999846701
--11607431376571,426181,35728078,LABTSH,2013-06-04 07:09:00,0,1,1,1,0.441860465116,1.0,1.0,0.754056846419,0.739698894684,0.733004863805,0.794972341409,0.49666999667,0.744173140806,0.513461538462,0.744460439005,0.798209887318,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.527684438646,0.514944355485,0.519994167151,0.49715714533,0.499808596663,0.740725964774,0.766861498718,0.761536867121,1.09505564819e-09
--6293623667828,442802,34874421,LABTSH,2013-05-07 17:46:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.835894293867,0.835050890356,0.862091731563,0.860714762034,0.853409637945,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.895458363254,0.903447686522,0.907007406771,0.999999801591
-12018686408782,373483,33123360,LABTSH,2013-03-11 09:49:00,0,1,1,1,0.162790697674,0.0,0.0,0.737955010488,0.730633828534,0.71424046518,0.51423956675,0.873616611771,0.681209710684,0.427838827839,0.55833493069,0.643791781168,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.547076457607,0.523813412205,0.519994167151,0.510731446054,0.503977181013,0.546837500398,0.545338318456,0.773105892717,1.43553127792e-42
-6133371573518,449331,32543557,LABTSH,2013-02-19 22:02:00,0,1,1,1,0.8125,0.0,0.0,0.684913673841,0.802868547455,0.794403003607,0.711032318743,0.834245059826,0.747965059339,0.391627321372,0.863467182308,0.800239973019,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.572618269085,0.543249216256,0.544333308272,0.510967461659,0.507584906615,0.835724426915,0.82700040296,0.826500898182,0.980221144908
-3472639956135,378192,34073127,LABTSH,2013-04-16 10:02:00,1,0,1,0,0.721247563353,0.926553672316,0.0,0.726403719405,0.710923990631,0.641572372069,0.70363412279,0.467558139535,0.675432555667,0.466666666667,0.37856051994,0.682474676739,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.542071411865,0.523991646118,0.523393024656,0.502916661225,0.504902652192,0.931251487229,0.984534013607,0.991944736301,3.95208701383e-50
--5302862187662,364784,33636306,LABTSH,2013-03-05 05:05:00,0,1,1,1,0.8125,0.929515418502,1.0,0.832248027839,0.755019655906,0.754926116383,0.796912962195,0.668172832204,0.714305716967,0.549950057767,0.570344467405,0.709905665448,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.565688547677,0.535015119586,0.529989690422,0.514391662355,0.504285737811,0.76212799803,0.828718123053,0.765161403943,5.63941006127e-146
--2475293061722,374741,33011771,LABTSH,2013-02-14 09:14:00,1,0,1,0,0.721247563353,1.0,1.0,0.739058577709,0.735693683939,0.715292856556,0.819186860315,0.833225805203,0.725985069096,0.6,0.756928780084,0.712052557471,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.54017883935,0.519584482921,0.537411938773,0.508535253551,0.502812865506,0.872971126919,0.902261205937,0.859843376995,1.91246786685e-57
-7449569794204,456904,35681625,LABTSH,2013-06-25 09:13:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.762821778301,0.727168308293,0.760012549828,0.774764926707,0.726388806786,0.755744294911,0.793230769231,0.814845442742,0.724412829679,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.535317017998,0.516954781034,0.519994167151,0.511361681843,0.503673097298,0.634594190131,0.598958761228,0.547650763232,0.144822161418
--10786299547738,413985,40356448,LABTSH,2013-12-12 18:42:00,0,1,1,1,0.8125,1.0,1.0,0.771738282698,0.806735645701,0.810513880189,0.68581636212,0.749397967035,0.832700852775,0.633461538462,0.791519140066,0.733012421934,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.559309068722,0.530902941579,0.519118175928,0.512747382985,0.509905945678,0.821589200209,0.829960293966,0.817230158506,0.99997503592
-3642813887293,419343,38083852,LABTSH,2013-10-04 01:55:00,0,1,1,1,0.8125,0.921052631579,1.0,0.824768585728,0.806703808061,0.804302480147,0.817833840453,0.808948990333,0.796007721517,0.800695748931,0.826596581622,0.774850571397,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.531356291374,0.529989690422,0.504104758378,0.50155214183,0.688111838653,0.681418408387,0.678662921509,0.999995554707
-3156804258432,407235,39604653,LABTSH,2013-11-26 22:00:00,1,0,1,0,0.721247563353,0.0,1.0,0.708877568791,0.710792769583,0.759587179038,0.783278981144,0.782949138839,0.728736212094,0.593230769231,0.714521688782,0.705749205678,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.559602837719,0.530471549151,0.522467456931,0.506493534534,0.501551665484,0.817615406295,0.831241544999,0.716590138808,9.0882159852e-36
--2606778709175,460737,34088345,LABTSH,2013-04-02 02:31:00,1,0,1,0,0.8125,0.830703012912,1.0,0.791202717594,0.767712272645,0.786124467624,0.703922487061,0.678373834763,0.764509266537,0.706855987096,0.892849462912,0.757065344953,0.782447187593,0.736457474868,0.682471893924,0.700503272381,0.548392402003,0.52287147007,0.510069898265,0.501732513996,0.500219490242,0.616982037118,0.655999291011,0.685470022139,0.984158684764
-1269329229924,381006,32605645,LABTSH,2013-02-09 14:42:00,0,1,1,1,0.8125,0.95,1.0,0.707164139084,0.815743927743,0.777767285495,0.899378572789,0.708573344962,0.846585014933,0.51851426413,0.838596044771,0.751758902073,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.543897825222,0.539002574476,0.513654920642,0.509797855448,0.932396858124,0.939411185028,0.941854178161,0.999969653385
-8502613688413,458222,37880197,LABTSH,2013-09-28 23:44:00,1,0,1,0,0.721247563353,0.9375,1.0,0.653560912771,0.622364009132,0.716521643936,0.83470889157,0.740523981981,0.75174196444,0.743606988503,0.676713418444,0.671980409454,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.561808566913,0.532189351255,0.522467456931,0.50436483092,0.504265170823,0.652516380825,0.669706981381,0.674296619211,2.83782995918e-37
--3657071316589,385427,38539474,LABTSH,2013-09-27 05:36:00,0,1,1,1,0.8125,0.444444444444,1.0,0.832248027839,0.794314029098,0.722480880611,0.772217718036,0.761781836905,0.804345516946,0.744011294506,0.918845003264,0.778593969392,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.530129585731,0.529989690422,0.502727217613,0.5008001916,0.787571321656,0.807963965875,0.821744069113,0.000667579185625
--284691036131,399917,32103909,LABTSH,2013-01-13 19:12:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.818843457826,0.768756917376,0.808948990333,0.82465042432,0.827158075229,0.746677862907,0.829054447579,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.505698681961,0.502946199006,0.810756072442,0.815857358128,0.817153610848,0.999997831065
--7087703699969,366306,37378961,LABTSH,2013-08-24 12:50:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.822395603613,0.823928435048,0.81820730512,0.825938385157,0.866395892252,0.849653514241,0.841346693286,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.815742146351,0.824869002448,0.827257033467,0.999997848953
-1158276448400,376115,34964752,LABTSH,2013-05-20 21:48:00,0,1,1,1,0.8125,0.0,1.0,0.824768585728,0.838556404444,0.816027016645,0.898524741666,0.835628259368,0.828934321181,0.943202232621,0.799209096103,0.866011445,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.592634953007,0.561239352497,0.541464288286,0.523248603149,0.513240533756,0.923175755679,0.917765875493,0.884518114507,0.929484057301
-3623544635958,409526,33597279,LABTSH,2013-03-21 17:38:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.835894293867,0.839456358688,0.814095389072,0.849653514241,0.849326872594,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.84167716597,0.85343544961,0.855138854465,0.999999527334
--6393388244510,367702,35644830,LABTSH,2013-06-01 10:58:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.795451219759,0.815941941005,0.883809710514,0.848267609399,0.850742806426,0.869570495427,0.887314827485,0.872156089505,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.539853406838,0.539002574476,0.507509612204,0.504547288822,0.864226860007,0.873229797902,0.880738031806,0.999999181497
-8000820752432,442455,40108575,LABTSH,2013-12-15 21:29:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.820946294004,0.875615909806,0.824026815733,0.836114294075,0.870249847627,0.834322808424,0.84258498143,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.537179687977,0.541464288286,0.508005045332,0.504099541569,0.844270438248,0.851294604008,0.858545368779,0.999999981657
--8733638599984,390750,36098088,LABTSH,2013-06-26 09:28:00,1,0,1,0,0.721247563353,0.0,0.0,0.667273950614,0.566639516617,0.465552936984,0.791803278689,0.503703703704,0.439283204234,0.6,0.5,0.427297264139,0.650240561102,0.616385848561,0.575111269937,0.58466802601,0.51197284888,0.503857386791,0.489033096195,0.491904804903,0.499894337686,0.444580788734,0.631649904443,0.782656965406,2.02977324114e-137
--118758323806,471827,37212982,LABTSH,2013-08-04 22:24:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.828835595448,0.824773882727,0.842240859777,0.833513185829,0.941846304639,0.906469020537,0.883677155654,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.567234053907,0.533286352734,0.529989690422,0.506016892982,0.50525760833,0.885365773557,0.889759597126,0.891151113223,0.999999999308
--7478047013010,424867,35319078,LABTSH,2013-05-28 10:22:00,0,1,1,1,0.721247563353,1.0,0.0,0.651570464752,0.566691812308,0.589847533496,0.411742424242,0.484743589744,0.499962966366,0.380753138075,0.302046837544,0.487843369859,0.650240561102,0.645760345789,0.618892348407,0.625324561677,0.531832478614,0.515865911455,0.50621658075,0.501750278264,0.502901848342,0.0740292957129,0.0399550722367,0.079109020158,6.61893935446e-230
-11359896669989,361990,36250218,LABTSH,2013-07-12 20:49:00,0,1,1,1,0.8125,0.0,0.0,0.825071553138,0.764383553701,0.788471042207,0.954302803992,0.904103744763,0.730211543535,0.780753138075,0.556543988713,0.579617126639,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561305234366,0.532830533466,0.544333308272,0.513773105908,0.505766332786,0.375282237101,0.167317782734,0.0370295903904,1.07534975145e-116
--1805702354644,396853,36759552,LABTSH,2013-07-24 04:56:00,0,1,1,1,0.8125,0.0,0.0,0.778720755112,0.802549237695,0.805570410802,0.854461745246,0.850011141332,0.788752546936,0.769196919692,0.80708139387,0.752927225007,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.557833936788,0.526561351611,0.518997554745,0.50369418249,0.501545139546,0.712207273346,0.685842149426,0.683602649503,0.000987903893003
--9127964852644,396634,40296446,LABTSH,2013-12-17 08:41:00,0,1,1,1,0.8125,0.921052631579,1.0,0.805421138254,0.806703808061,0.814692278417,0.823928435048,0.81820730512,0.803388826927,0.800695748931,0.710732688827,0.801139573986,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.532603319913,0.539002574476,0.505915822739,0.502457751127,0.71350488199,0.713417250438,0.705611028836,0.999931967878
--3121489653430,409442,39430140,LABTSH,2013-11-14 09:14:00,0,1,1,1,0.8125,0.0,0.0,0.633122003296,0.740965573344,0.730577005681,0.903444456854,0.746971392023,0.708835630215,0.666302748742,0.722791296718,0.703737709199,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.56880104907,0.530726304712,0.539002574476,0.504664480386,0.501333992333,0.655588159482,0.56026864289,0.576279560141,1.73660757148e-06
-2357746389094,461315,36402908,LABTSH,2013-07-23 22:00:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.683912678248,0.715954669535,0.859313500059,0.815356685921,0.766103533687,0.538461538462,0.571428612308,0.589328675868,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.553951490401,0.527990075001,0.529989690422,0.505698681961,0.496635526536,0.491315331421,0.587910408856,0.744268129741,1.01290993562e-29
--8155754333345,470301,31860155,LABTSH,2013-01-06 04:02:00,1,0,1,0,0.8125,1.0,1.0,0.768586592328,0.796715526386,0.812990931397,0.626291978553,0.827203797913,0.801426682137,0.765337346376,0.739735220154,0.655858421171,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.561430308262,0.526242806779,0.529989690422,0.502789325132,0.502541354293,0.650206323648,0.707949310935,0.772147036628,8.12695474525e-58
-10925155819495,416153,39186513,LABTSH,2013-11-17 22:00:00,0,1,1,1,0.8125,0.929515418502,1.0,0.794137003889,0.678364229249,0.704175766807,0.816222222222,0.910719701966,0.690694031511,0.969230769231,0.755015515943,0.596683845083,0.782447187593,0.718940845638,0.679071337105,0.688610573577,0.565784114673,0.535575405262,0.544333308272,0.510650574718,0.510002504372,0.46581684185,0.481171670043,0.721812471584,1.1499179751e-45
-8094239333802,420541,32084576,LABTSH,2013-01-27 07:34:00,0,1,1,1,0.8125,0.921052631579,1.0,0.805421138254,0.806703808061,0.811599984869,0.817833840453,0.835894293867,0.824244964284,0.806333339012,0.804714046813,0.818498690257,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572701760878,0.537790168734,0.539002574476,0.508141356859,0.503570699049,0.855625880038,0.862272879653,0.863478182767,0.99999994769
-3341415469487,458077,36957162,LABTSH,2013-08-21 02:04:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.795451219759,0.801677329724,0.661762246258,0.646347916722,0.82151966288,0.653145714424,0.794668269824,0.74555973727,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.538634184197,0.555751254867,0.509295441669,0.510068526939,0.876686421916,0.894780785091,0.911468370585,0.999999999978
--10037499292108,450994,38526382,LABTSH,2013-09-27 22:00:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.765233355411,0.812170875503,0.772217718036,0.835655221787,0.799210929877,0.884042951075,0.746284705099,0.80702296521,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.504239715778,0.504710542986,0.638540116379,0.58608818982,0.579961387553,1.23799376534e-21
-8268514990716,398513,37582907,LABTSH,2013-09-16 19:45:00,1,0,1,0,0.8125,1.0,1.0,0.797115458713,0.810499239495,0.810923531232,0.718786803315,0.853857529008,0.818994731116,0.377186963979,0.795900405383,0.704835251723,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.574069828986,0.539503334281,0.530502782545,0.508027211959,0.506773528046,0.941316378805,0.954815998619,0.96141741001,0.99999987948
--2254143165051,417287,40414614,LABTSH,2013-12-13 11:04:00,0,1,1,1,0.8125,0.0,0.0,0.56052098339,0.690209294753,0.724956009793,0.677035992937,0.492898324386,0.652294518604,0.864856711916,0.783080883081,0.59667759226,0.782447187593,0.710772579177,0.664096068439,0.671017582252,0.534693315162,0.516416478343,0.528316557067,0.505537573736,0.504856091115,0.458875705014,0.379282698759,0.313882231888,5.34429207715e-91
--8839370224204,364566,35006153,LABTSH,2013-05-04 21:55:00,0,1,1,1,0.8125,0.929515418502,1.0,0.857842790675,0.834204509092,0.755164659422,0.699437229437,0.854335772207,0.763295330662,0.906017317175,0.653224137744,0.778056084233,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.56003880051,0.525077579385,0.529989690422,0.504843168954,0.501647546356,0.800143205231,0.808815067254,0.806680697798,0.952607337893
--4334602490306,423425,33024604,LABTSH,2013-02-19 20:51:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.818589729764,0.824633663533,0.87383968791,0.856432999515,0.798780955112,0.939801079636,0.935206629003,0.901539632731,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.507316970896,0.502748586678,0.844707742862,0.870537261165,0.878660265923,0.999984730902
--3002485728660,365012,37027037,LABTSH,2013-08-06 02:18:00,0,1,1,1,0.8125,0.830703012912,1.0,0.768586592328,0.796684603414,0.798689970224,0.772217718036,0.801395937651,0.826580525536,0.842236259433,0.831615216027,0.797920879473,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.529495298978,0.529989690422,0.503368826877,0.501264701751,0.726929395507,0.726954024321,0.711116146244,0.999993328068
-818249236493,449301,33667207,LABTSH,2013-04-02 12:21:00,1,0,1,0,1.0,1.0,1.0,0.597155376592,0.558699275767,0.683921959842,0.727700954401,0.475094175897,0.539519646816,0.580753138075,0.47893078031,0.700933370377,0.782447187593,0.710772579177,0.664096068439,0.671017582252,0.53250181034,0.518525498313,0.496030185414,0.493045071304,0.501295202748,0.652901728385,0.512946001578,0.34591905954,1.48800309949e-65
--575283682746,464189,35013350,LABTSH,2013-05-07 21:26:00,0,1,1,1,0.8125,0.830703012912,1.0,0.798068202586,0.810765400565,0.811238333915,0.84652039834,0.8293371683,0.871269240218,0.827158075229,0.814304769925,0.845061633066,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.568536728729,0.535545805724,0.529989690422,0.51117536072,0.506847012279,0.853486168648,0.866295774492,0.881899014554,0.999998910484
--3111811921665,452049,33902223,LABTSH,2013-03-30 19:21:00,0,1,1,1,0.8125,0.0,0.0,0.797115458713,0.798999239495,0.810923531232,0.718786803315,0.836170540262,0.808504448297,0.74659206313,0.795900405383,0.781389601265,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.566494176865,0.534319063215,0.530502782545,0.505801663667,0.503583556396,0.857012970384,0.871651996119,0.887846697945,0.999933379856
--8433170494599,461275,38240938,LABTSH,2013-09-21 10:47:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.822395603613,0.875615909806,0.83328513052,0.837849148028,0.870249847627,0.865027757664,0.854931815052,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.591878617801,0.550713873318,0.550443778739,0.514327147124,0.507261793209,0.863788421735,0.875107216592,0.879025032077,0.999998948865
-8505301715487,356821,35159505,LABTSH,2013-06-06 15:04:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.835064709502,0.798836781442,0.866398886392,0.77367867055,0.832008164911,0.438461538462,0.89129268291,0.816550915126,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534472960217,0.539002574476,0.504997891287,0.502676316057,0.741215676159,0.762881052345,0.71515725397,0.99999523518
--10422014770839,381879,38838836,LABTSH,2013-10-16 14:30:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.789042006686,0.792549787726,0.827989417248,0.839237387555,0.81154898753,0.89658986251,0.887334061594,0.851097095836,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534955706867,0.539002574476,0.51102667772,0.506389550964,0.757308557186,0.752624356538,0.7145568492,0.978789554716
-11616879465361,447517,33599997,LABTSH,2013-03-19 17:28:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.816790020392,0.78266113847,0.855191528695,0.863185754424,0.780058487231,0.830867167025,0.823765775891,0.863147168962,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.539853406838,0.539002574476,0.506773789746,0.504259868758,0.892159201493,0.909282548431,0.914321993337,0.999995926717
--8291313067760,421939,40053309,LABTSH,2013-12-07 20:32:00,0,1,1,1,0.8125,0.95,1.0,0.819450097199,0.831446407082,0.818593106617,0.852546616866,0.900390597366,0.879800486425,0.956728983533,0.835838871257,0.8339880736,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571544464625,0.530827825539,0.529989690422,0.505797337281,0.501602089582,0.857833086082,0.852649064483,0.858125317688,0.999999238223
--2826027585302,414590,34533652,LABTSH,2013-04-20 07:01:00,0,1,1,1,0.721247563353,0.0,1.0,0.590268660921,0.66776488854,0.745645545163,0.732557720058,0.395578031036,0.608215256303,0.767741935484,0.667601426518,0.666592032252,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.536311686584,0.517208000243,0.531497075666,0.499463915564,0.500040837047,0.932380035769,0.96904709655,0.991346021324,5.0804917861e-32
--10232823590382,469781,37997855,LABTSH,2013-09-05 12:22:00,1,0,1,0,0.441860465116,0.0,0.0,0.72162051314,0.680976935127,0.687391641543,0.755195186807,0.758657139818,0.711675224125,0.623610280932,0.542739790271,0.817023033046,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.545946077264,0.526531546838,0.519994167151,0.501872587686,0.501818025756,0.69317571093,0.610933787622,0.517118226574,2.60878331761e-05
--8953597712807,384322,33044916,LABTSH,2013-02-14 21:00:00,1,0,1,0,0.8125,0.929515418502,1.0,0.798068202586,0.779598194348,0.800249769408,0.87383968791,0.885209168497,0.789682415626,0.882770007992,0.793455228048,0.763167900275,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.530942461088,0.529989690422,0.502924287524,0.501495773146,0.639639407166,0.642120310534,0.642889141779,9.17339728082e-09
--2505374412191,431423,39596641,LABTSH,2013-11-06 12:00:00,1,0,1,0,0.8125,0.830703012912,1.0,0.798068202586,0.724616065253,0.807187430419,0.750081006273,0.864580581736,0.781380825743,0.74659206313,0.691304218953,0.846791448732,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.568536728729,0.53571743504,0.539002574476,0.510737791307,0.508070917572,0.787904060525,0.822666025591,0.884226080783,5.67384399288e-08
--11554284258801,416591,35359181,LABTSH,2013-05-30 02:19:00,0,1,1,1,0.8125,0.929515418502,1.0,0.805421138254,0.806703808061,0.810129739924,0.79351078908,0.842240859777,0.843860703171,0.849501660978,0.859742052645,0.843929682847,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.527843762947,0.529989690422,0.502789325132,0.502202936521,0.936297335431,0.961633133752,0.969421934966,0.999999999999
-2055640568417,377655,33710559,LABTSH,2013-03-22 02:57:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.718534174804,0.721063237059,0.71989604594,0.512015477751,0.693313569259,0.64281505702,0.946512220252,0.744241669596,0.682967393087,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.523564328802,0.510946844091,0.506868806925,0.503778158578,0.768166702394,0.799594721522,0.805573572278,0.00247872085778
-3389746800850,456978,38208076,LABTSH,2013-09-29 06:34:00,1,0,1,0,0.441860465116,0.0,0.0,0.726403719405,0.693832447394,0.635958122536,0.471088503358,0.715120006078,0.503554318682,0.757940102055,0.740278189481,0.692659860397,0.650240561102,0.646281427091,0.615593043724,0.617481144548,0.53706993846,0.519459110235,0.520508298955,0.497594303509,0.49947620018,0.44714043761,0.178555132573,0.170348330695,6.48432455239e-64
-3208259661206,394442,38896284,LABTSH,2013-10-30 14:02:00,0,1,1,1,0.8125,0.929515418502,1.0,0.823942348299,0.742400969635,0.748489797605,0.596991444997,0.71451147035,0.789927373827,0.76397794102,0.727612924237,0.74454455814,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.572375507238,0.538572027526,0.52803351545,0.511186467164,0.505692530575,0.944426677169,0.96391260181,0.97376930182,0.999999886766
--7288373233889,362312,32728756,LABTSH,2013-02-09 07:04:00,0,1,1,1,0.8125,0.929515418502,1.0,0.779764997065,0.749596294136,0.806756245647,0.857638652196,0.844186413277,0.805468352145,0.910027488457,0.808281054722,0.793717630435,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.558505036448,0.527914096767,0.52803351545,0.512308275976,0.506830235171,0.772484896781,0.761017290861,0.768251286118,0.999999992011
-9897808953818,454308,38369139,LABTSH,2013-10-13 22:00:00,1,0,1,0,0.333333333333,1.0,1.0,0.543063331201,0.634489402863,0.680355567471,0.814841267149,0.171700667991,0.551342709809,0.4,0.472463594415,0.420200796293,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.539489185908,0.512801274844,0.497708960395,0.492568776528,0.498053691081,0.135660932587,0.173945119219,0.416409316006,2.4509474163e-125
--800623100187,384071,34208849,LABTSH,2013-04-04 11:29:00,1,0,1,0,0.441860465116,0.0,0.0,0.794137003889,0.728185530915,0.699922358765,0.874631005761,0.82746204624,0.711918782116,0.644633972992,0.678275451243,0.833211217325,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.558796382565,0.531914310248,0.531497075666,0.507058394698,0.504246816386,0.789507287403,0.877802452352,0.90250639666,1.63218449364e-07
--8062712267290,378168,33678080,LABTSH,2013-03-08 18:11:00,0,1,1,1,0.8125,0.929515418502,1.0,0.797115458713,0.74946073782,0.811205397034,0.687523709667,0.879366652741,0.72301774706,0.766963015647,0.734377654529,0.872547688183,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.564792565622,0.534019997462,0.52803351545,0.504570271583,0.50192070472,0.746859325849,0.766925490462,0.776376516023,0.999926798798
--7647810162077,361805,37720540,LABTSH,2013-08-25 22:27:00,0,1,1,1,0.8125,0.830703012912,1.0,0.794137003889,0.691386422198,0.779442263971,0.457326083458,0.580429802272,0.709908054388,0.927126609006,0.751942011487,0.772010890183,0.782447187593,0.710772579177,0.669122348657,0.671017582252,0.56289856986,0.538813465351,0.544333308272,0.515781923978,0.508461930318,0.97424516125,0.987246532907,0.992288427134,1.0
-6727378149210,465800,36938112,LABTSH,2013-08-17 20:29:00,0,1,1,1,0.8125,1.0,1.0,0.797115458713,0.799299446697,0.683399535069,0.719825829726,0.733604389603,0.68295075932,0.78021978022,0.729247776858,0.729260650316,0.782447187593,0.711240791414,0.66097372741,0.663616145716,0.552814786513,0.528377168492,0.518997554745,0.503132040762,0.499881822148,0.844962563265,0.815221734772,0.778001423805,1.03573743005e-09
-5670819854241,469771,34329160,LABTSH,2013-04-16 22:00:00,0,1,1,1,0.721247563353,0.0,0.0,0.665215924426,0.649021679818,0.730702177525,0.779203377306,0.722957739934,0.599179938157,0.727345201205,0.562962962963,0.615123600663,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.529508251346,0.512424168075,0.478644806321,0.497053929213,0.497863161613,0.51798959461,0.560565682967,0.552674755851,3.80042834216e-38
--9955809539917,415796,38506048,LABTSH,2013-09-25 23:03:00,1,0,1,0,0.8125,0.830703012912,1.0,0.798068202586,0.76214970469,0.806319056556,0.772217718036,0.735491014454,0.812294594476,0.873982291179,0.770889393574,0.797830597501,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.572618269085,0.540563772331,0.529989690422,0.515366131478,0.508674111622,0.607893765319,0.583507613031,0.551196013732,0.000189310466884
--8362987711697,358612,33987097,LABTSH,2013-04-04 07:35:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.805259989323,0.756793194865,0.707718267613,0.945414328553,0.709634394827,0.691627044564,0.949007106329,0.480926470941,0.727525495024,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.553573887675,0.52976884847,0.545834112185,0.507869992512,0.506659633782,0.723566980764,0.662483272654,0.53322558727,4.79928921856e-185
--3704394726568,385631,39302222,LABTSH,2013-11-05 15:39:00,0,1,1,1,0.8125,0.929515418502,1.0,0.797115458713,0.783427427947,0.783558067683,0.830855168838,0.824115480927,0.805757185417,0.870671536317,0.731654801861,0.872278714397,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.558772420396,0.5303419803,0.52803351545,0.509705152898,0.505793584871,0.728748450368,0.733766380064,0.727234683175,2.67690707413e-11
--12047264408531,467821,37514272,LABTSH,2013-08-27 14:26:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.816754850347,0.819303310065,0.768756917376,0.835894293867,0.834915984342,0.866395892252,0.849653514241,0.844984367702,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.904045961105,0.91374703616,0.915907936051,0.999999999919
--11385244242668,362055,33965626,LABTSH,2013-03-21 16:01:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.793398042835,0.7972914803,0.772217718036,0.74348806798,0.804887850435,0.514637851925,0.755993239913,0.804014083423,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.533839380957,0.539002574476,0.505314974473,0.502754328216,0.61963297167,0.530395516097,0.469279775175,0.999776937226
-2776515171266,354812,37305974,LABTSH,2013-08-26 01:04:00,0,1,1,1,0.8125,0.929515418502,1.0,0.797115458713,0.772950639254,0.726189902239,0.687523709667,0.610475103787,0.744179031052,0.695,0.654765795299,0.611242794696,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553903394042,0.525994245381,0.518997554745,0.50279879453,0.501044392986,0.689024021009,0.686493722385,0.678209726417,0.999230736966
-3406340724044,409449,35733829,LABTSH,2013-06-27 10:16:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.822395603613,0.855191528695,0.81820730512,0.829339238874,0.866395892252,0.849653514241,0.8459939643,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.539220837249,0.539002574476,0.507509612204,0.503851781014,0.821622050034,0.827300317508,0.827913411888,0.999998127515
-9124953203289,357083,39890061,LABTSH,2013-12-14 16:42:00,1,0,1,0,1.0,1.0,1.0,0.768710323289,0.790944780134,0.751902175263,0.72780129669,0.694095926928,0.762784994385,0.497014925373,0.661488140922,0.646318492713,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.549388361563,0.524286172666,0.518291022889,0.501158615909,0.501519962482,0.711555088724,0.554432057156,0.166979590438,2.24507759308e-14
--10809843771461,467256,36364348,LABTSH,2013-07-10 15:51:00,0,1,1,1,0.8125,0.929515418502,1.0,0.770415075572,0.773633115523,0.739563656978,0.783128564992,0.695698565564,0.860787641891,1.0,0.594800811553,0.677666803899,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.55507106451,0.525227317148,0.52803351545,0.507826911244,0.504123283176,0.748070255336,0.868091844541,0.903453949559,2.52573217735e-25
-6795528531679,384164,35880311,LABTSH,2013-07-08 10:14:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.661844332431,0.616566045911,0.735582437984,0.669620998272,0.782396670328,0.659874341917,0.922641509434,0.614532258534,0.780247742162,0.650240561102,0.674575323822,0.65500560385,0.656811041531,0.570235934657,0.537241339557,0.520508298955,0.507641691913,0.503771348981,0.870538834201,0.888752694893,0.904433096169,2.66121681645e-07
--12132659066985,433054,40069806,LABTSH,2013-12-04 09:06:00,0,1,1,1,1.0,1.0,1.0,0.321615407297,0.596605457673,0.667617842087,0.549979391822,0.436761133603,0.55467476513,0.580753138075,0.496153846154,0.553951629557,0.650240561102,0.616385848561,0.586995107235,0.58466802601,0.520303355183,0.510621179154,0.508984840768,0.500105349506,0.504453993198,0.984710083901,0.99297618413,0.993535267877,0.780234814616
-1208309262729,392791,34953345,LABTSH,2013-05-22 19:37:00,0,1,1,1,0.8125,0.0,0.0,0.735687862079,0.772659885746,0.748010771969,0.535262638672,0.541552829873,0.633679727465,0.763791514287,0.539653012689,0.509682283572,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.56003880051,0.525519541307,0.529989690422,0.496451963939,0.498405111063,0.721737167711,0.743747757982,0.752849246544,1.84364761776e-08
-9183481523321,364715,31728363,LABTSH,2013-01-11 10:54:00,0,1,1,1,0.8125,0.830703012912,1.0,0.805421138254,0.726004169943,0.820462278332,0.773687582681,0.807724120183,0.82488702411,0.684042951075,0.859126611537,0.836182913056,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.576509987974,0.542920379045,0.539002574476,0.509734924238,0.501475486454,0.884971844199,0.903943158434,0.915387406241,0.447971569081
-7967365655785,384091,36343116,LABTSH,2013-07-23 13:48:00,0,1,1,1,0.8125,1.0,1.0,0.802901935771,0.769512100548,0.73017755601,0.612175238625,0.78088614761,0.668027505533,0.887609125171,0.730231743807,0.754419748257,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.56436748407,0.532053997256,0.550443778739,0.509815716348,0.504158739735,0.895210939309,0.964261788378,0.987980246747,1.03679101414e-15
-4793918292335,446836,32437247,LABTSH,2013-02-15 03:09:00,1,0,1,0,0.8125,0.0,0.0,0.805421138254,0.806703808061,0.804302480147,0.823928435048,0.808948990333,0.797867650275,0.800695748931,0.804714046813,0.796408718159,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.531356291374,0.529989690422,0.504104758378,0.50155214183,0.718157908073,0.710327666563,0.702698576514,0.999946706149
-4493671974782,364538,34754647,LABTSH,2013-05-20 15:19:00,0,1,1,1,0.8125,0.0,0.0,0.752307514282,0.696597186522,0.733268900728,0.668806510695,0.707519695714,0.733188352923,0.23021978022,0.767098525781,0.679247159352,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553543883705,0.527734458171,0.52803351545,0.515029264485,0.500105953734,0.998143030684,0.999909015244,0.999990800435,4.68929782639e-15
-7816458038311,367698,34919168,LABTSH,2013-05-18 13:40:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.835894293867,0.844919765442,0.866395892252,0.860714762034,0.861356123284,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.903897201003,0.913231054573,0.916324933197,0.999999972313
-3391944593003,358630,32351895,LABTSH,2013-02-07 21:00:00,0,1,1,1,0.721247563353,0.0,1.0,0.7297753114,0.746303351382,0.735105616845,0.662661850616,0.749466300907,0.698724072604,0.780753138075,0.817852323115,0.648069985168,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.547294262624,0.524183032424,0.510946844091,0.502461790367,0.501952785448,0.533939271535,0.572527173542,0.589197717362,5.54701362281e-30
--928601619775,362653,35221491,LABTSH,2013-05-10 06:22:00,0,1,1,1,0.8125,1.0,1.0,0.672601437132,0.795341830287,0.815057632093,0.870553847536,0.697461739334,0.820129602038,0.706715506716,0.921727748458,0.789600728337,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.531312877761,0.550443778739,0.501349014635,0.500926572228,0.398964246521,0.350459204007,0.307173853758,5.70374979259e-72
-6339246696584,382940,38750392,LABTSH,2013-10-26 17:49:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.790600666774,0.820462278332,0.581920329454,0.81820730512,0.824758145881,0.534637851925,0.82368925989,0.832124788107,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.567869079446,0.534955074766,0.539002574476,0.50605077021,0.50237028437,0.853468613977,0.909407803666,0.92594596169,5.3034064927e-05
--3821876237156,439432,37012025,LABTSH,2013-08-04 22:18:00,1,0,1,0,0.721247563353,0.0,1.0,0.699040590745,0.679450618339,0.732024564871,0.605313273241,0.842294045374,0.773988940265,1.0,0.651167769214,0.826147785319,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.553813210242,0.52924411622,0.528395166143,0.504327877354,0.498473851756,0.82346274393,0.838965218631,0.832047631302,2.59786724535e-28
--4748306642735,357088,36458147,LABTSH,2013-07-02 23:44:00,1,0,1,0,1.0,1.0,1.0,0.79898989899,0.352846035918,0.470583389927,0.778787878788,0.681615245009,0.61086712927,0.6,0.371428571429,0.530262745098,0.580851119148,0.500098045511,0.512557019644,0.492623384824,0.512948956549,0.511219145681,0.448656635382,0.498943856276,0.5039732892,0.131416763027,0.106444749795,0.597510779859,0.0
--3436624415575,396855,34659045,LABTSH,2013-04-24 22:58:00,0,1,1,1,0.8125,0.830703012912,1.0,0.851595475314,0.777763314931,0.79617493884,0.847733656643,0.826704280121,0.834943791914,0.726900093933,0.873894726834,0.857826470601,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.507316970896,0.50291343369,0.826687800629,0.829733775113,0.835757324878,0.000235125702744
--5078640231689,453348,39471265,LABTSH,2013-11-08 13:35:00,0,1,1,1,0.8125,0.0,0.0,0.496216380724,0.741757691568,0.665790079605,0.764156862745,0.539273944292,0.470555771698,0.55060690943,0.541378128906,0.501784775098,0.782447187593,0.695161131498,0.648315919379,0.645974720261,0.559342465752,0.615567369121,0.473228744772,0.504023140415,0.499921999855,0.924321911072,0.928249560321,0.915457274059,1.30022223332e-30
-5852305907543,440922,36725082,LABTSH,2013-08-04 22:14:00,0,1,1,1,0.8125,0.830703012912,1.0,0.798068202586,0.793551485896,0.796023791569,0.79351078908,0.820037613454,0.811232976625,0.873982291179,0.702359217085,0.857204933706,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.568536728729,0.53491241602,0.529989690422,0.505799926429,0.502125972586,0.869954292429,0.840196074674,0.826803508953,0.999999991856
-8121657133754,429697,34799380,LABTSH,2013-05-19 19:57:00,0,1,1,1,0.721247563353,0.9375,1.0,0.754056846419,0.738671811254,0.748601779058,0.710803375107,0.860435611157,0.850644030103,0.893925593052,0.800906932343,0.805285400398,0.650240561102,0.674575323822,0.65176201513,0.656811041531,0.554069047837,0.52759645542,0.510946844091,0.503676639416,0.500222196216,0.770422284337,0.770855498842,0.756070053459,2.29997391283e-05
-4740480530752,380337,38184170,LABTSH,2013-10-03 07:41:00,1,0,1,0,0.8125,0.994152046784,1.0,0.657842790675,0.818667558642,0.667702246408,0.928612955906,0.75007195753,0.677790294725,0.852450887946,0.617530408212,0.547376018282,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.568915349898,0.532185083039,0.564675901386,0.507629707968,0.503314823073,0.695475803249,0.732201297872,0.764017400525,8.9867276678e-40
--1872296128547,426236,39882256,LABTSH,2013-12-01 17:08:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.805169267082,0.830012823073,0.827158075229,0.860714762034,0.842635174297,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.776894099477,0.774386380447,0.768277890667,0.999999963733
-5920435602162,372968,33410113,LABTSH,2013-03-12 22:00:00,0,1,1,1,0.441860465116,1.0,1.0,0.7297753114,0.743897094165,0.654517254216,0.635797478254,0.657388100021,0.690401901521,0.393230769231,0.57531835206,0.675364891986,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.549924222774,0.525325517434,0.522467456931,0.506227400228,0.503237482357,0.688663245345,0.643016269914,0.582632179689,6.63987508546e-12
-9420376715542,357995,34987528,LABTSH,2013-05-26 23:22:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.816754850347,0.812934723098,0.772217718036,0.791587662508,0.851363345105,0.836641358583,0.852039428459,0.873464797707,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.508950288353,0.50753460484,0.744109231656,0.742017602174,0.727794783416,0.999996403935
-6983176355698,467786,37027775,LABTSH,2013-08-10 22:00:00,0,1,1,1,0.162790697674,0.0,0.0,0.781882591093,0.782256853056,0.69470183054,0.437559077009,0.558719224631,0.799582428984,0.568253968254,0.478651685393,0.77225998436,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.55755214434,0.530303256516,0.554170608875,0.509522733577,0.505447955273,0.972992092595,0.987114878897,0.991933235258,5.16945903016e-18
-818658955660,468619,39412448,LABTSH,2013-11-26 00:24:00,1,0,1,0,0.8125,0.830703012912,1.0,0.771738282698,0.806703808061,0.801337009507,0.772217718036,0.73366744586,0.798369762747,0.832324052426,0.803950441058,0.696109745649,0.782447187593,0.736457474868,0.682471893924,0.700503272381,0.548392402003,0.52287147007,0.510069898265,0.500113956382,0.499556699826,0.620606625533,0.61125713513,0.609086600729,0.999992488291
--12090155318492,372164,34696510,LABTSH,2013-04-21 07:41:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.754056846419,0.705235807825,0.763533587805,0.800299454959,0.727646114082,0.830481866885,0.893925593052,0.737281186003,0.756380717617,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.525744439655,0.519994167151,0.502260109127,0.500629783444,0.738571179968,0.746302622354,0.747272914504,0.999697022608
--3445462326418,424798,40469858,LABTSH,2013-12-26 23:20:00,0,1,1,1,0.8125,0.830703012912,1.0,0.83725054821,0.816754850347,0.812934723098,0.826113821932,0.81006724903,0.839295905628,0.827158075229,0.856166689957,0.87329112549,0.782447187593,0.736457474868,0.684542054663,0.700503272381,0.553273067162,0.528669861804,0.52447598901,0.506226325427,0.505733146562,0.661049744248,0.457247035603,0.290159852577,0.999997219005
--8138427780422,452067,34388990,LABTSH,2013-04-13 22:02:00,1,0,1,0,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.802790605381,0.803480811683,0.808948990333,0.827950270037,0.827158075229,0.823261560631,0.82955709092,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.567234053907,0.534935624541,0.529989690422,0.505698681961,0.502946199006,0.782828849605,0.782799774767,0.775889411424,0.999954484867
--6963896650815,369737,34953464,LABTSH,2013-04-29 00:04:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.79824471596,0.772217718036,0.808948990333,0.798532102402,0.668641435425,0.81845043008,0.755462873667,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.529495298978,0.529989690422,0.504104758378,0.50155214183,0.691029548201,0.677274047448,0.667141482774,0.999999802854
-4479784976671,454336,35629953,LABTSH,2013-06-26 09:56:00,0,1,1,1,0.441860465116,0.0,0.0,0.708283321699,0.752500574034,0.730453056709,0.905856452559,0.830682997754,0.712106877846,0.68,0.757018411036,0.827906939058,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.537870103028,0.529452989759,0.537411938773,0.506542231037,0.503466525174,0.633191567518,0.502874441115,0.364802124738,1.71093595431e-24
--10607730806026,447535,34268446,LABTSH,2013-04-03 22:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.798068202586,0.818589729764,0.808850422584,0.878992136703,0.855409853149,0.816721332041,0.863122512744,0.893938507139,0.891998218605,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.57052867743,0.5413272816,0.544333308272,0.510967461659,0.506393661789,0.716523098364,0.721618110717,0.740021429155,0.999999917166
--5442903108776,447423,39144358,LABTSH,2013-10-25 21:12:00,1,0,1,0,0.8125,0.731707317073,0.0,0.824768585728,0.826809225038,0.820946294004,0.875615909806,0.824026815733,0.839263409856,0.870249847627,0.838635804054,0.835468397047,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.537179687977,0.541464288286,0.508005045332,0.504099541569,0.863507715806,0.871457262114,0.872861104777,0.999999994349
--8138911828531,437231,34036178,LABTSH,2013-04-01 16:10:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.811630285896,0.784233672676,0.81820730512,0.77896575034,0.827158075229,0.856185435066,0.758624571418,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.567869079446,0.534955074766,0.539002574476,0.507509612204,0.499776777953,0.409527845805,0.400508981896,0.409823158034,1.04054470898e-31
-363030338837,457354,38571976,LABTSH,2013-10-16 00:12:00,0,1,1,1,0.8125,0.830703012912,1.0,0.798068202586,0.793519648256,0.780780326026,0.692841953622,0.77872450366,0.813577103587,0.87459792258,0.866566286527,0.671665836372,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.528565168983,0.529989690422,0.505723165175,0.503056947986,0.748360510878,0.767994905677,0.782119021719,0.872280281876
-4248223969436,425270,33396384,LABTSH,2013-03-22 00:55:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.801337009507,0.772217718036,0.808948990333,0.797724601457,0.716637777916,0.778212803111,0.768658117242,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.529495298978,0.529989690422,0.504104758378,0.50155214183,0.712171896529,0.706861377068,0.705326583699,0.999999986394
-10069435249369,405669,39039342,LABTSH,2013-11-08 12:39:00,0,1,1,1,0.721247563353,0.0,1.0,0.805259989323,0.695171757865,0.730025338947,0.687145663118,0.819827960764,0.70787043012,0.728937728938,0.936133410394,0.807579315487,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.554069047837,0.529962372892,0.519994167151,0.506476785127,0.504114146746,0.738644526201,0.723388291183,0.681433000302,3.74413779451e-05
--10710521817283,459410,32962704,LABTSH,2013-02-17 07:16:00,0,1,1,1,0.721247563353,0.8,0.0,0.783093561777,0.706402341119,0.682883434306,0.563463012789,0.758942669149,0.6475273317,0.6,0.823563756064,0.617147276345,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.535421915307,0.517024104628,0.519994167151,0.500089961006,0.500148695028,0.710994042422,0.731763295792,0.661620621429,2.50447600857e-18
--3719051002708,364879,37666362,LABTSH,2013-09-01 18:04:00,0,1,1,1,0.8125,0.444444444444,0.0,0.824768585728,0.836055212229,0.813563611177,0.784233672676,0.583679483063,0.760675376528,0.722698383977,0.771895111056,0.764584472771,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.500842834061,0.499976388159,0.606574421043,0.643339753532,0.670353216887,2.1743187178e-05
-11240412089424,437901,32908687,LABTSH,2013-03-01 08:30:00,0,1,1,1,0.8125,0.830703012912,1.0,0.675702933064,0.695202503684,0.793680687213,0.787580036876,0.862893061219,0.80263518443,0.744594594595,0.624283337528,0.822721002207,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.531376999647,0.539002574476,0.512844978498,0.509243942878,0.783216228078,0.772884367749,0.775343904196,0.0070576876093
--294269296511,367454,38881742,LABTSH,2013-10-09 06:28:00,1,0,1,0,0.8125,1.0,1.0,0.857842790675,0.820285866883,0.818631234869,0.87383968791,0.876543784961,0.812767570201,0.720453723248,0.81884987067,0.86422949905,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.532162082227,0.539002574476,0.506903761251,0.501944910711,0.736575391339,0.759875344127,0.769768018882,0.999161389617
--1458652096218,398995,40398423,LABTSH,2013-12-13 19:36:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.817705333542,0.823928435048,0.853359620803,0.861536389054,0.885917401644,0.867145524058,0.865688673804,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571544464625,0.538412474867,0.529989690422,0.51117536072,0.508874009814,0.932350495239,0.94285941055,0.940875271088,0.999975729845
-363626077792,371922,37946608,LABTSH,2013-09-22 20:42:00,0,1,1,1,0.8125,0.0,0.0,0.771738282698,0.826809225038,0.812289745014,0.883809710514,0.717243758189,0.757794876644,0.538602018842,0.790366456513,0.724256964568,0.782447187593,0.736457474868,0.684542054663,0.700503272381,0.561015455709,0.531202251666,0.521591826683,0.5040147489,0.506308307404,0.665776255123,0.586139745395,0.556568905697,0.431418296071
--8157245263008,457184,39268175,LABTSH,2013-11-04 11:06:00,1,0,1,0,0.8125,1.0,1.0,0.646244283582,0.78985890952,0.754031039084,0.864535669279,0.879372579987,0.823753735196,0.590476190476,0.756044612715,0.790304714314,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.592634953007,0.559730202363,0.567675265501,0.521105258751,0.508028139343,0.908922369494,0.926035501075,0.924531680549,6.27032862057e-14
--11983949804393,396774,39206826,LABTSH,2013-10-28 13:21:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.835894293867,0.840304687715,0.866395892252,0.849653514241,0.861284128908,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.580771991934,0.544397069062,0.539002574476,0.509734924238,0.504964682872,0.884376308218,0.898240826828,0.903071136374,0.999999643422
--9094124845469,378599,39501181,LABTSH,2013-11-07 15:59:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.81820730512,0.830753500702,0.827158075229,0.849653514241,0.839529360889,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.827753389793,0.839128771353,0.842585680636,0.99999804638
-4466600995489,434916,34295553,LABTSH,2013-04-16 06:21:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.816758182752,0.814692278417,0.891696217527,0.820247092482,0.815481539493,0.823284558525,0.83820342162,0.808238793849,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.537190238198,0.550443778739,0.50822215822,0.503611108466,0.731108923436,0.721924282033,0.714546798584,0.999990670679
-10559021790052,382021,36923182,LABTSH,2013-08-08 19:34:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.815176932279,0.901407873098,0.824026815733,0.827350946946,0.870249847627,0.844415100766,0.849995521803,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.569567829113,0.53525406267,0.541464288286,0.508005045332,0.503347583999,0.847133830422,0.847757178844,0.847158069928,0.999999394988
-952807754469,405591,38437313,LABTSH,2013-10-12 09:26:00,1,0,1,0,0.441860465116,0.0,1.0,0.654214559387,0.657063360767,0.675105092755,0.838896254678,0.293851747887,0.62819995248,0.941773520203,0.886251361251,0.722079362967,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.53714046763,0.519935146955,0.534374830545,0.508260502853,0.505099152087,0.927275933871,0.971861824299,0.982992436477,1.29364238894e-27
--3781506831115,441696,37539398,LABTSH,2013-08-20 10:48:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.759486464602,0.652823685838,0.713632381103,0.682006282698,0.467650770914,0.609207084281,0.580753138075,0.570394211577,0.661464167081,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.547294262624,0.525619968468,0.519994167151,0.50485834816,0.501633316764,0.719099690451,0.730381863385,0.641779161329,1.31274540218e-23
--4716787243805,361260,34359353,LABTSH,2013-04-06 20:02:00,0,1,1,1,0.8125,1.0,1.0,0.773347415207,0.739627360208,0.794952221778,0.663407055117,0.834245059826,0.780419074758,0.706855987096,0.817889464516,0.709492193533,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.558162539088,0.524915499138,0.529989690422,0.504557945848,0.501477309351,0.548102609274,0.580847668649,0.651937716606,2.52235402022e-41
-8036198160322,360924,32557148,LABTSH,2013-02-05 20:55:00,0,1,1,1,0.8125,0.95,1.0,0.773347415207,0.754140968229,0.782865633279,0.852546616866,0.798924103144,0.799295346935,0.690049295453,0.743297053611,0.886106760443,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.568536728729,0.535545805724,0.529989690422,0.508154000905,0.504869533698,0.848239475541,0.837779108976,0.830709502543,0.098192713129
--12295127946402,362343,33128528,LABTSH,2013-02-25 11:06:00,0,1,1,1,0.8125,0.929515418502,1.0,0.831932059681,0.709206445838,0.765196126615,0.675527617587,0.63004698873,0.798757582069,0.92818698468,0.65692775402,0.82045021877,0.782447187593,0.736457474868,0.682471893924,0.700503272381,0.550249179735,0.519805211964,0.519118175928,0.504074953593,0.503189536535,0.755707228408,0.78430793839,0.794967333778,0.944737900605
--2066683474477,412819,37872302,LABTSH,2013-09-01 20:53:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.817456993434,0.772796425736,0.851746616866,0.843444254699,0.808695806256,0.867236259433,0.765752641336,0.816010912132,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.57052867743,0.540695014322,0.544333308272,0.511705407275,0.505023053893,0.850962937837,0.852173577352,0.847526057181,1.0
-7681240090675,425754,32847948,LABTSH,2013-02-19 18:36:00,1,0,1,0,0.8125,0.0,0.0,0.604812487645,0.739446815021,0.692522781392,0.367233978143,0.749404796821,0.598312999904,0.634637851925,0.381046218487,0.35224114277,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.561980540771,0.529253377569,0.519118175928,0.502267784082,0.503833450368,0.697648998531,0.709177321215,0.709633751279,0.0494934531505
--11465124379665,455881,35628778,LABTSH,2013-06-07 02:48:00,0,1,1,1,0.8125,1.0,1.0,0.805421138254,0.806703808061,0.804302480147,0.817902216522,0.795910952295,0.775885605566,0.752699406439,0.833890425989,0.72391513011,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.531356291374,0.529989690422,0.504104758378,0.50155214183,0.680993996814,0.687825291451,0.693612561011,0.99791068632
-8024415046371,385804,40283040,LABTSH,2013-12-21 21:43:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.830336909169,0.824819747062,0.869826727686,0.856432999515,0.854780422801,0.914214725691,0.899750112522,0.893783313901,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.534434879391,0.529989690422,0.509619903762,0.504907156688,0.761501075651,0.723387331882,0.715430898088,0.999982888167
--5302862187662,441095,34800551,LABTSH,2013-04-25 23:38:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.816754850347,0.750728769227,0.444451162535,0.726931737512,0.715521628218,0.677752976079,0.79729730628,0.677593599157,0.782447187593,0.736457474868,0.69034813276,0.700503272381,0.556787634298,0.527980418537,0.529989690422,0.50050745687,0.503072024628,0.70776243088,0.653281385546,0.635553651921,3.39158036151e-07
-11542157933889,466554,31870135,LABTSH,2013-01-10 16:15:00,0,1,1,1,0.8125,0.0,0.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.81820730512,0.830753500702,0.827158075229,0.849653514241,0.839529360889,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.877232270007,0.887634967593,0.892336247864,0.99999947505
-2022417333362,428590,38598457,LABTSH,2013-10-15 07:52:00,0,1,1,1,0.721247563353,1.0,1.0,0.758372774397,0.738677940577,0.739647471067,0.342999848358,0.913874285638,0.669758244657,0.732045482541,0.803493902987,0.61144480185,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.548933635232,0.527392773702,0.519994167151,0.506142950212,0.505577705589,0.88716239788,0.943653200322,0.969115097178,1.08113009971e-19
-6892699404983,422000,40339699,LABTSH,2013-12-28 04:16:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.683391008597,0.589823680137,0.58846941919,0.721632419918,0.610578881453,0.694489751433,0.575,0.782864238411,0.648797625435,0.650240561102,0.674575323822,0.643974295644,0.656811041531,0.536576998194,0.508130278673,0.483257590622,0.494014283404,0.496331466417,0.230166527301,0.223615308794,0.224874691132,5.13828271675e-322
-9296649107727,369599,37624284,LABTSH,2013-09-19 16:25:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.839263409856,0.870249847627,0.865027757664,0.841932539382,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.869892639865,0.882818113136,0.888124700902,0.999999006097
-9529418849874,400372,34152669,LABTSH,2013-04-12 20:14:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.828502029753,0.828649511919,0.870841340802,0.860223606957,0.868958645652,0.742786154263,0.790369204372,0.87152039261,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.534434879391,0.529989690422,0.508161254787,0.506535447404,0.77838422669,0.796222413317,0.80092430725,0.0203520741882
--11838304446822,419296,36832507,LABTSH,2013-07-26 02:30:00,0,1,1,1,0.721247563353,1.0,1.0,0.708283321699,0.680202803626,0.692562602095,0.78731648097,0.560983460931,0.664925383158,0.976671505422,0.6610140029,0.808107226016,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.546519614265,0.523167447072,0.525351144162,0.49591468917,0.503723875736,0.88718504322,0.85329873093,0.803854178397,5.32239712222e-39
-7060118137573,469651,32559304,LABTSH,2013-02-08 06:35:00,0,1,1,1,0.8125,0.929515418502,1.0,0.740933465313,0.798913395697,0.796000516616,0.911411402157,0.883912724028,0.782566596007,0.8,0.846685628024,0.945387448571,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553903394042,0.527877463202,0.52803351545,0.50355465168,0.503209263586,0.374067022081,0.272756309819,0.276400626623,1.85009620053e-19
-1298444913787,448212,36218510,LABTSH,2013-07-17 16:35:00,1,0,1,0,0.8125,0.0,0.0,0.824768585728,0.801441913674,0.820462278332,0.901407873098,0.83328513052,0.817754019067,0.684042951075,0.688011007868,0.775686539658,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.575552566025,0.539538866879,0.550443778739,0.509815716348,0.504253155539,0.865176433185,0.885450192971,0.888038370417,0.999999317427
--510227318657,441740,38186484,LABTSH,2013-09-29 08:07:00,0,1,1,1,0.8125,0.929515418502,1.0,0.759343861375,0.773066030998,0.772141282048,0.844102946696,0.600309429568,0.759297245116,0.4,0.837913508505,0.80278783676,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556892449246,0.528932548888,0.539002574476,0.513988318791,0.504674711826,0.840416771061,0.862404954925,0.86016248956,3.39541363758e-14
--11250658342924,385344,36820608,LABTSH,2013-07-16 22:22:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.816027016645,0.803480811683,0.808948990333,0.833413676791,0.827158075229,0.834322808424,0.836673675303,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.567234053907,0.534935624541,0.529989690422,0.505698681961,0.502946199006,0.862185063101,0.869651714665,0.872246972063,0.999996741094
-11268941402537,399033,34069029,LABTSH,2013-04-14 05:27:00,0,1,1,1,0.8125,0.830703012912,0.0,0.778720755112,0.792817505169,0.803350076848,0.772217718036,0.808948990333,0.802726375687,0.829389726966,0.760020074656,0.79912216079,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.530129585731,0.529989690422,0.502875525039,0.50093749879,0.6597048934,0.611060563551,0.587642513225,0.999995856048
--7545640367478,426361,37043977,LABTSH,2013-08-04 22:00:00,0,1,1,1,0.721247563353,0.9375,1.0,0.780125795053,0.737576718071,0.739504167741,0.678992136703,0.723521526817,0.667340921889,0.706855987096,0.496749543296,0.747013708803,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.549797690615,0.520111765835,0.525351144162,0.504457120866,0.50015613411,0.339344611547,0.337888310344,0.379904903407,1.06802268724e-19
--11256490683170,396841,34835493,LABTSH,2013-05-07 22:00:00,0,1,1,1,0.721247563353,0.0,0.0,0.650981440615,0.739710286782,0.530591412876,0.670003013226,0.529733922356,0.566650833337,0.767741935484,0.75748296202,0.645842881313,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.546189210608,0.519343803651,0.499929566985,0.507067518666,0.501708528788,0.534591241555,0.573257062668,0.687603045176,7.88296787463e-75
-3910449111260,450582,32169644,LABTSH,2013-02-02 22:39:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.812934723098,0.772217718036,0.808948990333,0.831555963131,0.827158075229,0.823261560631,0.833085489475,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.568536728729,0.53491241602,0.529989690422,0.507924242837,0.504059132757,0.801102591047,0.802742770075,0.799699177649,0.999999201006
-6015639363883,363757,34090330,LABTSH,2013-04-17 16:48:00,1,0,1,0,0.8125,0.830703012912,1.0,0.815525854775,0.804247212954,0.768218911318,0.654234546543,0.8426106939,0.827426169577,0.699946275946,0.889054296252,0.857778714798,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561754663375,0.530913245966,0.539002574476,0.50605077021,0.50375886509,0.836627984806,0.852410954607,0.86273135467,0.995141297588
-7098756842900,458331,37970209,LABTSH,2013-09-21 19:38:00,1,0,1,0,0.8125,0.830703012912,1.0,0.657599212178,0.786072789789,0.765381067761,0.723645797513,0.716516490121,0.632471924764,0.78021978022,0.71615046703,0.594246143206,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.557213402293,0.530005236586,0.529989690422,0.50717930383,0.505160938218,0.819333576918,0.875040856551,0.931206166683,6.2425586351e-68
-7826923593069,374700,38133778,LABTSH,2013-10-08 13:13:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.820462278332,0.790259891202,0.81820730512,0.81417020275,0.84119897256,0.859126611537,0.849278256855,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.567869079446,0.534955074766,0.539002574476,0.507509612204,0.503099818079,0.791409341551,0.811700126887,0.810715089851,0.999005384737
--3414478708961,426187,37154589,LABTSH,2013-08-15 18:07:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.835132737003,0.830012823073,0.827158075229,0.860714762034,0.851116938504,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.505272494752,0.798547309672,0.802786672144,0.802980506807,0.999998450874
--7042801553491,399097,34311214,LABTSH,2013-04-15 22:00:00,1,0,1,0,0.307692307692,0.0,0.0,0.6098891034,0.5843255652,0.7043414024,0.454997252531,0.475641180769,0.633279072101,0.654759560487,0.748433051005,0.618582015008,0.782447187593,0.736457474868,0.69034813276,0.700503272381,0.54805977353,0.519984948709,0.529989690422,0.496822410827,0.497755968412,0.31810469285,0.249448947643,0.208813229469,1.02994881928e-13
--10012635835904,416452,34467818,LABTSH,2013-04-15 13:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.816722657277,0.818399549761,0.849653514241,0.844365976153,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.788279184363,0.793975991033,0.792063380412,0.999969559189
-1849744711647,425219,31827598,LABTSH,2013-01-20 12:52:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.794196170668,0.817936742505,0.827989417248,0.839547908901,0.826645231493,0.941164905,0.884920938609,0.876443380755,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534472960217,0.539002574476,0.50605077021,0.50561392564,0.782895637448,0.814636224508,0.832866685612,0.990790303389
--1839433894685,383002,39242332,LABTSH,2013-10-27 04:10:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.620922222232,0.624922380377,0.720841351852,0.823377456351,0.792684837919,0.76248770124,0.893925593052,0.82366148945,0.81944607266,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.524495449261,0.528395166143,0.512361301493,0.505534914117,0.887572999603,0.905519350827,0.909525319785,0.00083194350041
--8165380344471,451014,40187882,LABTSH,2013-12-10 03:26:00,1,0,1,0,0.8125,0.929515418502,1.0,0.805421138254,0.735116506474,0.801239121631,0.483397500046,0.767521622637,0.777597382471,0.793230769231,0.506643949476,0.760055847029,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.555400265406,0.524379684437,0.529989690422,0.504422989936,0.501711267491,0.703770982131,0.70999403846,0.712400363803,0.999958260667
--1539333143099,384356,37262693,LABTSH,2013-08-15 08:34:00,0,1,1,1,0.8125,0.830703012912,1.0,0.70756324141,0.748980134469,0.755080661372,0.881168403941,0.682014002543,0.741299741588,0.588122750894,0.758131002676,0.817975272083,0.782447187593,0.736457474868,0.679970044804,0.700503272381,0.537911121158,0.518856421828,0.522517540536,0.504239910404,0.502561118978,0.792261889118,0.825119339445,0.82853540261,0.0308827805712
--2323271980195,472090,38912613,LABTSH,2013-10-21 20:31:00,1,0,1,0,0.441860465116,0.0,0.0,0.648193861404,0.600910205261,0.618729748345,0.575782770636,0.778023159636,0.625553274543,0.4,0.43,0.678619971991,0.650240561102,0.616385848561,0.575111269937,0.58466802601,0.522020761615,0.510046632171,0.479985795621,0.497704504998,0.49889009237,0.477294423376,0.522304495008,0.61860426945,1.6223272314e-76
--3005505839748,458413,38967135,LABTSH,2013-10-23 14:25:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.830012823073,0.866395892252,0.860714762034,0.851482560536,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.846952591,0.856581916325,0.85956982991,0.999998178126
--10306363969222,413740,38760423,LABTSH,2013-10-16 22:08:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.816027016645,0.823905192794,0.824026815733,0.841923585944,0.870249847627,0.834322808424,0.853142090923,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.575015677088,0.538885218704,0.541464288286,0.508005045332,0.504099541569,0.830731610564,0.831679853023,0.832307462834,0.999998837955
--8415795163867,460612,36314494,LABTSH,2013-07-05 20:12:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.816754850347,0.772380171691,0.66702740694,0.749020593144,0.797328660148,0.706855987096,0.875690066358,0.794761380456,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.567641163893,0.532380046744,0.529989690422,0.504447328343,0.503897426883,0.835531944619,0.844612933628,0.847984545645,0.996634202206
-317507696545,399964,35496145,LABTSH,2013-06-07 03:50:00,0,1,1,1,0.441860465116,0.0,1.0,0.720532580446,0.726059391544,0.647449320073,0.726549060959,0.47998280545,0.726546305327,0.719214676537,0.506412690126,0.632846001407,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.536547979196,0.514150326903,0.510946844091,0.502515379538,0.49826646126,0.557912938046,0.540655414707,0.684492180569,6.58263430063e-19
--7536584978738,468158,39167400,LABTSH,2013-11-02 19:25:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.790600666774,0.81711025756,0.761833840453,0.808948990333,0.788587911389,0.514846031384,0.821451002217,0.819066580107,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.504239715778,0.499578432075,0.76349650194,0.779358543591,0.779806182939,7.11354639671e-99
-8122668230080,418776,33002298,LABTSH,2013-02-22 10:26:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.557075219123,0.637138917293,0.673934342757,0.569671234121,0.466495494444,0.676483850788,0.2,0.453593703594,0.61857354221,0.650240561102,0.645760345789,0.618892348407,0.625324561677,0.516382784395,0.504437285514,0.476969273043,0.485964307447,0.495815521395,0.253305209405,0.237075506922,0.185096672219,3.47855867611e-121
-9154097186273,425404,31888175,LABTSH,2013-01-07 21:00:00,0,1,1,1,0.8125,0.731707317073,0.0,0.824768585728,0.826809225038,0.820946294004,0.855191528695,0.808948990333,0.827950270037,0.657961155537,0.843459501924,0.835658837814,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.567234053907,0.534935624541,0.529989690422,0.505698681961,0.501496774122,0.794129255478,0.785221785241,0.776104451478,0.999999854882
-5782424987310,362538,36287939,LABTSH,2013-07-30 07:36:00,0,1,1,1,0.8125,0.929515418502,1.0,0.768586592328,0.757684001407,0.797927446893,0.802589932927,0.742962549875,0.780767924563,0.88721755425,0.840343468468,0.792638485665,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556084968027,0.529407786228,0.539002574476,0.507396427765,0.502974998613,0.897287904713,0.88917815408,0.844567830814,0.999983659099
--2211877648414,452013,33652975,LABTSH,2013-03-06 08:00:00,0,1,1,1,0.8125,0.0,0.0,0.740933465313,0.80962077703,0.818708126802,0.860487963772,0.774757627037,0.822228183426,0.938788445576,0.86241831,0.844287513203,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.559937652909,0.529739171456,0.52803351545,0.510290656072,0.507003379242,0.829095087507,0.849595119115,0.850479601146,0.736344970103
--4458991386842,461131,39168020,LABTSH,2013-10-24 20:20:00,1,0,1,0,0.8125,0.830703012912,1.0,0.778552205004,0.644783044516,0.739087692379,0.729236420146,0.77878584416,0.646908370216,0.231111111111,0.873126556398,0.609848500149,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.557935186178,0.524223522557,0.529989690422,0.502221453354,0.501151058261,0.814445788122,0.82200896889,0.836805941265,0.0183612801264
-7975467158552,457902,36744645,LABTSH,2013-08-06 12:11:00,0,1,1,1,0.8125,1.0,1.0,0.797115458713,0.7847793736,0.757072005632,0.712123993625,0.833966292048,0.797190919483,0.4,0.898969253506,0.83734538831,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.558772420396,0.532093309452,0.52803351545,0.506051053782,0.502120362016,0.594742634764,0.539848113217,0.535209421333,0.000545418429757
-3845625699973,392065,36400714,LABTSH,2013-07-20 12:49:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.806085424776,0.79591271196,0.822214944189,0.842699770968,0.861176652409,0.947179601266,0.898977657262,0.863176330902,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.571658585874,0.539057409788,0.550443778739,0.508357087919,0.5047266479,0.883062920252,0.901319152749,0.908755645064,0.898121182694
-4656409654788,397862,36965190,LABTSH,2013-08-12 22:00:00,0,1,1,1,0.8125,1.0,1.0,0.84235274436,0.708905354777,0.683464224126,0.701979023299,0.757562918307,0.836313897891,0.63021978022,0.654703292082,0.661964491796,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.56273896605,0.530552527366,0.541464288286,0.508179639488,0.501123697553,0.739554864452,0.770616435579,0.795389242841,2.06367728928e-50
--11808100439458,461012,39416167,LABTSH,2013-11-12 01:06:00,1,0,1,0,0.8125,0.929515418502,1.0,0.819450097199,0.746060869179,0.805315718358,0.863397500046,0.825983161098,0.818953372696,0.745440932233,0.804228452625,0.6886202687,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.61181517113,0.529989690422,0.671183650893,0.586720090788,0.756440920776,0.647294492772,0.555027168221,1.0
--9770782257558,397173,36716204,LABTSH,2013-07-23 22:14:00,0,1,1,1,0.8125,0.0,0.0,0.67673974392,0.643607200521,0.729544389272,0.953168044077,0.806524729432,0.636558914218,0.534285714286,0.530411209611,0.533446228548,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.554843389535,0.524714145782,0.533381282937,0.508760657434,0.504559804095,0.811692677028,0.833596390824,0.850737570119,6.75549718524e-07
--2792865507497,403673,33930547,LABTSH,2013-03-28 10:24:00,1,0,1,0,0.721247563353,0.8,1.0,0.817741951805,0.721923449533,0.648670337517,0.885104718945,0.677927927928,0.62082817507,0.56940509915,0.639509768509,0.65114012575,0.650240561102,0.616385848561,0.577924023434,0.58466802601,0.520640869452,0.509278742362,0.500050363663,0.500786006863,0.499526980755,0.533576116593,0.419240372679,0.344035993097,2.64234215255e-34
-12059308994351,437954,37918263,LABTSH,2013-10-01 22:00:00,1,0,1,0,0.162790697674,0.0,1.0,0.720532580446,0.80153747827,0.739759494606,0.762085456568,0.643297030673,0.591285755989,0.413043478261,0.809222071798,0.463099316176,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.529423178078,0.51096497612,0.510946844091,0.50092823208,0.495433312809,0.106891722009,0.0839861075837,0.0829834932692,4.56737092918e-49
-1367910111504,422321,36365932,LABTSH,2013-07-13 16:22:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.804247212954,0.804206419301,0.897720128079,0.862833137694,0.826576923688,0.530681657177,0.892876659725,0.808916432945,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.536530430437,0.553302965396,0.51381007407,0.511123886947,0.982616217222,0.990883918644,0.994770562458,1.0
-2483455252213,437970,34327427,LABTSH,2013-04-22 11:55:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.783347000549,0.800968435468,0.846308154019,0.873711630867,0.787284713257,0.777186963979,0.908161931948,0.829228448154,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534472960217,0.539002574476,0.506948478557,0.506444057516,0.824594925786,0.80352867647,0.803713013077,0.999971768305
-12132578317500,458954,34982169,LABTSH,2013-05-27 10:57:00,0,1,1,1,0.8125,0.830703012912,1.0,0.805421138254,0.806703808061,0.819303310065,0.823928435048,0.81820730512,0.828598561245,0.866395892252,0.860714762034,0.845017012358,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.785980414428,0.783122647602,0.778597682773,0.999998158409
-2436542819081,468255,40138376,LABTSH,2013-12-22 16:45:00,1,0,1,0,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.810973145828,0.823905192794,0.75856589338,0.82021396042,0.684042951075,0.829802179512,0.839076853796,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.571658585874,0.538424681531,0.550443778739,0.509815716348,0.505005090357,0.836149184173,0.84596882167,0.849816757028,0.999998697012
-1229712737253,375384,35766880,LABTSH,2013-06-23 04:58:00,0,1,1,1,0.8125,0.921052631579,1.0,0.768586592328,0.806703808061,0.801210186599,0.823928435048,0.808948990333,0.807050809256,0.800695748931,0.84302711949,0.80267914788,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.531356291374,0.529989690422,0.504104758378,0.50155214183,0.629590686095,0.602369960257,0.618588650434,0.999997445872
-4080970342793,460536,32945028,LABTSH,2013-02-27 12:24:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.819303310065,0.817833840453,0.81820730512,0.82589633858,0.866395892252,0.860714762034,0.848463277669,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.79723341417,0.795375070314,0.788794549919,0.999997069753
-3679059694055,428198,36969168,LABTSH,2013-08-15 02:32:00,0,1,1,1,0.8125,1.0,1.0,0.768586592328,0.816758182752,0.815662408693,0.713158265596,0.854335772207,0.857330977025,0.887663427814,0.801678758942,0.832077743078,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.566821521737,0.534719572666,0.541464288286,0.505675434754,0.501466679935,0.85598060413,0.875877436501,0.883438281979,0.999998105465
--10150996020380,471041,39224996,LABTSH,2013-10-24 12:09:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.731678393726,0.764668737615,0.71914058062,0.525212961335,0.759914384989,0.763965301409,0.701587301587,0.767814119643,0.71613883321,0.650240561102,0.645760345789,0.610419418727,0.625324561677,0.536536553551,0.518803054274,0.500050363663,0.499878495142,0.505048670118,0.796457798158,0.81649687694,0.855498939016,5.38978196674e-05
--1749786233749,467742,36196619,LABTSH,2013-06-30 19:31:00,0,1,1,1,0.8125,0.731707317073,0.0,0.824768585728,0.826809225038,0.81711025756,0.875615909806,0.824026815733,0.83646017919,0.822253505136,0.838635804054,0.847920730948,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.575015677088,0.538885218704,0.541464288286,0.508005045332,0.504099541569,0.845040410782,0.855136601701,0.857745186142,0.999985690263
-7448034226664,414245,35772995,LABTSH,2013-07-01 06:48:00,0,1,1,1,0.8125,0.921052631579,1.0,0.805421138254,0.781336496697,0.811599984869,0.817833840453,0.835894293867,0.812607000258,0.806333339012,0.754654750998,0.812809878063,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572701760878,0.537790168734,0.539002574476,0.506682567195,0.502841173504,0.705704031784,0.684846757041,0.668122472357,0.999998546597
--1723877331771,426296,39701109,LABTSH,2013-11-20 22:00:00,1,0,1,0,0.162790697674,0.0,0.0,0.14316373728,0.540962351485,0.277962948982,0.379245283019,0.281402439024,0.184826847456,0.4,0.277272727273,0.194444444444,0.580851119148,0.324122475513,0.421308869015,0.0244904408297,0.331107310229,0.406248935348,0.434430515107,0.312868615605,0.402281520227,0.0460205979318,0.0276308322805,0.0281604078637,0.0
--5643927486083,447386,36875120,LABTSH,2013-07-28 07:39:00,0,1,1,1,0.721247563353,1.0,1.0,0.66445664472,0.679739415664,0.69500989457,0.363035714286,0.602547355047,0.572085385426,0.960972918295,0.87504015004,0.852344763522,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.530730653644,0.515538853556,0.519994167151,0.51783812475,0.512000659836,0.83187923665,0.838078475287,0.866822576935,1.44973303281e-240
-4712107475903,444197,31966587,LABTSH,2013-01-28 00:13:00,0,1,1,1,0.8125,1.0,0.0,0.824768585728,0.828505362158,0.810129739924,0.714026955529,0.842240859777,0.83437320111,0.92295213146,0.864328331592,0.849751321897,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.566821521737,0.535927402583,0.541464288286,0.510332342439,0.503659661927,0.776118415702,0.796132385797,0.808653177605,0.999991393211
--11905850038388,374487,33505272,LABTSH,2013-03-08 11:45:00,0,1,1,1,0.0,0.0,0.0,0.47695231146,0.568432159182,0.57223240631,0.869958297818,0.283769633508,0.493909378392,0.2,0.565489091368,0.44102057405,0.72832805097,0.574216773013,0.564270391016,0.54266394453,0.528110719401,0.514619512476,0.473228744772,0.495968244743,0.500294496012,0.104433640207,0.055841095678,0.0536246932524,3.15315424708e-233
-403917050840,379624,31876613,LABTSH,2013-01-18 07:12:00,0,1,1,1,0.8125,0.0,0.0,0.805421138254,0.816758182752,0.814692278417,0.875615909806,0.83328513052,0.818141715581,0.814491455076,0.804714046813,0.808238793849,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.537190238198,0.550443778739,0.50822215822,0.503611108466,0.828134679957,0.830544385663,0.83009035807,0.999997942586
--8559180493012,385290,40235099,LABTSH,2013-12-10 06:39:00,1,0,1,0,0.8125,0.731707317073,1.0,0.692181485768,0.711775411452,0.724027287612,0.728279981479,0.78491713288,0.712860797309,0.760972918295,0.463285486443,0.70199243897,0.782447187593,0.718940845638,0.679071337105,0.688610573577,0.561057565399,0.533532439657,0.550443778739,0.517580769168,0.506620718434,0.733179376844,0.648764769504,0.543868748661,7.82411174958e-06
--11633640124183,438604,39057294,LABTSH,2013-10-24 13:40:00,0,1,1,1,0.721247563353,1.0,1.0,0.774511391874,0.66732949736,0.644956538692,0.517999848358,0.637346611362,0.75482222233,0.366420024523,0.523947368421,0.664981744426,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.554069047837,0.530905130491,0.534374830545,0.510249346642,0.508968097632,0.918513366831,0.925046375847,0.913317813965,1.28925468905e-17
--2386176078205,390694,39633900,LABTSH,2013-11-10 20:35:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.779619042265,0.780712489134,0.870461648019,0.826710329118,0.802604832421,0.74659206313,0.657461014433,0.793178124275,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561858477317,0.534566364713,0.544333308272,0.511060223411,0.506801089133,0.802789892139,0.828276362716,0.780822467634,1.03119216283e-06
-10494981715492,418423,33152692,LABTSH,2013-03-04 10:20:00,0,1,1,1,0.721247563353,0.0,0.0,0.805259989323,0.762433098477,0.737124409151,0.750297297496,0.613679997432,0.512258382894,0.549007106329,0.669464164539,0.682613978805,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.545282175605,0.522190413236,0.519994167151,0.503863602359,0.501283584566,0.781574176423,0.705175752126,0.487336477576,1.07483904783e-31
-468746391156,435712,35631420,LABTSH,2013-06-15 17:05:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.81820730512,0.827352646985,0.827158075229,0.849653514241,0.835518589914,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.831365417247,0.838483103307,0.841525207022,0.99999936875
--3902441697258,363511,34557674,LABTSH,2013-04-16 16:51:00,1,0,1,0,0.8125,0.830703012912,1.0,0.771738282698,0.732457329686,0.783591147644,0.768756917376,0.807724120183,0.832856109751,0.827158075229,0.775391152189,0.79245807497,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.559309068722,0.530268773768,0.519118175928,0.506937386489,0.505499486169,0.864943684212,0.859555099857,0.856376800962,0.999196816254
-3744441631078,414122,36976860,LABTSH,2013-08-12 08:20:00,0,1,1,1,0.8125,0.0,0.0,0.886488712941,0.805225638921,0.779134123458,0.721349862259,0.89276179966,0.729788903636,0.575,0.626362527654,0.560682435991,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.550504668741,0.519536800706,0.519118175928,0.505893536328,0.50252898932,0.745083628049,0.732089652354,0.704221825245,7.98171734299e-07
-4901686869045,461150,32652316,LABTSH,2013-02-12 12:05:00,0,1,1,1,0.8125,1.0,1.0,0.794137003889,0.70774557592,0.738186705161,0.868700253558,0.800779744386,0.802181591557,0.780753138075,0.599678488716,0.802143965602,0.782447187593,0.69274736424,0.649009644303,0.650988479411,0.554218703706,0.5317301944,0.539002574476,0.509524711767,0.505480199303,0.72932445675,0.832310465968,0.837297410803,1.98085554202e-22
--6039021983984,395612,38181556,LABTSH,2013-09-24 19:46:00,1,0,1,0,0.721247563353,0.0,1.0,0.741106700858,0.765014826762,0.780482087209,0.804312415183,0.816157571193,0.75166905357,0.747368421053,0.650306913545,0.66155559082,0.650240561102,0.674575323822,0.640600755518,0.656811041531,0.543337998832,0.52097007871,0.490995096375,0.503704853431,0.503963713687,0.794234842701,0.817404351292,0.817455379428,0.999816078208
--2498085731924,472137,38712850,LABTSH,2013-10-13 22:26:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.816754850347,0.825148225919,0.81913062379,0.829202821739,0.848658907818,0.939801079636,0.891096914887,0.856873186032,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.507316970896,0.504599275086,0.800614113392,0.849058808213,0.856999949759,0.999793214817
--1784466296831,457849,34272944,LABTSH,2013-03-30 22:00:00,0,1,1,1,0.8125,0.929515418502,1.0,0.797115458713,0.63494106532,0.705727998441,0.885449471674,0.777608568263,0.679679671142,0.58021978022,0.574373877315,0.69188294596,0.782447187593,0.710772579177,0.653465387248,0.671017582252,0.535554029925,0.515699812752,0.499052234548,0.499657010237,0.501701111442,0.439615449326,0.405981175314,0.370140527036,1.4699893367e-144
-6062440589967,365159,36470024,LABTSH,2013-08-01 22:00:00,0,1,1,1,0.441860465116,1.0,1.0,0.719196431244,0.728620655324,0.660855173263,0.654987373737,0.701675127822,0.793145721748,0.780753138075,0.655023809524,0.871599441573,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.544263240679,0.523790233203,0.525351144162,0.506805362846,0.504161413422,0.853882223317,0.949771363423,0.991136277834,5.29683876608e-25
-6361496304962,437883,36433978,LABTSH,2013-07-16 17:24:00,0,1,1,1,0.8125,0.0,0.0,0.698534990455,0.574302481547,0.728453872493,0.881613413477,0.628093605707,0.68822967832,0.594594594595,0.471392986511,0.731284918243,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.561102043937,0.529485179728,0.52803351545,0.511132762621,0.504535066724,0.152875771765,0.13906813577,0.115091365501,8.32058692501e-256
-1295711421980,459577,31872067,LABTSH,2013-01-22 15:29:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.81820730512,0.831555963131,0.827158075229,0.849653514241,0.843057759445,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.578591973145,0.544841251108,0.539002574476,0.509734924238,0.504964682872,0.886267479968,0.89253175905,0.894780273871,0.999999999672
-5018656149363,382935,38172135,LABTSH,2013-09-29 17:38:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.791649617823,0.822395603613,0.920671643833,0.873878738819,0.863520001577,0.718715729874,0.877833520053,0.830254427252,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542693038507,0.539002574476,0.512985549635,0.506186315109,0.950589004683,0.967902922424,0.966334535551,0.900110200234
-11972016023828,381352,34853240,LABTSH,2013-05-15 13:27:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.827352646985,0.866395892252,0.849653514241,0.844365976153,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.811914982667,0.817269259065,0.817927342755,0.999998225066
--6318107916101,470862,38291497,LABTSH,2013-09-30 23:55:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.816027016645,0.794495304437,0.824026815733,0.841923585944,0.701052927935,0.834322808424,0.824275350428,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.585958478961,0.546445641426,0.541464288286,0.512517206114,0.508010226379,0.860121803517,0.865587087347,0.866014287269,0.999999090158
-6893420429493,370870,38229522,LABTSH,2013-09-30 14:11:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.827352646985,0.866395892252,0.849653514241,0.844365976153,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.509772522819,0.874981909015,0.890924509333,0.899742488753,0.999999941205
--6315010013845,428921,32223070,LABTSH,2013-01-18 12:40:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.806703808061,0.825374983319,0.843522051232,0.863185754424,0.83656996397,0.94659206313,0.904263576316,0.87125660921,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.537513893758,0.539002574476,0.507669028674,0.506141003605,0.82027162983,0.831774220689,0.835360777473,0.998005650868
--2982318315413,365678,35720321,LABTSH,2013-06-12 18:29:00,1,0,1,0,0.8125,0.929515418502,1.0,0.824768585728,0.816754850347,0.828523348363,0.87383968791,0.879277077105,0.851379875656,0.901543736694,0.830473795648,0.911308523931,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.537513893758,0.539002574476,0.510760830864,0.505020901664,0.876977320819,0.892049691541,0.897775357918,0.99999995779
-10521344181295,423567,36789072,LABTSH,2013-08-15 11:02:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.771108153669,0.786280393268,0.833954826264,0.79420815101,0.806404063641,0.927137021747,0.74115438515,0.741624703017,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.560883715632,0.531619424901,0.539002574476,0.509462147164,0.503474372033,0.841038253818,0.92387075802,0.96129248933,3.51169362323e-11
--1052282048389,451008,36908239,LABTSH,2013-07-30 18:53:00,1,0,1,0,0.8125,0.830703012912,1.0,0.835946990465,0.768895440095,0.756554773984,0.915861602958,0.726044862044,0.651610140653,0.52,0.714871540506,0.634478638426,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.560875013606,0.526314453403,0.52803351545,0.501092936368,0.502840141218,0.842822996529,0.863481074035,0.876275418265,0.00140609153584
-11589451297058,462638,38362495,LABTSH,2013-10-08 10:11:00,1,0,1,0,0.8125,1.0,1.0,0.805421138254,0.79340137524,0.758691745388,0.823905192794,0.677516521156,0.816993687313,0.703526740814,0.790861167599,0.787369542204,0.782447187593,0.684152284793,0.63641322029,0.632404586216,0.56781991282,0.5375379459,0.550443778739,0.509545888581,0.504870139038,0.408749046988,0.244157027536,0.0764063701873,3.36499726738e-08
-1311927730390,391660,39167875,LABTSH,2013-11-11 04:57:00,1,0,1,0,0.721247563353,1.0,0.0,0.74873835789,0.727537761463,0.709426576983,0.738741478218,0.778405023004,0.687207439244,0.6,0.7522890971,0.60599621607,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.544413991675,0.512892329462,0.522467456931,0.503078226736,0.506196767669,0.550270397592,0.500406823972,0.535128639813,3.39030483693e-05
--8676253263783,369529,36709473,LABTSH,2013-07-20 12:18:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.822395603613,0.823928435048,0.81820730512,0.828598561245,0.866395892252,0.860714762034,0.848463277669,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.869553083827,0.87486494832,0.875621624384,0.999999785397
--4955818346447,457881,33105398,LABTSH,2013-02-09 16:47:00,0,1,1,1,0.8125,0.994152046784,1.0,0.797115458713,0.79256925491,0.826349909422,0.888033055911,0.862441220858,0.84961594033,0.771781558574,0.823796586675,0.890653877388,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.572489572704,0.539811480983,0.539514376287,0.511533288421,0.503986573895,0.886443823931,0.903631093949,0.909346765219,0.999984754052
--4048930214562,470259,39429407,LABTSH,2013-11-14 13:23:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.869521315212,0.83328513052,0.836561187191,0.870249847627,0.865027757664,0.850779925622,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.813438152487,0.814294926263,0.813683409335,0.999996680762
--9044230247557,377234,35131794,LABTSH,2013-05-15 15:23:00,1,0,1,0,0.162790697674,0.0,1.0,0.485984057242,0.585232815223,0.621084164445,0.886561507741,0.586335614389,0.463191334838,0.342857142857,0.780952380952,0.438366544876,0.650240561102,0.645760345789,0.614099487619,0.625324561677,0.52936899614,0.510717436652,0.508984840768,0.496110993556,0.497400250313,0.422345133052,0.340050267427,0.337155103423,6.34800458514e-104
-5879621710580,368242,31706754,LABTSH,2013-01-10 13:36:00,0,1,1,1,0.721247563353,1.0,1.0,0.72162051314,0.676831073275,0.690605000459,0.705883421276,0.634509810606,0.749707245982,0.4,0.648033837071,0.682263361529,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.560568768578,0.533900207042,0.519994167151,0.504461497358,0.504457290241,0.494055118504,0.58921186928,0.782691324135,5.14038413483e-49
--1658580835090,371548,34714255,LABTSH,2013-05-01 13:08:00,1,0,1,0,0.8125,0.830703012912,1.0,0.835416380724,0.56455239465,0.679626578433,0.761745279459,0.542561954103,0.657012836367,0.2,0.290241604674,0.493039191632,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.562045647501,0.524820138186,0.553302965396,0.507917813641,0.499068033786,0.243920921701,0.144402242465,0.0867459212486,1.0
-2136644303720,462985,39996180,LABTSH,2013-12-15 15:01:00,1,0,1,0,0.721247563353,0.0,0.0,0.689497459446,0.778174038316,0.730989778216,0.80863917885,0.731697941523,0.779902161634,0.857961155537,0.626042877023,0.631429182072,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.560102845502,0.533949047602,0.534374830545,0.506579231219,0.508809538888,0.7945728716,0.72053556675,0.721492190999,1.0
--8967516783628,422667,36950811,LABTSH,2013-07-25 17:47:00,1,0,1,0,0.721247563353,0.926553672316,0.0,0.783093561777,0.730099728786,0.704599218282,0.61036932627,0.49962382985,0.727660936939,0.726716294458,0.662716560374,0.591006830042,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.551959725264,0.614992452821,0.519994167151,0.508016804555,0.59400918581,0.868553842357,0.888882059346,0.892824591272,1.0
-3951110015509,366871,36044832,LABTSH,2013-07-06 16:47:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.841923585944,0.870249847627,0.860714762034,0.863114360893,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.591878617801,0.550713873318,0.550443778739,0.514327147124,0.507261793209,0.89201246212,0.901550354459,0.905806698741,0.999999396427
--11880124844624,416547,33756565,LABTSH,2013-03-16 00:07:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.744814115466,0.688761194082,0.720810061906,0.914757727235,0.634257844973,0.83358774158,0.466666666667,0.785016033774,0.829568862684,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.558173515075,0.538641278502,0.539874895585,0.515834546678,0.507276866651,0.766590768748,0.7071539801,0.630636529475,9.80201417051e-12
--1033030092639,450034,37328483,LABTSH,2013-08-06 18:39:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.817369984784,0.800575099195,0.835132737003,0.819294739127,0.827158075229,0.859126611537,0.84644349902,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.567869079446,0.534955074766,0.539002574476,0.507509612204,0.504520567597,0.73952212954,0.743738879218,0.746431224524,0.999750467356
-2037193556104,376195,33252088,LABTSH,2013-03-12 18:52:00,1,0,1,0,0.8125,0.830703012912,1.0,0.798068202586,0.775073153597,0.795145620862,0.823928435048,0.891144811108,0.828481296617,0.886274602812,0.875134906047,0.878292739,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.544113600356,0.553302965396,0.515868001424,0.510148430734,0.887237345854,0.895365722218,0.898901948089,0.99999974525
-3494868608002,433131,33469605,LABTSH,2013-03-19 22:00:00,1,0,1,0,0.307692307692,0.0,0.0,0.67887900239,0.571971046908,0.609131243378,0.573245283019,0.527109193253,0.643011035122,0.2,0.588278388278,0.643426947432,0.782447187593,0.711240791414,0.66097372741,0.663616145716,0.549338278247,0.526764213308,0.518997554745,0.501672964148,0.504022198819,0.581570309133,0.64756813172,0.717221690571,1.78000349761e-29
--3312920219045,430497,33726550,LABTSH,2013-03-17 14:34:00,0,1,1,1,0.441860465116,1.0,1.0,0.757075219123,0.634383588326,0.604074852647,0.710461458897,0.750236085053,0.694381123578,0.686857142857,0.588278388278,0.602788792247,0.650240561102,0.645760345789,0.618892348407,0.625324561677,0.529059392029,0.515344024357,0.509268689392,0.500088621423,0.501615527966,0.417880182552,0.455965861539,0.567594784409,3.06873817377e-132
-2020918181327,465487,31355328,LABTSH,2013-01-01 09:32:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.754056846419,0.704579799731,0.724960551221,0.8138780982,0.83870690751,0.803013235623,0.893925593052,0.816696198453,0.816861087219,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.524813549125,0.519994167151,0.503893797043,0.502142203278,0.714206531024,0.718590303411,0.700913448997,4.27558666962e-05
-11967700110313,426566,37293521,LABTSH,2013-08-30 14:44:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.841923585944,0.870249847627,0.860714762034,0.857896510005,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.87530439839,0.887269004224,0.892839526183,0.999999117866
--3177148233086,451735,32274470,LABTSH,2013-01-10 15:24:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.606164126113,0.730245209359,0.701066346655,0.57763533103,0.729626769627,0.684053487709,0.737659067404,0.62993935724,0.665238485464,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.553741419968,0.525745822116,0.534374830545,0.502825256125,0.501362916734,0.724181556222,0.736768436381,0.760870328276,2.78989461785e-26
-8239903194023,462458,38425531,LABTSH,2013-10-13 10:18:00,1,0,1,0,0.8125,0.444444444444,0.0,0.857842790675,0.702691232659,0.708500564563,0.501515151515,0.685008858761,0.723466197974,0.75015015015,0.906107962792,0.55848406517,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.571034794586,0.533262339938,0.539002574476,0.499120342548,0.497944025797,0.761199103165,0.812873747414,0.873551624703,7.07105972401e-145
--5496247590037,467811,36868344,LABTSH,2013-07-28 01:02:00,0,1,1,1,0.8125,0.95,1.0,0.85177193628,0.823693293396,0.777306731815,0.825096032806,0.683316063415,0.801096836097,0.482240397953,0.771093719649,0.737169464151,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556441971091,0.527672179751,0.529989690422,0.505147185509,0.503409978881,0.861979046733,0.878642665009,0.88833518888,1.26234434794e-12
--6715240056737,397236,35204117,LABTSH,2013-05-29 13:34:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.817833840453,0.81820730512,0.828853740466,0.866395892252,0.874487217124,0.851905145684,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.578591973145,0.544841251108,0.539002574476,0.509734924238,0.504964682872,0.94114995956,0.958277367145,0.957163107317,1.0
--9128151034771,440744,40497165,LABTSH,2013-12-27 15:53:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.627211208836,0.6591596807,0.724802359623,0.515859895369,0.795197497982,0.697929870268,0.591300639659,0.731325115419,0.788326050023,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.56878112347,0.538201982314,0.519994167151,0.507713319596,0.503084659467,0.890885794815,0.904819957035,0.914145989821,1.42715653905e-06
--12109209302584,362649,40461171,LABTSH,2013-12-21 08:59:00,0,1,1,1,0.441860465116,0.0,0.0,0.726403719405,0.617604673544,0.690591152646,0.678048780488,0.734992712012,0.597097908432,0.576671505422,0.579944718257,0.805740222395,0.650240561102,0.646281427091,0.615593043724,0.617481144548,0.551618845194,0.528768618848,0.520508298955,0.507630287629,0.502709569936,0.803273581878,0.87407925401,0.880820909055,2.7072274924e-12
--7846484798454,464098,34933485,LABTSH,2013-05-12 08:21:00,0,1,1,1,0.721247563353,1.0,1.0,0.653311162522,0.695594410208,0.731340618976,0.809826774396,0.765084011925,0.697293425012,0.6,0.826484485316,0.763523343059,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.540681135872,0.516714097116,0.519994167151,0.501126871291,0.502611185474,0.784860613017,0.814216236782,0.843680701434,1.0
--7562012320777,456970,37952144,LABTSH,2013-09-07 04:36:00,0,1,1,1,0.8125,0.0,0.0,0.769478024159,0.73751393191,0.715767745946,0.908233554057,0.624981318834,0.760272168189,0.568253968254,0.654352602224,0.693544315871,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.5505218542,0.52105036785,0.544333308272,0.501616817162,0.503336946587,0.850510907531,0.884575700777,0.84119117235,3.83285371544e-31
-5258213935566,421188,38089494,LABTSH,2013-10-07 23:46:00,0,1,1,1,0.8125,0.830703012912,1.0,0.810550165068,0.802868547455,0.779958233877,0.799476198096,0.856060810023,0.793773100748,0.781459161699,0.894745763591,0.845339571661,0.782447187593,0.736457474868,0.684542054663,0.700503272381,0.553273067162,0.526608285123,0.510069898265,0.504960187581,0.506413485033,0.879403829995,0.906561640485,0.895653562789,1.0
-2253688290766,371626,34803628,LABTSH,2013-05-22 19:39:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.828644104455,0.818864301808,0.824915115919,0.854017015272,0.855328435973,0.955867167025,0.835012965049,0.814764866457,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.564397420605,0.532391896447,0.530502782545,0.507419943856,0.501482199722,0.788424376768,0.80293441214,0.804335476478,0.00184596039508
-11153105811349,401051,31649491,LABTSH,2013-01-02 04:53:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.754056846419,0.805894779014,0.761975057712,0.844861663694,0.743443448777,0.857812471249,0.889707187283,0.872327569434,0.868422544186,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.549189993202,0.524495449261,0.510946844091,0.50354167965,0.501769993804,0.752263455991,0.768852288364,0.775248049905,0.0152546289958
--1824607523055,428189,36240296,LABTSH,2013-06-25 17:29:00,0,1,1,1,0.8125,0.830703012912,1.0,0.771738282698,0.816754850347,0.822395603613,0.823928435048,0.807724120183,0.827352646985,0.827158075229,0.864329904396,0.818616327515,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.559309068722,0.530268773768,0.519118175928,0.503519224618,0.503790360859,0.778513635438,0.785089671515,0.787790431707,0.999996614259
--5848373338376,435602,36151749,LABTSH,2013-07-01 20:36:00,0,1,1,1,0.8125,1.0,1.0,0.773347415207,0.837809260591,0.792178058155,0.856297418527,0.844465491827,0.773531493371,0.737147034634,0.899549444383,0.756015368962,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.583340659231,0.544631717035,0.541464288286,0.513155632592,0.510010111623,0.857677826834,0.880596167551,0.887994927983,2.46581353078e-14
--10692983195898,387336,39018164,LABTSH,2013-10-20 01:23:00,1,0,1,0,0.8125,0.921052631579,1.0,0.743078728902,0.806703808061,0.799276861318,0.784233672676,0.808948990333,0.774495088102,0.631498829239,0.845176498352,0.742782117048,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.563021933408,0.529427661802,0.529989690422,0.504104758378,0.500800146716,0.567940440166,0.557031846781,0.57497263387,2.22879774128e-11
--11467493279271,357863,36143766,LABTSH,2013-06-23 14:53:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.839263409856,0.870249847627,0.865027757664,0.850779925622,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.8322360685,0.831912849341,0.831699245074,0.999997847231
-5465192565947,430832,33990619,LABTSH,2013-04-13 17:44:00,1,0,1,0,0.8125,0.888888888889,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.81820730512,0.830753500702,0.827158075229,0.849653514241,0.839529360889,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.888820477509,0.898728925266,0.90566067782,0.999999974509
-8214183265708,380347,38314790,LABTSH,2013-10-16 17:34:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.822856255829,0.840304687715,0.862091731563,0.849653514241,0.850171039115,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.580771991934,0.544397069062,0.539002574476,0.509734924238,0.504964682872,0.809453417635,0.819810148474,0.822316261435,0.999994422452
--11559410317330,467568,35595281,LABTSH,2013-06-06 22:00:00,1,0,1,0,0.8125,1.0,1.0,0.71347598253,0.795667514328,0.724949252229,0.921022332257,0.799321191808,0.803356888864,0.766420024523,0.747189538103,0.879804090587,0.782447187593,0.710772579177,0.672472076714,0.684819519785,0.557653154464,0.528034743903,0.530502782545,0.505801663667,0.505373630045,0.746818090964,0.759537960464,0.803909333951,0.0546256204123
--11854985732873,396268,32897160,LABTSH,2013-02-08 15:06:00,0,1,1,1,0.8125,0.444444444444,0.0,0.798068202586,0.777763314931,0.812687643523,0.823928435048,0.681336546469,0.846105884122,0.883150650981,0.861966506118,0.702734748975,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.5352325001,0.539002574476,0.498068236606,0.499627685558,0.73936511239,0.756404254483,0.773103586821,2.14001830261e-10
-1694293542468,456961,39663859,LABTSH,2013-11-30 22:30:00,0,1,1,1,0.8125,0.0,0.0,0.687630522138,0.798441680325,0.769655184533,0.897720128079,0.725315843054,0.671365540321,0.513043478261,0.613508384977,0.691212352949,0.782447187593,0.736457474868,0.684542054663,0.700503272381,0.557116929947,0.52474937824,0.52447598901,0.502708533082,0.500653222628,0.787149200127,0.777450533986,0.750265615975,1.0270688036e-28
--3285473297920,372876,32140901,LABTSH,2013-01-15 14:24:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806735645701,0.825191022985,0.849548269759,0.847989206297,0.867297517089,0.760442330424,0.779658173786,0.817724774148,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.629888505004,0.539002574476,0.516681976302,0.508264781401,0.917133972912,0.924013658724,0.922014444035,1.0
--8681350626996,403591,37678215,LABTSH,2013-09-02 09:05:00,0,1,1,1,0.8125,0.0,0.0,0.759343861375,0.794196170668,0.685264070758,0.723436196831,0.767534420574,0.857413765662,0.917878847624,0.666419080919,0.698435551036,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.548012971365,0.522173594657,0.539002574476,0.504456869427,0.500311728094,0.741415441022,0.858329120952,0.831465895337,9.0242483874e-13
--1831256370357,395523,39727447,LABTSH,2013-11-23 07:45:00,0,1,1,1,0.8125,0.830703012912,1.0,0.792797219249,0.739999428984,0.780364826141,0.778112284942,0.814034289712,0.794793679702,0.912798620616,0.947514512538,0.840525640485,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556084968027,0.53033773096,0.539002574476,0.510488291183,0.505216832683,0.616285138755,0.599294893168,0.600521973205,0.02510280572
-5798455498798,426219,32990637,LABTSH,2013-03-05 19:17:00,1,0,1,0,0.8125,0.0,0.0,0.83725054821,0.791684787868,0.823293682983,0.720671643833,0.843852587269,0.867149188542,0.749555385032,0.712972937335,0.884994309855,0.782447187593,0.736457474868,0.684542054663,0.700503272381,0.561015455709,0.533261553842,0.535967338887,0.504704473802,0.50241215191,0.693658445727,0.688715333858,0.687453844835,0.996941014425
-9820125271965,415606,34059729,LABTSH,2013-04-19 22:00:00,1,0,1,0,0.721247563353,0.9375,1.0,0.642764243221,0.677428946013,0.691056689488,0.682243206439,0.917069448942,0.71692585526,0.542857142857,0.858095399475,0.698686252262,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.546641617771,0.522294013261,0.514349932763,0.500369555095,0.5010083556,0.637148403985,0.661618340383,0.653604057975,6.00139411346e-13
-8536261802730,390491,33881247,LABTSH,2013-04-08 16:42:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.830012823073,0.827158075229,0.860714762034,0.842635174297,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.818048172,0.820353247358,0.819804127195,0.999998564136
-3881224372209,405366,37704804,LABTSH,2013-09-07 16:23:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.823159472773,0.810294487924,0.823928435048,0.844562261012,0.83868937911,0.938671701464,0.919514634518,0.872592134112,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507669028674,0.504691720199,0.818315780918,0.833982676145,0.846415199708,1.02886455925e-05
-10209418045619,397808,32927375,LABTSH,2013-02-25 18:09:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.837172524365,0.836114294075,0.879042951075,0.860714762034,0.857530887972,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.506425736731,0.813496405853,0.814784925394,0.813621821572,0.999995157458
-2728017178685,425592,35133525,LABTSH,2013-06-04 22:00:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.826048767051,0.805938210674,0.885642301022,0.841832527207,0.875340362722,0.77041773321,0.83400444139,0.854177264319,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.536739012764,0.541464288286,0.50435265781,0.503579719434,0.805997074167,0.796275344477,0.797145112368,0.99394972809
-4151369558026,431841,35370776,LABTSH,2013-06-11 22:44:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.823159472773,0.775919143934,0.676217185138,0.801340129505,0.727769889632,0.871120706833,0.751012070766,0.773093427367,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.564626899674,0.527843184544,0.529989690422,0.507699152822,0.503075734015,0.911256850619,0.918942705211,0.924114351319,7.03723128287e-07
-835392971472,450459,32738564,LABTSH,2013-02-19 09:02:00,0,1,1,1,0.8125,1.0,1.0,0.768586592328,0.804250545359,0.808981422614,0.859252510895,0.826199349517,0.822329158534,0.939801079636,0.930886599528,0.888619582865,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.562385274042,0.53372062684,0.550443778739,0.506763375585,0.502881583682,0.748534661021,0.76130804008,0.75748433978,0.998045470656
-4574354138376,459372,33847545,LABTSH,2013-04-05 13:27:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.772064509255,0.785125978414,0.770459740511,0.86846728569,0.721715576167,0.751060456694,0.911434795252,0.904767523899,0.839884531131,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.554069047837,0.528844617455,0.519994167151,0.505731918696,0.503741680946,0.822363997057,0.889390120267,0.923625183526,0.00249542077389
--2849482030521,461469,38284530,LABTSH,2013-09-21 09:25:00,0,1,1,1,0.826086956522,1.0,1.0,0.744814115466,0.721184191318,0.757643275041,0.404474219084,0.744997320598,0.742237700148,0.749950057767,0.937575076851,0.769803946603,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.525452029828,0.51219628244,0.519994167151,0.500992519221,0.500632615804,0.77706542436,0.815426782457,0.836595401849,3.87236574996e-13
-635153292152,361914,34126143,LABTSH,2013-04-16 10:18:00,0,1,1,1,0.8125,0.830703012912,1.0,0.742915332464,0.800459994951,0.653858000118,0.719041496201,0.888060672516,0.717225885263,0.647741935484,0.408462874639,0.700196455364,0.782447187593,0.710772579177,0.664436408228,0.671017582252,0.546872146902,0.524298336825,0.545414592285,0.509545620822,0.505592380637,0.726012611258,0.749434719699,0.774338780132,4.46790179559e-254
-4357693878562,356804,36992184,LABTSH,2013-08-11 22:00:00,1,0,1,0,0.721247563353,0.9375,1.0,0.754056846419,0.752971274912,0.713356116625,0.836179464671,0.695063633096,0.693998095877,0.94659206313,0.683633966738,0.706665877489,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.554069047837,0.52759645542,0.510946844091,0.501883492326,0.502874036761,0.622293979771,0.588603069312,0.59646692257,7.12810855494e-07
-7939532610242,414078,36358070,LABTSH,2013-07-17 07:36:00,0,1,1,1,0.721247563353,0.8,1.0,0.754056846419,0.739764109561,0.741368439673,0.787876935647,0.787651215387,0.794476192207,0.977186963979,0.836832491682,0.809354204323,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.537543863043,0.519889247853,0.519994167151,0.497316675474,0.499001937134,0.839128628213,0.849285475124,0.857503190901,0.101765156515
-12059308994351,437954,39935590,LABTSH,2013-12-20 22:00:00,1,0,1,0,0.721247563353,0.0,0.0,0.684001786679,0.527737388072,0.674507194939,0.456077198533,0.412728226515,0.435342667581,0.4,0.654513754359,0.574127193338,0.650240561102,0.616385848561,0.593279477016,0.599932757913,0.515484511023,0.502078493735,0.510946844091,0.497501324977,0.497389387912,0.0590484531766,0.0374913027969,0.0311797993117,1.33371536895e-85
--3629411009402,461403,39422330,LABTSH,2013-11-02 05:09:00,0,1,1,1,0.721247563353,1.0,0.0,0.737320037056,0.792113737849,0.689038574662,0.570892410342,0.822556957755,0.576348761037,0.950158237225,0.631827106349,0.663008404063,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.53122642906,0.514165016102,0.510946844091,0.505027934228,0.503097283398,0.33299361294,0.243459245769,0.204650023049,4.68555901112e-30
-9224364645132,387537,35481122,LABTSH,2013-06-17 15:53:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.841923585944,0.870249847627,0.860714762034,0.863114360893,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.591878617801,0.550713873318,0.550443778739,0.514327147124,0.507261793209,0.889577063394,0.89821306871,0.901829304394,0.999999416228
--11660161553201,466505,31977348,LABTSH,2013-01-12 12:35:00,1,0,1,0,0.721247563353,1.0,1.0,0.805259989323,0.593977555323,0.662952429073,0.698767788615,0.730693934104,0.570377592369,0.0,0.95814146531,0.681633589989,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.548859929099,0.524412096719,0.508984840768,0.506991778643,0.50275080948,0.733056308951,0.629313797632,0.50476034553,1.98849981383e-09
-9743997733415,460185,34884710,LABTSH,2013-05-19 00:02:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.817460325839,0.792120332066,0.808972032194,0.712218792926,0.795132763398,0.695240096879,0.786323514642,0.741929153021,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.566821521737,0.53408603646,0.541464288286,0.506570726178,0.501914387761,0.516182016777,0.474319538223,0.423689521075,9.63258459672e-49
-924560298345,448812,36974625,LABTSH,2013-08-16 23:10:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.804247212954,0.787511438249,0.806696346204,0.744693076298,0.725071835797,0.892046608584,0.705505622969,0.742846435367,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.505873298477,0.506201638638,0.807954890264,0.807706819537,0.763749405841,8.66131002346e-20
--6724482096603,418406,36379373,LABTSH,2013-07-04 08:06:00,0,1,1,1,0.721247563353,0.0,1.0,0.644042913992,0.733145842312,0.709335418825,0.733807990055,0.735760268545,0.705732886162,0.68253968254,0.781141482954,0.678752907504,0.650240561102,0.674575323822,0.65500560385,0.656811041531,0.550030258879,0.52014784061,0.508984840768,0.49875601192,0.498035687586,0.62898641885,0.624718660162,0.612981575152,0.0144039797868
--6293623667828,442802,34847840,LABTSH,2013-05-07 22:23:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.828502029753,0.819199052252,0.843406892976,0.842240859777,0.84934896941,0.9180765268,0.803053235894,0.850461147718,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.566442290797,0.534827485869,0.529989690422,0.508823654235,0.503756983804,0.870014845526,0.874540649444,0.87373289133,0.318226834387
--12376620840026,380936,36840154,LABTSH,2013-07-24 05:42:00,0,1,1,1,0.721247563353,0.0,1.0,0.599558081821,0.663181637409,0.662028887953,0.745564928026,0.695181962823,0.596745437746,0.647186963979,0.514396688855,0.570686721928,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.541853197543,0.518601744113,0.499929566985,0.505283305095,0.498923943743,0.531000562152,0.485432270666,0.441993846704,7.45687871167e-13
--237209027206,355734,35898855,LABTSH,2013-06-16 22:00:00,0,1,1,1,0.721247563353,0.0,1.0,0.604134042652,0.644472304356,0.663870890231,0.839729551818,0.5738536309,0.560564592762,0.966420024523,0.746901590705,0.67182127813,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.545830690837,0.523688207748,0.499929566985,0.504831585652,0.500772863406,0.756234335488,0.779852192274,0.781065607915,2.44682186506e-05
-4778974442533,413704,38315818,LABTSH,2013-10-06 02:12:00,0,1,1,1,0.8125,0.830703012912,1.0,0.696447162038,0.778784533717,0.791498964962,0.673652022272,0.798964243713,0.837585915721,0.67459792258,0.694641078812,0.739809102903,0.782447187593,0.736457474868,0.682471893924,0.700503272381,0.548392402003,0.52556956569,0.52447598901,0.504617251207,0.50253097284,0.612236517301,0.61481422219,0.630205116078,0.999883146311
-8993078937045,394356,40127480,LABTSH,2013-12-17 20:00:00,1,0,1,0,0.162790697674,0.0,0.0,0.656348659664,0.530935384848,0.643201904979,0.866327681671,0.723136223536,0.634889736688,0.523610280932,0.613474184519,0.482455141145,0.650240561102,0.646281427091,0.615593043724,0.617481144548,0.527848349428,0.511302850215,0.499929566985,0.506455438155,0.502414469806,0.73788463286,0.831102356055,0.874182380678,2.39325287135e-17
--8807605119284,419107,33902293,LABTSH,2013-03-27 22:39:00,1,0,1,0,0.8125,1.0,1.0,0.824768585728,0.838556404444,0.824819747062,0.824773882727,0.854335772207,0.853308589231,0.928235588078,0.879716502395,0.865287389763,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571804488446,0.539093410351,0.541464288286,0.51415008993,0.507173211222,0.832192430973,0.820519497734,0.814892725689,0.999836961019
-3365228305274,447207,35968661,LABTSH,2013-07-05 23:43:00,0,1,1,1,0.8125,0.830703012912,1.0,0.663035601012,0.743978308485,0.772984431353,0.857100298134,0.698245794986,0.773675486944,0.511369092407,0.707335047165,0.811044364548,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.563363429523,0.533475187294,0.533381282937,0.51039766784,0.505237467355,0.843992293256,0.842960723818,0.837945567784,0.999999998364
-1940383823502,440093,35444627,LABTSH,2013-06-17 12:05:00,1,0,1,0,0.8125,0.0,0.0,0.771738282698,0.816620173946,0.798450219577,0.903183388761,0.868580319248,0.862797991151,0.762427688923,0.752297266438,0.786389204438,0.782447187593,0.736457474868,0.682471893924,0.700503272381,0.561015455709,0.532449329425,0.53062312788,0.507862855507,0.504771773031,0.881221882903,0.895620742572,0.898666290379,8.31186786562e-13
-1653376319670,409492,33134797,LABTSH,2013-03-04 10:46:00,0,1,1,1,0.721247563353,0.0,0.0,0.726403719405,0.660416355417,0.62088731755,0.578048780488,0.505081980933,0.741869691035,0.688888888889,0.771412765733,0.676915526679,0.650240561102,0.674575323822,0.635730785253,0.656811041531,0.535001747584,0.514918888119,0.489033096195,0.495883449673,0.497644174746,0.569825983291,0.643489354508,0.69435156051,1.33019422075e-45
-4385698356305,415697,34869653,LABTSH,2013-05-11 12:38:00,1,0,1,0,0.8125,0.929515418502,1.0,0.824768585728,0.806703808061,0.821057354313,0.866945093315,0.851332299985,0.826600584628,0.930915292561,0.897855677367,0.88927992536,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.567869079446,0.535588456814,0.539002574476,0.510760830864,0.504725874951,0.831222231163,0.852191958754,0.857676287992,0.999919865916
--6017173961671,415041,40390572,LABTSH,2013-12-15 23:00:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826048767051,0.808941474667,0.782904682669,0.842240859777,0.866299010542,0.740973288766,0.906230932905,0.918590118721,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.569567829113,0.537094813561,0.541464288286,0.514453401641,0.512619581673,0.804722597916,0.814342248455,0.82389433398,0.00574021227383
--759158008238,444285,33276951,LABTSH,2013-02-17 22:49:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.810309572675,0.853372301992,0.853927439637,0.810355783642,0.563115124153,0.880296723004,0.825132983453,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.535284737427,0.544333308272,0.507847173142,0.504796631336,0.795194678013,0.776876521593,0.765675384349,0.999936501762
-8026072380942,472150,38568152,LABTSH,2013-10-25 18:39:00,0,1,1,1,0.8125,0.444444444444,0.0,0.824768585728,0.816754850347,0.764743740087,0.823928435048,0.815569844279,0.860754052952,0.867236259433,0.857422947526,0.839797964489,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.578591973145,0.54752262085,0.553302965396,0.509204949699,0.505944996816,0.901137004705,0.918129355333,0.923668845728,0.999320381384
-4170722765856,385463,31605056,LABTSH,2013-01-08 00:26:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.818450987467,0.801337009507,0.772217718036,0.841832527207,0.781526876432,0.890428720036,0.84656100356,0.792931089523,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563691317678,0.533659321628,0.529989690422,0.51025155368,0.506629937395,0.868710110482,0.879596037508,0.873488340328,0.999999128113
--6304518127540,376910,37993829,LABTSH,2013-09-20 14:21:00,0,1,1,1,0.721247563353,0.0,1.0,0.512637843604,0.634858260061,0.666677288426,0.411843220339,0.769716089279,0.531217321295,0.6,0.542227696352,0.568504096698,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.55658745802,0.52279648874,0.531497075666,0.504542313808,0.505859454076,0.52536883151,0.559886813412,0.646203832077,6.97162942315e-69
--1036050138478,395530,34104318,LABTSH,2013-03-23 14:49:00,0,1,1,1,0.8125,0.830703012912,0.0,0.771738282698,0.812438125039,0.786128391749,0.835743294913,0.871972488757,0.839741213961,0.734539905156,0.64421989803,0.705293681462,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.561980540771,0.529887700897,0.519118175928,0.50170646017,0.501956696446,0.898697850503,0.910804935778,0.920859282838,0.999999855659
-4357291704473,364655,32996043,LABTSH,2013-03-01 20:49:00,0,1,1,1,0.8125,0.830703012912,0.0,0.824768585728,0.816754850347,0.81455467679,0.768756917376,0.827203797913,0.845749279828,0.883150650981,0.872749456043,0.847265897152,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533226548227,0.529989690422,0.508950288353,0.504396958002,0.823493239835,0.817654664132,0.815234751173,0.999999849481
--7601020425991,414242,35744584,LABTSH,2013-06-10 14:41:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.835894293867,0.832390714267,0.866395892252,0.849653514241,0.854602830262,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.897046334803,0.90852093787,0.913002590942,0.99999998886
-3330601561087,405507,32685586,LABTSH,2013-02-26 10:53:00,0,1,1,1,1.0,0.0,0.0,0.7297753114,0.636926645284,0.668098611269,0.322916666667,0.845714964524,0.604717603376,0.4,0.623069028672,0.662983333333,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.542504854153,0.608492895508,0.519994167151,0.674291096313,0.593730230596,0.86394889762,0.978054037634,0.999883779107,2.29129800618e-187
-8759794999210,366242,37196419,LABTSH,2013-08-26 11:33:00,1,0,1,0,0.162790697674,0.0,0.0,0.755440434762,0.572340211373,0.603758260447,0.82215472808,0.539140261009,0.54667108713,0.447619047619,0.684432234432,0.603341835285,0.650240561102,0.645760345789,0.619252229858,0.625324561677,0.5324327026,0.517341322888,0.508984840768,0.503124654678,0.500907116004,0.300038261808,0.283733217069,0.230962891112,2.76415315294e-35
-75070823630,355530,31890236,LABTSH,2013-01-23 13:36:00,0,1,1,1,0.8125,0.929515418502,1.0,0.815525854775,0.740103587762,0.772631860604,0.694498082295,0.783865628478,0.78932594784,0.624853753212,0.751158834343,0.860048603487,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.558742399518,0.530024780186,0.539002574476,0.50605077021,0.504690245541,0.859203298275,0.871706450713,0.866245238451,3.16125199052e-08
--3233614462703,399026,33785425,LABTSH,2013-03-10 05:36:00,0,1,1,1,0.8125,0.8,0.0,0.805421138254,0.8124062874,0.793371526471,0.651019012023,0.808948990333,0.80713242006,0.777752976079,0.778212803111,0.769949724011,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.529495298978,0.529989690422,0.502645714566,0.500822590405,0.73271353278,0.727897637896,0.728823591566,0.9740448899
-4901856343409,422031,40124752,LABTSH,2013-12-13 17:31:00,0,1,1,1,0.8125,1.0,1.0,0.714228435112,0.803138538434,0.722247698887,0.430364152079,0.590533430001,0.674934228841,0.725674325674,0.576564689125,0.668575117639,0.782447187593,0.692262140043,0.649566236082,0.65852479141,0.541826314189,0.520620991274,0.522388752893,0.502715391322,0.501100627541,0.419008722796,0.485411875725,0.540764592845,1.22553576541e-78
--8246208056075,382766,36911169,LABTSH,2013-07-26 20:42:00,1,0,1,0,0.8125,0.929515418502,0.0,0.674006750459,0.727793559181,0.721296123684,0.880091121,0.863653010844,0.807525578783,0.469555385032,0.805392471793,0.756786182663,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.565545700701,0.529421200944,0.529989690422,0.504447328343,0.500561590889,0.835140243178,0.827465848929,0.853355284352,0.963983825153
--5078640231689,453348,39523091,LABTSH,2013-11-08 11:19:00,0,1,1,1,0.8125,0.0,0.0,0.562388245221,0.708088809741,0.677414942319,0.925168044077,0.758629637415,0.737767044308,0.631111111111,0.638588357333,0.687064304786,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.571544292887,0.533712934279,0.539002574476,0.507891789651,0.504937808956,0.865316625806,0.811063409998,0.811789063103,8.80649783102e-09
--5784847684834,444009,34920934,LABTSH,2013-05-11 09:15:00,0,1,1,1,0.8125,0.921052631579,1.0,0.805421138254,0.806703808061,0.814692278417,0.737668059338,0.81820730512,0.809380068492,0.800695748931,0.836424416292,0.810809903656,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.532603319913,0.539002574476,0.505915822739,0.502457751127,0.839541744131,0.858200389447,0.869144580844,0.262706810259
--2957186291267,430508,36813036,LABTSH,2013-07-30 01:35:00,0,1,1,1,0.441860465116,0.0,1.0,0.477044844649,0.608752475425,0.616017496713,0.267904290429,0.831651390115,0.493654695544,0.580753138075,0.493333333333,0.753597883598,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.536031642029,0.513134453971,0.499929566985,0.495472273369,0.497757835164,0.942322005211,0.922067889853,0.878949995392,1.37499301122e-71
--4219781369134,375480,32102047,LABTSH,2013-01-02 15:43:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.824128803879,0.823928435048,0.74348806798,0.827352646985,0.827158075229,0.82864538416,0.835518589914,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.813614350601,0.821477490804,0.823319552423,0.999998108236
-10934367070045,444640,32266387,LABTSH,2013-02-04 17:53:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.820462278332,0.648827997821,0.81820730512,0.826301285938,0.827158075229,0.859126611537,0.845500904344,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.576509987974,0.542920379045,0.539002574476,0.508276287278,0.503483234605,0.948133488995,0.963003063996,0.966430366825,0.999999999764
--8300845543805,382993,34953283,LABTSH,2013-04-30 17:19:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.754056846419,0.731359078525,0.75959988423,0.776993125613,0.830646527952,0.825236624548,0.737659067404,0.80485476601,0.810590005283,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.560102845502,0.530960526567,0.519994167151,0.506946618233,0.503570240676,0.805357259982,0.825316950002,0.842104682929,9.14333807051e-08
--8328205483487,377005,33301483,LABTSH,2013-02-18 22:01:00,0,1,1,1,0.8125,1.0,1.0,0.770415075572,0.746802509799,0.761575689853,0.818770757414,0.686570618406,0.791728253148,0.720044106837,0.600325025983,0.887153056133,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.55666754426,0.528917251079,0.518997554745,0.506609358181,0.502343112126,0.413442869283,0.321365971051,0.345050965144,1.04496951884e-08
--3939928986149,413599,32495697,LABTSH,2013-01-30 08:52:00,0,1,1,1,0.8125,0.921052631579,1.0,0.805421138254,0.806703808061,0.811599984869,0.817833840453,0.81820730512,0.806677845827,0.800695748931,0.79365279902,0.810809903656,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.532603319913,0.539002574476,0.505915822739,0.502457751127,0.745962023492,0.743811620278,0.739378547128,0.999997095782
--11165275032556,355138,36949282,LABTSH,2013-07-24 08:12:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.754056846419,0.754046736325,0.745064146186,0.851811096316,0.704817868003,0.769428605064,0.647619047619,0.729702000809,0.788540765056,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.545334837661,0.524489541268,0.531497075666,0.500359341246,0.499679392215,0.799097069299,0.78243967504,0.744448879863,7.95108616997e-10
--10952453743488,447430,38291779,LABTSH,2013-09-26 00:23:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.801337009507,0.772217718036,0.808948990333,0.802961765644,0.784327709935,0.81845043008,0.758629478122,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563691317678,0.531816817933,0.529989690422,0.506330489964,0.502665110406,0.791130778068,0.797408258906,0.798238364549,0.999999905154
--11858046225472,455898,33128334,LABTSH,2013-02-22 01:26:00,1,0,1,0,0.8125,0.0,0.0,0.85177193628,0.73024422561,0.754909644242,0.859271940181,0.848744338313,0.769992285151,0.928235588078,0.89653215271,0.684916337715,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.555172623888,0.523052476458,0.529989690422,0.506439348001,0.502800088272,0.675888902346,0.692160225803,0.705172440945,0.999277030429
-6505473355416,383558,33745538,LABTSH,2013-03-31 13:05:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.708283321699,0.679201945756,0.71418386636,0.652837294726,0.719108387704,0.594807347375,0.422222222222,0.77240145204,0.684294478853,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.560102845502,0.530960526567,0.519994167151,0.501674380824,0.503291975761,0.751812678081,0.763048705229,0.741488351149,3.21837748841e-09
-635153292152,361914,34067455,LABTSH,2013-04-16 10:52:00,0,0,1,0,0.8125,0.830703012912,1.0,0.742915332464,0.800459994951,0.653858000118,0.719041496201,0.788060672516,0.717225885263,0.647741935484,0.408462874639,0.666863122031,0.782447187593,0.710772579177,0.664436408228,0.671017582252,0.546872146902,0.524298336825,0.545414592285,0.509545620822,0.505592380637,0.726041411015,0.749494796293,0.774607480201,2.50637918489e-254
--8778304299923,404096,32567449,LABTSH,2013-02-01 11:36:00,0,1,1,1,0.8125,0.994152046784,1.0,0.819450097199,0.816758182752,0.809758766287,0.680625900953,0.888803741476,0.756789907908,0.918252379092,0.658649239465,0.767452541286,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.542094053251,0.550443778739,0.51180991513,0.506781306099,0.906247370625,0.91932932801,0.902040657026,0.999999937898
--1521725141870,402016,38090685,LABTSH,2013-09-09 08:03:00,0,1,1,1,0.8125,0.921052631579,1.0,0.69385186436,0.729002680122,0.706609677673,0.700217986589,0.62167590349,0.776069202231,0.714543695582,0.753927068654,0.850401094743,0.782447187593,0.710772579177,0.674818736657,0.671017582252,0.556170144055,0.527382779206,0.539002574476,0.50666068238,0.500990370034,0.563809720608,0.656353900111,0.724747292111,1.41504468621e-26
-523184669240,375471,36693940,LABTSH,2013-08-09 18:11:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.793398042835,0.813620185111,0.772217718036,0.822094698965,0.802227674346,0.903526740814,0.821515689845,0.830379263247,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.533839380957,0.539002574476,0.505314974473,0.504175092017,0.736665845312,0.736970071773,0.733987361327,0.999898851713
--9085648008151,414111,37064653,LABTSH,2013-07-31 23:11:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.826809225038,0.816027016645,0.803480811683,0.808948990333,0.833413676791,0.827158075229,0.834322808424,0.836673675303,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.571658585874,0.537179687977,0.541464288286,0.508005045332,0.505753611477,0.807174393711,0.808508098795,0.807088450065,0.999998272037
--2957186291267,431874,37437932,LABTSH,2013-08-24 05:49:00,0,1,1,1,0.162790697674,0.0,0.0,0.656348659664,0.672362187085,0.687312742502,0.751489178313,0.883582798439,0.567606221658,0.4,0.520904534062,0.574207515173,0.650240561102,0.674575323822,0.641965366904,0.656811041531,0.524905678054,0.507952393494,0.499929566985,0.49857418125,0.50384895111,0.771058088499,0.781616615369,0.743081821673,2.8520528673e-24
-4073282604750,459389,39599937,LABTSH,2013-12-09 01:17:00,0,1,1,1,0.8125,0.921052631579,0.0,0.743078728902,0.783347000549,0.804302480147,0.823928435048,0.808948990333,0.791446241957,0.631290649781,0.673705518609,0.760226087717,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.531356291374,0.529989690422,0.501909839039,0.500454645556,0.592085860764,0.574715135651,0.56507234592,0.999940681314
-11761558112296,455824,37728028,LABTSH,2013-09-16 19:51:00,0,1,1,1,0.721247563353,0.0,1.0,0.754056846419,0.807618046675,0.70555247584,0.901307397339,0.677210971403,0.848264061922,0.755989284347,0.850427892405,0.849347838082,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.576193509261,0.541469932693,0.522467456931,0.514013865303,0.507409078558,0.944787823797,0.950135920978,0.95636843617,2.24551454189e-09
-10551692243829,387385,40243063,LABTSH,2013-12-27 23:49:00,1,0,1,0,0.8125,1.0,1.0,0.707164139084,0.814051123028,0.796831683526,0.794495304437,0.776724218651,0.713901248385,0.97041773321,0.907381127384,0.706063147907,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.585958478961,0.546445641426,0.541464288286,0.515766908592,0.510504597164,0.730779851977,0.70868989589,0.687981259806,1.27844587311e-05
--496166376320,401998,34011299,LABTSH,2013-04-05 11:48:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.819303310065,0.823928435048,0.835894293867,0.840845327526,0.866395892252,0.849653514241,0.854254075065,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.819657087557,0.825240194016,0.824993999961,0.999999261241
--8851427962982,369234,37146059,LABTSH,2013-08-05 15:56:00,1,0,1,0,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.795085876303,0.680983491987,0.807724120183,0.833386492338,0.827158075229,0.871895111056,0.788455539664,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.539220837249,0.539002574476,0.507509612204,0.503851781014,0.937838357677,0.947504952505,0.948007641674,0.999999999971
-7638477886346,374695,37246486,LABTSH,2013-08-31 08:33:00,1,0,1,0,0.721247563353,0.926553672316,0.0,0.719196431244,0.656879043816,0.701472574765,0.729579815334,0.660096690392,0.796406956112,0.573983907306,0.571041722947,0.738734815134,0.650240561102,0.645760345789,0.630451905607,0.640015064438,0.553306999481,0.528503552103,0.548859578131,0.512626629712,0.507566010278,0.961551996508,0.972894207661,0.978784663978,0.993748455436
--2167953395536,437714,34980178,LABTSH,2013-05-07 16:48:00,0,1,1,1,0.8125,0.994152046784,1.0,0.798068202586,0.832566954662,0.815779937071,0.719871643833,0.870638861763,0.861890267822,0.925272266175,0.895904495833,0.878436760414,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.543299090014,0.550443778739,0.515368048961,0.50506321487,0.936394598262,0.948816022066,0.95202882757,0.999999999429
-6754173767628,361918,33753132,LABTSH,2013-04-03 23:17:00,0,1,1,1,0.8125,1.0,1.0,0.72682244133,0.737681759803,0.755772137514,0.860484268799,0.734695669603,0.757603116586,0.537147034634,0.631183522844,0.815111986023,0.782447187593,0.736457474868,0.692540781897,0.700503272381,0.55666754426,0.5298602453,0.533381282937,0.508172461487,0.505342341612,0.602432943631,0.52938458908,0.486119189216,2.4876075613e-25
-488469605323,363729,35170684,LABTSH,2013-06-03 17:07:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.75473622187,0.776509102446,0.859574660974,0.887807616759,0.755828730545,0.867688425011,0.844964067175,0.843559852801,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534472960217,0.556319119767,0.512464008453,0.507803884678,0.920677928002,0.950318626461,0.958449042326,4.28233318793e-47
--5612387960819,448404,31914872,LABTSH,2013-01-06 17:50:00,1,0,1,0,0.8125,0.929515418502,1.0,0.798068202586,0.802108089468,0.805254105138,0.647406892976,0.855698281277,0.860937661563,0.92159206313,0.900302966646,0.863348347609,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561858477317,0.533754425653,0.539002574476,0.511445526154,0.507245650695,0.892286918686,0.913564258693,0.92334213331,0.141739145342
-8296358175318,418151,36390376,LABTSH,2013-07-21 07:26:00,0,1,1,1,0.721247563353,0.8,1.0,0.744814115466,0.698990764644,0.719223452557,0.899818030177,0.650424610642,0.758843684799,0.569230769231,0.876589541281,0.726416842404,0.650240561102,0.616385848561,0.598528474777,0.599932757913,0.531649274675,0.516235068539,0.519994167151,0.501890371827,0.501229876263,0.809803819739,0.815001638813,0.781336533324,1.59533404613e-09
-6342891161031,451794,32994518,LABTSH,2013-02-27 11:18:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.806703808061,0.810973145828,0.794052580421,0.81820730512,0.8199127324,0.888831400312,0.829802179512,0.838491007699,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.533839380957,0.539002574476,0.507509612204,0.503851781014,0.891004955941,0.904977112206,0.910296730741,6.77279090924e-05
-1781398129514,433998,35090295,LABTSH,2013-05-29 23:39:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.734292209103,0.767470380793,0.803754962529,0.793362252834,0.729228951281,0.899946275946,0.725731864054,0.878206460775,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.561305234366,0.530771002939,0.529989690422,0.504878510202,0.504885734746,0.81315743778,0.816774074733,0.809129246549,0.038507988817
--2311017341031,376996,34709925,LABTSH,2013-04-26 15:24:00,1,0,1,0,0.0,0.0,0.0,0.329047007174,0.520134798382,0.486342333148,0.360302803992,0.358055347794,0.321967266205,0.4,0.3,0.286287190788,0.72832805097,0.423674177729,0.502549388931,0.0339872295197,0.363960787843,0.433081412428,0.484741860372,0.324958024368,0.410597765962,0.244020690989,0.111161351658,0.0786754665075,0.0
--2486809971770,449282,32201821,LABTSH,2013-01-12 15:34:00,0,1,1,1,0.441860465116,0.0,0.0,0.571942329638,0.596460224288,0.469671222669,0.294629364917,0.812001139581,0.525447812777,0.142857142857,0.393333333333,0.599633666011,0.650240561102,0.616385848561,0.583828564855,0.58466802601,0.509852265045,0.500430656341,0.480783037393,0.493259884838,0.495543222177,0.118593785649,0.0750730953798,0.0352963181272,0.0
-604504639113,431822,38007201,LABTSH,2013-09-28 13:31:00,0,1,1,1,0.8125,0.0,0.0,0.74021010101,0.686726028245,0.754922436774,0.932062638672,0.853600683193,0.567444333105,0.577186963979,0.842494378283,0.739694983069,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.566055085768,0.531658699921,0.556319119767,0.517270937146,0.507281932219,0.853397466624,0.86581153222,0.870996717138,2.84810802776e-12
-7529924193878,398940,34779705,LABTSH,2013-05-11 19:20:00,0,1,1,1,0.721247563353,0.9375,0.0,0.643723934725,0.623540877255,0.717450696067,0.904813977466,0.647915400144,0.636379418323,0.980753138075,0.663441497924,0.805842304861,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.559152933749,0.529920849881,0.522467456931,0.510940207659,0.506039837476,0.799056574178,0.732612230768,0.593107093873,1.47773038529e-42
-2629399022787,356766,32754480,LABTSH,2013-02-16 13:49:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.819303310065,0.823928435048,0.835894293867,0.842259589354,0.866395892252,0.849653514241,0.857273357933,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.853100573193,0.863158326385,0.865631906627,0.999999689688
-6320000657646,367395,38302865,LABTSH,2013-10-17 08:04:00,0,1,1,1,0.721247563353,1.0,1.0,0.7297753114,0.774214880221,0.665103299703,0.797332137647,0.747951173606,0.625850226965,0.616420024523,0.532159000155,0.504870188136,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.554839831926,0.527479851612,0.531497075666,0.509945720378,0.501429529505,0.787091435777,0.801150709616,0.734755128366,8.13893055829e-30
--9174472743387,420507,33514342,LABTSH,2013-03-02 10:46:00,0,1,1,1,0.721247563353,0.926553672316,1.0,0.628002019092,0.651540720745,0.657464378172,0.357905397355,0.738646511052,0.642185834868,0.36940509915,0.843335745149,0.763803374135,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.546641617771,0.522411307801,0.508984840768,0.500031832777,0.499121888056,0.740845921186,0.766934454245,0.799607648204,1.0
-4588493578841,434543,34359192,LABTSH,2013-04-28 14:59:00,1,0,1,0,0.721247563353,0.0,0.0,0.786468440719,0.434635286487,0.647707937298,0.948572118536,0.627446162904,0.625896579951,0.380753138075,0.67856051994,0.560799447741,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.531445328071,0.517049958349,0.551720634847,0.507485463907,0.500238100642,0.796344514963,0.782078069966,0.686947754641,7.61756790965e-38
--12025621575732,471877,40143199,LABTSH,2013-12-10 17:49:00,0,1,1,1,0.8125,0.0,0.0,0.729577616915,0.722015763612,0.751827694224,0.783019344139,0.807058500337,0.744410254111,0.648307947583,0.79723582948,0.748086450889,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536656450488,0.553302965396,0.515373935575,0.505070992552,0.663260284037,0.653585783526,0.608364162088,0.0460616660024
--660818407107,366240,35691498,LABTSH,2013-06-16 20:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.820946294004,0.823928435048,0.808948990333,0.830012823073,0.827158075229,0.834322808424,0.836171031962,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.532592756108,0.529989690422,0.505698681961,0.502946199006,0.849769257941,0.853298233216,0.854669768689,0.999999405335
--6655285524721,448680,37901686,LABTSH,2013-09-16 11:38:00,0,1,1,1,0.721247563353,1.0,1.0,0.615465046063,0.791535903821,0.756503930324,0.600924749544,0.803713224332,0.773634837038,0.928945004306,0.784544492882,0.770272825381,0.650240561102,0.674575323822,0.646528780405,0.656811041531,0.549398911352,0.525269774171,0.500050363663,0.499703855271,0.502578253855,0.73502382607,0.753286775344,0.768725967534,0.995562216038
--964450075897,428286,35449202,LABTSH,2013-05-28 06:27:00,0,1,1,1,1.0,1.0,0.0,0.721880574558,0.565337890633,0.626743592545,0.535829228243,0.803480796281,0.600561560222,0.647619047619,0.855555555556,0.591291409666,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.540344392835,0.519367393511,0.508984840768,0.505062650497,0.499894031439,0.680603272333,0.602565981007,0.480762430894,2.06053298925e-55
--1082949565646,470325,34817519,LABTSH,2013-04-22 17:26:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.669213459086,0.751055820862,0.725134608273,0.687071621756,0.719532429872,0.780753138075,0.665808490281,0.630654760413,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.558226884909,0.529162890398,0.539002574476,0.505857436219,0.501260648464,0.759943990396,0.893605062517,0.944644873274,1.0
-6870853311890,456980,38737056,LABTSH,2013-10-22 13:49:00,1,0,1,0,0.721247563353,0.8,1.0,0.689497459446,0.771612719256,0.760222724315,0.873920177218,0.785759056935,0.784428389277,0.997014925373,0.74845246227,0.863773271216,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.561808566913,0.533436109493,0.531497075666,0.505843702108,0.503862565436,0.927457278979,0.967081779383,0.981072993024,0.000302329686809
--5479606121264,369183,31777383,LABTSH,2013-01-04 09:59:00,0,1,1,1,0.8125,0.830703012912,1.0,0.805421138254,0.806703808061,0.814692278417,0.823928435048,0.835894293867,0.832246480702,0.862091731563,0.818309284908,0.829549175588,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572701760878,0.538972035251,0.539002574476,0.508141356859,0.503570699049,0.885773346127,0.894574596844,0.896791914294,0.999999956425
--5023114876861,463681,37843833,LABTSH,2013-08-29 23:00:00,0,1,1,1,0.8125,1.0,1.0,0.787313039113,0.814301587645,0.717498997024,0.443619683069,0.550253498954,0.593639045625,0.6,0.643068008317,0.508010511928,0.782447187593,0.710772579177,0.664278617596,0.671017582252,0.546831995053,0.518871509904,0.541464288286,0.49912916959,0.502740638096,0.63394492121,0.699705288461,0.736658302715,1.84813803969e-61
--12136311750284,422002,39966480,LABTSH,2013-11-25 15:18:00,1,0,1,0,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.81820730512,0.827950270037,0.827158075229,0.849653514241,0.839529360889,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.539220837249,0.539002574476,0.507509612204,0.503851781014,0.811043588337,0.824203981649,0.826405261608,0.999966178846
-10415657068218,454411,38535906,LABTSH,2013-10-25 22:00:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.755785172919,0.77259741535,0.606985355605,0.82463749441,0.766029750398,0.70519282343,0.805711282284,0.850049241336,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.561858477317,0.53129882622,0.529989690422,0.510013998755,0.504205847403,0.894999019424,0.904114981674,0.898821374503,0.162153425778
--9232759176221,435998,38791358,LABTSH,2013-10-09 22:00:00,0,1,1,1,0.721247563353,0.9375,1.0,0.702122184385,0.667224891807,0.673730814058,0.916508225025,0.817881188383,0.817649899827,0.590245694604,0.674283140198,0.666711719615,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.550056959833,0.528445951361,0.5114615621,0.511767893185,0.505071449601,0.815551949934,0.855834421053,0.890771787455,1.07288990513e-14
-3638512852193,421563,34497681,LABTSH,2013-04-25 22:01:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.828502029753,0.819714386174,0.772217718036,0.815108885483,0.854764359322,0.913532857364,0.879375067956,0.869720712086,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.533801293511,0.529989690422,0.509619903762,0.504907156688,0.804036699813,0.806715968091,0.795335954474,0.99995437703
-9592401025493,464254,32118791,LABTSH,2013-01-22 16:15:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.826809225038,0.822395603613,0.875615909806,0.83328513052,0.841923585944,0.870249847627,0.860714762034,0.849049123765,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.884254685794,0.893220433271,0.896971997449,0.999999129117
--2058293830326,413693,39795522,LABTSH,2013-11-23 11:46:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.819303310065,0.817833840453,0.81820730512,0.812078337973,0.818399549761,0.860714762034,0.848463277669,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.816606988137,0.824182891939,0.824378604229,0.999982654028
--2886819710661,439426,37124290,LABTSH,2013-08-08 10:01:00,0,1,1,1,0.441860465116,1.0,1.0,0.648193861404,0.608632727737,0.638602416872,0.534298780488,0.676525819466,0.661099637752,0.447619047619,0.736099515047,0.712388345015,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.543609870033,0.520885846626,0.508984840768,0.501490939695,0.500582499406,0.828836347462,0.889268175703,0.920443187361,4.79460287915e-11
-3302291467073,428521,38604759,LABTSH,2013-10-15 11:18:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.782300718624,0.805900975488,0.718446046707,0.851090841994,0.839819298485,0.907868621156,0.888661492653,0.895231617673,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563958012904,0.534955706867,0.539002574476,0.512066378107,0.507632015471,0.847828777192,0.859952257791,0.86473879614,0.783982708567
-5197792385534,451826,32853489,LABTSH,2013-03-03 00:27:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.409182498816,0.671459765833,0.614246671962,0.553817311528,0.780503664885,0.723503142085,0.794594594595,0.777569814704,0.602535753134,0.650240561102,0.674575323822,0.64695993465,0.656811041531,0.543970789578,0.519913008973,0.499929566985,0.497614625744,0.497289683526,0.706782000521,0.717110176029,0.730051679721,0.00016486906411
--6735113325564,435318,38771762,LABTSH,2013-10-04 20:00:00,0,1,1,1,0.162790697674,0.0,1.0,0.737320037056,0.64185073641,0.618511420919,0.579816513761,0.702758466545,0.579588585157,0.8,0.62796911541,0.609078309932,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.529319899636,0.512731261307,0.510946844091,0.520504814881,0.515846642527,0.706456696362,0.929008378173,0.997179408799,2.37564490317e-39
--460905420414,424917,34476983,LABTSH,2013-04-10 00:59:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.807405951148,0.775630587221,0.654926896333,0.816511103452,0.775479630176,0.60519282343,0.801021989333,0.785486641592,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556996506668,0.527565820141,0.529989690422,0.505723165175,0.502478482807,0.601522567481,0.574377468041,0.530786108947,8.83013171112e-16
--3254057754234,375429,36063156,LABTSH,2013-06-17 14:08:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.64972039834,0.807724120183,0.827325462532,0.866395892252,0.854951510322,0.813996961717,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.845536499247,0.859274274201,0.860005687984,0.999999930844
--10539776234554,457996,35938599,LABTSH,2013-06-13 23:35:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806735645701,0.799735309492,0.660494127535,0.595731416691,0.813716127825,0.744196919692,0.79395168091,0.778095789831,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.568536728729,0.535545805724,0.529989690422,0.515366131478,0.510211167719,0.846248672033,0.854867915698,0.845252432032,0.999829282433
--1789753038787,355924,36237875,LABTSH,2013-07-05 22:00:00,0,1,1,1,0.8125,0.929515418502,1.0,0.815525854775,0.814812030397,0.783852344431,0.827989417248,0.871373168738,0.79562260176,0.730681657177,0.634076022568,0.742503723689,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.558742399518,0.530837505229,0.544333308272,0.508582947075,0.505692545693,0.826485948599,0.871776131059,0.977109865138,1.0
-2357746389094,431560,33256434,LABTSH,2013-03-09 12:02:00,1,0,1,0,0.721247563353,0.926553672316,1.0,0.598294906848,0.632947274837,0.659257803568,0.464112488681,0.570560094241,0.634772875973,0.247619047619,0.440826327158,0.607980012051,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.533493287317,0.511055016279,0.519994167151,0.498171677194,0.497584234588,0.334978949663,0.547606150541,0.582141486565,2.58582348403e-132
-387295894286,428106,33842111,LABTSH,2013-04-10 22:46:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.838556404444,0.824819747062,0.901724741666,0.854335772207,0.830881863141,0.92546176162,0.906133200425,0.881443231498,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.575015677088,0.540723928082,0.541464288286,0.511925629489,0.504182839353,0.909194215091,0.92690709071,0.930230872268,0.999999731858
-11782588765590,464127,35545651,LABTSH,2013-06-13 07:08:00,1,0,1,0,0.8125,0.921052631579,0.0,0.805421138254,0.806703808061,0.809666659588,0.849652022272,0.835894293867,0.821692509756,0.884904767584,0.843982718331,0.812172406387,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.570612271331,0.535864888809,0.539002574476,0.508141356859,0.502818730475,0.764364799414,0.78394021135,0.789438328893,0.999991788039
--2862838179235,457625,32226051,LABTSH,2013-01-18 23:00:00,0,1,1,1,0.8125,1.0,1.0,0.824768585728,0.816754850347,0.799040270692,0.760627096268,0.730853648934,0.807752248819,0.684042951075,0.832734657927,0.782047391929,0.782447187593,0.711240791414,0.663550601005,0.663616145716,0.550271618617,0.524815370508,0.529989690422,0.5031098693,0.50255393383,0.678469046183,0.702606083341,0.708928540152,0.90023737647
-5152657745676,357192,32325079,LABTSH,2013-02-06 15:51:00,0,1,1,1,0.8125,0.888888888889,1.0,0.771738282698,0.74610958692,0.822395603613,0.691184772082,0.822801945583,0.836114294075,0.870249847627,0.875391152189,0.782177817115,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.567028710764,0.534858594021,0.53062312788,0.505825819737,0.500387144412,0.775922973983,0.793298364649,0.811437337192,0.956604565285
--11241013328560,355184,37790785,LABTSH,2013-09-09 08:21:00,0,1,1,1,0.8125,1.0,1.0,0.644670070126,0.773408884972,0.707913209237,0.452333333333,0.69050079141,0.668087462989,0.580753138075,0.399888610415,0.624504573416,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.548663763681,0.52390645491,0.52803351545,0.504457153018,0.502260001273,0.63805055875,0.681712944987,0.649798043485,6.01787846451e-14
-4905714596026,441701,37524386,LABTSH,2013-09-10 04:22:00,1,0,1,0,0.8125,1.0,1.0,0.737955010488,0.7873681505,0.73341014391,0.878187045425,0.736280722633,0.727267048542,0.757929689314,0.804297440326,0.663827449154,0.782447187593,0.718940845638,0.679071337105,0.688610573577,0.56406635549,0.527724914164,0.541464288286,0.499725448831,0.500206335342,0.33848485828,0.403976048929,0.461015892608,1.37277807071e-177
-9270375313018,451277,37742104,LABTSH,2013-09-16 00:22:00,1,0,1,0,0.8125,0.929515418502,1.0,0.815525854775,0.705540292602,0.740473769575,0.680805234004,0.717248825654,0.732213334146,0.193073593074,0.508194216946,0.699335875851,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.55478575048,0.524635371642,0.529989690422,0.517773452343,0.505929219787,0.52408431515,0.487225484764,0.422459115111,1.56173302413e-11
--1627978260881,468322,34675799,LABTSH,2013-05-01 11:25:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.816758182752,0.822395603613,0.885669999001,0.83328513052,0.836853009303,0.822253505136,0.865027757664,0.847760642754,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.505005090357,0.899024633879,0.907559363354,0.910096450506,0.999999970132
--8576387523146,399040,39657005,LABTSH,2013-11-14 16:41:00,1,0,1,0,0.8125,0.994152046784,1.0,0.771738282698,0.826809225038,0.822395603613,0.875615909806,0.809763907545,0.841923585944,0.879042951075,0.875391152189,0.817146861366,0.782447187593,0.736457474868,0.690095927488,0.700503272381,0.567028710764,0.534858594021,0.53062312788,0.505825819737,0.504943672693,0.810907237309,0.817895053022,0.819752205316,0.999992808046
-8022476701057,382239,38944047,LABTSH,2013-10-29 19:18:00,1,0,1,0,0.721247563353,0.0,0.0,0.655828312851,0.731826569227,0.775599281409,0.734868928522,0.746557284179,0.865699553665,0.380753138075,0.864946688102,0.642526373639,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.547294262624,0.526245669154,0.525351144162,0.514638362634,0.509331402918,0.917323391871,0.916046645315,0.918002037872,1.85228340601e-67
-7893495655810,452247,33043023,LABTSH,2013-02-26 23:41:00,1,0,1,0,0.8125,0.0,0.0,0.703312647031,0.78908199625,0.770868433452,0.744648318043,0.765384411794,0.82387520767,0.737659067404,0.624257573942,0.701947489568,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.564853495182,0.531863429247,0.544333308272,0.509493130461,0.505655846492,0.742959851093,0.75402791125,0.760217494398,4.55765226366e-16
--10710452930074,362592,38765748,LABTSH,2013-10-08 22:00:00,0,1,1,1,0.8125,0.830703012912,1.0,0.798068202586,0.734575607506,0.775185759318,0.852531107301,0.792047018501,0.810246280433,0.899574937137,0.825310449131,0.882026003445,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.557213402293,0.530935046747,0.529989690422,0.508638049078,0.504046471501,0.739191563297,0.799495997864,0.82946881285,1.4940616993e-10
-7779343947061,371675,38686949,LABTSH,2013-10-21 09:12:00,1,0,1,0,1.0,1.0,1.0,0.63520084147,0.746699222459,0.72528826146,0.717845425306,0.654694422803,0.717871585145,0.740049140049,0.709350239046,0.656984819537,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.558140815781,0.52791726727,0.519994167151,0.503011116042,0.499780872396,0.740875800015,0.766861787104,0.799054951946,1.95086528161e-38
--7110392581552,359289,37663072,LABTSH,2013-08-30 11:35:00,0,1,1,1,0.8125,0.0,0.0,0.734095168603,0.655336551227,0.624560010483,0.562693558881,0.622049371091,0.641812385424,0.568253968254,0.453190559753,0.579039887183,0.782447187593,0.736457474868,0.698015955124,0.700503272381,0.567746787182,0.536464518894,0.542385285598,0.499988287048,0.499612207252,0.884510424229,0.94206053201,0.971111560906,6.80877872977e-17
-4499521166583,361070,38634358,LABTSH,2013-10-20 17:18:00,0,1,1,1,0.8125,0.830703012912,1.0,0.6098891034,0.720062316236,0.784892007676,0.896552354263,0.841389466512,0.727416544128,0.876329747113,0.906013127902,0.74641498677,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.564184515396,0.529846113024,0.539002574476,0.504735427489,0.500559160891,0.580959098493,0.585153678539,0.564015845513,2.55403461671e-14
--5039664854744,425558,34037230,LABTSH,2013-03-29 18:53:00,0,1,1,1,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.855191528695,0.835132737003,0.833413676791,0.827158075229,0.860714762034,0.855127709479,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.509815716348,0.506425736731,0.844595802198,0.848768626559,0.849742008123,0.999998733024
-51159089443,409395,34634293,LABTSH,2013-05-04 14:19:00,0,1,1,1,0.8125,1.0,1.0,0.815525854775,0.826048767051,0.787998030842,0.803922487061,0.855101599159,0.815081295469,0.740833670579,0.861027576719,0.877082671487,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.583784253024,0.546608346463,0.550443778739,0.516628533357,0.508402215728,0.950814211536,0.961500322894,0.968161861033,0.977100122269
--3436624415575,396855,34693235,LABTSH,2013-04-25 09:41:00,0,1,1,1,0.8125,0.830703012912,1.0,0.832248027839,0.767712272645,0.796557071231,0.472945777855,0.8361900523,0.820996269896,0.577494994782,0.767050574527,0.879328223649,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556996506668,0.52881399083,0.539002574476,0.505915822739,0.500863721932,0.743609876557,0.734629573067,0.735644642677,5.10321527128e-26
-2961764115483,377007,38426216,LABTSH,2013-10-18 17:48:00,0,1,1,1,0.8125,0.830703012912,1.0,0.777928114197,0.755529647909,0.751526813734,0.74972039834,0.830904562628,0.798354233376,0.721795327199,0.928455542081,0.812771220488,0.782447187593,0.718940845638,0.686739542259,0.688610573577,0.569359527685,0.539067808035,0.553302965396,0.524074791383,0.511323596143,0.970298738259,0.969594658718,0.966884198175,0.99999999737
--7268969749474,451426,37865967,LABTSH,2013-09-15 13:00:00,1,0,1,0,0.8125,0.994152046784,1.0,0.824768585728,0.826809225038,0.822395603613,0.849096934101,0.81820730512,0.830711454125,0.866395892252,0.860714762034,0.851482560536,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.57322692598,0.539220837249,0.539002574476,0.507509612204,0.503851781014,0.859734698801,0.869493324869,0.873080238186,0.999998276324
--1645163611773,431574,40044096,LABTSH,2013-11-27 16:37:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.811736993423,0.735088373531,0.81820730512,0.815440184075,0.827158075229,0.794223356652,0.833016711737,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.812238341227,0.81848618205,0.813878197267,0.999999965653
-279415780882,372363,37016711,LABTSH,2013-08-11 07:16:00,0,1,1,1,0.8125,0.830703012912,1.0,0.792797219249,0.708045599049,0.691750998027,0.455084529506,0.538462349898,0.756515326798,0.6,0.655294368519,0.642366541736,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556084968027,0.530350670418,0.553302965396,0.507341321388,0.501257318396,0.927518898853,0.953008050617,0.918988633676,1.0
--11161387153505,381125,40327756,LABTSH,2013-12-19 19:56:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.788959456489,0.790774497862,0.67703968791,0.848318845052,0.830810378146,0.860517883113,0.76443989761,0.808228835873,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.534651301057,0.544333308272,0.511833895665,0.506858281035,0.785319211742,0.7988251259,0.797350541722,0.832357236552
--2132407506420,386529,37298155,LABTSH,2013-08-16 10:18:00,0,1,1,1,0.8125,0.830703012912,1.0,0.805421138254,0.806703808061,0.795085876303,0.655746616866,0.75284761082,0.784822334463,0.866395892252,0.860714762034,0.854890575105,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577522104052,0.542061061241,0.539002574476,0.509734924238,0.504964682872,0.82976649964,0.832946855051,0.852628656677,0.999999264801
--5132883171719,357016,34116205,LABTSH,2013-04-01 11:13:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.806703808061,0.822395603613,0.823928435048,0.81820730512,0.833826053985,0.866395892252,0.860714762034,0.838844815108,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.848243417312,0.847152101188,0.838742302778,0.99999999693
--12229305080263,423572,33734049,LABTSH,2013-03-16 09:16:00,1,0,1,0,0.8125,1.0,0.0,0.694128535055,0.818083759414,0.743333207303,0.507455825105,0.938531491884,0.801656705595,0.960972918295,0.85265618884,0.780772651102,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.556421990787,0.529229540212,0.539514376287,0.506863149469,0.503716410159,0.926835378479,0.963142941046,0.977110452203,1.0
--3808383155426,367969,40267255,LABTSH,2013-12-16 02:49:00,0,1,1,1,0.721247563353,0.926553672316,0.0,0.774789556414,0.759027681804,0.752285234076,0.48044077135,0.810286221286,0.740872237147,0.975,0.857638570385,0.754212782758,0.650240561102,0.674575323822,0.657604341426,0.656811041531,0.551301581759,0.519276125034,0.510946844091,0.497709756855,0.498436981751,0.699008088155,0.700021454365,0.69772169523,0.114204774697
-8704040961039,468083,39755810,LABTSH,2013-12-19 12:16:00,1,0,1,0,0.8125,0.830703012912,1.0,0.824768585728,0.814600661171,0.824835978834,0.655746616866,0.788792783588,0.811151818331,0.884042951075,0.867882253412,0.753996039212,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.537513893758,0.539002574476,0.514017914039,0.507216433771,0.883109515232,0.830976822864,0.815789023763,0.999996380296
--9941383909774,366267,36315460,LABTSH,2013-06-28 20:01:00,1,0,1,0,0.8125,0.929515418502,1.0,0.812144666724,0.804698891424,0.776854921238,0.663126911497,0.735073681962,0.826200823183,0.747498949138,0.844861743677,0.842230454054,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.563958012904,0.531663333375,0.529989690422,0.505858113871,0.505408993742,0.883439049026,0.891761310475,0.892407572436,1.05195032917e-05
-4777671823833,374682,32813137,LABTSH,2013-02-23 08:14:00,1,0,1,0,0.8125,0.0,0.0,0.778720755112,0.782798300522,0.813855109213,0.852546616866,0.758059925421,0.795037322016,0.644196919692,0.714075880629,0.800129775878,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.533237110261,0.539002574476,0.510056430021,0.505304516675,0.931791844452,0.952506697835,0.951927919482,1.0
--5960505844591,470355,34719813,LABTSH,2013-04-22 02:12:00,1,0,1,0,0.8125,1.0,1.0,0.740933465313,0.792322075504,0.792054904052,0.863654281588,0.847938289303,0.796537177374,0.715872278665,0.882847236921,0.861763989407,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.561643513551,0.531222976842,0.530502782545,0.505826146766,0.502412908021,0.704849421067,0.718397672601,0.722365764885,0.999885570412
--2838749559638,387373,34973972,LABTSH,2013-05-09 00:51:00,0,1,1,1,0.8125,0.929515418502,0.0,0.675659520016,0.667054025599,0.796455569142,0.878393369177,0.853931879047,0.773158811692,0.688253968254,0.872155398645,0.794335548396,0.782447187593,0.736457474868,0.690499379418,0.700503272381,0.553903394042,0.528690599603,0.533381282937,0.508037530153,0.504827235043,0.687581153706,0.703666975181,0.754153604499,1.0
-10707107594669,438599,38705194,LABTSH,2013-10-30 15:41:00,1,0,1,0,0.721247563353,1.0,0.0,0.488820358324,0.355777069803,0.444192538175,0.284848484848,0.343110662242,0.429825820634,0.4,0.605098039216,0.65141343203,0.650240561102,0.616385848561,0.583828564855,0.58466802601,0.512907327047,0.495654750818,0.480783037393,0.490073674043,0.493147002611,0.302055344074,0.356342114757,0.369612545832,3.25737480303e-320
-1429954379113,395515,38758992,LABTSH,2013-10-28 01:01:00,0,1,1,1,0.8125,0.731707317073,1.0,0.824768585728,0.816758182752,0.801337009507,0.778233347428,0.808948990333,0.80750538349,0.827158075229,0.81845043008,0.776312612914,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.562385274042,0.53184004573,0.529989690422,0.504104758378,0.50155214183,0.771850281292,0.777276525089,0.789060761318,0.999999962749
--9731068272561,468296,34680904,LABTSH,2013-05-01 22:00:00,0,1,1,1,0.721247563353,0.9375,1.0,0.66310404407,0.629610795645,0.694000215808,0.825082259832,0.785614479096,0.642812185012,0.8,0.712773967311,0.592692865305,0.650240561102,0.646281427091,0.618315664794,0.617481144548,0.548004315095,0.523410860552,0.522467456931,0.499406280578,0.499805978556,0.303330283212,0.20665701395,0.186309857396,1.76759763987e-18
--1131525097069,378798,36439175,LABTSH,2013-07-05 08:46:00,0,1,1,1,0.117647058824,0.0,0.0,0.691014084255,0.828505362158,0.788580856275,0.634411992799,0.650828499379,0.799459776947,0.913000091285,0.826878260458,0.841179096879,0.782447187593,0.710772579177,0.656069634837,0.671017582252,0.529238980749,0.516197584792,0.486813772678,0.502742817718,0.502345581064,0.727239071209,0.730592863534,0.669877942298,0.509128285564
-9766061197623,427520,39667911,LABTSH,2013-12-02 07:25:00,0,1,1,1,0.8125,0.929515418502,1.0,0.805421138254,0.790184910167,0.791843893098,0.79351078908,0.838622573759,0.846615967024,0.807912022751,0.770310689887,0.883666634288,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.530742906251,0.539002574476,0.507534093072,0.503267006701,0.787411501239,0.79990421076,0.794828381657,0.999954071211
--5816735075434,408668,38976028,LABTSH,2013-10-18 14:36:00,0,1,1,1,0.8125,0.994152046784,1.0,0.662116763866,0.828537199797,0.803231001457,0.899172029354,0.873878738819,0.824965967808,0.926048624098,0.895582877188,0.875019417333,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.580771991934,0.539739061208,0.539002574476,0.511712014969,0.504251650515,0.95943070026,0.964900977326,0.965174220384,1.0
--10964006180954,402149,39251817,LABTSH,2013-10-30 23:06:00,1,0,1,0,0.721247563353,1.0,1.0,0.831023907017,0.66753171359,0.709635252478,0.732711217868,0.660662116087,0.744026738358,0.886211845065,0.49928057554,0.708545820518,0.650240561102,0.674575323822,0.649142593505,0.656811041531,0.556617509168,0.522165823075,0.5114615621,0.502614609265,0.501853464189,0.742871832843,0.697209407589,0.566234798641,3.07837228448e-45
--4894166442439,433027,38549851,LABTSH,2013-10-04 17:39:00,0,1,1,1,0.8125,0.0,0.0,0.824768585728,0.743798710373,0.826082973142,0.821313082068,0.869238124024,0.760417180447,0.676526112239,0.799123090424,0.860468940336,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.577635809724,0.541461947623,0.550443778739,0.511608095829,0.505030813355,0.741250482945,0.761232974439,0.75910229118,0.000799034930614
-4535861704019,448712,39017254,LABTSH,2013-11-01 20:00:00,1,0,1,0,1.0,1.0,1.0,0.55528575753,0.452006841027,0.299095842404,0.214285714286,0.65690376569,0.394313186813,0.4,0.3,0.389898989899,0.650240561102,0.616385848561,0.569776179144,0.58466802601,0.493990851334,0.487408209495,0.479985795621,0.491324965333,0.491865274748,0.0222794767878,0.0170115676348,0.018125826072,1.57197517001e-187
--131827416186,380433,39095227,LABTSH,2013-10-22 02:20:00,0,1,1,1,0.8125,0.929515418502,1.0,0.755962673324,0.787631877755,0.754537427697,0.79351078908,0.850198945314,0.725175970904,0.993230769231,0.739343334802,0.798697921449,0.782447187593,0.718940845638,0.679071337105,0.688610573577,0.556335222434,0.52809835045,0.529989690422,0.504334559488,0.503181184246,0.661040685504,0.628763283394,0.612801530499,4.24242320625e-06
-1609054775487,423520,31831980,LABTSH,2013-01-20 04:56:00,0,1,1,1,0.8125,1.0,1.0,0.832248027839,0.768414415732,0.740903603403,0.68131475737,0.70336966495,0.716339720383,0.67459792258,0.881693463581,0.817254929679,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.556441971091,0.529098914207,0.544333308272,0.504535572764,0.501621072698,0.63920039797,0.619819741679,0.601351579173,2.95743827454e-10
--5023114876861,397595,38486311,LABTSH,2013-09-26 13:07:00,0,1,1,1,0.8125,0.994152046784,1.0,0.6098891034,0.826809225038,0.760189649742,0.659852771366,0.738229839662,0.697096749337,0.729637851925,0.739387652968,0.669639943973,0.782447187593,0.736457474868,0.695845203646,0.700503272381,0.56184881889,0.528881754139,0.550443778739,0.500940415814,0.499814977791,0.693720434677,0.798225948971,0.834854088624,3.3125072058e-15
-11865815267465,370413,34901040,LABTSH,2013-05-21 00:59:00,1,0,1,0,0.721247563353,1.0,1.0,0.724456822871,0.689274800746,0.716811352806,0.533684432273,0.805786221704,0.700563377577,0.988524590164,0.714127142621,0.600859970281,0.650240561102,0.674575323822,0.649586855192,0.656811041531,0.541303915457,0.520245090099,0.510946844091,0.507999416923,0.504099413884,0.710347142889,0.785100219847,0.833556075724,0.480706350172
--2253356289608,468218,39094273,LABTSH,2013-10-27 00:07:00,0,1,1,1,0.8125,0.929515418502,1.0,0.862116763866,0.824153466805,0.795099531611,0.835118237216,0.83179102136,0.778292391644,0.684235758601,0.616736360408,0.678923222096,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.563691317678,0.524854578122,0.529989690422,0.504203419927,0.502875570391,0.755084070505,0.753637305378,0.734784857348,0.999983737892
--12399070138294,411927,32213385,LABTSH,2013-01-15 06:08:00,1,0,1,0,0.8125,0.921052631579,1.0,0.661490326021,0.823108430487,0.774749668653,0.705015496934,0.780052037186,0.779088955211,0.937450887946,0.825671290807,0.818989366648,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.565120209018,0.530009294307,0.539002574476,0.507534093072,0.503091694144,0.686676566836,0.678245330536,0.696027476137,5.23347206294e-07
-6261683368557,436679,38396677,LABTSH,2013-10-18 07:27:00,0,1,1,1,0.8125,0.994152046784,1.0,0.805421138254,0.804250545359,0.786273255944,0.475413454686,0.868239479973,0.79668708616,0.524668435013,0.796746324615,0.752744636529,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.537823206089,0.550443778739,0.512368478005,0.509022137372,0.919277571893,0.94350882769,0.955520231729,5.54212130817e-31
--9019378105094,396414,32227054,LABTSH,2013-01-17 02:43:00,0,1,1,1,0.8125,0.921052631579,1.0,0.805421138254,0.781336496697,0.804302480147,0.817833840453,0.826635979079,0.788212424215,0.787633062774,0.725478371821,0.783337439163,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572701760878,0.536544936879,0.529989690422,0.506330489964,0.502665110406,0.695545479948,0.678833067195,0.666677159622,0.999976396091
--847088917496,437986,40141259,LABTSH,2013-12-06 08:08:00,1,0,1,0,0.826086956522,1.0,1.0,0.74070060235,0.700490397724,0.682053548973,0.679355935961,0.564857506834,0.714122005513,0.380753138075,0.612823842824,0.656897443013,0.650240561102,0.616385848561,0.58978530417,0.58466802601,0.527684438646,0.516064911296,0.519994167151,0.50025662957,0.501463581192,0.712296772207,0.756820715981,0.861448984413,1.8970072074e-122
-1998568949705,420540,34081499,LABTSH,2013-04-15 08:38:00,0,1,1,1,0.721247563353,1.0,1.0,0.754056846419,0.693117661696,0.74032296423,0.842725526539,0.950646687964,0.671959433195,0.795918367347,0.752045663201,0.701629179186,0.650240561102,0.645760345789,0.621964849634,0.625324561677,0.539300178087,0.518536774704,0.519994167151,0.501943079623,0.500039241129,0.836557120087,0.890095714398,0.886535204059,6.13906317827e-37
--6045786041226,458029,34624861,LABTSH,2013-04-22 02:30:00,0,1,1,1,0.8125,0.830703012912,1.0,0.831142407533,0.825699925873,0.784475332534,0.714096466716,0.860953394916,0.824675214685,0.75046176162,0.660679821882,0.753901210665,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.565688547677,0.535106920178,0.529989690422,0.508951724092,0.503048705029,0.780609278445,0.825308191036,0.855793097714,0.00209305451755
--8180040524895,373592,37791936,LABTSH,2013-08-28 08:08:00,0,1,1,1,0.8125,0.994152046784,1.0,0.857842790675,0.828505362158,0.8349594151,0.905102781557,0.880334392402,0.852149006596,0.745643218693,0.858375182726,0.852801717361,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.572815804269,0.5390299248,0.550443778739,0.51214267376,0.504538239558,0.852835539257,0.865795804466,0.869378659037,0.999983861202
--2266184951722,414185,39257928,LABTSH,2013-10-28 22:09:00,0,1,1,1,0.8125,0.830703012912,1.0,0.469504585956,0.567792966793,0.646709524169,0.749168044077,0.621882306148,0.677712715978,0.297186963979,0.610562749411,0.695915430645,0.782447187593,0.736457474868,0.694988440333,0.700503272381,0.557935186178,0.524223522557,0.529989690422,0.502396089533,0.501494239449,0.651461658053,0.553438428665,0.553912701715,8.17192459246e-22
--6230950886358,442435,33972519,LABTSH,2013-03-26 16:45:00,0,1,1,1,0.8125,0.830703012912,1.0,0.824768585728,0.816754850347,0.822395603613,0.823928435048,0.81820730512,0.830012823073,0.827158075229,0.860714762034,0.842635174297,0.782447187593,0.736457474868,0.700438880043,0.700503272381,0.569961857593,0.536880867027,0.539002574476,0.507509612204,0.503851781014,0.838296929726,0.842680494717,0.843559083233,0.999998580163
-11597975434938,419682,34635049,LABTSH,2013-05-12 09:45:00,0,1,1,1,0.8125,0.830703012912,1.0,0.7961784073,0.765365671377,0.743842859316,0.531915705964,0.893559864743,0.772594291488,0.247619047619,0.920975022072,0.782676671605,0.782447187593,0.710772579177,0.667000222298,0.671017582252,0.550125730486,0.52615494782,0.539002574476,0.509029736735,0.503872740578,0.439571354222,0.381221059119,0.302710813176,2.52713260879e-09
--11494610321364,453294,39489423,LABTSH,2013-11-15 00:56:00,0,1,1,1,0.8125,0.929515418502,1.0,0.824768585728,0.806703808061,0.795597676676,0.79351078908,0.787169587748,0.802594261026,0.861577349325,0.848388609039,0.783444917932,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.530129585731,0.529989690422,0.504104758378,0.50155214183,0.625318315872,0.610094150356,0.604210862253,0.99980059942
-6258796338363,428399,34077478,LABTSH,2013-04-16 19:20:00,0,1,1,1,0.117647058824,0.0,0.0,0.768710323289,0.838556404444,0.796325562199,0.920314399346,0.767521268628,0.820683036565,0.949097936256,0.814154485466,0.805886280909,0.782447187593,0.710772579177,0.669122348657,0.671017582252,0.549388361563,0.524603088036,0.509241894961,0.504794540864,0.502494091965,0.766484521196,0.783622260512,0.805131248314,0.994501983283
--4750324841712,435125,38044563,LABTSH,2013-09-13 07:12:00,0,1,1,1,0.8125,0.830703012912,1.0,0.766096836107,0.812461537684,0.79753460126,0.693372301992,0.78385479653,0.830924568794,0.991204764086,0.811327189184,0.802145580249,0.782447187593,0.736457474868,0.69295608408,0.700503272381,0.559101057988,0.531952023305,0.539002574476,0.512047988805,0.510332530834,0.850777300269,0.868158354452,0.870866907369,0.836673040414
diff --git a/scripts/LabTestAnalysis/machine_learning/processedFeatureMatrixData/processed_zero_sampleData.csv b/scripts/LabTestAnalysis/machine_learning/processedFeatureMatrixData/processed_zero_sampleData.csv
deleted file mode 100644
index 7edcdbc5..00000000
--- a/scripts/LabTestAnalysis/machine_learning/processedFeatureMatrixData/processed_zero_sampleData.csv
+++ /dev/null
@@ -1,19 +0,0 @@
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diff --git a/scripts/LabTestAnalysis/machine_learning/rawFeatureMatrixData/sampleData.csv b/scripts/LabTestAnalysis/machine_learning/rawFeatureMatrixData/sampleData.csv
deleted file mode 100644
index 1f5ba369..00000000
--- a/scripts/LabTestAnalysis/machine_learning/rawFeatureMatrixData/sampleData.csv
+++ /dev/null
@@ -1,19 +0,0 @@
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--1.24346E+13,407358,66355,11/21/10 22:24,,475.0666667,FALSE,TRUE,-27353.93333,,TRUE,FALSE,-27353.93333,,TRUE,FALSE,,,FALSE,FALSE,1,0,16.8,16.8,16.8,16.8,0,16.8,16.8,16.8,-0.171527778,-0.171527778,-0.171527778,1,1,40.8,40.8,40.8,40.8,0,40.8,40.8,40.8,-0.171527778,-0.171527778,-0.171527778,1,1,371,371,371,371,0,371,371,371,-0.171527778,-0.171527778,-0.171527778,1,0,124,124,124,124,0,124,124,124,-0.154861111,-0.154861111,-0.154861111,1,0,3.3,3.3,3.3,3.3,0,3.3,3.3,3.3,-0.154861111,-0.154861111,-0.154861111,1,1,27,27,27,27,0,27,27,27,-0.154861111,-0.154861111,-0.154861111,1,1,12,12,12,12,0,12,12,12,-0.154861111,-0.154861111,-0.154861111,1,0,0.9,0.9,0.9,0.9,0,0.9,0.9,0.9,-0.154861111,-0.154861111,-0.154861111,1,0,0.5,0.5,0.5,0.5,0,0.5,0.5,0.5,-0.154861111,-0.154861111,-0.154861111,1,0,3.4,3.4,3.4,3.4,0,3.4,3.4,3.4,-0.154861111,-0.154861111,-0.154861111,1,0,1.13,1.13,1.13,1.13,0,1.13,1.13,1.13,-0.179861111,-0.179861111,-0.179861111,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,-0.165972222,-0.165972222,-0.165972222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,7.46,7.46,7.46,7.46,0,7.46,7.46,7.46,-0.179861111,-0.179861111,-0.179861111,1,0,21,21,21,21,0,21,21,21,-0.179861111,-0.179861111,-0.179861111,1,1,42.2,42.2,42.2,42.2,0,42.2,42.2,42.2,-0.179861111,-0.179861111,-0.179861111
--1.24325E+13,454507,66355,6/27/10 10:31,,,FALSE,FALSE,-18440.43819,,TRUE,FALSE,-18440.43819,,TRUE,FALSE,,,FALSE,FALSE,3,3,6.2,9.4,6.7,7.433333333,1.405544576,9.4,6.2,6.2,-0.906944444,-0.293055556,-0.293055556,4,3,39.8,51,43.65,44.525,4.306608294,45.7,39.8,39.8,-0.906944444,-0.293055556,-0.293055556,3,3,181,226,207,204.6666667,18.44511378,226,181,181,-0.906944444,-0.293055556,-0.293055556,7,4,130,139,136,135.1428571,3.522522287,131,139,139,-0.872916667,-0.004166667,-0.004166667,7,7,3.5,5.1,4.1,4.242857143,0.531459293,5.1,3.9,3.9,-0.872916667,-0.004166667,-0.004166667,6,3,14,23,19,18.66666667,3.036811193,14,23,23,-0.872916667,-0.004166667,-0.004166667,6,6,9,14,11,11.33333333,1.699673171,14,9,9,-0.872916667,-0.004166667,-0.004166667,6,0,0.7,1,0.9,0.883333333,0.121335165,1,0.7,0.7,-0.872916667,-0.004166667,-0.004166667,2,0,0.6,0.8,0.7,0.7,0.1,0.8,0.6,0.6,-0.872916667,-0.271527778,-0.271527778,2,0,2.7,3.2,2.95,2.95,0.25,3.2,2.7,2.7,-0.872916667,-0.271527778,-0.271527778,5,0,1.1,1.5,1.29,1.278,0.159799875,1.29,1.1,1.1,-0.840277778,-0.038194444,-0.038194444,1,0,28,28,28,28,0,28,28,28,-0.849305556,-0.849305556,-0.849305556,1,0,5.7,5.7,5.7,5.7,0,5.7,5.7,5.7,-0.883333333,-0.883333333,-0.883333333,1,0,0,0,0,0,0,0,0,0,-0.870138889,-0.870138889,-0.870138889,2,0,7.32,7.33,7.325,7.325,0.005,7.33,7.32,7.32,-0.579861111,-0.302777778,-0.302777778,2,1,76,98,87,87,11,76,98,98,-0.579861111,-0.302777778,-0.302777778,2,2,37.4,41,39.2,39.2,1.8,37.4,41,41,-0.579861111,-0.302777778,-0.302777778,1,1,7.34,7.34,7.34,7.34,0,7.34,7.34,7.34,-0.840277778,-0.840277778,-0.840277778,1,0,86,86,86,86,0,86,86,86,-0.840277778,-0.840277778,-0.840277778,1,0,25.2,25.2,25.2,25.2,0,25.2,25.2,25.2,-0.840277778,-0.840277778,-0.840277778
--1.24325E+13,454507,66355,6/27/10 16:17,,,FALSE,FALSE,-18440.67847,,TRUE,FALSE,-18440.67847,,TRUE,FALSE,,,FALSE,FALSE,2,2,6.2,6.7,6.45,6.45,0.25,6.7,6.2,6.2,-0.813888889,-0.533333333,-0.533333333,2,1,39.8,41.6,40.7,40.7,0.9,41.6,39.8,39.8,-0.813888889,-0.533333333,-0.533333333,2,2,181,207,194,194,13,207,181,181,-0.813888889,-0.533333333,-0.533333333,6,5,133,139,138,137.1666667,2.114762923,133,138,138,-0.997222222,-0.013194444,-0.013194444,6,6,3.5,4.3,4,3.966666667,0.249443826,4.3,4.1,4.1,-0.997222222,-0.013194444,-0.013194444,6,4,16,23,20.5,19.83333333,2.266911751,16,21,21,-0.997222222,-0.013194444,-0.013194444,6,6,8,13,10.5,10.33333333,1.598610508,13,8,8,-0.997222222,-0.013194444,-0.013194444,6,0,0.7,1,0.8,0.833333333,0.124721913,1,0.7,0.7,-0.997222222,-0.013194444,-0.013194444,1,0,0.6,0.6,0.6,0.6,0,0.6,0.6,0.6,-0.511805556,-0.511805556,-0.511805556,1,0,2.7,2.7,2.7,2.7,0,2.7,2.7,2.7,-0.511805556,-0.511805556,-0.511805556,4,0,1.1,1.5,1.25,1.275,0.178535711,1.5,1.1,1.1,-0.803472222,-0.278472222,-0.278472222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,2,0,7.32,7.33,7.325,7.325,0.005,7.33,7.32,7.32,-0.820138889,-0.543055556,-0.543055556,2,1,76,98,87,87,11,76,98,98,-0.820138889,-0.543055556,-0.543055556,2,2,37.4,41,39.2,39.2,1.8,37.4,41,41,-0.820138889,-0.543055556,-0.543055556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24315E+13,425565,66355,2/12/13 11:29,,,FALSE,FALSE,-8808.478472,,TRUE,FALSE,-8808.478472,,TRUE,FALSE,,,FALSE,FALSE,1,0,12.1,12.1,12.1,12.1,0,12.1,12.1,12.1,-0.88125,-0.88125,-0.88125,1,0,32.6,32.6,32.6,32.6,0,32.6,32.6,32.6,-0.88125,-0.88125,-0.88125,1,1,397,397,397,397,0,397,397,397,-0.88125,-0.88125,-0.88125,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24285E+13,360335,66350,6/18/10 11:29,,7.521527778,FALSE,TRUE,-27562.47847,,TRUE,FALSE,-27562.47847,,TRUE,FALSE,,,FALSE,FALSE,1,0,13.8,13.8,13.8,13.8,0,13.8,13.8,13.8,-0.905555556,-0.905555556,-0.905555556,1,0,37.8,37.8,37.8,37.8,0,37.8,37.8,37.8,-0.905555556,-0.905555556,-0.905555556,2,2,232,293,262.5,262.5,30.5,293,232,232,-0.905555556,-0.236805556,-0.236805556,3,0,149,150,150,149.6666667,0.471404521,150,149,149,-0.875694444,-0.243055556,-0.243055556,3,3,3.7,4.1,3.9,3.9,0.163299316,4.1,3.7,3.7,-0.875694444,-0.243055556,-0.243055556,3,3,20,22,21,21,0.816496581,21,20,20,-0.875694444,-0.243055556,-0.243055556,3,0,41,41,41,41,0,41,41,41,-0.875694444,-0.243055556,-0.243055556,4,0,1.6,1.7,1.65,1.65,0.05,1.6,1.6,1.6,-0.910416667,-0.243055556,-0.243055556,1,0,1,1,1,1,0,1,1,1,-0.875694444,-0.875694444,-0.875694444,1,0,2.2,2.2,2.2,2.2,0,2.2,2.2,2.2,-0.875694444,-0.875694444,-0.875694444,4,0,2.6,3.88,3.535,3.3875,0.478193214,3.88,2.6,2.6,-0.905555556,-0.527083333,-0.527083333,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0.12,0.12,0.12,0.12,0,0.12,0.12,0.12,-0.884722222,-0.884722222,-0.884722222,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,2,2,7.38,7.4,7.39,7.39,0.01,7.38,7.4,7.4,-0.905555556,-0.870138889,-0.870138889,2,0,17,28,22.5,22.5,5.5,17,28,28,-0.905555556,-0.870138889,-0.870138889,2,0,36.9,38.8,37.85,37.85,0.95,38.8,36.9,36.9,-0.905555556,-0.870138889,-0.870138889
--1.24275E+13,434544,66355,3/27/13 14:41,,,FALSE,FALSE,-27479.61181,,TRUE,FALSE,-27479.61181,,TRUE,FALSE,,,FALSE,FALSE,2,2,6.8,7.2,7,7,0.2,6.8,7.2,7.2,-0.802777778,-0.29375,-0.29375,2,2,45.6,46.4,46,46,0.4,45.6,46.4,46.4,-0.802777778,-0.29375,-0.29375,2,2,203,206,204.5,204.5,1.5,203,206,206,-0.802777778,-0.29375,-0.29375,2,2,136,138,137,137,1,138,136,136,-0.788194444,-0.270833333,-0.270833333,2,1,3.4,3.6,3.5,3.5,0.1,3.6,3.4,3.4,-0.788194444,-0.270833333,-0.270833333,2,2,26,27,26.5,26.5,0.5,27,26,26,-0.788194444,-0.270833333,-0.270833333,2,2,13,14,13.5,13.5,0.5,14,13,13,-0.788194444,-0.270833333,-0.270833333,3,0,1,1.1,1.1,1.066666667,0.047140452,1,1.1,1.1,-0.80625,-0.270833333,-0.270833333,2,0,0.6,1,0.8,0.8,0.2,0.6,1,1,-0.788194444,-0.270833333,-0.270833333,2,2,3.8,3.9,3.85,3.85,0.05,3.8,3.9,3.9,-0.788194444,-0.270833333,-0.270833333,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0.1,0.1,0.1,0.1,0,0.1,0.1,0.1,-0.486805556,-0.486805556,-0.486805556,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24257E+13,399441,66355,4/3/11 9:57,,,FALSE,FALSE,-19815.41458,,TRUE,FALSE,,,FALSE,FALSE,-19815.41458,,TRUE,FALSE,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.2425E+13,361859,66355,3/13/11 14:10,,,FALSE,FALSE,-16142.59028,,TRUE,FALSE,-16142.59028,,TRUE,FALSE,,,FALSE,FALSE,1,1,5.3,5.3,5.3,5.3,0,5.3,5.3,5.3,-0.214583333,-0.214583333,-0.214583333,1,1,43.9,43.9,43.9,43.9,0,43.9,43.9,43.9,-0.214583333,-0.214583333,-0.214583333,1,1,224,224,224,224,0,224,224,224,-0.214583333,-0.214583333,-0.214583333,1,1,143,143,143,143,0,143,143,143,-0.175,-0.175,-0.175,1,1,4.1,4.1,4.1,4.1,0,4.1,4.1,4.1,-0.175,-0.175,-0.175,1,0,33,33,33,33,0,33,33,33,-0.175,-0.175,-0.175,1,1,14,14,14,14,0,14,14,14,-0.175,-0.175,-0.175,1,0,1.1,1.1,1.1,1.1,0,1.1,1.1,1.1,-0.175,-0.175,-0.175,1,0,0.5,0.5,0.5,0.5,0,0.5,0.5,0.5,-0.175,-0.175,-0.175,1,1,4.5,4.5,4.5,4.5,0,4.5,4.5,4.5,-0.175,-0.175,-0.175,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,
--1.24248E+13,434276,66355,9/16/13 10:55,,9.545138889,FALSE,TRUE,-32766.45486,,TRUE,FALSE,,,FALSE,FALSE,-32766.45486,,TRUE,FALSE,2,0,14.2,14.6,14.4,14.4,0.2,14.6,14.2,14.2,-0.561805556,-0.211111111,-0.211111111,3,0,27,32.4,27.2,28.86666667,2.499777768,32.4,27,27,-0.561805556,-0.031944444,-0.031944444,2,1,148,158,153,153,5,158,148,148,-0.561805556,-0.211111111,-0.211111111,3,2,144,146,145,145,0.816496581,146,145,145,-0.568055556,-0.007638889,-0.007638889,3,2,3.4,4.1,3.6,3.7,0.294392029,4.1,3.6,3.6,-0.568055556,-0.007638889,-0.007638889,2,1,30,34,32,32,2,34,30,30,-0.568055556,-0.007638889,-0.007638889,2,0,88,102,95,95,7,102,88,88,-0.568055556,-0.007638889,-0.007638889,2,0,3,3.7,3.35,3.35,0.35,3.7,3,3,-0.568055556,-0.007638889,-0.007638889,1,0,0.4,0.4,0.4,0.4,0,0.4,0.4,0.4,-0.568055556,-0.568055556,-0.568055556,1,0,1.8,1.8,1.8,1.8,0,1.8,1.8,1.8,-0.568055556,-0.568055556,-0.568055556,2,0,2.67,3.06,2.865,2.865,0.195,2.67,3.06,3.06,-0.531944444,-0.488194444,-0.488194444,0,0,,,,,,,,,,,,0,0,,,,,,,,,,,,1,0,0.05,0.05,0.05,0.05,0,0.05,0.05,0.05,-0.582638889,-0.582638889,-0.582638889,2,2,7.43,7.44,7.435,7.435,0.005,7.43,7.44,7.44,-0.281944444,-0.029166667,-0.029166667,2,1,101,107,104,104,3,101,107,107,-0.281944444,-0.029166667,-0.029166667,2,2,42.3,43.5,42.9,42.9,0.6,43.5,42.3,42.3,-0.281944444,-0.029166667,-0.029166667,3,2,7.38,7.45,7.41,7.413333333,0.028674418,7.41,7.38,7.38,-0.531944444,-0.031944444,-0.031944444,3,0,28,32.7,30,30.23333333,1.925847577,28,32.7,32.7,-0.531944444,-0.031944444,-0.031944444,3,2,47.9,53.8,49,50.23333333,2.561683474,53.8,47.9,47.9,-0.531944444,-0.031944444,-0.031944444
\ No newline at end of file
diff --git a/scripts/Labogram/labogram.ipynb b/scripts/Labogram/labogram.ipynb
new file mode 100644
index 00000000..6ba4124f
--- /dev/null
+++ b/scripts/Labogram/labogram.ipynb
@@ -0,0 +1,604 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "94a2d61a",
+   "metadata": {},
+   "source": [
+    "Labogram calculated using a 7-Day window\n",
+    "\n",
+    "For each lab look in the previous 7 days. Calculate how many immediately preceding normal results there were in a row.\n",
+    "Group by 'preceding' count to get normal prevalence for each group of the labogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7203ae9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
+      "  %reload_ext google.cloud.bigquery\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()\n",
+    "\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 100\n",
+    "pd.options.display.min_rows = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0f202548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def labQuery(basename, date_start, date_end, time_window = '7D'):\n",
+    "    query = '''\n",
+    "    SELECT pat_enc_csn_id_coded, base_name, result_flag, result_flag IS NULL AS normal,\n",
+    "        ord_value, reference_low, reference_high, result_time\n",
+    "        FROM `som-nero-phi-jonc101.shc_core_2021.lab_result`\n",
+    "    WHERE\n",
+    "        ordering_mode = 'Inpatient'\n",
+    "        AND order_type = 'Lab'\n",
+    "        AND ord_value IS NOT NULL\n",
+    "        AND lower(group_lab_name) NOT LIKE '%arterial%'\n",
+    "\n",
+    "        AND base_name = '{basename}'\n",
+    "        AND result_time < '{date_end}'\n",
+    "        AND result_time >= '{date_start}'\n",
+    "\n",
+    "    ORDER BY pat_enc_csn_id_coded asc, result_time asc\n",
+    "    '''.format_map({'basename': basename,\n",
+    "                    'date_start': (np.datetime64(date_start) - pd.Timedelta(time_window)).strftime('%Y-%m-%d'),\n",
+    "                    'date_end': date_end})\n",
+    "    query_job = client.query(query)\n",
+    "    df = query_job.to_dataframe()\n",
+    "    df.pat_enc_csn_id_coded = df.pat_enc_csn_id_coded.astype(int)\n",
+    "    df.reset_index(drop=True, inplace=True)    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d36a1618",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def windowedLabCounts(df_lab, date_start, time_window = '7D'):\n",
+    "    df_lab.reset_index()\n",
+    "    totenc = len(df_lab.pat_enc_csn_id_coded.unique())\n",
+    "    enc_num = 0\n",
+    "\n",
+    "    df_lab['preceding'] = 0\n",
+    "    for enc in df_lab.pat_enc_csn_id_coded.unique():\n",
+    "        enc_idx = (df_lab.pat_enc_csn_id_coded==enc)\n",
+    "        df = df_lab.loc[enc_idx, ['result_time', 'normal']]\n",
+    "        rownum = 0\n",
+    "        for (idx, row) in df.iterrows():\n",
+    "            if rownum>0:\n",
+    "                rtime = row.result_time\n",
+    "                dfslice = df.loc[(df.result_time <= rtime) & (df.result_time > rtime - pd.Timedelta(time_window))]\n",
+    "                if dfslice.shift().loc[idx, 'normal']==True:\n",
+    "                    y = dfslice.shift().normal\n",
+    "                    prec = y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)\n",
+    "                    df_lab.loc[idx, 'preceding'] = prec.values[-1]\n",
+    "            else:\n",
+    "                df_lab.loc[idx, 'preceding'] = -1\n",
+    "            rownum = rownum + 1\n",
+    "\n",
+    "        enc_num = enc_num + 1\n",
+    "    return df_lab.loc[df_lab.result_time >= date_start]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "fea5a82d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unitTestWindow():\n",
+    "    query = '''\n",
+    "    SELECT pat_enc_csn_id_coded, base_name, result_flag, result_flag IS NULL AS normal,\n",
+    "        ord_value, reference_low, reference_high, result_time\n",
+    "        FROM `som-nero-phi-jonc101.shc_core_2021.lab_result`\n",
+    "    WHERE\n",
+    "        ordering_mode = 'Inpatient'\n",
+    "        AND order_type = 'Lab'\n",
+    "        AND ord_value IS NOT NULL\n",
+    "        AND lower(group_lab_name) NOT LIKE '%arterial%'\n",
+    "\n",
+    "        AND base_name = 'NA'\n",
+    "        AND (pat_enc_csn_id_coded = 131290584921\n",
+    "        OR pat_enc_csn_id_coded = 131287053881)\n",
+    "\n",
+    "    ORDER BY pat_enc_csn_id_coded asc, result_time asc\n",
+    "    '''\n",
+    "    query_job = client.query(query)\n",
+    "    df = query_job.to_dataframe()\n",
+    "    df.pat_enc_csn_id_coded = df.pat_enc_csn_id_coded.astype(int)\n",
+    "    df.reset_index(drop=True, inplace=True)  \n",
+    "    \n",
+    "    df = windowedLabCounts(df, '2020-01-01')\n",
+    "    unit_test = pd.read_csv('unit_test_results.csv')\n",
+    "\n",
+    "    if (df.preceding == unit_test.preceding).all():\n",
+    "        print('Unit test passed')\n",
+    "        return True\n",
+    "    else:\n",
+    "        print('Unit test failed')\n",
+    "        return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "c64155b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def labogramGroupings(df, basename, max_streak = 7): \n",
+    "    df = df.groupby('preceding').agg({'base_name':'count', 'normal':'sum'}).rename(columns={'base_name':'cnt'})\n",
+    "    if len(df) > max_streak+2:\n",
+    "        df.iloc[max_streak+1] = df.iloc[max_streak+1:-1].sum()\n",
+    "        df['prop'] = df.normal / df.cnt\n",
+    "        df['basename'] = basename\n",
+    "        return df.iloc[0:max_streak+2][['basename', 'prop', 'normal', 'cnt']]\n",
+    "    df['prop'] = df.normal / df.cnt\n",
+    "    df['basename'] = basename\n",
+    "    return df[['basename', 'prop', 'normal', 'cnt']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1c63d2b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculateLabogram(basename, date_start, date_end):\n",
+    "    filename = basename + '_' + date_start + '_' + date_end\n",
+    "    df = labQuery(basename, date_start, date_end)\n",
+    "    df = windowedLabCounts(df, date_start)\n",
+    "    df = labogramGroupings(df, basename)\n",
+    "    df.to_csv('labogram.csv', mode='a', header=False, index=True)\n",
+    "    print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "325818d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             ALB  0.837009  110851  132437\n",
+      " 0             ALB  0.065996    4100   62125\n",
+      " 1             ALB  0.655087    9485   14479\n",
+      " 2             ALB  0.733176    4042    5513\n",
+      " 3             ALB  0.799864    2346    2933\n",
+      " 4             ALB  0.824772    1445    1752\n",
+      " 5             ALB  0.859574    1010    1175\n",
+      " 6             ALB  0.911765    1581    1734\n",
+      " 7             ALB  0.908502    2105    2317\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1            ALKP  0.789522  103545  131149\n",
+      " 0            ALKP  0.107027    3919   36617\n",
+      " 1            ALKP  0.920057   12821   13935\n",
+      " 2            ALKP  0.942559    7778    8252\n",
+      " 3            ALKP  0.951199    5633    5922\n",
+      " 4            ALKP  0.949367    4200    4424\n",
+      " 5            ALKP  0.954139    3412    3576\n",
+      " 6            ALKP  0.960374    5962    6208\n",
+      " 7            ALKP  0.960298   10812   11259\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             ALT  0.791019  103936  131395\n",
+      " 0             ALT  0.124845    4747   38023\n",
+      " 1             ALT  0.894094   13094   14645\n",
+      " 2             ALT  0.922000    7837    8500\n",
+      " 3             ALT  0.927735    5623    6061\n",
+      " 4             ALT  0.926207    4029    4350\n",
+      " 5             ALT  0.930664    3208    3447\n",
+      " 6             ALT  0.939910    5459    5808\n",
+      " 7             ALT  0.949120    8898    9375\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             AST  0.808109  105990  131158\n",
+      " 0             AST  0.157495    6545   41557\n",
+      " 1             AST  0.868708   12737   14662\n",
+      " 2             AST  0.897309    7270    8102\n",
+      " 3             AST  0.911874    5153    5651\n",
+      " 4             AST  0.912143    3613    3961\n",
+      " 5             AST  0.931158    2854    3065\n",
+      " 6             AST  0.939421    5257    5596\n",
+      " 7             AST  0.949231    7161    7544\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              AG  0.912135  136324  149456\n",
+      " 0              AG  0.497144   13926   28012\n",
+      " 1              AG  0.908905   36069   39684\n",
+      " 2              AG  0.937500   25380   27072\n",
+      " 3              AG  0.948398   18912   19941\n",
+      " 4              AG  0.956387   14495   15156\n",
+      " 5              AG  0.962011   11902   12372\n",
+      " 6              AG  0.968071   15372   15879\n",
+      " 7              AG  0.969483   53339   55018\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1            EGFR  0.733890  107542  146537\n",
+      " 0            EGFR  0.076791    6446   83942\n",
+      " 1            EGFR  0.929675   24919   26804\n",
+      " 2            EGFR  0.955301   17119   17920\n",
+      " 3            EGFR  0.966679   12765   13205\n",
+      " 4            EGFR  0.974551    9880   10138\n",
+      " 5            EGFR  0.979703    8254    8425\n",
+      " 6            EGFR  0.984527   11644   11827\n",
+      " 7            EGFR  0.984418   40181   40817\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             BUN  0.762352  114057  149612\n",
+      " 0             BUN  0.109498   11335  103518\n",
+      " 1             BUN  0.879932   28076   31907\n",
+      " 2             BUN  0.888994   17931   20170\n",
+      " 3             BUN  0.901910   12229   13559\n",
+      " 4             BUN  0.914351    8818    9644\n",
+      " 5             BUN  0.917801    6744    7348\n",
+      " 6             BUN  0.928106    7552    8137\n",
+      " 7             BUN  0.930967   18543   19918\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              CA  0.888029  133112  149896\n",
+      " 0              CA  0.252419   18626   73790\n",
+      " 1              CA  0.779960   33401   42824\n",
+      " 2              CA  0.859161   21406   24915\n",
+      " 3              CA  0.889123   14755   16595\n",
+      " 4              CA  0.901277   10517   11669\n",
+      " 5              CA  0.916705    8034    8764\n",
+      " 6              CA  0.930871    9251    9938\n",
+      " 7              CA  0.932970   24664   26436\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              CL  0.816638  122361  149835\n",
+      " 0              CL  0.277731   24760   89151\n",
+      " 1              CL  0.788594   35661   45221\n",
+      " 2              CL  0.850260   23099   27167\n",
+      " 3              CL  0.877386   16229   18497\n",
+      " 4              CL  0.892746   11803   13221\n",
+      " 5              CL  0.898703    9147   10178\n",
+      " 6              CL  0.909635    9583   10535\n",
+      " 7              CL  0.918407   33070   36008\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             CO2  0.819537  122629  149632\n",
+      " 0             CO2  0.317911   21053   66223\n",
+      " 1             CO2  0.817166   34942   42760\n",
+      " 2             CO2  0.869909   22896   26320\n",
+      " 3             CO2  0.886277   15984   18035\n",
+      " 4             CO2  0.896223   11555   12893\n",
+      " 5             CO2  0.901171    8772    9734\n",
+      " 6             CO2  0.915210    9844   10756\n",
+      " 7             CO2  0.911923   24176   26511\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              CR  0.673223  101341  150531\n",
+      " 0              CR  0.102155   11690  114434\n",
+      " 1              CR  0.832359   24786   29778\n",
+      " 2              CR  0.885740   15845   17889\n",
+      " 3              CR  0.902717   10866   12037\n",
+      " 4              CR  0.914521    7778    8505\n",
+      " 5              CR  0.921529    5954    6461\n",
+      " 6              CR  0.932417    6650    7132\n",
+      " 7              CR  0.934775   16682   17846\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             GLU  0.742526  111252  149829\n",
+      " 0             GLU  0.304154   34012  111825\n",
+      " 1             GLU  0.678210   35096   51748\n",
+      " 2             GLU  0.790684   21320   26964\n",
+      " 3             GLU  0.841755   14059   16702\n",
+      " 4             GLU  0.870793    9624   11052\n",
+      " 5             GLU  0.879259    7020    7984\n",
+      " 6             GLU  0.914736    7778    8503\n",
+      " 7             GLU  0.907463   14092   15529\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1            GLOB  0.940364  121401  129100\n",
+      " 0            GLOB  0.357328    3316    9280\n",
+      " 1            GLOB  0.915429   13812   15088\n",
+      " 2            GLOB  0.945212    8557    9053\n",
+      " 3            GLOB  0.961314    6262    6514\n",
+      " 4            GLOB  0.970797    4787    4931\n",
+      " 5            GLOB  0.977789    3918    4007\n",
+      " 6            GLOB  0.980103    8177    8343\n",
+      " 7            GLOB  0.982783   13129   13359\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1               K  0.918125  138277  150608\n",
+      " 0               K  0.571274   19068   33378\n",
+      " 1               K  0.874344   40128   45895\n",
+      " 2               K  0.909106   28155   30970\n",
+      " 3               K  0.921031   20982   22781\n",
+      " 4               K  0.929514   15983   17195\n",
+      " 5               K  0.937707   13021   13886\n",
+      " 6               K  0.948841   15264   16087\n",
+      " 7               K  0.959488   80834   84247\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              NA  0.862697  129308  149888\n",
+      " 0              NA  0.249460   21122   84671\n",
+      " 1              NA  0.828892   36550   44095\n",
+      " 2              NA  0.874874   24276   27748\n",
+      " 3              NA  0.894786   17247   19275\n",
+      " 4              NA  0.907234   12704   14003\n",
+      " 5              NA  0.911673    9981   10948\n",
+      " 6              NA  0.920643   10302   11190\n",
+      " 7              NA  0.933364   43281   46371\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1            TBIL  0.928556  122107  131502\n",
+      " 0            TBIL  0.131095    3522   26866\n",
+      " 1            TBIL  0.942380   15243   16175\n",
+      " 2            TBIL  0.957229    9176    9586\n",
+      " 3            TBIL  0.972054    6887    7085\n",
+      " 4            TBIL  0.977213    5189    5310\n",
+      " 5            TBIL  0.980176    4351    4439\n",
+      " 6            TBIL  0.985549    8593    8719\n",
+      " 7            TBIL  0.985233   13611   13815\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              TP  0.887169  116362  131161\n",
+      " 0              TP  0.134675    6071   45079\n",
+      " 1              TP  0.711333   12422   17463\n",
+      " 2              TP  0.797052    6327    7938\n",
+      " 3              TP  0.859815    4183    4865\n",
+      " 4              TP  0.895801    2923    3263\n",
+      " 5              TP  0.909992    2204    2422\n",
+      " 6              TP  0.932877    3544    3799\n",
+      " 7              TP  0.944606    5184    5488\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1            PHOS  0.709227   17286  24373\n",
+      " 0            PHOS  0.414322   10750  25946\n",
+      " 1            PHOS  0.707765   10792  15248\n",
+      " 2            PHOS  0.782594    6933   8859\n",
+      " 3            PHOS  0.819064    4898   5980\n",
+      " 4            PHOS  0.849339    3467   4082\n",
+      " 5            PHOS  0.854987    2606   3048\n",
+      " 6            PHOS  0.891239    3286   3687\n",
+      " 7            PHOS  0.878209    5884   6700\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1              MG  0.849455   38279  45063\n",
+      " 0              MG  0.434399    6400  14733\n",
+      " 1              MG  0.904415   15385  17011\n",
+      " 2              MG  0.937842   11316  12066\n",
+      " 3              MG  0.949590    9023   9502\n",
+      " 4              MG  0.955121    6938   7264\n",
+      " 5              MG  0.955943    5815   6083\n",
+      " 6              MG  0.964738    8372   8678\n",
+      " 7              MG  0.967439   31346  32401\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             HCT  0.458983   18072   39374\n",
+      " 0             HCT  0.028223    3349  118661\n",
+      " 1             HCT  0.638845    6016    9417\n",
+      " 2             HCT  0.752506    2928    3891\n",
+      " 3             HCT  0.797035    1559    1956\n",
+      " 4             HCT  0.832896     952    1143\n",
+      " 5             HCT  0.832857     583     700\n",
+      " 6             HCT  0.863799     482     558\n",
+      " 7             HCT  0.883721     988    1118\n",
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1             HGB  0.400516   15679   39147\n",
+      " 0             HGB  0.023321    2704  115948\n",
+      " 1             HGB  0.620271    4853    7824\n",
+      " 2             HGB  0.749417    2249    3001\n",
+      " 3             HGB  0.806878    1220    1512\n",
+      " 4             HGB  0.840629     749     891\n",
+      " 5             HGB  0.845196     475     562\n",
+      " 6             HGB  0.866051     375     433\n",
+      " 7             HGB  0.875676     810     925\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1             PLT  0.749739   29494  39339\n",
+      " 0             PLT  0.117785    4885  41474\n",
+      " 1             PLT  0.874662   15185  17361\n",
+      " 2             PLT  0.912875    9189  10066\n",
+      " 3             PLT  0.931170    6169   6625\n",
+      " 4             PLT  0.937039    4316   4606\n",
+      " 5             PLT  0.940134    3235   3441\n",
+      " 6             PLT  0.944387    3668   3884\n",
+      " 7             PLT  0.942848    9156   9711\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1             WBC  0.700934   27007  38530\n",
+      " 0             WBC  0.211312    8795  41621\n",
+      " 1             WBC  0.815751   15040  18437\n",
+      " 2             WBC  0.879322    8875  10093\n",
+      " 3             WBC  0.885334    5783   6532\n",
+      " 4             WBC  0.908675    3970   4369\n",
+      " 5             WBC  0.900129    2785   3094\n",
+      " 6             WBC  0.924741    3035   3282\n",
+      " 7             WBC  0.929032    6768   7285\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1             RBC  0.471786   17800  37729\n",
+      " 0             RBC  0.029633    2313  78054\n",
+      " 1             RBC  0.692127    5899   8523\n",
+      " 2             RBC  0.787870    2741   3479\n",
+      " 3             RBC  0.823221    1411   1714\n",
+      " 4             RBC  0.858511     807    940\n",
+      " 5             RBC  0.875000     490    560\n",
+      " 6             RBC  0.909894     515    566\n",
+      " 7             RBC  0.885835     419    473\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1              PT  0.632696   22269  35197\n",
+      " 0              PT  0.086318    3463  40119\n",
+      " 1              PT  0.731662    5077   6939\n",
+      " 2              PT  0.792056    2373   2996\n",
+      " 3              PT  0.832753    1434   1722\n",
+      " 4              PT  0.856736     903   1054\n",
+      " 5              PT  0.891185     647    726\n",
+      " 6              PT  0.906283     851    939\n",
+      " 7              PT  0.914759    1159   1267\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1             INR  0.699477   24616  35192\n",
+      " 0             INR  0.109839    3780  34414\n",
+      " 1             INR  0.797897    6451   8085\n",
+      " 2             INR  0.839340    3307   3940\n",
+      " 3             INR  0.866448    2115   2441\n",
+      " 4             INR  0.889925    1431   1608\n",
+      " 5             INR  0.900000    1053   1170\n",
+      " 6             INR  0.937150    1506   1607\n",
+      " 7             INR  0.928918    2300   2476\n",
+      "          basename      prop  normal    cnt\n",
+      "preceding                                  \n",
+      "-1            XPTT  0.715268   18205  25452\n",
+      " 0            XPTT  0.083166    3101  37287\n",
+      " 1            XPTT  0.603732    3527   5842\n",
+      " 2            XPTT  0.678482    1627   2398\n",
+      " 3            XPTT  0.789910    1049   1328\n",
+      " 4            XPTT  0.833137     709    851\n",
+      " 5            XPTT  0.861157     521    605\n",
+      " 6            XPTT  0.861237     571    663\n",
+      " 7            XPTT  0.888122    1286   1448\n"
+     ]
+    }
+   ],
+   "source": [
+    "date_start = '2020-01-01'\n",
+    "date_end = '2021-01-01'\n",
+    "labnames = ['ALB', 'ALKP', 'ALT', 'AST', 'AG', 'EGFR', 'BUN', 'CA', 'CL', 'CO2', 'CR', 'GLU', 'GLOB', 'K', 'NA', 'TBIL', 'TP', 'PHOS', 'MG', 'HCT', 'HGB', 'PLT', 'WBC', 'RBC', 'PT', 'INR', 'XPTT']\n",
+    "\n",
+    "for labname in labnames:\n",
+    "    calculateLabogram(labname, date_start, date_end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "9005b5d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          basename      prop  normal     cnt\n",
+      "preceding                                   \n",
+      "-1              NA  0.862697  129308  149888\n",
+      " 0              NA  0.249406   21115   84661\n",
+      " 1              NA  0.828911   36550   44094\n",
+      " 2              NA  0.874752   24277   27753\n",
+      " 3              NA  0.894802   17250   19278\n",
+      " 4              NA  0.907234   12704   14003\n",
+      " 5              NA  0.911765    9982   10948\n",
+      " 6              NA  0.920643   10302   11190\n",
+      " 7              NA  0.933346   43283   46374\n"
+     ]
+    }
+   ],
+   "source": [
+    "date_start = '2020-01-01'\n",
+    "date_end = '2021-01-01'\n",
+    "labnames = ['ALB', 'ALKP', 'ALT', 'AST', 'AG', 'EGFR', 'BUN', 'CA', 'CL', 'CO2', 'CR', 'GLU', 'GLOB', 'K', 'NA', 'TBIL', 'TP', 'PHOS', 'MG', 'HCT', 'HGB', 'PLT', 'WBC', 'RBC', 'PT', 'INR', 'XPTT']\n",
+    "\n",
+    "calculateLabogram('NA', date_start, date_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f67e15dd",
+   "metadata": {},
+   "source": [
+    "## Add Confidence Intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "4b239e09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.proportion import proportion_confint\n",
+    "\n",
+    "labogram_file = 'labogram_2022-04-19'\n",
+    "labogram_df = pd.read_csv(labogram_file+'.csv')\n",
+    "ci_df = pd.DataFrame(proportion_confint(labogram_df.normal, labogram_df.cnt, alpha=0.05)).T\n",
+    "labogram_df['cilb'] = ci_df[0]\n",
+    "labogram_df['ciub'] = ci_df[1]\n",
+    "\n",
+    "labogram_df[['preceding', 'basename', 'prop', 'cilb', 'ciub', 'normal', 'cnt']].to_csv(labogram_file+'_ci.csv', index=False, float_format='%.4f')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/Labogram/labogram_2022-04-19.csv b/scripts/Labogram/labogram_2022-04-19.csv
new file mode 100644
index 00000000..923afe57
--- /dev/null
+++ b/scripts/Labogram/labogram_2022-04-19.csv
@@ -0,0 +1,244 @@
+preceding,basename,prop,normal,cnt
+-1,ALB,0.837009295,110851,132437
+0,ALB,0.065995976,4100,62125
+1,ALB,0.655086677,9485,14479
+2,ALB,0.733176129,4042,5513
+3,ALB,0.799863621,2346,2933
+4,ALB,0.824771689,1445,1752
+5,ALB,0.859574468,1010,1175
+6,ALB,0.911764706,1581,1734
+7,ALB,0.908502374,2105,2317
+-1,ALKP,0.789521842,103545,131149
+0,ALKP,0.107026791,3919,36617
+1,ALKP,0.920057409,12821,13935
+2,ALKP,0.94255938,7778,8252
+3,ALKP,0.951198919,5633,5922
+4,ALKP,0.949367089,4200,4424
+5,ALKP,0.954138702,3412,3576
+6,ALKP,0.960373711,5962,6208
+7,ALKP,0.960298428,10812,11259
+-1,ALT,0.791019445,103936,131395
+0,ALT,0.124845488,4747,38023
+1,ALT,0.894093547,13094,14645
+2,ALT,0.922,7837,8500
+3,ALT,0.927734697,5623,6061
+4,ALT,0.926206897,4029,4350
+5,ALT,0.930664346,3208,3447
+6,ALT,0.939910468,5459,5808
+7,ALT,0.94912,8898,9375
+-1,AST,0.808109303,105990,131158
+0,AST,0.157494526,6545,41557
+1,AST,0.868708225,12737,14662
+2,AST,0.897309306,7270,8102
+3,AST,0.911874005,5153,5651
+4,AST,0.912143398,3613,3961
+5,AST,0.931158238,2854,3065
+6,AST,0.939421015,5257,5596
+7,AST,0.949231177,7161,7544
+-1,AG,0.912134675,136324,149456
+0,AG,0.497144081,13926,28012
+1,AG,0.908905352,36069,39684
+2,AG,0.9375,25380,27072
+3,AG,0.948397773,18912,19941
+4,AG,0.956386909,14495,15156
+5,AG,0.962010993,11902,12372
+6,AG,0.968071037,15372,15879
+7,AG,0.969482715,53339,55018
+-1,EGFR,0.733889734,107542,146537
+0,EGFR,0.076791118,6446,83942
+1,EGFR,0.929674675,24919,26804
+2,EGFR,0.955301339,17119,17920
+3,EGFR,0.966679288,12765,13205
+4,EGFR,0.974551194,9880,10138
+5,EGFR,0.979703264,8254,8425
+6,EGFR,0.98452693,11644,11827
+7,EGFR,0.984418257,40181,40817
+-1,BUN,0.76235195,114057,149612
+0,BUN,0.109497865,11335,103518
+1,BUN,0.879932303,28076,31907
+2,BUN,0.888993555,17931,20170
+3,BUN,0.90191017,12229,13559
+4,BUN,0.914350892,8818,9644
+5,BUN,0.917800762,6744,7348
+6,BUN,0.928106182,7552,8137
+7,BUN,0.930966965,18543,19918
+-1,CA,0.888029033,133112,149896
+0,CA,0.252419027,18626,73790
+1,CA,0.779959836,33401,42824
+2,CA,0.859161148,21406,24915
+3,CA,0.88912323,14755,16595
+4,CA,0.901276887,10517,11669
+5,CA,0.916704701,8034,8764
+6,CA,0.930871403,9251,9938
+7,CA,0.932970192,24664,26436
+-1,CL,0.816638302,122361,149835
+0,CL,0.277731041,24760,89151
+1,CL,0.788593795,35661,45221
+2,CL,0.850259506,23099,27167
+3,CL,0.877385522,16229,18497
+4,CL,0.892746388,11803,13221
+5,CL,0.898703085,9147,10178
+6,CL,0.909634551,9583,10535
+7,CL,0.918407021,33070,36008
+-1,CO2,0.819537265,122629,149632
+0,CO2,0.317910696,21053,66223
+1,CO2,0.817165575,34942,42760
+2,CO2,0.869908815,22896,26320
+3,CO2,0.886276684,15984,18035
+4,CO2,0.896222757,11555,12893
+5,CO2,0.901171153,8772,9734
+6,CO2,0.915210115,9844,10756
+7,CO2,0.911923353,24176,26511
+-1,CR,0.673223456,101341,150531
+0,CR,0.102154954,11690,114434
+1,CR,0.83235946,24786,29778
+2,CR,0.88573984,15845,17889
+3,CR,0.902716624,10866,12037
+4,CR,0.91452087,7778,8505
+5,CR,0.921529175,5954,6461
+6,CR,0.932417274,6650,7132
+7,CR,0.9347753,16682,17846
+-1,GLU,0.74252648,111252,149829
+0,GLU,0.304153812,34012,111825
+1,GLU,0.678209786,35096,51748
+2,GLU,0.790683875,21320,26964
+3,GLU,0.841755478,14059,16702
+4,GLU,0.870792617,9624,11052
+5,GLU,0.879258517,7020,7984
+6,GLU,0.914735976,7778,8503
+7,GLU,0.907463455,14092,15529
+-1,GLOB,0.940364059,121401,129100
+0,GLOB,0.357327586,3316,9280
+1,GLOB,0.91542948,13812,15088
+2,GLOB,0.945211532,8557,9053
+3,GLOB,0.961314093,6262,6514
+4,GLOB,0.970796999,4787,4931
+5,GLOB,0.977788869,3918,4007
+6,GLOB,0.98010308,8177,8343
+7,GLOB,0.982783142,13129,13359
+-1,K,0.918125199,138277,150608
+0,K,0.571274492,19068,33378
+1,K,0.87434361,40128,45895
+2,K,0.909105586,28155,30970
+3,K,0.921030683,20982,22781
+4,K,0.929514394,15983,17195
+5,K,0.937707043,13021,13886
+6,K,0.948840679,15264,16087
+7,K,0.959488172,80834,84247
+-1,NA,0.862697481,129308,149888
+0,NA,0.249459673,21122,84671
+1,NA,0.828892165,36550,44095
+2,NA,0.874873865,24276,27748
+3,NA,0.894785992,17247,19275
+4,NA,0.907234164,12704,14003
+5,NA,0.911673365,9981,10948
+6,NA,0.920643432,10302,11190
+7,NA,0.933363525,43281,46371
+-1,TBIL,0.92855622,122107,131502
+0,TBIL,0.131095064,3522,26866
+1,TBIL,0.942380216,15243,16175
+2,TBIL,0.957229293,9176,9586
+3,TBIL,0.972053634,6887,7085
+4,TBIL,0.977212806,5189,5310
+5,TBIL,0.980175715,4351,4439
+6,TBIL,0.985548801,8593,8719
+7,TBIL,0.985233442,13611,13815
+-1,TP,0.887169204,116362,131161
+0,TP,0.134674682,6071,45079
+1,TP,0.711332532,12422,17463
+2,TP,0.797052154,6327,7938
+3,TP,0.859815005,4183,4865
+4,TP,0.89580141,2923,3263
+5,TP,0.909991742,2204,2422
+6,TP,0.932877073,3544,3799
+7,TP,0.944606414,5184,5488
+-1,PHOS,0.709227424,17286,24373
+0,PHOS,0.414322053,10750,25946
+1,PHOS,0.707764953,10792,15248
+2,PHOS,0.782593972,6933,8859
+3,PHOS,0.819063545,4898,5980
+4,PHOS,0.84933856,3467,4082
+5,PHOS,0.854986877,2606,3048
+6,PHOS,0.89123949,3286,3687
+7,PHOS,0.878208955,5884,6700
+-1,MG,0.849455207,38279,45063
+0,MG,0.434398968,6400,14733
+1,MG,0.90441479,15385,17011
+2,MG,0.93784187,11316,12066
+3,MG,0.94958956,9023,9502
+4,MG,0.955121145,6938,7264
+5,MG,0.955942791,5815,6083
+6,MG,0.964738419,8372,8678
+7,MG,0.967439277,31346,32401
+-1,HCT,0.458983085,18072,39374
+0,HCT,0.028223258,3349,118661
+1,HCT,0.638844643,6016,9417
+2,HCT,0.752505783,2928,3891
+3,HCT,0.797034765,1559,1956
+4,HCT,0.832895888,952,1143
+5,HCT,0.832857143,583,700
+6,HCT,0.863799283,482,558
+7,HCT,0.88372093,988,1118
+-1,HGB,0.400516004,15679,39147
+0,HGB,0.023320799,2704,115948
+1,HGB,0.620270961,4853,7824
+2,HGB,0.749416861,2249,3001
+3,HGB,0.806878307,1220,1512
+4,HGB,0.840628507,749,891
+5,HGB,0.84519573,475,562
+6,HGB,0.866050808,375,433
+7,HGB,0.875675676,810,925
+-1,PLT,0.749739444,29494,39339
+0,PLT,0.117784636,4885,41474
+1,PLT,0.874661598,15185,17361
+2,PLT,0.912875025,9189,10066
+3,PLT,0.931169811,6169,6625
+4,PLT,0.937038645,4316,4606
+5,PLT,0.940133682,3235,3441
+6,PLT,0.94438723,3668,3884
+7,PLT,0.942848316,9156,9711
+-1,WBC,0.700934337,27007,38530
+0,WBC,0.211311598,8795,41621
+1,WBC,0.815750936,15040,18437
+2,WBC,0.879322303,8875,10093
+3,WBC,0.885333742,5783,6532
+4,WBC,0.908674754,3970,4369
+5,WBC,0.900129282,2785,3094
+6,WBC,0.924741012,3035,3282
+7,WBC,0.929032258,6768,7285
+-1,RBC,0.471785629,17800,37729
+0,RBC,0.029633331,2313,78054
+1,RBC,0.692127185,5899,8523
+2,RBC,0.787870078,2741,3479
+3,RBC,0.823220537,1411,1714
+4,RBC,0.858510638,807,940
+5,RBC,0.875,490,560
+6,RBC,0.909893993,515,566
+7,RBC,0.885835095,419,473
+-1,PT,0.632695968,22269,35197
+0,PT,0.086318203,3463,40119
+1,PT,0.731661623,5077,6939
+2,PT,0.792056075,2373,2996
+3,PT,0.832752613,1434,1722
+4,PT,0.856736243,903,1054
+5,PT,0.891184573,647,726
+6,PT,0.90628328,851,939
+7,PT,0.914759274,1159,1267
+-1,INR,0.699477154,24616,35192
+0,INR,0.109839019,3780,34414
+1,INR,0.797897341,6451,8085
+2,INR,0.839340102,3307,3940
+3,INR,0.866448177,2115,2441
+4,INR,0.889925373,1431,1608
+5,INR,0.9,1053,1170
+6,INR,0.937149969,1506,1607
+7,INR,0.928917609,2300,2476
+-1,XPTT,0.715267955,18205,25452
+0,XPTT,0.083165715,3101,37287
+1,XPTT,0.603731599,3527,5842
+2,XPTT,0.678482068,1627,2398
+3,XPTT,0.789909639,1049,1328
+4,XPTT,0.833137485,709,851
+5,XPTT,0.861157025,521,605
+6,XPTT,0.861236802,571,663
+7,XPTT,0.888121547,1286,1448
\ No newline at end of file
diff --git a/scripts/Labogram/labogram_2022-04-19_ci.csv b/scripts/Labogram/labogram_2022-04-19_ci.csv
new file mode 100644
index 00000000..4e04655f
--- /dev/null
+++ b/scripts/Labogram/labogram_2022-04-19_ci.csv
@@ -0,0 +1,244 @@
+preceding,basename,prop,cilb,ciub,normal,cnt
+-1,ALB,0.8370,0.8350,0.8390,110851,132437
+0,ALB,0.0660,0.0640,0.0679,4100,62125
+1,ALB,0.6551,0.6473,0.6628,9485,14479
+2,ALB,0.7332,0.7215,0.7449,4042,5513
+3,ALB,0.7999,0.7854,0.8143,2346,2933
+4,ALB,0.8248,0.8070,0.8426,1445,1752
+5,ALB,0.8596,0.8397,0.8794,1010,1175
+6,ALB,0.9118,0.8984,0.9251,1581,1734
+7,ALB,0.9085,0.8968,0.9202,2105,2317
+-1,ALKP,0.7895,0.7873,0.7917,103545,131149
+0,ALKP,0.1070,0.1039,0.1102,3919,36617
+1,ALKP,0.9201,0.9156,0.9246,12821,13935
+2,ALKP,0.9426,0.9375,0.9476,7778,8252
+3,ALKP,0.9512,0.9457,0.9567,5633,5922
+4,ALKP,0.9494,0.9429,0.9558,4200,4424
+5,ALKP,0.9541,0.9473,0.9610,3412,3576
+6,ALKP,0.9604,0.9555,0.9652,5962,6208
+7,ALKP,0.9603,0.9567,0.9639,10812,11259
+-1,ALT,0.7910,0.7888,0.7932,103936,131395
+0,ALT,0.1248,0.1215,0.1282,4747,38023
+1,ALT,0.8941,0.8891,0.8991,13094,14645
+2,ALT,0.9220,0.9163,0.9277,7837,8500
+3,ALT,0.9277,0.9212,0.9343,5623,6061
+4,ALT,0.9262,0.9184,0.9340,4029,4350
+5,ALT,0.9307,0.9222,0.9391,3208,3447
+6,ALT,0.9399,0.9338,0.9460,5459,5808
+7,ALT,0.9491,0.9447,0.9536,8898,9375
+-1,AST,0.8081,0.8060,0.8102,105990,131158
+0,AST,0.1575,0.1540,0.1610,6545,41557
+1,AST,0.8687,0.8632,0.8742,12737,14662
+2,AST,0.8973,0.8907,0.9039,7270,8102
+3,AST,0.9119,0.9045,0.9193,5153,5651
+4,AST,0.9121,0.9033,0.9210,3613,3961
+5,AST,0.9312,0.9222,0.9401,2854,3065
+6,AST,0.9394,0.9332,0.9457,5257,5596
+7,AST,0.9492,0.9443,0.9542,7161,7544
+-1,AG,0.9121,0.9107,0.9136,136324,149456
+0,AG,0.4971,0.4913,0.5030,13926,28012
+1,AG,0.9089,0.9061,0.9117,36069,39684
+2,AG,0.9375,0.9346,0.9404,25380,27072
+3,AG,0.9484,0.9453,0.9515,18912,19941
+4,AG,0.9564,0.9531,0.9596,14495,15156
+5,AG,0.9620,0.9586,0.9654,11902,12372
+6,AG,0.9681,0.9653,0.9708,15372,15879
+7,AG,0.9695,0.9680,0.9709,53339,55018
+-1,EGFR,0.7339,0.7316,0.7362,107542,146537
+0,EGFR,0.0768,0.0750,0.0786,6446,83942
+1,EGFR,0.9297,0.9266,0.9327,24919,26804
+2,EGFR,0.9553,0.9523,0.9583,17119,17920
+3,EGFR,0.9667,0.9636,0.9697,12765,13205
+4,EGFR,0.9746,0.9715,0.9776,9880,10138
+5,EGFR,0.9797,0.9767,0.9827,8254,8425
+6,EGFR,0.9845,0.9823,0.9868,11644,11827
+7,EGFR,0.9844,0.9832,0.9856,40181,40817
+-1,BUN,0.7624,0.7602,0.7645,114057,149612
+0,BUN,0.1095,0.1076,0.1114,11335,103518
+1,BUN,0.8799,0.8764,0.8835,28076,31907
+2,BUN,0.8890,0.8847,0.8933,17931,20170
+3,BUN,0.9019,0.8969,0.9069,12229,13559
+4,BUN,0.9144,0.9088,0.9199,8818,9644
+5,BUN,0.9178,0.9115,0.9241,6744,7348
+6,BUN,0.9281,0.9225,0.9337,7552,8137
+7,BUN,0.9310,0.9274,0.9345,18543,19918
+-1,CA,0.8880,0.8864,0.8896,133112,149896
+0,CA,0.2524,0.2493,0.2556,18626,73790
+1,CA,0.7800,0.7760,0.7839,33401,42824
+2,CA,0.8592,0.8548,0.8635,21406,24915
+3,CA,0.8891,0.8843,0.8939,14755,16595
+4,CA,0.9013,0.8959,0.9067,10517,11669
+5,CA,0.9167,0.9109,0.9225,8034,8764
+6,CA,0.9309,0.9259,0.9359,9251,9938
+7,CA,0.9330,0.9300,0.9360,24664,26436
+-1,CL,0.8166,0.8147,0.8186,122361,149835
+0,CL,0.2777,0.2748,0.2807,24760,89151
+1,CL,0.7886,0.7848,0.7924,35661,45221
+2,CL,0.8503,0.8460,0.8545,23099,27167
+3,CL,0.8774,0.8727,0.8821,16229,18497
+4,CL,0.8927,0.8875,0.8980,11803,13221
+5,CL,0.8987,0.8928,0.9046,9147,10178
+6,CL,0.9096,0.9042,0.9151,9583,10535
+7,CL,0.9184,0.9156,0.9212,33070,36008
+-1,CO2,0.8195,0.8176,0.8215,122629,149632
+0,CO2,0.3179,0.3144,0.3215,21053,66223
+1,CO2,0.8172,0.8135,0.8208,34942,42760
+2,CO2,0.8699,0.8658,0.8740,22896,26320
+3,CO2,0.8863,0.8816,0.8909,15984,18035
+4,CO2,0.8962,0.8910,0.9015,11555,12893
+5,CO2,0.9012,0.8952,0.9071,8772,9734
+6,CO2,0.9152,0.9099,0.9205,9844,10756
+7,CO2,0.9119,0.9085,0.9153,24176,26511
+-1,CR,0.6732,0.6709,0.6756,101341,150531
+0,CR,0.1022,0.1004,0.1039,11690,114434
+1,CR,0.8324,0.8281,0.8366,24786,29778
+2,CR,0.8857,0.8811,0.8904,15845,17889
+3,CR,0.9027,0.8974,0.9080,10866,12037
+4,CR,0.9145,0.9086,0.9205,7778,8505
+5,CR,0.9215,0.9150,0.9281,5954,6461
+6,CR,0.9324,0.9266,0.9382,6650,7132
+7,CR,0.9348,0.9312,0.9384,16682,17846
+-1,GLU,0.7425,0.7403,0.7447,111252,149829
+0,GLU,0.3042,0.3015,0.3069,34012,111825
+1,GLU,0.6782,0.6742,0.6822,35096,51748
+2,GLU,0.7907,0.7858,0.7955,21320,26964
+3,GLU,0.8418,0.8362,0.8473,14059,16702
+4,GLU,0.8708,0.8645,0.8770,9624,11052
+5,GLU,0.8793,0.8721,0.8864,7020,7984
+6,GLU,0.9147,0.9088,0.9207,7778,8503
+7,GLU,0.9075,0.9029,0.9120,14092,15529
+-1,GLOB,0.9404,0.9391,0.9417,121401,129100
+0,GLOB,0.3573,0.3476,0.3671,3316,9280
+1,GLOB,0.9154,0.9110,0.9199,13812,15088
+2,GLOB,0.9452,0.9405,0.9499,8557,9053
+3,GLOB,0.9613,0.9566,0.9660,6262,6514
+4,GLOB,0.9708,0.9661,0.9755,4787,4931
+5,GLOB,0.9778,0.9732,0.9824,3918,4007
+6,GLOB,0.9801,0.9771,0.9831,8177,8343
+7,GLOB,0.9828,0.9806,0.9850,13129,13359
+-1,K,0.9181,0.9167,0.9195,138277,150608
+0,K,0.5713,0.5660,0.5766,19068,33378
+1,K,0.8743,0.8713,0.8774,40128,45895
+2,K,0.9091,0.9059,0.9123,28155,30970
+3,K,0.9210,0.9175,0.9245,20982,22781
+4,K,0.9295,0.9257,0.9333,15983,17195
+5,K,0.9377,0.9337,0.9417,13021,13886
+6,K,0.9488,0.9454,0.9522,15264,16087
+7,K,0.9595,0.9582,0.9608,80834,84247
+-1,,0.8627,0.8610,0.8644,129308,149888
+0,,0.2495,0.2465,0.2524,21122,84671
+1,,0.8289,0.8254,0.8324,36550,44095
+2,,0.8749,0.8710,0.8788,24276,27748
+3,,0.8948,0.8905,0.8991,17247,19275
+4,,0.9072,0.9024,0.9120,12704,14003
+5,,0.9117,0.9064,0.9170,9981,10948
+6,,0.9206,0.9156,0.9257,10302,11190
+7,,0.9334,0.9311,0.9356,43281,46371
+-1,TBIL,0.9286,0.9272,0.9299,122107,131502
+0,TBIL,0.1311,0.1271,0.1351,3522,26866
+1,TBIL,0.9424,0.9388,0.9460,15243,16175
+2,TBIL,0.9572,0.9532,0.9613,9176,9586
+3,TBIL,0.9721,0.9682,0.9759,6887,7085
+4,TBIL,0.9772,0.9732,0.9812,5189,5310
+5,TBIL,0.9802,0.9761,0.9843,4351,4439
+6,TBIL,0.9855,0.9830,0.9881,8593,8719
+7,TBIL,0.9852,0.9832,0.9872,13611,13815
+-1,TP,0.8872,0.8855,0.8889,116362,131161
+0,TP,0.1347,0.1315,0.1378,6071,45079
+1,TP,0.7113,0.7046,0.7181,12422,17463
+2,TP,0.7971,0.7882,0.8059,6327,7938
+3,TP,0.8598,0.8501,0.8696,4183,4865
+4,TP,0.8958,0.8853,0.9063,2923,3263
+5,TP,0.9100,0.8986,0.9214,2204,2422
+6,TP,0.9329,0.9249,0.9408,3544,3799
+7,TP,0.9446,0.9386,0.9507,5184,5488
+-1,PHOS,0.7092,0.7035,0.7149,17286,24373
+0,PHOS,0.4143,0.4083,0.4203,10750,25946
+1,PHOS,0.7078,0.7005,0.7150,10792,15248
+2,PHOS,0.7826,0.7740,0.7912,6933,8859
+3,PHOS,0.8191,0.8093,0.8288,4898,5980
+4,PHOS,0.8493,0.8384,0.8603,3467,4082
+5,PHOS,0.8550,0.8425,0.8675,2606,3048
+6,PHOS,0.8912,0.8812,0.9013,3286,3687
+7,PHOS,0.8782,0.8704,0.8860,5884,6700
+-1,MG,0.8495,0.8462,0.8528,38279,45063
+0,MG,0.4344,0.4264,0.4424,6400,14733
+1,MG,0.9044,0.9000,0.9088,15385,17011
+2,MG,0.9378,0.9335,0.9421,11316,12066
+3,MG,0.9496,0.9452,0.9540,9023,9502
+4,MG,0.9551,0.9504,0.9599,6938,7264
+5,MG,0.9559,0.9508,0.9611,5815,6083
+6,MG,0.9647,0.9609,0.9686,8372,8678
+7,MG,0.9674,0.9655,0.9694,31346,32401
+-1,HCT,0.4590,0.4541,0.4639,18072,39374
+0,HCT,0.0282,0.0273,0.0292,3349,118661
+1,HCT,0.6388,0.6291,0.6485,6016,9417
+2,HCT,0.7525,0.7389,0.7661,2928,3891
+3,HCT,0.7970,0.7792,0.8149,1559,1956
+4,HCT,0.8329,0.8113,0.8545,952,1143
+5,HCT,0.8329,0.8052,0.8605,583,700
+6,HCT,0.8638,0.8353,0.8923,482,558
+7,HCT,0.8837,0.8649,0.9025,988,1118
+-1,HGB,0.4005,0.3957,0.4054,15679,39147
+0,HGB,0.0233,0.0225,0.0242,2704,115948
+1,HGB,0.6203,0.6095,0.6310,4853,7824
+2,HGB,0.7494,0.7339,0.7649,2249,3001
+3,HGB,0.8069,0.7870,0.8268,1220,1512
+4,HGB,0.8406,0.8166,0.8647,749,891
+5,HGB,0.8452,0.8153,0.8751,475,562
+6,HGB,0.8661,0.8340,0.8981,375,433
+7,HGB,0.8757,0.8544,0.8969,810,925
+-1,PLT,0.7497,0.7455,0.7540,29494,39339
+0,PLT,0.1178,0.1147,0.1209,4885,41474
+1,PLT,0.8747,0.8697,0.8796,15185,17361
+2,PLT,0.9129,0.9074,0.9184,9189,10066
+3,PLT,0.9312,0.9251,0.9373,6169,6625
+4,PLT,0.9370,0.9300,0.9441,4316,4606
+5,PLT,0.9401,0.9322,0.9481,3235,3441
+6,PLT,0.9444,0.9372,0.9516,3668,3884
+7,PLT,0.9428,0.9382,0.9475,9156,9711
+-1,WBC,0.7009,0.6964,0.7055,27007,38530
+0,WBC,0.2113,0.2074,0.2152,8795,41621
+1,WBC,0.8158,0.8102,0.8213,15040,18437
+2,WBC,0.8793,0.8730,0.8857,8875,10093
+3,WBC,0.8853,0.8776,0.8931,5783,6532
+4,WBC,0.9087,0.9001,0.9172,3970,4369
+5,WBC,0.9001,0.8896,0.9107,2785,3094
+6,WBC,0.9247,0.9157,0.9338,3035,3282
+7,WBC,0.9290,0.9231,0.9349,6768,7285
+-1,RBC,0.4718,0.4667,0.4768,17800,37729
+0,RBC,0.0296,0.0284,0.0308,2313,78054
+1,RBC,0.6921,0.6823,0.7019,5899,8523
+2,RBC,0.7879,0.7743,0.8015,2741,3479
+3,RBC,0.8232,0.8052,0.8413,1411,1714
+4,RBC,0.8585,0.8362,0.8808,807,940
+5,RBC,0.8750,0.8476,0.9024,490,560
+6,RBC,0.9099,0.8863,0.9335,515,566
+7,RBC,0.8858,0.8572,0.9145,419,473
+-1,PT,0.6327,0.6277,0.6377,22269,35197
+0,PT,0.0863,0.0836,0.0891,3463,40119
+1,PT,0.7317,0.7212,0.7421,5077,6939
+2,PT,0.7921,0.7775,0.8066,2373,2996
+3,PT,0.8328,0.8151,0.8504,1434,1722
+4,PT,0.8567,0.8356,0.8779,903,1054
+5,PT,0.8912,0.8685,0.9138,647,726
+6,PT,0.9063,0.8876,0.9249,851,939
+7,PT,0.9148,0.8994,0.9301,1159,1267
+-1,INR,0.6995,0.6947,0.7043,24616,35192
+0,INR,0.1098,0.1065,0.1131,3780,34414
+1,INR,0.7979,0.7891,0.8067,6451,8085
+2,INR,0.8393,0.8279,0.8508,3307,3940
+3,INR,0.8664,0.8530,0.8799,2115,2441
+4,INR,0.8899,0.8746,0.9052,1431,1608
+5,INR,0.9000,0.8828,0.9172,1053,1170
+6,INR,0.9371,0.9253,0.9490,1506,1607
+7,INR,0.9289,0.9188,0.9390,2300,2476
+-1,XPTT,0.7153,0.7097,0.7208,18205,25452
+0,XPTT,0.0832,0.0804,0.0860,3101,37287
+1,XPTT,0.6037,0.5912,0.6163,3527,5842
+2,XPTT,0.6785,0.6598,0.6972,1627,2398
+3,XPTT,0.7899,0.7680,0.8118,1049,1328
+4,XPTT,0.8331,0.8081,0.8582,709,851
+5,XPTT,0.8612,0.8336,0.8887,521,605
+6,XPTT,0.8612,0.8349,0.8876,571,663
+7,XPTT,0.8881,0.8719,0.9044,1286,1448
diff --git a/scripts/Labogram/unit_test_results.csv b/scripts/Labogram/unit_test_results.csv
new file mode 100644
index 00000000..acfca61a
--- /dev/null
+++ b/scripts/Labogram/unit_test_results.csv
@@ -0,0 +1,51 @@
+,index,pat_enc_csn_id_coded,preceding
+0,126625,131287053881,-1
+1,126626,131287053881,1
+2,126627,131287053881,2
+3,126628,131287053881,3
+4,126629,131287053881,4
+5,126630,131287053881,5
+6,126631,131287053881,6
+7,126632,131287053881,7
+8,126633,131287053881,7
+9,126634,131287053881,8
+10,126635,131287053881,7
+11,126636,131287053881,7
+12,126637,131287053881,0
+13,126638,131287053881,1
+14,126639,131287053881,2
+15,126640,131287053881,3
+16,126641,131287053881,0
+17,126642,131287053881,1
+18,126643,131287053881,2
+19,126644,131287053881,3
+20,126645,131287053881,4
+21,126646,131287053881,0
+22,126647,131287053881,0
+23,126648,131287053881,1
+24,126649,131287053881,2
+25,126650,131287053881,3
+26,126651,131287053881,4
+27,126652,131287053881,5
+28,126653,131287053881,5
+29,126654,131287053881,6
+30,208465,131290584921,-1
+31,208466,131290584921,1
+32,208467,131290584921,2
+33,208468,131290584921,3
+34,208469,131290584921,0
+35,208470,131290584921,1
+36,208471,131290584921,2
+37,208472,131290584921,3
+38,208473,131290584921,4
+39,208474,131290584921,5
+40,208475,131290584921,6
+41,208476,131290584921,7
+42,208477,131290584921,8
+43,208478,131290584921,9
+44,208479,131290584921,0
+45,208480,131290584921,0
+46,208481,131290584921,0
+47,208482,131290584921,0
+48,208483,131290584921,0
+49,208484,131290584921,0
diff --git a/scripts/Moore_Stroke/Aspirin.sql b/scripts/Moore_Stroke/Aspirin.sql
new file mode 100644
index 00000000..6fd88721
--- /dev/null
+++ b/scripts/Moore_Stroke/Aspirin.sql
@@ -0,0 +1,44 @@
+select distinct labels.jc_uid, labels.pat_enc_csn_id_coded, labels.emergencyAdmitTime, labels.tpaAdminTime, med.pat_enc_csn_id_coded as med_enc,
+                med.order_time_jittered, med.start_time_jittered, med.end_time_jittered, med.med_description
+from
+(select 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  starr_datalake2018.order_proc as op, 
+  starr_datalake2018.adt as admit, 
+  starr_datalake2018.order_proc as opCT,
+  starr_datalake2018.order_med as om,
+  starr_datalake2018.mar as mar,
+  starr_datalake2018.adt as inpatient,
+  starr_datalake2018.encounter as enc
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime) labels
+LEFT JOIN
+  (select jc_uid, pat_enc_csn_id_coded, order_time_jittered, start_time_jittered, end_time_jittered, med_description
+  from starr_datalake2018.order_med
+  where med_description like '%ASPIRIN%') med
+ON med.jc_uid = labels.jc_uid
+WHERE med.order_time_jittered < labels.tpaAdminTime
+ORDER BY labels.jc_uid, labels.pat_enc_csn_id_coded, med.order_time_jittered
+
diff --git a/scripts/Moore_Stroke/Demographics.sql b/scripts/Moore_Stroke/Demographics.sql
new file mode 100644
index 00000000..bddfc8e0
--- /dev/null
+++ b/scripts/Moore_Stroke/Demographics.sql
@@ -0,0 +1,42 @@
+select distinct labels.jc_uid, labels.pat_enc_csn_id_coded, labels.emergencyAdmitTime, labels.tpaAdminTime, 
+                demo.birth_date_jittered, demo.gender, demo.canonical_race, demo.canonical_ethnicity
+from
+(select 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  starr_datalake2018.order_proc as op, 
+  starr_datalake2018.adt as admit, 
+  starr_datalake2018.order_proc as opCT,
+  starr_datalake2018.order_med as om,
+  starr_datalake2018.mar as mar,
+  starr_datalake2018.adt as inpatient,
+  starr_datalake2018.encounter as enc
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime) labels
+LEFT JOIN
+  (select rit_uid, birth_date_jittered, gender, canonical_race, canonical_ethnicity
+  from starr_datalake2018.demographic) demo
+ON demo.rit_uid = labels.jc_uid
+ORDER BY labels.jc_uid, labels.pat_enc_csn_id_coded
+
diff --git a/scripts/Moore_Stroke/ExampleAccessLogJoin.sql b/scripts/Moore_Stroke/ExampleAccessLogJoin.sql
new file mode 100644
index 00000000..09faf26d
--- /dev/null
+++ b/scripts/Moore_Stroke/ExampleAccessLogJoin.sql
@@ -0,0 +1,49 @@
+select mg.name, count(distinct jc_uid) as patientCount, count(*) accessCount
+from
+(
+  select datetime_diff(al.access_time_jittered, cohort.tpaAdminTime, MINUTE) as accessTimeDiff, 
+    *
+  from
+  (
+    select 
+      op.jc_uid, op.pat_enc_csn_id_coded,
+      admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+      min(opCT.order_inst_jittered) as ctHeadOrderTime,
+      om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+      min(mar.taken_time_jittered) as tpaAdminTime,
+      inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+    from 
+      starr_datalake2018.order_proc as op, 
+      starr_datalake2018.adt as admit, 
+      starr_datalake2018.order_proc as opCT,
+      starr_datalake2018.order_med as om,
+      starr_datalake2018.mar as mar,
+      starr_datalake2018.adt as inpatient,
+      starr_datalake2018.encounter as enc
+    where op.display_name like 'Patient on TPA%'
+      and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+      and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+      and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+      and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+      and op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded
+      and om.order_med_id_coded = mar.order_med_id_coded
+      and admit.event_type_c = 1 -- Admission
+      and admit.pat_class_c = '112' -- Emergency Services
+      and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+      and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+      and inpatient.pat_class_c = '126' -- Inpatient
+    group by 
+      op.jc_uid, op.pat_enc_csn_id_coded, 
+      admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+      om.med_description,
+      inpatient.pat_class
+    order by emergencyAdmitTime
+  ) as cohort
+  join `starr_access_log.shc_access_log_de` as al on cohort.jc_uid  = al.rit_uid 
+where datetime_diff(al.access_time_jittered, cohort.tpaAdminTime, MINUTE) >= -360 
+  and datetime_diff(al.access_time_jittered, cohort.tpaAdminTime, MINUTE) < 0
+) as cohortAccessLog
+join `starr_access_log.access_log_metric` as alm using (metric_id)
+join `starr_access_log.zc_metric_group` as mg using (metric_group_c)
+group by mg.name
+order by patientCount desc
\ No newline at end of file
diff --git a/scripts/Moore_Stroke/HistoryOfAfib.sql b/scripts/Moore_Stroke/HistoryOfAfib.sql
new file mode 100644
index 00000000..fc697169
--- /dev/null
+++ b/scripts/Moore_Stroke/HistoryOfAfib.sql
@@ -0,0 +1,43 @@
+select distinct labels.jc_uid, labels.pat_enc_csn_id_coded, diag.pat_enc_csn_id_coded as diag_enc, labels.emergencyAdmitTime, labels.tpaAdminTime,
+                diag.timestamp, diag.dx_name, diag.icd9, diag.icd10
+from
+(select 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  starr_datalake2018.order_proc as op, 
+  starr_datalake2018.adt as admit, 
+  starr_datalake2018.order_proc as opCT,
+  starr_datalake2018.order_med as om,
+  starr_datalake2018.mar as mar,
+  starr_datalake2018.adt as inpatient,
+  starr_datalake2018.encounter as enc
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime) labels
+LEFT JOIN
+  (select jc_uid, pat_enc_csn_id_coded, timestamp, dx_name, icd9, icd10
+  from starr_datalake2018.diagnosis_code
+  where dx_name like '%Atrial fibrillation%') diag
+ON diag.jc_uid = labels.jc_uid
+WHERE diag.timestamp < labels.emergencyAdmitTime and diag.pat_enc_csn_id_coded != labels.pat_enc_csn_id_coded
+ORDER BY labels.jc_uid, labels.pat_enc_csn_id_coded, diag.timestamp
diff --git a/scripts/Moore_Stroke/Labels.sql b/scripts/Moore_Stroke/Labels.sql
new file mode 100644
index 00000000..5488549b
--- /dev/null
+++ b/scripts/Moore_Stroke/Labels.sql
@@ -0,0 +1,31 @@
+select 
+  op.jc_uid, op.pat_enc_csn_id_coded, 
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  starr_datalake2018.order_proc as op, 
+  starr_datalake2018.adt as admit, 
+  starr_datalake2018.order_proc as opCT,
+  starr_datalake2018.order_med as om,
+  starr_datalake2018.mar as mar,
+  starr_datalake2018.adt as inpatient
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, 
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime
\ No newline at end of file
diff --git a/scripts/Moore_Stroke/Moore_Stroke_Exploration.ipynb b/scripts/Moore_Stroke/Moore_Stroke_Exploration.ipynb
new file mode 100644
index 00000000..482bab59
--- /dev/null
+++ b/scripts/Moore_Stroke/Moore_Stroke_Exploration.ipynb
@@ -0,0 +1,983 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()\n",
+    "from scipy.stats import pearsonr\n",
+    "from scipy.stats import ttest_ind\n",
+    "import pdb\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in Datasets\n",
+    "df_labels = pd.read_csv('moore_stroke_labels.csv')\n",
+    "df_demographics = pd.read_csv('moore_stroke_demographics.csv')\n",
+    "df_meds = pd.read_csv('moore_stroke_aspirin.csv')\n",
+    "df_comorbids = pd.read_csv('moore_stroke_afib.csv')\n",
+    "df_vitals = pd.read_csv('moore_stroke_vitals.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Engineering\n",
+    "5 csv files here generated from sql queries on starr_datalake2018 which finds patient admitted to the ER who subsequently received a CT exam and were placed on tPA - and joins them to a serires of tables that contain potential predictor variables.   \n",
+    "\n",
+    "### Demographics\n",
+    "* Age\n",
+    "* Sex\n",
+    "* Race (White, Black, Asian, Native American, Pacific Islander, Other)\n",
+    "* Ethnicity (Non-Hispanic, Hispanic/Latino)\n",
+    "\n",
+    "### Comorbities\n",
+    "ICD9/10 codes from prior patient encounters. Looking specifically for history of atrial fibrillation. \n",
+    "* Patient has history of afib (0 or 1)\n",
+    "* Days since last known afib dx. \n",
+    "\n",
+    "### Medications\n",
+    "Joined cohort to medication table to look at history of aspirin use. \n",
+    "* Aspirin ordered during current encounter (0 or 1)\n",
+    "* Aspirin ordered at any point in patient history\n",
+    "    * Obviously patients can get aspirin over the counter and we wouldn't know about it, and they could get it at another insitution and we wouldn't know about it\n",
+    "* Days since last aspirin order (given patient has history of taking aspirin)\n",
+    "\n",
+    "### Vitals\n",
+    "Joined to flowsheets and engineer presenting values for\n",
+    "* Systolic BP\n",
+    "* Diastolic BP\n",
+    "* Heart Rate\n",
+    "* Urine (Barely any datapoints)\n",
+    "* Temp\n",
+    "* Glasgow Coma Scale Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate label: Time from ER Admit to tPA administration \n",
+    "df_labels['tpaAdminTime'] = pd.to_datetime(df_labels['tpaAdminTime'])\n",
+    "df_labels['emergencyAdmitTime']  = pd.to_datetime(df_labels['emergencyAdmitTime'])\n",
+    "df_labels['time_to_tPA'] = df_labels['tpaAdminTime'] - df_labels['emergencyAdmitTime']\n",
+    "df_labels['time_to_tPA'] = df_labels['time_to_tPA'].dt.seconds/60. # convert timedelta to min\n",
+    "df_labels = df_labels[['jc_uid', 'pat_enc_csn_id_coded', 'time_to_tPA']] # Keep useful columns\n",
+    "\n",
+    "# Feature Engineer Demographics and Merge with Labels DataFrame\n",
+    "df_demographics['emergencyAdmitTime'] = pd.to_datetime(df_demographics['emergencyAdmitTime'])\n",
+    "df_demographics['birth_date_jittered']  = pd.to_datetime(df_demographics['birth_date_jittered'])\n",
+    "df_demographics['age'] = df_demographics['emergencyAdmitTime'] - df_demographics['birth_date_jittered']\n",
+    "df_demographics['age'] = df_demographics['age'].dt.days/365.\n",
+    "df_demographics = df_demographics[['jc_uid', 'pat_enc_csn_id_coded', 'age', 'gender', 'canonical_race', 'canonical_ethnicity']]\n",
+    "df_labels = pd.merge(df_labels, df_demographics, on=['jc_uid', 'pat_enc_csn_id_coded'], how='left')\n",
+    "\n",
+    "# Feature Engineer History of Afib and time since last afib dx and merge with labels.\n",
+    "# CSV's should already be ordered by timestamp of ICD code, so okay to take last. \n",
+    "df_comorbids = df_comorbids.groupby(['jc_uid', 'pat_enc_csn_id_coded']).last().reset_index()\n",
+    "df_comorbids['emergencyAdmitTime'] = pd.to_datetime(df_comorbids['emergencyAdmitTime'])\n",
+    "df_comorbids['timestamp'] = pd.to_datetime(df_comorbids['timestamp'])\n",
+    "df_comorbids['days_since_afib_dx'] = df_comorbids['emergencyAdmitTime'] - df_comorbids['timestamp']\n",
+    "df_comorbids['days_since_afib_dx'] = df_comorbids['days_since_afib_dx'].dt.days\n",
+    "df_comorbids = df_comorbids[['jc_uid', 'pat_enc_csn_id_coded', 'days_since_afib_dx']]\n",
+    "df_labels = pd.merge(df_labels, df_comorbids, on=['jc_uid', 'pat_enc_csn_id_coded'], how='left')\n",
+    "df_labels['history_of_afib'] = df_labels['days_since_afib_dx'].notna().astype(int)\n",
+    "\n",
+    "# Feature Engineer Aspirin Ordered During ER admission\n",
+    "df_meds_admit = df_meds[df_meds['pat_enc_csn_id_coded'] == df_meds['med_enc']]\n",
+    "df_meds_admit = df_meds_admit[['jc_uid', 'pat_enc_csn_id_coded', 'med_description']].groupby(['jc_uid', 'pat_enc_csn_id_coded']).first().reset_index() # Don't care if multiple kinds of aspsirin ordered here so okay to just take first arbitrarily \n",
+    "df_labels = pd.merge(df_labels, df_meds_admit, on=['jc_uid', 'pat_enc_csn_id_coded'], how='left')\n",
+    "df_labels['aspirin_ord_during_admit'] = df_labels['med_description'].notna().astype(int)\n",
+    "df_labels = df_labels.drop('med_description', axis=1)\n",
+    "\n",
+    "# Feature Engineer History of Aspirin and merge to labels\n",
+    "df_meds = df_meds[df_meds['pat_enc_csn_id_coded'] != df_meds['med_enc']]\n",
+    "df_meds = df_meds.groupby(['jc_uid', 'pat_enc_csn_id_coded']).last().reset_index() # again csv should be ordered by timestamp, so just take last\n",
+    "df_meds['emergencyAdmitTime'] = pd.to_datetime(df_meds['emergencyAdmitTime'])\n",
+    "df_meds['order_time_jittered'] = pd.to_datetime(df_meds['order_time_jittered'])\n",
+    "df_meds['days_since_last_aspirin_order'] = df_meds['emergencyAdmitTime'] - df_meds['order_time_jittered']\n",
+    "df_meds['days_since_last_aspirin_order'] = df_meds['days_since_last_aspirin_order'].dt.days\n",
+    "df_meds = df_meds[['jc_uid', 'pat_enc_csn_id_coded', 'days_since_last_aspirin_order']]\n",
+    "df_labels = pd.merge(df_labels, df_meds, on=['jc_uid', 'pat_enc_csn_id_coded'], how='left')\n",
+    "df_labels['prior_asprin_order'] = df_labels['days_since_last_aspirin_order'].notna().astype(int)\n",
+    "\n",
+    "# Feature Engineer Presenting vitals and merge to labels\n",
+    "df_vitals = df_vitals.groupby(['jc_uid', 'pat_enc_csn_id_coded', 'row_disp_name']).first().reset_index() # Get first of each Vital Type Upon each Admission \n",
+    "df_vitals = df_vitals[['pat_enc_csn_id_coded', 'row_disp_name', 'meas_value']]\n",
+    "df_vitals = df_vitals.pivot(index='pat_enc_csn_id_coded', columns = 'row_disp_name', values='meas_value').reset_index() # Like spread in R\n",
+    "df_labels = pd.merge(df_labels, df_vitals, on=['pat_enc_csn_id_coded'], how='left')\n",
+    "\n",
+    "# Convert BP To Systolic and Diastolic BP Features\n",
+    "def get_diastolic(x):\n",
+    "    if '/' in x:\n",
+    "        x = x.split('/')[0]\n",
+    "    else:\n",
+    "        x = None\n",
+    "    return x\n",
+    "\n",
+    "def get_systolic(x):\n",
+    "    if '/' in x: \n",
+    "        x = x.split('/')[1].split()[0]\n",
+    "    else:\n",
+    "        x = None\n",
+    "    return x\n",
+    "\n",
+    "df_labels['Systolic_BP'] = df_labels['BP'].astype(str).map(get_diastolic).astype(float)\n",
+    "df_labels['Diastolic_BP'] = df_labels['BP'].astype(str).map(get_systolic).astype(float)\n",
+    "\n",
+    "# Convert Urine to Float\n",
+    "df_labels['Urine'] = df_labels['Urine'].astype(str).map(lambda x : x.split()[0]).astype(float)\n",
+    "\n",
+    "# Convert Temperature to Float (Some temperatures given in F and C so Just take F)\n",
+    "df_labels['Temp'] = df_labels['Temp'].astype(str).map(lambda x : x.split()[0]).astype(float)\n",
+    "\n",
+    "# Convert Glasgow Coma  and Heart Rate to Float\n",
+    "df_labels['Glasgow Coma Scale Score'] = df_labels['Glasgow Coma Scale Score'].astype(float)\n",
+    "df_labels['Heart Rate'] = df_labels['Heart Rate'].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Distribution of Time From ER Admit to tPA Administration\n",
+    "Here we show the full distribution from the sql query cohort, and note that there exist a few outliers where time to tPA is much greater than the contraindication limit of 4.5 hours since start of stroke. We filter our data at this 4.5 hour cutoff, and the new distribution is displayed on the right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Time to tPA administration outliers removed')"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style=\"whitegrid\", font_scale=2.0)\n",
+    "times = df_labels['time_to_tPA'].values\n",
+    "fig = plt.figure(figsize=(16, 8))\n",
+    "ax1 = plt.subplot(1, 2, 1)\n",
+    "ax1 = sns.distplot(times)\n",
+    "ax1.set_title('Time to tPA administration Full')\n",
+    "\n",
+    "df_labels = df_labels[df_labels['time_to_tPA'] < 60*4.5] # time_to_tPA should be less than 4.5 hours - filters outliers - how do we explain \n",
+    "ax2 = plt.subplot(1, 2, 2)\n",
+    "ax2 = sns.distplot(df_labels['time_to_tPA'].values)\n",
+    "ax2.set_title('Time to tPA administration outliers removed')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lets Create Some Plots of Time to tPA by Demographics\n",
+    "\n",
+    "Lets look at age, sex, and ethnicity. Two tailed T-test used to get pvals for sex and ethnicity.  Regression line fit through age, pearsonr and corresponding pvalue shown in fig. Sex and ethnicity significant with p < 0.05.  Easy to make up stories post hoc... but added language barrier prevents consent of tPA admin?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create figs\n",
+    "sns.set(style=\"whitegrid\", font_scale=2.0)\n",
+    "fig1 = plt.figure(figsize=(24, 8))\n",
+    "fig1.suptitle('ER Admit to tPA Administration Stratfied by Demographics', fontsize=30)\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1, 3, 1)\n",
+    "ax1 = sns.boxplot(x=\"gender\", y=\"time_to_tPA\", data=df_labels)\n",
+    "df_male = df_labels[df_labels['gender'] == 'Male']\n",
+    "df_female = df_labels[df_labels['gender'] == 'Female']\n",
+    "t, p = ttest_ind(df_male['time_to_tPA'], df_female['time_to_tPA'])\n",
+    "ax1.text(0.0001, 180,'p = %.2f' % p, fontsize=20)\n",
+    "ax1.set_xlabel('Sex')\n",
+    "ax1.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "# ax1.set_xticklabels(ax1.get_xticklabels(),rotation=45)\n",
+    "\n",
+    "ax2 = fig1.add_subplot(1, 3, 2)\n",
+    "ax2 = sns.regplot(x=\"age\", y=\"time_to_tPA\", data=df_labels)\n",
+    "pearson, pval = pearsonr(df_labels['age'], df_labels['time_to_tPA'])\n",
+    "ax2.text(30, 180,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax2.set_xlabel('Age')\n",
+    "ax2.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "\n",
+    "# Ethnicity has 3 unique vals: Non-Hispanic, Hispanci, and Unknown. Filter out unknowns\n",
+    "df_no_unk = df_labels[df_labels['canonical_ethnicity'] != 'Unknown']\n",
+    "ax3 = fig1.add_subplot(1, 3, 3)\n",
+    "ax3 = sns.boxplot(x=\"canonical_ethnicity\", y=\"time_to_tPA\", data=df_no_unk)\n",
+    "no_hisp = df_no_unk[df_no_unk['canonical_ethnicity'] == 'Non-Hispanic']\n",
+    "hisp = df_no_unk[df_no_unk['canonical_ethnicity'] == 'Hispanic/Latino']\n",
+    "t, p = ttest_ind(no_hisp['time_to_tPA'], hisp['time_to_tPA'])\n",
+    "ax3.text(0.1, 180,'p = %.2f' % p, fontsize=20)\n",
+    "ax3.set_xlabel('Ethnicity')\n",
+    "ax3.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "fig1.subplots_adjust(wspace=0.25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now Lets look at Comorbities (Specifically History of Afib)\n",
+    "\n",
+    "Will stratify by history of Afib and perform two tailed ttest to get pvalue.  Plot on left.  No significant difference.  Plot on right shows time till tPA admin by days since last dx of afib given there is a prior diagnosis of afib.  Not significant at p < 0.05 but downward trend. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create figs\n",
+    "sns.set(style=\"whitegrid\", font_scale=2.0)\n",
+    "fig1 = plt.figure(figsize=(16, 8))\n",
+    "fig1.suptitle('ER Admit to tPA Administration Stratified by History of Atrial Fibrillation', fontsize=30)\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1, 2, 1)\n",
+    "ax1 = sns.boxplot(x=\"history_of_afib\", y=\"time_to_tPA\", data=df_labels)\n",
+    "df_afib = df_labels[df_labels['history_of_afib'] == 1]\n",
+    "df_no_afib = df_labels[df_labels['history_of_afib'] ==  0]\n",
+    "t, p = ttest_ind(df_afib['time_to_tPA'], df_no_afib['time_to_tPA'])\n",
+    "ax1.text(1, 180,'p = %.2f' % p, fontsize=20)\n",
+    "ax1.set_xlabel('History of Atrial Fibrillation')\n",
+    "ax1.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "# ax1.set_xticklabels(ax1.get_xticklabels(),rotation=45)\n",
+    "\n",
+    "# Of the the people who had atrial fibrillation, does time since last atrial fibrillation matter?\n",
+    "ax2 = fig1.add_subplot(1, 2, 2)\n",
+    "ax2 = sns.regplot(x=\"days_since_afib_dx\", y=\"time_to_tPA\", data=df_afib)\n",
+    "pearson, pval = pearsonr(df_afib['days_since_afib_dx'], df_afib['time_to_tPA'])\n",
+    "ax2.text(1000, 148,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax2.set_xlabel('Days Since Last Afib Dx')\n",
+    "ax2.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "fig1.subplots_adjust(wspace=0.25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now Lets look at Prior Meds (Specifically Aspirin)\n",
+    "\n",
+    "First plot - time till tPA treatment stratified by whether the presenting patient was given Aspirin.  Longer delay if given aspirin.  Middle plot shows time till tPA treamtent stratified by whether the presenting patient had been given aspirin in the past. Not significant at p < 0.05, though the pvalue is low.  Diff in means greater than diff in medians - suggesting tails are playing a key role in why the pvalue is relatively small. Right plot shows time till tPA by time since last known aspirin in days.  No trend. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create figs\n",
+    "sns.set(style=\"whitegrid\", font_scale=2.0)\n",
+    "fig1 = plt.figure(figsize=(24, 8))\n",
+    "fig1.suptitle('ER Admit to tPA Administration Stratfied by Aspirin Delivery', fontsize=30)\n",
+    "\n",
+    "ax1 = fig1.add_subplot(1, 3, 1)\n",
+    "ax1 = sns.boxplot(x=\"aspirin_ord_during_admit\", y=\"time_to_tPA\", data=df_labels)\n",
+    "df_asp_at_admit = df_labels[df_labels['aspirin_ord_during_admit'] == 1]\n",
+    "df_no_asp_at_admit = df_labels[df_labels['aspirin_ord_during_admit'] == 0]\n",
+    "t, p = ttest_ind(df_asp_at_admit['time_to_tPA'], df_no_asp_at_admit['time_to_tPA'])\n",
+    "ax1.text(0.1, 180,'p = %.5f' % p, fontsize=20)\n",
+    "ax1.set_xlabel('Aspirin Ordered Between ER Admit and tPA Admin')\n",
+    "ax1.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "# ax1.set_xticklabels(ax1.get_xticklabels(),rotation=45)\n",
+    "\n",
+    "ax2 = fig1.add_subplot(1, 3, 2)\n",
+    "ax2 = sns.boxplot(x=\"prior_asprin_order\", y=\"time_to_tPA\", data=df_labels)\n",
+    "df_asp = df_labels[df_labels['prior_asprin_order'] == 1]\n",
+    "df_no_asp = df_labels[df_labels['prior_asprin_order'] == 0]\n",
+    "diff_in_mean =  np.mean(df_no_asp['time_to_tPA']) - np.mean(df_asp['time_to_tPA'])\n",
+    "t, p = ttest_ind(df_asp['time_to_tPA'], df_no_asp['time_to_tPA'])\n",
+    "ax2.text(0.05, 175,'p = %.2f\\nNo_Asp_bar - Asp_bar = %.2f' % (p, diff_in_mean), fontsize=20)\n",
+    "ax2.set_xlabel('Known History of Taking Aspirin')\n",
+    "ax2.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "# ax1.set_xticklabels(ax1.get_xticklabels(),rotation=45)\n",
+    "\n",
+    "ax3 = fig1.add_subplot(1, 3, 3)\n",
+    "ax3 = sns.regplot(x=\"days_since_last_aspirin_order\", y=\"time_to_tPA\", data=df_asp)\n",
+    "pearson, pval = pearsonr(df_asp['days_since_last_aspirin_order'], df_asp['time_to_tPA'])\n",
+    "ax3.text(1500, 155,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax3.set_xlabel('Days Since Last Aspirin Order')\n",
+    "ax3.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "\n",
+    "fig1.subplots_adjust(wspace=0.25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finally Looking at Vitals (Blood Pressure, Glasgow Coma, Heart Rate, Temp, Urine)\n",
+    "\n",
+    "Nothing but glasgow coma score is significantly associated with time from admit until tPA administration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'ER Admit to tPA Admin (Min)')"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x1152 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create figs\n",
+    "sns.set(style=\"whitegrid\", font_scale=2.0)\n",
+    "fig1 = plt.figure(figsize=(24, 16))\n",
+    "fig1.suptitle('ER Admit to tPA Administration Stratfied by Presenting Vitals', fontsize=30)\n",
+    "\n",
+    "df_bp_s = df_labels[df_labels['Systolic_BP'].notna()]\n",
+    "ax1 = fig1.add_subplot(2, 3, 1)\n",
+    "ax1 = sns.regplot(x=\"Systolic_BP\", y=\"time_to_tPA\", data=df_bp_s)\n",
+    "pearson, pval = pearsonr(df_bp_s['Systolic_BP'], df_bp_s['time_to_tPA'])\n",
+    "ax1.text(100, 170,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax1.set_xlabel('Presenting Systolic BP')\n",
+    "ax1.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "df_bp_d = df_labels[df_labels['Diastolic_BP'].notna()]\n",
+    "ax2 = fig1.add_subplot(2, 3, 2)\n",
+    "ax2 = sns.regplot(x=\"Diastolic_BP\", y=\"time_to_tPA\", data=df_bp_d)\n",
+    "pearson, pval = pearsonr(df_bp_d['Diastolic_BP'], df_bp_d['time_to_tPA'])\n",
+    "ax2.text(100, 170,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax2.set_xlabel('Presenting Diastolic BP')\n",
+    "ax2.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "df_hr = df_labels[df_labels['Heart Rate'].notna()]\n",
+    "ax3 = fig1.add_subplot(2, 3, 3)\n",
+    "ax3 = sns.regplot(x=\"Heart Rate\", y=\"time_to_tPA\", data=df_hr)\n",
+    "pearson, pval = pearsonr(df_hr['Heart Rate'], df_hr['time_to_tPA'])\n",
+    "ax3.text(75, 170,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax3.set_xlabel('Presenting Heart Rate')\n",
+    "ax3.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "df_temp = df_labels[df_labels['Temp'].notna()]\n",
+    "ax4 = fig1.add_subplot(2, 3, 4)\n",
+    "ax4 = sns.regplot(x=\"Temp\", y=\"time_to_tPA\", data=df_temp)\n",
+    "pearson, pval = pearsonr(df_temp['Temp'], df_temp['time_to_tPA'])\n",
+    "ax4.text(98, 170,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax4.set_xlabel('Presenting Temperature')\n",
+    "ax4.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "df_coma = df_labels[df_labels['Glasgow Coma Scale Score'].notna()]\n",
+    "ax5 = fig1.add_subplot(2, 3, 5)\n",
+    "ax5 = sns.regplot(x=\"Glasgow Coma Scale Score\", y=\"time_to_tPA\", data=df_coma)\n",
+    "pearson, pval = pearsonr(df_coma['Glasgow Coma Scale Score'], df_coma['time_to_tPA'])\n",
+    "ax5.text(7, 170,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax5.set_xlabel('Presenting Glasgow Coma Scale Score')\n",
+    "ax5.set_ylabel('ER Admit to tPA Admin (Min)')\n",
+    "\n",
+    "df_urine = df_labels[df_labels['Urine'].notna()]\n",
+    "ax6 = fig1.add_subplot(2, 3, 6)\n",
+    "ax6 = sns.regplot(x='Urine', y=\"time_to_tPA\", data=df_urine)\n",
+    "pearson, pval = pearsonr(df_urine['Urine'], df_urine['time_to_tPA'])\n",
+    "ax6.text(300, 170,'R = %.2f, p = %.2f' % (pearson, pval), fontsize=20)\n",
+    "ax6.set_xlabel('Presenting Urine')\n",
+    "ax6.set_ylabel('ER Admit to tPA Admin (Min)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Do a Glasgow Coma Scale Score JointPlot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_coma = df_labels[df_labels['Glasgow Coma Scale Score'].notna()]\n",
+    "ax5 = sns.jointplot(x=\"Glasgow Coma Scale Score\", y=\"time_to_tPA\", data=df_coma, kind='reg', height=12)\n",
+    "pearson, pval = pearsonr(df_coma['Glasgow Coma Scale Score'], df_coma['time_to_tPA'])\n",
+    "# ax5.text(7, 170,'R = %.2f, p = %.3f' % (pearson, pval), fontsize=20)\n",
+    "# ax5.set_xlabel('Presenting Glasgow Coma Scale Score')\n",
+    "# ax5.set_ylabel('ER Admit to tPA Admin (Min)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit Multiple Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inpute features we want in our model that need it\n",
+    "df_labels[['Glasgow Coma Scale Score', 'Heart Rate', 'Temp', 'Urine', 'Systolic_BP', 'Diastolic_BP']] = df_labels[['Glasgow Coma Scale Score', 'Heart Rate', 'Temp', 'Urine', 'Systolic_BP', 'Diastolic_BP']].fillna(df_labels[['Glasgow Coma Scale Score', 'Heart Rate', 'Temp', 'Urine', 'Systolic_BP', 'Diastolic_BP']].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>       <td>time_to_tPA</td>   <th>  R-squared:         </th> <td>   0.184</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.124</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3.056</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Mon, 02 Sep 2019</td> <th>  Prob (F-statistic):</th> <td>4.97e-05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>20:42:23</td>     <th>  Log-Likelihood:    </th> <td> -1311.4</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   263</td>      <th>  AIC:               </th> <td>   2661.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   244</td>      <th>  BIC:               </th> <td>   2729.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    18</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "                     <td></td>                       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>                              <td> -661.6337</td> <td>  432.557</td> <td>   -1.530</td> <td> 0.127</td> <td>-1513.655</td> <td>  190.388</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(gender)[T.Male]</th>                      <td>  -10.6953</td> <td>    4.763</td> <td>   -2.246</td> <td> 0.026</td> <td>  -20.077</td> <td>   -1.314</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_ethnicity)[T.Non-Hispanic]</th> <td>  -13.2908</td> <td>   11.811</td> <td>   -1.125</td> <td> 0.262</td> <td>  -36.555</td> <td>    9.973</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_ethnicity)[T.Unknown]</th>      <td>   -4.1301</td> <td>   14.837</td> <td>   -0.278</td> <td> 0.781</td> <td>  -33.356</td> <td>   25.096</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_race)[T.Black]</th>             <td>    3.7388</td> <td>   11.977</td> <td>    0.312</td> <td> 0.755</td> <td>  -19.853</td> <td>   27.331</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_race)[T.Native American]</th>   <td>   22.7346</td> <td>   40.067</td> <td>    0.567</td> <td> 0.571</td> <td>  -56.186</td> <td>  101.655</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_race)[T.Other]</th>             <td>   12.1540</td> <td>   12.778</td> <td>    0.951</td> <td> 0.342</td> <td>  -13.014</td> <td>   37.322</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_race)[T.Pacific Islander]</th>  <td>   -3.2911</td> <td>   20.403</td> <td>   -0.161</td> <td> 0.872</td> <td>  -43.480</td> <td>   36.898</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_race)[T.Unknown]</th>           <td>   -4.1301</td> <td>   14.837</td> <td>   -0.278</td> <td> 0.781</td> <td>  -33.356</td> <td>   25.096</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>C(canonical_race)[T.White]</th>             <td>   11.0284</td> <td>    7.779</td> <td>    1.418</td> <td> 0.158</td> <td>   -4.294</td> <td>   26.351</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>age</th>                                    <td>   -0.0598</td> <td>    0.191</td> <td>   -0.314</td> <td> 0.754</td> <td>   -0.435</td> <td>    0.316</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>history_of_afib</th>                        <td>   -6.1427</td> <td>    6.305</td> <td>   -0.974</td> <td> 0.331</td> <td>  -18.562</td> <td>    6.277</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>aspirin_ord_during_admit</th>               <td>   39.2856</td> <td>   10.589</td> <td>    3.710</td> <td> 0.000</td> <td>   18.428</td> <td>   60.144</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>prior_asprin_order</th>                     <td>   -6.1045</td> <td>    5.166</td> <td>   -1.182</td> <td> 0.238</td> <td>  -16.280</td> <td>    4.071</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Q(\"Glasgow Coma Scale Score\")</th>          <td>    3.3302</td> <td>    1.418</td> <td>    2.349</td> <td> 0.020</td> <td>    0.538</td> <td>    6.123</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Q(\"Heart Rate\")</th>                        <td>   -0.2762</td> <td>    0.239</td> <td>   -1.155</td> <td> 0.249</td> <td>   -0.747</td> <td>    0.195</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temp</th>                                   <td>    7.8563</td> <td>    4.368</td> <td>    1.799</td> <td> 0.073</td> <td>   -0.748</td> <td>   16.460</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Urine</th>                                  <td>   -0.2254</td> <td>    0.105</td> <td>   -2.147</td> <td> 0.033</td> <td>   -0.432</td> <td>   -0.019</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Systolic_BP</th>                            <td>    0.1399</td> <td>    0.096</td> <td>    1.458</td> <td> 0.146</td> <td>   -0.049</td> <td>    0.329</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Diastolic_BP</th>                           <td>   -0.0427</td> <td>    0.144</td> <td>   -0.296</td> <td> 0.767</td> <td>   -0.326</td> <td>    0.241</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>88.266</td> <th>  Durbin-Watson:     </th> <td>   1.896</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 260.098</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 1.483</td> <th>  Prob(JB):          </th> <td>3.32e-57</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.865</td> <th>  Cond. No.          </th> <td>2.07e+19</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.07e-31. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:            time_to_tPA   R-squared:                       0.184\n",
+       "Model:                            OLS   Adj. R-squared:                  0.124\n",
+       "Method:                 Least Squares   F-statistic:                     3.056\n",
+       "Date:                Mon, 02 Sep 2019   Prob (F-statistic):           4.97e-05\n",
+       "Time:                        20:42:23   Log-Likelihood:                -1311.4\n",
+       "No. Observations:                 263   AIC:                             2661.\n",
+       "Df Residuals:                     244   BIC:                             2729.\n",
+       "Df Model:                          18                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==========================================================================================================\n",
+       "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "----------------------------------------------------------------------------------------------------------\n",
+       "Intercept                               -661.6337    432.557     -1.530      0.127   -1513.655     190.388\n",
+       "C(gender)[T.Male]                        -10.6953      4.763     -2.246      0.026     -20.077      -1.314\n",
+       "C(canonical_ethnicity)[T.Non-Hispanic]   -13.2908     11.811     -1.125      0.262     -36.555       9.973\n",
+       "C(canonical_ethnicity)[T.Unknown]         -4.1301     14.837     -0.278      0.781     -33.356      25.096\n",
+       "C(canonical_race)[T.Black]                 3.7388     11.977      0.312      0.755     -19.853      27.331\n",
+       "C(canonical_race)[T.Native American]      22.7346     40.067      0.567      0.571     -56.186     101.655\n",
+       "C(canonical_race)[T.Other]                12.1540     12.778      0.951      0.342     -13.014      37.322\n",
+       "C(canonical_race)[T.Pacific Islander]     -3.2911     20.403     -0.161      0.872     -43.480      36.898\n",
+       "C(canonical_race)[T.Unknown]              -4.1301     14.837     -0.278      0.781     -33.356      25.096\n",
+       "C(canonical_race)[T.White]                11.0284      7.779      1.418      0.158      -4.294      26.351\n",
+       "age                                       -0.0598      0.191     -0.314      0.754      -0.435       0.316\n",
+       "history_of_afib                           -6.1427      6.305     -0.974      0.331     -18.562       6.277\n",
+       "aspirin_ord_during_admit                  39.2856     10.589      3.710      0.000      18.428      60.144\n",
+       "prior_asprin_order                        -6.1045      5.166     -1.182      0.238     -16.280       4.071\n",
+       "Q(\"Glasgow Coma Scale Score\")              3.3302      1.418      2.349      0.020       0.538       6.123\n",
+       "Q(\"Heart Rate\")                           -0.2762      0.239     -1.155      0.249      -0.747       0.195\n",
+       "Temp                                       7.8563      4.368      1.799      0.073      -0.748      16.460\n",
+       "Urine                                     -0.2254      0.105     -2.147      0.033      -0.432      -0.019\n",
+       "Systolic_BP                                0.1399      0.096      1.458      0.146      -0.049       0.329\n",
+       "Diastolic_BP                              -0.0427      0.144     -0.296      0.767      -0.326       0.241\n",
+       "==============================================================================\n",
+       "Omnibus:                       88.266   Durbin-Watson:                   1.896\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              260.098\n",
+       "Skew:                           1.483   Prob(JB):                     3.32e-57\n",
+       "Kurtosis:                       6.865   Cond. No.                     2.07e+19\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The smallest eigenvalue is 1.07e-31. This might indicate that there are\n",
+       "strong multicollinearity problems or that the design matrix is singular.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.regression.linear_model import OLS\n",
+    "formula = \"\"\"time_to_tPA ~ age + C(gender) + C(canonical_ethnicity) + C(canonical_race) + history_of_afib + aspirin_ord_during_admit + prior_asprin_order + Q(\"Glasgow Coma Scale Score\") + Q(\"Heart Rate\") + Temp + Urine + Systolic_BP + Diastolic_BP \"\"\"\n",
+    "model = OLS.from_formula(formula, data=df_labels)\n",
+    "results = model.fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvals = results.pvalues\n",
+    "coeff = results.params\n",
+    "conf_lower = results.conf_int()[0]\n",
+    "conf_higher = results.conf_int()[1]\n",
+    "\n",
+    "results_df = pd.DataFrame({\"pvals\":pvals,\n",
+    "                           \"coeff\":coeff,\n",
+    "                           \"conf_lower\":conf_lower,\n",
+    "                           \"conf_higher\":conf_higher\n",
+    "                            })\n",
+    "\n",
+    "#Reordering...\n",
+    "results_df = results_df[[\"coeff\",\"pvals\",\"conf_lower\",\"conf_higher\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coeff</th>\n",
+       "      <th>pvals</th>\n",
+       "      <th>conf_lower</th>\n",
+       "      <th>conf_higher</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>aspirin_ord_during_admit</th>\n",
+       "      <td>39.285583</td>\n",
+       "      <td>0.000257</td>\n",
+       "      <td>18.427643</td>\n",
+       "      <td>60.143522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q(\"Glasgow Coma Scale Score\")</th>\n",
+       "      <td>3.330183</td>\n",
+       "      <td>0.019617</td>\n",
+       "      <td>0.537815</td>\n",
+       "      <td>6.122551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(gender)[T.Male]</th>\n",
+       "      <td>-10.695302</td>\n",
+       "      <td>0.025623</td>\n",
+       "      <td>-20.076554</td>\n",
+       "      <td>-1.314050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urine</th>\n",
+       "      <td>-0.225429</td>\n",
+       "      <td>0.032741</td>\n",
+       "      <td>-0.432200</td>\n",
+       "      <td>-0.018658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Temp</th>\n",
+       "      <td>7.856344</td>\n",
+       "      <td>0.073318</td>\n",
+       "      <td>-0.747536</td>\n",
+       "      <td>16.460225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Intercept</th>\n",
+       "      <td>-661.633724</td>\n",
+       "      <td>0.127414</td>\n",
+       "      <td>-1513.655217</td>\n",
+       "      <td>190.387768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Systolic_BP</th>\n",
+       "      <td>0.139887</td>\n",
+       "      <td>0.146089</td>\n",
+       "      <td>-0.049081</td>\n",
+       "      <td>0.328855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_race)[T.White]</th>\n",
+       "      <td>11.028365</td>\n",
+       "      <td>0.157552</td>\n",
+       "      <td>-4.294225</td>\n",
+       "      <td>26.350955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prior_asprin_order</th>\n",
+       "      <td>-6.104517</td>\n",
+       "      <td>0.238473</td>\n",
+       "      <td>-16.279868</td>\n",
+       "      <td>4.070834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q(\"Heart Rate\")</th>\n",
+       "      <td>-0.276235</td>\n",
+       "      <td>0.249337</td>\n",
+       "      <td>-0.747442</td>\n",
+       "      <td>0.194972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_ethnicity)[T.Non-Hispanic]</th>\n",
+       "      <td>-13.290811</td>\n",
+       "      <td>0.261558</td>\n",
+       "      <td>-36.554693</td>\n",
+       "      <td>9.973070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>history_of_afib</th>\n",
+       "      <td>-6.142740</td>\n",
+       "      <td>0.330902</td>\n",
+       "      <td>-18.562215</td>\n",
+       "      <td>6.276735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_race)[T.Other]</th>\n",
+       "      <td>12.154034</td>\n",
+       "      <td>0.342442</td>\n",
+       "      <td>-13.014266</td>\n",
+       "      <td>37.322333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_race)[T.Native American]</th>\n",
+       "      <td>22.734573</td>\n",
+       "      <td>0.570950</td>\n",
+       "      <td>-56.185790</td>\n",
+       "      <td>101.654936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>-0.059781</td>\n",
+       "      <td>0.754162</td>\n",
+       "      <td>-0.435379</td>\n",
+       "      <td>0.315817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_race)[T.Black]</th>\n",
+       "      <td>3.738783</td>\n",
+       "      <td>0.755189</td>\n",
+       "      <td>-19.853336</td>\n",
+       "      <td>27.330901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diastolic_BP</th>\n",
+       "      <td>-0.042706</td>\n",
+       "      <td>0.767160</td>\n",
+       "      <td>-0.326491</td>\n",
+       "      <td>0.241078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_race)[T.Unknown]</th>\n",
+       "      <td>-4.130062</td>\n",
+       "      <td>0.780976</td>\n",
+       "      <td>-33.355829</td>\n",
+       "      <td>25.095706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_ethnicity)[T.Unknown]</th>\n",
+       "      <td>-4.130062</td>\n",
+       "      <td>0.780976</td>\n",
+       "      <td>-33.355829</td>\n",
+       "      <td>25.095706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C(canonical_race)[T.Pacific Islander]</th>\n",
+       "      <td>-3.291068</td>\n",
+       "      <td>0.871990</td>\n",
+       "      <td>-43.480000</td>\n",
+       "      <td>36.897863</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             coeff     pvals   conf_lower  \\\n",
+       "aspirin_ord_during_admit                 39.285583  0.000257    18.427643   \n",
+       "Q(\"Glasgow Coma Scale Score\")             3.330183  0.019617     0.537815   \n",
+       "C(gender)[T.Male]                       -10.695302  0.025623   -20.076554   \n",
+       "Urine                                    -0.225429  0.032741    -0.432200   \n",
+       "Temp                                      7.856344  0.073318    -0.747536   \n",
+       "Intercept                              -661.633724  0.127414 -1513.655217   \n",
+       "Systolic_BP                               0.139887  0.146089    -0.049081   \n",
+       "C(canonical_race)[T.White]               11.028365  0.157552    -4.294225   \n",
+       "prior_asprin_order                       -6.104517  0.238473   -16.279868   \n",
+       "Q(\"Heart Rate\")                          -0.276235  0.249337    -0.747442   \n",
+       "C(canonical_ethnicity)[T.Non-Hispanic]  -13.290811  0.261558   -36.554693   \n",
+       "history_of_afib                          -6.142740  0.330902   -18.562215   \n",
+       "C(canonical_race)[T.Other]               12.154034  0.342442   -13.014266   \n",
+       "C(canonical_race)[T.Native American]     22.734573  0.570950   -56.185790   \n",
+       "age                                      -0.059781  0.754162    -0.435379   \n",
+       "C(canonical_race)[T.Black]                3.738783  0.755189   -19.853336   \n",
+       "Diastolic_BP                             -0.042706  0.767160    -0.326491   \n",
+       "C(canonical_race)[T.Unknown]             -4.130062  0.780976   -33.355829   \n",
+       "C(canonical_ethnicity)[T.Unknown]        -4.130062  0.780976   -33.355829   \n",
+       "C(canonical_race)[T.Pacific Islander]    -3.291068  0.871990   -43.480000   \n",
+       "\n",
+       "                                        conf_higher  \n",
+       "aspirin_ord_during_admit                  60.143522  \n",
+       "Q(\"Glasgow Coma Scale Score\")              6.122551  \n",
+       "C(gender)[T.Male]                         -1.314050  \n",
+       "Urine                                     -0.018658  \n",
+       "Temp                                      16.460225  \n",
+       "Intercept                                190.387768  \n",
+       "Systolic_BP                                0.328855  \n",
+       "C(canonical_race)[T.White]                26.350955  \n",
+       "prior_asprin_order                         4.070834  \n",
+       "Q(\"Heart Rate\")                            0.194972  \n",
+       "C(canonical_ethnicity)[T.Non-Hispanic]     9.973070  \n",
+       "history_of_afib                            6.276735  \n",
+       "C(canonical_race)[T.Other]                37.322333  \n",
+       "C(canonical_race)[T.Native American]     101.654936  \n",
+       "age                                        0.315817  \n",
+       "C(canonical_race)[T.Black]                27.330901  \n",
+       "Diastolic_BP                               0.241078  \n",
+       "C(canonical_race)[T.Unknown]              25.095706  \n",
+       "C(canonical_ethnicity)[T.Unknown]         25.095706  \n",
+       "C(canonical_race)[T.Pacific Islander]     36.897863  "
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df.sort_values(by=\"pvals\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/Moore_Stroke/Vitals.sql b/scripts/Moore_Stroke/Vitals.sql
new file mode 100644
index 00000000..c354a0ae
--- /dev/null
+++ b/scripts/Moore_Stroke/Vitals.sql
@@ -0,0 +1,43 @@
+select distinct labels.jc_uid, labels.pat_enc_csn_id_coded, flow.inpatient_data_id_coded, labels.emergencyAdmitTime, flow.recorded_time_jittered, labels.tpaAdminTime, 
+       flow.row_disp_name, flow.meas_value
+from
+(select 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered as emergencyAdmitTime, 
+  min(opCT.order_inst_jittered) as ctHeadOrderTime,
+  om.med_description as tpaDescription, min(om.order_time_jittered) as tpaOrderTime,
+  min(mar.taken_time_jittered) as tpaAdminTime,
+  inpatient.pat_class as inptClass, min(inpatient.effective_time_jittered) as inpatientAdmitTime
+from 
+  starr_datalake2018.order_proc as op, 
+  starr_datalake2018.adt as admit, 
+  starr_datalake2018.order_proc as opCT,
+  starr_datalake2018.order_med as om,
+  starr_datalake2018.mar as mar,
+  starr_datalake2018.adt as inpatient,
+  starr_datalake2018.encounter as enc
+where op.display_name like 'Patient on TPA%'
+  and op.pat_enc_csn_id_coded = admit.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = opCT.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = inpatient.pat_enc_csn_id_coded
+  and op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded
+  and om.order_med_id_coded = mar.order_med_id_coded
+  and admit.event_type_c = 1 -- Admission
+  and admit.pat_class_c = '112' -- Emergency Services
+  and opCT.proc_code like 'IMGCTH%' -- CT Head orders
+  and om.medication_id = 86145 -- ALTEPLASE 100mg infusion
+  and inpatient.pat_class_c = '126' -- Inpatient
+group by 
+  op.jc_uid, op.pat_enc_csn_id_coded, inpatient.event_id_coded, enc.inpatient_data_id_coded,
+  admit.event_type, admit.pat_class, admit.effective_time_jittered, 
+  om.med_description,
+  inpatient.pat_class
+order by emergencyAdmitTime) labels
+LEFT JOIN
+  (select rit_uid, inpatient_data_id_coded, row_disp_name, meas_value, recorded_time_jittered
+  from starr_datalake2018.flowsheet
+  where row_disp_name in ('Heart Rate', 'FiO2', 'Glasgow Coma Scale' , 'Resp Rule', 'Urine', 'Temp', 'BP')) flow
+ON flow.rit_uid = labels.jc_uid and flow.inpatient_data_id_coded = labels.inpatient_data_id_coded
+WHERE flow.recorded_time_jittered < labels.tpaAdminTime
+ORDER BY labels.jc_uid, labels.pat_enc_csn_id_coded, flow.inpatient_data_id_coded, flow.recorded_time_jittered
\ No newline at end of file
diff --git a/scripts/OrderSetStats/orderser_stats.sql b/scripts/OrderSetStats/orderser_stats.sql
new file mode 100644
index 00000000..e149f18d
--- /dev/null
+++ b/scripts/OrderSetStats/orderser_stats.sql
@@ -0,0 +1,70 @@
+-- For proc_orderset
+
+WITH 
+proc_orderset_usage AS
+(
+    SELECT 
+        EXTRACT(YEAR FROM op.ordering_date_jittered) as orderYear, pos.protocol_id, pos.protocol_name, 
+        count(distinct pos.anon_id) as nPatientsPerOrderSet, count(distinct pos.pat_enc_csn_id_coded) as nEncountersPerOrderSet
+    FROM `som-nero-phi-jonc101.shc_core_2021.proc_orderset` as pos
+       INNER JOIN `som-nero-phi-jonc101.shc_core_2021.order_proc` as op USING (order_proc_id_coded)
+    WHERE ss_section_name <> 'Ad-hoc Orders'  -- These are not a part of the order set. Could be random orders that users add on after the fact
+    group by orderYear, pos.protocol_id, pos.protocol_name
+),
+
+proc_orderset_individual_order_usage AS
+(
+    SELECT 
+        EXTRACT(YEAR FROM op.ordering_date_jittered) as orderYear, pos.protocol_id, pos.protocol_name, 
+        op.order_type, op.proc_code, op.display_name, 
+        count(distinct op.anon_id) as nPatients, count(distinct op.pat_enc_csn_id_coded) as nEncounters, count(distinct op.order_proc_id_coded) as nOrders
+    FROM `som-nero-phi-jonc101.shc_core_2021.proc_orderset` as pos
+       INNER JOIN `som-nero-phi-jonc101.shc_core_2021.order_proc` as op USING (order_proc_id_coded)
+    WHERE ss_section_name <> 'Ad-hoc Orders'  
+    group by orderYear, pos.protocol_id, pos.protocol_name, op.order_type, op.proc_code, op.display_name
+)
+ 
+
+ select *
+ from proc_orderset_usage as pou
+ INNER JOIN proc_orderset_individual_order_usage as poiou USING (orderYear, protocol_id, protocol_name)
+ WHERE nPatients > 10 -- Ignore rare items to avoid exposing PHI
+ and orderYear > 2018 -- Avoid excessive old results
+ order by orderYear desc, nEncountersPerOrderSet desc, protocol_name, nOrders desc
+
+
+ -- Similarly for med_orderset
+ WITH 
+
+med_orderset_usage AS
+
+(
+    SELECT 
+
+        EXTRACT(YEAR FROM om.order_time_jittered) as orderYear, mos.protocol_id, mos.protocol_name, 
+        count(distinct mos.anon_id) as nPatientsPerOrderSet, count(distinct mos.pat_enc_csn_id_coded) as nEncountersPerOrderSet
+    FROM `som-nero-phi-jonc101.shc_core_2021.med_orderset` as mos
+       INNER JOIN `som-nero-phi-jonc101.shc_core_2021.order_med` as om USING (order_med_id_coded)
+    WHERE ss_section_name <> 'Ad-hoc Orders'  -- These are not a part of the order set. Could be random orders that users add on after the fact
+    group by orderYear, mos.protocol_id, mos.protocol_name
+),
+
+med_orderset_individual_order_usage AS
+(
+    SELECT 
+        EXTRACT(YEAR FROM om.order_time_jittered) as orderYear, mos.protocol_id, mos.protocol_name, 
+        om.pharm_class_abbr, om.pharm_class_name, 
+        count(distinct om.anon_id) as nPatients, count(distinct om.pat_enc_csn_id_coded) as nEncounters, count(distinct om.order_med_id_coded) as nOrders
+    FROM `som-nero-phi-jonc101.shc_core_2021.med_orderset` as mos
+       INNER JOIN `som-nero-phi-jonc101.shc_core_2021.order_med` as om USING (order_med_id_coded)
+    WHERE ss_section_name <> 'Ad-hoc Orders'  
+    group by orderYear, mos.protocol_id, mos.protocol_name, om.pharm_class_abbr, om.pharm_class_name
+)
+
+ 
+ select *
+ from med_orderset_usage as pou
+ INNER JOIN med_orderset_individual_order_usage as poiou USING (orderYear, protocol_id, protocol_name)
+ WHERE nPatients > 10 -- Ignore rare items to avoid exposing PHI
+ and orderYear > 2018 -- Avoid excessive old results
+ order by orderYear desc, nEncountersPerOrderSet desc, protocol_name, nOrders desc
\ No newline at end of file
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP1A_get_orderset_usage_instances.py b/scripts/OrdersetExpectedVsReality/Code/STEP1A_get_orderset_usage_instances.py
new file mode 100644
index 00000000..70559153
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP1A_get_orderset_usage_instances.py
@@ -0,0 +1,69 @@
+# Get all orderset usage instances from 2014-01-01 onwards; based on unique (patient_id, timestamp) pairings
+
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+
+os.chdir('/Users/jwang/Desktop/CDSS/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/CDSS')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+DATA_QUERY = """
+SELECT DISTINCT 
+    soom.protocol_id,
+    soom.protocol_name,
+    som.pat_id, 
+    som.ordering_date
+FROM 
+    stride_order_med AS som 
+    JOIN 
+        stride_orderset_order_med AS soom 
+    ON 
+        som.order_med_id = soom.order_med_id
+WHERE 
+    som.ordering_date::DATE > DATE '2014-01-01'
+    AND NOT section_name = 'Ad-hoc Orders'
+GROUP BY 
+    soom.protocol_id,
+    soom.protocol_name,
+    som.ordering_date, 
+    som.pat_id
+UNION 
+SELECT DISTINCT 
+    soop.protocol_id, 
+    soop.protocol_name,
+    sop.pat_id, 
+    sop.ordering_date
+FROM 
+    stride_orderset_order_proc AS soop
+    JOIN 
+        stride_order_proc AS sop 
+    ON 
+        soop.order_proc_id = sop.order_proc_id
+WHERE 
+    sop.ordering_date::DATE > DATE '2014-01-01'
+    AND NOT section_name = 'Ad-hoc Orders'
+GROUP BY 
+    soop.protocol_id, 
+    soop.protocol_name,
+    sop.pat_id, 
+    sop.ordering_date
+;
+"""
+DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/2014-2017"
+outf = open("{0}/orderset_usage_instances.csv".format(DATADIR), "w")
+outf.write("orderset_id,orderset_name,patient_id,ordering_date\n")
+
+results = DBUtil.execute(DATA_QUERY);
+for line in results:
+    outf.write("{0},{1},{2},{3}\n".format(line[0], line[1], line[2], line[3]))
+outf.close()
+
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP1B_define_orders_in_ordersets.py b/scripts/OrdersetExpectedVsReality/Code/STEP1B_define_orders_in_ordersets.py
new file mode 100644
index 00000000..6bbdd213
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP1B_define_orders_in_ordersets.py
@@ -0,0 +1,53 @@
+"""
+Find orders associated with each given orderset; output as a pickled map
+"""
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+import pickle as pkl
+
+DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/2014-2017"
+
+# Find medication_ids and proc_codes associated with a given orderset; these cannot be counted as "co-occurring" items
+orderset_orders = {} # key: orderset_id, value: medication_id/proc_code
+
+for medication_instances_file in glob.glob("{0}/orderset_order_pairings_MEDICATIONS/*".format(DATADIR)):
+	inf = open(medication_instances_file, "rU")
+	inf.readline()
+	for line in inf:
+		line = line.strip().split(",")
+		medication_id = int(line[2])
+		orderset_id = int(line[3])
+		orderset_name = "_".join(line[4].strip().split())
+		orderset = "{0}_{1}".format(orderset_id, orderset_name)
+
+		if (orderset not in orderset_orders):
+			orderset_orders[orderset] = {medication_id:1,} # initialize map data structure
+		else:
+			orderset_orders[orderset][medication_id] = 1
+
+for procedure_instances_file in glob.glob("{0}/orderset_order_pairings_PROCEDURES/*".format(DATADIR)):
+	# if (procedure_instances_file.strip().split("/")[-1] not in ordersets_of_interest):
+	# 	continue
+
+	inf = open(procedure_instances_file, "rU")
+	inf.readline()
+	for line in inf:
+		line = line.strip().split(",")
+		procedure_code = line[2]
+		orderset_id = int(line[3])
+		orderset_name = "_".join(line[4].strip().split())
+		orderset = "{0}_{1}".format(orderset_id, orderset_name)
+
+		if (orderset not in orderset_orders):
+			orderset_orders[orderset] = {procedure_code:1,} # initialize map data structure
+		else:
+			orderset_orders[orderset][procedure_code] = 1
+
+# Output pickle file
+# print(orderset_orders)
+pkl.dump(orderset_orders, open("{0}/orderset_orders.pkl".format(DATADIR), "wb"))
+
+	
\ No newline at end of file
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP2A_count_co_occurring_orders_not_in_orderset.py b/scripts/OrdersetExpectedVsReality/Code/STEP2A_count_co_occurring_orders_not_in_orderset.py
new file mode 100644
index 00000000..3ca723b0
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP2A_count_co_occurring_orders_not_in_orderset.py
@@ -0,0 +1,230 @@
+"""
+Find "Co-Occuring Orders": orders not listed in order set that are commonly ordered a la carte with the order set
+1) Find distinct orderset usage instances based on medication and procedure tables: result is a list of (patient_id, timestamp) "usage instances" for each orderset
+2) Count co-occurring medications and procedures (e.g. labs) (BINARY, co-occur or not) that occur within a X minute window before/after the "usage instance"; this requires counting from stride_order_med and stride_order_proc separately
+3) Exclude medication/procedures IDs that are already in the given order set in question (e.g. the one we are counting co-occurrences for)
+"""
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+import pickle as pkl
+
+os.chdir('/Users/jwang/Desktop/CDSS/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/CDSS')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# year_intervals = [(2008, 2010), (2010, 2012), (2012, 2014), (2014, 2016)]
+year_intervals = [(2014, 2017)]
+for tup in year_intervals:
+	start_year = tup[0]
+	end_year = tup[1]
+
+	DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/{0}-{1}/".format(start_year, end_year)
+	# ordersets_of_interest = ["625_ANE_PACU_(INPATIENT)_usage_instances.csv", "698_IP_MED_GENERAL__ADMIT_usage_instances.csv", "2457_IP_GEN_HEPARIN_PROTOCOLS_usage_instances.csv"] # use these as sanity check
+
+	#######################################################################
+	#                                                                     # 
+	#    Load in Distinct Order Set Usage Instances and Orderset Orders   # 
+	#                                                                     # 
+	#######################################################################
+
+	# Load distinct order set usage instances from medication and procedure tables
+	orderset_instances = {} # key: orderset, value: instance = (patient_id, timestamp)
+
+	orderset_usage_instances_inf = open("{0}/orderset_usage_instances.csv".format(DATADIR), "rU")
+	orderset_usage_instances_inf.readline()
+	for line in orderset_usage_instances_inf:
+		line = line.strip().split(",")
+		orderset_id = int(line[0])
+		orderset_name = "_".join(line[1].strip().split())
+		orderset = "{0}_{1}".format(orderset_id, orderset_name)
+		patient_id = line[2]
+		timestamp = line[3]
+		instance = (patient_id, timestamp)
+
+		if (orderset not in orderset_instances):
+			orderset_instances[orderset] = {instance:1,} # initialize map data structure
+		else: 
+			orderset_instances[orderset][instance] = 1
+
+	# Load medication_ids and proc_codes associated with a given orderset; these cannot be counted as "co-occurring" items
+	orderset_orders = pkl.load(open("{0}/orderset_orders.pkl".format(DATADIR), "rb")) # key: orderset_id, value: medication_id/proc_code
+
+	#############################
+	#                           # 
+	#    Count Co-Occurrences   # 
+	#                           # 
+	#############################
+
+	# windows = [10, 60, 120] # minutes
+	windows = [10]
+	for window in windows:
+		print("Starting co-occurrence counts for {0}-minute window...".format(window))
+		order_description_map = {} # key: medication_id/proc_code, value: description
+
+		for orderset in list(orderset_instances.keys()):
+			instances = list(orderset_instances[orderset].keys())
+			num_instances = len(instances)
+			
+			co_occurrence_medication_counts = {} 
+			print("Counting medication co-occurrences for orderset {0}...".format(orderset))
+			instances_queried = 0
+
+			for instance in instances:
+				instances_queried += 1
+				if (instances_queried % 1000 == 0):
+					print(instances_queried)
+
+				pat_id = instance[0]
+				try:
+					timestamp = parser.parse(instance[1])
+				except:
+					print("Unexpected error... skipping...")
+					continue
+				window_upper = timestamp + timedelta(minutes=window)
+				window_lower = timestamp - timedelta(minutes=window)
+
+				"""
+				SELECT
+					pat_id,
+					medication_id,
+					description,
+					COUNT(*) as medication_co_occurrence
+				FROM
+					stride_order_med
+				WHERE
+					pat_id = {0} AND
+					ordering_date >= {1}-Xmin AND
+					ordering_date <= {1}+Xmin
+				GROUP BY
+					pat_id, medication_id, description
+				"""
+				DATA_QUERY = SQLQuery();
+				DATA_QUERY.addSelect("pat_id")
+				DATA_QUERY.addSelect("medication_id")
+				DATA_QUERY.addSelect("description")
+				DATA_QUERY.addSelect("COUNT(*) as medication_co_occurrence")
+
+				DATA_QUERY.addFrom("stride_order_med")
+				DATA_QUERY.addWhere("pat_id = '{0}'".format(pat_id))
+				DATA_QUERY.addWhere("ordering_date >= '{0}'".format(window_lower))
+				DATA_QUERY.addWhere("ordering_date <= '{0}'".format(window_upper))
+
+				DATA_QUERY.addGroupBy("pat_id")
+				DATA_QUERY.addGroupBy("medication_id")
+				DATA_QUERY.addGroupBy("description")
+
+				# print(DATA_QUERY)
+				results = DBUtil.execute(DATA_QUERY);
+				for line in results:
+					medication = int(line[1])
+					description = line[2]
+					count = int(line[3])
+
+					order_description_map[medication] = description.replace(",", ";")
+
+					# consider only orders not already in orderset
+					if (orderset not in orderset_orders or medication not in list(orderset_orders[orderset].keys())): # orderset does not exist or medication is not yet in order set
+						if (medication not in co_occurrence_medication_counts):
+							co_occurrence_medication_counts[medication] = 1 # binary, consider whether the order occurred in the given time window or not
+						else:
+							co_occurrence_medication_counts[medication] += 1
+
+			outf = open("{0}/co_occurring_orders_MEDICATIONS/{1}/{2}.csv".format(DATADIR, window, orderset.replace(" ", "_").replace("/", "_")), "w")
+			outf.write("medication,description,order_type,co_occurrence_count,orderset_usage_count,ratio\n")
+
+			for medication, co_occurrence in co_occurrence_medication_counts.items():
+				outf.write("{0},{1},medication,{2},{3},{4}\n".format(medication, order_description_map[medication], co_occurrence, num_instances, float(co_occurrence)/num_instances))
+			outf.close()
+
+		for orderset in list(orderset_instances.keys()):
+			instances = list(orderset_instances[orderset].keys())
+			num_instances = len(instances)
+
+			co_occurrence_procedure_counts = {}
+			print("Counting procedure co-occurrences for orderset {0}...".format(orderset))
+			instances_queried = 0
+
+			for instance in instances:
+				instances_queried += 1
+				if (instances_queried % 1000 == 0):
+					print(instances_queried)
+
+				pat_id = instance[0]
+				try:
+					timestamp = parser.parse(instance[1])
+				except:
+					print("Unexpected error... skipping...")
+					continue
+				window_upper = timestamp + timedelta(minutes=window)
+				window_lower = timestamp - timedelta(minutes=window)
+
+				"""
+				SELECT
+					pat_id,
+					proc_id,
+					description,
+					COUNT(*) as procedure_co_occurrence
+				FROM
+					stride_order_proc
+				WHERE
+					pat_id = {0} AND
+					order_time >= {1}-Xmin AND
+					order_time <= {1}+Xmin AND
+					order_type IN ('Imaging', 'Blood Bank', 'Lab Panel', 'Microbiology', 'HIV Lab Restricted', 'Lab Only', 'Lab', 'Microbiology Culture', 'Point of Care Testing', 'ECHO')
+				GROUP BY
+					pat_id, proc_id, description
+				"""
+				DATA_QUERY = SQLQuery();
+				DATA_QUERY.addSelect("pat_id")
+				# DATA_QUERY.addSelect("proc_id")
+				DATA_QUERY.addSelect("proc_code")
+				DATA_QUERY.addSelect("description")
+				DATA_QUERY.addSelect("order_type")
+				DATA_QUERY.addSelect("COUNT(*) as procedure_co_occurrence")
+
+				DATA_QUERY.addFrom("stride_order_proc")
+				DATA_QUERY.addWhere("pat_id = '{0}'".format(pat_id))
+				DATA_QUERY.addWhere("order_time >= '{0}'".format(window_lower))
+				DATA_QUERY.addWhere("order_time <= '{0}'".format(window_upper))
+				DATA_QUERY.addWhere("order_type IN  ('Imaging', 'Blood Bank', 'Lab Panel', 'Microbiology', 'HIV Lab Restricted', 'Lab Only', 'Lab', 'Microbiology Culture', 'Point of Care Testing', 'ECHO')")
+
+				DATA_QUERY.addGroupBy("pat_id")
+				DATA_QUERY.addGroupBy("proc_code")
+				DATA_QUERY.addGroupBy("description")
+				DATA_QUERY.addGroupBy("order_type")
+
+				# print(DATA_QUERY)
+				results = DBUtil.execute(DATA_QUERY);
+				for line in results:
+					procedure = line[1]
+					description = line[2].replace(",", ";")
+					order_type = line[3]
+					count = int(line[4])
+
+					if (order_type == "Blood Bank" and procedure not in ["LABBBPCRY", "LABBBPFFP", "LABBBPPLT", "LABBBPRBC"]): # filter irrelevant blood bank orders (e.g. nursing orders)
+						continue 
+
+					order_description_map[procedure] = description
+
+					# consider only orders not already in orderset
+					if (orderset not in orderset_orders or procedure not in list(orderset_orders[orderset].keys())): # orderset does not exist or procedure is not yet in order set
+						if (procedure not in co_occurrence_procedure_counts):
+							co_occurrence_procedure_counts[procedure] = 1
+						else:
+							co_occurrence_procedure_counts[procedure] += 1
+
+			outf = open("{0}/co_occurring_orders_PROCEDURES/{1}/{2}.csv".format(DATADIR, window, orderset.replace(" ", "_").replace("/", "_")), "w")
+			outf.write("procedure,description,order_type,co_occurrence_count,orderset_usage_count,ratio\n")
+
+			for procedure, co_occurrence in co_occurrence_procedure_counts.items():
+				outf.write("{0},{1},{2},{3},{4},{5}\n".format(procedure, order_description_map[procedure], order_type, co_occurrence, num_instances, float(co_occurrence)/num_instances))
+			outf.close()
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP2B_compute_co_occurring_statistics.py b/scripts/OrdersetExpectedVsReality/Code/STEP2B_compute_co_occurring_statistics.py
new file mode 100644
index 00000000..b3105a5e
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP2B_compute_co_occurring_statistics.py
@@ -0,0 +1,114 @@
+# Aggregate co_occurrence results to create master spreadsheet with columns:
+# procedure_code/medication_id, description, orderset_X, yes_co_occurrence_with_orderset_X_usage, no_co_occurrence_with_orderset_X_usage, yes_co_occurrence_with_any_orderset_usage, no_co_occurrence_with_any_orderset_usage, orderset_X_usage_count, co_occurring_probability_orderset_X, co_occurring_probability_any_orderset, p_value
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+import numpy as np
+import scipy.stats as stats
+
+year_intervals = [(2014, 2017)]
+for tup in year_intervals:
+	start_year = tup[0]
+	end_year = tup[1]
+
+	# windows = [10, 60, 120] # minutes
+	windows = [10]
+	for window in windows:
+		# initialize data structures; store four counts for contingency table
+		co_occurrence_for_orderset_X_tracker = {} # first key = orderset X > second key = medication_id/proc_code, value = [yes, no] within co_occurrence window of specific orderset X usage instance
+		co_occurrence_for_any_orderset_tracker = {} # key = medication_id/proc_code, value = yes, within co_occurrence window of any orderset usage instance  
+		orderset_usage_instances = {} # key = orderset X, value = orderset X usage instances
+		descriptions = {} # key = medication_id/proc_code, value = description
+		order_types = {} # key = medication_id/proc_code, value = order_type
+
+		DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/{0}-{1}".format(start_year, end_year)
+
+		# MEDICATIONS
+		for medication_co_occurrence_file in glob.glob("{0}/co_occurring_orders_MEDICATIONS/{1}/*".format(DATADIR, window)):
+			orderset = medication_co_occurrence_file.split("/")[-1].split(".csv")[0]
+
+			inf = open(medication_co_occurrence_file, "rU")
+			inf.readline()
+			for line in inf:
+				line = line.strip().split(",")
+				medication_id = int(line[0])
+				description = line[1]
+				order_type = line[2]
+				co_occurrence_count = int(line[3])
+				orderset_usage_count = int(line[4])
+
+				orderset_usage_instances[orderset] = orderset_usage_count
+				descriptions[medication_id] = description
+				order_types[medication_id] = order_type
+
+				# update counts for orderset X tracker
+				if (orderset not in co_occurrence_for_orderset_X_tracker):
+					co_occurrence_for_orderset_X_tracker[orderset] = {}
+				if (medication_id not in co_occurrence_for_orderset_X_tracker[orderset]):
+					co_occurrence_for_orderset_X_tracker[orderset][medication_id] = [0,0]
+				co_occurrence_for_orderset_X_tracker[orderset][medication_id][0] = co_occurrence_count # yes, medication co-occurred with orderset X
+				co_occurrence_for_orderset_X_tracker[orderset][medication_id][1] = orderset_usage_count - co_occurrence_count # no, medication did no co-occur with orderset X
+
+				# update counts for all orderset tracker
+				if (medication_id not in co_occurrence_for_any_orderset_tracker):
+					co_occurrence_for_any_orderset_tracker[medication_id] = 0
+				co_occurrence_for_any_orderset_tracker[medication_id] += co_occurrence_count # yes, medication co-occurred with orderset X 
+			inf.close()
+
+		# PROCEDURES
+		for procedure_co_occurrence_file in glob.glob("{0}/co_occurring_orders_PROCEDURES/{1}/*".format(DATADIR, window)):
+			orderset = procedure_co_occurrence_file.split("/")[-1].split(".csv")[0]
+
+			inf = open(procedure_co_occurrence_file, "rU")
+			inf.readline()
+			for line in inf:
+				line = line.strip().split(",")
+				procedure_code = line[0]
+				description = line[1]
+				order_type = line[2]
+				co_occurrence_count = int(line[3])
+				orderset_usage_count = int(line[4])
+
+				orderset_usage_instances[orderset] = orderset_usage_count
+				descriptions[procedure_code] = description
+				order_types[procedure_code] = order_type
+
+				# update counts for orderset X tracker
+				if (orderset not in co_occurrence_for_orderset_X_tracker):
+					co_occurrence_for_orderset_X_tracker[orderset] = {}
+				if (procedure_code not in co_occurrence_for_orderset_X_tracker[orderset]):
+					co_occurrence_for_orderset_X_tracker[orderset][procedure_code] = [0,0]
+				co_occurrence_for_orderset_X_tracker[orderset][procedure_code][0] = co_occurrence_count # yes, procedure co-occurred with orderset X
+				co_occurrence_for_orderset_X_tracker[orderset][procedure_code][1] = orderset_usage_count - co_occurrence_count # no, procedure did no co-occur with orderset X
+
+				# update counts for all orderset tracker
+				if (procedure_code not in co_occurrence_for_any_orderset_tracker):
+					co_occurrence_for_any_orderset_tracker[procedure_code] = 0
+				co_occurrence_for_any_orderset_tracker[procedure_code] += co_occurrence_count # yes, procedure co-occurred with orderset X 
+			inf.close()
+
+		# compute total orderset usage instances
+		total_order_set_usage_instances = np.sum(np.array(list(orderset_usage_instances.values())))
+
+		# output files
+		for orderset, co_occurrence_map in co_occurrence_for_orderset_X_tracker.items():
+			outf = open("{0}/co_occurring_orders_MASTER/{1}/{2}.csv".format(DATADIR, window, orderset), "w")
+			outf.write("id_or_code,description,order_type,orderset_X,yes_co_occurrence_with_orderset_X_usage,no_co_occurrence_with_orderset_X_usage,yes_co_occurrence_with_any_orderset_usage,no_co_occurrence_with_any_orderset_usage,orderset_X_usage_count,co_occurring_probability_orderset_X,co_occurring_probability_any_orderset,p_value\n")
+
+			for id_or_code, co_occurrence_counts in co_occurrence_map.items():
+				yes_orderset_X = co_occurrence_for_orderset_X_tracker[orderset][id_or_code][0] # medication co-occurred within window of orderset X usage instance
+				no_orderset_X = co_occurrence_for_orderset_X_tracker[orderset][id_or_code][1] # medication did not co-occur within window of orderset X usage instance
+				yes_any_orderset = co_occurrence_for_any_orderset_tracker[id_or_code] # medication co-occurred within window of any orderset usage instance
+				no_any_orderset = total_order_set_usage_instances - co_occurrence_for_any_orderset_tracker[id_or_code] # medication did not co-occur within window of any orderset usage instance
+
+				# compute P-value
+				oddsratio, pvalue = stats.fisher_exact([[yes_orderset_X, no_orderset_X], [yes_any_orderset, no_any_orderset]])
+
+				outf.write("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11}\n".format(
+					id_or_code, descriptions[id_or_code], order_types[id_or_code], orderset, yes_orderset_X, no_orderset_X, yes_any_orderset,
+					no_any_orderset, orderset_usage_instances[orderset], float(yes_orderset_X)/(yes_orderset_X + no_orderset_X), 
+					float(yes_any_orderset)/(yes_any_orderset+no_any_orderset), pvalue))
+			outf.close()
+
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP2C_compute_median_ordering_prob_cutoffs.py b/scripts/OrdersetExpectedVsReality/Code/STEP2C_compute_median_ordering_prob_cutoffs.py
new file mode 100644
index 00000000..eb31f6b2
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP2C_compute_median_ordering_prob_cutoffs.py
@@ -0,0 +1,30 @@
+# Compute median ordering probabilities for each orderset
+# Can derive these thresholds from order set utilization spreadsheet (From Ron Li)
+# Output as pickle array
+
+import numpy as np
+import pickle as pkl
+
+median_ordering_probabilities = {} # key = orderset ID, value = median
+ordering_probabilities = {} # key = orderset ID, value = array of ordering probabilities
+inf = open("orderset_ordering_probabilities.csv", "rU")
+inf.readline()
+
+for line in inf:
+	line = line.strip().split(",")
+	orderset_id = int(line[1])
+	if ("NA" in line[-1]):
+		continue
+	ordering_prob = float(line[-1])
+
+	if (orderset_id not in ordering_probabilities):
+		ordering_probabilities[orderset_id] = []
+	ordering_probabilities[orderset_id].append(ordering_prob)
+inf.close()
+
+# compute median ordering probabilities
+for orderset_id, ordering_prob_array in ordering_probabilities.items():
+	median_ordering_probabilities[orderset_id] = np.median(np.array(ordering_prob_array))
+
+# print(median_ordering_probabilities)
+pkl.dump(median_ordering_probabilities, open("median_ordering_probabilities.pkl", "wb"))
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP2D_filter_co_occurring_orders.py b/scripts/OrdersetExpectedVsReality/Code/STEP2D_filter_co_occurring_orders.py
new file mode 100644
index 00000000..8ed3f785
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP2D_filter_co_occurring_orders.py
@@ -0,0 +1,55 @@
+# Filter co-occurring orders to only consider ones with co-occurring probability > median ordering probability of associated order set
+# And P-value (from Fisher Exact Test) < p_threshold defined below
+# Aggregate all results in one master spreadsheet
+
+import pickle as pkl
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+
+# Counter (to obtain Bonferroni Correction denominator)
+# denominator = 0 
+
+# Load in median ordering probabilities
+median_ordering_probabilities = pkl.load(open("median_ordering_probabilities.pkl", "rb"))
+p_threshold = 0.05/114017 # Bonferroni Correction
+print("Bonferroni Correction: P < {0}".format(p_threshold))
+
+year_intervals = [(2014, 2017)]
+for tup in year_intervals:
+	start_year = tup[0]
+	end_year = tup[1]
+
+	# windows = [10, 60, 120] # minutes
+	windows = [10]
+	for window in windows:
+
+		DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/{0}-{1}".format(start_year, end_year)
+		outf = open("{0}/co_occurring_orders_FINAL/{1}/co_occurring_orders_filtered.csv".format(DATADIR, window), "w")
+		outf.write("id_or_code,description,order_type,orderset_X,yes_co_occurrence_with_orderset_X_usage,no_co_occurrence_with_orderset_X_usage,yes_co_occurrence_with_any_orderset_usage,no_co_occurrence_with_any_orderset_usage,orderset_X_usage_count,co_occurring_probability_orderset_X,co_occurring_probability_any_orderset,p_value\n")
+
+		# Iterate over intermediate co-occurrence statistics file
+		for co_occurrence_file in glob.glob("{0}/co_occurring_orders_MASTER/{1}/*".format(DATADIR, window)):
+			orderset_id = int(co_occurrence_file.split("/")[-1].split(".csv")[0].split("_")[0])
+
+			if (orderset_id not in median_ordering_probabilities):
+				continue # skip
+			median_ordering_prob = median_ordering_probabilities[orderset_id]
+			
+			inf = open(co_occurrence_file, "rU")
+			header = inf.readline()
+			for raw_line in inf:
+				line = raw_line.strip().split(",")
+				p_value = float(line[-1])
+				co_occurring_prob = float(line[-3])
+
+				# denominator += 1
+
+				if (co_occurring_prob > median_ordering_prob and p_value < p_threshold): # filter
+					outf.write(raw_line)
+		outf.close()
+# print(denominator)
+
+
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP3A_find_order_ids_associated_with_ordersets.py b/scripts/OrdersetExpectedVsReality/Code/STEP3A_find_order_ids_associated_with_ordersets.py
new file mode 100644
index 00000000..9b1b4155
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP3A_find_order_ids_associated_with_ordersets.py
@@ -0,0 +1,89 @@
+# An a la carte order cannot be ordered from an order set although the order is present in the orderset; an orderset cannot be triggered
+# Extract all order_med_ids/order_proc_ids associated with a given patient_id
+# Output a pickled map that we can use to filter orders ordere from order sets
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+import pickle as pkl
+
+os.chdir('/Users/jwang/Desktop/CDSS/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/CDSS')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+# Medications
+orderset_order_med_ids = {} # key: patient_id, second_key: order_med_id associated with having been ordered from an orderset
+
+DATA_QUERY = """
+SELECT  
+	soom.order_med_id,
+	som.pat_id
+FROM 
+	stride_order_med AS som 
+	JOIN 
+		stride_orderset_order_med AS soom 
+	ON 
+		som.order_med_id = soom.order_med_id
+WHERE 
+	som.ordering_date::DATE > DATE '2014-01-01'
+	AND NOT section_name = 'Ad-hoc Orders'
+"""
+print(DATA_QUERY)
+DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/2014-2017"
+
+results = DBUtil.execute(DATA_QUERY);
+for line in results:
+	order_med_id = int(line[0])
+	patient_id = int(line[1])
+	# if (patient_id == 7072283518095):
+	# 	print(patient_id, order_med_id)
+	if (patient_id not in list(orderset_order_med_ids.keys())):
+		orderset_order_med_ids[patient_id] = {}
+	orderset_order_med_ids[patient_id][order_med_id] = 1
+
+pkl.dump(orderset_order_med_ids, open("{0}/orderset_order_med_ids.pkl".format(DATADIR), "wb"))
+
+print(orderset_order_med_ids)
+# sanity check
+# for patient_id, order_med_id_arr in orderset_order_med_ids.iteritems():
+# 	if (len(order_med_id_arr) > 1):
+# 		print(patient_id, len(order_med_id_arr))
+
+# Procedures
+orderset_order_proc_ids = {} # key: patient_id, second_key: order_proc_id associated with having been ordered from an orderset
+
+DATA_QUERY = """ 
+SELECT  
+	soop.order_proc_id, 
+	sop.pat_id
+FROM 
+	stride_orderset_order_proc AS soop
+	JOIN 
+		stride_order_proc AS sop 
+	ON 
+		soop.order_proc_id = sop.order_proc_id
+WHERE 
+	sop.ordering_date::DATE > DATE '2014-01-01'
+	AND NOT section_name = 'Ad-hoc Orders'
+;
+"""
+print(DATA_QUERY)
+
+results = DBUtil.execute(DATA_QUERY);
+for line in results:
+	order_proc_id = int(line[0])
+	patient_id = int(line[1])
+	if (patient_id not in list(orderset_order_proc_ids.keys())):
+		orderset_order_proc_ids[patient_id] = {}
+	orderset_order_proc_ids[patient_id][order_proc_id] = 1
+
+pkl.dump(orderset_order_proc_ids, open("{0}/orderset_order_proc_ids.pkl".format(DATADIR), "wb"))
+
+print(orderset_order_proc_ids)
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP3B_count_co_occurring_orders_already_in_orderset.py b/scripts/OrdersetExpectedVsReality/Code/STEP3B_count_co_occurring_orders_already_in_orderset.py
new file mode 100644
index 00000000..26a09818
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP3B_count_co_occurring_orders_already_in_orderset.py
@@ -0,0 +1,251 @@
+"""
+Find "A La Carte Orders": medications/procedures listed in order set that are more commonly ordered a la carte rather than with the order set
+1) Find distinct orderset usage instances based on medication and procedure tables: result is a list of (patient_id, timestamp) "usage instances" for each orderset
+2) Count co-occurring medications and procedures (e.g. labs) (BINARY, co-occur or not) that occur within a X minute window before/after the "usage instance" but NOT during; this requires counting from stride_order_med and stride_order_proc separately
+3) Only include IDs that are already in the given order set in question
+* To filter for just a la carte orders, we need to exclude all order_med_id/order_proc_ids that are from ordersets
+"""
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+import pickle as pkl
+import time
+
+os.chdir('/Users/jwang/Desktop/CDSS/medinfo/db')
+from Const import RUNNER_VERBOSITY;
+from Util import log;
+from Util import DBTestCase;
+
+os.chdir('/Users/jwang/Desktop/CDSS')
+from medinfo.common.test.Util import MedInfoTestCase;
+from medinfo.db import DBUtil
+from medinfo.db.Model import SQLQuery;
+
+year_intervals = [(2014, 2017)]
+for tup in year_intervals:
+	start_year = tup[0]
+	end_year = tup[1]
+
+	DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/{0}-{1}/".format(start_year, end_year)
+	# ordersets_of_interest = ["625_ANE_PACU_(INPATIENT)_usage_instances.csv", "698_IP_MED_GENERAL__ADMIT_usage_instances.csv", "2457_IP_GEN_HEPARIN_PROTOCOLS_usage_instances.csv"] # use these as sanity check
+
+	#######################################################################
+	#                                                                     # 
+	#    Load in Distinct Order Set Usage Instances and Orderset Orders   # 
+	#                                                                     # 
+	#######################################################################
+
+	# Load distinct order set usage instances from medication and procedure tables
+	orderset_instances = {} # key: orderset, value: instance = (patient_id, timestamp)
+
+	# Load orderset order ids; need to explicitly exclude these in our co-occurrence counting
+	orderset_order_med_ids = pkl.load(open("{0}/orderset_order_med_ids.pkl".format(DATADIR), "rb")) # key: patient_id, second_key: order_med_id associated with having been ordered from an orderset
+	orderset_order_proc_ids = pkl.load(open("{0}/orderset_order_proc_ids.pkl".format(DATADIR), "rb")) # key: patient_id, second_key: order_proc_id associated with having been ordered from an orderset
+
+	orderset_usage_instances_inf = open("{0}/orderset_usage_instances.csv".format(DATADIR), "rU")
+	orderset_usage_instances_inf.readline()
+	for line in orderset_usage_instances_inf:
+		line = line.strip().split(",")
+		orderset_id = int(line[0])
+		orderset_name = "_".join(line[1].strip().split())
+		orderset = "{0}_{1}".format(orderset_id, orderset_name)
+		patient_id = line[2]
+		timestamp = line[3]
+		instance = (patient_id, timestamp)
+
+		if (orderset not in orderset_instances):
+			orderset_instances[orderset] = {instance:1,} # initialize map data structure
+		else: 
+			orderset_instances[orderset][instance] = 1
+
+	# Load medication_ids and proc_codes associated with a given orderset; only these can be counted as "a la carte" items
+	orderset_orders = pkl.load(open("{0}/orderset_orders.pkl".format(DATADIR), "rb")) # key: orderset_id, value: medication_id/proc_code
+
+	#############################
+	#                           # 
+	#    Count Co-Occurrences   # 
+	#                           # 
+	#############################
+
+	windows = [10, 60, 120] # minutes
+	for window in windows:
+		print("Starting a la carte counts counts for {0}-minute window...".format(window))
+		order_description_map = {} # key: medication_id/proc_code, value: description
+
+		for orderset in list(orderset_instances.keys()):
+			instances = list(orderset_instances[orderset].keys())
+			num_instances = len(instances)
+			
+			co_occurrence_medication_counts = {} 
+			print("Querying medication a la carte counts for orderset {0}...".format(orderset))
+			instances_queried = 0
+
+			for instance in instances:
+				instances_queried += 1
+				if (instances_queried % 1000 == 0):
+					print(instances_queried)
+
+				try:
+					pat_id = int(instance[0])
+					timestamp = parser.parse(instance[1])
+				except:
+					print("Unexpected error... skipping...")
+					continue
+				window_upper = timestamp + timedelta(minutes=window)
+				window_lower = timestamp - timedelta(minutes=window)
+
+				order_med_id_str = ""
+				if (pat_id in list(orderset_order_med_ids.keys())):
+					for order_med_id in list(orderset_order_med_ids[pat_id].keys()):
+						order_med_id_str += "'{0}', ".format(order_med_id)
+					order_med_id_str = order_med_id_str[:-2]
+
+				"""
+				SELECT
+					pat_id,
+					medication_id,
+					description,
+					COUNT(*) as medication_co_occurrence
+				FROM
+					stride_order_med
+				WHERE
+					pat_id = {0} AND
+					ordering_date > {1}-Xmin AND
+					ordering_date < {1}+Xmin
+				GROUP BY
+					pat_id, medication_id, description
+				"""
+				DATA_QUERY = SQLQuery();
+				DATA_QUERY.addSelect("pat_id")
+				DATA_QUERY.addSelect("medication_id")
+				DATA_QUERY.addSelect("description")
+				DATA_QUERY.addSelect("COUNT(*) as medication_co_occurrence")
+
+				DATA_QUERY.addFrom("stride_order_med")
+				DATA_QUERY.addWhere("pat_id = '{0}'".format(pat_id))
+				DATA_QUERY.addWhere("ordering_date >= '{0}'".format(window_lower))
+				DATA_QUERY.addWhere("ordering_date <= '{0}'".format(window_upper))
+				if (pat_id in list(orderset_order_med_ids.keys())):
+					DATA_QUERY.addWhere("order_med_id NOT IN ({0})".format(order_med_id_str)) # do not count order instances ordered with an order set; we only want a la carte instances
+
+				DATA_QUERY.addGroupBy("pat_id")
+				DATA_QUERY.addGroupBy("medication_id")
+				DATA_QUERY.addGroupBy("description")
+
+				# print(DATA_QUERY)
+				results = DBUtil.execute(DATA_QUERY);
+				for line in results:
+					medication = int(line[1])
+					description = line[2]
+					count = int(line[3])
+
+					order_description_map[medication] = description.replace(",", ";")
+
+					# consider only orders already in orderset
+					if (orderset in orderset_orders and medication in list(orderset_orders[orderset].keys())): # medication is in order set
+						if (medication not in co_occurrence_medication_counts):
+							co_occurrence_medication_counts[medication] = 1 # binary, consider whether the order occurred in the given time window or not
+						else:
+							co_occurrence_medication_counts[medication] += 1
+
+			outf = open("{0}/a_la_carte_orders_MEDICATIONS/{1}/{2}.csv".format(DATADIR, window, orderset.replace(" ", "_").replace("/", "_")), "w")
+			outf.write("medication,description,order_type,a_la_carte_count,orderset_usage_count,ratio\n")
+
+			for medication, co_occurrence in co_occurrence_medication_counts.items():
+				outf.write("{0},{1},medication,{2},{3},{4}\n".format(medication, order_description_map[medication], co_occurrence, num_instances, float(co_occurrence)/num_instances))
+			outf.close()
+
+		for orderset in list(orderset_instances.keys()):
+			instances = list(orderset_instances[orderset].keys())
+			num_instances = len(instances)
+
+			co_occurrence_procedure_counts = {}
+			print("Querying procedure a la carte counts for orderset {0}...".format(orderset))
+			instances_queried = 0
+
+			for instance in instances:
+				instances_queried += 1
+				if (instances_queried % 1000 == 0):
+					print(instances_queried)
+
+				try:
+					pat_id = int(instance[0])
+					timestamp = parser.parse(instance[1])
+				except:
+					print("Unexpected error... skipping...")
+					continue
+				window_upper = timestamp + timedelta(minutes=window)
+				window_lower = timestamp - timedelta(minutes=window)
+
+				order_proc_id_str = ""
+				if (pat_id in list(orderset_order_proc_ids.keys())):
+					for order_proc_id in list(orderset_order_proc_ids[pat_id].keys()):
+						order_proc_id_str += "'{0}', ".format(order_proc_id)
+					order_proc_id_str = order_proc_id_str[:-2]
+
+				"""
+				SELECT
+					pat_id,
+					proc_id,
+					description,
+					COUNT(*) as procedure_co_occurrence
+				FROM
+					stride_order_proc
+				WHERE
+					pat_id = {0} AND
+					order_time >= {1}-Xmin AND
+					order_time <= {1}+Xmin AND
+					order_type IN ('Imaging', 'Blood Bank', 'Lab Panel', 'Microbiology', 'HIV Lab Restricted', 'Lab Only', 'Lab', 'Microbiology Culture', 'Point of Care Testing', 'ECHO')
+				GROUP BY
+					pat_id, proc_id, description
+				"""
+				DATA_QUERY = SQLQuery();
+				DATA_QUERY.addSelect("pat_id")
+				# DATA_QUERY.addSelect("proc_id")
+				DATA_QUERY.addSelect("proc_code")
+				DATA_QUERY.addSelect("description")
+				DATA_QUERY.addSelect("order_type")
+				DATA_QUERY.addSelect("COUNT(*) as procedure_co_occurrence")
+
+				DATA_QUERY.addFrom("stride_order_proc")
+				DATA_QUERY.addWhere("pat_id = '{0}'".format(pat_id))
+				DATA_QUERY.addWhere("order_time >= '{0}'".format(window_lower))
+				DATA_QUERY.addWhere("order_time <= '{0}'".format(window_upper))
+				DATA_QUERY.addWhere("order_type IN  ('Imaging', 'Blood Bank', 'Lab Panel', 'Microbiology', 'HIV Lab Restricted', 'Lab Only', 'Lab', 'Microbiology Culture', 'Point of Care Testing', 'ECHO')")
+				if (pat_id in list(orderset_order_proc_ids.keys())):
+					DATA_QUERY.addWhere("order_proc_id NOT IN ({0})".format(order_proc_id_str)) # do not count order instances ordered with an order set
+
+				DATA_QUERY.addGroupBy("pat_id")
+				DATA_QUERY.addGroupBy("proc_code")
+				DATA_QUERY.addGroupBy("description")
+				DATA_QUERY.addGroupBy("order_type")
+
+				# print(DATA_QUERY)
+				results = DBUtil.execute(DATA_QUERY);
+				for line in results:
+					procedure = line[1]
+					description = line[2].replace(",", ";")
+					order_type = line[3]
+					count = int(line[4])
+
+					# if (order_type == "Blood Bank" and procedure not in ["LABBBPCRY", "LABBBPFFP", "LABBBPPLT", "LABBBPRBC"]): # filter irrelevant blood bank orders (e.g. nursing orders)
+					# 	continue 
+
+					order_description_map[procedure] = description
+
+					# consider only orders already in orderset
+					if (orderset in orderset_orders and procedure in list(orderset_orders[orderset].keys())): # procedure is in order set
+						if (procedure not in co_occurrence_procedure_counts):
+							co_occurrence_procedure_counts[procedure] = 1
+						else:
+							co_occurrence_procedure_counts[procedure] += 1
+
+			outf = open("{0}/a_la_carte_orders_PROCEDURES/{1}/{2}.csv".format(DATADIR, window, orderset.replace(" ", "_").replace("/", "_")), "w")
+			outf.write("procedure,description,order_type,a_la_carte_count,orderset_usage_count,ratio\n")
+
+			for procedure, co_occurrence in co_occurrence_procedure_counts.items():
+				outf.write("{0},{1},{2},{3},{4},{5}\n".format(procedure, order_description_map[procedure], order_type, co_occurrence, num_instances, float(co_occurrence)/num_instances))
+			outf.close()
+
diff --git a/scripts/OrdersetExpectedVsReality/Code/STEP3C_filter_a_la_carte_orders.py b/scripts/OrdersetExpectedVsReality/Code/STEP3C_filter_a_la_carte_orders.py
new file mode 100644
index 00000000..823fb7b6
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Code/STEP3C_filter_a_la_carte_orders.py
@@ -0,0 +1,84 @@
+# Filter a la carte orders; orders associated with order sets such that:
+# Co-occuring probability a la carte > ordering probability with order set
+import pickle as pkl
+import glob
+import sys, os
+from io import StringIO
+from datetime import datetime, timedelta
+from dateutil import parser
+import scipy.stats as stats
+
+# Load in order set ordering probabilities
+ordering_probabilities = {} # key = orderset ID, second_key = medication_id/order_proc, value = ordering probability
+ordering_counts = {} # key = orderset ID, second_key = medication_id/order_proc, value = order count (from orderset)
+orderset_usage_counts = {} # key = orderset ID, value = orderset usage count
+orderset_names = {} # key = orderset id, value = orderset name
+
+inf = open("orderset_ordering_probabilities.csv", "rU")
+inf.readline()
+
+for line in inf:
+	line = line.strip().split(",")
+	orderset_id = int(line[1])
+	id_or_code = line[3].strip() # string
+	if ("NA" in line[-1]):
+		continue
+	ordering_prob = float(line[-1])
+	orderset_usage_count = int(line[-2])
+	ordering_count = int(line[-3])
+
+	if (orderset_id not in ordering_probabilities):
+		ordering_probabilities[orderset_id] = {}
+	ordering_probabilities[orderset_id][id_or_code] = ordering_prob
+
+	if (orderset_id not in ordering_counts):
+		ordering_counts[orderset_id] = {}
+	ordering_counts[orderset_id][id_or_code] = ordering_count
+
+	orderset_usage_counts[orderset_id] = orderset_usage_count
+
+	orderset_names[orderset_id] = line[2]
+inf.close()
+# print(ordering_probabilities[832].values())
+
+# Filter a la carte output
+year_intervals = [(2014, 2017)]
+for tup in year_intervals:
+	start_year = tup[0]
+	end_year = tup[1]
+
+	windows = [10, 60, 120] # minutes
+	for window in windows:
+
+		DATADIR = "/Users/jwang/Desktop/Chen/OrderSetOptimization/Results/{0}-{1}".format(start_year, end_year)
+		outf = open("{0}/a_la_carte_orders_FINAL/{1}/a_la_carte_orders_filtered.csv".format(DATADIR, window), "w")
+		outf.write("orderset_id,orderset_name,id_or_code,description,order_type,a_la_carte_count,orderset_usage_count,a_la_carte_prob,ordering_count_from_orderset,ordering_prob_from_orderset,p_value\n")
+
+		# Iterate over intermediate co-occurrence statistics file
+		for a_la_carte_file in glob.glob("{0}/a_la_carte_orders_MEDICATIONS/{1}/*".format(DATADIR, window)) + glob.glob("{0}/a_la_carte_orders_PROCEDURES/{1}/*".format(DATADIR, window)):
+			orderset_id = int(a_la_carte_file.split("/")[-1].split(".csv")[0].split("_")[0])
+
+			if (orderset_id not in ordering_probabilities):
+				continue # skip
+			
+			inf = open(a_la_carte_file, "rU")
+			# print(a_la_carte_file)
+			header = inf.readline()
+			for raw_line in inf:
+				line = raw_line.strip().split(",")
+				id_or_code = str(line[0].strip())
+				if (id_or_code not in ordering_probabilities[orderset_id]):
+					continue
+				ordering_count = int(ordering_counts[orderset_id][id_or_code])
+				a_la_carte_count = int(line[-3])
+				orderset_usage_count = int(orderset_usage_counts[orderset_id])
+				a_la_carte_prob = float(line[-1])
+				ordering_prob = float(ordering_probabilities[orderset_id][id_or_code])
+
+				if (a_la_carte_prob > ordering_prob): # filter
+					# compute P-value
+					# print([[a_la_carte_count, ordering_count], [orderset_usage_count - a_la_carte_count, orderset_usage_count - ordering_count]])
+					odds_ratio, p_value = stats.fisher_exact([[a_la_carte_count, ordering_count], [orderset_usage_count - a_la_carte_count, orderset_usage_count - ordering_count]])
+
+					outf.write("{0},{1},{2},{3},{4},{5}\n".format(orderset_id, orderset_names[orderset_id], raw_line[:-1], ordering_count, ordering_prob, p_value))
+		outf.close()
\ No newline at end of file
diff --git a/scripts/OrdersetExpectedVsReality/Data/orderset_ordering_probabilities.csv b/scripts/OrdersetExpectedVsReality/Data/orderset_ordering_probabilities.csv
new file mode 100644
index 00000000..d4fa0ddc
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Data/orderset_ordering_probabilities.csv
@@ -0,0 +1,11763 @@
+,protocol_id,protocol_name,order_id,description,ordertype,ct_order,ct_orderset,prob_order
+1,522,ED PNEUMONIA,9487,CEFTRIAXONE 1 GRAM IV SOLR,Medication,4,423,0.009456265
+2,522,ED PNEUMONIA,20943,AZITHROMYCIN 250 MG PO TABS,Medication,3,423,0.007092199
+3,522,ED PNEUMONIA,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,6,423,0.014184397
+4,522,ED PNEUMONIA,85920,CEFTRIAXONE 1 GRAM INJ SOLR,Medication,16,423,0.037825059
+5,522,ED PNEUMONIA,93461,MOXIFLOXACIN IN NACL (ISO-OSM) 400 MG/250 ML IV PGBK,Medication,1,423,0.002364066
+6,522,ED PNEUMONIA,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,7,423,0.016548463
+7,522,ED PNEUMONIA,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,68,423,0.160756501
+8,522,ED PNEUMONIA,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,423,0.002364066
+9,522,ED PNEUMONIA,115287,LEVOFLOXACIN IN D5W 500 MG/100 ML IV PGBK,Medication,14,423,0.033096927
+10,522,ED PNEUMONIA,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,81,423,0.191489362
+11,522,ED PNEUMONIA,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,5,423,0.011820331
+12,522,ED PNEUMONIA,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,130,423,0.307328605
+13,522,ED PNEUMONIA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,8,423,0.01891253
+14,522,ED PNEUMONIA,520039,AZITHROMYCIN IVPB,Medication,48,423,0.113475177
+15,522,ED PNEUMONIA,520102,CEFEPIME 2 GM IVPB,Medication,2,423,0.004728132
+16,522,ED PNEUMONIA,540102,NS IV BOLUS,Medication,29,423,0.06855792
+17,522,ED PNEUMONIA,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,423,0.002364066
+18,522,ED PNEUMONIA,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,4,423,0.009456265
+19,522,ED PNEUMONIA,540773,PIPERACILLIN-TAZOBACTAM (ZOSYN) 3.375 GRAM IN DEXTROSE IVPB,Medication,1,423,0.002364066
+20,522,ED PNEUMONIA,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,3,423,0.007092199
+21,522,ED PNEUMONIA,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,34,423,0.080378251
+22,522,ED PNEUMONIA,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,39,423,0.092198582
+23,522,ED PNEUMONIA,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,24,423,0.056737589
+24,522,ED PNEUMONIA,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,36,423,0.085106383
+25,522,ED PNEUMONIA,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,36,423,0.085106383
+26,522,ED PNEUMONIA,LABCBCD,CBC WITH DIFF,Lab,37,423,0.087470449
+27,522,ED PNEUMONIA,LABLEGU,"LEGIONELLA AG, URINE",Lab,3,423,0.007092199
+28,522,ED PNEUMONIA,LABMETB,"METABOLIC PANEL, BASIC",Lab,5,423,0.011820331
+29,522,ED PNEUMONIA,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,34,423,0.080378251
+30,522,ED PNEUMONIA,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,16,423,0.037825059
+31,522,ED PNEUMONIA,POC15,"POC ISTAT, ARTERIAL BLOOD GASES AND LACTATE (CG4)",Lab,2,423,0.004728132
+32,522,ED PNEUMONIA,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,33,423,0.078014184
+33,522,ED PNEUMONIA,POC24,POC ISTAT TROPONIN I,Lab,29,423,0.06855792
+34,522,ED PNEUMONIA,POC26,POC URINE PREGNANCY,Lab,1,423,0.002364066
+35,522,ED PNEUMONIA,IMGDXCH1,XR CHEST 1V,Imaging,27,423,0.063829787
+36,522,ED PNEUMONIA,IMGDXCH2,XR CHEST 2V,Imaging,11,423,0.026004728
+37,523,ED AMI-STEMI PROTOCOL,680,ASPIRIN 81 MG PO CHEW,Medication,32,203,0.157635468
+38,523,ED AMI-STEMI PROTOCOL,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,1,203,0.004926108
+39,523,ED AMI-STEMI PROTOCOL,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,58,203,0.285714286
+40,523,ED AMI-STEMI PROTOCOL,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,31,203,0.15270936
+41,523,ED AMI-STEMI PROTOCOL,77545,ATORVASTATIN 40 MG PO TABS,Medication,1,203,0.004926108
+42,523,ED AMI-STEMI PROTOCOL,83398,CLOPIDOGREL 75 MG PO TABS,Medication,1,203,0.004926108
+43,523,ED AMI-STEMI PROTOCOL,90097,NITROGLYCERIN 0.4 MG/DOSE TL SPRY,Medication,1,203,0.004926108
+44,523,ED AMI-STEMI PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,5,203,0.024630542
+45,523,ED AMI-STEMI PROTOCOL,203749,CLOPIDOGREL 75 MG PO TABS,Medication,17,203,0.083743842
+46,523,ED AMI-STEMI PROTOCOL,208212,PRASUGREL 10 MG PO TABS,Medication,15,203,0.073891626
+47,523,ED AMI-STEMI PROTOCOL,214201,TICAGRELOR 90 MG PO TABS,Medication,4,203,0.019704433
+48,523,ED AMI-STEMI PROTOCOL,540102,NS IV BOLUS,Medication,10,203,0.049261084
+49,523,ED AMI-STEMI PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,67,203,0.330049261
+50,523,ED AMI-STEMI PROTOCOL,LABDDIML,D - DIMER (ELISA),Lab,13,203,0.064039409
+51,523,ED AMI-STEMI PROTOCOL,LABLIPS,LIPASE,Lab,12,203,0.0591133
+52,523,ED AMI-STEMI PROTOCOL,LABMB,"CK, MB (MASS)",Lab,90,203,0.443349754
+53,523,ED AMI-STEMI PROTOCOL,LABMETB,"METABOLIC PANEL, BASIC",Lab,9,203,0.044334975
+54,523,ED AMI-STEMI PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,60,203,0.295566502
+55,523,ED AMI-STEMI PROTOCOL,LABNTBNP,NT - PROBNP,Lab,21,203,0.103448276
+56,523,ED AMI-STEMI PROTOCOL,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,12,203,0.0591133
+57,523,ED AMI-STEMI PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,67,203,0.330049261
+58,523,ED AMI-STEMI PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,63,203,0.310344828
+59,523,ED AMI-STEMI PROTOCOL,LABTNI,TROPONIN I,Lab,88,203,0.433497537
+60,523,ED AMI-STEMI PROTOCOL,LABTYPSNI,TYPE AND SCREEN,Lab,60,203,0.295566502
+61,523,ED AMI-STEMI PROTOCOL,POC1,POC URINE DIPSTICK,Lab,1,203,0.004926108
+62,523,ED AMI-STEMI PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,7,203,0.034482759
+63,523,ED AMI-STEMI PROTOCOL,POC15,"POC ISTAT, ARTERIAL BLOOD GASES AND LACTATE (CG4)",Lab,2,203,0.009852217
+64,523,ED AMI-STEMI PROTOCOL,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,5,203,0.024630542
+65,523,ED AMI-STEMI PROTOCOL,POC18,"POC ISTAT, ARTERIAL NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,2,203,0.009852217
+66,523,ED AMI-STEMI PROTOCOL,POC24,POC ISTAT TROPONIN I,Lab,84,203,0.413793103
+67,523,ED AMI-STEMI PROTOCOL,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,1,203,0.004926108
+68,523,ED AMI-STEMI PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,41,203,0.201970443
+69,524,ED SHORTNESS OF BREATH,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,628,0.001592357
+70,524,ED SHORTNESS OF BREATH,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,41,628,0.065286624
+71,524,ED SHORTNESS OF BREATH,6496,PREDNISONE 20 MG PO TABS,Medication,15,628,0.02388535
+72,524,ED SHORTNESS OF BREATH,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,130,628,0.207006369
+73,524,ED SHORTNESS OF BREATH,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,628,0.001592357
+74,524,ED SHORTNESS OF BREATH,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,4,628,0.006369427
+75,524,ED SHORTNESS OF BREATH,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,1,628,0.001592357
+76,524,ED SHORTNESS OF BREATH,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,19,628,0.030254777
+77,524,ED SHORTNESS OF BREATH,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,53,628,0.084394904
+78,524,ED SHORTNESS OF BREATH,520002,DEXAMETHASONE (CUSTOM DOSE) IVPB,Medication,8,628,0.012738854
+79,524,ED SHORTNESS OF BREATH,540102,NS IV BOLUS,Medication,34,628,0.054140127
+80,524,ED SHORTNESS OF BREATH,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,628,0.003184713
+81,524,ED SHORTNESS OF BREATH,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,87,628,0.138535032
+82,524,ED SHORTNESS OF BREATH,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,87,628,0.138535032
+83,524,ED SHORTNESS OF BREATH,LABCBCD,CBC WITH DIFF,Lab,332,628,0.52866242
+84,524,ED SHORTNESS OF BREATH,LABDDIM,D - DIMER,Lab,16,628,0.025477707
+85,524,ED SHORTNESS OF BREATH,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,4,628,0.006369427
+86,524,ED SHORTNESS OF BREATH,LABGRAM,GRAM STAIN,Lab,16,628,0.025477707
+87,524,ED SHORTNESS OF BREATH,LABMETB,"METABOLIC PANEL, BASIC",Lab,30,628,0.047770701
+88,524,ED SHORTNESS OF BREATH,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,309,628,0.492038217
+89,524,ED SHORTNESS OF BREATH,LABNTBNP,NT - PROBNP,Lab,174,628,0.277070064
+90,524,ED SHORTNESS OF BREATH,LABRESP,RESPIRATORY CULTURE,Lab,13,628,0.020700637
+91,524,ED SHORTNESS OF BREATH,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,95,628,0.151273885
+92,524,ED SHORTNESS OF BREATH,POC15,"POC ISTAT, ARTERIAL BLOOD GASES AND LACTATE (CG4)",Lab,7,628,0.011146497
+93,524,ED SHORTNESS OF BREATH,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,190,628,0.302547771
+94,524,ED SHORTNESS OF BREATH,POC26,POC URINE PREGNANCY,Lab,10,628,0.015923567
+95,524,ED SHORTNESS OF BREATH,POC46,POC ISTAT BHCG,Lab,1,628,0.001592357
+96,524,ED SHORTNESS OF BREATH,IMGCTPEP,CT PULMONARY EMBOLISM & LOWER EXTREMITY,Imaging,3,628,0.00477707
+97,524,ED SHORTNESS OF BREATH,IMGDXCH1,XR CHEST 1V,Imaging,216,628,0.343949045
+98,524,ED SHORTNESS OF BREATH,IMGDXCH2,XR CHEST 2V,Imaging,72,628,0.114649682
+99,525,ED ABDOMINAL PAIN ,3844,IBUPROFEN 600 MG PO TABS,Medication,2,1002,0.001996008
+100,525,ED ABDOMINAL PAIN ,4560,LOPERAMIDE 2 MG PO CAPS,Medication,2,1002,0.001996008
+101,525,ED ABDOMINAL PAIN ,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,1002,0.000998004
+102,525,ED ABDOMINAL PAIN ,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,1002,0.00499002
+103,525,ED ABDOMINAL PAIN ,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,1002,0.000998004
+104,525,ED ABDOMINAL PAIN ,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,2,1002,0.001996008
+105,525,ED ABDOMINAL PAIN ,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,1002,0.000998004
+106,525,ED ABDOMINAL PAIN ,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,6,1002,0.005988024
+107,525,ED ABDOMINAL PAIN ,112562,MORPHINE 10 MG/ML INJ SYRG,Medication,1,1002,0.000998004
+108,525,ED ABDOMINAL PAIN ,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,136,1002,0.135728543
+109,525,ED ABDOMINAL PAIN ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,229,1002,0.228542914
+110,525,ED ABDOMINAL PAIN ,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,1002,0.000998004
+111,525,ED ABDOMINAL PAIN ,540102,NS IV BOLUS,Medication,268,1002,0.26746507
+112,525,ED ABDOMINAL PAIN ,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,5,1002,0.00499002
+113,525,ED ABDOMINAL PAIN ,540141,GI COCKTAIL,Medication,10,1002,0.00998004
+114,525,ED ABDOMINAL PAIN ,540300,MORPHINE INJECTABLE SYRINGE,Medication,94,1002,0.093812375
+115,525,ED ABDOMINAL PAIN ,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,1,1002,0.000998004
+116,525,ED ABDOMINAL PAIN ,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,9,1002,0.008982036
+117,525,ED ABDOMINAL PAIN ,LABCBCD,CBC WITH DIFF,Lab,572,1002,0.570858283
+118,525,ED ABDOMINAL PAIN ,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,7,1002,0.006986028
+119,525,ED ABDOMINAL PAIN ,LABLIPS,LIPASE,Lab,433,1002,0.432135729
+120,525,ED ABDOMINAL PAIN ,LABMETB,"METABOLIC PANEL, BASIC",Lab,17,1002,0.016966068
+121,525,ED ABDOMINAL PAIN ,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,564,1002,0.562874251
+122,525,ED ABDOMINAL PAIN ,LABPT,PROTHROMBIN TIME,Lab,198,1002,0.19760479
+123,525,ED ABDOMINAL PAIN ,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,153,1002,0.152694611
+124,525,ED ABDOMINAL PAIN ,LABTYPSNI,TYPE AND SCREEN,Lab,46,1002,0.045908184
+125,525,ED ABDOMINAL PAIN ,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,418,1002,0.417165669
+126,525,ED ABDOMINAL PAIN ,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,143,1002,0.142714571
+127,525,ED ABDOMINAL PAIN ,POC26,POC URINE PREGNANCY,Lab,70,1002,0.069860279
+128,525,ED ABDOMINAL PAIN ,POC46,POC ISTAT BHCG,Lab,6,1002,0.005988024
+129,525,ED ABDOMINAL PAIN ,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,4,1002,0.003992016
+130,525,ED ABDOMINAL PAIN ,IMGCTAP,CT ABDOMEN PELVIS,Imaging,73,1002,0.072854291
+131,525,ED ABDOMINAL PAIN ,IMGDXAB3,"XR ABDOMEN W AP, UPRIGHT, DECUB & DIAPHRAGM",Imaging,6,1002,0.005988024
+132,525,ED ABDOMINAL PAIN ,IMGDXAB4,"XR 4 VIEW ABDOMEN (SUPINE, AP, LATERAL, OBLIQUES)",Imaging,3,1002,0.002994012
+133,525,ED ABDOMINAL PAIN ,IMGUSAB1,US ABDOMEN,Imaging,16,1002,0.015968064
+134,525,ED ABDOMINAL PAIN ,IMGUSP1,US PELVIS NON OB WO TRANSVAG,Imaging,4,1002,0.003992016
+135,526,ED GI BLEED ,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,377,0.00530504
+136,526,ED GI BLEED ,25122,OCTREOTIDE ACETATE 100 MCG/ML INJ SOLN,Medication,25,377,0.066312997
+137,526,ED GI BLEED ,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,124,377,0.328912467
+138,526,ED GI BLEED ,112420,DIAZEPAM 5 MG/ML INJ SYRG,Medication,1,377,0.00265252
+139,526,ED GI BLEED ,112562,MORPHINE 10 MG/ML INJ SYRG,Medication,1,377,0.00265252
+140,526,ED GI BLEED ,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,377,0.00795756
+141,526,ED GI BLEED ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,31,377,0.082228117
+142,526,ED GI BLEED ,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,2,377,0.00530504
+143,526,ED GI BLEED ,530014,PANTOPRAZOLE 0.8 MG/ML IV INFUSION,Medication,102,377,0.270557029
+144,526,ED GI BLEED ,530016,OCTREOTIDE 10 MCG/ML IV INFUSION,Medication,25,377,0.066312997
+145,526,ED GI BLEED ,540102,NS IV BOLUS,Medication,56,377,0.148541114
+146,526,ED GI BLEED ,540148,NS FLUSH BAG,Medication,1,377,0.00265252
+147,526,ED GI BLEED ,LABCBCD,CBC WITH DIFF,Lab,128,377,0.339522546
+148,526,ED GI BLEED ,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,3,377,0.00795756
+149,526,ED GI BLEED ,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,377,0.00530504
+150,526,ED GI BLEED ,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,128,377,0.339522546
+151,526,ED GI BLEED ,LABPT,PROTHROMBIN TIME,Lab,130,377,0.344827586
+152,526,ED GI BLEED ,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,120,377,0.318302387
+153,526,ED GI BLEED ,LABTYPSNI,TYPE AND SCREEN,Lab,151,377,0.400530504
+154,526,ED GI BLEED ,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,37,377,0.098143236
+155,526,ED GI BLEED ,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,56,377,0.148541114
+156,526,ED GI BLEED ,POC26,POC URINE PREGNANCY,Lab,2,377,0.00530504
+157,526,ED GI BLEED ,IMGCTA,CT ABDOMEN,Imaging,1,377,0.00265252
+158,526,ED GI BLEED ,IMGDXCH1,XR CHEST 1V,Imaging,8,377,0.021220159
+159,526,ED GI BLEED ,IMGDXCH2,XR CHEST 2V,Imaging,2,377,0.00530504
+160,526,ED GI BLEED ,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,377,0.00265252
+161,526,ED GI BLEED ,LABBBPRBC,RED BLOOD CELLS,Blood Product,42,377,0.111405836
+162,527,ED VAGINAL BLEEDING/INFECTION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3,13,0.230769231
+163,527,ED VAGINAL BLEEDING/INFECTION,540102,NS IV BOLUS,Medication,3,13,0.230769231
+164,527,ED VAGINAL BLEEDING/INFECTION,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,13,0.153846154
+165,527,ED VAGINAL BLEEDING/INFECTION,LABBHCG,"BETA HCG, QUANT",Lab,2,13,0.153846154
+166,527,ED VAGINAL BLEEDING/INFECTION,LABCBCD,CBC WITH DIFF,Lab,5,13,0.384615385
+167,527,ED VAGINAL BLEEDING/INFECTION,LABCLGC2,"CHLAMYDIA GC, MOLECULAR DETECTION",Lab,1,13,0.076923077
+168,527,ED VAGINAL BLEEDING/INFECTION,LABKOHP,KOH / CALCOFLUOR,Lab,2,13,0.153846154
+169,527,ED VAGINAL BLEEDING/INFECTION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,5,13,0.384615385
+170,527,ED VAGINAL BLEEDING/INFECTION,LABPT,PROTHROMBIN TIME,Lab,4,13,0.307692308
+171,527,ED VAGINAL BLEEDING/INFECTION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,13,0.230769231
+172,527,ED VAGINAL BLEEDING/INFECTION,LABTYPSNI,TYPE AND SCREEN,Lab,1,13,0.076923077
+173,527,ED VAGINAL BLEEDING/INFECTION,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,5,13,0.384615385
+174,527,ED VAGINAL BLEEDING/INFECTION,POC26,POC URINE PREGNANCY,Lab,4,13,0.307692308
+175,527,ED VAGINAL BLEEDING/INFECTION,IMGUSECT,US R/O ECTOPIC PREGNANCY,Imaging,1,13,0.076923077
+176,527,ED VAGINAL BLEEDING/INFECTION,IMGUSPTV,US PELVIS NON OB W TRANSVAG,Imaging,1,13,0.076923077
+177,528,ED TRAUMA,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,20,0.05
+178,528,ED TRAUMA,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1,20,0.05
+179,528,ED TRAUMA,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,20,0.15
+180,528,ED TRAUMA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,20,0.05
+181,528,ED TRAUMA,540102,NS IV BOLUS,Medication,2,20,0.1
+182,528,ED TRAUMA,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,20,0.05
+183,528,ED TRAUMA,LABCBCD,CBC WITH DIFF,Lab,11,20,0.55
+184,528,ED TRAUMA,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,1,20,0.05
+185,528,ED TRAUMA,LABLIPS,LIPASE,Lab,1,20,0.05
+186,528,ED TRAUMA,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,20,0.1
+187,528,ED TRAUMA,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,11,20,0.55
+188,528,ED TRAUMA,LABPT,PROTHROMBIN TIME,Lab,6,20,0.3
+189,528,ED TRAUMA,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,9,20,0.45
+190,528,ED TRAUMA,LABTYPSNI,TYPE AND SCREEN,Lab,5,20,0.25
+191,528,ED TRAUMA,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,3,20,0.15
+192,528,ED TRAUMA,LABVOLTL,VOLATILE SCREEN,Lab,3,20,0.15
+193,528,ED TRAUMA,POC1,POC URINE DIPSTICK,Lab,1,20,0.05
+194,528,ED TRAUMA,POC14,POC GLUCOSE BY METER,Lab,2,20,0.1
+195,528,ED TRAUMA,POC24,POC ISTAT TROPONIN I,Lab,1,20,0.05
+196,528,ED TRAUMA,IMGCTFAC,CT FACIAL BONES OR SINUS,Imaging,2,20,0.1
+197,528,ED TRAUMA,IMGCTH,CT HEAD,Imaging,3,20,0.15
+198,528,ED TRAUMA,IMGCTSC,CT SPINE CERVICAL,Imaging,3,20,0.15
+199,528,ED TRAUMA,IMGCTSL,CT SPINE LUMBAR,Imaging,2,20,0.1
+200,528,ED TRAUMA,IMGCTST,CT SPINE THORACIC,Imaging,2,20,0.1
+201,528,ED TRAUMA,IMGDXFEMRT,XR FEMUR RT,Imaging,1,20,0.05
+202,528,ED TRAUMA,IMGDXFORLT,XR FOREARM 2V LT,Imaging,1,20,0.05
+203,528,ED TRAUMA,IMGDXHUMLT,XR HUMERUS 2V LT,Imaging,1,20,0.05
+204,528,ED TRAUMA,IMGDXPEL,XR PELVIS 1V,Imaging,2,20,0.1
+205,528,ED TRAUMA,IMGDXSPL2,"XR SPINE, LUMBAR 2V",Imaging,1,20,0.05
+206,528,ED TRAUMA,IMGDXTIBRT,XR TIBIA AND FIBULA RT,Imaging,1,20,0.05
+207,530,ED UPPER EXTREMITY INJURY/PAIN,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,10,0.2
+208,530,ED UPPER EXTREMITY INJURY/PAIN,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3,10,0.3
+209,530,ED UPPER EXTREMITY INJURY/PAIN,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,10,0.1
+210,530,ED UPPER EXTREMITY INJURY/PAIN,LABCBCD,CBC WITH DIFF,Lab,2,10,0.2
+211,530,ED UPPER EXTREMITY INJURY/PAIN,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,10,0.1
+212,530,ED UPPER EXTREMITY INJURY/PAIN,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,10,0.1
+213,530,ED UPPER EXTREMITY INJURY/PAIN,IMGDXFORLT,XR FOREARM 2V LT,Imaging,1,10,0.1
+214,530,ED UPPER EXTREMITY INJURY/PAIN,IMGDXHUMLT,XR HUMERUS 2V LT,Imaging,1,10,0.1
+215,530,ED UPPER EXTREMITY INJURY/PAIN,IMGDXSH2LT,XR SHOULDER 2V LT,Imaging,1,10,0.1
+216,532,ED BACK PAIN,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,48,0.020833333
+217,532,ED BACK PAIN,2405,DIAZEPAM 5 MG PO TABS,Medication,3,48,0.0625
+218,532,ED BACK PAIN,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,3,48,0.0625
+219,532,ED BACK PAIN,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,48,0.020833333
+220,532,ED BACK PAIN,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,48,0.020833333
+221,532,ED BACK PAIN,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,4,48,0.083333333
+222,532,ED BACK PAIN,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,48,0.041666667
+223,532,ED BACK PAIN,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,12,48,0.25
+224,532,ED BACK PAIN,540102,NS IV BOLUS,Medication,6,48,0.125
+225,532,ED BACK PAIN,540300,MORPHINE INJECTABLE SYRINGE,Medication,5,48,0.104166667
+226,532,ED BACK PAIN,9000000,ZZZ IMS TEMPLATE,Medication,1,48,0.020833333
+227,532,ED BACK PAIN,LABCBCD,CBC WITH DIFF,Lab,17,48,0.354166667
+228,532,ED BACK PAIN,LABCRP,C - REACTIVE PROTEIN,Lab,2,48,0.041666667
+229,532,ED BACK PAIN,LABESRP,SEDIMENTATION RATE (ESR),Lab,1,48,0.020833333
+230,532,ED BACK PAIN,LABLIPS,LIPASE,Lab,3,48,0.0625
+231,532,ED BACK PAIN,LABMETB,"METABOLIC PANEL, BASIC",Lab,5,48,0.104166667
+232,532,ED BACK PAIN,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,12,48,0.25
+233,532,ED BACK PAIN,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,13,48,0.270833333
+234,532,ED BACK PAIN,POC26,POC URINE PREGNANCY,Lab,2,48,0.041666667
+235,532,ED BACK PAIN,IMGCTSL,CT SPINE LUMBAR,Imaging,2,48,0.041666667
+236,532,ED BACK PAIN,IMGDXCH2,XR CHEST 2V,Imaging,1,48,0.020833333
+237,532,ED BACK PAIN,IMGMRSL,MR L-SPINE,Imaging,2,48,0.041666667
+238,533,ED LACERATION,4452,LIDOCAINE HCL 10 MG/ML (1 %) INJ SOLN,Medication,2,15,0.133333333
+239,533,ED LACERATION,4454,LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN,Medication,1,15,0.066666667
+240,533,ED LACERATION,15954,"LIDOCAINE-EPINEPHRINE 2 %-1:100,000 INJ SOLN",Medication,3,15,0.2
+241,533,ED LACERATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,15,0.066666667
+242,533,ED LACERATION,115300,"BUPIVACAINE-EPINEPHRINE (PF) 0.25 %-1:200,000 INJ SOLN",Medication,1,15,0.066666667
+243,533,ED LACERATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,15,0.066666667
+244,533,ED LACERATION,541184,"DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM INJ",Medication,1,15,0.066666667
+245,533,ED LACERATION,LABCBCD,CBC WITH DIFF,Lab,2,15,0.133333333
+246,533,ED LACERATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,15,0.133333333
+247,534,ED CELLULITIS/ABSCESS,3844,IBUPROFEN 600 MG PO TABS,Medication,1,32,0.03125
+248,534,ED CELLULITIS/ABSCESS,9488,CEFTRIAXONE 2 GRAM IV SOLR,Medication,2,32,0.0625
+249,534,ED CELLULITIS/ABSCESS,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,32,0.03125
+250,534,ED CELLULITIS/ABSCESS,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,32,0.03125
+251,534,ED CELLULITIS/ABSCESS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,4,32,0.125
+252,534,ED CELLULITIS/ABSCESS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,32,0.0625
+253,534,ED CELLULITIS/ABSCESS,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,5,32,0.15625
+254,534,ED CELLULITIS/ABSCESS,540102,NS IV BOLUS,Medication,4,32,0.125
+255,534,ED CELLULITIS/ABSCESS,540300,MORPHINE INJECTABLE SYRINGE,Medication,3,32,0.09375
+256,534,ED CELLULITIS/ABSCESS,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,1,32,0.03125
+257,534,ED CELLULITIS/ABSCESS,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,32,0.03125
+258,534,ED CELLULITIS/ABSCESS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,9,32,0.28125
+259,534,ED CELLULITIS/ABSCESS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,9,32,0.28125
+260,534,ED CELLULITIS/ABSCESS,LABCBCD,CBC WITH DIFF,Lab,14,32,0.4375
+261,534,ED CELLULITIS/ABSCESS,LABMETB,"METABOLIC PANEL, BASIC",Lab,3,32,0.09375
+262,534,ED CELLULITIS/ABSCESS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,11,32,0.34375
+263,534,ED CELLULITIS/ABSCESS,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,3,32,0.09375
+264,534,ED CELLULITIS/ABSCESS,LABWDC,WOUND CULTURE,Lab,5,32,0.15625
+265,534,ED CELLULITIS/ABSCESS,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,8,32,0.25
+266,536,ED FEVER/NEUTROPENIA,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,237,0.008438819
+267,536,ED FEVER/NEUTROPENIA,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,237,0.004219409
+268,536,ED FEVER/NEUTROPENIA,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,6,237,0.025316456
+269,536,ED FEVER/NEUTROPENIA,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,1,237,0.004219409
+270,536,ED FEVER/NEUTROPENIA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,12,237,0.050632911
+271,536,ED FEVER/NEUTROPENIA,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,23,237,0.097046414
+272,536,ED FEVER/NEUTROPENIA,520102,CEFEPIME 2 GM IVPB,Medication,3,237,0.012658228
+273,536,ED FEVER/NEUTROPENIA,540102,NS IV BOLUS,Medication,66,237,0.278481013
+274,536,ED FEVER/NEUTROPENIA,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,237,0.004219409
+275,536,ED FEVER/NEUTROPENIA,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,43,237,0.181434599
+276,536,ED FEVER/NEUTROPENIA,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,77,237,0.324894515
+277,536,ED FEVER/NEUTROPENIA,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,77,237,0.324894515
+278,536,ED FEVER/NEUTROPENIA,LABCBCD,CBC WITH DIFF,Lab,102,237,0.430379747
+279,536,ED FEVER/NEUTROPENIA,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,3,237,0.012658228
+280,536,ED FEVER/NEUTROPENIA,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,2,237,0.008438819
+281,536,ED FEVER/NEUTROPENIA,LABMETB,"METABOLIC PANEL, BASIC",Lab,6,237,0.025316456
+282,536,ED FEVER/NEUTROPENIA,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,99,237,0.417721519
+283,536,ED FEVER/NEUTROPENIA,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,93,237,0.392405063
+284,536,ED FEVER/NEUTROPENIA,LABWDC,WOUND CULTURE,Lab,3,237,0.012658228
+285,536,ED FEVER/NEUTROPENIA,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,66,237,0.278481013
+286,536,ED FEVER/NEUTROPENIA,POC26,POC URINE PREGNANCY,Lab,4,237,0.016877637
+287,536,ED FEVER/NEUTROPENIA,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,1,237,0.004219409
+288,536,ED FEVER/NEUTROPENIA,IMGDXCH1,XR CHEST 1V,Imaging,42,237,0.17721519
+289,536,ED FEVER/NEUTROPENIA,IMGDXCH2,XR CHEST 2V,Imaging,35,237,0.147679325
+290,538,ED SEPTIC SHOCK,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,6,26,0.230769231
+291,538,ED SEPTIC SHOCK,93461,MOXIFLOXACIN IN NACL (ISO-OSM) 400 MG/250 ML IV PGBK,Medication,1,26,0.038461538
+292,538,ED SEPTIC SHOCK,95725,CEFTRIAXONE 2 GRAM INJ SOLR,Medication,1,26,0.038461538
+293,538,ED SEPTIC SHOCK,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,26,0.038461538
+294,538,ED SEPTIC SHOCK,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,1,26,0.038461538
+295,538,ED SEPTIC SHOCK,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,26,0.038461538
+296,538,ED SEPTIC SHOCK,541089,VANCOMYCIN 1 GRAM/100 ML CENTRAL LINE MINI-BAG PLUS,Medication,1,26,0.038461538
+297,538,ED SEPTIC SHOCK,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,1,26,0.038461538
+298,538,ED SEPTIC SHOCK,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,10,26,0.384615385
+299,538,ED SEPTIC SHOCK,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,7,26,0.269230769
+300,538,ED SEPTIC SHOCK,LABCBCD,CBC WITH DIFF,Lab,3,26,0.115384615
+301,538,ED SEPTIC SHOCK,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,1,26,0.038461538
+302,538,ED SEPTIC SHOCK,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,6,26,0.230769231
+303,538,ED SEPTIC SHOCK,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,26,0.038461538
+304,538,ED SEPTIC SHOCK,LABPT,PROTHROMBIN TIME,Lab,1,26,0.038461538
+305,538,ED SEPTIC SHOCK,LABRESP,RESPIRATORY CULTURE,Lab,2,26,0.076923077
+306,538,ED SEPTIC SHOCK,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,1,26,0.038461538
+307,538,ED SEPTIC SHOCK,LABTNI,TROPONIN I,Lab,2,26,0.076923077
+308,538,ED SEPTIC SHOCK,LABTYPSNI,TYPE AND SCREEN,Lab,1,26,0.038461538
+309,538,ED SEPTIC SHOCK,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,7,26,0.269230769
+310,538,ED SEPTIC SHOCK,LABURNC,URINE CULTURE,Lab,4,26,0.153846154
+311,538,ED SEPTIC SHOCK,POC14,POC GLUCOSE BY METER,Lab,1,26,0.038461538
+312,538,ED SEPTIC SHOCK,POC15,"POC ISTAT, ARTERIAL BLOOD GASES AND LACTATE (CG4)",Lab,3,26,0.115384615
+313,538,ED SEPTIC SHOCK,POC23,"POC ISTAT, VENOUS BLOOD GASES (G3+)",Lab,4,26,0.153846154
+314,538,ED SEPTIC SHOCK,POC24,POC ISTAT TROPONIN I,Lab,3,26,0.115384615
+315,538,ED SEPTIC SHOCK,POC26,POC URINE PREGNANCY,Lab,1,26,0.038461538
+316,538,ED SEPTIC SHOCK,IMGDXCH1,XR CHEST 1V,Imaging,3,26,0.115384615
+317,538,ED SEPTIC SHOCK,IMGDXCH2,XR CHEST 2V,Imaging,3,26,0.115384615
+318,539,ED SEPSIS PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,864,0.002314815
+319,539,ED SEPSIS PROTOCOL,102,ACETAMINOPHEN 500 MG PO TABS,Medication,7,864,0.008101852
+320,539,ED SEPSIS PROTOCOL,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,5,864,0.005787037
+321,539,ED SEPSIS PROTOCOL,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,1,864,0.001157407
+322,539,ED SEPSIS PROTOCOL,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,864,0.001157407
+323,539,ED SEPSIS PROTOCOL,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,864,0.002314815
+324,539,ED SEPSIS PROTOCOL,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,864,0.001157407
+325,539,ED SEPSIS PROTOCOL,89444,PIPERACILLIN-TAZOBACTAM-DEXTRS 2.25 GRAM/50 ML IV PGBK,Medication,1,864,0.001157407
+326,539,ED SEPSIS PROTOCOL,95725,CEFTRIAXONE 2 GRAM INJ SOLR,Medication,1,864,0.001157407
+327,539,ED SEPSIS PROTOCOL,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,864,0.001157407
+328,539,ED SEPSIS PROTOCOL,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,46,864,0.053240741
+329,539,ED SEPSIS PROTOCOL,112321,LINEZOLID 600 MG/300 ML IV SOLP,Medication,1,864,0.001157407
+330,539,ED SEPSIS PROTOCOL,112562,MORPHINE 10 MG/ML INJ SYRG,Medication,1,864,0.001157407
+331,539,ED SEPSIS PROTOCOL,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,864,0.003472222
+332,539,ED SEPSIS PROTOCOL,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,22,864,0.025462963
+333,539,ED SEPSIS PROTOCOL,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,39,864,0.045138889
+334,539,ED SEPSIS PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,22,864,0.025462963
+335,539,ED SEPSIS PROTOCOL,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,1,864,0.001157407
+336,539,ED SEPSIS PROTOCOL,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,3,864,0.003472222
+337,539,ED SEPSIS PROTOCOL,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,2,864,0.002314815
+338,539,ED SEPSIS PROTOCOL,520039,AZITHROMYCIN IVPB,Medication,12,864,0.013888889
+339,539,ED SEPSIS PROTOCOL,520058,CASPOFUNGIN 70 MG IVPB,Medication,1,864,0.001157407
+340,539,ED SEPSIS PROTOCOL,520102,CEFEPIME 2 GM IVPB,Medication,1,864,0.001157407
+341,539,ED SEPSIS PROTOCOL,530011,NOREPINEPHRINE IV INFUSION,Medication,2,864,0.002314815
+342,539,ED SEPSIS PROTOCOL,540102,NS IV BOLUS,Medication,211,864,0.244212963
+343,539,ED SEPSIS PROTOCOL,540115,LACTATED RINGERS IV BOLUS,Medication,1,864,0.001157407
+344,539,ED SEPSIS PROTOCOL,540300,MORPHINE INJECTABLE SYRINGE,Medication,3,864,0.003472222
+345,539,ED SEPSIS PROTOCOL,540773,PIPERACILLIN-TAZOBACTAM (ZOSYN) 3.375 GRAM IN DEXTROSE IVPB,Medication,1,864,0.001157407
+346,539,ED SEPSIS PROTOCOL,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,47,864,0.054398148
+347,539,ED SEPSIS PROTOCOL,541020,VANCOMYCIN ED ORDER ,Medication,77,864,0.08912037
+348,539,ED SEPSIS PROTOCOL,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,16,864,0.018518519
+349,539,ED SEPSIS PROTOCOL,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,9,864,0.010416667
+350,539,ED SEPSIS PROTOCOL,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,10,864,0.011574074
+351,539,ED SEPSIS PROTOCOL,541092,AMPICILLIN 2 GRAM/100 ML MINI-BAG PLUS,Medication,2,864,0.002314815
+352,539,ED SEPSIS PROTOCOL,541095,AZITHROMYCIN 500 MG/250 ML MINIBAG PLUS,Medication,6,864,0.006944444
+353,539,ED SEPSIS PROTOCOL,541097,AZTREONAM 2 GRAM/100 ML MINI-BAG PLUS,Medication,1,864,0.001157407
+354,539,ED SEPSIS PROTOCOL,541109,DOXYCYCLINE 100 MG/100 ML MINI-BAG PLUS,Medication,3,864,0.003472222
+355,539,ED SEPSIS PROTOCOL,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,864,0.002314815
+356,539,ED SEPSIS PROTOCOL,541136,MEROPENEM IN D5W 1 GRAM/100 ML MINIBAG PLUS,Medication,7,864,0.008101852
+357,539,ED SEPSIS PROTOCOL,541440,NS IV BOLUS - 500 ML,Medication,1,864,0.001157407
+358,539,ED SEPSIS PROTOCOL,541441,NS IV BOLUS - 1000 ML,Medication,11,864,0.012731481
+359,539,ED SEPSIS PROTOCOL,541442,NS IV BOLUS - 2000 ML,Medication,32,864,0.037037037
+360,539,ED SEPSIS PROTOCOL,541444,LR IV BOLUS - 500 ML,Medication,1,864,0.001157407
+361,539,ED SEPSIS PROTOCOL,541446,LR IV BOLUS - 2000 ML,Medication,2,864,0.002314815
+362,539,ED SEPSIS PROTOCOL,541447,NS IV BOLUS - 30 ML/KG,Medication,4,864,0.00462963
+363,539,ED SEPSIS PROTOCOL,541448,LR IV BOLUS - 30 ML/KG,Medication,8,864,0.009259259
+364,539,ED SEPSIS PROTOCOL,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,163,864,0.188657407
+365,539,ED SEPSIS PROTOCOL,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,170,864,0.196759259
+366,539,ED SEPSIS PROTOCOL,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,67,864,0.077546296
+367,539,ED SEPSIS PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,155,864,0.179398148
+368,539,ED SEPSIS PROTOCOL,LABDDIM,D - DIMER,Lab,4,864,0.00462963
+369,539,ED SEPSIS PROTOCOL,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,74,864,0.085648148
+370,539,ED SEPSIS PROTOCOL,LABLIPS,LIPASE,Lab,54,864,0.0625
+371,539,ED SEPSIS PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,150,864,0.173611111
+372,539,ED SEPSIS PROTOCOL,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,159,864,0.184027778
+373,539,ED SEPSIS PROTOCOL,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,123,864,0.142361111
+374,539,ED SEPSIS PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,102,864,0.118055556
+375,539,ED SEPSIS PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,64,864,0.074074074
+376,539,ED SEPSIS PROTOCOL,LABRESEARCH,EXTRA LAB DRAWS (DO NOT SEND TO LAB),Lab,6,864,0.006944444
+377,539,ED SEPSIS PROTOCOL,LABTYPSNI,TYPE AND SCREEN,Lab,70,864,0.081018519
+378,539,ED SEPSIS PROTOCOL,LABURNC,URINE CULTURE,Lab,96,864,0.111111111
+379,539,ED SEPSIS PROTOCOL,LABVBG,"BLOOD GASES, VENOUS",Lab,21,864,0.024305556
+380,539,ED SEPSIS PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,71,864,0.082175926
+381,539,ED SEPSIS PROTOCOL,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,134,864,0.155092593
+382,539,ED SEPSIS PROTOCOL,POC26,POC URINE PREGNANCY,Lab,24,864,0.027777778
+383,539,ED SEPSIS PROTOCOL,POC46,POC ISTAT BHCG,Lab,1,864,0.001157407
+384,539,ED SEPSIS PROTOCOL,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,1,864,0.001157407
+385,539,ED SEPSIS PROTOCOL,IMGCTAP,CT ABDOMEN PELVIS,Imaging,5,864,0.005787037
+386,539,ED SEPSIS PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,206,864,0.238425926
+387,539,ED SEPSIS PROTOCOL,IMGDXCH2,XR CHEST 2V,Imaging,65,864,0.075231481
+388,540,ED ALLERGIC REACTION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,3,10,0.3
+389,540,ED ALLERGIC REACTION,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,1,10,0.1
+390,540,ED ALLERGIC REACTION,122688,RACEPINEPHRINE 2.25 % INH NEBU,Medication,1,10,0.1
+391,540,ED ALLERGIC REACTION,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,5,10,0.5
+392,540,ED ALLERGIC REACTION,540102,NS IV BOLUS,Medication,1,10,0.1
+393,540,ED ALLERGIC REACTION,LABCBCD,CBC WITH DIFF,Lab,2,10,0.2
+394,540,ED ALLERGIC REACTION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,10,0.2
+395,540,ED ALLERGIC REACTION,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,10,0.1
+396,542,ED PSYCH/DRUG PROBLEM,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,400,0.0025
+397,542,ED PSYCH/DRUG PROBLEM,4573,LORAZEPAM 1 MG PO TABS,Medication,1,400,0.0025
+398,542,ED PSYCH/DRUG PROBLEM,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,2,400,0.005
+399,542,ED PSYCH/DRUG PROBLEM,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,400,0.0025
+400,542,ED PSYCH/DRUG PROBLEM,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,400,0.0025
+401,542,ED PSYCH/DRUG PROBLEM,123648,ACTIVATED CHARCOAL 50 GRAM/240 ML PO SUSP,Medication,1,400,0.0025
+402,542,ED PSYCH/DRUG PROBLEM,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,400,0.0025
+403,542,ED PSYCH/DRUG PROBLEM,540102,NS IV BOLUS,Medication,28,400,0.07
+404,542,ED PSYCH/DRUG PROBLEM,540505,HALOPERIDOL INTRAMUSCULAR INJECTION,Medication,2,400,0.005
+405,542,ED PSYCH/DRUG PROBLEM,LABACETA,"ACETAMINOPHEN, SERUM",Lab,246,400,0.615
+406,542,ED PSYCH/DRUG PROBLEM,LABCBCD,CBC WITH DIFF,Lab,249,400,0.6225
+407,542,ED PSYCH/DRUG PROBLEM,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,343,400,0.8575
+408,542,ED PSYCH/DRUG PROBLEM,LABLIL,LITHIUM,Lab,65,400,0.1625
+409,542,ED PSYCH/DRUG PROBLEM,LABLIPS,LIPASE,Lab,50,400,0.125
+410,542,ED PSYCH/DRUG PROBLEM,LABMETB,"METABOLIC PANEL, BASIC",Lab,10,400,0.025
+411,542,ED PSYCH/DRUG PROBLEM,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,329,400,0.8225
+412,542,ED PSYCH/DRUG PROBLEM,LABSALICY,SALICYLATE LEVEL,Lab,238,400,0.595
+413,542,ED PSYCH/DRUG PROBLEM,LABTSH,TSH,Lab,156,400,0.39
+414,542,ED PSYCH/DRUG PROBLEM,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,322,400,0.805
+415,542,ED PSYCH/DRUG PROBLEM,LABVOLTL,VOLATILE SCREEN,Lab,322,400,0.805
+416,542,ED PSYCH/DRUG PROBLEM,POC26,POC URINE PREGNANCY,Lab,61,400,0.1525
+417,542,ED PSYCH/DRUG PROBLEM,POC46,POC ISTAT BHCG,Lab,16,400,0.04
+418,542,ED PSYCH/DRUG PROBLEM,IMGCTH,CT HEAD,Imaging,5,400,0.0125
+419,542,ED PSYCH/DRUG PROBLEM,IMGDXCH1,XR CHEST 1V,Imaging,7,400,0.0175
+420,542,ED PSYCH/DRUG PROBLEM,IMGDXCH2,XR CHEST 2V,Imaging,1,400,0.0025
+421,543,ED ETOH/ALTERED MENTAL STATUS,4573,LORAZEPAM 1 MG PO TABS,Medication,1,147,0.006802721
+422,543,ED ETOH/ALTERED MENTAL STATUS,7876,THIAMINE HCL (VITAMIN B1) 100 MG/ML INJ SOLN,Medication,5,147,0.034013605
+423,543,ED ETOH/ALTERED MENTAL STATUS,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,8,147,0.054421769
+424,543,ED ETOH/ALTERED MENTAL STATUS,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,3,147,0.020408163
+425,543,ED ETOH/ALTERED MENTAL STATUS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,147,0.013605442
+426,543,ED ETOH/ALTERED MENTAL STATUS,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,147,0.06122449
+427,543,ED ETOH/ALTERED MENTAL STATUS,200345,MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV SOLN,Medication,1,147,0.006802721
+428,543,ED ETOH/ALTERED MENTAL STATUS,520094,"MULTIVITAMINS (+ FOLIC ACID, THIAMINE, VIT K) IVPB",Medication,6,147,0.040816327
+429,543,ED ETOH/ALTERED MENTAL STATUS,540102,NS IV BOLUS,Medication,31,147,0.210884354
+430,543,ED ETOH/ALTERED MENTAL STATUS,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,147,0.006802721
+431,543,ED ETOH/ALTERED MENTAL STATUS,540505,HALOPERIDOL INTRAMUSCULAR INJECTION,Medication,2,147,0.013605442
+432,543,ED ETOH/ALTERED MENTAL STATUS,LABBHCG,"BETA HCG, QUANT",Lab,5,147,0.034013605
+433,543,ED ETOH/ALTERED MENTAL STATUS,LABCBCD,CBC WITH DIFF,Lab,83,147,0.56462585
+434,543,ED ETOH/ALTERED MENTAL STATUS,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,25,147,0.170068027
+435,543,ED ETOH/ALTERED MENTAL STATUS,LABMETB,"METABOLIC PANEL, BASIC",Lab,5,147,0.034013605
+436,543,ED ETOH/ALTERED MENTAL STATUS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,83,147,0.56462585
+437,543,ED ETOH/ALTERED MENTAL STATUS,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,8,147,0.054421769
+438,543,ED ETOH/ALTERED MENTAL STATUS,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,71,147,0.482993197
+439,543,ED ETOH/ALTERED MENTAL STATUS,LABVOLTL,VOLATILE SCREEN,Lab,32,147,0.217687075
+440,543,ED ETOH/ALTERED MENTAL STATUS,POC1,POC URINE DIPSTICK,Lab,3,147,0.020408163
+441,543,ED ETOH/ALTERED MENTAL STATUS,POC14,POC GLUCOSE BY METER,Lab,5,147,0.034013605
+442,543,ED ETOH/ALTERED MENTAL STATUS,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,45,147,0.306122449
+443,543,ED ETOH/ALTERED MENTAL STATUS,POC24,POC ISTAT TROPONIN I,Lab,88,147,0.598639456
+444,543,ED ETOH/ALTERED MENTAL STATUS,IMGCTH,CT HEAD,Imaging,44,147,0.299319728
+445,543,ED ETOH/ALTERED MENTAL STATUS,IMGCTSC,CT SPINE CERVICAL,Imaging,2,147,0.013605442
+446,543,ED ETOH/ALTERED MENTAL STATUS,IMGDXCH1,XR CHEST 1V,Imaging,47,147,0.319727891
+447,543,ED ETOH/ALTERED MENTAL STATUS,IMGDXCH2,XR CHEST 2V,Imaging,8,147,0.054421769
+448,547,ED GENERAL COMPLAINT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,2569,0.000778513
+449,547,ED GENERAL COMPLAINT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,6,2569,0.002335539
+450,547,ED GENERAL COMPLAINT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,2569,0.000389257
+451,547,ED GENERAL COMPLAINT,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,1,2569,0.000389257
+452,547,ED GENERAL COMPLAINT,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,94,2569,0.036590113
+453,547,ED GENERAL COMPLAINT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,2569,0.000389257
+454,547,ED GENERAL COMPLAINT,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,10,2569,0.003892565
+455,547,ED GENERAL COMPLAINT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,4,2569,0.001557026
+456,547,ED GENERAL COMPLAINT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,2569,0.001946283
+457,547,ED GENERAL COMPLAINT,94176,ONDANSETRON 4 MG PO TBDL,Medication,3,2569,0.00116777
+458,547,ED GENERAL COMPLAINT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,2569,0.000389257
+459,547,ED GENERAL COMPLAINT,112562,MORPHINE 10 MG/ML INJ SYRG,Medication,2,2569,0.000778513
+460,547,ED GENERAL COMPLAINT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,103,2569,0.040093422
+461,547,ED GENERAL COMPLAINT,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,11,2569,0.004281822
+462,547,ED GENERAL COMPLAINT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,193,2569,0.075126508
+463,547,ED GENERAL COMPLAINT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,2569,0.000778513
+464,547,ED GENERAL COMPLAINT,540102,NS IV BOLUS,Medication,630,2569,0.245231608
+465,547,ED GENERAL COMPLAINT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,4,2569,0.001557026
+466,547,ED GENERAL COMPLAINT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,8,2569,0.003114052
+467,547,ED GENERAL COMPLAINT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,361,2569,0.140521604
+468,547,ED GENERAL COMPLAINT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,362,2569,0.14091086
+469,547,ED GENERAL COMPLAINT,LABCBCD,CBC WITH DIFF,Lab,1618,2569,0.629817049
+470,547,ED GENERAL COMPLAINT,LABCRP,C - REACTIVE PROTEIN,Lab,38,2569,0.014791748
+471,547,ED GENERAL COMPLAINT,LABDDIML,D - DIMER (ELISA),Lab,15,2569,0.005838848
+472,547,ED GENERAL COMPLAINT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,53,2569,0.020630596
+473,547,ED GENERAL COMPLAINT,LABESRP,SEDIMENTATION RATE (ESR),Lab,34,2569,0.013234722
+474,547,ED GENERAL COMPLAINT,LABLIPS,LIPASE,Lab,429,2569,0.166991047
+475,547,ED GENERAL COMPLAINT,LABMETB,"METABOLIC PANEL, BASIC",Lab,91,2569,0.035422343
+476,547,ED GENERAL COMPLAINT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1532,2569,0.596340989
+477,547,ED GENERAL COMPLAINT,LABPT,PROTHROMBIN TIME,Lab,735,2569,0.286103542
+478,547,ED GENERAL COMPLAINT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,534,2569,0.207862982
+479,547,ED GENERAL COMPLAINT,LABTYPSNI,TYPE AND SCREEN,Lab,122,2569,0.047489295
+480,547,ED GENERAL COMPLAINT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,831,2569,0.323472168
+481,547,ED GENERAL COMPLAINT,POC14,POC GLUCOSE BY METER,Lab,178,2569,0.069287661
+482,547,ED GENERAL COMPLAINT,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,359,2569,0.139743091
+483,547,ED GENERAL COMPLAINT,POC24,POC ISTAT TROPONIN I,Lab,554,2569,0.215648112
+484,547,ED GENERAL COMPLAINT,POC26,POC URINE PREGNANCY,Lab,80,2569,0.031140522
+485,547,ED GENERAL COMPLAINT,POC46,POC ISTAT BHCG,Lab,6,2569,0.002335539
+486,547,ED GENERAL COMPLAINT,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,11,2569,0.004281822
+487,547,ED GENERAL COMPLAINT,IMGCTAP,CT ABDOMEN PELVIS,Imaging,15,2569,0.005838848
+488,547,ED GENERAL COMPLAINT,IMGCTH,CT HEAD,Imaging,73,2569,0.028415726
+489,547,ED GENERAL COMPLAINT,IMGDXCH1,XR CHEST 1V,Imaging,241,2569,0.093810821
+490,547,ED GENERAL COMPLAINT,IMGDXCH2,XR CHEST 2V,Imaging,148,2569,0.057609965
+491,550,ED SEIZURE TREATMENT,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,9,37,0.243243243
+492,550,ED SEIZURE TREATMENT,112420,DIAZEPAM 5 MG/ML INJ SYRG,Medication,1,37,0.027027027
+493,550,ED SEIZURE TREATMENT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,37,0.054054054
+494,550,ED SEIZURE TREATMENT,203954,FOSPHENYTOIN 100 MG PE/2 ML INJ SOLN,Medication,2,37,0.054054054
+495,550,ED SEIZURE TREATMENT,222569,"LEVETIRACETAM IN NACL (ISO-OS) 1,500 MG/100 ML IV PGBK",Medication,1,37,0.027027027
+496,550,ED SEIZURE TREATMENT,520068,PHENYTOIN IVPB (500 MG OR LESS),Medication,2,37,0.054054054
+497,550,ED SEIZURE TREATMENT,520069,VALPROATE IVPB,Medication,1,37,0.027027027
+498,550,ED SEIZURE TREATMENT,540102,NS IV BOLUS,Medication,4,37,0.108108108
+499,550,ED SEIZURE TREATMENT,LABBHCG,"BETA HCG, QUANT",Lab,1,37,0.027027027
+500,550,ED SEIZURE TREATMENT,LABCBCD,CBC WITH DIFF,Lab,15,37,0.405405405
+501,550,ED SEIZURE TREATMENT,LABCZPL,"CARBAMAZEPINE, SERUM",Lab,1,37,0.027027027
+502,550,ED SEIZURE TREATMENT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,3,37,0.081081081
+503,550,ED SEIZURE TREATMENT,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,37,0.027027027
+504,550,ED SEIZURE TREATMENT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,15,37,0.405405405
+505,550,ED SEIZURE TREATMENT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,11,37,0.297297297
+506,550,ED SEIZURE TREATMENT,LABPB,PHENOBARBITAL LEVEL,Lab,1,37,0.027027027
+507,550,ED SEIZURE TREATMENT,LABPHEN,PHENYTOIN LEVEL,Lab,1,37,0.027027027
+508,550,ED SEIZURE TREATMENT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,12,37,0.324324324
+509,550,ED SEIZURE TREATMENT,LABVOLTL,VOLATILE SCREEN,Lab,3,37,0.081081081
+510,550,ED SEIZURE TREATMENT,LABVPAL,VALPROIC ACID,Lab,1,37,0.027027027
+511,550,ED SEIZURE TREATMENT,POC14,POC GLUCOSE BY METER,Lab,10,37,0.27027027
+512,550,ED SEIZURE TREATMENT,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,3,37,0.081081081
+513,550,ED SEIZURE TREATMENT,POC26,POC URINE PREGNANCY,Lab,3,37,0.081081081
+514,550,ED SEIZURE TREATMENT,IMGCTH,CT HEAD,Imaging,3,37,0.081081081
+515,550,ED SEIZURE TREATMENT,IMGDXCH1,XR CHEST 1V,Imaging,3,37,0.081081081
+516,567,PED ED ANAPHYLAXIS-ALLERGIC REACTION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,2,0.5
+517,567,PED ED ANAPHYLAXIS-ALLERGIC REACTION,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,2,0.5
+518,567,PED ED ANAPHYLAXIS-ALLERGIC REACTION,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,1,2,0.5
+519,567,PED ED ANAPHYLAXIS-ALLERGIC REACTION,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,1,2,0.5
+520,568,PED ED ASTHMA,251,ALBUTEROL SULFATE 5 MG/ML INH NEBU,Medication,1,4,0.25
+521,568,PED ED ASTHMA,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,2,4,0.5
+522,571,PED ED DKA,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,16,0.0625
+523,571,PED ED DKA,540102,NS IV BOLUS,Medication,4,16,0.25
+524,571,PED ED DKA,570056,INSULIN REGULAR 0.1 UNIT/ML PEDIATRIC IV INFUSION,Medication,2,16,0.125
+525,571,PED ED DKA,570057,INSULIN REGULAR 1 UNIT/ML PEDIATRIC IV INFUSION,Medication,2,16,0.125
+526,571,PED ED DKA,LABA1C,HEMOGLOBIN A1C,Lab,2,16,0.125
+527,571,PED ED DKA,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,1,16,0.0625
+528,571,PED ED DKA,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,1,16,0.0625
+529,571,PED ED DKA,LABCBCD,CBC WITH DIFF,Lab,4,16,0.25
+530,571,PED ED DKA,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,1,16,0.0625
+531,571,PED ED DKA,LABLIPS,LIPASE,Lab,3,16,0.1875
+532,571,PED ED DKA,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,16,0.25
+533,571,PED ED DKA,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,16,0.0625
+534,571,PED ED DKA,LABOSM,"OSMOLALITY, SERUM",Lab,1,16,0.0625
+535,571,PED ED DKA,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,7,16,0.4375
+536,571,PED ED DKA,LABUOSM,"OSMOLALITY, URINE",Lab,3,16,0.1875
+537,571,PED ED DKA,LABURNC,URINE CULTURE,Lab,3,16,0.1875
+538,571,PED ED DKA,POC1,POC URINE DIPSTICK,Lab,1,16,0.0625
+539,571,PED ED DKA,POC14,POC GLUCOSE BY METER,Lab,6,16,0.375
+540,571,PED ED DKA,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,3,16,0.1875
+541,571,PED ED DKA,POC26,POC URINE PREGNANCY,Lab,2,16,0.125
+542,572,PED ED FEVER,LABCBCD,CBC WITH DIFF,Lab,1,1,1
+543,572,PED ED FEVER,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,1,1
+544,572,PED ED FEVER,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,1,1
+545,573,RETIRED - PED ED GASTROENTERITIS,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,1,1,1
+546,573,RETIRED - PED ED GASTROENTERITIS,LABDVERTV,"ROTAVIRUS ANTIGEN, STOOL",Lab,1,1,1
+547,573,RETIRED - PED ED GASTROENTERITIS,LABGIARS,GIARDIA SCREEN,Lab,1,1,1
+548,573,RETIRED - PED ED GASTROENTERITIS,LABOAPE,OVA AND PARASITES,Lab,1,1,1
+549,573,RETIRED - PED ED GASTROENTERITIS,LABSTLCP,STOOL CULTURE,Lab,1,1,1
+550,580,PED ED PSYCHIATRIC EVALUATION,540102,NS IV BOLUS,Medication,1,19,0.052631579
+551,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,540102,NS IV BOLUS,Medication,1,19,0.052631579
+552,580,PED ED PSYCHIATRIC EVALUATION,LABACETA,"ACETAMINOPHEN, SERUM",Lab,14,19,0.736842105
+553,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABACETA,"ACETAMINOPHEN, SERUM",Lab,14,19,0.736842105
+554,580,PED ED PSYCHIATRIC EVALUATION,LABCBCD,CBC WITH DIFF,Lab,13,19,0.684210526
+555,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABCBCD,CBC WITH DIFF,Lab,13,19,0.684210526
+556,580,PED ED PSYCHIATRIC EVALUATION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,12,19,0.631578947
+557,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,12,19,0.631578947
+558,580,PED ED PSYCHIATRIC EVALUATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,13,19,0.684210526
+559,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,13,19,0.684210526
+560,580,PED ED PSYCHIATRIC EVALUATION,LABSALICY,SALICYLATE LEVEL,Lab,14,19,0.736842105
+561,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABSALICY,SALICYLATE LEVEL,Lab,14,19,0.736842105
+562,580,PED ED PSYCHIATRIC EVALUATION,LABTSH,TSH,Lab,14,19,0.736842105
+563,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABTSH,TSH,Lab,14,19,0.736842105
+564,580,PED ED PSYCHIATRIC EVALUATION,LABVOLTL,VOLATILE SCREEN,Lab,14,19,0.736842105
+565,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,LABVOLTL,VOLATILE SCREEN,Lab,14,19,0.736842105
+566,580,PED ED PSYCHIATRIC EVALUATION,POC14,POC GLUCOSE BY METER,Lab,8,19,0.421052632
+567,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,POC14,POC GLUCOSE BY METER,Lab,8,19,0.421052632
+568,580,PED ED PSYCHIATRIC EVALUATION,POC26,POC URINE PREGNANCY,Lab,9,19,0.473684211
+569,580,RETIRED - PED ED PSYCHIATRIC EVALUATION,POC26,POC URINE PREGNANCY,Lab,9,19,0.473684211
+570,585,PED ED SICKLE CELL FEVER,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,5,0.2
+571,585,PED ED SICKLE CELL FEVER,540102,NS IV BOLUS,Medication,1,5,0.2
+572,585,PED ED SICKLE CELL FEVER,LABCBCD,CBC WITH DIFF,Lab,4,5,0.8
+573,585,PED ED SICKLE CELL FEVER,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,3,5,0.6
+574,585,PED ED SICKLE CELL FEVER,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,5,0.2
+575,585,PED ED SICKLE CELL FEVER,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,5,0.6
+576,585,PED ED SICKLE CELL FEVER,LABPT,PROTHROMBIN TIME,Lab,2,5,0.4
+577,585,PED ED SICKLE CELL FEVER,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,5,0.6
+578,585,PED ED SICKLE CELL FEVER,LABRETIC,RETICULOCYTE COUNT,Lab,4,5,0.8
+579,585,PED ED SICKLE CELL FEVER,LABTYPSNI,TYPE AND SCREEN,Lab,1,5,0.2
+580,585,PED ED SICKLE CELL FEVER,POC26,POC URINE PREGNANCY,Lab,1,5,0.2
+581,585,PED ED SICKLE CELL FEVER,IMGDXCH2,XR CHEST 2V,Imaging,1,5,0.2
+582,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,9,0.333333333
+583,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,LABARI,ABO/RH (ARI),Lab,4,9,0.444444444
+584,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,LABBHCG,"BETA HCG, QUANT",Lab,3,9,0.333333333
+585,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,6,9,0.666666667
+586,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,9,0.333333333
+587,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,5,9,0.555555556
+588,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,LABUPREG,"PREGNANCY TEST, URINE",Lab,3,9,0.333333333
+589,590,NURSING TRIAGE VAGINAL COMPLAINT PROTOCOL,POC46,POC ISTAT BHCG,Lab,1,9,0.111111111
+590,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,680,ASPIRIN 81 MG PO CHEW,Medication,116,1097,0.105742935
+591,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,96,1097,0.087511395
+592,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,871,1097,0.793983592
+593,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,LABDIGL,"DIGOXIN, SERUM",Lab,11,1097,0.010027347
+594,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,881,1097,0.803099362
+595,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,367,1097,0.334548769
+596,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,365,1097,0.332725615
+597,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,POC24,POC ISTAT TROPONIN I,Lab,855,1097,0.779398359
+598,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,675,1097,0.615314494
+599,591,NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL,IMGDXCH2,XR CHEST 2V,Imaging,96,1097,0.087511395
+600,592,NURSING TRIAGE COPD PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,98,0.051020408
+601,592,NURSING TRIAGE COPD PROTOCOL,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,4,98,0.040816327
+602,592,NURSING TRIAGE COPD PROTOCOL,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,17,98,0.173469388
+603,592,NURSING TRIAGE COPD PROTOCOL,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,9,98,0.091836735
+604,592,NURSING TRIAGE COPD PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,68,98,0.693877551
+605,592,NURSING TRIAGE COPD PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,68,98,0.693877551
+606,592,NURSING TRIAGE COPD PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,48,98,0.489795918
+607,592,NURSING TRIAGE COPD PROTOCOL,IMGDXCH2,XR CHEST 2V,Imaging,12,98,0.12244898
+608,593,NURSING TRIAGE EXTREMITY DEFORMITY PROTOCOL,3757,HYDROMORPHONE (PF) 1 MG/ML INJ SOLN,Medication,1,9,0.111111111
+609,593,NURSING TRIAGE EXTREMITY DEFORMITY PROTOCOL,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,9,0.111111111
+610,593,NURSING TRIAGE EXTREMITY DEFORMITY PROTOCOL,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,9,0.111111111
+611,593,NURSING TRIAGE EXTREMITY DEFORMITY PROTOCOL,IMGDXAN3LT,XR ANKLE 3V LT,Imaging,1,9,0.111111111
+612,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABACETA,"ACETAMINOPHEN, SERUM",Lab,49,175,0.28
+613,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,160,175,0.914285714
+614,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,173,175,0.988571429
+615,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,160,175,0.914285714
+616,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABSALICY,SALICYLATE LEVEL,Lab,48,175,0.274285714
+617,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABTSH,TSH,Lab,168,175,0.96
+618,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,164,175,0.937142857
+619,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABUPREG,"PREGNANCY TEST, URINE",Lab,4,175,0.022857143
+620,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,LABVOLTL,VOLATILE SCREEN,Lab,167,175,0.954285714
+621,595,NURSING TRIAGE SUICIDAL/HOMICIDAL COMPLAINTS PROTOCOL,POC46,POC ISTAT BHCG,Lab,14,175,0.08
+622,596,NURSING TRIAGE URINARY TRACT INFECTION PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,18,18,1
+623,596,NURSING TRIAGE URINARY TRACT INFECTION PROTOCOL,LABUPREG,"PREGNANCY TEST, URINE",Lab,7,18,0.388888889
+624,596,NURSING TRIAGE URINARY TRACT INFECTION PROTOCOL,POC46,POC ISTAT BHCG,Lab,1,18,0.055555556
+625,597,NURSING TRIAGE  WOUND CARE PROTOCOL,115773,BACITRACIN ZINC 500 UNIT/G TP PACK,Medication,1,7,0.142857143
+626,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,102,ACETAMINOPHEN 500 MG PO TABS,Medication,3,760,0.003947368
+627,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,3757,HYDROMORPHONE (PF) 1 MG/ML INJ SOLN,Medication,2,760,0.002631579
+628,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,760,0.006578947
+629,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,81839,LIDOCAINE 4 % TP CREA,Medication,1,760,0.001315789
+630,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,94176,ONDANSETRON 4 MG PO TBDL,Medication,3,760,0.003947368
+631,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,95,760,0.125
+632,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,111,760,0.146052632
+633,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,696,760,0.915789474
+634,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,LABLIPS,LIPASE,Lab,309,760,0.406578947
+635,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,628,760,0.826315789
+636,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,305,760,0.401315789
+637,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,LABUPREG,"PREGNANCY TEST, URINE",Lab,36,760,0.047368421
+638,598,NURSING TRIAGE ABDOMINAL PAIN PROTOCOL,POC46,POC ISTAT BHCG,Lab,23,760,0.030263158
+639,609,IP PSY PSYCHIATRY INPATIENT ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,986,6251,0.157734762
+640,609,IP PSY PSYCHIATRY INPATIENT ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,26,6251,0.004159335
+641,609,IP PSY PSYCHIATRY INPATIENT ADMIT,1755,CLONIDINE HCL 0.1 MG PO TABS,Medication,9,6251,0.00143977
+642,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2403,DIAZEPAM 10 MG PO TABS,Medication,1,6251,0.000159974
+643,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2404,DIAZEPAM 2 MG PO TABS,Medication,1,6251,0.000159974
+644,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2405,DIAZEPAM 5 MG PO TABS,Medication,4,6251,0.000639898
+645,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2418,DICYCLOMINE 10 MG PO CAPS,Medication,9,6251,0.00143977
+646,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,118,6251,0.01887698
+647,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2510,DIPHENHYDRAMINE HCL 50 MG PO CAPS,Medication,13,6251,0.002079667
+648,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,695,6251,0.111182211
+649,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,338,6251,0.054071349
+650,609,IP PSY PSYCHIATRY INPATIENT ADMIT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,3,6251,0.000479923
+651,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3233,FOLIC ACID 1 MG PO TABS,Medication,84,6251,0.01343785
+652,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3772,HYDROXYZINE HCL 10 MG PO TABS,Medication,2,6251,0.000319949
+653,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3774,HYDROXYZINE HCL 25 MG PO TABS,Medication,473,6251,0.075667893
+654,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3775,HYDROXYZINE HCL 50 MG PO TABS,Medication,42,6251,0.006718925
+655,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3841,IBUPROFEN 200 MG PO TABS,Medication,6,6251,0.000959846
+656,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3843,IBUPROFEN 400 MG PO TABS,Medication,363,6251,0.058070709
+657,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3844,IBUPROFEN 600 MG PO TABS,Medication,20,6251,0.003199488
+658,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3845,IBUPROFEN 800 MG PO TABS,Medication,17,6251,0.002719565
+659,609,IP PSY PSYCHIATRY INPATIENT ADMIT,3897,INDOMETHACIN 25 MG PO CAPS,Medication,3,6251,0.000479923
+660,609,IP PSY PSYCHIATRY INPATIENT ADMIT,4560,LOPERAMIDE 2 MG PO CAPS,Medication,24,6251,0.003839386
+661,609,IP PSY PSYCHIATRY INPATIENT ADMIT,4572,LORAZEPAM 0.5 MG PO TABS,Medication,35,6251,0.005599104
+662,609,IP PSY PSYCHIATRY INPATIENT ADMIT,4573,LORAZEPAM 1 MG PO TABS,Medication,162,6251,0.025915853
+663,609,IP PSY PSYCHIATRY INPATIENT ADMIT,4574,LORAZEPAM 2 MG PO TABS,Medication,10,6251,0.001599744
+664,609,IP PSY PSYCHIATRY INPATIENT ADMIT,4972,METHOCARBAMOL 750 MG PO TABS,Medication,3,6251,0.000479923
+665,609,IP PSY PSYCHIATRY INPATIENT ADMIT,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,3,6251,0.000479923
+666,609,IP PSY PSYCHIATRY INPATIENT ADMIT,7876,THIAMINE HCL (VITAMIN B1) 100 MG/ML INJ SOLN,Medication,22,6251,0.003519437
+667,609,IP PSY PSYCHIATRY INPATIENT ADMIT,7877,THIAMINE HCL (VITAMIN B1) 100 MG PO TABS,Medication,76,6251,0.012158055
+668,609,IP PSY PSYCHIATRY INPATIENT ADMIT,8083,TRAZODONE 50 MG PO TABS,Medication,12,6251,0.001919693
+669,609,IP PSY PSYCHIATRY INPATIENT ADMIT,8085,TRAZODONE 50 MG PO TABS,Medication,178,6251,0.028475444
+670,609,IP PSY PSYCHIATRY INPATIENT ADMIT,9637,CLONAZEPAM 0.5 MG PO TABS,Medication,25,6251,0.00399936
+671,609,IP PSY PSYCHIATRY INPATIENT ADMIT,9638,CLONAZEPAM 1 MG PO TABS,Medication,5,6251,0.000799872
+672,609,IP PSY PSYCHIATRY INPATIENT ADMIT,10717,NICOTINE (POLACRILEX) 2 MG BUCCAL GUM,Medication,104,6251,0.016637338
+673,609,IP PSY PSYCHIATRY INPATIENT ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,584,6251,0.093425052
+674,609,IP PSY PSYCHIATRY INPATIENT ADMIT,16830,MELATONIN 3 MG PO TABS,Medication,157,6251,0.025115981
+675,609,IP PSY PSYCHIATRY INPATIENT ADMIT,27860,NICOTINE 7 MG/24 HR TD PT24,Medication,23,6251,0.003679411
+676,609,IP PSY PSYCHIATRY INPATIENT ADMIT,27862,NICOTINE 14 MG/24 HR TD PT24,Medication,52,6251,0.008318669
+677,609,IP PSY PSYCHIATRY INPATIENT ADMIT,27863,NICOTINE 21 MG/24 HR TD PT24,Medication,44,6251,0.007038874
+678,609,IP PSY PSYCHIATRY INPATIENT ADMIT,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,91,6251,0.014557671
+679,609,IP PSY PSYCHIATRY INPATIENT ADMIT,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,440,6251,0.070388738
+680,609,IP PSY PSYCHIATRY INPATIENT ADMIT,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,218,6251,0.03487442
+681,609,IP PSY PSYCHIATRY INPATIENT ADMIT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,14,6251,0.002239642
+682,609,IP PSY PSYCHIATRY INPATIENT ADMIT,84520,ONDANSETRON 8 MG PO TBDL,Medication,28,6251,0.004479283
+683,609,IP PSY PSYCHIATRY INPATIENT ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,30,6251,0.004799232
+684,609,IP PSY PSYCHIATRY INPATIENT ADMIT,90805,BISMUTH SUBSALICYLATE 262 MG PO CHEW,Medication,8,6251,0.001279795
+685,609,IP PSY PSYCHIATRY INPATIENT ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,37,6251,0.005919053
+686,609,IP PSY PSYCHIATRY INPATIENT ADMIT,93295,ZOLPIDEM 10 MG PO TABS,Medication,15,6251,0.002399616
+687,609,IP PSY PSYCHIATRY INPATIENT ADMIT,94176,ONDANSETRON 4 MG PO TBDL,Medication,40,6251,0.006398976
+688,609,IP PSY PSYCHIATRY INPATIENT ADMIT,116506,MELATONIN 1 MG PO TABS,Medication,2,6251,0.000319949
+689,609,IP PSY PSYCHIATRY INPATIENT ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,6251,0.000159974
+690,609,IP PSY PSYCHIATRY INPATIENT ADMIT,540479,CLONAZEPAM (PAIN COCKTAIL) 0.5 MG PO TABS,Medication,1,6251,0.000159974
+691,609,IP PSY PSYCHIATRY INPATIENT ADMIT,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,3,6251,0.000479923
+692,609,IP PSY PSYCHIATRY INPATIENT ADMIT,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,140,6251,0.022396417
+693,609,IP PSY PSYCHIATRY INPATIENT ADMIT,9000000,ZZZ IMS TEMPLATE,Medication,9,6251,0.00143977
+694,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABA1C,HEMOGLOBIN A1C,Lab,1043,6251,0.166853303
+695,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABB12,VITAMIN B12,Lab,600,6251,0.095984642
+696,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABCBCD,CBC WITH DIFF,Lab,306,6251,0.048952168
+697,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABCZPL,"CARBAMAZEPINE, SERUM",Lab,1,6251,0.000159974
+698,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,208,6251,0.033274676
+699,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABFOL,FOLIC ACID,Lab,548,6251,0.087665973
+700,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABGLF,GLUCOSE FASTING,Lab,2139,6251,0.34218525
+701,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABLIL,LITHIUM,Lab,70,6251,0.011198208
+702,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,4055,6251,0.648696209
+703,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,47,6251,0.007518797
+704,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,311,6251,0.04975204
+705,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,282,6251,0.045112782
+706,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,245,6251,0.039193729
+707,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABTSHFT4,TSH W/ REFLEX FT4,Lab,826,6251,0.132138858
+708,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,193,6251,0.03087506
+709,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABUPREG,"PREGNANCY TEST, URINE",Lab,133,6251,0.021276596
+710,609,IP PSY PSYCHIATRY INPATIENT ADMIT,LABVPAL,VALPROIC ACID,Lab,55,6251,0.008798592
+711,611,IP ASC PSY ECT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,1777,0.000562746
+712,611,IP ASC PSY ECT,4318,LACTATED RINGERS IV SOLP,Medication,1111,1777,0.62521103
+713,611,IP ASC PSY ECT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,1777,0.000562746
+714,611,IP ASC PSY ECT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,1777,0.000562746
+715,616,IP PAI EPIDURAL ANALGESIA POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,601,6394,0.09399437
+716,616,IP PAI EPIDURAL ANALGESIA POSTOP,5339,NALBUPHINE 10 MG/ML INJ SOLN,Medication,975,6394,0.152486706
+717,616,IP PAI EPIDURAL ANALGESIA POSTOP,5340,NALBUPHINE 20 MG/ML INJ SOLN,Medication,107,6394,0.016734439
+718,616,IP PAI EPIDURAL ANALGESIA POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,6,6394,0.00093838
+719,616,IP PAI EPIDURAL ANALGESIA POSTOP,29464,MORPHINE (PF) 0.5 MG/ML INJ SOLN,Medication,2,6394,0.000312793
+720,616,IP PAI EPIDURAL ANALGESIA POSTOP,530032,BUPIVACAINE 0.125% (1/8%) EPIDURAL INFUSION,Medication,2314,6394,0.361901783
+721,616,IP PAI EPIDURAL ANALGESIA POSTOP,530033,BUPIVACAINE 0.25% (1/4%) EPIDURAL INFUSION,Medication,79,6394,0.012355333
+722,616,IP PAI EPIDURAL ANALGESIA POSTOP,530051,LIDOCAINE 1% EPIDURAL INFUSION,Medication,190,6394,0.029715358
+723,616,IP PAI EPIDURAL ANALGESIA POSTOP,530104,HYDROMORPHONE 0.05 MG/ML EPIDURAL INFUSION,Medication,1683,6394,0.263215515
+724,616,IP PAI EPIDURAL ANALGESIA POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,33,6394,0.005161089
+725,616,IP PAI EPIDURAL ANALGESIA POSTOP,540107,FENTANYL 50 MCG/ML EPIDURAL PCEA,Medication,1763,6394,0.275727244
+726,616,IP PAI EPIDURAL ANALGESIA POSTOP,540204,LIDOCAINE 0.5% NERVE BLOCK INFUSION,Medication,1,6394,0.000156397
+727,616,IP PAI EPIDURAL ANALGESIA POSTOP,540205,LIDOCAINE 1% INFUSION,Medication,16,6394,0.002502346
+728,616,IP PAI EPIDURAL ANALGESIA POSTOP,540541,FENTANYL/BUPIVACAINE PCEA 30 ML SYRINGE,Medication,166,6394,0.025961839
+729,616,IP PAI EPIDURAL ANALGESIA POSTOP,541064,HYDROMORPHONE EPIDURAL INJECTION,Medication,284,6394,0.044416641
+730,616,IP PAI EPIDURAL ANALGESIA POSTOP,541125,LIDOCAINE 0.5% EPIDURAL INFUSION,Medication,5,6394,0.000781983
+731,616,IP PAI EPIDURAL ANALGESIA POSTOP,541126,ROPIVACAINE 0.1% EPIDURAL INFUSION,Medication,5,6394,0.000781983
+732,616,IP PAI EPIDURAL ANALGESIA POSTOP,541127,ROPIVACAINE 0.2% EPIDURAL INFUSION,Medication,3,6394,0.00046919
+733,616,IP PAI EPIDURAL ANALGESIA POSTOP,541604,FENTANYL 50 MCG/ML EPIDURAL PCEA,Medication,550,6394,0.086018142
+734,616,IP PAI EPIDURAL ANALGESIA POSTOP,541606,FENTANYL/BUPIVACAINE PCEA 30 ML SYRINGE,Medication,20,6394,0.003127932
+735,616,IP PAI EPIDURAL ANALGESIA POSTOP,800100,ZZ EXTEMPORANEOUS TEMPLATE(CUSTOM),Medication,1,6394,0.000156397
+736,617,IP PAI INTRATHECAL CHRONIC PAIN ADMIT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,7,43,0.162790698
+737,617,IP PAI INTRATHECAL CHRONIC PAIN ADMIT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,6,43,0.139534884
+738,617,IP PAI INTRATHECAL CHRONIC PAIN ADMIT,5339,NALBUPHINE 10 MG/ML INJ SOLN,Medication,6,43,0.139534884
+739,617,IP PAI INTRATHECAL CHRONIC PAIN ADMIT,96837,DOLASETRON 12.5 MG/0.625 ML IV SOLN,Medication,1,43,0.023255814
+740,617,IP PAI INTRATHECAL CHRONIC PAIN ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,6,43,0.139534884
+741,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1916,10324,0.185586982
+742,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,947,10324,0.091728012
+743,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,263,10324,0.025474622
+744,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,908,10324,0.087950407
+745,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,36,10324,0.003487021
+746,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,87,10324,0.008426966
+747,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,1,10324,9.69E-05
+748,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,1,10324,9.69E-05
+749,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,3956,10324,0.383184812
+750,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,10324,0.000193723
+751,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,7,10324,0.000678032
+752,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,3,10324,0.000290585
+753,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,10814,OXYCODONE 5 MG PO TABS,Medication,3774,10324,0.365555986
+754,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,718,10324,0.069546687
+755,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,2,10324,0.000193723
+756,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,18307,GABAPENTIN 400 MG PO CAPS,Medication,27,10324,0.002615265
+757,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,18308,GABAPENTIN 300 MG PO CAPS,Medication,1973,10324,0.191108098
+758,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,18309,GABAPENTIN 100 MG PO CAPS,Medication,845,10324,0.081848121
+759,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,1,10324,9.69E-05
+760,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,374,10324,0.036226269
+761,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,25855,GABAPENTIN 600 MG PO TABS,Medication,9,10324,0.000871755
+762,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,27920,OXYCODONE 10 MG PO TB12,Medication,288,10324,0.027896164
+763,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,27921,OXYCODONE 20 MG PO TB12,Medication,34,10324,0.003293297
+764,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,27922,OXYCODONE 40 MG PO TB12,Medication,19,10324,0.001840372
+765,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,28899,OXYCODONE 15 MG PO TABS,Medication,26,10324,0.002518404
+766,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,28900,OXYCODONE 30 MG PO TABS,Medication,5,10324,0.000484308
+767,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,12,10324,0.00116234
+768,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,77052,CELECOXIB 200 MG PO CAPS,Medication,237,10324,0.022956219
+769,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,82918,GABAPENTIN 250 MG/5 ML PO SOLN,Medication,5,10324,0.000484308
+770,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,110,10324,0.010654785
+771,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,3,10324,0.000290585
+772,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,93715,CELECOXIB 100 MG PO CAPS,Medication,986,10324,0.095505618
+773,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,125756,PREGABALIN 25 MG PO CAPS,Medication,5,10324,0.000484308
+774,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,125757,PREGABALIN 50 MG PO CAPS,Medication,5,10324,0.000484308
+775,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,125758,PREGABALIN 75 MG PO CAPS,Medication,39,10324,0.003777606
+776,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,125759,PREGABALIN 100 MG PO CAPS,Medication,5,10324,0.000484308
+777,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,825,10324,0.079910887
+778,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,10324,0.000193723
+779,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,211398,ROPIVACAINE (PF) 0.2 % 550 ML LINF ELPH,Medication,215,10324,0.020825262
+780,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,233900,OXYCODONE 10 MG PO TR12,Medication,65,10324,0.006296009
+781,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,233902,OXYCODONE 20 MG PO TR12,Medication,4,10324,0.000387447
+782,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,233904,OXYCODONE 40 MG PO TR12,Medication,1,10324,9.69E-05
+783,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,520183,BUPIVACAINE 0.125% (1/8%) NERVE BLOCK INFUSION,Medication,1525,10324,0.147714064
+784,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,520184,BUPIVACAINE 0.25% (1/4%) NERVE BLOCK INFUSION,Medication,1830,10324,0.177256877
+785,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540202,ROPIVACAINE 0.1% NERVE BLOCK INFUSION,Medication,209,10324,0.020244091
+786,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540203,ROPIVACAINE 0.2% NERVE BLOCK INFUSION,Medication,16,10324,0.001549787
+787,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540204,LIDOCAINE 0.5% NERVE BLOCK INFUSION,Medication,15,10324,0.001452925
+788,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540205,LIDOCAINE 1% INFUSION,Medication,71,10324,0.006877179
+789,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540206,ROPIVACAINE 0.1% ANESTHETIC PUMP,Medication,355,10324,0.034385897
+790,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540207,ROPIVACAINE 0.2% 400 ML 2-14 ML ANESTHETIC PUMP,Medication,171,10324,0.016563348
+791,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540470,BUPIVACAINE 0.125% 400 ML 2-14 ML ANESTHETIC PUMP,Medication,13,10324,0.001259202
+792,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,540475,BUPIVACAINE 0.25% 400 ML 2-14 ML ANESTHETIC PUMP,Medication,14,10324,0.001356064
+793,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,541178,ROPIVACAINE 0.2% NERVE BLOCK INFUSION (PREMIX),Medication,257,10324,0.024893452
+794,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,541286,ROPIVACAINE 0.2% VARIABLE RATE WITH ONDEMAND ANESTHETIC PUMP,Medication,7,10324,0.000678032
+795,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,541893,ROPIVACAINE INFUSION VIA NIMBUS PUMP,Medication,1,10324,9.69E-05
+796,618,IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT,9000000,ZZZ IMS TEMPLATE,Medication,22,10324,0.002130957
+797,619,IP PAI SCIPP CHRONIC PAIN ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,30,364,0.082417582
+798,619,IP PAI SCIPP CHRONIC PAIN ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,364,0.005494505
+799,619,IP PAI SCIPP CHRONIC PAIN ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,18,364,0.049450549
+800,619,IP PAI SCIPP CHRONIC PAIN ADMIT,3841,IBUPROFEN 200 MG PO TABS,Medication,2,364,0.005494505
+801,619,IP PAI SCIPP CHRONIC PAIN ADMIT,3843,IBUPROFEN 400 MG PO TABS,Medication,16,364,0.043956044
+802,619,IP PAI SCIPP CHRONIC PAIN ADMIT,3844,IBUPROFEN 600 MG PO TABS,Medication,1,364,0.002747253
+803,619,IP PAI SCIPP CHRONIC PAIN ADMIT,3845,IBUPROFEN 800 MG PO TABS,Medication,1,364,0.002747253
+804,619,IP PAI SCIPP CHRONIC PAIN ADMIT,5393,NAPROXEN 500 MG PO TABS,Medication,2,364,0.005494505
+805,619,IP PAI SCIPP CHRONIC PAIN ADMIT,8085,TRAZODONE 50 MG PO TABS,Medication,6,364,0.016483516
+806,619,IP PAI SCIPP CHRONIC PAIN ADMIT,18307,GABAPENTIN 400 MG PO CAPS,Medication,1,364,0.002747253
+807,619,IP PAI SCIPP CHRONIC PAIN ADMIT,18308,GABAPENTIN 300 MG PO CAPS,Medication,3,364,0.008241758
+808,619,IP PAI SCIPP CHRONIC PAIN ADMIT,84520,ONDANSETRON 8 MG PO TBDL,Medication,16,364,0.043956044
+809,619,IP PAI SCIPP CHRONIC PAIN ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,8,364,0.021978022
+810,619,IP PAI SCIPP CHRONIC PAIN ADMIT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,5,364,0.013736264
+811,619,IP PAI SCIPP CHRONIC PAIN ADMIT,93295,ZOLPIDEM 10 MG PO TABS,Medication,7,364,0.019230769
+812,619,IP PAI SCIPP CHRONIC PAIN ADMIT,94176,ONDANSETRON 4 MG PO TBDL,Medication,4,364,0.010989011
+813,619,IP PAI SCIPP CHRONIC PAIN ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,364,0.005494505
+814,619,IP PAI SCIPP CHRONIC PAIN ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,364,0.005494505
+815,619,IP PAI SCIPP CHRONIC PAIN ADMIT,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,10,364,0.027472527
+816,619,IP PAI SCIPP CHRONIC PAIN ADMIT,540000,PAIN COCKTAIL,Medication,105,364,0.288461538
+817,619,IP PAI SCIPP CHRONIC PAIN ADMIT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,133,364,0.365384615
+818,619,IP PAI SCIPP CHRONIC PAIN ADMIT,LABPLTS,PLATELET COUNT,Lab,1,364,0.002747253
+819,619,IP PAI SCIPP CHRONIC PAIN ADMIT,LABTES,TESTOSTERONE,Lab,28,364,0.076923077
+820,619,IP PAI SCIPP CHRONIC PAIN ADMIT,LABYSDA,"DRUG SCREEN, PLASMA",Lab,6,364,0.016483516
+821,623,IP PAI KETAMINE IV,1755,CLONIDINE HCL 0.1 MG PO TABS,Medication,41,358,0.11452514
+822,623,IP PAI KETAMINE IV,4572,LORAZEPAM 0.5 MG PO TABS,Medication,93,358,0.259776536
+823,623,IP PAI KETAMINE IV,4573,LORAZEPAM 1 MG PO TABS,Medication,3,358,0.008379888
+824,623,IP PAI KETAMINE IV,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,358,0.002793296
+825,623,IP PAI KETAMINE IV,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,358,0.01396648
+826,623,IP PAI KETAMINE IV,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,358,0.008379888
+827,623,IP PAI KETAMINE IV,530034,KETAMINE 5 MG/ML IV INFUSION,Medication,160,358,0.446927374
+828,623,IP PAI KETAMINE IV,LABCBCD,CBC WITH DIFF,Lab,99,358,0.276536313
+829,623,IP PAI KETAMINE IV,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,103,358,0.287709497
+830,623,IP PAI KETAMINE IV,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,107,358,0.298882682
+831,623,IP PAI KETAMINE IV,LABPLTS,PLATELET COUNT,Lab,2,358,0.005586592
+832,623,IP PAI KETAMINE IV,LABUPREG,"PREGNANCY TEST, URINE",Lab,54,358,0.150837989
+833,624,ANE PACU (OUTPATIENT),101,ACETAMINOPHEN 325 MG PO TABS,Medication,73,7783,0.009379417
+834,624,ANE PACU (OUTPATIENT),102,ACETAMINOPHEN 500 MG PO TABS,Medication,531,7783,0.06822562
+835,624,ANE PACU (OUTPATIENT),730,ATROPINE 0.1 MG/ML INJ SYRG,Medication,19,7783,0.002441218
+836,624,ANE PACU (OUTPATIENT),2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,314,7783,0.04034434
+837,624,ANE PACU (OUTPATIENT),2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,1,7783,0.000128485
+838,624,ANE PACU (OUTPATIENT),2833,EPHEDRINE SULFATE 50 MG/ML INJ SOLN,Medication,26,7783,0.003340614
+839,624,ANE PACU (OUTPATIENT),3497,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,Medication,318,7783,0.040858281
+840,624,ANE PACU (OUTPATIENT),3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,4,7783,0.000513941
+841,624,ANE PACU (OUTPATIENT),3843,IBUPROFEN 400 MG PO TABS,Medication,1,7783,0.000128485
+842,624,ANE PACU (OUTPATIENT),4318,LACTATED RINGERS IV SOLP,Medication,2947,7783,0.378645766
+843,624,ANE PACU (OUTPATIENT),5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,428,7783,0.054991648
+844,624,ANE PACU (OUTPATIENT),5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,132,7783,0.016960041
+845,624,ANE PACU (OUTPATIENT),5339,NALBUPHINE 10 MG/ML INJ SOLN,Medication,182,7783,0.023384299
+846,624,ANE PACU (OUTPATIENT),5340,NALBUPHINE 20 MG/ML INJ SOLN,Medication,1,7783,0.000128485
+847,624,ANE PACU (OUTPATIENT),5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1325,7783,0.170242837
+848,624,ANE PACU (OUTPATIENT),5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,237,7783,0.030450983
+849,624,ANE PACU (OUTPATIENT),6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1271,7783,0.163304638
+850,624,ANE PACU (OUTPATIENT),10372,LABETALOL 5 MG/ML IV SOLN,Medication,540,7783,0.069381986
+851,624,ANE PACU (OUTPATIENT),10814,OXYCODONE 5 MG PO TABS,Medication,1182,7783,0.151869459
+852,624,ANE PACU (OUTPATIENT),14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,20,7783,0.002569703
+853,624,ANE PACU (OUTPATIENT),18308,GABAPENTIN 300 MG PO CAPS,Medication,46,7783,0.005910317
+854,624,ANE PACU (OUTPATIENT),18309,GABAPENTIN 100 MG PO CAPS,Medication,2,7783,0.00025697
+855,624,ANE PACU (OUTPATIENT),22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,22,7783,0.002826674
+856,624,ANE PACU (OUTPATIENT),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,970,7783,0.124630605
+857,624,ANE PACU (OUTPATIENT),27920,OXYCODONE 10 MG PO TB12,Medication,18,7783,0.002312733
+858,624,ANE PACU (OUTPATIENT),27921,OXYCODONE 20 MG PO TB12,Medication,2,7783,0.00025697
+859,624,ANE PACU (OUTPATIENT),27922,OXYCODONE 40 MG PO TB12,Medication,1,7783,0.000128485
+860,624,ANE PACU (OUTPATIENT),28899,OXYCODONE 15 MG PO TABS,Medication,1,7783,0.000128485
+861,624,ANE PACU (OUTPATIENT),28900,OXYCODONE 30 MG PO TABS,Medication,1,7783,0.000128485
+862,624,ANE PACU (OUTPATIENT),34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,416,7783,0.053449827
+863,624,ANE PACU (OUTPATIENT),88080,ELECTROLYTE-R (PH 7.4) IV SOLP,Medication,12,7783,0.001541822
+864,624,ANE PACU (OUTPATIENT),93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,207,7783,0.026596428
+865,624,ANE PACU (OUTPATIENT),93715,CELECOXIB 100 MG PO CAPS,Medication,1,7783,0.000128485
+866,624,ANE PACU (OUTPATIENT),110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1494,7783,0.191956829
+867,624,ANE PACU (OUTPATIENT),112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,7783,0.000128485
+868,624,ANE PACU (OUTPATIENT),112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,615,7783,0.079018373
+869,624,ANE PACU (OUTPATIENT),124521,GRANISETRON (PF) 100 MCG/ML IV SOLN,Medication,36,7783,0.004625466
+870,624,ANE PACU (OUTPATIENT),125758,PREGABALIN 75 MG PO CAPS,Medication,3,7783,0.000385455
+871,624,ANE PACU (OUTPATIENT),125759,PREGABALIN 100 MG PO CAPS,Medication,1,7783,0.000128485
+872,624,ANE PACU (OUTPATIENT),126003,MEPERIDINE (PF) 50 MG/ML INJ SOLN,Medication,1,7783,0.000128485
+873,624,ANE PACU (OUTPATIENT),126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,534,7783,0.068611075
+874,624,ANE PACU (OUTPATIENT),126014,MEPERIDINE (PF) 100 MG/ML INJ SYRG,Medication,1,7783,0.000128485
+875,624,ANE PACU (OUTPATIENT),198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3276,7783,0.420917384
+876,624,ANE PACU (OUTPATIENT),200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,104,7783,0.013362457
+877,624,ANE PACU (OUTPATIENT),201096,HYDROMORPHONE (PF) 2 MG/ML INJ SYRG,Medication,1,7783,0.000128485
+878,624,ANE PACU (OUTPATIENT),203751,GRANISETRON HCL 1 MG/ML (1 ML) IV SOLN,Medication,1,7783,0.000128485
+879,624,ANE PACU (OUTPATIENT),212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,415,7783,0.053321341
+880,624,ANE PACU (OUTPATIENT),233900,OXYCODONE 10 MG PO TR12,Medication,2,7783,0.00025697
+881,624,ANE PACU (OUTPATIENT),540102,NS IV BOLUS,Medication,66,7783,0.008480021
+882,624,ANE PACU (OUTPATIENT),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,3437,7783,0.441603495
+883,624,ANE PACU (OUTPATIENT),540115,LACTATED RINGERS IV BOLUS,Medication,103,7783,0.013233971
+884,624,ANE PACU (OUTPATIENT),540300,MORPHINE INJECTABLE SYRINGE,Medication,106,7783,0.013619427
+885,624,ANE PACU (OUTPATIENT),540573,ACETAMINOPHEN 10 MG/ML IV (CUSTOM DOSE),Medication,12,7783,0.001541822
+886,624,ANE PACU (OUTPATIENT),541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,49,7783,0.006295773
+887,624,ANE PACU (OUTPATIENT),541229,ELECTROLYTE-R IV BOLUS,Medication,2,7783,0.00025697
+888,624,ANE PACU (OUTPATIENT),9000000,ZZZ IMS TEMPLATE,Medication,2,7783,0.00025697
+889,624,ANE PACU (OUTPATIENT),LABCBCO,CBC W/O DIFF,Lab,19,7783,0.002441218
+890,624,ANE PACU (OUTPATIENT),LABEP1,ELECTROLYTE PANEL,Lab,2,7783,0.00025697
+891,624,ANE PACU (OUTPATIENT),LABHCTX,HEMATOCRIT,Lab,5,7783,0.000642426
+892,624,ANE PACU (OUTPATIENT),LABHGBX,HEMOGLOBIN,Lab,4,7783,0.000513941
+893,624,ANE PACU (OUTPATIENT),LABMETB,"METABOLIC PANEL, BASIC",Lab,9,7783,0.001156366
+894,624,ANE PACU (OUTPATIENT),LABPT,PROTHROMBIN TIME,Lab,12,7783,0.001541822
+895,624,ANE PACU (OUTPATIENT),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,9,7783,0.001156366
+896,624,ANE PACU (OUTPATIENT),POC14,POC GLUCOSE BY METER,Lab,247,7783,0.031735835
+897,625,ANE PACU (INPATIENT),101,ACETAMINOPHEN 325 MG PO TABS,Medication,102,65802,0.001550105
+898,625,ANE PACU (INPATIENT),102,ACETAMINOPHEN 500 MG PO TABS,Medication,979,65802,0.014877967
+899,625,ANE PACU (INPATIENT),730,ATROPINE 0.1 MG/ML INJ SYRG,Medication,184,65802,0.002796268
+900,625,ANE PACU (INPATIENT),2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1729,65802,0.026275797
+901,625,ANE PACU (INPATIENT),2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,20,65802,0.000303942
+902,625,ANE PACU (INPATIENT),2833,EPHEDRINE SULFATE 50 MG/ML INJ SOLN,Medication,513,65802,0.007796116
+903,625,ANE PACU (INPATIENT),3497,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,Medication,1069,65802,0.016245707
+904,625,ANE PACU (INPATIENT),3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,2541,65802,0.038615848
+905,625,ANE PACU (INPATIENT),3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,38,65802,0.00057749
+906,625,ANE PACU (INPATIENT),3843,IBUPROFEN 400 MG PO TABS,Medication,4,65802,6.08E-05
+907,625,ANE PACU (INPATIENT),3844,IBUPROFEN 600 MG PO TABS,Medication,1,65802,1.52E-05
+908,625,ANE PACU (INPATIENT),3845,IBUPROFEN 800 MG PO TABS,Medication,2,65802,3.04E-05
+909,625,ANE PACU (INPATIENT),4318,LACTATED RINGERS IV SOLP,Medication,10108,65802,0.153612352
+910,625,ANE PACU (INPATIENT),5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,4764,65802,0.072399015
+911,625,ANE PACU (INPATIENT),5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,993,65802,0.015090727
+912,625,ANE PACU (INPATIENT),5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,65802,1.52E-05
+913,625,ANE PACU (INPATIENT),5339,NALBUPHINE 10 MG/ML INJ SOLN,Medication,1015,65802,0.015425063
+914,625,ANE PACU (INPATIENT),5340,NALBUPHINE 20 MG/ML INJ SOLN,Medication,7,65802,0.00010638
+915,625,ANE PACU (INPATIENT),5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,19532,65802,0.296829884
+916,625,ANE PACU (INPATIENT),5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,921,65802,0.013996535
+917,625,ANE PACU (INPATIENT),6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,13269,65802,0.201650406
+918,625,ANE PACU (INPATIENT),10372,LABETALOL 5 MG/ML IV SOLN,Medication,7960,65802,0.120968968
+919,625,ANE PACU (INPATIENT),10814,OXYCODONE 5 MG PO TABS,Medication,4998,65802,0.075955138
+920,625,ANE PACU (INPATIENT),14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,28,65802,0.000425519
+921,625,ANE PACU (INPATIENT),18307,GABAPENTIN 400 MG PO CAPS,Medication,6,65802,9.12E-05
+922,625,ANE PACU (INPATIENT),18308,GABAPENTIN 300 MG PO CAPS,Medication,454,65802,0.006899486
+923,625,ANE PACU (INPATIENT),18309,GABAPENTIN 100 MG PO CAPS,Medication,23,65802,0.000349533
+924,625,ANE PACU (INPATIENT),22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,81,65802,0.001230966
+925,625,ANE PACU (INPATIENT),22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,2,65802,3.04E-05
+926,625,ANE PACU (INPATIENT),25174,HETASTARCH 6 % IN 0.9 % NACL 6 % IV SOLN,Medication,40,65802,0.000607884
+927,625,ANE PACU (INPATIENT),25855,GABAPENTIN 600 MG PO TABS,Medication,1,65802,1.52E-05
+928,625,ANE PACU (INPATIENT),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,22930,65802,0.348469651
+929,625,ANE PACU (INPATIENT),27920,OXYCODONE 10 MG PO TB12,Medication,276,65802,0.004194401
+930,625,ANE PACU (INPATIENT),27921,OXYCODONE 20 MG PO TB12,Medication,5,65802,7.60E-05
+931,625,ANE PACU (INPATIENT),28899,OXYCODONE 15 MG PO TABS,Medication,5,65802,7.60E-05
+932,625,ANE PACU (INPATIENT),28900,OXYCODONE 30 MG PO TABS,Medication,9,65802,0.000136774
+933,625,ANE PACU (INPATIENT),34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1276,65802,0.019391508
+934,625,ANE PACU (INPATIENT),77052,CELECOXIB 200 MG PO CAPS,Medication,90,65802,0.00136774
+935,625,ANE PACU (INPATIENT),88080,ELECTROLYTE-R (PH 7.4) IV SOLP,Medication,223,65802,0.003388955
+936,625,ANE PACU (INPATIENT),93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,1010,65802,0.015349078
+937,625,ANE PACU (INPATIENT),93715,CELECOXIB 100 MG PO CAPS,Medication,8,65802,0.000121577
+938,625,ANE PACU (INPATIENT),95157,ELECTROLYTE-A IV SOLP,Medication,40,65802,0.000607884
+939,625,ANE PACU (INPATIENT),110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,16269,65802,0.247241725
+940,625,ANE PACU (INPATIENT),112564,MORPHINE 2 MG/ML INJ SYRG,Medication,3,65802,4.56E-05
+941,625,ANE PACU (INPATIENT),112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,9382,65802,0.142579253
+942,625,ANE PACU (INPATIENT),124521,GRANISETRON (PF) 100 MCG/ML IV SOLN,Medication,575,65802,0.008738336
+943,625,ANE PACU (INPATIENT),125756,PREGABALIN 25 MG PO CAPS,Medication,1,65802,1.52E-05
+944,625,ANE PACU (INPATIENT),125757,PREGABALIN 50 MG PO CAPS,Medication,13,65802,0.000197562
+945,625,ANE PACU (INPATIENT),125758,PREGABALIN 75 MG PO CAPS,Medication,15,65802,0.000227957
+946,625,ANE PACU (INPATIENT),125759,PREGABALIN 100 MG PO CAPS,Medication,2,65802,3.04E-05
+947,625,ANE PACU (INPATIENT),126003,MEPERIDINE (PF) 50 MG/ML INJ SOLN,Medication,1,65802,1.52E-05
+948,625,ANE PACU (INPATIENT),126006,MEPERIDINE (PF) 25 MG/0.5 ML INJ SOLN,Medication,3,65802,4.56E-05
+949,625,ANE PACU (INPATIENT),126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,5615,65802,0.085331753
+950,625,ANE PACU (INPATIENT),126010,MEPERIDINE (PF) 50 MG/ML INJ SYRG,Medication,2,65802,3.04E-05
+951,625,ANE PACU (INPATIENT),198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,29495,65802,0.448238655
+952,625,ANE PACU (INPATIENT),200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1355,65802,0.020592079
+953,625,ANE PACU (INPATIENT),201096,HYDROMORPHONE (PF) 2 MG/ML INJ SYRG,Medication,2,65802,3.04E-05
+954,625,ANE PACU (INPATIENT),203737,GRANISETRON (PF) 1 MG/ML (1 ML) IV SOLN,Medication,1,65802,1.52E-05
+955,625,ANE PACU (INPATIENT),203751,GRANISETRON HCL 1 MG/ML (1 ML) IV SOLN,Medication,4,65802,6.08E-05
+956,625,ANE PACU (INPATIENT),212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,4548,65802,0.06911644
+957,625,ANE PACU (INPATIENT),233900,OXYCODONE 10 MG PO TR12,Medication,8,65802,0.000121577
+958,625,ANE PACU (INPATIENT),233902,OXYCODONE 20 MG PO TR12,Medication,1,65802,1.52E-05
+959,625,ANE PACU (INPATIENT),233904,OXYCODONE 40 MG PO TR12,Medication,2,65802,3.04E-05
+960,625,ANE PACU (INPATIENT),540102,NS IV BOLUS,Medication,1080,65802,0.016412875
+961,625,ANE PACU (INPATIENT),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,32391,65802,0.492249476
+962,625,ANE PACU (INPATIENT),540115,LACTATED RINGERS IV BOLUS,Medication,940,65802,0.01428528
+963,625,ANE PACU (INPATIENT),540125,HYDROMORPHONE 1 MG/ML IV PCA,Medication,2,65802,3.04E-05
+964,625,ANE PACU (INPATIENT),540300,MORPHINE INJECTABLE SYRINGE,Medication,467,65802,0.007097049
+965,625,ANE PACU (INPATIENT),540303,MEPERIDINE INJECTABLE SYRINGE,Medication,2,65802,3.04E-05
+966,625,ANE PACU (INPATIENT),540573,ACETAMINOPHEN 10 MG/ML IV (CUSTOM DOSE),Medication,166,65802,0.00252272
+967,625,ANE PACU (INPATIENT),541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,878,65802,0.013343059
+968,625,ANE PACU (INPATIENT),541229,ELECTROLYTE-R IV BOLUS,Medication,17,65802,0.000258351
+969,625,ANE PACU (INPATIENT),9000000,ZZZ IMS TEMPLATE,Medication,9,65802,0.000136774
+970,625,ANE PACU (INPATIENT),LABCBCO,CBC W/O DIFF,Lab,315,65802,0.004787089
+971,625,ANE PACU (INPATIENT),LABEP1,ELECTROLYTE PANEL,Lab,56,65802,0.000851038
+972,625,ANE PACU (INPATIENT),LABHCTX,HEMATOCRIT,Lab,118,65802,0.001793259
+973,625,ANE PACU (INPATIENT),LABHGBX,HEMOGLOBIN,Lab,74,65802,0.001124586
+974,625,ANE PACU (INPATIENT),LABMETB,"METABOLIC PANEL, BASIC",Lab,155,65802,0.002355552
+975,625,ANE PACU (INPATIENT),LABPT,PROTHROMBIN TIME,Lab,74,65802,0.001124586
+976,625,ANE PACU (INPATIENT),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,54,65802,0.000820644
+977,625,ANE PACU (INPATIENT),POC14,POC GLUCOSE BY METER,Lab,4254,65802,0.064648491
+978,625,ANE PACU (INPATIENT),IMGDXCH1,XR CHEST 1V,Imaging,253,65802,0.003844868
+979,625,ANE PACU (INPATIENT),IMGDXCH2,XR CHEST 2V,Imaging,2,65802,3.04E-05
+980,626,IP ANE PED ANESTHESIA POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,2,8,0.25
+981,626,IP ANE PED ANESTHESIA POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,8,0.25
+982,626,IP ANE PED ANESTHESIA POSTOP,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,2,8,0.25
+983,626,IP ANE PED ANESTHESIA POSTOP,228220,ACETAMINOPHEN-CODEINE 300 MG-30 MG /12.5 ML PO SOLN,Medication,1,8,0.125
+984,626,IP ANE PED ANESTHESIA POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,4,8,0.5
+985,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,851,5576,0.152618364
+986,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,396,5576,0.071018651
+987,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,9,5576,0.00161406
+988,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,2,5576,0.00035868
+989,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,3843,IBUPROFEN 400 MG PO TABS,Medication,5,5576,0.0008967
+990,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,3844,IBUPROFEN 600 MG PO TABS,Medication,1,5576,0.00017934
+991,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,3845,IBUPROFEN 800 MG PO TABS,Medication,18,5576,0.003228121
+992,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1652,5576,0.296269727
+993,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,20,5576,0.003586801
+994,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,14,5576,0.00251076
+995,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,3,5576,0.00053802
+996,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,36,5576,0.006456241
+997,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1544,5576,0.276901004
+998,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,18,5576,0.003228121
+999,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,9,5576,0.00161406
+1000,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,18307,GABAPENTIN 400 MG PO CAPS,Medication,7,5576,0.00125538
+1001,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,18308,GABAPENTIN 300 MG PO CAPS,Medication,1093,5576,0.196018651
+1002,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,18309,GABAPENTIN 100 MG PO CAPS,Medication,46,5576,0.008249641
+1003,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,153,5576,0.027439024
+1004,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,679,5576,0.121771879
+1005,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,26,5576,0.004662841
+1006,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,25855,GABAPENTIN 600 MG PO TABS,Medication,4,5576,0.00071736
+1007,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,70,5576,0.012553802
+1008,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,90,5576,0.016140603
+1009,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,14,5576,0.00251076
+1010,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,152,5576,0.027259684
+1011,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,82918,GABAPENTIN 250 MG/5 ML PO SOLN,Medication,5,5576,0.0008967
+1012,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,87475,PIPERACILLIN-TAZOBACTAM 3.375 GRAM IV SOLR,Medication,8,5576,0.00143472
+1013,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,22,5576,0.003945481
+1014,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,32,5576,0.005738881
+1015,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,131,5576,0.023493544
+1016,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,22,5576,0.003945481
+1017,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,5576,0.00017934
+1018,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,3,5576,0.00053802
+1019,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,5576,0.00017934
+1020,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1179,5576,0.211441894
+1021,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,105,5576,0.018830703
+1022,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,112597,ENOXAPARIN 120 MG/0.8 ML SC SYRG,Medication,1,5576,0.00017934
+1023,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1759,5576,0.31545911
+1024,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,8,5576,0.00143472
+1025,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,9,5576,0.00161406
+1026,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,205597,ALVIMOPAN 12 MG PO CAPS,Medication,979,5576,0.175573888
+1027,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,5576,0.00035868
+1028,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,420,5576,0.075322812
+1029,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,613,5576,0.109935438
+1030,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,379,5576,0.067969871
+1031,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,520043,CEFOXITIN IVPB,Medication,1,5576,0.00017934
+1032,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,520160,ERTAPENEM 1 G IVPB,Medication,2,5576,0.00035868
+1033,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,540102,NS IV BOLUS,Medication,7,5576,0.00125538
+1034,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,9,5576,0.00161406
+1035,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,8,5576,0.00143472
+1036,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,8,5576,0.00143472
+1037,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,33,5576,0.005918221
+1038,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,17,5576,0.00304878
+1039,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,15,5576,0.0026901
+1040,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,34,5576,0.006097561
+1041,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,1,5576,0.00017934
+1042,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,1,5576,0.00017934
+1043,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,LABCBCO,CBC W/O DIFF,Lab,2910,5576,0.521879484
+1044,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,2909,5576,0.521700143
+1045,637,IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP,LABPLTS,PLATELET COUNT,Lab,83,5576,0.014885222
+1046,639,IP SUR COLECTOMY PREOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,4,37,0.108108108
+1047,639,IP SUR COLECTOMY PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,37,0.054054054
+1048,639,IP SUR COLECTOMY PREOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,37,0.027027027
+1049,639,IP SUR COLECTOMY PREOP,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,2,37,0.054054054
+1050,639,IP SUR COLECTOMY PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,37,0.027027027
+1051,639,IP SUR COLECTOMY PREOP,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,13,37,0.351351351
+1052,639,IP SUR COLECTOMY PREOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,1,37,0.027027027
+1053,639,IP SUR COLECTOMY PREOP,LABCBCD,CBC WITH DIFF,Lab,6,37,0.162162162
+1054,639,IP SUR COLECTOMY PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,7,37,0.189189189
+1055,639,IP SUR COLECTOMY PREOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,37,0.027027027
+1056,639,IP SUR COLECTOMY PREOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,1,37,0.027027027
+1057,639,IP SUR COLECTOMY PREOP,LABPT,PROTHROMBIN TIME,Lab,2,37,0.054054054
+1058,639,IP SUR COLECTOMY PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,37,0.054054054
+1059,639,IP SUR COLECTOMY PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,6,37,0.162162162
+1060,640,IP SUR ANO RECTAL SURGERY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,55,0.109090909
+1061,640,IP SUR ANO RECTAL SURGERY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,55,0.018181818
+1062,640,IP SUR ANO RECTAL SURGERY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,16,55,0.290909091
+1063,640,IP SUR ANO RECTAL SURGERY POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,2,55,0.036363636
+1064,640,IP SUR ANO RECTAL SURGERY POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,4,55,0.072727273
+1065,640,IP SUR ANO RECTAL SURGERY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,14,55,0.254545455
+1066,640,IP SUR ANO RECTAL SURGERY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,55,0.018181818
+1067,640,IP SUR ANO RECTAL SURGERY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,55,0.018181818
+1068,640,IP SUR ANO RECTAL SURGERY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,2,55,0.036363636
+1069,640,IP SUR ANO RECTAL SURGERY POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,2,55,0.036363636
+1070,640,IP SUR ANO RECTAL SURGERY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,55,0.036363636
+1071,640,IP SUR ANO RECTAL SURGERY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,6,55,0.109090909
+1072,640,IP SUR ANO RECTAL SURGERY POSTOP,81839,LIDOCAINE 4 % TP CREA,Medication,2,55,0.036363636
+1073,640,IP SUR ANO RECTAL SURGERY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,12,55,0.218181818
+1074,640,IP SUR ANO RECTAL SURGERY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,4,55,0.072727273
+1075,640,IP SUR ANO RECTAL SURGERY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,55,0.036363636
+1076,640,IP SUR ANO RECTAL SURGERY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,21,55,0.381818182
+1077,640,IP SUR ANO RECTAL SURGERY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,55,0.018181818
+1078,640,IP SUR ANO RECTAL SURGERY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,8,55,0.145454545
+1079,640,IP SUR ANO RECTAL SURGERY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,55,0.018181818
+1080,640,IP SUR ANO RECTAL SURGERY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,55,0.036363636
+1081,640,IP SUR ANO RECTAL SURGERY POSTOP,LABPLTS,PLATELET COUNT,Lab,4,55,0.072727273
+1082,647,IP SUR CHOLECYSTECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,123,0.024390244
+1083,647,IP SUR CHOLECYSTECTOMY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,123,0.008130081
+1084,647,IP SUR CHOLECYSTECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,39,123,0.317073171
+1085,647,IP SUR CHOLECYSTECTOMY POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,3,123,0.024390244
+1086,647,IP SUR CHOLECYSTECTOMY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,4,123,0.032520325
+1087,647,IP SUR CHOLECYSTECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,27,123,0.219512195
+1088,647,IP SUR CHOLECYSTECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,3,123,0.024390244
+1089,647,IP SUR CHOLECYSTECTOMY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,4,123,0.032520325
+1090,647,IP SUR CHOLECYSTECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,12,123,0.097560976
+1091,647,IP SUR CHOLECYSTECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,17,123,0.138211382
+1092,647,IP SUR CHOLECYSTECTOMY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,2,123,0.016260163
+1093,647,IP SUR CHOLECYSTECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,8,123,0.06504065
+1094,647,IP SUR CHOLECYSTECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,3,123,0.024390244
+1095,647,IP SUR CHOLECYSTECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,7,123,0.056910569
+1096,647,IP SUR CHOLECYSTECTOMY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,4,123,0.032520325
+1097,647,IP SUR CHOLECYSTECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,10,123,0.081300813
+1098,647,IP SUR CHOLECYSTECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,46,123,0.37398374
+1099,647,IP SUR CHOLECYSTECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,123,0.008130081
+1100,647,IP SUR CHOLECYSTECTOMY POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,123,0.008130081
+1101,647,IP SUR CHOLECYSTECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,30,123,0.243902439
+1102,647,IP SUR CHOLECYSTECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,4,123,0.032520325
+1103,647,IP SUR CHOLECYSTECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,123,0.008130081
+1104,647,IP SUR CHOLECYSTECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,25,123,0.203252033
+1105,647,IP SUR CHOLECYSTECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,12,123,0.097560976
+1106,647,IP SUR CHOLECYSTECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,13,123,0.105691057
+1107,647,IP SUR CHOLECYSTECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,21,123,0.170731707
+1108,647,IP SUR CHOLECYSTECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,5,123,0.040650407
+1109,647,IP SUR CHOLECYSTECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,123,0.016260163
+1110,662,IP CAPR 23 HOUR CARE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,20,0.2
+1111,662,IP CAPR 23 HOUR CARE,3841,IBUPROFEN 200 MG PO TABS,Medication,2,20,0.1
+1112,662,IP CAPR 23 HOUR CARE,3844,IBUPROFEN 600 MG PO TABS,Medication,1,20,0.05
+1113,662,IP CAPR 23 HOUR CARE,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,20,0.1
+1114,662,IP CAPR 23 HOUR CARE,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1,20,0.05
+1115,662,IP CAPR 23 HOUR CARE,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,2,20,0.1
+1116,662,IP CAPR 23 HOUR CARE,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,6,20,0.3
+1117,662,IP CAPR 23 HOUR CARE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,6,20,0.3
+1118,663,IP GU GENERAL ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,100,522,0.191570881
+1119,663,IP GU GENERAL ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,10,522,0.019157088
+1120,663,IP GU GENERAL ADMIT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,2,522,0.003831418
+1121,663,IP GU GENERAL ADMIT,324,ALPRAZOLAM 0.25 MG PO TABS,Medication,2,522,0.003831418
+1122,663,IP GU GENERAL ADMIT,325,ALPRAZOLAM 0.5 MG PO TABS,Medication,1,522,0.001915709
+1123,663,IP GU GENERAL ADMIT,472,AMPICILLIN SODIUM 2 GRAM INJ SOLR,Medication,2,522,0.003831418
+1124,663,IP GU GENERAL ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,18,522,0.034482759
+1125,663,IP GU GENERAL ADMIT,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,33,522,0.063218391
+1126,663,IP GU GENERAL ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,78,522,0.149425287
+1127,663,IP GU GENERAL ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,33,522,0.063218391
+1128,663,IP GU GENERAL ADMIT,4573,LORAZEPAM 1 MG PO TABS,Medication,2,522,0.003831418
+1129,663,IP GU GENERAL ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,53,522,0.101532567
+1130,663,IP GU GENERAL ADMIT,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,2,522,0.003831418
+1131,663,IP GU GENERAL ADMIT,5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,1,522,0.001915709
+1132,663,IP GU GENERAL ADMIT,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,5,522,0.009578544
+1133,663,IP GU GENERAL ADMIT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,14,522,0.026819923
+1134,663,IP GU GENERAL ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,3,522,0.005747126
+1135,663,IP GU GENERAL ADMIT,8442,"VANCOMYCIN 1,000 MG IV SOLR",Medication,5,522,0.009578544
+1136,663,IP GU GENERAL ADMIT,9461,CEFOXITIN 1 GRAM IV SOLR,Medication,2,522,0.003831418
+1137,663,IP GU GENERAL ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,522,0.001915709
+1138,663,IP GU GENERAL ADMIT,10246,IBUPROFEN 100 MG/5 ML PO SUSP,Medication,2,522,0.003831418
+1139,663,IP GU GENERAL ADMIT,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,522,0.001915709
+1140,663,IP GU GENERAL ADMIT,11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,1,522,0.001915709
+1141,663,IP GU GENERAL ADMIT,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,3,522,0.005747126
+1142,663,IP GU GENERAL ADMIT,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,6,522,0.011494253
+1143,663,IP GU GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,66,522,0.126436782
+1144,663,IP GU GENERAL ADMIT,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,1,522,0.001915709
+1145,663,IP GU GENERAL ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,10,522,0.019157088
+1146,663,IP GU GENERAL ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,14,522,0.026819923
+1147,663,IP GU GENERAL ADMIT,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,11,522,0.021072797
+1148,663,IP GU GENERAL ADMIT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,4,522,0.007662835
+1149,663,IP GU GENERAL ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,57,522,0.109195402
+1150,663,IP GU GENERAL ADMIT,27862,NICOTINE 14 MG/24 HR TD PT24,Medication,1,522,0.001915709
+1151,663,IP GU GENERAL ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,93,522,0.17816092
+1152,663,IP GU GENERAL ADMIT,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,22,522,0.042145594
+1153,663,IP GU GENERAL ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,13,522,0.024904215
+1154,663,IP GU GENERAL ADMIT,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,6,522,0.011494253
+1155,663,IP GU GENERAL ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2,522,0.003831418
+1156,663,IP GU GENERAL ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,12,522,0.022988506
+1157,663,IP GU GENERAL ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,26,522,0.049808429
+1158,663,IP GU GENERAL ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,81,522,0.155172414
+1159,663,IP GU GENERAL ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,3,522,0.005747126
+1160,663,IP GU GENERAL ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,37,522,0.070881226
+1161,663,IP GU GENERAL ADMIT,126157,GENTAMICIN SULFATE (PF) 100 MG/10 ML IV SOLN,Medication,8,522,0.01532567
+1162,663,IP GU GENERAL ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,162,522,0.310344828
+1163,663,IP GU GENERAL ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,3,522,0.005747126
+1164,663,IP GU GENERAL ADMIT,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,4,522,0.007662835
+1165,663,IP GU GENERAL ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,522,0.001915709
+1166,663,IP GU GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,71,522,0.136015326
+1167,663,IP GU GENERAL ADMIT,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,2,522,0.003831418
+1168,663,IP GU GENERAL ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,13,522,0.024904215
+1169,663,IP GU GENERAL ADMIT,541092,AMPICILLIN 2 GRAM/100 ML MINI-BAG PLUS,Medication,8,522,0.01532567
+1170,663,IP GU GENERAL ADMIT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,1,522,0.001915709
+1171,663,IP GU GENERAL ADMIT,LABEP1,ELECTROLYTE PANEL,Lab,1,522,0.001915709
+1172,663,IP GU GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,109,522,0.208812261
+1173,663,IP GU GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,16,522,0.030651341
+1174,663,IP GU GENERAL ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,8,522,0.01532567
+1175,663,IP GU GENERAL ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,1,522,0.001915709
+1176,663,IP GU GENERAL ADMIT,LABBBPRBC,RED BLOOD CELLS,Blood Product,6,522,0.011494253
+1177,664,RETIRED IP GU GENERAL PREOP,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,2,7,0.285714286
+1178,664,RETIRED IP GU GENERAL PREOP,LABCBCD,CBC WITH DIFF,Lab,2,7,0.285714286
+1179,664,RETIRED IP GU GENERAL PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,3,7,0.428571429
+1180,664,RETIRED IP GU GENERAL PREOP,LABPT,PROTHROMBIN TIME,Lab,2,7,0.285714286
+1181,664,RETIRED IP GU GENERAL PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,7,0.285714286
+1182,664,RETIRED IP GU GENERAL PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,3,7,0.428571429
+1183,664,RETIRED IP GU GENERAL PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,2,7,0.285714286
+1184,669,IP GU RADICAL CYSTECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,374,2684,0.139344262
+1185,669,IP GU RADICAL CYSTECTOMY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,59,2684,0.021982116
+1186,669,IP GU RADICAL CYSTECTOMY POSTOP,324,ALPRAZOLAM 0.25 MG PO TABS,Medication,1,2684,0.000372578
+1187,669,IP GU RADICAL CYSTECTOMY POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,46,2684,0.017138599
+1188,669,IP GU RADICAL CYSTECTOMY POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,1,2684,0.000372578
+1189,669,IP GU RADICAL CYSTECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,61,2684,0.022727273
+1190,669,IP GU RADICAL CYSTECTOMY POSTOP,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,3,2684,0.001117735
+1191,669,IP GU RADICAL CYSTECTOMY POSTOP,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,2,2684,0.000745156
+1192,669,IP GU RADICAL CYSTECTOMY POSTOP,4573,LORAZEPAM 1 MG PO TABS,Medication,1,2684,0.000372578
+1193,669,IP GU RADICAL CYSTECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,992,2684,0.369597615
+1194,669,IP GU RADICAL CYSTECTOMY POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,68,2684,0.02533532
+1195,669,IP GU RADICAL CYSTECTOMY POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,4,2684,0.001490313
+1196,669,IP GU RADICAL CYSTECTOMY POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,2,2684,0.000745156
+1197,669,IP GU RADICAL CYSTECTOMY POSTOP,7318,SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP,Medication,1,2684,0.000372578
+1198,669,IP GU RADICAL CYSTECTOMY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,18,2684,0.006706408
+1199,669,IP GU RADICAL CYSTECTOMY POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,2,2684,0.000745156
+1200,669,IP GU RADICAL CYSTECTOMY POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,100,2684,0.037257824
+1201,669,IP GU RADICAL CYSTECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,105,2684,0.039120715
+1202,669,IP GU RADICAL CYSTECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,34,2684,0.01266766
+1203,669,IP GU RADICAL CYSTECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,19,2684,0.007078987
+1204,669,IP GU RADICAL CYSTECTOMY POSTOP,16830,MELATONIN 3 MG PO TABS,Medication,2,2684,0.000745156
+1205,669,IP GU RADICAL CYSTECTOMY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,275,2684,0.102459016
+1206,669,IP GU RADICAL CYSTECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,322,2684,0.119970194
+1207,669,IP GU RADICAL CYSTECTOMY POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,68,2684,0.02533532
+1208,669,IP GU RADICAL CYSTECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,648,2684,0.2414307
+1209,669,IP GU RADICAL CYSTECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,2684,0.002608048
+1210,669,IP GU RADICAL CYSTECTOMY POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,8,2684,0.002980626
+1211,669,IP GU RADICAL CYSTECTOMY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,31,2684,0.011549925
+1212,669,IP GU RADICAL CYSTECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,141,2684,0.052533532
+1213,669,IP GU RADICAL CYSTECTOMY POSTOP,85848,"CALCIUM CARBONATE 400 MG (1,000 MG) PO CHEW",Medication,59,2684,0.021982116
+1214,669,IP GU RADICAL CYSTECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,59,2684,0.021982116
+1215,669,IP GU RADICAL CYSTECTOMY POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,2,2684,0.000745156
+1216,669,IP GU RADICAL CYSTECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,294,2684,0.109538003
+1217,669,IP GU RADICAL CYSTECTOMY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,12,2684,0.004470939
+1218,669,IP GU RADICAL CYSTECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,273,2684,0.10171386
+1219,669,IP GU RADICAL CYSTECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,990,2684,0.368852459
+1220,669,IP GU RADICAL CYSTECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,31,2684,0.011549925
+1221,669,IP GU RADICAL CYSTECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,50,2684,0.018628912
+1222,669,IP GU RADICAL CYSTECTOMY POSTOP,205597,ALVIMOPAN 12 MG PO CAPS,Medication,35,2684,0.013040238
+1223,669,IP GU RADICAL CYSTECTOMY POSTOP,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,44,2684,0.016393443
+1224,669,IP GU RADICAL CYSTECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,909,2684,0.338673621
+1225,669,IP GU RADICAL CYSTECTOMY POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,132,2684,0.049180328
+1226,669,IP GU RADICAL CYSTECTOMY POSTOP,520008,METOPROLOL IVPB,Medication,3,2684,0.001117735
+1227,669,IP GU RADICAL CYSTECTOMY POSTOP,520043,CEFOXITIN IVPB,Medication,19,2684,0.007078987
+1228,669,IP GU RADICAL CYSTECTOMY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,143,2684,0.053278689
+1229,669,IP GU RADICAL CYSTECTOMY POSTOP,540102,NS IV BOLUS,Medication,5,2684,0.001862891
+1230,669,IP GU RADICAL CYSTECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,66,2684,0.024590164
+1231,669,IP GU RADICAL CYSTECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,129,2684,0.048062593
+1232,669,IP GU RADICAL CYSTECTOMY POSTOP,541105,CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS,Medication,41,2684,0.015275708
+1233,669,IP GU RADICAL CYSTECTOMY POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,2,2684,0.000745156
+1234,669,IP GU RADICAL CYSTECTOMY POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,11,2684,0.004098361
+1235,669,IP GU RADICAL CYSTECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,384,2684,0.143070045
+1236,669,IP GU RADICAL CYSTECTOMY POSTOP,LABCBCO,CBC W/O DIFF,Lab,61,2684,0.022727273
+1237,669,IP GU RADICAL CYSTECTOMY POSTOP,LABFCR,"CREATININE, FLUID",Lab,8,2684,0.002980626
+1238,669,IP GU RADICAL CYSTECTOMY POSTOP,LABHCTX,HEMATOCRIT,Lab,240,2684,0.089418778
+1239,669,IP GU RADICAL CYSTECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,806,2684,0.300298063
+1240,669,IP GU RADICAL CYSTECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,8,2684,0.002980626
+1241,669,IP GU RADICAL CYSTECTOMY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,350,2684,0.130402385
+1242,669,IP GU RADICAL CYSTECTOMY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,31,2684,0.011549925
+1243,669,IP GU RADICAL CYSTECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,255,2684,0.095007452
+1244,669,IP GU RADICAL CYSTECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,16,2684,0.005961252
+1245,669,IP GU RADICAL CYSTECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,15,2684,0.005588674
+1246,669,IP GU RADICAL CYSTECTOMY POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,2,2684,0.000745156
+1247,669,IP GU RADICAL CYSTECTOMY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,2,2684,0.000745156
+1248,669,IP GU RADICAL CYSTECTOMY POSTOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,2,2684,0.000745156
+1249,672,IP GU RADICAL PROSTATECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,333,1059,0.314447592
+1250,672,IP GU RADICAL PROSTATECTOMY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,4,1059,0.003777148
+1251,672,IP GU RADICAL PROSTATECTOMY POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,4,1059,0.003777148
+1252,672,IP GU RADICAL PROSTATECTOMY POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,49,1059,0.046270066
+1253,672,IP GU RADICAL PROSTATECTOMY POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,3,1059,0.002832861
+1254,672,IP GU RADICAL PROSTATECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,153,1059,0.144475921
+1255,672,IP GU RADICAL PROSTATECTOMY POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,3,1059,0.002832861
+1256,672,IP GU RADICAL PROSTATECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,405,1059,0.382436261
+1257,672,IP GU RADICAL PROSTATECTOMY POSTOP,8085,TRAZODONE 50 MG PO TABS,Medication,2,1059,0.001888574
+1258,672,IP GU RADICAL PROSTATECTOMY POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,1059,0.003777148
+1259,672,IP GU RADICAL PROSTATECTOMY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,6,1059,0.005665722
+1260,672,IP GU RADICAL PROSTATECTOMY POSTOP,10246,IBUPROFEN 100 MG/5 ML PO SUSP,Medication,1,1059,0.000944287
+1261,672,IP GU RADICAL PROSTATECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,40,1059,0.037771483
+1262,672,IP GU RADICAL PROSTATECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,6,1059,0.005665722
+1263,672,IP GU RADICAL PROSTATECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,8,1059,0.007554297
+1264,672,IP GU RADICAL PROSTATECTOMY POSTOP,16830,MELATONIN 3 MG PO TABS,Medication,36,1059,0.033994334
+1265,672,IP GU RADICAL PROSTATECTOMY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,5,1059,0.004721435
+1266,672,IP GU RADICAL PROSTATECTOMY POSTOP,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,175,1059,0.165250236
+1267,672,IP GU RADICAL PROSTATECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,109,1059,0.10292729
+1268,672,IP GU RADICAL PROSTATECTOMY POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,154,1059,0.145420208
+1269,672,IP GU RADICAL PROSTATECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,187,1059,0.176581681
+1270,672,IP GU RADICAL PROSTATECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,295,1059,0.278564684
+1271,672,IP GU RADICAL PROSTATECTOMY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,1059,0.000944287
+1272,672,IP GU RADICAL PROSTATECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,130,1059,0.122757318
+1273,672,IP GU RADICAL PROSTATECTOMY POSTOP,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,1,1059,0.000944287
+1274,672,IP GU RADICAL PROSTATECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,47,1059,0.044381492
+1275,672,IP GU RADICAL PROSTATECTOMY POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,2,1059,0.001888574
+1276,672,IP GU RADICAL PROSTATECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,57,1059,0.053824363
+1277,672,IP GU RADICAL PROSTATECTOMY POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,3,1059,0.002832861
+1278,672,IP GU RADICAL PROSTATECTOMY POSTOP,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,1059,0.000944287
+1279,672,IP GU RADICAL PROSTATECTOMY POSTOP,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,2,1059,0.001888574
+1280,672,IP GU RADICAL PROSTATECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,314,1059,0.296506138
+1281,672,IP GU RADICAL PROSTATECTOMY POSTOP,116506,MELATONIN 1 MG PO TABS,Medication,1,1059,0.000944287
+1282,672,IP GU RADICAL PROSTATECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,408,1059,0.385269122
+1283,672,IP GU RADICAL PROSTATECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,1059,0.000944287
+1284,672,IP GU RADICAL PROSTATECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,31,1059,0.029272899
+1285,672,IP GU RADICAL PROSTATECTOMY POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,1059,0.001888574
+1286,672,IP GU RADICAL PROSTATECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,222,1059,0.209631728
+1287,672,IP GU RADICAL PROSTATECTOMY POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,156,1059,0.147308782
+1288,672,IP GU RADICAL PROSTATECTOMY POSTOP,520043,CEFOXITIN IVPB,Medication,1,1059,0.000944287
+1289,672,IP GU RADICAL PROSTATECTOMY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,103,1059,0.097261568
+1290,672,IP GU RADICAL PROSTATECTOMY POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,1059,0.000944287
+1291,672,IP GU RADICAL PROSTATECTOMY POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,24,1059,0.02266289
+1292,672,IP GU RADICAL PROSTATECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,7,1059,0.006610009
+1293,672,IP GU RADICAL PROSTATECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,1059,0.002832861
+1294,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,174,1059,0.164305949
+1295,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABCBCO,CBC W/O DIFF,Lab,39,1059,0.036827195
+1296,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABHCTX,HEMATOCRIT,Lab,20,1059,0.018885741
+1297,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,304,1059,0.287063267
+1298,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,1059,0.000944287
+1299,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,22,1059,0.020774315
+1300,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2,1059,0.001888574
+1301,672,IP GU RADICAL PROSTATECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,10,1059,0.009442871
+1302,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,9,0.222222222
+1303,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,1,9,0.111111111
+1304,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,3,9,0.333333333
+1305,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,1,9,0.111111111
+1306,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,9,0.222222222
+1307,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3,9,0.333333333
+1308,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,9,0.111111111
+1309,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,9,0.555555556
+1310,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,1,9,0.111111111
+1311,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,1,9,0.111111111
+1312,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,9,0.111111111
+1313,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,LABCBCD,CBC WITH DIFF,Lab,2,9,0.222222222
+1314,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,9,0.222222222
+1315,673,IP GU RETROPERITONEAL LYMPH NODE DISSECTION POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,9,0.111111111
+1316,675,RETIRED IP GU STONE PROCEDURE POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,5,0.2
+1317,675,IP GU STONE PROCEDURE POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,5,0.2
+1318,675,RETIRED IP GU STONE PROCEDURE POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,5,0.4
+1319,675,IP GU STONE PROCEDURE POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,5,0.4
+1320,675,RETIRED IP GU STONE PROCEDURE POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,5,0.2
+1321,675,IP GU STONE PROCEDURE POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,5,0.2
+1322,675,RETIRED IP GU STONE PROCEDURE POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,5,0.2
+1323,675,IP GU STONE PROCEDURE POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,5,0.2
+1324,675,RETIRED IP GU STONE PROCEDURE POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,5,0.2
+1325,675,IP GU STONE PROCEDURE POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,5,0.2
+1326,675,RETIRED IP GU STONE PROCEDURE POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,5,0.4
+1327,675,IP GU STONE PROCEDURE POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,5,0.4
+1328,675,RETIRED IP GU STONE PROCEDURE POSTOP,540102,NS IV BOLUS,Medication,1,5,0.2
+1329,675,IP GU STONE PROCEDURE POSTOP,540102,NS IV BOLUS,Medication,1,5,0.2
+1330,675,RETIRED IP GU STONE PROCEDURE POSTOP,LABCBCD,CBC WITH DIFF,Lab,2,5,0.4
+1331,675,IP GU STONE PROCEDURE POSTOP,LABCBCD,CBC WITH DIFF,Lab,2,5,0.4
+1332,675,RETIRED IP GU STONE PROCEDURE POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,5,0.4
+1333,675,IP GU STONE PROCEDURE POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,5,0.4
+1334,675,RETIRED IP GU STONE PROCEDURE POSTOP,IMGCTAP,CT ABDOMEN PELVIS,Imaging,2,5,0.4
+1335,675,IP GU STONE PROCEDURE POSTOP,IMGCTAP,CT ABDOMEN PELVIS,Imaging,2,5,0.4
+1336,679,IP GU TURP/TURBT POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,56,281,0.199288256
+1337,679,IP GU TURP/TURBT POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,281,0.007117438
+1338,679,IP GU TURP/TURBT POSTOP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,3,281,0.010676157
+1339,679,IP GU TURP/TURBT POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,1,281,0.003558719
+1340,679,IP GU TURP/TURBT POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,2,281,0.007117438
+1341,679,IP GU TURP/TURBT POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,8,281,0.028469751
+1342,679,IP GU TURP/TURBT POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,2,281,0.007117438
+1343,679,IP GU TURP/TURBT POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,69,281,0.245551601
+1344,679,IP GU TURP/TURBT POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,8,281,0.028469751
+1345,679,IP GU TURP/TURBT POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,1,281,0.003558719
+1346,679,IP GU TURP/TURBT POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,7,281,0.024911032
+1347,679,IP GU TURP/TURBT POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,6,281,0.021352313
+1348,679,IP GU TURP/TURBT POSTOP,7753,TEMAZEPAM 15 MG PO CAPS,Medication,1,281,0.003558719
+1349,679,IP GU TURP/TURBT POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,19,281,0.067615658
+1350,679,IP GU TURP/TURBT POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,1,281,0.003558719
+1351,679,IP GU TURP/TURBT POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,31,281,0.110320285
+1352,679,IP GU TURP/TURBT POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,12,281,0.042704626
+1353,679,IP GU TURP/TURBT POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,7,281,0.024911032
+1354,679,IP GU TURP/TURBT POSTOP,16830,MELATONIN 3 MG PO TABS,Medication,1,281,0.003558719
+1355,679,IP GU TURP/TURBT POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,8,281,0.028469751
+1356,679,IP GU TURP/TURBT POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,50,281,0.177935943
+1357,679,IP GU TURP/TURBT POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,4,281,0.014234875
+1358,679,IP GU TURP/TURBT POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,60,281,0.213523132
+1359,679,IP GU TURP/TURBT POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,34,281,0.120996441
+1360,679,IP GU TURP/TURBT POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,1,281,0.003558719
+1361,679,IP GU TURP/TURBT POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,9,281,0.03202847
+1362,679,IP GU TURP/TURBT POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,7,281,0.024911032
+1363,679,IP GU TURP/TURBT POSTOP,85848,"CALCIUM CARBONATE 400 MG (1,000 MG) PO CHEW",Medication,8,281,0.028469751
+1364,679,IP GU TURP/TURBT POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,6,281,0.021352313
+1365,679,IP GU TURP/TURBT POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,27,281,0.096085409
+1366,679,IP GU TURP/TURBT POSTOP,93295,ZOLPIDEM 10 MG PO TABS,Medication,1,281,0.003558719
+1367,679,IP GU TURP/TURBT POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,19,281,0.067615658
+1368,679,IP GU TURP/TURBT POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,281,0.003558719
+1369,679,IP GU TURP/TURBT POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,281,0.007117438
+1370,679,IP GU TURP/TURBT POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,10,281,0.035587189
+1371,679,IP GU TURP/TURBT POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,117,281,0.416370107
+1372,679,IP GU TURP/TURBT POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,281,0.003558719
+1373,679,IP GU TURP/TURBT POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,6,281,0.021352313
+1374,679,IP GU TURP/TURBT POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,45,281,0.160142349
+1375,679,IP GU TURP/TURBT POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,5,281,0.017793594
+1376,679,IP GU TURP/TURBT POSTOP,520043,CEFOXITIN IVPB,Medication,1,281,0.003558719
+1377,679,IP GU TURP/TURBT POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,18,281,0.06405694
+1378,679,IP GU TURP/TURBT POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,10,281,0.035587189
+1379,679,IP GU TURP/TURBT POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,281,0.017793594
+1380,679,IP GU TURP/TURBT POSTOP,LABCBCD,CBC WITH DIFF,Lab,47,281,0.167259786
+1381,679,IP GU TURP/TURBT POSTOP,LABCBCO,CBC W/O DIFF,Lab,3,281,0.010676157
+1382,679,IP GU TURP/TURBT POSTOP,LABHCTX,HEMATOCRIT,Lab,4,281,0.014234875
+1383,679,IP GU TURP/TURBT POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,51,281,0.181494662
+1384,679,IP GU TURP/TURBT POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,7,281,0.024911032
+1385,679,IP GU TURP/TURBT POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,1,281,0.003558719
+1386,679,IP GU TURP/TURBT POSTOP,LABPLTS,PLATELET COUNT,Lab,5,281,0.017793594
+1387,679,IP GU TURP/TURBT POSTOP,LABPT,PROTHROMBIN TIME,Lab,2,281,0.007117438
+1388,679,IP GU TURP/TURBT POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,281,0.003558719
+1389,679,IP GU TURP/TURBT POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,3,281,0.010676157
+1390,679,IP GU TURP/TURBT POSTOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,1,281,0.003558719
+1391,684,IP GU NEPHRECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,29,154,0.188311688
+1392,684,IP GU NEPHRECTOMY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,29,154,0.188311688
+1393,684,IP GU NEPHRECTOMY POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,3,154,0.019480519
+1394,684,IP GU NEPHRECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,34,154,0.220779221
+1395,684,IP GU NEPHRECTOMY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,27,154,0.175324675
+1396,684,IP GU NEPHRECTOMY POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,17,154,0.11038961
+1397,684,IP GU NEPHRECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,60,154,0.38961039
+1398,684,IP GU NEPHRECTOMY POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,2,154,0.012987013
+1399,684,IP GU NEPHRECTOMY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,154,0.012987013
+1400,684,IP GU NEPHRECTOMY POSTOP,7753,TEMAZEPAM 15 MG PO CAPS,Medication,4,154,0.025974026
+1401,684,IP GU NEPHRECTOMY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,154,0.006493506
+1402,684,IP GU NEPHRECTOMY POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,29,154,0.188311688
+1403,684,IP GU NEPHRECTOMY POSTOP,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,7,154,0.045454545
+1404,684,IP GU NEPHRECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,154,0.058441558
+1405,684,IP GU NEPHRECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,3,154,0.019480519
+1406,684,IP GU NEPHRECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,3,154,0.019480519
+1407,684,IP GU NEPHRECTOMY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,3,154,0.019480519
+1408,684,IP GU NEPHRECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,18,154,0.116883117
+1409,684,IP GU NEPHRECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,23,154,0.149350649
+1410,684,IP GU NEPHRECTOMY POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,2,154,0.012987013
+1411,684,IP GU NEPHRECTOMY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,154,0.006493506
+1412,684,IP GU NEPHRECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,15,154,0.097402597
+1413,684,IP GU NEPHRECTOMY POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,22,154,0.142857143
+1414,684,IP GU NEPHRECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,13,154,0.084415584
+1415,684,IP GU NEPHRECTOMY POSTOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,17,154,0.11038961
+1416,684,IP GU NEPHRECTOMY POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,8,154,0.051948052
+1417,684,IP GU NEPHRECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,18,154,0.116883117
+1418,684,IP GU NEPHRECTOMY POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,2,154,0.012987013
+1419,684,IP GU NEPHRECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,5,154,0.032467532
+1420,684,IP GU NEPHRECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,57,154,0.37012987
+1421,684,IP GU NEPHRECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,3,154,0.019480519
+1422,684,IP GU NEPHRECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,11,154,0.071428571
+1423,684,IP GU NEPHRECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,34,154,0.220779221
+1424,684,IP GU NEPHRECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,2,154,0.012987013
+1425,684,IP GU NEPHRECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,154,0.006493506
+1426,684,IP GU NEPHRECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,6,154,0.038961039
+1427,691,IP MED UTI/PYELONEPHRITIS,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,63,0.031746032
+1428,691,IP MED UTI/PYELONEPHRITIS,6193,PHENAZOPYRIDINE 100 MG PO TABS,Medication,2,63,0.031746032
+1429,691,IP MED UTI/PYELONEPHRITIS,6194,PHENAZOPYRIDINE 200 MG PO TABS,Medication,1,63,0.015873016
+1430,691,IP MED UTI/PYELONEPHRITIS,9500,CEPHALEXIN 500 MG PO CAPS,Medication,1,63,0.015873016
+1431,691,IP MED UTI/PYELONEPHRITIS,11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,1,63,0.015873016
+1432,691,IP MED UTI/PYELONEPHRITIS,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,1,63,0.015873016
+1433,691,IP MED UTI/PYELONEPHRITIS,33227,AMOXICILLIN-POT CLAVULANATE 500-125 MG PO TABS,Medication,1,63,0.015873016
+1434,691,IP MED UTI/PYELONEPHRITIS,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,3,63,0.047619048
+1435,691,IP MED UTI/PYELONEPHRITIS,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,63,0.015873016
+1436,691,IP MED UTI/PYELONEPHRITIS,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,63,0.015873016
+1437,691,IP MED UTI/PYELONEPHRITIS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,3,63,0.047619048
+1438,691,IP MED UTI/PYELONEPHRITIS,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,2,63,0.031746032
+1439,691,IP MED UTI/PYELONEPHRITIS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,9,63,0.142857143
+1440,691,IP MED UTI/PYELONEPHRITIS,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,16,63,0.253968254
+1441,691,IP MED UTI/PYELONEPHRITIS,540317,CEFTRIAXONE 2 GRAM/50 ML D5W IVPB,Medication,1,63,0.015873016
+1442,691,IP MED UTI/PYELONEPHRITIS,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,1,63,0.015873016
+1443,691,IP MED UTI/PYELONEPHRITIS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,1,63,0.015873016
+1444,691,IP MED UTI/PYELONEPHRITIS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,1,63,0.015873016
+1445,691,IP MED UTI/PYELONEPHRITIS,LABPLTS,PLATELET COUNT,Lab,4,63,0.063492063
+1446,691,IP MED UTI/PYELONEPHRITIS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,63,0.015873016
+1447,691,IP MED UTI/PYELONEPHRITIS,LABURNC,URINE CULTURE,Lab,4,63,0.063492063
+1448,691,IP MED UTI/PYELONEPHRITIS,IMGDXAB1,XR ABDOMEN 1V,Imaging,1,63,0.015873016
+1449,691,IP MED UTI/PYELONEPHRITIS,IMGUSAB1,US ABDOMEN,Imaging,2,63,0.031746032
+1450,692,IP MED SEPSIS,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,1,81,0.012345679
+1451,692,IP MED SEPSIS,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,3,81,0.037037037
+1452,692,IP MED SEPSIS,88175,OSELTAMIVIR 75 MG PO CAPS,Medication,3,81,0.037037037
+1453,692,IP MED SEPSIS,88533,AZTREONAM 2 GRAM INJ SOLR,Medication,1,81,0.012345679
+1454,692,IP MED SEPSIS,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,2,81,0.024691358
+1455,692,IP MED SEPSIS,520039,AZITHROMYCIN IVPB,Medication,3,81,0.037037037
+1456,692,IP MED SEPSIS,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,5,81,0.061728395
+1457,692,IP MED SEPSIS,520102,CEFEPIME 2 GM IVPB,Medication,1,81,0.012345679
+1458,692,IP MED SEPSIS,520107,AMPICILLIN 2 GM IVPB,Medication,1,81,0.012345679
+1459,692,IP MED SEPSIS,540102,NS IV BOLUS,Medication,3,81,0.037037037
+1460,692,IP MED SEPSIS,540317,CEFTRIAXONE 2 GRAM/50 ML D5W IVPB,Medication,3,81,0.037037037
+1461,692,IP MED SEPSIS,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,2,81,0.024691358
+1462,692,IP MED SEPSIS,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,1,81,0.012345679
+1463,692,IP MED SEPSIS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,10,81,0.12345679
+1464,692,IP MED SEPSIS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,7,81,0.086419753
+1465,692,IP MED SEPSIS,LABCBCD,CBC WITH DIFF,Lab,10,81,0.12345679
+1466,692,IP MED SEPSIS,LABGRAM,GRAM STAIN,Lab,1,81,0.012345679
+1467,692,IP MED SEPSIS,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,81,0.012345679
+1468,692,IP MED SEPSIS,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,48,81,0.592592593
+1469,692,IP MED SEPSIS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,81,0.049382716
+1470,692,IP MED SEPSIS,LABPROCT,PROCALCITONIN,Lab,2,81,0.024691358
+1471,692,IP MED SEPSIS,LABPT,PROTHROMBIN TIME,Lab,3,81,0.037037037
+1472,692,IP MED SEPSIS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,81,0.024691358
+1473,692,IP MED SEPSIS,LABRESP,RESPIRATORY CULTURE,Lab,6,81,0.074074074
+1474,692,IP MED SEPSIS,LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,20,81,0.24691358
+1475,692,IP MED SEPSIS,LABURNC,URINE CULTURE,Lab,10,81,0.12345679
+1476,692,IP MED SEPSIS,IMGDXCH1,XR CHEST 1V,Imaging,9,81,0.111111111
+1477,693,IP MED PANCREATITIS (ACUTE),101,ACETAMINOPHEN 325 MG PO TABS,Medication,14,95,0.147368421
+1478,693,IP MED PANCREATITIS (ACUTE),102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,95,0.021052632
+1479,693,IP MED PANCREATITIS (ACUTE),105,ACETAMINOPHEN 650 MG PR SUPP,Medication,3,95,0.031578947
+1480,693,IP MED PANCREATITIS (ACUTE),3757,HYDROMORPHONE (PF) 1 MG/ML INJ SOLN,Medication,2,95,0.021052632
+1481,693,IP MED PANCREATITIS (ACUTE),5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,7,95,0.073684211
+1482,693,IP MED PANCREATITIS (ACUTE),5172,MORPHINE 4 MG/ML INJ SYRG,Medication,9,95,0.094736842
+1483,693,IP MED PANCREATITIS (ACUTE),5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,27,95,0.284210526
+1484,693,IP MED PANCREATITIS (ACUTE),6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,2,95,0.021052632
+1485,693,IP MED PANCREATITIS (ACUTE),6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,5,95,0.052631579
+1486,693,IP MED PANCREATITIS (ACUTE),11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,1,95,0.010526316
+1487,693,IP MED PANCREATITIS (ACUTE),14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,4,95,0.042105263
+1488,693,IP MED PANCREATITIS (ACUTE),14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,5,95,0.052631579
+1489,693,IP MED PANCREATITIS (ACUTE),15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,95,0.010526316
+1490,693,IP MED PANCREATITIS (ACUTE),16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,6,95,0.063157895
+1491,693,IP MED PANCREATITIS (ACUTE),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,95,0.031578947
+1492,693,IP MED PANCREATITIS (ACUTE),110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,5,95,0.052631579
+1493,693,IP MED PANCREATITIS (ACUTE),110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,95,0.010526316
+1494,693,IP MED PANCREATITIS (ACUTE),110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,9,95,0.094736842
+1495,693,IP MED PANCREATITIS (ACUTE),112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,95,0.010526316
+1496,693,IP MED PANCREATITIS (ACUTE),112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,9,95,0.094736842
+1497,693,IP MED PANCREATITIS (ACUTE),200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,4,95,0.042105263
+1498,693,IP MED PANCREATITIS (ACUTE),212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,10,95,0.105263158
+1499,693,IP MED PANCREATITIS (ACUTE),540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,95,0.010526316
+1500,693,IP MED PANCREATITIS (ACUTE),LABAMY,"AMYLASE, SERUM",Lab,6,95,0.063157895
+1501,693,IP MED PANCREATITIS (ACUTE),LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,7,95,0.073684211
+1502,693,IP MED PANCREATITIS (ACUTE),LABCBCD,CBC WITH DIFF,Lab,10,95,0.105263158
+1503,693,IP MED PANCREATITIS (ACUTE),LABHFP,HEPATIC FUNCTION PANEL A,Lab,7,95,0.073684211
+1504,693,IP MED PANCREATITIS (ACUTE),LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,9,95,0.094736842
+1505,693,IP MED PANCREATITIS (ACUTE),LABLIPS,LIPASE,Lab,4,95,0.042105263
+1506,693,IP MED PANCREATITIS (ACUTE),LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,12,95,0.126315789
+1507,693,IP MED PANCREATITIS (ACUTE),LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,9,95,0.094736842
+1508,693,IP MED PANCREATITIS (ACUTE),LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,7,95,0.073684211
+1509,693,IP MED PANCREATITIS (ACUTE),LABPLTS,PLATELET COUNT,Lab,6,95,0.063157895
+1510,693,IP MED PANCREATITIS (ACUTE),LABPT,PROTHROMBIN TIME,Lab,2,95,0.021052632
+1511,693,IP MED PANCREATITIS (ACUTE),LABTYPSNI,TYPE AND SCREEN,Lab,1,95,0.010526316
+1512,693,IP MED PANCREATITIS (ACUTE),LABVOLTL,VOLATILE SCREEN,Lab,3,95,0.031578947
+1513,693,IP MED PANCREATITIS (ACUTE),IMGCTA,CT ABDOMEN,Imaging,1,95,0.010526316
+1514,693,IP MED PANCREATITIS (ACUTE),IMGDXCH1,XR CHEST 1V,Imaging,1,95,0.010526316
+1515,693,IP MED PANCREATITIS (ACUTE),IMGUSAB1,US ABDOMEN,Imaging,2,95,0.021052632
+1516,696,IP MED GI BLEED LOWER,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,65,0.030769231
+1517,696,IP MED GI BLEED LOWER,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,65,0.015384615
+1518,696,IP MED GI BLEED LOWER,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,65,0.030769231
+1519,696,IP MED GI BLEED LOWER,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,65,0.061538462
+1520,696,IP MED GI BLEED LOWER,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,3,65,0.046153846
+1521,696,IP MED GI BLEED LOWER,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,19,65,0.292307692
+1522,696,IP MED GI BLEED LOWER,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,6,65,0.092307692
+1523,696,IP MED GI BLEED LOWER,LABCBCD,CBC WITH DIFF,Lab,11,65,0.169230769
+1524,696,IP MED GI BLEED LOWER,LABHCTX,HEMATOCRIT,Lab,7,65,0.107692308
+1525,696,IP MED GI BLEED LOWER,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,65,0.015384615
+1526,696,IP MED GI BLEED LOWER,LABPT,PROTHROMBIN TIME,Lab,6,65,0.092307692
+1527,696,IP MED GI BLEED LOWER,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,4,65,0.061538462
+1528,696,IP MED GI BLEED LOWER,LABTYPSNI,TYPE AND SCREEN,Lab,11,65,0.169230769
+1529,696,IP MED GI BLEED LOWER,LABBBPRBC,RED BLOOD CELLS,Blood Product,3,65,0.046153846
+1530,697,IP MED GI BLEED UPPER,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,307,0.003257329
+1531,697,IP MED GI BLEED UPPER,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,5,307,0.016286645
+1532,697,IP MED GI BLEED UPPER,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,6,307,0.019543974
+1533,697,IP MED GI BLEED UPPER,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,4,307,0.013029316
+1534,697,IP MED GI BLEED UPPER,25122,OCTREOTIDE ACETATE 100 MCG/ML INJ SOLN,Medication,22,307,0.071661238
+1535,697,IP MED GI BLEED UPPER,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,15,307,0.048859935
+1536,697,IP MED GI BLEED UPPER,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,1,307,0.003257329
+1537,697,IP MED GI BLEED UPPER,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,5,307,0.016286645
+1538,697,IP MED GI BLEED UPPER,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,8,307,0.026058632
+1539,697,IP MED GI BLEED UPPER,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,51,307,0.166123779
+1540,697,IP MED GI BLEED UPPER,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,307,0.006514658
+1541,697,IP MED GI BLEED UPPER,530014,PANTOPRAZOLE 0.8 MG/ML IV INFUSION,Medication,54,307,0.175895765
+1542,697,IP MED GI BLEED UPPER,530016,OCTREOTIDE 10 MCG/ML IV INFUSION,Medication,28,307,0.091205212
+1543,697,IP MED GI BLEED UPPER,LABCBCD,CBC WITH DIFF,Lab,24,307,0.078175896
+1544,697,IP MED GI BLEED UPPER,LABCBCO,CBC W/O DIFF,Lab,99,307,0.32247557
+1545,697,IP MED GI BLEED UPPER,LABHCTX,HEMATOCRIT,Lab,18,307,0.058631922
+1546,697,IP MED GI BLEED UPPER,LABHELAG,"HELICOBACTER PYLORI AG, STOOL",Lab,36,307,0.117263844
+1547,697,IP MED GI BLEED UPPER,LABHELI,HELICOBACTER PYLORI AB,Lab,26,307,0.084690554
+1548,697,IP MED GI BLEED UPPER,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,307,0.003257329
+1549,697,IP MED GI BLEED UPPER,LABMETB,"METABOLIC PANEL, BASIC",Lab,30,307,0.09771987
+1550,697,IP MED GI BLEED UPPER,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,45,307,0.146579805
+1551,697,IP MED GI BLEED UPPER,LABPT,PROTHROMBIN TIME,Lab,38,307,0.123778502
+1552,697,IP MED GI BLEED UPPER,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,16,307,0.052117264
+1553,697,IP MED GI BLEED UPPER,LABSTOBGD,"OCCULT BLD, GUAIAC, STOOL, DIAG TEST",Lab,2,307,0.006514658
+1554,697,IP MED GI BLEED UPPER,LABTYPSNI,TYPE AND SCREEN,Lab,38,307,0.123778502
+1555,697,IP MED GI BLEED UPPER,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,307,0.003257329
+1556,697,IP MED GI BLEED UPPER,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,1,307,0.003257329
+1557,697,IP MED GI BLEED UPPER,LABBBPRBC,RED BLOOD CELLS,Blood Product,17,307,0.055374593
+1558,698,IP MED GENERAL ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,16668,71055,0.234578847
+1559,698,IP MED GENERAL ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,850,71055,0.011962564
+1560,698,IP MED GENERAL ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,4037,71055,0.056815143
+1561,698,IP MED GENERAL ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,11489,71055,0.161691647
+1562,698,IP MED GENERAL ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,529,71055,0.007444937
+1563,698,IP MED GENERAL ADMIT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,7,71055,9.85E-05
+1564,698,IP MED GENERAL ADMIT,3841,IBUPROFEN 200 MG PO TABS,Medication,34,71055,0.000478503
+1565,698,IP MED GENERAL ADMIT,3843,IBUPROFEN 400 MG PO TABS,Medication,316,71055,0.004447259
+1566,698,IP MED GENERAL ADMIT,3844,IBUPROFEN 600 MG PO TABS,Medication,30,71055,0.000422208
+1567,698,IP MED GENERAL ADMIT,3845,IBUPROFEN 800 MG PO TABS,Medication,7,71055,9.85E-05
+1568,698,IP MED GENERAL ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,873,71055,0.012286257
+1569,698,IP MED GENERAL ADMIT,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,196,71055,0.002758427
+1570,698,IP MED GENERAL ADMIT,5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,18,71055,0.000253325
+1571,698,IP MED GENERAL ADMIT,5087,MINERAL OIL PR ENEM,Medication,86,71055,0.00121033
+1572,698,IP MED GENERAL ADMIT,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,17,71055,0.000239251
+1573,698,IP MED GENERAL ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,428,71055,0.006023503
+1574,698,IP MED GENERAL ADMIT,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,85,71055,0.001196256
+1575,698,IP MED GENERAL ADMIT,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,304,71055,0.004278376
+1576,698,IP MED GENERAL ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,619,71055,0.008711561
+1577,698,IP MED GENERAL ADMIT,7321,SODIUM CHLORIDE 3 % 3 % IV SOLP,Medication,3,71055,4.22E-05
+1578,698,IP MED GENERAL ADMIT,8083,TRAZODONE 50 MG PO TABS,Medication,21,71055,0.000295546
+1579,698,IP MED GENERAL ADMIT,8085,TRAZODONE 50 MG PO TABS,Medication,1865,71055,0.026247273
+1580,698,IP MED GENERAL ADMIT,8749,WARFARIN 2 MG PO TABS,Medication,4,71055,5.63E-05
+1581,698,IP MED GENERAL ADMIT,8750,WARFARIN 2.5 MG PO TABS,Medication,1,71055,1.41E-05
+1582,698,IP MED GENERAL ADMIT,11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,52,71055,0.000731827
+1583,698,IP MED GENERAL ADMIT,11143,PROMETHAZINE 12.5 MG PR SUPP,Medication,7,71055,9.85E-05
+1584,698,IP MED GENERAL ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,444,71055,0.006248681
+1585,698,IP MED GENERAL ADMIT,11664,WARFARIN 1 MG PO TABS,Medication,13,71055,0.000182957
+1586,698,IP MED GENERAL ADMIT,14632,TRAMADOL 50 MG PO TABS,Medication,1795,71055,0.025262121
+1587,698,IP MED GENERAL ADMIT,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,339,71055,0.004770952
+1588,698,IP MED GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,601,71055,0.008458237
+1589,698,IP MED GENERAL ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,500,71055,0.007036802
+1590,698,IP MED GENERAL ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,613,71055,0.00862712
+1591,698,IP MED GENERAL ADMIT,20943,AZITHROMYCIN 250 MG PO TABS,Medication,31,71055,0.000436282
+1592,698,IP MED GENERAL ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,10366,71055,0.145886989
+1593,698,IP MED GENERAL ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3203,71055,0.045077757
+1594,698,IP MED GENERAL ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2580,71055,0.036309901
+1595,698,IP MED GENERAL ADMIT,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,182,71055,0.002561396
+1596,698,IP MED GENERAL ADMIT,80845,ONDANSETRON HCL 2 MG/ML IV SOLN,Medication,11,71055,0.00015481
+1597,698,IP MED GENERAL ADMIT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,3443,71055,0.048455422
+1598,698,IP MED GENERAL ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,71055,1.41E-05
+1599,698,IP MED GENERAL ADMIT,82268,MOXIFLOXACIN 400 MG PO TABS,Medication,7,71055,9.85E-05
+1600,698,IP MED GENERAL ADMIT,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,3,71055,4.22E-05
+1601,698,IP MED GENERAL ADMIT,84520,ONDANSETRON 8 MG PO TBDL,Medication,90,71055,0.001266624
+1602,698,IP MED GENERAL ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,1834,71055,0.025810991
+1603,698,IP MED GENERAL ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,944,71055,0.013285483
+1604,698,IP MED GENERAL ADMIT,93137,LEVOFLOXACIN 500 MG PO TABS,Medication,1,71055,1.41E-05
+1605,698,IP MED GENERAL ADMIT,93263,LEVOFLOXACIN 750 MG PO TABS,Medication,10,71055,0.000140736
+1606,698,IP MED GENERAL ADMIT,93295,ZOLPIDEM 10 MG PO TABS,Medication,28,71055,0.000394061
+1607,698,IP MED GENERAL ADMIT,93461,MOXIFLOXACIN IN NACL (ISO-OSM) 400 MG/250 ML IV PGBK,Medication,5,71055,7.04E-05
+1608,698,IP MED GENERAL ADMIT,94176,ONDANSETRON 4 MG PO TBDL,Medication,1080,71055,0.015199493
+1609,698,IP MED GENERAL ADMIT,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,35,71055,0.000492576
+1610,698,IP MED GENERAL ADMIT,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,9,71055,0.000126662
+1611,698,IP MED GENERAL ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1315,71055,0.018506791
+1612,698,IP MED GENERAL ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,390,71055,0.005488706
+1613,698,IP MED GENERAL ADMIT,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,12,71055,0.000168883
+1614,698,IP MED GENERAL ADMIT,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,14,71055,0.00019703
+1615,698,IP MED GENERAL ADMIT,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,10,71055,0.000140736
+1616,698,IP MED GENERAL ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,6215,71055,0.087467455
+1617,698,IP MED GENERAL ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1669,71055,0.023488847
+1618,698,IP MED GENERAL ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,941,71055,0.013243262
+1619,698,IP MED GENERAL ADMIT,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,48,71055,0.000675533
+1620,698,IP MED GENERAL ADMIT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,3,71055,4.22E-05
+1621,698,IP MED GENERAL ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,15022,71055,0.211413694
+1622,698,IP MED GENERAL ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,450,71055,0.006333122
+1623,698,IP MED GENERAL ADMIT,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,9,71055,0.000126662
+1624,698,IP MED GENERAL ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,58,71055,0.000816269
+1625,698,IP MED GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,7145,71055,0.100555907
+1626,698,IP MED GENERAL ADMIT,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,100,71055,0.00140736
+1627,698,IP MED GENERAL ADMIT,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,188,71055,0.002645838
+1628,698,IP MED GENERAL ADMIT,230510,FLU VAC TS 2013-14(36MOS+)(PF) 45 MCG (15 MCG X 3)/0.5 ML IM SYRG,Medication,21,71055,0.000295546
+1629,698,IP MED GENERAL ADMIT,520039,AZITHROMYCIN IVPB,Medication,35,71055,0.000492576
+1630,698,IP MED GENERAL ADMIT,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,86,71055,0.00121033
+1631,698,IP MED GENERAL ADMIT,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,71055,1.41E-05
+1632,698,IP MED GENERAL ADMIT,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,4,71055,5.63E-05
+1633,698,IP MED GENERAL ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,100,71055,0.00140736
+1634,698,IP MED GENERAL ADMIT,540769,SODIUM CHLORIDE 3 % IV BOLUS,Medication,7,71055,9.85E-05
+1635,698,IP MED GENERAL ADMIT,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,32,71055,0.000450355
+1636,698,IP MED GENERAL ADMIT,540998,HYDROMORPHONE (PF) SPECIFIC 10 MG/ML INJ SOLN,Medication,1,71055,1.41E-05
+1637,698,IP MED GENERAL ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,210,71055,0.002955457
+1638,698,IP MED GENERAL ADMIT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,6,71055,8.44E-05
+1639,698,IP MED GENERAL ADMIT,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,56,71055,0.000788122
+1640,698,IP MED GENERAL ADMIT,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,4,71055,5.63E-05
+1641,698,IP MED GENERAL ADMIT,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,38,71055,0.000534797
+1642,698,IP MED GENERAL ADMIT,580021,ENOXAPARIN (LOVENOX) ORDERABLE,Medication,1,71055,1.41E-05
+1643,698,IP MED GENERAL ADMIT,9000000,ZZZ IMS TEMPLATE,Medication,4,71055,5.63E-05
+1644,698,IP MED GENERAL ADMIT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,957,71055,0.01346844
+1645,698,IP MED GENERAL ADMIT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,955,71055,0.013440293
+1646,698,IP MED GENERAL ADMIT,LABCBCO,CBC W/O DIFF,Lab,20964,71055,0.295039054
+1647,698,IP MED GENERAL ADMIT,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,1328,71055,0.018689747
+1648,698,IP MED GENERAL ADMIT,LABLIPS,LIPASE,Lab,621,71055,0.008739709
+1649,698,IP MED GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,14532,71055,0.204517627
+1650,698,IP MED GENERAL ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,11165,71055,0.157131799
+1651,698,IP MED GENERAL ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,12011,71055,0.169038069
+1652,698,IP MED GENERAL ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,5700,71055,0.080219548
+1653,698,IP MED GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,5099,71055,0.071761312
+1654,698,IP MED GENERAL ADMIT,LABPT,PROTHROMBIN TIME,Lab,6941,71055,0.097684892
+1655,698,IP MED GENERAL ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2518,71055,0.035437337
+1656,698,IP MED GENERAL ADMIT,LABTNI,TROPONIN I,Lab,2888,71055,0.040644571
+1657,698,IP MED GENERAL ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,783,71055,0.011019633
+1658,698,IP MED GENERAL ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,631,71055,0.008880445
+1659,698,IP MED GENERAL ADMIT,LABURNC,URINE CULTURE,Lab,824,71055,0.01159665
+1660,698,IP MED GENERAL ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,1029,71055,0.014481739
+1661,698,IP MED GENERAL ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,700,71055,0.009851523
+1662,699,IP MED FEVER DIAGNOSTIC,LABANAS,ANTI - NUCLEAR AB,Lab,1,14,0.071428571
+1663,699,IP MED FEVER DIAGNOSTIC,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,9,14,0.642857143
+1664,699,IP MED FEVER DIAGNOSTIC,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,9,14,0.642857143
+1665,699,IP MED FEVER DIAGNOSTIC,LABCBCD,CBC WITH DIFF,Lab,1,14,0.071428571
+1666,699,IP MED FEVER DIAGNOSTIC,LABCK,"CREATINE KINASE, TOTAL",Lab,1,14,0.071428571
+1667,699,IP MED FEVER DIAGNOSTIC,LABGRAM,GRAM STAIN,Lab,1,14,0.071428571
+1668,699,IP MED FEVER DIAGNOSTIC,LABRESP,RESPIRATORY CULTURE,Lab,1,14,0.071428571
+1669,699,IP MED FEVER DIAGNOSTIC,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,1,14,0.071428571
+1670,699,IP MED FEVER DIAGNOSTIC,LABRF,RHEUMATOID FACTOR,Lab,1,14,0.071428571
+1671,699,IP MED FEVER DIAGNOSTIC,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,7,14,0.5
+1672,699,IP MED FEVER DIAGNOSTIC,LABURNC,URINE CULTURE,Lab,8,14,0.571428571
+1673,699,IP MED FEVER DIAGNOSTIC,IMGDXCH1,XR CHEST 1V,Imaging,2,14,0.142857143
+1674,701,IP MED DYSLIPIDEMIA,LABCHOHDL,CHOLESTEROL HDL / TOTAL,Lab,8,12,0.666666667
+1675,701,IP MED DYSLIPIDEMIA,LABCHOL,"CHOLESTEROL TOTAL, SERUM/PLASMA",Lab,8,12,0.666666667
+1676,701,IP MED DYSLIPIDEMIA,LABCK,"CREATINE KINASE, TOTAL",Lab,1,12,0.083333333
+1677,701,IP MED DYSLIPIDEMIA,LABHSCRP,HIGH SENSITIVITY C-REACTIVE PROTEIN,Lab,1,12,0.083333333
+1678,701,IP MED DYSLIPIDEMIA,LABLDLD,DIRECT LDL CHOLESTEROL,Lab,8,12,0.666666667
+1679,701,IP MED DYSLIPIDEMIA,LABLPA,LIPOPROTEIN,Lab,1,12,0.083333333
+1680,701,IP MED DYSLIPIDEMIA,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,4,12,0.333333333
+1681,701,IP MED DYSLIPIDEMIA,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,8,12,0.666666667
+1682,704,IP MED COPD,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,11,216,0.050925926
+1683,704,IP MED COPD,6494,PREDNISONE 10 MG PO TABS,Medication,1,216,0.00462963
+1684,704,IP MED COPD,6496,PREDNISONE 20 MG PO TABS,Medication,22,216,0.101851852
+1685,704,IP MED COPD,6498,PREDNISONE 50 MG PO TABS,Medication,1,216,0.00462963
+1686,704,IP MED COPD,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,3,216,0.013888889
+1687,704,IP MED COPD,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,2,216,0.009259259
+1688,704,IP MED COPD,77973,FLUTICASONE-SALMETEROL 250-50 MCG/DOSE INH DSDV,Medication,5,216,0.023148148
+1689,704,IP MED COPD,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,68,216,0.314814815
+1690,704,IP MED COPD,89401,FLUTICASONE-SALMETEROL 500-50 MCG/DOSE INH DSDV,Medication,3,216,0.013888889
+1691,704,IP MED COPD,95250,FLUTICASONE 110 MCG/ACTUATION INH AERO,Medication,1,216,0.00462963
+1692,704,IP MED COPD,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,2,216,0.009259259
+1693,704,IP MED COPD,115287,LEVOFLOXACIN IN D5W 500 MG/100 ML IV PGBK,Medication,1,216,0.00462963
+1694,704,IP MED COPD,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,5,216,0.023148148
+1695,704,IP MED COPD,199112,BUDESONIDE 180 MCG/ACTUATION INH AEPB,Medication,1,216,0.00462963
+1696,704,IP MED COPD,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,9,216,0.041666667
+1697,704,IP MED COPD,225590,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH AERO,Medication,1,216,0.00462963
+1698,704,IP MED COPD,233335,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH MIST,Medication,2,216,0.009259259
+1699,704,IP MED COPD,520039,AZITHROMYCIN IVPB,Medication,13,216,0.060185185
+1700,704,IP MED COPD,520051,DOXYCYCLINE IVPB,Medication,1,216,0.00462963
+1701,704,IP MED COPD,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,6,216,0.027777778
+1702,704,IP MED COPD,541109,DOXYCYCLINE 100 MG/100 ML MINI-BAG PLUS,Medication,4,216,0.018518519
+1703,704,IP MED COPD,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,2,216,0.009259259
+1704,704,IP MED COPD,9000000,ZZZ IMS TEMPLATE,Medication,33,216,0.152777778
+1705,704,IP MED COPD,LABABG,"BLOOD GASES, ARTERIAL",Lab,2,216,0.009259259
+1706,704,IP MED COPD,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,3,216,0.013888889
+1707,704,IP MED COPD,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,2,216,0.009259259
+1708,704,IP MED COPD,LABLEGU,"LEGIONELLA AG, URINE",Lab,3,216,0.013888889
+1709,704,IP MED COPD,LABRESP,RESPIRATORY CULTURE,Lab,3,216,0.013888889
+1710,704,IP MED COPD,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,10,216,0.046296296
+1711,704,IP MED COPD,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,2,216,0.009259259
+1712,704,IP MED COPD,IMGDXCH1,XR CHEST 1V,Imaging,1,216,0.00462963
+1713,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,20943,AZITHROMYCIN 250 MG PO TABS,Medication,17,192,0.088541667
+1714,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,82268,MOXIFLOXACIN 400 MG PO TABS,Medication,2,192,0.010416667
+1715,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,93137,LEVOFLOXACIN 500 MG PO TABS,Medication,1,192,0.005208333
+1716,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,93263,LEVOFLOXACIN 750 MG PO TABS,Medication,9,192,0.046875
+1717,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,93461,MOXIFLOXACIN IN NACL (ISO-OSM) 400 MG/250 ML IV PGBK,Medication,5,192,0.026041667
+1718,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,12,192,0.0625
+1719,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,3,192,0.015625
+1720,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,23,192,0.119791667
+1721,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,520039,AZITHROMYCIN IVPB,Medication,22,192,0.114583333
+1722,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,22,192,0.114583333
+1723,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,5,192,0.026041667
+1724,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,540317,CEFTRIAXONE 2 GRAM/50 ML D5W IVPB,Medication,3,192,0.015625
+1725,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,6,192,0.03125
+1726,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,8,192,0.041666667
+1727,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,28,192,0.145833333
+1728,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,3,192,0.015625
+1729,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,15,192,0.078125
+1730,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LAB305,"CULTURE, VIRUS",Lab,10,192,0.052083333
+1731,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LABABG,"BLOOD GASES, ARTERIAL",Lab,1,192,0.005208333
+1732,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,10,192,0.052083333
+1733,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,10,192,0.052083333
+1734,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LABLEGU,"LEGIONELLA AG, URINE",Lab,30,192,0.15625
+1735,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LABRESP,RESPIRATORY CULTURE,Lab,21,192,0.109375
+1736,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,24,192,0.125
+1737,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,IMGCTT,CT THORAX,Imaging,1,192,0.005208333
+1738,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,IMGDXCH1,XR CHEST 1V,Imaging,5,192,0.026041667
+1739,705,IP MED COMMUNITY ACQUIRED PNEUMONIA,IMGDXCH2,XR CHEST 2V,Imaging,2,192,0.010416667
+1740,706,IP MED CHEST PAIN,681,ASPIRIN 325 MG PO TABS,Medication,3,110,0.027272727
+1741,706,IP MED CHEST PAIN,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,26,110,0.236363636
+1742,706,IP MED CHEST PAIN,10011,FAMOTIDINE 20 MG PO TABS,Medication,2,110,0.018181818
+1743,706,IP MED CHEST PAIN,15065,FAMOTIDINE 10 MG PO TABS,Medication,2,110,0.018181818
+1744,706,IP MED CHEST PAIN,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,110,0.009090909
+1745,706,IP MED CHEST PAIN,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,110,0.009090909
+1746,706,IP MED CHEST PAIN,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,14,110,0.127272727
+1747,706,IP MED CHEST PAIN,90788,LIDOCAINE HCL 2 % VISCOUS ORAL SOLN,Medication,1,110,0.009090909
+1748,706,IP MED CHEST PAIN,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,6,110,0.054545455
+1749,706,IP MED CHEST PAIN,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,110,0.009090909
+1750,706,IP MED CHEST PAIN,540102,NS IV BOLUS,Medication,1,110,0.009090909
+1751,706,IP MED CHEST PAIN,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,1,110,0.009090909
+1752,706,IP MED CHEST PAIN,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,1,110,0.009090909
+1753,706,IP MED CHEST PAIN,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,1,110,0.009090909
+1754,706,IP MED CHEST PAIN,LABTNI,TROPONIN I,Lab,54,110,0.490909091
+1755,706,IP MED CHEST PAIN,LABTYPSNI,TYPE AND SCREEN,Lab,5,110,0.045454545
+1756,706,IP MED CHEST PAIN,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,110,0.009090909
+1757,706,IP MED CHEST PAIN,ECH29,ECHO - DOBUTAMINE STRESS ECHO,Imaging,8,110,0.072727273
+1758,708,IP MED ALCOHOL WITHDRAWAL,1622,CHLORDIAZEPOXIDE HCL 10 MG PO CAPS,Medication,3,2614,0.001147666
+1759,708,IP MED ALCOHOL WITHDRAWAL,1623,CHLORDIAZEPOXIDE HCL 25 MG PO CAPS,Medication,49,2614,0.018745218
+1760,708,IP MED ALCOHOL WITHDRAWAL,1755,CLONIDINE HCL 0.1 MG PO TABS,Medication,235,2614,0.089900536
+1761,708,IP MED ALCOHOL WITHDRAWAL,1756,CLONIDINE HCL 0.2 MG PO TABS,Medication,12,2614,0.004590666
+1762,708,IP MED ALCOHOL WITHDRAWAL,2403,DIAZEPAM 10 MG PO TABS,Medication,115,2614,0.043993879
+1763,708,IP MED ALCOHOL WITHDRAWAL,2404,DIAZEPAM 2 MG PO TABS,Medication,7,2614,0.002677888
+1764,708,IP MED ALCOHOL WITHDRAWAL,2405,DIAZEPAM 5 MG PO TABS,Medication,129,2614,0.049349656
+1765,708,IP MED ALCOHOL WITHDRAWAL,3233,FOLIC ACID 1 MG PO TABS,Medication,463,2614,0.177123183
+1766,708,IP MED ALCOHOL WITHDRAWAL,4572,LORAZEPAM 0.5 MG PO TABS,Medication,34,2614,0.013006886
+1767,708,IP MED ALCOHOL WITHDRAWAL,4573,LORAZEPAM 1 MG PO TABS,Medication,1745,2614,0.667559296
+1768,708,IP MED ALCOHOL WITHDRAWAL,4574,LORAZEPAM 2 MG PO TABS,Medication,200,2614,0.076511094
+1769,708,IP MED ALCOHOL WITHDRAWAL,7877,THIAMINE HCL (VITAMIN B1) 100 MG PO TABS,Medication,449,2614,0.171767406
+1770,708,IP MED ALCOHOL WITHDRAWAL,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,2181,2614,0.834353481
+1771,708,IP MED ALCOHOL WITHDRAWAL,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,42,2614,0.01606733
+1772,708,IP MED ALCOHOL WITHDRAWAL,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,29,2614,0.011094109
+1773,708,IP MED ALCOHOL WITHDRAWAL,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,30,2614,0.011476664
+1774,708,IP MED ALCOHOL WITHDRAWAL,111320,MULTIVITAMIN WITH IRON-MINERAL PO LIQD,Medication,5,2614,0.001912777
+1775,708,IP MED ALCOHOL WITHDRAWAL,111354,"MULTIVITAMIN,TX-IRON-CA-FA-MIN 27-0.4 MG PO TABS",Medication,1,2614,0.000382555
+1776,708,IP MED ALCOHOL WITHDRAWAL,112420,DIAZEPAM 5 MG/ML INJ SYRG,Medication,129,2614,0.049349656
+1777,708,IP MED ALCOHOL WITHDRAWAL,121429,MAGNESIUM L-LACTATE 84 MG PO TBSR,Medication,23,2614,0.008798776
+1778,708,IP MED ALCOHOL WITHDRAWAL,200346,MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK,Medication,69,2614,0.026396327
+1779,708,IP MED ALCOHOL WITHDRAWAL,222985,DIAZEPAM 5 MG/5 ML (5 ML) PO SOLN,Medication,2,2614,0.000765111
+1780,708,IP MED ALCOHOL WITHDRAWAL,226393,MULTIVITAMIN-FERROUS GLUCONATE 10 MG IRON/5 ML PO LIQD,Medication,2,2614,0.000765111
+1781,708,IP MED ALCOHOL WITHDRAWAL,230510,FLU VAC TS 2013-14(36MOS+)(PF) 45 MCG (15 MCG X 3)/0.5 ML IM SYRG,Medication,4,2614,0.001530222
+1782,708,IP MED ALCOHOL WITHDRAWAL,520094,"MULTIVITAMINS (+ FOLIC ACID, THIAMINE, VIT K) IVPB",Medication,240,2614,0.091813313
+1783,708,IP MED ALCOHOL WITHDRAWAL,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,3,2614,0.001147666
+1784,708,IP MED ALCOHOL WITHDRAWAL,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,425,2614,0.162586075
+1785,708,IP MED ALCOHOL WITHDRAWAL,9000000,ZZZ IMS TEMPLATE,Medication,1,2614,0.000382555
+1786,708,IP MED ALCOHOL WITHDRAWAL,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,106,2614,0.04055088
+1787,708,IP MED ALCOHOL WITHDRAWAL,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,102,2614,0.039020658
+1788,708,IP MED ALCOHOL WITHDRAWAL,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,106,2614,0.04055088
+1789,708,IP MED ALCOHOL WITHDRAWAL,LABVOLTL,VOLATILE SCREEN,Lab,71,2614,0.027161438
+1790,708,IP MED ALCOHOL WITHDRAWAL,IMGDXCH1,XR CHEST 1V,Imaging,11,2614,0.00420811
+1791,708,IP MED ALCOHOL WITHDRAWAL,IMGDXCH2,XR CHEST 2V,Imaging,2,2614,0.000765111
+1792,710,IP MED  ACUTE RENAL FAILURE,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,1,17,0.058823529
+1793,710,IP MED  ACUTE RENAL FAILURE,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,1,17,0.058823529
+1794,710,IP MED  ACUTE RENAL FAILURE,27999,SODIUM POLYSTYRENE SULFONATE 15 GRAM/60 ML PO SUSP,Medication,8,17,0.470588235
+1795,710,IP MED  ACUTE RENAL FAILURE,94556,SEVELAMER HCL 400 MG PO TABS,Medication,1,17,0.058823529
+1796,710,IP MED  ACUTE RENAL FAILURE,540775,POLYETHYLENE GLYCOL 3350 17 GM (FOR KAYEXALATE PANEL),Medication,6,17,0.352941176
+1797,710,IP MED  ACUTE RENAL FAILURE,LABRP,RENAL FUNCTION PANEL,Lab,1,17,0.058823529
+1798,710,IP MED  ACUTE RENAL FAILURE,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,17,0.058823529
+1799,710,IP MED  ACUTE RENAL FAILURE,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,17,0.058823529
+1800,710,IP MED  ACUTE RENAL FAILURE,LABUEP,"ELECTROLYTE PANEL, URINE WITH CREATININE",Lab,1,17,0.058823529
+1801,710,IP MED  ACUTE RENAL FAILURE,LABUTPRAN,"PROTEIN WITH CREATININE, URINE (RANDOM)",Lab,1,17,0.058823529
+1802,710,IP MED  ACUTE RENAL FAILURE,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,1,17,0.058823529
+1803,710,IP MED  ACUTE RENAL FAILURE,IMGDXCH1,XR CHEST 1V,Imaging,1,17,0.058823529
+1804,712,IP MED ASTHMA,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,6,56,0.107142857
+1805,712,IP MED ASTHMA,6496,PREDNISONE 20 MG PO TABS,Medication,7,56,0.125
+1806,712,IP MED ASTHMA,6498,PREDNISONE 50 MG PO TABS,Medication,1,56,0.017857143
+1807,712,IP MED ASTHMA,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,3,56,0.053571429
+1808,712,IP MED ASTHMA,70536,FLUTICASONE 50 MCG/ACTUATION NASAL SPSN,Medication,1,56,0.017857143
+1809,712,IP MED ASTHMA,77973,FLUTICASONE-SALMETEROL 250-50 MCG/DOSE INH DSDV,Medication,1,56,0.017857143
+1810,712,IP MED ASTHMA,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,10,56,0.178571429
+1811,712,IP MED ASTHMA,89401,FLUTICASONE-SALMETEROL 500-50 MCG/DOSE INH DSDV,Medication,1,56,0.017857143
+1812,712,IP MED ASTHMA,92914,FLUTICASONE-SALMETEROL 100-50 MCG/DOSE INH DSDV,Medication,1,56,0.017857143
+1813,712,IP MED ASTHMA,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,5,56,0.089285714
+1814,712,IP MED ASTHMA,9000000,ZZZ IMS TEMPLATE,Medication,10,56,0.178571429
+1815,713,IP GEN COMFORT CARE - NON ICU UNITS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,127,2998,0.042361574
+1816,713,IP GEN COMFORT CARE - NON ICU UNITS,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,146,2998,0.048699133
+1817,713,IP GEN COMFORT CARE - NON ICU UNITS,736,ATROPINE 1 % OPHT DROP,Medication,214,2998,0.071380921
+1818,713,IP GEN COMFORT CARE - NON ICU UNITS,1080,BISACODYL 10 MG PR SUPP,Medication,145,2998,0.048365577
+1819,713,IP GEN COMFORT CARE - NON ICU UNITS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,114,2998,0.03802535
+1820,713,IP GEN COMFORT CARE - NON ICU UNITS,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,4,2998,0.001334223
+1821,713,IP GEN COMFORT CARE - NON ICU UNITS,3497,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,Medication,431,2998,0.143762508
+1822,713,IP GEN COMFORT CARE - NON ICU UNITS,3578,HALOPERIDOL 0.5 MG PO TABS,Medication,47,2998,0.015677118
+1823,713,IP GEN COMFORT CARE - NON ICU UNITS,3579,HALOPERIDOL 1 MG PO TABS,Medication,2,2998,0.000667111
+1824,713,IP GEN COMFORT CARE - NON ICU UNITS,3584,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,Medication,157,2998,0.052368245
+1825,713,IP GEN COMFORT CARE - NON ICU UNITS,3760,HYDROMORPHONE 2 MG PO TABS,Medication,21,2998,0.00700467
+1826,713,IP GEN COMFORT CARE - NON ICU UNITS,4572,LORAZEPAM 0.5 MG PO TABS,Medication,141,2998,0.047031354
+1827,713,IP GEN COMFORT CARE - NON ICU UNITS,4573,LORAZEPAM 1 MG PO TABS,Medication,23,2998,0.007671781
+1828,713,IP GEN COMFORT CARE - NON ICU UNITS,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,237,2998,0.079052702
+1829,713,IP GEN COMFORT CARE - NON ICU UNITS,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,32,2998,0.010673783
+1830,713,IP GEN COMFORT CARE - NON ICU UNITS,5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,1,2998,0.000333556
+1831,713,IP GEN COMFORT CARE - NON ICU UNITS,5087,MINERAL OIL PR ENEM,Medication,1,2998,0.000333556
+1832,713,IP GEN COMFORT CARE - NON ICU UNITS,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,409,2998,0.136424283
+1833,713,IP GEN COMFORT CARE - NON ICU UNITS,5176,MORPHINE 10 MG/5 ML PO SOLN,Medication,114,2998,0.03802535
+1834,713,IP GEN COMFORT CARE - NON ICU UNITS,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,24,2998,0.008005337
+1835,713,IP GEN COMFORT CARE - NON ICU UNITS,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,38,2998,0.012675117
+1836,713,IP GEN COMFORT CARE - NON ICU UNITS,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,462,2998,0.154102735
+1837,713,IP GEN COMFORT CARE - NON ICU UNITS,11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,49,2998,0.016344229
+1838,713,IP GEN COMFORT CARE - NON ICU UNITS,11349,SENNOSIDES 8.6 MG PO TABS,Medication,136,2998,0.045363576
+1839,713,IP GEN COMFORT CARE - NON ICU UNITS,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,23,2998,0.007671781
+1840,713,IP GEN COMFORT CARE - NON ICU UNITS,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,109,2998,0.036357572
+1841,713,IP GEN COMFORT CARE - NON ICU UNITS,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,110,2998,0.036691127
+1842,713,IP GEN COMFORT CARE - NON ICU UNITS,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,145,2998,0.048365577
+1843,713,IP GEN COMFORT CARE - NON ICU UNITS,95795,ARTIFICIAL TEARS(HYPROMELLOSE) 0.5 % OPHT DROP,Medication,380,2998,0.126751167
+1844,713,IP GEN COMFORT CARE - NON ICU UNITS,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,101,2998,0.033689126
+1845,713,IP GEN COMFORT CARE - NON ICU UNITS,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,22,2998,0.007338225
+1846,713,IP GEN COMFORT CARE - NON ICU UNITS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,117,2998,0.039026017
+1847,713,IP GEN COMFORT CARE - NON ICU UNITS,117414,ARTIFICIAL SALIVA (YERBAS-LYT) MM SPRA,Medication,429,2998,0.143095397
+1848,713,IP GEN COMFORT CARE - NON ICU UNITS,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,37,2998,0.012341561
+1849,713,IP GEN COMFORT CARE - NON ICU UNITS,205617,SALIVA STIMULANT AGENTS COMB.3 MM SPRY,Medication,26,2998,0.008672448
+1850,713,IP GEN COMFORT CARE - NON ICU UNITS,206620,HYDROMORPHONE IN 0.9 % NACL 30 MG/30 ML (1 MG/ML) IV SPCA,Medication,1,2998,0.000333556
+1851,713,IP GEN COMFORT CARE - NON ICU UNITS,207949,MORPHINE (PF) 30 MG/30 ML (1 MG/ML) IV PCA,Medication,38,2998,0.012675117
+1852,713,IP GEN COMFORT CARE - NON ICU UNITS,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,2998,0.000333556
+1853,713,IP GEN COMFORT CARE - NON ICU UNITS,540125,HYDROMORPHONE 1 MG/ML IV PCA,Medication,17,2998,0.005670447
+1854,713,IP GEN COMFORT CARE - NON ICU UNITS,540989,MORPHINE (PF) (1 MG/ML) IV PCA CONTINUOUS INFUSION,Medication,148,2998,0.049366244
+1855,713,IP GEN COMFORT CARE - NON ICU UNITS,540991,HYDROMORPHONE 1 MG/ML IV PCA CONTINUOUS INFUSION,Medication,85,2998,0.028352235
+1856,713,IP GEN COMFORT CARE - NON ICU UNITS,540993,MORPHINE 5 MG/ML IV PCA CONTINUOUS INFUSION,Medication,2,2998,0.000667111
+1857,713,IP GEN COMFORT CARE - NON ICU UNITS,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,8,2998,0.002668446
+1858,713,IP GEN COMFORT CARE - NON ICU UNITS,541607,HYDROMORPHONE 1 MG/ML IV PCA,Medication,76,2998,0.025350233
+1859,713,IP GEN COMFORT CARE - NON ICU UNITS,541609,MORPHINE 30 MG/30 ML IV PCA,Medication,91,2998,0.030353569
+1860,715,IP GYN-ONC PREOP,4318,LACTATED RINGERS IV SOLP,Medication,5,59,0.084745763
+1861,715,IP GYN-ONC PREOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,59,0.016949153
+1862,715,IP GYN-ONC PREOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,59,0.016949153
+1863,715,IP GYN-ONC PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,59,0.06779661
+1864,715,IP GYN-ONC PREOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,1,59,0.016949153
+1865,715,IP GYN-ONC PREOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,3,59,0.050847458
+1866,715,IP GYN-ONC PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,15,59,0.254237288
+1867,715,IP GYN-ONC PREOP,520043,CEFOXITIN IVPB,Medication,2,59,0.033898305
+1868,715,IP GYN-ONC PREOP,540115,LACTATED RINGERS IV BOLUS,Medication,1,59,0.016949153
+1869,715,IP GYN-ONC PREOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,7,59,0.118644068
+1870,715,IP GYN-ONC PREOP,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,59,0.016949153
+1871,715,IP GYN-ONC PREOP,LABC125,CANCER AG 125,Lab,1,59,0.016949153
+1872,715,IP GYN-ONC PREOP,LABCBCD,CBC WITH DIFF,Lab,9,59,0.152542373
+1873,715,IP GYN-ONC PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,7,59,0.118644068
+1874,715,IP GYN-ONC PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,59,0.050847458
+1875,715,IP GYN-ONC PREOP,LABPLTS,PLATELET COUNT,Lab,8,59,0.13559322
+1876,715,IP GYN-ONC PREOP,LABPT,PROTHROMBIN TIME,Lab,1,59,0.016949153
+1877,715,IP GYN-ONC PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,59,0.016949153
+1878,715,IP GYN-ONC PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,10,59,0.169491525
+1879,715,IP GYN-ONC PREOP,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,1,59,0.016949153
+1880,715,IP GYN-ONC PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,5,59,0.084745763
+1881,716,IP GYN EXLAP/TAH/BSO GENERAL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,549,0.001821494
+1882,716,IP GYN EXLAP/TAH/BSO GENERAL,1080,BISACODYL 10 MG PR SUPP,Medication,39,549,0.071038251
+1883,716,IP GYN EXLAP/TAH/BSO GENERAL,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,145,549,0.264116576
+1884,716,IP GYN EXLAP/TAH/BSO GENERAL,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,8,549,0.014571949
+1885,716,IP GYN EXLAP/TAH/BSO GENERAL,4318,LACTATED RINGERS IV SOLP,Medication,91,549,0.16575592
+1886,716,IP GYN EXLAP/TAH/BSO GENERAL,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,210,549,0.382513661
+1887,716,IP GYN EXLAP/TAH/BSO GENERAL,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,11,549,0.02003643
+1888,716,IP GYN EXLAP/TAH/BSO GENERAL,10011,FAMOTIDINE 20 MG PO TABS,Medication,8,549,0.014571949
+1889,716,IP GYN EXLAP/TAH/BSO GENERAL,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,549,0.016393443
+1890,716,IP GYN EXLAP/TAH/BSO GENERAL,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,13,549,0.023679417
+1891,716,IP GYN EXLAP/TAH/BSO GENERAL,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,15,549,0.027322404
+1892,716,IP GYN EXLAP/TAH/BSO GENERAL,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,27,549,0.049180328
+1893,716,IP GYN EXLAP/TAH/BSO GENERAL,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,57,549,0.103825137
+1894,716,IP GYN EXLAP/TAH/BSO GENERAL,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,13,549,0.023679417
+1895,716,IP GYN EXLAP/TAH/BSO GENERAL,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,18,549,0.032786885
+1896,716,IP GYN EXLAP/TAH/BSO GENERAL,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,4,549,0.007285974
+1897,716,IP GYN EXLAP/TAH/BSO GENERAL,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,25,549,0.045537341
+1898,716,IP GYN EXLAP/TAH/BSO GENERAL,91562,ENOXAPARIN 150 MG/ML SC SYRG,Medication,1,549,0.001821494
+1899,716,IP GYN EXLAP/TAH/BSO GENERAL,92173,ZOLPIDEM 5 MG PO TABS,Medication,8,549,0.014571949
+1900,716,IP GYN EXLAP/TAH/BSO GENERAL,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,32,549,0.058287796
+1901,716,IP GYN EXLAP/TAH/BSO GENERAL,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,57,549,0.103825137
+1902,716,IP GYN EXLAP/TAH/BSO GENERAL,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,15,549,0.027322404
+1903,716,IP GYN EXLAP/TAH/BSO GENERAL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,204,549,0.371584699
+1904,716,IP GYN EXLAP/TAH/BSO GENERAL,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,7,549,0.012750455
+1905,716,IP GYN EXLAP/TAH/BSO GENERAL,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,39,549,0.071038251
+1906,716,IP GYN EXLAP/TAH/BSO GENERAL,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,549,0.001821494
+1907,716,IP GYN EXLAP/TAH/BSO GENERAL,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,6,549,0.010928962
+1908,716,IP GYN EXLAP/TAH/BSO GENERAL,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,549,0.001821494
+1909,716,IP GYN EXLAP/TAH/BSO GENERAL,LABCBCO,CBC W/O DIFF,Lab,163,549,0.296903461
+1910,716,IP GYN EXLAP/TAH/BSO GENERAL,LABMETB,"METABOLIC PANEL, BASIC",Lab,130,549,0.236794171
+1911,716,IP GYN EXLAP/TAH/BSO GENERAL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,5,549,0.009107468
+1912,716,IP GYN EXLAP/TAH/BSO GENERAL,LABPLTS,PLATELET COUNT,Lab,22,549,0.04007286
+1913,716,IP GYN EXLAP/TAH/BSO GENERAL,LABPT,PROTHROMBIN TIME,Lab,2,549,0.003642987
+1914,716,IP GYN EXLAP/TAH/BSO GENERAL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,549,0.003642987
+1915,716,IP GYN EXLAP/TAH/BSO GENERAL,LABTYPSNI,TYPE AND SCREEN,Lab,2,549,0.003642987
+1916,716,IP GYN EXLAP/TAH/BSO GENERAL,POC14,POC GLUCOSE BY METER,Lab,3,549,0.005464481
+1917,716,IP GYN EXLAP/TAH/BSO GENERAL,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,549,0.001821494
+1918,716,IP GYN EXLAP/TAH/BSO GENERAL,LABBBPRBC,RED BLOOD CELLS,Blood Product,2,549,0.003642987
+1919,720,IP GYN-ONC LAPAROSCOPY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,36,350,0.102857143
+1920,720,IP GYN-ONC LAPAROSCOPY POSTOP ,1080,BISACODYL 10 MG PR SUPP,Medication,36,350,0.102857143
+1921,720,IP GYN-ONC LAPAROSCOPY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,108,350,0.308571429
+1922,720,IP GYN-ONC LAPAROSCOPY POSTOP ,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,108,350,0.308571429
+1923,720,IP GYN-ONC LAPAROSCOPY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,6,350,0.017142857
+1924,720,IP GYN-ONC LAPAROSCOPY POSTOP ,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,6,350,0.017142857
+1925,720,IP GYN-ONC LAPAROSCOPY POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,82,350,0.234285714
+1926,720,IP GYN-ONC LAPAROSCOPY POSTOP ,4318,LACTATED RINGERS IV SOLP,Medication,82,350,0.234285714
+1927,720,IP GYN-ONC LAPAROSCOPY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,141,350,0.402857143
+1928,720,IP GYN-ONC LAPAROSCOPY POSTOP ,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,141,350,0.402857143
+1929,720,IP GYN-ONC LAPAROSCOPY POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,2,350,0.005714286
+1930,720,IP GYN-ONC LAPAROSCOPY POSTOP ,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,2,350,0.005714286
+1931,720,IP GYN-ONC LAPAROSCOPY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,350,0.002857143
+1932,720,IP GYN-ONC LAPAROSCOPY POSTOP ,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,350,0.002857143
+1933,720,IP GYN-ONC LAPAROSCOPY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,3,350,0.008571429
+1934,720,IP GYN-ONC LAPAROSCOPY POSTOP ,10011,FAMOTIDINE 20 MG PO TABS,Medication,3,350,0.008571429
+1935,720,IP GYN-ONC LAPAROSCOPY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,350,0.025714286
+1936,720,IP GYN-ONC LAPAROSCOPY POSTOP ,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,350,0.025714286
+1937,720,IP GYN-ONC LAPAROSCOPY POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,3,350,0.008571429
+1938,720,IP GYN-ONC LAPAROSCOPY POSTOP ,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,3,350,0.008571429
+1939,720,IP GYN-ONC LAPAROSCOPY POSTOP,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,12,350,0.034285714
+1940,720,IP GYN-ONC LAPAROSCOPY POSTOP ,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,12,350,0.034285714
+1941,720,IP GYN-ONC LAPAROSCOPY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,5,350,0.014285714
+1942,720,IP GYN-ONC LAPAROSCOPY POSTOP ,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,5,350,0.014285714
+1943,720,IP GYN-ONC LAPAROSCOPY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,48,350,0.137142857
+1944,720,IP GYN-ONC LAPAROSCOPY POSTOP ,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,48,350,0.137142857
+1945,720,IP GYN-ONC LAPAROSCOPY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,21,350,0.06
+1946,720,IP GYN-ONC LAPAROSCOPY POSTOP ,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,21,350,0.06
+1947,720,IP GYN-ONC LAPAROSCOPY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,27,350,0.077142857
+1948,720,IP GYN-ONC LAPAROSCOPY POSTOP ,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,27,350,0.077142857
+1949,720,IP GYN-ONC LAPAROSCOPY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,350,0.002857143
+1950,720,IP GYN-ONC LAPAROSCOPY POSTOP ,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,350,0.002857143
+1951,720,IP GYN-ONC LAPAROSCOPY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,19,350,0.054285714
+1952,720,IP GYN-ONC LAPAROSCOPY POSTOP ,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,19,350,0.054285714
+1953,720,IP GYN-ONC LAPAROSCOPY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,7,350,0.02
+1954,720,IP GYN-ONC LAPAROSCOPY POSTOP ,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,7,350,0.02
+1955,720,IP GYN-ONC LAPAROSCOPY POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,29,350,0.082857143
+1956,720,IP GYN-ONC LAPAROSCOPY POSTOP ,92173,ZOLPIDEM 5 MG PO TABS,Medication,29,350,0.082857143
+1957,720,IP GYN-ONC LAPAROSCOPY POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,350,0.002857143
+1958,720,IP GYN-ONC LAPAROSCOPY POSTOP ,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,350,0.002857143
+1959,720,IP GYN-ONC LAPAROSCOPY POSTOP,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,350,0.002857143
+1960,720,IP GYN-ONC LAPAROSCOPY POSTOP ,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,350,0.002857143
+1961,720,IP GYN-ONC LAPAROSCOPY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,50,350,0.142857143
+1962,720,IP GYN-ONC LAPAROSCOPY POSTOP ,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,50,350,0.142857143
+1963,720,IP GYN-ONC LAPAROSCOPY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,136,350,0.388571429
+1964,720,IP GYN-ONC LAPAROSCOPY POSTOP ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,136,350,0.388571429
+1965,720,IP GYN-ONC LAPAROSCOPY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,20,350,0.057142857
+1966,720,IP GYN-ONC LAPAROSCOPY POSTOP ,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,20,350,0.057142857
+1967,720,IP GYN-ONC LAPAROSCOPY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,16,350,0.045714286
+1968,720,IP GYN-ONC LAPAROSCOPY POSTOP ,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,16,350,0.045714286
+1969,720,IP GYN-ONC LAPAROSCOPY POSTOP,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,5,350,0.014285714
+1970,720,IP GYN-ONC LAPAROSCOPY POSTOP ,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,5,350,0.014285714
+1971,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,31,350,0.088571429
+1972,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,31,350,0.088571429
+1973,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABCBCD,CBC WITH DIFF,Lab,136,350,0.388571429
+1974,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABCBCD,CBC WITH DIFF,Lab,136,350,0.388571429
+1975,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,119,350,0.34
+1976,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABMETB,"METABOLIC PANEL, BASIC",Lab,119,350,0.34
+1977,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,10,350,0.028571429
+1978,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,10,350,0.028571429
+1979,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,93,350,0.265714286
+1980,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,93,350,0.265714286
+1981,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,91,350,0.26
+1982,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,91,350,0.26
+1983,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABPLTS,PLATELET COUNT,Lab,8,350,0.022857143
+1984,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABPLTS,PLATELET COUNT,Lab,8,350,0.022857143
+1985,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABPT,PROTHROMBIN TIME,Lab,2,350,0.005714286
+1986,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABPT,PROTHROMBIN TIME,Lab,2,350,0.005714286
+1987,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,350,0.005714286
+1988,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,350,0.005714286
+1989,720,IP GYN-ONC LAPAROSCOPY POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,2,350,0.005714286
+1990,720,IP GYN-ONC LAPAROSCOPY POSTOP ,LABTYPSNI,TYPE AND SCREEN,Lab,2,350,0.005714286
+1991,720,IP GYN-ONC LAPAROSCOPY POSTOP,POC14,POC GLUCOSE BY METER,Lab,3,350,0.008571429
+1992,720,IP GYN-ONC LAPAROSCOPY POSTOP ,POC14,POC GLUCOSE BY METER,Lab,3,350,0.008571429
+1993,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,244,0.008196721
+1994,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,20,244,0.081967213
+1995,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,73,244,0.299180328
+1996,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1,244,0.004098361
+1997,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,60,244,0.245901639
+1998,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,93,244,0.381147541
+1999,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,244,0.004098361
+2000,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,5,244,0.020491803
+2001,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,244,0.036885246
+2002,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,244,0.004098361
+2003,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,2,244,0.008196721
+2004,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,244,0.004098361
+2005,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,8,244,0.032786885
+2006,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,8,244,0.032786885
+2007,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,1,244,0.004098361
+2008,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,244,0.004098361
+2009,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,13,244,0.053278689
+2010,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,4,244,0.016393443
+2011,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,9,244,0.036885246
+2012,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,21,244,0.086065574
+2013,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,244,0.016393443
+2014,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,47,244,0.192622951
+2015,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,17,244,0.069672131
+2016,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,91,244,0.37295082
+2017,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,6,244,0.024590164
+2018,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,11,244,0.045081967
+2019,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,10,244,0.040983607
+2020,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,244,0.008196721
+2021,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,12,244,0.049180328
+2022,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,244,0.004098361
+2023,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,26,244,0.106557377
+2024,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,82,244,0.336065574
+2025,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,71,244,0.290983607
+2026,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,244,0.008196721
+2027,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,58,244,0.237704918
+2028,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,50,244,0.204918033
+2029,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,5,244,0.020491803
+2030,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,2,244,0.008196721
+2031,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,244,0.008196721
+2032,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,244,0.004098361
+2033,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,LABURNC,URINE CULTURE,Lab,1,244,0.004098361
+2034,721,IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP,POC14,POC GLUCOSE BY METER,Lab,6,244,0.024590164
+2035,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,1080,BISACODYL 10 MG PR SUPP,Medication,3,128,0.0234375
+2036,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,15,128,0.1171875
+2037,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,4318,LACTATED RINGERS IV SOLP,Medication,20,128,0.15625
+2038,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,51,128,0.3984375
+2039,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,128,0.015625
+2040,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,128,0.0078125
+2041,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,7,128,0.0546875
+2042,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,128,0.0078125
+2043,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,11,128,0.0859375
+2044,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2,128,0.015625
+2045,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,128,0.015625
+2046,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,92173,ZOLPIDEM 5 MG PO TABS,Medication,2,128,0.015625
+2047,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,23,128,0.1796875
+2048,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,128,0.03125
+2049,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,16,128,0.125
+2050,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,18,128,0.140625
+2051,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,45,128,0.3515625
+2052,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,128,0.015625
+2053,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,3,128,0.0234375
+2054,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,128,0.015625
+2055,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,128,0.0078125
+2056,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,3,128,0.0234375
+2057,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,LABCBCO,CBC W/O DIFF,Lab,9,128,0.0703125
+2058,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,LABMETB,"METABOLIC PANEL, BASIC",Lab,12,128,0.09375
+2059,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,128,0.0078125
+2060,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,13,128,0.1015625
+2061,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,13,128,0.1015625
+2062,724,RETIRED IP GYN-ONC  BOWEL OBSTRUCTION,LABPLTS,PLATELET COUNT,Lab,2,128,0.015625
+2063,725,RETIRED IP GYN  VAGINAL SURGERY,1080,BISACODYL 10 MG PR SUPP,Medication,1,134,0.007462687
+2064,725,RETIRED IP GYN  VAGINAL SURGERY,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,40,134,0.298507463
+2065,725,RETIRED IP GYN  VAGINAL SURGERY,4318,LACTATED RINGERS IV SOLP,Medication,32,134,0.23880597
+2066,725,RETIRED IP GYN  VAGINAL SURGERY,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,54,134,0.402985075
+2067,725,RETIRED IP GYN  VAGINAL SURGERY,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,1,134,0.007462687
+2068,725,RETIRED IP GYN  VAGINAL SURGERY,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,134,0.007462687
+2069,725,RETIRED IP GYN  VAGINAL SURGERY,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,134,0.007462687
+2070,725,RETIRED IP GYN  VAGINAL SURGERY,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,134,0.007462687
+2071,725,RETIRED IP GYN  VAGINAL SURGERY,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,2,134,0.014925373
+2072,725,RETIRED IP GYN  VAGINAL SURGERY,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,134,0.014925373
+2073,725,RETIRED IP GYN  VAGINAL SURGERY,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,4,134,0.029850746
+2074,725,RETIRED IP GYN  VAGINAL SURGERY,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,10,134,0.074626866
+2075,725,RETIRED IP GYN  VAGINAL SURGERY,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,9,134,0.067164179
+2076,725,RETIRED IP GYN  VAGINAL SURGERY,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,134,0.007462687
+2077,725,RETIRED IP GYN  VAGINAL SURGERY,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,1,134,0.007462687
+2078,725,RETIRED IP GYN  VAGINAL SURGERY,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,1,134,0.007462687
+2079,725,RETIRED IP GYN  VAGINAL SURGERY,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,3,134,0.02238806
+2080,725,RETIRED IP GYN  VAGINAL SURGERY,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,16,134,0.119402985
+2081,725,RETIRED IP GYN  VAGINAL SURGERY,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,134,0.037313433
+2082,725,RETIRED IP GYN  VAGINAL SURGERY,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,16,134,0.119402985
+2083,725,RETIRED IP GYN  VAGINAL SURGERY,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,53,134,0.395522388
+2084,725,RETIRED IP GYN  VAGINAL SURGERY,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,2,134,0.014925373
+2085,725,RETIRED IP GYN  VAGINAL SURGERY,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,134,0.037313433
+2086,725,RETIRED IP GYN  VAGINAL SURGERY,520043,CEFOXITIN IVPB,Medication,1,134,0.007462687
+2087,725,RETIRED IP GYN  VAGINAL SURGERY,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,4,134,0.029850746
+2088,725,RETIRED IP GYN  VAGINAL SURGERY,LABPLTS,PLATELET COUNT,Lab,1,134,0.007462687
+2089,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,1017,0.000983284
+2090,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,33,1017,0.032448378
+2091,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,263,1017,0.258603736
+2092,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,17,1017,0.016715831
+2093,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,3626,HEPARIN LOCK FLUSH (PORCINE) 10 UNIT/ML IV SOLN,Medication,2,1017,0.001966568
+2094,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,3841,IBUPROFEN 200 MG PO TABS,Medication,2,1017,0.001966568
+2095,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,3843,IBUPROFEN 400 MG PO TABS,Medication,9,1017,0.008849558
+2096,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,3844,IBUPROFEN 600 MG PO TABS,Medication,72,1017,0.07079646
+2097,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,3845,IBUPROFEN 800 MG PO TABS,Medication,1,1017,0.000983284
+2098,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,1,1017,0.000983284
+2099,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,377,1017,0.370698132
+2100,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,2,1017,0.001966568
+2101,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1,1017,0.000983284
+2102,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,56,1017,0.055063913
+2103,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,3,1017,0.002949853
+2104,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,4,1017,0.003933137
+2105,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,1017,0.000983284
+2106,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,132,1017,0.12979351
+2107,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,1,1017,0.000983284
+2108,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,81,1017,0.079646018
+2109,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,1017,0.000983284
+2110,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,28,1017,0.027531957
+2111,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2,1017,0.001966568
+2112,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,56,1017,0.055063913
+2113,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,1017,0.000983284
+2114,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,120,1017,0.1179941
+2115,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,1017,0.003933137
+2116,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,1017,0.000983284
+2117,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,98,1017,0.096361849
+2118,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,82,1017,0.080629302
+2119,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,399,1017,0.392330383
+2120,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,12,1017,0.01179941
+2121,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,8,1017,0.007866273
+2122,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,34,1017,0.033431662
+2123,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,1017,0.000983284
+2124,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,6,1017,0.005899705
+2125,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,540579,BENZOCAINE-MENTHOL SUGAR FREE MAXIMUM STRENGTH LOZG,Medication,7,1017,0.006882989
+2126,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,26,1017,0.025565388
+2127,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,1017,0.004916421
+2128,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,1,1017,0.000983284
+2129,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,32,1017,0.031465093
+2130,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABCBCO,CBC W/O DIFF,Lab,307,1017,0.30186824
+2131,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,191,1017,0.187807276
+2132,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,26,1017,0.025565388
+2133,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,138,1017,0.135693215
+2134,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,140,1017,0.137659784
+2135,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABPLTS,PLATELET COUNT,Lab,16,1017,0.015732547
+2136,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABPT,PROTHROMBIN TIME,Lab,23,1017,0.022615536
+2137,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,21,1017,0.020648968
+2138,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,1017,0.000983284
+2139,727,IP GYN MINIMALLY INVASIVE SURGERY POSTOP,POC14,POC GLUCOSE BY METER,Lab,3,1017,0.002949853
+2140,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,106,0.037735849
+2141,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,10814,OXYCODONE 5 MG PO TABS,Medication,6,106,0.056603774
+2142,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,13,106,0.122641509
+2143,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,12,106,0.113207547
+2144,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,106,0.009433962
+2145,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,7,106,0.066037736
+2146,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,12,106,0.113207547
+2147,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,49,106,0.462264151
+2148,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,LABMETB,"METABOLIC PANEL, BASIC",Lab,49,106,0.462264151
+2149,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,54,106,0.509433962
+2150,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,55,106,0.518867925
+2151,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,LABTYPSNI,TYPE AND SCREEN,Lab,50,106,0.471698113
+2152,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,50,106,0.471698113
+2153,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,30,106,0.283018868
+2154,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,IMGDXFEMLT,XR FEMUR LT,Imaging,2,106,0.018867925
+2155,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,IMGDXFEMRT,XR FEMUR RT,Imaging,1,106,0.009433962
+2156,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,IMGDXHIP2LT,XR HIP 2V LT,Imaging,6,106,0.056603774
+2157,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,IMGDXHIP2RT,XR HIP 2V RT,Imaging,5,106,0.047169811
+2158,728,NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL,IMGDXPEL,XR PELVIS 1V,Imaging,34,106,0.320754717
+2159,729,ED URI/PHARYNGITIS-ENT ABSCESS,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1,2,0.5
+2160,729,RETIRED ED URI/PHARYNGITIS-ENT ABSCESS,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1,2,0.5
+2161,729,ED URI/PHARYNGITIS-ENT ABSCESS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,2,0.5
+2162,729,RETIRED ED URI/PHARYNGITIS-ENT ABSCESS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,2,0.5
+2163,730,ED RAPID SEQUENCE INDUCTION INTUBATION,7536,SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN,Medication,2,7,0.285714286
+2164,730,ED RAPID SEQUENCE INDUCTION INTUBATION,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,2,7,0.285714286
+2165,730,ED RAPID SEQUENCE INDUCTION INTUBATION,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,1,7,0.142857143
+2166,730,ED RAPID SEQUENCE INDUCTION INTUBATION,20472,ETOMIDATE 2 MG/ML IV SOLN,Medication,1,7,0.142857143
+2167,730,ED RAPID SEQUENCE INDUCTION INTUBATION,77468,SUCCINYLCHOLINE CHLORIDE 100 MG/ML INJ SOLN,Medication,1,7,0.142857143
+2168,730,ED RAPID SEQUENCE INDUCTION INTUBATION,83055,ROCURONIUM 10 MG/ML IV SOLN,Medication,3,7,0.428571429
+2169,730,ED RAPID SEQUENCE INDUCTION INTUBATION,90587,KETAMINE 10 MG/ML INJ SOLN,Medication,1,7,0.142857143
+2170,730,ED RAPID SEQUENCE INDUCTION INTUBATION,112552,ETOMIDATE 2 MG/ML IV SYRG,Medication,4,7,0.571428571
+2171,730,ED RAPID SEQUENCE INDUCTION INTUBATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,7,0.142857143
+2172,732,ED TIA PATHWAY,10372,LABETALOL 5 MG/ML IV SOLN,Medication,3,385,0.007792208
+2173,732,IP/ED STROKE CODE,10372,LABETALOL 5 MG/ML IV SOLN,Medication,3,385,0.007792208
+2174,732,ED TIA PATHWAY,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,3,385,0.007792208
+2175,732,IP/ED STROKE CODE,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,3,385,0.007792208
+2176,732,ED TIA PATHWAY,530010,NICARDIPINE IV INFUSION,Medication,3,385,0.007792208
+2177,732,IP/ED STROKE CODE,530010,NICARDIPINE IV INFUSION,Medication,3,385,0.007792208
+2178,732,ED TIA PATHWAY,540119,LABETALOL 5 MG/ML IV INFUSION,Medication,1,385,0.002597403
+2179,732,IP/ED STROKE CODE,540119,LABETALOL 5 MG/ML IV INFUSION,Medication,1,385,0.002597403
+2180,732,ED TIA PATHWAY,LABA1C,HEMOGLOBIN A1C,Lab,81,385,0.21038961
+2181,732,IP/ED STROKE CODE,LABA1C,HEMOGLOBIN A1C,Lab,81,385,0.21038961
+2182,732,ED TIA PATHWAY,LABCBCD,CBC WITH DIFF,Lab,257,385,0.667532468
+2183,732,IP/ED STROKE CODE,LABCBCD,CBC WITH DIFF,Lab,257,385,0.667532468
+2184,732,ED TIA PATHWAY,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,81,385,0.21038961
+2185,732,IP/ED STROKE CODE,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,81,385,0.21038961
+2186,732,ED TIA PATHWAY,LABMB,"CK, MB (MASS)",Lab,1,385,0.002597403
+2187,732,IP/ED STROKE CODE,LABMB,"CK, MB (MASS)",Lab,1,385,0.002597403
+2188,732,ED TIA PATHWAY,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,257,385,0.667532468
+2189,732,IP/ED STROKE CODE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,257,385,0.667532468
+2190,732,ED TIA PATHWAY,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,242,385,0.628571429
+2191,732,IP/ED STROKE CODE,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,242,385,0.628571429
+2192,732,ED TIA PATHWAY,LABPT,PROTHROMBIN TIME,Lab,267,385,0.693506494
+2193,732,IP/ED STROKE CODE,LABPT,PROTHROMBIN TIME,Lab,267,385,0.693506494
+2194,732,ED TIA PATHWAY,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,57,385,0.148051948
+2195,732,IP/ED STROKE CODE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,57,385,0.148051948
+2196,732,ED TIA PATHWAY,LABTSH,TSH,Lab,78,385,0.202597403
+2197,732,IP/ED STROKE CODE,LABTSH,TSH,Lab,78,385,0.202597403
+2198,732,ED TIA PATHWAY,POC14,POC GLUCOSE BY METER,Lab,253,385,0.657142857
+2199,732,IP/ED STROKE CODE,POC14,POC GLUCOSE BY METER,Lab,253,385,0.657142857
+2200,732,ED TIA PATHWAY,POC25,POC ISTAT INR AND PROTIME,Lab,235,385,0.61038961
+2201,732,IP/ED STROKE CODE,POC25,POC ISTAT INR AND PROTIME,Lab,235,385,0.61038961
+2202,732,ED TIA PATHWAY,IMGCTH,CT HEAD,Imaging,254,385,0.65974026
+2203,732,IP/ED STROKE CODE,IMGCTH,CT HEAD,Imaging,254,385,0.65974026
+2204,732,ED TIA PATHWAY,IMGCTHPRW,CT HEAD PERFUSION W CONTRAST,Imaging,230,385,0.597402597
+2205,732,IP/ED STROKE CODE,IMGCTHPRW,CT HEAD PERFUSION W CONTRAST,Imaging,230,385,0.597402597
+2206,732,ED TIA PATHWAY,IMGDXCH1,XR CHEST 1V,Imaging,248,385,0.644155844
+2207,732,IP/ED STROKE CODE,IMGDXCH1,XR CHEST 1V,Imaging,248,385,0.644155844
+2208,732,ED TIA PATHWAY,IMGDXCH2,XR CHEST 2V,Imaging,3,385,0.007792208
+2209,732,IP/ED STROKE CODE,IMGDXCH2,XR CHEST 2V,Imaging,3,385,0.007792208
+2210,732,ED TIA PATHWAY,IMGMRAH,MR ANGIO HEAD,Imaging,36,385,0.093506494
+2211,732,IP/ED STROKE CODE,IMGMRAH,MR ANGIO HEAD,Imaging,36,385,0.093506494
+2212,732,ED TIA PATHWAY,IMGMRAK,MR ANGIO NECK,Imaging,35,385,0.090909091
+2213,732,IP/ED STROKE CODE,IMGMRAK,MR ANGIO NECK,Imaging,35,385,0.090909091
+2214,732,ED TIA PATHWAY,IMGMRH,MR BRAIN,Imaging,48,385,0.124675325
+2215,732,IP/ED STROKE CODE,IMGMRH,MR BRAIN,Imaging,48,385,0.124675325
+2216,732,ED TIA PATHWAY,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,1,385,0.002597403
+2217,732,IP/ED STROKE CODE,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,1,385,0.002597403
+2218,733,ED PROCEDURAL SEDATION MEDS,4237,KETAMINE 100 MG/ML INJ SOLN,Medication,1,54,0.018518519
+2219,733,ED PROCEDURAL SEDATION MEDS,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,2,54,0.037037037
+2220,733,ED PROCEDURAL SEDATION MEDS,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,11,54,0.203703704
+2221,733,ED PROCEDURAL SEDATION MEDS,20472,ETOMIDATE 2 MG/ML IV SOLN,Medication,1,54,0.018518519
+2222,733,ED PROCEDURAL SEDATION MEDS,77354,METHOHEXITAL 500 MG IV SOLR,Medication,9,54,0.166666667
+2223,733,ED PROCEDURAL SEDATION MEDS,90587,KETAMINE 10 MG/ML INJ SOLN,Medication,10,54,0.185185185
+2224,733,ED PROCEDURAL SEDATION MEDS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,9,54,0.166666667
+2225,733,ED PROCEDURAL SEDATION MEDS,540114,PROPOFOL 10 MG/ML IV INJECTION,Medication,2,54,0.037037037
+2226,734,ED CHEST PAIN,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,802,0.002493766
+2227,734,ED CHEST PAIN,680,ASPIRIN 81 MG PO CHEW,Medication,84,802,0.104738155
+2228,734,ED CHEST PAIN,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,1,802,0.001246883
+2229,734,ED CHEST PAIN,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,45,802,0.056109726
+2230,734,ED CHEST PAIN,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,25,802,0.03117207
+2231,734,ED CHEST PAIN,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,25,802,0.03117207
+2232,734,ED CHEST PAIN,15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,4,802,0.004987531
+2233,734,ED CHEST PAIN,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,802,0.001246883
+2234,734,ED CHEST PAIN,77795,EPTIFIBATIDE 2 MG/ML IV SOLN,Medication,1,802,0.001246883
+2235,734,ED CHEST PAIN,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,7,802,0.00872818
+2236,734,ED CHEST PAIN,83398,CLOPIDOGREL 75 MG PO TABS,Medication,1,802,0.001246883
+2237,734,ED CHEST PAIN,91451,NITROGLYCERIN 2 % TD OINT,Medication,3,802,0.003740648
+2238,734,ED CHEST PAIN,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,802,0.001246883
+2239,734,ED CHEST PAIN,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,2,802,0.002493766
+2240,734,ED CHEST PAIN,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,2,802,0.002493766
+2241,734,ED CHEST PAIN,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,11,802,0.013715711
+2242,734,ED CHEST PAIN,112597,ENOXAPARIN 120 MG/0.8 ML SC SYRG,Medication,1,802,0.001246883
+2243,734,ED CHEST PAIN,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,24,802,0.029925187
+2244,734,ED CHEST PAIN,203749,CLOPIDOGREL 75 MG PO TABS,Medication,4,802,0.004987531
+2245,734,ED CHEST PAIN,208212,PRASUGREL 10 MG PO TABS,Medication,1,802,0.001246883
+2246,734,ED CHEST PAIN,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,1,802,0.001246883
+2247,734,ED CHEST PAIN,520002,DEXAMETHASONE (CUSTOM DOSE) IVPB,Medication,3,802,0.003740648
+2248,734,ED CHEST PAIN,540102,NS IV BOLUS,Medication,42,802,0.052369077
+2249,734,ED CHEST PAIN,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,802,0.001246883
+2250,734,ED CHEST PAIN,540300,MORPHINE INJECTABLE SYRINGE,Medication,23,802,0.028678304
+2251,734,ED CHEST PAIN,LABCBCD,CBC WITH DIFF,Lab,521,802,0.649625935
+2252,734,ED CHEST PAIN,LABDDIML,D - DIMER (ELISA),Lab,21,802,0.026184539
+2253,734,ED CHEST PAIN,LABDIGL,"DIGOXIN, SERUM",Lab,7,802,0.00872818
+2254,734,ED CHEST PAIN,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,5,802,0.006234414
+2255,734,ED CHEST PAIN,LABLIPS,LIPASE,Lab,84,802,0.104738155
+2256,734,ED CHEST PAIN,LABMETB,"METABOLIC PANEL, BASIC",Lab,59,802,0.073566085
+2257,734,ED CHEST PAIN,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,468,802,0.583541147
+2258,734,ED CHEST PAIN,LABNTBNP,NT - PROBNP,Lab,156,802,0.194513716
+2259,734,ED CHEST PAIN,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,15,802,0.018703242
+2260,734,ED CHEST PAIN,LABPT,PROTHROMBIN TIME,Lab,286,802,0.356608479
+2261,734,ED CHEST PAIN,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,217,802,0.270573566
+2262,734,ED CHEST PAIN,LABTYPSNI,TYPE AND SCREEN,Lab,46,802,0.057356608
+2263,734,ED CHEST PAIN,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,110,802,0.137157107
+2264,734,ED CHEST PAIN,POC1,POC URINE DIPSTICK,Lab,3,802,0.003740648
+2265,734,ED CHEST PAIN,POC14,POC GLUCOSE BY METER,Lab,23,802,0.028678304
+2266,734,ED CHEST PAIN,POC15,"POC ISTAT, ARTERIAL BLOOD GASES AND LACTATE (CG4)",Lab,1,802,0.001246883
+2267,734,ED CHEST PAIN,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,47,802,0.058603491
+2268,734,ED CHEST PAIN,POC18,"POC ISTAT, ARTERIAL NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,1,802,0.001246883
+2269,734,ED CHEST PAIN,POC24,POC ISTAT TROPONIN I,Lab,442,802,0.551122195
+2270,734,ED CHEST PAIN,POC26,POC URINE PREGNANCY,Lab,11,802,0.013715711
+2271,734,ED CHEST PAIN,POC46,POC ISTAT BHCG,Lab,2,802,0.002493766
+2272,734,ED CHEST PAIN,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,2,802,0.002493766
+2273,734,ED CHEST PAIN,IMGCTPE,CT PULMONARY EMBOLISM CHEST ONLY,Imaging,2,802,0.002493766
+2274,734,ED CHEST PAIN,IMGDXCH1,XR CHEST 1V,Imaging,245,802,0.305486284
+2275,734,ED CHEST PAIN,IMGDXCH2,XR CHEST 2V,Imaging,183,802,0.228179551
+2276,734,ED CHEST PAIN,LABBBPRBC,RED BLOOD CELLS,Blood Product,3,802,0.003740648
+2277,737,IP PLS GENERAL POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,167,1019,0.163886163
+2278,737,IP PLS GENERAL POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,14,1019,0.01373896
+2279,737,IP PLS GENERAL POSTOP,324,ALPRAZOLAM 0.25 MG PO TABS,Medication,2,1019,0.001962709
+2280,737,IP PLS GENERAL POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,134,1019,0.131501472
+2281,737,IP PLS GENERAL POSTOP,1300,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",Medication,5,1019,0.004906771
+2282,737,IP PLS GENERAL POSTOP,2404,DIAZEPAM 2 MG PO TABS,Medication,3,1019,0.002944063
+2283,737,IP PLS GENERAL POSTOP,2405,DIAZEPAM 5 MG PO TABS,Medication,72,1019,0.070657507
+2284,737,IP PLS GENERAL POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,67,1019,0.065750736
+2285,737,IP PLS GENERAL POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,89,1019,0.08734053
+2286,737,IP PLS GENERAL POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,1,1019,0.000981354
+2287,737,IP PLS GENERAL POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,245,1019,0.240431796
+2288,737,IP PLS GENERAL POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,39,1019,0.038272816
+2289,737,IP PLS GENERAL POSTOP,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,1019,0.000981354
+2290,737,IP PLS GENERAL POSTOP,3777,HYDROXYZINE PAMOATE 25 MG PO CAPS,Medication,26,1019,0.025515211
+2291,737,IP PLS GENERAL POSTOP,3778,HYDROXYZINE PAMOATE 50 MG PO CAPS,Medication,1,1019,0.000981354
+2292,737,IP PLS GENERAL POSTOP,3841,IBUPROFEN 200 MG PO TABS,Medication,1,1019,0.000981354
+2293,737,IP PLS GENERAL POSTOP,3843,IBUPROFEN 400 MG PO TABS,Medication,2,1019,0.001962709
+2294,737,IP PLS GENERAL POSTOP,4573,LORAZEPAM 1 MG PO TABS,Medication,9,1019,0.008832188
+2295,737,IP PLS GENERAL POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,49,1019,0.048086359
+2296,737,IP PLS GENERAL POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,371,1019,0.364082434
+2297,737,IP PLS GENERAL POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,68,1019,0.06673209
+2298,737,IP PLS GENERAL POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,19,1019,0.018645731
+2299,737,IP PLS GENERAL POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,1019,0.003925417
+2300,737,IP PLS GENERAL POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,6,1019,0.005888126
+2301,737,IP PLS GENERAL POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,4,1019,0.003925417
+2302,737,IP PLS GENERAL POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,1019,0.000981354
+2303,737,IP PLS GENERAL POSTOP,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,2,1019,0.001962709
+2304,737,IP PLS GENERAL POSTOP,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,3,1019,0.002944063
+2305,737,IP PLS GENERAL POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,272,1019,0.266928361
+2306,737,IP PLS GENERAL POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,12,1019,0.011776251
+2307,737,IP PLS GENERAL POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,143,1019,0.14033366
+2308,737,IP PLS GENERAL POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,170,1019,0.166830226
+2309,737,IP PLS GENERAL POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,6,1019,0.005888126
+2310,737,IP PLS GENERAL POSTOP,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,42,1019,0.041216879
+2311,737,IP PLS GENERAL POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,11,1019,0.010794897
+2312,737,IP PLS GENERAL POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,46,1019,0.045142296
+2313,737,IP PLS GENERAL POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,8,1019,0.007850834
+2314,737,IP PLS GENERAL POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,11,1019,0.010794897
+2315,737,IP PLS GENERAL POSTOP,93295,ZOLPIDEM 10 MG PO TABS,Medication,1,1019,0.000981354
+2316,737,IP PLS GENERAL POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,137,1019,0.134445535
+2317,737,IP PLS GENERAL POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,78,1019,0.076545633
+2318,737,IP PLS GENERAL POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,13,1019,0.012757605
+2319,737,IP PLS GENERAL POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,89,1019,0.08734053
+2320,737,IP PLS GENERAL POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,23,1019,0.022571148
+2321,737,IP PLS GENERAL POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,85,1019,0.083415113
+2322,737,IP PLS GENERAL POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,383,1019,0.375858685
+2323,737,IP PLS GENERAL POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,14,1019,0.01373896
+2324,737,IP PLS GENERAL POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,5,1019,0.004906771
+2325,737,IP PLS GENERAL POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,80,1019,0.078508342
+2326,737,IP PLS GENERAL POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,13,1019,0.012757605
+2327,737,IP PLS GENERAL POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,15,1019,0.014720314
+2328,737,IP PLS GENERAL POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,10,1019,0.009813543
+2329,737,IP PLS GENERAL POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,54,1019,0.052993131
+2330,737,IP PLS GENERAL POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,14,1019,0.01373896
+2331,737,IP PLS GENERAL POSTOP,LABCBCD,CBC WITH DIFF,Lab,93,1019,0.091265947
+2332,737,IP PLS GENERAL POSTOP,LABK,"POTASSIUM, SERUM/PLASMA",Lab,2,1019,0.001962709
+2333,737,IP PLS GENERAL POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,82,1019,0.08047105
+2334,737,IP PLS GENERAL POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,38,1019,0.037291462
+2335,737,IP PLS GENERAL POSTOP,LABPLTS,PLATELET COUNT,Lab,39,1019,0.038272816
+2336,737,IP PLS GENERAL POSTOP,LABPT,PROTHROMBIN TIME,Lab,17,1019,0.016683023
+2337,737,IP PLS GENERAL POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,10,1019,0.009813543
+2338,738,IP PLS GENERAL PREOP,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,6,33,0.181818182
+2339,738,IP PLS GENERAL PREOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,33,0.03030303
+2340,738,IP PLS GENERAL PREOP,8442,"VANCOMYCIN 1,000 MG IV SOLR",Medication,1,33,0.03030303
+2341,738,IP PLS GENERAL PREOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,33,0.03030303
+2342,738,IP PLS GENERAL PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,33,0.060606061
+2343,738,IP PLS GENERAL PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,33,0.03030303
+2344,738,IP PLS GENERAL PREOP,LABCBCD,CBC WITH DIFF,Lab,10,33,0.303030303
+2345,738,IP PLS GENERAL PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,8,33,0.242424242
+2346,738,IP PLS GENERAL PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,33,0.03030303
+2347,738,IP PLS GENERAL PREOP,LABPT,PROTHROMBIN TIME,Lab,8,33,0.242424242
+2348,738,IP PLS GENERAL PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,7,33,0.212121212
+2349,738,IP PLS GENERAL PREOP,POC14,POC GLUCOSE BY METER,Lab,1,33,0.03030303
+2350,739,IP PLS OUTPT PROCEDURE PRE/POSTOP,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,4,13,0.307692308
+2351,739,IP PLS OUTPT PROCEDURE PRE/POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,13,0.153846154
+2352,739,IP PLS OUTPT PROCEDURE PRE/POSTOP,LABCBCD,CBC WITH DIFF,Lab,1,13,0.076923077
+2353,739,IP PLS OUTPT PROCEDURE PRE/POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,13,0.076923077
+2354,739,IP PLS OUTPT PROCEDURE PRE/POSTOP,LABPT,PROTHROMBIN TIME,Lab,1,13,0.076923077
+2355,740,IP VAS GENERAL ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,161,2310,0.06969697
+2356,740,IP VAS GENERAL ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,10,2310,0.004329004
+2357,740,IP VAS GENERAL ADMIT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,5,2310,0.002164502
+2358,740,IP VAS GENERAL ADMIT,681,ASPIRIN 325 MG PO TABS,Medication,10,2310,0.004329004
+2359,740,IP VAS GENERAL ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,465,2310,0.201298701
+2360,740,IP VAS GENERAL ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,480,2310,0.207792208
+2361,740,IP VAS GENERAL ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,169,2310,0.073160173
+2362,740,IP VAS GENERAL ADMIT,3843,IBUPROFEN 400 MG PO TABS,Medication,2,2310,0.000865801
+2363,740,IP VAS GENERAL ADMIT,3844,IBUPROFEN 600 MG PO TABS,Medication,1,2310,0.0004329
+2364,740,IP VAS GENERAL ADMIT,4318,LACTATED RINGERS IV SOLP,Medication,137,2310,0.059307359
+2365,740,IP VAS GENERAL ADMIT,4711,MAGNESIUM CITRATE PO SOLN,Medication,292,2310,0.126406926
+2366,740,IP VAS GENERAL ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,209,2310,0.09047619
+2367,740,IP VAS GENERAL ADMIT,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,5,2310,0.002164502
+2368,740,IP VAS GENERAL ADMIT,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,4,2310,0.001731602
+2369,740,IP VAS GENERAL ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,789,2310,0.341558442
+2370,740,IP VAS GENERAL ADMIT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,26,2310,0.011255411
+2371,740,IP VAS GENERAL ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,40,2310,0.017316017
+2372,740,IP VAS GENERAL ADMIT,9500,CEPHALEXIN 500 MG PO CAPS,Medication,1,2310,0.0004329
+2373,740,IP VAS GENERAL ADMIT,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,3,2310,0.001298701
+2374,740,IP VAS GENERAL ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,9,2310,0.003896104
+2375,740,IP VAS GENERAL ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,614,2310,0.265800866
+2376,740,IP VAS GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,153,2310,0.066233766
+2377,740,IP VAS GENERAL ADMIT,14903,D5W WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,2310,0.000865801
+2378,740,IP VAS GENERAL ADMIT,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,4,2310,0.001731602
+2379,740,IP VAS GENERAL ADMIT,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,2310,0.0004329
+2380,740,IP VAS GENERAL ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,25,2310,0.010822511
+2381,740,IP VAS GENERAL ADMIT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,61,2310,0.026406926
+2382,740,IP VAS GENERAL ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,1,2310,0.0004329
+2383,740,IP VAS GENERAL ADMIT,27862,NICOTINE 14 MG/24 HR TD PT24,Medication,3,2310,0.001298701
+2384,740,IP VAS GENERAL ADMIT,27920,OXYCODONE 10 MG PO TB12,Medication,1,2310,0.0004329
+2385,740,IP VAS GENERAL ADMIT,27921,OXYCODONE 20 MG PO TB12,Medication,2,2310,0.000865801
+2386,740,IP VAS GENERAL ADMIT,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,22,2310,0.00952381
+2387,740,IP VAS GENERAL ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,260,2310,0.112554113
+2388,740,IP VAS GENERAL ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,5,2310,0.002164502
+2389,740,IP VAS GENERAL ADMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,20,2310,0.008658009
+2390,740,IP VAS GENERAL ADMIT,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,1,2310,0.0004329
+2391,740,IP VAS GENERAL ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,418,2310,0.180952381
+2392,740,IP VAS GENERAL ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,11,2310,0.004761905
+2393,740,IP VAS GENERAL ADMIT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,232,2310,0.1004329
+2394,740,IP VAS GENERAL ADMIT,94176,ONDANSETRON 4 MG PO TBDL,Medication,13,2310,0.005627706
+2395,740,IP VAS GENERAL ADMIT,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,37,2310,0.016017316
+2396,740,IP VAS GENERAL ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,233,2310,0.100865801
+2397,740,IP VAS GENERAL ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,2310,0.0004329
+2398,740,IP VAS GENERAL ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,22,2310,0.00952381
+2399,740,IP VAS GENERAL ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,203,2310,0.087878788
+2400,740,IP VAS GENERAL ADMIT,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,30,2310,0.012987013
+2401,740,IP VAS GENERAL ADMIT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,5,2310,0.002164502
+2402,740,IP VAS GENERAL ADMIT,126583,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SYRG",Medication,2,2310,0.000865801
+2403,740,IP VAS GENERAL ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,401,2310,0.173593074
+2404,740,IP VAS GENERAL ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,12,2310,0.005194805
+2405,740,IP VAS GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,119,2310,0.051515152
+2406,740,IP VAS GENERAL ADMIT,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,60,2310,0.025974026
+2407,740,IP VAS GENERAL ADMIT,500010,LACTATED RINGERS WITH KCL IV INFUSION,Medication,3,2310,0.001298701
+2408,740,IP VAS GENERAL ADMIT,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,82,2310,0.035497835
+2409,740,IP VAS GENERAL ADMIT,530012,PHENYLEPHRINE IV INFUSION,Medication,2,2310,0.000865801
+2410,740,IP VAS GENERAL ADMIT,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,65,2310,0.028138528
+2411,740,IP VAS GENERAL ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,2310,0.0004329
+2412,740,IP VAS GENERAL ADMIT,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,17,2310,0.007359307
+2413,740,IP VAS GENERAL ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,37,2310,0.016017316
+2414,740,IP VAS GENERAL ADMIT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,20,2310,0.008658009
+2415,740,IP VAS GENERAL ADMIT,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,35,2310,0.015151515
+2416,740,IP VAS GENERAL ADMIT,9000000,ZZZ IMS TEMPLATE,Medication,2,2310,0.000865801
+2417,740,IP VAS GENERAL ADMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,99,2310,0.042857143
+2418,740,IP VAS GENERAL ADMIT,LABCBCD,CBC WITH DIFF,Lab,706,2310,0.305627706
+2419,740,IP VAS GENERAL ADMIT,LABCK,"CREATINE KINASE, TOTAL",Lab,32,2310,0.013852814
+2420,740,IP VAS GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,589,2310,0.254978355
+2421,740,IP VAS GENERAL ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,133,2310,0.057575758
+2422,740,IP VAS GENERAL ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,236,2310,0.102164502
+2423,740,IP VAS GENERAL ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,196,2310,0.084848485
+2424,740,IP VAS GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,80,2310,0.034632035
+2425,740,IP VAS GENERAL ADMIT,LABPT,PROTHROMBIN TIME,Lab,266,2310,0.115151515
+2426,740,IP VAS GENERAL ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,217,2310,0.093939394
+2427,740,IP VAS GENERAL ADMIT,LABVANPRL,VANCOMYCIN TROUGH LEVEL,Lab,36,2310,0.015584416
+2428,740,IP VAS GENERAL ADMIT,LABVANUNL,VANCOMYCIN SINGLE DAILY LEVEL,Lab,4,2310,0.001731602
+2429,740,IP VAS GENERAL ADMIT,IMGCT3AP,CT ANGIO ABDOMEN PELVIS,Imaging,1,2310,0.0004329
+2430,740,IP VAS GENERAL ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,63,2310,0.027272727
+2431,740,IP VAS GENERAL ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,165,2310,0.071428571
+2432,740,IP VAS GENERAL ADMIT,VSC02,VSC CAROTID VERTEBRAL ARTERY ULTRASOUND,Imaging,1,2310,0.0004329
+2433,740,IP VAS GENERAL ADMIT,VSC03,VSC ARTERIAL EXAM/ABIS,Imaging,19,2310,0.008225108
+2434,740,IP VAS GENERAL ADMIT,VSC04,VSC GRAFT W ABIS ART DUP,Imaging,1,2310,0.0004329
+2435,740,IP VAS GENERAL ADMIT,VSC05,VSC VENOUS ULTRASOUND,Imaging,5,2310,0.002164502
+2436,740,IP VAS GENERAL ADMIT,VSC06,VSC ABDO AORTA ULTRASOUND,Imaging,1,2310,0.0004329
+2437,741,IP VAS GENERAL POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,126,1423,0.088545327
+2438,741,IP VAS GENERAL POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,4,1423,0.002810963
+2439,741,IP VAS GENERAL POSTOP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,1423,0.000702741
+2440,741,IP VAS GENERAL POSTOP,693,ASPIRIN 300 MG PR SUPP,Medication,8,1423,0.005621926
+2441,741,IP VAS GENERAL POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,378,1423,0.26563598
+2442,741,IP VAS GENERAL POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,291,1423,0.20449754
+2443,741,IP VAS GENERAL POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,179,1423,0.125790583
+2444,741,IP VAS GENERAL POSTOP,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,2,1423,0.001405481
+2445,741,IP VAS GENERAL POSTOP,3843,IBUPROFEN 400 MG PO TABS,Medication,1,1423,0.000702741
+2446,741,IP VAS GENERAL POSTOP,3844,IBUPROFEN 600 MG PO TABS,Medication,2,1423,0.001405481
+2447,741,IP VAS GENERAL POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,164,1423,0.115249473
+2448,741,IP VAS GENERAL POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,317,1423,0.222768798
+2449,741,IP VAS GENERAL POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,434,1423,0.304989459
+2450,741,IP VAS GENERAL POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,56,1423,0.039353479
+2451,741,IP VAS GENERAL POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,465,1423,0.32677442
+2452,741,IP VAS GENERAL POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,67,1423,0.047083626
+2453,741,IP VAS GENERAL POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,4,1423,0.002810963
+2454,741,IP VAS GENERAL POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,2,1423,0.001405481
+2455,741,IP VAS GENERAL POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,434,1423,0.304989459
+2456,741,IP VAS GENERAL POSTOP,13654,ASPIRIN 325 MG PO TBEC,Medication,14,1423,0.00983837
+2457,741,IP VAS GENERAL POSTOP,14113,ASPIRIN 81 MG PO TBEC,Medication,51,1423,0.035839775
+2458,741,IP VAS GENERAL POSTOP,14862,POTASSIUM CHLORIDE-D5-0.2%NACL 20 MEQ/L IV SOLP,Medication,2,1423,0.001405481
+2459,741,IP VAS GENERAL POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,113,1423,0.079409698
+2460,741,IP VAS GENERAL POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,10,1423,0.007027407
+2461,741,IP VAS GENERAL POSTOP,15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,10,1423,0.007027407
+2462,741,IP VAS GENERAL POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,38,1423,0.026704146
+2463,741,IP VAS GENERAL POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,4,1423,0.002810963
+2464,741,IP VAS GENERAL POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,94,1423,0.066057625
+2465,741,IP VAS GENERAL POSTOP,27920,OXYCODONE 10 MG PO TB12,Medication,2,1423,0.001405481
+2466,741,IP VAS GENERAL POSTOP,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,50,1423,0.035137034
+2467,741,IP VAS GENERAL POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,249,1423,0.174982431
+2468,741,IP VAS GENERAL POSTOP,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,1,1423,0.000702741
+2469,741,IP VAS GENERAL POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,16,1423,0.011243851
+2470,741,IP VAS GENERAL POSTOP,83398,CLOPIDOGREL 75 MG PO TABS,Medication,17,1423,0.011946592
+2471,741,IP VAS GENERAL POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,380,1423,0.267041462
+2472,741,IP VAS GENERAL POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,22,1423,0.015460295
+2473,741,IP VAS GENERAL POSTOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,265,1423,0.186226283
+2474,741,IP VAS GENERAL POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,7,1423,0.004919185
+2475,741,IP VAS GENERAL POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,130,1423,0.09135629
+2476,741,IP VAS GENERAL POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,11,1423,0.007730148
+2477,741,IP VAS GENERAL POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,138,1423,0.096978215
+2478,741,IP VAS GENERAL POSTOP,112634,METOCLOPRAMIDE 5 MG/ML INJ SYRG,Medication,1,1423,0.000702741
+2479,741,IP VAS GENERAL POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,16,1423,0.011243851
+2480,741,IP VAS GENERAL POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,2,1423,0.001405481
+2481,741,IP VAS GENERAL POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,468,1423,0.328882642
+2482,741,IP VAS GENERAL POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,5,1423,0.003513703
+2483,741,IP VAS GENERAL POSTOP,203749,CLOPIDOGREL 75 MG PO TABS,Medication,4,1423,0.002810963
+2484,741,IP VAS GENERAL POSTOP,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,1,1423,0.000702741
+2485,741,IP VAS GENERAL POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,142,1423,0.099789178
+2486,741,IP VAS GENERAL POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,63,1423,0.044272663
+2487,741,IP VAS GENERAL POSTOP,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,8,1423,0.005621926
+2488,741,IP VAS GENERAL POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,47,1423,0.033028812
+2489,741,IP VAS GENERAL POSTOP,530012,PHENYLEPHRINE IV INFUSION,Medication,3,1423,0.002108222
+2490,741,IP VAS GENERAL POSTOP,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,9,1423,0.006324666
+2491,741,IP VAS GENERAL POSTOP,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,6,1423,0.004216444
+2492,741,IP VAS GENERAL POSTOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,7,1423,0.004919185
+2493,741,IP VAS GENERAL POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,47,1423,0.033028812
+2494,741,IP VAS GENERAL POSTOP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,1,1423,0.000702741
+2495,741,IP VAS GENERAL POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,101,1423,0.07097681
+2496,741,IP VAS GENERAL POSTOP,LABCBCD,CBC WITH DIFF,Lab,531,1423,0.373155306
+2497,741,IP VAS GENERAL POSTOP,LABCK,"CREATINE KINASE, TOTAL",Lab,20,1423,0.014054814
+2498,741,IP VAS GENERAL POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,417,1423,0.293042867
+2499,741,IP VAS GENERAL POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,84,1423,0.059030218
+2500,741,IP VAS GENERAL POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,202,1423,0.141953619
+2501,741,IP VAS GENERAL POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,100,1423,0.070274069
+2502,741,IP VAS GENERAL POSTOP,LABPLTS,PLATELET COUNT,Lab,82,1423,0.057624736
+2503,741,IP VAS GENERAL POSTOP,LABPT,PROTHROMBIN TIME,Lab,138,1423,0.096978215
+2504,741,IP VAS GENERAL POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,128,1423,0.089950808
+2505,741,IP VAS GENERAL POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,5,1423,0.003513703
+2506,741,IP VAS GENERAL POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,57,1423,0.040056219
+2507,741,IP VAS GENERAL POSTOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,1,1423,0.000702741
+2508,742,IP VAS GENERAL PREOP,4318,LACTATED RINGERS IV SOLP,Medication,2,53,0.037735849
+2509,742,IP VAS GENERAL PREOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,17,53,0.320754717
+2510,742,IP VAS GENERAL PREOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1,53,0.018867925
+2511,742,IP VAS GENERAL PREOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,53,0.018867925
+2512,742,IP VAS GENERAL PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,53,0.037735849
+2513,742,IP VAS GENERAL PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,7,53,0.132075472
+2514,742,IP VAS GENERAL PREOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,1,53,0.018867925
+2515,742,IP VAS GENERAL PREOP,520064,CEFAZOLIN 2 G IVPB,Medication,2,53,0.037735849
+2516,742,IP VAS GENERAL PREOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,6,53,0.113207547
+2517,742,IP VAS GENERAL PREOP,LABCBCD,CBC WITH DIFF,Lab,35,53,0.660377358
+2518,742,IP VAS GENERAL PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,35,53,0.660377358
+2519,742,IP VAS GENERAL PREOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,29,53,0.547169811
+2520,742,IP VAS GENERAL PREOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,10,53,0.188679245
+2521,742,IP VAS GENERAL PREOP,LABPLTS,PLATELET COUNT,Lab,6,53,0.113207547
+2522,742,IP VAS GENERAL PREOP,LABPT,PROTHROMBIN TIME,Lab,14,53,0.264150943
+2523,742,IP VAS GENERAL PREOP,LABPTTINH,PTT INHIBITOR SCREEN,Lab,5,53,0.094339623
+2524,742,IP VAS GENERAL PREOP,LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,7,53,0.132075472
+2525,742,IP VAS GENERAL PREOP,IMGDXCH1,XR CHEST 1V,Imaging,9,53,0.169811321
+2526,742,IP VAS GENERAL PREOP,IMGDXCH2,XR CHEST 2V,Imaging,3,53,0.056603774
+2527,753,IP NEU ICU ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,711,0.005625879
+2528,753,IP NEU ICU ADMIT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,22,711,0.030942335
+2529,753,IP NEU ICU ADMIT,680,ASPIRIN 81 MG PO CHEW,Medication,12,711,0.016877637
+2530,753,IP NEU ICU ADMIT,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,1,711,0.00140647
+2531,753,IP NEU ICU ADMIT,6256,PHENYTOIN SODIUM 50 MG/ML IV SOLN,Medication,1,711,0.00140647
+2532,753,IP NEU ICU ADMIT,7321,SODIUM CHLORIDE 3 % 3 % IV SOLP,Medication,1,711,0.00140647
+2533,753,IP NEU ICU ADMIT,10372,LABETALOL 5 MG/ML IV SOLN,Medication,20,711,0.028129395
+2534,753,IP NEU ICU ADMIT,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,1,711,0.00140647
+2535,753,IP NEU ICU ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,95,711,0.133614627
+2536,753,IP NEU ICU ADMIT,77545,ATORVASTATIN 40 MG PO TABS,Medication,22,711,0.030942335
+2537,753,IP NEU ICU ADMIT,78531,ATORVASTATIN 20 MG PO TABS,Medication,16,711,0.022503516
+2538,753,IP NEU ICU ADMIT,79507,ATORVASTATIN 10 MG PO TABS,Medication,3,711,0.004219409
+2539,753,IP NEU ICU ADMIT,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,8,711,0.011251758
+2540,753,IP NEU ICU ADMIT,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,91,711,0.127988748
+2541,753,IP NEU ICU ADMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,5,711,0.007032349
+2542,753,IP NEU ICU ADMIT,83628,AMLODIPINE 5 MG PO TABS,Medication,2,711,0.00281294
+2543,753,IP NEU ICU ADMIT,88045,SIMVASTATIN 20 MG PO TABS,Medication,1,711,0.00140647
+2544,753,IP NEU ICU ADMIT,92585,FAMOTIDINE 40 MG/5 ML PO SUSP,Medication,6,711,0.008438819
+2545,753,IP NEU ICU ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,2,711,0.00281294
+2546,753,IP NEU ICU ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,10,711,0.014064698
+2547,753,IP NEU ICU ADMIT,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,14,711,0.019690577
+2548,753,IP NEU ICU ADMIT,206226,NICARDIPINE IN SALINE (ISO-OS) 40 MG/200 ML IV PGBK,Medication,2,711,0.00281294
+2549,753,IP NEU ICU ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,103,711,0.144866385
+2550,753,IP NEU ICU ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,10,711,0.014064698
+2551,753,IP NEU ICU ADMIT,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,3,711,0.004219409
+2552,753,IP NEU ICU ADMIT,520067,FOSPHENYTOIN IVPB,Medication,1,711,0.00140647
+2553,753,IP NEU ICU ADMIT,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,1,711,0.00140647
+2554,753,IP NEU ICU ADMIT,530010,NICARDIPINE IV INFUSION,Medication,22,711,0.030942335
+2555,753,IP NEU ICU ADMIT,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,1,711,0.00140647
+2556,753,IP NEU ICU ADMIT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,711,0.00281294
+2557,753,IP NEU ICU ADMIT,LAB12802R,HEPARIN COFACTOR,Lab,1,711,0.00140647
+2558,753,IP NEU ICU ADMIT,LABA1C,HEMOGLOBIN A1C,Lab,124,711,0.17440225
+2559,753,IP NEU ICU ADMIT,LABACA,ANTI CARDIOLIPIN AB,Lab,5,711,0.007032349
+2560,753,IP NEU ICU ADMIT,LABANTIXA,LMWH ACTIVITY BY ANTIXA,Lab,1,711,0.00140647
+2561,753,IP NEU ICU ADMIT,LABAT3,ANTITHROMBIN III,Lab,7,711,0.009845288
+2562,753,IP NEU ICU ADMIT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,6,711,0.008438819
+2563,753,IP NEU ICU ADMIT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,7,711,0.009845288
+2564,753,IP NEU ICU ADMIT,LABCA,"CALCIUM, SERUM/PLASMA",Lab,190,711,0.267229255
+2565,753,IP NEU ICU ADMIT,LABCBCD,CBC WITH DIFF,Lab,170,711,0.239099859
+2566,753,IP NEU ICU ADMIT,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,1,711,0.00140647
+2567,753,IP NEU ICU ADMIT,LABCK,"CREATINE KINASE, TOTAL",Lab,80,711,0.112517581
+2568,753,IP NEU ICU ADMIT,LABDIC,DIC SCREEN,Lab,8,711,0.011251758
+2569,753,IP NEU ICU ADMIT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,109,711,0.153305204
+2570,753,IP NEU ICU ADMIT,LABDRVVTP,DILUTE RUSSELL VIPER VENOM PANEL,Lab,3,711,0.004219409
+2571,753,IP NEU ICU ADMIT,LABESRP,SEDIMENTATION RATE (ESR),Lab,4,711,0.005625879
+2572,753,IP NEU ICU ADMIT,LABFACT8,FACTOR VIII ASSAY,Lab,4,711,0.005625879
+2573,753,IP NEU ICU ADMIT,LABFIB,FIBRINOGEN,Lab,3,711,0.004219409
+2574,753,IP NEU ICU ADMIT,LABFT4,"T4, FREE",Lab,104,711,0.146272855
+2575,753,IP NEU ICU ADMIT,LABGRAM,GRAM STAIN,Lab,2,711,0.00281294
+2576,753,IP NEU ICU ADMIT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,167,711,0.23488045
+2577,753,IP NEU ICU ADMIT,LABLEID,FACTOR V LEIDEN,Lab,7,711,0.009845288
+2578,753,IP NEU ICU ADMIT,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,11,711,0.015471167
+2579,753,IP NEU ICU ADMIT,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,115,711,0.161744023
+2580,753,IP NEU ICU ADMIT,LABLUPUS,LUPUS ANTICOAGULANT,Lab,6,711,0.008438819
+2581,753,IP NEU ICU ADMIT,LABMB,"CK, MB (MASS)",Lab,81,711,0.113924051
+2582,753,IP NEU ICU ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,265,711,0.372714487
+2583,753,IP NEU ICU ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,259,711,0.364275668
+2584,753,IP NEU ICU ADMIT,LABNA,"SODIUM, SERUM / PLASMA",Lab,14,711,0.019690577
+2585,753,IP NEU ICU ADMIT,LABNH3,"AMMONIA, PLASMA",Lab,4,711,0.005625879
+2586,753,IP NEU ICU ADMIT,LABOSM,"OSMOLALITY, SERUM",Lab,3,711,0.004219409
+2587,753,IP NEU ICU ADMIT,LABP20210,PROTHROMBIN 20210A,Lab,5,711,0.007032349
+2588,753,IP NEU ICU ADMIT,LABPHEN,PHENYTOIN LEVEL,Lab,2,711,0.00281294
+2589,753,IP NEU ICU ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,234,711,0.329113924
+2590,753,IP NEU ICU ADMIT,LABPLTS,PLATELET COUNT,Lab,4,711,0.005625879
+2591,753,IP NEU ICU ADMIT,LABPROTC,PROTEIN C ACTIVITY,Lab,5,711,0.007032349
+2592,753,IP NEU ICU ADMIT,LABPROTS,PROTEIN S ACTIVITY,Lab,5,711,0.007032349
+2593,753,IP NEU ICU ADMIT,LABPT,PROTHROMBIN TIME,Lab,141,711,0.198312236
+2594,753,IP NEU ICU ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,26,711,0.036568214
+2595,753,IP NEU ICU ADMIT,LABRESP,RESPIRATORY CULTURE,Lab,2,711,0.00281294
+2596,753,IP NEU ICU ADMIT,LABROMRS,MRSA SCREEN,Lab,144,711,0.202531646
+2597,753,IP NEU ICU ADMIT,LABTNI,TROPONIN I,Lab,117,711,0.164556962
+2598,753,IP NEU ICU ADMIT,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,5,711,0.007032349
+2599,753,IP NEU ICU ADMIT,LABTSH,TSH,Lab,121,711,0.170182841
+2600,753,IP NEU ICU ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,7,711,0.009845288
+2601,753,IP NEU ICU ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,6,711,0.008438819
+2602,753,IP NEU ICU ADMIT,LABURNC,URINE CULTURE,Lab,6,711,0.008438819
+2603,753,IP NEU ICU ADMIT,LABVWSCN,VON WILLEBRAND SCREEN,Lab,4,711,0.005625879
+2604,753,IP NEU ICU ADMIT,LABYACIGA,"CARDIOLIPIN AB, IGA",Lab,1,711,0.00140647
+2605,753,IP NEU ICU ADMIT,POC14,POC GLUCOSE BY METER,Lab,34,711,0.047819972
+2606,753,IP NEU ICU ADMIT,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,50,711,0.070323488
+2607,753,IP NEU ICU ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,123,711,0.172995781
+2608,753,IP NEU ICU ADMIT,IMGCT3HN,CT ANGIO HEAD AND NECK,Imaging,3,711,0.004219409
+2609,753,IP NEU ICU ADMIT,IMGCTH,CT HEAD,Imaging,32,711,0.045007032
+2610,753,IP NEU ICU ADMIT,IMGDXAB1,XR ABDOMEN 1V,Imaging,36,711,0.050632911
+2611,753,IP NEU ICU ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,82,711,0.11533052
+2612,753,IP NEU ICU ADMIT,IMGMRH,MR BRAIN,Imaging,18,711,0.025316456
+2613,753,IP NEU ICU ADMIT,IMGMRIGEN,MRI UNLISTED EXAM,Imaging,2,711,0.00281294
+2614,755,IP NEU GENERAL WARD ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,652,3072,0.212239583
+2615,755,IP NEU GENERAL WARD ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,6,3072,0.001953125
+2616,755,IP NEU GENERAL WARD ADMIT,680,ASPIRIN 81 MG PO CHEW,Medication,21,3072,0.006835938
+2617,755,IP NEU GENERAL WARD ADMIT,681,ASPIRIN 325 MG PO TABS,Medication,5,3072,0.001627604
+2618,755,IP NEU GENERAL WARD ADMIT,693,ASPIRIN 300 MG PR SUPP,Medication,10,3072,0.003255208
+2619,755,IP NEU GENERAL WARD ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,79,3072,0.025716146
+2620,755,IP NEU GENERAL WARD ADMIT,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,94,3072,0.030598958
+2621,755,IP NEU GENERAL WARD ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,296,3072,0.096354167
+2622,755,IP NEU GENERAL WARD ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,7,3072,0.002278646
+2623,755,IP NEU GENERAL WARD ADMIT,11111,PRAVASTATIN 20 MG PO TABS,Medication,1,3072,0.000325521
+2624,755,IP NEU GENERAL WARD ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,199,3072,0.064778646
+2625,755,IP NEU GENERAL WARD ADMIT,13654,ASPIRIN 325 MG PO TBEC,Medication,1,3072,0.000325521
+2626,755,IP NEU GENERAL WARD ADMIT,14113,ASPIRIN 81 MG PO TBEC,Medication,83,3072,0.027018229
+2627,755,IP NEU GENERAL WARD ADMIT,16050,GLUCOSE 4 GRAM PO CHEW,Medication,95,3072,0.030924479
+2628,755,IP NEU GENERAL WARD ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,168,3072,0.0546875
+2629,755,IP NEU GENERAL WARD ADMIT,77545,ATORVASTATIN 40 MG PO TABS,Medication,103,3072,0.033528646
+2630,755,IP NEU GENERAL WARD ADMIT,77989,DIPYRIDAMOLE-ASPIRIN 200-25 MG PO CM12,Medication,1,3072,0.000325521
+2631,755,IP NEU GENERAL WARD ADMIT,78531,ATORVASTATIN 20 MG PO TABS,Medication,5,3072,0.001627604
+2632,755,IP NEU GENERAL WARD ADMIT,78748,INSULIN GLARGINE 100 UNIT/ML SC SOLN,Medication,19,3072,0.006184896
+2633,755,IP NEU GENERAL WARD ADMIT,79507,ATORVASTATIN 10 MG PO TABS,Medication,13,3072,0.004231771
+2634,755,IP NEU GENERAL WARD ADMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,13,3072,0.004231771
+2635,755,IP NEU GENERAL WARD ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,128,3072,0.041666667
+2636,755,IP NEU GENERAL WARD ADMIT,88045,SIMVASTATIN 20 MG PO TABS,Medication,3,3072,0.000976563
+2637,755,IP NEU GENERAL WARD ADMIT,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,14,3072,0.004557292
+2638,755,IP NEU GENERAL WARD ADMIT,89742,INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP,Medication,13,3072,0.004231771
+2639,755,IP NEU GENERAL WARD ADMIT,92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,107,3072,0.034830729
+2640,755,IP NEU GENERAL WARD ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,8,3072,0.002604167
+2641,755,IP NEU GENERAL WARD ADMIT,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,3072,0.000325521
+2642,755,IP NEU GENERAL WARD ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,337,3072,0.109700521
+2643,755,IP NEU GENERAL WARD ADMIT,116179,ROSUVASTATIN 40 MG PO TABS,Medication,1,3072,0.000325521
+2644,755,IP NEU GENERAL WARD ADMIT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,5,3072,0.001627604
+2645,755,IP NEU GENERAL WARD ADMIT,126583,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SYRG",Medication,1,3072,0.000325521
+2646,755,IP NEU GENERAL WARD ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,10,3072,0.003255208
+2647,755,IP NEU GENERAL WARD ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,42,3072,0.013671875
+2648,755,IP NEU GENERAL WARD ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,5,3072,0.001627604
+2649,755,IP NEU GENERAL WARD ADMIT,541452,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING,Medication,6,3072,0.001953125
+2650,755,IP NEU GENERAL WARD ADMIT,541453,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,1,3072,0.000325521
+2651,755,IP NEU GENERAL WARD ADMIT,541454,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,1,3072,0.000325521
+2652,755,IP NEU GENERAL WARD ADMIT,541456,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE NPO,Medication,2,3072,0.000651042
+2653,755,IP NEU GENERAL WARD ADMIT,541457,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO,Medication,1,3072,0.000325521
+2654,755,IP NEU GENERAL WARD ADMIT,541460,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE FEEDING,Medication,1,3072,0.000325521
+2655,755,IP NEU GENERAL WARD ADMIT,LABBHCG,"BETA HCG, QUANT",Lab,22,3072,0.007161458
+2656,755,IP NEU GENERAL WARD ADMIT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,31,3072,0.010091146
+2657,755,IP NEU GENERAL WARD ADMIT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,15,3072,0.004882813
+2658,755,IP NEU GENERAL WARD ADMIT,LABCBCD,CBC WITH DIFF,Lab,431,3072,0.140299479
+2659,755,IP NEU GENERAL WARD ADMIT,LABCBCO,CBC W/O DIFF,Lab,194,3072,0.063151042
+2660,755,IP NEU GENERAL WARD ADMIT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,107,3072,0.034830729
+2661,755,IP NEU GENERAL WARD ADMIT,LABFT4,"T4, FREE",Lab,179,3072,0.058268229
+2662,755,IP NEU GENERAL WARD ADMIT,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,183,3072,0.059570313
+2663,755,IP NEU GENERAL WARD ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,230,3072,0.074869792
+2664,755,IP NEU GENERAL WARD ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,444,3072,0.14453125
+2665,755,IP NEU GENERAL WARD ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,279,3072,0.090820313
+2666,755,IP NEU GENERAL WARD ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,207,3072,0.067382813
+2667,755,IP NEU GENERAL WARD ADMIT,LABPLTS,PLATELET COUNT,Lab,33,3072,0.010742188
+2668,755,IP NEU GENERAL WARD ADMIT,LABPT,PROTHROMBIN TIME,Lab,264,3072,0.0859375
+2669,755,IP NEU GENERAL WARD ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,123,3072,0.040039063
+2670,755,IP NEU GENERAL WARD ADMIT,LABRESP,RESPIRATORY CULTURE,Lab,4,3072,0.001302083
+2671,755,IP NEU GENERAL WARD ADMIT,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,2,3072,0.000651042
+2672,755,IP NEU GENERAL WARD ADMIT,LABTNI,TROPONIN I,Lab,49,3072,0.015950521
+2673,755,IP NEU GENERAL WARD ADMIT,LABTSH,TSH,Lab,346,3072,0.112630208
+2674,755,IP NEU GENERAL WARD ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,191,3072,0.062174479
+2675,755,IP NEU GENERAL WARD ADMIT,POC14,POC GLUCOSE BY METER,Lab,703,3072,0.228841146
+2676,755,IP NEU GENERAL WARD ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,70,3072,0.022786458
+2677,755,IP NEU GENERAL WARD ADMIT,ECH16,ECHO - TRANSESOPHAGEAL ECHO,Imaging,6,3072,0.001953125
+2678,755,IP NEU GENERAL WARD ADMIT,IMGCT3HN,CT ANGIO HEAD AND NECK,Imaging,13,3072,0.004231771
+2679,755,IP NEU GENERAL WARD ADMIT,IMGCTH,CT HEAD,Imaging,9,3072,0.002929688
+2680,755,IP NEU GENERAL WARD ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,127,3072,0.041341146
+2681,755,IP NEU GENERAL WARD ADMIT,IMGMRH,MR BRAIN,Imaging,156,3072,0.05078125
+2682,755,IP NEU GENERAL WARD ADMIT,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,4,3072,0.001302083
+2683,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1,152,0.006578947
+2684,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,29,152,0.190789474
+2685,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,8,152,0.052631579
+2686,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,34208,PHYTONADIONE 1 MG/0.5 ML INJ SOLN,Medication,1,152,0.006578947
+2687,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,5,152,0.032894737
+2688,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,29,152,0.190789474
+2689,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,203954,FOSPHENYTOIN 100 MG PE/2 ML INJ SOLN,Medication,1,152,0.006578947
+2690,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,206226,NICARDIPINE IN SALINE (ISO-OS) 40 MG/200 ML IV PGBK,Medication,1,152,0.006578947
+2691,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,35,152,0.230263158
+2692,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,530010,NICARDIPINE IV INFUSION,Medication,15,152,0.098684211
+2693,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,25,152,0.164473684
+2694,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,540353,FOSPHENYTOIN IN NS 100 ML IVPB,Medication,1,152,0.006578947
+2695,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABABG,"BLOOD GASES, ARTERIAL",Lab,25,152,0.164473684
+2696,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABCA,"CALCIUM, SERUM/PLASMA",Lab,29,152,0.190789474
+2697,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABCBCD,CBC WITH DIFF,Lab,39,152,0.256578947
+2698,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,23,152,0.151315789
+2699,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABFTRIGA,"TRIGLYCERIDES, NON-PLEURAL",Lab,1,152,0.006578947
+2700,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABHFP,HEPATIC FUNCTION PANEL A,Lab,24,152,0.157894737
+2701,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABMB,"CK, MB (MASS)",Lab,12,152,0.078947368
+2702,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABMETB,"METABOLIC PANEL, BASIC",Lab,43,152,0.282894737
+2703,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,33,152,0.217105263
+2704,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,31,152,0.203947368
+2705,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABPT,PROTHROMBIN TIME,Lab,39,152,0.256578947
+2706,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,39,152,0.256578947
+2707,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABTNI,TROPONIN I,Lab,24,152,0.157894737
+2708,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABTSH,TSH,Lab,32,152,0.210526316
+2709,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,152,0.006578947
+2710,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,POC14,POC GLUCOSE BY METER,Lab,44,152,0.289473684
+2711,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,30,152,0.197368421
+2712,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,IMGCT3HN,CT ANGIO HEAD AND NECK,Imaging,2,152,0.013157895
+2713,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,IMGCTH,CT HEAD,Imaging,9,152,0.059210526
+2714,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,IMGDXCH1,XR CHEST 1V,Imaging,15,152,0.098684211
+2715,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,IMGMRAH,MR ANGIO HEAD,Imaging,2,152,0.013157895
+2716,758,RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE,IMGMRH,MR BRAIN,Imaging,2,152,0.013157895
+2717,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,11634,VECURONIUM BROMIDE 10 MG IV SOLR,Medication,78,381,0.204724409
+2718,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,88548,CISATRACURIUM 2 MG/ML IV SOLN,Medication,7,381,0.018372703
+2719,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,530003,FENTANYL  10 MCG/ML IV INFUSION,Medication,46,381,0.120734908
+2720,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,76,381,0.199475066
+2721,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,530007,VECURONIUM IV INFUSION,Medication,80,381,0.209973753
+2722,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,530009,CISATRACURIUM IV INFUSION,Medication,16,381,0.041994751
+2723,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,540102,NS IV BOLUS,Medication,75,381,0.196850394
+2724,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,540638,FENTANYL 50 MCG/ML IV INFUSION,Medication,28,381,0.073490814
+2725,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,LAB11049R,NSE,Lab,125,381,0.32808399
+2726,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,11,381,0.028871391
+2727,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,LABMETB,"METABOLIC PANEL, BASIC",Lab,146,381,0.3832021
+2728,759,IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL,IMGCTH,CT HEAD,Imaging,7,381,0.018372703
+2729,761,RETIRED IP NEU STROKE ISCHEMIC ELEVATED ICP,4750,MANNITOL 25 % 25 % IV SOLN,Medication,1,4,0.25
+2730,761,IP NEU STROKE ISCHEMIC ELEVATED ICP,4750,MANNITOL 25 % 25 % IV SOLN,Medication,1,4,0.25
+2731,761,RETIRED IP NEU STROKE ISCHEMIC ELEVATED ICP,LABNA,"SODIUM, SERUM / PLASMA",Lab,1,4,0.25
+2732,761,IP NEU STROKE ISCHEMIC ELEVATED ICP,LABNA,"SODIUM, SERUM / PLASMA",Lab,1,4,0.25
+2733,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,8,37,0.216216216
+2734,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,3232,FOLIC ACID 5 MG/ML INJ SOLN,Medication,1,37,0.027027027
+2735,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,4749,MANNITOL 20 % 20 % IV SOLP,Medication,2,37,0.054054054
+2736,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,7322,SODIUM CHLORIDE 4 MEQ/ML IV SOLP,Medication,2,37,0.054054054
+2737,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,7876,THIAMINE HCL (VITAMIN B1) 100 MG/ML INJ SOLN,Medication,1,37,0.027027027
+2738,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,1,37,0.027027027
+2739,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,9,37,0.243243243
+2740,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,1,37,0.027027027
+2741,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,11,37,0.297297297
+2742,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,37,0.243243243
+2743,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,530010,NICARDIPINE IV INFUSION,Medication,1,37,0.027027027
+2744,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,530025,DEXMEDETOMIDINE IV INFUSION,Medication,1,37,0.027027027
+2745,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,1,37,0.027027027
+2746,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,540769,SODIUM CHLORIDE 3 % IV BOLUS,Medication,1,37,0.027027027
+2747,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,8,37,0.216216216
+2748,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABCA,"CALCIUM, SERUM/PLASMA",Lab,9,37,0.243243243
+2749,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,10,37,0.27027027
+2750,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,5,37,0.135135135
+2751,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABHFP,HEPATIC FUNCTION PANEL A,Lab,9,37,0.243243243
+2752,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,1,37,0.027027027
+2753,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABMETB,"METABOLIC PANEL, BASIC",Lab,11,37,0.297297297
+2754,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,10,37,0.27027027
+2755,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABOSM,"OSMOLALITY, SERUM",Lab,2,37,0.054054054
+2756,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,10,37,0.27027027
+2757,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,8,37,0.216216216
+2758,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,7,37,0.189189189
+2759,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,LABTYPSNI,TYPE AND SCREEN,Lab,1,37,0.027027027
+2760,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,11,37,0.297297297
+2761,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,5,37,0.135135135
+2762,765,IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL,IMGCTH,CT HEAD,Imaging,5,37,0.135135135
+2763,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,193,1475,0.130847458
+2764,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,1475,0.000677966
+2765,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,3,1475,0.002033898
+2766,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,6,1475,0.004067797
+2767,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1,1475,0.000677966
+2768,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,5,1475,0.003389831
+2769,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,168,1475,0.113898305
+2770,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,11,1475,0.007457627
+2771,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,188,1475,0.127457627
+2772,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,1475,0.001355932
+2773,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,530008,NITROPRUSSIDE IV INFUSION,Medication,11,1475,0.007457627
+2774,766,IP RAD NEURORADIOLOGY POST-PROCEDURE,IMGCTH,CT HEAD,Imaging,6,1475,0.004067797
+2775,775,IP SUR TRAUMA SURGICAL INTERVENTION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,28,125,0.224
+2776,775,IP SUR TRAUMA SURGICAL INTERVENTION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,6,125,0.048
+2777,775,IP SUR TRAUMA SURGICAL INTERVENTION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,2,125,0.016
+2778,775,IP SUR TRAUMA SURGICAL INTERVENTION,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1,125,0.008
+2779,775,IP SUR TRAUMA SURGICAL INTERVENTION,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,125,0.016
+2780,775,IP SUR TRAUMA SURGICAL INTERVENTION,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,16,125,0.128
+2781,775,IP SUR TRAUMA SURGICAL INTERVENTION,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,2,125,0.016
+2782,775,IP SUR TRAUMA SURGICAL INTERVENTION,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1,125,0.008
+2783,775,IP SUR TRAUMA SURGICAL INTERVENTION,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,1,125,0.008
+2784,775,IP SUR TRAUMA SURGICAL INTERVENTION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,125,0.008
+2785,775,IP SUR TRAUMA SURGICAL INTERVENTION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,125,0.016
+2786,775,IP SUR TRAUMA SURGICAL INTERVENTION,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,125,0.008
+2787,775,IP SUR TRAUMA SURGICAL INTERVENTION,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,125,0.008
+2788,775,IP SUR TRAUMA SURGICAL INTERVENTION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,8,125,0.064
+2789,775,IP SUR TRAUMA SURGICAL INTERVENTION,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,125,0.008
+2790,775,IP SUR TRAUMA SURGICAL INTERVENTION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,26,125,0.208
+2791,775,IP SUR TRAUMA SURGICAL INTERVENTION,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,1,125,0.008
+2792,775,IP SUR TRAUMA SURGICAL INTERVENTION,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,125,0.008
+2793,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABCA,"CALCIUM, SERUM/PLASMA",Lab,1,125,0.008
+2794,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABCBCD,CBC WITH DIFF,Lab,42,125,0.336
+2795,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABMETB,"METABOLIC PANEL, BASIC",Lab,25,125,0.2
+2796,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,18,125,0.144
+2797,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,41,125,0.328
+2798,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,41,125,0.328
+2799,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABPLTS,PLATELET COUNT,Lab,20,125,0.16
+2800,775,IP SUR TRAUMA SURGICAL INTERVENTION,LABPT,PROTHROMBIN TIME,Lab,13,125,0.104
+2801,775,IP SUR TRAUMA SURGICAL INTERVENTION,IMGDXCH1,XR CHEST 1V,Imaging,4,125,0.032
+2802,776,IP SUR APPENDICITIS ADMISSION ,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,36,0.055555556
+2803,776,IP SUR APPENDICITIS ADMISSION ,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,36,0.027777778
+2804,776,IP SUR APPENDICITIS ADMISSION ,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,2,36,0.055555556
+2805,776,IP SUR APPENDICITIS ADMISSION ,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,17,36,0.472222222
+2806,776,IP SUR APPENDICITIS ADMISSION ,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,11,36,0.305555556
+2807,776,IP SUR APPENDICITIS ADMISSION ,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,36,0.027777778
+2808,776,IP SUR APPENDICITIS ADMISSION ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,36,0.25
+2809,776,IP SUR APPENDICITIS ADMISSION ,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,36,0.055555556
+2810,776,IP SUR APPENDICITIS ADMISSION ,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,36,0.027777778
+2811,776,IP SUR APPENDICITIS ADMISSION ,520043,CEFOXITIN IVPB,Medication,1,36,0.027777778
+2812,776,IP SUR APPENDICITIS ADMISSION ,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,36,0.027777778
+2813,776,IP SUR APPENDICITIS ADMISSION ,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,36,0.138888889
+2814,776,IP SUR APPENDICITIS ADMISSION ,LABCBCD,CBC WITH DIFF,Lab,1,36,0.027777778
+2815,776,IP SUR APPENDICITIS ADMISSION ,LABPLTS,PLATELET COUNT,Lab,1,36,0.027777778
+2816,776,IP SUR APPENDICITIS ADMISSION ,LABTYPSNI,TYPE AND SCREEN,Lab,2,36,0.055555556
+2817,778,RETIRED IP SUR APPENDECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,15,0.066666667
+2818,778,RETIRED IP SUR APPENDECTOMY POSTOP,3845,IBUPROFEN 800 MG PO TABS,Medication,1,15,0.066666667
+2819,778,RETIRED IP SUR APPENDECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,6,15,0.4
+2820,778,RETIRED IP SUR APPENDECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,3,15,0.2
+2821,778,RETIRED IP SUR APPENDECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,3,15,0.2
+2822,778,RETIRED IP SUR APPENDECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,15,0.066666667
+2823,778,RETIRED IP SUR APPENDECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,5,15,0.333333333
+2824,778,RETIRED IP SUR APPENDECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,15,0.066666667
+2825,778,RETIRED IP SUR APPENDECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,15,0.133333333
+2826,778,RETIRED IP SUR APPENDECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,15,0.066666667
+2827,778,RETIRED IP SUR APPENDECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,1,15,0.066666667
+2828,778,RETIRED IP SUR APPENDECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,15,0.066666667
+2829,789,IP SUR ACUTE PANCREATITIS  ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,9,0.111111111
+2830,789,IP SUR ACUTE PANCREATITIS  ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,9,0.111111111
+2831,789,IP SUR ACUTE PANCREATITIS  ADMIT,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,1,9,0.111111111
+2832,789,IP SUR ACUTE PANCREATITIS  ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,9,0.111111111
+2833,789,IP SUR ACUTE PANCREATITIS  ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,9,0.333333333
+2834,789,IP SUR ACUTE PANCREATITIS  ADMIT,540302,POTASSIUM CHLORIDE IV SCALE,Medication,1,9,0.111111111
+2835,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABAMY,"AMYLASE, SERUM",Lab,3,9,0.333333333
+2836,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,4,9,0.444444444
+2837,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABCBCD,CBC WITH DIFF,Lab,2,9,0.222222222
+2838,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABGLNF,"GLUCOSE NON-FASTING, SERUM/PLASMA",Lab,2,9,0.222222222
+2839,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,3,9,0.333333333
+2840,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABLIPS,LIPASE,Lab,3,9,0.333333333
+2841,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABPLTS,PLATELET COUNT,Lab,4,9,0.444444444
+2842,789,IP SUR ACUTE PANCREATITIS  ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,1,9,0.111111111
+2843,789,IP SUR ACUTE PANCREATITIS  ADMIT,POC14,POC GLUCOSE BY METER,Lab,1,9,0.111111111
+2844,792,IP ICU GI BLEED,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,164,0.012195122
+2845,792,IP ICU GI BLEED,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,164,0.006097561
+2846,792,IP ICU GI BLEED,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,4,164,0.024390244
+2847,792,IP ICU GI BLEED,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,33,164,0.201219512
+2848,792,IP ICU GI BLEED,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,164,0.006097561
+2849,792,IP ICU GI BLEED,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,20,164,0.12195122
+2850,792,IP ICU GI BLEED,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,1,164,0.006097561
+2851,792,IP ICU GI BLEED,530014,PANTOPRAZOLE 0.8 MG/ML IV INFUSION,Medication,43,164,0.262195122
+2852,792,IP ICU GI BLEED,530016,OCTREOTIDE 10 MCG/ML IV INFUSION,Medication,20,164,0.12195122
+2853,792,IP ICU GI BLEED,541098,CEFEPIME 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,164,0.006097561
+2854,792,IP ICU GI BLEED,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,2,164,0.012195122
+2855,792,IP ICU GI BLEED,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,1,164,0.006097561
+2856,792,IP ICU GI BLEED,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,32,164,0.195121951
+2857,792,IP ICU GI BLEED,LABCBCO,CBC W/O DIFF,Lab,99,164,0.603658537
+2858,792,IP ICU GI BLEED,LABFIB,FIBRINOGEN,Lab,17,164,0.103658537
+2859,792,IP ICU GI BLEED,LABPT,PROTHROMBIN TIME,Lab,25,164,0.152439024
+2860,792,IP ICU GI BLEED,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,19,164,0.115853659
+2861,792,IP ICU GI BLEED,LABTYPSNI,TYPE AND SCREEN,Lab,15,164,0.091463415
+2862,792,IP ICU GI BLEED,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,5,164,0.030487805
+2863,792,IP ICU GI BLEED,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,3,164,0.018292683
+2864,792,IP ICU GI BLEED,LABBBPRBC,RED BLOOD CELLS,Blood Product,11,164,0.067073171
+2865,798,IP SUR GENERAL PREOP,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,296,664,0.445783133
+2866,798,IP SUR GENERAL PREOP,1743,CLINDAMYCIN PHOSPHATE 150 MG/ML INJ SOLN,Medication,3,664,0.004518072
+2867,798,IP SUR GENERAL PREOP,8442,"VANCOMYCIN 1,000 MG IV SOLR",Medication,12,664,0.018072289
+2868,798,IP SUR GENERAL PREOP,9461,CEFOXITIN 1 GRAM IV SOLR,Medication,7,664,0.010542169
+2869,798,IP SUR GENERAL PREOP,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,664,0.001506024
+2870,798,IP SUR GENERAL PREOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,664,0.001506024
+2871,798,IP SUR GENERAL PREOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,3,664,0.004518072
+2872,798,IP SUR GENERAL PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,10,664,0.015060241
+2873,798,IP SUR GENERAL PREOP,96098,ERTAPENEM 1 GRAM INJ SOLR,Medication,2,664,0.003012048
+2874,798,IP SUR GENERAL PREOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,664,0.001506024
+2875,798,IP SUR GENERAL PREOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,664,0.001506024
+2876,798,IP SUR GENERAL PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,664,0.001506024
+2877,798,IP SUR GENERAL PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,4,664,0.006024096
+2878,798,IP SUR GENERAL PREOP,LABCBCD,CBC WITH DIFF,Lab,10,664,0.015060241
+2879,798,IP SUR GENERAL PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,6,664,0.009036145
+2880,798,IP SUR GENERAL PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,664,0.004518072
+2881,798,IP SUR GENERAL PREOP,LABPLTS,PLATELET COUNT,Lab,2,664,0.003012048
+2882,798,IP SUR GENERAL PREOP,LABPT,PROTHROMBIN TIME,Lab,5,664,0.00753012
+2883,798,IP SUR GENERAL PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,664,0.009036145
+2884,798,IP SUR GENERAL PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,30,664,0.045180723
+2885,798,IP SUR GENERAL PREOP,IMGDXCH1,XR CHEST 1V,Imaging,1,664,0.001506024
+2886,798,IP SUR GENERAL PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,22,664,0.03313253
+2887,801,IP SUR OR GENERAL POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,930,3021,0.307845084
+2888,801,IP SUR OR GENERAL POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,6,3021,0.001986097
+2889,801,IP SUR OR GENERAL POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,22,3021,0.007282357
+2890,801,IP SUR OR GENERAL POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,10,3021,0.003310162
+2891,801,IP SUR OR GENERAL POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,5,3021,0.001655081
+2892,801,IP SUR OR GENERAL POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,5,3021,0.001655081
+2893,801,IP SUR OR GENERAL POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,247,3021,0.081761006
+2894,801,IP SUR OR GENERAL POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,3,3021,0.000993049
+2895,801,IP SUR OR GENERAL POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,53,3021,0.01754386
+2896,801,IP SUR OR GENERAL POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,38,3021,0.012578616
+2897,801,IP SUR OR GENERAL POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,46,3021,0.015226746
+2898,801,IP SUR OR GENERAL POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,190,3021,0.062893082
+2899,801,IP SUR OR GENERAL POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,156,3021,0.05163853
+2900,801,IP SUR OR GENERAL POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,6,3021,0.001986097
+2901,801,IP SUR OR GENERAL POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,40,3021,0.013240649
+2902,801,IP SUR OR GENERAL POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,100,3021,0.033101622
+2903,801,IP SUR OR GENERAL POSTOP,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,3021,0.000331016
+2904,801,IP SUR OR GENERAL POSTOP,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,2,3021,0.000662032
+2905,801,IP SUR OR GENERAL POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,76,3021,0.025157233
+2906,801,IP SUR OR GENERAL POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,31,3021,0.010261503
+2907,801,IP SUR OR GENERAL POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,126,3021,0.041708044
+2908,801,IP SUR OR GENERAL POSTOP,112597,ENOXAPARIN 120 MG/0.8 ML SC SYRG,Medication,1,3021,0.000331016
+2909,801,IP SUR OR GENERAL POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,8,3021,0.00264813
+2910,801,IP SUR OR GENERAL POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1003,3021,0.332009268
+2911,801,IP SUR OR GENERAL POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,20,3021,0.006620324
+2912,801,IP SUR OR GENERAL POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,600,3021,0.198609732
+2913,801,IP SUR OR GENERAL POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,5,3021,0.001655081
+2914,801,IP SUR OR GENERAL POSTOP,520043,CEFOXITIN IVPB,Medication,4,3021,0.001324065
+2915,801,IP SUR OR GENERAL POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,14,3021,0.004634227
+2916,801,IP SUR OR GENERAL POSTOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,10,3021,0.003310162
+2917,801,IP SUR OR GENERAL POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,46,3021,0.015226746
+2918,801,IP SUR OR GENERAL POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,3021,0.000993049
+2919,801,IP SUR OR GENERAL POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,33,3021,0.010923535
+2920,801,IP SUR OR GENERAL POSTOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,36,3021,0.011916584
+2921,801,IP SUR OR GENERAL POSTOP,LABABG,"BLOOD GASES, ARTERIAL",Lab,2,3021,0.000662032
+2922,801,IP SUR OR GENERAL POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,298,3021,0.098642833
+2923,801,IP SUR OR GENERAL POSTOP,LABCBCO,CBC W/O DIFF,Lab,994,3021,0.329030122
+2924,801,IP SUR OR GENERAL POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,417,3021,0.138033764
+2925,801,IP SUR OR GENERAL POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,599,3021,0.198278716
+2926,801,IP SUR OR GENERAL POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,764,3021,0.252896392
+2927,801,IP SUR OR GENERAL POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,629,3021,0.208209202
+2928,801,IP SUR OR GENERAL POSTOP,LABPLTS,PLATELET COUNT,Lab,241,3021,0.079774909
+2929,801,IP SUR OR GENERAL POSTOP,LABPT,PROTHROMBIN TIME,Lab,372,3021,0.123138034
+2930,801,IP SUR OR GENERAL POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,268,3021,0.088712347
+2931,805,IP SUR GENERAL ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1501,15690,0.095666029
+2932,805,IP SUR GENERAL ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,281,15690,0.017909496
+2933,805,IP SUR GENERAL ADMIT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,9,15690,0.000573614
+2934,805,IP SUR GENERAL ADMIT,324,ALPRAZOLAM 0.25 MG PO TABS,Medication,7,15690,0.000446144
+2935,805,IP SUR GENERAL ADMIT,325,ALPRAZOLAM 0.5 MG PO TABS,Medication,3,15690,0.000191205
+2936,805,IP SUR GENERAL ADMIT,326,ALPRAZOLAM 1 MG PO TABS,Medication,2,15690,0.00012747
+2937,805,IP SUR GENERAL ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,372,15690,0.023709369
+2938,805,IP SUR GENERAL ADMIT,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,43,15690,0.002740599
+2939,805,IP SUR GENERAL ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,113,15690,0.00720204
+2940,805,IP SUR GENERAL ADMIT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,385,15690,0.024537922
+2941,805,IP SUR GENERAL ADMIT,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,6,15690,0.000382409
+2942,805,IP SUR GENERAL ADMIT,3294,FUROSEMIDE 20 MG PO TABS,Medication,1,15690,6.37E-05
+2943,805,IP SUR GENERAL ADMIT,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,7,15690,0.000446144
+2944,805,IP SUR GENERAL ADMIT,3841,IBUPROFEN 200 MG PO TABS,Medication,1,15690,6.37E-05
+2945,805,IP SUR GENERAL ADMIT,3843,IBUPROFEN 400 MG PO TABS,Medication,40,15690,0.002549395
+2946,805,IP SUR GENERAL ADMIT,3844,IBUPROFEN 600 MG PO TABS,Medication,11,15690,0.000701083
+2947,805,IP SUR GENERAL ADMIT,3845,IBUPROFEN 800 MG PO TABS,Medication,1,15690,6.37E-05
+2948,805,IP SUR GENERAL ADMIT,4572,LORAZEPAM 0.5 MG PO TABS,Medication,6,15690,0.000382409
+2949,805,IP SUR GENERAL ADMIT,4573,LORAZEPAM 1 MG PO TABS,Medication,9,15690,0.000573614
+2950,805,IP SUR GENERAL ADMIT,4711,MAGNESIUM CITRATE PO SOLN,Medication,33,15690,0.00210325
+2951,805,IP SUR GENERAL ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1521,15690,0.096940727
+2952,805,IP SUR GENERAL ADMIT,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,51,15690,0.003250478
+2953,805,IP SUR GENERAL ADMIT,5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,34,15690,0.002166985
+2954,805,IP SUR GENERAL ADMIT,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,207,15690,0.013193117
+2955,805,IP SUR GENERAL ADMIT,5009,METOPROLOL TARTRATE 50 MG PO TABS,Medication,3,15690,0.000191205
+2956,805,IP SUR GENERAL ADMIT,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,183,15690,0.01166348
+2957,805,IP SUR GENERAL ADMIT,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,15690,6.37E-05
+2958,805,IP SUR GENERAL ADMIT,6427,POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK,Medication,1,15690,6.37E-05
+2959,805,IP SUR GENERAL ADMIT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,29,15690,0.001848311
+2960,805,IP SUR GENERAL ADMIT,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,1,15690,6.37E-05
+2961,805,IP SUR GENERAL ADMIT,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,1,15690,6.37E-05
+2962,805,IP SUR GENERAL ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,73,15690,0.004652645
+2963,805,IP SUR GENERAL ADMIT,7753,TEMAZEPAM 15 MG PO CAPS,Medication,2,15690,0.00012747
+2964,805,IP SUR GENERAL ADMIT,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,42,15690,0.002676864
+2965,805,IP SUR GENERAL ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,50,15690,0.003186743
+2966,805,IP SUR GENERAL ADMIT,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,26,15690,0.001657106
+2967,805,IP SUR GENERAL ADMIT,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,1,15690,6.37E-05
+2968,805,IP SUR GENERAL ADMIT,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,1,15690,6.37E-05
+2969,805,IP SUR GENERAL ADMIT,11664,WARFARIN 1 MG PO TABS,Medication,1,15690,6.37E-05
+2970,805,IP SUR GENERAL ADMIT,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,2,15690,0.00012747
+2971,805,IP SUR GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,3566,15690,0.227278521
+2972,805,IP SUR GENERAL ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,136,15690,0.008667941
+2973,805,IP SUR GENERAL ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,179,15690,0.01140854
+2974,805,IP SUR GENERAL ADMIT,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,76,15690,0.00484385
+2975,805,IP SUR GENERAL ADMIT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,325,15690,0.02071383
+2976,805,IP SUR GENERAL ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,607,15690,0.038687062
+2977,805,IP SUR GENERAL ADMIT,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,2,15690,0.00012747
+2978,805,IP SUR GENERAL ADMIT,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,9,15690,0.000573614
+2979,805,IP SUR GENERAL ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,164,15690,0.010452518
+2980,805,IP SUR GENERAL ADMIT,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,1107,15690,0.070554493
+2981,805,IP SUR GENERAL ADMIT,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,1,15690,6.37E-05
+2982,805,IP SUR GENERAL ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,100,15690,0.006373486
+2983,805,IP SUR GENERAL ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,184,15690,0.011727215
+2984,805,IP SUR GENERAL ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,24,15690,0.001529637
+2985,805,IP SUR GENERAL ADMIT,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,441,15690,0.028107075
+2986,805,IP SUR GENERAL ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1701,15690,0.108413002
+2987,805,IP SUR GENERAL ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,20,15690,0.001274697
+2988,805,IP SUR GENERAL ADMIT,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,4,15690,0.000254939
+2989,805,IP SUR GENERAL ADMIT,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,4,15690,0.000254939
+2990,805,IP SUR GENERAL ADMIT,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,2,15690,0.00012747
+2991,805,IP SUR GENERAL ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,359,15690,0.022880816
+2992,805,IP SUR GENERAL ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,267,15690,0.017017208
+2993,805,IP SUR GENERAL ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1017,15690,0.064818356
+2994,805,IP SUR GENERAL ADMIT,112597,ENOXAPARIN 120 MG/0.8 ML SC SYRG,Medication,1,15690,6.37E-05
+2995,805,IP SUR GENERAL ADMIT,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,144,15690,0.00917782
+2996,805,IP SUR GENERAL ADMIT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,182,15690,0.011599745
+2997,805,IP SUR GENERAL ADMIT,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,226,15690,0.014404079
+2998,805,IP SUR GENERAL ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,4590,15690,0.292543021
+2999,805,IP SUR GENERAL ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,185,15690,0.01179095
+3000,805,IP SUR GENERAL ADMIT,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,154,15690,0.009815169
+3001,805,IP SUR GENERAL ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,15690,0.00012747
+3002,805,IP SUR GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2268,15690,0.144550669
+3003,805,IP SUR GENERAL ADMIT,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,31,15690,0.001975781
+3004,805,IP SUR GENERAL ADMIT,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,2,15690,0.00012747
+3005,805,IP SUR GENERAL ADMIT,520008,METOPROLOL IVPB,Medication,1,15690,6.37E-05
+3006,805,IP SUR GENERAL ADMIT,520043,CEFOXITIN IVPB,Medication,13,15690,0.000828553
+3007,805,IP SUR GENERAL ADMIT,520160,ERTAPENEM 1 G IVPB,Medication,2,15690,0.00012747
+3008,805,IP SUR GENERAL ADMIT,540102,NS IV BOLUS,Medication,44,15690,0.002804334
+3009,805,IP SUR GENERAL ADMIT,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,15690,6.37E-05
+3010,805,IP SUR GENERAL ADMIT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,95,15690,0.006054812
+3011,805,IP SUR GENERAL ADMIT,540302,POTASSIUM CHLORIDE IV SCALE,Medication,247,15690,0.015742511
+3012,805,IP SUR GENERAL ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,2,15690,0.00012747
+3013,805,IP SUR GENERAL ADMIT,540773,PIPERACILLIN-TAZOBACTAM (ZOSYN) 3.375 GRAM IN DEXTROSE IVPB,Medication,42,15690,0.002676864
+3014,805,IP SUR GENERAL ADMIT,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,67,15690,0.004270236
+3015,805,IP SUR GENERAL ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,389,15690,0.024792862
+3016,805,IP SUR GENERAL ADMIT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,75,15690,0.004780115
+3017,805,IP SUR GENERAL ADMIT,541081,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS EXTENDED INF,Medication,141,15690,0.008986616
+3018,805,IP SUR GENERAL ADMIT,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,88,15690,0.005608668
+3019,805,IP SUR GENERAL ADMIT,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,20,15690,0.001274697
+3020,805,IP SUR GENERAL ADMIT,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,364,15690,0.02319949
+3021,805,IP SUR GENERAL ADMIT,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,9,15690,0.000573614
+3022,805,IP SUR GENERAL ADMIT,9000000,ZZZ IMS TEMPLATE,Medication,1,15690,6.37E-05
+3023,805,IP SUR GENERAL ADMIT,LABABG,"BLOOD GASES, ARTERIAL",Lab,16,15690,0.001019758
+3024,805,IP SUR GENERAL ADMIT,LABCBCO,CBC W/O DIFF,Lab,3751,15690,0.239069471
+3025,805,IP SUR GENERAL ADMIT,LABEP1,ELECTROLYTE PANEL,Lab,26,15690,0.001657106
+3026,805,IP SUR GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,2141,15690,0.136456342
+3027,805,IP SUR GENERAL ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1715,15690,0.10930529
+3028,805,IP SUR GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,969,15690,0.061759082
+3029,805,IP SUR GENERAL ADMIT,LABPT,PROTHROMBIN TIME,Lab,1605,15690,0.102294455
+3030,805,IP SUR GENERAL ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1330,15690,0.084767368
+3031,805,IP SUR GENERAL ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,508,15690,0.03237731
+3032,805,IP SUR GENERAL ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,69,15690,0.004397706
+3033,805,IP SUR GENERAL ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,61,15690,0.003887827
+3034,805,IP SUR GENERAL ADMIT,IMGCTAP,CT ABDOMEN PELVIS,Imaging,59,15690,0.003760357
+3035,805,IP SUR GENERAL ADMIT,IMGCTT,CT THORAX,Imaging,6,15690,0.000382409
+3036,805,IP SUR GENERAL ADMIT,IMGDXAB3,"XR ABDOMEN W AP, UPRIGHT, DECUB & DIAPHRAGM",Imaging,12,15690,0.000764818
+3037,805,IP SUR GENERAL ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,74,15690,0.00471638
+3038,805,IP SUR GENERAL ADMIT,IMGFLBE,XR BARIUM ENEMA,Imaging,4,15690,0.000254939
+3039,805,IP SUR GENERAL ADMIT,IMGUSAB1,US ABDOMEN,Imaging,4,15690,0.000254939
+3040,805,IP SUR GENERAL ADMIT,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,11,15690,0.000701083
+3041,805,IP SUR GENERAL ADMIT,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,4,15690,0.000254939
+3042,805,IP SUR GENERAL ADMIT,LABBBPRBC,RED BLOOD CELLS,Blood Product,70,15690,0.00446144
+3043,806,IP ICU SUR/TRAUMA  ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,241,7652,0.031495034
+3044,806,IP ICU SUR/TRAUMA  ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,56,7652,0.007318348
+3045,806,IP ICU SUR/TRAUMA  ADMIT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,5,7652,0.000653424
+3046,806,IP ICU SUR/TRAUMA  ADMIT,855,"BACITRACIN-POLYMYXIN B 500-10,000 UNIT/GRAM TP OINT",Medication,1,7652,0.000130685
+3047,806,IP ICU SUR/TRAUMA  ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,113,7652,0.014767381
+3048,806,IP ICU SUR/TRAUMA  ADMIT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,7652,0.00026137
+3049,806,IP ICU SUR/TRAUMA  ADMIT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,7652,0.000130685
+3050,806,IP ICU SUR/TRAUMA  ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,253,7652,0.033063251
+3051,806,IP ICU SUR/TRAUMA  ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,49,7652,0.006403555
+3052,806,IP ICU SUR/TRAUMA  ADMIT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,3,7652,0.000392054
+3053,806,IP ICU SUR/TRAUMA  ADMIT,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,3,7652,0.000392054
+3054,806,IP ICU SUR/TRAUMA  ADMIT,3584,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,Medication,12,7652,0.001568217
+3055,806,IP ICU SUR/TRAUMA  ADMIT,4318,LACTATED RINGERS IV SOLP,Medication,315,7652,0.041165708
+3056,806,IP ICU SUR/TRAUMA  ADMIT,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,82,7652,0.010716153
+3057,806,IP ICU SUR/TRAUMA  ADMIT,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,15,7652,0.001960272
+3058,806,IP ICU SUR/TRAUMA  ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,126,7652,0.016466283
+3059,806,IP ICU SUR/TRAUMA  ADMIT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,40,7652,0.005227392
+3060,806,IP ICU SUR/TRAUMA  ADMIT,7318,SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP,Medication,17,7652,0.002221641
+3061,806,IP ICU SUR/TRAUMA  ADMIT,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,313,7652,0.040904339
+3062,806,IP ICU SUR/TRAUMA  ADMIT,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,16,7652,0.002090957
+3063,806,IP ICU SUR/TRAUMA  ADMIT,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,6,7652,0.000784109
+3064,806,IP ICU SUR/TRAUMA  ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,8,7652,0.001045478
+3065,806,IP ICU SUR/TRAUMA  ADMIT,10372,LABETALOL 5 MG/ML IV SOLN,Medication,114,7652,0.014898066
+3066,806,IP ICU SUR/TRAUMA  ADMIT,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,9,7652,0.001176163
+3067,806,IP ICU SUR/TRAUMA  ADMIT,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,33,7652,0.004312598
+3068,806,IP ICU SUR/TRAUMA  ADMIT,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,87,7652,0.011369577
+3069,806,IP ICU SUR/TRAUMA  ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,191,7652,0.024960795
+3070,806,IP ICU SUR/TRAUMA  ADMIT,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,40,7652,0.005227392
+3071,806,IP ICU SUR/TRAUMA  ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,177,7652,0.023131208
+3072,806,IP ICU SUR/TRAUMA  ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,124,7652,0.016204914
+3073,806,IP ICU SUR/TRAUMA  ADMIT,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,79,7652,0.010324098
+3074,806,IP ICU SUR/TRAUMA  ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,55,7652,0.007187663
+3075,806,IP ICU SUR/TRAUMA  ADMIT,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,4,7652,0.000522739
+3076,806,IP ICU SUR/TRAUMA  ADMIT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,31,7652,0.004051228
+3077,806,IP ICU SUR/TRAUMA  ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,174,7652,0.022739153
+3078,806,IP ICU SUR/TRAUMA  ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,519,7652,0.067825405
+3079,806,IP ICU SUR/TRAUMA  ADMIT,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,5,7652,0.000653424
+3080,806,IP ICU SUR/TRAUMA  ADMIT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,20,7652,0.002613696
+3081,806,IP ICU SUR/TRAUMA  ADMIT,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,21,7652,0.002744381
+3082,806,IP ICU SUR/TRAUMA  ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,7652,0.000130685
+3083,806,IP ICU SUR/TRAUMA  ADMIT,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,621,7652,0.081155254
+3084,806,IP ICU SUR/TRAUMA  ADMIT,83588,SENNOSIDES 8.8 MG/5 ML PO SYRP,Medication,1,7652,0.000130685
+3085,806,IP ICU SUR/TRAUMA  ADMIT,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,1,7652,0.000130685
+3086,806,IP ICU SUR/TRAUMA  ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,22,7652,0.002875065
+3087,806,IP ICU SUR/TRAUMA  ADMIT,89065,LIDOCAINE 5 % TP PTMD,Medication,82,7652,0.010716153
+3088,806,IP ICU SUR/TRAUMA  ADMIT,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,2,7652,0.00026137
+3089,806,IP ICU SUR/TRAUMA  ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,263,7652,0.034370099
+3090,806,IP ICU SUR/TRAUMA  ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,7652,0.000522739
+3091,806,IP ICU SUR/TRAUMA  ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,71,7652,0.00927862
+3092,806,IP ICU SUR/TRAUMA  ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,261,7652,0.03410873
+3093,806,IP ICU SUR/TRAUMA  ADMIT,115683,LANSOPRAZOLE 30 MG PO TBLD,Medication,1,7652,0.000130685
+3094,806,IP ICU SUR/TRAUMA  ADMIT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,6,7652,0.000784109
+3095,806,IP ICU SUR/TRAUMA  ADMIT,126583,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SYRG",Medication,1,7652,0.000130685
+3096,806,IP ICU SUR/TRAUMA  ADMIT,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,625,7652,0.081677993
+3097,806,IP ICU SUR/TRAUMA  ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1280,7652,0.167276529
+3098,806,IP ICU SUR/TRAUMA  ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,87,7652,0.011369577
+3099,806,IP ICU SUR/TRAUMA  ADMIT,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,206,7652,0.026921066
+3100,806,IP ICU SUR/TRAUMA  ADMIT,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,5,7652,0.000653424
+3101,806,IP ICU SUR/TRAUMA  ADMIT,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,63,7652,0.008233142
+3102,806,IP ICU SUR/TRAUMA  ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,10,7652,0.001306848
+3103,806,IP ICU SUR/TRAUMA  ADMIT,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,50,7652,0.006534239
+3104,806,IP ICU SUR/TRAUMA  ADMIT,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,1407,7652,0.183873497
+3105,806,IP ICU SUR/TRAUMA  ADMIT,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,886,7652,0.115786722
+3106,806,IP ICU SUR/TRAUMA  ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,193,7652,0.025222164
+3107,806,IP ICU SUR/TRAUMA  ADMIT,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,193,7652,0.025222164
+3108,806,IP ICU SUR/TRAUMA  ADMIT,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,18,7652,0.002352326
+3109,806,IP ICU SUR/TRAUMA  ADMIT,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,234,7652,0.03058024
+3110,806,IP ICU SUR/TRAUMA  ADMIT,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,7,7652,0.000914794
+3111,806,IP ICU SUR/TRAUMA  ADMIT,500012,1/2 NS WITH KCL IV INFUSION,Medication,2,7652,0.00026137
+3112,806,IP ICU SUR/TRAUMA  ADMIT,520039,AZITHROMYCIN IVPB,Medication,1,7652,0.000130685
+3113,806,IP ICU SUR/TRAUMA  ADMIT,520043,CEFOXITIN IVPB,Medication,1,7652,0.000130685
+3114,806,IP ICU SUR/TRAUMA  ADMIT,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,12,7652,0.001568217
+3115,806,IP ICU SUR/TRAUMA  ADMIT,520064,CEFAZOLIN 2 G IVPB,Medication,20,7652,0.002613696
+3116,806,IP ICU SUR/TRAUMA  ADMIT,530003,FENTANYL  10 MCG/ML IV INFUSION,Medication,15,7652,0.001960272
+3117,806,IP ICU SUR/TRAUMA  ADMIT,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,8,7652,0.001045478
+3118,806,IP ICU SUR/TRAUMA  ADMIT,530006,EPINEPHRINE IV INFUSION,Medication,28,7652,0.003659174
+3119,806,IP ICU SUR/TRAUMA  ADMIT,530008,NITROPRUSSIDE IV INFUSION,Medication,9,7652,0.001176163
+3120,806,IP ICU SUR/TRAUMA  ADMIT,530011,NOREPINEPHRINE IV INFUSION,Medication,34,7652,0.004443283
+3121,806,IP ICU SUR/TRAUMA  ADMIT,530012,PHENYLEPHRINE IV INFUSION,Medication,36,7652,0.004704652
+3122,806,IP ICU SUR/TRAUMA  ADMIT,530025,DEXMEDETOMIDINE IV INFUSION,Medication,71,7652,0.00927862
+3123,806,IP ICU SUR/TRAUMA  ADMIT,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,48,7652,0.00627287
+3124,806,IP ICU SUR/TRAUMA  ADMIT,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,232,7652,0.030318871
+3125,806,IP ICU SUR/TRAUMA  ADMIT,540102,NS IV BOLUS,Medication,13,7652,0.001698902
+3126,806,IP ICU SUR/TRAUMA  ADMIT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,396,7652,0.051751176
+3127,806,IP ICU SUR/TRAUMA  ADMIT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,156,7652,0.020386827
+3128,806,IP ICU SUR/TRAUMA  ADMIT,540302,POTASSIUM CHLORIDE IV SCALE,Medication,1485,7652,0.194066911
+3129,806,IP ICU SUR/TRAUMA  ADMIT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,1504,7652,0.196549922
+3130,806,IP ICU SUR/TRAUMA  ADMIT,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,1,7652,0.000130685
+3131,806,IP ICU SUR/TRAUMA  ADMIT,540638,FENTANYL 50 MCG/ML IV INFUSION,Medication,9,7652,0.001176163
+3132,806,IP ICU SUR/TRAUMA  ADMIT,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,29,7652,0.003789859
+3133,806,IP ICU SUR/TRAUMA  ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,33,7652,0.004312598
+3134,806,IP ICU SUR/TRAUMA  ADMIT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,15,7652,0.001960272
+3135,806,IP ICU SUR/TRAUMA  ADMIT,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,17,7652,0.002221641
+3136,806,IP ICU SUR/TRAUMA  ADMIT,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,2,7652,0.00026137
+3137,806,IP ICU SUR/TRAUMA  ADMIT,541105,CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS,Medication,7,7652,0.000914794
+3138,806,IP ICU SUR/TRAUMA  ADMIT,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,8,7652,0.001045478
+3139,806,IP ICU SUR/TRAUMA  ADMIT,LABBBPCRY,CRYOPRECIPITATE,Lab,2,7652,0.00026137
+3140,806,IP ICU SUR/TRAUMA  ADMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,1441,7652,0.18831678
+3141,806,IP ICU SUR/TRAUMA  ADMIT,LABCBCD,CBC WITH DIFF,Lab,2238,7652,0.292472556
+3142,806,IP ICU SUR/TRAUMA  ADMIT,LABCO2,"CO2, SERUM/PLASMA",Lab,47,7652,0.006142185
+3143,806,IP ICU SUR/TRAUMA  ADMIT,LABK,"POTASSIUM, SERUM/PLASMA",Lab,254,7652,0.033193936
+3144,806,IP ICU SUR/TRAUMA  ADMIT,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,588,7652,0.076842656
+3145,806,IP ICU SUR/TRAUMA  ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1982,7652,0.25901725
+3146,806,IP ICU SUR/TRAUMA  ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1687,7652,0.220465238
+3147,806,IP ICU SUR/TRAUMA  ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,1604,7652,0.2096184
+3148,806,IP ICU SUR/TRAUMA  ADMIT,LABPLTS,PLATELET COUNT,Lab,78,7652,0.010193413
+3149,806,IP ICU SUR/TRAUMA  ADMIT,LABPT,PROTHROMBIN TIME,Lab,1344,7652,0.175640355
+3150,806,IP ICU SUR/TRAUMA  ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1104,7652,0.144276006
+3151,806,IP ICU SUR/TRAUMA  ADMIT,LABROMRS,MRSA SCREEN,Lab,1590,7652,0.207788813
+3152,806,IP ICU SUR/TRAUMA  ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,293,7652,0.038290643
+3153,806,IP ICU SUR/TRAUMA  ADMIT,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,781,7652,0.10206482
+3154,806,IP ICU SUR/TRAUMA  ADMIT,IMGCT3T,CT ANGIO CHEST,Imaging,4,7652,0.000522739
+3155,806,IP ICU SUR/TRAUMA  ADMIT,IMGCTAP,CT ABDOMEN PELVIS,Imaging,7,7652,0.000914794
+3156,806,IP ICU SUR/TRAUMA  ADMIT,IMGCTH,CT HEAD,Imaging,188,7652,0.02456874
+3157,806,IP ICU SUR/TRAUMA  ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,1307,7652,0.170805018
+3158,806,IP ICU SUR/TRAUMA  ADMIT,LABBBPCRY,CRYOPRECIPITATE,Blood Product,5,7652,0.000653424
+3159,806,IP ICU SUR/TRAUMA  ADMIT,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,32,7652,0.004181913
+3160,806,IP ICU SUR/TRAUMA  ADMIT,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,25,7652,0.00326712
+3161,806,IP ICU SUR/TRAUMA  ADMIT,LABBBPRBC,RED BLOOD CELLS,Blood Product,61,7652,0.007971772
+3162,811,IP PAMF ENT POSTOP,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,5,29,0.172413793
+3163,811,IP PAMF ENT POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,1,29,0.034482759
+3164,811,IP PAMF ENT POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,7,29,0.24137931
+3165,811,IP PAMF ENT POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,3,29,0.103448276
+3166,811,IP PAMF ENT POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,7,29,0.24137931
+3167,811,IP PAMF ENT POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,29,0.310344828
+3168,811,IP PAMF ENT POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,29,0.034482759
+3169,811,IP PAMF ENT POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,29,0.034482759
+3170,811,IP PAMF ENT POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,29,0.068965517
+3171,811,IP PAMF ENT POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,2,29,0.068965517
+3172,811,IP PAMF ENT POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,5,29,0.172413793
+3173,811,IP PAMF ENT POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,5,29,0.172413793
+3174,812,IP ENT GENERAL ADMISSION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,838,0.001193317
+3175,812,IP ENT GENERAL ADMISSION,1080,BISACODYL 10 MG PR SUPP,Medication,130,838,0.155131265
+3176,812,IP ENT GENERAL ADMISSION,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,31,838,0.03699284
+3177,812,IP ENT GENERAL ADMISSION,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,174,838,0.207637232
+3178,812,IP ENT GENERAL ADMISSION,4711,MAGNESIUM CITRATE PO SOLN,Medication,12,838,0.014319809
+3179,812,IP ENT GENERAL ADMISSION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,113,838,0.134844869
+3180,812,IP ENT GENERAL ADMISSION,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,1,838,0.001193317
+3181,812,IP ENT GENERAL ADMISSION,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,23,838,0.027446301
+3182,812,IP ENT GENERAL ADMISSION,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,2,838,0.002386635
+3183,812,IP ENT GENERAL ADMISSION,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,5,838,0.005966587
+3184,812,IP ENT GENERAL ADMISSION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,21,838,0.025059666
+3185,812,IP ENT GENERAL ADMISSION,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,35,838,0.04176611
+3186,812,IP ENT GENERAL ADMISSION,10246,IBUPROFEN 100 MG/5 ML PO SUSP,Medication,1,838,0.001193317
+3187,812,IP ENT GENERAL ADMISSION,11143,PROMETHAZINE 12.5 MG PR SUPP,Medication,1,838,0.001193317
+3188,812,IP ENT GENERAL ADMISSION,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,838,0.001193317
+3189,812,IP ENT GENERAL ADMISSION,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,21,838,0.025059666
+3190,812,IP ENT GENERAL ADMISSION,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,191,838,0.227923628
+3191,812,IP ENT GENERAL ADMISSION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,9,838,0.010739857
+3192,812,IP ENT GENERAL ADMISSION,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,46,838,0.054892601
+3193,812,IP ENT GENERAL ADMISSION,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,10,838,0.011933174
+3194,812,IP ENT GENERAL ADMISSION,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,71,838,0.084725537
+3195,812,IP ENT GENERAL ADMISSION,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,10,838,0.011933174
+3196,812,IP ENT GENERAL ADMISSION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,19,838,0.022673031
+3197,812,IP ENT GENERAL ADMISSION,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,57,838,0.068019093
+3198,812,IP ENT GENERAL ADMISSION,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,73,838,0.087112172
+3199,812,IP ENT GENERAL ADMISSION,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,25,838,0.029832936
+3200,812,IP ENT GENERAL ADMISSION,92173,ZOLPIDEM 5 MG PO TABS,Medication,1,838,0.001193317
+3201,812,IP ENT GENERAL ADMISSION,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,1,838,0.001193317
+3202,812,IP ENT GENERAL ADMISSION,94176,ONDANSETRON 4 MG PO TBDL,Medication,8,838,0.009546539
+3203,812,IP ENT GENERAL ADMISSION,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,5,838,0.005966587
+3204,812,IP ENT GENERAL ADMISSION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,38,838,0.045346062
+3205,812,IP ENT GENERAL ADMISSION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,3,838,0.003579952
+3206,812,IP ENT GENERAL ADMISSION,117862,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN,Medication,101,838,0.12052506
+3207,812,IP ENT GENERAL ADMISSION,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,62,838,0.07398568
+3208,812,IP ENT GENERAL ADMISSION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,277,838,0.330548926
+3209,812,IP ENT GENERAL ADMISSION,206739,HYDROCODONE-ACETAMINOPHEN 7.5-500 MG/15 ML(15 ML) PO SOLN,Medication,1,838,0.001193317
+3210,812,IP ENT GENERAL ADMISSION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,52,838,0.062052506
+3211,812,IP ENT GENERAL ADMISSION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,122,838,0.145584726
+3212,812,IP ENT GENERAL ADMISSION,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,21,838,0.025059666
+3213,812,IP ENT GENERAL ADMISSION,540300,MORPHINE INJECTABLE SYRINGE,Medication,174,838,0.207637232
+3214,812,IP ENT GENERAL ADMISSION,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,1,838,0.001193317
+3215,812,IP ENT GENERAL ADMISSION,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,1,838,0.001193317
+3216,812,IP ENT GENERAL ADMISSION,LABCA,"CALCIUM, SERUM/PLASMA",Lab,9,838,0.010739857
+3217,812,IP ENT GENERAL ADMISSION,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,1,838,0.001193317
+3218,812,IP ENT GENERAL ADMISSION,LABCBCD,CBC WITH DIFF,Lab,109,838,0.130071599
+3219,812,IP ENT GENERAL ADMISSION,LABEP1,ELECTROLYTE PANEL,Lab,1,838,0.001193317
+3220,812,IP ENT GENERAL ADMISSION,LABK,"POTASSIUM, SERUM/PLASMA",Lab,2,838,0.002386635
+3221,812,IP ENT GENERAL ADMISSION,LABMETB,"METABOLIC PANEL, BASIC",Lab,87,838,0.103818616
+3222,812,IP ENT GENERAL ADMISSION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,30,838,0.035799523
+3223,812,IP ENT GENERAL ADMISSION,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,23,838,0.027446301
+3224,812,IP ENT GENERAL ADMISSION,LABPLTS,PLATELET COUNT,Lab,93,838,0.11097852
+3225,812,IP ENT GENERAL ADMISSION,LABPT,PROTHROMBIN TIME,Lab,24,838,0.028639618
+3226,812,IP ENT GENERAL ADMISSION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,22,838,0.026252983
+3227,812,IP ENT GENERAL ADMISSION,POC14,POC GLUCOSE BY METER,Lab,14,838,0.016706444
+3228,812,IP ENT GENERAL ADMISSION,IMGDXAB1,XR ABDOMEN 1V,Imaging,20,838,0.023866348
+3229,812,IP ENT GENERAL ADMISSION,IMGDXCH1,XR CHEST 1V,Imaging,9,838,0.010739857
+3230,813,IP ENT GENERAL POSTOP,855,"BACITRACIN-POLYMYXIN B 500-10,000 UNIT/GRAM TP OINT",Medication,19,568,0.033450704
+3231,813,IP ENT GENERAL POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,33,568,0.058098592
+3232,813,IP ENT GENERAL POSTOP,1300,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",Medication,14,568,0.024647887
+3233,813,IP ENT GENERAL POSTOP,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,25,568,0.044014085
+3234,813,IP ENT GENERAL POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,568,0.001760563
+3235,813,IP ENT GENERAL POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,568,0.012323944
+3236,813,IP ENT GENERAL POSTOP,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,33,568,0.058098592
+3237,813,IP ENT GENERAL POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,7,568,0.012323944
+3238,813,IP ENT GENERAL POSTOP,4996,METHYLPREDNISOLONE ACETATE 80 MG/ML INJ SUSP,Medication,26,568,0.045774648
+3239,813,IP ENT GENERAL POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,127,568,0.223591549
+3240,813,IP ENT GENERAL POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,22,568,0.038732394
+3241,813,IP ENT GENERAL POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,2,568,0.003521127
+3242,813,IP ENT GENERAL POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,7,568,0.012323944
+3243,813,IP ENT GENERAL POSTOP,5943,OXYMETAZOLINE 0.05 % NASAL SPRA,Medication,10,568,0.017605634
+3244,813,IP ENT GENERAL POSTOP,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,9,568,0.01584507
+3245,813,IP ENT GENERAL POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,10,568,0.017605634
+3246,813,IP ENT GENERAL POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,3,568,0.00528169
+3247,813,IP ENT GENERAL POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,27,568,0.047535211
+3248,813,IP ENT GENERAL POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,3,568,0.00528169
+3249,813,IP ENT GENERAL POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,105,568,0.184859155
+3250,813,IP ENT GENERAL POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,568,0.003521127
+3251,813,IP ENT GENERAL POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,18,568,0.031690141
+3252,813,IP ENT GENERAL POSTOP,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,4,568,0.007042254
+3253,813,IP ENT GENERAL POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,40,568,0.070422535
+3254,813,IP ENT GENERAL POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,63,568,0.110915493
+3255,813,IP ENT GENERAL POSTOP,28809,WHITE PETROLATUM TP GEL,Medication,4,568,0.007042254
+3256,813,IP ENT GENERAL POSTOP,29676,SODIUM CHLORIDE 0.65 % NASAL SPRA,Medication,13,568,0.022887324
+3257,813,IP ENT GENERAL POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,1,568,0.001760563
+3258,813,IP ENT GENERAL POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,1,568,0.001760563
+3259,813,IP ENT GENERAL POSTOP,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,1,568,0.001760563
+3260,813,IP ENT GENERAL POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,29,568,0.051056338
+3261,813,IP ENT GENERAL POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,29,568,0.051056338
+3262,813,IP ENT GENERAL POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,8,568,0.014084507
+3263,813,IP ENT GENERAL POSTOP,88836,WHITE PETROLATUM TP OINT,Medication,2,568,0.003521127
+3264,813,IP ENT GENERAL POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,3,568,0.00528169
+3265,813,IP ENT GENERAL POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,568,0.007042254
+3266,813,IP ENT GENERAL POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,46,568,0.080985915
+3267,813,IP ENT GENERAL POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,3,568,0.00528169
+3268,813,IP ENT GENERAL POSTOP,117862,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN,Medication,45,568,0.079225352
+3269,813,IP ENT GENERAL POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,25,568,0.044014085
+3270,813,IP ENT GENERAL POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,213,568,0.375
+3271,813,IP ENT GENERAL POSTOP,200718,CARBOXYMETHYLCELLULOSE SODIUM 1 % OPHT DPGE,Medication,1,568,0.001760563
+3272,813,IP ENT GENERAL POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,38,568,0.066901408
+3273,813,IP ENT GENERAL POSTOP,206739,HYDROCODONE-ACETAMINOPHEN 7.5-500 MG/15 ML(15 ML) PO SOLN,Medication,8,568,0.014084507
+3274,813,IP ENT GENERAL POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,27,568,0.047535211
+3275,813,IP ENT GENERAL POSTOP,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,25,568,0.044014085
+3276,813,IP ENT GENERAL POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,20,568,0.035211268
+3277,813,IP ENT GENERAL POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,6,568,0.01056338
+3278,813,IP ENT GENERAL POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,6,568,0.01056338
+3279,813,IP ENT GENERAL POSTOP,540102,NS IV BOLUS,Medication,1,568,0.001760563
+3280,813,IP ENT GENERAL POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,118,568,0.207746479
+3281,813,IP ENT GENERAL POSTOP,541044,SODIUM BICARBONATE IN NS ORAL SOLUTION,Medication,5,568,0.008802817
+3282,813,IP ENT GENERAL POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,7,568,0.012323944
+3283,813,IP ENT GENERAL POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,1,568,0.001760563
+3284,813,IP ENT GENERAL POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,27,568,0.047535211
+3285,813,IP ENT GENERAL POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,2,568,0.003521127
+3286,813,IP ENT GENERAL POSTOP,LABCBCD,CBC WITH DIFF,Lab,18,568,0.031690141
+3287,813,IP ENT GENERAL POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,13,568,0.022887324
+3288,813,IP ENT GENERAL POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,11,568,0.019366197
+3289,813,IP ENT GENERAL POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,8,568,0.014084507
+3290,813,IP ENT GENERAL POSTOP,LABPLTS,PLATELET COUNT,Lab,12,568,0.021126761
+3291,813,IP ENT GENERAL POSTOP,LABPT,PROTHROMBIN TIME,Lab,2,568,0.003521127
+3292,813,IP ENT GENERAL POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,568,0.001760563
+3293,813,IP ENT GENERAL POSTOP,POC14,POC GLUCOSE BY METER,Lab,4,568,0.007042254
+3294,813,IP ENT GENERAL POSTOP,IMGDXAB1,XR ABDOMEN 1V,Imaging,11,568,0.019366197
+3295,813,IP ENT GENERAL POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,8,568,0.014084507
+3296,814,IP ENT HEAD AND NECK POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,293,3366,0.08704694
+3297,814,IP ENT HEAD AND NECK POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,13,3366,0.003862151
+3298,814,IP ENT HEAD AND NECK POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,334,3366,0.09922757
+3299,814,IP ENT HEAD AND NECK POSTOP,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,20,3366,0.005941771
+3300,814,IP ENT HEAD AND NECK POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,405,3366,0.120320856
+3301,814,IP ENT HEAD AND NECK POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,206,3366,0.061200238
+3302,814,IP ENT HEAD AND NECK POSTOP,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,270,3366,0.080213904
+3303,814,IP ENT HEAD AND NECK POSTOP,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,3366,0.000297089
+3304,814,IP ENT HEAD AND NECK POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,586,3366,0.17409388
+3305,814,IP ENT HEAD AND NECK POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,176,3366,0.052287582
+3306,814,IP ENT HEAD AND NECK POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,14,3366,0.004159239
+3307,814,IP ENT HEAD AND NECK POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,110,3366,0.032679739
+3308,814,IP ENT HEAD AND NECK POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,121,3366,0.035947712
+3309,814,IP ENT HEAD AND NECK POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,128,3366,0.038027332
+3310,814,IP ENT HEAD AND NECK POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,221,3366,0.065656566
+3311,814,IP ENT HEAD AND NECK POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,953,3366,0.283125371
+3312,814,IP ENT HEAD AND NECK POSTOP,14963,HYDROCODONE-ACETAMINOPHEN 7.5-500 MG PO TABS,Medication,6,3366,0.001782531
+3313,814,IP ENT HEAD AND NECK POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,8,3366,0.002376708
+3314,814,IP ENT HEAD AND NECK POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,445,3366,0.132204397
+3315,814,IP ENT HEAD AND NECK POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,58,3366,0.017231135
+3316,814,IP ENT HEAD AND NECK POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,14,3366,0.004159239
+3317,814,IP ENT HEAD AND NECK POSTOP,34544,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG PO TABS,Medication,70,3366,0.020796197
+3318,814,IP ENT HEAD AND NECK POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,167,3366,0.049613785
+3319,814,IP ENT HEAD AND NECK POSTOP,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,2,3366,0.000594177
+3320,814,IP ENT HEAD AND NECK POSTOP,85936,OFLOXACIN 0.3 % OTIC DROP,Medication,1,3366,0.000297089
+3321,814,IP ENT HEAD AND NECK POSTOP,87473,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DROP,Medication,1,3366,0.000297089
+3322,814,IP ENT HEAD AND NECK POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,33,3366,0.009803922
+3323,814,IP ENT HEAD AND NECK POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,5,3366,0.001485443
+3324,814,IP ENT HEAD AND NECK POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,95,3366,0.028223411
+3325,814,IP ENT HEAD AND NECK POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,36,3366,0.010695187
+3326,814,IP ENT HEAD AND NECK POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,187,3366,0.055555556
+3327,814,IP ENT HEAD AND NECK POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,19,3366,0.005644682
+3328,814,IP ENT HEAD AND NECK POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,41,3366,0.01218063
+3329,814,IP ENT HEAD AND NECK POSTOP,116120,CIPROFLOXACIN-DEXAMETHASONE 0.3-0.1 % OTIC DRPS,Medication,1,3366,0.000297089
+3330,814,IP ENT HEAD AND NECK POSTOP,117862,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN,Medication,314,3366,0.093285799
+3331,814,IP ENT HEAD AND NECK POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,20,3366,0.005941771
+3332,814,IP ENT HEAD AND NECK POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1114,3366,0.330956625
+3333,814,IP ENT HEAD AND NECK POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,5,3366,0.001485443
+3334,814,IP ENT HEAD AND NECK POSTOP,200718,CARBOXYMETHYLCELLULOSE SODIUM 1 % OPHT DPGE,Medication,13,3366,0.003862151
+3335,814,IP ENT HEAD AND NECK POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,362,3366,0.107546049
+3336,814,IP ENT HEAD AND NECK POSTOP,206739,HYDROCODONE-ACETAMINOPHEN 7.5-500 MG/15 ML(15 ML) PO SOLN,Medication,4,3366,0.001188354
+3337,814,IP ENT HEAD AND NECK POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,247,3366,0.073380867
+3338,814,IP ENT HEAD AND NECK POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,481,3366,0.142899584
+3339,814,IP ENT HEAD AND NECK POSTOP,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,19,3366,0.005644682
+3340,814,IP ENT HEAD AND NECK POSTOP,520002,DEXAMETHASONE (CUSTOM DOSE) IVPB,Medication,25,3366,0.007427213
+3341,814,IP ENT HEAD AND NECK POSTOP,520112,AMPICILLIN AND SULBACTAM 1.5 GM IVPB,Medication,1,3366,0.000297089
+3342,814,IP ENT HEAD AND NECK POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,780,3366,0.231729055
+3343,814,IP ENT HEAD AND NECK POSTOP,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,1,3366,0.000297089
+3344,814,IP ENT HEAD AND NECK POSTOP,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,3366,0.000297089
+3345,814,IP ENT HEAD AND NECK POSTOP,540744,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,Medication,13,3366,0.003862151
+3346,814,IP ENT HEAD AND NECK POSTOP,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,9,3366,0.002673797
+3347,814,IP ENT HEAD AND NECK POSTOP,541044,SODIUM BICARBONATE IN NS ORAL SOLUTION,Medication,48,3366,0.01426025
+3348,814,IP ENT HEAD AND NECK POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,46,3366,0.013666072
+3349,814,IP ENT HEAD AND NECK POSTOP,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,5,3366,0.001485443
+3350,814,IP ENT HEAD AND NECK POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,7,3366,0.00207962
+3351,814,IP ENT HEAD AND NECK POSTOP,541093,AMPICILLIN/SULBACTAM 1.5 GRAM/50 ML MINI-BAG PLUS,Medication,46,3366,0.013666072
+3352,814,IP ENT HEAD AND NECK POSTOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,5,3366,0.001485443
+3353,814,IP ENT HEAD AND NECK POSTOP,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,1,3366,0.000297089
+3354,814,IP ENT HEAD AND NECK POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,58,3366,0.017231135
+3355,814,IP ENT HEAD AND NECK POSTOP,LABCBCD,CBC WITH DIFF,Lab,288,3366,0.085561497
+3356,814,IP ENT HEAD AND NECK POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,336,3366,0.099821747
+3357,814,IP ENT HEAD AND NECK POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,241,3366,0.071598336
+3358,814,IP ENT HEAD AND NECK POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,201,3366,0.059714795
+3359,814,IP ENT HEAD AND NECK POSTOP,LABPLTS,PLATELET COUNT,Lab,332,3366,0.098633393
+3360,814,IP ENT HEAD AND NECK POSTOP,LABPT,PROTHROMBIN TIME,Lab,44,3366,0.013071895
+3361,814,IP ENT HEAD AND NECK POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,38,3366,0.011289364
+3362,814,IP ENT HEAD AND NECK POSTOP,IMGDXAB1,XR ABDOMEN 1V,Imaging,60,3366,0.017825312
+3363,814,IP ENT HEAD AND NECK POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,41,3366,0.01218063
+3364,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,10,246,0.040650407
+3365,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,1080,BISACODYL 10 MG PR SUPP,Medication,3,246,0.012195122
+3366,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,14,246,0.056910569
+3367,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,4,246,0.016260163
+3368,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,26,246,0.105691057
+3369,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,37,246,0.150406504
+3370,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,1,246,0.004065041
+3371,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,5087,MINERAL OIL PR ENEM,Medication,1,246,0.004065041
+3372,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,6,246,0.024390244
+3373,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,29,246,0.117886179
+3374,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,35,246,0.142276423
+3375,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,46,246,0.18699187
+3376,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,246,0.004065041
+3377,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,4,246,0.016260163
+3378,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,4,246,0.016260163
+3379,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,246,0.016260163
+3380,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,34544,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG PO TABS,Medication,7,246,0.028455285
+3381,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,246,0.004065041
+3382,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,246,0.004065041
+3383,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,6,246,0.024390244
+3384,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,9,246,0.036585366
+3385,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,246,0.004065041
+3386,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,117862,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN,Medication,32,246,0.130081301
+3387,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,246,0.004065041
+3388,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,49,246,0.199186992
+3389,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,6,246,0.024390244
+3390,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,12,246,0.048780488
+3391,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,33,246,0.134146341
+3392,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,30,246,0.12195122
+3393,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,1,246,0.004065041
+3394,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,520002,DEXAMETHASONE (CUSTOM DOSE) IVPB,Medication,28,246,0.113821138
+3395,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,35,246,0.142276423
+3396,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,2,246,0.008130081
+3397,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,541093,AMPICILLIN/SULBACTAM 1.5 GRAM/50 ML MINI-BAG PLUS,Medication,21,246,0.085365854
+3398,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,1,246,0.004065041
+3399,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,1,246,0.004065041
+3400,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABCBCD,CBC WITH DIFF,Lab,12,246,0.048780488
+3401,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,8,246,0.032520325
+3402,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,5,246,0.020325203
+3403,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,4,246,0.016260163
+3404,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABPLTS,PLATELET COUNT,Lab,13,246,0.052845528
+3405,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABPT,PROTHROMBIN TIME,Lab,4,246,0.016260163
+3406,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,246,0.008130081
+3407,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,5,246,0.020325203
+3408,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,IMGDXAB1,XR ABDOMEN 1V,Imaging,2,246,0.008130081
+3409,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,IMGDXCH1,XR CHEST 1V,Imaging,3,246,0.012195122
+3410,815,IP ENT - HEAD AND NECK ADMISSION-PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,4,246,0.016260163
+3411,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,2,751,0.002663116
+3412,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,751,0.002663116
+3413,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,8,751,0.010652463
+3414,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,352,751,0.468708389
+3415,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,540102,NS IV BOLUS,Medication,1,751,0.001331558
+3416,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,LABHCTX,HEMATOCRIT,Lab,25,751,0.033288948
+3417,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,LABPLTS,PLATELET COUNT,Lab,33,751,0.043941411
+3418,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,LABPREG,"PREGNANCY TEST, SERUM",Lab,133,751,0.177097204
+3419,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,336,751,0.447403462
+3420,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,POC17,POC ISTAT CREATININE,Lab,384,751,0.511318242
+3421,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,382,751,0.508655126
+3422,816,IP RAD NEURORADIOLOGY PRE-PROCEDURE,POC25,POC ISTAT INR AND PROTIME,Lab,388,751,0.516644474
+3423,817,IP RAD INTERVENTIONAL RADIOLOGY LUNG BIOPSY POST PROCEDURE,IMGDXCHPNB,XR CHEST POST NEEDLE BIOPSY,Imaging,29,24,1
+3424,818,IP RAD IR CATH ANGIO POST PROCEDURE,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,2,24,0.083333333
+3425,818,IP RAD IR CATH ANGIO POST PROCEDURE,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1,24,0.041666667
+3426,818,IP RAD IR CATH ANGIO POST PROCEDURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3,24,0.125
+3427,818,IP RAD IR CATH ANGIO POST PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,6,24,0.25
+3428,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,2,0.5
+3429,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,LABCR,"CREATININE, SERUM/PLASMA",Lab,2,2,1
+3430,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,LABHCTX,HEMATOCRIT,Lab,2,2,1
+3431,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,LABHGBX,HEMOGLOBIN,Lab,2,2,1
+3432,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,LABK,"POTASSIUM, SERUM/PLASMA",Lab,2,2,1
+3433,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,LABPT,PROTHROMBIN TIME,Lab,2,2,1
+3434,819,IP RAD CARDIOVASCULAR PRE-PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,1,2,0.5
+3435,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),101,ACETAMINOPHEN 325 MG PO TABS,Medication,25,2642,0.009462528
+3436,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,2642,0.000378501
+3437,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,2642,0.000757002
+3438,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,2642,0.000378501
+3439,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),3841,IBUPROFEN 200 MG PO TABS,Medication,1,2642,0.000378501
+3440,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),3843,IBUPROFEN 400 MG PO TABS,Medication,10,2642,0.003785011
+3441,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),3844,IBUPROFEN 600 MG PO TABS,Medication,1,2642,0.000378501
+3442,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),4318,LACTATED RINGERS IV SOLP,Medication,2,2642,0.000757002
+3443,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,8,2642,0.003028009
+3444,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,1,2642,0.000378501
+3445,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,16,2642,0.006056018
+3446,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,2,2642,0.000757002
+3447,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,33,2642,0.012490537
+3448,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),10814,OXYCODONE 5 MG PO TABS,Medication,63,2642,0.023845572
+3449,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,2,2642,0.000757002
+3450,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,1,2642,0.000378501
+3451,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,2,2642,0.000757002
+3452,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1,2642,0.000378501
+3453,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,18,2642,0.00681302
+3454,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),28899,OXYCODONE 15 MG PO TABS,Medication,1,2642,0.000378501
+3455,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,19,2642,0.007191522
+3456,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,2,2642,0.000757002
+3457,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,2642,0.000757002
+3458,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,1,2642,0.000378501
+3459,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,27,2642,0.010219531
+3460,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,2642,0.000378501
+3461,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,25,2642,0.009462528
+3462,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,2642,0.000757002
+3463,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,130,2642,0.049205148
+3464,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,2642,0.000757002
+3465,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,3,2642,0.001135503
+3466,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),540102,NS IV BOLUS,Medication,4,2642,0.001514005
+3467,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,19,2642,0.007191522
+3468,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),540300,MORPHINE INJECTABLE SYRINGE,Medication,62,2642,0.02346707
+3469,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,6,2642,0.002271007
+3470,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,1,2642,0.000378501
+3471,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),LABCBCD,CBC WITH DIFF,Lab,1,2642,0.000378501
+3472,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),LABFIB,FIBRINOGEN,Lab,1,2642,0.000378501
+3473,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,2642,0.000378501
+3474,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),POC14,POC GLUCOSE BY METER,Lab,1,2642,0.000378501
+3475,821,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE),IMGDXCHPNB,XR CHEST POST NEEDLE BIOPSY,Imaging,98,2642,0.037093111
+3476,822,IP RAD VENOUS THROMBOLYSIS,530040,ALTEPLASE INFUSION,Medication,40,55,0.727272727
+3477,822,IP RAD VENOUS THROMBOLYSIS,540669,HEPARIN IV INFUSION (INTERVENTIONAL RADIOLOGY),Medication,21,55,0.381818182
+3478,822,IP RAD VENOUS THROMBOLYSIS,LABFIB,FIBRINOGEN,Lab,13,55,0.236363636
+3479,822,IP RAD VENOUS THROMBOLYSIS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,11,55,0.2
+3480,823,IP RAD ARTERIAL THROMBOLYSIS,530040,ALTEPLASE INFUSION,Medication,5,13,0.384615385
+3481,823,IP RAD ARTERIAL THROMBOLYSIS,540669,HEPARIN IV INFUSION (INTERVENTIONAL RADIOLOGY),Medication,6,13,0.461538462
+3482,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,27,930,0.029032258
+3483,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,2,930,0.002150538
+3484,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,2510,DIPHENHYDRAMINE HCL 50 MG PO CAPS,Medication,4,930,0.004301075
+3485,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,4573,LORAZEPAM 1 MG PO TABS,Medication,53,930,0.056989247
+3486,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,6498,PREDNISONE 50 MG PO TABS,Medication,5,930,0.005376344
+3487,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,930,0.001075269
+3488,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,930,0.002150538
+3489,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,449,930,0.482795699
+3490,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,44,930,0.047311828
+3491,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,6,930,0.006451613
+3492,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,114906,APREPITANT 125 MG PO CAPS,Medication,10,930,0.010752688
+3493,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,10,930,0.010752688
+3494,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,8,930,0.008602151
+3495,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,520187,ONDANSETRON (+DEXAMETHASONE) IVPB,Medication,28,930,0.030107527
+3496,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,930,0.002150538
+3497,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,3,930,0.003225806
+3498,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,3,930,0.003225806
+3499,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABAFP,"ALPHA FETOPROTEIN, SERUM",Lab,8,930,0.008602151
+3500,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABC125,CANCER AG 125,Lab,1,930,0.001075269
+3501,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABC153,CANCER AG 15-3,Lab,1,930,0.001075269
+3502,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABCA199,CANCER AG 19-9,Lab,3,930,0.003225806
+3503,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABCBCD,CBC WITH DIFF,Lab,78,930,0.083870968
+3504,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABCEA,CEA,Lab,1,930,0.001075269
+3505,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABCR,"CREATININE, SERUM/PLASMA",Lab,1,930,0.001075269
+3506,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABHFP,HEPATIC FUNCTION PANEL A,Lab,4,930,0.004301075
+3507,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABMETB,"METABOLIC PANEL, BASIC",Lab,15,930,0.016129032
+3508,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,34,930,0.03655914
+3509,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABPLTS,PLATELET COUNT,Lab,64,930,0.068817204
+3510,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABPREG,"PREGNANCY TEST, SERUM",Lab,51,930,0.05483871
+3511,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABPT,PROTHROMBIN TIME,Lab,29,930,0.031182796
+3512,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,930,0.006451613
+3513,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABTYPSNI,TYPE AND SCREEN,Lab,28,930,0.030107527
+3514,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABYCHRA,CHROMOGRANIN A,Lab,1,930,0.001075269
+3515,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,111,930,0.119354839
+3516,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,POC17,POC ISTAT CREATININE,Lab,71,930,0.076344086
+3517,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,10,930,0.010752688
+3518,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,POC25,POC ISTAT INR AND PROTIME,Lab,213,930,0.229032258
+3519,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,9,930,0.009677419
+3520,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,22,930,0.023655914
+3521,824,IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE,LABBBPRBC,RED BLOOD CELLS,Blood Product,10,930,0.010752688
+3522,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,17,66,0.257575758
+3523,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,3,66,0.045454545
+3524,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,66,0.015151515
+3525,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,7,66,0.106060606
+3526,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,4,66,0.060606061
+3527,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5,66,0.075757576
+3528,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,66,0.075757576
+3529,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,66,0.015151515
+3530,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,66,0.045454545
+3531,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,19,66,0.287878788
+3532,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,19,66,0.287878788
+3533,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,66,0.075757576
+3534,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,66,0.03030303
+3535,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,16,66,0.242424242
+3536,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,66,0.015151515
+3537,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,17,66,0.257575758
+3538,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,15,66,0.227272727
+3539,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,2,66,0.03030303
+3540,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,66,0.015151515
+3541,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,POC14,POC GLUCOSE BY METER,Lab,20,66,0.303030303
+3542,827,RETIRED IP SUR ESOPHAGECTOMY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,18,66,0.272727273
+3543,828,RETIRED IP SUR GASTRECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,10,24,0.416666667
+3544,828,RETIRED IP SUR GASTRECTOMY POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,1,24,0.041666667
+3545,828,RETIRED IP SUR GASTRECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,3,24,0.125
+3546,828,RETIRED IP SUR GASTRECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,24,0.041666667
+3547,828,RETIRED IP SUR GASTRECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,24,0.083333333
+3548,828,RETIRED IP SUR GASTRECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,7,24,0.291666667
+3549,828,RETIRED IP SUR GASTRECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,6,24,0.25
+3550,828,RETIRED IP SUR GASTRECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,8,24,0.333333333
+3551,828,RETIRED IP SUR GASTRECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,24,0.125
+3552,828,RETIRED IP SUR GASTRECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,3,24,0.125
+3553,828,RETIRED IP SUR GASTRECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,4,24,0.166666667
+3554,828,RETIRED IP SUR GASTRECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,2,24,0.083333333
+3555,828,RETIRED IP SUR GASTRECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,1,24,0.041666667
+3556,828,RETIRED IP SUR GASTRECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,24,0.041666667
+3557,832,IP SUR HEPATECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,398,0.015075377
+3558,832,IP SUR HEPATECTOMY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,398,0.005025126
+3559,832,IP SUR HEPATECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,40,398,0.100502513
+3560,832,IP SUR HEPATECTOMY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,2,398,0.005025126
+3561,832,IP SUR HEPATECTOMY POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,66,398,0.165829146
+3562,832,IP SUR HEPATECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,99,398,0.248743719
+3563,832,IP SUR HEPATECTOMY POSTOP,14862,POTASSIUM CHLORIDE-D5-0.2%NACL 20 MEQ/L IV SOLP,Medication,1,398,0.002512563
+3564,832,IP SUR HEPATECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,32,398,0.08040201
+3565,832,IP SUR HEPATECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,398,0.005025126
+3566,832,IP SUR HEPATECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5,398,0.012562814
+3567,832,IP SUR HEPATECTOMY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,10,398,0.025125628
+3568,832,IP SUR HEPATECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,61,398,0.153266332
+3569,832,IP SUR HEPATECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,7,398,0.01758794
+3570,832,IP SUR HEPATECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,11,398,0.027638191
+3571,832,IP SUR HEPATECTOMY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,398,0.005025126
+3572,832,IP SUR HEPATECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,6,398,0.015075377
+3573,832,IP SUR HEPATECTOMY POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,398,0.005025126
+3574,832,IP SUR HEPATECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,111,398,0.278894472
+3575,832,IP SUR HEPATECTOMY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,398,0.002512563
+3576,832,IP SUR HEPATECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,398,0.002512563
+3577,832,IP SUR HEPATECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,89,398,0.22361809
+3578,832,IP SUR HEPATECTOMY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,2,398,0.005025126
+3579,832,IP SUR HEPATECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,398,0.002512563
+3580,832,IP SUR HEPATECTOMY POSTOP,LAB11041R,FREE HEMOGLOBIN,Lab,1,398,0.002512563
+3581,832,IP SUR HEPATECTOMY POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,4,398,0.010050251
+3582,832,IP SUR HEPATECTOMY POSTOP,LABALKP,ALKALINE PHOSPHATASE TOTAL SERUM/PLASMA,Lab,6,398,0.015075377
+3583,832,IP SUR HEPATECTOMY POSTOP,LABALT,"ALT, SERUM/PLASMA",Lab,6,398,0.015075377
+3584,832,IP SUR HEPATECTOMY POSTOP,LABAST,"AST, SERUM/PLASMA",Lab,6,398,0.015075377
+3585,832,IP SUR HEPATECTOMY POSTOP,LABBUN,"UREA NITROGEN, SERUM/PLASMA",Lab,1,398,0.002512563
+3586,832,IP SUR HEPATECTOMY POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,6,398,0.015075377
+3587,832,IP SUR HEPATECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,9,398,0.022613065
+3588,832,IP SUR HEPATECTOMY POSTOP,LABCBCO,CBC W/O DIFF,Lab,108,398,0.271356784
+3589,832,IP SUR HEPATECTOMY POSTOP,LABCR,"CREATININE, SERUM/PLASMA",Lab,1,398,0.002512563
+3590,832,IP SUR HEPATECTOMY POSTOP,LABDBIL,"BILIRUBIN DIRECT, SERUM/PLASMA",Lab,4,398,0.010050251
+3591,832,IP SUR HEPATECTOMY POSTOP,LABFIB,FIBRINOGEN,Lab,1,398,0.002512563
+3592,832,IP SUR HEPATECTOMY POSTOP,LABGLNF,"GLUCOSE NON-FASTING, SERUM/PLASMA",Lab,1,398,0.002512563
+3593,832,IP SUR HEPATECTOMY POSTOP,LABHCTX,HEMATOCRIT,Lab,1,398,0.002512563
+3594,832,IP SUR HEPATECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,24,398,0.060301508
+3595,832,IP SUR HEPATECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,113,398,0.283919598
+3596,832,IP SUR HEPATECTOMY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,100,398,0.251256281
+3597,832,IP SUR HEPATECTOMY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,101,398,0.253768844
+3598,832,IP SUR HEPATECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,45,398,0.113065327
+3599,832,IP SUR HEPATECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,88,398,0.221105528
+3600,832,IP SUR HEPATECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,18,398,0.045226131
+3601,832,IP SUR HEPATECTOMY POSTOP,LABTBIL,"BILIRUBIN TOTAL, SERUM/PLASMA",Lab,5,398,0.012562814
+3602,832,IP SUR HEPATECTOMY POSTOP,LABTNI,TROPONIN I,Lab,1,398,0.002512563
+3603,832,IP SUR HEPATECTOMY POSTOP,LABTP,"PROTEIN TOTAL, SERUM/PLASMA",Lab,1,398,0.002512563
+3604,832,IP SUR HEPATECTOMY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,5,398,0.012562814
+3605,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,319,0.003134796
+3606,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,319,0.003134796
+3607,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1,319,0.003134796
+3608,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,74,319,0.231974922
+3609,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,16,319,0.05015674
+3610,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,25122,OCTREOTIDE ACETATE 100 MCG/ML INJ SOLN,Medication,7,319,0.021943574
+3611,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,25124,OCTREOTIDE ACETATE 100 MCG/ML INJ SOLN,Medication,2,319,0.006269592
+3612,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,319,0.015673981
+3613,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,83,319,0.260188088
+3614,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,319,0.003134796
+3615,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,319,0.006269592
+3616,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,126583,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SYRG",Medication,1,319,0.003134796
+3617,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,1,319,0.003134796
+3618,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,95,319,0.297805643
+3619,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,92,319,0.288401254
+3620,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,2,319,0.006269592
+3621,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,4,319,0.012539185
+3622,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABAMY,"AMYLASE, SERUM",Lab,15,319,0.047021944
+3623,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABCBCD,CBC WITH DIFF,Lab,72,319,0.225705329
+3624,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,34,319,0.106583072
+3625,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,52,319,0.163009404
+3626,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,45,319,0.141065831
+3627,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,16,319,0.05015674
+3628,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,21,319,0.065830721
+3629,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,319,0.018808777
+3630,838,RETIRED: IP SUR PANCREATECTOMY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,1,319,0.003134796
+3631,846,IP ENT THYROIDECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,33,0.121212121
+3632,846,IP ENT THYROIDECTOMY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,3,33,0.090909091
+3633,846,IP ENT THYROIDECTOMY POSTOP,1300,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",Medication,9,33,0.272727273
+3634,846,IP ENT THYROIDECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,33,0.212121212
+3635,846,IP ENT THYROIDECTOMY POSTOP,4425,LEVOTHYROXINE 150 MCG PO TABS,Medication,1,33,0.03030303
+3636,846,IP ENT THYROIDECTOMY POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,4,33,0.121212121
+3637,846,IP ENT THYROIDECTOMY POSTOP,9350,CALCITRIOL 0.25 MCG PO CAPS,Medication,8,33,0.242424242
+3638,846,IP ENT THYROIDECTOMY POSTOP,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,33,0.03030303
+3639,846,IP ENT THYROIDECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,7,33,0.212121212
+3640,846,IP ENT THYROIDECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,33,0.060606061
+3641,846,IP ENT THYROIDECTOMY POSTOP,34544,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG PO TABS,Medication,2,33,0.060606061
+3642,846,IP ENT THYROIDECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,1,33,0.03030303
+3643,846,IP ENT THYROIDECTOMY POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,2,33,0.060606061
+3644,846,IP ENT THYROIDECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,33,0.272727273
+3645,846,IP ENT THYROIDECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,33,0.03030303
+3646,846,IP ENT THYROIDECTOMY POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,3,33,0.090909091
+3647,846,IP ENT THYROIDECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,33,0.151515152
+3648,846,IP ENT THYROIDECTOMY POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,3,33,0.090909091
+3649,846,IP ENT THYROIDECTOMY POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,4,33,0.121212121
+3650,846,IP ENT THYROIDECTOMY POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,7,33,0.212121212
+3651,846,IP ENT THYROIDECTOMY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2,33,0.060606061
+3652,846,IP ENT THYROIDECTOMY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2,33,0.060606061
+3653,846,IP ENT THYROIDECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,2,33,0.060606061
+3654,848,IP SUR BREAST PROCEDURES POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,36,325,0.110769231
+3655,848,IP SUR BREAST PROCEDURES POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,325,0.003076923
+3656,848,IP SUR BREAST PROCEDURES POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,37,325,0.113846154
+3657,848,IP SUR BREAST PROCEDURES POSTOP,3760,HYDROMORPHONE 2 MG PO TABS,Medication,7,325,0.021538462
+3658,848,IP SUR BREAST PROCEDURES POSTOP,3843,IBUPROFEN 400 MG PO TABS,Medication,4,325,0.012307692
+3659,848,IP SUR BREAST PROCEDURES POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,122,325,0.375384615
+3660,848,IP SUR BREAST PROCEDURES POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,5,325,0.015384615
+3661,848,IP SUR BREAST PROCEDURES POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,3,325,0.009230769
+3662,848,IP SUR BREAST PROCEDURES POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,59,325,0.181538462
+3663,848,IP SUR BREAST PROCEDURES POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,325,0.006153846
+3664,848,IP SUR BREAST PROCEDURES POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,1,325,0.003076923
+3665,848,IP SUR BREAST PROCEDURES POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,14,325,0.043076923
+3666,848,IP SUR BREAST PROCEDURES POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,12,325,0.036923077
+3667,848,IP SUR BREAST PROCEDURES POSTOP,11441,SUCRALFATE 100 MG/ML PO SUSP,Medication,1,325,0.003076923
+3668,848,IP SUR BREAST PROCEDURES POSTOP,14632,TRAMADOL 50 MG PO TABS,Medication,37,325,0.113846154
+3669,848,IP SUR BREAST PROCEDURES POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,121,325,0.372307692
+3670,848,IP SUR BREAST PROCEDURES POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,2,325,0.006153846
+3671,848,IP SUR BREAST PROCEDURES POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,9,325,0.027692308
+3672,848,IP SUR BREAST PROCEDURES POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,39,325,0.12
+3673,848,IP SUR BREAST PROCEDURES POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,1,325,0.003076923
+3674,848,IP SUR BREAST PROCEDURES POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,38,325,0.116923077
+3675,848,IP SUR BREAST PROCEDURES POSTOP,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,1,325,0.003076923
+3676,848,IP SUR BREAST PROCEDURES POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,5,325,0.015384615
+3677,848,IP SUR BREAST PROCEDURES POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,8,325,0.024615385
+3678,848,IP SUR BREAST PROCEDURES POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,40,325,0.123076923
+3679,848,IP SUR BREAST PROCEDURES POSTOP,93295,ZOLPIDEM 10 MG PO TABS,Medication,7,325,0.021538462
+3680,848,IP SUR BREAST PROCEDURES POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,42,325,0.129230769
+3681,848,IP SUR BREAST PROCEDURES POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,2,325,0.006153846
+3682,848,IP SUR BREAST PROCEDURES POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,4,325,0.012307692
+3683,848,IP SUR BREAST PROCEDURES POSTOP,112562,MORPHINE 10 MG/ML INJ SYRG,Medication,2,325,0.006153846
+3684,848,IP SUR BREAST PROCEDURES POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,3,325,0.009230769
+3685,848,IP SUR BREAST PROCEDURES POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,325,0.009230769
+3686,848,IP SUR BREAST PROCEDURES POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,130,325,0.4
+3687,848,IP SUR BREAST PROCEDURES POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,6,325,0.018461538
+3688,848,IP SUR BREAST PROCEDURES POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,22,325,0.067692308
+3689,848,IP SUR BREAST PROCEDURES POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,9,325,0.027692308
+3690,848,IP SUR BREAST PROCEDURES POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,12,325,0.036923077
+3691,848,IP SUR BREAST PROCEDURES POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,41,325,0.126153846
+3692,848,IP SUR BREAST PROCEDURES POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,19,325,0.058461538
+3693,848,IP SUR BREAST PROCEDURES POSTOP,LABPLTS,PLATELET COUNT,Lab,3,325,0.009230769
+3694,850,IP MED SOCIAL ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,2,0.5
+3695,850,RETIRED IP MED SOCIAL ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,2,0.5
+3696,851,IP MED CELLULITIS,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,36,0.111111111
+3697,851,IP MED CELLULITIS,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,2,36,0.055555556
+3698,851,IP MED CELLULITIS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,10,36,0.277777778
+3699,851,IP MED CELLULITIS,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,2,36,0.055555556
+3700,851,IP MED CELLULITIS,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,4,36,0.111111111
+3701,851,IP MED CELLULITIS,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,2,36,0.055555556
+3702,851,IP MED CELLULITIS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,4,36,0.111111111
+3703,851,IP MED CELLULITIS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,4,36,0.111111111
+3704,853,IP MED ALLERGIC REACTION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,10,59,0.169491525
+3705,853,IP MED ALLERGIC REACTION,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,7,59,0.118644068
+3706,853,IP MED ALLERGIC REACTION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,28,59,0.474576271
+3707,853,IP MED ALLERGIC REACTION,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,10,59,0.169491525
+3708,853,IP MED ALLERGIC REACTION,10011,FAMOTIDINE 20 MG PO TABS,Medication,6,59,0.101694915
+3709,853,IP MED ALLERGIC REACTION,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,4,59,0.06779661
+3710,853,IP MED ALLERGIC REACTION,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,59,0.016949153
+3711,853,IP MED ALLERGIC REACTION,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,36,59,0.610169492
+3712,853,IP MED ALLERGIC REACTION,9000000,ZZZ IMS TEMPLATE,Medication,3,59,0.050847458
+3713,855,IP MED SYNCOPE,LABTNI,TROPONIN I,Lab,5,28,0.178571429
+3714,855,IP MED SYNCOPE,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,14,28,0.5
+3715,855,IP MED SYNCOPE,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,1,28,0.035714286
+3716,856,IP MED DRUG OVERDOSE,520118,ACETYLCYSTEINE IV INFUSION,Medication,63,38,1
+3717,856,IP MED DRUG OVERDOSE,LABACETA,"ACETAMINOPHEN, SERUM",Lab,3,38,0.078947368
+3718,856,IP MED DRUG OVERDOSE,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,2,38,0.052631579
+3719,856,IP MED DRUG OVERDOSE,LABSALICY,SALICYLATE LEVEL,Lab,4,38,0.105263158
+3720,856,IP MED DRUG OVERDOSE,LABVOLTL,VOLATILE SCREEN,Lab,2,38,0.052631579
+3721,857,RETIRED IP MED ABDOMINAL PAIN,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,9,0.222222222
+3722,857,IP MED ABDOMINAL PAIN,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,9,0.222222222
+3723,857,RETIRED IP MED ABDOMINAL PAIN,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,9,0.111111111
+3724,857,IP MED ABDOMINAL PAIN,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,9,0.111111111
+3725,857,RETIRED IP MED ABDOMINAL PAIN,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,9,0.333333333
+3726,857,IP MED ABDOMINAL PAIN,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,9,0.333333333
+3727,859,IP CAR GENERAL ADMIT,11664,WARFARIN 1 MG PO TABS,Medication,6,1453,0.004129387
+3728,859,IP CAR GENERAL ADMIT,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,2,1453,0.001376462
+3729,859,IP CAR GENERAL ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,1453,0.000688231
+3730,859,IP CAR GENERAL ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,59,1453,0.040605643
+3731,859,IP CAR GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,217,1453,0.14934618
+3732,859,IP CAR GENERAL ADMIT,230510,FLU VAC TS 2013-14(36MOS+)(PF) 45 MCG (15 MCG X 3)/0.5 ML IM SYRG,Medication,2,1453,0.001376462
+3733,859,IP CAR GENERAL ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,12,1453,0.008258775
+3734,859,IP CAR GENERAL ADMIT,LABCBCO,CBC W/O DIFF,Lab,798,1453,0.549208534
+3735,859,IP CAR GENERAL ADMIT,LABDIGL,"DIGOXIN, SERUM",Lab,17,1453,0.011699931
+3736,859,IP CAR GENERAL ADMIT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,178,1453,0.122505162
+3737,859,IP CAR GENERAL ADMIT,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,578,1453,0.39779766
+3738,859,IP CAR GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,944,1453,0.649690296
+3739,859,IP CAR GENERAL ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,756,1453,0.520302822
+3740,859,IP CAR GENERAL ADMIT,LABNTBNP,NT - PROBNP,Lab,74,1453,0.050929112
+3741,859,IP CAR GENERAL ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,162,1453,0.111493462
+3742,859,IP CAR GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,158,1453,0.108740537
+3743,859,IP CAR GENERAL ADMIT,LABPT,PROTHROMBIN TIME,Lab,283,1453,0.194769443
+3744,859,IP CAR GENERAL ADMIT,LABTNI,TROPONIN I,Lab,352,1453,0.242257398
+3745,859,IP CAR GENERAL ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,50,1453,0.034411562
+3746,859,IP CAR GENERAL ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,10,1453,0.006882312
+3747,859,IP CAR GENERAL ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,158,1453,0.108740537
+3748,859,IP CAR GENERAL ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,61,1453,0.041982106
+3749,859,IP CAR GENERAL ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,29,1453,0.019958706
+3750,860,IP CAR HEART TRANSPLANT EVALUATION,LAB11136R,HTLV AB,Lab,126,164,0.768292683
+3751,860,IP CAR HEART TRANSPLANT EVALUATION,LAB11182R,NICOTINE & METABOLITES,Lab,76,164,0.463414634
+3752,860,IP CAR HEART TRANSPLANT EVALUATION,LABA1C,HEMOGLOBIN A1C,Lab,71,164,0.432926829
+3753,860,IP CAR HEART TRANSPLANT EVALUATION,LABASI,AB SCREEN (ASI),Lab,72,164,0.43902439
+3754,860,IP CAR HEART TRANSPLANT EVALUATION,LABCBCD,CBC WITH DIFF,Lab,22,164,0.134146341
+3755,860,IP CAR HEART TRANSPLANT EVALUATION,LABCMVIGG,CMV IGG,Lab,124,164,0.756097561
+3756,860,IP CAR HEART TRANSPLANT EVALUATION,LABCMVIGM,CMV IGM,Lab,127,164,0.774390244
+3757,860,IP CAR HEART TRANSPLANT EVALUATION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,54,164,0.329268293
+3758,860,IP CAR HEART TRANSPLANT EVALUATION,LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL,Lab,125,164,0.762195122
+3759,860,IP CAR HEART TRANSPLANT EVALUATION,LABHAVAG,HAV ANTIBODY IGG,Lab,80,164,0.487804878
+3760,860,IP CAR HEART TRANSPLANT EVALUATION,LABHAVM,IGM ANTI-HAV,Lab,86,164,0.524390244
+3761,860,IP CAR HEART TRANSPLANT EVALUATION,LABHBCT,ANTI-HBC,Lab,76,164,0.463414634
+3762,860,IP CAR HEART TRANSPLANT EVALUATION,LABHBSAG,HBSAG,Lab,79,164,0.481707317
+3763,860,IP CAR HEART TRANSPLANT EVALUATION,LABHBSQT,HEP B SURF AB QUANT,Lab,77,164,0.469512195
+3764,860,IP CAR HEART TRANSPLANT EVALUATION,LABHCVA,HEPATITIS C AB IGG,Lab,80,164,0.487804878
+3765,860,IP CAR HEART TRANSPLANT EVALUATION,LABHSVPAN,HERPES SIMPLEX VIRUS AB PANEL,Lab,127,164,0.774390244
+3766,860,IP CAR HEART TRANSPLANT EVALUATION,LABPSA,PSA,Lab,35,164,0.213414634
+3767,860,IP CAR HEART TRANSPLANT EVALUATION,LABPT,PROTHROMBIN TIME,Lab,51,164,0.31097561
+3768,860,IP CAR HEART TRANSPLANT EVALUATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,56,164,0.341463415
+3769,860,IP CAR HEART TRANSPLANT EVALUATION,LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,132,164,0.804878049
+3770,860,IP CAR HEART TRANSPLANT EVALUATION,LABSTSS,SYPHILIS TREPONEMAL SCREEN,Lab,130,164,0.792682927
+3771,860,IP CAR HEART TRANSPLANT EVALUATION,LABTOXIG,TOXOPLASMA IGG,Lab,131,164,0.798780488
+3772,860,IP CAR HEART TRANSPLANT EVALUATION,LABTP,"PROTEIN TOTAL, SERUM/PLASMA",Lab,109,164,0.664634146
+3773,860,IP CAR HEART TRANSPLANT EVALUATION,LABTYPSNI,TYPE AND SCREEN,Lab,82,164,0.5
+3774,860,IP CAR HEART TRANSPLANT EVALUATION,LABUCRT,"CREATININE, URINE (TIMED)",Lab,5,164,0.030487805
+3775,860,IP CAR HEART TRANSPLANT EVALUATION,LABUPREG,"PREGNANCY TEST, URINE",Lab,7,164,0.042682927
+3776,860,IP CAR HEART TRANSPLANT EVALUATION,LABUTP,"PROTEIN, URINE (TIMED)",Lab,4,164,0.024390244
+3777,860,IP CAR HEART TRANSPLANT EVALUATION,LABVOLTL,VOLATILE SCREEN,Lab,2,164,0.012195122
+3778,860,IP CAR HEART TRANSPLANT EVALUATION,LABVP5,COMP HEPATITIS PANEL,Lab,104,164,0.634146341
+3779,860,IP CAR HEART TRANSPLANT EVALUATION,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,1,164,0.006097561
+3780,860,IP CAR HEART TRANSPLANT EVALUATION,IMGCTT,CT THORAX,Imaging,25,164,0.152439024
+3781,860,IP CAR HEART TRANSPLANT EVALUATION,IMGMAB,MAMMO DIAGNOSTIC BILATERAL WITH US AS INDIC,Imaging,2,164,0.012195122
+3782,860,IP CAR HEART TRANSPLANT EVALUATION,IMGUSAA,US ANKLE ARM INDEX,Imaging,3,164,0.018292683
+3783,860,IP CAR HEART TRANSPLANT EVALUATION,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,40,164,0.243902439
+3784,860,IP CAR HEART TRANSPLANT EVALUATION,VSC03,VSC ARTERIAL EXAM/ABIS,Imaging,36,164,0.219512195
+3785,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,19,0.052631579
+3786,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,19,0.105263158
+3787,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,9869,DILTIAZEM HCL 5 MG/ML IV SOLN,Medication,1,19,0.052631579
+3788,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,11664,WARFARIN 1 MG PO TABS,Medication,1,19,0.052631579
+3789,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,4,19,0.210526316
+3790,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,19,0.052631579
+3791,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,530013,DILTIAZEM IV INFUSION,Medication,3,19,0.157894737
+3792,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,19,0.052631579
+3793,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABAHP2,ACUTE HEPATITIS PANEL,Lab,1,19,0.052631579
+3794,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABCA,"CALCIUM, SERUM/PLASMA",Lab,1,19,0.052631579
+3795,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABCBCD,CBC WITH DIFF,Lab,4,19,0.210526316
+3796,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,19,0.210526316
+3797,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,5,19,0.263157895
+3798,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABPT,PROTHROMBIN TIME,Lab,2,19,0.105263158
+3799,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABTNI,TROPONIN I,Lab,3,19,0.157894737
+3800,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABTSH,TSH,Lab,2,19,0.105263158
+3801,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,LABTT4,"T4, TOTAL",Lab,1,19,0.052631579
+3802,861,RETIRED IP CAR  ATRIAL FIBRILLATION CONVERSION,IMGDXCH1,XR CHEST 1V,Imaging,1,19,0.052631579
+3803,863,IP CAR EPS/ABLATION (POST-PROCEDURE),101,ACETAMINOPHEN 325 MG PO TABS,Medication,30,295,0.101694915
+3804,863,IP CAR EPS/ABLATION (POST-PROCEDURE),730,ATROPINE 0.1 MG/ML INJ SYRG,Medication,1,295,0.003389831
+3805,863,IP CAR EPS/ABLATION (POST-PROCEDURE),14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,2,295,0.006779661
+3806,863,IP CAR EPS/ABLATION (POST-PROCEDURE),15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,2,295,0.006779661
+3807,863,IP CAR EPS/ABLATION (POST-PROCEDURE),34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,24,295,0.081355932
+3808,863,IP CAR EPS/ABLATION (POST-PROCEDURE),198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,15,295,0.050847458
+3809,863,IP CAR EPS/ABLATION (POST-PROCEDURE),222102,RIVAROXABAN 20 MG PO TABS,Medication,3,295,0.010169492
+3810,863,IP CAR EPS/ABLATION (POST-PROCEDURE),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,12,295,0.040677966
+3811,863,IP CAR EPS/ABLATION (POST-PROCEDURE),540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,295,0.003389831
+3812,863,IP CAR EPS/ABLATION (POST-PROCEDURE),LABCBCO,CBC W/O DIFF,Lab,60,295,0.203389831
+3813,863,IP CAR EPS/ABLATION (POST-PROCEDURE),LABMETB,"METABOLIC PANEL, BASIC",Lab,61,295,0.206779661
+3814,863,IP CAR EPS/ABLATION (POST-PROCEDURE),LABPT,PROTHROMBIN TIME,Lab,46,295,0.155932203
+3815,863,IP CAR EPS/ABLATION (POST-PROCEDURE),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,36,295,0.122033898
+3816,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,908,0.002202643
+3817,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,319,908,0.351321586
+3818,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,58,908,0.063876652
+3819,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,13,908,0.014317181
+3820,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,3,908,0.003303965
+3821,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),520064,CEFAZOLIN 2 G IVPB,Medication,51,908,0.056167401
+3822,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),LABCBCD,CBC WITH DIFF,Lab,89,908,0.098017621
+3823,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),LABMETB,"METABOLIC PANEL, BASIC",Lab,94,908,0.103524229
+3824,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),LABPREG,"PREGNANCY TEST, SERUM",Lab,7,908,0.007709251
+3825,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),LABPT,PROTHROMBIN TIME,Lab,96,908,0.105726872
+3826,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,84,908,0.092511013
+3827,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),LABTYPSNI,TYPE AND SCREEN,Lab,50,908,0.055066079
+3828,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),POC17,POC ISTAT CREATININE,Lab,83,908,0.091409692
+3829,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),POC18,"POC ISTAT, ARTERIAL NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,83,908,0.091409692
+3830,864,IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE),POC25,POC ISTAT INR AND PROTIME,Lab,90,908,0.099118943
+3831,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),1740,CLINDAMYCIN HCL 150 MG PO CAPS,Medication,1,1314,0.000761035
+3832,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),9499,CEPHALEXIN 250 MG PO CAPS,Medication,18,1314,0.01369863
+3833,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),9500,CEPHALEXIN 500 MG PO CAPS,Medication,368,1314,0.280060883
+3834,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),9502,CEPHALEXIN 250 MG/5 ML PO SUSR,Medication,2,1314,0.00152207
+3835,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),9621,CLINDAMYCIN HCL 300 MG PO CAPS,Medication,43,1314,0.032724505
+3836,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,1314,0.000761035
+3837,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),25119,CIPROFLOXACIN 500 MG PO TABS,Medication,3,1314,0.002283105
+3838,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,473,1314,0.359969559
+3839,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,1314,0.000761035
+3840,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,71,1314,0.054033486
+3841,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,8,1314,0.00608828
+3842,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,1314,0.000761035
+3843,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,26,1314,0.01978691
+3844,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,1,1314,0.000761035
+3845,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),LABCBCO,CBC W/O DIFF,Lab,37,1314,0.028158295
+3846,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),LABMETB,"METABOLIC PANEL, BASIC",Lab,41,1314,0.031202435
+3847,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),LABPLTS,PLATELET COUNT,Lab,1,1314,0.000761035
+3848,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),LABPT,PROTHROMBIN TIME,Lab,30,1314,0.02283105
+3849,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,20,1314,0.0152207
+3850,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),IMGDXCH1,XR CHEST 1V,Imaging,393,1314,0.299086758
+3851,865,IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE),IMGDXCH2,XR CHEST 2V,Imaging,618,1314,0.470319635
+3852,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,401,0.004987531
+3853,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,401,0.002493766
+3854,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,79,401,0.197007481
+3855,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,401,0.007481297
+3856,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,4,401,0.009975062
+3857,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),520064,CEFAZOLIN 2 G IVPB,Medication,1,401,0.002493766
+3858,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),LABCBCD,CBC WITH DIFF,Lab,65,401,0.162094763
+3859,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),LABMETB,"METABOLIC PANEL, BASIC",Lab,64,401,0.159600998
+3860,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),LABPREG,"PREGNANCY TEST, SERUM",Lab,7,401,0.017456359
+3861,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),LABPT,PROTHROMBIN TIME,Lab,76,401,0.189526185
+3862,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,72,401,0.179551122
+3863,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),LABTYPSNI,TYPE AND SCREEN,Lab,183,401,0.456359102
+3864,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),POC14,POC GLUCOSE BY METER,Lab,154,401,0.3840399
+3865,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),POC17,POC ISTAT CREATININE,Lab,120,401,0.29925187
+3866,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),POC18,"POC ISTAT, ARTERIAL NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,120,401,0.29925187
+3867,867,IP CAR EPS/ABLATION (PRE-PROCEDURE),POC25,POC ISTAT INR AND PROTIME,Lab,121,401,0.301745636
+3868,868,IP PULM RH CATH POST,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,93,0.043010753
+3869,868,IP PULM RH CATH POST,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,93,0.010752688
+3870,868,IP PULM RH CATH POST,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,93,0.021505376
+3871,868,IP PULM RH CATH POST,LABPLTS,PLATELET COUNT,Lab,1,93,0.010752688
+3872,870,IP CAR ACUTE MI/STEMI ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,18,76,0.236842105
+3873,870,IP CAR ACUTE MI/STEMI ADMIT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,1,76,0.013157895
+3874,870,IP CAR ACUTE MI/STEMI ADMIT,5009,METOPROLOL TARTRATE 50 MG PO TABS,Medication,1,76,0.013157895
+3875,870,IP CAR ACUTE MI/STEMI ADMIT,14113,ASPIRIN 81 MG PO TBEC,Medication,18,76,0.236842105
+3876,870,IP CAR ACUTE MI/STEMI ADMIT,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,11,76,0.144736842
+3877,870,IP CAR ACUTE MI/STEMI ADMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,6,76,0.078947368
+3878,870,IP CAR ACUTE MI/STEMI ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1,76,0.013157895
+3879,870,IP CAR ACUTE MI/STEMI ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,6,76,0.078947368
+3880,870,IP CAR ACUTE MI/STEMI ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,6,76,0.078947368
+3881,870,IP CAR ACUTE MI/STEMI ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,76,0.026315789
+3882,870,IP CAR ACUTE MI/STEMI ADMIT,LABCBCD,CBC WITH DIFF,Lab,17,76,0.223684211
+3883,870,IP CAR ACUTE MI/STEMI ADMIT,LABHCTX,HEMATOCRIT,Lab,1,76,0.013157895
+3884,870,IP CAR ACUTE MI/STEMI ADMIT,LABK,"POTASSIUM, SERUM/PLASMA",Lab,1,76,0.013157895
+3885,870,IP CAR ACUTE MI/STEMI ADMIT,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,29,76,0.381578947
+3886,870,IP CAR ACUTE MI/STEMI ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,17,76,0.223684211
+3887,870,IP CAR ACUTE MI/STEMI ADMIT,LABPLTS,PLATELET COUNT,Lab,1,76,0.013157895
+3888,870,IP CAR ACUTE MI/STEMI ADMIT,LABPT,PROTHROMBIN TIME,Lab,4,76,0.052631579
+3889,870,IP CAR ACUTE MI/STEMI ADMIT,LABTNI,TROPONIN I,Lab,22,76,0.289473684
+3890,870,IP CAR ACUTE MI/STEMI ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,2,76,0.026315789
+3891,870,IP CAR ACUTE MI/STEMI ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,14,76,0.184210526
+3892,870,IP CAR ACUTE MI/STEMI ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,2,76,0.026315789
+3893,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,3,33,0.090909091
+3894,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,33,0.03030303
+3895,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,1,33,0.03030303
+3896,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,10,33,0.303030303
+3897,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,2,33,0.060606061
+3898,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",LABCBCD,CBC WITH DIFF,Lab,3,33,0.090909091
+3899,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",LABFK506L,TACROLIMUS (FK506),Lab,5,33,0.151515152
+3900,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",LABMETB,"METABOLIC PANEL, BASIC",Lab,4,33,0.121212121
+3901,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,3,33,0.090909091
+3902,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",LABPLTS,PLATELET COUNT,Lab,1,33,0.03030303
+3903,874,"IP TRS LUNG, HEART-LUNG TRANSPLANT TRANSFER",IMGDXCH1,XR CHEST 1V,Imaging,2,33,0.060606061
+3904,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,64,335,0.191044776
+3905,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,335,0.002985075
+3906,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,680,ASPIRIN 81 MG PO CHEW,Medication,37,335,0.110447761
+3907,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,1300,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",Medication,1,335,0.002985075
+3908,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,12,335,0.035820896
+3909,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,8,335,0.023880597
+3910,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,3,335,0.008955224
+3911,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,3,335,0.008955224
+3912,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,5009,METOPROLOL TARTRATE 50 MG PO TABS,Medication,1,335,0.002985075
+3913,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,101,335,0.301492537
+3914,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,1,335,0.002985075
+3915,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,335,0.002985075
+3916,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,9925,ENALAPRIL MALEATE 2.5 MG PO TABS,Medication,1,335,0.002985075
+3917,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,4,335,0.011940299
+3918,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,10,335,0.029850746
+3919,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,10181,"HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",Medication,11,335,0.032835821
+3920,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,11111,PRAVASTATIN 20 MG PO TABS,Medication,1,335,0.002985075
+3921,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,2,335,0.005970149
+3922,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,24,335,0.071641791
+3923,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,335,0.005970149
+3924,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,335,0.005970149
+3925,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,27472,NITROGLYCERIN 0.2 MG/HR TD PT24,Medication,1,335,0.002985075
+3926,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,7,335,0.020895522
+3927,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,10,335,0.029850746
+3928,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,9,335,0.026865672
+3929,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,77545,ATORVASTATIN 40 MG PO TABS,Medication,36,335,0.107462687
+3930,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,78531,ATORVASTATIN 20 MG PO TABS,Medication,2,335,0.005970149
+3931,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,79507,ATORVASTATIN 10 MG PO TABS,Medication,9,335,0.026865672
+3932,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,5,335,0.014925373
+3933,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,13,335,0.03880597
+3934,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,10,335,0.029850746
+3935,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,91451,NITROGLYCERIN 2 % TD OINT,Medication,6,335,0.017910448
+3936,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,335,0.011940299
+3937,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,3,335,0.008955224
+3938,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,7,335,0.020895522
+3939,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,30,335,0.089552239
+3940,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,203749,CLOPIDOGREL 75 MG PO TABS,Medication,4,335,0.011940299
+3941,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,208212,PRASUGREL 10 MG PO TABS,Medication,2,335,0.005970149
+3942,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,23,335,0.068656716
+3943,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,214201,TICAGRELOR 90 MG PO TABS,Medication,4,335,0.011940299
+3944,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,8,335,0.023880597
+3945,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,530030,NITROGLYCERIN IV INFUSION CUSTOM MIXTURE,Medication,2,335,0.005970149
+3946,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,5,335,0.014925373
+3947,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABCBCD,CBC WITH DIFF,Lab,52,335,0.155223881
+3948,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,107,335,0.319402985
+3949,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,63,335,0.188059701
+3950,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,15,335,0.044776119
+3951,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABNTBNP,NT - PROBNP,Lab,25,335,0.074626866
+3952,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABPLTS,PLATELET COUNT,Lab,22,335,0.065671642
+3953,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABPT,PROTHROMBIN TIME,Lab,42,335,0.125373134
+3954,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,LABTNI,TROPONIN I,Lab,144,335,0.429850746
+3955,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,1,335,0.002985075
+3956,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,23,335,0.068656716
+3957,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,ECH30,ECHO - EXERCISE STRESS ECHO,Imaging,2,335,0.005970149
+3958,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,3,335,0.008955224
+3959,875,IP CAR UNSTABLE ANGINA/NSTEMI ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,4,335,0.011940299
+3960,877,IP CAR CHEST PAIN LOW RISK ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,40,0.05
+3961,877,IP CAR CHEST PAIN LOW RISK ADMIT,681,ASPIRIN 325 MG PO TABS,Medication,3,40,0.075
+3962,877,IP CAR CHEST PAIN LOW RISK ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,12,40,0.3
+3963,877,IP CAR CHEST PAIN LOW RISK ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,40,0.175
+3964,877,IP CAR CHEST PAIN LOW RISK ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,7,40,0.175
+3965,877,IP CAR CHEST PAIN LOW RISK ADMIT,4711,MAGNESIUM CITRATE PO SOLN,Medication,12,40,0.3
+3966,877,IP CAR CHEST PAIN LOW RISK ADMIT,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,8,40,0.2
+3967,877,IP CAR CHEST PAIN LOW RISK ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,40,0.025
+3968,877,IP CAR CHEST PAIN LOW RISK ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,13,40,0.325
+3969,877,IP CAR CHEST PAIN LOW RISK ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,40,0.025
+3970,877,IP CAR CHEST PAIN LOW RISK ADMIT,24521,ISOSORBIDE MONONITRATE 30 MG PO TB24,Medication,1,40,0.025
+3971,877,IP CAR CHEST PAIN LOW RISK ADMIT,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,3,40,0.075
+3972,877,IP CAR CHEST PAIN LOW RISK ADMIT,77545,ATORVASTATIN 40 MG PO TABS,Medication,2,40,0.05
+3973,877,IP CAR CHEST PAIN LOW RISK ADMIT,78531,ATORVASTATIN 20 MG PO TABS,Medication,2,40,0.05
+3974,877,IP CAR CHEST PAIN LOW RISK ADMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,1,40,0.025
+3975,877,IP CAR CHEST PAIN LOW RISK ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,13,40,0.325
+3976,877,IP CAR CHEST PAIN LOW RISK ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1,40,0.025
+3977,877,IP CAR CHEST PAIN LOW RISK ADMIT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,9,40,0.225
+3978,877,IP CAR CHEST PAIN LOW RISK ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,1,40,0.025
+3979,877,IP CAR CHEST PAIN LOW RISK ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,40,0.05
+3980,877,IP CAR CHEST PAIN LOW RISK ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,4,40,0.1
+3981,877,IP CAR CHEST PAIN LOW RISK ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,40,0.125
+3982,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABA1C,HEMOGLOBIN A1C,Lab,4,40,0.1
+3983,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABCBCD,CBC WITH DIFF,Lab,9,40,0.225
+3984,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,40,0.025
+3985,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,10,40,0.25
+3986,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4,40,0.1
+3987,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABPLTS,PLATELET COUNT,Lab,3,40,0.075
+3988,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABPT,PROTHROMBIN TIME,Lab,2,40,0.05
+3989,877,IP CAR CHEST PAIN LOW RISK ADMIT,LABTNI,TROPONIN I,Lab,20,40,0.5
+3990,877,IP CAR CHEST PAIN LOW RISK ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,2,40,0.05
+3991,877,IP CAR CHEST PAIN LOW RISK ADMIT,ECH30,ECHO - EXERCISE STRESS ECHO,Imaging,2,40,0.05
+3992,877,IP CAR CHEST PAIN LOW RISK ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,1,40,0.025
+3993,878,IP PULM RH CATH PREOP,7318,SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP,Medication,26,162,0.160493827
+3994,878,IP PULM RH CATH PREOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,162,0.00617284
+3995,878,IP PULM RH CATH PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,162,0.012345679
+3996,878,IP PULM RH CATH PREOP,LABA1C,HEMOGLOBIN A1C,Lab,97,162,0.598765432
+3997,878,IP PULM RH CATH PREOP,LABCBCO,CBC W/O DIFF,Lab,53,162,0.327160494
+3998,878,IP PULM RH CATH PREOP,LABCR,"CREATININE, SERUM/PLASMA",Lab,31,162,0.191358025
+3999,878,IP PULM RH CATH PREOP,LABGLF,GLUCOSE FASTING,Lab,19,162,0.117283951
+4000,878,IP PULM RH CATH PREOP,LABHFP,HEPATIC FUNCTION PANEL A,Lab,71,162,0.438271605
+4001,878,IP PULM RH CATH PREOP,LABHMCY,"HOMOCYSTEINE TOTAL, PLASMA",Lab,22,162,0.135802469
+4002,878,IP PULM RH CATH PREOP,LABHSCRP,HIGH SENSITIVITY C-REACTIVE PROTEIN,Lab,90,162,0.555555556
+4003,878,IP PULM RH CATH PREOP,LABINSLF,INSULIN FASTING,Lab,94,162,0.580246914
+4004,878,IP PULM RH CATH PREOP,LABK,"POTASSIUM, SERUM/PLASMA",Lab,16,162,0.098765432
+4005,878,IP PULM RH CATH PREOP,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,90,162,0.555555556
+4006,878,IP PULM RH CATH PREOP,LABLPDPC,LIPID PLUS RISK PANEL WITH CALCULATED LDL,Lab,18,162,0.111111111
+4007,878,IP PULM RH CATH PREOP,LABMB,"CK, MB (MASS)",Lab,21,162,0.12962963
+4008,878,IP PULM RH CATH PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,48,162,0.296296296
+4009,878,IP PULM RH CATH PREOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,19,162,0.117283951
+4010,878,IP PULM RH CATH PREOP,LABNTBNP,NT - PROBNP,Lab,91,162,0.561728395
+4011,878,IP PULM RH CATH PREOP,LABPREG,"PREGNANCY TEST, SERUM",Lab,18,162,0.111111111
+4012,878,IP PULM RH CATH PREOP,LABPT,PROTHROMBIN TIME,Lab,75,162,0.462962963
+4013,878,IP PULM RH CATH PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,79,162,0.487654321
+4014,878,IP PULM RH CATH PREOP,LABTNI,TROPONIN I,Lab,21,162,0.12962963
+4015,878,IP PULM RH CATH PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,112,162,0.691358025
+4016,878,IP PULM RH CATH PREOP,POC14,POC GLUCOSE BY METER,Lab,2,162,0.012345679
+4017,878,IP PULM RH CATH PREOP,POC17,POC ISTAT CREATININE,Lab,3,162,0.018518519
+4018,878,IP PULM RH CATH PREOP,POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,2,162,0.012345679
+4019,878,IP PULM RH CATH PREOP,POC25,POC ISTAT INR AND PROTIME,Lab,5,162,0.030864198
+4020,878,IP PULM RH CATH PREOP,POC26,POC URINE PREGNANCY,Lab,24,162,0.148148148
+4021,881,RETIRED IP ORT HIP FRACTURE POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,29,0.103448276
+4022,881,IP ORT HIP FRACTURE POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,29,0.103448276
+4023,881,RETIRED IP ORT HIP FRACTURE POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,5,29,0.172413793
+4024,881,IP ORT HIP FRACTURE POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,5,29,0.172413793
+4025,881,RETIRED IP ORT HIP FRACTURE POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,9,29,0.310344828
+4026,881,IP ORT HIP FRACTURE POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,9,29,0.310344828
+4027,881,RETIRED IP ORT HIP FRACTURE POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,8,29,0.275862069
+4028,881,IP ORT HIP FRACTURE POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,8,29,0.275862069
+4029,881,RETIRED IP ORT HIP FRACTURE POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,3,29,0.103448276
+4030,881,IP ORT HIP FRACTURE POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,3,29,0.103448276
+4031,881,RETIRED IP ORT HIP FRACTURE POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,9,29,0.310344828
+4032,881,IP ORT HIP FRACTURE POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,9,29,0.310344828
+4033,881,RETIRED IP ORT HIP FRACTURE POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,7,29,0.24137931
+4034,881,IP ORT HIP FRACTURE POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,7,29,0.24137931
+4035,881,RETIRED IP ORT HIP FRACTURE POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,29,0.034482759
+4036,881,IP ORT HIP FRACTURE POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,29,0.034482759
+4037,881,RETIRED IP ORT HIP FRACTURE POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,1,29,0.034482759
+4038,881,IP ORT HIP FRACTURE POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,1,29,0.034482759
+4039,881,RETIRED IP ORT HIP FRACTURE POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,9,29,0.310344828
+4040,881,IP ORT HIP FRACTURE POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,9,29,0.310344828
+4041,881,RETIRED IP ORT HIP FRACTURE POSTOP,18309,GABAPENTIN 100 MG PO CAPS,Medication,4,29,0.137931034
+4042,881,IP ORT HIP FRACTURE POSTOP,18309,GABAPENTIN 100 MG PO CAPS,Medication,4,29,0.137931034
+4043,881,RETIRED IP ORT HIP FRACTURE POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5,29,0.172413793
+4044,881,IP ORT HIP FRACTURE POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5,29,0.172413793
+4045,881,RETIRED IP ORT HIP FRACTURE POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,10,29,0.344827586
+4046,881,IP ORT HIP FRACTURE POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,10,29,0.344827586
+4047,881,RETIRED IP ORT HIP FRACTURE POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,29,0.103448276
+4048,881,IP ORT HIP FRACTURE POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,29,0.103448276
+4049,881,RETIRED IP ORT HIP FRACTURE POSTOP,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,6,29,0.206896552
+4050,881,IP ORT HIP FRACTURE POSTOP,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,6,29,0.206896552
+4051,881,RETIRED IP ORT HIP FRACTURE POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,9,29,0.310344828
+4052,881,IP ORT HIP FRACTURE POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,9,29,0.310344828
+4053,881,RETIRED IP ORT HIP FRACTURE POSTOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,9,29,0.310344828
+4054,881,IP ORT HIP FRACTURE POSTOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,9,29,0.310344828
+4055,881,RETIRED IP ORT HIP FRACTURE POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,5,29,0.172413793
+4056,881,IP ORT HIP FRACTURE POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,5,29,0.172413793
+4057,881,RETIRED IP ORT HIP FRACTURE POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,29,0.172413793
+4058,881,IP ORT HIP FRACTURE POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,29,0.172413793
+4059,881,RETIRED IP ORT HIP FRACTURE POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,29,0.034482759
+4060,881,IP ORT HIP FRACTURE POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,29,0.034482759
+4061,881,RETIRED IP ORT HIP FRACTURE POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,5,29,0.172413793
+4062,881,IP ORT HIP FRACTURE POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,5,29,0.172413793
+4063,881,RETIRED IP ORT HIP FRACTURE POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,7,29,0.24137931
+4064,881,IP ORT HIP FRACTURE POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,7,29,0.24137931
+4065,881,RETIRED IP ORT HIP FRACTURE POSTOP,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,3,29,0.103448276
+4066,881,IP ORT HIP FRACTURE POSTOP,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,3,29,0.103448276
+4067,881,RETIRED IP ORT HIP FRACTURE POSTOP,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,3,29,0.103448276
+4068,881,IP ORT HIP FRACTURE POSTOP,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,3,29,0.103448276
+4069,881,RETIRED IP ORT HIP FRACTURE POSTOP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,8,29,0.275862069
+4070,881,IP ORT HIP FRACTURE POSTOP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,8,29,0.275862069
+4071,881,RETIRED IP ORT HIP FRACTURE POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,8,29,0.275862069
+4072,881,IP ORT HIP FRACTURE POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,8,29,0.275862069
+4073,881,RETIRED IP ORT HIP FRACTURE POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,3,29,0.103448276
+4074,881,IP ORT HIP FRACTURE POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,3,29,0.103448276
+4075,881,RETIRED IP ORT HIP FRACTURE POSTOP,LABCBCO,CBC W/O DIFF,Lab,8,29,0.275862069
+4076,881,IP ORT HIP FRACTURE POSTOP,LABCBCO,CBC W/O DIFF,Lab,8,29,0.275862069
+4077,881,RETIRED IP ORT HIP FRACTURE POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,8,29,0.275862069
+4078,881,IP ORT HIP FRACTURE POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,8,29,0.275862069
+4079,881,RETIRED IP ORT HIP FRACTURE POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,5,29,0.172413793
+4080,881,IP ORT HIP FRACTURE POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,5,29,0.172413793
+4081,882,RETIRED IP ORT HIP FRACTURE ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,136,0.066176471
+4082,882,IP ORT HIP FRACTURE ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,136,0.066176471
+4083,882,RETIRED IP ORT HIP FRACTURE ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,32,136,0.235294118
+4084,882,IP ORT HIP FRACTURE ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,32,136,0.235294118
+4085,882,RETIRED IP ORT HIP FRACTURE ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,45,136,0.330882353
+4086,882,IP ORT HIP FRACTURE ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,45,136,0.330882353
+4087,882,RETIRED IP ORT HIP FRACTURE ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,136,0.051470588
+4088,882,IP ORT HIP FRACTURE ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,136,0.051470588
+4089,882,RETIRED IP ORT HIP FRACTURE ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,39,136,0.286764706
+4090,882,IP ORT HIP FRACTURE ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,39,136,0.286764706
+4091,882,RETIRED IP ORT HIP FRACTURE ADMIT,4711,MAGNESIUM CITRATE PO SOLN,Medication,43,136,0.316176471
+4092,882,IP ORT HIP FRACTURE ADMIT,4711,MAGNESIUM CITRATE PO SOLN,Medication,43,136,0.316176471
+4093,882,RETIRED IP ORT HIP FRACTURE ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,19,136,0.139705882
+4094,882,IP ORT HIP FRACTURE ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,19,136,0.139705882
+4095,882,RETIRED IP ORT HIP FRACTURE ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,42,136,0.308823529
+4096,882,IP ORT HIP FRACTURE ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,42,136,0.308823529
+4097,882,RETIRED IP ORT HIP FRACTURE ADMIT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,136,0.014705882
+4098,882,IP ORT HIP FRACTURE ADMIT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,136,0.014705882
+4099,882,RETIRED IP ORT HIP FRACTURE ADMIT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,1,136,0.007352941
+4100,882,IP ORT HIP FRACTURE ADMIT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,1,136,0.007352941
+4101,882,RETIRED IP ORT HIP FRACTURE ADMIT,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,5,136,0.036764706
+4102,882,IP ORT HIP FRACTURE ADMIT,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,5,136,0.036764706
+4103,882,RETIRED IP ORT HIP FRACTURE ADMIT,10814,OXYCODONE 5 MG PO TABS,Medication,6,136,0.044117647
+4104,882,IP ORT HIP FRACTURE ADMIT,10814,OXYCODONE 5 MG PO TABS,Medication,6,136,0.044117647
+4105,882,RETIRED IP ORT HIP FRACTURE ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,51,136,0.375
+4106,882,IP ORT HIP FRACTURE ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,51,136,0.375
+4107,882,RETIRED IP ORT HIP FRACTURE ADMIT,16830,MELATONIN 3 MG PO TABS,Medication,1,136,0.007352941
+4108,882,IP ORT HIP FRACTURE ADMIT,16830,MELATONIN 3 MG PO TABS,Medication,1,136,0.007352941
+4109,882,RETIRED IP ORT HIP FRACTURE ADMIT,18309,GABAPENTIN 100 MG PO CAPS,Medication,7,136,0.051470588
+4110,882,IP ORT HIP FRACTURE ADMIT,18309,GABAPENTIN 100 MG PO CAPS,Medication,7,136,0.051470588
+4111,882,RETIRED IP ORT HIP FRACTURE ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,49,136,0.360294118
+4112,882,IP ORT HIP FRACTURE ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,49,136,0.360294118
+4113,882,RETIRED IP ORT HIP FRACTURE ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,41,136,0.301470588
+4114,882,IP ORT HIP FRACTURE ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,41,136,0.301470588
+4115,882,RETIRED IP ORT HIP FRACTURE ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,136,0.051470588
+4116,882,IP ORT HIP FRACTURE ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,136,0.051470588
+4117,882,RETIRED IP ORT HIP FRACTURE ADMIT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,14,136,0.102941176
+4118,882,IP ORT HIP FRACTURE ADMIT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,14,136,0.102941176
+4119,882,RETIRED IP ORT HIP FRACTURE ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,45,136,0.330882353
+4120,882,IP ORT HIP FRACTURE ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,45,136,0.330882353
+4121,882,RETIRED IP ORT HIP FRACTURE ADMIT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,41,136,0.301470588
+4122,882,IP ORT HIP FRACTURE ADMIT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,41,136,0.301470588
+4123,882,RETIRED IP ORT HIP FRACTURE ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,136,0.066176471
+4124,882,IP ORT HIP FRACTURE ADMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,136,0.066176471
+4125,882,RETIRED IP ORT HIP FRACTURE ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,13,136,0.095588235
+4126,882,IP ORT HIP FRACTURE ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,13,136,0.095588235
+4127,882,RETIRED IP ORT HIP FRACTURE ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,136,0.007352941
+4128,882,IP ORT HIP FRACTURE ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,136,0.007352941
+4129,882,RETIRED IP ORT HIP FRACTURE ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,7,136,0.051470588
+4130,882,IP ORT HIP FRACTURE ADMIT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,7,136,0.051470588
+4131,882,RETIRED IP ORT HIP FRACTURE ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,136,0.007352941
+4132,882,IP ORT HIP FRACTURE ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,136,0.007352941
+4133,882,RETIRED IP ORT HIP FRACTURE ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,48,136,0.352941176
+4134,882,IP ORT HIP FRACTURE ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,48,136,0.352941176
+4135,882,RETIRED IP ORT HIP FRACTURE ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,136,0.014705882
+4136,882,IP ORT HIP FRACTURE ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,136,0.014705882
+4137,882,RETIRED IP ORT HIP FRACTURE ADMIT,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,6,136,0.044117647
+4138,882,IP ORT HIP FRACTURE ADMIT,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,6,136,0.044117647
+4139,882,RETIRED IP ORT HIP FRACTURE ADMIT,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,1,136,0.007352941
+4140,882,IP ORT HIP FRACTURE ADMIT,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,1,136,0.007352941
+4141,882,RETIRED IP ORT HIP FRACTURE ADMIT,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,13,136,0.095588235
+4142,882,IP ORT HIP FRACTURE ADMIT,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,13,136,0.095588235
+4143,882,RETIRED IP ORT HIP FRACTURE ADMIT,540102,NS IV BOLUS,Medication,9,136,0.066176471
+4144,882,IP ORT HIP FRACTURE ADMIT,540102,NS IV BOLUS,Medication,9,136,0.066176471
+4145,882,RETIRED IP ORT HIP FRACTURE ADMIT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,136,0.007352941
+4146,882,IP ORT HIP FRACTURE ADMIT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,136,0.007352941
+4147,882,RETIRED IP ORT HIP FRACTURE ADMIT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,38,136,0.279411765
+4148,882,IP ORT HIP FRACTURE ADMIT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,38,136,0.279411765
+4149,882,RETIRED IP ORT HIP FRACTURE ADMIT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,37,136,0.272058824
+4150,882,IP ORT HIP FRACTURE ADMIT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,37,136,0.272058824
+4151,882,RETIRED IP ORT HIP FRACTURE ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,19,136,0.139705882
+4152,882,IP ORT HIP FRACTURE ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,19,136,0.139705882
+4153,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,10,136,0.073529412
+4154,882,IP ORT HIP FRACTURE ADMIT,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,10,136,0.073529412
+4155,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,1,136,0.007352941
+4156,882,IP ORT HIP FRACTURE ADMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,1,136,0.007352941
+4157,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABCBCO,CBC W/O DIFF,Lab,19,136,0.139705882
+4158,882,IP ORT HIP FRACTURE ADMIT,LABCBCO,CBC W/O DIFF,Lab,19,136,0.139705882
+4159,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,15,136,0.110294118
+4160,882,IP ORT HIP FRACTURE ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,15,136,0.110294118
+4161,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,15,136,0.110294118
+4162,882,IP ORT HIP FRACTURE ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,15,136,0.110294118
+4163,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABPT,PROTHROMBIN TIME,Lab,4,136,0.029411765
+4164,882,IP ORT HIP FRACTURE ADMIT,LABPT,PROTHROMBIN TIME,Lab,4,136,0.029411765
+4165,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,12,136,0.088235294
+4166,882,IP ORT HIP FRACTURE ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,12,136,0.088235294
+4167,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,4,136,0.029411765
+4168,882,IP ORT HIP FRACTURE ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,4,136,0.029411765
+4169,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,136,0.007352941
+4170,882,IP ORT HIP FRACTURE ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,136,0.007352941
+4171,882,RETIRED IP ORT HIP FRACTURE ADMIT,IMGDXFEMLT,XR FEMUR LT,Imaging,1,136,0.007352941
+4172,882,IP ORT HIP FRACTURE ADMIT,IMGDXFEMLT,XR FEMUR LT,Imaging,1,136,0.007352941
+4173,882,RETIRED IP ORT HIP FRACTURE ADMIT,IMGDXPEL,XR PELVIS 1V,Imaging,1,136,0.007352941
+4174,882,IP ORT HIP FRACTURE ADMIT,IMGDXPEL,XR PELVIS 1V,Imaging,1,136,0.007352941
+4175,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,136,0.007352941
+4176,882,IP ORT HIP FRACTURE ADMIT,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,136,0.007352941
+4177,882,RETIRED IP ORT HIP FRACTURE ADMIT,LABBBPRBC,RED BLOOD CELLS,Blood Product,7,136,0.051470588
+4178,882,IP ORT HIP FRACTURE ADMIT,LABBBPRBC,RED BLOOD CELLS,Blood Product,7,136,0.051470588
+4179,883,IP ORT TUMOR POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,26,160,0.1625
+4180,883,IP ORT TUMOR POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,7,160,0.04375
+4181,883,IP ORT TUMOR POSTOP,664,ASCORBIC ACID 500 MG PO TABS,Medication,5,160,0.03125
+4182,883,IP ORT TUMOR POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,8,160,0.05
+4183,883,IP ORT TUMOR POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,6,160,0.0375
+4184,883,IP ORT TUMOR POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,2,160,0.0125
+4185,883,IP ORT TUMOR POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,30,160,0.1875
+4186,883,IP ORT TUMOR POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,24,160,0.15
+4187,883,IP ORT TUMOR POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,46,160,0.2875
+4188,883,IP ORT TUMOR POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,6,160,0.0375
+4189,883,IP ORT TUMOR POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,3,160,0.01875
+4190,883,IP ORT TUMOR POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,160,0.00625
+4191,883,IP ORT TUMOR POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,160,0.00625
+4192,883,IP ORT TUMOR POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,17,160,0.10625
+4193,883,IP ORT TUMOR POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,22,160,0.1375
+4194,883,IP ORT TUMOR POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,23,160,0.14375
+4195,883,IP ORT TUMOR POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,160,0.00625
+4196,883,IP ORT TUMOR POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,18,160,0.1125
+4197,883,IP ORT TUMOR POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,4,160,0.025
+4198,883,IP ORT TUMOR POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,9,160,0.05625
+4199,883,IP ORT TUMOR POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,13,160,0.08125
+4200,883,IP ORT TUMOR POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,160,0.00625
+4201,883,IP ORT TUMOR POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,52,160,0.325
+4202,883,IP ORT TUMOR POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,4,160,0.025
+4203,883,IP ORT TUMOR POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,160,0.0125
+4204,883,IP ORT TUMOR POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,10,160,0.0625
+4205,883,IP ORT TUMOR POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,10,160,0.0625
+4206,883,IP ORT TUMOR POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,160,0.00625
+4207,883,IP ORT TUMOR POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,21,160,0.13125
+4208,883,IP ORT TUMOR POSTOP,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,1,160,0.00625
+4209,883,IP ORT TUMOR POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,7,160,0.04375
+4210,883,IP ORT TUMOR POSTOP,LABCBCD,CBC WITH DIFF,Lab,1,160,0.00625
+4211,883,IP ORT TUMOR POSTOP,LABCBCO,CBC W/O DIFF,Lab,48,160,0.3
+4212,883,IP ORT TUMOR POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,50,160,0.3125
+4213,883,IP ORT TUMOR POSTOP,LABPLTS,PLATELET COUNT,Lab,2,160,0.0125
+4214,883,IP ORT TUMOR POSTOP,LABPT,PROTHROMBIN TIME,Lab,3,160,0.01875
+4215,884,RETIRED IP ORT SPINAL FUSION POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,204,1500,0.136
+4216,884,RETIRED IP ORT SPINAL FUSION POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,129,1500,0.086
+4217,884,RETIRED IP ORT SPINAL FUSION POSTOP,664,ASCORBIC ACID 500 MG PO TABS,Medication,53,1500,0.035333333
+4218,884,RETIRED IP ORT SPINAL FUSION POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,485,1500,0.323333333
+4219,884,RETIRED IP ORT SPINAL FUSION POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,67,1500,0.044666667
+4220,884,RETIRED IP ORT SPINAL FUSION POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,162,1500,0.108
+4221,884,RETIRED IP ORT SPINAL FUSION POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,24,1500,0.016
+4222,884,RETIRED IP ORT SPINAL FUSION POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,265,1500,0.176666667
+4223,884,RETIRED IP ORT SPINAL FUSION POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,217,1500,0.144666667
+4224,884,RETIRED IP ORT SPINAL FUSION POSTOP,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,5,1500,0.003333333
+4225,884,RETIRED IP ORT SPINAL FUSION POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,482,1500,0.321333333
+4226,884,RETIRED IP ORT SPINAL FUSION POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,77,1500,0.051333333
+4227,884,RETIRED IP ORT SPINAL FUSION POSTOP,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,11,1500,0.007333333
+4228,884,RETIRED IP ORT SPINAL FUSION POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,3,1500,0.002
+4229,884,RETIRED IP ORT SPINAL FUSION POSTOP,7753,TEMAZEPAM 15 MG PO CAPS,Medication,2,1500,0.001333333
+4230,884,RETIRED IP ORT SPINAL FUSION POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,24,1500,0.016
+4231,884,RETIRED IP ORT SPINAL FUSION POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,1500,0.002666667
+4232,884,RETIRED IP ORT SPINAL FUSION POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,3,1500,0.002
+4233,884,RETIRED IP ORT SPINAL FUSION POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,61,1500,0.040666667
+4234,884,RETIRED IP ORT SPINAL FUSION POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,539,1500,0.359333333
+4235,884,RETIRED IP ORT SPINAL FUSION POSTOP,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,1500,0.000666667
+4236,884,RETIRED IP ORT SPINAL FUSION POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,1,1500,0.000666667
+4237,884,RETIRED IP ORT SPINAL FUSION POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,43,1500,0.028666667
+4238,884,RETIRED IP ORT SPINAL FUSION POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,1,1500,0.000666667
+4239,884,RETIRED IP ORT SPINAL FUSION POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,9,1500,0.006
+4240,884,RETIRED IP ORT SPINAL FUSION POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,112,1500,0.074666667
+4241,884,RETIRED IP ORT SPINAL FUSION POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,203,1500,0.135333333
+4242,884,RETIRED IP ORT SPINAL FUSION POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,505,1500,0.336666667
+4243,884,RETIRED IP ORT SPINAL FUSION POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,233,1500,0.155333333
+4244,884,RETIRED IP ORT SPINAL FUSION POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,25,1500,0.016666667
+4245,884,RETIRED IP ORT SPINAL FUSION POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,219,1500,0.146
+4246,884,RETIRED IP ORT SPINAL FUSION POSTOP,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,3,1500,0.002
+4247,884,RETIRED IP ORT SPINAL FUSION POSTOP,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,199,1500,0.132666667
+4248,884,RETIRED IP ORT SPINAL FUSION POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,494,1500,0.329333333
+4249,884,RETIRED IP ORT SPINAL FUSION POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,60,1500,0.04
+4250,884,RETIRED IP ORT SPINAL FUSION POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,128,1500,0.085333333
+4251,884,RETIRED IP ORT SPINAL FUSION POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,1500,0.000666667
+4252,884,RETIRED IP ORT SPINAL FUSION POSTOP,111320,MULTIVITAMIN WITH IRON-MINERAL PO LIQD,Medication,2,1500,0.001333333
+4253,884,RETIRED IP ORT SPINAL FUSION POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,23,1500,0.015333333
+4254,884,RETIRED IP ORT SPINAL FUSION POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,113,1500,0.075333333
+4255,884,RETIRED IP ORT SPINAL FUSION POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,18,1500,0.012
+4256,884,RETIRED IP ORT SPINAL FUSION POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,6,1500,0.004
+4257,884,RETIRED IP ORT SPINAL FUSION POSTOP,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,26,1500,0.017333333
+4258,884,RETIRED IP ORT SPINAL FUSION POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,489,1500,0.326
+4259,884,RETIRED IP ORT SPINAL FUSION POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,21,1500,0.014
+4260,884,RETIRED IP ORT SPINAL FUSION POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,1500,0.000666667
+4261,884,RETIRED IP ORT SPINAL FUSION POSTOP,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,97,1500,0.064666667
+4262,884,RETIRED IP ORT SPINAL FUSION POSTOP,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,295,1500,0.196666667
+4263,884,RETIRED IP ORT SPINAL FUSION POSTOP,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,3,1500,0.002
+4264,884,RETIRED IP ORT SPINAL FUSION POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,93,1500,0.062
+4265,884,RETIRED IP ORT SPINAL FUSION POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,67,1500,0.044666667
+4266,884,RETIRED IP ORT SPINAL FUSION POSTOP,540102,NS IV BOLUS,Medication,4,1500,0.002666667
+4267,884,RETIRED IP ORT SPINAL FUSION POSTOP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,268,1500,0.178666667
+4268,884,RETIRED IP ORT SPINAL FUSION POSTOP,540302,POTASSIUM CHLORIDE IV SCALE,Medication,28,1500,0.018666667
+4269,884,RETIRED IP ORT SPINAL FUSION POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,269,1500,0.179333333
+4270,884,RETIRED IP ORT SPINAL FUSION POSTOP,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,15,1500,0.01
+4271,884,RETIRED IP ORT SPINAL FUSION POSTOP,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,69,1500,0.046
+4272,884,RETIRED IP ORT SPINAL FUSION POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,81,1500,0.054
+4273,884,RETIRED IP ORT SPINAL FUSION POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,1500,0.002
+4274,884,RETIRED IP ORT SPINAL FUSION POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,83,1500,0.055333333
+4275,884,RETIRED IP ORT SPINAL FUSION POSTOP,LABCBCD,CBC WITH DIFF,Lab,320,1500,0.213333333
+4276,884,RETIRED IP ORT SPINAL FUSION POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,439,1500,0.292666667
+4277,884,RETIRED IP ORT SPINAL FUSION POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,293,1500,0.195333333
+4278,884,RETIRED IP ORT SPINAL FUSION POSTOP,LABPT,PROTHROMBIN TIME,Lab,17,1500,0.011333333
+4279,884,RETIRED IP ORT SPINAL FUSION POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,4,1500,0.002666667
+4280,884,RETIRED IP ORT SPINAL FUSION POSTOP,IMGCTST,CT SPINE THORACIC,Imaging,2,1500,0.001333333
+4281,884,RETIRED IP ORT SPINAL FUSION POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,7,1500,0.004666667
+4282,884,RETIRED IP ORT SPINAL FUSION POSTOP,IMGDXSPL2,"XR SPINE, LUMBAR 2V",Imaging,65,1500,0.043333333
+4283,884,RETIRED IP ORT SPINAL FUSION POSTOP,IMGDXSPT,"XR SPINE, THORACIC 2V",Imaging,2,1500,0.001333333
+4284,884,RETIRED IP ORT SPINAL FUSION POSTOP,IMGMRSL,MR L-SPINE,Imaging,1,1500,0.000666667
+4285,884,RETIRED IP ORT SPINAL FUSION POSTOP,IMGUSVUUPLT,US RADIOLOGY VEIN UPPER EXTREMITY LEFT RULE OUT DVT,Imaging,2,1500,0.001333333
+4286,885,IP ORT SPORTS ORTHO/REHAB POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,27,0.148148148
+4287,885,IP ORT SPORTS ORTHO/REHAB POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,27,0.074074074
+4288,885,IP ORT SPORTS ORTHO/REHAB POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,9,27,0.333333333
+4289,885,IP ORT SPORTS ORTHO/REHAB POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,6,27,0.222222222
+4290,885,IP ORT SPORTS ORTHO/REHAB POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,4,27,0.148148148
+4291,885,IP ORT SPORTS ORTHO/REHAB POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,5,27,0.185185185
+4292,885,IP ORT SPORTS ORTHO/REHAB POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,9,27,0.333333333
+4293,885,IP ORT SPORTS ORTHO/REHAB POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,9,27,0.333333333
+4294,885,IP ORT SPORTS ORTHO/REHAB POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,4,27,0.148148148
+4295,885,IP ORT SPORTS ORTHO/REHAB POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,9,27,0.333333333
+4296,885,IP ORT SPORTS ORTHO/REHAB POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,8,27,0.296296296
+4297,885,IP ORT SPORTS ORTHO/REHAB POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,7,27,0.259259259
+4298,885,IP ORT SPORTS ORTHO/REHAB POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,9,27,0.333333333
+4299,885,IP ORT SPORTS ORTHO/REHAB POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,27,0.074074074
+4300,885,IP ORT SPORTS ORTHO/REHAB POSTOP,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,3,27,0.111111111
+4301,885,IP ORT SPORTS ORTHO/REHAB POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,9,27,0.333333333
+4302,885,IP ORT SPORTS ORTHO/REHAB POSTOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,7,27,0.259259259
+4303,885,IP ORT SPORTS ORTHO/REHAB POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,2,27,0.074074074
+4304,885,IP ORT SPORTS ORTHO/REHAB POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,4,27,0.148148148
+4305,885,IP ORT SPORTS ORTHO/REHAB POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,27,0.333333333
+4306,885,IP ORT SPORTS ORTHO/REHAB POSTOP,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,3,27,0.111111111
+4307,885,IP ORT SPORTS ORTHO/REHAB POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,3,27,0.111111111
+4308,885,IP ORT SPORTS ORTHO/REHAB POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,4,27,0.148148148
+4309,885,IP ORT SPORTS ORTHO/REHAB POSTOP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,1,27,0.037037037
+4310,885,IP ORT SPORTS ORTHO/REHAB POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,1,27,0.037037037
+4311,886,IP ORT TRAUMA,101,ACETAMINOPHEN 325 MG PO TABS,Medication,502,9299,0.053984299
+4312,886,IP ORT TRAUMA,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1939,9299,0.208517045
+4313,886,IP ORT TRAUMA,1080,BISACODYL 10 MG PR SUPP,Medication,2628,9299,0.282611033
+4314,886,IP ORT TRAUMA,1801,CODEINE SULFATE 15 MG PO TABS,Medication,112,9299,0.012044306
+4315,886,IP ORT TRAUMA,1802,CODEINE SULFATE 30 MG PO TABS,Medication,7,9299,0.000752769
+4316,886,IP ORT TRAUMA,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1883,9299,0.202494892
+4317,886,IP ORT TRAUMA,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,816,9299,0.087751371
+4318,886,IP ORT TRAUMA,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,9299,0.000107538
+4319,886,IP ORT TRAUMA,4318,LACTATED RINGERS IV SOLP,Medication,305,9299,0.032799226
+4320,886,IP ORT TRAUMA,4711,MAGNESIUM CITRATE PO SOLN,Medication,2485,9299,0.267233036
+4321,886,IP ORT TRAUMA,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,938,9299,0.100871061
+4322,886,IP ORT TRAUMA,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,9299,0.000107538
+4323,886,IP ORT TRAUMA,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,2600,9299,0.279599957
+4324,886,IP ORT TRAUMA,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,21,9299,0.002258307
+4325,886,IP ORT TRAUMA,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,18,9299,0.001935692
+4326,886,IP ORT TRAUMA,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,12,9299,0.001290461
+4327,886,IP ORT TRAUMA,8085,TRAZODONE 50 MG PO TABS,Medication,9,9299,0.000967846
+4328,886,IP ORT TRAUMA,9152,"ASPIRIN, BUFFERED 325 MG PO TABS",Medication,7,9299,0.000752769
+4329,886,IP ORT TRAUMA,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,71,9299,0.00763523
+4330,886,IP ORT TRAUMA,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,13,9299,0.001398
+4331,886,IP ORT TRAUMA,10181,"HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",Medication,3,9299,0.000322615
+4332,886,IP ORT TRAUMA,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,14,9299,0.001505538
+4333,886,IP ORT TRAUMA,10814,OXYCODONE 5 MG PO TABS,Medication,3130,9299,0.336595333
+4334,886,IP ORT TRAUMA,11349,SENNOSIDES 8.6 MG PO TABS,Medication,2884,9299,0.310140875
+4335,886,IP ORT TRAUMA,11664,WARFARIN 1 MG PO TABS,Medication,1,9299,0.000107538
+4336,886,IP ORT TRAUMA,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,2,9299,0.000215077
+4337,886,IP ORT TRAUMA,14632,TRAMADOL 50 MG PO TABS,Medication,191,9299,0.020539843
+4338,886,IP ORT TRAUMA,16830,MELATONIN 3 MG PO TABS,Medication,642,9299,0.069039682
+4339,886,IP ORT TRAUMA,18307,GABAPENTIN 400 MG PO CAPS,Medication,17,9299,0.001828154
+4340,886,IP ORT TRAUMA,18308,GABAPENTIN 300 MG PO CAPS,Medication,386,9299,0.04150984
+4341,886,IP ORT TRAUMA,18309,GABAPENTIN 100 MG PO CAPS,Medication,560,9299,0.060221529
+4342,886,IP ORT TRAUMA,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1148,9299,0.123454135
+4343,886,IP ORT TRAUMA,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,2622,9299,0.281965803
+4344,886,IP ORT TRAUMA,25855,GABAPENTIN 600 MG PO TABS,Medication,8,9299,0.000860308
+4345,886,IP ORT TRAUMA,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2213,9299,0.237982579
+4346,886,IP ORT TRAUMA,28899,OXYCODONE 15 MG PO TABS,Medication,13,9299,0.001398
+4347,886,IP ORT TRAUMA,28900,OXYCODONE 30 MG PO TABS,Medication,6,9299,0.000645231
+4348,886,IP ORT TRAUMA,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,61,9299,0.006559845
+4349,886,IP ORT TRAUMA,35943,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,Medication,1,9299,0.000107538
+4350,886,IP ORT TRAUMA,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,73,9299,0.007850306
+4351,886,IP ORT TRAUMA,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,1999,9299,0.214969352
+4352,886,IP ORT TRAUMA,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2598,9299,0.27938488
+4353,886,IP ORT TRAUMA,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,1696,9299,0.182385203
+4354,886,IP ORT TRAUMA,92173,ZOLPIDEM 5 MG PO TABS,Medication,250,9299,0.026884611
+4355,886,IP ORT TRAUMA,93295,ZOLPIDEM 10 MG PO TABS,Medication,3,9299,0.000322615
+4356,886,IP ORT TRAUMA,93715,CELECOXIB 100 MG PO CAPS,Medication,9,9299,0.000967846
+4357,886,IP ORT TRAUMA,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1157,9299,0.124421981
+4358,886,IP ORT TRAUMA,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,36,9299,0.003871384
+4359,886,IP ORT TRAUMA,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,656,9299,0.07054522
+4360,886,IP ORT TRAUMA,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,48,9299,0.005161845
+4361,886,IP ORT TRAUMA,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,763,9299,0.082051834
+4362,886,IP ORT TRAUMA,116506,MELATONIN 1 MG PO TABS,Medication,7,9299,0.000752769
+4363,886,IP ORT TRAUMA,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,62,9299,0.006667384
+4364,886,IP ORT TRAUMA,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,11,9299,0.001182923
+4365,886,IP ORT TRAUMA,125758,PREGABALIN 75 MG PO CAPS,Medication,1,9299,0.000107538
+4366,886,IP ORT TRAUMA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2706,9299,0.290999032
+4367,886,IP ORT TRAUMA,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,101,9299,0.010861383
+4368,886,IP ORT TRAUMA,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,1437,9299,0.154532745
+4369,886,IP ORT TRAUMA,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,8,9299,0.000860308
+4370,886,IP ORT TRAUMA,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,111,9299,0.011936767
+4371,886,IP ORT TRAUMA,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,59,9299,0.006344768
+4372,886,IP ORT TRAUMA,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,1239,9299,0.133240133
+4373,886,IP ORT TRAUMA,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,9299,0.000107538
+4374,886,IP ORT TRAUMA,229996,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP,Medication,40,9299,0.004301538
+4375,886,IP ORT TRAUMA,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,106,9299,0.011399075
+4376,886,IP ORT TRAUMA,520064,CEFAZOLIN 2 G IVPB,Medication,240,9299,0.025809227
+4377,886,IP ORT TRAUMA,540102,NS IV BOLUS,Medication,1601,9299,0.17216905
+4378,886,IP ORT TRAUMA,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,63,9299,0.006774922
+4379,886,IP ORT TRAUMA,540125,HYDROMORPHONE 1 MG/ML IV PCA,Medication,1,9299,0.000107538
+4380,886,IP ORT TRAUMA,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,2400,9299,0.258092268
+4381,886,IP ORT TRAUMA,540304,MAGNESIUM SULFATE IV SCALE ,Medication,2337,9299,0.251317346
+4382,886,IP ORT TRAUMA,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,4,9299,0.000430154
+4383,886,IP ORT TRAUMA,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,308,9299,0.033121841
+4384,886,IP ORT TRAUMA,9000000,ZZZ IMS TEMPLATE,Medication,7,9299,0.000752769
+4385,886,IP ORT TRAUMA,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,905,9299,0.097322293
+4386,886,IP ORT TRAUMA,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,79,9299,0.008495537
+4387,886,IP ORT TRAUMA,LABCBCO,CBC W/O DIFF,Lab,2548,9299,0.274007958
+4388,886,IP ORT TRAUMA,LABHCTX,HEMATOCRIT,Lab,9,9299,0.000967846
+4389,886,IP ORT TRAUMA,LABMETB,"METABOLIC PANEL, BASIC",Lab,2518,9299,0.270781804
+4390,886,IP ORT TRAUMA,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2027,9299,0.217980428
+4391,886,IP ORT TRAUMA,LABPLTS,PLATELET COUNT,Lab,51,9299,0.005484461
+4392,886,IP ORT TRAUMA,LABPT,PROTHROMBIN TIME,Lab,121,9299,0.013012152
+4393,886,IP ORT TRAUMA,LABTYPSNI,TYPE AND SCREEN,Lab,474,9299,0.050973223
+4394,886,IP ORT TRAUMA,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,14,9299,0.001505538
+4395,886,IP ORT TRAUMA,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,13,9299,0.001398
+4396,886,IP ORT TRAUMA,IMGDXCH1,XR CHEST 1V,Imaging,10,9299,0.001075384
+4397,886,IP ORT TRAUMA,IMGDXFEMLT,XR FEMUR LT,Imaging,19,9299,0.00204323
+4398,886,IP ORT TRAUMA,IMGDXFEMRT,XR FEMUR RT,Imaging,18,9299,0.001935692
+4399,886,IP ORT TRAUMA,IMGDXFORLT,XR FOREARM 2V LT,Imaging,1,9299,0.000107538
+4400,886,IP ORT TRAUMA,IMGDXHIP2LT,XR HIP 2V LT,Imaging,7,9299,0.000752769
+4401,886,IP ORT TRAUMA,IMGDXHIP2RT,XR HIP 2V RT,Imaging,12,9299,0.001290461
+4402,886,IP ORT TRAUMA,IMGDXHUMLT,XR HUMERUS 2V LT,Imaging,2,9299,0.000215077
+4403,886,IP ORT TRAUMA,IMGDXHUMRT,XR HUMERUS 2V RT,Imaging,1,9299,0.000107538
+4404,886,IP ORT TRAUMA,IMGDXPEL,XR PELVIS 1V,Imaging,51,9299,0.005484461
+4405,886,IP ORT TRAUMA,IMGDXPEL2,XR PELVIS 2V,Imaging,4,9299,0.000430154
+4406,886,IP ORT TRAUMA,IMGDXTIBLT,XR TIBIA AND FIBULA LT,Imaging,8,9299,0.000860308
+4407,886,IP ORT TRAUMA,IMGDXTIBRT,XR TIBIA AND FIBULA RT,Imaging,8,9299,0.000860308
+4408,886,IP ORT TRAUMA,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,6,9299,0.000645231
+4409,886,IP ORT TRAUMA,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,1,9299,0.000107538
+4410,886,IP ORT TRAUMA,LABBBPRBC,RED BLOOD CELLS,Blood Product,193,9299,0.02075492
+4411,888,RETIRED IP ORT REPLANT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,7,51,0.137254902
+4412,888,RETIRED IP ORT REPLANT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,7,51,0.137254902
+4413,888,RETIRED IP ORT REPLANT,664,ASCORBIC ACID 500 MG PO TABS,Medication,7,51,0.137254902
+4414,888,RETIRED IP ORT REPLANT,681,ASPIRIN 325 MG PO TABS,Medication,6,51,0.117647059
+4415,888,RETIRED IP ORT REPLANT,1080,BISACODYL 10 MG PR SUPP,Medication,14,51,0.274509804
+4416,888,RETIRED IP ORT REPLANT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,6,51,0.117647059
+4417,888,RETIRED IP ORT REPLANT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,10,51,0.196078431
+4418,888,RETIRED IP ORT REPLANT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,4,51,0.078431373
+4419,888,RETIRED IP ORT REPLANT,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,2,51,0.039215686
+4420,888,RETIRED IP ORT REPLANT,4711,MAGNESIUM CITRATE PO SOLN,Medication,14,51,0.274509804
+4421,888,RETIRED IP ORT REPLANT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,13,51,0.254901961
+4422,888,RETIRED IP ORT REPLANT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,51,0.039215686
+4423,888,RETIRED IP ORT REPLANT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,2,51,0.039215686
+4424,888,RETIRED IP ORT REPLANT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,3,51,0.058823529
+4425,888,RETIRED IP ORT REPLANT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,14,51,0.274509804
+4426,888,RETIRED IP ORT REPLANT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,51,0.019607843
+4427,888,RETIRED IP ORT REPLANT,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,4,51,0.078431373
+4428,888,RETIRED IP ORT REPLANT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,6,51,0.117647059
+4429,888,RETIRED IP ORT REPLANT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5,51,0.098039216
+4430,888,RETIRED IP ORT REPLANT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,14,51,0.274509804
+4431,888,RETIRED IP ORT REPLANT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,7,51,0.137254902
+4432,888,RETIRED IP ORT REPLANT,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,1,51,0.019607843
+4433,888,RETIRED IP ORT REPLANT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,2,51,0.039215686
+4434,888,RETIRED IP ORT REPLANT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,14,51,0.274509804
+4435,888,RETIRED IP ORT REPLANT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,14,51,0.274509804
+4436,888,RETIRED IP ORT REPLANT,92173,ZOLPIDEM 5 MG PO TABS,Medication,1,51,0.019607843
+4437,888,RETIRED IP ORT REPLANT,93936,DEXTRAN 40 10 % IN 5% DEXTROSE 10 % IV SOLP,Medication,4,51,0.078431373
+4438,888,RETIRED IP ORT REPLANT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,3,51,0.058823529
+4439,888,RETIRED IP ORT REPLANT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,51,0.019607843
+4440,888,RETIRED IP ORT REPLANT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,7,51,0.137254902
+4441,888,RETIRED IP ORT REPLANT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,14,51,0.274509804
+4442,888,RETIRED IP ORT REPLANT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,3,51,0.058823529
+4443,888,RETIRED IP ORT REPLANT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,51,0.019607843
+4444,888,RETIRED IP ORT REPLANT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,51,0.019607843
+4445,888,RETIRED IP ORT REPLANT,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,1,51,0.019607843
+4446,888,RETIRED IP ORT REPLANT,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,6,51,0.117647059
+4447,888,RETIRED IP ORT REPLANT,520064,CEFAZOLIN 2 G IVPB,Medication,1,51,0.019607843
+4448,888,RETIRED IP ORT REPLANT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,51,0.019607843
+4449,888,RETIRED IP ORT REPLANT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,2,51,0.039215686
+4450,888,RETIRED IP ORT REPLANT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,3,51,0.058823529
+4451,888,RETIRED IP ORT REPLANT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,2,51,0.039215686
+4452,888,RETIRED IP ORT REPLANT,LABCBCD,CBC WITH DIFF,Lab,12,51,0.235294118
+4453,888,RETIRED IP ORT REPLANT,LABMETB,"METABOLIC PANEL, BASIC",Lab,12,51,0.235294118
+4454,888,RETIRED IP ORT REPLANT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4,51,0.078431373
+4455,888,RETIRED IP ORT REPLANT,LABPLTS,PLATELET COUNT,Lab,1,51,0.019607843
+4456,888,RETIRED IP ORT REPLANT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,5,51,0.098039216
+4457,891,IP ORT TOTAL JOINT REPLACEMENT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1160,17250,0.067246377
+4458,891,IP ORT TOTAL JOINT REPLACEMENT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2877,17250,0.166782609
+4459,891,IP ORT TOTAL JOINT REPLACEMENT,680,ASPIRIN 81 MG PO CHEW,Medication,2,17250,0.000115942
+4460,891,IP ORT TOTAL JOINT REPLACEMENT,1080,BISACODYL 10 MG PR SUPP,Medication,4915,17250,0.284927536
+4461,891,IP ORT TOTAL JOINT REPLACEMENT,1801,CODEINE SULFATE 15 MG PO TABS,Medication,130,17250,0.007536232
+4462,891,IP ORT TOTAL JOINT REPLACEMENT,1802,CODEINE SULFATE 30 MG PO TABS,Medication,5,17250,0.000289855
+4463,891,IP ORT TOTAL JOINT REPLACEMENT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,3753,17250,0.217565217
+4464,891,IP ORT TOTAL JOINT REPLACEMENT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1206,17250,0.069913043
+4465,891,IP ORT TOTAL JOINT REPLACEMENT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,1,17250,5.80E-05
+4466,891,IP ORT TOTAL JOINT REPLACEMENT,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,2,17250,0.000115942
+4467,891,IP ORT TOTAL JOINT REPLACEMENT,3760,HYDROMORPHONE 2 MG PO TABS,Medication,35,17250,0.002028986
+4468,891,IP ORT TOTAL JOINT REPLACEMENT,3761,HYDROMORPHONE 4 MG PO TABS,Medication,7,17250,0.000405797
+4469,891,IP ORT TOTAL JOINT REPLACEMENT,4318,LACTATED RINGERS IV SOLP,Medication,355,17250,0.02057971
+4470,891,IP ORT TOTAL JOINT REPLACEMENT,4711,MAGNESIUM CITRATE PO SOLN,Medication,4703,17250,0.272637681
+4471,891,IP ORT TOTAL JOINT REPLACEMENT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1177,17250,0.068231884
+4472,891,IP ORT TOTAL JOINT REPLACEMENT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1493,17250,0.086550725
+4473,891,IP ORT TOTAL JOINT REPLACEMENT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,9,17250,0.000521739
+4474,891,IP ORT TOTAL JOINT REPLACEMENT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,63,17250,0.003652174
+4475,891,IP ORT TOTAL JOINT REPLACEMENT,6434,POTASSIUM CHLORIDE 20 MEQ PO PACK,Medication,1,17250,5.80E-05
+4476,891,IP ORT TOTAL JOINT REPLACEMENT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,304,17250,0.017623188
+4477,891,IP ORT TOTAL JOINT REPLACEMENT,8083,TRAZODONE 50 MG PO TABS,Medication,1,17250,5.80E-05
+4478,891,IP ORT TOTAL JOINT REPLACEMENT,8085,TRAZODONE 50 MG PO TABS,Medication,129,17250,0.007478261
+4479,891,IP ORT TOTAL JOINT REPLACEMENT,8748,WARFARIN 10 MG PO TABS,Medication,8,17250,0.000463768
+4480,891,IP ORT TOTAL JOINT REPLACEMENT,8751,WARFARIN 5 MG PO TABS,Medication,3,17250,0.000173913
+4481,891,IP ORT TOTAL JOINT REPLACEMENT,8752,WARFARIN 7.5 MG PO TABS,Medication,6,17250,0.000347826
+4482,891,IP ORT TOTAL JOINT REPLACEMENT,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,113,17250,0.006550725
+4483,891,IP ORT TOTAL JOINT REPLACEMENT,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,33,17250,0.001913043
+4484,891,IP ORT TOTAL JOINT REPLACEMENT,10181,"HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",Medication,2,17250,0.000115942
+4485,891,IP ORT TOTAL JOINT REPLACEMENT,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,43,17250,0.002492754
+4486,891,IP ORT TOTAL JOINT REPLACEMENT,10814,OXYCODONE 5 MG PO TABS,Medication,5314,17250,0.308057971
+4487,891,IP ORT TOTAL JOINT REPLACEMENT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,6712,17250,0.389101449
+4488,891,IP ORT TOTAL JOINT REPLACEMENT,11664,WARFARIN 1 MG PO TABS,Medication,2,17250,0.000115942
+4489,891,IP ORT TOTAL JOINT REPLACEMENT,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,1,17250,5.80E-05
+4490,891,IP ORT TOTAL JOINT REPLACEMENT,14632,TRAMADOL 50 MG PO TABS,Medication,458,17250,0.026550725
+4491,891,IP ORT TOTAL JOINT REPLACEMENT,16830,MELATONIN 3 MG PO TABS,Medication,880,17250,0.051014493
+4492,891,IP ORT TOTAL JOINT REPLACEMENT,18307,GABAPENTIN 400 MG PO CAPS,Medication,12,17250,0.000695652
+4493,891,IP ORT TOTAL JOINT REPLACEMENT,18308,GABAPENTIN 300 MG PO CAPS,Medication,614,17250,0.035594203
+4494,891,IP ORT TOTAL JOINT REPLACEMENT,18309,GABAPENTIN 100 MG PO CAPS,Medication,1088,17250,0.063072464
+4495,891,IP ORT TOTAL JOINT REPLACEMENT,19434,WARFARIN 6 MG PO TABS,Medication,1,17250,5.80E-05
+4496,891,IP ORT TOTAL JOINT REPLACEMENT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,3569,17250,0.206898551
+4497,891,IP ORT TOTAL JOINT REPLACEMENT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,4985,17250,0.288985507
+4498,891,IP ORT TOTAL JOINT REPLACEMENT,25855,GABAPENTIN 600 MG PO TABS,Medication,2,17250,0.000115942
+4499,891,IP ORT TOTAL JOINT REPLACEMENT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4323,17250,0.250608696
+4500,891,IP ORT TOTAL JOINT REPLACEMENT,27920,OXYCODONE 10 MG PO TB12,Medication,307,17250,0.017797101
+4501,891,IP ORT TOTAL JOINT REPLACEMENT,27921,OXYCODONE 20 MG PO TB12,Medication,67,17250,0.003884058
+4502,891,IP ORT TOTAL JOINT REPLACEMENT,27922,OXYCODONE 40 MG PO TB12,Medication,26,17250,0.001507246
+4503,891,IP ORT TOTAL JOINT REPLACEMENT,28899,OXYCODONE 15 MG PO TABS,Medication,21,17250,0.001217391
+4504,891,IP ORT TOTAL JOINT REPLACEMENT,28900,OXYCODONE 30 MG PO TABS,Medication,17,17250,0.000985507
+4505,891,IP ORT TOTAL JOINT REPLACEMENT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,31,17250,0.001797101
+4506,891,IP ORT TOTAL JOINT REPLACEMENT,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,1,17250,5.80E-05
+4507,891,IP ORT TOTAL JOINT REPLACEMENT,35943,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,Medication,2,17250,0.000115942
+4508,891,IP ORT TOTAL JOINT REPLACEMENT,77052,CELECOXIB 200 MG PO CAPS,Medication,223,17250,0.012927536
+4509,891,IP ORT TOTAL JOINT REPLACEMENT,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,51,17250,0.002956522
+4510,891,IP ORT TOTAL JOINT REPLACEMENT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,4218,17250,0.244521739
+4511,891,IP ORT TOTAL JOINT REPLACEMENT,82918,GABAPENTIN 250 MG/5 ML PO SOLN,Medication,1,17250,5.80E-05
+4512,891,IP ORT TOTAL JOINT REPLACEMENT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,4876,17250,0.282666667
+4513,891,IP ORT TOTAL JOINT REPLACEMENT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,3566,17250,0.206724638
+4514,891,IP ORT TOTAL JOINT REPLACEMENT,92173,ZOLPIDEM 5 MG PO TABS,Medication,981,17250,0.056869565
+4515,891,IP ORT TOTAL JOINT REPLACEMENT,93295,ZOLPIDEM 10 MG PO TABS,Medication,7,17250,0.000405797
+4516,891,IP ORT TOTAL JOINT REPLACEMENT,93715,CELECOXIB 100 MG PO CAPS,Medication,342,17250,0.019826087
+4517,891,IP ORT TOTAL JOINT REPLACEMENT,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,17250,5.80E-05
+4518,891,IP ORT TOTAL JOINT REPLACEMENT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,806,17250,0.046724638
+4519,891,IP ORT TOTAL JOINT REPLACEMENT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,133,17250,0.007710145
+4520,891,IP ORT TOTAL JOINT REPLACEMENT,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,2,17250,0.000115942
+4521,891,IP ORT TOTAL JOINT REPLACEMENT,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,2,17250,0.000115942
+4522,891,IP ORT TOTAL JOINT REPLACEMENT,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,17250,5.80E-05
+4523,891,IP ORT TOTAL JOINT REPLACEMENT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1196,17250,0.069333333
+4524,891,IP ORT TOTAL JOINT REPLACEMENT,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,205,17250,0.011884058
+4525,891,IP ORT TOTAL JOINT REPLACEMENT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,17250,5.80E-05
+4526,891,IP ORT TOTAL JOINT REPLACEMENT,116506,MELATONIN 1 MG PO TABS,Medication,4,17250,0.000231884
+4527,891,IP ORT TOTAL JOINT REPLACEMENT,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,261,17250,0.015130435
+4528,891,IP ORT TOTAL JOINT REPLACEMENT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,6,17250,0.000347826
+4529,891,IP ORT TOTAL JOINT REPLACEMENT,125757,PREGABALIN 50 MG PO CAPS,Medication,1,17250,5.80E-05
+4530,891,IP ORT TOTAL JOINT REPLACEMENT,125758,PREGABALIN 75 MG PO CAPS,Medication,173,17250,0.010028986
+4531,891,IP ORT TOTAL JOINT REPLACEMENT,125759,PREGABALIN 100 MG PO CAPS,Medication,9,17250,0.000521739
+4532,891,IP ORT TOTAL JOINT REPLACEMENT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,4958,17250,0.28742029
+4533,891,IP ORT TOTAL JOINT REPLACEMENT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,64,17250,0.003710145
+4534,891,IP ORT TOTAL JOINT REPLACEMENT,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,2481,17250,0.143826087
+4535,891,IP ORT TOTAL JOINT REPLACEMENT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,17250,0.000115942
+4536,891,IP ORT TOTAL JOINT REPLACEMENT,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,103,17250,0.005971014
+4537,891,IP ORT TOTAL JOINT REPLACEMENT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,27,17250,0.001565217
+4538,891,IP ORT TOTAL JOINT REPLACEMENT,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,1845,17250,0.106956522
+4539,891,IP ORT TOTAL JOINT REPLACEMENT,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,185,17250,0.010724638
+4540,891,IP ORT TOTAL JOINT REPLACEMENT,229996,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP,Medication,146,17250,0.008463768
+4541,891,IP ORT TOTAL JOINT REPLACEMENT,233900,OXYCODONE 10 MG PO TR12,Medication,52,17250,0.003014493
+4542,891,IP ORT TOTAL JOINT REPLACEMENT,540102,NS IV BOLUS,Medication,2724,17250,0.157913043
+4543,891,IP ORT TOTAL JOINT REPLACEMENT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,48,17250,0.002782609
+4544,891,IP ORT TOTAL JOINT REPLACEMENT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,4212,17250,0.244173913
+4545,891,IP ORT TOTAL JOINT REPLACEMENT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,4141,17250,0.240057971
+4546,891,IP ORT TOTAL JOINT REPLACEMENT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,6,17250,0.000347826
+4547,891,IP ORT TOTAL JOINT REPLACEMENT,540579,BENZOCAINE-MENTHOL SUGAR FREE MAXIMUM STRENGTH LOZG,Medication,1,17250,5.80E-05
+4548,891,IP ORT TOTAL JOINT REPLACEMENT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,387,17250,0.022434783
+4549,891,IP ORT TOTAL JOINT REPLACEMENT,9000000,ZZZ IMS TEMPLATE,Medication,50,17250,0.002898551
+4550,891,IP ORT TOTAL JOINT REPLACEMENT,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,1623,17250,0.094086957
+4551,891,IP ORT TOTAL JOINT REPLACEMENT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,808,17250,0.04684058
+4552,891,IP ORT TOTAL JOINT REPLACEMENT,LABCBCO,CBC W/O DIFF,Lab,5051,17250,0.292811594
+4553,891,IP ORT TOTAL JOINT REPLACEMENT,LABHCTX,HEMATOCRIT,Lab,68,17250,0.003942029
+4554,891,IP ORT TOTAL JOINT REPLACEMENT,LABMETB,"METABOLIC PANEL, BASIC",Lab,5048,17250,0.292637681
+4555,891,IP ORT TOTAL JOINT REPLACEMENT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4612,17250,0.267362319
+4556,891,IP ORT TOTAL JOINT REPLACEMENT,LABPLTS,PLATELET COUNT,Lab,21,17250,0.001217391
+4557,891,IP ORT TOTAL JOINT REPLACEMENT,LABPT,PROTHROMBIN TIME,Lab,68,17250,0.003942029
+4558,891,IP ORT TOTAL JOINT REPLACEMENT,IMGDXHIP2LT,XR HIP 2V LT,Imaging,12,17250,0.000695652
+4559,891,IP ORT TOTAL JOINT REPLACEMENT,IMGDXHIP2RT,XR HIP 2V RT,Imaging,14,17250,0.000811594
+4560,891,IP ORT TOTAL JOINT REPLACEMENT,IMGDXKN2LT,XR KNEE 1 OR 2V LT,Imaging,37,17250,0.002144928
+4561,891,IP ORT TOTAL JOINT REPLACEMENT,IMGDXKN2RT,XR KNEE 1 OR 2V RT,Imaging,61,17250,0.003536232
+4562,891,IP ORT TOTAL JOINT REPLACEMENT,IMGDXPEL,XR PELVIS 1V,Imaging,363,17250,0.021043478
+4563,891,IP ORT TOTAL JOINT REPLACEMENT,IMGUSNVLOW,US VEIN LOWER EXTREMITY BILAT R/O DVT,Imaging,33,17250,0.001913043
+4564,891,IP ORT TOTAL JOINT REPLACEMENT,IMGXR0070,XR KNEE 2 VIEWS LEFT,Imaging,1,17250,5.80E-05
+4565,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,29,1620,0.017901235
+4566,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,272,1620,0.167901235
+4567,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,586,1620,0.361728395
+4568,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,1801,CODEINE SULFATE 15 MG PO TABS,Medication,105,1620,0.064814815
+4569,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,1802,CODEINE SULFATE 30 MG PO TABS,Medication,2,1620,0.001234568
+4570,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,426,1620,0.262962963
+4571,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,166,1620,0.102469136
+4572,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,1620,0.000617284
+4573,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,3760,HYDROMORPHONE 2 MG PO TABS,Medication,7,1620,0.004320988
+4574,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,135,1620,0.083333333
+4575,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,572,1620,0.35308642
+4576,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,15,1620,0.009259259
+4577,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,450,1620,0.277777778
+4578,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,1620,0.000617284
+4579,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,14,1620,0.008641975
+4580,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,8085,TRAZODONE 50 MG PO TABS,Medication,9,1620,0.005555556
+4581,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,35,1620,0.021604938
+4582,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,5,1620,0.00308642
+4583,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,334,1620,0.20617284
+4584,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,615,1620,0.37962963
+4585,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,1,1620,0.000617284
+4586,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,18308,GABAPENTIN 300 MG PO CAPS,Medication,135,1620,0.083333333
+4587,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,18309,GABAPENTIN 100 MG PO CAPS,Medication,22,1620,0.013580247
+4588,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,460,1620,0.283950617
+4589,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,543,1620,0.335185185
+4590,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,425,1620,0.262345679
+4591,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,27920,OXYCODONE 10 MG PO TB12,Medication,80,1620,0.049382716
+4592,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,27921,OXYCODONE 20 MG PO TB12,Medication,10,1620,0.00617284
+4593,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,27922,OXYCODONE 40 MG PO TB12,Medication,3,1620,0.001851852
+4594,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,1620,0.001234568
+4595,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,77052,CELECOXIB 200 MG PO CAPS,Medication,3,1620,0.001851852
+4596,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,26,1620,0.016049383
+4597,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,545,1620,0.336419753
+4598,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,583,1620,0.359876543
+4599,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,481,1620,0.29691358
+4600,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,73,1620,0.045061728
+4601,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,93715,CELECOXIB 100 MG PO CAPS,Medication,7,1620,0.004320988
+4602,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,56,1620,0.034567901
+4603,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,1620,0.000617284
+4604,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,1620,0.001234568
+4605,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,192,1620,0.118518519
+4606,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,57,1620,0.035185185
+4607,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,30,1620,0.018518519
+4608,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,563,1620,0.347530864
+4609,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,1620,0.000617284
+4610,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,206114,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,Medication,157,1620,0.09691358
+4611,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,1620,0.000617284
+4612,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,5,1620,0.00308642
+4613,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,169,1620,0.104320988
+4614,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,229996,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP,Medication,26,1620,0.016049383
+4615,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,233900,OXYCODONE 10 MG PO TR12,Medication,28,1620,0.017283951
+4616,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,233902,OXYCODONE 20 MG PO TR12,Medication,1,1620,0.000617284
+4617,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,233904,OXYCODONE 40 MG PO TR12,Medication,1,1620,0.000617284
+4618,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,540102,NS IV BOLUS,Medication,338,1620,0.208641975
+4619,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,8,1620,0.004938272
+4620,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,560,1620,0.345679012
+4621,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,527,1620,0.325308642
+4622,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,23,1620,0.014197531
+4623,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,2,1620,0.001234568
+4624,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,LABCBCO,CBC W/O DIFF,Lab,545,1620,0.336419753
+4625,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,579,1620,0.357407407
+4626,893,IP ORT SHOULDER ELBOW ADMISSION POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,499,1620,0.308024691
+4627,894,IP ICU COPD,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,3,91,0.032967033
+4628,894,IP ICU COPD,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,8,91,0.087912088
+4629,894,IP ICU COPD,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,91,0.010989011
+4630,894,IP ICU COPD,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,91,0.032967033
+4631,894,IP ICU COPD,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,32,91,0.351648352
+4632,894,IP ICU COPD,84263,BECLOMETHASONE DIPROPIONATE 80 MCG/ACTUATION INH AERO,Medication,1,91,0.010989011
+4633,894,IP ICU COPD,89401,FLUTICASONE-SALMETEROL 500-50 MCG/DOSE INH DSDV,Medication,3,91,0.032967033
+4634,894,IP ICU COPD,92642,LEVALBUTEROL HCL 1.25 MG/3 ML INH NEBU,Medication,3,91,0.032967033
+4635,894,IP ICU COPD,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,2,91,0.021978022
+4636,894,IP ICU COPD,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,91,0.054945055
+4637,894,IP ICU COPD,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,10,91,0.10989011
+4638,894,IP ICU COPD,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,6,91,0.065934066
+4639,894,IP ICU COPD,520039,AZITHROMYCIN IVPB,Medication,5,91,0.054945055
+4640,894,IP ICU COPD,520078,METHYLPREDNISOLONE IVPB,Medication,17,91,0.186813187
+4641,894,IP ICU COPD,540115,LACTATED RINGERS IV BOLUS,Medication,1,91,0.010989011
+4642,894,IP ICU COPD,LABPLTS,PLATELET COUNT,Lab,5,91,0.054945055
+4643,894,IP ICU COPD,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,9,91,0.098901099
+4644,894,IP ICU COPD,IMGDXCH1,XR CHEST 1V,Imaging,5,91,0.054945055
+4645,901,IP ICU PNEUMONIA,9487,CEFTRIAXONE 1 GRAM IV SOLR,Medication,5,124,0.040322581
+4646,901,IP ICU PNEUMONIA,9488,CEFTRIAXONE 2 GRAM IV SOLR,Medication,1,124,0.008064516
+4647,901,IP ICU PNEUMONIA,21063,AZITHROMYCIN 500 MG IV SOLR,Medication,7,124,0.056451613
+4648,901,IP ICU PNEUMONIA,88005,PIPERACILLIN-TAZOBACTAM 4.5 GRAM IV SOLR,Medication,5,124,0.040322581
+4649,901,IP ICU PNEUMONIA,88533,AZTREONAM 2 GRAM INJ SOLR,Medication,3,124,0.024193548
+4650,901,IP ICU PNEUMONIA,89444,PIPERACILLIN-TAZOBACTAM-DEXTRS 2.25 GRAM/50 ML IV PGBK,Medication,1,124,0.008064516
+4651,901,IP ICU PNEUMONIA,93461,MOXIFLOXACIN IN NACL (ISO-OSM) 400 MG/250 ML IV PGBK,Medication,2,124,0.016129032
+4652,901,IP ICU PNEUMONIA,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,8,124,0.064516129
+4653,901,IP ICU PNEUMONIA,112321,LINEZOLID 600 MG/300 ML IV SOLP,Medication,2,124,0.016129032
+4654,901,IP ICU PNEUMONIA,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,11,124,0.088709677
+4655,901,IP ICU PNEUMONIA,123696,CIPROFLOXACIN 400 MG/40 ML IV SOLN,Medication,1,124,0.008064516
+4656,901,IP ICU PNEUMONIA,520039,AZITHROMYCIN IVPB,Medication,3,124,0.024193548
+4657,901,IP ICU PNEUMONIA,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,13,124,0.10483871
+4658,901,IP ICU PNEUMONIA,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,7,124,0.056451613
+4659,901,IP ICU PNEUMONIA,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,2,124,0.016129032
+4660,901,IP ICU PNEUMONIA,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,124,0.016129032
+4661,901,IP ICU PNEUMONIA,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,1,124,0.008064516
+4662,901,IP ICU PNEUMONIA,541097,AZTREONAM 2 GRAM/100 ML MINI-BAG PLUS,Medication,3,124,0.024193548
+4663,901,IP ICU PNEUMONIA,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,7,124,0.056451613
+4664,901,IP ICU PNEUMONIA,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,12,124,0.096774194
+4665,901,IP ICU PNEUMONIA,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,2,124,0.016129032
+4666,901,IP ICU PNEUMONIA,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,43,124,0.346774194
+4667,901,IP ICU PNEUMONIA,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,43,124,0.346774194
+4668,901,IP ICU PNEUMONIA,LABLEGU,"LEGIONELLA AG, URINE",Lab,8,124,0.064516129
+4669,901,IP ICU PNEUMONIA,LABRESP,RESPIRATORY CULTURE,Lab,8,124,0.064516129
+4670,901,IP ICU PNEUMONIA,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,39,124,0.314516129
+4671,901,IP ICU PNEUMONIA,IMGCTT,CT THORAX,Imaging,1,124,0.008064516
+4672,901,IP ICU PNEUMONIA,IMGDXCH1,XR CHEST 1V,Imaging,19,124,0.153225806
+4673,902,IP ICU SEPSIS MANAGEMENT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,516,0.005813953
+4674,902,IP ICU SEPSIS MANAGEMENT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,516,0.003875969
+4675,902,IP ICU SEPSIS MANAGEMENT,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,4,516,0.007751938
+4676,902,IP ICU SEPSIS MANAGEMENT,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,1,516,0.001937984
+4677,902,IP ICU SEPSIS MANAGEMENT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,3,516,0.005813953
+4678,902,IP ICU SEPSIS MANAGEMENT,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,20,516,0.03875969
+4679,902,IP ICU SEPSIS MANAGEMENT,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,13,516,0.025193798
+4680,902,IP ICU SEPSIS MANAGEMENT,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,2,516,0.003875969
+4681,902,IP ICU SEPSIS MANAGEMENT,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,17,516,0.032945736
+4682,902,IP ICU SEPSIS MANAGEMENT,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,516,0.001937984
+4683,902,IP ICU SEPSIS MANAGEMENT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,9,516,0.01744186
+4684,902,IP ICU SEPSIS MANAGEMENT,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,1,516,0.001937984
+4685,902,IP ICU SEPSIS MANAGEMENT,520039,AZITHROMYCIN IVPB,Medication,3,516,0.005813953
+4686,902,IP ICU SEPSIS MANAGEMENT,520045,MEROPENEM CUSTOM IVPB,Medication,10,516,0.019379845
+4687,902,IP ICU SEPSIS MANAGEMENT,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,65,516,0.125968992
+4688,902,IP ICU SEPSIS MANAGEMENT,520054,DAPTOMYCIN IVPB,Medication,1,516,0.001937984
+4689,902,IP ICU SEPSIS MANAGEMENT,520057,CASPOFUNGIN IVPB (CUSTOM),Medication,2,516,0.003875969
+4690,902,IP ICU SEPSIS MANAGEMENT,520058,CASPOFUNGIN 70 MG IVPB,Medication,3,516,0.005813953
+4691,902,IP ICU SEPSIS MANAGEMENT,530006,EPINEPHRINE IV INFUSION,Medication,3,516,0.005813953
+4692,902,IP ICU SEPSIS MANAGEMENT,530011,NOREPINEPHRINE IV INFUSION,Medication,31,516,0.060077519
+4693,902,IP ICU SEPSIS MANAGEMENT,530012,PHENYLEPHRINE IV INFUSION,Medication,5,516,0.009689922
+4694,902,IP ICU SEPSIS MANAGEMENT,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,16,516,0.031007752
+4695,902,IP ICU SEPSIS MANAGEMENT,540102,NS IV BOLUS,Medication,19,516,0.036821705
+4696,902,IP ICU SEPSIS MANAGEMENT,540773,PIPERACILLIN-TAZOBACTAM (ZOSYN) 3.375 GRAM IN DEXTROSE IVPB,Medication,1,516,0.001937984
+4697,902,IP ICU SEPSIS MANAGEMENT,540960,MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS,Medication,1,516,0.001937984
+4698,902,IP ICU SEPSIS MANAGEMENT,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,25,516,0.048449612
+4699,902,IP ICU SEPSIS MANAGEMENT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,15,516,0.029069767
+4700,902,IP ICU SEPSIS MANAGEMENT,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,2,516,0.003875969
+4701,902,IP ICU SEPSIS MANAGEMENT,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,4,516,0.007751938
+4702,902,IP ICU SEPSIS MANAGEMENT,541092,AMPICILLIN 2 GRAM/100 ML MINI-BAG PLUS,Medication,1,516,0.001937984
+4703,902,IP ICU SEPSIS MANAGEMENT,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,15,516,0.029069767
+4704,902,IP ICU SEPSIS MANAGEMENT,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,4,516,0.007751938
+4705,902,IP ICU SEPSIS MANAGEMENT,541445,LR IV BOLUS - 1000 ML,Medication,1,516,0.001937984
+4706,902,IP ICU SEPSIS MANAGEMENT,541447,NS IV BOLUS - 30 ML/KG,Medication,1,516,0.001937984
+4707,902,IP ICU SEPSIS MANAGEMENT,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,3,516,0.005813953
+4708,902,IP ICU SEPSIS MANAGEMENT,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,3,516,0.005813953
+4709,902,IP ICU SEPSIS MANAGEMENT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,175,516,0.339147287
+4710,902,IP ICU SEPSIS MANAGEMENT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,161,516,0.312015504
+4711,902,IP ICU SEPSIS MANAGEMENT,LABBLCSTK,"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY",Lab,10,516,0.019379845
+4712,902,IP ICU SEPSIS MANAGEMENT,LABBLCTIP,BLOOD CULT CENTRAL LINE CATHETER BY NURSE,Lab,10,516,0.019379845
+4713,902,IP ICU SEPSIS MANAGEMENT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,3,516,0.005813953
+4714,902,IP ICU SEPSIS MANAGEMENT,LABCBCD,CBC WITH DIFF,Lab,59,516,0.114341085
+4715,902,IP ICU SEPSIS MANAGEMENT,LABGRAM,GRAM STAIN,Lab,10,516,0.019379845
+4716,902,IP ICU SEPSIS MANAGEMENT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,17,516,0.032945736
+4717,902,IP ICU SEPSIS MANAGEMENT,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,164,516,0.317829457
+4718,902,IP ICU SEPSIS MANAGEMENT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,40,516,0.07751938
+4719,902,IP ICU SEPSIS MANAGEMENT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4,516,0.007751938
+4720,902,IP ICU SEPSIS MANAGEMENT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,4,516,0.007751938
+4721,902,IP ICU SEPSIS MANAGEMENT,LABPLTS,PLATELET COUNT,Lab,9,516,0.01744186
+4722,902,IP ICU SEPSIS MANAGEMENT,LABPT,PROTHROMBIN TIME,Lab,28,516,0.054263566
+4723,902,IP ICU SEPSIS MANAGEMENT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,19,516,0.036821705
+4724,902,IP ICU SEPSIS MANAGEMENT,LABRESP,RESPIRATORY CULTURE,Lab,30,516,0.058139535
+4725,902,IP ICU SEPSIS MANAGEMENT,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,85,516,0.164728682
+4726,902,IP ICU SEPSIS MANAGEMENT,LABTYPSNI,TYPE AND SCREEN,Lab,2,516,0.003875969
+4727,902,IP ICU SEPSIS MANAGEMENT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,129,516,0.25
+4728,902,IP ICU SEPSIS MANAGEMENT,LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,99,516,0.191860465
+4729,902,IP ICU SEPSIS MANAGEMENT,LABURNC,URINE CULTURE,Lab,20,516,0.03875969
+4730,902,IP ICU SEPSIS MANAGEMENT,LABVANPOL,VANCOMYCIN PEAK LEVEL,Lab,5,516,0.009689922
+4731,902,IP ICU SEPSIS MANAGEMENT,LABVANPRL,VANCOMYCIN TROUGH LEVEL,Lab,20,516,0.03875969
+4732,902,IP ICU SEPSIS MANAGEMENT,LABVBG,"BLOOD GASES, VENOUS",Lab,7,516,0.013565891
+4733,902,IP ICU SEPSIS MANAGEMENT,IMGCTAP,CT ABDOMEN PELVIS,Imaging,2,516,0.003875969
+4734,902,IP ICU SEPSIS MANAGEMENT,IMGDXCH1,XR CHEST 1V,Imaging,28,516,0.054263566
+4735,902,IP ICU SEPSIS MANAGEMENT,IMGUSAB1,US ABDOMEN,Imaging,1,516,0.001937984
+4736,904,IP ICU/CCU GENERAL ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1342,22594,0.0593963
+4737,904,IP ICU/CCU GENERAL ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,71,22594,0.003142427
+4738,904,IP ICU/CCU GENERAL ADMIT,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,78,22594,0.003452244
+4739,904,IP ICU/CCU GENERAL ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,933,22594,0.041294149
+4740,904,IP ICU/CCU GENERAL ADMIT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,4,22594,0.000177038
+4741,904,IP ICU/CCU GENERAL ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,679,22594,0.030052226
+4742,904,IP ICU/CCU GENERAL ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,270,22594,0.011950075
+4743,904,IP ICU/CCU GENERAL ADMIT,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,766,22594,0.033902806
+4744,904,IP ICU/CCU GENERAL ADMIT,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,3,22594,0.000132779
+4745,904,IP ICU/CCU GENERAL ADMIT,4711,MAGNESIUM CITRATE PO SOLN,Medication,49,22594,0.002168717
+4746,904,IP ICU/CCU GENERAL ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,274,22594,0.012127113
+4747,904,IP ICU/CCU GENERAL ADMIT,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,10,22594,0.000442595
+4748,904,IP ICU/CCU GENERAL ADMIT,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,246,22594,0.010887846
+4749,904,IP ICU/CCU GENERAL ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,68,22594,0.003009649
+4750,904,IP ICU/CCU GENERAL ADMIT,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,4335,22594,0.191865097
+4751,904,IP ICU/CCU GENERAL ADMIT,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,4,22594,0.000177038
+4752,904,IP ICU/CCU GENERAL ADMIT,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,1,22594,4.43E-05
+4753,904,IP ICU/CCU GENERAL ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,86,22594,0.00380632
+4754,904,IP ICU/CCU GENERAL ADMIT,10246,IBUPROFEN 100 MG/5 ML PO SUSP,Medication,7,22594,0.000309817
+4755,904,IP ICU/CCU GENERAL ADMIT,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,89,22594,0.003939099
+4756,904,IP ICU/CCU GENERAL ADMIT,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,125,22594,0.005532442
+4757,904,IP ICU/CCU GENERAL ADMIT,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,218,22594,0.009648579
+4758,904,IP ICU/CCU GENERAL ADMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1177,22594,0.052093476
+4759,904,IP ICU/CCU GENERAL ADMIT,11664,WARFARIN 1 MG PO TABS,Medication,1,22594,4.43E-05
+4760,904,IP ICU/CCU GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,151,22594,0.00668319
+4761,904,IP ICU/CCU GENERAL ADMIT,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,22594,4.43E-05
+4762,904,IP ICU/CCU GENERAL ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,160,22594,0.007081526
+4763,904,IP ICU/CCU GENERAL ADMIT,15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,88,22594,0.003894839
+4764,904,IP ICU/CCU GENERAL ADMIT,15982,"DOBUTAMINE IN D5W 1,000 MG/250 ML (4,000 MCG/ML) IV SOLP",Medication,6,22594,0.000265557
+4765,904,IP ICU/CCU GENERAL ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,123,22594,0.005443923
+4766,904,IP ICU/CCU GENERAL ADMIT,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,9,22594,0.000398336
+4767,904,IP ICU/CCU GENERAL ADMIT,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,4,22594,0.000177038
+4768,904,IP ICU/CCU GENERAL ADMIT,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,9,22594,0.000398336
+4769,904,IP ICU/CCU GENERAL ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,904,22594,0.040010622
+4770,904,IP ICU/CCU GENERAL ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,362,22594,0.016021953
+4771,904,IP ICU/CCU GENERAL ADMIT,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,4,22594,0.000177038
+4772,904,IP ICU/CCU GENERAL ADMIT,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,1,22594,4.43E-05
+4773,904,IP ICU/CCU GENERAL ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,6,22594,0.000265557
+4774,904,IP ICU/CCU GENERAL ADMIT,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,1026,22594,0.045410286
+4775,904,IP ICU/CCU GENERAL ADMIT,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,1,22594,4.43E-05
+4776,904,IP ICU/CCU GENERAL ADMIT,84520,ONDANSETRON 8 MG PO TBDL,Medication,29,22594,0.001283527
+4777,904,IP ICU/CCU GENERAL ADMIT,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,2,22594,8.85E-05
+4778,904,IP ICU/CCU GENERAL ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,109,22594,0.00482429
+4779,904,IP ICU/CCU GENERAL ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,101,22594,0.004470213
+4780,904,IP ICU/CCU GENERAL ADMIT,88967,LANSOPRAZOLE 30 MG PO CPDR,Medication,1,22594,4.43E-05
+4781,904,IP ICU/CCU GENERAL ADMIT,92585,FAMOTIDINE 40 MG/5 ML PO SUSP,Medication,2,22594,8.85E-05
+4782,904,IP ICU/CCU GENERAL ADMIT,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,15,22594,0.000663893
+4783,904,IP ICU/CCU GENERAL ADMIT,94176,ONDANSETRON 4 MG PO TBDL,Medication,418,22594,0.018500487
+4784,904,IP ICU/CCU GENERAL ADMIT,94621,AZTREONAM IN DEXTROSE(ISO-OSM) 2 GRAM/50 ML IV PGBK,Medication,1,22594,4.43E-05
+4785,904,IP ICU/CCU GENERAL ADMIT,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,16,22594,0.000708153
+4786,904,IP ICU/CCU GENERAL ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,311,22594,0.013764716
+4787,904,IP ICU/CCU GENERAL ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,29,22594,0.001283527
+4788,904,IP ICU/CCU GENERAL ADMIT,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,3,22594,0.000132779
+4789,904,IP ICU/CCU GENERAL ADMIT,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,3,22594,0.000132779
+4790,904,IP ICU/CCU GENERAL ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,285,22594,0.012613968
+4791,904,IP ICU/CCU GENERAL ADMIT,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,350,22594,0.015490838
+4792,904,IP ICU/CCU GENERAL ADMIT,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,62,22594,0.002744091
+4793,904,IP ICU/CCU GENERAL ADMIT,115683,LANSOPRAZOLE 30 MG PO TBLD,Medication,11,22594,0.000486855
+4794,904,IP ICU/CCU GENERAL ADMIT,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,479,22594,0.021200319
+4795,904,IP ICU/CCU GENERAL ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3071,22594,0.135921041
+4796,904,IP ICU/CCU GENERAL ADMIT,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,143,22594,0.006329114
+4797,904,IP ICU/CCU GENERAL ADMIT,202543,WHITE PETROLATUM-MINERAL OIL 83-15 % OPHT OINT,Medication,5,22594,0.000221298
+4798,904,IP ICU/CCU GENERAL ADMIT,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,699,22594,0.030937417
+4799,904,IP ICU/CCU GENERAL ADMIT,203654,WHITE PETROLATUM-MINERAL OIL 56.8-42.5 % OPHT OINT,Medication,5,22594,0.000221298
+4800,904,IP ICU/CCU GENERAL ADMIT,205192,MILRINONE IN D5W 20 MG/100 ML IV PGBK,Medication,41,22594,0.001814641
+4801,904,IP ICU/CCU GENERAL ADMIT,206168,"DOPAMINE IN D5W 800 MG/250 ML (3,200 MCG/ML) IV SOLN",Medication,6,22594,0.000265557
+4802,904,IP ICU/CCU GENERAL ADMIT,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,80,22594,0.003540763
+4803,904,IP ICU/CCU GENERAL ADMIT,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,176,22594,0.007789679
+4804,904,IP ICU/CCU GENERAL ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,117,22594,0.005178366
+4805,904,IP ICU/CCU GENERAL ADMIT,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,60,22594,0.002655572
+4806,904,IP ICU/CCU GENERAL ADMIT,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,1698,22594,0.075152695
+4807,904,IP ICU/CCU GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1198,22594,0.053022926
+4808,904,IP ICU/CCU GENERAL ADMIT,222229,DOCUSATE SODIUM 283 MG/5 ML PR ENEM,Medication,6,22594,0.000265557
+4809,904,IP ICU/CCU GENERAL ADMIT,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,66,22594,0.00292113
+4810,904,IP ICU/CCU GENERAL ADMIT,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,489,22594,0.021642914
+4811,904,IP ICU/CCU GENERAL ADMIT,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,7,22594,0.000309817
+4812,904,IP ICU/CCU GENERAL ADMIT,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,7,22594,0.000309817
+4813,904,IP ICU/CCU GENERAL ADMIT,520011,TOBRAMYCIN IVPB,Medication,2,22594,8.85E-05
+4814,904,IP ICU/CCU GENERAL ADMIT,520039,AZITHROMYCIN IVPB,Medication,25,22594,0.001106488
+4815,904,IP ICU/CCU GENERAL ADMIT,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,270,22594,0.011950075
+4816,904,IP ICU/CCU GENERAL ADMIT,520064,CEFAZOLIN 2 G IVPB,Medication,4,22594,0.000177038
+4817,904,IP ICU/CCU GENERAL ADMIT,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,51,22594,0.002257236
+4818,904,IP ICU/CCU GENERAL ADMIT,530006,EPINEPHRINE IV INFUSION,Medication,107,22594,0.004735771
+4819,904,IP ICU/CCU GENERAL ADMIT,530011,NOREPINEPHRINE IV INFUSION,Medication,244,22594,0.010799327
+4820,904,IP ICU/CCU GENERAL ADMIT,530012,PHENYLEPHRINE IV INFUSION,Medication,101,22594,0.004470213
+4821,904,IP ICU/CCU GENERAL ADMIT,530025,DEXMEDETOMIDINE IV INFUSION,Medication,148,22594,0.006550412
+4822,904,IP ICU/CCU GENERAL ADMIT,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,154,22594,0.006815969
+4823,904,IP ICU/CCU GENERAL ADMIT,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,352,22594,0.015579357
+4824,904,IP ICU/CCU GENERAL ADMIT,540102,NS IV BOLUS,Medication,105,22594,0.004647251
+4825,904,IP ICU/CCU GENERAL ADMIT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,573,22594,0.025360715
+4826,904,IP ICU/CCU GENERAL ADMIT,540115,LACTATED RINGERS IV BOLUS,Medication,110,22594,0.004868549
+4827,904,IP ICU/CCU GENERAL ADMIT,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,1,22594,4.43E-05
+4828,904,IP ICU/CCU GENERAL ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,5,22594,0.000221298
+4829,904,IP ICU/CCU GENERAL ADMIT,540744,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,Medication,5177,22594,0.229131628
+4830,904,IP ICU/CCU GENERAL ADMIT,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,289,22594,0.012791006
+4831,904,IP ICU/CCU GENERAL ADMIT,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,44,22594,0.00194742
+4832,904,IP ICU/CCU GENERAL ADMIT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,145,22594,0.006417633
+4833,904,IP ICU/CCU GENERAL ADMIT,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,17,22594,0.000752412
+4834,904,IP ICU/CCU GENERAL ADMIT,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,28,22594,0.001239267
+4835,904,IP ICU/CCU GENERAL ADMIT,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,6,22594,0.000265557
+4836,904,IP ICU/CCU GENERAL ADMIT,541097,AZTREONAM 2 GRAM/100 ML MINI-BAG PLUS,Medication,9,22594,0.000398336
+4837,904,IP ICU/CCU GENERAL ADMIT,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,22594,8.85E-05
+4838,904,IP ICU/CCU GENERAL ADMIT,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,9,22594,0.000398336
+4839,904,IP ICU/CCU GENERAL ADMIT,LABBBPCRY,CRYOPRECIPITATE,Lab,2,22594,8.85E-05
+4840,904,IP ICU/CCU GENERAL ADMIT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,329,22594,0.014561388
+4841,904,IP ICU/CCU GENERAL ADMIT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,327,22594,0.014472869
+4842,904,IP ICU/CCU GENERAL ADMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,2098,22594,0.092856511
+4843,904,IP ICU/CCU GENERAL ADMIT,LABCBCD,CBC WITH DIFF,Lab,6633,22594,0.293573515
+4844,904,IP ICU/CCU GENERAL ADMIT,LABDDIM,D - DIMER,Lab,44,22594,0.00194742
+4845,904,IP ICU/CCU GENERAL ADMIT,LABDIC,DIC SCREEN,Lab,465,22594,0.020580685
+4846,904,IP ICU/CCU GENERAL ADMIT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,5340,22594,0.236345933
+4847,904,IP ICU/CCU GENERAL ADMIT,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,2482,22594,0.109852173
+4848,904,IP ICU/CCU GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,7441,22594,0.329335222
+4849,904,IP ICU/CCU GENERAL ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4054,22594,0.179428167
+4850,904,IP ICU/CCU GENERAL ADMIT,LABMYO,"MYOGLOBIN, SERUM / PLASMA",Lab,29,22594,0.001283527
+4851,904,IP ICU/CCU GENERAL ADMIT,LABNTBNP,NT - PROBNP,Lab,405,22594,0.017925113
+4852,904,IP ICU/CCU GENERAL ADMIT,LABPALB,PREALBUMIN,Lab,137,22594,0.006063557
+4853,904,IP ICU/CCU GENERAL ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2583,22594,0.114322386
+4854,904,IP ICU/CCU GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,817,22594,0.036160042
+4855,904,IP ICU/CCU GENERAL ADMIT,LABPT,PROTHROMBIN TIME,Lab,2009,22594,0.088917412
+4856,904,IP ICU/CCU GENERAL ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1085,22594,0.048021599
+4857,904,IP ICU/CCU GENERAL ADMIT,LABROMRS,MRSA SCREEN,Lab,4568,22594,0.202177569
+4858,904,IP ICU/CCU GENERAL ADMIT,LABTNI,TROPONIN I,Lab,1219,22594,0.053952377
+4859,904,IP ICU/CCU GENERAL ADMIT,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,43,22594,0.00190316
+4860,904,IP ICU/CCU GENERAL ADMIT,LABTYPSNI,TYPE AND SCREEN,Lab,1152,22594,0.050986988
+4861,904,IP ICU/CCU GENERAL ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,348,22594,0.015402319
+4862,904,IP ICU/CCU GENERAL ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,243,22594,0.010755068
+4863,904,IP ICU/CCU GENERAL ADMIT,LABURNC,URINE CULTURE,Lab,331,22594,0.014649907
+4864,904,IP ICU/CCU GENERAL ADMIT,POC14,POC GLUCOSE BY METER,Lab,1284,22594,0.056829247
+4865,904,IP ICU/CCU GENERAL ADMIT,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,1803,22594,0.079799947
+4866,904,IP ICU/CCU GENERAL ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,387,22594,0.017128441
+4867,904,IP ICU/CCU GENERAL ADMIT,IMGDXAB1,XR ABDOMEN 1V,Imaging,48,22594,0.002124458
+4868,904,IP ICU/CCU GENERAL ADMIT,IMGDXCH1,XR CHEST 1V,Imaging,1654,22594,0.073205276
+4869,904,IP ICU/CCU GENERAL ADMIT,LABBBPCRY,CRYOPRECIPITATE,Blood Product,8,22594,0.000354076
+4870,904,IP ICU/CCU GENERAL ADMIT,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,77,22594,0.003407984
+4871,904,IP ICU/CCU GENERAL ADMIT,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,59,22594,0.002611313
+4872,904,IP ICU/CCU GENERAL ADMIT,LABBBPRBC,RED BLOOD CELLS,Blood Product,155,22594,0.006860228
+4873,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,264,2337,0.11296534
+4874,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,2,2337,0.000855798
+4875,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,664,ASCORBIC ACID 500 MG PO TABS,Medication,110,2337,0.047068892
+4876,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,103,2337,0.044073599
+4877,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,1755,CLONIDINE HCL 0.1 MG PO TABS,Medication,23,2337,0.009841677
+4878,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,1756,CLONIDINE HCL 0.2 MG PO TABS,Medication,21,2337,0.008985879
+4879,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,152,2337,0.06504065
+4880,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,169,2337,0.072314934
+4881,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,67,2337,0.028669234
+4882,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,98,2337,0.041934104
+4883,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,273,2337,0.116816431
+4884,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,112,2337,0.04792469
+4885,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,5751,"NYSTATIN 100,000 UNIT/ML PO SUSP",Medication,292,2337,0.124946513
+4886,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,6493,PREDNISONE 1 MG PO TABS,Medication,3,2337,0.001283697
+4887,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,6494,PREDNISONE 10 MG PO TABS,Medication,77,2337,0.032948224
+4888,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,6496,PREDNISONE 20 MG PO TABS,Medication,390,2337,0.166880616
+4889,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,6497,PREDNISONE 5 MG PO TABS,Medication,13,2337,0.005562687
+4890,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,322,2337,0.137783483
+4891,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,8982,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",Medication,11,2337,0.004706889
+4892,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,9,2337,0.003851091
+4893,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,10050,FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK,Medication,1,2337,0.000427899
+4894,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,10181,"HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",Medication,8,2337,0.003423192
+4895,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,280,2337,0.119811724
+4896,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,61,2337,0.02610184
+4897,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,97,2337,0.041506205
+4898,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,10,2337,0.00427899
+4899,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,77883,VALGANCICLOVIR 450 MG PO TABS,Medication,338,2337,0.144629867
+4900,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,80562,MYCOPHENOLATE MOFETIL 250 MG PO CAPS,Medication,3,2337,0.001283697
+4901,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,81371,TACROLIMUS 5 MG PO CAPS,Medication,115,2337,0.049208387
+4902,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,14,2337,0.005990586
+4903,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,33,2337,0.014120668
+4904,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,94670,TACROLIMUS 1 MG PO CAPS,Medication,573,2337,0.245186136
+4905,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,95803,TACROLIMUS 0.5 MG PO CAPS,Medication,6,2337,0.002567394
+4906,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,97,2337,0.041506205
+4907,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,4,2337,0.001711596
+4908,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,111320,MULTIVITAMIN WITH IRON-MINERAL PO LIQD,Medication,2,2337,0.000855798
+4909,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,5,2337,0.002139495
+4910,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,7,2337,0.002995293
+4911,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,297,2337,0.127086008
+4912,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,212,2337,0.090714591
+4913,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,1,2337,0.000427899
+4914,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,3,2337,0.001283697
+4915,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,842,2337,0.360290971
+4916,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,209553,VALGANCICLOVIR 50 MG/ML PO SOLR,Medication,4,2337,0.001711596
+4917,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,142,2337,0.06076166
+4918,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,263,2337,0.112537441
+4919,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,11,2337,0.004706889
+4920,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,500017,1/2 NS WITH SODIUM BICARBONATE IV INFUSION,Medication,320,2337,0.136927685
+4921,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,12,2337,0.005134788
+4922,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,520072,BASILIXIMAB IVPB,Medication,5,2337,0.002139495
+4923,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,520073,MYCOPHENOLATE IVPB,Medication,208,2337,0.089002995
+4924,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540102,NS IV BOLUS,Medication,11,2337,0.004706889
+4925,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,8,2337,0.003423192
+4926,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540128,BUPIVACAINE 0.125 % ANESTHETIC PUMP,Medication,18,2337,0.007702182
+4927,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540134,CYCLOSPORINE MODIFIED (NEORAL) 25 MG PO CAPS,Medication,3,2337,0.001283697
+4928,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540135,CYCLOSPORINE MODIFIED (NEORAL) 100 MG PO CAPS,Medication,10,2337,0.00427899
+4929,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540302,POTASSIUM CHLORIDE IV SCALE,Medication,8,2337,0.003423192
+4930,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540489,BUPIVACAINE 0.5% ANESTHETIC PUMP 270 ML SINGLE SITE,Medication,1,2337,0.000427899
+4931,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540492,SHC TRANSPLANT BEDSIDE MEDS HELP ORDER,Medication,149,2337,0.063756953
+4932,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,153,2337,0.065468549
+4933,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,64,2337,0.027385537
+4934,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,55,2337,0.023534446
+4935,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,3,2337,0.001283697
+4936,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,107,2337,0.045785195
+4937,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,2,2337,0.000855798
+4938,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,417,2337,0.17843389
+4939,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABAMY,"AMYLASE, SERUM",Lab,11,2337,0.004706889
+4940,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,242,2337,0.103551562
+4941,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,293,2337,0.125374412
+4942,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABCBCD,CBC WITH DIFF,Lab,683,2337,0.292255028
+4943,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABCYCWBL,"CYCLOSPORINE, WHOLE BLOOD",Lab,13,2337,0.005562687
+4944,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABFK506L,TACROLIMUS (FK506),Lab,362,2337,0.154899444
+4945,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,358,2337,0.153187848
+4946,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,294,2337,0.125802311
+4947,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,380,2337,0.162601626
+4948,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABMYCPA,MYCOPHENOLIC ACID,Lab,92,2337,0.039366709
+4949,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,381,2337,0.163029525
+4950,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABPLTS,PLATELET COUNT,Lab,36,2337,0.015404365
+4951,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,LABSIROL,SIROLIMUS LEVEL,Lab,1,2337,0.000427899
+4952,918,IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,235,2337,0.100556269
+4953,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABAFP,"ALPHA FETOPROTEIN, SERUM",Lab,1,5,0.2
+4954,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABCBCD,CBC WITH DIFF,Lab,2,5,0.4
+4955,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABHBSAG,HBSAG,Lab,1,5,0.2
+4956,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABHCVA,HEPATITIS C AB IGG,Lab,1,5,0.2
+4957,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,5,0.2
+4958,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,5,0.2
+4959,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2,5,0.4
+4960,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABRPRQLT,RPR W/REFLEX TO TITER,Lab,2,5,0.4
+4961,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABTYPSNI,TYPE AND SCREEN,Lab,1,5,0.2
+4962,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,5,0.2
+4963,919,IP TRS LIVER TRANSPLANT EVAL MOTC,LABYA1AS,ALPHA - 1 - ANTITRYPSIN,Lab,1,5,0.2
+4964,919,IP TRS LIVER TRANSPLANT EVAL MOTC,IMGDXCH2,XR CHEST 2V,Imaging,1,5,0.2
+4965,920,IP TRS LIVER TRANSPLANT POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,187,2549,0.073362103
+4966,920,IP TRS LIVER TRANSPLANT POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,475,2549,0.186347587
+4967,920,IP TRS LIVER TRANSPLANT POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,27,2549,0.010592389
+4968,920,IP TRS LIVER TRANSPLANT POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,156,2549,0.061200471
+4969,920,IP TRS LIVER TRANSPLANT POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,201,2549,0.078854453
+4970,920,IP TRS LIVER TRANSPLANT POSTOP,5751,"NYSTATIN 100,000 UNIT/ML PO SUSP",Medication,115,2549,0.045115732
+4971,920,IP TRS LIVER TRANSPLANT POSTOP,10050,FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK,Medication,17,2549,0.006669282
+4972,920,IP TRS LIVER TRANSPLANT POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,2549,0.000392311
+4973,920,IP TRS LIVER TRANSPLANT POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,185,2549,0.072577481
+4974,920,IP TRS LIVER TRANSPLANT POSTOP,81371,TACROLIMUS 5 MG PO CAPS,Medication,17,2549,0.006669282
+4975,920,IP TRS LIVER TRANSPLANT POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,218,2549,0.085523735
+4976,920,IP TRS LIVER TRANSPLANT POSTOP,85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,1,2549,0.000392311
+4977,920,IP TRS LIVER TRANSPLANT POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,14,2549,0.00549235
+4978,920,IP TRS LIVER TRANSPLANT POSTOP,94670,TACROLIMUS 1 MG PO CAPS,Medication,63,2549,0.024715575
+4979,920,IP TRS LIVER TRANSPLANT POSTOP,95803,TACROLIMUS 0.5 MG PO CAPS,Medication,2,2549,0.000784621
+4980,920,IP TRS LIVER TRANSPLANT POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,104,2549,0.040800314
+4981,920,IP TRS LIVER TRANSPLANT POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,21,2549,0.008238525
+4982,920,IP TRS LIVER TRANSPLANT POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,7,2549,0.002746175
+4983,920,IP TRS LIVER TRANSPLANT POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,22,2549,0.008630836
+4984,920,IP TRS LIVER TRANSPLANT POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,51,2549,0.020007846
+4985,920,IP TRS LIVER TRANSPLANT POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,11,2549,0.004315418
+4986,920,IP TRS LIVER TRANSPLANT POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,321,2549,0.125931738
+4987,920,IP TRS LIVER TRANSPLANT POSTOP,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,474,2549,0.185955277
+4988,920,IP TRS LIVER TRANSPLANT POSTOP,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,39,2549,0.015300118
+4989,920,IP TRS LIVER TRANSPLANT POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,11,2549,0.004315418
+4990,920,IP TRS LIVER TRANSPLANT POSTOP,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,14,2549,0.00549235
+4991,920,IP TRS LIVER TRANSPLANT POSTOP,520045,MEROPENEM CUSTOM IVPB,Medication,1,2549,0.000392311
+4992,920,IP TRS LIVER TRANSPLANT POSTOP,520057,CASPOFUNGIN IVPB (CUSTOM),Medication,65,2549,0.025500196
+4993,920,IP TRS LIVER TRANSPLANT POSTOP,520058,CASPOFUNGIN 70 MG IVPB,Medication,13,2549,0.005100039
+4994,920,IP TRS LIVER TRANSPLANT POSTOP,520073,MYCOPHENOLATE IVPB,Medication,4,2549,0.001569243
+4995,920,IP TRS LIVER TRANSPLANT POSTOP,520074,ANTITHYMOCYTE GLOBULIN RABBIT IVPB,Medication,520,2549,0.204001569
+4996,920,IP TRS LIVER TRANSPLANT POSTOP,520180,"HEPATITIS B IMMUNE GLOBULIN (HEPAGAM B) IVPB (20,000 UNITS DOSE)",Medication,20,2549,0.007846214
+4997,920,IP TRS LIVER TRANSPLANT POSTOP,530006,EPINEPHRINE IV INFUSION,Medication,24,2549,0.009415457
+4998,920,IP TRS LIVER TRANSPLANT POSTOP,530008,NITROPRUSSIDE IV INFUSION,Medication,98,2549,0.03844645
+4999,920,IP TRS LIVER TRANSPLANT POSTOP,530012,PHENYLEPHRINE IV INFUSION,Medication,36,2549,0.014123186
+5000,920,IP TRS LIVER TRANSPLANT POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,134,2549,0.052569635
+5001,920,IP TRS LIVER TRANSPLANT POSTOP,540129,TACROLIMUS 1 MG/ML ORAL SUSPENSION,Medication,433,2549,0.169870537
+5002,920,IP TRS LIVER TRANSPLANT POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,2549,0.000784621
+5003,920,IP TRS LIVER TRANSPLANT POSTOP,540528,TACROLIMUS (GENERIC) 1 MG PO CAPS,Medication,2,2549,0.000784621
+5004,920,IP TRS LIVER TRANSPLANT POSTOP,540960,MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS,Medication,5,2549,0.001961554
+5005,920,IP TRS LIVER TRANSPLANT POSTOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,67,2549,0.026284818
+5006,920,IP TRS LIVER TRANSPLANT POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,12,2549,0.004707729
+5007,920,IP TRS LIVER TRANSPLANT POSTOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,102,2549,0.040015692
+5008,920,IP TRS LIVER TRANSPLANT POSTOP,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,1,2549,0.000392311
+5009,920,IP TRS LIVER TRANSPLANT POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,38,2549,0.014907807
+5010,920,IP TRS LIVER TRANSPLANT POSTOP,LABCBCO,CBC W/O DIFF,Lab,982,2549,0.385249117
+5011,920,IP TRS LIVER TRANSPLANT POSTOP,LABFIB,FIBRINOGEN,Lab,241,2549,0.094546881
+5012,920,IP TRS LIVER TRANSPLANT POSTOP,LABFK506L,TACROLIMUS (FK506),Lab,490,2549,0.192232248
+5013,920,IP TRS LIVER TRANSPLANT POSTOP,LABK,"POTASSIUM, SERUM/PLASMA",Lab,147,2549,0.057669674
+5014,920,IP TRS LIVER TRANSPLANT POSTOP,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,194,2549,0.076108278
+5015,920,IP TRS LIVER TRANSPLANT POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,7,2549,0.002746175
+5016,920,IP TRS LIVER TRANSPLANT POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,725,2549,0.284425265
+5017,920,IP TRS LIVER TRANSPLANT POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,495,2549,0.194193801
+5018,920,IP TRS LIVER TRANSPLANT POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,379,2549,0.148685759
+5019,920,IP TRS LIVER TRANSPLANT POSTOP,LABPLTS,PLATELET COUNT,Lab,4,2549,0.001569243
+5020,920,IP TRS LIVER TRANSPLANT POSTOP,LABPT,PROTHROMBIN TIME,Lab,608,2549,0.238524912
+5021,920,IP TRS LIVER TRANSPLANT POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,432,2549,0.169478227
+5022,920,IP TRS LIVER TRANSPLANT POSTOP,POC14,POC GLUCOSE BY METER,Lab,160,2549,0.062769714
+5023,920,IP TRS LIVER TRANSPLANT POSTOP,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,196,2549,0.076892899
+5024,920,IP TRS LIVER TRANSPLANT POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,221,2549,0.086700667
+5025,920,IP TRS LIVER TRANSPLANT POSTOP,IMGUSTXL,US LIVER TRANSPLANT,Imaging,216,2549,0.084739113
+5026,921,IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,27,147,0.183673469
+5027,921,IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,35,147,0.238095238
+5028,921,IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,1,147,0.006802721
+5029,921,IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,34,147,0.231292517
+5030,921,IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,40,147,0.272108844
+5031,921,IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP,LABHCTX,HEMATOCRIT,Lab,50,147,0.340136054
+5032,922,IP TRS LIVER TRANSPLANT PREOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,137,0.00729927
+5033,922,IP TRS LIVER TRANSPLANT PREOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,137,0.00729927
+5034,922,IP TRS LIVER TRANSPLANT PREOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,7,137,0.051094891
+5035,922,IP TRS LIVER TRANSPLANT PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,11,137,0.080291971
+5036,922,IP TRS LIVER TRANSPLANT PREOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,1,137,0.00729927
+5037,922,IP TRS LIVER TRANSPLANT PREOP,520064,CEFAZOLIN 2 G IVPB,Medication,1,137,0.00729927
+5038,922,IP TRS LIVER TRANSPLANT PREOP,10273R,HLA ORDER - WORKUP AT TIME OF TRANSPLANT,Lab,24,137,0.175182482
+5039,922,IP TRS LIVER TRANSPLANT PREOP,LABCBCD,CBC WITH DIFF,Lab,108,137,0.788321168
+5040,922,IP TRS LIVER TRANSPLANT PREOP,LABCMVIGG,CMV IGG,Lab,114,137,0.832116788
+5041,922,IP TRS LIVER TRANSPLANT PREOP,LABCMVIGM,CMV IGM,Lab,25,137,0.182481752
+5042,922,IP TRS LIVER TRANSPLANT PREOP,LABDCMVL,DONOR CMV- TOTAL AB,Lab,45,137,0.328467153
+5043,922,IP TRS LIVER TRANSPLANT PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,86,137,0.627737226
+5044,922,IP TRS LIVER TRANSPLANT PREOP,LABPREG,"PREGNANCY TEST, SERUM",Lab,24,137,0.175182482
+5045,922,IP TRS LIVER TRANSPLANT PREOP,LABPT,PROTHROMBIN TIME,Lab,110,137,0.802919708
+5046,922,IP TRS LIVER TRANSPLANT PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,107,137,0.781021898
+5047,922,IP TRS LIVER TRANSPLANT PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,106,137,0.773722628
+5048,922,IP TRS LIVER TRANSPLANT PREOP,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,49,137,0.357664234
+5049,922,IP TRS LIVER TRANSPLANT PREOP,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,52,137,0.379562044
+5050,922,IP TRS LIVER TRANSPLANT PREOP,IMGDXCH1,XR CHEST 1V,Imaging,40,137,0.291970803
+5051,922,IP TRS LIVER TRANSPLANT PREOP,IMGDXCH2,XR CHEST 2V,Imaging,55,137,0.401459854
+5052,922,IP TRS LIVER TRANSPLANT PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,112,137,0.817518248
+5053,925,IP GEN ABDOMINAL PAIN,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,11,0.090909091
+5054,925,IP GEN ABDOMINAL PAIN,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,11,0.090909091
+5055,925,IP GEN ABDOMINAL PAIN,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,11,0.181818182
+5056,925,IP GEN ABDOMINAL PAIN,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,11,0.090909091
+5057,925,IP GEN ABDOMINAL PAIN,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,5,11,0.454545455
+5058,925,IP GEN ABDOMINAL PAIN,540141,GI COCKTAIL,Medication,1,11,0.090909091
+5059,925,IP GEN ABDOMINAL PAIN,LABCBCD,CBC WITH DIFF,Lab,4,11,0.363636364
+5060,925,IP GEN ABDOMINAL PAIN,LABCBCO,CBC W/O DIFF,Lab,3,11,0.272727273
+5061,925,IP GEN ABDOMINAL PAIN,LABCK,"CREATINE KINASE, TOTAL",Lab,1,11,0.090909091
+5062,925,IP GEN ABDOMINAL PAIN,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,11,0.090909091
+5063,925,IP GEN ABDOMINAL PAIN,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,11,0.090909091
+5064,925,IP GEN ABDOMINAL PAIN,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,11,0.181818182
+5065,925,IP GEN ABDOMINAL PAIN,LABPT,PROTHROMBIN TIME,Lab,3,11,0.272727273
+5066,925,IP GEN ABDOMINAL PAIN,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,11,0.181818182
+5067,925,IP GEN ABDOMINAL PAIN,IMGDXAB2,ABDOMEN WITH DECUB AND/OR UPRIGHT,Imaging,1,11,0.090909091
+5068,926,RETIRED IP GEN ANEMIA (SHORT SET),101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,124,0.008064516
+5069,926,IP GEN ANEMIA (SHORT SET),101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,124,0.008064516
+5070,926,RETIRED IP GEN ANEMIA (SHORT SET),2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,124,0.008064516
+5071,926,IP GEN ANEMIA (SHORT SET),2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,124,0.008064516
+5072,926,RETIRED IP GEN ANEMIA (SHORT SET),3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,1,124,0.008064516
+5073,926,IP GEN ANEMIA (SHORT SET),3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,1,124,0.008064516
+5074,926,RETIRED IP GEN ANEMIA (SHORT SET),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,124,0.024193548
+5075,926,IP GEN ANEMIA (SHORT SET),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,124,0.024193548
+5076,926,RETIRED IP GEN ANEMIA (SHORT SET),LABCBCD,CBC WITH DIFF,Lab,6,124,0.048387097
+5077,926,IP GEN ANEMIA (SHORT SET),LABCBCD,CBC WITH DIFF,Lab,6,124,0.048387097
+5078,926,RETIRED IP GEN ANEMIA (SHORT SET),LABESRP,SEDIMENTATION RATE (ESR),Lab,22,124,0.177419355
+5079,926,IP GEN ANEMIA (SHORT SET),LABESRP,SEDIMENTATION RATE (ESR),Lab,22,124,0.177419355
+5080,926,RETIRED IP GEN ANEMIA (SHORT SET),LABFE,"IRON, TOTAL",Lab,109,124,0.879032258
+5081,926,IP GEN ANEMIA (SHORT SET),LABFE,"IRON, TOTAL",Lab,109,124,0.879032258
+5082,926,RETIRED IP GEN ANEMIA (SHORT SET),LABFER,FERRITIN,Lab,103,124,0.830645161
+5083,926,IP GEN ANEMIA (SHORT SET),LABFER,FERRITIN,Lab,103,124,0.830645161
+5084,926,RETIRED IP GEN ANEMIA (SHORT SET),LABHGHPLC,HGB QUANTITATIVE / FRACTIONATED HPLC,Lab,1,124,0.008064516
+5085,926,IP GEN ANEMIA (SHORT SET),LABHGHPLC,HGB QUANTITATIVE / FRACTIONATED HPLC,Lab,1,124,0.008064516
+5086,926,RETIRED IP GEN ANEMIA (SHORT SET),LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,5,124,0.040322581
+5087,926,IP GEN ANEMIA (SHORT SET),LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,5,124,0.040322581
+5088,926,RETIRED IP GEN ANEMIA (SHORT SET),LABPLTFUN,PLATELET FUNCTION SCREEN,Lab,1,124,0.008064516
+5089,926,IP GEN ANEMIA (SHORT SET),LABPLTFUN,PLATELET FUNCTION SCREEN,Lab,1,124,0.008064516
+5090,926,RETIRED IP GEN ANEMIA (SHORT SET),LABPT,PROTHROMBIN TIME,Lab,6,124,0.048387097
+5091,926,IP GEN ANEMIA (SHORT SET),LABPT,PROTHROMBIN TIME,Lab,6,124,0.048387097
+5092,926,RETIRED IP GEN ANEMIA (SHORT SET),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,7,124,0.056451613
+5093,926,IP GEN ANEMIA (SHORT SET),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,7,124,0.056451613
+5094,926,RETIRED IP GEN ANEMIA (SHORT SET),LABRETIC,RETICULOCYTE COUNT,Lab,75,124,0.60483871
+5095,926,IP GEN ANEMIA (SHORT SET),LABRETIC,RETICULOCYTE COUNT,Lab,75,124,0.60483871
+5096,926,RETIRED IP GEN ANEMIA (SHORT SET),LABTYPSNI,TYPE AND SCREEN,Lab,15,124,0.120967742
+5097,926,IP GEN ANEMIA (SHORT SET),LABTYPSNI,TYPE AND SCREEN,Lab,15,124,0.120967742
+5098,926,RETIRED IP GEN ANEMIA (SHORT SET),LABBBPRBC,RED BLOOD CELLS,Blood Product,12,124,0.096774194
+5099,926,IP GEN ANEMIA (SHORT SET),LABBBPRBC,RED BLOOD CELLS,Blood Product,12,124,0.096774194
+5100,927,IP GEN CONSTIPATION,1080,BISACODYL 10 MG PR SUPP,Medication,372,629,0.591414944
+5101,927,IP GEN CONSTIPATION,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,183,629,0.290937997
+5102,927,IP GEN CONSTIPATION,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,11,629,0.017488076
+5103,927,IP GEN CONSTIPATION,5087,MINERAL OIL PR ENEM,Medication,7,629,0.011128776
+5104,927,IP GEN CONSTIPATION,11349,SENNOSIDES 8.6 MG PO TABS,Medication,219,629,0.348171701
+5105,927,IP GEN CONSTIPATION,13632,BISACODYL 5 MG PO TBEC,Medication,234,629,0.372019078
+5106,927,IP GEN CONSTIPATION,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,288,629,0.457869634
+5107,927,IP GEN CONSTIPATION,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,307,629,0.488076312
+5108,927,IP GEN CONSTIPATION,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,174,629,0.276629571
+5109,927,IP GEN CONSTIPATION,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,7,629,0.011128776
+5110,927,IP GEN CONSTIPATION,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,58,629,0.092209857
+5111,927,IP GEN CONSTIPATION,230523,PSYLLIUM HUSK 6 GRAM PO PWPK,Medication,39,629,0.06200318
+5112,928,RETIRED IP GEN DELIRIUM,3584,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,Medication,1,179,0.005586592
+5113,928,RETIRED IP GEN DELIRIUM,77034,QUETIAPINE 25 MG PO TABS,Medication,7,179,0.039106145
+5114,928,RETIRED IP GEN DELIRIUM,LABCA,"CALCIUM, SERUM/PLASMA",Lab,1,179,0.005586592
+5115,928,RETIRED IP GEN DELIRIUM,LABCBCD,CBC WITH DIFF,Lab,6,179,0.033519553
+5116,928,RETIRED IP GEN DELIRIUM,LABHFP,HEPATIC FUNCTION PANEL A,Lab,1,179,0.005586592
+5117,928,RETIRED IP GEN DELIRIUM,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,179,0.011173184
+5118,928,RETIRED IP GEN DELIRIUM,LABNH3,"AMMONIA, PLASMA",Lab,3,179,0.016759777
+5119,928,RETIRED IP GEN DELIRIUM,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,179,0.005586592
+5120,928,RETIRED IP GEN DELIRIUM,IMGCTH,CT HEAD,Imaging,1,179,0.005586592
+5121,928,RETIRED IP GEN DELIRIUM,IMGDXCH1,XR CHEST 1V,Imaging,3,179,0.016759777
+5122,929,IP GEN DYSPNEA,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,7,39,0.179487179
+5123,929,IP GEN DYSPNEA,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,11,39,0.282051282
+5124,929,IP GEN DYSPNEA,LABABG,"BLOOD GASES, ARTERIAL",Lab,2,39,0.051282051
+5125,929,IP GEN DYSPNEA,LABMB,"CK, MB (MASS)",Lab,1,39,0.025641026
+5126,929,IP GEN DYSPNEA,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,39,0.025641026
+5127,929,IP GEN DYSPNEA,LABPT,PROTHROMBIN TIME,Lab,1,39,0.025641026
+5128,929,IP GEN DYSPNEA,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,39,0.025641026
+5129,929,IP GEN DYSPNEA,LABTNI,TROPONIN I,Lab,3,39,0.076923077
+5130,929,IP GEN DYSPNEA,IMGDXCH1,XR CHEST 1V,Imaging,1,39,0.025641026
+5131,930,IP GEN FEVER DIAGNOSTIC,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,6,7,0.857142857
+5132,930,IP GEN FEVER DIAGNOSTIC,LABURNC,URINE CULTURE,Lab,5,7,0.714285714
+5133,930,IP GEN FEVER DIAGNOSTIC,IMGDXCH1,XR CHEST 1V,Imaging,4,7,0.571428571
+5134,931,IP GEN HYPERCALCEMIA DIAGNOSTIC,LAB11098R,1 25 OH VITAMIN D,Lab,3,3,1
+5135,931,IP GEN HYPERCALCEMIA DIAGNOSTIC,LABPTH,PARATHYROID HORMONE,Lab,1,3,0.333333333
+5136,931,IP GEN HYPERCALCEMIA DIAGNOSTIC,LABSPIE,"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM",Lab,1,3,0.333333333
+5137,931,IP GEN HYPERCALCEMIA DIAGNOSTIC,LABUPIE,URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS,Lab,1,3,0.333333333
+5138,932,IP/ED GEN HYPERKALEMIA THERAPY,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,207,1546,0.13389392
+5139,932,IP/ED GEN HYPERKALEMIA THERAPY,251,ALBUTEROL SULFATE 5 MG/ML INH NEBU,Medication,1,1546,0.000646831
+5140,932,IP/ED GEN HYPERKALEMIA THERAPY,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,607,1546,0.392626132
+5141,932,IP/ED GEN HYPERKALEMIA THERAPY,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,111,1546,0.071798189
+5142,932,IP/ED GEN HYPERKALEMIA THERAPY,7309,SODIUM BICARBONATE 8.4 % IV SOLN,Medication,2,1546,0.001293661
+5143,932,IP/ED GEN HYPERKALEMIA THERAPY,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,4,1546,0.002587322
+5144,932,IP/ED GEN HYPERKALEMIA THERAPY,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,57,1546,0.03686934
+5145,932,IP/ED GEN HYPERKALEMIA THERAPY,27999,SODIUM POLYSTYRENE SULFONATE 15 GRAM/60 ML PO SUSP,Medication,263,1546,0.170116429
+5146,932,IP/ED GEN HYPERKALEMIA THERAPY,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,633,1546,0.409443726
+5147,932,IP/ED GEN HYPERKALEMIA THERAPY,111668,CALCIUM CHLORIDE 100 MG/ML (10 %) IV SYRG,Medication,5,1546,0.003234153
+5148,932,IP/ED GEN HYPERKALEMIA THERAPY,112287,DEXTROSE 50% IN WATER (D50W) IV SOLP,Medication,2,1546,0.001293661
+5149,932,IP/ED GEN HYPERKALEMIA THERAPY,112517,SODIUM BICARBONATE 8.4 % (1 MEQ/ML) IV SYRG,Medication,132,1546,0.08538163
+5150,932,IP/ED GEN HYPERKALEMIA THERAPY,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,7,1546,0.004527814
+5151,932,IP/ED GEN HYPERKALEMIA THERAPY,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,389,1546,0.251617076
+5152,932,IP/ED GEN HYPERKALEMIA THERAPY,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,40,1546,0.025873221
+5153,932,IP/ED GEN HYPERKALEMIA THERAPY,520097,SODIUM BICARBONATE IVPB,Medication,72,1546,0.046571798
+5154,932,IP/ED GEN HYPERKALEMIA THERAPY,540500,SODIUM BICARBONATE 50 MEQ / 50 ML IV BAG (DKA PROTOCOL),Medication,1,1546,0.000646831
+5155,932,IP/ED GEN HYPERKALEMIA THERAPY,540775,POLYETHYLENE GLYCOL 3350 17 GM (FOR KAYEXALATE PANEL),Medication,142,1546,0.091849935
+5156,932,IP/ED GEN HYPERKALEMIA THERAPY,LABMETB,"METABOLIC PANEL, BASIC",Lab,99,1546,0.064036223
+5157,932,IP/ED GEN HYPERKALEMIA THERAPY,POC14,POC GLUCOSE BY METER,Lab,576,1546,0.372574386
+5158,934,IP ELECTROLYTE REPLACEMENT SCALES,6427,POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK,Medication,24,15206,0.001578324
+5159,934,IP ELECTROLYTE REPLACEMENT SCALES,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,2673,15206,0.175785874
+5160,934,IP ELECTROLYTE REPLACEMENT SCALES,11076,POTASSIUM CHLORIDE 20 MEQ/100 ML IV PGBK,Medication,4,15206,0.000263054
+5161,934,IP ELECTROLYTE REPLACEMENT SCALES,11077,POTASSIUM CHLORIDE 20 MEQ/50 ML IV PGBK,Medication,9,15206,0.000591872
+5162,934,IP ELECTROLYTE REPLACEMENT SCALES,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,6,15206,0.000394581
+5163,934,IP ELECTROLYTE REPLACEMENT SCALES,35943,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,Medication,2,15206,0.000131527
+5164,934,IP ELECTROLYTE REPLACEMENT SCALES,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,3979,15206,0.261673024
+5165,934,IP ELECTROLYTE REPLACEMENT SCALES,200346,MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK,Medication,1,15206,6.58E-05
+5166,934,IP ELECTROLYTE REPLACEMENT SCALES,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,4187,15206,0.275351835
+5167,934,IP ELECTROLYTE REPLACEMENT SCALES,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,1690,15206,0.111140339
+5168,934,IP ELECTROLYTE REPLACEMENT SCALES,500006,SODIUM PHOSPHATE IVPB,Medication,468,15206,0.030777325
+5169,934,IP ELECTROLYTE REPLACEMENT SCALES,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,4147,15206,0.272721294
+5170,934,IP ELECTROLYTE REPLACEMENT SCALES,540302,POTASSIUM CHLORIDE IV SCALE,Medication,6537,15206,0.429896094
+5171,934,IP ELECTROLYTE REPLACEMENT SCALES,540304,MAGNESIUM SULFATE IV SCALE ,Medication,7601,15206,0.499868473
+5172,934,IP ELECTROLYTE REPLACEMENT SCALES,541351,POTASSIUM CHLORIDE TABLET ORAL REPLACEMENT 10-30 MEQ,Medication,2,15206,0.000131527
+5173,934,IP ELECTROLYTE REPLACEMENT SCALES,541352,POTASSIUM CHLORIDE TABLET ORAL REPLACEMENT 20-40 MEQ,Medication,2,15206,0.000131527
+5174,934,IP ELECTROLYTE REPLACEMENT SCALES,541355,POTASSIUM CHLORIDE 20 MEQ/15ML PO LIQUID ORAL SCALE 20MEQ,Medication,2,15206,0.000131527
+5175,934,IP ELECTROLYTE REPLACEMENT SCALES,541359,SODIUM PHOSPHATE IVPB 15 MMOL SCALE,Medication,1353,15206,0.088978035
+5176,934,IP ELECTROLYTE REPLACEMENT SCALES,541362,SODIUM PHOSPHATE IVPB SCALE 30 MMOL,Medication,1290,15206,0.084834934
+5177,934,IP ELECTROLYTE REPLACEMENT SCALES,541433,POTASSIUM IV CHLORIDE SCALE 10-30 MEQ,Medication,2,15206,0.000131527
+5178,935,IP GEN CHEST PAIN DIAGNOSTIC,540616,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (RT PANEL),Medication,1,9,0.111111111
+5179,935,IP GEN CHEST PAIN DIAGNOSTIC,LABABG,"BLOOD GASES, ARTERIAL",Lab,1,9,0.111111111
+5180,935,IP GEN CHEST PAIN DIAGNOSTIC,LABCBCD,CBC WITH DIFF,Lab,2,9,0.222222222
+5181,935,IP GEN CHEST PAIN DIAGNOSTIC,LABMB,"CK, MB (MASS)",Lab,5,9,0.555555556
+5182,935,IP GEN CHEST PAIN DIAGNOSTIC,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,9,0.111111111
+5183,935,IP GEN CHEST PAIN DIAGNOSTIC,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,9,0.111111111
+5184,935,IP GEN CHEST PAIN DIAGNOSTIC,LABPT,PROTHROMBIN TIME,Lab,2,9,0.222222222
+5185,935,IP GEN CHEST PAIN DIAGNOSTIC,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,9,0.222222222
+5186,935,IP GEN CHEST PAIN DIAGNOSTIC,LABTNI,TROPONIN I,Lab,7,9,0.777777778
+5187,935,IP GEN CHEST PAIN DIAGNOSTIC,IMGDXCH1,XR CHEST 1V,Imaging,4,9,0.444444444
+5188,936,IP GEN VOMITING/DIARRHEA,2516,DIPHENOXYLATE-ATROPINE 2.5-0.025 MG PO TABS,Medication,1,34,0.029411765
+5189,936,IP GEN VOMITING/DIARRHEA,4560,LOPERAMIDE 2 MG PO CAPS,Medication,2,34,0.058823529
+5190,936,IP GEN VOMITING/DIARRHEA,4572,LORAZEPAM 0.5 MG PO TABS,Medication,2,34,0.058823529
+5191,936,IP GEN VOMITING/DIARRHEA,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,7,34,0.205882353
+5192,936,IP GEN VOMITING/DIARRHEA,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,6,34,0.176470588
+5193,936,IP GEN VOMITING/DIARRHEA,7228,SIMETHICONE 40 MG/0.6 ML PO DRPS,Medication,1,34,0.029411765
+5194,936,IP GEN VOMITING/DIARRHEA,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,34,0.029411765
+5195,936,IP GEN VOMITING/DIARRHEA,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,5,34,0.147058824
+5196,936,IP GEN VOMITING/DIARRHEA,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,34,0.117647059
+5197,936,IP GEN VOMITING/DIARRHEA,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,2,34,0.058823529
+5198,936,IP GEN VOMITING/DIARRHEA,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1,34,0.029411765
+5199,936,IP GEN VOMITING/DIARRHEA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3,34,0.088235294
+5200,936,IP GEN VOMITING/DIARRHEA,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,34,0.029411765
+5201,936,IP GEN VOMITING/DIARRHEA,LAB385,GASTROENTERITIS PCR PANEL,Lab,2,34,0.058823529
+5202,936,IP GEN VOMITING/DIARRHEA,LABAMY,"AMYLASE, SERUM",Lab,1,34,0.029411765
+5203,936,IP GEN VOMITING/DIARRHEA,LABCBCD,CBC WITH DIFF,Lab,3,34,0.088235294
+5204,936,IP GEN VOMITING/DIARRHEA,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,4,34,0.117647059
+5205,936,IP GEN VOMITING/DIARRHEA,LABDVERTV,"ROTAVIRUS ANTIGEN, STOOL",Lab,4,34,0.117647059
+5206,936,IP GEN VOMITING/DIARRHEA,LABGASBLD,OCCULT BLOOD GASTRIC,Lab,1,34,0.029411765
+5207,936,IP GEN VOMITING/DIARRHEA,LABGIARS,GIARDIA SCREEN,Lab,2,34,0.058823529
+5208,936,IP GEN VOMITING/DIARRHEA,LABLIPS,LIPASE,Lab,3,34,0.088235294
+5209,936,IP GEN VOMITING/DIARRHEA,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,34,0.029411765
+5210,936,IP GEN VOMITING/DIARRHEA,POC14,POC GLUCOSE BY METER,Lab,1,34,0.029411765
+5211,936,IP GEN VOMITING/DIARRHEA,IMGDXAB1,XR ABDOMEN 1V,Imaging,4,34,0.117647059
+5212,936,IP GEN VOMITING/DIARRHEA,IMGUSAB1,US ABDOMEN,Imaging,1,34,0.029411765
+5213,937,RETIRED IP GEN BRONCHOSCOPY PRE PROCEDURE,4450,LIDOCAINE HCL 4 % TOPICAL SOLN,Medication,14,25,0.56
+5214,937,IP GEN BRONCHOSCOPY PRE PROCEDURE,4450,LIDOCAINE HCL 4 % TOPICAL SOLN,Medication,14,25,0.56
+5215,937,RETIRED IP GEN BRONCHOSCOPY PRE PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,25,0.2
+5216,937,IP GEN BRONCHOSCOPY PRE PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,25,0.2
+5217,937,RETIRED IP GEN BRONCHOSCOPY PRE PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,2,25,0.08
+5218,937,IP GEN BRONCHOSCOPY PRE PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,2,25,0.08
+5219,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,989,10114,0.097785248
+5220,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,4448,LIDOCAINE HCL 2 % JELLY,Medication,137,10114,0.01354558
+5221,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,4985,METHYLENE BLUE (ANTIDOTE) 1 % INJ SOLN,Medication,2,10114,0.000197746
+5222,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,10114,9.89E-05
+5223,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,175,10114,0.017302749
+5224,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,13,10114,0.001285347
+5225,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,7228,SIMETHICONE 40 MG/0.6 ML PO DRPS,Medication,638,10114,0.063080878
+5226,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,163,10114,0.016116274
+5227,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,2799,10114,0.276745106
+5228,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4963,10114,0.490705952
+5229,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,79552,INDOMETHACIN 50 MG PR SUPP,Medication,36,10114,0.003559423
+5230,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,80371,ONABOTULINUMTOXINA 100 UNIT INJ SOLR,Medication,13,10114,0.001285347
+5231,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,64,10114,0.006327862
+5232,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,82190,BUTAMBEN-TETRACAINE-BENZOCAINE 2-2-14 % TP SPRA,Medication,637,10114,0.062982005
+5233,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,83986,INDIGOTINDISULFONATE SODIUM 8 MG/ML INJ SOLN,Medication,2,10114,0.000197746
+5234,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,90788,LIDOCAINE HCL 2 % VISCOUS ORAL SOLN,Medication,251,10114,0.024817085
+5235,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,10114,0.000197746
+5236,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,124897,BENZOCAINE 20 % MM SPRY,Medication,15,10114,0.001483093
+5237,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,126003,MEPERIDINE (PF) 50 MG/ML INJ SOLN,Medication,1,10114,9.89E-05
+5238,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,126010,MEPERIDINE (PF) 50 MG/ML INJ SYRG,Medication,1,10114,9.89E-05
+5239,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,20,10114,0.001977457
+5240,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,222816,LIDOCAINE 5 % TP OINT,Medication,52,10114,0.005141388
+5241,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,514252,BOTULINUM TOXIN TYPE A 200 UNITS IN NS,Medication,8,10114,0.000790983
+5242,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,520043,CEFOXITIN IVPB,Medication,1,10114,9.89E-05
+5243,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2789,10114,0.275756377
+5244,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,540303,MEPERIDINE INJECTABLE SYRINGE,Medication,30,10114,0.002966185
+5245,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,540548,GLUCAGON 1 MG (1 UNIT) INJ SOLR,Medication,273,10114,0.026992288
+5246,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,541092,AMPICILLIN 2 GRAM/100 ML MINI-BAG PLUS,Medication,3,10114,0.000296619
+5247,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,541105,CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS,Medication,17,10114,0.001680838
+5248,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,541251,BOTULINUM TOXIN TYPE A 100 UNITS IN NS,Medication,61,10114,0.006031244
+5249,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,LAB11000R,"CEA, PANCREATIC CYST",Lab,1,10114,9.89E-05
+5250,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,LAB305,"CULTURE, VIRUS",Lab,2,10114,0.000197746
+5251,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,2490,10114,0.246193395
+5252,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,IMGFLCOLON,XR COLONOSCOPY W FLUOROSCOPY,Imaging,58,10114,0.005734625
+5253,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,IMGFLERCP,XR ERCP (PANCREAS ONLY),Imaging,124,10114,0.012260233
+5254,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,IMGFLERCPS,"XR ERCP W SPHICTEROTOMY, BILIARY AND PANCREAS",Imaging,1028,10114,0.101641289
+5255,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,IMGFLESOD,XR ESOPHAGEAL DILATATION PNEUMATIC,Imaging,25,10114,0.002471821
+5256,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,IMGFLSBEF,XR SMALL BOWEL ENDOSCOPY W FLUORO,Imaging,171,10114,0.016907257
+5257,944,IP GI ENDOSCOPY PRE/POSTOP PROCEDURE,IMGFLSBM,XR SMALL BOWEL MOTILITY,Imaging,2,10114,0.000197746
+5258,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,2,37671,5.31E-05
+5259,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,37671,2.65E-05
+5260,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,11746,37671,0.311804837
+5261,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,327,37671,0.008680417
+5262,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,75,37671,0.001990921
+5263,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,21,37671,0.000557458
+5264,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),207949,MORPHINE (PF) 30 MG/30 ML (1 MG/ML) IV PCA,Medication,580,37671,0.015396459
+5265,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),540101,FENTANYL 50 MCG/ML IV PCA,Medication,1940,37671,0.0514985
+5266,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,127,37671,0.003371294
+5267,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),540125,HYDROMORPHONE 1 MG/ML IV PCA,Medication,11475,37671,0.304610974
+5268,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),540300,MORPHINE INJECTABLE SYRINGE,Medication,32,37671,0.00084946
+5269,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,63,37671,0.001672374
+5270,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),541605,FENTANYL 50 MCG/ML IV PCA,Medication,831,37671,0.022059409
+5271,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),541607,HYDROMORPHONE 1 MG/ML IV PCA,Medication,4934,37671,0.130976082
+5272,946,IP PAI PATIENT CONTROLLED ANALGESIA (PCA),541609,MORPHINE 30 MG/30 ML IV PCA,Medication,214,37671,0.005680762
+5273,947,CDU DEHYDRATION OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,56,0.160714286
+5274,947,CDU DEHYDRATION OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,56,0.017857143
+5275,947,CDU DEHYDRATION OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,3,56,0.053571429
+5276,947,CDU DEHYDRATION OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,16,56,0.285714286
+5277,947,CDU DEHYDRATION OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,56,0.071428571
+5278,947,CDU DEHYDRATION OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,12,56,0.214285714
+5279,947,CDU DEHYDRATION OBSERVATION,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,56,0.017857143
+5280,947,CDU DEHYDRATION OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,3,56,0.053571429
+5281,947,CDU DEHYDRATION OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,27,56,0.482142857
+5282,947,CDU DEHYDRATION OBSERVATION,540102,NS IV BOLUS,Medication,2,56,0.035714286
+5283,947,CDU DEHYDRATION OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,56,0.035714286
+5284,947,CDU DEHYDRATION OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,6,56,0.107142857
+5285,947,CDU DEHYDRATION OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,7,56,0.125
+5286,947,CDU DEHYDRATION OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,56,0.035714286
+5287,948,CDU DIVERTICULITIS OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,4,0.5
+5288,948,CDU DIVERTICULITIS OBSERVATION,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,1,4,0.25
+5289,949,CDU HYPOGLYCEMIA OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,3,0.333333333
+5290,949,CDU HYPOGLYCEMIA OBSERVATION,POC14,POC GLUCOSE BY METER,Lab,1,3,0.333333333
+5291,950,CDU PYELONEPHRITIS OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,24,122,0.196721311
+5292,950,CDU PYELONEPHRITIS OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,3,122,0.024590164
+5293,950,CDU PYELONEPHRITIS OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,2,122,0.016393443
+5294,950,CDU PYELONEPHRITIS OBSERVATION,3844,IBUPROFEN 600 MG PO TABS,Medication,1,122,0.008196721
+5295,950,CDU PYELONEPHRITIS OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,122,0.008196721
+5296,950,CDU PYELONEPHRITIS OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,6,122,0.049180328
+5297,950,CDU PYELONEPHRITIS OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,122,0.016393443
+5298,950,CDU PYELONEPHRITIS OBSERVATION,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,1,122,0.008196721
+5299,950,CDU PYELONEPHRITIS OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,44,122,0.360655738
+5300,950,CDU PYELONEPHRITIS OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,12,122,0.098360656
+5301,950,CDU PYELONEPHRITIS OBSERVATION,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,7,122,0.057377049
+5302,950,CDU PYELONEPHRITIS OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,25,122,0.204918033
+5303,950,CDU PYELONEPHRITIS OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,4,122,0.032786885
+5304,950,CDU PYELONEPHRITIS OBSERVATION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,122,0.008196721
+5305,950,CDU PYELONEPHRITIS OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,6,122,0.049180328
+5306,950,CDU PYELONEPHRITIS OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,52,122,0.426229508
+5307,950,CDU PYELONEPHRITIS OBSERVATION,540102,NS IV BOLUS,Medication,3,122,0.024590164
+5308,950,CDU PYELONEPHRITIS OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,122,0.008196721
+5309,950,CDU PYELONEPHRITIS OBSERVATION,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,2,122,0.016393443
+5310,950,CDU PYELONEPHRITIS OBSERVATION,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,6,122,0.049180328
+5311,950,CDU PYELONEPHRITIS OBSERVATION,9000000,ZZZ IMS TEMPLATE,Medication,3,122,0.024590164
+5312,950,CDU PYELONEPHRITIS OBSERVATION,LABBHCG,"BETA HCG, QUANT",Lab,1,122,0.008196721
+5313,950,CDU PYELONEPHRITIS OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,45,122,0.368852459
+5314,950,CDU PYELONEPHRITIS OBSERVATION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,1,122,0.008196721
+5315,950,CDU PYELONEPHRITIS OBSERVATION,LABLIPS,LIPASE,Lab,1,122,0.008196721
+5316,950,CDU PYELONEPHRITIS OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,19,122,0.155737705
+5317,950,CDU PYELONEPHRITIS OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,14,122,0.114754098
+5318,950,CDU PYELONEPHRITIS OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,2,122,0.016393443
+5319,950,CDU PYELONEPHRITIS OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,122,0.008196721
+5320,951,CDU KIDNEY STONE OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,6,0.333333333
+5321,951,CDU KIDNEY STONE OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,6,0.333333333
+5322,951,CDU KIDNEY STONE OBSERVATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,6,0.166666667
+5323,951,CDU KIDNEY STONE OBSERVATION,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,1,6,0.166666667
+5324,951,CDU KIDNEY STONE OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,6,0.333333333
+5325,951,CDU KIDNEY STONE OBSERVATION,86943,TAMSULOSIN 0.4 MG PO CP24,Medication,1,6,0.166666667
+5326,951,CDU KIDNEY STONE OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,2,6,0.333333333
+5327,951,CDU KIDNEY STONE OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,6,0.166666667
+5328,951,CDU KIDNEY STONE OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,4,6,0.666666667
+5329,951,CDU KIDNEY STONE OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,6,0.166666667
+5330,952,CDU VERTIGO OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,29,0.172413793
+5331,952,CDU VERTIGO OBSERVATION,12025,MECLIZINE 25 MG PO TABS,Medication,7,29,0.24137931
+5332,952,CDU VERTIGO OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,29,0.068965517
+5333,952,CDU VERTIGO OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,29,0.034482759
+5334,952,CDU VERTIGO OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,8,29,0.275862069
+5335,952,CDU VERTIGO OBSERVATION,112420,DIAZEPAM 5 MG/ML INJ SYRG,Medication,2,29,0.068965517
+5336,952,CDU VERTIGO OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,10,29,0.344827586
+5337,952,CDU VERTIGO OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,29,0.034482759
+5338,952,CDU VERTIGO OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,1,29,0.034482759
+5339,952,CDU VERTIGO OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,29,0.034482759
+5340,952,CDU VERTIGO OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,29,0.034482759
+5341,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,7,0.142857143
+5342,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,7,0.142857143
+5343,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,7,0.142857143
+5344,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,7,0.285714286
+5345,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,4,7,0.571428571
+5346,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,1,7,0.142857143
+5347,953,CDU HYPEREMESIS GRAVIDARUM OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,7,0.142857143
+5348,954,CDU TOXICOLOGIC INGESTION,540102,NS IV BOLUS,Medication,1,3,0.333333333
+5349,954,CDU TOXICOLOGIC INGESTION,LABACETA,"ACETAMINOPHEN, SERUM",Lab,2,3,0.666666667
+5350,954,CDU TOXICOLOGIC INGESTION,LABCBCD,CBC WITH DIFF,Lab,1,3,0.333333333
+5351,954,CDU TOXICOLOGIC INGESTION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,1,3,0.333333333
+5352,954,CDU TOXICOLOGIC INGESTION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,3,0.333333333
+5353,954,CDU TOXICOLOGIC INGESTION,LABPT,PROTHROMBIN TIME,Lab,1,3,0.333333333
+5354,954,CDU TOXICOLOGIC INGESTION,LABSALICY,SALICYLATE LEVEL,Lab,2,3,0.666666667
+5355,954,CDU TOXICOLOGIC INGESTION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,2,3,0.666666667
+5356,954,CDU TOXICOLOGIC INGESTION,LABVOLTL,VOLATILE SCREEN,Lab,1,3,0.333333333
+5357,955,CDU SEIZURES OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,18,0.222222222
+5358,955,CDU SEIZURES OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,18,0.055555556
+5359,955,CDU SEIZURES OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,18,0.055555556
+5360,955,CDU SEIZURES OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,2,18,0.111111111
+5361,955,CDU SEIZURES OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,18,0.055555556
+5362,955,CDU SEIZURES OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,18,0.5
+5363,955,CDU SEIZURES OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,2,18,0.111111111
+5364,955,CDU SEIZURES OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,3,18,0.166666667
+5365,956,CDU CLOSED HEAD INJURY OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,45,0.111111111
+5366,956,CDU CLOSED HEAD INJURY OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,1,45,0.022222222
+5367,956,CDU CLOSED HEAD INJURY OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,45,0.044444444
+5368,956,CDU CLOSED HEAD INJURY OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,8,45,0.177777778
+5369,956,CDU CLOSED HEAD INJURY OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,45,0.088888889
+5370,956,CDU CLOSED HEAD INJURY OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,45,0.155555556
+5371,956,CDU CLOSED HEAD INJURY OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,2,45,0.044444444
+5372,956,CDU CLOSED HEAD INJURY OBSERVATION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,45,0.022222222
+5373,956,CDU CLOSED HEAD INJURY OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,45,0.044444444
+5374,956,CDU CLOSED HEAD INJURY OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,16,45,0.355555556
+5375,956,CDU CLOSED HEAD INJURY OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,45,0.022222222
+5376,956,CDU CLOSED HEAD INJURY OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,4,45,0.088888889
+5377,956,CDU CLOSED HEAD INJURY OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,45,0.044444444
+5378,956,CDU CLOSED HEAD INJURY OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,45,0.022222222
+5379,956,CDU CLOSED HEAD INJURY OBSERVATION,IMGCTH,CT HEAD,Imaging,1,45,0.022222222
+5380,957,CDU SYNCOPE OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,74,0.067567568
+5381,957,CDU SYNCOPE OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,74,0.013513514
+5382,957,CDU SYNCOPE OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,74,0.013513514
+5383,957,CDU SYNCOPE OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,74,0.067567568
+5384,957,CDU SYNCOPE OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,74,0.027027027
+5385,957,CDU SYNCOPE OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,74,0.121621622
+5386,957,CDU SYNCOPE OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,33,74,0.445945946
+5387,957,CDU SYNCOPE OBSERVATION,540102,NS IV BOLUS,Medication,1,74,0.013513514
+5388,957,CDU SYNCOPE OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,8,74,0.108108108
+5389,957,CDU SYNCOPE OBSERVATION,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,3,74,0.040540541
+5390,957,CDU SYNCOPE OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,5,74,0.067567568
+5391,957,CDU SYNCOPE OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,74,0.054054054
+5392,957,CDU SYNCOPE OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,3,74,0.040540541
+5393,957,CDU SYNCOPE OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,74,0.040540541
+5394,957,CDU SYNCOPE OBSERVATION,LABTYPSNI,TYPE AND SCREEN,Lab,1,74,0.013513514
+5395,957,CDU SYNCOPE OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,2,74,0.027027027
+5396,957,CDU SYNCOPE OBSERVATION,LABURNC,URINE CULTURE,Lab,3,74,0.040540541
+5397,958,CDU CELLULITIS OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,23,159,0.144654088
+5398,958,CDU CELLULITIS OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,3,159,0.018867925
+5399,958,CDU CELLULITIS OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,14,159,0.088050314
+5400,958,CDU CELLULITIS OBSERVATION,3844,IBUPROFEN 600 MG PO TABS,Medication,2,159,0.012578616
+5401,958,CDU CELLULITIS OBSERVATION,4318,LACTATED RINGERS IV SOLP,Medication,1,159,0.006289308
+5402,958,CDU CELLULITIS OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,9,159,0.056603774
+5403,958,CDU CELLULITIS OBSERVATION,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,159,0.025157233
+5404,958,CDU CELLULITIS OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,3,159,0.018867925
+5405,958,CDU CELLULITIS OBSERVATION,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,3,159,0.018867925
+5406,958,CDU CELLULITIS OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,11,159,0.06918239
+5407,958,CDU CELLULITIS OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,13,159,0.081761006
+5408,958,CDU CELLULITIS OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,32,159,0.201257862
+5409,958,CDU CELLULITIS OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,7,159,0.044025157
+5410,958,CDU CELLULITIS OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,9,159,0.056603774
+5411,958,CDU CELLULITIS OBSERVATION,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,6,159,0.037735849
+5412,958,CDU CELLULITIS OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,71,159,0.446540881
+5413,958,CDU CELLULITIS OBSERVATION,540102,NS IV BOLUS,Medication,3,159,0.018867925
+5414,958,CDU CELLULITIS OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,159,0.006289308
+5415,958,CDU CELLULITIS OBSERVATION,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,2,159,0.012578616
+5416,958,CDU CELLULITIS OBSERVATION,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,3,159,0.018867925
+5417,958,CDU CELLULITIS OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,38,159,0.238993711
+5418,958,CDU CELLULITIS OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,18,159,0.113207547
+5419,958,CDU CELLULITIS OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,8,159,0.050314465
+5420,958,CDU CELLULITIS OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,159,0.006289308
+5421,958,CDU CELLULITIS OBSERVATION,LABURNC,URINE CULTURE,Lab,1,159,0.006289308
+5422,959,CDU ASTHMA OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,10,103,0.097087379
+5423,959,CDU ASTHMA OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,103,0.009708738
+5424,959,CDU ASTHMA OBSERVATION,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,25,103,0.242718447
+5425,959,CDU ASTHMA OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,1,103,0.009708738
+5426,959,CDU ASTHMA OBSERVATION,6496,PREDNISONE 20 MG PO TABS,Medication,6,103,0.058252427
+5427,959,CDU ASTHMA OBSERVATION,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,25,103,0.242718447
+5428,959,CDU ASTHMA OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,103,0.019417476
+5429,959,CDU ASTHMA OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,103,0.029126214
+5430,959,CDU ASTHMA OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,103,0.019417476
+5431,959,CDU ASTHMA OBSERVATION,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,10,103,0.097087379
+5432,959,CDU ASTHMA OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,103,0.087378641
+5433,959,CDU ASTHMA OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,34,103,0.330097087
+5434,959,CDU ASTHMA OBSERVATION,540102,NS IV BOLUS,Medication,3,103,0.029126214
+5435,959,CDU ASTHMA OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,103,0.009708738
+5436,959,CDU ASTHMA OBSERVATION,9000000,ZZZ IMS TEMPLATE,Medication,15,103,0.145631068
+5437,959,CDU ASTHMA OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,10,103,0.097087379
+5438,959,CDU ASTHMA OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,11,103,0.106796117
+5439,959,CDU ASTHMA OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,103,0.009708738
+5440,959,CDU ASTHMA OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,1,103,0.009708738
+5441,959,CDU ASTHMA OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,103,0.009708738
+5442,959,CDU ASTHMA OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,103,0.009708738
+5443,960,CDU ABDOMINAL PAIN OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,17,253,0.067193676
+5444,960,CDU ABDOMINAL PAIN OBSERVATION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,253,0.003952569
+5445,960,CDU ABDOMINAL PAIN OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,2,253,0.007905138
+5446,960,CDU ABDOMINAL PAIN OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,9,253,0.035573123
+5447,960,CDU ABDOMINAL PAIN OBSERVATION,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,2,253,0.007905138
+5448,960,CDU ABDOMINAL PAIN OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,253,0.019762846
+5449,960,CDU ABDOMINAL PAIN OBSERVATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,3,253,0.011857708
+5450,960,CDU ABDOMINAL PAIN OBSERVATION,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,253,0.003952569
+5451,960,CDU ABDOMINAL PAIN OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,59,253,0.233201581
+5452,960,CDU ABDOMINAL PAIN OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,29,253,0.114624506
+5453,960,CDU ABDOMINAL PAIN OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,11,253,0.043478261
+5454,960,CDU ABDOMINAL PAIN OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,39,253,0.154150198
+5455,960,CDU ABDOMINAL PAIN OBSERVATION,93295,ZOLPIDEM 10 MG PO TABS,Medication,1,253,0.003952569
+5456,960,CDU ABDOMINAL PAIN OBSERVATION,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,253,0.003952569
+5457,960,CDU ABDOMINAL PAIN OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,14,253,0.055335968
+5458,960,CDU ABDOMINAL PAIN OBSERVATION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,2,253,0.007905138
+5459,960,CDU ABDOMINAL PAIN OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,15,253,0.059288538
+5460,960,CDU ABDOMINAL PAIN OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,121,253,0.47826087
+5461,960,CDU ABDOMINAL PAIN OBSERVATION,540102,NS IV BOLUS,Medication,1,253,0.003952569
+5462,960,CDU ABDOMINAL PAIN OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,15,253,0.059288538
+5463,960,CDU ABDOMINAL PAIN OBSERVATION,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,253,0.003952569
+5464,960,CDU ABDOMINAL PAIN OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,49,253,0.193675889
+5465,960,CDU ABDOMINAL PAIN OBSERVATION,LABLIPS,LIPASE,Lab,10,253,0.039525692
+5466,960,CDU ABDOMINAL PAIN OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,14,253,0.055335968
+5467,960,CDU ABDOMINAL PAIN OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,42,253,0.166007905
+5468,960,CDU ABDOMINAL PAIN OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,3,253,0.011857708
+5469,960,CDU ABDOMINAL PAIN OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,253,0.011857708
+5470,960,CDU ABDOMINAL PAIN OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,3,253,0.011857708
+5471,960,CDU ABDOMINAL PAIN OBSERVATION,LABURNC,URINE CULTURE,Lab,3,253,0.011857708
+5472,961,CDU CHEST PAIN OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,36,336,0.107142857
+5473,961,CDU CHEST PAIN OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,336,0.00297619
+5474,961,CDU CHEST PAIN OBSERVATION,680,ASPIRIN 81 MG PO CHEW,Medication,8,336,0.023809524
+5475,961,CDU CHEST PAIN OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,336,0.005952381
+5476,961,CDU CHEST PAIN OBSERVATION,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,117,336,0.348214286
+5477,961,CDU CHEST PAIN OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,3,336,0.008928571
+5478,961,CDU CHEST PAIN OBSERVATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,336,0.00297619
+5479,961,CDU CHEST PAIN OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,17,336,0.050595238
+5480,961,CDU CHEST PAIN OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,11,336,0.032738095
+5481,961,CDU CHEST PAIN OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,67,336,0.199404762
+5482,961,CDU CHEST PAIN OBSERVATION,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,336,0.00297619
+5483,961,CDU CHEST PAIN OBSERVATION,112562,MORPHINE 10 MG/ML INJ SYRG,Medication,2,336,0.005952381
+5484,961,CDU CHEST PAIN OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,336,0.005952381
+5485,961,CDU CHEST PAIN OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,151,336,0.449404762
+5486,961,CDU CHEST PAIN OBSERVATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,336,0.00297619
+5487,961,CDU CHEST PAIN OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,13,336,0.038690476
+5488,961,CDU CHEST PAIN OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,22,336,0.06547619
+5489,961,CDU CHEST PAIN OBSERVATION,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,2,336,0.005952381
+5490,961,CDU CHEST PAIN OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,10,336,0.029761905
+5491,961,CDU CHEST PAIN OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,12,336,0.035714286
+5492,961,CDU CHEST PAIN OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,4,336,0.011904762
+5493,961,CDU CHEST PAIN OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,4,336,0.011904762
+5494,961,CDU CHEST PAIN OBSERVATION,LABTYPSNI,TYPE AND SCREEN,Lab,1,336,0.00297619
+5495,961,CDU CHEST PAIN OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,6,336,0.017857143
+5496,961,CDU CHEST PAIN OBSERVATION,LABURNC,URINE CULTURE,Lab,8,336,0.023809524
+5497,961,CDU CHEST PAIN OBSERVATION,POC24,POC ISTAT TROPONIN I,Lab,135,336,0.401785714
+5498,961,CDU CHEST PAIN OBSERVATION,ECH29,ECHO - DOBUTAMINE STRESS ECHO,Imaging,10,336,0.029761905
+5499,961,CDU CHEST PAIN OBSERVATION,ECH30,ECHO - EXERCISE STRESS ECHO,Imaging,20,336,0.05952381
+5500,962,CDU GENERAL  PROTOCOL OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,118,1009,0.116947473
+5501,962,CDU GENERAL  PROTOCOL OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,9,1009,0.008919722
+5502,962,CDU GENERAL  PROTOCOL OBSERVATION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,2,1009,0.001982161
+5503,962,CDU GENERAL  PROTOCOL OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,20,1009,0.019821606
+5504,962,CDU GENERAL  PROTOCOL OBSERVATION,3844,IBUPROFEN 600 MG PO TABS,Medication,2,1009,0.001982161
+5505,962,CDU GENERAL  PROTOCOL OBSERVATION,3845,IBUPROFEN 800 MG PO TABS,Medication,4,1009,0.003964321
+5506,962,CDU GENERAL  PROTOCOL OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,5,1009,0.004955401
+5507,962,CDU GENERAL  PROTOCOL OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,42,1009,0.041625372
+5508,962,CDU GENERAL  PROTOCOL OBSERVATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,1009,0.00099108
+5509,962,CDU GENERAL  PROTOCOL OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,20,1009,0.019821606
+5510,962,CDU GENERAL  PROTOCOL OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,60,1009,0.059464817
+5511,962,CDU GENERAL  PROTOCOL OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,62,1009,0.061446977
+5512,962,CDU GENERAL  PROTOCOL OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,191,1009,0.189296333
+5513,962,CDU GENERAL  PROTOCOL OBSERVATION,93295,ZOLPIDEM 10 MG PO TABS,Medication,4,1009,0.003964321
+5514,962,CDU GENERAL  PROTOCOL OBSERVATION,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,1009,0.00099108
+5515,962,CDU GENERAL  PROTOCOL OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,26,1009,0.025768087
+5516,962,CDU GENERAL  PROTOCOL OBSERVATION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,2,1009,0.001982161
+5517,962,CDU GENERAL  PROTOCOL OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,22,1009,0.021803766
+5518,962,CDU GENERAL  PROTOCOL OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,436,1009,0.432111001
+5519,962,CDU GENERAL  PROTOCOL OBSERVATION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,1009,0.00099108
+5520,962,CDU GENERAL  PROTOCOL OBSERVATION,540102,NS IV BOLUS,Medication,10,1009,0.009910803
+5521,962,CDU GENERAL  PROTOCOL OBSERVATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,1009,0.00099108
+5522,962,CDU GENERAL  PROTOCOL OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,22,1009,0.021803766
+5523,962,CDU GENERAL  PROTOCOL OBSERVATION,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,1009,0.00099108
+5524,962,CDU GENERAL  PROTOCOL OBSERVATION,9000000,ZZZ IMS TEMPLATE,Medication,4,1009,0.003964321
+5525,962,CDU GENERAL  PROTOCOL OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,117,1009,0.115956392
+5526,962,CDU GENERAL  PROTOCOL OBSERVATION,LABDDIML,D - DIMER (ELISA),Lab,1,1009,0.00099108
+5527,962,CDU GENERAL  PROTOCOL OBSERVATION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,2,1009,0.001982161
+5528,962,CDU GENERAL  PROTOCOL OBSERVATION,LABLIPS,LIPASE,Lab,1,1009,0.00099108
+5529,962,CDU GENERAL  PROTOCOL OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,77,1009,0.076313181
+5530,962,CDU GENERAL  PROTOCOL OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,29,1009,0.028741328
+5531,962,CDU GENERAL  PROTOCOL OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,11,1009,0.010901883
+5532,962,CDU GENERAL  PROTOCOL OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,9,1009,0.008919722
+5533,962,CDU GENERAL  PROTOCOL OBSERVATION,LABTYPSNI,TYPE AND SCREEN,Lab,2,1009,0.001982161
+5534,962,CDU GENERAL  PROTOCOL OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,19,1009,0.018830525
+5535,962,CDU GENERAL  PROTOCOL OBSERVATION,LABURNC,URINE CULTURE,Lab,12,1009,0.011892963
+5536,962,CDU GENERAL  PROTOCOL OBSERVATION,POC14,POC GLUCOSE BY METER,Lab,1,1009,0.00099108
+5537,962,CDU GENERAL  PROTOCOL OBSERVATION,POC24,POC ISTAT TROPONIN I,Lab,7,1009,0.006937562
+5538,962,CDU GENERAL  PROTOCOL OBSERVATION,POC26,POC URINE PREGNANCY,Lab,1,1009,0.00099108
+5539,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,5,0.2
+5540,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,5,0.4
+5541,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,5,0.2
+5542,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,1,5,0.2
+5543,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,5,0.2
+5544,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,1,5,0.2
+5545,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,5,0.2
+5546,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,5,0.2
+5547,963,CDU VENOUS THROMBOEMBOLIC DISEASE OBSERVATION,LABURNC,URINE CULTURE,Lab,2,5,0.4
+5548,964,CDU ALLERGIC REACTION OBSERVATON,4318,LACTATED RINGERS IV SOLP,Medication,1,4,0.25
+5549,964,CDU ALLERGIC REACTION OBSERVATON,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,4,0.5
+5550,965,CDU PNEUMONIA OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,30,128,0.234375
+5551,965,CDU PNEUMONIA OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,4,128,0.03125
+5552,965,CDU PNEUMONIA OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,6,128,0.046875
+5553,965,CDU PNEUMONIA OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,3,128,0.0234375
+5554,965,CDU PNEUMONIA OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,4,128,0.03125
+5555,965,CDU PNEUMONIA OBSERVATION,20943,AZITHROMYCIN 250 MG PO TABS,Medication,7,128,0.0546875
+5556,965,CDU PNEUMONIA OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,15,128,0.1171875
+5557,965,CDU PNEUMONIA OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,15,128,0.1171875
+5558,965,CDU PNEUMONIA OBSERVATION,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,1,128,0.0078125
+5559,965,CDU PNEUMONIA OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,57,128,0.4453125
+5560,965,CDU PNEUMONIA OBSERVATION,540102,NS IV BOLUS,Medication,5,128,0.0390625
+5561,965,CDU PNEUMONIA OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,128,0.015625
+5562,965,CDU PNEUMONIA OBSERVATION,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,128,0.0390625
+5563,965,CDU PNEUMONIA OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,32,128,0.25
+5564,965,CDU PNEUMONIA OBSERVATION,LABDDIML,D - DIMER (ELISA),Lab,1,128,0.0078125
+5565,965,CDU PNEUMONIA OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,18,128,0.140625
+5566,965,CDU PNEUMONIA OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,10,128,0.078125
+5567,965,CDU PNEUMONIA OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,2,128,0.015625
+5568,965,CDU PNEUMONIA OBSERVATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,128,0.015625
+5569,965,CDU PNEUMONIA OBSERVATION,IMGDXCH2,XR CHEST 2V,Imaging,2,128,0.015625
+5570,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,107,1068,0.100187266
+5571,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,20,1068,0.018726592
+5572,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,6,1068,0.005617978
+5573,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,663,ASCORBIC ACID 250 MG PO TABS,Medication,7,1068,0.006554307
+5574,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,664,ASCORBIC ACID 500 MG PO TABS,Medication,232,1068,0.217228464
+5575,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,231,1068,0.216292135
+5576,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,34,1068,0.031835206
+5577,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,162,1068,0.151685393
+5578,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,4572,LORAZEPAM 0.5 MG PO TABS,Medication,2,1068,0.001872659
+5579,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,4573,LORAZEPAM 1 MG PO TABS,Medication,32,1068,0.029962547
+5580,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,309,1068,0.289325843
+5581,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,5086,MINERAL OIL PO OIL,Medication,53,1068,0.049625468
+5582,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,18,1068,0.016853933
+5583,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,3,1068,0.002808989
+5584,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,10011,FAMOTIDINE 20 MG PO TABS,Medication,13,1068,0.012172285
+5585,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,17,1068,0.015917603
+5586,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,13632,BISACODYL 5 MG PO TBEC,Medication,189,1068,0.176966292
+5587,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,332,1068,0.310861423
+5588,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,212,1068,0.198501873
+5589,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,16,1068,0.014981273
+5590,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,92,1068,0.086142322
+5591,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,13,1068,0.012172285
+5592,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,6,1068,0.005617978
+5593,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,92173,ZOLPIDEM 5 MG PO TABS,Medication,24,1068,0.02247191
+5594,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,17,1068,0.015917603
+5595,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,1068,0.00093633
+5596,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,1068,0.00093633
+5597,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,261,1068,0.244382022
+5598,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,2,1068,0.001872659
+5599,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,45,1068,0.042134831
+5600,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,1068,0.001872659
+5601,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,330,1068,0.308988764
+5602,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,7,1068,0.006554307
+5603,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,18,1068,0.016853933
+5604,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,3,1068,0.002808989
+5605,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,39,1068,0.036516854
+5606,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,1068,0.00093633
+5607,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,19,1068,0.017790262
+5608,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,520064,CEFAZOLIN 2 G IVPB,Medication,32,1068,0.029962547
+5609,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,88,1068,0.082397004
+5610,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,246,1068,0.230337079
+5611,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,21,1068,0.019662921
+5612,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,1,1068,0.00093633
+5613,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,272,1068,0.254681648
+5614,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,69,1068,0.064606742
+5615,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABCBCO,CBC W/O DIFF,Lab,455,1068,0.426029963
+5616,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABHCTX,HEMATOCRIT,Lab,147,1068,0.137640449
+5617,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABK,"POTASSIUM, SERUM/PLASMA",Lab,150,1068,0.140449438
+5618,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABMETB,"METABOLIC PANEL, BASIC",Lab,459,1068,0.429775281
+5619,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,414,1068,0.387640449
+5620,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,311,1068,0.291198502
+5621,972,IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP,LABPLTS,PLATELET COUNT,Lab,20,1068,0.018726592
+5622,973,IP CTS ICU CARDIAC SURGERY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1622,13311,0.121854106
+5623,973,IP CTS ICU CARDIAC SURGERY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,123,13311,0.009240478
+5624,973,IP CTS ICU CARDIAC SURGERY POSTOP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,9,13311,0.000676133
+5625,973,IP CTS ICU CARDIAC SURGERY POSTOP,680,ASPIRIN 81 MG PO CHEW,Medication,819,13311,0.061528059
+5626,973,IP CTS ICU CARDIAC SURGERY POSTOP,693,ASPIRIN 300 MG PR SUPP,Medication,2018,13311,0.151603937
+5627,973,IP CTS ICU CARDIAC SURGERY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,2623,13311,0.197055067
+5628,973,IP CTS ICU CARDIAC SURGERY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1859,13311,0.139658929
+5629,973,IP CTS ICU CARDIAC SURGERY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,775,13311,0.058222523
+5630,973,IP CTS ICU CARDIAC SURGERY POSTOP,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,4,13311,0.000300503
+5631,973,IP CTS ICU CARDIAC SURGERY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2580,13311,0.193824656
+5632,973,IP CTS ICU CARDIAC SURGERY POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,2551,13311,0.191646007
+5633,973,IP CTS ICU CARDIAC SURGERY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,13311,0.000150252
+5634,973,IP CTS ICU CARDIAC SURGERY POSTOP,8982,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",Medication,17,13311,0.001277139
+5635,973,IP CTS ICU CARDIAC SURGERY POSTOP,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,1301,13311,0.097738712
+5636,973,IP CTS ICU CARDIAC SURGERY POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,1,13311,7.51E-05
+5637,973,IP CTS ICU CARDIAC SURGERY POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,13,13311,0.000976636
+5638,973,IP CTS ICU CARDIAC SURGERY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,7,13311,0.000525881
+5639,973,IP CTS ICU CARDIAC SURGERY POSTOP,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,2519,13311,0.18924198
+5640,973,IP CTS ICU CARDIAC SURGERY POSTOP,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,14,13311,0.001051762
+5641,973,IP CTS ICU CARDIAC SURGERY POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,34,13311,0.002554278
+5642,973,IP CTS ICU CARDIAC SURGERY POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,5536,13311,0.415896627
+5643,973,IP CTS ICU CARDIAC SURGERY POSTOP,11111,PRAVASTATIN 20 MG PO TABS,Medication,2,13311,0.000150252
+5644,973,IP CTS ICU CARDIAC SURGERY POSTOP,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,46,13311,0.003455788
+5645,973,IP CTS ICU CARDIAC SURGERY POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,2970,13311,0.223123732
+5646,973,IP CTS ICU CARDIAC SURGERY POSTOP,14632,TRAMADOL 50 MG PO TABS,Medication,5,13311,0.000375629
+5647,973,IP CTS ICU CARDIAC SURGERY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,2457,13311,0.184584178
+5648,973,IP CTS ICU CARDIAC SURGERY POSTOP,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,13311,7.51E-05
+5649,973,IP CTS ICU CARDIAC SURGERY POSTOP,15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,1741,13311,0.13079408
+5650,973,IP CTS ICU CARDIAC SURGERY POSTOP,15863,DEXTROSE 5%-0.2 % SOD CHLORIDE IV SOLP,Medication,2,13311,0.000150252
+5651,973,IP CTS ICU CARDIAC SURGERY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1248,13311,0.093757043
+5652,973,IP CTS ICU CARDIAC SURGERY POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,2908,13311,0.21846593
+5653,973,IP CTS ICU CARDIAC SURGERY POSTOP,27920,OXYCODONE 10 MG PO TB12,Medication,1,13311,7.51E-05
+5654,973,IP CTS ICU CARDIAC SURGERY POSTOP,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,5,13311,0.000375629
+5655,973,IP CTS ICU CARDIAC SURGERY POSTOP,28899,OXYCODONE 15 MG PO TABS,Medication,16,13311,0.001202013
+5656,973,IP CTS ICU CARDIAC SURGERY POSTOP,28900,OXYCODONE 30 MG PO TABS,Medication,2,13311,0.000150252
+5657,973,IP CTS ICU CARDIAC SURGERY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,8,13311,0.000601007
+5658,973,IP CTS ICU CARDIAC SURGERY POSTOP,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,2,13311,0.000150252
+5659,973,IP CTS ICU CARDIAC SURGERY POSTOP,77545,ATORVASTATIN 40 MG PO TABS,Medication,4,13311,0.000300503
+5660,973,IP CTS ICU CARDIAC SURGERY POSTOP,78531,ATORVASTATIN 20 MG PO TABS,Medication,2,13311,0.000150252
+5661,973,IP CTS ICU CARDIAC SURGERY POSTOP,79507,ATORVASTATIN 10 MG PO TABS,Medication,16,13311,0.001202013
+5662,973,IP CTS ICU CARDIAC SURGERY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,7,13311,0.000525881
+5663,973,IP CTS ICU CARDIAC SURGERY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,2407,13311,0.180827887
+5664,973,IP CTS ICU CARDIAC SURGERY POSTOP,83398,CLOPIDOGREL 75 MG PO TABS,Medication,44,13311,0.003305537
+5665,973,IP CTS ICU CARDIAC SURGERY POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2549,13311,0.191495755
+5666,973,IP CTS ICU CARDIAC SURGERY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,23,13311,0.001727894
+5667,973,IP CTS ICU CARDIAC SURGERY POSTOP,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,1837,13311,0.13800616
+5668,973,IP CTS ICU CARDIAC SURGERY POSTOP,89444,PIPERACILLIN-TAZOBACTAM-DEXTRS 2.25 GRAM/50 ML IV PGBK,Medication,2,13311,0.000150252
+5669,973,IP CTS ICU CARDIAC SURGERY POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,6,13311,0.000450755
+5670,973,IP CTS ICU CARDIAC SURGERY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,70,13311,0.005258809
+5671,973,IP CTS ICU CARDIAC SURGERY POSTOP,112520,SODIUM CHLORIDE 0.9 % 0.9 % INJ SYRG,Medication,1432,13311,0.107580197
+5672,973,IP CTS ICU CARDIAC SURGERY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,39,13311,0.002929908
+5673,973,IP CTS ICU CARDIAC SURGERY POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,688,13311,0.051686575
+5674,973,IP CTS ICU CARDIAC SURGERY POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,15,13311,0.001126888
+5675,973,IP CTS ICU CARDIAC SURGERY POSTOP,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,1,13311,7.51E-05
+5676,973,IP CTS ICU CARDIAC SURGERY POSTOP,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,2606,13311,0.195777928
+5677,973,IP CTS ICU CARDIAC SURGERY POSTOP,126993,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,1,13311,7.51E-05
+5678,973,IP CTS ICU CARDIAC SURGERY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2623,13311,0.197055067
+5679,973,IP CTS ICU CARDIAC SURGERY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,13311,7.51E-05
+5680,973,IP CTS ICU CARDIAC SURGERY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,155,13311,0.011644505
+5681,973,IP CTS ICU CARDIAC SURGERY POSTOP,205192,MILRINONE IN D5W 20 MG/100 ML IV PGBK,Medication,237,13311,0.017804823
+5682,973,IP CTS ICU CARDIAC SURGERY POSTOP,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,1105,13311,0.083014049
+5683,973,IP CTS ICU CARDIAC SURGERY POSTOP,206196,NITROGLYCERIN IN 5 % DEXTROSE 100 MG/250 ML (400 MCG/ML) IV SOLN,Medication,3,13311,0.000225378
+5684,973,IP CTS ICU CARDIAC SURGERY POSTOP,206226,NICARDIPINE IN SALINE (ISO-OS) 40 MG/200 ML IV PGBK,Medication,71,13311,0.005333934
+5685,973,IP CTS ICU CARDIAC SURGERY POSTOP,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,77,13311,0.005784689
+5686,973,IP CTS ICU CARDIAC SURGERY POSTOP,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,2,13311,0.000150252
+5687,973,IP CTS ICU CARDIAC SURGERY POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,11,13311,0.000826384
+5688,973,IP CTS ICU CARDIAC SURGERY POSTOP,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,89,13311,0.006686199
+5689,973,IP CTS ICU CARDIAC SURGERY POSTOP,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,1441,13311,0.108256329
+5690,973,IP CTS ICU CARDIAC SURGERY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,15,13311,0.001126888
+5691,973,IP CTS ICU CARDIAC SURGERY POSTOP,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,2541,13311,0.190894749
+5692,973,IP CTS ICU CARDIAC SURGERY POSTOP,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,733,13311,0.055067238
+5693,973,IP CTS ICU CARDIAC SURGERY POSTOP,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,1649,13311,0.123882503
+5694,973,IP CTS ICU CARDIAC SURGERY POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,778,13311,0.0584479
+5695,973,IP CTS ICU CARDIAC SURGERY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,126,13311,0.009465855
+5696,973,IP CTS ICU CARDIAC SURGERY POSTOP,530001,AMIODARONE INFUSION PERIPH ADMIN,Medication,5,13311,0.000375629
+5697,973,IP CTS ICU CARDIAC SURGERY POSTOP,530002,AMIODARONE INFUSION CENTRAL LINE ADMIN,Medication,48,13311,0.00360604
+5698,973,IP CTS ICU CARDIAC SURGERY POSTOP,530006,EPINEPHRINE IV INFUSION,Medication,437,13311,0.03282999
+5699,973,IP CTS ICU CARDIAC SURGERY POSTOP,530008,NITROPRUSSIDE IV INFUSION,Medication,207,13311,0.015551048
+5700,973,IP CTS ICU CARDIAC SURGERY POSTOP,530010,NICARDIPINE IV INFUSION,Medication,12,13311,0.00090151
+5701,973,IP CTS ICU CARDIAC SURGERY POSTOP,530011,NOREPINEPHRINE IV INFUSION,Medication,24,13311,0.00180302
+5702,973,IP CTS ICU CARDIAC SURGERY POSTOP,530017,ISOPROTERENOL IV INFUSION,Medication,1,13311,7.51E-05
+5703,973,IP CTS ICU CARDIAC SURGERY POSTOP,530025,DEXMEDETOMIDINE IV INFUSION,Medication,626,13311,0.047028773
+5704,973,IP CTS ICU CARDIAC SURGERY POSTOP,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,117,13311,0.008789723
+5705,973,IP CTS ICU CARDIAC SURGERY POSTOP,530039,AMINOCAPROIC ACID IV INFUSION,Medication,2109,13311,0.158440388
+5706,973,IP CTS ICU CARDIAC SURGERY POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2853,13311,0.214334009
+5707,973,IP CTS ICU CARDIAC SURGERY POSTOP,540302,POTASSIUM CHLORIDE IV SCALE,Medication,2990,13311,0.224626249
+5708,973,IP CTS ICU CARDIAC SURGERY POSTOP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,2617,13311,0.196604312
+5709,973,IP CTS ICU CARDIAC SURGERY POSTOP,540964,PIPERACILLIN-TAZOBACTAM 2.25 GRAM/50 ML MINI-BAG PLUS,Medication,3,13311,0.000225378
+5710,973,IP CTS ICU CARDIAC SURGERY POSTOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,8,13311,0.000601007
+5711,973,IP CTS ICU CARDIAC SURGERY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,13311,7.51E-05
+5712,973,IP CTS ICU CARDIAC SURGERY POSTOP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,16,13311,0.001202013
+5713,973,IP CTS ICU CARDIAC SURGERY POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,1,13311,7.51E-05
+5714,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,5243,13311,0.393884757
+5715,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABCBCO,CBC W/O DIFF,Lab,6127,13311,0.460295996
+5716,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABCK,"CREATINE KINASE, TOTAL",Lab,38,13311,0.002854782
+5717,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABFIB,FIBRINOGEN,Lab,2539,13311,0.190744497
+5718,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABHCTX,HEMATOCRIT,Lab,5403,13311,0.405904891
+5719,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABK,"POTASSIUM, SERUM/PLASMA",Lab,7943,13311,0.596724514
+5720,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABMB,"CK, MB (MASS)",Lab,25,13311,0.001878146
+5721,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,6066,13311,0.45571332
+5722,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,50,13311,0.003756292
+5723,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,8464,13311,0.635865074
+5724,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,4160,13311,0.312523477
+5725,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABPLTS,PLATELET COUNT,Lab,2514,13311,0.188866351
+5726,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABPT,PROTHROMBIN TIME,Lab,8172,13311,0.61392833
+5727,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2801,13311,0.210427466
+5728,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABROMRS,MRSA SCREEN,Lab,2598,13311,0.195176921
+5729,973,IP CTS ICU CARDIAC SURGERY POSTOP,LABTEGCP,THROMBELASTOGRAPH CLOTTING PROFILE,Lab,7,13311,0.000525881
+5730,973,IP CTS ICU CARDIAC SURGERY POSTOP,POC14,POC GLUCOSE BY METER,Lab,5070,13311,0.380887987
+5731,973,IP CTS ICU CARDIAC SURGERY POSTOP,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,3471,13311,0.260761776
+5732,973,IP CTS ICU CARDIAC SURGERY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,5616,13311,0.421906694
+5733,974,IP CTS GEN ESOPHAGECTOMY POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,440,0.006818182
+5734,974,IP CTS GEN ESOPHAGECTOMY POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,440,0.004545455
+5735,974,IP CTS GEN ESOPHAGECTOMY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,95,440,0.215909091
+5736,974,IP CTS GEN ESOPHAGECTOMY POSTOP,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,2,440,0.004545455
+5737,974,IP CTS GEN ESOPHAGECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,84,440,0.190909091
+5738,974,IP CTS GEN ESOPHAGECTOMY POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,3,440,0.006818182
+5739,974,IP CTS GEN ESOPHAGECTOMY POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,440,0.004545455
+5740,974,IP CTS GEN ESOPHAGECTOMY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,440,0.020454545
+5741,974,IP CTS GEN ESOPHAGECTOMY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,440,0.002272727
+5742,974,IP CTS GEN ESOPHAGECTOMY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,64,440,0.145454545
+5743,974,IP CTS GEN ESOPHAGECTOMY POSTOP,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,15,440,0.034090909
+5744,974,IP CTS GEN ESOPHAGECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,23,440,0.052272727
+5745,974,IP CTS GEN ESOPHAGECTOMY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,440,0.002272727
+5746,974,IP CTS GEN ESOPHAGECTOMY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,50,440,0.113636364
+5747,974,IP CTS GEN ESOPHAGECTOMY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1,440,0.002272727
+5748,974,IP CTS GEN ESOPHAGECTOMY POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,5,440,0.011363636
+5749,974,IP CTS GEN ESOPHAGECTOMY POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,440,0.002272727
+5750,974,IP CTS GEN ESOPHAGECTOMY POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,1,440,0.002272727
+5751,974,IP CTS GEN ESOPHAGECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,85,440,0.193181818
+5752,974,IP CTS GEN ESOPHAGECTOMY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,11,440,0.025
+5753,974,IP CTS GEN ESOPHAGECTOMY POSTOP,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,1,440,0.002272727
+5754,974,IP CTS GEN ESOPHAGECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,84,440,0.190909091
+5755,974,IP CTS GEN ESOPHAGECTOMY POSTOP,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,2,440,0.004545455
+5756,974,IP CTS GEN ESOPHAGECTOMY POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,3,440,0.006818182
+5757,974,IP CTS GEN ESOPHAGECTOMY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,6,440,0.013636364
+5758,974,IP CTS GEN ESOPHAGECTOMY POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,440,0.006818182
+5759,974,IP CTS GEN ESOPHAGECTOMY POSTOP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,1,440,0.002272727
+5760,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABCBCO,CBC W/O DIFF,Lab,97,440,0.220454545
+5761,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,94,440,0.213636364
+5762,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,21,440,0.047727273
+5763,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,36,440,0.081818182
+5764,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABPT,PROTHROMBIN TIME,Lab,39,440,0.088636364
+5765,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,37,440,0.084090909
+5766,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,1,440,0.002272727
+5767,974,IP CTS GEN ESOPHAGECTOMY POSTOP,POC14,POC GLUCOSE BY METER,Lab,5,440,0.011363636
+5768,974,IP CTS GEN ESOPHAGECTOMY POSTOP,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,11,440,0.025
+5769,974,IP CTS GEN ESOPHAGECTOMY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,167,440,0.379545455
+5770,974,IP CTS GEN ESOPHAGECTOMY POSTOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,1,440,0.002272727
+5771,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,40,176,0.227272727
+5772,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,40,176,0.227272727
+5773,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,1,176,0.005681818
+5774,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,1,176,0.005681818
+5775,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,6,176,0.034090909
+5776,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,6,176,0.034090909
+5777,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,33,176,0.1875
+5778,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,33,176,0.1875
+5779,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,12,176,0.068181818
+5780,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,12,176,0.068181818
+5781,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,176,0.005681818
+5782,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,176,0.005681818
+5783,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,25,176,0.142045455
+5784,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,25,176,0.142045455
+5785,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,14,176,0.079545455
+5786,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,14,176,0.079545455
+5787,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,44,176,0.25
+5788,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,44,176,0.25
+5789,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,176,0.005681818
+5790,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,176,0.005681818
+5791,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,3,176,0.017045455
+5792,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,3,176,0.017045455
+5793,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,37,176,0.210227273
+5794,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,37,176,0.210227273
+5795,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,176,0.011363636
+5796,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,176,0.011363636
+5797,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABCBCD,CBC WITH DIFF,Lab,33,176,0.1875
+5798,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABCBCD,CBC WITH DIFF,Lab,33,176,0.1875
+5799,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABEP1,ELECTROLYTE PANEL,Lab,4,176,0.022727273
+5800,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABEP1,ELECTROLYTE PANEL,Lab,4,176,0.022727273
+5801,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABHCTX,HEMATOCRIT,Lab,4,176,0.022727273
+5802,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABHCTX,HEMATOCRIT,Lab,4,176,0.022727273
+5803,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,39,176,0.221590909
+5804,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,39,176,0.221590909
+5805,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,176,0.017045455
+5806,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,176,0.017045455
+5807,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,37,176,0.210227273
+5808,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,37,176,0.210227273
+5809,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,18,176,0.102272727
+5810,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,18,176,0.102272727
+5811,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPLTS,PLATELET COUNT,Lab,21,176,0.119318182
+5812,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPLTS,PLATELET COUNT,Lab,21,176,0.119318182
+5813,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPT,PROTHROMBIN TIME,Lab,6,176,0.034090909
+5814,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPT,PROTHROMBIN TIME,Lab,6,176,0.034090909
+5815,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,176,0.034090909
+5816,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,176,0.034090909
+5817,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,1,176,0.005681818
+5818,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,1,176,0.005681818
+5819,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,POC14,POC GLUCOSE BY METER,Lab,4,176,0.022727273
+5820,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,POC14,POC GLUCOSE BY METER,Lab,4,176,0.022727273
+5821,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,68,176,0.386363636
+5822,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,68,176,0.386363636
+5823,975,IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,IMGFLESO,XR ESOPHAGRAM,Imaging,1,176,0.005681818
+5824,975,RETIRED IP CTS ESOPHAGECTOMY STEPDOWN POSTOP,IMGFLESO,XR ESOPHAGRAM,Imaging,1,176,0.005681818
+5825,976,IP CTS LUNG RESECTION POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,423,4789,0.088327417
+5826,976,IP CTS LUNG RESECTION POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,97,4789,0.02025475
+5827,976,IP CTS LUNG RESECTION POSTOP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,4789,0.000208812
+5828,976,IP CTS LUNG RESECTION POSTOP,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,1,4789,0.000208812
+5829,976,IP CTS LUNG RESECTION POSTOP,325,ALPRAZOLAM 0.5 MG PO TABS,Medication,1,4789,0.000208812
+5830,976,IP CTS LUNG RESECTION POSTOP,717,ATENOLOL 25 MG PO TABS,Medication,2,4789,0.000417624
+5831,976,IP CTS LUNG RESECTION POSTOP,718,ATENOLOL 50 MG PO TABS,Medication,1,4789,0.000208812
+5832,976,IP CTS LUNG RESECTION POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,1069,4789,0.223219879
+5833,976,IP CTS LUNG RESECTION POSTOP,1300,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",Medication,3,4789,0.000626436
+5834,976,IP CTS LUNG RESECTION POSTOP,2475,DILTIAZEM HCL 30 MG PO TABS,Medication,277,4789,0.057840885
+5835,976,IP CTS LUNG RESECTION POSTOP,2476,DILTIAZEM HCL 60 MG PO TABS,Medication,56,4789,0.011693464
+5836,976,IP CTS LUNG RESECTION POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,6,4789,0.001252871
+5837,976,IP CTS LUNG RESECTION POSTOP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,952,4789,0.198788891
+5838,976,IP CTS LUNG RESECTION POSTOP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,35,4789,0.007308415
+5839,976,IP CTS LUNG RESECTION POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,759,4789,0.158488202
+5840,976,IP CTS LUNG RESECTION POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,314,4789,0.065566924
+5841,976,IP CTS LUNG RESECTION POSTOP,3843,IBUPROFEN 400 MG PO TABS,Medication,36,4789,0.007517227
+5842,976,IP CTS LUNG RESECTION POSTOP,3844,IBUPROFEN 600 MG PO TABS,Medication,6,4789,0.001252871
+5843,976,IP CTS LUNG RESECTION POSTOP,4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,4789,0.000208812
+5844,976,IP CTS LUNG RESECTION POSTOP,4573,LORAZEPAM 1 MG PO TABS,Medication,5,4789,0.001044059
+5845,976,IP CTS LUNG RESECTION POSTOP,4711,MAGNESIUM CITRATE PO SOLN,Medication,1,4789,0.000208812
+5846,976,IP CTS LUNG RESECTION POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1044,4789,0.217999582
+5847,976,IP CTS LUNG RESECTION POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,65,4789,0.013572771
+5848,976,IP CTS LUNG RESECTION POSTOP,5008,METOPROLOL TARTRATE 100 MG PO TABS,Medication,3,4789,0.000626436
+5849,976,IP CTS LUNG RESECTION POSTOP,5009,METOPROLOL TARTRATE 50 MG PO TABS,Medication,9,4789,0.001879307
+5850,976,IP CTS LUNG RESECTION POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,492,4789,0.102735435
+5851,976,IP CTS LUNG RESECTION POSTOP,6494,PREDNISONE 10 MG PO TABS,Medication,1,4789,0.000208812
+5852,976,IP CTS LUNG RESECTION POSTOP,6497,PREDNISONE 5 MG PO TABS,Medication,1,4789,0.000208812
+5853,976,IP CTS LUNG RESECTION POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,6,4789,0.001252871
+5854,976,IP CTS LUNG RESECTION POSTOP,7753,TEMAZEPAM 15 MG PO CAPS,Medication,363,4789,0.075798705
+5855,976,IP CTS LUNG RESECTION POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,24,4789,0.005011485
+5856,976,IP CTS LUNG RESECTION POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,8,4789,0.001670495
+5857,976,IP CTS LUNG RESECTION POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,21,4789,0.004385049
+5858,976,IP CTS LUNG RESECTION POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,3,4789,0.000626436
+5859,976,IP CTS LUNG RESECTION POSTOP,10717,NICOTINE (POLACRILEX) 2 MG BUCCAL GUM,Medication,1,4789,0.000208812
+5860,976,IP CTS LUNG RESECTION POSTOP,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,7,4789,0.001461683
+5861,976,IP CTS LUNG RESECTION POSTOP,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,4789,0.000208812
+5862,976,IP CTS LUNG RESECTION POSTOP,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,4,4789,0.000835247
+5863,976,IP CTS LUNG RESECTION POSTOP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,3,4789,0.000626436
+5864,976,IP CTS LUNG RESECTION POSTOP,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,969,4789,0.202338693
+5865,976,IP CTS LUNG RESECTION POSTOP,14862,POTASSIUM CHLORIDE-D5-0.2%NACL 20 MEQ/L IV SOLP,Medication,2,4789,0.000417624
+5866,976,IP CTS LUNG RESECTION POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,30,4789,0.006264356
+5867,976,IP CTS LUNG RESECTION POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,1,4789,0.000208812
+5868,976,IP CTS LUNG RESECTION POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,6,4789,0.001252871
+5869,976,IP CTS LUNG RESECTION POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,74,4789,0.015452078
+5870,976,IP CTS LUNG RESECTION POSTOP,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,122,4789,0.025475047
+5871,976,IP CTS LUNG RESECTION POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,822,4789,0.171643349
+5872,976,IP CTS LUNG RESECTION POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,29,4789,0.006055544
+5873,976,IP CTS LUNG RESECTION POSTOP,27862,NICOTINE 14 MG/24 HR TD PT24,Medication,1,4789,0.000208812
+5874,976,IP CTS LUNG RESECTION POSTOP,27863,NICOTINE 21 MG/24 HR TD PT24,Medication,1,4789,0.000208812
+5875,976,IP CTS LUNG RESECTION POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,23,4789,0.004802673
+5876,976,IP CTS LUNG RESECTION POSTOP,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,84,4789,0.017540196
+5877,976,IP CTS LUNG RESECTION POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,9,4789,0.001879307
+5878,976,IP CTS LUNG RESECTION POSTOP,38390,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,Medication,1017,4789,0.212361662
+5879,976,IP CTS LUNG RESECTION POSTOP,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,1096,4789,0.228857799
+5880,976,IP CTS LUNG RESECTION POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,79,4789,0.016496137
+5881,976,IP CTS LUNG RESECTION POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,1027,4789,0.214449781
+5882,976,IP CTS LUNG RESECTION POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,218,4789,0.045520986
+5883,976,IP CTS LUNG RESECTION POSTOP,89904,SALMETEROL 50 MCG/DOSE INH DSDV,Medication,2,4789,0.000417624
+5884,976,IP CTS LUNG RESECTION POSTOP,92173,ZOLPIDEM 5 MG PO TABS,Medication,6,4789,0.001252871
+5885,976,IP CTS LUNG RESECTION POSTOP,95250,FLUTICASONE 110 MCG/ACTUATION INH AERO,Medication,4,4789,0.000835247
+5886,976,IP CTS LUNG RESECTION POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,148,4789,0.030904155
+5887,976,IP CTS LUNG RESECTION POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,4789,0.000208812
+5888,976,IP CTS LUNG RESECTION POSTOP,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,4789,0.000208812
+5889,976,IP CTS LUNG RESECTION POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,4789,0.000417624
+5890,976,IP CTS LUNG RESECTION POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,48,4789,0.010022969
+5891,976,IP CTS LUNG RESECTION POSTOP,120980,TIOTROPIUM BROMIDE 18 MCG INH CPDV,Medication,1,4789,0.000208812
+5892,976,IP CTS LUNG RESECTION POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,39,4789,0.008143663
+5893,976,IP CTS LUNG RESECTION POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,12,4789,0.002505742
+5894,976,IP CTS LUNG RESECTION POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1168,4789,0.243892253
+5895,976,IP CTS LUNG RESECTION POSTOP,199112,BUDESONIDE 180 MCG/ACTUATION INH AEPB,Medication,5,4789,0.001044059
+5896,976,IP CTS LUNG RESECTION POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,20,4789,0.004176237
+5897,976,IP CTS LUNG RESECTION POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,7,4789,0.001461683
+5898,976,IP CTS LUNG RESECTION POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,4789,0.000208812
+5899,976,IP CTS LUNG RESECTION POSTOP,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,4789,0.000208812
+5900,976,IP CTS LUNG RESECTION POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,979,4789,0.204426811
+5901,976,IP CTS LUNG RESECTION POSTOP,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,2,4789,0.000417624
+5902,976,IP CTS LUNG RESECTION POSTOP,233335,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH MIST,Medication,1,4789,0.000208812
+5903,976,IP CTS LUNG RESECTION POSTOP,520008,METOPROLOL IVPB,Medication,24,4789,0.005011485
+5904,976,IP CTS LUNG RESECTION POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,7,4789,0.001461683
+5905,976,IP CTS LUNG RESECTION POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,11,4789,0.00229693
+5906,976,IP CTS LUNG RESECTION POSTOP,540640,IPRATROPIUM-ALBUTEROL 0.5MG-2.5MG BASE(3MG)/3ML INH NEB-PANEL USE ONLY,Medication,943,4789,0.196909584
+5907,976,IP CTS LUNG RESECTION POSTOP,540642,IPRATROPIUM-ALBUTEROL 18-103 MCG/ACTUATION INH AERO-PANEL USE ONLY,Medication,1,4789,0.000208812
+5908,976,IP CTS LUNG RESECTION POSTOP,540643,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,2,4789,0.000417624
+5909,976,IP CTS LUNG RESECTION POSTOP,540644,IPRATROPIUM BROMIDE 0.02 % INH SOLN PANEL USE ONLY,Medication,2,4789,0.000417624
+5910,976,IP CTS LUNG RESECTION POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,29,4789,0.006055544
+5911,976,IP CTS LUNG RESECTION POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,3,4789,0.000626436
+5912,976,IP CTS LUNG RESECTION POSTOP,LABCA,"CALCIUM, SERUM/PLASMA",Lab,20,4789,0.004176237
+5913,976,IP CTS LUNG RESECTION POSTOP,LABCBCD,CBC WITH DIFF,Lab,1763,4789,0.36813531
+5914,976,IP CTS LUNG RESECTION POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,1851,4789,0.386510754
+5915,976,IP CTS LUNG RESECTION POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,20,4789,0.004176237
+5916,976,IP CTS LUNG RESECTION POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,583,4789,0.121737315
+5917,976,IP CTS LUNG RESECTION POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,34,4789,0.007099603
+5918,976,IP CTS LUNG RESECTION POSTOP,LABPLTS,PLATELET COUNT,Lab,182,4789,0.038003759
+5919,976,IP CTS LUNG RESECTION POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,10,4789,0.002088119
+5920,976,IP CTS LUNG RESECTION POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,1986,4789,0.414700355
+5921,976,IP CTS LUNG RESECTION POSTOP,IMGDXCH2,XR CHEST 2V,Imaging,2,4789,0.000417624
+5922,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1999,12217,0.163624458
+5923,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,102,ACETAMINOPHEN 500 MG PO TABS,Medication,24,12217,0.001964476
+5924,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,333,12217,0.027257101
+5925,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,680,ASPIRIN 81 MG PO CHEW,Medication,2,12217,0.000163706
+5926,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,681,ASPIRIN 325 MG PO TABS,Medication,39,12217,0.003192273
+5927,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2325,DEXAMETHASONE 1.5 MG PO TABS,Medication,1,12217,8.19E-05
+5928,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2326,DEXAMETHASONE 2 MG PO TABS,Medication,6,12217,0.000491119
+5929,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2327,DEXAMETHASONE 4 MG PO TABS,Medication,61,12217,0.004993042
+5930,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,988,12217,0.080870918
+5931,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2404,DIAZEPAM 2 MG PO TABS,Medication,17,12217,0.001391504
+5932,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2405,DIAZEPAM 5 MG PO TABS,Medication,225,12217,0.01841696
+5933,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1877,12217,0.153638373
+5934,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,877,12217,0.071785217
+5935,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,7,12217,0.000572972
+5936,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,1889,12217,0.154620611
+5937,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,4711,MAGNESIUM CITRATE PO SOLN,Medication,362,12217,0.029630842
+5938,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2690,12217,0.220184988
+5939,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,1626,12217,0.133093231
+5940,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,2774,12217,0.227060653
+5941,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,137,12217,0.011213882
+5942,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,6427,POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK,Medication,1,12217,8.19E-05
+5943,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,750,12217,0.061389867
+5944,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,52,12217,0.004256364
+5945,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,7321,SODIUM CHLORIDE 3 % 3 % IV SOLP,Medication,41,12217,0.003355979
+5946,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,8958,BUTALBITAL-ACETAMINOPHEN-CAFF 50-325-40 MG PO TABS,Medication,193,12217,0.015797659
+5947,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,12217,0.000163706
+5948,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,5,12217,0.000409266
+5949,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,10011,FAMOTIDINE 20 MG PO TABS,Medication,51,12217,0.004174511
+5950,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,10054,FLUDROCORTISONE 0.1 MG PO TABS,Medication,1,12217,8.19E-05
+5951,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,2749,12217,0.225014324
+5952,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,10610,MIDODRINE 5 MG PO TABS,Medication,24,12217,0.001964476
+5953,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,10814,OXYCODONE 5 MG PO TABS,Medication,92,12217,0.00753049
+5954,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,11144,PROMETHAZINE 25 MG PR SUPP,Medication,31,12217,0.002537448
+5955,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,53,12217,0.004338217
+5956,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,11349,SENNOSIDES 8.6 MG PO TABS,Medication,3031,12217,0.248096914
+5957,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,17,12217,0.001391504
+5958,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,41,12217,0.003355979
+5959,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,12217,8.19E-05
+5960,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2489,12217,0.203732504
+5961,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,18307,GABAPENTIN 400 MG PO CAPS,Medication,2,12217,0.000163706
+5962,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,18308,GABAPENTIN 300 MG PO CAPS,Medication,11,12217,0.000900385
+5963,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,110,12217,0.009003847
+5964,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,854,12217,0.069902595
+5965,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,294,12217,0.024064828
+5966,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,24,12217,0.001964476
+5967,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2440,12217,0.199721699
+5968,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,1,12217,8.19E-05
+5969,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,2172,12217,0.177785054
+5970,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,81934,NIMODIPINE 30 MG PO CAPS,Medication,228,12217,0.018662519
+5971,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,118,12217,0.009658672
+5972,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,82918,GABAPENTIN 250 MG/5 ML PO SOLN,Medication,2,12217,0.000163706
+5973,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,83398,CLOPIDOGREL 75 MG PO TABS,Medication,28,12217,0.002291888
+5974,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,83588,SENNOSIDES 8.8 MG/5 ML PO SYRP,Medication,1,12217,8.19E-05
+5975,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,355,12217,0.02905787
+5976,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,50,12217,0.004092658
+5977,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,89065,LIDOCAINE 5 % TP PTMD,Medication,1,12217,8.19E-05
+5978,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,92585,FAMOTIDINE 40 MG/5 ML PO SUSP,Medication,3,12217,0.000245559
+5979,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,915,12217,0.074895637
+5980,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,54,12217,0.00442007
+5981,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,12217,8.19E-05
+5982,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,26,12217,0.002128182
+5983,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,5,12217,0.000409266
+5984,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,73,12217,0.00597528
+5985,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,117862,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN,Medication,9,12217,0.000736678
+5986,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,64,12217,0.005238602
+5987,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,147,12217,0.012032414
+5988,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,1805,12217,0.147744946
+5989,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2906,12217,0.23786527
+5990,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,8,12217,0.000654825
+5991,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,2311,12217,0.189162642
+5992,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,205766,CLEVIDIPINE 50 MG/100 ML IV EMUL,Medication,94,12217,0.007694197
+5993,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,206226,NICARDIPINE IN SALINE (ISO-OS) 40 MG/200 ML IV PGBK,Medication,717,12217,0.058688712
+5994,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,41,12217,0.003355979
+5995,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,2,12217,0.000163706
+5996,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,75,12217,0.006138987
+5997,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,203,12217,0.016616191
+5998,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,2110,12217,0.172710158
+5999,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,796,12217,0.065155112
+6000,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,22,12217,0.001800769
+6001,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,1189,12217,0.097323402
+6002,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,222567,LEVETIRACETAM IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,63,12217,0.005156749
+6003,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,222985,DIAZEPAM 5 MG/5 ML (5 ML) PO SOLN,Medication,2,12217,0.000163706
+6004,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,53,12217,0.004338217
+6005,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,230362,NIMODIPINE 60 MG/20 ML PO SOLN,Medication,43,12217,0.003519686
+6006,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,29,12217,0.002373742
+6007,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,520008,METOPROLOL IVPB,Medication,2,12217,0.000163706
+6008,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,520064,CEFAZOLIN 2 G IVPB,Medication,27,12217,0.002210035
+6009,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,520068,PHENYTOIN IVPB (500 MG OR LESS),Medication,14,12217,0.001145944
+6010,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,520301,CEFTRIAXONE 1 GRAM/50 ML NS IVPB,Medication,1388,12217,0.11361218
+6011,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,520302,CEFTRIAXONE 2 GRAM/50 ML NS IVPB,Medication,26,12217,0.002128182
+6012,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,530008,NITROPRUSSIDE IV INFUSION,Medication,1685,12217,0.137922567
+6013,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,530010,NICARDIPINE IV INFUSION,Medication,45,12217,0.003683392
+6014,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,530011,NOREPINEPHRINE IV INFUSION,Medication,13,12217,0.001064091
+6015,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,530012,PHENYLEPHRINE IV INFUSION,Medication,75,12217,0.006138987
+6016,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,530025,DEXMEDETOMIDINE IV INFUSION,Medication,6,12217,0.000491119
+6017,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,3100,12217,0.253744782
+6018,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,1212,12217,0.099206024
+6019,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540302,POTASSIUM CHLORIDE IV SCALE,Medication,1585,12217,0.129737251
+6020,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540304,MAGNESIUM SULFATE IV SCALE ,Medication,2137,12217,0.174920193
+6021,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540316,CEFTRIAXONE 1 GRAM/50 ML D5W IVPB,Medication,1,12217,8.19E-05
+6022,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540769,SODIUM CHLORIDE 3 % IV BOLUS,Medication,8,12217,0.000654825
+6023,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,540988,NIMODIPINE 60 MG/20 ML PO SOLN,Medication,3,12217,0.000245559
+6024,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,9,12217,0.000736678
+6025,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,541433,POTASSIUM IV CHLORIDE SCALE 10-30 MEQ,Medication,3,12217,0.000245559
+6026,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,541524,CEFAZOLIN IN NS IVPB (COMPOUNDED),Medication,330,12217,0.027011541
+6027,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,590432,CLEVIDIPINE 25 MG/50 ML ANES BOLUS (SHC OR),Medication,2,12217,0.000163706
+6028,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,9000000,ZZZ IMS TEMPLATE,Medication,1,12217,8.19E-05
+6029,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,911,12217,0.074568225
+6030,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABCBCD,CBC WITH DIFF,Lab,2705,12217,0.221412785
+6031,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABMETB,"METABOLIC PANEL, BASIC",Lab,2628,12217,0.215110092
+6032,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,353,12217,0.028894164
+6033,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,948,12217,0.077596791
+6034,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABPHEN,PHENYTOIN LEVEL,Lab,14,12217,0.001145944
+6035,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABPLTS,PLATELET COUNT,Lab,20,12217,0.001637063
+6036,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABPT,PROTHROMBIN TIME,Lab,2207,12217,0.180649914
+6037,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,684,12217,0.055987558
+6038,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,483,12217,0.039535074
+6039,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGCTH,CT HEAD,Imaging,178,12217,0.014569862
+6040,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGDXCH1,XR CHEST 1V,Imaging,1151,12217,0.094212982
+6041,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGMRH,MR BRAIN,Imaging,288,12217,0.023573709
+6042,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGMRSC,MR C-SPINE,Imaging,6,12217,0.000491119
+6043,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGMRSL,MR L-SPINE,Imaging,3,12217,0.000245559
+6044,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGMRST,MR T-SPINE,Imaging,5,12217,0.000409266
+6045,978,IP NSR ICU NEUROSURGERY POSTOPERATIVE,IMGXR0023,XR CHEST 1 VIEW,Imaging,1,12217,8.19E-05
+6046,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,309,0.01618123
+6047,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,1,309,0.003236246
+6048,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,309,0.003236246
+6049,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",8442,"VANCOMYCIN 1,000 MG IV SOLR",Medication,1,309,0.003236246
+6050,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,6,309,0.019417476
+6051,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,25,309,0.080906149
+6052,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,54,309,0.174757282
+6053,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",79363,PHYTONADIONE (VITAMIN K1) 10 MG/ML INJ SOLN,Medication,3,309,0.009708738
+6054,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,58,309,0.187702265
+6055,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,309,0.003236246
+6056,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,309,0.006472492
+6057,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,117,309,0.378640777
+6058,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,13,309,0.042071197
+6059,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,309,0.01618123
+6060,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,20,309,0.064724919
+6061,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",10045R,HLA ORDER - ANTIBODY SCREENING,Lab,133,309,0.430420712
+6062,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,25,309,0.080906149
+6063,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",10277R,HLA ORDER - PROSPECTIVE CROSSMATCH TESTING,Lab,5,309,0.01618123
+6064,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",10283R,HLA ORDER - TRANSPLANT EVENT COMMUNICATION,Lab,117,309,0.378640777
+6065,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABBBPCRY,CRYOPRECIPITATE,Lab,10,309,0.03236246
+6066,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABCBCD,CBC WITH DIFF,Lab,141,309,0.45631068
+6067,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABHFP,HEPATIC FUNCTION PANEL A,Lab,135,309,0.436893204
+6068,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,145,309,0.469255663
+6069,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABPLTS,PLATELET COUNT,Lab,143,309,0.462783172
+6070,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABPT,PROTHROMBIN TIME,Lab,144,309,0.466019417
+6071,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,145,309,0.469255663
+6072,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABTYPSNI,TYPE AND SCREEN,Lab,135,309,0.436893204
+6073,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,140,309,0.453074434
+6074,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABYWNILE,"WEST NILE VIRUS AB, IGG AND IGM",Lab,4,309,0.012944984
+6075,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",IMGDXCH1,XR CHEST 1V,Imaging,41,309,0.132686084
+6076,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",IMGDXCH2,XR CHEST 2V,Imaging,63,309,0.203883495
+6077,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABBBPCRY,CRYOPRECIPITATE,Blood Product,41,309,0.132686084
+6078,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,92,309,0.297734628
+6079,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,103,309,0.333333333
+6080,980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",LABBBPRBC,RED BLOOD CELLS,Blood Product,133,309,0.430420712
+6081,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,693,0.001443001
+6082,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",464,AMPHOTERICIN B 50 MG INJ SOLR,Medication,38,693,0.054834055
+6083,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",2364,DEXTROSE 5 % IN WATER (D5W) IV SOLP,Medication,8,693,0.011544012
+6084,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,98,693,0.141414141
+6085,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",5751,"NYSTATIN 100,000 UNIT/ML PO SUSP",Medication,10,693,0.014430014
+6086,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,693,0.007215007
+6087,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",6492,PREDNISONE 5 MG/5 ML PO SOLN,Medication,1,693,0.001443001
+6088,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",7555,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,Medication,9,693,0.012987013
+6089,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,12,693,0.017316017
+6090,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,2,693,0.002886003
+6091,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",11150,PROPOFOL 10 MG/ML IV EMUL,Medication,6,693,0.008658009
+6092,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,10,693,0.014430014
+6093,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,18,693,0.025974026
+6094,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,6,693,0.008658009
+6095,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,693,0.001443001
+6096,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,7,693,0.01010101
+6097,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",82544,ITRACONAZOLE 10 MG/ML PO SOLN,Medication,14,693,0.02020202
+6098,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,1,693,0.001443001
+6099,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,2,693,0.002886003
+6100,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,1,693,0.001443001
+6101,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,104,693,0.15007215
+6102,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,22,693,0.031746032
+6103,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,105,693,0.151515152
+6104,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,3,693,0.004329004
+6105,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,3,693,0.004329004
+6106,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,110,693,0.158730159
+6107,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,97,693,0.13997114
+6108,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,2,693,0.002886003
+6109,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,6,693,0.008658009
+6110,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,693,0.001443001
+6111,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,20,693,0.028860029
+6112,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,38,693,0.054834055
+6113,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",520055,GANCICLOVIR IVPB,Medication,105,693,0.151515152
+6114,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",520103,CEFEPIME 1 GM IVPB,Medication,5,693,0.007215007
+6115,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530001,AMIODARONE INFUSION PERIPH ADMIN,Medication,1,693,0.001443001
+6116,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530006,EPINEPHRINE IV INFUSION,Medication,35,693,0.050505051
+6117,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530008,NITROPRUSSIDE IV INFUSION,Medication,2,693,0.002886003
+6118,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530011,NOREPINEPHRINE IV INFUSION,Medication,3,693,0.004329004
+6119,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530012,PHENYLEPHRINE IV INFUSION,Medication,6,693,0.008658009
+6120,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530025,DEXMEDETOMIDINE IV INFUSION,Medication,12,693,0.017316017
+6121,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,14,693,0.02020202
+6122,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",530049,INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW),Medication,88,693,0.126984127
+6123,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,36,693,0.051948052
+6124,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",540129,TACROLIMUS 1 MG/ML ORAL SUSPENSION,Medication,17,693,0.024531025
+6125,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",540302,POTASSIUM CHLORIDE IV SCALE,Medication,45,693,0.064935065
+6126,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",540304,MAGNESIUM SULFATE IV SCALE ,Medication,43,693,0.062049062
+6127,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",540321,MYCOPHENOLATE MOFETIL 200 MG/ML ORAL SUSP,Medication,88,693,0.126984127
+6128,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",540492,SHC TRANSPLANT BEDSIDE MEDS HELP ORDER,Medication,9,693,0.012987013
+6129,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",541098,CEFEPIME 1 GRAM/50 ML MINI-BAG PLUS,Medication,55,693,0.079365079
+6130,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,5,693,0.007215007
+6131,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,1,693,0.001443001
+6132,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",541285,AMPHOTERICIN B 20 MG/4ML NEB SOLUTION,Medication,50,693,0.072150072
+6133,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",541543,CEFEPIME 1 G/50 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,17,693,0.024531025
+6134,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",10045R,HLA ORDER - ANTIBODY SCREENING,Lab,78,693,0.112554113
+6135,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,3,693,0.004329004
+6136,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",10283R,HLA ORDER - TRANSPLANT EVENT COMMUNICATION,Lab,62,693,0.089466089
+6137,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABCA,"CALCIUM, SERUM/PLASMA",Lab,109,693,0.157287157
+6138,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABCBCD,CBC WITH DIFF,Lab,136,693,0.196248196
+6139,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABFK506L,TACROLIMUS (FK506),Lab,155,693,0.223665224
+6140,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABHCTX,HEMATOCRIT,Lab,137,693,0.197691198
+6141,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABK,"POTASSIUM, SERUM/PLASMA",Lab,113,693,0.163059163
+6142,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABMETB,"METABOLIC PANEL, BASIC",Lab,193,693,0.278499278
+6143,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,115,693,0.165945166
+6144,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,154,693,0.222222222
+6145,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABPLTS,PLATELET COUNT,Lab,14,693,0.02020202
+6146,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABPT,PROTHROMBIN TIME,Lab,118,693,0.17027417
+6147,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,117,693,0.168831169
+6148,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",LABVANPRL,VANCOMYCIN TROUGH LEVEL,Lab,106,693,0.152958153
+6149,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",POC14,POC GLUCOSE BY METER,Lab,713,693,1
+6150,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,88,693,0.126984127
+6151,981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",IMGDXCH1,XR CHEST 1V,Imaging,197,693,0.284271284
+6152,982,IP TRS HEART TRANSPLANT PREOP,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1,441,0.002267574
+6153,982,IP TRS HEART TRANSPLANT PREOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,441,0.002267574
+6154,982,IP TRS HEART TRANSPLANT PREOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,14,441,0.031746032
+6155,982,IP TRS HEART TRANSPLANT PREOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,39,441,0.088435374
+6156,982,IP TRS HEART TRANSPLANT PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,23,441,0.052154195
+6157,982,IP TRS HEART TRANSPLANT PREOP,79363,PHYTONADIONE (VITAMIN K1) 10 MG/ML INJ SOLN,Medication,16,441,0.036281179
+6158,982,IP TRS HEART TRANSPLANT PREOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,112,441,0.253968254
+6159,982,IP TRS HEART TRANSPLANT PREOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,170,441,0.385487528
+6160,982,IP TRS HEART TRANSPLANT PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,7,441,0.015873016
+6161,982,IP TRS HEART TRANSPLANT PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,441,0.006802721
+6162,982,IP TRS HEART TRANSPLANT PREOP,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,16,441,0.036281179
+6163,982,IP TRS HEART TRANSPLANT PREOP,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,210,441,0.476190476
+6164,982,IP TRS HEART TRANSPLANT PREOP,10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,16,441,0.036281179
+6165,982,IP TRS HEART TRANSPLANT PREOP,10235R,HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING,Lab,199,441,0.451247166
+6166,982,IP TRS HEART TRANSPLANT PREOP,10260R,HLA ORDER - DECEASED DONOR TESTING NOTICE,Lab,183,441,0.414965986
+6167,982,IP TRS HEART TRANSPLANT PREOP,10273R,HLA ORDER - WORKUP AT TIME OF TRANSPLANT,Lab,77,441,0.174603175
+6168,982,IP TRS HEART TRANSPLANT PREOP,10277R,HLA ORDER - PROSPECTIVE CROSSMATCH TESTING,Lab,10,441,0.022675737
+6169,982,IP TRS HEART TRANSPLANT PREOP,10283R,HLA ORDER - TRANSPLANT EVENT COMMUNICATION,Lab,223,441,0.505668934
+6170,982,IP TRS HEART TRANSPLANT PREOP,10297R,HLA ORDER - DECEASED DONOR O.R. SPECIMEN,Lab,201,441,0.455782313
+6171,982,IP TRS HEART TRANSPLANT PREOP,LABBBPCRY,CRYOPRECIPITATE,Lab,26,441,0.058956916
+6172,982,IP TRS HEART TRANSPLANT PREOP,LABCBCD,CBC WITH DIFF,Lab,203,441,0.46031746
+6173,982,IP TRS HEART TRANSPLANT PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,223,441,0.505668934
+6174,982,IP TRS HEART TRANSPLANT PREOP,LABPLTS,PLATELET COUNT,Lab,2,441,0.004535147
+6175,982,IP TRS HEART TRANSPLANT PREOP,LABPT,PROTHROMBIN TIME,Lab,207,441,0.469387755
+6176,982,IP TRS HEART TRANSPLANT PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,205,441,0.464852608
+6177,982,IP TRS HEART TRANSPLANT PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,210,441,0.476190476
+6178,982,IP TRS HEART TRANSPLANT PREOP,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,238,441,0.53968254
+6179,982,IP TRS HEART TRANSPLANT PREOP,IMGDXCH1,XR CHEST 1V,Imaging,36,441,0.081632653
+6180,982,IP TRS HEART TRANSPLANT PREOP,IMGDXCH2,XR CHEST 2V,Imaging,157,441,0.35600907
+6181,982,IP TRS HEART TRANSPLANT PREOP,LABBBPCRY,CRYOPRECIPITATE,Blood Product,113,441,0.256235828
+6182,982,IP TRS HEART TRANSPLANT PREOP,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,199,441,0.451247166
+6183,982,IP TRS HEART TRANSPLANT PREOP,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,176,441,0.399092971
+6184,982,IP TRS HEART TRANSPLANT PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,211,441,0.47845805
+6185,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,19,0.052631579
+6186,1044,IP REN TRANSFUSION FOR DIALYSIS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,19,0.052631579
+6187,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,19,0.105263158
+6188,1044,IP REN TRANSFUSION FOR DIALYSIS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,19,0.105263158
+6189,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,19,0.105263158
+6190,1044,IP REN TRANSFUSION FOR DIALYSIS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,19,0.105263158
+6191,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,1,19,0.052631579
+6192,1044,IP REN TRANSFUSION FOR DIALYSIS,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,1,19,0.052631579
+6193,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,LABTYPSNI,TYPE AND SCREEN,Lab,7,19,0.368421053
+6194,1044,IP REN TRANSFUSION FOR DIALYSIS,LABTYPSNI,TYPE AND SCREEN,Lab,7,19,0.368421053
+6195,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,3,19,0.157894737
+6196,1044,IP REN TRANSFUSION FOR DIALYSIS,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,3,19,0.157894737
+6197,1044,RETIRED - IP REN TRANSFUSION FOR DIALYSIS,LABBBPRBC,RED BLOOD CELLS,Blood Product,10,19,0.526315789
+6198,1044,IP REN TRANSFUSION FOR DIALYSIS,LABBBPRBC,RED BLOOD CELLS,Blood Product,10,19,0.526315789
+6199,1045,IP ID HIV/AIDS CHRONIC DIAGNOSIS,LABC4C8,"CD4 / CD8 PANEL, BLOOD",Lab,8,8,1
+6200,1045,IP ID HIV/AIDS CHRONIC DIAGNOSIS,LABHIVQ2,HIV QUANTITATIVE PCR,Lab,6,8,0.75
+6201,1045,IP ID HIV/AIDS CHRONIC DIAGNOSIS,LABPT,PROTHROMBIN TIME,Lab,1,8,0.125
+6202,1045,IP ID HIV/AIDS CHRONIC DIAGNOSIS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,8,0.125
+6203,1045,IP ID HIV/AIDS CHRONIC DIAGNOSIS,LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,4,8,0.5
+6204,1046,IP ID HIV/AIDS NEW DIAGNOSIS,17387,AZITHROMYCIN 600 MG PO TABS,Medication,1,9,0.111111111
+6205,1046,IP ID HIV/AIDS NEW DIAGNOSIS,77878,TUBERCULIN PPD 5 TUB. UNIT /0.1 ML ID SOLN,Medication,1,9,0.111111111
+6206,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABAFBC,"AFB CULTURE, RESPIRATORY",Lab,1,9,0.111111111
+6207,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABAVRT,HIV - 1 GENOTYPING,Lab,1,9,0.111111111
+6208,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABC4C8,"CD4 / CD8 PANEL, BLOOD",Lab,2,9,0.222222222
+6209,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,1,9,0.111111111
+6210,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABCMVIGG,CMV IGG,Lab,1,9,0.111111111
+6211,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABCMVIGM,CMV IGM,Lab,1,9,0.111111111
+6212,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABGRAM,GRAM STAIN,Lab,1,9,0.111111111
+6213,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABHAVAG,HAV ANTIBODY IGG,Lab,1,9,0.111111111
+6214,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABHBCT,ANTI-HBC,Lab,3,9,0.333333333
+6215,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABHCVA,HEPATITIS C AB IGG,Lab,3,9,0.333333333
+6216,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABHIVQ2,HIV QUANTITATIVE PCR,Lab,4,9,0.444444444
+6217,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABOAPE,OVA AND PARASITES,Lab,1,9,0.111111111
+6218,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABPCYS,PNEUMOCYSTIS STAIN,Lab,2,9,0.222222222
+6219,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABPT,PROTHROMBIN TIME,Lab,1,9,0.111111111
+6220,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,9,0.111111111
+6221,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,2,9,0.222222222
+6222,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABREF1,LAB UNLISTED 1,Lab,1,9,0.111111111
+6223,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,1,9,0.111111111
+6224,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABROMSP,MICROSPORIDIUM SCREEN,Lab,1,9,0.111111111
+6225,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABRPRQLT,RPR W/REFLEX TO TITER,Lab,1,9,0.111111111
+6226,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABSTLCP,STOOL CULTURE,Lab,1,9,0.111111111
+6227,1046,IP ID HIV/AIDS NEW DIAGNOSIS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,9,0.111111111
+6228,1046,IP ID HIV/AIDS NEW DIAGNOSIS,POC13,POC PPD SKIN TEST (AMB),Lab,1,9,0.111111111
+6229,1047,IP MED LIVER TRANSPLANT EVALUATION,LAB11071R,MITOCHONDRIAL AB,Lab,9,42,0.214285714
+6230,1047,IP MED LIVER TRANSPLANT EVALUATION,LAB11075R,SMOOTH MUSCLE AB,Lab,14,42,0.333333333
+6231,1047,IP MED LIVER TRANSPLANT EVALUATION,LAB11106R,HBE AB,Lab,3,42,0.071428571
+6232,1047,IP MED LIVER TRANSPLANT EVALUATION,LAB11107R,HBE ANTIGEN,Lab,3,42,0.071428571
+6233,1047,IP MED LIVER TRANSPLANT EVALUATION,LAB11179R,"NICOTINE & METABOLITES, U",Lab,9,42,0.214285714
+6234,1047,IP MED LIVER TRANSPLANT EVALUATION,LABAFP,"ALPHA FETOPROTEIN, SERUM",Lab,27,42,0.642857143
+6235,1047,IP MED LIVER TRANSPLANT EVALUATION,LABARI,ABO/RH (ARI),Lab,25,42,0.595238095
+6236,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCA,"CALCIUM, SERUM/PLASMA",Lab,2,42,0.047619048
+6237,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCA199,CANCER AG 19-9,Lab,6,42,0.142857143
+6238,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCBCD,CBC WITH DIFF,Lab,8,42,0.19047619
+6239,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCEA,CEA,Lab,2,42,0.047619048
+6240,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCMVIGG,CMV IGG,Lab,28,42,0.666666667
+6241,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCMVIGM,CMV IGM,Lab,29,42,0.69047619
+6242,1047,IP MED LIVER TRANSPLANT EVALUATION,LABCRCL,CREATININE CLEARANCE,Lab,20,42,0.476190476
+6243,1047,IP MED LIVER TRANSPLANT EVALUATION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,14,42,0.333333333
+6244,1047,IP MED LIVER TRANSPLANT EVALUATION,LABFE,"IRON, TOTAL",Lab,27,42,0.642857143
+6245,1047,IP MED LIVER TRANSPLANT EVALUATION,LABFER,FERRITIN,Lab,27,42,0.642857143
+6246,1047,IP MED LIVER TRANSPLANT EVALUATION,LABFHVIG,"HIV 1 IGG-AB, WESTERN BLOT CONFIRM",Lab,10,42,0.238095238
+6247,1047,IP MED LIVER TRANSPLANT EVALUATION,LABGGT,GAMMA - GLUTAMYL TRANSFERASE,Lab,26,42,0.619047619
+6248,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHAVM,IGM ANTI-HAV,Lab,14,42,0.333333333
+6249,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHBCA,IGM ANTI-HBC,Lab,17,42,0.404761905
+6250,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHBPQ,HEPATITIS B PCR QUANTITATIVE,Lab,5,42,0.119047619
+6251,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHBSAG,HBSAG,Lab,27,42,0.642857143
+6252,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHBSQT,HEP B SURF AB QUANT,Lab,11,42,0.261904762
+6253,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHCPQ,HEPATITIS C RNA PCR QUANTITATIVE,Lab,11,42,0.261904762
+6254,1047,IP MED LIVER TRANSPLANT EVALUATION,LABHCVA,HEPATITIS C AB IGG,Lab,14,42,0.333333333
+6255,1047,IP MED LIVER TRANSPLANT EVALUATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,8,42,0.19047619
+6256,1047,IP MED LIVER TRANSPLANT EVALUATION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2,42,0.047619048
+6257,1047,IP MED LIVER TRANSPLANT EVALUATION,LABNH3,"AMMONIA, PLASMA",Lab,9,42,0.214285714
+6258,1047,IP MED LIVER TRANSPLANT EVALUATION,LABPSA,PSA,Lab,17,42,0.404761905
+6259,1047,IP MED LIVER TRANSPLANT EVALUATION,LABPT,PROTHROMBIN TIME,Lab,11,42,0.261904762
+6260,1047,IP MED LIVER TRANSPLANT EVALUATION,LABRF,RHEUMATOID FACTOR,Lab,3,42,0.071428571
+6261,1047,IP MED LIVER TRANSPLANT EVALUATION,LABRPRQLT,RPR W/REFLEX TO TITER,Lab,23,42,0.547619048
+6262,1047,IP MED LIVER TRANSPLANT EVALUATION,LABRPRQT,RPR QUANTITATIVE,Lab,4,42,0.095238095
+6263,1047,IP MED LIVER TRANSPLANT EVALUATION,LABSPIE,"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM",Lab,2,42,0.047619048
+6264,1047,IP MED LIVER TRANSPLANT EVALUATION,LABTES,TESTOSTERONE,Lab,1,42,0.023809524
+6265,1047,IP MED LIVER TRANSPLANT EVALUATION,LABTRFS,TRANSFERRIN SATURATION,Lab,27,42,0.642857143
+6266,1047,IP MED LIVER TRANSPLANT EVALUATION,LABTSH,TSH,Lab,13,42,0.30952381
+6267,1047,IP MED LIVER TRANSPLANT EVALUATION,LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,18,42,0.428571429
+6268,1047,IP MED LIVER TRANSPLANT EVALUATION,LABVOLTL,VOLATILE SCREEN,Lab,24,42,0.571428571
+6269,1047,IP MED LIVER TRANSPLANT EVALUATION,LABYA1APH,ALPHA - 1 - ANTITRYPSIN PHENOTYPE,Lab,4,42,0.095238095
+6270,1047,IP MED LIVER TRANSPLANT EVALUATION,LABYA1AS,ALPHA - 1 - ANTITRYPSIN,Lab,16,42,0.380952381
+6271,1047,IP MED LIVER TRANSPLANT EVALUATION,LABYCERU,CERULOPLASMIN,Lab,10,42,0.238095238
+6272,1047,IP MED LIVER TRANSPLANT EVALUATION,ECH43,ECHO - DOBUTAMINE STRESS ECHO +DOPPLER,Imaging,3,42,0.071428571
+6273,1047,IP MED LIVER TRANSPLANT EVALUATION,IMGDXCH2,XR CHEST 2V,Imaging,1,42,0.023809524
+6274,1047,IP MED LIVER TRANSPLANT EVALUATION,IMGUSAB1,US ABDOMEN,Imaging,2,42,0.047619048
+6275,1049,IP/ED GENERAL TRANSFUSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1345,52260,0.025736701
+6276,1049,IP/ED GENERAL TRANSFUSION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,4,52260,7.65E-05
+6277,1049,IP/ED GENERAL TRANSFUSION,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1270,52260,0.024301569
+6278,1049,IP/ED GENERAL TRANSFUSION,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,4,52260,7.65E-05
+6279,1049,IP/ED GENERAL TRANSFUSION,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,600,52260,0.011481056
+6280,1049,IP/ED GENERAL TRANSFUSION,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,4,52260,7.65E-05
+6281,1049,IP/ED GENERAL TRANSFUSION,112538,RHO D IMMUNE GLOBULIN 300 MCG IM SYRG,Medication,1,52260,1.91E-05
+6282,1049,IP/ED GENERAL TRANSFUSION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,6,52260,0.000114811
+6283,1049,IP/ED GENERAL TRANSFUSION,540148,NS FLUSH BAG,Medication,53,52260,0.00101416
+6284,1049,IP/ED GENERAL TRANSFUSION,LABBBPCRY,CRYOPRECIPITATE,Lab,775,52260,0.014829698
+6285,1049,IP/ED GENERAL TRANSFUSION,LABTYPSNI,TYPE AND SCREEN,Lab,17619,52260,0.337141217
+6286,1049,IP/ED GENERAL TRANSFUSION,LABBBPCRY,CRYOPRECIPITATE,Blood Product,2492,52260,0.047684654
+6287,1049,IP/ED GENERAL TRANSFUSION,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,9303,52260,0.178013777
+6288,1049,IP/ED GENERAL TRANSFUSION,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,22319,52260,0.427076158
+6289,1049,IP/ED GENERAL TRANSFUSION,LABBBPRBC,RED BLOOD CELLS,Blood Product,42335,52260,0.810084194
+6290,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,310,ALLOPURINOL 100 MG PO TABS,Medication,1,156,0.006410256
+6291,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,311,ALLOPURINOL 100 MG PO TABS,Medication,15,156,0.096153846
+6292,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,6,156,0.038461538
+6293,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,13,156,0.083333333
+6294,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,27999,SODIUM POLYSTYRENE SULFONATE 15 GRAM/60 ML PO SUSP,Medication,2,156,0.012820513
+6295,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,6,156,0.038461538
+6296,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,94556,SEVELAMER HCL 400 MG PO TABS,Medication,1,156,0.006410256
+6297,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,112517,SODIUM BICARBONATE 8.4 % (1 MEQ/ML) IV SYRG,Medication,2,156,0.012820513
+6298,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,1,156,0.006410256
+6299,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,123688,CALCIUM ACETATE 667 MG PO CAPS,Medication,2,156,0.012820513
+6300,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,6,156,0.038461538
+6301,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,1,156,0.006410256
+6302,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,520081,RASBURICASE IVPB,Medication,4,156,0.025641026
+6303,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,540775,POLYETHYLENE GLYCOL 3350 17 GM (FOR KAYEXALATE PANEL),Medication,2,156,0.012820513
+6304,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,74,156,0.474358974
+6305,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,95,156,0.608974359
+6306,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,LABMETB,"METABOLIC PANEL, BASIC",Lab,59,156,0.378205128
+6307,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,88,156,0.564102564
+6308,1090,IP ONC TUMOR LYSIS SYNDROME TREATMENT,LABURIC,"URIC ACID, SERUM / PLASMA",Lab,101,156,0.647435897
+6309,1091,IP ONC  HYPERCALCEMIA,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,9,0.111111111
+6310,1091,IP ONC  HYPERCALCEMIA,540360,PAMIDRONATE IVPB (90 MG),Medication,1,9,0.111111111
+6311,1091,IP ONC  HYPERCALCEMIA,LAB11098R,1 25 OH VITAMIN D,Lab,4,9,0.444444444
+6312,1091,IP ONC  HYPERCALCEMIA,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,2,9,0.222222222
+6313,1091,IP ONC  HYPERCALCEMIA,LABCA,"CALCIUM, SERUM/PLASMA",Lab,2,9,0.222222222
+6314,1091,IP ONC  HYPERCALCEMIA,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,3,9,0.333333333
+6315,1091,IP ONC  HYPERCALCEMIA,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,1,9,0.111111111
+6316,1091,IP ONC  HYPERCALCEMIA,LABPTH,PARATHYROID HORMONE,Lab,3,9,0.333333333
+6317,1091,IP ONC  HYPERCALCEMIA,LABSPIE,"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM",Lab,2,9,0.222222222
+6318,1091,IP ONC  HYPERCALCEMIA,LABUPIE,URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS,Lab,2,9,0.222222222
+6319,1093,IP ONC SPINAL CORD COMPRESSION,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,10,8,1
+6320,1093,IP ONC SPINAL CORD COMPRESSION,7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,1,8,0.125
+6321,1093,IP ONC SPINAL CORD COMPRESSION,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,2,8,0.25
+6322,1093,IP ONC SPINAL CORD COMPRESSION,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,8,0.25
+6323,1093,IP ONC SPINAL CORD COMPRESSION,IMGMRSF,MR SPINE FULL,Imaging,1,8,0.125
+6324,1096,CDU HEADACHE OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,8,61,0.131147541
+6325,1096,CDU HEADACHE OBSERVATION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,61,0.032786885
+6326,1096,CDU HEADACHE OBSERVATION,3843,IBUPROFEN 400 MG PO TABS,Medication,1,61,0.016393443
+6327,1096,CDU HEADACHE OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,61,0.016393443
+6328,1096,CDU HEADACHE OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,61,0.016393443
+6329,1096,CDU HEADACHE OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,61,0.081967213
+6330,1096,CDU HEADACHE OBSERVATION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,61,0.081967213
+6331,1096,CDU HEADACHE OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,61,0.147540984
+6332,1096,CDU HEADACHE OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,2,61,0.032786885
+6333,1096,CDU HEADACHE OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,26,61,0.426229508
+6334,1096,CDU HEADACHE OBSERVATION,540102,NS IV BOLUS,Medication,1,61,0.016393443
+6335,1096,CDU HEADACHE OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,4,61,0.06557377
+6336,1096,CDU HEADACHE OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,2,61,0.032786885
+6337,1096,CDU HEADACHE OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,61,0.016393443
+6338,1096,CDU HEADACHE OBSERVATION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,61,0.016393443
+6339,1098,CDU GASTROENTERITIS OBSERVATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,62,0.048387097
+6340,1098,CDU GASTROENTERITIS OBSERVATION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,7,62,0.112903226
+6341,1098,CDU GASTROENTERITIS OBSERVATION,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,62,0.016129032
+6342,1098,CDU GASTROENTERITIS OBSERVATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,62,0.016129032
+6343,1098,CDU GASTROENTERITIS OBSERVATION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,19,62,0.306451613
+6344,1098,CDU GASTROENTERITIS OBSERVATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3,62,0.048387097
+6345,1098,CDU GASTROENTERITIS OBSERVATION,92173,ZOLPIDEM 5 MG PO TABS,Medication,11,62,0.177419355
+6346,1098,CDU GASTROENTERITIS OBSERVATION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,62,0.016129032
+6347,1098,CDU GASTROENTERITIS OBSERVATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,62,0.016129032
+6348,1098,CDU GASTROENTERITIS OBSERVATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,26,62,0.419354839
+6349,1098,CDU GASTROENTERITIS OBSERVATION,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,62,0.016129032
+6350,1098,CDU GASTROENTERITIS OBSERVATION,LABCBCD,CBC WITH DIFF,Lab,16,62,0.258064516
+6351,1098,CDU GASTROENTERITIS OBSERVATION,LABLIPS,LIPASE,Lab,3,62,0.048387097
+6352,1098,CDU GASTROENTERITIS OBSERVATION,LABMETB,"METABOLIC PANEL, BASIC",Lab,3,62,0.048387097
+6353,1098,CDU GASTROENTERITIS OBSERVATION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,13,62,0.209677419
+6354,1098,CDU GASTROENTERITIS OBSERVATION,LABPT,PROTHROMBIN TIME,Lab,1,62,0.016129032
+6355,1101,IP ID PULMONARY NODULES DIAGNOSTIC(INFECTIOUS CAUSES),LAB11124R,COCCI CF,Lab,11,7,1
+6356,1101,IP ID PULMONARY NODULES DIAGNOSTIC(INFECTIOUS CAUSES),LABAGALA,ASPERGILLUS GALACTOMANNAN,Lab,8,7,1
+6357,1101,IP ID PULMONARY NODULES DIAGNOSTIC(INFECTIOUS CAUSES),LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,5,7,0.714285714
+6358,1101,IP ID PULMONARY NODULES DIAGNOSTIC(INFECTIOUS CAUSES),LABYBLAS,BLASTOMYCES AB,Lab,6,7,0.857142857
+6359,1101,IP ID PULMONARY NODULES DIAGNOSTIC(INFECTIOUS CAUSES),LABYHISTU,"HISTOPLASMA AG, URINE",Lab,9,7,1
+6360,1104,IP ID MYOCARDITIS DIAGNOSTIC (INFECTIOUS CAUSES),LAB12017R,"ECHOVIRUS AB, SERUM",Lab,1,2,0.5
+6361,1104,IP ID MYOCARDITIS DIAGNOSTIC (INFECTIOUS CAUSES),LABCOXPNL,COXSACKIE A AB PANEL,Lab,1,2,0.5
+6362,1104,IP ID MYOCARDITIS DIAGNOSTIC (INFECTIOUS CAUSES),LABFHHV6,HERPES VIRUS 6 IGG AND IGM AB,Lab,1,2,0.5
+6363,1104,IP ID MYOCARDITIS DIAGNOSTIC (INFECTIOUS CAUSES),LABFPARVO,PARVOVIRUS IGG / IGM AB,Lab,2,2,1
+6364,1105,IP ID TUBERCULOSIS THERAPY,4027,ISONIAZID 300 MG PO TABS,Medication,4,5,0.8
+6365,1105,IP ID TUBERCULOSIS THERAPY,6738,PYRAZINAMIDE 500 MG PO TABS,Medication,5,5,1
+6366,1105,IP ID TUBERCULOSIS THERAPY,6746,PYRIDOXINE 25 MG PO TABS,Medication,2,5,0.4
+6367,1105,IP ID TUBERCULOSIS THERAPY,6748,PYRIDOXINE (VITAMIN B6) 50 MG PO TABS,Medication,1,5,0.2
+6368,1105,IP ID TUBERCULOSIS THERAPY,9983,ETHAMBUTOL 400 MG PO TABS,Medication,6,5,1
+6369,1105,IP ID TUBERCULOSIS THERAPY,11293,RIFAMPIN 300 MG PO CAPS,Medication,4,5,0.8
+6370,1106,IP ID ACUTE ENCEPHALITIS,520041,ACYCLOVIR IVPB (<700 MG),Medication,2,7,0.285714286
+6371,1106,IP ID ACUTE ENCEPHALITIS,LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,2,7,0.285714286
+6372,1106,IP ID ACUTE ENCEPHALITIS,LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,1,7,0.142857143
+6373,1106,IP ID ACUTE ENCEPHALITIS,LABCSFC,CSF CULTURE AND GRAM STAIN,Lab,1,7,0.142857143
+6374,1106,IP ID ACUTE ENCEPHALITIS,LABCSFCT,"CELL COUNT AND DIFFERENTIAL, CSF",Lab,1,7,0.142857143
+6375,1106,IP ID ACUTE ENCEPHALITIS,LABCSFGL,"GLUCOSE, CSF",Lab,1,7,0.142857143
+6376,1106,IP ID ACUTE ENCEPHALITIS,LABCSFTP,"PROTEIN TOTAL, CSF",Lab,1,7,0.142857143
+6377,1106,IP ID ACUTE ENCEPHALITIS,LABEVPCR,ENTEROVIRUS BY PCR,Lab,2,7,0.285714286
+6378,1106,IP ID ACUTE ENCEPHALITIS,LABGRAM,GRAM STAIN,Lab,1,7,0.142857143
+6379,1106,IP ID ACUTE ENCEPHALITIS,LABHHV6QL,HERPES VIRAL 6 DNA QUALITATIVE,Lab,3,7,0.428571429
+6380,1106,IP ID ACUTE ENCEPHALITIS,LABWNVAB,"WEST NILE VIRUS AB PANEL, CSF",Lab,1,7,0.142857143
+6381,1136,IP ANE ANESTHESIA PREOP,10608,MIDAZOLAM 5 MG/ML INJ SOLN,Medication,12,340,0.035294118
+6382,1136,IP ANE ANESTHESIA PREOP,24176,MIDAZOLAM 2 MG/ML PO SYRP,Medication,1,340,0.002941176
+6383,1136,IP ANE ANESTHESIA PREOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,60,340,0.176470588
+6384,1136,IP ANE ANESTHESIA PREOP,540618,CHLORHEXIDINE GLUCONATE 4% (E-Z SCRUB 107) BATH/WIPE,Medication,3,340,0.008823529
+6385,1136,IP ANE ANESTHESIA PREOP,LABCBCD,CBC WITH DIFF,Lab,1,340,0.002941176
+6386,1136,IP ANE ANESTHESIA PREOP,LABCBCO,CBC W/O DIFF,Lab,20,340,0.058823529
+6387,1136,IP ANE ANESTHESIA PREOP,LABEP1,ELECTROLYTE PANEL,Lab,2,340,0.005882353
+6388,1136,IP ANE ANESTHESIA PREOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,18,340,0.052941176
+6389,1136,IP ANE ANESTHESIA PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,340,0.008823529
+6390,1136,IP ANE ANESTHESIA PREOP,LABPLTS,PLATELET COUNT,Lab,1,340,0.002941176
+6391,1136,IP ANE ANESTHESIA PREOP,LABPREG,"PREGNANCY TEST, SERUM",Lab,1,340,0.002941176
+6392,1136,IP ANE ANESTHESIA PREOP,LABPT,PROTHROMBIN TIME,Lab,10,340,0.029411765
+6393,1136,IP ANE ANESTHESIA PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,8,340,0.023529412
+6394,1136,IP ANE ANESTHESIA PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,71,340,0.208823529
+6395,1136,IP ANE ANESTHESIA PREOP,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,2,340,0.005882353
+6396,1136,IP ANE ANESTHESIA PREOP,LABUPREG,"PREGNANCY TEST, URINE",Lab,15,340,0.044117647
+6397,1136,IP ANE ANESTHESIA PREOP,POC14,POC GLUCOSE BY METER,Lab,41,340,0.120588235
+6398,1136,IP ANE ANESTHESIA PREOP,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,2,340,0.005882353
+6399,1136,IP ANE ANESTHESIA PREOP,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,1,340,0.002941176
+6400,1136,IP ANE ANESTHESIA PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,21,340,0.061764706
+6401,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,25,0.04
+6402,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,1,25,0.04
+6403,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,1,25,0.04
+6404,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,5,25,0.2
+6405,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,203954,FOSPHENYTOIN 100 MG PE/2 ML INJ SOLN,Medication,3,25,0.12
+6406,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,520067,FOSPHENYTOIN IVPB,Medication,2,25,0.08
+6407,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,520069,VALPROATE IVPB,Medication,1,25,0.04
+6408,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,520177,PENTOBARBITAL IVPB (LOADING DOSE),Medication,1,25,0.04
+6409,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,2,25,0.08
+6410,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,530041,PENTOBARBITAL IV INFUSION,Medication,2,25,0.08
+6411,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,7,25,0.28
+6412,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABCA,"CALCIUM, SERUM/PLASMA",Lab,8,25,0.32
+6413,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABCBCD,CBC WITH DIFF,Lab,5,25,0.2
+6414,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,4,25,0.16
+6415,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABHFP,HEPATIC FUNCTION PANEL A,Lab,7,25,0.28
+6416,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,2,25,0.08
+6417,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABMETB,"METABOLIC PANEL, BASIC",Lab,7,25,0.28
+6418,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,6,25,0.24
+6419,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABNH3,"AMMONIA, PLASMA",Lab,2,25,0.08
+6420,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABPB,PHENOBARBITAL LEVEL,Lab,1,25,0.04
+6421,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABPHEN,PHENYTOIN LEVEL,Lab,5,25,0.2
+6422,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,7,25,0.28
+6423,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABPT,PROTHROMBIN TIME,Lab,7,25,0.28
+6424,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,8,25,0.32
+6425,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,5,25,0.2
+6426,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABVOLTL,VOLATILE SCREEN,Lab,1,25,0.04
+6427,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,LABVPAL,VALPROIC ACID,Lab,2,25,0.08
+6428,1157,RETIRED IP NEU ICU STATUS EPILEPTICUS ,IMGMRH,MR BRAIN,Imaging,1,25,0.04
+6429,1161,IP NEU SHIVERING PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,102,0.058823529
+6430,1161,IP NEU SHIVERING PROTOCOL,1755,CLONIDINE HCL 0.1 MG PO TABS,Medication,6,102,0.058823529
+6431,1161,IP NEU SHIVERING PROTOCOL,1756,CLONIDINE HCL 0.2 MG PO TABS,Medication,1,102,0.009803922
+6432,1161,IP NEU SHIVERING PROTOCOL,9323,BUSPIRONE 10 MG PO TABS,Medication,46,102,0.450980392
+6433,1161,IP NEU SHIVERING PROTOCOL,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,2,102,0.019607843
+6434,1161,IP NEU SHIVERING PROTOCOL,14632,TRAMADOL 50 MG PO TABS,Medication,3,102,0.029411765
+6435,1161,IP NEU SHIVERING PROTOCOL,126006,MEPERIDINE (PF) 25 MG/0.5 ML INJ SOLN,Medication,13,102,0.12745098
+6436,1161,IP NEU SHIVERING PROTOCOL,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,6,102,0.058823529
+6437,1161,IP NEU SHIVERING PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,6,102,0.058823529
+6438,1161,IP NEU SHIVERING PROTOCOL,203672,MAGNESIUM SULFATE IN WATER 20 GRAM/500 ML (4 %) IV SOLP,Medication,16,102,0.156862745
+6439,1161,IP NEU SHIVERING PROTOCOL,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,6,102,0.058823529
+6440,1161,IP NEU SHIVERING PROTOCOL,530007,VECURONIUM IV INFUSION,Medication,3,102,0.029411765
+6441,1161,IP NEU SHIVERING PROTOCOL,540303,MEPERIDINE INJECTABLE SYRINGE,Medication,3,102,0.029411765
+6442,1161,IP NEU SHIVERING PROTOCOL,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,17,102,0.166666667
+6443,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,10372,LABETALOL 5 MG/ML IV SOLN,Medication,77,532,0.144736842
+6444,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,27,532,0.05075188
+6445,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,86145,ALTEPLASE 100 MG IV SOLR,Medication,191,532,0.359022556
+6446,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,540102,NS IV BOLUS,Medication,16,532,0.030075188
+6447,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,540994,ALTEPLASE STROKE BOLUS IV SYRINGE,Medication,26,532,0.04887218
+6448,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,540995,ALTEPLASE STROKE INFUSION,Medication,28,532,0.052631579
+6449,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,LABFIB,FIBRINOGEN,Lab,1,532,0.001879699
+6450,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,LABHCTX,HEMATOCRIT,Lab,1,532,0.001879699
+6451,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,532,0.001879699
+6452,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,LABTYPSNI,TYPE AND SCREEN,Lab,1,532,0.001879699
+6453,1238,IP/ED NEU TPA  ALTEPLASE PROTOCOL,IMGMRH,MR BRAIN,Imaging,1,532,0.001879699
+6454,1247,IP GEN PRE-BEDSIDE PROCEDURE ORDERS,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,21,98,0.214285714
+6455,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,147,647,0.227202473
+6456,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,630,647,0.973724884
+6457,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFAMY,"AMYLASE, FLUID",Lab,87,647,0.13446677
+6458,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFCUL,FUNGAL CULTURE,Lab,178,647,0.27511592
+6459,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFHCT,"HEMATOCRIT, FLUID",Lab,45,647,0.069551777
+6460,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,656,647,1
+6461,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFLDH,"LDH TOTAL, FLUID",Lab,622,647,0.961360124
+6462,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFLGL,"GLUCOSE, FLUID",Lab,629,647,0.972179289
+6463,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFTP,"PROTEIN TOTAL, FLUID",Lab,627,647,0.969088099
+6464,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFTRIG,"TRIGLYCERIDES, FLUID",Lab,5,647,0.007727975
+6465,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABFTRPL,"TRIGLYCERIDES, PLEURAL",Lab,90,647,0.139103555
+6466,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABKOHP,KOH / CALCOFLUOR,Lab,105,647,0.162287481
+6467,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,611,647,0.944358578
+6468,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABNTBNP,NT - PROBNP,Lab,17,647,0.026275116
+6469,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABPHME,PH BY METER,Lab,216,647,0.333848532
+6470,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,LABTP,"PROTEIN TOTAL, SERUM/PLASMA",Lab,590,647,0.911901082
+6471,1248,IP POST-PROCEDURE ORDERS THORACENTESIS,IMGDXCH1,XR CHEST 1V,Imaging,179,647,0.276661515
+6472,1266,IP RAD IR BODY PRE PROCEDURE ,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,2,0.5
+6473,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,19,132,0.143939394
+6474,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,40,132,0.303030303
+6475,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABCRYST,"CRYSTAL ANALYSIS, SYNOVIAL FLUID",Lab,140,132,1
+6476,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABFCUL,FUNGAL CULTURE,Lab,21,132,0.159090909
+6477,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,142,132,1
+6478,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABFLGL,"GLUCOSE, FLUID",Lab,17,132,0.128787879
+6479,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABKOHP,KOH / CALCOFLUOR,Lab,5,132,0.037878788
+6480,1272,IP POST-PROCEDURE ORDERS ARTHROCENTESIS,LABSYNCT,"CELL COUNT AND DIFF, SYNOVIAL FLUID",Lab,97,132,0.734848485
+6481,1273,IP POST PROCEDURE ORDERS PARACENTESIS,8981,"ALBUMIN, HUMAN 25 % 25 % IV SOLP",Medication,119,1054,0.112903226
+6482,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,50,1054,0.04743833
+6483,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,626,1054,0.593927894
+6484,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,952,1054,0.903225806
+6485,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFAL,"ALBUMIN, FLUID",Lab,868,1054,0.823529412
+6486,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFAMY,"AMYLASE, FLUID",Lab,107,1054,0.101518027
+6487,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFCUL,FUNGAL CULTURE,Lab,110,1054,0.104364326
+6488,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,948,1054,0.89943074
+6489,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFLDH,"LDH TOTAL, FLUID",Lab,318,1054,0.30170778
+6490,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFLGL,"GLUCOSE, FLUID",Lab,274,1054,0.259962049
+6491,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABFTP,"PROTEIN TOTAL, FLUID",Lab,423,1054,0.401328273
+6492,1273,IP POST PROCEDURE ORDERS PARACENTESIS,LABKOHP,KOH / CALCOFLUOR,Lab,65,1054,0.061669829
+6493,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LAB11049R,NSE,Lab,1,198,0.005050505
+6494,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LAB11068R,ACE,Lab,4,198,0.02020202
+6495,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LAB11123R,"COCCI CF AB, CSF",Lab,26,198,0.131313131
+6496,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,15,198,0.075757576
+6497,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,4,198,0.02020202
+6498,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,25,198,0.126262626
+6499,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABCSFC,CSF CULTURE AND GRAM STAIN,Lab,162,198,0.818181818
+6500,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABCSFCT,"CELL COUNT AND DIFFERENTIAL, CSF",Lab,195,198,0.984848485
+6501,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABCSFGL,"GLUCOSE, CSF",Lab,172,198,0.868686869
+6502,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABCSFTP,"PROTEIN TOTAL, CSF",Lab,177,198,0.893939394
+6503,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABEVPCR,ENTEROVIRUS BY PCR,Lab,26,198,0.131313131
+6504,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABFCUL,FUNGAL CULTURE,Lab,35,198,0.176767677
+6505,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABHHV6QL,HERPES VIRAL 6 DNA QUALITATIVE,Lab,24,198,0.121212121
+6506,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABIGG,"IGG, SERUM",Lab,5,198,0.025252525
+6507,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABOLIG,CSF PROTEIN & IMMUNOFIX ELECT.,Lab,7,198,0.035353535
+6508,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABTP,"PROTEIN TOTAL, SERUM/PLASMA",Lab,9,198,0.045454545
+6509,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABWNVAB,"WEST NILE VIRUS AB PANEL, CSF",Lab,13,198,0.065656566
+6510,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,LABYJCPCR,"JC VIRUS DNA, PCR",Lab,6,198,0.03030303
+6511,1274,IP POST PROCEDURE ORDERS LUMBAR PUNCTURE,POC14,POC GLUCOSE BY METER,Lab,144,198,0.727272727
+6512,1280,IP NEU LUMBAR PUNCTURE,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,53,367,0.144414169
+6513,1280,IP NEU LUMBAR PUNCTURE,LAB11019R,"MYELIN BASIC PROTEIN, CSF",Lab,8,367,0.021798365
+6514,1280,IP NEU LUMBAR PUNCTURE,LAB11028R,"ACE, CSF",Lab,32,367,0.08719346
+6515,1280,IP NEU LUMBAR PUNCTURE,LAB11049R,NSE,Lab,3,367,0.008174387
+6516,1280,IP NEU LUMBAR PUNCTURE,LAB11123R,"COCCI CF AB, CSF",Lab,45,367,0.122615804
+6517,1280,IP NEU LUMBAR PUNCTURE,LAB11530R,CEREBELLAR PARANEOPLASTIC AB,Lab,3,367,0.008174387
+6518,1280,IP NEU LUMBAR PUNCTURE,LAB12352R,NMO EVALUATION WITH REFLEX,Lab,9,367,0.024523161
+6519,1280,IP NEU LUMBAR PUNCTURE,LAB12362R,"PARANEOPLASTIC AUTOANTIBODY EVAL, CSF",Lab,43,367,0.117166213
+6520,1280,IP NEU LUMBAR PUNCTURE,LAB12607R,"N-METHYL-D-ASPARTATE RECEPTOR AB, CSF",Lab,22,367,0.059945504
+6521,1280,IP NEU LUMBAR PUNCTURE,LAB12851R,NMO/AQP4-IGG CBA,Lab,6,367,0.016348774
+6522,1280,IP NEU LUMBAR PUNCTURE,LAB13276R,CEREBELLAR PARANEOPLASTIC AB,Lab,2,367,0.005449591
+6523,1280,IP NEU LUMBAR PUNCTURE,LAB305,"CULTURE, VIRUS",Lab,7,367,0.019073569
+6524,1280,IP NEU LUMBAR PUNCTURE,LABADMARK,"ADMARK TAU/AB42, CSF",Lab,3,367,0.008174387
+6525,1280,IP NEU LUMBAR PUNCTURE,LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,35,367,0.095367847
+6526,1280,IP NEU LUMBAR PUNCTURE,LABB2M,"BETA - 2 - MICROGLOBULIN, SERUM",Lab,1,367,0.002724796
+6527,1280,IP NEU LUMBAR PUNCTURE,LABCLACT,"LACTIC ACID, CSF",Lab,14,367,0.038147139
+6528,1280,IP NEU LUMBAR PUNCTURE,LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,47,367,0.128065395
+6529,1280,IP NEU LUMBAR PUNCTURE,LABCSFC,CSF CULTURE AND GRAM STAIN,Lab,265,367,0.722070845
+6530,1280,IP NEU LUMBAR PUNCTURE,LABCSFCT,"CELL COUNT AND DIFFERENTIAL, CSF",Lab,490,367,1
+6531,1280,IP NEU LUMBAR PUNCTURE,LABCSFGL,"GLUCOSE, CSF",Lab,280,367,0.762942779
+6532,1280,IP NEU LUMBAR PUNCTURE,LABCSFLD,"LDH, CSF",Lab,15,367,0.040871935
+6533,1280,IP NEU LUMBAR PUNCTURE,LABCSFTP,"PROTEIN TOTAL, CSF",Lab,282,367,0.768392371
+6534,1280,IP NEU LUMBAR PUNCTURE,LABCYSCSF,"CYSTICERCUS AB REFLEX PANEL, CSF",Lab,5,367,0.013623978
+6535,1280,IP NEU LUMBAR PUNCTURE,LABEVPCR,ENTEROVIRUS BY PCR,Lab,37,367,0.100817439
+6536,1280,IP NEU LUMBAR PUNCTURE,LABFBBURG,"LYME WESTERN BLOT, CSF",Lab,33,367,0.089918256
+6537,1280,IP NEU LUMBAR PUNCTURE,LABFCUL,FUNGAL CULTURE,Lab,55,367,0.14986376
+6538,1280,IP NEU LUMBAR PUNCTURE,LABOLIG,CSF PROTEIN & IMMUNOFIX ELECT.,Lab,71,367,0.19346049
+6539,1280,IP NEU LUMBAR PUNCTURE,LABRPRQL,RPR QUALITATIVE,Lab,3,367,0.008174387
+6540,1280,IP NEU LUMBAR PUNCTURE,LABRPRQLT,RPR W/REFLEX TO TITER,Lab,4,367,0.010899183
+6541,1280,IP NEU LUMBAR PUNCTURE,LABTOXPCR,TOXOPLASMOSIS PCR,Lab,15,367,0.040871935
+6542,1280,IP NEU LUMBAR PUNCTURE,LABVCULF,"VIRAL CULTURE, FLUID",Lab,18,367,0.049046322
+6543,1280,IP NEU LUMBAR PUNCTURE,LABWNVAB,"WEST NILE VIRUS AB PANEL, CSF",Lab,29,367,0.079019074
+6544,1280,IP NEU LUMBAR PUNCTURE,LABYCPYRU,"PYRUVATE, CSF",Lab,2,367,0.005449591
+6545,1280,IP NEU LUMBAR PUNCTURE,LABYHISTC,"HISTOPLASMA AG, CSF",Lab,17,367,0.046321526
+6546,1280,IP NEU LUMBAR PUNCTURE,LABYJCPCR,"JC VIRUS DNA, PCR",Lab,23,367,0.0626703
+6547,1280,IP NEU LUMBAR PUNCTURE,LABYVDRLC,"VDRL, CSF",Lab,57,367,0.155313351
+6548,1280,IP NEU LUMBAR PUNCTURE,PATH24,"FLOW CYTOMETRY FOR LEUK/LYMPHOMA, MISC",Lab,75,367,0.204359673
+6549,1280,IP NEU LUMBAR PUNCTURE,POC14,POC GLUCOSE BY METER,Lab,49,367,0.133514986
+6550,1289,IP GEN VTE PROPHYLAXIS,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,1,1541,0.000648929
+6551,1289,IP GEN VTE PROPHYLAXIS,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,25,1541,0.016223232
+6552,1289,IP GEN VTE PROPHYLAXIS,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,1541,0.000648929
+6553,1289,IP GEN VTE PROPHYLAXIS,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,4,1541,0.002595717
+6554,1289,IP GEN VTE PROPHYLAXIS,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,2,1541,0.001297859
+6555,1289,IP GEN VTE PROPHYLAXIS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,334,1541,0.216742375
+6556,1289,IP GEN VTE PROPHYLAXIS,112597,ENOXAPARIN 120 MG/0.8 ML SC SYRG,Medication,1,1541,0.000648929
+6557,1289,IP GEN VTE PROPHYLAXIS,126583,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SYRG",Medication,1,1541,0.000648929
+6558,1289,IP GEN VTE PROPHYLAXIS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,368,1541,0.23880597
+6559,1289,IP GEN VTE PROPHYLAXIS,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,8,1541,0.005191434
+6560,1289,IP GEN VTE PROPHYLAXIS,LABPLTS,PLATELET COUNT,Lab,151,1541,0.097988319
+6561,1289,IP GEN VTE PROPHYLAXIS,LABPT,PROTHROMBIN TIME,Lab,6,1541,0.003893576
+6562,1307,IP BMT ALLOGENEIC READMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,672,2258,0.297608503
+6563,1307,IP BMT ALLOGENEIC READMISSION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,3,2258,0.001328609
+6564,1307,IP BMT ALLOGENEIC READMISSION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,635,2258,0.281222321
+6565,1307,IP BMT ALLOGENEIC READMISSION,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,645,2258,0.285651019
+6566,1307,IP BMT ALLOGENEIC READMISSION,4560,LOPERAMIDE 2 MG PO CAPS,Medication,641,2258,0.283879539
+6567,1307,IP BMT ALLOGENEIC READMISSION,4572,LORAZEPAM 0.5 MG PO TABS,Medication,619,2258,0.274136404
+6568,1307,IP BMT ALLOGENEIC READMISSION,4573,LORAZEPAM 1 MG PO TABS,Medication,17,2258,0.007528787
+6569,1307,IP BMT ALLOGENEIC READMISSION,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,4,2258,0.001771479
+6570,1307,IP BMT ALLOGENEIC READMISSION,6494,PREDNISONE 10 MG PO TABS,Medication,10,2258,0.004428698
+6571,1307,IP BMT ALLOGENEIC READMISSION,6496,PREDNISONE 20 MG PO TABS,Medication,24,2258,0.010628875
+6572,1307,IP BMT ALLOGENEIC READMISSION,6497,PREDNISONE 5 MG PO TABS,Medication,5,2258,0.002214349
+6573,1307,IP BMT ALLOGENEIC READMISSION,6498,PREDNISONE 50 MG PO TABS,Medication,3,2258,0.001328609
+6574,1307,IP BMT ALLOGENEIC READMISSION,7555,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,Medication,33,2258,0.014614703
+6575,1307,IP BMT ALLOGENEIC READMISSION,7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,27,2258,0.011957484
+6576,1307,IP BMT ALLOGENEIC READMISSION,8083,TRAZODONE 50 MG PO TABS,Medication,2,2258,0.00088574
+6577,1307,IP BMT ALLOGENEIC READMISSION,8085,TRAZODONE 50 MG PO TABS,Medication,121,2258,0.053587245
+6578,1307,IP BMT ALLOGENEIC READMISSION,9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,23,2258,0.010186005
+6579,1307,IP BMT ALLOGENEIC READMISSION,10011,FAMOTIDINE 20 MG PO TABS,Medication,371,2258,0.164304694
+6580,1307,IP BMT ALLOGENEIC READMISSION,10012,FAMOTIDINE 40 MG PO TABS,Medication,4,2258,0.001771479
+6581,1307,IP BMT ALLOGENEIC READMISSION,10045,FLUCONAZOLE 200 MG PO TABS,Medication,30,2258,0.013286094
+6582,1307,IP BMT ALLOGENEIC READMISSION,10048,FLUCONAZOLE IN DEXTROSE(ISO-O) 400 MG/200 ML IV PGBK,Medication,3,2258,0.001328609
+6583,1307,IP BMT ALLOGENEIC READMISSION,10050,FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK,Medication,1,2258,0.00044287
+6584,1307,IP BMT ALLOGENEIC READMISSION,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,631,2258,0.279450841
+6585,1307,IP BMT ALLOGENEIC READMISSION,10747,NORETHINDRONE ACETATE 5 MG PO TABS,Medication,7,2258,0.003100089
+6586,1307,IP BMT ALLOGENEIC READMISSION,11624,URSODIOL 300 MG PO CAPS,Medication,30,2258,0.013286094
+6587,1307,IP BMT ALLOGENEIC READMISSION,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,2,2258,0.00088574
+6588,1307,IP BMT ALLOGENEIC READMISSION,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,94,2258,0.041629761
+6589,1307,IP BMT ALLOGENEIC READMISSION,14866,POTASSIUM CHLORID-D5-0.45%NACL 40 MEQ/L IV SOLP,Medication,8,2258,0.003542958
+6590,1307,IP BMT ALLOGENEIC READMISSION,15065,FAMOTIDINE 10 MG PO TABS,Medication,2,2258,0.00088574
+6591,1307,IP BMT ALLOGENEIC READMISSION,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,195,2258,0.08635961
+6592,1307,IP BMT ALLOGENEIC READMISSION,16427,POTASSIUM CHLORIDE IN 0.9%NACL 40 MEQ/L IV SOLP,Medication,20,2258,0.008857396
+6593,1307,IP BMT ALLOGENEIC READMISSION,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,621,2258,0.275022143
+6594,1307,IP BMT ALLOGENEIC READMISSION,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,35,2258,0.015500443
+6595,1307,IP BMT ALLOGENEIC READMISSION,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,653,2258,0.289193977
+6596,1307,IP BMT ALLOGENEIC READMISSION,80562,MYCOPHENOLATE MOFETIL 250 MG PO CAPS,Medication,21,2258,0.009300266
+6597,1307,IP BMT ALLOGENEIC READMISSION,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,2,2258,0.00088574
+6598,1307,IP BMT ALLOGENEIC READMISSION,85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,20,2258,0.008857396
+6599,1307,IP BMT ALLOGENEIC READMISSION,92173,ZOLPIDEM 5 MG PO TABS,Medication,489,2258,0.21656333
+6600,1307,IP BMT ALLOGENEIC READMISSION,93295,ZOLPIDEM 10 MG PO TABS,Medication,1,2258,0.00044287
+6601,1307,IP BMT ALLOGENEIC READMISSION,93461,MOXIFLOXACIN IN NACL (ISO-OSM) 400 MG/250 ML IV PGBK,Medication,2,2258,0.00088574
+6602,1307,IP BMT ALLOGENEIC READMISSION,94670,TACROLIMUS 1 MG PO CAPS,Medication,53,2258,0.023472099
+6603,1307,IP BMT ALLOGENEIC READMISSION,95354,ATOVAQUONE 750 MG/5 ML PO SUSP,Medication,42,2258,0.018600531
+6604,1307,IP BMT ALLOGENEIC READMISSION,95803,TACROLIMUS 0.5 MG PO CAPS,Medication,30,2258,0.013286094
+6605,1307,IP BMT ALLOGENEIC READMISSION,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,24,2258,0.010628875
+6606,1307,IP BMT ALLOGENEIC READMISSION,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,15,2258,0.006643047
+6607,1307,IP BMT ALLOGENEIC READMISSION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,9,2258,0.003985828
+6608,1307,IP BMT ALLOGENEIC READMISSION,115287,LEVOFLOXACIN IN D5W 500 MG/100 ML IV PGBK,Medication,4,2258,0.001771479
+6609,1307,IP BMT ALLOGENEIC READMISSION,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,16,2258,0.007085917
+6610,1307,IP BMT ALLOGENEIC READMISSION,117152,"GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",Medication,648,2258,0.286979628
+6611,1307,IP BMT ALLOGENEIC READMISSION,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,17,2258,0.007528787
+6612,1307,IP BMT ALLOGENEIC READMISSION,124234,URSODIOL 500 MG PO TABS,Medication,2,2258,0.00088574
+6613,1307,IP BMT ALLOGENEIC READMISSION,125660,LOPERAMIDE 1 MG/7.5 ML PO LIQD,Medication,3,2258,0.001328609
+6614,1307,IP BMT ALLOGENEIC READMISSION,200345,MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV SOLN,Medication,678,2258,0.300265722
+6615,1307,IP BMT ALLOGENEIC READMISSION,200346,MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK,Medication,675,2258,0.298937112
+6616,1307,IP BMT ALLOGENEIC READMISSION,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,2258,0.00044287
+6617,1307,IP BMT ALLOGENEIC READMISSION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,663,2258,0.293622675
+6618,1307,IP BMT ALLOGENEIC READMISSION,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,5,2258,0.002214349
+6619,1307,IP BMT ALLOGENEIC READMISSION,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,34,2258,0.015057573
+6620,1307,IP BMT ALLOGENEIC READMISSION,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,623,2258,0.275907883
+6621,1307,IP BMT ALLOGENEIC READMISSION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,8,2258,0.003542958
+6622,1307,IP BMT ALLOGENEIC READMISSION,520038,AMPHOTERICIN B LIPOSOMAL IVPB,Medication,5,2258,0.002214349
+6623,1307,IP BMT ALLOGENEIC READMISSION,520045,MEROPENEM CUSTOM IVPB,Medication,8,2258,0.003542958
+6624,1307,IP BMT ALLOGENEIC READMISSION,520057,CASPOFUNGIN IVPB (CUSTOM),Medication,3,2258,0.001328609
+6625,1307,IP BMT ALLOGENEIC READMISSION,520061,VORICONAZOLE IVPB,Medication,12,2258,0.005314438
+6626,1307,IP BMT ALLOGENEIC READMISSION,520073,MYCOPHENOLATE IVPB,Medication,12,2258,0.005314438
+6627,1307,IP BMT ALLOGENEIC READMISSION,520098,TACROLIMUS IV INFUSION,Medication,48,2258,0.02125775
+6628,1307,IP BMT ALLOGENEIC READMISSION,520099,CYCLOSPORINE IV INFUSION,Medication,10,2258,0.004428698
+6629,1307,IP BMT ALLOGENEIC READMISSION,520102,CEFEPIME 2 GM IVPB,Medication,10,2258,0.004428698
+6630,1307,IP BMT ALLOGENEIC READMISSION,520104,VANCOMYCIN 1 GM CENTRAL LINE IVPB,Medication,20,2258,0.008857396
+6631,1307,IP BMT ALLOGENEIC READMISSION,540003,TRIPLE MIX,Medication,616,2258,0.272807795
+6632,1307,IP BMT ALLOGENEIC READMISSION,540132,CYCLOSPORINE MODIFIED (GENGRAF) 25 MG PO CAPS,Medication,9,2258,0.003985828
+6633,1307,IP BMT ALLOGENEIC READMISSION,540133,CYCLOSPORINE MODIFIED (GENGRAF) 100 MG PO CAPS,Medication,15,2258,0.006643047
+6634,1307,IP BMT ALLOGENEIC READMISSION,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,509,2258,0.225420726
+6635,1307,IP BMT ALLOGENEIC READMISSION,540302,POTASSIUM CHLORIDE IV SCALE,Medication,684,2258,0.302922941
+6636,1307,IP BMT ALLOGENEIC READMISSION,540304,MAGNESIUM SULFATE IV SCALE ,Medication,12,2258,0.005314438
+6637,1307,IP BMT ALLOGENEIC READMISSION,540960,MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS,Medication,6,2258,0.002657219
+6638,1307,IP BMT ALLOGENEIC READMISSION,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,4,2258,0.001771479
+6639,1307,IP BMT ALLOGENEIC READMISSION,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,6,2258,0.002657219
+6640,1307,IP BMT ALLOGENEIC READMISSION,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,26,2258,0.011514615
+6641,1307,IP BMT ALLOGENEIC READMISSION,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,69,2258,0.030558016
+6642,1307,IP BMT ALLOGENEIC READMISSION,541089,VANCOMYCIN 1 GRAM/100 ML CENTRAL LINE MINI-BAG PLUS,Medication,32,2258,0.014171833
+6643,1307,IP BMT ALLOGENEIC READMISSION,541119,MEROPENEM IN NS 500 MG/100 ML MINI-BAG PLUS,Medication,2,2258,0.00088574
+6644,1307,IP BMT ALLOGENEIC READMISSION,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,27,2258,0.011957484
+6645,1307,IP BMT ALLOGENEIC READMISSION,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,10,2258,0.004428698
+6646,1307,IP BMT ALLOGENEIC READMISSION,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,11,2258,0.004871568
+6647,1307,IP BMT ALLOGENEIC READMISSION,541546,MEROPENEM 2 G/100 ML EXTENDED INFUSION IVPB,Medication,1,2258,0.00044287
+6648,1307,IP BMT ALLOGENEIC READMISSION,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,8,2258,0.003542958
+6649,1307,IP BMT ALLOGENEIC READMISSION,541548,MEROPENEM 500 MG/50 ML EXTENDED INFUSION MINIBAG PLUS,Medication,1,2258,0.00044287
+6650,1307,IP BMT ALLOGENEIC READMISSION,9000000,ZZZ IMS TEMPLATE,Medication,49,2258,0.02170062
+6651,1307,IP BMT ALLOGENEIC READMISSION,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,43,2258,0.019043401
+6652,1307,IP BMT ALLOGENEIC READMISSION,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,43,2258,0.019043401
+6653,1307,IP BMT ALLOGENEIC READMISSION,LABBMT1,BMT PANEL 1,Lab,13,2258,0.005757307
+6654,1307,IP BMT ALLOGENEIC READMISSION,LABBMT2,BMT PANEL 2,Lab,15,2258,0.006643047
+6655,1307,IP BMT ALLOGENEIC READMISSION,LABCBCD,CBC WITH DIFF,Lab,768,2258,0.340124004
+6656,1307,IP BMT ALLOGENEIC READMISSION,LABCBCO,CBC W/O DIFF,Lab,16,2258,0.007085917
+6657,1307,IP BMT ALLOGENEIC READMISSION,LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,304,2258,0.134632418
+6658,1307,IP BMT ALLOGENEIC READMISSION,LABCYCWBL,"CYCLOSPORINE, WHOLE BLOOD",Lab,94,2258,0.041629761
+6659,1307,IP BMT ALLOGENEIC READMISSION,LABFCUL,FUNGAL CULTURE,Lab,7,2258,0.003100089
+6660,1307,IP BMT ALLOGENEIC READMISSION,LABFK506L,TACROLIMUS (FK506),Lab,275,2258,0.121789194
+6661,1307,IP BMT ALLOGENEIC READMISSION,LABIGG,"IGG, SERUM",Lab,218,2258,0.096545616
+6662,1307,IP BMT ALLOGENEIC READMISSION,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,8,2258,0.003542958
+6663,1307,IP BMT ALLOGENEIC READMISSION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,729,2258,0.322852081
+6664,1307,IP BMT ALLOGENEIC READMISSION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,795,2258,0.352081488
+6665,1307,IP BMT ALLOGENEIC READMISSION,LABPLTS,PLATELET COUNT,Lab,6,2258,0.002657219
+6666,1307,IP BMT ALLOGENEIC READMISSION,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,16,2258,0.007085917
+6667,1307,IP BMT ALLOGENEIC READMISSION,LABSIROL,SIROLIMUS LEVEL,Lab,62,2258,0.027457927
+6668,1307,IP BMT ALLOGENEIC READMISSION,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,68,2258,0.030115146
+6669,1307,IP BMT ALLOGENEIC READMISSION,IMGDXCH1,XR CHEST 1V,Imaging,41,2258,0.018157662
+6670,1307,IP BMT ALLOGENEIC READMISSION,IMGDXCH2,XR CHEST 2V,Imaging,462,2258,0.204605846
+6671,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,293,908,0.322687225
+6672,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,908,0.002202643
+6673,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,287,908,0.316079295
+6674,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,284,908,0.31277533
+6675,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,4560,LOPERAMIDE 2 MG PO CAPS,Medication,302,908,0.332599119
+6676,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,4572,LORAZEPAM 0.5 MG PO TABS,Medication,271,908,0.29845815
+6677,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,4573,LORAZEPAM 1 MG PO TABS,Medication,13,908,0.014317181
+6678,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,3,908,0.003303965
+6679,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,7555,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,Medication,3,908,0.003303965
+6680,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,3,908,0.003303965
+6681,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,8083,TRAZODONE 50 MG PO TABS,Medication,1,908,0.001101322
+6682,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,8085,TRAZODONE 50 MG PO TABS,Medication,53,908,0.058370044
+6683,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,26,908,0.028634361
+6684,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,10011,FAMOTIDINE 20 MG PO TABS,Medication,198,908,0.218061674
+6685,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,10012,FAMOTIDINE 40 MG PO TABS,Medication,1,908,0.001101322
+6686,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,283,908,0.311674009
+6687,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,10747,NORETHINDRONE ACETATE 5 MG PO TABS,Medication,2,908,0.002202643
+6688,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,71,908,0.078193833
+6689,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,14866,POTASSIUM CHLORID-D5-0.45%NACL 40 MEQ/L IV SOLP,Medication,10,908,0.011013216
+6690,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,908,0.001101322
+6691,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,187,908,0.205947137
+6692,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,86,908,0.094713656
+6693,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,16427,POTASSIUM CHLORIDE IN 0.9%NACL 40 MEQ/L IV SOLP,Medication,17,908,0.018722467
+6694,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,283,908,0.311674009
+6695,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,22560,SULFAMETHOXAZOLE-TRIMETHOPRIM 200-40 MG/5 ML PO SUSP,Medication,1,908,0.001101322
+6696,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,65,908,0.071585903
+6697,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,284,908,0.31277533
+6698,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,7,908,0.007709251
+6699,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,92173,ZOLPIDEM 5 MG PO TABS,Medication,218,908,0.240088106
+6700,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,95354,ATOVAQUONE 750 MG/5 ML PO SUSP,Medication,1,908,0.001101322
+6701,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,12,908,0.013215859
+6702,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,7,908,0.007709251
+6703,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,112593,FILGRASTIM 480 MCG/0.8 ML INJ SYRG,Medication,35,908,0.038546256
+6704,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,112594,FILGRASTIM 300 MCG/0.5 ML INJ SYRG,Medication,30,908,0.033039648
+6705,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,4,908,0.004405286
+6706,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,117152,"GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",Medication,285,908,0.313876652
+6707,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,15,908,0.016519824
+6708,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,200345,MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV SOLN,Medication,292,908,0.321585903
+6709,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,200346,MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK,Medication,289,908,0.318281938
+6710,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,290,908,0.31938326
+6711,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,279,908,0.307268722
+6712,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,908,0.001101322
+6713,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,520045,MEROPENEM CUSTOM IVPB,Medication,4,908,0.004405286
+6714,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,520102,CEFEPIME 2 GM IVPB,Medication,14,908,0.015418502
+6715,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,520104,VANCOMYCIN 1 GM CENTRAL LINE IVPB,Medication,10,908,0.011013216
+6716,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,540003,TRIPLE MIX,Medication,291,908,0.320484581
+6717,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,221,908,0.24339207
+6718,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,540302,POTASSIUM CHLORIDE IV SCALE,Medication,297,908,0.327092511
+6719,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,540304,MAGNESIUM SULFATE IV SCALE ,Medication,5,908,0.005506608
+6720,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,540960,MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS,Medication,2,908,0.002202643
+6721,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,2,908,0.002202643
+6722,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,18,908,0.019823789
+6723,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,75,908,0.082599119
+6724,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541089,VANCOMYCIN 1 GRAM/100 ML CENTRAL LINE MINI-BAG PLUS,Medication,49,908,0.053964758
+6725,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,12,908,0.013215859
+6726,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,7,908,0.007709251
+6727,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,14,908,0.015418502
+6728,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,3,908,0.003303965
+6729,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,9000000,ZZZ IMS TEMPLATE,Medication,11,908,0.012114537
+6730,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,24,908,0.026431718
+6731,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,24,908,0.026431718
+6732,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABCBCD,CBC WITH DIFF,Lab,316,908,0.348017621
+6733,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABFCUL,FUNGAL CULTURE,Lab,2,908,0.002202643
+6734,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,238,908,0.262114537
+6735,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,335,908,0.368942731
+6736,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,331,908,0.364537445
+6737,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABPLTS,PLATELET COUNT,Lab,1,908,0.001101322
+6738,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,8,908,0.008810573
+6739,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,46,908,0.050660793
+6740,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,IMGDXCH1,XR CHEST 1V,Imaging,33,908,0.036343612
+6741,1309,IP BMT AUTOLOGOUS READMISSION ORDERS,IMGDXCH2,XR CHEST 2V,Imaging,205,908,0.225770925
+6742,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,6,0.833333333
+6743,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,3,6,0.5
+6744,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,6,0.166666667
+6745,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,4560,LOPERAMIDE 2 MG PO CAPS,Medication,1,6,0.166666667
+6746,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,6,0.166666667
+6747,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,6,0.166666667
+6748,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,6,0.166666667
+6749,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,1,6,0.166666667
+6750,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,1,6,0.166666667
+6751,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,117152,"GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",Medication,1,6,0.166666667
+6752,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,6,0.166666667
+6753,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,6,0.166666667
+6754,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,4,6,0.666666667
+6755,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,540003,TRIPLE MIX,Medication,1,6,0.166666667
+6756,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,540302,POTASSIUM CHLORIDE IV SCALE,Medication,2,6,0.333333333
+6757,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,2,6,0.333333333
+6758,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,LABCBCD,CBC WITH DIFF,Lab,1,6,0.166666667
+6759,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,6,0.166666667
+6760,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,6,0.166666667
+6761,1310,RETIRED IP BMT READMISSION FOR NON-MYELOABLATIVE TRANSPLANT,IMGDXCH2,XR CHEST 2V,Imaging,3,6,0.5
+6762,1352,IP MED CYSTIC FIBROSIS ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,333,2469,0.134872418
+6763,1352,IP MED CYSTIC FIBROSIS ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,8,2469,0.003240178
+6764,1352,IP MED CYSTIC FIBROSIS ADMIT,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,62,2469,0.025111381
+6765,1352,IP MED CYSTIC FIBROSIS ADMIT,663,ASCORBIC ACID 250 MG PO TABS,Medication,1,2469,0.000405022
+6766,1352,IP MED CYSTIC FIBROSIS ADMIT,664,ASCORBIC ACID 500 MG PO TABS,Medication,5,2469,0.002025111
+6767,1352,IP MED CYSTIC FIBROSIS ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,9,2469,0.0036452
+6768,1352,IP MED CYSTIC FIBROSIS ADMIT,1300,"CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",Medication,51,2469,0.020656136
+6769,1352,IP MED CYSTIC FIBROSIS ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,30,2469,0.012150668
+6770,1352,IP MED CYSTIC FIBROSIS ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,60,2469,0.024301337
+6771,1352,IP MED CYSTIC FIBROSIS ADMIT,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,8,2469,0.003240178
+6772,1352,IP MED CYSTIC FIBROSIS ADMIT,3233,FOLIC ACID 1 MG PO TABS,Medication,4,2469,0.001620089
+6773,1352,IP MED CYSTIC FIBROSIS ADMIT,3843,IBUPROFEN 400 MG PO TABS,Medication,1,2469,0.000405022
+6774,1352,IP MED CYSTIC FIBROSIS ADMIT,3844,IBUPROFEN 600 MG PO TABS,Medication,1,2469,0.000405022
+6775,1352,IP MED CYSTIC FIBROSIS ADMIT,3845,IBUPROFEN 800 MG PO TABS,Medication,1,2469,0.000405022
+6776,1352,IP MED CYSTIC FIBROSIS ADMIT,5087,MINERAL OIL PR ENEM,Medication,1,2469,0.000405022
+6777,1352,IP MED CYSTIC FIBROSIS ADMIT,6494,PREDNISONE 10 MG PO TABS,Medication,3,2469,0.001215067
+6778,1352,IP MED CYSTIC FIBROSIS ADMIT,6496,PREDNISONE 20 MG PO TABS,Medication,3,2469,0.001215067
+6779,1352,IP MED CYSTIC FIBROSIS ADMIT,6497,PREDNISONE 5 MG PO TABS,Medication,6,2469,0.002430134
+6780,1352,IP MED CYSTIC FIBROSIS ADMIT,7326,SODIUM CHLORIDE 10 % INH NEBU,Medication,1,2469,0.000405022
+6781,1352,IP MED CYSTIC FIBROSIS ADMIT,7327,SODIUM CHLORIDE 3 % INH NEBU,Medication,76,2469,0.030781693
+6782,1352,IP MED CYSTIC FIBROSIS ADMIT,7994,TOBRAMYCIN SULFATE 40 MG/ML INJ SOLN,Medication,10,2469,0.004050223
+6783,1352,IP MED CYSTIC FIBROSIS ADMIT,8701,VITAMIN E 400 UNIT PO CAPS,Medication,2,2469,0.000810045
+6784,1352,IP MED CYSTIC FIBROSIS ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,37,2469,0.014985824
+6785,1352,IP MED CYSTIC FIBROSIS ADMIT,10466,LORATADINE 10 MG PO TABS,Medication,30,2469,0.012150668
+6786,1352,IP MED CYSTIC FIBROSIS ADMIT,10674,MUPIROCIN 2 % TP OINT,Medication,12,2469,0.004860267
+6787,1352,IP MED CYSTIC FIBROSIS ADMIT,11664,WARFARIN 1 MG PO TABS,Medication,1,2469,0.000405022
+6788,1352,IP MED CYSTIC FIBROSIS ADMIT,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,4,2469,0.001620089
+6789,1352,IP MED CYSTIC FIBROSIS ADMIT,13632,BISACODYL 5 MG PO TBEC,Medication,27,2469,0.010935601
+6790,1352,IP MED CYSTIC FIBROSIS ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,2469,0.000810045
+6791,1352,IP MED CYSTIC FIBROSIS ADMIT,19483,"CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",Medication,95,2469,0.038477116
+6792,1352,IP MED CYSTIC FIBROSIS ADMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,361,2469,0.146213042
+6793,1352,IP MED CYSTIC FIBROSIS ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,23,2469,0.009315512
+6794,1352,IP MED CYSTIC FIBROSIS ADMIT,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,19,2469,0.007695423
+6795,1352,IP MED CYSTIC FIBROSIS ADMIT,70536,FLUTICASONE 50 MCG/ACTUATION NASAL SPSN,Medication,36,2469,0.014580802
+6796,1352,IP MED CYSTIC FIBROSIS ADMIT,77973,FLUTICASONE-SALMETEROL 250-50 MCG/DOSE INH DSDV,Medication,28,2469,0.011340624
+6797,1352,IP MED CYSTIC FIBROSIS ADMIT,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,363,2469,0.147023086
+6798,1352,IP MED CYSTIC FIBROSIS ADMIT,78941,TOBRAMYCIN IN 0.225 % NACL 300 MG/5 ML INH NEBU,Medication,39,2469,0.015795869
+6799,1352,IP MED CYSTIC FIBROSIS ADMIT,79363,PHYTONADIONE (VITAMIN K1) 10 MG/ML INJ SOLN,Medication,1,2469,0.000405022
+6800,1352,IP MED CYSTIC FIBROSIS ADMIT,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,9,2469,0.0036452
+6801,1352,IP MED CYSTIC FIBROSIS ADMIT,84263,BECLOMETHASONE DIPROPIONATE 80 MCG/ACTUATION INH AERO,Medication,11,2469,0.004455245
+6802,1352,IP MED CYSTIC FIBROSIS ADMIT,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,8,2469,0.003240178
+6803,1352,IP MED CYSTIC FIBROSIS ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,7,2469,0.002835156
+6804,1352,IP MED CYSTIC FIBROSIS ADMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,157,2469,0.063588497
+6805,1352,IP MED CYSTIC FIBROSIS ADMIT,88175,OSELTAMIVIR 75 MG PO CAPS,Medication,1,2469,0.000405022
+6806,1352,IP MED CYSTIC FIBROSIS ADMIT,89401,FLUTICASONE-SALMETEROL 500-50 MCG/DOSE INH DSDV,Medication,60,2469,0.024301337
+6807,1352,IP MED CYSTIC FIBROSIS ADMIT,91619,MONTELUKAST 10 MG PO TABS,Medication,12,2469,0.004860267
+6808,1352,IP MED CYSTIC FIBROSIS ADMIT,91892,FLUTICASONE 220 MCG/ACTUATION INH AERO,Medication,1,2469,0.000405022
+6809,1352,IP MED CYSTIC FIBROSIS ADMIT,92642,LEVALBUTEROL HCL 1.25 MG/3 ML INH NEBU,Medication,31,2469,0.012555691
+6810,1352,IP MED CYSTIC FIBROSIS ADMIT,92914,FLUTICASONE-SALMETEROL 100-50 MCG/DOSE INH DSDV,Medication,1,2469,0.000405022
+6811,1352,IP MED CYSTIC FIBROSIS ADMIT,94621,AZTREONAM IN DEXTROSE(ISO-OSM) 2 GRAM/50 ML IV PGBK,Medication,5,2469,0.002025111
+6812,1352,IP MED CYSTIC FIBROSIS ADMIT,95901,PHYTONADIONE 5 MG PO TABS,Medication,52,2469,0.021061158
+6813,1352,IP MED CYSTIC FIBROSIS ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,2,2469,0.000810045
+6814,1352,IP MED CYSTIC FIBROSIS ADMIT,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,2469,0.000405022
+6815,1352,IP MED CYSTIC FIBROSIS ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,25,2469,0.010125557
+6816,1352,IP MED CYSTIC FIBROSIS ADMIT,111320,MULTIVITAMIN WITH IRON-MINERAL PO LIQD,Medication,1,2469,0.000405022
+6817,1352,IP MED CYSTIC FIBROSIS ADMIT,112321,LINEZOLID 600 MG/300 ML IV SOLP,Medication,72,2469,0.029161604
+6818,1352,IP MED CYSTIC FIBROSIS ADMIT,120980,TIOTROPIUM BROMIDE 18 MCG INH CPDV,Medication,5,2469,0.002025111
+6819,1352,IP MED CYSTIC FIBROSIS ADMIT,127719,FLUTICASONE-SALMETEROL 115-21 MCG/ACTUATION INH HFAA,Medication,8,2469,0.003240178
+6820,1352,IP MED CYSTIC FIBROSIS ADMIT,127720,FLUTICASONE-SALMETEROL 230-21 MCG/ACTUATION INH HFAA,Medication,17,2469,0.006885379
+6821,1352,IP MED CYSTIC FIBROSIS ADMIT,127907,SODIUM PHOSPHATES 7.2-2.7 GRAM/15 ML PO LIQD,Medication,2,2469,0.000810045
+6822,1352,IP MED CYSTIC FIBROSIS ADMIT,199112,BUDESONIDE 180 MCG/ACTUATION INH AEPB,Medication,2,2469,0.000810045
+6823,1352,IP MED CYSTIC FIBROSIS ADMIT,200453,SODIUM CHLORIDE 7 % INH NEBU,Medication,316,2469,0.127987039
+6824,1352,IP MED CYSTIC FIBROSIS ADMIT,208090,"LIPASE-PROTEASE-AMYLASE 6,000-19,000 -30,000 UNIT PO CPDR",Medication,1,2469,0.000405022
+6825,1352,IP MED CYSTIC FIBROSIS ADMIT,208091,"LIPASE-PROTEASE-AMYLASE 12,000-38,000 -60,000 UNIT PO CPDR",Medication,9,2469,0.0036452
+6826,1352,IP MED CYSTIC FIBROSIS ADMIT,208092,"LIPASE-PROTEASE-AMYLASE 24,000-76,000 -120,000 UNIT PO CPDR",Medication,137,2469,0.055488052
+6827,1352,IP MED CYSTIC FIBROSIS ADMIT,208942,"LIPASE-PROTEASE-AMYLASE 5,000-17,000 -27,000 UNIT PO CPDR",Medication,1,2469,0.000405022
+6828,1352,IP MED CYSTIC FIBROSIS ADMIT,208944,"LIPASE-PROTEASE-AMYLASE 15,000-51,000 -82,000 UNIT PO CPDR",Medication,1,2469,0.000405022
+6829,1352,IP MED CYSTIC FIBROSIS ADMIT,208945,"LIPASE-PROTEASE-AMYLASE 20,000-68,000 -109,000 UNIT PO CPDR",Medication,37,2469,0.014985824
+6830,1352,IP MED CYSTIC FIBROSIS ADMIT,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,2469,0.000405022
+6831,1352,IP MED CYSTIC FIBROSIS ADMIT,210750,"LIPASE-PROTEASE-AMYLASE 16,800-40,000 -70,000 UNIT PO CPDR",Medication,13,2469,0.00526529
+6832,1352,IP MED CYSTIC FIBROSIS ADMIT,210751,"LIPASE-PROTEASE-AMYLASE 21,000-37,000 -61,000 UNIT PO CPDR",Medication,9,2469,0.0036452
+6833,1352,IP MED CYSTIC FIBROSIS ADMIT,210752,"LIPASE-PROTEASE-AMYLASE 10,500-25,000 -43,750 UNIT PO CPDR",Medication,1,2469,0.000405022
+6834,1352,IP MED CYSTIC FIBROSIS ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,24,2469,0.009720535
+6835,1352,IP MED CYSTIC FIBROSIS ADMIT,221975,MULTIVIT-MIN-FA-COENZYME Q10 100-10 MCG-MG PO CAPS,Medication,157,2469,0.063588497
+6836,1352,IP MED CYSTIC FIBROSIS ADMIT,225590,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH AERO,Medication,3,2469,0.001215067
+6837,1352,IP MED CYSTIC FIBROSIS ADMIT,229996,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP,Medication,71,2469,0.028756582
+6838,1352,IP MED CYSTIC FIBROSIS ADMIT,233335,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH MIST,Medication,8,2469,0.003240178
+6839,1352,IP MED CYSTIC FIBROSIS ADMIT,520011,TOBRAMYCIN IVPB,Medication,201,2469,0.081409478
+6840,1352,IP MED CYSTIC FIBROSIS ADMIT,520045,MEROPENEM CUSTOM IVPB,Medication,86,2469,0.034831916
+6841,1352,IP MED CYSTIC FIBROSIS ADMIT,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,51,2469,0.020656136
+6842,1352,IP MED CYSTIC FIBROSIS ADMIT,520050,COLISTIMETHATE (COLISTIN) IVPB,Medication,50,2469,0.020251114
+6843,1352,IP MED CYSTIC FIBROSIS ADMIT,540102,NS IV BOLUS,Medication,321,2469,0.130012151
+6844,1352,IP MED CYSTIC FIBROSIS ADMIT,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,3,2469,0.001215067
+6845,1352,IP MED CYSTIC FIBROSIS ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,2469,0.000405022
+6846,1352,IP MED CYSTIC FIBROSIS ADMIT,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,2,2469,0.000810045
+6847,1352,IP MED CYSTIC FIBROSIS ADMIT,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,23,2469,0.009315512
+6848,1352,IP MED CYSTIC FIBROSIS ADMIT,540671,AZTREONAM IN NORMAL SALINE 2 GRAM/100 ML IVPB,Medication,4,2469,0.001620089
+6849,1352,IP MED CYSTIC FIBROSIS ADMIT,540672,CIPROFLOXACIN IN NS IV 400 MG/250 ML IV,Medication,74,2469,0.029971648
+6850,1352,IP MED CYSTIC FIBROSIS ADMIT,540674,LEVOFLOXACIN IN NS IV 750 MG/250 ML IV PGBK,Medication,75,2469,0.030376671
+6851,1352,IP MED CYSTIC FIBROSIS ADMIT,540675,CEFTAZIDIME IN NORMAL SALINE 2 GRAM/100 ML IV COMPOUNDED,Medication,21,2469,0.008505468
+6852,1352,IP MED CYSTIC FIBROSIS ADMIT,540676,PIPERACILLIN-TAZOBACTAM 2.25 GRAM IN NS IV SOLN,Medication,2,2469,0.000810045
+6853,1352,IP MED CYSTIC FIBROSIS ADMIT,540677,PIPERACILLIN-TAZOBACTAM 3.375 GRAM IN NS IV SOLN,Medication,36,2469,0.014580802
+6854,1352,IP MED CYSTIC FIBROSIS ADMIT,540678,PIPERACILLIN-TAZOBACTAM 4.5 GRAM IN NS IV SOLN,Medication,66,2469,0.02673147
+6855,1352,IP MED CYSTIC FIBROSIS ADMIT,540687,ACETYLCYSTEINE 20 % (200MG/ML) MISC SOLN (ACF USE ONLY),Medication,6,2469,0.002430134
+6856,1352,IP MED CYSTIC FIBROSIS ADMIT,540692,NS/BUDESONIDE NASAL IRRIGATION,Medication,321,2469,0.130012151
+6857,1352,IP MED CYSTIC FIBROSIS ADMIT,540699,CF ENEMA SOLUTION,Medication,15,2469,0.006075334
+6858,1352,IP MED CYSTIC FIBROSIS ADMIT,540958,CEFEPIME IN NORMAL SALINE 2 GRAM/100 ML IVPB MIXTURE,Medication,15,2469,0.006075334
+6859,1352,IP MED CYSTIC FIBROSIS ADMIT,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,6,2469,0.002430134
+6860,1352,IP MED CYSTIC FIBROSIS ADMIT,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,19,2469,0.007695423
+6861,1352,IP MED CYSTIC FIBROSIS ADMIT,541001,COLISTIMETHATE SODIUM 150 MG INJ SOLR (ACF PANEL USE ONLY),Medication,64,2469,0.025921426
+6862,1352,IP MED CYSTIC FIBROSIS ADMIT,541002,DORNASE ALFA 1 MG/ML INH SOLN (ACF PANEL USE ONLY),Medication,435,2469,0.17618469
+6863,1352,IP MED CYSTIC FIBROSIS ADMIT,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,21,2469,0.008505468
+6864,1352,IP MED CYSTIC FIBROSIS ADMIT,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,19,2469,0.007695423
+6865,1352,IP MED CYSTIC FIBROSIS ADMIT,541097,AZTREONAM 2 GRAM/100 ML MINI-BAG PLUS,Medication,74,2469,0.029971648
+6866,1352,IP MED CYSTIC FIBROSIS ADMIT,541098,CEFEPIME 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,2469,0.001215067
+6867,1352,IP MED CYSTIC FIBROSIS ADMIT,541107,CEFTAZIDIME 2 GRAM/50 ML MINI-BAG PLUS,Medication,92,2469,0.037262049
+6868,1352,IP MED CYSTIC FIBROSIS ADMIT,541546,MEROPENEM 2 G/100 ML EXTENDED INFUSION IVPB,Medication,27,2469,0.010935601
+6869,1352,IP MED CYSTIC FIBROSIS ADMIT,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,2,2469,0.000810045
+6870,1352,IP MED CYSTIC FIBROSIS ADMIT,541550,CEFEPIM 2 G/100 ML NS EI MINI-BAG PLUS,Medication,19,2469,0.007695423
+6871,1352,IP MED CYSTIC FIBROSIS ADMIT,9000000,ZZZ IMS TEMPLATE,Medication,1,2469,0.000405022
+6872,1352,IP MED CYSTIC FIBROSIS ADMIT,LABA1C,HEMOGLOBIN A1C,Lab,538,2469,0.217901985
+6873,1352,IP MED CYSTIC FIBROSIS ADMIT,LABAFBC,"AFB CULTURE, RESPIRATORY",Lab,189,2469,0.07654921
+6874,1352,IP MED CYSTIC FIBROSIS ADMIT,LABAFBCF,"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS",Lab,97,2469,0.039287161
+6875,1352,IP MED CYSTIC FIBROSIS ADMIT,LABALB,"ALBUMIN, SERUM/PLASMA",Lab,11,2469,0.004455245
+6876,1352,IP MED CYSTIC FIBROSIS ADMIT,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,59,2469,0.023896314
+6877,1352,IP MED CYSTIC FIBROSIS ADMIT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,59,2469,0.023896314
+6878,1352,IP MED CYSTIC FIBROSIS ADMIT,LABCBCD,CBC WITH DIFF,Lab,682,2469,0.276225192
+6879,1352,IP MED CYSTIC FIBROSIS ADMIT,LABCFRESP,CYSTIC FIBROSIS RESPIRATORY CULTURE,Lab,280,2469,0.113406237
+6880,1352,IP MED CYSTIC FIBROSIS ADMIT,LABCRP,C - REACTIVE PROTEIN,Lab,2,2469,0.000810045
+6881,1352,IP MED CYSTIC FIBROSIS ADMIT,LABESRP,SEDIMENTATION RATE (ESR),Lab,1,2469,0.000405022
+6882,1352,IP MED CYSTIC FIBROSIS ADMIT,LABFCUL,FUNGAL CULTURE,Lab,283,2469,0.114621304
+6883,1352,IP MED CYSTIC FIBROSIS ADMIT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,666,2469,0.269744836
+6884,1352,IP MED CYSTIC FIBROSIS ADMIT,LABIGET,TOTAL IGE,Lab,547,2469,0.221547185
+6885,1352,IP MED CYSTIC FIBROSIS ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,721,2469,0.292021061
+6886,1352,IP MED CYSTIC FIBROSIS ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,712,2469,0.288375861
+6887,1352,IP MED CYSTIC FIBROSIS ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,689,2469,0.279060348
+6888,1352,IP MED CYSTIC FIBROSIS ADMIT,LABPLTS,PLATELET COUNT,Lab,21,2469,0.008505468
+6889,1352,IP MED CYSTIC FIBROSIS ADMIT,LABPT,PROTHROMBIN TIME,Lab,635,2469,0.257189145
+6890,1352,IP MED CYSTIC FIBROSIS ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,508,2469,0.205751316
+6891,1352,IP MED CYSTIC FIBROSIS ADMIT,LABTBIL,"BILIRUBIN TOTAL, SERUM/PLASMA",Lab,6,2469,0.002430134
+6892,1352,IP MED CYSTIC FIBROSIS ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,9,2469,0.0036452
+6893,1352,IP MED CYSTIC FIBROSIS ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,17,2469,0.006885379
+6894,1352,IP MED CYSTIC FIBROSIS ADMIT,LABUPREG,"PREGNANCY TEST, URINE",Lab,279,2469,0.113001215
+6895,1352,IP MED CYSTIC FIBROSIS ADMIT,LABYFRUCM,FRUCTOSAMINE,Lab,70,2469,0.028351559
+6896,1352,IP MED CYSTIC FIBROSIS ADMIT,IMGCTFAC,CT FACIAL BONES OR SINUS,Imaging,1,2469,0.000405022
+6897,1352,IP MED CYSTIC FIBROSIS ADMIT,IMGCTT,CT THORAX,Imaging,22,2469,0.00891049
+6898,1352,IP MED CYSTIC FIBROSIS ADMIT,IMGDXAB2,ABDOMEN WITH DECUB AND/OR UPRIGHT,Imaging,38,2469,0.015390846
+6899,1352,IP MED CYSTIC FIBROSIS ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,280,2469,0.113406237
+6900,1352,IP MED CYSTIC FIBROSIS ADMIT,IMGFLBE,XR BARIUM ENEMA,Imaging,1,2469,0.000405022
+6901,1355,IP GEN NEUTROPENIC FEVERS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,25,407,0.061425061
+6902,1355,IP GEN NEUTROPENIC FEVERS,5016,METRONIDAZOLE 500 MG PO TABS,Medication,5,407,0.012285012
+6903,1355,IP GEN NEUTROPENIC FEVERS,7555,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,Medication,3,407,0.007371007
+6904,1355,IP GEN NEUTROPENIC FEVERS,9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,2,407,0.004914005
+6905,1355,IP GEN NEUTROPENIC FEVERS,10045,FLUCONAZOLE 200 MG PO TABS,Medication,1,407,0.002457002
+6906,1355,IP GEN NEUTROPENIC FEVERS,10049,FLUCONAZOLE IN SALINE(ISO-OSM) 200 MG/100 ML IV PGBK,Medication,1,407,0.002457002
+6907,1355,IP GEN NEUTROPENIC FEVERS,10050,FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK,Medication,1,407,0.002457002
+6908,1355,IP GEN NEUTROPENIC FEVERS,86670,FAMCICLOVIR 500 MG PO TABS,Medication,1,407,0.002457002
+6909,1355,IP GEN NEUTROPENIC FEVERS,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,27,407,0.066339066
+6910,1355,IP GEN NEUTROPENIC FEVERS,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,3,407,0.007371007
+6911,1355,IP GEN NEUTROPENIC FEVERS,112593,FILGRASTIM 480 MCG/0.8 ML INJ SYRG,Medication,6,407,0.014742015
+6912,1355,IP GEN NEUTROPENIC FEVERS,112594,FILGRASTIM 300 MCG/0.5 ML INJ SYRG,Medication,7,407,0.017199017
+6913,1355,IP GEN NEUTROPENIC FEVERS,520041,ACYCLOVIR IVPB (<700 MG),Medication,1,407,0.002457002
+6914,1355,IP GEN NEUTROPENIC FEVERS,520045,MEROPENEM CUSTOM IVPB,Medication,2,407,0.004914005
+6915,1355,IP GEN NEUTROPENIC FEVERS,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,46,407,0.113022113
+6916,1355,IP GEN NEUTROPENIC FEVERS,520102,CEFEPIME 2 GM IVPB,Medication,5,407,0.012285012
+6917,1355,IP GEN NEUTROPENIC FEVERS,540960,MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS,Medication,4,407,0.00982801
+6918,1355,IP GEN NEUTROPENIC FEVERS,540967,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS,Medication,4,407,0.00982801
+6919,1355,IP GEN NEUTROPENIC FEVERS,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,27,407,0.066339066
+6920,1355,IP GEN NEUTROPENIC FEVERS,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,95,407,0.233415233
+6921,1355,IP GEN NEUTROPENIC FEVERS,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,29,407,0.071253071
+6922,1355,IP GEN NEUTROPENIC FEVERS,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,3,407,0.007371007
+6923,1355,IP GEN NEUTROPENIC FEVERS,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,28,407,0.068796069
+6924,1355,IP GEN NEUTROPENIC FEVERS,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,4,407,0.00982801
+6925,1355,IP GEN NEUTROPENIC FEVERS,LAB385,GASTROENTERITIS PCR PANEL,Lab,3,407,0.007371007
+6926,1355,IP GEN NEUTROPENIC FEVERS,LABAGALA,ASPERGILLUS GALACTOMANNAN,Lab,1,407,0.002457002
+6927,1355,IP GEN NEUTROPENIC FEVERS,LABAHP2,ACUTE HEPATITIS PANEL,Lab,1,407,0.002457002
+6928,1355,IP GEN NEUTROPENIC FEVERS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,78,407,0.191646192
+6929,1355,IP GEN NEUTROPENIC FEVERS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,73,407,0.179361179
+6930,1355,IP GEN NEUTROPENIC FEVERS,LABBLCSTK,"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY",Lab,36,407,0.088452088
+6931,1355,IP GEN NEUTROPENIC FEVERS,LABBLCTIP,BLOOD CULT CENTRAL LINE CATHETER BY NURSE,Lab,36,407,0.088452088
+6932,1355,IP GEN NEUTROPENIC FEVERS,LABCBCD,CBC WITH DIFF,Lab,67,407,0.164619165
+6933,1355,IP GEN NEUTROPENIC FEVERS,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,5,407,0.012285012
+6934,1355,IP GEN NEUTROPENIC FEVERS,LABDVERTV,"ROTAVIRUS ANTIGEN, STOOL",Lab,3,407,0.007371007
+6935,1355,IP GEN NEUTROPENIC FEVERS,LABFADAG,"ADENOVIRUS AG, STOOL",Lab,3,407,0.007371007
+6936,1355,IP GEN NEUTROPENIC FEVERS,LABHBSAG,HBSAG,Lab,1,407,0.002457002
+6937,1355,IP GEN NEUTROPENIC FEVERS,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,45,407,0.110565111
+6938,1355,IP GEN NEUTROPENIC FEVERS,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,12,407,0.029484029
+6939,1355,IP GEN NEUTROPENIC FEVERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,43,407,0.105651106
+6940,1355,IP GEN NEUTROPENIC FEVERS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,32,407,0.078624079
+6941,1355,IP GEN NEUTROPENIC FEVERS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,27,407,0.066339066
+6942,1355,IP GEN NEUTROPENIC FEVERS,LABRESP,RESPIRATORY CULTURE,Lab,7,407,0.017199017
+6943,1355,IP GEN NEUTROPENIC FEVERS,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,2,407,0.004914005
+6944,1355,IP GEN NEUTROPENIC FEVERS,LABSTLCP,STOOL CULTURE,Lab,5,407,0.012285012
+6945,1355,IP GEN NEUTROPENIC FEVERS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,49,407,0.12039312
+6946,1355,IP GEN NEUTROPENIC FEVERS,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,82,407,0.201474201
+6947,1355,IP GEN NEUTROPENIC FEVERS,LABURNC,URINE CULTURE,Lab,130,407,0.319410319
+6948,1355,IP GEN NEUTROPENIC FEVERS,IMGCTT,CT THORAX,Imaging,4,407,0.00982801
+6949,1355,IP GEN NEUTROPENIC FEVERS,IMGDXCH1,XR CHEST 1V,Imaging,28,407,0.068796069
+6950,1355,IP GEN NEUTROPENIC FEVERS,IMGDXCH2,XR CHEST 2V,Imaging,50,407,0.122850123
+6951,1437,IP PICC LINE PLACEMENT / EXCHANGE,10434,LIDOCAINE-PRILOCAINE 2.5-2.5 % TP CREA,Medication,2,17694,0.000113033
+6952,1437,IP PICC LINE PLACEMENT / EXCHANGE,81839,LIDOCAINE 4 % TP CREA,Medication,8876,17694,0.501638974
+6953,1437,IP PICC LINE PLACEMENT / EXCHANGE,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,8914,17694,0.503786594
+6954,1437,IP PICC LINE PLACEMENT / EXCHANGE,212508,CHLOROPROCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,1,17694,5.65E-05
+6955,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,18,50,0.36
+6956,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABANER,ANAEROBIC CULTURE,Lab,11,50,0.22
+6957,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,36,50,0.72
+6958,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,9,50,0.18
+6959,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFAL,"ALBUMIN, FLUID",Lab,6,50,0.12
+6960,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFAMY,"AMYLASE, FLUID",Lab,1,50,0.02
+6961,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFCUL,FUNGAL CULTURE,Lab,14,50,0.28
+6962,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFHCT,"HEMATOCRIT, FLUID",Lab,10,50,0.2
+6963,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,34,50,0.68
+6964,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFLDH,"LDH TOTAL, FLUID",Lab,28,50,0.56
+6965,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFLGL,"GLUCOSE, FLUID",Lab,6,50,0.12
+6966,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFTBLYM,"T SUBSETS AND B LYMPHOCYTES, FLUID",Lab,1,50,0.02
+6967,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFTP,"PROTEIN TOTAL, FLUID",Lab,28,50,0.56
+6968,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFTRPL,"TRIGLYCERIDES, PLEURAL",Lab,3,50,0.06
+6969,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABFURICA,"URIC ACID, FLUID",Lab,1,50,0.02
+6970,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABGRAM,GRAM STAIN,Lab,17,50,0.34
+6971,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABKOHP,KOH / CALCOFLUOR,Lab,7,50,0.14
+6972,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABNTBNP,NT - PROBNP,Lab,1,50,0.02
+6973,1444,"IP LAB SPECIMEN TESTING, PERICARDIAL FLUID",LABPHME,PH BY METER,Lab,22,50,0.44
+6974,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,20,292,0.068493151
+6975,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABANER,ANAEROBIC CULTURE,Lab,60,292,0.205479452
+6976,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,190,292,0.650684932
+6977,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,1,292,0.003424658
+6978,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFAL,"ALBUMIN, FLUID",Lab,107,292,0.366438356
+6979,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFAMY,"AMYLASE, FLUID",Lab,21,292,0.071917808
+6980,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFBILA,"BILIRUBIN, TOTAL BODY FLUID",Lab,8,292,0.02739726
+6981,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFCUL,FUNGAL CULTURE,Lab,18,292,0.061643836
+6982,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFHCT,"HEMATOCRIT, FLUID",Lab,4,292,0.01369863
+6983,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFKOH,FUNGAL CULTURE AND KOH,Lab,14,292,0.047945205
+6984,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,220,292,0.753424658
+6985,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFLDH,"LDH TOTAL, FLUID",Lab,106,292,0.363013699
+6986,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFLGL,"GLUCOSE, FLUID",Lab,42,292,0.143835616
+6987,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFLNA,"SODIUM, FLUID",Lab,2,292,0.006849315
+6988,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFTP,"PROTEIN TOTAL, FLUID",Lab,151,292,0.517123288
+6989,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFTRIG,"TRIGLYCERIDES, FLUID",Lab,2,292,0.006849315
+6990,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABFTRPL,"TRIGLYCERIDES, PLEURAL",Lab,17,292,0.058219178
+6991,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABGRAM,GRAM STAIN,Lab,100,292,0.342465753
+6992,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABPHME,PH BY METER,Lab,53,292,0.181506849
+6993,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABYFMG,"MAGNESIUM, FLUID",Lab,1,292,0.003424658
+6994,1445,"IP LAB SPECIMEN TESTING, PERITONEAL FLUID",LABYFUN,"UREA NITROGEN, FLUID",Lab,1,292,0.003424658
+6995,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,6,82,0.073170732
+6996,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABANER,ANAEROBIC CULTURE,Lab,32,82,0.390243902
+6997,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,76,82,0.926829268
+6998,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABFCUL,FUNGAL CULTURE,Lab,9,82,0.109756098
+6999,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABFKOH,FUNGAL CULTURE AND KOH,Lab,1,82,0.012195122
+7000,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABFLDH,"LDH TOTAL, FLUID",Lab,18,82,0.219512195
+7001,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABFLGL,"GLUCOSE, FLUID",Lab,4,82,0.048780488
+7002,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABFTP,"PROTEIN TOTAL, FLUID",Lab,19,82,0.231707317
+7003,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABFURICA,"URIC ACID, FLUID",Lab,6,82,0.073170732
+7004,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABGRAM,GRAM STAIN,Lab,75,82,0.914634146
+7005,1446,"IP LAB SPECIMEN TESTING, SYNOVIAL FLUID",LABPHME,PH BY METER,Lab,12,82,0.146341463
+7006,1448,"IP LAB SPECIMEN TESTING, URINE",LAB12811R,"MYOGLOBIN, URINE",Lab,3,185,0.016216216
+7007,1448,"IP LAB SPECIMEN TESTING, URINE",LABGHBUR,"GAMMA - HYDROXYBUTYRIC ACID, URINE",Lab,1,185,0.005405405
+7008,1448,"IP LAB SPECIMEN TESTING, URINE",LABUA,URINALYSIS WITH MICROSCOPIC,Lab,22,185,0.118918919
+7009,1448,"IP LAB SPECIMEN TESTING, URINE",LABUALB,"ALBUMIN WITH CREATININE, URINE (RANDOM)",Lab,7,185,0.037837838
+7010,1448,"IP LAB SPECIMEN TESTING, URINE",LABUALBT,"ALBUMIN, URINE (TIMED)",Lab,1,185,0.005405405
+7011,1448,"IP LAB SPECIMEN TESTING, URINE",LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,51,185,0.275675676
+7012,1448,"IP LAB SPECIMEN TESTING, URINE",LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,11,185,0.059459459
+7013,1448,"IP LAB SPECIMEN TESTING, URINE",LABUCAT,"CALCIUM, URINE (TIMED)",Lab,1,185,0.005405405
+7014,1448,"IP LAB SPECIMEN TESTING, URINE",LABUCL,"CHLORIDE WITH CREATININE, URINE (RANDOM)",Lab,1,185,0.005405405
+7015,1448,"IP LAB SPECIMEN TESTING, URINE",LABUCR,"CREATININE, URINE (RANDOM)",Lab,69,185,0.372972973
+7016,1448,"IP LAB SPECIMEN TESTING, URINE",LABUCRT,"CREATININE, URINE (TIMED)",Lab,8,185,0.043243243
+7017,1448,"IP LAB SPECIMEN TESTING, URINE",LABUEOS,"EOSINOPHILS, URINE",Lab,14,185,0.075675676
+7018,1448,"IP LAB SPECIMEN TESTING, URINE",LABUEP,"ELECTROLYTE PANEL, URINE WITH CREATININE",Lab,19,185,0.102702703
+7019,1448,"IP LAB SPECIMEN TESTING, URINE",LABUHGB,"HEMOGLOBIN, URINE",Lab,2,185,0.010810811
+7020,1448,"IP LAB SPECIMEN TESTING, URINE",LABUK,"POTASSIUM WITH CREATININE, URINE (RANDOM)",Lab,3,185,0.016216216
+7021,1448,"IP LAB SPECIMEN TESTING, URINE",LABUNA,"SODIUM WITH CREATININE, URINE (RANDOM)",Lab,19,185,0.102702703
+7022,1448,"IP LAB SPECIMEN TESTING, URINE",LABUNAT,"SODIUM, URINE (TIMED)",Lab,2,185,0.010810811
+7023,1448,"IP LAB SPECIMEN TESTING, URINE",LABUOSM,"OSMOLALITY, URINE",Lab,49,185,0.264864865
+7024,1448,"IP LAB SPECIMEN TESTING, URINE",LABUPHOS,"PHOSPHATE WITH CREATININE, URINE (RANDOM)",Lab,3,185,0.016216216
+7025,1448,"IP LAB SPECIMEN TESTING, URINE",LABUPHOST,"PHOSPHORUS, URINE (TIMED)",Lab,1,185,0.005405405
+7026,1448,"IP LAB SPECIMEN TESTING, URINE",LABURCA,"CALCIUM, URINE (RANDOM)",Lab,6,185,0.032432432
+7027,1448,"IP LAB SPECIMEN TESTING, URINE",LABURCL,"CHLORIDE, URINE (RANDOM)",Lab,14,185,0.075675676
+7028,1448,"IP LAB SPECIMEN TESTING, URINE",LABURGL,"GLUCOSE, URINE (RANDOM)",Lab,4,185,0.021621622
+7029,1448,"IP LAB SPECIMEN TESTING, URINE",LABURK,"POTASSIUM, URINE (RANDOM)",Lab,18,185,0.097297297
+7030,1448,"IP LAB SPECIMEN TESTING, URINE",LABURMAG,"MAGNESIUM, URINE (RANDOM)",Lab,3,185,0.016216216
+7031,1448,"IP LAB SPECIMEN TESTING, URINE",LABURNA,"SODIUM, URINE (RANDOM)",Lab,68,185,0.367567568
+7032,1448,"IP LAB SPECIMEN TESTING, URINE",LABURPHOS,"PHOSPHATE, URINE (RANDOM)",Lab,2,185,0.010810811
+7033,1448,"IP LAB SPECIMEN TESTING, URINE",LABURTP,"PROTEIN, URINE (RANDOM)",Lab,15,185,0.081081081
+7034,1448,"IP LAB SPECIMEN TESTING, URINE",LABURUN,"UREA, URINE (RANDOM)",Lab,35,185,0.189189189
+7035,1448,"IP LAB SPECIMEN TESTING, URINE",LABURURIC,"URIC ACID, URINE (RANDOM)",Lab,2,185,0.010810811
+7036,1448,"IP LAB SPECIMEN TESTING, URINE",LABUTP,"PROTEIN, URINE (TIMED)",Lab,2,185,0.010810811
+7037,1448,"IP LAB SPECIMEN TESTING, URINE",LABUTPRAN,"PROTEIN WITH CREATININE, URINE (RANDOM)",Lab,13,185,0.07027027
+7038,1448,"IP LAB SPECIMEN TESTING, URINE",LABUUN,"UREA WITH CREATININE, URINE (RANDOM)",Lab,8,185,0.043243243
+7039,1448,"IP LAB SPECIMEN TESTING, URINE",LABUUNT,"UREA NITROGEN, URINE (TIMED)",Lab,9,185,0.048648649
+7040,1448,"IP LAB SPECIMEN TESTING, URINE",LABUURIC,"URIC ACID WITH CREATININE, URINE (RANDOM)",Lab,1,185,0.005405405
+7041,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB11096R,"ALPHA 1 ANTITRYPSIN, STOOL",Lab,9,246,0.036585366
+7042,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB11118R,"ELASTASE, STOOL",Lab,19,246,0.077235772
+7043,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB13006R,"REDUCING SUBSTANCE, FECES",Lab,23,246,0.093495935
+7044,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB385,GASTROENTERITIS PCR PANEL,Lab,66,246,0.268292683
+7045,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB388,DIENTAMOEBA FRAGILIS MICROSCOPIC EXAM,Lab,10,246,0.040650407
+7046,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB389,ISOSPORA MICROSCOPIC EXAM,Lab,6,246,0.024390244
+7047,1449,"IP LAB SPECIMEN TESTING, STOOL",LAB390,HELMINTHIC OVA AND LARVAE,Lab,53,246,0.215447154
+7048,1449,"IP LAB SPECIMEN TESTING, STOOL",LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,82,246,0.333333333
+7049,1449,"IP LAB SPECIMEN TESTING, STOOL",LABDVERTV,"ROTAVIRUS ANTIGEN, STOOL",Lab,25,246,0.101626016
+7050,1449,"IP LAB SPECIMEN TESTING, STOOL",LABGIARS,GIARDIA SCREEN,Lab,75,246,0.304878049
+7051,1449,"IP LAB SPECIMEN TESTING, STOOL",LABHELAG,"HELICOBACTER PYLORI AG, STOOL",Lab,11,246,0.044715447
+7052,1449,"IP LAB SPECIMEN TESTING, STOOL",LABOAPE,OVA AND PARASITES,Lab,109,246,0.443089431
+7053,1449,"IP LAB SPECIMEN TESTING, STOOL",LABPORFEC,"PORPHYRINS FRACTIONATED, FECAL",Lab,3,246,0.012195122
+7054,1449,"IP LAB SPECIMEN TESTING, STOOL",LABROMSP,MICROSPORIDIUM SCREEN,Lab,21,246,0.085365854
+7055,1449,"IP LAB SPECIMEN TESTING, STOOL",LABROPK,ISOSPORA/CYCLOSPORA SCREEN,Lab,23,246,0.093495935
+7056,1449,"IP LAB SPECIMEN TESTING, STOOL",LABSTFAT,"FAT STAIN, STOOL",Lab,40,246,0.162601626
+7057,1449,"IP LAB SPECIMEN TESTING, STOOL",LABSTLCP,STOOL CULTURE,Lab,94,246,0.382113821
+7058,1449,"IP LAB SPECIMEN TESTING, STOOL",LABYLACTS,"LACTOFERRIN DETECTION, STOOL",Lab,1,246,0.004065041
+7059,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABAFBC,"AFB CULTURE, RESPIRATORY",Lab,488,2137,0.228357511
+7060,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABAFBSO,AFB SMEAR ONLY,Lab,9,2137,0.004211511
+7061,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,35,2137,0.0163781
+7062,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABFCUL,FUNGAL CULTURE,Lab,705,2137,0.329901731
+7063,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABFKOH,FUNGAL CULTURE AND KOH,Lab,502,2137,0.234908751
+7064,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABGRAM,GRAM STAIN,Lab,741,2137,0.346747777
+7065,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABLCVCUL,"CULTURE, VIRAL, GENERAL",Lab,36,2137,0.016846046
+7066,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABLEGDA,LEGIONELLA CULTURE,Lab,175,2137,0.081890501
+7067,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABPCYS,PNEUMOCYSTIS STAIN,Lab,221,2137,0.103416004
+7068,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABRESP,RESPIRATORY CULTURE,Lab,730,2137,0.341600374
+7069,1451,"IP LAB SPECIMEN TESTING, RESPIRATORY SECRETIONS",LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,1369,2137,0.640617688
+7070,1453,"IP LAB SPECIMEN TESTING, CSF",LAB11019R,"MYELIN BASIC PROTEIN, CSF",Lab,12,1767,0.006791171
+7071,1453,"IP LAB SPECIMEN TESTING, CSF",LABAACSF,"AMINO ACIDS, CSF",Lab,4,1767,0.002263724
+7072,1453,"IP LAB SPECIMEN TESTING, CSF",LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,174,1767,0.098471986
+7073,1453,"IP LAB SPECIMEN TESTING, CSF",LABCIGA,"IGA, CSF",Lab,6,1767,0.003395586
+7074,1453,"IP LAB SPECIMEN TESTING, CSF",LABCIGMT,"IGM, CSF",Lab,8,1767,0.004527448
+7075,1453,"IP LAB SPECIMEN TESTING, CSF",LABCLACT,"LACTIC ACID, CSF",Lab,121,1767,0.068477646
+7076,1453,"IP LAB SPECIMEN TESTING, CSF",LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,178,1767,0.10073571
+7077,1453,"IP LAB SPECIMEN TESTING, CSF",LABCSFC,CSF CULTURE AND GRAM STAIN,Lab,1276,1767,0.7221279
+7078,1453,"IP LAB SPECIMEN TESTING, CSF",LABCSFCT,"CELL COUNT AND DIFFERENTIAL, CSF",Lab,1641,1767,0.928692699
+7079,1453,"IP LAB SPECIMEN TESTING, CSF",LABCSFGL,"GLUCOSE, CSF",Lab,1434,1767,0.811544992
+7080,1453,"IP LAB SPECIMEN TESTING, CSF",LABCSFLD,"LDH, CSF",Lab,38,1767,0.021505376
+7081,1453,"IP LAB SPECIMEN TESTING, CSF",LABCSFTP,"PROTEIN TOTAL, CSF",Lab,1462,1767,0.827391058
+7082,1453,"IP LAB SPECIMEN TESTING, CSF",LABEVPCR,ENTEROVIRUS BY PCR,Lab,204,1767,0.115449915
+7083,1453,"IP LAB SPECIMEN TESTING, CSF",LABFBBURG,"LYME WESTERN BLOT, CSF",Lab,39,1767,0.022071307
+7084,1453,"IP LAB SPECIMEN TESTING, CSF",LABFCUL,FUNGAL CULTURE,Lab,285,1767,0.161290323
+7085,1453,"IP LAB SPECIMEN TESTING, CSF",LABHSVQL,"HSV DNA PCR QUAL, NON LESION",Lab,238,1767,0.134691568
+7086,1453,"IP LAB SPECIMEN TESTING, CSF",LABOLIG,CSF PROTEIN & IMMUNOFIX ELECT.,Lab,92,1767,0.052065648
+7087,1453,"IP LAB SPECIMEN TESTING, CSF",LABVCUL,VIRAL CULTURE,Lab,1,1767,0.000565931
+7088,1453,"IP LAB SPECIMEN TESTING, CSF",LABWNVAB,"WEST NILE VIRUS AB PANEL, CSF",Lab,151,1767,0.085455574
+7089,1453,"IP LAB SPECIMEN TESTING, CSF",LABYAFCSF,"ALPHA FETOPROTEIN, CSF",Lab,6,1767,0.003395586
+7090,1453,"IP LAB SPECIMEN TESTING, CSF",LABYCEAC,"CEA, CSF",Lab,4,1767,0.002263724
+7091,1453,"IP LAB SPECIMEN TESTING, CSF",LABYCPYRU,"PYRUVATE, CSF",Lab,3,1767,0.001697793
+7092,1453,"IP LAB SPECIMEN TESTING, CSF",LABYHCCSF,"HCG BETA-SUBUNIT QUANTITATIVE, CSF",Lab,1,1767,0.000565931
+7093,1454,"IP LAB SPECIMEN TESTING, BONE MARROW",LABBLCA,"AFB CULTURE, BLOOD",Lab,10,13,0.769230769
+7094,1454,"IP LAB SPECIMEN TESTING, BONE MARROW",LABBMC,BONE MARROW CULTURE,Lab,10,13,0.769230769
+7095,1454,"IP LAB SPECIMEN TESTING, BONE MARROW",LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,6,13,0.461538462
+7096,1454,"IP LAB SPECIMEN TESTING, BONE MARROW",LABFCUL,FUNGAL CULTURE,Lab,10,13,0.769230769
+7097,1454,"IP LAB SPECIMEN TESTING, BONE MARROW",LABKOHP,KOH / CALCOFLUOR,Lab,3,13,0.230769231
+7098,1455,"IP LAB SPECIMEN TESTING, BLOOD CULTURE - INFECTIOUS AGENT DETECTION",LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,3964,3848,1
+7099,1455,"IP LAB SPECIMEN TESTING, BLOOD CULTURE - INFECTIOUS AGENT DETECTION",LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,3937,3848,1
+7100,1455,"IP LAB SPECIMEN TESTING, BLOOD CULTURE - INFECTIOUS AGENT DETECTION",LABBLCA,"AFB CULTURE, BLOOD",Lab,12,3848,0.003118503
+7101,1455,"IP LAB SPECIMEN TESTING, BLOOD CULTURE - INFECTIOUS AGENT DETECTION",LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,7,3848,0.001819127
+7102,1455,"IP LAB SPECIMEN TESTING, BLOOD CULTURE - INFECTIOUS AGENT DETECTION",LABISOF,ISOLATOR FUNGUS,Lab,23,3848,0.005977131
+7103,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,175,827,0.211608222
+7104,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,106,827,0.128174123
+7105,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,2510,DIPHENHYDRAMINE HCL 50 MG PO CAPS,Medication,53,827,0.064087062
+7106,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,6494,PREDNISONE 10 MG PO TABS,Medication,1,827,0.00120919
+7107,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,6496,PREDNISONE 20 MG PO TABS,Medication,3,827,0.00362757
+7108,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,6498,PREDNISONE 50 MG PO TABS,Medication,145,827,0.175332527
+7109,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,827,0.00120919
+7110,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,166,827,0.200725514
+7111,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,2,827,0.00241838
+7112,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,121,827,0.146311971
+7113,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,107,827,0.129383313
+7114,1458,RADIOLOGY CONTRAST DYE PREMEDICATION,9000000,ZZZ IMS TEMPLATE,Medication,42,827,0.050785973
+7115,1480,"IP LAB SPECIMEN TESTING, CSF SHUNT FLUID",LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,1,22,0.045454545
+7116,1480,"IP LAB SPECIMEN TESTING, CSF SHUNT FLUID",LABANER,ANAEROBIC CULTURE,Lab,17,22,0.772727273
+7117,1480,"IP LAB SPECIMEN TESTING, CSF SHUNT FLUID",LABCSFC,CSF CULTURE AND GRAM STAIN,Lab,18,22,0.818181818
+7118,1480,"IP LAB SPECIMEN TESTING, CSF SHUNT FLUID",LABFCUL,FUNGAL CULTURE,Lab,4,22,0.181818182
+7119,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,26,97,0.268041237
+7120,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,14,97,0.144329897
+7121,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,9,97,0.092783505
+7122,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,97,0.010309278
+7123,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,97,0.010309278
+7124,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,97,0.020618557
+7125,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,7,97,0.072164948
+7126,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,25,97,0.257731959
+7127,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),95795,ARTIFICIAL TEARS(HYPROMELLOSE) 0.5 % OPHT DROP,Medication,25,97,0.257731959
+7128,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,18,97,0.18556701
+7129,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,10,97,0.103092784
+7130,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,7,97,0.072164948
+7131,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,26,97,0.268041237
+7132,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),222249,LEVOTHYROXINE 100 MCG IV SOLR,Medication,19,97,0.195876289
+7133,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),520078,METHYLPREDNISOLONE IVPB,Medication,30,97,0.309278351
+7134,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),520087,DESMOPRESSIN IVPB,Medication,9,97,0.092783505
+7135,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),520201,LEVOTHYROXINE IV INFUSION,Medication,29,97,0.298969072
+7136,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),530012,PHENYLEPHRINE IV INFUSION,Medication,11,97,0.113402062
+7137,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),530049,INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW),Medication,20,97,0.206185567
+7138,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),540148,NS FLUSH BAG,Medication,1,97,0.010309278
+7139,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),540302,POTASSIUM CHLORIDE IV SCALE,Medication,25,97,0.257731959
+7140,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),540304,MAGNESIUM SULFATE IV SCALE ,Medication,26,97,0.268041237
+7141,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),540910,INSULIN REGULAR IV BOLUS,Medication,5,97,0.051546392
+7142,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),541081,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS EXTENDED INF,Medication,9,97,0.092783505
+7143,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,18,97,0.18556701
+7144,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABABG,"BLOOD GASES, ARTERIAL",Lab,43,97,0.443298969
+7145,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABAMY,"AMYLASE, SERUM",Lab,25,97,0.257731959
+7146,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,24,97,0.24742268
+7147,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,24,97,0.24742268
+7148,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABCBCD,CBC WITH DIFF,Lab,34,97,0.350515464
+7149,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABCK,"CREATINE KINASE, TOTAL",Lab,31,97,0.319587629
+7150,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABFKOH,FUNGAL CULTURE AND KOH,Lab,26,97,0.268041237
+7151,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABLACWB,"LACTATE, WHOLE BLOOD",Lab,33,97,0.340206186
+7152,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABLIPS,LIPASE,Lab,26,97,0.268041237
+7153,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,35,97,0.360824742
+7154,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABNA,"SODIUM, SERUM / PLASMA",Lab,14,97,0.144329897
+7155,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABPT,PROTHROMBIN TIME,Lab,33,97,0.340206186
+7156,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,33,97,0.340206186
+7157,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABRESP,RESPIRATORY CULTURE,Lab,8,97,0.082474227
+7158,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,18,97,0.18556701
+7159,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABTNI,TROPONIN I,Lab,30,97,0.309278351
+7160,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABTYPSNI,TYPE AND SCREEN,Lab,20,97,0.206185567
+7161,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABUA,URINALYSIS WITH MICROSCOPIC,Lab,28,97,0.288659794
+7162,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABURNC,URINE CULTURE,Lab,25,97,0.257731959
+7163,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),POC14,POC GLUCOSE BY METER,Lab,35,97,0.360824742
+7164,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),IMGDXCH1,XR CHEST 1V,Imaging,26,97,0.268041237
+7165,1489,IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG),LABBBPRBC,RED BLOOD CELLS,Blood Product,5,97,0.051546392
+7166,1492,DRUG DESENSITIZATION PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,15,50,0.3
+7167,1492,DRUG DESENSITIZATION PROTOCOL,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,21,50,0.42
+7168,1492,DRUG DESENSITIZATION PROTOCOL,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,17,50,0.34
+7169,1492,DRUG DESENSITIZATION PROTOCOL,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,18,50,0.36
+7170,1492,DRUG DESENSITIZATION PROTOCOL,520178,DESENSITIZATION DRUG MIXTURE,Medication,75,50,1
+7171,1492,DRUG DESENSITIZATION PROTOCOL,540138,DESENSITIZATION DRUG HELP ORDER,Medication,9,50,0.18
+7172,1518,IP SUR DAY OF SURGERY ORDERS,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,3,930,0.003225806
+7173,1518,IP SUR DAY OF SURGERY ORDERS,4318,LACTATED RINGERS IV SOLP,Medication,93,930,0.1
+7174,1518,IP SUR DAY OF SURGERY ORDERS,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,16,930,0.017204301
+7175,1518,IP SUR DAY OF SURGERY ORDERS,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,6,930,0.006451613
+7176,1518,IP SUR DAY OF SURGERY ORDERS,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,7,930,0.007526882
+7177,1518,IP SUR DAY OF SURGERY ORDERS,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,4,930,0.004301075
+7178,1518,IP SUR DAY OF SURGERY ORDERS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,197,930,0.211827957
+7179,1518,IP SUR DAY OF SURGERY ORDERS,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,6,930,0.006451613
+7180,1518,IP SUR DAY OF SURGERY ORDERS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,930,0.001075269
+7181,1518,IP SUR DAY OF SURGERY ORDERS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,16,930,0.017204301
+7182,1518,IP SUR DAY OF SURGERY ORDERS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,14,930,0.015053763
+7183,1518,IP SUR DAY OF SURGERY ORDERS,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,41,930,0.044086022
+7184,1518,IP SUR DAY OF SURGERY ORDERS,520043,CEFOXITIN IVPB,Medication,7,930,0.007526882
+7185,1518,IP SUR DAY OF SURGERY ORDERS,520064,CEFAZOLIN 2 G IVPB,Medication,35,930,0.037634409
+7186,1518,IP SUR DAY OF SURGERY ORDERS,520160,ERTAPENEM 1 G IVPB,Medication,16,930,0.017204301
+7187,1518,IP SUR DAY OF SURGERY ORDERS,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,20,930,0.021505376
+7188,1518,IP SUR DAY OF SURGERY ORDERS,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,103,930,0.110752688
+7189,1518,IP SUR DAY OF SURGERY ORDERS,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,1,930,0.001075269
+7190,1518,IP SUR DAY OF SURGERY ORDERS,LABCBCD,CBC WITH DIFF,Lab,28,930,0.030107527
+7191,1518,IP SUR DAY OF SURGERY ORDERS,LABCBCO,CBC W/O DIFF,Lab,6,930,0.006451613
+7192,1518,IP SUR DAY OF SURGERY ORDERS,LABCR,"CREATININE, SERUM/PLASMA",Lab,5,930,0.005376344
+7193,1518,IP SUR DAY OF SURGERY ORDERS,LABMETB,"METABOLIC PANEL, BASIC",Lab,14,930,0.015053763
+7194,1518,IP SUR DAY OF SURGERY ORDERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,14,930,0.015053763
+7195,1518,IP SUR DAY OF SURGERY ORDERS,LABPREG,"PREGNANCY TEST, SERUM",Lab,2,930,0.002150538
+7196,1518,IP SUR DAY OF SURGERY ORDERS,LABPT,PROTHROMBIN TIME,Lab,30,930,0.032258065
+7197,1518,IP SUR DAY OF SURGERY ORDERS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,12,930,0.012903226
+7198,1518,IP SUR DAY OF SURGERY ORDERS,LABTYPSNI,TYPE AND SCREEN,Lab,71,930,0.076344086
+7199,1518,IP SUR DAY OF SURGERY ORDERS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,3,930,0.003225806
+7200,1518,IP SUR DAY OF SURGERY ORDERS,LABUPREG,"PREGNANCY TEST, URINE",Lab,2,930,0.002150538
+7201,1518,IP SUR DAY OF SURGERY ORDERS,POC14,POC GLUCOSE BY METER,Lab,4,930,0.004301075
+7202,1518,IP SUR DAY OF SURGERY ORDERS,IMGDXCH2,XR CHEST 2V,Imaging,1,930,0.001075269
+7203,1518,IP SUR DAY OF SURGERY ORDERS,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,930,0.001075269
+7204,1518,IP SUR DAY OF SURGERY ORDERS,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,1,930,0.001075269
+7205,1518,IP SUR DAY OF SURGERY ORDERS,LABBBPRBC,RED BLOOD CELLS,Blood Product,13,930,0.013978495
+7206,1521,NURSING TRIAGE FEVER PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,114,0.052631579
+7207,1521,NURSING TRIAGE FEVER PROTOCOL,102,ACETAMINOPHEN 500 MG PO TABS,Medication,34,114,0.298245614
+7208,1521,NURSING TRIAGE FEVER PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,48,114,0.421052632
+7209,1521,NURSING TRIAGE FEVER PROTOCOL,POC26,POC URINE PREGNANCY,Lab,10,114,0.087719298
+7210,1522,NURSING TRIAGE FLANK PAIN PROTOCOL,540102,NS IV BOLUS,Medication,8,26,0.307692308
+7211,1522,NURSING TRIAGE FLANK PAIN PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,17,26,0.653846154
+7212,1522,NURSING TRIAGE FLANK PAIN PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,20,26,0.769230769
+7213,1522,NURSING TRIAGE FLANK PAIN PROTOCOL,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,17,26,0.653846154
+7214,1522,NURSING TRIAGE FLANK PAIN PROTOCOL,POC46,POC ISTAT BHCG,Lab,1,26,0.038461538
+7215,1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,70,187,0.374331551
+7216,1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",LABCBCO,CBC W/O DIFF,Lab,110,187,0.588235294
+7217,1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,111,187,0.593582888
+7218,1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",LABPT,PROTHROMBIN TIME,Lab,106,187,0.56684492
+7219,1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,104,187,0.556149733
+7220,1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",LABTYPSNI,TYPE AND SCREEN,Lab,107,187,0.572192513
+7221,1524,NURSING TRIAGE HYPOGLYCEMIA PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,10,15,0.666666667
+7222,1524,NURSING TRIAGE HYPOGLYCEMIA PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,10,15,0.666666667
+7223,1524,NURSING TRIAGE HYPOGLYCEMIA PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,24,15,1
+7224,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,4,345,0.011594203
+7225,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,144,345,0.417391304
+7226,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,540102,NS IV BOLUS,Medication,94,345,0.272463768
+7227,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,LABCBCO,CBC W/O DIFF,Lab,98,345,0.284057971
+7228,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,102,345,0.295652174
+7229,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,POC14,POC GLUCOSE BY METER,Lab,57,345,0.165217391
+7230,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,POC26,POC URINE PREGNANCY,Lab,1,345,0.002898551
+7231,1526,NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ,POC46,POC ISTAT BHCG,Lab,1,345,0.002898551
+7232,1528,NURSING TRIAGE OVERDOSE PROTOCOL,LABACETA,"ACETAMINOPHEN, SERUM",Lab,8,9,0.888888889
+7233,1528,NURSING TRIAGE OVERDOSE PROTOCOL,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,7,9,0.777777778
+7234,1528,NURSING TRIAGE OVERDOSE PROTOCOL,LABEP1,ELECTROLYTE PANEL,Lab,6,9,0.666666667
+7235,1528,NURSING TRIAGE OVERDOSE PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,7,9,0.777777778
+7236,1528,NURSING TRIAGE OVERDOSE PROTOCOL,LABSALICY,SALICYLATE LEVEL,Lab,8,9,0.888888889
+7237,1528,NURSING TRIAGE OVERDOSE PROTOCOL,LABVOLTL,VOLATILE SCREEN,Lab,5,9,0.555555556
+7238,1528,NURSING TRIAGE OVERDOSE PROTOCOL,POC26,POC URINE PREGNANCY,Lab,1,9,0.111111111
+7239,1548,IP LAB COAGULATION TESTING,LABACA,ANTI CARDIOLIPIN AB,Lab,1,47,0.021276596
+7240,1548,IP LAB COAGULATION TESTING,LABAPC,ACTIVATED PROTEIN C RESISTANCE,Lab,2,47,0.042553191
+7241,1548,IP LAB COAGULATION TESTING,LABAPHSA2,ANTI PHOSPHOLIPID AB PANEL,Lab,4,47,0.085106383
+7242,1548,IP LAB COAGULATION TESTING,LABAT3,ANTITHROMBIN III,Lab,6,47,0.127659574
+7243,1548,IP LAB COAGULATION TESTING,LABDDIM,D - DIMER,Lab,4,47,0.085106383
+7244,1548,IP LAB COAGULATION TESTING,LABDIC,DIC SCREEN,Lab,11,47,0.234042553
+7245,1548,IP LAB COAGULATION TESTING,LABDRVVTP,DILUTE RUSSELL VIPER VENOM PANEL,Lab,1,47,0.021276596
+7246,1548,IP LAB COAGULATION TESTING,LABF8INH,FACTOR VIII INHIBITOR HUMAN,Lab,1,47,0.021276596
+7247,1548,IP LAB COAGULATION TESTING,LABF8POR,FACTOR VIII INHIBITOR PORCINE,Lab,1,47,0.021276596
+7248,1548,IP LAB COAGULATION TESTING,LABFACT10,FACTOR X ASSAY,Lab,2,47,0.042553191
+7249,1548,IP LAB COAGULATION TESTING,LABFACT11,FACTOR XI ASSAY,Lab,1,47,0.021276596
+7250,1548,IP LAB COAGULATION TESTING,LABFACT12,FACTOR XII ASSAY,Lab,1,47,0.021276596
+7251,1548,IP LAB COAGULATION TESTING,LABFACT13,FACTOR XIII SCREEN,Lab,2,47,0.042553191
+7252,1548,IP LAB COAGULATION TESTING,LABFACT2,FACTOR II ASSAY,Lab,1,47,0.021276596
+7253,1548,IP LAB COAGULATION TESTING,LABFACT5,FACTOR V ASSAY,Lab,2,47,0.042553191
+7254,1548,IP LAB COAGULATION TESTING,LABFACT8,FACTOR VIII ASSAY,Lab,1,47,0.021276596
+7255,1548,IP LAB COAGULATION TESTING,LABFACT9,FACTOR IX ASSAY,Lab,1,47,0.021276596
+7256,1548,IP LAB COAGULATION TESTING,LABFDP,FIBRIN DEGRADATION PRODUCTS,Lab,1,47,0.021276596
+7257,1548,IP LAB COAGULATION TESTING,LABFIB,FIBRINOGEN,Lab,11,47,0.234042553
+7258,1548,IP LAB COAGULATION TESTING,LABLEID,FACTOR V LEIDEN,Lab,4,47,0.085106383
+7259,1548,IP LAB COAGULATION TESTING,LABLUPUS,LUPUS ANTICOAGULANT,Lab,3,47,0.063829787
+7260,1548,IP LAB COAGULATION TESTING,LABP20210,PROTHROMBIN 20210A,Lab,4,47,0.085106383
+7261,1548,IP LAB COAGULATION TESTING,LABPLASMN,PLASMINOGEN,Lab,1,47,0.021276596
+7262,1548,IP LAB COAGULATION TESTING,LABPLTFUN,PLATELET FUNCTION SCREEN,Lab,1,47,0.021276596
+7263,1548,IP LAB COAGULATION TESTING,LABPROTC,PROTEIN C ACTIVITY,Lab,7,47,0.14893617
+7264,1548,IP LAB COAGULATION TESTING,LABPROTS,PROTEIN S ACTIVITY,Lab,7,47,0.14893617
+7265,1548,IP LAB COAGULATION TESTING,LABPT,PROTHROMBIN TIME,Lab,30,47,0.638297872
+7266,1548,IP LAB COAGULATION TESTING,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,30,47,0.638297872
+7267,1548,IP LAB COAGULATION TESTING,LABTEGCP,THROMBELASTOGRAPH CLOTTING PROFILE,Lab,1,47,0.021276596
+7268,1548,IP LAB COAGULATION TESTING,LABTT,THROMBIN TIME,Lab,5,47,0.106382979
+7269,1548,IP LAB COAGULATION TESTING,LABVWSCN,VON WILLEBRAND SCREEN,Lab,2,47,0.042553191
+7270,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,9,336,0.026785714
+7271,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,336,0.00297619
+7272,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,336,0.00297619
+7273,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,24,336,0.071428571
+7274,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,336,0.014880952
+7275,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,10273R,HLA ORDER - WORKUP AT TIME OF TRANSPLANT,Lab,153,336,0.455357143
+7276,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,10280R,HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING_CROSSMATCH,Lab,312,336,0.928571429
+7277,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABCBCD,CBC WITH DIFF,Lab,309,336,0.919642857
+7278,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABCMVIGG,CMV IGG,Lab,293,336,0.87202381
+7279,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABDGHBP,DIAG. HEPATITIS B PANEL,Lab,291,336,0.866071429
+7280,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL,Lab,292,336,0.869047619
+7281,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABHCVA,HEPATITIS C AB IGG,Lab,290,336,0.863095238
+7282,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,311,336,0.925595238
+7283,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,270,336,0.803571429
+7284,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,262,336,0.779761905
+7285,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABPLTS,PLATELET COUNT,Lab,6,336,0.017857143
+7286,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABPREG,"PREGNANCY TEST, SERUM",Lab,101,336,0.300595238
+7287,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABPT,PROTHROMBIN TIME,Lab,310,336,0.922619048
+7288,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,310,336,0.922619048
+7289,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,295,336,0.87797619
+7290,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,153,336,0.455357143
+7291,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,158,336,0.470238095
+7292,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABVZVIGG,VZ IGG,Lab,278,336,0.827380952
+7293,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,IMGDXCH2,XR CHEST 2V,Imaging,309,336,0.919642857
+7294,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABBBPCRY,CRYOPRECIPITATE,Blood Product,2,336,0.005952381
+7295,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,10,336,0.029761905
+7296,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,5,336,0.014880952
+7297,1553,IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,246,336,0.732142857
+7298,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,7,295,0.023728814
+7299,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,72,295,0.244067797
+7300,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,2,295,0.006779661
+7301,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,4318,LACTATED RINGERS IV SOLP,Medication,79,295,0.26779661
+7302,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,115,295,0.389830508
+7303,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,9,295,0.030508475
+7304,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,295,0.003389831
+7305,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,11,295,0.037288136
+7306,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,5,295,0.016949153
+7307,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,295,0.003389831
+7308,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,20,295,0.06779661
+7309,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,24,295,0.081355932
+7310,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,1,295,0.003389831
+7311,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,295,0.006779661
+7312,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,5,295,0.016949153
+7313,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,1,295,0.003389831
+7314,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,8,295,0.027118644
+7315,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,15,295,0.050847458
+7316,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,127,295,0.430508475
+7317,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,7,295,0.023728814
+7318,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,295,0.010169492
+7319,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,540102,NS IV BOLUS,Medication,1,295,0.003389831
+7320,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,295,0.003389831
+7321,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,LABCBCD,CBC WITH DIFF,Lab,99,295,0.33559322
+7322,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,11,295,0.037288136
+7323,1554,RETIRED IP GYN LAPAROSCOPY POSTOP,LABPLTS,PLATELET COUNT,Lab,1,295,0.003389831
+7324,1557,IP ATIC INTRAVENOUS GAMMA GLOBULIN,101,ACETAMINOPHEN 325 MG PO TABS,Medication,53,111,0.477477477
+7325,1557,IP ATIC INTRAVENOUS GAMMA GLOBULIN,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,111,0.009009009
+7326,1557,IP ATIC INTRAVENOUS GAMMA GLOBULIN,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,96,111,0.864864865
+7327,1557,IP ATIC INTRAVENOUS GAMMA GLOBULIN,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,41,111,0.369369369
+7328,1557,IP ATIC INTRAVENOUS GAMMA GLOBULIN,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,46,111,0.414414414
+7329,1557,IP ATIC INTRAVENOUS GAMMA GLOBULIN,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,26,111,0.234234234
+7330,1558,IP ATIC INFLIXIMAB (REMICADE) INFUSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,8,22,0.363636364
+7331,1558,IP ATIC INFLIXIMAB (REMICADE) INFUSION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,7,22,0.318181818
+7332,1558,IP ATIC INFLIXIMAB (REMICADE) INFUSION,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,10,22,0.454545455
+7333,1558,IP ATIC INFLIXIMAB (REMICADE) INFUSION,90870,METHYLPREDNISOLONE SODIUM SUCC 40 MG INJ SOLR,Medication,1,22,0.045454545
+7334,1558,IP ATIC INFLIXIMAB (REMICADE) INFUSION,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,1,22,0.045454545
+7335,1558,IP ATIC INFLIXIMAB (REMICADE) INFUSION,520116,INFLIXIMAB IV INFUSION,Medication,11,22,0.5
+7336,1564,IP CAPR CARDIOVERSION,7318,SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP,Medication,3,58,0.051724138
+7337,1564,IP CAPR CARDIOVERSION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,26,58,0.448275862
+7338,1564,IP CAPR CARDIOVERSION,LABPT,PROTHROMBIN TIME,Lab,4,58,0.068965517
+7339,1564,IP CAPR CARDIOVERSION,POC17,POC ISTAT CREATININE,Lab,24,58,0.413793103
+7340,1564,IP CAPR CARDIOVERSION,POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,24,58,0.413793103
+7341,1564,IP CAPR CARDIOVERSION,POC25,POC ISTAT INR AND PROTIME,Lab,15,58,0.25862069
+7342,1566,IP SUR BOWEL PREP,127907,SODIUM PHOSPHATES 7.2-2.7 GRAM/15 ML PO LIQD,Medication,1,18,0.055555556
+7343,1566,IP SUR BOWEL PREP,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,9,18,0.5
+7344,1566,IP SUR BOWEL PREP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,18,0.055555556
+7345,1566,IP SUR BOWEL PREP,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,18,0.055555556
+7346,1582,IP REN HEMODIALYSIS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,21812,0.000183385
+7347,1582,IP REN HEMODIALYSIS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,3,21812,0.000137539
+7348,1582,IP REN HEMODIALYSIS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,227,21812,0.010407115
+7349,1582,IP REN HEMODIALYSIS,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,4,21812,0.000183385
+7350,1582,IP REN HEMODIALYSIS,4749,MANNITOL 20 % 20 % IV SOLP,Medication,1,21812,4.58E-05
+7351,1582,IP REN HEMODIALYSIS,4750,MANNITOL 25 % 25 % IV SOLN,Medication,303,21812,0.013891436
+7352,1582,IP REN HEMODIALYSIS,8981,"ALBUMIN, HUMAN 25 % 25 % IV SOLP",Medication,128,21812,0.005868329
+7353,1582,IP REN HEMODIALYSIS,8982,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",Medication,29,21812,0.001329543
+7354,1582,IP REN HEMODIALYSIS,9348,CALCITRIOL 1 MCG/ML IV SOLN,Medication,98,21812,0.00449294
+7355,1582,IP REN HEMODIALYSIS,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,973,21812,0.044608472
+7356,1582,IP REN HEMODIALYSIS,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,1,21812,4.58E-05
+7357,1582,IP REN HEMODIALYSIS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,21812,9.17E-05
+7358,1582,IP REN HEMODIALYSIS,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,21812,4.58E-05
+7359,1582,IP REN HEMODIALYSIS,77542,"EPOETIN ALFA 2,000 UNIT/ML INJ SOLN",Medication,352,21812,0.016137906
+7360,1582,IP REN HEMODIALYSIS,77641,"EPOETIN ALFA 4,000 UNIT/ML INJ SOLN",Medication,878,21812,0.040253072
+7361,1582,IP REN HEMODIALYSIS,86299,"EPOETIN ALFA 20,000 UNIT/ML INJ SOLN",Medication,45,21812,0.002063085
+7362,1582,IP REN HEMODIALYSIS,91568,"EPOETIN ALFA 3,000 UNIT/ML INJ SOLN",Medication,564,21812,0.025857326
+7363,1582,IP REN HEMODIALYSIS,94501,"EPOETIN ALFA 10,000 UNIT/ML INJ SOLN",Medication,843,21812,0.03864845
+7364,1582,IP REN HEMODIALYSIS,94623,ALTEPLASE 2 MG INJ SOLR,Medication,1098,21812,0.050339263
+7365,1582,IP REN HEMODIALYSIS,95223,VANCOMYCIN IN D5W 500 MG/100 ML IV PGBK,Medication,4,21812,0.000183385
+7366,1582,IP REN HEMODIALYSIS,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,21812,4.58E-05
+7367,1582,IP REN HEMODIALYSIS,126580,"HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",Medication,7403,21812,0.33940033
+7368,1582,IP REN HEMODIALYSIS,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,866,21812,0.039702916
+7369,1582,IP REN HEMODIALYSIS,127600,DARBEPOETIN ALFA IN POLYSORBAT 200 MCG/0.4 ML INJ SYRG,Medication,1,21812,4.58E-05
+7370,1582,IP REN HEMODIALYSIS,127601,DARBEPOETIN ALFA IN POLYSORBAT 40 MCG/0.4 ML INJ SYRG,Medication,2,21812,9.17E-05
+7371,1582,IP REN HEMODIALYSIS,127602,DARBEPOETIN ALFA IN POLYSORBAT 25 MCG/0.42 ML INJ SYRG,Medication,7,21812,0.000320924
+7372,1582,IP REN HEMODIALYSIS,127608,DARBEPOETIN ALFA IN POLYSORBAT 60 MCG/0.3 ML INJ SYRG,Medication,1,21812,4.58E-05
+7373,1582,IP REN HEMODIALYSIS,127609,DARBEPOETIN ALFA IN POLYSORBAT 100 MCG/0.5 ML INJ SYRG,Medication,6,21812,0.000275078
+7374,1582,IP REN HEMODIALYSIS,127696,DARBEPOETIN ALFA IN POLYSORBAT 150 MCG/0.3 ML INJ SYRG,Medication,1,21812,4.58E-05
+7375,1582,IP REN HEMODIALYSIS,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,9,21812,0.000412617
+7376,1582,IP REN HEMODIALYSIS,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,9,21812,0.000412617
+7377,1582,IP REN HEMODIALYSIS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,8,21812,0.000366771
+7378,1582,IP REN HEMODIALYSIS,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,2,21812,9.17E-05
+7379,1582,IP REN HEMODIALYSIS,520124,IRON SUCROSE IVPB (<300MG),Medication,96,21812,0.004401247
+7380,1582,IP REN HEMODIALYSIS,520126,FERRIC GLUCONATE COMPLEX IV INFUSION,Medication,86,21812,0.003942784
+7381,1582,IP REN HEMODIALYSIS,540136,HEPARIN 500 UNIT/ML SYRINGE,Medication,656,21812,0.030075188
+7382,1582,IP REN HEMODIALYSIS,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,21812,4.58E-05
+7383,1582,IP REN HEMODIALYSIS,10205R,HLA ORDER - DESENSITIZATION MONITORING PRE IVIG,Lab,2,21812,9.17E-05
+7384,1582,IP REN HEMODIALYSIS,10210R,HLA ORDER - DESENSITIZATION AB MONITORING-POST IVIG,Lab,2,21812,9.17E-05
+7385,1582,IP REN HEMODIALYSIS,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,2,21812,9.17E-05
+7386,1582,IP REN HEMODIALYSIS,10225R,HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*,Lab,2,21812,9.17E-05
+7387,1582,IP REN HEMODIALYSIS,LAB420,"BUN, RECIRCULATION STUDY",Lab,24,21812,0.001100312
+7388,1582,IP REN HEMODIALYSIS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,63,21812,0.002888318
+7389,1582,IP REN HEMODIALYSIS,LABBUN,"UREA NITROGEN, SERUM/PLASMA",Lab,1070,21812,0.049055566
+7390,1582,IP REN HEMODIALYSIS,LABCBCD,CBC WITH DIFF,Lab,108,21812,0.004951403
+7391,1582,IP REN HEMODIALYSIS,LABFE,"IRON, TOTAL",Lab,33,21812,0.001512929
+7392,1582,IP REN HEMODIALYSIS,LABFER,FERRITIN,Lab,80,21812,0.003667706
+7393,1582,IP REN HEMODIALYSIS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,1,21812,4.58E-05
+7394,1582,IP REN HEMODIALYSIS,LABHBSAG,HBSAG,Lab,996,21812,0.045662938
+7395,1582,IP REN HEMODIALYSIS,LABHBSQT,HEP B SURF AB QUANT,Lab,555,21812,0.025444709
+7396,1582,IP REN HEMODIALYSIS,LABK,"POTASSIUM, SERUM/PLASMA",Lab,50,21812,0.002292316
+7397,1582,IP REN HEMODIALYSIS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,200,21812,0.009169265
+7398,1582,IP REN HEMODIALYSIS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,43,21812,0.001971392
+7399,1582,IP REN HEMODIALYSIS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,134,21812,0.006143407
+7400,1582,IP REN HEMODIALYSIS,LABPT,PROTHROMBIN TIME,Lab,9,21812,0.000412617
+7401,1582,IP REN HEMODIALYSIS,LABPTH,PARATHYROID HORMONE,Lab,71,21812,0.003255089
+7402,1582,IP REN HEMODIALYSIS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,7,21812,0.000320924
+7403,1582,IP REN HEMODIALYSIS,LABTRFS,TRANSFERRIN SATURATION,Lab,78,21812,0.003576013
+7404,1582,IP REN HEMODIALYSIS,LABVANPRL,VANCOMYCIN TROUGH LEVEL,Lab,13,21812,0.000596002
+7405,1582,IP REN HEMODIALYSIS,LABVANUNL,VANCOMYCIN SINGLE DAILY LEVEL,Lab,2,21812,9.17E-05
+7406,1582,IP REN HEMODIALYSIS,LABWDC,WOUND CULTURE,Lab,6,21812,0.000275078
+7407,1586,IP REN HEMODIALYSIS/PUF,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,14,3043,0.004600723
+7408,1586,IP REN HEMODIALYSIS/PUF,4750,MANNITOL 25 % 25 % IV SOLN,Medication,24,3043,0.007886954
+7409,1586,IP REN HEMODIALYSIS/PUF,8981,"ALBUMIN, HUMAN 25 % 25 % IV SOLP",Medication,24,3043,0.007886954
+7410,1586,IP REN HEMODIALYSIS/PUF,8982,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",Medication,6,3043,0.001971738
+7411,1586,IP REN HEMODIALYSIS/PUF,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,222,3043,0.072954321
+7412,1586,IP REN HEMODIALYSIS/PUF,77542,"EPOETIN ALFA 2,000 UNIT/ML INJ SOLN",Medication,3,3043,0.000985869
+7413,1586,IP REN HEMODIALYSIS/PUF,77641,"EPOETIN ALFA 4,000 UNIT/ML INJ SOLN",Medication,10,3043,0.003286231
+7414,1586,IP REN HEMODIALYSIS/PUF,91568,"EPOETIN ALFA 3,000 UNIT/ML INJ SOLN",Medication,13,3043,0.0042721
+7415,1586,IP REN HEMODIALYSIS/PUF,94501,"EPOETIN ALFA 10,000 UNIT/ML INJ SOLN",Medication,20,3043,0.006572461
+7416,1586,IP REN HEMODIALYSIS/PUF,94623,ALTEPLASE 2 MG INJ SOLR,Medication,134,3043,0.044035491
+7417,1586,IP REN HEMODIALYSIS/PUF,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,3043,0.000328623
+7418,1586,IP REN HEMODIALYSIS/PUF,126580,"HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",Medication,807,3043,0.265198817
+7419,1586,IP REN HEMODIALYSIS/PUF,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,72,3043,0.023660861
+7420,1586,IP REN HEMODIALYSIS/PUF,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,3043,0.000985869
+7421,1586,IP REN HEMODIALYSIS/PUF,520124,IRON SUCROSE IVPB (<300MG),Medication,3,3043,0.000985869
+7422,1586,IP REN HEMODIALYSIS/PUF,520126,FERRIC GLUCONATE COMPLEX IV INFUSION,Medication,6,3043,0.001971738
+7423,1586,IP REN HEMODIALYSIS/PUF,540136,HEPARIN 500 UNIT/ML SYRINGE,Medication,19,3043,0.006243838
+7424,1586,IP REN HEMODIALYSIS/PUF,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,2,3043,0.000657246
+7425,1586,IP REN HEMODIALYSIS/PUF,LABBUN,"UREA NITROGEN, SERUM/PLASMA",Lab,102,3043,0.033519553
+7426,1586,IP REN HEMODIALYSIS/PUF,LABCBCD,CBC WITH DIFF,Lab,3,3043,0.000985869
+7427,1586,IP REN HEMODIALYSIS/PUF,LABFE,"IRON, TOTAL",Lab,1,3043,0.000328623
+7428,1586,IP REN HEMODIALYSIS/PUF,LABFER,FERRITIN,Lab,3,3043,0.000985869
+7429,1586,IP REN HEMODIALYSIS/PUF,LABHBSAG,HBSAG,Lab,116,3043,0.038120276
+7430,1586,IP REN HEMODIALYSIS/PUF,LABHBSQT,HEP B SURF AB QUANT,Lab,70,3043,0.023003615
+7431,1586,IP REN HEMODIALYSIS/PUF,LABK,"POTASSIUM, SERUM/PLASMA",Lab,7,3043,0.002300361
+7432,1586,IP REN HEMODIALYSIS/PUF,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,3043,0.001314492
+7433,1586,IP REN HEMODIALYSIS/PUF,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,3043,0.000328623
+7434,1586,IP REN HEMODIALYSIS/PUF,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,7,3043,0.002300361
+7435,1586,IP REN HEMODIALYSIS/PUF,LABPTH,PARATHYROID HORMONE,Lab,4,3043,0.001314492
+7436,1586,IP REN HEMODIALYSIS/PUF,LABTRFS,TRANSFERRIN SATURATION,Lab,4,3043,0.001314492
+7437,1586,IP REN HEMODIALYSIS/PUF,LABWDC,WOUND CULTURE,Lab,1,3043,0.000328623
+7438,1594,IP SUR BARIATRIC SURGERY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,829,2248,0.368772242
+7439,1594,IP SUR BARIATRIC SURGERY POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,279,2248,0.12411032
+7440,1594,IP SUR BARIATRIC SURGERY POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,2248,0.00044484
+7441,1594,IP SUR BARIATRIC SURGERY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,560,2248,0.24911032
+7442,1594,IP SUR BARIATRIC SURGERY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,11,2248,0.004893238
+7443,1594,IP SUR BARIATRIC SURGERY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,76,2248,0.033807829
+7444,1594,IP SUR BARIATRIC SURGERY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,174,2248,0.077402135
+7445,1594,IP SUR BARIATRIC SURGERY POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,2248,0.00044484
+7446,1594,IP SUR BARIATRIC SURGERY POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,810,2248,0.360320285
+7447,1594,IP SUR BARIATRIC SURGERY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,14,2248,0.006227758
+7448,1594,IP SUR BARIATRIC SURGERY POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,11,2248,0.004893238
+7449,1594,IP SUR BARIATRIC SURGERY POSTOP,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,2248,0.00044484
+7450,1594,IP SUR BARIATRIC SURGERY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,12,2248,0.005338078
+7451,1594,IP SUR BARIATRIC SURGERY POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,4,2248,0.001779359
+7452,1594,IP SUR BARIATRIC SURGERY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,970,2248,0.431494662
+7453,1594,IP SUR BARIATRIC SURGERY POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,2248,0.00088968
+7454,1594,IP SUR BARIATRIC SURGERY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,823,2248,0.366103203
+7455,1594,IP SUR BARIATRIC SURGERY POSTOP,520008,METOPROLOL IVPB,Medication,3,2248,0.00133452
+7456,1594,IP SUR BARIATRIC SURGERY POSTOP,520043,CEFOXITIN IVPB,Medication,1,2248,0.00044484
+7457,1594,IP SUR BARIATRIC SURGERY POSTOP,540102,NS IV BOLUS,Medication,15,2248,0.006672598
+7458,1594,IP SUR BARIATRIC SURGERY POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,9,2248,0.004003559
+7459,1594,IP SUR BARIATRIC SURGERY POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,2248,0.00044484
+7460,1594,IP SUR BARIATRIC SURGERY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,10,2248,0.004448399
+7461,1594,IP SUR BARIATRIC SURGERY POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,6,2248,0.002669039
+7462,1594,IP SUR BARIATRIC SURGERY POSTOP,LABCBCD,CBC WITH DIFF,Lab,40,2248,0.017793594
+7463,1594,IP SUR BARIATRIC SURGERY POSTOP,LABHFP,HEPATIC FUNCTION PANEL A,Lab,2,2248,0.00088968
+7464,1594,IP SUR BARIATRIC SURGERY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,40,2248,0.017793594
+7465,1594,IP SUR BARIATRIC SURGERY POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,3,2248,0.00133452
+7466,1594,IP SUR BARIATRIC SURGERY POSTOP,LABPLTS,PLATELET COUNT,Lab,502,2248,0.223309609
+7467,1594,IP SUR BARIATRIC SURGERY POSTOP,LABPT,PROTHROMBIN TIME,Lab,4,2248,0.001779359
+7468,1594,IP SUR BARIATRIC SURGERY POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,2248,0.00133452
+7469,1594,IP SUR BARIATRIC SURGERY POSTOP,LABTYPSNI,TYPE AND SCREEN,Lab,1,2248,0.00044484
+7470,1594,IP SUR BARIATRIC SURGERY POSTOP,POC14,POC GLUCOSE BY METER,Lab,856,2248,0.380782918
+7471,1594,IP SUR BARIATRIC SURGERY POSTOP,IMGDXAB1,XR ABDOMEN 1V,Imaging,1,2248,0.00044484
+7472,1594,IP SUR BARIATRIC SURGERY POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,3,2248,0.00133452
+7473,1594,IP SUR BARIATRIC SURGERY POSTOP,IMGFLESO,XR ESOPHAGRAM,Imaging,897,2248,0.399021352
+7474,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,94,272,0.345588235
+7475,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,10,272,0.036764706
+7476,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,1,272,0.003676471
+7477,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,63,272,0.231617647
+7478,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,3,272,0.011029412
+7479,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,7,272,0.025735294
+7480,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,9,272,0.033088235
+7481,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,20,272,0.073529412
+7482,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,1,272,0.003676471
+7483,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,68,272,0.25
+7484,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,5,272,0.018382353
+7485,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,272,0.018382353
+7486,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,10,272,0.036764706
+7487,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,105,272,0.386029412
+7488,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,272,0.003676471
+7489,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,70,272,0.257352941
+7490,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,272,0.003676471
+7491,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,17,272,0.0625
+7492,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,2,272,0.007352941
+7493,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,10,272,0.036764706
+7494,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,LABCBCD,CBC WITH DIFF,Lab,18,272,0.066176471
+7495,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,21,272,0.077205882
+7496,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,272,0.007352941
+7497,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,LABPLTS,PLATELET COUNT,Lab,38,272,0.139705882
+7498,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,LABPT,PROTHROMBIN TIME,Lab,4,272,0.014705882
+7499,1598,IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP,IMGFLGI,XR UPPER GI,Imaging,34,272,0.125
+7500,1608,IP REN CAPD CONT AMB PERITONEAL DIALYSIS,198393,PERIT. DIALYSIS 7-DEX 2.5 % CA 2.5 MEQ/L- MG 0.5 MEQ/L IP SOLN,Medication,27,161,0.167701863
+7501,1608,IP REN CAPD CONT AMB PERITONEAL DIALYSIS,198468,PERIT. DIALYSIS 6 & 1.5 % DEX CA 2.5 MEQ/L- MG 0.5 MEQ/L IP SOLN,Medication,40,161,0.248447205
+7502,1608,IP REN CAPD CONT AMB PERITONEAL DIALYSIS,500030,PERITONEAL DIALYSIS SOLUTION,Medication,16,161,0.099378882
+7503,1608,IP REN CAPD CONT AMB PERITONEAL DIALYSIS,800100,ZZ EXTEMPORANEOUS TEMPLATE(CUSTOM),Medication,4,161,0.02484472
+7504,1608,IP REN CAPD CONT AMB PERITONEAL DIALYSIS,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,27,161,0.167701863
+7505,1608,IP REN CAPD CONT AMB PERITONEAL DIALYSIS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,24,161,0.149068323
+7506,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,198389,PERIT. DIALYSIS 8 & 4.25 % DEX CA 2.5 MEQ/L- MG 0.5 MEQ/L IP SOLN,Medication,20,728,0.027472527
+7507,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,198393,PERIT. DIALYSIS 7-DEX 2.5 % CA 2.5 MEQ/L- MG 0.5 MEQ/L IP SOLN,Medication,168,728,0.230769231
+7508,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,198468,PERIT. DIALYSIS 6 & 1.5 % DEX CA 2.5 MEQ/L- MG 0.5 MEQ/L IP SOLN,Medication,165,728,0.226648352
+7509,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,500030,PERITONEAL DIALYSIS SOLUTION,Medication,75,728,0.103021978
+7510,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,800100,ZZ EXTEMPORANEOUS TEMPLATE(CUSTOM),Medication,4,728,0.005494505
+7511,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,67,728,0.092032967
+7512,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,49,728,0.067307692
+7513,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,LABHBSQT,HEP B SURF AB QUANT,Lab,9,728,0.012362637
+7514,1612,IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS,LABWDC,WOUND CULTURE,Lab,3,728,0.004120879
+7515,1618,IP OPH OPHTHALMIC PREOP LEFT EYE,POC14,POC GLUCOSE BY METER,Lab,1,1,1
+7516,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABACA,ANTI CARDIOLIPIN AB,Lab,62,69,0.898550725
+7517,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABAT3,ANTITHROMBIN III,Lab,63,69,0.913043478
+7518,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABFACT8,FACTOR VIII ASSAY,Lab,4,69,0.057971014
+7519,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABFIB,FIBRINOGEN,Lab,4,69,0.057971014
+7520,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABHMCY,"HOMOCYSTEINE TOTAL, PLASMA",Lab,60,69,0.869565217
+7521,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABLEID,FACTOR V LEIDEN,Lab,58,69,0.84057971
+7522,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABLPA,LIPOPROTEIN,Lab,5,69,0.072463768
+7523,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABLUPUS,LUPUS ANTICOAGULANT,Lab,62,69,0.898550725
+7524,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABNJAK2,"JANUS KINASE 2 V617F MUTATION, NON-BLOOD",Lab,12,69,0.173913043
+7525,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABP20210,PROTHROMBIN 20210A,Lab,63,69,0.913043478
+7526,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABPROTC,PROTEIN C ACTIVITY,Lab,55,69,0.797101449
+7527,1623,IP GEN HYPERCOAGULABLE EVALUATION,LABPROTS,PROTEIN S ACTIVITY,Lab,56,69,0.811594203
+7528,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABAFBD,"AFB CULTURE, NON - RESPIRATORY",Lab,68,396,0.171717172
+7529,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABANER,ANAEROBIC CULTURE,Lab,95,396,0.23989899
+7530,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,246,396,0.621212121
+7531,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABCMVQL,"CMV DNA PCR QUAL, NON-PLASMA",Lab,9,396,0.022727273
+7532,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFAL,"ALBUMIN, FLUID",Lab,40,396,0.101010101
+7533,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFAMY,"AMYLASE, FLUID",Lab,48,396,0.121212121
+7534,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFBILA,"BILIRUBIN, TOTAL BODY FLUID",Lab,16,396,0.04040404
+7535,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFCUL,FUNGAL CULTURE,Lab,79,396,0.199494949
+7536,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFHCT,"HEMATOCRIT, FLUID",Lab,16,396,0.04040404
+7537,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFKOH,FUNGAL CULTURE AND KOH,Lab,40,396,0.101010101
+7538,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFLCL,"CHLORIDE, FLUID",Lab,3,396,0.007575758
+7539,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,287,396,0.724747475
+7540,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFLDH,"LDH TOTAL, FLUID",Lab,243,396,0.613636364
+7541,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFLGL,"GLUCOSE, FLUID",Lab,103,396,0.26010101
+7542,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFLK,"POTASSIUM, FLUID",Lab,3,396,0.007575758
+7543,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFLNA,"SODIUM, FLUID",Lab,2,396,0.005050505
+7544,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFTBLYM,"T SUBSETS AND B LYMPHOCYTES, FLUID",Lab,1,396,0.002525253
+7545,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFTP,"PROTEIN TOTAL, FLUID",Lab,238,396,0.601010101
+7546,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFTRIG,"TRIGLYCERIDES, FLUID",Lab,11,396,0.027777778
+7547,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFTRPL,"TRIGLYCERIDES, PLEURAL",Lab,64,396,0.161616162
+7548,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABFURICA,"URIC ACID, FLUID",Lab,1,396,0.002525253
+7549,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABGRAM,GRAM STAIN,Lab,139,396,0.351010101
+7550,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABLEGD,"LEGIONELLA CULTURE, DIRECT",Lab,8,396,0.02020202
+7551,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABLEGDA,LEGIONELLA CULTURE,Lab,11,396,0.027777778
+7552,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABPHME,PH BY METER,Lab,183,396,0.462121212
+7553,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABYFMG,"MAGNESIUM, FLUID",Lab,3,396,0.007575758
+7554,1625,"IP LAB SPECIMEN TESTING, PLEURAL FLUID",LABYFUN,"UREA NITROGEN, FLUID",Lab,2,396,0.005050505
+7555,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,855,"BACITRACIN-POLYMYXIN B 500-10,000 UNIT/GRAM TP OINT",Medication,43,1203,0.035743973
+7556,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,136,1203,0.113050707
+7557,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,416,1203,0.345802161
+7558,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,5943,OXYMETAZOLINE 0.05 % NASAL SPRA,Medication,192,1203,0.159600998
+7559,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,111,1203,0.092269327
+7560,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,37,1203,0.030756442
+7561,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,3,1203,0.002493766
+7562,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,1203,0.000831255
+7563,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,10372,LABETALOL 5 MG/ML IV SOLN,Medication,129,1203,0.10723192
+7564,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,306,1203,0.25436409
+7565,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,310,1203,0.257689111
+7566,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,46,1203,0.038237739
+7567,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,39,1203,0.032418953
+7568,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,321,1203,0.266832918
+7569,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,12,1203,0.009975062
+7570,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,28809,WHITE PETROLATUM TP GEL,Medication,10,1203,0.008312552
+7571,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,29676,SODIUM CHLORIDE 0.65 % NASAL SPRA,Medication,162,1203,0.134663342
+7572,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,88836,WHITE PETROLATUM TP OINT,Medication,7,1203,0.005818786
+7573,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,1203,0.000831255
+7574,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,284,1203,0.236076475
+7575,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,1203,0.000831255
+7576,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,439,1203,0.364921031
+7577,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,63,1203,0.052369077
+7578,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,1203,0.002493766
+7579,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,228220,ACETAMINOPHEN-CODEINE 300 MG-30 MG /12.5 ML PO SOLN,Medication,41,1203,0.034081463
+7580,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,20,1203,0.016625104
+7581,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,29,1203,0.024106401
+7582,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,530008,NITROPRUSSIDE IV INFUSION,Medication,1,1203,0.000831255
+7583,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,540303,MEPERIDINE INJECTABLE SYRINGE,Medication,67,1203,0.055694098
+7584,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,541044,SODIUM BICARBONATE IN NS ORAL SOLUTION,Medication,9,1203,0.007481297
+7585,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,1203,0.002493766
+7586,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,LABHCTX,HEMATOCRIT,Lab,99,1203,0.082294264
+7587,1628,IP ENT MAXILLOFACIAL SURGERY POSTOP,LABPLTS,PLATELET COUNT,Lab,1,1203,0.000831255
+7588,1629,ED FLOOR  HOLDING ORDERS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,4,0.25
+7589,1629,ED FLOOR  HOLDING ORDERS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,1,4,0.25
+7590,1629,ED FLOOR  HOLDING ORDERS,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,4,0.5
+7591,1629,ED FLOOR  HOLDING ORDERS,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,4,0.25
+7592,1632,IP ORAL STEROID TAPERS,2322,DEXAMETHASONE 0.5 MG PO TABS,Medication,69,1405,0.04911032
+7593,1632,IP ORAL STEROID TAPERS,2323,DEXAMETHASONE 0.75 MG PO TABS,Medication,76,1405,0.054092527
+7594,1632,IP ORAL STEROID TAPERS,2324,DEXAMETHASONE 1 MG PO TABS,Medication,3134,1405,1
+7595,1632,IP ORAL STEROID TAPERS,2325,DEXAMETHASONE 1.5 MG PO TABS,Medication,1534,1405,1
+7596,1632,IP ORAL STEROID TAPERS,2326,DEXAMETHASONE 2 MG PO TABS,Medication,949,1405,0.67544484
+7597,1632,IP ORAL STEROID TAPERS,2327,DEXAMETHASONE 4 MG PO TABS,Medication,1096,1405,0.780071174
+7598,1632,IP ORAL STEROID TAPERS,3734,HYDROCORTISONE 20 MG PO TABS,Medication,2,1405,0.001423488
+7599,1632,IP ORAL STEROID TAPERS,6493,PREDNISONE 1 MG PO TABS,Medication,2,1405,0.001423488
+7600,1632,IP ORAL STEROID TAPERS,6494,PREDNISONE 10 MG PO TABS,Medication,38,1405,0.027046263
+7601,1632,IP ORAL STEROID TAPERS,6496,PREDNISONE 20 MG PO TABS,Medication,81,1405,0.057651246
+7602,1632,IP ORAL STEROID TAPERS,6497,PREDNISONE 5 MG PO TABS,Medication,37,1405,0.02633452
+7603,1632,IP ORAL STEROID TAPERS,6498,PREDNISONE 50 MG PO TABS,Medication,19,1405,0.013523132
+7604,1632,IP ORAL STEROID TAPERS,14887,METHYLPREDNISOLONE 4 MG PO DSPK,Medication,659,1405,0.469039146
+7605,1632,IP ORAL STEROID TAPERS,92437,HYDROCORTISONE 10 MG PO TABS,Medication,1,1405,0.000711744
+7606,1632,IP ORAL STEROID TAPERS,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,1405,0.000711744
+7607,1632,IP ORAL STEROID TAPERS,9000000,ZZZ IMS TEMPLATE,Medication,91,1405,0.064768683
+7608,1633,NURSING TRIAGE SEIZURE PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,17,19,0.894736842
+7609,1633,NURSING TRIAGE SEIZURE PROTOCOL,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,13,19,0.684210526
+7610,1633,NURSING TRIAGE SEIZURE PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,16,19,0.842105263
+7611,1633,NURSING TRIAGE SEIZURE PROTOCOL,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,15,19,0.789473684
+7612,1633,NURSING TRIAGE SEIZURE PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,15,19,0.789473684
+7613,1633,NURSING TRIAGE SEIZURE PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,15,19,0.789473684
+7614,1633,NURSING TRIAGE SEIZURE PROTOCOL,POC26,POC URINE PREGNANCY,Lab,5,19,0.263157895
+7615,1633,NURSING TRIAGE SEIZURE PROTOCOL,POC46,POC ISTAT BHCG,Lab,2,19,0.105263158
+7616,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,32,149,0.214765101
+7617,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,32,149,0.214765101
+7618,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,148,149,0.993288591
+7619,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,150,149,1
+7620,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,49,149,0.32885906
+7621,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,LABUAS,URINALYSIS MACROSCOPIC ONLY,Lab,5,149,0.033557047
+7622,1634,NURSING TRIAGE ALTERED LEVEL OF CONSCIOUSNESS PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,99,149,0.66442953
+7623,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,7,62,0.112903226
+7624,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,62,0.016129032
+7625,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,41,62,0.661290323
+7626,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,41,62,0.661290323
+7627,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,43,62,0.693548387
+7628,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,47,62,0.758064516
+7629,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,5,62,0.080645161
+7630,1635,NURSING TRIAGE PNEUMONIA PROTOCOL,IMGDXCH2,XR CHEST 2V,Imaging,9,62,0.14516129
+7631,1636,NURSING TRIAGE SYNCOPE PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,34,60,0.566666667
+7632,1636,NURSING TRIAGE SYNCOPE PROTOCOL,POC26,POC URINE PREGNANCY,Lab,2,60,0.033333333
+7633,1637,NURSING TRIAGE ASTHMA PROTOCOL,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,7,28,0.25
+7634,1637,NURSING TRIAGE ASTHMA PROTOCOL,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,7,28,0.25
+7635,1637,NURSING TRIAGE ASTHMA PROTOCOL,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,2,28,0.071428571
+7636,1638,NURSING TRIAGE STROKE PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,172,463,0.371490281
+7637,1638,NURSING TRIAGE STROKE PROTOCOL,LABCBCO,CBC W/O DIFF,Lab,279,463,0.602591793
+7638,1638,NURSING TRIAGE STROKE PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,454,463,0.980561555
+7639,1638,NURSING TRIAGE STROKE PROTOCOL,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,440,463,0.950323974
+7640,1638,NURSING TRIAGE STROKE PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,458,463,0.989200864
+7641,1638,NURSING TRIAGE STROKE PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,459,463,0.991360691
+7642,1638,NURSING TRIAGE STROKE PROTOCOL,POC24,POC ISTAT TROPONIN I,Lab,378,463,0.816414687
+7643,1638,NURSING TRIAGE STROKE PROTOCOL,POC25,POC ISTAT INR AND PROTIME,Lab,436,463,0.941684665
+7644,1638,NURSING TRIAGE STROKE PROTOCOL,IMGCTH,CT HEAD,Imaging,133,463,0.287257019
+7645,1638,NURSING TRIAGE STROKE PROTOCOL,IMGCTHPRW,CT HEAD PERFUSION W CONTRAST,Imaging,414,463,0.894168467
+7646,1638,NURSING TRIAGE STROKE PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,447,463,0.965442765
+7647,1644,IP NSR PITUITARY POSTOP ,101,ACETAMINOPHEN 325 MG PO TABS,Medication,132,777,0.16988417
+7648,1644,IP NSR PITUITARY POSTOP ,1080,BISACODYL 10 MG PR SUPP,Medication,149,777,0.191763192
+7649,1644,IP NSR PITUITARY POSTOP ,2405,DIAZEPAM 5 MG PO TABS,Medication,5,777,0.006435006
+7650,1644,IP NSR PITUITARY POSTOP ,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,121,777,0.155727156
+7651,1644,IP NSR PITUITARY POSTOP ,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,44,777,0.056628057
+7652,1644,IP NSR PITUITARY POSTOP ,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,95,777,0.122265122
+7653,1644,IP NSR PITUITARY POSTOP ,4421,LEVOTHYROXINE 50 MCG PO TABS,Medication,3,777,0.003861004
+7654,1644,IP NSR PITUITARY POSTOP ,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,76,777,0.097812098
+7655,1644,IP NSR PITUITARY POSTOP ,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,216,777,0.277992278
+7656,1644,IP NSR PITUITARY POSTOP ,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,17,777,0.021879022
+7657,1644,IP NSR PITUITARY POSTOP ,5943,OXYMETAZOLINE 0.05 % NASAL SPRA,Medication,5,777,0.006435006
+7658,1644,IP NSR PITUITARY POSTOP ,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,57,777,0.073359073
+7659,1644,IP NSR PITUITARY POSTOP ,8958,BUTALBITAL-ACETAMINOPHEN-CAFF 50-325-40 MG PO TABS,Medication,28,777,0.036036036
+7660,1644,IP NSR PITUITARY POSTOP ,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,210,777,0.27027027
+7661,1644,IP NSR PITUITARY POSTOP ,10814,OXYCODONE 5 MG PO TABS,Medication,6,777,0.007722008
+7662,1644,IP NSR PITUITARY POSTOP ,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,1,777,0.001287001
+7663,1644,IP NSR PITUITARY POSTOP ,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,9,777,0.011583012
+7664,1644,IP NSR PITUITARY POSTOP ,16052,DESMOPRESSIN 0.1 MG PO TABS,Medication,2,777,0.002574003
+7665,1644,IP NSR PITUITARY POSTOP ,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,155,777,0.199485199
+7666,1644,IP NSR PITUITARY POSTOP ,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,33,777,0.042471042
+7667,1644,IP NSR PITUITARY POSTOP ,29676,SODIUM CHLORIDE 0.65 % NASAL SPRA,Medication,19,777,0.024453024
+7668,1644,IP NSR PITUITARY POSTOP ,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,217,777,0.279279279
+7669,1644,IP NSR PITUITARY POSTOP ,77082,HYDROCORTISONE 5 MG PO TABS,Medication,1,777,0.001287001
+7670,1644,IP NSR PITUITARY POSTOP ,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,4,777,0.005148005
+7671,1644,IP NSR PITUITARY POSTOP ,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,12,777,0.015444015
+7672,1644,IP NSR PITUITARY POSTOP ,126656,ARTIFICIAL TEARS WITH LANOLIN OPHT OINT,Medication,7,777,0.009009009
+7673,1644,IP NSR PITUITARY POSTOP ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,195,777,0.250965251
+7674,1644,IP NSR PITUITARY POSTOP ,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,777,0.001287001
+7675,1644,IP NSR PITUITARY POSTOP ,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,58,777,0.074646075
+7676,1644,IP NSR PITUITARY POSTOP ,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,37,777,0.047619048
+7677,1644,IP NSR PITUITARY POSTOP ,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,777,0.002574003
+7678,1644,IP NSR PITUITARY POSTOP ,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,193,777,0.248391248
+7679,1644,IP NSR PITUITARY POSTOP ,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,81,777,0.104247104
+7680,1644,IP NSR PITUITARY POSTOP ,540744,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,Medication,2,777,0.002574003
+7681,1644,IP NSR PITUITARY POSTOP ,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,777,0.001287001
+7682,1644,IP NSR PITUITARY POSTOP ,9000000,ZZZ IMS TEMPLATE,Medication,2,777,0.002574003
+7683,1644,IP NSR PITUITARY POSTOP ,LABACTHO,ADRENOCORTICOTROPIC HORMONE (ACTH),Lab,16,777,0.020592021
+7684,1644,IP NSR PITUITARY POSTOP ,LABCORTS,"CORTISOL, POST OP",Lab,171,777,0.22007722
+7685,1644,IP NSR PITUITARY POSTOP ,LABNA,"SODIUM, SERUM / PLASMA",Lab,275,777,0.353925354
+7686,1644,IP NSR PITUITARY POSTOP ,LABPLTS,PLATELET COUNT,Lab,2,777,0.002574003
+7687,1644,IP NSR PITUITARY POSTOP ,LABUSPG,"SPECIFIC GRAVITY, URINE",Lab,261,777,0.335907336
+7688,1644,IP NSR PITUITARY POSTOP ,POC14,POC GLUCOSE BY METER,Lab,215,777,0.276705277
+7689,1651,IP CTS CARDIAC SURGERY ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1012,3389,0.29861316
+7690,1651,IP CTS CARDIAC SURGERY ADMISSION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,8,3389,0.002360578
+7691,1651,IP CTS CARDIAC SURGERY ADMISSION,680,ASPIRIN 81 MG PO CHEW,Medication,23,3389,0.006786663
+7692,1651,IP CTS CARDIAC SURGERY ADMISSION,1080,BISACODYL 10 MG PR SUPP,Medication,34,3389,0.010032458
+7693,1651,IP CTS CARDIAC SURGERY ADMISSION,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,564,3389,0.166420773
+7694,1651,IP CTS CARDIAC SURGERY ADMISSION,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,3,3389,0.000885217
+7695,1651,IP CTS CARDIAC SURGERY ADMISSION,4711,MAGNESIUM CITRATE PO SOLN,Medication,32,3389,0.009442313
+7696,1651,IP CTS CARDIAC SURGERY ADMISSION,7753,TEMAZEPAM 15 MG PO CAPS,Medication,40,3389,0.011802892
+7697,1651,IP CTS CARDIAC SURGERY ADMISSION,7754,TEMAZEPAM 30 MG PO CAPS,Medication,1,3389,0.000295072
+7698,1651,IP CTS CARDIAC SURGERY ADMISSION,8085,TRAZODONE 50 MG PO TABS,Medication,7,3389,0.002065506
+7699,1651,IP CTS CARDIAC SURGERY ADMISSION,8442,"VANCOMYCIN 1,000 MG IV SOLR",Medication,136,3389,0.040129832
+7700,1651,IP CTS CARDIAC SURGERY ADMISSION,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,1218,3389,0.359398053
+7701,1651,IP CTS CARDIAC SURGERY ADMISSION,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,3389,0.001180289
+7702,1651,IP CTS CARDIAC SURGERY ADMISSION,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,2,3389,0.000590145
+7703,1651,IP CTS CARDIAC SURGERY ADMISSION,13654,ASPIRIN 325 MG PO TBEC,Medication,1,3389,0.000295072
+7704,1651,IP CTS CARDIAC SURGERY ADMISSION,14113,ASPIRIN 81 MG PO TBEC,Medication,15,3389,0.004426084
+7705,1651,IP CTS CARDIAC SURGERY ADMISSION,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,8,3389,0.002360578
+7706,1651,IP CTS CARDIAC SURGERY ADMISSION,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,2,3389,0.000590145
+7707,1651,IP CTS CARDIAC SURGERY ADMISSION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,3389,0.000590145
+7708,1651,IP CTS CARDIAC SURGERY ADMISSION,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,20,3389,0.005901446
+7709,1651,IP CTS CARDIAC SURGERY ADMISSION,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,28,3389,0.008262024
+7710,1651,IP CTS CARDIAC SURGERY ADMISSION,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,15,3389,0.004426084
+7711,1651,IP CTS CARDIAC SURGERY ADMISSION,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,956,3389,0.282089112
+7712,1651,IP CTS CARDIAC SURGERY ADMISSION,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,16,3389,0.004721157
+7713,1651,IP CTS CARDIAC SURGERY ADMISSION,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,39,3389,0.011507819
+7714,1651,IP CTS CARDIAC SURGERY ADMISSION,92173,ZOLPIDEM 5 MG PO TABS,Medication,53,3389,0.015638832
+7715,1651,IP CTS CARDIAC SURGERY ADMISSION,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,41,3389,0.012097964
+7716,1651,IP CTS CARDIAC SURGERY ADMISSION,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,17,3389,0.005016229
+7717,1651,IP CTS CARDIAC SURGERY ADMISSION,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,3389,0.000295072
+7718,1651,IP CTS CARDIAC SURGERY ADMISSION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,3389,0.000295072
+7719,1651,IP CTS CARDIAC SURGERY ADMISSION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,5,3389,0.001475361
+7720,1651,IP CTS CARDIAC SURGERY ADMISSION,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,400,3389,0.118028917
+7721,1651,IP CTS CARDIAC SURGERY ADMISSION,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,18,3389,0.005311301
+7722,1651,IP CTS CARDIAC SURGERY ADMISSION,541228,CEFAZOLIN IVPB (COMPOUNDED),Medication,10,3389,0.002950723
+7723,1651,IP CTS CARDIAC SURGERY ADMISSION,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,2,3389,0.000590145
+7724,1651,IP CTS CARDIAC SURGERY ADMISSION,LABA1C,HEMOGLOBIN A1C,Lab,1060,3389,0.31277663
+7725,1651,IP CTS CARDIAC SURGERY ADMISSION,LABCBCD,CBC WITH DIFF,Lab,1107,3389,0.326645028
+7726,1651,IP CTS CARDIAC SURGERY ADMISSION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,4,3389,0.001180289
+7727,1651,IP CTS CARDIAC SURGERY ADMISSION,LABHAP,HAPTOGLOBIN,Lab,71,3389,0.020950133
+7728,1651,IP CTS CARDIAC SURGERY ADMISSION,LABMB,"CK, MB (MASS)",Lab,4,3389,0.001180289
+7729,1651,IP CTS CARDIAC SURGERY ADMISSION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1089,3389,0.321333727
+7730,1651,IP CTS CARDIAC SURGERY ADMISSION,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,689,3389,0.20330481
+7731,1651,IP CTS CARDIAC SURGERY ADMISSION,LABNTBNP,NT - PROBNP,Lab,341,3389,0.100619652
+7732,1651,IP CTS CARDIAC SURGERY ADMISSION,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,436,3389,0.12865152
+7733,1651,IP CTS CARDIAC SURGERY ADMISSION,LABPLTS,PLATELET COUNT,Lab,4,3389,0.001180289
+7734,1651,IP CTS CARDIAC SURGERY ADMISSION,LABPT,PROTHROMBIN TIME,Lab,1089,3389,0.321333727
+7735,1651,IP CTS CARDIAC SURGERY ADMISSION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1090,3389,0.321628799
+7736,1651,IP CTS CARDIAC SURGERY ADMISSION,LABTNI,TROPONIN I,Lab,161,3389,0.047506639
+7737,1651,IP CTS CARDIAC SURGERY ADMISSION,LABTYPSNI,TYPE AND SCREEN,Lab,1207,3389,0.356152257
+7738,1651,IP CTS CARDIAC SURGERY ADMISSION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,656,3389,0.193567424
+7739,1651,IP CTS CARDIAC SURGERY ADMISSION,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,430,3389,0.126881086
+7740,1651,IP CTS CARDIAC SURGERY ADMISSION,LABUPREG,"PREGNANCY TEST, URINE",Lab,3,3389,0.000885217
+7741,1651,IP CTS CARDIAC SURGERY ADMISSION,LABYHGBP,"HEMOGLOBIN, PLASMA",Lab,3,3389,0.000885217
+7742,1651,IP CTS CARDIAC SURGERY ADMISSION,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,1,3389,0.000295072
+7743,1651,IP CTS CARDIAC SURGERY ADMISSION,IMGDXCH1,XR CHEST 1V,Imaging,137,3389,0.040424904
+7744,1651,IP CTS CARDIAC SURGERY ADMISSION,IMGDXCH2,XR CHEST 2V,Imaging,1024,3389,0.302154028
+7745,1651,IP CTS CARDIAC SURGERY ADMISSION,VSC02,VSC CAROTID VERTEBRAL ARTERY ULTRASOUND,Imaging,5,3389,0.001475361
+7746,1651,IP CTS CARDIAC SURGERY ADMISSION,VSC03,VSC ARTERIAL EXAM/ABIS,Imaging,18,3389,0.005311301
+7747,1651,IP CTS CARDIAC SURGERY ADMISSION,VSC05,VSC VENOUS ULTRASOUND,Imaging,6,3389,0.001770434
+7748,1651,IP CTS CARDIAC SURGERY ADMISSION,LABBBPCRY,CRYOPRECIPITATE,Blood Product,2,3389,0.000590145
+7749,1651,IP CTS CARDIAC SURGERY ADMISSION,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,9,3389,0.002655651
+7750,1651,IP CTS CARDIAC SURGERY ADMISSION,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,8,3389,0.002360578
+7751,1651,IP CTS CARDIAC SURGERY ADMISSION,LABBBPRBC,RED BLOOD CELLS,Blood Product,1068,3389,0.315137209
+7752,1652,IP IV STEROID TAPERS,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1007,236,1
+7753,1652,IP IV STEROID TAPERS,6494,PREDNISONE 10 MG PO TABS,Medication,2,236,0.008474576
+7754,1652,IP IV STEROID TAPERS,6496,PREDNISONE 20 MG PO TABS,Medication,1,236,0.004237288
+7755,1652,IP IV STEROID TAPERS,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,21,236,0.088983051
+7756,1652,IP IV STEROID TAPERS,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,42,236,0.177966102
+7757,1652,IP IV STEROID TAPERS,9000000,ZZZ IMS TEMPLATE,Medication,1,236,0.004237288
+7758,1653,IP TRS HEART TRANSPLANT POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,1393,0.002153625
+7759,1653,IP TRS HEART TRANSPLANT POSTOP,464,AMPHOTERICIN B 50 MG INJ SOLR,Medication,71,1393,0.050969131
+7760,1653,IP TRS HEART TRANSPLANT POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,198,1393,0.142139268
+7761,1653,IP TRS HEART TRANSPLANT POSTOP,2364,DEXTROSE 5 % IN WATER (D5W) IV SOLP,Medication,6,1393,0.004307251
+7762,1653,IP TRS HEART TRANSPLANT POSTOP,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,182,1393,0.130653266
+7763,1653,IP TRS HEART TRANSPLANT POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,167,1393,0.11988514
+7764,1653,IP TRS HEART TRANSPLANT POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,87,1393,0.062455133
+7765,1653,IP TRS HEART TRANSPLANT POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,64,1393,0.045944006
+7766,1653,IP TRS HEART TRANSPLANT POSTOP,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,62,1393,0.044508256
+7767,1653,IP TRS HEART TRANSPLANT POSTOP,5751,"NYSTATIN 100,000 UNIT/ML PO SUSP",Medication,116,1393,0.08327351
+7768,1653,IP TRS HEART TRANSPLANT POSTOP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,8,1393,0.005743001
+7769,1653,IP TRS HEART TRANSPLANT POSTOP,6492,PREDNISONE 5 MG/5 ML PO SOLN,Medication,176,1393,0.126346016
+7770,1653,IP TRS HEART TRANSPLANT POSTOP,6494,PREDNISONE 10 MG PO TABS,Medication,1,1393,0.000717875
+7771,1653,IP TRS HEART TRANSPLANT POSTOP,6496,PREDNISONE 20 MG PO TABS,Medication,40,1393,0.028715004
+7772,1653,IP TRS HEART TRANSPLANT POSTOP,7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,140,1393,0.100502513
+7773,1653,IP TRS HEART TRANSPLANT POSTOP,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,181,1393,0.129935391
+7774,1653,IP TRS HEART TRANSPLANT POSTOP,9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,16,1393,0.011486001
+7775,1653,IP TRS HEART TRANSPLANT POSTOP,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,2,1393,0.00143575
+7776,1653,IP TRS HEART TRANSPLANT POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,146,1393,0.104809763
+7777,1653,IP TRS HEART TRANSPLANT POSTOP,15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,13,1393,0.009332376
+7778,1653,IP TRS HEART TRANSPLANT POSTOP,15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,2,1393,0.00143575
+7779,1653,IP TRS HEART TRANSPLANT POSTOP,22560,SULFAMETHOXAZOLE-TRIMETHOPRIM 200-40 MG/5 ML PO SUSP,Medication,5,1393,0.003589375
+7780,1653,IP TRS HEART TRANSPLANT POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,25,1393,0.017946877
+7781,1653,IP TRS HEART TRANSPLANT POSTOP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,3,1393,0.002153625
+7782,1653,IP TRS HEART TRANSPLANT POSTOP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,4,1393,0.0028715
+7783,1653,IP TRS HEART TRANSPLANT POSTOP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,47,1393,0.033740129
+7784,1653,IP TRS HEART TRANSPLANT POSTOP,82544,ITRACONAZOLE 10 MG/ML PO SOLN,Medication,54,1393,0.038765255
+7785,1653,IP TRS HEART TRANSPLANT POSTOP,85809,CYTOMEGALOVIRUS IMMUNE GLOB 50 MG/ML IV SOLN,Medication,11,1393,0.007896626
+7786,1653,IP TRS HEART TRANSPLANT POSTOP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,11,1393,0.007896626
+7787,1653,IP TRS HEART TRANSPLANT POSTOP,94670,TACROLIMUS 1 MG PO CAPS,Medication,9,1393,0.006460876
+7788,1653,IP TRS HEART TRANSPLANT POSTOP,95803,TACROLIMUS 0.5 MG PO CAPS,Medication,2,1393,0.00143575
+7789,1653,IP TRS HEART TRANSPLANT POSTOP,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,75,1393,0.053840632
+7790,1653,IP TRS HEART TRANSPLANT POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,164,1393,0.117731515
+7791,1653,IP TRS HEART TRANSPLANT POSTOP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,102,1393,0.073223259
+7792,1653,IP TRS HEART TRANSPLANT POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,201,1393,0.144292893
+7793,1653,IP TRS HEART TRANSPLANT POSTOP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,101,1393,0.072505384
+7794,1653,IP TRS HEART TRANSPLANT POSTOP,205192,MILRINONE IN D5W 20 MG/100 ML IV PGBK,Medication,38,1393,0.027279253
+7795,1653,IP TRS HEART TRANSPLANT POSTOP,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,118,1393,0.084709261
+7796,1653,IP TRS HEART TRANSPLANT POSTOP,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,1393,0.000717875
+7797,1653,IP TRS HEART TRANSPLANT POSTOP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,339,1393,0.243359655
+7798,1653,IP TRS HEART TRANSPLANT POSTOP,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,189,1393,0.135678392
+7799,1653,IP TRS HEART TRANSPLANT POSTOP,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,127,1393,0.091170136
+7800,1653,IP TRS HEART TRANSPLANT POSTOP,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,14,1393,0.010050251
+7801,1653,IP TRS HEART TRANSPLANT POSTOP,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,42,1393,0.030150754
+7802,1653,IP TRS HEART TRANSPLANT POSTOP,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,29,1393,0.020818378
+7803,1653,IP TRS HEART TRANSPLANT POSTOP,520055,GANCICLOVIR IVPB,Medication,182,1393,0.130653266
+7804,1653,IP TRS HEART TRANSPLANT POSTOP,520057,CASPOFUNGIN IVPB (CUSTOM),Medication,3,1393,0.002153625
+7805,1653,IP TRS HEART TRANSPLANT POSTOP,520058,CASPOFUNGIN 70 MG IVPB,Medication,2,1393,0.00143575
+7806,1653,IP TRS HEART TRANSPLANT POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,17,1393,0.012203877
+7807,1653,IP TRS HEART TRANSPLANT POSTOP,520073,MYCOPHENOLATE IVPB,Medication,89,1393,0.063890883
+7808,1653,IP TRS HEART TRANSPLANT POSTOP,520074,ANTITHYMOCYTE GLOBULIN RABBIT IVPB,Medication,152,1393,0.109117014
+7809,1653,IP TRS HEART TRANSPLANT POSTOP,530006,EPINEPHRINE IV INFUSION,Medication,120,1393,0.086145011
+7810,1653,IP TRS HEART TRANSPLANT POSTOP,530008,NITROPRUSSIDE IV INFUSION,Medication,15,1393,0.010768126
+7811,1653,IP TRS HEART TRANSPLANT POSTOP,530017,ISOPROTERENOL IV INFUSION,Medication,11,1393,0.007896626
+7812,1653,IP TRS HEART TRANSPLANT POSTOP,530025,DEXMEDETOMIDINE IV INFUSION,Medication,8,1393,0.005743001
+7813,1653,IP TRS HEART TRANSPLANT POSTOP,530049,INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW),Medication,176,1393,0.126346016
+7814,1653,IP TRS HEART TRANSPLANT POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,11,1393,0.007896626
+7815,1653,IP TRS HEART TRANSPLANT POSTOP,540129,TACROLIMUS 1 MG/ML ORAL SUSPENSION,Medication,180,1393,0.129217516
+7816,1653,IP TRS HEART TRANSPLANT POSTOP,540321,MYCOPHENOLATE MOFETIL 200 MG/ML ORAL SUSP,Medication,109,1393,0.078248385
+7817,1653,IP TRS HEART TRANSPLANT POSTOP,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,83,1393,0.059583632
+7818,1653,IP TRS HEART TRANSPLANT POSTOP,541285,AMPHOTERICIN B 20 MG/4ML NEB SOLUTION,Medication,106,1393,0.07609476
+7819,1653,IP TRS HEART TRANSPLANT POSTOP,9000000,ZZZ IMS TEMPLATE,Medication,11,1393,0.007896626
+7820,1653,IP TRS HEART TRANSPLANT POSTOP,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,35,1393,0.025125628
+7821,1653,IP TRS HEART TRANSPLANT POSTOP,10283R,HLA ORDER - TRANSPLANT EVENT COMMUNICATION,Lab,152,1393,0.109117014
+7822,1653,IP TRS HEART TRANSPLANT POSTOP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,210,1393,0.150753769
+7823,1653,IP TRS HEART TRANSPLANT POSTOP,LABCBCD,CBC WITH DIFF,Lab,199,1393,0.142857143
+7824,1653,IP TRS HEART TRANSPLANT POSTOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,236,1393,0.169418521
+7825,1653,IP TRS HEART TRANSPLANT POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,284,1393,0.203876525
+7826,1653,IP TRS HEART TRANSPLANT POSTOP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,204,1393,0.146446518
+7827,1653,IP TRS HEART TRANSPLANT POSTOP,LABPT,PROTHROMBIN TIME,Lab,189,1393,0.135678392
+7828,1653,IP TRS HEART TRANSPLANT POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,188,1393,0.134960517
+7829,1653,IP TRS HEART TRANSPLANT POSTOP,LABYITRA,ITRACONAZOLE LEVEL,Lab,63,1393,0.045226131
+7830,1653,IP TRS HEART TRANSPLANT POSTOP,POC14,POC GLUCOSE BY METER,Lab,1109,1393,0.796123475
+7831,1653,IP TRS HEART TRANSPLANT POSTOP,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,30,1393,0.021536253
+7832,1653,IP TRS HEART TRANSPLANT POSTOP,IMGDXCH1,XR CHEST 1V,Imaging,371,1393,0.266331658
+7833,1654,IP HEM / ONC HYPERCOAGUABILITY,LABAPC,ACTIVATED PROTEIN C RESISTANCE,Lab,7,9,0.777777778
+7834,1654,IP HEM / ONC HYPERCOAGUABILITY,LABAPHSA2,ANTI PHOSPHOLIPID AB PANEL,Lab,9,9,1
+7835,1654,IP HEM / ONC HYPERCOAGUABILITY,LABHMCY,"HOMOCYSTEINE TOTAL, PLASMA",Lab,2,9,0.222222222
+7836,1654,IP HEM / ONC HYPERCOAGUABILITY,LABLEID,FACTOR V LEIDEN,Lab,2,9,0.222222222
+7837,1654,IP HEM / ONC HYPERCOAGUABILITY,LABP20210,PROTHROMBIN 20210A,Lab,8,9,0.888888889
+7838,1654,IP HEM / ONC HYPERCOAGUABILITY,LABPROTC,PROTEIN C ACTIVITY,Lab,4,9,0.444444444
+7839,1654,IP HEM / ONC HYPERCOAGUABILITY,LABPROTS,PROTEIN S ACTIVITY,Lab,4,9,0.444444444
+7840,1654,IP HEM / ONC HYPERCOAGUABILITY,LABYAT3AG,"ANTITHROMBIN AG, PLASMA",Lab,4,9,0.444444444
+7841,1660,IP GEN TPN/PPN  ADULT PROTOCOL,78963,FAT EMULSION 20 % IV EMUL,Medication,1312,2935,0.447018739
+7842,1660,IP GEN TPN/PPN  ADULT PROTOCOL,550000,TPN ADULT STANDARD,Medication,1113,2935,0.379216354
+7843,1660,IP GEN TPN/PPN  ADULT PROTOCOL,550001,PPN ADULT STANDARD,Medication,163,2935,0.055536627
+7844,1660,IP GEN TPN/PPN  ADULT PROTOCOL,550002,TPN BMT,Medication,108,2935,0.036797274
+7845,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,23,2935,0.007836457
+7846,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABCRP,C - REACTIVE PROTEIN,Lab,341,2935,0.116183986
+7847,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,179,2935,0.060988075
+7848,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,854,2935,0.290971039
+7849,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABPALB,PREALBUMIN,Lab,366,2935,0.124701874
+7850,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,1005,2935,0.34241908
+7851,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,6,2935,0.002044293
+7852,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,1208,2935,0.411584327
+7853,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABYCHROM,CHROMIUM,Lab,6,2935,0.002044293
+7854,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABYCOPPR,"COPPER, SERUM",Lab,7,2935,0.002385009
+7855,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABYMANG,MANGANESE,Lab,6,2935,0.002044293
+7856,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABYSES,"SELENIUM, SERUM",Lab,5,2935,0.001703578
+7857,1660,IP GEN TPN/PPN  ADULT PROTOCOL,LABYZINCS,ZINC,Lab,8,2935,0.002725724
+7858,1660,IP GEN TPN/PPN  ADULT PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,225,2935,0.076660988
+7859,1660,IP GEN TPN/PPN  ADULT PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,5,2935,0.001703578
+7860,1665,IP TRS KIDNEY DONOR PREOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,5,221,0.022624434
+7861,1665,IP TRS KIDNEY DONOR PREOP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,114,221,0.515837104
+7862,1665,IP TRS KIDNEY DONOR PREOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,11,221,0.049773756
+7863,1665,IP TRS KIDNEY DONOR PREOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,8,221,0.036199095
+7864,1665,IP TRS KIDNEY DONOR PREOP,LABCBCO,CBC W/O DIFF,Lab,115,221,0.520361991
+7865,1665,IP TRS KIDNEY DONOR PREOP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,115,221,0.520361991
+7866,1665,IP TRS KIDNEY DONOR PREOP,LABPLTS,PLATELET COUNT,Lab,9,221,0.040723982
+7867,1665,IP TRS KIDNEY DONOR PREOP,LABPREG,"PREGNANCY TEST, SERUM",Lab,25,221,0.113122172
+7868,1665,IP TRS KIDNEY DONOR PREOP,LABTYPSNI,TYPE AND SCREEN,Lab,106,221,0.479638009
+7869,1665,IP TRS KIDNEY DONOR PREOP,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,114,221,0.515837104
+7870,1665,IP TRS KIDNEY DONOR PREOP,IMGDXCH2,XR CHEST 2V,Imaging,113,221,0.511312217
+7871,1665,IP TRS KIDNEY DONOR PREOP,LABBBPRBC,RED BLOOD CELLS,Blood Product,87,221,0.393665158
+7872,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,86,328,0.262195122
+7873,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,28,328,0.085365854
+7874,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,96,328,0.292682927
+7875,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,3,328,0.009146341
+7876,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,328,0.00304878
+7877,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,71,328,0.216463415
+7878,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,2,328,0.006097561
+7879,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,328,0.006097561
+7880,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,125,328,0.381097561
+7881,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,118,328,0.359756098
+7882,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,16,328,0.048780488
+7883,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,520064,CEFAZOLIN 2 G IVPB,Medication,20,328,0.06097561
+7884,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,4,328,0.012195122
+7885,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,2,328,0.006097561
+7886,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,2,328,0.006097561
+7887,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,LABCBCO,CBC W/O DIFF,Lab,248,328,0.756097561
+7888,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,241,328,0.734756098
+7889,1666,IP TRS LAP DONOR NEPHRECTOMY POSTOP,LABPLTS,PLATELET COUNT,Lab,52,328,0.158536585
+7890,1667,IP GYN  GENERAL ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,382,2898,0.131815045
+7891,1667,IP GYN  GENERAL ADMISSION,102,ACETAMINOPHEN 500 MG PO TABS,Medication,139,2898,0.047964113
+7892,1667,IP GYN  GENERAL ADMISSION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,3,2898,0.001035197
+7893,1667,IP GYN  GENERAL ADMISSION,324,ALPRAZOLAM 0.25 MG PO TABS,Medication,10,2898,0.003450656
+7894,1667,IP GYN  GENERAL ADMISSION,1080,BISACODYL 10 MG PR SUPP,Medication,105,2898,0.036231884
+7895,1667,IP GYN  GENERAL ADMISSION,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,8,2898,0.002760524
+7896,1667,IP GYN  GENERAL ADMISSION,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,120,2898,0.041407867
+7897,1667,IP GYN  GENERAL ADMISSION,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,172,2898,0.059351277
+7898,1667,IP GYN  GENERAL ADMISSION,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,1,2898,0.000345066
+7899,1667,IP GYN  GENERAL ADMISSION,3294,FUROSEMIDE 20 MG PO TABS,Medication,1,2898,0.000345066
+7900,1667,IP GYN  GENERAL ADMISSION,3841,IBUPROFEN 200 MG PO TABS,Medication,9,2898,0.00310559
+7901,1667,IP GYN  GENERAL ADMISSION,3843,IBUPROFEN 400 MG PO TABS,Medication,62,2898,0.021394065
+7902,1667,IP GYN  GENERAL ADMISSION,3844,IBUPROFEN 600 MG PO TABS,Medication,125,2898,0.043133195
+7903,1667,IP GYN  GENERAL ADMISSION,3845,IBUPROFEN 800 MG PO TABS,Medication,4,2898,0.001380262
+7904,1667,IP GYN  GENERAL ADMISSION,4318,LACTATED RINGERS IV SOLP,Medication,508,2898,0.175293306
+7905,1667,IP GYN  GENERAL ADMISSION,4572,LORAZEPAM 0.5 MG PO TABS,Medication,9,2898,0.00310559
+7906,1667,IP GYN  GENERAL ADMISSION,4573,LORAZEPAM 1 MG PO TABS,Medication,52,2898,0.017943409
+7907,1667,IP GYN  GENERAL ADMISSION,4711,MAGNESIUM CITRATE PO SOLN,Medication,24,2898,0.008281573
+7908,1667,IP GYN  GENERAL ADMISSION,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,412,2898,0.142167012
+7909,1667,IP GYN  GENERAL ADMISSION,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,39,2898,0.013457557
+7910,1667,IP GYN  GENERAL ADMISSION,5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,52,2898,0.017943409
+7911,1667,IP GYN  GENERAL ADMISSION,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,72,2898,0.02484472
+7912,1667,IP GYN  GENERAL ADMISSION,5009,METOPROLOL TARTRATE 50 MG PO TABS,Medication,2,2898,0.000690131
+7913,1667,IP GYN  GENERAL ADMISSION,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,3,2898,0.001035197
+7914,1667,IP GYN  GENERAL ADMISSION,5087,MINERAL OIL PR ENEM,Medication,2,2898,0.000690131
+7915,1667,IP GYN  GENERAL ADMISSION,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,7,2898,0.002415459
+7916,1667,IP GYN  GENERAL ADMISSION,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,6,2898,0.002070393
+7917,1667,IP GYN  GENERAL ADMISSION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,37,2898,0.012767426
+7918,1667,IP GYN  GENERAL ADMISSION,7753,TEMAZEPAM 15 MG PO CAPS,Medication,1,2898,0.000345066
+7919,1667,IP GYN  GENERAL ADMISSION,8750,WARFARIN 2.5 MG PO TABS,Medication,1,2898,0.000345066
+7920,1667,IP GYN  GENERAL ADMISSION,10011,FAMOTIDINE 20 MG PO TABS,Medication,16,2898,0.005521049
+7921,1667,IP GYN  GENERAL ADMISSION,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,45,2898,0.01552795
+7922,1667,IP GYN  GENERAL ADMISSION,11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,1,2898,0.000345066
+7923,1667,IP GYN  GENERAL ADMISSION,11349,SENNOSIDES 8.6 MG PO TABS,Medication,12,2898,0.004140787
+7924,1667,IP GYN  GENERAL ADMISSION,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,15,2898,0.005175983
+7925,1667,IP GYN  GENERAL ADMISSION,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,7,2898,0.002415459
+7926,1667,IP GYN  GENERAL ADMISSION,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,37,2898,0.012767426
+7927,1667,IP GYN  GENERAL ADMISSION,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,27,2898,0.00931677
+7928,1667,IP GYN  GENERAL ADMISSION,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,147,2898,0.050724638
+7929,1667,IP GYN  GENERAL ADMISSION,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,2,2898,0.000690131
+7930,1667,IP GYN  GENERAL ADMISSION,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,80,2898,0.027605245
+7931,1667,IP GYN  GENERAL ADMISSION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,132,2898,0.045548654
+7932,1667,IP GYN  GENERAL ADMISSION,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,4,2898,0.001380262
+7933,1667,IP GYN  GENERAL ADMISSION,80845,ONDANSETRON HCL 2 MG/ML IV SOLN,Medication,1,2898,0.000345066
+7934,1667,IP GYN  GENERAL ADMISSION,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,90,2898,0.031055901
+7935,1667,IP GYN  GENERAL ADMISSION,84520,ONDANSETRON 8 MG PO TBDL,Medication,5,2898,0.001725328
+7936,1667,IP GYN  GENERAL ADMISSION,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,37,2898,0.012767426
+7937,1667,IP GYN  GENERAL ADMISSION,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,53,2898,0.018288475
+7938,1667,IP GYN  GENERAL ADMISSION,92173,ZOLPIDEM 5 MG PO TABS,Medication,64,2898,0.022084196
+7939,1667,IP GYN  GENERAL ADMISSION,93295,ZOLPIDEM 10 MG PO TABS,Medication,1,2898,0.000345066
+7940,1667,IP GYN  GENERAL ADMISSION,94176,ONDANSETRON 4 MG PO TBDL,Medication,80,2898,0.027605245
+7941,1667,IP GYN  GENERAL ADMISSION,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,219,2898,0.075569358
+7942,1667,IP GYN  GENERAL ADMISSION,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,17,2898,0.005866115
+7943,1667,IP GYN  GENERAL ADMISSION,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,2898,0.000345066
+7944,1667,IP GYN  GENERAL ADMISSION,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,2,2898,0.000690131
+7945,1667,IP GYN  GENERAL ADMISSION,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,5,2898,0.001725328
+7946,1667,IP GYN  GENERAL ADMISSION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,259,2898,0.089371981
+7947,1667,IP GYN  GENERAL ADMISSION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,25,2898,0.008626639
+7948,1667,IP GYN  GENERAL ADMISSION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,248,2898,0.085576259
+7949,1667,IP GYN  GENERAL ADMISSION,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,76,2898,0.026224983
+7950,1667,IP GYN  GENERAL ADMISSION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,700,2898,0.241545894
+7951,1667,IP GYN  GENERAL ADMISSION,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,5,2898,0.001725328
+7952,1667,IP GYN  GENERAL ADMISSION,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,17,2898,0.005866115
+7953,1667,IP GYN  GENERAL ADMISSION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,2898,0.000345066
+7954,1667,IP GYN  GENERAL ADMISSION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,42,2898,0.014492754
+7955,1667,IP GYN  GENERAL ADMISSION,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,97,2898,0.03347136
+7956,1667,IP GYN  GENERAL ADMISSION,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,2,2898,0.000690131
+7957,1667,IP GYN  GENERAL ADMISSION,540102,NS IV BOLUS,Medication,9,2898,0.00310559
+7958,1667,IP GYN  GENERAL ADMISSION,540115,LACTATED RINGERS IV BOLUS,Medication,15,2898,0.005175983
+7959,1667,IP GYN  GENERAL ADMISSION,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,5,2898,0.001725328
+7960,1667,IP GYN  GENERAL ADMISSION,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,2,2898,0.000690131
+7961,1667,IP GYN  GENERAL ADMISSION,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,23,2898,0.007936508
+7962,1667,IP GYN  GENERAL ADMISSION,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,2898,0.000690131
+7963,1667,IP GYN  GENERAL ADMISSION,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,2898,0.000345066
+7964,1667,IP GYN  GENERAL ADMISSION,9000000,ZZZ IMS TEMPLATE,Medication,3,2898,0.001035197
+7965,1667,IP GYN  GENERAL ADMISSION,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,10,2898,0.003450656
+7966,1667,IP GYN  GENERAL ADMISSION,LABCBCD,CBC WITH DIFF,Lab,605,2898,0.208764665
+7967,1667,IP GYN  GENERAL ADMISSION,LABCBCO,CBC W/O DIFF,Lab,329,2898,0.11352657
+7968,1667,IP GYN  GENERAL ADMISSION,LABMETB,"METABOLIC PANEL, BASIC",Lab,521,2898,0.179779158
+7969,1667,IP GYN  GENERAL ADMISSION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,170,2898,0.058661146
+7970,1667,IP GYN  GENERAL ADMISSION,LABPLTS,PLATELET COUNT,Lab,23,2898,0.007936508
+7971,1667,IP GYN  GENERAL ADMISSION,LABPT,PROTHROMBIN TIME,Lab,76,2898,0.026224983
+7972,1667,IP GYN  GENERAL ADMISSION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,73,2898,0.025189786
+7973,1667,IP GYN  GENERAL ADMISSION,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,18,2898,0.00621118
+7974,1667,IP GYN  GENERAL ADMISSION,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,16,2898,0.005521049
+7975,1667,IP GYN  GENERAL ADMISSION,LABURNC,URINE CULTURE,Lab,20,2898,0.006901311
+7976,1667,IP GYN  GENERAL ADMISSION,IMGCT3T,CT ANGIO CHEST,Imaging,1,2898,0.000345066
+7977,1667,IP GYN  GENERAL ADMISSION,IMGCTAP,CT ABDOMEN PELVIS,Imaging,9,2898,0.00310559
+7978,1667,IP GYN  GENERAL ADMISSION,IMGCTT,CT THORAX,Imaging,2,2898,0.000690131
+7979,1667,IP GYN  GENERAL ADMISSION,IMGDXAB3,"XR ABDOMEN W AP, UPRIGHT, DECUB & DIAPHRAGM",Imaging,2,2898,0.000690131
+7980,1667,IP GYN  GENERAL ADMISSION,IMGDXCH2,XR CHEST 2V,Imaging,7,2898,0.002415459
+7981,1667,IP GYN  GENERAL ADMISSION,IMGUSP1,US PELVIS NON OB WO TRANSVAG,Imaging,1,2898,0.000345066
+7982,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,2,0.5
+7983,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,2,0.5
+7984,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,4560,LOPERAMIDE 2 MG PO CAPS,Medication,1,2,0.5
+7985,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,2,0.5
+7986,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,2,0.5
+7987,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,1,2,0.5
+7988,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,117152,"GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",Medication,1,2,0.5
+7989,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,2,0.5
+7990,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,2,0.5
+7991,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,520045,MEROPENEM CUSTOM IVPB,Medication,1,2,0.5
+7992,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,540302,POTASSIUM CHLORIDE IV SCALE,Medication,2,2,1
+7993,1673,PHARM IP BMT ALLOGENEIC READMISSION ORDERS,540304,MAGNESIUM SULFATE IV SCALE ,Medication,2,2,1
+7994,1674,IP PHARM IDARUBICIN CYTARABINE 3 + 7 INDUCTION,4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,1,1
+7995,1674,IP PHARM IDARUBICIN CYTARABINE 3 + 7 INDUCTION,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,1,1
+7996,1676,IP MOLECULAR PATHOLOGY - HEMATOLOGY AND GENETIC,LABBMCEBP,"DETECTION OF CEBPA, BONE MARROW",Lab,2,2,1
+7997,1676,IP MOLECULAR PATHOLOGY - HEMATOLOGY AND GENETIC,LABCKITMU,"KIT MUTATIONS, EXONS 8 & 17, BLOOD",Lab,2,2,1
+7998,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2755,11718,0.23510838
+7999,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,61,11718,0.005205666
+8000,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,405,11718,0.034562212
+8001,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,681,ASPIRIN 325 MG PO TABS,Medication,6,11718,0.000512033
+8002,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,1080,BISACODYL 10 MG PR SUPP,Medication,5,11718,0.000426694
+8003,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2324,DEXAMETHASONE 1 MG PO TABS,Medication,2,11718,0.000170678
+8004,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2325,DEXAMETHASONE 1.5 MG PO TABS,Medication,1,11718,8.53E-05
+8005,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2326,DEXAMETHASONE 2 MG PO TABS,Medication,5,11718,0.000426694
+8006,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2327,DEXAMETHASONE 4 MG PO TABS,Medication,35,11718,0.002986858
+8007,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,242,11718,0.020651988
+8008,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2403,DIAZEPAM 10 MG PO TABS,Medication,6,11718,0.000512033
+8009,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2404,DIAZEPAM 2 MG PO TABS,Medication,53,11718,0.004522956
+8010,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2405,DIAZEPAM 5 MG PO TABS,Medication,1083,11718,0.092421915
+8011,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,11718,8.53E-05
+8012,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,2718,11718,0.231950845
+8013,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,957,11718,0.081669227
+8014,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,2997,11718,0.255760369
+8015,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,11718,8.53E-05
+8016,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,4711,MAGNESIUM CITRATE PO SOLN,Medication,458,11718,0.039085168
+8017,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,4993,METHYLPREDNISOLONE 4 MG PO TABS,Medication,2,11718,0.000170678
+8018,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,3534,11718,0.301587302
+8019,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,2487,11718,0.212237583
+8020,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,5087,MINERAL OIL PR ENEM,Medication,5,11718,0.000426694
+8021,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,3702,11718,0.315924219
+8022,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,573,11718,0.04889913
+8023,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,6427,POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK,Medication,1,11718,8.53E-05
+8024,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,673,11718,0.057433009
+8025,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,38,11718,0.003242874
+8026,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,7321,SODIUM CHLORIDE 3 % 3 % IV SOLP,Medication,4,11718,0.000341355
+8027,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,8958,BUTALBITAL-ACETAMINOPHEN-CAFF 50-325-40 MG PO TABS,Medication,70,11718,0.005973716
+8028,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,23,11718,0.001962792
+8029,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,10,11718,0.000853388
+8030,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,10011,FAMOTIDINE 20 MG PO TABS,Medication,615,11718,0.052483359
+8031,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,3693,11718,0.31515617
+8032,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,1,11718,8.53E-05
+8033,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,10814,OXYCODONE 5 MG PO TABS,Medication,577,11718,0.049240485
+8034,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,11144,PROMETHAZINE 25 MG PR SUPP,Medication,15,11718,0.001280082
+8035,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,4130,11718,0.352449223
+8036,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,22,11718,0.001877453
+8037,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,14632,TRAMADOL 50 MG PO TABS,Medication,2,11718,0.000170678
+8038,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,38,11718,0.003242874
+8039,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2729,11718,0.232889572
+8040,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,18307,GABAPENTIN 400 MG PO CAPS,Medication,4,11718,0.000341355
+8041,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,18308,GABAPENTIN 300 MG PO CAPS,Medication,48,11718,0.004096262
+8042,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,18309,GABAPENTIN 100 MG PO CAPS,Medication,3,11718,0.000256016
+8043,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,20920,MORPHINE 15 MG PO TBSR,Medication,1,11718,8.53E-05
+8044,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,20921,MORPHINE 30 MG PO TBSR,Medication,1,11718,8.53E-05
+8045,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,20922,MORPHINE 60 MG PO TBSR,Medication,1,11718,8.53E-05
+8046,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,873,11718,0.074500768
+8047,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,1537,11718,0.131165728
+8048,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,25855,GABAPENTIN 600 MG PO TABS,Medication,4,11718,0.000341355
+8049,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,221,11718,0.018859874
+8050,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,27920,OXYCODONE 10 MG PO TB12,Medication,6,11718,0.000512033
+8051,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,27921,OXYCODONE 20 MG PO TB12,Medication,1,11718,8.53E-05
+8052,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,702,11718,0.059907834
+8053,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,28899,OXYCODONE 15 MG PO TABS,Medication,6,11718,0.000512033
+8054,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,28900,OXYCODONE 30 MG PO TABS,Medication,1,11718,8.53E-05
+8055,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,56,11718,0.004778973
+8056,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2129,11718,0.181686295
+8057,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,3,11718,0.000256016
+8058,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,36063,0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,16,11718,0.001365421
+8059,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,81934,NIMODIPINE 30 MG PO CAPS,Medication,7,11718,0.000597372
+8060,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,77,11718,0.006571087
+8061,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,83398,CLOPIDOGREL 75 MG PO TABS,Medication,2,11718,0.000170678
+8062,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,452,11718,0.038573135
+8063,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,87473,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DROP,Medication,476,11718,0.040621266
+8064,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,206,11718,0.017579792
+8065,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,89065,LIDOCAINE 5 % TP PTMD,Medication,19,11718,0.001621437
+8066,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,1004,11718,0.08568015
+8067,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,94176,ONDANSETRON 4 MG PO TBDL,Medication,5,11718,0.000426694
+8068,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,587,11718,0.050093873
+8069,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,8,11718,0.00068271
+8070,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,110889,ENOXAPARIN 60 MG/0.6 ML SC SYRG,Medication,1,11718,8.53E-05
+8071,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,11718,8.53E-05
+8072,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,484,11718,0.041303977
+8073,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,36,11718,0.003072197
+8074,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,517,11718,0.044120157
+8075,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,112597,ENOXAPARIN 120 MG/0.8 ML SC SYRG,Medication,1,11718,8.53E-05
+8076,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,113246,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DPET,Medication,1,11718,8.53E-05
+8077,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,138,11718,0.011776754
+8078,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,8,11718,0.00068271
+8079,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,1145,11718,0.09771292
+8080,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3796,11718,0.323946066
+8081,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,19,11718,0.001621437
+8082,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,202543,WHITE PETROLATUM-MINERAL OIL 83-15 % OPHT OINT,Medication,2,11718,0.000170678
+8083,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,2239,11718,0.191073562
+8084,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,6,11718,0.000512033
+8085,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,583,11718,0.049752517
+8086,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,243,11718,0.020737327
+8087,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,5,11718,0.000426694
+8088,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,222567,LEVETIRACETAM IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,16,11718,0.001365421
+8089,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,233900,OXYCODONE 10 MG PO TR12,Medication,1,11718,8.53E-05
+8090,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,233904,OXYCODONE 40 MG PO TR12,Medication,1,11718,8.53E-05
+8091,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,346,11718,0.029527223
+8092,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,520008,METOPROLOL IVPB,Medication,2,11718,0.000170678
+8093,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,520064,CEFAZOLIN 2 G IVPB,Medication,279,11718,0.023809524
+8094,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,520068,PHENYTOIN IVPB (500 MG OR LESS),Medication,4,11718,0.000341355
+8095,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,520301,CEFTRIAXONE 1 GRAM/50 ML NS IVPB,Medication,550,11718,0.046936337
+8096,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,520302,CEFTRIAXONE 2 GRAM/50 ML NS IVPB,Medication,26,11718,0.002218809
+8097,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1171,11718,0.099931729
+8098,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,1106,11718,0.094384707
+8099,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,540302,POTASSIUM CHLORIDE IV SCALE,Medication,1112,11718,0.09489674
+8100,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,540744,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,Medication,165,11718,0.014080901
+8101,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,540769,SODIUM CHLORIDE 3 % IV BOLUS,Medication,1,11718,8.53E-05
+8102,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,122,11718,0.010411333
+8103,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,541433,POTASSIUM IV CHLORIDE SCALE 10-30 MEQ,Medication,1,11718,8.53E-05
+8104,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,541434,POTASSIUM CHLORIDE IV SCALE 20-40MEQ IVPB,Medication,1,11718,8.53E-05
+8105,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,4,11718,0.000341355
+8106,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,9000000,ZZZ IMS TEMPLATE,Medication,3,11718,0.000256016
+8107,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABCBCD,CBC WITH DIFF,Lab,2236,11718,0.190817546
+8108,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABHCTX,HEMATOCRIT,Lab,147,11718,0.012544803
+8109,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABMETB,"METABOLIC PANEL, BASIC",Lab,2132,11718,0.181942311
+8110,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABPHEN,PHENYTOIN LEVEL,Lab,3,11718,0.000256016
+8111,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABPLTS,PLATELET COUNT,Lab,208,11718,0.017750469
+8112,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABPT,PROTHROMBIN TIME,Lab,1665,11718,0.142089094
+8113,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,399,11718,0.034050179
+8114,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABTYPSNI,TYPE AND SCREEN,Lab,594,11718,0.050691244
+8115,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,342,11718,0.029185868
+8116,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGCTH,CT HEAD,Imaging,212,11718,0.018091825
+8117,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGDXCH1,XR CHEST 1V,Imaging,503,11718,0.042925414
+8118,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGDXSPC2,"XR SPINE, CERVICAL 2V",Imaging,2,11718,0.000170678
+8119,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGDXSPL2,"XR SPINE, LUMBAR 2V",Imaging,1,11718,8.53E-05
+8120,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGMRH,MR BRAIN,Imaging,93,11718,0.007936508
+8121,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGMRSC,MR C-SPINE,Imaging,17,11718,0.00145076
+8122,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGMRSL,MR L-SPINE,Imaging,14,11718,0.001194743
+8123,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,IMGMRST,MR T-SPINE,Imaging,7,11718,0.000597372
+8124,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,5,11718,0.000426694
+8125,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,20,11718,0.001706776
+8126,1677,IP NSR NEUROSURGERY ADMISSION/POST-OP,LABBBPRBC,RED BLOOD CELLS,Blood Product,83,11718,0.00708312
+8127,1687,IP VAS ANGIO POST- CATH,101,ACETAMINOPHEN 325 MG PO TABS,Medication,36,462,0.077922078
+8128,1687,IP VAS ANGIO POST- CATH,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,2,462,0.004329004
+8129,1687,IP VAS ANGIO POST- CATH,693,ASPIRIN 300 MG PR SUPP,Medication,1,462,0.002164502
+8130,1687,IP VAS ANGIO POST- CATH,1080,BISACODYL 10 MG PR SUPP,Medication,89,462,0.192640693
+8131,1687,IP VAS ANGIO POST- CATH,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,94,462,0.203463203
+8132,1687,IP VAS ANGIO POST- CATH,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,32,462,0.069264069
+8133,1687,IP VAS ANGIO POST- CATH,3843,IBUPROFEN 400 MG PO TABS,Medication,1,462,0.002164502
+8134,1687,IP VAS ANGIO POST- CATH,4318,LACTATED RINGERS IV SOLP,Medication,46,462,0.0995671
+8135,1687,IP VAS ANGIO POST- CATH,4711,MAGNESIUM CITRATE PO SOLN,Medication,73,462,0.158008658
+8136,1687,IP VAS ANGIO POST- CATH,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,115,462,0.248917749
+8137,1687,IP VAS ANGIO POST- CATH,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,10,462,0.021645022
+8138,1687,IP VAS ANGIO POST- CATH,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,135,462,0.292207792
+8139,1687,IP VAS ANGIO POST- CATH,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,16,462,0.034632035
+8140,1687,IP VAS ANGIO POST- CATH,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,462,0.004329004
+8141,1687,IP VAS ANGIO POST- CATH,11349,SENNOSIDES 8.6 MG PO TABS,Medication,127,462,0.274891775
+8142,1687,IP VAS ANGIO POST- CATH,14113,ASPIRIN 81 MG PO TBEC,Medication,15,462,0.032467532
+8143,1687,IP VAS ANGIO POST- CATH,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,18,462,0.038961039
+8144,1687,IP VAS ANGIO POST- CATH,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,3,462,0.006493506
+8145,1687,IP VAS ANGIO POST- CATH,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,14,462,0.03030303
+8146,1687,IP VAS ANGIO POST- CATH,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,9,462,0.019480519
+8147,1687,IP VAS ANGIO POST- CATH,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,82,462,0.177489177
+8148,1687,IP VAS ANGIO POST- CATH,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,1,462,0.002164502
+8149,1687,IP VAS ANGIO POST- CATH,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,8,462,0.017316017
+8150,1687,IP VAS ANGIO POST- CATH,83398,CLOPIDOGREL 75 MG PO TABS,Medication,19,462,0.041125541
+8151,1687,IP VAS ANGIO POST- CATH,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,91,462,0.196969697
+8152,1687,IP VAS ANGIO POST- CATH,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,8,462,0.017316017
+8153,1687,IP VAS ANGIO POST- CATH,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,60,462,0.12987013
+8154,1687,IP VAS ANGIO POST- CATH,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,50,462,0.108225108
+8155,1687,IP VAS ANGIO POST- CATH,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,2,462,0.004329004
+8156,1687,IP VAS ANGIO POST- CATH,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,24,462,0.051948052
+8157,1687,IP VAS ANGIO POST- CATH,115141,"ESMOLOL IN NACL (ISO-OSM) 2,000 MG/100 ML IV SOLP",Medication,1,462,0.002164502
+8158,1687,IP VAS ANGIO POST- CATH,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,462,0.004329004
+8159,1687,IP VAS ANGIO POST- CATH,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,138,462,0.298701299
+8160,1687,IP VAS ANGIO POST- CATH,203749,CLOPIDOGREL 75 MG PO TABS,Medication,6,462,0.012987013
+8161,1687,IP VAS ANGIO POST- CATH,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,1,462,0.002164502
+8162,1687,IP VAS ANGIO POST- CATH,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,13,462,0.028138528
+8163,1687,IP VAS ANGIO POST- CATH,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,4,462,0.008658009
+8164,1687,IP VAS ANGIO POST- CATH,500010,LACTATED RINGERS WITH KCL IV INFUSION,Medication,1,462,0.002164502
+8165,1687,IP VAS ANGIO POST- CATH,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,8,462,0.017316017
+8166,1687,IP VAS ANGIO POST- CATH,520064,CEFAZOLIN 2 G IVPB,Medication,15,462,0.032467532
+8167,1687,IP VAS ANGIO POST- CATH,530040,ALTEPLASE INFUSION,Medication,20,462,0.043290043
+8168,1687,IP VAS ANGIO POST- CATH,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,9,462,0.019480519
+8169,1687,IP VAS ANGIO POST- CATH,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,1,462,0.002164502
+8170,1687,IP VAS ANGIO POST- CATH,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,10,462,0.021645022
+8171,1687,IP VAS ANGIO POST- CATH,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,3,462,0.006493506
+8172,1687,IP VAS ANGIO POST- CATH,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,2,462,0.004329004
+8173,1687,IP VAS ANGIO POST- CATH,LABCBCD,CBC WITH DIFF,Lab,81,462,0.175324675
+8174,1687,IP VAS ANGIO POST- CATH,LABFIB,FIBRINOGEN,Lab,20,462,0.043290043
+8175,1687,IP VAS ANGIO POST- CATH,LABMETB,"METABOLIC PANEL, BASIC",Lab,96,462,0.207792208
+8176,1687,IP VAS ANGIO POST- CATH,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,10,462,0.021645022
+8177,1687,IP VAS ANGIO POST- CATH,LABPLTS,PLATELET COUNT,Lab,8,462,0.017316017
+8178,1687,IP VAS ANGIO POST- CATH,LABPT,PROTHROMBIN TIME,Lab,27,462,0.058441558
+8179,1687,IP VAS ANGIO POST- CATH,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,31,462,0.067099567
+8180,1690,IP FLOW CYTOMETRY,PATH22,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BLD",Lab,2,3,0.666666667
+8181,1690,IP FLOW CYTOMETRY,PATH23,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BM",Lab,1,3,0.333333333
+8182,1695,IP GEN TUBE FEEDING,LABCRP,C - REACTIVE PROTEIN,Lab,21,3874,0.005420754
+8183,1695,IP GEN TUBE FEEDING,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,19,3874,0.004904491
+8184,1695,IP GEN TUBE FEEDING,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,29,3874,0.007485803
+8185,1695,IP GEN TUBE FEEDING,LABPALB,PREALBUMIN,Lab,46,3874,0.011874032
+8186,1695,IP GEN TUBE FEEDING,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,34,3874,0.008776458
+8187,1695,IP GEN TUBE FEEDING,POC14,POC GLUCOSE BY METER,Lab,10,3874,0.002581311
+8188,1695,IP GEN TUBE FEEDING,IMGDXAB1,XR ABDOMEN 1V,Imaging,460,3874,0.11874032
+8189,1697,PHARM IP HIGH DOSE METHOTREXATE,500018,D5 1/2 NS WITH KCL AND SOD BICARBONATE IV INFUSION,Medication,1,1,1
+8190,1701,PHARM IP GENERIC CHEMOTHERAPY ORDER TEMPLATE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,2,1
+8191,1701,PHARM IP GENERIC CHEMOTHERAPY ORDER TEMPLATE,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,2,2,1
+8192,1701,PHARM IP GENERIC CHEMOTHERAPY ORDER TEMPLATE,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,4,2,1
+8193,1702,PHARM IP MEC,2327,DEXAMETHASONE 4 MG PO TABS,Medication,2,4,0.5
+8194,1702,PHARM IP MEC,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,4,0.25
+8195,1702,PHARM IP MEC,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,2,4,0.5
+8196,1702,PHARM IP MEC,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,3,4,0.75
+8197,1702,PHARM IP MEC,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,1,4,0.25
+8198,1702,PHARM IP MEC,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,1,4,0.25
+8199,1702,PHARM IP MEC,510001,CYTARABINE IV CHEMO INFUSION,Medication,2,4,0.5
+8200,1702,PHARM IP MEC,510012,ETOPOSIDE IV CHEMO INFUSION,Medication,2,4,0.5
+8201,1702,PHARM IP MEC,510017,MITOXANTRONE IV CHEMO INFUSION,Medication,2,4,0.5
+8202,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,202,6335,0.031886346
+8203,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),91456,"SOD PHOS DI, MONO-K PHOS MONO 250 MG PO TABS",Medication,2,6335,0.000315706
+8204,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),94623,ALTEPLASE 2 MG INJ SOLR,Medication,115,6335,0.018153118
+8205,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),96471,CITRATE DEXTROSE SOLUTION MISC SOLN,Medication,1010,6335,0.159431728
+8206,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),126580,"HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",Medication,519,6335,0.081925809
+8207,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),203465,POTASSIUM & SODIUM PHOSPHATES 280-160-250 MG PO PWPK,Medication,7,6335,0.001104972
+8208,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),205894,PRISMASOL SOLUTION (BGK 2/0) (32 MEQ BICARB/L),Medication,1542,6335,0.243409629
+8209,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),210695,"BICAR DIAL SOL W-O CA,K,DEX#13 MG (1.2 MEQ/L) HEMODIAL SOLN",Medication,292,6335,0.046093133
+8210,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),210698,BICARB DIALYS SOLN #15 W-O CA K (4 MEQ/L) -MG (1.2 MEQ/L) HEMODIAL SOLN,Medication,2050,6335,0.323599053
+8211,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),211166,BICARB DIALY SOLN W-O CA NO.16 K (4 MEQ/L) -MG (1.5 MEQ/L) HEMODIAL SOLN,Medication,1375,6335,0.217048145
+8212,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,1,6335,0.000157853
+8213,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),230501,DEXTROSE-SOD CITRATE-CITRIC AC 2.45-2.2 GRAM- 730 MG/100 ML MISC SOLN,Medication,391,6335,0.0617206
+8214,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),236703,"PHOS DIALY SOLN W-O CA,DEX #3 K (4)-MG (1.5 MEQ/L)-PO4 (1) HEMODIAL SOLN",Medication,185,6335,0.029202841
+8215,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),500006,SODIUM PHOSPHATE IVPB,Medication,2821,6335,0.445303867
+8216,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),500034,CALCIUM CHLORIDE 8 G/1000 ML NS IV INFUSION,Medication,669,6335,0.105603788
+8217,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),540911,"CALCIUM GLUCONATE 107.6 MEQ NS 1,000 ML (CRRT)",Medication,638,6335,0.100710339
+8218,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),541062,POTASSIUM & SODIUM PHOSPHATES 280-160-250 MG PO PWPK (CRRT),Medication,119,6335,0.01878453
+8219,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),541063,SODIUM GLYCEROPHOSPHATE (GLYCOPHOS) IN NS IVPB (CRRT),Medication,457,6335,0.072138911
+8220,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,1140,6335,0.179952644
+8221,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),LABHBSAG,HBSAG,Lab,271,6335,0.042778216
+8222,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),LABHBSQT,HEP B SURF AB QUANT,Lab,123,6335,0.019415943
+8223,1705,IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT),LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2345,6335,0.370165746
+8224,1706,DRUG DESENSITIZATION PROTOCOL HIGH ALERT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,39,113,0.345132743
+8225,1706,DRUG DESENSITIZATION PROTOCOL HIGH ALERT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,51,113,0.451327434
+8226,1706,DRUG DESENSITIZATION PROTOCOL HIGH ALERT,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,38,113,0.336283186
+8227,1706,DRUG DESENSITIZATION PROTOCOL HIGH ALERT,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,44,113,0.389380531
+8228,1706,DRUG DESENSITIZATION PROTOCOL HIGH ALERT,520186,DESENSITIZATION DRUG MIXTURE-HIGH ALERT MED,Medication,285,113,1
+8229,1706,DRUG DESENSITIZATION PROTOCOL HIGH ALERT,540138,DESENSITIZATION DRUG HELP ORDER,Medication,6,113,0.053097345
+8230,1713,IP CTS CARDIAC SURGERY TRANSFER,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2726,8978,0.303631098
+8231,1713,IP CTS CARDIAC SURGERY TRANSFER,102,ACETAMINOPHEN 500 MG PO TABS,Medication,39,8978,0.004343952
+8232,1713,IP CTS CARDIAC SURGERY TRANSFER,663,ASCORBIC ACID 250 MG PO TABS,Medication,1472,8978,0.163956338
+8233,1713,IP CTS CARDIAC SURGERY TRANSFER,664,ASCORBIC ACID 500 MG PO TABS,Medication,889,8978,0.099019826
+8234,1713,IP CTS CARDIAC SURGERY TRANSFER,680,ASPIRIN 81 MG PO CHEW,Medication,441,8978,0.049120071
+8235,1713,IP CTS CARDIAC SURGERY TRANSFER,681,ASPIRIN 325 MG PO TABS,Medication,1,8978,0.000111383
+8236,1713,IP CTS CARDIAC SURGERY TRANSFER,1080,BISACODYL 10 MG PR SUPP,Medication,2275,8978,0.253397193
+8237,1713,IP CTS CARDIAC SURGERY TRANSFER,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1624,8978,0.180886612
+8238,1713,IP CTS CARDIAC SURGERY TRANSFER,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,416,8978,0.046335487
+8239,1713,IP CTS CARDIAC SURGERY TRANSFER,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,2,8978,0.000222767
+8240,1713,IP CTS CARDIAC SURGERY TRANSFER,2571,DOCUSATE SODIUM 60 MG/15 ML PO SYRP,Medication,1,8978,0.000111383
+8241,1713,IP CTS CARDIAC SURGERY TRANSFER,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,2,8978,0.000222767
+8242,1713,IP CTS CARDIAC SURGERY TRANSFER,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,2203,8978,0.24537759
+8243,1713,IP CTS CARDIAC SURGERY TRANSFER,7227,SIMETHICONE 80 MG PO CHEW,Medication,3,8978,0.00033415
+8244,1713,IP CTS CARDIAC SURGERY TRANSFER,8083,TRAZODONE 50 MG PO TABS,Medication,5,8978,0.000556917
+8245,1713,IP CTS CARDIAC SURGERY TRANSFER,8084,TRAZODONE 150 MG PO TABS,Medication,2,8978,0.000222767
+8246,1713,IP CTS CARDIAC SURGERY TRANSFER,8085,TRAZODONE 50 MG PO TABS,Medication,2317,8978,0.258075295
+8247,1713,IP CTS CARDIAC SURGERY TRANSFER,10011,FAMOTIDINE 20 MG PO TABS,Medication,82,8978,0.009133437
+8248,1713,IP CTS CARDIAC SURGERY TRANSFER,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,1960,8978,0.218311428
+8249,1713,IP CTS CARDIAC SURGERY TRANSFER,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,6,8978,0.0006683
+8250,1713,IP CTS CARDIAC SURGERY TRANSFER,10814,OXYCODONE 5 MG PO TABS,Medication,2154,8978,0.239919804
+8251,1713,IP CTS CARDIAC SURGERY TRANSFER,11349,SENNOSIDES 8.6 MG PO TABS,Medication,2186,8978,0.243484072
+8252,1713,IP CTS CARDIAC SURGERY TRANSFER,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,1457,8978,0.162285587
+8253,1713,IP CTS CARDIAC SURGERY TRANSFER,13654,ASPIRIN 325 MG PO TBEC,Medication,3,8978,0.00033415
+8254,1713,IP CTS CARDIAC SURGERY TRANSFER,14113,ASPIRIN 81 MG PO TBEC,Medication,878,8978,0.097794609
+8255,1713,IP CTS CARDIAC SURGERY TRANSFER,14632,TRAMADOL 50 MG PO TABS,Medication,2328,8978,0.259300512
+8256,1713,IP CTS CARDIAC SURGERY TRANSFER,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,2091,8978,0.232902651
+8257,1713,IP CTS CARDIAC SURGERY TRANSFER,28899,OXYCODONE 15 MG PO TABS,Medication,13,8978,0.001447984
+8258,1713,IP CTS CARDIAC SURGERY TRANSFER,28900,OXYCODONE 30 MG PO TABS,Medication,1,8978,0.000111383
+8259,1713,IP CTS CARDIAC SURGERY TRANSFER,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,103,8978,0.011472488
+8260,1713,IP CTS CARDIAC SURGERY TRANSFER,35943,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,Medication,2,8978,0.000222767
+8261,1713,IP CTS CARDIAC SURGERY TRANSFER,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,2302,8978,0.256404544
+8262,1713,IP CTS CARDIAC SURGERY TRANSFER,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,1,8978,0.000111383
+8263,1713,IP CTS CARDIAC SURGERY TRANSFER,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2284,8978,0.254399644
+8264,1713,IP CTS CARDIAC SURGERY TRANSFER,87348,"EPOETIN ALFA 40,000 UNIT/ML INJ SOLN",Medication,1200,8978,0.133660058
+8265,1713,IP CTS CARDIAC SURGERY TRANSFER,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1639,8978,0.182557362
+8266,1713,IP CTS CARDIAC SURGERY TRANSFER,92585,FAMOTIDINE 40 MG/5 ML PO SUSP,Medication,1,8978,0.000111383
+8267,1713,IP CTS CARDIAC SURGERY TRANSFER,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,260,8978,0.028959679
+8268,1713,IP CTS CARDIAC SURGERY TRANSFER,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,8978,0.000111383
+8269,1713,IP CTS CARDIAC SURGERY TRANSFER,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,6,8978,0.0006683
+8270,1713,IP CTS CARDIAC SURGERY TRANSFER,111320,MULTIVITAMIN WITH IRON-MINERAL PO LIQD,Medication,11,8978,0.001225217
+8271,1713,IP CTS CARDIAC SURGERY TRANSFER,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,631,8978,0.070282914
+8272,1713,IP CTS CARDIAC SURGERY TRANSFER,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,5,8978,0.000556917
+8273,1713,IP CTS CARDIAC SURGERY TRANSFER,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2628,8978,0.292715527
+8274,1713,IP CTS CARDIAC SURGERY TRANSFER,208037,FERROUS SULFATE 142 MG (45 MG IRON) PO TBSR,Medication,1016,8978,0.113165516
+8275,1713,IP CTS CARDIAC SURGERY TRANSFER,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,14,8978,0.001559367
+8276,1713,IP CTS CARDIAC SURGERY TRANSFER,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,7,8978,0.000779684
+8277,1713,IP CTS CARDIAC SURGERY TRANSFER,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,11,8978,0.001225217
+8278,1713,IP CTS CARDIAC SURGERY TRANSFER,213228,FERROUS SULFATE 140 MG (45 MG IRON) PO TBSR,Medication,1263,8978,0.140677211
+8279,1713,IP CTS CARDIAC SURGERY TRANSFER,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,3,8978,0.00033415
+8280,1713,IP CTS CARDIAC SURGERY TRANSFER,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1399,8978,0.155825351
+8281,1713,IP CTS CARDIAC SURGERY TRANSFER,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,2309,8978,0.257184228
+8282,1713,IP CTS CARDIAC SURGERY TRANSFER,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,27,8978,0.003007351
+8283,1713,IP CTS CARDIAC SURGERY TRANSFER,540579,BENZOCAINE-MENTHOL SUGAR FREE MAXIMUM STRENGTH LOZG,Medication,2136,8978,0.237914903
+8284,1713,IP CTS CARDIAC SURGERY TRANSFER,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,2303,8978,0.256515928
+8285,1713,IP CTS CARDIAC SURGERY TRANSFER,LABCBCD,CBC WITH DIFF,Lab,3188,8978,0.355090221
+8286,1713,IP CTS CARDIAC SURGERY TRANSFER,LABMETB,"METABOLIC PANEL, BASIC",Lab,2825,8978,0.314658053
+8287,1713,IP CTS CARDIAC SURGERY TRANSFER,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2794,8978,0.311205168
+8288,1713,IP CTS CARDIAC SURGERY TRANSFER,LABPLTS,PLATELET COUNT,Lab,9,8978,0.00100245
+8289,1713,IP CTS CARDIAC SURGERY TRANSFER,LABPT,PROTHROMBIN TIME,Lab,280,8978,0.031187347
+8290,1713,IP CTS CARDIAC SURGERY TRANSFER,POC14,POC GLUCOSE BY METER,Lab,566,8978,0.063042994
+8291,1713,IP CTS CARDIAC SURGERY TRANSFER,IMGDXCH2,XR CHEST 2V,Imaging,2859,8978,0.318445088
+8292,1716,IP ATIC RITUXIMAB (RITUXAN),101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,28,0.071428571
+8293,1716,IP ATIC RITUXIMAB (RITUXAN),102,ACETAMINOPHEN 500 MG PO TABS,Medication,8,28,0.285714286
+8294,1716,IP ATIC RITUXIMAB (RITUXAN),2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,14,28,0.5
+8295,1716,IP ATIC RITUXIMAB (RITUXAN),209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,7,28,0.25
+8296,1716,IP ATIC RITUXIMAB (RITUXAN),213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,10,28,0.357142857
+8297,1716,IP ATIC RITUXIMAB (RITUXAN),520000,RITUXIMAB IV STANDARD INFUSION,Medication,14,28,0.5
+8298,1717,IP ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN (CYTOGAM),101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,3,1
+8299,1717,IP ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN (CYTOGAM),2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,3,0.333333333
+8300,1717,IP ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN (CYTOGAM),2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,2,3,0.666666667
+8301,1717,IP ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN (CYTOGAM),213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,1,3,0.333333333
+8302,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,122,ACETYLCYSTEINE 100 MG/ML (10 %) MISC SOLN,Medication,21,1310,0.016030534
+8303,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,123,ACETYLCYSTEINE 200 MG/ML (20 %) MISC SOLN,Medication,7,1310,0.005343511
+8304,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,154,1310,0.117557252
+8305,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,251,ALBUTEROL SULFATE 5 MG/ML INH NEBU,Medication,7,1310,0.005343511
+8306,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,7325,SODIUM CHLORIDE 0.9 % INH NEBU,Medication,6,1310,0.004580153
+8307,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,7326,SODIUM CHLORIDE 10 % INH NEBU,Medication,15,1310,0.011450382
+8308,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,7327,SODIUM CHLORIDE 3 % INH NEBU,Medication,12,1310,0.009160305
+8309,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,74,1310,0.05648855
+8310,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,18,1310,0.013740458
+8311,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,77973,FLUTICASONE-SALMETEROL 250-50 MCG/DOSE INH DSDV,Medication,2,1310,0.001526718
+8312,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,386,1310,0.294656489
+8313,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,89401,FLUTICASONE-SALMETEROL 500-50 MCG/DOSE INH DSDV,Medication,2,1310,0.001526718
+8314,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,89904,SALMETEROL 50 MCG/DOSE INH DSDV,Medication,1,1310,0.000763359
+8315,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,90913,BUDESONIDE 0.5 MG/2 ML INH NBSP,Medication,5,1310,0.003816794
+8316,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,91892,FLUTICASONE 220 MCG/ACTUATION INH AERO,Medication,1,1310,0.000763359
+8317,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,92642,LEVALBUTEROL HCL 1.25 MG/3 ML INH NEBU,Medication,39,1310,0.029770992
+8318,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,94683,BECLOMETHASONE DIPROPIONATE 40 MCG/ACTUATION INH AERO,Medication,1,1310,0.000763359
+8319,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,120980,TIOTROPIUM BROMIDE 18 MCG INH CPDV,Medication,5,1310,0.003816794
+8320,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,122688,RACEPINEPHRINE 2.25 % INH NEBU,Medication,6,1310,0.004580153
+8321,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,1,1310,0.000763359
+8322,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,123759,LEVALBUTEROL HCL 1.25 MG/0.5 ML INH NEBU,Medication,4,1310,0.003053435
+8323,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,125355,IPRATROPIUM BROMIDE 17 MCG/ACTUATION INH HFAA,Medication,8,1310,0.00610687
+8324,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,199053,ARFORMOTEROL 15 MCG/2 ML INH NEBU,Medication,5,1310,0.003816794
+8325,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,199112,BUDESONIDE 180 MCG/ACTUATION INH AEPB,Medication,2,1310,0.001526718
+8326,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,225590,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH AERO,Medication,6,1310,0.004580153
+8327,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,233335,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH MIST,Medication,9,1310,0.006870229
+8328,1724,IP RESP INHALED MEDICATION NEBULIZER/MDI,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,2,1310,0.001526718
+8329,1725,"IP PHARM HYPERCVAD CYCLES 1 , 3 ,  5 , 7",4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,1,1
+8330,1725,"IP PHARM HYPERCVAD CYCLES 1 , 3 ,  5 , 7",6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,1,1,1
+8331,1725,"IP PHARM HYPERCVAD CYCLES 1 , 3 ,  5 , 7",221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,1,1
+8332,1729,RETIRED IP ITA PHARM RITUXIMAB,101,ACETAMINOPHEN 325 MG PO TABS,Medication,47,58,0.810344828
+8333,1729,IP ITA PHARM RITUXIMAB,101,ACETAMINOPHEN 325 MG PO TABS,Medication,47,58,0.810344828
+8334,1729,RETIRED IP ITA PHARM RITUXIMAB,2327,DEXAMETHASONE 4 MG PO TABS,Medication,5,58,0.086206897
+8335,1729,IP ITA PHARM RITUXIMAB,2327,DEXAMETHASONE 4 MG PO TABS,Medication,5,58,0.086206897
+8336,1729,RETIRED IP ITA PHARM RITUXIMAB,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,41,58,0.706896552
+8337,1729,IP ITA PHARM RITUXIMAB,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,41,58,0.706896552
+8338,1729,RETIRED IP ITA PHARM RITUXIMAB,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,25,58,0.431034483
+8339,1729,IP ITA PHARM RITUXIMAB,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,25,58,0.431034483
+8340,1729,RETIRED IP ITA PHARM RITUXIMAB,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,23,58,0.396551724
+8341,1729,IP ITA PHARM RITUXIMAB,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,23,58,0.396551724
+8342,1729,RETIRED IP ITA PHARM RITUXIMAB,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,37,58,0.637931034
+8343,1729,IP ITA PHARM RITUXIMAB,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,37,58,0.637931034
+8344,1729,RETIRED IP ITA PHARM RITUXIMAB,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,33,58,0.568965517
+8345,1729,IP ITA PHARM RITUXIMAB,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,33,58,0.568965517
+8346,1729,RETIRED IP ITA PHARM RITUXIMAB,520000,RITUXIMAB IV STANDARD INFUSION,Medication,30,58,0.517241379
+8347,1729,IP ITA PHARM RITUXIMAB,520000,RITUXIMAB IV STANDARD INFUSION,Medication,30,58,0.517241379
+8348,1729,RETIRED IP ITA PHARM RITUXIMAB,520020,RITUXIMAB IV RAPID INFUSION,Medication,1,58,0.017241379
+8349,1729,IP ITA PHARM RITUXIMAB,520020,RITUXIMAB IV RAPID INFUSION,Medication,1,58,0.017241379
+8350,1729,RETIRED IP ITA PHARM RITUXIMAB,9000000,ZZZ IMS TEMPLATE,Medication,3,58,0.051724138
+8351,1729,IP ITA PHARM RITUXIMAB,9000000,ZZZ IMS TEMPLATE,Medication,3,58,0.051724138
+8352,1742,IP NUC I-131 THYROID THERAPY ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,17,50,0.34
+8353,1742,IP NUC I-131 THYROID THERAPY ADMISSION,11138,PROCHLORPERAZINE 25 MG PR SUPP,Medication,8,50,0.16
+8354,1742,IP NUC I-131 THYROID THERAPY ADMISSION,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2,50,0.04
+8355,1742,IP NUC I-131 THYROID THERAPY ADMISSION,9000000,ZZZ IMS TEMPLATE,Medication,1,50,0.02
+8356,1745,IP PAMF ORTHO HIP FRACTURE POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,2,0.5
+8357,1745,IP PAMF ORTHO HIP FRACTURE POSTOP,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,1,2,0.5
+8358,1745,IP PAMF ORTHO HIP FRACTURE POSTOP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1,2,0.5
+8359,1745,IP PAMF ORTHO HIP FRACTURE POSTOP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,2,0.5
+8360,1745,IP PAMF ORTHO HIP FRACTURE POSTOP,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,2,0.5
+8361,1745,IP PAMF ORTHO HIP FRACTURE POSTOP,LABHCTX,HEMATOCRIT,Lab,1,2,0.5
+8362,1746,IP PAMF ORTHO TRAUMA,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,5,0.2
+8363,1746,IP PAMF ORTHO TRAUMA,1080,BISACODYL 10 MG PR SUPP,Medication,1,5,0.2
+8364,1746,IP PAMF ORTHO TRAUMA,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1,5,0.2
+8365,1746,IP PAMF ORTHO TRAUMA,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,2,5,0.4
+8366,1746,IP PAMF ORTHO TRAUMA,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,5,0.2
+8367,1746,IP PAMF ORTHO TRAUMA,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,5,0.2
+8368,1746,IP PAMF ORTHO TRAUMA,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,5,0.4
+8369,1746,IP PAMF ORTHO TRAUMA,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,5,0.2
+8370,1746,IP PAMF ORTHO TRAUMA,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,1,5,0.2
+8371,1746,IP PAMF ORTHO TRAUMA,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,1,5,0.2
+8372,1746,IP PAMF ORTHO TRAUMA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,5,0.2
+8373,1746,IP PAMF ORTHO TRAUMA,540300,MORPHINE INJECTABLE SYRINGE,Medication,2,5,0.4
+8374,1746,IP PAMF ORTHO TRAUMA,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,2,5,0.4
+8375,1746,IP PAMF ORTHO TRAUMA,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,5,0.2
+8376,1746,IP PAMF ORTHO TRAUMA,LABPT,PROTHROMBIN TIME,Lab,1,5,0.2
+8377,1746,IP PAMF ORTHO TRAUMA,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,5,0.2
+8378,1746,IP PAMF ORTHO TRAUMA,LABTYPSNI,TYPE AND SCREEN,Lab,1,5,0.2
+8379,1746,IP PAMF ORTHO TRAUMA,IMGUSNVLOW,US VEIN LOWER EXTREMITY BILAT R/O DVT,Imaging,1,5,0.2
+8380,1746,IP PAMF ORTHO TRAUMA,LABBBPRBC,RED BLOOD CELLS,Blood Product,1,5,0.2
+8381,1747,RETIRED IP PAMF ORTHO TKA,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,144,0.0625
+8382,1747,RETIRED IP PAMF ORTHO TKA,102,ACETAMINOPHEN 500 MG PO TABS,Medication,35,144,0.243055556
+8383,1747,RETIRED IP PAMF ORTHO TKA,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,5,144,0.034722222
+8384,1747,RETIRED IP PAMF ORTHO TKA,1080,BISACODYL 10 MG PR SUPP,Medication,41,144,0.284722222
+8385,1747,RETIRED IP PAMF ORTHO TKA,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,40,144,0.277777778
+8386,1747,RETIRED IP PAMF ORTHO TKA,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1,144,0.006944444
+8387,1747,RETIRED IP PAMF ORTHO TKA,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,5,144,0.034722222
+8388,1747,RETIRED IP PAMF ORTHO TKA,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,144,0.034722222
+8389,1747,RETIRED IP PAMF ORTHO TKA,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,37,144,0.256944444
+8390,1747,RETIRED IP PAMF ORTHO TKA,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,39,144,0.270833333
+8391,1747,RETIRED IP PAMF ORTHO TKA,7753,TEMAZEPAM 15 MG PO CAPS,Medication,28,144,0.194444444
+8392,1747,RETIRED IP PAMF ORTHO TKA,8751,WARFARIN 5 MG PO TABS,Medication,12,144,0.083333333
+8393,1747,RETIRED IP PAMF ORTHO TKA,10814,OXYCODONE 5 MG PO TABS,Medication,39,144,0.270833333
+8394,1747,RETIRED IP PAMF ORTHO TKA,11349,SENNOSIDES 8.6 MG PO TABS,Medication,41,144,0.284722222
+8395,1747,RETIRED IP PAMF ORTHO TKA,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,144,0.013888889
+8396,1747,RETIRED IP PAMF ORTHO TKA,27920,OXYCODONE 10 MG PO TB12,Medication,1,144,0.006944444
+8397,1747,RETIRED IP PAMF ORTHO TKA,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,41,144,0.284722222
+8398,1747,RETIRED IP PAMF ORTHO TKA,77052,CELECOXIB 200 MG PO CAPS,Medication,15,144,0.104166667
+8399,1747,RETIRED IP PAMF ORTHO TKA,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,34,144,0.236111111
+8400,1747,RETIRED IP PAMF ORTHO TKA,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,40,144,0.277777778
+8401,1747,RETIRED IP PAMF ORTHO TKA,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,17,144,0.118055556
+8402,1747,RETIRED IP PAMF ORTHO TKA,92173,ZOLPIDEM 5 MG PO TABS,Medication,1,144,0.006944444
+8403,1747,RETIRED IP PAMF ORTHO TKA,93715,CELECOXIB 100 MG PO CAPS,Medication,8,144,0.055555556
+8404,1747,RETIRED IP PAMF ORTHO TKA,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,144,0.006944444
+8405,1747,RETIRED IP PAMF ORTHO TKA,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,15,144,0.104166667
+8406,1747,RETIRED IP PAMF ORTHO TKA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,41,144,0.284722222
+8407,1747,RETIRED IP PAMF ORTHO TKA,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,33,144,0.229166667
+8408,1747,RETIRED IP PAMF ORTHO TKA,233900,OXYCODONE 10 MG PO TR12,Medication,21,144,0.145833333
+8409,1747,RETIRED IP PAMF ORTHO TKA,233902,OXYCODONE 20 MG PO TR12,Medication,3,144,0.020833333
+8410,1747,RETIRED IP PAMF ORTHO TKA,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,33,144,0.229166667
+8411,1747,RETIRED IP PAMF ORTHO TKA,520064,CEFAZOLIN 2 G IVPB,Medication,4,144,0.027777778
+8412,1747,RETIRED IP PAMF ORTHO TKA,540300,MORPHINE INJECTABLE SYRINGE,Medication,35,144,0.243055556
+8413,1747,RETIRED IP PAMF ORTHO TKA,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,41,144,0.284722222
+8414,1747,RETIRED IP PAMF ORTHO TKA,LABHCTX,HEMATOCRIT,Lab,43,144,0.298611111
+8415,1747,RETIRED IP PAMF ORTHO TKA,LABMETB,"METABOLIC PANEL, BASIC",Lab,42,144,0.291666667
+8416,1747,RETIRED IP PAMF ORTHO TKA,LABPT,PROTHROMBIN TIME,Lab,12,144,0.083333333
+8417,1747,RETIRED IP PAMF ORTHO TKA,LABTYPSNI,TYPE AND SCREEN,Lab,2,144,0.013888889
+8418,1747,RETIRED IP PAMF ORTHO TKA,IMGDXKN2LT,XR KNEE 1 OR 2V LT,Imaging,6,144,0.041666667
+8419,1747,RETIRED IP PAMF ORTHO TKA,IMGDXKN2RT,XR KNEE 1 OR 2V RT,Imaging,6,144,0.041666667
+8420,1748,RETIRED IP PAMF ORTHO THA,101,ACETAMINOPHEN 325 MG PO TABS,Medication,449,2288,0.196241259
+8421,1748,RETIRED IP PAMF ORTHO THA,102,ACETAMINOPHEN 500 MG PO TABS,Medication,225,2288,0.098339161
+8422,1748,RETIRED IP PAMF ORTHO THA,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,202,2288,0.088286713
+8423,1748,RETIRED IP PAMF ORTHO THA,1080,BISACODYL 10 MG PR SUPP,Medication,823,2288,0.359702797
+8424,1748,RETIRED IP PAMF ORTHO THA,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1398,2288,0.611013986
+8425,1748,RETIRED IP PAMF ORTHO THA,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,823,2288,0.359702797
+8426,1748,RETIRED IP PAMF ORTHO THA,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,437,2288,0.190996503
+8427,1748,RETIRED IP PAMF ORTHO THA,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,382,2288,0.166958042
+8428,1748,RETIRED IP PAMF ORTHO THA,3769,HYDROXYZINE HCL 25 MG/ML IM SOLN,Medication,42,2288,0.018356643
+8429,1748,RETIRED IP PAMF ORTHO THA,3770,HYDROXYZINE HCL 50 MG/ML IM SOLN,Medication,516,2288,0.225524476
+8430,1748,RETIRED IP PAMF ORTHO THA,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,5,2288,0.002185315
+8431,1748,RETIRED IP PAMF ORTHO THA,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,5,2288,0.002185315
+8432,1748,RETIRED IP PAMF ORTHO THA,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,34,2288,0.01486014
+8433,1748,RETIRED IP PAMF ORTHO THA,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,644,2288,0.281468531
+8434,1748,RETIRED IP PAMF ORTHO THA,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,3,2288,0.001311189
+8435,1748,RETIRED IP PAMF ORTHO THA,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,802,2288,0.350524476
+8436,1748,RETIRED IP PAMF ORTHO THA,7753,TEMAZEPAM 15 MG PO CAPS,Medication,172,2288,0.075174825
+8437,1748,RETIRED IP PAMF ORTHO THA,8748,WARFARIN 10 MG PO TABS,Medication,1,2288,0.000437063
+8438,1748,RETIRED IP PAMF ORTHO THA,8750,WARFARIN 2.5 MG PO TABS,Medication,1,2288,0.000437063
+8439,1748,RETIRED IP PAMF ORTHO THA,8751,WARFARIN 5 MG PO TABS,Medication,4,2288,0.001748252
+8440,1748,RETIRED IP PAMF ORTHO THA,8752,WARFARIN 7.5 MG PO TABS,Medication,2,2288,0.000874126
+8441,1748,RETIRED IP PAMF ORTHO THA,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,2288,0.000874126
+8442,1748,RETIRED IP PAMF ORTHO THA,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,5,2288,0.002185315
+8443,1748,RETIRED IP PAMF ORTHO THA,10676,NABUMETONE 500 MG PO TABS,Medication,7,2288,0.003059441
+8444,1748,RETIRED IP PAMF ORTHO THA,10814,OXYCODONE 5 MG PO TABS,Medication,849,2288,0.371066434
+8445,1748,RETIRED IP PAMF ORTHO THA,11349,SENNOSIDES 8.6 MG PO TABS,Medication,873,2288,0.381555944
+8446,1748,RETIRED IP PAMF ORTHO THA,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,1,2288,0.000437063
+8447,1748,RETIRED IP PAMF ORTHO THA,11664,WARFARIN 1 MG PO TABS,Medication,1,2288,0.000437063
+8448,1748,RETIRED IP PAMF ORTHO THA,13410,ACETAMINOPHEN 650 MG PO TBSR,Medication,15,2288,0.006555944
+8449,1748,RETIRED IP PAMF ORTHO THA,14858,D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,546,2288,0.238636364
+8450,1748,RETIRED IP PAMF ORTHO THA,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,174,2288,0.076048951
+8451,1748,RETIRED IP PAMF ORTHO THA,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,4,2288,0.001748252
+8452,1748,RETIRED IP PAMF ORTHO THA,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,61,2288,0.026660839
+8453,1748,RETIRED IP PAMF ORTHO THA,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,611,2288,0.267045455
+8454,1748,RETIRED IP PAMF ORTHO THA,27920,OXYCODONE 10 MG PO TB12,Medication,685,2288,0.299388112
+8455,1748,RETIRED IP PAMF ORTHO THA,27921,OXYCODONE 20 MG PO TB12,Medication,24,2288,0.01048951
+8456,1748,RETIRED IP PAMF ORTHO THA,27922,OXYCODONE 40 MG PO TB12,Medication,4,2288,0.001748252
+8457,1748,RETIRED IP PAMF ORTHO THA,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,24,2288,0.01048951
+8458,1748,RETIRED IP PAMF ORTHO THA,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,11,2288,0.004807692
+8459,1748,RETIRED IP PAMF ORTHO THA,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,834,2288,0.36451049
+8460,1748,RETIRED IP PAMF ORTHO THA,77052,CELECOXIB 200 MG PO CAPS,Medication,398,2288,0.173951049
+8461,1748,RETIRED IP PAMF ORTHO THA,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,259,2288,0.113199301
+8462,1748,RETIRED IP PAMF ORTHO THA,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,824,2288,0.36013986
+8463,1748,RETIRED IP PAMF ORTHO THA,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,481,2288,0.210227273
+8464,1748,RETIRED IP PAMF ORTHO THA,92173,ZOLPIDEM 5 MG PO TABS,Medication,570,2288,0.249125874
+8465,1748,RETIRED IP PAMF ORTHO THA,93295,ZOLPIDEM 10 MG PO TABS,Medication,3,2288,0.001311189
+8466,1748,RETIRED IP PAMF ORTHO THA,93715,CELECOXIB 100 MG PO CAPS,Medication,44,2288,0.019230769
+8467,1748,RETIRED IP PAMF ORTHO THA,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,129,2288,0.056381119
+8468,1748,RETIRED IP PAMF ORTHO THA,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,521,2288,0.22770979
+8469,1748,RETIRED IP PAMF ORTHO THA,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,9,2288,0.003933566
+8470,1748,RETIRED IP PAMF ORTHO THA,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,8,2288,0.003496503
+8471,1748,RETIRED IP PAMF ORTHO THA,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,23,2288,0.010052448
+8472,1748,RETIRED IP PAMF ORTHO THA,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,18,2288,0.007867133
+8473,1748,RETIRED IP PAMF ORTHO THA,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,830,2288,0.362762238
+8474,1748,RETIRED IP PAMF ORTHO THA,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,11,2288,0.004807692
+8475,1748,RETIRED IP PAMF ORTHO THA,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,2288,0.000437063
+8476,1748,RETIRED IP PAMF ORTHO THA,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,2288,0.000437063
+8477,1748,RETIRED IP PAMF ORTHO THA,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,497,2288,0.21722028
+8478,1748,RETIRED IP PAMF ORTHO THA,233900,OXYCODONE 10 MG PO TR12,Medication,5,2288,0.002185315
+8479,1748,RETIRED IP PAMF ORTHO THA,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,17,2288,0.00743007
+8480,1748,RETIRED IP PAMF ORTHO THA,520064,CEFAZOLIN 2 G IVPB,Medication,170,2288,0.074300699
+8481,1748,RETIRED IP PAMF ORTHO THA,540300,MORPHINE INJECTABLE SYRINGE,Medication,741,2288,0.323863636
+8482,1748,RETIRED IP PAMF ORTHO THA,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,17,2288,0.00743007
+8483,1748,RETIRED IP PAMF ORTHO THA,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,71,2288,0.031031469
+8484,1748,RETIRED IP PAMF ORTHO THA,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,752,2288,0.328671329
+8485,1748,RETIRED IP PAMF ORTHO THA,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,44,2288,0.019230769
+8486,1748,RETIRED IP PAMF ORTHO THA,9000000,ZZZ IMS TEMPLATE,Medication,6,2288,0.002622378
+8487,1748,RETIRED IP PAMF ORTHO THA,LABHCTX,HEMATOCRIT,Lab,654,2288,0.285839161
+8488,1748,RETIRED IP PAMF ORTHO THA,LABMETB,"METABOLIC PANEL, BASIC",Lab,828,2288,0.361888112
+8489,1748,RETIRED IP PAMF ORTHO THA,LABPLTS,PLATELET COUNT,Lab,175,2288,0.076486014
+8490,1748,RETIRED IP PAMF ORTHO THA,LABPT,PROTHROMBIN TIME,Lab,261,2288,0.114073427
+8491,1748,RETIRED IP PAMF ORTHO THA,LABTYPSNI,TYPE AND SCREEN,Lab,1,2288,0.000437063
+8492,1748,RETIRED IP PAMF ORTHO THA,IMGUSNVLOW,US VEIN LOWER EXTREMITY BILAT R/O DVT,Imaging,186,2288,0.081293706
+8493,1750,RETIRED IP PAMF ORTHO SPINE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,126,407,0.30958231
+8494,1750,RETIRED IP PAMF ORTHO SPINE,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,407,0.004914005
+8495,1750,RETIRED IP PAMF ORTHO SPINE,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,133,407,0.326781327
+8496,1750,RETIRED IP PAMF ORTHO SPINE,1080,BISACODYL 10 MG PR SUPP,Medication,145,407,0.356265356
+8497,1750,RETIRED IP PAMF ORTHO SPINE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,144,407,0.353808354
+8498,1750,RETIRED IP PAMF ORTHO SPINE,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,143,407,0.351351351
+8499,1750,RETIRED IP PAMF ORTHO SPINE,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,78,407,0.191646192
+8500,1750,RETIRED IP PAMF ORTHO SPINE,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,68,407,0.167076167
+8501,1750,RETIRED IP PAMF ORTHO SPINE,3769,HYDROXYZINE HCL 25 MG/ML IM SOLN,Medication,14,407,0.034398034
+8502,1750,RETIRED IP PAMF ORTHO SPINE,3770,HYDROXYZINE HCL 50 MG/ML IM SOLN,Medication,129,407,0.316953317
+8503,1750,RETIRED IP PAMF ORTHO SPINE,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,127,407,0.312039312
+8504,1750,RETIRED IP PAMF ORTHO SPINE,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,17,407,0.041769042
+8505,1750,RETIRED IP PAMF ORTHO SPINE,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,134,407,0.329238329
+8506,1750,RETIRED IP PAMF ORTHO SPINE,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,142,407,0.348894349
+8507,1750,RETIRED IP PAMF ORTHO SPINE,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,1,407,0.002457002
+8508,1750,RETIRED IP PAMF ORTHO SPINE,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,144,407,0.353808354
+8509,1750,RETIRED IP PAMF ORTHO SPINE,7753,TEMAZEPAM 15 MG PO CAPS,Medication,125,407,0.307125307
+8510,1750,RETIRED IP PAMF ORTHO SPINE,8751,WARFARIN 5 MG PO TABS,Medication,2,407,0.004914005
+8511,1750,RETIRED IP PAMF ORTHO SPINE,11349,SENNOSIDES 8.6 MG PO TABS,Medication,148,407,0.363636364
+8512,1750,RETIRED IP PAMF ORTHO SPINE,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,1,407,0.002457002
+8513,1750,RETIRED IP PAMF ORTHO SPINE,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,120,407,0.294840295
+8514,1750,RETIRED IP PAMF ORTHO SPINE,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,407,0.002457002
+8515,1750,RETIRED IP PAMF ORTHO SPINE,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,6,407,0.014742015
+8516,1750,RETIRED IP PAMF ORTHO SPINE,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,77,407,0.189189189
+8517,1750,RETIRED IP PAMF ORTHO SPINE,27920,OXYCODONE 10 MG PO TB12,Medication,1,407,0.002457002
+8518,1750,RETIRED IP PAMF ORTHO SPINE,27921,OXYCODONE 20 MG PO TB12,Medication,2,407,0.004914005
+8519,1750,RETIRED IP PAMF ORTHO SPINE,27922,OXYCODONE 40 MG PO TB12,Medication,1,407,0.002457002
+8520,1750,RETIRED IP PAMF ORTHO SPINE,36063,0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,22,407,0.054054054
+8521,1750,RETIRED IP PAMF ORTHO SPINE,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,145,407,0.356265356
+8522,1750,RETIRED IP PAMF ORTHO SPINE,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,146,407,0.358722359
+8523,1750,RETIRED IP PAMF ORTHO SPINE,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,107,407,0.262899263
+8524,1750,RETIRED IP PAMF ORTHO SPINE,92173,ZOLPIDEM 5 MG PO TABS,Medication,9,407,0.022113022
+8525,1750,RETIRED IP PAMF ORTHO SPINE,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,407,0.002457002
+8526,1750,RETIRED IP PAMF ORTHO SPINE,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,6,407,0.014742015
+8527,1750,RETIRED IP PAMF ORTHO SPINE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,146,407,0.358722359
+8528,1750,RETIRED IP PAMF ORTHO SPINE,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,407,0.002457002
+8529,1750,RETIRED IP PAMF ORTHO SPINE,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,4,407,0.00982801
+8530,1750,RETIRED IP PAMF ORTHO SPINE,520064,CEFAZOLIN 2 G IVPB,Medication,63,407,0.154791155
+8531,1750,RETIRED IP PAMF ORTHO SPINE,540101,FENTANYL 50 MCG/ML IV PCA,Medication,15,407,0.036855037
+8532,1750,RETIRED IP PAMF ORTHO SPINE,540300,MORPHINE INJECTABLE SYRINGE,Medication,135,407,0.331695332
+8533,1750,RETIRED IP PAMF ORTHO SPINE,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,15,407,0.036855037
+8534,1750,RETIRED IP PAMF ORTHO SPINE,540598,THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS,Medication,131,407,0.321867322
+8535,1750,RETIRED IP PAMF ORTHO SPINE,LABHCTX,HEMATOCRIT,Lab,292,407,0.717444717
+8536,1750,RETIRED IP PAMF ORTHO SPINE,LABMETB,"METABOLIC PANEL, BASIC",Lab,149,407,0.366093366
+8537,1750,RETIRED IP PAMF ORTHO SPINE,LABPT,PROTHROMBIN TIME,Lab,2,407,0.004914005
+8538,1750,RETIRED IP PAMF ORTHO SPINE,LABTYPSNI,TYPE AND SCREEN,Lab,142,407,0.348894349
+8539,1757,IP MODERATE SEDATION MEDICATIONS,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1,4,0.25
+8540,1757,IP MODERATE SEDATION MEDICATIONS,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,3,4,0.75
+8541,1757,IP MODERATE SEDATION MEDICATIONS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,4,4,1
+8542,1764,IP GEN PACU ORDERS,LABCBCD,CBC WITH DIFF,Lab,1,2,0.5
+8543,1764,IP GEN PACU ORDERS,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,2,0.5
+8544,1764,IP GEN PACU ORDERS,IMGDXCH2,XR CHEST 2V,Imaging,1,2,0.5
+8545,1770,"PHARM IP HYPERCVAD CYCLES 2 , 4 , 6 , 8",4573,LORAZEPAM 1 MG PO TABS,Medication,1,1,1
+8546,1770,"PHARM IP HYPERCVAD CYCLES 2 , 4 , 6 , 8",10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,1,1
+8547,1770,"PHARM IP HYPERCVAD CYCLES 2 , 4 , 6 , 8",221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,1,1
+8548,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,20,0.45
+8549,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,20,0.05
+8550,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,9,20,0.45
+8551,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,5,20,0.25
+8552,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,3,20,0.15
+8553,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,1,20,0.05
+8554,1772,PHARM IP THYMOGLOBULIN CYCLOSPORINE,520074,ANTITHYMOCYTE GLOBULIN RABBIT IVPB,Medication,12,20,0.6
+8555,1777,IP REH PHASE I EPILEPSY ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,487,1313,0.370906321
+8556,1777,IP REH PHASE I EPILEPSY ADMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,1313,0.000761615
+8557,1777,IP REH PHASE I EPILEPSY ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,81,1313,0.061690784
+8558,1777,IP REH PHASE I EPILEPSY ADMIT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,22,1313,0.016755522
+8559,1777,IP REH PHASE I EPILEPSY ADMIT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,246,1313,0.187357197
+8560,1777,IP REH PHASE I EPILEPSY ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,107,1313,0.081492765
+8561,1777,IP REH PHASE I EPILEPSY ADMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,68,1313,0.051789794
+8562,1777,IP REH PHASE I EPILEPSY ADMIT,4573,LORAZEPAM 1 MG PO TABS,Medication,1,1313,0.000761615
+8563,1777,IP REH PHASE I EPILEPSY ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,45,1313,0.034272658
+8564,1777,IP REH PHASE I EPILEPSY ADMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,1,1313,0.000761615
+8565,1777,IP REH PHASE I EPILEPSY ADMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,13,1313,0.00990099
+8566,1777,IP REH PHASE I EPILEPSY ADMIT,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,4,1313,0.003046458
+8567,1777,IP REH PHASE I EPILEPSY ADMIT,10608,MIDAZOLAM 5 MG/ML INJ SOLN,Medication,2,1313,0.001523229
+8568,1777,IP REH PHASE I EPILEPSY ADMIT,13632,BISACODYL 5 MG PO TBEC,Medication,333,1313,0.253617669
+8569,1777,IP REH PHASE I EPILEPSY ADMIT,15053,GLYCERIN (ADULT) PR SUPP,Medication,3,1313,0.002284844
+8570,1777,IP REH PHASE I EPILEPSY ADMIT,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,69,1313,0.052551409
+8571,1777,IP REH PHASE I EPILEPSY ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,74,1313,0.056359482
+8572,1777,IP REH PHASE I EPILEPSY ADMIT,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,1,1313,0.000761615
+8573,1777,IP REH PHASE I EPILEPSY ADMIT,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,7,1313,0.005331302
+8574,1777,IP REH PHASE I EPILEPSY ADMIT,540533,LORAZEPAM 2 MG/ML INJ (EPILEPSY ORDER SET),Medication,658,1313,0.501142422
+8575,1777,IP REH PHASE I EPILEPSY ADMIT,540535,MIDAZOLAM 5 MG/ML INJ SOLN (EPILEPSY ORDER SET),Medication,8,1313,0.006092917
+8576,1777,IP REH PHASE I EPILEPSY ADMIT,LAB11034R,ETHOSUXIMIDE LEVEL,Lab,1,1313,0.000761615
+8577,1777,IP REH PHASE I EPILEPSY ADMIT,LAB11079R,GABAPENTIN,Lab,41,1313,0.0312262
+8578,1777,IP REH PHASE I EPILEPSY ADMIT,LAB11084R,LORAZEPAM LEVEL,Lab,2,1313,0.001523229
+8579,1777,IP REH PHASE I EPILEPSY ADMIT,LAB11170R,PREGABALIN,Lab,16,1313,0.012185834
+8580,1777,IP REH PHASE I EPILEPSY ADMIT,LAB12238R,LACOSAMIDE,Lab,127,1313,0.096725057
+8581,1777,IP REH PHASE I EPILEPSY ADMIT,LAB12584R,TIAGABINE CONCENTRATION,Lab,1,1313,0.000761615
+8582,1777,IP REH PHASE I EPILEPSY ADMIT,LABCBCD,CBC WITH DIFF,Lab,643,1313,0.489718203
+8583,1777,IP REH PHASE I EPILEPSY ADMIT,LABCZPL,"CARBAMAZEPINE, SERUM",Lab,52,1313,0.03960396
+8584,1777,IP REH PHASE I EPILEPSY ADMIT,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,450,1313,0.34272658
+8585,1777,IP REH PHASE I EPILEPSY ADMIT,LABESRP,SEDIMENTATION RATE (ESR),Lab,12,1313,0.009139375
+8586,1777,IP REH PHASE I EPILEPSY ADMIT,LABLAMOL,LAMOTRIGINE LEVEL,Lab,179,1313,0.136329018
+8587,1777,IP REH PHASE I EPILEPSY ADMIT,LABLEVETL,LEVETIRACETAM LEVEL,Lab,209,1313,0.159177456
+8588,1777,IP REH PHASE I EPILEPSY ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,643,1313,0.489718203
+8589,1777,IP REH PHASE I EPILEPSY ADMIT,LABPB,PHENOBARBITAL LEVEL,Lab,14,1313,0.010662605
+8590,1777,IP REH PHASE I EPILEPSY ADMIT,LABPHEN,PHENYTOIN LEVEL,Lab,48,1313,0.036557502
+8591,1777,IP REH PHASE I EPILEPSY ADMIT,LABPROL,PROLACTIN,Lab,1,1313,0.000761615
+8592,1777,IP REH PHASE I EPILEPSY ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,25,1313,0.019040366
+8593,1777,IP REH PHASE I EPILEPSY ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,186,1313,0.14166032
+8594,1777,IP REH PHASE I EPILEPSY ADMIT,LABUPREG,"PREGNANCY TEST, URINE",Lab,139,1313,0.105864433
+8595,1777,IP REH PHASE I EPILEPSY ADMIT,LABVPAL,VALPROIC ACID,Lab,68,1313,0.051789794
+8596,1777,IP REH PHASE I EPILEPSY ADMIT,LABYCLON,"CLONAZEPAM, SERUM",Lab,29,1313,0.022086824
+8597,1777,IP REH PHASE I EPILEPSY ADMIT,LABYPRIMI,PRIMIDONE AND PHENOBARBITAL,Lab,2,1313,0.001523229
+8598,1777,IP REH PHASE I EPILEPSY ADMIT,LABYTOPIR,TOPIRAMATE,Lab,60,1313,0.045696877
+8599,1777,IP REH PHASE I EPILEPSY ADMIT,LABYZONIS,ZONISAMIDE,Lab,66,1313,0.050266565
+8600,1779,IP REH PHASE II EPILEPSY ADMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,61,0.147540984
+8601,1779,IP REH PHASE II EPILEPSY ADMIT,1080,BISACODYL 10 MG PR SUPP,Medication,5,61,0.081967213
+8602,1779,IP REH PHASE II EPILEPSY ADMIT,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1,61,0.016393443
+8603,1779,IP REH PHASE II EPILEPSY ADMIT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,5,61,0.081967213
+8604,1779,IP REH PHASE II EPILEPSY ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,61,0.114754098
+8605,1779,IP REH PHASE II EPILEPSY ADMIT,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,3,61,0.049180328
+8606,1779,IP REH PHASE II EPILEPSY ADMIT,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,1,61,0.016393443
+8607,1779,IP REH PHASE II EPILEPSY ADMIT,13632,BISACODYL 5 MG PO TBEC,Medication,7,61,0.114754098
+8608,1779,IP REH PHASE II EPILEPSY ADMIT,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,6,61,0.098360656
+8609,1779,IP REH PHASE II EPILEPSY ADMIT,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,7,61,0.114754098
+8610,1779,IP REH PHASE II EPILEPSY ADMIT,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,3,61,0.049180328
+8611,1779,IP REH PHASE II EPILEPSY ADMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,3,61,0.049180328
+8612,1779,IP REH PHASE II EPILEPSY ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,7,61,0.114754098
+8613,1779,IP REH PHASE II EPILEPSY ADMIT,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,3,61,0.049180328
+8614,1779,IP REH PHASE II EPILEPSY ADMIT,540533,LORAZEPAM 2 MG/ML INJ (EPILEPSY ORDER SET),Medication,20,61,0.327868852
+8615,1779,IP REH PHASE II EPILEPSY ADMIT,541100,CEFOTAXIME 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,61,0.032786885
+8616,1779,IP REH PHASE II EPILEPSY ADMIT,LABCBCD,CBC WITH DIFF,Lab,10,61,0.163934426
+8617,1779,IP REH PHASE II EPILEPSY ADMIT,LABEP1,ELECTROLYTE PANEL,Lab,2,61,0.032786885
+8618,1779,IP REH PHASE II EPILEPSY ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,7,61,0.114754098
+8619,1779,IP REH PHASE II EPILEPSY ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,4,61,0.06557377
+8620,1779,IP REH PHASE II EPILEPSY ADMIT,LABYTOPIR,TOPIRAMATE,Lab,1,61,0.016393443
+8621,1779,IP REH PHASE II EPILEPSY ADMIT,IMGCTH,CT HEAD,Imaging,1,61,0.016393443
+8622,1789,IP BMT LABS,LABCBCD,CBC WITH DIFF,Lab,4,5,0.8
+8623,1789,IP BMT LABS,LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,2,5,0.4
+8624,1789,IP BMT LABS,LABFK506L,TACROLIMUS (FK506),Lab,2,5,0.4
+8625,1789,IP BMT LABS,LABIGG,"IGG, SERUM",Lab,1,5,0.2
+8626,1789,IP BMT LABS,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,1,5,0.2
+8627,1789,IP BMT LABS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,5,5,1
+8628,1789,IP BMT LABS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,5,0.2
+8629,1789,IP BMT LABS,LABSIROL,SIROLIMUS LEVEL,Lab,1,5,0.2
+8630,1789,IP BMT LABS,LABTYPSNI,TYPE AND SCREEN,Lab,1,5,0.2
+8631,1791,IP ICU END OF LIFE CARE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,27,2449,0.011024908
+8632,1791,IP ICU END OF LIFE CARE,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,23,2449,0.009391588
+8633,1791,IP ICU END OF LIFE CARE,736,ATROPINE 1 % OPHT DROP,Medication,140,2449,0.05716619
+8634,1791,IP ICU END OF LIFE CARE,3497,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,Medication,660,2449,0.269497754
+8635,1791,IP ICU END OF LIFE CARE,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,220,2449,0.089832585
+8636,1791,IP ICU END OF LIFE CARE,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,3,2449,0.00122499
+8637,1791,IP ICU END OF LIFE CARE,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,120,2449,0.048999592
+8638,1791,IP ICU END OF LIFE CARE,7108,SCOPOLAMINE HBR 0.4 MG/ML INJ SOLN,Medication,93,2449,0.037974684
+8639,1791,IP ICU END OF LIFE CARE,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,552,2449,0.225398122
+8640,1791,IP ICU END OF LIFE CARE,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,352,2449,0.143732136
+8641,1791,IP ICU END OF LIFE CARE,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,125,2449,0.051041241
+8642,1791,IP ICU END OF LIFE CARE,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,184,2449,0.075132707
+8643,1791,IP ICU END OF LIFE CARE,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,390,2449,0.159248673
+8644,1791,IP ICU END OF LIFE CARE,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,177,2449,0.072274398
+8645,1791,IP ICU END OF LIFE CARE,126132,MORPHINE (PF) IN D5W 100 MG/100 ML (1 MG/ML) IV SOLP,Medication,245,2449,0.100040833
+8646,1791,IP ICU END OF LIFE CARE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,577,2449,0.23560637
+8647,1791,IP ICU END OF LIFE CARE,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,20,2449,0.008166599
+8648,1791,IP ICU END OF LIFE CARE,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,6,2449,0.00244998
+8649,1791,IP ICU END OF LIFE CARE,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,334,2449,0.136382197
+8650,1791,IP ICU END OF LIFE CARE,231233,MIDAZOLAM (PF) 1 MG/ML INJ SOLN,Medication,1,2449,0.00040833
+8651,1791,IP ICU END OF LIFE CARE,530003,FENTANYL  10 MCG/ML IV INFUSION,Medication,23,2449,0.009391588
+8652,1791,IP ICU END OF LIFE CARE,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,141,2449,0.05757452
+8653,1791,IP ICU END OF LIFE CARE,530019,LORAZEPAM 0.5 MG/ML IV INFUSION,Medication,72,2449,0.029399755
+8654,1791,IP ICU END OF LIFE CARE,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,199,2449,0.081257656
+8655,1791,IP ICU END OF LIFE CARE,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,162,2449,0.066149449
+8656,1791,IP ICU END OF LIFE CARE,540300,MORPHINE INJECTABLE SYRINGE,Medication,1,2449,0.00040833
+8657,1791,IP ICU END OF LIFE CARE,540638,FENTANYL 50 MCG/ML IV INFUSION,Medication,9,2449,0.003674969
+8658,1791,IP ICU END OF LIFE CARE,540989,MORPHINE (PF) (1 MG/ML) IV PCA CONTINUOUS INFUSION,Medication,1,2449,0.00040833
+8659,1791,IP ICU END OF LIFE CARE,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,31,2449,0.012658228
+8660,1791,IP ICU END OF LIFE CARE,541350,MORPHINE 1 MG/ML IV INFUSION,Medication,63,2449,0.025724786
+8661,1791,IP ICU END OF LIFE CARE,541635,MORPHINE 1MG/ML IV INFUSION ICU END OF LIFE,Medication,47,2449,0.019191507
+8662,1791,IP ICU END OF LIFE CARE,541636,FENTANYL 10 MCG/ML IV INFUSION ICU END OF LIFE,Medication,12,2449,0.004899959
+8663,1791,IP ICU END OF LIFE CARE,541637,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC) ICU END OF LIFE,Medication,56,2449,0.022866476
+8664,1791,IP ICU END OF LIFE CARE,541639,MIDAZOLAM 100 MG IN D5W 100 ML IV INFUSION ICU END OF LIFE,Medication,52,2449,0.021233156
+8665,1791,IP ICU END OF LIFE CARE,541640,LORAZEPAM 0.5 MG /ML IV INFUSION ICU END OF LIFE,Medication,10,2449,0.004083299
+8666,1799,IP ICU CRISIS,7536,SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN,Medication,2,111,0.018018018
+8667,1799,IP ICU CRISIS,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,1,111,0.009009009
+8668,1799,IP ICU CRISIS,20472,ETOMIDATE 2 MG/ML IV SOLN,Medication,2,111,0.018018018
+8669,1799,IP ICU CRISIS,83055,ROCURONIUM 10 MG/ML IV SOLN,Medication,1,111,0.009009009
+8670,1799,IP ICU CRISIS,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,2,111,0.018018018
+8671,1799,IP ICU CRISIS,96560,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,Medication,1,111,0.009009009
+8672,1799,IP ICU CRISIS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,111,0.009009009
+8673,1799,IP ICU CRISIS,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,1,111,0.009009009
+8674,1799,IP ICU CRISIS,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,1,111,0.009009009
+8675,1799,IP ICU CRISIS,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,2,111,0.018018018
+8676,1799,IP ICU CRISIS,530003,FENTANYL  10 MCG/ML IV INFUSION,Medication,1,111,0.009009009
+8677,1799,IP ICU CRISIS,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,1,111,0.009009009
+8678,1799,IP ICU CRISIS,540102,NS IV BOLUS,Medication,3,111,0.027027027
+8679,1799,IP ICU CRISIS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,111,0.009009009
+8680,1799,IP ICU CRISIS,540114,PROPOFOL 10 MG/ML IV INJECTION,Medication,2,111,0.018018018
+8681,1799,IP ICU CRISIS,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,1,111,0.009009009
+8682,1799,IP ICU CRISIS,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,2,111,0.018018018
+8683,1799,IP ICU CRISIS,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,1,111,0.009009009
+8684,1799,IP ICU CRISIS,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,19,111,0.171171171
+8685,1799,IP ICU CRISIS,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,16,111,0.144144144
+8686,1799,IP ICU CRISIS,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,60,111,0.540540541
+8687,1799,IP ICU CRISIS,LABCBCD,CBC WITH DIFF,Lab,61,111,0.54954955
+8688,1799,IP ICU CRISIS,LABDIC,DIC SCREEN,Lab,9,111,0.081081081
+8689,1799,IP ICU CRISIS,LABGRAM,GRAM STAIN,Lab,1,111,0.009009009
+8690,1799,IP ICU CRISIS,LABHFP,HEPATIC FUNCTION PANEL A,Lab,43,111,0.387387387
+8691,1799,IP ICU CRISIS,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,59,111,0.531531532
+8692,1799,IP ICU CRISIS,LABMETB,"METABOLIC PANEL, BASIC",Lab,61,111,0.54954955
+8693,1799,IP ICU CRISIS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,65,111,0.585585586
+8694,1799,IP ICU CRISIS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,47,111,0.423423423
+8695,1799,IP ICU CRISIS,LABPT,PROTHROMBIN TIME,Lab,51,111,0.459459459
+8696,1799,IP ICU CRISIS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,39,111,0.351351351
+8697,1799,IP ICU CRISIS,LABRESP,RESPIRATORY CULTURE,Lab,3,111,0.027027027
+8698,1799,IP ICU CRISIS,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,9,111,0.081081081
+8699,1799,IP ICU CRISIS,LABTNI,TROPONIN I,Lab,22,111,0.198198198
+8700,1799,IP ICU CRISIS,LABTYPSNI,TYPE AND SCREEN,Lab,5,111,0.045045045
+8701,1799,IP ICU CRISIS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,4,111,0.036036036
+8702,1799,IP ICU CRISIS,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,12,111,0.108108108
+8703,1799,IP ICU CRISIS,LABURNC,URINE CULTURE,Lab,4,111,0.036036036
+8704,1799,IP ICU CRISIS,LABVBG,"BLOOD GASES, VENOUS",Lab,11,111,0.099099099
+8705,1799,IP ICU CRISIS,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,18,111,0.162162162
+8706,1799,IP ICU CRISIS,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,2,111,0.018018018
+8707,1799,IP ICU CRISIS,IMGCTH,CT HEAD,Imaging,1,111,0.009009009
+8708,1799,IP ICU CRISIS,IMGDXCH1,XR CHEST 1V,Imaging,22,111,0.198198198
+8709,1799,IP ICU CRISIS,LABBBPRBC,RED BLOOD CELLS,Blood Product,2,111,0.018018018
+8710,1800,IP ITA PHARM APHERESIS,520147,ALBUMIN 5% (PHARMACY COMPOUNDED MIXTURE),Medication,505,631,0.800316957
+8711,1800,IP ITA PHARM APHERESIS,530044,HEPARIN SOLUTION FOR APHERESIS,Medication,126,631,0.199683043
+8712,1803,IP ICU INTUBATION,4238,KETAMINE 50 MG/ML INJ SOLN,Medication,1,922,0.001084599
+8713,1803,IP ICU INTUBATION,7536,SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN,Medication,75,922,0.081344902
+8714,1803,IP ICU INTUBATION,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,41,922,0.044468547
+8715,1803,IP ICU INTUBATION,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,27,922,0.029284165
+8716,1803,IP ICU INTUBATION,20472,ETOMIDATE 2 MG/ML IV SOLN,Medication,116,922,0.125813449
+8717,1803,IP ICU INTUBATION,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,4,922,0.004338395
+8718,1803,IP ICU INTUBATION,78668,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,Medication,14,922,0.015184382
+8719,1803,IP ICU INTUBATION,83055,ROCURONIUM 10 MG/ML IV SOLN,Medication,116,922,0.125813449
+8720,1803,IP ICU INTUBATION,210226,PHENYLEPHRINE IN 0.9% NACL(PF) 0.5 MG/5 ML (100 MCG/ML) IV SYRG,Medication,9,922,0.009761388
+8721,1803,IP ICU INTUBATION,222138,EPINEPHRINE HCL-0.9% NACL (PF) 100 MCG/10 ML (10 MCG/ML) IV SYRG,Medication,5,922,0.005422993
+8722,1803,IP ICU INTUBATION,225590,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH AERO,Medication,7,922,0.007592191
+8723,1803,IP ICU INTUBATION,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,40,922,0.043383948
+8724,1803,IP ICU INTUBATION,236616,SUGAMMADEX 100 MG/ML IV SOLN,Medication,10,922,0.010845987
+8725,1803,IP ICU INTUBATION,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,6,922,0.006507592
+8726,1803,IP ICU INTUBATION,530025,DEXMEDETOMIDINE IV INFUSION,Medication,32,922,0.034707158
+8727,1803,IP ICU INTUBATION,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,46,922,0.04989154
+8728,1803,IP ICU INTUBATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,89,922,0.096529284
+8729,1803,IP ICU INTUBATION,540114,PROPOFOL 10 MG/ML IV INJECTION,Medication,105,922,0.113882863
+8730,1803,IP ICU INTUBATION,540115,LACTATED RINGERS IV BOLUS,Medication,1,922,0.001084599
+8731,1803,IP ICU INTUBATION,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,8,922,0.00867679
+8732,1803,IP ICU INTUBATION,IMGDXCH1,XR CHEST 1V,Imaging,248,922,0.268980477
+8733,1807,IP LABS CULTURES,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,3667,4208,0.871435361
+8734,1807,IP LABS CULTURES,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,3634,4208,0.863593156
+8735,1807,IP LABS CULTURES,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,2121,4208,0.504039924
+8736,1807,IP LABS CULTURES,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,2162,4208,0.51378327
+8737,1817,IP GYN/ONC PEB,LABCBCD,CBC WITH DIFF,Lab,1,2,0.5
+8738,1817,IP GYN/ONC PEB,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,2,0.5
+8739,1817,IP GYN/ONC PEB,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,2,0.5
+8740,1817,IP GYN/ONC PEB,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,1,2,0.5
+8741,1823,PHARM ATIC INFLIXIMAB (REMICADE) INFUSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,2,0.5
+8742,1823,PHARM ATIC INFLIXIMAB (REMICADE) INFUSION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,2,0.5
+8743,1823,PHARM ATIC INFLIXIMAB (REMICADE) INFUSION,520116,INFLIXIMAB IV INFUSION,Medication,2,2,1
+8744,1825,PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,4,0.5
+8745,1825,PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,4,0.5
+8746,1825,PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,4,4,1
+8747,1825,PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN,85809,CYTOMEGALOVIRUS IMMUNE GLOB 50 MG/ML IV SOLN,Medication,1,4,0.25
+8748,1825,PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,2,4,0.5
+8749,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,101,ACETAMINOPHEN 325 MG PO TABS,Medication,114,168,0.678571429
+8750,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,168,0.005952381
+8751,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,205,168,1
+8752,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,5,168,0.029761905
+8753,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,102,168,0.607142857
+8754,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,127,168,0.755952381
+8755,1828,PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,97,168,0.577380952
+8756,1829,PHARM ATIC RITUXIMAB (RITUXAN),102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,3,0.666666667
+8757,1829,PHARM ATIC RITUXIMAB (RITUXAN),2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,3,3,1
+8758,1829,PHARM ATIC RITUXIMAB (RITUXAN),209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,2,3,0.666666667
+8759,1829,PHARM ATIC RITUXIMAB (RITUXAN),213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,3,3,1
+8760,1829,PHARM ATIC RITUXIMAB (RITUXAN),520000,RITUXIMAB IV STANDARD INFUSION,Medication,1,3,0.333333333
+8761,1839,IP ATIC HEPATITIS B IMMUNE GLOBULIN S/P LIVER TRANSPLANT,LABHBSAG,HBSAG,Lab,1,3,0.333333333
+8762,1839,IP ATIC HEPATITIS B IMMUNE GLOBULIN S/P LIVER TRANSPLANT,LABHBSQT,HEP B SURF AB QUANT,Lab,2,3,0.666666667
+8763,1841,IP SUBCUTANEOUS INSULIN ORDER SET,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,21973,65457,0.335686023
+8764,1841,IP SUBCUTANEOUS INSULIN ORDER SET,16050,GLUCOSE 4 GRAM PO CHEW,Medication,20986,65457,0.320607422
+8765,1841,IP SUBCUTANEOUS INSULIN ORDER SET,78748,INSULIN GLARGINE 100 UNIT/ML SC SOLN,Medication,1754,65457,0.026796217
+8766,1841,IP SUBCUTANEOUS INSULIN ORDER SET,81586,DEXTROSE 40 % PO GEL,Medication,1,65457,1.53E-05
+8767,1841,IP SUBCUTANEOUS INSULIN ORDER SET,87878,INSULIN ASPART 100 UNIT/ML SC SOLN,Medication,27,65457,0.000412485
+8768,1841,IP SUBCUTANEOUS INSULIN ORDER SET,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,5062,65457,0.077333211
+8769,1841,IP SUBCUTANEOUS INSULIN ORDER SET,89742,INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP,Medication,2832,65457,0.043265044
+8770,1841,IP SUBCUTANEOUS INSULIN ORDER SET,92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,17102,65457,0.261270758
+8771,1841,IP SUBCUTANEOUS INSULIN ORDER SET,112287,DEXTROSE 50% IN WATER (D50W) IV SOLP,Medication,23,65457,0.000351376
+8772,1841,IP SUBCUTANEOUS INSULIN ORDER SET,125813,INSULIN DETEMIR 100 UNIT/ML SC SOLN,Medication,678,65457,0.010357945
+8773,1841,IP SUBCUTANEOUS INSULIN ORDER SET,540371,INSULIN ASPART 100 UNIT/ML AGGRESSIVE SCALE FEEDING,Medication,1,65457,1.53E-05
+8774,1841,IP SUBCUTANEOUS INSULIN ORDER SET,540571,INSULIN REG HUMAN 100 UNITS/ML INJ,Medication,1,65457,1.53E-05
+8775,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541452,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING,Medication,1680,65457,0.025665704
+8776,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541453,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,2484,65457,0.037948577
+8777,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541454,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,1803,65457,0.0275448
+8778,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541455,INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING,Medication,534,65457,0.008158027
+8779,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541456,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE NPO,Medication,894,65457,0.013657821
+8780,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541457,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO,Medication,1147,65457,0.017522954
+8781,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541458,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE NPO,Medication,828,65457,0.012649526
+8782,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541459,INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE NPO,Medication,346,65457,0.005285913
+8783,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541460,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE FEEDING,Medication,332,65457,0.005072032
+8784,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541461,INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,621,65457,0.009487144
+8785,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541462,INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,342,65457,0.005224804
+8786,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541463,INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING,Medication,106,65457,0.001619384
+8787,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541464,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE NPO,Medication,364,65457,0.005560903
+8788,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541465,INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE NPO,Medication,548,65457,0.008371908
+8789,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541466,INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE NPO,Medication,350,65457,0.005347022
+8790,1841,IP SUBCUTANEOUS INSULIN ORDER SET,541467,INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE NPO,Medication,136,65457,0.0020777
+8791,1841,IP SUBCUTANEOUS INSULIN ORDER SET,LABA1C,HEMOGLOBIN A1C,Lab,10873,65457,0.166109049
+8792,1841,IP SUBCUTANEOUS INSULIN ORDER SET,POC14,POC GLUCOSE BY METER,Lab,40105,65457,0.612692302
+8793,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,78748,INSULIN GLARGINE 100 UNIT/ML SC SOLN,Medication,319,19111,0.016691958
+8794,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,87878,INSULIN ASPART 100 UNIT/ML SC SOLN,Medication,12,19111,0.000627911
+8795,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,1830,19111,0.095756371
+8796,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,89742,INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP,Medication,1566,19111,0.081942337
+8797,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,6488,19111,0.339490346
+8798,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,125813,INSULIN DETEMIR 100 UNIT/ML SC SOLN,Medication,909,19111,0.04756423
+8799,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541452,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING,Medication,407,19111,0.021296635
+8800,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541453,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,786,19111,0.041128146
+8801,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541454,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,636,19111,0.033279263
+8802,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541455,INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING,Medication,247,19111,0.012924494
+8803,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541456,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE NPO,Medication,193,19111,0.010098896
+8804,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541457,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO,Medication,326,19111,0.017058239
+8805,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541458,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE NPO,Medication,316,19111,0.01653498
+8806,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541459,INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE NPO,Medication,124,19111,0.00648841
+8807,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541460,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE FEEDING,Medication,50,19111,0.002616294
+8808,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541461,INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,114,19111,0.005965151
+8809,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541462,INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,86,19111,0.004500026
+8810,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541463,INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING,Medication,28,19111,0.001465125
+8811,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541464,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE NPO,Medication,68,19111,0.00355816
+8812,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541465,INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE NPO,Medication,163,19111,0.008529119
+8813,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541466,INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE NPO,Medication,164,19111,0.008581445
+8814,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,541467,INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE NPO,Medication,67,19111,0.003505834
+8815,1842,IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET,POC14,POC GLUCOSE BY METER,Lab,11290,19111,0.590759249
+8816,1850,IP HEM/ONC R-ICE,LABCBCD,CBC WITH DIFF,Lab,2,1,1
+8817,1850,IP HEM/ONC R-ICE,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,1,1
+8818,1850,IP HEM/ONC R-ICE,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4,1,1
+8819,1850,IP HEM/ONC R-ICE,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,4,1,1
+8820,1850,IP HEM/ONC R-ICE,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,2,1,1
+8821,2029,CC  LUNG MED ONC CLINIC DISCHARGE ,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,1,3,0.333333333
+8822,2029,CC  LUNG MED ONC CLINIC DISCHARGE ,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,2,3,0.666666667
+8823,2030,SHC AMB ONC BMT,7555,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,Medication,1,9,0.111111111
+8824,2030,SHC AMB ONC BMT,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,2,9,0.222222222
+8825,2030,SHC AMB ONC BMT,85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,4,9,0.444444444
+8826,2030,SHC AMB ONC BMT,94670,TACROLIMUS 1 MG PO CAPS,Medication,5,9,0.555555556
+8827,2030,SHC AMB ONC BMT,95803,TACROLIMUS 0.5 MG PO CAPS,Medication,1,9,0.111111111
+8828,2133,CC APHERESIS HPC-A COLLECTION,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,1,41,0.024390244
+8829,2133,CC APHERESIS HPC-A COLLECTION,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,4,41,0.097560976
+8830,2133,CC APHERESIS HPC-A COLLECTION,94623,ALTEPLASE 2 MG INJ SOLR,Medication,2,41,0.048780488
+8831,2133,CC APHERESIS HPC-A COLLECTION,96471,CITRATE DEXTROSE SOLUTION MISC SOLN,Medication,19,41,0.463414634
+8832,2133,CC APHERESIS HPC-A COLLECTION,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,2,41,0.048780488
+8833,2133,CC APHERESIS HPC-A COLLECTION,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,7,41,0.170731707
+8834,2133,CC APHERESIS HPC-A COLLECTION,230501,DEXTROSE-SOD CITRATE-CITRIC AC 2.45-2.2 GRAM- 730 MG/100 ML MISC SOLN,Medication,1,41,0.024390244
+8835,2133,CC APHERESIS HPC-A COLLECTION,LABCBCO,CBC W/O DIFF,Lab,2,41,0.048780488
+8836,2133,CC APHERESIS HPC-A COLLECTION,LABFCD34,"CD34 PANEL, FLUID",Lab,4,41,0.097560976
+8837,2133,CC APHERESIS HPC-A COLLECTION,LABFTBLYM,"T SUBSETS AND B LYMPHOCYTES, FLUID",Lab,1,41,0.024390244
+8838,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,2405,DIAZEPAM 5 MG PO TABS,Medication,1,24,0.041666667
+8839,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,7,24,0.291666667
+8840,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,10015R,HLA ORDER - DSA SCREENING,Lab,8,24,0.333333333
+8841,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABCR,"CREATININE, SERUM/PLASMA",Lab,3,24,0.125
+8842,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABHCTX,HEMATOCRIT,Lab,3,24,0.125
+8843,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABPLTFUN,PLATELET FUNCTION SCREEN,Lab,5,24,0.208333333
+8844,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABPLTS,PLATELET COUNT,Lab,3,24,0.125
+8845,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABPT,PROTHROMBIN TIME,Lab,7,24,0.291666667
+8846,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,7,24,0.291666667
+8847,2204,IP TRANSPLANT KIDNEY BIOPSY PRE-PROCEDURE,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,3,24,0.125
+8848,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,4,0.25
+8849,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1,4,0.25
+8850,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,4,0.25
+8851,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,1,4,0.25
+8852,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,3,4,0.75
+8853,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,4,0.25
+8854,2209,CC ITA COMPREHENSIVE GENERAL ADMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,4,0.25
+8855,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,3760,HYDROMORPHONE 2 MG PO TABS,Medication,1,6,0.166666667
+8856,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,4573,LORAZEPAM 1 MG PO TABS,Medication,1,6,0.166666667
+8857,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,5176,MORPHINE 10 MG/5 ML PO SOLN,Medication,1,6,0.166666667
+8858,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,LABAMLP,AML PROGNOSIS ASSAY,Lab,2,6,0.333333333
+8859,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,LABBMAML,"AML PROGNOSIS ASSAY, BONE MARROW",Lab,2,6,0.333333333
+8860,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,LABBMCEBP,"DETECTION OF CEBPA, BONE MARROW",Lab,1,6,0.166666667
+8861,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,LABCBCD,CBC WITH DIFF,Lab,7,6,1
+8862,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,LABCKITMU,"KIT MUTATIONS, EXONS 8 & 17, BLOOD",Lab,1,6,0.166666667
+8863,2252,CC HEME BONE MARROW BIOPSY AND ASPIRATE,PATH23,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BM",Lab,4,6,0.666666667
+8864,2264,CC BMT POST BONE MARROW HARVEST,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,4,0.25
+8865,2264,CC BMT POST BONE MARROW HARVEST,4573,LORAZEPAM 1 MG PO TABS,Medication,1,4,0.25
+8866,2264,CC BMT POST BONE MARROW HARVEST,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,1,4,0.25
+8867,2264,CC BMT POST BONE MARROW HARVEST,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,4,0.25
+8868,2264,CC BMT POST BONE MARROW HARVEST,14087,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,Medication,3,4,0.75
+8869,2264,CC BMT POST BONE MARROW HARVEST,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,4,0.25
+8870,2264,CC BMT POST BONE MARROW HARVEST,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,2,4,0.5
+8871,2264,CC BMT POST BONE MARROW HARVEST,203751,GRANISETRON HCL 1 MG/ML (1 ML) IV SOLN,Medication,1,4,0.25
+8872,2264,CC BMT POST BONE MARROW HARVEST,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,4,0.25
+8873,2264,CC BMT POST BONE MARROW HARVEST,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,4,0.25
+8874,2264,CC BMT POST BONE MARROW HARVEST,LABCBCD,CBC WITH DIFF,Lab,3,4,0.75
+8875,2275,CC APHERESIS TRANSFUSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,8,324,0.024691358
+8876,2275,CC APHERESIS TRANSFUSION,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,8,324,0.024691358
+8877,2275,CC APHERESIS TRANSFUSION,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,1,324,0.00308642
+8878,2275,CC APHERESIS TRANSFUSION,LABARI,ABO/RH (ARI),Lab,12,324,0.037037037
+8879,2275,CC APHERESIS TRANSFUSION,LABBBPCRY,CRYOPRECIPITATE,Lab,1,324,0.00308642
+8880,2275,CC APHERESIS TRANSFUSION,LABTYPSNI,TYPE AND SCREEN,Lab,43,324,0.132716049
+8881,2275,CC APHERESIS TRANSFUSION,LABBBPCRY,CRYOPRECIPITATE,Blood Product,9,324,0.027777778
+8882,2275,CC APHERESIS TRANSFUSION,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,191,324,0.589506173
+8883,2275,CC APHERESIS TRANSFUSION,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,57,324,0.175925926
+8884,2275,CC APHERESIS TRANSFUSION,LABBBPRBC,RED BLOOD CELLS,Blood Product,86,324,0.265432099
+8885,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,3760,HYDROMORPHONE 2 MG PO TABS,Medication,10,280,0.035714286
+8886,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,4454,LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN,Medication,39,280,0.139285714
+8887,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,4573,LORAZEPAM 1 MG PO TABS,Medication,14,280,0.05
+8888,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,5176,MORPHINE 10 MG/5 ML PO SOLN,Medication,7,280,0.025
+8889,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,10075R,HLA ORDER - STR/CHIMERISM (SOLID ORGAN),Lab,90,280,0.321428571
+8890,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,10090R,HLA ORDER - STR/CHIMERISM (BONE MARROW),Lab,6,280,0.021428571
+8891,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LAB392,"MINIMAL RESIDUAL DISEASE, BM",Lab,8,280,0.028571429
+8892,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABAMLP,AML PROGNOSIS ASSAY,Lab,1,280,0.003571429
+8893,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBCRQT,"BCR-ABL, BLOOD",Lab,1,280,0.003571429
+8894,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBM1418,"T(14;18), BONE MARROW",Lab,1,280,0.003571429
+8895,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBM1517,"T(15;17) PML/RAR, BONE MARROW",Lab,2,280,0.007142857
+8896,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBMAML,"AML PROGNOSIS ASSAY, BONE MARROW",Lab,1,280,0.003571429
+8897,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBMBCRQ,"BCR - ABL QUANTITATIVE, BONE MARROW",Lab,4,280,0.014285714
+8898,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBMCEBP,"DETECTION OF CEBPA, BONE MARROW",Lab,13,280,0.046428571
+8899,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABBMTCLO,"T-CELL CLONALITY PCR, BONE MARROW",Lab,3,280,0.010714286
+8900,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABCBCD,CBC WITH DIFF,Lab,73,280,0.260714286
+8901,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABFK506L,TACROLIMUS (FK506),Lab,1,280,0.003571429
+8902,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,280,0.003571429
+8903,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,1,280,0.003571429
+8904,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,LABTCLON,"T-CELL CLONALITY PCR, BLOOD",Lab,1,280,0.003571429
+8905,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,PATH22,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BLD",Lab,2,280,0.007142857
+8906,2327,CC BMT BONE MARROW BIOPSY AND ASPIRATE,PATH23,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BM",Lab,43,280,0.153571429
+8907,2328,CC RAD ONC HIGH DOSE RATE BRACHYTHERAPY,4573,LORAZEPAM 1 MG PO TABS,Medication,1,4,0.25
+8908,2328,CC RAD ONC HIGH DOSE RATE BRACHYTHERAPY,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,4,0.25
+8909,2328,CC RAD ONC HIGH DOSE RATE BRACHYTHERAPY,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,4,0.25
+8910,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,4573,LORAZEPAM 1 MG PO TABS,Medication,17,189,0.08994709
+8911,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,18,189,0.095238095
+8912,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,10011,FAMOTIDINE 20 MG PO TABS,Medication,17,189,0.08994709
+8913,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,11265,RANITIDINE HCL 150 MG PO TABS,Medication,10,189,0.052910053
+8914,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,54,189,0.285714286
+8915,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,7,189,0.037037037
+8916,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,97,189,0.513227513
+8917,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,112593,FILGRASTIM 480 MCG/0.8 ML INJ SYRG,Medication,8,189,0.042328042
+8918,2342,CC BMT PRESCRIPTION MEDS HPC - APHERESIS,112594,FILGRASTIM 300 MCG/0.5 ML INJ SYRG,Medication,13,189,0.068783069
+8919,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,4572,LORAZEPAM 0.5 MG PO TABS,Medication,2,101,0.01980198
+8920,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,4573,LORAZEPAM 1 MG PO TABS,Medication,5,101,0.04950495
+8921,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,1,101,0.00990099
+8922,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,8971,ACYCLOVIR 400 MG PO TABS,Medication,29,101,0.287128713
+8923,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,10011,FAMOTIDINE 20 MG PO TABS,Medication,7,101,0.069306931
+8924,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,10045,FLUCONAZOLE 200 MG PO TABS,Medication,4,101,0.03960396
+8925,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,10747,NORETHINDRONE ACETATE 5 MG PO TABS,Medication,1,101,0.00990099
+8926,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,11265,RANITIDINE HCL 150 MG PO TABS,Medication,1,101,0.00990099
+8927,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,9,101,0.089108911
+8928,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,12,101,0.118811881
+8929,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,80562,MYCOPHENOLATE MOFETIL 250 MG PO CAPS,Medication,11,101,0.108910891
+8930,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,83993,GRANISETRON HCL 1 MG PO TABS,Medication,1,101,0.00990099
+8931,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,27,101,0.267326733
+8932,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,540132,CYCLOSPORINE MODIFIED (GENGRAF) 25 MG PO CAPS,Medication,17,101,0.168316832
+8933,2345,CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE,540133,CYCLOSPORINE MODIFIED (GENGRAF) 100 MG PO CAPS,Medication,33,101,0.326732673
+8934,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,58,0.017241379
+8935,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,4573,LORAZEPAM 1 MG PO TABS,Medication,11,58,0.189655172
+8936,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,6582,PROCHLORPERAZINE MALEATE 10 MG PO TABS,Medication,9,58,0.155172414
+8937,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,1,58,0.017241379
+8938,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,10011,FAMOTIDINE 20 MG PO TABS,Medication,10,58,0.172413793
+8939,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,10747,NORETHINDRONE ACETATE 5 MG PO TABS,Medication,6,58,0.103448276
+8940,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,11265,RANITIDINE HCL 150 MG PO TABS,Medication,5,58,0.086206897
+8941,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,11624,URSODIOL 300 MG PO CAPS,Medication,40,58,0.689655172
+8942,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,10,58,0.172413793
+8943,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,25119,CIPROFLOXACIN 500 MG PO TABS,Medication,1,58,0.017241379
+8944,2353,CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT,83993,GRANISETRON HCL 1 MG PO TABS,Medication,1,58,0.017241379
+8945,2360,CC LUMBAR PUNCTURE,LABCSFCT,"CELL COUNT AND DIFFERENTIAL, CSF",Lab,1,1,1
+8946,2360,CC LUMBAR PUNCTURE,LABCSFGL,"GLUCOSE, CSF",Lab,1,1,1
+8947,2360,CC LUMBAR PUNCTURE,LABCSFTP,"PROTEIN TOTAL, CSF",Lab,1,1,1
+8948,2398,CC BMT PRESCRIPTION MEDS POST ABLATIVE AUTOLOGOUS HPCT ,4573,LORAZEPAM 1 MG PO TABS,Medication,1,10,0.1
+8949,2398,CC BMT PRESCRIPTION MEDS POST ABLATIVE AUTOLOGOUS HPCT ,8971,ACYCLOVIR 400 MG PO TABS,Medication,6,10,0.6
+8950,2398,CC BMT PRESCRIPTION MEDS POST ABLATIVE AUTOLOGOUS HPCT ,112593,FILGRASTIM 480 MCG/0.8 ML INJ SYRG,Medication,3,10,0.3
+8951,2409,AMB CAR CONGESTIVE HEART FAILURE,14113,ASPIRIN 81 MG PO TBEC,Medication,1,1,1
+8952,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,168,0.005952381
+8953,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),3774,HYDROXYZINE HCL 25 MG PO TABS,Medication,1,168,0.005952381
+8954,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,168,0.005952381
+8955,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,6,168,0.035714286
+8956,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,39,168,0.232142857
+8957,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),77934,METHOXSALEN 20 MCG/ML INJ SOLN,Medication,76,168,0.452380952
+8958,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,5,168,0.029761905
+8959,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),540102,NS IV BOLUS,Medication,9,168,0.053571429
+8960,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),LABCBCD,CBC WITH DIFF,Lab,1,168,0.005952381
+8961,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),LABCHOL,"CHOLESTEROL TOTAL, SERUM/PLASMA",Lab,1,168,0.005952381
+8962,2415,CC APHERESIS EXTRACORPOREAL PHOTOPHERESIS (ECP),LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,1,168,0.005952381
+8963,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,101,ACETAMINOPHEN 325 MG PO TABS,Medication,20,702,0.028490028
+8964,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,102,ACETAMINOPHEN 500 MG PO TABS,Medication,5,702,0.007122507
+8965,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,3757,HYDROMORPHONE (PF) 1 MG/ML INJ SOLN,Medication,3,702,0.004273504
+8966,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,57,702,0.081196581
+8967,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,94176,ONDANSETRON 4 MG PO TBDL,Medication,3,702,0.004273504
+8968,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,197,702,0.280626781
+8969,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,91,702,0.12962963
+8970,2440,NURSING TRIAGE PAIN MANAGEMENT PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,200,702,0.284900285
+8971,2457,IP GEN HEPARIN PROTOCOLS,10181,"HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",Medication,2,18442,0.000108448
+8972,2457,IP GEN HEPARIN PROTOCOLS,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,12,18442,0.000650689
+8973,2457,IP GEN HEPARIN PROTOCOLS,540323,HEPARIN IV BOLUS (ACS PROTOCOL),Medication,1015,18442,0.055037415
+8974,2457,IP GEN HEPARIN PROTOCOLS,540325,HEPARIN IV BOLUS (DVT/PE PROTOCOL),Medication,852,18442,0.046198894
+8975,2457,IP GEN HEPARIN PROTOCOLS,540326,"HEPARIN IV BOLUS (A-FIB PROTOCOL, A-FIB & INR < 2)",Medication,485,18442,0.026298666
+8976,2457,IP GEN HEPARIN PROTOCOLS,540328,HEPARIN IV BOLUS (NEURO CEREBRAL SINUS THROMBOSIS PROTOCOL),Medication,13,18442,0.000704913
+8977,2457,IP GEN HEPARIN PROTOCOLS,540329,HEPARIN IV INFUSION (ACS PROTOCOL),Medication,1329,18442,0.072063767
+8978,2457,IP GEN HEPARIN PROTOCOLS,540330,"HEPARIN IV REBOLUS (ACS PROTOCOL, HAL < 0.1)",Medication,1115,18442,0.06045982
+8979,2457,IP GEN HEPARIN PROTOCOLS,540331,"HEPARIN IV REBOLUS (ACS PROTOCOL, HAL 0.1-0.19)",Medication,1116,18442,0.060514044
+8980,2457,IP GEN HEPARIN PROTOCOLS,540332,HEPARIN IV BOLUS (MD TO SPECIFY PARAMETERS),Medication,70,18442,0.003795684
+8981,2457,IP GEN HEPARIN PROTOCOLS,540333,HEPARIN IV INFUSION (MD TO SPECIFY PARAMETERS),Medication,534,18442,0.028955645
+8982,2457,IP GEN HEPARIN PROTOCOLS,540336,HEPARIN IV INFUSION (DVT/PE PROTOCOL),Medication,1374,18442,0.07450385
+8983,2457,IP GEN HEPARIN PROTOCOLS,540337,"HEPARIN IV REBOLUS (DVT/PE PROTOCOL, HAL < 0.3)",Medication,1042,18442,0.056501464
+8984,2457,IP GEN HEPARIN PROTOCOLS,540338,"HEPARIN IV INFUSION (A-FIB PROTOCOL, A-FIB & INR > 2)",Medication,147,18442,0.007970936
+8985,2457,IP GEN HEPARIN PROTOCOLS,540339,"HEPARIN IV REBOLUS (A-FIB PROTOCOL, HAL < 0.1)",Medication,725,18442,0.039312439
+8986,2457,IP GEN HEPARIN PROTOCOLS,540340,"HEPARIN IV REBOLUS (A-FIB PROTOCOL, HAL 0.1 - 0.19)",Medication,727,18442,0.039420887
+8987,2457,IP GEN HEPARIN PROTOCOLS,540441,"HEPARIN IV INFUSION (A-FIB PROTOCOL, A-FIB & INR < 2)",Medication,805,18442,0.043650363
+8988,2457,IP GEN HEPARIN PROTOCOLS,540444,"HEPARIN IV BOLUS (MHV PROTOCOL, INR IS NOT THERAPEUTIC)",Medication,55,18442,0.002982323
+8989,2457,IP GEN HEPARIN PROTOCOLS,540445,"HEPARIN IV INFUSION (MHV PROTOCOL, INR IS NOT THERAPEUTIC)",Medication,121,18442,0.006561111
+8990,2457,IP GEN HEPARIN PROTOCOLS,540447,"HEPARIN IV REBOLUS (MECH HEART VALVE PROTOCOL, HAL < 0.2)",Medication,184,18442,0.009977226
+8991,2457,IP GEN HEPARIN PROTOCOLS,540448,"HEPARIN IV INFUSION (MHV PROTOCOL, INR IS THERAPEUTIC)",Medication,158,18442,0.0085674
+8992,2457,IP GEN HEPARIN PROTOCOLS,540451,HEPARIN IV INFUSION (NEURO POST STROKE PROTOCOL),Medication,49,18442,0.002656979
+8993,2457,IP GEN HEPARIN PROTOCOLS,540452,HEPARIN IV INFUSION (NEURO POST IR PROCEDURE PROTOCOL),Medication,6,18442,0.000325344
+8994,2457,IP GEN HEPARIN PROTOCOLS,540453,HEPARIN IV INFUSION (NEURO CEREBRAL SINUS THROMBOSIS),Medication,44,18442,0.002385858
+8995,2457,IP GEN HEPARIN PROTOCOLS,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,194,18442,0.010519466
+8996,2457,IP GEN HEPARIN PROTOCOLS,540646,HEPARIN IV INFUSION (EP PROTOCOL),Medication,272,18442,0.014748943
+8997,2457,IP GEN HEPARIN PROTOCOLS,540653,"HEPARIN IV REBOLUS (MECH HEART VALVE PROTOCOL, HAL 0.2-0.29)",Medication,186,18442,0.010085674
+8998,2457,IP GEN HEPARIN PROTOCOLS,540665,HEPARIN IV INFUSION (DVT/PE PROTOCOL-HIGH BLEEDING RISK/POST PROCEDURE),Medication,823,18442,0.044626396
+8999,2457,IP GEN HEPARIN PROTOCOLS,540681,HEPARIN IV INFUSION (HIGH BLEEDING RISK PROTOCOL),Medication,875,18442,0.047446047
+9000,2457,IP GEN HEPARIN PROTOCOLS,540736,HEPARIN IV INFUSION (VASCULAR SURGERY HIGH BLEEDING RISK),Medication,182,18442,0.009868778
+9001,2457,IP GEN HEPARIN PROTOCOLS,540738,HEPARIN IV INFUSION (VASCULAR SURGERY LOW BLEEDING RISK),Medication,203,18442,0.011007483
+9002,2457,IP GEN HEPARIN PROTOCOLS,540740,HEPARIN IV BOLUS (VASCULAR SURGERY LOW BLEEDING RISK),Medication,108,18442,0.005856198
+9003,2457,IP GEN HEPARIN PROTOCOLS,540741,HEPARIN IV REBOLUS 1000 UNITS(VASCULAR SURGERY LOW BLEEDING RISK),Medication,140,18442,0.007591368
+9004,2457,IP GEN HEPARIN PROTOCOLS,540742,HEPARIN IV REBOLUS 2000 UNITS (VASCULAR SURGERY LOW BLEEDING RISK),Medication,131,18442,0.007103351
+9005,2457,IP GEN HEPARIN PROTOCOLS,540755,HEPARIN IV BOLUS (DVT/PE PROTOCOL HIGH BLEEDING RISK/POST PROCEDURE),Medication,220,18442,0.011929292
+9006,2457,IP GEN HEPARIN PROTOCOLS,541066,HEPARIN INFUSION LVAD HIGH RISK BLEED,Medication,625,18442,0.033890034
+9007,2457,IP GEN HEPARIN PROTOCOLS,541074,HEPARIN INFUSION LVAD NORMAL RISK,Medication,1194,18442,0.06474352
+9008,2457,IP GEN HEPARIN PROTOCOLS,541075,HEPARIN INFUSION LVAD HIGH PUMP THROMBOSIS RISK,Medication,112,18442,0.006073094
+9009,2457,IP GEN HEPARIN PROTOCOLS,541764,HEPARIN IV INFUSION (LVAD PROTOCOL - HIGH BLEED RISK),Medication,159,18442,0.008621625
+9010,2457,IP GEN HEPARIN PROTOCOLS,541765,HEPARIN IV INFUSION (LVAD PROTOCOL - NORMAL RISK),Medication,329,18442,0.017839714
+9011,2457,IP GEN HEPARIN PROTOCOLS,541766,HEPARIN IV INFUSION (LVAD PROTOCOL - HIGH PUMP THROMBOSIS RISK),Medication,34,18442,0.001843618
+9012,2457,IP GEN HEPARIN PROTOCOLS,800100,ZZ EXTEMPORANEOUS TEMPLATE(CUSTOM),Medication,1,18442,5.42E-05
+9013,2457,IP GEN HEPARIN PROTOCOLS,LABCBCO,CBC W/O DIFF,Lab,11481,18442,0.622546362
+9014,2457,IP GEN HEPARIN PROTOCOLS,LABHEPAR,"HEPARIN, UNFRACTIONATED, ANTI XA",Lab,15614,18442,0.846654376
+9015,2457,IP GEN HEPARIN PROTOCOLS,LABPLTS,PLATELET COUNT,Lab,10967,18442,0.594675198
+9016,2457,IP GEN HEPARIN PROTOCOLS,LABPT,PROTHROMBIN TIME,Lab,6471,18442,0.350883852
+9017,2457,IP GEN HEPARIN PROTOCOLS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,22912,18442,1
+9018,2457,IP GEN HEPARIN PROTOCOLS,POC5,POC STOOL - OCCULT BLOOD EXAM (UNIT PERFORMED),Lab,6361,18442,0.344919206
+9019,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,16,1283,0.012470772
+9020,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,196,1283,0.152766952
+9021,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,18,1283,0.014029618
+9022,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,4573,LORAZEPAM 1 MG PO TABS,Medication,15,1283,0.011691348
+9023,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,462,1283,0.360093531
+9024,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,2,1283,0.001558846
+9025,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,57,1283,0.044427124
+9026,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,81839,LIDOCAINE 4 % TP CREA,Medication,2,1283,0.001558846
+9027,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,125595,LIDOCAINE-TETRACAINE 70-70 MG TP PTMH,Medication,1,1283,0.000779423
+9028,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,17,1283,0.013250195
+9029,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,149,1283,0.116134061
+9030,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,5,1283,0.003897116
+9031,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,467,1283,0.363990647
+9032,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,230501,DEXTROSE-SOD CITRATE-CITRIC AC 2.45-2.2 GRAM- 730 MG/100 ML MISC SOLN,Medication,57,1283,0.044427124
+9033,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABARI,ABO/RH (ARI),Lab,7,1283,0.005455963
+9034,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,48,1283,0.037412315
+9035,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABCBCD,CBC WITH DIFF,Lab,6,1283,0.004676539
+9036,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABCBCO,CBC W/O DIFF,Lab,9,1283,0.007014809
+9037,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABFIB,FIBRINOGEN,Lab,46,1283,0.035853468
+9038,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABHFP,HEPATIC FUNCTION PANEL A,Lab,3,1283,0.00233827
+9039,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,5,1283,0.003897116
+9040,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,1283,0.001558846
+9041,2473,CC APHERESIS THERAPEUTIC PLASMA EXCHANGE,LABPT,PROTHROMBIN TIME,Lab,11,1283,0.008573655
+9042,2513,CC APHERESIS RED CELL EXCHANGE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,41,0.146341463
+9043,2513,CC APHERESIS RED CELL EXCHANGE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,11,41,0.268292683
+9044,2513,CC APHERESIS RED CELL EXCHANGE,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,4,41,0.097560976
+9045,2513,CC APHERESIS RED CELL EXCHANGE,4573,LORAZEPAM 1 MG PO TABS,Medication,1,41,0.024390244
+9046,2513,CC APHERESIS RED CELL EXCHANGE,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,4,41,0.097560976
+9047,2513,CC APHERESIS RED CELL EXCHANGE,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,9,41,0.219512195
+9048,2513,CC APHERESIS RED CELL EXCHANGE,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,3,41,0.073170732
+9049,2513,CC APHERESIS RED CELL EXCHANGE,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,8,41,0.195121951
+9050,2513,CC APHERESIS RED CELL EXCHANGE,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,4,41,0.097560976
+9051,2513,CC APHERESIS RED CELL EXCHANGE,LABCBCO,CBC W/O DIFF,Lab,8,41,0.195121951
+9052,2513,CC APHERESIS RED CELL EXCHANGE,LABHGBSQ,HGB S QUANTITATIVE,Lab,22,41,0.536585366
+9053,2514,CC APHERESIS WBC DEPLETION ,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,6,27,0.222222222
+9054,2514,CC APHERESIS WBC DEPLETION ,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,1,27,0.037037037
+9055,2514,CC APHERESIS WBC DEPLETION ,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,4,27,0.148148148
+9056,2514,CC APHERESIS WBC DEPLETION ,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,4,27,0.148148148
+9057,2514,CC APHERESIS WBC DEPLETION ,LABCBCD,CBC WITH DIFF,Lab,1,27,0.037037037
+9058,2514,CC APHERESIS WBC DEPLETION ,LABCBCO,CBC W/O DIFF,Lab,51,27,1
+9059,2516,CC APHERESIS PLATELET DEPLETION,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,2,15,0.133333333
+9060,2516,CC APHERESIS PLATELET DEPLETION,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,1,15,0.066666667
+9061,2516,CC APHERESIS PLATELET DEPLETION,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,4,15,0.266666667
+9062,2516,CC APHERESIS PLATELET DEPLETION,LABCBCO,CBC W/O DIFF,Lab,9,15,0.6
+9063,2516,CC APHERESIS PLATELET DEPLETION,LABPLTS,PLATELET COUNT,Lab,5,15,0.333333333
+9064,2529,AMB PRE-OP ORDERS ,540619,SHC AMB CHLORHEXIDINE 4% SCRUBS PACKET WITH INSTRUCTIONS,Medication,1,1,1
+9065,2763,IP PHARM BMT PROTOCOL 9022 BU CY (2),2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,1,1
+9066,2763,IP PHARM BMT PROTOCOL 9022 BU CY (2),210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,1,1,1
+9067,2763,IP PHARM BMT PROTOCOL 9022 BU CY (2),540302,POTASSIUM CHLORIDE IV SCALE,Medication,1,1,1
+9068,2763,IP PHARM BMT PROTOCOL 9022 BU CY (2),540304,MAGNESIUM SULFATE IV SCALE ,Medication,1,1,1
+9069,2804,IP ONC TUMOR LYSIS PRECAUTIONS,310,ALLOPURINOL 100 MG PO TABS,Medication,5,307,0.016286645
+9070,2804,IP ONC TUMOR LYSIS PRECAUTIONS,311,ALLOPURINOL 100 MG PO TABS,Medication,50,307,0.16286645
+9071,2804,IP ONC TUMOR LYSIS PRECAUTIONS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,9,307,0.029315961
+9072,2804,IP ONC TUMOR LYSIS PRECAUTIONS,224461,SODIUM CHLORIDE 0.9% IV SOLP,Medication,37,307,0.120521173
+9073,2804,IP ONC TUMOR LYSIS PRECAUTIONS,520081,RASBURICASE IVPB,Medication,5,307,0.016286645
+9074,2804,IP ONC TUMOR LYSIS PRECAUTIONS,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,203,307,0.661237785
+9075,2804,IP ONC TUMOR LYSIS PRECAUTIONS,LABMETB,"METABOLIC PANEL, BASIC",Lab,171,307,0.557003257
+9076,2804,IP ONC TUMOR LYSIS PRECAUTIONS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,205,307,0.667752443
+9077,2804,IP ONC TUMOR LYSIS PRECAUTIONS,LABURIC,"URIC ACID, SERUM / PLASMA",Lab,222,307,0.723127036
+9078,2863,AMB LIV PRE-OP ORDERS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,31,72,0.430555556
+9079,2863,AMB LIV PRE-OP ORDERS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,5,72,0.069444444
+9080,2873,AMB LAB KID TOX SCREEN,LABVOLTL,VOLATILE SCREEN,Lab,1,1,1
+9081,2955,IP INSULIN TRANSITION OFF IV INFUSION,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,2995,11359,0.263667576
+9082,2955,IP INSULIN TRANSITION OFF IV INFUSION,16050,GLUCOSE 4 GRAM PO CHEW,Medication,2969,11359,0.261378642
+9083,2955,IP INSULIN TRANSITION OFF IV INFUSION,78748,INSULIN GLARGINE 100 UNIT/ML SC SOLN,Medication,124,11359,0.010916454
+9084,2955,IP INSULIN TRANSITION OFF IV INFUSION,81586,DEXTROSE 40 % PO GEL,Medication,2,11359,0.000176072
+9085,2955,IP INSULIN TRANSITION OFF IV INFUSION,88904,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,Medication,1183,11359,0.104146492
+9086,2955,IP INSULIN TRANSITION OFF IV INFUSION,89742,INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP,Medication,7231,11359,0.636587728
+9087,2955,IP INSULIN TRANSITION OFF IV INFUSION,92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,3772,11359,0.332071485
+9088,2955,IP INSULIN TRANSITION OFF IV INFUSION,125813,INSULIN DETEMIR 100 UNIT/ML SC SOLN,Medication,195,11359,0.017167004
+9089,2955,IP INSULIN TRANSITION OFF IV INFUSION,541452,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING,Medication,88,11359,0.007747161
+9090,2955,IP INSULIN TRANSITION OFF IV INFUSION,541453,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,175,11359,0.015406286
+9091,2955,IP INSULIN TRANSITION OFF IV INFUSION,541454,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,95,11359,0.008363412
+9092,2955,IP INSULIN TRANSITION OFF IV INFUSION,541455,INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING,Medication,12,11359,0.001056431
+9093,2955,IP INSULIN TRANSITION OFF IV INFUSION,541456,INSULIN LISPRO 100 UNIT/ML SC MILD SCALE NPO,Medication,30,11359,0.002641078
+9094,2955,IP INSULIN TRANSITION OFF IV INFUSION,541457,INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO,Medication,61,11359,0.005370191
+9095,2955,IP INSULIN TRANSITION OFF IV INFUSION,541458,INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE NPO,Medication,52,11359,0.004577868
+9096,2955,IP INSULIN TRANSITION OFF IV INFUSION,541459,INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE NPO,Medication,17,11359,0.001496611
+9097,2955,IP INSULIN TRANSITION OFF IV INFUSION,541460,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE FEEDING,Medication,17,11359,0.001496611
+9098,2955,IP INSULIN TRANSITION OFF IV INFUSION,541461,INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE FEEDING,Medication,23,11359,0.002024826
+9099,2955,IP INSULIN TRANSITION OFF IV INFUSION,541462,INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING,Medication,18,11359,0.001584647
+9100,2955,IP INSULIN TRANSITION OFF IV INFUSION,541463,INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING,Medication,5,11359,0.00044018
+9101,2955,IP INSULIN TRANSITION OFF IV INFUSION,541464,INSULIN REGULAR 100 UNIT/ML SC MILD SCALE NPO,Medication,22,11359,0.00193679
+9102,2955,IP INSULIN TRANSITION OFF IV INFUSION,541465,INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE NPO,Medication,35,11359,0.003081257
+9103,2955,IP INSULIN TRANSITION OFF IV INFUSION,541466,INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE NPO,Medication,19,11359,0.001672682
+9104,2955,IP INSULIN TRANSITION OFF IV INFUSION,541467,INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE NPO,Medication,3,11359,0.000264108
+9105,2955,IP INSULIN TRANSITION OFF IV INFUSION,POC14,POC GLUCOSE BY METER,Lab,4639,11359,0.408398627
+9106,2956,IP INSULIN CONTINUOUS IV INFUSION,2364,DEXTROSE 5 % IN WATER (D5W) IV SOLP,Medication,325,8645,0.037593985
+9107,2956,IP INSULIN CONTINUOUS IV INFUSION,2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,4067,8645,0.470445344
+9108,2956,IP INSULIN CONTINUOUS IV INFUSION,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,531,8645,0.061422788
+9109,2956,IP INSULIN CONTINUOUS IV INFUSION,112287,DEXTROSE 50% IN WATER (D50W) IV SOLP,Medication,2,8645,0.000231348
+9110,2956,IP INSULIN CONTINUOUS IV INFUSION,112318,DEXTROSE 10% IN WATER (D10W) IV SOLP,Medication,125,8645,0.014459225
+9111,2956,IP INSULIN CONTINUOUS IV INFUSION,530049,INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW),Medication,4418,8645,0.511046848
+9112,2956,IP INSULIN CONTINUOUS IV INFUSION,540910,INSULIN REGULAR IV BOLUS,Medication,2763,8645,0.319606709
+9113,2956,IP INSULIN CONTINUOUS IV INFUSION,POC14,POC GLUCOSE BY METER,Lab,24796,8645,1
+9114,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,446,950,0.469473684
+9115,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),6427,POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK,Medication,22,950,0.023157895
+9116,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),7309,SODIUM BICARBONATE 8.4 % IV SOLN,Medication,2,950,0.002105263
+9117,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),7318,SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP,Medication,18,950,0.018947368
+9118,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),11077,POTASSIUM CHLORIDE 20 MEQ/50 ML IV PGBK,Medication,6,950,0.006315789
+9119,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),11079,POTASSIUM CHLORIDE 40 MEQ/100 ML IV PGBK,Medication,2,950,0.002105263
+9120,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,54,950,0.056842105
+9121,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,28,950,0.029473684
+9122,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),36063,0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,42,950,0.044210526
+9123,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),112517,SODIUM BICARBONATE 8.4 % (1 MEQ/ML) IV SYRG,Medication,5,950,0.005263158
+9124,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,202,950,0.212631579
+9125,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),500006,SODIUM PHOSPHATE IVPB,Medication,37,950,0.038947368
+9126,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),500032,1/2 NS WITH 30 MEQ KCL IV INFUSION (DKA PROTOCOL),Medication,40,950,0.042105263
+9127,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),500033,NS WITH 30 MEQ KCL IV INFUSION (DKA PROTOCOL),Medication,33,950,0.034736842
+9128,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),530048,INSULIN REGULAR 1 UNIT/ML IV INFUSION (DKA/HHS PROTOCOL),Medication,451,950,0.474736842
+9129,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),540102,NS IV BOLUS,Medication,53,950,0.055789474
+9130,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),540500,SODIUM BICARBONATE 50 MEQ / 50 ML IV BAG (DKA PROTOCOL),Medication,28,950,0.029473684
+9131,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),540501,SODIUM BICARBONATE 100 MEQ / 100 ML BAG (DKA PROTOCOL),Medication,16,950,0.016842105
+9132,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),540502,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN (DKA PROTOCOL),Medication,105,950,0.110526316
+9133,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABA1C,HEMOGLOBIN A1C,Lab,327,950,0.344210526
+9134,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABABG,"BLOOD GASES, ARTERIAL",Lab,13,950,0.013684211
+9135,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABBHB,BETA-HYDROXYBUTYRATE,Lab,34,950,0.035789474
+9136,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,24,950,0.025263158
+9137,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABCBCO,CBC W/O DIFF,Lab,121,950,0.127368421
+9138,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABHFP,HEPATIC FUNCTION PANEL A,Lab,5,950,0.005263158
+9139,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABK,"POTASSIUM, SERUM/PLASMA",Lab,36,950,0.037894737
+9140,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABLACWB,"LACTATE, WHOLE BLOOD",Lab,32,950,0.033684211
+9141,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABLIPS,LIPASE,Lab,12,950,0.012631579
+9142,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABMETB,"METABOLIC PANEL, BASIC",Lab,354,950,0.372631579
+9143,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,319,950,0.335789474
+9144,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABOSM,"OSMOLALITY, SERUM",Lab,14,950,0.014736842
+9145,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,372,950,0.391578947
+9146,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABTNI,TROPONIN I,Lab,8,950,0.008421053
+9147,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABUA,URINALYSIS WITH MICROSCOPIC,Lab,168,950,0.176842105
+9148,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABURNC,URINE CULTURE,Lab,2,950,0.002105263
+9149,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),LABVBG,"BLOOD GASES, VENOUS",Lab,204,950,0.214736842
+9150,2957,IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS),POC14,POC GLUCOSE BY METER,Lab,480,950,0.505263158
+9151,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),2365,DEXTROSE 50 % IN WATER (D50W) IV SYRG,Medication,419,983,0.426246185
+9152,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),16050,GLUCOSE 4 GRAM PO CHEW,Medication,400,983,0.406917599
+9153,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),87878,INSULIN ASPART 100 UNIT/ML SC SOLN,Medication,3,983,0.003051882
+9154,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,1,983,0.001017294
+9155,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),126472,INSULIN GLULISINE 100 UNIT/ML SC SOLN,Medication,8,983,0.008138352
+9156,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),540499,INSULIN LISPRO SUBCUTANEOUS PUMP,Medication,282,983,0.286876907
+9157,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),540503,INSULIN ASPART SUBCUTANEOUS PUMP,Medication,136,983,0.138351984
+9158,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),540512,INSULIN ASPART SUBCUTANEOUS PUMP BOLUS,Medication,271,983,0.275686673
+9159,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),540513,INSULIN LISPRO SUBCUTANEOUS PUMP BOLUS,Medication,531,983,0.540183113
+9160,2958,IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII)),POC14,POC GLUCOSE BY METER,Lab,676,983,0.687690743
+9161,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,1792,COCAINE 4 % TP SOLN,Medication,27,2290,0.011790393
+9162,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,4318,LACTATED RINGERS IV SOLP,Medication,44,2290,0.019213974
+9163,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,4448,LIDOCAINE HCL 2 % JELLY,Medication,914,2290,0.399126638
+9164,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,4450,LIDOCAINE HCL 4 % TOPICAL SOLN,Medication,1,2290,0.000436681
+9165,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,4452,LIDOCAINE HCL 10 MG/ML (1 %) INJ SOLN,Medication,1,2290,0.000436681
+9166,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,4455,LIDOCAINE (PF) 40 MG/ML (4 %) INJ SOLN,Medication,931,2290,0.406550218
+9167,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,450,2290,0.19650655
+9168,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,6,2290,0.002620087
+9169,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,878,2290,0.383406114
+9170,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,496,2290,0.216593886
+9171,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,780,2290,0.340611354
+9172,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,976,2290,0.426200873
+9173,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,171,2290,0.074672489
+9174,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,65,2290,0.028384279
+9175,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,1283,2290,0.560262009
+9176,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,885,2290,0.386462882
+9177,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,984,2290,0.429694323
+9178,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,540300,MORPHINE INJECTABLE SYRINGE,Medication,15,2290,0.006550218
+9179,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,540897,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN(ORDERSET USE ONLY),Medication,988,2290,0.431441048
+9180,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,LABHCTX,HEMATOCRIT,Lab,1,2290,0.000436681
+9181,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,LABPLTS,PLATELET COUNT,Lab,2,2290,0.000873362
+9182,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,LABPT,PROTHROMBIN TIME,Lab,3,2290,0.001310044
+9183,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,2290,0.000873362
+9184,2961,IP GEN BRONCHOSCOPY PRE/INTRA/POST,POC14,POC GLUCOSE BY METER,Lab,16,2290,0.0069869
+9185,2972,IP CAR CARDIOLOGY POST PROCEDURE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,141,2659,0.053027454
+9186,2972,IP CAR CARDIOLOGY POST PROCEDURE,102,ACETAMINOPHEN 500 MG PO TABS,Medication,5,2659,0.001880406
+9187,2972,IP CAR CARDIOLOGY POST PROCEDURE,730,ATROPINE 0.1 MG/ML INJ SYRG,Medication,2,2659,0.000752162
+9188,2972,IP CAR CARDIOLOGY POST PROCEDURE,731,ATROPINE 0.4 MG/ML INJ SOLN,Medication,11,2659,0.004136894
+9189,2972,IP CAR CARDIOLOGY POST PROCEDURE,1080,BISACODYL 10 MG PR SUPP,Medication,6,2659,0.002256487
+9190,2972,IP CAR CARDIOLOGY POST PROCEDURE,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,1,2659,0.000376081
+9191,2972,IP CAR CARDIOLOGY POST PROCEDURE,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,2659,0.002632569
+9192,2972,IP CAR CARDIOLOGY POST PROCEDURE,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,37,2659,0.013915006
+9193,2972,IP CAR CARDIOLOGY POST PROCEDURE,5087,MINERAL OIL PR ENEM,Medication,5,2659,0.001880406
+9194,2972,IP CAR CARDIOLOGY POST PROCEDURE,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,17,2659,0.006393381
+9195,2972,IP CAR CARDIOLOGY POST PROCEDURE,10011,FAMOTIDINE 20 MG PO TABS,Medication,10,2659,0.003760812
+9196,2972,IP CAR CARDIOLOGY POST PROCEDURE,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,2659,0.000376081
+9197,2972,IP CAR CARDIOLOGY POST PROCEDURE,11500,TEMAZEPAM 7.5 MG PO CAPS,Medication,1,2659,0.000376081
+9198,2972,IP CAR CARDIOLOGY POST PROCEDURE,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,2659,0.000376081
+9199,2972,IP CAR CARDIOLOGY POST PROCEDURE,13654,ASPIRIN 325 MG PO TBEC,Medication,5,2659,0.001880406
+9200,2972,IP CAR CARDIOLOGY POST PROCEDURE,14113,ASPIRIN 81 MG PO TBEC,Medication,228,2659,0.085746521
+9201,2972,IP CAR CARDIOLOGY POST PROCEDURE,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,2659,0.000376081
+9202,2972,IP CAR CARDIOLOGY POST PROCEDURE,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,2659,0.000376081
+9203,2972,IP CAR CARDIOLOGY POST PROCEDURE,16830,MELATONIN 3 MG PO TABS,Medication,1,2659,0.000376081
+9204,2972,IP CAR CARDIOLOGY POST PROCEDURE,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,22,2659,0.008273787
+9205,2972,IP CAR CARDIOLOGY POST PROCEDURE,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,5,2659,0.001880406
+9206,2972,IP CAR CARDIOLOGY POST PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,88,2659,0.033095149
+9207,2972,IP CAR CARDIOLOGY POST PROCEDURE,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,25,2659,0.009402031
+9208,2972,IP CAR CARDIOLOGY POST PROCEDURE,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,10,2659,0.003760812
+9209,2972,IP CAR CARDIOLOGY POST PROCEDURE,83398,CLOPIDOGREL 75 MG PO TABS,Medication,254,2659,0.095524633
+9210,2972,IP CAR CARDIOLOGY POST PROCEDURE,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,2659,0.000752162
+9211,2972,IP CAR CARDIOLOGY POST PROCEDURE,89797,EPTIFIBATIDE 0.75 MG/ML IV SOLN,Medication,15,2659,0.005641219
+9212,2972,IP CAR CARDIOLOGY POST PROCEDURE,92173,ZOLPIDEM 5 MG PO TABS,Medication,14,2659,0.005265137
+9213,2972,IP CAR CARDIOLOGY POST PROCEDURE,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,2659,0.000376081
+9214,2972,IP CAR CARDIOLOGY POST PROCEDURE,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,18,2659,0.006769462
+9215,2972,IP CAR CARDIOLOGY POST PROCEDURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,118,2659,0.044377586
+9216,2972,IP CAR CARDIOLOGY POST PROCEDURE,203749,CLOPIDOGREL 75 MG PO TABS,Medication,15,2659,0.005641219
+9217,2972,IP CAR CARDIOLOGY POST PROCEDURE,207215,TIROFIBAN-0.9% SODIUM CHLORIDE 5 MG/100 ML (50 MCG/ML) IV SOLN,Medication,24,2659,0.00902595
+9218,2972,IP CAR CARDIOLOGY POST PROCEDURE,208212,PRASUGREL 10 MG PO TABS,Medication,65,2659,0.02444528
+9219,2972,IP CAR CARDIOLOGY POST PROCEDURE,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,2659,0.001128244
+9220,2972,IP CAR CARDIOLOGY POST PROCEDURE,214201,TICAGRELOR 90 MG PO TABS,Medication,36,2659,0.013538924
+9221,2972,IP CAR CARDIOLOGY POST PROCEDURE,500003,D5W WITH SODIUM BICARBONATE IV INFUSION,Medication,5,2659,0.001880406
+9222,2972,IP CAR CARDIOLOGY POST PROCEDURE,530035,BIVALIRUDIN IV INFUSION,Medication,12,2659,0.004512975
+9223,2972,IP CAR CARDIOLOGY POST PROCEDURE,540102,NS IV BOLUS,Medication,12,2659,0.004512975
+9224,2972,IP CAR CARDIOLOGY POST PROCEDURE,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,131,2659,0.049266642
+9225,2972,IP CAR CARDIOLOGY POST PROCEDURE,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,1,2659,0.000376081
+9226,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABCBCO,CBC W/O DIFF,Lab,86,2659,0.032342986
+9227,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,34,2659,0.012786762
+9228,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABMB,"CK, MB (MASS)",Lab,110,2659,0.041368936
+9229,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABMETB,"METABOLIC PANEL, BASIC",Lab,80,2659,0.030086499
+9230,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABPLTS,PLATELET COUNT,Lab,4,2659,0.001504325
+9231,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABTNI,TROPONIN I,Lab,4,2659,0.001504325
+9232,2972,IP CAR CARDIOLOGY POST PROCEDURE,IMGDXCH1,XR CHEST 1V,Imaging,6,2659,0.002256487
+9233,2972,IP CAR CARDIOLOGY POST PROCEDURE,IMGDXCH2,XR CHEST 2V,Imaging,28,2659,0.010530275
+9234,2972,IP CAR CARDIOLOGY POST PROCEDURE,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,1,2659,0.000376081
+9235,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,7,3317,0.002110341
+9236,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,3317,0.000301477
+9237,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,680,ASPIRIN 81 MG PO CHEW,Medication,1,3317,0.000301477
+9238,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,681,ASPIRIN 325 MG PO TABS,Medication,685,3317,0.206511908
+9239,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,2404,DIAZEPAM 2 MG PO TABS,Medication,24,3317,0.007235454
+9240,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,2405,DIAZEPAM 5 MG PO TABS,Medication,1208,3317,0.364184504
+9241,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,35,3317,0.010551703
+9242,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1271,3317,0.38317757
+9243,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,3317,0.000301477
+9244,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,14113,ASPIRIN 81 MG PO TBEC,Medication,6,3317,0.001808863
+9245,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,2,3317,0.000602954
+9246,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1081,3317,0.325896895
+9247,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,83398,CLOPIDOGREL 75 MG PO TABS,Medication,16,3317,0.004823636
+9248,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,3,3317,0.000904432
+9249,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,43,3317,0.012963521
+9250,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,203749,CLOPIDOGREL 75 MG PO TABS,Medication,14,3317,0.004220681
+9251,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,208212,PRASUGREL 10 MG PO TABS,Medication,1,3317,0.000301477
+9252,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,45,3317,0.013566476
+9253,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,6,3317,0.001808863
+9254,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,5,3317,0.001507386
+9255,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,540304,MAGNESIUM SULFATE IV SCALE ,Medication,1,3317,0.000301477
+9256,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,LABCBCO,CBC W/O DIFF,Lab,17,3317,0.005125113
+9257,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,LABMETB,"METABOLIC PANEL, BASIC",Lab,5,3317,0.001507386
+9258,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,LABPREG,"PREGNANCY TEST, SERUM",Lab,34,3317,0.010250226
+9259,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,LABPT,PROTHROMBIN TIME,Lab,58,3317,0.01748568
+9260,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,14,3317,0.004220681
+9261,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,LABTYPSNI,TYPE AND SCREEN,Lab,1300,3317,0.39192041
+9262,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,POC14,POC GLUCOSE BY METER,Lab,79,3317,0.023816702
+9263,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,POC17,POC ISTAT CREATININE,Lab,651,3317,0.196261682
+9264,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,666,3317,0.200783841
+9265,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,POC25,POC ISTAT INR AND PROTIME,Lab,680,3317,0.205004522
+9266,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,IMGDXCH1,XR CHEST 1V,Imaging,1,3317,0.000301477
+9267,2973,IP CAR CARDIOLOGY PREOP PROCEDURE,IMGDXCH2,XR CHEST 2V,Imaging,1,3317,0.000301477
+9268,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),8748,WARFARIN 10 MG PO TABS,Medication,60,3364,0.01783591
+9269,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),8749,WARFARIN 2 MG PO TABS,Medication,201,3364,0.059750297
+9270,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),8750,WARFARIN 2.5 MG PO TABS,Medication,322,3364,0.095719382
+9271,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),8751,WARFARIN 5 MG PO TABS,Medication,458,3364,0.136147444
+9272,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),8752,WARFARIN 7.5 MG PO TABS,Medication,204,3364,0.060642093
+9273,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),11664,WARFARIN 1 MG PO TABS,Medication,211,3364,0.062722949
+9274,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),19433,WARFARIN 3 MG PO TABS,Medication,197,3364,0.058561237
+9275,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),19434,WARFARIN 6 MG PO TABS,Medication,113,3364,0.033590963
+9276,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),21372,WARFARIN 4 MG PO TABS,Medication,200,3364,0.059453032
+9277,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),9000000,ZZZ IMS TEMPLATE,Medication,67,3364,0.019916766
+9278,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),LABCBCO,CBC W/O DIFF,Lab,160,3364,0.047562426
+9279,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),LABHFP,HEPATIC FUNCTION PANEL A,Lab,52,3364,0.015457788
+9280,3015,RXWARFARIN PROTOCOL (PHARMACY USE ONLY),LABPT,PROTHROMBIN TIME,Lab,1360,3364,0.404280618
+9281,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,12,1075,0.011162791
+9282,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,21,1075,0.019534884
+9283,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,84,1075,0.078139535
+9284,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,12,1075,0.011162791
+9285,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,1075,0.000930233
+9286,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,5339,NALBUPHINE 10 MG/ML INJ SOLN,Medication,230,1075,0.213953488
+9287,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,5340,NALBUPHINE 20 MG/ML INJ SOLN,Medication,25,1075,0.023255814
+9288,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,517,1075,0.480930233
+9289,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,48,1075,0.044651163
+9290,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,18308,GABAPENTIN 300 MG PO CAPS,Medication,29,1075,0.026976744
+9291,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,18309,GABAPENTIN 100 MG PO CAPS,Medication,4,1075,0.00372093
+9292,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,27920,OXYCODONE 10 MG PO TB12,Medication,3,1075,0.002790698
+9293,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,4,1075,0.00372093
+9294,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,125758,PREGABALIN 75 MG PO CAPS,Medication,4,1075,0.00372093
+9295,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,54,1075,0.050232558
+9296,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,2,1075,0.001860465
+9297,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,7,1075,0.006511628
+9298,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,3,1075,0.002790698
+9299,3035,IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,1075,0.000930233
+9300,3200,OP TRANSFUSION ORDERS RECURRING,LABBBPCRY,CRYOPRECIPITATE,Lab,1,189,0.005291005
+9301,3200,OP TRANSFUSION ORDERS RECURRING,LABTYPSNI,TYPE AND SCREEN,Lab,126,189,0.666666667
+9302,3200,OP TRANSFUSION ORDERS RECURRING,LABBBPCRY,CRYOPRECIPITATE,Blood Product,3,189,0.015873016
+9303,3200,OP TRANSFUSION ORDERS RECURRING,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,3,189,0.015873016
+9304,3200,OP TRANSFUSION ORDERS RECURRING,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,99,189,0.523809524
+9305,3200,OP TRANSFUSION ORDERS RECURRING,LABBBPRBC,RED BLOOD CELLS,Blood Product,128,189,0.677248677
+9306,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,9,17,0.529411765
+9307,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,7,17,0.411764706
+9308,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,5,17,0.294117647
+9309,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,17,0.117647059
+9310,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,17,0.117647059
+9311,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,520035,ONDANSETRON IVPB,Medication,5,17,0.294117647
+9312,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,LABCBCD,CBC WITH DIFF,Lab,1,17,0.058823529
+9313,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,LABCR,"CREATININE, SERUM/PLASMA",Lab,1,17,0.058823529
+9314,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,LABFLL,FSH,Lab,2,17,0.117647059
+9315,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,LABPREG,"PREGNANCY TEST, SERUM",Lab,1,17,0.058823529
+9316,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,LABPT,PROTHROMBIN TIME,Lab,1,17,0.058823529
+9317,3369,IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,17,0.058823529
+9318,3386,RETIRED IP ICU SPONTANEOUS AWAKE TRIAL AND SPONTANEOUS BREATHING TRIAL,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,61,334,0.182634731
+9319,3388,IP CAR IMPELLA,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,9,51,0.176470588
+9320,3388,IP CAR IMPELLA,221680,ARGATROBAN IN NACL (ISO-OS) 50 MG/50 ML (1 MG/ML) IV SOLN,Medication,2,51,0.039215686
+9321,3388,IP CAR IMPELLA,540613,PVAD PURGE SOLN: DEXTROSE 20% + HEPARIN 50 UNITS/ML,Medication,12,51,0.235294118
+9322,3388,IP CAR IMPELLA,540973,PVAD PURGE SOLN: DEXTROSE 20% + HEPARIN 25 UNITS/ML,Medication,18,51,0.352941176
+9323,3388,IP CAR IMPELLA,LABPT,PROTHROMBIN TIME,Lab,10,51,0.196078431
+9324,3388,IP CAR IMPELLA,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,12,51,0.235294118
+9325,3388,IP CAR IMPELLA,POC45,ACTIVATED CLOTTING TIME (ACT),Lab,13,51,0.254901961
+9326,3388,IP CAR IMPELLA,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,1,51,0.019607843
+9327,3389,CDU TIA,LABA1C,HEMOGLOBIN A1C,Lab,28,179,0.156424581
+9328,3389,CDU TIA,LABCHOL,"CHOLESTEROL TOTAL, SERUM/PLASMA",Lab,19,179,0.106145251
+9329,3389,CDU TIA,LABFT4,"T4, FREE",Lab,14,179,0.078212291
+9330,3389,CDU TIA,LABHDL,"HDL, SERUM",Lab,20,179,0.111731844
+9331,3389,CDU TIA,LABHFP,HEPATIC FUNCTION PANEL A,Lab,11,179,0.061452514
+9332,3389,CDU TIA,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,20,179,0.111731844
+9333,3389,CDU TIA,LABTSH,TSH,Lab,27,179,0.150837989
+9334,3389,CDU TIA,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,2,179,0.011173184
+9335,3389,CDU TIA,IMGCT3HN,CT ANGIO HEAD AND NECK,Imaging,1,179,0.005586592
+9336,3389,CDU TIA,IMGCTH,CT HEAD,Imaging,1,179,0.005586592
+9337,3389,CDU TIA,IMGMRAH,MR ANGIO HEAD,Imaging,36,179,0.201117318
+9338,3389,CDU TIA,IMGMRAK,MR ANGIO NECK,Imaging,34,179,0.189944134
+9339,3389,CDU TIA,IMGMRH,MR BRAIN,Imaging,48,179,0.268156425
+9340,3389,CDU TIA,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,2,179,0.011173184
+9341,3390,CDU CONTRAST DYE ALLERGY,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,3,11,0.272727273
+9342,3390,CDU CONTRAST DYE ALLERGY,2510,DIPHENHYDRAMINE HCL 50 MG PO CAPS,Medication,2,11,0.181818182
+9343,3390,CDU CONTRAST DYE ALLERGY,6496,PREDNISONE 20 MG PO TABS,Medication,3,11,0.272727273
+9344,3390,CDU CONTRAST DYE ALLERGY,6498,PREDNISONE 50 MG PO TABS,Medication,6,11,0.545454545
+9345,3390,CDU CONTRAST DYE ALLERGY,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,11,0.181818182
+9346,3390,CDU CONTRAST DYE ALLERGY,9000000,ZZZ IMS TEMPLATE,Medication,6,11,0.545454545
+9347,3390,CDU CONTRAST DYE ALLERGY,IMGCT3T,CT ANGIO CHEST,Imaging,1,11,0.090909091
+9348,3409,IP GI ENDOSCOPY MANOMETRY,4448,LIDOCAINE HCL 2 % JELLY,Medication,7,48,0.145833333
+9349,3409,IP GI ENDOSCOPY MANOMETRY,7228,SIMETHICONE 40 MG/0.6 ML PO DRPS,Medication,4,48,0.083333333
+9350,3409,IP GI ENDOSCOPY MANOMETRY,82190,BUTAMBEN-TETRACAINE-BENZOCAINE 2-2-14 % TP SPRA,Medication,4,48,0.083333333
+9351,3409,IP GI ENDOSCOPY MANOMETRY,90788,LIDOCAINE HCL 2 % VISCOUS ORAL SOLN,Medication,1,48,0.020833333
+9352,3417,IP TRS IVIG DESENSITIZATION ORDERS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,54,0.037037037
+9353,3417,IP TRS IVIG DESENSITIZATION ORDERS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,24,54,0.444444444
+9354,3417,IP TRS IVIG DESENSITIZATION ORDERS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,3,54,0.055555556
+9355,3417,IP TRS IVIG DESENSITIZATION ORDERS,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,19,54,0.351851852
+9356,3417,IP TRS IVIG DESENSITIZATION ORDERS,2510,DIPHENHYDRAMINE HCL 50 MG PO CAPS,Medication,7,54,0.12962963
+9357,3417,IP TRS IVIG DESENSITIZATION ORDERS,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,12,54,0.222222222
+9358,3417,IP TRS IVIG DESENSITIZATION ORDERS,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,1,54,0.018518519
+9359,3417,IP TRS IVIG DESENSITIZATION ORDERS,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,29,54,0.537037037
+9360,3417,IP TRS IVIG DESENSITIZATION ORDERS,10205R,HLA ORDER - DESENSITIZATION MONITORING PRE IVIG,Lab,6,54,0.111111111
+9361,3417,IP TRS IVIG DESENSITIZATION ORDERS,10210R,HLA ORDER - DESENSITIZATION AB MONITORING-POST IVIG,Lab,10,54,0.185185185
+9362,3417,IP TRS IVIG DESENSITIZATION ORDERS,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,4,54,0.074074074
+9363,3417,IP TRS IVIG DESENSITIZATION ORDERS,10225R,HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*,Lab,7,54,0.12962963
+9364,3417,IP TRS IVIG DESENSITIZATION ORDERS,LABCBCD,CBC WITH DIFF,Lab,1,54,0.018518519
+9365,3437,IP EMERGENT WARFARIN REVERSAL,83422,FACTOR IX COMPLEX 500 (+/-) UNIT IV SOLR,Medication,4,383,0.010443864
+9366,3437,IP EMERGENT WARFARIN REVERSAL,230057,HUMAN PROTHROMBIN COMPLEX(PCC) 500 UNIT (FII 380-800 UNIT) IV KIT,Medication,145,383,0.378590078
+9367,3437,IP EMERGENT WARFARIN REVERSAL,540660,FACTOR IX COMPLEX IV FOR INR < 4,Medication,6,383,0.015665796
+9368,3437,IP EMERGENT WARFARIN REVERSAL,540661,FACTOR IX COMPLEX IV FOR INR > OR = 4,Medication,3,383,0.007832898
+9369,3437,IP EMERGENT WARFARIN REVERSAL,540688,PHYTONADIONE (VITAMIN K) IVPB (WARFARIN PROTOCOL),Medication,170,383,0.44386423
+9370,3437,IP EMERGENT WARFARIN REVERSAL,LABMB,"CK, MB (MASS)",Lab,6,383,0.015665796
+9371,3437,IP EMERGENT WARFARIN REVERSAL,LABPT,PROTHROMBIN TIME,Lab,160,383,0.417754569
+9372,3437,IP EMERGENT WARFARIN REVERSAL,LABTNI,TROPONIN I,Lab,46,383,0.120104439
+9373,3437,IP EMERGENT WARFARIN REVERSAL,LABTYPSNI,TYPE AND SCREEN,Lab,3,383,0.007832898
+9374,3439,IP DENTAL DISCHARGE,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,1,7,0.142857143
+9375,3439,IP DENTAL DISCHARGE,33227,AMOXICILLIN-POT CLAVULANATE 500-125 MG PO TABS,Medication,1,7,0.142857143
+9376,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,2516,DIPHENOXYLATE-ATROPINE 2.5-0.025 MG PO TABS,Medication,85,233,0.364806867
+9377,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,18,233,0.077253219
+9378,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,45,233,0.193133047
+9379,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,6622,PROMETHAZINE 25 MG PO TABS,Medication,37,233,0.158798283
+9380,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,80840,ONDANSETRON HCL 8 MG PO TABS,Medication,53,233,0.227467811
+9381,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,81141,ONDANSETRON HCL 4 MG PO TABS,Medication,16,233,0.068669528
+9382,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,84520,ONDANSETRON 8 MG PO TBDL,Medication,16,233,0.068669528
+9383,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,94176,ONDANSETRON 4 MG PO TBDL,Medication,8,233,0.034334764
+9384,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,59,233,0.253218884
+9385,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,540680,DIHYDROERGOTAMINE IN NORMAL SALINE 1 MG/ML IN 100 ML IVPB,Medication,97,233,0.416309013
+9386,3449,IP DIHYDROERGOTAMINE (DHE) PROTOCOL,LABUPREG,"PREGNANCY TEST, URINE",Lab,34,233,0.145922747
+9387,3450,IP MAINTENANCE DIHYDROERGOTAMINE (DHE) INFUSIONS (BASED ON VAS),80840,ONDANSETRON HCL 8 MG PO TABS,Medication,40,106,0.377358491
+9388,3450,IP MAINTENANCE DIHYDROERGOTAMINE (DHE) INFUSIONS (BASED ON VAS),81141,ONDANSETRON HCL 4 MG PO TABS,Medication,16,106,0.150943396
+9389,3450,IP MAINTENANCE DIHYDROERGOTAMINE (DHE) INFUSIONS (BASED ON VAS),540680,DIHYDROERGOTAMINE IN NORMAL SALINE 1 MG/ML IN 100 ML IVPB,Medication,124,106,1
+9390,3452,IP CAR CONGESTIVE HEART FAILURE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,30,921,0.03257329
+9391,3452,IP CAR CONGESTIVE HEART FAILURE,102,ACETAMINOPHEN 500 MG PO TABS,Medication,4,921,0.004343105
+9392,3452,IP CAR CONGESTIVE HEART FAILURE,1080,BISACODYL 10 MG PR SUPP,Medication,23,921,0.024972856
+9393,3452,IP CAR CONGESTIVE HEART FAILURE,2444,DIGOXIN 125 MCG PO TABS,Medication,3,921,0.003257329
+9394,3452,IP CAR CONGESTIVE HEART FAILURE,2445,DIGOXIN 250 MCG PO TABS,Medication,1,921,0.001085776
+9395,3452,IP CAR CONGESTIVE HEART FAILURE,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,2,921,0.002171553
+9396,3452,IP CAR CONGESTIVE HEART FAILURE,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,23,921,0.024972856
+9397,3452,IP CAR CONGESTIVE HEART FAILURE,3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,96,921,0.104234528
+9398,3452,IP CAR CONGESTIVE HEART FAILURE,3294,FUROSEMIDE 20 MG PO TABS,Medication,2,921,0.002171553
+9399,3452,IP CAR CONGESTIVE HEART FAILURE,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,5,921,0.005428882
+9400,3452,IP CAR CONGESTIVE HEART FAILURE,3698,HYDRALAZINE 10 MG PO TABS,Medication,5,921,0.005428882
+9401,3452,IP CAR CONGESTIVE HEART FAILURE,3700,HYDRALAZINE 25 MG PO TABS,Medication,11,921,0.01194354
+9402,3452,IP CAR CONGESTIVE HEART FAILURE,3701,HYDRALAZINE 50 MG PO TABS,Medication,4,921,0.004343105
+9403,3452,IP CAR CONGESTIVE HEART FAILURE,4064,ISOSORBIDE DINITRATE 10 MG PO TABS,Medication,14,921,0.015200869
+9404,3452,IP CAR CONGESTIVE HEART FAILURE,4065,ISOSORBIDE DINITRATE 20 MG PO TABS,Medication,14,921,0.015200869
+9405,3452,IP CAR CONGESTIVE HEART FAILURE,4526,LISINOPRIL 20 MG PO TABS,Medication,3,921,0.003257329
+9406,3452,IP CAR CONGESTIVE HEART FAILURE,4711,MAGNESIUM CITRATE PO SOLN,Medication,5,921,0.005428882
+9407,3452,IP CAR CONGESTIVE HEART FAILURE,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,921,0.001085776
+9408,3452,IP CAR CONGESTIVE HEART FAILURE,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,1,921,0.001085776
+9409,3452,IP CAR CONGESTIVE HEART FAILURE,5009,METOPROLOL TARTRATE 50 MG PO TABS,Medication,1,921,0.001085776
+9410,3452,IP CAR CONGESTIVE HEART FAILURE,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,1,921,0.001085776
+9411,3452,IP CAR CONGESTIVE HEART FAILURE,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,1,921,0.001085776
+9412,3452,IP CAR CONGESTIVE HEART FAILURE,6427,POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK,Medication,1,921,0.001085776
+9413,3452,IP CAR CONGESTIVE HEART FAILURE,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,1,921,0.001085776
+9414,3452,IP CAR CONGESTIVE HEART FAILURE,7437,SPIRONOLACTONE 25 MG PO TABS,Medication,10,921,0.010857763
+9415,3452,IP CAR CONGESTIVE HEART FAILURE,9221,BENAZEPRIL 20 MG PO TABS,Medication,1,921,0.001085776
+9416,3452,IP CAR CONGESTIVE HEART FAILURE,9308,BUMETANIDE 0.25 MG/ML INJ SOLN,Medication,2,921,0.002171553
+9417,3452,IP CAR CONGESTIVE HEART FAILURE,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,921,0.001085776
+9418,3452,IP CAR CONGESTIVE HEART FAILURE,10449,LISINOPRIL 10 MG PO TABS,Medication,3,921,0.003257329
+9419,3452,IP CAR CONGESTIVE HEART FAILURE,10451,LISINOPRIL 5 MG PO TABS,Medication,3,921,0.003257329
+9420,3452,IP CAR CONGESTIVE HEART FAILURE,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,921,0.001085776
+9421,3452,IP CAR CONGESTIVE HEART FAILURE,11426,SPIRONOLACTONE 50 MG PO TABS,Medication,1,921,0.001085776
+9422,3452,IP CAR CONGESTIVE HEART FAILURE,13089,LISINOPRIL 2.5 MG PO TABS,Medication,3,921,0.003257329
+9423,3452,IP CAR CONGESTIVE HEART FAILURE,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,921,0.001085776
+9424,3452,IP CAR CONGESTIVE HEART FAILURE,15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,6,921,0.006514658
+9425,3452,IP CAR CONGESTIVE HEART FAILURE,18293,TORSEMIDE 20 MG PO TABS,Medication,1,921,0.001085776
+9426,3452,IP CAR CONGESTIVE HEART FAILURE,35943,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,Medication,8,921,0.008686211
+9427,3452,IP CAR CONGESTIVE HEART FAILURE,37637,METOPROLOL TARTRATE 50 MG PO TABS,Medication,2,921,0.002171553
+9428,3452,IP CAR CONGESTIVE HEART FAILURE,78198,CARVEDILOL 6.25 MG PO TABS,Medication,2,921,0.002171553
+9429,3452,IP CAR CONGESTIVE HEART FAILURE,78559,CARVEDILOL 12.5 MG PO TABS,Medication,1,921,0.001085776
+9430,3452,IP CAR CONGESTIVE HEART FAILURE,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,1,921,0.001085776
+9431,3452,IP CAR CONGESTIVE HEART FAILURE,84475,CARVEDILOL 3.125 MG PO TABS,Medication,3,921,0.003257329
+9432,3452,IP CAR CONGESTIVE HEART FAILURE,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,7,921,0.007600434
+9433,3452,IP CAR CONGESTIVE HEART FAILURE,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,3,921,0.003257329
+9434,3452,IP CAR CONGESTIVE HEART FAILURE,91451,NITROGLYCERIN 2 % TD OINT,Medication,1,921,0.001085776
+9435,3452,IP CAR CONGESTIVE HEART FAILURE,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,921,0.004343105
+9436,3452,IP CAR CONGESTIVE HEART FAILURE,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,44,921,0.047774159
+9437,3452,IP CAR CONGESTIVE HEART FAILURE,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,3,921,0.003257329
+9438,3452,IP CAR CONGESTIVE HEART FAILURE,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,921,0.001085776
+9439,3452,IP CAR CONGESTIVE HEART FAILURE,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,24,921,0.026058632
+9440,3452,IP CAR CONGESTIVE HEART FAILURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,38,921,0.041259501
+9441,3452,IP CAR CONGESTIVE HEART FAILURE,200345,MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV SOLN,Medication,4,921,0.004343105
+9442,3452,IP CAR CONGESTIVE HEART FAILURE,205192,MILRINONE IN D5W 20 MG/100 ML IV PGBK,Medication,8,921,0.008686211
+9443,3452,IP CAR CONGESTIVE HEART FAILURE,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,1,921,0.001085776
+9444,3452,IP CAR CONGESTIVE HEART FAILURE,206196,NITROGLYCERIN IN 5 % DEXTROSE 100 MG/250 ML (400 MCG/ML) IV SOLN,Medication,1,921,0.001085776
+9445,3452,IP CAR CONGESTIVE HEART FAILURE,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,129,921,0.140065147
+9446,3452,IP CAR CONGESTIVE HEART FAILURE,530006,EPINEPHRINE IV INFUSION,Medication,4,921,0.004343105
+9447,3452,IP CAR CONGESTIVE HEART FAILURE,540964,PIPERACILLIN-TAZOBACTAM 2.25 GRAM/50 ML MINI-BAG PLUS,Medication,1,921,0.001085776
+9448,3452,IP CAR CONGESTIVE HEART FAILURE,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,921,0.001085776
+9449,3452,IP CAR CONGESTIVE HEART FAILURE,9000000,ZZZ IMS TEMPLATE,Medication,1,921,0.001085776
+9450,3452,IP CAR CONGESTIVE HEART FAILURE,LABCBCO,CBC W/O DIFF,Lab,386,921,0.419109663
+9451,3452,IP CAR CONGESTIVE HEART FAILURE,LABDIGL,"DIGOXIN, SERUM",Lab,10,921,0.010857763
+9452,3452,IP CAR CONGESTIVE HEART FAILURE,LABHFP,HEPATIC FUNCTION PANEL A,Lab,262,921,0.284473398
+9453,3452,IP CAR CONGESTIVE HEART FAILURE,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,181,921,0.196525516
+9454,3452,IP CAR CONGESTIVE HEART FAILURE,LABMB,"CK, MB (MASS)",Lab,15,921,0.016286645
+9455,3452,IP CAR CONGESTIVE HEART FAILURE,LABMETB,"METABOLIC PANEL, BASIC",Lab,516,921,0.560260586
+9456,3452,IP CAR CONGESTIVE HEART FAILURE,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,475,921,0.515743757
+9457,3452,IP CAR CONGESTIVE HEART FAILURE,LABNTBNP,NT - PROBNP,Lab,87,921,0.094462541
+9458,3452,IP CAR CONGESTIVE HEART FAILURE,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,264,921,0.286644951
+9459,3452,IP CAR CONGESTIVE HEART FAILURE,LABPLTS,PLATELET COUNT,Lab,100,921,0.108577633
+9460,3452,IP CAR CONGESTIVE HEART FAILURE,LABPT,PROTHROMBIN TIME,Lab,284,921,0.308360478
+9461,3452,IP CAR CONGESTIVE HEART FAILURE,LABTNI,TROPONIN I,Lab,173,921,0.187839305
+9462,3452,IP CAR CONGESTIVE HEART FAILURE,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,21,921,0.022801303
+9463,3452,IP CAR CONGESTIVE HEART FAILURE,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,15,921,0.016286645
+9464,3452,IP CAR CONGESTIVE HEART FAILURE,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,88,921,0.095548317
+9465,3452,IP CAR CONGESTIVE HEART FAILURE,IMGDXCH1,XR CHEST 1V,Imaging,52,921,0.056460369
+9466,3452,IP CAR CONGESTIVE HEART FAILURE,IMGDXCH2,XR CHEST 2V,Imaging,31,921,0.033659066
+9467,3471,OAC ENCOUNTER,8750,WARFARIN 2.5 MG PO TABS,Medication,1,8,0.125
+9468,3471,OAC ENCOUNTER,8751,WARFARIN 5 MG PO TABS,Medication,1,8,0.125
+9469,3471,OAC ENCOUNTER,11664,WARFARIN 1 MG PO TABS,Medication,3,8,0.375
+9470,3471,OAC ENCOUNTER,21372,WARFARIN 4 MG PO TABS,Medication,1,8,0.125
+9471,3471,OAC ENCOUNTER,110890,ENOXAPARIN 80 MG/0.8 ML SC SYRG,Medication,1,8,0.125
+9472,3471,OAC ENCOUNTER,110891,ENOXAPARIN 100 MG/ML SC SYRG,Medication,1,8,0.125
+9473,3491,ITA ROUTINE PREMEDICATIONS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,8,0.75
+9474,3491,ITA ROUTINE PREMEDICATIONS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,8,0.125
+9475,3491,ITA ROUTINE PREMEDICATIONS,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,3,8,0.375
+9476,3491,ITA ROUTINE PREMEDICATIONS,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,1,8,0.125
+9477,3491,ITA ROUTINE PREMEDICATIONS,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,8,0.125
+9478,3491,ITA ROUTINE PREMEDICATIONS,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,2,8,0.25
+9479,3491,ITA ROUTINE PREMEDICATIONS,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,1,8,0.125
+9480,3491,ITA ROUTINE PREMEDICATIONS,540142,DIPHENHYDRAMINE 25 MG/50 ML NS IVPB (CANCER CENTER ONLY),Medication,2,8,0.25
+9481,3491,ITA ROUTINE PREMEDICATIONS,540143,DIPHENHYDRAMINE 50 MG/50 ML NS IVPB,Medication,1,8,0.125
+9482,3509,PRE-ADMISSION/PRE-OP ORDERS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1353,38646,0.035010092
+9483,3509,PRE-ADMISSION/PRE-OP ORDERS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1880,38646,0.04864669
+9484,3509,PRE-ADMISSION/PRE-OP ORDERS,1445,CEFAZOLIN 1 GRAM INJ SOLR,Medication,92,38646,0.002380583
+9485,3509,PRE-ADMISSION/PRE-OP ORDERS,4318,LACTATED RINGERS IV SOLP,Medication,2047,38646,0.052967966
+9486,3509,PRE-ADMISSION/PRE-OP ORDERS,4454,LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN,Medication,2,38646,5.18E-05
+9487,3509,PRE-ADMISSION/PRE-OP ORDERS,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,866,38646,0.022408529
+9488,3509,PRE-ADMISSION/PRE-OP ORDERS,8442,"VANCOMYCIN 1,000 MG IV SOLR",Medication,18,38646,0.000465766
+9489,3509,PRE-ADMISSION/PRE-OP ORDERS,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,798,38646,0.020648968
+9490,3509,PRE-ADMISSION/PRE-OP ORDERS,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,355,38646,0.009185944
+9491,3509,PRE-ADMISSION/PRE-OP ORDERS,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,467,38646,0.012084045
+9492,3509,PRE-ADMISSION/PRE-OP ORDERS,10814,OXYCODONE 5 MG PO TABS,Medication,361,38646,0.0093412
+9493,3509,PRE-ADMISSION/PRE-OP ORDERS,11664,WARFARIN 1 MG PO TABS,Medication,1,38646,2.59E-05
+9494,3509,PRE-ADMISSION/PRE-OP ORDERS,18307,GABAPENTIN 400 MG PO CAPS,Medication,2,38646,5.18E-05
+9495,3509,PRE-ADMISSION/PRE-OP ORDERS,18308,GABAPENTIN 300 MG PO CAPS,Medication,3469,38646,0.089763494
+9496,3509,PRE-ADMISSION/PRE-OP ORDERS,18309,GABAPENTIN 100 MG PO CAPS,Medication,9,38646,0.000232883
+9497,3509,PRE-ADMISSION/PRE-OP ORDERS,24176,MIDAZOLAM 2 MG/ML PO SYRP,Medication,7,38646,0.000181131
+9498,3509,PRE-ADMISSION/PRE-OP ORDERS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5074,38646,0.131294312
+9499,3509,PRE-ADMISSION/PRE-OP ORDERS,25855,GABAPENTIN 600 MG PO TABS,Medication,113,38646,0.002923977
+9500,3509,PRE-ADMISSION/PRE-OP ORDERS,25856,GABAPENTIN 800 MG PO TABS,Medication,1,38646,2.59E-05
+9501,3509,PRE-ADMISSION/PRE-OP ORDERS,27297,CEFAZOLIN 1 GRAM IV SOLR,Medication,1,38646,2.59E-05
+9502,3509,PRE-ADMISSION/PRE-OP ORDERS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,3583,38646,0.092713347
+9503,3509,PRE-ADMISSION/PRE-OP ORDERS,77052,CELECOXIB 200 MG PO CAPS,Medication,3020,38646,0.078145216
+9504,3509,PRE-ADMISSION/PRE-OP ORDERS,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,124,38646,0.003208611
+9505,3509,PRE-ADMISSION/PRE-OP ORDERS,83728,FONDAPARINUX 2.5 MG/0.5 ML SC SYRG,Medication,2,38646,5.18E-05
+9506,3509,PRE-ADMISSION/PRE-OP ORDERS,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,49,38646,0.001267919
+9507,3509,PRE-ADMISSION/PRE-OP ORDERS,93715,CELECOXIB 100 MG PO CAPS,Medication,5,38646,0.000129379
+9508,3509,PRE-ADMISSION/PRE-OP ORDERS,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,55,38646,0.001423174
+9509,3509,PRE-ADMISSION/PRE-OP ORDERS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,159,38646,0.004114268
+9510,3509,PRE-ADMISSION/PRE-OP ORDERS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,639,38646,0.0165347
+9511,3509,PRE-ADMISSION/PRE-OP ORDERS,125757,PREGABALIN 50 MG PO CAPS,Medication,1,38646,2.59E-05
+9512,3509,PRE-ADMISSION/PRE-OP ORDERS,125758,PREGABALIN 75 MG PO CAPS,Medication,587,38646,0.015189153
+9513,3509,PRE-ADMISSION/PRE-OP ORDERS,126993,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,2732,38646,0.070692957
+9514,3509,PRE-ADMISSION/PRE-OP ORDERS,205597,ALVIMOPAN 12 MG PO CAPS,Medication,478,38646,0.01236868
+9515,3509,PRE-ADMISSION/PRE-OP ORDERS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3144,38646,0.081353827
+9516,3509,PRE-ADMISSION/PRE-OP ORDERS,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,4737,38646,0.122574134
+9517,3509,PRE-ADMISSION/PRE-OP ORDERS,520043,CEFOXITIN IVPB,Medication,138,38646,0.003570874
+9518,3509,PRE-ADMISSION/PRE-OP ORDERS,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,67,38646,0.001733685
+9519,3509,PRE-ADMISSION/PRE-OP ORDERS,520064,CEFAZOLIN 2 G IVPB,Medication,3440,38646,0.089013093
+9520,3509,PRE-ADMISSION/PRE-OP ORDERS,520160,ERTAPENEM 1 G IVPB,Medication,88,38646,0.002277079
+9521,3509,PRE-ADMISSION/PRE-OP ORDERS,540619,SHC AMB CHLORHEXIDINE 4% SCRUBS PACKET WITH INSTRUCTIONS,Medication,601,38646,0.015551415
+9522,3509,PRE-ADMISSION/PRE-OP ORDERS,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,396,38646,0.010246856
+9523,3509,PRE-ADMISSION/PRE-OP ORDERS,541105,CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS,Medication,999,38646,0.025850023
+9524,3509,PRE-ADMISSION/PRE-OP ORDERS,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,558,38646,0.014438752
+9525,3509,PRE-ADMISSION/PRE-OP ORDERS,541128,PATCH CHECK,Medication,49,38646,0.001267919
+9526,3509,PRE-ADMISSION/PRE-OP ORDERS,541228,CEFAZOLIN IVPB (COMPOUNDED),Medication,447,38646,0.011566527
+9527,3509,PRE-ADMISSION/PRE-OP ORDERS,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,85,38646,0.002199451
+9528,3509,PRE-ADMISSION/PRE-OP ORDERS,LABBTVI,BLOOD TYPE VERIFICATION,Lab,1061,38646,0.027454329
+9529,3509,PRE-ADMISSION/PRE-OP ORDERS,LABC125,CANCER AG 125,Lab,1,38646,2.59E-05
+9530,3509,PRE-ADMISSION/PRE-OP ORDERS,LABCBCD,CBC WITH DIFF,Lab,1,38646,2.59E-05
+9531,3509,PRE-ADMISSION/PRE-OP ORDERS,LABCBCO,CBC W/O DIFF,Lab,872,38646,0.022563784
+9532,3509,PRE-ADMISSION/PRE-OP ORDERS,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,50,38646,0.001293795
+9533,3509,PRE-ADMISSION/PRE-OP ORDERS,LABHFP,HEPATIC FUNCTION PANEL A,Lab,22,38646,0.00056927
+9534,3509,PRE-ADMISSION/PRE-OP ORDERS,LABK,"POTASSIUM, SERUM/PLASMA",Lab,105,38646,0.002716969
+9535,3509,PRE-ADMISSION/PRE-OP ORDERS,LABMETB,"METABOLIC PANEL, BASIC",Lab,528,38646,0.013662475
+9536,3509,PRE-ADMISSION/PRE-OP ORDERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,244,38646,0.006313719
+9537,3509,PRE-ADMISSION/PRE-OP ORDERS,LABPLTS,PLATELET COUNT,Lab,679,38646,0.017569736
+9538,3509,PRE-ADMISSION/PRE-OP ORDERS,LABPREG,"PREGNANCY TEST, SERUM",Lab,130,38646,0.003363867
+9539,3509,PRE-ADMISSION/PRE-OP ORDERS,LABPT,PROTHROMBIN TIME,Lab,853,38646,0.022072142
+9540,3509,PRE-ADMISSION/PRE-OP ORDERS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,544,38646,0.014076489
+9541,3509,PRE-ADMISSION/PRE-OP ORDERS,LABTYPSNI,TYPE AND SCREEN,Lab,2155,38646,0.055762563
+9542,3509,PRE-ADMISSION/PRE-OP ORDERS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,130,38646,0.003363867
+9543,3509,PRE-ADMISSION/PRE-OP ORDERS,LABUPREG,"PREGNANCY TEST, URINE",Lab,822,38646,0.021269989
+9544,3509,PRE-ADMISSION/PRE-OP ORDERS,LABURNC,URINE CULTURE,Lab,19,38646,0.000491642
+9545,3509,PRE-ADMISSION/PRE-OP ORDERS,POC14,POC GLUCOSE BY METER,Lab,2223,38646,0.057522124
+9546,3509,PRE-ADMISSION/PRE-OP ORDERS,IMGDXCH2,XR CHEST 2V,Imaging,104,38646,0.002691094
+9547,3509,PRE-ADMISSION/PRE-OP ORDERS,IMGXR0031,XR CHEST 2 VIEWS,Imaging,2,38646,5.18E-05
+9548,3509,PRE-ADMISSION/PRE-OP ORDERS,LABBBPCRY,CRYOPRECIPITATE,Blood Product,7,38646,0.000181131
+9549,3509,PRE-ADMISSION/PRE-OP ORDERS,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,40,38646,0.001035036
+9550,3509,PRE-ADMISSION/PRE-OP ORDERS,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,45,38646,0.001164415
+9551,3509,PRE-ADMISSION/PRE-OP ORDERS,LABBBPRBC,RED BLOOD CELLS,Blood Product,1189,38646,0.030766444
+9552,3521,MU2 ISCHEMIC STROKE - ATRIAL FIB/FLUTTER ANTICOAGULATION ,8749,WARFARIN 2 MG PO TABS,Medication,1,7,0.142857143
+9553,3521,MU2 ISCHEMIC STROKE - ATRIAL FIB/FLUTTER ANTICOAGULATION ,8752,WARFARIN 7.5 MG PO TABS,Medication,1,7,0.142857143
+9554,3521,MU2 ISCHEMIC STROKE - ATRIAL FIB/FLUTTER ANTICOAGULATION ,222101,RIVAROXABAN 15 MG PO TABS,Medication,1,7,0.142857143
+9555,3521,MU2 ISCHEMIC STROKE - ATRIAL FIB/FLUTTER ANTICOAGULATION ,222102,RIVAROXABAN 20 MG PO TABS,Medication,1,7,0.142857143
+9556,3521,MU2 ISCHEMIC STROKE - ATRIAL FIB/FLUTTER ANTICOAGULATION ,228451,APIXABAN 5 MG PO TABS,Medication,3,7,0.428571429
+9557,3533,IP ECHO EXERCISE STRESS TEST,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,18,45,0.4
+9558,3533,IP ECHO EXERCISE STRESS TEST,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,20,45,0.444444444
+9559,3533,IP ECHO EXERCISE STRESS TEST,8975,ADENOSINE 3 MG/ML IV SOLN,Medication,1,45,0.022222222
+9560,3533,IP ECHO EXERCISE STRESS TEST,9869,DILTIAZEM HCL 5 MG/ML IV SOLN,Medication,17,45,0.377777778
+9561,3533,IP ECHO EXERCISE STRESS TEST,111387,ADENOSINE 3 MG/ML IV SYRG,Medication,16,45,0.355555556
+9562,3533,IP ECHO EXERCISE STRESS TEST,ECH30,ECHO - EXERCISE STRESS ECHO,Imaging,26,45,0.577777778
+9563,3534,IP ECHO DOBUTAMINE STRESS TEST,730,ATROPINE 0.1 MG/ML INJ SYRG,Medication,41,86,0.476744186
+9564,3534,IP ECHO DOBUTAMINE STRESS TEST,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,38,86,0.441860465
+9565,3534,IP ECHO DOBUTAMINE STRESS TEST,5604,NITROGLYCERIN 0.4 MG SL SUBL,Medication,41,86,0.476744186
+9566,3534,IP ECHO DOBUTAMINE STRESS TEST,8975,ADENOSINE 3 MG/ML IV SOLN,Medication,1,86,0.011627907
+9567,3534,IP ECHO DOBUTAMINE STRESS TEST,9869,DILTIAZEM HCL 5 MG/ML IV SOLN,Medication,40,86,0.465116279
+9568,3534,IP ECHO DOBUTAMINE STRESS TEST,15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,2,86,0.023255814
+9569,3534,IP ECHO DOBUTAMINE STRESS TEST,111387,ADENOSINE 3 MG/ML IV SYRG,Medication,41,86,0.476744186
+9570,3534,IP ECHO DOBUTAMINE STRESS TEST,206098,DOBUTAMINE IN D5W 250 MG/250 ML (1 MG/ML) IV SOLP,Medication,41,86,0.476744186
+9571,3534,IP ECHO DOBUTAMINE STRESS TEST,ECH29,ECHO - DOBUTAMINE STRESS ECHO,Imaging,48,86,0.558139535
+9572,3542,IP PAI INTRATHECAL INFUSION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,18,137,0.131386861
+9573,3542,IP PAI INTRATHECAL INFUSION,5339,NALBUPHINE 10 MG/ML INJ SOLN,Medication,10,137,0.072992701
+9574,3542,IP PAI INTRATHECAL INFUSION,92173,ZOLPIDEM 5 MG PO TABS,Medication,3,137,0.02189781
+9575,3542,IP PAI INTRATHECAL INFUSION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,61,137,0.445255474
+9576,3542,IP PAI INTRATHECAL INFUSION,530100,CLONIDINE 10 MCG/ML INTRATHECAL INFUSION,Medication,8,137,0.058394161
+9577,3542,IP PAI INTRATHECAL INFUSION,530106,BACLOFEN 10 MCG/ML INTRATHECAL INFUSION,Medication,5,137,0.03649635
+9578,3542,IP PAI INTRATHECAL INFUSION,540830,ZICONOTIDE 25MCG IN 100ML NS,Medication,35,137,0.255474453
+9579,3542,IP PAI INTRATHECAL INFUSION,540831,HYDROMORPHONE (MINI DOSE) 0.05 MG/ML INTRATHECAL INFUSION,Medication,14,137,0.102189781
+9580,3542,IP PAI INTRATHECAL INFUSION,540832,HYDROMORPHONE (REGULAR) 0.5 MG/ML INTRATHECAL INFUSION,Medication,9,137,0.065693431
+9581,3542,IP PAI INTRATHECAL INFUSION,540834,MORPHINE (REGULAR) 1 MG/ML INTRATHECAL INFUSION,Medication,1,137,0.00729927
+9582,3542,IP PAI INTRATHECAL INFUSION,540835,MORPHINE (MINI DOSE) 0.2 MG/ML INTRATHECAL INFUSION,Medication,1,137,0.00729927
+9583,3542,IP PAI INTRATHECAL INFUSION,540836,BUPIVACAINE 1.25MG/ML INTRATHECAL INFUSION,Medication,11,137,0.080291971
+9584,3542,IP PAI INTRATHECAL INFUSION,540855,BUPIVACAINE 5 MG/ML INTRATHECAL INFUSION,Medication,3,137,0.02189781
+9585,3542,IP PAI INTRATHECAL INFUSION,541648,SODIUM CHLORIDE 0.9 % INFUSION,Medication,6,137,0.04379562
+9586,3550,IP VAD GENERAL ADMIT,680,ASPIRIN 81 MG PO CHEW,Medication,2,689,0.002902758
+9587,3550,IP VAD GENERAL ADMIT,681,ASPIRIN 325 MG PO TABS,Medication,11,689,0.015965167
+9588,3550,IP VAD GENERAL ADMIT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,2,689,0.002902758
+9589,3550,IP VAD GENERAL ADMIT,8748,WARFARIN 10 MG PO TABS,Medication,3,689,0.004354136
+9590,3550,IP VAD GENERAL ADMIT,8750,WARFARIN 2.5 MG PO TABS,Medication,1,689,0.001451379
+9591,3550,IP VAD GENERAL ADMIT,11664,WARFARIN 1 MG PO TABS,Medication,3,689,0.004354136
+9592,3550,IP VAD GENERAL ADMIT,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,689,0.001451379
+9593,3550,IP VAD GENERAL ADMIT,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,4,689,0.005805515
+9594,3550,IP VAD GENERAL ADMIT,15861,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,Medication,1,689,0.001451379
+9595,3550,IP VAD GENERAL ADMIT,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,6,689,0.008708273
+9596,3550,IP VAD GENERAL ADMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,18,689,0.026124819
+9597,3550,IP VAD GENERAL ADMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,689,0.001451379
+9598,3550,IP VAD GENERAL ADMIT,112520,SODIUM CHLORIDE 0.9 % 0.9 % INJ SYRG,Medication,2,689,0.002902758
+9599,3550,IP VAD GENERAL ADMIT,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,2,689,0.002902758
+9600,3550,IP VAD GENERAL ADMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,689,0.004354136
+9601,3550,IP VAD GENERAL ADMIT,540102,NS IV BOLUS,Medication,15,689,0.021770682
+9602,3550,IP VAD GENERAL ADMIT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,82,689,0.119013062
+9603,3550,IP VAD GENERAL ADMIT,540302,POTASSIUM CHLORIDE IV SCALE,Medication,9,689,0.013062409
+9604,3550,IP VAD GENERAL ADMIT,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,159,689,0.230769231
+9605,3550,IP VAD GENERAL ADMIT,LAB11041R,FREE HEMOGLOBIN,Lab,25,689,0.03628447
+9606,3550,IP VAD GENERAL ADMIT,LABCBCO,CBC W/O DIFF,Lab,360,689,0.522496372
+9607,3550,IP VAD GENERAL ADMIT,LABDIGL,"DIGOXIN, SERUM",Lab,25,689,0.03628447
+9608,3550,IP VAD GENERAL ADMIT,LABHAP,HAPTOGLOBIN,Lab,33,689,0.047895501
+9609,3550,IP VAD GENERAL ADMIT,LABHFP,HEPATIC FUNCTION PANEL A,Lab,16,689,0.023222061
+9610,3550,IP VAD GENERAL ADMIT,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,321,689,0.465892598
+9611,3550,IP VAD GENERAL ADMIT,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,3,689,0.004354136
+9612,3550,IP VAD GENERAL ADMIT,LABMB,"CK, MB (MASS)",Lab,2,689,0.002902758
+9613,3550,IP VAD GENERAL ADMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,158,689,0.229317852
+9614,3550,IP VAD GENERAL ADMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,222,689,0.322206096
+9615,3550,IP VAD GENERAL ADMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,246,689,0.357039187
+9616,3550,IP VAD GENERAL ADMIT,LABNTBNP,NT - PROBNP,Lab,33,689,0.047895501
+9617,3550,IP VAD GENERAL ADMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,52,689,0.075471698
+9618,3550,IP VAD GENERAL ADMIT,LABPLTS,PLATELET COUNT,Lab,4,689,0.005805515
+9619,3550,IP VAD GENERAL ADMIT,LABPT,PROTHROMBIN TIME,Lab,302,689,0.438316401
+9620,3550,IP VAD GENERAL ADMIT,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,77,689,0.111756168
+9621,3550,IP VAD GENERAL ADMIT,LABTBILFR,"BILIRUBIN TOTAL AND FRACTIONATED, SERUM",Lab,17,689,0.02467344
+9622,3550,IP VAD GENERAL ADMIT,LABTEGCP,THROMBELASTOGRAPH CLOTTING PROFILE,Lab,58,689,0.084179971
+9623,3550,IP VAD GENERAL ADMIT,LABTNI,TROPONIN I,Lab,7,689,0.010159652
+9624,3550,IP VAD GENERAL ADMIT,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,18,689,0.026124819
+9625,3550,IP VAD GENERAL ADMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,35,689,0.050798258
+9626,3550,IP VAD GENERAL ADMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,14,689,0.020319303
+9627,3550,IP VAD GENERAL ADMIT,IMGDXCH2,XR CHEST 2V,Imaging,67,689,0.09724238
+9628,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,10372,LABETALOL 5 MG/ML IV SOLN,Medication,5,37,0.135135135
+9629,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,86145,ALTEPLASE 100 MG IV SOLR,Medication,8,37,0.216216216
+9630,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,530008,NITROPRUSSIDE IV INFUSION,Medication,1,37,0.027027027
+9631,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,530010,NICARDIPINE IV INFUSION,Medication,1,37,0.027027027
+9632,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,540102,NS IV BOLUS,Medication,2,37,0.054054054
+9633,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABA1C,HEMOGLOBIN A1C,Lab,15,37,0.405405405
+9634,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABCBCD,CBC WITH DIFF,Lab,12,37,0.324324324
+9635,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABCBCO,CBC W/O DIFF,Lab,8,37,0.216216216
+9636,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,15,37,0.405405405
+9637,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,12,37,0.324324324
+9638,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,10,37,0.27027027
+9639,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABPLTS,PLATELET COUNT,Lab,1,37,0.027027027
+9640,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABPT,PROTHROMBIN TIME,Lab,13,37,0.351351351
+9641,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2,37,0.054054054
+9642,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABTSH,TSH,Lab,15,37,0.405405405
+9643,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,LABTYPSNI,TYPE AND SCREEN,Lab,1,37,0.027027027
+9644,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,POC14,POC GLUCOSE BY METER,Lab,9,37,0.243243243
+9645,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,POC25,POC ISTAT INR AND PROTIME,Lab,2,37,0.054054054
+9646,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,IMGCTH,CT HEAD,Imaging,6,37,0.162162162
+9647,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,IMGCTHPRW,CT HEAD PERFUSION W CONTRAST,Imaging,5,37,0.135135135
+9648,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,IMGDXCH1,XR CHEST 1V,Imaging,2,37,0.054054054
+9649,3552,RETIRED IP ED THROMBOLYTIC THERAPY/STROKE,IMGMRH,MR BRAIN,Imaging,1,37,0.027027027
+9650,3564,ANE ECT PACU ORDERS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,526,1096,0.479927007
+9651,3564,ANE ECT PACU ORDERS,4318,LACTATED RINGERS IV SOLP,Medication,594,1096,0.541970803
+9652,3564,ANE ECT PACU ORDERS,10372,LABETALOL 5 MG/ML IV SOLN,Medication,578,1096,0.527372263
+9653,3564,ANE ECT PACU ORDERS,14966,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,Medication,9,1096,0.008211679
+9654,3564,ANE ECT PACU ORDERS,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,469,1096,0.427919708
+9655,3564,ANE ECT PACU ORDERS,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,571,1096,0.520985401
+9656,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10005R,HLA ORDER - TYPING - BMT EVALUATION (RECIPIENT),Lab,258,734,0.351498638
+9657,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10035R,HLA ORDER - TYPING - BMT CONFIRMATORY (RECIPIENT),Lab,296,734,0.403269755
+9658,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10040R,HLA ORDER - TYPING - BMT CONFIRMATORY (RELATED DONOR),Lab,3,734,0.004087193
+9659,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,60,734,0.081743869
+9660,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10065R,HLA ORDER - CROSSMATCH (W/RELATED DONOR INFO),Lab,1,734,0.001362398
+9661,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10075R,HLA ORDER - STR/CHIMERISM (SOLID ORGAN),Lab,81,734,0.110354223
+9662,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10090R,HLA ORDER - STR/CHIMERISM (BONE MARROW),Lab,4,734,0.005449591
+9663,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10100R,HLA ORDER - PLATELET COMPATIBILITY,Lab,218,734,0.297002725
+9664,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10110R,HLA ORDER - FLOW AUTO CROSSMATCH,Lab,10,734,0.013623978
+9665,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10120R,HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT)-(TYPING),Lab,3,734,0.004087193
+9666,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10145R,HLA ORDER - UNRELATED DONOR PROCESSING,Lab,1,734,0.001362398
+9667,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10150R,HLA ORDER - SUMMARIES,Lab,260,734,0.354223433
+9668,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10170R,HLA ORDER - NMDP ADDITIONAL SPECIMEN,Lab,147,734,0.20027248
+9669,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10270R,HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT),Lab,42,734,0.057220708
+9670,3566,CC BMT HLA HISTOCOMPATIBILITY TESTING,10275R,HLA ORDER - SPECIMEN IN LAB ORDER NOTICE,Lab,18,734,0.024523161
+9671,3567,IP BONE MARROW AND ASPIRATE,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,7,1299,0.005388761
+9672,3567,IP BONE MARROW AND ASPIRATE,3760,HYDROMORPHONE 2 MG PO TABS,Medication,29,1299,0.022324865
+9673,3567,IP BONE MARROW AND ASPIRATE,3761,HYDROMORPHONE 4 MG PO TABS,Medication,3,1299,0.002309469
+9674,3567,IP BONE MARROW AND ASPIRATE,4454,LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN,Medication,6,1299,0.004618938
+9675,3567,IP BONE MARROW AND ASPIRATE,4572,LORAZEPAM 0.5 MG PO TABS,Medication,68,1299,0.05234796
+9676,3567,IP BONE MARROW AND ASPIRATE,4573,LORAZEPAM 1 MG PO TABS,Medication,12,1299,0.009237875
+9677,3567,IP BONE MARROW AND ASPIRATE,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,10,1299,0.007698229
+9678,3567,IP BONE MARROW AND ASPIRATE,7309,SODIUM BICARBONATE 8.4 % IV SOLN,Medication,26,1299,0.020015396
+9679,3567,IP BONE MARROW AND ASPIRATE,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,109,1299,0.083910701
+9680,3567,IP BONE MARROW AND ASPIRATE,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,98,1299,0.075442648
+9681,3567,IP BONE MARROW AND ASPIRATE,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,34,1299,0.02617398
+9682,3567,IP BONE MARROW AND ASPIRATE,126993,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,40,1299,0.030792918
+9683,3567,IP BONE MARROW AND ASPIRATE,200439,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,Medication,1,1299,0.000769823
+9684,3567,IP BONE MARROW AND ASPIRATE,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,21,1299,0.016166282
+9685,3567,IP BONE MARROW AND ASPIRATE,LAB13258R,CLONOSIGHT MRD,Lab,3,1299,0.002309469
+9686,3567,IP BONE MARROW AND ASPIRATE,LAB392,"MINIMAL RESIDUAL DISEASE, BM",Lab,18,1299,0.013856813
+9687,3567,IP BONE MARROW AND ASPIRATE,LABAMLP,AML PROGNOSIS ASSAY,Lab,162,1299,0.124711316
+9688,3567,IP BONE MARROW AND ASPIRATE,LABBCRABL,"BCR - ABL, BLOOD",Lab,3,1299,0.002309469
+9689,3567,IP BONE MARROW AND ASPIRATE,LABBCRKDM,BCR - ABL KINASE DOMAIN MUTATION ANALYSIS,Lab,15,1299,0.011547344
+9690,3567,IP BONE MARROW AND ASPIRATE,LABBCRQT,"BCR-ABL, BLOOD",Lab,4,1299,0.003079292
+9691,3567,IP BONE MARROW AND ASPIRATE,LABBM1418,"T(14;18), BONE MARROW",Lab,6,1299,0.004618938
+9692,3567,IP BONE MARROW AND ASPIRATE,LABBM1517,"T(15;17) PML/RAR, BONE MARROW",Lab,20,1299,0.015396459
+9693,3567,IP BONE MARROW AND ASPIRATE,LABBMAML,"AML PROGNOSIS ASSAY, BONE MARROW",Lab,157,1299,0.120862202
+9694,3567,IP BONE MARROW AND ASPIRATE,LABBMBCLO,"B-CELL CLONALITY, NON-BLOOD",Lab,9,1299,0.006928406
+9695,3567,IP BONE MARROW AND ASPIRATE,LABBMBCR,"BCR - ABL, BONE MARROW",Lab,12,1299,0.009237875
+9696,3567,IP BONE MARROW AND ASPIRATE,LABBMBCRQ,"BCR - ABL QUANTITATIVE, BONE MARROW",Lab,41,1299,0.031562741
+9697,3567,IP BONE MARROW AND ASPIRATE,LABBMCEBP,"DETECTION OF CEBPA, BONE MARROW",Lab,95,1299,0.073133179
+9698,3567,IP BONE MARROW AND ASPIRATE,LABBMTCLO,"T-CELL CLONALITY PCR, BONE MARROW",Lab,24,1299,0.018475751
+9699,3567,IP BONE MARROW AND ASPIRATE,LABCEBPA,"CEBPA, BLOOD",Lab,16,1299,0.012317167
+9700,3567,IP BONE MARROW AND ASPIRATE,LABCKITMU,"KIT MUTATIONS, EXONS 8 & 17, BLOOD",Lab,114,1299,0.087759815
+9701,3567,IP BONE MARROW AND ASPIRATE,LABCSMPBM,"CANCER SOMATIC MUTATION PNL, BM",Lab,56,1299,0.043110085
+9702,3567,IP BONE MARROW AND ASPIRATE,LABMGMT,"MGMT, METHYLATION SPECIFIC PCR",Lab,11,1299,0.008468052
+9703,3567,IP BONE MARROW AND ASPIRATE,LABNJAK2,"JANUS KINASE 2 V617F MUTATION, NON-BLOOD",Lab,19,1299,0.014626636
+9704,3567,IP BONE MARROW AND ASPIRATE,LABNVHHA,"VH HYPERMUTATION ASSAY, NON-BLOOD",Lab,1,1299,0.000769823
+9705,3567,IP BONE MARROW AND ASPIRATE,LABT1418,"T(14;18), BLOOD",Lab,1,1299,0.000769823
+9706,3567,IP BONE MARROW AND ASPIRATE,LABT1517,"T(15;17) PML/RAR, BLOOD",Lab,5,1299,0.003849115
+9707,3567,IP BONE MARROW AND ASPIRATE,LABTCLON,"T-CELL CLONALITY PCR, BLOOD",Lab,4,1299,0.003079292
+9708,3567,IP BONE MARROW AND ASPIRATE,PATH22,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BLD",Lab,18,1299,0.013856813
+9709,3567,IP BONE MARROW AND ASPIRATE,PATH23,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BM",Lab,400,1299,0.307929176
+9710,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,126,0.015873016
+9711,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,1080,BISACODYL 10 MG PR SUPP,Medication,5,126,0.03968254
+9712,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,38,126,0.301587302
+9713,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1,126,0.007936508
+9714,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,3844,IBUPROFEN 600 MG PO TABS,Medication,4,126,0.031746032
+9715,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,4318,LACTATED RINGERS IV SOLP,Medication,21,126,0.166666667
+9716,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,54,126,0.428571429
+9717,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,4,126,0.031746032
+9718,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,10011,FAMOTIDINE 20 MG PO TABS,Medication,2,126,0.015873016
+9719,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,1,126,0.007936508
+9720,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,15882,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,Medication,9,126,0.071428571
+9721,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,126,0.015873016
+9722,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,17362,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",Medication,1,126,0.007936508
+9723,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,4,126,0.031746032
+9724,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,15,126,0.119047619
+9725,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,126,0.03968254
+9726,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,4,126,0.031746032
+9727,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,3,126,0.023809524
+9728,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,92173,ZOLPIDEM 5 MG PO TABS,Medication,1,126,0.007936508
+9729,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,3,126,0.023809524
+9730,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,20,126,0.158730159
+9731,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,51,126,0.404761905
+9732,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,3,126,0.023809524
+9733,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,500009,D5 LR WITH KCL IV INFUSION,Medication,1,126,0.007936508
+9734,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,126,0.007936508
+9735,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,15,126,0.119047619
+9736,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABCBCD,CBC WITH DIFF,Lab,43,126,0.341269841
+9737,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABMETB,"METABOLIC PANEL, BASIC",Lab,40,126,0.317460317
+9738,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,126,0.015873016
+9739,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,37,126,0.293650794
+9740,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,37,126,0.293650794
+9741,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABPLTS,PLATELET COUNT,Lab,2,126,0.015873016
+9742,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,126,0.007936508
+9743,3568,RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP,POC14,POC GLUCOSE BY METER,Lab,2,126,0.015873016
+9744,3571,ED TRAUMA 95,LAB219,RAINBOW DRAW (FOR ED/RRT/CODE BLUE ONLY),Lab,38,253,0.150197628
+9745,3571,ED TRAUMA 95,LABCBCD,CBC WITH DIFF,Lab,245,253,0.968379447
+9746,3571,ED TRAUMA 95,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,79,253,0.312252964
+9747,3571,ED TRAUMA 95,LABPREG,"PREGNANCY TEST, SERUM",Lab,8,253,0.031620553
+9748,3571,ED TRAUMA 95,LABTYPSNI,TYPE AND SCREEN,Lab,211,253,0.833992095
+9749,3571,ED TRAUMA 95,POC46,POC ISTAT BHCG,Lab,2,253,0.007905138
+9750,3571,ED TRAUMA 95,IMGDXCH1,XR CHEST 1V,Imaging,94,253,0.371541502
+9751,3572,ED TRAUMA 97,LAB219,RAINBOW DRAW (FOR ED/RRT/CODE BLUE ONLY),Lab,170,1431,0.118798043
+9752,3572,ED TRAUMA 97,LABCBCD,CBC WITH DIFF,Lab,1422,1431,0.993710692
+9753,3572,ED TRAUMA 97,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1411,1431,0.98602376
+9754,3572,ED TRAUMA 97,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,1293,1431,0.903563941
+9755,3572,ED TRAUMA 97,LABPREG,"PREGNANCY TEST, SERUM",Lab,59,1431,0.041229909
+9756,3572,ED TRAUMA 97,LABTYPSNI,TYPE AND SCREEN,Lab,1377,1431,0.962264151
+9757,3572,ED TRAUMA 97,POC25,POC ISTAT INR AND PROTIME,Lab,1052,1431,0.735150245
+9758,3572,ED TRAUMA 97,POC46,POC ISTAT BHCG,Lab,16,1431,0.011180992
+9759,3572,ED TRAUMA 97,IMGDXCH1,XR CHEST 1V,Imaging,1319,1431,0.921733054
+9760,3572,ED TRAUMA 97,IMGDXPEL,XR PELVIS 1V,Imaging,320,1431,0.223619846
+9761,3573,ED TRAUMA 99,LAB219,RAINBOW DRAW (FOR ED/RRT/CODE BLUE ONLY),Lab,25,407,0.061425061
+9762,3573,ED TRAUMA 99,LABCBCD,CBC WITH DIFF,Lab,343,407,0.842751843
+9763,3573,ED TRAUMA 99,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,346,407,0.85012285
+9764,3573,ED TRAUMA 99,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,342,407,0.84029484
+9765,3573,ED TRAUMA 99,LABPCCH8,"ISTAT CR, HCT/HB, CL , BUN, NA, K, GLUCOSE, ICA, TCO2",Lab,341,407,0.837837838
+9766,3573,ED TRAUMA 99,LABPREG,"PREGNANCY TEST, SERUM",Lab,9,407,0.022113022
+9767,3573,ED TRAUMA 99,LABTYPSNI,TYPE AND SCREEN,Lab,335,407,0.823095823
+9768,3573,ED TRAUMA 99,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,346,407,0.85012285
+9769,3573,ED TRAUMA 99,LABVOLTL,VOLATILE SCREEN,Lab,345,407,0.847665848
+9770,3573,ED TRAUMA 99,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,349,407,0.857493857
+9771,3573,ED TRAUMA 99,POC25,POC ISTAT INR AND PROTIME,Lab,334,407,0.820638821
+9772,3573,ED TRAUMA 99,POC46,POC ISTAT BHCG,Lab,2,407,0.004914005
+9773,3573,ED TRAUMA 99,IMGDXCH1,XR CHEST 1V,Imaging,339,407,0.832923833
+9774,3573,ED TRAUMA 99,IMGDXPEL,XR PELVIS 1V,Imaging,330,407,0.810810811
+9775,3573,ED TRAUMA 99,IMGDXSP1,XR SPINE 1V,Imaging,17,407,0.041769042
+9776,3574,ED TRAUMA RADIOLOGY,IMGCT3N,CT ANGIO NECK,Imaging,108,1471,0.073419443
+9777,3574,ED TRAUMA RADIOLOGY,IMGCT3T,CT ANGIO CHEST,Imaging,657,1471,0.446634942
+9778,3574,ED TRAUMA RADIOLOGY,IMGCTAP,CT ABDOMEN PELVIS,Imaging,730,1471,0.496261047
+9779,3574,ED TRAUMA RADIOLOGY,IMGCTFAC,CT FACIAL BONES OR SINUS,Imaging,270,1471,0.183548606
+9780,3574,ED TRAUMA RADIOLOGY,IMGCTH,CT HEAD,Imaging,154,1471,0.104690687
+9781,3574,ED TRAUMA RADIOLOGY,IMGCTHSC,CT HEAD C SPINE,Imaging,1108,1471,0.753229096
+9782,3574,ED TRAUMA RADIOLOGY,IMGCTSL,CT SPINE LUMBAR,Imaging,523,1471,0.355540449
+9783,3574,ED TRAUMA RADIOLOGY,IMGCTST,CT SPINE THORACIC,Imaging,524,1471,0.356220258
+9784,3574,ED TRAUMA RADIOLOGY,IMGDXCH1,XR CHEST 1V,Imaging,368,1471,0.250169952
+9785,3574,ED TRAUMA RADIOLOGY,IMGDXPEL,XR PELVIS 1V,Imaging,471,1471,0.320190347
+9786,3574,ED TRAUMA RADIOLOGY,IMGDXSPL2,"XR SPINE, LUMBAR 2V",Imaging,14,1471,0.009517335
+9787,3574,ED TRAUMA RADIOLOGY,IMGDXSPT,"XR SPINE, THORACIC 2V",Imaging,16,1471,0.010876954
+9788,3575,IP ICU VAD POST OP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,22,1605,0.013707165
+9789,3575,IP ICU VAD POST OP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,1605,0.000623053
+9790,3575,IP ICU VAD POST OP,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,7,1605,0.004361371
+9791,3575,IP ICU VAD POST OP,680,ASPIRIN 81 MG PO CHEW,Medication,17,1605,0.0105919
+9792,3575,IP ICU VAD POST OP,681,ASPIRIN 325 MG PO TABS,Medication,31,1605,0.019314642
+9793,3575,IP ICU VAD POST OP,693,ASPIRIN 300 MG PR SUPP,Medication,6,1605,0.003738318
+9794,3575,IP ICU VAD POST OP,1080,BISACODYL 10 MG PR SUPP,Medication,32,1605,0.019937695
+9795,3575,IP ICU VAD POST OP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,31,1605,0.019314642
+9796,3575,IP ICU VAD POST OP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,9,1605,0.005607477
+9797,3575,IP ICU VAD POST OP,4711,MAGNESIUM CITRATE PO SOLN,Medication,3,1605,0.001869159
+9798,3575,IP ICU VAD POST OP,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,1605,0.004361371
+9799,3575,IP ICU VAD POST OP,8982,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",Medication,3,1605,0.001869159
+9800,3575,IP ICU VAD POST OP,9066,AMIODARONE 200 MG PO TABS,Medication,1,1605,0.000623053
+9801,3575,IP ICU VAD POST OP,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,182,1605,0.113395639
+9802,3575,IP ICU VAD POST OP,10050,FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK,Medication,178,1605,0.110903427
+9803,3575,IP ICU VAD POST OP,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,2,1605,0.001246106
+9804,3575,IP ICU VAD POST OP,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,4,1605,0.002492212
+9805,3575,IP ICU VAD POST OP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,1,1605,0.000623053
+9806,3575,IP ICU VAD POST OP,14862,POTASSIUM CHLORIDE-D5-0.2%NACL 20 MEQ/L IV SOLP,Medication,149,1605,0.092834891
+9807,3575,IP ICU VAD POST OP,15846,"HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",Medication,7,1605,0.004361371
+9808,3575,IP ICU VAD POST OP,15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,1,1605,0.000623053
+9809,3575,IP ICU VAD POST OP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,6,1605,0.003738318
+9810,3575,IP ICU VAD POST OP,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,93,1605,0.057943925
+9811,3575,IP ICU VAD POST OP,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,24,1605,0.014953271
+9812,3575,IP ICU VAD POST OP,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,1605,0.001246106
+9813,3575,IP ICU VAD POST OP,112520,SODIUM CHLORIDE 0.9 % 0.9 % INJ SYRG,Medication,2,1605,0.001246106
+9814,3575,IP ICU VAD POST OP,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,6,1605,0.003738318
+9815,3575,IP ICU VAD POST OP,125505,SILDENAFIL 20 MG PO TABS,Medication,160,1605,0.099688474
+9816,3575,IP ICU VAD POST OP,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,2,1605,0.001246106
+9817,3575,IP ICU VAD POST OP,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,2,1605,0.001246106
+9818,3575,IP ICU VAD POST OP,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,2,1605,0.001246106
+9819,3575,IP ICU VAD POST OP,205192,MILRINONE IN D5W 20 MG/100 ML IV PGBK,Medication,35,1605,0.021806854
+9820,3575,IP ICU VAD POST OP,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,96,1605,0.059813084
+9821,3575,IP ICU VAD POST OP,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,2,1605,0.001246106
+9822,3575,IP ICU VAD POST OP,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,1,1605,0.000623053
+9823,3575,IP ICU VAD POST OP,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,194,1605,0.120872274
+9824,3575,IP ICU VAD POST OP,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,197,1605,0.122741433
+9825,3575,IP ICU VAD POST OP,222229,DOCUSATE SODIUM 283 MG/5 ML PR ENEM,Medication,1,1605,0.000623053
+9826,3575,IP ICU VAD POST OP,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,23,1605,0.014330218
+9827,3575,IP ICU VAD POST OP,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,51,1605,0.031775701
+9828,3575,IP ICU VAD POST OP,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,27,1605,0.01682243
+9829,3575,IP ICU VAD POST OP,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,214,1605,0.133333333
+9830,3575,IP ICU VAD POST OP,520102,CEFEPIME 2 GM IVPB,Medication,17,1605,0.0105919
+9831,3575,IP ICU VAD POST OP,530001,AMIODARONE INFUSION PERIPH ADMIN,Medication,3,1605,0.001869159
+9832,3575,IP ICU VAD POST OP,530002,AMIODARONE INFUSION CENTRAL LINE ADMIN,Medication,5,1605,0.003115265
+9833,3575,IP ICU VAD POST OP,530006,EPINEPHRINE IV INFUSION,Medication,142,1605,0.08847352
+9834,3575,IP ICU VAD POST OP,530008,NITROPRUSSIDE IV INFUSION,Medication,19,1605,0.011838006
+9835,3575,IP ICU VAD POST OP,530011,NOREPINEPHRINE IV INFUSION,Medication,1,1605,0.000623053
+9836,3575,IP ICU VAD POST OP,530012,PHENYLEPHRINE IV INFUSION,Medication,2,1605,0.001246106
+9837,3575,IP ICU VAD POST OP,530025,DEXMEDETOMIDINE IV INFUSION,Medication,12,1605,0.007476636
+9838,3575,IP ICU VAD POST OP,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,27,1605,0.01682243
+9839,3575,IP ICU VAD POST OP,530039,AMINOCAPROIC ACID IV INFUSION,Medication,2,1605,0.001246106
+9840,3575,IP ICU VAD POST OP,540004,STANFORD BOWEL SOLUTION,Medication,1,1605,0.000623053
+9841,3575,IP ICU VAD POST OP,540102,NS IV BOLUS,Medication,118,1605,0.073520249
+9842,3575,IP ICU VAD POST OP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,25,1605,0.015576324
+9843,3575,IP ICU VAD POST OP,540115,LACTATED RINGERS IV BOLUS,Medication,118,1605,0.073520249
+9844,3575,IP ICU VAD POST OP,540302,POTASSIUM CHLORIDE IV SCALE,Medication,211,1605,0.131464174
+9845,3575,IP ICU VAD POST OP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,196,1605,0.12211838
+9846,3575,IP ICU VAD POST OP,540493,WARFARIN PER PHARMACY PROTOCOL,Medication,4,1605,0.002492212
+9847,3575,IP ICU VAD POST OP,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,82,1605,0.051090343
+9848,3575,IP ICU VAD POST OP,541106,CEFTAZIDIME 1 GRAM/50 ML MINI-BAG PLUS,Medication,101,1605,0.062928349
+9849,3575,IP ICU VAD POST OP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,704,1605,0.438629283
+9850,3575,IP ICU VAD POST OP,LABCBCD,CBC WITH DIFF,Lab,5,1605,0.003115265
+9851,3575,IP ICU VAD POST OP,LABCBCO,CBC W/O DIFF,Lab,656,1605,0.408722741
+9852,3575,IP ICU VAD POST OP,LABCK,"CREATINE KINASE, TOTAL",Lab,4,1605,0.002492212
+9853,3575,IP ICU VAD POST OP,LABCRP,C - REACTIVE PROTEIN,Lab,245,1605,0.152647975
+9854,3575,IP ICU VAD POST OP,LABFIB,FIBRINOGEN,Lab,208,1605,0.129595016
+9855,3575,IP ICU VAD POST OP,LABHAP,HAPTOGLOBIN,Lab,447,1605,0.278504673
+9856,3575,IP ICU VAD POST OP,LABHCTX,HEMATOCRIT,Lab,236,1605,0.147040498
+9857,3575,IP ICU VAD POST OP,LABK,"POTASSIUM, SERUM/PLASMA",Lab,647,1605,0.403115265
+9858,3575,IP ICU VAD POST OP,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,513,1605,0.319626168
+9859,3575,IP ICU VAD POST OP,LABMETB,"METABOLIC PANEL, BASIC",Lab,334,1605,0.208099688
+9860,3575,IP ICU VAD POST OP,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,519,1605,0.323364486
+9861,3575,IP ICU VAD POST OP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,798,1605,0.497196262
+9862,3575,IP ICU VAD POST OP,LABPALB,PREALBUMIN,Lab,248,1605,0.154517134
+9863,3575,IP ICU VAD POST OP,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,507,1605,0.31588785
+9864,3575,IP ICU VAD POST OP,LABPLTS,PLATELET COUNT,Lab,196,1605,0.12211838
+9865,3575,IP ICU VAD POST OP,LABPT,PROTHROMBIN TIME,Lab,707,1605,0.440498442
+9866,3575,IP ICU VAD POST OP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,694,1605,0.432398754
+9867,3575,IP ICU VAD POST OP,LABTBILFR,"BILIRUBIN TOTAL AND FRACTIONATED, SERUM",Lab,437,1605,0.272274143
+9868,3575,IP ICU VAD POST OP,LABTEGCP,THROMBELASTOGRAPH CLOTTING PROFILE,Lab,455,1605,0.283489097
+9869,3575,IP ICU VAD POST OP,POC14,POC GLUCOSE BY METER,Lab,403,1605,0.251090343
+9870,3575,IP ICU VAD POST OP,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,582,1605,0.362616822
+9871,3575,IP ICU VAD POST OP,IMGDXCH1,XR CHEST 1V,Imaging,285,1605,0.177570093
+9872,3579,CC BMT HLA KIT ORDER REQUESTS,10125R,HLA ORDER - KIT REQUEST (DONOR),Lab,3,6,0.5
+9873,3579,CC BMT HLA KIT ORDER REQUESTS,10130R,HLA ORDER - KIT REQUEST (RECIPIENT),Lab,5,6,0.833333333
+9874,3579,CC BMT HLA KIT ORDER REQUESTS,10150R,HLA ORDER - SUMMARIES,Lab,4,6,0.666666667
+9875,3579,CC BMT HLA KIT ORDER REQUESTS,10160R,HLA ORDER - KIT PROCESSING DONOR,Lab,3,6,0.5
+9876,3579,CC BMT HLA KIT ORDER REQUESTS,10175R,HLA ORDER - KIT PROCESSING RECIPIENT,Lab,6,6,1
+9877,3579,CC BMT HLA KIT ORDER REQUESTS,10180R,HLA ORDER - KIT REQUEST NOTICE,Lab,2,6,0.333333333
+9878,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,189,379,0.498680739
+9879,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,188,379,0.496042216
+9880,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,188,379,0.496042216
+9881,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,82190,BUTAMBEN-TETRACAINE-BENZOCAINE 2-2-14 % TP SPRA,Medication,175,379,0.461741425
+9882,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,90788,LIDOCAINE HCL 2 % VISCOUS ORAL SOLN,Medication,186,379,0.490765172
+9883,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,231233,MIDAZOLAM (PF) 1 MG/ML INJ SOLN,Medication,1,379,0.002638522
+9884,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,540102,NS IV BOLUS,Medication,180,379,0.474934037
+9885,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,191,379,0.503957784
+9886,3580,IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET,ECH16,ECHO - TRANSESOPHAGEAL ECHO,Imaging,36,379,0.094986807
+9887,3584,IP RESP INHALED EPOPROSTENOL (VELETRI),221561,EPOPROSTENOL INHALATION BAG,Medication,698,2101,0.332222751
+9888,3591,IP TRS HEART TRANSPLANT READMIT,101,ACETAMINOPHEN 325 MG PO TABS,Medication,51,548,0.093065693
+9889,3591,IP TRS HEART TRANSPLANT READMIT,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,548,0.001824818
+9890,3591,IP TRS HEART TRANSPLANT READMIT,1080,BISACODYL 10 MG PR SUPP,Medication,14,548,0.025547445
+9891,3591,IP TRS HEART TRANSPLANT READMIT,1756,CLONIDINE HCL 0.2 MG PO TABS,Medication,2,548,0.003649635
+9892,3591,IP TRS HEART TRANSPLANT READMIT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,8,548,0.01459854
+9893,3591,IP TRS HEART TRANSPLANT READMIT,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,19,548,0.034671533
+9894,3591,IP TRS HEART TRANSPLANT READMIT,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,13,548,0.023722628
+9895,3591,IP TRS HEART TRANSPLANT READMIT,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,3,548,0.005474453
+9896,3591,IP TRS HEART TRANSPLANT READMIT,3294,FUROSEMIDE 20 MG PO TABS,Medication,3,548,0.005474453
+9897,3591,IP TRS HEART TRANSPLANT READMIT,3295,FUROSEMIDE 40 MG PO TABS,Medication,1,548,0.001824818
+9898,3591,IP TRS HEART TRANSPLANT READMIT,3296,FUROSEMIDE 80 MG PO TABS,Medication,1,548,0.001824818
+9899,3591,IP TRS HEART TRANSPLANT READMIT,3720,HYDROCHLOROTHIAZIDE 25 MG PO TABS,Medication,3,548,0.005474453
+9900,3591,IP TRS HEART TRANSPLANT READMIT,4065,ISOSORBIDE DINITRATE 20 MG PO TABS,Medication,1,548,0.001824818
+9901,3591,IP TRS HEART TRANSPLANT READMIT,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,5,548,0.009124088
+9902,3591,IP TRS HEART TRANSPLANT READMIT,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,1,548,0.001824818
+9903,3591,IP TRS HEART TRANSPLANT READMIT,6493,PREDNISONE 1 MG PO TABS,Medication,3,548,0.005474453
+9904,3591,IP TRS HEART TRANSPLANT READMIT,6494,PREDNISONE 10 MG PO TABS,Medication,1,548,0.001824818
+9905,3591,IP TRS HEART TRANSPLANT READMIT,6496,PREDNISONE 20 MG PO TABS,Medication,1,548,0.001824818
+9906,3591,IP TRS HEART TRANSPLANT READMIT,6497,PREDNISONE 5 MG PO TABS,Medication,2,548,0.003649635
+9907,3591,IP TRS HEART TRANSPLANT READMIT,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,5,548,0.009124088
+9908,3591,IP TRS HEART TRANSPLANT READMIT,7437,SPIRONOLACTONE 25 MG PO TABS,Medication,3,548,0.005474453
+9909,3591,IP TRS HEART TRANSPLANT READMIT,7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,3,548,0.005474453
+9910,3591,IP TRS HEART TRANSPLANT READMIT,7753,TEMAZEPAM 15 MG PO CAPS,Medication,2,548,0.003649635
+9911,3591,IP TRS HEART TRANSPLANT READMIT,8085,TRAZODONE 50 MG PO TABS,Medication,7,548,0.012773723
+9912,3591,IP TRS HEART TRANSPLANT READMIT,9308,BUMETANIDE 0.25 MG/ML INJ SOLN,Medication,3,548,0.005474453
+9913,3591,IP TRS HEART TRANSPLANT READMIT,9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,2,548,0.003649635
+9914,3591,IP TRS HEART TRANSPLANT READMIT,9924,ENALAPRIL MALEATE 10 MG PO TABS,Medication,1,548,0.001824818
+9915,3591,IP TRS HEART TRANSPLANT READMIT,9925,ENALAPRIL MALEATE 2.5 MG PO TABS,Medication,1,548,0.001824818
+9916,3591,IP TRS HEART TRANSPLANT READMIT,10011,FAMOTIDINE 20 MG PO TABS,Medication,7,548,0.012773723
+9917,3591,IP TRS HEART TRANSPLANT READMIT,10117,GLIPIZIDE 5 MG PO TABS,Medication,1,548,0.001824818
+9918,3591,IP TRS HEART TRANSPLANT READMIT,11111,PRAVASTATIN 20 MG PO TABS,Medication,10,548,0.018248175
+9919,3591,IP TRS HEART TRANSPLANT READMIT,11349,SENNOSIDES 8.6 MG PO TABS,Medication,32,548,0.058394161
+9920,3591,IP TRS HEART TRANSPLANT READMIT,11426,SPIRONOLACTONE 50 MG PO TABS,Medication,4,548,0.00729927
+9921,3591,IP TRS HEART TRANSPLANT READMIT,13089,LISINOPRIL 2.5 MG PO TABS,Medication,1,548,0.001824818
+9922,3591,IP TRS HEART TRANSPLANT READMIT,13654,ASPIRIN 325 MG PO TBEC,Medication,1,548,0.001824818
+9923,3591,IP TRS HEART TRANSPLANT READMIT,14113,ASPIRIN 81 MG PO TBEC,Medication,4,548,0.00729927
+9924,3591,IP TRS HEART TRANSPLANT READMIT,15065,FAMOTIDINE 10 MG PO TABS,Medication,1,548,0.001824818
+9925,3591,IP TRS HEART TRANSPLANT READMIT,15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,1,548,0.001824818
+9926,3591,IP TRS HEART TRANSPLANT READMIT,18294,TORSEMIDE 100 MG PO TABS,Medication,1,548,0.001824818
+9927,3591,IP TRS HEART TRANSPLANT READMIT,19483,"CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",Medication,7,548,0.012773723
+9928,3591,IP TRS HEART TRANSPLANT READMIT,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,13,548,0.023722628
+9929,3591,IP TRS HEART TRANSPLANT READMIT,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,12,548,0.02189781
+9930,3591,IP TRS HEART TRANSPLANT READMIT,29272,DILTIAZEM HCL 180 MG PO CP24,Medication,1,548,0.001824818
+9931,3591,IP TRS HEART TRANSPLANT READMIT,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,13,548,0.023722628
+9932,3591,IP TRS HEART TRANSPLANT READMIT,77012,LOSARTAN 50 MG PO TABS,Medication,1,548,0.001824818
+9933,3591,IP TRS HEART TRANSPLANT READMIT,77545,ATORVASTATIN 40 MG PO TABS,Medication,2,548,0.003649635
+9934,3591,IP TRS HEART TRANSPLANT READMIT,77883,VALGANCICLOVIR 450 MG PO TABS,Medication,3,548,0.005474453
+9935,3591,IP TRS HEART TRANSPLANT READMIT,78198,CARVEDILOL 6.25 MG PO TABS,Medication,1,548,0.001824818
+9936,3591,IP TRS HEART TRANSPLANT READMIT,78748,INSULIN GLARGINE 100 UNIT/ML SC SOLN,Medication,7,548,0.012773723
+9937,3591,IP TRS HEART TRANSPLANT READMIT,80562,MYCOPHENOLATE MOFETIL 250 MG PO CAPS,Medication,7,548,0.012773723
+9938,3591,IP TRS HEART TRANSPLANT READMIT,81839,LIDOCAINE 4 % TP CREA,Medication,40,548,0.072992701
+9939,3591,IP TRS HEART TRANSPLANT READMIT,82544,ITRACONAZOLE 10 MG/ML PO SOLN,Medication,8,548,0.01459854
+9940,3591,IP TRS HEART TRANSPLANT READMIT,83398,CLOPIDOGREL 75 MG PO TABS,Medication,1,548,0.001824818
+9941,3591,IP TRS HEART TRANSPLANT READMIT,83628,AMLODIPINE 5 MG PO TABS,Medication,4,548,0.00729927
+9942,3591,IP TRS HEART TRANSPLANT READMIT,84475,CARVEDILOL 3.125 MG PO TABS,Medication,2,548,0.003649635
+9943,3591,IP TRS HEART TRANSPLANT READMIT,84953,PANTOPRAZOLE 20 MG PO TBEC,Medication,2,548,0.003649635
+9944,3591,IP TRS HEART TRANSPLANT READMIT,85078,MYCOPHENOLATE MOFETIL 500 MG PO TABS,Medication,3,548,0.005474453
+9945,3591,IP TRS HEART TRANSPLANT READMIT,86699,SIROLIMUS 1 MG PO TABS,Medication,1,548,0.001824818
+9946,3591,IP TRS HEART TRANSPLANT READMIT,87650,AMLODIPINE 10 MG PO TABS,Medication,1,548,0.001824818
+9947,3591,IP TRS HEART TRANSPLANT READMIT,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,9,548,0.016423358
+9948,3591,IP TRS HEART TRANSPLANT READMIT,89742,INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP,Medication,7,548,0.012773723
+9949,3591,IP TRS HEART TRANSPLANT READMIT,92173,ZOLPIDEM 5 MG PO TABS,Medication,18,548,0.032846715
+9950,3591,IP TRS HEART TRANSPLANT READMIT,92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,20,548,0.03649635
+9951,3591,IP TRS HEART TRANSPLANT READMIT,94670,TACROLIMUS 1 MG PO CAPS,Medication,6,548,0.010948905
+9952,3591,IP TRS HEART TRANSPLANT READMIT,95354,ATOVAQUONE 750 MG/5 ML PO SUSP,Medication,4,548,0.00729927
+9953,3591,IP TRS HEART TRANSPLANT READMIT,95803,TACROLIMUS 0.5 MG PO CAPS,Medication,11,548,0.020072993
+9954,3591,IP TRS HEART TRANSPLANT READMIT,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,548,0.001824818
+9955,3591,IP TRS HEART TRANSPLANT READMIT,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,6,548,0.010948905
+9956,3591,IP TRS HEART TRANSPLANT READMIT,116178,ROSUVASTATIN 20 MG PO TABS,Medication,1,548,0.001824818
+9957,3591,IP TRS HEART TRANSPLANT READMIT,118023,MYCOPHENOLATE SODIUM 180 MG PO TBEC,Medication,2,548,0.003649635
+9958,3591,IP TRS HEART TRANSPLANT READMIT,118024,MYCOPHENOLATE SODIUM 360 MG PO TBEC,Medication,2,548,0.003649635
+9959,3591,IP TRS HEART TRANSPLANT READMIT,125813,INSULIN DETEMIR 100 UNIT/ML SC SOLN,Medication,10,548,0.018248175
+9960,3591,IP TRS HEART TRANSPLANT READMIT,126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,2,548,0.003649635
+9961,3591,IP TRS HEART TRANSPLANT READMIT,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,38,548,0.069343066
+9962,3591,IP TRS HEART TRANSPLANT READMIT,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,80,548,0.145985401
+9963,3591,IP TRS HEART TRANSPLANT READMIT,206098,DOBUTAMINE IN D5W 250 MG/250 ML (1 MG/ML) IV SOLP,Medication,2,548,0.003649635
+9964,3591,IP TRS HEART TRANSPLANT READMIT,206169,"DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",Medication,2,548,0.003649635
+9965,3591,IP TRS HEART TRANSPLANT READMIT,208479,MAGNESIUM OXIDE-MG AA CHELATE 133 MG PO TABS,Medication,8,548,0.01459854
+9966,3591,IP TRS HEART TRANSPLANT READMIT,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,7,548,0.012773723
+9967,3591,IP TRS HEART TRANSPLANT READMIT,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,2,548,0.003649635
+9968,3591,IP TRS HEART TRANSPLANT READMIT,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,1,548,0.001824818
+9969,3591,IP TRS HEART TRANSPLANT READMIT,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,99,548,0.180656934
+9970,3591,IP TRS HEART TRANSPLANT READMIT,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,1,548,0.001824818
+9971,3591,IP TRS HEART TRANSPLANT READMIT,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,4,548,0.00729927
+9972,3591,IP TRS HEART TRANSPLANT READMIT,520055,GANCICLOVIR IVPB,Medication,4,548,0.00729927
+9973,3591,IP TRS HEART TRANSPLANT READMIT,520074,ANTITHYMOCYTE GLOBULIN RABBIT IVPB,Medication,2,548,0.003649635
+9974,3591,IP TRS HEART TRANSPLANT READMIT,520078,METHYLPREDNISOLONE IVPB,Medication,10,548,0.018248175
+9975,3591,IP TRS HEART TRANSPLANT READMIT,540132,CYCLOSPORINE MODIFIED (GENGRAF) 25 MG PO CAPS,Medication,3,548,0.005474453
+9976,3591,IP TRS HEART TRANSPLANT READMIT,540133,CYCLOSPORINE MODIFIED (GENGRAF) 100 MG PO CAPS,Medication,2,548,0.003649635
+9977,3591,IP TRS HEART TRANSPLANT READMIT,540134,CYCLOSPORINE MODIFIED (NEORAL) 25 MG PO CAPS,Medication,4,548,0.00729927
+9978,3591,IP TRS HEART TRANSPLANT READMIT,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,28,548,0.051094891
+9979,3591,IP TRS HEART TRANSPLANT READMIT,540302,POTASSIUM CHLORIDE IV SCALE,Medication,3,548,0.005474453
+9980,3591,IP TRS HEART TRANSPLANT READMIT,540304,MAGNESIUM SULFATE IV SCALE ,Medication,30,548,0.054744526
+9981,3591,IP TRS HEART TRANSPLANT READMIT,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,8,548,0.01459854
+9982,3591,IP TRS HEART TRANSPLANT READMIT,541285,AMPHOTERICIN B 20 MG/4ML NEB SOLUTION,Medication,2,548,0.003649635
+9983,3591,IP TRS HEART TRANSPLANT READMIT,9000000,ZZZ IMS TEMPLATE,Medication,4,548,0.00729927
+9984,3591,IP TRS HEART TRANSPLANT READMIT,10015R,HLA ORDER - DSA SCREENING,Lab,10,548,0.018248175
+9985,3591,IP TRS HEART TRANSPLANT READMIT,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,2,548,0.003649635
+9986,3591,IP TRS HEART TRANSPLANT READMIT,10230R,HLA ORDER - DESENSITIZATION/DSA MONITORING-NO IVIG,Lab,7,548,0.012773723
+9987,3591,IP TRS HEART TRANSPLANT READMIT,LAB12853R,"EVEROLIMUS, BLOOD",Lab,7,548,0.012773723
+9988,3591,IP TRS HEART TRANSPLANT READMIT,LABAMY,"AMYLASE, SERUM",Lab,14,548,0.025547445
+9989,3591,IP TRS HEART TRANSPLANT READMIT,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,14,548,0.025547445
+9990,3591,IP TRS HEART TRANSPLANT READMIT,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,23,548,0.041970803
+9991,3591,IP TRS HEART TRANSPLANT READMIT,LABCBCD,CBC WITH DIFF,Lab,202,548,0.368613139
+9992,3591,IP TRS HEART TRANSPLANT READMIT,LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,10,548,0.018248175
+9993,3591,IP TRS HEART TRANSPLANT READMIT,LABCHOL,"CHOLESTEROL TOTAL, SERUM/PLASMA",Lab,14,548,0.025547445
+9994,3591,IP TRS HEART TRANSPLANT READMIT,LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,54,548,0.098540146
+9995,3591,IP TRS HEART TRANSPLANT READMIT,LABCYCWBL,"CYCLOSPORINE, WHOLE BLOOD",Lab,26,548,0.047445255
+9996,3591,IP TRS HEART TRANSPLANT READMIT,LABEBVQP,EBV DNA PCR QUANT,Lab,13,548,0.023722628
+9997,3591,IP TRS HEART TRANSPLANT READMIT,LABFK506L,TACROLIMUS (FK506),Lab,149,548,0.27189781
+9998,3591,IP TRS HEART TRANSPLANT READMIT,LABFT4,"T4, FREE",Lab,7,548,0.012773723
+9999,3591,IP TRS HEART TRANSPLANT READMIT,LABHDL,"HDL, SERUM",Lab,14,548,0.025547445
+10000,3591,IP TRS HEART TRANSPLANT READMIT,LABLIPS,LIPASE,Lab,15,548,0.027372263
+10001,3591,IP TRS HEART TRANSPLANT READMIT,LABMETB,"METABOLIC PANEL, BASIC",Lab,21,548,0.038321168
+10002,3591,IP TRS HEART TRANSPLANT READMIT,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,229,548,0.417883212
+10003,3591,IP TRS HEART TRANSPLANT READMIT,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,124,548,0.226277372
+10004,3591,IP TRS HEART TRANSPLANT READMIT,LABMYCPA,MYCOPHENOLIC ACID,Lab,23,548,0.041970803
+10005,3591,IP TRS HEART TRANSPLANT READMIT,LABNTBNP,NT - PROBNP,Lab,38,548,0.069343066
+10006,3591,IP TRS HEART TRANSPLANT READMIT,LABPALB,PREALBUMIN,Lab,11,548,0.020072993
+10007,3591,IP TRS HEART TRANSPLANT READMIT,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,41,548,0.074817518
+10008,3591,IP TRS HEART TRANSPLANT READMIT,LABPLTS,PLATELET COUNT,Lab,57,548,0.104014599
+10009,3591,IP TRS HEART TRANSPLANT READMIT,LABPT,PROTHROMBIN TIME,Lab,3,548,0.005474453
+10010,3591,IP TRS HEART TRANSPLANT READMIT,LABSIROL,SIROLIMUS LEVEL,Lab,66,548,0.120437956
+10011,3591,IP TRS HEART TRANSPLANT READMIT,LABSTLCP,STOOL CULTURE,Lab,5,548,0.009124088
+10012,3591,IP TRS HEART TRANSPLANT READMIT,LABTNI,TROPONIN I,Lab,17,548,0.031021898
+10013,3591,IP TRS HEART TRANSPLANT READMIT,LABTRIG,"TRIGLYCERIDES, SERUM / PLASMA",Lab,13,548,0.023722628
+10014,3591,IP TRS HEART TRANSPLANT READMIT,LABTSH,TSH,Lab,27,548,0.049270073
+10015,3591,IP TRS HEART TRANSPLANT READMIT,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,33,548,0.060218978
+10016,3591,IP TRS HEART TRANSPLANT READMIT,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,13,548,0.023722628
+10017,3591,IP TRS HEART TRANSPLANT READMIT,IMGDXCH1,XR CHEST 1V,Imaging,32,548,0.058394161
+10018,3591,IP TRS HEART TRANSPLANT READMIT,IMGDXCH2,XR CHEST 2V,Imaging,34,548,0.062043796
+10019,3593,IP/ED MASSIVE TRANSFUSION ADULT (>50 KG),LABABG,"BLOOD GASES, ARTERIAL",Lab,528,487,1
+10020,3593,IP/ED MASSIVE TRANSFUSION ADULT (>50 KG),LABCBCO,CBC W/O DIFF,Lab,542,487,1
+10021,3593,IP/ED MASSIVE TRANSFUSION ADULT (>50 KG),LABDIC,DIC SCREEN,Lab,548,487,1
+10022,3593,IP/ED MASSIVE TRANSFUSION ADULT (>50 KG),LABTYPSNI,TYPE AND SCREEN,Lab,446,487,0.915811088
+10023,3594,IP/ED MASSIVE TRANSFUSION PEDS (<50 KG),LABABG,"BLOOD GASES, ARTERIAL",Lab,1,1,1
+10024,3594,IP/ED MASSIVE TRANSFUSION PEDS (<50 KG),LABCBCO,CBC W/O DIFF,Lab,1,1,1
+10025,3594,IP/ED MASSIVE TRANSFUSION PEDS (<50 KG),LABDIC,DIC SCREEN,Lab,1,1,1
+10026,3594,IP/ED MASSIVE TRANSFUSION PEDS (<50 KG),LABTYPSNI,TYPE AND SCREEN,Lab,1,1,1
+10027,3595,IP/ED EMERGENCY RELEASE BLOOD PRODUCTS,LABTYPSNI,TYPE AND SCREEN,Lab,393,546,0.71978022
+10028,3600,OPH CATARACT PERIOPERATIVE ORDERS LEFT EYE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,5,1
+10029,3600,OPH CATARACT PERIOPERATIVE ORDERS LEFT EYE,540975,KETOROLAC 0.5 % OPHT DROP (SHC BEI),Medication,1,5,0.2
+10030,3600,OPH CATARACT PERIOPERATIVE ORDERS LEFT EYE,540976,GATIFLOXACIN 0.5 % OPHT DROP (SHC BEI),Medication,1,5,0.2
+10031,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,9,12,0.75
+10032,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,6246,PHENYLEPHRINE HCL 2.5 % OPHT DROP,Medication,1,12,0.083333333
+10033,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,8250,TROPICAMIDE 1 % OPHT DROP,Medication,1,12,0.083333333
+10034,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,84991,KETOROLAC 0.5 % OPHT DROP,Medication,1,12,0.083333333
+10035,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,210606,GATIFLOXACIN 0.5 % OPHT DROP,Medication,1,12,0.083333333
+10036,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,212876,TETRACAINE HCL (PF) 0.5 % OPHT DROP,Medication,1,12,0.083333333
+10037,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,540975,KETOROLAC 0.5 % OPHT DROP (SHC BEI),Medication,2,12,0.166666667
+10038,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,540976,GATIFLOXACIN 0.5 % OPHT DROP (SHC BEI),Medication,2,12,0.166666667
+10039,3601,OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE,POC14,POC GLUCOSE BY METER,Lab,1,12,0.083333333
+10040,3603,IP HEM ACUTE LEUKEMIA,310,ALLOPURINOL 100 MG PO TABS,Medication,2,612,0.003267974
+10041,3603,IP HEM ACUTE LEUKEMIA,311,ALLOPURINOL 100 MG PO TABS,Medication,95,612,0.155228758
+10042,3603,IP HEM ACUTE LEUKEMIA,10747,NORETHINDRONE ACETATE 5 MG PO TABS,Medication,12,612,0.019607843
+10043,3603,IP HEM ACUTE LEUKEMIA,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,56,612,0.091503268
+10044,3603,IP HEM ACUTE LEUKEMIA,78681,LEUPROLIDE 3.75 MG IM SYKT,Medication,4,612,0.006535948
+10045,3603,IP HEM ACUTE LEUKEMIA,10005R,HLA ORDER - TYPING - BMT EVALUATION (RECIPIENT),Lab,128,612,0.209150327
+10046,3603,IP HEM ACUTE LEUKEMIA,LABBCRABL,"BCR - ABL, BLOOD",Lab,23,612,0.037581699
+10047,3603,IP HEM ACUTE LEUKEMIA,LABBMPTP,BMT PRETRANSPLANT PANEL,Lab,2,612,0.003267974
+10048,3603,IP HEM ACUTE LEUKEMIA,LABBMPTP1,BMT PRETRANSPLANT PANEL,Lab,9,612,0.014705882
+10049,3603,IP HEM ACUTE LEUKEMIA,LABBMPTP2,BMT PRETRANSPLANT PANEL,Lab,60,612,0.098039216
+10050,3603,IP HEM ACUTE LEUKEMIA,LABCBCD,CBC WITH DIFF,Lab,205,612,0.33496732
+10051,3603,IP HEM ACUTE LEUKEMIA,LABCEBPA,"CEBPA, BLOOD",Lab,76,612,0.124183007
+10052,3603,IP HEM ACUTE LEUKEMIA,LABCSMPB,"CANCER SOMATIC MUTATION PANEL, BLOOD",Lab,90,612,0.147058824
+10053,3603,IP HEM ACUTE LEUKEMIA,LABDIC,DIC SCREEN,Lab,258,612,0.421568627
+10054,3603,IP HEM ACUTE LEUKEMIA,LABLDH,"LDH TOTAL, SERUM / PLASMA",Lab,231,612,0.37745098
+10055,3603,IP HEM ACUTE LEUKEMIA,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,179,612,0.29248366
+10056,3603,IP HEM ACUTE LEUKEMIA,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,247,612,0.403594771
+10057,3603,IP HEM ACUTE LEUKEMIA,LABSHIV12,RAPID HIV-1/2 ANTIBODY,Lab,59,612,0.096405229
+10058,3603,IP HEM ACUTE LEUKEMIA,LABTYPSNI,TYPE AND SCREEN,Lab,250,612,0.408496732
+10059,3603,IP HEM ACUTE LEUKEMIA,LABUPREG,"PREGNANCY TEST, URINE",Lab,19,612,0.031045752
+10060,3603,IP HEM ACUTE LEUKEMIA,LABURIC,"URIC ACID, SERUM / PLASMA",Lab,284,612,0.464052288
+10061,3603,IP HEM ACUTE LEUKEMIA,PATH22,"FLOW CYTOMETRY FOR LEUKEMIA/LYMPHOMA,BLD",Lab,67,612,0.109477124
+10062,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABACA,ANTI CARDIOLIPIN AB,Lab,31,37,0.837837838
+10063,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABANTIXA,LMWH ACTIVITY BY ANTIXA,Lab,12,37,0.324324324
+10064,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABAT3,ANTITHROMBIN III,Lab,33,37,0.891891892
+10065,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABDRVVTP,DILUTE RUSSELL VIPER VENOM PANEL,Lab,18,37,0.486486486
+10066,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABESRP,SEDIMENTATION RATE (ESR),Lab,20,37,0.540540541
+10067,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABFACT8,FACTOR VIII ASSAY,Lab,16,37,0.432432432
+10068,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABLEID,FACTOR V LEIDEN,Lab,35,37,0.945945946
+10069,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABLUPUS,LUPUS ANTICOAGULANT,Lab,32,37,0.864864865
+10070,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABP20210,PROTHROMBIN 20210A,Lab,32,37,0.864864865
+10071,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABPROTC,PROTEIN C ACTIVITY,Lab,28,37,0.756756757
+10072,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABPROTS,PROTEIN S ACTIVITY,Lab,28,37,0.756756757
+10073,3604,RETIRED IP NEU HYPERCOAGULABLE LABS,LABVWSCN,VON WILLEBRAND SCREEN,Lab,16,37,0.432432432
+10074,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,180,517,0.348162476
+10075,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,11111,PRAVASTATIN 20 MG PO TABS,Medication,162,517,0.313346228
+10076,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,13654,ASPIRIN 325 MG PO TBEC,Medication,1,517,0.001934236
+10077,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,14113,ASPIRIN 81 MG PO TBEC,Medication,131,517,0.253384913
+10078,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,19483,"CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",Medication,177,517,0.342359768
+10079,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,208479,MAGNESIUM OXIDE-MG AA CHELATE 133 MG PO TABS,Medication,176,517,0.340425532
+10080,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,520082,PAMIDRONATE IVPB,Medication,95,517,0.183752418
+10081,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,540492,SHC TRANSPLANT BEDSIDE MEDS HELP ORDER,Medication,167,517,0.323017408
+10082,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,174,517,0.33655706
+10083,3622,IP TRS HEART TRANSPLANT TRANSFER FROM ICU,IMGDXCH2,XR CHEST 2V,Imaging,186,517,0.359767892
+10084,3661,IP CTS TAVR CV POSTOP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,103,258,0.399224806
+10085,3661,IP CTS TAVR CV POSTOP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,258,0.003875969
+10086,3661,IP CTS TAVR CV POSTOP,1080,BISACODYL 10 MG PR SUPP,Medication,9,258,0.034883721
+10087,3661,IP CTS TAVR CV POSTOP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,11,258,0.042635659
+10088,3661,IP CTS TAVR CV POSTOP,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,6,258,0.023255814
+10089,3661,IP CTS TAVR CV POSTOP,8083,TRAZODONE 50 MG PO TABS,Medication,1,258,0.003875969
+10090,3661,IP CTS TAVR CV POSTOP,8085,TRAZODONE 50 MG PO TABS,Medication,84,258,0.325581395
+10091,3661,IP CTS TAVR CV POSTOP,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,2,258,0.007751938
+10092,3661,IP CTS TAVR CV POSTOP,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,1,258,0.003875969
+10093,3661,IP CTS TAVR CV POSTOP,10814,OXYCODONE 5 MG PO TABS,Medication,5,258,0.019379845
+10094,3661,IP CTS TAVR CV POSTOP,11349,SENNOSIDES 8.6 MG PO TABS,Medication,4,258,0.015503876
+10095,3661,IP CTS TAVR CV POSTOP,12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,1,258,0.003875969
+10096,3661,IP CTS TAVR CV POSTOP,14113,ASPIRIN 81 MG PO TBEC,Medication,35,258,0.135658915
+10097,3661,IP CTS TAVR CV POSTOP,14632,TRAMADOL 50 MG PO TABS,Medication,15,258,0.058139535
+10098,3661,IP CTS TAVR CV POSTOP,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,9,258,0.034883721
+10099,3661,IP CTS TAVR CV POSTOP,35942,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,Medication,1,258,0.003875969
+10100,3661,IP CTS TAVR CV POSTOP,35943,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,Medication,1,258,0.003875969
+10101,3661,IP CTS TAVR CV POSTOP,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,89,258,0.34496124
+10102,3661,IP CTS TAVR CV POSTOP,83398,CLOPIDOGREL 75 MG PO TABS,Medication,25,258,0.096899225
+10103,3661,IP CTS TAVR CV POSTOP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,2,258,0.007751938
+10104,3661,IP CTS TAVR CV POSTOP,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,14,258,0.054263566
+10105,3661,IP CTS TAVR CV POSTOP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,105,258,0.406976744
+10106,3661,IP CTS TAVR CV POSTOP,213228,FERROUS SULFATE 140 MG (45 MG IRON) PO TBSR,Medication,1,258,0.003875969
+10107,3661,IP CTS TAVR CV POSTOP,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,98,258,0.379844961
+10108,3661,IP CTS TAVR CV POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,4,258,0.015503876
+10109,3661,IP CTS TAVR CV POSTOP,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,104,258,0.403100775
+10110,3661,IP CTS TAVR CV POSTOP,540579,BENZOCAINE-MENTHOL SUGAR FREE MAXIMUM STRENGTH LOZG,Medication,101,258,0.391472868
+10111,3661,IP CTS TAVR CV POSTOP,540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,101,258,0.391472868
+10112,3661,IP CTS TAVR CV POSTOP,LABCBCD,CBC WITH DIFF,Lab,211,258,0.817829457
+10113,3661,IP CTS TAVR CV POSTOP,LABMETB,"METABOLIC PANEL, BASIC",Lab,213,258,0.825581395
+10114,3661,IP CTS TAVR CV POSTOP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,211,258,0.817829457
+10115,3661,IP CTS TAVR CV POSTOP,LABPT,PROTHROMBIN TIME,Lab,5,258,0.019379845
+10116,3661,IP CTS TAVR CV POSTOP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,5,258,0.019379845
+10117,3661,IP CTS TAVR CV POSTOP,LABTNI,TROPONIN I,Lab,1,258,0.003875969
+10118,3661,IP CTS TAVR CV POSTOP,POC14,POC GLUCOSE BY METER,Lab,2,258,0.007751938
+10119,3661,IP CTS TAVR CV POSTOP,ECH24,ECHO - TAVR (TTE),Imaging,106,258,0.410852713
+10120,3661,IP CTS TAVR CV POSTOP,IMGDXCH2,XR CHEST 2V,Imaging,100,258,0.387596899
+10121,3719,AMB IMM ADULT,83705,"MENINGOCOCCAL VAC A,C,Y,W-135 50 MCG SC SOLR",Medication,4,9,0.444444444
+10122,3719,AMB IMM ADULT,95140,PNEUMOCOCCAL 23-VALPS VACCINE 25 MCG/0.5 ML INJ INJ,Medication,3,9,0.333333333
+10123,3719,AMB IMM ADULT,125357,"DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM SUSP",Medication,1,9,0.111111111
+10124,3724,IP TRS PRE TRANSPLANT CARE,3294,FUROSEMIDE 20 MG PO TABS,Medication,1,71,0.014084507
+10125,3724,IP TRS PRE TRANSPLANT CARE,3295,FUROSEMIDE 40 MG PO TABS,Medication,1,71,0.014084507
+10126,3724,IP TRS PRE TRANSPLANT CARE,25121,OCTREOTIDE ACETATE 50 MCG/ML INJ SOLN,Medication,1,71,0.014084507
+10127,3724,IP TRS PRE TRANSPLANT CARE,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,2,71,0.028169014
+10128,3724,IP TRS PRE TRANSPLANT CARE,127761,ZINC SULFATE 220 (50) MG PO CAPS,Medication,8,71,0.112676056
+10129,3724,IP TRS PRE TRANSPLANT CARE,210426,RIFAXIMIN 550 MG PO TABS,Medication,2,71,0.028169014
+10130,3724,IP TRS PRE TRANSPLANT CARE,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,2,71,0.028169014
+10131,3724,IP TRS PRE TRANSPLANT CARE,520200,LACTULOSE ENEMA,Medication,1,71,0.014084507
+10132,3724,IP TRS PRE TRANSPLANT CARE,530014,PANTOPRAZOLE 0.8 MG/ML IV INFUSION,Medication,2,71,0.028169014
+10133,3724,IP TRS PRE TRANSPLANT CARE,530016,OCTREOTIDE 10 MCG/ML IV INFUSION,Medication,3,71,0.042253521
+10134,3724,IP TRS PRE TRANSPLANT CARE,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,4,71,0.056338028
+10135,3724,IP TRS PRE TRANSPLANT CARE,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,7,71,0.098591549
+10136,3724,IP TRS PRE TRANSPLANT CARE,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,4,71,0.056338028
+10137,3724,IP TRS PRE TRANSPLANT CARE,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,4,71,0.056338028
+10138,3724,IP TRS PRE TRANSPLANT CARE,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,9,71,0.126760563
+10139,3724,IP TRS PRE TRANSPLANT CARE,LABFAL,"ALBUMIN, FLUID",Lab,4,71,0.056338028
+10140,3724,IP TRS PRE TRANSPLANT CARE,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,7,71,0.098591549
+10141,3724,IP TRS PRE TRANSPLANT CARE,LABFTP,"PROTEIN TOTAL, FLUID",Lab,4,71,0.056338028
+10142,3724,IP TRS PRE TRANSPLANT CARE,LABTYPSNI,TYPE AND SCREEN,Lab,8,71,0.112676056
+10143,3724,IP TRS PRE TRANSPLANT CARE,IMGDXCH1,XR CHEST 1V,Imaging,1,71,0.014084507
+10144,3732,RETIRED ENT PERIOPERATIVE ORDERS,4206,KEFLEX 500 MG PO CAPS,Medication,1,120,0.008333333
+10145,3732,RETIRED ENT PERIOPERATIVE ORDERS,4318,LACTATED RINGERS IV SOLP,Medication,18,120,0.15
+10146,3732,RETIRED ENT PERIOPERATIVE ORDERS,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,3,120,0.025
+10147,3732,RETIRED ENT PERIOPERATIVE ORDERS,5943,OXYMETAZOLINE 0.05 % NASAL SPRA,Medication,4,120,0.033333333
+10148,3732,RETIRED ENT PERIOPERATIVE ORDERS,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,4,120,0.033333333
+10149,3732,RETIRED ENT PERIOPERATIVE ORDERS,16390,AUGMENTIN 875-125 MG PO TABS,Medication,1,120,0.008333333
+10150,3732,RETIRED ENT PERIOPERATIVE ORDERS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,5,120,0.041666667
+10151,3732,RETIRED ENT PERIOPERATIVE ORDERS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,20,120,0.166666667
+10152,3732,RETIRED ENT PERIOPERATIVE ORDERS,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,120,0.008333333
+10153,3732,RETIRED ENT PERIOPERATIVE ORDERS,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,1,120,0.008333333
+10154,3732,RETIRED ENT PERIOPERATIVE ORDERS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,19,120,0.158333333
+10155,3732,RETIRED ENT PERIOPERATIVE ORDERS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,2,120,0.016666667
+10156,3732,RETIRED ENT PERIOPERATIVE ORDERS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,120,0.016666667
+10157,3732,RETIRED ENT PERIOPERATIVE ORDERS,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,31,120,0.258333333
+10158,3732,RETIRED ENT PERIOPERATIVE ORDERS,520064,CEFAZOLIN 2 G IVPB,Medication,6,120,0.05
+10159,3732,RETIRED ENT PERIOPERATIVE ORDERS,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,120,0.008333333
+10160,3732,RETIRED ENT PERIOPERATIVE ORDERS,541228,CEFAZOLIN IVPB (COMPOUNDED),Medication,1,120,0.008333333
+10161,3732,RETIRED ENT PERIOPERATIVE ORDERS,541537,"CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",Medication,4,120,0.033333333
+10162,3732,RETIRED ENT PERIOPERATIVE ORDERS,LABBBPRBC,RED BLOOD CELLS,Blood Product,1,120,0.008333333
+10163,3734,ED TRAUMA MEDICATION,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,5,499,0.01002004
+10164,3734,ED TRAUMA MEDICATION,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,24,499,0.048096192
+10165,3734,ED TRAUMA MEDICATION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,3,499,0.006012024
+10166,3734,ED TRAUMA MEDICATION,125356,"DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM SYRG",Medication,4,499,0.008016032
+10167,3734,ED TRAUMA MEDICATION,125357,"DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM SUSP",Medication,25,499,0.0501002
+10168,3734,ED TRAUMA MEDICATION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,190,499,0.380761523
+10169,3734,ED TRAUMA MEDICATION,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,1,499,0.002004008
+10170,3734,ED TRAUMA MEDICATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,265,499,0.531062124
+10171,3734,ED TRAUMA MEDICATION,541184,"DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM INJ",Medication,158,499,0.316633267
+10172,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LAB11132R,DONOR HEP B SURF. AG,Lab,1,1,1
+10173,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LAB11133R,DONOR HBC AB,Lab,1,1,1
+10174,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LAB11134R,DONOR HCV AB,Lab,1,1,1
+10175,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LAB11136R,HTLV AB,Lab,1,1,1
+10176,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LAB12854R,"DONOR, WEST NILE VIRUS NAT",Lab,1,1,1
+10177,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LAB12858R,"DONOR, NAT MPX, HIV, HCV, HBV",Lab,1,1,1
+10178,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LABCMVIGG,CMV IGG,Lab,1,1,1
+10179,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LABCMVIGM,CMV IGM,Lab,1,1,1
+10180,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LABDCMVL,DONOR CMV- TOTAL AB,Lab,1,1,1
+10181,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LABDHIVB,DONOR HIV AB,Lab,1,1,1
+10182,3739,RETIRED INPT/AMB REI DONOR VIROLOGY (MALE),LABDRPRS,"DONOR, RPR",Lab,1,1,1
+10183,3743,IP CAR PERIPHERAL AMIODARONE IV,4454,LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN,Medication,91,1203,0.075644223
+10184,3743,IP CAR PERIPHERAL AMIODARONE IV,7309,SODIUM BICARBONATE 8.4 % IV SOLN,Medication,186,1203,0.154613466
+10185,3743,IP CAR PERIPHERAL AMIODARONE IV,10434,LIDOCAINE-PRILOCAINE 2.5-2.5 % TP CREA,Medication,1,1203,0.000831255
+10186,3743,IP CAR PERIPHERAL AMIODARONE IV,81839,LIDOCAINE 4 % TP CREA,Medication,119,1203,0.098919368
+10187,3743,IP CAR PERIPHERAL AMIODARONE IV,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,3,1203,0.002493766
+10188,3743,IP CAR PERIPHERAL AMIODARONE IV,126993,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,2,1203,0.00166251
+10189,3743,IP CAR PERIPHERAL AMIODARONE IV,530001,AMIODARONE INFUSION PERIPH ADMIN,Medication,1011,1203,0.840399002
+10190,3743,IP CAR PERIPHERAL AMIODARONE IV,530200,AMIODARONE PERIPHERAL INFUSION,Medication,726,1203,0.603491272
+10191,3755,IP PLS FREE FLAP,101,ACETAMINOPHEN 325 MG PO TABS,Medication,117,721,0.162274619
+10192,3755,IP PLS FREE FLAP,102,ACETAMINOPHEN 500 MG PO TABS,Medication,13,721,0.018030513
+10193,3755,IP PLS FREE FLAP,681,ASPIRIN 325 MG PO TABS,Medication,107,721,0.148404993
+10194,3755,IP PLS FREE FLAP,693,ASPIRIN 300 MG PR SUPP,Medication,90,721,0.12482663
+10195,3755,IP PLS FREE FLAP,1080,BISACODYL 10 MG PR SUPP,Medication,140,721,0.194174757
+10196,3755,IP PLS FREE FLAP,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,4,721,0.00554785
+10197,3755,IP PLS FREE FLAP,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,110,721,0.152565881
+10198,3755,IP PLS FREE FLAP,2511,DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX,Medication,2,721,0.002773925
+10199,3755,IP PLS FREE FLAP,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,120,721,0.166435506
+10200,3755,IP PLS FREE FLAP,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,10,721,0.013869626
+10201,3755,IP PLS FREE FLAP,5172,MORPHINE 4 MG/ML INJ SYRG,Medication,1,721,0.001386963
+10202,3755,IP PLS FREE FLAP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,7,721,0.009708738
+10203,3755,IP PLS FREE FLAP,12556,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,721,0.001386963
+10204,3755,IP PLS FREE FLAP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,129,721,0.178918169
+10205,3755,IP PLS FREE FLAP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,86,721,0.119278779
+10206,3755,IP PLS FREE FLAP,36063,0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,3,721,0.004160888
+10207,3755,IP PLS FREE FLAP,93936,DEXTRAN 40 10 % IN 5% DEXTROSE 10 % IV SOLP,Medication,14,721,0.019417476
+10208,3755,IP PLS FREE FLAP,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,14,721,0.019417476
+10209,3755,IP PLS FREE FLAP,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,4,721,0.00554785
+10210,3755,IP PLS FREE FLAP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,91,721,0.126213592
+10211,3755,IP PLS FREE FLAP,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,4,721,0.00554785
+10212,3755,IP PLS FREE FLAP,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,28,721,0.038834951
+10213,3755,IP PLS FREE FLAP,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,149,721,0.20665742
+10214,3755,IP PLS FREE FLAP,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,2,721,0.002773925
+10215,3755,IP PLS FREE FLAP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,30,721,0.041608877
+10216,3755,IP PLS FREE FLAP,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,113,721,0.156726768
+10217,3755,IP PLS FREE FLAP,540142,DIPHENHYDRAMINE 25 MG/50 ML NS IVPB (CANCER CENTER ONLY),Medication,15,721,0.020804438
+10218,3755,IP PLS FREE FLAP,540300,MORPHINE INJECTABLE SYRINGE,Medication,7,721,0.009708738
+10219,3755,IP PLS FREE FLAP,540304,MAGNESIUM SULFATE IV SCALE ,Medication,23,721,0.031900139
+10220,3755,IP PLS FREE FLAP,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,1,721,0.001386963
+10221,3755,IP PLS FREE FLAP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,120,721,0.166435506
+10222,3755,IP PLS FREE FLAP,LABCBCO,CBC W/O DIFF,Lab,158,721,0.219140083
+10223,3755,IP PLS FREE FLAP,LABMETB,"METABOLIC PANEL, BASIC",Lab,156,721,0.216366158
+10224,3755,IP PLS FREE FLAP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,140,721,0.194174757
+10225,3755,IP PLS FREE FLAP,LABPLTS,PLATELET COUNT,Lab,18,721,0.024965326
+10226,3756,IP PLS ICU HEAND AND NECK FREE FLAP,680,ASPIRIN 81 MG PO CHEW,Medication,1,203,0.004926108
+10227,3756,IP PLS ICU HEAND AND NECK FREE FLAP,681,ASPIRIN 325 MG PO TABS,Medication,22,203,0.108374384
+10228,3756,IP PLS ICU HEAND AND NECK FREE FLAP,693,ASPIRIN 300 MG PR SUPP,Medication,29,203,0.142857143
+10229,3756,IP PLS ICU HEAND AND NECK FREE FLAP,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,27,203,0.133004926
+10230,3756,IP PLS ICU HEAND AND NECK FREE FLAP,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,19,203,0.093596059
+10231,3756,IP PLS ICU HEAND AND NECK FREE FLAP,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,2,203,0.009852217
+10232,3756,IP PLS ICU HEAND AND NECK FREE FLAP,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,29,203,0.142857143
+10233,3756,IP PLS ICU HEAND AND NECK FREE FLAP,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,36,203,0.177339901
+10234,3756,IP PLS ICU HEAND AND NECK FREE FLAP,36063,0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,2,203,0.009852217
+10235,3756,IP PLS ICU HEAND AND NECK FREE FLAP,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,20,203,0.098522167
+10236,3756,IP PLS ICU HEAND AND NECK FREE FLAP,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,23,203,0.113300493
+10237,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,31,203,0.15270936
+10238,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABCBCO,CBC W/O DIFF,Lab,52,203,0.256157635
+10239,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABMETB,"METABOLIC PANEL, BASIC",Lab,48,203,0.236453202
+10240,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,42,203,0.206896552
+10241,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABPLTS,PLATELET COUNT,Lab,14,203,0.068965517
+10242,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABPT,PROTHROMBIN TIME,Lab,31,203,0.15270936
+10243,3756,IP PLS ICU HEAND AND NECK FREE FLAP,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,31,203,0.15270936
+10244,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,540616,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (RT PANEL),Medication,3,55,0.054545455
+10245,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,540616,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (RT PANEL),Medication,3,55,0.054545455
+10246,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABABG,"BLOOD GASES, ARTERIAL",Lab,3,55,0.054545455
+10247,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABABG,"BLOOD GASES, ARTERIAL",Lab,3,55,0.054545455
+10248,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABANAS,ANTI - NUCLEAR AB,Lab,30,55,0.545454545
+10249,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABANAS,ANTI - NUCLEAR AB,Lab,30,55,0.545454545
+10250,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABANTIM,ANTI - TPO AB,Lab,28,55,0.509090909
+10251,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABANTIM,ANTI - TPO AB,Lab,28,55,0.509090909
+10252,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABANTITG,ANTI - THYROGLOBULIN AB,Lab,29,55,0.527272727
+10253,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABANTITG,ANTI - THYROGLOBULIN AB,Lab,29,55,0.527272727
+10254,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABAPHSA2,ANTI PHOSPHOLIPID AB PANEL,Lab,22,55,0.4
+10255,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABAPHSA2,ANTI PHOSPHOLIPID AB PANEL,Lab,22,55,0.4
+10256,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABAT3,ANTITHROMBIN III,Lab,10,55,0.181818182
+10257,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABAT3,ANTITHROMBIN III,Lab,10,55,0.181818182
+10258,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABCBCD,CBC WITH DIFF,Lab,3,55,0.054545455
+10259,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABCBCD,CBC WITH DIFF,Lab,3,55,0.054545455
+10260,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABCBCO,CBC W/O DIFF,Lab,1,55,0.018181818
+10261,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABCBCO,CBC W/O DIFF,Lab,1,55,0.018181818
+10262,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABCENABY,ANTI CENTROMERE ANTIBODY,Lab,35,55,0.636363636
+10263,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABCENABY,ANTI CENTROMERE ANTIBODY,Lab,35,55,0.636363636
+10264,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABDIGL,"DIGOXIN, SERUM",Lab,1,55,0.018181818
+10265,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABDIGL,"DIGOXIN, SERUM",Lab,1,55,0.018181818
+10266,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,13,55,0.236363636
+10267,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,13,55,0.236363636
+10268,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABDSDNA,ANTI - DSDNA AB,Lab,35,55,0.636363636
+10269,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABDSDNA,ANTI - DSDNA AB,Lab,35,55,0.636363636
+10270,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABEP1,ELECTROLYTE PANEL,Lab,1,55,0.018181818
+10271,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABEP1,ELECTROLYTE PANEL,Lab,1,55,0.018181818
+10272,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABESRP,SEDIMENTATION RATE (ESR),Lab,36,55,0.654545455
+10273,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABESRP,SEDIMENTATION RATE (ESR),Lab,36,55,0.654545455
+10274,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABFT4,"T4, FREE",Lab,33,55,0.6
+10275,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABFT4,"T4, FREE",Lab,33,55,0.6
+10276,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHAVM,IGM ANTI-HAV,Lab,2,55,0.036363636
+10277,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHAVM,IGM ANTI-HAV,Lab,2,55,0.036363636
+10278,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHBCT,ANTI-HBC,Lab,1,55,0.018181818
+10279,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHBCT,ANTI-HBC,Lab,1,55,0.018181818
+10280,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHBSAG,HBSAG,Lab,1,55,0.018181818
+10281,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHBSAG,HBSAG,Lab,1,55,0.018181818
+10282,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHBSQT,HEP B SURF AB QUANT,Lab,2,55,0.036363636
+10283,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHBSQT,HEP B SURF AB QUANT,Lab,2,55,0.036363636
+10284,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHCVA,HEPATITIS C AB IGG,Lab,2,55,0.036363636
+10285,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHCVA,HEPATITIS C AB IGG,Lab,2,55,0.036363636
+10286,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHFP,HEPATIC FUNCTION PANEL A,Lab,5,55,0.090909091
+10287,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHFP,HEPATIC FUNCTION PANEL A,Lab,5,55,0.090909091
+10288,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHMCY,"HOMOCYSTEINE TOTAL, PLASMA",Lab,22,55,0.4
+10289,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHMCY,"HOMOCYSTEINE TOTAL, PLASMA",Lab,22,55,0.4
+10290,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABHSCRP,HIGH SENSITIVITY C-REACTIVE PROTEIN,Lab,27,55,0.490909091
+10291,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABHSCRP,HIGH SENSITIVITY C-REACTIVE PROTEIN,Lab,27,55,0.490909091
+10292,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABLEID,FACTOR V LEIDEN,Lab,5,55,0.090909091
+10293,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABLEID,FACTOR V LEIDEN,Lab,5,55,0.090909091
+10294,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABLPA,LIPOPROTEIN,Lab,17,55,0.309090909
+10295,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABLPA,LIPOPROTEIN,Lab,17,55,0.309090909
+10296,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,19,55,0.345454545
+10297,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,19,55,0.345454545
+10298,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABLPDPC,LIPID PLUS RISK PANEL WITH CALCULATED LDL,Lab,11,55,0.2
+10299,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABLPDPC,LIPID PLUS RISK PANEL WITH CALCULATED LDL,Lab,11,55,0.2
+10300,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,55,0.018181818
+10301,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,55,0.018181818
+10302,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,55,0.072727273
+10303,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,4,55,0.072727273
+10304,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4,55,0.072727273
+10305,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,4,55,0.072727273
+10306,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABNTBNP,NT - PROBNP,Lab,8,55,0.145454545
+10307,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABNTBNP,NT - PROBNP,Lab,8,55,0.145454545
+10308,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABP20210,PROTHROMBIN 20210A,Lab,5,55,0.090909091
+10309,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABP20210,PROTHROMBIN 20210A,Lab,5,55,0.090909091
+10310,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABPREG,"PREGNANCY TEST, SERUM",Lab,3,55,0.054545455
+10311,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABPREG,"PREGNANCY TEST, SERUM",Lab,3,55,0.054545455
+10312,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABPROTC,PROTEIN C ACTIVITY,Lab,14,55,0.254545455
+10313,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABPROTC,PROTEIN C ACTIVITY,Lab,14,55,0.254545455
+10314,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABPROTS,PROTEIN S ACTIVITY,Lab,14,55,0.254545455
+10315,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABPROTS,PROTEIN S ACTIVITY,Lab,14,55,0.254545455
+10316,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABPT,PROTHROMBIN TIME,Lab,3,55,0.054545455
+10317,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABPT,PROTHROMBIN TIME,Lab,3,55,0.054545455
+10318,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,55,0.054545455
+10319,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,55,0.054545455
+10320,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABRF,RHEUMATOID FACTOR,Lab,17,55,0.309090909
+10321,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABRF,RHEUMATOID FACTOR,Lab,17,55,0.309090909
+10322,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABRNPABY,ANTI-RNP ANTIBODY,Lab,22,55,0.4
+10323,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABRNPABY,ANTI-RNP ANTIBODY,Lab,22,55,0.4
+10324,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABSCLABY,ANTI-SCL70 ANTIBODY,Lab,31,55,0.563636364
+10325,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABSCLABY,ANTI-SCL70 ANTIBODY,Lab,31,55,0.563636364
+10326,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABT3,"T3, TOTAL",Lab,28,55,0.509090909
+10327,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABT3,"T3, TOTAL",Lab,28,55,0.509090909
+10328,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABTSH,TSH,Lab,34,55,0.618181818
+10329,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABTSH,TSH,Lab,34,55,0.618181818
+10330,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,7,55,0.127272727
+10331,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,7,55,0.127272727
+10332,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABUPREG,"PREGNANCY TEST, URINE",Lab,1,55,0.018181818
+10333,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABUPREG,"PREGNANCY TEST, URINE",Lab,1,55,0.018181818
+10334,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,LABVP5,COMP HEPATITIS PANEL,Lab,22,55,0.4
+10335,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,LABVP5,COMP HEPATITIS PANEL,Lab,22,55,0.4
+10336,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,4,55,0.072727273
+10337,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,4,55,0.072727273
+10338,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,IMGCTHR,CT CHEST HIGH RESOLUTION,Imaging,2,55,0.036363636
+10339,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,IMGCTHR,CT CHEST HIGH RESOLUTION,Imaging,2,55,0.036363636
+10340,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,IMGDXCH2,XR CHEST 2V,Imaging,1,55,0.018181818
+10341,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,IMGDXCH2,XR CHEST 2V,Imaging,1,55,0.018181818
+10342,3761,IP PULM PULMONARY HYPERTENSION SCREENING ORDERS,IMGNPVQ,NUC LUNG V/Q,Imaging,5,55,0.090909091
+10343,3761,IP PULM PULMONARY HYPERTENSION SCREENING LABS,IMGNPVQ,NUC LUNG V/Q,Imaging,5,55,0.090909091
+10344,3771,IP ENT EPISTAXIS,5943,OXYMETAZOLINE 0.05 % NASAL SPRA,Medication,1,7,0.142857143
+10345,3771,IP ENT EPISTAXIS,198837,SILVER NITRATE APPLICATORS 75-25 % TP STCK,Medication,2,7,0.285714286
+10346,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),3291,FUROSEMIDE 10 MG/ML INJ SOLN,Medication,2,32,0.0625
+10347,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),3294,FUROSEMIDE 20 MG PO TABS,Medication,1,32,0.03125
+10348,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,1,32,0.03125
+10349,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,2,32,0.0625
+10350,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),205192,MILRINONE IN D5W 20 MG/100 ML IV PGBK,Medication,1,32,0.03125
+10351,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),LABDIGL,"DIGOXIN, SERUM",Lab,2,32,0.0625
+10352,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,16,32,0.5
+10353,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),LABMB,"CK, MB (MASS)",Lab,1,32,0.03125
+10354,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),LABNTBNP,NT - PROBNP,Lab,3,32,0.09375
+10355,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),LABTNI,TROPONIN I,Lab,6,32,0.1875
+10356,3786,RETIRED IP CAR CONGESTIVE HEART FAILURE (ADD ON),ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,2,32,0.0625
+10357,3812,CC HDR - T AND O,22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,1,1,1
+10358,3812,CC HDR - T AND O,80845,ONDANSETRON HCL 2 MG/ML IV SOLN,Medication,1,1,1
+10359,3812,CC HDR - T AND O,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,1,1
+10360,3821,ED HIP FRACTURE SYMPTOMS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,164,0.030487805
+10361,3821,ED HIP FRACTURE SYMPTOMS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,11,164,0.067073171
+10362,3821,ED HIP FRACTURE SYMPTOMS,10814,OXYCODONE 5 MG PO TABS,Medication,4,164,0.024390244
+10363,3821,ED HIP FRACTURE SYMPTOMS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,17,164,0.103658537
+10364,3821,ED HIP FRACTURE SYMPTOMS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,7,164,0.042682927
+10365,3821,ED HIP FRACTURE SYMPTOMS,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,10,164,0.06097561
+10366,3821,ED HIP FRACTURE SYMPTOMS,540102,NS IV BOLUS,Medication,8,164,0.048780488
+10367,3821,ED HIP FRACTURE SYMPTOMS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,9,164,0.054878049
+10368,3821,ED HIP FRACTURE SYMPTOMS,LABCBCO,CBC W/O DIFF,Lab,72,164,0.43902439
+10369,3821,ED HIP FRACTURE SYMPTOMS,LABMETB,"METABOLIC PANEL, BASIC",Lab,71,164,0.432926829
+10370,3821,ED HIP FRACTURE SYMPTOMS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,97,164,0.591463415
+10371,3821,ED HIP FRACTURE SYMPTOMS,LABPT,PROTHROMBIN TIME,Lab,92,164,0.56097561
+10372,3821,ED HIP FRACTURE SYMPTOMS,LABTYPSNI,TYPE AND SCREEN,Lab,89,164,0.542682927
+10373,3821,ED HIP FRACTURE SYMPTOMS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,89,164,0.542682927
+10374,3821,ED HIP FRACTURE SYMPTOMS,IMGCTP,CT PELVIS,Imaging,2,164,0.012195122
+10375,3821,ED HIP FRACTURE SYMPTOMS,IMGDXCH1,XR CHEST 1V,Imaging,37,164,0.225609756
+10376,3821,ED HIP FRACTURE SYMPTOMS,IMGDXFEMLT,XR FEMUR LT,Imaging,16,164,0.097560976
+10377,3821,ED HIP FRACTURE SYMPTOMS,IMGDXFEMRT,XR FEMUR RT,Imaging,19,164,0.115853659
+10378,3821,ED HIP FRACTURE SYMPTOMS,IMGDXHIP2LT,XR HIP 2V LT,Imaging,18,164,0.109756098
+10379,3821,ED HIP FRACTURE SYMPTOMS,IMGDXHIP2RT,XR HIP 2V RT,Imaging,21,164,0.12804878
+10380,3821,ED HIP FRACTURE SYMPTOMS,IMGDXPEL,XR PELVIS 1V,Imaging,35,164,0.213414634
+10381,3824,IP VAS VASCULAR ICU,101,ACETAMINOPHEN 325 MG PO TABS,Medication,17,549,0.030965392
+10382,3824,IP VAS VASCULAR ICU,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,549,0.001821494
+10383,3824,IP VAS VASCULAR ICU,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,549,0.001821494
+10384,3824,IP VAS VASCULAR ICU,680,ASPIRIN 81 MG PO CHEW,Medication,15,549,0.027322404
+10385,3824,IP VAS VASCULAR ICU,693,ASPIRIN 300 MG PR SUPP,Medication,19,549,0.034608379
+10386,3824,IP VAS VASCULAR ICU,696,ASPIRIN 600 MG PR SUPP,Medication,1,549,0.001821494
+10387,3824,IP VAS VASCULAR ICU,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,12,549,0.021857923
+10388,3824,IP VAS VASCULAR ICU,4318,LACTATED RINGERS IV SOLP,Medication,56,549,0.102003643
+10389,3824,IP VAS VASCULAR ICU,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,17,549,0.030965392
+10390,3824,IP VAS VASCULAR ICU,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,126,549,0.229508197
+10391,3824,IP VAS VASCULAR ICU,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,13,549,0.023679417
+10392,3824,IP VAS VASCULAR ICU,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,16,549,0.029143898
+10393,3824,IP VAS VASCULAR ICU,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,2,549,0.003642987
+10394,3824,IP VAS VASCULAR ICU,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,3,549,0.005464481
+10395,3824,IP VAS VASCULAR ICU,10011,FAMOTIDINE 20 MG PO TABS,Medication,4,549,0.007285974
+10396,3824,IP VAS VASCULAR ICU,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,3,549,0.005464481
+10397,3824,IP VAS VASCULAR ICU,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,10,549,0.018214936
+10398,3824,IP VAS VASCULAR ICU,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,18,549,0.032786885
+10399,3824,IP VAS VASCULAR ICU,15859,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,Medication,7,549,0.012750455
+10400,3824,IP VAS VASCULAR ICU,15980,"DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",Medication,1,549,0.001821494
+10401,3824,IP VAS VASCULAR ICU,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,9,549,0.016393443
+10402,3824,IP VAS VASCULAR ICU,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,21,549,0.038251366
+10403,3824,IP VAS VASCULAR ICU,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,43,549,0.078324226
+10404,3824,IP VAS VASCULAR ICU,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,3,549,0.005464481
+10405,3824,IP VAS VASCULAR ICU,96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,5,549,0.009107468
+10406,3824,IP VAS VASCULAR ICU,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,549,0.001821494
+10407,3824,IP VAS VASCULAR ICU,112520,SODIUM CHLORIDE 0.9 % 0.9 % INJ SYRG,Medication,72,549,0.131147541
+10408,3824,IP VAS VASCULAR ICU,115141,"ESMOLOL IN NACL (ISO-OSM) 2,000 MG/100 ML IV SOLP",Medication,8,549,0.014571949
+10409,3824,IP VAS VASCULAR ICU,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,9,549,0.016393443
+10410,3824,IP VAS VASCULAR ICU,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,46,549,0.083788707
+10411,3824,IP VAS VASCULAR ICU,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,85,549,0.154826958
+10412,3824,IP VAS VASCULAR ICU,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,26,549,0.047358834
+10413,3824,IP VAS VASCULAR ICU,207694,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,8,549,0.014571949
+10414,3824,IP VAS VASCULAR ICU,210741,PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN,Medication,10,549,0.018214936
+10415,3824,IP VAS VASCULAR ICU,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,54,549,0.098360656
+10416,3824,IP VAS VASCULAR ICU,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,59,549,0.107468124
+10417,3824,IP VAS VASCULAR ICU,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,20,549,0.036429872
+10418,3824,IP VAS VASCULAR ICU,221562,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,Medication,24,549,0.043715847
+10419,3824,IP VAS VASCULAR ICU,226058,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,Medication,3,549,0.005464481
+10420,3824,IP VAS VASCULAR ICU,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,16,549,0.029143898
+10421,3824,IP VAS VASCULAR ICU,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,18,549,0.032786885
+10422,3824,IP VAS VASCULAR ICU,500010,LACTATED RINGERS WITH KCL IV INFUSION,Medication,2,549,0.003642987
+10423,3824,IP VAS VASCULAR ICU,500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,5,549,0.009107468
+10424,3824,IP VAS VASCULAR ICU,520064,CEFAZOLIN 2 G IVPB,Medication,13,549,0.023679417
+10425,3824,IP VAS VASCULAR ICU,530001,AMIODARONE INFUSION PERIPH ADMIN,Medication,1,549,0.001821494
+10426,3824,IP VAS VASCULAR ICU,530006,EPINEPHRINE IV INFUSION,Medication,2,549,0.003642987
+10427,3824,IP VAS VASCULAR ICU,530008,NITROPRUSSIDE IV INFUSION,Medication,2,549,0.003642987
+10428,3824,IP VAS VASCULAR ICU,530010,NICARDIPINE IV INFUSION,Medication,5,549,0.009107468
+10429,3824,IP VAS VASCULAR ICU,530011,NOREPINEPHRINE IV INFUSION,Medication,5,549,0.009107468
+10430,3824,IP VAS VASCULAR ICU,530012,PHENYLEPHRINE IV INFUSION,Medication,5,549,0.009107468
+10431,3824,IP VAS VASCULAR ICU,530025,DEXMEDETOMIDINE IV INFUSION,Medication,4,549,0.007285974
+10432,3824,IP VAS VASCULAR ICU,530031,VASOPRESSIN 1 UNIT/ML IV INFUSION,Medication,4,549,0.007285974
+10433,3824,IP VAS VASCULAR ICU,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,47,549,0.0856102
+10434,3824,IP VAS VASCULAR ICU,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,1,549,0.001821494
+10435,3824,IP VAS VASCULAR ICU,540302,POTASSIUM CHLORIDE IV SCALE,Medication,74,549,0.134790528
+10436,3824,IP VAS VASCULAR ICU,540304,MAGNESIUM SULFATE IV SCALE ,Medication,77,549,0.140255009
+10437,3824,IP VAS VASCULAR ICU,540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,3,549,0.005464481
+10438,3824,IP VAS VASCULAR ICU,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,549,0.009107468
+10439,3824,IP VAS VASCULAR ICU,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,123,549,0.224043716
+10440,3824,IP VAS VASCULAR ICU,LABCBCD,CBC WITH DIFF,Lab,241,549,0.438979964
+10441,3824,IP VAS VASCULAR ICU,LABCK,"CREATINE KINASE, TOTAL",Lab,9,549,0.016393443
+10442,3824,IP VAS VASCULAR ICU,LABK,"POTASSIUM, SERUM/PLASMA",Lab,16,549,0.029143898
+10443,3824,IP VAS VASCULAR ICU,LABMETB,"METABOLIC PANEL, BASIC",Lab,79,549,0.143897996
+10444,3824,IP VAS VASCULAR ICU,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,131,549,0.238615665
+10445,3824,IP VAS VASCULAR ICU,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,192,549,0.349726776
+10446,3824,IP VAS VASCULAR ICU,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,125,549,0.227686703
+10447,3824,IP VAS VASCULAR ICU,LABPLTS,PLATELET COUNT,Lab,14,549,0.025500911
+10448,3824,IP VAS VASCULAR ICU,LABPT,PROTHROMBIN TIME,Lab,178,549,0.324225865
+10449,3824,IP VAS VASCULAR ICU,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,122,549,0.222222222
+10450,3824,IP VAS VASCULAR ICU,LABTEGCP,THROMBELASTOGRAPH CLOTTING PROFILE,Lab,1,549,0.001821494
+10451,3824,IP VAS VASCULAR ICU,POC14,POC GLUCOSE BY METER,Lab,17,549,0.030965392
+10452,3824,IP VAS VASCULAR ICU,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,29,549,0.052823315
+10453,3824,IP VAS VASCULAR ICU,IMGDXCH1,XR CHEST 1V,Imaging,92,549,0.167577413
+10454,3841,AMB UHA AMIODARONE TRACK,LABHFP,HEPATIC FUNCTION PANEL A,Lab,2,14,0.142857143
+10455,3841,AMB UHA AMIODARONE TRACK,LABLCTFT,TSH AND FREE T4,Lab,2,14,0.142857143
+10456,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABABG,"BLOOD GASES, ARTERIAL",Lab,25567,14678,1
+10457,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABARVI,BLOOD TYPE VERIFICATION,Lab,56,14678,0.003815234
+10458,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABBBPCRY,CRYOPRECIPITATE,Lab,552,14678,0.037607303
+10459,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABBTVI,BLOOD TYPE VERIFICATION,Lab,1262,14678,0.085979016
+10460,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABCBCO,CBC W/O DIFF,Lab,1100,14678,0.07494209
+10461,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABDIC,DIC SCREEN,Lab,117,14678,0.007971113
+10462,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABFIB,FIBRINOGEN,Lab,798,14678,0.05436708
+10463,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABIOPTH,INTRA OPERATIVE PTH,Lab,46,14678,0.003133942
+10464,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABMETB,"METABOLIC PANEL, BASIC",Lab,252,14678,0.017168552
+10465,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABPT,PROTHROMBIN TIME,Lab,1143,14678,0.077871645
+10466,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1054,14678,0.071808148
+10467,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABTYPSNI,TYPE AND SCREEN,Lab,1595,14678,0.108666031
+10468,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABVBG,"BLOOD GASES, VENOUS",Lab,5113,14678,0.348344461
+10469,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABBBPCRY,CRYOPRECIPITATE,Blood Product,2139,14678,0.145728301
+10470,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,6975,14678,0.475200981
+10471,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,4856,14678,0.330835264
+10472,3852,INTRA-OPERATIVE BLOOD PRODUCT AND TRANSFUSION ORDERS,LABBBPRBC,RED BLOOD CELLS,Blood Product,15899,14678,1
+10473,3870,ED ACUTE HEADACHE AND MIGRAINE (GREATER OR EQUAL TO 18 YEARS),3844,IBUPROFEN 600 MG PO TABS,Medication,1,4,0.25
+10474,3870,ED ACUTE HEADACHE AND MIGRAINE (GREATER OR EQUAL TO 18 YEARS),5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,4,0.5
+10475,3870,ED ACUTE HEADACHE AND MIGRAINE (GREATER OR EQUAL TO 18 YEARS),22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,1,4,0.25
+10476,3870,ED ACUTE HEADACHE AND MIGRAINE (GREATER OR EQUAL TO 18 YEARS),86020,SUMATRIPTAN SUCCINATE 6 MG/0.5 ML SC SOLN,Medication,1,4,0.25
+10477,3870,ED ACUTE HEADACHE AND MIGRAINE (GREATER OR EQUAL TO 18 YEARS),541441,NS IV BOLUS - 1000 ML,Medication,2,4,0.5
+10478,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,4711,MAGNESIUM CITRATE PO SOLN,Medication,15,269,0.055762082
+10479,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,11349,SENNOSIDES 8.6 MG PO TABS,Medication,17,269,0.063197026
+10480,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,13632,BISACODYL 5 MG PO TBEC,Medication,19,269,0.07063197
+10481,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,20943,AZITHROMYCIN 250 MG PO TABS,Medication,1,269,0.003717472
+10482,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,33,269,0.12267658
+10483,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,20,269,0.074349442
+10484,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,126639,LUBIPROSTONE 24 MCG PO CAPS,Medication,2,269,0.007434944
+10485,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,40,269,0.148698885
+10486,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,2,269,0.007434944
+10487,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,204675,LUBIPROSTONE 8 MCG PO CAPS,Medication,3,269,0.011152416
+10488,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,5,269,0.018587361
+10489,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,500011,POTASSIUM PHOSPHATE IVPB,Medication,63,269,0.234200743
+10490,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540102,NS IV BOLUS,Medication,32,269,0.118959108
+10491,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,1,269,0.003717472
+10492,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,55,269,0.204460967
+10493,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540302,POTASSIUM CHLORIDE IV SCALE,Medication,41,269,0.152416357
+10494,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540304,MAGNESIUM SULFATE IV SCALE ,Medication,56,269,0.208178439
+10495,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540687,ACETYLCYSTEINE 20 % (200MG/ML) MISC SOLN (ACF USE ONLY),Medication,92,269,0.342007435
+10496,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,540699,CF ENEMA SOLUTION,Medication,96,269,0.356877323
+10497,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,LABCBCO,CBC W/O DIFF,Lab,32,269,0.118959108
+10498,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,49,269,0.182156134
+10499,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,49,269,0.182156134
+10500,3881,IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS,IMGDXAB2,ABDOMEN WITH DECUB AND/OR UPRIGHT,Imaging,95,269,0.353159851
+10501,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,3,101,0.02970297
+10502,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,8,101,0.079207921
+10503,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,126639,LUBIPROSTONE 24 MCG PO CAPS,Medication,3,101,0.02970297
+10504,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,12,101,0.118811881
+10505,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,500011,POTASSIUM PHOSPHATE IVPB,Medication,22,101,0.217821782
+10506,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540102,NS IV BOLUS,Medication,14,101,0.138613861
+10507,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540184,ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN,Medication,1,101,0.00990099
+10508,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,1,101,0.00990099
+10509,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540302,POTASSIUM CHLORIDE IV SCALE,Medication,19,101,0.188118812
+10510,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540304,MAGNESIUM SULFATE IV SCALE ,Medication,17,101,0.168316832
+10511,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540687,ACETYLCYSTEINE 20 % (200MG/ML) MISC SOLN (ACF USE ONLY),Medication,17,101,0.168316832
+10512,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,540699,CF ENEMA SOLUTION,Medication,19,101,0.188118812
+10513,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,LABCBCO,CBC W/O DIFF,Lab,13,101,0.128712871
+10514,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,15,101,0.148514851
+10515,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,LABMETB,"METABOLIC PANEL, BASIC",Lab,25,101,0.247524752
+10516,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,26,101,0.257425743
+10517,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,26,101,0.257425743
+10518,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,IMGCTAP,CT ABDOMEN PELVIS,Imaging,2,101,0.01980198
+10519,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,IMGDXAB2,ABDOMEN WITH DECUB AND/OR UPRIGHT,Imaging,18,101,0.178217822
+10520,3885,IP MED CYSTIC FIBROSIS COMPLETE DIOS,IMGFLTBE,THERAPEUTIC BARIUM ENEMA,Imaging,32,101,0.316831683
+10521,3886,ED PSYCH HOLDING,101,ACETAMINOPHEN 325 MG PO TABS,Medication,19,62,0.306451613
+10522,3886,ED PSYCH HOLDING,84520,ONDANSETRON 8 MG PO TBDL,Medication,8,62,0.129032258
+10523,3886,ED PSYCH HOLDING,94176,ONDANSETRON 4 MG PO TBDL,Medication,11,62,0.177419355
+10524,3886,ED PSYCH HOLDING,95933,OLANZAPINE 5 MG PO TBDL,Medication,14,62,0.225806452
+10525,3887,AMB BARIATRIC LABS,LAB12586R,"VITAMIN B1(THIAMINE),WHOLE BLOOD",Lab,1,1,1
+10526,3887,AMB BARIATRIC LABS,LABB12,VITAMIN B12,Lab,1,1,1
+10527,3887,AMB BARIATRIC LABS,LABFE,"IRON, TOTAL",Lab,1,1,1
+10528,3887,AMB BARIATRIC LABS,LABFOL,FOLIC ACID,Lab,1,1,1
+10529,3887,AMB BARIATRIC LABS,LABTSH,TSH,Lab,1,1,1
+10530,3889,ED PEDS STROKE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,5,10,0.5
+10531,3889,ED PEDS STROKE,209036,ACETAMINOPHEN 325 MG/10.15 ML PO SOLN,Medication,1,10,0.1
+10532,3889,ED PEDS STROKE,541038,ALTEPLASE PEDIATRIC STROKE BOLUS,Medication,1,10,0.1
+10533,3889,ED PEDS STROKE,541039,ALTEPLASE PEDIATRIC STROKE INFUSION,Medication,1,10,0.1
+10534,3889,ED PEDS STROKE,LABCBCD,CBC WITH DIFF,Lab,5,10,0.5
+10535,3889,ED PEDS STROKE,LABDDIM,D - DIMER,Lab,3,10,0.3
+10536,3889,ED PEDS STROKE,LABLPDC,LIPID PANEL WITH CALCULATED LDL,Lab,6,10,0.6
+10537,3889,ED PEDS STROKE,LABMETB,"METABOLIC PANEL, BASIC",Lab,5,10,0.5
+10538,3889,ED PEDS STROKE,LABPT,PROTHROMBIN TIME,Lab,7,10,0.7
+10539,3889,ED PEDS STROKE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,10,0.6
+10540,3889,ED PEDS STROKE,LABTSH,TSH,Lab,6,10,0.6
+10541,3889,ED PEDS STROKE,LABTYPSNI,TYPE AND SCREEN,Lab,1,10,0.1
+10542,3889,ED PEDS STROKE,POC14,POC GLUCOSE BY METER,Lab,5,10,0.5
+10543,3889,ED PEDS STROKE,POC17,POC ISTAT CREATININE,Lab,3,10,0.3
+10544,3889,ED PEDS STROKE,IMGCT3HN,CT ANGIO HEAD AND NECK,Imaging,1,10,0.1
+10545,3889,ED PEDS STROKE,IMGCTH,CT HEAD,Imaging,3,10,0.3
+10546,3889,ED PEDS STROKE,IMGMRAH,MR ANGIO HEAD,Imaging,2,10,0.2
+10547,3889,ED PEDS STROKE,IMGMRAK,MR ANGIO NECK,Imaging,2,10,0.2
+10548,3889,ED PEDS STROKE,IMGMRH,MR BRAIN,Imaging,2,10,0.2
+10549,3890,IP NEU SHINE CONTROL,541318,INV  INSULIN 100 UNITS OR PLACEBO IN NORMAL SALINE 100 ML INFUSION CONTROL,Medication,4,8,0.5
+10550,3890,IP NEU SHINE CONTROL,541320,INV DEXTROSE 50% WATER FOR INJECTION CONTROL,Medication,4,8,0.5
+10551,3890,IP NEU SHINE CONTROL,541325,INV - INSULIN REGULAR CONTROL,Medication,4,8,0.5
+10552,3890,IP NEU SHINE CONTROL,POC14,POC GLUCOSE BY METER,Lab,12,8,1
+10553,3897,IP NEU SHINE INTERVENTION,541317,INV - INSULIN/PLACEBO INFUSION INTERVENTION,Medication,8,21,0.380952381
+10554,3897,IP NEU SHINE INTERVENTION,541324,INV - D50W INTERVENTION INTERVENTION,Medication,7,21,0.333333333
+10555,3897,IP NEU SHINE INTERVENTION,541338, INV - INSULIN LISPRO SC (ORAL DIETS/BOLUS FEEDS) INTERVENTION,Medication,6,21,0.285714286
+10556,3897,IP NEU SHINE INTERVENTION,541339,INV - NORMAL SALINE SC (NPO/CONTINUOUS FEEDS) INTERVENTION,Medication,7,21,0.333333333
+10557,3897,IP NEU SHINE INTERVENTION,LABA1C,HEMOGLOBIN A1C,Lab,1,21,0.047619048
+10558,3897,IP NEU SHINE INTERVENTION,POC14,POC GLUCOSE BY METER,Lab,27,21,1
+10559,3938,IP PHARM PENICILLIN SKIN TEST,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,26,86,0.302325581
+10560,3938,IP PHARM PENICILLIN SKIN TEST,681,ASPIRIN 325 MG PO TABS,Medication,16,86,0.186046512
+10561,3938,IP PHARM PENICILLIN SKIN TEST,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,36,86,0.418604651
+10562,3938,IP PHARM PENICILLIN SKIN TEST,3774,HYDROXYZINE HCL 25 MG PO TABS,Medication,22,86,0.255813953
+10563,3938,IP PHARM PENICILLIN SKIN TEST,4572,LORAZEPAM 0.5 MG PO TABS,Medication,12,86,0.139534884
+10564,3938,IP PHARM PENICILLIN SKIN TEST,4573,LORAZEPAM 1 MG PO TABS,Medication,1,86,0.011627907
+10565,3938,IP PHARM PENICILLIN SKIN TEST,6085,PENICILLIN G POTASSIUM 20 MILLION UNIT INJ SOLR,Medication,1,86,0.011627907
+10566,3938,IP PHARM PENICILLIN SKIN TEST,6092,PENICILLIN V POTASSIUM 250 MG PO TABS,Medication,3,86,0.034883721
+10567,3938,IP PHARM PENICILLIN SKIN TEST,6496,PREDNISONE 20 MG PO TABS,Medication,18,86,0.209302326
+10568,3938,IP PHARM PENICILLIN SKIN TEST,7319,SODIUM CHLORIDE 0.9 % 0.9 % INJ SOLN,Medication,34,86,0.395348837
+10569,3938,IP PHARM PENICILLIN SKIN TEST,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,17,86,0.197674419
+10570,3938,IP PHARM PENICILLIN SKIN TEST,30450,FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK,Medication,1,86,0.011627907
+10571,3938,IP PHARM PENICILLIN SKIN TEST,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,19,86,0.220930233
+10572,3938,IP PHARM PENICILLIN SKIN TEST,90488,BENZYLPENICILLOYL POLYLYSINE 0.25 ML ID SOLN,Medication,36,86,0.418604651
+10573,3938,IP PHARM PENICILLIN SKIN TEST,91619,MONTELUKAST 10 MG PO TABS,Medication,19,86,0.220930233
+10574,3938,IP PHARM PENICILLIN SKIN TEST,122689,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,Medication,6,86,0.069767442
+10575,3938,IP PHARM PENICILLIN SKIN TEST,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,27,86,0.313953488
+10576,3938,IP PHARM PENICILLIN SKIN TEST,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,1,86,0.011627907
+10577,3938,IP PHARM PENICILLIN SKIN TEST,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,25,86,0.290697674
+10578,3938,IP PHARM PENICILLIN SKIN TEST,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,36,86,0.418604651
+10579,3938,IP PHARM PENICILLIN SKIN TEST,541032,"PENICILLIN G 10,000 UNITS/ML (ID)",Medication,36,86,0.418604651
+10580,3938,IP PHARM PENICILLIN SKIN TEST,541033,AMPICILLIN 10 MG/ML (ID2) INJECTION,Medication,15,86,0.174418605
+10581,3938,IP PHARM PENICILLIN SKIN TEST,541034,AMPICILLIN 1 MG/ML (ID1) INJECTION,Medication,15,86,0.174418605
+10582,3938,IP PHARM PENICILLIN SKIN TEST,541035,NAFCILLIN 25 MG/ML (ID) INJECTION,Medication,4,86,0.046511628
+10583,3938,IP PHARM PENICILLIN SKIN TEST,541036,NAFCILLIN 0.0025 MG/ML (ID1) INJECTION,Medication,4,86,0.046511628
+10584,3938,IP PHARM PENICILLIN SKIN TEST,541037,NAFCILLIN 0.025 MG/ML (ID) INJECTION,Medication,4,86,0.046511628
+10585,3938,IP PHARM PENICILLIN SKIN TEST,541040,CEFAZOLIN 330 MG/ML (ID) INJECTION ,Medication,9,86,0.104651163
+10586,3938,IP PHARM PENICILLIN SKIN TEST,541041,CEFAZOLIN 3 MG/ML (ID) INJECTION,Medication,9,86,0.104651163
+10587,3938,IP PHARM PENICILLIN SKIN TEST,541042,CEFAZOLIN 33 MG/ML (ID) INJECTION,Medication,9,86,0.104651163
+10588,3938,IP PHARM PENICILLIN SKIN TEST,541047,HISTAMINE PHOSPHATE (2.75 MG/ML) ID SOLN,Medication,15,86,0.174418605
+10589,3950,IP CAR TANDEMHEART,540343,HEPARIN 45000 UNITS IN NS 500 ML,Medication,11,16,0.6875
+10590,3950,IP CAR TANDEMHEART,LABPT,PROTHROMBIN TIME,Lab,4,16,0.25
+10591,3950,IP CAR TANDEMHEART,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,6,16,0.375
+10592,3950,IP CAR TANDEMHEART,POC45,ACTIVATED CLOTTING TIME (ACT),Lab,5,16,0.3125
+10593,3950,IP CAR TANDEMHEART,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,1,16,0.0625
+10594,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,540616,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (RT PANEL),Medication,14,157,0.089171975
+10595,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",540616,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (RT PANEL),Medication,14,157,0.089171975
+10596,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,10000R,HLA ORDER - TYPING - SOLID ORGAN EVALUATION (RECIPIENT),Lab,69,157,0.439490446
+10597,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",10000R,HLA ORDER - TYPING - SOLID ORGAN EVALUATION (RECIPIENT),Lab,69,157,0.439490446
+10598,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,10150R,HLA ORDER - SUMMARIES,Lab,67,157,0.426751592
+10599,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",10150R,HLA ORDER - SUMMARIES,Lab,67,157,0.426751592
+10600,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LAB11125R,COCCI ID,Lab,67,157,0.426751592
+10601,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LAB11125R,COCCI ID,Lab,67,157,0.426751592
+10602,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LAB11136R,HTLV AB,Lab,65,157,0.414012739
+10603,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LAB11136R,HTLV AB,Lab,65,157,0.414012739
+10604,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LAB310,"HISTOPLASMA ANTIBODY, SERUM",Lab,11,157,0.070063694
+10605,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LAB310,"HISTOPLASMA ANTIBODY, SERUM",Lab,11,157,0.070063694
+10606,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABA1C,HEMOGLOBIN A1C,Lab,54,157,0.343949045
+10607,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABA1C,HEMOGLOBIN A1C,Lab,54,157,0.343949045
+10608,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABABG,"BLOOD GASES, ARTERIAL",Lab,14,157,0.089171975
+10609,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABABG,"BLOOD GASES, ARTERIAL",Lab,14,157,0.089171975
+10610,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABAFBC,"AFB CULTURE, RESPIRATORY",Lab,2,157,0.012738854
+10611,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABAFBC,"AFB CULTURE, RESPIRATORY",Lab,2,157,0.012738854
+10612,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABAFBCF,"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS",Lab,2,157,0.012738854
+10613,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABAFBCF,"AFB CULTURE, RESPIRATORY FOR CYSTIC FIBROSIS",Lab,2,157,0.012738854
+10614,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABANAS,ANTI - NUCLEAR AB,Lab,52,157,0.331210191
+10615,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABANAS,ANTI - NUCLEAR AB,Lab,52,157,0.331210191
+10616,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABCBCD,CBC WITH DIFF,Lab,21,157,0.133757962
+10617,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABCBCD,CBC WITH DIFF,Lab,21,157,0.133757962
+10618,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABCMVIGG,CMV IGG,Lab,71,157,0.452229299
+10619,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABCMVIGG,CMV IGG,Lab,71,157,0.452229299
+10620,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABCMVIGM,CMV IGM,Lab,71,157,0.452229299
+10621,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABCMVIGM,CMV IGM,Lab,71,157,0.452229299
+10622,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABCRCL,CREATININE CLEARANCE,Lab,45,157,0.286624204
+10623,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABCRCL,CREATININE CLEARANCE,Lab,45,157,0.286624204
+10624,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,46,157,0.292993631
+10625,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,46,157,0.292993631
+10626,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL,Lab,62,157,0.394904459
+10627,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL,Lab,62,157,0.394904459
+10628,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABEBVQP,EBV DNA PCR QUANT,Lab,68,157,0.433121019
+10629,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABEBVQP,EBV DNA PCR QUANT,Lab,68,157,0.433121019
+10630,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABFCUL,FUNGAL CULTURE,Lab,4,157,0.025477707
+10631,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABFCUL,FUNGAL CULTURE,Lab,4,157,0.025477707
+10632,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABHAVAG,HAV ANTIBODY IGG,Lab,60,157,0.382165605
+10633,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABHAVAG,HAV ANTIBODY IGG,Lab,60,157,0.382165605
+10634,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABHAVT,ANTI-HAV,Lab,3,157,0.01910828
+10635,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABHAVT,ANTI-HAV,Lab,3,157,0.01910828
+10636,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABHBCT,ANTI-HBC,Lab,64,157,0.407643312
+10637,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABHBCT,ANTI-HBC,Lab,64,157,0.407643312
+10638,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABHBSAG,HBSAG,Lab,65,157,0.414012739
+10639,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABHBSAG,HBSAG,Lab,65,157,0.414012739
+10640,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABHCVA,HEPATITIS C AB IGG,Lab,63,157,0.401273885
+10641,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABHCVA,HEPATITIS C AB IGG,Lab,63,157,0.401273885
+10642,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABHSVIGG,HERPES SIMPLEX VIRUS IGG PANEL,Lab,66,157,0.420382166
+10643,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABHSVIGG,HERPES SIMPLEX VIRUS IGG PANEL,Lab,66,157,0.420382166
+10644,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABLPDP,LIPID PANEL PLUS RISK,Lab,51,157,0.324840764
+10645,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABLPDP,LIPID PANEL PLUS RISK,Lab,51,157,0.324840764
+10646,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,22,157,0.140127389
+10647,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,22,157,0.140127389
+10648,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABNTBNP,NT - PROBNP,Lab,47,157,0.299363057
+10649,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABNTBNP,NT - PROBNP,Lab,47,157,0.299363057
+10650,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABPSA,PSA,Lab,30,157,0.191082803
+10651,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABPSA,PSA,Lab,30,157,0.191082803
+10652,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABPT,PROTHROMBIN TIME,Lab,29,157,0.184713376
+10653,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABPT,PROTHROMBIN TIME,Lab,29,157,0.184713376
+10654,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,32,157,0.203821656
+10655,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,32,157,0.203821656
+10656,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,65,157,0.414012739
+10657,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,65,157,0.414012739
+10658,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABRAAS,RHEUMATOID ARTHRITIS AB SCREEN,Lab,24,157,0.152866242
+10659,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABRAAS,RHEUMATOID ARTHRITIS AB SCREEN,Lab,24,157,0.152866242
+10660,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABRESP,RESPIRATORY CULTURE,Lab,2,157,0.012738854
+10661,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABRESP,RESPIRATORY CULTURE,Lab,2,157,0.012738854
+10662,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,2,157,0.012738854
+10663,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,2,157,0.012738854
+10664,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABRPRQL,RPR QUALITATIVE,Lab,15,157,0.095541401
+10665,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABRPRQL,RPR QUALITATIVE,Lab,15,157,0.095541401
+10666,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABRPRQLT,RPR W/REFLEX TO TITER,Lab,51,157,0.324840764
+10667,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABRPRQLT,RPR W/REFLEX TO TITER,Lab,51,157,0.324840764
+10668,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABSTOBGC,"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.",Lab,57,157,0.363057325
+10669,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABSTOBGC,"OCCULT BLD, GUAIAC, STOOL, COLORECTAL SC.",Lab,57,157,0.363057325
+10670,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABTNI,TROPONIN I,Lab,44,157,0.280254777
+10671,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABTNI,TROPONIN I,Lab,44,157,0.280254777
+10672,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABTOXIG,TOXOPLASMA IGG,Lab,73,157,0.464968153
+10673,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABTOXIG,TOXOPLASMA IGG,Lab,73,157,0.464968153
+10674,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABTYPSNI,TYPE AND SCREEN,Lab,124,157,0.789808917
+10675,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABTYPSNI,TYPE AND SCREEN,Lab,124,157,0.789808917
+10676,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,21,157,0.133757962
+10677,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABUA,URINALYSIS WITH MICROSCOPIC,Lab,21,157,0.133757962
+10678,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,22,157,0.140127389
+10679,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,22,157,0.140127389
+10680,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABUCRT,"CREATININE, URINE (TIMED)",Lab,34,157,0.21656051
+10681,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABUCRT,"CREATININE, URINE (TIMED)",Lab,34,157,0.21656051
+10682,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABUTP,"PROTEIN, URINE (TIMED)",Lab,77,157,0.49044586
+10683,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABUTP,"PROTEIN, URINE (TIMED)",Lab,77,157,0.49044586
+10684,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABVOLTL,VOLATILE SCREEN,Lab,44,157,0.280254777
+10685,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABVOLTL,VOLATILE SCREEN,Lab,44,157,0.280254777
+10686,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABVZVIGG,VZ IGG,Lab,67,157,0.426751592
+10687,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABVZVIGG,VZ IGG,Lab,67,157,0.426751592
+10688,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABYFRUCM,FRUCTOSAMINE,Lab,45,157,0.286624204
+10689,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABYFRUCM,FRUCTOSAMINE,Lab,45,157,0.286624204
+10690,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,LABYHIST,HISTOPLASMA AB SCREEN,Lab,58,157,0.369426752
+10691,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",LABYHIST,HISTOPLASMA AB SCREEN,Lab,58,157,0.369426752
+10692,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,26,157,0.165605096
+10693,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,26,157,0.165605096
+10694,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,ECH34,ECHO - 3-DIMENSIONAL ECHO,Imaging,13,157,0.082802548
+10695,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",ECH34,ECHO - 3-DIMENSIONAL ECHO,Imaging,13,157,0.082802548
+10696,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGCTA,CT ABDOMEN,Imaging,12,157,0.076433121
+10697,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGCTA,CT ABDOMEN,Imaging,12,157,0.076433121
+10698,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGCTFAC,CT FACIAL BONES OR SINUS,Imaging,10,157,0.063694268
+10699,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGCTFAC,CT FACIAL BONES OR SINUS,Imaging,10,157,0.063694268
+10700,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGCTP,CT PELVIS,Imaging,4,157,0.025477707
+10701,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGCTP,CT PELVIS,Imaging,4,157,0.025477707
+10702,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGCTT,CT THORAX,Imaging,7,157,0.044585987
+10703,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGCTT,CT THORAX,Imaging,7,157,0.044585987
+10704,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGCTTA,CT CHEST ABDOMEN,Imaging,18,157,0.114649682
+10705,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGCTTA,CT CHEST ABDOMEN,Imaging,18,157,0.114649682
+10706,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGCTTAP,CT CHEST ABDOMEN PELVIS,Imaging,13,157,0.082802548
+10707,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGCTTAP,CT CHEST ABDOMEN PELVIS,Imaging,13,157,0.082802548
+10708,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGDXCH2,XR CHEST 2V,Imaging,19,157,0.121019108
+10709,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGDXCH2,XR CHEST 2V,Imaging,19,157,0.121019108
+10710,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGFLESO,XR ESOPHAGRAM,Imaging,27,157,0.171974522
+10711,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGFLESO,XR ESOPHAGRAM,Imaging,27,157,0.171974522
+10712,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGMASDC,MAMMO SCREENING,Imaging,6,157,0.038216561
+10713,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGMASDC,MAMMO SCREENING,Imaging,6,157,0.038216561
+10714,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGNBONEDENS,BONE DENSITY ADULT,Imaging,11,157,0.070063694
+10715,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGNBONEDENS,BONE DENSITY ADULT,Imaging,11,157,0.070063694
+10716,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,IMGNPVQ,NUC LUNG V/Q,Imaging,27,157,0.171974522
+10717,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",IMGNPVQ,NUC LUNG V/Q,Imaging,27,157,0.171974522
+10718,3955,IP TRS LUNG/HEART -LUNG PRE EVALUATION LABS AND STUDIES,VSC02,VSC CAROTID VERTEBRAL ARTERY ULTRASOUND,Imaging,39,157,0.248407643
+10719,3955,"IP TRS LUNG, HEART-LUNG PRE EVALUATION LABS AND STUDIES",VSC02,VSC CAROTID VERTEBRAL ARTERY ULTRASOUND,Imaging,39,157,0.248407643
+10720,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10000R,HLA ORDER - TYPING - SOLID ORGAN EVALUATION (RECIPIENT),Lab,40,238,0.168067227
+10721,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10015R,HLA ORDER - DSA SCREENING,Lab,64,238,0.268907563
+10722,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,81,238,0.340336134
+10723,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10075R,HLA ORDER - STR/CHIMERISM (SOLID ORGAN),Lab,4,238,0.016806723
+10724,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10085R,HLA ORDER -  TYPING ADD ON LOCUS/AUTO CROSSMATCH,Lab,9,238,0.037815126
+10725,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10110R,HLA ORDER - FLOW AUTO CROSSMATCH,Lab,20,238,0.084033613
+10726,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10150R,HLA ORDER - SUMMARIES,Lab,39,238,0.163865546
+10727,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,27,238,0.113445378
+10728,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10195R,HLA ORDER - WAIT LIST STATUS CHANGE,Lab,4,238,0.016806723
+10729,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10210R,HLA ORDER - DESENSITIZATION AB MONITORING-POST IVIG,Lab,1,238,0.004201681
+10730,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,3,238,0.012605042
+10731,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10225R,HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*,Lab,9,238,0.037815126
+10732,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10230R,HLA ORDER - DESENSITIZATION/DSA MONITORING-NO IVIG,Lab,6,238,0.025210084
+10733,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10235R,HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING,Lab,24,238,0.100840336
+10734,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10260R,HLA ORDER - DECEASED DONOR TESTING NOTICE,Lab,24,238,0.100840336
+10735,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10270R,HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT),Lab,14,238,0.058823529
+10736,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10275R,HLA ORDER - SPECIMEN IN LAB ORDER NOTICE,Lab,10,238,0.042016807
+10737,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10280R,HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING_CROSSMATCH,Lab,5,238,0.021008403
+10738,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10287R,HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT,Lab,4,238,0.016806723
+10739,3958,IP KID HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10297R,HLA ORDER - DECEASED DONOR O.R. SPECIMEN,Lab,25,238,0.105042017
+10740,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,386,0.002590674
+10741,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,3758,HYDROMORPHONE 2 MG/ML INJ SOLN,Medication,1,386,0.002590674
+10742,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,5,386,0.012953368
+10743,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,52,386,0.134715026
+10744,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,89065,LIDOCAINE 5 % TP PTMD,Medication,1,386,0.002590674
+10745,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,12,386,0.031088083
+10746,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,9,386,0.023316062
+10747,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,7,386,0.018134715
+10748,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,229762,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,Medication,74,386,0.191709845
+10749,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,24,386,0.062176166
+10750,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,530025,DEXMEDETOMIDINE IV INFUSION,Medication,44,386,0.113989637
+10751,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,111,386,0.287564767
+10752,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,540125,HYDROMORPHONE 1 MG/ML IV PCA,Medication,2,386,0.005181347
+10753,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,4,386,0.010362694
+10754,3960,IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS,541607,HYDROMORPHONE 1 MG/ML IV PCA,Medication,1,386,0.002590674
+10755,3962,IP PHARMA RESCUE MEDS,250,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,Medication,1,1,1
+10756,3962,IP PHARMA RESCUE MEDS,681,ASPIRIN 325 MG PO TABS,Medication,1,1,1
+10757,3962,IP PHARMA RESCUE MEDS,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,1,1
+10758,3962,IP PHARMA RESCUE MEDS,3774,HYDROXYZINE HCL 25 MG PO TABS,Medication,1,1,1
+10759,3962,IP PHARMA RESCUE MEDS,4572,LORAZEPAM 0.5 MG PO TABS,Medication,1,1,1
+10760,3962,IP PHARMA RESCUE MEDS,6496,PREDNISONE 20 MG PO TABS,Medication,1,1,1
+10761,3962,IP PHARMA RESCUE MEDS,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,1,1,1
+10762,3962,IP PHARMA RESCUE MEDS,40196,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,Medication,1,1,1
+10763,3962,IP PHARMA RESCUE MEDS,91619,MONTELUKAST 10 MG PO TABS,Medication,1,1,1
+10764,3962,IP PHARMA RESCUE MEDS,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,1,1
+10765,3962,IP PHARMA RESCUE MEDS,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,1,1,1
+10766,3962,IP PHARMA RESCUE MEDS,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,1,1,1
+10767,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),101,ACETAMINOPHEN 325 MG PO TABS,Medication,53,1118,0.047406082
+10768,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,1118,0.000894454
+10769,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),105,ACETAMINOPHEN 650 MG PR SUPP,Medication,1,1118,0.000894454
+10770,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,5,1118,0.004472272
+10771,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),3843,IBUPROFEN 400 MG PO TABS,Medication,9,1118,0.008050089
+10772,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),3844,IBUPROFEN 600 MG PO TABS,Medication,2,1118,0.001788909
+10773,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),4318,LACTATED RINGERS IV SOLP,Medication,2,1118,0.001788909
+10774,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,10,1118,0.008944544
+10775,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,4,1118,0.003577818
+10776,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),5006,METOCLOPRAMIDE HCL 5 MG PO TABS,Medication,1,1118,0.000894454
+10777,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,11,1118,0.009838998
+10778,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),6583,PROCHLORPERAZINE MALEATE 5 MG PO TABS,Medication,4,1118,0.003577818
+10779,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,73,1118,0.06529517
+10780,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,3,1118,0.002683363
+10781,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),10814,OXYCODONE 5 MG PO TABS,Medication,154,1118,0.137745975
+10782,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,5,1118,0.004472272
+10783,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),22472,KETOROLAC 15 MG/ML INJ SOLN,Medication,17,1118,0.015205725
+10784,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,5,1118,0.004472272
+10785,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,3,1118,0.002683363
+10786,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,72,1118,0.064400716
+10787,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),28899,OXYCODONE 15 MG PO TABS,Medication,2,1118,0.001788909
+10788,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,17,1118,0.015205725
+10789,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,20,1118,0.017889088
+10790,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),94176,ONDANSETRON 4 MG PO TBDL,Medication,1,1118,0.000894454
+10791,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),96559,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,Medication,3,1118,0.002683363
+10792,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,29,1118,0.025939177
+10793,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),112564,MORPHINE 2 MG/ML INJ SYRG,Medication,1,1118,0.000894454
+10794,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,32,1118,0.02862254
+10795,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),114905,APREPITANT 80 MG PO CAPS,Medication,1,1118,0.000894454
+10796,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),114906,APREPITANT 125 MG PO CAPS,Medication,1,1118,0.000894454
+10797,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,1118,0.000894454
+10798,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,218,1118,0.194991055
+10799,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),212629,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",Medication,1,1118,0.000894454
+10800,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,1118,0.000894454
+10801,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),500027,D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY,Medication,1,1118,0.000894454
+10802,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),520064,CEFAZOLIN 2 G IVPB,Medication,1,1118,0.000894454
+10803,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),540102,NS IV BOLUS,Medication,5,1118,0.004472272
+10804,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,31,1118,0.027728086
+10805,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),540300,MORPHINE INJECTABLE SYRINGE,Medication,74,1118,0.066189624
+10806,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),540965,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS,Medication,1,1118,0.000894454
+10807,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),541048,HYDROMORPHONE VARIABLE DOSE INJECTION,Medication,7,1118,0.006261181
+10808,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,4,1118,0.003577818
+10809,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),LABCBCD,CBC WITH DIFF,Lab,15,1118,0.013416816
+10810,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),LABCR,"CREATININE, SERUM/PLASMA",Lab,4,1118,0.003577818
+10811,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),LABFIB,FIBRINOGEN,Lab,1,1118,0.000894454
+10812,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),LABPREG,"PREGNANCY TEST, SERUM",Lab,1,1118,0.000894454
+10813,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),LABPT,PROTHROMBIN TIME,Lab,2,1118,0.001788909
+10814,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,4,1118,0.003577818
+10815,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),POC14,POC GLUCOSE BY METER,Lab,7,1118,0.006261181
+10816,3970,IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION),IMGDXCHPNB,XR CHEST POST NEEDLE BIOPSY,Imaging,31,1118,0.027728086
+10817,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,172,0.029069767
+10818,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,3,172,0.01744186
+10819,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",6494,PREDNISONE 10 MG PO TABS,Medication,7,172,0.040697674
+10820,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",6497,PREDNISONE 5 MG PO TABS,Medication,7,172,0.040697674
+10821,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",7557,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,Medication,2,172,0.011627907
+10822,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,5,172,0.029069767
+10823,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",19483,"CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",Medication,2,172,0.011627907
+10824,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",77883,VALGANCICLOVIR 450 MG PO TABS,Medication,10,172,0.058139535
+10825,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",78748,INSULIN GLARGINE 100 UNIT/ML SC SOLN,Medication,1,172,0.005813953
+10826,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",80562,MYCOPHENOLATE MOFETIL 250 MG PO CAPS,Medication,4,172,0.023255814
+10827,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",82544,ITRACONAZOLE 10 MG/ML PO SOLN,Medication,8,172,0.046511628
+10828,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",83177,AMLODIPINE 2.5 MG PO TABS,Medication,1,172,0.005813953
+10829,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,9,172,0.052325581
+10830,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",89742,INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP,Medication,2,172,0.011627907
+10831,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,8,172,0.046511628
+10832,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",94670,TACROLIMUS 1 MG PO CAPS,Medication,14,172,0.081395349
+10833,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",95354,ATOVAQUONE 750 MG/5 ML PO SUSP,Medication,2,172,0.011627907
+10834,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",95803,TACROLIMUS 0.5 MG PO CAPS,Medication,4,172,0.023255814
+10835,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,8,172,0.046511628
+10836,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,7,172,0.040697674
+10837,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",208479,MAGNESIUM OXIDE-MG AA CHELATE 133 MG PO TABS,Medication,2,172,0.011627907
+10838,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,38,172,0.220930233
+10839,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",520078,METHYLPREDNISOLONE IVPB,Medication,2,172,0.011627907
+10840,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",540302,POTASSIUM CHLORIDE IV SCALE,Medication,6,172,0.034883721
+10841,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",540304,MAGNESIUM SULFATE IV SCALE ,Medication,8,172,0.046511628
+10842,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",540597,THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS,Medication,2,172,0.011627907
+10843,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",9000000,ZZZ IMS TEMPLATE,Medication,7,172,0.040697674
+10844,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",10015R,HLA ORDER - DSA SCREENING,Lab,8,172,0.046511628
+10845,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,1,172,0.005813953
+10846,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,9,172,0.052325581
+10847,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,5,172,0.029069767
+10848,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABCBCD,CBC WITH DIFF,Lab,59,172,0.343023256
+10849,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABCDTPCR,"C.DIFF TOXIN B GENE, QUAL. REAL-TIME PCR",Lab,4,172,0.023255814
+10850,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,29,172,0.168604651
+10851,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABFK506L,TACROLIMUS (FK506),Lab,48,172,0.279069767
+10852,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABMETB,"METABOLIC PANEL, BASIC",Lab,10,172,0.058139535
+10853,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,65,172,0.377906977
+10854,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,41,172,0.238372093
+10855,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,25,172,0.145348837
+10856,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABPLTS,PLATELET COUNT,Lab,27,172,0.156976744
+10857,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABPT,PROTHROMBIN TIME,Lab,2,172,0.011627907
+10858,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABRESPCR,RESPIRATORY VIRUS PCR PANEL,Lab,8,172,0.046511628
+10859,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,8,172,0.046511628
+10860,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",IMGDXCH1,XR CHEST 1V,Imaging,11,172,0.063953488
+10861,3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",IMGDXCH2,XR CHEST 2V,Imaging,10,172,0.058139535
+10862,3974,IP GEN SEPSIS COMPREHENSIVE,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,6,1072,0.005597015
+10863,3974,IP GEN SEPSIS COMPREHENSIVE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,7,1072,0.006529851
+10864,3974,IP GEN SEPSIS COMPREHENSIVE,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,4,1072,0.003731343
+10865,3974,IP GEN SEPSIS COMPREHENSIVE,112321,LINEZOLID 600 MG/300 ML IV SOLP,Medication,1,1072,0.000932836
+10866,3974,IP GEN SEPSIS COMPREHENSIVE,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,5,1072,0.004664179
+10867,3974,IP GEN SEPSIS COMPREHENSIVE,520039,AZITHROMYCIN IVPB,Medication,4,1072,0.003731343
+10868,3974,IP GEN SEPSIS COMPREHENSIVE,520045,MEROPENEM CUSTOM IVPB,Medication,1,1072,0.000932836
+10869,3974,IP GEN SEPSIS COMPREHENSIVE,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,58,1072,0.054104478
+10870,3974,IP GEN SEPSIS COMPREHENSIVE,520057,CASPOFUNGIN IVPB (CUSTOM),Medication,2,1072,0.001865672
+10871,3974,IP GEN SEPSIS COMPREHENSIVE,520058,CASPOFUNGIN 70 MG IVPB,Medication,2,1072,0.001865672
+10872,3974,IP GEN SEPSIS COMPREHENSIVE,540102,NS IV BOLUS,Medication,46,1072,0.042910448
+10873,3974,IP GEN SEPSIS COMPREHENSIVE,540115,LACTATED RINGERS IV BOLUS,Medication,6,1072,0.005597015
+10874,3974,IP GEN SEPSIS COMPREHENSIVE,540616,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN (RT PANEL),Medication,10,1072,0.009328358
+10875,3974,IP GEN SEPSIS COMPREHENSIVE,540674,LEVOFLOXACIN IN NS IV 750 MG/250 ML IV PGBK,Medication,1,1072,0.000932836
+10876,3974,IP GEN SEPSIS COMPREHENSIVE,540773,PIPERACILLIN-TAZOBACTAM (ZOSYN) 3.375 GRAM IN DEXTROSE IVPB,Medication,1,1072,0.000932836
+10877,3974,IP GEN SEPSIS COMPREHENSIVE,541020,VANCOMYCIN ED ORDER ,Medication,33,1072,0.030783582
+10878,3974,IP GEN SEPSIS COMPREHENSIVE,541061,VANCOMYCIN PER PHARMACY PROTOCOL,Medication,40,1072,0.037313433
+10879,3974,IP GEN SEPSIS COMPREHENSIVE,541081,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS EXTENDED INF,Medication,1,1072,0.000932836
+10880,3974,IP GEN SEPSIS COMPREHENSIVE,541082,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS EXTENDED INF,Medication,10,1072,0.009328358
+10881,3974,IP GEN SEPSIS COMPREHENSIVE,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,7,1072,0.006529851
+10882,3974,IP GEN SEPSIS COMPREHENSIVE,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,19,1072,0.017723881
+10883,3974,IP GEN SEPSIS COMPREHENSIVE,541086,CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS,Medication,2,1072,0.001865672
+10884,3974,IP GEN SEPSIS COMPREHENSIVE,541095,AZITHROMYCIN 500 MG/250 ML MINIBAG PLUS,Medication,2,1072,0.001865672
+10885,3974,IP GEN SEPSIS COMPREHENSIVE,541097,AZTREONAM 2 GRAM/100 ML MINI-BAG PLUS,Medication,3,1072,0.002798507
+10886,3974,IP GEN SEPSIS COMPREHENSIVE,541109,DOXYCYCLINE 100 MG/100 ML MINI-BAG PLUS,Medication,4,1072,0.003731343
+10887,3974,IP GEN SEPSIS COMPREHENSIVE,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,1072,0.000932836
+10888,3974,IP GEN SEPSIS COMPREHENSIVE,541136,MEROPENEM IN D5W 1 GRAM/100 ML MINIBAG PLUS,Medication,1,1072,0.000932836
+10889,3974,IP GEN SEPSIS COMPREHENSIVE,541271,PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION,Medication,3,1072,0.002798507
+10890,3974,IP GEN SEPSIS COMPREHENSIVE,541273,PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION,Medication,64,1072,0.059701493
+10891,3974,IP GEN SEPSIS COMPREHENSIVE,541440,NS IV BOLUS - 500 ML,Medication,3,1072,0.002798507
+10892,3974,IP GEN SEPSIS COMPREHENSIVE,541441,NS IV BOLUS - 1000 ML,Medication,33,1072,0.030783582
+10893,3974,IP GEN SEPSIS COMPREHENSIVE,541442,NS IV BOLUS - 2000 ML,Medication,11,1072,0.010261194
+10894,3974,IP GEN SEPSIS COMPREHENSIVE,541444,LR IV BOLUS - 500 ML,Medication,2,1072,0.001865672
+10895,3974,IP GEN SEPSIS COMPREHENSIVE,541445,LR IV BOLUS - 1000 ML,Medication,6,1072,0.005597015
+10896,3974,IP GEN SEPSIS COMPREHENSIVE,541447,NS IV BOLUS - 30 ML/KG,Medication,5,1072,0.004664179
+10897,3974,IP GEN SEPSIS COMPREHENSIVE,541544,CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,12,1072,0.01119403
+10898,3974,IP GEN SEPSIS COMPREHENSIVE,541546,MEROPENEM 2 G/100 ML EXTENDED INFUSION IVPB,Medication,1,1072,0.000932836
+10899,3974,IP GEN SEPSIS COMPREHENSIVE,541547,MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS,Medication,10,1072,0.009328358
+10900,3974,IP GEN SEPSIS COMPREHENSIVE,541804,NORMOSOL-R IV BOLUS - 1000 ML,Medication,1,1072,0.000932836
+10901,3974,IP GEN SEPSIS COMPREHENSIVE,LABABG,"BLOOD GASES, ARTERIAL",Lab,9,1072,0.008395522
+10902,3974,IP GEN SEPSIS COMPREHENSIVE,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,243,1072,0.226679104
+10903,3974,IP GEN SEPSIS COMPREHENSIVE,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,237,1072,0.22108209
+10904,3974,IP GEN SEPSIS COMPREHENSIVE,LABCBCD,CBC WITH DIFF,Lab,69,1072,0.064365672
+10905,3974,IP GEN SEPSIS COMPREHENSIVE,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,532,1072,0.496268657
+10906,3974,IP GEN SEPSIS COMPREHENSIVE,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,41,1072,0.038246269
+10907,3974,IP GEN SEPSIS COMPREHENSIVE,LABPROCT,PROCALCITONIN,Lab,95,1072,0.088619403
+10908,3974,IP GEN SEPSIS COMPREHENSIVE,LABPT,PROTHROMBIN TIME,Lab,18,1072,0.016791045
+10909,3974,IP GEN SEPSIS COMPREHENSIVE,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,11,1072,0.010261194
+10910,3974,IP GEN SEPSIS COMPREHENSIVE,LABRESP,RESPIRATORY CULTURE,Lab,4,1072,0.003731343
+10911,3974,IP GEN SEPSIS COMPREHENSIVE,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,49,1072,0.045708955
+10912,3974,IP GEN SEPSIS COMPREHENSIVE,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,147,1072,0.137126866
+10913,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10000R,HLA ORDER - TYPING - SOLID ORGAN EVALUATION (RECIPIENT),Lab,121,407,0.297297297
+10914,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10015R,HLA ORDER - DSA SCREENING,Lab,65,407,0.15970516
+10915,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,150,407,0.368550369
+10916,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10070R,HLA ORDER - IVIG IN VITRO INHIBITION ASSAY,Lab,13,407,0.031941032
+10917,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10085R,HLA ORDER -  TYPING ADD ON LOCUS/AUTO CROSSMATCH,Lab,7,407,0.017199017
+10918,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10150R,HLA ORDER - SUMMARIES,Lab,120,407,0.294840295
+10919,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,25,407,0.061425061
+10920,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10195R,HLA ORDER - WAIT LIST STATUS CHANGE,Lab,1,407,0.002457002
+10921,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10205R,HLA ORDER - DESENSITIZATION MONITORING PRE IVIG,Lab,26,407,0.063882064
+10922,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10210R,HLA ORDER - DESENSITIZATION AB MONITORING-POST IVIG,Lab,27,407,0.066339066
+10923,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10215R,HLA ORDER - DESENSITIZATION MONITORING,Lab,11,407,0.027027027
+10924,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,11,407,0.027027027
+10925,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10225R,HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*,Lab,14,407,0.034398034
+10926,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10230R,HLA ORDER - DESENSITIZATION/DSA MONITORING-NO IVIG,Lab,15,407,0.036855037
+10927,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10235R,HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING,Lab,27,407,0.066339066
+10928,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10260R,HLA ORDER - DECEASED DONOR TESTING NOTICE,Lab,27,407,0.066339066
+10929,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10270R,HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT),Lab,20,407,0.049140049
+10930,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10275R,HLA ORDER - SPECIMEN IN LAB ORDER NOTICE,Lab,20,407,0.049140049
+10931,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10277R,HLA ORDER - PROSPECTIVE CROSSMATCH TESTING,Lab,6,407,0.014742015
+10932,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10280R,HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING_CROSSMATCH,Lab,7,407,0.017199017
+10933,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10283R,HLA ORDER - TRANSPLANT EVENT COMMUNICATION,Lab,13,407,0.031941032
+10934,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10287R,HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT,Lab,10,407,0.024570025
+10935,3976,IP HEART HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10297R,HLA ORDER - DECEASED DONOR O.R. SPECIMEN,Lab,36,407,0.088452088
+10936,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10000R,HLA ORDER - TYPING - SOLID ORGAN EVALUATION (RECIPIENT),Lab,35,485,0.072164948
+10937,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10015R,HLA ORDER - DSA SCREENING,Lab,156,485,0.321649485
+10938,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,111,485,0.228865979
+10939,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10070R,HLA ORDER - IVIG IN VITRO INHIBITION ASSAY,Lab,5,485,0.010309278
+10940,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10085R,HLA ORDER -  TYPING ADD ON LOCUS/AUTO CROSSMATCH,Lab,19,485,0.039175258
+10941,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10150R,HLA ORDER - SUMMARIES,Lab,36,485,0.074226804
+10942,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,71,485,0.146391753
+10943,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10195R,HLA ORDER - WAIT LIST STATUS CHANGE,Lab,1,485,0.002061856
+10944,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10205R,HLA ORDER - DESENSITIZATION MONITORING PRE IVIG,Lab,10,485,0.020618557
+10945,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10210R,HLA ORDER - DESENSITIZATION AB MONITORING-POST IVIG,Lab,7,485,0.01443299
+10946,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10215R,HLA ORDER - DESENSITIZATION MONITORING,Lab,8,485,0.016494845
+10947,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,35,485,0.072164948
+10948,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10225R,HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*,Lab,45,485,0.092783505
+10949,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10230R,HLA ORDER - DESENSITIZATION/DSA MONITORING-NO IVIG,Lab,30,485,0.06185567
+10950,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10235R,HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING,Lab,108,485,0.222680412
+10951,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10260R,HLA ORDER - DECEASED DONOR TESTING NOTICE,Lab,97,485,0.2
+10952,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10270R,HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT),Lab,25,485,0.051546392
+10953,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10275R,HLA ORDER - SPECIMEN IN LAB ORDER NOTICE,Lab,25,485,0.051546392
+10954,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10277R,HLA ORDER - PROSPECTIVE CROSSMATCH TESTING,Lab,16,485,0.032989691
+10955,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10280R,HLA ORDER - ADDITIONAL SPECIMEN (RECIPIENT) - AB SCREENING_CROSSMATCH,Lab,49,485,0.101030928
+10956,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10283R,HLA ORDER - TRANSPLANT EVENT COMMUNICATION,Lab,43,485,0.088659794
+10957,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10287R,HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT,Lab,2,485,0.004123711
+10958,3977,IP LUNG HLA RECIPIENT HISTOCOMPATIBILITY TESTING,10297R,HLA ORDER - DECEASED DONOR O.R. SPECIMEN,Lab,173,485,0.356701031
+10959,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB11132R,DONOR HEP B SURF. AG,Lab,3,7,0.428571429
+10960,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB11133R,DONOR HBC AB,Lab,3,7,0.428571429
+10961,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB11134R,DONOR HCV AB,Lab,4,7,0.571428571
+10962,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB11136R,HTLV AB,Lab,3,7,0.428571429
+10963,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB12854R,"DONOR, WEST NILE VIRUS NAT",Lab,3,7,0.428571429
+10964,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB12856R,"DONOR, T CRUZI AB (CHAGAS)",Lab,3,7,0.428571429
+10965,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LAB12858R,"DONOR, NAT MPX, HIV, HCV, HBV",Lab,6,7,0.857142857
+10966,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABDCMVL,DONOR CMV- TOTAL AB,Lab,5,7,0.714285714
+10967,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABDHIVB,DONOR HIV AB,Lab,7,7,1
+10968,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABDRPRS,"DONOR, RPR",Lab,5,7,0.714285714
+10969,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL,Lab,4,7,0.571428571
+10970,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABHAVAG,HAV ANTIBODY IGG,Lab,1,7,0.142857143
+10971,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABHSVIGG,HERPES SIMPLEX VIRUS IGG PANEL,Lab,4,7,0.571428571
+10972,3998,INPT/AMB BMT DONOR/AUTOLOGOUS PT VIROLOGY,LABVZPAN,VZV IGG IGM PANEL,Lab,6,7,0.857142857
+10973,3999,IP COMMUNITY ACQUIRED PNEUMONIA ANTIBIOTICS,115288,LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK,Medication,1,2,0.5
+10974,3999,IP COMMUNITY ACQUIRED PNEUMONIA ANTIBIOTICS,520039,AZITHROMYCIN IVPB,Medication,1,2,0.5
+10975,3999,IP COMMUNITY ACQUIRED PNEUMONIA ANTIBIOTICS,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,1,2,0.5
+10976,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,5,0.4
+10977,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,5,0.2
+10978,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,5,0.2
+10979,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,LABCBCD,CBC WITH DIFF,Lab,1,5,0.2
+10980,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,5,0.2
+10981,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,LABPT,PROTHROMBIN TIME,Lab,1,5,0.2
+10982,4000,RETIRED IP NEU SUBARACHNOID HEMORRHAGE TRANSFER,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,5,0.2
+10983,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,101,ACETAMINOPHEN 325 MG PO TABS,Medication,12,91,0.131868132
+10984,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,1080,BISACODYL 10 MG PR SUPP,Medication,10,91,0.10989011
+10985,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,7,91,0.076923077
+10986,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,2,91,0.021978022
+10987,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,4,91,0.043956044
+10988,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,4,91,0.043956044
+10989,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,19,91,0.208791209
+10990,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,91,0.010989011
+10991,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,15,91,0.164835165
+10992,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,2,91,0.021978022
+10993,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,8,91,0.087912088
+10994,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,14,91,0.153846154
+10995,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,81934,NIMODIPINE 30 MG PO CAPS,Medication,18,91,0.197802198
+10996,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,7,91,0.076923077
+10997,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,4,91,0.043956044
+10998,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,13,91,0.142857143
+10999,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,7,91,0.076923077
+11000,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,12,91,0.131868132
+11001,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,3,91,0.032967033
+11002,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,230362,NIMODIPINE 60 MG/20 ML PO SOLN,Medication,8,91,0.087912088
+11003,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,530008,NITROPRUSSIDE IV INFUSION,Medication,15,91,0.164835165
+11004,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,530010,NICARDIPINE IV INFUSION,Medication,3,91,0.032967033
+11005,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,17,91,0.186813187
+11006,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,12,91,0.131868132
+11007,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,540302,POTASSIUM CHLORIDE IV SCALE,Medication,15,91,0.164835165
+11008,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,7,91,0.076923077
+11009,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABCBCD,CBC WITH DIFF,Lab,20,91,0.21978022
+11010,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABHCTX,HEMATOCRIT,Lab,1,91,0.010989011
+11011,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABMETB,"METABOLIC PANEL, BASIC",Lab,19,91,0.208791209
+11012,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABPT,PROTHROMBIN TIME,Lab,16,91,0.175824176
+11013,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,10,91,0.10989011
+11014,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABTYPSNI,TYPE AND SCREEN,Lab,14,91,0.153846154
+11015,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,6,91,0.065934066
+11016,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,91,0.010989011
+11017,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,IMGCTH,CT HEAD,Imaging,1,91,0.010989011
+11018,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,IMGDXCH1,XR CHEST 1V,Imaging,13,91,0.142857143
+11019,4001,RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ,LABBBPRBC,RED BLOOD CELLS,Blood Product,6,91,0.065934066
+11020,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,2567,DOCUSATE SODIUM 250 MG PO CAPS,Medication,1,9,0.111111111
+11021,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,1,9,0.111111111
+11022,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,9,0.111111111
+11023,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,9,0.111111111
+11024,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,81421,"ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",Medication,1,9,0.111111111
+11025,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,81934,NIMODIPINE 30 MG PO CAPS,Medication,2,9,0.222222222
+11026,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,9,0.111111111
+11027,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,9,0.111111111
+11028,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,9,0.111111111
+11029,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,211084,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,Medication,1,9,0.111111111
+11030,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,530008,NITROPRUSSIDE IV INFUSION,Medication,1,9,0.111111111
+11031,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,530010,NICARDIPINE IV INFUSION,Medication,1,9,0.111111111
+11032,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,540302,POTASSIUM CHLORIDE IV SCALE,Medication,1,9,0.111111111
+11033,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,540304,MAGNESIUM SULFATE IV SCALE ,Medication,1,9,0.111111111
+11034,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,2,9,0.222222222
+11035,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,LABCBCD,CBC WITH DIFF,Lab,1,9,0.111111111
+11036,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,LABMETB,"METABOLIC PANEL, BASIC",Lab,1,9,0.111111111
+11037,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,POC14,POC GLUCOSE BY METER,Lab,1,9,0.111111111
+11038,4002,IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ,IMGCTH,CT HEAD,Imaging,1,9,0.111111111
+11039,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,89,126,0.706349206
+11040,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,97,126,0.76984127
+11041,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,209508,METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR,Medication,33,126,0.261904762
+11042,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,209509,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,Medication,30,126,0.238095238
+11043,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,24,126,0.19047619
+11044,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,20,126,0.158730159
+11045,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,23,126,0.182539683
+11046,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,520000,RITUXIMAB IV STANDARD INFUSION,Medication,14,126,0.111111111
+11047,4004,IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION,520074,ANTITHYMOCYTE GLOBULIN RABBIT IVPB,Medication,66,126,0.523809524
+11048,4005,IP ID SPERM BANKING TESTS,LAB11136R,HTLV AB,Lab,17,20,0.85
+11049,4005,IP ID SPERM BANKING TESTS,LABHBSAG,HBSAG,Lab,12,20,0.6
+11050,4005,IP ID SPERM BANKING TESTS,LABHCVA,HEPATITIS C AB IGG,Lab,15,20,0.75
+11051,4005,IP ID SPERM BANKING TESTS,LABRPRQL,RPR QUALITATIVE,Lab,2,20,0.1
+11052,4005,IP ID SPERM BANKING TESTS,LABRPRQLT,RPR W/REFLEX TO TITER,Lab,17,20,0.85
+11053,4006,IP/ED TPA  ADVERSE ACTIONS,IMGMRH,MR BRAIN,Imaging,1,1,1
+11054,4007,IP ICU ECMO ORDERSET,8981,"ALBUMIN, HUMAN 25 % 25 % IV SOLP",Medication,1,15,0.066666667
+11055,4007,IP ICU ECMO ORDERSET,8982,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",Medication,1,15,0.066666667
+11056,4007,IP ICU ECMO ORDERSET,11077,POTASSIUM CHLORIDE 20 MEQ/50 ML IV PGBK,Medication,1,15,0.066666667
+11057,4007,IP ICU ECMO ORDERSET,11079,POTASSIUM CHLORIDE 40 MEQ/100 ML IV PGBK,Medication,1,15,0.066666667
+11058,4007,IP ICU ECMO ORDERSET,111668,CALCIUM CHLORIDE 100 MG/ML (10 %) IV SYRG,Medication,1,15,0.066666667
+11059,4007,IP ICU ECMO ORDERSET,540333,HEPARIN IV INFUSION (MD TO SPECIFY PARAMETERS),Medication,6,15,0.4
+11060,4012,ED POST-INTUBATION,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,10,88,0.113636364
+11061,4012,ED POST-INTUBATION,11150,PROPOFOL 10 MG/ML IV EMUL,Medication,21,88,0.238636364
+11062,4012,ED POST-INTUBATION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,9,88,0.102272727
+11063,4012,ED POST-INTUBATION,530003,FENTANYL  10 MCG/ML IV INFUSION,Medication,6,88,0.068181818
+11064,4012,ED POST-INTUBATION,530004,MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION,Medication,10,88,0.113636364
+11065,4012,ED POST-INTUBATION,530120,HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC),Medication,15,88,0.170454545
+11066,4012,ED POST-INTUBATION,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,6,88,0.068181818
+11067,4012,ED POST-INTUBATION,POC22,"POC ISTAT, ARTERIAL BLOOD GASES (G3+)",Lab,11,88,0.125
+11068,4012,ED POST-INTUBATION,POC23,"POC ISTAT, VENOUS BLOOD GASES (G3+)",Lab,10,88,0.113636364
+11069,4012,ED POST-INTUBATION,IMGDXCH1,XR CHEST 1V,Imaging,25,88,0.284090909
+11070,4023,IP NEU STROKE ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,98,1147,0.085440279
+11071,4023,IP NEU STROKE ADMISSION,693,ASPIRIN 300 MG PR SUPP,Medication,25,1147,0.02179599
+11072,4023,IP NEU STROKE ADMISSION,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,27,1147,0.023539669
+11073,4023,IP NEU STROKE ADMISSION,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,22,1147,0.019180471
+11074,4023,IP NEU STROKE ADMISSION,10372,LABETALOL 5 MG/ML IV SOLN,Medication,28,1147,0.024411508
+11075,4023,IP NEU STROKE ADMISSION,11349,SENNOSIDES 8.6 MG PO TABS,Medication,36,1147,0.031386225
+11076,4023,IP NEU STROKE ADMISSION,13654,ASPIRIN 325 MG PO TBEC,Medication,24,1147,0.02092415
+11077,4023,IP NEU STROKE ADMISSION,14113,ASPIRIN 81 MG PO TBEC,Medication,169,1147,0.147340889
+11078,4023,IP NEU STROKE ADMISSION,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,15,1147,0.013077594
+11079,4023,IP NEU STROKE ADMISSION,77545,ATORVASTATIN 40 MG PO TABS,Medication,34,1147,0.029642546
+11080,4023,IP NEU STROKE ADMISSION,78531,ATORVASTATIN 20 MG PO TABS,Medication,1,1147,0.00087184
+11081,4023,IP NEU STROKE ADMISSION,83398,CLOPIDOGREL 75 MG PO TABS,Medication,9,1147,0.007846556
+11082,4023,IP NEU STROKE ADMISSION,110888,ENOXAPARIN 30 MG/0.3 ML SC SYRG,Medication,6,1147,0.005231037
+11083,4023,IP NEU STROKE ADMISSION,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,260,1147,0.226678291
+11084,4023,IP NEU STROKE ADMISSION,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,97,1147,0.084568439
+11085,4023,IP NEU STROKE ADMISSION,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,8,1147,0.006974717
+11086,4023,IP NEU STROKE ADMISSION,222567,LEVETIRACETAM IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,1,1147,0.00087184
+11087,4023,IP NEU STROKE ADMISSION,LABA1C,HEMOGLOBIN A1C,Lab,336,1147,0.292938099
+11088,4023,IP NEU STROKE ADMISSION,LABCBCO,CBC W/O DIFF,Lab,293,1147,0.255448997
+11089,4023,IP NEU STROKE ADMISSION,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,5,1147,0.004359198
+11090,4023,IP NEU STROKE ADMISSION,LABLPD,LIPID PANEL WITH DIRECT LDL,Lab,331,1147,0.288578901
+11091,4023,IP NEU STROKE ADMISSION,LABMETB,"METABOLIC PANEL, BASIC",Lab,290,1147,0.252833479
+11092,4023,IP NEU STROKE ADMISSION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,39,1147,0.034001744
+11093,4023,IP NEU STROKE ADMISSION,LABPLTS,PLATELET COUNT,Lab,5,1147,0.004359198
+11094,4023,IP NEU STROKE ADMISSION,LABPT,PROTHROMBIN TIME,Lab,227,1147,0.197907585
+11095,4023,IP NEU STROKE ADMISSION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,220,1147,0.191804708
+11096,4023,IP NEU STROKE ADMISSION,LABTSH,TSH,Lab,283,1147,0.246730602
+11097,4023,IP NEU STROKE ADMISSION,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,70,1147,0.061028771
+11098,4023,IP NEU STROKE ADMISSION,IMGCT3HN,CT ANGIO HEAD AND NECK,Imaging,6,1147,0.005231037
+11099,4023,IP NEU STROKE ADMISSION,IMGCTH,CT HEAD,Imaging,8,1147,0.006974717
+11100,4023,IP NEU STROKE ADMISSION,IMGDXCH1,XR CHEST 1V,Imaging,2,1147,0.001743679
+11101,4023,IP NEU STROKE ADMISSION,IMGMRAH,MR ANGIO HEAD,Imaging,34,1147,0.029642546
+11102,4023,IP NEU STROKE ADMISSION,IMGMRAK,MR ANGIO NECK,Imaging,2,1147,0.001743679
+11103,4023,IP NEU STROKE ADMISSION,IMGMRH,MR BRAIN,Imaging,103,1147,0.089799477
+11104,4023,IP NEU STROKE ADMISSION,IMGUSCAR,US RADIOLOGY CAROTID ARTERY BILATERAL,Imaging,9,1147,0.007846556
+11105,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,9504,CEPHALEXIN 500 MG PO TABS,Medication,1,35,0.028571429
+11106,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,1,35,0.028571429
+11107,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,27297,CEFAZOLIN 1 GRAM IV SOLR,Medication,4,35,0.114285714
+11108,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,3,35,0.085714286
+11109,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,35,0.028571429
+11110,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,LABCBCO,CBC W/O DIFF,Lab,1,35,0.028571429
+11111,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,35,0.028571429
+11112,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,LABPREG,"PREGNANCY TEST, SERUM",Lab,1,35,0.028571429
+11113,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,LABPT,PROTHROMBIN TIME,Lab,1,35,0.028571429
+11114,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,35,0.028571429
+11115,4027,PHC OCULOPLASTICS PERIOPERATIVE ORDERS,POC14,POC GLUCOSE BY METER,Lab,12,35,0.342857143
+11116,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,15,0.333333333
+11117,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,736,ATROPINE 1 % OPHT DROP,Medication,1,15,0.066666667
+11118,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,4318,LACTATED RINGERS IV SOLP,Medication,1,15,0.066666667
+11119,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,6246,PHENYLEPHRINE HCL 2.5 % OPHT DROP,Medication,6,15,0.4
+11120,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,6487,PREDNISOLONE ACETATE 1 % OPHT DRPS,Medication,6,15,0.4
+11121,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,8250,TROPICAMIDE 1 % OPHT DROP,Medication,6,15,0.4
+11122,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,6,15,0.4
+11123,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,212876,TETRACAINE HCL (PF) 0.5 % OPHT DROP,Medication,5,15,0.333333333
+11124,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,221881,ATROPINE SULFATE (PF) 1 % OPHT DROP,Medication,4,15,0.266666667
+11125,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,540977,MOXIFLOXACIN 0.5 % OPHT DROP (SHC BEI),Medication,3,15,0.2
+11126,4028,PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE,POC14,POC GLUCOSE BY METER,Lab,3,15,0.2
+11127,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,5,18,0.277777778
+11128,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,4318,LACTATED RINGERS IV SOLP,Medication,3,18,0.166666667
+11129,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,6246,PHENYLEPHRINE HCL 2.5 % OPHT DROP,Medication,6,18,0.333333333
+11130,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,6487,PREDNISOLONE ACETATE 1 % OPHT DRPS,Medication,5,18,0.277777778
+11131,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,8250,TROPICAMIDE 1 % OPHT DROP,Medication,6,18,0.333333333
+11132,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,4,18,0.222222222
+11133,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,212876,TETRACAINE HCL (PF) 0.5 % OPHT DROP,Medication,6,18,0.333333333
+11134,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,221881,ATROPINE SULFATE (PF) 1 % OPHT DROP,Medication,5,18,0.277777778
+11135,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,540976,GATIFLOXACIN 0.5 % OPHT DROP (SHC BEI),Medication,1,18,0.055555556
+11136,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,540977,MOXIFLOXACIN 0.5 % OPHT DROP (SHC BEI),Medication,2,18,0.111111111
+11137,4030,PHC RETINA PERIOPERATIVE ORDERS LEFT EYE,POC14,POC GLUCOSE BY METER,Lab,6,18,0.333333333
+11138,4031,PHC GLAUCOMA PERIOPERATIVE ORDERS LEFT EYE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,3,3,1
+11139,4032,PHC GLAUCOMA PERIOPERATIVE ORDERS RIGHT EYE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,2,1
+11140,4032,PHC GLAUCOMA PERIOPERATIVE ORDERS RIGHT EYE,6280,PILOCARPINE HCL 2 % OPHT DROP,Medication,1,2,0.5
+11141,4032,PHC GLAUCOMA PERIOPERATIVE ORDERS RIGHT EYE,115155,MOXIFLOXACIN 0.5 % OPHT DROP,Medication,1,2,0.5
+11142,4032,PHC GLAUCOMA PERIOPERATIVE ORDERS RIGHT EYE,212876,TETRACAINE HCL (PF) 0.5 % OPHT DROP,Medication,1,2,0.5
+11143,4032,PHC GLAUCOMA PERIOPERATIVE ORDERS RIGHT EYE,POC14,POC GLUCOSE BY METER,Lab,1,2,0.5
+11144,4045,IP VAD PRE-OP/DAY OF SURGERY,101,ACETAMINOPHEN 325 MG PO TABS,Medication,113,598,0.188963211
+11145,4045,IP VAD PRE-OP/DAY OF SURGERY,102,ACETAMINOPHEN 500 MG PO TABS,Medication,3,598,0.005016722
+11146,4045,IP VAD PRE-OP/DAY OF SURGERY,1080,BISACODYL 10 MG PR SUPP,Medication,4,598,0.006688963
+11147,4045,IP VAD PRE-OP/DAY OF SURGERY,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,6,598,0.010033445
+11148,4045,IP VAD PRE-OP/DAY OF SURGERY,7753,TEMAZEPAM 15 MG PO CAPS,Medication,1,598,0.001672241
+11149,4045,IP VAD PRE-OP/DAY OF SURGERY,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,190,598,0.317725753
+11150,4045,IP VAD PRE-OP/DAY OF SURGERY,10050,FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK,Medication,184,598,0.307692308
+11151,4045,IP VAD PRE-OP/DAY OF SURGERY,10674,MUPIROCIN 2 % TP OINT,Medication,191,598,0.319397993
+11152,4045,IP VAD PRE-OP/DAY OF SURGERY,11349,SENNOSIDES 8.6 MG PO TABS,Medication,4,598,0.006688963
+11153,4045,IP VAD PRE-OP/DAY OF SURGERY,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,13,598,0.02173913
+11154,4045,IP VAD PRE-OP/DAY OF SURGERY,38285,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,Medication,123,598,0.205685619
+11155,4045,IP VAD PRE-OP/DAY OF SURGERY,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2,598,0.003344482
+11156,4045,IP VAD PRE-OP/DAY OF SURGERY,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,3,598,0.005016722
+11157,4045,IP VAD PRE-OP/DAY OF SURGERY,90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,1,598,0.001672241
+11158,4045,IP VAD PRE-OP/DAY OF SURGERY,92173,ZOLPIDEM 5 MG PO TABS,Medication,1,598,0.001672241
+11159,4045,IP VAD PRE-OP/DAY OF SURGERY,111940,"FOAM BANDAGE 4 X 4 "" TP BNDG",Medication,36,598,0.060200669
+11160,4045,IP VAD PRE-OP/DAY OF SURGERY,200273,PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR,Medication,3,598,0.005016722
+11161,4045,IP VAD PRE-OP/DAY OF SURGERY,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,188,598,0.314381271
+11162,4045,IP VAD PRE-OP/DAY OF SURGERY,520102,CEFEPIME 2 GM IVPB,Medication,13,598,0.02173913
+11163,4045,IP VAD PRE-OP/DAY OF SURGERY,541084,CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS,Medication,49,598,0.081939799
+11164,4045,IP VAD PRE-OP/DAY OF SURGERY,541106,CEFTAZIDIME 1 GRAM/50 ML MINI-BAG PLUS,Medication,112,598,0.18729097
+11165,4045,IP VAD PRE-OP/DAY OF SURGERY,LABA1C,HEMOGLOBIN A1C,Lab,115,598,0.192307692
+11166,4045,IP VAD PRE-OP/DAY OF SURGERY,LABBBPCRY,CRYOPRECIPITATE,Lab,33,598,0.055183946
+11167,4045,IP VAD PRE-OP/DAY OF SURGERY,LABCBCD,CBC WITH DIFF,Lab,102,598,0.170568562
+11168,4045,IP VAD PRE-OP/DAY OF SURGERY,LABHAP,HAPTOGLOBIN,Lab,1,598,0.001672241
+11169,4045,IP VAD PRE-OP/DAY OF SURGERY,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,110,598,0.183946488
+11170,4045,IP VAD PRE-OP/DAY OF SURGERY,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,114,598,0.190635452
+11171,4045,IP VAD PRE-OP/DAY OF SURGERY,LABNTBNP,NT - PROBNP,Lab,112,598,0.18729097
+11172,4045,IP VAD PRE-OP/DAY OF SURGERY,LABPT,PROTHROMBIN TIME,Lab,110,598,0.183946488
+11173,4045,IP VAD PRE-OP/DAY OF SURGERY,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,106,598,0.177257525
+11174,4045,IP VAD PRE-OP/DAY OF SURGERY,LABTYPSNI,TYPE AND SCREEN,Lab,192,598,0.321070234
+11175,4045,IP VAD PRE-OP/DAY OF SURGERY,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,156,598,0.260869565
+11176,4045,IP VAD PRE-OP/DAY OF SURGERY,IMGDXCH1,XR CHEST 1V,Imaging,8,598,0.013377926
+11177,4045,IP VAD PRE-OP/DAY OF SURGERY,IMGDXCH2,XR CHEST 2V,Imaging,97,598,0.162207358
+11178,4045,IP VAD PRE-OP/DAY OF SURGERY,LABBBPCRY,CRYOPRECIPITATE,Blood Product,135,598,0.225752508
+11179,4045,IP VAD PRE-OP/DAY OF SURGERY,LABBBPFFP,FRESH FROZEN PLASMA PRODUCT,Blood Product,150,598,0.25083612
+11180,4045,IP VAD PRE-OP/DAY OF SURGERY,LABBBPPLT,PLATELET APHERESIS PRODUCT,Blood Product,171,598,0.285953177
+11181,4045,IP VAD PRE-OP/DAY OF SURGERY,LABBBPRBC,RED BLOOD CELLS,Blood Product,199,598,0.33277592
+11182,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,21,0.047619048
+11183,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,21,0.095238095
+11184,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,1,21,0.047619048
+11185,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,9859,DIHYDROERGOTAMINE 1 MG/ML INJ SOLN,Medication,1,21,0.047619048
+11186,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,22473,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,Medication,2,21,0.095238095
+11187,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,21,0.047619048
+11188,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,21,0.047619048
+11189,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,21,0.047619048
+11190,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,540102,NS IV BOLUS,Medication,3,21,0.142857143
+11191,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,540148,NS FLUSH BAG,Medication,2,21,0.095238095
+11192,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,LABCBCD,CBC WITH DIFF,Lab,7,21,0.333333333
+11193,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,LABMETB,"METABOLIC PANEL, BASIC",Lab,2,21,0.095238095
+11194,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,5,21,0.238095238
+11195,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,LABPT,PROTHROMBIN TIME,Lab,6,21,0.285714286
+11196,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,4,21,0.19047619
+11197,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,1,21,0.047619048
+11198,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,POC14,POC GLUCOSE BY METER,Lab,2,21,0.095238095
+11199,4047,RETIRED ED PRIMARY HEADACHE ORDERSET,POC26,POC URINE PREGNANCY,Lab,1,21,0.047619048
+11200,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10000R,HLA ORDER - TYPING - SOLID ORGAN EVALUATION (RECIPIENT),Lab,1,46,0.02173913
+11201,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10015R,HLA ORDER - DSA SCREENING,Lab,21,46,0.456521739
+11202,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10045R,HLA ORDER - ANTIBODY SCREENING,Lab,2,46,0.043478261
+11203,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10085R,HLA ORDER -  TYPING ADD ON LOCUS/AUTO CROSSMATCH,Lab,9,46,0.195652174
+11204,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10110R,HLA ORDER - FLOW AUTO CROSSMATCH,Lab,2,46,0.043478261
+11205,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10150R,HLA ORDER - SUMMARIES,Lab,1,46,0.02173913
+11206,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10190R,HLA ORDER - COMMUNICATION/UPDATE,Lab,2,46,0.043478261
+11207,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10220R,HLA ORDER - DESENSITIZATION/DSA MONITORING-PRE IVIG,Lab,1,46,0.02173913
+11208,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10225R,HLA ORDER - DESENSITIZATION MONITORING POST IVIG (POS*,Lab,8,46,0.173913043
+11209,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10230R,HLA ORDER - DESENSITIZATION/DSA MONITORING-NO IVIG,Lab,6,46,0.130434783
+11210,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10235R,HLA ORDER - DECEASED DONOR CROSSMATCH AND TESTING,Lab,2,46,0.043478261
+11211,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10270R,HLA ORDER - SPECIMEN IN LAB ORDERS (RECIPIENT),Lab,4,46,0.086956522
+11212,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10275R,HLA ORDER - SPECIMEN IN LAB ORDER NOTICE,Lab,3,46,0.065217391
+11213,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10287R,HLA ORDER - REQUEST FOR SUMMARY INFO. - RECIPIENT,Lab,5,46,0.108695652
+11214,4078,IP LVR/INTESTINE RECIPIENT HISTOCOMPATIBILITY TESTING,10297R,HLA ORDER - DECEASED DONOR O.R. SPECIMEN,Lab,1,46,0.02173913
+11215,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,540102,NS IV BOLUS,Medication,1385,5327,0.259996246
+11216,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,541440,NS IV BOLUS - 500 ML,Medication,9,5327,0.001689506
+11217,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,541441,NS IV BOLUS - 1000 ML,Medication,871,5327,0.163506664
+11218,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,2518,5327,0.472686315
+11219,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,2528,5327,0.474563544
+11220,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABBLCSTK,"BLOOD CULT - FIRST SET, VIA PHLEBOTOMY",Lab,244,5327,0.045804393
+11221,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABBLCTIP,BLOOD CULT CENTRAL LINE CATHETER BY NURSE,Lab,244,5327,0.045804393
+11222,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABCBCD,CBC WITH DIFF,Lab,2529,5327,0.474751267
+11223,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2543,5327,0.477379388
+11224,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABPT,PROTHROMBIN TIME,Lab,2845,5327,0.53407171
+11225,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,2857,5327,0.536324385
+11226,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABRESEARCH,EXTRA LAB DRAWS (DO NOT SEND TO LAB),Lab,789,5327,0.148113385
+11227,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,2738,5327,0.513985358
+11228,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,POC16,"POC ISTAT,VENOUS BLOOD GASES AND LACTATE (CG4)",Lab,2682,5327,0.503472874
+11229,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,IMGDXCH1,XR CHEST 1V,Imaging,27,5327,0.005068519
+11230,4085,NURSING TRIAGE SEPSIS NURSING PROTOCOL,IMGDXCH2,XR CHEST 2V,Imaging,181,5327,0.033977849
+11231,4111,SHC RIS MRI CONTRAST SMART SET,127241,GADOBENATE DIMEGLUMINE 529 MG/ML (0.1MMOL/0.2ML) IV SOLN,Medication,1,1,1
+11232,4124,IP TRS PRE-TRANSPLANT ORDERS,LAB11179R,"NICOTINE & METABOLITES, U",Lab,56,134,0.417910448
+11233,4124,IP TRS PRE-TRANSPLANT ORDERS,LABAFP,"ALPHA FETOPROTEIN, SERUM",Lab,80,134,0.597014925
+11234,4124,IP TRS PRE-TRANSPLANT ORDERS,LABARI,ABO/RH (ARI),Lab,53,134,0.395522388
+11235,4124,IP TRS PRE-TRANSPLANT ORDERS,LABCBCD,CBC WITH DIFF,Lab,12,134,0.089552239
+11236,4124,IP TRS PRE-TRANSPLANT ORDERS,LABCMVIGG,CMV IGG,Lab,20,134,0.149253731
+11237,4124,IP TRS PRE-TRANSPLANT ORDERS,LABCMVIGM,CMV IGM,Lab,21,134,0.156716418
+11238,4124,IP TRS PRE-TRANSPLANT ORDERS,LABDBIL,"BILIRUBIN DIRECT, SERUM/PLASMA",Lab,44,134,0.328358209
+11239,4124,IP TRS PRE-TRANSPLANT ORDERS,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,66,134,0.492537313
+11240,4124,IP TRS PRE-TRANSPLANT ORDERS,LABEBVPAN,EPSTEIN-BARR VIRUS AB PANEL,Lab,20,134,0.149253731
+11241,4124,IP TRS PRE-TRANSPLANT ORDERS,LABFER,FERRITIN,Lab,67,134,0.5
+11242,4124,IP TRS PRE-TRANSPLANT ORDERS,LABGGT,GAMMA - GLUTAMYL TRANSFERASE,Lab,80,134,0.597014925
+11243,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHAVAG,HAV ANTIBODY IGG,Lab,83,134,0.619402985
+11244,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHBCT,ANTI-HBC,Lab,76,134,0.567164179
+11245,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHBSAG,HBSAG,Lab,77,134,0.574626866
+11246,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHBSQT,HEP B SURF AB QUANT,Lab,80,134,0.597014925
+11247,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHCHGA,HEMOCHROMATOSIS GENOTYPING ANALYSIS,Lab,14,134,0.104477612
+11248,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHCVA,HEPATITIS C AB IGG,Lab,80,134,0.597014925
+11249,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHEVG,HEPATITIS E AB IGG PANEL,Lab,67,134,0.5
+11250,4124,IP TRS PRE-TRANSPLANT ORDERS,LABHEVM,HEPATITIS E AB IGM PANEL,Lab,67,134,0.5
+11251,4124,IP TRS PRE-TRANSPLANT ORDERS,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,13,134,0.097014925
+11252,4124,IP TRS PRE-TRANSPLANT ORDERS,LABPSA,PSA,Lab,45,134,0.335820896
+11253,4124,IP TRS PRE-TRANSPLANT ORDERS,LABPT,PROTHROMBIN TIME,Lab,19,134,0.141791045
+11254,4124,IP TRS PRE-TRANSPLANT ORDERS,LABQFTBG,QUANTIFERON TEST FOR LATENT TB,Lab,91,134,0.679104478
+11255,4124,IP TRS PRE-TRANSPLANT ORDERS,LABSTSS,SYPHILIS TREPONEMAL SCREEN,Lab,71,134,0.529850746
+11256,4124,IP TRS PRE-TRANSPLANT ORDERS,LABTRFN,TRANSFERRIN,Lab,67,134,0.5
+11257,4124,IP TRS PRE-TRANSPLANT ORDERS,LABTRFS,TRANSFERRIN SATURATION,Lab,67,134,0.5
+11258,4124,IP TRS PRE-TRANSPLANT ORDERS,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,50,134,0.373134328
+11259,4124,IP TRS PRE-TRANSPLANT ORDERS,LABUCAT,"CALCIUM, URINE (TIMED)",Lab,70,134,0.52238806
+11260,4124,IP TRS PRE-TRANSPLANT ORDERS,LABUCRT,"CREATININE, URINE (TIMED)",Lab,71,134,0.529850746
+11261,4124,IP TRS PRE-TRANSPLANT ORDERS,LABUNAT,"SODIUM, URINE (TIMED)",Lab,75,134,0.559701493
+11262,4124,IP TRS PRE-TRANSPLANT ORDERS,LABVOLTL,VOLATILE SCREEN,Lab,71,134,0.529850746
+11263,4124,IP TRS PRE-TRANSPLANT ORDERS,LABYA1AS,ALPHA - 1 - ANTITRYPSIN,Lab,82,134,0.611940299
+11264,4124,IP TRS PRE-TRANSPLANT ORDERS,LABYCERU,CERULOPLASMIN,Lab,65,134,0.485074627
+11265,4124,IP TRS PRE-TRANSPLANT ORDERS,ECH43,ECHO - DOBUTAMINE STRESS ECHO +DOPPLER,Imaging,12,134,0.089552239
+11266,4124,IP TRS PRE-TRANSPLANT ORDERS,IMGCTLMP,CT TRIPHASIC LIVER MASS PROTOCOL,Imaging,2,134,0.014925373
+11267,4124,IP TRS PRE-TRANSPLANT ORDERS,IMGDXCH2,XR CHEST 2V,Imaging,3,134,0.02238806
+11268,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABA1C,HEMOGLOBIN A1C,Lab,3,6,0.5
+11269,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABCBCD,CBC WITH DIFF,Lab,1,6,0.166666667
+11270,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,6,0.166666667
+11271,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,3,6,0.5
+11272,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABPT,PROTHROMBIN TIME,Lab,1,6,0.166666667
+11273,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1,6,0.166666667
+11274,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABTYPSNI,TYPE AND SCREEN,Lab,2,6,0.333333333
+11275,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,2,6,0.333333333
+11276,4218,AMB SHC PRE-OP CARDIAC SMARTSET,IMGDXCH2,XR CHEST 2V,Imaging,2,6,0.333333333
+11277,4218,AMB SHC PRE-OP CARDIAC SMARTSET,LABBBPRBC,RED BLOOD CELLS,Blood Product,4,6,0.666666667
+11278,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,2,60,0.033333333
+11279,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),94623,ALTEPLASE 2 MG INJ SOLR,Medication,5,60,0.083333333
+11280,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),96471,CITRATE DEXTROSE SOLUTION MISC SOLN,Medication,15,60,0.25
+11281,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),126580,"HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",Medication,9,60,0.15
+11282,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),205894,PRISMASOL SOLUTION (BGK 2/0) (32 MEQ BICARB/L),Medication,32,60,0.533333333
+11283,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),205897,BICARB DIALY SOLN W-O K NO.11 CA (2.5 MEQ/L) -MG (1.5 MEQ/L) HEMODIAL SOLN,Medication,8,60,0.133333333
+11284,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),210695,"BICAR DIAL SOL W-O CA,K,DEX#13 MG (1.2 MEQ/L) HEMODIAL SOLN",Medication,61,60,1
+11285,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),210698,BICARB DIALYS SOLN #15 W-O CA K (4 MEQ/L) -MG (1.2 MEQ/L) HEMODIAL SOLN,Medication,5,60,0.083333333
+11286,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),211166,BICARB DIALY SOLN W-O CA NO.16 K (4 MEQ/L) -MG (1.5 MEQ/L) HEMODIAL SOLN,Medication,5,60,0.083333333
+11287,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),230501,DEXTROSE-SOD CITRATE-CITRIC AC 2.45-2.2 GRAM- 730 MG/100 ML MISC SOLN,Medication,6,60,0.1
+11288,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),500006,SODIUM PHOSPHATE IVPB,Medication,48,60,0.8
+11289,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),500034,CALCIUM CHLORIDE 8 G/1000 ML NS IV INFUSION,Medication,22,60,0.366666667
+11290,4223,OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT),LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,22,60,0.366666667
+11291,4233,RETIRED - ED/IP REN HEMODIALYSIS,10176,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",Medication,1,7,0.142857143
+11292,4233,RETIRED - ED/IP REN HEMODIALYSIS,126580,"HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",Medication,6,7,0.857142857
+11293,4235,ORT OSC SHORT STAY,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,3,0.333333333
+11294,4235,ORT OSC SHORT STAY,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,3,0.333333333
+11295,4235,ORT OSC SHORT STAY,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,1,3,0.333333333
+11296,4235,ORT OSC SHORT STAY,10814,OXYCODONE 5 MG PO TABS,Medication,1,3,0.333333333
+11297,4235,ORT OSC SHORT STAY,18309,GABAPENTIN 100 MG PO CAPS,Medication,1,3,0.333333333
+11298,4235,ORT OSC SHORT STAY,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,1,3,0.333333333
+11299,4235,ORT OSC SHORT STAY,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,3,0.333333333
+11300,4235,ORT OSC SHORT STAY,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,3,0.333333333
+11301,4235,ORT OSC SHORT STAY,110872,HYDROMORPHONE 2 MG/ML INJ SYRG,Medication,1,3,0.333333333
+11302,4235,ORT OSC SHORT STAY,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,3,0.333333333
+11303,4235,ORT OSC SHORT STAY,214400,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,Medication,1,3,0.333333333
+11304,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),1080,BISACODYL 10 MG PR SUPP,Medication,19,2122,0.008953817
+11305,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,17,2122,0.00801131
+11306,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),3584,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,Medication,38,2122,0.017907634
+11307,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),5087,MINERAL OIL PR ENEM,Medication,3,2122,0.001413761
+11308,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),6432,POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD,Medication,1,2122,0.000471254
+11309,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),8083,TRAZODONE 50 MG PO TABS,Medication,3,2122,0.001413761
+11310,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),8085,TRAZODONE 50 MG PO TABS,Medication,70,2122,0.032987747
+11311,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),11349,SENNOSIDES 8.6 MG PO TABS,Medication,19,2122,0.008953817
+11312,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),13632,BISACODYL 5 MG PO TBEC,Medication,4,2122,0.001885014
+11313,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),16830,MELATONIN 3 MG PO TABS,Medication,605,2122,0.285108388
+11314,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,18,2122,0.008482564
+11315,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),77034,QUETIAPINE 25 MG PO TABS,Medication,106,2122,0.049952875
+11316,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,3,2122,0.001413761
+11317,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),90206,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,Medication,2,2122,0.000942507
+11318,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),116506,MELATONIN 1 MG PO TABS,Medication,12,2122,0.005655042
+11319,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,5,2122,0.002356268
+11320,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),540301,POTASSIUM CHLORIDE ORAL SCALE,Medication,5,2122,0.002356268
+11321,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),540304,MAGNESIUM SULFATE IV SCALE ,Medication,3,2122,0.001413761
+11322,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),9000000,ZZZ IMS TEMPLATE,Medication,38,2122,0.017907634
+11323,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,4,2122,0.001885014
+11324,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,4,2122,0.001885014
+11325,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABCBCD,CBC WITH DIFF,Lab,18,2122,0.008482564
+11326,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,2,2122,0.000942507
+11327,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,11,2122,0.005183789
+11328,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,12,2122,0.005655042
+11329,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,10,2122,0.004712535
+11330,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,13,2122,0.006126296
+11331,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),IMGCTH,CT HEAD,Imaging,2,2122,0.000942507
+11332,4236,IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY),IMGDXCH1,XR CHEST 1V,Imaging,1,2122,0.000471254
+11333,4237,ED PEDS ACUTE ASTHMA EXACERBATION (18 MO - 18 YEARS),251,ALBUTEROL SULFATE 5 MG/ML INH NEBU,Medication,1,2,0.5
+11334,4237,ED PEDS ACUTE ASTHMA EXACERBATION (18 MO - 18 YEARS),12580,IPRATROPIUM BROMIDE 0.02 % INH SOLN,Medication,1,2,0.5
+11335,4237,ED PEDS ACUTE ASTHMA EXACERBATION (18 MO - 18 YEARS),541071,DEXAMETHASONE SODIUM PHOSPHATE 4MG/ML INJ CROUP ORDER,Medication,1,2,0.5
+11336,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,81839,LIDOCAINE 4 % TP CREA,Medication,1,2,0.5
+11337,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,125595,LIDOCAINE-TETRACAINE 70-70 MG TP PTMH,Medication,1,2,0.5
+11338,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,520045,MEROPENEM CUSTOM IVPB,Medication,1,2,0.5
+11339,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,540102,NS IV BOLUS,Medication,1,2,0.5
+11340,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,570051,VANCOMYCIN PEDIATRIC IV INFUSION,Medication,1,2,0.5
+11341,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,2,0.5
+11342,4259,ED PEDS FEVER IN PEDIATRIC ONCOLOGY,LABURNC,URINE CULTURE,Lab,1,2,0.5
+11343,4260,ED PEDS CROUP - LARYNGOTRACHEITIS (6 MO - 6 YEARS),122688,RACEPINEPHRINE 2.25 % INH NEBU,Medication,1,2,0.5
+11344,4265,IP ICU VOLUME BASED TUBE FEEDING (ICU ONLY),541258,PRO-STAT SUGAR FREE - VANILLA FLAVORED,Medication,6,20,0.3
+11345,4265,IP ICU VOLUME BASED TUBE FEEDING (ICU ONLY),LABMETB,"METABOLIC PANEL, BASIC",Lab,2,20,0.1
+11346,4265,IP ICU VOLUME BASED TUBE FEEDING (ICU ONLY),LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2,20,0.1
+11347,4265,IP ICU VOLUME BASED TUBE FEEDING (ICU ONLY),LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2,20,0.1
+11348,4277,IP ARGATROBAN INFUSION,221680,ARGATROBAN IN NACL (ISO-OS) 50 MG/50 ML (1 MG/ML) IV SOLN,Medication,103,1006,0.102385686
+11349,4277,IP ARGATROBAN INFUSION,541261,ARGATROBAN IV INFUSION (LVAD HIGH BLEED RISK),Medication,135,1006,0.134194831
+11350,4277,IP ARGATROBAN INFUSION,541262,ARGATROBAN IV INFUSION (LVAD NORMAL BLEED RISK),Medication,153,1006,0.152087475
+11351,4277,IP ARGATROBAN INFUSION,541263,ARGATROBAN IV INFUSION (LVAD HIGH PUMP THROMBOSIS RISK),Medication,34,1006,0.033797217
+11352,4277,IP ARGATROBAN INFUSION,541265,ARGATROBAN IV INFUSION (MD TO SPECIFY PARAMETERS),Medication,32,1006,0.031809145
+11353,4277,IP ARGATROBAN INFUSION,LABCBCO,CBC W/O DIFF,Lab,587,1006,0.583499006
+11354,4277,IP ARGATROBAN INFUSION,LABPT,PROTHROMBIN TIME,Lab,686,1006,0.681908549
+11355,4277,IP ARGATROBAN INFUSION,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,1069,1006,1
+11356,4310,IP/ED GEN  FINGER STICK SOURCE PATIENT,LAB11107R,HBE ANTIGEN,Lab,2,5,0.4
+11357,4310,IP/ED GEN  FINGER STICK SOURCE PATIENT,LABHBSAG,HBSAG,Lab,4,5,0.8
+11358,4310,IP/ED GEN  FINGER STICK SOURCE PATIENT,LABHCPQ,HEPATITIS C RNA PCR QUANTITATIVE,Lab,1,5,0.2
+11359,4310,IP/ED GEN  FINGER STICK SOURCE PATIENT,LABHCVA,HEPATITIS C AB IGG,Lab,5,5,1
+11360,4310,IP/ED GEN  FINGER STICK SOURCE PATIENT,LABSHIV12,RAPID HIV-1/2 ANTIBODY,Lab,2,5,0.4
+11361,4317,SHC TXP DIABETES,10116,GLIPIZIDE 10 MG PO TABS,Medication,1,151,0.006622517
+11362,4317,SHC TXP DIABETES,10117,GLIPIZIDE 5 MG PO TABS,Medication,3,151,0.01986755
+11363,4317,SHC TXP DIABETES,16027,SHARPS CONTAINER MISC MISC,Medication,68,151,0.450331126
+11364,4317,SHC TXP DIABETES,16050,GLUCOSE 4 GRAM PO CHEW,Medication,6,151,0.039735099
+11365,4317,SHC TXP DIABETES,28314,FREESTYLE LANCETS MISC MISC,Medication,3,151,0.01986755
+11366,4317,SHC TXP DIABETES,32410,ALCOHOL SWABS TP PADM,Medication,79,151,0.523178808
+11367,4317,SHC TXP DIABETES,34952,NOVOLOG FLEXPEN 100 UNIT/ML SC INPN,Medication,15,151,0.099337748
+11368,4317,SHC TXP DIABETES,37706,"BD INSULIN PEN NEEDLE UF MINI 31 X 3/16 "" MISC NDLE",Medication,68,151,0.450331126
+11369,4317,SHC TXP DIABETES,70824,LEVEMIR FLEXPEN 100 UNIT/ML (3 ML) SC INPN,Medication,9,151,0.059602649
+11370,4317,SHC TXP DIABETES,198413,SITAGLIPTIN 50 MG PO TABS,Medication,1,151,0.006622517
+11371,4317,SHC TXP DIABETES,200409,FREESTYLE LITE METER MISC KIT,Medication,1,151,0.006622517
+11372,4317,SHC TXP DIABETES,200410,FREESTYLE LITE STRIPS MISC STRP,Medication,3,151,0.01986755
+11373,4317,SHC TXP DIABETES,201231,LANTUS SOLOSTAR 100 UNIT/ML (3 ML) SC INPN,Medication,32,151,0.21192053
+11374,4317,SHC TXP DIABETES,203639,HUMALOG KWIKPEN 100 UNIT/ML SC INPN,Medication,56,151,0.370860927
+11375,4317,SHC TXP DIABETES,205787,TRUERESULT BLOOD GLUCOSE SYSTM MISC KIT,Medication,5,151,0.033112583
+11376,4317,SHC TXP DIABETES,205788,TRUETEST TEST STRIPS MISC STRP,Medication,9,151,0.059602649
+11377,4317,SHC TXP DIABETES,210305,ONETOUCH DELICA LANCETS 33 GAUGE MISC MISC,Medication,16,151,0.105960265
+11378,4317,SHC TXP DIABETES,221626,ACCU-CHEK FASTCLIX MISC MISC,Medication,32,151,0.21192053
+11379,4317,SHC TXP DIABETES,222402,ONE TOUCH VERIO IQ METER MISC KIT,Medication,8,151,0.052980132
+11380,4317,SHC TXP DIABETES,222403,ONETOUCH VERIO MISC STRP,Medication,19,151,0.125827815
+11381,4317,SHC TXP DIABETES,222770,ACCU-CHEK NANO MISC MISC,Medication,12,151,0.079470199
+11382,4317,SHC TXP DIABETES,222771,ACCU-CHEK SMARTVIEW TEST STRIP MISC STRP,Medication,28,151,0.185430464
+11383,4317,SHC TXP DIABETES,229186,UNIVERSAL 1 LANCETS 30 GAUGE MISC MISC,Medication,4,151,0.026490066
+11384,4317,SHC TXP DIABETES,229959,TECHLITE LANCETS 30 GAUGE MISC MISC,Medication,21,151,0.139072848
+11385,4317,SHC TXP DIABETES,231561,HUMULIN N KWIKPEN 100 UNIT/ML (3 ML) SC INPN,Medication,26,151,0.17218543
+11386,4317,SHC TXP DIABETES,233001,TRUE METRIX GLUCOSE TEST STRIP MISC STRP,Medication,6,151,0.039735099
+11387,4317,SHC TXP DIABETES,233002,TRUE METRIX GLUCOSE METER MISC MISC,Medication,3,151,0.01986755
+11388,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,15,447,0.033557047
+11389,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,111,447,0.248322148
+11390,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,4318,LACTATED RINGERS IV SOLP,Medication,3,447,0.006711409
+11391,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,7,447,0.015659955
+11392,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,1,447,0.002237136
+11393,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,1,447,0.002237136
+11394,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,10814,OXYCODONE 5 MG PO TABS,Medication,1,447,0.002237136
+11395,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,18308,GABAPENTIN 300 MG PO CAPS,Medication,131,447,0.293064877
+11396,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,24176,MIDAZOLAM 2 MG/ML PO SYRP,Medication,1,447,0.002237136
+11397,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,25,447,0.055928412
+11398,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,447,0.002237136
+11399,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,77052,CELECOXIB 200 MG PO CAPS,Medication,95,447,0.212527964
+11400,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,81505,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,Medication,6,447,0.013422819
+11401,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,4,447,0.008948546
+11402,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,125758,PREGABALIN 75 MG PO CAPS,Medication,1,447,0.002237136
+11403,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,126993,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,1,447,0.002237136
+11404,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,205597,ALVIMOPAN 12 MG PO CAPS,Medication,129,447,0.288590604
+11405,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,64,447,0.143176734
+11406,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,520064,CEFAZOLIN 2 G IVPB,Medication,16,447,0.035794183
+11407,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,541104,CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS,Medication,5,447,0.011185682
+11408,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,541105,CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS,Medication,3,447,0.006711409
+11409,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,541110,ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS,Medication,143,447,0.319910515
+11410,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABCBCO,CBC W/O DIFF,Lab,3,447,0.006711409
+11411,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABMETB,"METABOLIC PANEL, BASIC",Lab,3,447,0.006711409
+11412,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABPLTS,PLATELET COUNT,Lab,31,447,0.06935123
+11413,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABPREG,"PREGNANCY TEST, SERUM",Lab,1,447,0.002237136
+11414,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABPT,PROTHROMBIN TIME,Lab,6,447,0.013422819
+11415,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,3,447,0.006711409
+11416,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABTYPSNI,TYPE AND SCREEN,Lab,8,447,0.017897092
+11417,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABUPREG,"PREGNANCY TEST, URINE",Lab,9,447,0.020134228
+11418,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,POC14,POC GLUCOSE BY METER,Lab,21,447,0.046979866
+11419,4319,INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS,LABBBPRBC,RED BLOOD CELLS,Blood Product,3,447,0.006711409
+11420,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,540102,NS IV BOLUS,Medication,28,243,0.115226337
+11421,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,541440,NS IV BOLUS - 500 ML,Medication,2,243,0.008230453
+11422,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,541441,NS IV BOLUS - 1000 ML,Medication,10,243,0.041152263
+11423,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,541447,NS IV BOLUS - 30 ML/KG,Medication,2,243,0.008230453
+11424,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,86,243,0.353909465
+11425,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,74,243,0.304526749
+11426,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,161,243,0.66255144
+11427,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,LABPROCT,PROCALCITONIN,Lab,33,243,0.135802469
+11428,4330,IP GEN SEVERE SEPSIS BUNDLE RAPID,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,46,243,0.189300412
+11429,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,2331,DEXAMETHASONE SODIUM PHOSPHATE 10 MG/ML INJ SOLN,Medication,11,47,0.234042553
+11430,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,8,47,0.170212766
+11431,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,19,47,0.404255319
+11432,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,19,47,0.404255319
+11433,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,78302,OCTREOTIDE ACETATE 30 MG IM KIT,Medication,3,47,0.063829787
+11434,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,19,47,0.404255319
+11435,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,221565,FOSAPREPITANT 150 MG IVPB,Medication,19,47,0.404255319
+11436,4343,IP NUC MED PRRT LU177-DOTATATE THERAPY,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,38,47,0.808510638
+11437,4344,IP GEN 72 HOUR FAST PROTOCOL,540548,GLUCAGON 1 MG (1 UNIT) INJ SOLR,Medication,14,36,0.388888889
+11438,4344,IP GEN 72 HOUR FAST PROTOCOL,LAB12418R,SULFONYLUREA HYPOGLYCEMICS PNL,Lab,17,36,0.472222222
+11439,4344,IP GEN 72 HOUR FAST PROTOCOL,LAB13194R,INSULIN AUTOANTIBODY,Lab,17,36,0.472222222
+11440,4344,IP GEN 72 HOUR FAST PROTOCOL,LABBHB,BETA-HYDROXYBUTYRATE,Lab,17,36,0.472222222
+11441,4344,IP GEN 72 HOUR FAST PROTOCOL,LABCORT,"CORTISOL, SERUM",Lab,15,36,0.416666667
+11442,4344,IP GEN 72 HOUR FAST PROTOCOL,LABGLNF,"GLUCOSE NON-FASTING, SERUM/PLASMA",Lab,32,36,0.888888889
+11443,4344,IP GEN 72 HOUR FAST PROTOCOL,LABINSL,INSULIN UNSPECIFIED,Lab,18,36,0.5
+11444,4344,IP GEN 72 HOUR FAST PROTOCOL,LABMETB,"METABOLIC PANEL, BASIC",Lab,15,36,0.416666667
+11445,4344,IP GEN 72 HOUR FAST PROTOCOL,LABYCP,"C - PEPTIDE, SERUM",Lab,18,36,0.5
+11446,4344,IP GEN 72 HOUR FAST PROTOCOL,LABYIGF2,INSULIN LIKE GROWTH FACTOR II,Lab,17,36,0.472222222
+11447,4344,IP GEN 72 HOUR FAST PROTOCOL,LABYPROIN,"PROINSULIN, PLASMA",Lab,20,36,0.555555556
+11448,4344,IP GEN 72 HOUR FAST PROTOCOL,POC14,POC GLUCOSE BY METER,Lab,26,36,0.722222222
+11449,4347,IP ICU SPINAL CORD ISCHEMIA PREVENTION PROTOCOL,541085,CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS,Medication,7,20,0.35
+11450,4364,ED PEDS PROCEDURAL SEDATION - KETAMINE,81839,LIDOCAINE 4 % TP CREA,Medication,1,3,0.333333333
+11451,4364,ED PEDS PROCEDURAL SEDATION - KETAMINE,90587,KETAMINE 10 MG/ML INJ SOLN,Medication,1,3,0.333333333
+11452,4364,ED PEDS PROCEDURAL SEDATION - KETAMINE,125595,LIDOCAINE-TETRACAINE 70-70 MG TP PTMH,Medication,1,3,0.333333333
+11453,4364,ED PEDS PROCEDURAL SEDATION - KETAMINE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,3,0.333333333
+11454,4365,NEUROSCIENCES CLINIC ADMINISTERED MEDICATION,541533,"BACLOFEN 20,000 MCG IN 20 ML (1000 MCG/ML) INTRATHECAL PUMP REFILL SYRINGE",Medication,1,2,0.5
+11455,4365,NEUROSCIENCES CLINIC ADMINISTERED MEDICATION,541561,BACLOFEN 2000 MCG/ML INTRATHECAL PUMP REFIILL SYRINGE,Medication,1,2,0.5
+11456,4432,IP ENT CAROTID BLOW,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,3,11,0.272727273
+11457,4432,IP ENT CAROTID BLOW,540102,NS IV BOLUS,Medication,3,11,0.272727273
+11458,4432,IP ENT CAROTID BLOW,LABTYPSNI,TYPE AND SCREEN,Lab,2,11,0.181818182
+11459,4432,IP ENT CAROTID BLOW,LABBBPRBC,RED BLOOD CELLS,Blood Product,3,11,0.272727273
+11460,4445,SHC IP ENDO LIVER BIOPSY POSTOP,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,4,0.5
+11461,4466,AMB ORT XRAY,IMGDXHIP1LT,XR HIP 1V LT,Imaging,1,3,0.333333333
+11462,4466,AMB ORT XRAY,IMGDXPEL,XR PELVIS 1V,Imaging,1,3,0.333333333
+11463,4466,AMB ORT XRAY,IMGDXPEL3,XR PELVIS 3V,Imaging,1,3,0.333333333
+11464,4466,AMB ORT XRAY,IMGXR0062,XR HAND 3 VIEWS RIGHT,Imaging,1,3,0.333333333
+11465,4565,SHC AMB AI TESTING & TREATMENT,2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,1,1,1
+11466,4565,SHC AMB AI TESTING & TREATMENT,2509,DIPHENHYDRAMINE HCL 25 MG PO CAPS,Medication,1,1,1
+11467,4565,SHC AMB AI TESTING & TREATMENT,92888,"EPINEPHRINE 0.3 MG/0.3 ML (1:1,000) INJ ATIN",Medication,1,1,1
+11468,4572,SHC PAI PRE-OP/MODERATE SEDATION ORDERS,10607,MIDAZOLAM 1 MG/ML INJ SOLN,Medication,4,36,0.111111111
+11469,4572,SHC PAI PRE-OP/MODERATE SEDATION ORDERS,126993,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,Medication,19,36,0.527777778
+11470,4572,SHC PAI PRE-OP/MODERATE SEDATION ORDERS,231233,MIDAZOLAM (PF) 1 MG/ML INJ SOLN,Medication,9,36,0.25
+11471,4572,SHC PAI PRE-OP/MODERATE SEDATION ORDERS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,15,36,0.416666667
+11472,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",101,ACETAMINOPHEN 325 MG PO TABS,Medication,4,9,0.444444444
+11473,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",2508,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,Medication,2,9,0.222222222
+11474,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",3074,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,Medication,1,9,0.111111111
+11475,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",3233,FOLIC ACID 1 MG PO TABS,Medication,1,9,0.111111111
+11476,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",7555,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,Medication,1,9,0.111111111
+11477,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",9644,CLOTRIMAZOLE 10 MG MM TROC,Medication,1,9,0.111111111
+11478,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",19483,"CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",Medication,1,9,0.111111111
+11479,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",77883,VALGANCICLOVIR 450 MG PO TABS,Medication,2,9,0.222222222
+11480,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",80562,MYCOPHENOLATE MOFETIL 250 MG PO CAPS,Medication,1,9,0.111111111
+11481,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",85860,RIBAVIRIN 6 GRAM INH SOLR,Medication,2,9,0.222222222
+11482,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1,9,0.111111111
+11483,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",92835,INSULIN LISPRO 100 UNIT/ML SC SOLN,Medication,1,9,0.111111111
+11484,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",94670,TACROLIMUS 1 MG PO CAPS,Medication,1,9,0.111111111
+11485,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",95803,TACROLIMUS 0.5 MG PO CAPS,Medication,2,9,0.222222222
+11486,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",126009,MEPERIDINE (PF) 25 MG/ML INJ SYRG,Medication,2,9,0.222222222
+11487,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",208479,MAGNESIUM OXIDE-MG AA CHELATE 133 MG PO TABS,Medication,1,9,0.111111111
+11488,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",210379,HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR,Medication,4,9,0.444444444
+11489,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",212610,"IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",Medication,3,9,0.333333333
+11490,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,9,0.222222222
+11491,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",213662,"EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",Medication,2,9,0.222222222
+11492,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",231412,POSACONAZOLE 100 MG PO TBEC,Medication,1,9,0.111111111
+11493,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",520078,METHYLPREDNISOLONE IVPB,Medication,1,9,0.111111111
+11494,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",LABCBCD,CBC WITH DIFF,Lab,2,9,0.222222222
+11495,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",LABCMVQT,CYTOMEGALOVIRUS DNA PCR QUANT,Lab,2,9,0.222222222
+11496,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",LABFK506L,TACROLIMUS (FK506),Lab,4,9,0.444444444
+11497,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,2,9,0.222222222
+11498,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2,9,0.222222222
+11499,4601,"IP TRS LUNG, HEART-LUNG TRANSPLANT CARV",LABPLTS,PLATELET COUNT,Lab,2,9,0.222222222
+11500,4614,ED/IP NURSING NG INSERTION PAIN PROTOCOL,4448,LIDOCAINE HCL 2 % JELLY,Medication,166,370,0.448648649
+11501,4614,ED/IP NURSING NG INSERTION PAIN PROTOCOL,4450,LIDOCAINE HCL 4 % TOPICAL SOLN,Medication,15,370,0.040540541
+11502,4614,ED/IP NURSING NG INSERTION PAIN PROTOCOL,4455,LIDOCAINE (PF) 40 MG/ML (4 %) INJ SOLN,Medication,117,370,0.316216216
+11503,4619,IP NSR LUMBAR MICRODISC,101,ACETAMINOPHEN 325 MG PO TABS,Medication,10,30,0.333333333
+11504,4619,IP NSR LUMBAR MICRODISC,1080,BISACODYL 10 MG PR SUPP,Medication,8,30,0.266666667
+11505,4619,IP NSR LUMBAR MICRODISC,2017,CYCLOBENZAPRINE 10 MG PO TABS,Medication,1,30,0.033333333
+11506,4619,IP NSR LUMBAR MICRODISC,2405,DIAZEPAM 5 MG PO TABS,Medication,1,30,0.033333333
+11507,4619,IP NSR LUMBAR MICRODISC,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,10,30,0.333333333
+11508,4619,IP NSR LUMBAR MICRODISC,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,3,30,0.1
+11509,4619,IP NSR LUMBAR MICRODISC,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,2,30,0.066666667
+11510,4619,IP NSR LUMBAR MICRODISC,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,2,30,0.066666667
+11511,4619,IP NSR LUMBAR MICRODISC,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,11,30,0.366666667
+11512,4619,IP NSR LUMBAR MICRODISC,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,30,0.033333333
+11513,4619,IP NSR LUMBAR MICRODISC,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,11,30,0.366666667
+11514,4619,IP NSR LUMBAR MICRODISC,10814,OXYCODONE 5 MG PO TABS,Medication,1,30,0.033333333
+11515,4619,IP NSR LUMBAR MICRODISC,11349,SENNOSIDES 8.6 MG PO TABS,Medication,11,30,0.366666667
+11516,4619,IP NSR LUMBAR MICRODISC,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,1,30,0.033333333
+11517,4619,IP NSR LUMBAR MICRODISC,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,8,30,0.266666667
+11518,4619,IP NSR LUMBAR MICRODISC,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,30,0.233333333
+11519,4619,IP NSR LUMBAR MICRODISC,94176,ONDANSETRON 4 MG PO TBDL,Medication,5,30,0.166666667
+11520,4619,IP NSR LUMBAR MICRODISC,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,6,30,0.2
+11521,4619,IP NSR LUMBAR MICRODISC,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,9,30,0.3
+11522,4619,IP NSR LUMBAR MICRODISC,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,1,30,0.033333333
+11523,4619,IP NSR LUMBAR MICRODISC,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,8,30,0.266666667
+11524,4619,IP NSR LUMBAR MICRODISC,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,1,30,0.033333333
+11525,4628,PULM/CARD BRONCHOSCOPY LABS,LABAFBC,"AFB CULTURE, RESPIRATORY",Lab,8,11,0.727272727
+11526,4628,PULM/CARD BRONCHOSCOPY LABS,LABBFCT,"CELL COUNT AND DIFF, BODY FLUID",Lab,7,11,0.636363636
+11527,4628,PULM/CARD BRONCHOSCOPY LABS,LABBXTG,BIOPSY / TISSUE CULTURE AND GRAM STAIN,Lab,1,11,0.090909091
+11528,4628,PULM/CARD BRONCHOSCOPY LABS,LABFCUL,FUNGAL CULTURE,Lab,10,11,0.909090909
+11529,4628,PULM/CARD BRONCHOSCOPY LABS,LABKOHP,KOH / CALCOFLUOR,Lab,3,11,0.272727273
+11530,4628,PULM/CARD BRONCHOSCOPY LABS,LABLCVCUL,"CULTURE, VIRAL, GENERAL",Lab,5,11,0.454545455
+11531,4628,PULM/CARD BRONCHOSCOPY LABS,LABLEGDA,LEGIONELLA CULTURE,Lab,6,11,0.545454545
+11532,4628,PULM/CARD BRONCHOSCOPY LABS,LABPCYS,PNEUMOCYSTIS STAIN,Lab,7,11,0.636363636
+11533,4628,PULM/CARD BRONCHOSCOPY LABS,LABRESPCR,RESPIRATORY VIRUS PCR PANEL,Lab,6,11,0.545454545
+11534,4628,PULM/CARD BRONCHOSCOPY LABS,LABRESPG,RESPIRATORY CULTURE AND GRAM STAIN,Lab,11,11,1
+11535,4632,IP NSR LAMINECTOMY/DISKECTOMY,101,ACETAMINOPHEN 325 MG PO TABS,Medication,16,92,0.173913043
+11536,4632,IP NSR LAMINECTOMY/DISKECTOMY,102,ACETAMINOPHEN 500 MG PO TABS,Medication,1,92,0.010869565
+11537,4632,IP NSR LAMINECTOMY/DISKECTOMY,1080,BISACODYL 10 MG PR SUPP,Medication,22,92,0.239130435
+11538,4632,IP NSR LAMINECTOMY/DISKECTOMY,2405,DIAZEPAM 5 MG PO TABS,Medication,1,92,0.010869565
+11539,4632,IP NSR LAMINECTOMY/DISKECTOMY,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,23,92,0.25
+11540,4632,IP NSR LAMINECTOMY/DISKECTOMY,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,9,92,0.097826087
+11541,4632,IP NSR LAMINECTOMY/DISKECTOMY,4711,MAGNESIUM CITRATE PO SOLN,Medication,2,92,0.02173913
+11542,4632,IP NSR LAMINECTOMY/DISKECTOMY,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,4,92,0.043478261
+11543,4632,IP NSR LAMINECTOMY/DISKECTOMY,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,7,92,0.076086957
+11544,4632,IP NSR LAMINECTOMY/DISKECTOMY,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,24,92,0.260869565
+11545,4632,IP NSR LAMINECTOMY/DISKECTOMY,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,92,0.02173913
+11546,4632,IP NSR LAMINECTOMY/DISKECTOMY,10011,FAMOTIDINE 20 MG PO TABS,Medication,1,92,0.010869565
+11547,4632,IP NSR LAMINECTOMY/DISKECTOMY,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,25,92,0.27173913
+11548,4632,IP NSR LAMINECTOMY/DISKECTOMY,10814,OXYCODONE 5 MG PO TABS,Medication,22,92,0.239130435
+11549,4632,IP NSR LAMINECTOMY/DISKECTOMY,11349,SENNOSIDES 8.6 MG PO TABS,Medication,25,92,0.27173913
+11550,4632,IP NSR LAMINECTOMY/DISKECTOMY,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,5,92,0.054347826
+11551,4632,IP NSR LAMINECTOMY/DISKECTOMY,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,2,92,0.02173913
+11552,4632,IP NSR LAMINECTOMY/DISKECTOMY,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,24,92,0.260869565
+11553,4632,IP NSR LAMINECTOMY/DISKECTOMY,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,1,92,0.010869565
+11554,4632,IP NSR LAMINECTOMY/DISKECTOMY,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,7,92,0.076086957
+11555,4632,IP NSR LAMINECTOMY/DISKECTOMY,35184,CYCLOBENZAPRINE 5 MG PO TABS,Medication,6,92,0.065217391
+11556,4632,IP NSR LAMINECTOMY/DISKECTOMY,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,2,92,0.02173913
+11557,4632,IP NSR LAMINECTOMY/DISKECTOMY,94176,ONDANSETRON 4 MG PO TBDL,Medication,1,92,0.010869565
+11558,4632,IP NSR LAMINECTOMY/DISKECTOMY,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,1,92,0.010869565
+11559,4632,IP NSR LAMINECTOMY/DISKECTOMY,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,23,92,0.25
+11560,4632,IP NSR LAMINECTOMY/DISKECTOMY,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,16,92,0.173913043
+11561,4632,IP NSR LAMINECTOMY/DISKECTOMY,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,6,92,0.065217391
+11562,4632,IP NSR LAMINECTOMY/DISKECTOMY,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,18,92,0.195652174
+11563,4632,IP NSR LAMINECTOMY/DISKECTOMY,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,2,92,0.02173913
+11564,4632,IP NSR LAMINECTOMY/DISKECTOMY,LABPLTS,PLATELET COUNT,Lab,6,92,0.065217391
+11565,4654,IP HNS FREE FLAP ORDERSET,101,ACETAMINOPHEN 325 MG PO TABS,Medication,6,372,0.016129032
+11566,4654,IP HNS FREE FLAP ORDERSET,102,ACETAMINOPHEN 500 MG PO TABS,Medication,4,372,0.010752688
+11567,4654,IP HNS FREE FLAP ORDERSET,680,ASPIRIN 81 MG PO CHEW,Medication,68,372,0.182795699
+11568,4654,IP HNS FREE FLAP ORDERSET,1080,BISACODYL 10 MG PR SUPP,Medication,39,372,0.10483871
+11569,4654,IP HNS FREE FLAP ORDERSET,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,1,372,0.002688172
+11570,4654,IP HNS FREE FLAP ORDERSET,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,14,372,0.037634409
+11571,4654,IP HNS FREE FLAP ORDERSET,2569,DOCUSATE SODIUM 50 MG/5 ML PO LIQD,Medication,24,372,0.064516129
+11572,4654,IP HNS FREE FLAP ORDERSET,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,56,372,0.150537634
+11573,4654,IP HNS FREE FLAP ORDERSET,5018,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,Medication,39,372,0.10483871
+11574,4654,IP HNS FREE FLAP ORDERSET,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,1,372,0.002688172
+11575,4654,IP HNS FREE FLAP ORDERSET,9626,CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK,Medication,10,372,0.02688172
+11576,4654,IP HNS FREE FLAP ORDERSET,10011,FAMOTIDINE 20 MG PO TABS,Medication,11,372,0.029569892
+11577,4654,IP HNS FREE FLAP ORDERSET,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,68,372,0.182795699
+11578,4654,IP HNS FREE FLAP ORDERSET,14863,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,Medication,62,372,0.166666667
+11579,4654,IP HNS FREE FLAP ORDERSET,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,47,372,0.126344086
+11580,4654,IP HNS FREE FLAP ORDERSET,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,6,372,0.016129032
+11581,4654,IP HNS FREE FLAP ORDERSET,34544,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG PO TABS,Medication,1,372,0.002688172
+11582,4654,IP HNS FREE FLAP ORDERSET,82384,PANTOPRAZOLE 40 MG IV SOLR,Medication,4,372,0.010752688
+11583,4654,IP HNS FREE FLAP ORDERSET,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,1,372,0.002688172
+11584,4654,IP HNS FREE FLAP ORDERSET,92585,FAMOTIDINE 40 MG/5 ML PO SUSP,Medication,1,372,0.002688172
+11585,4654,IP HNS FREE FLAP ORDERSET,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,6,372,0.016129032
+11586,4654,IP HNS FREE FLAP ORDERSET,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,6,372,0.016129032
+11587,4654,IP HNS FREE FLAP ORDERSET,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,30,372,0.080645161
+11588,4654,IP HNS FREE FLAP ORDERSET,117862,HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN,Medication,24,372,0.064516129
+11589,4654,IP HNS FREE FLAP ORDERSET,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,1,372,0.002688172
+11590,4654,IP HNS FREE FLAP ORDERSET,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,82,372,0.220430108
+11591,4654,IP HNS FREE FLAP ORDERSET,200718,CARBOXYMETHYLCELLULOSE SODIUM 1 % OPHT DPGE,Medication,10,372,0.02688172
+11592,4654,IP HNS FREE FLAP ORDERSET,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,34,372,0.091397849
+11593,4654,IP HNS FREE FLAP ORDERSET,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,48,372,0.129032258
+11594,4654,IP HNS FREE FLAP ORDERSET,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,56,372,0.150537634
+11595,4654,IP HNS FREE FLAP ORDERSET,221636,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,Medication,1,372,0.002688172
+11596,4654,IP HNS FREE FLAP ORDERSET,540300,MORPHINE INJECTABLE SYRINGE,Medication,51,372,0.137096774
+11597,4654,IP HNS FREE FLAP ORDERSET,540744,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,Medication,6,372,0.016129032
+11598,4654,IP HNS FREE FLAP ORDERSET,541044,SODIUM BICARBONATE IN NS ORAL SOLUTION,Medication,4,372,0.010752688
+11599,4654,IP HNS FREE FLAP ORDERSET,541093,AMPICILLIN/SULBACTAM 1.5 GRAM/50 ML MINI-BAG PLUS,Medication,5,372,0.01344086
+11600,4654,IP HNS FREE FLAP ORDERSET,LABCAI,"CALCIUM IONIZED, SERUM/PLASMA",Lab,17,372,0.045698925
+11601,4654,IP HNS FREE FLAP ORDERSET,LABCBCD,CBC WITH DIFF,Lab,99,372,0.266129032
+11602,4654,IP HNS FREE FLAP ORDERSET,LABMETB,"METABOLIC PANEL, BASIC",Lab,93,372,0.25
+11603,4654,IP HNS FREE FLAP ORDERSET,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,71,372,0.190860215
+11604,4654,IP HNS FREE FLAP ORDERSET,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,70,372,0.188172043
+11605,4654,IP HNS FREE FLAP ORDERSET,LABPLTS,PLATELET COUNT,Lab,32,372,0.086021505
+11606,4654,IP HNS FREE FLAP ORDERSET,IMGDXAB1,XR ABDOMEN 1V,Imaging,14,372,0.037634409
+11607,4654,IP HNS FREE FLAP ORDERSET,IMGDXCH1,XR CHEST 1V,Imaging,4,372,0.010752688
+11608,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,9516,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,Medication,258,640,0.403125
+11609,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,3,640,0.0046875
+11610,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,21,640,0.0328125
+11611,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,2,640,0.003125
+11612,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,263,640,0.4109375
+11613,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,520046,VANCOMYCIN IVPB (CUSTOM DOSE),Medication,3,640,0.0046875
+11614,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABBTVI,BLOOD TYPE VERIFICATION,Lab,170,640,0.265625
+11615,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABCBCD,CBC WITH DIFF,Lab,16,640,0.025
+11616,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABDOA,"DRUGS OF ABUSE SCREEN, URINE",Lab,2,640,0.003125
+11617,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABHA1C,HGB A1C W/ EST MEAN GLUCOSE,Lab,24,640,0.0375
+11618,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,21,640,0.0328125
+11619,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,23,640,0.0359375
+11620,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABPT,PROTHROMBIN TIME,Lab,20,640,0.03125
+11621,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,20,640,0.03125
+11622,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABTYPSNI,TYPE AND SCREEN,Lab,40,640,0.0625
+11623,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABUAPRN,"URINALYSIS, SCREEN FOR CULTURE",Lab,24,640,0.0375
+11624,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABUPREG,"PREGNANCY TEST, URINE",Lab,3,640,0.0046875
+11625,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,IMGDXCH2,XR CHEST 2V,Imaging,17,640,0.0265625
+11626,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,IMGXR0031,XR CHEST 2 VIEWS,Imaging,5,640,0.0078125
+11627,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,VSC03,VSC ARTERIAL EXAM/ABIS,Imaging,2,640,0.003125
+11628,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,VSC05,VSC VENOUS ULTRASOUND,Imaging,1,640,0.0015625
+11629,4658,PRE-ADMISSION/PRE-OP ORDERS CV SURGERY,LABBBPRBC,RED BLOOD CELLS,Blood Product,42,640,0.065625
+11630,4678,IP NUR SEVERE SEPSIS NURSE PROTOCOL,541440,NS IV BOLUS - 500 ML,Medication,27,512,0.052734375
+11631,4678,IP NUR SEVERE SEPSIS NURSE PROTOCOL,LABBLC,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),Lab,76,512,0.1484375
+11632,4678,IP NUR SEVERE SEPSIS NURSE PROTOCOL,LABBLC2,BLOOD CULTURE (2 AEROBIC BOTTLES),Lab,75,512,0.146484375
+11633,4678,IP NUR SEVERE SEPSIS NURSE PROTOCOL,LABLACWB,"LACTATE, WHOLE BLOOD",Lab,376,512,0.734375
+11634,4683,IP VAD TPA ALTEPLASE LVAD THROMBUS PROTOCOL,541066,HEPARIN INFUSION LVAD HIGH RISK BLEED,Medication,8,19,0.421052632
+11635,4683,IP VAD TPA ALTEPLASE LVAD THROMBUS PROTOCOL,541530,ALTEPLASE (TPA) 30MG/100ML NS (VAD THROMBUS),Medication,5,19,0.263157895
+11636,4683,IP VAD TPA ALTEPLASE LVAD THROMBUS PROTOCOL,541531,ALTEPLASE (TPA) 10MG IN NS 1000ML INFUSION (VAD THROMBUS),Medication,4,19,0.210526316
+11637,4683,IP VAD TPA ALTEPLASE LVAD THROMBUS PROTOCOL,541764,HEPARIN IV INFUSION (LVAD PROTOCOL - HIGH BLEED RISK),Medication,1,19,0.052631579
+11638,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),81839,LIDOCAINE 4 % TP CREA,Medication,1,2,0.5
+11639,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),125595,LIDOCAINE-TETRACAINE 70-70 MG TP PTMH,Medication,1,2,0.5
+11640,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),LABBHB,BETA-HYDROXYBUTYRATE,Lab,2,2,1
+11641,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),LABCA,"CALCIUM, SERUM/PLASMA",Lab,2,2,1
+11642,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,2,2,1
+11643,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2,2,1
+11644,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),LABUA,URINALYSIS WITH MICROSCOPIC,Lab,1,2,0.5
+11645,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),LABVBG,"BLOOD GASES, VENOUS",Lab,1,2,0.5
+11646,4718,ED PEDS SUSPECTED/UNCONFIRMED DIABETIC KETOACIDOSIS - DKA (1-18 YEARS),POC14,POC GLUCOSE BY METER,Lab,1,2,0.5
+11647,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,101,ACETAMINOPHEN 325 MG PO TABS,Medication,53,337,0.15727003
+11648,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,102,ACETAMINOPHEN 500 MG PO TABS,Medication,2,337,0.005934718
+11649,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,1080,BISACODYL 10 MG PR SUPP,Medication,64,337,0.189910979
+11650,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,2017,CYCLOBENZAPRINE 10 MG PO TABS,Medication,2,337,0.005934718
+11651,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,2404,DIAZEPAM 2 MG PO TABS,Medication,5,337,0.014836795
+11652,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,2405,DIAZEPAM 5 MG PO TABS,Medication,24,337,0.071216617
+11653,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,2566,DOCUSATE SODIUM 100 MG PO CAPS,Medication,79,337,0.234421365
+11654,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,3697,HYDRALAZINE 20 MG/ML INJ SOLN,Medication,38,337,0.112759644
+11655,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,4711,MAGNESIUM CITRATE PO SOLN,Medication,7,337,0.020771513
+11656,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,5002,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,Medication,37,337,0.109792285
+11657,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,8,337,0.023738872
+11658,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,5007,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,Medication,21,337,0.06231454
+11659,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,5373,NALOXONE 0.4 MG/ML INJ SOLN,Medication,83,337,0.246290801
+11660,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,5940,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,24,337,0.071216617
+11661,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,9627,CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK,Medication,1,337,0.002967359
+11662,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,10055,FLUMAZENIL 0.1 MG/ML IV SOLN,Medication,80,337,0.237388724
+11663,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,10814,OXYCODONE 5 MG PO TABS,Medication,49,337,0.145400593
+11664,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,11143,PROMETHAZINE 12.5 MG PR SUPP,Medication,1,337,0.002967359
+11665,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,11349,SENNOSIDES 8.6 MG PO TABS,Medication,105,337,0.3115727
+11666,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,16426,NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP,Medication,63,337,0.18694362
+11667,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,25365,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,Medication,17,337,0.050445104
+11668,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,87,337,0.258160237
+11669,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,28384,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,Medication,8,337,0.023738872
+11670,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,28899,OXYCODONE 15 MG PO TABS,Medication,1,337,0.002967359
+11671,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,34505,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,Medication,2,337,0.005934718
+11672,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,35184,CYCLOBENZAPRINE 5 MG PO TABS,Medication,11,337,0.03264095
+11673,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,86533,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,Medication,8,337,0.023738872
+11674,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,88043,PANTOPRAZOLE 40 MG PO TBEC,Medication,2,337,0.005934718
+11675,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,94176,ONDANSETRON 4 MG PO TBDL,Medication,11,337,0.03264095
+11676,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,110892,ENOXAPARIN 40 MG/0.4 ML SC SYRG,Medication,5,337,0.014836795
+11677,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,35,337,0.103857567
+11678,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,124216,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,Medication,4,337,0.011869436
+11679,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,124873,SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP,Medication,2,337,0.005934718
+11680,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,126992,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,Medication,10,337,0.029673591
+11681,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,72,337,0.213649852
+11682,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,202606,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,Medication,11,337,0.03264095
+11683,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,209037,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,Medication,3,337,0.008902077
+11684,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,212945,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",Medication,32,337,0.09495549
+11685,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,236263,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,Medication,49,337,0.145400593
+11686,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,540106,FENTANYL (PF) 50 MCG/ML INJECTION,Medication,5,337,0.014836795
+11687,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,540302,POTASSIUM CHLORIDE IV SCALE,Medication,11,337,0.03264095
+11688,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABCA,"CALCIUM, SERUM/PLASMA",Lab,2,337,0.005934718
+11689,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABCBCD,CBC WITH DIFF,Lab,2,337,0.005934718
+11690,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABCBCO,CBC W/O DIFF,Lab,59,337,0.175074184
+11691,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABMETB,"METABOLIC PANEL, BASIC",Lab,68,337,0.201780415
+11692,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABMGN,"MAGNESIUM, SERUM/PLASMA",Lab,10,337,0.029673591
+11693,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABPHOS,"PHOSPHORUS, SERUM/PLASMA",Lab,2,337,0.005934718
+11694,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABPLTS,PLATELET COUNT,Lab,28,337,0.083086053
+11695,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABPT,PROTHROMBIN TIME,Lab,49,337,0.145400593
+11696,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,LABPTT,PTT PARTIAL THROMBOPLASTIN TIME,Lab,24,337,0.071216617
+11697,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,IMGDXSPC2,"XR SPINE, CERVICAL 2V",Imaging,22,337,0.065281899
+11698,4727,IP NSR CERVICAL AND LUMBAR FUSIONS,IMGDXSPL2,"XR SPINE, LUMBAR 2V",Imaging,26,337,0.077151335
+11699,4766,IP GEN HOSPICE ADMISSION,101,ACETAMINOPHEN 325 MG PO TABS,Medication,2,109,0.018348624
+11700,4766,IP GEN HOSPICE ADMISSION,105,ACETAMINOPHEN 650 MG PR SUPP,Medication,9,109,0.082568807
+11701,4766,IP GEN HOSPICE ADMISSION,1080,BISACODYL 10 MG PR SUPP,Medication,24,109,0.220183486
+11702,4766,IP GEN HOSPICE ADMISSION,2332,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,Medication,3,109,0.027522936
+11703,4766,IP GEN HOSPICE ADMISSION,3497,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,Medication,20,109,0.183486239
+11704,4766,IP GEN HOSPICE ADMISSION,3578,HALOPERIDOL 0.5 MG PO TABS,Medication,5,109,0.04587156
+11705,4766,IP GEN HOSPICE ADMISSION,3584,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,Medication,27,109,0.247706422
+11706,4766,IP GEN HOSPICE ADMISSION,5005,METOCLOPRAMIDE HCL 10 MG PO TABS,Medication,2,109,0.018348624
+11707,4766,IP GEN HOSPICE ADMISSION,10467,LORAZEPAM 2 MG/ML INJ SOLN,Medication,15,109,0.137614679
+11708,4766,IP GEN HOSPICE ADMISSION,10813,OXYCODONE 5 MG/5 ML PO SOLN,Medication,9,109,0.082568807
+11709,4766,IP GEN HOSPICE ADMISSION,11349,SENNOSIDES 8.6 MG PO TABS,Medication,12,109,0.110091743
+11710,4766,IP GEN HOSPICE ADMISSION,25424,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,Medication,5,109,0.04587156
+11711,4766,IP GEN HOSPICE ADMISSION,93305,SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D,Medication,2,109,0.018348624
+11712,4766,IP GEN HOSPICE ADMISSION,94176,ONDANSETRON 4 MG PO TBDL,Medication,5,109,0.04587156
+11713,4766,IP GEN HOSPICE ADMISSION,95795,ARTIFICIAL TEARS(HYPROMELLOSE) 0.5 % OPHT DROP,Medication,5,109,0.04587156
+11714,4766,IP GEN HOSPICE ADMISSION,95933,OLANZAPINE 5 MG PO TBDL,Medication,1,109,0.009174312
+11715,4766,IP GEN HOSPICE ADMISSION,112564,MORPHINE 2 MG/ML INJ SYRG,Medication,11,109,0.100917431
+11716,4766,IP GEN HOSPICE ADMISSION,112586,HYDROMORPHONE 1 MG/ML INJ SYRG,Medication,58,109,0.532110092
+11717,4766,IP GEN HOSPICE ADMISSION,117414,ARTIFICIAL SALIVA (YERBAS-LYT) MM SPRA,Medication,13,109,0.119266055
+11718,4766,IP GEN HOSPICE ADMISSION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,17,109,0.155963303
+11719,4766,IP GEN HOSPICE ADMISSION,222272,LACTULOSE 20 GRAM/30 ML PO SOLN,Medication,4,109,0.036697248
+11720,4766,IP GEN HOSPICE ADMISSION,541607,HYDROMORPHONE 1 MG/ML IV PCA,Medication,9,109,0.082568807
+11721,4766,IP GEN HOSPICE ADMISSION,541609,MORPHINE 30 MG/30 ML IV PCA,Medication,2,109,0.018348624
+11722,4766,IP GEN HOSPICE ADMISSION,LABROMRS,MRSA SCREEN,Lab,1,109,0.009174312
+11723,4770,IP RAD NEURORADIOLOGY LP/MYELOGRAM POST PROCEDURE,101,ACETAMINOPHEN 325 MG PO TABS,Medication,1,3,0.333333333
+11724,4770,IP RAD NEURORADIOLOGY LP/MYELOGRAM POST PROCEDURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,3,0.333333333
+11725,4771,IP RAD NEURORADIOLOGY LP/MYELOGRAM PRE PROCEDURE,27838,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,Medication,1,3,0.333333333
+11726,4771,IP RAD NEURORADIOLOGY LP/MYELOGRAM PRE PROCEDURE,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,1,3,0.333333333
+11727,4771,IP RAD NEURORADIOLOGY LP/MYELOGRAM PRE PROCEDURE,POC19,"POC ISTAT, VENOUS NA, K, GLUCOSE, HCT, HEMOGLOBIN (EC4)",Lab,1,3,0.333333333
+11728,4771,IP RAD NEURORADIOLOGY LP/MYELOGRAM PRE PROCEDURE,POC20,"POC ISTAT, ARTERIAL NA, K, BLOOD GASES, IONIZED CA, HCT, HEMOGLOBIN (EG7)",Lab,1,3,0.333333333
+11729,4771,IP RAD NEURORADIOLOGY LP/MYELOGRAM PRE PROCEDURE,POC25,POC ISTAT INR AND PROTIME,Lab,1,3,0.333333333
+11730,210000001,SHC AMB NEUROPATHIC PAIN,LABCSFCT,"CELL COUNT AND DIFFERENTIAL, CSF",Lab,1,4,0.25
+11731,210000001,SHC AMB NEUROPATHIC PAIN,LABCSFGL,"GLUCOSE, CSF",Lab,2,4,0.5
+11732,210000001,SHC AMB NEUROPATHIC PAIN,LABCSFTP,"PROTEIN TOTAL, CSF",Lab,2,4,0.5
+11733,210000001,SHC AMB NEUROPATHIC PAIN,LABLCCORTISOL,"CORTISOL, AM",Lab,2,4,0.5
+11734,210000001,SHC AMB NEUROPATHIC PAIN,LABPROL,PROLACTIN,Lab,3,4,0.75
+11735,210000001,SHC AMB NEUROPATHIC PAIN,LABTSH,TSH,Lab,2,4,0.5
+11736,210000001,SHC AMB NEUROPATHIC PAIN,IMGCTSCMYELO,CT MYELOGRAM C-SPINE,Imaging,2,4,0.5
+11737,210000001,SHC AMB NEUROPATHIC PAIN,IMGCTSLMYELO,CT MYELOGRAM L-SPINE,Imaging,1,4,0.25
+11738,210000001,SHC AMB NEUROPATHIC PAIN,IMGCTSTMYELO,CT MYELOGRAM T-SPINE,Imaging,2,4,0.5
+11739,210000001,SHC AMB NEUROPATHIC PAIN,IMGFLLCON,FLUOROSCOPY LUMBAR CONTRAST INJECTION ONLY,Imaging,1,4,0.25
+11740,210000001,SHC AMB NEUROPATHIC PAIN,IMGMRHSF,MR BRAIN AND FULL SPINE,Imaging,3,4,0.75
+11741,1150000008,SHC AMB ONC AMYLOID LABS,LABFLCR,FREE LIGHT CHAIN RATIO,Lab,1,2,0.5
+11742,1150000008,SHC AMB ONC AMYLOID LABS,LABSPIE,"PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM",Lab,1,2,0.5
+11743,1150000008,SHC AMB ONC AMYLOID LABS,LABUPIE,URINE PROTEIN IMMUNOFIXATION ELECTROPHORESIS,Lab,2,2,1
+11744,1150000920,SHC AMB FERTILITY CRYOPRESERVATION,LAB11136R,HTLV AB,Lab,2,4,0.5
+11745,1150000920,SHC AMB FERTILITY CRYOPRESERVATION,LABHBSAG,HBSAG,Lab,2,4,0.5
+11746,1150000920,SHC AMB FERTILITY CRYOPRESERVATION,LABHCVA,HEPATITIS C AB IGG,Lab,2,4,0.5
+11747,1150000920,SHC AMB FERTILITY CRYOPRESERVATION,LABSTSS,SYPHILIS TREPONEMAL SCREEN,Lab,2,4,0.5
+11748,1650000010,SHC TXP PRE-LIVER ANNUAL WORK-UP,LABDGHBP,DIAG. HEPATITIS B PANEL,Lab,1,1,1
+11749,1650000010,SHC TXP PRE-LIVER ANNUAL WORK-UP,LABHAVAG,HAV ANTIBODY IGG,Lab,1,1,1
+11750,1650000010,SHC TXP PRE-LIVER ANNUAL WORK-UP,LABHCVA,HEPATITIS C AB IGG,Lab,1,1,1
+11751,1653000001,SHC TXP LUNG UNOS LIST RENEWAL,LABCMVIGG,CMV IGG,Lab,1,1,1
+11752,1653000001,SHC TXP LUNG UNOS LIST RENEWAL,LABCMVIGM,CMV IGM,Lab,1,1,1
+11753,1653000001,SHC TXP LUNG UNOS LIST RENEWAL,LABHSVIGG,HERPES SIMPLEX VIRUS IGG PANEL,Lab,1,1,1
+11754,1653000001,SHC TXP LUNG UNOS LIST RENEWAL,ECH15,ECHO - TRANSTHORACIC ECHO +DOPPLER,Imaging,1,1,1
+11755,2100012001,AMB PAIN LIDOCAINE INFUSION,6618,PROMETHAZINE 25 MG/ML INJ SOLN,Medication,3,11,0.272727273
+11756,2100012001,AMB PAIN LIDOCAINE INFUSION,198509,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,Medication,2,11,0.181818182
+11757,2100012001,AMB PAIN LIDOCAINE INFUSION,541167,LIDOCAINE INFUSION PAIN CLINIC,Medication,5,11,0.454545455
+11758,2100012001,AMB PAIN LIDOCAINE INFUSION,LABMETC,"METABOLIC PANEL, COMPREHENSIVE",Lab,1,11,0.090909091
+11759,21090000550,AMB AWCC WOUND CARE LAB AND RAD ORDERS,LABBXTG,BIOPSY / TISSUE CULTURE AND GRAM STAIN,Lab,1,1,1
+11760,21090000550,AMB AWCC WOUND CARE LAB AND RAD ORDERS,LABFCUL,FUNGAL CULTURE,Lab,1,1,1
+11761,21090000550,AMB AWCC WOUND CARE LAB AND RAD ORDERS,LABFLDC,FLUID CULTURE AND GRAM STAIN,Lab,1,1,1
+11762,21090000550,AMB AWCC WOUND CARE LAB AND RAD ORDERS,LABWDC,WOUND CULTURE,Lab,1,1,1
\ No newline at end of file
diff --git a/scripts/OrdersetExpectedVsReality/Data/retraction_spreadsheet.csv b/scripts/OrdersetExpectedVsReality/Data/retraction_spreadsheet.csv
new file mode 100644
index 00000000..eb885183
--- /dev/null
+++ b/scripts/OrdersetExpectedVsReality/Data/retraction_spreadsheet.csv
@@ -0,0 +1,2629 @@
+"","protocol_id","protocol_name","ct_orderset","order_id","description","ct_dw30m","ct_order","prob_order","ct_dw30m_noorderset","ct_med_noorderset","rate_dw30m_orderset","rate_dw30m_noorderset","RR_dw30m","ct_nodw30m_orderset","ct_nodw30m_noorderset","RR_pvalue"
+"1",522,"ED PNEUMONIA",423,"9487","CEFTRIAXONE 1 GRAM IV SOLR",3,4,0.00945626477541371,13,272,0.75,0.0477941176470588,15.6923076923077,1,259,1.01652852668246e-06
+"2",522,"ED PNEUMONIA",423,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,7,0.016548463356974,1067,10067,0.142857142857143,0.105989867885169,1.34783772928103,6,9000,0.999999999999992
+"3",522,"ED PNEUMONIA",423,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",1,68,0.160756501182033,290,2601,0.0147058823529412,0.111495578623606,0.131896551724138,67,2311,0.0197573814605168
+"4",522,"ED PNEUMONIA",423,"115287","LEVOFLOXACIN IN D5W 500 MG/100 ML IV PGBK",6,14,0.033096926713948,156,1438,0.428571428571429,0.108484005563282,3.95054945054945,8,1282,0.0007817771890473
+"5",522,"ED PNEUMONIA",423,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",2,81,0.191489361702128,208,3699,0.0246913580246914,0.0562314138956475,0.439102564102564,79,3491,0.326737262386161
+"6",522,"ED PNEUMONIA",423,"122689","ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU",1,5,0.0118203309692671,142,1409,0.2,0.100780695528744,1.98450704225352,4,1267,0.999999999999998
+"7",522,"ED PNEUMONIA",423,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",16,130,0.307328605200946,1951,16917,0.123076923076923,0.115327776792576,1.06719236683358,114,14966,0.890473267593354
+"8",522,"ED PNEUMONIA",423,"520039","AZITHROMYCIN IVPB",3,48,0.113475177304965,127,2522,0.0625,0.0503568596352102,1.24114173228346,45,2395,0.961827304710995
+"9",522,"ED PNEUMONIA",423,"540102","NS IV BOLUS",2,29,0.0685579196217494,2225,80957,0.0689655172413793,0.0274837259285794,2.50932196822937,27,78732,0.424926564469396
+"10",522,"ED PNEUMONIA",423,"540967","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS",5,34,0.0803782505910166,135,2073,0.147058823529412,0.065123010130246,2.25816993464052,29,1938,0.119795551317201
+"11",522,"ED PNEUMONIA",423,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",5,39,0.0921985815602837,277,3459,0.128205128205128,0.0800809482509396,1.60094418217162,34,3182,0.42255456033726
+"12",522,"ED PNEUMONIA",423,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",1,24,0.0567375886524823,411,7130,0.0416666666666667,0.0576437587657784,0.722830494728305,23,6719,0.999999999999745
+"13",523,"ED AMI-STEMI PROTOCOL",203,"680","ASPIRIN 81 MG PO CHEW",6,32,0.157635467980296,357,11203,0.1875,0.031866464339909,5.88392857142857,26,10846,7.77346854982215e-06
+"14",523,"ED AMI-STEMI PROTOCOL",203,"10176","HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",3,58,0.285714285714286,164,2898,0.0517241379310345,0.0565907522429262,0.914003364171573,55,2734,0.999999999999931
+"15",523,"ED AMI-STEMI PROTOCOL",203,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",1,31,0.152709359605911,68,899,0.032258064516129,0.0756395995550612,0.426470588235294,30,831,0.577113320157024
+"16",523,"ED AMI-STEMI PROTOCOL",203,"77545","ATORVASTATIN 40 MG PO TABS",1,1,0.00492610837438424,159,9613,1,0.0165401019452824,60.4591194968553,0,9454,0.000157731528933068
+"17",523,"ED AMI-STEMI PROTOCOL",203,"90097","NITROGLYCERIN 0.4 MG/DOSE TL SPRY",1,1,0.00492610837438424,1,33,1,0.0303030303030303,33,0,32,0.0570148352207188
+"18",523,"ED AMI-STEMI PROTOCOL",203,"203749","CLOPIDOGREL 75 MG PO TABS",1,17,0.083743842364532,40,500,0.0588235294117647,0.08,0.735294117647059,16,460,1
+"19",523,"ED AMI-STEMI PROTOCOL",203,"208212","PRASUGREL 10 MG PO TABS",1,15,0.0738916256157636,24,451,0.0666666666666667,0.0532150776053215,1.25277777777778,14,427,0.999999999999999
+"20",523,"ED AMI-STEMI PROTOCOL",203,"214201","TICAGRELOR 90 MG PO TABS",1,4,0.0197044334975369,23,361,0.25,0.0637119113573407,3.92391304347826,3,338,0.63071241331896
+"21",524,"ED SHORTNESS OF BREATH",628,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",7,41,0.0652866242038217,106,3939,0.170731707317073,0.0269103833460269,6.3444546709618,34,3833,4.57330089358468e-07
+"22",524,"ED SHORTNESS OF BREATH",628,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",2,130,0.207006369426752,493,18456,0.0153846153846154,0.0267121803207629,0.575940084256514,128,17963,0.598869850205443
+"23",524,"ED SHORTNESS OF BREATH",628,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",1,53,0.0843949044585987,275,8000,0.0188679245283019,0.034375,0.548885077186964,52,7725,0.810540610602515
+"24",524,"ED SHORTNESS OF BREATH",628,"520002","DEXAMETHASONE (CUSTOM DOSE) IVPB",5,8,0.0127388535031847,168,1360,0.625,0.123529411764706,5.05952380952381,3,1192,0.000198015923642377
+"25",524,"ED SHORTNESS OF BREATH",628,"540102","NS IV BOLUS",1,34,0.054140127388535,2225,80957,0.0294117647058824,0.0274837259285794,1.07015201586252,33,78732,0.999999999997799
+"26",525,"ED ABDOMINAL PAIN ",1002,"3844","IBUPROFEN 600 MG PO TABS",1,2,0.00199600798403194,98,3745,0.5,0.0261682242990654,19.1071428571429,1,3647,0.0486130032760359
+"27",525,"ED ABDOMINAL PAIN ",1002,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,2,0.00199600798403194,688,12925,1,0.0532301740812379,18.7863372093023,0,12237,1.16997135943892e-05
+"28",525,"ED ABDOMINAL PAIN ",1002,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",8,136,0.135728542914172,1219,33427,0.0588235294117647,0.036467526251234,1.61303865270472,128,32208,0.247089678427697
+"29",525,"ED ABDOMINAL PAIN ",1002,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,229,0.228542914171657,705,36939,0.00873362445414847,0.0190855193697718,0.457604757038001,227,36234,0.36778953274521
+"30",525,"ED ABDOMINAL PAIN ",1002,"540102","NS IV BOLUS",2,268,0.267465069860279,2225,80957,0.00746268656716418,0.0274837259285794,0.271531108502432,266,78732,0.0692980754713673
+"31",525,"ED ABDOMINAL PAIN ",1002,"540141","GI COCKTAIL",1,10,0.00998003992015968,39,917,0.1,0.0425299890948746,2.35128205128205,9,878,0.914641606875305
+"32",525,"ED ABDOMINAL PAIN ",1002,"540300","MORPHINE INJECTABLE SYRINGE",3,94,0.093812375249501,61,1002,0.0319148936170213,0.0608782435129741,0.524241367282874,91,941,0.360177192335494
+"33",526,"ED GI BLEED ",377,"82384","PANTOPRAZOLE 40 MG IV SOLR",4,124,0.328912466843501,688,12925,0.032258064516129,0.0532301740812379,0.60601087771943,120,12237,0.40324632809615
+"34",526,"ED GI BLEED ",377,"530014","PANTOPRAZOLE 0.8 MG/ML IV INFUSION",2,102,0.270557029177719,72,1751,0.0196078431372549,0.0411193603655054,0.476851851851852,100,1679,0.4130877929008
+"35",526,"ED GI BLEED ",377,"540102","NS IV BOLUS",1,56,0.148541114058355,2225,80957,0.0178571428571429,0.0274837259285794,0.649735152487961,55,78732,0.974743155880719
+"36",534,"ED CELLULITIS/ABSCESS",32,"9488","CEFTRIAXONE 2 GRAM IV SOLR",1,2,0.0625,23,339,0.5,0.0678466076696165,7.3695652173913,1,316,0.319245865298994
+"37",536,"ED FEVER/NEUTROPENIA",237,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",5,23,0.0970464135021097,1808,23328,0.217391304347826,0.0775034293552812,2.80492497114275,18,21520,0.034349581581189
+"38",536,"ED FEVER/NEUTROPENIA",237,"540102","NS IV BOLUS",1,66,0.278481012658228,2225,80957,0.0151515151515152,0.0274837259285794,0.551290432414028,65,78732,0.813435685574951
+"39",536,"ED FEVER/NEUTROPENIA",237,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",5,43,0.181434599156118,277,3459,0.116279069767442,0.0800809482509396,1.45201914196961,38,3182,0.558542641171762
+"40",538,"ED SEPTIC SHOCK",26,"95725","CEFTRIAXONE 2 GRAM INJ SOLR",1,1,0.0384615384615385,31,319,1,0.0971786833855799,10.2903225806452,0,288,0.181738411507938
+"41",538,"ED SEPTIC SHOCK",26,"541089","VANCOMYCIN 1 GRAM/100 ML CENTRAL LINE MINI-BAG PLUS",1,1,0.0384615384615385,112,785,1,0.142675159235669,7.00892857142857,0,673,0.309636054941106
+"42",539,"ED SEPSIS PROTOCOL",864,"5018","METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",1,5,0.00578703703703704,223,3847,0.2,0.0579672472056148,3.45022421524664,4,3624,0.689078870446924
+"43",539,"ED SEPSIS PROTOCOL",864,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",1,46,0.0532407407407407,290,2601,0.0217391304347826,0.111495578623606,0.194977511244378,45,2311,0.0907672691055761
+"44",539,"ED SEPSIS PROTOCOL",864,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,22,0.025462962962963,208,3699,0.0454545454545455,0.0562314138956475,0.808347902097902,21,3491,0.999999999999994
+"45",539,"ED SEPSIS PROTOCOL",864,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",6,39,0.0451388888888889,1951,16917,0.153846153846154,0.115327776792576,1.33399045854197,33,14966,0.61629915644815
+"46",539,"ED SEPSIS PROTOCOL",864,"520039","AZITHROMYCIN IVPB",1,12,0.0138888888888889,127,2522,0.0833333333333333,0.0503568596352102,1.65485564304462,11,2395,0.999999999999999
+"47",539,"ED SEPSIS PROTOCOL",864,"520058","CASPOFUNGIN 70 MG IVPB",1,1,0.00115740740740741,56,1656,1,0.0338164251207729,29.5714285714286,0,1600,0.0106046631275878
+"48",539,"ED SEPSIS PROTOCOL",864,"540102","NS IV BOLUS",9,211,0.244212962962963,2225,80957,0.042654028436019,0.0274837259285794,1.55197401352575,202,78732,0.25657982421943
+"49",539,"ED SEPSIS PROTOCOL",864,"540967","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS",5,47,0.0543981481481481,135,2073,0.106382978723404,0.065123010130246,1.63356973995272,42,1938,0.406931664366999
+"50",539,"ED SEPSIS PROTOCOL",864,"541020","VANCOMYCIN ED ORDER ",12,77,0.0891203703703704,213,1807,0.155844155844156,0.117874930824571,1.3221145052131,65,1594,0.408360276740031
+"51",539,"ED SEPSIS PROTOCOL",864,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",2,16,0.0185185185185185,277,3459,0.125,0.0800809482509396,1.56092057761733,14,3182,0.842558049696187
+"52",539,"ED SEPSIS PROTOCOL",864,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",2,9,0.0104166666666667,411,7130,0.222222222222222,0.0576437587657784,3.85509597188429,7,6719,0.161762888221869
+"53",539,"ED SEPSIS PROTOCOL",864,"541086","CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS",3,10,0.0115740740740741,174,2732,0.3,0.0636896046852123,4.71034482758621,7,2558,0.0168089936072586
+"54",539,"ED SEPSIS PROTOCOL",864,"541110","ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS",1,2,0.00231481481481481,72,1340,0.5,0.0537313432835821,9.30555555555556,1,1268,0.222234783408579
+"55",539,"ED SEPSIS PROTOCOL",864,"541442","NS IV BOLUS - 2000 ML",6,32,0.037037037037037,5,120,0.1875,0.0416666666666667,4.5,26,115,0.0144814201647833
+"56",539,"ED SEPSIS PROTOCOL",864,"541448","LR IV BOLUS - 30 ML/KG",2,8,0.00925925925925926,1,6,0.25,0.166666666666667,1.5,6,5,1
+"57",540,"ED ALLERGIC REACTION",10,"213662","EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",1,5,0.5,69,5047,0.2,0.0136714880126808,14.6289855072464,4,4978,0.099211920834331
+"58",543,"ED ETOH/ALTERED MENTAL STATUS",147,"10467","LORAZEPAM 2 MG/ML INJ SOLN",1,8,0.054421768707483,749,20621,0.125,0.0363221958197954,3.44142189586115,7,19872,0.692745738725335
+"59",543,"ED ETOH/ALTERED MENTAL STATUS",147,"520094","MULTIVITAMINS (+ FOLIC ACID, THIAMINE, VIT K) IVPB",1,6,0.0408163265306122,109,1474,0.166666666666667,0.0739484396200814,2.25382262996942,5,1365,0.932816017719159
+"60",543,"ED ETOH/ALTERED MENTAL STATUS",147,"540102","NS IV BOLUS",2,31,0.210884353741497,2225,80957,0.0645161290322581,0.0274837259285794,2.3474302283436,29,78732,0.476859945095999
+"61",543,"ED ETOH/ALTERED MENTAL STATUS",147,"540300","MORPHINE INJECTABLE SYRINGE",1,1,0.00680272108843537,61,1002,1,0.0608782435129741,16.4262295081967,0,941,0.0686866302622316
+"62",547,"ED GENERAL COMPLAINT",2569,"5172","MORPHINE 4 MG/ML INJ SYRG",4,94,0.0365901128843908,238,5949,0.0425531914893617,0.0400067238191293,1.06365099231182,90,5711,0.999999999999996
+"63",547,"ED GENERAL COMPLAINT",2569,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",1,10,0.00389256520046711,127,3899,0.1,0.0325724544755065,3.07007874015748,9,3772,0.758847960380584
+"64",547,"ED GENERAL COMPLAINT",2569,"94176","ONDANSETRON 4 MG PO TBDL",1,3,0.00116776956014013,153,4277,0.333333333333333,0.0357727379003975,9.31808278867102,2,4124,0.224064876529558
+"65",547,"ED GENERAL COMPLAINT",2569,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,103,0.0400934215648112,1219,33427,0.00970873786407767,0.036467526251234,0.266229680543498,102,32208,0.23617666798263
+"66",547,"ED GENERAL COMPLAINT",2569,"122689","ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU",1,11,0.00428182172051382,142,1409,0.0909090909090909,0.100780695528744,0.902048655569782,10,1267,0.999999999999965
+"67",547,"ED GENERAL COMPLAINT",2569,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,193,0.0751265083690152,705,36939,0.00518134715025907,0.0190855193697718,0.271480542387829,192,36234,0.251598965076661
+"68",547,"ED GENERAL COMPLAINT",2569,"540102","NS IV BOLUS",11,630,0.245231607629428,2225,80957,0.0174603174603175,0.0274837259285794,0.635296593543785,619,78732,0.157796097679712
+"69",550,"ED SEIZURE TREATMENT",37,"10467","LORAZEPAM 2 MG/ML INJ SOLN",2,9,0.243243243243243,749,20621,0.222222222222222,0.0363221958197954,6.11808337041982,7,19872,0.0368890631652922
+"70",550,"ED SEIZURE TREATMENT",37,"203954","FOSPHENYTOIN 100 MG PE/2 ML INJ SOLN",2,2,0.0540540540540541,9,107,1,0.0841121495327103,11.8888888888889,0,98,0.0020996190510804
+"71",571,"PED ED DKA",16,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,1,0.0625,692,19620,1,0.0352701325178389,28.3526011560694,0,18928,0.0118194121841506
+"72",571,"PED ED DKA",16,"570056","INSULIN REGULAR 0.1 UNIT/ML PEDIATRIC IV INFUSION",1,2,0.125,2,3,0.5,0.666666666666667,0.75,1,1,1
+"73",571,"PED ED DKA",16,"570057","INSULIN REGULAR 1 UNIT/ML PEDIATRIC IV INFUSION",1,2,0.125,6,10,0.5,0.6,0.833333333333333,1,4,1
+"74",591,"NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL",1097,"680","ASPIRIN 81 MG PO CHEW",15,116,0.105742935278031,357,11203,0.129310344827586,0.031866464339909,4.05788177339902,101,10846,2.20966158120887e-08
+"75",591,"NURSING TRIAGE CARDIAC COMPLAINTS PROTOCOL",1097,"5604","NITROGLYCERIN 0.4 MG SL SUBL",10,96,0.0875113947128532,77,2947,0.104166666666667,0.0261282660332542,3.98674242424242,86,2870,2.62239881186508e-05
+"76",592,"NURSING TRIAGE COPD PROTOCOL",98,"101","ACETAMINOPHEN 325 MG PO TABS",2,5,0.0510204081632653,981,42092,0.4,0.0233060914187969,17.1628950050968,3,41111,4.19328604996452e-05
+"77",592,"NURSING TRIAGE COPD PROTOCOL",98,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",1,17,0.173469387755102,127,3899,0.0588235294117647,0.0325724544755065,1.80592867068087,16,3772,0.999999999999899
+"78",592,"NURSING TRIAGE COPD PROTOCOL",98,"122689","ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU",2,9,0.0918367346938776,142,1409,0.222222222222222,0.100780695528744,2.20500782472613,7,1267,0.516481576855816
+"79",598,"NURSING TRIAGE ABDOMINAL PAIN PROTOCOL",760,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",8,95,0.125,1219,33427,0.0842105263157895,0.036467526251234,2.3091921765036,87,32208,0.0277376599148412
+"80",598,"NURSING TRIAGE ABDOMINAL PAIN PROTOCOL",760,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,111,0.146052631578947,705,36939,0.00900900900900901,0.0190855193697718,0.472033735863523,110,36234,0.66887279964401
+"81",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"101","ACETAMINOPHEN 325 MG PO TABS",9,986,0.15773476243801,981,42092,0.00912778904665314,0.0233060914187969,0.391648212590952,977,41111,0.00466314526492194
+"82",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"102","ACETAMINOPHEN 500 MG PO TABS",1,26,0.00415933450647896,392,15968,0.0384615384615385,0.0245490981963928,1.56671899529042,25,15576,0.999999999999926
+"83",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"1755","CLONIDINE HCL 0.1 MG PO TABS",1,9,0.0014397696368581,104,2839,0.111111111111111,0.0366326171187038,3.03311965811966,8,2735,0.765714822380169
+"84",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",2,118,0.0188769796832507,356,12049,0.0169491525423729,0.0295460204166321,0.573652637592839,116,11693,0.594665094718473
+"85",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"2566","DOCUSATE SODIUM 100 MG PO CAPS",4,695,0.111182210846265,542,37509,0.00575539568345324,0.0144498653656456,0.398300990204147,691,36967,0.0797307046210229
+"86",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"2567","DOCUSATE SODIUM 250 MG PO CAPS",4,338,0.0540713485842265,296,17760,0.0118343195266272,0.0166666666666667,0.710059171597633,334,17464,0.635305546859246
+"87",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"3233","FOLIC ACID 1 MG PO TABS",1,84,0.013437849944009,58,4621,0.0119047619047619,0.0125513958017745,0.948481116584565,83,4563,0.999999999999999
+"88",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"3774","HYDROXYZINE HCL 25 MG PO TABS",7,473,0.0756678931370981,133,4979,0.0147991543340381,0.0267121912030528,0.554022476911094,466,4846,0.157582770437956
+"89",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"3843","IBUPROFEN 400 MG PO TABS",6,363,0.0580707086866101,148,4216,0.0165289256198347,0.0351043643263757,0.470851016305562,357,4068,0.08327534912658
+"90",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"3845","IBUPROFEN 800 MG PO TABS",1,17,0.00271956486962086,25,565,0.0588235294117647,0.0442477876106195,1.32941176470588,16,540,0.999999999999999
+"91",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"4572","LORAZEPAM 0.5 MG PO TABS",2,35,0.00559910414333707,349,13238,0.0571428571428571,0.0263634990179785,2.16749897666803,33,12889,0.544552135408463
+"92",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"4573","LORAZEPAM 1 MG PO TABS",7,162,0.0259158534634459,337,8494,0.0432098765432099,0.0396750647515894,1.08909403963806,155,8157,0.979942716961932
+"93",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"4574","LORAZEPAM 2 MG PO TABS",1,10,0.00159974404095345,31,618,0.1,0.0501618122977346,1.99354838709677,9,587,0.999999999999963
+"94",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"7876","THIAMINE HCL (VITAMIN B1) 100 MG/ML INJ SOLN",1,22,0.00351943689009758,145,981,0.0454545454545455,0.147808358817533,0.307523510971787,21,836,0.298044905960772
+"95",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"7877","THIAMINE HCL (VITAMIN B1) 100 MG PO TABS",1,76,0.0121580547112462,100,3808,0.0131578947368421,0.0262605042016807,0.501052631578947,75,3708,0.728802690204205
+"96",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"8085","TRAZODONE 50 MG PO TABS",3,178,0.0284754439289714,334,11344,0.0168539325842697,0.0294428772919605,0.572428177353159,175,11010,0.444336713158395
+"97",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"9637","CLONAZEPAM 0.5 MG PO TABS",2,25,0.00399936010238362,108,3358,0.08,0.0321620011911852,2.48740740740741,23,3250,0.436757766040088
+"98",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"10717","NICOTINE (POLACRILEX) 2 MG BUCCAL GUM",1,104,0.0166373380259159,19,871,0.00961538461538462,0.0218140068886338,0.440789473684211,103,852,0.642969990222206
+"99",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"11349","SENNOSIDES 8.6 MG PO TABS",5,584,0.0934250519916813,610,35793,0.00856164383561644,0.0170424384656218,0.502371996406917,579,35183,0.157033728530506
+"100",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"16830","MELATONIN 3 MG PO TABS",3,157,0.0251159814429691,456,17087,0.0191082802547771,0.0266869549950255,0.716015755950385,154,16631,0.735203404709836
+"101",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"27860","NICOTINE 7 MG/24 HR TD PT24",1,23,0.00367941129419293,23,953,0.0434782608695652,0.0241343126967471,1.80151228733459,22,930,0.999999999999978
+"102",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"27862","NICOTINE 14 MG/24 HR TD PT24",1,52,0.00831866901295793,28,1507,0.0192307692307692,0.0185799601857996,1.03502747252747,51,1479,0.999999999999997
+"103",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"27863","NICOTINE 21 MG/24 HR TD PT24",1,44,0.00703887378019517,23,1042,0.0227272727272727,0.0220729366602687,1.0296442687747,43,1019,0.999999999999999
+"104",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"38390","ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP",2,440,0.0703887378019517,14,882,0.00454545454545455,0.0158730158730159,0.286363636363636,438,868,0.131543168359831
+"105",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"80840","ONDANSETRON HCL 8 MG PO TABS",1,218,0.0348744200927852,170,10077,0.00458715596330275,0.0168701002282425,0.271910415542364,217,9907,0.255923738497241
+"106",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"81141","ONDANSETRON HCL 4 MG PO TABS",1,14,0.00223964165733483,87,4024,0.0714285714285714,0.0216202783300199,3.30377668308703,13,3937,0.720810576386406
+"107",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"84520","ONDANSETRON 8 MG PO TBDL",2,28,0.00447928331466965,153,4394,0.0714285714285714,0.0348202093764224,2.05135387488329,26,4241,0.592970534874936
+"108",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"92173","ZOLPIDEM 5 MG PO TABS",3,37,0.00591905295152776,159,8203,0.0810810810810811,0.019383152505181,4.18306986231515,34,8044,0.035398517062158
+"109",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"540598","THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS",1,140,0.0223964165733483,17,1250,0.00714285714285714,0.0136,0.525210084033613,139,1233,0.805142527868802
+"110",609,"IP PSY PSYCHIATRY INPATIENT ADMIT",6251,"9000000","ZZZ IMS TEMPLATE",1,9,0.0014397696368581,1035,28824,0.111111111111111,0.0359075770191507,3.09436392914654,8,27789,0.751719783873197
+"111",611,"IP ASC PSY ECT",1777,"4318","LACTATED RINGERS IV SOLP",94,1111,0.625211029825549,135,4847,0.0846084608460846,0.0278522797606767,3.03775710904424,1017,4712,1.5072019695937e-18
+"112",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",9,601,0.093994369721614,399,16019,0.0149750415973378,0.0249079218428117,0.601216018415423,592,15620,0.158351002475462
+"113",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"5339","NALBUPHINE 10 MG/ML INJ SOLN",10,975,0.152486706287144,3,308,0.0102564102564103,0.00974025974025974,1.05299145299145,965,305,1
+"114",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"5340","NALBUPHINE 20 MG/ML INJ SOLN",1,107,0.0167344385361276,1,129,0.00934579439252336,0.00775193798449612,1.20560747663551,106,128,0.999999999999993
+"115",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"29464","MORPHINE (PF) 0.5 MG/ML INJ SOLN",1,2,0.000312793243665937,7,23,0.5,0.304347826086957,1.64285714285714,1,16,0.999999999999999
+"116",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"530032","BUPIVACAINE 0.125% (1/8%) EPIDURAL INFUSION",30,2314,0.361901782921489,5,132,0.0129645635263613,0.0378787878787879,0.342264477095938,2284,127,0.0491208316502799
+"117",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"530033","BUPIVACAINE 0.25% (1/4%) EPIDURAL INFUSION",2,79,0.0123553331248045,1,18,0.0253164556962025,0.0555555555555556,0.455696202531646,77,17,0.999999999999994
+"118",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"530051","LIDOCAINE 1% EPIDURAL INFUSION",7,190,0.029715358148264,1,20,0.0368421052631579,0.05,0.736842105263158,183,19,0.999999999999999
+"119",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"530104","HYDROMORPHONE 0.05 MG/ML EPIDURAL INFUSION",35,1683,0.263215514544886,9,116,0.0207961972667855,0.0775862068965517,0.268039875883013,1648,107,0.000432806595323392
+"120",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",2,33,0.00516108852048796,461,18637,0.0606060606060606,0.0247357407308043,2.45014132649707,31,18176,0.445060061892013
+"121",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"540107","FENTANYL 50 MCG/ML EPIDURAL PCEA",52,1763,0.275727244291523,5,55,0.029495178672717,0.0909090909090909,0.324446965399887,1711,50,0.0291968227685402
+"122",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"540205","LIDOCAINE 1% INFUSION",4,16,0.00250234594932749,1,16,0.25,0.0625,4,12,15,0.330191118603343
+"123",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"540541","FENTANYL/BUPIVACAINE PCEA 30 ML SYRINGE",13,166,0.0259618392242728,4,20,0.0783132530120482,0.2,0.391566265060241,153,16,0.169644769901168
+"124",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"541064","HYDROMORPHONE EPIDURAL INJECTION",13,284,0.044416640600563,4,82,0.0457746478873239,0.0487804878048781,0.938380281690141,271,78,0.999999999999999
+"125",616,"IP PAI EPIDURAL ANALGESIA POSTOP",6394,"541604","FENTANYL 50 MCG/ML EPIDURAL PCEA",15,550,0.0860181420081326,4,26,0.0272727272727273,0.153846153846154,0.177272727272727,535,22,0.00298483030712576
+"126",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"101","ACETAMINOPHEN 325 MG PO TABS",24,1916,0.185586981790004,981,42092,0.0125260960334029,0.0233060914187969,0.537460177612636,1892,41111,0.00260356874758031
+"127",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"102","ACETAMINOPHEN 500 MG PO TABS",21,947,0.0917280123982952,392,15968,0.0221752903907075,0.0245490981963928,0.903303665711269,926,15576,0.725196596015492
+"128",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"1080","BISACODYL 10 MG PR SUPP",3,263,0.0254746222394421,344,22442,0.0114068441064639,0.0153284021031994,0.7441639402246,260,22098,0.792850101750838
+"129",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"2566","DOCUSATE SODIUM 100 MG PO CAPS",8,908,0.0879504068190624,542,37509,0.00881057268722467,0.0144498653656456,0.609733894695775,900,36967,0.203336329899897
+"130",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"5373","NALOXONE 0.4 MG/ML INJ SOLN",53,3956,0.383184812088338,83,1774,0.0133973710819009,0.0467869222096956,0.286348630111954,3903,1691,3.38636059699585e-14
+"131",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,2,0.000193723363037582,218,7862,0.5,0.0277283134062579,18.0321100917431,1,7644,0.0561791108300188
+"132",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"10814","OXYCODONE 5 MG PO TABS",104,3774,0.365555986051918,1478,56614,0.0275569687334393,0.0261066167379094,1.0555549579668,3670,55136,0.625903474582416
+"133",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"11349","SENNOSIDES 8.6 MG PO TABS",10,718,0.0695466873304921,610,35793,0.0139275766016713,0.0170424384656218,0.817229097219051,708,35183,0.621478989492255
+"134",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"18308","GABAPENTIN 300 MG PO CAPS",53,1973,0.191108097636575,588,19458,0.026862645717182,0.0302189330866482,0.888934286334909,1920,18870,0.444515253342906
+"135",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"18309","GABAPENTIN 100 MG PO CAPS",15,845,0.0818481208833785,199,6318,0.0177514792899408,0.0314973092750871,0.563587166602242,830,6119,0.0360192118449499
+"136",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",3,374,0.0362262688880279,712,36910,0.00802139037433155,0.0192901652668653,0.41582797572553,371,36198,0.164059436991192
+"137",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"27920","OXYCODONE 10 MG PO TB12",14,288,0.0278961642774119,258,7208,0.0486111111111111,0.0357935627081021,1.35809646856159,274,6950,0.327099500909072
+"138",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"27921","OXYCODONE 20 MG PO TB12",1,34,0.0032932971716389,93,2927,0.0294117647058824,0.0317731465664503,0.925679949399114,33,2834,0.999999999999996
+"139",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"27922","OXYCODONE 40 MG PO TB12",1,19,0.00184037194885703,47,1081,0.0526315789473684,0.0434782608695652,1.21052631578947,18,1034,0.999999999999998
+"140",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"28899","OXYCODONE 15 MG PO TABS",1,26,0.00251840371948857,51,1050,0.0384615384615385,0.0485714285714286,0.79185520361991,25,999,0.999999999999983
+"141",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,12,0.00116234017822549,603,24164,0.0833333333333333,0.0249544777354743,3.33941404090658,11,23561,0.711100430991499
+"142",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"77052","CELECOXIB 200 MG PO CAPS",13,237,0.0229562185199535,20,1192,0.0548523206751055,0.0167785234899329,3.26919831223629,224,1172,0.000876659532486715
+"143",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"93715","CELECOXIB 100 MG PO CAPS",25,986,0.0955056179775281,42,1272,0.0253549695740365,0.0330188679245283,0.767893364242249,961,1230,0.347502902880716
+"144",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"125758","PREGABALIN 75 MG PO CAPS",1,39,0.00377760557923286,31,1474,0.0256410256410256,0.0210312075983718,1.2191894127378,38,1443,1
+"145",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"125759","PREGABALIN 100 MG PO CAPS",1,5,0.000484308407593956,13,833,0.2,0.0156062424969988,12.8153846153846,4,820,0.144974943390625
+"146",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",11,825,0.0799108872530027,705,36939,0.0133333333333333,0.0190855193697718,0.698609929078014,814,36234,0.285038086590935
+"147",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"211398","ROPIVACAINE (PF) 0.2 % 550 ML LINF ELPH",5,215,0.0208252615265401,20,425,0.0232558139534884,0.0470588235294118,0.494186046511628,210,405,0.210561129552813
+"148",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"233900","OXYCODONE 10 MG PO TR12",2,65,0.00629600929872143,38,949,0.0307692307692308,0.0400421496311907,0.768421052631579,63,911,0.966322168493397
+"149",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"520183","BUPIVACAINE 0.125% (1/8%) NERVE BLOCK INFUSION",27,1525,0.147714064316157,8,165,0.0177049180327869,0.0484848484848485,0.365163934426229,1498,157,0.018796710488868
+"150",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"520184","BUPIVACAINE 0.25% (1/4%) NERVE BLOCK INFUSION",22,1830,0.177256877179388,6,202,0.0120218579234973,0.0297029702970297,0.404735883424408,1808,196,0.0840395314076258
+"151",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"540202","ROPIVACAINE 0.1% NERVE BLOCK INFUSION",16,209,0.0202440914374274,2,13,0.076555023923445,0.153846153846154,0.497607655502392,193,11,0.640500919916039
+"152",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"540205","LIDOCAINE 1% INFUSION",3,71,0.00687717938783417,1,16,0.0422535211267606,0.0625,0.676056338028169,68,15,0.999999999999994
+"153",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"540207","ROPIVACAINE 0.2% 400 ML 2-14 ML ANESTHETIC PUMP",16,171,0.0165633475397133,2,30,0.0935672514619883,0.0666666666666667,1.40350877192982,155,28,0.897094286567087
+"154",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"540470","BUPIVACAINE 0.125% 400 ML 2-14 ML ANESTHETIC PUMP",2,13,0.00125920185974429,1,24,0.153846153846154,0.0416666666666667,3.69230769230769,11,23,0.573700502098691
+"155",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"540475","BUPIVACAINE 0.25% 400 ML 2-14 ML ANESTHETIC PUMP",5,14,0.00135606354126308,1,34,0.357142857142857,0.0294117647058824,12.1428571428571,9,33,0.00827769135810255
+"156",618,"IP PAI PERIPHERAL NERVE CATHETER/REGIONAL ANALGESIA ADMIT",10324,"541178","ROPIVACAINE 0.2% NERVE BLOCK INFUSION (PREMIX)",9,257,0.0248934521503293,1,6,0.0350194552529183,0.166666666666667,0.21011673151751,248,5,0.55716433822452
+"157",619,"IP PAI SCIPP CHRONIC PAIN ADMIT",364,"93295","ZOLPIDEM 10 MG PO TABS",1,7,0.0192307692307692,33,1662,0.142857142857143,0.01985559566787,7.19480519480519,6,1629,0.337940134100052
+"158",619,"IP PAI SCIPP CHRONIC PAIN ADMIT",364,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,2,0.00549450549450549,499,13896,0.5,0.0359096142774899,13.9238476953908,1,13397,0.10408135842914
+"159",619,"IP PAI SCIPP CHRONIC PAIN ADMIT",364,"540000","PAIN COCKTAIL",1,105,0.288461538461538,57,1525,0.00952380952380952,0.0373770491803279,0.254803675856307,104,1468,0.223251461731937
+"160",623,"IP PAI KETAMINE IV",358,"4572","LORAZEPAM 0.5 MG PO TABS",3,93,0.259776536312849,349,13238,0.032258064516129,0.0263634990179785,1.22358813199002,90,12889,0.97702679729596
+"161",623,"IP PAI KETAMINE IV",358,"530034","KETAMINE 5 MG/ML IV INFUSION",2,160,0.446927374301676,31,480,0.0125,0.0645833333333333,0.193548387096774,158,449,0.0176160532687648
+"162",624,"ANE PACU (OUTPATIENT)",7783,"101","ACETAMINOPHEN 325 MG PO TABS",1,73,0.00937941667737376,981,42092,0.0136986301369863,0.0233060914187969,0.587770376886878,72,41111,0.876473725484369
+"163",624,"ANE PACU (OUTPATIENT)",7783,"102","ACETAMINOPHEN 500 MG PO TABS",23,531,0.0682256199408968,392,15968,0.0433145009416196,0.0245490981963928,1.76440293631577,508,15576,0.0100000979565167
+"164",624,"ANE PACU (OUTPATIENT)",7783,"730","ATROPINE 0.1 MG/ML INJ SYRG",1,19,0.00244121803931646,11,381,0.0526315789473684,0.0288713910761155,1.82296650717703,18,370,1
+"165",624,"ANE PACU (OUTPATIENT)",7783,"2332","DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",10,314,0.0403443402287036,390,6737,0.0318471337579618,0.0578892682202761,0.550138820839458,304,6347,0.067974568413664
+"166",624,"ANE PACU (OUTPATIENT)",7783,"3497","GLYCOPYRROLATE 0.2 MG/ML INJ SOLN",13,318,0.0408582808685597,61,1031,0.0408805031446541,0.0591658583899127,0.690947520362924,305,970,0.266534744562113
+"167",624,"ANE PACU (OUTPATIENT)",7783,"4318","LACTATED RINGERS IV SOLP",96,2947,0.378645766413979,135,4847,0.0325755005089922,0.0278522797606767,1.16958111827471,2851,4712,0.261247472573188
+"168",624,"ANE PACU (OUTPATIENT)",7783,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",9,428,0.0549916484646023,346,10128,0.0210280373831776,0.0341627172195893,0.615525903516828,419,9782,0.180382589128349
+"169",624,"ANE PACU (OUTPATIENT)",7783,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",5,132,0.0169600411152512,745,12385,0.0378787878787879,0.0601534113847396,0.629703070978239,127,11640,0.374409186028367
+"170",624,"ANE PACU (OUTPATIENT)",7783,"5339","NALBUPHINE 10 MG/ML INJ SOLN",7,182,0.0233842991134524,3,308,0.0384615384615385,0.00974025974025974,3.94871794871795,175,305,0.0654693216055104
+"171",624,"ANE PACU (OUTPATIENT)",7783,"5373","NALOXONE 0.4 MG/ML INJ SOLN",39,1325,0.170242836952332,83,1774,0.0294339622641509,0.0467869222096956,0.629106615139804,1286,1691,0.0180701276701051
+"172",624,"ANE PACU (OUTPATIENT)",7783,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",9,237,0.0304509829114737,218,7862,0.0379746835443038,0.0277283134062579,1.36952734873998,228,7644,0.458153945400246
+"173",624,"ANE PACU (OUTPATIENT)",7783,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",38,1271,0.163304638314275,289,5149,0.029897718332022,0.056127403379297,0.532675957410316,1233,4860,0.000185694397555465
+"174",624,"ANE PACU (OUTPATIENT)",7783,"10372","LABETALOL 5 MG/ML IV SOLN",14,540,0.069381986380573,370,5898,0.0259259259259259,0.0627331298745337,0.413273273273273,526,5528,0.000774217216845612
+"175",624,"ANE PACU (OUTPATIENT)",7783,"10814","OXYCODONE 5 MG PO TABS",23,1182,0.151869459077477,1478,56614,0.0194585448392555,0.0261066167379094,0.745349159356976,1159,55136,0.183548937693976
+"176",624,"ANE PACU (OUTPATIENT)",7783,"18308","GABAPENTIN 300 MG PO CAPS",1,46,0.00591031735834511,588,19458,0.0217391304347826,0.0302189330866482,0.719387755102041,45,18870,0.999999999999906
+"177",624,"ANE PACU (OUTPATIENT)",7783,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",33,970,0.124630605165103,692,19620,0.034020618556701,0.0352701325178389,0.964573029020916,937,18928,0.906957223457993
+"178",624,"ANE PACU (OUTPATIENT)",7783,"27920","OXYCODONE 10 MG PO TB12",1,18,0.00231273287935243,258,7208,0.0555555555555556,0.0357935627081021,1.55211024978467,17,6950,0.999999999999816
+"179",624,"ANE PACU (OUTPATIENT)",7783,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",14,416,0.0534498265450341,603,24164,0.0336538461538462,0.0249544777354743,1.34860951651996,402,23561,0.333771740303041
+"180",624,"ANE PACU (OUTPATIENT)",7783,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",8,207,0.026596428112553,93,5289,0.0386473429951691,0.0175836642087351,2.19791179678978,199,5196,0.0512112057252685
+"181",624,"ANE PACU (OUTPATIENT)",7783,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",43,1494,0.191956828986252,738,23934,0.0287817938420348,0.0308347956881424,0.93341931411282,1451,23196,0.712191483211845
+"182",624,"ANE PACU (OUTPATIENT)",7783,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,615,0.0790183733778749,1219,33427,0.0032520325203252,0.036467526251234,0.0891761206373343,613,32208,1.86942285079479e-05
+"183",624,"ANE PACU (OUTPATIENT)",7783,"125758","PREGABALIN 75 MG PO CAPS",1,3,0.000385455479892072,31,1474,0.333333333333333,0.0210312075983718,15.8494623655914,2,1443,0.0842014042056066
+"184",624,"ANE PACU (OUTPATIENT)",7783,"126009","MEPERIDINE (PF) 25 MG/ML INJ SYRG",12,534,0.0686110754207889,164,2764,0.0224719101123595,0.0593342981186686,0.378733899698548,522,2600,0.000767063463258679
+"185",624,"ANE PACU (OUTPATIENT)",7783,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",103,3276,0.420917384042143,705,36939,0.0314407814407814,0.0190855193697718,1.64736315693763,3173,36234,1.88597344093615e-06
+"186",624,"ANE PACU (OUTPATIENT)",7783,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",2,104,0.0133624566362585,142,3356,0.0192307692307692,0.0423122765196663,0.454496208017335,102,3214,0.362038163099978
+"187",624,"ANE PACU (OUTPATIENT)",7783,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",8,415,0.05332134138507,510,18335,0.0192771084337349,0.0278156531224434,0.693030947318687,407,17825,0.369170675854479
+"188",624,"ANE PACU (OUTPATIENT)",7783,"540102","NS IV BOLUS",3,66,0.00848002055762559,2225,80957,0.0454545454545455,0.0274837259285794,1.65387129724208,63,78732,0.60592437582615
+"189",624,"ANE PACU (OUTPATIENT)",7783,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",106,3437,0.441603494796351,461,18637,0.0308408495781205,0.0247357407308043,1.24681326157794,3331,18176,0.0433614247349804
+"190",624,"ANE PACU (OUTPATIENT)",7783,"540115","LACTATED RINGERS IV BOLUS",1,103,0.0132339714762945,400,12941,0.00970873786407767,0.0309095124024419,0.314101941747573,102,12541,0.33957564791445
+"191",624,"ANE PACU (OUTPATIENT)",7783,"540300","MORPHINE INJECTABLE SYRINGE",4,106,0.0136194269561866,61,1002,0.0377358490566038,0.0608782435129741,0.619857717290442,102,941,0.455135617190192
+"192",624,"ANE PACU (OUTPATIENT)",7783,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",2,49,0.00629577283823718,104,5739,0.0408163265306122,0.0181216239763025,2.25235478806907,47,5635,0.519060642384769
+"193",624,"ANE PACU (OUTPATIENT)",7783,"541229","ELECTROLYTE-R IV BOLUS",1,2,0.000256970319928048,92,2706,0.5,0.0339985218033999,14.7065217391304,1,2614,0.0938614899016372
+"194",625,"ANE PACU (INPATIENT)",65802,"101","ACETAMINOPHEN 325 MG PO TABS",3,102,0.00155010486003465,981,42092,0.0294117647058824,0.0233060914187969,1.26197757390418,99,41111,0.936507419701278
+"195",625,"ANE PACU (INPATIENT)",65802,"102","ACETAMINOPHEN 500 MG PO TABS",27,979,0.0148779672350384,392,15968,0.0275791624106231,0.0245490981963928,1.12342873819599,952,15576,0.626519005321447
+"196",625,"ANE PACU (INPATIENT)",65802,"730","ATROPINE 0.1 MG/ML INJ SYRG",2,184,0.00279626759065074,11,381,0.0108695652173913,0.0288713910761155,0.376482213438735,182,370,0.299249251144985
+"197",625,"ANE PACU (INPATIENT)",65802,"2332","DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",33,1729,0.0262757970882344,390,6737,0.0190861769809138,0.0578892682202761,0.329701472616452,1696,6347,5.97303709140253e-11
+"198",625,"ANE PACU (INPATIENT)",65802,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",1,20,0.000303942129418559,134,3551,0.05,0.0377358490566038,1.325,19,3417,0.999999999999998
+"199",625,"ANE PACU (INPATIENT)",65802,"2833","EPHEDRINE SULFATE 50 MG/ML INJ SOLN",9,513,0.00779611561958603,4,76,0.0175438596491228,0.0526315789473684,0.333333333333333,504,72,0.127310377220761
+"200",625,"ANE PACU (INPATIENT)",65802,"3497","GLYCOPYRROLATE 0.2 MG/ML INJ SOLN",17,1069,0.016245706817422,61,1031,0.0159027128157156,0.0591658583899127,0.268781916606603,1052,970,2.96723335216683e-07
+"201",625,"ANE PACU (INPATIENT)",65802,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",45,2541,0.0386158475426279,537,15149,0.0177095631641086,0.0354478843488019,0.499594361961046,2496,14612,4.67758413886206e-06
+"202",625,"ANE PACU (INPATIENT)",65802,"3758","HYDROMORPHONE 2 MG/ML INJ SOLN",2,38,0.000577490045895261,2,86,0.0526315789473684,0.0232558139534884,2.26315789473684,36,84,0.762428700080518
+"203",625,"ANE PACU (INPATIENT)",65802,"4318","LACTATED RINGERS IV SOLP",172,10108,0.15361235220814,135,4847,0.0170162247724575,0.0278522797606767,0.610945492385936,9936,4712,1.61522693420453e-05
+"204",625,"ANE PACU (INPATIENT)",65802,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",76,4764,0.0723990152275007,346,10128,0.0159529806884971,0.0341627172195893,0.466970486743058,4688,9782,5.8815691664221e-10
+"205",625,"ANE PACU (INPATIENT)",65802,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",16,993,0.0150907267256314,745,12385,0.0161127895266868,0.0601534113847396,0.26786160844029,977,11640,1.2422157612242e-08
+"206",625,"ANE PACU (INPATIENT)",65802,"5339","NALBUPHINE 10 MG/ML INJ SOLN",19,1015,0.0154250630679919,3,308,0.0187192118226601,0.00974025974025974,1.92183908045977,996,305,0.40937577052107
+"207",625,"ANE PACU (INPATIENT)",65802,"5373","NALOXONE 0.4 MG/ML INJ SOLN",306,19532,0.296829883590164,83,1774,0.0156665984026213,0.0467869222096956,0.334849946581328,19226,1691,1.67421444364117e-20
+"208",625,"ANE PACU (INPATIENT)",65802,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",17,921,0.0139965350597246,218,7862,0.0184581976112921,0.0277283134062579,0.665680502843937,904,7644,0.123188250514051
+"209",625,"ANE PACU (INPATIENT)",65802,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",196,13269,0.201650405762743,289,5149,0.0147712713844299,0.056127403379297,0.263173966638164,13073,4860,2.09561403171358e-55
+"210",625,"ANE PACU (INPATIENT)",65802,"10372","LABETALOL 5 MG/ML IV SOLN",122,7960,0.120968967508586,370,5898,0.0153266331658291,0.0627331298745337,0.244314817329893,7838,5528,5.57661177781799e-50
+"211",625,"ANE PACU (INPATIENT)",65802,"10814","OXYCODONE 5 MG PO TABS",86,4998,0.0759551381416978,1478,56614,0.0172068827531012,0.0261066167379094,0.659100446673933,4912,55136,0.000152149947468476
+"212",625,"ANE PACU (INPATIENT)",65802,"18308","GABAPENTIN 300 MG PO CAPS",4,454,0.00689948633780128,588,19458,0.00881057268722467,0.0302189330866482,0.291558032904792,450,18870,0.0118976158674495
+"213",625,"ANE PACU (INPATIENT)",65802,"22472","KETOROLAC 15 MG/ML INJ SOLN",1,81,0.00123096562414516,215,5031,0.0123456790123457,0.0427350427350427,0.288888888888889,80,4816,0.284442380219894
+"214",625,"ANE PACU (INPATIENT)",65802,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",468,22930,0.348469651378378,692,19620,0.020409943305713,0.0352701325178389,0.57867498216487,22462,18928,8.49580403140399e-21
+"215",625,"ANE PACU (INPATIENT)",65802,"27920","OXYCODONE 10 MG PO TB12",2,276,0.00419440138597611,258,7208,0.0072463768115942,0.0357935627081021,0.202449163015392,274,6950,0.0175876712783988
+"216",625,"ANE PACU (INPATIENT)",65802,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",11,1276,0.019391507856904,603,24164,0.00862068965517241,0.0249544777354743,0.34545662492137,1265,23561,0.000304233981342938
+"217",625,"ANE PACU (INPATIENT)",65802,"77052","CELECOXIB 200 MG PO CAPS",1,90,0.00136773958238351,20,1192,0.0111111111111111,0.0167785234899329,0.662222222222222,89,1172,0.999999999999944
+"218",625,"ANE PACU (INPATIENT)",65802,"88080","ELECTROLYTE-R (PH 7.4) IV SOLP",2,223,0.00338895474301693,17,349,0.00896860986547085,0.0487106017191977,0.184120284885255,221,332,0.0188915611767859
+"219",625,"ANE PACU (INPATIENT)",65802,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",15,1010,0.0153490775356372,93,5289,0.0148514851485149,0.0175836642087351,0.844618332801022,995,5196,0.630763796138196
+"220",625,"ANE PACU (INPATIENT)",65802,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",297,16269,0.247241725175527,738,23934,0.0182555780933063,0.0308347956881424,0.592044723692673,15972,23196,6.98074914191548e-15
+"221",625,"ANE PACU (INPATIENT)",65802,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",169,9382,0.142579252910246,1219,33427,0.0180132167981241,0.036467526251234,0.493952254233711,9213,32208,6.40370180834498e-19
+"222",625,"ANE PACU (INPATIENT)",65802,"124521","GRANISETRON (PF) 100 MCG/ML IV SOLN",12,575,0.00873833622078356,2,11,0.0208695652173913,0.181818181818182,0.114782608695652,563,9,0.0136628204161151
+"223",625,"ANE PACU (INPATIENT)",65802,"126006","MEPERIDINE (PF) 25 MG/0.5 ML INJ SOLN",1,3,4.55913194127838e-05,34,293,0.333333333333333,0.116040955631399,2.87254901960784,2,259,0.794034526188974
+"224",625,"ANE PACU (INPATIENT)",65802,"126009","MEPERIDINE (PF) 25 MG/ML INJ SYRG",60,5615,0.0853317528342604,164,2764,0.0106856634016028,0.0593342981186686,0.180092522207502,5555,2600,4.05834579795508e-38
+"225",625,"ANE PACU (INPATIENT)",65802,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",505,29495,0.448238655360019,705,36939,0.01712154602475,0.0190855193697718,0.897096154054239,28990,36234,0.0640775769260076
+"226",625,"ANE PACU (INPATIENT)",65802,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",18,1355,0.0205920792681074,142,3356,0.0132841328413284,0.0423122765196663,0.31395457616548,1337,3214,1.00752622467964e-06
+"227",625,"ANE PACU (INPATIENT)",65802,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",72,4548,0.0691164402297803,510,18335,0.0158311345646438,0.0278156531224434,0.569144808319106,4476,17825,5.55680556263582e-06
+"228",625,"ANE PACU (INPATIENT)",65802,"540102","NS IV BOLUS",21,1080,0.0164128749886022,2225,80957,0.0194444444444444,0.0274837259285794,0.707489388264669,1059,78732,0.129901806926317
+"229",625,"ANE PACU (INPATIENT)",65802,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",584,32391,0.492249475699827,461,18637,0.0180296996079158,0.0247357407308043,0.728892649875762,31807,18176,3.09656288651642e-07
+"230",625,"ANE PACU (INPATIENT)",65802,"540115","LACTATED RINGERS IV BOLUS",16,940,0.0142852800826723,400,12941,0.0170212765957447,0.0309095124024419,0.55068085106383,924,12541,0.0207632368603546
+"231",625,"ANE PACU (INPATIENT)",65802,"540300","MORPHINE INJECTABLE SYRINGE",7,467,0.00709704872192335,61,1002,0.0149892933618844,0.0608782435129741,0.246217572928002,460,941,0.00016679520965692
+"232",625,"ANE PACU (INPATIENT)",65802,"540573","ACETAMINOPHEN 10 MG/ML IV (CUSTOM DOSE)",6,166,0.00252271967417404,248,2951,0.036144578313253,0.0840393087089122,0.430091333074232,160,2703,0.0404742131613519
+"233",625,"ANE PACU (INPATIENT)",65802,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",13,878,0.0133430594814747,104,5739,0.0148063781321185,0.0181216239763025,0.817055808656037,865,5635,0.57774492981894
+"234",625,"ANE PACU (INPATIENT)",65802,"541229","ELECTROLYTE-R IV BOLUS",1,17,0.000258350810005775,92,2706,0.0588235294117647,0.0339985218033999,1.73017902813299,16,2614,0.999999999999999
+"235",625,"ANE PACU (INPATIENT)",65802,"9000000","ZZZ IMS TEMPLATE",1,9,0.000136773958238351,1035,28824,0.111111111111111,0.0359075770191507,3.09436392914654,8,27789,0.751719783873197
+"236",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"101","ACETAMINOPHEN 325 MG PO TABS",18,851,0.152618364418938,981,42092,0.0211515863689777,0.0233060914187969,0.907556140105003,833,41111,0.765739724623315
+"237",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"102","ACETAMINOPHEN 500 MG PO TABS",2,396,0.0710186513629842,392,15968,0.00505050505050505,0.0245490981963928,0.205730777159349,394,15576,0.0195679878212455
+"238",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"3843","IBUPROFEN 400 MG PO TABS",1,5,0.000896700143472023,148,4216,0.2,0.0351043643263757,5.6972972972973,4,4068,0.432774211971368
+"239",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",10,1652,0.296269727403156,346,10128,0.00605326876513317,0.0341627172195893,0.177189323853378,1642,9782,9.92519223695702e-10
+"240",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"14087","ACETAMINOPHEN-CODEINE 300-30 MG PO TABS",1,36,0.00645624103299857,28,1655,0.0277777777777778,0.0169184290030211,1.64186507936508,35,1627,0.999999999999962
+"241",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",7,1544,0.276901004304161,314,8762,0.00453367875647668,0.035836566993837,0.126509851160028,1537,8448,1.12382923833331e-10
+"242",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"18308","GABAPENTIN 300 MG PO CAPS",6,1093,0.196018651362984,588,19458,0.00548947849954254,0.0302189330866482,0.181656926265474,1087,18870,3.22960346992825e-06
+"243",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"22473","KETOROLAC 30 MG/ML (1 ML) INJ SOLN",2,679,0.121771879483501,132,2852,0.00294550810014728,0.0462833099579243,0.0636408265274245,677,2720,1.99440115014707e-07
+"244",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,152,0.0272596843615495,688,12925,0.00657894736842105,0.0532301740812379,0.12359432374541,151,12237,0.0174616132912249
+"245",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"87475","PIPERACILLIN-TAZOBACTAM 3.375 GRAM IV SOLR",4,8,0.00143472022955524,49,686,0.5,0.0714285714285714,7,4,637,0.000109596921679685
+"246",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",2,32,0.00573888091822095,1067,10067,0.0625,0.105989867885169,0.58967900656045,30,9000,0.609604110338662
+"247",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,22,0.0039454806312769,73,1394,0.0454545454545455,0.0523672883787661,0.86799501867995,21,1321,0.999999999999997
+"248",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",9,1179,0.211441893830703,499,13896,0.00763358778625954,0.0359096142774899,0.212577827410546,1170,13397,3.74105792003645e-07
+"249",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,105,0.0188307030129125,1219,33427,0.00952380952380952,0.036467526251234,0.261158639009336,104,32208,0.225819358848272
+"250",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",13,1759,0.315459110473458,705,36939,0.00739056281978397,0.0190855193697718,0.387234042553191,1746,36234,0.000538564735518951
+"251",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"205597","ALVIMOPAN 12 MG PO CAPS",3,979,0.175573888091822,28,1117,0.00306435137895812,0.0250671441360788,0.122245731796294,976,1089,6.83145425217705e-05
+"252",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",10,420,0.0753228120516499,313,9164,0.0238095238095238,0.0341553906591008,0.697094173132512,410,8851,0.312194477640444
+"253",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",1,613,0.10993543758967,510,18335,0.00163132137030995,0.0278156531224434,0.0586476025973195,612,17825,0.000138954725962682
+"254",637,"IP SUR COLON/RECTAL/SMALL BOWEL RESECTION POSTOP",5576,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",8,379,0.0679698708751793,452,13480,0.0211081794195251,0.0335311572700297,0.629509421626544,371,13028,0.235572820981245
+"255",639,"IP SUR COLECTOMY PREOP",37,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,1,0.027027027027027,499,13896,1,0.0359096142774899,27.8476953907816,0,13397,0.0127154320473097
+"256",647,"IP SUR CHOLECYSTECTOMY POSTOP",123,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,39,0.317073170731707,346,10128,0.0256410256410256,0.0341627172195893,0.750555802578924,38,9782,0.999999999999992
+"257",647,"IP SUR CHOLECYSTECTOMY POSTOP",123,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,4,0.032520325203252,218,7862,0.25,0.0277283134062579,9.01605504587156,3,7644,0.237431625195555
+"258",647,"IP SUR CHOLECYSTECTOMY POSTOP",123,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,46,0.373983739837398,705,36939,0.0217391304347826,0.0190855193697718,1.13903792784459,45,36234,0.999999999999958
+"259",663,"IP GU GENERAL ADMIT",522,"472","AMPICILLIN SODIUM 2 GRAM INJ SOLR",2,2,0.00383141762452107,2,32,1,0.0625,16,0,30,0.00422224166133054
+"260",663,"IP GU GENERAL ADMIT",522,"1445","CEFAZOLIN 1 GRAM INJ SOLR",14,33,0.0632183908045977,47,571,0.424242424242424,0.0823117338003503,5.15409413281754,19,524,1.53044826066995e-09
+"261",663,"IP GU GENERAL ADMIT",522,"2566","DOCUSATE SODIUM 100 MG PO CAPS",2,78,0.149425287356322,542,37509,0.0256410256410256,0.0144498653656456,1.77448197558899,76,36967,0.724694712809625
+"262",663,"IP GU GENERAL ADMIT",522,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",3,14,0.0268199233716475,218,7862,0.214285714285714,0.0277283134062579,7.72804718217562,11,7644,0.000642135617261398
+"263",663,"IP GU GENERAL ADMIT",522,"8442","VANCOMYCIN 1,000 MG IV SOLR",3,5,0.00957854406130268,248,2879,0.6,0.0861410211879125,6.96532258064516,2,2631,0.00104259691406982
+"264",663,"IP GU GENERAL ADMIT",522,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,14,0.0268199233716475,118,4415,0.0714285714285714,0.026727066817667,2.6725181598063,13,4297,0.83755954003275
+"265",663,"IP GU GENERAL ADMIT",522,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,13,0.024904214559387,242,3682,0.0769230769230769,0.0657251493753395,1.17037507946599,12,3440,0.999999999999852
+"266",663,"IP GU GENERAL ADMIT",522,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",3,81,0.155172413793103,738,23934,0.037037037037037,0.0308347956881424,1.20114423366456,78,23196,0.999647165929283
+"267",663,"IP GU GENERAL ADMIT",522,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,162,0.310344827586207,705,36939,0.00617283950617284,0.0190855193697718,0.323430522721303,161,36234,0.36169749511122
+"268",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"101","ACETAMINOPHEN 325 MG PO TABS",1,374,0.139344262295082,981,42092,0.00267379679144385,0.0233060914187969,0.114725233991289,373,41111,0.0135006738522522
+"269",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",2,46,0.0171385991058122,399,16019,0.0434782608695652,0.0249079218428117,1.74555955105154,44,15620,0.739169168530361
+"270",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"3758","HYDROMORPHONE 2 MG/ML INJ SOLN",1,2,0.000745156482861401,2,86,0.5,0.0232558139534884,21.5,1,84,0.0887325490861772
+"271",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",9,992,0.369597615499255,346,10128,0.00907258064516129,0.0341627172195893,0.265569643856051,983,9782,2.7247982488948e-05
+"272",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"10814","OXYCODONE 5 MG PO TABS",1,100,0.03725782414307,1478,56614,0.01,0.0261066167379094,0.383044654939107,99,55136,0.486586525277349
+"273",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",2,19,0.00707898658718331,118,4415,0.105263157894737,0.026727066817667,3.93844781445138,17,4297,0.162501221072004
+"274",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"22472","KETOROLAC 15 MG/ML INJ SOLN",1,275,0.102459016393443,215,5031,0.00363636363636364,0.0427350427350427,0.0850909090909091,274,4816,0.00238010343936673
+"275",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,322,0.119970193740686,258,5335,0.0062111801242236,0.0483598875351453,0.128436612258655,320,5077,0.000750117305920331
+"276",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",5,648,0.241430700447094,692,19620,0.00771604938271605,0.0352701325178389,0.218770070648683,643,18928,0.00023549829143579
+"277",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"82384","PANTOPRAZOLE 40 MG IV SOLR",4,141,0.0525335320417288,688,12925,0.0283687943262411,0.0532301740812379,0.532945736434108,137,12237,0.261865360486221
+"278",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",5,294,0.109538002980626,738,23934,0.0170068027210884,0.0308347956881424,0.551545821580665,289,23196,0.231444011120535
+"279",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,273,0.101713859910581,1219,33427,0.00732600732600733,0.036467526251234,0.200891260776413,271,32208,0.0162326432461977
+"280",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",9,990,0.368852459016393,705,36939,0.00909090909090909,0.0190855193697718,0.476324951644101,981,36234,0.0303853978232914
+"281",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",1,31,0.0115499254843517,142,3356,0.032258064516129,0.0423122765196663,0.762380736029078,30,3214,0.999999999999999
+"282",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",4,909,0.338673621460507,452,13480,0.0044004400440044,0.0335311572700297,0.131234362374291,905,13028,1.98497760007842e-06
+"283",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",2,132,0.0491803278688525,111,2584,0.0151515151515152,0.0429566563467492,0.352716352716353,130,2473,0.181221865606894
+"284",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"520064","CEFAZOLIN 2 G IVPB",1,143,0.0532786885245902,109,2674,0.00699300699300699,0.0407629020194465,0.171553217424777,142,2565,0.0703646985060621
+"285",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,66,0.0245901639344262,104,5739,0.0151515151515152,0.0181216239763025,0.836101398601399,65,5635,0.999999999999998
+"286",669,"IP GU RADICAL CYSTECTOMY POSTOP",2684,"541104","CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS",1,129,0.0480625931445604,9,214,0.00775193798449612,0.0420560747663551,0.184323858742463,128,205,0.134139353198699
+"287",672,"IP GU RADICAL PROSTATECTOMY POSTOP",1059,"101","ACETAMINOPHEN 325 MG PO TABS",1,333,0.314447592067989,981,42092,0.003003003003003,0.0233060914187969,0.128850563101328,332,41111,0.0231297659363506
+"288",672,"IP GU RADICAL PROSTATECTOMY POSTOP",1059,"22473","KETOROLAC 30 MG/ML (1 ML) INJ SOLN",1,175,0.165250236071766,132,2852,0.00571428571428571,0.0462833099579243,0.123463203463203,174,2720,0.0186885122964422
+"289",672,"IP GU RADICAL PROSTATECTOMY POSTOP",1059,"82384","PANTOPRAZOLE 40 MG IV SOLR",6,130,0.12275731822474,688,12925,0.0461538461538462,0.0532301740812379,0.867061717352415,124,12237,0.871790447504682
+"290",679,"IP GU TURP/TURBT POSTOP",281,"101","ACETAMINOPHEN 325 MG PO TABS",3,56,0.199288256227758,981,42092,0.0535714285714286,0.0233060914187969,2.29860200961118,53,41111,0.290914041510297
+"291",679,"IP GU TURP/TURBT POSTOP",281,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",1,8,0.0284697508896797,745,12385,0.125,0.0601534113847396,2.07802013422819,7,11640,0.978127892210357
+"292",679,"IP GU TURP/TURBT POSTOP",281,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,2,0.00711743772241993,499,13896,0.5,0.0359096142774899,13.9238476953908,1,13397,0.10408135842914
+"293",679,"IP GU TURP/TURBT POSTOP",281,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,117,0.416370106761566,705,36939,0.00854700854700855,0.0190855193697718,0.447826877614112,116,36234,0.621414245141014
+"294",679,"IP GU TURP/TURBT POSTOP",281,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",1,5,0.0177935943060498,111,2584,0.2,0.0429566563467492,4.65585585585586,4,2473,0.532467187505868
+"295",684,"IP GU NEPHRECTOMY POSTOP",154,"2566","DOCUSATE SODIUM 100 MG PO CAPS",2,34,0.220779220779221,542,37509,0.0588235294117647,0.0144498653656456,4.0708704145865,32,36967,0.148084901231214
+"296",684,"IP GU NEPHRECTOMY POSTOP",154,"2567","DOCUSATE SODIUM 250 MG PO CAPS",1,27,0.175324675324675,296,17760,0.037037037037037,0.0166666666666667,2.22222222222222,26,17464,0.941091277901481
+"297",691,"IP MED UTI/PYELONEPHRITIS",63,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,2,0.0317460317460317,208,3699,0.5,0.0562314138956475,8.89182692307692,1,3491,0.235620077804616
+"298",692,"IP MED SEPSIS",81,"88175","OSELTAMIVIR 75 MG PO CAPS",1,3,0.037037037037037,41,1269,0.333333333333333,0.0323089046493302,10.3170731707317,2,1228,0.194625929450645
+"299",692,"IP MED SEPSIS",81,"88533","AZTREONAM 2 GRAM INJ SOLR",1,1,0.0123456790123457,17,47,1,0.361702127659574,2.76470588235294,0,30,0.794145541405665
+"300",692,"IP MED SEPSIS",81,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",1,2,0.0246913580246914,290,2601,0.5,0.111495578623606,4.48448275862069,1,2311,0.534928171423576
+"301",692,"IP MED SEPSIS",81,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",1,2,0.0246913580246914,235,4195,0.5,0.0560190703218117,8.92553191489362,1,3960,0.234114040528826
+"302",693,"IP MED PANCREATITIS (ACUTE)",95,"101","ACETAMINOPHEN 325 MG PO TABS",1,14,0.147368421052632,981,42092,0.0714285714285714,0.0233060914187969,3.06480267948158,13,41111,0.758635264760877
+"303",693,"IP MED PANCREATITIS (ACUTE)",95,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,6,0.0631578947368421,118,4415,0.166666666666667,0.026727066817667,6.23587570621469,5,4297,0.392854981090241
+"304",697,"IP MED GI BLEED UPPER",307,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,5,0.0162866449511401,314,8762,0.4,0.035836566993837,11.1617834394904,3,8448,0.00153845256023476
+"305",697,"IP MED GI BLEED UPPER",307,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,6,0.0195439739413681,158,4300,0.166666666666667,0.0367441860465116,4.53586497890295,5,4142,0.546357696621898
+"306",697,"IP MED GI BLEED UPPER",307,"25122","OCTREOTIDE ACETATE 100 MCG/ML INJ SOLN",2,22,0.0716612377850163,62,895,0.0909090909090909,0.0692737430167598,1.31231671554252,20,833,0.999999999999975
+"307",697,"IP MED GI BLEED UPPER",307,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,15,0.0488599348534202,692,19620,0.0666666666666667,0.0352701325178389,1.89017341040462,14,18928,0.999999999999522
+"308",697,"IP MED GI BLEED UPPER",307,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,8,0.0260586319218241,242,3682,0.125,0.0657251493753395,1.90185950413223,7,3440,0.999999999999995
+"309",697,"IP MED GI BLEED UPPER",307,"82384","PANTOPRAZOLE 40 MG IV SOLR",6,51,0.166123778501629,688,12925,0.117647058823529,0.0532301740812379,2.21015731874145,45,12237,0.0838450749163841
+"310",697,"IP MED GI BLEED UPPER",307,"530014","PANTOPRAZOLE 0.8 MG/ML IV INFUSION",5,54,0.175895765472313,72,1751,0.0925925925925926,0.0411193603655054,2.25180041152263,49,1679,0.133186522251737
+"311",698,"IP MED GENERAL ADMIT",71055,"101","ACETAMINOPHEN 325 MG PO TABS",209,16668,0.234578847371754,981,42092,0.0125389968802496,0.0233060914187969,0.538013717312401,16459,41111,8.80538535241591e-17
+"312",698,"IP MED GENERAL ADMIT",71055,"102","ACETAMINOPHEN 500 MG PO TABS",18,850,0.0119625642108226,392,15968,0.0211764705882353,0.0245490981963928,0.862617046818727,832,15576,0.612062274003116
+"313",698,"IP MED GENERAL ADMIT",71055,"1080","BISACODYL 10 MG PR SUPP",33,4037,0.0568151431989304,344,22442,0.00817438692098093,0.0153284021031994,0.533283695583296,4004,22098,0.000539951572324925
+"314",698,"IP MED GENERAL ADMIT",71055,"2566","DOCUSATE SODIUM 100 MG PO CAPS",141,11489,0.16169164731546,542,37509,0.012272608582122,0.0144498653656456,0.849323386174936,11348,36967,0.0898562830961058
+"315",698,"IP MED GENERAL ADMIT",71055,"2567","DOCUSATE SODIUM 250 MG PO CAPS",6,529,0.00744493702061783,296,17760,0.0113421550094518,0.0166666666666667,0.680529300567108,523,17464,0.439007262424072
+"316",698,"IP MED GENERAL ADMIT",71055,"3843","IBUPROFEN 400 MG PO TABS",10,316,0.00444725916543523,148,4216,0.0316455696202532,0.0351043643263757,0.901471091344509,306,4068,0.869485301897334
+"317",698,"IP MED GENERAL ADMIT",71055,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",15,873,0.0122862571247625,346,10128,0.0171821305841924,0.0341627172195893,0.502949764614743,858,9782,0.00923644044140893
+"318",698,"IP MED GENERAL ADMIT",71055,"5005","METOCLOPRAMIDE HCL 10 MG PO TABS",8,196,0.00275842657096615,127,2872,0.0408163265306122,0.0442200557103064,0.923027478708019,188,2745,0.964252756038656
+"319",698,"IP MED GENERAL ADMIT",71055,"5373","NALOXONE 0.4 MG/ML INJ SOLN",2,428,0.00602350292027303,83,1774,0.00467289719626168,0.0467869222096956,0.0998761400743159,426,1691,8.86784842492836e-05
+"320",698,"IP MED GENERAL ADMIT",71055,"6582","PROCHLORPERAZINE MALEATE 10 MG PO TABS",1,85,0.00119625642108226,56,5593,0.0117647058823529,0.0100125156445557,1.175,84,5537,0.999999999999994
+"321",698,"IP MED GENERAL ADMIT",71055,"6583","PROCHLORPERAZINE MALEATE 5 MG PO TABS",2,304,0.00427837590598832,18,681,0.00657894736842105,0.026431718061674,0.24890350877193,302,663,0.0724739191978432
+"322",698,"IP MED GENERAL ADMIT",71055,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",11,619,0.00871156146646964,289,5149,0.0177705977382876,0.056127403379297,0.316611791537864,608,4860,7.34642357462109e-05
+"323",698,"IP MED GENERAL ADMIT",71055,"8085","TRAZODONE 50 MG PO TABS",16,1865,0.0262472732390402,334,11344,0.00857908847184987,0.0294428772919605,0.291380777319356,1849,11010,3.03709111058037e-07
+"324",698,"IP MED GENERAL ADMIT",71055,"8750","WARFARIN 2.5 MG PO TABS",1,1,1.40736049539089e-05,86,3039,1,0.0282987824942415,35.3372093023256,0,2953,0.00468904412440495
+"325",698,"IP MED GENERAL ADMIT",71055,"11138","PROCHLORPERAZINE 25 MG PR SUPP",1,52,0.000731827457603265,6,140,0.0192307692307692,0.0428571428571429,0.448717948717949,51,134,0.73161657388473
+"326",698,"IP MED GENERAL ADMIT",71055,"11349","SENNOSIDES 8.6 MG PO TABS",9,444,0.00624868059953557,610,35793,0.0202702702702703,0.0170424384656218,1.18939964554719,435,35183,0.735805334371769
+"327",698,"IP MED GENERAL ADMIT",71055,"11664","WARFARIN 1 MG PO TABS",5,13,0.000182956864400816,98,3895,0.384615384615385,0.0251604621309371,15.2864992150706,8,3797,5.59938914247756e-13
+"328",698,"IP MED GENERAL ADMIT",71055,"14632","TRAMADOL 50 MG PO TABS",23,1795,0.0252621208922666,304,13808,0.0128133704735376,0.0220162224797219,0.581996774666471,1772,13504,0.0133968611588831
+"329",698,"IP MED GENERAL ADMIT",71055,"14858","D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",12,339,0.00477095207937513,58,2031,0.0353982300884956,0.0285573609059577,1.23954836740922,327,1973,0.606256011765557
+"330",698,"IP MED GENERAL ADMIT",71055,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",8,601,0.00845823657729928,314,8762,0.0133111480865225,0.035836566993837,0.371440380681878,593,8448,0.00486622789172391
+"331",698,"IP MED GENERAL ADMIT",71055,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",20,500,0.00703680247695447,158,4300,0.04,0.0367441860465116,1.08860759493671,480,4142,0.810620074050711
+"332",698,"IP MED GENERAL ADMIT",71055,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",12,613,0.00862711983674618,118,4415,0.0195758564437194,0.026727066817667,0.732435645754417,601,4297,0.363019562630004
+"333",698,"IP MED GENERAL ADMIT",71055,"20943","AZITHROMYCIN 250 MG PO TABS",1,31,0.000436281753571177,132,3557,0.032258064516129,0.0371099240933371,0.869257086999022,30,3425,0.999999999999998
+"334",698,"IP MED GENERAL ADMIT",71055,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",79,10366,0.14588698895222,712,36910,0.00762106887902759,0.0192901652668653,0.395075354388916,10287,36198,3.91580181706609e-16
+"335",698,"IP MED GENERAL ADMIT",71055,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",75,3203,0.0450777566673704,692,19620,0.0234155479238214,0.0352701325178389,0.663891691134937,3128,18928,0.000676627642620302
+"336",698,"IP MED GENERAL ADMIT",71055,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",46,2580,0.0363099007810851,603,24164,0.0178294573643411,0.0249544777354743,0.71447928317071,2534,23561,0.0301364625138016
+"337",698,"IP MED GENERAL ADMIT",71055,"80840","ONDANSETRON HCL 8 MG PO TABS",3,182,0.00256139610161143,170,10077,0.0164835164835165,0.0168701002282425,0.977084680025857,179,9907,0.999999999999991
+"338",698,"IP MED GENERAL ADMIT",71055,"80845","ONDANSETRON HCL 2 MG/ML IV SOLN",1,11,0.000154809654492998,1,52,0.0909090909090909,0.0192307692307692,4.72727272727273,10,51,0.775306503342273
+"339",698,"IP MED GENERAL ADMIT",71055,"81141","ONDANSETRON HCL 4 MG PO TABS",32,3443,0.0484554218563085,87,4024,0.00929422015683997,0.0216202783300199,0.429884389783035,3411,3937,3.36822571528808e-05
+"340",698,"IP MED GENERAL ADMIT",71055,"84520","ONDANSETRON 8 MG PO TBDL",7,90,0.0012666244458518,153,4394,0.0777777777777778,0.0348202093764224,2.23369644153958,83,4241,0.0590545946041133
+"341",698,"IP MED GENERAL ADMIT",71055,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",9,1834,0.025810991485469,111,6618,0.00490730643402399,0.0167724388032638,0.292581567390728,1825,6507,0.000225088628344927
+"342",698,"IP MED GENERAL ADMIT",71055,"92173","ZOLPIDEM 5 MG PO TABS",7,944,0.01328548307649,159,8203,0.00741525423728814,0.019383152505181,0.382561827097324,937,8044,0.0131419805287129
+"343",698,"IP MED GENERAL ADMIT",71055,"93263","LEVOFLOXACIN 750 MG PO TABS",1,10,0.000140736049539089,172,3978,0.1,0.0432378079436903,2.31279069767442,9,3806,0.91804921723451
+"344",698,"IP MED GENERAL ADMIT",71055,"94176","ONDANSETRON 4 MG PO TBDL",9,1080,0.0151994933502217,153,4277,0.00833333333333333,0.0357727379003975,0.232952069716776,1071,4124,4.11187065968056e-06
+"345",698,"IP MED GENERAL ADMIT",71055,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",10,35,0.000492576173386813,1067,10067,0.285714285714286,0.105989867885169,2.69567545856206,25,9000,0.00155124580503254
+"346",698,"IP MED GENERAL ADMIT",71055,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",44,1315,0.0185067905143903,738,23934,0.0334600760456274,0.0308347956881424,1.08514018980494,1271,23196,0.650348167299227
+"347",698,"IP MED GENERAL ADMIT",71055,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",27,390,0.00548870593202449,73,1394,0.0692307692307692,0.0523672883787661,1.32202318229715,363,1321,0.247980228530301
+"348",698,"IP MED GENERAL ADMIT",71055,"110890","ENOXAPARIN 80 MG/0.8 ML SC SYRG",1,14,0.000197030469354725,147,2839,0.0714285714285714,0.0517787953504755,1.37949465500486,13,2692,0.999999999999815
+"349",698,"IP MED GENERAL ADMIT",71055,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",288,6215,0.0874674547885441,499,13896,0.0463395012067578,0.0359096142774899,1.29044831416655,5927,13397,0.000491431847521699
+"350",698,"IP MED GENERAL ADMIT",71055,"112564","MORPHINE 2 MG/ML INJ SYRG",31,1669,0.023488846668074,276,6156,0.018573996405033,0.0448343079922027,0.414280876338344,1638,5880,1.36470473882025e-06
+"351",698,"IP MED GENERAL ADMIT",71055,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",35,941,0.0132432622616283,1219,33427,0.0371944739638682,0.036467526251234,1.01993411090256,906,32208,0.976748079314111
+"352",698,"IP MED GENERAL ADMIT",71055,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,48,0.000675533037787629,208,3699,0.0208333333333333,0.0562314138956475,0.370492788461538,47,3491,0.456108877367217
+"353",698,"IP MED GENERAL ADMIT",71055,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",148,15022,0.21141369361762,705,36939,0.00985221674876847,0.0190855193697718,0.516214233308878,14874,36234,7.96215580813808e-14
+"354",698,"IP MED GENERAL ADMIT",71055,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",22,450,0.00633312222925903,142,3356,0.0488888888888889,0.0423122765196663,1.1554303599374,428,3214,0.601991401344729
+"355",698,"IP MED GENERAL ADMIT",71055,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",4,58,0.000816269087326719,192,8611,0.0689655172413793,0.0222970618975729,3.0930316091954,54,8419,0.0524107520025368
+"356",698,"IP MED GENERAL ADMIT",71055,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",185,7145,0.100555907395679,452,13480,0.0258922323303009,0.0335311572700297,0.772184273921363,6960,13028,0.00292965162423249
+"357",698,"IP MED GENERAL ADMIT",71055,"221636","PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN",3,100,0.00140736049539089,177,6874,0.03,0.0257491998836194,1.16508474576271,97,6697,0.999999999999925
+"358",698,"IP MED GENERAL ADMIT",71055,"222272","LACTULOSE 20 GRAM/30 ML PO SOLN",5,188,0.00264583773133488,398,10561,0.0265957446808511,0.0376858252059464,0.70572276274992,183,10163,0.548659676692611
+"359",698,"IP MED GENERAL ADMIT",71055,"520039","AZITHROMYCIN IVPB",2,35,0.000492576173386813,127,2522,0.0571428571428571,0.0503568596352102,1.1347581552306,33,2395,0.999999999999994
+"360",698,"IP MED GENERAL ADMIT",71055,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",14,86,0.00121033002603617,1808,23328,0.162790697674419,0.0775034293552812,2.10043218769294,72,21520,0.00604328522983912
+"361",698,"IP MED GENERAL ADMIT",71055,"540493","WARFARIN PER PHARMACY PROTOCOL",1,100,0.00140736049539089,218,6316,0.01,0.0345155161494617,0.289724770642202,99,6098,0.288201632816547
+"362",698,"IP MED GENERAL ADMIT",71055,"540769","SODIUM CHLORIDE 3 % IV BOLUS",1,7,9.85152346773626e-05,71,876,0.142857142857143,0.0810502283105023,1.7625754527163,6,805,0.999999999999994
+"363",698,"IP MED GENERAL ADMIT",71055,"540967","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS",3,32,0.000450355358525086,135,2073,0.09375,0.065123010130246,1.43958333333333,29,1938,0.772255856388379
+"364",698,"IP MED GENERAL ADMIT",71055,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",7,210,0.00295545704032088,104,5739,0.0333333333333333,0.0181216239763025,1.83942307692308,203,5635,0.180100219681251
+"365",698,"IP MED GENERAL ADMIT",71055,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",3,56,0.000788121877418901,411,7130,0.0535714285714286,0.0576437587657784,0.929353493222106,53,6719,0.999999999999857
+"366",704,"IP MED COPD",216,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",4,68,0.314814814814815,493,18456,0.0588235294117647,0.0267121803207629,2.20212385156903,64,17963,0.207745350642925
+"367",704,"IP MED COPD",216,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",2,2,0.00925925925925926,1067,10067,1,0.105989867885169,9.4348641049672,0,9000,0.00311637347350603
+"368",704,"IP MED COPD",216,"541109","DOXYCYCLINE 100 MG/100 ML MINI-BAG PLUS",2,4,0.0185185185185185,13,330,0.5,0.0393939393939394,12.6923076923077,2,317,0.00134178901939379
+"369",704,"IP MED COPD",216,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,2,0.00925925925925926,466,8270,0.5,0.0563482466747279,8.87339055793991,1,7804,0.235590749930388
+"370",704,"IP MED COPD",216,"9000000","ZZZ IMS TEMPLATE",1,33,0.152777777777778,1035,28824,0.0303030303030303,0.0359075770191507,0.843917435221783,32,27789,0.999999999998816
+"371",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"93263","LEVOFLOXACIN 750 MG PO TABS",2,9,0.046875,172,3978,0.222222222222222,0.0432378079436903,5.13953488372093,7,3806,0.0705128825964503
+"372",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",4,12,0.0625,1067,10067,0.333333333333333,0.105989867885169,3.14495470165573,8,9000,0.0370362557208241
+"373",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",1,3,0.015625,290,2601,0.333333333333333,0.111495578623606,2.98965517241379,2,2311,0.762596011988471
+"374",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",2,23,0.119791666666667,208,3699,0.0869565217391304,0.0562314138956475,1.54640468227425,21,3491,0.854487209222801
+"375",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"520039","AZITHROMYCIN IVPB",2,22,0.114583333333333,127,2522,0.0909090909090909,0.0503568596352102,1.80529706513958,20,2395,0.707513481329213
+"376",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",1,22,0.114583333333333,1808,23328,0.0454545454545455,0.0775034293552812,0.586484312148029,21,21520,0.870446024921406
+"377",705,"IP MED COMMUNITY ACQUIRED PNEUMONIA",192,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",2,28,0.145833333333333,411,7130,0.0714285714285714,0.0576437587657784,1.23913799096281,26,6719,0.999999999999948
+"378",706,"IP MED CHEST PAIN",110,"5604","NITROGLYCERIN 0.4 MG SL SUBL",2,26,0.236363636363636,77,2947,0.0769230769230769,0.0261282660332542,2.94405594405594,24,2870,0.321697156616492
+"379",706,"IP MED CHEST PAIN",110,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,1,0.00909090909090909,692,19620,1,0.0352701325178389,28.3526011560694,0,18928,0.0118194121841506
+"380",708,"IP MED ALCOHOL WITHDRAWAL",2614,"1755","CLONIDINE HCL 0.1 MG PO TABS",11,235,0.0899005355776588,104,2839,0.0468085106382979,0.0366326171187038,1.27778232405892,224,2735,0.54110884859247
+"381",708,"IP MED ALCOHOL WITHDRAWAL",2614,"2403","DIAZEPAM 10 MG PO TABS",9,115,0.0439938791124713,37,532,0.0782608695652174,0.0695488721804511,1.12526439482961,106,495,0.896904085330291
+"382",708,"IP MED ALCOHOL WITHDRAWAL",2614,"2404","DIAZEPAM 2 MG PO TABS",1,7,0.0026778882938026,52,1512,0.142857142857143,0.0343915343915344,4.15384615384615,6,1460,0.597494144712902
+"383",708,"IP MED ALCOHOL WITHDRAWAL",2614,"2405","DIAZEPAM 5 MG PO TABS",9,129,0.0493496557000765,152,5041,0.0697674418604651,0.0301527474707399,2.31380048959608,120,4889,0.0213840021461713
+"384",708,"IP MED ALCOHOL WITHDRAWAL",2614,"3233","FOLIC ACID 1 MG PO TABS",11,463,0.177123182861515,58,4621,0.0237580993520518,0.0125513958017745,1.89286512251434,452,4563,0.0756892447566907
+"385",708,"IP MED ALCOHOL WITHDRAWAL",2614,"4572","LORAZEPAM 0.5 MG PO TABS",3,34,0.0130068859984698,349,13238,0.0882352941176471,0.0263634990179785,3.34687341985505,31,12889,0.0876299915774757
+"386",708,"IP MED ALCOHOL WITHDRAWAL",2614,"4573","LORAZEPAM 1 MG PO TABS",55,1745,0.667559296097934,337,8494,0.0315186246418338,0.0396750647515894,0.794418984295954,1690,8157,0.121428733321273
+"387",708,"IP MED ALCOHOL WITHDRAWAL",2614,"4574","LORAZEPAM 2 MG PO TABS",7,200,0.0765110941086457,31,618,0.035,0.0501618122977346,0.697741935483871,193,587,0.488777364316041
+"388",708,"IP MED ALCOHOL WITHDRAWAL",2614,"7877","THIAMINE HCL (VITAMIN B1) 100 MG PO TABS",10,449,0.17176740627391,100,3808,0.022271714922049,0.0262605042016807,0.848106904231626,439,3708,0.728891853855425
+"389",708,"IP MED ALCOHOL WITHDRAWAL",2614,"10467","LORAZEPAM 2 MG/ML INJ SOLN",72,2181,0.834353481254782,749,20621,0.0330123796423659,0.0363221958197954,0.90887620908575,2109,19872,0.466263819203233
+"390",708,"IP MED ALCOHOL WITHDRAWAL",2614,"14858","D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,42,0.0160673297628156,58,2031,0.0238095238095238,0.0285573609059577,0.833743842364532,41,1973,0.999999999999998
+"391",708,"IP MED ALCOHOL WITHDRAWAL",2614,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",3,29,0.0110941086457536,314,8762,0.103448275862069,0.035836566993837,2.8866681309027,26,8448,0.146816153806712
+"392",708,"IP MED ALCOHOL WITHDRAWAL",2614,"111354","MULTIVITAMIN,TX-IRON-CA-FA-MIN 27-0.4 MG PO TABS",1,1,0.000382555470543229,1,3,1,0.333333333333333,3,0,2,1
+"393",708,"IP MED ALCOHOL WITHDRAWAL",2614,"112420","DIAZEPAM 5 MG/ML INJ SYRG",10,129,0.0493496557000765,98,1948,0.0775193798449612,0.0503080082135524,1.54089542793862,119,1850,0.252887252565962
+"394",708,"IP MED ALCOHOL WITHDRAWAL",2614,"200346","MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK",2,69,0.0263963274674828,578,30211,0.0289855072463768,0.0191321042004568,1.51501930695552,67,29633,0.875394983302025
+"395",708,"IP MED ALCOHOL WITHDRAWAL",2614,"520094","MULTIVITAMINS (+ FOLIC ACID, THIAMINE, VIT K) IVPB",12,240,0.0918133129303749,109,1474,0.05,0.0739484396200814,0.676146788990826,228,1365,0.227311086921812
+"396",708,"IP MED ALCOHOL WITHDRAWAL",2614,"540598","THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS",9,425,0.162586074980872,17,1250,0.0211764705882353,0.0136,1.55709342560554,416,1233,0.387372163120008
+"397",712,"IP MED ASTHMA",56,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",1,6,0.107142857142857,106,3939,0.166666666666667,0.0269103833460269,6.19339622641509,5,3833,0.396297098695298
+"398",712,"IP MED ASTHMA",56,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",1,10,0.178571428571429,493,18456,0.1,0.0267121803207629,3.74361054766734,9,17963,0.64857719149223
+"399",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"101","ACETAMINOPHEN 325 MG PO TABS",6,127,0.042361574382922,981,42092,0.047244094488189,0.0233060914187969,2.02711358327915,121,41111,0.13659686883043
+"400",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"105","ACETAMINOPHEN 650 MG PR SUPP",2,146,0.0486991327551701,59,929,0.0136986301369863,0.0635091496232508,0.215695379614581,144,870,0.0260153724506647
+"401",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"736","ATROPINE 1 % OPHT DROP",1,214,0.0713809206137425,13,270,0.00467289719626168,0.0481481481481481,0.0970524802300503,213,257,0.0104305391765004
+"402",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"1080","BISACODYL 10 MG PR SUPP",1,145,0.0483655770513676,344,22442,0.00689655172413793,0.0153284021031994,0.449919807538091,144,22098,0.627279379275667
+"403",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,114,0.038025350233489,399,16019,0.0087719298245614,0.0249079218428117,0.352174295387592,113,15620,0.422658466658886
+"404",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"3497","GLYCOPYRROLATE 0.2 MG/ML INJ SOLN",2,431,0.143762508338893,61,1031,0.00464037122969838,0.0591658583899127,0.0784298809478529,429,970,5.62496732215015e-06
+"405",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"3578","HALOPERIDOL 0.5 MG PO TABS",1,47,0.0156771180787191,36,753,0.0212765957446809,0.047808764940239,0.445035460992908,46,717,0.629587366190285
+"406",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"3584","HALOPERIDOL LACTATE 5 MG/ML INJ SOLN",4,157,0.052368245496998,513,10794,0.0254777070063694,0.047526403557532,0.536074794204194,153,10281,0.26971024314078
+"407",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"4572","LORAZEPAM 0.5 MG PO TABS",3,141,0.0470313542361574,349,13238,0.0212765957446809,0.0263634990179785,0.807047491312565,138,12889,0.911680199736014
+"408",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"4573","LORAZEPAM 1 MG PO TABS",1,23,0.00767178118745831,337,8494,0.0434782608695652,0.0396750647515894,1.09585859889047,22,8157,0.999999999999998
+"409",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",3,237,0.0790527018012008,346,10128,0.0126582278481013,0.0341627172195893,0.370527548108583,234,9782,0.102664656162082
+"410",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"5172","MORPHINE 4 MG/ML INJ SYRG",21,409,0.136424282855237,238,5949,0.0513447432762836,0.0400067238191293,1.28340284769164,388,5711,0.320843252507469
+"411",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"5176","MORPHINE 10 MG/5 ML PO SOLN",6,114,0.038025350233489,156,3537,0.0526315789473684,0.0441051738761662,1.19331983805668,108,3381,0.838280977645218
+"412",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"6583","PROCHLORPERAZINE MALEATE 5 MG PO TABS",1,38,0.0126751167444963,18,681,0.0263157894736842,0.026431718061674,0.995614035087719,37,663,0.999999999999999
+"413",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"10467","LORAZEPAM 2 MG/ML INJ SOLN",10,462,0.154102735156771,749,20621,0.0216450216450216,0.0363221958197954,0.595917211404528,452,19872,0.121496091724913
+"414",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"11349","SENNOSIDES 8.6 MG PO TABS",3,136,0.0453635757171448,610,35793,0.0220588235294118,0.0170424384656218,1.29434667309547,133,35183,0.905133339866658
+"415",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"12556","DIPHENHYDRAMINE HCL 25 MG PO CAPS",2,23,0.00767178118745831,29,344,0.0869565217391304,0.0843023255813953,1.03148425787106,21,315,0.999999999999999
+"416",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",2,109,0.0363575717144763,111,6618,0.018348623853211,0.0167724388032638,1.09397470865361,107,6507,0.999999999999997
+"417",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",3,110,0.0366911274182789,101,3581,0.0272727272727273,0.02820441217537,0.966966696669667,107,3480,0.99999999999992
+"418",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",7,145,0.0483655770513676,93,5289,0.0482758620689655,0.0175836642087351,2.74549499443826,138,5196,0.0164067403754389
+"419",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"95795","ARTIFICIAL TEARS(HYPROMELLOSE) 0.5 % OPHT DROP",3,380,0.126751167444963,38,2949,0.00789473684210526,0.0128857239742286,0.612673130193906,377,2911,0.559777542002666
+"420",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",4,101,0.033689126084056,738,23934,0.0396039603960396,0.0308347956881424,1.28439185381953,97,23196,0.825720100418033
+"421",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",12,117,0.0390260173448966,1219,33427,0.102564102564103,0.036467526251234,2.81247765086978,105,32208,0.000385847248599031
+"422",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"117414","ARTIFICIAL SALIVA (YERBAS-LYT) MM SPRA",30,429,0.143095396931288,38,452,0.0699300699300699,0.084070796460177,0.8317997791682,399,414,0.509392880193195
+"423",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",2,37,0.0123415610406938,142,3356,0.0540540540540541,0.0423122765196663,1.27750285496764,35,3214,0.999999999999998
+"424",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"207949","MORPHINE (PF) 30 MG/30 ML (1 MG/ML) IV PCA",2,38,0.0126751167444963,59,774,0.0526315789473684,0.0762273901808786,0.690454950936664,36,715,0.823087978989777
+"425",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"540991","HYDROMORPHONE 1 MG/ML IV PCA CONTINUOUS INFUSION",4,85,0.0283522348232155,1,48,0.0470588235294118,0.0208333333333333,2.25882352941176,81,47,0.772549328119066
+"426",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,8,0.00266844563042028,104,5739,0.125,0.0181216239763025,6.89783653846154,7,5635,0.349922694280945
+"427",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"541607","HYDROMORPHONE 1 MG/ML IV PCA",5,76,0.0253502334889927,124,1939,0.0657894736842105,0.0639504899432697,1.02875636672326,71,1815,0.999999999999998
+"428",713,"IP GEN COMFORT CARE - NON ICU UNITS",2998,"541609","MORPHINE 30 MG/30 ML IV PCA",7,91,0.0303535690460307,12,223,0.0769230769230769,0.0538116591928251,1.42948717948718,84,211,0.604184636049917
+"429",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,210,0.382513661202186,346,10128,0.00476190476190476,0.0341627172195893,0.139388934764657,209,9782,0.0317235271647411
+"430",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,57,0.103825136612022,692,19620,0.0175438596491228,0.0352701325178389,0.497414055369638,56,18928,0.714980269303959
+"431",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"88043","PANTOPRAZOLE 40 MG PO TBEC",1,25,0.0455373406193078,624,31884,0.04,0.0195709446744449,2.04384615384615,24,31260,0.988104616819441
+"432",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,32,0.058287795992714,738,23934,0.03125,0.0308347956881424,1.01346544715447,31,23196,0.99999999999907
+"433",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,7,0.0127504553734062,288,4280,0.142857142857143,0.0672897196261682,2.12301587301587,6,3992,0.966175414383934
+"434",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,39,0.0710382513661202,452,13480,0.0256410256410256,0.0335311572700297,0.764692534604039,38,13028,0.999999999999991
+"435",716,"IP GYN EXLAP/TAH/BSO GENERAL",549,"541104","CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS",1,1,0.00182149362477231,9,214,1,0.0420560747663551,23.7777777777778,0,205,0.0308936201712197
+"436",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,141,0.402857142857143,346,10128,0.00709219858156028,0.0341627172195893,0.207600541138851,140,9782,0.125505970066428
+"437",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,141,0.402857142857143,346,10128,0.00709219858156028,0.0341627172195893,0.207600541138851,140,9782,0.125505970066428
+"438",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"22472","KETOROLAC 15 MG/ML INJ SOLN",2,48,0.137142857142857,215,5031,0.0416666666666667,0.0427350427350427,0.975,46,4816,0.999999999999994
+"439",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"22472","KETOROLAC 15 MG/ML INJ SOLN",2,48,0.137142857142857,215,5031,0.0416666666666667,0.0427350427350427,0.975,46,4816,0.999999999999994
+"440",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,21,0.06,692,19620,0.0476190476190476,0.0352701325178389,1.35012386457473,20,18928,0.99999999999996
+"441",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,21,0.06,692,19620,0.0476190476190476,0.0352701325178389,1.35012386457473,20,18928,0.99999999999996
+"442",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,27,0.0771428571428571,603,24164,0.037037037037037,0.0249544777354743,1.4841840181807,26,23561,0.999999999998922
+"443",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,27,0.0771428571428571,603,24164,0.037037037037037,0.0249544777354743,1.4841840181807,26,23561,0.999999999998922
+"444",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,50,0.142857142857143,499,13896,0.04,0.0359096142774899,1.11390781563126,48,13397,0.999999999999989
+"445",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,50,0.142857142857143,499,13896,0.04,0.0359096142774899,1.11390781563126,48,13397,0.999999999999989
+"446",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,136,0.388571428571429,705,36939,0.0147058823529412,0.0190855193697718,0.770525657071339,134,36234,0.953195192475283
+"447",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,136,0.388571428571429,705,36939,0.0147058823529412,0.0190855193697718,0.770525657071339,134,36234,0.953195192475283
+"448",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",3,20,0.0571428571428571,288,4280,0.15,0.0672897196261682,2.22916666666667,17,3992,0.306303270163958
+"449",720,"IP GYN-ONC LAPAROSCOPY POSTOP",350,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",3,20,0.0571428571428571,288,4280,0.15,0.0672897196261682,2.22916666666667,17,3992,0.306303270163958
+"450",721,"IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP",244,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,73,0.299180327868852,542,37509,0.0136986301369863,0.0144498653656456,0.948010918465349,72,36967,0.999999999998483
+"451",721,"IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP",244,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",4,93,0.381147540983607,346,10128,0.043010752688172,0.0341627172195893,1.25899683013239,89,9782,0.856636271657988
+"452",721,"IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP",244,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",3,47,0.192622950819672,499,13896,0.0638297872340425,0.0359096142774899,1.77751247175201,44,13397,0.526365873320431
+"453",721,"IP GYN-ONC OPEN ABDOMINAL HYSTERECTOMY POSTOP",244,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,91,0.372950819672131,705,36939,0.010989010989011,0.0190855193697718,0.575777414075286,90,36234,0.856888421801969
+"454",724,"RETIRED IP GYN-ONC  BOWEL OBSTRUCTION",128,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,51,0.3984375,346,10128,0.0196078431372549,0.0341627172195893,0.573954437266236,50,9782,0.85356733622982
+"455",724,"RETIRED IP GYN-ONC  BOWEL OBSTRUCTION",128,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,23,0.1796875,738,23934,0.0434782608695652,0.0308347956881424,1.41003888299753,22,23196,0.999999999999992
+"456",724,"RETIRED IP GYN-ONC  BOWEL OBSTRUCTION",128,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,16,0.125,499,13896,0.0625,0.0359096142774899,1.74048096192385,15,13397,0.999999999999996
+"457",724,"RETIRED IP GYN-ONC  BOWEL OBSTRUCTION",128,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,18,0.140625,1219,33427,0.0555555555555556,0.036467526251234,1.52342539422113,17,32208,0.999999999999581
+"458",724,"RETIRED IP GYN-ONC  BOWEL OBSTRUCTION",128,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,3,0.0234375,288,4280,0.333333333333333,0.0672897196261682,4.9537037037037,2,3992,0.493256486855763
+"459",724,"RETIRED IP GYN-ONC  BOWEL OBSTRUCTION",128,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,2,0.015625,452,13480,0.5,0.0335311572700297,14.9115044247788,1,13028,0.0894223689889605
+"460",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"2566","DOCUSATE SODIUM 100 MG PO CAPS",3,263,0.258603736479843,542,37509,0.0114068441064639,0.0144498653656456,0.789408331345671,260,36967,0.878444695678504
+"461",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"3844","IBUPROFEN 600 MG PO TABS",1,72,0.0707964601769911,98,3745,0.0138888888888889,0.0261682242990654,0.530753968253968,71,3647,0.783283735686736
+"462",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",8,377,0.370698131760079,346,10128,0.0212201591511936,0.0341627172195893,0.621149629720489,369,9782,0.221684848383796
+"463",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"22472","KETOROLAC 15 MG/ML INJ SOLN",2,132,0.129793510324484,215,5031,0.0151515151515152,0.0427350427350427,0.354545454545455,130,4816,0.180462699926563
+"464",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,81,0.079646017699115,603,24164,0.0246913580246914,0.0249544777354743,0.989456012120468,79,23561,0.999999999999341
+"465",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,28,0.0275319567354966,688,12925,0.0357142857142857,0.0532301740812379,0.67094061461794,27,12237,0.999999999999773
+"466",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"88043","PANTOPRAZOLE 40 MG PO TBEC",2,56,0.0550639134709931,624,31884,0.0357142857142857,0.0195709446744449,1.82486263736264,54,31260,0.697793040275935
+"467",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,120,0.117994100294985,738,23934,0.00833333333333333,0.0308347956881424,0.270257452574526,119,23196,0.246184795274719
+"468",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,98,0.0963618485742379,499,13896,0.0102040816326531,0.0359096142774899,0.284160157048791,97,13397,0.274355035909218
+"469",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,82,0.0806293018682399,1219,33427,0.024390243902439,0.036467526251234,0.668820904780008,80,32208,0.773418459248555
+"470",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",7,399,0.392330383480826,705,36939,0.0175438596491228,0.0190855193697718,0.919223590892124,392,36234,0.968135072220526
+"471",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",1,12,0.0117994100294985,142,3356,0.0833333333333333,0.0423122765196663,1.96948356807512,11,3214,0.999999999999998
+"472",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,34,0.0334316617502458,452,13480,0.0294117647058824,0.0335311572700297,0.877147319104633,33,13028,0.999999999999965
+"473",727,"IP GYN MINIMALLY INVASIVE SURGERY POSTOP",1017,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,26,0.0255653883972468,104,5739,0.0384615384615385,0.0181216239763025,2.12241124260355,25,5635,0.968983521688516
+"474",728,"NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL",106,"101","ACETAMINOPHEN 325 MG PO TABS",1,4,0.0377358490566038,981,42092,0.25,0.0233060914187969,10.7268093781855,3,41111,0.177904126837166
+"475",728,"NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL",106,"10814","OXYCODONE 5 MG PO TABS",2,6,0.0566037735849057,1478,56614,0.333333333333333,0.0261066167379094,12.7681551646369,4,55136,0.000588146887172436
+"476",728,"NURSING TRIAGE HIP FRACTURE SYMPTOMS PROTOCOL",106,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,12,0.113207547169811,1219,33427,0.0833333333333333,0.036467526251234,2.28513809133169,11,32208,0.923706605188143
+"477",730,"ED RAPID SEQUENCE INDUCTION INTUBATION",7,"7536","SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN",1,2,0.285714285714286,28,870,0.5,0.032183908045977,15.5357142857143,1,842,0.086999225120579
+"478",730,"ED RAPID SEQUENCE INDUCTION INTUBATION",7,"112552","ETOMIDATE 2 MG/ML IV SYRG",1,4,0.571428571428571,5,94,0.25,0.0531914893617021,4.7,3,89,0.586966425239004
+"479",732,"ED TIA PATHWAY",385,"10372","LABETALOL 5 MG/ML IV SOLN",1,3,0.00779220779220779,370,5898,0.333333333333333,0.0627331298745337,5.31351351351351,2,5528,0.458787148513899
+"480",732,"ED TIA PATHWAY",385,"10372","LABETALOL 5 MG/ML IV SOLN",1,3,0.00779220779220779,370,5898,0.333333333333333,0.0627331298745337,5.31351351351351,2,5528,0.458787148513899
+"481",732,"ED TIA PATHWAY",385,"530010","NICARDIPINE IV INFUSION",1,3,0.00779220779220779,257,676,0.333333333333333,0.380177514792899,0.876783398184176,2,419,0.999999999999976
+"482",732,"ED TIA PATHWAY",385,"530010","NICARDIPINE IV INFUSION",1,3,0.00779220779220779,257,676,0.333333333333333,0.380177514792899,0.876783398184176,2,419,0.999999999999976
+"483",733,"ED PROCEDURAL SEDATION MEDS",54,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",2,11,0.203703703703704,155,5616,0.181818181818182,0.0275997150997151,6.58768328445748,9,5461,0.0287847158219268
+"484",733,"ED PROCEDURAL SEDATION MEDS",54,"90587","KETAMINE 10 MG/ML INJ SOLN",1,10,0.185185185185185,24,389,0.1,0.0616966580976864,1.62083333333333,9,365,0.999999999999999
+"485",734,"ED CHEST PAIN",802,"101","ACETAMINOPHEN 325 MG PO TABS",1,2,0.00249376558603491,981,42092,0.5,0.0233060914187969,21.453618756371,1,41111,0.0336916546422699
+"486",734,"ED CHEST PAIN",802,"680","ASPIRIN 81 MG PO CHEW",2,84,0.104738154613466,357,11203,0.0238095238095238,0.031866464339909,0.747165532879819,82,10846,0.914642865731449
+"487",734,"ED CHEST PAIN",802,"5604","NITROGLYCERIN 0.4 MG SL SUBL",2,45,0.0561097256857855,77,2947,0.0444444444444444,0.0261282660332542,1.7010101010101,43,2870,0.770184058410116
+"488",734,"ED CHEST PAIN",802,"10176","HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",5,25,0.0311720698254364,164,2898,0.2,0.0565907522429262,3.53414634146342,20,2734,0.00857006953426814
+"489",734,"ED CHEST PAIN",802,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",6,25,0.0311720698254364,68,899,0.24,0.0756395995550612,3.17294117647059,19,831,0.00897627499745524
+"490",734,"ED CHEST PAIN",802,"15859","NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN",2,4,0.00498753117206983,71,1104,0.5,0.0643115942028985,7.77464788732394,2,1033,0.012539977241793
+"491",734,"ED CHEST PAIN",802,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",1,7,0.00872817955112219,493,18456,0.142857142857143,0.0267121803207629,5.3480150680962,6,17963,0.463822858416398
+"492",734,"ED CHEST PAIN",802,"83398","CLOPIDOGREL 75 MG PO TABS",1,1,0.00124688279301746,234,7279,1,0.0321472729770573,31.1068376068376,0,7045,0.00813285995730957
+"493",734,"ED CHEST PAIN",802,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,11,0.013715710723192,1219,33427,0.0909090909090909,0.036467526251234,2.49287791781639,10,32208,0.873919262213904
+"494",734,"ED CHEST PAIN",802,"520002","DEXAMETHASONE (CUSTOM DOSE) IVPB",3,3,0.00374064837905237,168,1360,1,0.123529411764706,8.09523809523809,0,1192,0.000210914477633671
+"495",734,"ED CHEST PAIN",802,"540102","NS IV BOLUS",2,42,0.0523690773067332,2225,80957,0.0476190476190476,0.0274837259285794,1.73262707330123,40,78732,0.744520095036611
+"496",737,"IP PLS GENERAL POSTOP",1019,"2566","DOCUSATE SODIUM 100 MG PO CAPS",5,245,0.240431795878312,542,37509,0.0204081632653061,0.0144498653656456,1.41234279689736,240,36967,0.61023015410504
+"497",737,"IP PLS GENERAL POSTOP",1019,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,371,0.364082433758587,346,10128,0.00539083557951483,0.0341627172195893,0.157798794073197,369,9782,0.0038173242845358
+"498",737,"IP PLS GENERAL POSTOP",1019,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",1,19,0.0186457311089303,70,1432,0.0526315789473684,0.0488826815642458,1.07669172932331,18,1362,0.999999999999937
+"499",737,"IP PLS GENERAL POSTOP",1019,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,143,0.140333660451423,258,5335,0.00699300699300699,0.0483598875351453,0.144603458556947,142,5077,0.0356833746070618
+"500",737,"IP PLS GENERAL POSTOP",1019,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,170,0.166830225711482,603,24164,0.0117647058823529,0.0249544777354743,0.471446688127987,168,23561,0.393402442892753
+"501",737,"IP PLS GENERAL POSTOP",1019,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",3,137,0.134445534838077,738,23934,0.0218978102189781,0.0308347956881424,0.710165568808973,134,23196,0.721952711328159
+"502",737,"IP PLS GENERAL POSTOP",1019,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,78,0.0765456329735034,499,13896,0.0256410256410256,0.0359096142774899,0.714043471558502,76,13397,0.856317351013013
+"503",737,"IP PLS GENERAL POSTOP",1019,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,89,0.0873405299313052,1219,33427,0.0112359550561798,0.036467526251234,0.308108506696408,88,32208,0.324172733799476
+"504",737,"IP PLS GENERAL POSTOP",1019,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",2,23,0.0225711481844946,1951,16917,0.0869565217391304,0.115327776792576,0.753994607001983,21,14966,0.921074617335404
+"505",737,"IP PLS GENERAL POSTOP",1019,"124873","SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP",1,85,0.0834151128557409,90,4112,0.0117647058823529,0.0218871595330739,0.537516339869281,84,4022,0.796363226334638
+"506",737,"IP PLS GENERAL POSTOP",1019,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,383,0.37585868498528,705,36939,0.00522193211488251,0.0190855193697718,0.27360702183212,381,36234,0.073197160881978
+"507",739,"IP PLS OUTPT PROCEDURE PRE/POSTOP",13,"1445","CEFAZOLIN 1 GRAM INJ SOLR",1,4,0.307692307692308,47,571,0.25,0.0823117338003503,3.03723404255319,3,524,0.763204685121939
+"508",739,"IP PLS OUTPT PROCEDURE PRE/POSTOP",13,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,2,0.153846153846154,258,5335,0.5,0.0483598875351453,10.3391472868217,1,5077,0.184772868055709
+"509",740,"IP VAS GENERAL ADMIT",2310,"101","ACETAMINOPHEN 325 MG PO TABS",4,161,0.0696969696969697,981,42092,0.0248447204968944,0.0233060914187969,1.06601832329794,157,41111,0.999999999999125
+"510",740,"IP VAS GENERAL ADMIT",2310,"105","ACETAMINOPHEN 650 MG PR SUPP",1,5,0.00216450216450216,59,929,0.2,0.0635091496232508,3.14915254237288,4,870,0.743657659348636
+"511",740,"IP VAS GENERAL ADMIT",2310,"681","ASPIRIN 325 MG PO TABS",1,10,0.00432900432900433,303,7114,0.1,0.0425920719707619,2.34785478547855,9,6811,0.908665509248172
+"512",740,"IP VAS GENERAL ADMIT",2310,"1080","BISACODYL 10 MG PR SUPP",3,465,0.201298701298701,344,22442,0.00645161290322581,0.0153284021031994,0.420892723180795,462,22098,0.174022821673567
+"513",740,"IP VAS GENERAL ADMIT",2310,"2566","DOCUSATE SODIUM 100 MG PO CAPS",14,480,0.207792207792208,542,37509,0.0291666666666667,0.0144498653656456,2.01847324723247,466,36967,0.0132630339395493
+"514",740,"IP VAS GENERAL ADMIT",2310,"2567","DOCUSATE SODIUM 250 MG PO CAPS",2,169,0.0731601731601732,296,17760,0.0118343195266272,0.0166666666666667,0.710059171597633,167,17464,0.851830838414866
+"515",740,"IP VAS GENERAL ADMIT",2310,"3843","IBUPROFEN 400 MG PO TABS",1,2,0.000865800865800866,148,4216,0.5,0.0351043643263757,14.2432432432432,1,4068,0.0999670321487003
+"516",740,"IP VAS GENERAL ADMIT",2310,"4318","LACTATED RINGERS IV SOLP",2,137,0.0593073593073593,135,4847,0.0145985401459854,0.0278522797606767,0.524141659908083,135,4712,0.502384453299298
+"517",740,"IP VAS GENERAL ADMIT",2310,"4711","MAGNESIUM CITRATE PO SOLN",4,292,0.126406926406926,131,5481,0.0136986301369863,0.0239007480386791,0.573146502143679,288,5350,0.354793030987847
+"518",740,"IP VAS GENERAL ADMIT",2310,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,209,0.0904761904761905,346,10128,0.00956937799043062,0.0341627172195893,0.280111734933761,207,9782,0.0788381985435139
+"519",740,"IP VAS GENERAL ADMIT",2310,"5373","NALOXONE 0.4 MG/ML INJ SOLN",4,789,0.341558441558442,83,1774,0.00506970849176172,0.0467869222096956,0.108357383908257,785,1691,1.39874882379723e-07
+"520",740,"IP VAS GENERAL ADMIT",2310,"11349","SENNOSIDES 8.6 MG PO TABS",2,614,0.265800865800866,610,35793,0.00325732899022801,0.0170424384656218,0.191130453356117,612,35183,0.013278685189965
+"521",740,"IP VAS GENERAL ADMIT",2310,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",3,153,0.0662337662337662,314,8762,0.0196078431372549,0.035836566993837,0.547146247033845,150,8448,0.392848320210679
+"522",740,"IP VAS GENERAL ADMIT",2310,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,61,0.0264069264069264,258,5335,0.0327868852459016,0.0483598875351453,0.677976871266997,59,5077,0.791710077752991
+"523",740,"IP VAS GENERAL ADMIT",2310,"28384","HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS",1,22,0.00952380952380952,335,9576,0.0454545454545455,0.0349832915622389,1.29932157394844,21,9241,0.999999999999408
+"524",740,"IP VAS GENERAL ADMIT",2310,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,260,0.112554112554113,603,24164,0.00769230769230769,0.0249544777354743,0.308253603775992,258,23561,0.113943301644733
+"525",740,"IP VAS GENERAL ADMIT",2310,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",4,418,0.180952380952381,111,6618,0.00956937799043062,0.0167724388032638,0.570541833699728,414,6507,0.353645071947417
+"526",740,"IP VAS GENERAL ADMIT",2310,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",4,232,0.1004329004329,101,3581,0.0172413793103448,0.02820441217537,0.611300785250939,228,3480,0.43427917618779
+"527",740,"IP VAS GENERAL ADMIT",2310,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",3,37,0.016017316017316,1067,10067,0.0810810810810811,0.105989867885169,0.764988981483827,34,9000,0.822859489102926
+"528",740,"IP VAS GENERAL ADMIT",2310,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",4,233,0.100865800865801,738,23934,0.0171673819742489,0.0308347956881424,0.556753550368122,229,23196,0.311209139853855
+"529",740,"IP VAS GENERAL ADMIT",2310,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,203,0.0878787878787879,1219,33427,0.00985221674876847,0.036467526251234,0.270164109320003,201,32208,0.0668052816908448
+"530",740,"IP VAS GENERAL ADMIT",2310,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",3,401,0.173593073593074,705,36939,0.00748129675810474,0.0190855193697718,0.391988114819335,398,36234,0.130903014268123
+"531",740,"IP VAS GENERAL ADMIT",2310,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",1,12,0.00519480519480519,142,3356,0.0833333333333333,0.0423122765196663,1.96948356807512,11,3214,0.999999999999998
+"532",740,"IP VAS GENERAL ADMIT",2310,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,119,0.0515151515151515,452,13480,0.00840336134453781,0.0335311572700297,0.250613519744181,118,13028,0.206128642482156
+"533",740,"IP VAS GENERAL ADMIT",2310,"221636","PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN",1,60,0.025974025974026,177,6874,0.0166666666666667,0.0257491998836194,0.647269303201507,59,6697,0.973683858705024
+"534",740,"IP VAS GENERAL ADMIT",2310,"500027","D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY",7,82,0.0354978354978355,54,1287,0.0853658536585366,0.041958041958042,2.03455284552846,75,1233,0.116153048493325
+"535",740,"IP VAS GENERAL ADMIT",2310,"540184","ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN",3,65,0.0281385281385281,77,1458,0.0461538461538462,0.0528120713305898,0.873926073926074,62,1381,0.999999999999998
+"536",740,"IP VAS GENERAL ADMIT",2310,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,37,0.016017316017316,104,5739,0.027027027027027,0.0181216239763025,1.49142411642412,36,5635,0.999999999999645
+"537",741,"IP VAS GENERAL POSTOP",1423,"1080","BISACODYL 10 MG PR SUPP",3,378,0.265635980323261,344,22442,0.00793650793650794,0.0153284021031994,0.517764857881137,375,22098,0.340727403014271
+"538",741,"IP VAS GENERAL POSTOP",1423,"2566","DOCUSATE SODIUM 100 MG PO CAPS",3,291,0.20449754040759,542,37509,0.0103092783505155,0.0144498653656456,0.713451515958458,288,36967,0.731289225609196
+"539",741,"IP VAS GENERAL POSTOP",1423,"2567","DOCUSATE SODIUM 250 MG PO CAPS",1,179,0.125790583274772,296,17760,0.00558659217877095,0.0166666666666667,0.335195530726257,178,17464,0.38891089624702
+"540",741,"IP VAS GENERAL POSTOP",1423,"4318","LACTATED RINGERS IV SOLP",5,164,0.115249472944483,135,4847,0.0304878048780488,0.0278522797606767,1.0946251129178,159,4712,0.999999999999993
+"541",741,"IP VAS GENERAL POSTOP",1423,"4711","MAGNESIUM CITRATE PO SOLN",4,317,0.222768798313422,131,5481,0.0126182965299685,0.0239007480386791,0.527945673898909,313,5350,0.269768678647199
+"542",741,"IP VAS GENERAL POSTOP",1423,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,434,0.30498945888967,346,10128,0.00460829493087558,0.0341627172195893,0.134892517514184,432,9782,0.00119386062651741
+"543",741,"IP VAS GENERAL POSTOP",1423,"5373","NALOXONE 0.4 MG/ML INJ SOLN",5,465,0.326774420238932,83,1774,0.010752688172043,0.0467869222096956,0.229822515869931,460,1691,0.000613886477919461
+"544",741,"IP VAS GENERAL POSTOP",1423,"11349","SENNOSIDES 8.6 MG PO TABS",4,434,0.30498945888967,610,35793,0.00921658986175115,0.0170424384656218,0.540802296592884,430,35183,0.285340880303211
+"545",741,"IP VAS GENERAL POSTOP",1423,"14113","ASPIRIN 81 MG PO TBEC",2,51,0.0358397751229796,391,18448,0.0392156862745098,0.0211947094535993,1.85025826187252,49,18057,0.685441410821198
+"546",741,"IP VAS GENERAL POSTOP",1423,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",3,113,0.0794096978215039,314,8762,0.0265486725663717,0.035836566993837,0.740826334479454,110,8448,0.78444938253605
+"547",741,"IP VAS GENERAL POSTOP",1423,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,38,0.0267041461700632,118,4415,0.0263157894736842,0.026727066817667,0.984611953612846,37,4297,0.999999999999842
+"548",741,"IP VAS GENERAL POSTOP",1423,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,94,0.0660576247364722,258,5335,0.0212765957446809,0.0483598875351453,0.439963714332839,92,5077,0.329377764467245
+"549",741,"IP VAS GENERAL POSTOP",1423,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",2,380,0.267041461700632,111,6618,0.00526315789473684,0.0167724388032638,0.313798008534851,378,6507,0.128071962356406
+"550",741,"IP VAS GENERAL POSTOP",1423,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",4,265,0.186226282501757,101,3581,0.0150943396226415,0.02820441217537,0.535176536521577,261,3480,0.285350847751275
+"551",741,"IP VAS GENERAL POSTOP",1423,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,130,0.0913562895291637,738,23934,0.00769230769230769,0.0308347956881424,0.249468417761101,129,23196,0.203948354111077
+"552",741,"IP VAS GENERAL POSTOP",1423,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,138,0.0969782150386507,1219,33427,0.0072463768115942,0.036467526251234,0.198707660115799,137,32208,0.109043814644376
+"553",741,"IP VAS GENERAL POSTOP",1423,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,468,0.328882642304989,705,36939,0.00427350427350427,0.0190855193697718,0.223913438807056,466,36234,0.0301914699230266
+"554",741,"IP VAS GENERAL POSTOP",1423,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,142,0.0997891777933942,452,13480,0.00704225352112676,0.0335311572700297,0.210021189081391,141,13028,0.12951768984696
+"555",741,"IP VAS GENERAL POSTOP",1423,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",2,63,0.0442726633872101,111,2584,0.0317460317460317,0.0429566563467492,0.739024739024739,61,2473,0.904874673985534
+"556",742,"IP VAS GENERAL PREOP",53,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,17,0.320754716981132,314,8762,0.0588235294117647,0.035836566993837,1.64143874110154,16,8448,0.999999999999312
+"557",753,"IP NEU ICU ADMIT",711,"680","ASPIRIN 81 MG PO CHEW",1,12,0.0168776371308017,357,11203,0.0833333333333333,0.031866464339909,2.61507936507937,11,10846,0.847633955534062
+"558",753,"IP NEU ICU ADMIT",711,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",1,1,0.00140646976090014,745,12385,1,0.0601534113847396,16.6241610738255,0,11640,0.0645241703957725
+"559",753,"IP NEU ICU ADMIT",711,"10372","LABETALOL 5 MG/ML IV SOLN",1,20,0.0281293952180028,370,5898,0.05,0.0627331298745337,0.797027027027027,19,5528,0.999999999999927
+"560",753,"IP NEU ICU ADMIT",711,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",4,95,0.133614627285513,692,19620,0.0421052631578947,0.0352701325178389,1.19379373288713,91,18928,0.93505919674055
+"561",753,"IP NEU ICU ADMIT",711,"78531","ATORVASTATIN 20 MG PO TABS",2,16,0.0225035161744023,60,3749,0.125,0.0160042678047479,7.81041666666667,14,3689,0.0149242488790371
+"562",753,"IP NEU ICU ADMIT",711,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,91,0.127988748241913,688,12925,0.010989010989011,0.0532301740812379,0.206443266036289,90,12237,0.11912324401649
+"563",753,"IP NEU ICU ADMIT",711,"83628","AMLODIPINE 5 MG PO TABS",1,2,0.00281293952180028,435,12391,0.5,0.0351061254136067,14.2425287356322,1,11956,0.0991290716213317
+"564",753,"IP NEU ICU ADMIT",711,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,14,0.019690576652602,288,4280,0.0714285714285714,0.0672897196261682,1.06150793650794,13,3992,0.999999999999999
+"565",753,"IP NEU ICU ADMIT",711,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",4,103,0.144866385372714,192,8611,0.0388349514563107,0.0222970618975729,1.7417071197411,99,8419,0.428953747623454
+"566",753,"IP NEU ICU ADMIT",711,"530010","NICARDIPINE IV INFUSION",9,22,0.0309423347398031,257,676,0.409090909090909,0.380177514792899,1.07605235231694,13,419,0.9587150767995
+"567",755,"IP NEU GENERAL WARD ADMIT",3072,"101","ACETAMINOPHEN 325 MG PO TABS",8,652,0.212239583333333,981,42092,0.0122699386503067,0.0233060914187969,0.526469171935486,644,41111,0.0838438781195414
+"568",755,"IP NEU GENERAL WARD ADMIT",3072,"102","ACETAMINOPHEN 500 MG PO TABS",2,6,0.001953125,392,15968,0.333333333333333,0.0245490981963928,13.578231292517,4,15576,0.000371810366111544
+"569",755,"IP NEU GENERAL WARD ADMIT",3072,"681","ASPIRIN 325 MG PO TABS",1,5,0.00162760416666667,303,7114,0.2,0.0425920719707619,4.6957095709571,4,6811,0.52614532361672
+"570",755,"IP NEU GENERAL WARD ADMIT",3072,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",1,94,0.0305989583333333,134,3551,0.0106382978723404,0.0377358490566038,0.281914893617021,93,3417,0.272895388249547
+"571",755,"IP NEU GENERAL WARD ADMIT",3072,"2566","DOCUSATE SODIUM 100 MG PO CAPS",2,296,0.0963541666666667,542,37509,0.00675675675675676,0.0144498653656456,0.467599980053855,294,36967,0.388661526656623
+"572",755,"IP NEU GENERAL WARD ADMIT",3072,"11349","SENNOSIDES 8.6 MG PO TABS",3,199,0.0647786458333333,610,35793,0.0150753768844221,0.0170424384656218,0.884578630859214,196,35183,0.99999999999888
+"573",755,"IP NEU GENERAL WARD ADMIT",3072,"16050","GLUCOSE 4 GRAM PO CHEW",1,95,0.0309244791666667,17,1600,0.0105263157894737,0.010625,0.990712074303405,94,1583,0.999999999999999
+"574",755,"IP NEU GENERAL WARD ADMIT",3072,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",3,168,0.0546875,118,4415,0.0178571428571429,0.026727066817667,0.668129539951574,165,4297,0.646470202194337
+"575",755,"IP NEU GENERAL WARD ADMIT",3072,"77545","ATORVASTATIN 40 MG PO TABS",1,103,0.0335286458333333,159,9613,0.00970873786407767,0.0165401019452824,0.586981742687916,102,9454,0.878639999658873
+"576",755,"IP NEU GENERAL WARD ADMIT",3072,"78531","ATORVASTATIN 20 MG PO TABS",2,5,0.00162760416666667,60,3749,0.4,0.0160042678047479,24.9933333333333,3,3689,6.45645617120291e-07
+"577",755,"IP NEU GENERAL WARD ADMIT",3072,"78748","INSULIN GLARGINE 100 UNIT/ML SC SOLN",1,19,0.00618489583333333,348,8601,0.0526315789473684,0.0404604115800488,1.3008166969147,18,8253,0.999999999999695
+"578",755,"IP NEU GENERAL WARD ADMIT",3072,"79507","ATORVASTATIN 10 MG PO TABS",1,13,0.00423177083333333,96,5116,0.0769230769230769,0.0187646598905395,4.09935897435897,12,5020,0.604371300142608
+"579",755,"IP NEU GENERAL WARD ADMIT",3072,"88045","SIMVASTATIN 20 MG PO TABS",1,3,0.0009765625,56,5146,0.333333333333333,0.0108822386319471,30.6309523809524,2,5090,0.00998091480525756
+"580",755,"IP NEU GENERAL WARD ADMIT",3072,"89742","INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP",2,13,0.00423177083333333,703,13345,0.153846153846154,0.0526789059572874,2.92045081518766,11,12642,0.312454608462987
+"581",755,"IP NEU GENERAL WARD ADMIT",3072,"92835","INSULIN LISPRO 100 UNIT/ML SC SOLN",5,107,0.0348307291666667,516,10567,0.0467289719626168,0.0488312671524558,0.956947764978628,102,10051,0.999999999999998
+"582",755,"IP NEU GENERAL WARD ADMIT",3072,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",16,337,0.109700520833333,499,13896,0.0474777448071217,0.0359096142774899,1.32214577522999,321,13397,0.329054261035644
+"583",758,"RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE",152,"81421","ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",1,5,0.0328947368421053,72,1479,0.2,0.0486815415821501,4.10833333333333,4,1407,0.598737948541823
+"584",758,"RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE",152,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,29,0.190789473684211,688,12925,0.0344827586206897,0.0532301740812379,0.647804731355253,28,12237,0.971941949244833
+"585",758,"RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE",152,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",3,35,0.230263157894737,192,8611,0.0857142857142857,0.0222970618975729,3.84419642857143,32,8419,0.0510109151962715
+"586",758,"RETIRED IP NEU ICU INTRACEREBRAL HEMORRHAGE",152,"530010","NICARDIPINE IV INFUSION",8,15,0.0986842105263158,257,676,0.533333333333333,0.380177514792899,1.40285343709468,7,419,0.348159375247847
+"587",759,"IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL",381,"11634","VECURONIUM BROMIDE 10 MG IV SOLR",4,78,0.204724409448819,6,198,0.0512820512820513,0.0303030303030303,1.69230769230769,74,192,0.629727309070216
+"588",759,"IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL",381,"530003","FENTANYL  10 MCG/ML IV INFUSION",5,46,0.120734908136483,51,585,0.108695652173913,0.0871794871794872,1.24680306905371,41,534,0.822088869287681
+"589",759,"IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL",381,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",7,76,0.199475065616798,65,1105,0.0921052631578947,0.0588235294117647,1.56578947368421,69,1040,0.354884627763689
+"590",759,"IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL",381,"530007","VECURONIUM IV INFUSION",10,80,0.20997375328084,4,99,0.125,0.0404040404040404,3.09375,70,95,0.0694060958177766
+"591",759,"IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL",381,"540102","NS IV BOLUS",5,75,0.196850393700787,2225,80957,0.0666666666666667,0.0274837259285794,2.42567790262172,70,78732,0.0853929283371095
+"592",759,"IP NEU HYPOTHERMIA AFTER CARDIAC ARREST PROTOCOL",381,"540638","FENTANYL 50 MCG/ML IV INFUSION",1,28,0.073490813648294,23,146,0.0357142857142857,0.157534246575342,0.226708074534162,27,123,0.157591069616907
+"593",761,"RETIRED IP NEU STROKE ISCHEMIC ELEVATED ICP",4,"4750","MANNITOL 25 % 25 % IV SOLN",1,1,0.25,21,64,1,0.328125,3.04761904761905,0,43,0.730815320888868
+"594",761,"RETIRED IP NEU STROKE ISCHEMIC ELEVATED ICP",4,"4750","MANNITOL 25 % 25 % IV SOLN",1,1,0.25,21,64,1,0.328125,3.04761904761905,0,43,0.730815320888868
+"595",765,"IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL",37,"7322","SODIUM CHLORIDE 4 MEQ/ML IV SOLP",2,2,0.0540540540540541,22,515,1,0.0427184466019417,23.4090909090909,0,493,2.15425542810016e-06
+"596",765,"IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL",37,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,9,0.243243243243243,118,4415,0.111111111111111,0.026727066817667,4.15725047080979,8,4297,0.594781221103919
+"597",765,"IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL",37,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,11,0.297297297297297,688,12925,0.0909090909090909,0.0532301740812379,1.7078488372093,10,12237,0.999999999999995
+"598",765,"IP NEU ICU TRAUMATIC BRAIN INJURY PROTOCOL",37,"530025","DEXMEDETOMIDINE IV INFUSION",1,1,0.027027027027027,530,2902,1,0.18263266712612,5.47547169811321,0,2372,0.412022185201503
+"599",766,"IP RAD NEURORADIOLOGY POST-PROCEDURE",1475,"101","ACETAMINOPHEN 325 MG PO TABS",6,193,0.130847457627119,981,42092,0.0310880829015544,0.0233060914187969,1.33390375687281,187,41111,0.634445666173618
+"600",766,"IP RAD NEURORADIOLOGY POST-PROCEDURE",1475,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",9,168,0.113898305084746,603,24164,0.0535714285714286,0.0249544777354743,2.14676616915423,159,23561,0.034569256466848
+"601",766,"IP RAD NEURORADIOLOGY POST-PROCEDURE",1475,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",6,188,0.127457627118644,705,36939,0.0319148936170213,0.0190855193697718,1.67220461747397,182,36234,0.310826157485817
+"602",775,"IP SUR TRAUMA SURGICAL INTERVENTION",125,"101","ACETAMINOPHEN 325 MG PO TABS",1,28,0.224,981,42092,0.0357142857142857,0.0233060914187969,1.53240133974079,27,41111,0.999999999997374
+"603",775,"IP SUR TRAUMA SURGICAL INTERVENTION",125,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,16,0.128,314,8762,0.0625,0.035836566993837,1.74402866242038,15,8448,0.999999999999738
+"604",778,"RETIRED IP SUR APPENDECTOMY POSTOP",15,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,5,0.333333333333333,705,36939,0.2,0.0190855193697718,10.4791489361702,4,36234,0.186434590271818
+"605",792,"IP ICU GI BLEED",164,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,4,0.024390243902439,242,3682,0.25,0.0657251493753395,3.80371900826446,3,3440,0.63380464589299
+"606",792,"IP ICU GI BLEED",164,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,33,0.201219512195122,688,12925,0.0303030303030303,0.0532301740812379,0.569282945736434,32,12237,0.843176527768213
+"607",792,"IP ICU GI BLEED",164,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,1,0.00609756097560976,1067,10067,1,0.105989867885169,9.4348641049672,0,9000,0.200796619232221
+"608",792,"IP ICU GI BLEED",164,"530014","PANTOPRAZOLE 0.8 MG/ML IV INFUSION",1,43,0.26219512195122,72,1751,0.0232558139534884,0.0411193603655054,0.565568475452196,42,1679,0.845313678607907
+"609",792,"IP ICU GI BLEED",164,"530016","OCTREOTIDE 10 MCG/ML IV INFUSION",1,20,0.121951219512195,29,929,0.05,0.031216361679225,1.60172413793103,19,900,0.999999999999992
+"610",798,"IP SUR GENERAL PREOP",664,"1445","CEFAZOLIN 1 GRAM INJ SOLR",14,296,0.44578313253012,47,571,0.0472972972972973,0.0823117338003503,0.574611845888442,282,524,0.0764714359579744
+"611",798,"IP SUR GENERAL PREOP",664,"8442","VANCOMYCIN 1,000 MG IV SOLR",1,12,0.0180722891566265,248,2879,0.0833333333333333,0.0861410211879125,0.967405913978495,11,2631,0.999999999999978
+"612",801,"IP SUR OR GENERAL POSTOP",3021,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",5,930,0.307845084409136,346,10128,0.00537634408602151,0.0341627172195893,0.157374603766549,925,9782,2.67199896098858e-06
+"613",801,"IP SUR OR GENERAL POSTOP",3021,"5373","NALOXONE 0.4 MG/ML INJ SOLN",3,22,0.00728235683548494,83,1774,0.136363636363636,0.0467869222096956,2.91456736035049,19,1691,0.146138981754399
+"614",801,"IP SUR OR GENERAL POSTOP",3021,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,247,0.0817610062893082,314,8762,0.00809716599190283,0.035836566993837,0.225947033188066,245,8448,0.0306458565876691
+"615",801,"IP SUR OR GENERAL POSTOP",3021,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,53,0.0175438596491228,158,4300,0.0188679245283019,0.0367441860465116,0.513494148555051,52,4142,0.748106104570498
+"616",801,"IP SUR OR GENERAL POSTOP",3021,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,38,0.0125786163522013,118,4415,0.0263157894736842,0.026727066817667,0.984611953612846,37,4297,0.999999999999842
+"617",801,"IP SUR OR GENERAL POSTOP",3021,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,190,0.0628930817610063,692,19620,0.00526315789473684,0.0352701325178389,0.149224216610891,189,18928,0.041155150877909
+"618",801,"IP SUR OR GENERAL POSTOP",3021,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",5,156,0.0516385302879841,603,24164,0.032051282051282,0.0249544777354743,1.2843900157333,151,23561,0.757561974670018
+"619",801,"IP SUR OR GENERAL POSTOP",3021,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",7,40,0.0132406487917908,1067,10067,0.175,0.105989867885169,1.65110121836926,33,9000,0.247507366178126
+"620",801,"IP SUR OR GENERAL POSTOP",3021,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,76,0.0251572327044025,499,13896,0.0263157894736842,0.0359096142774899,0.732834089231094,74,13397,0.889221602839886
+"621",801,"IP SUR OR GENERAL POSTOP",3021,"112564","MORPHINE 2 MG/ML INJ SYRG",2,31,0.0102615028136379,276,6156,0.0645161290322581,0.0448343079922027,1.43899018232819,29,5880,0.925846813009105
+"622",801,"IP SUR OR GENERAL POSTOP",3021,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",3,126,0.041708043694141,1219,33427,0.0238095238095238,0.036467526251234,0.652896597523341,123,32208,0.603888963986084
+"623",801,"IP SUR OR GENERAL POSTOP",3021,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",9,1003,0.332009268454154,705,36939,0.00897308075772682,0.0190855193697718,0.470151248382512,994,36234,0.0272586764460906
+"624",801,"IP SUR OR GENERAL POSTOP",3021,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",6,600,0.198609731876862,452,13480,0.01,0.0335311572700297,0.298230088495575,594,13028,0.00220187478585295
+"625",801,"IP SUR OR GENERAL POSTOP",3021,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",1,10,0.0033101621979477,235,4195,0.1,0.0560190703218117,1.78510638297872,9,3960,0.999999999999757
+"626",801,"IP SUR OR GENERAL POSTOP",3021,"541104","CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS",1,33,0.0109235352532274,9,214,0.0303030303030303,0.0420560747663551,0.720538720538721,32,205,0.999999999999999
+"627",801,"IP SUR OR GENERAL POSTOP",3021,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",3,36,0.0119165839126117,466,8270,0.0833333333333333,0.0563482466747279,1.47889842632332,33,7804,0.735270767075586
+"628",805,"IP SUR GENERAL ADMIT",15690,"101","ACETAMINOPHEN 325 MG PO TABS",24,1501,0.095666029318037,981,42092,0.0159893404397069,0.0233060914187969,0.686058427918595,1477,41111,0.0769710423238707
+"629",805,"IP SUR GENERAL ADMIT",15690,"102","ACETAMINOPHEN 500 MG PO TABS",4,281,0.0179094964945825,392,15968,0.0142348754448399,0.0245490981963928,0.579853293630619,277,15576,0.359453104807696
+"630",805,"IP SUR GENERAL ADMIT",15690,"326","ALPRAZOLAM 1 MG PO TABS",1,2,0.000127469725940089,15,450,0.5,0.0333333333333333,15,1,435,0.0997507862408433
+"631",805,"IP SUR GENERAL ADMIT",15690,"1080","BISACODYL 10 MG PR SUPP",3,372,0.0237093690248566,344,22442,0.00806451612903226,0.0153284021031994,0.526115903975994,369,22098,0.356634648120451
+"632",805,"IP SUR GENERAL ADMIT",15690,"2511","DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX",1,43,0.00274059910771192,57,714,0.0232558139534884,0.0798319327731092,0.291309669522644,42,657,0.289404662178956
+"633",805,"IP SUR GENERAL ADMIT",15690,"2569","DOCUSATE SODIUM 50 MG/5 ML PO LIQD",7,385,0.0245379222434672,170,7328,0.0181818181818182,0.0231986899563319,0.783743315508021,378,7158,0.641079245614626
+"634",805,"IP SUR GENERAL ADMIT",15690,"3758","HYDROMORPHONE 2 MG/ML INJ SOLN",1,7,0.000446144040790312,2,86,0.142857142857143,0.0232558139534884,6.14285714285714,6,84,0.541888145731545
+"635",805,"IP SUR GENERAL ADMIT",15690,"3843","IBUPROFEN 400 MG PO TABS",1,40,0.00254939451880178,148,4216,0.025,0.0351043643263757,0.712162162162162,39,4068,0.999999999999999
+"636",805,"IP SUR GENERAL ADMIT",15690,"4573","LORAZEPAM 1 MG PO TABS",1,9,0.000573613766730402,337,8494,0.111111111111111,0.0396750647515894,2.80052753049786,8,8157,0.808147579013301
+"637",805,"IP SUR GENERAL ADMIT",15690,"4711","MAGNESIUM CITRATE PO SOLN",1,33,0.00210325047801147,131,5481,0.0303030303030303,0.0239007480386791,1.26786953504511,32,5350,0.999999999999987
+"638",805,"IP SUR GENERAL ADMIT",15690,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",11,1521,0.0969407265774379,346,10128,0.00723208415516108,0.0341627172195893,0.211695226368414,1510,9782,2.11670270319495e-08
+"639",805,"IP SUR GENERAL ADMIT",15690,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",6,207,0.0131931166347992,745,12385,0.0289855072463768,0.0601534113847396,0.481859741270304,201,11640,0.083641068738824
+"640",805,"IP SUR GENERAL ADMIT",15690,"5009","METOPROLOL TARTRATE 50 MG PO TABS",1,3,0.000191204588910134,199,5315,0.333333333333333,0.0374412041392286,8.90284757118928,2,5116,0.239869977722474
+"641",805,"IP SUR GENERAL ADMIT",15690,"5018","METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",6,183,0.0116634799235182,223,3847,0.0327868852459016,0.0579672472056148,0.565610527089613,177,3624,0.202599740063018
+"642",805,"IP SUR GENERAL ADMIT",15690,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",1,73,0.00465264499681326,289,5149,0.0136986301369863,0.056127403379297,0.244063136938901,72,4860,0.188695724494378
+"643",805,"IP SUR GENERAL ADMIT",15690,"10011","FAMOTIDINE 20 MG PO TABS",1,50,0.00318674314850223,472,16960,0.02,0.0278301886792453,0.71864406779661,49,16488,0.999999999999992
+"644",805,"IP SUR GENERAL ADMIT",15690,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",78,3566,0.227278521351179,314,8762,0.0218732473359506,0.035836566993837,0.610361124705731,3488,8448,7.82160731063692e-05
+"645",805,"IP SUR GENERAL ADMIT",15690,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",3,136,0.00866794136392607,158,4300,0.0220588235294118,0.0367441860465116,0.600335070737156,133,4142,0.503668006870966
+"646",805,"IP SUR GENERAL ADMIT",15690,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",6,179,0.011408540471638,118,4415,0.0335195530726257,0.026727066817667,1.25414260013256,173,4297,0.753142312638744
+"647",805,"IP SUR GENERAL ADMIT",15690,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",6,325,0.0207138304652645,258,5335,0.0184615384615385,0.0483598875351453,0.38175313059034,319,5077,0.018971134437389
+"648",805,"IP SUR GENERAL ADMIT",15690,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",17,607,0.0386870618228171,692,19620,0.028006589785832,0.0352701325178389,0.79405966993934,590,18928,0.39738961652896
+"649",805,"IP SUR GENERAL ADMIT",15690,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",7,164,0.0104525175270873,242,3682,0.0426829268292683,0.0657251493753395,0.649415440435396,157,3440,0.31193266711288
+"650",805,"IP SUR GENERAL ADMIT",15690,"82384","PANTOPRAZOLE 40 MG IV SOLR",25,1107,0.0705544933078394,688,12925,0.022583559168925,0.0532301740812379,0.424262357933657,1082,12237,1.16114060225011e-05
+"651",805,"IP SUR GENERAL ADMIT",15690,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",1,100,0.00637348629700446,111,6618,0.01,0.0167724388032638,0.596216216216216,99,6507,0.895348088274773
+"652",805,"IP SUR GENERAL ADMIT",15690,"88043","PANTOPRAZOLE 40 MG PO TBEC",5,184,0.0117272147864882,624,31884,0.0271739130434783,0.0195709446744449,1.38848244147157,179,31260,0.634788866146878
+"653",805,"IP SUR GENERAL ADMIT",15690,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",115,441,0.0281070745697897,1067,10067,0.260770975056689,0.105989867885169,2.46033871217965,326,9000,1.65019718732282e-23
+"654",805,"IP SUR GENERAL ADMIT",15690,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",42,1701,0.108413001912046,738,23934,0.0246913580246914,0.0308347956881424,0.800762822443039,1659,23196,0.176265783746354
+"655",805,"IP SUR GENERAL ADMIT",15690,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",3,20,0.00127469725940089,73,1394,0.15,0.0523672883787661,2.86438356164384,17,1321,0.154723220886906
+"656",805,"IP SUR GENERAL ADMIT",15690,"110889","ENOXAPARIN 60 MG/0.6 ML SC SYRG",2,4,0.000254939451880178,119,2159,0.5,0.0551181102362205,9.07142857142857,2,2040,0.00544722030967489
+"657",805,"IP SUR GENERAL ADMIT",15690,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",12,359,0.022880815806246,499,13896,0.0334261838440111,0.0359096142774899,0.930842185764286,347,13397,0.915480009551411
+"658",805,"IP SUR GENERAL ADMIT",15690,"112564","MORPHINE 2 MG/ML INJ SYRG",4,267,0.0170172084130019,276,6156,0.0149812734082397,0.0448343079922027,0.334147532975086,263,5880,0.0288356806772582
+"659",805,"IP SUR GENERAL ADMIT",15690,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",33,1017,0.0648183556405354,1219,33427,0.0324483775811209,0.036467526251234,0.889788283350394,984,32208,0.555447962197049
+"660",805,"IP SUR GENERAL ADMIT",15690,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",21,144,0.00917782026768642,1951,16917,0.145833333333333,0.115327776792576,1.26451178882624,123,14966,0.31286895629104
+"661",805,"IP SUR GENERAL ADMIT",15690,"124873","SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP",3,182,0.0115997450605481,90,4112,0.0164835164835165,0.0218871595330739,0.753113553113553,179,4022,0.81817798394437
+"662",805,"IP SUR GENERAL ADMIT",15690,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",42,4590,0.292543021032505,705,36939,0.00915032679738562,0.0190855193697718,0.479438186622167,4548,36234,2.38661362893501e-06
+"663",805,"IP SUR GENERAL ADMIT",15690,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",2,185,0.0117909496494583,142,3356,0.0108108108108108,0.0423122765196663,0.255500570993529,183,3214,0.0547737347814238
+"664",805,"IP SUR GENERAL ADMIT",15690,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",19,154,0.00981516889738687,288,4280,0.123376623376623,0.0672897196261682,1.83351370851371,135,3992,0.0113352184638885
+"665",805,"IP SUR GENERAL ADMIT",15690,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",32,2268,0.144550669216061,452,13480,0.0141093474426808,0.0335311572700297,0.420783193644551,2236,13028,9.96571133614345e-07
+"666",805,"IP SUR GENERAL ADMIT",15690,"540102","NS IV BOLUS",2,44,0.00280433397068196,2225,80957,0.0454545454545455,0.0274837259285794,1.65387129724208,42,78732,0.788925979790686
+"667",805,"IP SUR GENERAL ADMIT",15690,"540301","POTASSIUM CHLORIDE ORAL SCALE",2,95,0.00605481198215424,105,3653,0.0210526315789474,0.0287434984943882,0.732431077694236,93,3548,0.894694418522955
+"668",805,"IP SUR GENERAL ADMIT",15690,"540302","POTASSIUM CHLORIDE IV SCALE",2,247,0.015742511153601,150,4660,0.00809716599190283,0.0321888412017167,0.251551956815115,245,4510,0.0522252470200412
+"669",805,"IP SUR GENERAL ADMIT",15690,"540773","PIPERACILLIN-TAZOBACTAM (ZOSYN) 3.375 GRAM IN DEXTROSE IVPB",1,42,0.00267686424474187,2,9,0.0238095238095238,0.222222222222222,0.107142857142857,41,7,0.129727787606415
+"670",805,"IP SUR GENERAL ADMIT",15690,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",2,67,0.00427023581899299,235,4195,0.0298507462686567,0.0560190703218117,0.532867577008574,65,3960,0.510125714553768
+"671",805,"IP SUR GENERAL ADMIT",15690,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",7,389,0.0247928616953474,104,5739,0.0179948586118252,0.0181216239763025,0.993004745896777,382,5635,0.999999999999991
+"672",805,"IP SUR GENERAL ADMIT",15690,"541061","VANCOMYCIN PER PHARMACY PROTOCOL",2,75,0.00478011472275335,239,5475,0.0266666666666667,0.0436529680365297,0.610878661087866,73,5236,0.665976695646873
+"673",805,"IP SUR GENERAL ADMIT",15690,"541081","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS EXTENDED INF",10,141,0.00898661567877629,9,216,0.0709219858156028,0.0416666666666667,1.70212765957447,131,207,0.335746587401922
+"674",805,"IP SUR GENERAL ADMIT",15690,"541104","CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS",3,88,0.00560866794136393,9,214,0.0340909090909091,0.0420560747663551,0.810606060606061,85,205,0.999999999999993
+"675",805,"IP SUR GENERAL ADMIT",15690,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",13,364,0.0231994901210962,466,8270,0.0357142857142857,0.0563482466747279,0.633813611281422,351,7804,0.117317912639822
+"676",805,"IP SUR GENERAL ADMIT",15690,"541273","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION",1,9,0.000573613766730402,148,1260,0.111111111111111,0.117460317460317,0.945945945945946,8,1112,0.999999999999997
+"677",805,"IP SUR GENERAL ADMIT",15690,"9000000","ZZZ IMS TEMPLATE",1,1,6.37348629700446e-05,1035,28824,1,0.0359075770191507,27.8492753623188,0,27789,0.012664547841265
+"678",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"101","ACETAMINOPHEN 325 MG PO TABS",7,241,0.031495033978045,981,42092,0.029045643153527,0.0233060914187969,1.24626830949873,234,41111,0.707997828671172
+"679",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"1080","BISACODYL 10 MG PR SUPP",1,113,0.0147673810768427,344,22442,0.00884955752212389,0.0153284021031994,0.577330726486932,112,22098,0.860652328009889
+"680",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"2566","DOCUSATE SODIUM 100 MG PO CAPS",8,253,0.0330632514375327,542,37509,0.0316205533596838,0.0144498653656456,2.18829397780144,245,36967,0.0445591219633671
+"681",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"2567","DOCUSATE SODIUM 250 MG PO CAPS",1,49,0.00640355462624151,296,17760,0.0204081632653061,0.0166666666666667,1.22448979591837,48,17464,0.999999999999984
+"682",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"4318","LACTATED RINGERS IV SOLP",11,315,0.0411657083115525,135,4847,0.0349206349206349,0.0278522797606767,1.25378012933568,304,4712,0.576911169444291
+"683",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",3,82,0.0107161526398327,745,12385,0.0365853658536585,0.0601534113847396,0.608201014896055,79,11640,0.50769563473753
+"684",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"5373","NALOXONE 0.4 MG/ML INJ SOLN",1,126,0.016466283324621,83,1774,0.00793650793650794,0.0467869222096956,0.169630904570664,125,1691,0.067900590642174
+"685",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"6432","POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD",11,40,0.00522739153162572,425,8090,0.275,0.0525339925834363,5.23470588235294,29,7665,4.1451540235663e-09
+"686",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"7318","SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP",2,17,0.00222164140094093,51,783,0.117647058823529,0.0651340996168582,1.80622837370242,15,732,0.712579435190513
+"687",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",7,313,0.0409043387349712,89,4372,0.0223642172523962,0.0203568161024703,1.09861076210647,306,4283,0.971554441551882
+"688",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",3,16,0.00209095661265029,70,1432,0.1875,0.0488826815642458,3.83571428571429,13,1362,0.0516990858450319
+"689",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"9627","CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK",1,6,0.000784108729743858,29,474,0.166666666666667,0.0611814345991561,2.72413793103448,5,445,0.831991098086384
+"690",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"10011","FAMOTIDINE 20 MG PO TABS",1,8,0.00104547830632514,472,16960,0.125,0.0278301886792453,4.49152542372881,7,16488,0.551814556720466
+"691",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"10372","LABETALOL 5 MG/ML IV SOLN",3,114,0.0148980658651333,370,5898,0.0263157894736842,0.0627331298745337,0.419487908961593,111,5528,0.161363279740659
+"692",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"10467","LORAZEPAM 2 MG/ML INJ SOLN",1,9,0.00117616309461579,749,20621,0.111111111111111,0.0363221958197954,3.05904168520991,8,19872,0.758220717101787
+"693",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",1,33,0.00431259801359122,155,5616,0.0303030303030303,0.0275997150997151,1.09794721407625,32,5461,0.999999999999999
+"694",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"11150","PROPOFOL 10 MG/ML IV EMUL",8,87,0.0113695765812859,135,3592,0.0919540229885057,0.0375835189309577,2.44665815240528,79,3457,0.0207833291554443
+"695",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"11349","SENNOSIDES 8.6 MG PO TABS",5,191,0.0249607945635128,610,35793,0.0261780104712042,0.0170424384656218,1.53604840786199,186,35183,0.489154735355037
+"696",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"14858","D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,40,0.00522739153162572,58,2031,0.025,0.0285573609059577,0.875431034482759,39,1973,1
+"697",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",9,177,0.0231312075274438,314,8762,0.0508474576271186,0.035836566993837,1.41887077620641,168,8448,0.391966511725676
+"698",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",6,124,0.0162049137480397,158,4300,0.0483870967741935,0.0367441860465116,1.31686402613312,118,4142,0.663219124583502
+"699",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"15882","D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP",2,79,0.0103240982749608,137,3533,0.0253164556962025,0.0387772431361449,0.652868890326157,77,3396,0.749394730419365
+"700",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",2,55,0.00718766335598536,118,4415,0.0363636363636364,0.026727066817667,1.36055469953775,53,4297,0.984268395097246
+"701",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,31,0.00405122843700993,258,5335,0.032258064516129,0.0483598875351453,0.66704176044011,30,5077,0.999999999999986
+"702",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",1,174,0.0227391531625719,712,36910,0.00574712643678161,0.0192901652668653,0.297930388738215,173,36198,0.307161847496578
+"703",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",21,519,0.0678254051228437,692,19620,0.0404624277456647,0.0352701325178389,1.14721507567911,498,18928,0.6090234048952
+"704",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"81421","ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",1,21,0.0027443805541035,72,1479,0.0476190476190476,0.0486815415821501,0.978174603174603,20,1407,0.999999999999947
+"705",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"82384","PANTOPRAZOLE 40 MG IV SOLR",20,621,0.0811552535284893,688,12925,0.0322061191626409,0.0532301740812379,0.605035014792345,601,12237,0.0273052553255269
+"706",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"88043","PANTOPRAZOLE 40 MG PO TBEC",5,22,0.00287506534239415,624,31884,0.227272727272727,0.0195709446744449,11.6127622377622,17,31260,4.42154302175863e-10
+"707",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"89065","LIDOCAINE 5 % TP PTMD",2,82,0.0107161526398327,197,10907,0.024390243902439,0.018061795177409,1.35037761545128,80,10710,0.990011300261359
+"708",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",9,263,0.0343700993204391,738,23934,0.0342205323193916,0.0308347956881424,1.10980246684596,254,23196,0.891437077866896
+"709",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",8,71,0.00927861996863565,499,13896,0.112676056338028,0.0359096142774899,3.13776849473595,63,13397,0.00173890127062974
+"710",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",9,261,0.0341087297438578,1219,33427,0.0344827586206897,0.036467526251234,0.945574382620011,252,32208,0.996293400039863
+"711",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",6,625,0.0816779926816519,457,11068,0.0096,0.0412902059992772,0.232500656455142,619,10611,0.000119485679129508
+"712",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",18,1280,0.167276529012023,705,36939,0.0140625,0.0190855193697718,0.736815159574468,1262,36234,0.233063242254762
+"713",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",3,87,0.0113695765812859,142,3356,0.0344827586206897,0.0423122765196663,0.814958717824186,84,3214,0.929364322353941
+"714",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",12,206,0.0269210663878725,288,4280,0.058252427184466,0.0672897196261682,0.865695792880259,194,3992,0.715552429650592
+"715",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"207694","NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",4,63,0.00823314166231051,56,1641,0.0634920634920635,0.0341255332114564,1.86054421768707,59,1585,0.371983940100794
+"716",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"210741","PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN",1,50,0.00653423941453215,43,1494,0.02,0.0287817938420348,0.694883720930233,49,1451,0.999999999999957
+"717",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",14,1407,0.183873497124935,326,12268,0.00995024875621891,0.0265731985653733,0.374446784482496,1393,11942,0.000213484073701903
+"718",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",21,886,0.11578672242551,510,18335,0.0237020316027088,0.0278156531224434,0.852111273403267,865,17825,0.532155356877485
+"719",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",8,193,0.0252221641400941,452,13480,0.0414507772020725,0.0335311572700297,1.23618689531845,185,13028,0.685594889966275
+"720",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",19,193,0.0252221641400941,156,2315,0.0984455958549223,0.0673866090712743,1.46090740002657,174,2159,0.138842247760953
+"721",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"226058","EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",2,18,0.00235232618923157,56,1218,0.111111111111111,0.0459770114942529,2.41666666666667,16,1162,0.46186486587034
+"722",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",12,234,0.0305802404600105,291,6612,0.0512820512820513,0.044010889292196,1.16521279407877,222,6321,0.71155514998999
+"723",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",1,12,0.00156821745948772,1808,23328,0.0833333333333333,0.0775034293552812,1.07522123893805,11,21520,0.999999999999919
+"724",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"520064","CEFAZOLIN 2 G IVPB",1,20,0.00261369576581286,109,2674,0.05,0.0407629020194465,1.22660550458716,19,2565,0.99999999999988
+"725",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530003","FENTANYL  10 MCG/ML IV INFUSION",1,15,0.00196027182435964,51,585,0.0666666666666667,0.0871794871794872,0.764705882352941,14,534,0.999999999999966
+"726",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",1,8,0.00104547830632514,65,1105,0.125,0.0588235294117647,2.125,7,1040,0.969313034010182
+"727",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530006","EPINEPHRINE IV INFUSION",1,28,0.003659174072138,99,2540,0.0357142857142857,0.0389763779527559,0.916305916305916,27,2441,0.999999999999998
+"728",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530008","NITROPRUSSIDE IV INFUSION",1,9,0.00117616309461579,79,2082,0.111111111111111,0.0379442843419789,2.92827004219409,8,2003,0.786320916896849
+"729",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530011","NOREPINEPHRINE IV INFUSION",2,34,0.00444328280188186,92,2909,0.0588235294117647,0.0316259883121348,1.85997442455243,32,2817,0.684623638193078
+"730",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530025","DEXMEDETOMIDINE IV INFUSION",29,71,0.00927861996863565,530,2902,0.408450704225352,0.18263266712612,2.23646027106032,42,2372,3.19988743234354e-06
+"731",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530031","VASOPRESSIN 1 UNIT/ML IV INFUSION",4,48,0.00627286983795086,215,4489,0.0833333333333333,0.0478948540877701,1.73992248062015,44,4274,0.423168878710224
+"732",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"530120","HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)",13,232,0.0303188708834292,130,2894,0.0560344827586207,0.0449205252246026,1.24741379310345,219,2764,0.53770326790398
+"733",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"540102","NS IV BOLUS",1,13,0.00169890224777836,2225,80957,0.0769230769230769,0.0274837259285794,2.79885911840968,12,78732,0.808846785615324
+"734",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",10,396,0.0517511761630946,461,18637,0.0252525252525253,0.0247357407308043,1.02089221937378,386,18176,0.999999999999999
+"735",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"540301","POTASSIUM CHLORIDE ORAL SCALE",4,156,0.0203868269733403,105,3653,0.0256410256410256,0.0287434984943882,0.892063492063492,152,3548,0.999999999999997
+"736",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"540302","POTASSIUM CHLORIDE IV SCALE",13,1485,0.194066910611605,150,4660,0.00875420875420875,0.0321888412017167,0.271964085297419,1472,4510,1.57706245781378e-06
+"737",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"540304","MAGNESIUM SULFATE IV SCALE ",12,1504,0.196549921589127,78,4104,0.00797872340425532,0.0190058479532164,0.419803600654664,1492,4026,0.00524917240290593
+"738",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",4,29,0.00378985886042865,235,4195,0.137931034482759,0.0560190703218117,2.46221570066031,25,3960,0.13376814737282
+"739",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,33,0.00431259801359122,104,5739,0.0303030303030303,0.0181216239763025,1.6722027972028,32,5635,0.999999999999626
+"740",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"541061","VANCOMYCIN PER PHARMACY PROTOCOL",2,15,0.00196027182435964,239,5475,0.133333333333333,0.0436529680365297,3.05439330543933,13,5236,0.288214446244473
+"741",806,"IP ICU SUR/TRAUMA  ADMIT",7652,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",4,17,0.00222164140094093,411,7130,0.235294117647059,0.0576437587657784,4.0818663231716,13,6719,0.00907805814411235
+"742",811,"IP PAMF ENT POSTOP",29,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,1,0.0344827586206897,499,13896,1,0.0359096142774899,27.8476953907816,0,13397,0.0127154320473097
+"743",812,"IP ENT GENERAL ADMISSION",838,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,113,0.134844868735084,346,10128,0.00884955752212389,0.0341627172195893,0.259041383190956,112,9782,0.22338234210862
+"744",812,"IP ENT GENERAL ADMISSION",838,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",5,191,0.227923627684964,314,8762,0.0261780104712042,0.035836566993837,0.730483209390736,186,8448,0.606487539711785
+"745",812,"IP ENT GENERAL ADMISSION",838,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,9,0.0107398568019093,158,4300,0.111111111111111,0.0367441860465116,3.0239099859353,8,4142,0.766313882271774
+"746",812,"IP ENT GENERAL ADMISSION",838,"22473","KETOROLAC 30 MG/ML (1 ML) INJ SOLN",1,10,0.0119331742243437,132,2852,0.1,0.0462833099579243,2.16060606060606,9,2720,0.957646452386939
+"747",812,"IP ENT GENERAL ADMISSION",838,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",3,71,0.0847255369928401,258,5335,0.0422535211267606,0.0483598875351453,0.87373075663282,68,5077,0.999999999999915
+"748",812,"IP ENT GENERAL ADMISSION",838,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,19,0.022673031026253,692,19620,0.0526315789473684,0.0352701325178389,1.49224216610891,18,18928,0.999999999999997
+"749",812,"IP ENT GENERAL ADMISSION",838,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,38,0.045346062052506,499,13896,0.0526315789473684,0.0359096142774899,1.46566817846219,36,13397,0.907132317225053
+"750",812,"IP ENT GENERAL ADMISSION",838,"117862","HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN",2,101,0.120525059665871,79,3308,0.0198019801980198,0.0238814993954051,0.82917658854493,99,3229,0.999999999999999
+"751",812,"IP ENT GENERAL ADMISSION",838,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",3,277,0.33054892601432,705,36939,0.0108303249097473,0.0190855193697718,0.56746293878178,274,36234,0.434651831255433
+"752",812,"IP ENT GENERAL ADMISSION",838,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",1,52,0.0620525059665871,192,8611,0.0192307692307692,0.0222970618975729,0.862479967948718,51,8419,0.999999999999797
+"753",812,"IP ENT GENERAL ADMISSION",838,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,122,0.145584725536993,452,13480,0.00819672131147541,0.0335311572700297,0.244450892209488,121,13028,0.193906294425452
+"754",813,"IP ENT GENERAL POSTOP",568,"2332","DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",1,25,0.0440140845070423,390,6737,0.04,0.0578892682202761,0.690974358974359,24,6347,0.999999999999999
+"755",813,"IP ENT GENERAL POSTOP",568,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,7,0.0123239436619718,542,37509,0.142857142857143,0.0144498653656456,9.8863995782815,6,36967,0.207017404340548
+"756",813,"IP ENT GENERAL POSTOP",568,"4996","METHYLPREDNISOLONE ACETATE 80 MG/ML INJ SUSP",5,26,0.0457746478873239,7,34,0.192307692307692,0.205882352941176,0.934065934065934,21,27,1
+"757",813,"IP ENT GENERAL POSTOP",568,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,105,0.184859154929577,314,8762,0.00952380952380952,0.035836566993837,0.265756748559296,104,8448,0.236891494773988
+"758",813,"IP ENT GENERAL POSTOP",568,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",3,46,0.0809859154929577,499,13896,0.0652173913043478,0.0359096142774899,1.81615404722488,43,13397,0.503614165131861
+"759",813,"IP ENT GENERAL POSTOP",568,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,213,0.375,705,36939,0.00469483568075117,0.0190855193697718,0.245989411647188,212,36234,0.199776337594964
+"760",813,"IP ENT GENERAL POSTOP",568,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",2,38,0.0669014084507042,288,4280,0.0526315789473684,0.0672897196261682,0.782163742690058,36,3992,0.972940285668882
+"761",813,"IP ENT GENERAL POSTOP",568,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",4,25,0.0440140845070423,275,8000,0.16,0.034375,4.65454545454545,21,7725,0.00401758229732467
+"762",813,"IP ENT GENERAL POSTOP",568,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,20,0.0352112676056338,452,13480,0.05,0.0335311572700297,1.49115044247788,19,13028,0.999999999999998
+"763",813,"IP ENT GENERAL POSTOP",568,"540300","MORPHINE INJECTABLE SYRINGE",1,118,0.207746478873239,61,1002,0.00847457627118644,0.0608782435129741,0.139205334815226,117,941,0.0322145271620259
+"764",814,"IP ENT HEAD AND NECK POSTOP",3366,"101","ACETAMINOPHEN 325 MG PO TABS",2,293,0.0870469399881165,981,42092,0.0068259385665529,0.0233060914187969,0.292882167322472,291,41111,0.0943308363978631
+"765",814,"IP ENT HEAD AND NECK POSTOP",3366,"2566","DOCUSATE SODIUM 100 MG PO CAPS",2,405,0.120320855614973,542,37509,0.00493827160493827,0.0144498653656456,0.341752084187509,403,36967,0.164243641396504
+"766",814,"IP ENT HEAD AND NECK POSTOP",3366,"2569","DOCUSATE SODIUM 50 MG/5 ML PO LIQD",6,270,0.0802139037433155,170,7328,0.0222222222222222,0.0231986899563319,0.957908496732026,264,7158,0.999999999999996
+"767",814,"IP ENT HEAD AND NECK POSTOP",3366,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",5,586,0.174093879976233,346,10128,0.00853242320819113,0.0341627172195893,0.24975833020971,581,9782,0.0010775992471599
+"768",814,"IP ENT HEAD AND NECK POSTOP",3366,"5172","MORPHINE 4 MG/ML INJ SYRG",2,14,0.0041592394533571,238,5949,0.142857142857143,0.0400067238191293,3.57082833133253,12,5711,0.202306104824998
+"769",814,"IP ENT HEAD AND NECK POSTOP",3366,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",4,121,0.0359477124183007,70,1432,0.0330578512396694,0.0488826815642458,0.676269185360095,117,1362,0.573800009466831
+"770",814,"IP ENT HEAD AND NECK POSTOP",3366,"10813","OXYCODONE 5 MG/5 ML PO SOLN",3,221,0.0656565656565657,328,8370,0.0135746606334842,0.0391875746714456,0.346402163116654,218,8042,0.075788934790251
+"771",814,"IP ENT HEAD AND NECK POSTOP",3366,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",9,953,0.283125371360665,314,8762,0.00944386149003148,0.035836566993837,0.263525841960687,944,8448,2.43448110165578e-05
+"772",814,"IP ENT HEAD AND NECK POSTOP",3366,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",7,445,0.132204396910279,258,5335,0.0157303370786517,0.0483598875351453,0.325276543855065,438,5077,0.00233620426151259
+"773",814,"IP ENT HEAD AND NECK POSTOP",3366,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,58,0.0172311348781937,692,19620,0.0172413793103448,0.0352701325178389,0.488837950966713,57,18928,0.698694626515309
+"774",814,"IP ENT HEAD AND NECK POSTOP",3366,"82384","PANTOPRAZOLE 40 MG IV SOLR",6,167,0.0496137849079026,688,12925,0.0359281437125748,0.0532301740812379,0.674958223088706,161,12237,0.413495545294657
+"775",814,"IP ENT HEAD AND NECK POSTOP",3366,"88043","PANTOPRAZOLE 40 MG PO TBEC",1,33,0.00980392156862745,624,31884,0.0303030303030303,0.0195709446744449,1.5483682983683,32,31260,0.999999999999993
+"776",814,"IP ENT HEAD AND NECK POSTOP",3366,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,5,0.00148544266191325,1067,10067,0.2,0.105989867885169,1.88697282099344,4,9000,0.999999999999995
+"777",814,"IP ENT HEAD AND NECK POSTOP",3366,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",2,95,0.0282234105763518,738,23934,0.0210526315789474,0.0308347956881424,0.68275566966196,93,23196,0.800062794329204
+"778",814,"IP ENT HEAD AND NECK POSTOP",3366,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",3,187,0.0555555555555556,499,13896,0.0160427807486631,0.0359096142774899,0.446754471509865,184,13397,0.208750457555335
+"779",814,"IP ENT HEAD AND NECK POSTOP",3366,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,41,0.0121806298276887,1219,33427,0.0487804878048781,0.036467526251234,1.33764180956002,39,32208,0.997198245733373
+"780",814,"IP ENT HEAD AND NECK POSTOP",3366,"117862","HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN",2,314,0.0932857991681521,79,3308,0.00636942675159236,0.0238814993954051,0.266709667016045,312,3229,0.0709243074459005
+"781",814,"IP ENT HEAD AND NECK POSTOP",3366,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,20,0.005941770647653,1951,16917,0.05,0.115327776792576,0.43354689902614,19,14966,0.572724609086698
+"782",814,"IP ENT HEAD AND NECK POSTOP",3366,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",16,1114,0.330956625074272,705,36939,0.0143626570915619,0.0190855193697718,0.752542113908094,1098,36234,0.304130057403062
+"783",814,"IP ENT HEAD AND NECK POSTOP",3366,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",51,362,0.107546048722519,288,4280,0.140883977900552,0.0672897196261682,2.09369244935543,311,3992,4.14030083048459e-07
+"784",814,"IP ENT HEAD AND NECK POSTOP",3366,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",7,247,0.0733808674985146,192,8611,0.0283400809716599,0.0222970618975729,1.27102311066127,240,8419,0.678769249218201
+"785",814,"IP ENT HEAD AND NECK POSTOP",3366,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",5,481,0.142899584076055,452,13480,0.0103950103950104,0.0335311572700297,0.310010487001637,476,13028,0.00754743555964161
+"786",814,"IP ENT HEAD AND NECK POSTOP",3366,"520002","DEXAMETHASONE (CUSTOM DOSE) IVPB",11,25,0.00742721330956625,168,1360,0.44,0.123529411764706,3.56190476190476,14,1192,1.22395765645211e-05
+"787",814,"IP ENT HEAD AND NECK POSTOP",3366,"540300","MORPHINE INJECTABLE SYRINGE",4,780,0.231729055258467,61,1002,0.00512820512820513,0.0608782435129741,0.0842370744010088,776,941,1.05737027979507e-09
+"788",814,"IP ENT HEAD AND NECK POSTOP",3366,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,46,0.0136660724896019,104,5739,0.0217391304347826,0.0181216239763025,1.1996237458194,45,5635,0.999999999999998
+"789",814,"IP ENT HEAD AND NECK POSTOP",3366,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",1,7,0.00207961972667855,411,7130,0.142857142857143,0.0576437587657784,2.47827598192562,6,6719,0.876423662391561
+"790",814,"IP ENT HEAD AND NECK POSTOP",3366,"541093","AMPICILLIN/SULBACTAM 1.5 GRAM/50 ML MINI-BAG PLUS",3,46,0.0136660724896019,29,343,0.0652173913043478,0.0845481049562682,0.77136431784108,43,314,0.8710450445486
+"791",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,37,0.150406504065041,346,10128,0.027027027027027,0.0341627172195893,0.791126386502109,36,9782,0.999999999999995
+"792",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"10813","OXYCODONE 5 MG/5 ML PO SOLN",1,35,0.142276422764228,328,8370,0.0285714285714286,0.0391875746714456,0.729094076655052,34,8042,0.999999999999812
+"793",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,46,0.186991869918699,314,8762,0.0217391304347826,0.035836566993837,0.606618665189698,45,8448,0.908044363650959
+"794",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,1,0.0040650406504065,1067,10067,1,0.105989867885169,9.4348641049672,0,9000,0.200796619232221
+"795",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,9,0.0365853658536585,499,13896,0.111111111111111,0.0359096142774899,3.09418837675351,8,13397,0.752100320369339
+"796",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,49,0.199186991869919,705,36939,0.0204081632653061,0.0190855193697718,1.0693009118541,48,36234,0.999999999999991
+"797",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,33,0.134146341463415,452,13480,0.0303030303030303,0.0335311572700297,0.903727540895683,32,13028,0.999999999999999
+"798",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"221636","PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN",2,30,0.121951219512195,177,6874,0.0666666666666667,0.0257491998836194,2.58907721280603,28,6697,0.405688675786015
+"799",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"520002","DEXAMETHASONE (CUSTOM DOSE) IVPB",8,28,0.113821138211382,168,1360,0.285714285714286,0.123529411764706,2.31292517006803,20,1192,0.0234457148758245
+"800",815,"IP ENT - HEAD AND NECK ADMISSION-PREOP",246,"540300","MORPHINE INJECTABLE SYRINGE",2,35,0.142276422764228,61,1002,0.0571428571428571,0.0608782435129741,0.93864168618267,33,941,0.999999999999999
+"801",816,"IP RAD NEURORADIOLOGY PRE-PROCEDURE",751,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,8,0.0106524633821571,118,4415,0.125,0.026727066817667,4.67690677966102,7,4297,0.533427194484823
+"802",816,"IP RAD NEURORADIOLOGY PRE-PROCEDURE",751,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",4,352,0.468708388814913,692,19620,0.0113636363636364,0.0352701325178389,0.322188649500788,348,18928,0.02276242924129
+"803",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"101","ACETAMINOPHEN 325 MG PO TABS",2,25,0.00946252838758516,981,42092,0.08,0.0233060914187969,3.43257900101937,23,41111,0.224581279646696
+"804",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"4318","LACTATED RINGERS IV SOLP",1,2,0.000757002271006813,135,4847,0.5,0.0278522797606767,17.9518518518519,1,4712,0.0572346957934298
+"805",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"10814","OXYCODONE 5 MG PO TABS",3,63,0.0238455715367146,1478,56614,0.0476190476190476,0.0261066167379094,1.8240221663767,60,55136,0.499885808590635
+"806",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,18,0.00681302043906132,692,19620,0.0555555555555556,0.0352701325178389,1.57514450867052,17,18928,0.999999999999956
+"807",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,2,0.000757002271006813,242,3682,0.5,0.0657251493753395,7.60743801652893,1,3440,0.29424298677062
+"808",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",2,27,0.010219530658592,738,23934,0.0740740740740741,0.0308347956881424,2.40228846732912,25,23196,0.458422233477916
+"809",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",3,25,0.00946252838758516,1219,33427,0.12,0.036467526251234,3.29059885151764,22,32208,0.0906035340407528
+"810",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,2,0.000757002271006813,1951,16917,0.5,0.115327776792576,4.3354689902614,1,14966,0.551181481032122
+"811",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,130,0.0492051476154428,705,36939,0.0153846153846154,0.0190855193697718,0.806088379705401,128,36234,0.999999999998231
+"812",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"540300","MORPHINE INJECTABLE SYRINGE",1,62,0.0234670704012112,61,1002,0.0161290322580645,0.0608782435129741,0.264939185616076,61,941,0.237864383154073
+"813",821,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (SAME DAY DISCHARGE)",2642,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,6,0.00227100681302044,104,5739,0.166666666666667,0.0181216239763025,9.19711538461539,5,5635,0.233935935882488
+"814",823,"IP RAD ARTERIAL THROMBOLYSIS",13,"530040","ALTEPLASE INFUSION",1,5,0.384615384615385,22,207,0.2,0.106280193236715,1.88181818181818,4,185,0.999999999999999
+"815",824,"IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE",930,"1445","CEFAZOLIN 1 GRAM INJ SOLR",9,27,0.0290322580645161,47,571,0.333333333333333,0.0823117338003503,4.04964539007092,18,524,5.4164591812481e-05
+"816",824,"IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE",930,"4573","LORAZEPAM 1 MG PO TABS",1,53,0.056989247311828,337,8494,0.0188679245283019,0.0396750647515894,0.475561278763787,52,8157,0.673511833013784
+"817",824,"IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE",930,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",16,449,0.482795698924731,692,19620,0.0356347438752784,0.0352701325178389,1.01033768039445,433,18928,0.999999999999778
+"818",824,"IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE",930,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",2,44,0.0473118279569892,242,3682,0.0454545454545455,0.0657251493753395,0.691585274229902,42,3440,0.815142979018486
+"819",824,"IP RAD INTERVENTIONAL RADIOLOGY PRE-PROCEDURE",930,"520187","ONDANSETRON (+DEXAMETHASONE) IVPB",2,28,0.0301075268817204,28,2757,0.0714285714285714,0.0101559666303954,7.03316326530612,26,2729,0.0274517956627377
+"820",838,"RETIRED: IP SUR PANCREATECTOMY POSTOP",319,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,95,0.297805642633229,705,36939,0.0105263157894737,0.0190855193697718,0.551534154535274,94,36234,0.815247584969921
+"821",838,"RETIRED: IP SUR PANCREATECTOMY POSTOP",319,"541104","CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS",1,4,0.0125391849529781,9,214,0.25,0.0420560747663551,5.94444444444444,3,205,0.445165528196408
+"822",848,"IP SUR BREAST PROCEDURES POSTOP",325,"5172","MORPHINE 4 MG/ML INJ SYRG",1,3,0.00923076923076923,238,5949,0.333333333333333,0.0400067238191293,8.33193277310924,2,5711,0.264235346629724
+"823",848,"IP SUR BREAST PROCEDURES POSTOP",325,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,2,0.00615384615384615,73,1394,0.5,0.0523672883787661,9.54794520547945,1,1321,0.213386719932846
+"824",848,"IP SUR BREAST PROCEDURES POSTOP",325,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",3,22,0.0676923076923077,288,4280,0.136363636363636,0.0672897196261682,2.02651515151515,19,3992,0.389112832378695
+"825",851,"IP MED CELLULITIS",36,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",2,10,0.277777777777778,1951,16917,0.2,0.115327776792576,1.73418759610456,8,14966,0.731747653063538
+"826",853,"IP MED ALLERGIC REACTION",59,"101","ACETAMINOPHEN 325 MG PO TABS",1,10,0.169491525423729,981,42092,0.1,0.0233060914187969,4.29072375127421,9,41111,0.576182885786226
+"827",853,"IP MED ALLERGIC REACTION",59,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,28,0.474576271186441,399,16019,0.0357142857142857,0.0249079218428117,1.43385248836377,27,15620,0.999999999999995
+"828",853,"IP MED ALLERGIC REACTION",59,"213662","EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",2,36,0.610169491525424,69,5047,0.0555555555555556,0.0136714880126808,4.06360708534622,34,4978,0.155276031094858
+"829",853,"IP MED ALLERGIC REACTION",59,"9000000","ZZZ IMS TEMPLATE",1,3,0.0508474576271186,1035,28824,0.333333333333333,0.0359075770191507,9.28309178743961,2,27789,0.223786333563954
+"830",856,"IP MED DRUG OVERDOSE",38,"520118","ACETYLCYSTEINE IV INFUSION",1,63,1,44,493,0.0158730158730159,0.0892494929006085,0.177849927849928,62,449,0.0774754553305128
+"831",857,"RETIRED IP MED ABDOMINAL PAIN",9,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,1,0.111111111111111,738,23934,1,0.0308347956881424,32.4308943089431,0,23196,0.00668630199023242
+"832",857,"RETIRED IP MED ABDOMINAL PAIN",9,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,1,0.111111111111111,738,23934,1,0.0308347956881424,32.4308943089431,0,23196,0.00668630199023242
+"833",859,"IP CAR GENERAL ADMIT",1453,"11664","WARFARIN 1 MG PO TABS",1,6,0.00412938747419133,98,3895,0.166666666666667,0.0251604621309371,6.62414965986394,5,3797,0.36634158623887
+"834",859,"IP CAR GENERAL ADMIT",1453,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,59,0.0406056434962147,499,13896,0.0338983050847458,0.0359096142774899,0.943989674263782,57,13397,0.999999999999797
+"835",859,"IP CAR GENERAL ADMIT",1453,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",8,217,0.149346180316586,452,13480,0.0368663594470046,0.0335311572700297,1.09946576403899,209,13028,0.935738085702399
+"836",863,"IP CAR EPS/ABLATION (POST-PROCEDURE)",295,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",1,2,0.00677966101694915,68,899,0.5,0.0756395995550612,6.61029411764706,1,831,0.355863592746064
+"837",864,"IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE)",908,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,319,0.351321585903084,258,5335,0.00626959247648903,0.0483598875351453,0.129644480085539,317,5077,0.000811533444844564
+"838",864,"IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE)",908,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",2,58,0.0638766519823789,1951,16917,0.0344827586206897,0.115327776792576,0.298997861397338,56,14966,0.0854015840537023
+"839",864,"IP CAR ICD/PACEMAKER IMPLANT(PRE-PROCEDURE)",908,"520064","CEFAZOLIN 2 G IVPB",1,51,0.0561674008810573,109,2674,0.0196078431372549,0.0407629020194465,0.481021766504767,50,2565,0.688217346209749
+"840",865,"IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE)",1314,"9499","CEPHALEXIN 250 MG PO CAPS",2,18,0.0136986301369863,36,616,0.111111111111111,0.0584415584415584,1.90123456790123,16,580,0.671388840446334
+"841",865,"IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE)",1314,"9500","CEPHALEXIN 500 MG PO CAPS",7,368,0.280060882800609,175,4220,0.0190217391304348,0.0414691943127962,0.458695652173913,361,4045,0.0480769481334383
+"842",865,"IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE)",1314,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",6,473,0.359969558599696,258,5335,0.0126849894291755,0.0483598875351453,0.262303948080043,467,5077,0.000550751485739096
+"843",865,"IP CAR ICD/PACEMAKER IMPLANT (POST-PROCEDURE)",1314,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,71,0.0540334855403349,1951,16917,0.0140845070422535,0.115327776792576,0.122125887049617,70,14966,0.0130284235065569
+"844",867,"IP CAR EPS/ABLATION (PRE-PROCEDURE)",401,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,79,0.197007481296758,258,5335,0.0126582278481013,0.0483598875351453,0.261750564223334,78,5077,0.22612501858523
+"845",868,"IP PULM RH CATH POST",93,"101","ACETAMINOPHEN 325 MG PO TABS",1,4,0.043010752688172,981,42092,0.25,0.0233060914187969,10.7268093781855,3,41111,0.177904126837166
+"846",870,"IP CAR ACUTE MI/STEMI ADMIT",76,"14113","ASPIRIN 81 MG PO TBEC",1,18,0.236842105263158,391,18448,0.0555555555555556,0.0211947094535993,2.62119920431941,17,18057,0.847061095470676
+"847",870,"IP CAR ACUTE MI/STEMI ADMIT",76,"37637","METOPROLOL TARTRATE 50 MG PO TABS",1,11,0.144736842105263,809,19725,0.0909090909090909,0.0410139416983523,2.21654118440274,10,18916,0.941174673185643
+"848",870,"IP CAR ACUTE MI/STEMI ADMIT",76,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,2,0.0263157894736842,452,13480,0.5,0.0335311572700297,14.9115044247788,1,13028,0.0894223689889605
+"849",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"101","ACETAMINOPHEN 325 MG PO TABS",1,64,0.191044776119403,981,42092,0.015625,0.0233060914187969,0.670425586136595,63,41111,0.999999999999965
+"850",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"680","ASPIRIN 81 MG PO CHEW",2,37,0.11044776119403,357,11203,0.0540540540540541,0.031866464339909,1.6962676962677,35,10846,0.765684219250243
+"851",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"5604","NITROGLYCERIN 0.4 MG SL SUBL",2,101,0.301492537313433,77,2947,0.0198019801980198,0.0261282660332542,0.757875787578758,99,2870,0.940204918774548
+"852",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"10176","HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",1,10,0.0298507462686567,164,2898,0.1,0.0565907522429262,1.76707317073171,9,2734,0.999999999999997
+"853",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"10181","HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",2,11,0.0328358208955224,156,510,0.181818181818182,0.305882352941176,0.594405594405594,9,354,0.57946187630536
+"854",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",4,24,0.0716417910447761,68,899,0.166666666666667,0.0756395995550612,2.20343137254902,20,831,0.209316061518262
+"855",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"79507","ATORVASTATIN 10 MG PO TABS",3,9,0.026865671641791,96,5116,0.333333333333333,0.0187646598905395,17.7638888888889,6,5020,1.71552159490559e-08
+"856",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"83398","CLOPIDOGREL 75 MG PO TABS",1,13,0.0388059701492537,234,7279,0.0769230769230769,0.0321472729770573,2.39283366206443,12,7045,0.898626171043624
+"857",875,"IP CAR UNSTABLE ANGINA/NSTEMI ADMIT",335,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,23,0.0686567164179104,452,13480,0.0434782608695652,0.0335311572700297,1.29665255867641,22,13028,0.999999999999998
+"858",882,"RETIRED IP ORT HIP FRACTURE ADMIT",136,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,19,0.139705882352941,346,10128,0.0526315789473684,0.0341627172195893,1.54061454213569,18,9782,0.999999999999976
+"859",882,"RETIRED IP ORT HIP FRACTURE ADMIT",136,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,19,0.139705882352941,346,10128,0.0526315789473684,0.0341627172195893,1.54061454213569,18,9782,0.999999999999976
+"860",882,"RETIRED IP ORT HIP FRACTURE ADMIT",136,"10813","OXYCODONE 5 MG/5 ML PO SOLN",1,5,0.0367647058823529,328,8370,0.2,0.0391875746714456,5.10365853658537,4,8042,0.484511293371825
+"861",882,"RETIRED IP ORT HIP FRACTURE ADMIT",136,"10813","OXYCODONE 5 MG/5 ML PO SOLN",1,5,0.0367647058823529,328,8370,0.2,0.0391875746714456,5.10365853658537,4,8042,0.484511293371825
+"862",883,"IP ORT TUMOR POSTOP",160,"101","ACETAMINOPHEN 325 MG PO TABS",2,26,0.1625,981,42092,0.0769230769230769,0.0233060914187969,3.30055673174939,24,41111,0.245815928316414
+"863",883,"IP ORT TUMOR POSTOP",160,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,9,0.05625,499,13896,0.111111111111111,0.0359096142774899,3.09418837675351,8,13397,0.752100320369339
+"864",883,"IP ORT TUMOR POSTOP",160,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,21,0.13125,104,5739,0.0476190476190476,0.0181216239763025,2.62774725274725,20,5635,0.848131282070756
+"865",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"102","ACETAMINOPHEN 500 MG PO TABS",1,129,0.086,392,15968,0.00775193798449612,0.0245490981963928,0.315772820756209,128,15576,0.344760866407146
+"866",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"1080","BISACODYL 10 MG PR SUPP",2,485,0.323333333333333,344,22442,0.00412371134020619,0.0153284021031994,0.269024214816591,483,22098,0.0696405308147918
+"867",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"2511","DIPHENHYDRAMINE HCL 12.5 MG/5 ML PO ELIX",1,24,0.016,57,714,0.0416666666666667,0.0798319327731092,0.521929824561403,23,657,0.765842641801687
+"868",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,265,0.176666666666667,542,37509,0.00377358490566038,0.0144498653656456,0.261150177539511,264,36967,0.231687800589354
+"869",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"2567","DOCUSATE SODIUM 250 MG PO CAPS",3,217,0.144666666666667,296,17760,0.0138248847926267,0.0166666666666667,0.829493087557604,214,17464,0.953486410128214
+"870",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,482,0.321333333333333,346,10128,0.004149377593361,0.0341627172195893,0.121459237761735,480,9782,0.000494615298894693
+"871",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,77,0.0513333333333333,218,7862,0.012987012987013,0.0277283134062579,0.468366495889432,76,7644,0.662579046521306
+"872",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"10813","OXYCODONE 5 MG/5 ML PO SOLN",1,61,0.0406666666666667,328,8370,0.0163934426229508,0.0391875746714456,0.418332666933227,60,8042,0.559079894607559
+"873",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"11349","SENNOSIDES 8.6 MG PO TABS",3,539,0.359333333333333,610,35793,0.00556586270871985,0.0170424384656218,0.326588399890508,536,35183,0.0594416447604967
+"874",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,203,0.135333333333333,258,5335,0.00492610837438424,0.0483598875351453,0.101863520067209,202,5077,0.00678218845751698
+"875",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",5,505,0.336666666666667,712,36910,0.0099009900990099,0.0192901652668653,0.513266214261876,500,36198,0.172196093234049
+"876",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"80840","ONDANSETRON HCL 8 MG PO TABS",1,3,0.002,170,10077,0.333333333333333,0.0168701002282425,19.7588235294118,2,9907,0.0446247092886682
+"877",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"81141","ONDANSETRON HCL 4 MG PO TABS",2,199,0.132666666666667,87,4024,0.0100502512562814,0.0216202783300199,0.464853000635361,197,3937,0.391758355622167
+"878",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",1,494,0.329333333333333,111,6618,0.00202429149797571,0.0167724388032638,0.120691541744173,493,6507,0.0186476698330876
+"879",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"92173","ZOLPIDEM 5 MG PO TABS",1,60,0.04,159,8203,0.0166666666666667,0.019383152505181,0.859853249475891,59,8044,0.999999999999998
+"880",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",2,128,0.0853333333333333,738,23934,0.015625,0.0308347956881424,0.506732723577236,126,23196,0.460891619754251
+"881",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,489,0.326,705,36939,0.00204498977505112,0.0190855193697718,0.10714876212853,488,36234,0.00975281518514197
+"882",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",1,97,0.0646666666666667,326,12268,0.0103092783505155,0.0265731985653733,0.387957750932895,96,11942,0.498584130765061
+"883",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",1,3,0.002,156,2315,0.333333333333333,0.0673866090712743,4.9465811965812,2,2159,0.494993206218388
+"884",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",1,93,0.062,111,2584,0.010752688172043,0.0429566563467492,0.250314830959992,92,2473,0.207536331826898
+"885",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"520064","CEFAZOLIN 2 G IVPB",1,67,0.0446666666666667,109,2674,0.0149253731343284,0.0407629020194465,0.366150896891688,66,2565,0.453737079851465
+"886",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"540304","MAGNESIUM SULFATE IV SCALE ",1,269,0.179333333333333,78,4104,0.00371747211895911,0.0190058479532164,0.195596225336002,268,4026,0.112384753878924
+"887",884,"RETIRED IP ORT SPINAL FUSION POSTOP",1500,"540598","THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS",1,69,0.046,17,1250,0.0144927536231884,0.0136,1.06564364876385,68,1233,0.999999999999926
+"888",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"1080","BISACODYL 10 MG PR SUPP",1,9,0.333333333333333,344,22442,0.111111111111111,0.0153284021031994,7.24870801033592,8,22098,0.326910264620884
+"889",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,6,0.222222222222222,356,12049,0.166666666666667,0.0295460204166321,5.64091760299625,5,11693,0.437508676520942
+"890",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,4,0.148148148148148,542,37509,0.25,0.0144498653656456,17.3011992619926,3,36967,0.0641913323211949
+"891",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"4711","MAGNESIUM CITRATE PO SOLN",1,9,0.333333333333333,131,5481,0.111111111111111,0.0239007480386791,4.64885496183206,8,5350,0.536812769990336
+"892",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,9,0.333333333333333,346,10128,0.111111111111111,0.0341627172195893,3.252408477842,8,9782,0.724835763883227
+"893",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"11349","SENNOSIDES 8.6 MG PO TABS",1,9,0.333333333333333,610,35793,0.111111111111111,0.0170424384656218,6.51967213114754,8,35183,0.37258689843434
+"894",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",1,9,0.333333333333333,712,36910,0.111111111111111,0.0192901652668653,5.75998751560549,8,36198,0.429436178459759
+"895",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"81141","ONDANSETRON HCL 4 MG PO TABS",1,3,0.111111111111111,87,4024,0.333333333333333,0.0216202783300199,15.4176245210728,2,3937,0.0861160990834944
+"896",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",1,9,0.333333333333333,111,6618,0.111111111111111,0.0167724388032638,6.62462462462462,8,6507,0.36797660845888
+"897",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",1,7,0.259259259259259,101,3581,0.142857142857143,0.02820441217537,5.06506364922206,6,3480,0.493200143304714
+"898",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,4,0.148148148148148,499,13896,0.25,0.0359096142774899,6.96192384769539,3,13397,0.338913857321974
+"899",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,9,0.333333333333333,705,36939,0.111111111111111,0.0190855193697718,5.82174940898345,8,36234,0.424419424581305
+"900",885,"IP ORT SPORTS ORTHO/REHAB POSTOP",27,"520064","CEFAZOLIN 2 G IVPB",1,3,0.111111111111111,109,2674,0.333333333333333,0.0407629020194465,8.17737003058104,2,2565,0.27292534734676
+"901",886,"IP ORT TRAUMA",9299,"101","ACETAMINOPHEN 325 MG PO TABS",10,502,0.0539842993870309,981,42092,0.0199203187250996,0.0233060914187969,0.854725846867373,492,41111,0.725340161580053
+"902",886,"IP ORT TRAUMA",9299,"102","ACETAMINOPHEN 500 MG PO TABS",25,1939,0.208517044843532,392,15968,0.0128932439401753,0.0245490981963928,0.525202344991633,1914,15576,0.00172452863744216
+"903",886,"IP ORT TRAUMA",9299,"1080","BISACODYL 10 MG PR SUPP",13,2628,0.282611033444456,344,22442,0.00494672754946728,0.0153284021031994,0.322716452514955,2615,22098,3.14074442353618e-05
+"904",886,"IP ORT TRAUMA",9299,"1801","CODEINE SULFATE 15 MG PO TABS",4,112,0.0120443058393376,8,181,0.0357142857142857,0.0441988950276243,0.808035714285714,108,173,0.957896299707526
+"905",886,"IP ORT TRAUMA",9299,"2566","DOCUSATE SODIUM 100 MG PO CAPS",18,1883,0.202494891923863,542,37509,0.00955921402018056,0.0144498653656456,0.66154346620471,1865,36967,0.0990258132773922
+"906",886,"IP ORT TRAUMA",9299,"2567","DOCUSATE SODIUM 250 MG PO CAPS",5,816,0.0877513711151737,296,17760,0.00612745098039216,0.0166666666666667,0.367647058823529,811,17464,0.0285432643507768
+"907",886,"IP ORT TRAUMA",9299,"4318","LACTATED RINGERS IV SOLP",4,305,0.032799225723196,135,4847,0.0131147540983607,0.0278522797606767,0.470868245294475,301,4712,0.174269797711285
+"908",886,"IP ORT TRAUMA",9299,"4711","MAGNESIUM CITRATE PO SOLN",12,2485,0.267233035810302,131,5481,0.00482897384305835,0.0239007480386791,0.202042791097731,2473,5350,4.96248186285625e-09
+"909",886,"IP ORT TRAUMA",9299,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",3,938,0.100871061404452,346,10128,0.00319829424307036,0.0341627172195893,0.0936194337971579,935,9782,3.5130856223318e-07
+"910",886,"IP ORT TRAUMA",9299,"5373","NALOXONE 0.4 MG/ML INJ SOLN",19,2600,0.279599956984622,83,1774,0.00730769230769231,0.0467869222096956,0.156190917516219,2581,1691,4.74590469384353e-17
+"911",886,"IP ORT TRAUMA",9299,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,21,0.00225830734487579,218,7862,0.0476190476190476,0.0277283134062579,1.71734381826125,20,7644,0.999999999999973
+"912",886,"IP ORT TRAUMA",9299,"10181","HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",1,3,0.000322615334982256,156,510,0.333333333333333,0.305882352941176,1.08974358974359,2,354,1
+"913",886,"IP ORT TRAUMA",9299,"10814","OXYCODONE 5 MG PO TABS",66,3130,0.336595332831487,1478,56614,0.0210862619808307,0.0261066167379094,0.807697994440289,3064,55136,0.095849408672613
+"914",886,"IP ORT TRAUMA",9299,"11349","SENNOSIDES 8.6 MG PO TABS",32,2884,0.310140875362942,610,35793,0.0110957004160888,0.0170424384656218,0.651062959005025,2852,35183,0.0198671676254282
+"915",886,"IP ORT TRAUMA",9299,"14632","TRAMADOL 50 MG PO TABS",4,191,0.0205398429938703,304,13808,0.0209424083769634,0.0220162224797219,0.951226233122072,187,13504,0.999999999999985
+"916",886,"IP ORT TRAUMA",9299,"16830","MELATONIN 3 MG PO TABS",4,642,0.0690396816862028,456,17087,0.00623052959501558,0.0266869549950255,0.233467235065858,638,16631,0.00210948018811381
+"917",886,"IP ORT TRAUMA",9299,"18307","GABAPENTIN 400 MG PO CAPS",3,17,0.00182815356489945,41,1580,0.176470588235294,0.0259493670886076,6.80057388809182,14,1539,0.00247434256542444
+"918",886,"IP ORT TRAUMA",9299,"18308","GABAPENTIN 300 MG PO CAPS",7,386,0.041509839767717,588,19458,0.0181347150259067,0.0302189330866482,0.600111028867506,379,18870,0.219513601634146
+"919",886,"IP ORT TRAUMA",9299,"18309","GABAPENTIN 100 MG PO CAPS",12,560,0.0602215291966878,199,6318,0.0214285714285714,0.0314973092750871,0.680330222541278,548,6119,0.231521251160569
+"920",886,"IP ORT TRAUMA",9299,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",23,1148,0.12345413485321,258,5335,0.0200348432055749,0.0483598875351453,0.414286389541636,1125,5077,2.72243346160393e-05
+"921",886,"IP ORT TRAUMA",9299,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",17,2622,0.281965802774492,712,36910,0.0064836003051106,0.0192901652668653,0.336109111322517,2605,36198,3.57540889547944e-06
+"922",886,"IP ORT TRAUMA",9299,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",16,2213,0.237982578771911,692,19620,0.00723000451875282,0.0352701325178389,0.204989434476778,2197,18928,2.63359798228466e-12
+"923",886,"IP ORT TRAUMA",9299,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,61,0.00655984514463921,603,24164,0.0327868852459016,0.0249544777354743,1.31386781937308,59,23561,0.999999999999991
+"924",886,"IP ORT TRAUMA",9299,"81141","ONDANSETRON HCL 4 MG PO TABS",12,1999,0.214969351543177,87,4024,0.00600300150075038,0.0216202783300199,0.277656069414017,1987,3937,1.18075919473151e-05
+"925",886,"IP ORT TRAUMA",9299,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",11,2598,0.279384880094634,111,6618,0.00423402617397998,0.0167724388032638,0.252439506481077,2587,6507,3.53162828186957e-06
+"926",886,"IP ORT TRAUMA",9299,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",7,1696,0.182385202709969,101,3581,0.00412735849056604,0.02820441217537,0.146337334205119,1689,3480,1.47146356849465e-08
+"927",886,"IP ORT TRAUMA",9299,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",17,1157,0.124421980858157,738,23934,0.0146931719965428,0.0308347956881424,0.476512708083001,1140,23196,0.0022820502549331
+"928",886,"IP ORT TRAUMA",9299,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,36,0.00387138401978707,73,1394,0.0277777777777778,0.0523672883787661,0.530441400304414,35,1321,0.782109341983428
+"929",886,"IP ORT TRAUMA",9299,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",13,656,0.07054521991612,499,13896,0.0198170731707317,0.0359096142774899,0.551859817195366,643,13397,0.0377421910626108
+"930",886,"IP ORT TRAUMA",9299,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",16,763,0.0820518335304871,1219,33427,0.0209698558322412,0.036467526251234,0.57502819598386,747,32208,0.0299796825993686
+"931",886,"IP ORT TRAUMA",9299,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",4,62,0.00666738358963329,1951,16917,0.0645161290322581,0.115327776792576,0.559415353582117,58,14966,0.292868163410533
+"932",886,"IP ORT TRAUMA",9299,"124873","SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP",1,11,0.00118292289493494,90,4112,0.0909090909090909,0.0218871595330739,4.15353535353535,10,4022,0.597094506391832
+"933",886,"IP ORT TRAUMA",9299,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",19,2706,0.290999032153995,705,36939,0.00702143385070214,0.0190855193697718,0.367893255334874,2687,36234,8.59707240299176e-06
+"934",886,"IP ORT TRAUMA",9299,"206114","ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS",127,1437,0.154532745456501,70,2981,0.0883785664578984,0.0234820530023482,3.76366438015707,1310,2911,2.66302552330558e-22
+"935",886,"IP ORT TRAUMA",9299,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,59,0.00634476825465104,452,13480,0.0169491525423729,0.0335311572700297,0.505474726263687,58,13028,0.730879114821355
+"936",886,"IP ORT TRAUMA",9299,"214400","BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG",3,1239,0.133240133347672,71,6103,0.00242130750605327,0.0116336228084549,0.20813013675272,1236,6032,0.00505031626060686
+"937",886,"IP ORT TRAUMA",9299,"520064","CEFAZOLIN 2 G IVPB",3,240,0.0258092267985805,109,2674,0.0125,0.0407629020194465,0.306651376146789,237,2565,0.0448037694122282
+"938",886,"IP ORT TRAUMA",9299,"540102","NS IV BOLUS",4,1601,0.172169050435531,2225,80957,0.00249843847595253,0.0274837259285794,0.0909061050326692,1597,78732,1.63863190920993e-09
+"939",886,"IP ORT TRAUMA",9299,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",1,63,0.00677492203462738,461,18637,0.0158730158730159,0.0247357407308043,0.641703680749234,62,18176,0.963381178407588
+"940",886,"IP ORT TRAUMA",9299,"540301","POTASSIUM CHLORIDE ORAL SCALE",14,2400,0.258092267985805,105,3653,0.00583333333333333,0.0287434984943882,0.202944444444444,2386,3548,6.17481562429228e-10
+"941",886,"IP ORT TRAUMA",9299,"540304","MAGNESIUM SULFATE IV SCALE ",13,2337,0.251317345951178,78,4104,0.00556268720581943,0.0190058479532164,0.292682926829268,2324,4026,1.82195336175567e-05
+"942",886,"IP ORT TRAUMA",9299,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",3,308,0.0331218410581783,104,5739,0.00974025974025974,0.0181216239763025,0.537493756243756,305,5635,0.386990304819418
+"943",886,"IP ORT TRAUMA",9299,"9000000","ZZZ IMS TEMPLATE",1,7,0.000752769114958598,1035,28824,0.142857142857143,0.0359075770191507,3.97846790890269,6,27789,0.613824700660452
+"944",888,"RETIRED IP ORT REPLANT",51,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,10,0.196078431372549,542,37509,0.1,0.0144498653656456,6.92047970479705,9,36967,0.346791199110908
+"945",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"101","ACETAMINOPHEN 325 MG PO TABS",15,1160,0.0672463768115942,981,42092,0.0129310344827586,0.0233060914187969,0.554834967837182,1145,41111,0.0260912165365678
+"946",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"102","ACETAMINOPHEN 500 MG PO TABS",48,2877,0.166782608695652,392,15968,0.0166840458811262,0.0245490981963928,0.679619501606691,2829,15576,0.0122617060583818
+"947",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"1080","BISACODYL 10 MG PR SUPP",20,4915,0.284927536231884,344,22442,0.00406917599186165,0.0153284021031994,0.265466417469067,4895,22098,6.78981822556026e-10
+"948",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"1801","CODEINE SULFATE 15 MG PO TABS",4,130,0.00753623188405797,8,181,0.0307692307692308,0.0441988950276243,0.696153846153846,126,173,0.758045993439382
+"949",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"2566","DOCUSATE SODIUM 100 MG PO CAPS",18,3753,0.217565217391304,542,37509,0.00479616306954436,0.0144498653656456,0.331917491836789,3735,36967,1.59199076818039e-06
+"950",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"2567","DOCUSATE SODIUM 250 MG PO CAPS",3,1206,0.0699130434782609,296,17760,0.00248756218905473,0.0166666666666667,0.149253731343284,1203,17464,0.000210726226895637
+"951",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"3760","HYDROMORPHONE 2 MG PO TABS",2,35,0.00202898550724638,241,5912,0.0571428571428571,0.0407645466847091,1.40177830468287,33,5671,0.952290278631641
+"952",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"4318","LACTATED RINGERS IV SOLP",5,355,0.0205797101449275,135,4847,0.0140845070422535,0.0278522797606767,0.505685967657799,350,4712,0.16838341147435
+"953",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"4711","MAGNESIUM CITRATE PO SOLN",11,4703,0.27263768115942,131,5481,0.00233893259621518,0.0239007480386791,0.0978602256477512,4692,5350,4.87962488772023e-20
+"954",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",4,1177,0.068231884057971,346,10128,0.00339847068819031,0.0341627172195893,0.0994789339017095,1173,9782,1.35758656982961e-08
+"955",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"5373","NALOXONE 0.4 MG/ML INJ SOLN",24,1493,0.0865507246376812,83,1774,0.0160750167448091,0.0467869222096956,0.343579273557727,1469,1691,1.47640026984859e-06
+"956",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",3,304,0.0176231884057971,289,5149,0.00986842105263158,0.056127403379297,0.175821799307958,301,4860,0.000807214229634768
+"957",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"8752","WARFARIN 7.5 MG PO TABS",1,6,0.000347826086956522,54,1901,0.166666666666667,0.0284061020515518,5.86728395061728,5,1847,0.424400813282763
+"958",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",1,113,0.00655072463768116,70,1432,0.00884955752212389,0.0488826815642458,0.181036662452592,112,1362,0.0848282514079184
+"959",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"10813","OXYCODONE 5 MG/5 ML PO SOLN",4,43,0.00249275362318841,328,8370,0.0930232558139535,0.0391875746714456,2.37379466817924,39,8042,0.156789914211618
+"960",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"10814","OXYCODONE 5 MG PO TABS",136,5314,0.308057971014493,1478,56614,0.0255927738050433,0.0261066167379094,0.980317521108742,5178,55136,0.857334574750836
+"961",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"11349","SENNOSIDES 8.6 MG PO TABS",190,6712,0.389101449275362,610,35793,0.0283075089392133,0.0170424384656218,1.66100109419879,6522,35183,6.27997711796277e-10
+"962",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"14632","TRAMADOL 50 MG PO TABS",8,458,0.0265507246376812,304,13808,0.0174672489082969,0.0220162224797219,0.793380831992645,450,13504,0.622384363548258
+"963",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"16830","MELATONIN 3 MG PO TABS",6,880,0.0510144927536232,456,17087,0.00681818181818182,0.0266869549950255,0.255487440191388,874,16631,0.000427881038073498
+"964",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"18308","GABAPENTIN 300 MG PO CAPS",8,614,0.0355942028985507,588,19458,0.0130293159609121,0.0302189330866482,0.431163996543243,606,18870,0.018774895553894
+"965",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"18309","GABAPENTIN 100 MG PO CAPS",34,1088,0.0630724637681159,199,6318,0.03125,0.0314973092750871,0.99214824120603,1054,6119,0.999999999999966
+"966",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",53,3569,0.206898550724638,258,5335,0.0148500980666853,0.0483598875351453,0.307074702270412,3516,5077,5.23476227949762e-17
+"967",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",11,4985,0.288985507246377,712,36910,0.00220661985957874,0.0192901652668653,0.114390925585746,4974,36198,5.84483845440267e-18
+"968",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",14,4323,0.250608695652174,692,19620,0.00323849178811011,0.0352701325178389,0.0918196660154918,4309,18928,3.24564128618127e-29
+"969",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"27920","OXYCODONE 10 MG PO TB12",5,307,0.0177971014492754,258,7208,0.0162866449511401,0.0357935627081021,0.455016034138828,302,6950,0.0963286865719873
+"970",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"27921","OXYCODONE 20 MG PO TB12",2,67,0.00388405797101449,93,2927,0.0298507462686567,0.0317731465664503,0.939496068046862,65,2834,0.99999999999988
+"971",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"27922","OXYCODONE 40 MG PO TB12",2,26,0.00150724637681159,47,1081,0.0769230769230769,0.0434782608695652,1.76923076923077,24,1034,0.736202868384599
+"972",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"28899","OXYCODONE 15 MG PO TABS",2,21,0.00121739130434783,51,1050,0.0952380952380952,0.0485714285714286,1.96078431372549,19,999,0.639615082149031
+"973",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"28900","OXYCODONE 30 MG PO TABS",1,17,0.000985507246376812,32,591,0.0588235294117647,0.0541455160744501,1.08639705882353,16,559,0.999999999999998
+"974",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",3,31,0.00179710144927536,603,24164,0.0967741935483871,0.0249544777354743,3.87802920879474,28,23561,0.0474514071055678
+"975",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"77052","CELECOXIB 200 MG PO CAPS",4,223,0.0129275362318841,20,1192,0.0179372197309417,0.0167785234899329,1.06905829596413,219,1172,0.999999999999998
+"976",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"81141","ONDANSETRON HCL 4 MG PO TABS",12,4218,0.244521739130435,87,4024,0.00284495021337127,0.0216202783300199,0.131587122512712,4206,3937,1.16228018366552e-14
+"977",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",14,4876,0.282666666666667,111,6618,0.00287120590648072,0.0167724388032638,0.171185952153959,4862,6507,2.36944257473611e-12
+"978",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",15,3566,0.206724637681159,101,3581,0.00420639371845205,0.02820441217537,0.149139563423532,3551,3480,2.12000390181469e-15
+"979",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"92173","ZOLPIDEM 5 MG PO TABS",2,981,0.0568695652173913,159,8203,0.00203873598369011,0.019383152505181,0.105180825623962,979,8044,0.000154716513949341
+"980",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"93715","CELECOXIB 100 MG PO CAPS",10,342,0.0198260869565217,42,1272,0.0292397660818713,0.0330188679245283,0.885547201336675,332,1230,0.858026544782622
+"981",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",7,806,0.0467246376811594,738,23934,0.0086848635235732,0.0308347956881424,0.281657891020598,799,23196,0.00044075732717436
+"982",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",29,1196,0.0693333333333333,499,13896,0.024247491638796,0.0359096142774899,0.675236761147713,1167,13397,0.0429474542972889
+"983",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"112564","MORPHINE 2 MG/ML INJ SYRG",5,205,0.0118840579710145,276,6156,0.024390243902439,0.0448343079922027,0.544008483563097,200,5880,0.219217568849752
+"984",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",11,261,0.0151304347826087,1951,16917,0.0421455938697318,0.115327776792576,0.365441830596747,250,14966,0.000329848848190787
+"985",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"125758","PREGABALIN 75 MG PO CAPS",1,173,0.0100289855072464,31,1474,0.00578034682080925,0.0210312075983718,0.274846168189446,172,1443,0.278491194241998
+"986",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",30,4958,0.287420289855072,705,36939,0.00605082694634933,0.0190855193697718,0.317037583788933,4928,36234,7.67620314430365e-11
+"987",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"206114","ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS",67,2481,0.143826086956522,70,2981,0.0270052398226522,0.0234820530023482,1.15003742730466,2414,2911,0.457986139611049
+"988",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",4,103,0.00597101449275362,510,18335,0.0388349514563107,0.0278156531224434,1.39615457833619,99,17825,0.705929426847423
+"989",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"214400","BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG",2,1845,0.10695652173913,71,6103,0.0010840108401084,0.0116336228084549,0.0931791289743883,1843,6032,5.74141587525913e-05
+"990",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"540102","NS IV BOLUS",3,2724,0.157913043478261,2225,80957,0.00110132158590308,0.0274837259285794,0.0400717715190813,2721,78732,6.68721155443136e-17
+"991",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"540301","POTASSIUM CHLORIDE ORAL SCALE",28,4212,0.244173913043478,105,3653,0.00664767331433998,0.0287434984943882,0.231275720164609,4184,3548,6.77754623967285e-14
+"992",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"540304","MAGNESIUM SULFATE IV SCALE ",26,4141,0.240057971014493,78,4104,0.00627867664815262,0.0190058479532164,0.330354986718184,4115,4026,3.79605974345796e-07
+"993",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,387,0.0224347826086957,104,5739,0.00258397932816537,0.0181216239763025,0.142590936195587,386,5635,0.0377950039908229
+"994",891,"IP ORT TOTAL JOINT REPLACEMENT",17250,"9000000","ZZZ IMS TEMPLATE",5,50,0.00289855072463768,1035,28824,0.1,0.0359075770191507,2.78492753623188,45,27789,0.0403393769871987
+"995",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"102","ACETAMINOPHEN 500 MG PO TABS",5,272,0.167901234567901,392,15968,0.0183823529411765,0.0245490981963928,0.748799519807923,267,15576,0.649062005457801
+"996",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"1080","BISACODYL 10 MG PR SUPP",8,586,0.361728395061728,344,22442,0.0136518771331058,0.0153284021031994,0.890626240177792,578,22098,0.876015272088449
+"997",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"1801","CODEINE SULFATE 15 MG PO TABS",5,105,0.0648148148148148,8,181,0.0476190476190476,0.0441988950276243,1.07738095238095,100,173,0.999999999999996
+"998",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"1802","CODEINE SULFATE 30 MG PO TABS",1,2,0.00123456790123457,3,90,0.5,0.0333333333333333,15,1,87,0.147615778588567
+"999",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"2566","DOCUSATE SODIUM 100 MG PO CAPS",4,426,0.262962962962963,542,37509,0.00938967136150235,0.0144498653656456,0.649810300919911,422,36967,0.50451371213845
+"1000",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"2567","DOCUSATE SODIUM 250 MG PO CAPS",2,166,0.102469135802469,296,17760,0.0120481927710843,0.0166666666666667,0.72289156626506,164,17464,0.874217922048999
+"1001",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"4318","LACTATED RINGERS IV SOLP",2,135,0.0833333333333333,135,4847,0.0148148148148148,0.0278522797606767,0.531906721536351,133,4712,0.517705264860257
+"1002",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"4711","MAGNESIUM CITRATE PO SOLN",1,572,0.353086419753086,131,5481,0.00174825174825175,0.0239007480386791,0.0731463193295254,571,5350,0.000961956127089027
+"1003",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"5373","NALOXONE 0.4 MG/ML INJ SOLN",5,450,0.277777777777778,83,1774,0.0111111111111111,0.0467869222096956,0.237483266398929,445,1691,0.000862707743100295
+"1004",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"10814","OXYCODONE 5 MG PO TABS",19,334,0.206172839506173,1478,56614,0.0568862275449102,0.0261066167379094,2.17899654007276,315,55136,0.000855549113940065
+"1005",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"11349","SENNOSIDES 8.6 MG PO TABS",2,615,0.37962962962963,610,35793,0.0032520325203252,0.0170424384656218,0.190819672131148,613,35183,0.0131571309034887
+"1006",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"18308","GABAPENTIN 300 MG PO CAPS",7,135,0.0833333333333333,588,19458,0.0518518518518519,0.0302189330866482,1.71587301587302,128,18870,0.227019992658544
+"1007",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"18309","GABAPENTIN 100 MG PO CAPS",1,22,0.0135802469135802,199,6318,0.0454545454545455,0.0314973092750871,1.4431247144815,21,6119,0.999999999999986
+"1008",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",14,460,0.283950617283951,258,5335,0.0304347826086957,0.0483598875351453,0.629339400067408,446,5077,0.103273242343713
+"1009",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",2,543,0.335185185185185,712,36910,0.00368324125230203,0.0192901652668653,0.190938812672005,541,36198,0.013063302228479
+"1010",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,425,0.262345679012346,692,19620,0.00470588235294118,0.0352701325178389,0.133424005440326,423,18928,0.00105396409812573
+"1011",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"27920","OXYCODONE 10 MG PO TB12",3,80,0.0493827160493827,258,7208,0.0375,0.0357935627081021,1.04767441860465,77,6950,0.999999999999834
+"1012",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,2,0.00123456790123457,603,24164,0.5,0.0249544777354743,20.0364842454395,1,23561,0.0415013714707364
+"1013",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"81141","ONDANSETRON HCL 4 MG PO TABS",4,545,0.33641975308642,87,4024,0.0073394495412844,0.0216202783300199,0.339470631656649,541,3937,0.0378905895758156
+"1014",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",2,583,0.359876543209877,111,6618,0.00343053173241852,0.0167724388032638,0.204533864911223,581,6507,0.0208269420877777
+"1015",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",1,481,0.296913580246914,101,3581,0.00207900207900208,0.02820441217537,0.0737119449990737,480,3480,0.00102620712113615
+"1016",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"92173","ZOLPIDEM 5 MG PO TABS",1,73,0.0450617283950617,159,8203,0.0136986301369863,0.019383152505181,0.706728698199362,72,8044,0.999999999999963
+"1017",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"112564","MORPHINE 2 MG/ML INJ SYRG",6,192,0.118518518518519,276,6156,0.03125,0.0448343079922027,0.697010869565217,186,5880,0.470408048214631
+"1018",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,57,0.0351851851851852,1219,33427,0.0175438596491228,0.036467526251234,0.481081703438251,56,32208,0.683200846020416
+"1019",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,30,0.0185185185185185,1951,16917,0.0333333333333333,0.115327776792576,0.289031266017427,29,14966,0.263000724082718
+"1020",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",4,563,0.347530864197531,705,36939,0.00710479573712256,0.0190855193697718,0.372261063451873,559,36234,0.0554062209147166
+"1021",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"206114","ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS",4,157,0.0969135802469136,70,2981,0.0254777070063694,0.0234820530023482,1.08498635122839,153,2911,0.999999999999998
+"1022",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"233900","OXYCODONE 10 MG PO TR12",2,28,0.0172839506172839,38,949,0.0714285714285714,0.0400421496311907,1.78383458646617,26,911,0.732198360123568
+"1023",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"540102","NS IV BOLUS",2,338,0.208641975308642,2225,80957,0.00591715976331361,0.0274837259285794,0.215296855262283,336,78732,0.0240038769419564
+"1024",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"540301","POTASSIUM CHLORIDE ORAL SCALE",1,560,0.345679012345679,105,3653,0.00178571428571429,0.0287434984943882,0.062125850340136,559,3548,0.000264165480303086
+"1025",893,"IP ORT SHOULDER ELBOW ADMISSION POSTOP",1620,"540304","MAGNESIUM SULFATE IV SCALE ",2,527,0.325308641975309,78,4104,0.00379506641366224,0.0190058479532164,0.199678878995767,525,4026,0.0190105151237844
+"1026",894,"IP ICU COPD",91,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",1,3,0.032967032967033,106,3939,0.333333333333333,0.0269103833460269,12.3867924528302,2,3833,0.136831202158949
+"1027",894,"IP ICU COPD",91,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",2,8,0.0879120879120879,127,3899,0.25,0.0325724544755065,7.6751968503937,6,3772,0.0143707656330183
+"1028",894,"IP ICU COPD",91,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",1,32,0.351648351648352,493,18456,0.03125,0.0267121803207629,1.16987829614604,31,17963,0.999999999999976
+"1029",894,"IP ICU COPD",91,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,10,0.10989010989011,208,3699,0.1,0.0562314138956475,1.77836538461538,9,3491,0.999999999999955
+"1030",894,"IP ICU COPD",91,"520078","METHYLPREDNISOLONE IVPB",3,17,0.186813186813187,342,2212,0.176470588235294,0.154611211573237,1.14138286893705,14,1870,0.999999999999947
+"1031",901,"IP ICU PNEUMONIA",124,"9487","CEFTRIAXONE 1 GRAM IV SOLR",2,5,0.0403225806451613,13,272,0.4,0.0477941176470588,8.36923076923077,3,259,0.0142352858346717
+"1032",901,"IP ICU PNEUMONIA",124,"9488","CEFTRIAXONE 2 GRAM IV SOLR",1,1,0.00806451612903226,23,339,1,0.0678466076696165,14.7391304347826,0,316,0.0931576142406838
+"1033",901,"IP ICU PNEUMONIA",124,"21063","AZITHROMYCIN 500 MG IV SOLR",4,7,0.0564516129032258,1,9,0.571428571428571,0.111111111111111,5.14285714285714,3,8,0.153576396549984
+"1034",901,"IP ICU PNEUMONIA",124,"89444","PIPERACILLIN-TAZOBACTAM-DEXTRS 2.25 GRAM/50 ML IV PGBK",1,1,0.00806451612903226,63,534,1,0.117977528089888,8.47619047619048,0,471,0.240717581906376
+"1035",901,"IP ICU PNEUMONIA",124,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",1,8,0.0645161290322581,290,2601,0.125,0.111495578623606,1.12112068965517,7,2311,0.999999999999932
+"1036",901,"IP ICU PNEUMONIA",124,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,7,0.0564516129032258,466,8270,0.142857142857143,0.0563482466747279,2.53525444512569,6,7804,0.863315459000189
+"1037",901,"IP ICU PNEUMONIA",124,"541273","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION",2,12,0.0967741935483871,148,1260,0.166666666666667,0.117460317460317,1.41891891891892,10,1112,0.939135008698663
+"1038",902,"IP ICU SEPSIS MANAGEMENT",516,"101","ACETAMINOPHEN 325 MG PO TABS",1,3,0.00581395348837209,981,42092,0.333333333333333,0.0233060914187969,14.3024125042474,2,41111,0.100002477677862
+"1039",902,"IP ICU SEPSIS MANAGEMENT",516,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",3,20,0.0387596899224806,1067,10067,0.15,0.105989867885169,1.41522961574508,17,9000,0.783248409508895
+"1040",902,"IP ICU SEPSIS MANAGEMENT",516,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,13,0.0251937984496124,208,3699,0.0769230769230769,0.0562314138956475,1.36797337278107,12,3491,0.999999999999999
+"1041",902,"IP ICU SEPSIS MANAGEMENT",516,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,9,0.0174418604651163,452,13480,0.111111111111111,0.0335311572700297,3.31366764995084,8,13028,0.714348887042159
+"1042",902,"IP ICU SEPSIS MANAGEMENT",516,"520045","MEROPENEM CUSTOM IVPB",2,10,0.0193798449612403,100,1654,0.2,0.060459492140266,3.308,8,1554,0.240842756264353
+"1043",902,"IP ICU SEPSIS MANAGEMENT",516,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",15,65,0.125968992248062,1808,23328,0.230769230769231,0.0775034293552812,2.97753573859769,50,21520,1.23351137875983e-05
+"1044",902,"IP ICU SEPSIS MANAGEMENT",516,"530011","NOREPINEPHRINE IV INFUSION",3,31,0.060077519379845,92,2909,0.0967741935483871,0.0316259883121348,3.05995792426367,28,2817,0.126040709154815
+"1045",902,"IP ICU SEPSIS MANAGEMENT",516,"530012","PHENYLEPHRINE IV INFUSION",1,5,0.00968992248062015,61,2180,0.2,0.0279816513761468,7.14754098360656,4,2119,0.334212411522971
+"1046",902,"IP ICU SEPSIS MANAGEMENT",516,"540102","NS IV BOLUS",2,19,0.0368217054263566,2225,80957,0.105263157894737,0.0274837259285794,3.83001774098167,17,78732,0.170266250564635
+"1047",902,"IP ICU SEPSIS MANAGEMENT",516,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",1,25,0.0484496124031008,235,4195,0.04,0.0560190703218117,0.714042553191489,24,3960,0.999999999999906
+"1048",902,"IP ICU SEPSIS MANAGEMENT",516,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",1,2,0.00387596899224806,277,3459,0.5,0.0800809482509396,6.24368231046931,1,3182,0.377168029010213
+"1049",902,"IP ICU SEPSIS MANAGEMENT",516,"541273","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION",1,4,0.00775193798449612,148,1260,0.25,0.117460317460317,2.12837837837838,3,1112,0.964719992261303
+"1050",902,"IP ICU SEPSIS MANAGEMENT",516,"541544","CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",1,3,0.00581395348837209,60,934,0.333333333333333,0.0642398286937901,5.18888888888889,2,874,0.475098406599495
+"1051",904,"IP ICU/CCU GENERAL ADMIT",22594,"101","ACETAMINOPHEN 325 MG PO TABS",30,1342,0.059396299902629,981,42092,0.022354694485842,0.0233060914187969,0.95917818582881,1312,41111,0.892139466038518
+"1052",904,"IP ICU/CCU GENERAL ADMIT",22594,"102","ACETAMINOPHEN 500 MG PO TABS",1,71,0.0031424271930601,392,15968,0.0140845070422535,0.0245490981963928,0.573728082782409,70,15576,0.853694601189435
+"1053",904,"IP ICU/CCU GENERAL ADMIT",22594,"1080","BISACODYL 10 MG PR SUPP",15,933,0.0412941488890856,344,22442,0.0160771704180064,0.0153284021031994,1.04884842593285,918,22098,0.963004088720522
+"1054",904,"IP ICU/CCU GENERAL ADMIT",22594,"2566","DOCUSATE SODIUM 100 MG PO CAPS",10,679,0.0300522262547579,542,37509,0.0147275405007364,0.0144498653656456,1.01921645136923,669,36967,0.999999999999989
+"1055",904,"IP ICU/CCU GENERAL ADMIT",22594,"2567","DOCUSATE SODIUM 250 MG PO CAPS",7,270,0.0119500752412145,296,17760,0.0259259259259259,0.0166666666666667,1.55555555555556,263,17464,0.349163424007352
+"1056",904,"IP ICU/CCU GENERAL ADMIT",22594,"2569","DOCUSATE SODIUM 50 MG/5 ML PO LIQD",17,766,0.0339028060547048,170,7328,0.0221932114882507,0.0231986899563319,0.956657963446475,749,7158,0.960224761510593
+"1057",904,"IP ICU/CCU GENERAL ADMIT",22594,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",7,274,0.0121271133929362,346,10128,0.0255474452554745,0.0341627172195893,0.747816547824986,267,9782,0.543119607038416
+"1058",904,"IP ICU/CCU GENERAL ADMIT",22594,"5018","METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",1,10,0.00044259537930424,223,3847,0.1,0.0579672472056148,1.72511210762332,9,3624,0.999999999999997
+"1059",904,"IP ICU/CCU GENERAL ADMIT",22594,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",123,4335,0.191865096928388,89,4372,0.0283737024221453,0.0203568161024703,1.39381828078224,4212,4283,0.0184124744921784
+"1060",904,"IP ICU/CCU GENERAL ADMIT",22594,"10011","FAMOTIDINE 20 MG PO TABS",10,86,0.00380632026201646,472,16960,0.116279069767442,0.0278301886792453,4.17816318486401,76,16488,4.03182877226094e-06
+"1061",904,"IP ICU/CCU GENERAL ADMIT",22594,"10467","LORAZEPAM 2 MG/ML INJ SOLN",5,89,0.00393909887580774,749,20621,0.0561797752808989,0.0363221958197954,1.54670647004995,84,19872,0.474945550083053
+"1062",904,"IP ICU/CCU GENERAL ADMIT",22594,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",1,125,0.005532442241303,155,5616,0.008,0.0275997150997151,0.289858064516129,124,5461,0.291462071230317
+"1063",904,"IP ICU/CCU GENERAL ADMIT",22594,"11150","PROPOFOL 10 MG/ML IV EMUL",10,218,0.00964857926883243,135,3592,0.0458715596330275,0.0375835189309577,1.22052327556915,208,3457,0.660866634540611
+"1064",904,"IP ICU/CCU GENERAL ADMIT",22594,"11349","SENNOSIDES 8.6 MG PO TABS",19,1177,0.0520934761441091,610,35793,0.016142735768904,0.0170424384656218,0.947208100617017,1158,35183,0.904233972066567
+"1065",904,"IP ICU/CCU GENERAL ADMIT",22594,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",11,151,0.00668319022749402,314,8762,0.0728476821192053,0.035836566993837,2.03277512970852,140,8448,0.0287581125003283
+"1066",904,"IP ICU/CCU GENERAL ADMIT",22594,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",3,160,0.00708152606886784,158,4300,0.01875,0.0367441860465116,0.510284810126582,157,4142,0.325953803569839
+"1067",904,"IP ICU/CCU GENERAL ADMIT",22594,"15980","DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",3,88,0.00389483933787731,33,665,0.0340909090909091,0.0496240601503759,0.68698347107438,85,632,0.706938606399145
+"1068",904,"IP ICU/CCU GENERAL ADMIT",22594,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",12,123,0.00544392316544215,118,4415,0.0975609756097561,0.026727066817667,3.65026870607689,111,4297,1.23601054326176e-05
+"1069",904,"IP ICU/CCU GENERAL ADMIT",22594,"22472","KETOROLAC 15 MG/ML INJ SOLN",2,9,0.000398335841373816,215,5031,0.222222222222222,0.0427350427350427,5.2,7,4816,0.0674657403493547
+"1070",904,"IP ICU/CCU GENERAL ADMIT",22594,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",12,904,0.0400106222891033,712,36910,0.0132743362831858,0.0192901652668653,0.688140101421895,892,36198,0.237530868640426
+"1071",904,"IP ICU/CCU GENERAL ADMIT",22594,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",16,362,0.0160219527308135,692,19620,0.0441988950276243,0.0352701325178389,1.25315364225721,346,18928,0.443016094802387
+"1072",904,"IP ICU/CCU GENERAL ADMIT",22594,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,6,0.000265557227582544,242,3682,0.166666666666667,0.0657251493753395,2.53581267217631,5,3440,0.863144842274486
+"1073",904,"IP ICU/CCU GENERAL ADMIT",22594,"82384","PANTOPRAZOLE 40 MG IV SOLR",38,1026,0.045410285916615,688,12925,0.037037037037037,0.0532301740812379,0.695790267011197,988,12237,0.0296468338742933
+"1074",904,"IP ICU/CCU GENERAL ADMIT",22594,"88043","PANTOPRAZOLE 40 MG PO TBEC",5,101,0.00447021333097282,624,31884,0.0495049504950495,0.0195709446744449,2.52951256664128,96,31260,0.0711799644532455
+"1075",904,"IP ICU/CCU GENERAL ADMIT",22594,"94176","ONDANSETRON 4 MG PO TBDL",14,418,0.0185004868549172,153,4277,0.0334928229665072,0.0357727379003975,0.936266691684648,404,4124,0.918865137950959
+"1076",904,"IP ICU/CCU GENERAL ADMIT",22594,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,16,0.000708152606886784,1067,10067,0.0625,0.105989867885169,0.58967900656045,15,9000,0.874201348569363
+"1077",904,"IP ICU/CCU GENERAL ADMIT",22594,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",8,311,0.0137647162963619,738,23934,0.0257234726688103,0.0308347956881424,0.834235223381173,303,23196,0.723815274593506
+"1078",904,"IP ICU/CCU GENERAL ADMIT",22594,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",2,29,0.0012835265999823,73,1394,0.0689655172413793,0.0523672883787661,1.31695795937648,27,1321,0.999999999999995
+"1079",904,"IP ICU/CCU GENERAL ADMIT",22594,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",21,285,0.0126139683101708,499,13896,0.0736842105263158,0.0359096142774899,2.05193544984706,264,13397,0.00137638297092591
+"1080",904,"IP ICU/CCU GENERAL ADMIT",22594,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",12,350,0.0154908382756484,1219,33427,0.0342857142857143,0.036467526251234,0.940171100433611,338,32208,0.941549383532015
+"1081",904,"IP ICU/CCU GENERAL ADMIT",22594,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",3,62,0.00274409135168629,208,3699,0.0483870967741935,0.0562314138956475,0.860499379652605,59,3491,0.999999999999909
+"1082",904,"IP ICU/CCU GENERAL ADMIT",22594,"115683","LANSOPRAZOLE 30 MG PO TBLD",1,11,0.000486854917234664,42,1961,0.0909090909090909,0.0214176440591535,4.24458874458874,10,1919,0.590189428502539
+"1083",904,"IP ICU/CCU GENERAL ADMIT",22594,"124873","SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP",6,479,0.0212003186686731,90,4112,0.0125260960334029,0.0218871595330739,0.572303409881698,473,4022,0.235465957713068
+"1084",904,"IP ICU/CCU GENERAL ADMIT",22594,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",65,3071,0.135921040984332,705,36939,0.0211657440573103,0.0190855193697718,1.10899492160707,3006,36234,0.46057737624689
+"1085",904,"IP ICU/CCU GENERAL ADMIT",22594,"200439","HYDROMORPHONE (PF) 2 MG/ML INJ SOLN",8,143,0.00632911392405063,142,3356,0.0559440559440559,0.0423122765196663,1.32217078695952,135,3214,0.563691618395656
+"1086",904,"IP ICU/CCU GENERAL ADMIT",22594,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",62,699,0.0309374170133664,288,4280,0.0886981402002861,0.0672897196261682,1.31815291686536,637,3992,0.0484987084568089
+"1087",904,"IP ICU/CCU GENERAL ADMIT",22594,"205192","MILRINONE IN D5W 20 MG/100 ML IV PGBK",1,41,0.00181464105514738,27,851,0.024390243902439,0.0317273795534665,0.768744354110208,40,824,0.999999999999969
+"1088",904,"IP ICU/CCU GENERAL ADMIT",22594,"206169","DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",8,80,0.00354076303443392,62,1778,0.1,0.0348706411698538,2.86774193548387,72,1716,0.00708801770275415
+"1089",904,"IP ICU/CCU GENERAL ADMIT",22594,"207694","NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",9,176,0.00778967867575463,56,1641,0.0511363636363636,0.0341255332114564,1.49847808441558,167,1585,0.346591839697404
+"1090",904,"IP ICU/CCU GENERAL ADMIT",22594,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",1,117,0.00517836593785961,192,8611,0.00854700854700855,0.0222970618975729,0.38332443019943,116,8419,0.491363755749711
+"1091",904,"IP ICU/CCU GENERAL ADMIT",22594,"210741","PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN",2,60,0.00265557227582544,43,1494,0.0333333333333333,0.0287817938420348,1.15813953488372,58,1451,0.999999999999999
+"1092",904,"IP ICU/CCU GENERAL ADMIT",22594,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",28,1698,0.07515269540586,510,18335,0.016489988221437,0.0278156531224434,0.592831243215779,1670,17825,0.00728885880174614
+"1093",904,"IP ICU/CCU GENERAL ADMIT",22594,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",63,1198,0.053022926440648,452,13480,0.0525876460767947,0.0335311572700297,1.56832183432565,1135,13028,0.000798238246509855
+"1094",904,"IP ICU/CCU GENERAL ADMIT",22594,"226058","EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",5,66,0.00292112950340798,56,1218,0.0757575757575758,0.0459770114942529,1.64772727272727,61,1162,0.417556541799695
+"1095",904,"IP ICU/CCU GENERAL ADMIT",22594,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",26,489,0.0216429140479773,291,6612,0.0531697341513292,0.044010889292196,1.20810406257247,463,6321,0.404912418495319
+"1096",904,"IP ICU/CCU GENERAL ADMIT",22594,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",1,7,0.000309816765512968,111,2584,0.142857142857143,0.0429566563467492,3.32561132561133,6,2473,0.713322158583556
+"1097",904,"IP ICU/CCU GENERAL ADMIT",22594,"520039","AZITHROMYCIN IVPB",1,25,0.0011064884482606,127,2522,0.04,0.0503568596352102,0.794330708661417,24,2395,0.999999999999895
+"1098",904,"IP ICU/CCU GENERAL ADMIT",22594,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",43,270,0.0119500752412145,1808,23328,0.159259259259259,0.0775034293552812,2.05486725663717,227,21520,1.20944806737125e-06
+"1099",904,"IP ICU/CCU GENERAL ADMIT",22594,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",4,51,0.00225723643445162,65,1105,0.0784313725490196,0.0588235294117647,1.33333333333333,47,1040,0.782852982237912
+"1100",904,"IP ICU/CCU GENERAL ADMIT",22594,"530006","EPINEPHRINE IV INFUSION",11,107,0.00473577055855537,99,2540,0.102803738317757,0.0389763779527559,2.63759086188993,96,2441,0.00275861247789234
+"1101",904,"IP ICU/CCU GENERAL ADMIT",22594,"530011","NOREPINEPHRINE IV INFUSION",7,244,0.0107993272550235,92,2909,0.0286885245901639,0.0316259883121348,0.907118674269423,237,2817,0.950852751804297
+"1102",904,"IP ICU/CCU GENERAL ADMIT",22594,"530012","PHENYLEPHRINE IV INFUSION",7,101,0.00447021333097282,61,2180,0.0693069306930693,0.0279816513761468,2.47687063788346,94,2119,0.0367851411726822
+"1103",904,"IP ICU/CCU GENERAL ADMIT",22594,"530025","DEXMEDETOMIDINE IV INFUSION",54,148,0.00655041161370275,530,2902,0.364864864864865,0.18263266712612,1.99780724120347,94,2372,7.08606523644049e-08
+"1104",904,"IP ICU/CCU GENERAL ADMIT",22594,"530031","VASOPRESSIN 1 UNIT/ML IV INFUSION",10,154,0.0068159688412853,215,4489,0.0649350649350649,0.0478948540877701,1.35578375113259,144,4274,0.436882908323381
+"1105",904,"IP ICU/CCU GENERAL ADMIT",22594,"530120","HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)",22,352,0.0155793573515093,130,2894,0.0625,0.0449205252246026,1.39134615384615,330,2764,0.180090206516709
+"1106",904,"IP ICU/CCU GENERAL ADMIT",22594,"540102","NS IV BOLUS",9,105,0.00464725148269452,2225,80957,0.0857142857142857,0.0274837259285794,3.11872873194222,96,78732,0.000825075790146498
+"1107",904,"IP ICU/CCU GENERAL ADMIT",22594,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",13,573,0.025360715234133,461,18637,0.0226876090750436,0.0247357407308043,0.917199501803879,560,18176,0.861405041745891
+"1108",904,"IP ICU/CCU GENERAL ADMIT",22594,"540115","LACTATED RINGERS IV BOLUS",5,110,0.00486854917234664,400,12941,0.0454545454545455,0.0309095124024419,1.47056818181818,105,12541,0.548554848460368
+"1109",904,"IP ICU/CCU GENERAL ADMIT",22594,"540744","WHITE PETROLATUM-MINERAL OIL OPHTH OINT",89,5177,0.229131627865805,46,2959,0.0171914236044041,0.0155457924974654,1.1058570096833,5088,2913,0.639218149129303
+"1110",904,"IP ICU/CCU GENERAL ADMIT",22594,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",27,289,0.0127910064618925,235,4195,0.0934256055363322,0.0560190703218117,1.66774644776559,262,3960,0.0126784110232515
+"1111",904,"IP ICU/CCU GENERAL ADMIT",22594,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,44,0.00194741966893866,104,5739,0.0227272727272727,0.0181216239763025,1.2541520979021,43,5635,0.999999999999998
+"1112",904,"IP ICU/CCU GENERAL ADMIT",22594,"541061","VANCOMYCIN PER PHARMACY PROTOCOL",1,145,0.00641763299991148,239,5475,0.00689655172413793,0.0436529680365297,0.157985860626172,144,5236,0.050871180088259
+"1113",904,"IP ICU/CCU GENERAL ADMIT",22594,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",2,17,0.000752412144817208,277,3459,0.117647058823529,0.0800809482509396,1.46910172011043,15,3182,0.903491721200297
+"1114",904,"IP ICU/CCU GENERAL ADMIT",22594,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",1,28,0.00123926706205187,411,7130,0.0357142857142857,0.0576437587657784,0.619568995481404,27,6719,0.927691167926472
+"1115",904,"IP ICU/CCU GENERAL ADMIT",22594,"541097","AZTREONAM 2 GRAM/100 ML MINI-BAG PLUS",1,9,0.000398335841373816,22,369,0.111111111111111,0.0596205962059621,1.86363636363636,8,347,1
+"1116",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"101","ACETAMINOPHEN 325 MG PO TABS",3,264,0.112965340179718,981,42092,0.0113636363636364,0.0233060914187969,0.487582244462978,261,41111,0.280504467455729
+"1117",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"664","ASCORBIC ACID 500 MG PO TABS",1,110,0.047068891741549,59,3135,0.00909090909090909,0.0188197767145136,0.483050847457627,109,3076,0.700648050749879
+"1118",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"1755","CLONIDINE HCL 0.1 MG PO TABS",2,23,0.00984167736414206,104,2839,0.0869565217391304,0.0366326171187038,2.37374581939799,21,2735,0.472435992629164
+"1119",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"1756","CLONIDINE HCL 0.2 MG PO TABS",1,21,0.00898587933247754,50,1163,0.0476190476190476,0.0429922613929493,1.10761904761905,20,1113,0.999999999999979
+"1120",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",2,273,0.116816431322208,537,15149,0.00732600732600733,0.0354478843488019,0.206669804435168,271,14612,0.019217222122169
+"1121",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"6493","PREDNISONE 1 MG PO TABS",1,3,0.00128369704749679,32,729,0.333333333333333,0.0438957475994513,7.59375,2,697,0.309124577341044
+"1122",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"6496","PREDNISONE 20 MG PO TABS",3,390,0.166880616174583,276,7517,0.00769230769230769,0.0367167753092989,0.209503901895206,387,7241,0.00387228191130221
+"1123",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"7557","SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS",2,322,0.137783483097989,112,3269,0.0062111801242236,0.0342612419700214,0.181288819875776,320,3157,0.0100932660388602
+"1124",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"10181","HEPARIN (PORCINE) 5,000 UNIT/ML INJ SOLN",1,8,0.00342319212665811,156,510,0.125,0.305882352941176,0.408653846153846,7,354,0.473427343114025
+"1125",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,280,0.119811724433034,158,4300,0.00357142857142857,0.0367441860465116,0.0971971066907776,279,4142,0.00561157171357923
+"1126",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"81371","TACROLIMUS 5 MG PO CAPS",1,115,0.0492083868207103,25,898,0.00869565217391304,0.0278396436525612,0.312347826086956,114,873,0.363268887136621
+"1127",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"85078","MYCOPHENOLATE MOFETIL 500 MG PO TABS",1,33,0.0141206675224647,45,1479,0.0303030303030303,0.0304259634888438,0.995959595959596,32,1434,0.999999999999953
+"1128",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"94670","TACROLIMUS 1 MG PO CAPS",8,573,0.245186136071887,322,9479,0.0139616055846422,0.0339698280409326,0.41100018427585,565,9157,0.012796070483047
+"1129",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",6,97,0.0415062045357296,1067,10067,0.0618556701030928,0.105989867885169,0.583599841544363,91,9000,0.214316613811756
+"1130",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,4,0.00171159606332905,738,23934,0.25,0.0308347956881424,8.10772357723577,3,23196,0.276385076814835
+"1131",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,5,0.00213949507916132,1219,33427,0.2,0.036467526251234,5.48433141919606,4,32208,0.448816183989845
+"1132",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,297,0.127086007702182,705,36939,0.00336700336700337,0.0190855193697718,0.176416648757074,296,36234,0.077615537594197
+"1133",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",16,212,0.0907145913564399,288,4280,0.0754716981132075,0.0672897196261682,1.12159329140461,196,3992,0.746780024228561
+"1134",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",13,842,0.360290971330766,275,8000,0.0154394299287411,0.034375,0.449147052472468,829,7725,0.00448042340805032
+"1135",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",2,263,0.112537441163885,452,13480,0.00760456273764259,0.0335311572700297,0.22679094182173,261,13028,0.0311064054343653
+"1136",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"500017","1/2 NS WITH SODIUM BICARBONATE IV INFUSION",6,320,0.136927685066324,46,1402,0.01875,0.0328102710413695,0.571467391304348,314,1356,0.252144112607234
+"1137",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"540135","CYCLOSPORINE MODIFIED (NEORAL) 100 MG PO CAPS",1,10,0.00427899015832264,8,57,0.1,0.140350877192982,0.7125,9,49,1
+"1138",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"540492","SHC TRANSPLANT BEDSIDE MEDS HELP ORDER",7,149,0.0637569533590073,3,27,0.0469798657718121,0.111111111111111,0.422818791946309,142,24,0.382814326457375
+"1139",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",2,55,0.0235344458707745,235,4195,0.0363636363636364,0.0560190703218117,0.649129593810445,53,3960,0.737327812857295
+"1140",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",3,107,0.0457851946940522,466,8270,0.0280373831775701,0.0563482466747279,0.497573302314388,104,7804,0.291853563761605
+"1141",918,"IP TRS KIDNEY AND/OR  PANCREAS RECIPIENT POSTOP",2337,"9000000","ZZZ IMS TEMPLATE",6,417,0.178433889602054,1035,28824,0.0143884892086331,0.0359075770191507,0.400708998018976,411,27789,0.0263175190578988
+"1142",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"101","ACETAMINOPHEN 325 MG PO TABS",4,187,0.073362102785406,981,42092,0.0213903743315508,0.0233060914187969,0.917801871930312,183,41111,0.999999999999981
+"1143",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",10,475,0.186347587289133,399,16019,0.0210526315789474,0.0249079218428117,0.84521830893022,465,15620,0.701874749780684
+"1144",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",3,156,0.0612004707728521,745,12385,0.0192307692307692,0.0601534113847396,0.319695405265875,153,11640,0.0483072255517419
+"1145",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"5373","NALOXONE 0.4 MG/ML INJ SOLN",5,201,0.0788544527265594,83,1774,0.0248756218905473,0.0467869222096956,0.531678954624468,196,1691,0.212556021041443
+"1146",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"5751","NYSTATIN 100,000 UNIT/ML PO SUSP",1,115,0.0451157316594743,68,4056,0.00869565217391304,0.0167652859960552,0.518670076726343,114,3988,0.765438598721171
+"1147",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"10050","FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK",1,17,0.00666928207140055,40,1102,0.0588235294117647,0.0362976406533575,1.62058823529412,16,1062,0.999999999999998
+"1148",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",3,185,0.0725774813652413,158,4300,0.0162162162162162,0.0367441860465116,0.44132740335272,182,4142,0.204881339913124
+"1149",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"82384","PANTOPRAZOLE 40 MG IV SOLR",5,218,0.08552373479796,688,12925,0.0229357798165138,0.0532301740812379,0.430879293791338,213,12237,0.0669612949388706
+"1150",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"94670","TACROLIMUS 1 MG PO CAPS",2,63,0.0247155747351903,322,9479,0.0317460317460317,0.0339698280409326,0.934536133293897,61,9157,0.999999999999521
+"1151",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",16,104,0.0408003138485681,1067,10067,0.153846153846154,0.105989867885169,1.45151755461034,88,9000,0.157258339862497
+"1152",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,21,0.00823852491173009,738,23934,0.0476190476190476,0.0308347956881424,1.54432830042586,20,23196,0.999999999999994
+"1153",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",14,321,0.125931737936446,192,8611,0.043613707165109,0.0222970618975729,1.95602933541018,307,8419,0.0209507700165365
+"1154",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",6,474,0.185955276579051,313,9164,0.0126582278481013,0.0341553906591008,0.370607028753994,468,8851,0.0155445201618769
+"1155",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"520045","MEROPENEM CUSTOM IVPB",1,1,0.000392310710082385,100,1654,1,0.060459492140266,16.54,0,1554,0.0666030171596958
+"1156",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"520074","ANTITHYMOCYTE GLOBULIN RABBIT IVPB",15,520,0.20400156924284,75,1781,0.0288461538461538,0.0421111734980348,0.685,505,1706,0.213436953816056
+"1157",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"530006","EPINEPHRINE IV INFUSION",1,24,0.00941545704197725,99,2540,0.0416666666666667,0.0389763779527559,1.06902356902357,23,2441,0.999999999999904
+"1158",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"530008","NITROPRUSSIDE IV INFUSION",1,98,0.0384464495880738,79,2082,0.0102040816326531,0.0379442843419789,0.268922758977009,97,2003,0.249120605160857
+"1159",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"530012","PHENYLEPHRINE IV INFUSION",1,36,0.0141231855629659,61,2180,0.0277777777777778,0.0279816513761468,0.992714025500911,35,2119,0.999999999999999
+"1160",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",4,134,0.0525696351510396,461,18637,0.0298507462686567,0.0247357407308043,1.20678602648363,130,18176,0.919796320410462
+"1161",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"540129","TACROLIMUS 1 MG/ML ORAL SUSPENSION",24,433,0.169870537465673,147,4881,0.0554272517321016,0.0301167793484942,1.84041099118631,409,4734,0.00656430308550519
+"1162",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"540960","MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS",2,5,0.00196155355041193,302,3726,0.4,0.0810520665593129,4.93509933774834,3,3424,0.0738874555304097
+"1163",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",1,67,0.0262848175755198,235,4195,0.0149253731343284,0.0560190703218117,0.266433788504287,66,3960,0.234080545732343
+"1164",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",4,102,0.0400156924284033,466,8270,0.0392156862745098,0.0563482466747279,0.695952200622738,98,7804,0.595629650423754
+"1165",920,"IP TRS LIVER TRANSPLANT POSTOP",2549,"9000000","ZZZ IMS TEMPLATE",2,38,0.0149078069831306,1035,28824,0.0526315789473684,0.0359075770191507,1.46575133485889,36,27789,0.906492996491915
+"1166",921,"IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP",147,"102","ACETAMINOPHEN 500 MG PO TABS",1,27,0.183673469387755,392,15968,0.037037037037037,0.0245490981963928,1.50869236583522,26,15576,0.999999999999152
+"1167",921,"IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP",147,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,35,0.238095238095238,346,10128,0.0571428571428571,0.0341627172195893,1.67266721717589,33,9782,0.778892148690187
+"1168",921,"IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP",147,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,34,0.231292517006803,603,24164,0.0588235294117647,0.0249544777354743,2.35723344063994,32,23561,0.474978820653102
+"1169",921,"IP TRS KIDNEY TRANSPLANT  BIOPSY POSTOP",147,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,40,0.272108843537415,705,36939,0.025,0.0190855193697718,1.30989361702128,39,36234,0.999999999999993
+"1170",922,"IP TRS LIVER TRANSPLANT PREOP",137,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,7,0.0510948905109489,258,5335,0.142857142857143,0.0483598875351453,2.95404208194906,6,5077,0.777313352992631
+"1171",927,"IP GEN CONSTIPATION",629,"1080","BISACODYL 10 MG PR SUPP",2,372,0.591414944356121,344,22442,0.00537634408602151,0.0153284021031994,0.350743935983996,370,22098,0.178989654502174
+"1172",927,"IP GEN CONSTIPATION",629,"2566","DOCUSATE SODIUM 100 MG PO CAPS",6,183,0.29093799682035,542,37509,0.0327868852459016,0.0144498653656456,2.26900973927772,177,36967,0.0787813446024106
+"1173",927,"IP GEN CONSTIPATION",629,"11349","SENNOSIDES 8.6 MG PO TABS",5,219,0.348171701112878,610,35793,0.0228310502283105,0.0170424384656218,1.33965865708511,214,35183,0.6909289204608
+"1174",927,"IP GEN CONSTIPATION",629,"13632","BISACODYL 5 MG PO TBEC",2,234,0.372019077901431,49,3268,0.00854700854700855,0.0149938800489596,0.570033141461713,232,3219,0.608096134829171
+"1175",927,"IP GEN CONSTIPATION",629,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",7,288,0.457869634340223,712,36910,0.0243055555555556,0.0192901652668653,1.2599972690387,281,36198,0.688434795027657
+"1176",927,"IP GEN CONSTIPATION",629,"90206","SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM",1,174,0.276629570747218,101,3581,0.00574712643678161,0.02820441217537,0.20376692841698,173,3480,0.123366906550634
+"1177",927,"IP GEN CONSTIPATION",629,"230523","PSYLLIUM HUSK 6 GRAM PO PWPK",2,39,0.0620031796502385,26,1435,0.0512820512820513,0.0181184668989547,2.83037475345168,37,1409,0.366780065003667
+"1178",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",12,207,0.133893919793014,106,3939,0.0579710144927536,0.0269103833460269,2.15422477440525,195,3833,0.0161688728819056
+"1179",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",23,607,0.392626131953428,134,3551,0.0378912685337726,0.0377358490566038,1.00411861614498,584,3417,0.999999999999998
+"1180",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"3291","FUROSEMIDE 10 MG/ML INJ SOLN",3,111,0.0717981888745149,1985,57715,0.027027027027027,0.0343931386987785,0.785826128395398,108,55730,0.869092346426563
+"1181",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",4,57,0.036869340232859,692,19620,0.0701754385964912,0.0352701325178389,1.98965622147855,53,18928,0.286626909013089
+"1182",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"27999","SODIUM POLYSTYRENE SULFONATE 15 GRAM/60 ML PO SUSP",26,263,0.170116429495472,177,2440,0.0988593155893536,0.0725409836065574,1.36280638439561,237,2263,0.156918625107138
+"1183",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"88904","INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN",29,633,0.409443725743855,482,5722,0.0458135860979463,0.0842362810206222,0.543869999278939,604,5240,0.000979718876657221
+"1184",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"112517","SODIUM BICARBONATE 8.4 % (1 MEQ/ML) IV SYRG",10,132,0.0853816300129366,65,2665,0.0757575757575758,0.024390243902439,3.10606060606061,122,2600,0.00100147610841819
+"1185",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",19,389,0.251617076326003,326,12268,0.0488431876606684,0.0265731985653733,1.83806204362294,370,11942,0.0125062355173994
+"1186",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",9,40,0.0258732212160414,156,2315,0.225,0.0673866090712743,3.33894230769231,31,2159,0.000371462526406656
+"1187",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"520097","SODIUM BICARBONATE IVPB",11,72,0.0465717981888745,69,856,0.152777777777778,0.080607476635514,1.89533011272142,61,787,0.0605281673261796
+"1188",932,"IP/ED GEN HYPERKALEMIA THERAPY",1546,"540775","POLYETHYLENE GLYCOL 3350 17 GM (FOR KAYEXALATE PANEL)",13,142,0.091849935316947,151,2483,0.0915492957746479,0.0608135320177205,1.50540994310232,129,2332,0.195807153029078
+"1189",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"6427","POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK",1,24,0.00157832434565303,1228,18599,0.0416666666666667,0.0660250551104898,0.631073561346363,23,17371,0.945003323200909
+"1190",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"6432","POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD",72,2673,0.175785873997106,425,8090,0.0269360269360269,0.0525339925834363,0.512735195088136,2601,7665,6.16065469076659e-08
+"1191",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"35943","POTASSIUM CHLORIDE 20 MEQ PO TBTQ",1,2,0.000131527028804419,1006,28886,0.5,0.0348265595790348,14.3568588469185,1,27880,0.0971497641435672
+"1192",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",73,3979,0.261673023806392,457,11068,0.0183463181703946,0.0412902059992772,0.444326147724129,3906,10611,2.33610081016045e-11
+"1193",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",48,4187,0.275351834802052,326,12268,0.0114640554096011,0.0265731985653733,0.431414207868058,4139,11942,2.09334228676735e-08
+"1194",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",143,1690,0.111140339339734,156,2315,0.0846153846153846,0.0673866090712743,1.25567061143984,1547,2159,0.0468258271554514
+"1195",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"500006","SODIUM PHOSPHATE IVPB",25,468,0.0307773247402341,296,4785,0.0534188034188034,0.0618599791013584,0.863543832293832,443,4489,0.530972186213114
+"1196",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"540301","POTASSIUM CHLORIDE ORAL SCALE",101,4147,0.272721294225963,105,3653,0.0243549553894381,0.0287434984943882,0.847320495596358,4046,3548,0.256221895594361
+"1197",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"540302","POTASSIUM CHLORIDE IV SCALE",188,6537,0.429896093647245,150,4660,0.0287593697414716,0.0321888412017167,0.893457753301719,6349,4510,0.322451220140277
+"1198",934,"IP ELECTROLYTE REPLACEMENT SCALES",15206,"540304","MAGNESIUM SULFATE IV SCALE ",135,7601,0.499868472971196,78,4104,0.0177608209446126,0.0190058479532164,0.934492425085768,7466,4026,0.682982522218742
+"1199",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",16,989,0.0977852481708523,399,16019,0.0161779575328615,0.0249079218428117,0.64951053062383,973,15620,0.105076893071504
+"1200",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"4448","LIDOCAINE HCL 2 % JELLY",1,137,0.0135455803836267,66,3270,0.0072992700729927,0.0201834862385321,0.361645653616457,136,3204,0.453239801108443
+"1201",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"5373","NALOXONE 0.4 MG/ML INJ SOLN",2,175,0.0173027486652165,83,1774,0.0114285714285714,0.0467869222096956,0.244268502581756,173,1691,0.0464733423132068
+"1202",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"7228","SIMETHICONE 40 MG/0.6 ML PO DRPS",7,638,0.0630808779909037,18,574,0.0109717868338558,0.0313588850174216,0.349878091257402,631,556,0.0219668654467539
+"1203",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",1,163,0.0161162744710303,14,238,0.00613496932515337,0.0588235294117647,0.104294478527607,162,224,0.0137715954875524
+"1204",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",27,2799,0.276745105793949,155,5616,0.00964630225080386,0.0275997150997151,0.349507312519448,2772,5461,1.48238941005652e-07
+"1205",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",69,4963,0.490705952145541,692,19620,0.0139028813217812,0.0352701325178389,0.394182849036628,4894,18928,1.17704515691033e-14
+"1206",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"79552","INDOMETHACIN 50 MG PR SUPP",1,36,0.00355942258255883,4,121,0.0277777777777778,0.0330578512396694,0.840277777777778,35,117,1
+"1207",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,64,0.00632786236899347,242,3682,0.015625,0.0657251493753395,0.237732438016529,63,3440,0.174652765989568
+"1208",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"82190","BUTAMBEN-TETRACAINE-BENZOCAINE 2-2-14 % TP SPRA",3,637,0.0629820051413882,12,304,0.00470957613814757,0.0394736842105263,0.119309262166405,634,292,0.000212622177597693
+"1209",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"90788","LIDOCAINE HCL 2 % VISCOUS ORAL SOLN",2,251,0.0248170852283963,77,1939,0.00796812749003984,0.0397111913357401,0.200651937703731,249,1862,0.0183823555104757
+"1210",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"222816","LIDOCAINE 5 % TP OINT",4,52,0.0051413881748072,26,543,0.0769230769230769,0.0478821362799263,1.60650887573965,48,517,0.56017357158383
+"1211",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",33,2789,0.275756377298794,461,18637,0.0118321979204016,0.0247357407308043,0.478344192283133,2756,18176,3.08548829943551e-05
+"1212",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"540303","MEPERIDINE INJECTABLE SYRINGE",1,30,0.00296618548546569,5,60,0.0333333333333333,0.0833333333333333,0.4,29,55,0.654001120664955
+"1213",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"540548","GLUCAGON 1 MG (1 UNIT) INJ SOLR",1,273,0.0269922879177378,16,252,0.00366300366300366,0.0634920634920635,0.0576923076923077,272,236,0.000291875729669113
+"1214",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"541092","AMPICILLIN 2 GRAM/100 ML MINI-BAG PLUS",1,3,0.000296618548546569,29,480,0.333333333333333,0.0604166666666667,5.51724137931034,2,451,0.4516580154346
+"1215",944,"IP GI ENDOSCOPY PRE/POSTOP PROCEDURE",10114,"541105","CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS",2,17,0.00168083844176389,25,530,0.117647058823529,0.0471698113207547,2.49411764705882,15,505,0.452219553738857
+"1216",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"5373","NALOXONE 0.4 MG/ML INJ SOLN",267,11746,0.311804836611717,83,1774,0.0227311425166014,0.0467869222096956,0.485843937643986,11479,1691,4.44118086500507e-09
+"1217",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",5,327,0.00868041729712511,738,23934,0.0152905198776758,0.0308347956881424,0.495885234081699,322,23196,0.144622271901603
+"1218",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,75,0.00199092139842319,1219,33427,0.0266666666666667,0.036467526251234,0.731244189226142,73,32208,0.885508592855815
+"1219",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"207949","MORPHINE (PF) 30 MG/30 ML (1 MG/ML) IV PCA",25,580,0.0153964588144727,59,774,0.0431034482758621,0.0762273901808786,0.565458796025716,555,715,0.0170105948138206
+"1220",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"540101","FENTANYL 50 MCG/ML IV PCA",66,1940,0.0514985001725465,39,965,0.034020618556701,0.0404145077720207,0.841792228390166,1874,926,0.444810585162447
+"1221",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",4,127,0.0033712935679966,461,18637,0.031496062992126,0.0247357407308043,1.27330179172289,123,18176,0.839889921209452
+"1222",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"540125","HYDROMORPHONE 1 MG/ML IV PCA",302,11475,0.304610973958748,338,7089,0.026318082788671,0.0476795034560587,0.55197896120973,11173,6751,1.26751183226826e-14
+"1223",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"541605","FENTANYL 50 MCG/ML IV PCA",45,831,0.0220594090945289,11,240,0.0541516245487365,0.0458333333333333,1.18148999015425,786,229,0.729846094621287
+"1224",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"541607","HYDROMORPHONE 1 MG/ML IV PCA",221,4934,0.1309760823976,124,1939,0.0447912444264289,0.0639504899432697,0.700405023732625,4713,1815,0.00131660612884581
+"1225",946,"IP PAI PATIENT CONTROLLED ANALGESIA (PCA)",37671,"541609","MORPHINE 30 MG/30 ML IV PCA",15,214,0.0056807623901675,12,223,0.0700934579439252,0.0538116591928251,1.30257009345794,199,211,0.611480367775465
+"1226",947,"CDU DEHYDRATION OBSERVATION",56,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",3,16,0.285714285714286,158,4300,0.1875,0.0367441860465116,5.10284810126582,13,4142,0.011890582399978
+"1227",947,"CDU DEHYDRATION OBSERVATION",56,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,3,0.0535714285714286,1219,33427,0.333333333333333,0.036467526251234,9.14055236532677,2,32208,0.229196268956555
+"1228",950,"CDU PYELONEPHRITIS OBSERVATION",122,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",1,6,0.0491803278688525,411,7130,0.166666666666667,0.0576437587657784,2.89132197891322,5,6719,0.787975986634405
+"1229",951,"CDU KIDNEY STONE OBSERVATION",6,"86943","TAMSULOSIN 0.4 MG PO CP24",1,1,0.166666666666667,238,11835,1,0.0201098436839882,49.7268907563025,0,11597,0.000646566653151083
+"1230",956,"CDU CLOSED HEAD INJURY OBSERVATION",45,"101","ACETAMINOPHEN 325 MG PO TABS",1,5,0.111111111111111,981,42092,0.2,0.0233060914187969,8.58144750254842,4,41111,0.255989696965087
+"1231",956,"CDU CLOSED HEAD INJURY OBSERVATION",45,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,8,0.177777777777778,158,4300,0.125,0.0367441860465116,3.40189873417722,7,4142,0.700764929972869
+"1232",957,"CDU SYNCOPE OBSERVATION",74,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,5,0.0675675675675676,692,19620,0.2,0.0352701325178389,5.67052023121387,4,18928,0.43315438725597
+"1233",957,"CDU SYNCOPE OBSERVATION",74,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,2,0.027027027027027,603,24164,0.5,0.0249544777354743,20.0364842454395,1,23561,0.0415013714707364
+"1234",957,"CDU SYNCOPE OBSERVATION",74,"92173","ZOLPIDEM 5 MG PO TABS",1,9,0.121621621621622,159,8203,0.111111111111111,0.019383152505181,5.73235499650594,8,8044,0.433411779437914
+"1235",958,"CDU CELLULITIS OBSERVATION",159,"101","ACETAMINOPHEN 325 MG PO TABS",3,23,0.144654088050314,981,42092,0.130434782608696,0.0233060914187969,5.59659619731419,20,41111,0.00673148764119779
+"1236",958,"CDU CELLULITIS OBSERVATION",159,"9627","CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK",1,4,0.0251572327044025,29,474,0.25,0.0611814345991561,4.08620689655172,3,445,0.606275443907692
+"1237",958,"CDU CELLULITIS OBSERVATION",159,"92173","ZOLPIDEM 5 MG PO TABS",1,32,0.20125786163522,159,8203,0.03125,0.019383152505181,1.6122248427673,31,8044,0.999999999999998
+"1238",958,"CDU CELLULITIS OBSERVATION",159,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",2,6,0.0377358490566038,1951,16917,0.333333333333333,0.115327776792576,2.89031266017427,4,14966,0.302053233450265
+"1239",958,"CDU CELLULITIS OBSERVATION",159,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,71,0.446540880503145,705,36939,0.0140845070422535,0.0190855193697718,0.737968234941564,70,36234,0.999999999999974
+"1240",959,"CDU ASTHMA OBSERVATION",103,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",4,25,0.242718446601942,106,3939,0.16,0.0269103833460269,5.94566037735849,21,3833,0.000608544230157317
+"1241",959,"CDU ASTHMA OBSERVATION",103,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",4,25,0.242718446601942,127,3899,0.16,0.0325724544755065,4.91212598425197,21,3772,0.00291070104984472
+"1242",959,"CDU ASTHMA OBSERVATION",103,"9000000","ZZZ IMS TEMPLATE",1,15,0.145631067961165,1035,28824,0.0666666666666667,0.0359075770191507,1.85661835748792,14,27789,0.999999999999337
+"1243",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,9,0.0355731225296443,346,10128,0.222222222222222,0.0341627172195893,6.50481695568401,7,9782,0.0291498670524629
+"1244",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,5,0.0197628458498024,218,7862,0.2,0.0277283134062579,7.21284403669725,4,7644,0.326507028774615
+"1245",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",4,59,0.233201581027668,158,4300,0.0677966101694915,0.0367441860465116,1.84509761853679,55,4142,0.365003688313926
+"1246",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,29,0.114624505928854,692,19620,0.0689655172413793,0.0352701325178389,1.95535180386685,27,18928,0.63198565991831
+"1247",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,11,0.0434782608695652,603,24164,0.0909090909090909,0.0249544777354743,3.64299713553445,10,23561,0.663501364559018
+"1248",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,15,0.0592885375494071,1219,33427,0.0666666666666667,0.036467526251234,1.82811047306535,14,32208,0.999999999999995
+"1249",960,"CDU ABDOMINAL PAIN OBSERVATION",253,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,121,0.478260869565217,705,36939,0.00826446280991736,0.0190855193697718,0.433022683312819,120,36234,0.591775289384449
+"1250",961,"CDU CHEST PAIN OBSERVATION",336,"680","ASPIRIN 81 MG PO CHEW",1,8,0.0238095238095238,357,11203,0.125,0.031866464339909,3.92261904761905,7,10846,0.622786012463415
+"1251",961,"CDU CHEST PAIN OBSERVATION",336,"5604","NITROGLYCERIN 0.4 MG SL SUBL",2,117,0.348214285714286,77,2947,0.0170940170940171,0.0261282660332542,0.654234654234654,115,2870,0.758618254529157
+"1252",961,"CDU CHEST PAIN OBSERVATION",336,"92173","ZOLPIDEM 5 MG PO TABS",3,67,0.199404761904762,159,8203,0.0447761194029851,0.019383152505181,2.31005350605463,64,8044,0.293183438040247
+"1253",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"101","ACETAMINOPHEN 325 MG PO TABS",1,118,0.116947472745292,981,42092,0.00847457627118644,0.0233060914187969,0.363620656887645,117,41111,0.446349343974249
+"1254",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"3845","IBUPROFEN 800 MG PO TABS",1,4,0.00396432111000991,25,565,0.25,0.0442477876106195,5.65,3,540,0.445915943407428
+"1255",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,42,0.0416253716551041,218,7862,0.0476190476190476,0.0277283134062579,1.71734381826125,40,7644,0.755580249102666
+"1256",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,60,0.0594648166501487,692,19620,0.0333333333333333,0.0352701325178389,0.945086705202312,58,18928,0.999999999999305
+"1257",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"92173","ZOLPIDEM 5 MG PO TABS",12,191,0.189296333002973,159,8203,0.06282722513089,0.019383152505181,3.24133162106095,179,8044,8.06566292922893e-05
+"1258",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",3,26,0.0257680872150644,738,23934,0.115384615384615,0.0308347956881424,3.74202626641651,23,23196,0.0545754568363725
+"1259",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",9,436,0.43211100099108,705,36939,0.0206422018348624,0.0190855193697718,1.08156353699004,427,36234,0.952071810352023
+"1260",962,"CDU GENERAL  PROTOCOL OBSERVATION",1009,"9000000","ZZZ IMS TEMPLATE",1,4,0.00396432111000991,1035,28824,0.25,0.0359075770191507,6.96231884057971,3,27789,0.338546273868797
+"1261",965,"CDU PNEUMONIA OBSERVATION",128,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,4,0.03125,158,4300,0.25,0.0367441860465116,6.80379746835443,3,4142,0.350233555143549
+"1262",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"101","ACETAMINOPHEN 325 MG PO TABS",2,107,0.100187265917603,981,42092,0.0186915887850467,0.0233060914187969,0.802004439490506,105,41111,0.9999999999999
+"1263",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"663","ASCORBIC ACID 250 MG PO TABS",1,7,0.00655430711610487,39,2351,0.142857142857143,0.0165886856656742,8.61172161172161,6,2312,0.26375639320522
+"1264",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"664","ASCORBIC ACID 500 MG PO TABS",1,232,0.217228464419476,59,3135,0.00431034482758621,0.0188197767145136,0.229032729398013,231,3076,0.175491207942454
+"1265",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,231,0.216292134831461,542,37509,0.00432900432900433,0.0144498653656456,0.29958786600853,230,36967,0.312177110018948
+"1266",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"3074","FERROUS SULFATE 325 MG (65 MG IRON) PO TABS",1,162,0.151685393258427,97,6557,0.00617283950617284,0.0147933506176605,0.417271223113148,161,6460,0.567043408268712
+"1267",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"4573","LORAZEPAM 1 MG PO TABS",1,32,0.0299625468164794,337,8494,0.03125,0.0396750647515894,0.787648367952522,31,8157,0.999999999999994
+"1268",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",4,309,0.289325842696629,346,10128,0.0129449838187702,0.0341627172195893,0.378921376059263,305,9782,0.0600442688592135
+"1269",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,332,0.310861423220974,314,8762,0.00602409638554217,0.035836566993837,0.168099148185097,330,8448,0.00580190528799988
+"1270",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,212,0.198501872659176,258,5335,0.00943396226415094,0.0483598875351453,0.195078250694749,210,5077,0.0137369621611587
+"1271",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,13,0.0121722846441948,688,12925,0.0769230769230769,0.0532301740812379,1.44510286225403,12,12237,0.999999999999887
+"1272",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",5,330,0.308988764044944,705,36939,0.0151515151515152,0.0190855193697718,0.793874919406834,325,36234,0.75032256166878
+"1273",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",2,18,0.0168539325842697,288,4280,0.111111111111111,0.0672897196261682,1.65123456790123,16,3992,0.788070488790243
+"1274",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"520064","CEFAZOLIN 2 G IVPB",1,32,0.0299625468164794,109,2674,0.03125,0.0407629020194465,0.766628440366972,31,2565,0.999999999999996
+"1275",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"540598","THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS",1,246,0.230337078651685,17,1250,0.0040650406504065,0.0136,0.298900047824008,245,1233,0.350331044586876
+"1276",972,"IP PLS BREAST RECONSTRUCT UNILAT/BILATERAL  FREE TRAM FLAP",1068,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,21,0.0196629213483146,104,5739,0.0476190476190476,0.0181216239763025,2.62774725274725,20,5635,0.848131282070756
+"1277",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"101","ACETAMINOPHEN 325 MG PO TABS",32,1622,0.121854105626925,981,42092,0.0197287299630086,0.0233060914187969,0.846505302347563,1590,41111,0.392231905161933
+"1278",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"102","ACETAMINOPHEN 500 MG PO TABS",5,123,0.00924047780031553,392,15968,0.040650406504065,0.0245490981963928,1.6558818649411,118,15576,0.392549316206799
+"1279",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"680","ASPIRIN 81 MG PO CHEW",12,819,0.0615280594996619,357,11203,0.0146520146520147,0.031866464339909,0.459794174079888,807,10846,0.00799668365693572
+"1280",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"693","ASPIRIN 300 MG PR SUPP",49,2018,0.151603936593795,70,1872,0.0242814667988107,0.0373931623931624,0.649355797819623,1969,1802,0.0226347793454293
+"1281",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"1080","BISACODYL 10 MG PR SUPP",16,2623,0.197055067237623,344,22442,0.00609988562714449,0.0153284021031994,0.397946608268537,2607,22098,0.000240557734800305
+"1282",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"2566","DOCUSATE SODIUM 100 MG PO CAPS",17,1859,0.139658928705582,542,37509,0.00914470145239376,0.0144498653656456,0.632857208077191,1842,36967,0.0739810295570757
+"1283",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"2567","DOCUSATE SODIUM 250 MG PO CAPS",2,775,0.0582225227255653,296,17760,0.00258064516129032,0.0166666666666667,0.154838709677419,773,17464,0.00366045238524324
+"1284",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",21,2580,0.193824656299301,346,10128,0.00813953488372093,0.0341627172195893,0.238257830353542,2559,9782,2.9417370589807e-12
+"1285",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"5373","NALOXONE 0.4 MG/ML INJ SOLN",22,2551,0.191646007061829,83,1774,0.00862406899255194,0.0467869222096956,0.184326486660086,2529,1691,2.36898691524741e-15
+"1286",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",17,1301,0.0977387123431748,89,4372,0.0130668716372022,0.0203568161024703,0.641891716829751,1284,4283,0.112268588003431
+"1287",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"9627","CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK",1,13,0.000976635865073999,29,474,0.0769230769230769,0.0611814345991561,1.25729442970822,12,445,0.999999999999975
+"1288",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"10011","FAMOTIDINE 20 MG PO TABS",1,7,0.000525880850424461,472,16960,0.142857142857143,0.0278301886792453,5.13317191283293,6,16488,0.483877497244309
+"1289",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",24,2519,0.189241980317031,14,238,0.00952759031361651,0.0588235294117647,0.161969035331481,2495,224,2.77865094700502e-09
+"1290",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"10813","OXYCODONE 5 MG/5 ML PO SOLN",2,34,0.00255427841634738,328,8370,0.0588235294117647,0.0391875746714456,1.50107604017217,32,8042,0.8839877519749
+"1291",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"10814","OXYCODONE 5 MG PO TABS",85,5536,0.415896626849974,1478,56614,0.0153540462427746,0.0261066167379094,0.588128534498267,5451,55136,1.35368834644734e-06
+"1292",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"11150","PROPOFOL 10 MG/ML IV EMUL",3,46,0.00345578844564646,135,3592,0.0652173913043478,0.0375835189309577,1.73526570048309,43,3457,0.557538341161427
+"1293",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"11349","SENNOSIDES 8.6 MG PO TABS",23,2970,0.223123732251521,610,35793,0.00774410774410774,0.0170424384656218,0.454401390958768,2947,35183,0.000165450984146201
+"1294",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",40,2457,0.184584178498986,314,8762,0.0162800162800163,0.035836566993837,0.454285040272301,2417,8448,1.32883014693635e-06
+"1295",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"15859","NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN",67,1741,0.130794080084141,71,1104,0.038483630097645,0.0643115942028985,0.598393346870424,1674,1033,0.00240276325207342
+"1296",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",37,1248,0.0937570430471039,258,5335,0.0296474358974359,0.0483598875351453,0.61305841284039,1211,5077,0.0051037351096309
+"1297",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",21,2908,0.218465930433476,712,36910,0.00722145804676754,0.0192901652668653,0.374359573744649,2887,36198,4.4384341492318e-06
+"1298",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,8,0.000601006686199384,603,24164,0.125,0.0249544777354743,5.00912106135987,7,23561,0.496588042560405
+"1299",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,7,0.000525880850424461,242,3682,0.142857142857143,0.0657251493753395,2.17355371900826,6,3440,0.952691421578972
+"1300",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"82384","PANTOPRAZOLE 40 MG IV SOLR",125,2407,0.18082788671024,688,12925,0.0519318653926049,0.0532301740812379,0.975609535173573,2282,12237,0.832534463417621
+"1301",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"83398","CLOPIDOGREL 75 MG PO TABS",2,44,0.00330553677409661,234,7279,0.0454545454545455,0.0321472729770573,1.41394716394716,42,7045,0.944025339048358
+"1302",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",16,2549,0.191495755390279,111,6618,0.00627697136131816,0.0167724388032638,0.374243211434267,2533,6507,0.000175286331345698
+"1303",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"88904","INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN",30,1837,0.138006160318534,482,5722,0.0163309744148067,0.0842362810206222,0.19387102821893,1807,5240,1.20110293613179e-23
+"1304",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",3,6,0.000450755014649538,1067,10067,0.5,0.105989867885169,4.7174320524836,3,9000,0.0135625029865434
+"1305",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",3,70,0.00525880850424461,738,23934,0.0428571428571429,0.0308347956881424,1.38989547038328,67,23196,0.814460674369173
+"1306",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"112520","SODIUM CHLORIDE 0.9 % 0.9 % INJ SYRG",10,1432,0.10758019682969,11,154,0.00698324022346369,0.0714285714285714,0.0977653631284916,1422,143,3.4449746667665e-10
+"1307",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,39,0.002929907595222,1219,33427,0.0256410256410256,0.036467526251234,0.703119412717444,38,32208,0.999999999998899
+"1308",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",34,688,0.051686575013147,1951,16917,0.0494186046511628,0.115327776792576,0.428505656014209,654,14966,1.18061264176275e-07
+"1309",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",17,2606,0.195777928029449,457,11068,0.00652340752110514,0.0412902059992772,0.157989221977225,2589,10611,4.3456111785577e-18
+"1310",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",17,2623,0.197055067237623,705,36939,0.00648112847884102,0.0190855193697718,0.339583553021147,2606,36234,4.54832457262351e-06
+"1311",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",7,155,0.0116445045451131,288,4280,0.0451612903225806,0.0672897196261682,0.671146953405018,148,3992,0.356504047063812
+"1312",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"205192","MILRINONE IN D5W 20 MG/100 ML IV PGBK",8,237,0.0178048230786567,27,851,0.0337552742616034,0.0317273795534665,1.06391623691202,229,824,0.999999999999999
+"1313",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"206169","DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",47,1105,0.0830140485312899,62,1778,0.0425339366515837,0.0348706411698538,1.21976353816961,1058,1716,0.342904769699969
+"1314",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"206226","NICARDIPINE IN SALINE (ISO-OS) 40 MG/200 ML IV PGBK",3,71,0.00533393434001953,65,2286,0.0422535211267606,0.0284339457567804,1.48602383531961,68,2221,0.745069215117669
+"1315",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"207694","NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",5,77,0.00578468935466907,56,1641,0.0649350649350649,0.0341255332114564,1.9028293135436,72,1585,0.265811710926495
+"1316",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",29,1441,0.108256329351664,510,18335,0.0201249132546842,0.0278156531224434,0.723510361813011,1412,17825,0.10050527980017
+"1317",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,15,0.00112688753662384,452,13480,0.0666666666666667,0.0335311572700297,1.9882005899705,14,13028,0.999999999999989
+"1318",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",123,2541,0.190894748704079,156,2315,0.0484061393152302,0.0673866090712743,0.718334695607423,2418,2159,0.00548554382398013
+"1319",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"226058","EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",19,733,0.0550672376230186,56,1218,0.0259208731241473,0.0459770114942529,0.563778990450205,714,1162,0.0348611667639702
+"1320",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",49,1649,0.123882503192848,291,6612,0.0297149787750152,0.044010889292196,0.675173332166324,1600,6321,0.0109234061800515
+"1321",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",60,778,0.0584479002328901,111,2584,0.077120822622108,0.0429566563467492,1.79531716806781,718,2473,0.000207898475084467
+"1322",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"520064","CEFAZOLIN 2 G IVPB",1,126,0.0094658553076403,109,2674,0.00793650793650794,0.0407629020194465,0.194699286442406,125,2565,0.105470656041367
+"1323",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530001","AMIODARONE INFUSION PERIPH ADMIN",2,5,0.000375629178874615,7,168,0.4,0.0416666666666667,9.6,3,161,0.0112842138074326
+"1324",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530002","AMIODARONE INFUSION CENTRAL LINE ADMIN",1,48,0.0036060401171963,277,3542,0.0208333333333333,0.0782044042913608,0.266395908543923,47,3265,0.228089530283929
+"1325",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530006","EPINEPHRINE IV INFUSION",7,437,0.0328299902336414,99,2540,0.0160183066361556,0.0389763779527559,0.410974735917528,430,2441,0.0242876754439259
+"1326",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530008","NITROPRUSSIDE IV INFUSION",9,207,0.0155510480054091,79,2082,0.0434782608695652,0.0379442843419789,1.14584479911943,198,2003,0.837243978198321
+"1327",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530010","NICARDIPINE IV INFUSION",4,12,0.000901510029299076,257,676,0.333333333333333,0.380177514792899,0.876783398184176,8,419,0.974946567538517
+"1328",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530011","NOREPINEPHRINE IV INFUSION",1,24,0.00180302005859815,92,2909,0.0416666666666667,0.0316259883121348,1.31748188405797,23,2817,0.999999999999994
+"1329",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530025","DEXMEDETOMIDINE IV INFUSION",24,626,0.0470287731951018,530,2902,0.0383386581469649,0.18263266712612,0.209922237627343,602,2372,3.92581856325473e-19
+"1330",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530031","VASOPRESSIN 1 UNIT/ML IV INFUSION",4,117,0.00878972278566599,215,4489,0.0341880341880342,0.0478948540877701,0.713814351023653,113,4274,0.639951077356986
+"1331",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"530039","AMINOCAPROIC ACID IV INFUSION",47,2109,0.158440387649313,10,148,0.0222854433380749,0.0675675675675676,0.329824561403509,2062,138,0.00179004920207259
+"1332",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",59,2853,0.214334009465855,461,18637,0.0206799859796705,0.0247357407308043,0.836036656622819,2794,18176,0.212224289432385
+"1333",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"540302","POTASSIUM CHLORIDE IV SCALE",19,2990,0.22462624896702,150,4660,0.00635451505016722,0.0321888412017167,0.197413600891862,2971,4510,1.15772106428612e-13
+"1334",973,"IP CTS ICU CARDIAC SURGERY POSTOP",13311,"540304","MAGNESIUM SULFATE IV SCALE ",23,2617,0.196604312222973,78,4104,0.00878868933893772,0.0190058479532164,0.462420269833338,2594,4026,0.00113679391135
+"1335",974,"IP CTS GEN ESOPHAGECTOMY POSTOP",440,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,84,0.190909090909091,346,10128,0.0119047619047619,0.0341627172195893,0.348472336911643,83,9782,0.412809105013071
+"1336",974,"IP CTS GEN ESOPHAGECTOMY POSTOP",440,"5373","NALOXONE 0.4 MG/ML INJ SOLN",1,3,0.00681818181818182,83,1774,0.333333333333333,0.0467869222096956,7.12449799196787,2,1691,0.329413318578587
+"1337",974,"IP CTS GEN ESOPHAGECTOMY POSTOP",440,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,1,0.00227272727272727,692,19620,1,0.0352701325178389,28.3526011560694,0,18928,0.0118194121841506
+"1338",974,"IP CTS GEN ESOPHAGECTOMY POSTOP",440,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,50,0.113636363636364,688,12925,0.04,0.0532301740812379,0.751453488372093,48,12237,0.920044840798298
+"1339",974,"IP CTS GEN ESOPHAGECTOMY POSTOP",440,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,85,0.193181818181818,705,36939,0.0117647058823529,0.0190855193697718,0.616420525657071,84,36234,0.923564261846866
+"1340",974,"IP CTS GEN ESOPHAGECTOMY POSTOP",440,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,84,0.190909090909091,452,13480,0.0119047619047619,0.0335311572700297,0.35503581963759,83,13028,0.426511675816412
+"1341",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,40,0.227272727272727,346,10128,0.05,0.0341627172195893,1.4635838150289,38,9782,0.909117038852275
+"1342",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,40,0.227272727272727,346,10128,0.05,0.0341627172195893,1.4635838150289,38,9782,0.909117038852275
+"1343",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,25,0.142045454545455,688,12925,0.08,0.0532301740812379,1.50290697674419,23,12237,0.880996607334719
+"1344",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,25,0.142045454545455,688,12925,0.08,0.0532301740812379,1.50290697674419,23,12237,0.880996607334719
+"1345",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",2,14,0.0795454545454545,738,23934,0.142857142857143,0.0308347956881424,4.63298490127758,12,23196,0.0991457517564912
+"1346",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",2,14,0.0795454545454545,738,23934,0.142857142857143,0.0308347956881424,4.63298490127758,12,23196,0.0991457517564912
+"1347",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,44,0.25,705,36939,0.0227272727272727,0.0190855193697718,1.19081237911025,43,36234,0.999999999997223
+"1348",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,44,0.25,705,36939,0.0227272727272727,0.0190855193697718,1.19081237911025,43,36234,0.999999999997223
+"1349",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",2,37,0.210227272727273,452,13480,0.0540540540540541,0.0335311572700297,1.61205453240851,35,13028,0.814148147348766
+"1350",975,"IP CTS ESOPHAGECTOMY STEPDOWN POSTOP",176,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",2,37,0.210227272727273,452,13480,0.0540540540540541,0.0335311572700297,1.61205453240851,35,13028,0.814148147348766
+"1351",976,"IP CTS LUNG RESECTION POSTOP",4789,"101","ACETAMINOPHEN 325 MG PO TABS",5,423,0.0883274169972854,981,42092,0.0118203309692671,0.0233060914187969,0.507177748377566,418,41111,0.16171394476303
+"1352",976,"IP CTS LUNG RESECTION POSTOP",4789,"102","ACETAMINOPHEN 500 MG PO TABS",2,97,0.0202547504698267,392,15968,0.0206185567010309,0.0245490981963928,0.839890595413423,95,15576,0.999999999999576
+"1353",976,"IP CTS LUNG RESECTION POSTOP",4789,"1080","BISACODYL 10 MG PR SUPP",7,1069,0.223219878889121,344,22442,0.00654817586529467,0.0153284021031994,0.427192333630648,1062,22098,0.0289822980268318
+"1354",976,"IP CTS LUNG RESECTION POSTOP",4789,"2475","DILTIAZEM HCL 30 MG PO TABS",3,277,0.0578408853622886,91,1786,0.0108303249097473,0.0509518477043673,0.212560003173722,274,1695,0.00473519852522684
+"1355",976,"IP CTS LUNG RESECTION POSTOP",4789,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",3,952,0.198788891209021,356,12049,0.00315126050420168,0.0295460204166321,0.106656005098669,949,11693,2.84276472507585e-06
+"1356",976,"IP CTS LUNG RESECTION POSTOP",4789,"2566","DOCUSATE SODIUM 100 MG PO CAPS",6,759,0.158488202129881,542,37509,0.00790513833992095,0.0144498653656456,0.547073494450359,753,36967,0.17758497572446
+"1357",976,"IP CTS LUNG RESECTION POSTOP",4789,"2567","DOCUSATE SODIUM 250 MG PO CAPS",2,314,0.0655669242012946,296,17760,0.00636942675159236,0.0166666666666667,0.382165605095541,312,17464,0.231360025798157
+"1358",976,"IP CTS LUNG RESECTION POSTOP",4789,"3843","IBUPROFEN 400 MG PO TABS",2,36,0.00751722697849238,148,4216,0.0555555555555556,0.0351043643263757,1.58258258258258,34,4068,0.834694435703878
+"1359",976,"IP CTS LUNG RESECTION POSTOP",4789,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",6,1044,0.217999582376279,346,10128,0.00574712643678161,0.0341627172195893,0.168228024715966,1038,9782,9.04613251104081e-07
+"1360",976,"IP CTS LUNG RESECTION POSTOP",4789,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",1,65,0.013572770933389,745,12385,0.0153846153846154,0.0601534113847396,0.2557563242127,64,11640,0.209547464318446
+"1361",976,"IP CTS LUNG RESECTION POSTOP",4789,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",11,492,0.102735435372729,218,7862,0.0223577235772358,0.0277283134062579,0.806313865890953,481,7644,0.571764072765455
+"1362",976,"IP CTS LUNG RESECTION POSTOP",4789,"7753","TEMAZEPAM 15 MG PO CAPS",7,363,0.0757987053664648,24,941,0.0192837465564738,0.0255047821466525,0.756083562901745,356,917,0.64685174312441
+"1363",976,"IP CTS LUNG RESECTION POSTOP",4789,"14858","D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",10,969,0.202338692837753,58,2031,0.0103199174406605,0.0285573609059577,0.361375040034162,959,1973,0.00263651471266067
+"1364",976,"IP CTS LUNG RESECTION POSTOP",4789,"22473","KETOROLAC 30 MG/ML (1 ML) INJ SOLN",3,122,0.0254750469826686,132,2852,0.0245901639344262,0.0462833099579243,0.531296572280179,119,2720,0.365397017248819
+"1365",976,"IP CTS LUNG RESECTION POSTOP",4789,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",17,822,0.171643349342243,258,5335,0.0206812652068127,0.0483598875351453,0.427653294102114,805,5077,0.000491491859621128
+"1366",976,"IP CTS LUNG RESECTION POSTOP",4789,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,29,0.00605554395489664,692,19620,0.0689655172413793,0.0352701325178389,1.95535180386685,27,18928,0.63198565991831
+"1367",976,"IP CTS LUNG RESECTION POSTOP",4789,"37637","METOPROLOL TARTRATE 50 MG PO TABS",1,84,0.0175401962831489,809,19725,0.0119047619047619,0.0410139416983523,0.290261345576549,83,18916,0.28540608579144
+"1368",976,"IP CTS LUNG RESECTION POSTOP",4789,"38390","ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP",4,1017,0.21236166214241,14,882,0.00393313667649951,0.0158730158730159,0.247787610619469,1013,868,0.0146600291505708
+"1369",976,"IP CTS LUNG RESECTION POSTOP",4789,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",17,1096,0.22885779912299,493,18456,0.0155109489051095,0.0267121803207629,0.580669519254971,1079,17963,0.0305477242337149
+"1370",976,"IP CTS LUNG RESECTION POSTOP",4789,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,79,0.0164961369805805,688,12925,0.0253164556962025,0.0532301740812379,0.475603473653223,77,12237,0.394357133601174
+"1371",976,"IP CTS LUNG RESECTION POSTOP",4789,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",7,1027,0.214449780747546,111,6618,0.0068159688412853,0.0167724388032638,0.406379115239875,1020,6507,0.0230768581392515
+"1372",976,"IP CTS LUNG RESECTION POSTOP",4789,"88043","PANTOPRAZOLE 40 MG PO TBEC",2,218,0.0455209855919816,624,31884,0.00917431192660551,0.0195709446744449,0.468772053634439,216,31260,0.389447593739177
+"1373",976,"IP CTS LUNG RESECTION POSTOP",4789,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,148,0.0309041553560242,738,23934,0.00675675675675676,0.0308347956881424,0.219127664249615,147,23196,0.145904989988356
+"1374",976,"IP CTS LUNG RESECTION POSTOP",4789,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,2,0.000417623721027354,499,13896,0.5,0.0359096142774899,13.9238476953908,1,13397,0.10408135842914
+"1375",976,"IP CTS LUNG RESECTION POSTOP",4789,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",4,48,0.0100229693046565,1219,33427,0.0833333333333333,0.036467526251234,2.28513809133169,44,32208,0.178800482073504
+"1376",976,"IP CTS LUNG RESECTION POSTOP",4789,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,39,0.00814366256003341,1951,16917,0.0256410256410256,0.115327776792576,0.222331743090328,38,14966,0.13317668456428
+"1377",976,"IP CTS LUNG RESECTION POSTOP",4789,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",21,1168,0.243892253079975,705,36939,0.0179794520547945,0.0190855193697718,0.942046779364617,1147,36234,0.870090183148089
+"1378",976,"IP CTS LUNG RESECTION POSTOP",4789,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,7,0.00146168302359574,288,4280,0.142857142857143,0.0672897196261682,2.12301587301587,6,3992,0.966175414383934
+"1379",976,"IP CTS LUNG RESECTION POSTOP",4789,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",10,979,0.20442681144289,452,13480,0.0102145045965271,0.0335311572700297,0.304627260976073,969,13028,9.18464011025904e-05
+"1380",976,"IP CTS LUNG RESECTION POSTOP",4789,"540640","IPRATROPIUM-ALBUTEROL 0.5MG-2.5MG BASE(3MG)/3ML INH NEB-PANEL USE ONLY",28,943,0.196909584464398,1,27,0.0296924708377519,0.037037037037037,0.8016967126193,915,26,0.999999999999999
+"1381",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"101","ACETAMINOPHEN 325 MG PO TABS",56,1999,0.163624457722845,981,42092,0.0280140070035018,0.0233060914187969,1.20200365218287,1943,41111,0.199989874335533
+"1382",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"105","ACETAMINOPHEN 650 MG PR SUPP",6,333,0.0272571007612343,59,929,0.018018018018018,0.0635091496232508,0.283707436249809,327,870,0.00208438406182113
+"1383",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"681","ASPIRIN 325 MG PO TABS",3,39,0.00319227306212655,303,7114,0.0769230769230769,0.0425920719707619,1.80604214267581,36,6811,0.509344002381142
+"1384",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"2327","DEXAMETHASONE 4 MG PO TABS",4,61,0.00499304248178767,277,6991,0.0655737704918033,0.0396223716206551,1.65496833757472,57,6714,0.482039979975244
+"1385",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"2332","DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",10,988,0.0808709175738725,390,6737,0.0101214574898785,0.0578892682202761,0.174841690023876,978,6347,4.07650912192305e-10
+"1386",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"2405","DIAZEPAM 5 MG PO TABS",4,225,0.018416959973807,152,5041,0.0177777777777778,0.0301527474707399,0.589590643274854,221,4889,0.384171282479482
+"1387",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"2566","DOCUSATE SODIUM 100 MG PO CAPS",27,1877,0.15363837275927,542,37509,0.0143846563665424,0.0144498653656456,0.99548722445136,1850,36967,0.999999999999982
+"1388",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"2567","DOCUSATE SODIUM 250 MG PO CAPS",13,877,0.0717852173201277,296,17760,0.0148232611174458,0.0166666666666667,0.88939566704675,864,17464,0.778024636203177
+"1389",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",15,1889,0.15462061062454,537,15149,0.00794070937003706,0.0354478843488019,0.224010812377451,1874,14612,3.01294715172823e-10
+"1390",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"4711","MAGNESIUM CITRATE PO SOLN",2,362,0.0296308422689695,131,5481,0.00552486187845304,0.0239007480386791,0.231158534013749,360,5350,0.0367531724299289
+"1391",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",23,2690,0.220184988131292,346,10128,0.00855018587360595,0.0341627172195893,0.250278273201968,2667,9782,2.61469240733343e-12
+"1392",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",14,1626,0.133093230744045,745,12385,0.00861008610086101,0.0601534113847396,0.14313545820022,1612,11640,9.94532030676625e-18
+"1393",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"5373","NALOXONE 0.4 MG/ML INJ SOLN",24,2774,0.22706065318818,83,1774,0.00865176640230714,0.0467869222096956,0.184918477080637,2750,1691,2.93512580703755e-16
+"1394",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"8958","BUTALBITAL-ACETAMINOPHEN-CAFF 50-325-40 MG PO TABS",1,193,0.0157976589997544,58,2595,0.00518134715025907,0.0223506743737958,0.231820618188315,192,2537,0.180345201898584
+"1395",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"9627","CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK",1,5,0.000409265777195711,29,474,0.2,0.0611814345991561,3.26896551724138,4,445,0.728829318099584
+"1396",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"10011","FAMOTIDINE 20 MG PO TABS",1,51,0.00417451092739625,472,16960,0.0196078431372549,0.0278301886792453,0.704553007643735,50,16488,0.999999999999115
+"1397",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",20,2749,0.225014324302202,14,238,0.00727537286285922,0.0588235294117647,0.123681338668607,2729,224,6.27385096849317e-12
+"1398",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"10610","MIDODRINE 5 MG PO TABS",1,24,0.00196447573053941,154,4184,0.0416666666666667,0.0368068833652008,1.13203463203463,23,4030,0.999999999999999
+"1399",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"10814","OXYCODONE 5 MG PO TABS",1,92,0.00753049030040108,1478,56614,0.0108695652173913,0.0261066167379094,0.416352885803377,91,55136,0.555924614567641
+"1400",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"11150","PROPOFOL 10 MG/ML IV EMUL",3,53,0.00433821723827454,135,3592,0.0566037735849057,0.0375835189309577,1.50607966457023,50,3457,0.720551346961699
+"1401",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"11349","SENNOSIDES 8.6 MG PO TABS",34,3031,0.24809691413604,610,35793,0.0112174199934015,0.0170424384656218,0.658205104629214,2997,35183,0.0194475428003885
+"1402",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",23,2489,0.203732503888025,118,4415,0.00924065889915629,0.026727066817667,0.345741602031992,2466,4297,1.27446820673438e-06
+"1403",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",8,110,0.00900384709830564,258,5335,0.0727272727272727,0.0483598875351453,1.50387596899225,102,5077,0.342042452877117
+"1404",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",8,854,0.0699025947450274,712,36910,0.00936768149882904,0.0192901652668653,0.485619556350814,846,36198,0.0488770989170476
+"1405",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"28384","HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS",9,294,0.0240648276991078,335,9576,0.0306122448979592,0.0349832915622389,0.875053304904051,285,9241,0.809482976585641
+"1406",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",27,2440,0.199721699271507,603,24164,0.0110655737704918,0.0249544777354743,0.443430389038414,2413,23561,2.33406648689411e-05
+"1407",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"81421","ESMOLOL IN NACL (ISO-OSM) 2,500 MG/250 ML (10 MG/ML) IV SOLP",29,2172,0.177785053613817,72,1479,0.0133517495395948,0.0486815415821501,0.274267188459177,2143,1407,3.23647654593304e-10
+"1408",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"81934","NIMODIPINE 30 MG PO CAPS",11,228,0.0186625194401244,23,372,0.0482456140350877,0.0618279569892473,0.780320366132723,217,349,0.605456801089997
+"1409",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"82384","PANTOPRAZOLE 40 MG IV SOLR",6,118,0.00965867234181878,688,12925,0.0508474576271186,0.0532301740812379,0.955237485218762,112,12237,0.999999999999903
+"1410",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",3,355,0.0290578701808955,111,6618,0.00845070422535211,0.0167724388032638,0.503844689760183,352,6507,0.322306189945883
+"1411",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"88043","PANTOPRAZOLE 40 MG PO TBEC",1,50,0.00409265777195711,624,31884,0.02,0.0195709446744449,1.02192307692308,49,31260,0.999999999999959
+"1412",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",15,915,0.0748956372268151,93,5289,0.0163934426229508,0.0175836642087351,0.932310946589106,900,5196,0.906630634939426
+"1413",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,26,0.0021281820414177,499,13896,0.0384615384615385,0.0359096142774899,1.07106520733775,25,13397,0.999999999999289
+"1414",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",3,73,0.00597528034705738,1219,33427,0.0410958904109589,0.036467526251234,1.1269174149033,70,32208,0.999999999999863
+"1415",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"117862","HYDROCODONE-ACETAMINOPHEN 7.5-325 MG/15 ML PO SOLN",2,9,0.00073667839895228,79,3308,0.222222222222222,0.0238814993954051,9.30520393811533,7,3229,0.00563055500863467
+"1416",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",4,64,0.0052386019481051,1951,16917,0.0625,0.115327776792576,0.541933623782676,60,14966,0.260412994910942
+"1417",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"124873","SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP",3,147,0.0120324138495539,90,4112,0.0204081632653061,0.0218871595330739,0.932426303854875,144,4022,0.999999999999917
+"1418",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",6,1805,0.147744945567652,457,11068,0.00332409972299169,0.0412902059992772,0.0805057674706171,1799,10611,1.66557892726479e-15
+"1419",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",46,2906,0.237865269706147,705,36939,0.0158293186510668,0.0190855193697718,0.829388938513128,2860,36234,0.241198248154265
+"1420",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",248,2311,0.189162642219858,288,4280,0.107312851579403,0.0672897196261682,1.59478821097168,2063,3992,1.8528398080245e-08
+"1421",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"206226","NICARDIPINE IN SALINE (ISO-OS) 40 MG/200 ML IV PGBK",6,717,0.0586887124498649,65,2286,0.00836820083682008,0.0284339457567804,0.294303186353396,711,2221,0.00323377842553168
+"1422",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",3,75,0.00613898665793566,192,8611,0.04,0.0222970618975729,1.79395833333333,72,8419,0.522821843481971
+"1423",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"210741","PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN",1,203,0.0166161905541459,43,1494,0.00492610837438424,0.0287817938420348,0.171153625844885,202,1451,0.076493453137841
+"1424",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",6,2110,0.17271015797659,326,12268,0.0028436018957346,0.0265731985653733,0.107010147413718,2104,11942,3.46401588149896e-11
+"1425",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",9,796,0.0651551117295572,510,18335,0.0113065326633166,0.0278156531224434,0.406480934082176,787,17825,0.00703129546987584
+"1426",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,22,0.00180076941966113,452,13480,0.0454545454545455,0.0335311572700297,1.35559131134352,21,13028,0.999999999999979
+"1427",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",179,1189,0.0973234018171401,156,2315,0.150546677880572,0.0673866090712743,2.23407409803541,1010,2159,3.66744470065855e-15
+"1428",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"222567","LEVETIRACETAM IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",3,63,0.00515674879266596,50,1098,0.0476190476190476,0.0455373406193078,1.04571428571429,60,1048,0.999999999999986
+"1429",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",2,53,0.00433821723827454,291,6612,0.0377358490566038,0.044010889292196,0.857420735265513,51,6321,0.99999999999985
+"1430",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"230362","NIMODIPINE 60 MG/20 ML PO SOLN",1,43,0.00351968568388311,12,316,0.0232558139534884,0.0379746835443038,0.612403100775194,42,304,0.960374476934782
+"1431",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",1,29,0.00237374150773512,111,2584,0.0344827586206897,0.0429566563467492,0.80273376825101,28,2473,0.999999999999865
+"1432",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"520064","CEFAZOLIN 2 G IVPB",2,27,0.00221003519685684,109,2674,0.0740740740740741,0.0407629020194465,1.81719334012912,25,2565,0.703652146706697
+"1433",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"520068","PHENYTOIN IVPB (500 MG OR LESS)",4,14,0.00114594417614799,153,533,0.285714285714286,0.287054409005629,0.995331465919701,10,380,0.999999999999993
+"1434",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"520301","CEFTRIAXONE 1 GRAM/50 ML NS IVPB",22,1388,0.113612179749529,3,146,0.015850144092219,0.0205479452054795,0.771373679154659,1366,143,0.933954713653156
+"1435",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"530008","NITROPRUSSIDE IV INFUSION",36,1685,0.137922566914955,79,2082,0.0213649851632047,0.0379442843419789,0.563062014048004,1649,2003,0.00443099950431445
+"1436",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"530010","NICARDIPINE IV INFUSION",14,45,0.0036833919947614,257,676,0.311111111111111,0.380177514792899,0.818331171638565,31,419,0.442896914856134
+"1437",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"530011","NOREPINEPHRINE IV INFUSION",1,13,0.00106409102070885,92,2909,0.0769230769230769,0.0316259883121348,2.43227424749164,12,2817,0.89137470465442
+"1438",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"530012","PHENYLEPHRINE IV INFUSION",1,75,0.00613898665793566,61,2180,0.0133333333333333,0.0279816513761468,0.476502732240437,74,2119,0.686441233729763
+"1439",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"530025","DEXMEDETOMIDINE IV INFUSION",1,6,0.000491118932634853,530,2902,0.166666666666667,0.18263266712612,0.912578616352201,5,2372,0.999999999999906
+"1440",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",44,3100,0.253744781861341,461,18637,0.0141935483870968,0.0247357407308043,0.573807291302218,3056,18176,0.000394776050842226
+"1441",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"540301","POTASSIUM CHLORIDE ORAL SCALE",5,1212,0.0992060243922403,105,3653,0.00412541254125413,0.0287434984943882,0.143525066792394,1207,3548,1.03822160800388e-06
+"1442",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"540302","POTASSIUM CHLORIDE IV SCALE",6,1585,0.12973725137104,150,4660,0.00378548895899054,0.0321888412017167,0.117602523659306,1579,4510,7.00893255161969e-10
+"1443",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"540304","MAGNESIUM SULFATE IV SCALE ",6,2137,0.174920193173447,78,4104,0.00280767430978007,0.0190058479532164,0.147726863683813,2131,4026,2.55238263847669e-07
+"1444",978,"IP NSR ICU NEUROSURGERY POSTOPERATIVE",12217,"541524","CEFAZOLIN IN NS IVPB (COMPOUNDED)",10,330,0.0270115412949169,1,61,0.0303030303030303,0.0163934426229508,1.84848484848485,320,60,0.855463074100031
+"1445",980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",309,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",1,6,0.0194174757281553,70,1432,0.166666666666667,0.0488826815642458,3.40952380952381,5,1362,0.700417481270438
+"1446",980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",309,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,25,0.0809061488673139,258,5335,0.04,0.0483598875351453,0.827131782945737,24,5077,0.999999999999747
+"1447",980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",309,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,54,0.174757281553398,692,19620,0.037037037037037,0.0352701325178389,1.05009633911368,52,18928,0.999999999999991
+"1448",980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",309,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",2,58,0.187702265372168,1067,10067,0.0344827586206897,0.105989867885169,0.325340141550593,56,9000,0.12046861892082
+"1449",980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",309,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,1,0.00323624595469256,73,1394,1,0.0523672883787661,19.0958904109589,0,1321,0.0460518955900975
+"1450",980,"IP TRS LUNG, HEART-LUNG TRANSPLANT PREOP",309,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",4,117,0.378640776699029,1951,16917,0.0341880341880342,0.115327776792576,0.296442324120438,113,14966,0.00936334181490198
+"1451",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"464","AMPHOTERICIN B 50 MG INJ SOLR",1,38,0.0548340548340548,4,131,0.0263157894736842,0.0305343511450382,0.861842105263158,37,127,0.999999999999998
+"1452",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",5,98,0.141414141414141,134,3551,0.0510204081632653,0.0377358490566038,1.35204081632653,93,3417,0.681612643367292
+"1453",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"5751","NYSTATIN 100,000 UNIT/ML PO SUSP",1,10,0.0144300144300144,68,4056,0.1,0.0167652859960552,5.96470588235294,9,3988,0.418118011459487
+"1454",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"6492","PREDNISONE 5 MG/5 ML PO SOLN",1,1,0.00144300144300144,72,1096,1,0.0656934306569343,15.2222222222222,0,1024,0.0818675297324781
+"1455",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"7555","SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS",1,9,0.012987012987013,273,6399,0.111111111111111,0.0426629160806376,2.6043956043956,8,6126,0.849398398069859
+"1456",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"9644","CLOTRIMAZOLE 10 MG MM TROC",1,12,0.0173160173160173,29,2750,0.0833333333333333,0.0105454545454545,7.90229885057471,11,2721,0.302183507116337
+"1457",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",1,2,0.00288600288600289,155,5616,0.5,0.0275997150997151,18.1161290322581,1,5461,0.0557324080954827
+"1458",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,6,0.00865800865800866,692,19620,0.333333333333333,0.0352701325178389,9.45086705202312,4,18928,0.0044118382989373
+"1459",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"82544","ITRACONAZOLE 10 MG/ML PO SOLN",3,14,0.0202020202020202,27,1462,0.214285714285714,0.0184678522571819,11.6031746031746,11,1435,2.48589772422308e-05
+"1460",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,2,0.00288600288600289,1067,10067,0.5,0.105989867885169,4.7174320524836,1,9000,0.508544984834858
+"1461",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",10,104,0.15007215007215,1951,16917,0.0961538461538462,0.115327776792576,0.833744036588732,94,14966,0.648009707065288
+"1462",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",5,105,0.151515151515152,705,36939,0.0476190476190476,0.0190855193697718,2.49503546099291,100,36234,0.0762198324783069
+"1463",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",9,110,0.158730158730159,192,8611,0.0818181818181818,0.0222970618975729,3.66946022727273,101,8419,0.000136641588029632
+"1464",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",6,97,0.13997113997114,275,8000,0.0618556701030928,0.034375,1.79943767572634,91,7725,0.233729188766881
+"1465",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,6,0.00865800865800866,452,13480,0.166666666666667,0.0335311572700297,4.97050147492625,5,13028,0.498774860641588
+"1466",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",1,38,0.0548340548340548,291,6612,0.0263157894736842,0.044010889292196,0.597938144329897,37,6321,0.893524076248935
+"1467",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"520055","GANCICLOVIR IVPB",4,105,0.151515151515152,62,1312,0.0380952380952381,0.0472560975609756,0.806144393241167,101,1250,0.850882253038808
+"1468",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"530006","EPINEPHRINE IV INFUSION",1,35,0.0505050505050505,99,2540,0.0285714285714286,0.0389763779527559,0.733044733044733,34,2441,0.999999999999966
+"1469",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"530025","DEXMEDETOMIDINE IV INFUSION",1,12,0.0173160173160173,530,2902,0.0833333333333333,0.18263266712612,0.456289308176101,11,2372,0.606854534700818
+"1470",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"530031","VASOPRESSIN 1 UNIT/ML IV INFUSION",2,14,0.0202020202020202,215,4489,0.142857142857143,0.0478948540877701,2.98272425249169,12,4274,0.302282910915641
+"1471",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"530049","INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW)",4,88,0.126984126984127,23,2672,0.0454545454545455,0.00860778443113772,5.28063241106719,84,2649,0.00367119062010235
+"1472",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"540129","TACROLIMUS 1 MG/ML ORAL SUSPENSION",2,17,0.0245310245310245,147,4881,0.117647058823529,0.0301167793484942,3.90636254501801,15,4734,0.164404887473504
+"1473",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"540302","POTASSIUM CHLORIDE IV SCALE",2,45,0.0649350649350649,150,4660,0.0444444444444444,0.0321888412017167,1.38074074074074,43,4510,0.968760915256859
+"1474",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"540304","MAGNESIUM SULFATE IV SCALE ",3,43,0.062049062049062,78,4104,0.0697674418604651,0.0190058479532164,3.67084078711986,40,4026,0.0659191769968006
+"1475",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"540321","MYCOPHENOLATE MOFETIL 200 MG/ML ORAL SUSP",4,88,0.126984126984127,49,786,0.0454545454545455,0.0623409669211196,0.7291280148423,84,737,0.693638135329594
+"1476",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"540492","SHC TRANSPLANT BEDSIDE MEDS HELP ORDER",1,9,0.012987012987013,3,27,0.111111111111111,0.111111111111111,1,8,24,1
+"1477",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"541098","CEFEPIME 1 GRAM/50 ML MINI-BAG PLUS",9,55,0.0793650793650794,199,1387,0.163636363636364,0.143475126171593,1.14052078574692,46,1188,0.824535439194576
+"1478",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",3,5,0.00721500721500722,466,8270,0.6,0.0563482466747279,10.6480686695279,2,7804,1.79894491474564e-05
+"1479",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"541285","AMPHOTERICIN B 20 MG/4ML NEB SOLUTION",3,50,0.0721500721500721,2,126,0.06,0.0158730158730159,3.78,47,124,0.277449367755867
+"1480",981,"IP TRS LUNG, HEART-LUNG TRANSPLANT POSTOP",693,"541543","CEFEPIME 1 G/50 ML EXTENDED INFUSION MINI-BAG PLUS",6,17,0.0245310245310245,39,371,0.352941176470588,0.105121293800539,3.35746606334842,11,332,0.006274241137796
+"1481",982,"IP TRS HEART TRANSPLANT PREOP",441,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,39,0.0884353741496599,258,5335,0.0512820512820513,0.0483598875351453,1.06042536275094,37,5077,0.999999999999991
+"1482",982,"IP TRS HEART TRANSPLANT PREOP",441,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",5,112,0.253968253968254,1067,10067,0.0446428571428571,0.105989867885169,0.421199290400321,107,9000,0.0513406024347579
+"1483",982,"IP TRS HEART TRANSPLANT PREOP",441,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",6,170,0.385487528344671,1951,16917,0.0352941176470588,0.115327776792576,0.306033105194923,164,14966,0.00169281603350242
+"1484",982,"IP TRS HEART TRANSPLANT PREOP",441,"540965","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML MINI-BAG PLUS",1,16,0.036281179138322,235,4195,0.0625,0.0560190703218117,1.1156914893617,15,3960,0.99999999999999
+"1485",1049,"IP/ED GENERAL TRANSFUSION",52260,"101","ACETAMINOPHEN 325 MG PO TABS",16,1345,0.0257367011098354,981,42092,0.0118959107806691,0.0233060914187969,0.51042066929656,1329,41111,0.00785482005932001
+"1486",1049,"IP/ED GENERAL TRANSFUSION",52260,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",23,1270,0.0243015690776885,356,12049,0.0181102362204724,0.0295460204166321,0.6129501017429,1247,11693,0.0249258068818054
+"1487",1049,"IP/ED GENERAL TRANSFUSION",52260,"3291","FUROSEMIDE 10 MG/ML INJ SOLN",19,600,0.0114810562571757,1985,57715,0.0316666666666667,0.0343931386987785,0.920726280436608,581,55730,0.800971723559704
+"1488",1049,"IP/ED GENERAL TRANSFUSION",52260,"540148","NS FLUSH BAG",2,53,0.00101415996938385,4,133,0.0377358490566038,0.0300751879699248,1.25471698113208,51,129,0.999999999999994
+"1489",1096,"CDU HEADACHE OBSERVATION",61,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,5,0.0819672131147541,603,24164,0.2,0.0249544777354743,8.01459369817579,4,23561,0.282548586581163
+"1490",1098,"CDU GASTROENTERITIS OBSERVATION",62,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,26,0.419354838709677,705,36939,0.0384615384615385,0.0190855193697718,2.0152209492635,25,36234,0.996086325541603
+"1491",1136,"IP ANE ANESTHESIA PREOP",340,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,60,0.176470588235294,288,4280,0.0166666666666667,0.0672897196261682,0.247685185185185,59,3992,0.19318824784803
+"1492",1157,"RETIRED IP NEU ICU STATUS EPILEPTICUS ",25,"11150","PROPOFOL 10 MG/ML IV EMUL",1,1,0.04,135,3592,1,0.0375835189309577,26.6074074074074,0,3457,0.0154339194241659
+"1493",1157,"RETIRED IP NEU ICU STATUS EPILEPTICUS ",25,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,5,0.2,688,12925,0.2,0.0532301740812379,3.75726744186047,4,12237,0.641827997743328
+"1494",1157,"RETIRED IP NEU ICU STATUS EPILEPTICUS ",25,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",1,2,0.08,65,1105,0.5,0.0588235294117647,8.5,1,1040,0.255081467105994
+"1495",1161,"IP NEU SHIVERING PROTOCOL",102,"9323","BUSPIRONE 10 MG PO TABS",5,46,0.450980392156863,12,457,0.108695652173913,0.0262582056892779,4.13949275362319,41,445,0.0116957790189496
+"1496",1161,"IP NEU SHIVERING PROTOCOL",102,"203672","MAGNESIUM SULFATE IN WATER 20 GRAM/500 ML (4 %) IV SOLP",4,16,0.156862745098039,16,65,0.25,0.246153846153846,1.015625,12,49,0.999999999999998
+"1497",1238,"IP/ED NEU TPA  ALTEPLASE PROTOCOL",532,"10372","LABETALOL 5 MG/ML IV SOLN",2,77,0.144736842105263,370,5898,0.025974025974026,0.0627331298745337,0.414040014040014,75,5528,0.276167024896776
+"1498",1238,"IP/ED NEU TPA  ALTEPLASE PROTOCOL",532,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,27,0.0507518796992481,692,19620,0.0740740740740741,0.0352701325178389,2.10019267822736,25,18928,0.568745291136034
+"1499",1238,"IP/ED NEU TPA  ALTEPLASE PROTOCOL",532,"86145","ALTEPLASE 100 MG IV SOLR",15,191,0.359022556390977,1,14,0.0785340314136126,0.0714285714285714,1.09947643979058,176,13,0.999999999999999
+"1500",1273,"IP POST PROCEDURE ORDERS PARACENTESIS",1054,"8981","ALBUMIN, HUMAN 25 % 25 % IV SOLP",2,119,0.112903225806452,341,9016,0.0168067226890756,0.0378216503992901,0.444367776436088,117,8675,0.339402903091301
+"1501",1280,"IP NEU LUMBAR PUNCTURE",367,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",1,53,0.14441416893733,457,11068,0.0188679245283019,0.0412902059992772,0.456958837372528,52,10611,0.636173839296513
+"1502",1289,"IP GEN VTE PROPHYLAXIS",1541,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,25,0.0162232316677482,73,1394,0.04,0.0523672883787661,0.763835616438356,24,1321,0.99999999999998
+"1503",1289,"IP GEN VTE PROPHYLAXIS",1541,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",21,334,0.216742375081116,499,13896,0.062874251497006,0.0359096142774899,1.75090300361201,313,13397,0.0143734137641049
+"1504",1289,"IP GEN VTE PROPHYLAXIS",1541,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",18,368,0.238805970149254,452,13480,0.0489130434782609,0.0335311572700297,1.45873412851097,350,13028,0.143771588777915
+"1505",1289,"IP GEN VTE PROPHYLAXIS",1541,"540493","WARFARIN PER PHARMACY PROTOCOL",1,8,0.00519143413367943,218,6316,0.125,0.0345155161494617,3.62155963302752,7,6098,0.66617469253489
+"1506",1307,"IP BMT ALLOGENEIC READMISSION",2258,"101","ACETAMINOPHEN 325 MG PO TABS",16,672,0.297608503100089,981,42092,0.0238095238095238,0.0233060914187969,1.02160089316053,656,41111,0.999999999999996
+"1507",1307,"IP BMT ALLOGENEIC READMISSION",2258,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",10,635,0.281222320637733,399,16019,0.015748031496063,0.0249079218428117,0.632249916128905,625,15620,0.182889829985637
+"1508",1307,"IP BMT ALLOGENEIC READMISSION",2258,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",12,645,0.285651018600531,356,12049,0.0186046511627907,0.0295460204166321,0.629683825450745,633,11693,0.135400259955151
+"1509",1307,"IP BMT ALLOGENEIC READMISSION",2258,"4560","LOPERAMIDE 2 MG PO CAPS",10,641,0.283879539415412,119,5178,0.015600624024961,0.0229818462726922,0.678823791607126,631,5059,0.291366133601707
+"1510",1307,"IP BMT ALLOGENEIC READMISSION",2258,"4572","LORAZEPAM 0.5 MG PO TABS",14,619,0.274136403897254,349,13238,0.0226171243941842,0.0263634990179785,0.857895394642435,605,12889,0.658731815180138
+"1511",1307,"IP BMT ALLOGENEIC READMISSION",2258,"6494","PREDNISONE 10 MG PO TABS",1,10,0.00442869796279894,104,4234,0.1,0.0245630609352858,4.07115384615385,9,4130,0.606671125930196
+"1512",1307,"IP BMT ALLOGENEIC READMISSION",2258,"6496","PREDNISONE 20 MG PO TABS",2,24,0.0106288751107174,276,7517,0.0833333333333333,0.0367167753092989,2.26962560386473,22,7241,0.504427230065266
+"1513",1307,"IP BMT ALLOGENEIC READMISSION",2258,"6497","PREDNISONE 5 MG PO TABS",1,5,0.00221434898139947,108,4678,0.2,0.023086789226165,8.66296296296296,4,4570,0.254933830423203
+"1514",1307,"IP BMT ALLOGENEIC READMISSION",2258,"7555","SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS",2,33,0.0146147032772365,273,6399,0.0606060606060606,0.0426629160806376,1.42057942057942,31,6126,0.938740084001063
+"1515",1307,"IP BMT ALLOGENEIC READMISSION",2258,"7557","SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS",1,27,0.0119574844995571,112,3269,0.037037037037037,0.0342612419700214,1.08101851851852,26,3157,0.999999999999994
+"1516",1307,"IP BMT ALLOGENEIC READMISSION",2258,"8085","TRAZODONE 50 MG PO TABS",2,121,0.0535872453498671,334,11344,0.0165289256198347,0.0294428772919605,0.561389617459296,119,11010,0.570823799990368
+"1517",1307,"IP BMT ALLOGENEIC READMISSION",2258,"9644","CLOTRIMAZOLE 10 MG MM TROC",1,23,0.0101860053144376,29,2750,0.0434782608695652,0.0105454545454545,4.12293853073463,22,2721,0.611183754823578
+"1518",1307,"IP BMT ALLOGENEIC READMISSION",2258,"10011","FAMOTIDINE 20 MG PO TABS",31,371,0.164304694419841,472,16960,0.0835579514824798,0.0278301886792453,3.00242130750605,340,16488,6.87182853963978e-10
+"1519",1307,"IP BMT ALLOGENEIC READMISSION",2258,"10467","LORAZEPAM 2 MG/ML INJ SOLN",15,631,0.279450841452613,749,20621,0.0237717908082409,0.0363221958197954,0.654470091130488,616,19872,0.118856867997309
+"1520",1307,"IP BMT ALLOGENEIC READMISSION",2258,"11624","URSODIOL 300 MG PO CAPS",3,30,0.0132860938883968,49,2387,0.1,0.0205278592375367,4.87142857142857,27,2338,0.0188590604912754
+"1521",1307,"IP BMT ALLOGENEIC READMISSION",2258,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",5,94,0.04162976085031,314,8762,0.0531914893617021,0.035836566993837,1.4842797126982,89,8448,0.535300369913008
+"1522",1307,"IP BMT ALLOGENEIC READMISSION",2258,"15065","FAMOTIDINE 10 MG PO TABS",1,2,0.000885739592559787,11,326,0.5,0.0337423312883436,14.8181818181818,1,315,0.106847309250935
+"1523",1307,"IP BMT ALLOGENEIC READMISSION",2258,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",5,195,0.0863596102745793,118,4415,0.0256410256410256,0.026727066817667,0.959365493263798,190,4297,0.999999999999877
+"1524",1307,"IP BMT ALLOGENEIC READMISSION",2258,"16427","POTASSIUM CHLORIDE IN 0.9%NACL 40 MEQ/L IV SOLP",1,20,0.00885739592559787,32,752,0.05,0.0425531914893617,1.175,19,720,1
+"1525",1307,"IP BMT ALLOGENEIC READMISSION",2258,"17362","HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",8,621,0.275022143489814,202,8865,0.0128824476650564,0.0227862380146644,0.565360883914478,613,8663,0.138736107205413
+"1526",1307,"IP BMT ALLOGENEIC READMISSION",2258,"25119","CIPROFLOXACIN 500 MG PO TABS",1,35,0.0155004428697963,442,10593,0.0285714285714286,0.0417256678938922,0.684744667097608,34,10151,0.999999999999496
+"1527",1307,"IP BMT ALLOGENEIC READMISSION",2258,"38285","ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",9,653,0.289193976970771,71,5546,0.0137825421133231,0.0128020194734944,1.07659124733085,644,5475,0.978700211134168
+"1528",1307,"IP BMT ALLOGENEIC READMISSION",2258,"80562","MYCOPHENOLATE MOFETIL 250 MG PO CAPS",1,21,0.00930026572187777,120,3882,0.0476190476190476,0.0309119010819165,1.54047619047619,20,3762,0.999999999999939
+"1529",1307,"IP BMT ALLOGENEIC READMISSION",2258,"85078","MYCOPHENOLATE MOFETIL 500 MG PO TABS",1,20,0.00885739592559787,45,1479,0.05,0.0304259634888438,1.64333333333333,19,1434,0.999999999999998
+"1530",1307,"IP BMT ALLOGENEIC READMISSION",2258,"92173","ZOLPIDEM 5 MG PO TABS",16,489,0.216563330380868,159,8203,0.032719836400818,0.019383152505181,1.68805545909377,473,8044,0.0609188299254834
+"1531",1307,"IP BMT ALLOGENEIC READMISSION",2258,"94670","TACROLIMUS 1 MG PO CAPS",2,53,0.0234720992028344,322,9479,0.0377358490566038,0.0339698280409326,1.1108637056135,51,9157,0.999999999999745
+"1532",1307,"IP BMT ALLOGENEIC READMISSION",2258,"95354","ATOVAQUONE 750 MG/5 ML PO SUSP",1,42,0.0186005314437555,36,1709,0.0238095238095238,0.0210649502633119,1.13029100529101,41,1673,0.999999999999965
+"1533",1307,"IP BMT ALLOGENEIC READMISSION",2258,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",5,24,0.0106288751107174,1067,10067,0.208333333333333,0.105989867885169,1.96559668853483,19,9000,0.195810777381399
+"1534",1307,"IP BMT ALLOGENEIC READMISSION",2258,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",2,15,0.00664304694419841,290,2601,0.133333333333333,0.111495578623606,1.19586206896552,13,2311,0.999999999999874
+"1535",1307,"IP BMT ALLOGENEIC READMISSION",2258,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,9,0.00398582816651904,499,13896,0.111111111111111,0.0359096142774899,3.09418837675351,8,13397,0.752100320369339
+"1536",1307,"IP BMT ALLOGENEIC READMISSION",2258,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,16,0.0070859167404783,208,3699,0.0625,0.0562314138956475,1.11147836538462,15,3491,0.999999999999879
+"1537",1307,"IP BMT ALLOGENEIC READMISSION",2258,"117152","GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",8,648,0.286979627989371,19,1429,0.0123456790123457,0.013296011196641,0.928525016244314,640,1410,0.999999999999991
+"1538",1307,"IP BMT ALLOGENEIC READMISSION",2258,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,17,0.00752878653675819,1951,16917,0.0588235294117647,0.115327776792576,0.510055175324871,16,14966,0.726914034307092
+"1539",1307,"IP BMT ALLOGENEIC READMISSION",2258,"200345","MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV SOLN",10,678,0.300265721877768,410,20810,0.0147492625368732,0.019702066314272,0.748615008273977,668,20400,0.437853390705382
+"1540",1307,"IP BMT ALLOGENEIC READMISSION",2258,"200346","MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK",10,675,0.298937112488928,578,30211,0.0148148148148148,0.0191321042004568,0.774343201332821,665,29633,0.503256354849592
+"1541",1307,"IP BMT ALLOGENEIC READMISSION",2258,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",17,663,0.29362267493357,192,8611,0.0256410256410256,0.0222970618975729,1.14997329059829,646,8419,0.672124228988929
+"1542",1307,"IP BMT ALLOGENEIC READMISSION",2258,"209508","METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR",1,5,0.00221434898139947,155,3480,0.2,0.0445402298850575,4.49032258064516,4,3325,0.550017114954847
+"1543",1307,"IP BMT ALLOGENEIC READMISSION",2258,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",2,34,0.0150575730735164,275,8000,0.0588235294117647,0.034375,1.71122994652406,32,7725,0.757544925102445
+"1544",1307,"IP BMT ALLOGENEIC READMISSION",2258,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",11,623,0.275907883082374,313,9164,0.0176565008025682,0.0341553906591008,0.516946240749953,612,8851,0.0347214379294398
+"1545",1307,"IP BMT ALLOGENEIC READMISSION",2258,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,8,0.00354295837023915,452,13480,0.125,0.0335311572700297,3.72787610619469,7,13028,0.649770163706163
+"1546",1307,"IP BMT ALLOGENEIC READMISSION",2258,"520073","MYCOPHENOLATE IVPB",1,12,0.00531443755535872,37,865,0.0833333333333333,0.0427745664739884,1.9481981981982,11,828,0.999999999999938
+"1547",1307,"IP BMT ALLOGENEIC READMISSION",2258,"520098","TACROLIMUS IV INFUSION",3,48,0.0212577502214349,87,3271,0.0625,0.0265973708346072,2.34985632183908,45,3184,0.283384271835328
+"1548",1307,"IP BMT ALLOGENEIC READMISSION",2258,"540003","TRIPLE MIX",9,616,0.272807794508415,60,2236,0.0146103896103896,0.0268336314847943,0.544480519480519,607,2176,0.109557712168388
+"1549",1307,"IP BMT ALLOGENEIC READMISSION",2258,"540132","CYCLOSPORINE MODIFIED (GENGRAF) 25 MG PO CAPS",2,9,0.00398582816651904,30,642,0.222222222222222,0.0467289719626168,4.75555555555556,7,612,0.100580442178359
+"1550",1307,"IP BMT ALLOGENEIC READMISSION",2258,"540301","POTASSIUM CHLORIDE ORAL SCALE",7,509,0.225420726306466,105,3653,0.0137524557956778,0.0287434984943882,0.478454485920105,502,3548,0.0700026675845892
+"1551",1307,"IP BMT ALLOGENEIC READMISSION",2258,"540302","POTASSIUM CHLORIDE IV SCALE",12,684,0.302922940655447,150,4660,0.0175438596491228,0.0321888412017167,0.545029239766082,672,4510,0.0492180586423604
+"1552",1307,"IP BMT ALLOGENEIC READMISSION",2258,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",10,69,0.0305580159433127,277,3459,0.144927536231884,0.0800809482509396,1.80976298854183,59,3182,0.0838650523281628
+"1553",1307,"IP BMT ALLOGENEIC READMISSION",2258,"541089","VANCOMYCIN 1 GRAM/100 ML CENTRAL LINE MINI-BAG PLUS",6,32,0.0141718334809566,112,785,0.1875,0.142675159235669,1.31417410714286,26,673,0.652313513205365
+"1554",1307,"IP BMT ALLOGENEIC READMISSION",2258,"541119","MEROPENEM IN NS 500 MG/100 ML MINI-BAG PLUS",1,2,0.000885739592559787,19,408,0.5,0.0465686274509804,10.7368421052632,1,389,0.185406353297182
+"1555",1307,"IP BMT ALLOGENEIC READMISSION",2258,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,27,0.0119574844995571,466,8270,0.037037037037037,0.0563482466747279,0.657288189477031,26,7804,0.986850030574088
+"1556",1307,"IP BMT ALLOGENEIC READMISSION",2258,"541273","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION",2,10,0.00442869796279894,148,1260,0.2,0.117460317460317,1.7027027027027,8,1112,0.753747555693581
+"1557",1307,"IP BMT ALLOGENEIC READMISSION",2258,"541544","CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",2,11,0.00487156775907883,60,934,0.181818181818182,0.0642398286937901,2.83030303030303,9,874,0.340412506597774
+"1558",1307,"IP BMT ALLOGENEIC READMISSION",2258,"9000000","ZZZ IMS TEMPLATE",1,49,0.0217006200177148,1035,28824,0.0204081632653061,0.0359075770191507,0.56835255841467,48,27789,0.842676157488176
+"1559",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"101","ACETAMINOPHEN 325 MG PO TABS",9,293,0.322687224669604,981,42092,0.0307167235494881,0.0233060914187969,1.31796975295112,284,41111,0.520298485618769
+"1560",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",6,287,0.316079295154185,399,16019,0.0209059233449477,0.0249079218428117,0.839328285871473,281,15620,0.80997931342325
+"1561",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",8,284,0.312775330396476,356,12049,0.028169014084507,0.0295460204166321,0.953394524450071,276,11693,0.999999999999995
+"1562",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"4560","LOPERAMIDE 2 MG PO CAPS",8,302,0.332599118942731,119,5178,0.0264900662251656,0.0229818462726922,1.1526517891925,294,5059,0.843706940510217
+"1563",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"4572","LORAZEPAM 0.5 MG PO TABS",8,271,0.298458149779736,349,13238,0.029520295202952,0.0263634990179785,1.11974116875839,263,12889,0.897013986579153
+"1564",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"8085","TRAZODONE 50 MG PO TABS",2,53,0.0583700440528634,334,11344,0.0377358490566038,0.0294428772919605,1.28166308891651,51,11010,0.999999999999436
+"1565",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"9644","CLOTRIMAZOLE 10 MG MM TROC",1,26,0.0286343612334802,29,2750,0.0384615384615385,0.0105454545454545,3.64721485411141,25,2721,0.676387998128135
+"1566",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"10011","FAMOTIDINE 20 MG PO TABS",14,198,0.218061674008811,472,16960,0.0707070707070707,0.0278301886792453,2.54066084574559,184,16488,0.000673248863469876
+"1567",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"10467","LORAZEPAM 2 MG/ML INJ SOLN",8,283,0.311674008810573,749,20621,0.0282685512367491,0.0363221958197954,0.778272089523369,275,19872,0.575411540903291
+"1568",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,71,0.0781938325991189,314,8762,0.028169014084507,0.035836566993837,0.78604108728806,69,8448,0.979515837381003
+"1569",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",7,187,0.205947136563877,68,899,0.0374331550802139,0.0756395995550612,0.494888329663416,180,831,0.0861128035508223
+"1570",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"17362","HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",7,283,0.311674008810573,202,8865,0.0247349823321555,0.0227862380146644,1.08552286324039,276,8663,0.988896766420461
+"1571",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"25119","CIPROFLOXACIN 500 MG PO TABS",2,65,0.0715859030837004,442,10593,0.0307692307692308,0.0417256678938922,0.737417333797424,63,10151,0.897036023289475
+"1572",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"38285","ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",8,284,0.312775330396476,71,5546,0.028169014084507,0.0128020194734944,2.20035707200952,276,5475,0.0546604450998462
+"1573",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"92173","ZOLPIDEM 5 MG PO TABS",4,218,0.240088105726872,159,8203,0.018348623853211,0.019383152505181,0.94662743061566,214,8044,0.999999999999827
+"1574",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,12,0.013215859030837,1067,10067,0.0833333333333333,0.105989867885169,0.786238675413933,11,9000,0.999999999999996
+"1575",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"96560","PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK",1,7,0.00770925110132159,290,2601,0.142857142857143,0.111495578623606,1.28128078817734,6,2311,0.999999999999998
+"1576",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"112593","FILGRASTIM 480 MCG/0.8 ML INJ SYRG",1,35,0.0385462555066079,70,1940,0.0285714285714286,0.0360824742268041,0.791836734693878,34,1870,0.999999999999998
+"1577",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"117152","GLYCERIN-DIMETHICONE-PETRO, WH TP CREA",8,285,0.313876651982379,19,1429,0.0280701754385965,0.013296011196641,2.11117266851339,277,1410,0.116770705816033
+"1578",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,15,0.0165198237885463,1951,16917,0.0666666666666667,0.115327776792576,0.578062532034854,14,14966,0.852882890539151
+"1579",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"200345","MAGNESIUM SULFATE IN 0.9 %NACL 1 GRAM/50 ML IV SOLN",6,292,0.3215859030837,410,20810,0.0205479452054795,0.019702066314272,1.0429335115269,286,20400,0.999999999999534
+"1580",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"200346","MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK",5,289,0.318281938325991,578,30211,0.0173010380622837,0.0191321042004568,0.90429353096826,284,29633,0.991678055315456
+"1581",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",9,290,0.319383259911894,192,8611,0.0310344827586207,0.0222970618975729,1.39186422413793,281,8419,0.432953033915548
+"1582",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",7,279,0.30726872246696,313,9164,0.025089605734767,0.0341553906591008,0.734572354483722,272,8851,0.511497923947275
+"1583",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"540003","TRIPLE MIX",8,291,0.320484581497797,60,2236,0.0274914089347079,0.0268336314847943,1.02451317296678,283,2176,1
+"1584",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"540301","POTASSIUM CHLORIDE ORAL SCALE",2,221,0.243392070484582,105,3653,0.00904977375565611,0.0287434984943882,0.31484593837535,219,3548,0.127656189878634
+"1585",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"540302","POTASSIUM CHLORIDE IV SCALE",6,297,0.327092511013216,150,4660,0.0202020202020202,0.0321888412017167,0.627609427609428,291,4510,0.329138537876547
+"1586",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"540960","MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS",1,2,0.00220264317180617,302,3726,0.5,0.0810520665593129,6.16887417218543,1,3424,0.382426046187656
+"1587",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",7,75,0.0825991189427313,277,3459,0.0933333333333333,0.0800809482509396,1.16548736462094,68,3182,0.839132503510895
+"1588",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"541089","VANCOMYCIN 1 GRAM/100 ML CENTRAL LINE MINI-BAG PLUS",5,49,0.0539647577092511,112,785,0.102040816326531,0.142675159235669,0.715196793002915,44,673,0.560151959674523
+"1589",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"541544","CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",1,14,0.0154185022026432,60,934,0.0714285714285714,0.0642398286937901,1.11190476190476,13,874,1
+"1590",1309,"IP BMT AUTOLOGOUS READMISSION ORDERS",908,"9000000","ZZZ IMS TEMPLATE",4,11,0.0121145374449339,1035,28824,0.363636363636364,0.0359075770191507,10.1270092226614,7,27789,5.11288846215371e-07
+"1591",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",1,62,0.0251113811259619,106,3939,0.0161290322580645,0.0269103833460269,0.599360925136945,61,3833,0.900191212820415
+"1592",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"1300","CALCIUM CARBONATE 500 MG CALCIUM (1,250 MG) PO TABS",2,51,0.0206561360874848,94,2563,0.0392156862745098,0.03667577058135,1.06925323320818,49,2469,0.999999999999896
+"1593",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"7327","SODIUM CHLORIDE 3 % INH NEBU",2,76,0.0307816929931146,13,454,0.0263157894736842,0.0286343612334802,0.919028340080972,74,441,0.999999999999997
+"1594",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"7994","TOBRAMYCIN SULFATE 40 MG/ML INJ SOLN",6,10,0.00405022276225192,8,37,0.6,0.216216216216216,2.775,4,29,0.0494232061382861
+"1595",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"19483","CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",2,95,0.0384771162413933,27,2802,0.0210526315789474,0.00963597430406852,2.18479532163743,93,2775,0.565062349187606
+"1596",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",7,361,0.146213041717294,712,36910,0.0193905817174515,0.0192901652668653,1.00520557751564,354,36198,0.999999999999476
+"1597",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"70536","FLUTICASONE 50 MCG/ACTUATION NASAL SPSN",1,36,0.0145808019441069,57,3947,0.0277777777777778,0.0144413478591335,1.92348927875244,35,3890,0.999999999999916
+"1598",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"77973","FLUTICASONE-SALMETEROL 250-50 MCG/DOSE INH DSDV",1,28,0.0113406237343054,39,2261,0.0357142857142857,0.0172490048651039,2.07051282051282,27,2222,0.987607539026879
+"1599",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",5,363,0.147023086269745,493,18456,0.0137741046831956,0.0267121803207629,0.515648835766852,358,17963,0.175163425799323
+"1600",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"78941","TOBRAMYCIN IN 0.225 % NACL 300 MG/5 ML INH NEBU",2,39,0.0157958687727825,9,345,0.0512820512820513,0.0260869565217391,1.96581196581197,37,336,0.698241665637532
+"1601",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",1,9,0.00364520048602673,242,3682,0.111111111111111,0.0657251493753395,1.69054178145087,8,3440,0.999999999999996
+"1602",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"84953","PANTOPRAZOLE 20 MG PO TBEC",1,8,0.00324017820980154,41,1209,0.125,0.0339123242349049,3.6859756097561,7,1168,0.663474579820909
+"1603",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"88043","PANTOPRAZOLE 40 MG PO TBEC",2,157,0.0635884973673552,624,31884,0.0127388535031847,0.0195709446744449,0.650906418422342,155,31260,0.742929252082316
+"1604",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"89401","FLUTICASONE-SALMETEROL 500-50 MCG/DOSE INH DSDV",2,60,0.0243013365735115,15,1230,0.0333333333333333,0.0121951219512195,2.73333333333333,58,1215,0.410885905715656
+"1605",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"92642","LEVALBUTEROL HCL 1.25 MG/3 ML INH NEBU",1,31,0.012555690562981,46,1262,0.032258064516129,0.0364500792393027,0.884992987377279,30,1216,0.999999999999997
+"1606",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"94621","AZTREONAM IN DEXTROSE(ISO-OSM) 2 GRAM/50 ML IV PGBK",1,5,0.00202511138112596,3,54,0.2,0.0555555555555556,3.6,4,51,0.764632446680992
+"1607",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"95901","PHYTONADIONE 5 MG PO TABS",3,52,0.02106115836371,137,2851,0.0576923076923077,0.0480533146264469,1.20058955642897,49,2714,0.999999999999996
+"1608",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",4,25,0.0101255569056298,499,13896,0.16,0.0359096142774899,4.45563126252505,21,13397,0.00534668750390256
+"1609",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"112321","LINEZOLID 600 MG/300 ML IV SOLP",2,72,0.0291616038882139,51,1417,0.0277777777777778,0.0359915314043754,0.771786492374728,70,1366,0.967340362301028
+"1610",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"200453","SODIUM CHLORIDE 7 % INH NEBU",2,316,0.127987039287161,9,470,0.00632911392405063,0.0191489361702128,0.330520393811533,314,461,0.23384274417268
+"1611",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"208092","LIPASE-PROTEASE-AMYLASE 24,000-76,000 -120,000 UNIT PO CPDR",4,137,0.0554880518428514,55,1851,0.0291970802919708,0.0297136682874122,0.982614465826145,133,1796,0.999999999999999
+"1612",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"210750","LIPASE-PROTEASE-AMYLASE 16,800-40,000 -70,000 UNIT PO CPDR",3,13,0.0052652895909275,5,140,0.230769230769231,0.0357142857142857,6.46153846153846,10,135,0.0177466687799448
+"1613",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"221975","MULTIVIT-MIN-FA-COENZYME Q10 100-10 MCG-MG PO CAPS",2,157,0.0635884973673552,10,590,0.0127388535031847,0.0169491525423729,0.751592356687898,155,580,0.987412016512947
+"1614",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"229996","LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP",1,71,0.0287565816119887,52,4244,0.0140845070422535,0.0122525918944392,1.14951245937161,70,4192,0.999999999999999
+"1615",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"520011","TOBRAMYCIN IVPB",3,201,0.0814094775212637,35,711,0.0149253731343284,0.0492264416315049,0.30319829424307,198,676,0.0513089878097182
+"1616",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"520045","MEROPENEM CUSTOM IVPB",2,86,0.0348319157553665,100,1654,0.0232558139534884,0.060459492140266,0.384651162790698,84,1554,0.231494356961273
+"1617",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",7,51,0.0206561360874848,1808,23328,0.137254901960784,0.0775034293552812,1.77095262883915,44,21520,0.183205579435171
+"1618",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"520050","COLISTIMETHATE (COLISTIN) IVPB",3,50,0.0202511138112596,10,147,0.06,0.0680272108843537,0.882,47,137,0.999999999999998
+"1619",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"540672","CIPROFLOXACIN IN NS IV 400 MG/250 ML IV",13,74,0.0299716484406642,1,39,0.175675675675676,0.0256410256410256,6.85135135135135,61,38,0.0453788184777699
+"1620",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"540677","PIPERACILLIN-TAZOBACTAM 3.375 GRAM IN NS IV SOLN",8,36,0.0145808019441069,17,277,0.222222222222222,0.0613718411552347,3.62091503267974,28,260,0.00250886094884938
+"1621",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"540678","PIPERACILLIN-TAZOBACTAM 4.5 GRAM IN NS IV SOLN",4,66,0.0267314702308627,8,95,0.0606060606060606,0.0842105263157895,0.71969696969697,62,87,0.798105891081707
+"1622",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"540692","NS/BUDESONIDE NASAL IRRIGATION",2,321,0.130012150668287,14,318,0.00623052959501558,0.0440251572327044,0.141522029372497,319,304,0.0050451149066541
+"1623",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"540699","CF ENEMA SOLUTION",2,15,0.00607533414337789,8,509,0.133333333333333,0.0157170923379175,8.48333333333333,13,501,0.0201253347998847
+"1624",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"540967","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS",1,19,0.00769542324827866,135,2073,0.0526315789473684,0.065123010130246,0.808187134502924,18,1938,0.999999999999999
+"1625",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"541098","CEFEPIME 1 GRAM/50 ML MINI-BAG PLUS",2,3,0.00121506682867558,199,1387,0.666666666666667,0.143475126171593,4.6465661641541,1,1188,0.0797504412836874
+"1626",1352,"IP MED CYSTIC FIBROSIS ADMIT",2469,"541107","CEFTAZIDIME 2 GRAM/50 ML MINI-BAG PLUS",2,92,0.0372620494127177,18,257,0.0217391304347826,0.0700389105058366,0.310386473429952,90,239,0.14731719676617
+"1627",1355,"IP GEN NEUTROPENIC FEVERS",407,"101","ACETAMINOPHEN 325 MG PO TABS",1,25,0.0614250614250614,981,42092,0.04,0.0233060914187969,1.71628950050968,24,41111,0.999999999999996
+"1628",1355,"IP GEN NEUTROPENIC FEVERS",407,"7555","SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS",2,3,0.00737100737100737,273,6399,0.666666666666667,0.0426629160806376,15.6263736263736,1,6126,9.41364222614971e-05
+"1629",1355,"IP GEN NEUTROPENIC FEVERS",407,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",10,27,0.0663390663390663,1067,10067,0.37037037037037,0.105989867885169,3.49439411295081,17,9000,3.60097047567243e-05
+"1630",1355,"IP GEN NEUTROPENIC FEVERS",407,"112593","FILGRASTIM 480 MCG/0.8 ML INJ SYRG",2,6,0.0147420147420147,70,1940,0.333333333333333,0.0360824742268041,9.23809523809524,4,1870,0.00563423744922943
+"1631",1355,"IP GEN NEUTROPENIC FEVERS",407,"520045","MEROPENEM CUSTOM IVPB",1,2,0.00491400491400491,100,1654,0.5,0.060459492140266,8.27,1,1554,0.263727453442821
+"1632",1355,"IP GEN NEUTROPENIC FEVERS",407,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",13,46,0.113022113022113,1808,23328,0.282608695652174,0.0775034293552812,3.64640246248557,33,21520,9.12077991868073e-07
+"1633",1355,"IP GEN NEUTROPENIC FEVERS",407,"540960","MEROPENEM IN NS 1 GRAM/100 ML MINI-BAG PLUS",1,4,0.00982800982800983,302,3726,0.25,0.0810520665593129,3.08443708609272,3,3424,0.748527920988681
+"1634",1355,"IP GEN NEUTROPENIC FEVERS",407,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",4,95,0.233415233415233,277,3459,0.0421052631578947,0.0800809482509396,0.525783773513205,91,3182,0.245828693449978
+"1635",1355,"IP GEN NEUTROPENIC FEVERS",407,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,29,0.0712530712530713,466,8270,0.0344827586206897,0.0563482466747279,0.611957969513098,28,7804,0.915218235797089
+"1636",1355,"IP GEN NEUTROPENIC FEVERS",407,"541544","CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",2,28,0.0687960687960688,60,934,0.0714285714285714,0.0642398286937901,1.11190476190476,26,874,0.999999999999999
+"1637",1355,"IP GEN NEUTROPENIC FEVERS",407,"541547","MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",1,4,0.00982800982800983,71,1342,0.25,0.0529061102831595,4.72535211267606,3,1271,0.524424189782573
+"1638",1437,"IP PICC LINE PLACEMENT / EXCHANGE",17694,"81839","LIDOCAINE 4 % TP CREA",67,8876,0.501638973663389,31,996,0.00754844524560613,0.0311244979919679,0.242524240794313,8809,965,3.70751194033956e-12
+"1639",1437,"IP PICC LINE PLACEMENT / EXCHANGE",17694,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",62,8914,0.50378659432576,457,11068,0.00695535113304914,0.0412902059992772,0.168450385865619,8852,10611,1.1323991717171e-51
+"1640",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",14,175,0.211608222490931,399,16019,0.08,0.0249079218428117,3.21182957393484,161,15620,1.3194538993301e-05
+"1641",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",6,106,0.128174123337364,356,12049,0.0566037735849057,0.0295460204166321,1.91578333686665,100,11693,0.178717316407456
+"1642",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"2510","DIPHENHYDRAMINE HCL 50 MG PO CAPS",5,53,0.064087061668682,37,867,0.0943396226415094,0.0426758938869665,2.21060683324834,48,830,0.158446369870284
+"1643",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"6498","PREDNISONE 50 MG PO TABS",13,145,0.175332527206771,34,1011,0.0896551724137931,0.0336300692383778,2.66592292089249,132,977,0.00298083412425215
+"1644",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"209508","METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR",18,166,0.200725513905683,155,3480,0.108433734939759,0.0445402298850575,2.43451224251846,148,3325,0.000322985646411063
+"1645",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"500027","D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY",9,121,0.146311970979444,54,1287,0.0743801652892562,0.041958041958042,1.77272727272727,112,1233,0.155808210365092
+"1646",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"540184","ACETYLCYSTEINE 20 % (200MG/ML) ORAL SOLN",7,107,0.129383313180169,77,1458,0.0654205607476635,0.0528120713305898,1.23874256584537,100,1381,0.736599369022655
+"1647",1458,"RADIOLOGY CONTRAST DYE PREMEDICATION",827,"9000000","ZZZ IMS TEMPLATE",3,42,0.0507859733978235,1035,28824,0.0714285714285714,0.0359075770191507,1.98923395445135,39,27789,0.411749914843252
+"1648",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",1,26,0.268041237113402,106,3939,0.0384615384615385,0.0269103833460269,1.42924528301887,25,3833,0.999999999999999
+"1649",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,9,0.0927835051546392,314,8762,0.111111111111111,0.035836566993837,3.10049539985846,8,8448,0.751367403549212
+"1650",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"40196","ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA",1,7,0.0721649484536082,110,6310,0.142857142857143,0.01743264659271,8.19480519480519,6,6200,0.277871628909863
+"1651",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"82384","PANTOPRAZOLE 40 MG IV SOLR",3,25,0.257731958762887,688,12925,0.12,0.0532301740812379,2.25436046511628,22,12237,0.298977802158394
+"1652",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,18,0.185567010309278,1067,10067,0.0555555555555556,0.105989867885169,0.524159116942622,17,9000,0.755480554599885
+"1653",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"210741","PHENYLEPHRINE IN 0.9% NACL(PF) 40 MG/250 ML (160 MCG/ML) IV SOLN",1,7,0.0721649484536082,43,1494,0.142857142857143,0.0287817938420348,4.96345514950166,6,1451,0.507907526234309
+"1654",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"222249","LEVOTHYROXINE 100 MCG IV SOLR",1,19,0.195876288659794,49,1184,0.0526315789473684,0.0413851351351351,1.27175080558539,18,1135,0.999999999999963
+"1655",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"520078","METHYLPREDNISOLONE IVPB",2,30,0.309278350515464,342,2212,0.0666666666666667,0.154611211573237,0.431189083820663,28,1870,0.28347147239036
+"1656",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"520087","DESMOPRESSIN IVPB",1,9,0.0927835051546392,42,903,0.111111111111111,0.0465116279069767,2.38888888888889,8,861,0.904820506446234
+"1657",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"540304","MAGNESIUM SULFATE IV SCALE ",1,26,0.268041237113402,78,4104,0.0384615384615385,0.0190058479532164,2.02366863905325,25,4026,0.996947738800466
+"1658",1489,"IP ICU ADULT ORGAN DONOR ORDERS (GREATER THAN 40KG)",97,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,18,0.185567010309278,466,8270,0.0555555555555556,0.0563482466747279,0.985932284215546,17,7804,0.999999999999831
+"1659",1492,"DRUG DESENSITIZATION PROTOCOL",50,"540138","DESENSITIZATION DRUG HELP ORDER",5,9,0.18,6,38,0.555555555555556,0.157894736842105,3.51851851851852,4,32,0.0361038039953819
+"1660",1518,"IP SUR DAY OF SURGERY ORDERS",930,"4318","LACTATED RINGERS IV SOLP",6,93,0.1,135,4847,0.0645161290322581,0.0278522797606767,2.31636798088411,87,4712,0.0736261847040524
+"1661",1518,"IP SUR DAY OF SURGERY ORDERS",930,"5018","METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",2,16,0.0172043010752688,223,3847,0.125,0.0579672472056148,2.15639013452915,14,3624,0.543418938908519
+"1662",1518,"IP SUR DAY OF SURGERY ORDERS",930,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",1,6,0.00645161290322581,70,1432,0.166666666666667,0.0488826815642458,3.40952380952381,5,1362,0.700417481270438
+"1663",1518,"IP SUR DAY OF SURGERY ORDERS",930,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",8,197,0.211827956989247,258,5335,0.0406091370558376,0.0483598875351453,0.839727698422067,189,5077,0.741556171851041
+"1664",1518,"IP SUR DAY OF SURGERY ORDERS",930,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",2,6,0.00645161290322581,242,3682,0.333333333333333,0.0657251493753395,5.07162534435262,4,3440,0.0698095490274859
+"1665",1518,"IP SUR DAY OF SURGERY ORDERS",930,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",8,41,0.0440860215053763,111,2584,0.195121951219512,0.0429566563467492,4.54229839595693,33,2473,1.9680707993012e-05
+"1666",1518,"IP SUR DAY OF SURGERY ORDERS",930,"520064","CEFAZOLIN 2 G IVPB",1,35,0.0376344086021505,109,2674,0.0285714285714286,0.0407629020194465,0.70091743119266,34,2565,0.999999999999997
+"1667",1518,"IP SUR DAY OF SURGERY ORDERS",930,"541110","ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS",7,103,0.110752688172043,72,1340,0.0679611650485437,0.0537313432835821,1.26483279395901,96,1268,0.698738490921498
+"1668",1521,"NURSING TRIAGE FEVER PROTOCOL",114,"102","ACETAMINOPHEN 500 MG PO TABS",1,34,0.298245614035088,392,15968,0.0294117647058824,0.0245490981963928,1.19807923169268,33,15576,0.999999999999967
+"1669",1522,"NURSING TRIAGE FLANK PAIN PROTOCOL",26,"540102","NS IV BOLUS",2,8,0.307692307692308,2225,80957,0.25,0.0274837259285794,9.09629213483146,6,78732,0.00565672474764662
+"1670",1523,"NURSING TRIAGE  GASTROINTESTINAL BLEEDING, SYMPTOMS OF PROTOCOL",187,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",5,70,0.374331550802139,692,19620,0.0714285714285714,0.0352701325178389,2.0251857968621,65,18928,0.190107485653738
+"1671",1526,"NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ",345,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,4,0.0115942028985507,692,19620,0.25,0.0352701325178389,7.08815028901734,3,18928,0.331086991670267
+"1672",1526,"NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ",345,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",3,144,0.417391304347826,705,36939,0.0208333333333333,0.0190855193697718,1.0915780141844,141,36234,0.999999999998327
+"1673",1526,"NURSING TRIAGE ADULT NAUSEA/VOMITING  PROTOCOL ",345,"540102","NS IV BOLUS",6,94,0.272463768115942,2225,80957,0.0638297872340425,0.0274837259285794,2.32245756633995,88,78732,0.0661816350094859
+"1674",1553,"IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP",336,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,9,0.0267857142857143,346,10128,0.111111111111111,0.0341627172195893,3.252408477842,8,9782,0.724835763883227
+"1675",1553,"IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP",336,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,1,0.00297619047619048,499,13896,1,0.0359096142774899,27.8476953907816,0,13397,0.0127154320473097
+"1676",1553,"IP TRS  KIDNEY/PANCREAS TRANSPLANT RECIPIENT PREOP",336,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,24,0.0714285714285714,705,36939,0.0416666666666667,0.0190855193697718,2.18315602836879,23,36234,0.950521822820235
+"1677",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,72,0.24406779661017,542,37509,0.0138888888888889,0.0144498653656456,0.961177736777368,71,36967,0.999999999997141
+"1678",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"4318","LACTATED RINGERS IV SOLP",1,79,0.267796610169492,135,4847,0.0126582278481013,0.0278522797606767,0.454477262072199,78,4712,0.637306379563023
+"1679",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,115,0.389830508474576,346,10128,0.0173913043478261,0.0341627172195893,0.509072631314401,113,9782,0.466394749419217
+"1680",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,9,0.0305084745762712,218,7862,0.222222222222222,0.0277283134062579,8.01427115188583,7,7644,0.0115326983692341
+"1681",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"22472","KETOROLAC 15 MG/ML INJ SOLN",1,20,0.0677966101694915,215,5031,0.05,0.0427350427350427,1.17,19,4816,0.999999999999908
+"1682",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,2,0.00677966101694915,603,24164,0.5,0.0249544777354743,20.0364842454395,1,23561,0.0415013714707364
+"1683",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,127,0.430508474576271,705,36939,0.015748031496063,0.0190855193697718,0.825129837493718,125,36234,0.999999999999972
+"1684",1554,"RETIRED IP GYN LAPAROSCOPY POSTOP",295,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",2,7,0.023728813559322,288,4280,0.285714285714286,0.0672897196261682,4.24603174603175,5,3992,0.122076800308768
+"1685",1557,"IP ATIC INTRAVENOUS GAMMA GLOBULIN",111,"101","ACETAMINOPHEN 325 MG PO TABS",3,53,0.477477477477477,981,42092,0.0566037735849057,0.0233060914187969,2.42871155732502,50,41111,0.25047730831482
+"1686",1557,"IP ATIC INTRAVENOUS GAMMA GLOBULIN",111,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",4,96,0.864864864864865,399,16019,0.0416666666666667,0.0249079218428117,1.67282790309106,92,15620,0.471126835987811
+"1687",1557,"IP ATIC INTRAVENOUS GAMMA GLOBULIN",111,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",1,41,0.369369369369369,313,9164,0.024390243902439,0.0341553906591008,0.714096470038183,40,8851,0.999999999999969
+"1688",1557,"IP ATIC INTRAVENOUS GAMMA GLOBULIN",111,"212610","IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",3,46,0.414414414414414,73,1504,0.0652173913043478,0.0485372340425532,1.34365693865396,43,1431,0.865410437053311
+"1689",1558,"IP ATIC INFLIXIMAB (REMICADE) INFUSION",22,"101","ACETAMINOPHEN 325 MG PO TABS",2,8,0.363636363636364,981,42092,0.25,0.0233060914187969,10.7268093781855,6,41111,0.00210608249038196
+"1690",1558,"IP ATIC INFLIXIMAB (REMICADE) INFUSION",22,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",2,7,0.318181818181818,399,16019,0.285714285714286,0.0249079218428117,11.4708199069101,5,15620,0.00134279952479121
+"1691",1558,"IP ATIC INFLIXIMAB (REMICADE) INFUSION",22,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",2,10,0.454545454545455,356,12049,0.2,0.0295460204166321,6.76910112359551,8,11693,0.0249233406134419
+"1692",1558,"IP ATIC INFLIXIMAB (REMICADE) INFUSION",22,"520116","INFLIXIMAB IV INFUSION",2,11,0.5,3,64,0.181818181818182,0.046875,3.87878787878788,9,61,0.315774846185011
+"1693",1564,"IP CAPR CARDIOVERSION",58,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,26,0.448275862068966,692,19620,0.0384615384615385,0.0352701325178389,1.09048465984882,25,18928,0.999999999999981
+"1694",1582,"IP REN HEMODIALYSIS",21812,"101","ACETAMINOPHEN 325 MG PO TABS",1,4,0.000183385292499542,981,42092,0.25,0.0233060914187969,10.7268093781855,3,41111,0.177904126837166
+"1695",1582,"IP REN HEMODIALYSIS",21812,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",4,227,0.010407115349349,399,16019,0.0176211453744493,0.0249079218428117,0.707451448003268,223,15620,0.626936377240407
+"1696",1582,"IP REN HEMODIALYSIS",21812,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,4,0.000183385292499542,356,12049,0.25,0.0295460204166321,8.46137640449438,3,11693,0.260421137830905
+"1697",1582,"IP REN HEMODIALYSIS",21812,"4750","MANNITOL 25 % 25 % IV SOLN",6,303,0.0138914359068403,21,64,0.0198019801980198,0.328125,0.0603488920320603,297,43,8.70020259553855e-17
+"1698",1582,"IP REN HEMODIALYSIS",21812,"8981","ALBUMIN, HUMAN 25 % 25 % IV SOLP",1,128,0.00586832935998533,341,9016,0.0078125,0.0378216503992901,0.206561583577713,127,8675,0.123024383578926
+"1699",1582,"IP REN HEMODIALYSIS",21812,"8982","ALBUMIN, HUMAN 5 % 5 % IV SOLP",3,29,0.00132954337062168,537,23182,0.103448275862069,0.0231645241998102,4.46580620304373,26,22645,0.0244520441925182
+"1700",1582,"IP REN HEMODIALYSIS",21812,"9348","CALCITRIOL 1 MCG/ML IV SOLN",2,98,0.00449293966623877,3,54,0.0204081632653061,0.0555555555555556,0.36734693877551,96,51,0.491679751591955
+"1701",1582,"IP REN HEMODIALYSIS",21812,"10176","HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",2,973,0.0446084724005135,164,2898,0.00205549845837616,0.0565907522429262,0.0363221617827689,971,2734,7.29777517086442e-13
+"1702",1582,"IP REN HEMODIALYSIS",21812,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,2,9.16926462497708e-05,258,5335,0.5,0.0483598875351453,10.3391472868217,1,5077,0.184772868055709
+"1703",1582,"IP REN HEMODIALYSIS",21812,"77542","EPOETIN ALFA 2,000 UNIT/ML INJ SOLN",13,352,0.0161379057399597,58,299,0.0369318181818182,0.193979933110368,0.190389890282132,339,241,3.38901089079543e-10
+"1704",1582,"IP REN HEMODIALYSIS",21812,"77641","EPOETIN ALFA 4,000 UNIT/ML INJ SOLN",13,878,0.0402530717036494,35,370,0.0148063781321185,0.0945945945945946,0.156524568825252,865,335,6.45485902553526e-11
+"1705",1582,"IP REN HEMODIALYSIS",21812,"91568","EPOETIN ALFA 3,000 UNIT/ML INJ SOLN",7,564,0.0258573262424354,7,186,0.0124113475177305,0.0376344086021505,0.329787234042553,557,179,0.0585329650096354
+"1706",1582,"IP REN HEMODIALYSIS",21812,"94501","EPOETIN ALFA 10,000 UNIT/ML INJ SOLN",17,843,0.0386484503942784,43,1003,0.0201660735468565,0.0428713858424726,0.470385389941792,826,960,0.00909371604090168
+"1707",1582,"IP REN HEMODIALYSIS",21812,"94623","ALTEPLASE 2 MG INJ SOLR",11,1098,0.0503392627911242,201,12455,0.0100182149362477,0.0161380971497391,0.620780432989878,1087,12254,0.14992408003314
+"1708",1582,"IP REN HEMODIALYSIS",21812,"126580","HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",67,7403,0.339400330093526,58,653,0.00905038497906254,0.0888208269525268,0.101894851574618,7336,595,3.57869534903023e-55
+"1709",1582,"IP REN HEMODIALYSIS",21812,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",3,866,0.0397029158261507,457,11068,0.00346420323325635,0.0412902059992772,0.0838989089402217,863,10611,4.32881030771955e-08
+"1710",1582,"IP REN HEMODIALYSIS",21812,"127602","DARBEPOETIN ALFA IN POLYSORBAT 25 MCG/0.42 ML INJ SYRG",1,7,0.000320924261874198,2,21,0.142857142857143,0.0952380952380952,1.5,6,19,1
+"1711",1582,"IP REN HEMODIALYSIS",21812,"209508","METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR",1,9,0.000412616908123968,155,3480,0.111111111111111,0.0445402298850575,2.49462365591398,8,3325,0.874766125316319
+"1712",1582,"IP REN HEMODIALYSIS",21812,"212610","IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",1,9,0.000412616908123968,73,1504,0.111111111111111,0.0485372340425532,2.28919330289193,8,1431,0.926125466583651
+"1713",1582,"IP REN HEMODIALYSIS",21812,"520124","IRON SUCROSE IVPB (<300MG)",2,96,0.004401247019989,26,603,0.0208333333333333,0.0431177446102819,0.483173076923077,94,577,0.450858089674223
+"1714",1582,"IP REN HEMODIALYSIS",21812,"520126","FERRIC GLUCONATE COMPLEX IV INFUSION",3,86,0.00394278378874014,5,68,0.0348837209302326,0.0735294117647059,0.474418604651163,83,63,0.479260790516986
+"1715",1586,"IP REN HEMODIALYSIS/PUF",3043,"94623","ALTEPLASE 2 MG INJ SOLR",3,134,0.0440354912914887,201,12455,0.0223880597014925,0.0161380971497391,1.38728001782134,131,12254,0.821188123424298
+"1716",1586,"IP REN HEMODIALYSIS/PUF",3043,"126580","HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",7,807,0.26519881695695,58,653,0.00867410161090459,0.0888208269525268,0.0976584198607016,800,595,4.01548369440731e-13
+"1717",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",3,829,0.368772241992883,346,10128,0.00361881785283474,0.0341627172195893,0.105928864778931,826,9782,2.45344741474375e-06
+"1718",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",2,279,0.124110320284698,745,12385,0.00716845878136201,0.0601534113847396,0.119169613432441,277,11640,0.000335278359126871
+"1719",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,560,0.249110320284698,314,8762,0.00357142857142857,0.035836566993837,0.0996587807097361,558,8448,7.18572219509831e-05
+"1720",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,11,0.0048932384341637,158,4300,0.0909090909090909,0.0367441860465116,2.4741081703107,10,4142,0.879944222901443
+"1721",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,76,0.0338078291814947,118,4415,0.0131578947368421,0.026727066817667,0.492305976806423,75,4297,0.711302620206601
+"1722",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,174,0.0774021352313167,692,19620,0.00574712643678161,0.0352701325178389,0.162945983655571,173,18928,0.0571377483112076
+"1723",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"82384","PANTOPRAZOLE 40 MG IV SOLR",8,810,0.360320284697509,688,12925,0.00987654320987654,0.0532301740812379,0.185544071202986,802,12237,7.67354631727913e-08
+"1724",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,12,0.00533807829181495,499,13896,0.166666666666667,0.0359096142774899,4.64128256513026,10,13397,0.0979704173770042
+"1725",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",8,970,0.431494661921708,705,36939,0.00824742268041237,0.0190855193697718,0.432129853037947,962,36234,0.0196445934148457
+"1726",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",5,823,0.366103202846975,452,13480,0.00607533414337789,0.0335311572700297,0.181184743921978,818,13028,2.17863758270411e-05
+"1727",1594,"IP SUR BARIATRIC SURGERY POSTOP",2248,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,10,0.00444839857651246,104,5739,0.1,0.0181216239763025,5.51826923076923,9,5635,0.453179301393086
+"1728",1598,"IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP",272,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,94,0.345588235294118,346,10128,0.0106382978723404,0.0341627172195893,0.311400811708277,93,9782,0.333263768616171
+"1729",1598,"IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP",272,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,68,0.25,688,12925,0.0294117647058824,0.0532301740812379,0.552539329685363,66,12237,0.546850709768956
+"1730",1598,"IP SUR MINIMALLY INVASIVE FOREGUT  POSTOP",272,"540300","MORPHINE INJECTABLE SYRINGE",1,17,0.0625,61,1002,0.0588235294117647,0.0608782435129741,0.966248794599807,16,941,0.999999999999999
+"1731",1612,"IP REN CCPD CONT CYCLE PERITONEAL DIALYSIS",728,"500030","PERITONEAL DIALYSIS SOLUTION",5,75,0.103021978021978,4,20,0.0666666666666667,0.2,0.333333333333333,70,16,0.167746683137264
+"1732",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",2,136,0.113050706566916,537,15149,0.0147058823529412,0.0354478843488019,0.4148592397853,134,14612,0.283667996935446
+"1733",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",9,416,0.345802161263508,346,10128,0.0216346153846154,0.0341627172195893,0.633281458425967,407,9782,0.211396778766618
+"1734",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"5943","OXYMETAZOLINE 0.05 % NASAL SPRA",1,192,0.159600997506234,106,2713,0.00520833333333333,0.0390711389605603,0.133303852201258,191,2607,0.0271608282990777
+"1735",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"10372","LABETALOL 5 MG/ML IV SOLN",6,129,0.107231920199501,370,5898,0.0465116279069767,0.0627331298745337,0.7414204902577,123,5528,0.568959157773201
+"1736",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"10813","OXYCODONE 5 MG/5 ML PO SOLN",11,306,0.254364089775561,328,8370,0.0359477124183007,0.0391875746714456,0.917324246771879,295,8042,0.890954419664741
+"1737",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",16,310,0.257689110556941,314,8762,0.0516129032258065,0.035836566993837,1.44023012122457,294,8448,0.192326492571792
+"1738",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",3,46,0.0382377389858687,158,4300,0.0652173913043478,0.0367441860465116,1.77490368739681,43,4142,0.53221508538873
+"1739",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,39,0.0324189526184539,118,4415,0.0256410256410256,0.026727066817667,0.959365493263798,38,4297,0.999999999999994
+"1740",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",4,321,0.266832917705736,258,5335,0.0124610591900312,0.0483598875351453,0.257673452631071,317,5077,0.00457846789623891
+"1741",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"29676","SODIUM CHLORIDE 0.65 % NASAL SPRA",1,162,0.134663341645885,51,3278,0.00617283950617284,0.0155582672361196,0.396756233357541,161,3227,0.531390871604489
+"1742",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"112564","MORPHINE 2 MG/ML INJ SYRG",6,284,0.236076475477972,276,6156,0.0211267605633803,0.0448343079922027,0.471218616044091,278,5880,0.0782986161193152
+"1743",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",1,1,0.000831255195344971,1951,16917,1,0.115327776792576,8.67093798052281,0,14966,0.228617033124343
+"1744",1628,"IP ENT MAXILLOFACIAL SURGERY POSTOP",1203,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",9,439,0.364921030756442,705,36939,0.020501138952164,0.0190855193697718,1.07417244220424,430,36234,0.968061769445117
+"1745",1632,"IP ORAL STEROID TAPERS",1405,"2322","DEXAMETHASONE 0.5 MG PO TABS",5,69,0.0491103202846975,3,154,0.072463768115942,0.0194805194805195,3.71980676328502,64,151,0.114770992385177
+"1746",1632,"IP ORAL STEROID TAPERS",1405,"2323","DEXAMETHASONE 0.75 MG PO TABS",6,76,0.0540925266903915,1,70,0.0789473684210526,0.0142857142857143,5.52631578947368,70,69,0.150082290097862
+"1747",1632,"IP ORAL STEROID TAPERS",1405,"2324","DEXAMETHASONE 1 MG PO TABS",100,3134,1,59,2074,0.0319081046585833,0.0284474445515911,1.12165100104918,3034,2015,0.529761860988916
+"1748",1632,"IP ORAL STEROID TAPERS",1405,"2325","DEXAMETHASONE 1.5 MG PO TABS",33,1534,1,14,255,0.0215123859191656,0.0549019607843137,0.391832743527659,1501,241,0.00403373894585234
+"1749",1632,"IP ORAL STEROID TAPERS",1405,"2326","DEXAMETHASONE 2 MG PO TABS",23,949,0.675444839857651,47,1734,0.0242360379346681,0.0271049596309112,0.894155101674775,926,1687,0.749673188618769
+"1750",1632,"IP ORAL STEROID TAPERS",1405,"2327","DEXAMETHASONE 4 MG PO TABS",58,1096,0.780071174377224,277,6991,0.0529197080291971,0.0396223716206551,1.3356017286358,1038,6714,0.0485525371752012
+"1751",1632,"IP ORAL STEROID TAPERS",1405,"6494","PREDNISONE 10 MG PO TABS",5,38,0.0270462633451957,104,4234,0.131578947368421,0.0245630609352858,5.35678137651822,33,4130,0.000263978387692845
+"1752",1632,"IP ORAL STEROID TAPERS",1405,"6496","PREDNISONE 20 MG PO TABS",4,81,0.0576512455516014,276,7517,0.0493827160493827,0.0367167753092989,1.34496332080873,77,7241,0.760088051446617
+"1753",1632,"IP ORAL STEROID TAPERS",1405,"6497","PREDNISONE 5 MG PO TABS",1,37,0.0263345195729537,108,4678,0.027027027027027,0.023086789226165,1.17067067067067,36,4570,0.999999999999815
+"1754",1632,"IP ORAL STEROID TAPERS",1405,"6498","PREDNISONE 50 MG PO TABS",1,19,0.0135231316725979,34,1011,0.0526315789473684,0.0336300692383778,1.56501547987616,18,977,0.999999999999989
+"1755",1632,"IP ORAL STEROID TAPERS",1405,"14887","METHYLPREDNISOLONE 4 MG PO DSPK",47,659,0.469039145907473,93,1087,0.0713201820940819,0.0855565777368905,0.833602558454485,612,994,0.331595638318061
+"1756",1632,"IP ORAL STEROID TAPERS",1405,"9000000","ZZZ IMS TEMPLATE",6,91,0.0647686832740214,1035,28824,0.0659340659340659,0.0359075770191507,1.83621595795509,85,27789,0.210091478332606
+"1757",1635,"NURSING TRIAGE PNEUMONIA PROTOCOL",62,"101","ACETAMINOPHEN 325 MG PO TABS",3,7,0.112903225806452,981,42092,0.428571428571429,0.0233060914187969,18.3888160768895,4,41111,5.05701134475349e-09
+"1758",1637,"NURSING TRIAGE ASTHMA PROTOCOL",28,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",3,7,0.25,106,3939,0.428571428571429,0.0269103833460269,15.9258760107817,4,3833,1.01330953657042e-07
+"1759",1637,"NURSING TRIAGE ASTHMA PROTOCOL",28,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",1,7,0.25,127,3899,0.142857142857143,0.0325724544755065,4.38582677165354,6,3772,0.56528085366827
+"1760",1644,"IP NSR PITUITARY POSTOP ",777,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",1,95,0.122265122265122,537,15149,0.0105263157894737,0.0354478843488019,0.296951876898951,94,14612,0.301400037305725
+"1761",1644,"IP NSR PITUITARY POSTOP ",777,"5373","NALOXONE 0.4 MG/ML INJ SOLN",6,216,0.277992277992278,83,1774,0.0277777777777778,0.0467869222096956,0.593708165997323,210,1691,0.270531398853502
+"1762",1644,"IP NSR PITUITARY POSTOP ",777,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",5,210,0.27027027027027,14,238,0.0238095238095238,0.0588235294117647,0.404761904761905,205,224,0.109541391797849
+"1763",1644,"IP NSR PITUITARY POSTOP ",777,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,155,0.199485199485199,118,4415,0.00645161290322581,0.026727066817667,0.241388737014762,154,4297,0.193124390092768
+"1764",1644,"IP NSR PITUITARY POSTOP ",777,"28384","HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS",1,33,0.0424710424710425,335,9576,0.0303030303030303,0.0349832915622389,0.866214382632293,32,9241,0.999999999999972
+"1765",1644,"IP NSR PITUITARY POSTOP ",777,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",7,217,0.279279279279279,603,24164,0.032258064516129,0.0249544777354743,1.29267640293158,210,23561,0.640149992244382
+"1766",1644,"IP NSR PITUITARY POSTOP ",777,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,195,0.250965250965251,705,36939,0.0102564102564103,0.0190855193697718,0.537392253136934,193,36234,0.524060734882241
+"1767",1644,"IP NSR PITUITARY POSTOP ",777,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",2,58,0.0746460746460746,313,9164,0.0344827586206897,0.0341553906591008,1.00958466453674,56,8851,0.999999999999543
+"1768",1644,"IP NSR PITUITARY POSTOP ",777,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",3,193,0.248391248391248,461,18637,0.0155440414507772,0.0247357407308043,0.628404122598991,190,18176,0.557811442544071
+"1769",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"101","ACETAMINOPHEN 325 MG PO TABS",28,1012,0.298613160224255,981,42092,0.0276679841897233,0.0233060914187969,1.18715676912725,984,41111,0.422719367519552
+"1770",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"680","ASPIRIN 81 MG PO CHEW",1,23,0.00678666273236943,357,11203,0.0434782608695652,0.031866464339909,1.36438923395445,22,10846,0.999999999999973
+"1771",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"1080","BISACODYL 10 MG PR SUPP",1,34,0.0100324579521983,344,22442,0.0294117647058824,0.0153284021031994,1.9187756497948,33,22098,0.999999999998974
+"1772",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"1445","CEFAZOLIN 1 GRAM INJ SOLR",53,564,0.166420773089407,47,571,0.0939716312056738,0.0823117338003503,1.1416553493285,511,524,0.556402308636085
+"1773",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"4711","MAGNESIUM CITRATE PO SOLN",1,32,0.00944231336677486,131,5481,0.03125,0.0239007480386791,1.30749045801527,31,5350,0.999999999999981
+"1774",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"7753","TEMAZEPAM 15 MG PO CAPS",1,40,0.0118028917084686,24,941,0.025,0.0255047821466525,0.980208333333333,39,917,0.999999999999956
+"1775",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"8442","VANCOMYCIN 1,000 MG IV SOLR",27,136,0.0401298318087932,248,2879,0.198529411764706,0.0861410211879125,2.30470232447818,109,2631,1.73823423691276e-05
+"1776",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",22,1218,0.359398052522868,89,4372,0.0180623973727422,0.0203568161024703,0.887289902400325,1196,4283,0.69543603720579
+"1777",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"13654","ASPIRIN 325 MG PO TBEC",1,1,0.000295072292711714,16,582,1,0.0274914089347079,36.375,0,566,0.00509757346214272
+"1778",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"14113","ASPIRIN 81 MG PO TBEC",1,15,0.00442608439067572,391,18448,0.0666666666666667,0.0211947094535993,3.14543904518329,14,18057,0.744981297783516
+"1779",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,8,0.00236057834169371,314,8762,0.125,0.035836566993837,3.48805732484076,7,8448,0.686049487184666
+"1780",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,28,0.008262024195928,603,24164,0.0357142857142857,0.0249544777354743,1.43117744610282,27,23561,0.999999999999994
+"1781",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"38285","ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",15,956,0.282089111832399,71,5546,0.0156903765690377,0.0128020194734944,1.22561730213919,941,5475,0.569563925095733
+"1782",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",2,41,0.0120979640011803,1951,16917,0.0487804878048781,0.115327776792576,0.422972584415747,39,14966,0.276458484219928
+"1783",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"200273","PEG 3350-ELECTROLYTES 236-22.74-6.74 -5.86 GRAM PO SOLR",7,17,0.00501622897609914,89,2251,0.411764705882353,0.039537983118614,10.4144084600132,10,2162,2.75855830941002e-12
+"1784",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,5,0.00147536146355857,452,13480,0.2,0.0335311572700297,5.96460176991151,4,13028,0.40977862268031
+"1785",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",13,400,0.118028917084686,111,2584,0.0325,0.0429566563467492,0.756576576576577,387,2473,0.400605256386296
+"1786",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",1,18,0.00531130126881086,1808,23328,0.0555555555555556,0.0775034293552812,0.716814159292035,17,21520,0.999999999999637
+"1787",1651,"IP CTS CARDIAC SURGERY ADMISSION",3389,"541228","CEFAZOLIN IVPB (COMPOUNDED)",2,10,0.00295072292711714,10,117,0.2,0.0854700854700855,2.34,8,107,0.531802452773848
+"1788",1652,"IP IV STEROID TAPERS",236,"2332","DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",51,1007,1,390,6737,0.0506454816285998,0.0578892682202761,0.874868230081736,956,6347,0.394055971883017
+"1789",1652,"IP IV STEROID TAPERS",236,"6494","PREDNISONE 10 MG PO TABS",1,2,0.00847457627118644,104,4234,0.5,0.0245630609352858,20.3557692307692,1,4130,0.0404613232657077
+"1790",1652,"IP IV STEROID TAPERS",236,"209508","METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR",3,21,0.0889830508474576,155,3480,0.142857142857143,0.0445402298850575,3.2073732718894,18,3325,0.101700602359505
+"1791",1652,"IP IV STEROID TAPERS",236,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",9,42,0.177966101694915,275,8000,0.214285714285714,0.034375,6.23376623376623,33,7725,4.06811539464871e-09
+"1792",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"464","AMPHOTERICIN B 50 MG INJ SOLR",1,71,0.0509691313711414,4,131,0.0140845070422535,0.0305343511450382,0.461267605633803,70,127,0.807097511279137
+"1793",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"1080","BISACODYL 10 MG PR SUPP",4,198,0.142139267767408,344,22442,0.0202020202020202,0.0153284021031994,1.31794691097017,194,22098,0.791095313230563
+"1794",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",7,182,0.130653266331658,134,3551,0.0384615384615385,0.0377358490566038,1.01923076923077,175,3417,0.999999999999999
+"1795",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",4,167,0.119885139985642,399,16019,0.0239520958083832,0.0249079218428117,0.961625620938574,163,15620,0.999999999999651
+"1796",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"2566","DOCUSATE SODIUM 100 MG PO CAPS",3,87,0.0624551328068916,542,37509,0.0344827586206897,0.0144498653656456,2.38637231199898,84,36967,0.265925346985864
+"1797",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"5751","NYSTATIN 100,000 UNIT/ML PO SUSP",1,116,0.0832735104091888,68,4056,0.00862068965517241,0.0167652859960552,0.51419878296146,115,3988,0.757319016485468
+"1798",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"6492","PREDNISONE 5 MG/5 ML PO SOLN",9,176,0.126346015793252,72,1096,0.0511363636363636,0.0656934306569343,0.778409090909091,167,1024,0.570128172395983
+"1799",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"6496","PREDNISONE 20 MG PO TABS",1,40,0.0287150035893754,276,7517,0.025,0.0367167753092989,0.68088768115942,39,7241,0.999999999999998
+"1800",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"7557","SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS",7,140,0.100502512562814,112,3269,0.05,0.0342612419700214,1.459375,133,3157,0.448194315838588
+"1801",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",7,181,0.129935391241924,89,4372,0.0386740331491713,0.0203568161024703,1.89980756099075,174,4283,0.156520542372094
+"1802",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"11349","SENNOSIDES 8.6 MG PO TABS",1,146,0.10480976310122,610,35793,0.00684931506849315,0.0170424384656218,0.401897597125533,145,35183,0.528665367815338
+"1803",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",3,25,0.0179468772433597,258,5335,0.12,0.0483598875351453,2.48139534883721,22,5077,0.232208286002477
+"1804",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"82384","PANTOPRAZOLE 40 MG IV SOLR",3,47,0.0337401292175162,688,12925,0.0638297872340425,0.0532301740812379,1.19912790697674,44,12237,0.999999999999904
+"1805",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",10,75,0.053840631730079,1067,10067,0.133333333333333,0.105989867885169,1.25798188066229,65,9000,0.563478190727038
+"1806",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",13,164,0.117731514716439,1951,16917,0.0792682926829268,0.115327776792576,0.687330449675589,151,14966,0.187618408060115
+"1807",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"124873","SENNA LEAF EXTRACT 176 MG/5 ML PO SYRP",2,102,0.0732232591529074,90,4112,0.0196078431372549,0.0218871595330739,0.895860566448802,100,4022,0.999999999999972
+"1808",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",5,201,0.144292893036612,705,36939,0.0248756218905473,0.0190855193697718,1.3033767333545,196,36234,0.734158030233505
+"1809",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",4,101,0.072505384063173,288,4280,0.0396039603960396,0.0672897196261682,0.588558855885589,97,3992,0.367690783504833
+"1810",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"205192","MILRINONE IN D5W 20 MG/100 ML IV PGBK",4,38,0.0272792534099067,27,851,0.105263157894737,0.0317273795534665,3.317738791423,34,824,0.0493349750467536
+"1811",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"206169","DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",3,118,0.0847092605886576,62,1778,0.0254237288135593,0.0348706411698538,0.729086932750137,115,1716,0.775700890588419
+"1812",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",8,339,0.243359655419957,192,8611,0.023598820058997,0.0222970618975729,1.05838249754179,331,8419,0.999999999999994
+"1813",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",3,189,0.135678391959799,275,8000,0.0158730158730159,0.034375,0.461760461760462,186,7725,0.235988842201653
+"1814",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",7,127,0.091170136396267,313,9164,0.0551181102362205,0.0341553906591008,1.61374556614928,120,8851,0.297607710302788
+"1815",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"226058","EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",2,42,0.0301507537688442,56,1218,0.0476190476190476,0.0459770114942529,1.03571428571429,40,1162,0.999999999999964
+"1816",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"520055","GANCICLOVIR IVPB",11,182,0.130653266331658,62,1312,0.0604395604395604,0.0472560975609756,1.2789790854307,171,1250,0.555414328972694
+"1817",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"520073","MYCOPHENOLATE IVPB",2,89,0.0638908829863604,37,865,0.0224719101123595,0.0427745664739884,0.525356817491649,87,828,0.522193550845754
+"1818",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"520074","ANTITHYMOCYTE GLOBULIN RABBIT IVPB",12,152,0.109117013639627,75,1781,0.0789473684210526,0.0421111734980348,1.87473684210526,140,1706,0.0575819754430648
+"1819",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"530006","EPINEPHRINE IV INFUSION",3,120,0.0861450107681263,99,2540,0.025,0.0389763779527559,0.641414141414141,117,2441,0.59205594080498
+"1820",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"530017","ISOPROTERENOL IV INFUSION",2,11,0.00789662598707825,4,147,0.181818181818182,0.0272108843537415,6.68181818181818,9,143,0.0767278479133054
+"1821",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"530025","DEXMEDETOMIDINE IV INFUSION",2,8,0.00574300071787509,530,2902,0.25,0.18263266712612,1.3688679245283,6,2372,0.972630150069924
+"1822",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"530049","INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW)",7,176,0.126346015793252,23,2672,0.0397727272727273,0.00860778443113772,4.62055335968379,169,2649,0.000397827426019971
+"1823",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"540129","TACROLIMUS 1 MG/ML ORAL SUSPENSION",5,180,0.12921751615219,147,4881,0.0277777777777778,0.0301167793484942,0.922335600907029,175,4734,0.999999999999996
+"1824",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"540321","MYCOPHENOLATE MOFETIL 200 MG/ML ORAL SUSP",5,109,0.0782483847810481,49,786,0.0458715596330275,0.0623409669211196,0.73581726268489,104,737,0.64400360664012
+"1825",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,83,0.0595836324479541,466,8270,0.0120481927710843,0.0563482466747279,0.213816639950359,82,7804,0.131586168541194
+"1826",1653,"IP TRS HEART TRANSPLANT POSTOP",1393,"541285","AMPHOTERICIN B 20 MG/4ML NEB SOLUTION",3,106,0.0760947595118449,2,126,0.0283018867924528,0.0158730158730159,1.78301886792453,103,124,0.844920083669902
+"1827",1660,"IP GEN TPN/PPN  ADULT PROTOCOL",2935,"78963","FAT EMULSION 20 % IV EMUL",86,1312,0.447018739352641,242,20224,0.0655487804878049,0.0119659810126582,5.47792783712961,1226,19982,1.88309123946763e-52
+"1828",1660,"IP GEN TPN/PPN  ADULT PROTOCOL",2935,"550000","TPN ADULT STANDARD",140,1113,0.379216354344123,779,15481,0.125786163522013,0.0503197467863833,2.49973760909407,973,14702,4.36879556441377e-26
+"1829",1660,"IP GEN TPN/PPN  ADULT PROTOCOL",2935,"550001","PPN ADULT STANDARD",16,163,0.0555366269165247,104,921,0.098159509202454,0.112920738327904,0.869277961302501,147,817,0.675777446045014
+"1830",1660,"IP GEN TPN/PPN  ADULT PROTOCOL",2935,"550002","TPN BMT",10,108,0.0367972742759796,155,3565,0.0925925925925926,0.0434782608695652,2.12962962962963,98,3410,0.0283871373905396
+"1831",1665,"IP TRS KIDNEY DONOR PREOP",221,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,5,0.0226244343891403,346,10128,0.2,0.0341627172195893,5.85433526011561,4,9782,0.418680009960003
+"1832",1665,"IP TRS KIDNEY DONOR PREOP",221,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,114,0.515837104072398,692,19620,0.0087719298245614,0.0352701325178389,0.248707027684819,113,18928,0.201457982947326
+"1833",1665,"IP TRS KIDNEY DONOR PREOP",221,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,11,0.0497737556561086,705,36939,0.0909090909090909,0.0190855193697718,4.76324951644101,10,36234,0.523209561272467
+"1834",1665,"IP TRS KIDNEY DONOR PREOP",221,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,8,0.0361990950226244,452,13480,0.125,0.0335311572700297,3.72787610619469,7,13028,0.649770163706163
+"1835",1667,"IP GYN  GENERAL ADMISSION",2898,"101","ACETAMINOPHEN 325 MG PO TABS",3,382,0.131815044858523,981,42092,0.00785340314136126,0.0233060914187969,0.336967833869702,379,41111,0.067582140783348
+"1836",1667,"IP GYN  GENERAL ADMISSION",2898,"1080","BISACODYL 10 MG PR SUPP",1,105,0.036231884057971,344,22442,0.00952380952380952,0.0153284021031994,0.621317829457364,104,22098,0.932273959917987
+"1837",1667,"IP GYN  GENERAL ADMISSION",2898,"2566","DOCUSATE SODIUM 100 MG PO CAPS",3,120,0.041407867494824,542,37509,0.025,0.0144498653656456,1.73011992619926,117,36967,0.559795294602241
+"1838",1667,"IP GYN  GENERAL ADMISSION",2898,"2569","DOCUSATE SODIUM 50 MG/5 ML PO LIQD",2,172,0.0593512767425811,170,7328,0.0116279069767442,0.0231986899563319,0.501231190150479,170,7158,0.456635357723867
+"1839",1667,"IP GYN  GENERAL ADMISSION",2898,"3843","IBUPROFEN 400 MG PO TABS",1,62,0.0213940648723257,148,4216,0.0161290322580645,0.0351043643263757,0.459459459459459,61,4068,0.645437203913096
+"1840",1667,"IP GYN  GENERAL ADMISSION",2898,"3844","IBUPROFEN 600 MG PO TABS",1,125,0.0431331953071084,98,3745,0.008,0.0261682242990654,0.305714285714286,124,3647,0.32823557915257
+"1841",1667,"IP GYN  GENERAL ADMISSION",2898,"4318","LACTATED RINGERS IV SOLP",4,508,0.175293305728088,135,4847,0.0078740157480315,0.0278522797606767,0.28270632837562,504,4712,0.0108480809526651
+"1842",1667,"IP GYN  GENERAL ADMISSION",2898,"4573","LORAZEPAM 1 MG PO TABS",3,52,0.0179434092477571,337,8494,0.0576923076923077,0.0396750647515894,1.45412006391235,49,8157,0.758941524192599
+"1843",1667,"IP GYN  GENERAL ADMISSION",2898,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",4,412,0.142167011732229,346,10128,0.00970873786407767,0.0341627172195893,0.284191032044447,408,9782,0.0100150342679743
+"1844",1667,"IP GYN  GENERAL ADMISSION",2898,"5005","METOCLOPRAMIDE HCL 10 MG PO TABS",2,39,0.0134575569358178,127,2872,0.0512820512820513,0.0442200557103064,1.15970119119725,37,2745,0.999999999999996
+"1845",1667,"IP GYN  GENERAL ADMISSION",2898,"5006","METOCLOPRAMIDE HCL 5 MG PO TABS",1,52,0.0179434092477571,28,997,0.0192307692307692,0.0280842527582748,0.684752747252747,51,969,0.999999999999999
+"1846",1667,"IP GYN  GENERAL ADMISSION",2898,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",1,37,0.0127674258109041,289,5149,0.027027027027027,0.056127403379297,0.481529972879454,36,4860,0.682829714355745
+"1847",1667,"IP GYN  GENERAL ADMISSION",2898,"10467","LORAZEPAM 2 MG/ML INJ SOLN",1,45,0.015527950310559,749,20621,0.0222222222222222,0.0363221958197954,0.611808337041982,44,19872,0.91540398067136
+"1848",1667,"IP GYN  GENERAL ADMISSION",2898,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",2,27,0.0093167701863354,118,4415,0.0740740740740741,0.026727066817667,2.7715003138732,25,4297,0.358869779142219
+"1849",1667,"IP GYN  GENERAL ADMISSION",2898,"22472","KETOROLAC 15 MG/ML INJ SOLN",3,147,0.0507246376811594,215,5031,0.0204081632653061,0.0427350427350427,0.477551020408163,144,4816,0.262558910503654
+"1850",1667,"IP GYN  GENERAL ADMISSION",2898,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",1,80,0.0276052449965493,712,36910,0.0125,0.0192901652668653,0.647998595505618,79,36198,0.972700770015304
+"1851",1667,"IP GYN  GENERAL ADMISSION",2898,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,132,0.0455486542443064,692,19620,0.00757575757575758,0.0352701325178389,0.214792433000525,131,18928,0.137225501556941
+"1852",1667,"IP GYN  GENERAL ADMISSION",2898,"37637","METOPROLOL TARTRATE 50 MG PO TABS",2,4,0.00138026224982747,809,19725,0.5,0.0410139416983523,12.1909765142151,2,18916,0.00076859522863466
+"1853",1667,"IP GYN  GENERAL ADMISSION",2898,"82384","PANTOPRAZOLE 40 MG IV SOLR",2,90,0.031055900621118,688,12925,0.0222222222222222,0.0532301740812379,0.417474160206718,88,12237,0.28359373425909
+"1854",1667,"IP GYN  GENERAL ADMISSION",2898,"94176","ONDANSETRON 4 MG PO TBDL",1,80,0.0276052449965493,153,4277,0.0125,0.0357727379003975,0.349428104575163,79,4124,0.417168253271253
+"1855",1667,"IP GYN  GENERAL ADMISSION",2898,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",7,219,0.0755693581780538,738,23934,0.0319634703196347,0.0308347956881424,1.03660392768311,212,23196,0.999999999999547
+"1856",1667,"IP GYN  GENERAL ADMISSION",2898,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",12,259,0.0893719806763285,499,13896,0.0463320463320463,0.0359096142774899,1.2902407130864,247,13397,0.469789877789493
+"1857",1667,"IP GYN  GENERAL ADMISSION",2898,"112564","MORPHINE 2 MG/ML INJ SYRG",1,25,0.00862663906142167,276,6156,0.04,0.0448343079922027,0.892173913043478,24,5880,0.999999999999999
+"1858",1667,"IP GYN  GENERAL ADMISSION",2898,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",3,248,0.085576259489303,1219,33427,0.0120967741935484,0.036467526251234,0.331713593902988,245,32208,0.0608873827173452
+"1859",1667,"IP GYN  GENERAL ADMISSION",2898,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",11,700,0.241545893719807,705,36939,0.0157142857142857,0.0190855193697718,0.82336170212766,689,36234,0.612021660464862
+"1860",1667,"IP GYN  GENERAL ADMISSION",2898,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,17,0.00586611456176674,288,4280,0.0588235294117647,0.0672897196261682,0.874183006535948,16,3992,0.999999999999928
+"1861",1667,"IP GYN  GENERAL ADMISSION",2898,"221636","PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN",1,97,0.0334713595583161,177,6874,0.0103092783505155,0.0257491998836194,0.400372764866911,96,6697,0.526612165834081
+"1862",1667,"IP GYN  GENERAL ADMISSION",2898,"540102","NS IV BOLUS",1,9,0.0031055900621118,2225,80957,0.111111111111111,0.0274837259285794,4.04279650436954,8,78732,0.606629709610948
+"1863",1667,"IP GYN  GENERAL ADMISSION",2898,"540115","LACTATED RINGERS IV BOLUS",2,15,0.005175983436853,400,12941,0.133333333333333,0.0309095124024419,4.31366666666667,13,12541,0.123201424528627
+"1864",1667,"IP GYN  GENERAL ADMISSION",2898,"540493","WARFARIN PER PHARMACY PROTOCOL",1,2,0.000690131124913734,218,6316,0.5,0.0345155161494617,14.4862385321101,1,6098,0.0958999965627139
+"1865",1667,"IP GYN  GENERAL ADMISSION",2898,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,23,0.00793650793650794,104,5739,0.0434782608695652,0.0181216239763025,2.3992474916388,22,5635,0.899471566849783
+"1866",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"101","ACETAMINOPHEN 325 MG PO TABS",41,2755,0.235108380269671,981,42092,0.0148820326678766,0.0233060914187969,0.638546910352968,2714,41111,0.00503727187360037
+"1867",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"102","ACETAMINOPHEN 500 MG PO TABS",3,61,0.00520566649598908,392,15968,0.0491803278688525,0.0245490981963928,2.0033456005353,58,15576,0.40949635414139
+"1868",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"105","ACETAMINOPHEN 650 MG PR SUPP",4,405,0.0345622119815668,59,929,0.00987654320987654,0.0635091496232508,0.155513705796192,401,870,4.02860421269216e-05
+"1869",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"2327","DEXAMETHASONE 4 MG PO TABS",2,35,0.0029868578255675,277,6991,0.0571428571428571,0.0396223716206551,1.44218669417225,33,6714,0.923842459178569
+"1870",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"2332","DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN",6,242,0.0206519883939239,390,6737,0.0247933884297521,0.0578892682202761,0.428289891926256,236,6347,0.0408440857662951
+"1871",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"2404","DIAZEPAM 2 MG PO TABS",2,53,0.00452295613585936,52,1512,0.0377358490566038,0.0343915343915344,1.09724238026125,51,1460,0.999999999999993
+"1872",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"2405","DIAZEPAM 5 MG PO TABS",11,1083,0.0924219150025602,152,5041,0.0101569713758079,0.0301527474707399,0.336850609904262,1072,4889,0.000312012822312252
+"1873",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"2566","DOCUSATE SODIUM 100 MG PO CAPS",35,2718,0.231950844854071,542,37509,0.0128771155261221,0.0144498653656456,0.891158166548553,2683,36967,0.560325568452843
+"1874",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"2567","DOCUSATE SODIUM 250 MG PO CAPS",15,957,0.0816692268305172,296,17760,0.0156739811912226,0.0166666666666667,0.940438871473354,942,17464,0.917000096794691
+"1875",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",15,2997,0.255760368663594,537,15149,0.005005005005005,0.0354478843488019,0.141193334861864,2982,14612,1.26889736008059e-18
+"1876",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"4711","MAGNESIUM CITRATE PO SOLN",1,458,0.0390851681174262,131,5481,0.00218340611353712,0.0239007480386791,0.0913530451015034,457,5350,0.00418626494838588
+"1877",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",38,3534,0.301587301587302,346,10128,0.010752688172043,0.0341627172195893,0.314749207533097,3496,9782,6.44913338639265e-13
+"1878",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",30,2487,0.212237583205325,745,12385,0.0120627261761158,0.0601534113847396,0.2005327029412,2457,11640,1.14984333926097e-22
+"1879",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"5373","NALOXONE 0.4 MG/ML INJ SOLN",71,3702,0.315924219150026,83,1774,0.0191788222582388,0.0467869222096956,0.409918442001393,3631,1691,1.22772057139769e-08
+"1880",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",5,573,0.0488991295442908,218,7862,0.0087260034904014,0.0277283134062579,0.314696511199705,568,7644,0.00925735937355009
+"1881",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"6432","POTASSIUM CHLORIDE 20 MEQ/15 ML PO LIQD",3,673,0.0574330090459123,425,8090,0.00445765230312036,0.0525339925834363,0.0848527226641028,670,7665,4.58214067051246e-08
+"1882",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"8958","BUTALBITAL-ACETAMINOPHEN-CAFF 50-325-40 MG PO TABS",1,70,0.00597371565113501,58,2595,0.0142857142857143,0.0223506743737958,0.639162561576355,69,2537,0.967352314946852
+"1883",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"10011","FAMOTIDINE 20 MG PO TABS",6,615,0.0524833589349718,472,16960,0.00975609756097561,0.0278301886792453,0.350558081852005,609,16488,0.00985792266244222
+"1884",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",34,3693,0.31515616999488,14,238,0.00920660709450311,0.0588235294117647,0.156512320606553,3659,224,1.11117922007225e-10
+"1885",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"10814","OXYCODONE 5 MG PO TABS",6,577,0.0492404847243557,1478,56614,0.0103986135181976,0.0261066167379094,0.398313332692312,571,55136,0.0257628356754892
+"1886",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"11349","SENNOSIDES 8.6 MG PO TABS",59,4130,0.352449223416965,610,35793,0.0142857142857143,0.0170424384656218,0.838243559718969,4071,35183,0.213930290275971
+"1887",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",25,2729,0.232889571599249,118,4415,0.00916086478563576,0.026727066817667,0.342756084987982,2704,4297,4.1100841647006e-07
+"1888",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"18308","GABAPENTIN 300 MG PO CAPS",1,48,0.00409626216077829,588,19458,0.0208333333333333,0.0302189330866482,0.689413265306122,47,18870,0.999999999999994
+"1889",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",14,873,0.0745007680491551,258,5335,0.0160366552119129,0.0483598875351453,0.33161068044789,859,5077,2.27245599006582e-05
+"1890",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",11,1537,0.131165727939921,712,36910,0.00715679895901106,0.0192901652668653,0.371007653900419,1526,36198,0.000851904844664665
+"1891",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,221,0.0188598736985834,692,19620,0.00904977375565611,0.0352701325178389,0.256584625846782,219,18928,0.0541443366413855
+"1892",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"28384","HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS",8,702,0.0599078341013825,335,9576,0.0113960113960114,0.0349832915622389,0.325755836203597,694,9241,0.00115496764254581
+"1893",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"30450","FAMOTIDINE(PF) IN SAL (ISO-OS) 20 MG/50 ML IV PGBK",1,56,0.004778972520908,35,839,0.0178571428571429,0.0417163289630513,0.428061224489796,55,804,0.597083692744417
+"1894",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",34,2129,0.18168629458952,603,24164,0.0159699389384688,0.0249544777354743,0.639962859882519,2095,23561,0.0120299713035686
+"1895",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"81934","NIMODIPINE 30 MG PO CAPS",1,7,0.000597371565113501,23,372,0.142857142857143,0.0618279569892473,2.31055900621118,6,349,0.929191308179688
+"1896",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"82384","PANTOPRAZOLE 40 MG IV SOLR",5,77,0.00657108721624851,688,12925,0.0649350649350649,0.0532301740812379,1.21989202657807,72,12237,0.840333046841788
+"1897",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"87473","CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DROP",1,476,0.040621266427718,6,455,0.00210084033613445,0.0131868131868132,0.159313725490196,475,449,0.114593293675995
+"1898",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"88043","PANTOPRAZOLE 40 MG PO TBEC",2,206,0.0175797917733402,624,31884,0.00970873786407767,0.0195709446744449,0.496079163554892,204,31260,0.442786757001597
+"1899",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",22,1004,0.0856801501962792,93,5289,0.0219123505976096,0.0175836642087351,1.24617658398663,982,5196,0.417781457498066
+"1900",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",14,587,0.0500938726745178,738,23934,0.0238500851788756,0.0308347956881424,0.773479591695406,573,23196,0.396143142423579
+"1901",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,8,0.000682710360129715,73,1394,0.125,0.0523672883787661,2.38698630136986,7,1321,0.90188164607657
+"1902",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",2,484,0.0413039767878478,499,13896,0.00413223140495868,0.0359096142774899,0.115073121449511,482,13397,0.000292739548466053
+"1903",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",10,517,0.0441201570233828,1219,33427,0.0193423597678917,0.036467526251234,0.530399556982211,507,32208,0.0511878139345419
+"1904",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",4,138,0.0117767537122376,1951,16917,0.0289855072463768,0.115327776792576,0.251331535667328,134,14966,0.00239106640202645
+"1905",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",7,1145,0.0977129202935655,457,11068,0.00611353711790393,0.0412902059992772,0.148062645122452,1138,10611,5.03746372104995e-09
+"1906",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",37,3796,0.32394606588155,705,36939,0.00974710221285564,0.0190855193697718,0.510706678922942,3759,36234,5.49988752412626e-05
+"1907",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",178,2239,0.191073562041304,288,4280,0.0794997766860205,0.0672897196261682,1.18145501463947,2061,3992,0.0773152057363409
+"1908",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",5,583,0.049752517494453,510,18335,0.00857632933104631,0.0278156531224434,0.308327447617126,578,17825,0.00733913001907508
+"1909",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",3,243,0.0207373271889401,452,13480,0.0123456790123457,0.0335311572700297,0.368185294438982,240,13028,0.0994847076367003
+"1910",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"222567","LEVETIRACETAM IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",1,16,0.00136542072025943,50,1098,0.0625,0.0455373406193078,1.3725,15,1048,0.999999999999998
+"1911",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",3,346,0.0295272230756102,111,2584,0.00867052023121387,0.0429566563467492,0.201843461959069,343,2473,0.00318627503392502
+"1912",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"520064","CEFAZOLIN 2 G IVPB",2,279,0.0238095238095238,109,2674,0.00716845878136201,0.0407629020194465,0.175857420012495,277,2565,0.00824090532791913
+"1913",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"520068","PHENYTOIN IVPB (500 MG OR LESS)",1,4,0.000341355180064858,153,533,0.25,0.287054409005629,0.870915032679739,3,380,0.99999999999998
+"1914",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"520301","CEFTRIAXONE 1 GRAM/50 ML NS IVPB",7,550,0.0469363372589179,3,146,0.0127272727272727,0.0205479452054795,0.619393939393939,543,143,0.752964089521779
+"1915",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",8,1171,0.099931728963987,461,18637,0.00683176771989752,0.0247357407308043,0.276190140988569,1163,18176,0.000139175256136937
+"1916",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"540301","POTASSIUM CHLORIDE ORAL SCALE",8,1106,0.0943847072879331,105,3653,0.00723327305605787,0.0287434984943882,0.251649014035994,1098,3548,6.23400195873438e-05
+"1917",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"540302","POTASSIUM CHLORIDE IV SCALE",7,1112,0.0948967400580304,150,4660,0.00629496402877698,0.0321888412017167,0.195563549160671,1105,4510,3.05626428664881e-06
+"1918",1677,"IP NSR NEUROSURGERY ADMISSION/POST-OP",11718,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",3,122,0.0104113329919782,104,5739,0.0245901639344262,0.0181216239763025,1.35695145018916,119,5635,0.852138581297455
+"1919",1687,"IP VAS ANGIO POST- CATH",462,"1080","BISACODYL 10 MG PR SUPP",2,89,0.192640692640693,344,22442,0.0224719101123595,0.0153284021031994,1.46603083355108,87,22098,0.908364940240793
+"1920",1687,"IP VAS ANGIO POST- CATH",462,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,94,0.203463203463203,542,37509,0.0106382978723404,0.0144498653656456,0.736221245191175,93,36967,0.999999999999979
+"1921",1687,"IP VAS ANGIO POST- CATH",462,"2567","DOCUSATE SODIUM 250 MG PO CAPS",2,32,0.0692640692640693,296,17760,0.0625,0.0166666666666667,3.75,30,17464,0.183794454853095
+"1922",1687,"IP VAS ANGIO POST- CATH",462,"4318","LACTATED RINGERS IV SOLP",3,46,0.0995670995670996,135,4847,0.0652173913043478,0.0278522797606767,2.34154589371981,43,4712,0.281866675149382
+"1923",1687,"IP VAS ANGIO POST- CATH",462,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,115,0.248917748917749,346,10128,0.00869565217391304,0.0341627172195893,0.2545363156572,114,9782,0.214268164033043
+"1924",1687,"IP VAS ANGIO POST- CATH",462,"5373","NALOXONE 0.4 MG/ML INJ SOLN",1,135,0.292207792207792,83,1774,0.00740740740740741,0.0467869222096956,0.158322177599286,134,1691,0.0532510541436137
+"1925",1687,"IP VAS ANGIO POST- CATH",462,"11349","SENNOSIDES 8.6 MG PO TABS",2,127,0.274891774891775,610,35793,0.015748031496063,0.0170424384656218,0.92404801858784,125,35183,0.999999999999337
+"1926",1687,"IP VAS ANGIO POST- CATH",462,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,14,0.0303030303030303,258,5335,0.0714285714285714,0.0483598875351453,1.47702104097453,13,5077,0.999999999999558
+"1927",1687,"IP VAS ANGIO POST- CATH",462,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",1,91,0.196969696969697,111,6618,0.010989010989011,0.0167724388032638,0.655182655182655,90,6507,0.987411101440567
+"1928",1687,"IP VAS ANGIO POST- CATH",462,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,50,0.108225108225108,738,23934,0.02,0.0308347956881424,0.648617886178862,49,23196,0.973460051416947
+"1929",1687,"IP VAS ANGIO POST- CATH",462,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,2,0.00432900432900433,499,13896,0.5,0.0359096142774899,13.9238476953908,1,13397,0.10408135842914
+"1930",1687,"IP VAS ANGIO POST- CATH",462,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,24,0.051948051948052,1219,33427,0.0416666666666667,0.036467526251234,1.14256904566585,23,32208,0.999999999999907
+"1931",1687,"IP VAS ANGIO POST- CATH",462,"115141","ESMOLOL IN NACL (ISO-OSM) 2,000 MG/100 ML IV SOLP",1,1,0.00216450216450216,1,114,1,0.0087719298245614,114,0,113,0.000208934116823056
+"1932",1687,"IP VAS ANGIO POST- CATH",462,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,138,0.298701298701299,705,36939,0.0144927536231884,0.0190855193697718,0.759358618563059,136,36234,0.934674342676417
+"1933",1687,"IP VAS ANGIO POST- CATH",462,"530040","ALTEPLASE INFUSION",1,20,0.0432900432900433,22,207,0.05,0.106280193236715,0.470454545454545,19,185,0.682900001968871
+"1934",1687,"IP VAS ANGIO POST- CATH",462,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",1,3,0.00649350649350649,411,7130,0.333333333333333,0.0576437587657784,5.78264395782644,2,6719,0.418658985621735
+"1935",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"94623","ALTEPLASE 2 MG INJ SOLR",2,115,0.0181531176006314,201,12455,0.0173913043478261,0.0161380971497391,1.07765520224962,113,12254,0.999999999999579
+"1936",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"96471","CITRATE DEXTROSE SOLUTION MISC SOLN",25,1010,0.159431728492502,3,75,0.0247524752475248,0.04,0.618811881188119,985,72,0.670033836355298
+"1937",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"126580","HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",19,519,0.0819258089976322,58,653,0.0366088631984586,0.0888208269525268,0.412165304630922,500,595,0.000530349445720894
+"1938",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"205894","PRISMASOL SOLUTION (BGK 2/0) (32 MEQ BICARB/L)",50,1542,0.243409629044988,4,19,0.0324254215304799,0.210526315789474,0.15402075226978,1492,15,0.0003299145056225
+"1939",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"210698","BICARB DIALYS SOLN #15 W-O CA K (4 MEQ/L) -MG (1.2 MEQ/L) HEMODIAL SOLN",62,2050,0.323599052880821,4,19,0.0302439024390244,0.210526315789474,0.143658536585366,1988,15,0.00014739498307499
+"1940",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"211166","BICARB DIALY SOLN W-O CA NO.16 K (4 MEQ/L) -MG (1.5 MEQ/L) HEMODIAL SOLN",41,1375,0.217048145224941,2,20,0.0298181818181818,0.1,0.298181818181818,1334,18,0.249612536766091
+"1941",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"230501","DEXTROSE-SOD CITRATE-CITRIC AC 2.45-2.2 GRAM- 730 MG/100 ML MISC SOLN",7,391,0.0617205998421468,2,37,0.0179028132992327,0.0540540540540541,0.331202046035806,384,35,0.386769024639731
+"1942",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"500006","SODIUM PHOSPHATE IVPB",134,2821,0.445303867403315,296,4785,0.0475008862105636,0.0618599791013584,0.767877501748469,2687,4489,0.0102337146106378
+"1943",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"500034","CALCIUM CHLORIDE 8 G/1000 ML NS IV INFUSION",285,669,0.105603788476717,9,166,0.426008968609865,0.0542168674698795,7.85749875435974,384,157,6.31375959309468e-19
+"1944",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"540911","CALCIUM GLUCONATE 107.6 MEQ NS 1,000 ML (CRRT)",98,638,0.100710339384373,10,519,0.153605015673981,0.0192678227360308,7.97210031347962,540,509,1.25506487926645e-14
+"1945",1705,"IP REN CONTINUOUS RENAL REPLACEMENT THERAPY(CRRT)",6335,"541063","SODIUM GLYCEROPHOSPHATE (GLYCOPHOS) IN NS IVPB (CRRT)",47,457,0.072138910812944,5,39,0.102844638949672,0.128205128205128,0.80218818380744,410,34,0.822781611787748
+"1946",1706,"DRUG DESENSITIZATION PROTOCOL HIGH ALERT",113,"213662","EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",1,44,0.389380530973451,69,5047,0.0227272727272727,0.0136714880126808,1.66238471673254,43,4978,0.999999999999998
+"1947",1706,"DRUG DESENSITIZATION PROTOCOL HIGH ALERT",113,"520186","DESENSITIZATION DRUG MIXTURE-HIGH ALERT MED",18,285,1,7,39,0.0631578947368421,0.179487179487179,0.35187969924812,267,32,0.0255197774977627
+"1948",1706,"DRUG DESENSITIZATION PROTOCOL HIGH ALERT",113,"540138","DESENSITIZATION DRUG HELP ORDER",3,6,0.0530973451327434,6,38,0.5,0.157894736842105,3.16666666666667,3,32,0.165719194946547
+"1949",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"101","ACETAMINOPHEN 325 MG PO TABS",102,2726,0.303631098240143,981,42092,0.0374174614820249,0.0233060914187969,1.60547990693312,2624,41111,4.53127229301411e-06
+"1950",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"663","ASCORBIC ACID 250 MG PO TABS",26,1472,0.163956337714413,39,2351,0.0176630434782609,0.0165886856656742,1.06476449275362,1446,2312,0.903306413953408
+"1951",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"664","ASCORBIC ACID 500 MG PO TABS",17,889,0.0990198262419247,59,3135,0.0191226096737908,0.0188197767145136,1.01609120893787,872,3076,0.999999999999997
+"1952",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"680","ASPIRIN 81 MG PO CHEW",21,441,0.0491200712853642,357,11203,0.0476190476190476,0.031866464339909,1.49433106575964,420,10846,0.0902822725419434
+"1953",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"1080","BISACODYL 10 MG PR SUPP",24,2275,0.253397193138784,344,22442,0.0105494505494505,0.0153284021031994,0.688228980322004,2251,22098,0.0886424615097297
+"1954",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"2566","DOCUSATE SODIUM 100 MG PO CAPS",43,1624,0.180886611717532,542,37509,0.0264778325123153,0.0144498653656456,1.83239302528493,1581,36967,0.000141126267651831
+"1955",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"2567","DOCUSATE SODIUM 250 MG PO CAPS",11,416,0.0463354867453776,296,17760,0.0264423076923077,0.0166666666666667,1.58653846153846,405,17464,0.181215245732847
+"1956",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"5373","NALOXONE 0.4 MG/ML INJ SOLN",32,2203,0.245377589663622,83,1774,0.0145256468452111,0.0467869222096956,0.310463825342222,2171,1691,2.85075030351632e-09
+"1957",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"8084","TRAZODONE 150 MG PO TABS",1,2,0.000222766763198931,1,155,0.5,0.00645161290322581,77.5,1,154,0.00260188853700249
+"1958",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"8085","TRAZODONE 50 MG PO TABS",38,2317,0.258075295165961,334,11344,0.0164005179110919,0.0294428772919605,0.557028368812655,2279,11010,0.000571128408784577
+"1959",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"10011","FAMOTIDINE 20 MG PO TABS",5,82,0.00913343729115616,472,16960,0.0609756097560976,0.0278301886792453,2.19098801157503,77,16488,0.138943468465541
+"1960",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",26,1960,0.218311427934952,14,238,0.013265306122449,0.0588235294117647,0.225510204081633,1934,224,2.49563032520026e-06
+"1961",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"10813","OXYCODONE 5 MG/5 ML PO SOLN",1,6,0.000668300289596792,328,8370,0.166666666666667,0.0391875746714456,4.2530487804878,5,8042,0.578413987906119
+"1962",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"10814","OXYCODONE 5 MG PO TABS",55,2154,0.239919803965248,1478,56614,0.0255338904363974,0.0261066167379094,0.97806202514628,2099,55136,0.924459842962023
+"1963",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"11349","SENNOSIDES 8.6 MG PO TABS",56,2186,0.243484072176431,610,35793,0.0256175663311985,0.0170424384656218,1.50316319949605,2130,35183,0.003959392234265
+"1964",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",44,1457,0.162285586990421,127,3899,0.0301990391214825,0.0325724544755065,0.927134279800475,1413,3772,0.724580691794922
+"1965",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"14113","ASPIRIN 81 MG PO TBEC",42,878,0.0977946090443306,391,18448,0.0478359908883827,0.0211947094535993,2.25697790258027,836,18057,3.49309940523784e-07
+"1966",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"14632","TRAMADOL 50 MG PO TABS",29,2328,0.259300512363555,304,13808,0.0124570446735395,0.0220162224797219,0.565812081750769,2299,13504,0.00347431260175956
+"1967",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",46,2091,0.232902650924482,712,36910,0.021999043519847,0.0192901652668653,1.14042794426622,2045,36198,0.428661614041204
+"1968",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"28899","OXYCODONE 15 MG PO TABS",1,13,0.00144798396079305,51,1050,0.0769230769230769,0.0485714285714286,1.58371040723982,12,999,0.999999999999986
+"1969",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"35942","POTASSIUM CHLORIDE 10 MEQ PO TBTQ",1,103,0.0114724883047449,648,18469,0.00970873786407767,0.035085819481293,0.276714011746374,102,17821,0.258668115326521
+"1970",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"38285","ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",45,2302,0.256404544441969,71,5546,0.0195482189400521,0.0128020194734944,1.52696369354266,2257,5475,0.0313924094175265
+"1971",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",39,2284,0.254399643573179,111,6618,0.0170753064798599,0.0167724388032638,1.01805746201543,2245,6507,0.997853769617888
+"1972",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"87348","EPOETIN ALFA 40,000 UNIT/ML INJ SOLN",67,1200,0.133660057919358,19,641,0.0558333333333333,0.0296411856474259,1.88364035087719,1133,622,0.015471964813697
+"1973",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"88043","PANTOPRAZOLE 40 MG PO TBEC",48,1639,0.182557362441524,624,31884,0.0292861500915192,0.0195709446744449,1.49640963063782,1591,31260,0.00813466767890558
+"1974",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",11,260,0.028959679215861,738,23934,0.0423076923076923,0.0308347956881424,1.37207629768605,249,23196,0.377603683454133
+"1975",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"111320","MULTIVITAMIN WITH IRON-MINERAL PO LIQD",1,11,0.00122521719759412,36,1196,0.0909090909090909,0.0301003344481605,3.02020202020202,10,1160,0.774830487294761
+"1976",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",38,631,0.0702829137892626,1219,33427,0.0602218700475436,0.036467526251234,1.65138347012243,593,32208,0.0024543110120748
+"1977",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",55,2628,0.292715526843395,705,36939,0.0209284627092846,0.0190855193697718,1.09656238867839,2573,36234,0.554163926674737
+"1978",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"208037","FERROUS SULFATE 142 MG (45 MG IRON) PO TBSR",25,1016,0.113165515705057,13,984,0.0246062992125984,0.0132113821138211,1.86250757116899,991,971,0.088704775083491
+"1979",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"209036","ACETAMINOPHEN 325 MG/10.15 ML PO SOLN",1,14,0.00155936734239252,95,1460,0.0714285714285714,0.0650684931506849,1.09774436090226,13,1365,1
+"1980",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,11,0.00122521719759412,452,13480,0.0909090909090909,0.0335311572700297,2.71118262268705,10,13028,0.826841950122822
+"1981",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"213228","FERROUS SULFATE 140 MG (45 MG IRON) PO TBSR",28,1263,0.140677210960125,14,999,0.0221694378463975,0.014014014014014,1.58194774346793,1235,985,0.204079331761425
+"1982",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",37,1399,0.155825350857652,461,18637,0.0264474624731951,0.0247357407308043,1.06920034297817,1362,18176,0.758389797090711
+"1983",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"540301","POTASSIUM CHLORIDE ORAL SCALE",46,2309,0.257184228113165,105,3653,0.0199220441749675,0.0287434984943882,0.693097403534822,2263,3548,0.0426395755168543
+"1984",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"540579","BENZOCAINE-MENTHOL SUGAR FREE MAXIMUM STRENGTH LOZG",19,2136,0.237914903096458,13,677,0.00889513108614232,0.0192023633677991,0.463231057332181,2117,664,0.0459642460900526
+"1985",1713,"IP CTS CARDIAC SURGERY TRANSFER",8978,"540597","THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS",39,2303,0.256515927823569,106,7394,0.0169344333478072,0.0143359480659995,1.18125660541214,2264,7288,0.424349319575144
+"1986",1716,"IP ATIC RITUXIMAB (RITUXAN)",28,"101","ACETAMINOPHEN 325 MG PO TABS",1,2,0.0714285714285714,981,42092,0.5,0.0233060914187969,21.453618756371,1,41111,0.0336916546422699
+"1987",1716,"IP ATIC RITUXIMAB (RITUXAN)",28,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",2,7,0.25,275,8000,0.285714285714286,0.034375,8.31168831168831,5,7725,0.00925230614654436
+"1988",1716,"IP ATIC RITUXIMAB (RITUXAN)",28,"520000","RITUXIMAB IV STANDARD INFUSION",3,14,0.5,14,1252,0.214285714285714,0.0111821086261981,19.1632653061224,11,1238,6.72254757385862e-08
+"1989",1717,"IP ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN (CYTOGAM)",3,"101","ACETAMINOPHEN 325 MG PO TABS",1,3,1,981,42092,0.333333333333333,0.0233060914187969,14.3024125042474,2,41111,0.100002477677862
+"1990",1717,"IP ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN (CYTOGAM)",3,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,2,0.666666666666667,356,12049,0.5,0.0295460204166321,16.9227528089888,1,11693,0.0660143530444397
+"1991",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"122","ACETYLCYSTEINE 100 MG/ML (10 %) MISC SOLN",4,21,0.016030534351145,39,431,0.19047619047619,0.0904872389791183,2.10500610500611,17,392,0.252548476433774
+"1992",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",5,154,0.117557251908397,106,3939,0.0324675324675325,0.0269103833460269,1.20650575839255,149,3833,0.870009244882793
+"1993",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"251","ALBUTEROL SULFATE 5 MG/ML INH NEBU",1,7,0.00534351145038168,100,358,0.142857142857143,0.279329608938547,0.511428571428571,6,258,0.709311338353464
+"1994",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"12580","IPRATROPIUM BROMIDE 0.02 % INH SOLN",1,74,0.0564885496183206,127,3899,0.0135135135135135,0.0325724544755065,0.414875505426687,73,3772,0.556847868196874
+"1995",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"78668","IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU",12,386,0.294656488549618,493,18456,0.0310880829015544,0.0267121803207629,1.16381675057016,374,17963,0.713149495328523
+"1996",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"92642","LEVALBUTEROL HCL 1.25 MG/3 ML INH NEBU",2,39,0.0297709923664122,46,1262,0.0512820512820513,0.0364500792393027,1.40691192865106,37,1216,0.957967358926503
+"1997",1724,"IP RESP INHALED MEDICATION NEBULIZER/MDI",1310,"225590","IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH AERO",3,6,0.00458015267175573,49,718,0.5,0.0682451253481894,7.3265306122449,3,669,0.00101923339490384
+"1998",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"2327","DEXAMETHASONE 4 MG PO TABS",1,5,0.0862068965517241,277,6991,0.2,0.0396223716206551,5.04765342960289,4,6714,0.490144863674697
+"1999",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"2327","DEXAMETHASONE 4 MG PO TABS",1,5,0.0862068965517241,277,6991,0.2,0.0396223716206551,5.04765342960289,4,6714,0.490144863674697
+"2000",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,41,0.706896551724138,399,16019,0.024390243902439,0.0249079218428117,0.979216333516719,40,15620,0.999999999999356
+"2001",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,41,0.706896551724138,399,16019,0.024390243902439,0.0249079218428117,0.979216333516719,40,15620,0.999999999999356
+"2002",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"520000","RITUXIMAB IV STANDARD INFUSION",1,30,0.517241379310345,14,1252,0.0333333333333333,0.0111821086261981,2.98095238095238,29,1238,0.797977833102665
+"2003",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"520000","RITUXIMAB IV STANDARD INFUSION",1,30,0.517241379310345,14,1252,0.0333333333333333,0.0111821086261981,2.98095238095238,29,1238,0.797977833102665
+"2004",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"9000000","ZZZ IMS TEMPLATE",1,3,0.0517241379310345,1035,28824,0.333333333333333,0.0359075770191507,9.28309178743961,2,27789,0.223786333563954
+"2005",1729,"RETIRED IP ITA PHARM RITUXIMAB",58,"9000000","ZZZ IMS TEMPLATE",1,3,0.0517241379310345,1035,28824,0.333333333333333,0.0359075770191507,9.28309178743961,2,27789,0.223786333563954
+"2006",1742,"IP NUC I-131 THYROID THERAPY ADMISSION",50,"11138","PROCHLORPERAZINE 25 MG PR SUPP",2,8,0.16,6,140,0.25,0.0428571428571429,5.83333333333333,6,134,0.0861239795831721
+"2007",1747,"RETIRED IP PAMF ORTHO TKA",144,"8751","WARFARIN 5 MG PO TABS",1,12,0.0833333333333333,131,4609,0.0833333333333333,0.0284226513343458,2.93193384223919,11,4478,0.785005273568251
+"2008",1748,"RETIRED IP PAMF ORTHO THA",2288,"101","ACETAMINOPHEN 325 MG PO TABS",1,449,0.196241258741259,981,42092,0.0022271714922049,0.0233060914187969,0.0955617761976439,448,41111,0.00510020537562935
+"2009",1748,"RETIRED IP PAMF ORTHO THA",2288,"102","ACETAMINOPHEN 500 MG PO TABS",1,225,0.0983391608391608,392,15968,0.00444444444444444,0.0245490981963928,0.181043083900227,224,15576,0.0840045859027544
+"2010",1748,"RETIRED IP PAMF ORTHO THA",2288,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",5,1398,0.611013986013986,399,16019,0.00357653791130186,0.0249079218428117,0.143590377947731,1393,15620,6.0720780059539e-07
+"2011",1748,"RETIRED IP PAMF ORTHO THA",2288,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,823,0.359702797202797,356,12049,0.00121506682867558,0.0295460204166321,0.0411245511761574,822,11693,2.88396956474914e-06
+"2012",1748,"RETIRED IP PAMF ORTHO THA",2288,"3770","HYDROXYZINE HCL 50 MG/ML IM SOLN",91,516,0.225524475524476,18,59,0.176356589147287,0.305084745762712,0.578057708871662,425,41,0.0267988800228766
+"2013",1748,"RETIRED IP PAMF ORTHO THA",2288,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",1,802,0.350524475524476,289,5149,0.00124688279301746,0.056127403379297,0.0222152231877055,801,4860,3.44014265870588e-11
+"2014",1748,"RETIRED IP PAMF ORTHO THA",2288,"7753","TEMAZEPAM 15 MG PO CAPS",1,172,0.0751748251748252,24,941,0.00581395348837209,0.0255047821466525,0.227955426356589,171,917,0.185954367135408
+"2015",1748,"RETIRED IP PAMF ORTHO THA",2288,"10814","OXYCODONE 5 MG PO TABS",1,849,0.371066433566434,1478,56614,0.00117785630153121,0.0261066167379094,0.0451171560587876,848,55136,8.83865982595184e-06
+"2016",1748,"RETIRED IP PAMF ORTHO THA",2288,"13410","ACETAMINOPHEN 650 MG PO TBSR",4,15,0.00655594405594406,8,179,0.266666666666667,0.0446927374301676,5.96666666666667,11,171,0.00410291235049868
+"2017",1748,"RETIRED IP PAMF ORTHO THA",2288,"14858","D5-NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",3,546,0.238636363636364,58,2031,0.00549450549450549,0.0285573609059577,0.192402425161046,543,1973,0.00280367154534546
+"2018",1748,"RETIRED IP PAMF ORTHO THA",2288,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,174,0.0760489510489511,314,8762,0.0114942528735632,0.035836566993837,0.320740903433633,172,8448,0.129958958139285
+"2019",1748,"RETIRED IP PAMF ORTHO THA",2288,"27920","OXYCODONE 10 MG PO TB12",3,685,0.299388111888112,258,7208,0.00437956204379562,0.0357935627081021,0.122356136479375,682,6950,1.85075520514323e-05
+"2020",1748,"RETIRED IP PAMF ORTHO THA",2288,"27922","OXYCODONE 40 MG PO TB12",1,4,0.00174825174825175,47,1081,0.25,0.0434782608695652,5.75,3,1034,0.431308047100132
+"2021",1748,"RETIRED IP PAMF ORTHO THA",2288,"83728","FONDAPARINUX 2.5 MG/0.5 ML SC SYRG",2,259,0.113199300699301,10,158,0.00772200772200772,0.0632911392405063,0.122007722007722,257,148,0.00278189350527088
+"2022",1748,"RETIRED IP PAMF ORTHO THA",2288,"88043","PANTOPRAZOLE 40 MG PO TBEC",2,481,0.210227272727273,624,31884,0.00415800415800416,0.0195709446744449,0.212458020150328,479,31260,0.0232476554663384
+"2023",1748,"RETIRED IP PAMF ORTHO THA",2288,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",5,521,0.22770979020979,73,1394,0.00959692898272553,0.0523672883787661,0.183261904135882,516,1321,4.42807044110727e-05
+"2024",1748,"RETIRED IP PAMF ORTHO THA",2288,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,830,0.362762237762238,705,36939,0.00240963855421687,0.0190855193697718,0.126254806459882,828,36234,0.000735134760208414
+"2025",1748,"RETIRED IP PAMF ORTHO THA",2288,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",1,497,0.21722027972028,510,18335,0.00201207243460765,0.0278156531224434,0.0723359766441788,496,17825,0.000797644895544126
+"2026",1748,"RETIRED IP PAMF ORTHO THA",2288,"540598","THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS",10,752,0.328671328671329,17,1250,0.0132978723404255,0.0136,0.977784730913642,742,1233,0.999999999999999
+"2027",1750,"RETIRED IP PAMF ORTHO SPINE",407,"105","ACETAMINOPHEN 650 MG PR SUPP",1,133,0.326781326781327,59,929,0.0075187969924812,0.0635091496232508,0.118389193322289,132,870,0.0157354797045704
+"2028",1750,"RETIRED IP PAMF ORTHO SPINE",407,"3770","HYDROXYZINE HCL 50 MG/ML IM SOLN",17,129,0.316953316953317,18,59,0.131782945736434,0.305084745762712,0.431955211024978,112,41,0.00851437994107084
+"2029",1750,"RETIRED IP PAMF ORTHO SPINE",407,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",1,134,0.329238329238329,218,7862,0.00746268656716418,0.0277283134062579,0.269135971518554,133,7644,0.246722210737541
+"2030",1750,"RETIRED IP PAMF ORTHO SPINE",407,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,120,0.294840294840295,314,8762,0.00833333333333333,0.035836566993837,0.232537154989384,119,8448,0.17085325180059
+"2031",1750,"RETIRED IP PAMF ORTHO SPINE",407,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,1,0.00245700245700246,158,4300,1,0.0367441860465116,27.2151898734177,0,4142,0.0141160166500026
+"2032",1750,"RETIRED IP PAMF ORTHO SPINE",407,"27920","OXYCODONE 10 MG PO TB12",1,1,0.00245700245700246,258,7208,1,0.0357935627081021,27.937984496124,0,6950,0.0126409795875591
+"2033",1750,"RETIRED IP PAMF ORTHO SPINE",407,"27921","OXYCODONE 20 MG PO TB12",1,2,0.00491400491400491,93,2927,0.5,0.0317731465664503,15.7365591397849,1,2834,0.0802743980708211
+"2034",1750,"RETIRED IP PAMF ORTHO SPINE",407,"36063","0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",1,22,0.0540540540540541,24,759,0.0454545454545455,0.0316205533596838,1.4375,21,735,0.999999999999999
+"2035",1750,"RETIRED IP PAMF ORTHO SPINE",407,"88043","PANTOPRAZOLE 40 MG PO TBEC",1,107,0.262899262899263,624,31884,0.00934579439252336,0.0195709446744449,0.477534148094896,106,31260,0.679527553737912
+"2036",1750,"RETIRED IP PAMF ORTHO SPINE",407,"520064","CEFAZOLIN 2 G IVPB",1,63,0.154791154791155,109,2674,0.0158730158730159,0.0407629020194465,0.389398572884811,62,2565,0.503028085770801
+"2037",1750,"RETIRED IP PAMF ORTHO SPINE",407,"540101","FENTANYL 50 MCG/ML IV PCA",1,15,0.0368550368550369,39,965,0.0666666666666667,0.0404145077720207,1.64957264957265,14,926,0.999999999999997
+"2038",1750,"RETIRED IP PAMF ORTHO SPINE",407,"540598","THERAPEUTIC MULTIVITAMIN & MINERALS (GENERIC) PO TABS",1,131,0.321867321867322,17,1250,0.00763358778625954,0.0136,0.561293219577907,130,1233,0.866602343928154
+"2039",1772,"PHARM IP THYMOGLOBULIN CYCLOSPORINE",20,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",2,5,0.25,275,8000,0.4,0.034375,11.6363636363636,3,7725,0.00116256084009681
+"2040",1772,"PHARM IP THYMOGLOBULIN CYCLOSPORINE",20,"520074","ANTITHYMOCYTE GLOBULIN RABBIT IVPB",2,12,0.6,75,1781,0.166666666666667,0.0421111734980348,3.95777777777778,10,1706,0.159487740318429
+"2041",1777,"IP REH PHASE I EPILEPSY ADMIT",1313,"101","ACETAMINOPHEN 325 MG PO TABS",3,487,0.370906321401371,981,42092,0.00616016427104723,0.0233060914187969,0.264315631495331,484,41111,0.0186644102701994
+"2042",1777,"IP REH PHASE I EPILEPSY ADMIT",1313,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",3,246,0.187357197258187,356,12049,0.0121951219512195,0.0295460204166321,0.412750068511921,243,11693,0.158878922374058
+"2043",1777,"IP REH PHASE I EPILEPSY ADMIT",1313,"13632","BISACODYL 5 MG PO TBEC",1,333,0.253617669459254,49,3268,0.003003003003003,0.0149938800489596,0.200281914567629,332,3219,0.124632530557371
+"2044",1779,"IP REH PHASE II EPILEPSY ADMIT",61,"2566","DOCUSATE SODIUM 100 MG PO CAPS",2,7,0.114754098360656,542,37509,0.285714285714286,0.0144498653656456,19.772799156563,5,36967,9.77232785515998e-06
+"2045",1779,"IP REH PHASE II EPILEPSY ADMIT",61,"86533","MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP",1,7,0.114754098360656,111,6618,0.142857142857143,0.0167724388032638,8.51737451737452,6,6507,0.262905074602558
+"2046",1779,"IP REH PHASE II EPILEPSY ADMIT",61,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,3,0.0491803278688525,499,13896,0.333333333333333,0.0359096142774899,9.28256513026052,2,13397,0.2241042464862
+"2047",1791,"IP ICU END OF LIFE CARE",2449,"101","ACETAMINOPHEN 325 MG PO TABS",1,27,0.0110249081257656,981,42092,0.037037037037037,0.0233060914187969,1.58915694491637,26,41111,0.999999999999999
+"2048",1791,"IP ICU END OF LIFE CARE",2449,"736","ATROPINE 1 % OPHT DROP",3,140,0.0571661902817477,13,270,0.0214285714285714,0.0481481481481481,0.445054945054945,137,257,0.291001665696747
+"2049",1791,"IP ICU END OF LIFE CARE",2449,"3497","GLYCOPYRROLATE 0.2 MG/ML INJ SOLN",17,660,0.269497754185382,61,1031,0.0257575757575758,0.0591658583899127,0.435345255837059,643,970,0.00209718885569453
+"2050",1791,"IP ICU END OF LIFE CARE",2449,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",5,220,0.0898325847284606,346,10128,0.0227272727272727,0.0341627172195893,0.665265370467683,215,9782,0.460065526232024
+"2051",1791,"IP ICU END OF LIFE CARE",2449,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",1,120,0.0489995916700694,289,5149,0.00833333333333333,0.056127403379297,0.148471741637832,119,4860,0.0387353822305805
+"2052",1791,"IP ICU END OF LIFE CARE",2449,"7108","SCOPOLAMINE HBR 0.4 MG/ML INJ SOLN",3,93,0.0379746835443038,6,29,0.032258064516129,0.206896551724138,0.155913978494624,90,23,0.00624931461878801
+"2053",1791,"IP ICU END OF LIFE CARE",2449,"10467","LORAZEPAM 2 MG/ML INJ SOLN",14,552,0.225398121682319,749,20621,0.0253623188405797,0.0363221958197954,0.698259515102262,538,19872,0.212124792503016
+"2054",1791,"IP ICU END OF LIFE CARE",2449,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",7,352,0.143732135565537,155,5616,0.0198863636363636,0.0275997150997151,0.720527859237537,345,5461,0.487174797205163
+"2055",1791,"IP ICU END OF LIFE CARE",2449,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",2,125,0.051041241322989,93,5289,0.016,0.0175836642087351,0.909935483870968,123,5196,0.999999999999982
+"2056",1791,"IP ICU END OF LIFE CARE",2449,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",8,184,0.0751327072274398,738,23934,0.0434782608695652,0.0308347956881424,1.41003888299753,176,23196,0.439466696201839
+"2057",1791,"IP ICU END OF LIFE CARE",2449,"112564","MORPHINE 2 MG/ML INJ SYRG",21,390,0.159248672927726,276,6156,0.0538461538461538,0.0448343079922027,1.20100334448161,369,5880,0.48153989578227
+"2058",1791,"IP ICU END OF LIFE CARE",2449,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",8,177,0.0722743977133524,1219,33427,0.0451977401129944,0.036467526251234,1.23939693089177,169,32208,0.676889913160514
+"2059",1791,"IP ICU END OF LIFE CARE",2449,"126132","MORPHINE (PF) IN D5W 100 MG/100 ML (1 MG/ML) IV SOLP",20,245,0.100040832993058,21,123,0.0816326530612245,0.170731707317073,0.478134110787172,225,102,0.0169906108909208
+"2060",1791,"IP ICU END OF LIFE CARE",2449,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",12,577,0.235606369946917,705,36939,0.0207972270363951,0.0190855193697718,1.08968619786865,565,36234,0.884890396999334
+"2061",1791,"IP ICU END OF LIFE CARE",2449,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",11,334,0.136382196815027,510,18335,0.0329341317365269,0.0278156531224434,1.18401432429259,323,17825,0.692677454862874
+"2062",1791,"IP ICU END OF LIFE CARE",2449,"530003","FENTANYL  10 MCG/ML IV INFUSION",1,23,0.00939158840342997,51,585,0.0434782608695652,0.0871794871794872,0.498721227621483,22,534,0.722552320993599
+"2063",1791,"IP ICU END OF LIFE CARE",2449,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",7,141,0.0575745202123316,65,1105,0.049645390070922,0.0588235294117647,0.843971631205674,134,1040,0.80396207345777
+"2064",1791,"IP ICU END OF LIFE CARE",2449,"530019","LORAZEPAM 0.5 MG/ML IV INFUSION",6,72,0.0293997550020417,2,31,0.0833333333333333,0.0645161290322581,1.29166666666667,66,29,0.999999999999999
+"2065",1791,"IP ICU END OF LIFE CARE",2449,"530120","HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)",23,199,0.0812576561861984,130,2894,0.115577889447236,0.0449205252246026,2.57294163123309,176,2764,1.89115975697312e-05
+"2066",1791,"IP ICU END OF LIFE CARE",2449,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",6,162,0.0661494487545937,461,18637,0.037037037037037,0.0247357407308043,1.49730858841488,156,18176,0.454386201384984
+"2067",1791,"IP ICU END OF LIFE CARE",2449,"541048","HYDROMORPHONE VARIABLE DOSE INJECTION",1,31,0.0126582278481013,104,5739,0.032258064516129,0.0181216239763025,1.78008684863524,30,5635,0.999999999999996
+"2068",1791,"IP ICU END OF LIFE CARE",2449,"541350","MORPHINE 1 MG/ML IV INFUSION",10,63,0.0257247856267864,6,60,0.158730158730159,0.1,1.58730158730159,53,54,0.484096623601727
+"2069",1799,"IP ICU CRISIS",111,"7536","SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN",1,2,0.018018018018018,28,870,0.5,0.032183908045977,15.5357142857143,1,842,0.086999225120579
+"2070",1799,"IP ICU CRISIS",111,"20472","ETOMIDATE 2 MG/ML IV SOLN",1,2,0.018018018018018,32,1350,0.5,0.0237037037037037,21.09375,1,1318,0.0385476780770988
+"2071",1799,"IP ICU CRISIS",111,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",1,1,0.00900900900900901,461,18637,1,0.0247357407308043,40.4273318872017,0,18176,0.00223917762549783
+"2072",1799,"IP ICU CRISIS",111,"540114","PROPOFOL 10 MG/ML IV INJECTION",1,2,0.018018018018018,44,2013,0.5,0.0218579234972678,22.875,1,1969,0.0292534248787832
+"2073",1800,"IP ITA PHARM APHERESIS",631,"520147","ALBUMIN 5% (PHARMACY COMPOUNDED MIXTURE)",15,505,0.800316957210777,12,333,0.0297029702970297,0.036036036036036,0.824257425742574,490,321,0.757950790058368
+"2074",1803,"IP ICU INTUBATION",922,"7536","SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN",4,75,0.0813449023861171,28,870,0.0533333333333333,0.032183908045977,1.65714285714286,71,842,0.522860218687502
+"2075",1803,"IP ICU INTUBATION",922,"20472","ETOMIDATE 2 MG/ML IV SOLN",5,116,0.125813449023861,32,1350,0.0431034482758621,0.0237037037037037,1.81842672413793,111,1318,0.332098455052689
+"2076",1803,"IP ICU INTUBATION",922,"83055","ROCURONIUM 10 MG/ML IV SOLN",2,116,0.125813449023861,45,1570,0.0172413793103448,0.0286624203821656,0.601532567049808,114,1525,0.668049748930588
+"2077",1803,"IP ICU INTUBATION",922,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",2,40,0.0433839479392625,291,6612,0.05,0.044010889292196,1.1360824742268,38,6321,0.999999999999839
+"2078",1803,"IP ICU INTUBATION",922,"236616","SUGAMMADEX 100 MG/ML IV SOLN",1,10,0.0108459869848156,1,12,0.1,0.0833333333333333,1.2,9,11,1
+"2079",1803,"IP ICU INTUBATION",922,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",2,6,0.00650759219088937,65,1105,0.333333333333333,0.0588235294117647,5.66666666666667,4,1040,0.0503259496702918
+"2080",1803,"IP ICU INTUBATION",922,"530025","DEXMEDETOMIDINE IV INFUSION",14,32,0.03470715835141,530,2902,0.4375,0.18263266712612,2.39551886792453,18,2372,0.00053850907472089
+"2081",1803,"IP ICU INTUBATION",922,"530120","HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)",3,46,0.0498915401301518,130,2894,0.0652173913043478,0.0449205252246026,1.45183946488294,43,2764,0.764446833372048
+"2082",1803,"IP ICU INTUBATION",922,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",1,89,0.096529284164859,461,18637,0.0112359550561798,0.0247357407308043,0.454239684125862,88,18176,0.633664481051764
+"2083",1803,"IP ICU INTUBATION",922,"540114","PROPOFOL 10 MG/ML IV INJECTION",2,105,0.113882863340564,44,2013,0.019047619047619,0.0218579234972678,0.871428571428572,103,1969,0.999999999999999
+"2084",1825,"PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN",4,"101","ACETAMINOPHEN 325 MG PO TABS",1,2,0.5,981,42092,0.5,0.0233060914187969,21.453618756371,1,41111,0.0336916546422699
+"2085",1825,"PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN",4,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",3,4,1,356,12049,0.75,0.0295460204166321,25.3841292134831,1,11693,2.48867735141298e-12
+"2086",1825,"PHARM ATIC CYTOMEGALOVIRUS IMMUNE GLOBULIN",4,"85809","CYTOMEGALOVIRUS IMMUNE GLOB 50 MG/ML IV SOLN",1,1,0.25,5,301,1,0.0166112956810631,60.2,0,296,0.000568091033889762
+"2087",1828,"PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN",168,"101","ACETAMINOPHEN 325 MG PO TABS",4,114,0.678571428571429,981,42092,0.0350877192982456,0.0233060914187969,1.50551710571025,110,41111,0.602029702409117
+"2088",1828,"PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN",168,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",8,205,1,399,16019,0.0390243902439024,0.0249079218428117,1.56674613362675,197,15620,0.289373484111485
+"2089",1828,"PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN",168,"210379","HYDROCORTISONE SOD SUCC (PF) 100 MG/2 ML INJ SOLR",4,102,0.607142857142857,313,9164,0.0392156862745098,0.0341553906591008,1.14815510868884,98,8851,0.9954249478111
+"2090",1828,"PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN",168,"212610","IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",7,127,0.755952380952381,73,1504,0.0551181102362205,0.0485372340425532,1.13558407938734,120,1431,0.907795737006622
+"2091",1828,"PHARM ATIC INTRAVENOUS IMMUNE GLOBULIN",168,"213662","EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",2,97,0.577380952380952,69,5047,0.0206185567010309,0.0136714880126808,1.5081428357986,95,4978,0.887401727677434
+"2092",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",469,21973,0.335686022885253,134,3551,0.021344377190188,0.0377358490566038,0.565625995539981,21504,3417,3.46997676995392e-09
+"2093",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"16050","GLUCOSE 4 GRAM PO CHEW",372,20986,0.320607421666132,17,1600,0.0177261031163633,0.010625,1.66833911683419,20614,1583,0.044981953045891
+"2094",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"78748","INSULIN GLARGINE 100 UNIT/ML SC SOLN",75,1754,0.0267962173640711,348,8601,0.0427594070695553,0.0404604115800488,1.05682086265875,1679,8253,0.706071098482633
+"2095",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"88904","INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN",224,5062,0.0773332111156943,482,5722,0.0442512840774397,0.0842362810206222,0.525323335043797,4838,5240,7.50206592030752e-17
+"2096",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"89742","INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP",135,2832,0.0432650442275081,703,13345,0.0476694915254237,0.0526789059572874,0.904906635002532,2697,12642,0.29564227217485
+"2097",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"92835","INSULIN LISPRO 100 UNIT/ML SC SOLN",700,17102,0.261270757902134,516,10567,0.0409308852765758,0.0488312671524558,0.83821059053794,16402,10051,0.00203766484568977
+"2098",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"125813","INSULIN DETEMIR 100 UNIT/ML SC SOLN",37,678,0.0103579449103992,215,3971,0.0545722713864307,0.0541425333669101,1.00793716128147,641,3756,0.999999999999999
+"2099",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541452","INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING",36,1680,0.0256657042027591,1,117,0.0214285714285714,0.00854700854700855,2.50714285714286,1644,116,0.54050327154616
+"2100",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541453","INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING",43,2484,0.0379485769283652,6,257,0.0173107890499195,0.0233463035019455,0.741478797638218,2441,251,0.654236691737368
+"2101",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541454","INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING",50,1803,0.0275448004033182,6,238,0.0277315585135885,0.0252100840336134,1.10001848770568,1753,232,0.98985003943755
+"2102",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541455","INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING",11,534,0.00815802740730556,1,100,0.0205992509363296,0.01,2.05992509363296,523,99,0.753489250943397
+"2103",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541457","INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO",19,1147,0.0175229540003361,1,57,0.016564952048823,0.0175438596491228,0.944202266782912,1128,56,0.99999999999999
+"2104",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541461","INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE FEEDING",20,621,0.0094871442320913,2,36,0.0322061191626409,0.0555555555555556,0.579710144927536,601,34,0.778977199519159
+"2105",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541462","INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING",19,342,0.00522480406984738,1,16,0.0555555555555556,0.0625,0.888888888888889,323,15,0.999999999999993
+"2106",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541463","INSULIN REGULAR 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING",8,106,0.00161938371755504,1,12,0.0754716981132075,0.0833333333333333,0.905660377358491,98,11,0.999999999999997
+"2107",1841,"IP SUBCUTANEOUS INSULIN ORDER SET",65457,"541464","INSULIN REGULAR 100 UNIT/ML SC MILD SCALE NPO",7,364,0.00556090257726446,1,11,0.0192307692307692,0.0909090909090909,0.211538461538462,357,10,0.574135998325866
+"2108",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"78748","INSULIN GLARGINE 100 UNIT/ML SC SOLN",17,319,0.0166919575113809,348,8601,0.0532915360501567,0.0404604115800488,1.31712787806724,302,8253,0.321176357111829
+"2109",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"88904","INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN",100,1830,0.0957563706765737,482,5722,0.0546448087431694,0.0842362810206222,0.648708704623268,1730,5240,4.47922086108798e-05
+"2110",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"89742","INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP",60,1566,0.0819423368740516,703,13345,0.0383141762452107,0.0526789059572874,0.7273153371157,1506,12642,0.0173133479194475
+"2111",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"92835","INSULIN LISPRO 100 UNIT/ML SC SOLN",239,6488,0.339490345874104,516,10567,0.0368372379778052,0.0488312671524558,0.754378088588115,6249,10051,0.000253490787844653
+"2112",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"125813","INSULIN DETEMIR 100 UNIT/ML SC SOLN",50,909,0.0475642300245932,215,3971,0.055005500550055,0.0541425333669101,1.01593880318264,859,3756,0.982094905606407
+"2113",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541452","INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING",6,407,0.0212966354455549,1,117,0.0147420147420147,0.00854700854700855,1.72481572481572,401,116,0.954112373434769
+"2114",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541453","INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING",20,786,0.0411281460938726,6,257,0.0254452926208651,0.0233463035019455,1.08990670059372,766,251,0.999999999999984
+"2115",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541454","INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING",17,636,0.0332792632515305,6,238,0.0267295597484277,0.0252100840336134,1.06027253668763,619,232,0.999999999999999
+"2116",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541455","INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING",12,247,0.0129244937470567,1,100,0.048582995951417,0.01,4.8582995951417,235,99,0.160877211609823
+"2117",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541457","INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO",7,326,0.0170582387106902,1,57,0.0214723926380368,0.0175438596491228,1.2239263803681,319,56,0.999999999999999
+"2118",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541461","INSULIN REGULAR 100 UNIT/ML SC MODERATE SCALE FEEDING",4,114,0.00596515096018,2,36,0.0350877192982456,0.0555555555555556,0.631578947368421,110,34,0.953321199346075
+"2119",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541462","INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING",3,86,0.00450002616294281,1,16,0.0348837209302326,0.0625,0.558139534883721,83,15,1
+"2120",1842,"IP SUBCUTANEOUS INSULIN ADJUSTMENT ORDER SET",19111,"541464","INSULIN REGULAR 100 UNIT/ML SC MILD SCALE NPO",3,68,0.00355816022186175,1,11,0.0441176470588235,0.0909090909090909,0.485294117647059,65,10,1
+"2121",2030,"SHC AMB ONC BMT",9,"94670","TACROLIMUS 1 MG PO CAPS",1,5,0.555555555555556,322,9479,0.2,0.0339698280409326,5.88757763975155,4,9157,0.416117012664054
+"2122",2264,"CC BMT POST BONE MARROW HARVEST",4,"15861","D5 %-0.45 % SODIUM CHLORIDE IV SOLP",1,1,0.25,158,4300,1,0.0367441860465116,27.2151898734177,0,4142,0.0141160166500026
+"2123",2342,"CC BMT PRESCRIPTION MEDS HPC - APHERESIS",189,"80840","ONDANSETRON HCL 8 MG PO TABS",5,97,0.513227513227513,170,10077,0.0515463917525773,0.0168701002282425,3.0554881746513,92,9907,0.0262950235200866
+"2124",2342,"CC BMT PRESCRIPTION MEDS HPC - APHERESIS",189,"112594","FILGRASTIM 300 MCG/0.5 ML INJ SYRG",1,13,0.0687830687830688,96,2178,0.0769230769230769,0.0440771349862259,1.74519230769231,12,2082,0.999999999999997
+"2125",2345,"CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE",101,"4572","LORAZEPAM 0.5 MG PO TABS",1,2,0.0198019801980198,349,13238,0.5,0.0263634990179785,18.9656160458453,1,12889,0.0487278170363927
+"2126",2345,"CC BMT PRESCRIPTION MEDS NON-MYELOABLATIVE",101,"8971","ACYCLOVIR 400 MG PO TABS",1,29,0.287128712871287,144,5498,0.0344827586206897,0.0261913423062932,1.31657088122605,28,5354,0.99999999999967
+"2127",2353,"CC BMT PRESCRIPTION MEDS ABLATIVE ALLOGENEIC OUTPATIENT",58,"11624","URSODIOL 300 MG PO CAPS",3,40,0.689655172413793,49,2387,0.075,0.0205278592375367,3.65357142857143,37,2338,0.070446257859639
+"2128",2398,"CC BMT PRESCRIPTION MEDS POST ABLATIVE AUTOLOGOUS HPCT ",10,"8971","ACYCLOVIR 400 MG PO TABS",2,6,0.6,144,5498,0.333333333333333,0.0261913423062932,12.7268518518519,4,5354,0.000653652535428509
+"2129",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"101","ACETAMINOPHEN 325 MG PO TABS",2,20,0.0284900284900285,981,42092,0.1,0.0233060914187969,4.29072375127421,18,41111,0.12591645547746
+"2130",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"3757","HYDROMORPHONE (PF) 1 MG/ML INJ SOLN",2,3,0.00427350427350427,6,25,0.666666666666667,0.24,2.77777777777778,1,19,0.384583218571728
+"2131",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",12,57,0.0811965811965812,603,24164,0.210526315789474,0.0249544777354743,8.43641441913241,45,23561,2.36636554441755e-17
+"2132",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"94176","ONDANSETRON 4 MG PO TBDL",2,3,0.00427350427350427,153,4277,0.666666666666667,0.0357727379003975,18.636165577342,1,4124,1.698305825378e-05
+"2133",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"112564","MORPHINE 2 MG/ML INJ SYRG",22,197,0.280626780626781,276,6156,0.111675126903553,0.0448343079922027,2.49084087397925,175,5880,2.71071048179241e-05
+"2134",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",10,91,0.12962962962963,1219,33427,0.10989010989011,0.036467526251234,3.01336891164619,81,32208,0.000576618272092833
+"2135",2440,"NURSING TRIAGE PAIN MANAGEMENT PROTOCOL",702,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",10,200,0.284900284900285,705,36939,0.05,0.0190855193697718,2.61978723404255,190,36234,0.00355535139243901
+"2136",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540325","HEPARIN IV BOLUS (DVT/PE PROTOCOL)",82,852,0.0461988938293027,3,16,0.0962441314553991,0.1875,0.513302034428795,770,13,0.428201625108347
+"2137",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540326","HEPARIN IV BOLUS (A-FIB PROTOCOL, A-FIB & INR < 2)",40,485,0.0262986660882768,1,9,0.0824742268041237,0.111111111111111,0.742268041237113,445,8,0.999999999999998
+"2138",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540329","HEPARIN IV INFUSION (ACS PROTOCOL)",74,1329,0.0720637674872573,3,28,0.0556809631301731,0.107142857142857,0.519688989214949,1255,25,0.451973634839307
+"2139",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540336","HEPARIN IV INFUSION (DVT/PE PROTOCOL)",97,1374,0.0745038499078191,3,49,0.0705967976710335,0.0612244897959184,1.15308102862688,1277,46,0.99999999999999
+"2140",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540338","HEPARIN IV INFUSION (A-FIB PROTOCOL, A-FIB & INR > 2)",10,147,0.00797093590716842,1,9,0.0680272108843537,0.111111111111111,0.612244897959184,137,8,0.999999999999998
+"2141",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540448","HEPARIN IV INFUSION (MHV PROTOCOL, INR IS THERAPEUTIC)",11,158,0.0085674004988613,1,6,0.069620253164557,0.166666666666667,0.417721518987342,147,5,0.922418293401398
+"2142",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540451","HEPARIN IV INFUSION (NEURO POST STROKE PROTOCOL)",2,49,0.00265697863572281,3,5,0.0408163265306122,0.6,0.0680272108843537,47,2,0.000969218764119146
+"2143",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540493","WARFARIN PER PHARMACY PROTOCOL",14,194,0.0105194664353107,218,6316,0.0721649484536082,0.0345155161494617,2.0907973139128,180,6098,0.00960781491345187
+"2144",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540646","HEPARIN IV INFUSION (EP PROTOCOL)",4,272,0.0147489426309511,1,11,0.0147058823529412,0.0909090909090909,0.161764705882353,268,10,0.47550881819877
+"2145",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540665","HEPARIN IV INFUSION (DVT/PE PROTOCOL-HIGH BLEEDING RISK/POST PROCEDURE)",49,823,0.0446263962693851,3,50,0.0595382746051033,0.06,0.992304576751721,774,47,0.999999999999991
+"2146",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540681","HEPARIN IV INFUSION (HIGH BLEEDING RISK PROTOCOL)",68,875,0.0474460470664787,4,36,0.0777142857142857,0.111111111111111,0.699428571428571,807,32,0.679803681045291
+"2147",2457,"IP GEN HEPARIN PROTOCOLS",18442,"540736","HEPARIN IV INFUSION (VASCULAR SURGERY HIGH BLEEDING RISK)",13,182,0.00986877778982757,1,6,0.0714285714285714,0.166666666666667,0.428571428571429,169,5,0.933002636507205
+"2148",2457,"IP GEN HEPARIN PROTOCOLS",18442,"541066","HEPARIN INFUSION LVAD HIGH RISK BLEED",40,625,0.0338900336189134,1,22,0.064,0.0454545454545455,1.408,585,21,0.999999999999973
+"2149",2457,"IP GEN HEPARIN PROTOCOLS",18442,"541074","HEPARIN INFUSION LVAD NORMAL RISK",60,1194,0.0647435202255721,2,56,0.050251256281407,0.0357142857142857,1.4070351758794,1134,54,0.861223247715599
+"2150",2457,"IP GEN HEPARIN PROTOCOLS",18442,"541075","HEPARIN INFUSION LVAD HIGH PUMP THROMBOSIS RISK",10,112,0.00607309402450927,1,6,0.0892857142857143,0.166666666666667,0.535714285714286,102,5,0.999999999999992
+"2151",2457,"IP GEN HEPARIN PROTOCOLS",18442,"541765","HEPARIN IV INFUSION (LVAD PROTOCOL - NORMAL RISK)",17,329,0.017839713696996,1,18,0.0516717325227964,0.0555555555555556,0.930091185410334,312,17,0.999999999999998
+"2152",2473,"CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",1283,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,196,0.152766952455183,399,16019,0.00510204081632653,0.0249079218428117,0.204836069766252,195,15620,0.122315056252192
+"2153",2473,"CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",1283,"10176","HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",5,462,0.360093530787217,164,2898,0.0108225108225108,0.0565907522429262,0.191241685144124,457,2734,4.79095380279545e-05
+"2154",2473,"CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",1283,"17362","HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",1,57,0.0444271239282931,202,8865,0.0175438596491228,0.0227862380146644,0.769932256383533,56,8663,0.999999999999918
+"2155",2473,"CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",1283,"81839","LIDOCAINE 4 % TP CREA",1,2,0.00155884645362432,31,996,0.5,0.0311244979919679,16.0645161290323,1,965,0.079904133800439
+"2156",2473,"CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",1283,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",1,17,0.0132501948558067,457,11068,0.0588235294117647,0.0412902059992772,1.42463637533788,16,10611,0.999999999999292
+"2157",2473,"CC APHERESIS THERAPEUTIC PLASMA EXCHANGE",1283,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",11,467,0.363990646921278,156,2315,0.0235546038543897,0.0673866090712743,0.34954428155713,456,2159,0.000414336850502976
+"2158",2804,"IP ONC TUMOR LYSIS PRECAUTIONS",307,"310","ALLOPURINOL 100 MG PO TABS",1,5,0.0162866449511401,47,2267,0.2,0.0207322452580503,9.6468085106383,4,2220,0.219537159618252
+"2159",2804,"IP ONC TUMOR LYSIS PRECAUTIONS",307,"311","ALLOPURINOL 100 MG PO TABS",2,50,0.162866449511401,63,3080,0.04,0.0204545454545455,1.95555555555556,48,3017,0.644412456020657
+"2160",2804,"IP ONC TUMOR LYSIS PRECAUTIONS",307,"224461","SODIUM CHLORIDE 0.9% IV SOLP",10,37,0.120521172638436,3,52,0.27027027027027,0.0576923076923077,4.68468468468468,27,49,0.0126281728874262
+"2161",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",86,2995,0.263667576371159,134,3551,0.0287145242070117,0.0377358490566038,0.76093489148581,2909,3417,0.0513113289708187
+"2162",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"16050","GLUCOSE 4 GRAM PO CHEW",34,2969,0.261378642486134,17,1600,0.0114516672280229,0.010625,1.07780397440216,2935,1583,0.915488030084227
+"2163",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"78748","INSULIN GLARGINE 100 UNIT/ML SC SOLN",13,124,0.0109164539131966,348,8601,0.104838709677419,0.0404604115800488,2.59114293659622,111,8253,0.000817322864644299
+"2164",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"88904","INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN",35,1183,0.104146491768642,482,5722,0.029585798816568,0.0842362810206222,0.351223943627391,1148,5240,1.18817177191213e-10
+"2165",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"89742","INSULIN NPH HUMAN RECOMB 100 UNIT/ML SC SUSP",265,7231,0.636587727792939,703,13345,0.0366477665606417,0.0526789059572874,0.695681998224414,6966,12642,2.59764587041692e-07
+"2166",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"92835","INSULIN LISPRO 100 UNIT/ML SC SOLN",92,3772,0.332071485165948,516,10567,0.024390243902439,0.0488312671524558,0.499480052940064,3680,10051,2.18282808562984e-10
+"2167",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"125813","INSULIN DETEMIR 100 UNIT/ML SC SOLN",8,195,0.0171670041376882,215,3971,0.041025641025641,0.0541425333669101,0.75773404889684,187,3756,0.527673772873589
+"2168",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"541452","INSULIN LISPRO 100 UNIT/ML SC MILD SCALE FEEDING",2,88,0.00774716084162338,1,117,0.0227272727272727,0.00854700854700855,2.65909090909091,86,116,0.803096170304546
+"2169",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"541453","INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE FEEDING",2,175,0.015406285764592,6,257,0.0114285714285714,0.0233463035019455,0.48952380952381,173,251,0.590238206942538
+"2170",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"541454","INSULIN LISPRO 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING",2,95,0.00836341227220706,6,238,0.0210526315789474,0.0252100840336134,0.835087719298246,93,232,0.999999999999999
+"2171",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"541455","INSULIN LISPRO 100 UNIT/ML SC VERY AGGRESSIVE SCALE FEEDING",1,12,0.00105643102385773,1,100,0.0833333333333333,0.01,8.33333333333333,11,99,0.509825986881472
+"2172",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"541457","INSULIN LISPRO 100 UNIT/ML SC MODERATE SCALE NPO",1,61,0.00537019103794348,1,57,0.0163934426229508,0.0175438596491228,0.934426229508197,60,56,1
+"2173",2955,"IP INSULIN TRANSITION OFF IV INFUSION",11359,"541462","INSULIN REGULAR 100 UNIT/ML SC AGGRESSIVE SCALE FEEDING",1,18,0.0015846465357866,1,16,0.0555555555555556,0.0625,0.888888888888889,17,15,1
+"2174",2956,"IP INSULIN CONTINUOUS IV INFUSION",8645,"2364","DEXTROSE 5 % IN WATER (D5W) IV SOLP",22,325,0.037593984962406,239,2728,0.0676923076923077,0.0876099706744868,0.772655294496299,303,2489,0.267439319017064
+"2175",2956,"IP INSULIN CONTINUOUS IV INFUSION",8645,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",147,4067,0.470445344129555,134,3551,0.036144578313253,0.0377358490566038,0.957831325301205,3920,3417,0.759098990851062
+"2176",2956,"IP INSULIN CONTINUOUS IV INFUSION",8645,"15882","D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP",42,531,0.0614227877385772,137,3533,0.0790960451977401,0.0387772431361449,2.03975421666873,489,3396,3.98701134661701e-05
+"2177",2956,"IP INSULIN CONTINUOUS IV INFUSION",8645,"112318","DEXTROSE 10% IN WATER (D10W) IV SOLP",10,125,0.0144592249855408,40,720,0.08,0.0555555555555556,1.44,115,680,0.387659581491372
+"2178",2956,"IP INSULIN CONTINUOUS IV INFUSION",8645,"530049","INSULIN REGULAR 1 UNIT/ML IV INFUSION (NEW)",154,4418,0.511046847888953,23,2672,0.034857401539158,0.00860778443113772,4.04952073533175,4264,2649,1.14707745056797e-11
+"2179",2956,"IP INSULIN CONTINUOUS IV INFUSION",8645,"540910","INSULIN REGULAR IV BOLUS",376,2763,0.319606709080393,1,13,0.136083966702859,0.0769230769230769,1.76909156713717,2387,12,0.829423141663085
+"2180",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",48,446,0.469473684210526,134,3551,0.10762331838565,0.0377358490566038,2.85201793721973,398,3417,5.65478656082149e-11
+"2181",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"6427","POTASSIUM CHLORIDE 10 MEQ/100 ML IV PGBK",4,22,0.0231578947368421,1228,18599,0.181818181818182,0.0660250551104898,2.75377554042049,18,17371,0.0793265394506523
+"2182",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"7318","SODIUM CHLORIDE 0.45 % 0.45 % IV SOLP",2,18,0.0189473684210526,51,783,0.111111111111111,0.0651340996168582,1.70588235294118,16,732,0.766972881243892
+"2183",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"11077","POTASSIUM CHLORIDE 20 MEQ/50 ML IV PGBK",3,6,0.00631578947368421,480,9377,0.5,0.0511890796630052,9.76770833333333,3,8897,5.1315213131977e-05
+"2184",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",7,54,0.0568421052631579,118,4415,0.12962962962963,0.026727066817667,4.85012554927809,47,4297,3.42738531158235e-05
+"2185",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",6,28,0.0294736842105263,692,19620,0.214285714285714,0.0352701325178389,6.07555739058629,22,18928,4.16474319489946e-06
+"2186",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"36063","0.45 % NACL-POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",6,42,0.0442105263157895,24,759,0.142857142857143,0.0316205533596838,4.51785714285714,36,735,0.00104367593895542
+"2187",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",23,202,0.212631578947368,457,11068,0.113861386138614,0.0412902059992772,2.75758823147085,179,10611,1.02903733061937e-06
+"2188",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"500006","SODIUM PHOSPHATE IVPB",4,37,0.0389473684210526,296,4785,0.108108108108108,0.0618599791013584,1.74762600438276,33,4489,0.413039957058599
+"2189",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"530048","INSULIN REGULAR 1 UNIT/ML IV INFUSION (DKA/HHS PROTOCOL)",49,451,0.474736842105263,1,17,0.108647450110865,0.0588235294117647,1.8470066518847,402,16,0.800323016015479
+"2190",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"540102","NS IV BOLUS",5,53,0.0557894736842105,2225,80957,0.0943396226415094,0.0274837259285794,3.43256306974772,48,78732,0.0106521594078379
+"2191",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"540500","SODIUM BICARBONATE 50 MEQ / 50 ML IV BAG (DKA PROTOCOL)",6,28,0.0294736842105263,1,1,0.214285714285714,1,0.214285714285714,22,0,0.538511914882501
+"2192",2957,"IP/ED INSULIN - DIABETIC KETOACIDOSIS (DKA)/ HYPEROSMOLAR HYPERGLYCEMIC STATE (HHS)",950,"540501","SODIUM BICARBONATE 100 MEQ / 100 ML BAG (DKA PROTOCOL)",2,16,0.0168421052631579,1,1,0.125,1,0.125,14,0,0.381689600162199
+"2193",2958,"IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII))",983,"2365","DEXTROSE 50 % IN WATER (D50W) IV SYRG",32,419,0.426246185147508,134,3551,0.0763723150357995,0.0377358490566038,2.02386634844869,387,3417,0.000308834415880041
+"2194",2958,"IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII))",983,"16050","GLUCOSE 4 GRAM PO CHEW",27,400,0.406917599186165,17,1600,0.0675,0.010625,6.35294117647059,373,1583,1.52446081767711e-11
+"2195",2958,"IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII))",983,"540499","INSULIN LISPRO SUBCUTANEOUS PUMP",12,282,0.286876907426246,1,38,0.0425531914893617,0.0263157894736842,1.61702127659574,270,37,0.969452214726594
+"2196",2958,"IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII))",983,"540503","INSULIN ASPART SUBCUTANEOUS PUMP",5,136,0.138351983723296,2,24,0.0367647058823529,0.0833333333333333,0.441176470588235,131,22,0.626183851917404
+"2197",2958,"IP INSULIN PUMP (CONTINUOUS SUBCUTANEOUS INSULIN INFUSION (CSII))",983,"540513","INSULIN LISPRO SUBCUTANEOUS PUMP BOLUS",21,531,0.540183112919634,4,44,0.0395480225988701,0.0909090909090909,0.435028248587571,510,40,0.222164599427395
+"2198",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"4448","LIDOCAINE HCL 2 % JELLY",4,914,0.399126637554585,66,3270,0.00437636761487965,0.0201834862385321,0.216829122737219,910,3204,0.00164351061685011
+"2199",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"4455","LIDOCAINE (PF) 40 MG/ML (4 %) INJ SOLN",5,931,0.406550218340611,50,604,0.00537056928034372,0.0827814569536424,0.0648764769065521,926,554,4.8438381562432e-15
+"2200",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",3,450,0.196506550218341,346,10128,0.00666666666666667,0.0341627172195893,0.19514450867052,447,9782,0.00221014862179927
+"2201",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"5373","NALOXONE 0.4 MG/ML INJ SOLN",5,878,0.383406113537118,83,1774,0.00569476082004556,0.0467869222096956,0.121716936081456,873,1691,5.18798973756169e-08
+"2202",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",4,496,0.216593886462882,289,5149,0.00806451612903226,0.056127403379297,0.143682330617256,492,4860,6.71730354399157e-06
+"2203",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",5,780,0.34061135371179,14,238,0.00641025641025641,0.0588235294117647,0.108974358974359,775,224,7.18524672091159e-07
+"2204",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",8,976,0.426200873362445,155,5616,0.00819672131147541,0.0275997150997151,0.296985721840296,968,5461,0.000480810158245336
+"2205",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,171,0.0746724890829694,692,19620,0.0116959064327485,0.0352701325178389,0.331609370246425,169,18928,0.144331398260173
+"2206",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"112564","MORPHINE 2 MG/ML INJ SYRG",2,65,0.0283842794759825,276,6156,0.0307692307692308,0.0448343079922027,0.68628762541806,63,5880,0.807066053420282
+"2207",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",8,1283,0.560262008733624,457,11068,0.00623538581449727,0.0412902059992772,0.151013676575177,1275,10611,6.95546425140251e-10
+"2208",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",6,885,0.38646288209607,705,36939,0.00677966101694915,0.0190855193697718,0.355225387666787,879,36234,0.0111286321971242
+"2209",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",7,984,0.429694323144105,461,18637,0.00711382113821138,0.0247357407308043,0.287592808140663,977,18176,0.000618215251008821
+"2210",2961,"IP GEN BRONCHOSCOPY PRE/INTRA/POST",2290,"540897","LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN(ORDERSET USE ONLY)",5,988,0.431441048034934,2,5,0.00506072874493927,0.4,0.0126518218623482,983,3,4.17975907934535e-15
+"2211",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"101","ACETAMINOPHEN 325 MG PO TABS",1,141,0.0530274539300489,981,42092,0.00709219858156028,0.0233060914187969,0.30430664902654,140,41111,0.319476878675311
+"2212",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"10011","FAMOTIDINE 20 MG PO TABS",1,10,0.00376081233546446,472,16960,0.1,0.0278301886792453,3.59322033898305,9,16488,0.670682587745672
+"2213",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"14113","ASPIRIN 81 MG PO TBEC",10,228,0.0857465212485897,391,18448,0.043859649122807,0.0211947094535993,2.06936779288374,218,18057,0.0342820777841748
+"2214",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,22,0.00827378713802181,258,5335,0.0454545454545455,0.0483598875351453,0.939922480620155,21,5077,0.999999999999996
+"2215",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"83398","CLOPIDOGREL 75 MG PO TABS",12,254,0.0955246333207973,234,7279,0.047244094488189,0.0321472729770573,1.46961437512619,242,7045,0.249677494975112
+"2216",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"89797","EPTIFIBATIDE 0.75 MG/ML IV SOLN",1,15,0.00564121850319669,10,150,0.0666666666666667,0.0666666666666667,1,14,140,1
+"2217",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"208212","PRASUGREL 10 MG PO TABS",2,65,0.024445280180519,24,451,0.0307692307692308,0.0532150776053215,0.578205128205128,63,427,0.63823336154273
+"2218",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"214201","TICAGRELOR 90 MG PO TABS",3,36,0.0135389244076721,23,361,0.0833333333333333,0.0637119113573407,1.30797101449275,33,338,0.919910787331353
+"2219",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"530035","BIVALIRUDIN IV INFUSION",3,12,0.00451297480255735,1,17,0.25,0.0588235294117647,4.25,9,16,0.355621424238384
+"2220",2972,"IP CAR CARDIOLOGY POST PROCEDURE",2659,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",2,131,0.0492666415945844,461,18637,0.0152671755725191,0.0247357407308043,0.617211173850408,129,18176,0.679170648428019
+"2221",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"681","ASPIRIN 325 MG PO TABS",31,685,0.20651190835092,303,7114,0.0452554744525547,0.0425920719707619,1.06253282262533,654,6811,0.818065758904137
+"2222",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"2405","DIAZEPAM 5 MG PO TABS",13,1208,0.364184504069943,152,5041,0.0107615894039735,0.0301527474707399,0.356902448588358,1195,4889,0.000237371976747181
+"2223",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,35,0.0105517033463973,399,16019,0.0285714285714286,0.0249079218428117,1.14708199069101,34,15620,0.999999999999997
+"2224",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",13,1271,0.383177570093458,356,12049,0.010228166797797,0.0295460204166321,0.34617747681645,1258,11693,9.56902612292414e-05
+"2225",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",30,1081,0.325896894784444,692,19620,0.0277520814061055,0.0352701325178389,0.786843695358077,1051,18928,0.220018751449356
+"2226",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"83398","CLOPIDOGREL 75 MG PO TABS",1,16,0.00482363581549593,234,7279,0.0625,0.0321472729770573,1.94417735042735,15,7045,0.999999999999997
+"2227",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,43,0.0129635212541453,288,4280,0.0232558139534884,0.0672897196261682,0.345607235142119,42,3992,0.398944804246623
+"2228",2973,"IP CAR CARDIOLOGY PREOP PROCEDURE",3317,"208212","PRASUGREL 10 MG PO TABS",1,1,0.000301477238468496,24,451,1,0.0532150776053215,18.7916666666667,0,427,0.051466443890782
+"2229",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"8749","WARFARIN 2 MG PO TABS",8,201,0.0597502972651605,73,2789,0.0398009950248756,0.0261742560057368,1.52061609759422,193,2716,0.355288941727004
+"2230",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"8750","WARFARIN 2.5 MG PO TABS",12,322,0.0957193816884661,86,3039,0.0372670807453416,0.0282987824942415,1.3169146323848,310,2953,0.462117804367895
+"2231",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"8751","WARFARIN 5 MG PO TABS",12,458,0.136147443519619,131,4609,0.0262008733624454,0.0284226513343458,0.921830727690923,446,4478,0.899802809549842
+"2232",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"8752","WARFARIN 7.5 MG PO TABS",4,204,0.0606420927467301,54,1901,0.0196078431372549,0.0284061020515518,0.690268700072622,200,1847,0.613905327954678
+"2233",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"11664","WARFARIN 1 MG PO TABS",5,211,0.0627229488703924,98,3895,0.023696682464455,0.0251604621309371,0.94182222652094,206,3797,0.999999999999926
+"2234",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"19433","WARFARIN 3 MG PO TABS",3,197,0.0585612366230678,62,2384,0.0152284263959391,0.026006711409396,0.585557556901916,194,2322,0.489333703726952
+"2235",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"19434","WARFARIN 6 MG PO TABS",2,113,0.0335909631391201,30,1362,0.0176991150442478,0.0220264317180617,0.80353982300885,111,1332,0.999999999999968
+"2236",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"21372","WARFARIN 4 MG PO TABS",6,200,0.0594530321046373,63,2611,0.03,0.0241286863270777,1.24333333333333,194,2548,0.779410933103489
+"2237",3015,"RXWARFARIN PROTOCOL (PHARMACY USE ONLY)",3364,"9000000","ZZZ IMS TEMPLATE",3,67,0.0199167657550535,1035,28824,0.0447761194029851,0.0359075770191507,1.2469824789098,64,27789,0.951361439376485
+"2238",3035,"IP PAI SINGLE SHOT INTRATHECAL NARCOTICS POSTOP",1075,"5373","NALOXONE 0.4 MG/ML INJ SOLN",10,517,0.48093023255814,83,1774,0.0193423597678917,0.0467869222096956,0.413413809978793,507,1691,0.0079103000271017
+"2239",3369,"IP RAD INTERVENTIONAL RADIOLOGY UTERINE EMBOLIZATION",17,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",2,9,0.529411764705882,399,16019,0.222222222222222,0.0249079218428117,8.92174881648566,7,15620,0.00649685024127391
+"2240",3388,"IP CAR IMPELLA",51,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",2,9,0.176470588235294,68,899,0.222222222222222,0.0756395995550612,2.93790849673203,7,831,0.311313704894935
+"2241",3388,"IP CAR IMPELLA",51,"540613","PVAD PURGE SOLN: DEXTROSE 20% + HEPARIN 50 UNITS/ML",2,12,0.235294117647059,1,1,0.166666666666667,1,0.166666666666667,10,0,0.505985043644893
+"2242",3390,"CDU CONTRAST DYE ALLERGY",11,"2510","DIPHENHYDRAMINE HCL 50 MG PO CAPS",1,2,0.181818181818182,37,867,0.5,0.0426758938869665,11.7162162162162,1,830,0.153240770636041
+"2243",3390,"CDU CONTRAST DYE ALLERGY",11,"9000000","ZZZ IMS TEMPLATE",4,6,0.545454545454545,1035,28824,0.666666666666667,0.0359075770191507,18.5661835748792,2,27789,6.32552680054908e-13
+"2244",3409,"IP GI ENDOSCOPY MANOMETRY",48,"7228","SIMETHICONE 40 MG/0.6 ML PO DRPS",2,4,0.0833333333333333,18,574,0.5,0.0313588850174216,15.9444444444444,2,556,0.000185608600123402
+"2245",3417,"IP TRS IVIG DESENSITIZATION ORDERS",54,"102","ACETAMINOPHEN 500 MG PO TABS",1,24,0.444444444444444,392,15968,0.0416666666666667,0.0245490981963928,1.69727891156463,23,15576,0.999999999999833
+"2246",3417,"IP TRS IVIG DESENSITIZATION ORDERS",54,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,19,0.351851851851852,356,12049,0.0526315789473684,0.0295460204166321,1.78134240094619,18,11693,0.999999999999854
+"2247",3417,"IP TRS IVIG DESENSITIZATION ORDERS",54,"209508","METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR",3,12,0.222222222222222,155,3480,0.25,0.0445402298850575,5.61290322580645,9,3325,0.00647253806809044
+"2248",3417,"IP TRS IVIG DESENSITIZATION ORDERS",54,"212610","IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",2,29,0.537037037037037,73,1504,0.0689655172413793,0.0485372340425532,1.42087860179499,27,1431,0.943728605250382
+"2249",3437,"IP EMERGENT WARFARIN REVERSAL",383,"83422","FACTOR IX COMPLEX 500 (+/-) UNIT IV SOLR",1,4,0.010443864229765,2,9,0.25,0.222222222222222,1.125,3,7,1
+"2250",3437,"IP EMERGENT WARFARIN REVERSAL",383,"230057","HUMAN PROTHROMBIN COMPLEX(PCC) 500 UNIT (FII 380-800 UNIT) IV KIT",15,145,0.378590078328982,29,240,0.103448275862069,0.120833333333333,0.856123662306778,130,211,0.723181295991719
+"2251",3437,"IP EMERGENT WARFARIN REVERSAL",383,"540688","PHYTONADIONE (VITAMIN K) IVPB (WARFARIN PROTOCOL)",14,170,0.443864229765013,1,4,0.0823529411764706,0.25,0.329411764705882,156,3,0.779733731570073
+"2252",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"2516","DIPHENOXYLATE-ATROPINE 2.5-0.025 MG PO TABS",3,85,0.36480686695279,44,1787,0.0352941176470588,0.0246222719641858,1.43342245989305,82,1743,0.795132350930165
+"2253",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,18,0.0772532188841202,346,10128,0.111111111111111,0.0341627172195893,3.252408477842,16,9782,0.252590256766003
+"2254",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"6618","PROMETHAZINE 25 MG/ML INJ SOLN",4,45,0.1931330472103,289,5149,0.0888888888888889,0.056127403379297,1.5836985774702,41,4860,0.532654895398626
+"2255",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"6622","PROMETHAZINE 25 MG PO TABS",1,37,0.158798283261803,35,1459,0.027027027027027,0.023989033584647,1.12664092664093,36,1424,0.999999999999914
+"2256",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"80840","ONDANSETRON HCL 8 MG PO TABS",3,53,0.227467811158798,170,10077,0.0566037735849057,0.0168701002282425,3.35527192008879,50,9907,0.090017684063262
+"2257",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"81141","ONDANSETRON HCL 4 MG PO TABS",1,16,0.0686695278969957,87,4024,0.0625,0.0216202783300199,2.89080459770115,15,3937,0.79489610825475
+"2258",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",4,59,0.253218884120172,705,36939,0.0677966101694915,0.0190855193697718,3.55225387666787,55,36234,0.0243377023976432
+"2259",3449,"IP DIHYDROERGOTAMINE (DHE) PROTOCOL",233,"540680","DIHYDROERGOTAMINE IN NORMAL SALINE 1 MG/ML IN 100 ML IVPB",8,97,0.416309012875536,4,47,0.0824742268041237,0.0851063829787234,0.969072164948454,89,43,0.999999999999997
+"2260",3450,"IP MAINTENANCE DIHYDROERGOTAMINE (DHE) INFUSIONS (BASED ON VAS)",106,"80840","ONDANSETRON HCL 8 MG PO TABS",1,40,0.377358490566038,170,10077,0.025,0.0168701002282425,1.48191176470588,39,9907,0.999999999999696
+"2261",3450,"IP MAINTENANCE DIHYDROERGOTAMINE (DHE) INFUSIONS (BASED ON VAS)",106,"81141","ONDANSETRON HCL 4 MG PO TABS",3,16,0.150943396226415,87,4024,0.1875,0.0216202783300199,8.67241379310345,13,3937,0.000274432132688052
+"2262",3450,"IP MAINTENANCE DIHYDROERGOTAMINE (DHE) INFUSIONS (BASED ON VAS)",106,"540680","DIHYDROERGOTAMINE IN NORMAL SALINE 1 MG/ML IN 100 ML IVPB",14,124,1,4,47,0.112903225806452,0.0851063829787234,1.32661290322581,110,43,0.802819710159758
+"2263",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"3291","FUROSEMIDE 10 MG/ML INJ SOLN",2,96,0.104234527687296,1985,57715,0.0208333333333333,0.0343931386987785,0.605740973971452,94,55730,0.653922288472766
+"2264",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"3700","HYDRALAZINE 25 MG PO TABS",2,11,0.011943539630836,151,3336,0.181818181818182,0.0452637889688249,4.01685731487056,9,3185,0.149338591940786
+"2265",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"4065","ISOSORBIDE DINITRATE 20 MG PO TABS",2,14,0.0152008686210641,35,930,0.142857142857143,0.0376344086021505,3.79591836734694,12,895,0.186856932657342
+"2266",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"4526","LISINOPRIL 20 MG PO TABS",1,3,0.00325732899022801,112,3482,0.333333333333333,0.032165422171166,10.3630952380952,2,3370,0.189090181570672
+"2267",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"7437","SPIRONOLACTONE 25 MG PO TABS",1,10,0.01085776330076,182,6011,0.1,0.0302778239893529,3.30274725274725,9,5829,0.717749936803831
+"2268",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"10449","LISINOPRIL 10 MG PO TABS",1,3,0.00325732899022801,199,5437,0.333333333333333,0.03660106676476,9.107202680067,2,5238,0.23171461807719
+"2269",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"15980","DOBUTAMINE IN D5W 500 MG/250 ML (2,000 MCG/ML) IV SOLP",1,6,0.00651465798045603,33,665,0.166666666666667,0.0496240601503759,3.35858585858586,5,632,0.714044005788535
+"2270",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"78198","CARVEDILOL 6.25 MG PO TABS",1,2,0.00217155266015201,136,4444,0.5,0.0306030603060306,16.3382352941176,1,4308,0.0727976253722989
+"2271",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"78559","CARVEDILOL 12.5 MG PO TABS",1,1,0.001085776330076,92,2497,1,0.0368442130556668,27.1413043478261,0,2405,0.0144926994956575
+"2272",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,44,0.0477741585233442,499,13896,0.0227272727272727,0.0359096142774899,0.632902167972308,43,13397,0.949376708469257
+"2273",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",3,129,0.140065146579805,452,13480,0.0232558139534884,0.0335311572700297,0.693558345338547,126,13028,0.689108187408437
+"2274",3452,"IP CAR CONGESTIVE HEART FAILURE",921,"9000000","ZZZ IMS TEMPLATE",1,1,0.001085776330076,1035,28824,1,0.0359075770191507,27.8492753623188,0,27789,0.012664547841265
+"2275",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"101","ACETAMINOPHEN 325 MG PO TABS",17,1353,0.0350100916006831,981,42092,0.0125646711012565,0.0233060914187969,0.539115327211098,1336,41111,0.012288826541504
+"2276",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"102","ACETAMINOPHEN 500 MG PO TABS",33,1880,0.0486466904724939,392,15968,0.0175531914893617,0.0245490981963928,0.715023881893183,1847,15576,0.0715619453175153
+"2277",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"4318","LACTATED RINGERS IV SOLP",53,2047,0.0529679656368059,135,4847,0.0258915486077186,0.0278522797606767,0.929602489641571,1994,4712,0.707076731463051
+"2278",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"5018","METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",31,866,0.0224085286963722,223,3847,0.035796766743649,0.0579672472056148,0.617534357232366,835,3624,0.0115001066143191
+"2279",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",19,798,0.0206489675516224,89,4372,0.0238095238095238,0.0203568161024703,1.16960941680043,779,4283,0.622316541243438
+"2280",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"9626","CLINDAMYCIN IN 5 % DEXTROSE 600 MG/50 ML IV PGBK",15,355,0.00918594421156135,70,1432,0.0422535211267606,0.0488826815642458,0.864386317907445,340,1362,0.699471405873588
+"2281",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"9627","CLINDAMYCIN IN DEXTROSE 5% 900 MG/50 ML IV PGBK",12,467,0.012084044920561,29,474,0.0256959314775161,0.0611814345991561,0.419995569666987,455,445,0.0121958134575149
+"2282",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"10814","OXYCODONE 5 MG PO TABS",1,361,0.00934119960668633,1478,56614,0.00277008310249307,0.0261066167379094,0.106106552614711,360,55136,0.00896050333274044
+"2283",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"18308","GABAPENTIN 300 MG PO CAPS",46,3469,0.0897634942814263,588,19458,0.013260305563563,0.0302189330866482,0.438807866761579,3423,18870,2.76972049057185e-08
+"2284",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"24176","MIDAZOLAM 2 MG/ML PO SYRP",1,7,0.000181131294312477,1,8,0.142857142857143,0.125,1.14285714285714,6,7,1
+"2285",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",156,5074,0.131294312477359,258,5335,0.030744974379188,0.0483598875351453,0.635753636871969,4918,5077,5.45659395080979e-06
+"2286",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"25855","GABAPENTIN 600 MG PO TABS",1,113,0.00292397660818713,296,6004,0.00884955752212389,0.0493004663557628,0.179502511360918,112,5708,0.0782093969712637
+"2287",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",183,3583,0.0927133467888009,692,19620,0.051074518559866,0.0352701325178389,1.44809545396614,3400,18928,6.2155280885203e-06
+"2288",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"77052","CELECOXIB 200 MG PO CAPS",37,3020,0.0781452155462402,20,1192,0.0122516556291391,0.0167785234899329,0.730198675496689,2983,1172,0.318578557278556
+"2289",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",4,124,0.0032086114992496,242,3682,0.032258064516129,0.0657251493753395,0.490802452679286,120,3440,0.191854663073926
+"2290",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"93305","SCOPOLAMINE BASE 1.5 MG (1 MG OVER 3 DAYS) TD PT3D",1,49,0.00126791906018734,93,5289,0.0204081632653061,0.0175836642087351,1.16063199473338,48,5196,0.999999999999994
+"2291",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,55,0.00142317445531232,73,1394,0.0181818181818182,0.0523672883787661,0.34719800747198,54,1321,0.413729481211886
+"2292",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",16,159,0.00411426797081199,499,13896,0.10062893081761,0.0359096142774899,2.80228381290884,143,13397,4.01333108789565e-05
+"2293",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",13,639,0.0165346995808104,1951,16917,0.0203442879499218,0.115327776792576,0.176404059071669,626,14966,1.23020963334095e-13
+"2294",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"125758","PREGABALIN 75 MG PO CAPS",15,587,0.0151891528230606,31,1474,0.0255536626916525,0.0210312075983718,1.21503544540309,572,1443,0.644018682230468
+"2295",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"126993","LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN",167,2732,0.0706929565802412,163,1180,0.0611273792093704,0.138135593220339,0.442517223724277,2565,1017,2.98438365657354e-15
+"2296",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"205597","ALVIMOPAN 12 MG PO CAPS",12,478,0.0123686798116235,28,1117,0.0251046025104603,0.0250671441360788,1.001494321578,466,1089,0.999999999999997
+"2297",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",90,3144,0.0813538270454898,452,13480,0.0286259541984733,0.0335311572700297,0.853712085388097,3054,13028,0.180631285119874
+"2298",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",213,4737,0.122574134451172,111,2584,0.0449651678277391,0.0429566563467492,1.0467566997016,4524,2473,0.733953797975155
+"2299",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"520064","CEFAZOLIN 2 G IVPB",91,3440,0.0890130932049889,109,2674,0.026453488372093,0.0407629020194465,0.648959889054832,3349,2565,0.00230539781714132
+"2300",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"520160","ERTAPENEM 1 G IVPB",7,88,0.00227707912849972,3,142,0.0795454545454545,0.0211267605633803,3.76515151515152,81,139,0.0752624382770112
+"2301",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"541104","CEFOXITIN 1 GRAM/50 ML MINI-BAG PLUS",24,396,0.0102468560782487,9,214,0.0606060606060606,0.0420560747663551,1.44107744107744,372,205,0.435974729006661
+"2302",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"541105","CEFOXITIN 2 GRAM/50 ML MINI-BAG PLUS",35,999,0.0258500232883093,25,530,0.035035035035035,0.0471698113207547,0.742742742742743,964,505,0.305555323402237
+"2303",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"541110","ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS",26,558,0.0144387517466232,72,1340,0.046594982078853,0.0537313432835821,0.867184388689765,532,1268,0.59871044324706
+"2304",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"541128","PATCH CHECK",2,49,0.00126791906018734,312,8338,0.0408163265306122,0.0374190453346126,1.09079016221873,47,8026,0.999999999999928
+"2305",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"541228","CEFAZOLIN IVPB (COMPOUNDED)",10,447,0.0115665269368111,10,117,0.0223713646532438,0.0854700854700855,0.261744966442953,437,107,0.00265863896342189
+"2306",3509,"PRE-ADMISSION/PRE-OP ORDERS",38646,"541537","CEFAZOLIN (KEFZOL,ANCEF) 2 GM/100 ML PREMIX BAG",1,85,0.00219945143093722,2,47,0.0117647058823529,0.0425531914893617,0.276470588235294,84,45,0.598414883182385
+"2307",3533,"IP ECHO EXERCISE STRESS TEST",45,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",2,18,0.4,745,12385,0.111111111111111,0.0601534113847396,1.84712900820283,16,11640,0.680081876033735
+"2308",3533,"IP ECHO EXERCISE STRESS TEST",45,"5604","NITROGLYCERIN 0.4 MG SL SUBL",3,20,0.444444444444444,77,2947,0.15,0.0261282660332542,5.74090909090909,17,2870,0.00660859683101423
+"2309",3533,"IP ECHO EXERCISE STRESS TEST",45,"9869","DILTIAZEM HCL 5 MG/ML IV SOLN",2,17,0.377777777777778,148,2043,0.117647058823529,0.0724424865394028,1.62400635930048,15,1895,0.805913921933792
+"2310",3533,"IP ECHO EXERCISE STRESS TEST",45,"111387","ADENOSINE 3 MG/ML IV SYRG",3,16,0.355555555555556,8,116,0.1875,0.0689655172413793,2.71875,13,108,0.260284833869649
+"2311",3534,"IP ECHO DOBUTAMINE STRESS TEST",86,"730","ATROPINE 0.1 MG/ML INJ SYRG",4,41,0.476744186046512,11,381,0.0975609756097561,0.0288713910761155,3.37915742793792,37,370,0.0697877649017353
+"2312",3534,"IP ECHO DOBUTAMINE STRESS TEST",86,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",2,38,0.441860465116279,745,12385,0.0526315789473684,0.0601534113847396,0.874955845990816,36,11640,0.999999999999992
+"2313",3534,"IP ECHO DOBUTAMINE STRESS TEST",86,"5604","NITROGLYCERIN 0.4 MG SL SUBL",3,41,0.476744186046512,77,2947,0.0731707317073171,0.0261282660332542,2.80044345898004,38,2870,0.171909910676004
+"2314",3534,"IP ECHO DOBUTAMINE STRESS TEST",86,"9869","DILTIAZEM HCL 5 MG/ML IV SOLN",3,40,0.465116279069767,148,2043,0.075,0.0724424865394028,1.03530405405405,37,1895,0.999999999999999
+"2315",3534,"IP ECHO DOBUTAMINE STRESS TEST",86,"111387","ADENOSINE 3 MG/ML IV SYRG",4,41,0.476744186046512,8,116,0.0975609756097561,0.0689655172413793,1.41463414634146,37,108,0.802238881864101
+"2316",3534,"IP ECHO DOBUTAMINE STRESS TEST",86,"206098","DOBUTAMINE IN D5W 250 MG/250 ML (1 MG/ML) IV SOLP",8,41,0.476744186046512,1,6,0.195121951219512,0.166666666666667,1.17073170731707,33,5,0.999999999999998
+"2317",3542,"IP PAI INTRATHECAL INFUSION",137,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",4,61,0.445255474452555,705,36939,0.0655737704918033,0.0190855193697718,3.43578653644925,57,36234,0.0293399804470907
+"2318",3550,"IP VAD GENERAL ADMIT",689,"681","ASPIRIN 325 MG PO TABS",1,11,0.0159651669085631,303,7114,0.0909090909090909,0.0425920719707619,2.13441344134413,10,6811,0.963481062676902
+"2319",3550,"IP VAD GENERAL ADMIT",689,"11664","WARFARIN 1 MG PO TABS",1,3,0.00435413642960813,98,3895,0.333333333333333,0.0251604621309371,13.2482993197279,2,3797,0.119746633668864
+"2320",3550,"IP VAD GENERAL ADMIT",689,"15846","HEPARIN (PORCINE) IN 5 % DEX 25,000 UNIT/250 ML(100 UNIT/ML) IV SOLP",1,4,0.0058055152394775,68,899,0.25,0.0756395995550612,3.30514705882353,3,831,0.713910403416097
+"2321",3550,"IP VAD GENERAL ADMIT",689,"540493","WARFARIN PER PHARMACY PROTOCOL",6,159,0.230769230769231,218,6316,0.0377358490566038,0.0345155161494617,1.09330102129133,153,6098,0.999999999999875
+"2322",3564,"ANE ECT PACU ORDERS",1096,"102","ACETAMINOPHEN 500 MG PO TABS",105,526,0.47992700729927,392,15968,0.199619771863118,0.0245490981963928,8.13145029875068,421,15576,7.34881060238927e-117
+"2323",3564,"ANE ECT PACU ORDERS",1096,"4318","LACTATED RINGERS IV SOLP",114,594,0.541970802919708,135,4847,0.191919191919192,0.0278522797606767,6.89060980172091,480,4712,4.29141661989186e-72
+"2324",3564,"ANE ECT PACU ORDERS",1096,"10372","LABETALOL 5 MG/ML IV SOLN",115,578,0.527372262773723,370,5898,0.198961937716263,0.0627331298745337,3.17156083419059,463,5528,4.3032715454859e-32
+"2325",3564,"ANE ECT PACU ORDERS",1096,"34505","HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS",95,469,0.427919708029197,603,24164,0.202558635394456,0.0249544777354743,8.11712581371748,374,23561,3.05739684624923e-115
+"2326",3564,"ANE ECT PACU ORDERS",1096,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",112,571,0.520985401459854,705,36939,0.19614711033275,0.0190855193697718,10.2772739128815,459,36234,3.79039726590927e-180
+"2327",3567,"IP BONE MARROW AND ASPIRATE",1299,"3758","HYDROMORPHONE 2 MG/ML INJ SOLN",3,7,0.00538876058506543,2,86,0.428571428571429,0.0232558139534884,18.4285714285714,4,84,0.000215004748018797
+"2328",3567,"IP BONE MARROW AND ASPIRATE",1299,"4573","LORAZEPAM 1 MG PO TABS",1,12,0.0092378752886836,337,8494,0.0833333333333333,0.0396750647515894,2.10039564787339,11,8157,0.972677457746971
+"2329",3567,"IP BONE MARROW AND ASPIRATE",1299,"10467","LORAZEPAM 2 MG/ML INJ SOLN",1,109,0.0839107005388761,749,20621,0.00917431192660551,0.0363221958197954,0.252581423549442,108,19872,0.208868711661628
+"2330",3567,"IP BONE MARROW AND ASPIRATE",1299,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",1,98,0.0754426481909161,738,23934,0.0102040816326531,0.0308347956881424,0.330927492948399,97,23196,0.374851506493692
+"2331",3568,"RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP",126,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",2,54,0.428571428571429,346,10128,0.037037037037037,0.0341627172195893,1.08413615928067,52,9782,0.999999999999999
+"2332",3568,"RETIRED IP GYN-ONC SMAC LAPAROSCOPY POST-OP",126,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,51,0.404761904761905,705,36939,0.0392156862745098,0.0190855193697718,2.05473508552357,49,36234,0.590919861559563
+"2333",3575,"IP ICU VAD POST OP",1605,"680","ASPIRIN 81 MG PO CHEW",1,17,0.0105919003115265,357,11203,0.0588235294117647,0.031866464339909,1.84593837535014,16,10846,0.99999999999904
+"2334",3575,"IP ICU VAD POST OP",1605,"2566","DOCUSATE SODIUM 100 MG PO CAPS",3,31,0.0193146417445483,542,37509,0.0967741935483871,0.0144498653656456,6.69723842399714,28,36967,0.00207420387343343
+"2335",3575,"IP ICU VAD POST OP",1605,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",5,182,0.113395638629283,89,4372,0.0274725274725275,0.0203568161024703,1.34954932707742,177,4283,0.692472865624093
+"2336",3575,"IP ICU VAD POST OP",1605,"10050","FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK",9,178,0.110903426791277,40,1102,0.050561797752809,0.0362976406533575,1.39297752808989,169,1062,0.477835705309591
+"2337",3575,"IP ICU VAD POST OP",1605,"14862","POTASSIUM CHLORIDE-D5-0.2%NACL 20 MEQ/L IV SOLP",5,149,0.0928348909657321,10,92,0.0335570469798658,0.108695652173913,0.308724832214765,144,82,0.0383390893075648
+"2338",3575,"IP ICU VAD POST OP",1605,"82384","PANTOPRAZOLE 40 MG IV SOLR",5,93,0.0579439252336449,688,12925,0.0537634408602151,0.0532301740812379,1.01001812953238,88,12237,0.99999999999967
+"2339",3575,"IP ICU VAD POST OP",1605,"125505","SILDENAFIL 20 MG PO TABS",9,160,0.0996884735202492,60,1707,0.05625,0.0351493848857645,1.6003125,151,1647,0.256942271366103
+"2340",3575,"IP ICU VAD POST OP",1605,"206169","DOPAMINE IN 5 % DEXTROSE 400 MG/250 ML (1,600 MCG/ML) IV SOLN",5,96,0.0598130841121495,62,1778,0.0520833333333333,0.0348706411698538,1.49361559139785,91,1716,0.546788870574038
+"2341",3575,"IP ICU VAD POST OP",1605,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",5,194,0.120872274143302,326,12268,0.0257731958762887,0.0265731985653733,0.969894377332237,189,11942,0.999999999999995
+"2342",3575,"IP ICU VAD POST OP",1605,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",12,197,0.122741433021807,156,2315,0.0609137055837563,0.0673866090712743,0.903943771964077,185,2159,0.841024836992993
+"2343",3575,"IP ICU VAD POST OP",1605,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",1,27,0.0168224299065421,291,6612,0.037037037037037,0.044010889292196,0.841542573501336,26,6321,0.999999999999986
+"2344",3575,"IP ICU VAD POST OP",1605,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",41,214,0.133333333333333,1808,23328,0.191588785046729,0.0775034293552812,2.47200396989496,173,21520,1.46816624421624e-09
+"2345",3575,"IP ICU VAD POST OP",1605,"520102","CEFEPIME 2 GM IVPB",1,17,0.0105919003115265,26,498,0.0588235294117647,0.0522088353413655,1.12669683257919,16,472,0.999999999999999
+"2346",3575,"IP ICU VAD POST OP",1605,"530001","AMIODARONE INFUSION PERIPH ADMIN",1,3,0.00186915887850467,7,168,0.333333333333333,0.0416666666666667,8,2,161,0.32118924111968
+"2347",3575,"IP ICU VAD POST OP",1605,"530006","EPINEPHRINE IV INFUSION",6,142,0.0884735202492212,99,2540,0.0422535211267606,0.0389763779527559,1.08408023900982,136,2441,0.999999999999989
+"2348",3575,"IP ICU VAD POST OP",1605,"530031","VASOPRESSIN 1 UNIT/ML IV INFUSION",4,27,0.0168224299065421,215,4489,0.148148148148148,0.0478948540877701,3.0931955211025,23,4274,0.049006296476892
+"2349",3575,"IP ICU VAD POST OP",1605,"540102","NS IV BOLUS",8,118,0.0735202492211838,2225,80957,0.0677966101694915,0.0274837259285794,2.46679108741192,110,78732,0.0167401284113686
+"2350",3575,"IP ICU VAD POST OP",1605,"540115","LACTATED RINGERS IV BOLUS",7,118,0.0735202492211838,400,12941,0.0593220338983051,0.0309095124024419,1.91921610169492,111,12541,0.133088905951855
+"2351",3575,"IP ICU VAD POST OP",1605,"540302","POTASSIUM CHLORIDE IV SCALE",5,211,0.131464174454829,150,4660,0.023696682464455,0.0321888412017167,0.736176935229068,206,4510,0.626328886608543
+"2352",3575,"IP ICU VAD POST OP",1605,"540304","MAGNESIUM SULFATE IV SCALE ",6,196,0.122118380062305,78,4104,0.0306122448979592,0.0190058479532164,1.61067503924647,190,4026,0.377303191158551
+"2353",3575,"IP ICU VAD POST OP",1605,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",4,82,0.0510903426791277,277,3459,0.0487804878048781,0.0800809482509396,0.609139737606762,78,3182,0.406692674850542
+"2354",3575,"IP ICU VAD POST OP",1605,"541106","CEFTAZIDIME 1 GRAM/50 ML MINI-BAG PLUS",6,101,0.0629283489096573,14,162,0.0594059405940594,0.0864197530864197,0.687411598302687,95,148,0.572290782108354
+"2355",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"5373","NALOXONE 0.4 MG/ML INJ SOLN",2,189,0.49868073878628,83,1774,0.0105820105820106,0.0467869222096956,0.226174539427551,187,1691,0.0326149813292961
+"2356",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"10055","FLUMAZENIL 0.1 MG/ML IV SOLN",2,188,0.496042216358839,14,238,0.0106382978723404,0.0588235294117647,0.180851063829787,186,224,0.019244147468067
+"2357",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",2,188,0.496042216358839,155,5616,0.0106382978723404,0.0275997150997151,0.385449553877831,186,5461,0.237356928296537
+"2358",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"82190","BUTAMBEN-TETRACAINE-BENZOCAINE 2-2-14 % TP SPRA",2,175,0.461741424802111,12,304,0.0114285714285714,0.0394736842105263,0.28952380952381,173,292,0.140754297108009
+"2359",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"90788","LIDOCAINE HCL 2 % VISCOUS ORAL SOLN",2,186,0.490765171503958,77,1939,0.010752688172043,0.0397111913357401,0.270772238514174,184,1862,0.073263897337984
+"2360",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"540102","NS IV BOLUS",3,180,0.474934036939314,2225,80957,0.0166666666666667,0.0274837259285794,0.606419475655431,177,78732,0.510041803319919
+"2361",3580,"IP TRANSESOPHAGEAL ECHO (TEE) ORDER SET",379,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",3,191,0.503957783641161,461,18637,0.0157068062827225,0.0247357407308043,0.634984270479608,188,18176,0.57125081720517
+"2362",3584,"IP RESP INHALED EPOPROSTENOL (VELETRI)",2101,"221561","EPOPROSTENOL INHALATION BAG",23,698,0.332222751070919,64,1336,0.0329512893982808,0.0479041916167665,0.687858166189112,675,1272,0.142404021371034
+"2363",3591,"IP TRS HEART TRANSPLANT READMIT",548,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",2,12,0.0218978102189781,692,19620,0.166666666666667,0.0352701325178389,4.72543352601156,10,18928,0.0925213229768856
+"2364",3591,"IP TRS HEART TRANSPLANT READMIT",548,"81839","LIDOCAINE 4 % TP CREA",1,40,0.072992700729927,31,996,0.025,0.0311244979919679,0.803225806451613,39,965,0.999999999999988
+"2365",3591,"IP TRS HEART TRANSPLANT READMIT",548,"92173","ZOLPIDEM 5 MG PO TABS",1,18,0.0328467153284672,159,8203,0.0555555555555556,0.019383152505181,2.86617749825297,17,8044,0.7982115215886
+"2366",3591,"IP TRS HEART TRANSPLANT READMIT",548,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,1,0.00182481751824818,73,1394,1,0.0523672883787661,19.0958904109589,0,1321,0.0460518955900975
+"2367",3591,"IP TRS HEART TRANSPLANT READMIT",548,"125813","INSULIN DETEMIR 100 UNIT/ML SC SOLN",1,10,0.0182481751824818,215,3971,0.1,0.0541425333669101,1.84697674418605,9,3756,0.999999999999999
+"2368",3591,"IP TRS HEART TRANSPLANT READMIT",548,"126009","MEPERIDINE (PF) 25 MG/ML INJ SYRG",1,2,0.00364963503649635,164,2764,0.5,0.0593342981186686,8.42682926829268,1,2600,0.255540661714246
+"2369",3591,"IP TRS HEART TRANSPLANT READMIT",548,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",1,38,0.0693430656934307,457,11068,0.0263157894736842,0.0412902059992772,0.637337325809052,37,10611,0.956281570770742
+"2370",3591,"IP TRS HEART TRANSPLANT READMIT",548,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",2,80,0.145985401459854,705,36939,0.025,0.0190855193697718,1.30989361702128,78,36234,0.999999999999973
+"2371",3591,"IP TRS HEART TRANSPLANT READMIT",548,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,99,0.180656934306569,452,13480,0.0101010101010101,0.0335311572700297,0.301242513631894,98,13028,0.311247997415857
+"2372",3591,"IP TRS HEART TRANSPLANT READMIT",548,"540133","CYCLOSPORINE MODIFIED (GENGRAF) 100 MG PO CAPS",1,2,0.00364963503649635,18,484,0.5,0.0371900826446281,13.4444444444444,1,466,0.123060908374911
+"2373",3601,"OPH CATARACT PERIOPERATIVE ORDERS RIGHT EYE",12,"101","ACETAMINOPHEN 325 MG PO TABS",1,9,0.75,981,42092,0.111111111111111,0.0233060914187969,4.76747083474912,8,41111,0.521719524918104
+"2374",3603,"IP HEM ACUTE LEUKEMIA",612,"310","ALLOPURINOL 100 MG PO TABS",1,2,0.00326797385620915,47,2267,0.5,0.0207322452580503,24.1170212765957,1,2220,0.0244471119328747
+"2375",3603,"IP HEM ACUTE LEUKEMIA",612,"10747","NORETHINDRONE ACETATE 5 MG PO TABS",1,12,0.0196078431372549,48,702,0.0833333333333333,0.0683760683760684,1.21875,11,654,0.999999999999957
+"2376",3603,"IP HEM ACUTE LEUKEMIA",612,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",3,56,0.0915032679738562,692,19620,0.0535714285714286,0.0352701325178389,1.51888934764657,53,18928,0.705138476617487
+"2377",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"3074","FERROUS SULFATE 325 MG (65 MG IRON) PO TABS",6,180,0.34816247582205,97,6557,0.0333333333333333,0.0147933506176605,2.253264604811,174,6460,0.0906264715081994
+"2378",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"11111","PRAVASTATIN 20 MG PO TABS",3,162,0.313346228239845,38,2869,0.0185185185185185,0.0132450331125828,1.39814814814815,159,2831,0.829168817657079
+"2379",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"14113","ASPIRIN 81 MG PO TBEC",4,131,0.253384912959381,391,18448,0.0305343511450382,0.0211947094535993,1.4406591046641,127,18057,0.663914527476671
+"2380",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"19483","CALCIUM CARBONATE-VITAMIN D3 500 MG(1,250MG) -200 UNIT PO TABS",3,177,0.342359767891683,27,2802,0.0169491525423729,0.00963597430406852,1.75894538606403,174,2775,0.577555935886173
+"2381",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"208479","MAGNESIUM OXIDE-MG AA CHELATE 133 MG PO TABS",7,176,0.340425531914894,69,3355,0.0397727272727273,0.0205663189269747,1.9338768115942,169,3286,0.148449423944246
+"2382",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"520082","PAMIDRONATE IVPB",1,95,0.183752417794971,10,243,0.0105263157894737,0.0411522633744856,0.255789473684211,94,233,0.277727216514622
+"2383",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"540492","SHC TRANSPLANT BEDSIDE MEDS HELP ORDER",35,167,0.323017408123791,3,27,0.209580838323353,0.111111111111111,1.88622754491018,132,24,0.34987205137997
+"2384",3622,"IP TRS HEART TRANSPLANT TRANSFER FROM ICU",517,"540597","THERAPEUTIC MULTIVITAMIN (GENERIC) PO TABS",5,174,0.336557059961315,106,7394,0.028735632183908,0.0143359480659995,2.00444589026242,169,7288,0.213954394301614
+"2385",3661,"IP CTS TAVR CV POSTOP",258,"101","ACETAMINOPHEN 325 MG PO TABS",2,103,0.39922480620155,981,42092,0.0194174757281553,0.0233060914187969,0.833150242965866,101,41111,0.999999999999974
+"2386",3661,"IP CTS TAVR CV POSTOP",258,"1080","BISACODYL 10 MG PR SUPP",1,9,0.0348837209302326,344,22442,0.111111111111111,0.0153284021031994,7.24870801033592,8,22098,0.326910264620884
+"2387",3661,"IP CTS TAVR CV POSTOP",258,"14113","ASPIRIN 81 MG PO TBEC",2,35,0.135658914728682,391,18448,0.0571428571428571,0.0211947094535993,2.69609061015711,33,18057,0.37538759383229
+"2388",3661,"IP CTS TAVR CV POSTOP",258,"38285","ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",3,89,0.344961240310078,71,5546,0.0337078651685393,0.0128020194734944,2.63301155246083,86,5475,0.211501635639844
+"2389",3661,"IP CTS TAVR CV POSTOP",258,"124216","VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK",2,14,0.0542635658914729,1951,16917,0.142857142857143,0.115327776792576,1.23870542578897,12,14966,0.999999999999967
+"2390",3661,"IP CTS TAVR CV POSTOP",258,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,105,0.406976744186047,705,36939,0.00952380952380952,0.0190855193697718,0.499007092198582,104,36234,0.720203004610847
+"2391",3661,"IP CTS TAVR CV POSTOP",258,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",3,98,0.37984496124031,111,2584,0.0306122448979592,0.0429566563467492,0.712630998345284,95,2473,0.734222144791123
+"2392",3661,"IP CTS TAVR CV POSTOP",258,"540301","POTASSIUM CHLORIDE ORAL SCALE",1,104,0.403100775193798,105,3653,0.00961538461538462,0.0287434984943882,0.33452380952381,103,3548,0.389036053917337
+"2393",3724,"IP TRS PRE TRANSPLANT CARE",71,"25121","OCTREOTIDE ACETATE 50 MCG/ML INJ SOLN",1,1,0.0140845070422535,16,150,1,0.106666666666667,9.375,0,134,0.218785413317582
+"2394",3724,"IP TRS PRE TRANSPLANT CARE",71,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",2,4,0.0563380281690141,411,7130,0.5,0.0576437587657784,8.67396593673966,2,6719,0.00659877935716515
+"2395",3732,"RETIRED ENT PERIOPERATIVE ORDERS",120,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,5,0.0416666666666667,258,5335,0.2,0.0483598875351453,4.13565891472868,4,5077,0.591760756758056
+"2396",3732,"RETIRED ENT PERIOPERATIVE ORDERS",120,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,2,0.0166666666666667,452,13480,0.5,0.0335311572700297,14.9115044247788,1,13028,0.0894223689889605
+"2397",3732,"RETIRED ENT PERIOPERATIVE ORDERS",120,"520064","CEFAZOLIN 2 G IVPB",1,6,0.05,109,2674,0.166666666666667,0.0407629020194465,4.08868501529052,5,2565,0.601179202717766
+"2398",3734,"ED TRAUMA MEDICATION",499,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",5,24,0.0480961923847695,258,5335,0.208333333333333,0.0483598875351453,4.30797803617571,19,5077,0.00165425194613992
+"2399",3734,"ED TRAUMA MEDICATION",499,"125357","DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM SUSP",2,25,0.0501002004008016,5,120,0.08,0.0416666666666667,1.92,23,115,0.76370117989197
+"2400",3734,"ED TRAUMA MEDICATION",499,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",3,190,0.380761523046092,705,36939,0.0157894736842105,0.0190855193697718,0.827301231802912,187,36234,0.947826671808323
+"2401",3734,"ED TRAUMA MEDICATION",499,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",4,265,0.531062124248497,461,18637,0.0150943396226415,0.0247357407308043,0.610223877542668,261,18176,0.420014962881312
+"2402",3734,"ED TRAUMA MEDICATION",499,"541184","DIPHTH,PERTUS(ACELL),TETANUS 2.5-8-5 LF-MCG-LF/0.5ML IM INJ",3,158,0.316633266533066,26,783,0.0189873417721519,0.033205619412516,0.571811100292113,155,757,0.489570195893051
+"2403",3743,"IP CAR PERIPHERAL AMIODARONE IV",1203,"4454","LIDOCAINE HCL 20 MG/ML (2 %) INJ SOLN",5,91,0.0756442227763924,97,1404,0.0549450549450549,0.0690883190883191,0.795287187039764,86,1307,0.761091582560623
+"2404",3743,"IP CAR PERIPHERAL AMIODARONE IV",1203,"7309","SODIUM BICARBONATE 8.4 % IV SOLN",20,186,0.154613466334165,186,3436,0.10752688172043,0.0541327124563446,1.98635680425483,166,3250,0.00373338261587687
+"2405",3743,"IP CAR PERIPHERAL AMIODARONE IV",1203,"81839","LIDOCAINE 4 % TP CREA",7,119,0.0989193682460515,31,996,0.0588235294117647,0.0311244979919679,1.8899430740038,112,965,0.19130957991189
+"2406",3743,"IP CAR PERIPHERAL AMIODARONE IV",1203,"530001","AMIODARONE INFUSION PERIPH ADMIN",72,1011,0.840399002493766,7,168,0.0712166172106825,0.0416666666666667,1.70919881305638,939,161,0.210604348461921
+"2407",3743,"IP CAR PERIPHERAL AMIODARONE IV",1203,"530200","AMIODARONE PERIPHERAL INFUSION",56,726,0.603491271820449,4,45,0.0771349862258953,0.0888888888888889,0.867768595041322,670,41,0.999999999999999
+"2408",3755,"IP PLS FREE FLAP",721,"101","ACETAMINOPHEN 325 MG PO TABS",4,117,0.162274618585298,981,42092,0.0341880341880342,0.0233060914187969,1.46691410299973,113,41111,0.636944191783983
+"2409",3755,"IP PLS FREE FLAP",721,"681","ASPIRIN 325 MG PO TABS",2,107,0.148404993065187,303,7114,0.0186915887850467,0.0425920719707619,0.438851361771691,105,6811,0.328112669478814
+"2410",3755,"IP PLS FREE FLAP",721,"693","ASPIRIN 300 MG PR SUPP",1,90,0.124826629680999,70,1872,0.0111111111111111,0.0373931623931624,0.297142857142857,89,1802,0.310021574213686
+"2411",3755,"IP PLS FREE FLAP",721,"1080","BISACODYL 10 MG PR SUPP",5,140,0.194174757281553,344,22442,0.0357142857142857,0.0153284021031994,2.32994186046512,135,22098,0.108334310531256
+"2412",3755,"IP PLS FREE FLAP",721,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,110,0.152565880721221,356,12049,0.00909090909090909,0.0295460204166321,0.307686414708887,109,11693,0.32640737170282
+"2413",3755,"IP PLS FREE FLAP",721,"2566","DOCUSATE SODIUM 100 MG PO CAPS",3,120,0.166435506241331,542,37509,0.025,0.0144498653656456,1.73011992619926,117,36967,0.559795294602241
+"2414",3755,"IP PLS FREE FLAP",721,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",2,129,0.178918169209431,314,8762,0.0155038759689922,0.035836566993837,0.432627265096529,127,8448,0.317927888492
+"2415",3755,"IP PLS FREE FLAP",721,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,86,0.119278779472954,258,5335,0.0116279069767442,0.0483598875351453,0.24044528574004,85,5077,0.183680815322227
+"2416",3755,"IP PLS FREE FLAP",721,"110888","ENOXAPARIN 30 MG/0.3 ML SC SYRG",1,4,0.00554785020804438,73,1394,0.25,0.0523672883787661,4.77397260273973,3,1321,0.5191411227568
+"2417",3755,"IP PLS FREE FLAP",721,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",4,149,0.206657420249653,705,36939,0.0268456375838926,0.0190855193697718,1.4065971726403,145,36234,0.696077623915495
+"2418",3755,"IP PLS FREE FLAP",721,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",2,30,0.0416088765603329,452,13480,0.0666666666666667,0.0335311572700297,1.9882005899705,28,13028,0.617873639429136
+"2419",3755,"IP PLS FREE FLAP",721,"214400","BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG",1,113,0.156726768377254,71,6103,0.00884955752212389,0.0116336228084549,0.760688021936931,112,6032,0.999999999999997
+"2420",3755,"IP PLS FREE FLAP",721,"540142","DIPHENHYDRAMINE 25 MG/50 ML NS IVPB (CANCER CENTER ONLY)",5,15,0.0208044382801664,95,264,0.333333333333333,0.359848484848485,0.926315789473684,10,169,0.999999999999999
+"2421",3755,"IP PLS FREE FLAP",721,"540304","MAGNESIUM SULFATE IV SCALE ",1,23,0.0319001386962552,78,4104,0.0434782608695652,0.0190058479532164,2.2876254180602,22,4026,0.92737739040887
+"2422",3756,"IP PLS ICU HEAND AND NECK FREE FLAP",203,"681","ASPIRIN 325 MG PO TABS",2,22,0.108374384236453,303,7114,0.0909090909090909,0.0425920719707619,2.13441344134413,20,6811,0.554622837003834
+"2423",3756,"IP PLS ICU HEAND AND NECK FREE FLAP",203,"693","ASPIRIN 300 MG PR SUPP",2,29,0.142857142857143,70,1872,0.0689655172413793,0.0373931623931624,1.84433497536946,27,1802,0.693796073270945
+"2424",3756,"IP PLS ICU HEAND AND NECK FREE FLAP",203,"5018","METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK",1,27,0.133004926108374,223,3847,0.037037037037037,0.0579672472056148,0.638930410230859,26,3624,0.95962901316619
+"2425",3756,"IP PLS ICU HEAND AND NECK FREE FLAP",203,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",2,19,0.0935960591133005,89,4372,0.105263157894737,0.0203568161024703,5.17090479006505,17,4283,0.0742049906423438
+"2426",3756,"IP PLS ICU HEAND AND NECK FREE FLAP",203,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",1,20,0.0985221674876847,499,13896,0.05,0.0359096142774899,1.39238476953908,19,13397,0.999999999999994
+"2427",3756,"IP PLS ICU HEAND AND NECK FREE FLAP",203,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,23,0.113300492610837,452,13480,0.0434782608695652,0.0335311572700297,1.29665255867641,22,13028,0.999999999999998
+"2428",3821,"ED HIP FRACTURE SYMPTOMS",164,"101","ACETAMINOPHEN 325 MG PO TABS",1,5,0.0304878048780488,981,42092,0.2,0.0233060914187969,8.58144750254842,4,41111,0.255989696965087
+"2429",3821,"ED HIP FRACTURE SYMPTOMS",164,"102","ACETAMINOPHEN 500 MG PO TABS",2,11,0.0670731707317073,392,15968,0.181818181818182,0.0245490981963928,7.40630797773655,9,15576,0.0168550323143895
+"2430",3821,"ED HIP FRACTURE SYMPTOMS",164,"10814","OXYCODONE 5 MG PO TABS",1,4,0.024390243902439,1478,56614,0.25,0.0261066167379094,9.57611637347767,3,55136,0.21501509130241
+"2431",3821,"ED HIP FRACTURE SYMPTOMS",164,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",1,9,0.0548780487804878,461,18637,0.111111111111111,0.0247357407308043,4.49192576524464,8,18176,0.552419012168307
+"2432",3824,"IP VAS VASCULAR ICU",549,"101","ACETAMINOPHEN 325 MG PO TABS",1,17,0.0309653916211293,981,42092,0.0588235294117647,0.0233060914187969,2.52395514780836,16,41111,0.867803897759476
+"2433",3824,"IP VAS VASCULAR ICU",549,"696","ASPIRIN 600 MG PR SUPP",1,1,0.00182149362477231,5,94,1,0.0531914893617021,18.8,0,89,0.0710108722424495
+"2434",3824,"IP VAS VASCULAR ICU",549,"4318","LACTATED RINGERS IV SOLP",3,56,0.10200364298725,135,4847,0.0535714285714286,0.0278522797606767,1.9234126984127,53,4712,0.452819409364064
+"2435",3824,"IP VAS VASCULAR ICU",549,"5007","METOPROLOL TARTRATE 5 MG/5 ML IV SOLN",1,17,0.0309653916211293,745,12385,0.0588235294117647,0.0601534113847396,0.977891827872088,16,11640,0.999999999999649
+"2436",3824,"IP VAS VASCULAR ICU",549,"5373","NALOXONE 0.4 MG/ML INJ SOLN",4,126,0.229508196721311,83,1774,0.0317460317460317,0.0467869222096956,0.678523618282654,122,1691,0.575527397449695
+"2437",3824,"IP VAS VASCULAR ICU",549,"5940","OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS",2,13,0.0236794171220401,218,7862,0.153846153846154,0.0277283134062579,5.54834156669019,11,7644,0.0555044828568314
+"2438",3824,"IP VAS VASCULAR ICU",549,"10011","FAMOTIDINE 20 MG PO TABS",1,4,0.00728597449908925,472,16960,0.25,0.0278301886792453,8.98305084745763,3,16488,0.238032036632024
+"2439",3824,"IP VAS VASCULAR ICU",549,"11150","PROPOFOL 10 MG/ML IV EMUL",2,10,0.0182149362477231,135,3592,0.2,0.0375835189309577,5.32148148148148,8,3457,0.0637939704387655
+"2440",3824,"IP VAS VASCULAR ICU",549,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,18,0.0327868852459016,314,8762,0.0555555555555556,0.035836566993837,1.55024769992923,17,8448,0.999999999999547
+"2441",3824,"IP VAS VASCULAR ICU",549,"16426","NS WITH POTASSIUM CHLORIDE 20 MEQ/L IV SOLP",2,9,0.0163934426229508,118,4415,0.222222222222222,0.026727066817667,8.31450094161958,7,4297,0.00989079003251237
+"2442",3824,"IP VAS VASCULAR ICU",549,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,21,0.0382513661202186,258,5335,0.0952380952380952,0.0483598875351453,1.96936138796604,19,5077,0.624888148633429
+"2443",3824,"IP VAS VASCULAR ICU",549,"82384","PANTOPRAZOLE 40 MG IV SOLR",1,43,0.0783242258652095,688,12925,0.0232558139534884,0.0532301740812379,0.436891563007031,42,12237,0.593095583034376
+"2444",3824,"IP VAS VASCULAR ICU",549,"112520","SODIUM CHLORIDE 0.9 % 0.9 % INJ SYRG",2,72,0.131147540983607,11,154,0.0277777777777778,0.0714285714285714,0.388888888888889,70,143,0.314145847080304
+"2445",3824,"IP VAS VASCULAR ICU",549,"115141","ESMOLOL IN NACL (ISO-OSM) 2,000 MG/100 ML IV SOLP",3,8,0.0145719489981785,1,114,0.375,0.0087719298245614,42.75,5,113,4.30807177337261e-06
+"2446",3824,"IP VAS VASCULAR ICU",549,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",1,46,0.0837887067395264,457,11068,0.0217391304347826,0.0412902059992772,0.526496051755304,45,10611,0.768705653930637
+"2447",3824,"IP VAS VASCULAR ICU",549,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,85,0.154826958105647,705,36939,0.0117647058823529,0.0190855193697718,0.616420525657071,84,36234,0.923564261846866
+"2448",3824,"IP VAS VASCULAR ICU",549,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",5,26,0.0473588342440801,288,4280,0.192307692307692,0.0672897196261682,2.85790598290598,21,3992,0.0329087744073592
+"2449",3824,"IP VAS VASCULAR ICU",549,"207694","NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN",2,8,0.0145719489981785,56,1641,0.25,0.0341255332114564,7.32589285714286,6,1585,0.0190526848165772
+"2450",3824,"IP VAS VASCULAR ICU",549,"211084","CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN",2,54,0.0983606557377049,326,12268,0.037037037037037,0.0265731985653733,1.3937741422404,52,11942,0.957720587781296
+"2451",3824,"IP VAS VASCULAR ICU",549,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",5,59,0.107468123861566,510,18335,0.0847457627118644,0.0278156531224434,3.04669325357262,54,17825,0.0243682329488737
+"2452",3824,"IP VAS VASCULAR ICU",549,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",3,20,0.0364298724954463,452,13480,0.15,0.0335311572700297,4.47345132743363,17,13028,0.023567798559628
+"2453",3824,"IP VAS VASCULAR ICU",549,"221562","CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB",1,24,0.0437158469945355,156,2315,0.0416666666666667,0.0673866090712743,0.61832264957265,23,2159,0.927517101050204
+"2454",3824,"IP VAS VASCULAR ICU",549,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",1,16,0.029143897996357,291,6612,0.0625,0.044010889292196,1.42010309278351,15,6321,0.99999999999999
+"2455",3824,"IP VAS VASCULAR ICU",549,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",2,18,0.0327868852459016,111,2584,0.111111111111111,0.0429566563467492,2.58658658658659,16,2473,0.404537733261115
+"2456",3824,"IP VAS VASCULAR ICU",549,"500027","D5W WITH SODIUM BICARBONATE 154 MEQ/L  IV INFUSION FOR CONTRAST STUDY",1,5,0.00910746812386157,54,1287,0.2,0.041958041958042,4.76666666666667,4,1233,0.52390959410828
+"2457",3824,"IP VAS VASCULAR ICU",549,"530008","NITROPRUSSIDE IV INFUSION",1,2,0.00364298724954463,79,2082,0.5,0.0379442843419789,13.1772151898734,1,2003,0.119147561001289
+"2458",3824,"IP VAS VASCULAR ICU",549,"530010","NICARDIPINE IV INFUSION",2,5,0.00910746812386157,257,676,0.4,0.380177514792899,1.05214007782101,3,419,0.999999999999979
+"2459",3824,"IP VAS VASCULAR ICU",549,"530011","NOREPINEPHRINE IV INFUSION",1,5,0.00910746812386157,92,2909,0.2,0.0316259883121348,6.32391304347826,4,2817,0.386010407234897
+"2460",3824,"IP VAS VASCULAR ICU",549,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",2,47,0.0856102003642987,461,18637,0.0425531914893617,0.0247357407308043,1.72031199520007,45,18176,0.752744074908311
+"2461",3824,"IP VAS VASCULAR ICU",549,"540302","POTASSIUM CHLORIDE IV SCALE",3,74,0.134790528233151,150,4660,0.0405405405405405,0.0321888412017167,1.25945945945946,71,4510,0.942764708260785
+"2462",3824,"IP VAS VASCULAR ICU",549,"540304","MAGNESIUM SULFATE IV SCALE ",3,77,0.140255009107468,78,4104,0.038961038961039,0.0190058479532164,2.04995004995005,74,4026,0.40012042076953
+"2463",3881,"IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS",269,"500011","POTASSIUM PHOSPHATE IVPB",2,63,0.234200743494424,574,9157,0.0317460317460317,0.0626842852462597,0.506443227697583,61,8583,0.453243189572797
+"2464",3881,"IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS",269,"540302","POTASSIUM CHLORIDE IV SCALE",1,41,0.152416356877323,150,4660,0.024390243902439,0.0321888412017167,0.757723577235772,40,4510,0.999999999999999
+"2465",3881,"IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS",269,"540304","MAGNESIUM SULFATE IV SCALE ",1,56,0.20817843866171,78,4104,0.0178571428571429,0.0190058479532164,0.93956043956044,55,4026,0.999999999999989
+"2466",3881,"IP MED CYSTIC FIBROSIS INCOMPLETE/PARTIAL DIOS",269,"540699","CF ENEMA SOLUTION",3,96,0.356877323420074,8,509,0.03125,0.0157170923379175,1.98828125,93,501,0.529743623818338
+"2467",3885,"IP MED CYSTIC FIBROSIS COMPLETE DIOS",101,"126992","LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN",1,12,0.118811881188119,457,11068,0.0833333333333333,0.0412902059992772,2.018234865062,11,10611,0.995402739330949
+"2468",3885,"IP MED CYSTIC FIBROSIS COMPLETE DIOS",101,"500011","POTASSIUM PHOSPHATE IVPB",1,22,0.217821782178218,574,9157,0.0454545454545455,0.0626842852462597,0.725134621476085,21,8583,0.999999999999613
+"2469",3885,"IP MED CYSTIC FIBROSIS COMPLETE DIOS",101,"540302","POTASSIUM CHLORIDE IV SCALE",1,19,0.188118811881188,150,4660,0.0526315789473684,0.0321888412017167,1.63508771929825,18,4510,0.999999999999997
+"2470",3885,"IP MED CYSTIC FIBROSIS COMPLETE DIOS",101,"540304","MAGNESIUM SULFATE IV SCALE ",1,17,0.168316831683168,78,4104,0.0588235294117647,0.0190058479532164,3.09502262443439,16,4026,0.757632603093063
+"2471",3885,"IP MED CYSTIC FIBROSIS COMPLETE DIOS",101,"540699","CF ENEMA SOLUTION",4,19,0.188118811881188,8,509,0.210526315789474,0.0157170923379175,13.3947368421053,15,501,1.50631725845031e-06
+"2472",3886,"ED PSYCH HOLDING",62,"101","ACETAMINOPHEN 325 MG PO TABS",2,19,0.306451612903226,981,42092,0.105263157894737,0.0233060914187969,4.51655131713075,17,41111,0.10836523363797
+"2473",3886,"ED PSYCH HOLDING",62,"94176","ONDANSETRON 4 MG PO TBDL",1,11,0.17741935483871,153,4277,0.0909090909090909,0.0357727379003975,2.54129530600119,10,4124,0.86480082417744
+"2474",3886,"ED PSYCH HOLDING",62,"95933","OLANZAPINE 5 MG PO TBDL",1,14,0.225806451612903,152,3857,0.0714285714285714,0.0394088669950739,1.8125,13,3705,0.999999999999998
+"2475",3938,"IP PHARM PENICILLIN SKIN TEST",86,"250","ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU",1,26,0.302325581395349,106,3939,0.0384615384615385,0.0269103833460269,1.42924528301887,25,3833,0.999999999999999
+"2476",3938,"IP PHARM PENICILLIN SKIN TEST",86,"681","ASPIRIN 325 MG PO TABS",1,16,0.186046511627907,303,7114,0.0625,0.0425920719707619,1.46740924092409,15,6811,0.999999999999998
+"2477",3938,"IP PHARM PENICILLIN SKIN TEST",86,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,36,0.418604651162791,399,16019,0.0277777777777778,0.0249079218428117,1.11521860206071,35,15620,0.999999999999436
+"2478",3938,"IP PHARM PENICILLIN SKIN TEST",86,"3774","HYDROXYZINE HCL 25 MG PO TABS",2,22,0.255813953488372,133,4979,0.0909090909090909,0.0267121912030528,3.40328092959672,20,4846,0.232229843493797
+"2479",3938,"IP PHARM PENICILLIN SKIN TEST",86,"4572","LORAZEPAM 0.5 MG PO TABS",1,12,0.13953488372093,349,13238,0.0833333333333333,0.0263634990179785,3.16093600764088,11,12889,0.741701195801289
+"2480",3938,"IP PHARM PENICILLIN SKIN TEST",86,"6496","PREDNISONE 20 MG PO TABS",1,18,0.209302325581395,276,7517,0.0555555555555556,0.0367167753092989,1.51308373590982,17,7241,0.999999999999599
+"2481",3938,"IP PHARM PENICILLIN SKIN TEST",86,"7319","SODIUM CHLORIDE 0.9 % 0.9 % INJ SOLN",1,34,0.395348837209302,59,937,0.0294117647058824,0.0629669156883671,0.467098703888335,33,878,0.66304068465386
+"2482",3938,"IP PHARM PENICILLIN SKIN TEST",86,"10467","LORAZEPAM 2 MG/ML INJ SOLN",1,17,0.197674418604651,749,20621,0.0588235294117647,0.0363221958197954,1.61949265687583,16,19872,0.999999999999946
+"2483",3938,"IP PHARM PENICILLIN SKIN TEST",86,"40196","ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA",1,19,0.22093023255814,110,6310,0.0526315789473684,0.01743264659271,3.01913875598086,18,6200,0.770356216441304
+"2484",3938,"IP PHARM PENICILLIN SKIN TEST",86,"90488","BENZYLPENICILLOYL POLYLYSINE 0.25 ML ID SOLN",2,36,0.418604651162791,1,4,0.0555555555555556,0.25,0.222222222222222,34,3,0.689009104052038
+"2485",3938,"IP PHARM PENICILLIN SKIN TEST",86,"91619","MONTELUKAST 10 MG PO TABS",1,19,0.22093023255814,23,2145,0.0526315789473684,0.0107226107226107,4.90846681922197,18,2122,0.524449055752342
+"2486",3938,"IP PHARM PENICILLIN SKIN TEST",86,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",2,27,0.313953488372093,288,4280,0.0740740740740741,0.0672897196261682,1.10082304526749,25,3992,0.999999999999998
+"2487",3938,"IP PHARM PENICILLIN SKIN TEST",86,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",1,25,0.290697674418605,275,8000,0.04,0.034375,1.16363636363636,24,7725,0.999999999999998
+"2488",3938,"IP PHARM PENICILLIN SKIN TEST",86,"213662","EPINEPHRINE 1 MG/ML (1:1,000) (1ML) INJ SOLN",2,36,0.418604651162791,69,5047,0.0555555555555556,0.0136714880126808,4.06360708534622,34,4978,0.155276031094858
+"2489",3960,"IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS",386,"11150","PROPOFOL 10 MG/ML IV EMUL",4,52,0.134715025906736,135,3592,0.0769230769230769,0.0375835189309577,2.04672364672365,48,3457,0.268810634619297
+"2490",3960,"IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS",386,"89065","LIDOCAINE 5 % TP PTMD",1,1,0.00259067357512953,197,10907,1,0.018061795177409,55.3654822335025,0,10710,0.00030677904673701
+"2491",3960,"IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS",386,"212629","ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",1,7,0.0181347150259067,510,18335,0.142857142857143,0.0278156531224434,5.13585434173669,6,17825,0.483564867012546
+"2492",3960,"IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS",386,"229762","DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN",2,74,0.191709844559585,291,6612,0.027027027027027,0.044010889292196,0.614098634717191,72,6321,0.671392724161987
+"2493",3960,"IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS",386,"530025","DEXMEDETOMIDINE IV INFUSION",22,44,0.113989637305699,530,2902,0.5,0.18263266712612,2.7377358490566,22,2372,2.47072505713368e-07
+"2494",3960,"IP ICU SEDATION MANAGEMENT FOR MECHANICALLY VENTILATED PATIENTS",386,"530120","HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)",5,111,0.287564766839378,130,2894,0.045045045045045,0.0449205252246026,1.002772002772,106,2764,0.999999999999939
+"2495",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"101","ACETAMINOPHEN 325 MG PO TABS",1,53,0.0474060822898032,981,42092,0.0188679245283019,0.0233060914187969,0.809570519108341,52,41111,0.999999999999175
+"2496",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"5002","METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN",1,10,0.00894454382826476,346,10128,0.1,0.0341627172195893,2.9271676300578,9,9782,0.78372849785235
+"2497",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"10814","OXYCODONE 5 MG PO TABS",10,154,0.137745974955277,1478,56614,0.0649350649350649,0.0261066167379094,2.48730295415004,144,55136,0.00579157629032886
+"2498",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"22472","KETOROLAC 15 MG/ML INJ SOLN",1,17,0.0152057245080501,215,5031,0.0588235294117647,0.0427350427350427,1.37647058823529,16,4816,0.999999999999666
+"2499",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,72,0.0644007155635063,692,19620,0.0138888888888889,0.0352701325178389,0.39378612716763,71,18928,0.507703214577724
+"2500",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"96559","PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK",1,3,0.00268336314847943,1067,10067,0.333333333333333,0.105989867885169,3.14495470165573,2,9000,0.733113684460442
+"2501",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"110872","HYDROMORPHONE 2 MG/ML INJ SYRG",2,29,0.0259391771019678,738,23934,0.0689655172413793,0.0308347956881424,2.23661340061676,27,23196,0.516195656370201
+"2502",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,32,0.0286225402504472,1219,33427,0.03125,0.036467526251234,0.856926784249385,31,32208,0.999999999998817
+"2503",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",4,218,0.194991055456172,705,36939,0.018348623853211,0.0190855193697718,0.961389810657818,214,36234,0.999999999999996
+"2504",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"540102","NS IV BOLUS",1,5,0.00447227191413238,2225,80957,0.2,0.0274837259285794,7.27703370786517,4,78732,0.32143108681697
+"2505",3970,"IP RAD INTERVENTIONAL RADIOLOGY POSTOP (WITH ADMISSION)",1118,"541271","PIPERACILLIN-TAZOBACTAM 3.375 GRAM/50 ML IVPB EXTENDED INFUSION",1,4,0.0035778175313059,466,8270,0.25,0.0563482466747279,4.43669527896996,3,7804,0.552308510520013
+"2506",3972,"IP TRS LUNG, HEART-LUNG TRANSPLANT ",172,"94670","TACROLIMUS 1 MG PO CAPS",1,14,0.0813953488372093,322,9479,0.0714285714285714,0.0339698280409326,2.10270629991127,13,9157,0.972168357343368
+"2507",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"27838","SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP",1,7,0.00652985074626866,692,19620,0.142857142857143,0.0352701325178389,4.05037159372419,6,18928,0.604531998684468
+"2508",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"81505","CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK",2,4,0.00373134328358209,242,3682,0.5,0.0657251493753395,7.60743801652893,2,3440,0.0129391816449724
+"2509",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"115288","LEVOFLOXACIN IN D5W 750 MG/150 ML IV PGBK",1,5,0.00466417910447761,208,3699,0.2,0.0562314138956475,3.55673076923077,4,3491,0.672618631294719
+"2510",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",8,58,0.0541044776119403,1808,23328,0.137931034482759,0.0775034293552812,1.77967653341471,50,21520,0.141068529299521
+"2511",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"540102","NS IV BOLUS",1,46,0.042910447761194,2225,80957,0.0217391304347826,0.0274837259285794,0.790981924767953,45,78732,0.999999999999518
+"2512",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541020","VANCOMYCIN ED ORDER ",6,33,0.0307835820895522,213,1807,0.181818181818182,0.117874930824571,1.54246692274861,27,1594,0.393704284281002
+"2513",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541061","VANCOMYCIN PER PHARMACY PROTOCOL",1,40,0.0373134328358209,239,5475,0.025,0.0436529680365297,0.572698744769874,39,5236,0.851482690206986
+"2514",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541082","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML MINI-BAG PLUS EXTENDED INF",1,10,0.00932835820895522,6,49,0.1,0.122448979591837,0.816666666666667,9,43,1
+"2515",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",1,7,0.00652985074626866,277,3459,0.142857142857143,0.0800809482509396,1.78390923156266,6,3182,0.999999999999988
+"2516",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541085","CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS",1,19,0.0177238805970149,411,7130,0.0526315789473684,0.0576437587657784,0.913049045972596,18,6719,0.999999999999998
+"2517",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541086","CEFTRIAXONE 2 GRAM/50 ML MINI-BAG PLUS",1,2,0.00186567164179104,174,2732,0.5,0.0636896046852123,7.85057471264368,1,2558,0.282372255725699
+"2518",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541273","PIPERACILLIN-TAZOBACTAM 4.5 GRAM/100 ML IVPB EXTENDED INFUSION",12,64,0.0597014925373134,148,1260,0.1875,0.117460317460317,1.59628378378378,52,1112,0.138761575826802
+"2519",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541441","NS IV BOLUS - 1000 ML",1,33,0.0307835820895522,95,3670,0.0303030303030303,0.0258855585831063,1.17065390749601,32,3575,0.999999999999997
+"2520",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541442","NS IV BOLUS - 2000 ML",1,11,0.0102611940298507,5,120,0.0909090909090909,0.0416666666666667,2.18181818181818,10,115,0.999999999999992
+"2521",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541544","CEFEPIME 2 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",4,12,0.0111940298507463,60,934,0.333333333333333,0.0642398286937901,5.18888888888889,8,874,0.00187345125100521
+"2522",3974,"IP GEN SEPSIS COMPREHENSIVE",1072,"541547","MEROPENEM 1 G/100 ML EXTENDED INFUSION MINI-BAG PLUS",2,10,0.00932835820895522,71,1342,0.2,0.0529061102831595,3.78028169014085,8,1271,0.177558375243284
+"2523",4001,"RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ",91,"81934","NIMODIPINE 30 MG PO CAPS",1,18,0.197802197802198,23,372,0.0555555555555556,0.0618279569892473,0.898550724637681,17,349,0.999999999999999
+"2524",4001,"RETIRED IP NSR STROKE: SUBARACHNOID HEMORRHAGE ",91,"530010","NICARDIPINE IV INFUSION",3,3,0.032967032967033,257,676,1,0.380177514792899,2.63035019455253,0,419,0.107732010681911
+"2525",4002,"IP NSR ICU STROKE: SUBARACHANOID HEMORRHAGE ",9,"530010","NICARDIPINE IV INFUSION",1,1,0.111111111111111,257,676,1,0.380177514792899,2.63035019455253,0,419,0.806441615303317
+"2526",4004,"IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION",126,"101","ACETAMINOPHEN 325 MG PO TABS",2,89,0.706349206349206,981,42092,0.0224719101123595,0.0233060914187969,0.964207584556002,87,41111,0.999999999999504
+"2527",4004,"IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION",126,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",3,97,0.76984126984127,399,16019,0.0309278350515464,0.0249079218428117,1.24168669095419,94,15620,0.958119278856827
+"2528",4004,"IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION",126,"209508","METHYLPREDNISOLONE SOD SUC(PF) 40 MG/ML INJ SOLR",3,33,0.261904761904762,155,3480,0.0909090909090909,0.0445402298850575,2.04105571847507,30,3325,0.391311223426467
+"2529",4004,"IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION",126,"209509","METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR",6,30,0.238095238095238,275,8000,0.2,0.034375,5.81818181818182,24,7725,9.43837559730567e-06
+"2530",4004,"IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION",126,"212610","IMMUNE GLOBULIN(HUM),CAPR(IGG) 10 GRAM/100 ML (10 %) INJ SOLN",1,20,0.158730158730159,73,1504,0.05,0.0485372340425532,1.03013698630137,19,1431,0.999999999999995
+"2531",4004,"IP TRS KIDNEY/PANCREAS INDUCTION/REJECTION MEDICATION",126,"520074","ANTITHYMOCYTE GLOBULIN RABBIT IVPB",1,66,0.523809523809524,75,1781,0.0151515151515152,0.0421111734980348,0.35979797979798,65,1706,0.442943805864892
+"2532",4012,"ED POST-INTUBATION",88,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",2,10,0.113636363636364,155,5616,0.2,0.0275997150997151,7.24645161290323,8,5461,0.0189629000753321
+"2533",4012,"ED POST-INTUBATION",88,"11150","PROPOFOL 10 MG/ML IV EMUL",3,21,0.238636363636364,135,3592,0.142857142857143,0.0375835189309577,3.8010582010582,18,3457,0.0525336461350647
+"2534",4012,"ED POST-INTUBATION",88,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",2,9,0.102272727272727,1219,33427,0.222222222222222,0.036467526251234,6.09370157688451,7,32208,0.0373566114204867
+"2535",4012,"ED POST-INTUBATION",88,"530004","MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION",1,10,0.113636363636364,65,1105,0.1,0.0588235294117647,1.7,9,1040,0.999999999999986
+"2536",4012,"ED POST-INTUBATION",88,"530120","HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)",1,15,0.170454545454545,130,2894,0.0666666666666667,0.0449205252246026,1.48410256410256,14,2764,0.999999999999992
+"2537",4023,"IP NEU STROKE ADMISSION",1147,"101","ACETAMINOPHEN 325 MG PO TABS",2,98,0.0854402789886661,981,42092,0.0204081632653061,0.0233060914187969,0.875657908423308,96,41111,0.999999999999981
+"2538",4023,"IP NEU STROKE ADMISSION",1147,"3697","HYDRALAZINE 20 MG/ML INJ SOLN",1,22,0.019180470793374,537,15149,0.0454545454545455,0.0354478843488019,1.28229219570002,21,14612,0.999999999999961
+"2539",4023,"IP NEU STROKE ADMISSION",1147,"10372","LABETALOL 5 MG/ML IV SOLN",1,28,0.024411508282476,370,5898,0.0357142857142857,0.0627331298745337,0.569305019305019,27,5528,0.843203048948744
+"2540",4023,"IP NEU STROKE ADMISSION",1147,"11349","SENNOSIDES 8.6 MG PO TABS",1,36,0.031386224934612,610,35793,0.0277777777777778,0.0170424384656218,1.62991803278689,35,35183,0.999999999999991
+"2541",4023,"IP NEU STROKE ADMISSION",1147,"13654","ASPIRIN 325 MG PO TBEC",5,24,0.020924149956408,16,582,0.208333333333333,0.0274914089347079,7.578125,19,566,2.94673070081973e-05
+"2542",4023,"IP NEU STROKE ADMISSION",1147,"14113","ASPIRIN 81 MG PO TBEC",5,169,0.147340889276373,391,18448,0.029585798816568,0.0211947094535993,1.39590490170856,164,18057,0.627814358358413
+"2543",4023,"IP NEU STROKE ADMISSION",1147,"110892","ENOXAPARIN 40 MG/0.4 ML SC SYRG",14,260,0.22667829119442,499,13896,0.0538461538461538,0.0359096142774899,1.49949129027285,246,13397,0.171992430971743
+"2544",4023,"IP NEU STROKE ADMISSION",1147,"209037","ACETAMINOPHEN 650 MG/20.3 ML PO SOLN",2,97,0.0845684394071491,192,8611,0.0206185567010309,0.0222970618975729,0.924720790378007,95,8419,0.999999999999782
+"2545",4023,"IP NEU STROKE ADMISSION",1147,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",1,8,0.00697471665213601,452,13480,0.125,0.0335311572700297,3.72787610619469,7,13028,0.649770163706163
+"2546",4028,"PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE",15,"101","ACETAMINOPHEN 325 MG PO TABS",1,5,0.333333333333333,981,42092,0.2,0.0233060914187969,8.58144750254842,4,41111,0.255989696965087
+"2547",4028,"PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE",15,"6487","PREDNISOLONE ACETATE 1 % OPHT DRPS",1,6,0.4,25,1088,0.166666666666667,0.0229779411764706,7.25333333333333,5,1063,0.336775064954837
+"2548",4028,"PHC RETINA PERIOPERATIVE ORDERS RIGHT EYE",15,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,6,0.4,705,36939,0.166666666666667,0.0190855193697718,8.73262411347518,5,36234,0.25049882357025
+"2549",4030,"PHC RETINA PERIOPERATIVE ORDERS LEFT EYE",18,"6487","PREDNISOLONE ACETATE 1 % OPHT DRPS",1,5,0.277777777777778,25,1088,0.2,0.0229779411764706,8.704,4,1063,0.262341545790685
+"2550",4031,"PHC GLAUCOMA PERIOPERATIVE ORDERS LEFT EYE",3,"101","ACETAMINOPHEN 325 MG PO TABS",1,3,1,981,42092,0.333333333333333,0.0233060914187969,14.3024125042474,2,41111,0.100002477677862
+"2551",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"101","ACETAMINOPHEN 325 MG PO TABS",2,113,0.188963210702341,981,42092,0.0176991150442478,0.0233060914187969,0.759420132968887,111,41111,0.934350258042478
+"2552",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"1080","BISACODYL 10 MG PR SUPP",1,4,0.00668896321070234,344,22442,0.25,0.0153284021031994,16.3095930232558,3,22098,0.0746736552710219
+"2553",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",1,190,0.317725752508361,89,4372,0.00526315789473684,0.0203568161024703,0.258545239503253,189,4283,0.230861016242538
+"2554",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"10050","FLUCONAZOLE IN NACL (ISO-OSM) 400 MG/200 ML IV PGBK",6,184,0.307692307692308,40,1102,0.0326086956521739,0.0362976406533575,0.898369565217391,178,1062,0.972069953788611
+"2555",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"38285","ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP",1,123,0.205685618729097,71,5546,0.00813008130081301,0.0128020194734944,0.635062406962098,122,5475,0.959627970850596
+"2556",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"111940","FOAM BANDAGE 4 X 4 "" TP BNDG",2,36,0.0602006688963211,1,6,0.0555555555555556,0.166666666666667,0.333333333333333,34,5,0.902661513079929
+"2557",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"520046","VANCOMYCIN IVPB (CUSTOM DOSE)",13,188,0.31438127090301,1808,23328,0.0691489361702128,0.0775034293552812,0.892204857842214,175,21520,0.771909941925248
+"2558",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"541084","CEFEPIME 2 GRAM/100 ML MINI-BAG PLUS",1,49,0.0819397993311037,277,3459,0.0204081632653061,0.0800809482509396,0.254844175937523,48,3182,0.20436033533536
+"2559",4045,"IP VAD PRE-OP/DAY OF SURGERY",598,"541106","CEFTAZIDIME 1 GRAM/50 ML MINI-BAG PLUS",5,112,0.187290969899666,14,162,0.0446428571428571,0.0864197530864197,0.516581632653061,107,148,0.272920941217379
+"2560",4047,"RETIRED ED PRIMARY HEADACHE ORDERSET",21,"540102","NS IV BOLUS",1,3,0.142857142857143,2225,80957,0.333333333333333,0.0274837259285794,12.1283895131086,2,78732,0.140437186609798
+"2561",4047,"RETIRED ED PRIMARY HEADACHE ORDERSET",21,"540148","NS FLUSH BAG",1,2,0.0952380952380952,4,133,0.5,0.0300751879699248,16.625,1,129,0.108118213611607
+"2562",4085,"NURSING TRIAGE SEPSIS NURSING PROTOCOL",5327,"540102","NS IV BOLUS",57,1385,0.259996245541581,2225,80957,0.0411552346570397,0.0274837259285794,1.49744014927189,1328,78732,0.00278231660795891
+"2563",4085,"NURSING TRIAGE SEPSIS NURSING PROTOCOL",5327,"541440","NS IV BOLUS - 500 ML",1,9,0.00168950628871785,189,5436,0.111111111111111,0.0347682119205298,3.1957671957672,8,5247,0.735332023092462
+"2564",4085,"NURSING TRIAGE SEPSIS NURSING PROTOCOL",5327,"541441","NS IV BOLUS - 1000 ML",55,871,0.163506664163694,95,3670,0.0631458094144661,0.0258855585831063,2.43942232159043,816,3575,5.76406030314642e-08
+"2565",4223,"OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT)",60,"126580","HEPARIN, PORCINE (PF) 1,000 UNIT/ML INJ SOLN",1,9,0.15,58,653,0.111111111111111,0.0888208269525268,1.25095785440613,8,595,0.999999999999992
+"2566",4223,"OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT)",60,"205894","PRISMASOL SOLUTION (BGK 2/0) (32 MEQ BICARB/L)",3,32,0.533333333333333,4,19,0.09375,0.210526315789474,0.4453125,29,15,0.452726126603559
+"2567",4223,"OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT)",60,"230501","DEXTROSE-SOD CITRATE-CITRIC AC 2.45-2.2 GRAM- 730 MG/100 ML MISC SOLN",1,6,0.1,2,37,0.166666666666667,0.0540540540540541,3.08333333333333,5,35,0.888173199669631
+"2568",4223,"OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT)",60,"500006","SODIUM PHOSPHATE IVPB",4,48,0.8,296,4785,0.0833333333333333,0.0618599791013584,1.34712837837838,44,4489,0.754350025258409
+"2569",4223,"OR REN CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT)",60,"500034","CALCIUM CHLORIDE 8 G/1000 ML NS IV INFUSION",8,22,0.366666666666667,9,166,0.363636363636364,0.0542168674698795,6.70707070707071,14,157,1.30275682672201e-05
+"2570",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"1080","BISACODYL 10 MG PR SUPP",1,19,0.00895381715362865,344,22442,0.0526315789473684,0.0153284021031994,3.43359853121175,18,22098,0.697658520502197
+"2571",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"2566","DOCUSATE SODIUM 100 MG PO CAPS",1,17,0.00801131008482564,542,37509,0.0588235294117647,0.0144498653656456,4.0708704145865,16,36967,0.605848366383774
+"2572",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"3584","HALOPERIDOL LACTATE 5 MG/ML INJ SOLN",1,38,0.0179076343072573,513,10794,0.0263157894736842,0.047526403557532,0.553708833487227,37,10281,0.816742388133581
+"2573",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"8085","TRAZODONE 50 MG PO TABS",2,70,0.0329877474081056,334,11344,0.0285714285714286,0.0294428772919605,0.970402053036784,68,11010,0.99999999999962
+"2574",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"11349","SENNOSIDES 8.6 MG PO TABS",2,19,0.00895381715362865,610,35793,0.105263157894737,0.0170424384656218,6.17653149266609,17,35183,0.0374346780043848
+"2575",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"16830","MELATONIN 3 MG PO TABS",10,605,0.285108388312912,456,17087,0.0165289256198347,0.0266869549950255,0.619363491373061,595,16631,0.160282172805896
+"2576",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",2,18,0.00848256361922714,712,36910,0.111111111111111,0.0192901652668653,5.75998751560549,16,36198,0.0485718595653844
+"2577",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"77034","QUETIAPINE 25 MG PO TABS",9,106,0.0499528746465598,544,15894,0.0849056603773585,0.0342267522335473,2.48068118756937,97,15350,0.00987560306250189
+"2578",4236,"IP GEN DELIRIUM (FOR NON-ICU PATIENTS ONLY)",2122,"9000000","ZZZ IMS TEMPLATE",1,38,0.0179076343072573,1035,28824,0.0263157894736842,0.0359075770191507,0.732875667429443,37,27789,0.999999999999435
+"2579",4237,"ED PEDS ACUTE ASTHMA EXACERBATION (18 MO - 18 YEARS)",2,"251","ALBUTEROL SULFATE 5 MG/ML INH NEBU",1,1,0.5,100,358,1,0.279329608938547,3.58,0,258,0.626277508490792
+"2580",4259,"ED PEDS FEVER IN PEDIATRIC ONCOLOGY",2,"520045","MEROPENEM CUSTOM IVPB",1,1,0.5,100,1654,1,0.060459492140266,16.54,0,1554,0.0666030171596958
+"2581",4259,"ED PEDS FEVER IN PEDIATRIC ONCOLOGY",2,"570051","VANCOMYCIN PEDIATRIC IV INFUSION",1,1,0.5,4,8,1,0.5,2,0,4,1
+"2582",4277,"IP ARGATROBAN INFUSION",1006,"221680","ARGATROBAN IN NACL (ISO-OS) 50 MG/50 ML (1 MG/ML) IV SOLN",9,103,0.102385685884692,6,136,0.087378640776699,0.0441176470588235,1.98058252427184,94,130,0.272953389191892
+"2583",4277,"IP ARGATROBAN INFUSION",1006,"541261","ARGATROBAN IV INFUSION (LVAD HIGH BLEED RISK)",16,135,0.134194831013916,1,1,0.118518518518519,1,0.118518518518519,119,0,0.255084875767801
+"2584",4317,"SHC TXP DIABETES",151,"16027","SHARPS CONTAINER MISC MISC",2,68,0.450331125827815,1,83,0.0294117647058824,0.0120481927710843,2.44117647058824,66,82,0.861349154844946
+"2585",4317,"SHC TXP DIABETES",151,"37706","BD INSULIN PEN NEEDLE UF MINI 31 X 3/16 "" MISC NDLE",2,68,0.450331125827815,1,84,0.0294117647058824,0.0119047619047619,2.47058823529412,66,83,0.853090833545455
+"2586",4317,"SHC TXP DIABETES",151,"203639","HUMALOG KWIKPEN 100 UNIT/ML SC INPN",4,56,0.370860927152318,11,419,0.0714285714285714,0.0262529832935561,2.72077922077922,52,408,0.158886833207661
+"2587",4317,"SHC TXP DIABETES",151,"205788","TRUETEST TEST STRIPS MISC STRP",1,9,0.0596026490066225,2,69,0.111111111111111,0.0289855072463768,3.83333333333333,8,67,0.776774355704329
+"2588",4317,"SHC TXP DIABETES",151,"210305","ONETOUCH DELICA LANCETS 33 GAUGE MISC MISC",2,16,0.105960264900662,1,142,0.125,0.00704225352112676,17.75,14,141,0.0208152946810593
+"2589",4317,"SHC TXP DIABETES",151,"222403","ONETOUCH VERIO MISC STRP",1,19,0.125827814569536,2,201,0.0526315789473684,0.00995024875621891,5.28947368421053,18,199,0.618085786749939
+"2590",4317,"SHC TXP DIABETES",151,"231561","HUMULIN N KWIKPEN 100 UNIT/ML (3 ML) SC INPN",2,26,0.172185430463576,5,191,0.0769230769230769,0.0261780104712042,2.93846153846154,24,186,0.433989164089528
+"2591",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"101","ACETAMINOPHEN 325 MG PO TABS",2,15,0.0335570469798658,981,42092,0.133333333333333,0.0233060914187969,5.72096500169895,13,41111,0.0492420443702524
+"2592",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"102","ACETAMINOPHEN 500 MG PO TABS",1,111,0.248322147651007,392,15968,0.00900900900900901,0.0245490981963928,0.366979224122081,110,15576,0.454331734928635
+"2593",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"18308","GABAPENTIN 300 MG PO CAPS",3,131,0.293064876957494,588,19458,0.0229007633587786,0.0302189330866482,0.757828322168562,128,18870,0.816706007553129
+"2594",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",2,25,0.0559284116331096,258,5335,0.08,0.0483598875351453,1.65426356589147,23,5077,0.788623617631712
+"2595",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"77052","CELECOXIB 200 MG PO CAPS",2,95,0.212527964205817,20,1192,0.0210526315789474,0.0167785234899329,1.25473684210526,93,1172,0.99999999999999
+"2596",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"205597","ALVIMOPAN 12 MG PO CAPS",3,129,0.288590604026846,28,1117,0.0232558139534884,0.0250671441360788,0.927740863787375,126,1089,0.999999999999973
+"2597",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"212945","HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",8,64,0.143176733780761,452,13480,0.125,0.0335311572700297,3.72787610619469,56,13028,0.000229251772992304
+"2598",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"520064","CEFAZOLIN 2 G IVPB",2,16,0.0357941834451902,109,2674,0.125,0.0407629020194465,3.06651376146789,14,2565,0.289745751459594
+"2599",4319,"INTERVENTIONAL HOME PRE-ANESTHESIA/PRE-OP ORDERS",447,"541110","ERTAPENEM 1 GRAM/50 ML MINI-BAG PLUS",5,143,0.319910514541387,72,1340,0.034965034965035,0.0537313432835821,0.650738150738151,138,1268,0.445340125556059
+"2600",4330,"IP GEN SEVERE SEPSIS BUNDLE RAPID",243,"540102","NS IV BOLUS",1,28,0.11522633744856,2225,80957,0.0357142857142857,0.0274837259285794,1.29947030497592,27,78732,0.999999999999245
+"2601",4330,"IP GEN SEVERE SEPSIS BUNDLE RAPID",243,"541441","NS IV BOLUS - 1000 ML",1,10,0.0411522633744856,95,3670,0.1,0.0258855585831063,3.86315789473684,9,3575,0.634739944926865
+"2602",4565,"SHC AMB AI TESTING & TREATMENT",1,"2508","DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN",1,1,1,399,16019,1,0.0249079218428117,40.1478696741855,0,15620,0.0023300376053999
+"2603",4565,"SHC AMB AI TESTING & TREATMENT",1,"2509","DIPHENHYDRAMINE HCL 25 MG PO CAPS",1,1,1,356,12049,1,0.0295460204166321,33.8455056179775,0,11693,0.00553235077773476
+"2604",4565,"SHC AMB AI TESTING & TREATMENT",1,"92888","EPINEPHRINE 0.3 MG/0.3 ML (1:1,000) INJ ATIN",1,1,1,10,73,1,0.136986301369863,7.3,0,63,0.320028118179172
+"2605",4572,"SHC PAI PRE-OP/MODERATE SEDATION ORDERS",36,"10607","MIDAZOLAM 1 MG/ML INJ SOLN",1,4,0.111111111111111,155,5616,0.25,0.0275997150997151,9.05806451612903,3,5461,0.236299296530011
+"2606",4572,"SHC PAI PRE-OP/MODERATE SEDATION ORDERS",36,"540106","FENTANYL (PF) 50 MCG/ML INJECTION",1,15,0.416666666666667,461,18637,0.0666666666666667,0.0247357407308043,2.69515545914678,14,18176,0.830944400791521
+"2607",4614,"ED/IP NURSING NG INSERTION PAIN PROTOCOL",370,"4450","LIDOCAINE HCL 4 % TOPICAL SOLN",1,15,0.0405405405405405,52,381,0.0666666666666667,0.136482939632546,0.488461538461538,14,329,0.694748308212545
+"2608",4614,"ED/IP NURSING NG INSERTION PAIN PROTOCOL",370,"4455","LIDOCAINE (PF) 40 MG/ML (4 %) INJ SOLN",3,117,0.316216216216216,50,604,0.0256410256410256,0.0827814569536424,0.30974358974359,114,554,0.0483633462764456
+"2609",4632,"IP NSR LAMINECTOMY/DISKECTOMY",92,"10814","OXYCODONE 5 MG PO TABS",1,22,0.239130434782609,1478,56614,0.0454545454545455,0.0261066167379094,1.74111206790503,21,55136,0.999999999999992
+"2610",4632,"IP NSR LAMINECTOMY/DISKECTOMY",92,"11349","SENNOSIDES 8.6 MG PO TABS",1,25,0.271739130434783,610,35793,0.04,0.0170424384656218,2.34708196721311,24,35183,0.909537480551858
+"2611",4654,"IP HNS FREE FLAP ORDERSET",372,"101","ACETAMINOPHEN 325 MG PO TABS",1,6,0.0161290322580645,981,42092,0.166666666666667,0.0233060914187969,7.15120625212368,5,41111,0.330113946258882
+"2612",4654,"IP HNS FREE FLAP ORDERSET",372,"680","ASPIRIN 81 MG PO CHEW",1,68,0.182795698924731,357,11203,0.0147058823529412,0.031866464339909,0.461484593837535,67,10846,0.647173988568757
+"2613",4654,"IP HNS FREE FLAP ORDERSET",372,"10011","FAMOTIDINE 20 MG PO TABS",1,11,0.0295698924731183,472,16960,0.0909090909090909,0.0278301886792453,3.26656394453005,10,16488,0.72303347734181
+"2614",4654,"IP HNS FREE FLAP ORDERSET",372,"10813","OXYCODONE 5 MG/5 ML PO SOLN",1,68,0.182795698924731,328,8370,0.0147058823529412,0.0391875746714456,0.375269010043042,67,8042,0.468937573997431
+"2615",4654,"IP HNS FREE FLAP ORDERSET",372,"14863","POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP",1,62,0.166666666666667,314,8762,0.0161290322580645,0.035836566993837,0.450071912882679,61,8448,0.624156731904624
+"2616",4654,"IP HNS FREE FLAP ORDERSET",372,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,47,0.126344086021505,258,5335,0.0212765957446809,0.0483598875351453,0.439963714332839,46,5077,0.602040926000093
+"2617",4654,"IP HNS FREE FLAP ORDERSET",372,"112586","HYDROMORPHONE 1 MG/ML INJ SYRG",1,30,0.0806451612903226,1219,33427,0.0333333333333333,0.036467526251234,0.914055236532677,29,32208,0.999999999999988
+"2618",4654,"IP HNS FREE FLAP ORDERSET",372,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,82,0.220430107526882,705,36939,0.0121951219512195,0.0190855193697718,0.63897249610794,81,36234,0.958896288341337
+"2619",4654,"IP HNS FREE FLAP ORDERSET",372,"202606","FAMOTIDINE (PF) 20 MG/2 ML IV SOLN",1,34,0.0913978494623656,288,4280,0.0294117647058824,0.0672897196261682,0.437091503267974,33,3992,0.592236034907383
+"2620",4658,"PRE-ADMISSION/PRE-OP ORDERS CV SURGERY",640,"9516","CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH",9,258,0.403125,89,4372,0.0348837209302326,0.0203568161024703,1.7136137967076,249,4283,0.176145467525591
+"2621",4658,"PRE-ADMISSION/PRE-OP ORDERS CV SURGERY",640,"236263","CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK",4,263,0.4109375,111,2584,0.0152091254752852,0.0429566563467492,0.354057479532765,259,2473,0.0441043178286998
+"2622",4678,"IP NUR SEVERE SEPSIS NURSE PROTOCOL",512,"541440","NS IV BOLUS - 500 ML",4,27,0.052734375,189,5436,0.148148148148148,0.0347682119205298,4.26102292768959,23,5247,0.00779420295506083
+"2623",4683,"IP VAD TPA ALTEPLASE LVAD THROMBUS PROTOCOL",19,"541066","HEPARIN INFUSION LVAD HIGH RISK BLEED",1,8,0.421052631578947,1,22,0.125,0.0454545454545455,2.75,7,21,0.999999999999998
+"2624",4727,"IP NSR CERVICAL AND LUMBAR FUSIONS",337,"25365","CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK",1,17,0.0504451038575668,258,5335,0.0588235294117647,0.0483598875351453,1.21637026903785,16,5077,0.999999999999997
+"2625",4727,"IP NSR CERVICAL AND LUMBAR FUSIONS",337,"198509","ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN",1,72,0.213649851632047,705,36939,0.0138888888888889,0.0190855193697718,0.727718676122931,71,36234,0.999999999998187
+"2626",4766,"IP GEN HOSPICE ADMISSION",109,"3584","HALOPERIDOL LACTATE 5 MG/ML INJ SOLN",1,27,0.247706422018349,513,10794,0.037037037037037,0.047526403557532,0.779293913796838,26,10281,0.999999999999865
+"2627",4766,"IP GEN HOSPICE ADMISSION",109,"25424","POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK",1,5,0.0458715596330275,712,36910,0.2,0.0192901652668653,10.3679775280899,4,36198,0.189859515999083
+"2628",4766,"IP GEN HOSPICE ADMISSION",109,"117414","ARTIFICIAL SALIVA (YERBAS-LYT) MM SPRA",1,13,0.119266055045872,38,452,0.0769230769230769,0.084070796460177,0.91497975708502,12,414,0.999999999999979
diff --git a/scripts/OutpatientLabPrediction/OutpatientAutomatedMLCode.ipynb b/scripts/OutpatientLabPrediction/OutpatientAutomatedMLCode.ipynb
new file mode 100644
index 00000000..76399d55
--- /dev/null
+++ b/scripts/OutpatientLabPrediction/OutpatientAutomatedMLCode.ipynb
@@ -0,0 +1,905 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from datetime import datetime\n",
+    "from xgboost import XGBClassifier\n",
+    "from google.cloud import bigquery\n",
+    "from google.cloud.bigquery import dbapi;\n",
+    "\n",
+    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
+    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getQuery(proc_code, features):\n",
+    "    \"\"\"\n",
+    "    Dynamically create feature matrix query using features in param list 'features'\n",
+    "    \"\"\"\n",
+    "    #values\n",
+    "    lab1_key, lab2_key, lab3_key, lab4_key, lab5_key, \\\n",
+    "    lab6_key, lab7_key, lab8_key, lab9_key, lab10_key = features[0]\n",
+    "    \n",
+    "    dx1, dx2, dx3, dx4, dx5, dx6, dx7, dx8, dx9, dx10 = features[1]\n",
+    "    \n",
+    "    hx1, hx2, hx3 = features[2]\n",
+    "    \n",
+    "    med1, med2, med3, med4, med5, med6, med7, med8, med9, med10 = features[3]\n",
+    "    \n",
+    "    for ft in features:\n",
+    "        if None in ft: return None\n",
+    "    \n",
+    "    #names\n",
+    "    dx_name1, dx_name2, dx_name3, dx_name4, dx_name5, dx_name6, dx_name7, \\\n",
+    "    dx_name8, dx_name9, dx_name10 = [\"dx\" + str(n) for n in range(1, 11)]\n",
+    "    \n",
+    "    hx_name1, hx_name2, hx_name3 = [\"hx\" + str(n) for n in range(1, 4)]\n",
+    "    \n",
+    "    med_name1, med_name2, med_name3, med_name4, med_name5, med_name6, \\\n",
+    "    med_name7, med_name8, med_name9, med_name10 = [\"med\" + str(n) for n in range(1, 11)]\n",
+    "    \n",
+    "    query = \"\"\"\n",
+    "WITH\n",
+    "\n",
+    "--Generate the labels table (1 if result_flag is true and all other columns are null, else 0)\n",
+    "label_labs AS \n",
+    "(\n",
+    "  SELECT\n",
+    "    anon_id, taken_time, result_time,\n",
+    "    max(case when (lab_result.result_in_range_yn is null and lab_result.result_flag is not null) then 1 else 0 end) as label\n",
+    "\n",
+    "  FROM\n",
+    "    shc_core.lab_result\n",
+    "  WHERE\n",
+    "    proc_code = '\"\"\" + proc_code + \"\"\"' and not (reference_low is null and reference_high is null)\n",
+    "  GROUP BY\n",
+    "    anon_id, taken_time, result_time\n",
+    "),\n",
+    "\n",
+    "--identify all cases when cohort (patients that have taken the target lab test) had one of the top 10 diagnoses\n",
+    "--before the target lab test was taken\n",
+    "labels_dx AS \n",
+    "  --note that the naming tells you what tables have been merged\n",
+    "(\n",
+    "  SELECT *\n",
+    "  FROM\n",
+    "  (\n",
+    "    SELECT\n",
+    "      anon_id as dx_anon_id, start_date_utc,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx1 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name1 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx2 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name2 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx3 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name3 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx4 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name4 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx5 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name5 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx6 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name6 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx7 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name7 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx8 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name8 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx9 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name9 + \"\"\"_hist,\n",
+    "      case when (diagnosis_code.dx_name like '%\"\"\" + dx10 + \"\"\"%') then 1 else 0 end as \"\"\" + dx_name10 + \"\"\"_hist\n",
+    "    FROM\n",
+    "      shc_core.diagnosis_code\n",
+    "  ) AS dx\n",
+    "  RIGHT JOIN \n",
+    "  (\n",
+    "    SELECT *\n",
+    "    FROM label_labs\n",
+    "  ) AS labels\n",
+    "  ON dx.dx_anon_id = labels.anon_id\n",
+    "  AND CAST(dx.start_date_utc as DATETIME) < CAST(labels.taken_time as DATETIME)\n",
+    "),\n",
+    "\n",
+    "--merge diagnoses results (1 or 0) to one row per patient/lab timestamp. So if a patient has had three out of the ten\n",
+    "--top dx before their target lab test date, they should have one row with three 1's and not three rows w/ one 1 each\n",
+    "labels_dx_part2 AS\n",
+    "(\n",
+    "  SELECT *\n",
+    "  FROM\n",
+    "  (\n",
+    "    SELECT \n",
+    "      max(\"\"\" + dx_name1 + \"\"\"_hist) as \"\"\" + dx_name1 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name2 + \"\"\"_hist) as \"\"\" + dx_name2 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name3 + \"\"\"_hist) as \"\"\" + dx_name3 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name4 + \"\"\"_hist) as \"\"\" + dx_name4 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name5 + \"\"\"_hist) as \"\"\" + dx_name5 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name6 + \"\"\"_hist) as \"\"\" + dx_name6 + \"\"\"_dx,  \n",
+    "      max(\"\"\" + dx_name7 + \"\"\"_hist) as \"\"\" + dx_name7 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name8 + \"\"\"_hist) as \"\"\" + dx_name8 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name9 + \"\"\"_hist) as \"\"\" + dx_name9 + \"\"\"_dx, \n",
+    "      max(\"\"\" + dx_name10 + \"\"\"_hist) as \"\"\" + dx_name10 + \"\"\"_dx, \n",
+    "      taken_time as labels_taken_time, result_time as labels_result_time, label, anon_id\n",
+    "    FROM \n",
+    "     labels_dx\n",
+    "    GROUP BY \n",
+    "      anon_id, taken_time, result_time, label\n",
+    "  )\n",
+    "),\n",
+    "\n",
+    "--identify all cases when cohort had one of the top 10 meds ordered before the target lab test was taken. \n",
+    "--Merge so all meds for one patient/timestamp are in one row.\n",
+    "labels_dx_fam AS\n",
+    "(\n",
+    "  SELECT *\n",
+    "  FROM\n",
+    "  ( \n",
+    "     SELECT anon_id,\n",
+    "      max(case when (family_hx.medical_hx like '%\"\"\" + hx1 + \"\"\"%') then 1 else 0 end) as \"\"\" + hx_name1 + \"\"\"_hist,\n",
+    "      max(case when (family_hx.medical_hx like '%\"\"\" + hx2 + \"\"\"%') then 1 else 0 end) as \"\"\" + hx_name2 + \"\"\"_hist,\n",
+    "      max(case when (family_hx.medical_hx like '%\"\"\" + hx3 + \"\"\"%') then 1 else 0 end) as \"\"\" + hx_name3 + \"\"\"_hist,\n",
+    "     FROM\n",
+    "      shc_core.family_hx\n",
+    "     GROUP BY\n",
+    "      anon_id\n",
+    "  ) as fam_hist\n",
+    "  RIGHT JOIN\n",
+    "  (\n",
+    "    SELECT *\n",
+    "    FROM labels_dx_part2\n",
+    "  ) USING(anon_id)\n",
+    "),\n",
+    "\n",
+    "--get birthdate and gender\n",
+    "labels_dx_fam_demog AS\n",
+    "(\n",
+    "  SELECT *, \n",
+    "  FROM\n",
+    "  (\n",
+    "    SELECT \n",
+    "      anon_id, birth_date_jittered,\n",
+    "      (case when demographic.gender = \"Male\" then 1 else 0 end) as genderMale\n",
+    "    FROM\n",
+    "      shc_core.demographic\n",
+    "  )\n",
+    "  RIGHT JOIN\n",
+    "  (\n",
+    "    SELECT *\n",
+    "    FROM labels_dx_fam\n",
+    "  ) as hm\n",
+    "  USING(anon_id)\n",
+    "),\n",
+    "\n",
+    "--identify all cases when cohort had one of the top 10 meds ordered before the target lab test was taken\n",
+    "labels_dx_fam_demog_meds AS\n",
+    "(  \n",
+    "  SELECT *\n",
+    "  FROM \n",
+    "  (\n",
+    "    SELECT \n",
+    "      anon_id as meds_anon_id, start_time_jittered,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med1 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name1 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med2 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name2 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med3 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name3 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med4 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name4 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med5 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name5 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med6 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name6 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med7 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name7 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med8 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name8 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med9 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name9 + \"\"\"_med,\n",
+    "      case when (order_med.med_description like '%\"\"\" + med10 + \"\"\"%') then 1 else 0 end as \"\"\" + med_name10 + \"\"\"_med,\n",
+    "    FROM\n",
+    "      shc_core.order_med\n",
+    "  ) AS meds\n",
+    "  RIGHT JOIN\n",
+    "  (\n",
+    "    SELECT \n",
+    "      * except(birth_date_jittered), \n",
+    "      DATETIME_DIFF(CAST(labels_taken_time as DATETIME), CAST(birth_date_jittered as DATETIME),YEAR) as age\n",
+    "      --use the date gained from the labels table with the birthdate to generate age\n",
+    "    FROM labels_dx_fam_demog\n",
+    "  ) AS labels\n",
+    "  ON meds.meds_anon_id = labels.anon_id\n",
+    "  AND CAST(meds.start_time_jittered as DATETIME) < DATETIME_SUB(CAST(labels.labels_taken_time as DATETIME), INTERVAL 1 DAY)\n",
+    "  AND CAST(meds.start_time_jittered as DATETIME) > DATETIME_SUB(CAST(labels.labels_taken_time as DATETIME), INTERVAL 6 MONTH)\n",
+    "),\n",
+    "\n",
+    "--Merge so all meds for one patient/timestamp are in one row.\n",
+    "--note: couldn't do select * except b/c needed to modify by using aggregate functions\n",
+    "labels_dx_fam_demog_meds_v2 AS\n",
+    "(\n",
+    "  SELECT \n",
+    "      anon_id as anon_id2, labels_taken_time, labels_result_time, label, age, \"\"\" + dx_name1 +\"\"\"_dx, \"\"\" + dx_name2 +\"\"\"_dx, \"\"\" + dx_name3 +\"\"\"_dx, \n",
+    "      \"\"\" + dx_name4 +\"\"\"_dx, \"\"\" + dx_name5 +\"\"\"_dx, \"\"\" + dx_name6 +\"\"\"_dx, \"\"\" + dx_name7 +\"\"\"_dx, \"\"\" + dx_name8 +\"\"\"_dx, \n",
+    "      \"\"\" + dx_name9 +\"\"\"_dx, \"\"\" + dx_name10 +\"\"\"_dx, genderMale, \"\"\" + hx_name1 +\"\"\"_hist, \"\"\" + hx_name2 +\"\"\"_hist, \"\"\" + hx_name3 +\"\"\"_hist,\n",
+    "      max(\"\"\" + med_name1 +\"\"\"_med) as \"\"\" + med_name1 +\"\"\"_med, max(\"\"\" + med_name2 +\"\"\"_med) as \"\"\" + med_name2 +\"\"\"_med, \n",
+    "      max(\"\"\" + med_name3 +\"\"\"_med) as \"\"\" + med_name3 +\"\"\"_med, max(\"\"\" + med_name4 +\"\"\"_med) as \"\"\" + med_name4 +\"\"\"_med, \n",
+    "      max(\"\"\" + med_name5 +\"\"\"_med) as \"\"\" + med_name5 +\"\"\"_med, max(\"\"\" + med_name6 +\"\"\"_med) as \"\"\" + med_name6 +\"\"\"_med, \n",
+    "      max(\"\"\" + med_name7 +\"\"\"_med) as \"\"\" + med_name7 +\"\"\"_med, max(\"\"\" + med_name9 +\"\"\"_med) as \"\"\" + med_name8 +\"\"\"_med, \n",
+    "      max(\"\"\" + med_name9 +\"\"\"_med) as \"\"\" + med_name9 +\"\"\"_med, max(\"\"\" + med_name10 +\"\"\"_med) as \"\"\" + med_name10 +\"\"\"_med\n",
+    "  FROM\n",
+    "    labels_dx_fam_demog_meds\n",
+    "  GROUP BY \n",
+    "    anon_id2, labels_taken_time, labels_result_time, age, \n",
+    "    \"\"\" + dx_name1 +\"\"\"_dx, \"\"\" + dx_name2 +\"\"\"_dx, \"\"\" + dx_name3 +\"\"\"_dx, \"\"\" + dx_name4 +\"\"\"_dx, \"\"\" + dx_name5 +\"\"\"_dx, \"\"\" + dx_name6 +\"\"\"_dx,\n",
+    "    \"\"\" + dx_name7 +\"\"\"_dx, \"\"\" + dx_name8 +\"\"\"_dx, \"\"\" + dx_name9 +\"\"\"_dx, \"\"\" + dx_name10 +\"\"\"_dx, genderMale, \n",
+    "    \"\"\" + hx_name1 +\"\"\"_hist, \"\"\" + hx_name2 +\"\"\"_hist, \"\"\" + hx_name3 +\"\"\"_hist, label\n",
+    "),\n",
+    "\n",
+    "--repeat of creating the labels table. Temporarily left bc of downstream usage of this table name. \n",
+    "hs AS \n",
+    "(   \n",
+    "  SELECT\n",
+    "    anon_id, taken_time, result_time,\n",
+    "    max(case when (lab_result.result_in_range_yn is null and lab_result.result_flag is not null) then 1 else 0 end) as label\n",
+    "\n",
+    "  FROM\n",
+    "    shc_core.lab_result\n",
+    "  WHERE\n",
+    "    proc_code = '\"\"\" + proc_code + \"\"\"' and not (reference_low is null and reference_high is null)\n",
+    "  GROUP BY\n",
+    "    anon_id, taken_time, result_time\n",
+    "),\n",
+    "\n",
+    "--logic is a bit different from how hx, dx, and meds were found since we don't simply want to indicate 1/0 for\n",
+    "--having taken these feature lab tests or not. Instead start by reducing the shc_core labs table to only include\n",
+    "--the top ten labs \n",
+    "labs AS\n",
+    "(\n",
+    "  SELECT \n",
+    "    anon_id as labs_anon_id, lab_name as labs_lab_name, ord_value as lab_ord_value, \n",
+    "    ord_num_value, reference_low, reference_high, taken_time as labs_taken_time\n",
+    "  FROM\n",
+    "    shc_core.lab_result\n",
+    "  WHERE \n",
+    "    lab_result.ord_num_value != 9999999 and\n",
+    "    (\n",
+    "      lab_result.lab_name like '%\"\"\" + lab1_key + \"\"\"%' or lab_result.lab_name like '%\"\"\" + lab2_key + \"\"\"%' \n",
+    "      or lab_result.lab_name like '%\"\"\" + lab3_key + \"\"\"%' or lab_result.lab_name like '%\"\"\" + lab4_key + \"\"\"%' \n",
+    "      or lab_result.lab_name like '%\"\"\" + lab5_key + \"\"\"%' or lab_result.lab_name like '%\"\"\" + lab6_key + \"\"\"%' \n",
+    "      or lab_result.lab_name like '%\"\"\" + lab7_key + \"\"\"%' or lab_result.lab_name like '%\"\"\" + lab8_key + \"\"\"%' \n",
+    "      or lab_result.lab_name like '%\"\"\" + lab9_key + \"\"\"%' or lab_result.lab_name like '%\"\"\" + lab10_key + \"\"\"%' \n",
+    "    )\n",
+    "  ORDER BY\n",
+    "    anon_id\n",
+    "),\n",
+    "\n",
+    "\n",
+    "--record the order value and time taken for each lab. Also only include lab tests taken within certain time period\n",
+    "--before the date the target lab test was taken. Note that there is some repetitive code here that will be\n",
+    "--streamlined in future iterations.\n",
+    "hs_labs AS\n",
+    "(\n",
+    "  SELECT *\n",
+    "  FROM \n",
+    "  (\n",
+    "    SELECT \n",
+    "      labs_anon_id, labs_taken_time, ord_num_value,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab1_key + \"\"\"%\" then labs.ord_num_value else null end as lab1_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab1_key + \"\"\"%\" then labs.ord_num_value else null end as lab1_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab1_key + \"\"\"%\" then labs.ord_num_value else null end as lab1_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab1_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab1_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab1_key + \"\"\"%\" then labs.labs_taken_time else null end as lab1_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab1_key + \"\"\"%\" then labs.labs_taken_time else null end as lab1_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab2_key + \"\"\"%\" then labs.ord_num_value else null end as lab2_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab2_key + \"\"\"%\" then labs.ord_num_value else null end as lab2_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab2_key + \"\"\"%\" then labs.ord_num_value else null end as lab2_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab2_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab2_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab2_key + \"\"\"%\" then labs.labs_taken_time else null end as lab2_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab2_key + \"\"\"%\" then labs.labs_taken_time else null end as lab2_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab3_key + \"\"\"%\" then labs.ord_num_value else null end as lab3_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab3_key + \"\"\"%\" then labs.ord_num_value else null end as lab3_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab3_key + \"\"\"%\" then labs.ord_num_value else null end as lab3_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab3_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab3_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab3_key + \"\"\"%\" then labs.labs_taken_time else null end as lab3_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab3_key + \"\"\"%\" then labs.labs_taken_time else null end as lab3_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab4_key + \"\"\"%\" then labs.ord_num_value else null end as lab4_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab4_key + \"\"\"%\" then labs.ord_num_value else null end as lab4_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab4_key + \"\"\"%\" then labs.ord_num_value else null end as lab4_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab4_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab4_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab4_key + \"\"\"%\" then labs.labs_taken_time else null end as lab4_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab4_key + \"\"\"%\" then labs.labs_taken_time else null end as lab4_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab5_key + \"\"\"%\" then labs.ord_num_value else null end as lab5_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab5_key + \"\"\"%\" then labs.ord_num_value else null end as lab5_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab5_key + \"\"\"%\" then labs.ord_num_value else null end as lab5_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab5_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab5_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab5_key + \"\"\"%\" then labs.labs_taken_time else null end as lab5_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab5_key + \"\"\"%\" then labs.labs_taken_time else null end as lab5_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab6_key + \"\"\"%\" then labs.ord_num_value else null end as lab6_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab6_key + \"\"\"%\" then labs.ord_num_value else null end as lab6_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab6_key + \"\"\"%\" then labs.ord_num_value else null end as lab6_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab6_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab6_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab6_key + \"\"\"%\" then labs.labs_taken_time else null end as lab6_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab6_key + \"\"\"%\" then labs.labs_taken_time else null end as lab6_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab7_key + \"\"\"%\" then labs.ord_num_value else null end as lab7_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab7_key + \"\"\"%\" then labs.ord_num_value else null end as lab7_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab7_key + \"\"\"%\" then labs.ord_num_value else null end as lab7_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab7_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab7_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab7_key + \"\"\"%\" then labs.labs_taken_time else null end as lab7_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab7_key + \"\"\"%\" then labs.labs_taken_time else null end as lab7_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab8_key + \"\"\"%\" then labs.ord_num_value else null end as lab8_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab8_key + \"\"\"%\" then labs.ord_num_value else null end as lab8_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab8_key + \"\"\"%\" then labs.ord_num_value else null end as lab8_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab8_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab8_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab8_key + \"\"\"%\" then labs.labs_taken_time else null end as lab8_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab8_key + \"\"\"%\" then labs.labs_taken_time else null end as lab8_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab9_key + \"\"\"%\" then labs.ord_num_value else null end as lab9_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab9_key + \"\"\"%\" then labs.ord_num_value else null end as lab9_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab9_key + \"\"\"%\" then labs.ord_num_value else null end as lab9_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab9_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab9_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab9_key + \"\"\"%\" then labs.labs_taken_time else null end as lab9_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab9_key + \"\"\"%\" then labs.labs_taken_time else null end as lab9_oldest_time,\n",
+    "      \n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab10_key + \"\"\"%\" then labs.ord_num_value else null end as lab10_max,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab10_key + \"\"\"%\" then labs.ord_num_value else null end as lab10_min,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab10_key + \"\"\"%\" then labs.ord_num_value else null end as lab10_avg,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab10_key + \"\"\"%\" then labs.ord_num_value else 0 end as lab10_sum,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab10_key + \"\"\"%\" then labs.labs_taken_time else null end as lab10_recent_time,\n",
+    "      case when labs.labs_lab_name like \"%\"\"\" + lab10_key + \"\"\"%\" then labs.labs_taken_time else null end as lab10_oldest_time\n",
+    "      \n",
+    "      \n",
+    "      \n",
+    "    FROM \n",
+    "      labs\n",
+    "  )AS labs_table\n",
+    "  INNER JOIN \n",
+    "  (\n",
+    "    SELECT *\n",
+    "    FROM hs\n",
+    "  ) AS hs_table\n",
+    "  ON labs_table.labs_anon_id = hs_table.anon_id\n",
+    "  AND CAST(labs_table.labs_taken_time as DATETIME) <  DATETIME_SUB( CAST(hs_table.taken_time as DATETIME), INTERVAL 1 DAY) \n",
+    "  AND CAST(labs_table.labs_taken_time as DATETIME) > DATETIME_SUB( CAST(hs_table.taken_time as DATETIME), INTERVAL 12 MONTH) \n",
+    "),\n",
+    "\n",
+    "--Aggregate to get the max, min, avg, and sum of the lab order values and to get the max and min of the time taken\n",
+    "lab_features AS\n",
+    "(\n",
+    "  SELECT\n",
+    "    anon_id, taken_time as hs_taken_time, result_time as hs_result_time,\n",
+    "    \n",
+    "    max(lab1_max) as lab1_max, min(lab1_min) as lab1_min, \n",
+    "    avg(lab1_avg) as lab1_avg, sum(lab1_sum) as lab1_sum, \n",
+    "    max(lab1_recent_time) as lab1_recent_time, min(lab1_oldest_time) as lab1_oldest_time,\n",
+    "    \n",
+    "    max(lab2_max) as lab2_max, min(lab2_min) as lab2_min, \n",
+    "    avg(lab2_avg) as lab2_avg, sum(lab2_sum) as lab2_sum, \n",
+    "    max(lab2_recent_time) as lab2_recent_time, min(lab2_oldest_time) as lab2_oldest_time,\n",
+    "    \n",
+    "    max(lab3_max) as lab3_max, min(lab3_min) as lab3_min, \n",
+    "    avg(lab3_avg) as lab3_avg, sum(lab3_sum) as lab3_sum, \n",
+    "    max(lab3_recent_time) as lab3_recent_time, min(lab3_oldest_time) as lab3_oldest_time,\n",
+    "    \n",
+    "    max(lab4_max) as lab4_max, min(lab4_min) as lab4_min, \n",
+    "    avg(lab4_avg) as lab4_avg, sum(lab4_sum) as lab4_sum, \n",
+    "    max(lab4_recent_time) as lab4_recent_time, min(lab4_oldest_time) as lab4_oldest_time,\n",
+    "    \n",
+    "    max(lab5_max) as lab5_max, min(lab5_min) as lab5_min, \n",
+    "    avg(lab5_avg) as lab5_avg, sum(lab5_sum) as lab5_sum, \n",
+    "    max(lab5_recent_time) as lab5_recent_time, min(lab5_oldest_time) as lab5_oldest_time,\n",
+    "    \n",
+    "    max(lab6_max) as lab6_max, min(lab6_min) as lab6_min, \n",
+    "    avg(lab6_avg) as lab6_avg, sum(lab6_sum) as lab6_sum, \n",
+    "    max(lab6_recent_time) as lab6_recent_time, min(lab6_oldest_time) as lab6_oldest_time,\n",
+    "    \n",
+    "    max(lab7_max) as lab7_max, min(lab7_min) as lab7_min, \n",
+    "    avg(lab7_avg) as lab7_avg, sum(lab7_sum) as lab7_sum, \n",
+    "    max(lab7_recent_time) as lab7_recent_time, min(lab7_oldest_time) as lab7_oldest_time,\n",
+    "    \n",
+    "    max(lab8_max) as lab8_max, min(lab8_min) as lab8_min, \n",
+    "    avg(lab8_avg) as lab8_avg, sum(lab8_sum) as lab8_sum, \n",
+    "    max(lab8_recent_time) as lab8_recent_time, min(lab8_oldest_time) as lab8_oldest_time,\n",
+    "    \n",
+    "    max(lab9_max) as lab9_max, min(lab9_min) as lab9_min, \n",
+    "    avg(lab9_avg) as lab9_avg, sum(lab9_sum) as lab9_sum, \n",
+    "    max(lab9_recent_time) as lab9_recent_time, min(lab9_oldest_time) as lab9_oldest_time,\n",
+    "    \n",
+    "    max(lab10_max) as lab10_max, min(lab10_min) as lab10_min, \n",
+    "    avg(lab10_avg) as lab10_avg, sum(lab10_sum) as lab10_sum, \n",
+    "    max(lab10_recent_time) as lab10_recent_time, min(lab10_oldest_time) as lab10_oldest_time,\n",
+    "    \n",
+    "  FROM\n",
+    "    hs_labs\n",
+    "  GROUP BY anon_id, taken_time, result_time\n",
+    "),\n",
+    "\n",
+    "--Get the lab order values at the first (oldest) and last (recent) time the test was taken.\n",
+    "--If the same lab was taken multiple times on the first or last day, average the results.\n",
+    "lab_features_v2 AS\n",
+    "(\n",
+    "  SELECT \n",
+    "    anon_id, hs_taken_time, \n",
+    "    \n",
+    "    lab1_max, lab1_min, lab1_avg, \n",
+    "    lab1_sum, lab1_recent_time, lab1_oldest_time,\n",
+    "    avg(case when lab1_ord_num_value is not null and labs_taken_time = lab1_recent_time then ord_num_value else null end) as lab1_recent_time_val,\n",
+    "    avg(case when lab1_ord_num_value is not null and labs_taken_time = lab1_oldest_time then ord_num_value else null end) as lab1_oldest_time_val,\n",
+    "    \n",
+    "    lab2_max, lab2_min, lab2_avg, \n",
+    "    lab2_sum, lab2_recent_time, lab2_oldest_time,\n",
+    "    avg(case when lab2_ord_num_value is not null and labs_taken_time = lab2_recent_time then ord_num_value else null end) as lab2_recent_time_val,\n",
+    "    avg(case when lab2_ord_num_value is not null and labs_taken_time = lab2_oldest_time then ord_num_value else null end) as lab2_oldest_time_val,\n",
+    "    \n",
+    "    lab3_max, lab3_min, lab3_avg, \n",
+    "    lab3_sum, lab3_recent_time, lab3_oldest_time,\n",
+    "    avg(case when lab3_ord_num_value is not null and labs_taken_time = lab3_recent_time then ord_num_value else null end) as lab3_recent_time_val,\n",
+    "    avg(case when lab3_ord_num_value is not null and labs_taken_time = lab3_oldest_time then ord_num_value else null end) as lab3_oldest_time_val,\n",
+    "    \n",
+    "    lab4_max, lab4_min, lab4_avg, \n",
+    "    lab4_sum, lab4_recent_time, lab4_oldest_time,\n",
+    "    avg(case when lab4_ord_num_value is not null and labs_taken_time = lab4_recent_time then ord_num_value else null end) as lab4_recent_time_val,\n",
+    "    avg(case when lab4_ord_num_value is not null and labs_taken_time = lab4_oldest_time then ord_num_value else null end) as lab4_oldest_time_val,\n",
+    "    \n",
+    "    lab5_max, lab5_min, lab5_avg, \n",
+    "    lab5_sum, lab5_recent_time, lab5_oldest_time,\n",
+    "    avg(case when lab5_ord_num_value is not null and labs_taken_time = lab5_recent_time then ord_num_value else null end) as lab5_recent_time_val,\n",
+    "    avg(case when lab5_ord_num_value is not null and labs_taken_time = lab5_oldest_time then ord_num_value else null end) as lab5_oldest_time_val,\n",
+    "    \n",
+    "    lab6_max, lab6_min, lab6_avg, \n",
+    "    lab6_sum, lab6_recent_time, lab6_oldest_time,\n",
+    "    avg(case when lab6_ord_num_value is not null and labs_taken_time = lab6_recent_time then ord_num_value else null end) as lab6_recent_time_val,\n",
+    "    avg(case when lab6_ord_num_value is not null and labs_taken_time = lab6_oldest_time then ord_num_value else null end) as lab6_oldest_time_val,\n",
+    "    \n",
+    "    lab7_max, lab7_min, lab7_avg, \n",
+    "    lab7_sum, lab7_recent_time, lab7_oldest_time,\n",
+    "    avg(case when lab7_ord_num_value is not null and labs_taken_time = lab7_recent_time then ord_num_value else null end) as lab7_recent_time_val,\n",
+    "    avg(case when lab7_ord_num_value is not null and labs_taken_time = lab7_oldest_time then ord_num_value else null end) as lab7_oldest_time_val,\n",
+    "    \n",
+    "    lab8_max, lab8_min, lab8_avg, \n",
+    "    lab8_sum, lab8_recent_time, lab8_oldest_time,\n",
+    "    avg(case when lab8_ord_num_value is not null and labs_taken_time = lab8_recent_time then ord_num_value else null end) as lab8_recent_time_val,\n",
+    "    avg(case when lab8_ord_num_value is not null and labs_taken_time = lab8_oldest_time then ord_num_value else null end) as lab8_oldest_time_val,\n",
+    "    \n",
+    "    lab9_max, lab9_min, lab9_avg, \n",
+    "    lab9_sum, lab9_recent_time, lab9_oldest_time,\n",
+    "    avg(case when lab9_ord_num_value is not null and labs_taken_time = lab9_recent_time then ord_num_value else null end) as lab9_recent_time_val,\n",
+    "    avg(case when lab9_ord_num_value is not null and labs_taken_time = lab9_oldest_time then ord_num_value else null end) as lab9_oldest_time_val,\n",
+    "    \n",
+    "    lab10_max, lab10_min, lab10_avg, \n",
+    "    lab10_sum, lab10_recent_time, lab10_oldest_time,\n",
+    "    avg(case when lab10_ord_num_value is not null and labs_taken_time = lab10_recent_time then ord_num_value else null end) as lab10_recent_time_val,\n",
+    "    avg(case when lab10_ord_num_value is not null and labs_taken_time = lab10_oldest_time then ord_num_value else null end) as lab10_oldest_time_val,\n",
+    "     \n",
+    "  FROM\n",
+    "  (\n",
+    "    SELECT *\n",
+    "    FROM lab_features\n",
+    "    LEFT JOIN\n",
+    "    (\n",
+    "      --drop all cases where the lab order value was null\n",
+    "      SELECT \n",
+    "        labs_anon_id, taken_time, ord_num_value, labs_taken_time, \n",
+    "        case when lab1_max is not null then ord_num_value else null end as lab1_ord_num_value,\n",
+    "        case when lab2_max is not null then ord_num_value else null end as lab2_ord_num_value,\n",
+    "        case when lab3_max is not null then ord_num_value else null end as lab3_ord_num_value,\n",
+    "        case when lab4_max is not null then ord_num_value else null end as lab4_ord_num_value,\n",
+    "        case when lab5_max is not null then ord_num_value else null end as lab5_ord_num_value,\n",
+    "        case when lab6_max is not null then ord_num_value else null end as lab6_ord_num_value,\n",
+    "        case when lab7_max is not null then ord_num_value else null end as lab7_ord_num_value,\n",
+    "        case when lab8_max is not null then ord_num_value else null end as lab8_ord_num_value,\n",
+    "        case when lab9_max is not null then ord_num_value else null end as lab9_ord_num_value,\n",
+    "        case when lab10_max is not null then ord_num_value else null end as lab10_ord_num_value\n",
+    "        \n",
+    "      FROM hs_labs\n",
+    "    ) ON labs_anon_id = anon_id and taken_time = hs_taken_time\n",
+    "  )\n",
+    "  GROUP BY\n",
+    "    anon_id, hs_taken_time, \n",
+    "    \n",
+    "    lab1_recent_time, lab1_oldest_time, lab1_max, \n",
+    "    lab1_min, lab1_avg, lab1_sum,\n",
+    "    \n",
+    "    lab2_recent_time, lab2_oldest_time, lab2_max, \n",
+    "    lab2_min, lab2_avg, lab2_sum,\n",
+    "    \n",
+    "    lab3_recent_time, lab3_oldest_time, lab3_max, \n",
+    "    lab3_min, lab3_avg, lab3_sum,\n",
+    "    \n",
+    "    lab4_recent_time, lab4_oldest_time, lab4_max, \n",
+    "    lab4_min, lab4_avg, lab4_sum,\n",
+    "\n",
+    "    lab5_recent_time, lab5_oldest_time, lab5_max, \n",
+    "    lab5_min, lab5_avg, lab5_sum,\n",
+    "    \n",
+    "    lab6_recent_time, lab6_oldest_time, lab6_max, \n",
+    "    lab6_min, lab6_avg, lab6_sum,\n",
+    "    \n",
+    "    lab7_recent_time, lab7_oldest_time, lab7_max, \n",
+    "    lab7_min, lab7_avg, lab7_sum,\n",
+    "    \n",
+    "    lab8_recent_time, lab8_oldest_time, lab8_max, \n",
+    "    lab8_min, lab8_avg, lab8_sum,\n",
+    "\n",
+    "    lab9_recent_time, lab9_oldest_time, lab9_max, \n",
+    "    lab9_min, lab9_avg, lab9_sum,\n",
+    "    \n",
+    "    lab10_recent_time, lab10_oldest_time, lab10_max, \n",
+    "    lab10_min, lab10_avg, lab10_sum\n",
+    "),\n",
+    "\n",
+    "--use the oldest value, recent value, oldest time, and recent time to calculate the slope with the time difference\n",
+    "--in terms of months\n",
+    "lab_features_v3 AS\n",
+    "(\n",
+    "  SELECT \n",
+    "    * except(\n",
+    "        lab1_recent_time, lab1_oldest_time, lab2_recent_time, lab2_oldest_time, \n",
+    "        lab3_recent_time, lab3_oldest_time, lab4_recent_time, lab4_oldest_time, \n",
+    "        lab5_recent_time, lab5_oldest_time, lab6_recent_time, lab6_oldest_time, \n",
+    "        lab7_recent_time, lab7_oldest_time, lab8_recent_time, lab8_oldest_time, \n",
+    "        lab9_recent_time, lab9_oldest_time, lab10_recent_time, lab10_oldest_time\n",
+    "        ),\n",
+    "    \n",
+    "    case when (lab1_oldest_time_val = lab1_recent_time_val) or DATETIME_DIFF(lab1_recent_time, lab1_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab1_recent_time_val - lab1_oldest_time_val)/DATETIME_DIFF(lab1_recent_time, lab1_oldest_time, MONTH) end as lab1_slope,\n",
+    "      \n",
+    "    case when (lab2_oldest_time_val = lab2_recent_time_val) or DATETIME_DIFF(lab2_recent_time, lab2_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab2_recent_time_val - lab2_oldest_time_val)/DATETIME_DIFF(lab2_recent_time, lab2_oldest_time, MONTH) end as lab2_slope,\n",
+    "      \n",
+    "    case when (lab3_oldest_time_val = lab3_recent_time_val) or DATETIME_DIFF(lab3_recent_time, lab3_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab3_recent_time_val - lab3_oldest_time_val)/DATETIME_DIFF(lab3_recent_time, lab3_oldest_time, MONTH) end as lab3_slope,\n",
+    "      \n",
+    "    case when (lab4_oldest_time_val = lab4_recent_time_val) or DATETIME_DIFF(lab4_recent_time, lab4_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab4_recent_time_val - lab4_oldest_time_val)/DATETIME_DIFF(lab4_recent_time, lab4_oldest_time, MONTH) end as lab4_slope,\n",
+    "      \n",
+    "    case when (lab5_oldest_time_val = lab5_recent_time_val) or DATETIME_DIFF(lab5_recent_time, lab5_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab5_recent_time_val - lab5_oldest_time_val)/DATETIME_DIFF(lab5_recent_time, lab5_oldest_time, MONTH) end as lab5_slope,\n",
+    "         \n",
+    "   case when (lab6_oldest_time_val = lab6_recent_time_val) or DATETIME_DIFF(lab6_recent_time, lab6_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab6_recent_time_val - lab6_oldest_time_val)/DATETIME_DIFF(lab6_recent_time, lab6_oldest_time, MONTH) end as lab6_slope,\n",
+    "        \n",
+    "    case when (lab7_oldest_time_val = lab7_recent_time_val) or DATETIME_DIFF(lab7_recent_time, lab7_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab7_recent_time_val - lab7_oldest_time_val)/DATETIME_DIFF(lab7_recent_time, lab7_oldest_time, MONTH) end as lab7_slope,\n",
+    "        \n",
+    "    case when (lab8_oldest_time_val = lab8_recent_time_val) or DATETIME_DIFF(lab8_recent_time, lab8_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab8_recent_time_val - lab8_oldest_time_val)/DATETIME_DIFF(lab8_recent_time, lab8_oldest_time, MONTH) end as lab8_slope,\n",
+    "       \n",
+    "    case when (lab9_oldest_time_val = lab9_recent_time_val) or DATETIME_DIFF(lab9_recent_time, lab9_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab9_recent_time_val - lab9_oldest_time_val)/DATETIME_DIFF(lab9_recent_time, lab9_oldest_time, MONTH) end as lab9_slope,\n",
+    "          \n",
+    "   case when (lab10_oldest_time_val = lab10_recent_time_val) or DATETIME_DIFF(lab10_recent_time, lab10_oldest_time, MONTH) = 0 then 0 \n",
+    "      else (lab10_recent_time_val - lab10_oldest_time_val)/DATETIME_DIFF(lab10_recent_time, lab10_oldest_time, MONTH) end as lab10_slope,\n",
+    "         \n",
+    "  FROM \n",
+    "    lab_features_v2\n",
+    "),\n",
+    "\n",
+    "--join the lab features table with the dx/hx/meds features table\n",
+    "combined_table AS\n",
+    "(\n",
+    "  SELECT *\n",
+    "  FROM\n",
+    "    (\n",
+    "      labels_dx_fam_demog_meds_v2 INNER JOIN lab_features_v3 \n",
+    "      ON anon_id2 = anon_id AND labels_taken_time = hs_taken_time\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "--return the combined feature table\n",
+    "SELECT * except(anon_id, hs_taken_time)\n",
+    "FROM combined_table\n",
+    "order by anon_id\n",
+    "\n",
+    "\n",
+    "\"\"\";\n",
+    "    return query\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def queryData(query, dxFeature=False, totalFeatures=False):\n",
+    "    client = bigquery.Client(\"som-nero-phi-jonc101\");\n",
+    "    if dxFeature:\n",
+    "        client = bigquery.Client(\"mining-clinical-decisions\");\n",
+    "    conn = dbapi.connect(client);\n",
+    "    cursor = conn.cursor();\n",
+    "    cursor.execute(query);\n",
+    "    results_table = cursor.fetchall();\n",
+    "    \n",
+    "    if totalFeatures:\n",
+    "        df_all = (\n",
+    "                client.query(query)\n",
+    "                .result()\n",
+    "                .to_dataframe()\n",
+    "        )\n",
+    "        return df_all\n",
+    "\n",
+    "    results_df = pd.DataFrame(results_table)\n",
+    "    results = []\n",
+    "    for i in range(len(results_df)):\n",
+    "        results.append(results_df[0][i][0])\n",
+    "            \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getData(proc_code):\n",
+    "    \n",
+    "    #get top labs\n",
+    "    lab_query = \"\"\"\n",
+    "    SELECT lab_name, count(*)\n",
+    "    FROM \n",
+    "      (\n",
+    "        select anon_id as labs_anon_id\n",
+    "        from shc_core.lab_result\n",
+    "        where proc_code = '\"\"\" + proc_code + \"\"\"'\n",
+    "      ) LEFT JOIN\n",
+    "      (\n",
+    "        select anon_id, lab_name\n",
+    "        from shc_core.lab_result\n",
+    "      ) on labs_anon_id = anon_id\n",
+    "    WHERE lab_name is not null\n",
+    "    GROUP BY lab_name\n",
+    "    ORDER BY count(*) DESC\n",
+    "    limit 10\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    \n",
+    "    #get top diagnoses after transforming diagnosis codes into AHRQ Category Descriptions\n",
+    "    dx_query = \"\"\"\n",
+    "    SELECT Category_Description, count(*)\n",
+    "    FROM\n",
+    "    (\n",
+    "      SELECT *\n",
+    "      FROM \n",
+    "        (\n",
+    "          select anon_id as labs_anon_id\n",
+    "          from shc_core.lab_result\n",
+    "          where proc_code = '\"\"\" + proc_code + \"\"\"'\n",
+    "        ) LEFT JOIN\n",
+    "        (\n",
+    "          select anon_id, icd10\n",
+    "          from shc_core.diagnosis_code\n",
+    "        ) on labs_anon_id = anon_id\n",
+    "    ) as dx \n",
+    "    INNER JOIN\n",
+    "    (\n",
+    "      select ICD10, Category_Description \n",
+    "      from ahrq_ccsr.diagnosis_code\n",
+    "    ) as codes \n",
+    "    ON dx.icd10 = codes.ICD10\n",
+    "    WHERE Category_Description is not null\n",
+    "    GROUP BY Category_Description\n",
+    "    ORDER BY count(*) DESC\n",
+    "    limit 10\n",
+    "    \"\"\"\n",
+    "  \n",
+    "    \n",
+    "    #get top histories\n",
+    "    hx_query = \"\"\"\n",
+    "    SELECT medical_hx, count(*)\n",
+    "    FROM \n",
+    "      (\n",
+    "        select anon_id as labs_anon_id\n",
+    "        from shc_core.lab_result\n",
+    "        where proc_code = '\"\"\" + proc_code + \"\"\"'\n",
+    "      ) LEFT JOIN\n",
+    "      (\n",
+    "        select anon_id, medical_hx\n",
+    "        from shc_core.family_hx\n",
+    "      ) on labs_anon_id = anon_id\n",
+    "    WHERE medical_hx is not null\n",
+    "    GROUP BY medical_hx\n",
+    "    ORDER BY count(*) DESC\n",
+    "    limit 3\n",
+    "    \"\"\"\n",
+    "   \n",
+    "    \n",
+    "    #get top medications\n",
+    "    med_query = \"\"\"\n",
+    "    SELECT med_description, count(*)\n",
+    "    FROM \n",
+    "      (\n",
+    "        select anon_id as labs_anon_id\n",
+    "        from shc_core.lab_result\n",
+    "        where proc_code = '\"\"\" + proc_code + \"\"\"'\n",
+    "      ) LEFT JOIN\n",
+    "      (\n",
+    "        select anon_id, med_description\n",
+    "        from shc_core.order_med\n",
+    "      ) on labs_anon_id = anon_id\n",
+    "    WHERE med_description is not null\n",
+    "    GROUP BY med_description\n",
+    "    ORDER BY count(*) DESC\n",
+    "    limit 10\n",
+    "    \"\"\"\n",
+    "   \n",
+    "    return queryData(lab_query), queryData(dx_query, True, False), queryData(hx_query), queryData(med_query) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def processData(np_all):\n",
+    "    np_all = np_all[np.argsort(np_all[:, 1])] #sort by lab taken time so most recent tests are at bottom\n",
+    "    X_all = np_all[:,4:] #selects all except the row num, anon_id, timestamp, and labels columns\n",
+    "    Y_all = np_all[:,3] #selects the labels column\n",
+    "    \n",
+    "    N_train = round(len(X_all) * 0.8)\n",
+    "    \n",
+    "    X_train = X_all[:N_train, :]\n",
+    "    I_train = np.random.permutation(np.shape(X_train)[0])\n",
+    "    X_train = X_train[I_train, :] #shuffles the training data\n",
+    "    \n",
+    "    X_test = X_all[N_train:, :]\n",
+    "    I_test = np.random.permutation(np.shape(X_test)[0])\n",
+    "    X_test = X_test[I_test, :] #shuffles the test data\n",
+    "    \n",
+    "    Y_train = Y_all[:N_train]\n",
+    "    Y_train = Y_train[I_train]\n",
+    "    \n",
+    "    Y_test = Y_all[N_train:]\n",
+    "    Y_test = Y_test[I_test]\n",
+    "       \n",
+    "    return X_train, X_test, Y_train, Y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def balanceDataHelper(smallerClass, largerClass, X, Y):\n",
+    "    largeN = len(largerClass)\n",
+    "    upsampledSmall = np.random.choice(smallerClass, size=largeN, replace=True)\n",
+    "    X = np.concatenate((X[largerClass], X[upsampledSmall]))\n",
+    "    Y = np.concatenate((Y[largerClass], Y[upsampledSmall]))\n",
+    "    return X, Y\n",
+    "    \n",
+    "def balanceData(X_train, X_test, Y_train, Y_test):\n",
+    "    \"\"\"\n",
+    "    Upsample the minority class by randomly selecting samples from the minority class until it has the same\n",
+    "    number of samples as the majority class\n",
+    "    \"\"\"\n",
+    "    train0 = np.where(Y_train == 0)[0]\n",
+    "    train1 = np.where(Y_train == 1)[0]\n",
+    "    \n",
+    "    smallerClass = train0\n",
+    "    largerClass = train1\n",
+    "    if len(train0) > len(train1):\n",
+    "        smallerClass = train1\n",
+    "        largerClass = train0\n",
+    "    X_train, Y_train = balanceDataHelper(smallerClass, largerClass, X_train, Y_train)\n",
+    "        \n",
+    "    test0 = np.where(Y_test == 0)[0]\n",
+    "    test1 = np.where(Y_test == 1)[0]\n",
+    "    \n",
+    "    smallerClass = test0\n",
+    "    largerClass = test1\n",
+    "    if len(test0) > len(test1):\n",
+    "        smallerClass = test1\n",
+    "        largerClass = test0\n",
+    "    X_test, Y_test = balanceDataHelper(smallerClass, largerClass, X_test, Y_test)\n",
+    "\n",
+    "    return X_train.astype(float), X_test.astype(float), Y_train.astype(int), Y_test.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def runModel(X_train, Y_train):\n",
+    "    model = XGBClassifier(n_estimators = 300, learning_rate = 0.03, max_depth=5, subsample = 0.5)\n",
+    "    auc_cv_scores = cross_val_score(estimator = model, \n",
+    "                                    X = X_train, \n",
+    "                                    y = Y_train, \n",
+    "                                    scoring = \"roc_auc\"\n",
+    "                                   )\n",
+    "    \n",
+    "    auc_avg = auc_cv_scores.mean()\n",
+    "    \n",
+    "    y_pred = cross_val_predict(model, X_train, Y_train)\n",
+    "    \n",
+    "    conf_mat = confusion_matrix(Y_train, y_pred)\n",
+    "    true_neg = conf_mat[0][0]\n",
+    "    false_pos = conf_mat[0][1]\n",
+    "    false_neg = conf_mat[1][0]\n",
+    "    TNR = true_neg / (true_neg + false_neg)\n",
+    "    NPV = true_neg / (true_neg + false_pos)\n",
+    "\n",
+    "    return auc_avg, TNR, NPV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_dict = {} #key: lab name value: labs, dx, hx, meds, auroc, TNR, NPV\n",
+    "\n",
+    "###CODE FOR USER TO MODIFY###\n",
+    "label_labs = [\"LABTBIL\", \"LABA1C\"] \n",
+    "###END###\n",
+    "\n",
+    "for proc_code in label_labs:\n",
+    "    labFeatures, dxFeatures, hxFeatures, medFeatures = getData(proc_code) #run query to select top 10 per ft. category\n",
+    "    features = [labFeatures, dxFeatures, hxFeatures, medFeatures]\n",
+    "    query = getQuery(proc_code, features) #dyanmically generate the feature matrix query\n",
+    "    if query is None:\n",
+    "        continue\n",
+    "    \n",
+    "    df_all = queryData(query, False, True) #create feature feature matrix\n",
+    "    np_all = np.array(df_all.replace([None], np.nan))\n",
+    "    X_train, X_test, Y_train, Y_test = processData(np_all) #divide and shuffle data\n",
+    "    X_train, X_test, Y_train, Y_test = balanceData(X_train, X_test, Y_train, Y_test)\n",
+    "    \n",
+    "    #leave the test data untouched for now\n",
+    "    auc_avg, TNR, NPV = runModel(X_train, Y_train)\n",
+    "    \n",
+    "    results_dict[proc_code] = features + [auc_avg, TNR, NPV]\n",
+    "    \n",
+    "results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/OutpatientReferral/__init__.py b/scripts/OutpatientReferral/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/OutpatientReferral/data_config.py b/scripts/OutpatientReferral/data_config.py
new file mode 100644
index 00000000..2c4fe6d6
--- /dev/null
+++ b/scripts/OutpatientReferral/data_config.py
@@ -0,0 +1,27 @@
+referral_to_specialty_tuples =\
+[
+    ('REFERRAL TO DERMATOLOGY', 'Dermatology'),
+    ('REFERRAL TO GASTROENTEROLOGY', 'Gastroenterology'),
+    ('REFERRAL TO EYE', 'Ophthalmology'),
+    # REFERRAL TO PAIN CLINIC PROCEDURES,   Pain Management #(cnt: 525, but Neurosurgery has 224)
+    ('REFERRAL TO ORTHOPEDICS', 'Orthopedic Surgery'),
+    ('REFERRAL TO CARDIOLOGY', 'Cardiology'),
+    ('REFERRAL TO PSYCHIATRY', 'Psychiatry'),
+    ('SLEEP CLINIC REFERRAL', 'Sleep Center'),
+    ('REFERRAL TO ENT/OTOLARYNGOLOGY', 'ENT-Otolaryngology'),  # (cnt: 2170, but Oncology has 480)
+    ('REFERRAL TO PAIN CLINIC', 'Pain Management'),
+    ('REFERRAL TO UROLOGY CLINIC', 'Urology'),  # (cnt: 2827, but Oncology has 605)
+    ('REFERRAL TO ENDOCRINE CLINIC', 'Endocrinology'), # Suggested by Jon Chen
+    ('REFERRAL TO HEMATOLOGY', 'Hematology')
+]
+referral_to_specialty_dict = dict(referral_to_specialty_tuples)
+
+import pandas as pd
+
+def get_icd10_category_mapping():
+    df = pd.read_csv('mapping/icd10_categories.csv', keep_default_na=False, header=None)
+    icd10_category_mapping = dict(list(zip(df[0], df[1])))
+    return icd10_category_mapping
+
+if __name__ == '__main__':
+    print(get_icd10_category_mapping())
\ No newline at end of file
diff --git a/scripts/OutpatientReferral/data_utils.py b/scripts/OutpatientReferral/data_utils.py
new file mode 100644
index 00000000..524cd00d
--- /dev/null
+++ b/scripts/OutpatientReferral/data_utils.py
@@ -0,0 +1,453 @@
+
+''''''
+
+import os
+import pandas as pd
+pd.set_option('display.width', 1000)
+pd.set_option('display.max_columns', 1000)
+
+from . import data_config
+
+from datetime import datetime
+import matplotlib as mpl
+mpl.use('TkAgg')
+import matplotlib.pyplot as plt
+time_format = '%Y-%m-%d %H:%M:%S'
+
+from . import queries
+
+from google.cloud import bigquery
+
+testdf = pd.DataFrame({'test':[1,2,3]})
+
+result_folderpath = 'queried_results'
+if not os.path.exists(result_folderpath):
+    os.mkdir(result_folderpath)
+
+def setup_client(jsonkey_filepath):
+    print('Setting up client...')
+    client = bigquery.Client.from_service_account_json(jsonkey_filepath)
+    return client
+
+def make_bigquery(query, client, project_id):
+    print('Making bigquery...')
+    df = client.query(query, project=project_id).to_dataframe()
+
+    return df
+
+def get_queried_data(query):
+    ''''''
+
+    '''
+    Alert: Python hash function which potentially could be random? (not now)
+    '''
+    query_id = hash(query)
+
+    cached_filepath = os.path.join(result_folderpath, 'queried_data_%d.csv'%query_id)
+
+    if os.path.exists(cached_filepath):
+        print("Found cached query data, now loading...")
+        df = pd.read_csv(cached_filepath, comment='#', sep='\t')
+
+    else:
+        print("Making new query...")
+        print("set client path to gcp key") 
+        # client = setup_client('/Users/jonc101/Documents/Biomedical_Data_Science/gcp/gcp_key.json')
+        client = setup_client('MiningClinicalDecisions_Song.json')
+        project_id = 'mining-clinical-decisions'
+        df = make_bigquery(query, client=client, project_id=project_id)
+
+        f = open(cached_filepath, 'a')
+        f.write('#' + query.replace('\n', '\n#') + '\n')
+        df.to_csv(f, index=False, sep='\t', encoding = 'utf-8')
+        f.close()
+
+    return df
+
+def calc_PC_cnts(df_full):
+    order_typeCnt_global = df_full[['specialty_order', 'specialty_name']] \
+        .groupby('specialty_order')['specialty_name'] \
+        .apply(list).apply(Counter).to_dict()
+    order_isPCCnt_global = {}
+    # # TODO: be careful of missing specialty departments, could potentially all be PC?
+    for order, cnter in list(order_typeCnt_global.items()):
+        order_isPCCnt_global[order] = {'PC_cnt': cnter['Primary Care'],
+                                            'nonPC_cnt': sum(cnter.values()) - cnter['Primary Care']}
+    return order_isPCCnt_global
+
+from .data_config import referral_to_specialty_dict
+from collections import Counter
+
+def truncate_icd10(icd10):
+    try:
+        return icd10.split('.')[0]
+    except AttributeError: # empty value 'nan'
+        return ''
+
+def plot_visit_cnts(df):
+    df_visitCnts = df[['referral_enc_id', 'specialty_enc_id']]\
+                    .groupby('referral_enc_id')['specialty_enc_id']\
+                    .count().reset_index().rename(columns={'specialty_enc_id':'visit_cnt'})
+    plt.hist(df_visitCnts['visit_cnt'])
+    plt.show()
+    pass
+
+def test_plotVisitCnts():
+    df = pd.read_csv('queried_results/queried_data_2690237133563743535_sample.csv')
+    print(df.head())
+    plot_visit_cnts(df)
+
+
+
+
+class ReferralDataMunger():
+
+    def __init__(self, referral, df_full, newPatientOnly=False, to_truncate_icd10=True, verbose=False):
+        self.referral = referral
+        self.referral_code = referral.replace("REFERRAL TO ", "").replace(" ","-").replace("/","-")
+        self.specialty = referral_to_specialty_dict[referral]
+        self.df_full = df_full
+        if to_truncate_icd10:
+            self.df_full['referral_icd10'] = self.df_full['referral_icd10'].fillna('NA').apply(lambda x: truncate_icd10(x))
+
+        '''Making df local (only to the current referral)'''
+        self.df = self.df_full[(self.df_full['referral_name'] == referral)
+                               & (self.df_full['specialty_name'] == self.specialty)].copy()
+
+        self.order_isPCCnt_global = calc_PC_cnts(self.df_full)
+
+    def plot_waiting_times(self, ax):
+
+        df_tmp_timediff = self.df[
+            ['referral_enc_id', 'referral_time', 'specialty_time']].copy().drop_duplicates()
+        df_tmp_timediff['specialty_timestamp'] = \
+            df_tmp_timediff['specialty_time'].apply(lambda x: datetime.strptime(x, time_format))
+        df_tmp_timediff['referral_timestamp'] = \
+            df_tmp_timediff['referral_time'].apply(lambda x: datetime.strptime(x, time_format))
+        df_tmp_timediff['time_diff'] = df_tmp_timediff['specialty_timestamp'] \
+                                       - df_tmp_timediff['referral_timestamp']
+
+        print('Train sample size for waiting time:', df_tmp_timediff['time_diff'].shape[0])
+        all_waiting_days = df_tmp_timediff['time_diff'].apply(lambda x: x.days)
+
+        if not ax:
+            plt.hist(all_waiting_days, bins=15)
+            plt.xlabel('Waiting days for %s' % self.referral)
+            plt.xlim([0, 30*6]) # six month by default
+
+            plt.savefig('figures/waiting_time_%s.png' % self.referral_code)
+            plt.clf()
+        else:
+            ax.hist(all_waiting_days, bins=15)
+            ax.set_title(self.referral_code)
+            ax.set_xlim([0, 30 * 6])  # six month by default
+
+            ax.get_xaxis().set_ticks([])
+
+        # return median and IQR
+        return all_waiting_days.median(), \
+               all_waiting_days.quantile(0.25), \
+               all_waiting_days.quantile(0.75)
+
+    def explore_referral(self, top_k=5, rank_by='abs'):
+        icd10_category_mapping = data_config.get_icd10_category_mapping()
+        #print 'Top icd10s (categories) and %s_cnt:'%rank_by
+        # if rank_by == 'abs':
+        #     icd10_cnts = self.N_to_ri.most_common(top_k)
+        # elif rank_by == 'tfidf':
+        #     icd10_cnts = self.N_to_ri_tfidf.most_common(top_k)
+        icd10_cnts = self.get_most_common_icd10s(top_k, rank_by=rank_by)
+
+        icd10s = [x[0] for x in icd10_cnts]
+        cnts = [x[1] for x in icd10_cnts]
+        categories = [icd10_category_mapping[x] for x in icd10s]
+
+        pd.DataFrame({'icd10':icd10s,
+                      '%s_cnt'%rank_by:cnts,
+                      'category':categories
+                        })[['icd10', 'category', '%s_cnt'%rank_by]]\
+            .to_csv('explore_%s_by_%s.csv'%(self.referral, rank_by), index=False)
+        # for i in range(len(icd10s)):
+        #     print "%s (%s), %f" % (icd10s[i], categories[i], icd10_cnts[i][1])
+
+    def get_cnt(self, referral=None, order=None, icd10=None, order_type='proc'):
+        if order_type == 'proc':
+            order_col = 'specialty_order'
+        elif order_type == 'med':
+            order_col = 'medication'
+
+        cur_df = self.df_full[['referral_enc_id', 'referral_name', 'specialty_name',
+                               order_col, 'referral_icd10']].copy()
+
+        included_columns = ['referral_enc_id']
+        if referral:
+            specialty = referral_to_specialty_dict[referral]
+            cur_df = cur_df[(cur_df['referral_name'] == referral) & \
+                            (cur_df['specialty_name'] == specialty)]
+            included_columns.append('referral_name')
+
+        if order:
+            cur_df = cur_df[cur_df[order_col] == order]
+            included_columns.append(order_col)
+
+        if icd10:
+            cur_df = cur_df[cur_df['referral_icd10'] == icd10]
+            included_columns.append('referral_icd10')
+
+        return float(cur_df[included_columns].drop_duplicates().shape[0]) #
+
+    def get_most_common_orders(self, icd10, top_k, rank_by='abs', order_type='proc'):
+        if order_type == 'proc':
+            order_col = 'specialty_order'
+        elif order_type == 'med':
+            order_col = 'medication'
+        cur_df = self.df[self.df['referral_icd10'] == icd10][['referral_enc_id', 'referral_name', 'specialty_name',
+                                                              order_col, 'referral_icd10']]\
+                                                            .drop_duplicates()
+        order_abscnts = Counter(cur_df[order_col])
+
+        if rank_by=='abs':
+            return order_abscnts.most_common(top_k)
+
+        else:
+            order_tfidfs = Counter()
+            for order, abscnt in list(order_abscnts.items()):
+                order_tfidfs[order] = float(abscnt) * self.get_cnt() \
+                                    / (self.get_cnt(order=order)
+                                       * self.get_cnt(referral=self.referral, icd10=icd10))
+            return order_tfidfs.most_common(top_k)
+
+    def get_most_common_icd10s(self, top_k, rank_by='abs'):
+        cur_df = self.df
+        icd10_abscnts = Counter(cur_df['referral_icd10'])
+
+        if rank_by == 'abs':
+            return icd10_abscnts.most_common(top_k)
+
+        else:
+            icd10_tfidfs = Counter()
+            for icd10, abscnt in list(icd10_abscnts.items()):
+                icd10_tfidfs[icd10] = float(abscnt) * self.get_cnt() \
+                                      / (self.get_cnt(icd10=icd10)
+                                         * self.get_cnt(referral=self.referral))
+            return icd10_tfidfs.most_common(top_k)
+
+
+    def generate_order_stats(self, icd10, top_k=5, rank_by='abs', order_type='proc'):
+        ''''''
+
+        '''
+        Find out the top k orders for that icd10
+        '''
+        df_res = pd.DataFrame(columns=['order', 'N(o)', 'N(o,r)', 'N(o,r,i)', 'N(r,i)',
+                                       'PPV', 'RelaRisk', 'TFIDF', 'PrimaryCareRatio'
+                                       #'PC_cnt', 'nonPC_cnt'
+                                       ])
+        # print self.N_to_ri.most_common(top_k)
+        # if rank_by == 'abs':
+        #     common_absCnt_locals = self.N_to_rio[icd10].most_common(top_k)
+        # elif rank_by == 'tfidf':
+        #     common_absCnt_locals = self.N_to_rio_tfidf[icd10].most_common(top_k)
+
+        top_orders_cnts = self.get_most_common_orders(icd10, top_k, rank_by=rank_by, order_type=order_type)
+        print(top_orders_cnts)
+
+        for order, _ in top_orders_cnts:  # TODO: when tfidf?
+            '''
+            Order_name
+            '''
+            cur_order_summary = {'order':order}
+
+            '''
+            Preva
+            '''
+            cur_order_summary['N(o)'] = self.get_cnt(order=order, order_type=order_type)
+
+            '''
+            Preva_referrel
+            '''
+            cur_order_summary['N(o,r)'] = self.get_cnt(order=order,
+                                                       referral=self.referral, order_type=order_type)
+
+            '''
+            Preva_referrel_icd10
+            '''
+            cur_order_summary['N(o,r,i)'] = self.get_cnt(order=order,
+                                                       referral=self.referral,
+                                                         icd10=icd10, order_type=order_type)
+            print(order, cur_order_summary['N(o,r,i)'])
+
+            cur_order_summary['N(r,i)'] = self.get_cnt(referral=self.referral,
+                                                         icd10=icd10, order_type=order_type)
+
+            '''
+            PPV = P(order|referral, diagnose)=P(o|rd): When (1 referral, 1 icd10 appear)
+            predicting order is in the actual order list, the fraction of time correct.
+            '''
+            # print self.df_referID_orders.head()
+            # df_referID_icd10_orders = self.df_referID_orders[self.df_referID_orders['referral_icd10']==icd10]
+                                         #self.df_referID_orders['specialty_order'].str.contains('PARATHYROID')].head()
+            # TODO: how is this wrong?
+            # num_icd10_encs = df_referID_icd10_orders.shape[0]
+            # num_icd10_encs_wOrder = df_referID_icd10_orders[df_referID_icd10_orders['specialty_order']
+            #                         .map(lambda x: order in x)].shape[0]
+            # PPV = float(num_icd10_encs_wOrder)/num_icd10_encs
+            PPV = (cur_order_summary['N(o,r,i)']) / (cur_order_summary['N(r,i)'])
+            cur_order_summary['PPV'] = '%.2f' % PPV
+
+            '''
+
+            Relative risk = PPV / P(order|!ri)
+
+            According to Bayes formula:
+            P(o|ri)P(ri) + p(o|!ri)P(!ri) = P(o)
+            So:
+            denominator = p(o|!ri) = (P(o)-P(o|ri)P(ri))/P(!ri)
+            where:
+                P(o) = self.N_to_o[order]/self.N
+                P(o|ri) = PPV
+                P(ri) = self.N_to_ri[icd10]/self.N
+                P(!ri) = 1 - P(ri)
+            '''
+            P_o = self.get_cnt(order=order, order_type=order_type)/self.get_cnt(order_type=order_type)
+            P_ri = self.get_cnt(referral=self.referral, icd10=icd10, order_type=order_type)/self.get_cnt()
+            RelaRisk = PPV / ( (P_o-PPV*P_ri)/(1.-P_ri+0.00001) +0.00001)
+            cur_order_summary['RelaRisk'] = '%.2f' % RelaRisk #'%d' % int(round(RelaRisk))
+
+            TFIDF = cur_order_summary['N(o,r,i)']*self.get_cnt()/\
+                    (cur_order_summary['N(r,i)'] * cur_order_summary['N(o)'])
+            cur_order_summary['TFIDF'] = '%.2f' % TFIDF #'%d' % int(round(TFIDF))
+
+            # cur_order_summary['PC_cnt'] =  self.order_isPCCnt_global[order]['PC_cnt']
+            # cur_order_summary['nonPC_cnt'] = self.order_isPCCnt_global[order]['nonPC_cnt']
+            if order_type == 'proc':
+                cur_order_summary['PrimaryCareRatio'] = '%.2f' % (float(self.order_isPCCnt_global[order]['PC_cnt'])
+                                                              /float(self.order_isPCCnt_global[order]['nonPC_cnt']))
+            else:
+                cur_order_summary['PrimaryCareRatio'] = 'NaN'
+
+            df_res = df_res.append(cur_order_summary, ignore_index=True)
+        if not os.path.exists("tables"):
+            os.mkdir("tables")
+        df_res.to_csv('tables/%s_%s_%s_%s.csv' % (order_type, self.referral_code, icd10, rank_by), index=False)
+
+def test_query():
+    query = queries.query_for_recent6months(include_med=True)
+    df = get_queried_data(query)
+    print(df.shape)
+    print(df.head())
+    df.to_csv('order_med.csv', index=False)
+
+def load_data(test_mode=False, newPatientOnly=True, referral_name=None, include_med=False):
+    if test_mode:
+        df = pd.read_csv(os.path.join(result_folderpath, 'queried_data_2690237133563743535_sample.csv'))
+    else:
+        query = queries.query_for_recent6months(newPatientOnly=newPatientOnly, referral_name=referral_name, include_med=include_med)
+        df = get_queried_data(query)
+    df['specialty_order'] = df['specialty_order'].fillna('MissingProcOrder')
+    df['medication'] = df['medication'].fillna('MissingMedOrder')
+    return df
+
+def test_munger(referral, icd10, test_mode=False, include_med=False):
+    print("loading data into test munger for referral:  %s  with icd10: %s " %(referral,icd10))
+    df = load_data(test_mode=test_mode, newPatientOnly=True, include_med=include_med)
+    print("running data munger")
+    munger = ReferralDataMunger(referral=referral,
+                                df_full=df)
+    print("generate order stats by abs")
+    munger.generate_order_stats(icd10=icd10, top_k=10, rank_by='abs', order_type='med')
+
+    # print "generate order stats by tfidf"
+    # munger.generate_order_stats(icd10=icd10, top_k=10, rank_by='tfidf')
+    print("test munger complete")
+
+def plot_waiting_times(col=3):
+    df = load_data()
+    row = len(data_config.referral_to_specialty_tuples)/col
+
+    referrals = []
+    medians = []
+    Q1s, Q3s = [], []
+    fig, axes = plt.subplots(row, col)
+    for i, pair in enumerate(data_config.referral_to_specialty_tuples):
+        referral = pair[0]
+        referrals.append(referral)
+        print("plot_waiting_times for %s" % referral)
+
+        munger = ReferralDataMunger(referral=referral,
+                                    df_full=df)
+        median, Q1, Q3 = munger.plot_waiting_times(axes[i/col, i%col])
+
+        medians.append(median)
+        Q1s.append(Q1)
+        Q3s.append(Q3)
+
+    pd.DataFrame({'referral':referrals, 'median':medians, 'Q1':Q1s, 'Q3':Q3s})\
+        [['referral', 'median', 'Q1', 'Q3']].to_csv('waiting_time_stats.csv', index=False)
+    plt.tight_layout()
+    # fig.suptitle('waiting days (max 6 months)', verticalalignment='bottom')
+    plt.show()
+
+def explore_referrals(referral, top_k=5):
+    df = load_data(test_mode=False)
+    print("reading data into munger")
+    munger = ReferralDataMunger(referral=referral,
+                                df_full=df)
+    print("calculating by abs")
+    munger.explore_referral(top_k=top_k, rank_by='abs')
+
+    print("calculating tfidf")
+    munger.explore_referral(top_k=top_k, rank_by='tfidf')
+    print("finished explore_referral")
+
+def explore_savable_frac():
+    df = load_data(test_mode=False)
+    pc_cnts = calc_PC_cnts(df)
+    df = df[['specialty_enc_id', 'specialty_name', 'specialty_order']]
+
+    df['savable_order'] = df['specialty_order']\
+        .apply(lambda x: float(pc_cnts[x]['PC_cnt'])/(pc_cnts[x]['nonPC_cnt']+0.1) >= 0.1)
+
+    df_tmp = df[['specialty_enc_id', 'specialty_name', 'savable_order']]\
+              .groupby(['specialty_enc_id', 'specialty_name'])['savable_order']\
+        .all().reset_index().rename(columns={'savable_order':'savable_enc'})
+    df_tmp.groupby(['specialty_name'])['savable_enc'].mean().reset_index()\
+        .rename(columns={'savable_enc':'savable_frac'}).to_csv('savable_frac.csv', index=False)
+
+def explore_savable_time(specialty='Hematology'):
+    df = load_data(newPatientOnly=False, referral_name='REFERRAL TO HEMATOLOGY')
+    print(df.head())
+    pass
+
+def explore_PC_freq():
+    df = load_data(test_mode=False)
+    top_orders_cnts = Counter(df['specialty_order']).most_common(1000)
+    PC_dict = calc_PC_cnts(df)
+
+    orders = []
+    PC_cnts = []
+    nonPC_cnts = []
+    for order, cnt in top_orders_cnts:
+        orders.append(order)
+        PC_cnts.append(PC_dict[order]['PC_cnt'])
+        nonPC_cnts.append(PC_dict[order]['nonPC_cnt'])
+        # print order, PC_dict[order]
+    pd.DataFrame({'order':orders, 'PC_cnt':PC_cnts, 'nonPC_cnt':nonPC_cnts})\
+        [['order', 'PC_cnt', 'nonPC_cnt']].to_csv('PC_freqs.csv', index=False)
+    pass
+
+if __name__ == '__main__':
+    # test_query()
+
+    # REFERRAL TO ENDOCRINE CLINIC, 'E11'
+    # 'REFERRAL TO HEMATOLOGY', 'D69'
+
+    # explore_referrals('REFERRAL TO HEMATOLOGY', top_k=10)
+    test_munger('REFERRAL TO ENDOCRINE CLINIC', 'E11', test_mode=False, include_med=True)
+    # plot_waiting_times()
+
+    # test_plotVisitTimes()
+
+    # explore_savable_frac()
+    # explore_savable_time()
+    # explore_PC_freq(
diff --git a/scripts/OutpatientReferral/eConsult_frequent_meds.sql b/scripts/OutpatientReferral/eConsult_frequent_meds.sql
new file mode 100644
index 00000000..b70aff18
--- /dev/null
+++ b/scripts/OutpatientReferral/eConsult_frequent_meds.sql
@@ -0,0 +1,323 @@
+WITH 
+
+    -- Find all (New Patient) clinic visits (with info on specialty)
+
+    newPatientEncounter AS
+
+    (
+
+        select enc.anon_id, enc.pat_enc_csn_id_coded,
+
+            dep.specialty_dep_c, dep.specialty, -- Groups different clinics into specialty groupings. E.g,. dep.specialty_dep_c = '7' -- dep.specialty like 'Endocrin%'
+
+            enc.appt_when_jittered as encounterDateTime, extract(YEAR from enc.appt_time_jittered) as encounterYear
+
+        from `som-nero-phi-jonc101.shc_core_2021.encounter` as enc
+
+            join `som-nero-phi-jonc101.shc_core_2021.dep_map` as dep on enc.department_id = dep.department_id
+
+        where visit_type like 'NEW PATIENT%' -- Naturally screens to only 'Office Visit' enc_type
+
+        -- and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc.
+
+        and appt_status = 'Completed'
+
+        and extract(YEAR from enc.appt_time_jittered) = 2019    -- Avoid 2020 for now to avoid weird Covid patterns
+
+    ),
+
+ 
+
+    -- Find all diagnoses associated with new patient encounters
+
+    newPatientEncounterDiagnosis AS
+
+    (
+
+        select newEnc.*, dx.icd9, dx.icd10, dx_name
+
+        from newPatientEncounter as newEnc
+
+            join `som-nero-phi-jonc101.shc_core_2021.diagnosis` as dx on newEnc.pat_enc_csn_id_coded = dx.pat_enc_csn_id_jittered
+
+    ),
+
+    -- Order Med from encounters
+
+    newPatientEncounterMed AS
+
+    (
+
+        select newEnc.*, medication_id, med_description, pharm_class_name
+
+        from newPatientEncounter as newEnc
+
+          join `som-nero-phi-jonc101.shc_core_2021.order_med` as om on newEnc.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+
+        where order_class_c <> 3 -- 'Historical Med'
+
+    ),
+
+ 
+
+    -- Encounter * Diagnosis * Order Med (will have redundancies, because one encounter can have multiple diagnoses, so later need to count unique by encounter or patient ID)
+
+    newPatientEncounterDiagnosisMed AS
+
+    (
+
+        select newEnc.*,
+
+            dx.icd9, dx.icd10, dx_name,
+
+            om.medication_id, om.med_description
+
+        from newPatientEncounter as newEnc
+
+            join `som-nero-phi-jonc101.shc_core_2021.diagnosis` as dx on newEnc.pat_enc_csn_id_coded = dx.pat_enc_csn_id_jittered
+
+            join `som-nero-phi-jonc101.shc_core_2021.order_med` as om on newEnc.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded
+
+    ),
+
+   
+
+    -- Start accumulating counts by different strata
+
+    newPatientEncounterCount AS -- For baseline prevalence numbers, get total patient encounter counts of any type
+
+    (
+
+        select
+
+            count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+
+        from newPatientEncounter
+
+    ),
+
+    topSpecialty AS
+
+    (
+
+        select 
+
+            specialty_dep_c, specialty,
+
+            count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+
+        from newPatientEncounter
+
+        group by
+
+            specialty_dep_c, specialty
+
+        order by nEncounters desc
+
+    ),
+
+ 
+
+    topDiagnosisPerSpecialty AS
+
+    (
+
+        select 
+
+            specialty_dep_c, specialty,
+
+            icd9, icd10, dx_name,
+
+            count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+
+        from newPatientEncounterDiagnosis
+
+        group by
+
+            specialty_dep_c, specialty,
+
+            icd9, icd10, dx_name
+
+        order by nEncounters desc
+
+    ),
+
+ 
+
+    -- Break down (proc) order prevalence by different strata. Start with across all New Patient visits, any specialty, any diagnosis
+
+    topMed AS 
+
+    (
+
+        select
+
+            medication_id, med_description,
+
+            count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+
+        from newPatientEncounterMed
+
+        group by
+
+            medication_id, med_description
+
+        order by nEncounters desc
+
+    ),
+
+ 
+
+    topMedPerSpecialty AS
+
+    (
+
+        select
+
+            specialty_dep_c, specialty,
+
+            medication_id, med_description,
+
+            count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+
+        from newPatientEncounterMed
+
+        group by
+
+            specialty_dep_c, specialty,
+
+            medication_id, med_description
+
+        order by nEncounters desc
+
+    ),
+
+   
+
+    topMedPerSpecialtyDiagnosis AS
+
+    (
+
+        select
+
+            specialty_dep_c, specialty,
+
+            icd9, icd10, dx_name,
+
+            medication_id, med_description,
+
+            count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+
+        from newPatientEncounterDiagnosisMed
+
+        group by
+
+            specialty_dep_c, specialty,
+
+            icd9, icd10, dx_name,
+
+            medication_id, med_description
+
+        order by nEncounters desc
+
+    ),
+
+    -- Compare proc order rate per specialty-diagnosis vs. overall most common rate
+
+    medCountsPerSpecialtyDiagnosis AS
+
+    (
+
+        select
+
+            specialty_dep_c, specialty,
+
+            icd9, icd10, dx_name,
+
+            medication_id, med_description,
+
+            newEncCount.nPatients as nPatientsAny, newEncCount.nEncounters as nEncountersAny,
+
+            ts.nPatients as nPatientsPerSpecialty, ts.nEncounters as nEncountersPerSpecialty,          
+
+            tds.nPatients as nPatientsPerSpecialtyDiagnosis, tds.nEncounters as nEncountersPerSpecialtyDiagnosis,
+
+            tp.nPatients as nPatientsMedPerAny, tp.nEncounters as nEncountersMedPerAny,
+
+            tmsd.nPatients as nPatientsMedPerSpecialtyDiagnosis, tmsd.nEncounters as nEncountersMedPerSpecialtyDiagnosis
+
+        from 
+
+            topMedPerSpecialtyDiagnosis as tmsd join 
+
+            topMed as tp using (medication_id, med_description) join
+
+            topDiagnosisPerSpecialty as tds using (specialty_dep_c, specialty, icd9, icd10, dx_name) join
+
+            topSpecialty as ts using (specialty_dep_c, specialty),
+
+            newPatientEncounterCount as newEncCount     -- Last one is not a joint, since no criteria. Should be a single count row product against all
+
+    ),
+
+ 
+
+    medOrderRatePerSpecialtyDiagnosis AS
+
+    (
+
+        select
+
+            specialty_dep_c, specialty,
+
+            icd9, icd10, dx_name,
+
+            medication_id, med_description,
+
+ 
+
+            (nEncountersMedPerSpecialtyDiagnosis / nEncountersPerSpecialtyDiagnosis) AS medRatePerSpecialtyDiagnosis,   -- AKA Confidence / PPV / Conditional Prevalence
+
+            (nEncountersMedPerAny / nEncountersAny) AS medRatePerAny, -- AKA Baseline Prevalence ~ Support
+
+            (nEncountersMedPerSpecialtyDiagnosis / nEncountersPerSpecialtyDiagnosis) / (nEncountersMedPerAny / nEncountersAny) AS medRateLiftPerSpecialtyDiagnosisVsAny,    -- AKA Lift / Interest (similar to relative risk)
+
+            -- To Do -- Add p-value / (negative log-p) value to further sort by significance of association to avoid spurious
+
+ 
+
+            nPatientsAny, nEncountersAny,
+
+            nPatientsPerSpecialty, nEncountersPerSpecialty,        
+
+            nPatientsPerSpecialtyDiagnosis, nEncountersPerSpecialtyDiagnosis,
+
+            nPatientsMedPerAny, nEncountersMedPerAny,
+
+            nPatientsMedPerSpecialtyDiagnosis, nEncountersMedPerSpecialtyDiagnosis,
+
+ 
+
+        from medCountsPerSpecialtyDiagnosis
+
+        where nPatientsMedPerSpecialtyDiagnosis > 10 -- Ignore small cases by only looking at items that are ordered for more than 10 different patients
+
+        order by
+
+            nEncountersPerSpecialty desc, specialty,    -- Sort by most common specialty type first
+
+            nEncountersPerSpecialtyDiagnosis desc, icd10, dx_name,  -- Then sort by most common diagnosis per specialty
+
+            medRatePerSpecialtyDiagnosis desc  -- Then sort by most common proc orders per specialty-diagnosis
+
+    ),
+
+    spacer AS (select * from newPatientEncounter limit 10)
+
+ 
+
+    select * 
+
+    from medOrderRatePerSpecialtyDiagnosis
+
+   
\ No newline at end of file
diff --git a/scripts/OutpatientReferral/mapping/EConsult.SmartText.Endrocrinology.txt b/scripts/OutpatientReferral/mapping/EConsult.SmartText.Endrocrinology.txt
new file mode 100644
index 00000000..b86f4d1d
--- /dev/null
+++ b/scripts/OutpatientReferral/mapping/EConsult.SmartText.Endrocrinology.txt
@@ -0,0 +1,239 @@
+{Press F3 to expand, then F2 to select a diagnosis:210101591} 
+	{Adrenal Mass :TXT,210101593}
+		My Clinical Question (condition: adrenal mass): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}. 
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on: 
+		+ MRI or CT abdomen results
+		+ Does the patient have a history of hypertension
+
+		Tests recommended by endocrinology:
+		+ 1 mg overnight dexamethasone suppression test (DST)
+		+ Serum metanephrines
+		+ AM  aldosterone concentration and plasma renin activity
+		+ CMP}
+
+	{Adrenal Insufficiency :TXT,210101594}
+		My Clinical Question (condition: adrenal insufficiency): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}. 
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Tests recommended by endocrinology:
+
+		+ Any prior glucocorticoid exposure  
+		+ 8 am (non fasting) ACTH, cortisol
+		+ If available (not necessary) - cosyntropin stimulated cortisol (cortisol 30-60 min after cosyntropin 250 mcg)}
+
+
+	{Diabetes :TXT,210101595}
+
+		My Clinical Question (condition: diabetes): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on:
+
+		+Insulin (including pump) or non-insulin dependent 
+		+Diabetic complications 
+		+Glucose logs (if available)
+
+		Tests recommended by endocrinology:
+		+HA1c
+		+Lipid profile 
+		+CMP (creatinine)}
+
+
+	{Hyperaldosteronism :TXT,210101596}
+
+		My Clinical Question (condition: hyperaldosteronism): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on:
+
+		+ Medications (mineralocorticoid receptor antagonists -- spironolactone, eplerenone; ACEs/ARBs)
+
+		Tests recommended by endocrinology:
+		+ Aldosterone 
+		+ Plasma renin activity (renin)
+		+ CMP (potassium)} 
+
+	{Hypercalcemia/Hyperparathyroidism :TXT,210101597}
+
+		My Clinical Question (condition: hypercalcemia/hyperparathyroidism): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Thiazide or lithium use
+		+ Calcium or vitamin D supplements 
+		+ Any other OTC medications or supplements 
+		+ History of malignancy 
+
+		Tests recommended by endocrinology: 
+
+		+ Serum calcium
+		+ Intact PTH
+		+ 1,25-dihydroxyvitamin D
+		+ 25-hydroxyvitamin D
+		+ CMP
+		+ TSH}
+
+	{Hyperprolactinemia :TXT,210101598}
+
+		My Clinical Question (condition: hyperprolactinemia): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Use of medications that cause hyperprolactinemia (estrogen, antipsychotics, antidepressants, antiemetics, verapamil)
+		+ Desire for pregnancy 
+		+ Headache or visual symptoms
+
+		Tests recommended by endocrinology:
+		+ Prolactin x2
+		+ Pregnancy test
+		+ Brain MRI -- pituitary protocol}
+
+	{Hyperthyroidism: TXT,210101599}
+		My Clinical Question (condition: hyperthyroidism): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Presence of symptoms (palpitations, tremor, heat intolerance, diarrhea, ophthalmopathy etc.)
+		+ Is the patient pregnant
+		+ Does the patient wish to conceive
+		+ Thyroid exam (enlarged, nodular)
+		+ Medication history of: 
+			+ Immunotherapy for chemo (such as pembrolizumab) 
+			+ Amiodarone 
+			+ Supplements
+			+ Thyroid medications
+
+		Tests recommended by endocrinology:
+
+		+ TSH
+		+ Free T4
+		+ Thyroid stimulating immunoglobin (TSI)}  
+		
+	{Hypothyroidism	:TXT,210101600}
+
+		My Clinical Question (condition: hypothyroidism): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Presence of symptoms (fatigue, weight gain, constipation, cold intolerance etc.) 
+		+ Is the patient pregnant
+		+ Is the patient trying to conceive 
+		+ Thyroid medications (types, dosing)
+		+ Medication history of: 
+			+ Immunotherapy for chemo (such as pembrolizumab) 
+			+ Amiodarone 
+			+ Supplements
+			+ Thyroid medications
+
+		Tests recommended by endocrinology:
+
+		+ TSH
+		+ Free T4
+		+ Thyroid peroxidase antibody (TPO)}
+
+	{Pituitary Mass :TXT,210101601}
+
+		My Clinical Question (condition: pituitary mass): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Neurological symptoms
+
+		Tests recommended by endocrinology:
+
+		+ Brain MRI - pituitary protocol (not just brain MRI)
+		+ Prolactin
+		+ IGF-1
+		+ TSH
+		+ Free T4
+		+ LH
+		+ FSH
+		+ FSH, LH, morning testosterone for men
+		+ 8am non fasting ACTH and cortisol 
+		+ 24 hour urine free cortisol is not necessary unless suspecting Cushing's; 1 mg DST would be another option if suspecting Cushing's} 
+
+	{Thyroid Nodule :TXT,210101602}
+
+		My Clinical Question (condition: thyroid nodule): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Symptoms of hyper- or hypothyroidism 
+		+ Imaging results 
+		+ Has the patient had any prior FNAs; if so, what were the results / when were they done 
+
+		Tests recommended by endocrinology:
+
+		+ TSH
+		+ Free T4}
+
+	{Vitamin D Deficiency :TXT,210101603}
+
+		My Clinical Question (condition: vitamin D deficiency): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Is the patient is menopausal 
+		+ History of osteoporosis/prior fractures or CKD
+		+ Please include any information on any malabsorption issues or intestinal surgeries
+
+		Tests recommended by endocrinology:
+
+		+ 25-hydroxyvitamin D}
+
+	{Unspecified :TXT,2101011623}
+
+		My Clinical Question (condition: unspecified): *** 
+
+		My most current assessment of this problem can be found in the note dated {Date:40773::"8/12/2021"}.
+
+		{INFORMATIONAL: PRESS F2 and DELETE KEY before signing
+
+		Please include information on (in your clinical question or note):
+
+		+ Pertinent labs
+		+ Pertinent medications
+		+ Pertinent physical exam features}
+
diff --git a/scripts/OutpatientReferral/mapping/icd10_categories.csv b/scripts/OutpatientReferral/mapping/icd10_categories.csv
new file mode 100755
index 00000000..8219f852
--- /dev/null
+++ b/scripts/OutpatientReferral/mapping/icd10_categories.csv
@@ -0,0 +1,24628 @@
+A00,"Cholera"
+A01,"Typhoid and paratyphoid fevers"
+A010,"Typhoid fever"
+A011,"Paratyphoid fever A"
+A012,"Paratyphoid fever B"
+A013,"Paratyphoid fever C"
+A014,"Paratyphoid fever, unspecified"
+A02,"Other salmonella infections"
+A022,"Localized salmonella infections"
+A028,"Other specified salmonella infections"
+A029,"Salmonella infection, unspecified"
+A03,"Shigellosis"
+A04,"Other bacterial intestinal infections"
+A047,"Enterocolitis due to Clostridium difficile"
+A048,"Other specified bacterial intestinal infections"
+A049,"Bacterial intestinal infection, unspecified"
+A05,"Other bacterial foodborne intoxications, not elsewhere classified"
+A06,"Amebiasis"
+A068,"Amebic infection of other sites"
+A069,"Amebiasis, unspecified"
+A07,"Other protozoal intestinal diseases"
+A08,"Viral and other specified intestinal infections"
+A081,"Acute gastroenteropathy due to Norwalk agent and other small round viruses"
+A082,"Adenoviral enteritis"
+A083,"Other viral enteritis"
+A084,"Viral intestinal infection, unspecified"
+A088,"Other specified intestinal infections"
+A09,"Infectious gastroenteritis and colitis, unspecified"
+A15,"Respiratory tuberculosis"
+A17,"Tuberculosis of nervous system"
+A178,"Other tuberculosis of nervous system"
+A179,"Tuberculosis of nervous system, unspecified"
+A18,"Tuberculosis of other organs"
+A180,"Tuberculosis of bones and joints"
+A181,"Tuberculosis of genitourinary system"
+A182,"Tuberculous peripheral lymphadenopathy"
+A183,"Tuberculosis of intestines, peritoneum and mesenteric glands"
+A184,"Tuberculosis of skin and subcutaneous tissue"
+A185,"Tuberculosis of eye"
+A186,"Tuberculosis of (inner) (middle) ear"
+A187,"Tuberculosis of adrenal glands"
+A188,"Tuberculosis of other specified organs"
+A19,"Miliary tuberculosis"
+A20,"Plague"
+A21,"Tularemia"
+A22,"Anthrax"
+A23,"Brucellosis"
+A24,"Glanders and melioidosis"
+A25,"Rat-bite fevers"
+A26,"Erysipeloid"
+A27,"Leptospirosis"
+A278,"Other forms of leptospirosis"
+A279,"Leptospirosis, unspecified"
+A28,"Other zoonotic bacterial diseases, not elsewhere classified"
+A30,"Leprosy [Hansen's disease]"
+A31,"Infection due to other mycobacteria"
+A32,"Listeriosis"
+A321,"Listerial meningitis and meningoencephalitis"
+A327,"Listerial sepsis"
+A328,"Other forms of listeriosis"
+A329,"Listeriosis, unspecified"
+A33,"Tetanus neonatorum"
+A34,"Obstetrical tetanus"
+A35,"Other tetanus"
+A36,"Diphtheria"
+A368,"Other diphtheria"
+A369,"Diphtheria, unspecified"
+A37,"Whooping cough"
+A370,"Whooping cough due to Bordetella pertussis"
+A371,"Whooping cough due to Bordetella parapertussis"
+A378,"Whooping cough due to other Bordetella species"
+A379,"Whooping cough, unspecified species"
+A38,"Scarlet fever"
+A39,"Meningococcal infection"
+A395,"Meningococcal heart disease"
+A398,"Other meningococcal infections"
+A399,"Meningococcal infection, unspecified"
+A40,"Streptococcal sepsis"
+A41,"Other sepsis"
+A410,"Sepsis due to Staphylococcus aureus"
+A411,"Sepsis due to other specified staphylococcus"
+A412,"Sepsis due to unspecified staphylococcus"
+A413,"Sepsis due to Hemophilus influenzae"
+A414,"Sepsis due to anaerobes"
+A415,"Sepsis due to other Gram-negative organisms"
+A418,"Other specified sepsis"
+A419,"Sepsis, unspecified organism"
+A42,"Actinomycosis"
+A428,"Other forms of actinomycosis"
+A429,"Actinomycosis, unspecified"
+A43,"Nocardiosis"
+A44,"Bartonellosis"
+A46,"Erysipelas"
+A48,"Other bacterial diseases, not elsewhere classified"
+A485,"Other specified botulism"
+A488,"Other specified bacterial diseases"
+A49,"Bacterial infection of unspecified site"
+A490,"Staphylococcal infection, unspecified site"
+A491,"Streptococcal infection, unspecified site"
+A492,"Hemophilus influenzae infection, unspecified site"
+A493,"Mycoplasma infection, unspecified site"
+A498,"Other bacterial infections of unspecified site"
+A499,"Bacterial infection, unspecified"
+A50,"Congenital syphilis"
+A500,"Early congenital syphilis, symptomatic"
+A501,"Early congenital syphilis, latent"
+A502,"Early congenital syphilis, unspecified"
+A503,"Late congenital syphilitic oculopathy"
+A504,"Late congenital neurosyphilis [juvenile neurosyphilis]"
+A505,"Other late congenital syphilis, symptomatic"
+A506,"Late congenital syphilis, latent"
+A507,"Late congenital syphilis, unspecified"
+A509,"Congenital syphilis, unspecified"
+A51,"Early syphilis"
+A513,"Secondary syphilis of skin and mucous membranes"
+A514,"Other secondary syphilis"
+A515,"Early syphilis, latent"
+A519,"Early syphilis, unspecified"
+A52,"Late syphilis"
+A520,"Cardiovascular and cerebrovascular syphilis"
+A521,"Symptomatic neurosyphilis"
+A522,"Asymptomatic neurosyphilis"
+A523,"Neurosyphilis, unspecified"
+A527,"Other symptomatic late syphilis"
+A528,"Late syphilis, latent"
+A529,"Late syphilis, unspecified"
+A53,"Other and unspecified syphilis"
+A54,"Gonococcal infection"
+A540,"Gonococcal infection of lower genitourinary tract without periurethral or accessory gland abscess"
+A541,"Gonococcal infection of lower genitourinary tract with periurethral and accessory gland abscess"
+A542,"Gonococcal pelviperitonitis and other gonococcal genitourinary infection"
+A543,"Gonococcal infection of eye"
+A544,"Gonococcal infection of musculoskeletal system"
+A545,"Gonococcal pharyngitis"
+A546,"Gonococcal infection of anus and rectum"
+A548,"Other gonococcal infections"
+A549,"Gonococcal infection, unspecified"
+A55,"Chlamydial lymphogranuloma (venereum)"
+A56,"Other sexually transmitted chlamydial diseases"
+A560,"Chlamydial infection of lower genitourinary tract"
+A561,"Chlamydial infection of pelviperitoneum and other genitourinary organs"
+A562,"Chlamydial infection of genitourinary tract, unspecified"
+A563,"Chlamydial infection of anus and rectum"
+A564,"Chlamydial infection of pharynx"
+A568,"Sexually transmitted chlamydial infection of other sites"
+A57,"Chancroid"
+A58,"Granuloma inguinale"
+A59,"Trichomoniasis"
+A590,"Urogenital trichomoniasis"
+A598,"Trichomoniasis of other sites"
+A599,"Trichomoniasis, unspecified"
+A60,"Anogenital herpesviral [herpes simplex] infections"
+A600,"Herpesviral infection of genitalia and urogenital tract"
+A601,"Herpesviral infection of perianal skin and rectum"
+A609,"Anogenital herpesviral infection, unspecified"
+A63,"Other predominantly sexually transmitted diseases, not elsewhere classified"
+A64,"Unspecified sexually transmitted disease"
+A65,"Nonvenereal syphilis"
+A66,"Yaws"
+A67,"Pinta [carate]"
+A68,"Relapsing fevers"
+A69,"Other spirochetal infections"
+A692,"Lyme disease"
+A698,"Other specified spirochetal infections"
+A699,"Spirochetal infection, unspecified"
+A70,"Chlamydia psittaci infections"
+A71,"Trachoma"
+A74,"Other diseases caused by chlamydiae"
+A748,"Other chlamydial diseases"
+A749,"Chlamydial infection, unspecified"
+A75,"Typhus fever"
+A77,"Spotted fever [tick-borne rickettsioses]"
+A774,"Ehrlichiosis"
+A778,"Other spotted fevers"
+A779,"Spotted fever, unspecified"
+A78,"Q fever"
+A79,"Other rickettsioses"
+A798,"Other specified rickettsioses"
+A799,"Rickettsiosis, unspecified"
+A80,"Acute poliomyelitis"
+A803,"Acute paralytic poliomyelitis, other and unspecified"
+A804,"Acute nonparalytic poliomyelitis"
+A809,"Acute poliomyelitis, unspecified"
+A81,"Atypical virus infections of central nervous system"
+A810,"Creutzfeldt-Jakob disease"
+A811,"Subacute sclerosing panencephalitis"
+A812,"Progressive multifocal leukoencephalopathy"
+A818,"Other atypical virus infections of central nervous system"
+A819,"Atypical virus infection of central nervous system, unspecified"
+A82,"Rabies"
+A83,"Mosquito-borne viral encephalitis"
+A84,"Tick-borne viral encephalitis"
+A85,"Other viral encephalitis, not elsewhere classified"
+A86,"Unspecified viral encephalitis"
+A87,"Viral meningitis"
+A88,"Other viral infections of central nervous system, not elsewhere classified"
+A89,"Unspecified viral infection of central nervous system"
+A90,"Dengue fever [classical dengue]"
+A91,"Dengue hemorrhagic fever"
+A92,"Other mosquito-borne viral fevers"
+A923,"West Nile virus infection"
+A924,"Rift Valley fever"
+A925,"Zika virus disease"
+A928,"Other specified mosquito-borne viral fevers"
+A929,"Mosquito-borne viral fever, unspecified"
+A93,"Other arthropod-borne viral fevers, not elsewhere classified"
+A94,"Unspecified arthropod-borne viral fever"
+A95,"Yellow fever"
+A96,"Arenaviral hemorrhagic fever"
+A98,"Other viral hemorrhagic fevers, not elsewhere classified"
+A99,"Unspecified viral hemorrhagic fever"
+B00,"Herpesviral [herpes simplex] infections"
+B005,"Herpesviral ocular disease"
+B007,"Disseminated herpesviral disease"
+B008,"Other forms of herpesviral infections"
+B009,"Herpesviral infection, unspecified"
+B01,"Varicella [chickenpox]"
+B011,"Varicella encephalitis, myelitis and encephalomyelitis"
+B012,"Varicella pneumonia"
+B018,"Varicella with other complications"
+B019,"Varicella without complication"
+B02,"Zoster [herpes zoster]"
+B022,"Zoster with other nervous system involvement"
+B023,"Zoster ocular disease"
+B027,"Disseminated zoster"
+B028,"Zoster with other complications"
+B029,"Zoster without complications"
+B03,"Smallpox"
+B04,"Monkeypox"
+B05,"Measles"
+B058,"Measles with other complications"
+B059,"Measles without complication"
+B06,"Rubella [German measles]"
+B060,"Rubella with neurological complications"
+B068,"Rubella with other complications"
+B069,"Rubella without complication"
+B07,"Viral warts"
+B08,"Other viral infections characterized by skin and mucous membrane lesions, not elsewhere classified"
+B080,"Other orthopoxvirus infections"
+B0801,"Cowpox and vaccinia not from vaccine"
+B0802,"Orf virus disease"
+B0803,"Pseudocowpox [milker's node]"
+B0804,"Paravaccinia, unspecified"
+B0809,"Other orthopoxvirus infections"
+B081,"Molluscum contagiosum"
+B082,"Exanthema subitum [sixth disease]"
+B083,"Erythema infectiosum [fifth disease]"
+B084,"Enteroviral vesicular stomatitis with exanthem"
+B085,"Enteroviral vesicular pharyngitis"
+B086,"Parapoxvirus infections"
+B087,"Yatapoxvirus infections"
+B088,"Other specified viral infections characterized by skin and mucous membrane lesions"
+B09,"Unspecified viral infection characterized by skin and mucous membrane lesions"
+B10,"Other human herpesviruses"
+B100,"Other human herpesvirus encephalitis"
+B108,"Other human herpesvirus infection"
+B15,"Acute hepatitis A"
+B16,"Acute hepatitis B"
+B17,"Other acute viral hepatitis"
+B171,"Acute hepatitis C"
+B172,"Acute hepatitis E"
+B178,"Other specified acute viral hepatitis"
+B179,"Acute viral hepatitis, unspecified"
+B18,"Chronic viral hepatitis"
+B19,"Unspecified viral hepatitis"
+B191,"Unspecified viral hepatitis B"
+B192,"Unspecified viral hepatitis C"
+B199,"Unspecified viral hepatitis without hepatic coma"
+B20,"Human immunodeficiency virus [HIV] disease"
+B25,"Cytomegaloviral disease"
+B26,"Mumps"
+B268,"Mumps with other complications"
+B269,"Mumps without complication"
+B27,"Infectious mononucleosis"
+B270,"Gammaherpesviral mononucleosis"
+B271,"Cytomegaloviral mononucleosis"
+B278,"Other infectious mononucleosis"
+B279,"Infectious mononucleosis, unspecified"
+B30,"Viral conjunctivitis"
+B33,"Other viral diseases, not elsewhere classified"
+B332,"Viral carditis"
+B333,"Retrovirus infections, not elsewhere classified"
+B334,"Hantavirus (cardio)-pulmonary syndrome [HPS] [HCPS]"
+B338,"Other specified viral diseases"
+B34,"Viral infection of unspecified site"
+B35,"Dermatophytosis"
+B36,"Other superficial mycoses"
+B37,"Candidiasis"
+B374,"Candidiasis of other urogenital sites"
+B375,"Candidal meningitis"
+B376,"Candidal endocarditis"
+B377,"Candidal sepsis"
+B378,"Candidiasis of other sites"
+B379,"Candidiasis, unspecified"
+B38,"Coccidioidomycosis"
+B388,"Other forms of coccidioidomycosis"
+B389,"Coccidioidomycosis, unspecified"
+B39,"Histoplasmosis"
+B40,"Blastomycosis"
+B408,"Other forms of blastomycosis"
+B409,"Blastomycosis, unspecified"
+B41,"Paracoccidioidomycosis"
+B42,"Sporotrichosis"
+B428,"Other forms of sporotrichosis"
+B429,"Sporotrichosis, unspecified"
+B43,"Chromomycosis and pheomycotic abscess"
+B44,"Aspergillosis"
+B448,"Other forms of aspergillosis"
+B449,"Aspergillosis, unspecified"
+B45,"Cryptococcosis"
+B46,"Zygomycosis"
+B47,"Mycetoma"
+B48,"Other mycoses, not elsewhere classified"
+B49,"Unspecified mycosis"
+B50,"Plasmodium falciparum malaria"
+B51,"Plasmodium vivax malaria"
+B52,"Plasmodium malariae malaria"
+B53,"Other specified malaria"
+B54,"Unspecified malaria"
+B55,"Leishmaniasis"
+B56,"African trypanosomiasis"
+B57,"Chagas' disease"
+B573,"Chagas' disease (chronic) with digestive system involvement"
+B574,"Chagas' disease (chronic) with nervous system involvement"
+B575,"Chagas' disease (chronic) with other organ involvement"
+B58,"Toxoplasmosis"
+B580,"Toxoplasma oculopathy"
+B581,"Toxoplasma hepatitis"
+B582,"Toxoplasma meningoencephalitis"
+B583,"Pulmonary toxoplasmosis"
+B588,"Toxoplasmosis with other organ involvement"
+B589,"Toxoplasmosis, unspecified"
+B59,"Pneumocystosis"
+B60,"Other protozoal diseases, not elsewhere classified"
+B601,"Acanthamebiasis"
+B602,"Naegleriasis"
+B608,"Other specified protozoal diseases"
+B64,"Unspecified protozoal disease"
+B65,"Schistosomiasis [bilharziasis]"
+B66,"Other fluke infections"
+B67,"Echinococcosis"
+B673,"Echinococcus granulosus infection, other and multiple sites"
+B674,"Echinococcus granulosus infection, unspecified"
+B675,"Echinococcus multilocularis infection of liver"
+B676,"Echinococcus multilocularis infection, other and multiple sites"
+B677,"Echinococcus multilocularis infection, unspecified"
+B678,"Echinococcosis, unspecified, of liver"
+B679,"Echinococcosis, other and unspecified"
+B68,"Taeniasis"
+B69,"Cysticercosis"
+B698,"Cysticercosis of other sites"
+B699,"Cysticercosis, unspecified"
+B70,"Diphyllobothriasis and sparganosis"
+B71,"Other cestode infections"
+B72,"Dracunculiasis"
+B73,"Onchocerciasis"
+B730,"Onchocerciasis with eye disease"
+B731,"Onchocerciasis without eye disease"
+B74,"Filariasis"
+B75,"Trichinellosis"
+B76,"Hookworm diseases"
+B77,"Ascariasis"
+B778,"Ascariasis with other complications"
+B779,"Ascariasis, unspecified"
+B78,"Strongyloidiasis"
+B79,"Trichuriasis"
+B80,"Enterobiasis"
+B81,"Other intestinal helminthiases, not elsewhere classified"
+B82,"Unspecified intestinal parasitism"
+B83,"Other helminthiases"
+B85,"Pediculosis and phthiriasis"
+B86,"Scabies"
+B87,"Myiasis"
+B878,"Myiasis of other sites"
+B879,"Myiasis, unspecified"
+B88,"Other infestations"
+B89,"Unspecified parasitic disease"
+B90,"Sequelae of tuberculosis"
+B91,"Sequelae of poliomyelitis"
+B92,"Sequelae of leprosy"
+B94,"Sequelae of other and unspecified infectious and parasitic diseases"
+B95,"Streptococcus, Staphylococcus, and Enterococcus as the cause of diseases classified elsewhere"
+B956,"Staphylococcus aureus as the cause of diseases classified elsewhere"
+B957,"Other staphylococcus as the cause of diseases classified elsewhere"
+B958,"Unspecified staphylococcus as the cause of diseases classified elsewhere"
+B96,"Other bacterial agents as the cause of diseases classified elsewhere"
+B962,"Escherichia coli [E. coli ] as the cause of diseases classified elsewhere"
+B963,"Hemophilus influenzae [H. influenzae] as the cause of diseases classified elsewhere"
+B964,"Proteus (mirabilis) (morganii) as the cause of diseases classified elsewhere"
+B965,"Pseudomonas (aeruginosa) (mallei) (pseudomallei) as the cause of diseases classified elsewhere"
+B966,"Bacteroides fragilis [B. fragilis] as the cause of diseases classified elsewhere"
+B967,"Clostridium perfringens [C. perfringens] as the cause of diseases classified elsewhere"
+B968,"Other specified bacterial agents as the cause of diseases classified elsewhere"
+B97,"Viral agents as the cause of diseases classified elsewhere"
+B971,"Enterovirus as the cause of diseases classified elsewhere"
+B972,"Coronavirus as the cause of diseases classified elsewhere"
+B973,"Retrovirus as the cause of diseases classified elsewhere"
+B974,"Respiratory syncytial virus as the cause of diseases classified elsewhere"
+B975,"Reovirus as the cause of diseases classified elsewhere"
+B976,"Parvovirus as the cause of diseases classified elsewhere"
+B977,"Papillomavirus as the cause of diseases classified elsewhere"
+B978,"Other viral agents as the cause of diseases classified elsewhere"
+B99,"Other and unspecified infectious diseases"
+C00,"Malignant neoplasm of lip"
+C01,"Malignant neoplasm of base of tongue"
+C02,"Malignant neoplasm of other and unspecified parts of tongue"
+C03,"Malignant neoplasm of gum"
+C04,"Malignant neoplasm of floor of mouth"
+C05,"Malignant neoplasm of palate"
+C06,"Malignant neoplasm of other and unspecified parts of mouth"
+C068,"Malignant neoplasm of overlapping sites of other and unspecified parts of mouth"
+C069,"Malignant neoplasm of mouth, unspecified"
+C07,"Malignant neoplasm of parotid gland"
+C08,"Malignant neoplasm of other and unspecified major salivary glands"
+C09,"Malignant neoplasm of tonsil"
+C10,"Malignant neoplasm of oropharynx"
+C11,"Malignant neoplasm of nasopharynx"
+C12,"Malignant neoplasm of pyriform sinus"
+C13,"Malignant neoplasm of hypopharynx"
+C14,"Malignant neoplasm of other and ill-defined sites in the lip, oral cavity and pharynx"
+C15,"Malignant neoplasm of esophagus"
+C16,"Malignant neoplasm of stomach"
+C17,"Malignant neoplasm of small intestine"
+C18,"Malignant neoplasm of colon"
+C19,"Malignant neoplasm of rectosigmoid junction"
+C20,"Malignant neoplasm of rectum"
+C21,"Malignant neoplasm of anus and anal canal"
+C22,"Malignant neoplasm of liver and intrahepatic bile ducts"
+C23,"Malignant neoplasm of gallbladder"
+C24,"Malignant neoplasm of other and unspecified parts of biliary tract"
+C25,"Malignant neoplasm of pancreas"
+C26,"Malignant neoplasm of other and ill-defined digestive organs"
+C30,"Malignant neoplasm of nasal cavity and middle ear"
+C31,"Malignant neoplasm of accessory sinuses"
+C32,"Malignant neoplasm of larynx"
+C33,"Malignant neoplasm of trachea"
+C34,"Malignant neoplasm of bronchus and lung"
+C340,"Malignant neoplasm of main bronchus"
+C341,"Malignant neoplasm of upper lobe, bronchus or lung"
+C342,"Malignant neoplasm of middle lobe, bronchus or lung"
+C343,"Malignant neoplasm of lower lobe, bronchus or lung"
+C348,"Malignant neoplasm of overlapping sites of bronchus and lung"
+C349,"Malignant neoplasm of unspecified part of bronchus or lung"
+C37,"Malignant neoplasm of thymus"
+C38,"Malignant neoplasm of heart, mediastinum and pleura"
+C39,"Malignant neoplasm of other and ill-defined sites in the respiratory system and intrathoracic organs"
+C40,"Malignant neoplasm of bone and articular cartilage of limbs"
+C400,"Malignant neoplasm of scapula and long bones of upper limb"
+C401,"Malignant neoplasm of short bones of upper limb"
+C402,"Malignant neoplasm of long bones of lower limb"
+C403,"Malignant neoplasm of short bones of lower limb"
+C408,"Malignant neoplasm of overlapping sites of bone and articular cartilage of limb"
+C409,"Malignant neoplasm of unspecified bones and articular cartilage of limb"
+C41,"Malignant neoplasm of bone and articular cartilage of other and unspecified sites"
+C43,"Malignant melanoma of skin"
+C431,"Malignant melanoma of eyelid, including canthus"
+C432,"Malignant melanoma of ear and external auricular canal"
+C433,"Malignant melanoma of other and unspecified parts of face"
+C434,"Malignant melanoma of scalp and neck"
+C435,"Malignant melanoma of trunk"
+C436,"Malignant melanoma of upper limb, including shoulder"
+C437,"Malignant melanoma of lower limb, including hip"
+C438,"Malignant melanoma of overlapping sites of skin"
+C439,"Malignant melanoma of skin, unspecified"
+C4A,"Merkel cell carcinoma"
+C4A1,"Merkel cell carcinoma of eyelid, including canthus"
+C4A2,"Merkel cell carcinoma of ear and external auricular canal"
+C4A3,"Merkel cell carcinoma of other and unspecified parts of face"
+C4A4,"Merkel cell carcinoma of scalp and neck"
+C4A5,"Merkel cell carcinoma of trunk"
+C4A6,"Merkel cell carcinoma of upper limb, including shoulder"
+C4A7,"Merkel cell carcinoma of lower limb, including hip"
+C4A8,"Merkel cell carcinoma of overlapping sites"
+C4A9,"Merkel cell carcinoma, unspecified"
+C44,"Other and unspecified malignant neoplasm of skin"
+C440,"Other and unspecified malignant neoplasm of skin of lip"
+C441,"Other and unspecified malignant neoplasm of skin of eyelid, including canthus"
+C4410,"Unspecified malignant neoplasm of skin of eyelid, including canthus"
+C4411,"Basal cell carcinoma of skin of eyelid, including canthus"
+C4412,"Squamous cell carcinoma of skin of eyelid, including canthus"
+C4419,"Other specified malignant neoplasm of skin of eyelid, including canthus"
+C442,"Other and unspecified malignant neoplasm of skin of ear and external auricular canal"
+C4420,"Unspecified malignant neoplasm of skin of ear and external auricular canal"
+C4421,"Basal cell carcinoma of skin of ear and external auricular canal"
+C4422,"Squamous cell carcinoma of skin of ear and external auricular canal"
+C4429,"Other specified malignant neoplasm of skin of ear and external auricular canal"
+C443,"Other and unspecified malignant neoplasm of skin of other and unspecified parts of face"
+C4430,"Unspecified malignant neoplasm of skin of other and unspecified parts of face"
+C4431,"Basal cell carcinoma of skin of other and unspecified parts of face"
+C4432,"Squamous cell carcinoma of skin of other and unspecified parts of face"
+C4439,"Other specified malignant neoplasm of skin of other and unspecified parts of face"
+C444,"Other and unspecified malignant neoplasm of skin of scalp and neck"
+C445,"Other and unspecified malignant neoplasm of skin of trunk"
+C4450,"Unspecified malignant neoplasm of skin of trunk"
+C4451,"Basal cell carcinoma of skin of trunk"
+C4452,"Squamous cell carcinoma of skin of trunk"
+C4459,"Other specified malignant neoplasm of skin of trunk"
+C446,"Other and unspecified malignant neoplasm of skin of upper limb, including shoulder"
+C4460,"Unspecified malignant neoplasm of skin of upper limb, including shoulder"
+C4461,"Basal cell carcinoma of skin of upper limb, including shoulder"
+C4462,"Squamous cell carcinoma of skin of upper limb, including shoulder"
+C4469,"Other specified malignant neoplasm of skin of upper limb, including shoulder"
+C447,"Other and unspecified malignant neoplasm of skin of lower limb, including hip"
+C4470,"Unspecified malignant neoplasm of skin of lower limb, including hip"
+C4471,"Basal cell carcinoma of skin of lower limb, including hip"
+C4472,"Squamous cell carcinoma of skin of lower limb, including hip"
+C4479,"Other specified malignant neoplasm of skin of lower limb, including hip"
+C448,"Other and unspecified malignant neoplasm of overlapping sites of skin"
+C449,"Other and unspecified malignant neoplasm of skin, unspecified"
+C45,"Mesothelioma"
+C46,"Kaposi's sarcoma"
+C465,"Kaposi's sarcoma of lung"
+C467,"Kaposi's sarcoma of other sites"
+C469,"Kaposi's sarcoma, unspecified"
+C47,"Malignant neoplasm of peripheral nerves and autonomic nervous system"
+C471,"Malignant neoplasm of peripheral nerves of upper limb, including shoulder"
+C472,"Malignant neoplasm of peripheral nerves of lower limb, including hip"
+C473,"Malignant neoplasm of peripheral nerves of thorax"
+C474,"Malignant neoplasm of peripheral nerves of abdomen"
+C475,"Malignant neoplasm of peripheral nerves of pelvis"
+C476,"Malignant neoplasm of peripheral nerves of trunk, unspecified"
+C478,"Malignant neoplasm of overlapping sites of peripheral nerves and autonomic nervous system"
+C479,"Malignant neoplasm of peripheral nerves and autonomic nervous system, unspecified"
+C48,"Malignant neoplasm of retroperitoneum and peritoneum"
+C49,"Malignant neoplasm of other connective and soft tissue"
+C491,"Malignant neoplasm of connective and soft tissue of upper limb, including shoulder"
+C492,"Malignant neoplasm of connective and soft tissue of lower limb, including hip"
+C493,"Malignant neoplasm of connective and soft tissue of thorax"
+C494,"Malignant neoplasm of connective and soft tissue of abdomen"
+C495,"Malignant neoplasm of connective and soft tissue of pelvis"
+C496,"Malignant neoplasm of connective and soft tissue of trunk, unspecified"
+C498,"Malignant neoplasm of overlapping sites of connective and soft tissue"
+C499,"Malignant neoplasm of connective and soft tissue, unspecified"
+C49A,"Gastrointestinal stromal tumor"
+C50,"Malignant neoplasm of breast"
+C500,"Malignant neoplasm of nipple and areola"
+C5001,"Malignant neoplasm of nipple and areola, female"
+C5002,"Malignant neoplasm of nipple and areola, male"
+C501,"Malignant neoplasm of central portion of breast"
+C5011,"Malignant neoplasm of central portion of breast, female"
+C5012,"Malignant neoplasm of central portion of breast, male"
+C502,"Malignant neoplasm of upper-inner quadrant of breast"
+C5021,"Malignant neoplasm of upper-inner quadrant of breast, female"
+C5022,"Malignant neoplasm of upper-inner quadrant of breast, male"
+C503,"Malignant neoplasm of lower-inner quadrant of breast"
+C5031,"Malignant neoplasm of lower-inner quadrant of breast, female"
+C5032,"Malignant neoplasm of lower-inner quadrant of breast, male"
+C504,"Malignant neoplasm of upper-outer quadrant of breast"
+C5041,"Malignant neoplasm of upper-outer quadrant of breast, female"
+C5042,"Malignant neoplasm of upper-outer quadrant of breast, male"
+C505,"Malignant neoplasm of lower-outer quadrant of breast"
+C5051,"Malignant neoplasm of lower-outer quadrant of breast, female"
+C5052,"Malignant neoplasm of lower-outer quadrant of breast, male"
+C506,"Malignant neoplasm of axillary tail of breast"
+C5061,"Malignant neoplasm of axillary tail of breast, female"
+C5062,"Malignant neoplasm of axillary tail of breast, male"
+C508,"Malignant neoplasm of overlapping sites of breast"
+C5081,"Malignant neoplasm of overlapping sites of breast, female"
+C5082,"Malignant neoplasm of overlapping sites of breast, male"
+C509,"Malignant neoplasm of breast of unspecified site"
+C5091,"Malignant neoplasm of breast of unspecified site, female"
+C5092,"Malignant neoplasm of breast of unspecified site, male"
+C51,"Malignant neoplasm of vulva"
+C52,"Malignant neoplasm of vagina"
+C53,"Malignant neoplasm of cervix uteri"
+C54,"Malignant neoplasm of corpus uteri"
+C55,"Malignant neoplasm of uterus, part unspecified"
+C56,"Malignant neoplasm of ovary"
+C57,"Malignant neoplasm of other and unspecified female genital organs"
+C570,"Malignant neoplasm of fallopian tube"
+C571,"Malignant neoplasm of broad ligament"
+C572,"Malignant neoplasm of round ligament"
+C573,"Malignant neoplasm of parametrium"
+C574,"Malignant neoplasm of uterine adnexa, unspecified"
+C577,"Malignant neoplasm of other specified female genital organs"
+C578,"Malignant neoplasm of overlapping sites of female genital organs"
+C579,"Malignant neoplasm of female genital organ, unspecified"
+C58,"Malignant neoplasm of placenta"
+C60,"Malignant neoplasm of penis"
+C61,"Malignant neoplasm of prostate"
+C62,"Malignant neoplasm of testis"
+C620,"Malignant neoplasm of undescended testis"
+C621,"Malignant neoplasm of descended testis"
+C629,"Malignant neoplasm of testis, unspecified whether descended or undescended"
+C63,"Malignant neoplasm of other and unspecified male genital organs"
+C630,"Malignant neoplasm of epididymis"
+C631,"Malignant neoplasm of spermatic cord"
+C632,"Malignant neoplasm of scrotum"
+C637,"Malignant neoplasm of other specified male genital organs"
+C638,"Malignant neoplasm of overlapping sites of male genital organs"
+C639,"Malignant neoplasm of male genital organ, unspecified"
+C64,"Malignant neoplasm of kidney, except renal pelvis"
+C65,"Malignant neoplasm of renal pelvis"
+C66,"Malignant neoplasm of ureter"
+C67,"Malignant neoplasm of bladder"
+C68,"Malignant neoplasm of other and unspecified urinary organs"
+C69,"Malignant neoplasm of eye and adnexa"
+C690,"Malignant neoplasm of conjunctiva"
+C691,"Malignant neoplasm of cornea"
+C692,"Malignant neoplasm of retina"
+C693,"Malignant neoplasm of choroid"
+C694,"Malignant neoplasm of ciliary body"
+C695,"Malignant neoplasm of lacrimal gland and duct"
+C696,"Malignant neoplasm of orbit"
+C698,"Malignant neoplasm of overlapping sites of eye and adnexa"
+C699,"Malignant neoplasm of unspecified site of eye"
+C70,"Malignant neoplasm of meninges"
+C71,"Malignant neoplasm of brain"
+C72,"Malignant neoplasm of spinal cord, cranial nerves and other parts of central nervous system"
+C722,"Malignant neoplasm of olfactory nerve"
+C723,"Malignant neoplasm of optic nerve"
+C724,"Malignant neoplasm of acoustic nerve"
+C725,"Malignant neoplasm of other and unspecified cranial nerves"
+C729,"Malignant neoplasm of central nervous system, unspecified"
+C73,"Malignant neoplasm of thyroid gland"
+C74,"Malignant neoplasm of adrenal gland"
+C740,"Malignant neoplasm of cortex of adrenal gland"
+C741,"Malignant neoplasm of medulla of adrenal gland"
+C749,"Malignant neoplasm of unspecified part of adrenal gland"
+C75,"Malignant neoplasm of other endocrine glands and related structures"
+C7A,"Malignant neuroendocrine tumors"
+C7A0,"Malignant carcinoid tumors"
+C7A01,"Malignant carcinoid tumors of the small intestine"
+C7A02,"Malignant carcinoid tumors of the appendix, large intestine, and rectum"
+C7A09,"Malignant carcinoid tumors of other sites"
+C7A1,"Malignant poorly differentiated neuroendocrine tumors"
+C7A8,"Other malignant neuroendocrine tumors"
+C7B,"Secondary neuroendocrine tumors"
+C7B0,"Secondary carcinoid tumors"
+C7B1,"Secondary Merkel cell carcinoma"
+C7B8,"Other secondary neuroendocrine tumors"
+C76,"Malignant neoplasm of other and ill-defined sites"
+C764,"Malignant neoplasm of upper limb"
+C765,"Malignant neoplasm of lower limb"
+C768,"Malignant neoplasm of other specified ill-defined sites"
+C77,"Secondary and unspecified malignant neoplasm of lymph nodes"
+C78,"Secondary malignant neoplasm of respiratory and digestive organs"
+C780,"Secondary malignant neoplasm of lung"
+C781,"Secondary malignant neoplasm of mediastinum"
+C782,"Secondary malignant neoplasm of pleura"
+C783,"Secondary malignant neoplasm of other and unspecified respiratory organs"
+C784,"Secondary malignant neoplasm of small intestine"
+C785,"Secondary malignant neoplasm of large intestine and rectum"
+C786,"Secondary malignant neoplasm of retroperitoneum and peritoneum"
+C787,"Secondary malignant neoplasm of liver and intrahepatic bile duct"
+C788,"Secondary malignant neoplasm of other and unspecified digestive organs"
+C79,"Secondary malignant neoplasm of other and unspecified sites"
+C790,"Secondary malignant neoplasm of kidney and renal pelvis"
+C791,"Secondary malignant neoplasm of bladder and other and unspecified urinary organs"
+C792,"Secondary malignant neoplasm of skin"
+C793,"Secondary malignant neoplasm of brain and cerebral meninges"
+C794,"Secondary malignant neoplasm of other and unspecified parts of nervous system"
+C795,"Secondary malignant neoplasm of bone and bone marrow"
+C796,"Secondary malignant neoplasm of ovary"
+C797,"Secondary malignant neoplasm of adrenal gland"
+C798,"Secondary malignant neoplasm of other specified sites"
+C799,"Secondary malignant neoplasm of unspecified site"
+C80,"Malignant neoplasm without specification of site"
+C81,"Hodgkin lymphoma"
+C810,"Nodular lymphocyte predominant Hodgkin lymphoma"
+C811,"Nodular sclerosis Hodgkin lymphoma"
+C812,"Mixed cellularity Hodgkin lymphoma"
+C813,"Lymphocyte depleted Hodgkin lymphoma"
+C814,"Lymphocyte-rich Hodgkin lymphoma"
+C817,"Other Hodgkin lymphoma"
+C819,"Hodgkin lymphoma, unspecified"
+C82,"Follicular lymphoma"
+C820,"Follicular lymphoma grade I"
+C821,"Follicular lymphoma grade II"
+C822,"Follicular lymphoma grade III, unspecified"
+C823,"Follicular lymphoma grade IIIa"
+C824,"Follicular lymphoma grade IIIb"
+C825,"Diffuse follicle center lymphoma"
+C826,"Cutaneous follicle center lymphoma"
+C828,"Other types of follicular lymphoma"
+C829,"Follicular lymphoma, unspecified"
+C83,"Non-follicular lymphoma"
+C830,"Small cell B-cell lymphoma"
+C831,"Mantle cell lymphoma"
+C833,"Diffuse large B-cell lymphoma"
+C835,"Lymphoblastic (diffuse) lymphoma"
+C837,"Burkitt lymphoma"
+C838,"Other non-follicular lymphoma"
+C839,"Non-follicular (diffuse) lymphoma, unspecified"
+C84,"Mature T/NK-cell lymphomas"
+C840,"Mycosis fungoides"
+C841,"Sezary disease"
+C844,"Peripheral T-cell lymphoma, not classified"
+C846,"Anaplastic large cell lymphoma, ALK-positive"
+C847,"Anaplastic large cell lymphoma, ALK-negative"
+C84A,"Cutaneous T-cell lymphoma, unspecified"
+C84Z,"Other mature T/NK-cell lymphomas"
+C849,"Mature T/NK-cell lymphomas, unspecified"
+C85,"Other specified and unspecified types of non-Hodgkin lymphoma"
+C851,"Unspecified B-cell lymphoma"
+C852,"Mediastinal (thymic) large B-cell lymphoma"
+C858,"Other specified types of non-Hodgkin lymphoma"
+C859,"Non-Hodgkin lymphoma, unspecified"
+C86,"Other specified types of T/NK-cell lymphoma"
+C88,"Malignant immunoproliferative diseases and certain other B-cell lymphomas"
+C90,"Multiple myeloma and malignant plasma cell neoplasms"
+C900,"Multiple myeloma"
+C901,"Plasma cell leukemia"
+C902,"Extramedullary plasmacytoma"
+C903,"Solitary plasmacytoma"
+C91,"Lymphoid leukemia"
+C910,"Acute lymphoblastic leukemia [ALL]"
+C911,"Chronic lymphocytic leukemia of B-cell type"
+C913,"Prolymphocytic leukemia of B-cell type"
+C914,"Hairy cell leukemia"
+C915,"Adult T-cell lymphoma/leukemia (HTLV-1-associated)"
+C916,"Prolymphocytic leukemia of T-cell type"
+C91A,"Mature B-cell leukemia Burkitt-type"
+C91Z,"Other lymphoid leukemia"
+C919,"Lymphoid leukemia, unspecified"
+C92,"Myeloid leukemia"
+C920,"Acute myeloblastic leukemia"
+C921,"Chronic myeloid leukemia, BCR/ABL-positive"
+C922,"Atypical chronic myeloid leukemia, BCR/ABL-negative"
+C923,"Myeloid sarcoma"
+C924,"Acute promyelocytic leukemia"
+C925,"Acute myelomonocytic leukemia"
+C926,"Acute myeloid leukemia with 11q23-abnormality"
+C92A,"Acute myeloid leukemia with multilineage dysplasia"
+C92Z,"Other myeloid leukemia"
+C929,"Myeloid leukemia, unspecified"
+C93,"Monocytic leukemia"
+C930,"Acute monoblastic/monocytic leukemia"
+C931,"Chronic myelomonocytic leukemia"
+C933,"Juvenile myelomonocytic leukemia"
+C93Z,"Other monocytic leukemia"
+C939,"Monocytic leukemia, unspecified"
+C94,"Other leukemias of specified cell type"
+C940,"Acute erythroid leukemia"
+C942,"Acute megakaryoblastic leukemia"
+C943,"Mast cell leukemia"
+C944,"Acute panmyelosis with myelofibrosis"
+C946,"Myelodysplastic disease, not classified"
+C948,"Other specified leukemias"
+C95,"Leukemia of unspecified cell type"
+C950,"Acute leukemia of unspecified cell type"
+C951,"Chronic leukemia of unspecified cell type"
+C959,"Leukemia, unspecified"
+C96,"Other and unspecified malignant neoplasms of lymphoid, hematopoietic and related tissue"
+C962,"Malignant mast cell neoplasm"
+C964,"Sarcoma of dendritic cells (accessory cells)"
+C965,"Multifocal and unisystemic Langerhans-cell histiocytosis"
+C966,"Unifocal Langerhans-cell histiocytosis"
+C96A,"Histiocytic sarcoma"
+C96Z,"Other specified malignant neoplasms of lymphoid, hematopoietic and related tissue"
+C969,"Malignant neoplasm of lymphoid, hematopoietic and related tissue, unspecified"
+D00,"Carcinoma in situ of oral cavity, esophagus and stomach"
+D000,"Carcinoma in situ of lip, oral cavity and pharynx"
+D001,"Carcinoma in situ of esophagus"
+D002,"Carcinoma in situ of stomach"
+D01,"Carcinoma in situ of other and unspecified digestive organs"
+D014,"Carcinoma in situ of other and unspecified parts of intestine"
+D015,"Carcinoma in situ of liver, gallbladder and bile ducts"
+D017,"Carcinoma in situ of other specified digestive organs"
+D019,"Carcinoma in situ of digestive organ, unspecified"
+D02,"Carcinoma in situ of middle ear and respiratory system"
+D022,"Carcinoma in situ of bronchus and lung"
+D023,"Carcinoma in situ of other parts of respiratory system"
+D024,"Carcinoma in situ of respiratory system, unspecified"
+D03,"Melanoma in situ"
+D031,"Melanoma in situ of eyelid, including canthus"
+D032,"Melanoma in situ of ear and external auricular canal"
+D033,"Melanoma in situ of other and unspecified parts of face"
+D034,"Melanoma in situ of scalp and neck"
+D035,"Melanoma in situ of trunk"
+D036,"Melanoma in situ of upper limb, including shoulder"
+D037,"Melanoma in situ of lower limb, including hip"
+D038,"Melanoma in situ of other sites"
+D039,"Melanoma in situ, unspecified"
+D04,"Carcinoma in situ of skin"
+D041,"Carcinoma in situ of skin of eyelid, including canthus"
+D042,"Carcinoma in situ of skin of ear and external auricular canal"
+D043,"Carcinoma in situ of skin of other and unspecified parts of face"
+D044,"Carcinoma in situ of skin of scalp and neck"
+D045,"Carcinoma in situ of skin of trunk"
+D046,"Carcinoma in situ of skin of upper limb, including shoulder"
+D047,"Carcinoma in situ of skin of lower limb, including hip"
+D048,"Carcinoma in situ of skin of other sites"
+D049,"Carcinoma in situ of skin, unspecified"
+D05,"Carcinoma in situ of breast"
+D050,"Lobular carcinoma in situ of breast"
+D051,"Intraductal carcinoma in situ of breast"
+D058,"Other specified type of carcinoma in situ of breast"
+D059,"Unspecified type of carcinoma in situ of breast"
+D06,"Carcinoma in situ of cervix uteri"
+D07,"Carcinoma in situ of other and unspecified genital organs"
+D073,"Carcinoma in situ of other and unspecified female genital organs"
+D074,"Carcinoma in situ of penis"
+D075,"Carcinoma in situ of prostate"
+D076,"Carcinoma in situ of other and unspecified male genital organs"
+D09,"Carcinoma in situ of other and unspecified sites"
+D091,"Carcinoma in situ of other and unspecified urinary organs"
+D092,"Carcinoma in situ of eye"
+D093,"Carcinoma in situ of thyroid and other endocrine glands"
+D098,"Carcinoma in situ of other specified sites"
+D099,"Carcinoma in situ, unspecified"
+D10,"Benign neoplasm of mouth and pharynx"
+D103,"Benign neoplasm of other and unspecified parts of mouth"
+D104,"Benign neoplasm of tonsil"
+D105,"Benign neoplasm of other parts of oropharynx"
+D106,"Benign neoplasm of nasopharynx"
+D107,"Benign neoplasm of hypopharynx"
+D109,"Benign neoplasm of pharynx, unspecified"
+D11,"Benign neoplasm of major salivary glands"
+D12,"Benign neoplasm of colon, rectum, anus and anal canal"
+D13,"Benign neoplasm of other and ill-defined parts of digestive system"
+D133,"Benign neoplasm of other and unspecified parts of small intestine"
+D134,"Benign neoplasm of liver"
+D135,"Benign neoplasm of extrahepatic bile ducts"
+D136,"Benign neoplasm of pancreas"
+D137,"Benign neoplasm of endocrine pancreas"
+D139,"Benign neoplasm of ill-defined sites within the digestive system"
+D14,"Benign neoplasm of middle ear and respiratory system"
+D143,"Benign neoplasm of bronchus and lung"
+D144,"Benign neoplasm of respiratory system, unspecified"
+D15,"Benign neoplasm of other and unspecified intrathoracic organs"
+D16,"Benign neoplasm of bone and articular cartilage"
+D160,"Benign neoplasm of scapula and long bones of upper limb"
+D161,"Benign neoplasm of short bones of upper limb"
+D162,"Benign neoplasm of long bones of lower limb"
+D163,"Benign neoplasm of short bones of lower limb"
+D164,"Benign neoplasm of bones of skull and face"
+D165,"Benign neoplasm of lower jaw bone"
+D166,"Benign neoplasm of vertebral column"
+D167,"Benign neoplasm of ribs, sternum and clavicle"
+D168,"Benign neoplasm of pelvic bones, sacrum and coccyx"
+D169,"Benign neoplasm of bone and articular cartilage, unspecified"
+D17,"Benign lipomatous neoplasm"
+D172,"Benign lipomatous neoplasm of skin and subcutaneous tissue of limb"
+D173,"Benign lipomatous neoplasm of skin and subcutaneous tissue of other and unspecified sites"
+D174,"Benign lipomatous neoplasm of intrathoracic organs"
+D175,"Benign lipomatous neoplasm of intra-abdominal organs"
+D176,"Benign lipomatous neoplasm of spermatic cord"
+D177,"Benign lipomatous neoplasm of other sites"
+D179,"Benign lipomatous neoplasm, unspecified"
+D18,"Hemangioma and lymphangioma, any site"
+D180,"Hemangioma"
+D181,"Lymphangioma, any site"
+D19,"Benign neoplasm of mesothelial tissue"
+D20,"Benign neoplasm of soft tissue of retroperitoneum and peritoneum"
+D21,"Other benign neoplasms of connective and other soft tissue"
+D211,"Benign neoplasm of connective and other soft tissue of upper limb, including shoulder"
+D212,"Benign neoplasm of connective and other soft tissue of lower limb, including hip"
+D213,"Benign neoplasm of connective and other soft tissue of thorax"
+D214,"Benign neoplasm of connective and other soft tissue of abdomen"
+D215,"Benign neoplasm of connective and other soft tissue of pelvis"
+D216,"Benign neoplasm of connective and other soft tissue of trunk, unspecified"
+D219,"Benign neoplasm of connective and other soft tissue, unspecified"
+D22,"Melanocytic nevi"
+D221,"Melanocytic nevi of eyelid, including canthus"
+D222,"Melanocytic nevi of ear and external auricular canal"
+D223,"Melanocytic nevi of other and unspecified parts of face"
+D224,"Melanocytic nevi of scalp and neck"
+D225,"Melanocytic nevi of trunk"
+D226,"Melanocytic nevi of upper limb, including shoulder"
+D227,"Melanocytic nevi of lower limb, including hip"
+D229,"Melanocytic nevi, unspecified"
+D23,"Other benign neoplasms of skin"
+D231,"Other benign neoplasm of skin of eyelid, including canthus"
+D232,"Other benign neoplasm of skin of ear and external auricular canal"
+D233,"Other benign neoplasm of skin of other and unspecified parts of face"
+D234,"Other benign neoplasm of skin of scalp and neck"
+D235,"Other benign neoplasm of skin of trunk"
+D236,"Other benign neoplasm of skin of upper limb, including shoulder"
+D237,"Other benign neoplasm of skin of lower limb, including hip"
+D239,"Other benign neoplasm of skin, unspecified"
+D24,"Benign neoplasm of breast"
+D25,"Leiomyoma of uterus"
+D26,"Other benign neoplasms of uterus"
+D27,"Benign neoplasm of ovary"
+D28,"Benign neoplasm of other and unspecified female genital organs"
+D29,"Benign neoplasm of male genital organs"
+D292,"Benign neoplasm of testis"
+D293,"Benign neoplasm of epididymis"
+D294,"Benign neoplasm of scrotum"
+D298,"Benign neoplasm of other specified male genital organs"
+D299,"Benign neoplasm of male genital organ, unspecified"
+D30,"Benign neoplasm of urinary organs"
+D300,"Benign neoplasm of kidney"
+D301,"Benign neoplasm of renal pelvis"
+D302,"Benign neoplasm of ureter"
+D303,"Benign neoplasm of bladder"
+D304,"Benign neoplasm of urethra"
+D308,"Benign neoplasm of other specified urinary organs"
+D309,"Benign neoplasm of urinary organ, unspecified"
+D31,"Benign neoplasm of eye and adnexa"
+D310,"Benign neoplasm of conjunctiva"
+D311,"Benign neoplasm of cornea"
+D312,"Benign neoplasm of retina"
+D313,"Benign neoplasm of choroid"
+D314,"Benign neoplasm of ciliary body"
+D315,"Benign neoplasm of lacrimal gland and duct"
+D316,"Benign neoplasm of unspecified site of orbit"
+D319,"Benign neoplasm of unspecified part of eye"
+D32,"Benign neoplasm of meninges"
+D33,"Benign neoplasm of brain and other parts of central nervous system"
+D34,"Benign neoplasm of thyroid gland"
+D35,"Benign neoplasm of other and unspecified endocrine glands"
+D350,"Benign neoplasm of adrenal gland"
+D351,"Benign neoplasm of parathyroid gland"
+D352,"Benign neoplasm of pituitary gland"
+D353,"Benign neoplasm of craniopharyngeal duct"
+D354,"Benign neoplasm of pineal gland"
+D355,"Benign neoplasm of carotid body"
+D356,"Benign neoplasm of aortic body and other paraganglia"
+D357,"Benign neoplasm of other specified endocrine glands"
+D359,"Benign neoplasm of endocrine gland, unspecified"
+D36,"Benign neoplasm of other and unspecified sites"
+D361,"Benign neoplasm of peripheral nerves and autonomic nervous system"
+D367,"Benign neoplasm of other specified sites"
+D369,"Benign neoplasm, unspecified site"
+D3A,"Benign neuroendocrine tumors"
+D3A0,"Benign carcinoid tumors"
+D3A01,"Benign carcinoid tumors of the small intestine"
+D3A02,"Benign carcinoid tumors of the appendix, large intestine, and rectum"
+D3A09,"Benign carcinoid tumors of other sites"
+D3A8,"Other benign neuroendocrine tumors"
+D37,"Neoplasm of uncertain behavior of oral cavity and digestive organs"
+D370,"Neoplasm of uncertain behavior of lip, oral cavity and pharynx"
+D3703,"Neoplasm of uncertain behavior of the major salivary glands"
+D3704,"Neoplasm of uncertain behavior of the minor salivary glands"
+D3705,"Neoplasm of uncertain behavior of pharynx"
+D3709,"Neoplasm of uncertain behavior of other specified sites of the oral cavity"
+D371,"Neoplasm of uncertain behavior of stomach"
+D372,"Neoplasm of uncertain behavior of small intestine"
+D373,"Neoplasm of uncertain behavior of appendix"
+D374,"Neoplasm of uncertain behavior of colon"
+D375,"Neoplasm of uncertain behavior of rectum"
+D376,"Neoplasm of uncertain behavior of liver, gallbladder and bile ducts"
+D378,"Neoplasm of uncertain behavior of other specified digestive organs"
+D379,"Neoplasm of uncertain behavior of digestive organ, unspecified"
+D38,"Neoplasm of uncertain behavior of middle ear and respiratory and intrathoracic organs"
+D39,"Neoplasm of uncertain behavior of female genital organs"
+D391,"Neoplasm of uncertain behavior of ovary"
+D392,"Neoplasm of uncertain behavior of placenta"
+D398,"Neoplasm of uncertain behavior of other specified female genital organs"
+D399,"Neoplasm of uncertain behavior of female genital organ, unspecified"
+D40,"Neoplasm of uncertain behavior of male genital organs"
+D401,"Neoplasm of uncertain behavior of testis"
+D408,"Neoplasm of uncertain behavior of other specified male genital organs"
+D409,"Neoplasm of uncertain behavior of male genital organ, unspecified"
+D41,"Neoplasm of uncertain behavior of urinary organs"
+D410,"Neoplasm of uncertain behavior of kidney"
+D411,"Neoplasm of uncertain behavior of renal pelvis"
+D412,"Neoplasm of uncertain behavior of ureter"
+D413,"Neoplasm of uncertain behavior of urethra"
+D414,"Neoplasm of uncertain behavior of bladder"
+D418,"Neoplasm of uncertain behavior of other specified urinary organs"
+D419,"Neoplasm of uncertain behavior of unspecified urinary organ"
+D42,"Neoplasm of uncertain behavior of meninges"
+D43,"Neoplasm of uncertain behavior of brain and central nervous system"
+D44,"Neoplasm of uncertain behavior of endocrine glands"
+D441,"Neoplasm of uncertain behavior of adrenal gland"
+D442,"Neoplasm of uncertain behavior of parathyroid gland"
+D443,"Neoplasm of uncertain behavior of pituitary gland"
+D444,"Neoplasm of uncertain behavior of craniopharyngeal duct"
+D445,"Neoplasm of uncertain behavior of pineal gland"
+D446,"Neoplasm of uncertain behavior of carotid body"
+D447,"Neoplasm of uncertain behavior of aortic body and other paraganglia"
+D449,"Neoplasm of uncertain behavior of unspecified endocrine gland"
+D45,"Polycythemia vera"
+D46,"Myelodysplastic syndromes"
+D462,"Refractory anemia with excess of blasts [RAEB]"
+D46A,"Refractory cytopenia with multilineage dysplasia"
+D46B,"Refractory cytopenia with multilineage dysplasia and ring sideroblasts"
+D46C,"Myelodysplastic syndrome with isolated del(5q) chromosomal abnormality"
+D464,"Refractory anemia, unspecified"
+D46Z,"Other myelodysplastic syndromes"
+D469,"Myelodysplastic syndrome, unspecified"
+D47,"Other neoplasms of uncertain behavior of lymphoid, hematopoietic and related tissue"
+D470,"Mast cell neoplasms of uncertain behavior"
+D471,"Chronic myeloproliferative disease"
+D472,"Monoclonal gammopathy"
+D473,"Essential (hemorrhagic) thrombocythemia"
+D474,"Osteomyelofibrosis"
+D47Z,"Other specified neoplasms of uncertain behavior of lymphoid, hematopoietic and related tissue"
+D479,"Neoplasm of uncertain behavior of lymphoid, hematopoietic and related tissue, unspecified"
+D48,"Neoplasm of uncertain behavior of other and unspecified sites"
+D486,"Neoplasm of uncertain behavior of breast"
+D487,"Neoplasm of uncertain behavior of other specified sites"
+D489,"Neoplasm of uncertain behavior, unspecified"
+D49,"Neoplasms of unspecified behavior"
+D495,"Neoplasm of unspecified behavior of other genitourinary organs"
+D4951,"Neoplasm of unspecified behavior of kidney"
+D4959,"Neoplasm of unspecified behavior of other genitourinary organ"
+D496,"Neoplasm of unspecified behavior of brain"
+D497,"Neoplasm of unspecified behavior of endocrine glands and other parts of nervous system"
+D498,"Neoplasm of unspecified behavior of other specified sites"
+D499,"Neoplasm of unspecified behavior of unspecified site"
+D50,"Iron deficiency anemia"
+D51,"Vitamin B12 deficiency anemia"
+D52,"Folate deficiency anemia"
+D53,"Other nutritional anemias"
+D55,"Anemia due to enzyme disorders"
+D56,"Thalassemia"
+D57,"Sickle-cell disorders"
+D570,"Hb-SS disease with crisis"
+D571,"Sickle-cell disease without crisis"
+D572,"Sickle-cell/Hb-C disease"
+D5721,"Sickle-cell/Hb-C disease with crisis"
+D573,"Sickle-cell trait"
+D574,"Sickle-cell thalassemia"
+D5741,"Sickle-cell thalassemia with crisis"
+D578,"Other sickle-cell disorders"
+D5781,"Other sickle-cell disorders with crisis"
+D58,"Other hereditary hemolytic anemias"
+D59,"Acquired hemolytic anemia"
+D60,"Acquired pure red cell aplasia [erythroblastopenia]"
+D61,"Other aplastic anemias and other bone marrow failure syndromes"
+D610,"Constitutional aplastic anemia"
+D611,"Drug-induced aplastic anemia"
+D612,"Aplastic anemia due to other external agents"
+D613,"Idiopathic aplastic anemia"
+D618,"Other specified aplastic anemias and other bone marrow failure syndromes"
+D6181,"Pancytopenia"
+D6182,"Myelophthisis"
+D6189,"Other specified aplastic anemias and other bone marrow failure syndromes"
+D619,"Aplastic anemia, unspecified"
+D62,"Acute posthemorrhagic anemia"
+D63,"Anemia in chronic diseases classified elsewhere"
+D64,"Other anemias"
+D648,"Other specified anemias"
+D649,"Anemia, unspecified"
+D65,"Disseminated intravascular coagulation [defibrination syndrome]"
+D66,"Hereditary factor VIII deficiency"
+D67,"Hereditary factor IX deficiency"
+D68,"Other coagulation defects"
+D683,"Hemorrhagic disorder due to circulating anticoagulants"
+D6831,"Hemorrhagic disorder due to intrinsic circulating anticoagulants, antibodies, or inhibitors"
+D6832,"Hemorrhagic disorder due to extrinsic circulating anticoagulants"
+D684,"Acquired coagulation factor deficiency"
+D685,"Primary thrombophilia"
+D686,"Other thrombophilia"
+D688,"Other specified coagulation defects"
+D689,"Coagulation defect, unspecified"
+D69,"Purpura and other hemorrhagic conditions"
+D694,"Other primary thrombocytopenia"
+D695,"Secondary thrombocytopenia"
+D696,"Thrombocytopenia, unspecified"
+D698,"Other specified hemorrhagic conditions"
+D699,"Hemorrhagic condition, unspecified"
+D70,"Neutropenia"
+D71,"Functional disorders of polymorphonuclear neutrophils"
+D72,"Other disorders of white blood cells"
+D728,"Other specified disorders of white blood cells"
+D7281,"Decreased white blood cell count"
+D7282,"Elevated white blood cell count"
+D7289,"Other specified disorders of white blood cells"
+D729,"Disorder of white blood cells, unspecified"
+D73,"Diseases of spleen"
+D738,"Other diseases of spleen"
+D739,"Disease of spleen, unspecified"
+D74,"Methemoglobinemia"
+D75,"Other and unspecified diseases of blood and blood-forming organs"
+D758,"Other specified diseases of blood and blood-forming organs"
+D759,"Disease of blood and blood-forming organs, unspecified"
+D76,"Other specified diseases with participation of lymphoreticular and reticulohistiocytic tissue"
+D77,"Other disorders of blood and blood-forming organs in diseases classified elsewhere"
+D78,"Intraoperative and postprocedural complications of the spleen"
+D780,"Intraoperative hemorrhage and hematoma of the spleen complicating a procedure"
+D781,"Accidental puncture and laceration of the spleen during a procedure"
+D782,"Postprocedural hemorrhage of the spleen following a procedure"
+D783,"Postprocedural hematoma and seroma of the spleen following a procedure"
+D788,"Other intraoperative and postprocedural complications of the spleen"
+D80,"Immunodeficiency with predominantly antibody defects"
+D81,"Combined immunodeficiencies"
+D818,"Other combined immunodeficiencies"
+D8181,"Biotin-dependent carboxylase deficiency"
+D8189,"Other combined immunodeficiencies"
+D819,"Combined immunodeficiency, unspecified"
+D82,"Immunodeficiency associated with other major defects"
+D83,"Common variable immunodeficiency"
+D84,"Other immunodeficiencies"
+D86,"Sarcoidosis"
+D868,"Sarcoidosis of other sites"
+D869,"Sarcoidosis, unspecified"
+D89,"Other disorders involving the immune mechanism, not elsewhere classified"
+D894,"Mast cell activation syndrome and related disorders"
+D898,"Other specified disorders involving the immune mechanism, not elsewhere classified"
+D8981,"Graft-versus-host disease"
+D8982,"Autoimmune lymphoproliferative syndrome [ALPS]"
+D8989,"Other specified disorders involving the immune mechanism, not elsewhere classified"
+D899,"Disorder involving the immune mechanism, unspecified"
+E00,"Congenital iodine-deficiency syndrome"
+E01,"Iodine-deficiency related thyroid disorders and allied conditions"
+E02,"Subclinical iodine-deficiency hypothyroidism"
+E03,"Other hypothyroidism"
+E04,"Other nontoxic goiter"
+E05,"Thyrotoxicosis [hyperthyroidism]"
+E050,"Thyrotoxicosis with diffuse goiter"
+E051,"Thyrotoxicosis with toxic single thyroid nodule"
+E052,"Thyrotoxicosis with toxic multinodular goiter"
+E053,"Thyrotoxicosis from ectopic thyroid tissue"
+E054,"Thyrotoxicosis factitia"
+E058,"Other thyrotoxicosis"
+E059,"Thyrotoxicosis, unspecified"
+E06,"Thyroiditis"
+E07,"Other disorders of thyroid"
+E078,"Other specified disorders of thyroid"
+E079,"Disorder of thyroid, unspecified"
+E08,"Diabetes mellitus due to underlying condition"
+E080,"Diabetes mellitus due to underlying condition with hyperosmolarity"
+E081,"Diabetes mellitus due to underlying condition with ketoacidosis"
+E082,"Diabetes mellitus due to underlying condition with kidney complications"
+E083,"Diabetes mellitus due to underlying condition with ophthalmic complications"
+E0831,"Diabetes mellitus due to underlying condition with unspecified diabetic retinopathy"
+E0832,"Diabetes mellitus due to underlying condition with mild nonproliferative diabetic retinopathy"
+E08321,"Diabetes mellitus due to underlying condition with mild nonproliferative diabetic retinopathy with macular edema"
+E08329,"Diabetes mellitus due to underlying condition with mild nonproliferative diabetic retinopathy without macular edema"
+E0833,"Diabetes mellitus due to underlying condition with moderate nonproliferative diabetic retinopathy"
+E08331,"Diabetes mellitus due to underlying condition with moderate nonproliferative diabetic retinopathy with macular edema"
+E08339,"Diabetes mellitus due to underlying condition with moderate nonproliferative diabetic retinopathy without macular edema"
+E0834,"Diabetes mellitus due to underlying condition with severe nonproliferative diabetic retinopathy"
+E08341,"Diabetes mellitus due to underlying condition with severe nonproliferative diabetic retinopathy with macular edema"
+E08349,"Diabetes mellitus due to underlying condition with severe nonproliferative diabetic retinopathy without macular edema"
+E0835,"Diabetes mellitus due to underlying condition with proliferative diabetic retinopathy"
+E08351,"Diabetes mellitus due to underlying condition with proliferative diabetic retinopathy with macular edema"
+E08352,"Diabetes mellitus due to underlying condition with proliferative diabetic retinopathy with traction retinal detachment involving the macula"
+E08353,"Diabetes mellitus due to underlying condition with proliferative diabetic retinopathy with traction retinal detachment not involving the macula"
+E08354,"Diabetes mellitus due to underlying condition with proliferative diabetic retinopathy with combined traction retinal detachment and rhegmatogenous retinal detachment"
+E08355,"Diabetes mellitus due to underlying condition with stable proliferative diabetic retinopathy"
+E08359,"Diabetes mellitus due to underlying condition with proliferative diabetic retinopathy without macular edema"
+E0836,"Diabetes mellitus due to underlying condition with diabetic cataract"
+E0837,"Diabetes mellitus due to underlying condition with diabetic macular edema, resolved following treatment"
+E0839,"Diabetes mellitus due to underlying condition with other diabetic ophthalmic complication"
+E084,"Diabetes mellitus due to underlying condition with neurological complications"
+E085,"Diabetes mellitus due to underlying condition with circulatory complications"
+E086,"Diabetes mellitus due to underlying condition with other specified complications"
+E0861,"Diabetes mellitus due to underlying condition with diabetic arthropathy"
+E0862,"Diabetes mellitus due to underlying condition with skin complications"
+E0863,"Diabetes mellitus due to underlying condition with oral complications"
+E0864,"Diabetes mellitus due to underlying condition with hypoglycemia"
+E0865,"Diabetes mellitus due to underlying condition with hyperglycemia"
+E0869,"Diabetes mellitus due to underlying condition with other specified complication"
+E088,"Diabetes mellitus due to underlying condition with unspecified complications"
+E089,"Diabetes mellitus due to underlying condition without complications"
+E09,"Drug or chemical induced diabetes mellitus"
+E090,"Drug or chemical induced diabetes mellitus with hyperosmolarity"
+E091,"Drug or chemical induced diabetes mellitus with ketoacidosis"
+E092,"Drug or chemical induced diabetes mellitus with kidney complications"
+E093,"Drug or chemical induced diabetes mellitus with ophthalmic complications"
+E0931,"Drug or chemical induced diabetes mellitus with unspecified diabetic retinopathy"
+E0932,"Drug or chemical induced diabetes mellitus with mild nonproliferative diabetic retinopathy"
+E09321,"Drug or chemical induced diabetes mellitus with mild nonproliferative diabetic retinopathy with macular edema"
+E09329,"Drug or chemical induced diabetes mellitus with mild nonproliferative diabetic retinopathy without macular edema"
+E0933,"Drug or chemical induced diabetes mellitus with moderate nonproliferative diabetic retinopathy"
+E09331,"Drug or chemical induced diabetes mellitus with moderate nonproliferative diabetic retinopathy with macular edema"
+E09339,"Drug or chemical induced diabetes mellitus with moderate nonproliferative diabetic retinopathy without macular edema"
+E0934,"Drug or chemical induced diabetes mellitus with severe nonproliferative diabetic retinopathy"
+E09341,"Drug or chemical induced diabetes mellitus with severe nonproliferative diabetic retinopathy with macular edema"
+E09349,"Drug or chemical induced diabetes mellitus with severe nonproliferative diabetic retinopathy without macular edema"
+E0935,"Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy"
+E09351,"Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy with macular edema"
+E09352,"Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment involving the macula"
+E09353,"Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment not involving the macula"
+E09354,"Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy with combined traction retinal detachment and rhegmatogenous retinal detachment"
+E09355,"Drug or chemical induced diabetes mellitus with stable proliferative diabetic retinopathy"
+E09359,"Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy without macular edema"
+E0936,"Drug or chemical induced diabetes mellitus with diabetic cataract"
+E0937,"Drug or chemical induced diabetes mellitus with diabetic macular edema, resolved following treatment"
+E0939,"Drug or chemical induced diabetes mellitus with other diabetic ophthalmic complication"
+E094,"Drug or chemical induced diabetes mellitus with neurological complications"
+E095,"Drug or chemical induced diabetes mellitus with circulatory complications"
+E096,"Drug or chemical induced diabetes mellitus with other specified complications"
+E0961,"Drug or chemical induced diabetes mellitus with diabetic arthropathy"
+E0962,"Drug or chemical induced diabetes mellitus with skin complications"
+E0963,"Drug or chemical induced diabetes mellitus with oral complications"
+E0964,"Drug or chemical induced diabetes mellitus with hypoglycemia"
+E0965,"Drug or chemical induced diabetes mellitus with hyperglycemia"
+E0969,"Drug or chemical induced diabetes mellitus with other specified complication"
+E098,"Drug or chemical induced diabetes mellitus with unspecified complications"
+E099,"Drug or chemical induced diabetes mellitus without complications"
+E10,"Type 1 diabetes mellitus"
+E101,"Type 1 diabetes mellitus with ketoacidosis"
+E102,"Type 1 diabetes mellitus with kidney complications"
+E103,"Type 1 diabetes mellitus with ophthalmic complications"
+E1031,"Type 1 diabetes mellitus with unspecified diabetic retinopathy"
+E1032,"Type 1 diabetes mellitus with mild nonproliferative diabetic retinopathy"
+E10321,"Type 1 diabetes mellitus with mild nonproliferative diabetic retinopathy with macular edema"
+E10329,"Type 1 diabetes mellitus with mild nonproliferative diabetic retinopathy without macular edema"
+E1033,"Type 1 diabetes mellitus with moderate nonproliferative diabetic retinopathy"
+E10331,"Type 1 diabetes mellitus with moderate nonproliferative diabetic retinopathy with macular edema"
+E10339,"Type 1 diabetes mellitus with moderate nonproliferative diabetic retinopathy without macular edema"
+E1034,"Type 1 diabetes mellitus with severe nonproliferative diabetic retinopathy"
+E10341,"Type 1 diabetes mellitus with severe nonproliferative diabetic retinopathy with macular edema"
+E10349,"Type 1 diabetes mellitus with severe nonproliferative diabetic retinopathy without macular edema"
+E1035,"Type 1 diabetes mellitus with proliferative diabetic retinopathy"
+E10351,"Type 1 diabetes mellitus with proliferative diabetic retinopathy with macular edema"
+E10352,"Type 1 diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment involving the macula"
+E10353,"Type 1 diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment not involving the macula"
+E10354,"Type 1 diabetes mellitus with proliferative diabetic retinopathy with combined traction retinal detachment and rhegmatogenous retinal detachment"
+E10355,"Type 1 diabetes mellitus with stable proliferative diabetic retinopathy"
+E10359,"Type 1 diabetes mellitus with proliferative diabetic retinopathy without macular edema"
+E1036,"Type 1 diabetes mellitus with diabetic cataract"
+E1037,"Type 1 diabetes mellitus with diabetic macular edema, resolved following treatment"
+E1039,"Type 1 diabetes mellitus with other diabetic ophthalmic complication"
+E104,"Type 1 diabetes mellitus with neurological complications"
+E105,"Type 1 diabetes mellitus with circulatory complications"
+E106,"Type 1 diabetes mellitus with other specified complications"
+E1061,"Type 1 diabetes mellitus with diabetic arthropathy"
+E1062,"Type 1 diabetes mellitus with skin complications"
+E1063,"Type 1 diabetes mellitus with oral complications"
+E1064,"Type 1 diabetes mellitus with hypoglycemia"
+E1065,"Type 1 diabetes mellitus with hyperglycemia"
+E1069,"Type 1 diabetes mellitus with other specified complication"
+E108,"Type 1 diabetes mellitus with unspecified complications"
+E109,"Type 1 diabetes mellitus without complications"
+E11,"Type 2 diabetes mellitus"
+E110,"Type 2 diabetes mellitus with hyperosmolarity"
+E111,"Type 2 diabetes mellitus with ketoacidosis"
+E112,"Type 2 diabetes mellitus with kidney complications"
+E113,"Type 2 diabetes mellitus with ophthalmic complications"
+E1131,"Type 2 diabetes mellitus with unspecified diabetic retinopathy"
+E1132,"Type 2 diabetes mellitus with mild nonproliferative diabetic retinopathy"
+E11321,"Type 2 diabetes mellitus with mild nonproliferative diabetic retinopathy with macular edema"
+E11329,"Type 2 diabetes mellitus with mild nonproliferative diabetic retinopathy without macular edema"
+E1133,"Type 2 diabetes mellitus with moderate nonproliferative diabetic retinopathy"
+E11331,"Type 2 diabetes mellitus with moderate nonproliferative diabetic retinopathy with macular edema"
+E11339,"Type 2 diabetes mellitus with moderate nonproliferative diabetic retinopathy without macular edema"
+E1134,"Type 2 diabetes mellitus with severe nonproliferative diabetic retinopathy"
+E11341,"Type 2 diabetes mellitus with severe nonproliferative diabetic retinopathy with macular edema"
+E11349,"Type 2 diabetes mellitus with severe nonproliferative diabetic retinopathy without macular edema"
+E1135,"Type 2 diabetes mellitus with proliferative diabetic retinopathy"
+E11351,"Type 2 diabetes mellitus with proliferative diabetic retinopathy with macular edema"
+E11352,"Type 2 diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment involving the macula"
+E11353,"Type 2 diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment not involving the macula"
+E11354,"Type 2 diabetes mellitus with proliferative diabetic retinopathy with combined traction retinal detachment and rhegmatogenous retinal detachment"
+E11355,"Type 2 diabetes mellitus with stable proliferative diabetic retinopathy"
+E11359,"Type 2 diabetes mellitus with proliferative diabetic retinopathy without macular edema"
+E1136,"Type 2 diabetes mellitus with diabetic cataract"
+E1137,"Type 2 diabetes mellitus with diabetic macular edema, resolved following treatment"
+E1139,"Type 2 diabetes mellitus with other diabetic ophthalmic complication"
+E114,"Type 2 diabetes mellitus with neurological complications"
+E115,"Type 2 diabetes mellitus with circulatory complications"
+E116,"Type 2 diabetes mellitus with other specified complications"
+E1161,"Type 2 diabetes mellitus with diabetic arthropathy"
+E1162,"Type 2 diabetes mellitus with skin complications"
+E1163,"Type 2 diabetes mellitus with oral complications"
+E1164,"Type 2 diabetes mellitus with hypoglycemia"
+E1165,"Type 2 diabetes mellitus with hyperglycemia"
+E1169,"Type 2 diabetes mellitus with other specified complication"
+E118,"Type 2 diabetes mellitus with unspecified complications"
+E119,"Type 2 diabetes mellitus without complications"
+E13,"Other specified diabetes mellitus"
+E130,"Other specified diabetes mellitus with hyperosmolarity"
+E131,"Other specified diabetes mellitus with ketoacidosis"
+E132,"Other specified diabetes mellitus with kidney complications"
+E133,"Other specified diabetes mellitus with ophthalmic complications"
+E1331,"Other specified diabetes mellitus with unspecified diabetic retinopathy"
+E1332,"Other specified diabetes mellitus with mild nonproliferative diabetic retinopathy"
+E13321,"Other specified diabetes mellitus with mild nonproliferative diabetic retinopathy with macular edema"
+E13329,"Other specified diabetes mellitus with mild nonproliferative diabetic retinopathy without macular edema"
+E1333,"Other specified diabetes mellitus with moderate nonproliferative diabetic retinopathy"
+E13331,"Other specified diabetes mellitus with moderate nonproliferative diabetic retinopathy with macular edema"
+E13339,"Other specified diabetes mellitus with moderate nonproliferative diabetic retinopathy without macular edema"
+E1334,"Other specified diabetes mellitus with severe nonproliferative diabetic retinopathy"
+E13341,"Other specified diabetes mellitus with severe nonproliferative diabetic retinopathy with macular edema"
+E13349,"Other specified diabetes mellitus with severe nonproliferative diabetic retinopathy without macular edema"
+E1335,"Other specified diabetes mellitus with proliferative diabetic retinopathy"
+E13351,"Other specified diabetes mellitus with proliferative diabetic retinopathy with macular edema"
+E13352,"Other specified diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment involving the macula"
+E13353,"Other specified diabetes mellitus with proliferative diabetic retinopathy with traction retinal detachment not involving the macula"
+E13354,"Other specified diabetes mellitus with proliferative diabetic retinopathy with combined traction retinal detachment and rhegmatogenous retinal detachment"
+E13355,"Other specified diabetes mellitus with stable proliferative diabetic retinopathy"
+E13359,"Other specified diabetes mellitus with proliferative diabetic retinopathy without macular edema"
+E1336,"Other specified diabetes mellitus with diabetic cataract"
+E1337,"Other specified diabetes mellitus with diabetic macular edema, resolved following treatment"
+E1339,"Other specified diabetes mellitus with other diabetic ophthalmic complication"
+E134,"Other specified diabetes mellitus with neurological complications"
+E135,"Other specified diabetes mellitus with circulatory complications"
+E136,"Other specified diabetes mellitus with other specified complications"
+E1361,"Other specified diabetes mellitus with diabetic arthropathy"
+E1362,"Other specified diabetes mellitus with skin complications"
+E1363,"Other specified diabetes mellitus with oral complications"
+E1364,"Other specified diabetes mellitus with hypoglycemia"
+E1365,"Other specified diabetes mellitus with hyperglycemia"
+E1369,"Other specified diabetes mellitus with other specified complication"
+E138,"Other specified diabetes mellitus with unspecified complications"
+E139,"Other specified diabetes mellitus without complications"
+E15,"Nondiabetic hypoglycemic coma"
+E16,"Other disorders of pancreatic internal secretion"
+E20,"Hypoparathyroidism"
+E21,"Hyperparathyroidism and other disorders of parathyroid gland"
+E22,"Hyperfunction of pituitary gland"
+E23,"Hypofunction and other disorders of the pituitary gland"
+E24,"Cushing's syndrome"
+E25,"Adrenogenital disorders"
+E26,"Hyperaldosteronism"
+E260,"Primary hyperaldosteronism"
+E261,"Secondary hyperaldosteronism"
+E268,"Other hyperaldosteronism"
+E269,"Hyperaldosteronism, unspecified"
+E27,"Other disorders of adrenal gland"
+E274,"Other and unspecified adrenocortical insufficiency"
+E275,"Adrenomedullary hyperfunction"
+E278,"Other specified disorders of adrenal gland"
+E279,"Disorder of adrenal gland, unspecified"
+E28,"Ovarian dysfunction"
+E283,"Primary ovarian failure"
+E2831,"Premature menopause"
+E2839,"Other primary ovarian failure"
+E288,"Other ovarian dysfunction"
+E289,"Ovarian dysfunction, unspecified"
+E29,"Testicular dysfunction"
+E30,"Disorders of puberty, not elsewhere classified"
+E31,"Polyglandular dysfunction"
+E312,"Multiple endocrine neoplasia [MEN] syndromes"
+E318,"Other polyglandular dysfunction"
+E319,"Polyglandular dysfunction, unspecified"
+E32,"Diseases of thymus"
+E34,"Other endocrine disorders"
+E345,"Androgen insensitivity syndrome"
+E348,"Other specified endocrine disorders"
+E349,"Endocrine disorder, unspecified"
+E35,"Disorders of endocrine glands in diseases classified elsewhere"
+E36,"Intraoperative complications of endocrine system"
+E360,"Intraoperative hemorrhage and hematoma of an endocrine system organ or structure complicating a procedure"
+E361,"Accidental puncture and laceration of an endocrine system organ or structure during a procedure"
+E368,"Other intraoperative complications of endocrine system"
+E40,"Kwashiorkor"
+E41,"Nutritional marasmus"
+E42,"Marasmic kwashiorkor"
+E43,"Unspecified severe protein-calorie malnutrition"
+E44,"Protein-calorie malnutrition of moderate and mild degree"
+E45,"Retarded development following protein-calorie malnutrition"
+E46,"Unspecified protein-calorie malnutrition"
+E50,"Vitamin A deficiency"
+E51,"Thiamine deficiency"
+E511,"Beriberi"
+E512,"Wernicke's encephalopathy"
+E518,"Other manifestations of thiamine deficiency"
+E519,"Thiamine deficiency, unspecified"
+E52,"Niacin deficiency [pellagra]"
+E53,"Deficiency of other B group vitamins"
+E54,"Ascorbic acid deficiency"
+E55,"Vitamin D deficiency"
+E56,"Other vitamin deficiencies"
+E58,"Dietary calcium deficiency"
+E59,"Dietary selenium deficiency"
+E60,"Dietary zinc deficiency"
+E61,"Deficiency of other nutrient elements"
+E63,"Other nutritional deficiencies"
+E64,"Sequelae of malnutrition and other nutritional deficiencies"
+E65,"Localized adiposity"
+E66,"Overweight and obesity"
+E660,"Obesity due to excess calories"
+E661,"Drug-induced obesity"
+E662,"Morbid (severe) obesity with alveolar hypoventilation"
+E663,"Overweight"
+E668,"Other obesity"
+E669,"Obesity, unspecified"
+E67,"Other hyperalimentation"
+E68,"Sequelae of hyperalimentation"
+E70,"Disorders of aromatic amino-acid metabolism"
+E702,"Disorders of tyrosine metabolism"
+E703,"Albinism"
+E7031,"Ocular albinism"
+E7032,"Oculocutaneous albinism"
+E7033,"Albinism with hematologic abnormality"
+E7039,"Other specified albinism"
+E704,"Disorders of histidine metabolism"
+E705,"Disorders of tryptophan metabolism"
+E708,"Other disorders of aromatic amino-acid metabolism"
+E709,"Disorder of aromatic amino-acid metabolism, unspecified"
+E71,"Disorders of branched-chain amino-acid metabolism and fatty-acid metabolism"
+E711,"Other disorders of branched-chain amino-acid metabolism"
+E7111,"Branched-chain organic acidurias"
+E7112,"Disorders of propionate metabolism"
+E7119,"Other disorders of branched-chain amino-acid metabolism"
+E712,"Disorder of branched-chain amino-acid metabolism, unspecified"
+E713,"Disorders of fatty-acid metabolism"
+E7131,"Disorders of fatty-acid oxidation"
+E7132,"Disorders of ketone metabolism"
+E7139,"Other disorders of fatty-acid metabolism"
+E714,"Disorders of carnitine metabolism"
+E7144,"Other secondary carnitine deficiency"
+E715,"Peroxisomal disorders"
+E7151,"Disorders of peroxisome biogenesis"
+E7152,"X-linked adrenoleukodystrophy"
+E7153,"Other group 2 peroxisomal disorders"
+E7154,"Other peroxisomal disorders"
+E72,"Other disorders of amino-acid metabolism"
+E720,"Disorders of amino-acid transport"
+E721,"Disorders of sulfur-bearing amino-acid metabolism"
+E722,"Disorders of urea cycle metabolism"
+E723,"Disorders of lysine and hydroxylysine metabolism"
+E724,"Disorders of ornithine metabolism"
+E725,"Disorders of glycine metabolism"
+E728,"Other specified disorders of amino-acid metabolism"
+E729,"Disorder of amino-acid metabolism, unspecified"
+E73,"Lactose intolerance"
+E74,"Other disorders of carbohydrate metabolism"
+E740,"Glycogen storage disease"
+E741,"Disorders of fructose metabolism"
+E742,"Disorders of galactose metabolism"
+E743,"Other disorders of intestinal carbohydrate absorption"
+E744,"Disorders of pyruvate metabolism and gluconeogenesis"
+E748,"Other specified disorders of carbohydrate metabolism"
+E749,"Disorder of carbohydrate metabolism, unspecified"
+E75,"Disorders of sphingolipid metabolism and other lipid storage disorders"
+E750,"GM2 gangliosidosis"
+E751,"Other and unspecified gangliosidosis"
+E752,"Other sphingolipidosis"
+E7524,"Niemann-Pick disease"
+E7525,"Metachromatic leukodystrophy"
+E7529,"Other sphingolipidosis"
+E753,"Sphingolipidosis, unspecified"
+E754,"Neuronal ceroid lipofuscinosis"
+E755,"Other lipid storage disorders"
+E756,"Lipid storage disorder, unspecified"
+E76,"Disorders of glycosaminoglycan metabolism"
+E760,"Mucopolysaccharidosis, type I"
+E761,"Mucopolysaccharidosis, type II"
+E762,"Other mucopolysaccharidoses"
+E7621,"Morquio mucopolysaccharidoses"
+E7622,"Sanfilippo mucopolysaccharidoses"
+E7629,"Other mucopolysaccharidoses"
+E763,"Mucopolysaccharidosis, unspecified"
+E768,"Other disorders of glucosaminoglycan metabolism"
+E769,"Glucosaminoglycan metabolism disorder, unspecified"
+E77,"Disorders of glycoprotein metabolism"
+E78,"Disorders of lipoprotein metabolism and other lipidemias"
+E780,"Pure hypercholesterolemia"
+E781,"Pure hyperglyceridemia"
+E782,"Mixed hyperlipidemia"
+E783,"Hyperchylomicronemia"
+E784,"Other hyperlipidemia"
+E785,"Hyperlipidemia, unspecified"
+E786,"Lipoprotein deficiency"
+E787,"Disorders of bile acid and cholesterol metabolism"
+E788,"Other disorders of lipoprotein metabolism"
+E789,"Disorder of lipoprotein metabolism, unspecified"
+E79,"Disorders of purine and pyrimidine metabolism"
+E80,"Disorders of porphyrin and bilirubin metabolism"
+E802,"Other and unspecified porphyria"
+E803,"Defects of catalase and peroxidase"
+E804,"Gilbert syndrome"
+E805,"Crigler-Najjar syndrome"
+E806,"Other disorders of bilirubin metabolism"
+E807,"Disorder of bilirubin metabolism, unspecified"
+E83,"Disorders of mineral metabolism"
+E830,"Disorders of copper metabolism"
+E831,"Disorders of iron metabolism"
+E8311,"Hemochromatosis"
+E8319,"Other disorders of iron metabolism"
+E832,"Disorders of zinc metabolism"
+E833,"Disorders of phosphorus metabolism and phosphatases"
+E834,"Disorders of magnesium metabolism"
+E835,"Disorders of calcium metabolism"
+E838,"Other disorders of mineral metabolism"
+E839,"Disorder of mineral metabolism, unspecified"
+E84,"Cystic fibrosis"
+E841,"Cystic fibrosis with intestinal manifestations"
+E848,"Cystic fibrosis with other manifestations"
+E849,"Cystic fibrosis, unspecified"
+E85,"Amyloidosis"
+E858,"Other amyloidosis"
+E859,"Amyloidosis, unspecified"
+E86,"Volume depletion"
+E87,"Other disorders of fluid, electrolyte and acid-base balance"
+E877,"Fluid overload"
+E878,"Other disorders of electrolyte and fluid balance, not elsewhere classified"
+E88,"Other and unspecified metabolic disorders"
+E880,"Disorders of plasma-protein metabolism, not elsewhere classified"
+E881,"Lipodystrophy, not elsewhere classified"
+E882,"Lipomatosis, not elsewhere classified"
+E883,"Tumor lysis syndrome"
+E884,"Mitochondrial metabolism disorders"
+E888,"Other specified metabolic disorders"
+E889,"Metabolic disorder, unspecified"
+E89,"Postprocedural endocrine and metabolic complications and disorders, not elsewhere classified"
+E894,"Postprocedural ovarian failure"
+E895,"Postprocedural testicular hypofunction"
+E896,"Postprocedural adrenocortical (-medullary) hypofunction"
+E898,"Other postprocedural endocrine and metabolic complications and disorders"
+E8981,"Postprocedural hemorrhage of an endocrine system organ or structure following a procedure"
+E8982,"Postprocedural hematoma and seroma of an endocrine system organ or structure"
+E8989,"Other postprocedural endocrine and metabolic complications and disorders"
+F01,"Vascular dementia"
+F015,"Vascular dementia"
+F02,"Dementia in other diseases classified elsewhere"
+F028,"Dementia in other diseases classified elsewhere"
+F03,"Unspecified dementia"
+F039,"Unspecified dementia"
+F04,"Amnestic disorder due to known physiological condition"
+F05,"Delirium due to known physiological condition"
+F06,"Other mental disorders due to known physiological condition"
+F063,"Mood disorder due to known physiological condition"
+F064,"Anxiety disorder due to known physiological condition"
+F068,"Other specified mental disorders due to known physiological condition"
+F07,"Personality and behavioral disorders due to known physiological condition"
+F078,"Other personality and behavioral disorders due to known physiological condition"
+F079,"Unspecified personality and behavioral disorder due to known physiological condition"
+F09,"Unspecified mental disorder due to known physiological condition"
+F10,"Alcohol related disorders"
+F101,"Alcohol abuse"
+F1012,"Alcohol abuse with intoxication"
+F1014,"Alcohol abuse with alcohol-induced mood disorder"
+F1015,"Alcohol abuse with alcohol-induced psychotic disorder"
+F1018,"Alcohol abuse with other alcohol-induced disorders"
+F1019,"Alcohol abuse with unspecified alcohol-induced disorder"
+F102,"Alcohol dependence"
+F1022,"Alcohol dependence with intoxication"
+F1023,"Alcohol dependence with withdrawal"
+F1024,"Alcohol dependence with alcohol-induced mood disorder"
+F1025,"Alcohol dependence with alcohol-induced psychotic disorder"
+F1026,"Alcohol dependence with alcohol-induced persisting amnestic disorder"
+F1027,"Alcohol dependence with alcohol-induced persisting dementia"
+F1028,"Alcohol dependence with other alcohol-induced disorders"
+F1029,"Alcohol dependence with unspecified alcohol-induced disorder"
+F109,"Alcohol use, unspecified"
+F1092,"Alcohol use, unspecified with intoxication"
+F1094,"Alcohol use, unspecified with alcohol-induced mood disorder"
+F1095,"Alcohol use, unspecified with alcohol-induced psychotic disorder"
+F1096,"Alcohol use, unspecified with alcohol-induced persisting amnestic disorder"
+F1097,"Alcohol use, unspecified with alcohol-induced persisting dementia"
+F1098,"Alcohol use, unspecified with other alcohol-induced disorders"
+F1099,"Alcohol use, unspecified with unspecified alcohol-induced disorder"
+F11,"Opioid related disorders"
+F111,"Opioid abuse"
+F1112,"Opioid abuse with intoxication"
+F1114,"Opioid abuse with opioid-induced mood disorder"
+F1115,"Opioid abuse with opioid-induced psychotic disorder"
+F1118,"Opioid abuse with other opioid-induced disorder"
+F1119,"Opioid abuse with unspecified opioid-induced disorder"
+F112,"Opioid dependence"
+F1122,"Opioid dependence with intoxication"
+F1123,"Opioid dependence with withdrawal"
+F1124,"Opioid dependence with opioid-induced mood disorder"
+F1125,"Opioid dependence with opioid-induced psychotic disorder"
+F1128,"Opioid dependence with other opioid-induced disorder"
+F1129,"Opioid dependence with unspecified opioid-induced disorder"
+F119,"Opioid use, unspecified"
+F1192,"Opioid use, unspecified with intoxication"
+F1193,"Opioid use, unspecified with withdrawal"
+F1194,"Opioid use, unspecified with opioid-induced mood disorder"
+F1195,"Opioid use, unspecified with opioid-induced psychotic disorder"
+F1198,"Opioid use, unspecified with other specified opioid-induced disorder"
+F1199,"Opioid use, unspecified with unspecified opioid-induced disorder"
+F12,"Cannabis related disorders"
+F121,"Cannabis abuse"
+F1212,"Cannabis abuse with intoxication"
+F1215,"Cannabis abuse with psychotic disorder"
+F1218,"Cannabis abuse with other cannabis-induced disorder"
+F1219,"Cannabis abuse with unspecified cannabis-induced disorder"
+F122,"Cannabis dependence"
+F1222,"Cannabis dependence with intoxication"
+F1225,"Cannabis dependence with psychotic disorder"
+F1228,"Cannabis dependence with other cannabis-induced disorder"
+F1229,"Cannabis dependence with unspecified cannabis-induced disorder"
+F129,"Cannabis use, unspecified"
+F1292,"Cannabis use, unspecified with intoxication"
+F1295,"Cannabis use, unspecified with psychotic disorder"
+F1298,"Cannabis use, unspecified with other cannabis-induced disorder"
+F1299,"Cannabis use, unspecified with unspecified cannabis-induced disorder"
+F13,"Sedative, hypnotic, or anxiolytic related disorders"
+F131,"Sedative, hypnotic or anxiolytic-related abuse"
+F1312,"Sedative, hypnotic or anxiolytic abuse with intoxication"
+F1314,"Sedative, hypnotic or anxiolytic abuse with sedative, hypnotic or anxiolytic-induced mood disorder"
+F1315,"Sedative, hypnotic or anxiolytic abuse with sedative, hypnotic or anxiolytic-induced psychotic disorder"
+F1318,"Sedative, hypnotic or anxiolytic abuse with other sedative, hypnotic or anxiolytic-induced disorders"
+F1319,"Sedative, hypnotic or anxiolytic abuse with unspecified sedative, hypnotic or anxiolytic-induced disorder"
+F132,"Sedative, hypnotic or anxiolytic-related dependence"
+F1322,"Sedative, hypnotic or anxiolytic dependence with intoxication"
+F1323,"Sedative, hypnotic or anxiolytic dependence with withdrawal"
+F1324,"Sedative, hypnotic or anxiolytic dependence with sedative, hypnotic or anxiolytic-induced mood disorder"
+F1325,"Sedative, hypnotic or anxiolytic dependence with sedative, hypnotic or anxiolytic-induced psychotic disorder"
+F1326,"Sedative, hypnotic or anxiolytic dependence with sedative, hypnotic or anxiolytic-induced persisting amnestic disorder"
+F1327,"Sedative, hypnotic or anxiolytic dependence with sedative, hypnotic or anxiolytic-induced persisting dementia"
+F1328,"Sedative, hypnotic or anxiolytic dependence with other sedative, hypnotic or anxiolytic-induced disorders"
+F1329,"Sedative, hypnotic or anxiolytic dependence with unspecified sedative, hypnotic or anxiolytic-induced disorder"
+F139,"Sedative, hypnotic or anxiolytic-related use, unspecified"
+F1392,"Sedative, hypnotic or anxiolytic use, unspecified with intoxication"
+F1393,"Sedative, hypnotic or anxiolytic use, unspecified with withdrawal"
+F1394,"Sedative, hypnotic or anxiolytic use, unspecified with sedative, hypnotic or anxiolytic-induced mood disorder"
+F1395,"Sedative, hypnotic or anxiolytic use, unspecified with sedative, hypnotic or anxiolytic-induced psychotic disorder"
+F1396,"Sedative, hypnotic or anxiolytic use, unspecified with sedative, hypnotic or anxiolytic-induced persisting amnestic disorder"
+F1397,"Sedative, hypnotic or anxiolytic use, unspecified with sedative, hypnotic or anxiolytic-induced persisting dementia"
+F1398,"Sedative, hypnotic or anxiolytic use, unspecified with other sedative, hypnotic or anxiolytic-induced disorders"
+F1399,"Sedative, hypnotic or anxiolytic use, unspecified with unspecified sedative, hypnotic or anxiolytic-induced disorder"
+F14,"Cocaine related disorders"
+F141,"Cocaine abuse"
+F1412,"Cocaine abuse with intoxication"
+F1414,"Cocaine abuse with cocaine-induced mood disorder"
+F1415,"Cocaine abuse with cocaine-induced psychotic disorder"
+F1418,"Cocaine abuse with other cocaine-induced disorder"
+F1419,"Cocaine abuse with unspecified cocaine-induced disorder"
+F142,"Cocaine dependence"
+F1422,"Cocaine dependence with intoxication"
+F1423,"Cocaine dependence with withdrawal"
+F1424,"Cocaine dependence with cocaine-induced mood disorder"
+F1425,"Cocaine dependence with cocaine-induced psychotic disorder"
+F1428,"Cocaine dependence with other cocaine-induced disorder"
+F1429,"Cocaine dependence with unspecified cocaine-induced disorder"
+F149,"Cocaine use, unspecified"
+F1492,"Cocaine use, unspecified with intoxication"
+F1494,"Cocaine use, unspecified with cocaine-induced mood disorder"
+F1495,"Cocaine use, unspecified with cocaine-induced psychotic disorder"
+F1498,"Cocaine use, unspecified with other specified cocaine-induced disorder"
+F1499,"Cocaine use, unspecified with unspecified cocaine-induced disorder"
+F15,"Other stimulant related disorders"
+F151,"Other stimulant abuse"
+F1512,"Other stimulant abuse with intoxication"
+F1514,"Other stimulant abuse with stimulant-induced mood disorder"
+F1515,"Other stimulant abuse with stimulant-induced psychotic disorder"
+F1518,"Other stimulant abuse with other stimulant-induced disorder"
+F1519,"Other stimulant abuse with unspecified stimulant-induced disorder"
+F152,"Other stimulant dependence"
+F1522,"Other stimulant dependence with intoxication"
+F1523,"Other stimulant dependence with withdrawal"
+F1524,"Other stimulant dependence with stimulant-induced mood disorder"
+F1525,"Other stimulant dependence with stimulant-induced psychotic disorder"
+F1528,"Other stimulant dependence with other stimulant-induced disorder"
+F1529,"Other stimulant dependence with unspecified stimulant-induced disorder"
+F159,"Other stimulant use, unspecified"
+F1592,"Other stimulant use, unspecified with intoxication"
+F1593,"Other stimulant use, unspecified with withdrawal"
+F1594,"Other stimulant use, unspecified with stimulant-induced mood disorder"
+F1595,"Other stimulant use, unspecified with stimulant-induced psychotic disorder"
+F1598,"Other stimulant use, unspecified with other stimulant-induced disorder"
+F1599,"Other stimulant use, unspecified with unspecified stimulant-induced disorder"
+F16,"Hallucinogen related disorders"
+F161,"Hallucinogen abuse"
+F1612,"Hallucinogen abuse with intoxication"
+F1614,"Hallucinogen abuse with hallucinogen-induced mood disorder"
+F1615,"Hallucinogen abuse with hallucinogen-induced psychotic disorder"
+F1618,"Hallucinogen abuse with other hallucinogen-induced disorder"
+F1619,"Hallucinogen abuse with unspecified hallucinogen-induced disorder"
+F162,"Hallucinogen dependence"
+F1622,"Hallucinogen dependence with intoxication"
+F1624,"Hallucinogen dependence with hallucinogen-induced mood disorder"
+F1625,"Hallucinogen dependence with hallucinogen-induced psychotic disorder"
+F1628,"Hallucinogen dependence with other hallucinogen-induced disorder"
+F1629,"Hallucinogen dependence with unspecified hallucinogen-induced disorder"
+F169,"Hallucinogen use, unspecified"
+F1692,"Hallucinogen use, unspecified with intoxication"
+F1694,"Hallucinogen use, unspecified with hallucinogen-induced mood disorder"
+F1695,"Hallucinogen use, unspecified with hallucinogen-induced psychotic disorder"
+F1698,"Hallucinogen use, unspecified with other specified hallucinogen-induced disorder"
+F1699,"Hallucinogen use, unspecified with unspecified hallucinogen-induced disorder"
+F17,"Nicotine dependence"
+F172,"Nicotine dependence"
+F1720,"Nicotine dependence, unspecified"
+F1721,"Nicotine dependence, cigarettes"
+F1722,"Nicotine dependence, chewing tobacco"
+F1729,"Nicotine dependence, other tobacco product"
+F18,"Inhalant related disorders"
+F181,"Inhalant abuse"
+F1812,"Inhalant abuse with intoxication"
+F1814,"Inhalant abuse with inhalant-induced mood disorder"
+F1815,"Inhalant abuse with inhalant-induced psychotic disorder"
+F1817,"Inhalant abuse with inhalant-induced dementia"
+F1818,"Inhalant abuse with other inhalant-induced disorders"
+F1819,"Inhalant abuse with unspecified inhalant-induced disorder"
+F182,"Inhalant dependence"
+F1822,"Inhalant dependence with intoxication"
+F1824,"Inhalant dependence with inhalant-induced mood disorder"
+F1825,"Inhalant dependence with inhalant-induced psychotic disorder"
+F1827,"Inhalant dependence with inhalant-induced dementia"
+F1828,"Inhalant dependence with other inhalant-induced disorders"
+F1829,"Inhalant dependence with unspecified inhalant-induced disorder"
+F189,"Inhalant use, unspecified"
+F1892,"Inhalant use, unspecified with intoxication"
+F1894,"Inhalant use, unspecified with inhalant-induced mood disorder"
+F1895,"Inhalant use, unspecified with inhalant-induced psychotic disorder"
+F1897,"Inhalant use, unspecified with inhalant-induced persisting dementia"
+F1898,"Inhalant use, unspecified with other inhalant-induced disorders"
+F1899,"Inhalant use, unspecified with unspecified inhalant-induced disorder"
+F19,"Other psychoactive substance related disorders"
+F191,"Other psychoactive substance abuse"
+F1912,"Other psychoactive substance abuse with intoxication"
+F1914,"Other psychoactive substance abuse with psychoactive substance-induced mood disorder"
+F1915,"Other psychoactive substance abuse with psychoactive substance-induced psychotic disorder"
+F1916,"Other psychoactive substance abuse with psychoactive substance-induced persisting amnestic disorder"
+F1917,"Other psychoactive substance abuse with psychoactive substance-induced persisting dementia"
+F1918,"Other psychoactive substance abuse with other psychoactive substance-induced disorders"
+F1919,"Other psychoactive substance abuse with unspecified psychoactive substance-induced disorder"
+F192,"Other psychoactive substance dependence"
+F1922,"Other psychoactive substance dependence with intoxication"
+F1923,"Other psychoactive substance dependence with withdrawal"
+F1924,"Other psychoactive substance dependence with psychoactive substance-induced mood disorder"
+F1925,"Other psychoactive substance dependence with psychoactive substance-induced psychotic disorder"
+F1926,"Other psychoactive substance dependence with psychoactive substance-induced persisting amnestic disorder"
+F1927,"Other psychoactive substance dependence with psychoactive substance-induced persisting dementia"
+F1928,"Other psychoactive substance dependence with other psychoactive substance-induced disorders"
+F1929,"Other psychoactive substance dependence with unspecified psychoactive substance-induced disorder"
+F199,"Other psychoactive substance use, unspecified"
+F1992,"Other psychoactive substance use, unspecified with intoxication"
+F1993,"Other psychoactive substance use, unspecified with withdrawal"
+F1994,"Other psychoactive substance use, unspecified with psychoactive substance-induced mood disorder"
+F1995,"Other psychoactive substance use, unspecified with psychoactive substance-induced psychotic disorder"
+F1996,"Other psychoactive substance use, unspecified with psychoactive substance-induced persisting amnestic disorder"
+F1997,"Other psychoactive substance use, unspecified with psychoactive substance-induced persisting dementia"
+F1998,"Other psychoactive substance use, unspecified with other psychoactive substance-induced disorders"
+F1999,"Other psychoactive substance use, unspecified with unspecified psychoactive substance-induced disorder"
+F20,"Schizophrenia"
+F208,"Other schizophrenia"
+F209,"Schizophrenia, unspecified"
+F21,"Schizotypal disorder"
+F22,"Delusional disorders"
+F23,"Brief psychotic disorder"
+F24,"Shared psychotic disorder"
+F25,"Schizoaffective disorders"
+F28,"Other psychotic disorder not due to a substance or known physiological condition"
+F29,"Unspecified psychosis not due to a substance or known physiological condition"
+F30,"Manic episode"
+F301,"Manic episode without psychotic symptoms"
+F302,"Manic episode, severe with psychotic symptoms"
+F303,"Manic episode in partial remission"
+F304,"Manic episode in full remission"
+F308,"Other manic episodes"
+F309,"Manic episode, unspecified"
+F31,"Bipolar disorder"
+F311,"Bipolar disorder, current episode manic without psychotic features"
+F312,"Bipolar disorder, current episode manic severe with psychotic features"
+F313,"Bipolar disorder, current episode depressed, mild or moderate severity"
+F314,"Bipolar disorder, current episode depressed, severe, without psychotic features"
+F315,"Bipolar disorder, current episode depressed, severe, with psychotic features"
+F316,"Bipolar disorder, current episode mixed"
+F317,"Bipolar disorder, currently in remission"
+F318,"Other bipolar disorders"
+F319,"Bipolar disorder, unspecified"
+F32,"Major depressive disorder, single episode"
+F328,"Other depressive episodes"
+F329,"Major depressive disorder, single episode, unspecified"
+F33,"Major depressive disorder, recurrent"
+F334,"Major depressive disorder, recurrent, in remission"
+F338,"Other recurrent depressive disorders"
+F339,"Major depressive disorder, recurrent, unspecified"
+F34,"Persistent mood [affective] disorders"
+F348,"Other persistent mood [affective] disorders"
+F349,"Persistent mood [affective] disorder, unspecified"
+F39,"Unspecified mood [affective] disorder"
+F40,"Phobic anxiety disorders"
+F400,"Agoraphobia"
+F401,"Social phobias"
+F402,"Specific (isolated) phobias"
+F4021,"Animal type phobia"
+F4022,"Natural environment type phobia"
+F4023,"Blood, injection, injury type phobia"
+F4024,"Situational type phobia"
+F4029,"Other specified phobia"
+F408,"Other phobic anxiety disorders"
+F409,"Phobic anxiety disorder, unspecified"
+F41,"Other anxiety disorders"
+F42,"Obsessive-compulsive disorder"
+F43,"Reaction to severe stress, and adjustment disorders"
+F431,"Post-traumatic stress disorder (PTSD)"
+F432,"Adjustment disorders"
+F438,"Other reactions to severe stress"
+F439,"Reaction to severe stress, unspecified"
+F44,"Dissociative and conversion disorders"
+F448,"Other dissociative and conversion disorders"
+F449,"Dissociative and conversion disorder, unspecified"
+F45,"Somatoform disorders"
+F452,"Hypochondriacal disorders"
+F454,"Pain disorders related to psychological factors"
+F458,"Other somatoform disorders"
+F459,"Somatoform disorder, unspecified"
+F48,"Other nonpsychotic mental disorders"
+F50,"Eating disorders"
+F500,"Anorexia nervosa"
+F502,"Bulimia nervosa"
+F508,"Other eating disorders"
+F509,"Eating disorder, unspecified"
+F51,"Sleep disorders not due to a substance or known physiological condition"
+F510,"Insomnia not due to a substance or known physiological condition"
+F511,"Hypersomnia not due to a substance or known physiological condition"
+F513,"Sleepwalking [somnambulism]"
+F514,"Sleep terrors [night terrors]"
+F515,"Nightmare disorder"
+F518,"Other sleep disorders not due to a substance or known physiological condition"
+F519,"Sleep disorder not due to a substance or known physiological condition, unspecified"
+F52,"Sexual dysfunction not due to a substance or known physiological condition"
+F522,"Sexual arousal disorders"
+F523,"Orgasmic disorder"
+F524,"Premature ejaculation"
+F525,"Vaginismus not due to a substance or known physiological condition"
+F526,"Dyspareunia not due to a substance or known physiological condition"
+F528,"Other sexual dysfunction not due to a substance or known physiological condition"
+F529,"Unspecified sexual dysfunction not due to a substance or known physiological condition"
+F53,"Puerperal psychosis"
+F54,"Psychological and behavioral factors associated with disorders or diseases classified elsewhere"
+F55,"Abuse of non-psychoactive substances"
+F59,"Unspecified behavioral syndromes associated with physiological disturbances and physical factors"
+F60,"Specific personality disorders"
+F608,"Other specific personality disorders"
+F609,"Personality disorder, unspecified"
+F63,"Impulse disorders"
+F638,"Other impulse disorders"
+F639,"Impulse disorder, unspecified"
+F64,"Gender identity disorders"
+F65,"Paraphilias"
+F655,"Sadomasochism"
+F658,"Other paraphilias"
+F659,"Paraphilia, unspecified"
+F66,"Other sexual disorders"
+F68,"Other disorders of adult personality and behavior"
+F681,"Factitious disorder"
+F688,"Other specified disorders of adult personality and behavior"
+F69,"Unspecified disorder of adult personality and behavior"
+F70,"Mild intellectual disabilities"
+F71,"Moderate intellectual disabilities"
+F72,"Severe intellectual disabilities"
+F73,"Profound intellectual disabilities"
+F78,"Other intellectual disabilities"
+F79,"Unspecified intellectual disabilities"
+F80,"Specific developmental disorders of speech and language"
+F808,"Other developmental disorders of speech and language"
+F809,"Developmental disorder of speech and language, unspecified"
+F81,"Specific developmental disorders of scholastic skills"
+F818,"Other developmental disorders of scholastic skills"
+F819,"Developmental disorder of scholastic skills, unspecified"
+F82,"Specific developmental disorder of motor function"
+F84,"Pervasive developmental disorders"
+F88,"Other disorders of psychological development"
+F89,"Unspecified disorder of psychological development"
+F90,"Attention-deficit hyperactivity disorders"
+F91,"Conduct disorders"
+F93,"Emotional disorders with onset specific to childhood"
+F94,"Disorders of social functioning with onset specific to childhood and adolescence"
+F95,"Tic disorder"
+F98,"Other behavioral and emotional disorders with onset usually occurring in childhood and adolescence"
+F982,"Other feeding disorders of infancy and childhood"
+F983,"Pica of infancy and childhood"
+F984,"Stereotyped movement disorders"
+F985,"Adult onset fluency disorder"
+F988,"Other specified behavioral and emotional disorders with onset usually occurring in childhood and adolescence"
+F989,"Unspecified behavioral and emotional disorders with onset usually occurring in childhood and adolescence"
+F99,"Mental disorder, not otherwise specified"
+G00,"Bacterial meningitis, not elsewhere classified"
+G01,"Meningitis in bacterial diseases classified elsewhere"
+G02,"Meningitis in other infectious and parasitic diseases classified elsewhere"
+G03,"Meningitis due to other and unspecified causes"
+G04,"Encephalitis, myelitis and encephalomyelitis"
+G040,"Acute disseminated encephalitis and encephalomyelitis (ADEM)"
+G041,"Tropical spastic paraplegia"
+G042,"Bacterial meningoencephalitis and meningomyelitis, not elsewhere classified"
+G043,"Acute necrotizing hemorrhagic encephalopathy"
+G048,"Other encephalitis, myelitis and encephalomyelitis"
+G049,"Encephalitis, myelitis and encephalomyelitis, unspecified"
+G05,"Encephalitis, myelitis and encephalomyelitis in diseases classified elsewhere"
+G06,"Intracranial and intraspinal abscess and granuloma"
+G07,"Intracranial and intraspinal abscess and granuloma in diseases classified elsewhere"
+G08,"Intracranial and intraspinal phlebitis and thrombophlebitis"
+G09,"Sequelae of inflammatory diseases of central nervous system"
+G10,"Huntington's disease"
+G11,"Hereditary ataxia"
+G12,"Spinal muscular atrophy and related syndromes"
+G122,"Motor neuron disease"
+G128,"Other spinal muscular atrophies and related syndromes"
+G129,"Spinal muscular atrophy, unspecified"
+G13,"Systemic atrophies primarily affecting central nervous system in diseases classified elsewhere"
+G14,"Postpolio syndrome"
+G20,"Parkinson's disease"
+G21,"Secondary parkinsonism"
+G211,"Other drug-induced secondary parkinsonism"
+G212,"Secondary parkinsonism due to other external agents"
+G213,"Postencephalitic parkinsonism"
+G214,"Vascular parkinsonism"
+G218,"Other secondary parkinsonism"
+G219,"Secondary parkinsonism, unspecified"
+G23,"Other degenerative diseases of basal ganglia"
+G24,"Dystonia"
+G240,"Drug induced dystonia"
+G241,"Genetic torsion dystonia"
+G242,"Idiopathic nonfamilial dystonia"
+G243,"Spasmodic torticollis"
+G244,"Idiopathic orofacial dystonia"
+G245,"Blepharospasm"
+G248,"Other dystonia"
+G249,"Dystonia, unspecified"
+G25,"Other extrapyramidal and movement disorders"
+G256,"Drug induced tics and other tics of organic origin"
+G257,"Other and unspecified drug induced movement disorders"
+G258,"Other specified extrapyramidal and movement disorders"
+G259,"Extrapyramidal and movement disorder, unspecified"
+G26,"Extrapyramidal and movement disorders in diseases classified elsewhere"
+G30,"Alzheimer's disease"
+G31,"Other degenerative diseases of nervous system, not elsewhere classified"
+G310,"Frontotemporal dementia"
+G311,"Senile degeneration of brain, not elsewhere classified"
+G312,"Degeneration of nervous system due to alcohol"
+G318,"Other specified degenerative diseases of nervous system"
+G319,"Degenerative disease of nervous system, unspecified"
+G32,"Other degenerative disorders of nervous system in diseases classified elsewhere"
+G328,"Other specified degenerative disorders of nervous system in diseases classified elsewhere"
+G35,"Multiple sclerosis"
+G36,"Other acute disseminated demyelination"
+G37,"Other demyelinating diseases of central nervous system"
+G40,"Epilepsy and recurrent seizures"
+G400,"Localization-related (focal) (partial) idiopathic epilepsy and epileptic syndromes with seizures of localized onset"
+G4000,"Localization-related (focal) (partial) idiopathic epilepsy and epileptic syndromes with seizures of localized onset, not intractable"
+G4001,"Localization-related (focal) (partial) idiopathic epilepsy and epileptic syndromes with seizures of localized onset, intractable"
+G401,"Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with simple partial seizures"
+G4010,"Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with simple partial seizures, not intractable"
+G4011,"Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with simple partial seizures, intractable"
+G402,"Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with complex partial seizures"
+G4020,"Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with complex partial seizures, not intractable"
+G4021,"Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with complex partial seizures, intractable"
+G403,"Generalized idiopathic epilepsy and epileptic syndromes"
+G4030,"Generalized idiopathic epilepsy and epileptic syndromes, not intractable"
+G4031,"Generalized idiopathic epilepsy and epileptic syndromes, intractable"
+G40A,"Absence epileptic syndrome"
+G40A0,"Absence epileptic syndrome, not intractable"
+G40A1,"Absence epileptic syndrome, intractable"
+G40B,"Juvenile myoclonic epilepsy [impulsive petit mal]"
+G40B0,"Juvenile myoclonic epilepsy, not intractable"
+G40B1,"Juvenile myoclonic epilepsy, intractable"
+G404,"Other generalized epilepsy and epileptic syndromes"
+G4040,"Other generalized epilepsy and epileptic syndromes, not intractable"
+G4041,"Other generalized epilepsy and epileptic syndromes, intractable"
+G405,"Epileptic seizures related to external causes"
+G4050,"Epileptic seizures related to external causes, not intractable"
+G408,"Other epilepsy and recurrent seizures"
+G4080,"Other epilepsy"
+G4081,"Lennox-Gastaut syndrome"
+G4082,"Epileptic spasms"
+G4089,"Other seizures"
+G409,"Epilepsy, unspecified"
+G4090,"Epilepsy, unspecified, not intractable"
+G4091,"Epilepsy, unspecified, intractable"
+G43,"Migraine"
+G430,"Migraine without aura"
+G4300,"Migraine without aura, not intractable"
+G4301,"Migraine without aura, intractable"
+G431,"Migraine with aura"
+G4310,"Migraine with aura, not intractable"
+G4311,"Migraine with aura, intractable"
+G434,"Hemiplegic migraine"
+G4340,"Hemiplegic migraine, not intractable"
+G4341,"Hemiplegic migraine, intractable"
+G435,"Persistent migraine aura without cerebral infarction"
+G4350,"Persistent migraine aura without cerebral infarction, not intractable"
+G4351,"Persistent migraine aura without cerebral infarction, intractable"
+G436,"Persistent migraine aura with cerebral infarction"
+G4360,"Persistent migraine aura with cerebral infarction, not intractable"
+G4361,"Persistent migraine aura with cerebral infarction, intractable"
+G437,"Chronic migraine without aura"
+G4370,"Chronic migraine without aura, not intractable"
+G4371,"Chronic migraine without aura, intractable"
+G43A,"Cyclical vomiting"
+G43B,"Ophthalmoplegic migraine"
+G43C,"Periodic headache syndromes in child or adult"
+G43D,"Abdominal migraine"
+G438,"Other migraine"
+G4380,"Other migraine, not intractable"
+G4381,"Other migraine, intractable"
+G4382,"Menstrual migraine, not intractable"
+G4383,"Menstrual migraine, intractable"
+G439,"Migraine, unspecified"
+G4390,"Migraine, unspecified, not intractable"
+G4391,"Migraine, unspecified, intractable"
+G44,"Other headache syndromes"
+G440,"Cluster headaches and other trigeminal autonomic cephalgias (TAC)"
+G4400,"Cluster headache syndrome, unspecified"
+G4401,"Episodic cluster headache"
+G4402,"Chronic cluster headache"
+G4403,"Episodic paroxysmal hemicrania"
+G4404,"Chronic paroxysmal hemicrania"
+G4405,"Short lasting unilateral neuralgiform headache with conjunctival injection and tearing (SUNCT)"
+G4409,"Other trigeminal autonomic cephalgias (TAC)"
+G441,"Vascular headache, not elsewhere classified"
+G442,"Tension-type headache"
+G4420,"Tension-type headache, unspecified"
+G4421,"Episodic tension-type headache"
+G4422,"Chronic tension-type headache"
+G443,"Post-traumatic headache"
+G4430,"Post-traumatic headache, unspecified"
+G4431,"Acute post-traumatic headache"
+G4432,"Chronic post-traumatic headache"
+G444,"Drug-induced headache, not elsewhere classified"
+G445,"Complicated headache syndromes"
+G448,"Other specified headache syndromes"
+G45,"Transient cerebral ischemic attacks and related syndromes"
+G46,"Vascular syndromes of brain in cerebrovascular diseases"
+G47,"Sleep disorders"
+G470,"Insomnia"
+G471,"Hypersomnia"
+G472,"Circadian rhythm sleep disorders"
+G473,"Sleep apnea"
+G474,"Narcolepsy and cataplexy"
+G4741,"Narcolepsy"
+G4742,"Narcolepsy in conditions classified elsewhere"
+G475,"Parasomnia"
+G476,"Sleep related movement disorders"
+G478,"Other sleep disorders"
+G479,"Sleep disorder, unspecified"
+G50,"Disorders of trigeminal nerve"
+G51,"Facial nerve disorders"
+G52,"Disorders of other cranial nerves"
+G53,"Cranial nerve disorders in diseases classified elsewhere"
+G54,"Nerve root and plexus disorders"
+G55,"Nerve root and plexus compressions in diseases classified elsewhere"
+G56,"Mononeuropathies of upper limb"
+G560,"Carpal tunnel syndrome"
+G561,"Other lesions of median nerve"
+G562,"Lesion of ulnar nerve"
+G563,"Lesion of radial nerve"
+G564,"Causalgia of upper limb"
+G568,"Other specified mononeuropathies of upper limb"
+G569,"Unspecified mononeuropathy of upper limb"
+G57,"Mononeuropathies of lower limb"
+G570,"Lesion of sciatic nerve"
+G571,"Meralgia paresthetica"
+G572,"Lesion of femoral nerve"
+G573,"Lesion of lateral popliteal nerve"
+G574,"Lesion of medial popliteal nerve"
+G575,"Tarsal tunnel syndrome"
+G576,"Lesion of plantar nerve"
+G577,"Causalgia of lower limb"
+G578,"Other specified mononeuropathies of lower limb"
+G579,"Unspecified mononeuropathy of lower limb"
+G58,"Other mononeuropathies"
+G59,"Mononeuropathy in diseases classified elsewhere"
+G60,"Hereditary and idiopathic neuropathy"
+G61,"Inflammatory polyneuropathy"
+G618,"Other inflammatory polyneuropathies"
+G619,"Inflammatory polyneuropathy, unspecified"
+G62,"Other and unspecified polyneuropathies"
+G628,"Other specified polyneuropathies"
+G629,"Polyneuropathy, unspecified"
+G63,"Polyneuropathy in diseases classified elsewhere"
+G64,"Other disorders of peripheral nervous system"
+G65,"Sequelae of inflammatory and toxic polyneuropathies"
+G70,"Myasthenia gravis and other myoneural disorders"
+G700,"Myasthenia gravis"
+G701,"Toxic myoneural disorders"
+G702,"Congenital and developmental myasthenia"
+G708,"Other specified myoneural disorders"
+G709,"Myoneural disorder, unspecified"
+G71,"Primary disorders of muscles"
+G711,"Myotonic disorders"
+G712,"Congenital myopathies"
+G713,"Mitochondrial myopathy, not elsewhere classified"
+G718,"Other primary disorders of muscles"
+G719,"Primary disorder of muscle, unspecified"
+G72,"Other and unspecified myopathies"
+G724,"Inflammatory and immune myopathies, not elsewhere classified"
+G728,"Other specified myopathies"
+G729,"Myopathy, unspecified"
+G73,"Disorders of myoneural junction and muscle in diseases classified elsewhere"
+G80,"Cerebral palsy"
+G81,"Hemiplegia and hemiparesis"
+G810,"Flaccid hemiplegia"
+G811,"Spastic hemiplegia"
+G819,"Hemiplegia, unspecified"
+G82,"Paraplegia (paraparesis) and quadriplegia (quadriparesis)"
+G822,"Paraplegia"
+G825,"Quadriplegia"
+G83,"Other paralytic syndromes"
+G831,"Monoplegia of lower limb"
+G832,"Monoplegia of upper limb"
+G833,"Monoplegia, unspecified"
+G834,"Cauda equina syndrome"
+G835,"Locked-in state"
+G838,"Other specified paralytic syndromes"
+G839,"Paralytic syndrome, unspecified"
+G89,"Pain, not elsewhere classified"
+G891,"Acute pain, not elsewhere classified"
+G892,"Chronic pain, not elsewhere classified"
+G893,"Neoplasm related pain (acute) (chronic)"
+G894,"Chronic pain syndrome"
+G90,"Disorders of autonomic nervous system"
+G900,"Idiopathic peripheral autonomic neuropathy"
+G901,"Familial dysautonomia [Riley-Day]"
+G902,"Horner's syndrome"
+G903,"Multi-system degeneration of the autonomic nervous system"
+G904,"Autonomic dysreflexia"
+G905,"Complex regional pain syndrome I (CRPS I)"
+G9051,"Complex regional pain syndrome I of upper limb"
+G9052,"Complex regional pain syndrome I of lower limb"
+G9059,"Complex regional pain syndrome I of other specified site"
+G908,"Other disorders of autonomic nervous system"
+G909,"Disorder of the autonomic nervous system, unspecified"
+G91,"Hydrocephalus"
+G92,"Toxic encephalopathy"
+G93,"Other disorders of brain"
+G934,"Other and unspecified encephalopathy"
+G935,"Compression of brain"
+G936,"Cerebral edema"
+G937,"Reye's syndrome"
+G938,"Other specified disorders of brain"
+G939,"Disorder of brain, unspecified"
+G94,"Other disorders of brain in diseases classified elsewhere"
+G95,"Other and unspecified diseases of spinal cord"
+G951,"Vascular myelopathies"
+G952,"Other and unspecified cord compression"
+G958,"Other specified diseases of spinal cord"
+G959,"Disease of spinal cord, unspecified"
+G96,"Other disorders of central nervous system"
+G961,"Disorders of meninges, not elsewhere classified"
+G968,"Other specified disorders of central nervous system"
+G969,"Disorder of central nervous system, unspecified"
+G97,"Intraoperative and postprocedural complications and disorders of nervous system, not elsewhere classified"
+G973,"Intraoperative hemorrhage and hematoma of a nervous system organ or structure complicating a procedure"
+G974,"Accidental puncture and laceration of a nervous system organ or structure during a procedure"
+G975,"Postprocedural hemorrhage of a nervous system organ or structure following a procedure"
+G976,"Postprocedural hematoma and seroma of a nervous system organ or structure following a procedure"
+G978,"Other intraoperative and postprocedural complications and disorders of nervous system"
+G98,"Other disorders of nervous system not elsewhere classified"
+G99,"Other disorders of nervous system in diseases classified elsewhere"
+H00,"Hordeolum and chalazion"
+H000,"Hordeolum (externum) (internum) of eyelid"
+H0001,"Hordeolum externum"
+H0002,"Hordeolum internum"
+H0003,"Abscess of eyelid"
+H001,"Chalazion"
+H01,"Other inflammation of eyelid"
+H010,"Blepharitis"
+H0100,"Unspecified blepharitis"
+H0101,"Ulcerative blepharitis"
+H0102,"Squamous blepharitis"
+H011,"Noninfectious dermatoses of eyelid"
+H0111,"Allergic dermatitis of eyelid"
+H0112,"Discoid lupus erythematosus of eyelid"
+H0113,"Eczematous dermatitis of eyelid"
+H0114,"Xeroderma of eyelid"
+H018,"Other specified inflammations of eyelid"
+H019,"Unspecified inflammation of eyelid"
+H02,"Other disorders of eyelid"
+H020,"Entropion and trichiasis of eyelid"
+H0200,"Unspecified entropion of eyelid"
+H0201,"Cicatricial entropion of eyelid"
+H0202,"Mechanical entropion of eyelid"
+H0203,"Senile entropion of eyelid"
+H0204,"Spastic entropion of eyelid"
+H0205,"Trichiasis without entropion"
+H021,"Ectropion of eyelid"
+H0210,"Unspecified ectropion of eyelid"
+H0211,"Cicatricial ectropion of eyelid"
+H0212,"Mechanical ectropion of eyelid"
+H0213,"Senile ectropion of eyelid"
+H0214,"Spastic ectropion of eyelid"
+H022,"Lagophthalmos"
+H0220,"Unspecified lagophthalmos"
+H0221,"Cicatricial lagophthalmos"
+H0222,"Mechanical lagophthalmos"
+H0223,"Paralytic lagophthalmos"
+H023,"Blepharochalasis"
+H024,"Ptosis of eyelid"
+H0240,"Unspecified ptosis of eyelid"
+H0241,"Mechanical ptosis of eyelid"
+H0242,"Myogenic ptosis of eyelid"
+H0243,"Paralytic ptosis of eyelid"
+H025,"Other disorders affecting eyelid function"
+H0251,"Abnormal innervation syndrome"
+H0252,"Blepharophimosis"
+H0253,"Eyelid retraction"
+H0259,"Other disorders affecting eyelid function"
+H026,"Xanthelasma of eyelid"
+H027,"Other and unspecified degenerative disorders of eyelid and periocular area"
+H0271,"Chloasma of eyelid and periocular area"
+H0272,"Madarosis of eyelid and periocular area"
+H0273,"Vitiligo of eyelid and periocular area"
+H0279,"Other degenerative disorders of eyelid and periocular area"
+H028,"Other specified disorders of eyelid"
+H0281,"Retained foreign body in eyelid"
+H0282,"Cysts of eyelid"
+H0283,"Dermatochalasis of eyelid"
+H0284,"Edema of eyelid"
+H0285,"Elephantiasis of eyelid"
+H0286,"Hypertrichosis of eyelid"
+H0287,"Vascular anomalies of eyelid"
+H0289,"Other specified disorders of eyelid"
+H029,"Unspecified disorder of eyelid"
+H04,"Disorders of lacrimal system"
+H040,"Dacryoadenitis"
+H0400,"Unspecified dacryoadenitis"
+H0401,"Acute dacryoadenitis"
+H0402,"Chronic dacryoadenitis"
+H0403,"Chronic enlargement of lacrimal gland"
+H041,"Other disorders of lacrimal gland"
+H0411,"Dacryops"
+H0412,"Dry eye syndrome"
+H0413,"Lacrimal cyst"
+H0414,"Primary lacrimal gland atrophy"
+H0415,"Secondary lacrimal gland atrophy"
+H0416,"Lacrimal gland dislocation"
+H0419,"Other specified disorders of lacrimal gland"
+H042,"Epiphora"
+H0420,"Unspecified epiphora"
+H0421,"Epiphora due to excess lacrimation"
+H0422,"Epiphora due to insufficient drainage"
+H043,"Acute and unspecified inflammation of lacrimal passages"
+H0430,"Unspecified dacryocystitis"
+H0431,"Phlegmonous dacryocystitis"
+H0432,"Acute dacryocystitis"
+H0433,"Acute lacrimal canaliculitis"
+H044,"Chronic inflammation of lacrimal passages"
+H0441,"Chronic dacryocystitis"
+H0442,"Chronic lacrimal canaliculitis"
+H0443,"Chronic lacrimal mucocele"
+H045,"Stenosis and insufficiency of lacrimal passages"
+H0451,"Dacryolith"
+H0452,"Eversion of lacrimal punctum"
+H0453,"Neonatal obstruction of nasolacrimal duct"
+H0454,"Stenosis of lacrimal canaliculi"
+H0455,"Acquired stenosis of nasolacrimal duct"
+H0456,"Stenosis of lacrimal punctum"
+H0457,"Stenosis of lacrimal sac"
+H046,"Other changes of lacrimal passages"
+H0461,"Lacrimal fistula"
+H0469,"Other changes of lacrimal passages"
+H048,"Other disorders of lacrimal system"
+H0481,"Granuloma of lacrimal passages"
+H0489,"Other disorders of lacrimal system"
+H049,"Disorder of lacrimal system, unspecified"
+H05,"Disorders of orbit"
+H050,"Acute inflammation of orbit"
+H0501,"Cellulitis of orbit"
+H0502,"Osteomyelitis of orbit"
+H0503,"Periostitis of orbit"
+H0504,"Tenonitis of orbit"
+H051,"Chronic inflammatory disorders of orbit"
+H0511,"Granuloma of orbit"
+H0512,"Orbital myositis"
+H052,"Exophthalmic conditions"
+H0521,"Displacement (lateral) of globe"
+H0522,"Edema of orbit"
+H0523,"Hemorrhage of orbit"
+H0524,"Constant exophthalmos"
+H0525,"Intermittent exophthalmos"
+H0526,"Pulsating exophthalmos"
+H053,"Deformity of orbit"
+H0531,"Atrophy of orbit"
+H0532,"Deformity of orbit due to bone disease"
+H0533,"Deformity of orbit due to trauma or surgery"
+H0534,"Enlargement of orbit"
+H0535,"Exostosis of orbit"
+H054,"Enophthalmos"
+H0540,"Unspecified enophthalmos"
+H0541,"Enophthalmos due to atrophy of orbital tissue"
+H0542,"Enophthalmos due to trauma or surgery"
+H055,"Retained (old) foreign body following penetrating wound of orbit"
+H058,"Other disorders of orbit"
+H0581,"Cyst of orbit"
+H0582,"Myopathy of extraocular muscles"
+H0589,"Other disorders of orbit"
+H059,"Unspecified disorder of orbit"
+H10,"Conjunctivitis"
+H100,"Mucopurulent conjunctivitis"
+H1001,"Acute follicular conjunctivitis"
+H1002,"Other mucopurulent conjunctivitis"
+H101,"Acute atopic conjunctivitis"
+H102,"Other acute conjunctivitis"
+H1021,"Acute toxic conjunctivitis"
+H1022,"Pseudomembranous conjunctivitis"
+H1023,"Serous conjunctivitis, except viral"
+H103,"Unspecified acute conjunctivitis"
+H104,"Chronic conjunctivitis"
+H1040,"Unspecified chronic conjunctivitis"
+H1041,"Chronic giant papillary conjunctivitis"
+H1042,"Simple chronic conjunctivitis"
+H1043,"Chronic follicular conjunctivitis"
+H1044,"Vernal conjunctivitis"
+H1045,"Other chronic allergic conjunctivitis"
+H105,"Blepharoconjunctivitis"
+H1050,"Unspecified blepharoconjunctivitis"
+H1051,"Ligneous conjunctivitis"
+H1052,"Angular blepharoconjunctivitis"
+H1053,"Contact blepharoconjunctivitis"
+H108,"Other conjunctivitis"
+H1081,"Pingueculitis"
+H1089,"Other conjunctivitis"
+H109,"Unspecified conjunctivitis"
+H11,"Other disorders of conjunctiva"
+H110,"Pterygium of eye"
+H1100,"Unspecified pterygium of eye"
+H1101,"Amyloid pterygium"
+H1102,"Central pterygium of eye"
+H1103,"Double pterygium of eye"
+H1104,"Peripheral pterygium of eye, stationary"
+H1105,"Peripheral pterygium of eye, progressive"
+H1106,"Recurrent pterygium of eye"
+H111,"Conjunctival degenerations and deposits"
+H1111,"Conjunctival deposits"
+H1112,"Conjunctival concretions"
+H1113,"Conjunctival pigmentations"
+H1114,"Conjunctival xerosis, unspecified"
+H1115,"Pinguecula"
+H112,"Conjunctival scars"
+H1121,"Conjunctival adhesions and strands (localized)"
+H1122,"Conjunctival granuloma"
+H1123,"Symblepharon"
+H1124,"Scarring of conjunctiva"
+H113,"Conjunctival hemorrhage"
+H114,"Other conjunctival vascular disorders and cysts"
+H1141,"Vascular abnormalities of conjunctiva"
+H1142,"Conjunctival edema"
+H1143,"Conjunctival hyperemia"
+H1144,"Conjunctival cysts"
+H118,"Other specified disorders of conjunctiva"
+H1181,"Pseudopterygium of conjunctiva"
+H1182,"Conjunctivochalasis"
+H1189,"Other specified disorders of conjunctiva"
+H119,"Unspecified disorder of conjunctiva"
+H15,"Disorders of sclera"
+H150,"Scleritis"
+H1500,"Unspecified scleritis"
+H1501,"Anterior scleritis"
+H1502,"Brawny scleritis"
+H1503,"Posterior scleritis"
+H1504,"Scleritis with corneal involvement"
+H1505,"Scleromalacia perforans"
+H1509,"Other scleritis"
+H151,"Episcleritis"
+H1510,"Unspecified episcleritis"
+H1511,"Episcleritis periodica fugax"
+H1512,"Nodular episcleritis"
+H158,"Other disorders of sclera"
+H1581,"Equatorial staphyloma"
+H1582,"Localized anterior staphyloma"
+H1583,"Staphyloma posticum"
+H1584,"Scleral ectasia"
+H1585,"Ring staphyloma"
+H1589,"Other disorders of sclera"
+H159,"Unspecified disorder of sclera"
+H16,"Keratitis"
+H160,"Corneal ulcer"
+H1600,"Unspecified corneal ulcer"
+H1601,"Central corneal ulcer"
+H1602,"Ring corneal ulcer"
+H1603,"Corneal ulcer with hypopyon"
+H1604,"Marginal corneal ulcer"
+H1605,"Mooren's corneal ulcer"
+H1606,"Mycotic corneal ulcer"
+H1607,"Perforated corneal ulcer"
+H161,"Other and unspecified superficial keratitis without conjunctivitis"
+H1610,"Unspecified superficial keratitis"
+H1611,"Macular keratitis"
+H1612,"Filamentary keratitis"
+H1613,"Photokeratitis"
+H1614,"Punctate keratitis"
+H162,"Keratoconjunctivitis"
+H1620,"Unspecified keratoconjunctivitis"
+H1621,"Exposure keratoconjunctivitis"
+H1622,"Keratoconjunctivitis sicca, not specified as Sjogren's"
+H1623,"Neurotrophic keratoconjunctivitis"
+H1624,"Ophthalmia nodosa"
+H1625,"Phlyctenular keratoconjunctivitis"
+H1626,"Vernal keratoconjunctivitis, with limbar and corneal involvement"
+H1629,"Other keratoconjunctivitis"
+H163,"Interstitial and deep keratitis"
+H1630,"Unspecified interstitial keratitis"
+H1631,"Corneal abscess"
+H1632,"Diffuse interstitial keratitis"
+H1633,"Sclerosing keratitis"
+H1639,"Other interstitial and deep keratitis"
+H164,"Corneal neovascularization"
+H1640,"Unspecified corneal neovascularization"
+H1641,"Ghost vessels (corneal)"
+H1642,"Pannus (corneal)"
+H1643,"Localized vascularization of cornea"
+H1644,"Deep vascularization of cornea"
+H168,"Other keratitis"
+H169,"Unspecified keratitis"
+H17,"Corneal scars and opacities"
+H170,"Adherent leukoma"
+H171,"Central corneal opacity"
+H178,"Other corneal scars and opacities"
+H1781,"Minor opacity of cornea"
+H1782,"Peripheral opacity of cornea"
+H1789,"Other corneal scars and opacities"
+H179,"Unspecified corneal scar and opacity"
+H18,"Other disorders of cornea"
+H180,"Corneal pigmentations and deposits"
+H1800,"Unspecified corneal deposit"
+H1801,"Anterior corneal pigmentations"
+H1802,"Argentous corneal deposits"
+H1803,"Corneal deposits in metabolic disorders"
+H1804,"Kayser-Fleischer ring"
+H1805,"Posterior corneal pigmentations"
+H1806,"Stromal corneal pigmentations"
+H181,"Bullous keratopathy"
+H182,"Other and unspecified corneal edema"
+H1821,"Corneal edema secondary to contact lens"
+H1822,"Idiopathic corneal edema"
+H1823,"Secondary corneal edema"
+H183,"Changes of corneal membranes"
+H1831,"Folds and rupture in Bowman's membrane"
+H1832,"Folds in Descemet's membrane"
+H1833,"Rupture in Descemet's membrane"
+H184,"Corneal degeneration"
+H1841,"Arcus senilis"
+H1842,"Band keratopathy"
+H1843,"Other calcerous corneal degeneration"
+H1844,"Keratomalacia"
+H1845,"Nodular corneal degeneration"
+H1846,"Peripheral corneal degeneration"
+H1849,"Other corneal degeneration"
+H185,"Hereditary corneal dystrophies"
+H186,"Keratoconus"
+H1860,"Keratoconus, unspecified"
+H1861,"Keratoconus, stable"
+H1862,"Keratoconus, unstable"
+H187,"Other and unspecified corneal deformities"
+H1871,"Corneal ectasia"
+H1872,"Corneal staphyloma"
+H1873,"Descemetocele"
+H1879,"Other corneal deformities"
+H188,"Other specified disorders of cornea"
+H1881,"Anesthesia and hypoesthesia of cornea"
+H1882,"Corneal disorder due to contact lens"
+H1883,"Recurrent erosion of cornea"
+H1889,"Other specified disorders of cornea"
+H189,"Unspecified disorder of cornea"
+H20,"Iridocyclitis"
+H200,"Acute and subacute iridocyclitis"
+H2001,"Primary iridocyclitis"
+H2002,"Recurrent acute iridocyclitis"
+H2003,"Secondary infectious iridocyclitis"
+H2004,"Secondary noninfectious iridocyclitis"
+H2005,"Hypopyon"
+H201,"Chronic iridocyclitis"
+H202,"Lens-induced iridocyclitis"
+H208,"Other iridocyclitis"
+H2081,"Fuchs' heterochromic cyclitis"
+H2082,"Vogt-Koyanagi syndrome"
+H209,"Unspecified iridocyclitis"
+H21,"Other disorders of iris and ciliary body"
+H210,"Hyphema"
+H211,"Other vascular disorders of iris and ciliary body"
+H211X,"Other vascular disorders of iris and ciliary body"
+H212,"Degeneration of iris and ciliary body"
+H2121,"Degeneration of chamber angle"
+H2122,"Degeneration of ciliary body"
+H2123,"Degeneration of iris (pigmentary)"
+H2124,"Degeneration of pupillary margin"
+H2125,"Iridoschisis"
+H2126,"Iris atrophy (essential) (progressive)"
+H2127,"Miotic pupillary cyst"
+H2129,"Other iris atrophy"
+H213,"Cyst of iris, ciliary body and anterior chamber"
+H2130,"Idiopathic cysts of iris, ciliary body or anterior chamber"
+H2131,"Exudative cysts of iris or anterior chamber"
+H2132,"Implantation cysts of iris, ciliary body or anterior chamber"
+H2133,"Parasitic cyst of iris, ciliary body or anterior chamber"
+H2134,"Primary cyst of pars plana"
+H2135,"Exudative cyst of pars plana"
+H214,"Pupillary membranes"
+H215,"Other and unspecified adhesions and disruptions of iris and ciliary body"
+H2150,"Unspecified adhesions of iris"
+H2151,"Anterior synechiae (iris)"
+H2152,"Goniosynechiae"
+H2153,"Iridodialysis"
+H2154,"Posterior synechiae (iris)"
+H2155,"Recession of chamber angle"
+H2156,"Pupillary abnormalities"
+H218,"Other specified disorders of iris and ciliary body"
+H219,"Unspecified disorder of iris and ciliary body"
+H22,"Disorders of iris and ciliary body in diseases classified elsewhere"
+H25,"Age-related cataract"
+H250,"Age-related incipient cataract"
+H2501,"Cortical age-related cataract"
+H2503,"Anterior subcapsular polar age-related cataract"
+H2504,"Posterior subcapsular polar age-related cataract"
+H2509,"Other age-related incipient cataract"
+H251,"Age-related nuclear cataract"
+H252,"Age-related cataract, morgagnian type"
+H258,"Other age-related cataract"
+H2581,"Combined forms of age-related cataract"
+H2589,"Other age-related cataract"
+H259,"Unspecified age-related cataract"
+H26,"Other cataract"
+H260,"Infantile and juvenile cataract"
+H2600,"Unspecified infantile and juvenile cataract"
+H2601,"Infantile and juvenile cortical, lamellar, or zonular cataract"
+H2603,"Infantile and juvenile nuclear cataract"
+H2604,"Anterior subcapsular polar infantile and juvenile cataract"
+H2605,"Posterior subcapsular polar infantile and juvenile cataract"
+H2606,"Combined forms of infantile and juvenile cataract"
+H2609,"Other infantile and juvenile cataract"
+H261,"Traumatic cataract"
+H2610,"Unspecified traumatic cataract"
+H2611,"Localized traumatic opacities"
+H2612,"Partially resolved traumatic cataract"
+H2613,"Total traumatic cataract"
+H262,"Complicated cataract"
+H2621,"Cataract with neovascularization"
+H2622,"Cataract secondary to ocular disorders (degenerative) (inflammatory)"
+H2623,"Glaucomatous flecks (subcapsular)"
+H263,"Drug-induced cataract"
+H264,"Secondary cataract"
+H2641,"Soemmering's ring"
+H2649,"Other secondary cataract"
+H268,"Other specified cataract"
+H269,"Unspecified cataract"
+H27,"Other disorders of lens"
+H270,"Aphakia"
+H271,"Dislocation of lens"
+H2711,"Subluxation of lens"
+H2712,"Anterior dislocation of lens"
+H2713,"Posterior dislocation of lens"
+H278,"Other specified disorders of lens"
+H279,"Unspecified disorder of lens"
+H28,"Cataract in diseases classified elsewhere"
+H30,"Chorioretinal inflammation"
+H300,"Focal chorioretinal inflammation"
+H3000,"Unspecified focal chorioretinal inflammation"
+H3001,"Focal chorioretinal inflammation, juxtapapillary"
+H3002,"Focal chorioretinal inflammation of posterior pole"
+H3003,"Focal chorioretinal inflammation, peripheral"
+H3004,"Focal chorioretinal inflammation, macular or paramacular"
+H301,"Disseminated chorioretinal inflammation"
+H3010,"Unspecified disseminated chorioretinal inflammation"
+H3011,"Disseminated chorioretinal inflammation of posterior pole"
+H3012,"Disseminated chorioretinal inflammation, peripheral"
+H3013,"Disseminated chorioretinal inflammation, generalized"
+H3014,"Acute posterior multifocal placoid pigment epitheliopathy"
+H302,"Posterior cyclitis"
+H308,"Other chorioretinal inflammations"
+H3081,"Harada's disease"
+H3089,"Other chorioretinal inflammations"
+H309,"Unspecified chorioretinal inflammation"
+H31,"Other disorders of choroid"
+H310,"Chorioretinal scars"
+H3100,"Unspecified chorioretinal scars"
+H3101,"Macula scars of posterior pole (postinflammatory) (post-traumatic)"
+H3102,"Solar retinopathy"
+H3109,"Other chorioretinal scars"
+H311,"Choroidal degeneration"
+H3110,"Unspecified choroidal degeneration"
+H3111,"Age-related choroidal atrophy"
+H3112,"Diffuse secondary atrophy of choroid"
+H312,"Hereditary choroidal dystrophy"
+H313,"Choroidal hemorrhage and rupture"
+H3130,"Unspecified choroidal hemorrhage"
+H3131,"Expulsive choroidal hemorrhage"
+H3132,"Choroidal rupture"
+H314,"Choroidal detachment"
+H3140,"Unspecified choroidal detachment"
+H3141,"Hemorrhagic choroidal detachment"
+H3142,"Serous choroidal detachment"
+H318,"Other specified disorders of choroid"
+H319,"Unspecified disorder of choroid"
+H32,"Chorioretinal disorders in diseases classified elsewhere"
+H33,"Retinal detachments and breaks"
+H330,"Retinal detachment with retinal break"
+H3300,"Unspecified retinal detachment with retinal break"
+H3301,"Retinal detachment with single break"
+H3302,"Retinal detachment with multiple breaks"
+H3303,"Retinal detachment with giant retinal tear"
+H3304,"Retinal detachment with retinal dialysis"
+H3305,"Total retinal detachment"
+H331,"Retinoschisis and retinal cysts"
+H3310,"Unspecified retinoschisis"
+H3311,"Cyst of ora serrata"
+H3312,"Parasitic cyst of retina"
+H3319,"Other retinoschisis and retinal cysts"
+H332,"Serous retinal detachment"
+H333,"Retinal breaks without detachment"
+H3330,"Unspecified retinal break"
+H3331,"Horseshoe tear of retina without detachment"
+H3332,"Round hole of retina without detachment"
+H3333,"Multiple defects of retina without detachment"
+H334,"Traction detachment of retina"
+H338,"Other retinal detachments"
+H34,"Retinal vascular occlusions"
+H340,"Transient retinal artery occlusion"
+H341,"Central retinal artery occlusion"
+H342,"Other retinal artery occlusions"
+H3421,"Partial retinal artery occlusion"
+H3423,"Retinal artery branch occlusion"
+H348,"Other retinal vascular occlusions"
+H3481,"Central retinal vein occlusion"
+H34811,"Central retinal vein occlusion, right eye"
+H34812,"Central retinal vein occlusion, left eye"
+H34813,"Central retinal vein occlusion, bilateral"
+H34819,"Central retinal vein occlusion, unspecified eye"
+H3482,"Venous engorgement"
+H3483,"Tributary (branch) retinal vein occlusion"
+H34831,"Tributary (branch) retinal vein occlusion, right eye"
+H34832,"Tributary (branch) retinal vein occlusion, left eye"
+H34833,"Tributary (branch) retinal vein occlusion, bilateral"
+H34839,"Tributary (branch) retinal vein occlusion, unspecified eye"
+H349,"Unspecified retinal vascular occlusion"
+H35,"Other retinal disorders"
+H350,"Background retinopathy and retinal vascular changes"
+H3501,"Changes in retinal vascular appearance"
+H3502,"Exudative retinopathy"
+H3503,"Hypertensive retinopathy"
+H3504,"Retinal micro-aneurysms, unspecified"
+H3505,"Retinal neovascularization, unspecified"
+H3506,"Retinal vasculitis"
+H3507,"Retinal telangiectasis"
+H3509,"Other intraretinal microvascular abnormalities"
+H351,"Retinopathy of prematurity"
+H3510,"Retinopathy of prematurity, unspecified"
+H3511,"Retinopathy of prematurity, stage 0"
+H3512,"Retinopathy of prematurity, stage 1"
+H3513,"Retinopathy of prematurity, stage 2"
+H3514,"Retinopathy of prematurity, stage 3"
+H3515,"Retinopathy of prematurity, stage 4"
+H3516,"Retinopathy of prematurity, stage 5"
+H3517,"Retrolental fibroplasia"
+H352,"Other non-diabetic proliferative retinopathy"
+H353,"Degeneration of macula and posterior pole"
+H3531,"Nonexudative age-related macular degeneration"
+H35311,"Nonexudative age-related macular degeneration, right eye"
+H35312,"Nonexudative age-related macular degeneration, left eye"
+H35313,"Nonexudative age-related macular degeneration, bilateral"
+H35319,"Nonexudative age-related macular degeneration, unspecified eye"
+H3532,"Exudative age-related macular degeneration"
+H35321,"Exudative age-related macular degeneration, right eye"
+H35322,"Exudative age-related macular degeneration, left eye"
+H35323,"Exudative age-related macular degeneration, bilateral"
+H35329,"Exudative age-related macular degeneration, unspecified eye"
+H3533,"Angioid streaks of macula"
+H3534,"Macular cyst, hole, or pseudohole"
+H3535,"Cystoid macular degeneration"
+H3536,"Drusen (degenerative) of macula"
+H3537,"Puckering of macula"
+H3538,"Toxic maculopathy"
+H354,"Peripheral retinal degeneration"
+H3541,"Lattice degeneration of retina"
+H3542,"Microcystoid degeneration of retina"
+H3543,"Paving stone degeneration of retina"
+H3544,"Age-related reticular degeneration of retina"
+H3545,"Secondary pigmentary degeneration"
+H3546,"Secondary vitreoretinal degeneration"
+H355,"Hereditary retinal dystrophy"
+H356,"Retinal hemorrhage"
+H357,"Separation of retinal layers"
+H3571,"Central serous chorioretinopathy"
+H3572,"Serous detachment of retinal pigment epithelium"
+H3573,"Hemorrhagic detachment of retinal pigment epithelium"
+H358,"Other specified retinal disorders"
+H359,"Unspecified retinal disorder"
+H36,"Retinal disorders in diseases classified elsewhere"
+H40,"Glaucoma"
+H400,"Glaucoma suspect"
+H4000,"Preglaucoma, unspecified"
+H4001,"Open angle with borderline findings, low risk"
+H4002,"Open angle with borderline findings, high risk"
+H4003,"Anatomical narrow angle"
+H4004,"Steroid responder"
+H4005,"Ocular hypertension"
+H4006,"Primary angle closure without glaucoma damage"
+H401,"Open-angle glaucoma"
+H4010,"Unspecified open-angle glaucoma"
+H4011,"Primary open-angle glaucoma"
+H40111,"Primary open-angle glaucoma, right eye"
+H40112,"Primary open-angle glaucoma, left eye"
+H40113,"Primary open-angle glaucoma, bilateral"
+H40119,"Primary open-angle glaucoma, unspecified eye"
+H4012,"Low-tension glaucoma"
+H40121,"Low-tension glaucoma, right eye"
+H40122,"Low-tension glaucoma, left eye"
+H40123,"Low-tension glaucoma, bilateral"
+H40129,"Low-tension glaucoma, unspecified eye"
+H4013,"Pigmentary glaucoma"
+H40131,"Pigmentary glaucoma, right eye"
+H40132,"Pigmentary glaucoma, left eye"
+H40133,"Pigmentary glaucoma, bilateral"
+H40139,"Pigmentary glaucoma, unspecified eye"
+H4014,"Capsular glaucoma with pseudoexfoliation of lens"
+H40141,"Capsular glaucoma with pseudoexfoliation of lens, right eye"
+H40142,"Capsular glaucoma with pseudoexfoliation of lens, left eye"
+H40143,"Capsular glaucoma with pseudoexfoliation of lens, bilateral"
+H40149,"Capsular glaucoma with pseudoexfoliation of lens, unspecified eye"
+H4015,"Residual stage of open-angle glaucoma"
+H402,"Primary angle-closure glaucoma"
+H4020,"Unspecified primary angle-closure glaucoma"
+H4021,"Acute angle-closure glaucoma"
+H4022,"Chronic angle-closure glaucoma"
+H40221,"Chronic angle-closure glaucoma, right eye"
+H40222,"Chronic angle-closure glaucoma, left eye"
+H40223,"Chronic angle-closure glaucoma, bilateral"
+H40229,"Chronic angle-closure glaucoma, unspecified eye"
+H4023,"Intermittent angle-closure glaucoma"
+H4024,"Residual stage of angle-closure glaucoma"
+H403,"Glaucoma secondary to eye trauma"
+H4030,"Glaucoma secondary to eye trauma, unspecified eye"
+H4031,"Glaucoma secondary to eye trauma, right eye"
+H4032,"Glaucoma secondary to eye trauma, left eye"
+H4033,"Glaucoma secondary to eye trauma, bilateral"
+H404,"Glaucoma secondary to eye inflammation"
+H4040,"Glaucoma secondary to eye inflammation, unspecified eye"
+H4041,"Glaucoma secondary to eye inflammation, right eye"
+H4042,"Glaucoma secondary to eye inflammation, left eye"
+H4043,"Glaucoma secondary to eye inflammation, bilateral"
+H405,"Glaucoma secondary to other eye disorders"
+H4050,"Glaucoma secondary to other eye disorders, unspecified eye"
+H4051,"Glaucoma secondary to other eye disorders, right eye"
+H4052,"Glaucoma secondary to other eye disorders, left eye"
+H4053,"Glaucoma secondary to other eye disorders, bilateral"
+H406,"Glaucoma secondary to drugs"
+H4060,"Glaucoma secondary to drugs, unspecified eye"
+H4061,"Glaucoma secondary to drugs, right eye"
+H4062,"Glaucoma secondary to drugs, left eye"
+H4063,"Glaucoma secondary to drugs, bilateral"
+H408,"Other glaucoma"
+H4081,"Glaucoma with increased episcleral venous pressure"
+H4082,"Hypersecretion glaucoma"
+H4083,"Aqueous misdirection"
+H4089,"Other specified glaucoma"
+H409,"Unspecified glaucoma"
+H42,"Glaucoma in diseases classified elsewhere"
+H43,"Disorders of vitreous body"
+H430,"Vitreous prolapse"
+H431,"Vitreous hemorrhage"
+H432,"Crystalline deposits in vitreous body"
+H433,"Other vitreous opacities"
+H4331,"Vitreous membranes and strands"
+H4339,"Other vitreous opacities"
+H438,"Other disorders of vitreous body"
+H4381,"Vitreous degeneration"
+H4382,"Vitreomacular adhesion"
+H4389,"Other disorders of vitreous body"
+H439,"Unspecified disorder of vitreous body"
+H44,"Disorders of globe"
+H440,"Purulent endophthalmitis"
+H4400,"Unspecified purulent endophthalmitis"
+H4401,"Panophthalmitis (acute)"
+H4402,"Vitreous abscess (chronic)"
+H441,"Other endophthalmitis"
+H4411,"Panuveitis"
+H4412,"Parasitic endophthalmitis, unspecified"
+H4413,"Sympathetic uveitis"
+H4419,"Other endophthalmitis"
+H442,"Degenerative myopia"
+H442A,"Degenerative myopia with choroidal neovascularization"
+H442B,"Degenerative myopia with macular hole"
+H442C,"Degenerative myopia with retinal detachment"
+H442D,"Degenerative myopia with foveoschisis"
+H442E,"Degenerative myopia with other maculopathy"
+H443,"Other and unspecified degenerative disorders of globe"
+H4431,"Chalcosis"
+H4432,"Siderosis of eye"
+H4439,"Other degenerative disorders of globe"
+H444,"Hypotony of eye"
+H4441,"Flat anterior chamber hypotony of eye"
+H4442,"Hypotony of eye due to ocular fistula"
+H4443,"Hypotony of eye due to other ocular disorders"
+H4444,"Primary hypotony of eye"
+H445,"Degenerated conditions of globe"
+H4451,"Absolute glaucoma"
+H4452,"Atrophy of globe"
+H4453,"Leucocoria"
+H446,"Retained (old) intraocular foreign body, magnetic"
+H4460,"Unspecified retained (old) intraocular foreign body, magnetic"
+H4461,"Retained (old) magnetic foreign body in anterior chamber"
+H4462,"Retained (old) magnetic foreign body in iris or ciliary body"
+H4463,"Retained (old) magnetic foreign body in lens"
+H4464,"Retained (old) magnetic foreign body in posterior wall of globe"
+H4465,"Retained (old) magnetic foreign body in vitreous body"
+H4469,"Retained (old) intraocular foreign body, magnetic, in other or multiple sites"
+H447,"Retained (old) intraocular foreign body, nonmagnetic"
+H4470,"Unspecified retained (old) intraocular foreign body, nonmagnetic"
+H4471,"Retained (nonmagnetic) (old) foreign body in anterior chamber"
+H4472,"Retained (nonmagnetic) (old) foreign body in iris or ciliary body"
+H4473,"Retained (nonmagnetic) (old) foreign body in lens"
+H4474,"Retained (nonmagnetic) (old) foreign body in posterior wall of globe"
+H4475,"Retained (nonmagnetic) (old) foreign body in vitreous body"
+H4479,"Retained (old) intraocular foreign body, nonmagnetic, in other or multiple sites"
+H448,"Other disorders of globe"
+H4481,"Hemophthalmos"
+H4482,"Luxation of globe"
+H4489,"Other disorders of globe"
+H449,"Unspecified disorder of globe"
+H46,"Optic neuritis"
+H460,"Optic papillitis"
+H461,"Retrobulbar neuritis"
+H462,"Nutritional optic neuropathy"
+H463,"Toxic optic neuropathy"
+H468,"Other optic neuritis"
+H469,"Unspecified optic neuritis"
+H47,"Other disorders of optic [2nd] nerve and visual pathways"
+H470,"Disorders of optic nerve, not elsewhere classified"
+H4701,"Ischemic optic neuropathy"
+H4702,"Hemorrhage in optic nerve sheath"
+H4703,"Optic nerve hypoplasia"
+H4709,"Other disorders of optic nerve, not elsewhere classified"
+H471,"Papilledema"
+H4714,"Foster-Kennedy syndrome"
+H472,"Optic atrophy"
+H4721,"Primary optic atrophy"
+H4722,"Hereditary optic atrophy"
+H4723,"Glaucomatous optic atrophy"
+H4729,"Other optic atrophy"
+H473,"Other disorders of optic disc"
+H4731,"Coloboma of optic disc"
+H4732,"Drusen of optic disc"
+H4733,"Pseudopapilledema of optic disc"
+H4739,"Other disorders of optic disc"
+H474,"Disorders of optic chiasm"
+H475,"Disorders of other visual pathways"
+H4751,"Disorders of visual pathways in (due to) inflammatory disorders"
+H4752,"Disorders of visual pathways in (due to) neoplasm"
+H4753,"Disorders of visual pathways in (due to) vascular disorders"
+H476,"Disorders of visual cortex"
+H4761,"Cortical blindness"
+H4762,"Disorders of visual cortex in (due to) inflammatory disorders"
+H4763,"Disorders of visual cortex in (due to) neoplasm"
+H4764,"Disorders of visual cortex in (due to) vascular disorders"
+H479,"Unspecified disorder of visual pathways"
+H49,"Paralytic strabismus"
+H490,"Third [oculomotor] nerve palsy"
+H491,"Fourth [trochlear] nerve palsy"
+H492,"Sixth [abducent] nerve palsy"
+H493,"Total (external) ophthalmoplegia"
+H494,"Progressive external ophthalmoplegia"
+H498,"Other paralytic strabismus"
+H4981,"Kearns-Sayre syndrome"
+H4988,"Other paralytic strabismus"
+H499,"Unspecified paralytic strabismus"
+H50,"Other strabismus"
+H500,"Esotropia"
+H5001,"Monocular esotropia"
+H5002,"Monocular esotropia with A pattern"
+H5003,"Monocular esotropia with V pattern"
+H5004,"Monocular esotropia with other noncomitancies"
+H5005,"Alternating esotropia"
+H5006,"Alternating esotropia with A pattern"
+H5007,"Alternating esotropia with V pattern"
+H5008,"Alternating esotropia with other noncomitancies"
+H501,"Exotropia"
+H5011,"Monocular exotropia"
+H5012,"Monocular exotropia with A pattern"
+H5013,"Monocular exotropia with V pattern"
+H5014,"Monocular exotropia with other noncomitancies"
+H5015,"Alternating exotropia"
+H5016,"Alternating exotropia with A pattern"
+H5017,"Alternating exotropia with V pattern"
+H5018,"Alternating exotropia with other noncomitancies"
+H502,"Vertical strabismus"
+H503,"Intermittent heterotropia"
+H5031,"Intermittent monocular esotropia"
+H5032,"Intermittent alternating esotropia"
+H5033,"Intermittent monocular exotropia"
+H5034,"Intermittent alternating exotropia"
+H504,"Other and unspecified heterotropia"
+H5041,"Cyclotropia"
+H5042,"Monofixation syndrome"
+H5043,"Accommodative component in esotropia"
+H505,"Heterophoria"
+H506,"Mechanical strabismus"
+H5061,"Brown's sheath syndrome"
+H5069,"Other mechanical strabismus"
+H508,"Other specified strabismus"
+H5081,"Duane's syndrome"
+H5089,"Other specified strabismus"
+H509,"Unspecified strabismus"
+H51,"Other disorders of binocular movement"
+H511,"Convergence insufficiency and excess"
+H512,"Internuclear ophthalmoplegia"
+H518,"Other specified disorders of binocular movement"
+H519,"Unspecified disorder of binocular movement"
+H52,"Disorders of refraction and accommodation"
+H520,"Hypermetropia"
+H521,"Myopia"
+H522,"Astigmatism"
+H5220,"Unspecified astigmatism"
+H5221,"Irregular astigmatism"
+H5222,"Regular astigmatism"
+H523,"Anisometropia and aniseikonia"
+H524,"Presbyopia"
+H525,"Disorders of accommodation"
+H5251,"Internal ophthalmoplegia (complete) (total)"
+H5252,"Paresis of accommodation"
+H5253,"Spasm of accommodation"
+H526,"Other disorders of refraction"
+H527,"Unspecified disorder of refraction"
+H53,"Visual disturbances"
+H530,"Amblyopia ex anopsia"
+H5300,"Unspecified amblyopia"
+H5301,"Deprivation amblyopia"
+H5302,"Refractive amblyopia"
+H5303,"Strabismic amblyopia"
+H5304,"Amblyopia suspect"
+H531,"Subjective visual disturbances"
+H5312,"Transient visual loss"
+H5313,"Sudden visual loss"
+H5314,"Visual discomfort"
+H5315,"Visual distortions of shape and size"
+H5316,"Psychophysical visual disturbances"
+H5319,"Other subjective visual disturbances"
+H532,"Diplopia"
+H533,"Other and unspecified disorders of binocular vision"
+H534,"Visual field defects"
+H5341,"Scotoma involving central area"
+H5342,"Scotoma of blind spot area"
+H5343,"Sector or arcuate defects"
+H5345,"Other localized visual field defect"
+H5346,"Homonymous bilateral field defects"
+H5347,"Heteronymous bilateral field defects"
+H5348,"Generalized contraction of visual field"
+H535,"Color vision deficiencies"
+H536,"Night blindness"
+H537,"Vision sensitivity deficiencies"
+H538,"Other visual disturbances"
+H539,"Unspecified visual disturbance"
+H54,"Blindness and low vision"
+H540,"Blindness, both eyes"
+H540X,"Blindness, both eyes, different category levels"
+H540X3,"Blindness right eye, category 3"
+H540X4,"Blindness right eye, category 4"
+H540X5,"Blindness right eye, category 5"
+H541,"Blindness, one eye, low vision other eye"
+H5411,"Blindness, right eye, low vision left eye"
+H54113,"Blindness right eye category 3, low vision left eye"
+H54114,"Blindness right eye category 4, low vision left eye"
+H54115,"Blindness right eye category 5, low vision left eye"
+H5412,"Blindness, left eye, low vision right eye"
+H54121,"Low vision right eye category 1, blindness left eye"
+H54122,"Low vision right eye category 2, blindness left eye"
+H542,"Low vision, both eyes"
+H542X,"Low vision, both eyes, different category levels"
+H542X1,"Low vision, right eye, category 1"
+H542X2,"Low vision, right eye, category 2"
+H543,"Unqualified visual loss, both eyes"
+H544,"Blindness, one eye"
+H5441,"Blindness, right eye, normal vision left eye"
+H54413,"Blindness, right eye, category 3"
+H54414,"Blindness, right eye, category  4"
+H54415,"Blindness, right eye, category 5"
+H5442,"Blindness, left eye, normal vision right eye"
+H5442A,"Blindness, left eye, category 3-5"
+H545,"Low vision, one eye"
+H5451,"Low vision, right eye, normal vision left eye"
+H54511,"Low vision, right eye, category 1-2"
+H54512A,"Low vision right eye category 2, normal vision left eye"
+H5452,"Low vision, left eye, normal vision right eye"
+H5452A,"Low vision, left eye, category 1-2"
+H546,"Unqualified visual loss, one eye"
+H547,"Unspecified visual loss"
+H548,"Legal blindness, as defined in USA"
+H55,"Nystagmus and other irregular eye movements"
+H550,"Nystagmus"
+H558,"Other irregular eye movements"
+H57,"Other disorders of eye and adnexa"
+H570,"Anomalies of pupillary function"
+H5705,"Tonic pupil"
+H5709,"Other anomalies of pupillary function"
+H571,"Ocular pain"
+H578,"Other specified disorders of eye and adnexa"
+H579,"Unspecified disorder of eye and adnexa"
+H59,"Intraoperative and postprocedural complications and disorders of eye and adnexa, not elsewhere classified"
+H590,"Disorders of the eye following cataract surgery"
+H5901,"Keratopathy (bullous aphakic) following cataract surgery"
+H5902,"Cataract (lens) fragments in eye following cataract surgery"
+H5903,"Cystoid macular edema following cataract surgery"
+H5909,"Other disorders of the eye following cataract surgery"
+H591,"Intraoperative hemorrhage and hematoma of eye and adnexa complicating a procedure"
+H5911,"Intraoperative hemorrhage and hematoma of eye and adnexa complicating an ophthalmic procedure"
+H5912,"Intraoperative hemorrhage and hematoma of eye and adnexa complicating other procedure"
+H592,"Accidental puncture and laceration of eye and adnexa during a procedure"
+H5921,"Accidental puncture and laceration of eye and adnexa during an ophthalmic procedure"
+H5922,"Accidental puncture and laceration of eye and adnexa during other procedure"
+H593,"Postprocedural hemorrhage, hematoma, and seroma of eye and adnexa following a procedure"
+H5931,"Postprocedural hemorrhage of eye and adnexa following an ophthalmic procedure"
+H5932,"Postprocedural hemorrhage of eye and adnexa following other procedure"
+H5933,"Postprocedural hematoma of eye and adnexa following an ophthalmic procedure"
+H5934,"Postprocedural hematoma of eye and adnexa following other procedure"
+H5935,"Postprocedural seroma of eye and adnexa following an ophthalmic procedure"
+H5936,"Postprocedural seroma of eye and adnexa following other procedure"
+H594,"Inflammation (infection) of postprocedural bleb"
+H598,"Other intraoperative and postprocedural complications and disorders of eye and adnexa, not elsewhere classified"
+H5981,"Chorioretinal scars after surgery for detachment"
+H5988,"Other intraoperative complications of eye and adnexa, not elsewhere classified"
+H5989,"Other postprocedural complications and disorders of eye and adnexa, not elsewhere classified"
+H60,"Otitis externa"
+H600,"Abscess of external ear"
+H601,"Cellulitis of external ear"
+H602,"Malignant otitis externa"
+H603,"Other infective otitis externa"
+H6031,"Diffuse otitis externa"
+H6032,"Hemorrhagic otitis externa"
+H6033,"Swimmer's ear"
+H6039,"Other infective otitis externa"
+H604,"Cholesteatoma of external ear"
+H605,"Acute noninfective otitis externa"
+H6050,"Unspecified acute noninfective otitis externa"
+H6051,"Acute actinic otitis externa"
+H6052,"Acute chemical otitis externa"
+H6053,"Acute contact otitis externa"
+H6054,"Acute eczematoid otitis externa"
+H6055,"Acute reactive otitis externa"
+H6059,"Other noninfective acute otitis externa"
+H606,"Unspecified chronic otitis externa"
+H608,"Other otitis externa"
+H608X,"Other otitis externa"
+H609,"Unspecified otitis externa"
+H61,"Other disorders of external ear"
+H610,"Chondritis and perichondritis of external ear"
+H6100,"Unspecified perichondritis of external ear"
+H6101,"Acute perichondritis of external ear"
+H6102,"Chronic perichondritis of external ear"
+H6103,"Chondritis of external ear"
+H611,"Noninfective disorders of pinna"
+H6110,"Unspecified noninfective disorders of pinna"
+H6111,"Acquired deformity of pinna"
+H6112,"Hematoma of pinna"
+H6119,"Other noninfective disorders of pinna"
+H612,"Impacted cerumen"
+H613,"Acquired stenosis of external ear canal"
+H6130,"Acquired stenosis of external ear canal, unspecified"
+H6131,"Acquired stenosis of external ear canal secondary to trauma"
+H6132,"Acquired stenosis of external ear canal secondary to inflammation and infection"
+H6139,"Other acquired stenosis of external ear canal"
+H618,"Other specified disorders of external ear"
+H6181,"Exostosis of external canal"
+H6189,"Other specified disorders of external ear"
+H619,"Disorder of external ear, unspecified"
+H62,"Disorders of external ear in diseases classified elsewhere"
+H624,"Otitis externa in other diseases classified elsewhere"
+H628,"Other disorders of external ear in diseases classified elsewhere"
+H628X,"Other disorders of external ear in diseases classified elsewhere"
+H65,"Nonsuppurative otitis media"
+H650,"Acute serous otitis media"
+H651,"Other acute nonsuppurative otitis media"
+H6511,"Acute and subacute allergic otitis media (mucoid) (sanguinous) (serous)"
+H6519,"Other acute nonsuppurative otitis media"
+H652,"Chronic serous otitis media"
+H653,"Chronic mucoid otitis media"
+H654,"Other chronic nonsuppurative otitis media"
+H6541,"Chronic allergic otitis media"
+H6549,"Other chronic nonsuppurative otitis media"
+H659,"Unspecified nonsuppurative otitis media"
+H66,"Suppurative and unspecified otitis media"
+H660,"Acute suppurative otitis media"
+H6600,"Acute suppurative otitis media without spontaneous rupture of ear drum"
+H6601,"Acute suppurative otitis media with spontaneous rupture of ear drum"
+H661,"Chronic tubotympanic suppurative otitis media"
+H662,"Chronic atticoantral suppurative otitis media"
+H663,"Other chronic suppurative otitis media"
+H663X,"Other chronic suppurative otitis media"
+H664,"Suppurative otitis media, unspecified"
+H669,"Otitis media, unspecified"
+H67,"Otitis media in diseases classified elsewhere"
+H68,"Eustachian salpingitis and obstruction"
+H680,"Eustachian salpingitis"
+H6800,"Unspecified Eustachian salpingitis"
+H6801,"Acute Eustachian salpingitis"
+H6802,"Chronic Eustachian salpingitis"
+H681,"Obstruction of Eustachian tube"
+H6810,"Unspecified obstruction of Eustachian tube"
+H6811,"Osseous obstruction of Eustachian tube"
+H6812,"Intrinsic cartilagenous obstruction of Eustachian tube"
+H6813,"Extrinsic cartilagenous obstruction of Eustachian tube"
+H69,"Other and unspecified disorders of Eustachian tube"
+H690,"Patulous Eustachian tube"
+H698,"Other specified disorders of Eustachian tube"
+H699,"Unspecified Eustachian tube disorder"
+H70,"Mastoiditis and related conditions"
+H700,"Acute mastoiditis"
+H7000,"Acute mastoiditis without complications"
+H7001,"Subperiosteal abscess of mastoid"
+H7009,"Acute mastoiditis with other complications"
+H701,"Chronic mastoiditis"
+H702,"Petrositis"
+H7020,"Unspecified petrositis"
+H7021,"Acute petrositis"
+H7022,"Chronic petrositis"
+H708,"Other mastoiditis and related conditions"
+H7081,"Postauricular fistula"
+H7089,"Other mastoiditis and related conditions"
+H709,"Unspecified mastoiditis"
+H71,"Cholesteatoma of middle ear"
+H710,"Cholesteatoma of attic"
+H711,"Cholesteatoma of tympanum"
+H712,"Cholesteatoma of mastoid"
+H713,"Diffuse cholesteatosis"
+H719,"Unspecified cholesteatoma"
+H72,"Perforation of tympanic membrane"
+H720,"Central perforation of tympanic membrane"
+H721,"Attic perforation of tympanic membrane"
+H722,"Other marginal perforations of tympanic membrane"
+H722X,"Other marginal perforations of tympanic membrane"
+H728,"Other perforations of tympanic membrane"
+H7281,"Multiple perforations of tympanic membrane"
+H7282,"Total perforations of tympanic membrane"
+H729,"Unspecified perforation of tympanic membrane"
+H73,"Other disorders of tympanic membrane"
+H730,"Acute myringitis"
+H7300,"Unspecified acute myringitis"
+H7301,"Bullous myringitis"
+H7309,"Other acute myringitis"
+H731,"Chronic myringitis"
+H732,"Unspecified myringitis"
+H738,"Other specified disorders of tympanic membrane"
+H7381,"Atrophic flaccid tympanic membrane"
+H7382,"Atrophic nonflaccid tympanic membrane"
+H7389,"Other specified disorders of tympanic membrane"
+H739,"Unspecified disorder of tympanic membrane"
+H74,"Other disorders of middle ear mastoid"
+H740,"Tympanosclerosis"
+H741,"Adhesive middle ear disease"
+H742,"Discontinuity and dislocation of ear ossicles"
+H743,"Other acquired abnormalities of ear ossicles"
+H7431,"Ankylosis of ear ossicles"
+H7432,"Partial loss of ear ossicles"
+H7439,"Other acquired abnormalities of ear ossicles"
+H744,"Polyp of middle ear"
+H748,"Other specified disorders of middle ear and mastoid"
+H748X,"Other specified disorders of middle ear and mastoid"
+H749,"Unspecified disorder of middle ear and mastoid"
+H75,"Other disorders of middle ear and mastoid in diseases classified elsewhere"
+H750,"Mastoiditis in infectious and parasitic diseases classified elsewhere"
+H758,"Other specified disorders of middle ear and mastoid in diseases classified elsewhere"
+H80,"Otosclerosis"
+H800,"Otosclerosis involving oval window, nonobliterative"
+H801,"Otosclerosis involving oval window, obliterative"
+H802,"Cochlear otosclerosis"
+H808,"Other otosclerosis"
+H809,"Unspecified otosclerosis"
+H81,"Disorders of vestibular function"
+H810,"Meniere's disease"
+H811,"Benign paroxysmal vertigo"
+H812,"Vestibular neuronitis"
+H813,"Other peripheral vertigo"
+H8131,"Aural vertigo"
+H8139,"Other peripheral vertigo"
+H814,"Vertigo of central origin"
+H818,"Other disorders of vestibular function"
+H818X,"Other disorders of vestibular function"
+H819,"Unspecified disorder of vestibular function"
+H82,"Vertiginous syndromes in diseases classified elsewhere"
+H83,"Other diseases of inner ear"
+H830,"Labyrinthitis"
+H831,"Labyrinthine fistula"
+H832,"Labyrinthine dysfunction"
+H832X,"Labyrinthine dysfunction"
+H833,"Noise effects on inner ear"
+H833X,"Noise effects on inner ear"
+H838,"Other specified diseases of inner ear"
+H838X,"Other specified diseases of inner ear"
+H839,"Unspecified disease of inner ear"
+H90,"Conductive and sensorineural hearing loss"
+H901,"Conductive hearing loss, unilateral with unrestricted hearing on the contralateral side"
+H902,"Conductive hearing loss, unspecified"
+H903,"Sensorineural hearing loss, bilateral"
+H904,"Sensorineural hearing loss, unilateral with unrestricted hearing on the contralateral side"
+H905,"Unspecified sensorineural hearing loss"
+H906,"Mixed conductive and sensorineural hearing loss, bilateral"
+H907,"Mixed conductive and sensorineural hearing loss, unilateral with unrestricted hearing on the contralateral side"
+H908,"Mixed conductive and sensorineural hearing loss, unspecified"
+H90A,"Conductive and sensorineural hearing loss with restricted hearing on the contralateral side"
+H90A1,"Conductive hearing loss, unilateral, with restricted hearing on the contralateral side"
+H90A2,"Sensorineural hearing loss, unilateral, with restricted hearing on the contralateral side"
+H90A3,"Mixed conductive and sensorineural hearing loss, unilateral with restricted hearing on the contralateral side"
+H91,"Other and unspecified hearing loss"
+H910,"Ototoxic hearing loss"
+H911,"Presbycusis"
+H912,"Sudden idiopathic hearing loss"
+H913,"Deaf nonspeaking, not elsewhere classified"
+H918,"Other specified hearing loss"
+H918X,"Other specified hearing loss"
+H919,"Unspecified hearing loss"
+H92,"Otalgia and effusion of ear"
+H920,"Otalgia"
+H921,"Otorrhea"
+H922,"Otorrhagia"
+H93,"Other disorders of ear, not elsewhere classified"
+H930,"Degenerative and vascular disorders of ear"
+H9301,"Transient ischemic deafness"
+H9309,"Unspecified degenerative and vascular disorders of ear"
+H931,"Tinnitus"
+H93A,"Pulsatile tinnitus"
+H932,"Other abnormal auditory perceptions"
+H9321,"Auditory recruitment"
+H9322,"Diplacusis"
+H9323,"Hyperacusis"
+H9324,"Temporary auditory threshold shift"
+H9325,"Central auditory processing disorder"
+H9329,"Other abnormal auditory perceptions"
+H933,"Disorders of acoustic nerve"
+H933X,"Disorders of acoustic nerve"
+H938,"Other specified disorders of ear"
+H938X,"Other specified disorders of ear"
+H939,"Unspecified disorder of ear"
+H94,"Other disorders of ear in diseases classified elsewhere"
+H940,"Acoustic neuritis in infectious and parasitic diseases classified elsewhere"
+H948,"Other specified disorders of ear in diseases classified elsewhere"
+H95,"Intraoperative and postprocedural complications and disorders of ear and mastoid process, not elsewhere classified"
+H950,"Recurrent cholesteatoma of postmastoidectomy cavity"
+H951,"Other disorders of ear and mastoid process following mastoidectomy"
+H9511,"Chronic inflammation of postmastoidectomy cavity"
+H9512,"Granulation of postmastoidectomy cavity"
+H9513,"Mucosal cyst of postmastoidectomy cavity"
+H9519,"Other disorders following mastoidectomy"
+H952,"Intraoperative hemorrhage and hematoma of ear and mastoid process complicating a procedure"
+H953,"Accidental puncture and laceration of ear and mastoid process during a procedure"
+H954,"Postprocedural hemorrhage of ear and mastoid process following a procedure"
+H955,"Postprocedural hematoma and seroma of ear and mastoid process following a procedure"
+H958,"Other intraoperative and postprocedural complications and disorders of the ear and mastoid process, not elsewhere classified"
+H9581,"Postprocedural stenosis of external ear canal"
+H9588,"Other intraoperative complications and disorders of the ear and mastoid process, not elsewhere classified"
+H9589,"Other postprocedural complications and disorders of the ear and mastoid process, not elsewhere classified"
+I00,"Rheumatic fever without heart involvement"
+I01,"Rheumatic fever with heart involvement"
+I02,"Rheumatic chorea"
+I05,"Rheumatic mitral valve diseases"
+I06,"Rheumatic aortic valve diseases"
+I07,"Rheumatic tricuspid valve diseases"
+I08,"Multiple valve diseases"
+I09,"Other rheumatic heart diseases"
+I098,"Other specified rheumatic heart diseases"
+I099,"Rheumatic heart disease, unspecified"
+I10,"Essential (primary) hypertension"
+I11,"Hypertensive heart disease"
+I12,"Hypertensive chronic kidney disease"
+I13,"Hypertensive heart and chronic kidney disease"
+I131,"Hypertensive heart and chronic kidney disease without heart failure"
+I132,"Hypertensive heart and chronic kidney disease with heart failure and with stage 5 chronic kidney disease, or end stage renal disease"
+I15,"Secondary hypertension"
+I16,"Hypertensive crisis"
+I20,"Angina pectoris"
+I21,"Acute myocardial infarction"
+I210,"ST elevation (STEMI) myocardial infarction of anterior wall"
+I211,"ST elevation (STEMI) myocardial infarction of inferior wall"
+I212,"ST elevation (STEMI) myocardial infarction of other sites"
+I213,"ST elevation (STEMI) myocardial infarction of unspecified site"
+I214,"Non-ST elevation (NSTEMI) myocardial infarction"
+I219,"Acute myocardial infarction, unspecified"
+I21A,"Other type of myocardial infarction"
+I22,"Subsequent ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction"
+I23,"Certain current complications following ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction (within the 28 day period)"
+I24,"Other acute ischemic heart diseases"
+I25,"Chronic ischemic heart disease"
+I251,"Atherosclerotic heart disease of native coronary artery"
+I2511,"Atherosclerotic heart disease of native coronary artery with angina pectoris"
+I252,"Old myocardial infarction"
+I253,"Aneurysm of heart"
+I254,"Coronary artery aneurysm and dissection"
+I255,"Ischemic cardiomyopathy"
+I256,"Silent myocardial ischemia"
+I257,"Atherosclerosis of coronary artery bypass graft(s) and coronary artery of transplanted heart with angina pectoris"
+I2570,"Atherosclerosis of coronary artery bypass graft(s), unspecified, with angina pectoris"
+I2571,"Atherosclerosis of autologous vein coronary artery bypass graft(s) with angina pectoris"
+I2572,"Atherosclerosis of autologous artery coronary artery bypass graft(s) with angina pectoris"
+I2573,"Atherosclerosis of nonautologous biological coronary artery bypass graft(s) with angina pectoris"
+I2575,"Atherosclerosis of native coronary artery of transplanted heart with angina pectoris"
+I2576,"Atherosclerosis of bypass graft of coronary artery of transplanted heart with angina pectoris"
+I2579,"Atherosclerosis of other coronary artery bypass graft(s) with angina pectoris"
+I258,"Other forms of chronic ischemic heart disease"
+I2581,"Atherosclerosis of other coronary vessels without angina pectoris"
+I2582,"Chronic total occlusion of coronary artery"
+I2583,"Coronary atherosclerosis due to lipid rich plaque"
+I2584,"Coronary atherosclerosis due to calcified coronary lesion"
+I2589,"Other forms of chronic ischemic heart disease"
+I259,"Chronic ischemic heart disease, unspecified"
+I26,"Pulmonary embolism"
+I260,"Pulmonary embolism with acute cor pulmonale"
+I269,"Pulmonary embolism without acute cor pulmonale"
+I27,"Other pulmonary heart diseases"
+I272,"Other secondary pulmonary hypertension"
+I278,"Other specified pulmonary heart diseases"
+I279,"Pulmonary heart disease, unspecified"
+I28,"Other diseases of pulmonary vessels"
+I30,"Acute pericarditis"
+I31,"Other diseases of pericardium"
+I32,"Pericarditis in diseases classified elsewhere"
+I33,"Acute and subacute endocarditis"
+I34,"Nonrheumatic mitral valve disorders"
+I35,"Nonrheumatic aortic valve disorders"
+I36,"Nonrheumatic tricuspid valve disorders"
+I37,"Nonrheumatic pulmonary valve disorders"
+I38,"Endocarditis, valve unspecified"
+I39,"Endocarditis and heart valve disorders in diseases classified elsewhere"
+I40,"Acute myocarditis"
+I41,"Myocarditis in diseases classified elsewhere"
+I42,"Cardiomyopathy"
+I43,"Cardiomyopathy in diseases classified elsewhere"
+I44,"Atrioventricular and left bundle-branch block"
+I443,"Other and unspecified atrioventricular block"
+I444,"Left anterior fascicular block"
+I445,"Left posterior fascicular block"
+I446,"Other and unspecified fascicular block"
+I447,"Left bundle-branch block, unspecified"
+I45,"Other conduction disorders"
+I451,"Other and unspecified right bundle-branch block"
+I452,"Bifascicular block"
+I453,"Trifascicular block"
+I454,"Nonspecific intraventricular block"
+I455,"Other specified heart block"
+I456,"Pre-excitation syndrome"
+I458,"Other specified conduction disorders"
+I459,"Conduction disorder, unspecified"
+I46,"Cardiac arrest"
+I47,"Paroxysmal tachycardia"
+I48,"Atrial fibrillation and flutter"
+I489,"Unspecified atrial fibrillation and atrial flutter"
+I49,"Other cardiac arrhythmias"
+I490,"Ventricular fibrillation and flutter"
+I491,"Atrial premature depolarization"
+I492,"Junctional premature depolarization"
+I493,"Ventricular premature depolarization"
+I494,"Other and unspecified premature depolarization"
+I495,"Sick sinus syndrome"
+I498,"Other specified cardiac arrhythmias"
+I499,"Cardiac arrhythmia, unspecified"
+I50,"Heart failure"
+I502,"Systolic (congestive) heart failure"
+I503,"Diastolic (congestive) heart failure"
+I504,"Combined systolic (congestive) and diastolic (congestive) heart failure"
+I508,"Other heart failure"
+I5081,"Right heart failure"
+I5082,"Biventricular heart failure"
+I5083,"High output heart failure"
+I5084,"End stage heart failure"
+I5089,"Other heart failure"
+I509,"Heart failure, unspecified"
+I51,"Complications and ill-defined descriptions of heart disease"
+I518,"Other ill-defined heart diseases"
+I519,"Heart disease, unspecified"
+I52,"Other heart disorders in diseases classified elsewhere"
+I60,"Nontraumatic subarachnoid hemorrhage"
+I600,"Nontraumatic subarachnoid hemorrhage from carotid siphon and bifurcation"
+I601,"Nontraumatic subarachnoid hemorrhage from middle cerebral artery"
+I602,"Nontraumatic subarachnoid hemorrhage from anterior communicating artery"
+I603,"Nontraumatic subarachnoid hemorrhage from posterior communicating artery"
+I604,"Nontraumatic subarachnoid hemorrhage from basilar artery"
+I605,"Nontraumatic subarachnoid hemorrhage from vertebral artery"
+I606,"Nontraumatic subarachnoid hemorrhage from other intracranial arteries"
+I607,"Nontraumatic subarachnoid hemorrhage from unspecified intracranial artery"
+I608,"Other nontraumatic subarachnoid hemorrhage"
+I609,"Nontraumatic subarachnoid hemorrhage, unspecified"
+I61,"Nontraumatic intracerebral hemorrhage"
+I62,"Other and unspecified nontraumatic intracranial hemorrhage"
+I620,"Nontraumatic subdural hemorrhage"
+I621,"Nontraumatic extradural hemorrhage"
+I629,"Nontraumatic intracranial hemorrhage, unspecified"
+I63,"Cerebral infarction"
+I630,"Cerebral infarction due to thrombosis of precerebral arteries"
+I6301,"Cerebral infarction due to thrombosis of vertebral artery"
+I6302,"Cerebral infarction due to thrombosis of basilar artery"
+I6303,"Cerebral infarction due to thrombosis of carotid artery"
+I6309,"Cerebral infarction due to thrombosis of other precerebral artery"
+I631,"Cerebral infarction due to embolism of precerebral arteries"
+I6311,"Cerebral infarction due to embolism of vertebral artery"
+I6312,"Cerebral infarction due to embolism of basilar artery"
+I6313,"Cerebral infarction due to embolism of carotid artery"
+I6319,"Cerebral infarction due to embolism of other precerebral artery"
+I632,"Cerebral infarction due to unspecified occlusion or stenosis of precerebral arteries"
+I6321,"Cerebral infarction due to unspecified occlusion or stenosis of vertebral arteries"
+I6322,"Cerebral infarction due to unspecified occlusion or stenosis of basilar artery"
+I6323,"Cerebral infarction due to unspecified occlusion or stenosis of carotid arteries"
+I6329,"Cerebral infarction due to unspecified occlusion or stenosis of other precerebral arteries"
+I633,"Cerebral infarction due to thrombosis of cerebral arteries"
+I6331,"Cerebral infarction due to thrombosis of middle cerebral artery"
+I6332,"Cerebral infarction due to thrombosis of anterior cerebral artery"
+I6333,"Cerebral infarction due to thrombosis of posterior cerebral artery"
+I6334,"Cerebral infarction due to thrombosis of cerebellar artery"
+I6339,"Cerebral infarction due to thrombosis of other cerebral artery"
+I634,"Cerebral infarction due to embolism of cerebral arteries"
+I6341,"Cerebral infarction due to embolism of middle cerebral artery"
+I6342,"Cerebral infarction due to embolism of anterior cerebral artery"
+I6343,"Cerebral infarction due to embolism of posterior cerebral artery"
+I6344,"Cerebral infarction due to embolism of cerebellar artery"
+I6349,"Cerebral infarction due to embolism of other cerebral artery"
+I635,"Cerebral infarction due to unspecified occlusion or stenosis of cerebral arteries"
+I6351,"Cerebral infarction due to unspecified occlusion or stenosis of middle cerebral artery"
+I6352,"Cerebral infarction due to unspecified occlusion or stenosis of anterior cerebral artery"
+I6353,"Cerebral infarction due to unspecified occlusion or stenosis of posterior cerebral artery"
+I6354,"Cerebral infarction due to unspecified occlusion or stenosis of cerebellar artery"
+I6359,"Cerebral infarction due to unspecified occlusion or stenosis of other cerebral artery"
+I636,"Cerebral infarction due to cerebral venous thrombosis, nonpyogenic"
+I638,"Other cerebral infarction"
+I639,"Cerebral infarction, unspecified"
+I65,"Occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction"
+I650,"Occlusion and stenosis of vertebral artery"
+I651,"Occlusion and stenosis of basilar artery"
+I652,"Occlusion and stenosis of carotid artery"
+I658,"Occlusion and stenosis of other precerebral arteries"
+I659,"Occlusion and stenosis of unspecified precerebral artery"
+I66,"Occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction"
+I660,"Occlusion and stenosis of middle cerebral artery"
+I661,"Occlusion and stenosis of anterior cerebral artery"
+I662,"Occlusion and stenosis of posterior cerebral artery"
+I663,"Occlusion and stenosis of cerebellar arteries"
+I668,"Occlusion and stenosis of other cerebral arteries"
+I669,"Occlusion and stenosis of unspecified cerebral artery"
+I67,"Other cerebrovascular diseases"
+I678,"Other specified cerebrovascular diseases"
+I6784,"Cerebral vasospasm and vasoconstriction"
+I6789,"Other cerebrovascular disease"
+I679,"Cerebrovascular disease, unspecified"
+I68,"Cerebrovascular disorders in diseases classified elsewhere"
+I69,"Sequelae of cerebrovascular disease"
+I690,"Sequelae of nontraumatic subarachnoid hemorrhage"
+I6901,"Cognitive deficits following nontraumatic subarachnoid hemorrhage"
+I6902,"Speech and language deficits following nontraumatic subarachnoid hemorrhage"
+I6903,"Monoplegia of upper limb following nontraumatic subarachnoid hemorrhage"
+I6904,"Monoplegia of lower limb following nontraumatic subarachnoid hemorrhage"
+I6905,"Hemiplegia and hemiparesis following nontraumatic subarachnoid hemorrhage"
+I6906,"Other paralytic syndrome following nontraumatic subarachnoid hemorrhage"
+I6909,"Other sequelae of nontraumatic subarachnoid hemorrhage"
+I691,"Sequelae of nontraumatic intracerebral hemorrhage"
+I6911,"Cognitive deficits following nontraumatic intracerebral hemorrhage"
+I6912,"Speech and language deficits following nontraumatic intracerebral hemorrhage"
+I6913,"Monoplegia of upper limb following nontraumatic intracerebral hemorrhage"
+I6914,"Monoplegia of lower limb following nontraumatic intracerebral hemorrhage"
+I6915,"Hemiplegia and hemiparesis following nontraumatic intracerebral hemorrhage"
+I6916,"Other paralytic syndrome following nontraumatic intracerebral hemorrhage"
+I6919,"Other sequelae of nontraumatic intracerebral hemorrhage"
+I692,"Sequelae of other nontraumatic intracranial hemorrhage"
+I6921,"Cognitive deficits following other nontraumatic intracranial hemorrhage"
+I6922,"Speech and language deficits following other nontraumatic intracranial hemorrhage"
+I6923,"Monoplegia of upper limb following other nontraumatic intracranial hemorrhage"
+I6924,"Monoplegia of lower limb following other nontraumatic intracranial hemorrhage"
+I6925,"Hemiplegia and hemiparesis following other nontraumatic intracranial hemorrhage"
+I6926,"Other paralytic syndrome following other nontraumatic intracranial hemorrhage"
+I6929,"Other sequelae of other nontraumatic intracranial hemorrhage"
+I693,"Sequelae of cerebral infarction"
+I6931,"Cognitive deficits following cerebral infarction"
+I6932,"Speech and language deficits following cerebral infarction"
+I6933,"Monoplegia of upper limb following cerebral infarction"
+I6934,"Monoplegia of lower limb following cerebral infarction"
+I6935,"Hemiplegia and hemiparesis following cerebral infarction"
+I6936,"Other paralytic syndrome following cerebral infarction"
+I6939,"Other sequelae of cerebral infarction"
+I698,"Sequelae of other cerebrovascular diseases"
+I6981,"Cognitive deficits following other cerebrovascular disease"
+I6982,"Speech and language deficits following other cerebrovascular disease"
+I6983,"Monoplegia of upper limb following other cerebrovascular disease"
+I6984,"Monoplegia of lower limb following other cerebrovascular disease"
+I6985,"Hemiplegia and hemiparesis following other cerebrovascular disease"
+I6986,"Other paralytic syndrome following other cerebrovascular disease"
+I6989,"Other sequelae of other cerebrovascular disease"
+I699,"Sequelae of unspecified cerebrovascular diseases"
+I6991,"Cognitive deficits following unspecified cerebrovascular disease"
+I6992,"Speech and language deficits following unspecified cerebrovascular disease"
+I6993,"Monoplegia of upper limb following unspecified cerebrovascular disease"
+I6994,"Monoplegia of lower limb following unspecified cerebrovascular disease"
+I6995,"Hemiplegia and hemiparesis following unspecified cerebrovascular disease"
+I6996,"Other paralytic syndrome following unspecified cerebrovascular disease"
+I6999,"Other sequelae of unspecified cerebrovascular disease"
+I70,"Atherosclerosis"
+I702,"Atherosclerosis of native arteries of the extremities"
+I7020,"Unspecified atherosclerosis of native arteries of extremities"
+I7021,"Atherosclerosis of native arteries of extremities with intermittent claudication"
+I7022,"Atherosclerosis of native arteries of extremities with rest pain"
+I7023,"Atherosclerosis of native arteries of right leg with ulceration"
+I7024,"Atherosclerosis of native arteries of left leg with ulceration"
+I7025,"Atherosclerosis of native arteries of other extremities with ulceration"
+I7026,"Atherosclerosis of native arteries of extremities with gangrene"
+I7029,"Other atherosclerosis of native arteries of extremities"
+I703,"Atherosclerosis of unspecified type of bypass graft(s) of the extremities"
+I7030,"Unspecified atherosclerosis of unspecified type of bypass graft(s) of the extremities"
+I7031,"Atherosclerosis of unspecified type of bypass graft(s) of the extremities with intermittent claudication"
+I7032,"Atherosclerosis of unspecified type of bypass graft(s) of the extremities with rest pain"
+I7033,"Atherosclerosis of unspecified type of bypass graft(s) of the right leg with ulceration"
+I7034,"Atherosclerosis of unspecified type of bypass graft(s) of the left leg with ulceration"
+I7035,"Atherosclerosis of unspecified type of bypass graft(s) of other extremity with ulceration"
+I7036,"Atherosclerosis of unspecified type of bypass graft(s) of the extremities with gangrene"
+I7039,"Other atherosclerosis of unspecified type of bypass graft(s) of the extremities"
+I704,"Atherosclerosis of autologous vein bypass graft(s) of the extremities"
+I7040,"Unspecified atherosclerosis of autologous vein bypass graft(s) of the extremities"
+I7041,"Atherosclerosis of autologous vein bypass graft(s) of the extremities with intermittent claudication"
+I7042,"Atherosclerosis of autologous vein bypass graft(s) of the extremities with rest pain"
+I7043,"Atherosclerosis of autologous vein bypass graft(s) of the right leg with ulceration"
+I7044,"Atherosclerosis of autologous vein bypass graft(s) of the left leg with ulceration"
+I7045,"Atherosclerosis of autologous vein bypass graft(s) of other extremity with ulceration"
+I7046,"Atherosclerosis of autologous vein bypass graft(s) of the extremities with gangrene"
+I7049,"Other atherosclerosis of autologous vein bypass graft(s) of the extremities"
+I705,"Atherosclerosis of nonautologous biological bypass graft(s) of the extremities"
+I7050,"Unspecified atherosclerosis of nonautologous biological bypass graft(s) of the extremities"
+I7051,"Atherosclerosis of nonautologous biological bypass graft(s) of the extremities intermittent claudication"
+I7052,"Atherosclerosis of nonautologous biological bypass graft(s) of the extremities with rest pain"
+I7053,"Atherosclerosis of nonautologous biological bypass graft(s) of the right leg with ulceration"
+I7054,"Atherosclerosis of nonautologous biological bypass graft(s) of the left leg with ulceration"
+I7055,"Atherosclerosis of nonautologous biological bypass graft(s) of other extremity with ulceration"
+I7056,"Atherosclerosis of nonautologous biological bypass graft(s) of the extremities with gangrene"
+I7059,"Other atherosclerosis of nonautologous biological bypass graft(s) of the extremities"
+I706,"Atherosclerosis of nonbiological bypass graft(s) of the extremities"
+I7060,"Unspecified atherosclerosis of nonbiological bypass graft(s) of the extremities"
+I7061,"Atherosclerosis of nonbiological bypass graft(s) of the extremities with intermittent claudication"
+I7062,"Atherosclerosis of nonbiological bypass graft(s) of the extremities with rest pain"
+I7063,"Atherosclerosis of nonbiological bypass graft(s) of the right leg with ulceration"
+I7064,"Atherosclerosis of nonbiological bypass graft(s) of the left leg with ulceration"
+I7065,"Atherosclerosis of nonbiological bypass graft(s) of other extremity with ulceration"
+I7066,"Atherosclerosis of nonbiological bypass graft(s) of the extremities with gangrene"
+I7069,"Other atherosclerosis of nonbiological bypass graft(s) of the extremities"
+I707,"Atherosclerosis of other type of bypass graft(s) of the extremities"
+I7070,"Unspecified atherosclerosis of other type of bypass graft(s) of the extremities"
+I7071,"Atherosclerosis of other type of bypass graft(s) of the extremities with intermittent claudication"
+I7072,"Atherosclerosis of other type of bypass graft(s) of the extremities with rest pain"
+I7073,"Atherosclerosis of other type of bypass graft(s) of the right leg with ulceration"
+I7074,"Atherosclerosis of other type of bypass graft(s) of the left leg with ulceration"
+I7075,"Atherosclerosis of other type of bypass graft(s) of other extremity with ulceration"
+I7076,"Atherosclerosis of other type of bypass graft(s) of the extremities with gangrene"
+I7079,"Other atherosclerosis of other type of bypass graft(s) of the extremities"
+I708,"Atherosclerosis of other arteries"
+I709,"Other and unspecified atherosclerosis"
+I71,"Aortic aneurysm and dissection"
+I710,"Dissection of aorta"
+I711,"Thoracic aortic aneurysm, ruptured"
+I712,"Thoracic aortic aneurysm, without rupture"
+I713,"Abdominal aortic aneurysm, ruptured"
+I714,"Abdominal aortic aneurysm, without rupture"
+I715,"Thoracoabdominal aortic aneurysm, ruptured"
+I716,"Thoracoabdominal aortic aneurysm, without rupture"
+I718,"Aortic aneurysm of unspecified site, ruptured"
+I719,"Aortic aneurysm of unspecified site, without rupture"
+I72,"Other aneurysm"
+I73,"Other peripheral vascular diseases"
+I730,"Raynaud's syndrome"
+I731,"Thromboangiitis obliterans [Buerger's disease]"
+I738,"Other specified peripheral vascular diseases"
+I739,"Peripheral vascular disease, unspecified"
+I74,"Arterial embolism and thrombosis"
+I740,"Embolism and thrombosis of abdominal aorta"
+I741,"Embolism and thrombosis of other and unspecified parts of aorta"
+I742,"Embolism and thrombosis of arteries of the upper extremities"
+I743,"Embolism and thrombosis of arteries of the lower extremities"
+I744,"Embolism and thrombosis of arteries of extremities, unspecified"
+I745,"Embolism and thrombosis of iliac artery"
+I748,"Embolism and thrombosis of other arteries"
+I749,"Embolism and thrombosis of unspecified artery"
+I75,"Atheroembolism"
+I750,"Atheroembolism of extremities"
+I7501,"Atheroembolism of upper extremity"
+I7502,"Atheroembolism of lower extremity"
+I758,"Atheroembolism of other sites"
+I76,"Septic arterial embolism"
+I77,"Other disorders of arteries and arterioles"
+I777,"Other arterial dissection"
+I778,"Other specified disorders of arteries and arterioles"
+I7781,"Aortic ectasia"
+I7789,"Other specified disorders of arteries and arterioles"
+I779,"Disorder of arteries and arterioles, unspecified"
+I78,"Diseases of capillaries"
+I79,"Disorders of arteries, arterioles and capillaries in diseases classified elsewhere"
+I80,"Phlebitis and thrombophlebitis"
+I800,"Phlebitis and thrombophlebitis of superficial vessels of lower extremities"
+I801,"Phlebitis and thrombophlebitis of femoral vein"
+I802,"Phlebitis and thrombophlebitis of other and unspecified deep vessels of lower extremities"
+I8020,"Phlebitis and thrombophlebitis of unspecified deep vessels of lower extremities"
+I8021,"Phlebitis and thrombophlebitis of iliac vein"
+I8022,"Phlebitis and thrombophlebitis of popliteal vein"
+I8023,"Phlebitis and thrombophlebitis of tibial vein"
+I8029,"Phlebitis and thrombophlebitis of other deep vessels of lower extremities"
+I803,"Phlebitis and thrombophlebitis of lower extremities, unspecified"
+I808,"Phlebitis and thrombophlebitis of other sites"
+I809,"Phlebitis and thrombophlebitis of unspecified site"
+I81,"Portal vein thrombosis"
+I82,"Other venous embolism and thrombosis"
+I822,"Embolism and thrombosis of vena cava and other thoracic veins"
+I8221,"Embolism and thrombosis of superior vena cava"
+I8222,"Embolism and thrombosis of inferior vena cava"
+I8229,"Embolism and thrombosis of other thoracic veins"
+I823,"Embolism and thrombosis of renal vein"
+I824,"Acute embolism and thrombosis of deep veins of lower extremity"
+I8240,"Acute embolism and thrombosis of unspecified deep veins of lower extremity"
+I8241,"Acute embolism and thrombosis of femoral vein"
+I8242,"Acute embolism and thrombosis of iliac vein"
+I8243,"Acute embolism and thrombosis of popliteal vein"
+I8244,"Acute embolism and thrombosis of tibial vein"
+I8249,"Acute embolism and thrombosis of other specified deep vein of lower extremity"
+I824Y,"Acute embolism and thrombosis of unspecified deep veins of proximal lower extremity"
+I824Z,"Acute embolism and thrombosis of unspecified deep veins of distal lower extremity"
+I825,"Chronic embolism and thrombosis of deep veins of lower extremity"
+I8250,"Chronic embolism and thrombosis of unspecified deep veins of lower extremity"
+I8251,"Chronic embolism and thrombosis of femoral vein"
+I8252,"Chronic embolism and thrombosis of iliac vein"
+I8253,"Chronic embolism and thrombosis of popliteal vein"
+I8254,"Chronic embolism and thrombosis of tibial vein"
+I8259,"Chronic embolism and thrombosis of other specified deep vein of lower extremity"
+I825Y,"Chronic embolism and thrombosis of unspecified deep veins of proximal lower extremity"
+I825Z,"Chronic embolism and thrombosis of unspecified deep veins of distal lower extremity"
+I826,"Acute embolism and thrombosis of veins of upper extremity"
+I8260,"Acute embolism and thrombosis of unspecified veins of upper extremity"
+I8261,"Acute embolism and thrombosis of superficial veins of upper extremity"
+I8262,"Acute embolism and thrombosis of deep veins of upper extremity"
+I827,"Chronic embolism and thrombosis of veins of upper extremity"
+I8270,"Chronic embolism and thrombosis of unspecified veins of upper extremity"
+I8271,"Chronic embolism and thrombosis of superficial veins of upper extremity"
+I8272,"Chronic embolism and thrombosis of deep veins of upper extremity"
+I82A,"Embolism and thrombosis of axillary vein"
+I82A1,"Acute embolism and thrombosis of axillary vein"
+I82A2,"Chronic embolism and thrombosis of axillary vein"
+I82B,"Embolism and thrombosis of subclavian vein"
+I82B1,"Acute embolism and thrombosis of subclavian vein"
+I82B2,"Chronic embolism and thrombosis of subclavian vein"
+I82C,"Embolism and thrombosis of internal jugular vein"
+I82C1,"Acute embolism and thrombosis of internal jugular vein"
+I82C2,"Chronic embolism and thrombosis of internal jugular vein"
+I828,"Embolism and thrombosis of other specified veins"
+I8281,"Embolism and thrombosis of superficial veins of lower extremities"
+I8289,"Embolism and thrombosis of other specified veins"
+I829,"Embolism and thrombosis of unspecified vein"
+I83,"Varicose veins of lower extremities"
+I830,"Varicose veins of lower extremities with ulcer"
+I8300,"Varicose veins of unspecified lower extremity with ulcer"
+I8301,"Varicose veins of right lower extremity with ulcer"
+I8302,"Varicose veins of left lower extremity with ulcer"
+I831,"Varicose veins of lower extremities with inflammation"
+I832,"Varicose veins of lower extremities with both ulcer and inflammation"
+I8320,"Varicose veins of unspecified lower extremity with both ulcer and inflammation"
+I8321,"Varicose veins of right lower extremity with both ulcer and inflammation"
+I8322,"Varicose veins of left lower extremity with both ulcer and inflammation"
+I838,"Varicose veins of lower extremities with other complications"
+I8381,"Varicose veins of lower extremities with pain"
+I8389,"Varicose veins of lower extremities with other complications"
+I839,"Asymptomatic varicose veins of lower extremities"
+I85,"Esophageal varices"
+I850,"Esophageal varices"
+I851,"Secondary esophageal varices"
+I86,"Varicose veins of other sites"
+I87,"Other disorders of veins"
+I870,"Postthrombotic syndrome"
+I8700,"Postthrombotic syndrome without complications"
+I8701,"Postthrombotic syndrome with ulcer"
+I8702,"Postthrombotic syndrome with inflammation"
+I8703,"Postthrombotic syndrome with ulcer and inflammation"
+I8709,"Postthrombotic syndrome with other complications"
+I871,"Compression of vein"
+I872,"Venous insufficiency (chronic) (peripheral)"
+I873,"Chronic venous hypertension (idiopathic)"
+I8730,"Chronic venous hypertension (idiopathic) without complications"
+I8731,"Chronic venous hypertension (idiopathic) with ulcer"
+I8732,"Chronic venous hypertension (idiopathic) with inflammation"
+I8733,"Chronic venous hypertension (idiopathic) with ulcer and inflammation"
+I8739,"Chronic venous hypertension (idiopathic) with other complications"
+I878,"Other specified disorders of veins"
+I879,"Disorder of vein, unspecified"
+I88,"Nonspecific lymphadenitis"
+I89,"Other noninfective disorders of lymphatic vessels and lymph nodes"
+I95,"Hypotension"
+I958,"Other hypotension"
+I959,"Hypotension, unspecified"
+I96,"Gangrene, not elsewhere classified"
+I97,"Intraoperative and postprocedural complications and disorders of circulatory system, not elsewhere classified"
+I971,"Other postprocedural cardiac functional disturbances"
+I9711,"Postprocedural cardiac insufficiency"
+I9712,"Postprocedural cardiac arrest"
+I9713,"Postprocedural heart failure"
+I9719,"Other postprocedural cardiac functional disturbances"
+I972,"Postmastectomy lymphedema syndrome"
+I973,"Postprocedural hypertension"
+I974,"Intraoperative hemorrhage and hematoma of a circulatory system organ or structure complicating a procedure"
+I9741,"Intraoperative hemorrhage and hematoma of a circulatory system organ or structure complicating a circulatory system procedure"
+I9742,"Intraoperative hemorrhage and hematoma of a circulatory system organ or structure complicating other procedure"
+I975,"Accidental puncture and laceration of a circulatory system organ or structure during a procedure"
+I976,"Postprocedural hemorrhage, hematoma and seroma of a circulatory system organ or structure following a procedure"
+I9761,"Postprocedural hemorrhage of a circulatory system organ or structure following a circulatory system procedure"
+I9762,"Postprocedural hemorrhage, hematoma and seroma of a circulatory system organ or structure following other procedure"
+I9763,"Postprocedural hematoma of a circulatory system organ or structure following a circulatory system procedure"
+I9764,"Postprocedural seroma of a circulatory system organ  or structure following a circulatory system procedure"
+I977,"Intraoperative cardiac functional disturbances"
+I9771,"Intraoperative cardiac arrest"
+I9779,"Other intraoperative cardiac functional disturbances"
+I978,"Other intraoperative and postprocedural complications and disorders of the circulatory system, not elsewhere classified"
+I9781,"Intraoperative cerebrovascular infarction"
+I9782,"Postprocedural cerebrovascular infarction"
+I9788,"Other intraoperative complications of the circulatory system, not elsewhere classified"
+I9789,"Other postprocedural complications and disorders of the circulatory system, not elsewhere classified"
+I99,"Other and unspecified disorders of circulatory system"
+J00,"Acute nasopharyngitis [common cold]"
+J01,"Acute sinusitis"
+J010,"Acute maxillary sinusitis"
+J011,"Acute frontal sinusitis"
+J012,"Acute ethmoidal sinusitis"
+J013,"Acute sphenoidal sinusitis"
+J014,"Acute pansinusitis"
+J018,"Other acute sinusitis"
+J019,"Acute sinusitis, unspecified"
+J02,"Acute pharyngitis"
+J03,"Acute tonsillitis"
+J030,"Streptococcal tonsillitis"
+J038,"Acute tonsillitis due to other specified organisms"
+J039,"Acute tonsillitis, unspecified"
+J04,"Acute laryngitis and tracheitis"
+J041,"Acute tracheitis"
+J042,"Acute laryngotracheitis"
+J043,"Supraglottitis, unspecified"
+J05,"Acute obstructive laryngitis [croup] and epiglottitis"
+J051,"Acute epiglottitis"
+J06,"Acute upper respiratory infections of multiple and unspecified sites"
+J09,"Influenza due to certain identified influenza viruses"
+J09X,"Influenza due to identified novel influenza A virus"
+J10,"Influenza due to other identified influenza virus"
+J100,"Influenza due to other identified influenza virus with pneumonia"
+J101,"Influenza due to other identified influenza virus with other respiratory manifestations"
+J102,"Influenza due to other identified influenza virus with gastrointestinal manifestations"
+J108,"Influenza due to other identified influenza virus with other manifestations"
+J11,"Influenza due to unidentified influenza virus"
+J110,"Influenza due to unidentified influenza virus with pneumonia"
+J111,"Influenza due to unidentified influenza virus with other respiratory manifestations"
+J112,"Influenza due to unidentified influenza virus with gastrointestinal manifestations"
+J118,"Influenza due to unidentified influenza virus with other manifestations"
+J12,"Viral pneumonia, not elsewhere classified"
+J128,"Other viral pneumonia"
+J129,"Viral pneumonia, unspecified"
+J13,"Pneumonia due to Streptococcus pneumoniae"
+J14,"Pneumonia due to Hemophilus influenzae"
+J15,"Bacterial pneumonia, not elsewhere classified"
+J152,"Pneumonia due to staphylococcus"
+J1521,"Pneumonia due to staphylococcus aureus"
+J1529,"Pneumonia due to other staphylococcus"
+J153,"Pneumonia due to streptococcus, group B"
+J154,"Pneumonia due to other streptococci"
+J155,"Pneumonia due to Escherichia coli"
+J156,"Pneumonia due to other Gram-negative bacteria"
+J157,"Pneumonia due to Mycoplasma pneumoniae"
+J158,"Pneumonia due to other specified bacteria"
+J159,"Unspecified bacterial pneumonia"
+J16,"Pneumonia due to other infectious organisms, not elsewhere classified"
+J17,"Pneumonia in diseases classified elsewhere"
+J18,"Pneumonia, unspecified organism"
+J20,"Acute bronchitis"
+J21,"Acute bronchiolitis"
+J22,"Unspecified acute lower respiratory infection"
+J30,"Vasomotor and allergic rhinitis"
+J308,"Other allergic rhinitis"
+J309,"Allergic rhinitis, unspecified"
+J31,"Chronic rhinitis, nasopharyngitis and pharyngitis"
+J32,"Chronic sinusitis"
+J33,"Nasal polyp"
+J34,"Other and unspecified disorders of nose and nasal sinuses"
+J348,"Other specified disorders of nose and nasal sinuses"
+J349,"Unspecified disorder of nose and nasal sinuses"
+J35,"Chronic diseases of tonsils and adenoids"
+J350,"Chronic tonsillitis and adenoiditis"
+J351,"Hypertrophy of tonsils"
+J352,"Hypertrophy of adenoids"
+J353,"Hypertrophy of tonsils with hypertrophy of adenoids"
+J358,"Other chronic diseases of tonsils and adenoids"
+J359,"Chronic disease of tonsils and adenoids, unspecified"
+J36,"Peritonsillar abscess"
+J37,"Chronic laryngitis and laryngotracheitis"
+J38,"Diseases of vocal cords and larynx, not elsewhere classified"
+J380,"Paralysis of vocal cords and larynx"
+J381,"Polyp of vocal cord and larynx"
+J382,"Nodules of vocal cords"
+J383,"Other diseases of vocal cords"
+J384,"Edema of larynx"
+J385,"Laryngeal spasm"
+J386,"Stenosis of larynx"
+J387,"Other diseases of larynx"
+J39,"Other diseases of upper respiratory tract"
+J40,"Bronchitis, not specified as acute or chronic"
+J41,"Simple and mucopurulent chronic bronchitis"
+J42,"Unspecified chronic bronchitis"
+J43,"Emphysema"
+J44,"Other chronic obstructive pulmonary disease"
+J45,"Asthma"
+J452,"Mild intermittent asthma"
+J453,"Mild persistent asthma"
+J454,"Moderate persistent asthma"
+J455,"Severe persistent asthma"
+J459,"Other and unspecified asthma"
+J4590,"Unspecified asthma"
+J4599,"Other asthma"
+J47,"Bronchiectasis"
+J60,"Coalworker's pneumoconiosis"
+J61,"Pneumoconiosis due to asbestos and other mineral fibers"
+J62,"Pneumoconiosis due to dust containing silica"
+J63,"Pneumoconiosis due to other inorganic dusts"
+J64,"Unspecified pneumoconiosis"
+J65,"Pneumoconiosis associated with tuberculosis"
+J66,"Airway disease due to specific organic dust"
+J67,"Hypersensitivity pneumonitis due to organic dust"
+J68,"Respiratory conditions due to inhalation of chemicals, gases, fumes and vapors"
+J69,"Pneumonitis due to solids and liquids"
+J70,"Respiratory conditions due to other external agents"
+J80,"Acute respiratory distress syndrome"
+J81,"Pulmonary edema"
+J82,"Pulmonary eosinophilia, not elsewhere classified"
+J84,"Other interstitial pulmonary diseases"
+J840,"Alveolar and parieto-alveolar conditions"
+J841,"Other interstitial pulmonary diseases with fibrosis"
+J8411,"Idiopathic interstitial pneumonia"
+J8417,"Other interstitial pulmonary diseases with fibrosis in diseases classified elsewhere"
+J842,"Lymphoid interstitial pneumonia"
+J848,"Other specified interstitial pulmonary diseases"
+J8484,"Other interstitial  lung diseases of childhood"
+J8489,"Other specified interstitial pulmonary diseases"
+J849,"Interstitial pulmonary disease, unspecified"
+J85,"Abscess of lung and mediastinum"
+J86,"Pyothorax"
+J90,"Pleural effusion, not elsewhere classified"
+J91,"Pleural effusion in conditions classified elsewhere"
+J92,"Pleural plaque"
+J93,"Pneumothorax and air leak"
+J931,"Other spontaneous pneumothorax"
+J938,"Other pneumothorax and air leak"
+J939,"Pneumothorax, unspecified"
+J94,"Other pleural conditions"
+J95,"Intraoperative and postprocedural complications and disorders of respiratory system, not elsewhere classified"
+J950,"Tracheostomy complications"
+J951,"Acute pulmonary insufficiency following thoracic surgery"
+J952,"Acute pulmonary insufficiency following nonthoracic surgery"
+J953,"Chronic pulmonary insufficiency following surgery"
+J954,"Chemical pneumonitis due to anesthesia"
+J955,"Postprocedural subglottic stenosis"
+J956,"Intraoperative hemorrhage and hematoma of a respiratory system organ or structure complicating a procedure"
+J957,"Accidental puncture and laceration of a respiratory system organ or structure during a procedure"
+J958,"Other intraoperative and postprocedural complications and disorders of respiratory system, not elsewhere classified"
+J9581,"Postprocedural pneumothorax and air leak"
+J9582,"Postprocedural respiratory failure"
+J9583,"Postprocedural hemorrhage of a respiratory system organ or structure following a procedure"
+J9584,"Transfusion-related acute lung injury (TRALI)"
+J9585,"Complication of respirator [ventilator]"
+J9586,"Postprocedural hematoma and seroma of a respiratory system organ or structure following a procedure"
+J9588,"Other intraoperative complications of respiratory system, not elsewhere classified"
+J9589,"Other postprocedural complications and disorders of respiratory system, not elsewhere classified"
+J96,"Respiratory failure, not elsewhere classified"
+J960,"Acute respiratory failure"
+J961,"Chronic respiratory failure"
+J962,"Acute and chronic respiratory failure"
+J969,"Respiratory failure, unspecified"
+J98,"Other respiratory disorders"
+J980,"Diseases of bronchus, not elsewhere classified"
+J981,"Pulmonary collapse"
+J982,"Interstitial emphysema"
+J983,"Compensatory emphysema"
+J984,"Other disorders of lung"
+J985,"Diseases of mediastinum, not elsewhere classified"
+J986,"Disorders of diaphragm"
+J988,"Other specified respiratory disorders"
+J989,"Respiratory disorder, unspecified"
+J99,"Respiratory disorders in diseases classified elsewhere"
+K00,"Disorders of tooth development and eruption"
+K01,"Embedded and impacted teeth"
+K02,"Dental caries"
+K025,"Dental caries on pit and fissure surface"
+K026,"Dental caries on smooth surface"
+K027,"Dental root caries"
+K029,"Dental caries, unspecified"
+K03,"Other diseases of hard tissues of teeth"
+K038,"Other specified diseases of hard tissues of teeth"
+K039,"Disease of hard tissues of teeth, unspecified"
+K04,"Diseases of pulp and periapical tissues"
+K040,"Pulpitis"
+K041,"Necrosis of pulp"
+K042,"Pulp degeneration"
+K043,"Abnormal hard tissue formation in pulp"
+K044,"Acute apical periodontitis of pulpal origin"
+K045,"Chronic apical periodontitis"
+K046,"Periapical abscess with sinus"
+K047,"Periapical abscess without sinus"
+K048,"Radicular cyst"
+K049,"Other and unspecified diseases of pulp and periapical tissues"
+K05,"Gingivitis and periodontal diseases"
+K050,"Acute gingivitis"
+K051,"Chronic gingivitis"
+K052,"Aggressive periodontitis"
+K0521,"Aggressive periodontitis, localized"
+K0522,"Aggressive periodontitis, generalized"
+K053,"Chronic periodontitis"
+K0531,"Chronic periodontitis, localized"
+K0532,"Chronic periodontitis, generalized"
+K054,"Periodontosis"
+K055,"Other periodontal diseases"
+K056,"Periodontal disease, unspecified"
+K06,"Other disorders of gingiva and edentulous alveolar ridge"
+K060,"Gingival recession"
+K0601,"Gingival recession, localized"
+K0602,"Gingival recession, generalized"
+K061,"Gingival enlargement"
+K062,"Gingival and edentulous alveolar ridge lesions associated with trauma"
+K063,"Horizontal alveolar bone loss"
+K068,"Other specified disorders of gingiva and edentulous alveolar ridge"
+K069,"Disorder of gingiva and edentulous alveolar ridge, unspecified"
+K08,"Other disorders of teeth and supporting structures"
+K081,"Complete loss of teeth"
+K0810,"Complete loss of teeth, unspecified cause"
+K0811,"Complete loss of teeth due to trauma"
+K0812,"Complete loss of teeth due to periodontal diseases"
+K0813,"Complete loss of teeth due to caries"
+K0819,"Complete loss of teeth due to other specified cause"
+K082,"Atrophy of edentulous alveolar ridge"
+K083,"Retained dental root"
+K084,"Partial loss of teeth"
+K0840,"Partial loss of teeth, unspecified cause"
+K0841,"Partial loss of teeth due to trauma"
+K0842,"Partial loss of teeth due to periodontal diseases"
+K0843,"Partial loss of teeth due to caries"
+K0849,"Partial loss of teeth due to other specified cause"
+K085,"Unsatisfactory restoration of tooth"
+K0853,"Fractured dental restorative material"
+K0854,"Contour of existing restoration of tooth biologically incompatible with oral health"
+K0855,"Allergy to existing dental restorative material"
+K0856,"Poor aesthetic of existing restoration of tooth"
+K0859,"Other unsatisfactory restoration of tooth"
+K088,"Other specified disorders of teeth and supporting structures"
+K089,"Disorder of teeth and supporting structures, unspecified"
+K09,"Cysts of oral region, not elsewhere classified"
+K11,"Diseases of salivary glands"
+K112,"Sialoadenitis"
+K113,"Abscess of salivary gland"
+K114,"Fistula of salivary gland"
+K115,"Sialolithiasis"
+K116,"Mucocele of salivary gland"
+K117,"Disturbances of salivary secretion"
+K118,"Other diseases of salivary glands"
+K119,"Disease of salivary gland, unspecified"
+K12,"Stomatitis and related lesions"
+K123,"Oral mucositis (ulcerative)"
+K13,"Other diseases of lip and oral mucosa"
+K132,"Leukoplakia and other disturbances of oral epithelium, including tongue"
+K133,"Hairy leukoplakia"
+K134,"Granuloma and granuloma-like lesions of oral mucosa"
+K135,"Oral submucous fibrosis"
+K136,"Irritative hyperplasia of oral mucosa"
+K137,"Other and unspecified lesions of oral mucosa"
+K14,"Diseases of tongue"
+K20,"Esophagitis"
+K21,"Gastro-esophageal reflux disease"
+K22,"Other diseases of esophagus"
+K221,"Ulcer of esophagus"
+K222,"Esophageal obstruction"
+K223,"Perforation of esophagus"
+K224,"Dyskinesia of esophagus"
+K225,"Diverticulum of esophagus, acquired"
+K226,"Gastro-esophageal laceration-hemorrhage syndrome"
+K227,"Barrett's esophagus"
+K2271,"Barrett's esophagus with dysplasia"
+K228,"Other specified diseases of esophagus"
+K229,"Disease of esophagus, unspecified"
+K23,"Disorders of esophagus in diseases classified elsewhere"
+K25,"Gastric ulcer"
+K26,"Duodenal ulcer"
+K27,"Peptic ulcer, site unspecified"
+K28,"Gastrojejunal ulcer"
+K29,"Gastritis and duodenitis"
+K290,"Acute gastritis"
+K292,"Alcoholic gastritis"
+K293,"Chronic superficial gastritis"
+K294,"Chronic atrophic gastritis"
+K295,"Unspecified chronic gastritis"
+K296,"Other gastritis"
+K297,"Gastritis, unspecified"
+K298,"Duodenitis"
+K299,"Gastroduodenitis, unspecified"
+K30,"Functional dyspepsia"
+K31,"Other diseases of stomach and duodenum"
+K318,"Other specified diseases of stomach and duodenum"
+K3181,"Angiodysplasia of stomach and duodenum"
+K3182,"Dieulafoy lesion (hemorrhagic) of stomach and duodenum"
+K3183,"Achlorhydria"
+K3184,"Gastroparesis"
+K3189,"Other diseases of stomach and duodenum"
+K319,"Disease of stomach and duodenum, unspecified"
+K35,"Acute appendicitis"
+K358,"Other and unspecified acute appendicitis"
+K36,"Other appendicitis"
+K37,"Unspecified appendicitis"
+K38,"Other diseases of appendix"
+K40,"Inguinal hernia"
+K400,"Bilateral inguinal hernia, with obstruction, without gangrene"
+K401,"Bilateral inguinal hernia, with gangrene"
+K402,"Bilateral inguinal hernia, without obstruction or gangrene"
+K403,"Unilateral inguinal hernia, with obstruction, without gangrene"
+K404,"Unilateral inguinal hernia, with gangrene"
+K409,"Unilateral inguinal hernia, without obstruction or gangrene"
+K41,"Femoral hernia"
+K410,"Bilateral femoral hernia, with obstruction, without gangrene"
+K411,"Bilateral femoral hernia, with gangrene"
+K412,"Bilateral femoral hernia, without obstruction or gangrene"
+K413,"Unilateral femoral hernia, with obstruction, without gangrene"
+K414,"Unilateral femoral hernia, with gangrene"
+K419,"Unilateral femoral hernia, without obstruction or gangrene"
+K42,"Umbilical hernia"
+K43,"Ventral hernia"
+K44,"Diaphragmatic hernia"
+K45,"Other abdominal hernia"
+K46,"Unspecified abdominal hernia"
+K50,"Crohn's disease [regional enteritis]"
+K500,"Crohn's disease of small intestine"
+K5001,"Crohn's disease of small intestine with complications"
+K501,"Crohn's disease of large intestine"
+K5011,"Crohn's disease of large intestine with complications"
+K508,"Crohn's disease of both small and large intestine"
+K5081,"Crohn's disease of both small and large intestine with complications"
+K509,"Crohn's disease, unspecified"
+K5091,"Crohn's disease, unspecified, with complications"
+K51,"Ulcerative colitis"
+K510,"Ulcerative (chronic) pancolitis"
+K5101,"Ulcerative (chronic) pancolitis with complications"
+K512,"Ulcerative (chronic) proctitis"
+K5121,"Ulcerative (chronic) proctitis with complications"
+K513,"Ulcerative (chronic) rectosigmoiditis"
+K5131,"Ulcerative (chronic) rectosigmoiditis with complications"
+K514,"Inflammatory polyps of colon"
+K5141,"Inflammatory polyps of colon with complications"
+K515,"Left sided colitis"
+K5151,"Left sided colitis with complications"
+K518,"Other ulcerative colitis"
+K5181,"Other ulcerative colitis with complications"
+K519,"Ulcerative colitis, unspecified"
+K5191,"Ulcerative colitis, unspecified, with complications"
+K52,"Other and unspecified noninfective gastroenteritis and colitis"
+K522,"Allergic and dietetic gastroenteritis and colitis"
+K523,"Indeterminate colitis"
+K528,"Other specified noninfective gastroenteritis and colitis"
+K5283,"Microscopic colitis"
+K5289,"Other specified noninfective gastroenteritis and colitis"
+K529,"Noninfective gastroenteritis and colitis, unspecified"
+K55,"Vascular disorders of intestine"
+K550,"Acute vascular disorders of intestine"
+K5501,"Acute (reversible) ischemia of small intestine"
+K5502,"Acute infarction of small intestine"
+K5503,"Acute (reversible) ischemia of large intestine"
+K5504,"Acute infarction of large intestine"
+K5505,"Acute (reversible) ischemia of intestine, part unspecified"
+K5506,"Acute infarction of intestine, part unspecified"
+K551,"Chronic vascular disorders of intestine"
+K552,"Angiodysplasia of colon"
+K553,"Necrotizing enterocolitis"
+K558,"Other vascular disorders of intestine"
+K559,"Vascular disorder of intestine, unspecified"
+K56,"Paralytic ileus and intestinal obstruction without hernia"
+K564,"Other impaction of intestine"
+K565,"Intestinal adhesions [bands] with obstruction (postinfection)"
+K566,"Other and unspecified intestinal obstruction"
+K5660,"Unspecified intestinal obstruction"
+K5669,"Other intestinal obstruction"
+K567,"Ileus, unspecified"
+K57,"Diverticular disease of intestine"
+K570,"Diverticulitis of small intestine with perforation and abscess"
+K571,"Diverticular disease of small intestine without perforation or abscess"
+K572,"Diverticulitis of large intestine with perforation and abscess"
+K573,"Diverticular disease of large intestine without perforation or abscess"
+K574,"Diverticulitis of both small and large intestine with perforation and abscess"
+K575,"Diverticular disease of both small and large intestine without perforation or abscess"
+K578,"Diverticulitis of intestine, part unspecified, with perforation and abscess"
+K579,"Diverticular disease of intestine, part unspecified, without perforation or abscess"
+K58,"Irritable bowel syndrome"
+K59,"Other functional intestinal disorders"
+K590,"Constipation"
+K591,"Functional diarrhea"
+K592,"Neurogenic bowel, not elsewhere classified"
+K593,"Megacolon, not elsewhere classified"
+K594,"Anal spasm"
+K598,"Other specified functional intestinal disorders"
+K599,"Functional intestinal disorder, unspecified"
+K60,"Fissure and fistula of anal and rectal regions"
+K61,"Abscess of anal and rectal regions"
+K62,"Other diseases of anus and rectum"
+K628,"Other specified diseases of anus and rectum"
+K629,"Disease of anus and rectum, unspecified"
+K63,"Other diseases of intestine"
+K638,"Other specified diseases of intestine"
+K639,"Disease of intestine, unspecified"
+K64,"Hemorrhoids and perianal venous thrombosis"
+K65,"Peritonitis"
+K66,"Other disorders of peritoneum"
+K67,"Disorders of peritoneum in infectious diseases classified elsewhere"
+K68,"Disorders of retroperitoneum"
+K681,"Retroperitoneal abscess"
+K689,"Other disorders of retroperitoneum"
+K70,"Alcoholic liver disease"
+K701,"Alcoholic hepatitis"
+K702,"Alcoholic fibrosis and sclerosis of liver"
+K703,"Alcoholic cirrhosis of liver"
+K704,"Alcoholic hepatic failure"
+K709,"Alcoholic liver disease, unspecified"
+K71,"Toxic liver disease"
+K711,"Toxic liver disease with hepatic necrosis"
+K712,"Toxic liver disease with acute hepatitis"
+K713,"Toxic liver disease with chronic persistent hepatitis"
+K714,"Toxic liver disease with chronic lobular hepatitis"
+K715,"Toxic liver disease with chronic active hepatitis"
+K716,"Toxic liver disease with hepatitis, not elsewhere classified"
+K717,"Toxic liver disease with fibrosis and cirrhosis of liver"
+K718,"Toxic liver disease with other disorders of liver"
+K719,"Toxic liver disease, unspecified"
+K72,"Hepatic failure, not elsewhere classified"
+K720,"Acute and subacute hepatic failure"
+K721,"Chronic hepatic failure"
+K729,"Hepatic failure, unspecified"
+K73,"Chronic hepatitis, not elsewhere classified"
+K74,"Fibrosis and cirrhosis of liver"
+K746,"Other and unspecified cirrhosis of liver"
+K75,"Other inflammatory liver diseases"
+K758,"Other specified inflammatory liver diseases"
+K759,"Inflammatory liver disease, unspecified"
+K76,"Other diseases of liver"
+K768,"Other specified diseases of liver"
+K769,"Liver disease, unspecified"
+K77,"Liver disorders in diseases classified elsewhere"
+K80,"Cholelithiasis"
+K800,"Calculus of gallbladder with acute cholecystitis"
+K801,"Calculus of gallbladder with other cholecystitis"
+K802,"Calculus of gallbladder without cholecystitis"
+K803,"Calculus of bile duct with cholangitis"
+K804,"Calculus of bile duct with cholecystitis"
+K805,"Calculus of bile duct without cholangitis or cholecystitis"
+K806,"Calculus of gallbladder and bile duct with cholecystitis"
+K807,"Calculus of gallbladder and bile duct without cholecystitis"
+K808,"Other cholelithiasis"
+K81,"Cholecystitis"
+K82,"Other diseases of gallbladder"
+K83,"Other diseases of biliary tract"
+K85,"Acute pancreatitis"
+K850,"Idiopathic acute pancreatitis"
+K851,"Biliary acute pancreatitis"
+K852,"Alcohol induced acute pancreatitis"
+K853,"Drug induced acute pancreatitis"
+K858,"Other acute pancreatitis"
+K859,"Acute pancreatitis, unspecified"
+K86,"Other diseases of pancreas"
+K868,"Other specified diseases of pancreas"
+K869,"Disease of pancreas, unspecified"
+K87,"Disorders of gallbladder, biliary tract and pancreas in diseases classified elsewhere"
+K90,"Intestinal malabsorption"
+K904,"Other malabsorption due to intolerance"
+K908,"Other intestinal malabsorption"
+K909,"Intestinal malabsorption, unspecified"
+K91,"Intraoperative and postprocedural complications and disorders of digestive system, not elsewhere classified"
+K913,"Postprocedural intestinal obstruction"
+K915,"Postcholecystectomy syndrome"
+K916,"Intraoperative hemorrhage and hematoma of a digestive system organ or structure complicating a procedure"
+K917,"Accidental puncture and laceration of a digestive system organ or structure during a procedure"
+K918,"Other intraoperative and postprocedural complications and disorders of digestive system"
+K9184,"Postprocedural hemorrhage of a digestive system organ or structure following a procedure"
+K9185,"Complications of intestinal pouch"
+K9186,"Retained cholelithiasis following cholecystectomy"
+K9187,"Postprocedural hematoma and seroma of a digestive system organ or structure following a procedure"
+K9189,"Other postprocedural complications and disorders of digestive system"
+K92,"Other diseases of digestive system"
+K928,"Other specified diseases of the digestive system"
+K929,"Disease of digestive system, unspecified"
+K94,"Complications of artificial openings of the digestive system"
+K940,"Colostomy complications"
+K941,"Enterostomy complications"
+K942,"Gastrostomy complications"
+K943,"Esophagostomy complications"
+K95,"Complications of bariatric procedures"
+K950,"Complications of gastric band procedure"
+K958,"Complications of other bariatric procedure"
+L00,"Staphylococcal scalded skin syndrome"
+L01,"Impetigo"
+L010,"Impetigo"
+L011,"Impetiginization of other dermatoses"
+L02,"Cutaneous abscess, furuncle and carbuncle"
+L020,"Cutaneous abscess, furuncle and carbuncle of face"
+L021,"Cutaneous abscess, furuncle and carbuncle of neck"
+L022,"Cutaneous abscess, furuncle and carbuncle of trunk"
+L0221,"Cutaneous abscess of trunk"
+L0222,"Furuncle of trunk"
+L0223,"Carbuncle of trunk"
+L023,"Cutaneous abscess, furuncle and carbuncle of buttock"
+L024,"Cutaneous abscess, furuncle and carbuncle of limb"
+L0241,"Cutaneous abscess of limb"
+L0242,"Furuncle of limb"
+L0243,"Carbuncle of limb"
+L025,"Cutaneous abscess, furuncle and carbuncle of hand"
+L0251,"Cutaneous abscess of hand"
+L0252,"Furuncle hand"
+L0253,"Carbuncle of hand"
+L026,"Cutaneous abscess, furuncle and carbuncle of foot"
+L0261,"Cutaneous abscess of foot"
+L0262,"Furuncle of foot"
+L0263,"Carbuncle of foot"
+L028,"Cutaneous abscess, furuncle and carbuncle of other sites"
+L0281,"Cutaneous abscess of other sites"
+L0282,"Furuncle of other sites"
+L0283,"Carbuncle of other sites"
+L029,"Cutaneous abscess, furuncle and carbuncle, unspecified"
+L03,"Cellulitis and acute lymphangitis"
+L030,"Cellulitis and acute lymphangitis of finger and toe"
+L0301,"Cellulitis of finger"
+L0302,"Acute lymphangitis of finger"
+L0303,"Cellulitis of toe"
+L0304,"Acute lymphangitis of toe"
+L031,"Cellulitis and acute lymphangitis of other parts of limb"
+L0311,"Cellulitis of other parts of limb"
+L0312,"Acute lymphangitis of other parts of limb"
+L032,"Cellulitis and acute lymphangitis of face and neck"
+L0321,"Cellulitis and acute lymphangitis of face"
+L0322,"Cellulitis and acute lymphangitis of neck"
+L033,"Cellulitis and acute lymphangitis of trunk"
+L0331,"Cellulitis of trunk"
+L0332,"Acute lymphangitis of trunk"
+L038,"Cellulitis and acute lymphangitis of other sites"
+L0381,"Cellulitis of other sites"
+L0389,"Acute lymphangitis of other sites"
+L039,"Cellulitis and acute lymphangitis, unspecified"
+L04,"Acute lymphadenitis"
+L05,"Pilonidal cyst and sinus"
+L050,"Pilonidal cyst and sinus with abscess"
+L059,"Pilonidal cyst and sinus without abscess"
+L08,"Other local infections of skin and subcutaneous tissue"
+L088,"Other specified local infections of the skin and subcutaneous tissue"
+L089,"Local infection of the skin and subcutaneous tissue, unspecified"
+L10,"Pemphigus"
+L108,"Other pemphigus"
+L109,"Pemphigus, unspecified"
+L11,"Other acantholytic disorders"
+L12,"Pemphigoid"
+L123,"Acquired epidermolysis bullosa"
+L128,"Other pemphigoid"
+L129,"Pemphigoid, unspecified"
+L13,"Other bullous disorders"
+L14,"Bullous disorders in diseases classified elsewhere"
+L20,"Atopic dermatitis"
+L208,"Other atopic dermatitis"
+L209,"Atopic dermatitis, unspecified"
+L21,"Seborrheic dermatitis"
+L22,"Diaper dermatitis"
+L23,"Allergic contact dermatitis"
+L238,"Allergic contact dermatitis due to other agents"
+L239,"Allergic contact dermatitis, unspecified cause"
+L24,"Irritant contact dermatitis"
+L248,"Irritant contact dermatitis due to other agents"
+L249,"Irritant contact dermatitis, unspecified cause"
+L25,"Unspecified contact dermatitis"
+L26,"Exfoliative dermatitis"
+L27,"Dermatitis due to substances taken internally"
+L28,"Lichen simplex chronicus and prurigo"
+L29,"Pruritus"
+L30,"Other and unspecified dermatitis"
+L40,"Psoriasis"
+L405,"Arthropathic psoriasis"
+L408,"Other psoriasis"
+L409,"Psoriasis, unspecified"
+L41,"Parapsoriasis"
+L42,"Pityriasis rosea"
+L43,"Lichen planus"
+L44,"Other papulosquamous disorders"
+L45,"Papulosquamous disorders in diseases classified elsewhere"
+L49,"Exfoliation due to erythematous conditions according to extent of body surface involved"
+L50,"Urticaria"
+L51,"Erythema multiforme"
+L52,"Erythema nodosum"
+L53,"Other erythematous conditions"
+L54,"Erythema in diseases classified elsewhere"
+L55,"Sunburn"
+L56,"Other acute skin changes due to ultraviolet radiation"
+L57,"Skin changes due to chronic exposure to nonionizing radiation"
+L58,"Radiodermatitis"
+L59,"Other disorders of skin and subcutaneous tissue related to radiation"
+L60,"Nail disorders"
+L62,"Nail disorders in diseases classified elsewhere"
+L63,"Alopecia areata"
+L64,"Androgenic alopecia"
+L65,"Other nonscarring hair loss"
+L66,"Cicatricial alopecia [scarring hair loss]"
+L67,"Hair color and hair shaft abnormalities"
+L68,"Hypertrichosis"
+L70,"Acne"
+L71,"Rosacea"
+L72,"Follicular cysts of skin and subcutaneous tissue"
+L721,"Pilar and trichodermal cyst"
+L722,"Steatocystoma multiplex"
+L723,"Sebaceous cyst"
+L728,"Other follicular cysts of the skin and subcutaneous tissue"
+L729,"Follicular cyst of the skin and subcutaneous tissue, unspecified"
+L73,"Other follicular disorders"
+L74,"Eccrine sweat disorders"
+L745,"Focal hyperhidrosis"
+L7451,"Primary focal hyperhidrosis"
+L7452,"Secondary focal hyperhidrosis"
+L748,"Other eccrine sweat disorders"
+L749,"Eccrine sweat disorder, unspecified"
+L75,"Apocrine sweat disorders"
+L76,"Intraoperative and postprocedural complications of skin and subcutaneous tissue"
+L760,"Intraoperative hemorrhage and hematoma of skin and subcutaneous tissue complicating a procedure"
+L761,"Accidental puncture and laceration of skin and subcutaneous tissue during a procedure"
+L762,"Postprocedural hemorrhage of skin and subcutaneous tissue following a procedure"
+L763,"Postprocedural hematoma and seroma of skin and subcutaneous tissue following a procedure"
+L768,"Other intraoperative and postprocedural complications of skin and subcutaneous tissue"
+L80,"Vitiligo"
+L81,"Other disorders of pigmentation"
+L82,"Seborrheic keratosis"
+L83,"Acanthosis nigricans"
+L84,"Corns and callosities"
+L85,"Other epidermal thickening"
+L86,"Keratoderma in diseases classified elsewhere"
+L87,"Transepidermal elimination disorders"
+L88,"Pyoderma gangrenosum"
+L89,"Pressure ulcer"
+L890,"Pressure ulcer of elbow"
+L8900,"Pressure ulcer of unspecified elbow"
+L8901,"Pressure ulcer of right elbow"
+L8902,"Pressure ulcer of left elbow"
+L891,"Pressure ulcer of back"
+L8910,"Pressure ulcer of unspecified part of back"
+L8911,"Pressure ulcer of right upper back"
+L8912,"Pressure ulcer of left upper back"
+L8913,"Pressure ulcer of right lower back"
+L8914,"Pressure ulcer of left lower back"
+L8915,"Pressure ulcer of sacral region"
+L892,"Pressure ulcer of hip"
+L8920,"Pressure ulcer of unspecified hip"
+L8921,"Pressure ulcer of right hip"
+L8922,"Pressure ulcer of left hip"
+L893,"Pressure ulcer of buttock"
+L8930,"Pressure ulcer of unspecified buttock"
+L8931,"Pressure ulcer of right buttock"
+L8932,"Pressure ulcer of left buttock"
+L894,"Pressure ulcer of contiguous site of back, buttock and hip"
+L895,"Pressure ulcer of ankle"
+L8950,"Pressure ulcer of unspecified ankle"
+L8951,"Pressure ulcer of right ankle"
+L8952,"Pressure ulcer of left ankle"
+L896,"Pressure ulcer of heel"
+L8960,"Pressure ulcer of unspecified heel"
+L8961,"Pressure ulcer of right heel"
+L8962,"Pressure ulcer of left heel"
+L898,"Pressure ulcer of other site"
+L8981,"Pressure ulcer of head"
+L8989,"Pressure ulcer of other site"
+L899,"Pressure ulcer of unspecified site"
+L90,"Atrophic disorders of skin"
+L91,"Hypertrophic disorders of skin"
+L92,"Granulomatous disorders of skin and subcutaneous tissue"
+L93,"Lupus erythematosus"
+L94,"Other localized connective tissue disorders"
+L95,"Vasculitis limited to skin, not elsewhere classified"
+L97,"Non-pressure chronic ulcer of lower limb, not elsewhere classified"
+L971,"Non-pressure chronic ulcer of thigh"
+L9710,"Non-pressure chronic ulcer of unspecified thigh"
+L9711,"Non-pressure chronic ulcer of right thigh"
+L9712,"Non-pressure chronic ulcer of left thigh"
+L972,"Non-pressure chronic ulcer of calf"
+L9720,"Non-pressure chronic ulcer of unspecified calf"
+L9721,"Non-pressure chronic ulcer of right calf"
+L9722,"Non-pressure chronic ulcer of left calf"
+L973,"Non-pressure chronic ulcer of ankle"
+L9730,"Non-pressure chronic ulcer of unspecified ankle"
+L9731,"Non-pressure chronic ulcer of right ankle"
+L9732,"Non-pressure chronic ulcer of left ankle"
+L974,"Non-pressure chronic ulcer of heel and midfoot"
+L9740,"Non-pressure chronic ulcer of unspecified heel and midfoot"
+L9741,"Non-pressure chronic ulcer of right heel and midfoot"
+L9742,"Non-pressure chronic ulcer of left heel and midfoot"
+L975,"Non-pressure chronic ulcer of other part of foot"
+L9750,"Non-pressure chronic ulcer of other part of unspecified foot"
+L9751,"Non-pressure chronic ulcer of other part of right foot"
+L9752,"Non-pressure chronic ulcer of other part of left foot"
+L978,"Non-pressure chronic ulcer of other part of lower leg"
+L9780,"Non-pressure chronic ulcer of other part of unspecified lower leg"
+L9781,"Non-pressure chronic ulcer of other part of right lower leg"
+L9782,"Non-pressure chronic ulcer of other part of left lower leg"
+L979,"Non-pressure chronic ulcer of unspecified part of lower leg"
+L9790,"Non-pressure chronic ulcer of unspecified part of unspecified lower leg"
+L9791,"Non-pressure chronic ulcer of unspecified part of right lower leg"
+L9792,"Non-pressure chronic ulcer of unspecified part of left lower leg"
+L98,"Other disorders of skin and subcutaneous tissue, not elsewhere classified"
+L984,"Non-pressure chronic ulcer of skin, not elsewhere classified"
+L9841,"Non-pressure chronic ulcer of buttock"
+L9842,"Non-pressure chronic ulcer of back"
+L9849,"Non-pressure chronic ulcer of skin of other sites"
+L985,"Mucinosis of the skin"
+L986,"Other infiltrative disorders of the skin and subcutaneous tissue"
+L987,"Excessive and redundant skin and subcutaneous tissue"
+L988,"Other specified disorders of the skin and subcutaneous tissue"
+L989,"Disorder of the skin and subcutaneous tissue, unspecified"
+L99,"Other disorders of skin and subcutaneous tissue in diseases classified elsewhere"
+M00,"Pyogenic arthritis"
+M000,"Staphylococcal arthritis and polyarthritis"
+M0001,"Staphylococcal arthritis, shoulder"
+M0002,"Staphylococcal arthritis, elbow"
+M0003,"Staphylococcal arthritis, wrist"
+M0004,"Staphylococcal arthritis, hand"
+M0005,"Staphylococcal arthritis, hip"
+M0006,"Staphylococcal arthritis, knee"
+M0007,"Staphylococcal arthritis, ankle and foot"
+M0008,"Staphylococcal arthritis, vertebrae"
+M0009,"Staphylococcal polyarthritis"
+M001,"Pneumococcal arthritis and polyarthritis"
+M0011,"Pneumococcal arthritis, shoulder"
+M0012,"Pneumococcal arthritis, elbow"
+M0013,"Pneumococcal arthritis, wrist"
+M0014,"Pneumococcal arthritis, hand"
+M0015,"Pneumococcal arthritis, hip"
+M0016,"Pneumococcal arthritis, knee"
+M0017,"Pneumococcal arthritis, ankle and foot"
+M0018,"Pneumococcal arthritis, vertebrae"
+M0019,"Pneumococcal polyarthritis"
+M002,"Other streptococcal arthritis and polyarthritis"
+M0021,"Other streptococcal arthritis, shoulder"
+M0022,"Other streptococcal arthritis, elbow"
+M0023,"Other streptococcal arthritis, wrist"
+M0024,"Other streptococcal arthritis, hand"
+M0025,"Other streptococcal arthritis, hip"
+M0026,"Other streptococcal arthritis, knee"
+M0027,"Other streptococcal arthritis, ankle and foot"
+M0028,"Other streptococcal arthritis, vertebrae"
+M0029,"Other streptococcal polyarthritis"
+M008,"Arthritis and polyarthritis due to other bacteria"
+M0081,"Arthritis due to other bacteria, shoulder"
+M0082,"Arthritis due to other bacteria, elbow"
+M0083,"Arthritis due to other bacteria, wrist"
+M0084,"Arthritis due to other bacteria, hand"
+M0085,"Arthritis due to other bacteria, hip"
+M0086,"Arthritis due to other bacteria, knee"
+M0087,"Arthritis due to other bacteria, ankle and foot"
+M0088,"Arthritis due to other bacteria, vertebrae"
+M0089,"Polyarthritis due to other bacteria"
+M009,"Pyogenic arthritis, unspecified"
+M01,"Direct infections of joint in infectious and parasitic diseases classified elsewhere"
+M01X,"Direct infection of joint in infectious and parasitic diseases classified elsewhere"
+M01X1,"Direct infection of shoulder joint in infectious and parasitic diseases classified elsewhere"
+M01X2,"Direct infection of elbow in infectious and parasitic diseases classified elsewhere"
+M01X3,"Direct infection of wrist in infectious and parasitic diseases classified elsewhere"
+M01X4,"Direct infection of hand in infectious and parasitic diseases classified elsewhere"
+M01X5,"Direct infection of hip in infectious and parasitic diseases classified elsewhere"
+M01X6,"Direct infection of knee in infectious and parasitic diseases classified elsewhere"
+M01X7,"Direct infection of ankle and foot in infectious and parasitic diseases classified elsewhere"
+M01X8,"Direct infection of vertebrae in infectious and parasitic diseases classified elsewhere"
+M01X9,"Direct infection of multiple joints in infectious and parasitic diseases classified elsewhere"
+M02,"Postinfective and reactive arthropathies"
+M020,"Arthropathy following intestinal bypass"
+M0201,"Arthropathy following intestinal bypass, shoulder"
+M0202,"Arthropathy following intestinal bypass, elbow"
+M0203,"Arthropathy following intestinal bypass, wrist"
+M0204,"Arthropathy following intestinal bypass, hand"
+M0205,"Arthropathy following intestinal bypass, hip"
+M0206,"Arthropathy following intestinal bypass, knee"
+M0207,"Arthropathy following intestinal bypass, ankle and foot"
+M0208,"Arthropathy following intestinal bypass, vertebrae"
+M0209,"Arthropathy following intestinal bypass, multiple sites"
+M021,"Postdysenteric arthropathy"
+M0211,"Postdysenteric arthropathy, shoulder"
+M0212,"Postdysenteric arthropathy, elbow"
+M0213,"Postdysenteric arthropathy, wrist"
+M0214,"Postdysenteric arthropathy, hand"
+M0215,"Postdysenteric arthropathy, hip"
+M0216,"Postdysenteric arthropathy, knee"
+M0217,"Postdysenteric arthropathy, ankle and foot"
+M0218,"Postdysenteric arthropathy, vertebrae"
+M0219,"Postdysenteric arthropathy, multiple sites"
+M022,"Postimmunization arthropathy"
+M0221,"Postimmunization arthropathy, shoulder"
+M0222,"Postimmunization arthropathy, elbow"
+M0223,"Postimmunization arthropathy, wrist"
+M0224,"Postimmunization arthropathy, hand"
+M0225,"Postimmunization arthropathy, hip"
+M0226,"Postimmunization arthropathy, knee"
+M0227,"Postimmunization arthropathy, ankle and foot"
+M0228,"Postimmunization arthropathy, vertebrae"
+M0229,"Postimmunization arthropathy, multiple sites"
+M023,"Reiter's disease"
+M0231,"Reiter's disease, shoulder"
+M0232,"Reiter's disease, elbow"
+M0233,"Reiter's disease, wrist"
+M0234,"Reiter's disease, hand"
+M0235,"Reiter's disease, hip"
+M0236,"Reiter's disease, knee"
+M0237,"Reiter's disease, ankle and foot"
+M0238,"Reiter's disease, vertebrae"
+M0239,"Reiter's disease, multiple sites"
+M028,"Other reactive arthropathies"
+M0281,"Other reactive arthropathies, shoulder"
+M0282,"Other reactive arthropathies, elbow"
+M0283,"Other reactive arthropathies, wrist"
+M0284,"Other reactive arthropathies, hand"
+M0285,"Other reactive arthropathies, hip"
+M0286,"Other reactive arthropathies, knee"
+M0287,"Other reactive arthropathies, ankle and foot"
+M0288,"Other reactive arthropathies, vertebrae"
+M0289,"Other reactive arthropathies, multiple sites"
+M029,"Reactive arthropathy, unspecified"
+M04,"Autoinflammatory syndromes"
+M05,"Rheumatoid arthritis with rheumatoid factor"
+M050,"Felty's syndrome"
+M0501,"Felty's syndrome, shoulder"
+M0502,"Felty's syndrome, elbow"
+M0503,"Felty's syndrome, wrist"
+M0504,"Felty's syndrome, hand"
+M0505,"Felty's syndrome, hip"
+M0506,"Felty's syndrome, knee"
+M0507,"Felty's syndrome, ankle and foot"
+M0509,"Felty's syndrome, multiple sites"
+M051,"Rheumatoid lung disease with rheumatoid arthritis"
+M0511,"Rheumatoid lung disease with rheumatoid arthritis of shoulder"
+M0512,"Rheumatoid lung disease with rheumatoid arthritis of elbow"
+M0513,"Rheumatoid lung disease with rheumatoid arthritis of wrist"
+M0514,"Rheumatoid lung disease with rheumatoid arthritis of hand"
+M0515,"Rheumatoid lung disease with rheumatoid arthritis of hip"
+M0516,"Rheumatoid lung disease with rheumatoid arthritis of knee"
+M0517,"Rheumatoid lung disease with rheumatoid arthritis of ankle and foot"
+M0519,"Rheumatoid lung disease with rheumatoid arthritis of multiple sites"
+M052,"Rheumatoid vasculitis with rheumatoid arthritis"
+M0521,"Rheumatoid vasculitis with rheumatoid arthritis of shoulder"
+M0522,"Rheumatoid vasculitis with rheumatoid arthritis of elbow"
+M0523,"Rheumatoid vasculitis with rheumatoid arthritis of wrist"
+M0524,"Rheumatoid vasculitis with rheumatoid arthritis of hand"
+M0525,"Rheumatoid vasculitis with rheumatoid arthritis of hip"
+M0526,"Rheumatoid vasculitis with rheumatoid arthritis of knee"
+M0527,"Rheumatoid vasculitis with rheumatoid arthritis of ankle and foot"
+M0529,"Rheumatoid vasculitis with rheumatoid arthritis of multiple sites"
+M053,"Rheumatoid heart disease with rheumatoid arthritis"
+M0531,"Rheumatoid heart disease with rheumatoid arthritis of shoulder"
+M0532,"Rheumatoid heart disease with rheumatoid arthritis of elbow"
+M0533,"Rheumatoid heart disease with rheumatoid arthritis of wrist"
+M0534,"Rheumatoid heart disease with rheumatoid arthritis of hand"
+M0535,"Rheumatoid heart disease with rheumatoid arthritis of hip"
+M0536,"Rheumatoid heart disease with rheumatoid arthritis of knee"
+M0537,"Rheumatoid heart disease with rheumatoid arthritis of ankle and foot"
+M0539,"Rheumatoid heart disease with rheumatoid arthritis of multiple sites"
+M054,"Rheumatoid myopathy with rheumatoid arthritis"
+M0541,"Rheumatoid myopathy with rheumatoid arthritis of shoulder"
+M0542,"Rheumatoid myopathy with rheumatoid arthritis of elbow"
+M0543,"Rheumatoid myopathy with rheumatoid arthritis of wrist"
+M0544,"Rheumatoid myopathy with rheumatoid arthritis of hand"
+M0545,"Rheumatoid myopathy with rheumatoid arthritis of hip"
+M0546,"Rheumatoid myopathy with rheumatoid arthritis of knee"
+M0547,"Rheumatoid myopathy with rheumatoid arthritis of ankle and foot"
+M0549,"Rheumatoid myopathy with rheumatoid arthritis of multiple sites"
+M055,"Rheumatoid polyneuropathy with rheumatoid arthritis"
+M0551,"Rheumatoid polyneuropathy with rheumatoid arthritis of shoulder"
+M0552,"Rheumatoid polyneuropathy with rheumatoid arthritis of elbow"
+M0553,"Rheumatoid polyneuropathy with rheumatoid arthritis of wrist"
+M0554,"Rheumatoid polyneuropathy with rheumatoid arthritis of hand"
+M0555,"Rheumatoid polyneuropathy with rheumatoid arthritis of hip"
+M0556,"Rheumatoid polyneuropathy with rheumatoid arthritis of knee"
+M0557,"Rheumatoid polyneuropathy with rheumatoid arthritis of ankle and foot"
+M0559,"Rheumatoid polyneuropathy with rheumatoid arthritis of multiple sites"
+M056,"Rheumatoid arthritis with involvement of other organs and systems"
+M0561,"Rheumatoid arthritis of shoulder with involvement of other organs and systems"
+M0562,"Rheumatoid arthritis of elbow with involvement of other organs and systems"
+M0563,"Rheumatoid arthritis of wrist with involvement of other organs and systems"
+M0564,"Rheumatoid arthritis of hand with involvement of other organs and systems"
+M0565,"Rheumatoid arthritis of hip with involvement of other organs and systems"
+M0566,"Rheumatoid arthritis of knee with involvement of other organs and systems"
+M0567,"Rheumatoid arthritis of ankle and foot with involvement of other organs and systems"
+M0569,"Rheumatoid arthritis of multiple sites with involvement of other organs and systems"
+M057,"Rheumatoid arthritis with rheumatoid factor without organ or systems involvement"
+M0571,"Rheumatoid arthritis with rheumatoid factor of shoulder without organ or systems involvement"
+M0572,"Rheumatoid arthritis with rheumatoid factor of elbow without organ or systems involvement"
+M0573,"Rheumatoid arthritis with rheumatoid factor of wrist without organ or systems involvement"
+M0574,"Rheumatoid arthritis with rheumatoid factor of hand without organ or systems involvement"
+M0575,"Rheumatoid arthritis with rheumatoid factor of hip without organ or systems involvement"
+M0576,"Rheumatoid arthritis with rheumatoid factor of knee without organ or systems involvement"
+M0577,"Rheumatoid arthritis with rheumatoid factor of ankle and foot without organ or systems involvement"
+M0579,"Rheumatoid arthritis with rheumatoid factor of multiple sites without organ or systems involvement"
+M058,"Other rheumatoid arthritis with rheumatoid factor"
+M0581,"Other rheumatoid arthritis with rheumatoid factor of shoulder"
+M0582,"Other rheumatoid arthritis with rheumatoid factor of elbow"
+M0583,"Other rheumatoid arthritis with rheumatoid factor of wrist"
+M0584,"Other rheumatoid arthritis with rheumatoid factor of hand"
+M0585,"Other rheumatoid arthritis with rheumatoid factor of hip"
+M0586,"Other rheumatoid arthritis with rheumatoid factor of knee"
+M0587,"Other rheumatoid arthritis with rheumatoid factor of ankle and foot"
+M0589,"Other rheumatoid arthritis with rheumatoid factor of multiple sites"
+M059,"Rheumatoid arthritis with rheumatoid factor, unspecified"
+M06,"Other rheumatoid arthritis"
+M060,"Rheumatoid arthritis without rheumatoid factor"
+M0601,"Rheumatoid arthritis without rheumatoid factor, shoulder"
+M0602,"Rheumatoid arthritis without rheumatoid factor, elbow"
+M0603,"Rheumatoid arthritis without rheumatoid factor, wrist"
+M0604,"Rheumatoid arthritis without rheumatoid factor, hand"
+M0605,"Rheumatoid arthritis without rheumatoid factor, hip"
+M0606,"Rheumatoid arthritis without rheumatoid factor, knee"
+M0607,"Rheumatoid arthritis without rheumatoid factor, ankle and foot"
+M0608,"Rheumatoid arthritis without rheumatoid factor, vertebrae"
+M0609,"Rheumatoid arthritis without rheumatoid factor, multiple sites"
+M061,"Adult-onset Still's disease"
+M062,"Rheumatoid bursitis"
+M0621,"Rheumatoid bursitis, shoulder"
+M0622,"Rheumatoid bursitis, elbow"
+M0623,"Rheumatoid bursitis, wrist"
+M0624,"Rheumatoid bursitis, hand"
+M0625,"Rheumatoid bursitis, hip"
+M0626,"Rheumatoid bursitis, knee"
+M0627,"Rheumatoid bursitis, ankle and foot"
+M0628,"Rheumatoid bursitis, vertebrae"
+M0629,"Rheumatoid bursitis, multiple sites"
+M063,"Rheumatoid nodule"
+M0631,"Rheumatoid nodule, shoulder"
+M0632,"Rheumatoid nodule, elbow"
+M0633,"Rheumatoid nodule, wrist"
+M0634,"Rheumatoid nodule, hand"
+M0635,"Rheumatoid nodule, hip"
+M0636,"Rheumatoid nodule, knee"
+M0637,"Rheumatoid nodule, ankle and foot"
+M0638,"Rheumatoid nodule, vertebrae"
+M0639,"Rheumatoid nodule, multiple sites"
+M064,"Inflammatory polyarthropathy"
+M068,"Other specified rheumatoid arthritis"
+M0681,"Other specified rheumatoid arthritis, shoulder"
+M0682,"Other specified rheumatoid arthritis, elbow"
+M0683,"Other specified rheumatoid arthritis, wrist"
+M0684,"Other specified rheumatoid arthritis, hand"
+M0685,"Other specified rheumatoid arthritis, hip"
+M0686,"Other specified rheumatoid arthritis, knee"
+M0687,"Other specified rheumatoid arthritis, ankle and foot"
+M0688,"Other specified rheumatoid arthritis, vertebrae"
+M0689,"Other specified rheumatoid arthritis, multiple sites"
+M069,"Rheumatoid arthritis, unspecified"
+M07,"Enteropathic arthropathies"
+M076,"Enteropathic arthropathies"
+M0761,"Enteropathic arthropathies, shoulder"
+M0762,"Enteropathic arthropathies, elbow"
+M0763,"Enteropathic arthropathies, wrist"
+M0764,"Enteropathic arthropathies, hand"
+M0765,"Enteropathic arthropathies, hip"
+M0766,"Enteropathic arthropathies, knee"
+M0767,"Enteropathic arthropathies, ankle and foot"
+M0768,"Enteropathic arthropathies, vertebrae"
+M0769,"Enteropathic arthropathies, multiple sites"
+M08,"Juvenile arthritis"
+M080,"Unspecified juvenile rheumatoid arthritis"
+M0801,"Unspecified juvenile rheumatoid arthritis, shoulder"
+M0802,"Unspecified juvenile rheumatoid arthritis of elbow"
+M0803,"Unspecified juvenile rheumatoid arthritis, wrist"
+M0804,"Unspecified juvenile rheumatoid arthritis, hand"
+M0805,"Unspecified juvenile rheumatoid arthritis, hip"
+M0806,"Unspecified juvenile rheumatoid arthritis, knee"
+M0807,"Unspecified juvenile rheumatoid arthritis, ankle and foot"
+M0808,"Unspecified juvenile rheumatoid arthritis, vertebrae"
+M0809,"Unspecified juvenile rheumatoid arthritis, multiple sites"
+M081,"Juvenile ankylosing spondylitis"
+M082,"Juvenile rheumatoid arthritis with systemic onset"
+M0821,"Juvenile rheumatoid arthritis with systemic onset, shoulder"
+M0822,"Juvenile rheumatoid arthritis with systemic onset, elbow"
+M0823,"Juvenile rheumatoid arthritis with systemic onset, wrist"
+M0824,"Juvenile rheumatoid arthritis with systemic onset, hand"
+M0825,"Juvenile rheumatoid arthritis with systemic onset, hip"
+M0826,"Juvenile rheumatoid arthritis with systemic onset, knee"
+M0827,"Juvenile rheumatoid arthritis with systemic onset, ankle and foot"
+M0828,"Juvenile rheumatoid arthritis with systemic onset, vertebrae"
+M0829,"Juvenile rheumatoid arthritis with systemic onset, multiple sites"
+M083,"Juvenile rheumatoid polyarthritis (seronegative)"
+M084,"Pauciarticular juvenile rheumatoid arthritis"
+M0841,"Pauciarticular juvenile rheumatoid arthritis, shoulder"
+M0842,"Pauciarticular juvenile rheumatoid arthritis, elbow"
+M0843,"Pauciarticular juvenile rheumatoid arthritis, wrist"
+M0844,"Pauciarticular juvenile rheumatoid arthritis, hand"
+M0845,"Pauciarticular juvenile rheumatoid arthritis, hip"
+M0846,"Pauciarticular juvenile rheumatoid arthritis, knee"
+M0847,"Pauciarticular juvenile rheumatoid arthritis, ankle and foot"
+M0848,"Pauciarticular juvenile rheumatoid arthritis, vertebrae"
+M088,"Other juvenile arthritis"
+M0881,"Other juvenile arthritis, shoulder"
+M0882,"Other juvenile arthritis, elbow"
+M0883,"Other juvenile arthritis, wrist"
+M0884,"Other juvenile arthritis, hand"
+M0885,"Other juvenile arthritis, hip"
+M0886,"Other juvenile arthritis, knee"
+M0887,"Other juvenile arthritis, ankle and foot"
+M0888,"Other juvenile arthritis, other specified site"
+M0889,"Other juvenile arthritis, multiple sites"
+M089,"Juvenile arthritis, unspecified"
+M0891,"Juvenile arthritis, unspecified, shoulder"
+M0892,"Juvenile arthritis, unspecified, elbow"
+M0893,"Juvenile arthritis, unspecified, wrist"
+M0894,"Juvenile arthritis, unspecified, hand"
+M0895,"Juvenile arthritis, unspecified, hip"
+M0896,"Juvenile arthritis, unspecified, knee"
+M0897,"Juvenile arthritis, unspecified, ankle and foot"
+M0898,"Juvenile arthritis, unspecified, vertebrae"
+M0899,"Juvenile arthritis, unspecified, multiple sites"
+M1A,"Chronic gout"
+M1A0,"Idiopathic chronic gout"
+M1A00,"Idiopathic chronic gout, unspecified site"
+M1A01,"Idiopathic chronic gout, shoulder"
+M1A011,"Idiopathic chronic gout, right shoulder"
+M1A012,"Idiopathic chronic gout, left shoulder"
+M1A019,"Idiopathic chronic gout, unspecified shoulder"
+M1A02,"Idiopathic chronic gout, elbow"
+M1A021,"Idiopathic chronic gout, right elbow"
+M1A022,"Idiopathic chronic gout, left elbow"
+M1A029,"Idiopathic chronic gout, unspecified elbow"
+M1A03,"Idiopathic chronic gout, wrist"
+M1A031,"Idiopathic chronic gout, right wrist"
+M1A032,"Idiopathic chronic gout, left wrist"
+M1A039,"Idiopathic chronic gout, unspecified wrist"
+M1A04,"Idiopathic chronic gout, hand"
+M1A041,"Idiopathic chronic gout, right hand"
+M1A042,"Idiopathic chronic gout, left hand"
+M1A049,"Idiopathic chronic gout, unspecified hand"
+M1A05,"Idiopathic chronic gout, hip"
+M1A051,"Idiopathic chronic gout, right hip"
+M1A052,"Idiopathic chronic gout, left hip"
+M1A059,"Idiopathic chronic gout, unspecified hip"
+M1A06,"Idiopathic chronic gout, knee"
+M1A061,"Idiopathic chronic gout, right knee"
+M1A062,"Idiopathic chronic gout, left knee"
+M1A069,"Idiopathic chronic gout, unspecified knee"
+M1A07,"Idiopathic chronic gout, ankle and foot"
+M1A071,"Idiopathic chronic gout, right ankle and foot"
+M1A072,"Idiopathic chronic gout, left ankle and foot"
+M1A079,"Idiopathic chronic gout, unspecified ankle and foot"
+M1A08,"Idiopathic chronic gout, vertebrae"
+M1A09,"Idiopathic chronic gout, multiple sites"
+M1A1,"Lead-induced chronic gout"
+M1A10,"Lead-induced chronic gout, unspecified site"
+M1A11,"Lead-induced chronic gout, shoulder"
+M1A111,"Lead-induced chronic gout, right shoulder"
+M1A112,"Lead-induced chronic gout, left shoulder"
+M1A119,"Lead-induced chronic gout, unspecified shoulder"
+M1A12,"Lead-induced chronic gout, elbow"
+M1A121,"Lead-induced chronic gout, right elbow"
+M1A122,"Lead-induced chronic gout, left elbow"
+M1A129,"Lead-induced chronic gout, unspecified elbow"
+M1A13,"Lead-induced chronic gout, wrist"
+M1A131,"Lead-induced chronic gout, right wrist"
+M1A132,"Lead-induced chronic gout, left wrist"
+M1A139,"Lead-induced chronic gout, unspecified wrist"
+M1A14,"Lead-induced chronic gout, hand"
+M1A141,"Lead-induced chronic gout, right hand"
+M1A142,"Lead-induced chronic gout, left hand"
+M1A149,"Lead-induced chronic gout, unspecified hand"
+M1A15,"Lead-induced chronic gout, hip"
+M1A151,"Lead-induced chronic gout, right hip"
+M1A152,"Lead-induced chronic gout, left hip"
+M1A159,"Lead-induced chronic gout, unspecified hip"
+M1A16,"Lead-induced chronic gout, knee"
+M1A161,"Lead-induced chronic gout, right knee"
+M1A162,"Lead-induced chronic gout, left knee"
+M1A169,"Lead-induced chronic gout, unspecified knee"
+M1A17,"Lead-induced chronic gout, ankle and foot"
+M1A171,"Lead-induced chronic gout, right ankle and foot"
+M1A172,"Lead-induced chronic gout, left ankle and foot"
+M1A179,"Lead-induced chronic gout, unspecified ankle and foot"
+M1A18,"Lead-induced chronic gout, vertebrae"
+M1A19,"Lead-induced chronic gout, multiple sites"
+M1A2,"Drug-induced chronic gout"
+M1A20,"Drug-induced chronic gout, unspecified site"
+M1A21,"Drug-induced chronic gout, shoulder"
+M1A211,"Drug-induced chronic gout, right shoulder"
+M1A212,"Drug-induced chronic gout, left shoulder"
+M1A219,"Drug-induced chronic gout, unspecified shoulder"
+M1A22,"Drug-induced chronic gout, elbow"
+M1A221,"Drug-induced chronic gout, right elbow"
+M1A222,"Drug-induced chronic gout, left elbow"
+M1A229,"Drug-induced chronic gout, unspecified elbow"
+M1A23,"Drug-induced chronic gout, wrist"
+M1A231,"Drug-induced chronic gout, right wrist"
+M1A232,"Drug-induced chronic gout, left wrist"
+M1A239,"Drug-induced chronic gout, unspecified wrist"
+M1A24,"Drug-induced chronic gout, hand"
+M1A241,"Drug-induced chronic gout, right hand"
+M1A242,"Drug-induced chronic gout, left hand"
+M1A249,"Drug-induced chronic gout, unspecified hand"
+M1A25,"Drug-induced chronic gout, hip"
+M1A251,"Drug-induced chronic gout, right hip"
+M1A252,"Drug-induced chronic gout, left hip"
+M1A259,"Drug-induced chronic gout, unspecified hip"
+M1A26,"Drug-induced chronic gout, knee"
+M1A261,"Drug-induced chronic gout, right knee"
+M1A262,"Drug-induced chronic gout, left knee"
+M1A269,"Drug-induced chronic gout, unspecified knee"
+M1A27,"Drug-induced chronic gout, ankle and foot"
+M1A271,"Drug-induced chronic gout, right ankle and foot"
+M1A272,"Drug-induced chronic gout, left ankle and foot"
+M1A279,"Drug-induced chronic gout, unspecified ankle and foot"
+M1A28,"Drug-induced chronic gout, vertebrae"
+M1A29,"Drug-induced chronic gout, multiple sites"
+M1A3,"Chronic gout due to renal impairment"
+M1A30,"Chronic gout due to renal impairment, unspecified site"
+M1A31,"Chronic gout due to renal impairment, shoulder"
+M1A311,"Chronic gout due to renal impairment, right shoulder"
+M1A312,"Chronic gout due to renal impairment, left shoulder"
+M1A319,"Chronic gout due to renal impairment, unspecified shoulder"
+M1A32,"Chronic gout due to renal impairment, elbow"
+M1A321,"Chronic gout due to renal impairment, right elbow"
+M1A322,"Chronic gout due to renal impairment, left elbow"
+M1A329,"Chronic gout due to renal impairment, unspecified elbow"
+M1A33,"Chronic gout due to renal impairment, wrist"
+M1A331,"Chronic gout due to renal impairment, right wrist"
+M1A332,"Chronic gout due to renal impairment, left wrist"
+M1A339,"Chronic gout due to renal impairment, unspecified wrist"
+M1A34,"Chronic gout due to renal impairment, hand"
+M1A341,"Chronic gout due to renal impairment, right hand"
+M1A342,"Chronic gout due to renal impairment, left hand"
+M1A349,"Chronic gout due to renal impairment, unspecified hand"
+M1A35,"Chronic gout due to renal impairment, hip"
+M1A351,"Chronic gout due to renal impairment, right hip"
+M1A352,"Chronic gout due to renal impairment, left hip"
+M1A359,"Chronic gout due to renal impairment, unspecified hip"
+M1A36,"Chronic gout due to renal impairment, knee"
+M1A361,"Chronic gout due to renal impairment, right knee"
+M1A362,"Chronic gout due to renal impairment, left knee"
+M1A369,"Chronic gout due to renal impairment, unspecified knee"
+M1A37,"Chronic gout due to renal impairment, ankle and foot"
+M1A371,"Chronic gout due to renal impairment, right ankle and foot"
+M1A372,"Chronic gout due to renal impairment, left ankle and foot"
+M1A379,"Chronic gout due to renal impairment, unspecified ankle and foot"
+M1A38,"Chronic gout due to renal impairment, vertebrae"
+M1A39,"Chronic gout due to renal impairment, multiple sites"
+M1A4,"Other secondary chronic gout"
+M1A40,"Other secondary chronic gout, unspecified site"
+M1A41,"Other secondary chronic gout, shoulder"
+M1A411,"Other secondary chronic gout, right shoulder"
+M1A412,"Other secondary chronic gout, left shoulder"
+M1A419,"Other secondary chronic gout, unspecified shoulder"
+M1A42,"Other secondary chronic gout, elbow"
+M1A421,"Other secondary chronic gout, right elbow"
+M1A422,"Other secondary chronic gout, left elbow"
+M1A429,"Other secondary chronic gout, unspecified elbow"
+M1A43,"Other secondary chronic gout, wrist"
+M1A431,"Other secondary chronic gout, right wrist"
+M1A432,"Other secondary chronic gout, left wrist"
+M1A439,"Other secondary chronic gout, unspecified wrist"
+M1A44,"Other secondary chronic gout, hand"
+M1A441,"Other secondary chronic gout, right hand"
+M1A442,"Other secondary chronic gout, left hand"
+M1A449,"Other secondary chronic gout, unspecified hand"
+M1A45,"Other secondary chronic gout, hip"
+M1A451,"Other secondary chronic gout, right hip"
+M1A452,"Other secondary chronic gout, left hip"
+M1A459,"Other secondary chronic gout, unspecified hip"
+M1A46,"Other secondary chronic gout, knee"
+M1A461,"Other secondary chronic gout, right knee"
+M1A462,"Other secondary chronic gout, left knee"
+M1A469,"Other secondary chronic gout, unspecified knee"
+M1A47,"Other secondary chronic gout, ankle and foot"
+M1A471,"Other secondary chronic gout, right ankle and foot"
+M1A472,"Other secondary chronic gout, left ankle and foot"
+M1A479,"Other secondary chronic gout, unspecified ankle and foot"
+M1A48,"Other secondary chronic gout, vertebrae"
+M1A49,"Other secondary chronic gout, multiple sites"
+M1A9,"Chronic gout, unspecified"
+M10,"Gout"
+M100,"Idiopathic gout"
+M1001,"Idiopathic gout, shoulder"
+M1002,"Idiopathic gout, elbow"
+M1003,"Idiopathic gout, wrist"
+M1004,"Idiopathic gout, hand"
+M1005,"Idiopathic gout, hip"
+M1006,"Idiopathic gout, knee"
+M1007,"Idiopathic gout, ankle and foot"
+M1008,"Idiopathic gout, vertebrae"
+M1009,"Idiopathic gout, multiple sites"
+M101,"Lead-induced gout"
+M1011,"Lead-induced gout, shoulder"
+M1012,"Lead-induced gout, elbow"
+M1013,"Lead-induced gout, wrist"
+M1014,"Lead-induced gout, hand"
+M1015,"Lead-induced gout, hip"
+M1016,"Lead-induced gout, knee"
+M1017,"Lead-induced gout, ankle and foot"
+M1018,"Lead-induced gout, vertebrae"
+M1019,"Lead-induced gout, multiple sites"
+M102,"Drug-induced gout"
+M1021,"Drug-induced gout, shoulder"
+M1022,"Drug-induced gout, elbow"
+M1023,"Drug-induced gout, wrist"
+M1024,"Drug-induced gout, hand"
+M1025,"Drug-induced gout, hip"
+M1026,"Drug-induced gout, knee"
+M1027,"Drug-induced gout, ankle and foot"
+M1028,"Drug-induced gout, vertebrae"
+M1029,"Drug-induced gout, multiple sites"
+M103,"Gout due to renal impairment"
+M1031,"Gout due to renal impairment, shoulder"
+M1032,"Gout due to renal impairment, elbow"
+M1033,"Gout due to renal impairment, wrist"
+M1034,"Gout due to renal impairment, hand"
+M1035,"Gout due to renal impairment, hip"
+M1036,"Gout due to renal impairment, knee"
+M1037,"Gout due to renal impairment, ankle and foot"
+M1038,"Gout due to renal impairment, vertebrae"
+M1039,"Gout due to renal impairment, multiple sites"
+M104,"Other secondary gout"
+M1041,"Other secondary gout, shoulder"
+M1042,"Other secondary gout, elbow"
+M1043,"Other secondary gout, wrist"
+M1044,"Other secondary gout, hand"
+M1045,"Other secondary gout, hip"
+M1046,"Other secondary gout, knee"
+M1047,"Other secondary gout, ankle and foot"
+M1048,"Other secondary gout, vertebrae"
+M1049,"Other secondary gout, multiple sites"
+M109,"Gout, unspecified"
+M11,"Other crystal arthropathies"
+M110,"Hydroxyapatite deposition disease"
+M1101,"Hydroxyapatite deposition disease, shoulder"
+M1102,"Hydroxyapatite deposition disease, elbow"
+M1103,"Hydroxyapatite deposition disease, wrist"
+M1104,"Hydroxyapatite deposition disease, hand"
+M1105,"Hydroxyapatite deposition disease, hip"
+M1106,"Hydroxyapatite deposition disease, knee"
+M1107,"Hydroxyapatite deposition disease, ankle and foot"
+M1108,"Hydroxyapatite deposition disease, vertebrae"
+M1109,"Hydroxyapatite deposition disease, multiple sites"
+M111,"Familial chondrocalcinosis"
+M1111,"Familial chondrocalcinosis, shoulder"
+M1112,"Familial chondrocalcinosis, elbow"
+M1113,"Familial chondrocalcinosis, wrist"
+M1114,"Familial chondrocalcinosis, hand"
+M1115,"Familial chondrocalcinosis, hip"
+M1116,"Familial chondrocalcinosis, knee"
+M1117,"Familial chondrocalcinosis, ankle and foot"
+M1118,"Familial chondrocalcinosis, vertebrae"
+M1119,"Familial chondrocalcinosis, multiple sites"
+M112,"Other chondrocalcinosis"
+M1121,"Other chondrocalcinosis, shoulder"
+M1122,"Other chondrocalcinosis, elbow"
+M1123,"Other chondrocalcinosis, wrist"
+M1124,"Other chondrocalcinosis, hand"
+M1125,"Other chondrocalcinosis, hip"
+M1126,"Other chondrocalcinosis, knee"
+M1127,"Other chondrocalcinosis, ankle and foot"
+M1128,"Other chondrocalcinosis, vertebrae"
+M1129,"Other chondrocalcinosis, multiple sites"
+M118,"Other specified crystal arthropathies"
+M1181,"Other specified crystal arthropathies, shoulder"
+M1182,"Other specified crystal arthropathies, elbow"
+M1183,"Other specified crystal arthropathies, wrist"
+M1184,"Other specified crystal arthropathies, hand"
+M1185,"Other specified crystal arthropathies, hip"
+M1186,"Other specified crystal arthropathies, knee"
+M1187,"Other specified crystal arthropathies, ankle and foot"
+M1188,"Other specified crystal arthropathies, vertebrae"
+M1189,"Other specified crystal arthropathies, multiple sites"
+M119,"Crystal arthropathy, unspecified"
+M12,"Other and unspecified arthropathy"
+M120,"Chronic postrheumatic arthropathy [Jaccoud]"
+M1201,"Chronic postrheumatic arthropathy [Jaccoud], shoulder"
+M1202,"Chronic postrheumatic arthropathy [Jaccoud], elbow"
+M1203,"Chronic postrheumatic arthropathy [Jaccoud], wrist"
+M1204,"Chronic postrheumatic arthropathy [Jaccoud], hand"
+M1205,"Chronic postrheumatic arthropathy [Jaccoud], hip"
+M1206,"Chronic postrheumatic arthropathy [Jaccoud], knee"
+M1207,"Chronic postrheumatic arthropathy [Jaccoud], ankle and foot"
+M1208,"Chronic postrheumatic arthropathy [Jaccoud], other specified site"
+M1209,"Chronic postrheumatic arthropathy [Jaccoud], multiple sites"
+M121,"Kaschin-Beck disease"
+M1211,"Kaschin-Beck disease, shoulder"
+M1212,"Kaschin-Beck disease, elbow"
+M1213,"Kaschin-Beck disease, wrist"
+M1214,"Kaschin-Beck disease, hand"
+M1215,"Kaschin-Beck disease, hip"
+M1216,"Kaschin-Beck disease, knee"
+M1217,"Kaschin-Beck disease, ankle and foot"
+M1218,"Kaschin-Beck disease, vertebrae"
+M1219,"Kaschin-Beck disease, multiple sites"
+M122,"Villonodular synovitis (pigmented)"
+M1221,"Villonodular synovitis (pigmented), shoulder"
+M1222,"Villonodular synovitis (pigmented), elbow"
+M1223,"Villonodular synovitis (pigmented), wrist"
+M1224,"Villonodular synovitis (pigmented), hand"
+M1225,"Villonodular synovitis (pigmented), hip"
+M1226,"Villonodular synovitis (pigmented), knee"
+M1227,"Villonodular synovitis (pigmented), ankle and foot"
+M1228,"Villonodular synovitis (pigmented), other specified site"
+M1229,"Villonodular synovitis (pigmented), multiple sites"
+M123,"Palindromic rheumatism"
+M1231,"Palindromic rheumatism, shoulder"
+M1232,"Palindromic rheumatism, elbow"
+M1233,"Palindromic rheumatism, wrist"
+M1234,"Palindromic rheumatism, hand"
+M1235,"Palindromic rheumatism, hip"
+M1236,"Palindromic rheumatism, knee"
+M1237,"Palindromic rheumatism, ankle and foot"
+M1238,"Palindromic rheumatism, other specified site"
+M1239,"Palindromic rheumatism, multiple sites"
+M124,"Intermittent hydrarthrosis"
+M1241,"Intermittent hydrarthrosis, shoulder"
+M1242,"Intermittent hydrarthrosis, elbow"
+M1243,"Intermittent hydrarthrosis, wrist"
+M1244,"Intermittent hydrarthrosis, hand"
+M1245,"Intermittent hydrarthrosis, hip"
+M1246,"Intermittent hydrarthrosis, knee"
+M1247,"Intermittent hydrarthrosis, ankle and foot"
+M1248,"Intermittent hydrarthrosis, other site"
+M1249,"Intermittent hydrarthrosis, multiple sites"
+M125,"Traumatic arthropathy"
+M1251,"Traumatic arthropathy, shoulder"
+M1252,"Traumatic arthropathy, elbow"
+M1253,"Traumatic arthropathy, wrist"
+M1254,"Traumatic arthropathy, hand"
+M1255,"Traumatic arthropathy, hip"
+M1256,"Traumatic arthropathy, knee"
+M1257,"Traumatic arthropathy, ankle and foot"
+M1258,"Traumatic arthropathy, other specified site"
+M1259,"Traumatic arthropathy, multiple sites"
+M128,"Other specific arthropathies, not elsewhere classified"
+M1281,"Other specific arthropathies, not elsewhere classified, shoulder"
+M1282,"Other specific arthropathies, not elsewhere classified, elbow"
+M1283,"Other specific arthropathies, not elsewhere classified, wrist"
+M1284,"Other specific arthropathies, not elsewhere classified, hand"
+M1285,"Other specific arthropathies, not elsewhere classified, hip"
+M1286,"Other specific arthropathies, not elsewhere classified, knee"
+M1287,"Other specific arthropathies, not elsewhere classified, ankle and foot"
+M1288,"Other specific arthropathies, not elsewhere classified, other specified site"
+M1289,"Other specific arthropathies, not elsewhere classified, multiple sites"
+M129,"Arthropathy, unspecified"
+M13,"Other arthritis"
+M131,"Monoarthritis, not elsewhere classified"
+M1311,"Monoarthritis, not elsewhere classified, shoulder"
+M1312,"Monoarthritis, not elsewhere classified, elbow"
+M1313,"Monoarthritis, not elsewhere classified, wrist"
+M1314,"Monoarthritis, not elsewhere classified, hand"
+M1315,"Monoarthritis, not elsewhere classified, hip"
+M1316,"Monoarthritis, not elsewhere classified, knee"
+M1317,"Monoarthritis, not elsewhere classified, ankle and foot"
+M138,"Other specified arthritis"
+M1381,"Other specified arthritis, shoulder"
+M1382,"Other specified arthritis, elbow"
+M1383,"Other specified arthritis, wrist"
+M1384,"Other specified arthritis, hand"
+M1385,"Other specified arthritis, hip"
+M1386,"Other specified arthritis, knee"
+M1387,"Other specified arthritis, ankle and foot"
+M1388,"Other specified arthritis, other site"
+M1389,"Other specified arthritis, multiple sites"
+M14,"Arthropathies in other diseases classified elsewhere"
+M146,"Charcot's joint"
+M1461,"Charcot's joint, shoulder"
+M1462,"Charcot's joint, elbow"
+M1463,"Charcot's joint, wrist"
+M1464,"Charcot's joint, hand"
+M1465,"Charcot's joint, hip"
+M1466,"Charcot's joint, knee"
+M1467,"Charcot's joint, ankle and foot"
+M1468,"Charcot's joint, vertebrae"
+M1469,"Charcot's joint, multiple sites"
+M148,"Arthropathies in other specified diseases classified elsewhere"
+M1481,"Arthropathies in other specified diseases classified elsewhere, shoulder"
+M1482,"Arthropathies in other specified diseases classified elsewhere, elbow"
+M1483,"Arthropathies in other specified diseases classified elsewhere, wrist"
+M1484,"Arthropathies in other specified diseases classified elsewhere, hand"
+M1485,"Arthropathies in other specified diseases classified elsewhere, hip"
+M1486,"Arthropathies in other specified diseases classified elsewhere, knee"
+M1487,"Arthropathies in other specified diseases classified elsewhere, ankle and foot"
+M1488,"Arthropathies in other specified diseases classified elsewhere, vertebrae"
+M1489,"Arthropathies in other specified diseases classified elsewhere, multiple sites"
+M15,"Polyosteoarthritis"
+M16,"Osteoarthritis of hip"
+M161,"Unilateral primary osteoarthritis of hip"
+M162,"Bilateral osteoarthritis resulting from hip dysplasia"
+M163,"Unilateral osteoarthritis resulting from hip dysplasia"
+M164,"Bilateral post-traumatic osteoarthritis of hip"
+M165,"Unilateral post-traumatic osteoarthritis of hip"
+M166,"Other bilateral secondary osteoarthritis of hip"
+M167,"Other unilateral secondary osteoarthritis of hip"
+M169,"Osteoarthritis of hip, unspecified"
+M17,"Osteoarthritis of knee"
+M171,"Unilateral primary osteoarthritis of knee"
+M172,"Bilateral post-traumatic osteoarthritis of knee"
+M173,"Unilateral post-traumatic osteoarthritis of knee"
+M174,"Other bilateral secondary osteoarthritis of knee"
+M175,"Other unilateral secondary osteoarthritis of knee"
+M179,"Osteoarthritis of knee, unspecified"
+M18,"Osteoarthritis of first carpometacarpal joint"
+M181,"Unilateral primary osteoarthritis of first carpometacarpal joint"
+M182,"Bilateral post-traumatic osteoarthritis of first carpometacarpal joints"
+M183,"Unilateral post-traumatic osteoarthritis of first carpometacarpal joint"
+M184,"Other bilateral secondary osteoarthritis of first carpometacarpal joints"
+M185,"Other unilateral secondary osteoarthritis of first carpometacarpal joint"
+M189,"Osteoarthritis of first carpometacarpal joint, unspecified"
+M19,"Other and unspecified osteoarthritis"
+M190,"Primary osteoarthritis of other joints"
+M1901,"Primary osteoarthritis, shoulder"
+M1902,"Primary osteoarthritis, elbow"
+M1903,"Primary osteoarthritis, wrist"
+M1904,"Primary osteoarthritis, hand"
+M1907,"Primary osteoarthritis ankle and foot"
+M191,"Post-traumatic osteoarthritis of other joints"
+M1911,"Post-traumatic osteoarthritis, shoulder"
+M1912,"Post-traumatic osteoarthritis, elbow"
+M1913,"Post-traumatic osteoarthritis, wrist"
+M1914,"Post-traumatic osteoarthritis, hand"
+M1917,"Post-traumatic osteoarthritis, ankle and foot"
+M192,"Secondary osteoarthritis of other joints"
+M1921,"Secondary osteoarthritis, shoulder"
+M1922,"Secondary osteoarthritis, elbow"
+M1923,"Secondary osteoarthritis, wrist"
+M1924,"Secondary osteoarthritis, hand"
+M1927,"Secondary osteoarthritis, ankle and foot"
+M199,"Osteoarthritis, unspecified site"
+M20,"Acquired deformities of fingers and toes"
+M200,"Deformity of finger(s)"
+M2000,"Unspecified deformity of finger(s)"
+M2001,"Mallet finger"
+M2002,"Boutonniere deformity"
+M2003,"Swan-neck deformity"
+M2009,"Other deformity of finger(s)"
+M201,"Hallux valgus (acquired)"
+M202,"Hallux rigidus"
+M203,"Hallux varus (acquired)"
+M204,"Other hammer toe(s) (acquired)"
+M205,"Other deformities of toe(s) (acquired)"
+M205X,"Other deformities of toe(s) (acquired)"
+M206,"Acquired deformities of toe(s), unspecified"
+M21,"Other acquired deformities of limbs"
+M210,"Valgus deformity, not elsewhere classified"
+M2102,"Valgus deformity, not elsewhere classified, elbow"
+M2105,"Valgus deformity, not elsewhere classified, hip"
+M2106,"Valgus deformity, not elsewhere classified, knee"
+M2107,"Valgus deformity, not elsewhere classified, ankle"
+M211,"Varus deformity, not elsewhere classified"
+M2112,"Varus deformity, not elsewhere classified, elbow"
+M2115,"Varus deformity, not elsewhere classified, hip"
+M2116,"Varus deformity, not elsewhere classified, knee"
+M2117,"Varus deformity, not elsewhere classified, ankle"
+M212,"Flexion deformity"
+M2121,"Flexion deformity, shoulder"
+M2122,"Flexion deformity, elbow"
+M2123,"Flexion deformity, wrist"
+M2124,"Flexion deformity, finger joints"
+M2125,"Flexion deformity, hip"
+M2126,"Flexion deformity, knee"
+M2127,"Flexion deformity, ankle and toes"
+M213,"Wrist or foot drop (acquired)"
+M2133,"Wrist drop (acquired)"
+M2137,"Foot drop (acquired)"
+M214,"Flat foot [pes planus] (acquired)"
+M215,"Acquired clawhand, clubhand, clawfoot and clubfoot"
+M2151,"Acquired clawhand"
+M2152,"Acquired clubhand"
+M2153,"Acquired clawfoot"
+M2154,"Acquired clubfoot"
+M216,"Other acquired deformities of foot"
+M2161,"Bunion"
+M2162,"Bunionette"
+M216X,"Other acquired deformities of foot"
+M217,"Unequal limb length (acquired)"
+M2172,"Unequal limb length (acquired), humerus"
+M2173,"Unequal limb length (acquired), ulna and radius"
+M2175,"Unequal limb length (acquired), femur"
+M2176,"Unequal limb length (acquired), tibia and fibula"
+M218,"Other specified acquired deformities of limbs"
+M2182,"Other specified acquired deformities of upper arm"
+M2183,"Other specified acquired deformities of forearm"
+M2185,"Other specified acquired deformities of thigh"
+M2186,"Other specified acquired deformities of lower leg"
+M219,"Unspecified acquired deformity of limb and hand"
+M2192,"Unspecified acquired deformity of upper arm"
+M2193,"Unspecified acquired deformity of forearm"
+M2194,"Unspecified acquired deformity of hand"
+M2195,"Unspecified acquired deformity of thigh"
+M2196,"Unspecified acquired deformity of lower leg"
+M22,"Disorder of patella"
+M220,"Recurrent dislocation of patella"
+M221,"Recurrent subluxation of patella"
+M222,"Patellofemoral disorders"
+M222X,"Patellofemoral disorders"
+M223,"Other derangements of patella"
+M223X,"Other derangements of patella"
+M224,"Chondromalacia patellae"
+M228,"Other disorders of patella"
+M228X,"Other disorders of patella"
+M229,"Unspecified disorder of patella"
+M23,"Internal derangement of knee"
+M230,"Cystic meniscus"
+M2300,"Cystic meniscus, unspecified meniscus"
+M2301,"Cystic meniscus, anterior horn of medial meniscus"
+M2302,"Cystic meniscus, posterior horn of medial meniscus"
+M2303,"Cystic meniscus, other medial meniscus"
+M2304,"Cystic meniscus, anterior horn of lateral meniscus"
+M2305,"Cystic meniscus, posterior horn of lateral meniscus"
+M2306,"Cystic meniscus, other lateral meniscus"
+M232,"Derangement of meniscus due to old tear or injury"
+M2320,"Derangement of unspecified meniscus due to old tear or injury"
+M2321,"Derangement of anterior horn of medial meniscus due to old tear or injury"
+M2322,"Derangement of posterior horn of medial meniscus due to old tear or injury"
+M2323,"Derangement of other medial meniscus due to old tear or injury"
+M2324,"Derangement of anterior horn of lateral meniscus due to old tear or injury"
+M2325,"Derangement of posterior horn of lateral meniscus due to old tear or injury"
+M2326,"Derangement of other lateral meniscus due to old tear or injury"
+M233,"Other meniscus derangements"
+M2330,"Other meniscus derangements, unspecified meniscus"
+M2331,"Other meniscus derangements, anterior horn of medial meniscus"
+M2332,"Other meniscus derangements, posterior horn of medial meniscus"
+M2333,"Other meniscus derangements, other medial meniscus"
+M2334,"Other meniscus derangements, anterior horn of lateral meniscus"
+M2335,"Other meniscus derangements, posterior horn of lateral meniscus"
+M2336,"Other meniscus derangements, other lateral meniscus"
+M234,"Loose body in knee"
+M235,"Chronic instability of knee"
+M236,"Other spontaneous disruption of ligament(s) of knee"
+M2360,"Other spontaneous disruption of unspecified ligament of knee"
+M2361,"Other spontaneous disruption of anterior cruciate ligament of knee"
+M2362,"Other spontaneous disruption of posterior cruciate ligament of knee"
+M2363,"Other spontaneous disruption of medial collateral ligament of knee"
+M2364,"Other spontaneous disruption of lateral collateral ligament of knee"
+M2367,"Other spontaneous disruption of capsular ligament of knee"
+M238,"Other internal derangements of knee"
+M238X,"Other internal derangements of knee"
+M239,"Unspecified internal derangement of knee"
+M24,"Other specific joint derangements"
+M240,"Loose body in joint"
+M2401,"Loose body in shoulder"
+M2402,"Loose body in elbow"
+M2403,"Loose body in wrist"
+M2404,"Loose body in finger joints"
+M2405,"Loose body in hip"
+M2407,"Loose body in ankle and toe joints"
+M2408,"Loose body, other site"
+M241,"Other articular cartilage disorders"
+M2411,"Other articular cartilage disorders, shoulder"
+M2412,"Other articular cartilage disorders, elbow"
+M2413,"Other articular cartilage disorders, wrist"
+M2414,"Other articular cartilage disorders, hand"
+M2415,"Other articular cartilage disorders, hip"
+M2417,"Other articular cartilage disorders, ankle and foot"
+M242,"Disorder of ligament"
+M2421,"Disorder of ligament, shoulder"
+M2422,"Disorder of ligament, elbow"
+M2423,"Disorder of ligament, wrist"
+M2424,"Disorder of ligament, hand"
+M2425,"Disorder of ligament, hip"
+M2427,"Disorder of ligament, ankle and foot"
+M2428,"Disorder of ligament, vertebrae"
+M243,"Pathological dislocation of joint, not elsewhere classified"
+M2431,"Pathological dislocation of shoulder, not elsewhere classified"
+M2432,"Pathological dislocation of elbow, not elsewhere classified"
+M2433,"Pathological dislocation of wrist, not elsewhere classified"
+M2434,"Pathological dislocation of hand, not elsewhere classified"
+M2435,"Pathological dislocation of hip, not elsewhere classified"
+M2436,"Pathological dislocation of knee, not elsewhere classified"
+M2437,"Pathological dislocation of ankle and foot, not elsewhere classified"
+M244,"Recurrent dislocation of joint"
+M2441,"Recurrent dislocation, shoulder"
+M2442,"Recurrent dislocation, elbow"
+M2443,"Recurrent dislocation, wrist"
+M2444,"Recurrent dislocation, hand and finger(s)"
+M2445,"Recurrent dislocation, hip"
+M2446,"Recurrent dislocation, knee"
+M2447,"Recurrent dislocation, ankle, foot and toes"
+M245,"Contracture of joint"
+M2451,"Contracture, shoulder"
+M2452,"Contracture, elbow"
+M2453,"Contracture, wrist"
+M2454,"Contracture, hand"
+M2455,"Contracture, hip"
+M2456,"Contracture, knee"
+M2457,"Contracture, ankle and foot"
+M246,"Ankylosis of joint"
+M2461,"Ankylosis, shoulder"
+M2462,"Ankylosis, elbow"
+M2463,"Ankylosis, wrist"
+M2464,"Ankylosis, hand"
+M2465,"Ankylosis, hip"
+M2466,"Ankylosis, knee"
+M2467,"Ankylosis, ankle and foot"
+M247,"Protrusio acetabuli"
+M248,"Other specific joint derangements, not elsewhere classified"
+M2481,"Other specific joint derangements of shoulder, not elsewhere classified"
+M2482,"Other specific joint derangements of elbow, not elsewhere classified"
+M2483,"Other specific joint derangements of wrist, not elsewhere classified"
+M2484,"Other specific joint derangements of hand, not elsewhere classified"
+M2485,"Other specific joint derangements of hip, not elsewhere classified"
+M2487,"Other specific joint derangements of ankle and foot, not elsewhere classified"
+M249,"Joint derangement, unspecified"
+M25,"Other joint disorder, not elsewhere classified"
+M250,"Hemarthrosis"
+M2501,"Hemarthrosis, shoulder"
+M2502,"Hemarthrosis, elbow"
+M2503,"Hemarthrosis, wrist"
+M2504,"Hemarthrosis, hand"
+M2505,"Hemarthrosis, hip"
+M2506,"Hemarthrosis, knee"
+M2507,"Hemarthrosis, ankle and foot"
+M2508,"Hemarthrosis, other specified site"
+M251,"Fistula of joint"
+M2511,"Fistula, shoulder"
+M2512,"Fistula, elbow"
+M2513,"Fistula, wrist"
+M2514,"Fistula, hand"
+M2515,"Fistula, hip"
+M2516,"Fistula, knee"
+M2517,"Fistula, ankle and foot"
+M2518,"Fistula, other specified site"
+M252,"Flail joint"
+M2521,"Flail joint, shoulder"
+M2522,"Flail joint, elbow"
+M2523,"Flail joint, wrist"
+M2524,"Flail joint, hand"
+M2525,"Flail joint, hip"
+M2526,"Flail joint, knee"
+M2527,"Flail joint, ankle and foot"
+M2528,"Flail joint, other site"
+M253,"Other instability of joint"
+M2531,"Other instability, shoulder"
+M2532,"Other instability, elbow"
+M2533,"Other instability, wrist"
+M2534,"Other instability, hand"
+M2535,"Other instability, hip"
+M2536,"Other instability, knee"
+M2537,"Other instability, ankle and foot"
+M254,"Effusion of joint"
+M2541,"Effusion, shoulder"
+M2542,"Effusion, elbow"
+M2543,"Effusion, wrist"
+M2544,"Effusion, hand"
+M2545,"Effusion, hip"
+M2546,"Effusion, knee"
+M2547,"Effusion, ankle and foot"
+M2548,"Effusion, other site"
+M255,"Pain in joint"
+M2551,"Pain in shoulder"
+M2552,"Pain in elbow"
+M2553,"Pain in wrist"
+M2554,"Pain in joints of hand"
+M2555,"Pain in hip"
+M2556,"Pain in knee"
+M2557,"Pain in ankle and joints of foot"
+M256,"Stiffness of joint, not elsewhere classified"
+M2561,"Stiffness of shoulder, not elsewhere classified"
+M2562,"Stiffness of elbow, not elsewhere classified"
+M2563,"Stiffness of wrist, not elsewhere classified"
+M2564,"Stiffness of hand, not elsewhere classified"
+M2565,"Stiffness of hip, not elsewhere classified"
+M2566,"Stiffness of knee, not elsewhere classified"
+M2567,"Stiffness of ankle and foot, not elsewhere classified"
+M257,"Osteophyte"
+M2571,"Osteophyte, shoulder"
+M2572,"Osteophyte, elbow"
+M2573,"Osteophyte, wrist"
+M2574,"Osteophyte, hand"
+M2575,"Osteophyte, hip"
+M2576,"Osteophyte, knee"
+M2577,"Osteophyte, ankle and foot"
+M2578,"Osteophyte, vertebrae"
+M258,"Other specified joint disorders"
+M2581,"Other specified joint disorders, shoulder"
+M2582,"Other specified joint disorders, elbow"
+M2583,"Other specified joint disorders, wrist"
+M2584,"Other specified joint disorders, hand"
+M2585,"Other specified joint disorders, hip"
+M2586,"Other specified joint disorders, knee"
+M2587,"Other specified joint disorders, ankle and foot"
+M259,"Joint disorder, unspecified"
+M26,"Dentofacial anomalies [including malocclusion]"
+M260,"Major anomalies of jaw size"
+M261,"Anomalies of jaw-cranial base relationship"
+M262,"Anomalies of dental arch relationship"
+M2621,"Malocclusion, Angle's class"
+M2622,"Open occlusal relationship"
+M2623,"Excessive horizontal overlap"
+M2624,"Reverse articulation"
+M2625,"Anomalies of interarch distance"
+M2629,"Other anomalies of dental arch relationship"
+M263,"Anomalies of tooth position of fully erupted tooth or teeth"
+M264,"Malocclusion, unspecified"
+M265,"Dentofacial functional abnormalities"
+M266,"Temporomandibular joint disorders"
+M2660,"Temporomandibular joint disorder, unspecified"
+M2661,"Adhesions and ankylosis of temporomandibular joint"
+M2662,"Arthralgia of temporomandibular joint"
+M2663,"Articular disc disorder of temporomandibular joint"
+M2669,"Other specified disorders of temporomandibular joint"
+M267,"Dental alveolar anomalies"
+M268,"Other dentofacial anomalies"
+M269,"Dentofacial anomaly, unspecified"
+M27,"Other diseases of jaws"
+M274,"Other and unspecified cysts of jaw"
+M275,"Periradicular pathology associated with previous endodontic treatment"
+M276,"Endosseous dental implant failure"
+M278,"Other specified diseases of jaws"
+M279,"Disease of jaws, unspecified"
+M30,"Polyarteritis nodosa and related conditions"
+M31,"Other necrotizing vasculopathies"
+M313,"Wegener's granulomatosis"
+M314,"Aortic arch syndrome [Takayasu]"
+M315,"Giant cell arteritis with polymyalgia rheumatica"
+M316,"Other giant cell arteritis"
+M317,"Microscopic polyangiitis"
+M318,"Other specified necrotizing vasculopathies"
+M319,"Necrotizing vasculopathy, unspecified"
+M32,"Systemic lupus erythematosus (SLE)"
+M321,"Systemic lupus erythematosus with organ or system involvement"
+M328,"Other forms of systemic lupus erythematosus"
+M329,"Systemic lupus erythematosus, unspecified"
+M33,"Dermatopolymyositis"
+M330,"Juvenile dermatomyositis"
+M331,"Other dermatomyositis"
+M332,"Polymyositis"
+M339,"Dermatopolymyositis, unspecified"
+M34,"Systemic sclerosis [scleroderma]"
+M348,"Other forms of systemic sclerosis"
+M349,"Systemic sclerosis, unspecified"
+M35,"Other systemic involvement of connective tissue"
+M350,"Sicca syndrome [Sjogren]"
+M351,"Other overlap syndromes"
+M352,"Behcet's disease"
+M353,"Polymyalgia rheumatica"
+M354,"Diffuse (eosinophilic) fasciitis"
+M355,"Multifocal fibrosclerosis"
+M356,"Relapsing panniculitis [Weber-Christian]"
+M357,"Hypermobility syndrome"
+M358,"Other specified systemic involvement of connective tissue"
+M359,"Systemic involvement of connective tissue, unspecified"
+M36,"Systemic disorders of connective tissue in diseases classified elsewhere"
+M40,"Kyphosis and lordosis"
+M400,"Postural kyphosis"
+M401,"Other secondary kyphosis"
+M402,"Other and unspecified kyphosis"
+M4020,"Unspecified kyphosis"
+M4029,"Other kyphosis"
+M403,"Flatback syndrome"
+M404,"Postural lordosis"
+M405,"Lordosis, unspecified"
+M41,"Scoliosis"
+M410,"Infantile idiopathic scoliosis"
+M411,"Juvenile and adolescent idiopathic scoliosis"
+M4111,"Juvenile idiopathic scoliosis"
+M4112,"Adolescent scoliosis"
+M412,"Other idiopathic scoliosis"
+M413,"Thoracogenic scoliosis"
+M414,"Neuromuscular scoliosis"
+M415,"Other secondary scoliosis"
+M418,"Other forms of scoliosis"
+M419,"Scoliosis, unspecified"
+M42,"Spinal osteochondrosis"
+M420,"Juvenile osteochondrosis of spine"
+M421,"Adult osteochondrosis of spine"
+M429,"Spinal osteochondrosis, unspecified"
+M43,"Other deforming dorsopathies"
+M430,"Spondylolysis"
+M431,"Spondylolisthesis"
+M432,"Fusion of spine"
+M433,"Recurrent atlantoaxial dislocation with myelopathy"
+M434,"Other recurrent atlantoaxial dislocation"
+M435,"Other recurrent vertebral dislocation"
+M435X,"Other recurrent vertebral dislocation"
+M436,"Torticollis"
+M438,"Other specified deforming dorsopathies"
+M438X,"Other specified deforming dorsopathies"
+M439,"Deforming dorsopathy, unspecified"
+M45,"Ankylosing spondylitis"
+M46,"Other inflammatory spondylopathies"
+M460,"Spinal enthesopathy"
+M461,"Sacroiliitis, not elsewhere classified"
+M462,"Osteomyelitis of vertebra"
+M463,"Infection of intervertebral disc (pyogenic)"
+M464,"Discitis, unspecified"
+M465,"Other infective spondylopathies"
+M468,"Other specified inflammatory spondylopathies"
+M469,"Unspecified inflammatory spondylopathy"
+M47,"Spondylosis"
+M470,"Anterior spinal and vertebral artery compression syndromes"
+M4701,"Anterior spinal artery compression syndromes"
+M4702,"Vertebral artery compression syndromes"
+M471,"Other spondylosis with myelopathy"
+M472,"Other spondylosis with radiculopathy"
+M478,"Other spondylosis"
+M4781,"Spondylosis without myelopathy or radiculopathy"
+M4789,"Other spondylosis"
+M479,"Spondylosis, unspecified"
+M48,"Other spondylopathies"
+M480,"Spinal stenosis"
+M4806,"Spinal stenosis, lumbar region"
+M4807,"Spinal stenosis, lumbosacral region"
+M4808,"Spinal stenosis, sacral and sacrococcygeal region"
+M481,"Ankylosing hyperostosis [Forestier]"
+M482,"Kissing spine"
+M483,"Traumatic spondylopathy"
+M484,"Fatigue fracture of vertebra"
+M4840,"Fatigue fracture of vertebra, site unspecified"
+M4841,"Fatigue fracture of vertebra, occipito-atlanto-axial region"
+M4842,"Fatigue fracture of vertebra, cervical region"
+M4843,"Fatigue fracture of vertebra, cervicothoracic region"
+M4844,"Fatigue fracture of vertebra, thoracic region"
+M4845,"Fatigue fracture of vertebra, thoracolumbar region"
+M4846,"Fatigue fracture of vertebra, lumbar region"
+M4847,"Fatigue fracture of vertebra, lumbosacral region"
+M4848,"Fatigue fracture of vertebra, sacral and sacrococcygeal region"
+M485,"Collapsed vertebra, not elsewhere classified"
+M4850,"Collapsed vertebra, not elsewhere classified, site unspecified"
+M4851,"Collapsed vertebra, not elsewhere classified, occipito-atlanto-axial region"
+M4852,"Collapsed vertebra, not elsewhere classified, cervical region"
+M4853,"Collapsed vertebra, not elsewhere classified, cervicothoracic region"
+M4854,"Collapsed vertebra, not elsewhere classified, thoracic region"
+M4855,"Collapsed vertebra, not elsewhere classified, thoracolumbar region"
+M4856,"Collapsed vertebra, not elsewhere classified, lumbar region"
+M4857,"Collapsed vertebra, not elsewhere classified, lumbosacral region"
+M4858,"Collapsed vertebra, not elsewhere classified, sacral and sacrococcygeal region"
+M488,"Other specified spondylopathies"
+M488X,"Other specified spondylopathies"
+M489,"Spondylopathy, unspecified"
+M49,"Spondylopathies in diseases classified elsewhere"
+M498,"Spondylopathy in diseases classified elsewhere"
+M50,"Cervical disc disorders"
+M500,"Cervical disc disorder with myelopathy"
+M5002,"Cervical disc disorder with myelopathy, mid-cervical region"
+M5003,"Cervical disc disorder with myelopathy, cervicothoracic region"
+M501,"Cervical disc disorder with radiculopathy"
+M5012,"Cervical disc disorder with radiculopathy, mid-cervical region"
+M5013,"Cervical disc disorder with radiculopathy, cervicothoracic region"
+M502,"Other cervical disc displacement"
+M5022,"Other cervical disc displacement, mid-cervical region"
+M5023,"Other cervical disc displacement, cervicothoracic region"
+M503,"Other cervical disc degeneration"
+M5032,"Other cervical disc degeneration, mid-cervical region"
+M5033,"Other cervical disc degeneration, cervicothoracic region"
+M508,"Other cervical disc disorders"
+M5082,"Other cervical disc disorders, mid-cervical region"
+M5083,"Other cervical disc disorders, cervicothoracic region"
+M509,"Cervical disc disorder, unspecified"
+M5092,"Cervical disc disorder, unspecified, mid-cervical region"
+M5093,"Cervical disc disorder, unspecified, cervicothoracic region"
+M51,"Thoracic, thoracolumbar, and lumbosacral intervertebral disc disorders"
+M510,"Thoracic, thoracolumbar and lumbosacral intervertebral disc disorders with myelopathy"
+M511,"Thoracic, thoracolumbar and lumbosacral intervertebral disc disorders with radiculopathy"
+M512,"Other thoracic, thoracolumbar and lumbosacral intervertebral disc displacement"
+M513,"Other thoracic, thoracolumbar and lumbosacral intervertebral disc degeneration"
+M514,"Schmorl's nodes"
+M518,"Other thoracic, thoracolumbar and lumbosacral intervertebral disc disorders"
+M519,"Unspecified thoracic, thoracolumbar and lumbosacral intervertebral disc disorder"
+M53,"Other and unspecified dorsopathies, not elsewhere classified"
+M532,"Spinal instabilities"
+M532X,"Spinal instabilities"
+M533,"Sacrococcygeal disorders, not elsewhere classified"
+M538,"Other specified dorsopathies"
+M539,"Dorsopathy, unspecified"
+M54,"Dorsalgia"
+M540,"Panniculitis affecting regions of neck and back"
+M541,"Radiculopathy"
+M542,"Cervicalgia"
+M543,"Sciatica"
+M544,"Lumbago with sciatica"
+M545,"Low back pain"
+M546,"Pain in thoracic spine"
+M548,"Other dorsalgia"
+M549,"Dorsalgia, unspecified"
+M60,"Myositis"
+M600,"Infective myositis"
+M6000,"Infective myositis, unspecified site"
+M6001,"Infective myositis, shoulder"
+M6002,"Infective myositis, upper arm"
+M6003,"Infective myositis, forearm"
+M6004,"Infective myositis, hand and fingers"
+M6005,"Infective myositis, thigh"
+M6006,"Infective myositis, lower leg"
+M6007,"Infective myositis, ankle, foot and toes"
+M6008,"Infective myositis, other site"
+M6009,"Infective myositis, multiple sites"
+M601,"Interstitial myositis"
+M6011,"Interstitial myositis, shoulder"
+M6012,"Interstitial myositis, upper arm"
+M6013,"Interstitial myositis, forearm"
+M6014,"Interstitial myositis, hand"
+M6015,"Interstitial myositis, thigh"
+M6016,"Interstitial myositis, lower leg"
+M6017,"Interstitial myositis, ankle and foot"
+M6018,"Interstitial myositis, other site"
+M6019,"Interstitial myositis, multiple sites"
+M602,"Foreign body granuloma of soft tissue, not elsewhere classified"
+M6021,"Foreign body granuloma of soft tissue, not elsewhere classified, shoulder"
+M6022,"Foreign body granuloma of soft tissue, not elsewhere classified, upper arm"
+M6023,"Foreign body granuloma of soft tissue, not elsewhere classified, forearm"
+M6024,"Foreign body granuloma of soft tissue, not elsewhere classified, hand"
+M6025,"Foreign body granuloma of soft tissue, not elsewhere classified, thigh"
+M6026,"Foreign body granuloma of soft tissue, not elsewhere classified, lower leg"
+M6027,"Foreign body granuloma of soft tissue, not elsewhere classified, ankle and foot"
+M6028,"Foreign body granuloma of soft tissue, not elsewhere classified, other site"
+M608,"Other myositis"
+M6081,"Other myositis shoulder"
+M6082,"Other myositis, upper arm"
+M6083,"Other myositis, forearm"
+M6084,"Other myositis, hand"
+M6085,"Other myositis, thigh"
+M6086,"Other myositis, lower leg"
+M6087,"Other myositis, ankle and foot"
+M6088,"Other myositis, other site"
+M6089,"Other myositis, multiple sites"
+M609,"Myositis, unspecified"
+M61,"Calcification and ossification of muscle"
+M610,"Myositis ossificans traumatica"
+M6101,"Myositis ossificans traumatica, shoulder"
+M6102,"Myositis ossificans traumatica, upper arm"
+M6103,"Myositis ossificans traumatica, forearm"
+M6104,"Myositis ossificans traumatica, hand"
+M6105,"Myositis ossificans traumatica, thigh"
+M6106,"Myositis ossificans traumatica, lower leg"
+M6107,"Myositis ossificans traumatica, ankle and foot"
+M6108,"Myositis ossificans traumatica, other site"
+M6109,"Myositis ossificans traumatica, multiple sites"
+M611,"Myositis ossificans progressiva"
+M6111,"Myositis ossificans progressiva, shoulder"
+M6112,"Myositis ossificans progressiva, upper arm"
+M6113,"Myositis ossificans progressiva, forearm"
+M6114,"Myositis ossificans progressiva, hand and finger(s)"
+M6115,"Myositis ossificans progressiva, thigh"
+M6116,"Myositis ossificans progressiva, lower leg"
+M6117,"Myositis ossificans progressiva, ankle, foot and toe(s)"
+M6118,"Myositis ossificans progressiva, other site"
+M6119,"Myositis ossificans progressiva, multiple sites"
+M612,"Paralytic calcification and ossification of muscle"
+M6121,"Paralytic calcification and ossification of muscle, shoulder"
+M6122,"Paralytic calcification and ossification of muscle, upper arm"
+M6123,"Paralytic calcification and ossification of muscle, forearm"
+M6124,"Paralytic calcification and ossification of muscle, hand"
+M6125,"Paralytic calcification and ossification of muscle, thigh"
+M6126,"Paralytic calcification and ossification of muscle, lower leg"
+M6127,"Paralytic calcification and ossification of muscle, ankle and foot"
+M6128,"Paralytic calcification and ossification of muscle, other site"
+M6129,"Paralytic calcification and ossification of muscle, multiple sites"
+M613,"Calcification and ossification of muscles associated with burns"
+M6131,"Calcification and ossification of muscles associated with burns, shoulder"
+M6132,"Calcification and ossification of muscles associated with burns, upper arm"
+M6133,"Calcification and ossification of muscles associated with burns, forearm"
+M6134,"Calcification and ossification of muscles associated with burns, hand"
+M6135,"Calcification and ossification of muscles associated with burns, thigh"
+M6136,"Calcification and ossification of muscles associated with burns, lower leg"
+M6137,"Calcification and ossification of muscles associated with burns, ankle and foot"
+M6138,"Calcification and ossification of muscles associated with burns, other site"
+M6139,"Calcification and ossification of muscles associated with burns, multiple sites"
+M614,"Other calcification of muscle"
+M6141,"Other calcification of muscle, shoulder"
+M6142,"Other calcification of muscle, upper arm"
+M6143,"Other calcification of muscle, forearm"
+M6144,"Other calcification of muscle, hand"
+M6145,"Other calcification of muscle, thigh"
+M6146,"Other calcification of muscle, lower leg"
+M6147,"Other calcification of muscle, ankle and foot"
+M6148,"Other calcification of muscle, other site"
+M6149,"Other calcification of muscle, multiple sites"
+M615,"Other ossification of muscle"
+M6151,"Other ossification of muscle, shoulder"
+M6152,"Other ossification of muscle, upper arm"
+M6153,"Other ossification of muscle, forearm"
+M6154,"Other ossification of muscle, hand"
+M6155,"Other ossification of muscle, thigh"
+M6156,"Other ossification of muscle, lower leg"
+M6157,"Other ossification of muscle, ankle and foot"
+M6158,"Other ossification of muscle, other site"
+M6159,"Other ossification of muscle, multiple sites"
+M619,"Calcification and ossification of muscle, unspecified"
+M62,"Other disorders of muscle"
+M620,"Separation of muscle (nontraumatic)"
+M6201,"Separation of muscle (nontraumatic), shoulder"
+M6202,"Separation of muscle (nontraumatic), upper arm"
+M6203,"Separation of muscle (nontraumatic), forearm"
+M6204,"Separation of muscle (nontraumatic), hand"
+M6205,"Separation of muscle (nontraumatic), thigh"
+M6206,"Separation of muscle (nontraumatic), lower leg"
+M6207,"Separation of muscle (nontraumatic), ankle and foot"
+M6208,"Separation of muscle (nontraumatic), other site"
+M621,"Other rupture of muscle (nontraumatic)"
+M6211,"Other rupture of muscle (nontraumatic), shoulder"
+M6212,"Other rupture of muscle (nontraumatic), upper arm"
+M6213,"Other rupture of muscle (nontraumatic), forearm"
+M6214,"Other rupture of muscle (nontraumatic), hand"
+M6215,"Other rupture of muscle (nontraumatic), thigh"
+M6216,"Other rupture of muscle (nontraumatic), lower leg"
+M6217,"Other rupture of muscle (nontraumatic), ankle and foot"
+M6218,"Other rupture of muscle (nontraumatic), other site"
+M622,"Nontraumatic ischemic infarction of muscle"
+M6221,"Nontraumatic ischemic infarction of muscle, shoulder"
+M6222,"Nontraumatic ischemic infarction of muscle, upper arm"
+M6223,"Nontraumatic ischemic infarction of muscle, forearm"
+M6224,"Nontraumatic ischemic infarction of muscle, hand"
+M6225,"Nontraumatic ischemic infarction of muscle, thigh"
+M6226,"Nontraumatic ischemic infarction of muscle, lower leg"
+M6227,"Nontraumatic ischemic infarction of muscle, ankle and foot"
+M6228,"Nontraumatic ischemic infarction of muscle, other site"
+M623,"Immobility syndrome (paraplegic)"
+M624,"Contracture of muscle"
+M6241,"Contracture of muscle, shoulder"
+M6242,"Contracture of muscle, upper arm"
+M6243,"Contracture of muscle, forearm"
+M6244,"Contracture of muscle, hand"
+M6245,"Contracture of muscle, thigh"
+M6246,"Contracture of muscle, lower leg"
+M6247,"Contracture of muscle, ankle and foot"
+M6248,"Contracture of muscle, other site"
+M6249,"Contracture of muscle, multiple sites"
+M625,"Muscle wasting and atrophy, not elsewhere classified"
+M6251,"Muscle wasting and atrophy, not elsewhere classified, shoulder"
+M6252,"Muscle wasting and atrophy, not elsewhere classified, upper arm"
+M6253,"Muscle wasting and atrophy, not elsewhere classified, forearm"
+M6254,"Muscle wasting and atrophy, not elsewhere classified, hand"
+M6255,"Muscle wasting and atrophy, not elsewhere classified, thigh"
+M6256,"Muscle wasting and atrophy, not elsewhere classified, lower leg"
+M6257,"Muscle wasting and atrophy, not elsewhere classified, ankle and foot"
+M6258,"Muscle wasting and atrophy, not elsewhere classified, other site"
+M6259,"Muscle wasting and atrophy, not elsewhere classified, multiple sites"
+M628,"Other specified disorders of muscle"
+M6283,"Muscle spasm"
+M6284,"Sarcopenia"
+M6289,"Other specified disorders of muscle"
+M629,"Disorder of muscle, unspecified"
+M63,"Disorders of muscle in diseases classified elsewhere"
+M638,"Disorders of muscle in diseases classified elsewhere"
+M6381,"Disorders of muscle in diseases classified elsewhere, shoulder"
+M6382,"Disorders of muscle in diseases classified elsewhere, upper arm"
+M6383,"Disorders of muscle in diseases classified elsewhere, forearm"
+M6384,"Disorders of muscle in diseases classified elsewhere, hand"
+M6385,"Disorders of muscle in diseases classified elsewhere, thigh"
+M6386,"Disorders of muscle in diseases classified elsewhere, lower leg"
+M6387,"Disorders of muscle in diseases classified elsewhere, ankle and foot"
+M6388,"Disorders of muscle in diseases classified elsewhere, other site"
+M6389,"Disorders of muscle in diseases classified elsewhere, multiple sites"
+M65,"Synovitis and tenosynovitis"
+M650,"Abscess of tendon sheath"
+M6501,"Abscess of tendon sheath, shoulder"
+M6502,"Abscess of tendon sheath, upper arm"
+M6503,"Abscess of tendon sheath, forearm"
+M6504,"Abscess of tendon sheath, hand"
+M6505,"Abscess of tendon sheath, thigh"
+M6506,"Abscess of tendon sheath, lower leg"
+M6507,"Abscess of tendon sheath, ankle and foot"
+M6508,"Abscess of tendon sheath, other site"
+M651,"Other infective (teno)synovitis"
+M6511,"Other infective (teno)synovitis, shoulder"
+M6512,"Other infective (teno)synovitis, elbow"
+M6513,"Other infective (teno)synovitis, wrist"
+M6514,"Other infective (teno)synovitis, hand"
+M6515,"Other infective (teno)synovitis, hip"
+M6516,"Other infective (teno)synovitis, knee"
+M6517,"Other infective (teno)synovitis, ankle and foot"
+M6518,"Other infective (teno)synovitis, other site"
+M6519,"Other infective (teno)synovitis, multiple sites"
+M652,"Calcific tendinitis"
+M6522,"Calcific tendinitis, upper arm"
+M6523,"Calcific tendinitis, forearm"
+M6524,"Calcific tendinitis, hand"
+M6525,"Calcific tendinitis, thigh"
+M6526,"Calcific tendinitis, lower leg"
+M6527,"Calcific tendinitis, ankle and foot"
+M6528,"Calcific tendinitis, other site"
+M6529,"Calcific tendinitis, multiple sites"
+M653,"Trigger finger"
+M6531,"Trigger thumb"
+M6532,"Trigger finger, index finger"
+M6533,"Trigger finger, middle finger"
+M6534,"Trigger finger, ring finger"
+M6535,"Trigger finger, little finger"
+M654,"Radial styloid tenosynovitis [de Quervain]"
+M658,"Other synovitis and tenosynovitis"
+M6581,"Other synovitis and tenosynovitis, shoulder"
+M6582,"Other synovitis and tenosynovitis, upper arm"
+M6583,"Other synovitis and tenosynovitis, forearm"
+M6584,"Other synovitis and tenosynovitis, hand"
+M6585,"Other synovitis and tenosynovitis, thigh"
+M6586,"Other synovitis and tenosynovitis, lower leg"
+M6587,"Other synovitis and tenosynovitis, ankle and foot"
+M6588,"Other synovitis and tenosynovitis, other site"
+M6589,"Other synovitis and tenosynovitis, multiple sites"
+M659,"Synovitis and tenosynovitis, unspecified"
+M66,"Spontaneous rupture of synovium and tendon"
+M661,"Rupture of synovium"
+M6611,"Rupture of synovium, shoulder"
+M6612,"Rupture of synovium, elbow"
+M6613,"Rupture of synovium, wrist"
+M6614,"Rupture of synovium, hand and fingers"
+M6615,"Rupture of synovium, hip"
+M6617,"Rupture of synovium, ankle, foot and toes"
+M6618,"Rupture of synovium, other site"
+M662,"Spontaneous rupture of extensor tendons"
+M6621,"Spontaneous rupture of extensor tendons, shoulder"
+M6622,"Spontaneous rupture of extensor tendons, upper arm"
+M6623,"Spontaneous rupture of extensor tendons, forearm"
+M6624,"Spontaneous rupture of extensor tendons, hand"
+M6625,"Spontaneous rupture of extensor tendons, thigh"
+M6626,"Spontaneous rupture of extensor tendons, lower leg"
+M6627,"Spontaneous rupture of extensor tendons, ankle and foot"
+M6628,"Spontaneous rupture of extensor tendons, other site"
+M6629,"Spontaneous rupture of extensor tendons, multiple sites"
+M663,"Spontaneous rupture of flexor tendons"
+M6631,"Spontaneous rupture of flexor tendons, shoulder"
+M6632,"Spontaneous rupture of flexor tendons, upper arm"
+M6633,"Spontaneous rupture of flexor tendons, forearm"
+M6634,"Spontaneous rupture of flexor tendons, hand"
+M6635,"Spontaneous rupture of flexor tendons, thigh"
+M6636,"Spontaneous rupture of flexor tendons, lower leg"
+M6637,"Spontaneous rupture of flexor tendons, ankle and foot"
+M6638,"Spontaneous rupture of flexor tendons, other site"
+M6639,"Spontaneous rupture of flexor tendons, multiple sites"
+M668,"Spontaneous rupture of other tendons"
+M6681,"Spontaneous rupture of other tendons, shoulder"
+M6682,"Spontaneous rupture of other tendons, upper arm"
+M6683,"Spontaneous rupture of other tendons, forearm"
+M6684,"Spontaneous rupture of other tendons, hand"
+M6685,"Spontaneous rupture of other tendons, thigh"
+M6686,"Spontaneous rupture of other tendons, lower leg"
+M6687,"Spontaneous rupture of other tendons, ankle and foot"
+M6688,"Spontaneous rupture of other tendons, other"
+M6689,"Spontaneous rupture of other tendons, multiple sites"
+M669,"Spontaneous rupture of unspecified tendon"
+M67,"Other disorders of synovium and tendon"
+M670,"Short Achilles tendon (acquired)"
+M672,"Synovial hypertrophy, not elsewhere classified"
+M6721,"Synovial hypertrophy, not elsewhere classified, shoulder"
+M6722,"Synovial hypertrophy, not elsewhere classified, upper arm"
+M6723,"Synovial hypertrophy, not elsewhere classified, forearm"
+M6724,"Synovial hypertrophy, not elsewhere classified, hand"
+M6725,"Synovial hypertrophy, not elsewhere classified, thigh"
+M6726,"Synovial hypertrophy, not elsewhere classified, lower leg"
+M6727,"Synovial hypertrophy, not elsewhere classified, ankle and foot"
+M6728,"Synovial hypertrophy, not elsewhere classified, other site"
+M6729,"Synovial hypertrophy, not elsewhere classified, multiple sites"
+M673,"Transient synovitis"
+M6731,"Transient synovitis, shoulder"
+M6732,"Transient synovitis, elbow"
+M6733,"Transient synovitis, wrist"
+M6734,"Transient synovitis, hand"
+M6735,"Transient synovitis, hip"
+M6736,"Transient synovitis, knee"
+M6737,"Transient synovitis, ankle and foot"
+M6738,"Transient synovitis, other site"
+M6739,"Transient synovitis, multiple sites"
+M674,"Ganglion"
+M6741,"Ganglion, shoulder"
+M6742,"Ganglion, elbow"
+M6743,"Ganglion, wrist"
+M6744,"Ganglion, hand"
+M6745,"Ganglion, hip"
+M6746,"Ganglion, knee"
+M6747,"Ganglion, ankle and foot"
+M6748,"Ganglion, other site"
+M6749,"Ganglion, multiple sites"
+M675,"Plica syndrome"
+M678,"Other specified disorders of synovium and tendon"
+M6781,"Other specified disorders of synovium and tendon, shoulder"
+M6782,"Other specified disorders of synovium and tendon, elbow"
+M6783,"Other specified disorders of synovium and tendon, wrist"
+M6784,"Other specified disorders of synovium and tendon, hand"
+M6785,"Other specified disorders of synovium and tendon, hip"
+M6786,"Other specified disorders of synovium and tendon, knee"
+M6787,"Other specified disorders of synovium and tendon, ankle and foot"
+M6788,"Other specified disorders of synovium and tendon, other site"
+M6789,"Other specified disorders of synovium and tendon, multiple sites"
+M679,"Unspecified disorder of synovium and tendon"
+M6791,"Unspecified disorder of synovium and tendon, shoulder"
+M6792,"Unspecified disorder of synovium and tendon, upper arm"
+M6793,"Unspecified disorder of synovium and tendon, forearm"
+M6794,"Unspecified disorder of synovium and tendon, hand"
+M6795,"Unspecified disorder of synovium and tendon, thigh"
+M6796,"Unspecified disorder of synovium and tendon, lower leg"
+M6797,"Unspecified disorder of synovium and tendon, ankle and foot"
+M6798,"Unspecified disorder of synovium and tendon, other site"
+M6799,"Unspecified disorder of synovium and tendon, multiple sites"
+M70,"Soft tissue disorders related to use, overuse and pressure"
+M700,"Crepitant synovitis (acute) (chronic) of hand and wrist"
+M7003,"Crepitant synovitis (acute) (chronic), wrist"
+M7004,"Crepitant synovitis (acute) (chronic), hand"
+M701,"Bursitis of hand"
+M702,"Olecranon bursitis"
+M703,"Other bursitis of elbow"
+M704,"Prepatellar bursitis"
+M705,"Other bursitis of knee"
+M706,"Trochanteric bursitis"
+M707,"Other bursitis of hip"
+M708,"Other soft tissue disorders related to use, overuse and pressure"
+M7081,"Other soft tissue disorders related to use, overuse and pressure of shoulder"
+M7082,"Other soft tissue disorders related to use, overuse and pressure of upper arm"
+M7083,"Other soft tissue disorders related to use, overuse and pressure of forearm"
+M7084,"Other soft tissue disorders related to use, overuse and pressure of hand"
+M7085,"Other soft tissue disorders related to use, overuse and pressure of thigh"
+M7086,"Other soft tissue disorders related to use, overuse and pressure lower leg"
+M7087,"Other soft tissue disorders related to use, overuse and pressure of ankle and foot"
+M7088,"Other soft tissue disorders related to use, overuse and pressure other site"
+M7089,"Other soft tissue disorders related to use, overuse and pressure multiple sites"
+M709,"Unspecified soft tissue disorder related to use, overuse and pressure"
+M7091,"Unspecified soft tissue disorder related to use, overuse and pressure of shoulder"
+M7092,"Unspecified soft tissue disorder related to use, overuse and pressure of upper arm"
+M7093,"Unspecified soft tissue disorder related to use, overuse and pressure of forearm"
+M7094,"Unspecified soft tissue disorder related to use, overuse and pressure of hand"
+M7095,"Unspecified soft tissue disorder related to use, overuse and pressure of thigh"
+M7096,"Unspecified soft tissue disorder related to use, overuse and pressure lower leg"
+M7097,"Unspecified soft tissue disorder related to use, overuse and pressure of ankle and foot"
+M7098,"Unspecified soft tissue disorder related to use, overuse and pressure other"
+M7099,"Unspecified soft tissue disorder related to use, overuse and pressure multiple sites"
+M71,"Other bursopathies"
+M710,"Abscess of bursa"
+M7101,"Abscess of bursa, shoulder"
+M7102,"Abscess of bursa, elbow"
+M7103,"Abscess of bursa, wrist"
+M7104,"Abscess of bursa, hand"
+M7105,"Abscess of bursa, hip"
+M7106,"Abscess of bursa, knee"
+M7107,"Abscess of bursa, ankle and foot"
+M7108,"Abscess of bursa, other site"
+M7109,"Abscess of bursa, multiple sites"
+M711,"Other infective bursitis"
+M7111,"Other infective bursitis, shoulder"
+M7112,"Other infective bursitis, elbow"
+M7113,"Other infective bursitis, wrist"
+M7114,"Other infective bursitis, hand"
+M7115,"Other infective bursitis, hip"
+M7116,"Other infective bursitis, knee"
+M7117,"Other infective bursitis, ankle and foot"
+M7118,"Other infective bursitis, other site"
+M7119,"Other infective bursitis, multiple sites"
+M712,"Synovial cyst of popliteal space [Baker]"
+M713,"Other bursal cyst"
+M7131,"Other bursal cyst, shoulder"
+M7132,"Other bursal cyst, elbow"
+M7133,"Other bursal cyst, wrist"
+M7134,"Other bursal cyst, hand"
+M7135,"Other bursal cyst, hip"
+M7137,"Other bursal cyst, ankle and foot"
+M7138,"Other bursal cyst, other site"
+M7139,"Other bursal cyst, multiple sites"
+M714,"Calcium deposit in bursa"
+M7142,"Calcium deposit in bursa, elbow"
+M7143,"Calcium deposit in bursa, wrist"
+M7144,"Calcium deposit in bursa, hand"
+M7145,"Calcium deposit in bursa, hip"
+M7146,"Calcium deposit in bursa, knee"
+M7147,"Calcium deposit in bursa, ankle and foot"
+M7148,"Calcium deposit in bursa, other site"
+M7149,"Calcium deposit in bursa, multiple sites"
+M715,"Other bursitis, not elsewhere classified"
+M7152,"Other bursitis, not elsewhere classified, elbow"
+M7153,"Other bursitis, not elsewhere classified, wrist"
+M7154,"Other bursitis, not elsewhere classified, hand"
+M7155,"Other bursitis, not elsewhere classified, hip"
+M7156,"Other bursitis, not elsewhere classified, knee"
+M7157,"Other bursitis, not elsewhere classified, ankle and foot"
+M7158,"Other bursitis, not elsewhere classified, other site"
+M718,"Other specified bursopathies"
+M7181,"Other specified bursopathies, shoulder"
+M7182,"Other specified bursopathies, elbow"
+M7183,"Other specified bursopathies, wrist"
+M7184,"Other specified bursopathies, hand"
+M7185,"Other specified bursopathies, hip"
+M7186,"Other specified bursopathies, knee"
+M7187,"Other specified bursopathies, ankle and foot"
+M7188,"Other specified bursopathies, other site"
+M7189,"Other specified bursopathies, multiple sites"
+M719,"Bursopathy, unspecified"
+M72,"Fibroblastic disorders"
+M75,"Shoulder lesions"
+M750,"Adhesive capsulitis of shoulder"
+M751,"Rotator cuff tear or rupture, not specified as traumatic"
+M7510,"Unspecified rotator cuff tear or rupture, not specified as traumatic"
+M7511,"Incomplete rotator cuff tear or rupture not specified as traumatic"
+M7512,"Complete rotator cuff tear or rupture not specified as traumatic"
+M752,"Bicipital tendinitis"
+M753,"Calcific tendinitis of shoulder"
+M754,"Impingement syndrome of shoulder"
+M755,"Bursitis of shoulder"
+M758,"Other shoulder lesions"
+M759,"Shoulder lesion, unspecified"
+M76,"Enthesopathies, lower limb, excluding foot"
+M760,"Gluteal tendinitis"
+M761,"Psoas tendinitis"
+M762,"Iliac crest spur"
+M763,"Iliotibial band syndrome"
+M764,"Tibial collateral bursitis [Pellegrini-Stieda]"
+M765,"Patellar tendinitis"
+M766,"Achilles tendinitis"
+M767,"Peroneal tendinitis"
+M768,"Other specified enthesopathies of lower limb, excluding foot"
+M7681,"Anterior tibial syndrome"
+M7682,"Posterior tibial tendinitis"
+M7689,"Other specified enthesopathies of lower limb, excluding foot"
+M769,"Unspecified enthesopathy, lower limb, excluding foot"
+M77,"Other enthesopathies"
+M770,"Medial epicondylitis"
+M771,"Lateral epicondylitis"
+M772,"Periarthritis of wrist"
+M773,"Calcaneal spur"
+M774,"Metatarsalgia"
+M775,"Other enthesopathy of foot"
+M778,"Other enthesopathies, not elsewhere classified"
+M779,"Enthesopathy, unspecified"
+M79,"Other and unspecified soft tissue disorders, not elsewhere classified"
+M796,"Pain in limb, hand, foot, fingers and toes"
+M7960,"Pain in limb, unspecified"
+M7962,"Pain in upper arm"
+M7963,"Pain in forearm"
+M7964,"Pain in hand and fingers"
+M7965,"Pain in thigh"
+M7966,"Pain in lower leg"
+M7967,"Pain in foot and toes"
+M797,"Fibromyalgia"
+M79A,"Nontraumatic compartment syndrome"
+M79A1,"Nontraumatic compartment syndrome of upper extremity"
+M79A2,"Nontraumatic compartment syndrome of lower extremity"
+M79A3,"Nontraumatic compartment syndrome of abdomen"
+M79A9,"Nontraumatic compartment syndrome of other sites"
+M798,"Other specified soft tissue disorders"
+M799,"Soft tissue disorder, unspecified"
+M80,"Osteoporosis with current pathological fracture"
+M800,"Age-related osteoporosis with current pathological fracture"
+M8000,"Age-related osteoporosis with current pathological fracture, unspecified site"
+M8001,"Age-related osteoporosis with current pathological fracture, shoulder"
+M80011,"Age-related osteoporosis with current pathological fracture, right shoulder"
+M80012,"Age-related osteoporosis with current pathological fracture, left shoulder"
+M80019,"Age-related osteoporosis with current pathological fracture, unspecified shoulder"
+M8002,"Age-related osteoporosis with current pathological fracture, humerus"
+M80021,"Age-related osteoporosis with current pathological fracture, right humerus"
+M80022,"Age-related osteoporosis with current pathological fracture, left humerus"
+M80029,"Age-related osteoporosis with current pathological fracture, unspecified humerus"
+M8003,"Age-related osteoporosis with current pathological fracture, forearm"
+M80031,"Age-related osteoporosis with current pathological fracture, right forearm"
+M80032,"Age-related osteoporosis with current pathological fracture, left forearm"
+M80039,"Age-related osteoporosis with current pathological fracture, unspecified forearm"
+M8004,"Age-related osteoporosis with current pathological fracture, hand"
+M80041,"Age-related osteoporosis with current pathological fracture, right hand"
+M80042,"Age-related osteoporosis with current pathological fracture, left hand"
+M80049,"Age-related osteoporosis with current pathological fracture, unspecified hand"
+M8005,"Age-related osteoporosis with current pathological fracture, femur"
+M80051,"Age-related osteoporosis with current pathological fracture, right femur"
+M80052,"Age-related osteoporosis with current pathological fracture, left femur"
+M80059,"Age-related osteoporosis with current pathological fracture, unspecified femur"
+M8006,"Age-related osteoporosis with current pathological fracture, lower leg"
+M80061,"Age-related osteoporosis with current pathological fracture, right lower leg"
+M80062,"Age-related osteoporosis with current pathological fracture, left lower leg"
+M80069,"Age-related osteoporosis with current pathological fracture, unspecified lower leg"
+M8007,"Age-related osteoporosis with current pathological fracture, ankle and foot"
+M80071,"Age-related osteoporosis with current pathological fracture, right ankle and foot"
+M80072,"Age-related osteoporosis with current pathological fracture, left ankle and foot"
+M80079,"Age-related osteoporosis with current pathological fracture, unspecified ankle and foot"
+M8008,"Age-related osteoporosis with current pathological fracture, vertebra(e)"
+M808,"Other osteoporosis with current pathological fracture"
+M8080,"Other osteoporosis with current pathological fracture, unspecified site"
+M8081,"Other osteoporosis with pathological fracture, shoulder"
+M80811,"Other osteoporosis with current pathological fracture, right shoulder"
+M80812,"Other osteoporosis with current pathological fracture, left shoulder"
+M80819,"Other osteoporosis with current pathological fracture, unspecified shoulder"
+M8082,"Other osteoporosis with current pathological fracture, humerus"
+M80821,"Other osteoporosis with current pathological fracture, right humerus"
+M80822,"Other osteoporosis with current pathological fracture, left humerus"
+M80829,"Other osteoporosis with current pathological fracture, unspecified humerus"
+M8083,"Other osteoporosis with current pathological fracture, forearm"
+M80831,"Other osteoporosis with current pathological fracture, right forearm"
+M80832,"Other osteoporosis with current pathological fracture, left forearm"
+M80839,"Other osteoporosis with current pathological fracture, unspecified forearm"
+M8084,"Other osteoporosis with current pathological fracture, hand"
+M80841,"Other osteoporosis with current pathological fracture, right hand"
+M80842,"Other osteoporosis with current pathological fracture, left hand"
+M80849,"Other osteoporosis with current pathological fracture, unspecified hand"
+M8085,"Other osteoporosis with current pathological fracture, femur"
+M80851,"Other osteoporosis with current pathological fracture, right femur"
+M80852,"Other osteoporosis with current pathological fracture, left femur"
+M80859,"Other osteoporosis with current pathological fracture, unspecified femur"
+M8086,"Other osteoporosis with current pathological fracture, lower leg"
+M80861,"Other osteoporosis with current pathological fracture, right lower leg"
+M80862,"Other osteoporosis with current pathological fracture, left lower leg"
+M80869,"Other osteoporosis with current pathological fracture, unspecified lower leg"
+M8087,"Other osteoporosis with current pathological fracture, ankle and foot"
+M80871,"Other osteoporosis with current pathological fracture, right ankle and foot"
+M80872,"Other osteoporosis with current pathological fracture, left ankle and foot"
+M80879,"Other osteoporosis with current pathological fracture, unspecified ankle and foot"
+M8088,"Other osteoporosis with current pathological fracture, vertebra(e)"
+M81,"Osteoporosis without current pathological fracture"
+M83,"Adult osteomalacia"
+M84,"Disorder of continuity of bone"
+M843,"Stress fracture"
+M8430,"Stress fracture, unspecified site"
+M8431,"Stress fracture, shoulder"
+M84311,"Stress fracture, right shoulder"
+M84312,"Stress fracture, left shoulder"
+M84319,"Stress fracture, unspecified shoulder"
+M8432,"Stress fracture, humerus"
+M84321,"Stress fracture, right humerus"
+M84322,"Stress fracture, left humerus"
+M84329,"Stress fracture, unspecified humerus"
+M8433,"Stress fracture, ulna and radius"
+M84331,"Stress fracture, right ulna"
+M84332,"Stress fracture, left ulna"
+M84333,"Stress fracture, right radius"
+M84334,"Stress fracture, left radius"
+M84339,"Stress fracture, unspecified ulna and radius"
+M8434,"Stress fracture, hand and fingers"
+M84341,"Stress fracture, right hand"
+M84342,"Stress fracture, left hand"
+M84343,"Stress fracture, unspecified hand"
+M84344,"Stress fracture, right finger(s)"
+M84345,"Stress fracture, left finger(s)"
+M84346,"Stress fracture, unspecified finger(s)"
+M8435,"Stress fracture, pelvis and femur"
+M84350,"Stress fracture, pelvis"
+M84351,"Stress fracture, right femur"
+M84352,"Stress fracture, left femur"
+M84353,"Stress fracture, unspecified femur"
+M84359,"Stress fracture, hip, unspecified"
+M8436,"Stress fracture, tibia and fibula"
+M84361,"Stress fracture, right tibia"
+M84362,"Stress fracture, left tibia"
+M84363,"Stress fracture, right fibula"
+M84364,"Stress fracture, left fibula"
+M84369,"Stress fracture, unspecified tibia and fibula"
+M8437,"Stress fracture, ankle, foot and toes"
+M84371,"Stress fracture, right ankle"
+M84372,"Stress fracture, left ankle"
+M84373,"Stress fracture, unspecified ankle"
+M84374,"Stress fracture, right foot"
+M84375,"Stress fracture, left foot"
+M84376,"Stress fracture, unspecified foot"
+M84377,"Stress fracture, right toe(s)"
+M84378,"Stress fracture, left toe(s)"
+M84379,"Stress fracture, unspecified toe(s)"
+M8438,"Stress fracture, other site"
+M844,"Pathological fracture, not elsewhere classified"
+M8440,"Pathological fracture, unspecified site"
+M8441,"Pathological fracture, shoulder"
+M84411,"Pathological fracture, right shoulder"
+M84412,"Pathological fracture, left shoulder"
+M84419,"Pathological fracture, unspecified shoulder"
+M8442,"Pathological fracture, humerus"
+M84421,"Pathological fracture, right humerus"
+M84422,"Pathological fracture, left humerus"
+M84429,"Pathological fracture, unspecified humerus"
+M8443,"Pathological fracture, ulna and radius"
+M84431,"Pathological fracture, right ulna"
+M84432,"Pathological fracture, left ulna"
+M84433,"Pathological fracture, right radius"
+M84434,"Pathological fracture, left radius"
+M84439,"Pathological fracture, unspecified ulna and radius"
+M8444,"Pathological fracture, hand and fingers"
+M84441,"Pathological fracture, right hand"
+M84442,"Pathological fracture, left hand"
+M84443,"Pathological fracture, unspecified hand"
+M84444,"Pathological fracture, right finger(s)"
+M84445,"Pathological fracture, left finger(s)"
+M84446,"Pathological fracture, unspecified finger(s)"
+M8445,"Pathological fracture, femur and pelvis"
+M84451,"Pathological fracture, right femur"
+M84452,"Pathological fracture, left femur"
+M84453,"Pathological fracture, unspecified femur"
+M84454,"Pathological fracture, pelvis"
+M84459,"Pathological fracture, hip, unspecified"
+M8446,"Pathological fracture, tibia and fibula"
+M84461,"Pathological fracture, right tibia"
+M84462,"Pathological fracture, left tibia"
+M84463,"Pathological fracture, right fibula"
+M84464,"Pathological fracture, left fibula"
+M84469,"Pathological fracture, unspecified tibia and fibula"
+M8447,"Pathological fracture, ankle, foot and toes"
+M84471,"Pathological fracture, right ankle"
+M84472,"Pathological fracture, left ankle"
+M84473,"Pathological fracture, unspecified ankle"
+M84474,"Pathological fracture, right foot"
+M84475,"Pathological fracture, left foot"
+M84476,"Pathological fracture, unspecified foot"
+M84477,"Pathological fracture, right toe(s)"
+M84478,"Pathological fracture, left toe(s)"
+M84479,"Pathological fracture, unspecified toe(s)"
+M8448,"Pathological fracture, other site"
+M845,"Pathological fracture in neoplastic disease"
+M8450,"Pathological fracture in neoplastic disease, unspecified site"
+M8451,"Pathological fracture in neoplastic disease, shoulder"
+M84511,"Pathological fracture in neoplastic disease, right shoulder"
+M84512,"Pathological fracture in neoplastic disease, left shoulder"
+M84519,"Pathological fracture in neoplastic disease, unspecified shoulder"
+M8452,"Pathological fracture in neoplastic disease, humerus"
+M84521,"Pathological fracture in neoplastic disease, right humerus"
+M84522,"Pathological fracture in neoplastic disease, left humerus"
+M84529,"Pathological fracture in neoplastic disease, unspecified humerus"
+M8453,"Pathological fracture in neoplastic disease, ulna and radius"
+M84531,"Pathological fracture in neoplastic disease, right ulna"
+M84532,"Pathological fracture in neoplastic disease, left ulna"
+M84533,"Pathological fracture in neoplastic disease, right radius"
+M84534,"Pathological fracture in neoplastic disease, left radius"
+M84539,"Pathological fracture in neoplastic disease, unspecified ulna and radius"
+M8454,"Pathological fracture in neoplastic disease, hand"
+M84541,"Pathological fracture in neoplastic disease, right hand"
+M84542,"Pathological fracture in neoplastic disease, left hand"
+M84549,"Pathological fracture in neoplastic disease, unspecified hand"
+M8455,"Pathological fracture in neoplastic disease, pelvis and femur"
+M84550,"Pathological fracture in neoplastic disease, pelvis"
+M84551,"Pathological fracture in neoplastic disease, right femur"
+M84552,"Pathological fracture in neoplastic disease, left femur"
+M84553,"Pathological fracture in neoplastic disease, unspecified femur"
+M84559,"Pathological fracture in neoplastic disease, hip, unspecified"
+M8456,"Pathological fracture in neoplastic disease, tibia and fibula"
+M84561,"Pathological fracture in neoplastic disease, right tibia"
+M84562,"Pathological fracture in neoplastic disease, left tibia"
+M84563,"Pathological fracture in neoplastic disease, right fibula"
+M84564,"Pathological fracture in neoplastic disease, left fibula"
+M84569,"Pathological fracture in neoplastic disease, unspecified tibia and fibula"
+M8457,"Pathological fracture in neoplastic disease, ankle and foot"
+M84571,"Pathological fracture in neoplastic disease, right ankle"
+M84572,"Pathological fracture in neoplastic disease, left ankle"
+M84573,"Pathological fracture in neoplastic disease, unspecified ankle"
+M84574,"Pathological fracture in neoplastic disease, right foot"
+M84575,"Pathological fracture in neoplastic disease, left foot"
+M84576,"Pathological fracture in neoplastic disease, unspecified foot"
+M8458,"Pathological fracture in neoplastic disease, other specified site"
+M846,"Pathological fracture in other disease"
+M8460,"Pathological fracture in other disease, unspecified site"
+M8461,"Pathological fracture in other disease, shoulder"
+M84611,"Pathological fracture in other disease, right shoulder"
+M84612,"Pathological fracture in other disease, left shoulder"
+M84619,"Pathological fracture in other disease, unspecified shoulder"
+M8462,"Pathological fracture in other disease, humerus"
+M84621,"Pathological fracture in other disease, right humerus"
+M84622,"Pathological fracture in other disease, left humerus"
+M84629,"Pathological fracture in other disease, unspecified humerus"
+M8463,"Pathological fracture in other disease, ulna and radius"
+M84631,"Pathological fracture in other disease, right ulna"
+M84632,"Pathological fracture in other disease, left ulna"
+M84633,"Pathological fracture in other disease, right radius"
+M84634,"Pathological fracture in other disease, left radius"
+M84639,"Pathological fracture in other disease, unspecified ulna and radius"
+M8464,"Pathological fracture in other disease, hand"
+M84641,"Pathological fracture in other disease, right hand"
+M84642,"Pathological fracture in other disease, left hand"
+M84649,"Pathological fracture in other disease, unspecified hand"
+M8465,"Pathological fracture in other disease, pelvis and femur"
+M84650,"Pathological fracture in other disease, pelvis"
+M84651,"Pathological fracture in other disease, right femur"
+M84652,"Pathological fracture in other disease, left femur"
+M84653,"Pathological fracture in other disease, unspecified femur"
+M84659,"Pathological fracture in other disease, hip, unspecified"
+M8466,"Pathological fracture in other disease, tibia and fibula"
+M84661,"Pathological fracture in other disease, right tibia"
+M84662,"Pathological fracture in other disease, left tibia"
+M84663,"Pathological fracture in other disease, right fibula"
+M84664,"Pathological fracture in other disease, left fibula"
+M84669,"Pathological fracture in other disease, unspecified tibia and fibula"
+M8467,"Pathological fracture in other disease, ankle and foot"
+M84671,"Pathological fracture in other disease, right ankle"
+M84672,"Pathological fracture in other disease, left ankle"
+M84673,"Pathological fracture in other disease, unspecified ankle"
+M84674,"Pathological fracture in other disease, right foot"
+M84675,"Pathological fracture in other disease, left foot"
+M84676,"Pathological fracture in other disease, unspecified foot"
+M8468,"Pathological fracture in other disease, other site"
+M847,"Nontraumatic fracture, not elsewhere classified"
+M8475,"Atypical femoral fracture"
+M84750,"Atypical femoral fracture, unspecified"
+M84751,"Incomplete atypical femoral fracture, right leg"
+M84752,"Incomplete atypical femoral fracture, left leg"
+M84753,"Incomplete atypical femoral fracture, unspecified leg"
+M84754,"Complete transverse atypical femoral fracture, right leg"
+M84755,"Complete transverse atypical femoral fracture, left leg"
+M84756,"Complete transverse atypical femoral fracture, unspecified leg"
+M84757,"Complete oblique atypical femoral fracture, right leg"
+M84758,"Complete oblique atypical femoral fracture, left leg"
+M84759,"Complete oblique atypical femoral fracture, unspecified leg"
+M848,"Other disorders of continuity of bone"
+M8481,"Other disorders of continuity of bone, shoulder"
+M8482,"Other disorders of continuity of bone, humerus"
+M8483,"Other disorders of continuity of bone, ulna and radius"
+M8484,"Other disorders of continuity of bone, hand"
+M8485,"Other disorders of continuity of bone, pelvic region and thigh"
+M8486,"Other disorders of continuity of bone, tibia and fibula"
+M8487,"Other disorders of continuity of bone, ankle and foot"
+M8488,"Other disorders of continuity of bone, other site"
+M849,"Disorder of continuity of bone, unspecified"
+M85,"Other disorders of bone density and structure"
+M850,"Fibrous dysplasia (monostotic)"
+M8501,"Fibrous dysplasia (monostotic), shoulder"
+M8502,"Fibrous dysplasia (monostotic), upper arm"
+M8503,"Fibrous dysplasia (monostotic), forearm"
+M8504,"Fibrous dysplasia (monostotic), hand"
+M8505,"Fibrous dysplasia (monostotic), thigh"
+M8506,"Fibrous dysplasia (monostotic), lower leg"
+M8507,"Fibrous dysplasia (monostotic), ankle and foot"
+M8508,"Fibrous dysplasia (monostotic), other site"
+M8509,"Fibrous dysplasia (monostotic), multiple sites"
+M851,"Skeletal fluorosis"
+M8511,"Skeletal fluorosis, shoulder"
+M8512,"Skeletal fluorosis, upper arm"
+M8513,"Skeletal fluorosis, forearm"
+M8514,"Skeletal fluorosis, hand"
+M8515,"Skeletal fluorosis, thigh"
+M8516,"Skeletal fluorosis, lower leg"
+M8517,"Skeletal fluorosis, ankle and foot"
+M8518,"Skeletal fluorosis, other site"
+M8519,"Skeletal fluorosis, multiple sites"
+M852,"Hyperostosis of skull"
+M853,"Osteitis condensans"
+M8531,"Osteitis condensans, shoulder"
+M8532,"Osteitis condensans, upper arm"
+M8533,"Osteitis condensans, forearm"
+M8534,"Osteitis condensans, hand"
+M8535,"Osteitis condensans, thigh"
+M8536,"Osteitis condensans, lower leg"
+M8537,"Osteitis condensans, ankle and foot"
+M8538,"Osteitis condensans, other site"
+M8539,"Osteitis condensans, multiple sites"
+M854,"Solitary bone cyst"
+M8541,"Solitary bone cyst, shoulder"
+M8542,"Solitary bone cyst, humerus"
+M8543,"Solitary bone cyst, ulna and radius"
+M8544,"Solitary bone cyst, hand"
+M8545,"Solitary bone cyst, pelvis"
+M8546,"Solitary bone cyst, tibia and fibula"
+M8547,"Solitary bone cyst, ankle and foot"
+M8548,"Solitary bone cyst, other site"
+M855,"Aneurysmal bone cyst"
+M8551,"Aneurysmal bone cyst, shoulder"
+M8552,"Aneurysmal bone cyst, upper arm"
+M8553,"Aneurysmal bone cyst, forearm"
+M8554,"Aneurysmal bone cyst, hand"
+M8555,"Aneurysmal bone cyst, thigh"
+M8556,"Aneurysmal bone cyst, lower leg"
+M8557,"Aneurysmal bone cyst, ankle and foot"
+M8558,"Aneurysmal bone cyst, other site"
+M8559,"Aneurysmal bone cyst, multiple sites"
+M856,"Other cyst of bone"
+M8561,"Other cyst of bone, shoulder"
+M8562,"Other cyst of bone, upper arm"
+M8563,"Other cyst of bone, forearm"
+M8564,"Other cyst of bone, hand"
+M8565,"Other cyst of bone, thigh"
+M8566,"Other cyst of bone, lower leg"
+M8567,"Other cyst of bone, ankle and foot"
+M8568,"Other cyst of bone, other site"
+M8569,"Other cyst of bone, multiple sites"
+M858,"Other specified disorders of bone density and structure"
+M8581,"Other specified disorders of bone density and structure, shoulder"
+M8582,"Other specified disorders of bone density and structure, upper arm"
+M8583,"Other specified disorders of bone density and structure, forearm"
+M8584,"Other specified disorders of bone density and structure, hand"
+M8585,"Other specified disorders of bone density and structure, thigh"
+M8586,"Other specified disorders of bone density and structure, lower leg"
+M8587,"Other specified disorders of bone density and structure, ankle and foot"
+M8588,"Other specified disorders of bone density and structure, other site"
+M8589,"Other specified disorders of bone density and structure, multiple sites"
+M859,"Disorder of bone density and structure, unspecified"
+M86,"Osteomyelitis"
+M860,"Acute hematogenous osteomyelitis"
+M8601,"Acute hematogenous osteomyelitis, shoulder"
+M8602,"Acute hematogenous osteomyelitis, humerus"
+M8603,"Acute hematogenous osteomyelitis, radius and ulna"
+M8604,"Acute hematogenous osteomyelitis, hand"
+M8605,"Acute hematogenous osteomyelitis, femur"
+M8606,"Acute hematogenous osteomyelitis, tibia and fibula"
+M8607,"Acute hematogenous osteomyelitis, ankle and foot"
+M8608,"Acute hematogenous osteomyelitis, other sites"
+M8609,"Acute hematogenous osteomyelitis, multiple sites"
+M861,"Other acute osteomyelitis"
+M8611,"Other acute osteomyelitis, shoulder"
+M8612,"Other acute osteomyelitis, humerus"
+M8613,"Other acute osteomyelitis, radius and ulna"
+M8614,"Other acute osteomyelitis, hand"
+M8615,"Other acute osteomyelitis, femur"
+M8616,"Other acute osteomyelitis, tibia and fibula"
+M8617,"Other acute osteomyelitis, ankle and foot"
+M8618,"Other acute osteomyelitis, other site"
+M8619,"Other acute osteomyelitis, multiple sites"
+M862,"Subacute osteomyelitis"
+M8621,"Subacute osteomyelitis, shoulder"
+M8622,"Subacute osteomyelitis, humerus"
+M8623,"Subacute osteomyelitis, radius and ulna"
+M8624,"Subacute osteomyelitis, hand"
+M8625,"Subacute osteomyelitis, femur"
+M8626,"Subacute osteomyelitis, tibia and fibula"
+M8627,"Subacute osteomyelitis, ankle and foot"
+M8628,"Subacute osteomyelitis, other site"
+M8629,"Subacute osteomyelitis, multiple sites"
+M863,"Chronic multifocal osteomyelitis"
+M8631,"Chronic multifocal osteomyelitis, shoulder"
+M8632,"Chronic multifocal osteomyelitis, humerus"
+M8633,"Chronic multifocal osteomyelitis, radius and ulna"
+M8634,"Chronic multifocal osteomyelitis, hand"
+M8635,"Chronic multifocal osteomyelitis, femur"
+M8636,"Chronic multifocal osteomyelitis, tibia and fibula"
+M8637,"Chronic multifocal osteomyelitis, ankle and foot"
+M8638,"Chronic multifocal osteomyelitis, other site"
+M8639,"Chronic multifocal osteomyelitis, multiple sites"
+M864,"Chronic osteomyelitis with draining sinus"
+M8641,"Chronic osteomyelitis with draining sinus, shoulder"
+M8642,"Chronic osteomyelitis with draining sinus, humerus"
+M8643,"Chronic osteomyelitis with draining sinus, radius and ulna"
+M8644,"Chronic osteomyelitis with draining sinus, hand"
+M8645,"Chronic osteomyelitis with draining sinus, femur"
+M8646,"Chronic osteomyelitis with draining sinus, tibia and fibula"
+M8647,"Chronic osteomyelitis with draining sinus, ankle and foot"
+M8648,"Chronic osteomyelitis with draining sinus, other site"
+M8649,"Chronic osteomyelitis with draining sinus, multiple sites"
+M865,"Other chronic hematogenous osteomyelitis"
+M8651,"Other chronic hematogenous osteomyelitis, shoulder"
+M8652,"Other chronic hematogenous osteomyelitis, humerus"
+M8653,"Other chronic hematogenous osteomyelitis, radius and ulna"
+M8654,"Other chronic hematogenous osteomyelitis, hand"
+M8655,"Other chronic hematogenous osteomyelitis, femur"
+M8656,"Other chronic hematogenous osteomyelitis, tibia and fibula"
+M8657,"Other chronic hematogenous osteomyelitis, ankle and foot"
+M8658,"Other chronic hematogenous osteomyelitis, other site"
+M8659,"Other chronic hematogenous osteomyelitis, multiple sites"
+M866,"Other chronic osteomyelitis"
+M8661,"Other chronic osteomyelitis, shoulder"
+M8662,"Other chronic osteomyelitis,  humerus"
+M8663,"Other chronic osteomyelitis, radius and ulna"
+M8664,"Other chronic osteomyelitis, hand"
+M8665,"Other chronic osteomyelitis, thigh"
+M8666,"Other chronic osteomyelitis, tibia and fibula"
+M8667,"Other chronic osteomyelitis, ankle and foot"
+M8668,"Other chronic osteomyelitis, other site"
+M8669,"Other chronic osteomyelitis, multiple sites"
+M868,"Other osteomyelitis"
+M868X,"Other osteomyelitis"
+M869,"Osteomyelitis, unspecified"
+M87,"Osteonecrosis"
+M870,"Idiopathic aseptic necrosis of bone"
+M8701,"Idiopathic aseptic necrosis of shoulder"
+M8702,"Idiopathic aseptic necrosis of humerus"
+M8703,"Idiopathic aseptic necrosis of radius, ulna and carpus"
+M8704,"Idiopathic aseptic necrosis of hand and fingers"
+M8705,"Idiopathic aseptic necrosis of pelvis and femur"
+M8706,"Idiopathic aseptic necrosis of tibia and fibula"
+M8707,"Idiopathic aseptic necrosis of ankle, foot and toes"
+M8708,"Idiopathic aseptic necrosis of bone, other site"
+M8709,"Idiopathic aseptic necrosis of bone, multiple sites"
+M871,"Osteonecrosis due to drugs"
+M8711,"Osteonecrosis due to drugs, shoulder"
+M8712,"Osteonecrosis due to drugs, humerus"
+M8713,"Osteonecrosis due to drugs of radius, ulna and carpus"
+M8714,"Osteonecrosis due to drugs, hand and fingers"
+M8715,"Osteonecrosis due to drugs, pelvis and femur"
+M8716,"Osteonecrosis due to drugs, tibia and fibula"
+M8717,"Osteonecrosis due to drugs, ankle, foot and toes"
+M8718,"Osteonecrosis due to drugs, other site"
+M8719,"Osteonecrosis due to drugs, multiple sites"
+M872,"Osteonecrosis due to previous trauma"
+M8721,"Osteonecrosis due to previous trauma, shoulder"
+M8722,"Osteonecrosis due to previous trauma, humerus"
+M8723,"Osteonecrosis due to previous trauma of radius, ulna and carpus"
+M8724,"Osteonecrosis due to previous trauma, hand and fingers"
+M8725,"Osteonecrosis due to previous trauma, pelvis and femur"
+M8726,"Osteonecrosis due to previous trauma, tibia and fibula"
+M8727,"Osteonecrosis due to previous trauma, ankle, foot and toes"
+M8728,"Osteonecrosis due to previous trauma, other site"
+M8729,"Osteonecrosis due to previous trauma, multiple sites"
+M873,"Other secondary osteonecrosis"
+M8731,"Other secondary osteonecrosis, shoulder"
+M8732,"Other secondary osteonecrosis, humerus"
+M8733,"Other secondary osteonecrosis of radius, ulna and carpus"
+M8734,"Other secondary osteonecrosis, hand and fingers"
+M8735,"Other secondary osteonecrosis, pelvis and femur"
+M8736,"Other secondary osteonecrosis, tibia and fibula"
+M8737,"Other secondary osteonecrosis, ankle and foot"
+M8738,"Other secondary osteonecrosis, other site"
+M8739,"Other secondary osteonecrosis, multiple sites"
+M878,"Other osteonecrosis"
+M8781,"Other osteonecrosis, shoulder"
+M8782,"Other osteonecrosis, humerus"
+M8783,"Other osteonecrosis of radius, ulna and carpus"
+M8784,"Other osteonecrosis, hand and fingers"
+M8785,"Other osteonecrosis, pelvis and femur"
+M8786,"Other osteonecrosis, tibia and fibula"
+M8787,"Other osteonecrosis, ankle, foot and toes"
+M8788,"Other osteonecrosis, other site"
+M8789,"Other osteonecrosis, multiple sites"
+M879,"Osteonecrosis, unspecified"
+M88,"Osteitis deformans [Paget's disease of bone]"
+M888,"Osteitis deformans of other bones"
+M8881,"Osteitis deformans of shoulder"
+M8882,"Osteitis deformans of upper arm"
+M8883,"Osteitis deformans of forearm"
+M8884,"Osteitis deformans of hand"
+M8885,"Osteitis deformans of thigh"
+M8886,"Osteitis deformans of lower leg"
+M8887,"Osteitis deformans of ankle and foot"
+M8888,"Osteitis deformans of other bones"
+M8889,"Osteitis deformans of multiple sites"
+M889,"Osteitis deformans of unspecified bone"
+M89,"Other disorders of bone"
+M890,"Algoneurodystrophy"
+M8901,"Algoneurodystrophy, shoulder"
+M8902,"Algoneurodystrophy, upper arm"
+M8903,"Algoneurodystrophy, forearm"
+M8904,"Algoneurodystrophy, hand"
+M8905,"Algoneurodystrophy, thigh"
+M8906,"Algoneurodystrophy, lower leg"
+M8907,"Algoneurodystrophy, ankle and foot"
+M8908,"Algoneurodystrophy, other site"
+M8909,"Algoneurodystrophy, multiple sites"
+M891,"Physeal arrest"
+M8912,"Physeal arrest, humerus"
+M8913,"Physeal arrest, forearm"
+M8915,"Physeal arrest, femur"
+M8916,"Physeal arrest, lower leg"
+M8918,"Physeal arrest, other site"
+M892,"Other disorders of bone development and growth"
+M8921,"Other disorders of bone development and growth, shoulder"
+M8922,"Other disorders of bone development and growth, humerus"
+M8923,"Other disorders of bone development and growth, ulna and radius"
+M8924,"Other disorders of bone development and growth, hand"
+M8925,"Other disorders of bone development and growth, femur"
+M8926,"Other disorders of bone development and growth, tibia and fibula"
+M8927,"Other disorders of bone development and growth, ankle and foot"
+M8928,"Other disorders of bone development and growth, other site"
+M8929,"Other disorders of bone development and growth, multiple sites"
+M893,"Hypertrophy of bone"
+M8931,"Hypertrophy of bone, shoulder"
+M8932,"Hypertrophy of bone, humerus"
+M8933,"Hypertrophy of bone, ulna and radius"
+M8934,"Hypertrophy of bone, hand"
+M8935,"Hypertrophy of bone, femur"
+M8936,"Hypertrophy of bone, tibia and fibula"
+M8937,"Hypertrophy of bone, ankle and foot"
+M8938,"Hypertrophy of bone, other site"
+M8939,"Hypertrophy of bone, multiple sites"
+M894,"Other hypertrophic osteoarthropathy"
+M8941,"Other hypertrophic osteoarthropathy, shoulder"
+M8942,"Other hypertrophic osteoarthropathy, upper arm"
+M8943,"Other hypertrophic osteoarthropathy, forearm"
+M8944,"Other hypertrophic osteoarthropathy, hand"
+M8945,"Other hypertrophic osteoarthropathy, thigh"
+M8946,"Other hypertrophic osteoarthropathy, lower leg"
+M8947,"Other hypertrophic osteoarthropathy, ankle and foot"
+M8948,"Other hypertrophic osteoarthropathy, other site"
+M8949,"Other hypertrophic osteoarthropathy, multiple sites"
+M895,"Osteolysis"
+M8951,"Osteolysis, shoulder"
+M8952,"Osteolysis, upper arm"
+M8953,"Osteolysis, forearm"
+M8954,"Osteolysis, hand"
+M8955,"Osteolysis, thigh"
+M8956,"Osteolysis, lower leg"
+M8957,"Osteolysis, ankle and foot"
+M8958,"Osteolysis, other site"
+M8959,"Osteolysis, multiple sites"
+M896,"Osteopathy after poliomyelitis"
+M8961,"Osteopathy after poliomyelitis, shoulder"
+M8962,"Osteopathy after poliomyelitis, upper arm"
+M8963,"Osteopathy after poliomyelitis, forearm"
+M8964,"Osteopathy after poliomyelitis, hand"
+M8965,"Osteopathy after poliomyelitis, thigh"
+M8966,"Osteopathy after poliomyelitis, lower leg"
+M8967,"Osteopathy after poliomyelitis, ankle and foot"
+M8968,"Osteopathy after poliomyelitis, other site"
+M8969,"Osteopathy after poliomyelitis, multiple sites"
+M897,"Major osseous defect"
+M8971,"Major osseous defect, shoulder region"
+M8972,"Major osseous defect, humerus"
+M8973,"Major osseous defect, forearm"
+M8974,"Major osseous defect, hand"
+M8975,"Major osseous defect, pelvic region and thigh"
+M8976,"Major osseous defect, lower leg"
+M8977,"Major osseous defect, ankle and foot"
+M8978,"Major osseous defect, other site"
+M8979,"Major osseous defect, multiple sites"
+M898,"Other specified disorders of bone"
+M898X,"Other specified disorders of bone"
+M899,"Disorder of bone, unspecified"
+M90,"Osteopathies in diseases classified elsewhere"
+M905,"Osteonecrosis in diseases classified elsewhere"
+M9051,"Osteonecrosis in diseases classified elsewhere, shoulder"
+M9052,"Osteonecrosis in diseases classified elsewhere, upper arm"
+M9053,"Osteonecrosis in diseases classified elsewhere, forearm"
+M9054,"Osteonecrosis in diseases classified elsewhere, hand"
+M9055,"Osteonecrosis in diseases classified elsewhere, thigh"
+M9056,"Osteonecrosis in diseases classified elsewhere, lower leg"
+M9057,"Osteonecrosis in diseases classified elsewhere, ankle and foot"
+M9058,"Osteonecrosis in diseases classified elsewhere, other site"
+M9059,"Osteonecrosis in diseases classified elsewhere, multiple sites"
+M906,"Osteitis deformans in neoplastic diseases"
+M9061,"Osteitis deformans in neoplastic diseases, shoulder"
+M9062,"Osteitis deformans in neoplastic diseases, upper arm"
+M9063,"Osteitis deformans in neoplastic diseases, forearm"
+M9064,"Osteitis deformans in neoplastic diseases, hand"
+M9065,"Osteitis deformans in neoplastic diseases, thigh"
+M9066,"Osteitis deformans in neoplastic diseases, lower leg"
+M9067,"Osteitis deformans in neoplastic diseases, ankle and foot"
+M9068,"Osteitis deformans in neoplastic diseases, other site"
+M9069,"Osteitis deformans in neoplastic diseases, multiple sites"
+M908,"Osteopathy in diseases classified elsewhere"
+M9081,"Osteopathy in diseases classified elsewhere, shoulder"
+M9082,"Osteopathy in diseases classified elsewhere, upper arm"
+M9083,"Osteopathy in diseases classified elsewhere, forearm"
+M9084,"Osteopathy in diseases classified elsewhere, hand"
+M9085,"Osteopathy in diseases classified elsewhere, thigh"
+M9086,"Osteopathy in diseases classified elsewhere, lower leg"
+M9087,"Osteopathy in diseases classified elsewhere, ankle and foot"
+M9088,"Osteopathy in diseases classified elsewhere, other site"
+M9089,"Osteopathy in diseases classified elsewhere, multiple sites"
+M91,"Juvenile osteochondrosis of hip and pelvis"
+M911,"Juvenile osteochondrosis of head of femur [Legg-Calve-Perthes]"
+M912,"Coxa plana"
+M913,"Pseudocoxalgia"
+M914,"Coxa magna"
+M918,"Other juvenile osteochondrosis of hip and pelvis"
+M919,"Juvenile osteochondrosis of hip and pelvis, unspecified"
+M92,"Other juvenile osteochondrosis"
+M920,"Juvenile osteochondrosis of humerus"
+M921,"Juvenile osteochondrosis of radius and ulna"
+M922,"Juvenile osteochondrosis, hand"
+M9220,"Unspecified juvenile osteochondrosis, hand"
+M9221,"Osteochondrosis (juvenile) of carpal lunate [Kienbock]"
+M9222,"Osteochondrosis (juvenile) of metacarpal heads [Mauclaire]"
+M9229,"Other juvenile osteochondrosis, hand"
+M923,"Other juvenile osteochondrosis, upper limb"
+M924,"Juvenile osteochondrosis of patella"
+M925,"Juvenile osteochondrosis of tibia and fibula"
+M926,"Juvenile osteochondrosis of tarsus"
+M927,"Juvenile osteochondrosis of metatarsus"
+M928,"Other specified juvenile osteochondrosis"
+M929,"Juvenile osteochondrosis, unspecified"
+M93,"Other osteochondropathies"
+M930,"Slipped upper femoral epiphysis (nontraumatic)"
+M9300,"Unspecified slipped upper femoral epiphysis (nontraumatic)"
+M9301,"Acute slipped upper femoral epiphysis (nontraumatic)"
+M9302,"Chronic slipped upper femoral epiphysis (nontraumatic)"
+M9303,"Acute on chronic slipped upper femoral epiphysis (nontraumatic)"
+M931,"Kienbock's disease of adults"
+M932,"Osteochondritis dissecans"
+M9321,"Osteochondritis dissecans of shoulder"
+M9322,"Osteochondritis dissecans of elbow"
+M9323,"Osteochondritis dissecans of wrist"
+M9324,"Osteochondritis dissecans of joints of hand"
+M9325,"Osteochondritis dissecans of hip"
+M9326,"Osteochondritis dissecans knee"
+M9327,"Osteochondritis dissecans of ankle and joints of foot"
+M9328,"Osteochondritis dissecans other site"
+M9329,"Osteochondritis dissecans multiple sites"
+M938,"Other specified osteochondropathies"
+M9381,"Other specified osteochondropathies of shoulder"
+M9382,"Other specified osteochondropathies of upper arm"
+M9383,"Other specified osteochondropathies of forearm"
+M9384,"Other specified osteochondropathies of hand"
+M9385,"Other specified osteochondropathies of thigh"
+M9386,"Other specified osteochondropathies lower leg"
+M9387,"Other specified osteochondropathies of ankle and foot"
+M9388,"Other specified osteochondropathies other"
+M9389,"Other specified osteochondropathies multiple sites"
+M939,"Osteochondropathy, unspecified"
+M9391,"Osteochondropathy, unspecified of shoulder"
+M9392,"Osteochondropathy, unspecified of upper arm"
+M9393,"Osteochondropathy, unspecified of forearm"
+M9394,"Osteochondropathy, unspecified of hand"
+M9395,"Osteochondropathy, unspecified of thigh"
+M9396,"Osteochondropathy, unspecified lower leg"
+M9397,"Osteochondropathy, unspecified of ankle and foot"
+M9398,"Osteochondropathy, unspecified other"
+M9399,"Osteochondropathy, unspecified multiple sites"
+M94,"Other disorders of cartilage"
+M942,"Chondromalacia"
+M9421,"Chondromalacia, shoulder"
+M9422,"Chondromalacia, elbow"
+M9423,"Chondromalacia, wrist"
+M9424,"Chondromalacia, joints of hand"
+M9425,"Chondromalacia, hip"
+M9426,"Chondromalacia, knee"
+M9427,"Chondromalacia, ankle and joints of foot"
+M9428,"Chondromalacia, other site"
+M9429,"Chondromalacia, multiple sites"
+M943,"Chondrolysis"
+M9435,"Chondrolysis, hip"
+M948,"Other specified disorders of cartilage"
+M948X,"Other specified disorders of cartilage"
+M949,"Disorder of cartilage, unspecified"
+M95,"Other acquired deformities of musculoskeletal system and connective tissue"
+M951,"Cauliflower ear"
+M952,"Other acquired deformity of head"
+M953,"Acquired deformity of neck"
+M954,"Acquired deformity of chest and rib"
+M955,"Acquired deformity of pelvis"
+M958,"Other specified acquired deformities of musculoskeletal system"
+M959,"Acquired deformity of musculoskeletal system, unspecified"
+M96,"Intraoperative and postprocedural complications and disorders of musculoskeletal system, not elsewhere classified"
+M966,"Fracture of bone following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M9662,"Fracture of humerus following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M9663,"Fracture of radius or ulna following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M9665,"Fracture of pelvis following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M9666,"Fracture of femur following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M9667,"Fracture of tibia or fibula following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M9669,"Fracture of other bone following insertion of orthopedic implant, joint prosthesis, or bone plate"
+M968,"Other intraoperative and postprocedural complications and disorders of  musculoskeletal system, not elsewhere classified"
+M9681,"Intraoperative hemorrhage and hematoma of a musculoskeletal structure complicating a procedure"
+M9682,"Accidental puncture and laceration of a musculoskeletal structure during a procedure"
+M9683,"Postprocedural hemorrhage of a musculoskeletal structure following a procedure"
+M9684,"Postprocedural hematoma and seroma of a musculoskeletal structure following a procedure"
+M9689,"Other intraoperative and postprocedural complications and disorders of the musculoskeletal system"
+M97,"Periprosthetic fracture around internal prosthetic joint"
+M970,"Periprosthetic fracture around internal prosthetic hip joint"
+M9701,"Periprosthetic fracture around internal prosthetic right hip joint"
+M9702,"Periprosthetic fracture around internal prosthetic left hip joint"
+M971,"Periprosthetic fracture around internal prosthetic knee joint"
+M9711,"Periprosthetic fracture around internal prosthetic right knee joint"
+M9712,"Periprosthetic fracture around internal prosthetic left knee joint"
+M972,"Periprosthetic fracture around internal prosthetic ankle joint"
+M9721,"Periprosthetic fracture around internal prosthetic right ankle joint"
+M9722,"Periprosthetic fracture around internal prosthetic left ankle joint"
+M973,"Periprosthetic fracture around internal prosthetic shoulder joint"
+M9731,"Periprosthetic fracture around internal prosthetic right shoulder joint"
+M9732,"Periprosthetic fracture around internal prosthetic left shoulder joint"
+M974,"Periprosthetic fracture around internal prosthetic elbow joint"
+M9741,"Periprosthetic fracture around internal prosthetic right elbow joint"
+M9742,"Periprosthetic fracture around internal prosthetic left elbow joint"
+M978,"Periprosthetic fracture around other internal prosthetic joint"
+M979,"Periprosthetic fracture around unspecified internal prosthetic joint"
+M99,"Biomechanical lesions, not elsewhere classified"
+M990,"Segmental and somatic dysfunction"
+M991,"Subluxation complex (vertebral)"
+M992,"Subluxation stenosis of neural canal"
+M993,"Osseous stenosis of neural canal"
+M994,"Connective tissue stenosis of neural canal"
+M995,"Intervertebral disc stenosis of neural canal"
+M996,"Osseous and subluxation stenosis of intervertebral foramina"
+M997,"Connective tissue and disc stenosis of intervertebral foramina"
+M998,"Other biomechanical lesions"
+M999,"Biomechanical lesion, unspecified"
+N00,"Acute nephritic syndrome"
+N01,"Rapidly progressive nephritic syndrome"
+N02,"Recurrent and persistent hematuria"
+N03,"Chronic nephritic syndrome"
+N04,"Nephrotic syndrome"
+N05,"Unspecified nephritic syndrome"
+N06,"Isolated proteinuria with specified morphological lesion"
+N07,"Hereditary nephropathy, not elsewhere classified"
+N08,"Glomerular disorders in diseases classified elsewhere"
+N10,"Acute pyelonephritis"
+N11,"Chronic tubulo-interstitial nephritis"
+N12,"Tubulo-interstitial nephritis, not specified as acute or chronic"
+N13,"Obstructive and reflux uropathy"
+N133,"Other and unspecified hydronephrosis"
+N134,"Hydroureter"
+N135,"Crossing vessel and stricture of ureter without hydronephrosis"
+N136,"Pyonephrosis"
+N137,"Vesicoureteral-reflux"
+N1372,"Vesicoureteral-reflux with reflux nephropathy without hydroureter"
+N1373,"Vesicoureteral-reflux with reflux nephropathy with hydroureter"
+N138,"Other obstructive and reflux uropathy"
+N139,"Obstructive and reflux uropathy, unspecified"
+N14,"Drug- and heavy-metal-induced tubulo-interstitial and tubular conditions"
+N15,"Other renal tubulo-interstitial diseases"
+N16,"Renal tubulo-interstitial disorders in diseases classified elsewhere"
+N17,"Acute kidney failure"
+N18,"Chronic kidney disease (CKD)"
+N19,"Unspecified kidney failure"
+N20,"Calculus of kidney and ureter"
+N21,"Calculus of lower urinary tract"
+N22,"Calculus of urinary tract in diseases classified elsewhere"
+N23,"Unspecified renal colic"
+N25,"Disorders resulting from impaired renal tubular function"
+N258,"Other disorders resulting from impaired renal tubular function"
+N259,"Disorder resulting from impaired renal tubular function, unspecified"
+N26,"Unspecified contracted kidney"
+N27,"Small kidney of unknown cause"
+N28,"Other disorders of kidney and ureter, not elsewhere classified"
+N288,"Other specified disorders of kidney and ureter"
+N289,"Disorder of kidney and ureter, unspecified"
+N29,"Other disorders of kidney and ureter in diseases classified elsewhere"
+N30,"Cystitis"
+N300,"Acute cystitis"
+N301,"Interstitial cystitis (chronic)"
+N302,"Other chronic cystitis"
+N303,"Trigonitis"
+N304,"Irradiation cystitis"
+N308,"Other cystitis"
+N309,"Cystitis, unspecified"
+N31,"Neuromuscular dysfunction of bladder, not elsewhere classified"
+N32,"Other disorders of bladder"
+N328,"Other specified disorders of bladder"
+N329,"Bladder disorder, unspecified"
+N33,"Bladder disorders in diseases classified elsewhere"
+N34,"Urethritis and urethral syndrome"
+N35,"Urethral stricture"
+N350,"Post-traumatic urethral stricture"
+N3501,"Post-traumatic urethral stricture, male"
+N3502,"Post-traumatic urethral stricture, female"
+N351,"Postinfective urethral stricture, not elsewhere classified"
+N3511,"Postinfective urethral stricture, not elsewhere classified, male"
+N3512,"Postinfective urethral stricture, not elsewhere classified, female"
+N358,"Other urethral stricture"
+N359,"Urethral stricture, unspecified"
+N36,"Other disorders of urethra"
+N364,"Urethral functional and muscular disorders"
+N365,"Urethral false passage"
+N368,"Other specified disorders of urethra"
+N369,"Urethral disorder, unspecified"
+N37,"Urethral disorders in diseases classified elsewhere"
+N39,"Other disorders of urinary system"
+N394,"Other specified urinary incontinence"
+N3949,"Other specified urinary incontinence"
+N398,"Other specified disorders of urinary system"
+N399,"Disorder of urinary system, unspecified"
+N40,"Benign prostatic hyperplasia"
+N41,"Inflammatory diseases of prostate"
+N42,"Other and unspecified disorders of prostate"
+N423,"Dysplasia of prostate"
+N428,"Other specified disorders of prostate"
+N429,"Disorder of prostate, unspecified"
+N43,"Hydrocele and spermatocele"
+N434,"Spermatocele of epididymis"
+N44,"Noninflammatory disorders of testis"
+N440,"Torsion of testis"
+N441,"Cyst of tunica albuginea testis"
+N442,"Benign cyst of testis"
+N448,"Other noninflammatory disorders of the testis"
+N45,"Orchitis and epididymitis"
+N46,"Male infertility"
+N460,"Azoospermia"
+N4602,"Azoospermia due to extratesticular causes"
+N461,"Oligospermia"
+N4612,"Oligospermia due to extratesticular causes"
+N468,"Other male infertility"
+N469,"Male infertility, unspecified"
+N47,"Disorders of prepuce"
+N48,"Other disorders of penis"
+N482,"Other inflammatory disorders of penis"
+N483,"Priapism"
+N485,"Ulcer of penis"
+N486,"Induration penis plastica"
+N488,"Other specified disorders of penis"
+N489,"Disorder of penis, unspecified"
+N49,"Inflammatory disorders of male genital organs, not elsewhere classified"
+N50,"Other and unspecified disorders of male genital organs"
+N508,"Other specified disorders of male genital organs"
+N5081,"Testicular pain"
+N5082,"Scrotal pain"
+N5089,"Other specified disorders of the male genital organs"
+N509,"Disorder of male genital organs, unspecified"
+N51,"Disorders of male genital organs in diseases classified elsewhere"
+N52,"Male erectile dysfunction"
+N520,"Vasculogenic erectile dysfunction"
+N521,"Erectile dysfunction due to diseases classified elsewhere"
+N522,"Drug-induced erectile dysfunction"
+N523,"Postprocedural erectile dysfunction"
+N528,"Other male erectile dysfunction"
+N529,"Male erectile dysfunction, unspecified"
+N53,"Other male sexual dysfunction"
+N531,"Ejaculatory dysfunction"
+N538,"Other male sexual dysfunction"
+N539,"Unspecified male sexual dysfunction"
+N60,"Benign mammary dysplasia"
+N600,"Solitary cyst of breast"
+N601,"Diffuse cystic mastopathy"
+N602,"Fibroadenosis of breast"
+N603,"Fibrosclerosis of breast"
+N604,"Mammary duct ectasia"
+N608,"Other benign mammary dysplasias"
+N609,"Unspecified benign mammary dysplasia"
+N61,"Inflammatory disorders of breast"
+N62,"Hypertrophy of breast"
+N63,"Unspecified lump in breast"
+N631,"Unspecified lump in the right breast"
+N632,"Unspecified lump in the left breast"
+N633,"Unspecified lump in axillary tail"
+N634,"Unspecified lump in breast, subareolar"
+N64,"Other disorders of breast"
+N645,"Other signs and symptoms in breast"
+N648,"Other specified disorders of breast"
+N649,"Disorder of breast, unspecified"
+N65,"Deformity and disproportion of reconstructed breast"
+N70,"Salpingitis and oophoritis"
+N700,"Acute salpingitis and oophoritis"
+N701,"Chronic salpingitis and oophoritis"
+N709,"Salpingitis and oophoritis, unspecified"
+N71,"Inflammatory disease of uterus, except cervix"
+N72,"Inflammatory disease of cervix uteri"
+N73,"Other female pelvic inflammatory diseases"
+N74,"Female pelvic inflammatory disorders in diseases classified elsewhere"
+N75,"Diseases of Bartholin's gland"
+N76,"Other inflammation of vagina and vulva"
+N768,"Other specified inflammation of vagina and vulva"
+N77,"Vulvovaginal ulceration and inflammation in diseases classified elsewhere"
+N80,"Endometriosis"
+N81,"Female genital prolapse"
+N811,"Cystocele"
+N812,"Incomplete uterovaginal prolapse"
+N813,"Complete uterovaginal prolapse"
+N814,"Uterovaginal prolapse, unspecified"
+N815,"Vaginal enterocele"
+N816,"Rectocele"
+N818,"Other female genital prolapse"
+N819,"Female genital prolapse, unspecified"
+N82,"Fistulae involving female genital tract"
+N83,"Noninflammatory disorders of ovary, fallopian tube and broad ligament"
+N830,"Follicular cyst of ovary"
+N831,"Corpus luteum cyst"
+N832,"Other and unspecified ovarian cysts"
+N8320,"Unspecified ovarian cysts"
+N8329,"Other ovarian cysts"
+N833,"Acquired atrophy of ovary and fallopian tube"
+N8331,"Acquired atrophy of ovary"
+N8332,"Acquired atrophy of fallopian tube"
+N8333,"Acquired atrophy of ovary and fallopian tube"
+N834,"Prolapse and hernia of ovary and fallopian tube"
+N835,"Torsion of ovary, ovarian pedicle and fallopian tube"
+N8351,"Torsion of ovary and ovarian pedicle"
+N8352,"Torsion of fallopian tube"
+N8353,"Torsion of ovary, ovarian pedicle and fallopian tube"
+N836,"Hematosalpinx"
+N837,"Hematoma of broad ligament"
+N838,"Other noninflammatory disorders of ovary, fallopian tube and broad ligament"
+N839,"Noninflammatory disorder of ovary, fallopian tube and broad ligament, unspecified"
+N84,"Polyp of female genital tract"
+N85,"Other noninflammatory disorders of uterus, except cervix"
+N850,"Endometrial hyperplasia"
+N852,"Hypertrophy of uterus"
+N853,"Subinvolution of uterus"
+N854,"Malposition of uterus"
+N855,"Inversion of uterus"
+N856,"Intrauterine synechiae"
+N857,"Hematometra"
+N858,"Other specified noninflammatory disorders of uterus"
+N859,"Noninflammatory disorder of uterus, unspecified"
+N86,"Erosion and ectropion of cervix uteri"
+N87,"Dysplasia of cervix uteri"
+N88,"Other noninflammatory disorders of cervix uteri"
+N89,"Other noninflammatory disorders of vagina"
+N90,"Other noninflammatory disorders of vulva and perineum"
+N906,"Hypertrophy of vulva"
+N907,"Vulvar cyst"
+N908,"Other specified noninflammatory disorders of vulva and perineum"
+N9081,"Female genital mutilation status"
+N9089,"Other specified noninflammatory disorders of vulva and perineum"
+N909,"Noninflammatory disorder of vulva and perineum, unspecified"
+N91,"Absent, scanty and rare menstruation"
+N92,"Excessive, frequent and irregular menstruation"
+N93,"Other abnormal uterine and vaginal bleeding"
+N94,"Pain and other conditions associated with female genital organs and menstrual cycle"
+N941,"Dyspareunia"
+N942,"Vaginismus"
+N943,"Premenstrual tension syndrome"
+N944,"Primary dysmenorrhea"
+N945,"Secondary dysmenorrhea"
+N946,"Dysmenorrhea, unspecified"
+N948,"Other specified conditions associated with female genital organs and menstrual cycle"
+N9481,"Vulvodynia"
+N9489,"Other specified conditions associated with female genital organs and menstrual cycle"
+N949,"Unspecified condition associated with female genital organs and menstrual cycle"
+N95,"Menopausal and other perimenopausal disorders"
+N96,"Recurrent pregnancy loss"
+N97,"Female infertility"
+N98,"Complications associated with artificial fertilization"
+N99,"Intraoperative and postprocedural complications and disorders of genitourinary system, not elsewhere classified"
+N991,"Postprocedural urethral stricture"
+N9911,"Postprocedural urethral stricture, male"
+N9912,"Postprocedural urethral stricture, female"
+N992,"Postprocedural adhesions of vagina"
+N993,"Prolapse of vaginal vault after hysterectomy"
+N994,"Postprocedural pelvic peritoneal adhesions"
+N995,"Complications of stoma of urinary tract"
+N9951,"Complication of cystostomy"
+N9952,"Complication of incontinent external stoma of urinary tract"
+N9953,"Complication of continent stoma of urinary tract"
+N996,"Intraoperative hemorrhage and hematoma of a genitourinary system organ or structure complicating a procedure"
+N997,"Accidental puncture and laceration of a genitourinary system organ or structure during a procedure"
+N998,"Other intraoperative and postprocedural complications and disorders of genitourinary system"
+N9982,"Postprocedural hemorrhage of a genitourinary system organ or structure following a procedure"
+N9983,"Residual ovary syndrome"
+N9984,"Postprocedural hematoma and seroma of a genitourinary system organ or structure following a procedure"
+N9989,"Other postprocedural complications and disorders of genitourinary system"
+O00,"Ectopic pregnancy"
+O000,"Abdominal pregnancy"
+O001,"Tubal pregnancy"
+O0010,"Tubal pregnancy without intrauterine pregnancy"
+O0011,"Tubal pregnancy with intrauterine pregnancy"
+O002,"Ovarian pregnancy"
+O0020,"Ovarian pregnancy without intrauterine pregnancy"
+O0021,"Ovarian pregnancy with intrauterine pregnancy"
+O008,"Other ectopic pregnancy"
+O009,"Ectopic pregnancy, unspecified"
+O01,"Hydatidiform mole"
+O02,"Other abnormal products of conception"
+O028,"Other specified abnormal products of conception"
+O029,"Abnormal product of conception, unspecified"
+O03,"Spontaneous abortion"
+O033,"Other and unspecified complications following incomplete spontaneous abortion"
+O034,"Incomplete spontaneous abortion without complication"
+O035,"Genital tract and pelvic infection following complete or unspecified spontaneous abortion"
+O036,"Delayed or excessive hemorrhage following complete or unspecified spontaneous abortion"
+O037,"Embolism following complete or unspecified spontaneous abortion"
+O038,"Other and unspecified complications following complete or unspecified spontaneous abortion"
+O039,"Complete or unspecified spontaneous abortion without complication"
+O04,"Complications following (induced) termination of pregnancy"
+O048,"(Induced) termination of pregnancy with other and unspecified complications"
+O07,"Failed attempted termination of pregnancy"
+O073,"Failed attempted termination of pregnancy with other and unspecified complications"
+O074,"Failed attempted termination of pregnancy without complication"
+O08,"Complications following ectopic and molar pregnancy"
+O088,"Other complications following an ectopic and molar pregnancy"
+O089,"Unspecified complication following an ectopic and molar pregnancy"
+O09,"Supervision of high risk pregnancy"
+O090,"Supervision of pregnancy with history of infertility"
+O091,"Supervision of pregnancy with history of ectopic pregnancy"
+O09A,"Supervision of pregnancy with history of molar pregnancy"
+O092,"Supervision of pregnancy with other poor reproductive or obstetric history"
+O0921,"Supervision of pregnancy with history of pre-term labor"
+O0929,"Supervision of pregnancy with other poor reproductive or obstetric history"
+O093,"Supervision of pregnancy with insufficient antenatal care"
+O094,"Supervision of pregnancy with grand multiparity"
+O095,"Supervision of elderly primigravida and multigravida"
+O0951,"Supervision of elderly primigravida"
+O0952,"Supervision of elderly multigravida"
+O096,"Supervision of young primigravida and multigravida"
+O0961,"Supervision of young primigravida"
+O0962,"Supervision of young multigravida"
+O097,"Supervision of high risk pregnancy due to social problems"
+O098,"Supervision of other high risk pregnancies"
+O0981,"Supervision of pregnancy resulting from assisted reproductive technology"
+O0982,"Supervision of pregnancy with history of in utero procedure during previous pregnancy"
+O0989,"Supervision of other high risk pregnancies"
+O099,"Supervision of high risk pregnancy, unspecified"
+O10,"Pre-existing hypertension complicating pregnancy, childbirth and the puerperium"
+O100,"Pre-existing essential hypertension complicating pregnancy, childbirth and the puerperium"
+O1001,"Pre-existing essential hypertension complicating pregnancy,"
+O1002,"Pre-existing essential hypertension complicating childbirth"
+O1003,"Pre-existing essential hypertension complicating the puerperium"
+O101,"Pre-existing hypertensive heart disease complicating pregnancy, childbirth and the puerperium"
+O1011,"Pre-existing hypertensive heart disease complicating pregnancy"
+O1012,"Pre-existing hypertensive heart disease complicating childbirth"
+O1013,"Pre-existing hypertensive heart disease complicating the puerperium"
+O102,"Pre-existing hypertensive chronic kidney disease complicating pregnancy, childbirth and the puerperium"
+O1021,"Pre-existing hypertensive chronic kidney disease complicating pregnancy"
+O1022,"Pre-existing hypertensive chronic kidney disease complicating childbirth"
+O1023,"Pre-existing hypertensive chronic kidney disease complicating the puerperium"
+O103,"Pre-existing hypertensive heart and chronic kidney disease complicating pregnancy, childbirth and the puerperium"
+O1031,"Pre-existing hypertensive heart and chronic kidney disease complicating pregnancy"
+O1032,"Pre-existing hypertensive heart and chronic kidney disease complicating childbirth"
+O1033,"Pre-existing hypertensive heart and chronic kidney disease complicating the puerperium"
+O104,"Pre-existing secondary hypertension complicating pregnancy, childbirth and the puerperium"
+O1041,"Pre-existing secondary hypertension complicating pregnancy"
+O1042,"Pre-existing secondary hypertension complicating childbirth"
+O1043,"Pre-existing secondary hypertension complicating the puerperium"
+O109,"Unspecified pre-existing hypertension complicating pregnancy, childbirth and the puerperium"
+O1091,"Unspecified pre-existing hypertension complicating pregnancy"
+O1092,"Unspecified pre-existing hypertension complicating childbirth"
+O1093,"Unspecified pre-existing hypertension complicating the puerperium"
+O11,"Pre-existing hypertension with pre-eclampsia"
+O12,"Gestational [pregnancy-induced] edema and proteinuria without hypertension"
+O120,"Gestational edema"
+O121,"Gestational proteinuria"
+O122,"Gestational edema with proteinuria"
+O13,"Gestational [pregnancy-induced] hypertension without significant proteinuria"
+O14,"Pre-eclampsia"
+O140,"Mild to moderate pre-eclampsia"
+O141,"Severe pre-eclampsia"
+O142,"HELLP syndrome"
+O149,"Unspecified pre-eclampsia"
+O15,"Eclampsia"
+O150,"Eclampsia complicating pregnancy"
+O151,"Eclampsia complicating labor"
+O152,"Eclampsia complicating the puerperium"
+O159,"Eclampsia, unspecified as to time period"
+O16,"Unspecified maternal hypertension"
+O20,"Hemorrhage in early pregnancy"
+O21,"Excessive vomiting in pregnancy"
+O22,"Venous complications and hemorrhoids in pregnancy"
+O220,"Varicose veins of lower extremity in pregnancy"
+O221,"Genital varices in pregnancy"
+O222,"Superficial thrombophlebitis in pregnancy"
+O223,"Deep phlebothrombosis in pregnancy"
+O224,"Hemorrhoids in pregnancy"
+O225,"Cerebral venous thrombosis in pregnancy"
+O228,"Other venous complications in pregnancy"
+O228X,"Other venous complications in pregnancy"
+O229,"Venous complication in pregnancy, unspecified"
+O23,"Infections of genitourinary tract in pregnancy"
+O230,"Infections of kidney in pregnancy"
+O231,"Infections of bladder in pregnancy"
+O232,"Infections of urethra in pregnancy"
+O233,"Infections of other parts of urinary tract in pregnancy"
+O234,"Unspecified infection of urinary tract in pregnancy"
+O235,"Infections of the genital tract in pregnancy"
+O2351,"Infection of cervix in pregnancy"
+O2352,"Salpingo-oophoritis in pregnancy"
+O2359,"Infection of other part of genital tract in pregnancy"
+O239,"Unspecified genitourinary tract infection in pregnancy"
+O24,"Diabetes mellitus in pregnancy, childbirth, and the puerperium"
+O240,"Pre-existing type 1 diabetes mellitus, in pregnancy, childbirth and the puerperium"
+O2401,"Pre-existing type 1 diabetes mellitus, in pregnancy"
+O2402,"Pre-existing type 1 diabetes mellitus, in childbirth"
+O2403,"Pre-existing type 1 diabetes mellitus, in the puerperium"
+O241,"Pre-existing type 2 diabetes mellitus, in pregnancy, childbirth and the puerperium"
+O2411,"Pre-existing type 2 diabetes mellitus, in pregnancy"
+O2412,"Pre-existing type 2 diabetes mellitus, in childbirth"
+O2413,"Pre-existing type 2 diabetes mellitus, in the puerperium"
+O243,"Unspecified pre-existing diabetes mellitus in pregnancy, childbirth and the puerperium"
+O2431,"Unspecified pre-existing diabetes mellitus in pregnancy"
+O2432,"Unspecified pre-existing diabetes mellitus in childbirth"
+O2433,"Unspecified pre-existing diabetes mellitus in the puerperium"
+O244,"Gestational diabetes mellitus"
+O2441,"Gestational diabetes mellitus in pregnancy"
+O2442,"Gestational diabetes mellitus in childbirth"
+O2443,"Gestational diabetes mellitus in the puerperium"
+O248,"Other pre-existing diabetes mellitus in pregnancy, childbirth, and the puerperium"
+O2481,"Other pre-existing diabetes mellitus in pregnancy"
+O2482,"Other pre-existing diabetes mellitus in childbirth"
+O2483,"Other pre-existing diabetes mellitus in the puerperium"
+O249,"Unspecified diabetes mellitus in pregnancy, childbirth and the puerperium"
+O2491,"Unspecified diabetes mellitus in pregnancy"
+O2492,"Unspecified diabetes mellitus in childbirth"
+O2493,"Unspecified diabetes mellitus in the puerperium"
+O25,"Malnutrition in pregnancy, childbirth and the puerperium"
+O251,"Malnutrition in pregnancy"
+O252,"Malnutrition in childbirth"
+O253,"Malnutrition in the puerperium"
+O26,"Maternal care for other conditions predominantly related to pregnancy"
+O260,"Excessive weight gain in pregnancy"
+O261,"Low weight gain in pregnancy"
+O262,"Pregnancy care for patient with recurrent pregnancy loss"
+O263,"Retained intrauterine contraceptive device in pregnancy"
+O264,"Herpes gestationis"
+O265,"Maternal hypotension syndrome"
+O266,"Liver and biliary tract disorders in pregnancy, childbirth and the puerperium"
+O2661,"Liver and biliary tract disorders in pregnancy"
+O2662,"Liver and biliary tract disorders in childbirth"
+O2663,"Liver and biliary tract disorders in the puerperium"
+O267,"Subluxation of symphysis (pubis) in pregnancy, childbirth and the puerperium"
+O2671,"Subluxation of symphysis (pubis) in pregnancy"
+O2672,"Subluxation of symphysis (pubis) in childbirth"
+O2673,"Subluxation of symphysis (pubis) in the puerperium"
+O268,"Other specified pregnancy related conditions"
+O2681,"Pregnancy related exhaustion and fatigue"
+O2682,"Pregnancy related peripheral neuritis"
+O2683,"Pregnancy related renal disease"
+O2684,"Uterine size-date discrepancy complicating pregnancy"
+O2685,"Spotting complicating pregnancy"
+O2686,"Pruritic urticarial papules and plaques of pregnancy (PUPPP)"
+O2687,"Cervical shortening"
+O2689,"Other specified pregnancy related conditions"
+O269,"Pregnancy related conditions, unspecified"
+O28,"Abnormal findings on antenatal screening of mother"
+O29,"Complications of anesthesia during pregnancy"
+O290,"Pulmonary complications of anesthesia during pregnancy"
+O2901,"Aspiration pneumonitis due to anesthesia during pregnancy"
+O2902,"Pressure collapse of lung due to anesthesia during pregnancy"
+O2909,"Other pulmonary complications of anesthesia during pregnancy"
+O291,"Cardiac complications of anesthesia during pregnancy"
+O2911,"Cardiac arrest due to anesthesia during pregnancy"
+O2912,"Cardiac failure due to anesthesia during pregnancy"
+O2919,"Other cardiac complications of anesthesia during pregnancy"
+O292,"Central nervous system complications of anesthesia during pregnancy"
+O2921,"Cerebral anoxia due to anesthesia during pregnancy"
+O2929,"Other central nervous system complications of anesthesia during pregnancy"
+O293,"Toxic reaction to local anesthesia during pregnancy"
+O293X,"Toxic reaction to local anesthesia during pregnancy"
+O294,"Spinal and epidural anesthesia induced headache during pregnancy"
+O295,"Other complications of spinal and epidural anesthesia during pregnancy"
+O295X,"Other complications of spinal and epidural anesthesia during pregnancy"
+O296,"Failed or difficult intubation for anesthesia during pregnancy"
+O298,"Other complications of anesthesia during pregnancy"
+O298X,"Other complications of anesthesia during pregnancy"
+O299,"Unspecified complication of anesthesia during pregnancy"
+O30,"Multiple gestation"
+O300,"Twin pregnancy"
+O3000,"Twin pregnancy, unspecified number of placenta and unspecified number of amniotic sacs"
+O3001,"Twin pregnancy, monochorionic/monoamniotic"
+O3002,"Conjoined twin pregnancy"
+O3003,"Twin pregnancy, monochorionic/diamniotic"
+O3004,"Twin pregnancy, dichorionic/diamniotic"
+O3009,"Twin pregnancy, unable to determine number of placenta and number of amniotic sacs"
+O301,"Triplet pregnancy"
+O3010,"Triplet pregnancy, unspecified number of placenta and unspecified number of amniotic sacs"
+O3011,"Triplet pregnancy with two or more monochorionic fetuses"
+O3012,"Triplet pregnancy with two or more monoamniotic fetuses"
+O3019,"Triplet pregnancy, unable to determine number of placenta and number of amniotic sacs"
+O302,"Quadruplet pregnancy"
+O3020,"Quadruplet pregnancy, unspecified number of placenta and unspecified number of amniotic sacs"
+O3021,"Quadruplet pregnancy with two or more monochorionic fetuses"
+O3022,"Quadruplet pregnancy with two or more monoamniotic fetuses"
+O3029,"Quadruplet pregnancy, unable to determine number of placenta and number of amniotic sacs"
+O308,"Other specified multiple gestation"
+O3080,"Other specified multiple gestation, unspecified number of placenta and unspecified number of amniotic sacs"
+O3081,"Other specified multiple gestation with two or more monochorionic fetuses"
+O3082,"Other specified multiple gestation with two or more monoamniotic fetuses"
+O3089,"Other specified multiple gestation, unable to determine number of placenta and number of amniotic sacs"
+O309,"Multiple gestation, unspecified"
+O31,"Complications specific to multiple gestation"
+O310,"Papyraceous fetus"
+O3100,"Papyraceous fetus, unspecified trimester"
+O3101,"Papyraceous fetus, first trimester"
+O3102,"Papyraceous fetus, second trimester"
+O3103,"Papyraceous fetus, third trimester"
+O311,"Continuing pregnancy after spontaneous abortion of one fetus or more"
+O3110,"Continuing pregnancy after spontaneous abortion of one fetus or more, unspecified trimester"
+O3111,"Continuing pregnancy after spontaneous abortion of one fetus or more, first trimester"
+O3112,"Continuing pregnancy after spontaneous abortion of one fetus or more, second trimester"
+O3113,"Continuing pregnancy after spontaneous abortion of one fetus or more, third trimester"
+O312,"Continuing pregnancy after intrauterine death of one fetus or more"
+O3120,"Continuing pregnancy after intrauterine death of one fetus or more, unspecified trimester"
+O3121,"Continuing pregnancy after intrauterine death of one fetus or more, first trimester"
+O3122,"Continuing pregnancy after intrauterine death of one fetus or more, second trimester"
+O3123,"Continuing pregnancy after intrauterine death of one fetus or more, third trimester"
+O313,"Continuing pregnancy after elective fetal reduction of one fetus or more"
+O3130,"Continuing pregnancy after elective fetal reduction of one fetus or more, unspecified trimester"
+O3131,"Continuing pregnancy after elective fetal reduction of one fetus or more, first trimester"
+O3132,"Continuing pregnancy after elective fetal reduction of one fetus or more, second trimester"
+O3133,"Continuing pregnancy after elective fetal reduction of one fetus or more, third trimester"
+O318,"Other complications specific to multiple gestation"
+O318X,"Other complications specific to multiple gestation"
+O318X1,"Other complications specific to multiple gestation, first trimester"
+O318X2,"Other complications specific to multiple gestation, second trimester"
+O318X3,"Other complications specific to multiple gestation, third trimester"
+O318X9,"Other complications specific to multiple gestation, unspecified trimester"
+O32,"Maternal care for malpresentation of fetus"
+O320,"Maternal care for unstable lie"
+O321,"Maternal care for breech presentation"
+O322,"Maternal care for transverse and oblique lie"
+O323,"Maternal care for face, brow and chin presentation"
+O324,"Maternal care for high head at term"
+O326,"Maternal care for compound presentation"
+O328,"Maternal care for other malpresentation of fetus"
+O329,"Maternal care for malpresentation of fetus, unspecified"
+O33,"Maternal care for disproportion"
+O333,"Maternal care for disproportion due to outlet contraction of pelvis"
+O334,"Maternal care for disproportion of mixed maternal and fetal origin"
+O335,"Maternal care for disproportion due to unusually large fetus"
+O336,"Maternal care for disproportion due to hydrocephalic fetus"
+O337,"Maternal care for disproportion due to other fetal deformities"
+O338,"Maternal care for disproportion of other origin"
+O339,"Maternal care for disproportion, unspecified"
+O34,"Maternal care for abnormality of pelvic organs"
+O340,"Maternal care for congenital malformation of uterus"
+O341,"Maternal care for benign tumor of corpus uteri"
+O342,"Maternal care due to uterine scar from previous surgery"
+O3421,"Maternal care for scar from previous cesarean delivery"
+O3429,"Maternal care due to uterine scar from other previous surgery"
+O343,"Maternal care for cervical incompetence"
+O344,"Maternal care for other abnormalities of cervix"
+O345,"Maternal care for other abnormalities of gravid uterus"
+O3451,"Maternal care for incarceration of gravid uterus"
+O3452,"Maternal care for prolapse of gravid uterus"
+O3453,"Maternal care for retroversion of gravid uterus"
+O3459,"Maternal care for other abnormalities of gravid uterus"
+O346,"Maternal care for abnormality of vagina"
+O347,"Maternal care for abnormality of vulva and perineum"
+O348,"Maternal care for other abnormalities of pelvic organs"
+O349,"Maternal care for abnormality of pelvic organ, unspecified"
+O35,"Maternal care for known or suspected fetal abnormality and damage"
+O350,"Maternal care for (suspected) central nervous system malformation in fetus"
+O351,"Maternal care for (suspected) chromosomal abnormality in fetus"
+O352,"Maternal care for (suspected) hereditary disease in fetus"
+O353,"Maternal care for (suspected) damage to fetus from viral disease in mother"
+O354,"Maternal care for (suspected) damage to fetus from alcohol"
+O355,"Maternal care for (suspected) damage to fetus by drugs"
+O356,"Maternal care for (suspected) damage to fetus by radiation"
+O357,"Maternal care for (suspected) damage to fetus by other medical procedures"
+O358,"Maternal care for other (suspected) fetal abnormality and damage"
+O359,"Maternal care for (suspected) fetal abnormality and damage, unspecified"
+O36,"Maternal care for other fetal problems"
+O360,"Maternal care for rhesus isoimmunization"
+O3601,"Maternal care for anti-D [Rh] antibodies"
+O36011,"Maternal care for anti-D [Rh] antibodies, first trimester"
+O36012,"Maternal care for anti-D [Rh] antibodies, second trimester"
+O36013,"Maternal care for anti-D [Rh] antibodies, third trimester"
+O36019,"Maternal care for anti-D [Rh] antibodies, unspecified trimester"
+O3609,"Maternal care for other rhesus isoimmunization"
+O36091,"Maternal care for other rhesus isoimmunization, first trimester"
+O36092,"Maternal care for other rhesus isoimmunization, second trimester"
+O36093,"Maternal care for other rhesus isoimmunization, third trimester"
+O36099,"Maternal care for other rhesus isoimmunization, unspecified trimester"
+O361,"Maternal care for other isoimmunization"
+O3611,"Maternal care for Anti-A sensitization"
+O36111,"Maternal care for Anti-A sensitization, first trimester"
+O36112,"Maternal care for Anti-A sensitization, second trimester"
+O36113,"Maternal care for Anti-A sensitization, third trimester"
+O36119,"Maternal care for Anti-A sensitization, unspecified trimester"
+O3619,"Maternal care for other isoimmunization"
+O36191,"Maternal care for other isoimmunization, first trimester"
+O36192,"Maternal care for other isoimmunization, second trimester"
+O36193,"Maternal care for other isoimmunization, third trimester"
+O36199,"Maternal care for other isoimmunization, unspecified trimester"
+O362,"Maternal care for hydrops fetalis"
+O3620,"Maternal care for hydrops fetalis, unspecified trimester"
+O3621,"Maternal care for hydrops fetalis, first trimester"
+O3622,"Maternal care for hydrops fetalis, second trimester"
+O3623,"Maternal care for hydrops fetalis, third trimester"
+O364,"Maternal care for intrauterine death"
+O365,"Maternal care for known or suspected poor fetal growth"
+O3651,"Maternal care for known or suspected placental insufficiency"
+O36511,"Maternal care for known or suspected placental insufficiency, first trimester"
+O36512,"Maternal care for known or suspected placental insufficiency, second trimester"
+O36513,"Maternal care for known or suspected placental insufficiency, third trimester"
+O36519,"Maternal care for known or suspected placental insufficiency, unspecified trimester"
+O3659,"Maternal care for other known or suspected poor fetal growth"
+O36591,"Maternal care for other known or suspected poor fetal growth, first trimester"
+O36592,"Maternal care for other known or suspected poor fetal growth, second trimester"
+O36593,"Maternal care for other known or suspected poor fetal growth, third trimester"
+O36599,"Maternal care for other known or suspected poor fetal growth, unspecified trimester"
+O366,"Maternal care for excessive fetal growth"
+O3660,"Maternal care for excessive fetal growth, unspecified trimester"
+O3661,"Maternal care for excessive fetal growth, first trimester"
+O3662,"Maternal care for excessive fetal growth, second trimester"
+O3663,"Maternal care for excessive fetal growth, third trimester"
+O367,"Maternal care for viable fetus in abdominal pregnancy"
+O3670,"Maternal care for viable fetus in abdominal pregnancy, unspecified trimester"
+O3671,"Maternal care for viable fetus in abdominal pregnancy, first trimester"
+O3672,"Maternal care for viable fetus in abdominal pregnancy, second trimester"
+O3673,"Maternal care for viable fetus in abdominal pregnancy, third trimester"
+O368,"Maternal care for other specified fetal problems"
+O3680,"Pregnancy with inconclusive fetal viability"
+O3681,"Decreased fetal movements"
+O36812,"Decreased fetal movements, second trimester"
+O36813,"Decreased fetal movements, third trimester"
+O36819,"Decreased fetal movements, unspecified trimester"
+O3682,"Fetal anemia and thrombocytopenia"
+O36821,"Fetal anemia and thrombocytopenia, first trimester"
+O36822,"Fetal anemia and thrombocytopenia, second trimester"
+O36823,"Fetal anemia and thrombocytopenia, third trimester"
+O36829,"Fetal anemia and thrombocytopenia, unspecified trimester"
+O3683,"Maternal care for abnormalities of the fetal heart rate or rhythm"
+O36831,"Maternal care for abnormalities of the fetal heart rate or rhythm, first trimester"
+O36832,"Maternal care for abnormalities of the fetal heart rate or rhythm, second trimester"
+O36833,"Maternal care for abnormalities of the fetal heart rate or rhythm, third trimester"
+O36839,"Maternal care for abnormalities of the fetal heart rate or rhythm, unspecified trimester"
+O3689,"Maternal care for other specified fetal problems"
+O36891,"Maternal care for other specified fetal problems, first trimester"
+O36892,"Maternal care for other specified fetal problems, second trimester"
+O36893,"Maternal care for other specified fetal problems, third trimester"
+O36899,"Maternal care for other specified fetal problems, unspecified trimester"
+O369,"Maternal care for fetal problem, unspecified"
+O3690,"Maternal care for fetal problem, unspecified, unspecified trimester"
+O3691,"Maternal care for fetal problem, unspecified, first trimester"
+O3692,"Maternal care for fetal problem, unspecified, second trimester"
+O3693,"Maternal care for fetal problem, unspecified, third trimester"
+O40,"Polyhydramnios"
+O401,"Polyhydramnios, first trimester"
+O402,"Polyhydramnios, second trimester"
+O403,"Polyhydramnios, third trimester"
+O409,"Polyhydramnios, unspecified trimester"
+O41,"Other disorders of amniotic fluid and membranes"
+O410,"Oligohydramnios"
+O4100,"Oligohydramnios, unspecified trimester"
+O4101,"Oligohydramnios, first trimester"
+O4102,"Oligohydramnios, second trimester"
+O4103,"Oligohydramnios, third trimester"
+O411,"Infection of amniotic sac and membranes"
+O4110,"Infection of amniotic sac and membranes, unspecified"
+O41101,"Infection of amniotic sac and membranes, unspecified, first trimester"
+O41102,"Infection of amniotic sac and membranes, unspecified, second trimester"
+O41103,"Infection of amniotic sac and membranes, unspecified, third trimester"
+O41109,"Infection of amniotic sac and membranes, unspecified, unspecified trimester"
+O4112,"Chorioamnionitis"
+O41121,"Chorioamnionitis, first trimester"
+O41122,"Chorioamnionitis, second trimester"
+O41123,"Chorioamnionitis, third trimester"
+O41129,"Chorioamnionitis, unspecified trimester"
+O4114,"Placentitis"
+O41141,"Placentitis, first trimester"
+O41142,"Placentitis, second trimester"
+O41143,"Placentitis, third trimester"
+O41149,"Placentitis, unspecified trimester"
+O418,"Other specified disorders of amniotic fluid and membranes"
+O418X,"Other specified disorders of amniotic fluid and membranes"
+O418X1,"Other specified disorders of amniotic fluid and membranes, first trimester"
+O418X2,"Other specified disorders of amniotic fluid and membranes, second trimester"
+O418X3,"Other specified disorders of amniotic fluid and membranes, third trimester"
+O418X9,"Other specified disorders of amniotic fluid and membranes, unspecified trimester"
+O419,"Disorder of amniotic fluid and membranes, unspecified"
+O4190,"Disorder of amniotic fluid and membranes, unspecified, unspecified trimester"
+O4191,"Disorder of amniotic fluid and membranes, unspecified, first trimester"
+O4192,"Disorder of amniotic fluid and membranes, unspecified, second trimester"
+O4193,"Disorder of amniotic fluid and membranes, unspecified, third trimester"
+O42,"Premature rupture of membranes"
+O420,"Premature rupture of membranes, onset of labor within 24 hours of rupture"
+O4201,"Preterm premature rupture of membranes, onset of labor within 24 hours of rupture"
+O4202,"Full-term premature rupture of membranes, onset of labor within 24 hours of rupture"
+O421,"Premature rupture of membranes, onset of labor more than 24 hours following rupture"
+O4211,"Preterm premature rupture of membranes, onset of labor more than 24 hours following rupture"
+O4212,"Full-term premature rupture of membranes, onset of labor more than 24 hours following rupture"
+O429,"Premature rupture of membranes, unspecified as to length of time between rupture and onset of labor"
+O4291,"Preterm premature rupture of membranes, unspecified as to length of time between rupture and onset of labor"
+O4292,"Full-term premature rupture of membranes, unspecified as to length of time between rupture and onset of labor"
+O43,"Placental disorders"
+O430,"Placental transfusion syndromes"
+O4301,"Fetomaternal placental transfusion syndrome"
+O4302,"Fetus-to-fetus placental transfusion syndrome"
+O431,"Malformation of placenta"
+O4310,"Malformation of placenta, unspecified"
+O4311,"Circumvallate placenta"
+O4312,"Velamentous insertion of umbilical cord"
+O4319,"Other malformation of placenta"
+O432,"Morbidly adherent placenta"
+O4321,"Placenta accreta"
+O4322,"Placenta increta"
+O4323,"Placenta percreta"
+O438,"Other placental disorders"
+O4381,"Placental infarction"
+O4389,"Other placental disorders"
+O439,"Unspecified placental disorder"
+O44,"Placenta previa"
+O440,"Complete placenta previa NOS or without hemorrhage"
+O441,"Complete placenta previa with hemorrhage"
+O442,"Partial placenta previa without hemorrhage"
+O443,"Partial placenta previa with hemorrhage"
+O444,"Low lying placenta NOS or without hemorrhage"
+O445,"Low lying placenta with hemorrhage"
+O45,"Premature separation of placenta [abruptio placentae]"
+O450,"Premature separation of placenta with coagulation defect"
+O4500,"Premature separation of placenta with coagulation defect, unspecified"
+O4501,"Premature separation of placenta with afibrinogenemia"
+O4502,"Premature separation of placenta with disseminated intravascular coagulation"
+O4509,"Premature separation of placenta with other coagulation defect"
+O458,"Other premature separation of placenta"
+O458X,"Other premature separation of placenta"
+O459,"Premature separation of placenta, unspecified"
+O46,"Antepartum hemorrhage, not elsewhere classified"
+O460,"Antepartum hemorrhage with coagulation defect"
+O4600,"Antepartum hemorrhage with coagulation defect, unspecified"
+O4601,"Antepartum hemorrhage with afibrinogenemia"
+O4602,"Antepartum hemorrhage with disseminated intravascular coagulation"
+O4609,"Antepartum hemorrhage with other coagulation defect"
+O468,"Other antepartum hemorrhage"
+O468X,"Other antepartum hemorrhage"
+O469,"Antepartum hemorrhage, unspecified"
+O47,"False labor"
+O470,"False labor before 37 completed weeks of gestation"
+O471,"False labor at or after 37 completed weeks of gestation"
+O479,"False labor, unspecified"
+O48,"Late pregnancy"
+O60,"Preterm labor"
+O600,"Preterm labor without delivery"
+O601,"Preterm labor with preterm delivery"
+O6010,"Preterm labor with preterm delivery, unspecified trimester"
+O6012,"Preterm labor second trimester with preterm delivery second trimester"
+O6013,"Preterm labor second trimester with preterm delivery third trimester"
+O6014,"Preterm labor third trimester with preterm delivery third trimester"
+O602,"Term delivery with preterm labor"
+O6020,"Term delivery with preterm labor, unspecified trimester"
+O6022,"Term delivery with preterm labor, second trimester"
+O6023,"Term delivery with preterm labor, third trimester"
+O61,"Failed induction of labor"
+O62,"Abnormalities of forces of labor"
+O63,"Long labor"
+O64,"Obstructed labor due to malposition and malpresentation of fetus"
+O640,"Obstructed labor due to incomplete rotation of fetal head"
+O641,"Obstructed labor due to breech presentation"
+O642,"Obstructed labor due to face presentation"
+O643,"Obstructed labor due to brow presentation"
+O644,"Obstructed labor due to shoulder presentation"
+O645,"Obstructed labor due to compound presentation"
+O648,"Obstructed labor due to other malposition and malpresentation"
+O649,"Obstructed labor due to malposition and malpresentation, unspecified"
+O65,"Obstructed labor due to maternal pelvic abnormality"
+O66,"Other obstructed labor"
+O664,"Failed trial of labor"
+O665,"Attempted application of vacuum extractor and forceps"
+O666,"Obstructed labor due to other multiple fetuses"
+O668,"Other specified obstructed labor"
+O669,"Obstructed labor, unspecified"
+O67,"Labor and delivery complicated by intrapartum hemorrhage, not elsewhere classified"
+O68,"Labor and delivery complicated by abnormality of fetal acid-base balance"
+O69,"Labor and delivery complicated by umbilical cord complications"
+O690,"Labor and delivery complicated by prolapse of cord"
+O691,"Labor and delivery complicated by cord around neck, with compression"
+O692,"Labor and delivery complicated by other cord entanglement, with compression"
+O693,"Labor and delivery complicated by short cord"
+O694,"Labor and delivery complicated by vasa previa"
+O695,"Labor and delivery complicated by vascular lesion of cord"
+O698,"Labor and delivery complicated by other cord complications"
+O6981,"Labor and delivery complicated by cord around neck, without compression"
+O6982,"Labor and delivery complicated by other cord entanglement, without compression"
+O6989,"Labor and delivery complicated by other cord complications"
+O699,"Labor and delivery complicated by cord complication, unspecified"
+O70,"Perineal laceration during delivery"
+O702,"Third degree perineal laceration during delivery"
+O703,"Fourth degree perineal laceration during delivery"
+O704,"Anal sphincter tear complicating delivery, not associated with third degree laceration"
+O709,"Perineal laceration during delivery, unspecified"
+O71,"Other obstetric trauma"
+O710,"Rupture of uterus (spontaneous) before onset of labor"
+O711,"Rupture of uterus during labor"
+O712,"Postpartum inversion of uterus"
+O713,"Obstetric laceration of cervix"
+O714,"Obstetric high vaginal laceration alone"
+O715,"Other obstetric injury to pelvic organs"
+O716,"Obstetric damage to pelvic joints and ligaments"
+O717,"Obstetric hematoma of pelvis"
+O718,"Other specified obstetric trauma"
+O719,"Obstetric trauma, unspecified"
+O72,"Postpartum hemorrhage"
+O73,"Retained placenta and membranes, without hemorrhage"
+O74,"Complications of anesthesia during labor and delivery"
+O75,"Other complications of labor and delivery, not elsewhere classified"
+O758,"Other specified complications of labor and delivery"
+O759,"Complication of labor and delivery, unspecified"
+O76,"Abnormality in fetal heart rate and rhythm complicating labor and delivery"
+O77,"Other fetal stress complicating labor and delivery"
+O80,"Encounter for full-term uncomplicated delivery"
+O82,"Encounter for cesarean delivery without indication"
+O85,"Puerperal sepsis"
+O86,"Other puerperal infections"
+O861,"Other infection of genital tract following delivery"
+O862,"Urinary tract infection following delivery"
+O864,"Pyrexia of unknown origin following delivery"
+O868,"Other specified puerperal infections"
+O87,"Venous complications and hemorrhoids in the puerperium"
+O88,"Obstetric embolism"
+O880,"Obstetric air embolism"
+O8801,"Obstetric air embolism in pregnancy"
+O8802,"Air embolism in childbirth"
+O8803,"Air embolism in the puerperium"
+O881,"Amniotic fluid embolism"
+O8811,"Amniotic fluid embolism in pregnancy"
+O8812,"Amniotic fluid embolism in childbirth"
+O8813,"Amniotic fluid embolism in the puerperium"
+O882,"Obstetric thromboembolism"
+O8821,"Thromboembolism in pregnancy"
+O8822,"Thromboembolism in childbirth"
+O8823,"Thromboembolism in the puerperium"
+O883,"Obstetric pyemic and septic embolism"
+O8831,"Pyemic and septic embolism in pregnancy"
+O8832,"Pyemic and septic embolism in childbirth"
+O8833,"Pyemic and septic embolism in the puerperium"
+O888,"Other obstetric embolism"
+O8881,"Other embolism in pregnancy"
+O8882,"Other embolism in childbirth"
+O8883,"Other embolism in the puerperium"
+O89,"Complications of anesthesia during the puerperium"
+O890,"Pulmonary complications of anesthesia during the puerperium"
+O891,"Cardiac complications of anesthesia during the puerperium"
+O892,"Central nervous system complications of anesthesia during the puerperium"
+O893,"Toxic reaction to local anesthesia during the puerperium"
+O894,"Spinal and epidural anesthesia-induced headache during the puerperium"
+O895,"Other complications of spinal and epidural anesthesia during the puerperium"
+O896,"Failed or difficult intubation for anesthesia during the puerperium"
+O898,"Other complications of anesthesia during the puerperium"
+O899,"Complication of anesthesia during the puerperium, unspecified"
+O90,"Complications of the puerperium, not elsewhere classified"
+O908,"Other complications of the puerperium, not elsewhere classified"
+O909,"Complication of the puerperium, unspecified"
+O91,"Infections of breast associated with pregnancy, the puerperium and lactation"
+O910,"Infection of nipple associated with pregnancy, the puerperium and lactation"
+O9101,"Infection of nipple associated with pregnancy"
+O9102,"Infection of nipple associated with the puerperium"
+O9103,"Infection of nipple associated with lactation"
+O911,"Abscess of breast associated with pregnancy, the puerperium and lactation"
+O9111,"Abscess of breast associated with pregnancy"
+O9112,"Abscess of breast associated with the puerperium"
+O9113,"Abscess of breast associated with lactation"
+O912,"Nonpurulent mastitis associated with pregnancy, the puerperium and lactation"
+O9121,"Nonpurulent mastitis associated with pregnancy"
+O9122,"Nonpurulent mastitis associated with the puerperium"
+O9123,"Nonpurulent mastitis associated with lactation"
+O92,"Other disorders of breast and disorders of lactation associated with pregnancy and the puerperium"
+O920,"Retracted nipple associated with pregnancy, the puerperium, and lactation"
+O9201,"Retracted nipple associated with pregnancy"
+O9202,"Retracted nipple associated with the puerperium"
+O9203,"Retracted nipple associated with lactation"
+O921,"Cracked nipple associated with pregnancy, the puerperium, and lactation"
+O9211,"Cracked nipple associated with pregnancy"
+O9212,"Cracked nipple associated with the puerperium"
+O9213,"Cracked nipple associated with lactation"
+O922,"Other and unspecified disorders of breast associated with pregnancy and the puerperium"
+O923,"Agalactia"
+O924,"Hypogalactia"
+O925,"Suppressed lactation"
+O926,"Galactorrhea"
+O927,"Other and unspecified disorders of lactation"
+O94,"Sequelae of complication of pregnancy, childbirth, and the puerperium"
+O98,"Maternal infectious and parasitic diseases classifiable elsewhere but complicating pregnancy, childbirth and the puerperium"
+O980,"Tuberculosis complicating pregnancy, childbirth and the puerperium"
+O9801,"Tuberculosis complicating pregnancy"
+O9802,"Tuberculosis complicating childbirth"
+O9803,"Tuberculosis complicating the puerperium"
+O981,"Syphilis complicating pregnancy, childbirth and the puerperium"
+O9811,"Syphilis complicating pregnancy"
+O9812,"Syphilis complicating childbirth"
+O9813,"Syphilis complicating the puerperium"
+O982,"Gonorrhea complicating pregnancy, childbirth and the puerperium"
+O9821,"Gonorrhea complicating pregnancy"
+O9822,"Gonorrhea complicating childbirth"
+O9823,"Gonorrhea complicating the puerperium"
+O983,"Other infections with a predominantly sexual mode of transmission complicating pregnancy, childbirth and the puerperium"
+O9831,"Other infections with a predominantly sexual mode of transmission complicating pregnancy"
+O9832,"Other infections with a predominantly sexual mode of transmission complicating childbirth"
+O9833,"Other infections with a predominantly sexual mode of transmission complicating the puerperium"
+O984,"Viral hepatitis complicating pregnancy, childbirth and the puerperium"
+O9841,"Viral hepatitis complicating pregnancy"
+O9842,"Viral hepatitis complicating childbirth"
+O9843,"Viral hepatitis complicating the puerperium"
+O985,"Other viral diseases complicating pregnancy, childbirth and the puerperium"
+O9851,"Other viral diseases complicating pregnancy"
+O9852,"Other viral diseases complicating childbirth"
+O9853,"Other viral diseases complicating the puerperium"
+O986,"Protozoal diseases complicating pregnancy, childbirth and the puerperium"
+O9861,"Protozoal diseases complicating pregnancy"
+O9862,"Protozoal diseases complicating childbirth"
+O9863,"Protozoal diseases complicating the puerperium"
+O987,"Human immunodeficiency virus [HIV] disease complicating pregnancy, childbirth and the puerperium"
+O9871,"Human immunodeficiency virus [HIV] disease complicating pregnancy"
+O9872,"Human immunodeficiency virus [HIV] disease complicating childbirth"
+O9873,"Human immunodeficiency virus [HIV] disease complicating the puerperium"
+O988,"Other maternal infectious and parasitic diseases complicating pregnancy, childbirth and the puerperium"
+O9881,"Other maternal infectious and parasitic diseases complicating pregnancy"
+O9882,"Other maternal infectious and parasitic diseases complicating childbirth"
+O9883,"Other maternal infectious and parasitic diseases complicating the puerperium"
+O989,"Unspecified maternal infectious and parasitic disease complicating pregnancy, childbirth and the puerperium"
+O9891,"Unspecified maternal infectious and parasitic disease complicating pregnancy"
+O9892,"Unspecified maternal infectious and parasitic disease complicating childbirth"
+O9893,"Unspecified maternal infectious and parasitic disease complicating the puerperium"
+O99,"Other maternal diseases classifiable elsewhere but complicating pregnancy, childbirth and the puerperium"
+O990,"Anemia complicating pregnancy, childbirth and the puerperium"
+O9901,"Anemia complicating pregnancy"
+O9902,"Anemia complicating childbirth"
+O9903,"Anemia complicating the puerperium"
+O991,"Other diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism complicating pregnancy, childbirth and the puerperium"
+O9911,"Other diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism complicating pregnancy"
+O9912,"Other diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism complicating childbirth"
+O9913,"Other diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism complicating the puerperium"
+O992,"Endocrine, nutritional and metabolic diseases complicating pregnancy, childbirth and the puerperium"
+O9921,"Obesity complicating pregnancy, childbirth, and the puerperium"
+O9928,"Other endocrine, nutritional and metabolic diseases complicating pregnancy, childbirth and the puerperium"
+O993,"Mental disorders and diseases of the nervous system complicating pregnancy, childbirth and the puerperium"
+O9931,"Alcohol use complicating pregnancy, childbirth, and the puerperium"
+O9932,"Drug use complicating pregnancy, childbirth, and the puerperium"
+O9933,"Tobacco use disorder complicating pregnancy, childbirth, and the puerperium"
+O9934,"Other mental disorders complicating pregnancy, childbirth, and the puerperium"
+O9935,"Diseases of the nervous system complicating pregnancy, childbirth, and the puerperium"
+O994,"Diseases of the circulatory system complicating pregnancy, childbirth and the puerperium"
+O9941,"Diseases of the circulatory system complicating pregnancy"
+O9942,"Diseases of the circulatory system complicating childbirth"
+O9943,"Diseases of the circulatory system complicating the puerperium"
+O995,"Diseases of the respiratory system complicating pregnancy, childbirth and the puerperium"
+O9951,"Diseases of the respiratory system complicating pregnancy"
+O9952,"Diseases of the respiratory system complicating childbirth"
+O9953,"Diseases of the respiratory system complicating the puerperium"
+O996,"Diseases of the digestive system complicating pregnancy, childbirth and the puerperium"
+O9961,"Diseases of the digestive system complicating pregnancy"
+O9962,"Diseases of the digestive system complicating childbirth"
+O9963,"Diseases of the digestive system complicating the puerperium"
+O997,"Diseases of the skin and subcutaneous tissue complicating pregnancy, childbirth and the puerperium"
+O9971,"Diseases of the skin and subcutaneous tissue complicating pregnancy"
+O9972,"Diseases of the skin and subcutaneous tissue complicating childbirth"
+O9973,"Diseases of the skin and subcutaneous tissue complicating the puerperium"
+O998,"Other specified diseases and conditions complicating pregnancy, childbirth and the puerperium"
+O9981,"Abnormal glucose complicating pregnancy, childbirth and the puerperium"
+O9982,"Streptococcus B carrier state complicating pregnancy, childbirth and the puerperium"
+O9983,"Other infection carrier state complicating pregnancy, childbirth and the puerperium"
+O9984,"Bariatric surgery status complicating pregnancy, childbirth and the puerperium"
+O9989,"Other specified diseases and conditions complicating pregnancy, childbirth and the puerperium"
+O9A,"Maternal malignant neoplasms, traumatic injuries and abuse classifiable elsewhere but complicating pregnancy, childbirth and the puerperium"
+O9A1,"Malignant neoplasm complicating pregnancy, childbirth and the puerperium"
+O9A11,"Malignant neoplasm complicating pregnancy"
+O9A12,"Malignant neoplasm complicating childbirth"
+O9A13,"Malignant neoplasm complicating the puerperium"
+O9A2,"Injury, poisoning and certain other consequences of external causes complicating pregnancy, childbirth and the puerperium"
+O9A21,"Injury, poisoning and certain other consequences of external causes complicating pregnancy"
+O9A22,"Injury, poisoning and certain other consequences of external causes complicating childbirth"
+O9A23,"Injury, poisoning and certain other consequences of external causes complicating the puerperium"
+O9A3,"Physical abuse complicating pregnancy, childbirth and the puerperium"
+O9A31,"Physical abuse complicating pregnancy"
+O9A32,"Physical abuse complicating childbirth"
+O9A33,"Physical abuse complicating the puerperium"
+O9A4,"Sexual abuse complicating pregnancy, childbirth and the puerperium"
+O9A41,"Sexual abuse complicating pregnancy"
+O9A42,"Sexual abuse complicating childbirth"
+O9A43,"Sexual abuse complicating the puerperium"
+O9A5,"Psychological abuse complicating pregnancy, childbirth and the puerperium"
+O9A51,"Psychological abuse complicating pregnancy"
+O9A52,"Psychological abuse complicating childbirth"
+O9A53,"Psychological abuse complicating the puerperium"
+P00,"Newborn affected by maternal conditions that may be unrelated to present pregnancy"
+P008,"Newborn affected by other maternal conditions"
+P009,"Newborn affected by unspecified maternal condition"
+P01,"Newborn affected by maternal complications of pregnancy"
+P02,"Newborn affected by complications of placenta, cord and membranes"
+P022,"Newborn affected by other and unspecified morphological and functional abnormalities of placenta"
+P023,"Newborn affected by placental transfusion syndromes"
+P024,"Newborn affected by prolapsed cord"
+P025,"Newborn affected by other compression of umbilical cord"
+P026,"Newborn affected by other and unspecified conditions of umbilical cord"
+P027,"Newborn affected by chorioamnionitis"
+P028,"Newborn affected by other abnormalities of membranes"
+P029,"Newborn affected by abnormality of membranes, unspecified"
+P03,"Newborn affected by other complications of labor and delivery"
+P038,"Newborn affected by other specified complications of labor and delivery"
+P0381,"Newborn affected by abnormality in fetal (intrauterine) heart rate or rhythm"
+P0382,"Meconium passage during delivery"
+P0389,"Newborn affected by other specified complications of labor and delivery"
+P039,"Newborn affected by complication of labor and delivery, unspecified"
+P04,"Newborn affected by noxious substances transmitted via placenta or breast milk"
+P044,"Newborn  affected by maternal use of drugs of addiction"
+P045,"Newborn affected by maternal use of nutritional chemical substances"
+P046,"Newborn affected by maternal exposure to environmental chemical substances"
+P048,"Newborn affected by other maternal noxious substances"
+P049,"Newborn affected by maternal noxious substance, unspecified"
+P05,"Disorders of newborn related to slow fetal growth and fetal malnutrition"
+P050,"Newborn light for gestational age"
+P051,"Newborn small for gestational age"
+P052,"Newborn affected by fetal (intrauterine) malnutrition not light or small for gestational age"
+P059,"Newborn affected by slow intrauterine growth, unspecified"
+P07,"Disorders of newborn related to short gestation and low birth weight, not elsewhere classified"
+P070,"Extremely low birth weight newborn"
+P071,"Other low birth weight newborn"
+P072,"Extreme immaturity of newborn"
+P073,"Preterm [premature] newborn [other]"
+P08,"Disorders of newborn related to long gestation and high birth weight"
+P082,"Late newborn, not heavy for gestational age"
+P09,"Abnormal findings on neonatal screening"
+P10,"Intracranial laceration and hemorrhage due to birth injury"
+P11,"Other birth injuries to central nervous system"
+P12,"Birth injury to scalp"
+P128,"Other birth injuries to scalp"
+P129,"Birth injury to scalp, unspecified"
+P13,"Birth injury to skeleton"
+P14,"Birth injury to peripheral nervous system"
+P15,"Other birth injuries"
+P19,"Metabolic acidemia in newborn"
+P22,"Respiratory distress of newborn"
+P23,"Congenital pneumonia"
+P24,"Neonatal aspiration"
+P240,"Meconium aspiration"
+P241,"Neonatal aspiration of (clear) amniotic fluid and mucus"
+P242,"Neonatal aspiration of blood"
+P243,"Neonatal aspiration of milk and regurgitated food"
+P248,"Other neonatal aspiration"
+P249,"Neonatal aspiration, unspecified"
+P25,"Interstitial emphysema and related conditions originating in the perinatal period"
+P26,"Pulmonary hemorrhage originating in the perinatal period"
+P27,"Chronic respiratory disease originating in the perinatal period"
+P28,"Other respiratory conditions originating in the perinatal period"
+P281,"Other and unspecified atelectasis of newborn"
+P282,"Cyanotic attacks of newborn"
+P283,"Primary sleep apnea of newborn"
+P284,"Other apnea of newborn"
+P285,"Respiratory failure of newborn"
+P288,"Other specified respiratory conditions of newborn"
+P289,"Respiratory condition of newborn, unspecified"
+P29,"Cardiovascular disorders originating in the perinatal period"
+P291,"Neonatal cardiac dysrhythmia"
+P292,"Neonatal hypertension"
+P293,"Persistent fetal circulation"
+P294,"Transient myocardial ischemia in newborn"
+P298,"Other cardiovascular disorders originating in the perinatal period"
+P299,"Cardiovascular disorder originating in the perinatal period, unspecified"
+P35,"Congenital viral diseases"
+P36,"Bacterial sepsis of newborn"
+P361,"Sepsis of newborn due to other and unspecified streptococci"
+P362,"Sepsis of newborn due to Staphylococcus aureus"
+P363,"Sepsis of newborn due to other and unspecified staphylococci"
+P364,"Sepsis of newborn due to Escherichia coli"
+P365,"Sepsis of newborn due to anaerobes"
+P368,"Other bacterial sepsis of newborn"
+P369,"Bacterial sepsis of newborn, unspecified"
+P37,"Other congenital infectious and parasitic diseases"
+P38,"Omphalitis of newborn"
+P39,"Other infections specific to the perinatal period"
+P50,"Newborn affected by intrauterine (fetal) blood loss"
+P51,"Umbilical hemorrhage of newborn"
+P52,"Intracranial nontraumatic hemorrhage of newborn"
+P522,"Intraventricular (nontraumatic) hemorrhage, grade 3 and grade 4, of newborn"
+P523,"Unspecified intraventricular (nontraumatic) hemorrhage of newborn"
+P524,"Intracerebral (nontraumatic) hemorrhage of newborn"
+P525,"Subarachnoid (nontraumatic) hemorrhage of newborn"
+P526,"Cerebellar (nontraumatic) and posterior fossa hemorrhage of newborn"
+P528,"Other intracranial (nontraumatic) hemorrhages of newborn"
+P529,"Intracranial (nontraumatic) hemorrhage of newborn, unspecified"
+P53,"Hemorrhagic disease of newborn"
+P54,"Other neonatal hemorrhages"
+P55,"Hemolytic disease of newborn"
+P56,"Hydrops fetalis due to hemolytic disease"
+P569,"Hydrops fetalis due to other and unspecified hemolytic disease"
+P57,"Kernicterus"
+P58,"Neonatal jaundice due to other excessive hemolysis"
+P584,"Neonatal jaundice due to drugs or toxins transmitted from mother or given to newborn"
+P585,"Neonatal jaundice due to swallowed maternal blood"
+P588,"Neonatal jaundice due to other specified excessive hemolysis"
+P589,"Neonatal jaundice due to excessive hemolysis, unspecified"
+P59,"Neonatal jaundice from other and unspecified causes"
+P592,"Neonatal jaundice from other and unspecified hepatocellular damage"
+P593,"Neonatal jaundice from breast milk inhibitor"
+P598,"Neonatal jaundice from other specified causes"
+P599,"Neonatal jaundice, unspecified"
+P60,"Disseminated intravascular coagulation of newborn"
+P61,"Other perinatal hematological disorders"
+P70,"Transitory disorders of carbohydrate metabolism specific to newborn"
+P71,"Transitory neonatal disorders of calcium and magnesium metabolism"
+P72,"Other transitory neonatal endocrine disorders"
+P74,"Other transitory neonatal electrolyte and metabolic disturbances"
+P76,"Other intestinal obstruction of newborn"
+P77,"Necrotizing enterocolitis of newborn"
+P78,"Other perinatal digestive system disorders"
+P788,"Other specified perinatal digestive system disorders"
+P789,"Perinatal digestive system disorder, unspecified"
+P80,"Hypothermia of newborn"
+P81,"Other disturbances of temperature regulation of newborn"
+P83,"Other conditions of integument specific to newborn"
+P833,"Other and unspecified edema specific to newborn"
+P834,"Breast engorgement of newborn"
+P835,"Congenital hydrocele"
+P836,"Umbilical polyp of newborn"
+P838,"Other specified conditions of integument specific to newborn"
+P839,"Condition of the integument specific to newborn, unspecified"
+P84,"Other problems with newborn"
+P90,"Convulsions of newborn"
+P91,"Other disturbances of cerebral status of newborn"
+P916,"Hypoxic ischemic encephalopathy [HIE]"
+P918,"Other specified disturbances of cerebral status of newborn"
+P9181,"Neonatal encephalopathy"
+P9188,"Other specified disturbances of cerebral status of newborn"
+P919,"Disturbance of cerebral status of newborn, unspecified"
+P92,"Feeding problems of newborn"
+P920,"Vomiting of newborn"
+P921,"Regurgitation and rumination of newborn"
+P922,"Slow feeding of newborn"
+P923,"Underfeeding of newborn"
+P924,"Overfeeding of newborn"
+P925,"Neonatal difficulty in feeding at breast"
+P926,"Failure to thrive in newborn"
+P928,"Other feeding problems of newborn"
+P929,"Feeding problem of newborn, unspecified"
+P93,"Reactions and intoxications due to drugs administered to newborn"
+P94,"Disorders of muscle tone of newborn"
+P95,"Stillbirth"
+P96,"Other conditions originating in the perinatal period"
+P968,"Other specified conditions originating in the perinatal period"
+P969,"Condition originating in the perinatal period, unspecified"
+Q00,"Anencephaly and similar malformations"
+Q01,"Encephalocele"
+Q02,"Microcephaly"
+Q03,"Congenital hydrocephalus"
+Q04,"Other congenital malformations of brain"
+Q05,"Spina bifida"
+Q06,"Other congenital malformations of spinal cord"
+Q07,"Other congenital malformations of nervous system"
+Q070,"Arnold-Chiari syndrome"
+Q078,"Other specified congenital malformations of nervous system"
+Q079,"Congenital malformation of nervous system, unspecified"
+Q10,"Congenital malformations of eyelid, lacrimal apparatus and orbit"
+Q11,"Anophthalmos, microphthalmos and macrophthalmos"
+Q12,"Congenital lens malformations"
+Q13,"Congenital malformations of anterior segment of eye"
+Q138,"Other congenital malformations of anterior segment of eye"
+Q139,"Congenital malformation of anterior segment of eye, unspecified"
+Q14,"Congenital malformations of posterior segment of eye"
+Q15,"Other congenital malformations of eye"
+Q16,"Congenital malformations of ear causing impairment of hearing"
+Q17,"Other congenital malformations of ear"
+Q18,"Other congenital malformations of face and neck"
+Q20,"Congenital malformations of cardiac chambers and connections"
+Q21,"Congenital malformations of cardiac septa"
+Q22,"Congenital malformations of pulmonary and tricuspid valves"
+Q23,"Congenital malformations of aortic and mitral valves"
+Q24,"Other congenital malformations of heart"
+Q25,"Congenital malformations of great arteries"
+Q252,"Atresia of aorta"
+Q253,"Supravalvular aortic stenosis"
+Q254,"Other congenital malformations of aorta"
+Q255,"Atresia of pulmonary artery"
+Q256,"Stenosis of pulmonary artery"
+Q257,"Other congenital malformations of pulmonary artery"
+Q258,"Other congenital malformations of other great arteries"
+Q259,"Congenital malformation of great arteries, unspecified"
+Q26,"Congenital malformations of great veins"
+Q27,"Other congenital malformations of peripheral vascular system"
+Q273,"Arteriovenous malformation (peripheral)"
+Q274,"Congenital phlebectasia"
+Q278,"Other specified congenital malformations of peripheral vascular system"
+Q279,"Congenital malformation of peripheral vascular system, unspecified"
+Q28,"Other congenital malformations of circulatory system"
+Q30,"Congenital malformations of nose"
+Q31,"Congenital malformations of larynx"
+Q32,"Congenital malformations of trachea and bronchus"
+Q33,"Congenital malformations of lung"
+Q34,"Other congenital malformations of respiratory system"
+Q35,"Cleft palate"
+Q36,"Cleft lip"
+Q37,"Cleft palate with cleft lip"
+Q38,"Other congenital malformations of tongue, mouth and pharynx"
+Q39,"Congenital malformations of esophagus"
+Q40,"Other congenital malformations of upper alimentary tract"
+Q41,"Congenital absence, atresia and stenosis of small intestine"
+Q42,"Congenital absence, atresia and stenosis of large intestine"
+Q43,"Other congenital malformations of intestine"
+Q44,"Congenital malformations of gallbladder, bile ducts and liver"
+Q45,"Other congenital malformations of digestive system"
+Q50,"Congenital malformations of ovaries, fallopian tubes and broad ligaments"
+Q500,"Congenital absence of ovary"
+Q501,"Developmental ovarian cyst"
+Q502,"Congenital torsion of ovary"
+Q503,"Other congenital malformations of ovary"
+Q504,"Embryonic cyst of fallopian tube"
+Q505,"Embryonic cyst of broad ligament"
+Q506,"Other congenital malformations of fallopian tube and broad ligament"
+Q51,"Congenital malformations of uterus and cervix"
+Q511,"Doubling of uterus with doubling of cervix and vagina"
+Q512,"Other doubling of uterus"
+Q513,"Bicornate uterus"
+Q514,"Unicornate uterus"
+Q515,"Agenesis and aplasia of cervix"
+Q516,"Embryonic cyst of cervix"
+Q517,"Congenital fistulae between uterus and digestive and urinary tracts"
+Q518,"Other congenital malformations of uterus and cervix"
+Q5181,"Other congenital malformations of uterus"
+Q5182,"Other congenital malformations of cervix"
+Q519,"Congenital malformation of uterus and cervix, unspecified"
+Q52,"Other congenital malformations of female genitalia"
+Q521,"Doubling of vagina"
+Q5212,"Longitudinal vaginal septum"
+Q522,"Congenital rectovaginal fistula"
+Q523,"Imperforate hymen"
+Q524,"Other congenital malformations of vagina"
+Q525,"Fusion of labia"
+Q526,"Congenital malformation of clitoris"
+Q527,"Other and unspecified congenital malformations of vulva"
+Q528,"Other specified congenital malformations of female genitalia"
+Q529,"Congenital malformation of female genitalia, unspecified"
+Q53,"Undescended and ectopic testicle"
+Q530,"Ectopic testis"
+Q531,"Undescended testicle, unilateral"
+Q5311,"Abdominal testis, unilateral"
+Q5312,"Ectopic perineal testis, unilateral"
+Q5313,"Unilateral high scrotal testis"
+Q532,"Undescended testicle, bilateral"
+Q5321,"Abdominal testis, bilateral"
+Q5322,"Ectopic perineal testis, bilateral"
+Q5323,"Bilateral high scrotal testes"
+Q539,"Undescended testicle, unspecified"
+Q54,"Hypospadias"
+Q55,"Other congenital malformations of male genital organs"
+Q552,"Other and unspecified congenital malformations of testis and scrotum"
+Q553,"Atresia of vas deferens"
+Q554,"Other congenital malformations of vas deferens, epididymis, seminal vesicles and prostate"
+Q555,"Congenital absence and aplasia of penis"
+Q556,"Other congenital malformations of penis"
+Q557,"Congenital vasocutaneous fistula"
+Q558,"Other specified congenital malformations of male genital organs"
+Q559,"Congenital malformation of male genital organ, unspecified"
+Q56,"Indeterminate sex and pseudohermaphroditism"
+Q60,"Renal agenesis and other reduction defects of kidney"
+Q61,"Cystic kidney disease"
+Q610,"Congenital renal cyst"
+Q611,"Polycystic kidney, infantile type"
+Q612,"Polycystic kidney, adult type"
+Q613,"Polycystic kidney, unspecified"
+Q614,"Renal dysplasia"
+Q615,"Medullary cystic kidney"
+Q618,"Other cystic kidney diseases"
+Q619,"Cystic kidney disease, unspecified"
+Q62,"Congenital obstructive defects of renal pelvis and congenital malformations of ureter"
+Q621,"Congenital occlusion of ureter"
+Q622,"Congenital megaureter"
+Q623,"Other obstructive defects of renal pelvis and ureter"
+Q624,"Agenesis of ureter"
+Q625,"Duplication of ureter"
+Q626,"Malposition of ureter"
+Q627,"Congenital vesico-uretero-renal reflux"
+Q628,"Other congenital malformations of ureter"
+Q63,"Other congenital malformations of kidney"
+Q64,"Other congenital malformations of urinary system"
+Q641,"Exstrophy of urinary bladder"
+Q642,"Congenital posterior urethral valves"
+Q643,"Other atresia and stenosis of urethra and bladder neck"
+Q644,"Malformation of urachus"
+Q645,"Congenital absence of bladder and urethra"
+Q646,"Congenital diverticulum of bladder"
+Q647,"Other and unspecified congenital malformations of bladder and urethra"
+Q648,"Other specified congenital malformations of urinary system"
+Q649,"Congenital malformation of urinary system, unspecified"
+Q65,"Congenital deformities of hip"
+Q650,"Congenital dislocation of hip, unilateral"
+Q651,"Congenital dislocation of hip, bilateral"
+Q652,"Congenital dislocation of hip, unspecified"
+Q653,"Congenital partial dislocation of hip, unilateral"
+Q654,"Congenital partial dislocation of hip, bilateral"
+Q655,"Congenital partial dislocation of hip, unspecified"
+Q656,"Congenital unstable hip"
+Q658,"Other congenital deformities of hip"
+Q659,"Congenital deformity of hip, unspecified"
+Q66,"Congenital deformities of feet"
+Q662,"Congenital metatarsus (primus) varus"
+Q663,"Other congenital varus deformities of feet"
+Q664,"Congenital talipes calcaneovalgus"
+Q665,"Congenital pes planus"
+Q666,"Other congenital valgus deformities of feet"
+Q667,"Congenital pes cavus"
+Q668,"Other congenital deformities of feet"
+Q669,"Congenital deformity of feet, unspecified"
+Q67,"Congenital musculoskeletal deformities of head, face, spine and chest"
+Q68,"Other congenital musculoskeletal deformities"
+Q69,"Polydactyly"
+Q70,"Syndactyly"
+Q700,"Fused fingers"
+Q701,"Webbed fingers"
+Q702,"Fused toes"
+Q703,"Webbed toes"
+Q704,"Polysyndactyly, unspecified"
+Q709,"Syndactyly, unspecified"
+Q71,"Reduction defects of upper limb"
+Q710,"Congenital complete absence of upper limb"
+Q711,"Congenital absence of upper arm and forearm with hand present"
+Q712,"Congenital absence of both forearm and hand"
+Q713,"Congenital absence of hand and finger"
+Q714,"Longitudinal reduction defect of radius"
+Q715,"Longitudinal reduction defect of ulna"
+Q716,"Lobster-claw hand"
+Q718,"Other reduction defects of upper limb"
+Q7181,"Congenital shortening of upper limb"
+Q7189,"Other reduction defects of upper limb"
+Q719,"Unspecified reduction defect of upper limb"
+Q72,"Reduction defects of lower limb"
+Q720,"Congenital complete absence of lower limb"
+Q721,"Congenital absence of thigh and lower leg with foot present"
+Q722,"Congenital absence of both lower leg and foot"
+Q723,"Congenital absence of foot and toe(s)"
+Q724,"Longitudinal reduction defect of femur"
+Q725,"Longitudinal reduction defect of tibia"
+Q726,"Longitudinal reduction defect of fibula"
+Q727,"Split foot"
+Q728,"Other reduction defects of lower limb"
+Q7281,"Congenital shortening of lower limb"
+Q7289,"Other reduction defects of lower limb"
+Q729,"Unspecified reduction defect of lower limb"
+Q73,"Reduction defects of unspecified limb"
+Q74,"Other congenital malformations of limb(s)"
+Q75,"Other congenital malformations of skull and face bones"
+Q76,"Congenital malformations of spine and bony thorax"
+Q764,"Other congenital malformations of spine, not associated with scoliosis"
+Q7641,"Congenital kyphosis"
+Q7642,"Congenital lordosis"
+Q7649,"Other congenital malformations of spine, not associated with scoliosis"
+Q765,"Cervical rib"
+Q766,"Other congenital malformations of ribs"
+Q767,"Congenital malformation of sternum"
+Q768,"Other congenital malformations of bony thorax"
+Q769,"Congenital malformation of bony thorax, unspecified"
+Q77,"Osteochondrodysplasia with defects of growth of tubular bones and spine"
+Q78,"Other osteochondrodysplasias"
+Q79,"Congenital malformations of musculoskeletal system, not elsewhere classified"
+Q795,"Other congenital malformations of abdominal wall"
+Q796,"Ehlers-Danlos syndrome"
+Q798,"Other congenital malformations of musculoskeletal system"
+Q799,"Congenital malformation of musculoskeletal system, unspecified"
+Q80,"Congenital ichthyosis"
+Q81,"Epidermolysis bullosa"
+Q82,"Other congenital malformations of skin"
+Q83,"Congenital malformations of breast"
+Q84,"Other congenital malformations of integument"
+Q85,"Phakomatoses, not elsewhere classified"
+Q850,"Neurofibromatosis (nonmalignant)"
+Q851,"Tuberous sclerosis"
+Q858,"Other phakomatoses, not elsewhere classified"
+Q859,"Phakomatosis, unspecified"
+Q86,"Congenital malformation syndromes due to known exogenous causes, not elsewhere classified"
+Q87,"Other specified congenital malformation syndromes affecting multiple systems"
+Q874,"Marfan's syndrome"
+Q8741,"Marfan's syndrome with cardiovascular manifestations"
+Q8742,"Marfan's syndrome with ocular manifestations"
+Q8743,"Marfan's syndrome with skeletal manifestation"
+Q875,"Other congenital malformation syndromes with other skeletal changes"
+Q878,"Other specified congenital malformation syndromes, not elsewhere classified"
+Q89,"Other congenital malformations, not elsewhere classified"
+Q890,"Congenital absence and malformations of spleen"
+Q891,"Congenital malformations of adrenal gland"
+Q892,"Congenital malformations of other endocrine glands"
+Q893,"Situs inversus"
+Q894,"Conjoined twins"
+Q897,"Multiple congenital malformations, not elsewhere classified"
+Q898,"Other specified congenital malformations"
+Q899,"Congenital malformation, unspecified"
+Q90,"Down syndrome"
+Q91,"Trisomy 18 and Trisomy 13"
+Q92,"Other trisomies and partial trisomies of the autosomes, not elsewhere classified"
+Q926,"Marker chromosomes"
+Q927,"Triploidy and polyploidy"
+Q928,"Other specified trisomies and partial trisomies of autosomes"
+Q929,"Trisomy and partial trisomy of autosomes, unspecified"
+Q93,"Monosomies and deletions from the autosomes, not elsewhere classified"
+Q938,"Other deletions from the autosomes"
+Q939,"Deletion from autosomes, unspecified"
+Q95,"Balanced rearrangements and structural markers, not elsewhere classified"
+Q96,"Turner's syndrome"
+Q97,"Other sex chromosome abnormalities, female phenotype, not elsewhere classified"
+Q98,"Other sex chromosome abnormalities, male phenotype, not elsewhere classified"
+Q99,"Other chromosome abnormalities, not elsewhere classified"
+R00,"Abnormalities of heart beat"
+R01,"Cardiac murmurs and other cardiac sounds"
+R03,"Abnormal blood-pressure reading, without diagnosis"
+R04,"Hemorrhage from respiratory passages"
+R048,"Hemorrhage from other sites in respiratory passages"
+R049,"Hemorrhage from respiratory passages, unspecified"
+R05,"Cough"
+R06,"Abnormalities of breathing"
+R060,"Dyspnea"
+R061,"Stridor"
+R062,"Wheezing"
+R063,"Periodic breathing"
+R064,"Hyperventilation"
+R065,"Mouth breathing"
+R066,"Hiccough"
+R067,"Sneezing"
+R068,"Other abnormalities of breathing"
+R069,"Unspecified abnormalities of breathing"
+R07,"Pain in throat and chest"
+R078,"Other chest pain"
+R079,"Chest pain, unspecified"
+R09,"Other symptoms and signs involving the circulatory and respiratory system"
+R090,"Asphyxia and hypoxemia"
+R091,"Pleurisy"
+R092,"Respiratory arrest"
+R093,"Abnormal sputum"
+R098,"Other specified symptoms and signs involving the circulatory and respiratory systems"
+R10,"Abdominal and pelvic pain"
+R101,"Pain localized to upper abdomen"
+R102,"Pelvic and perineal pain"
+R103,"Pain localized to other parts of lower abdomen"
+R108,"Other abdominal pain"
+R1081,"Abdominal tenderness"
+R1082,"Rebound abdominal tenderness"
+R1083,"Colic"
+R1084,"Generalized abdominal pain"
+R109,"Unspecified abdominal pain"
+R11,"Nausea and vomiting"
+R111,"Vomiting"
+R112,"Nausea with vomiting, unspecified"
+R12,"Heartburn"
+R13,"Aphagia and dysphagia"
+R131,"Dysphagia"
+R14,"Flatulence and related conditions"
+R15,"Fecal incontinence"
+R16,"Hepatomegaly and splenomegaly, not elsewhere classified"
+R17,"Unspecified jaundice"
+R18,"Ascites"
+R19,"Other symptoms and signs involving the digestive system and abdomen"
+R190,"Intra-abdominal and pelvic swelling, mass and lump"
+R191,"Abnormal bowel sounds"
+R192,"Visible peristalsis"
+R193,"Abdominal rigidity"
+R194,"Change in bowel habit"
+R195,"Other fecal abnormalities"
+R196,"Halitosis"
+R197,"Diarrhea, unspecified"
+R198,"Other specified symptoms and signs involving the digestive system and abdomen"
+R20,"Disturbances of skin sensation"
+R21,"Rash and other nonspecific skin eruption"
+R22,"Localized swelling, mass and lump of skin and subcutaneous tissue"
+R223,"Localized swelling, mass and lump, upper limb"
+R224,"Localized swelling, mass and lump, lower limb"
+R229,"Localized swelling, mass and lump, unspecified"
+R23,"Other skin changes"
+R25,"Abnormal involuntary movements"
+R26,"Abnormalities of gait and mobility"
+R268,"Other abnormalities of gait and mobility"
+R269,"Unspecified abnormalities of gait and mobility"
+R27,"Other lack of coordination"
+R29,"Other symptoms and signs involving the nervous and musculoskeletal systems"
+R297,"National Institutes of Health Stroke Scale (NIHSS) score"
+R2970,"NIHSS score 0-9"
+R2971,"NIHSS score 10-19"
+R2972,"NIHSS score 20-29"
+R2973,"NIHSS score 30-39"
+R2974,"NIHSS score 40-42"
+R298,"Other symptoms and signs involving the nervous and musculoskeletal systems"
+R2981,"Other symptoms and signs involving the nervous system"
+R2989,"Other symptoms and signs involving the musculoskeletal system"
+R299,"Unspecified symptoms and signs involving the nervous and musculoskeletal systems"
+R30,"Pain associated with micturition"
+R31,"Hematuria"
+R312,"Other microscopic hematuria"
+R319,"Hematuria, unspecified"
+R32,"Unspecified urinary incontinence"
+R33,"Retention of urine"
+R34,"Anuria and oliguria"
+R35,"Polyuria"
+R36,"Urethral discharge"
+R37,"Sexual dysfunction, unspecified"
+R39,"Other and unspecified symptoms and signs involving the genitourinary system"
+R391,"Other difficulties with micturition"
+R3919,"Other difficulties with micturition"
+R392,"Extrarenal uremia"
+R398,"Other symptoms and signs involving the genitourinary system"
+R399,"Unspecified symptoms and signs involving the genitourinary system"
+R40,"Somnolence, stupor and coma"
+R402,"Coma"
+R4021,"Coma scale, eyes open"
+R40211,"Coma scale, eyes open, never"
+R40212,"Coma scale, eyes open, to pain"
+R40213,"Coma scale, eyes open, to sound"
+R40214,"Coma scale, eyes open, spontaneous"
+R4022,"Coma scale, best verbal response"
+R40221,"Coma scale, best verbal response, none"
+R40222,"Coma scale, best verbal response, incomprehensible words"
+R40223,"Coma scale, best verbal response, inappropriate words"
+R40224,"Coma scale, best verbal response, confused conversation"
+R40225,"Coma scale, best verbal response, oriented"
+R4023,"Coma scale, best motor response"
+R40231,"Coma scale, best motor response, none"
+R40232,"Coma scale, best motor response, extension"
+R40233,"Coma scale, best motor response, abnormal"
+R40234,"Coma scale, best motor response, flexion withdrawal"
+R40235,"Coma scale, best motor response, localizes pain"
+R40236,"Coma scale, best motor response, obeys commands"
+R4024,"Glasgow coma scale, total score"
+R40241,"Glasgow coma scale score 13-15"
+R40242,"Glasgow coma scale score 9-12"
+R40243,"Glasgow coma scale score 3-8"
+R40244,"Other coma, without documented Glasgow coma scale score, or with partial score reported"
+R403,"Persistent vegetative state"
+R404,"Transient alteration of awareness"
+R41,"Other symptoms and signs involving cognitive functions and awareness"
+R418,"Other symptoms and signs involving cognitive functions and awareness"
+R4184,"Other specified cognitive deficit"
+R4189,"Other symptoms and signs involving cognitive functions and awareness"
+R419,"Unspecified symptoms and signs involving cognitive functions and awareness"
+R42,"Dizziness and giddiness"
+R43,"Disturbances of smell and taste"
+R44,"Other symptoms and signs involving general sensations and perceptions"
+R45,"Symptoms and signs involving emotional state"
+R458,"Other symptoms and signs involving emotional state"
+R4585,"Homicidal and suicidal ideations"
+R4586,"Emotional lability"
+R4587,"Impulsiveness"
+R4589,"Other symptoms and signs involving emotional state"
+R46,"Symptoms and signs involving appearance and behavior"
+R468,"Other symptoms and signs involving appearance and behavior"
+R47,"Speech disturbances, not elsewhere classified"
+R470,"Dysphasia and aphasia"
+R471,"Dysarthria and anarthria"
+R478,"Other speech disturbances"
+R479,"Unspecified speech disturbances"
+R48,"Dyslexia and other symbolic dysfunctions, not elsewhere classified"
+R49,"Voice and resonance disorders"
+R492,"Hypernasality and hyponasality"
+R498,"Other voice and resonance disorders"
+R499,"Unspecified voice and resonance disorder"
+R50,"Fever of other and unknown origin"
+R508,"Other specified fever"
+R509,"Fever, unspecified"
+R51,"Headache"
+R52,"Pain, unspecified"
+R53,"Malaise and fatigue"
+R538,"Other malaise and fatigue"
+R54,"Age-related physical debility"
+R55,"Syncope and collapse"
+R56,"Convulsions, not elsewhere classified"
+R560,"Febrile convulsions"
+R561,"Post traumatic seizures"
+R569,"Unspecified convulsions"
+R57,"Shock, not elsewhere classified"
+R58,"Hemorrhage, not elsewhere classified"
+R59,"Enlarged lymph nodes"
+R60,"Edema, not elsewhere classified"
+R61,"Generalized hyperhidrosis"
+R62,"Lack of expected normal physiological development in childhood and adults"
+R625,"Other and unspecified lack of expected normal physiological development in childhood"
+R627,"Adult failure to thrive"
+R63,"Symptoms and signs concerning food and fluid intake"
+R64,"Cachexia"
+R65,"Symptoms and signs specifically associated with systemic inflammation and infection"
+R651,"Systemic inflammatory response syndrome (SIRS) of non-infectious origin"
+R652,"Severe sepsis"
+R68,"Other general symptoms and signs"
+R681,"Nonspecific symptoms peculiar to infancy"
+R682,"Dry mouth, unspecified"
+R683,"Clubbing of fingers"
+R688,"Other general symptoms and signs"
+R69,"Illness, unspecified"
+R70,"Elevated erythrocyte sedimentation rate and abnormality of plasma viscosity"
+R71,"Abnormality of red blood cells"
+R73,"Elevated blood glucose level"
+R730,"Abnormal glucose"
+R739,"Hyperglycemia, unspecified"
+R74,"Abnormal serum enzyme levels"
+R75,"Inconclusive laboratory evidence of human immunodeficiency virus [HIV]"
+R76,"Other abnormal immunological findings in serum"
+R761,"Nonspecific reaction to test for tuberculosis"
+R768,"Other specified abnormal immunological findings in serum"
+R769,"Abnormal immunological finding in serum, unspecified"
+R77,"Other abnormalities of plasma proteins"
+R78,"Findings of drugs and other substances, not normally found in blood"
+R787,"Finding of abnormal level of heavy metals in blood"
+R788,"Finding of other specified substances, not normally found in blood"
+R789,"Finding of unspecified substance, not normally found in blood"
+R79,"Other abnormal findings of blood chemistry"
+R798,"Other specified abnormal findings of blood chemistry"
+R799,"Abnormal finding of blood chemistry, unspecified"
+R80,"Proteinuria"
+R81,"Glycosuria"
+R82,"Other and unspecified abnormal findings in urine"
+R827,"Abnormal findings on microbiological examination of urine"
+R828,"Abnormal findings on cytological and histological examination of urine"
+R829,"Other and unspecified abnormal findings in urine"
+R83,"Abnormal findings in cerebrospinal fluid"
+R84,"Abnormal findings in specimens from respiratory organs and thorax"
+R85,"Abnormal findings in specimens from digestive organs and abdominal cavity"
+R856,"Abnormal cytological findings in specimens from digestive organs and abdominal cavity"
+R8561,"Abnormal cytologic smear of anus"
+R8569,"Abnormal cytological findings in specimens from other digestive organs and abdominal cavity"
+R857,"Abnormal histological findings in specimens from digestive organs and abdominal cavity"
+R858,"Other abnormal findings in specimens from digestive organs and abdominal cavity"
+R859,"Unspecified abnormal finding in specimens from digestive organs and abdominal cavity"
+R86,"Abnormal findings in specimens from male genital organs"
+R87,"Abnormal findings in specimens from female genital organs"
+R876,"Abnormal cytological findings in specimens from female genital organs"
+R8761,"Abnormal cytological findings in specimens from cervix uteri"
+R8762,"Abnormal cytological findings in specimens from vagina"
+R8769,"Abnormal cytological findings in specimens from other female genital organs"
+R877,"Abnormal histological findings in specimens from female genital organs"
+R878,"Other abnormal findings in specimens from female genital organs"
+R8781,"High risk human papillomavirus (HPV) DNA test positive from female genital organs"
+R8782,"Low risk human papillomavirus (HPV) DNA test positive from female genital organs"
+R8789,"Other abnormal findings in specimens from female genital organs"
+R879,"Unspecified abnormal finding in specimens from female genital organs"
+R88,"Abnormal findings in other body fluids and substances"
+R89,"Abnormal findings in specimens from other organs, systems and tissues"
+R90,"Abnormal findings on diagnostic imaging of central nervous system"
+R908,"Other abnormal findings on diagnostic imaging of central nervous system"
+R91,"Abnormal findings on diagnostic imaging of lung"
+R92,"Abnormal and inconclusive findings on diagnostic imaging of breast"
+R93,"Abnormal findings on diagnostic imaging of other body structures"
+R934,"Abnormal findings on diagnostic imaging of urinary organs"
+R9342,"Abnormal radiologic findings on diagnostic imaging of kidney"
+R9349,"Abnormal radiologic findings on diagnostic imaging of other urinary organs"
+R935,"Abnormal findings on diagnostic imaging of other abdominal regions, including retroperitoneum"
+R936,"Abnormal findings on diagnostic imaging of limbs"
+R937,"Abnormal findings on diagnostic imaging of other parts of musculoskeletal system"
+R938,"Abnormal findings on diagnostic imaging of other specified body structures"
+R939,"Diagnostic imaging inconclusive due to excess body fat of patient"
+R94,"Abnormal results of function studies"
+R940,"Abnormal results of function studies of central nervous system"
+R941,"Abnormal results of function studies of peripheral nervous system and special senses"
+R9411,"Abnormal results of function studies of eye"
+R9412,"Abnormal results of function studies of ear and other special senses"
+R9413,"Abnormal results of function studies of peripheral nervous system"
+R942,"Abnormal results of pulmonary function studies"
+R943,"Abnormal results of cardiovascular function studies"
+R944,"Abnormal results of kidney function studies"
+R945,"Abnormal results of liver function studies"
+R946,"Abnormal results of thyroid function studies"
+R947,"Abnormal results of other endocrine function studies"
+R948,"Abnormal results of function studies of other organs and systems"
+R97,"Abnormal tumor markers"
+R972,"Elevated prostate specific antigen [PSA]"
+R978,"Other abnormal tumor markers"
+R99,"Ill-defined and unknown cause of mortality"
+S00,"Superficial injury of head"
+S000,"Superficial injury of scalp"
+S0000,"Unspecified superficial injury of scalp"
+S0001,"Abrasion of scalp"
+S0002,"Blister (nonthermal) of scalp"
+S0003,"Contusion of scalp"
+S0004,"External constriction of part of scalp"
+S0005,"Superficial foreign body of scalp"
+S0006,"Insect bite (nonvenomous) of scalp"
+S0007,"Other superficial bite of scalp"
+S001,"Contusion of eyelid and periocular area"
+S0010,"Contusion of unspecified eyelid and periocular area"
+S0011,"Contusion of right eyelid and periocular area"
+S0012,"Contusion of left eyelid and periocular area"
+S002,"Other and unspecified superficial injuries of eyelid and periocular area"
+S0020,"Unspecified superficial injury of eyelid and periocular area"
+S00201,"Unspecified superficial injury of right eyelid and periocular area"
+S00202,"Unspecified superficial injury of left eyelid and periocular area"
+S00209,"Unspecified superficial injury of unspecified eyelid and periocular area"
+S0021,"Abrasion of eyelid and periocular area"
+S00211,"Abrasion of right eyelid and periocular area"
+S00212,"Abrasion of left eyelid and periocular area"
+S00219,"Abrasion of unspecified eyelid and periocular area"
+S0022,"Blister (nonthermal) of eyelid and periocular area"
+S00221,"Blister (nonthermal) of right eyelid and periocular area"
+S00222,"Blister (nonthermal) of left eyelid and periocular area"
+S00229,"Blister (nonthermal) of unspecified eyelid and periocular area"
+S0024,"External constriction of eyelid and periocular area"
+S00241,"External constriction of right eyelid and periocular area"
+S00242,"External constriction of left eyelid and periocular area"
+S00249,"External constriction of unspecified eyelid and periocular area"
+S0025,"Superficial foreign body of eyelid and periocular area"
+S00251,"Superficial foreign body of right eyelid and periocular area"
+S00252,"Superficial foreign body of left eyelid and periocular area"
+S00259,"Superficial foreign body of unspecified eyelid and periocular area"
+S0026,"Insect bite (nonvenomous) of eyelid and periocular area"
+S00261,"Insect bite (nonvenomous) of right eyelid and periocular area"
+S00262,"Insect bite (nonvenomous) of left eyelid and periocular area"
+S00269,"Insect bite (nonvenomous) of unspecified eyelid and periocular area"
+S0027,"Other superficial bite of eyelid and periocular area"
+S00271,"Other superficial bite of right eyelid and periocular area"
+S00272,"Other superficial bite of left eyelid and periocular area"
+S00279,"Other superficial bite of unspecified eyelid and periocular area"
+S003,"Superficial injury of nose"
+S0030,"Unspecified superficial injury of nose"
+S0031,"Abrasion of nose"
+S0032,"Blister (nonthermal) of nose"
+S0033,"Contusion of nose"
+S0034,"External constriction of nose"
+S0035,"Superficial foreign body of nose"
+S0036,"Insect bite (nonvenomous) of nose"
+S0037,"Other superficial bite of nose"
+S004,"Superficial injury of ear"
+S0040,"Unspecified superficial injury of ear"
+S00401,"Unspecified superficial injury of right ear"
+S00402,"Unspecified superficial injury of left ear"
+S00409,"Unspecified superficial injury of unspecified ear"
+S0041,"Abrasion of ear"
+S00411,"Abrasion of right ear"
+S00412,"Abrasion of left ear"
+S00419,"Abrasion of unspecified ear"
+S0042,"Blister (nonthermal) of ear"
+S00421,"Blister (nonthermal) of right ear"
+S00422,"Blister (nonthermal) of left ear"
+S00429,"Blister (nonthermal) of unspecified ear"
+S0043,"Contusion of ear"
+S00431,"Contusion of right ear"
+S00432,"Contusion of left ear"
+S00439,"Contusion of unspecified ear"
+S0044,"External constriction of ear"
+S00441,"External constriction of right ear"
+S00442,"External constriction of left ear"
+S00449,"External constriction of unspecified ear"
+S0045,"Superficial foreign body of ear"
+S00451,"Superficial foreign body of right ear"
+S00452,"Superficial foreign body of left ear"
+S00459,"Superficial foreign body of unspecified ear"
+S0046,"Insect bite (nonvenomous) of ear"
+S00461,"Insect bite (nonvenomous) of right ear"
+S00462,"Insect bite (nonvenomous) of left ear"
+S00469,"Insect bite (nonvenomous) of unspecified ear"
+S0047,"Other superficial bite of ear"
+S00471,"Other superficial bite of right ear"
+S00472,"Other superficial bite of left ear"
+S00479,"Other superficial bite of unspecified ear"
+S005,"Superficial injury of lip and oral cavity"
+S0050,"Unspecified superficial injury of lip and oral cavity"
+S00501,"Unspecified superficial injury of lip"
+S00502,"Unspecified superficial injury of oral cavity"
+S0051,"Abrasion of lip and oral cavity"
+S00511,"Abrasion of lip"
+S00512,"Abrasion of oral cavity"
+S0052,"Blister (nonthermal) of lip and oral cavity"
+S00521,"Blister (nonthermal) of lip"
+S00522,"Blister (nonthermal) of oral cavity"
+S0053,"Contusion of lip and oral cavity"
+S00531,"Contusion of lip"
+S00532,"Contusion of oral cavity"
+S0054,"External constriction of lip and oral cavity"
+S00541,"External constriction of lip"
+S00542,"External constriction of oral cavity"
+S0055,"Superficial foreign body of lip and oral cavity"
+S00551,"Superficial foreign body of lip"
+S00552,"Superficial foreign body of oral cavity"
+S0056,"Insect bite (nonvenomous) of lip and oral cavity"
+S00561,"Insect bite (nonvenomous) of lip"
+S00562,"Insect bite (nonvenomous) of oral cavity"
+S0057,"Other superficial bite of lip and oral cavity"
+S00571,"Other superficial bite of lip"
+S00572,"Other superficial bite of oral cavity"
+S008,"Superficial injury of other parts of head"
+S0080,"Unspecified superficial injury of other part of head"
+S0081,"Abrasion of other part of head"
+S0082,"Blister (nonthermal) of other part of head"
+S0083,"Contusion of other part of head"
+S0084,"External constriction of other part of head"
+S0085,"Superficial foreign body of other part of head"
+S0086,"Insect bite (nonvenomous) of other part of head"
+S0087,"Other superficial bite of other part of head"
+S009,"Superficial injury of unspecified part of head"
+S0090,"Unspecified superficial injury of unspecified part of head"
+S0091,"Abrasion of unspecified part of head"
+S0092,"Blister (nonthermal) of unspecified part of head"
+S0093,"Contusion of unspecified part of head"
+S0094,"External constriction of unspecified part of head"
+S0095,"Superficial foreign body of unspecified part of head"
+S0096,"Insect bite (nonvenomous) of unspecified part of head"
+S0097,"Other superficial bite of unspecified part of head"
+S01,"Open wound of head"
+S010,"Open wound of scalp"
+S0100,"Unspecified open wound of scalp"
+S0101,"Laceration without foreign body of scalp"
+S0102,"Laceration with foreign body of scalp"
+S0103,"Puncture wound without foreign body of scalp"
+S0104,"Puncture wound with foreign body of scalp"
+S0105,"Open bite of scalp"
+S011,"Open wound of eyelid and periocular area"
+S0110,"Unspecified open wound of eyelid and periocular area"
+S01101,"Unspecified open wound of right eyelid and periocular area"
+S01102,"Unspecified open wound of left eyelid and periocular area"
+S01109,"Unspecified open wound of unspecified eyelid and periocular area"
+S0111,"Laceration without foreign body of eyelid and periocular area"
+S01111,"Laceration without foreign body of right eyelid and periocular area"
+S01112,"Laceration without foreign body of left eyelid and periocular area"
+S01119,"Laceration without foreign body of unspecified eyelid and periocular area"
+S0112,"Laceration with foreign body of eyelid and periocular area"
+S01121,"Laceration with foreign body of right eyelid and periocular area"
+S01122,"Laceration with foreign body of left eyelid and periocular area"
+S01129,"Laceration with foreign body of unspecified eyelid and periocular area"
+S0113,"Puncture wound without foreign body of eyelid and periocular area"
+S01131,"Puncture wound without foreign body of right eyelid and periocular area"
+S01132,"Puncture wound without foreign body of left eyelid and periocular area"
+S01139,"Puncture wound without foreign body of unspecified eyelid and periocular area"
+S0114,"Puncture wound with foreign body of eyelid and periocular area"
+S01141,"Puncture wound with foreign body of right eyelid and periocular area"
+S01142,"Puncture wound with foreign body of left eyelid and periocular area"
+S01149,"Puncture wound with foreign body of unspecified eyelid and periocular area"
+S0115,"Open bite of eyelid and periocular area"
+S01151,"Open bite of right eyelid and periocular area"
+S01152,"Open bite of left eyelid and periocular area"
+S01159,"Open bite of unspecified eyelid and periocular area"
+S012,"Open wound of nose"
+S0120,"Unspecified open wound of nose"
+S0121,"Laceration without foreign body of nose"
+S0122,"Laceration with foreign body of nose"
+S0123,"Puncture wound without foreign body of nose"
+S0124,"Puncture wound with foreign body of nose"
+S0125,"Open bite of nose"
+S013,"Open wound of ear"
+S0130,"Unspecified open wound of ear"
+S01301,"Unspecified open wound of right ear"
+S01302,"Unspecified open wound of left ear"
+S01309,"Unspecified open wound of unspecified ear"
+S0131,"Laceration without foreign body of ear"
+S01311,"Laceration without foreign body of right ear"
+S01312,"Laceration without foreign body of left ear"
+S01319,"Laceration without foreign body of unspecified ear"
+S0132,"Laceration with foreign body of ear"
+S01321,"Laceration with foreign body of right ear"
+S01322,"Laceration with foreign body of left ear"
+S01329,"Laceration with foreign body of unspecified ear"
+S0133,"Puncture wound without foreign body of ear"
+S01331,"Puncture wound without foreign body of right ear"
+S01332,"Puncture wound without foreign body of left ear"
+S01339,"Puncture wound without foreign body of unspecified ear"
+S0134,"Puncture wound with foreign body of ear"
+S01341,"Puncture wound with foreign body of right ear"
+S01342,"Puncture wound with foreign body of left ear"
+S01349,"Puncture wound with foreign body of unspecified ear"
+S0135,"Open bite of ear"
+S01351,"Open bite of right ear"
+S01352,"Open bite of left ear"
+S01359,"Open bite of unspecified ear"
+S014,"Open wound of cheek and temporomandibular area"
+S0140,"Unspecified open wound of cheek and temporomandibular area"
+S01401,"Unspecified open wound of right cheek and temporomandibular area"
+S01402,"Unspecified open wound of left cheek and temporomandibular area"
+S01409,"Unspecified open wound of unspecified cheek and temporomandibular area"
+S0141,"Laceration without foreign body of cheek and temporomandibular area"
+S01411,"Laceration without foreign body of right cheek and temporomandibular area"
+S01412,"Laceration without foreign body of left cheek and temporomandibular area"
+S01419,"Laceration without foreign body of unspecified cheek and temporomandibular area"
+S0142,"Laceration with foreign body of cheek and temporomandibular area"
+S01421,"Laceration with foreign body of right cheek and temporomandibular area"
+S01422,"Laceration with foreign body of left cheek and temporomandibular area"
+S01429,"Laceration with foreign body of unspecified cheek and temporomandibular area"
+S0143,"Puncture wound without foreign body of cheek and temporomandibular area"
+S01431,"Puncture wound without foreign body of right cheek and temporomandibular area"
+S01432,"Puncture wound without foreign body of left cheek and temporomandibular area"
+S01439,"Puncture wound without foreign body of unspecified cheek and temporomandibular area"
+S0144,"Puncture wound with foreign body of cheek and temporomandibular area"
+S01441,"Puncture wound with foreign body of right cheek and temporomandibular area"
+S01442,"Puncture wound with foreign body of left cheek and temporomandibular area"
+S01449,"Puncture wound with foreign body of unspecified cheek and temporomandibular area"
+S0145,"Open bite of cheek and temporomandibular area"
+S01451,"Open bite of right cheek and temporomandibular area"
+S01452,"Open bite of left cheek and temporomandibular area"
+S01459,"Open bite of unspecified cheek and temporomandibular area"
+S015,"Open wound of lip and oral cavity"
+S0150,"Unspecified open wound of lip and oral cavity"
+S01501,"Unspecified open wound of lip"
+S01502,"Unspecified open wound of oral cavity"
+S0151,"Laceration of lip and oral cavity without foreign body"
+S01511,"Laceration without foreign body of lip"
+S01512,"Laceration without foreign body of oral cavity"
+S0152,"Laceration of lip and oral cavity with foreign body"
+S01521,"Laceration with foreign body of lip"
+S01522,"Laceration with foreign body of oral cavity"
+S0153,"Puncture wound of lip and oral cavity without foreign body"
+S01531,"Puncture wound without foreign body of lip"
+S01532,"Puncture wound without foreign body of oral cavity"
+S0154,"Puncture wound of lip and oral cavity with foreign body"
+S01541,"Puncture wound with foreign body of lip"
+S01542,"Puncture wound with foreign body of oral cavity"
+S0155,"Open bite of lip and oral cavity"
+S01551,"Open bite of lip"
+S01552,"Open bite of oral cavity"
+S018,"Open wound of other parts of head"
+S0180,"Unspecified open wound of other part of head"
+S0181,"Laceration without foreign body of other part of head"
+S0182,"Laceration with foreign body of other part of head"
+S0183,"Puncture wound without foreign body of other part of head"
+S0184,"Puncture wound with foreign body of other part of head"
+S0185,"Open bite of other part of head"
+S019,"Open wound of unspecified part of head"
+S0190,"Unspecified open wound of unspecified part of head"
+S0191,"Laceration without foreign body of unspecified part of head"
+S0192,"Laceration with foreign body of unspecified part of head"
+S0193,"Puncture wound without foreign body of unspecified part of head"
+S0194,"Puncture wound with foreign body of unspecified part of head"
+S0195,"Open bite of unspecified part of head"
+S02,"Fracture of skull and facial bones"
+S020,"Fracture of vault of skull"
+S021,"Fracture of base of skull"
+S0210,"Unspecified fracture of base of skull"
+S02101,"Fracture of base of skull, right side"
+S02102,"Fracture of base of skull, left side"
+S02109,"Fracture of base of skull, unspecified side"
+S0211,"Fracture of occiput"
+S02110,"Type I occipital condyle fracture, unspecified side"
+S02111,"Type II occipital condyle fracture, unspecified side"
+S02112,"Type III occipital condyle fracture, unspecified side"
+S02113,"Unspecified occipital condyle fracture"
+S02118,"Other fracture of occiput, unspecified side"
+S02119,"Unspecified fracture of occiput"
+S0211A,"Type I occipital condyle fracture, right side"
+S0211B,"Type I occipital condyle fracture, left side"
+S0211C,"Type II occipital condyle fracture, right side"
+S0211D,"Type II occipital condyle fracture, left side"
+S0211E,"Type III occipital condyle fracture, right side"
+S0211F,"Type III occipital condyle fracture, left side"
+S0211G,"Other fracture of occiput, right side"
+S0211H,"Other fracture of occiput, left side"
+S0219,"Other fracture of base of skull"
+S022,"Fracture of nasal bones"
+S023,"Fracture of orbital floor"
+S0230,"Fracture of orbital floor, unspecified side"
+S0231,"Fracture of orbital floor, right side"
+S0232,"Fracture of orbital floor, left side"
+S024,"Fracture of malar, maxillary and zygoma bones"
+S0240,"Fracture of malar, maxillary and zygoma bones, unspecified"
+S02400,"Malar fracture, unspecified side"
+S02401,"Maxillary fracture, unspecified side"
+S02402,"Zygomatic fracture, unspecified side"
+S0240A,"Malar fracture, right side"
+S0240B,"Malar fracture, left side"
+S0240C,"Maxillary fracture, right side"
+S0240D,"Maxillary fracture, left side"
+S0240E,"Zygomatic fracture, right side"
+S0240F,"Zygomatic fracture, left side"
+S0241,"LeFort fracture"
+S02411,"LeFort I fracture"
+S02412,"LeFort II fracture"
+S02413,"LeFort III fracture"
+S0242,"Fracture of alveolus of maxilla"
+S025,"Fracture of tooth (traumatic)"
+S026,"Fracture of mandible"
+S0260,"Fracture of mandible, unspecified"
+S02600,"Fracture of unspecified part of body of mandible, unspecified side"
+S02601,"Fracture of unspecified part of body of right mandible"
+S02602,"Fracture of unspecified part of body of left mandible"
+S02609,"Fracture of mandible, unspecified"
+S0261,"Fracture of condylar process of mandible"
+S02610,"Fracture of condylar process of mandible, unspecified side"
+S02611,"Fracture of condylar process of right mandible"
+S02612,"Fracture of condylar process of left mandible"
+S0262,"Fracture of subcondylar process of mandible"
+S02620,"Fracture of subcondylar process of mandible, unspecified side"
+S02621,"Fracture of subcondylar process of right mandible"
+S02622,"Fracture of subcondylar process of left mandible"
+S0263,"Fracture of coronoid process of mandible"
+S02630,"Fracture of coronoid process of mandible, unspecified side"
+S02631,"Fracture of coronoid process of right mandible"
+S02632,"Fracture of coronoid process of left mandible"
+S0264,"Fracture of ramus of mandible"
+S02640,"Fracture of ramus of mandible, unspecified side"
+S02641,"Fracture of ramus of right mandible"
+S02642,"Fracture of ramus of left mandible"
+S0265,"Fracture of angle of mandible"
+S02650,"Fracture of angle of mandible, unspecified side"
+S02651,"Fracture of angle of right mandible"
+S02652,"Fracture of angle of left mandible"
+S0266,"Fracture of symphysis of mandible"
+S0267,"Fracture of alveolus of mandible"
+S02670,"Fracture of alveolus of mandible, unspecified side"
+S02671,"Fracture of alveolus of right mandible"
+S02672,"Fracture of alveolus of left mandible"
+S0269,"Fracture of mandible of other specified site"
+S028,"Fractures of other specified skull and facial bones"
+S0280,"Fracture of other specified skull and facial bones, unspecified side"
+S0281,"Fracture of other specified skull and facial bones, right side"
+S0282,"Fracture of other specified skull and facial bones, left side"
+S029,"Fracture of unspecified skull and facial bones"
+S0291,"Unspecified fracture of skull"
+S0292,"Unspecified fracture of facial bones"
+S03,"Dislocation and sprain of joints and ligaments of head"
+S030,"Dislocation of jaw"
+S0300,"Dislocation of jaw, unspecified side"
+S0301,"Dislocation of jaw, right side"
+S0302,"Dislocation of jaw, left side"
+S0303,"Dislocation of jaw, bilateral"
+S031,"Dislocation of septal cartilage of nose"
+S032,"Dislocation of tooth"
+S034,"Sprain of jaw"
+S0340,"Sprain of jaw, unspecified side"
+S0341,"Sprain of jaw, right side"
+S0342,"Sprain of jaw, left side"
+S0343,"Sprain of jaw, bilateral"
+S038,"Sprain of joints and ligaments of other parts of head"
+S039,"Sprain of joints and ligaments of unspecified parts of head"
+S04,"Injury of cranial nerve"
+S040,"Injury of optic nerve and pathways"
+S0401,"Injury of optic nerve"
+S04011,"Injury of optic nerve, right eye"
+S04012,"Injury of optic nerve, left eye"
+S04019,"Injury of optic nerve, unspecified eye"
+S0402,"Injury of optic chiasm"
+S0403,"Injury of optic tract and pathways"
+S04031,"Injury of optic tract and pathways, right side"
+S04032,"Injury of optic tract and pathways, left side"
+S04039,"Injury of optic tract and pathways, unspecified side"
+S0404,"Injury of visual cortex"
+S04041,"Injury of visual cortex, right side"
+S04042,"Injury of visual cortex, left side"
+S04049,"Injury of visual cortex, unspecified side"
+S041,"Injury of oculomotor nerve"
+S0410,"Injury of oculomotor nerve, unspecified side"
+S0411,"Injury of oculomotor nerve, right side"
+S0412,"Injury of oculomotor nerve, left side"
+S042,"Injury of trochlear nerve"
+S0420,"Injury of trochlear nerve, unspecified side"
+S0421,"Injury of trochlear nerve, right side"
+S0422,"Injury of trochlear nerve, left side"
+S043,"Injury of trigeminal nerve"
+S0430,"Injury of trigeminal nerve, unspecified side"
+S0431,"Injury of trigeminal nerve, right side"
+S0432,"Injury of trigeminal nerve, left side"
+S044,"Injury of abducent nerve"
+S0440,"Injury of abducent nerve, unspecified side"
+S0441,"Injury of abducent nerve, right side"
+S0442,"Injury of abducent nerve, left side"
+S045,"Injury of facial nerve"
+S0450,"Injury of facial nerve, unspecified side"
+S0451,"Injury of facial nerve, right side"
+S0452,"Injury of facial nerve, left side"
+S046,"Injury of acoustic nerve"
+S0460,"Injury of acoustic nerve, unspecified side"
+S0461,"Injury of acoustic nerve, right side"
+S0462,"Injury of acoustic nerve, left side"
+S047,"Injury of accessory nerve"
+S0470,"Injury of accessory nerve, unspecified side"
+S0471,"Injury of accessory nerve, right side"
+S0472,"Injury of accessory nerve, left side"
+S048,"Injury of other cranial nerves"
+S0481,"Injury of olfactory [1st ] nerve"
+S04811,"Injury of olfactory [1st ] nerve, right side"
+S04812,"Injury of olfactory [1st ] nerve, left side"
+S04819,"Injury of olfactory [1st ] nerve, unspecified side"
+S0489,"Injury of other cranial nerves"
+S04891,"Injury of other cranial nerves, right side"
+S04892,"Injury of other cranial nerves, left side"
+S04899,"Injury of other cranial nerves, unspecified side"
+S049,"Injury of unspecified cranial nerve"
+S05,"Injury of eye and orbit"
+S050,"Injury of conjunctiva and corneal abrasion without foreign body"
+S0500,"Injury of conjunctiva and corneal abrasion without foreign body, unspecified eye"
+S0501,"Injury of conjunctiva and corneal abrasion without foreign body, right eye"
+S0502,"Injury of conjunctiva and corneal abrasion without foreign body, left eye"
+S051,"Contusion of eyeball and orbital tissues"
+S0510,"Contusion of eyeball and orbital tissues, unspecified eye"
+S0511,"Contusion of eyeball and orbital tissues, right eye"
+S0512,"Contusion of eyeball and orbital tissues, left eye"
+S052,"Ocular laceration and rupture with prolapse or loss of intraocular tissue"
+S0520,"Ocular laceration and rupture with prolapse or loss of intraocular tissue, unspecified eye"
+S0521,"Ocular laceration and rupture with prolapse or loss of intraocular tissue, right eye"
+S0522,"Ocular laceration and rupture with prolapse or loss of intraocular tissue, left eye"
+S053,"Ocular laceration without prolapse or loss of intraocular tissue"
+S0530,"Ocular laceration without prolapse or loss of intraocular tissue, unspecified eye"
+S0531,"Ocular laceration without prolapse or loss of intraocular tissue, right eye"
+S0532,"Ocular laceration without prolapse or loss of intraocular tissue, left eye"
+S054,"Penetrating wound of orbit with or without foreign body"
+S0540,"Penetrating wound of orbit with or without foreign body, unspecified eye"
+S0541,"Penetrating wound of orbit with or without foreign body, right eye"
+S0542,"Penetrating wound of orbit with or without foreign body, left eye"
+S055,"Penetrating wound with foreign body of eyeball"
+S0550,"Penetrating wound with foreign body of unspecified eyeball"
+S0551,"Penetrating wound with foreign body of right eyeball"
+S0552,"Penetrating wound with foreign body of left eyeball"
+S056,"Penetrating wound without foreign body of eyeball"
+S0560,"Penetrating wound without foreign body of unspecified eyeball"
+S0561,"Penetrating wound without foreign body of right eyeball"
+S0562,"Penetrating wound without foreign body of left eyeball"
+S057,"Avulsion of eye"
+S0570,"Avulsion of unspecified eye"
+S0571,"Avulsion of right eye"
+S0572,"Avulsion of left eye"
+S058,"Other injuries of eye and orbit"
+S058X,"Other injuries of eye and orbit"
+S058X1,"Other injuries of right eye and orbit"
+S058X2,"Other injuries of left eye and orbit"
+S058X9,"Other injuries of unspecified eye and orbit"
+S059,"Unspecified injury of eye and orbit"
+S0590,"Unspecified injury of unspecified eye and orbit"
+S0591,"Unspecified injury of right eye and orbit"
+S0592,"Unspecified injury of left eye and orbit"
+S06,"Intracranial injury"
+S060,"Concussion"
+S060X,"Concussion"
+S060X0,"Concussion without loss of consciousness"
+S060X1,"Concussion with loss of consciousness of 30 minutes or less"
+S060X9,"Concussion with loss of consciousness of unspecified duration"
+S061,"Traumatic cerebral edema"
+S061X,"Traumatic cerebral edema"
+S061X0,"Traumatic cerebral edema without loss of consciousness"
+S061X1,"Traumatic cerebral edema with loss of consciousness of 30 minutes or less"
+S061X2,"Traumatic cerebral edema with loss of consciousness of 31 minutes to 59 minutes"
+S061X3,"Traumatic cerebral edema with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S061X4,"Traumatic cerebral edema with loss of consciousness of 6 hours to 24 hours"
+S061X5,"Traumatic cerebral edema with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S061X6,"Traumatic cerebral edema with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S061X7,"Traumatic cerebral edema with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S061X8,"Traumatic cerebral edema with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S061X9,"Traumatic cerebral edema with loss of consciousness of unspecified duration"
+S062,"Diffuse traumatic brain injury"
+S062X,"Diffuse traumatic brain injury"
+S062X0,"Diffuse traumatic brain injury without loss of consciousness"
+S062X1,"Diffuse traumatic brain injury with loss of consciousness of 30 minutes or less"
+S062X2,"Diffuse traumatic brain injury with loss of consciousness of 31 minutes to 59 minutes"
+S062X3,"Diffuse traumatic brain injury with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S062X4,"Diffuse traumatic brain injury with loss of consciousness of 6 hours to 24 hours"
+S062X5,"Diffuse traumatic brain injury with loss of consciousness greater than 24 hours with return to pre-existing conscious levels"
+S062X6,"Diffuse traumatic brain injury with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S062X7,"Diffuse traumatic brain injury with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S062X8,"Diffuse traumatic brain injury with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S062X9,"Diffuse traumatic brain injury with loss of consciousness of unspecified duration"
+S063,"Focal traumatic brain injury"
+S0630,"Unspecified focal traumatic brain injury"
+S06300,"Unspecified focal traumatic brain injury without loss of consciousness"
+S06301,"Unspecified focal traumatic brain injury with loss of consciousness of 30 minutes or less"
+S06302,"Unspecified focal traumatic brain injury with loss of consciousness of 31 minutes to 59 minutes"
+S06303,"Unspecified focal traumatic brain injury with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06304,"Unspecified focal traumatic brain injury with loss of consciousness of 6 hours to 24 hours"
+S06305,"Unspecified focal traumatic brain injury with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06306,"Unspecified focal traumatic brain injury with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06307,"Unspecified focal traumatic brain injury with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06308,"Unspecified focal traumatic brain injury with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06309,"Unspecified focal traumatic brain injury with loss of consciousness of unspecified duration"
+S0631,"Contusion and laceration of right cerebrum"
+S06310,"Contusion and laceration of right cerebrum without loss of consciousness"
+S06311,"Contusion and laceration of right cerebrum with loss of consciousness of 30 minutes or less"
+S06312,"Contusion and laceration of right cerebrum with loss of consciousness of 31 minutes to 59 minutes"
+S06313,"Contusion and laceration of right cerebrum with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06314,"Contusion and laceration of right cerebrum with loss of consciousness of 6 hours to 24 hours"
+S06315,"Contusion and laceration of right cerebrum with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06316,"Contusion and laceration of right cerebrum with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06317,"Contusion and laceration of right cerebrum with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06318,"Contusion and laceration of right cerebrum with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06319,"Contusion and laceration of right cerebrum with loss of consciousness of unspecified duration"
+S0632,"Contusion and laceration of left cerebrum"
+S06320,"Contusion and laceration of left cerebrum without loss of consciousness"
+S06321,"Contusion and laceration of left cerebrum with loss of consciousness of 30 minutes or less"
+S06322,"Contusion and laceration of left cerebrum with loss of consciousness of 31 minutes to 59 minutes"
+S06323,"Contusion and laceration of left cerebrum with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06324,"Contusion and laceration of left cerebrum with loss of consciousness of 6 hours to 24 hours"
+S06325,"Contusion and laceration of left cerebrum with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06326,"Contusion and laceration of left cerebrum with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06327,"Contusion and laceration of left cerebrum with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06328,"Contusion and laceration of left cerebrum with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06329,"Contusion and laceration of left cerebrum with loss of consciousness of unspecified duration"
+S0633,"Contusion and laceration of cerebrum, unspecified"
+S06330,"Contusion and laceration of cerebrum, unspecified, without loss of consciousness"
+S06331,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of 30 minutes or less"
+S06332,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of 31 minutes to 59 minutes"
+S06333,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06334,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of 6 hours to 24 hours"
+S06335,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06336,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06337,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06338,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06339,"Contusion and laceration of cerebrum, unspecified, with loss of consciousness of unspecified duration"
+S0634,"Traumatic hemorrhage of right cerebrum"
+S06340,"Traumatic hemorrhage of right cerebrum without loss of consciousness"
+S06341,"Traumatic hemorrhage of right cerebrum with loss of consciousness of 30 minutes or less"
+S06342,"Traumatic hemorrhage of right cerebrum with loss of consciousness of 31 minutes to 59 minutes"
+S06343,"Traumatic hemorrhage of right cerebrum with loss of consciousness of 1 hours to 5 hours 59 minutes"
+S06344,"Traumatic hemorrhage of right cerebrum with loss of consciousness of 6 hours to 24 hours"
+S06345,"Traumatic hemorrhage of right cerebrum with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06346,"Traumatic hemorrhage of right cerebrum with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06347,"Traumatic hemorrhage of right cerebrum with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06348,"Traumatic hemorrhage of right cerebrum with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06349,"Traumatic hemorrhage of right cerebrum with loss of consciousness of unspecified duration"
+S0635,"Traumatic hemorrhage of left cerebrum"
+S06350,"Traumatic hemorrhage of left cerebrum without loss of consciousness"
+S06351,"Traumatic hemorrhage of left cerebrum with loss of consciousness of 30 minutes or less"
+S06352,"Traumatic hemorrhage of left cerebrum with loss of consciousness of 31 minutes to 59 minutes"
+S06353,"Traumatic hemorrhage of left cerebrum with loss of consciousness of 1 hours to 5 hours 59 minutes"
+S06354,"Traumatic hemorrhage of left cerebrum with loss of consciousness of 6 hours to 24 hours"
+S06355,"Traumatic hemorrhage of left cerebrum with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06356,"Traumatic hemorrhage of left cerebrum with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06357,"Traumatic hemorrhage of left cerebrum with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06358,"Traumatic hemorrhage of left cerebrum with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06359,"Traumatic hemorrhage of left cerebrum with loss of consciousness of unspecified duration"
+S0636,"Traumatic hemorrhage of cerebrum, unspecified"
+S06360,"Traumatic hemorrhage of cerebrum, unspecified, without loss of consciousness"
+S06361,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of 30 minutes or less"
+S06362,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of 31 minutes to 59 minutes"
+S06363,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of 1 hours to 5 hours 59 minutes"
+S06364,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of 6 hours to 24 hours"
+S06365,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06366,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06367,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06368,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06369,"Traumatic hemorrhage of cerebrum, unspecified, with loss of consciousness of unspecified duration"
+S0637,"Contusion, laceration, and hemorrhage of cerebellum"
+S06370,"Contusion, laceration, and hemorrhage of cerebellum without loss of consciousness"
+S06371,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of 30 minutes or less"
+S06372,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of 31 minutes to 59 minutes"
+S06373,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06374,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of 6 hours to 24 hours"
+S06375,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06376,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06377,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06378,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06379,"Contusion, laceration, and hemorrhage of cerebellum with loss of consciousness of unspecified duration"
+S0638,"Contusion, laceration, and hemorrhage of brainstem"
+S06380,"Contusion, laceration, and hemorrhage of brainstem without loss of consciousness"
+S06381,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of 30 minutes or less"
+S06382,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of 31 minutes to 59 minutes"
+S06383,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06384,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of 6 hours to 24 hours"
+S06385,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06386,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06387,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06388,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06389,"Contusion, laceration, and hemorrhage of brainstem with loss of consciousness of unspecified duration"
+S064,"Epidural hemorrhage"
+S064X,"Epidural hemorrhage"
+S064X0,"Epidural hemorrhage without loss of consciousness"
+S064X1,"Epidural hemorrhage with loss of consciousness of 30 minutes or less"
+S064X2,"Epidural hemorrhage with loss of consciousness of 31 minutes to 59 minutes"
+S064X3,"Epidural hemorrhage with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S064X4,"Epidural hemorrhage with loss of consciousness of 6 hours to 24 hours"
+S064X5,"Epidural hemorrhage with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S064X6,"Epidural hemorrhage with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S064X7,"Epidural hemorrhage with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S064X8,"Epidural hemorrhage with loss of consciousness of any duration with death due to other causes prior to regaining consciousness"
+S064X9,"Epidural hemorrhage with loss of consciousness of unspecified duration"
+S065,"Traumatic subdural hemorrhage"
+S065X,"Traumatic subdural hemorrhage"
+S065X0,"Traumatic subdural hemorrhage without loss of consciousness"
+S065X1,"Traumatic subdural hemorrhage with loss of consciousness of 30 minutes or less"
+S065X2,"Traumatic subdural hemorrhage with loss of consciousness of 31 minutes to 59 minutes"
+S065X3,"Traumatic subdural hemorrhage with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S065X4,"Traumatic subdural hemorrhage with loss of consciousness of 6 hours to 24 hours"
+S065X5,"Traumatic subdural hemorrhage with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S065X6,"Traumatic subdural hemorrhage with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S065X7,"Traumatic subdural hemorrhage with loss of consciousness of any duration with death due to brain injury before regaining consciousness"
+S065X8,"Traumatic subdural hemorrhage with loss of consciousness of any duration with death due to other cause before regaining consciousness"
+S065X9,"Traumatic subdural hemorrhage with loss of consciousness of unspecified duration"
+S066,"Traumatic subarachnoid hemorrhage"
+S066X,"Traumatic subarachnoid hemorrhage"
+S066X0,"Traumatic subarachnoid hemorrhage without loss of consciousness"
+S066X1,"Traumatic subarachnoid hemorrhage with loss of consciousness of 30 minutes or less"
+S066X2,"Traumatic subarachnoid hemorrhage with loss of consciousness of 31 minutes to 59 minutes"
+S066X3,"Traumatic subarachnoid hemorrhage with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S066X4,"Traumatic subarachnoid hemorrhage with loss of consciousness of 6 hours to 24 hours"
+S066X5,"Traumatic subarachnoid hemorrhage with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S066X6,"Traumatic subarachnoid hemorrhage with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S066X7,"Traumatic subarachnoid hemorrhage with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S066X8,"Traumatic subarachnoid hemorrhage with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S066X9,"Traumatic subarachnoid hemorrhage with loss of consciousness of unspecified duration"
+S068,"Other specified intracranial injuries"
+S0681,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified"
+S06810,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified without loss of consciousness"
+S06811,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 30 minutes or less"
+S06812,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 31 minutes to 59 minutes"
+S06813,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06814,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 6 hours to 24 hours"
+S06815,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06816,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06817,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06818,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06819,"Injury of right internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of unspecified duration"
+S0682,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified"
+S06820,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified without loss of consciousness"
+S06821,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 30 minutes or less"
+S06822,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 31 minutes to 59 minutes"
+S06823,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06824,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of 6 hours to 24 hours"
+S06825,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06826,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06827,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06828,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06829,"Injury of left internal carotid artery, intracranial portion, not elsewhere classified with loss of consciousness of unspecified duration"
+S0689,"Other specified intracranial injury"
+S06890,"Other specified intracranial injury without loss of consciousness"
+S06891,"Other specified intracranial injury with loss of consciousness of 30 minutes or less"
+S06892,"Other specified intracranial injury with loss of consciousness of 31 minutes to 59 minutes"
+S06893,"Other specified intracranial injury with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S06894,"Other specified intracranial injury with loss of consciousness of 6 hours to 24 hours"
+S06895,"Other specified intracranial injury with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S06896,"Other specified intracranial injury with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S06897,"Other specified intracranial injury with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S06898,"Other specified intracranial injury with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S06899,"Other specified intracranial injury with loss of consciousness of unspecified duration"
+S069,"Unspecified intracranial injury"
+S069X,"Unspecified intracranial injury"
+S069X0,"Unspecified intracranial injury without loss of consciousness"
+S069X1,"Unspecified intracranial injury with loss of consciousness of 30 minutes or less"
+S069X2,"Unspecified intracranial injury with loss of consciousness of 31 minutes to 59 minutes"
+S069X3,"Unspecified intracranial injury with loss of consciousness of 1 hour to 5 hours 59 minutes"
+S069X4,"Unspecified intracranial injury with loss of consciousness of 6 hours to 24 hours"
+S069X5,"Unspecified intracranial injury with loss of consciousness greater than 24 hours with return to pre-existing conscious level"
+S069X6,"Unspecified intracranial injury with loss of consciousness greater than 24 hours without return to pre-existing conscious level with patient surviving"
+S069X7,"Unspecified intracranial injury with loss of consciousness of any duration with death due to brain injury prior to regaining consciousness"
+S069X8,"Unspecified intracranial injury with loss of consciousness of any duration with death due to other cause prior to regaining consciousness"
+S069X9,"Unspecified intracranial injury with loss of consciousness of unspecified duration"
+S07,"Crushing injury of head"
+S070,"Crushing injury of face"
+S071,"Crushing injury of skull"
+S078,"Crushing injury of other parts of head"
+S079,"Crushing injury of head, part unspecified"
+S08,"Avulsion and traumatic amputation of part of head"
+S080,"Avulsion of scalp"
+S081,"Traumatic amputation of ear"
+S0811,"Complete traumatic amputation of ear"
+S08111,"Complete traumatic amputation of right ear"
+S08112,"Complete traumatic amputation of left ear"
+S08119,"Complete traumatic amputation of unspecified ear"
+S0812,"Partial traumatic amputation of ear"
+S08121,"Partial traumatic amputation of right ear"
+S08122,"Partial traumatic amputation of left ear"
+S08129,"Partial traumatic amputation of unspecified ear"
+S088,"Traumatic amputation of other parts of head"
+S0881,"Traumatic amputation of nose"
+S08811,"Complete traumatic amputation of nose"
+S08812,"Partial traumatic amputation of nose"
+S0889,"Traumatic amputation of other parts of head"
+S09,"Other and unspecified injuries of head"
+S090,"Injury of blood vessels of head, not elsewhere classified"
+S091,"Injury of muscle and tendon of head"
+S0910,"Unspecified injury of muscle and tendon of head"
+S0911,"Strain of muscle and tendon of head"
+S0912,"Laceration of muscle and tendon of head"
+S0919,"Other specified injury of muscle and tendon of head"
+S092,"Traumatic rupture of ear drum"
+S0920,"Traumatic rupture of unspecified ear drum"
+S0921,"Traumatic rupture of right ear drum"
+S0922,"Traumatic rupture of left ear drum"
+S093,"Other specified and unspecified injury of middle and inner ear"
+S0930,"Unspecified injury of middle and inner ear"
+S09301,"Unspecified injury of right middle and inner ear"
+S09302,"Unspecified injury of left middle and inner ear"
+S09309,"Unspecified injury of unspecified middle and inner ear"
+S0931,"Primary blast injury of ear"
+S09311,"Primary blast injury of right ear"
+S09312,"Primary blast injury of left ear"
+S09313,"Primary blast injury of ear, bilateral"
+S09319,"Primary blast injury of unspecified ear"
+S0939,"Other specified injury of middle and inner ear"
+S09391,"Other specified injury of right middle and inner ear"
+S09392,"Other specified injury of left middle and inner ear"
+S09399,"Other specified injury of unspecified middle and inner ear"
+S098,"Other specified injuries of head"
+S099,"Unspecified injury of face and head"
+S0990,"Unspecified injury of head"
+S0991,"Unspecified injury of ear"
+S0992,"Unspecified injury of nose"
+S0993,"Unspecified injury of face"
+S10,"Superficial injury of neck"
+S100,"Contusion of throat"
+S101,"Other and unspecified superficial injuries of throat"
+S1010,"Unspecified superficial injuries of throat"
+S1011,"Abrasion of throat"
+S1012,"Blister (nonthermal) of throat"
+S1014,"External constriction of part of throat"
+S1015,"Superficial foreign body of throat"
+S1016,"Insect bite (nonvenomous) of throat"
+S1017,"Other superficial bite of throat"
+S108,"Superficial injury of other specified parts of neck"
+S1080,"Unspecified superficial injury of other specified part of neck"
+S1081,"Abrasion of other specified part of neck"
+S1082,"Blister (nonthermal) of other specified part of neck"
+S1083,"Contusion of other specified part of neck"
+S1084,"External constriction of other specified part of neck"
+S1085,"Superficial foreign body of other specified part of neck"
+S1086,"Insect bite of other specified part of neck"
+S1087,"Other superficial bite of other specified part of neck"
+S109,"Superficial injury of unspecified part of neck"
+S1090,"Unspecified superficial injury of unspecified part of neck"
+S1091,"Abrasion of unspecified part of neck"
+S1092,"Blister (nonthermal) of unspecified part of neck"
+S1093,"Contusion of unspecified part of neck"
+S1094,"External constriction of unspecified part of neck"
+S1095,"Superficial foreign body of unspecified part of neck"
+S1096,"Insect bite of unspecified part of neck"
+S1097,"Other superficial bite of unspecified part of neck"
+S11,"Open wound of neck"
+S110,"Open wound of larynx and trachea"
+S1101,"Open wound of larynx"
+S11011,"Laceration without foreign body of larynx"
+S11012,"Laceration with foreign body of larynx"
+S11013,"Puncture wound without foreign body of larynx"
+S11014,"Puncture wound with foreign body of larynx"
+S11015,"Open bite of larynx"
+S11019,"Unspecified open wound of larynx"
+S1102,"Open wound of trachea"
+S11021,"Laceration without foreign body of trachea"
+S11022,"Laceration with foreign body of trachea"
+S11023,"Puncture wound without foreign body of trachea"
+S11024,"Puncture wound with foreign body of trachea"
+S11025,"Open bite of trachea"
+S11029,"Unspecified open wound of trachea"
+S1103,"Open wound of vocal cord"
+S11031,"Laceration without foreign body of vocal cord"
+S11032,"Laceration with foreign body of vocal cord"
+S11033,"Puncture wound without foreign body of vocal cord"
+S11034,"Puncture wound with foreign body of vocal cord"
+S11035,"Open bite of vocal cord"
+S11039,"Unspecified open wound of vocal cord"
+S111,"Open wound of thyroid gland"
+S1110,"Unspecified open wound of thyroid gland"
+S1111,"Laceration without foreign body of thyroid gland"
+S1112,"Laceration with foreign body of thyroid gland"
+S1113,"Puncture wound without foreign body of thyroid gland"
+S1114,"Puncture wound with foreign body of thyroid gland"
+S1115,"Open bite of thyroid gland"
+S112,"Open wound of pharynx and cervical esophagus"
+S1120,"Unspecified open wound of pharynx and cervical esophagus"
+S1121,"Laceration without foreign body of pharynx and cervical esophagus"
+S1122,"Laceration with foreign body of pharynx and cervical esophagus"
+S1123,"Puncture wound without foreign body of pharynx and cervical esophagus"
+S1124,"Puncture wound with foreign body of pharynx and cervical esophagus"
+S1125,"Open bite of pharynx and cervical esophagus"
+S118,"Open wound of other specified parts of neck"
+S1180,"Unspecified open wound of other specified part of neck"
+S1181,"Laceration without foreign body of other specified part of neck"
+S1182,"Laceration with foreign body of other specified part of neck"
+S1183,"Puncture wound without foreign body of other specified part of neck"
+S1184,"Puncture wound with foreign body of other specified part of neck"
+S1185,"Open bite of other specified part of neck"
+S1189,"Other open wound of other specified part of neck"
+S119,"Open wound of unspecified part of neck"
+S1190,"Unspecified open wound of unspecified part of neck"
+S1191,"Laceration without foreign body of unspecified part of neck"
+S1192,"Laceration with foreign body of unspecified part of neck"
+S1193,"Puncture wound without foreign body of unspecified part of neck"
+S1194,"Puncture wound with foreign body of unspecified part of neck"
+S1195,"Open bite of unspecified part of neck"
+S12,"Fracture of cervical vertebra and other parts of neck"
+S120,"Fracture of first cervical vertebra"
+S1200,"Unspecified fracture of first cervical vertebra"
+S12000,"Unspecified displaced fracture of first cervical vertebra"
+S12001,"Unspecified nondisplaced fracture of first cervical vertebra"
+S1201,"Stable burst fracture of first cervical vertebra"
+S1202,"Unstable burst fracture of first cervical vertebra"
+S1203,"Posterior arch fracture of first cervical vertebra"
+S12030,"Displaced posterior arch fracture of first cervical vertebra"
+S12031,"Nondisplaced posterior arch fracture of first cervical vertebra"
+S1204,"Lateral mass fracture of first cervical vertebra"
+S12040,"Displaced lateral mass fracture of first cervical vertebra"
+S12041,"Nondisplaced lateral mass fracture of first cervical vertebra"
+S1209,"Other fracture of first cervical vertebra"
+S12090,"Other displaced fracture of first cervical vertebra"
+S12091,"Other nondisplaced fracture of first cervical vertebra"
+S121,"Fracture of second cervical vertebra"
+S1210,"Unspecified fracture of second cervical vertebra"
+S12100,"Unspecified displaced fracture of second cervical vertebra"
+S12101,"Unspecified nondisplaced fracture of second cervical vertebra"
+S1211,"Type II dens fracture"
+S12110,"Anterior displaced Type II dens fracture"
+S12111,"Posterior displaced Type II dens fracture"
+S12112,"Nondisplaced Type II dens fracture"
+S1212,"Other dens fracture"
+S12120,"Other displaced dens fracture"
+S12121,"Other nondisplaced dens fracture"
+S1213,"Unspecified traumatic spondylolisthesis of second cervical vertebra"
+S12130,"Unspecified traumatic displaced spondylolisthesis of second cervical vertebra"
+S12131,"Unspecified traumatic nondisplaced spondylolisthesis of second cervical vertebra"
+S1214,"Type III traumatic spondylolisthesis of second cervical vertebra"
+S1215,"Other traumatic spondylolisthesis of second cervical vertebra"
+S12150,"Other traumatic displaced spondylolisthesis of second cervical vertebra"
+S12151,"Other traumatic nondisplaced spondylolisthesis of second cervical vertebra"
+S1219,"Other fracture of second cervical vertebra"
+S12190,"Other displaced fracture of second cervical vertebra"
+S12191,"Other nondisplaced fracture of second cervical vertebra"
+S122,"Fracture of third cervical vertebra"
+S1220,"Unspecified fracture of third cervical vertebra"
+S12200,"Unspecified displaced fracture of third cervical vertebra"
+S12201,"Unspecified nondisplaced fracture of third cervical vertebra"
+S1223,"Unspecified traumatic spondylolisthesis of third cervical vertebra"
+S12230,"Unspecified traumatic displaced spondylolisthesis of third cervical vertebra"
+S12231,"Unspecified traumatic nondisplaced spondylolisthesis of third cervical vertebra"
+S1224,"Type III traumatic spondylolisthesis of third cervical vertebra"
+S1225,"Other traumatic spondylolisthesis of third cervical vertebra"
+S12250,"Other traumatic displaced spondylolisthesis of third cervical vertebra"
+S12251,"Other traumatic nondisplaced spondylolisthesis of third cervical vertebra"
+S1229,"Other fracture of third cervical vertebra"
+S12290,"Other displaced fracture of third cervical vertebra"
+S12291,"Other nondisplaced fracture of third cervical vertebra"
+S123,"Fracture of fourth cervical vertebra"
+S1230,"Unspecified fracture of fourth cervical vertebra"
+S12300,"Unspecified displaced fracture of fourth cervical vertebra"
+S12301,"Unspecified nondisplaced fracture of fourth cervical vertebra"
+S1233,"Unspecified traumatic spondylolisthesis of fourth cervical vertebra"
+S12330,"Unspecified traumatic displaced spondylolisthesis of fourth cervical vertebra"
+S12331,"Unspecified traumatic nondisplaced spondylolisthesis of fourth cervical vertebra"
+S1234,"Type III traumatic spondylolisthesis of fourth cervical vertebra"
+S1235,"Other traumatic spondylolisthesis of fourth cervical vertebra"
+S12350,"Other traumatic displaced spondylolisthesis of fourth cervical vertebra"
+S12351,"Other traumatic nondisplaced spondylolisthesis of fourth cervical vertebra"
+S1239,"Other fracture of fourth cervical vertebra"
+S12390,"Other displaced fracture of fourth cervical vertebra"
+S12391,"Other nondisplaced fracture of fourth cervical vertebra"
+S124,"Fracture of fifth cervical vertebra"
+S1240,"Unspecified fracture of fifth cervical vertebra"
+S12400,"Unspecified displaced fracture of fifth cervical vertebra"
+S12401,"Unspecified nondisplaced fracture of fifth cervical vertebra"
+S1243,"Unspecified traumatic spondylolisthesis of fifth cervical vertebra"
+S12430,"Unspecified traumatic displaced spondylolisthesis of fifth cervical vertebra"
+S12431,"Unspecified traumatic nondisplaced spondylolisthesis of fifth cervical vertebra"
+S1244,"Type III traumatic spondylolisthesis of fifth cervical vertebra"
+S1245,"Other traumatic spondylolisthesis of fifth cervical vertebra"
+S12450,"Other traumatic displaced spondylolisthesis of fifth cervical vertebra"
+S12451,"Other traumatic nondisplaced spondylolisthesis of fifth cervical vertebra"
+S1249,"Other fracture of fifth cervical vertebra"
+S12490,"Other displaced fracture of fifth cervical vertebra"
+S12491,"Other nondisplaced fracture of fifth cervical vertebra"
+S125,"Fracture of sixth cervical vertebra"
+S1250,"Unspecified fracture of sixth cervical vertebra"
+S12500,"Unspecified displaced fracture of sixth cervical vertebra"
+S12501,"Unspecified nondisplaced fracture of sixth cervical vertebra"
+S1253,"Unspecified traumatic spondylolisthesis of sixth cervical vertebra"
+S12530,"Unspecified traumatic displaced spondylolisthesis of sixth cervical vertebra"
+S12531,"Unspecified traumatic nondisplaced spondylolisthesis of sixth cervical vertebra"
+S1254,"Type III traumatic spondylolisthesis of sixth cervical vertebra"
+S1255,"Other traumatic spondylolisthesis of sixth cervical vertebra"
+S12550,"Other traumatic displaced spondylolisthesis of sixth cervical vertebra"
+S12551,"Other traumatic nondisplaced spondylolisthesis of sixth cervical vertebra"
+S1259,"Other fracture of sixth cervical vertebra"
+S12590,"Other displaced fracture of sixth cervical vertebra"
+S12591,"Other nondisplaced fracture of sixth cervical vertebra"
+S126,"Fracture of seventh cervical vertebra"
+S1260,"Unspecified fracture of seventh cervical vertebra"
+S12600,"Unspecified displaced fracture of seventh cervical vertebra"
+S12601,"Unspecified nondisplaced fracture of seventh cervical vertebra"
+S1263,"Unspecified traumatic spondylolisthesis of seventh cervical vertebra"
+S12630,"Unspecified traumatic displaced spondylolisthesis of seventh cervical vertebra"
+S12631,"Unspecified traumatic nondisplaced spondylolisthesis of seventh cervical vertebra"
+S1264,"Type III traumatic spondylolisthesis of seventh cervical vertebra"
+S1265,"Other traumatic spondylolisthesis of seventh cervical vertebra"
+S12650,"Other traumatic displaced spondylolisthesis of seventh cervical vertebra"
+S12651,"Other traumatic nondisplaced spondylolisthesis of seventh cervical vertebra"
+S1269,"Other fracture of seventh cervical vertebra"
+S12690,"Other displaced fracture of seventh cervical vertebra"
+S12691,"Other nondisplaced fracture of seventh cervical vertebra"
+S128,"Fracture of other parts of neck"
+S129,"Fracture of neck, unspecified"
+S13,"Dislocation and sprain of joints and ligaments at neck level"
+S130,"Traumatic rupture of cervical intervertebral disc"
+S131,"Subluxation and dislocation of cervical vertebrae"
+S1310,"Subluxation and dislocation of unspecified cervical vertebrae"
+S13100,"Subluxation of unspecified cervical vertebrae"
+S13101,"Dislocation of unspecified cervical vertebrae"
+S1311,"Subluxation and dislocation of C0/C1 cervical vertebrae"
+S13110,"Subluxation of C0/C1 cervical vertebrae"
+S13111,"Dislocation of C0/C1 cervical vertebrae"
+S1312,"Subluxation and dislocation of C1/C2 cervical vertebrae"
+S13120,"Subluxation of C1/C2 cervical vertebrae"
+S13121,"Dislocation of C1/C2 cervical vertebrae"
+S1313,"Subluxation and dislocation of C2/C3 cervical vertebrae"
+S13130,"Subluxation of C2/C3 cervical vertebrae"
+S13131,"Dislocation of C2/C3 cervical vertebrae"
+S1314,"Subluxation and dislocation of C3/C4 cervical vertebrae"
+S13140,"Subluxation of C3/C4 cervical vertebrae"
+S13141,"Dislocation of C3/C4 cervical vertebrae"
+S1315,"Subluxation and dislocation of C4/C5 cervical vertebrae"
+S13150,"Subluxation of C4/C5 cervical vertebrae"
+S13151,"Dislocation of C4/C5 cervical vertebrae"
+S1316,"Subluxation and dislocation of C5/C6 cervical vertebrae"
+S13160,"Subluxation of C5/C6 cervical vertebrae"
+S13161,"Dislocation of C5/C6 cervical vertebrae"
+S1317,"Subluxation and dislocation of C6/C7 cervical vertebrae"
+S13170,"Subluxation of C6/C7 cervical vertebrae"
+S13171,"Dislocation of C6/C7 cervical vertebrae"
+S1318,"Subluxation and dislocation of C7/T1 cervical vertebrae"
+S13180,"Subluxation of C7/T1 cervical vertebrae"
+S13181,"Dislocation of C7/T1 cervical vertebrae"
+S132,"Dislocation of other and unspecified parts of neck"
+S1320,"Dislocation of unspecified parts of neck"
+S1329,"Dislocation of other parts of neck"
+S134,"Sprain of ligaments of cervical spine"
+S135,"Sprain of thyroid region"
+S138,"Sprain of joints and ligaments of other parts of neck"
+S139,"Sprain of joints and ligaments of unspecified parts of neck"
+S14,"Injury of nerves and spinal cord at neck level"
+S140,"Concussion and edema of cervical spinal cord"
+S141,"Other and unspecified injuries of cervical spinal cord"
+S1410,"Unspecified injury of cervical spinal cord"
+S14101,"Unspecified injury at C1 level of cervical spinal cord"
+S14102,"Unspecified injury at C2 level of cervical spinal cord"
+S14103,"Unspecified injury at C3 level of cervical spinal cord"
+S14104,"Unspecified injury at C4 level of cervical spinal cord"
+S14105,"Unspecified injury at C5 level of cervical spinal cord"
+S14106,"Unspecified injury at C6 level of cervical spinal cord"
+S14107,"Unspecified injury at C7 level of cervical spinal cord"
+S14108,"Unspecified injury at C8 level of cervical spinal cord"
+S14109,"Unspecified injury at unspecified level of cervical spinal cord"
+S1411,"Complete lesion of cervical spinal cord"
+S14111,"Complete lesion at C1 level of cervical spinal cord"
+S14112,"Complete lesion at C2 level of cervical spinal cord"
+S14113,"Complete lesion at C3 level of cervical spinal cord"
+S14114,"Complete lesion at C4 level of cervical spinal cord"
+S14115,"Complete lesion at C5 level of cervical spinal cord"
+S14116,"Complete lesion at C6 level of cervical spinal cord"
+S14117,"Complete lesion at C7 level of cervical spinal cord"
+S14118,"Complete lesion at C8 level of cervical spinal cord"
+S14119,"Complete lesion at unspecified level of cervical spinal cord"
+S1412,"Central cord syndrome of cervical spinal cord"
+S14121,"Central cord syndrome at C1 level of cervical spinal cord"
+S14122,"Central cord syndrome at C2 level of cervical spinal cord"
+S14123,"Central cord syndrome at C3 level of cervical spinal cord"
+S14124,"Central cord syndrome at C4 level of cervical spinal cord"
+S14125,"Central cord syndrome at C5 level of cervical spinal cord"
+S14126,"Central cord syndrome at C6 level of cervical spinal cord"
+S14127,"Central cord syndrome at C7 level of cervical spinal cord"
+S14128,"Central cord syndrome at C8 level of cervical spinal cord"
+S14129,"Central cord syndrome at unspecified level of cervical spinal cord"
+S1413,"Anterior cord syndrome of cervical spinal cord"
+S14131,"Anterior cord syndrome at C1 level of cervical spinal cord"
+S14132,"Anterior cord syndrome at C2 level of cervical spinal cord"
+S14133,"Anterior cord syndrome at C3 level of cervical spinal cord"
+S14134,"Anterior cord syndrome at C4 level of cervical spinal cord"
+S14135,"Anterior cord syndrome at C5 level of cervical spinal cord"
+S14136,"Anterior cord syndrome at C6 level of cervical spinal cord"
+S14137,"Anterior cord syndrome at C7 level of cervical spinal cord"
+S14138,"Anterior cord syndrome at C8 level of cervical spinal cord"
+S14139,"Anterior cord syndrome at unspecified level of cervical spinal cord"
+S1414,"Brown-Sequard syndrome of cervical spinal cord"
+S14141,"Brown-Sequard syndrome at C1 level of cervical spinal cord"
+S14142,"Brown-Sequard syndrome at C2 level of cervical spinal cord"
+S14143,"Brown-Sequard syndrome at C3 level of cervical spinal cord"
+S14144,"Brown-Sequard syndrome at C4 level of cervical spinal cord"
+S14145,"Brown-Sequard syndrome at C5 level of cervical spinal cord"
+S14146,"Brown-Sequard syndrome at C6 level of cervical spinal cord"
+S14147,"Brown-Sequard syndrome at C7 level of cervical spinal cord"
+S14148,"Brown-Sequard syndrome at C8 level of cervical spinal cord"
+S14149,"Brown-Sequard syndrome at unspecified level of cervical spinal cord"
+S1415,"Other incomplete lesions of cervical spinal cord"
+S14151,"Other incomplete lesion at C1 level of cervical spinal cord"
+S14152,"Other incomplete lesion at C2 level of cervical spinal cord"
+S14153,"Other incomplete lesion at C3 level of cervical spinal cord"
+S14154,"Other incomplete lesion at C4 level of cervical spinal cord"
+S14155,"Other incomplete lesion at C5 level of cervical spinal cord"
+S14156,"Other incomplete lesion at C6 level of cervical spinal cord"
+S14157,"Other incomplete lesion at C7 level of cervical spinal cord"
+S14158,"Other incomplete lesion at C8 level of cervical spinal cord"
+S14159,"Other incomplete lesion at unspecified level of cervical spinal cord"
+S142,"Injury of nerve root of cervical spine"
+S143,"Injury of brachial plexus"
+S144,"Injury of peripheral nerves of neck"
+S145,"Injury of cervical sympathetic nerves"
+S148,"Injury of other specified nerves of neck"
+S149,"Injury of unspecified nerves of neck"
+S15,"Injury of blood vessels at neck level"
+S150,"Injury of carotid artery of neck"
+S1500,"Unspecified injury of carotid artery"
+S15001,"Unspecified injury of right carotid artery"
+S15002,"Unspecified injury of left carotid artery"
+S15009,"Unspecified injury of unspecified carotid artery"
+S1501,"Minor laceration of carotid artery"
+S15011,"Minor laceration of right carotid artery"
+S15012,"Minor laceration of left carotid artery"
+S15019,"Minor laceration of unspecified carotid artery"
+S1502,"Major laceration of carotid artery"
+S15021,"Major laceration of right carotid artery"
+S15022,"Major laceration of left carotid artery"
+S15029,"Major laceration of unspecified carotid artery"
+S1509,"Other specified injury of carotid artery"
+S15091,"Other specified injury of right carotid artery"
+S15092,"Other specified injury of left carotid artery"
+S15099,"Other specified injury of unspecified carotid artery"
+S151,"Injury of vertebral artery"
+S1510,"Unspecified injury of vertebral artery"
+S15101,"Unspecified injury of right vertebral artery"
+S15102,"Unspecified injury of left vertebral artery"
+S15109,"Unspecified injury of unspecified vertebral artery"
+S1511,"Minor laceration of vertebral artery"
+S15111,"Minor laceration of right vertebral artery"
+S15112,"Minor laceration of left vertebral artery"
+S15119,"Minor laceration of unspecified vertebral artery"
+S1512,"Major laceration of vertebral artery"
+S15121,"Major laceration of right vertebral artery"
+S15122,"Major laceration of left vertebral artery"
+S15129,"Major laceration of unspecified vertebral artery"
+S1519,"Other specified injury of vertebral artery"
+S15191,"Other specified injury of right vertebral artery"
+S15192,"Other specified injury of left vertebral artery"
+S15199,"Other specified injury of unspecified vertebral artery"
+S152,"Injury of external jugular vein"
+S1520,"Unspecified injury of external jugular vein"
+S15201,"Unspecified injury of right external jugular vein"
+S15202,"Unspecified injury of left external jugular vein"
+S15209,"Unspecified injury of unspecified external jugular vein"
+S1521,"Minor laceration of external jugular vein"
+S15211,"Minor laceration of right external jugular vein"
+S15212,"Minor laceration of left external jugular vein"
+S15219,"Minor laceration of unspecified external jugular vein"
+S1522,"Major laceration of external jugular vein"
+S15221,"Major laceration of right external jugular vein"
+S15222,"Major laceration of left external jugular vein"
+S15229,"Major laceration of unspecified external jugular vein"
+S1529,"Other specified injury of external jugular vein"
+S15291,"Other specified injury of right external jugular vein"
+S15292,"Other specified injury of left external jugular vein"
+S15299,"Other specified injury of unspecified external jugular vein"
+S153,"Injury of internal jugular vein"
+S1530,"Unspecified injury of internal jugular vein"
+S15301,"Unspecified injury of right internal jugular vein"
+S15302,"Unspecified injury of left internal jugular vein"
+S15309,"Unspecified injury of unspecified internal jugular vein"
+S1531,"Minor laceration of internal jugular vein"
+S15311,"Minor laceration of right internal jugular vein"
+S15312,"Minor laceration of left internal jugular vein"
+S15319,"Minor laceration of unspecified internal jugular vein"
+S1532,"Major laceration of internal jugular vein"
+S15321,"Major laceration of right internal jugular vein"
+S15322,"Major laceration of left internal jugular vein"
+S15329,"Major laceration of unspecified internal jugular vein"
+S1539,"Other specified injury of internal jugular vein"
+S15391,"Other specified injury of right internal jugular vein"
+S15392,"Other specified injury of left internal jugular vein"
+S15399,"Other specified injury of unspecified internal jugular vein"
+S158,"Injury of other specified blood vessels at neck level"
+S159,"Injury of unspecified blood vessel at neck level"
+S16,"Injury of muscle, fascia and tendon at neck level"
+S161,"Strain of muscle, fascia and tendon at neck level"
+S162,"Laceration of muscle, fascia and tendon at neck level"
+S168,"Other specified injury of muscle, fascia and tendon at neck level"
+S169,"Unspecified injury of muscle, fascia and tendon at neck level"
+S17,"Crushing injury of neck"
+S170,"Crushing injury of larynx and trachea"
+S178,"Crushing injury of other specified parts of neck"
+S179,"Crushing injury of neck, part unspecified"
+S19,"Other specified and unspecified injuries of neck"
+S198,"Other specified injuries of neck"
+S1980,"Other specified injuries of unspecified part of neck"
+S1981,"Other specified injuries of larynx"
+S1982,"Other specified injuries of cervical trachea"
+S1983,"Other specified injuries of vocal cord"
+S1984,"Other specified injuries of thyroid gland"
+S1985,"Other specified injuries of pharynx and cervical esophagus"
+S1989,"Other specified injuries of other specified part of neck"
+S199,"Unspecified injury of neck"
+S20,"Superficial injury of thorax"
+S200,"Contusion of breast"
+S2000,"Contusion of breast, unspecified breast"
+S2001,"Contusion of right breast"
+S2002,"Contusion of left breast"
+S201,"Other and unspecified superficial injuries of breast"
+S2010,"Unspecified superficial injuries of breast"
+S20101,"Unspecified superficial injuries of breast, right breast"
+S20102,"Unspecified superficial injuries of breast, left breast"
+S20109,"Unspecified superficial injuries of breast, unspecified breast"
+S2011,"Abrasion of breast"
+S20111,"Abrasion of breast, right breast"
+S20112,"Abrasion of breast, left breast"
+S20119,"Abrasion of breast, unspecified breast"
+S2012,"Blister (nonthermal) of breast"
+S20121,"Blister (nonthermal) of breast, right breast"
+S20122,"Blister (nonthermal) of breast, left breast"
+S20129,"Blister (nonthermal) of breast, unspecified breast"
+S2014,"External constriction of part of breast"
+S20141,"External constriction of part of breast, right breast"
+S20142,"External constriction of part of breast, left breast"
+S20149,"External constriction of part of breast, unspecified breast"
+S2015,"Superficial foreign body of breast"
+S20151,"Superficial foreign body of breast, right breast"
+S20152,"Superficial foreign body of breast, left breast"
+S20159,"Superficial foreign body of breast, unspecified breast"
+S2016,"Insect bite (nonvenomous) of breast"
+S20161,"Insect bite (nonvenomous) of breast, right breast"
+S20162,"Insect bite (nonvenomous) of breast, left breast"
+S20169,"Insect bite (nonvenomous) of breast, unspecified breast"
+S2017,"Other superficial bite of breast"
+S20171,"Other superficial bite of breast, right breast"
+S20172,"Other superficial bite of breast, left breast"
+S20179,"Other superficial bite of breast, unspecified breast"
+S202,"Contusion of thorax"
+S2020,"Contusion of thorax, unspecified"
+S2021,"Contusion of front wall of thorax"
+S20211,"Contusion of right front wall of thorax"
+S20212,"Contusion of left front wall of thorax"
+S20219,"Contusion of unspecified front wall of thorax"
+S2022,"Contusion of back wall of thorax"
+S20221,"Contusion of right back wall of thorax"
+S20222,"Contusion of left back wall of thorax"
+S20229,"Contusion of unspecified back wall of thorax"
+S203,"Other and unspecified superficial injuries of front wall of thorax"
+S2030,"Unspecified superficial injuries of front wall of thorax"
+S20301,"Unspecified superficial injuries of right front wall of thorax"
+S20302,"Unspecified superficial injuries of left front wall of thorax"
+S20309,"Unspecified superficial injuries of unspecified front wall of thorax"
+S2031,"Abrasion of front wall of thorax"
+S20311,"Abrasion of right front wall of thorax"
+S20312,"Abrasion of left front wall of thorax"
+S20319,"Abrasion of unspecified front wall of thorax"
+S2032,"Blister (nonthermal) of front wall of thorax"
+S20321,"Blister (nonthermal) of right front wall of thorax"
+S20322,"Blister (nonthermal) of left front wall of thorax"
+S20329,"Blister (nonthermal) of unspecified front wall of thorax"
+S2034,"External constriction of front wall of thorax"
+S20341,"External constriction of right front wall of thorax"
+S20342,"External constriction of left front wall of thorax"
+S20349,"External constriction of unspecified front wall of thorax"
+S2035,"Superficial foreign body of front wall of thorax"
+S20351,"Superficial foreign body of right front wall of thorax"
+S20352,"Superficial foreign body of left front wall of thorax"
+S20359,"Superficial foreign body of unspecified front wall of thorax"
+S2036,"Insect bite (nonvenomous) of front wall of thorax"
+S20361,"Insect bite (nonvenomous) of right front wall of thorax"
+S20362,"Insect bite (nonvenomous) of left front wall of thorax"
+S20369,"Insect bite (nonvenomous) of unspecified front wall of thorax"
+S2037,"Other superficial bite of front wall of thorax"
+S20371,"Other superficial bite of right front wall of thorax"
+S20372,"Other superficial bite of left front wall of thorax"
+S20379,"Other superficial bite of unspecified front wall of thorax"
+S204,"Other and unspecified superficial injuries of back wall of thorax"
+S2040,"Unspecified superficial injuries of back wall of thorax"
+S20401,"Unspecified superficial injuries of right back wall of thorax"
+S20402,"Unspecified superficial injuries of left back wall of thorax"
+S20409,"Unspecified superficial injuries of unspecified back wall of thorax"
+S2041,"Abrasion of back wall of thorax"
+S20411,"Abrasion of right back wall of thorax"
+S20412,"Abrasion of left back wall of thorax"
+S20419,"Abrasion of unspecified back wall of thorax"
+S2042,"Blister (nonthermal) of back wall of thorax"
+S20421,"Blister (nonthermal) of right back wall of thorax"
+S20422,"Blister (nonthermal) of left back wall of thorax"
+S20429,"Blister (nonthermal) of unspecified back wall of thorax"
+S2044,"External constriction of back wall of thorax"
+S20441,"External constriction of right back wall of thorax"
+S20442,"External constriction of left back wall of thorax"
+S20449,"External constriction of unspecified back wall of thorax"
+S2045,"Superficial foreign body of back wall of thorax"
+S20451,"Superficial foreign body of right back wall of thorax"
+S20452,"Superficial foreign body of left back wall of thorax"
+S20459,"Superficial foreign body of unspecified back wall of thorax"
+S2046,"Insect bite (nonvenomous) of back wall of thorax"
+S20461,"Insect bite (nonvenomous) of right back wall of thorax"
+S20462,"Insect bite (nonvenomous) of left back wall of thorax"
+S20469,"Insect bite (nonvenomous) of unspecified back wall of thorax"
+S2047,"Other superficial bite of back wall of thorax"
+S20471,"Other superficial bite of right back wall of thorax"
+S20472,"Other superficial bite of left back wall of thorax"
+S20479,"Other superficial bite of unspecified back wall of thorax"
+S209,"Superficial injury of unspecified parts of thorax"
+S2090,"Unspecified superficial injury of unspecified parts of thorax"
+S2091,"Abrasion of unspecified parts of thorax"
+S2092,"Blister (nonthermal) of unspecified parts of thorax"
+S2094,"External constriction of unspecified parts of thorax"
+S2095,"Superficial foreign body of unspecified parts of thorax"
+S2096,"Insect bite (nonvenomous) of unspecified parts of thorax"
+S2097,"Other superficial bite of unspecified parts of thorax"
+S21,"Open wound of thorax"
+S210,"Open wound of breast"
+S2100,"Unspecified open wound of breast"
+S21001,"Unspecified open wound of right breast"
+S21002,"Unspecified open wound of left breast"
+S21009,"Unspecified open wound of unspecified breast"
+S2101,"Laceration without foreign body of breast"
+S21011,"Laceration without foreign body of right breast"
+S21012,"Laceration without foreign body of left breast"
+S21019,"Laceration without foreign body of unspecified breast"
+S2102,"Laceration with foreign body of breast"
+S21021,"Laceration with foreign body of right breast"
+S21022,"Laceration with foreign body of left breast"
+S21029,"Laceration with foreign body of unspecified breast"
+S2103,"Puncture wound without foreign body of breast"
+S21031,"Puncture wound without foreign body of right breast"
+S21032,"Puncture wound without foreign body of left breast"
+S21039,"Puncture wound without foreign body of unspecified breast"
+S2104,"Puncture wound with foreign body of breast"
+S21041,"Puncture wound with foreign body of right breast"
+S21042,"Puncture wound with foreign body of left breast"
+S21049,"Puncture wound with foreign body of unspecified breast"
+S2105,"Open bite of breast"
+S21051,"Open bite of right breast"
+S21052,"Open bite of left breast"
+S21059,"Open bite of unspecified breast"
+S211,"Open wound of front wall of thorax without penetration into thoracic cavity"
+S2110,"Unspecified open wound of front wall of thorax without penetration into thoracic cavity"
+S21101,"Unspecified open wound of right front wall of thorax without penetration into thoracic cavity"
+S21102,"Unspecified open wound of left front wall of thorax without penetration into thoracic cavity"
+S21109,"Unspecified open wound of unspecified front wall of thorax without penetration into thoracic cavity"
+S2111,"Laceration without foreign body of front wall of thorax without penetration into thoracic cavity"
+S21111,"Laceration without foreign body of right front wall of thorax without penetration into thoracic cavity"
+S21112,"Laceration without foreign body of left front wall of thorax without penetration into thoracic cavity"
+S21119,"Laceration without foreign body of unspecified front wall of thorax without penetration into thoracic cavity"
+S2112,"Laceration with foreign body of front wall of thorax without penetration into thoracic cavity"
+S21121,"Laceration with foreign body of right front wall of thorax without penetration into thoracic cavity"
+S21122,"Laceration with foreign body of left front wall of thorax without penetration into thoracic cavity"
+S21129,"Laceration with foreign body of unspecified front wall of thorax without penetration into thoracic cavity"
+S2113,"Puncture wound without foreign body of front wall of thorax without penetration into thoracic cavity"
+S21131,"Puncture wound without foreign body of right front wall of thorax without penetration into thoracic cavity"
+S21132,"Puncture wound without foreign body of left front wall of thorax without penetration into thoracic cavity"
+S21139,"Puncture wound without foreign body of unspecified front wall of thorax without penetration into thoracic cavity"
+S2114,"Puncture wound with foreign body of front wall of thorax without penetration into thoracic cavity"
+S21141,"Puncture wound with foreign body of right front wall of thorax without penetration into thoracic cavity"
+S21142,"Puncture wound with foreign body of left front wall of thorax without penetration into thoracic cavity"
+S21149,"Puncture wound with foreign body of unspecified front wall of thorax without penetration into thoracic cavity"
+S2115,"Open bite of front wall of thorax without penetration into thoracic cavity"
+S21151,"Open bite of right front wall of thorax without penetration into thoracic cavity"
+S21152,"Open bite of left front wall of thorax without penetration into thoracic cavity"
+S21159,"Open bite of unspecified front wall of thorax without penetration into thoracic cavity"
+S212,"Open wound of back wall of thorax without penetration into thoracic cavity"
+S2120,"Unspecified open wound of back wall of thorax without penetration into thoracic cavity"
+S21201,"Unspecified open wound of right back wall of thorax without penetration into thoracic cavity"
+S21202,"Unspecified open wound of left back wall of thorax without penetration into thoracic cavity"
+S21209,"Unspecified open wound of unspecified back wall of thorax without penetration into thoracic cavity"
+S2121,"Laceration without foreign body of back wall of thorax without penetration into thoracic cavity"
+S21211,"Laceration without foreign body of right back wall of thorax without penetration into thoracic cavity"
+S21212,"Laceration without foreign body of left back wall of thorax without penetration into thoracic cavity"
+S21219,"Laceration without foreign body of unspecified back wall of thorax without penetration into thoracic cavity"
+S2122,"Laceration with foreign body of back wall of thorax without penetration into thoracic cavity"
+S21221,"Laceration with foreign body of right back wall of thorax without penetration into thoracic cavity"
+S21222,"Laceration with foreign body of left back wall of thorax without penetration into thoracic cavity"
+S21229,"Laceration with foreign body of unspecified back wall of thorax without penetration into thoracic cavity"
+S2123,"Puncture wound without foreign body of back wall of thorax without penetration into thoracic cavity"
+S21231,"Puncture wound without foreign body of right back wall of thorax without penetration into thoracic cavity"
+S21232,"Puncture wound without foreign body of left back wall of thorax without penetration into thoracic cavity"
+S21239,"Puncture wound without foreign body of unspecified back wall of thorax without penetration into thoracic cavity"
+S2124,"Puncture wound with foreign body of back wall of thorax without penetration into thoracic cavity"
+S21241,"Puncture wound with foreign body of right back wall of thorax without penetration into thoracic cavity"
+S21242,"Puncture wound with foreign body of left back wall of thorax without penetration into thoracic cavity"
+S21249,"Puncture wound with foreign body of unspecified back wall of thorax without penetration into thoracic cavity"
+S2125,"Open bite of back wall of thorax without penetration into thoracic cavity"
+S21251,"Open bite of right back wall of thorax without penetration into thoracic cavity"
+S21252,"Open bite of left back wall of thorax without penetration into thoracic cavity"
+S21259,"Open bite of unspecified back wall of thorax without penetration into thoracic cavity"
+S213,"Open wound of front wall of thorax with penetration into thoracic cavity"
+S2130,"Unspecified open wound of front wall of thorax with penetration into thoracic cavity"
+S21301,"Unspecified open wound of right front wall of thorax with penetration into thoracic cavity"
+S21302,"Unspecified open wound of left front wall of thorax with penetration into thoracic cavity"
+S21309,"Unspecified open wound of unspecified front wall of thorax with penetration into thoracic cavity"
+S2131,"Laceration without foreign body of front wall of thorax with penetration into thoracic cavity"
+S21311,"Laceration without foreign body of right front wall of thorax with penetration into thoracic cavity"
+S21312,"Laceration without foreign body of left front wall of thorax with penetration into thoracic cavity"
+S21319,"Laceration without foreign body of unspecified front wall of thorax with penetration into thoracic cavity"
+S2132,"Laceration with foreign body of front wall of thorax with penetration into thoracic cavity"
+S21321,"Laceration with foreign body of right front wall of thorax with penetration into thoracic cavity"
+S21322,"Laceration with foreign body of left front wall of thorax with penetration into thoracic cavity"
+S21329,"Laceration with foreign body of unspecified front wall of thorax with penetration into thoracic cavity"
+S2133,"Puncture wound without foreign body of front wall of thorax with penetration into thoracic cavity"
+S21331,"Puncture wound without foreign body of right front wall of thorax with penetration into thoracic cavity"
+S21332,"Puncture wound without foreign body of left front wall of thorax with penetration into thoracic cavity"
+S21339,"Puncture wound without foreign body of unspecified front wall of thorax with penetration into thoracic cavity"
+S2134,"Puncture wound with foreign body of front wall of thorax with penetration into thoracic cavity"
+S21341,"Puncture wound with foreign body of right front wall of thorax with penetration into thoracic cavity"
+S21342,"Puncture wound with foreign body of left front wall of thorax with penetration into thoracic cavity"
+S21349,"Puncture wound with foreign body of unspecified front wall of thorax with penetration into thoracic cavity"
+S2135,"Open bite of front wall of thorax with penetration into thoracic cavity"
+S21351,"Open bite of right front wall of thorax with penetration into thoracic cavity"
+S21352,"Open bite of left front wall of thorax with penetration into thoracic cavity"
+S21359,"Open bite of unspecified front wall of thorax with penetration into thoracic cavity"
+S214,"Open wound of back wall of thorax with penetration into thoracic cavity"
+S2140,"Unspecified open wound of back wall of thorax with penetration into thoracic cavity"
+S21401,"Unspecified open wound of right back wall of thorax with penetration into thoracic cavity"
+S21402,"Unspecified open wound of left back wall of thorax with penetration into thoracic cavity"
+S21409,"Unspecified open wound of unspecified back wall of thorax with penetration into thoracic cavity"
+S2141,"Laceration without foreign body of back wall of thorax with penetration into thoracic cavity"
+S21411,"Laceration without foreign body of right back wall of thorax with penetration into thoracic cavity"
+S21412,"Laceration without foreign body of left back wall of thorax with penetration into thoracic cavity"
+S21419,"Laceration without foreign body of unspecified back wall of thorax with penetration into thoracic cavity"
+S2142,"Laceration with foreign body of back wall of thorax with penetration into thoracic cavity"
+S21421,"Laceration with foreign body of right back wall of thorax with penetration into thoracic cavity"
+S21422,"Laceration with foreign body of left back wall of thorax with penetration into thoracic cavity"
+S21429,"Laceration with foreign body of unspecified back wall of thorax with penetration into thoracic cavity"
+S2143,"Puncture wound without foreign body of back wall of thorax with penetration into thoracic cavity"
+S21431,"Puncture wound without foreign body of right back wall of thorax with penetration into thoracic cavity"
+S21432,"Puncture wound without foreign body of left back wall of thorax with penetration into thoracic cavity"
+S21439,"Puncture wound without foreign body of unspecified back wall of thorax with penetration into thoracic cavity"
+S2144,"Puncture wound with foreign body of back wall of thorax with penetration into thoracic cavity"
+S21441,"Puncture wound with foreign body of right back wall of thorax with penetration into thoracic cavity"
+S21442,"Puncture wound with foreign body of left back wall of thorax with penetration into thoracic cavity"
+S21449,"Puncture wound with foreign body of unspecified back wall of thorax with penetration into thoracic cavity"
+S2145,"Open bite of back wall of thorax with penetration into thoracic cavity"
+S21451,"Open bite of right back wall of thorax with penetration into thoracic cavity"
+S21452,"Open bite of left back wall of thorax with penetration into thoracic cavity"
+S21459,"Open bite of unspecified back wall of thorax with penetration into thoracic cavity"
+S219,"Open wound of unspecified part of thorax"
+S2190,"Unspecified open wound of unspecified part of thorax"
+S2191,"Laceration without foreign body of unspecified part of thorax"
+S2192,"Laceration with foreign body of unspecified part of thorax"
+S2193,"Puncture wound without foreign body of unspecified part of thorax"
+S2194,"Puncture wound with foreign body of unspecified part of thorax"
+S2195,"Open bite of unspecified part of thorax"
+S22,"Fracture of rib(s), sternum and thoracic spine"
+S220,"Fracture of thoracic vertebra"
+S2200,"Fracture of unspecified thoracic vertebra"
+S22000,"Wedge compression fracture of unspecified thoracic vertebra"
+S22001,"Stable burst fracture of unspecified thoracic vertebra"
+S22002,"Unstable burst fracture of unspecified thoracic vertebra"
+S22008,"Other fracture of unspecified thoracic vertebra"
+S22009,"Unspecified fracture of unspecified thoracic vertebra"
+S2201,"Fracture of first thoracic vertebra"
+S22010,"Wedge compression fracture of first thoracic vertebra"
+S22011,"Stable burst fracture of first thoracic vertebra"
+S22012,"Unstable burst fracture of first thoracic vertebra"
+S22018,"Other fracture of first thoracic vertebra"
+S22019,"Unspecified fracture of first thoracic vertebra"
+S2202,"Fracture of second thoracic vertebra"
+S22020,"Wedge compression fracture of second thoracic vertebra"
+S22021,"Stable burst fracture of second thoracic vertebra"
+S22022,"Unstable burst fracture of second thoracic vertebra"
+S22028,"Other fracture of second thoracic vertebra"
+S22029,"Unspecified fracture of second thoracic vertebra"
+S2203,"Fracture of third thoracic vertebra"
+S22030,"Wedge compression fracture of third thoracic vertebra"
+S22031,"Stable burst fracture of third thoracic vertebra"
+S22032,"Unstable burst fracture of third thoracic vertebra"
+S22038,"Other fracture of third thoracic vertebra"
+S22039,"Unspecified fracture of third thoracic vertebra"
+S2204,"Fracture of fourth thoracic vertebra"
+S22040,"Wedge compression fracture of fourth thoracic vertebra"
+S22041,"Stable burst fracture of fourth thoracic vertebra"
+S22042,"Unstable burst fracture of fourth thoracic vertebra"
+S22048,"Other fracture of fourth thoracic vertebra"
+S22049,"Unspecified fracture of fourth thoracic vertebra"
+S2205,"Fracture of T5-T6 vertebra"
+S22050,"Wedge compression fracture of T5-T6 vertebra"
+S22051,"Stable burst fracture of T5-T6 vertebra"
+S22052,"Unstable burst fracture of T5-T6 vertebra"
+S22058,"Other fracture of T5-T6 vertebra"
+S22059,"Unspecified fracture of T5-T6 vertebra"
+S2206,"Fracture of T7-T8 vertebra"
+S22060,"Wedge compression fracture of T7-T8 vertebra"
+S22061,"Stable burst fracture of T7-T8 vertebra"
+S22062,"Unstable burst fracture of T7-T8 vertebra"
+S22068,"Other fracture of T7-T8 thoracic vertebra"
+S22069,"Unspecified fracture of T7-T8 vertebra"
+S2207,"Fracture of T9-T10 vertebra"
+S22070,"Wedge compression fracture of T9-T10 vertebra"
+S22071,"Stable burst fracture of T9-T10 vertebra"
+S22072,"Unstable burst fracture of T9-T10 vertebra"
+S22078,"Other fracture of T9-T10 vertebra"
+S22079,"Unspecified fracture of T9-T10 vertebra"
+S2208,"Fracture of T11-T12 vertebra"
+S22080,"Wedge compression fracture of T11-T12 vertebra"
+S22081,"Stable burst fracture of T11-T12 vertebra"
+S22082,"Unstable burst fracture of T11-T12 vertebra"
+S22088,"Other fracture of T11-T12 vertebra"
+S22089,"Unspecified fracture of T11-T12 vertebra"
+S222,"Fracture of sternum"
+S2220,"Unspecified fracture of sternum"
+S2221,"Fracture of manubrium"
+S2222,"Fracture of body of sternum"
+S2223,"Sternal manubrial dissociation"
+S2224,"Fracture of xiphoid process"
+S223,"Fracture of one rib"
+S2231,"Fracture of one rib, right side"
+S2232,"Fracture of one rib, left side"
+S2239,"Fracture of one rib, unspecified side"
+S224,"Multiple fractures of ribs"
+S2241,"Multiple fractures of ribs, right side"
+S2242,"Multiple fractures of ribs, left side"
+S2243,"Multiple fractures of ribs, bilateral"
+S2249,"Multiple fractures of ribs, unspecified side"
+S225,"Flail chest"
+S229,"Fracture of bony thorax, part unspecified"
+S23,"Dislocation and sprain of joints and ligaments of thorax"
+S230,"Traumatic rupture of thoracic intervertebral disc"
+S231,"Subluxation and dislocation of thoracic vertebra"
+S2310,"Subluxation and dislocation of unspecified thoracic vertebra"
+S23100,"Subluxation of unspecified thoracic vertebra"
+S23101,"Dislocation of unspecified thoracic vertebra"
+S2311,"Subluxation and dislocation of T1/T2 thoracic vertebra"
+S23110,"Subluxation of T1/T2 thoracic vertebra"
+S23111,"Dislocation of T1/T2 thoracic vertebra"
+S2312,"Subluxation and dislocation of T2/T3-T3/T4 thoracic vertebra"
+S23120,"Subluxation of T2/T3 thoracic vertebra"
+S23121,"Dislocation of T2/T3 thoracic vertebra"
+S23122,"Subluxation of T3/T4 thoracic vertebra"
+S23123,"Dislocation of T3/T4 thoracic vertebra"
+S2313,"Subluxation and dislocation of T4/T5-T5/T6 thoracic vertebra"
+S23130,"Subluxation of T4/T5 thoracic vertebra"
+S23131,"Dislocation of T4/T5 thoracic vertebra"
+S23132,"Subluxation of T5/T6 thoracic vertebra"
+S23133,"Dislocation of T5/T6 thoracic vertebra"
+S2314,"Subluxation and dislocation of T6/T7-T7/T8 thoracic vertebra"
+S23140,"Subluxation of T6/T7 thoracic vertebra"
+S23141,"Dislocation of T6/T7 thoracic vertebra"
+S23142,"Subluxation of T7/T8 thoracic vertebra"
+S23143,"Dislocation of T7/T8 thoracic vertebra"
+S2315,"Subluxation and dislocation of T8/T9-T9/T10 thoracic vertebra"
+S23150,"Subluxation of T8/T9 thoracic vertebra"
+S23151,"Dislocation of T8/T9 thoracic vertebra"
+S23152,"Subluxation of T9/T10 thoracic vertebra"
+S23153,"Dislocation of T9/T10 thoracic vertebra"
+S2316,"Subluxation and dislocation of T10/T11-T11/T12 thoracic vertebra"
+S23160,"Subluxation of T10/T11 thoracic vertebra"
+S23161,"Dislocation of T10/T11 thoracic vertebra"
+S23162,"Subluxation of T11/T12 thoracic vertebra"
+S23163,"Dislocation of T11/T12 thoracic vertebra"
+S2317,"Subluxation and dislocation of T12/L1 thoracic vertebra"
+S23170,"Subluxation of T12/L1 thoracic vertebra"
+S23171,"Dislocation of T12/L1 thoracic vertebra"
+S232,"Dislocation of other and unspecified parts of thorax"
+S2320,"Dislocation of unspecified part of thorax"
+S2329,"Dislocation of other parts of thorax"
+S233,"Sprain of ligaments of thoracic spine"
+S234,"Sprain of ribs and sternum"
+S2341,"Sprain of ribs"
+S2342,"Sprain of sternum"
+S23420,"Sprain of sternoclavicular (joint) (ligament)"
+S23421,"Sprain of chondrosternal joint"
+S23428,"Other sprain of sternum"
+S23429,"Unspecified sprain of sternum"
+S238,"Sprain of other specified parts of thorax"
+S239,"Sprain of unspecified parts of thorax"
+S24,"Injury of nerves and spinal cord at thorax level"
+S240,"Concussion and edema of thoracic spinal cord"
+S241,"Other and unspecified injuries of thoracic spinal cord"
+S2410,"Unspecified injury of thoracic spinal cord"
+S24101,"Unspecified injury at T1 level of thoracic spinal cord"
+S24102,"Unspecified injury at T2-T6 level of thoracic spinal cord"
+S24103,"Unspecified injury at T7-T10 level of thoracic spinal cord"
+S24104,"Unspecified injury at T11-T12 level of thoracic spinal cord"
+S24109,"Unspecified injury at unspecified level of thoracic spinal cord"
+S2411,"Complete lesion of thoracic spinal cord"
+S24111,"Complete lesion at T1 level of thoracic spinal cord"
+S24112,"Complete lesion at T2-T6 level of thoracic spinal cord"
+S24113,"Complete lesion at T7-T10 level of thoracic spinal cord"
+S24114,"Complete lesion at T11-T12 level of thoracic spinal cord"
+S24119,"Complete lesion at unspecified level of thoracic spinal cord"
+S2413,"Anterior cord syndrome of thoracic spinal cord"
+S24131,"Anterior cord syndrome at T1 level of thoracic spinal cord"
+S24132,"Anterior cord syndrome at T2-T6 level of thoracic spinal cord"
+S24133,"Anterior cord syndrome at T7-T10 level of thoracic spinal cord"
+S24134,"Anterior cord syndrome at T11-T12 level of thoracic spinal cord"
+S24139,"Anterior cord syndrome at unspecified level of thoracic spinal cord"
+S2414,"Brown-Sequard syndrome of thoracic spinal cord"
+S24141,"Brown-Sequard syndrome at T1 level of thoracic spinal cord"
+S24142,"Brown-Sequard syndrome at T2-T6 level of thoracic spinal cord"
+S24143,"Brown-Sequard syndrome at T7-T10 level of thoracic spinal cord"
+S24144,"Brown-Sequard syndrome at T11-T12 level of thoracic spinal cord"
+S24149,"Brown-Sequard syndrome at unspecified level of thoracic spinal cord"
+S2415,"Other incomplete lesions of thoracic spinal cord"
+S24151,"Other incomplete lesion at T1 level of thoracic spinal cord"
+S24152,"Other incomplete lesion at T2-T6 level of thoracic spinal cord"
+S24153,"Other incomplete lesion at T7-T10 level of thoracic spinal cord"
+S24154,"Other incomplete lesion at T11-T12 level of thoracic spinal cord"
+S24159,"Other incomplete lesion at unspecified level of thoracic spinal cord"
+S242,"Injury of nerve root of thoracic spine"
+S243,"Injury of peripheral nerves of thorax"
+S244,"Injury of thoracic sympathetic nervous system"
+S248,"Injury of other specified nerves of thorax"
+S249,"Injury of unspecified nerve of thorax"
+S25,"Injury of blood vessels of thorax"
+S250,"Injury of thoracic aorta"
+S2500,"Unspecified injury of thoracic aorta"
+S2501,"Minor laceration of thoracic aorta"
+S2502,"Major laceration of thoracic aorta"
+S2509,"Other specified injury of thoracic aorta"
+S251,"Injury of innominate or subclavian artery"
+S2510,"Unspecified injury of innominate or subclavian artery"
+S25101,"Unspecified injury of right innominate or subclavian artery"
+S25102,"Unspecified injury of left innominate or subclavian artery"
+S25109,"Unspecified injury of unspecified innominate or subclavian artery"
+S2511,"Minor laceration of innominate or subclavian artery"
+S25111,"Minor laceration of right innominate or subclavian artery"
+S25112,"Minor laceration of left innominate or subclavian artery"
+S25119,"Minor laceration of unspecified innominate or subclavian artery"
+S2512,"Major laceration of innominate or subclavian artery"
+S25121,"Major laceration of right innominate or subclavian artery"
+S25122,"Major laceration of left innominate or subclavian artery"
+S25129,"Major laceration of unspecified innominate or subclavian artery"
+S2519,"Other specified injury of innominate or subclavian artery"
+S25191,"Other specified injury of right innominate or subclavian artery"
+S25192,"Other specified injury of left innominate or subclavian artery"
+S25199,"Other specified injury of unspecified innominate or subclavian artery"
+S252,"Injury of superior vena cava"
+S2520,"Unspecified injury of superior vena cava"
+S2521,"Minor laceration of superior vena cava"
+S2522,"Major laceration of superior vena cava"
+S2529,"Other specified injury of superior vena cava"
+S253,"Injury of innominate or subclavian vein"
+S2530,"Unspecified injury of innominate or subclavian vein"
+S25301,"Unspecified injury of right innominate or subclavian vein"
+S25302,"Unspecified injury of left innominate or subclavian vein"
+S25309,"Unspecified injury of unspecified innominate or subclavian vein"
+S2531,"Minor laceration of innominate or subclavian vein"
+S25311,"Minor laceration of right innominate or subclavian vein"
+S25312,"Minor laceration of left innominate or subclavian vein"
+S25319,"Minor laceration of unspecified innominate or subclavian vein"
+S2532,"Major laceration of innominate or subclavian vein"
+S25321,"Major laceration of right innominate or subclavian vein"
+S25322,"Major laceration of left innominate or subclavian vein"
+S25329,"Major laceration of unspecified innominate or subclavian vein"
+S2539,"Other specified injury of innominate or subclavian vein"
+S25391,"Other specified injury of right innominate or subclavian vein"
+S25392,"Other specified injury of left innominate or subclavian vein"
+S25399,"Other specified injury of unspecified innominate or subclavian vein"
+S254,"Injury of pulmonary blood vessels"
+S2540,"Unspecified injury of pulmonary blood vessels"
+S25401,"Unspecified injury of right pulmonary blood vessels"
+S25402,"Unspecified injury of left pulmonary blood vessels"
+S25409,"Unspecified injury of unspecified pulmonary blood vessels"
+S2541,"Minor laceration of pulmonary blood vessels"
+S25411,"Minor laceration of right pulmonary blood vessels"
+S25412,"Minor laceration of left pulmonary blood vessels"
+S25419,"Minor laceration of unspecified pulmonary blood vessels"
+S2542,"Major laceration of pulmonary blood vessels"
+S25421,"Major laceration of right pulmonary blood vessels"
+S25422,"Major laceration of left pulmonary blood vessels"
+S25429,"Major laceration of unspecified pulmonary blood vessels"
+S2549,"Other specified injury of pulmonary blood vessels"
+S25491,"Other specified injury of right pulmonary blood vessels"
+S25492,"Other specified injury of left pulmonary blood vessels"
+S25499,"Other specified injury of unspecified pulmonary blood vessels"
+S255,"Injury of intercostal blood vessels"
+S2550,"Unspecified injury of intercostal blood vessels"
+S25501,"Unspecified injury of intercostal blood vessels, right side"
+S25502,"Unspecified injury of intercostal blood vessels, left side"
+S25509,"Unspecified injury of intercostal blood vessels, unspecified side"
+S2551,"Laceration of intercostal blood vessels"
+S25511,"Laceration of intercostal blood vessels, right side"
+S25512,"Laceration of intercostal blood vessels, left side"
+S25519,"Laceration of intercostal blood vessels, unspecified side"
+S2559,"Other specified injury of intercostal blood vessels"
+S25591,"Other specified injury of intercostal blood vessels, right side"
+S25592,"Other specified injury of intercostal blood vessels, left side"
+S25599,"Other specified injury of intercostal blood vessels, unspecified side"
+S258,"Injury of other blood vessels of thorax"
+S2580,"Unspecified injury of other blood vessels of thorax"
+S25801,"Unspecified injury of other blood vessels of thorax, right side"
+S25802,"Unspecified injury of other blood vessels of thorax, left side"
+S25809,"Unspecified injury of other blood vessels of thorax, unspecified side"
+S2581,"Laceration of other blood vessels of thorax"
+S25811,"Laceration of other blood vessels of thorax, right side"
+S25812,"Laceration of other blood vessels of thorax, left side"
+S25819,"Laceration of other blood vessels of thorax, unspecified side"
+S2589,"Other specified injury of other blood vessels of thorax"
+S25891,"Other specified injury of other blood vessels of thorax, right side"
+S25892,"Other specified injury of other blood vessels of thorax, left side"
+S25899,"Other specified injury of other blood vessels of thorax, unspecified side"
+S259,"Injury of unspecified blood vessel of thorax"
+S2590,"Unspecified injury of unspecified blood vessel of thorax"
+S2591,"Laceration of unspecified blood vessel of thorax"
+S2599,"Other specified injury of unspecified blood vessel of thorax"
+S26,"Injury of heart"
+S260,"Injury of heart with hemopericardium"
+S2600,"Unspecified injury of heart with hemopericardium"
+S2601,"Contusion of heart with hemopericardium"
+S2602,"Laceration of heart with hemopericardium"
+S26020,"Mild laceration of heart with hemopericardium"
+S26021,"Moderate laceration of heart with hemopericardium"
+S26022,"Major laceration of heart with hemopericardium"
+S2609,"Other injury of heart with hemopericardium"
+S261,"Injury of heart without hemopericardium"
+S2610,"Unspecified injury of heart without hemopericardium"
+S2611,"Contusion of heart without hemopericardium"
+S2612,"Laceration of heart without hemopericardium"
+S2619,"Other injury of heart without hemopericardium"
+S269,"Injury of heart, unspecified with or without hemopericardium"
+S2690,"Unspecified injury of heart, unspecified with or without hemopericardium"
+S2691,"Contusion of heart, unspecified with or without hemopericardium"
+S2692,"Laceration of heart, unspecified with or without hemopericardium"
+S2699,"Other injury of heart, unspecified with or without hemopericardium"
+S27,"Injury of other and unspecified intrathoracic organs"
+S270,"Traumatic pneumothorax"
+S271,"Traumatic hemothorax"
+S272,"Traumatic hemopneumothorax"
+S273,"Other and unspecified injuries of lung"
+S2730,"Unspecified injury of lung"
+S27301,"Unspecified injury of lung, unilateral"
+S27302,"Unspecified injury of lung, bilateral"
+S27309,"Unspecified injury of lung, unspecified"
+S2731,"Primary blast injury of lung"
+S27311,"Primary blast injury of lung, unilateral"
+S27312,"Primary blast injury of lung, bilateral"
+S27319,"Primary blast injury of lung, unspecified"
+S2732,"Contusion of lung"
+S27321,"Contusion of lung, unilateral"
+S27322,"Contusion of lung, bilateral"
+S27329,"Contusion of lung, unspecified"
+S2733,"Laceration of lung"
+S27331,"Laceration of lung, unilateral"
+S27332,"Laceration of lung, bilateral"
+S27339,"Laceration of lung, unspecified"
+S2739,"Other injuries of lung"
+S27391,"Other injuries of lung, unilateral"
+S27392,"Other injuries of lung, bilateral"
+S27399,"Other injuries of lung, unspecified"
+S274,"Injury of bronchus"
+S2740,"Unspecified injury of bronchus"
+S27401,"Unspecified injury of bronchus, unilateral"
+S27402,"Unspecified injury of bronchus, bilateral"
+S27409,"Unspecified injury of bronchus, unspecified"
+S2741,"Primary blast injury of bronchus"
+S27411,"Primary blast injury of bronchus, unilateral"
+S27412,"Primary blast injury of bronchus, bilateral"
+S27419,"Primary blast injury of bronchus, unspecified"
+S2742,"Contusion of bronchus"
+S27421,"Contusion of bronchus, unilateral"
+S27422,"Contusion of bronchus, bilateral"
+S27429,"Contusion of bronchus, unspecified"
+S2743,"Laceration of bronchus"
+S27431,"Laceration of bronchus, unilateral"
+S27432,"Laceration of bronchus, bilateral"
+S27439,"Laceration of bronchus, unspecified"
+S2749,"Other injury of bronchus"
+S27491,"Other injury of bronchus, unilateral"
+S27492,"Other injury of bronchus, bilateral"
+S27499,"Other injury of bronchus, unspecified"
+S275,"Injury of thoracic trachea"
+S2750,"Unspecified injury of thoracic trachea"
+S2751,"Primary blast injury of thoracic trachea"
+S2752,"Contusion of thoracic trachea"
+S2753,"Laceration of thoracic trachea"
+S2759,"Other injury of thoracic trachea"
+S276,"Injury of pleura"
+S2760,"Unspecified injury of pleura"
+S2763,"Laceration of pleura"
+S2769,"Other injury of pleura"
+S278,"Injury of other specified intrathoracic organs"
+S2780,"Injury of diaphragm"
+S27802,"Contusion of diaphragm"
+S27803,"Laceration of diaphragm"
+S27808,"Other injury of diaphragm"
+S27809,"Unspecified injury of diaphragm"
+S2781,"Injury of esophagus (thoracic part)"
+S27812,"Contusion of esophagus (thoracic part)"
+S27813,"Laceration of esophagus (thoracic part)"
+S27818,"Other injury of esophagus (thoracic part)"
+S27819,"Unspecified injury of esophagus (thoracic part)"
+S2789,"Injury of other specified intrathoracic organs"
+S27892,"Contusion of other specified intrathoracic organs"
+S27893,"Laceration of other specified intrathoracic organs"
+S27898,"Other injury of other specified intrathoracic organs"
+S27899,"Unspecified injury of other specified intrathoracic organs"
+S279,"Injury of unspecified intrathoracic organ"
+S28,"Crushing injury of thorax, and traumatic amputation of part of thorax"
+S280,"Crushed chest"
+S281,"Traumatic amputation (partial) of part of thorax, except breast"
+S282,"Traumatic amputation of breast"
+S2821,"Complete traumatic amputation of breast"
+S28211,"Complete traumatic amputation of right breast"
+S28212,"Complete traumatic amputation of left breast"
+S28219,"Complete traumatic amputation of unspecified breast"
+S2822,"Partial traumatic amputation of breast"
+S28221,"Partial traumatic amputation of right breast"
+S28222,"Partial traumatic amputation of left breast"
+S28229,"Partial traumatic amputation of unspecified breast"
+S29,"Other and unspecified injuries of thorax"
+S290,"Injury of muscle and tendon at thorax level"
+S2900,"Unspecified injury of muscle and tendon of thorax"
+S29001,"Unspecified injury of muscle and tendon of front wall of thorax"
+S29002,"Unspecified injury of muscle and tendon of back wall of thorax"
+S29009,"Unspecified injury of muscle and tendon of unspecified wall of thorax"
+S2901,"Strain of muscle and tendon of thorax"
+S29011,"Strain of muscle and tendon of front wall of thorax"
+S29012,"Strain of muscle and tendon of back wall of thorax"
+S29019,"Strain of muscle and tendon of unspecified wall of thorax"
+S2902,"Laceration of muscle and tendon of thorax"
+S29021,"Laceration of muscle and tendon of front wall of thorax"
+S29022,"Laceration of muscle and tendon of back wall of thorax"
+S29029,"Laceration of muscle and tendon of unspecified wall of thorax"
+S2909,"Other injury of muscle and tendon of thorax"
+S29091,"Other injury of muscle and tendon of front wall of thorax"
+S29092,"Other injury of muscle and tendon of back wall of thorax"
+S29099,"Other injury of muscle and tendon of unspecified wall of thorax"
+S298,"Other specified injuries of thorax"
+S299,"Unspecified injury of thorax"
+S30,"Superficial injury of abdomen, lower back, pelvis and external genitals"
+S300,"Contusion of lower back and pelvis"
+S301,"Contusion of abdominal wall"
+S302,"Contusion of external genital organs"
+S3020,"Contusion of unspecified external genital organ"
+S30201,"Contusion of unspecified external genital organ, male"
+S30202,"Contusion of unspecified external genital organ, female"
+S3021,"Contusion of penis"
+S3022,"Contusion of scrotum and testes"
+S3023,"Contusion of vagina and vulva"
+S303,"Contusion of anus"
+S308,"Other superficial injuries of abdomen, lower back, pelvis and external genitals"
+S3081,"Abrasion of abdomen, lower back, pelvis and external genitals"
+S30810,"Abrasion of lower back and pelvis"
+S30811,"Abrasion of abdominal wall"
+S30812,"Abrasion of penis"
+S30813,"Abrasion of scrotum and testes"
+S30814,"Abrasion of vagina and vulva"
+S30815,"Abrasion of unspecified external genital organs, male"
+S30816,"Abrasion of unspecified external genital organs, female"
+S30817,"Abrasion of anus"
+S3082,"Blister (nonthermal) of abdomen, lower back, pelvis and external genitals"
+S30820,"Blister (nonthermal) of lower back and pelvis"
+S30821,"Blister (nonthermal) of abdominal wall"
+S30822,"Blister (nonthermal) of penis"
+S30823,"Blister (nonthermal) of scrotum and testes"
+S30824,"Blister (nonthermal) of vagina and vulva"
+S30825,"Blister (nonthermal) of unspecified external genital organs, male"
+S30826,"Blister (nonthermal) of unspecified external genital organs, female"
+S30827,"Blister (nonthermal) of anus"
+S3084,"External constriction of abdomen, lower back, pelvis and external genitals"
+S30840,"External constriction of lower back and pelvis"
+S30841,"External constriction of abdominal wall"
+S30842,"External constriction of penis"
+S30843,"External constriction of scrotum and testes"
+S30844,"External constriction of vagina and vulva"
+S30845,"External constriction of unspecified external genital organs, male"
+S30846,"External constriction of unspecified external genital organs, female"
+S3085,"Superficial foreign body of abdomen, lower back, pelvis and external genitals"
+S30850,"Superficial foreign body of lower back and pelvis"
+S30851,"Superficial foreign body of abdominal wall"
+S30852,"Superficial foreign body of penis"
+S30853,"Superficial foreign body of scrotum and testes"
+S30854,"Superficial foreign body of vagina and vulva"
+S30855,"Superficial foreign body of unspecified external genital organs, male"
+S30856,"Superficial foreign body of unspecified external genital organs, female"
+S30857,"Superficial foreign body of anus"
+S3086,"Insect bite (nonvenomous) of abdomen, lower back, pelvis and external genitals"
+S30860,"Insect bite (nonvenomous) of lower back and pelvis"
+S30861,"Insect bite (nonvenomous) of abdominal wall"
+S30862,"Insect bite (nonvenomous) of penis"
+S30863,"Insect bite (nonvenomous) of scrotum and testes"
+S30864,"Insect bite (nonvenomous) of vagina and vulva"
+S30865,"Insect bite (nonvenomous) of unspecified external genital organs, male"
+S30866,"Insect bite (nonvenomous) of unspecified external genital organs, female"
+S30867,"Insect bite (nonvenomous) of anus"
+S3087,"Other superficial bite of abdomen, lower back, pelvis and external genitals"
+S30870,"Other superficial bite of lower back and pelvis"
+S30871,"Other superficial bite of abdominal wall"
+S30872,"Other superficial bite of penis"
+S30873,"Other superficial bite of scrotum and testes"
+S30874,"Other superficial bite of vagina and vulva"
+S30875,"Other superficial bite of unspecified external genital organs, male"
+S30876,"Other superficial bite of unspecified external genital organs, female"
+S30877,"Other superficial bite of anus"
+S309,"Unspecified superficial injury of abdomen, lower back, pelvis and external genitals"
+S3091,"Unspecified superficial injury of lower back and pelvis"
+S3092,"Unspecified superficial injury of abdominal wall"
+S3093,"Unspecified superficial injury of penis"
+S3094,"Unspecified superficial injury of scrotum and testes"
+S3095,"Unspecified superficial injury of vagina and vulva"
+S3096,"Unspecified superficial injury of unspecified external genital organs, male"
+S3097,"Unspecified superficial injury of unspecified external genital organs, female"
+S3098,"Unspecified superficial injury of anus"
+S31,"Open wound of abdomen, lower back, pelvis and external genitals"
+S310,"Open wound of lower back and pelvis"
+S3100,"Unspecified open wound of lower back and pelvis"
+S31000,"Unspecified open wound of lower back and pelvis without penetration into retroperitoneum"
+S31001,"Unspecified open wound of lower back and pelvis with penetration into retroperitoneum"
+S3101,"Laceration without foreign body of lower back and pelvis"
+S31010,"Laceration without foreign body of lower back and pelvis without penetration into retroperitoneum"
+S31011,"Laceration without foreign body of lower back and pelvis with penetration into retroperitoneum"
+S3102,"Laceration with foreign body of lower back and pelvis"
+S31020,"Laceration with foreign body of lower back and pelvis without penetration into retroperitoneum"
+S31021,"Laceration with foreign body of lower back and pelvis with penetration into retroperitoneum"
+S3103,"Puncture wound without foreign body of lower back and pelvis"
+S31030,"Puncture wound without foreign body of lower back and pelvis without penetration into retroperitoneum"
+S31031,"Puncture wound without foreign body of lower back and pelvis with penetration into retroperitoneum"
+S3104,"Puncture wound with foreign body of lower back and pelvis"
+S31040,"Puncture wound with foreign body of lower back and pelvis without penetration into retroperitoneum"
+S31041,"Puncture wound with foreign body of lower back and pelvis with penetration into retroperitoneum"
+S3105,"Open bite of lower back and pelvis"
+S31050,"Open bite of lower back and pelvis without penetration into retroperitoneum"
+S31051,"Open bite of lower back and pelvis with penetration into retroperitoneum"
+S311,"Open wound of abdominal wall without penetration into peritoneal cavity"
+S3110,"Unspecified open wound of abdominal wall without penetration into peritoneal cavity"
+S31100,"Unspecified open wound of abdominal wall, right upper quadrant without penetration into peritoneal cavity"
+S31101,"Unspecified open wound of abdominal wall, left upper quadrant without penetration into peritoneal cavity"
+S31102,"Unspecified open wound of abdominal wall, epigastric region without penetration into peritoneal cavity"
+S31103,"Unspecified open wound of abdominal wall, right lower quadrant without penetration into peritoneal cavity"
+S31104,"Unspecified open wound of abdominal wall, left lower quadrant without penetration into peritoneal cavity"
+S31105,"Unspecified open wound of abdominal wall, periumbilic region without penetration into peritoneal cavity"
+S31109,"Unspecified open wound of abdominal wall, unspecified quadrant without penetration into peritoneal cavity"
+S3111,"Laceration without foreign body of abdominal wall without penetration into peritoneal cavity"
+S31110,"Laceration without foreign body of abdominal wall, right upper quadrant without penetration into peritoneal cavity"
+S31111,"Laceration without foreign body of abdominal wall, left upper quadrant without penetration into peritoneal cavity"
+S31112,"Laceration without foreign body of abdominal wall, epigastric region without penetration into peritoneal cavity"
+S31113,"Laceration without foreign body of abdominal wall, right lower quadrant without penetration into peritoneal cavity"
+S31114,"Laceration without foreign body of abdominal wall, left lower quadrant without penetration into peritoneal cavity"
+S31115,"Laceration without foreign body of abdominal wall, periumbilic region without penetration into peritoneal cavity"
+S31119,"Laceration without foreign body of abdominal wall, unspecified quadrant without penetration into peritoneal cavity"
+S3112,"Laceration with foreign body of abdominal wall without penetration into peritoneal cavity"
+S31120,"Laceration of abdominal wall with foreign body, right upper quadrant without penetration into peritoneal cavity"
+S31121,"Laceration of abdominal wall with foreign body, left upper quadrant without penetration into peritoneal cavity"
+S31122,"Laceration of abdominal wall with foreign body, epigastric region without penetration into peritoneal cavity"
+S31123,"Laceration of abdominal wall with foreign body, right lower quadrant without penetration into peritoneal cavity"
+S31124,"Laceration of abdominal wall with foreign body, left lower quadrant without penetration into peritoneal cavity"
+S31125,"Laceration of abdominal wall with foreign body, periumbilic region without penetration into peritoneal cavity"
+S31129,"Laceration of abdominal wall with foreign body, unspecified quadrant without penetration into peritoneal cavity"
+S3113,"Puncture wound of abdominal wall without foreign body without penetration into peritoneal cavity"
+S31130,"Puncture wound of abdominal wall without foreign body, right upper quadrant without penetration into peritoneal cavity"
+S31131,"Puncture wound of abdominal wall without foreign body, left upper quadrant without penetration into peritoneal cavity"
+S31132,"Puncture wound of abdominal wall without foreign body, epigastric region without penetration into peritoneal cavity"
+S31133,"Puncture wound of abdominal wall without foreign body, right lower quadrant without penetration into peritoneal cavity"
+S31134,"Puncture wound of abdominal wall without foreign body, left lower quadrant without penetration into peritoneal cavity"
+S31135,"Puncture wound of abdominal wall without foreign body, periumbilic region without penetration into peritoneal cavity"
+S31139,"Puncture wound of abdominal wall without foreign body, unspecified quadrant without penetration into peritoneal cavity"
+S3114,"Puncture wound of abdominal wall with foreign body without penetration into peritoneal cavity"
+S31140,"Puncture wound of abdominal wall with foreign body, right upper quadrant without penetration into peritoneal cavity"
+S31141,"Puncture wound of abdominal wall with foreign body, left upper quadrant without penetration into peritoneal cavity"
+S31142,"Puncture wound of abdominal wall with foreign body, epigastric region without penetration into peritoneal cavity"
+S31143,"Puncture wound of abdominal wall with foreign body, right lower quadrant without penetration into peritoneal cavity"
+S31144,"Puncture wound of abdominal wall with foreign body, left lower quadrant without penetration into peritoneal cavity"
+S31145,"Puncture wound of abdominal wall with foreign body, periumbilic region without penetration into peritoneal cavity"
+S31149,"Puncture wound of abdominal wall with foreign body, unspecified quadrant without penetration into peritoneal cavity"
+S3115,"Open bite of abdominal wall without penetration into peritoneal cavity"
+S31150,"Open bite of abdominal wall, right upper quadrant without penetration into peritoneal cavity"
+S31151,"Open bite of abdominal wall, left upper quadrant without penetration into peritoneal cavity"
+S31152,"Open bite of abdominal wall, epigastric region without penetration into peritoneal cavity"
+S31153,"Open bite of abdominal wall, right lower quadrant without penetration into peritoneal cavity"
+S31154,"Open bite of abdominal wall, left lower quadrant without penetration into peritoneal cavity"
+S31155,"Open bite of abdominal wall, periumbilic region without penetration into peritoneal cavity"
+S31159,"Open bite of abdominal wall, unspecified quadrant without penetration into peritoneal cavity"
+S312,"Open wound of penis"
+S3120,"Unspecified open wound of penis"
+S3121,"Laceration without foreign body of penis"
+S3122,"Laceration with foreign body of penis"
+S3123,"Puncture wound without foreign body of penis"
+S3124,"Puncture wound with foreign body of penis"
+S3125,"Open bite of penis"
+S313,"Open wound of scrotum and testes"
+S3130,"Unspecified open wound of scrotum and testes"
+S3131,"Laceration without foreign body of scrotum and testes"
+S3132,"Laceration with foreign body of scrotum and testes"
+S3133,"Puncture wound without foreign body of scrotum and testes"
+S3134,"Puncture wound with foreign body of scrotum and testes"
+S3135,"Open bite of scrotum and testes"
+S314,"Open wound of vagina and vulva"
+S3140,"Unspecified open wound of vagina and vulva"
+S3141,"Laceration without foreign body of vagina and vulva"
+S3142,"Laceration with foreign body of vagina and vulva"
+S3143,"Puncture wound without foreign body of vagina and vulva"
+S3144,"Puncture wound with foreign body of vagina and vulva"
+S3145,"Open bite of vagina and vulva"
+S315,"Open wound of unspecified external genital organs"
+S3150,"Unspecified open wound of unspecified external genital organs"
+S31501,"Unspecified open wound of unspecified external genital organs, male"
+S31502,"Unspecified open wound of unspecified external genital organs, female"
+S3151,"Laceration without foreign body of unspecified external genital organs"
+S31511,"Laceration without foreign body of unspecified external genital organs, male"
+S31512,"Laceration without foreign body of unspecified external genital organs, female"
+S3152,"Laceration with foreign body of unspecified external genital organs"
+S31521,"Laceration with foreign body of unspecified external genital organs, male"
+S31522,"Laceration with foreign body of unspecified external genital organs, female"
+S3153,"Puncture wound without foreign body of unspecified external genital organs"
+S31531,"Puncture wound without foreign body of unspecified external genital organs, male"
+S31532,"Puncture wound without foreign body of unspecified external genital organs, female"
+S3154,"Puncture wound with foreign body of unspecified external genital organs"
+S31541,"Puncture wound with foreign body of unspecified external genital organs, male"
+S31542,"Puncture wound with foreign body of unspecified external genital organs, female"
+S3155,"Open bite of unspecified external genital organs"
+S31551,"Open bite of unspecified external genital organs, male"
+S31552,"Open bite of unspecified external genital organs, female"
+S316,"Open wound of abdominal wall with penetration into peritoneal cavity"
+S3160,"Unspecified open wound of abdominal wall with penetration into peritoneal cavity"
+S31600,"Unspecified open wound of abdominal wall, right upper quadrant with penetration into peritoneal cavity"
+S31601,"Unspecified open wound of abdominal wall, left upper quadrant with penetration into peritoneal cavity"
+S31602,"Unspecified open wound of abdominal wall, epigastric region with penetration into peritoneal cavity"
+S31603,"Unspecified open wound of abdominal wall, right lower quadrant with penetration into peritoneal cavity"
+S31604,"Unspecified open wound of abdominal wall, left lower quadrant with penetration into peritoneal cavity"
+S31605,"Unspecified open wound of abdominal wall, periumbilic region with penetration into peritoneal cavity"
+S31609,"Unspecified open wound of abdominal wall, unspecified quadrant with penetration into peritoneal cavity"
+S3161,"Laceration without foreign body of abdominal wall with penetration into peritoneal cavity"
+S31610,"Laceration without foreign body of abdominal wall, right upper quadrant with penetration into peritoneal cavity"
+S31611,"Laceration without foreign body of abdominal wall, left upper quadrant with penetration into peritoneal cavity"
+S31612,"Laceration without foreign body of abdominal wall, epigastric region with penetration into peritoneal cavity"
+S31613,"Laceration without foreign body of abdominal wall, right lower quadrant with penetration into peritoneal cavity"
+S31614,"Laceration without foreign body of abdominal wall, left lower quadrant with penetration into peritoneal cavity"
+S31615,"Laceration without foreign body of abdominal wall, periumbilic region with penetration into peritoneal cavity"
+S31619,"Laceration without foreign body of abdominal wall, unspecified quadrant with penetration into peritoneal cavity"
+S3162,"Laceration with foreign body of abdominal wall with penetration into peritoneal cavity"
+S31620,"Laceration with foreign body of abdominal wall, right upper quadrant with penetration into peritoneal cavity"
+S31621,"Laceration with foreign body of abdominal wall, left upper quadrant with penetration into peritoneal cavity"
+S31622,"Laceration with foreign body of abdominal wall, epigastric region with penetration into peritoneal cavity"
+S31623,"Laceration with foreign body of abdominal wall, right lower quadrant with penetration into peritoneal cavity"
+S31624,"Laceration with foreign body of abdominal wall, left lower quadrant with penetration into peritoneal cavity"
+S31625,"Laceration with foreign body of abdominal wall, periumbilic region with penetration into peritoneal cavity"
+S31629,"Laceration with foreign body of abdominal wall, unspecified quadrant with penetration into peritoneal cavity"
+S3163,"Puncture wound without foreign body of abdominal wall with penetration into peritoneal cavity"
+S31630,"Puncture wound without foreign body of abdominal wall, right upper quadrant with penetration into peritoneal cavity"
+S31631,"Puncture wound without foreign body of abdominal wall, left upper quadrant with penetration into peritoneal cavity"
+S31632,"Puncture wound without foreign body of abdominal wall, epigastric region with penetration into peritoneal cavity"
+S31633,"Puncture wound without foreign body of abdominal wall, right lower quadrant with penetration into peritoneal cavity"
+S31634,"Puncture wound without foreign body of abdominal wall, left lower quadrant with penetration into peritoneal cavity"
+S31635,"Puncture wound without foreign body of abdominal wall, periumbilic region with penetration into peritoneal cavity"
+S31639,"Puncture wound without foreign body of abdominal wall, unspecified quadrant with penetration into peritoneal cavity"
+S3164,"Puncture wound with foreign body of abdominal wall with penetration into peritoneal cavity"
+S31640,"Puncture wound with foreign body of abdominal wall, right upper quadrant with penetration into peritoneal cavity"
+S31641,"Puncture wound with foreign body of abdominal wall, left upper quadrant with penetration into peritoneal cavity"
+S31642,"Puncture wound with foreign body of abdominal wall, epigastric region with penetration into peritoneal cavity"
+S31643,"Puncture wound with foreign body of abdominal wall, right lower quadrant with penetration into peritoneal cavity"
+S31644,"Puncture wound with foreign body of abdominal wall, left lower quadrant with penetration into peritoneal cavity"
+S31645,"Puncture wound with foreign body of abdominal wall, periumbilic region with penetration into peritoneal cavity"
+S31649,"Puncture wound with foreign body of abdominal wall, unspecified quadrant with penetration into peritoneal cavity"
+S3165,"Open bite of abdominal wall with penetration into peritoneal cavity"
+S31650,"Open bite of abdominal wall, right upper quadrant with penetration into peritoneal cavity"
+S31651,"Open bite of abdominal wall, left upper quadrant with penetration into peritoneal cavity"
+S31652,"Open bite of abdominal wall, epigastric region with penetration into peritoneal cavity"
+S31653,"Open bite of abdominal wall, right lower quadrant with penetration into peritoneal cavity"
+S31654,"Open bite of abdominal wall, left lower quadrant with penetration into peritoneal cavity"
+S31655,"Open bite of abdominal wall, periumbilic region with penetration into peritoneal cavity"
+S31659,"Open bite of abdominal wall, unspecified quadrant with penetration into peritoneal cavity"
+S318,"Open wound of other parts of abdomen, lower back and pelvis"
+S3180,"Open wound of unspecified buttock"
+S31801,"Laceration without foreign body of unspecified buttock"
+S31802,"Laceration with foreign body of unspecified buttock"
+S31803,"Puncture wound without foreign body of unspecified buttock"
+S31804,"Puncture wound with foreign body of unspecified buttock"
+S31805,"Open bite of unspecified buttock"
+S31809,"Unspecified open wound of unspecified buttock"
+S3181,"Open wound of right buttock"
+S31811,"Laceration without foreign body of right buttock"
+S31812,"Laceration with foreign body of right buttock"
+S31813,"Puncture wound without foreign body of right buttock"
+S31814,"Puncture wound with foreign body of right buttock"
+S31815,"Open bite of right buttock"
+S31819,"Unspecified open wound of right buttock"
+S3182,"Open wound of left buttock"
+S31821,"Laceration without foreign body of left buttock"
+S31822,"Laceration with foreign body of left buttock"
+S31823,"Puncture wound without foreign body of left buttock"
+S31824,"Puncture wound with foreign body of left buttock"
+S31825,"Open bite of left buttock"
+S31829,"Unspecified open wound of left buttock"
+S3183,"Open wound of anus"
+S31831,"Laceration without foreign body of anus"
+S31832,"Laceration with foreign body of anus"
+S31833,"Puncture wound without foreign body of anus"
+S31834,"Puncture wound with foreign body of anus"
+S31835,"Open bite of anus"
+S31839,"Unspecified open wound of anus"
+S32,"Fracture of lumbar spine and pelvis"
+S320,"Fracture of lumbar vertebra"
+S3200,"Fracture of unspecified lumbar vertebra"
+S32000,"Wedge compression fracture of unspecified lumbar vertebra"
+S32001,"Stable burst fracture of unspecified lumbar vertebra"
+S32002,"Unstable burst fracture of unspecified lumbar vertebra"
+S32008,"Other fracture of unspecified lumbar vertebra"
+S32009,"Unspecified fracture of unspecified lumbar vertebra"
+S3201,"Fracture of first lumbar vertebra"
+S32010,"Wedge compression fracture of first lumbar vertebra"
+S32011,"Stable burst fracture of first lumbar vertebra"
+S32012,"Unstable burst fracture of first lumbar vertebra"
+S32018,"Other fracture of first lumbar vertebra"
+S32019,"Unspecified fracture of first lumbar vertebra"
+S3202,"Fracture of second lumbar vertebra"
+S32020,"Wedge compression fracture of second lumbar vertebra"
+S32021,"Stable burst fracture of second lumbar vertebra"
+S32022,"Unstable burst fracture of second lumbar vertebra"
+S32028,"Other fracture of second lumbar vertebra"
+S32029,"Unspecified fracture of second lumbar vertebra"
+S3203,"Fracture of third lumbar vertebra"
+S32030,"Wedge compression fracture of third lumbar vertebra"
+S32031,"Stable burst fracture of third lumbar vertebra"
+S32032,"Unstable burst fracture of third lumbar vertebra"
+S32038,"Other fracture of third lumbar vertebra"
+S32039,"Unspecified fracture of third lumbar vertebra"
+S3204,"Fracture of fourth lumbar vertebra"
+S32040,"Wedge compression fracture of fourth lumbar vertebra"
+S32041,"Stable burst fracture of fourth lumbar vertebra"
+S32042,"Unstable burst fracture of fourth lumbar vertebra"
+S32048,"Other fracture of fourth lumbar vertebra"
+S32049,"Unspecified fracture of fourth lumbar vertebra"
+S3205,"Fracture of fifth lumbar vertebra"
+S32050,"Wedge compression fracture of fifth lumbar vertebra"
+S32051,"Stable burst fracture of fifth lumbar vertebra"
+S32052,"Unstable burst fracture of fifth lumbar vertebra"
+S32058,"Other fracture of fifth lumbar vertebra"
+S32059,"Unspecified fracture of fifth lumbar vertebra"
+S321,"Fracture of sacrum"
+S3210,"Unspecified fracture of sacrum"
+S3211,"Zone I fracture of sacrum"
+S32110,"Nondisplaced Zone I fracture of sacrum"
+S32111,"Minimally displaced Zone I fracture of sacrum"
+S32112,"Severely displaced Zone I fracture of sacrum"
+S32119,"Unspecified Zone I fracture of sacrum"
+S3212,"Zone II fracture of sacrum"
+S32120,"Nondisplaced Zone II fracture of sacrum"
+S32121,"Minimally displaced Zone II fracture of sacrum"
+S32122,"Severely displaced Zone II fracture of sacrum"
+S32129,"Unspecified Zone II fracture of sacrum"
+S3213,"Zone III fracture of sacrum"
+S32130,"Nondisplaced Zone III fracture of sacrum"
+S32131,"Minimally displaced Zone III fracture of sacrum"
+S32132,"Severely displaced Zone III fracture of sacrum"
+S32139,"Unspecified Zone III fracture of sacrum"
+S3214,"Type 1 fracture of sacrum"
+S3215,"Type 2 fracture of sacrum"
+S3216,"Type 3 fracture of sacrum"
+S3217,"Type 4 fracture of sacrum"
+S3219,"Other fracture of sacrum"
+S322,"Fracture of coccyx"
+S323,"Fracture of ilium"
+S3230,"Unspecified fracture of ilium"
+S32301,"Unspecified fracture of right ilium"
+S32302,"Unspecified fracture of left ilium"
+S32309,"Unspecified fracture of unspecified ilium"
+S3231,"Avulsion fracture of ilium"
+S32311,"Displaced avulsion fracture of right ilium"
+S32312,"Displaced avulsion fracture of left ilium"
+S32313,"Displaced avulsion fracture of unspecified ilium"
+S32314,"Nondisplaced avulsion fracture of right ilium"
+S32315,"Nondisplaced avulsion fracture of left ilium"
+S32316,"Nondisplaced avulsion fracture of unspecified ilium"
+S3239,"Other fracture of ilium"
+S32391,"Other fracture of right ilium"
+S32392,"Other fracture of left ilium"
+S32399,"Other fracture of unspecified ilium"
+S324,"Fracture of acetabulum"
+S3240,"Unspecified fracture of acetabulum"
+S32401,"Unspecified fracture of right acetabulum"
+S32402,"Unspecified fracture of left acetabulum"
+S32409,"Unspecified fracture of unspecified acetabulum"
+S3241,"Fracture of anterior wall of acetabulum"
+S32411,"Displaced fracture of anterior wall of right acetabulum"
+S32412,"Displaced fracture of anterior wall of left acetabulum"
+S32413,"Displaced fracture of anterior wall of unspecified acetabulum"
+S32414,"Nondisplaced fracture of anterior wall of right acetabulum"
+S32415,"Nondisplaced fracture of anterior wall of left acetabulum"
+S32416,"Nondisplaced fracture of anterior wall of unspecified acetabulum"
+S3242,"Fracture of posterior wall of acetabulum"
+S32421,"Displaced fracture of posterior wall of right acetabulum"
+S32422,"Displaced fracture of posterior wall of left acetabulum"
+S32423,"Displaced fracture of posterior wall of unspecified acetabulum"
+S32424,"Nondisplaced fracture of posterior wall of right acetabulum"
+S32425,"Nondisplaced fracture of posterior wall of left acetabulum"
+S32426,"Nondisplaced fracture of posterior wall of unspecified acetabulum"
+S3243,"Fracture of anterior column [iliopubic] of acetabulum"
+S32431,"Displaced fracture of anterior column [iliopubic] of right acetabulum"
+S32432,"Displaced fracture of anterior column [iliopubic] of left acetabulum"
+S32433,"Displaced fracture of anterior column [iliopubic] of unspecified acetabulum"
+S32434,"Nondisplaced fracture of anterior column [iliopubic] of right acetabulum"
+S32435,"Nondisplaced fracture of anterior column [iliopubic] of left acetabulum"
+S32436,"Nondisplaced fracture of anterior column [iliopubic] of unspecified acetabulum"
+S3244,"Fracture of posterior column [ilioischial] of acetabulum"
+S32441,"Displaced fracture of posterior column [ilioischial] of right acetabulum"
+S32442,"Displaced fracture of posterior column [ilioischial] of left acetabulum"
+S32443,"Displaced fracture of posterior column [ilioischial] of unspecified acetabulum"
+S32444,"Nondisplaced fracture of posterior column [ilioischial] of right acetabulum"
+S32445,"Nondisplaced fracture of posterior column [ilioischial] of left acetabulum"
+S32446,"Nondisplaced fracture of posterior column [ilioischial] of unspecified acetabulum"
+S3245,"Transverse fracture of acetabulum"
+S32451,"Displaced transverse fracture of right acetabulum"
+S32452,"Displaced transverse fracture of left acetabulum"
+S32453,"Displaced transverse fracture of unspecified acetabulum"
+S32454,"Nondisplaced transverse fracture of right acetabulum"
+S32455,"Nondisplaced transverse fracture of left acetabulum"
+S32456,"Nondisplaced transverse fracture of unspecified acetabulum"
+S3246,"Associated transverse-posterior fracture of acetabulum"
+S32461,"Displaced associated transverse-posterior fracture of right acetabulum"
+S32462,"Displaced associated transverse-posterior fracture of left acetabulum"
+S32463,"Displaced associated transverse-posterior fracture of unspecified acetabulum"
+S32464,"Nondisplaced associated transverse-posterior fracture of right acetabulum"
+S32465,"Nondisplaced associated transverse-posterior fracture of left acetabulum"
+S32466,"Nondisplaced associated transverse-posterior fracture of unspecified acetabulum"
+S3247,"Fracture of medial wall of acetabulum"
+S32471,"Displaced fracture of medial wall of right acetabulum"
+S32472,"Displaced fracture of medial wall of left acetabulum"
+S32473,"Displaced fracture of medial wall of unspecified acetabulum"
+S32474,"Nondisplaced fracture of medial wall of right acetabulum"
+S32475,"Nondisplaced fracture of medial wall of left acetabulum"
+S32476,"Nondisplaced fracture of medial wall of unspecified acetabulum"
+S3248,"Dome fracture of acetabulum"
+S32481,"Displaced dome fracture of right acetabulum"
+S32482,"Displaced dome fracture of left acetabulum"
+S32483,"Displaced dome fracture of unspecified acetabulum"
+S32484,"Nondisplaced dome fracture of right acetabulum"
+S32485,"Nondisplaced dome fracture of left acetabulum"
+S32486,"Nondisplaced dome fracture of unspecified acetabulum"
+S3249,"Other specified fracture of acetabulum"
+S32491,"Other specified fracture of right acetabulum"
+S32492,"Other specified fracture of left acetabulum"
+S32499,"Other specified fracture of unspecified acetabulum"
+S325,"Fracture of pubis"
+S3250,"Unspecified fracture of pubis"
+S32501,"Unspecified fracture of right pubis"
+S32502,"Unspecified fracture of left pubis"
+S32509,"Unspecified fracture of unspecified pubis"
+S3251,"Fracture of superior rim of pubis"
+S32511,"Fracture of superior rim of right pubis"
+S32512,"Fracture of superior rim of left pubis"
+S32519,"Fracture of superior rim of unspecified pubis"
+S3259,"Other specified fracture of pubis"
+S32591,"Other specified fracture of right pubis"
+S32592,"Other specified fracture of left pubis"
+S32599,"Other specified fracture of unspecified pubis"
+S326,"Fracture of ischium"
+S3260,"Unspecified fracture of ischium"
+S32601,"Unspecified fracture of right ischium"
+S32602,"Unspecified fracture of left ischium"
+S32609,"Unspecified fracture of unspecified ischium"
+S3261,"Avulsion fracture of ischium"
+S32611,"Displaced avulsion fracture of right ischium"
+S32612,"Displaced avulsion fracture of left ischium"
+S32613,"Displaced avulsion fracture of unspecified ischium"
+S32614,"Nondisplaced avulsion fracture of right ischium"
+S32615,"Nondisplaced avulsion fracture of left ischium"
+S32616,"Nondisplaced avulsion fracture of unspecified ischium"
+S3269,"Other specified fracture of ischium"
+S32691,"Other specified fracture of right ischium"
+S32692,"Other specified fracture of left ischium"
+S32699,"Other specified fracture of unspecified ischium"
+S328,"Fracture of other parts of pelvis"
+S3281,"Multiple fractures of pelvis with disruption of pelvic ring"
+S32810,"Multiple fractures of pelvis with stable disruption of pelvic ring"
+S32811,"Multiple fractures of pelvis with unstable disruption of pelvic ring"
+S3282,"Multiple fractures of pelvis without disruption of pelvic ring"
+S3289,"Fracture of other parts of pelvis"
+S329,"Fracture of unspecified parts of lumbosacral spine and pelvis"
+S33,"Dislocation and sprain of joints and ligaments of lumbar spine and pelvis"
+S330,"Traumatic rupture of lumbar intervertebral disc"
+S331,"Subluxation and dislocation of lumbar vertebra"
+S3310,"Subluxation and dislocation of unspecified lumbar vertebra"
+S33100,"Subluxation of unspecified lumbar vertebra"
+S33101,"Dislocation of unspecified lumbar vertebra"
+S3311,"Subluxation and dislocation of L1/L2 lumbar vertebra"
+S33110,"Subluxation of L1/L2 lumbar vertebra"
+S33111,"Dislocation of L1/L2 lumbar vertebra"
+S3312,"Subluxation and dislocation of L2/L3 lumbar vertebra"
+S33120,"Subluxation of L2/L3 lumbar vertebra"
+S33121,"Dislocation of L2/L3 lumbar vertebra"
+S3313,"Subluxation and dislocation of L3/L4 lumbar vertebra"
+S33130,"Subluxation of L3/L4 lumbar vertebra"
+S33131,"Dislocation of L3/L4 lumbar vertebra"
+S3314,"Subluxation and dislocation of L4/L5 lumbar vertebra"
+S33140,"Subluxation of L4/L5 lumbar vertebra"
+S33141,"Dislocation of L4/L5 lumbar vertebra"
+S332,"Dislocation of sacroiliac and sacrococcygeal joint"
+S333,"Dislocation of other and unspecified parts of lumbar spine and pelvis"
+S3330,"Dislocation of unspecified parts of lumbar spine and pelvis"
+S3339,"Dislocation of other parts of lumbar spine and pelvis"
+S334,"Traumatic rupture of symphysis pubis"
+S335,"Sprain of ligaments of lumbar spine"
+S336,"Sprain of sacroiliac joint"
+S338,"Sprain of other parts of lumbar spine and pelvis"
+S339,"Sprain of unspecified parts of lumbar spine and pelvis"
+S34,"Injury of lumbar and sacral spinal cord and nerves at abdomen, lower back and pelvis level"
+S340,"Concussion and edema of lumbar and sacral spinal cord"
+S3401,"Concussion and edema of lumbar spinal cord"
+S3402,"Concussion and edema of sacral spinal cord"
+S341,"Other and unspecified injury of lumbar and sacral spinal cord"
+S3410,"Unspecified injury to lumbar spinal cord"
+S34101,"Unspecified injury to L1 level of lumbar spinal cord"
+S34102,"Unspecified injury to L2 level of lumbar spinal cord"
+S34103,"Unspecified injury to L3 level of lumbar spinal cord"
+S34104,"Unspecified injury to L4 level of lumbar spinal cord"
+S34105,"Unspecified injury to L5 level of lumbar spinal cord"
+S34109,"Unspecified injury to unspecified level of lumbar spinal cord"
+S3411,"Complete lesion of lumbar spinal cord"
+S34111,"Complete lesion of L1 level of lumbar spinal cord"
+S34112,"Complete lesion of L2 level of lumbar spinal cord"
+S34113,"Complete lesion of L3 level of lumbar spinal cord"
+S34114,"Complete lesion of L4 level of lumbar spinal cord"
+S34115,"Complete lesion of L5 level of lumbar spinal cord"
+S34119,"Complete lesion of unspecified level of lumbar spinal cord"
+S3412,"Incomplete lesion of lumbar spinal cord"
+S34121,"Incomplete lesion of L1 level of lumbar spinal cord"
+S34122,"Incomplete lesion of L2 level of lumbar spinal cord"
+S34123,"Incomplete lesion of L3 level of lumbar spinal cord"
+S34124,"Incomplete lesion of L4 level of lumbar spinal cord"
+S34125,"Incomplete lesion of L5 level of lumbar spinal cord"
+S34129,"Incomplete lesion of unspecified level of lumbar spinal cord"
+S3413,"Other and unspecified injury to sacral spinal cord"
+S34131,"Complete lesion of sacral spinal cord"
+S34132,"Incomplete lesion of sacral spinal cord"
+S34139,"Unspecified injury to sacral spinal cord"
+S342,"Injury of nerve root of lumbar and sacral spine"
+S3421,"Injury of nerve root of lumbar spine"
+S3422,"Injury of nerve root of sacral spine"
+S343,"Injury of cauda equina"
+S344,"Injury of lumbosacral plexus"
+S345,"Injury of lumbar, sacral and pelvic sympathetic nerves"
+S346,"Injury of peripheral nerve(s) at abdomen, lower back and pelvis level"
+S348,"Injury of other nerves at abdomen, lower back and pelvis level"
+S349,"Injury of unspecified nerves at abdomen, lower back and pelvis level"
+S35,"Injury of blood vessels at abdomen, lower back and pelvis level"
+S350,"Injury of abdominal aorta"
+S3500,"Unspecified injury of abdominal aorta"
+S3501,"Minor laceration of abdominal aorta"
+S3502,"Major laceration of abdominal aorta"
+S3509,"Other injury of abdominal aorta"
+S351,"Injury of inferior vena cava"
+S3510,"Unspecified injury of inferior vena cava"
+S3511,"Minor laceration of inferior vena cava"
+S3512,"Major laceration of inferior vena cava"
+S3519,"Other injury of inferior vena cava"
+S352,"Injury of celiac or mesenteric artery and branches"
+S3521,"Injury of celiac artery"
+S35211,"Minor laceration of celiac artery"
+S35212,"Major laceration of celiac artery"
+S35218,"Other injury of celiac artery"
+S35219,"Unspecified injury of celiac artery"
+S3522,"Injury of superior mesenteric artery"
+S35221,"Minor laceration of superior mesenteric artery"
+S35222,"Major laceration of superior mesenteric artery"
+S35228,"Other injury of superior mesenteric artery"
+S35229,"Unspecified injury of superior mesenteric artery"
+S3523,"Injury of inferior mesenteric artery"
+S35231,"Minor laceration of inferior mesenteric artery"
+S35232,"Major laceration of inferior mesenteric artery"
+S35238,"Other injury of inferior mesenteric artery"
+S35239,"Unspecified injury of inferior mesenteric artery"
+S3529,"Injury of branches of celiac and mesenteric artery"
+S35291,"Minor laceration of branches of celiac and mesenteric artery"
+S35292,"Major laceration of branches of celiac and mesenteric artery"
+S35298,"Other injury of branches of celiac and mesenteric artery"
+S35299,"Unspecified injury of branches of celiac and mesenteric artery"
+S353,"Injury of portal or splenic vein and branches"
+S3531,"Injury of portal vein"
+S35311,"Laceration of portal vein"
+S35318,"Other specified injury of portal vein"
+S35319,"Unspecified injury of portal vein"
+S3532,"Injury of splenic vein"
+S35321,"Laceration of splenic vein"
+S35328,"Other specified injury of splenic vein"
+S35329,"Unspecified injury of splenic vein"
+S3533,"Injury of superior mesenteric vein"
+S35331,"Laceration of superior mesenteric vein"
+S35338,"Other specified injury of superior mesenteric vein"
+S35339,"Unspecified injury of superior mesenteric vein"
+S3534,"Injury of inferior mesenteric vein"
+S35341,"Laceration of inferior mesenteric vein"
+S35348,"Other specified injury of inferior mesenteric vein"
+S35349,"Unspecified injury of inferior mesenteric vein"
+S354,"Injury of renal blood vessels"
+S3540,"Unspecified injury of renal blood vessel"
+S35401,"Unspecified injury of right renal artery"
+S35402,"Unspecified injury of left renal artery"
+S35403,"Unspecified injury of unspecified renal artery"
+S35404,"Unspecified injury of right renal vein"
+S35405,"Unspecified injury of left renal vein"
+S35406,"Unspecified injury of unspecified renal vein"
+S3541,"Laceration of renal blood vessel"
+S35411,"Laceration of right renal artery"
+S35412,"Laceration of left renal artery"
+S35413,"Laceration of unspecified renal artery"
+S35414,"Laceration of right renal vein"
+S35415,"Laceration of left renal vein"
+S35416,"Laceration of unspecified renal vein"
+S3549,"Other specified injury of renal blood vessel"
+S35491,"Other specified injury of right renal artery"
+S35492,"Other specified injury of left renal artery"
+S35493,"Other specified injury of unspecified renal artery"
+S35494,"Other specified injury of right renal vein"
+S35495,"Other specified injury of left renal vein"
+S35496,"Other specified injury of unspecified renal vein"
+S355,"Injury of iliac blood vessels"
+S3550,"Injury of unspecified iliac blood vessel(s)"
+S3551,"Injury of iliac artery or vein"
+S35511,"Injury of right iliac artery"
+S35512,"Injury of left iliac artery"
+S35513,"Injury of unspecified iliac artery"
+S35514,"Injury of right iliac vein"
+S35515,"Injury of left iliac vein"
+S35516,"Injury of unspecified iliac vein"
+S3553,"Injury of uterine artery or vein"
+S35531,"Injury of right uterine artery"
+S35532,"Injury of left uterine artery"
+S35533,"Injury of unspecified uterine artery"
+S35534,"Injury of right uterine vein"
+S35535,"Injury of left uterine vein"
+S35536,"Injury of unspecified uterine vein"
+S3559,"Injury of other iliac blood vessels"
+S358,"Injury of other blood vessels at abdomen, lower back and pelvis level"
+S358X,"Injury of other blood vessels at abdomen, lower back and pelvis level"
+S358X1,"Laceration of other blood vessels at abdomen, lower back and pelvis level"
+S358X8,"Other specified injury of other blood vessels at abdomen, lower back and pelvis level"
+S358X9,"Unspecified injury of other blood vessels at abdomen, lower back and pelvis level"
+S359,"Injury of unspecified blood vessel at abdomen, lower back and pelvis level"
+S3590,"Unspecified injury of unspecified blood vessel at abdomen, lower back and pelvis level"
+S3591,"Laceration of unspecified blood vessel at abdomen, lower back and pelvis level"
+S3599,"Other specified injury of unspecified blood vessel at abdomen, lower back and pelvis level"
+S36,"Injury of intra-abdominal organs"
+S360,"Injury of spleen"
+S3600,"Unspecified injury of spleen"
+S3602,"Contusion of spleen"
+S36020,"Minor contusion of spleen"
+S36021,"Major contusion of spleen"
+S36029,"Unspecified contusion of spleen"
+S3603,"Laceration of spleen"
+S36030,"Superficial (capsular) laceration of spleen"
+S36031,"Moderate laceration of spleen"
+S36032,"Major laceration of spleen"
+S36039,"Unspecified laceration of spleen"
+S3609,"Other injury of spleen"
+S361,"Injury of liver and gallbladder and bile duct"
+S3611,"Injury of liver"
+S36112,"Contusion of liver"
+S36113,"Laceration of liver, unspecified degree"
+S36114,"Minor laceration of liver"
+S36115,"Moderate laceration of liver"
+S36116,"Major laceration of liver"
+S36118,"Other injury of liver"
+S36119,"Unspecified injury of liver"
+S3612,"Injury of gallbladder"
+S36122,"Contusion of gallbladder"
+S36123,"Laceration of gallbladder"
+S36128,"Other injury of gallbladder"
+S36129,"Unspecified injury of gallbladder"
+S3613,"Injury of bile duct"
+S362,"Injury of pancreas"
+S3620,"Unspecified injury of pancreas"
+S36200,"Unspecified injury of head of pancreas"
+S36201,"Unspecified injury of body of pancreas"
+S36202,"Unspecified injury of tail of pancreas"
+S36209,"Unspecified injury of unspecified part of pancreas"
+S3622,"Contusion of pancreas"
+S36220,"Contusion of head of pancreas"
+S36221,"Contusion of body of pancreas"
+S36222,"Contusion of tail of pancreas"
+S36229,"Contusion of unspecified part of pancreas"
+S3623,"Laceration of pancreas, unspecified degree"
+S36230,"Laceration of head of pancreas, unspecified degree"
+S36231,"Laceration of body of pancreas, unspecified degree"
+S36232,"Laceration of tail of pancreas, unspecified degree"
+S36239,"Laceration of unspecified part of pancreas, unspecified degree"
+S3624,"Minor laceration of pancreas"
+S36240,"Minor laceration of head of pancreas"
+S36241,"Minor laceration of body of pancreas"
+S36242,"Minor laceration of tail of pancreas"
+S36249,"Minor laceration of unspecified part of pancreas"
+S3625,"Moderate laceration of pancreas"
+S36250,"Moderate laceration of head of pancreas"
+S36251,"Moderate laceration of body of pancreas"
+S36252,"Moderate laceration of tail of pancreas"
+S36259,"Moderate laceration of unspecified part of pancreas"
+S3626,"Major laceration of pancreas"
+S36260,"Major laceration of head of pancreas"
+S36261,"Major laceration of body of pancreas"
+S36262,"Major laceration of tail of pancreas"
+S36269,"Major laceration of unspecified part of pancreas"
+S3629,"Other injury of pancreas"
+S36290,"Other injury of head of pancreas"
+S36291,"Other injury of body of pancreas"
+S36292,"Other injury of tail of pancreas"
+S36299,"Other injury of unspecified part of pancreas"
+S363,"Injury of stomach"
+S3630,"Unspecified injury of stomach"
+S3632,"Contusion of stomach"
+S3633,"Laceration of stomach"
+S3639,"Other injury of stomach"
+S364,"Injury of small intestine"
+S3640,"Unspecified injury of small intestine"
+S36400,"Unspecified injury of duodenum"
+S36408,"Unspecified injury of other part of small intestine"
+S36409,"Unspecified injury of unspecified part of small intestine"
+S3641,"Primary blast injury of small intestine"
+S36410,"Primary blast injury of duodenum"
+S36418,"Primary blast injury of other part of small intestine"
+S36419,"Primary blast injury of unspecified part of small intestine"
+S3642,"Contusion of small intestine"
+S36420,"Contusion of duodenum"
+S36428,"Contusion of other part of small intestine"
+S36429,"Contusion of unspecified part of small intestine"
+S3643,"Laceration of small intestine"
+S36430,"Laceration of duodenum"
+S36438,"Laceration of other part of small intestine"
+S36439,"Laceration of unspecified part of small intestine"
+S3649,"Other injury of small intestine"
+S36490,"Other injury of duodenum"
+S36498,"Other injury of other part of small intestine"
+S36499,"Other injury of unspecified part of small intestine"
+S365,"Injury of colon"
+S3650,"Unspecified injury of colon"
+S36500,"Unspecified injury of ascending [right] colon"
+S36501,"Unspecified injury of transverse colon"
+S36502,"Unspecified injury of descending [left] colon"
+S36503,"Unspecified injury of sigmoid colon"
+S36508,"Unspecified injury of other part of colon"
+S36509,"Unspecified injury of unspecified part of colon"
+S3651,"Primary blast injury of colon"
+S36510,"Primary blast injury of ascending [right] colon"
+S36511,"Primary blast injury of transverse colon"
+S36512,"Primary blast injury of descending [left] colon"
+S36513,"Primary blast injury of sigmoid colon"
+S36518,"Primary blast injury of other part of colon"
+S36519,"Primary blast injury of unspecified part of colon"
+S3652,"Contusion of colon"
+S36520,"Contusion of ascending [right] colon"
+S36521,"Contusion of transverse colon"
+S36522,"Contusion of descending [left] colon"
+S36523,"Contusion of sigmoid colon"
+S36528,"Contusion of other part of colon"
+S36529,"Contusion of unspecified part of colon"
+S3653,"Laceration of colon"
+S36530,"Laceration of ascending [right] colon"
+S36531,"Laceration of transverse colon"
+S36532,"Laceration of descending [left] colon"
+S36533,"Laceration of sigmoid colon"
+S36538,"Laceration of other part of colon"
+S36539,"Laceration of unspecified part of colon"
+S3659,"Other injury of colon"
+S36590,"Other injury of ascending [right] colon"
+S36591,"Other injury of transverse colon"
+S36592,"Other injury of descending [left] colon"
+S36593,"Other injury of sigmoid colon"
+S36598,"Other injury of other part of colon"
+S36599,"Other injury of unspecified part of colon"
+S366,"Injury of rectum"
+S3660,"Unspecified injury of rectum"
+S3661,"Primary blast injury of rectum"
+S3662,"Contusion of rectum"
+S3663,"Laceration of rectum"
+S3669,"Other injury of rectum"
+S368,"Injury of other intra-abdominal organs"
+S3681,"Injury of peritoneum"
+S3689,"Injury of other intra-abdominal organs"
+S36892,"Contusion of other intra-abdominal organs"
+S36893,"Laceration of other intra-abdominal organs"
+S36898,"Other injury of other intra-abdominal organs"
+S36899,"Unspecified injury of other intra-abdominal organs"
+S369,"Injury of unspecified intra-abdominal organ"
+S3690,"Unspecified injury of unspecified intra-abdominal organ"
+S3692,"Contusion of unspecified intra-abdominal organ"
+S3693,"Laceration of unspecified intra-abdominal organ"
+S3699,"Other injury of unspecified intra-abdominal organ"
+S37,"Injury of urinary and pelvic organs"
+S370,"Injury of kidney"
+S3700,"Unspecified injury of kidney"
+S37001,"Unspecified injury of right kidney"
+S37002,"Unspecified injury of left kidney"
+S37009,"Unspecified injury of unspecified kidney"
+S3701,"Minor contusion of kidney"
+S37011,"Minor contusion of right kidney"
+S37012,"Minor contusion of left kidney"
+S37019,"Minor contusion of unspecified kidney"
+S3702,"Major contusion of kidney"
+S37021,"Major contusion of right kidney"
+S37022,"Major contusion of left kidney"
+S37029,"Major contusion of unspecified kidney"
+S3703,"Laceration of kidney, unspecified degree"
+S37031,"Laceration of right kidney, unspecified degree"
+S37032,"Laceration of left kidney, unspecified degree"
+S37039,"Laceration of unspecified kidney, unspecified degree"
+S3704,"Minor laceration of kidney"
+S37041,"Minor laceration of right kidney"
+S37042,"Minor laceration of left kidney"
+S37049,"Minor laceration of unspecified kidney"
+S3705,"Moderate laceration of kidney"
+S37051,"Moderate laceration of right kidney"
+S37052,"Moderate laceration of left kidney"
+S37059,"Moderate laceration of unspecified kidney"
+S3706,"Major laceration of kidney"
+S37061,"Major laceration of right kidney"
+S37062,"Major laceration of left kidney"
+S37069,"Major laceration of unspecified kidney"
+S3709,"Other injury of kidney"
+S37091,"Other injury of right kidney"
+S37092,"Other injury of left kidney"
+S37099,"Other injury of unspecified kidney"
+S371,"Injury of ureter"
+S3710,"Unspecified injury of ureter"
+S3712,"Contusion of ureter"
+S3713,"Laceration of ureter"
+S3719,"Other injury of ureter"
+S372,"Injury of bladder"
+S3720,"Unspecified injury of bladder"
+S3722,"Contusion of bladder"
+S3723,"Laceration of bladder"
+S3729,"Other injury of bladder"
+S373,"Injury of urethra"
+S3730,"Unspecified injury of urethra"
+S3732,"Contusion of urethra"
+S3733,"Laceration of urethra"
+S3739,"Other injury of urethra"
+S374,"Injury of ovary"
+S3740,"Unspecified injury of ovary"
+S37401,"Unspecified injury of ovary, unilateral"
+S37402,"Unspecified injury of ovary, bilateral"
+S37409,"Unspecified injury of ovary, unspecified"
+S3742,"Contusion of ovary"
+S37421,"Contusion of ovary, unilateral"
+S37422,"Contusion of ovary, bilateral"
+S37429,"Contusion of ovary, unspecified"
+S3743,"Laceration of ovary"
+S37431,"Laceration of ovary, unilateral"
+S37432,"Laceration of ovary, bilateral"
+S37439,"Laceration of ovary, unspecified"
+S3749,"Other injury of ovary"
+S37491,"Other injury of ovary, unilateral"
+S37492,"Other injury of ovary, bilateral"
+S37499,"Other injury of ovary, unspecified"
+S375,"Injury of fallopian tube"
+S3750,"Unspecified injury of fallopian tube"
+S37501,"Unspecified injury of fallopian tube, unilateral"
+S37502,"Unspecified injury of fallopian tube, bilateral"
+S37509,"Unspecified injury of fallopian tube, unspecified"
+S3751,"Primary blast injury of fallopian tube"
+S37511,"Primary blast injury of fallopian tube, unilateral"
+S37512,"Primary blast injury of fallopian tube, bilateral"
+S37519,"Primary blast injury of fallopian tube, unspecified"
+S3752,"Contusion of fallopian tube"
+S37521,"Contusion of fallopian tube, unilateral"
+S37522,"Contusion of fallopian tube, bilateral"
+S37529,"Contusion of fallopian tube, unspecified"
+S3753,"Laceration of fallopian tube"
+S37531,"Laceration of fallopian tube, unilateral"
+S37532,"Laceration of fallopian tube, bilateral"
+S37539,"Laceration of fallopian tube, unspecified"
+S3759,"Other injury of fallopian tube"
+S37591,"Other injury of fallopian tube, unilateral"
+S37592,"Other injury of fallopian tube, bilateral"
+S37599,"Other injury of fallopian tube, unspecified"
+S376,"Injury of uterus"
+S3760,"Unspecified injury of uterus"
+S3762,"Contusion of uterus"
+S3763,"Laceration of uterus"
+S3769,"Other injury of uterus"
+S378,"Injury of other urinary and pelvic organs"
+S3781,"Injury of adrenal gland"
+S37812,"Contusion of adrenal gland"
+S37813,"Laceration of adrenal gland"
+S37818,"Other injury of adrenal gland"
+S37819,"Unspecified injury of adrenal gland"
+S3782,"Injury of prostate"
+S37822,"Contusion of prostate"
+S37823,"Laceration of prostate"
+S37828,"Other injury of prostate"
+S37829,"Unspecified injury of prostate"
+S3789,"Injury of other urinary and pelvic organ"
+S37892,"Contusion of other urinary and pelvic organ"
+S37893,"Laceration of other urinary and pelvic organ"
+S37898,"Other injury of other urinary and pelvic organ"
+S37899,"Unspecified injury of other urinary and pelvic organ"
+S379,"Injury of unspecified urinary and pelvic organ"
+S3790,"Unspecified injury of unspecified urinary and pelvic organ"
+S3792,"Contusion of unspecified urinary and pelvic organ"
+S3793,"Laceration of unspecified urinary and pelvic organ"
+S3799,"Other injury of unspecified urinary and pelvic organ"
+S38,"Crushing injury and traumatic amputation of abdomen, lower back, pelvis and external genitals"
+S380,"Crushing injury of external genital organs"
+S3800,"Crushing injury of unspecified external genital organs"
+S38001,"Crushing injury of unspecified external genital organs, male"
+S38002,"Crushing injury of unspecified external genital organs, female"
+S3801,"Crushing injury of penis"
+S3802,"Crushing injury of scrotum and testis"
+S3803,"Crushing injury of vulva"
+S381,"Crushing injury of abdomen, lower back, and pelvis"
+S382,"Traumatic amputation of external genital organs"
+S3821,"Traumatic amputation of female external genital organs"
+S38211,"Complete traumatic amputation of female external genital organs"
+S38212,"Partial traumatic amputation of female external genital organs"
+S3822,"Traumatic amputation of penis"
+S38221,"Complete traumatic amputation of penis"
+S38222,"Partial traumatic amputation of penis"
+S3823,"Traumatic amputation of scrotum and testis"
+S38231,"Complete traumatic amputation of scrotum and testis"
+S38232,"Partial traumatic amputation of scrotum and testis"
+S383,"Transection (partial) of abdomen"
+S39,"Other and unspecified injuries of abdomen, lower back, pelvis and external genitals"
+S390,"Injury of muscle, fascia and tendon of abdomen, lower back and pelvis"
+S3900,"Unspecified injury of muscle, fascia and tendon of abdomen, lower back and pelvis"
+S39001,"Unspecified injury of muscle, fascia and tendon of abdomen"
+S39002,"Unspecified injury of muscle, fascia and tendon of lower back"
+S39003,"Unspecified injury of muscle, fascia and tendon of pelvis"
+S3901,"Strain of muscle, fascia and tendon of abdomen, lower back and pelvis"
+S39011,"Strain of muscle, fascia and tendon of abdomen"
+S39012,"Strain of muscle, fascia and tendon of lower back"
+S39013,"Strain of muscle, fascia and tendon of pelvis"
+S3902,"Laceration of muscle, fascia and tendon of abdomen, lower back and pelvis"
+S39021,"Laceration of muscle, fascia and tendon of abdomen"
+S39022,"Laceration of muscle, fascia and tendon of lower back"
+S39023,"Laceration of muscle, fascia and tendon of pelvis"
+S3909,"Other injury of muscle, fascia and tendon of abdomen, lower back and pelvis"
+S39091,"Other injury of muscle, fascia and tendon of abdomen"
+S39092,"Other injury of muscle, fascia and tendon of lower back"
+S39093,"Other injury of muscle, fascia and tendon of pelvis"
+S398,"Other specified injuries of abdomen, lower back, pelvis and external genitals"
+S3981,"Other specified injuries of abdomen"
+S3982,"Other specified injuries of lower back"
+S3983,"Other specified injuries of pelvis"
+S3984,"Other specified injuries of external genitals"
+S39840,"Fracture of corpus cavernosum penis"
+S39848,"Other specified injuries of external genitals"
+S399,"Unspecified injury of abdomen, lower back, pelvis and external genitals"
+S3991,"Unspecified injury of abdomen"
+S3992,"Unspecified injury of lower back"
+S3993,"Unspecified injury of pelvis"
+S3994,"Unspecified injury of external genitals"
+S40,"Superficial injury of shoulder and upper arm"
+S400,"Contusion of shoulder and upper arm"
+S4001,"Contusion of shoulder"
+S40011,"Contusion of right shoulder"
+S40012,"Contusion of left shoulder"
+S40019,"Contusion of unspecified shoulder"
+S4002,"Contusion of upper arm"
+S40021,"Contusion of right upper arm"
+S40022,"Contusion of left upper arm"
+S40029,"Contusion of unspecified upper arm"
+S402,"Other superficial injuries of shoulder"
+S4021,"Abrasion of shoulder"
+S40211,"Abrasion of right shoulder"
+S40212,"Abrasion of left shoulder"
+S40219,"Abrasion of unspecified shoulder"
+S4022,"Blister (nonthermal) of shoulder"
+S40221,"Blister (nonthermal) of right shoulder"
+S40222,"Blister (nonthermal) of left shoulder"
+S40229,"Blister (nonthermal) of unspecified shoulder"
+S4024,"External constriction of shoulder"
+S40241,"External constriction of right shoulder"
+S40242,"External constriction of left shoulder"
+S40249,"External constriction of unspecified shoulder"
+S4025,"Superficial foreign body of shoulder"
+S40251,"Superficial foreign body of right shoulder"
+S40252,"Superficial foreign body of left shoulder"
+S40259,"Superficial foreign body of unspecified shoulder"
+S4026,"Insect bite (nonvenomous) of shoulder"
+S40261,"Insect bite (nonvenomous) of right shoulder"
+S40262,"Insect bite (nonvenomous) of left shoulder"
+S40269,"Insect bite (nonvenomous) of unspecified shoulder"
+S4027,"Other superficial bite of shoulder"
+S40271,"Other superficial bite of right shoulder"
+S40272,"Other superficial bite of left shoulder"
+S40279,"Other superficial bite of unspecified shoulder"
+S408,"Other superficial injuries of upper arm"
+S4081,"Abrasion of upper arm"
+S40811,"Abrasion of right upper arm"
+S40812,"Abrasion of left upper arm"
+S40819,"Abrasion of unspecified upper arm"
+S4082,"Blister (nonthermal) of upper arm"
+S40821,"Blister (nonthermal) of right upper arm"
+S40822,"Blister (nonthermal) of left upper arm"
+S40829,"Blister (nonthermal) of unspecified upper arm"
+S4084,"External constriction of upper arm"
+S40841,"External constriction of right upper arm"
+S40842,"External constriction of left upper arm"
+S40849,"External constriction of unspecified upper arm"
+S4085,"Superficial foreign body of upper arm"
+S40851,"Superficial foreign body of right upper arm"
+S40852,"Superficial foreign body of left upper arm"
+S40859,"Superficial foreign body of unspecified upper arm"
+S4086,"Insect bite (nonvenomous) of upper arm"
+S40861,"Insect bite (nonvenomous) of right upper arm"
+S40862,"Insect bite (nonvenomous) of left upper arm"
+S40869,"Insect bite (nonvenomous) of unspecified upper arm"
+S4087,"Other superficial bite of upper arm"
+S40871,"Other superficial bite of right upper arm"
+S40872,"Other superficial bite of left upper arm"
+S40879,"Other superficial bite of unspecified upper arm"
+S409,"Unspecified superficial injury of shoulder and upper arm"
+S4091,"Unspecified superficial injury of shoulder"
+S40911,"Unspecified superficial injury of right shoulder"
+S40912,"Unspecified superficial injury of left shoulder"
+S40919,"Unspecified superficial injury of unspecified shoulder"
+S4092,"Unspecified superficial injury of upper arm"
+S40921,"Unspecified superficial injury of right upper arm"
+S40922,"Unspecified superficial injury of left upper arm"
+S40929,"Unspecified superficial injury of unspecified upper arm"
+S41,"Open wound of shoulder and upper arm"
+S410,"Open wound of shoulder"
+S4100,"Unspecified open wound of shoulder"
+S41001,"Unspecified open wound of right shoulder"
+S41002,"Unspecified open wound of left shoulder"
+S41009,"Unspecified open wound of unspecified shoulder"
+S4101,"Laceration without foreign body of shoulder"
+S41011,"Laceration without foreign body of right shoulder"
+S41012,"Laceration without foreign body of left shoulder"
+S41019,"Laceration without foreign body of unspecified shoulder"
+S4102,"Laceration with foreign body of shoulder"
+S41021,"Laceration with foreign body of right shoulder"
+S41022,"Laceration with foreign body of left shoulder"
+S41029,"Laceration with foreign body of unspecified shoulder"
+S4103,"Puncture wound without foreign body of shoulder"
+S41031,"Puncture wound without foreign body of right shoulder"
+S41032,"Puncture wound without foreign body of left shoulder"
+S41039,"Puncture wound without foreign body of unspecified shoulder"
+S4104,"Puncture wound with foreign body of shoulder"
+S41041,"Puncture wound with foreign body of right shoulder"
+S41042,"Puncture wound with foreign body of left shoulder"
+S41049,"Puncture wound with foreign body of unspecified shoulder"
+S4105,"Open bite of shoulder"
+S41051,"Open bite of right shoulder"
+S41052,"Open bite of left shoulder"
+S41059,"Open bite of unspecified shoulder"
+S411,"Open wound of upper arm"
+S4110,"Unspecified open wound of upper arm"
+S41101,"Unspecified open wound of right upper arm"
+S41102,"Unspecified open wound of left upper arm"
+S41109,"Unspecified open wound of unspecified upper arm"
+S4111,"Laceration without foreign body of upper arm"
+S41111,"Laceration without foreign body of right upper arm"
+S41112,"Laceration without foreign body of left upper arm"
+S41119,"Laceration without foreign body of unspecified upper arm"
+S4112,"Laceration with foreign body of upper arm"
+S41121,"Laceration with foreign body of right upper arm"
+S41122,"Laceration with foreign body of left upper arm"
+S41129,"Laceration with foreign body of unspecified upper arm"
+S4113,"Puncture wound without foreign body of upper arm"
+S41131,"Puncture wound without foreign body of right upper arm"
+S41132,"Puncture wound without foreign body of left upper arm"
+S41139,"Puncture wound without foreign body of unspecified upper arm"
+S4114,"Puncture wound with foreign body of upper arm"
+S41141,"Puncture wound with foreign body of right upper arm"
+S41142,"Puncture wound with foreign body of left upper arm"
+S41149,"Puncture wound with foreign body of unspecified upper arm"
+S4115,"Open bite of upper arm"
+S41151,"Open bite of right upper arm"
+S41152,"Open bite of left upper arm"
+S41159,"Open bite of unspecified upper arm"
+S42,"Fracture of shoulder and upper arm"
+S420,"Fracture of clavicle"
+S4200,"Fracture of unspecified part of clavicle"
+S42001,"Fracture of unspecified part of right clavicle"
+S42002,"Fracture of unspecified part of left clavicle"
+S42009,"Fracture of unspecified part of unspecified clavicle"
+S4201,"Fracture of sternal end of clavicle"
+S42011,"Anterior displaced fracture of sternal end of right clavicle"
+S42012,"Anterior displaced fracture of sternal end of left clavicle"
+S42013,"Anterior displaced fracture of sternal end of unspecified clavicle"
+S42014,"Posterior displaced fracture of sternal end of right clavicle"
+S42015,"Posterior displaced fracture of sternal end of left clavicle"
+S42016,"Posterior displaced fracture of sternal end of unspecified clavicle"
+S42017,"Nondisplaced fracture of sternal end of right clavicle"
+S42018,"Nondisplaced fracture of sternal end of left clavicle"
+S42019,"Nondisplaced fracture of sternal end of unspecified clavicle"
+S4202,"Fracture of shaft of clavicle"
+S42021,"Displaced fracture of shaft of right clavicle"
+S42022,"Displaced fracture of shaft of left clavicle"
+S42023,"Displaced fracture of shaft of unspecified clavicle"
+S42024,"Nondisplaced fracture of shaft of right clavicle"
+S42025,"Nondisplaced fracture of shaft of left clavicle"
+S42026,"Nondisplaced fracture of shaft of unspecified clavicle"
+S4203,"Fracture of lateral end of clavicle"
+S42031,"Displaced fracture of lateral end of right clavicle"
+S42032,"Displaced fracture of lateral end of left clavicle"
+S42033,"Displaced fracture of lateral end of unspecified clavicle"
+S42034,"Nondisplaced fracture of lateral end of right clavicle"
+S42035,"Nondisplaced fracture of lateral end of left clavicle"
+S42036,"Nondisplaced fracture of lateral end of unspecified clavicle"
+S421,"Fracture of scapula"
+S4210,"Fracture of unspecified part of scapula"
+S42101,"Fracture of unspecified part of scapula, right shoulder"
+S42102,"Fracture of unspecified part of scapula, left shoulder"
+S42109,"Fracture of unspecified part of scapula, unspecified shoulder"
+S4211,"Fracture of body of scapula"
+S42111,"Displaced fracture of body of scapula, right shoulder"
+S42112,"Displaced fracture of body of scapula, left shoulder"
+S42113,"Displaced fracture of body of scapula, unspecified shoulder"
+S42114,"Nondisplaced fracture of body of scapula, right shoulder"
+S42115,"Nondisplaced fracture of body of scapula, left shoulder"
+S42116,"Nondisplaced fracture of body of scapula, unspecified shoulder"
+S4212,"Fracture of acromial process"
+S42121,"Displaced fracture of acromial process, right shoulder"
+S42122,"Displaced fracture of acromial process, left shoulder"
+S42123,"Displaced fracture of acromial process, unspecified shoulder"
+S42124,"Nondisplaced fracture of acromial process, right shoulder"
+S42125,"Nondisplaced fracture of acromial process, left shoulder"
+S42126,"Nondisplaced fracture of acromial process, unspecified shoulder"
+S4213,"Fracture of coracoid process"
+S42131,"Displaced fracture of coracoid process, right shoulder"
+S42132,"Displaced fracture of coracoid process, left shoulder"
+S42133,"Displaced fracture of coracoid process, unspecified shoulder"
+S42134,"Nondisplaced fracture of coracoid process, right shoulder"
+S42135,"Nondisplaced fracture of coracoid process, left shoulder"
+S42136,"Nondisplaced fracture of coracoid process, unspecified shoulder"
+S4214,"Fracture of glenoid cavity of scapula"
+S42141,"Displaced fracture of glenoid cavity of scapula, right shoulder"
+S42142,"Displaced fracture of glenoid cavity of scapula, left shoulder"
+S42143,"Displaced fracture of glenoid cavity of scapula, unspecified shoulder"
+S42144,"Nondisplaced fracture of glenoid cavity of scapula, right shoulder"
+S42145,"Nondisplaced fracture of glenoid cavity of scapula, left shoulder"
+S42146,"Nondisplaced fracture of glenoid cavity of scapula, unspecified shoulder"
+S4215,"Fracture of neck of scapula"
+S42151,"Displaced fracture of neck of scapula, right shoulder"
+S42152,"Displaced fracture of neck of scapula, left shoulder"
+S42153,"Displaced fracture of neck of scapula, unspecified shoulder"
+S42154,"Nondisplaced fracture of neck of scapula, right shoulder"
+S42155,"Nondisplaced fracture of neck of scapula, left shoulder"
+S42156,"Nondisplaced fracture of neck of scapula, unspecified shoulder"
+S4219,"Fracture of other part of scapula"
+S42191,"Fracture of other part of scapula, right shoulder"
+S42192,"Fracture of other part of scapula, left shoulder"
+S42199,"Fracture of other part of scapula, unspecified shoulder"
+S422,"Fracture of upper end of humerus"
+S4220,"Unspecified fracture of upper end of humerus"
+S42201,"Unspecified fracture of upper end of right humerus"
+S42202,"Unspecified fracture of upper end of left humerus"
+S42209,"Unspecified fracture of upper end of unspecified humerus"
+S4221,"Unspecified fracture of surgical neck of humerus"
+S42211,"Unspecified displaced fracture of surgical neck of right humerus"
+S42212,"Unspecified displaced fracture of surgical neck of left humerus"
+S42213,"Unspecified displaced fracture of surgical neck of unspecified humerus"
+S42214,"Unspecified nondisplaced fracture of surgical neck of right humerus"
+S42215,"Unspecified nondisplaced fracture of surgical neck of left humerus"
+S42216,"Unspecified nondisplaced fracture of surgical neck of unspecified humerus"
+S4222,"2-part fracture of surgical neck of humerus"
+S42221,"2-part displaced fracture of surgical neck of right humerus"
+S42222,"2-part displaced fracture of surgical neck of left humerus"
+S42223,"2-part displaced fracture of surgical neck of unspecified humerus"
+S42224,"2-part nondisplaced fracture of surgical neck of right humerus"
+S42225,"2-part nondisplaced fracture of surgical neck of left humerus"
+S42226,"2-part nondisplaced fracture of surgical neck of unspecified humerus"
+S4223,"3-part fracture of surgical neck of humerus"
+S42231,"3-part fracture of surgical neck of right humerus"
+S42232,"3-part fracture of surgical neck of left humerus"
+S42239,"3-part fracture of surgical neck of unspecified humerus"
+S4224,"4-part fracture of surgical neck of humerus"
+S42241,"4-part fracture of surgical neck of right humerus"
+S42242,"4-part fracture of surgical neck of left humerus"
+S42249,"4-part fracture of surgical neck of unspecified humerus"
+S4225,"Fracture of greater tuberosity of humerus"
+S42251,"Displaced fracture of greater tuberosity of right humerus"
+S42252,"Displaced fracture of greater tuberosity of left humerus"
+S42253,"Displaced fracture of greater tuberosity of unspecified humerus"
+S42254,"Nondisplaced fracture of greater tuberosity of right humerus"
+S42255,"Nondisplaced fracture of greater tuberosity of left humerus"
+S42256,"Nondisplaced fracture of greater tuberosity of unspecified humerus"
+S4226,"Fracture of lesser tuberosity of humerus"
+S42261,"Displaced fracture of lesser tuberosity of right humerus"
+S42262,"Displaced fracture of lesser tuberosity of left humerus"
+S42263,"Displaced fracture of lesser tuberosity of unspecified humerus"
+S42264,"Nondisplaced fracture of lesser tuberosity of right humerus"
+S42265,"Nondisplaced fracture of lesser tuberosity of left humerus"
+S42266,"Nondisplaced fracture of lesser tuberosity of unspecified humerus"
+S4227,"Torus fracture of upper end of humerus"
+S42271,"Torus fracture of upper end of right humerus"
+S42272,"Torus fracture of upper end of left humerus"
+S42279,"Torus fracture of upper end of unspecified humerus"
+S4229,"Other fracture of upper end of humerus"
+S42291,"Other displaced fracture of upper end of right humerus"
+S42292,"Other displaced fracture of upper end of left humerus"
+S42293,"Other displaced fracture of upper end of unspecified humerus"
+S42294,"Other nondisplaced fracture of upper end of right humerus"
+S42295,"Other nondisplaced fracture of upper end of left humerus"
+S42296,"Other nondisplaced fracture of upper end of unspecified humerus"
+S423,"Fracture of shaft of humerus"
+S4230,"Unspecified fracture of shaft of humerus"
+S42301,"Unspecified fracture of shaft of humerus, right arm"
+S42302,"Unspecified fracture of shaft of humerus, left arm"
+S42309,"Unspecified fracture of shaft of humerus, unspecified arm"
+S4231,"Greenstick fracture of shaft of humerus"
+S42311,"Greenstick fracture of shaft of humerus, right arm"
+S42312,"Greenstick fracture of shaft of humerus, left arm"
+S42319,"Greenstick fracture of shaft of humerus, unspecified arm"
+S4232,"Transverse fracture of shaft of humerus"
+S42321,"Displaced transverse fracture of shaft of humerus, right arm"
+S42322,"Displaced transverse fracture of shaft of humerus, left arm"
+S42323,"Displaced transverse fracture of shaft of humerus, unspecified arm"
+S42324,"Nondisplaced transverse fracture of shaft of humerus, right arm"
+S42325,"Nondisplaced transverse fracture of shaft of humerus, left arm"
+S42326,"Nondisplaced transverse fracture of shaft of humerus, unspecified arm"
+S4233,"Oblique fracture of shaft of humerus"
+S42331,"Displaced oblique fracture of shaft of humerus, right arm"
+S42332,"Displaced oblique fracture of shaft of humerus, left arm"
+S42333,"Displaced oblique fracture of shaft of humerus, unspecified arm"
+S42334,"Nondisplaced oblique fracture of shaft of humerus, right arm"
+S42335,"Nondisplaced oblique fracture of shaft of humerus, left arm"
+S42336,"Nondisplaced oblique fracture of shaft of humerus, unspecified arm"
+S4234,"Spiral fracture of shaft of humerus"
+S42341,"Displaced spiral fracture of shaft of humerus, right arm"
+S42342,"Displaced spiral fracture of shaft of humerus, left arm"
+S42343,"Displaced spiral fracture of shaft of humerus, unspecified arm"
+S42344,"Nondisplaced spiral fracture of shaft of humerus, right arm"
+S42345,"Nondisplaced spiral fracture of shaft of humerus, left arm"
+S42346,"Nondisplaced spiral fracture of shaft of humerus, unspecified arm"
+S4235,"Comminuted fracture of shaft of humerus"
+S42351,"Displaced comminuted fracture of shaft of humerus, right arm"
+S42352,"Displaced comminuted fracture of shaft of humerus, left arm"
+S42353,"Displaced comminuted fracture of shaft of humerus, unspecified arm"
+S42354,"Nondisplaced comminuted fracture of shaft of humerus, right arm"
+S42355,"Nondisplaced comminuted fracture of shaft of humerus, left arm"
+S42356,"Nondisplaced comminuted fracture of shaft of humerus, unspecified arm"
+S4236,"Segmental fracture of shaft of humerus"
+S42361,"Displaced segmental fracture of shaft of humerus, right arm"
+S42362,"Displaced segmental fracture of shaft of humerus, left arm"
+S42363,"Displaced segmental fracture of shaft of humerus, unspecified arm"
+S42364,"Nondisplaced segmental fracture of shaft of humerus, right arm"
+S42365,"Nondisplaced segmental fracture of shaft of humerus, left arm"
+S42366,"Nondisplaced segmental fracture of shaft of humerus, unspecified arm"
+S4239,"Other fracture of shaft of humerus"
+S42391,"Other fracture of shaft of right humerus"
+S42392,"Other fracture of shaft of left humerus"
+S42399,"Other fracture of shaft of unspecified humerus"
+S424,"Fracture of lower end of humerus"
+S4240,"Unspecified fracture of lower end of humerus"
+S42401,"Unspecified fracture of lower end of right humerus"
+S42402,"Unspecified fracture of lower end of left humerus"
+S42409,"Unspecified fracture of lower end of unspecified humerus"
+S4241,"Simple supracondylar fracture without intercondylar fracture of humerus"
+S42411,"Displaced simple supracondylar fracture without intercondylar fracture of right humerus"
+S42412,"Displaced simple supracondylar fracture without intercondylar fracture of left humerus"
+S42413,"Displaced simple supracondylar fracture without intercondylar fracture of unspecified humerus"
+S42414,"Nondisplaced simple supracondylar fracture without intercondylar fracture of right humerus"
+S42415,"Nondisplaced simple supracondylar fracture without intercondylar fracture of left humerus"
+S42416,"Nondisplaced simple supracondylar fracture without intercondylar fracture of unspecified humerus"
+S4242,"Comminuted supracondylar fracture without intercondylar fracture of humerus"
+S42421,"Displaced comminuted supracondylar fracture without intercondylar fracture of right humerus"
+S42422,"Displaced comminuted supracondylar fracture without intercondylar fracture of left humerus"
+S42423,"Displaced comminuted supracondylar fracture without intercondylar fracture of unspecified humerus"
+S42424,"Nondisplaced comminuted supracondylar fracture without intercondylar fracture of right humerus"
+S42425,"Nondisplaced comminuted supracondylar fracture without intercondylar fracture of left humerus"
+S42426,"Nondisplaced comminuted supracondylar fracture without intercondylar fracture of unspecified humerus"
+S4243,"Fracture (avulsion) of lateral epicondyle of humerus"
+S42431,"Displaced fracture (avulsion) of lateral epicondyle of right humerus"
+S42432,"Displaced fracture (avulsion) of lateral epicondyle of left humerus"
+S42433,"Displaced fracture (avulsion) of lateral epicondyle of unspecified humerus"
+S42434,"Nondisplaced fracture (avulsion) of lateral epicondyle of right humerus"
+S42435,"Nondisplaced fracture (avulsion) of lateral epicondyle of left humerus"
+S42436,"Nondisplaced fracture (avulsion) of lateral epicondyle of unspecified humerus"
+S4244,"Fracture (avulsion) of medial epicondyle of humerus"
+S42441,"Displaced fracture (avulsion) of medial epicondyle of right humerus"
+S42442,"Displaced fracture (avulsion) of medial epicondyle of left humerus"
+S42443,"Displaced fracture (avulsion) of medial epicondyle of unspecified humerus"
+S42444,"Nondisplaced fracture (avulsion) of medial epicondyle of right humerus"
+S42445,"Nondisplaced fracture (avulsion) of medial epicondyle of left humerus"
+S42446,"Nondisplaced fracture (avulsion) of medial epicondyle of unspecified humerus"
+S42447,"Incarcerated fracture (avulsion) of medial epicondyle of right humerus"
+S42448,"Incarcerated fracture (avulsion) of medial epicondyle of left humerus"
+S42449,"Incarcerated fracture (avulsion) of medial epicondyle of unspecified humerus"
+S4245,"Fracture of lateral condyle of humerus"
+S42451,"Displaced fracture of lateral condyle of right humerus"
+S42452,"Displaced fracture of lateral condyle of left humerus"
+S42453,"Displaced fracture of lateral condyle of unspecified humerus"
+S42454,"Nondisplaced fracture of lateral condyle of right humerus"
+S42455,"Nondisplaced fracture of lateral condyle of left humerus"
+S42456,"Nondisplaced fracture of lateral condyle of unspecified humerus"
+S4246,"Fracture of medial condyle of humerus"
+S42461,"Displaced fracture of medial condyle of right humerus"
+S42462,"Displaced fracture of medial condyle of left humerus"
+S42463,"Displaced fracture of medial condyle of unspecified humerus"
+S42464,"Nondisplaced fracture of medial condyle of right humerus"
+S42465,"Nondisplaced fracture of medial condyle of left humerus"
+S42466,"Nondisplaced fracture of medial condyle of unspecified humerus"
+S4247,"Transcondylar fracture of humerus"
+S42471,"Displaced transcondylar fracture of right humerus"
+S42472,"Displaced transcondylar fracture of left humerus"
+S42473,"Displaced transcondylar fracture of unspecified humerus"
+S42474,"Nondisplaced transcondylar fracture of right humerus"
+S42475,"Nondisplaced transcondylar fracture of left humerus"
+S42476,"Nondisplaced transcondylar fracture of unspecified humerus"
+S4248,"Torus fracture of lower end of humerus"
+S42481,"Torus fracture of lower end of right humerus"
+S42482,"Torus fracture of lower end of left humerus"
+S42489,"Torus fracture of lower end of unspecified humerus"
+S4249,"Other fracture of lower end of humerus"
+S42491,"Other displaced fracture of lower end of right humerus"
+S42492,"Other displaced fracture of lower end of left humerus"
+S42493,"Other displaced fracture of lower end of unspecified humerus"
+S42494,"Other nondisplaced fracture of lower end of right humerus"
+S42495,"Other nondisplaced fracture of lower end of left humerus"
+S42496,"Other nondisplaced fracture of lower end of unspecified humerus"
+S429,"Fracture of shoulder girdle, part unspecified"
+S4290,"Fracture of unspecified shoulder girdle, part unspecified"
+S4291,"Fracture of right shoulder girdle, part unspecified"
+S4292,"Fracture of left shoulder girdle, part unspecified"
+S43,"Dislocation and sprain of joints and ligaments of shoulder girdle"
+S430,"Subluxation and dislocation of shoulder joint"
+S4300,"Unspecified subluxation and dislocation of shoulder joint"
+S43001,"Unspecified subluxation of right shoulder joint"
+S43002,"Unspecified subluxation of left shoulder joint"
+S43003,"Unspecified subluxation of unspecified shoulder joint"
+S43004,"Unspecified dislocation of right shoulder joint"
+S43005,"Unspecified dislocation of left shoulder joint"
+S43006,"Unspecified dislocation of unspecified shoulder joint"
+S4301,"Anterior subluxation and dislocation of humerus"
+S43011,"Anterior subluxation of right humerus"
+S43012,"Anterior subluxation of left humerus"
+S43013,"Anterior subluxation of unspecified humerus"
+S43014,"Anterior dislocation of right humerus"
+S43015,"Anterior dislocation of left humerus"
+S43016,"Anterior dislocation of unspecified humerus"
+S4302,"Posterior subluxation and dislocation of humerus"
+S43021,"Posterior subluxation of right humerus"
+S43022,"Posterior subluxation of left humerus"
+S43023,"Posterior subluxation of unspecified humerus"
+S43024,"Posterior dislocation of right humerus"
+S43025,"Posterior dislocation of left humerus"
+S43026,"Posterior dislocation of unspecified humerus"
+S4303,"Inferior subluxation and dislocation of humerus"
+S43031,"Inferior subluxation of right humerus"
+S43032,"Inferior subluxation of left humerus"
+S43033,"Inferior subluxation of unspecified humerus"
+S43034,"Inferior dislocation of right humerus"
+S43035,"Inferior dislocation of left humerus"
+S43036,"Inferior dislocation of unspecified humerus"
+S4308,"Other subluxation and dislocation of shoulder joint"
+S43081,"Other subluxation of right shoulder joint"
+S43082,"Other subluxation of left shoulder joint"
+S43083,"Other subluxation of unspecified shoulder joint"
+S43084,"Other dislocation of right shoulder joint"
+S43085,"Other dislocation of left shoulder joint"
+S43086,"Other dislocation of unspecified shoulder joint"
+S431,"Subluxation and dislocation of acromioclavicular joint"
+S4310,"Unspecified dislocation of acromioclavicular joint"
+S43101,"Unspecified dislocation of right acromioclavicular joint"
+S43102,"Unspecified dislocation of left acromioclavicular joint"
+S43109,"Unspecified dislocation of unspecified acromioclavicular joint"
+S4311,"Subluxation of acromioclavicular joint"
+S43111,"Subluxation of right acromioclavicular joint"
+S43112,"Subluxation of left acromioclavicular joint"
+S43119,"Subluxation of unspecified acromioclavicular joint"
+S4312,"Dislocation of acromioclavicular joint, 100%-200% displacement"
+S43121,"Dislocation of right acromioclavicular joint, 100%-200% displacement"
+S43122,"Dislocation of left acromioclavicular joint, 100%-200% displacement"
+S43129,"Dislocation of unspecified acromioclavicular joint, 100%-200% displacement"
+S4313,"Dislocation of acromioclavicular joint, greater than 200% displacement"
+S43131,"Dislocation of right acromioclavicular joint, greater than 200% displacement"
+S43132,"Dislocation of left acromioclavicular joint, greater than 200% displacement"
+S43139,"Dislocation of unspecified acromioclavicular joint, greater than 200% displacement"
+S4314,"Inferior dislocation of acromioclavicular joint"
+S43141,"Inferior dislocation of right acromioclavicular joint"
+S43142,"Inferior dislocation of left acromioclavicular joint"
+S43149,"Inferior dislocation of unspecified acromioclavicular joint"
+S4315,"Posterior dislocation of acromioclavicular joint"
+S43151,"Posterior dislocation of right acromioclavicular joint"
+S43152,"Posterior dislocation of left acromioclavicular joint"
+S43159,"Posterior dislocation of unspecified acromioclavicular joint"
+S432,"Subluxation and dislocation of sternoclavicular joint"
+S4320,"Unspecified subluxation and dislocation of sternoclavicular joint"
+S43201,"Unspecified subluxation of right sternoclavicular joint"
+S43202,"Unspecified subluxation of left sternoclavicular joint"
+S43203,"Unspecified subluxation of unspecified sternoclavicular joint"
+S43204,"Unspecified dislocation of right sternoclavicular joint"
+S43205,"Unspecified dislocation of left sternoclavicular joint"
+S43206,"Unspecified dislocation of unspecified sternoclavicular joint"
+S4321,"Anterior subluxation and dislocation of sternoclavicular joint"
+S43211,"Anterior subluxation of right sternoclavicular joint"
+S43212,"Anterior subluxation of left sternoclavicular joint"
+S43213,"Anterior subluxation of unspecified sternoclavicular joint"
+S43214,"Anterior dislocation of right sternoclavicular joint"
+S43215,"Anterior dislocation of left sternoclavicular joint"
+S43216,"Anterior dislocation of unspecified sternoclavicular joint"
+S4322,"Posterior subluxation and dislocation of sternoclavicular joint"
+S43221,"Posterior subluxation of right sternoclavicular joint"
+S43222,"Posterior subluxation of left sternoclavicular joint"
+S43223,"Posterior subluxation of unspecified sternoclavicular joint"
+S43224,"Posterior dislocation of right sternoclavicular joint"
+S43225,"Posterior dislocation of left sternoclavicular joint"
+S43226,"Posterior dislocation of unspecified sternoclavicular joint"
+S433,"Subluxation and dislocation of other and unspecified parts of shoulder girdle"
+S4330,"Subluxation and dislocation of unspecified parts of shoulder girdle"
+S43301,"Subluxation of unspecified parts of right shoulder girdle"
+S43302,"Subluxation of unspecified parts of left shoulder girdle"
+S43303,"Subluxation of unspecified parts of unspecified shoulder girdle"
+S43304,"Dislocation of unspecified parts of right shoulder girdle"
+S43305,"Dislocation of unspecified parts of left shoulder girdle"
+S43306,"Dislocation of unspecified parts of unspecified shoulder girdle"
+S4331,"Subluxation and dislocation of scapula"
+S43311,"Subluxation of right scapula"
+S43312,"Subluxation of left scapula"
+S43313,"Subluxation of unspecified scapula"
+S43314,"Dislocation of right scapula"
+S43315,"Dislocation of left scapula"
+S43316,"Dislocation of unspecified scapula"
+S4339,"Subluxation and dislocation of other parts of shoulder girdle"
+S43391,"Subluxation of other parts of right shoulder girdle"
+S43392,"Subluxation of other parts of left shoulder girdle"
+S43393,"Subluxation of other parts of unspecified shoulder girdle"
+S43394,"Dislocation of other parts of right shoulder girdle"
+S43395,"Dislocation of other parts of left shoulder girdle"
+S43396,"Dislocation of other parts of unspecified shoulder girdle"
+S434,"Sprain of shoulder joint"
+S4340,"Unspecified sprain of shoulder joint"
+S43401,"Unspecified sprain of right shoulder joint"
+S43402,"Unspecified sprain of left shoulder joint"
+S43409,"Unspecified sprain of unspecified shoulder joint"
+S4341,"Sprain of coracohumeral (ligament)"
+S43411,"Sprain of right coracohumeral (ligament)"
+S43412,"Sprain of left coracohumeral (ligament)"
+S43419,"Sprain of unspecified coracohumeral (ligament)"
+S4342,"Sprain of rotator cuff capsule"
+S43421,"Sprain of right rotator cuff capsule"
+S43422,"Sprain of left rotator cuff capsule"
+S43429,"Sprain of unspecified rotator cuff capsule"
+S4343,"Superior glenoid labrum lesion"
+S43431,"Superior glenoid labrum lesion of right shoulder"
+S43432,"Superior glenoid labrum lesion of left shoulder"
+S43439,"Superior glenoid labrum lesion of unspecified shoulder"
+S4349,"Other sprain of shoulder joint"
+S43491,"Other sprain of right shoulder joint"
+S43492,"Other sprain of left shoulder joint"
+S43499,"Other sprain of unspecified shoulder joint"
+S435,"Sprain of acromioclavicular joint"
+S4350,"Sprain of unspecified acromioclavicular joint"
+S4351,"Sprain of right acromioclavicular joint"
+S4352,"Sprain of left acromioclavicular joint"
+S436,"Sprain of sternoclavicular joint"
+S4360,"Sprain of unspecified sternoclavicular joint"
+S4361,"Sprain of right sternoclavicular joint"
+S4362,"Sprain of left sternoclavicular joint"
+S438,"Sprain of other specified parts of shoulder girdle"
+S4380,"Sprain of other specified parts of unspecified shoulder girdle"
+S4381,"Sprain of other specified parts of right shoulder girdle"
+S4382,"Sprain of other specified parts of left shoulder girdle"
+S439,"Sprain of unspecified parts of shoulder girdle"
+S4390,"Sprain of unspecified parts of unspecified shoulder girdle"
+S4391,"Sprain of unspecified parts of right shoulder girdle"
+S4392,"Sprain of unspecified parts of left shoulder girdle"
+S44,"Injury of nerves at shoulder and upper arm level"
+S440,"Injury of ulnar nerve at upper arm level"
+S4400,"Injury of ulnar nerve at upper arm level, unspecified arm"
+S4401,"Injury of ulnar nerve at upper arm level, right arm"
+S4402,"Injury of ulnar nerve at upper arm level, left arm"
+S441,"Injury of median nerve at upper arm level"
+S4410,"Injury of median nerve at upper arm level, unspecified arm"
+S4411,"Injury of median nerve at upper arm level, right arm"
+S4412,"Injury of median nerve at upper arm level, left arm"
+S442,"Injury of radial nerve at upper arm level"
+S4420,"Injury of radial nerve at upper arm level, unspecified arm"
+S4421,"Injury of radial nerve at upper arm level, right arm"
+S4422,"Injury of radial nerve at upper arm level, left arm"
+S443,"Injury of axillary nerve"
+S4430,"Injury of axillary nerve, unspecified arm"
+S4431,"Injury of axillary nerve, right arm"
+S4432,"Injury of axillary nerve, left arm"
+S444,"Injury of musculocutaneous nerve"
+S4440,"Injury of musculocutaneous nerve, unspecified arm"
+S4441,"Injury of musculocutaneous nerve, right arm"
+S4442,"Injury of musculocutaneous nerve, left arm"
+S445,"Injury of cutaneous sensory nerve at shoulder and upper arm level"
+S4450,"Injury of cutaneous sensory nerve at shoulder and upper arm level, unspecified arm"
+S4451,"Injury of cutaneous sensory nerve at shoulder and upper arm level, right arm"
+S4452,"Injury of cutaneous sensory nerve at shoulder and upper arm level, left arm"
+S448,"Injury of other nerves at shoulder and upper arm level"
+S448X,"Injury of other nerves at shoulder and upper arm level"
+S448X1,"Injury of other nerves at shoulder and upper arm level, right arm"
+S448X2,"Injury of other nerves at shoulder and upper arm level, left arm"
+S448X9,"Injury of other nerves at shoulder and upper arm level, unspecified arm"
+S449,"Injury of unspecified nerve at shoulder and upper arm level"
+S4490,"Injury of unspecified nerve at shoulder and upper arm level, unspecified arm"
+S4491,"Injury of unspecified nerve at shoulder and upper arm level, right arm"
+S4492,"Injury of unspecified nerve at shoulder and upper arm level, left arm"
+S45,"Injury of blood vessels at shoulder and upper arm level"
+S450,"Injury of axillary artery"
+S4500,"Unspecified injury of axillary artery"
+S45001,"Unspecified injury of axillary artery, right side"
+S45002,"Unspecified injury of axillary artery, left side"
+S45009,"Unspecified injury of axillary artery, unspecified side"
+S4501,"Laceration of axillary artery"
+S45011,"Laceration of axillary artery, right side"
+S45012,"Laceration of axillary artery, left side"
+S45019,"Laceration of axillary artery, unspecified side"
+S4509,"Other specified injury of axillary artery"
+S45091,"Other specified injury of axillary artery, right side"
+S45092,"Other specified injury of axillary artery, left side"
+S45099,"Other specified injury of axillary artery, unspecified side"
+S451,"Injury of brachial artery"
+S4510,"Unspecified injury of brachial artery"
+S45101,"Unspecified injury of brachial artery, right side"
+S45102,"Unspecified injury of brachial artery, left side"
+S45109,"Unspecified injury of brachial artery, unspecified side"
+S4511,"Laceration of brachial artery"
+S45111,"Laceration of brachial artery, right side"
+S45112,"Laceration of brachial artery, left side"
+S45119,"Laceration of brachial artery, unspecified side"
+S4519,"Other specified injury of brachial artery"
+S45191,"Other specified injury of brachial artery, right side"
+S45192,"Other specified injury of brachial artery, left side"
+S45199,"Other specified injury of brachial artery, unspecified side"
+S452,"Injury of axillary or brachial vein"
+S4520,"Unspecified injury of axillary or brachial vein"
+S45201,"Unspecified injury of axillary or brachial vein, right side"
+S45202,"Unspecified injury of axillary or brachial vein, left side"
+S45209,"Unspecified injury of axillary or brachial vein, unspecified side"
+S4521,"Laceration of axillary or brachial vein"
+S45211,"Laceration of axillary or brachial vein, right side"
+S45212,"Laceration of axillary or brachial vein, left side"
+S45219,"Laceration of axillary or brachial vein, unspecified side"
+S4529,"Other specified injury of axillary or brachial vein"
+S45291,"Other specified injury of axillary or brachial vein, right side"
+S45292,"Other specified injury of axillary or brachial vein, left side"
+S45299,"Other specified injury of axillary or brachial vein, unspecified side"
+S453,"Injury of superficial vein at shoulder and upper arm level"
+S4530,"Unspecified injury of superficial vein at shoulder and upper arm level"
+S45301,"Unspecified injury of superficial vein at shoulder and upper arm level, right arm"
+S45302,"Unspecified injury of superficial vein at shoulder and upper arm level, left arm"
+S45309,"Unspecified injury of superficial vein at shoulder and upper arm level, unspecified arm"
+S4531,"Laceration of superficial vein at shoulder and upper arm level"
+S45311,"Laceration of superficial vein at shoulder and upper arm level, right arm"
+S45312,"Laceration of superficial vein at shoulder and upper arm level, left arm"
+S45319,"Laceration of superficial vein at shoulder and upper arm level, unspecified arm"
+S4539,"Other specified injury of superficial vein at shoulder and upper arm level"
+S45391,"Other specified injury of superficial vein at shoulder and upper arm level, right arm"
+S45392,"Other specified injury of superficial vein at shoulder and upper arm level, left arm"
+S45399,"Other specified injury of superficial vein at shoulder and upper arm level, unspecified arm"
+S458,"Injury of other specified blood vessels at shoulder and upper arm level"
+S4580,"Unspecified injury of other specified blood vessels at shoulder and upper arm level"
+S45801,"Unspecified injury of other specified blood vessels at shoulder and upper arm level, right arm"
+S45802,"Unspecified injury of other specified blood vessels at shoulder and upper arm level, left arm"
+S45809,"Unspecified injury of other specified blood vessels at shoulder and upper arm level, unspecified arm"
+S4581,"Laceration of other specified blood vessels at shoulder and upper arm level"
+S45811,"Laceration of other specified blood vessels at shoulder and upper arm level, right arm"
+S45812,"Laceration of other specified blood vessels at shoulder and upper arm level, left arm"
+S45819,"Laceration of other specified blood vessels at shoulder and upper arm level, unspecified arm"
+S4589,"Other specified injury of other specified blood vessels at shoulder and upper arm level"
+S45891,"Other specified injury of other specified blood vessels at shoulder and upper arm level, right arm"
+S45892,"Other specified injury of other specified blood vessels at shoulder and upper arm level, left arm"
+S45899,"Other specified injury of other specified blood vessels at shoulder and upper arm level, unspecified arm"
+S459,"Injury of unspecified blood vessel at shoulder and upper arm level"
+S4590,"Unspecified injury of unspecified blood vessel at shoulder and upper arm level"
+S45901,"Unspecified injury of unspecified blood vessel at shoulder and upper arm level, right arm"
+S45902,"Unspecified injury of unspecified blood vessel at shoulder and upper arm level, left arm"
+S45909,"Unspecified injury of unspecified blood vessel at shoulder and upper arm level, unspecified arm"
+S4591,"Laceration of unspecified blood vessel at shoulder and upper arm level"
+S45911,"Laceration of unspecified blood vessel at shoulder and upper arm level, right arm"
+S45912,"Laceration of unspecified blood vessel at shoulder and upper arm level, left arm"
+S45919,"Laceration of unspecified blood vessel at shoulder and upper arm level, unspecified arm"
+S4599,"Other specified injury of unspecified blood vessel at shoulder and upper arm level"
+S45991,"Other specified injury of unspecified blood vessel at shoulder and upper arm level, right arm"
+S45992,"Other specified injury of unspecified blood vessel at shoulder and upper arm level, left arm"
+S45999,"Other specified injury of unspecified blood vessel at shoulder and upper arm level, unspecified arm"
+S46,"Injury of muscle, fascia and tendon at shoulder and upper arm level"
+S460,"Injury of muscle(s) and tendon(s) of the rotator cuff of shoulder"
+S4600,"Unspecified injury of muscle(s) and tendon(s) of the rotator cuff of shoulder"
+S46001,"Unspecified injury of muscle(s) and tendon(s) of the rotator cuff of right shoulder"
+S46002,"Unspecified injury of muscle(s) and tendon(s) of the rotator cuff of left shoulder"
+S46009,"Unspecified injury of muscle(s) and tendon(s) of the rotator cuff of unspecified shoulder"
+S4601,"Strain of muscle(s) and tendon(s) of the rotator cuff of shoulder"
+S46011,"Strain of muscle(s) and tendon(s) of the rotator cuff of right shoulder"
+S46012,"Strain of muscle(s) and tendon(s) of the rotator cuff of left shoulder"
+S46019,"Strain of muscle(s) and tendon(s) of the rotator cuff of unspecified shoulder"
+S4602,"Laceration of muscle(s) and tendon(s) of the rotator cuff of shoulder"
+S46021,"Laceration of muscle(s) and tendon(s) of the rotator cuff of right shoulder"
+S46022,"Laceration of muscle(s) and tendon(s) of the rotator cuff of left shoulder"
+S46029,"Laceration of muscle(s) and tendon(s) of the rotator cuff of unspecified shoulder"
+S4609,"Other injury of muscle(s) and tendon(s) of the rotator cuff of shoulder"
+S46091,"Other injury of muscle(s) and tendon(s) of the rotator cuff of right shoulder"
+S46092,"Other injury of muscle(s) and tendon(s) of the rotator cuff of left shoulder"
+S46099,"Other injury of muscle(s) and tendon(s) of the rotator cuff of unspecified shoulder"
+S461,"Injury of muscle, fascia and tendon of long head of biceps"
+S4610,"Unspecified injury of muscle, fascia and tendon of long head of biceps"
+S46101,"Unspecified injury of muscle, fascia and tendon of long head of biceps, right arm"
+S46102,"Unspecified injury of muscle, fascia and tendon of long head of biceps, left arm"
+S46109,"Unspecified injury of muscle, fascia and tendon of long head of biceps, unspecified arm"
+S4611,"Strain of muscle, fascia and tendon of long head of biceps"
+S46111,"Strain of muscle, fascia and tendon of long head of biceps, right arm"
+S46112,"Strain of muscle, fascia and tendon of long head of biceps, left arm"
+S46119,"Strain of muscle, fascia and tendon of long head of biceps, unspecified arm"
+S4612,"Laceration of muscle, fascia and tendon of long head of biceps"
+S46121,"Laceration of muscle, fascia and tendon of long head of biceps, right arm"
+S46122,"Laceration of muscle, fascia and tendon of long head of biceps, left arm"
+S46129,"Laceration of muscle, fascia and tendon of long head of biceps, unspecified arm"
+S4619,"Other injury of muscle, fascia and tendon of long head of biceps"
+S46191,"Other injury of muscle, fascia and tendon of long head of biceps, right arm"
+S46192,"Other injury of muscle, fascia and tendon of long head of biceps, left arm"
+S46199,"Other injury of muscle, fascia and tendon of long head of biceps, unspecified arm"
+S462,"Injury of muscle, fascia and tendon of other parts of biceps"
+S4620,"Unspecified injury of muscle, fascia and tendon of other parts of biceps"
+S46201,"Unspecified injury of muscle, fascia and tendon of other parts of biceps, right arm"
+S46202,"Unspecified injury of muscle, fascia and tendon of other parts of biceps, left arm"
+S46209,"Unspecified injury of muscle, fascia and tendon of other parts of biceps, unspecified arm"
+S4621,"Strain of muscle, fascia and tendon of other parts of biceps"
+S46211,"Strain of muscle, fascia and tendon of other parts of biceps, right arm"
+S46212,"Strain of muscle, fascia and tendon of other parts of biceps, left arm"
+S46219,"Strain of muscle, fascia and tendon of other parts of biceps, unspecified arm"
+S4622,"Laceration of muscle, fascia and tendon of other parts of biceps"
+S46221,"Laceration of muscle, fascia and tendon of other parts of biceps, right arm"
+S46222,"Laceration of muscle, fascia and tendon of other parts of biceps, left arm"
+S46229,"Laceration of muscle, fascia and tendon of other parts of biceps, unspecified arm"
+S4629,"Other injury of muscle, fascia and tendon of other parts of biceps"
+S46291,"Other injury of muscle, fascia and tendon of other parts of biceps, right arm"
+S46292,"Other injury of muscle, fascia and tendon of other parts of biceps, left arm"
+S46299,"Other injury of muscle, fascia and tendon of other parts of biceps, unspecified arm"
+S463,"Injury of muscle, fascia and tendon of triceps"
+S4630,"Unspecified injury of muscle, fascia and tendon of triceps"
+S46301,"Unspecified injury of muscle, fascia and tendon of triceps, right arm"
+S46302,"Unspecified injury of muscle, fascia and tendon of triceps, left arm"
+S46309,"Unspecified injury of muscle, fascia and tendon of triceps, unspecified arm"
+S4631,"Strain of muscle, fascia and tendon of triceps"
+S46311,"Strain of muscle, fascia and tendon of triceps, right arm"
+S46312,"Strain of muscle, fascia and tendon of triceps, left arm"
+S46319,"Strain of muscle, fascia and tendon of triceps, unspecified arm"
+S4632,"Laceration of muscle, fascia and tendon of triceps"
+S46321,"Laceration of muscle, fascia and tendon of triceps, right arm"
+S46322,"Laceration of muscle, fascia and tendon of triceps, left arm"
+S46329,"Laceration of muscle, fascia and tendon of triceps, unspecified arm"
+S4639,"Other injury of muscle, fascia and tendon of triceps"
+S46391,"Other injury of muscle, fascia and tendon of triceps, right arm"
+S46392,"Other injury of muscle, fascia and tendon of triceps, left arm"
+S46399,"Other injury of muscle, fascia and tendon of triceps, unspecified arm"
+S468,"Injury of other muscles, fascia and tendons at shoulder and upper arm level"
+S4680,"Unspecified injury of other muscles, fascia and tendons at shoulder and upper arm level"
+S46801,"Unspecified injury of other muscles, fascia and tendons at shoulder and upper arm level, right arm"
+S46802,"Unspecified injury of other muscles, fascia and tendons at shoulder and upper arm level, left arm"
+S46809,"Unspecified injury of other muscles, fascia and tendons at shoulder and upper arm level, unspecified arm"
+S4681,"Strain of other muscles, fascia and tendons at shoulder and upper arm level"
+S46811,"Strain of other muscles, fascia and tendons at shoulder and upper arm level, right arm"
+S46812,"Strain of other muscles, fascia and tendons at shoulder and upper arm level, left arm"
+S46819,"Strain of other muscles, fascia and tendons at shoulder and upper arm level, unspecified arm"
+S4682,"Laceration of other muscles, fascia and tendons at shoulder and upper arm level"
+S46821,"Laceration of other muscles, fascia and tendons at shoulder and upper arm level, right arm"
+S46822,"Laceration of other muscles, fascia and tendons at shoulder and upper arm level, left arm"
+S46829,"Laceration of other muscles, fascia and tendons at shoulder and upper arm level, unspecified arm"
+S4689,"Other injury of other muscles, fascia and tendons at shoulder and upper arm level"
+S46891,"Other injury of other muscles, fascia and tendons at shoulder and upper arm level, right arm"
+S46892,"Other injury of other muscles, fascia and tendons at shoulder and upper arm level, left arm"
+S46899,"Other injury of other muscles, fascia and tendons at shoulder and upper arm level, unspecified arm"
+S469,"Injury of unspecified muscle, fascia and tendon at shoulder and upper arm level"
+S4690,"Unspecified injury of unspecified muscle, fascia and tendon at shoulder and upper arm level"
+S46901,"Unspecified injury of unspecified muscle, fascia and tendon at shoulder and upper arm level, right arm"
+S46902,"Unspecified injury of unspecified muscle, fascia and tendon at shoulder and upper arm level, left arm"
+S46909,"Unspecified injury of unspecified muscle, fascia and tendon at shoulder and upper arm level, unspecified arm"
+S4691,"Strain of unspecified muscle, fascia and tendon at shoulder and upper arm level"
+S46911,"Strain of unspecified muscle, fascia and tendon at shoulder and upper arm level, right arm"
+S46912,"Strain of unspecified muscle, fascia and tendon at shoulder and upper arm level, left arm"
+S46919,"Strain of unspecified muscle, fascia and tendon at shoulder and upper arm level, unspecified arm"
+S4692,"Laceration of unspecified muscle, fascia and tendon at shoulder and upper arm level"
+S46921,"Laceration of unspecified muscle, fascia and tendon at shoulder and upper arm level, right arm"
+S46922,"Laceration of unspecified muscle, fascia and tendon at shoulder and upper arm level, left arm"
+S46929,"Laceration of unspecified muscle, fascia and tendon at shoulder and upper arm level, unspecified arm"
+S4699,"Other injury of unspecified muscle, fascia and tendon at shoulder and upper arm level"
+S46991,"Other injury of unspecified muscle, fascia and tendon at shoulder and upper arm level, right arm"
+S46992,"Other injury of unspecified muscle, fascia and tendon at shoulder and upper arm level, left arm"
+S46999,"Other injury of unspecified muscle, fascia and tendon at shoulder and upper arm level, unspecified arm"
+S47,"Crushing injury of shoulder and upper arm"
+S471,"Crushing injury of right shoulder and upper arm"
+S472,"Crushing injury of left shoulder and upper arm"
+S479,"Crushing injury of shoulder and upper arm, unspecified arm"
+S48,"Traumatic amputation of shoulder and upper arm"
+S480,"Traumatic amputation at shoulder joint"
+S4801,"Complete traumatic amputation at shoulder joint"
+S48011,"Complete traumatic amputation at right shoulder joint"
+S48012,"Complete traumatic amputation at left shoulder joint"
+S48019,"Complete traumatic amputation at unspecified shoulder joint"
+S4802,"Partial traumatic amputation at shoulder joint"
+S48021,"Partial traumatic amputation at right shoulder joint"
+S48022,"Partial traumatic amputation at left shoulder joint"
+S48029,"Partial traumatic amputation at unspecified shoulder joint"
+S481,"Traumatic amputation at level between shoulder and elbow"
+S4811,"Complete traumatic amputation at level between shoulder and elbow"
+S48111,"Complete traumatic amputation at level between right shoulder and elbow"
+S48112,"Complete traumatic amputation at level between left shoulder and elbow"
+S48119,"Complete traumatic amputation at level between unspecified shoulder and elbow"
+S4812,"Partial traumatic amputation at level between shoulder and elbow"
+S48121,"Partial traumatic amputation at level between right shoulder and elbow"
+S48122,"Partial traumatic amputation at level between left shoulder and elbow"
+S48129,"Partial traumatic amputation at level between unspecified shoulder and elbow"
+S489,"Traumatic amputation of shoulder and upper arm, level unspecified"
+S4891,"Complete traumatic amputation of shoulder and upper arm, level unspecified"
+S48911,"Complete traumatic amputation of right shoulder and upper arm, level unspecified"
+S48912,"Complete traumatic amputation of left shoulder and upper arm, level unspecified"
+S48919,"Complete traumatic amputation of unspecified shoulder and upper arm, level unspecified"
+S4892,"Partial traumatic amputation of shoulder and upper arm, level unspecified"
+S48921,"Partial traumatic amputation of right shoulder and upper arm, level unspecified"
+S48922,"Partial traumatic amputation of left shoulder and upper arm, level unspecified"
+S48929,"Partial traumatic amputation of unspecified shoulder and upper arm, level unspecified"
+S49,"Other and unspecified injuries of shoulder and upper arm"
+S490,"Physeal fracture of upper end of humerus"
+S4900,"Unspecified physeal fracture of upper end of humerus"
+S49001,"Unspecified physeal fracture of upper end of humerus, right arm"
+S49002,"Unspecified physeal fracture of upper end of humerus, left arm"
+S49009,"Unspecified physeal fracture of upper end of humerus, unspecified arm"
+S4901,"Salter-Harris Type I physeal fracture of upper end of humerus"
+S49011,"Salter-Harris Type I physeal fracture of upper end of humerus, right arm"
+S49012,"Salter-Harris Type I physeal fracture of upper end of humerus, left arm"
+S49019,"Salter-Harris Type I physeal fracture of upper end of humerus, unspecified arm"
+S4902,"Salter-Harris Type II physeal fracture of upper end of humerus"
+S49021,"Salter-Harris Type II physeal fracture of upper end of humerus, right arm"
+S49022,"Salter-Harris Type II physeal fracture of upper end of humerus, left arm"
+S49029,"Salter-Harris Type II physeal fracture of upper end of humerus, unspecified arm"
+S4903,"Salter-Harris Type III physeal fracture of upper end of humerus"
+S49031,"Salter-Harris Type III physeal fracture of upper end of humerus, right arm"
+S49032,"Salter-Harris Type III physeal fracture of upper end of humerus, left arm"
+S49039,"Salter-Harris Type III physeal fracture of upper end of humerus, unspecified arm"
+S4904,"Salter-Harris Type IV physeal fracture of upper end of humerus"
+S49041,"Salter-Harris Type IV physeal fracture of upper end of humerus, right arm"
+S49042,"Salter-Harris Type IV physeal fracture of upper end of humerus, left arm"
+S49049,"Salter-Harris Type IV physeal fracture of upper end of humerus, unspecified arm"
+S4909,"Other physeal fracture of upper end of humerus"
+S49091,"Other physeal fracture of upper end of humerus, right arm"
+S49092,"Other physeal fracture of upper end of humerus, left arm"
+S49099,"Other physeal fracture of upper end of humerus, unspecified arm"
+S491,"Physeal fracture of lower end of humerus"
+S4910,"Unspecified physeal fracture of lower end of humerus"
+S49101,"Unspecified physeal fracture of lower end of humerus, right arm"
+S49102,"Unspecified physeal fracture of lower end of humerus, left arm"
+S49109,"Unspecified physeal fracture of lower end of humerus, unspecified arm"
+S4911,"Salter-Harris Type I physeal fracture of lower end of humerus"
+S49111,"Salter-Harris Type I physeal fracture of lower end of humerus, right arm"
+S49112,"Salter-Harris Type I physeal fracture of lower end of humerus, left arm"
+S49119,"Salter-Harris Type I physeal fracture of lower end of humerus, unspecified arm"
+S4912,"Salter-Harris Type II physeal fracture of lower end of humerus"
+S49121,"Salter-Harris Type II physeal fracture of lower end of humerus, right arm"
+S49122,"Salter-Harris Type II physeal fracture of lower end of humerus, left arm"
+S49129,"Salter-Harris Type II physeal fracture of lower end of humerus, unspecified arm"
+S4913,"Salter-Harris Type III physeal fracture of lower end of humerus"
+S49131,"Salter-Harris Type III physeal fracture of lower end of humerus, right arm"
+S49132,"Salter-Harris Type III physeal fracture of lower end of humerus, left arm"
+S49139,"Salter-Harris Type III physeal fracture of lower end of humerus, unspecified arm"
+S4914,"Salter-Harris Type IV physeal fracture of lower end of humerus"
+S49141,"Salter-Harris Type IV physeal fracture of lower end of humerus, right arm"
+S49142,"Salter-Harris Type IV physeal fracture of lower end of humerus, left arm"
+S49149,"Salter-Harris Type IV physeal fracture of lower end of humerus, unspecified arm"
+S4919,"Other physeal fracture of lower end of humerus"
+S49191,"Other physeal fracture of lower end of humerus, right arm"
+S49192,"Other physeal fracture of lower end of humerus, left arm"
+S49199,"Other physeal fracture of lower end of humerus, unspecified arm"
+S498,"Other specified injuries of shoulder and upper arm"
+S4980,"Other specified injuries of shoulder and upper arm, unspecified arm"
+S4981,"Other specified injuries of right shoulder and upper arm"
+S4982,"Other specified injuries of left shoulder and upper arm"
+S499,"Unspecified injury of shoulder and upper arm"
+S4990,"Unspecified injury of shoulder and upper arm, unspecified arm"
+S4991,"Unspecified injury of right shoulder and upper arm"
+S4992,"Unspecified injury of left shoulder and upper arm"
+S50,"Superficial injury of elbow and forearm"
+S500,"Contusion of elbow"
+S5000,"Contusion of unspecified elbow"
+S5001,"Contusion of right elbow"
+S5002,"Contusion of left elbow"
+S501,"Contusion of forearm"
+S5010,"Contusion of unspecified forearm"
+S5011,"Contusion of right forearm"
+S5012,"Contusion of left forearm"
+S503,"Other superficial injuries of elbow"
+S5031,"Abrasion of elbow"
+S50311,"Abrasion of right elbow"
+S50312,"Abrasion of left elbow"
+S50319,"Abrasion of unspecified elbow"
+S5032,"Blister (nonthermal) of elbow"
+S50321,"Blister (nonthermal) of right elbow"
+S50322,"Blister (nonthermal) of left elbow"
+S50329,"Blister (nonthermal) of unspecified elbow"
+S5034,"External constriction of elbow"
+S50341,"External constriction of right elbow"
+S50342,"External constriction of left elbow"
+S50349,"External constriction of unspecified elbow"
+S5035,"Superficial foreign body of elbow"
+S50351,"Superficial foreign body of right elbow"
+S50352,"Superficial foreign body of left elbow"
+S50359,"Superficial foreign body of unspecified elbow"
+S5036,"Insect bite (nonvenomous) of elbow"
+S50361,"Insect bite (nonvenomous) of right elbow"
+S50362,"Insect bite (nonvenomous) of left elbow"
+S50369,"Insect bite (nonvenomous) of unspecified elbow"
+S5037,"Other superficial bite of elbow"
+S50371,"Other superficial bite of right elbow"
+S50372,"Other superficial bite of left elbow"
+S50379,"Other superficial bite of unspecified elbow"
+S508,"Other superficial injuries of forearm"
+S5081,"Abrasion of forearm"
+S50811,"Abrasion of right forearm"
+S50812,"Abrasion of left forearm"
+S50819,"Abrasion of unspecified forearm"
+S5082,"Blister (nonthermal) of forearm"
+S50821,"Blister (nonthermal) of right forearm"
+S50822,"Blister (nonthermal) of left forearm"
+S50829,"Blister (nonthermal) of unspecified forearm"
+S5084,"External constriction of forearm"
+S50841,"External constriction of right forearm"
+S50842,"External constriction of left forearm"
+S50849,"External constriction of unspecified forearm"
+S5085,"Superficial foreign body of forearm"
+S50851,"Superficial foreign body of right forearm"
+S50852,"Superficial foreign body of left forearm"
+S50859,"Superficial foreign body of unspecified forearm"
+S5086,"Insect bite (nonvenomous) of forearm"
+S50861,"Insect bite (nonvenomous) of right forearm"
+S50862,"Insect bite (nonvenomous) of left forearm"
+S50869,"Insect bite (nonvenomous) of unspecified forearm"
+S5087,"Other superficial bite of forearm"
+S50871,"Other superficial bite of right forearm"
+S50872,"Other superficial bite of left forearm"
+S50879,"Other superficial bite of unspecified forearm"
+S509,"Unspecified superficial injury of elbow and forearm"
+S5090,"Unspecified superficial injury of elbow"
+S50901,"Unspecified superficial injury of right elbow"
+S50902,"Unspecified superficial injury of left elbow"
+S50909,"Unspecified superficial injury of unspecified elbow"
+S5091,"Unspecified superficial injury of forearm"
+S50911,"Unspecified superficial injury of right forearm"
+S50912,"Unspecified superficial injury of left forearm"
+S50919,"Unspecified superficial injury of unspecified forearm"
+S51,"Open wound of elbow and forearm"
+S510,"Open wound of elbow"
+S5100,"Unspecified open wound of elbow"
+S51001,"Unspecified open wound of right elbow"
+S51002,"Unspecified open wound of left elbow"
+S51009,"Unspecified open wound of unspecified elbow"
+S5101,"Laceration without foreign body of elbow"
+S51011,"Laceration without foreign body of right elbow"
+S51012,"Laceration without foreign body of left elbow"
+S51019,"Laceration without foreign body of unspecified elbow"
+S5102,"Laceration with foreign body of elbow"
+S51021,"Laceration with foreign body of right elbow"
+S51022,"Laceration with foreign body of left elbow"
+S51029,"Laceration with foreign body of unspecified elbow"
+S5103,"Puncture wound without foreign body of elbow"
+S51031,"Puncture wound without foreign body of right elbow"
+S51032,"Puncture wound without foreign body of left elbow"
+S51039,"Puncture wound without foreign body of unspecified elbow"
+S5104,"Puncture wound with foreign body of elbow"
+S51041,"Puncture wound with foreign body of right elbow"
+S51042,"Puncture wound with foreign body of left elbow"
+S51049,"Puncture wound with foreign body of unspecified elbow"
+S5105,"Open bite of elbow"
+S51051,"Open bite, right elbow"
+S51052,"Open bite, left elbow"
+S51059,"Open bite, unspecified elbow"
+S518,"Open wound of forearm"
+S5180,"Unspecified open wound of forearm"
+S51801,"Unspecified open wound of right forearm"
+S51802,"Unspecified open wound of left forearm"
+S51809,"Unspecified open wound of unspecified forearm"
+S5181,"Laceration without foreign body of forearm"
+S51811,"Laceration without foreign body of right forearm"
+S51812,"Laceration without foreign body of left forearm"
+S51819,"Laceration without foreign body of unspecified forearm"
+S5182,"Laceration with foreign body of forearm"
+S51821,"Laceration with foreign body of right forearm"
+S51822,"Laceration with foreign body of left forearm"
+S51829,"Laceration with foreign body of unspecified forearm"
+S5183,"Puncture wound without foreign body of forearm"
+S51831,"Puncture wound without foreign body of right forearm"
+S51832,"Puncture wound without foreign body of left forearm"
+S51839,"Puncture wound without foreign body of unspecified forearm"
+S5184,"Puncture wound with foreign body of forearm"
+S51841,"Puncture wound with foreign body of right forearm"
+S51842,"Puncture wound with foreign body of left forearm"
+S51849,"Puncture wound with foreign body of unspecified forearm"
+S5185,"Open bite of forearm"
+S51851,"Open bite of right forearm"
+S51852,"Open bite of left forearm"
+S51859,"Open bite of unspecified forearm"
+S52,"Fracture of forearm"
+S520,"Fracture of upper end of ulna"
+S5200,"Unspecified fracture of upper end of ulna"
+S52001,"Unspecified fracture of upper end of right ulna"
+S52002,"Unspecified fracture of upper end of left ulna"
+S52009,"Unspecified fracture of upper end of unspecified ulna"
+S5201,"Torus fracture of upper end of ulna"
+S52011,"Torus fracture of upper end of right ulna"
+S52012,"Torus fracture of upper end of left ulna"
+S52019,"Torus fracture of upper end of unspecified ulna"
+S5202,"Fracture of olecranon process without intraarticular extension of ulna"
+S52021,"Displaced fracture of olecranon process without intraarticular extension of right ulna"
+S52022,"Displaced fracture of olecranon process without intraarticular extension of left ulna"
+S52023,"Displaced fracture of olecranon process without intraarticular extension of unspecified ulna"
+S52024,"Nondisplaced fracture of olecranon process without intraarticular extension of right ulna"
+S52025,"Nondisplaced fracture of olecranon process without intraarticular extension of left ulna"
+S52026,"Nondisplaced fracture of olecranon process without intraarticular extension of unspecified ulna"
+S5203,"Fracture of olecranon process with intraarticular extension of ulna"
+S52031,"Displaced fracture of olecranon process with intraarticular extension of right ulna"
+S52032,"Displaced fracture of olecranon process with intraarticular extension of left ulna"
+S52033,"Displaced fracture of olecranon process with intraarticular extension of unspecified ulna"
+S52034,"Nondisplaced fracture of olecranon process with intraarticular extension of right ulna"
+S52035,"Nondisplaced fracture of olecranon process with intraarticular extension of left ulna"
+S52036,"Nondisplaced fracture of olecranon process with intraarticular extension of unspecified ulna"
+S5204,"Fracture of coronoid process of ulna"
+S52041,"Displaced fracture of coronoid process of right ulna"
+S52042,"Displaced fracture of coronoid process of left ulna"
+S52043,"Displaced fracture of coronoid process of unspecified ulna"
+S52044,"Nondisplaced fracture of coronoid process of right ulna"
+S52045,"Nondisplaced fracture of coronoid process of left ulna"
+S52046,"Nondisplaced fracture of coronoid process of unspecified ulna"
+S5209,"Other fracture of upper end of ulna"
+S52091,"Other fracture of upper end of right ulna"
+S52092,"Other fracture of upper end of left ulna"
+S52099,"Other fracture of upper end of unspecified ulna"
+S521,"Fracture of upper end of radius"
+S5210,"Unspecified fracture of upper end of radius"
+S52101,"Unspecified fracture of upper end of right radius"
+S52102,"Unspecified fracture of upper end of left radius"
+S52109,"Unspecified fracture of upper end of unspecified radius"
+S5211,"Torus fracture of upper end of radius"
+S52111,"Torus fracture of upper end of right radius"
+S52112,"Torus fracture of upper end of left radius"
+S52119,"Torus fracture of upper end of unspecified radius"
+S5212,"Fracture of head of radius"
+S52121,"Displaced fracture of head of right radius"
+S52122,"Displaced fracture of head of left radius"
+S52123,"Displaced fracture of head of unspecified radius"
+S52124,"Nondisplaced fracture of head of right radius"
+S52125,"Nondisplaced fracture of head of left radius"
+S52126,"Nondisplaced fracture of head of unspecified radius"
+S5213,"Fracture of neck of radius"
+S52131,"Displaced fracture of neck of right radius"
+S52132,"Displaced fracture of neck of left radius"
+S52133,"Displaced fracture of neck of unspecified radius"
+S52134,"Nondisplaced fracture of neck of right radius"
+S52135,"Nondisplaced fracture of neck of left radius"
+S52136,"Nondisplaced fracture of neck of unspecified radius"
+S5218,"Other fracture of upper end of radius"
+S52181,"Other fracture of upper end of right radius"
+S52182,"Other fracture of upper end of left radius"
+S52189,"Other fracture of upper end of unspecified radius"
+S522,"Fracture of shaft of ulna"
+S5220,"Unspecified fracture of shaft of ulna"
+S52201,"Unspecified fracture of shaft of right ulna"
+S52202,"Unspecified fracture of shaft of left ulna"
+S52209,"Unspecified fracture of shaft of unspecified ulna"
+S5221,"Greenstick fracture of shaft of ulna"
+S52211,"Greenstick fracture of shaft of right ulna"
+S52212,"Greenstick fracture of shaft of left ulna"
+S52219,"Greenstick fracture of shaft of unspecified ulna"
+S5222,"Transverse fracture of shaft of ulna"
+S52221,"Displaced transverse fracture of shaft of right ulna"
+S52222,"Displaced transverse fracture of shaft of left ulna"
+S52223,"Displaced transverse fracture of shaft of unspecified ulna"
+S52224,"Nondisplaced transverse fracture of shaft of right ulna"
+S52225,"Nondisplaced transverse fracture of shaft of left ulna"
+S52226,"Nondisplaced transverse fracture of shaft of unspecified ulna"
+S5223,"Oblique fracture of shaft of ulna"
+S52231,"Displaced oblique fracture of shaft of right ulna"
+S52232,"Displaced oblique fracture of shaft of left ulna"
+S52233,"Displaced oblique fracture of shaft of unspecified ulna"
+S52234,"Nondisplaced oblique fracture of shaft of right ulna"
+S52235,"Nondisplaced oblique fracture of shaft of left ulna"
+S52236,"Nondisplaced oblique fracture of shaft of unspecified ulna"
+S5224,"Spiral fracture of shaft of ulna"
+S52241,"Displaced spiral fracture of shaft of ulna, right arm"
+S52242,"Displaced spiral fracture of shaft of ulna, left arm"
+S52243,"Displaced spiral fracture of shaft of ulna, unspecified arm"
+S52244,"Nondisplaced spiral fracture of shaft of ulna, right arm"
+S52245,"Nondisplaced spiral fracture of shaft of ulna, left arm"
+S52246,"Nondisplaced spiral fracture of shaft of ulna, unspecified arm"
+S5225,"Comminuted fracture of shaft of ulna"
+S52251,"Displaced comminuted fracture of shaft of ulna, right arm"
+S52252,"Displaced comminuted fracture of shaft of ulna, left arm"
+S52253,"Displaced comminuted fracture of shaft of ulna, unspecified arm"
+S52254,"Nondisplaced comminuted fracture of shaft of ulna, right arm"
+S52255,"Nondisplaced comminuted fracture of shaft of ulna, left arm"
+S52256,"Nondisplaced comminuted fracture of shaft of ulna, unspecified arm"
+S5226,"Segmental fracture of shaft of ulna"
+S52261,"Displaced segmental fracture of shaft of ulna, right arm"
+S52262,"Displaced segmental fracture of shaft of ulna, left arm"
+S52263,"Displaced segmental fracture of shaft of ulna, unspecified arm"
+S52264,"Nondisplaced segmental fracture of shaft of ulna, right arm"
+S52265,"Nondisplaced segmental fracture of shaft of ulna, left arm"
+S52266,"Nondisplaced segmental fracture of shaft of ulna, unspecified arm"
+S5227,"Monteggia's fracture of ulna"
+S52271,"Monteggia's fracture of right ulna"
+S52272,"Monteggia's fracture of left ulna"
+S52279,"Monteggia's fracture of unspecified ulna"
+S5228,"Bent bone of ulna"
+S52281,"Bent bone of right ulna"
+S52282,"Bent bone of left ulna"
+S52283,"Bent bone of unspecified ulna"
+S5229,"Other fracture of shaft of ulna"
+S52291,"Other fracture of shaft of right ulna"
+S52292,"Other fracture of shaft of left ulna"
+S52299,"Other fracture of shaft of unspecified ulna"
+S523,"Fracture of shaft of radius"
+S5230,"Unspecified fracture of shaft of radius"
+S52301,"Unspecified fracture of shaft of right radius"
+S52302,"Unspecified fracture of shaft of left radius"
+S52309,"Unspecified fracture of shaft of unspecified radius"
+S5231,"Greenstick fracture of shaft of radius"
+S52311,"Greenstick fracture of shaft of radius, right arm"
+S52312,"Greenstick fracture of shaft of radius, left arm"
+S52319,"Greenstick fracture of shaft of radius, unspecified arm"
+S5232,"Transverse fracture of shaft of radius"
+S52321,"Displaced transverse fracture of shaft of right radius"
+S52322,"Displaced transverse fracture of shaft of left radius"
+S52323,"Displaced transverse fracture of shaft of unspecified radius"
+S52324,"Nondisplaced transverse fracture of shaft of right radius"
+S52325,"Nondisplaced transverse fracture of shaft of left radius"
+S52326,"Nondisplaced transverse fracture of shaft of unspecified radius"
+S5233,"Oblique fracture of shaft of radius"
+S52331,"Displaced oblique fracture of shaft of right radius"
+S52332,"Displaced oblique fracture of shaft of left radius"
+S52333,"Displaced oblique fracture of shaft of unspecified radius"
+S52334,"Nondisplaced oblique fracture of shaft of right radius"
+S52335,"Nondisplaced oblique fracture of shaft of left radius"
+S52336,"Nondisplaced oblique fracture of shaft of unspecified radius"
+S5234,"Spiral fracture of shaft of radius"
+S52341,"Displaced spiral fracture of shaft of radius, right arm"
+S52342,"Displaced spiral fracture of shaft of radius, left arm"
+S52343,"Displaced spiral fracture of shaft of radius, unspecified arm"
+S52344,"Nondisplaced spiral fracture of shaft of radius, right arm"
+S52345,"Nondisplaced spiral fracture of shaft of radius, left arm"
+S52346,"Nondisplaced spiral fracture of shaft of radius, unspecified arm"
+S5235,"Comminuted fracture of shaft of radius"
+S52351,"Displaced comminuted fracture of shaft of radius, right arm"
+S52352,"Displaced comminuted fracture of shaft of radius, left arm"
+S52353,"Displaced comminuted fracture of shaft of radius, unspecified arm"
+S52354,"Nondisplaced comminuted fracture of shaft of radius, right arm"
+S52355,"Nondisplaced comminuted fracture of shaft of radius, left arm"
+S52356,"Nondisplaced comminuted fracture of shaft of radius, unspecified arm"
+S5236,"Segmental fracture of shaft of radius"
+S52361,"Displaced segmental fracture of shaft of radius, right arm"
+S52362,"Displaced segmental fracture of shaft of radius, left arm"
+S52363,"Displaced segmental fracture of shaft of radius, unspecified arm"
+S52364,"Nondisplaced segmental fracture of shaft of radius, right arm"
+S52365,"Nondisplaced segmental fracture of shaft of radius, left arm"
+S52366,"Nondisplaced segmental fracture of shaft of radius, unspecified arm"
+S5237,"Galeazzi's fracture"
+S52371,"Galeazzi's fracture of right radius"
+S52372,"Galeazzi's fracture of left radius"
+S52379,"Galeazzi's fracture of unspecified radius"
+S5238,"Bent bone of radius"
+S52381,"Bent bone of right radius"
+S52382,"Bent bone of left radius"
+S52389,"Bent bone of unspecified radius"
+S5239,"Other fracture of shaft of radius"
+S52391,"Other fracture of shaft of radius, right arm"
+S52392,"Other fracture of shaft of radius, left arm"
+S52399,"Other fracture of shaft of radius, unspecified arm"
+S525,"Fracture of lower end of radius"
+S5250,"Unspecified fracture of the lower end of radius"
+S52501,"Unspecified fracture of the lower end of right radius"
+S52502,"Unspecified fracture of the lower end of left radius"
+S52509,"Unspecified fracture of the lower end of unspecified radius"
+S5251,"Fracture of radial styloid process"
+S52511,"Displaced fracture of right radial styloid process"
+S52512,"Displaced fracture of left radial styloid process"
+S52513,"Displaced fracture of unspecified radial styloid process"
+S52514,"Nondisplaced fracture of right radial styloid process"
+S52515,"Nondisplaced fracture of left radial styloid process"
+S52516,"Nondisplaced fracture of unspecified radial styloid process"
+S5252,"Torus fracture of lower end of radius"
+S52521,"Torus fracture of lower end of right radius"
+S52522,"Torus fracture of lower end of left radius"
+S52529,"Torus fracture of lower end of unspecified radius"
+S5253,"Colles' fracture"
+S52531,"Colles' fracture of right radius"
+S52532,"Colles' fracture of left radius"
+S52539,"Colles' fracture of unspecified radius"
+S5254,"Smith's fracture"
+S52541,"Smith's fracture of right radius"
+S52542,"Smith's fracture of left radius"
+S52549,"Smith's fracture of unspecified radius"
+S5255,"Other extraarticular fracture of lower end of radius"
+S52551,"Other extraarticular fracture of lower end of right radius"
+S52552,"Other extraarticular fracture of lower end of left radius"
+S52559,"Other extraarticular fracture of lower end of unspecified radius"
+S5256,"Barton's fracture"
+S52561,"Barton's fracture of right radius"
+S52562,"Barton's fracture of left radius"
+S52569,"Barton's fracture of unspecified radius"
+S5257,"Other intraarticular fracture of lower end of radius"
+S52571,"Other intraarticular fracture of lower end of right radius"
+S52572,"Other intraarticular fracture of lower end of left radius"
+S52579,"Other intraarticular fracture of lower end of unspecified radius"
+S5259,"Other fractures of lower end of radius"
+S52591,"Other fractures of lower end of right radius"
+S52592,"Other fractures of lower end of left radius"
+S52599,"Other fractures of lower end of unspecified radius"
+S526,"Fracture of lower end of ulna"
+S5260,"Unspecified fracture of lower end of ulna"
+S52601,"Unspecified fracture of lower end of right ulna"
+S52602,"Unspecified fracture of lower end of left ulna"
+S52609,"Unspecified fracture of lower end of unspecified ulna"
+S5261,"Fracture of ulna styloid process"
+S52611,"Displaced fracture of right ulna styloid process"
+S52612,"Displaced fracture of left ulna styloid process"
+S52613,"Displaced fracture of unspecified ulna styloid process"
+S52614,"Nondisplaced fracture of right ulna styloid process"
+S52615,"Nondisplaced fracture of left ulna styloid process"
+S52616,"Nondisplaced fracture of unspecified ulna styloid process"
+S5262,"Torus fracture of lower end of ulna"
+S52621,"Torus fracture of lower end of right ulna"
+S52622,"Torus fracture of lower end of left ulna"
+S52629,"Torus fracture of lower end of unspecified ulna"
+S5269,"Other fracture of lower end of ulna"
+S52691,"Other fracture of lower end of right ulna"
+S52692,"Other fracture of lower end of left ulna"
+S52699,"Other fracture of lower end of unspecified ulna"
+S529,"Unspecified fracture of forearm"
+S5290,"Unspecified fracture of unspecified forearm"
+S5291,"Unspecified fracture of right forearm"
+S5292,"Unspecified fracture of left forearm"
+S53,"Dislocation and sprain of joints and ligaments of elbow"
+S530,"Subluxation and dislocation of radial head"
+S5300,"Unspecified subluxation and dislocation of radial head"
+S53001,"Unspecified subluxation of right radial head"
+S53002,"Unspecified subluxation of left radial head"
+S53003,"Unspecified subluxation of unspecified radial head"
+S53004,"Unspecified dislocation of right radial head"
+S53005,"Unspecified dislocation of left radial head"
+S53006,"Unspecified dislocation of unspecified radial head"
+S5301,"Anterior subluxation and dislocation of radial head"
+S53011,"Anterior subluxation of right radial head"
+S53012,"Anterior subluxation of left radial head"
+S53013,"Anterior subluxation of unspecified radial head"
+S53014,"Anterior dislocation of right radial head"
+S53015,"Anterior dislocation of left radial head"
+S53016,"Anterior dislocation of unspecified radial head"
+S5302,"Posterior subluxation and dislocation of radial head"
+S53021,"Posterior subluxation of right radial head"
+S53022,"Posterior subluxation of left radial head"
+S53023,"Posterior subluxation of unspecified radial head"
+S53024,"Posterior dislocation of right radial head"
+S53025,"Posterior dislocation of left radial head"
+S53026,"Posterior dislocation of unspecified radial head"
+S5303,"Nursemaid's elbow"
+S53031,"Nursemaid's elbow, right elbow"
+S53032,"Nursemaid's elbow, left elbow"
+S53033,"Nursemaid's elbow, unspecified elbow"
+S5309,"Other subluxation and dislocation of radial head"
+S53091,"Other subluxation of right radial head"
+S53092,"Other subluxation of left radial head"
+S53093,"Other subluxation of unspecified radial head"
+S53094,"Other dislocation of right radial head"
+S53095,"Other dislocation of left radial head"
+S53096,"Other dislocation of unspecified radial head"
+S531,"Subluxation and dislocation of ulnohumeral joint"
+S5310,"Unspecified subluxation and dislocation of ulnohumeral joint"
+S53101,"Unspecified subluxation of right ulnohumeral joint"
+S53102,"Unspecified subluxation of left ulnohumeral joint"
+S53103,"Unspecified subluxation of unspecified ulnohumeral joint"
+S53104,"Unspecified dislocation of right ulnohumeral joint"
+S53105,"Unspecified dislocation of left ulnohumeral joint"
+S53106,"Unspecified dislocation of unspecified ulnohumeral joint"
+S5311,"Anterior subluxation and dislocation of ulnohumeral joint"
+S53111,"Anterior subluxation of right ulnohumeral joint"
+S53112,"Anterior subluxation of left ulnohumeral joint"
+S53113,"Anterior subluxation of unspecified ulnohumeral joint"
+S53114,"Anterior dislocation of right ulnohumeral joint"
+S53115,"Anterior dislocation of left ulnohumeral joint"
+S53116,"Anterior dislocation of unspecified ulnohumeral joint"
+S5312,"Posterior subluxation and dislocation of ulnohumeral joint"
+S53121,"Posterior subluxation of right ulnohumeral joint"
+S53122,"Posterior subluxation of left ulnohumeral joint"
+S53123,"Posterior subluxation of unspecified ulnohumeral joint"
+S53124,"Posterior dislocation of right ulnohumeral joint"
+S53125,"Posterior dislocation of left ulnohumeral joint"
+S53126,"Posterior dislocation of unspecified ulnohumeral joint"
+S5313,"Medial subluxation and dislocation of ulnohumeral joint"
+S53131,"Medial subluxation of right ulnohumeral joint"
+S53132,"Medial subluxation of left ulnohumeral joint"
+S53133,"Medial subluxation of unspecified ulnohumeral joint"
+S53134,"Medial dislocation of right ulnohumeral joint"
+S53135,"Medial dislocation of left ulnohumeral joint"
+S53136,"Medial dislocation of unspecified ulnohumeral joint"
+S5314,"Lateral subluxation and dislocation of ulnohumeral joint"
+S53141,"Lateral subluxation of right ulnohumeral joint"
+S53142,"Lateral subluxation of left ulnohumeral joint"
+S53143,"Lateral subluxation of unspecified ulnohumeral joint"
+S53144,"Lateral dislocation of right ulnohumeral joint"
+S53145,"Lateral dislocation of left ulnohumeral joint"
+S53146,"Lateral dislocation of unspecified ulnohumeral joint"
+S5319,"Other subluxation and dislocation of ulnohumeral joint"
+S53191,"Other subluxation of right ulnohumeral joint"
+S53192,"Other subluxation of left ulnohumeral joint"
+S53193,"Other subluxation of unspecified ulnohumeral joint"
+S53194,"Other dislocation of right ulnohumeral joint"
+S53195,"Other dislocation of left ulnohumeral joint"
+S53196,"Other dislocation of unspecified ulnohumeral joint"
+S532,"Traumatic rupture of radial collateral ligament"
+S5320,"Traumatic rupture of unspecified radial collateral ligament"
+S5321,"Traumatic rupture of right radial collateral ligament"
+S5322,"Traumatic rupture of left radial collateral ligament"
+S533,"Traumatic rupture of ulnar collateral ligament"
+S5330,"Traumatic rupture of unspecified ulnar collateral ligament"
+S5331,"Traumatic rupture of right ulnar collateral ligament"
+S5332,"Traumatic rupture of left ulnar collateral ligament"
+S534,"Sprain of elbow"
+S5340,"Unspecified sprain of elbow"
+S53401,"Unspecified sprain of right elbow"
+S53402,"Unspecified sprain of left elbow"
+S53409,"Unspecified sprain of unspecified elbow"
+S5341,"Radiohumeral (joint) sprain"
+S53411,"Radiohumeral (joint) sprain of right elbow"
+S53412,"Radiohumeral (joint) sprain of left elbow"
+S53419,"Radiohumeral (joint) sprain of unspecified elbow"
+S5342,"Ulnohumeral (joint) sprain"
+S53421,"Ulnohumeral (joint) sprain of right elbow"
+S53422,"Ulnohumeral (joint) sprain of left elbow"
+S53429,"Ulnohumeral (joint) sprain of unspecified elbow"
+S5343,"Radial collateral ligament sprain"
+S53431,"Radial collateral ligament sprain of right elbow"
+S53432,"Radial collateral ligament sprain of left elbow"
+S53439,"Radial collateral ligament sprain of unspecified elbow"
+S5344,"Ulnar collateral ligament sprain"
+S53441,"Ulnar collateral ligament sprain of right elbow"
+S53442,"Ulnar collateral ligament sprain of left elbow"
+S53449,"Ulnar collateral ligament sprain of unspecified elbow"
+S5349,"Other sprain of elbow"
+S53491,"Other sprain of right elbow"
+S53492,"Other sprain of left elbow"
+S53499,"Other sprain of unspecified elbow"
+S54,"Injury of nerves at forearm level"
+S540,"Injury of ulnar nerve at forearm level"
+S5400,"Injury of ulnar nerve at forearm level, unspecified arm"
+S5401,"Injury of ulnar nerve at forearm level, right arm"
+S5402,"Injury of ulnar nerve at forearm level, left arm"
+S541,"Injury of median nerve at forearm level"
+S5410,"Injury of median nerve at forearm level, unspecified arm"
+S5411,"Injury of median nerve at forearm level, right arm"
+S5412,"Injury of median nerve at forearm level, left arm"
+S542,"Injury of radial nerve at forearm level"
+S5420,"Injury of radial nerve at forearm level, unspecified arm"
+S5421,"Injury of radial nerve at forearm level, right arm"
+S5422,"Injury of radial nerve at forearm level, left arm"
+S543,"Injury of cutaneous sensory nerve at forearm level"
+S5430,"Injury of cutaneous sensory nerve at forearm level, unspecified arm"
+S5431,"Injury of cutaneous sensory nerve at forearm level, right arm"
+S5432,"Injury of cutaneous sensory nerve at forearm level, left arm"
+S548,"Injury of other nerves at forearm level"
+S548X,"Injury of other nerves at forearm level"
+S548X1,"Injury of other nerves at forearm level, right arm"
+S548X2,"Injury of other nerves at forearm level, left arm"
+S548X9,"Injury of other nerves at forearm level, unspecified arm"
+S549,"Injury of unspecified nerve at forearm level"
+S5490,"Injury of unspecified nerve at forearm level, unspecified arm"
+S5491,"Injury of unspecified nerve at forearm level, right arm"
+S5492,"Injury of unspecified nerve at forearm level, left arm"
+S55,"Injury of blood vessels at forearm level"
+S550,"Injury of ulnar artery at forearm level"
+S5500,"Unspecified injury of ulnar artery at forearm level"
+S55001,"Unspecified injury of ulnar artery at forearm level, right arm"
+S55002,"Unspecified injury of ulnar artery at forearm level, left arm"
+S55009,"Unspecified injury of ulnar artery at forearm level, unspecified arm"
+S5501,"Laceration of ulnar artery at forearm level"
+S55011,"Laceration of ulnar artery at forearm level, right arm"
+S55012,"Laceration of ulnar artery at forearm level, left arm"
+S55019,"Laceration of ulnar artery at forearm level, unspecified arm"
+S5509,"Other specified injury of ulnar artery at forearm level"
+S55091,"Other specified injury of ulnar artery at forearm level, right arm"
+S55092,"Other specified injury of ulnar artery at forearm level, left arm"
+S55099,"Other specified injury of ulnar artery at forearm level, unspecified arm"
+S551,"Injury of radial artery at forearm level"
+S5510,"Unspecified injury of radial artery at forearm level"
+S55101,"Unspecified injury of radial artery at forearm level, right arm"
+S55102,"Unspecified injury of radial artery at forearm level, left arm"
+S55109,"Unspecified injury of radial artery at forearm level, unspecified arm"
+S5511,"Laceration of radial artery at forearm level"
+S55111,"Laceration of radial artery at forearm level, right arm"
+S55112,"Laceration of radial artery at forearm level, left arm"
+S55119,"Laceration of radial artery at forearm level, unspecified arm"
+S5519,"Other specified injury of radial artery at forearm level"
+S55191,"Other specified injury of radial artery at forearm level, right arm"
+S55192,"Other specified injury of radial artery at forearm level, left arm"
+S55199,"Other specified injury of radial artery at forearm level, unspecified arm"
+S552,"Injury of vein at forearm level"
+S5520,"Unspecified injury of vein at forearm level"
+S55201,"Unspecified injury of vein at forearm level, right arm"
+S55202,"Unspecified injury of vein at forearm level, left arm"
+S55209,"Unspecified injury of vein at forearm level, unspecified arm"
+S5521,"Laceration of vein at forearm level"
+S55211,"Laceration of vein at forearm level, right arm"
+S55212,"Laceration of vein at forearm level, left arm"
+S55219,"Laceration of vein at forearm level, unspecified arm"
+S5529,"Other specified injury of vein at forearm level"
+S55291,"Other specified injury of vein at forearm level, right arm"
+S55292,"Other specified injury of vein at forearm level, left arm"
+S55299,"Other specified injury of vein at forearm level, unspecified arm"
+S558,"Injury of other blood vessels at forearm level"
+S5580,"Unspecified injury of other blood vessels at forearm level"
+S55801,"Unspecified injury of other blood vessels at forearm level, right arm"
+S55802,"Unspecified injury of other blood vessels at forearm level, left arm"
+S55809,"Unspecified injury of other blood vessels at forearm level, unspecified arm"
+S5581,"Laceration of other blood vessels at forearm level"
+S55811,"Laceration of other blood vessels at forearm level, right arm"
+S55812,"Laceration of other blood vessels at forearm level, left arm"
+S55819,"Laceration of other blood vessels at forearm level, unspecified arm"
+S5589,"Other specified injury of other blood vessels at forearm level"
+S55891,"Other specified injury of other blood vessels at forearm level, right arm"
+S55892,"Other specified injury of other blood vessels at forearm level, left arm"
+S55899,"Other specified injury of other blood vessels at forearm level, unspecified arm"
+S559,"Injury of unspecified blood vessel at forearm level"
+S5590,"Unspecified injury of unspecified blood vessel at forearm level"
+S55901,"Unspecified injury of unspecified blood vessel at forearm level, right arm"
+S55902,"Unspecified injury of unspecified blood vessel at forearm level, left arm"
+S55909,"Unspecified injury of unspecified blood vessel at forearm level, unspecified arm"
+S5591,"Laceration of unspecified blood vessel at forearm level"
+S55911,"Laceration of unspecified blood vessel at forearm level, right arm"
+S55912,"Laceration of unspecified blood vessel at forearm level, left arm"
+S55919,"Laceration of unspecified blood vessel at forearm level, unspecified arm"
+S5599,"Other specified injury of unspecified blood vessel at forearm level"
+S55991,"Other specified injury of unspecified blood vessel at forearm level, right arm"
+S55992,"Other specified injury of unspecified blood vessel at forearm level, left arm"
+S55999,"Other specified injury of unspecified blood vessel at forearm level, unspecified arm"
+S56,"Injury of muscle, fascia and tendon at forearm level"
+S560,"Injury of flexor muscle, fascia and tendon of thumb at forearm level"
+S5600,"Unspecified injury of flexor muscle, fascia and tendon of thumb at forearm level"
+S56001,"Unspecified injury of flexor muscle, fascia and tendon of right thumb at forearm level"
+S56002,"Unspecified injury of flexor muscle, fascia and tendon of left thumb at forearm level"
+S56009,"Unspecified injury of flexor muscle, fascia and tendon of unspecified thumb at forearm level"
+S5601,"Strain of flexor muscle, fascia and tendon of thumb at forearm level"
+S56011,"Strain of flexor muscle, fascia and tendon of right thumb at forearm level"
+S56012,"Strain of flexor muscle, fascia and tendon of left thumb at forearm level"
+S56019,"Strain of flexor muscle, fascia and tendon of unspecified thumb at forearm level"
+S5602,"Laceration of flexor muscle, fascia and tendon of thumb at forearm level"
+S56021,"Laceration of flexor muscle, fascia and tendon of right thumb at forearm level"
+S56022,"Laceration of flexor muscle, fascia and tendon of left thumb at forearm level"
+S56029,"Laceration of flexor muscle, fascia and tendon of unspecified thumb at forearm level"
+S5609,"Other injury of flexor muscle, fascia and tendon of thumb at forearm level"
+S56091,"Other injury of flexor muscle, fascia and tendon of right thumb at forearm level"
+S56092,"Other injury of flexor muscle, fascia and tendon of left thumb at forearm level"
+S56099,"Other injury of flexor muscle, fascia and tendon of unspecified thumb at forearm level"
+S561,"Injury of flexor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S5610,"Unspecified injury of flexor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56101,"Unspecified injury of flexor muscle, fascia and tendon of right index finger at forearm level"
+S56102,"Unspecified injury of flexor muscle, fascia and tendon of left index finger at forearm level"
+S56103,"Unspecified injury of flexor muscle, fascia and tendon of right middle finger at forearm level"
+S56104,"Unspecified injury of flexor muscle, fascia and tendon of left middle finger at forearm level"
+S56105,"Unspecified injury of flexor muscle, fascia and tendon of right ring finger at forearm level"
+S56106,"Unspecified injury of flexor muscle, fascia and tendon of left ring finger at forearm level"
+S56107,"Unspecified injury of flexor muscle, fascia and tendon of right little finger at forearm level"
+S56108,"Unspecified injury of flexor muscle, fascia and tendon of left little finger at forearm level"
+S56109,"Unspecified injury of flexor muscle, fascia and tendon of unspecified finger at forearm level"
+S5611,"Strain of flexor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56111,"Strain of flexor muscle, fascia and tendon of right index finger at forearm level"
+S56112,"Strain of flexor muscle, fascia and tendon of left index finger at forearm level"
+S56113,"Strain of flexor muscle, fascia and tendon of right middle finger at forearm level"
+S56114,"Strain of flexor muscle, fascia and tendon of left middle finger at forearm level"
+S56115,"Strain of flexor muscle, fascia and tendon of right ring finger at forearm level"
+S56116,"Strain of flexor muscle, fascia and tendon of left ring finger at forearm level"
+S56117,"Strain of flexor muscle, fascia and tendon of right little finger at forearm level"
+S56118,"Strain of flexor muscle, fascia and tendon of left little finger at forearm level"
+S56119,"Strain of flexor muscle, fascia and tendon of finger of unspecified finger at forearm level"
+S5612,"Laceration of flexor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56121,"Laceration of flexor muscle, fascia and tendon of right index finger at forearm level"
+S56122,"Laceration of flexor muscle, fascia and tendon of left index finger at forearm level"
+S56123,"Laceration of flexor muscle, fascia and tendon of right middle finger at forearm level"
+S56124,"Laceration of flexor muscle, fascia and tendon of left middle finger at forearm level"
+S56125,"Laceration of flexor muscle, fascia and tendon of right ring finger at forearm level"
+S56126,"Laceration of flexor muscle, fascia and tendon of left ring finger at forearm level"
+S56127,"Laceration of flexor muscle, fascia and tendon of right little finger at forearm level"
+S56128,"Laceration of flexor muscle, fascia and tendon of left little finger at forearm level"
+S56129,"Laceration of flexor muscle, fascia and tendon of unspecified finger at forearm level"
+S5619,"Other injury of flexor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56191,"Other injury of flexor muscle, fascia and tendon of right index finger at forearm level"
+S56192,"Other injury of flexor muscle, fascia and tendon of left index finger at forearm level"
+S56193,"Other injury of flexor muscle, fascia and tendon of right middle finger at forearm level"
+S56194,"Other injury of flexor muscle, fascia and tendon of left middle finger at forearm level"
+S56195,"Other injury of flexor muscle, fascia and tendon of right ring finger at forearm level"
+S56196,"Other injury of flexor muscle, fascia and tendon of left ring finger at forearm level"
+S56197,"Other injury of flexor muscle, fascia and tendon of right little finger at forearm level"
+S56198,"Other injury of flexor muscle, fascia and tendon of left little finger at forearm level"
+S56199,"Other injury of flexor muscle, fascia and tendon of unspecified finger at forearm level"
+S562,"Injury of other flexor muscle, fascia and tendon at forearm level"
+S5620,"Unspecified injury of other flexor muscle, fascia and tendon at forearm level"
+S56201,"Unspecified injury of other flexor muscle, fascia and tendon at forearm level, right arm"
+S56202,"Unspecified injury of other flexor muscle, fascia and tendon at forearm level, left arm"
+S56209,"Unspecified injury of other flexor muscle, fascia and tendon at forearm level, unspecified arm"
+S5621,"Strain of other flexor muscle, fascia and tendon at forearm level"
+S56211,"Strain of other flexor muscle, fascia and tendon at forearm level, right arm"
+S56212,"Strain of other flexor muscle, fascia and tendon at forearm level, left arm"
+S56219,"Strain of other flexor muscle, fascia and tendon at forearm level, unspecified arm"
+S5622,"Laceration of other flexor muscle, fascia and tendon at forearm level"
+S56221,"Laceration of other flexor muscle, fascia and tendon at forearm level, right arm"
+S56222,"Laceration of other flexor muscle, fascia and tendon at forearm level, left arm"
+S56229,"Laceration of other flexor muscle, fascia and tendon at forearm level, unspecified arm"
+S5629,"Other injury of other flexor muscle, fascia and tendon at forearm level"
+S56291,"Other injury of other flexor muscle, fascia and tendon at forearm level, right arm"
+S56292,"Other injury of other flexor muscle, fascia and tendon at forearm level, left arm"
+S56299,"Other injury of other flexor muscle, fascia and tendon at forearm level, unspecified arm"
+S563,"Injury of extensor or abductor muscles, fascia and tendons of thumb at forearm level"
+S5630,"Unspecified injury of extensor or abductor muscles, fascia and tendons of thumb at forearm level"
+S56301,"Unspecified injury of extensor or abductor muscles, fascia and tendons of right thumb at forearm level"
+S56302,"Unspecified injury of extensor or abductor muscles, fascia and tendons of left thumb at forearm level"
+S56309,"Unspecified injury of extensor or abductor muscles, fascia and tendons of unspecified thumb at forearm level"
+S5631,"Strain of extensor or abductor muscles, fascia and tendons of thumb at forearm level"
+S56311,"Strain of extensor or abductor muscles, fascia and tendons of right thumb at forearm level"
+S56312,"Strain of extensor or abductor muscles, fascia and tendons of left thumb at forearm level"
+S56319,"Strain of extensor or abductor muscles, fascia and tendons of unspecified thumb at forearm level"
+S5632,"Laceration of extensor or abductor muscles, fascia and tendons of thumb at forearm level"
+S56321,"Laceration of extensor or abductor muscles, fascia and tendons of right thumb at forearm level"
+S56322,"Laceration of extensor or abductor muscles, fascia and tendons of left thumb at forearm level"
+S56329,"Laceration of extensor or abductor muscles, fascia and tendons of unspecified thumb at forearm level"
+S5639,"Other injury of extensor or abductor muscles, fascia and tendons of thumb at forearm level"
+S56391,"Other injury of extensor or abductor muscles, fascia and tendons of right thumb at forearm level"
+S56392,"Other injury of extensor or abductor muscles, fascia and tendons of left thumb at forearm level"
+S56399,"Other injury of extensor or abductor muscles, fascia and tendons of unspecified thumb at forearm level"
+S564,"Injury of extensor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S5640,"Unspecified injury of extensor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56401,"Unspecified injury of extensor muscle, fascia and tendon of right index finger at forearm level"
+S56402,"Unspecified injury of extensor muscle, fascia and tendon of left index finger at forearm level"
+S56403,"Unspecified injury of extensor muscle, fascia and tendon of right middle finger at forearm level"
+S56404,"Unspecified injury of extensor muscle, fascia and tendon of left middle finger at forearm level"
+S56405,"Unspecified injury of extensor muscle, fascia and tendon of right ring finger at forearm level"
+S56406,"Unspecified injury of extensor muscle, fascia and tendon of left ring finger at forearm level"
+S56407,"Unspecified injury of extensor muscle, fascia and tendon of right little finger at forearm level"
+S56408,"Unspecified injury of extensor muscle, fascia and tendon of left little finger at forearm level"
+S56409,"Unspecified injury of extensor muscle, fascia and tendon of unspecified finger at forearm level"
+S5641,"Strain of extensor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56411,"Strain of extensor muscle, fascia and tendon of right index finger at forearm level"
+S56412,"Strain of extensor muscle, fascia and tendon of left index finger at forearm level"
+S56413,"Strain of extensor muscle, fascia and tendon of right middle finger at forearm level"
+S56414,"Strain of extensor muscle, fascia and tendon of left middle finger at forearm level"
+S56415,"Strain of extensor muscle, fascia and tendon of right ring finger at forearm level"
+S56416,"Strain of extensor muscle, fascia and tendon of left ring finger at forearm level"
+S56417,"Strain of extensor muscle, fascia and tendon of right little finger at forearm level"
+S56418,"Strain of extensor muscle, fascia and tendon of left little finger at forearm level"
+S56419,"Strain of extensor muscle, fascia and tendon of finger, unspecified finger at forearm level"
+S5642,"Laceration of extensor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56421,"Laceration of extensor muscle, fascia and tendon of right index finger at forearm level"
+S56422,"Laceration of extensor muscle, fascia and tendon of left index finger at forearm level"
+S56423,"Laceration of extensor muscle, fascia and tendon of right middle finger at forearm level"
+S56424,"Laceration of extensor muscle, fascia and tendon of left middle finger at forearm level"
+S56425,"Laceration of extensor muscle, fascia and tendon of right ring finger at forearm level"
+S56426,"Laceration of extensor muscle, fascia and tendon of left ring finger at forearm level"
+S56427,"Laceration of extensor muscle, fascia and tendon of right little finger at forearm level"
+S56428,"Laceration of extensor muscle, fascia and tendon of left little finger at forearm level"
+S56429,"Laceration of extensor muscle, fascia and tendon of unspecified finger at forearm level"
+S5649,"Other injury of extensor muscle, fascia and tendon of other and unspecified finger at forearm level"
+S56491,"Other injury of extensor muscle, fascia and tendon of right index finger at forearm level"
+S56492,"Other injury of extensor muscle, fascia and tendon of left index finger at forearm level"
+S56493,"Other injury of extensor muscle, fascia and tendon of right middle finger at forearm level"
+S56494,"Other injury of extensor muscle, fascia and tendon of left middle finger at forearm level"
+S56495,"Other injury of extensor muscle, fascia and tendon of right ring finger at forearm level"
+S56496,"Other injury of extensor muscle, fascia and tendon of left ring finger at forearm level"
+S56497,"Other injury of extensor muscle, fascia and tendon of right little finger at forearm level"
+S56498,"Other injury of extensor muscle, fascia and tendon of left little finger at forearm level"
+S56499,"Other injury of extensor muscle, fascia and tendon of unspecified finger at forearm level"
+S565,"Injury of other extensor muscle, fascia and tendon at forearm level"
+S5650,"Unspecified injury of other extensor muscle, fascia and tendon at forearm level"
+S56501,"Unspecified injury of other extensor muscle, fascia and tendon at forearm level, right arm"
+S56502,"Unspecified injury of other extensor muscle, fascia and tendon at forearm level, left arm"
+S56509,"Unspecified injury of other extensor muscle, fascia and tendon at forearm level, unspecified arm"
+S5651,"Strain of other extensor muscle, fascia and tendon at forearm level"
+S56511,"Strain of other extensor muscle, fascia and tendon at forearm level, right arm"
+S56512,"Strain of other extensor muscle, fascia and tendon at forearm level, left arm"
+S56519,"Strain of other extensor muscle, fascia and tendon at forearm level, unspecified arm"
+S5652,"Laceration of other extensor muscle, fascia and tendon at forearm level"
+S56521,"Laceration of other extensor muscle, fascia and tendon at forearm level, right arm"
+S56522,"Laceration of other extensor muscle, fascia and tendon at forearm level, left arm"
+S56529,"Laceration of other extensor muscle, fascia and tendon at forearm level, unspecified arm"
+S5659,"Other injury of other extensor muscle, fascia and tendon at forearm level"
+S56591,"Other injury of other extensor muscle, fascia and tendon at forearm level, right arm"
+S56592,"Other injury of other extensor muscle, fascia and tendon at forearm level, left arm"
+S56599,"Other injury of other extensor muscle, fascia and tendon at forearm level, unspecified arm"
+S568,"Injury of other muscles, fascia and tendons at forearm level"
+S5680,"Unspecified injury of other muscles, fascia and tendons at forearm level"
+S56801,"Unspecified injury of other muscles, fascia and tendons at forearm level, right arm"
+S56802,"Unspecified injury of other muscles, fascia and tendons at forearm level, left arm"
+S56809,"Unspecified injury of other muscles, fascia and tendons at forearm level, unspecified arm"
+S5681,"Strain of other muscles, fascia and tendons at forearm level"
+S56811,"Strain of other muscles, fascia and tendons at forearm level, right arm"
+S56812,"Strain of other muscles, fascia and tendons at forearm level, left arm"
+S56819,"Strain of other muscles, fascia and tendons at forearm level, unspecified arm"
+S5682,"Laceration of other muscles, fascia and tendons at forearm level"
+S56821,"Laceration of other muscles, fascia and tendons at forearm level, right arm"
+S56822,"Laceration of other muscles, fascia and tendons at forearm level, left arm"
+S56829,"Laceration of other muscles, fascia and tendons at forearm level, unspecified arm"
+S5689,"Other injury of other muscles, fascia and tendons at forearm level"
+S56891,"Other injury of other muscles, fascia and tendons at forearm level, right arm"
+S56892,"Other injury of other muscles, fascia and tendons at forearm level, left arm"
+S56899,"Other injury of other muscles, fascia and tendons at forearm level, unspecified arm"
+S569,"Injury of unspecified muscles, fascia and tendons at forearm level"
+S5690,"Unspecified injury of unspecified muscles, fascia and tendons at forearm level"
+S56901,"Unspecified injury of unspecified muscles, fascia and tendons at forearm level, right arm"
+S56902,"Unspecified injury of unspecified muscles, fascia and tendons at forearm level, left arm"
+S56909,"Unspecified injury of unspecified muscles, fascia and tendons at forearm level, unspecified arm"
+S5691,"Strain of unspecified muscles, fascia and tendons at forearm level"
+S56911,"Strain of unspecified muscles, fascia and tendons at forearm level, right arm"
+S56912,"Strain of unspecified muscles, fascia and tendons at forearm level, left arm"
+S56919,"Strain of unspecified muscles, fascia and tendons at forearm level, unspecified arm"
+S5692,"Laceration of unspecified muscles, fascia and tendons at forearm level"
+S56921,"Laceration of unspecified muscles, fascia and tendons at forearm level, right arm"
+S56922,"Laceration of unspecified muscles, fascia and tendons at forearm level, left arm"
+S56929,"Laceration of unspecified muscles, fascia and tendons at forearm level, unspecified arm"
+S5699,"Other injury of unspecified muscles, fascia and tendons at forearm level"
+S56991,"Other injury of unspecified muscles, fascia and tendons at forearm level, right arm"
+S56992,"Other injury of unspecified muscles, fascia and tendons at forearm level, left arm"
+S56999,"Other injury of unspecified muscles, fascia and tendons at forearm level, unspecified arm"
+S57,"Crushing injury of elbow and forearm"
+S570,"Crushing injury of elbow"
+S5700,"Crushing injury of unspecified elbow"
+S5701,"Crushing injury of right elbow"
+S5702,"Crushing injury of left elbow"
+S578,"Crushing injury of forearm"
+S5780,"Crushing injury of unspecified forearm"
+S5781,"Crushing injury of right forearm"
+S5782,"Crushing injury of left forearm"
+S58,"Traumatic amputation of elbow and forearm"
+S580,"Traumatic amputation at elbow level"
+S5801,"Complete traumatic amputation at elbow level"
+S58011,"Complete traumatic amputation at elbow level, right arm"
+S58012,"Complete traumatic amputation at elbow level, left arm"
+S58019,"Complete traumatic amputation at elbow level, unspecified arm"
+S5802,"Partial traumatic amputation at elbow level"
+S58021,"Partial traumatic amputation at elbow level, right arm"
+S58022,"Partial traumatic amputation at elbow level, left arm"
+S58029,"Partial traumatic amputation at elbow level, unspecified arm"
+S581,"Traumatic amputation at level between elbow and wrist"
+S5811,"Complete traumatic amputation at level between elbow and wrist"
+S58111,"Complete traumatic amputation at level between elbow and wrist, right arm"
+S58112,"Complete traumatic amputation at level between elbow and wrist, left arm"
+S58119,"Complete traumatic amputation at level between elbow and wrist, unspecified arm"
+S5812,"Partial traumatic amputation at level between elbow and wrist"
+S58121,"Partial traumatic amputation at level between elbow and wrist, right arm"
+S58122,"Partial traumatic amputation at level between elbow and wrist, left arm"
+S58129,"Partial traumatic amputation at level between elbow and wrist, unspecified arm"
+S589,"Traumatic amputation of forearm, level unspecified"
+S5891,"Complete traumatic amputation of forearm, level unspecified"
+S58911,"Complete traumatic amputation of right forearm, level unspecified"
+S58912,"Complete traumatic amputation of left forearm, level unspecified"
+S58919,"Complete traumatic amputation of unspecified forearm, level unspecified"
+S5892,"Partial traumatic amputation of forearm, level unspecified"
+S58921,"Partial traumatic amputation of right forearm, level unspecified"
+S58922,"Partial traumatic amputation of left forearm, level unspecified"
+S58929,"Partial traumatic amputation of unspecified forearm, level unspecified"
+S59,"Other and unspecified injuries of elbow and forearm"
+S590,"Physeal fracture of lower end of ulna"
+S5900,"Unspecified physeal fracture of lower end of ulna"
+S59001,"Unspecified physeal fracture of lower end of ulna, right arm"
+S59002,"Unspecified physeal fracture of lower end of ulna, left arm"
+S59009,"Unspecified physeal fracture of lower end of ulna, unspecified arm"
+S5901,"Salter-Harris Type I physeal fracture of lower end of ulna"
+S59011,"Salter-Harris Type I physeal fracture of lower end of ulna, right arm"
+S59012,"Salter-Harris Type I physeal fracture of lower end of ulna, left arm"
+S59019,"Salter-Harris Type I physeal fracture of lower end of ulna, unspecified arm"
+S5902,"Salter-Harris Type II physeal fracture of lower end of ulna"
+S59021,"Salter-Harris Type II physeal fracture of lower end of ulna, right arm"
+S59022,"Salter-Harris Type II physeal fracture of lower end of ulna, left arm"
+S59029,"Salter-Harris Type II physeal fracture of lower end of ulna, unspecified arm"
+S5903,"Salter-Harris Type III physeal fracture of lower end of ulna"
+S59031,"Salter-Harris Type III physeal fracture of lower end of ulna, right arm"
+S59032,"Salter-Harris Type III physeal fracture of lower end of ulna, left arm"
+S59039,"Salter-Harris Type III physeal fracture of lower end of ulna, unspecified arm"
+S5904,"Salter-Harris Type IV physeal fracture of lower end of ulna"
+S59041,"Salter-Harris Type IV physeal fracture of lower end of ulna, right arm"
+S59042,"Salter-Harris Type IV physeal fracture of lower end of ulna, left arm"
+S59049,"Salter-Harris Type IV physeal fracture of lower end of ulna, unspecified arm"
+S5909,"Other physeal fracture of lower end of ulna"
+S59091,"Other physeal fracture of lower end of ulna, right arm"
+S59092,"Other physeal fracture of lower end of ulna, left arm"
+S59099,"Other physeal fracture of lower end of ulna, unspecified arm"
+S591,"Physeal fracture of upper end of radius"
+S5910,"Unspecified physeal fracture of upper end of radius"
+S59101,"Unspecified physeal fracture of upper end of radius, right arm"
+S59102,"Unspecified physeal fracture of upper end of radius, left arm"
+S59109,"Unspecified physeal fracture of upper end of radius, unspecified arm"
+S5911,"Salter-Harris Type I physeal fracture of upper end of radius"
+S59111,"Salter-Harris Type I physeal fracture of upper end of radius, right arm"
+S59112,"Salter-Harris Type I physeal fracture of upper end of radius, left arm"
+S59119,"Salter-Harris Type I physeal fracture of upper end of radius, unspecified arm"
+S5912,"Salter-Harris Type II physeal fracture of upper end of radius"
+S59121,"Salter-Harris Type II physeal fracture of upper end of radius, right arm"
+S59122,"Salter-Harris Type II physeal fracture of upper end of radius, left arm"
+S59129,"Salter-Harris Type II physeal fracture of upper end of radius, unspecified arm"
+S5913,"Salter-Harris Type III physeal fracture of upper end of radius"
+S59131,"Salter-Harris Type III physeal fracture of upper end of radius, right arm"
+S59132,"Salter-Harris Type III physeal fracture of upper end of radius, left arm"
+S59139,"Salter-Harris Type III physeal fracture of upper end of radius, unspecified arm"
+S5914,"Salter-Harris Type IV physeal fracture of upper end of radius"
+S59141,"Salter-Harris Type IV physeal fracture of upper end of radius, right arm"
+S59142,"Salter-Harris Type IV physeal fracture of upper end of radius, left arm"
+S59149,"Salter-Harris Type IV physeal fracture of upper end of radius, unspecified arm"
+S5919,"Other physeal fracture of upper end of radius"
+S59191,"Other physeal fracture of upper end of radius, right arm"
+S59192,"Other physeal fracture of upper end of radius, left arm"
+S59199,"Other physeal fracture of upper end of radius, unspecified arm"
+S592,"Physeal fracture of lower end of radius"
+S5920,"Unspecified physeal fracture of lower end of radius"
+S59201,"Unspecified physeal fracture of lower end of radius, right arm"
+S59202,"Unspecified physeal fracture of lower end of radius, left arm"
+S59209,"Unspecified physeal fracture of lower end of radius, unspecified arm"
+S5921,"Salter-Harris Type I physeal fracture of lower end of radius"
+S59211,"Salter-Harris Type I physeal fracture of lower end of radius, right arm"
+S59212,"Salter-Harris Type I physeal fracture of lower end of radius, left arm"
+S59219,"Salter-Harris Type I physeal fracture of lower end of radius, unspecified arm"
+S5922,"Salter-Harris Type II physeal fracture of lower end of radius"
+S59221,"Salter-Harris Type II physeal fracture of lower end of radius, right arm"
+S59222,"Salter-Harris Type II physeal fracture of lower end of radius, left arm"
+S59229,"Salter-Harris Type II physeal fracture of lower end of radius, unspecified arm"
+S5923,"Salter-Harris Type III physeal fracture of lower end of radius"
+S59231,"Salter-Harris Type III physeal fracture of lower end of radius, right arm"
+S59232,"Salter-Harris Type III physeal fracture of lower end of radius, left arm"
+S59239,"Salter-Harris Type III physeal fracture of lower end of radius, unspecified arm"
+S5924,"Salter-Harris Type IV physeal fracture of lower end of radius"
+S59241,"Salter-Harris Type IV physeal fracture of lower end of radius, right arm"
+S59242,"Salter-Harris Type IV physeal fracture of lower end of radius, left arm"
+S59249,"Salter-Harris Type IV physeal fracture of lower end of radius, unspecified arm"
+S5929,"Other physeal fracture of lower end of radius"
+S59291,"Other physeal fracture of lower end of radius, right arm"
+S59292,"Other physeal fracture of lower end of radius, left arm"
+S59299,"Other physeal fracture of lower end of radius, unspecified arm"
+S598,"Other specified injuries of elbow and forearm"
+S5980,"Other specified injuries of elbow"
+S59801,"Other specified injuries of right elbow"
+S59802,"Other specified injuries of left elbow"
+S59809,"Other specified injuries of unspecified elbow"
+S5981,"Other specified injuries of forearm"
+S59811,"Other specified injuries right forearm"
+S59812,"Other specified injuries left forearm"
+S59819,"Other specified injuries unspecified forearm"
+S599,"Unspecified injury of elbow and forearm"
+S5990,"Unspecified injury of elbow"
+S59901,"Unspecified injury of right elbow"
+S59902,"Unspecified injury of left elbow"
+S59909,"Unspecified injury of unspecified elbow"
+S5991,"Unspecified injury of forearm"
+S59911,"Unspecified injury of right forearm"
+S59912,"Unspecified injury of left forearm"
+S59919,"Unspecified injury of unspecified forearm"
+S60,"Superficial injury of wrist, hand and fingers"
+S600,"Contusion of finger without damage to nail"
+S6000,"Contusion of unspecified finger without damage to nail"
+S6001,"Contusion of thumb without damage to nail"
+S60011,"Contusion of right thumb without damage to nail"
+S60012,"Contusion of left thumb without damage to nail"
+S60019,"Contusion of unspecified thumb without damage to nail"
+S6002,"Contusion of index finger without damage to nail"
+S60021,"Contusion of right index finger without damage to nail"
+S60022,"Contusion of left index finger without damage to nail"
+S60029,"Contusion of unspecified index finger without damage to nail"
+S6003,"Contusion of middle finger without damage to nail"
+S60031,"Contusion of right middle finger without damage to nail"
+S60032,"Contusion of left middle finger without damage to nail"
+S60039,"Contusion of unspecified middle finger without damage to nail"
+S6004,"Contusion of ring finger without damage to nail"
+S60041,"Contusion of right ring finger without damage to nail"
+S60042,"Contusion of left ring finger without damage to nail"
+S60049,"Contusion of unspecified ring finger without damage to nail"
+S6005,"Contusion of little finger without damage to nail"
+S60051,"Contusion of right little finger without damage to nail"
+S60052,"Contusion of left little finger without damage to nail"
+S60059,"Contusion of unspecified little finger without damage to nail"
+S601,"Contusion of finger with damage to nail"
+S6010,"Contusion of unspecified finger with damage to nail"
+S6011,"Contusion of thumb with damage to nail"
+S60111,"Contusion of right thumb with damage to nail"
+S60112,"Contusion of left thumb with damage to nail"
+S60119,"Contusion of unspecified thumb with damage to nail"
+S6012,"Contusion of index finger with damage to nail"
+S60121,"Contusion of right index finger with damage to nail"
+S60122,"Contusion of left index finger with damage to nail"
+S60129,"Contusion of unspecified index finger with damage to nail"
+S6013,"Contusion of middle finger with damage to nail"
+S60131,"Contusion of right middle finger with damage to nail"
+S60132,"Contusion of left middle finger with damage to nail"
+S60139,"Contusion of unspecified middle finger with damage to nail"
+S6014,"Contusion of ring finger with damage to nail"
+S60141,"Contusion of right ring finger with damage to nail"
+S60142,"Contusion of left ring finger with damage to nail"
+S60149,"Contusion of unspecified ring finger with damage to nail"
+S6015,"Contusion of little finger with damage to nail"
+S60151,"Contusion of right little finger with damage to nail"
+S60152,"Contusion of left little finger with damage to nail"
+S60159,"Contusion of unspecified little finger with damage to nail"
+S602,"Contusion of wrist and hand"
+S6021,"Contusion of wrist"
+S60211,"Contusion of right wrist"
+S60212,"Contusion of left wrist"
+S60219,"Contusion of unspecified wrist"
+S6022,"Contusion of hand"
+S60221,"Contusion of right hand"
+S60222,"Contusion of left hand"
+S60229,"Contusion of unspecified hand"
+S603,"Other superficial injuries of thumb"
+S6031,"Abrasion of thumb"
+S60311,"Abrasion of right thumb"
+S60312,"Abrasion of left thumb"
+S60319,"Abrasion of unspecified thumb"
+S6032,"Blister (nonthermal) of thumb"
+S60321,"Blister (nonthermal) of right thumb"
+S60322,"Blister (nonthermal) of left thumb"
+S60329,"Blister (nonthermal) of unspecified thumb"
+S6034,"External constriction of thumb"
+S60341,"External constriction of right thumb"
+S60342,"External constriction of left thumb"
+S60349,"External constriction of unspecified thumb"
+S6035,"Superficial foreign body of thumb"
+S60351,"Superficial foreign body of right thumb"
+S60352,"Superficial foreign body of left thumb"
+S60359,"Superficial foreign body of unspecified thumb"
+S6036,"Insect bite (nonvenomous) of thumb"
+S60361,"Insect bite (nonvenomous) of right thumb"
+S60362,"Insect bite (nonvenomous) of left thumb"
+S60369,"Insect bite (nonvenomous) of unspecified thumb"
+S6037,"Other superficial bite of thumb"
+S60371,"Other superficial bite of right thumb"
+S60372,"Other superficial bite of left thumb"
+S60379,"Other superficial bite of unspecified thumb"
+S6039,"Other superficial injuries of thumb"
+S60391,"Other superficial injuries of right thumb"
+S60392,"Other superficial injuries of left thumb"
+S60399,"Other superficial injuries of unspecified thumb"
+S604,"Other superficial injuries of other fingers"
+S6041,"Abrasion of fingers"
+S60410,"Abrasion of right index finger"
+S60411,"Abrasion of left index finger"
+S60412,"Abrasion of right middle finger"
+S60413,"Abrasion of left middle finger"
+S60414,"Abrasion of right ring finger"
+S60415,"Abrasion of left ring finger"
+S60416,"Abrasion of right little finger"
+S60417,"Abrasion of left little finger"
+S60418,"Abrasion of other finger"
+S60419,"Abrasion of unspecified finger"
+S6042,"Blister (nonthermal) of fingers"
+S60420,"Blister (nonthermal) of right index finger"
+S60421,"Blister (nonthermal) of left index finger"
+S60422,"Blister (nonthermal) of right middle finger"
+S60423,"Blister (nonthermal) of left middle finger"
+S60424,"Blister (nonthermal) of right ring finger"
+S60425,"Blister (nonthermal) of left ring finger"
+S60426,"Blister (nonthermal) of right little finger"
+S60427,"Blister (nonthermal) of left little finger"
+S60428,"Blister (nonthermal) of other finger"
+S60429,"Blister (nonthermal) of unspecified finger"
+S6044,"External constriction of fingers"
+S60440,"External constriction of right index finger"
+S60441,"External constriction of left index finger"
+S60442,"External constriction of right middle finger"
+S60443,"External constriction of left middle finger"
+S60444,"External constriction of right ring finger"
+S60445,"External constriction of left ring finger"
+S60446,"External constriction of right little finger"
+S60447,"External constriction of left little finger"
+S60448,"External constriction of other finger"
+S60449,"External constriction of unspecified finger"
+S6045,"Superficial foreign body of fingers"
+S60450,"Superficial foreign body of right index finger"
+S60451,"Superficial foreign body of left index finger"
+S60452,"Superficial foreign body of right middle finger"
+S60453,"Superficial foreign body of left middle finger"
+S60454,"Superficial foreign body of right ring finger"
+S60455,"Superficial foreign body of left ring finger"
+S60456,"Superficial foreign body of right little finger"
+S60457,"Superficial foreign body of left little finger"
+S60458,"Superficial foreign body of other finger"
+S60459,"Superficial foreign body of unspecified finger"
+S6046,"Insect bite (nonvenomous) of fingers"
+S60460,"Insect bite (nonvenomous) of right index finger"
+S60461,"Insect bite (nonvenomous) of left index finger"
+S60462,"Insect bite (nonvenomous) of right middle finger"
+S60463,"Insect bite (nonvenomous) of left middle finger"
+S60464,"Insect bite (nonvenomous) of right ring finger"
+S60465,"Insect bite (nonvenomous) of left ring finger"
+S60466,"Insect bite (nonvenomous) of right little finger"
+S60467,"Insect bite (nonvenomous) of left little finger"
+S60468,"Insect bite (nonvenomous) of other finger"
+S60469,"Insect bite (nonvenomous) of unspecified finger"
+S6047,"Other superficial bite of fingers"
+S60470,"Other superficial bite of right index finger"
+S60471,"Other superficial bite of left index finger"
+S60472,"Other superficial bite of right middle finger"
+S60473,"Other superficial bite of left middle finger"
+S60474,"Other superficial bite of right ring finger"
+S60475,"Other superficial bite of left ring finger"
+S60476,"Other superficial bite of right little finger"
+S60477,"Other superficial bite of left little finger"
+S60478,"Other superficial bite of other finger"
+S60479,"Other superficial bite of unspecified finger"
+S605,"Other superficial injuries of hand"
+S6051,"Abrasion of hand"
+S60511,"Abrasion of right hand"
+S60512,"Abrasion of left hand"
+S60519,"Abrasion of unspecified hand"
+S6052,"Blister (nonthermal) of hand"
+S60521,"Blister (nonthermal) of right hand"
+S60522,"Blister (nonthermal) of left hand"
+S60529,"Blister (nonthermal) of unspecified hand"
+S6054,"External constriction of hand"
+S60541,"External constriction of right hand"
+S60542,"External constriction of left hand"
+S60549,"External constriction of unspecified hand"
+S6055,"Superficial foreign body of hand"
+S60551,"Superficial foreign body of right hand"
+S60552,"Superficial foreign body of left hand"
+S60559,"Superficial foreign body of unspecified hand"
+S6056,"Insect bite (nonvenomous) of hand"
+S60561,"Insect bite (nonvenomous) of right hand"
+S60562,"Insect bite (nonvenomous) of left hand"
+S60569,"Insect bite (nonvenomous) of unspecified hand"
+S6057,"Other superficial bite of hand"
+S60571,"Other superficial bite of hand of right hand"
+S60572,"Other superficial bite of hand of left hand"
+S60579,"Other superficial bite of hand of unspecified hand"
+S608,"Other superficial injuries of wrist"
+S6081,"Abrasion of wrist"
+S60811,"Abrasion of right wrist"
+S60812,"Abrasion of left wrist"
+S60819,"Abrasion of unspecified wrist"
+S6082,"Blister (nonthermal) of wrist"
+S60821,"Blister (nonthermal) of right wrist"
+S60822,"Blister (nonthermal) of left wrist"
+S60829,"Blister (nonthermal) of unspecified wrist"
+S6084,"External constriction of wrist"
+S60841,"External constriction of right wrist"
+S60842,"External constriction of left wrist"
+S60849,"External constriction of unspecified wrist"
+S6085,"Superficial foreign body of wrist"
+S60851,"Superficial foreign body of right wrist"
+S60852,"Superficial foreign body of left wrist"
+S60859,"Superficial foreign body of unspecified wrist"
+S6086,"Insect bite (nonvenomous) of wrist"
+S60861,"Insect bite (nonvenomous) of right wrist"
+S60862,"Insect bite (nonvenomous) of left wrist"
+S60869,"Insect bite (nonvenomous) of unspecified wrist"
+S6087,"Other superficial bite of wrist"
+S60871,"Other superficial bite of right wrist"
+S60872,"Other superficial bite of left wrist"
+S60879,"Other superficial bite of unspecified wrist"
+S609,"Unspecified superficial injury of wrist, hand and fingers"
+S6091,"Unspecified superficial injury of wrist"
+S60911,"Unspecified superficial injury of right wrist"
+S60912,"Unspecified superficial injury of left wrist"
+S60919,"Unspecified superficial injury of unspecified wrist"
+S6092,"Unspecified superficial injury of hand"
+S60921,"Unspecified superficial injury of right hand"
+S60922,"Unspecified superficial injury of left hand"
+S60929,"Unspecified superficial injury of unspecified hand"
+S6093,"Unspecified superficial injury of thumb"
+S60931,"Unspecified superficial injury of right thumb"
+S60932,"Unspecified superficial injury of left thumb"
+S60939,"Unspecified superficial injury of unspecified thumb"
+S6094,"Unspecified superficial injury of other fingers"
+S60940,"Unspecified superficial injury of right index finger"
+S60941,"Unspecified superficial injury of left index finger"
+S60942,"Unspecified superficial injury of right middle finger"
+S60943,"Unspecified superficial injury of left middle finger"
+S60944,"Unspecified superficial injury of right ring finger"
+S60945,"Unspecified superficial injury of left ring finger"
+S60946,"Unspecified superficial injury of right little finger"
+S60947,"Unspecified superficial injury of left little finger"
+S60948,"Unspecified superficial injury of other finger"
+S60949,"Unspecified superficial injury of unspecified finger"
+S61,"Open wound of wrist, hand and fingers"
+S610,"Open wound of thumb without damage to nail"
+S6100,"Unspecified open wound of thumb without damage to nail"
+S61001,"Unspecified open wound of right thumb without damage to nail"
+S61002,"Unspecified open wound of left thumb without damage to nail"
+S61009,"Unspecified open wound of unspecified thumb without damage to nail"
+S6101,"Laceration without foreign body of thumb without damage to nail"
+S61011,"Laceration without foreign body of right thumb without damage to nail"
+S61012,"Laceration without foreign body of left thumb without damage to nail"
+S61019,"Laceration without foreign body of unspecified thumb without damage to nail"
+S6102,"Laceration with foreign body of thumb without damage to nail"
+S61021,"Laceration with foreign body of right thumb without damage to nail"
+S61022,"Laceration with foreign body of left thumb without damage to nail"
+S61029,"Laceration with foreign body of unspecified thumb without damage to nail"
+S6103,"Puncture wound without foreign body of thumb without damage to nail"
+S61031,"Puncture wound without foreign body of right thumb without damage to nail"
+S61032,"Puncture wound without foreign body of left thumb without damage to nail"
+S61039,"Puncture wound without foreign body of unspecified thumb without damage to nail"
+S6104,"Puncture wound with foreign body of thumb without damage to nail"
+S61041,"Puncture wound with foreign body of right thumb without damage to nail"
+S61042,"Puncture wound with foreign body of left thumb without damage to nail"
+S61049,"Puncture wound with foreign body of unspecified thumb without damage to nail"
+S6105,"Open bite of thumb without damage to nail"
+S61051,"Open bite of right thumb without damage to nail"
+S61052,"Open bite of left thumb without damage to nail"
+S61059,"Open bite of unspecified thumb without damage to nail"
+S611,"Open wound of thumb with damage to nail"
+S6110,"Unspecified open wound of thumb with damage to nail"
+S61101,"Unspecified open wound of right thumb with damage to nail"
+S61102,"Unspecified open wound of left thumb with damage to nail"
+S61109,"Unspecified open wound of unspecified thumb with damage to nail"
+S6111,"Laceration without foreign body of thumb with damage to nail"
+S61111,"Laceration without foreign body of right thumb with damage to nail"
+S61112,"Laceration without foreign body of left thumb with damage to nail"
+S61119,"Laceration without foreign body of unspecified thumb with damage to nail"
+S6112,"Laceration with foreign body of thumb with damage to nail"
+S61121,"Laceration with foreign body of right thumb with damage to nail"
+S61122,"Laceration with foreign body of left thumb with damage to nail"
+S61129,"Laceration with foreign body of unspecified thumb with damage to nail"
+S6113,"Puncture wound without foreign body of thumb with damage to nail"
+S61131,"Puncture wound without foreign body of right thumb with damage to nail"
+S61132,"Puncture wound without foreign body of left thumb with damage to nail"
+S61139,"Puncture wound without foreign body of unspecified thumb with damage to nail"
+S6114,"Puncture wound with foreign body of thumb with damage to nail"
+S61141,"Puncture wound with foreign body of right thumb with damage to nail"
+S61142,"Puncture wound with foreign body of left thumb with damage to nail"
+S61149,"Puncture wound with foreign body of unspecified thumb with damage to nail"
+S6115,"Open bite of thumb with damage to nail"
+S61151,"Open bite of right thumb with damage to nail"
+S61152,"Open bite of left thumb with damage to nail"
+S61159,"Open bite of unspecified thumb with damage to nail"
+S612,"Open wound of other finger without damage to nail"
+S6120,"Unspecified open wound of other finger without damage to nail"
+S61200,"Unspecified open wound of right index finger without damage to nail"
+S61201,"Unspecified open wound of left index finger without damage to nail"
+S61202,"Unspecified open wound of right middle finger without damage to nail"
+S61203,"Unspecified open wound of left middle finger without damage to nail"
+S61204,"Unspecified open wound of right ring finger without damage to nail"
+S61205,"Unspecified open wound of left ring finger without damage to nail"
+S61206,"Unspecified open wound of right little finger without damage to nail"
+S61207,"Unspecified open wound of left little finger without damage to nail"
+S61208,"Unspecified open wound of other finger without damage to nail"
+S61209,"Unspecified open wound of unspecified finger without damage to nail"
+S6121,"Laceration without foreign body of finger without damage to nail"
+S61210,"Laceration without foreign body of right index finger without damage to nail"
+S61211,"Laceration without foreign body of left index finger without damage to nail"
+S61212,"Laceration without foreign body of right middle finger without damage to nail"
+S61213,"Laceration without foreign body of left middle finger without damage to nail"
+S61214,"Laceration without foreign body of right ring finger without damage to nail"
+S61215,"Laceration without foreign body of left ring finger without damage to nail"
+S61216,"Laceration without foreign body of right little finger without damage to nail"
+S61217,"Laceration without foreign body of left little finger without damage to nail"
+S61218,"Laceration without foreign body of other finger without damage to nail"
+S61219,"Laceration without foreign body of unspecified finger without damage to nail"
+S6122,"Laceration with foreign body of finger without damage to nail"
+S61220,"Laceration with foreign body of right index finger without damage to nail"
+S61221,"Laceration with foreign body of left index finger without damage to nail"
+S61222,"Laceration with foreign body of right middle finger without damage to nail"
+S61223,"Laceration with foreign body of left middle finger without damage to nail"
+S61224,"Laceration with foreign body of right ring finger without damage to nail"
+S61225,"Laceration with foreign body of left ring finger without damage to nail"
+S61226,"Laceration with foreign body of right little finger without damage to nail"
+S61227,"Laceration with foreign body of left little finger without damage to nail"
+S61228,"Laceration with foreign body of other finger without damage to nail"
+S61229,"Laceration with foreign body of unspecified finger without damage to nail"
+S6123,"Puncture wound without foreign body of finger without damage to nail"
+S61230,"Puncture wound without foreign body of right index finger without damage to nail"
+S61231,"Puncture wound without foreign body of left index finger without damage to nail"
+S61232,"Puncture wound without foreign body of right middle finger without damage to nail"
+S61233,"Puncture wound without foreign body of left middle finger without damage to nail"
+S61234,"Puncture wound without foreign body of right ring finger without damage to nail"
+S61235,"Puncture wound without foreign body of left ring finger without damage to nail"
+S61236,"Puncture wound without foreign body of right little finger without damage to nail"
+S61237,"Puncture wound without foreign body of left little finger without damage to nail"
+S61238,"Puncture wound without foreign body of other finger without damage to nail"
+S61239,"Puncture wound without foreign body of unspecified finger without damage to nail"
+S6124,"Puncture wound with foreign body of finger without damage to nail"
+S61240,"Puncture wound with foreign body of right index finger without damage to nail"
+S61241,"Puncture wound with foreign body of left index finger without damage to nail"
+S61242,"Puncture wound with foreign body of right middle finger without damage to nail"
+S61243,"Puncture wound with foreign body of left middle finger without damage to nail"
+S61244,"Puncture wound with foreign body of right ring finger without damage to nail"
+S61245,"Puncture wound with foreign body of left ring finger without damage to nail"
+S61246,"Puncture wound with foreign body of right little finger without damage to nail"
+S61247,"Puncture wound with foreign body of left little finger without damage to nail"
+S61248,"Puncture wound with foreign body of other finger without damage to nail"
+S61249,"Puncture wound with foreign body of unspecified finger without damage to nail"
+S6125,"Open bite of finger without damage to nail"
+S61250,"Open bite of right index finger without damage to nail"
+S61251,"Open bite of left index finger without damage to nail"
+S61252,"Open bite of right middle finger without damage to nail"
+S61253,"Open bite of left middle finger without damage to nail"
+S61254,"Open bite of right ring finger without damage to nail"
+S61255,"Open bite of left ring finger without damage to nail"
+S61256,"Open bite of right little finger without damage to nail"
+S61257,"Open bite of left little finger without damage to nail"
+S61258,"Open bite of other finger without damage to nail"
+S61259,"Open bite of unspecified finger without damage to nail"
+S613,"Open wound of other finger with damage to nail"
+S6130,"Unspecified open wound of finger with damage to nail"
+S61300,"Unspecified open wound of right index finger with damage to nail"
+S61301,"Unspecified open wound of left index finger with damage to nail"
+S61302,"Unspecified open wound of right middle finger with damage to nail"
+S61303,"Unspecified open wound of left middle finger with damage to nail"
+S61304,"Unspecified open wound of right ring finger with damage to nail"
+S61305,"Unspecified open wound of left ring finger with damage to nail"
+S61306,"Unspecified open wound of right little finger with damage to nail"
+S61307,"Unspecified open wound of left little finger with damage to nail"
+S61308,"Unspecified open wound of other finger with damage to nail"
+S61309,"Unspecified open wound of unspecified finger with damage to nail"
+S6131,"Laceration without foreign body of finger with damage to nail"
+S61310,"Laceration without foreign body of right index finger with damage to nail"
+S61311,"Laceration without foreign body of left index finger with damage to nail"
+S61312,"Laceration without foreign body of right middle finger with damage to nail"
+S61313,"Laceration without foreign body of left middle finger with damage to nail"
+S61314,"Laceration without foreign body of right ring finger with damage to nail"
+S61315,"Laceration without foreign body of left ring finger with damage to nail"
+S61316,"Laceration without foreign body of right little finger with damage to nail"
+S61317,"Laceration without foreign body of left little finger with damage to nail"
+S61318,"Laceration without foreign body of other finger with damage to nail"
+S61319,"Laceration without foreign body of unspecified finger with damage to nail"
+S6132,"Laceration with foreign body of finger with damage to nail"
+S61320,"Laceration with foreign body of right index finger with damage to nail"
+S61321,"Laceration with foreign body of left index finger with damage to nail"
+S61322,"Laceration with foreign body of right middle finger with damage to nail"
+S61323,"Laceration with foreign body of left middle finger with damage to nail"
+S61324,"Laceration with foreign body of right ring finger with damage to nail"
+S61325,"Laceration with foreign body of left ring finger with damage to nail"
+S61326,"Laceration with foreign body of right little finger with damage to nail"
+S61327,"Laceration with foreign body of left little finger with damage to nail"
+S61328,"Laceration with foreign body of other finger with damage to nail"
+S61329,"Laceration with foreign body of unspecified finger with damage to nail"
+S6133,"Puncture wound without foreign body of finger with damage to nail"
+S61330,"Puncture wound without foreign body of right index finger with damage to nail"
+S61331,"Puncture wound without foreign body of left index finger with damage to nail"
+S61332,"Puncture wound without foreign body of right middle finger with damage to nail"
+S61333,"Puncture wound without foreign body of left middle finger with damage to nail"
+S61334,"Puncture wound without foreign body of right ring finger with damage to nail"
+S61335,"Puncture wound without foreign body of left ring finger with damage to nail"
+S61336,"Puncture wound without foreign body of right little finger with damage to nail"
+S61337,"Puncture wound without foreign body of left little finger with damage to nail"
+S61338,"Puncture wound without foreign body of other finger with damage to nail"
+S61339,"Puncture wound without foreign body of unspecified finger with damage to nail"
+S6134,"Puncture wound with foreign body of finger with damage to nail"
+S61340,"Puncture wound with foreign body of right index finger with damage to nail"
+S61341,"Puncture wound with foreign body of left index finger with damage to nail"
+S61342,"Puncture wound with foreign body of right middle finger with damage to nail"
+S61343,"Puncture wound with foreign body of left middle finger with damage to nail"
+S61344,"Puncture wound with foreign body of right ring finger with damage to nail"
+S61345,"Puncture wound with foreign body of left ring finger with damage to nail"
+S61346,"Puncture wound with foreign body of right little finger with damage to nail"
+S61347,"Puncture wound with foreign body of left little finger with damage to nail"
+S61348,"Puncture wound with foreign body of other finger with damage to nail"
+S61349,"Puncture wound with foreign body of unspecified finger with damage to nail"
+S6135,"Open bite of finger with damage to nail"
+S61350,"Open bite of right index finger with damage to nail"
+S61351,"Open bite of left index finger with damage to nail"
+S61352,"Open bite of right middle finger with damage to nail"
+S61353,"Open bite of left middle finger with damage to nail"
+S61354,"Open bite of right ring finger with damage to nail"
+S61355,"Open bite of left ring finger with damage to nail"
+S61356,"Open bite of right little finger with damage to nail"
+S61357,"Open bite of left little finger with damage to nail"
+S61358,"Open bite of other finger with damage to nail"
+S61359,"Open bite of unspecified finger with damage to nail"
+S614,"Open wound of hand"
+S6140,"Unspecified open wound of hand"
+S61401,"Unspecified open wound of right hand"
+S61402,"Unspecified open wound of left hand"
+S61409,"Unspecified open wound of unspecified hand"
+S6141,"Laceration without foreign body of hand"
+S61411,"Laceration without foreign body of right hand"
+S61412,"Laceration without foreign body of left hand"
+S61419,"Laceration without foreign body of unspecified hand"
+S6142,"Laceration with foreign body of hand"
+S61421,"Laceration with foreign body of right hand"
+S61422,"Laceration with foreign body of left hand"
+S61429,"Laceration with foreign body of unspecified hand"
+S6143,"Puncture wound without foreign body of hand"
+S61431,"Puncture wound without foreign body of right hand"
+S61432,"Puncture wound without foreign body of left hand"
+S61439,"Puncture wound without foreign body of unspecified hand"
+S6144,"Puncture wound with foreign body of hand"
+S61441,"Puncture wound with foreign body of right hand"
+S61442,"Puncture wound with foreign body of left hand"
+S61449,"Puncture wound with foreign body of unspecified hand"
+S6145,"Open bite of hand"
+S61451,"Open bite of right hand"
+S61452,"Open bite of left hand"
+S61459,"Open bite of unspecified hand"
+S615,"Open wound of wrist"
+S6150,"Unspecified open wound of wrist"
+S61501,"Unspecified open wound of right wrist"
+S61502,"Unspecified open wound of left wrist"
+S61509,"Unspecified open wound of unspecified wrist"
+S6151,"Laceration without foreign body of wrist"
+S61511,"Laceration without foreign body of right wrist"
+S61512,"Laceration without foreign body of left wrist"
+S61519,"Laceration without foreign body of unspecified wrist"
+S6152,"Laceration with foreign body of wrist"
+S61521,"Laceration with foreign body of right wrist"
+S61522,"Laceration with foreign body of left wrist"
+S61529,"Laceration with foreign body of unspecified wrist"
+S6153,"Puncture wound without foreign body of wrist"
+S61531,"Puncture wound without foreign body of right wrist"
+S61532,"Puncture wound without foreign body of left wrist"
+S61539,"Puncture wound without foreign body of unspecified wrist"
+S6154,"Puncture wound with foreign body of wrist"
+S61541,"Puncture wound with foreign body of right wrist"
+S61542,"Puncture wound with foreign body of left wrist"
+S61549,"Puncture wound with foreign body of unspecified wrist"
+S6155,"Open bite of wrist"
+S61551,"Open bite of right wrist"
+S61552,"Open bite of left wrist"
+S61559,"Open bite of unspecified wrist"
+S62,"Fracture at wrist and hand level"
+S620,"Fracture of navicular [scaphoid] bone of wrist"
+S6200,"Unspecified fracture of navicular [scaphoid] bone of wrist"
+S62001,"Unspecified fracture of navicular [scaphoid] bone of right wrist"
+S62002,"Unspecified fracture of navicular [scaphoid] bone of left wrist"
+S62009,"Unspecified fracture of navicular [scaphoid] bone of unspecified wrist"
+S6201,"Fracture of distal pole of navicular [scaphoid] bone of wrist"
+S62011,"Displaced fracture of distal pole of navicular [scaphoid] bone of right wrist"
+S62012,"Displaced fracture of distal pole of navicular [scaphoid] bone of left wrist"
+S62013,"Displaced fracture of distal pole of navicular [scaphoid] bone of unspecified wrist"
+S62014,"Nondisplaced fracture of distal pole of navicular [scaphoid] bone of right wrist"
+S62015,"Nondisplaced fracture of distal pole of navicular [scaphoid] bone of left wrist"
+S62016,"Nondisplaced fracture of distal pole of navicular [scaphoid] bone of unspecified wrist"
+S6202,"Fracture of middle third of navicular [scaphoid] bone of wrist"
+S62021,"Displaced fracture of middle third of navicular [scaphoid] bone of right wrist"
+S62022,"Displaced fracture of middle third of navicular [scaphoid] bone of left wrist"
+S62023,"Displaced fracture of middle third of navicular [scaphoid] bone of unspecified wrist"
+S62024,"Nondisplaced fracture of middle third of navicular [scaphoid] bone of right wrist"
+S62025,"Nondisplaced fracture of middle third of navicular [scaphoid] bone of left wrist"
+S62026,"Nondisplaced fracture of middle third of navicular [scaphoid] bone of unspecified wrist"
+S6203,"Fracture of proximal third of navicular [scaphoid] bone of wrist"
+S62031,"Displaced fracture of proximal third of navicular [scaphoid] bone of right wrist"
+S62032,"Displaced fracture of proximal third of navicular [scaphoid] bone of left wrist"
+S62033,"Displaced fracture of proximal third of navicular [scaphoid] bone of unspecified wrist"
+S62034,"Nondisplaced fracture of proximal third of navicular [scaphoid] bone of right wrist"
+S62035,"Nondisplaced fracture of proximal third of navicular [scaphoid] bone of left wrist"
+S62036,"Nondisplaced fracture of proximal third of navicular [scaphoid] bone of unspecified wrist"
+S621,"Fracture of other and unspecified carpal bone(s)"
+S6210,"Fracture of unspecified carpal bone"
+S62101,"Fracture of unspecified carpal bone, right wrist"
+S62102,"Fracture of unspecified carpal bone, left wrist"
+S62109,"Fracture of unspecified carpal bone, unspecified wrist"
+S6211,"Fracture of triquetrum [cuneiform] bone of wrist"
+S62111,"Displaced fracture of triquetrum [cuneiform] bone, right wrist"
+S62112,"Displaced fracture of triquetrum [cuneiform] bone, left wrist"
+S62113,"Displaced fracture of triquetrum [cuneiform] bone, unspecified wrist"
+S62114,"Nondisplaced fracture of triquetrum [cuneiform] bone, right wrist"
+S62115,"Nondisplaced fracture of triquetrum [cuneiform] bone, left wrist"
+S62116,"Nondisplaced fracture of triquetrum [cuneiform] bone, unspecified wrist"
+S6212,"Fracture of lunate [semilunar]"
+S62121,"Displaced fracture of lunate [semilunar], right wrist"
+S62122,"Displaced fracture of lunate [semilunar], left wrist"
+S62123,"Displaced fracture of lunate [semilunar], unspecified wrist"
+S62124,"Nondisplaced fracture of lunate [semilunar], right wrist"
+S62125,"Nondisplaced fracture of lunate [semilunar], left wrist"
+S62126,"Nondisplaced fracture of lunate [semilunar], unspecified wrist"
+S6213,"Fracture of capitate [os magnum] bone"
+S62131,"Displaced fracture of capitate [os magnum] bone, right wrist"
+S62132,"Displaced fracture of capitate [os magnum] bone, left wrist"
+S62133,"Displaced fracture of capitate [os magnum] bone, unspecified wrist"
+S62134,"Nondisplaced fracture of capitate [os magnum] bone, right wrist"
+S62135,"Nondisplaced fracture of capitate [os magnum] bone, left wrist"
+S62136,"Nondisplaced fracture of capitate [os magnum] bone, unspecified wrist"
+S6214,"Fracture of body of hamate [unciform] bone"
+S62141,"Displaced fracture of body of hamate [unciform] bone, right wrist"
+S62142,"Displaced fracture of body of hamate [unciform] bone, left wrist"
+S62143,"Displaced fracture of body of hamate [unciform] bone, unspecified wrist"
+S62144,"Nondisplaced fracture of body of hamate [unciform] bone, right wrist"
+S62145,"Nondisplaced fracture of body of hamate [unciform] bone, left wrist"
+S62146,"Nondisplaced fracture of body of hamate [unciform] bone, unspecified wrist"
+S6215,"Fracture of hook process of hamate [unciform] bone"
+S62151,"Displaced fracture of hook process of hamate [unciform] bone, right wrist"
+S62152,"Displaced fracture of hook process of hamate [unciform] bone, left wrist"
+S62153,"Displaced fracture of hook process of hamate [unciform] bone, unspecified wrist"
+S62154,"Nondisplaced fracture of hook process of hamate [unciform] bone, right wrist"
+S62155,"Nondisplaced fracture of hook process of hamate [unciform] bone, left wrist"
+S62156,"Nondisplaced fracture of hook process of hamate [unciform] bone, unspecified wrist"
+S6216,"Fracture of pisiform"
+S62161,"Displaced fracture of pisiform, right wrist"
+S62162,"Displaced fracture of pisiform, left wrist"
+S62163,"Displaced fracture of pisiform, unspecified wrist"
+S62164,"Nondisplaced fracture of pisiform, right wrist"
+S62165,"Nondisplaced fracture of pisiform, left wrist"
+S62166,"Nondisplaced fracture of pisiform, unspecified wrist"
+S6217,"Fracture of trapezium [larger multangular]"
+S62171,"Displaced fracture of trapezium [larger multangular], right wrist"
+S62172,"Displaced fracture of trapezium [larger multangular], left wrist"
+S62173,"Displaced fracture of trapezium [larger multangular], unspecified wrist"
+S62174,"Nondisplaced fracture of trapezium [larger multangular], right wrist"
+S62175,"Nondisplaced fracture of trapezium [larger multangular], left wrist"
+S62176,"Nondisplaced fracture of trapezium [larger multangular], unspecified wrist"
+S6218,"Fracture of trapezoid [smaller multangular]"
+S62181,"Displaced fracture of trapezoid [smaller multangular], right wrist"
+S62182,"Displaced fracture of trapezoid [smaller multangular], left wrist"
+S62183,"Displaced fracture of trapezoid [smaller multangular], unspecified wrist"
+S62184,"Nondisplaced fracture of trapezoid [smaller multangular], right wrist"
+S62185,"Nondisplaced fracture of trapezoid [smaller multangular], left wrist"
+S62186,"Nondisplaced fracture of trapezoid [smaller multangular], unspecified wrist"
+S622,"Fracture of first metacarpal bone"
+S6220,"Unspecified fracture of first metacarpal bone"
+S62201,"Unspecified fracture of first metacarpal bone, right hand"
+S62202,"Unspecified fracture of first metacarpal bone, left hand"
+S62209,"Unspecified fracture of first metacarpal bone, unspecified hand"
+S6221,"Bennett's fracture"
+S62211,"Bennett's fracture, right hand"
+S62212,"Bennett's fracture, left hand"
+S62213,"Bennett's fracture, unspecified hand"
+S6222,"Rolando's fracture"
+S62221,"Displaced Rolando's fracture, right hand"
+S62222,"Displaced Rolando's fracture, left hand"
+S62223,"Displaced Rolando's fracture, unspecified hand"
+S62224,"Nondisplaced Rolando's fracture, right hand"
+S62225,"Nondisplaced Rolando's fracture, left hand"
+S62226,"Nondisplaced Rolando's fracture, unspecified hand"
+S6223,"Other fracture of base of first metacarpal bone"
+S62231,"Other displaced fracture of base of first metacarpal bone, right hand"
+S62232,"Other displaced fracture of base of first metacarpal bone, left hand"
+S62233,"Other displaced fracture of base of first metacarpal bone, unspecified hand"
+S62234,"Other nondisplaced fracture of base of first metacarpal bone, right hand"
+S62235,"Other nondisplaced fracture of base of first metacarpal bone, left hand"
+S62236,"Other nondisplaced fracture of base of first metacarpal bone, unspecified hand"
+S6224,"Fracture of shaft of first metacarpal bone"
+S62241,"Displaced fracture of shaft of first metacarpal bone, right hand"
+S62242,"Displaced fracture of shaft of first metacarpal bone, left hand"
+S62243,"Displaced fracture of shaft of first metacarpal bone, unspecified hand"
+S62244,"Nondisplaced fracture of shaft of first metacarpal bone, right hand"
+S62245,"Nondisplaced fracture of shaft of first metacarpal bone, left hand"
+S62246,"Nondisplaced fracture of shaft of first metacarpal bone, unspecified hand"
+S6225,"Fracture of neck of first metacarpal bone"
+S62251,"Displaced fracture of neck of first metacarpal bone, right hand"
+S62252,"Displaced fracture of neck of first metacarpal bone, left hand"
+S62253,"Displaced fracture of neck of first metacarpal bone, unspecified hand"
+S62254,"Nondisplaced fracture of neck of first metacarpal bone, right hand"
+S62255,"Nondisplaced fracture of neck of first metacarpal bone, left hand"
+S62256,"Nondisplaced fracture of neck of first metacarpal bone, unspecified hand"
+S6229,"Other fracture of first metacarpal bone"
+S62291,"Other fracture of first metacarpal bone, right hand"
+S62292,"Other fracture of first metacarpal bone, left hand"
+S62299,"Other fracture of first metacarpal bone, unspecified hand"
+S623,"Fracture of other and unspecified metacarpal bone"
+S6230,"Unspecified fracture of other metacarpal bone"
+S62300,"Unspecified fracture of second metacarpal bone, right hand"
+S62301,"Unspecified fracture of second metacarpal bone, left hand"
+S62302,"Unspecified fracture of third metacarpal bone, right hand"
+S62303,"Unspecified fracture of third metacarpal bone, left hand"
+S62304,"Unspecified fracture of fourth metacarpal bone, right hand"
+S62305,"Unspecified fracture of fourth metacarpal bone, left hand"
+S62306,"Unspecified fracture of fifth metacarpal bone, right hand"
+S62307,"Unspecified fracture of fifth metacarpal bone, left hand"
+S62308,"Unspecified fracture of other metacarpal bone"
+S62309,"Unspecified fracture of unspecified metacarpal bone"
+S6231,"Displaced fracture of base of other metacarpal bone"
+S62310,"Displaced fracture of base of second metacarpal bone, right hand"
+S62311,"Displaced fracture of base of second metacarpal bone, left hand"
+S62312,"Displaced fracture of base of third metacarpal bone, right hand"
+S62313,"Displaced fracture of base of third metacarpal bone, left hand"
+S62314,"Displaced fracture of base of fourth metacarpal bone, right hand"
+S62315,"Displaced fracture of base of fourth metacarpal bone, left hand"
+S62316,"Displaced fracture of base of fifth metacarpal bone, right hand"
+S62317,"Displaced fracture of base of fifth metacarpal bone, left hand"
+S62318,"Displaced fracture of base of other metacarpal bone"
+S62319,"Displaced fracture of base of unspecified metacarpal bone"
+S6232,"Displaced fracture of shaft of other metacarpal bone"
+S62320,"Displaced fracture of shaft of second metacarpal bone, right hand"
+S62321,"Displaced fracture of shaft of second metacarpal bone, left hand"
+S62322,"Displaced fracture of shaft of third metacarpal bone, right hand"
+S62323,"Displaced fracture of shaft of third metacarpal bone, left hand"
+S62324,"Displaced fracture of shaft of fourth metacarpal bone, right hand"
+S62325,"Displaced fracture of shaft of fourth metacarpal bone, left hand"
+S62326,"Displaced fracture of shaft of fifth metacarpal bone, right hand"
+S62327,"Displaced fracture of shaft of fifth metacarpal bone, left hand"
+S62328,"Displaced fracture of shaft of other metacarpal bone"
+S62329,"Displaced fracture of shaft of unspecified metacarpal bone"
+S6233,"Displaced fracture of neck of other metacarpal bone"
+S62330,"Displaced fracture of neck of second metacarpal bone, right hand"
+S62331,"Displaced fracture of neck of second metacarpal bone, left hand"
+S62332,"Displaced fracture of neck of third metacarpal bone, right hand"
+S62333,"Displaced fracture of neck of third metacarpal bone, left hand"
+S62334,"Displaced fracture of neck of fourth metacarpal bone, right hand"
+S62335,"Displaced fracture of neck of fourth metacarpal bone, left hand"
+S62336,"Displaced fracture of neck of fifth metacarpal bone, right hand"
+S62337,"Displaced fracture of neck of fifth metacarpal bone, left hand"
+S62338,"Displaced fracture of neck of other metacarpal bone"
+S62339,"Displaced fracture of neck of unspecified metacarpal bone"
+S6234,"Nondisplaced fracture of base of other metacarpal bone"
+S62340,"Nondisplaced fracture of base of second metacarpal bone, right hand"
+S62341,"Nondisplaced fracture of base of second metacarpal bone, left hand"
+S62342,"Nondisplaced fracture of base of third metacarpal bone, right hand"
+S62343,"Nondisplaced fracture of base of third metacarpal bone, left hand"
+S62344,"Nondisplaced fracture of base of fourth metacarpal bone, right hand"
+S62345,"Nondisplaced fracture of base of fourth metacarpal bone, left hand"
+S62346,"Nondisplaced fracture of base of fifth metacarpal bone, right hand"
+S62347,"Nondisplaced fracture of base of fifth metacarpal bone, left hand"
+S62348,"Nondisplaced fracture of base of other metacarpal bone"
+S62349,"Nondisplaced fracture of base of unspecified metacarpal bone"
+S6235,"Nondisplaced fracture of shaft of other metacarpal bone"
+S62350,"Nondisplaced fracture of shaft of second metacarpal bone, right hand"
+S62351,"Nondisplaced fracture of shaft of second metacarpal bone, left hand"
+S62352,"Nondisplaced fracture of shaft of third metacarpal bone, right hand"
+S62353,"Nondisplaced fracture of shaft of third metacarpal bone, left hand"
+S62354,"Nondisplaced fracture of shaft of fourth metacarpal bone, right hand"
+S62355,"Nondisplaced fracture of shaft of fourth metacarpal bone, left hand"
+S62356,"Nondisplaced fracture of shaft of fifth metacarpal bone, right hand"
+S62357,"Nondisplaced fracture of shaft of fifth metacarpal bone, left hand"
+S62358,"Nondisplaced fracture of shaft of other metacarpal bone"
+S62359,"Nondisplaced fracture of shaft of unspecified metacarpal bone"
+S6236,"Nondisplaced fracture of neck of other metacarpal bone"
+S62360,"Nondisplaced fracture of neck of second metacarpal bone, right hand"
+S62361,"Nondisplaced fracture of neck of second metacarpal bone, left hand"
+S62362,"Nondisplaced fracture of neck of third metacarpal bone, right hand"
+S62363,"Nondisplaced fracture of neck of third metacarpal bone, left hand"
+S62364,"Nondisplaced fracture of neck of fourth metacarpal bone, right hand"
+S62365,"Nondisplaced fracture of neck of fourth metacarpal bone, left hand"
+S62366,"Nondisplaced fracture of neck of fifth metacarpal bone, right hand"
+S62367,"Nondisplaced fracture of neck of fifth metacarpal bone, left hand"
+S62368,"Nondisplaced fracture of neck of other metacarpal bone"
+S62369,"Nondisplaced fracture of neck of unspecified metacarpal bone"
+S6239,"Other fracture of other metacarpal bone"
+S62390,"Other fracture of second metacarpal bone, right hand"
+S62391,"Other fracture of second metacarpal bone, left hand"
+S62392,"Other fracture of third metacarpal bone, right hand"
+S62393,"Other fracture of third metacarpal bone, left hand"
+S62394,"Other fracture of fourth metacarpal bone, right hand"
+S62395,"Other fracture of fourth metacarpal bone, left hand"
+S62396,"Other fracture of fifth metacarpal bone, right hand"
+S62397,"Other fracture of fifth metacarpal bone, left hand"
+S62398,"Other fracture of other metacarpal bone"
+S62399,"Other fracture of unspecified metacarpal bone"
+S625,"Fracture of thumb"
+S6250,"Fracture of unspecified phalanx of thumb"
+S62501,"Fracture of unspecified phalanx of right thumb"
+S62502,"Fracture of unspecified phalanx of left thumb"
+S62509,"Fracture of unspecified phalanx of unspecified thumb"
+S6251,"Fracture of proximal phalanx of thumb"
+S62511,"Displaced fracture of proximal phalanx of right thumb"
+S62512,"Displaced fracture of proximal phalanx of left thumb"
+S62513,"Displaced fracture of proximal phalanx of unspecified thumb"
+S62514,"Nondisplaced fracture of proximal phalanx of right thumb"
+S62515,"Nondisplaced fracture of proximal phalanx of left thumb"
+S62516,"Nondisplaced fracture of proximal phalanx of unspecified thumb"
+S6252,"Fracture of distal phalanx of thumb"
+S62521,"Displaced fracture of distal phalanx of right thumb"
+S62522,"Displaced fracture of distal phalanx of left thumb"
+S62523,"Displaced fracture of distal phalanx of unspecified thumb"
+S62524,"Nondisplaced fracture of distal phalanx of right thumb"
+S62525,"Nondisplaced fracture of distal phalanx of left thumb"
+S62526,"Nondisplaced fracture of distal phalanx of unspecified thumb"
+S626,"Fracture of other and unspecified finger(s)"
+S6260,"Fracture of unspecified phalanx of finger"
+S62600,"Fracture of unspecified phalanx of right index finger"
+S62601,"Fracture of unspecified phalanx of left index finger"
+S62602,"Fracture of unspecified phalanx of right middle finger"
+S62603,"Fracture of unspecified phalanx of left middle finger"
+S62604,"Fracture of unspecified phalanx of right ring finger"
+S62605,"Fracture of unspecified phalanx of left ring finger"
+S62606,"Fracture of unspecified phalanx of right little finger"
+S62607,"Fracture of unspecified phalanx of left little finger"
+S62608,"Fracture of unspecified phalanx of other finger"
+S62609,"Fracture of unspecified phalanx of unspecified finger"
+S6261,"Displaced fracture of proximal phalanx of finger"
+S62610,"Displaced fracture of proximal phalanx of right index finger"
+S62611,"Displaced fracture of proximal phalanx of left index finger"
+S62612,"Displaced fracture of proximal phalanx of right middle finger"
+S62613,"Displaced fracture of proximal phalanx of left middle finger"
+S62614,"Displaced fracture of proximal phalanx of right ring finger"
+S62615,"Displaced fracture of proximal phalanx of left ring finger"
+S62616,"Displaced fracture of proximal phalanx of right little finger"
+S62617,"Displaced fracture of proximal phalanx of left little finger"
+S62618,"Displaced fracture of proximal phalanx of other finger"
+S62619,"Displaced fracture of proximal phalanx of unspecified finger"
+S6262,"Displaced fracture of middle phalanx of finger"
+S62620,"Displaced fracture of middle phalanx of right index finger"
+S62621,"Displaced fracture of middle phalanx of left index finger"
+S62622,"Displaced fracture of middle phalanx of right middle finger"
+S62623,"Displaced fracture of middle phalanx of left middle finger"
+S62624,"Displaced fracture of middle phalanx of right ring finger"
+S62625,"Displaced fracture of middle phalanx of left ring finger"
+S62626,"Displaced fracture of medial phalanx of right little finger"
+S62627,"Displaced fracture of medial phalanx of left little finger"
+S62628,"Displaced fracture of medial phalanx of other finger"
+S62629,"Displaced fracture of medial phalanx of unspecified finger"
+S6263,"Displaced fracture of distal phalanx of finger"
+S62630,"Displaced fracture of distal phalanx of right index finger"
+S62631,"Displaced fracture of distal phalanx of left index finger"
+S62632,"Displaced fracture of distal phalanx of right middle finger"
+S62633,"Displaced fracture of distal phalanx of left middle finger"
+S62634,"Displaced fracture of distal phalanx of right ring finger"
+S62635,"Displaced fracture of distal phalanx of left ring finger"
+S62636,"Displaced fracture of distal phalanx of right little finger"
+S62637,"Displaced fracture of distal phalanx of left little finger"
+S62638,"Displaced fracture of distal phalanx of other finger"
+S62639,"Displaced fracture of distal phalanx of unspecified finger"
+S6264,"Nondisplaced fracture of proximal phalanx of finger"
+S62640,"Nondisplaced fracture of proximal phalanx of right index finger"
+S62641,"Nondisplaced fracture of proximal phalanx of left index finger"
+S62642,"Nondisplaced fracture of proximal phalanx of right middle finger"
+S62643,"Nondisplaced fracture of proximal phalanx of left middle finger"
+S62644,"Nondisplaced fracture of proximal phalanx of right ring finger"
+S62645,"Nondisplaced fracture of proximal phalanx of left ring finger"
+S62646,"Nondisplaced fracture of proximal phalanx of right little finger"
+S62647,"Nondisplaced fracture of proximal phalanx of left little finger"
+S62648,"Nondisplaced fracture of proximal phalanx of other finger"
+S62649,"Nondisplaced fracture of proximal phalanx of unspecified finger"
+S6265,"Nondisplaced fracture of middle phalanx of finger"
+S62650,"Nondisplaced fracture of middle phalanx of right index finger"
+S62651,"Nondisplaced fracture of middle phalanx of left index finger"
+S62652,"Nondisplaced fracture of middle phalanx of right middle finger"
+S62653,"Nondisplaced fracture of middle phalanx of left middle finger"
+S62654,"Nondisplaced fracture of medial phalanx of right ring finger"
+S62655,"Nondisplaced fracture of medial phalanx of left ring finger"
+S62656,"Nondisplaced fracture of medial phalanx of right little finger"
+S62657,"Nondisplaced fracture of medial phalanx of left little finger"
+S62658,"Nondisplaced fracture of medial phalanx of other finger"
+S62659,"Nondisplaced fracture of medial phalanx of unspecified finger"
+S6266,"Nondisplaced fracture of distal phalanx of finger"
+S62660,"Nondisplaced fracture of distal phalanx of right index finger"
+S62661,"Nondisplaced fracture of distal phalanx of left index finger"
+S62662,"Nondisplaced fracture of distal phalanx of right middle finger"
+S62663,"Nondisplaced fracture of distal phalanx of left middle finger"
+S62664,"Nondisplaced fracture of distal phalanx of right ring finger"
+S62665,"Nondisplaced fracture of distal phalanx of left ring finger"
+S62666,"Nondisplaced fracture of distal phalanx of right little finger"
+S62667,"Nondisplaced fracture of distal phalanx of left little finger"
+S62668,"Nondisplaced fracture of distal phalanx of other finger"
+S62669,"Nondisplaced fracture of distal phalanx of unspecified finger"
+S629,"Unspecified fracture of wrist and hand"
+S6290,"Unspecified fracture of unspecified wrist and hand"
+S6291,"Unspecified fracture of right wrist and hand"
+S6292,"Unspecified fracture of left wrist and hand"
+S63,"Dislocation and sprain of joints and ligaments at wrist and hand level"
+S630,"Subluxation and dislocation of wrist and hand joints"
+S6300,"Unspecified subluxation and dislocation of wrist and hand"
+S63001,"Unspecified subluxation of right wrist and hand"
+S63002,"Unspecified subluxation of left wrist and hand"
+S63003,"Unspecified subluxation of unspecified wrist and hand"
+S63004,"Unspecified dislocation of right wrist and hand"
+S63005,"Unspecified dislocation of left wrist and hand"
+S63006,"Unspecified dislocation of unspecified wrist and hand"
+S6301,"Subluxation and dislocation of distal radioulnar joint"
+S63011,"Subluxation of distal radioulnar joint of right wrist"
+S63012,"Subluxation of distal radioulnar joint of left wrist"
+S63013,"Subluxation of distal radioulnar joint of unspecified wrist"
+S63014,"Dislocation of distal radioulnar joint of right wrist"
+S63015,"Dislocation of distal radioulnar joint of left wrist"
+S63016,"Dislocation of distal radioulnar joint of unspecified wrist"
+S6302,"Subluxation and dislocation of radiocarpal joint"
+S63021,"Subluxation of radiocarpal joint of right wrist"
+S63022,"Subluxation of radiocarpal joint of left wrist"
+S63023,"Subluxation of radiocarpal joint of unspecified wrist"
+S63024,"Dislocation of radiocarpal joint of right wrist"
+S63025,"Dislocation of radiocarpal joint of left wrist"
+S63026,"Dislocation of radiocarpal joint of unspecified wrist"
+S6303,"Subluxation and dislocation of midcarpal joint"
+S63031,"Subluxation of midcarpal joint of right wrist"
+S63032,"Subluxation of midcarpal joint of left wrist"
+S63033,"Subluxation of midcarpal joint of unspecified wrist"
+S63034,"Dislocation of midcarpal joint of right wrist"
+S63035,"Dislocation of midcarpal joint of left wrist"
+S63036,"Dislocation of midcarpal joint of unspecified wrist"
+S6304,"Subluxation and dislocation of carpometacarpal joint of thumb"
+S63041,"Subluxation of carpometacarpal joint of right thumb"
+S63042,"Subluxation of carpometacarpal joint of left thumb"
+S63043,"Subluxation of carpometacarpal joint of unspecified thumb"
+S63044,"Dislocation of carpometacarpal joint of right thumb"
+S63045,"Dislocation of carpometacarpal joint of left thumb"
+S63046,"Dislocation of carpometacarpal joint of unspecified thumb"
+S6305,"Subluxation and dislocation of other carpometacarpal joint"
+S63051,"Subluxation of other carpometacarpal joint of right hand"
+S63052,"Subluxation of other carpometacarpal joint of left hand"
+S63053,"Subluxation of other carpometacarpal joint of unspecified hand"
+S63054,"Dislocation of other carpometacarpal joint of right hand"
+S63055,"Dislocation of other carpometacarpal joint of left hand"
+S63056,"Dislocation of other carpometacarpal joint of unspecified hand"
+S6306,"Subluxation and dislocation of metacarpal (bone), proximal end"
+S63061,"Subluxation of metacarpal (bone), proximal end of right hand"
+S63062,"Subluxation of metacarpal (bone), proximal end of left hand"
+S63063,"Subluxation of metacarpal (bone), proximal end of unspecified hand"
+S63064,"Dislocation of metacarpal (bone), proximal end of right hand"
+S63065,"Dislocation of metacarpal (bone), proximal end of left hand"
+S63066,"Dislocation of metacarpal (bone), proximal end of unspecified hand"
+S6307,"Subluxation and dislocation of distal end of ulna"
+S63071,"Subluxation of distal end of right ulna"
+S63072,"Subluxation of distal end of left ulna"
+S63073,"Subluxation of distal end of unspecified ulna"
+S63074,"Dislocation of distal end of right ulna"
+S63075,"Dislocation of distal end of left ulna"
+S63076,"Dislocation of distal end of unspecified ulna"
+S6309,"Other subluxation and dislocation of wrist and hand"
+S63091,"Other subluxation of right wrist and hand"
+S63092,"Other subluxation of left wrist and hand"
+S63093,"Other subluxation of unspecified wrist and hand"
+S63094,"Other dislocation of right wrist and hand"
+S63095,"Other dislocation of left wrist and hand"
+S63096,"Other dislocation of unspecified wrist and hand"
+S631,"Subluxation and dislocation of thumb"
+S6310,"Unspecified subluxation and dislocation of thumb"
+S63101,"Unspecified subluxation of right thumb"
+S63102,"Unspecified subluxation of left thumb"
+S63103,"Unspecified subluxation of unspecified thumb"
+S63104,"Unspecified dislocation of right thumb"
+S63105,"Unspecified dislocation of left thumb"
+S63106,"Unspecified dislocation of unspecified thumb"
+S6311,"Subluxation and dislocation of metacarpophalangeal joint of thumb"
+S63111,"Subluxation of metacarpophalangeal joint of right thumb"
+S63112,"Subluxation of metacarpophalangeal joint of left thumb"
+S63113,"Subluxation of metacarpophalangeal joint of unspecified thumb"
+S63114,"Dislocation of metacarpophalangeal joint of right thumb"
+S63115,"Dislocation of metacarpophalangeal joint of left thumb"
+S63116,"Dislocation of metacarpophalangeal joint of unspecified thumb"
+S6312,"Subluxation and dislocation of interphalangeal joint of thumb"
+S63121,"Subluxation of interphalangeal joint of right thumb"
+S63122,"Subluxation of interphalangeal joint of left thumb"
+S63123,"Subluxation of interphalangeal joint of unspecified thumb"
+S63124,"Dislocation of interphalangeal joint of right thumb"
+S63125,"Dislocation of interphalangeal joint of left thumb"
+S63126,"Dislocation of interphalangeal joint of unspecified thumb"
+S632,"Subluxation and dislocation of other finger(s)"
+S6320,"Unspecified subluxation of other finger"
+S63200,"Unspecified subluxation of right index finger"
+S63201,"Unspecified subluxation of left index finger"
+S63202,"Unspecified subluxation of right middle finger"
+S63203,"Unspecified subluxation of left middle finger"
+S63204,"Unspecified subluxation of right ring finger"
+S63205,"Unspecified subluxation of left ring finger"
+S63206,"Unspecified subluxation of right little finger"
+S63207,"Unspecified subluxation of left little finger"
+S63208,"Unspecified subluxation of other finger"
+S63209,"Unspecified subluxation of unspecified finger"
+S6321,"Subluxation of metacarpophalangeal joint of finger"
+S63210,"Subluxation of metacarpophalangeal joint of right index finger"
+S63211,"Subluxation of metacarpophalangeal joint of left index finger"
+S63212,"Subluxation of metacarpophalangeal joint of right middle finger"
+S63213,"Subluxation of metacarpophalangeal joint of left middle finger"
+S63214,"Subluxation of metacarpophalangeal joint of right ring finger"
+S63215,"Subluxation of metacarpophalangeal joint of left ring finger"
+S63216,"Subluxation of metacarpophalangeal joint of right little finger"
+S63217,"Subluxation of metacarpophalangeal joint of left little finger"
+S63218,"Subluxation of metacarpophalangeal joint of other finger"
+S63219,"Subluxation of metacarpophalangeal joint of unspecified finger"
+S6322,"Subluxation of unspecified interphalangeal joint of finger"
+S63220,"Subluxation of unspecified interphalangeal joint of right index finger"
+S63221,"Subluxation of unspecified interphalangeal joint of left index finger"
+S63222,"Subluxation of unspecified interphalangeal joint of right middle finger"
+S63223,"Subluxation of unspecified interphalangeal joint of left middle finger"
+S63224,"Subluxation of unspecified interphalangeal joint of right ring finger"
+S63225,"Subluxation of unspecified interphalangeal joint of left ring finger"
+S63226,"Subluxation of unspecified interphalangeal joint of right little finger"
+S63227,"Subluxation of unspecified interphalangeal joint of left little finger"
+S63228,"Subluxation of unspecified interphalangeal joint of other finger"
+S63229,"Subluxation of unspecified interphalangeal joint of unspecified finger"
+S6323,"Subluxation of proximal interphalangeal joint of finger"
+S63230,"Subluxation of proximal interphalangeal joint of right index finger"
+S63231,"Subluxation of proximal interphalangeal joint of left index finger"
+S63232,"Subluxation of proximal interphalangeal joint of right middle finger"
+S63233,"Subluxation of proximal interphalangeal joint of left middle finger"
+S63234,"Subluxation of proximal interphalangeal joint of right ring finger"
+S63235,"Subluxation of proximal interphalangeal joint of left ring finger"
+S63236,"Subluxation of proximal interphalangeal joint of right little finger"
+S63237,"Subluxation of proximal interphalangeal joint of left little finger"
+S63238,"Subluxation of proximal interphalangeal joint of other finger"
+S63239,"Subluxation of proximal interphalangeal joint of unspecified finger"
+S6324,"Subluxation of distal interphalangeal joint of finger"
+S63240,"Subluxation of distal interphalangeal joint of right index finger"
+S63241,"Subluxation of distal interphalangeal joint of left index finger"
+S63242,"Subluxation of distal interphalangeal joint of right middle finger"
+S63243,"Subluxation of distal interphalangeal joint of left middle finger"
+S63244,"Subluxation of distal interphalangeal joint of right ring finger"
+S63245,"Subluxation of distal interphalangeal joint of left ring finger"
+S63246,"Subluxation of distal interphalangeal joint of right little finger"
+S63247,"Subluxation of distal interphalangeal joint of left little finger"
+S63248,"Subluxation of distal interphalangeal joint of other finger"
+S63249,"Subluxation of distal interphalangeal joint of unspecified finger"
+S6325,"Unspecified dislocation of other finger"
+S63250,"Unspecified dislocation of right index finger"
+S63251,"Unspecified dislocation of left index finger"
+S63252,"Unspecified dislocation of right middle finger"
+S63253,"Unspecified dislocation of left middle finger"
+S63254,"Unspecified dislocation of right ring finger"
+S63255,"Unspecified dislocation of left ring finger"
+S63256,"Unspecified dislocation of right little finger"
+S63257,"Unspecified dislocation of left little finger"
+S63258,"Unspecified dislocation of other finger"
+S63259,"Unspecified dislocation of unspecified finger"
+S6326,"Dislocation of metacarpophalangeal joint of finger"
+S63260,"Dislocation of metacarpophalangeal joint of right index finger"
+S63261,"Dislocation of metacarpophalangeal joint of left index finger"
+S63262,"Dislocation of metacarpophalangeal joint of right middle finger"
+S63263,"Dislocation of metacarpophalangeal joint of left middle finger"
+S63264,"Dislocation of metacarpophalangeal joint of right ring finger"
+S63265,"Dislocation of metacarpophalangeal joint of left ring finger"
+S63266,"Dislocation of metacarpophalangeal joint of right little finger"
+S63267,"Dislocation of metacarpophalangeal joint of left little finger"
+S63268,"Dislocation of metacarpophalangeal joint of other finger"
+S63269,"Dislocation of metacarpophalangeal joint of unspecified finger"
+S6327,"Dislocation of unspecified interphalangeal joint of finger"
+S63270,"Dislocation of unspecified interphalangeal joint of right index finger"
+S63271,"Dislocation of unspecified interphalangeal joint of left index finger"
+S63272,"Dislocation of unspecified interphalangeal joint of right middle finger"
+S63273,"Dislocation of unspecified interphalangeal joint of left middle finger"
+S63274,"Dislocation of unspecified interphalangeal joint of right ring finger"
+S63275,"Dislocation of unspecified interphalangeal joint of left ring finger"
+S63276,"Dislocation of unspecified interphalangeal joint of right little finger"
+S63277,"Dislocation of unspecified interphalangeal joint of left little finger"
+S63278,"Dislocation of unspecified interphalangeal joint of other finger"
+S63279,"Dislocation of unspecified interphalangeal joint of unspecified finger"
+S6328,"Dislocation of proximal interphalangeal joint of finger"
+S63280,"Dislocation of proximal interphalangeal joint of right index finger"
+S63281,"Dislocation of proximal interphalangeal joint of left index finger"
+S63282,"Dislocation of proximal interphalangeal joint of right middle finger"
+S63283,"Dislocation of proximal interphalangeal joint of left middle finger"
+S63284,"Dislocation of proximal interphalangeal joint of right ring finger"
+S63285,"Dislocation of proximal interphalangeal joint of left ring finger"
+S63286,"Dislocation of proximal interphalangeal joint of right little finger"
+S63287,"Dislocation of proximal interphalangeal joint of left little finger"
+S63288,"Dislocation of proximal interphalangeal joint of other finger"
+S63289,"Dislocation of proximal interphalangeal joint of unspecified finger"
+S6329,"Dislocation of distal interphalangeal joint of finger"
+S63290,"Dislocation of distal interphalangeal joint of right index finger"
+S63291,"Dislocation of distal interphalangeal joint of left index finger"
+S63292,"Dislocation of distal interphalangeal joint of right middle finger"
+S63293,"Dislocation of distal interphalangeal joint of left middle finger"
+S63294,"Dislocation of distal interphalangeal joint of right ring finger"
+S63295,"Dislocation of distal interphalangeal joint of left ring finger"
+S63296,"Dislocation of distal interphalangeal joint of right little finger"
+S63297,"Dislocation of distal interphalangeal joint of left little finger"
+S63298,"Dislocation of distal interphalangeal joint of other finger"
+S63299,"Dislocation of distal interphalangeal joint of unspecified finger"
+S633,"Traumatic rupture of ligament of wrist"
+S6330,"Traumatic rupture of unspecified ligament of wrist"
+S63301,"Traumatic rupture of unspecified ligament of right wrist"
+S63302,"Traumatic rupture of unspecified ligament of left wrist"
+S63309,"Traumatic rupture of unspecified ligament of unspecified wrist"
+S6331,"Traumatic rupture of collateral ligament of wrist"
+S63311,"Traumatic rupture of collateral ligament of right wrist"
+S63312,"Traumatic rupture of collateral ligament of left wrist"
+S63319,"Traumatic rupture of collateral ligament of unspecified wrist"
+S6332,"Traumatic rupture of radiocarpal ligament"
+S63321,"Traumatic rupture of right radiocarpal ligament"
+S63322,"Traumatic rupture of left radiocarpal ligament"
+S63329,"Traumatic rupture of unspecified radiocarpal ligament"
+S6333,"Traumatic rupture of ulnocarpal (palmar) ligament"
+S63331,"Traumatic rupture of right ulnocarpal (palmar) ligament"
+S63332,"Traumatic rupture of left ulnocarpal (palmar) ligament"
+S63339,"Traumatic rupture of unspecified ulnocarpal (palmar) ligament"
+S6339,"Traumatic rupture of other ligament of wrist"
+S63391,"Traumatic rupture of other ligament of right wrist"
+S63392,"Traumatic rupture of other ligament of left wrist"
+S63399,"Traumatic rupture of other ligament of unspecified wrist"
+S634,"Traumatic rupture of ligament of finger at metacarpophalangeal and interphalangeal joint(s)"
+S6340,"Traumatic rupture of unspecified ligament of finger at metacarpophalangeal and interphalangeal joint"
+S63400,"Traumatic rupture of unspecified ligament of right index finger at metacarpophalangeal and interphalangeal joint"
+S63401,"Traumatic rupture of unspecified ligament of left index finger at metacarpophalangeal and interphalangeal joint"
+S63402,"Traumatic rupture of unspecified ligament of right middle finger at metacarpophalangeal and interphalangeal joint"
+S63403,"Traumatic rupture of unspecified ligament of left middle finger at metacarpophalangeal and interphalangeal joint"
+S63404,"Traumatic rupture of unspecified ligament of right ring finger at metacarpophalangeal and interphalangeal joint"
+S63405,"Traumatic rupture of unspecified ligament of left ring finger at metacarpophalangeal and interphalangeal joint"
+S63406,"Traumatic rupture of unspecified ligament of right little finger at metacarpophalangeal and interphalangeal joint"
+S63407,"Traumatic rupture of unspecified ligament of left little finger at metacarpophalangeal and interphalangeal joint"
+S63408,"Traumatic rupture of unspecified ligament of other finger at metacarpophalangeal and interphalangeal joint"
+S63409,"Traumatic rupture of unspecified ligament of unspecified finger at metacarpophalangeal and interphalangeal joint"
+S6341,"Traumatic rupture of collateral ligament of finger at metacarpophalangeal and interphalangeal joint"
+S63410,"Traumatic rupture of collateral ligament of right index finger at metacarpophalangeal and interphalangeal joint"
+S63411,"Traumatic rupture of collateral ligament of left index finger at metacarpophalangeal and interphalangeal joint"
+S63412,"Traumatic rupture of collateral ligament of right middle finger at metacarpophalangeal and interphalangeal joint"
+S63413,"Traumatic rupture of collateral ligament of left middle finger at metacarpophalangeal and interphalangeal joint"
+S63414,"Traumatic rupture of collateral ligament of right ring finger at metacarpophalangeal and interphalangeal joint"
+S63415,"Traumatic rupture of collateral ligament of left ring finger at metacarpophalangeal and interphalangeal joint"
+S63416,"Traumatic rupture of collateral ligament of right little finger at metacarpophalangeal and interphalangeal joint"
+S63417,"Traumatic rupture of collateral ligament of left little finger at metacarpophalangeal and interphalangeal joint"
+S63418,"Traumatic rupture of collateral ligament of other finger at metacarpophalangeal and interphalangeal joint"
+S63419,"Traumatic rupture of collateral ligament of unspecified finger at metacarpophalangeal and interphalangeal joint"
+S6342,"Traumatic rupture of palmar ligament of finger at metacarpophalangeal and interphalangeal joint"
+S63420,"Traumatic rupture of palmar ligament of right index finger at metacarpophalangeal and interphalangeal joint"
+S63421,"Traumatic rupture of palmar ligament of left index finger at metacarpophalangeal and interphalangeal joint"
+S63422,"Traumatic rupture of palmar ligament of right middle finger at metacarpophalangeal and interphalangeal joint"
+S63423,"Traumatic rupture of palmar ligament of left middle finger at metacarpophalangeal and interphalangeal joint"
+S63424,"Traumatic rupture of palmar ligament of right ring finger at metacarpophalangeal and interphalangeal joint"
+S63425,"Traumatic rupture of palmar ligament of left ring finger at metacarpophalangeal and interphalangeal joint"
+S63426,"Traumatic rupture of palmar ligament of right little finger at metacarpophalangeal and interphalangeal joint"
+S63427,"Traumatic rupture of palmar ligament of left little finger at metacarpophalangeal and interphalangeal joint"
+S63428,"Traumatic rupture of palmar ligament of other finger at metacarpophalangeal and interphalangeal joint"
+S63429,"Traumatic rupture of palmar ligament of unspecified finger at metacarpophalangeal and interphalangeal joint"
+S6343,"Traumatic rupture of volar plate of finger at metacarpophalangeal and interphalangeal joint"
+S63430,"Traumatic rupture of volar plate of right index finger at metacarpophalangeal and interphalangeal joint"
+S63431,"Traumatic rupture of volar plate of left index finger at metacarpophalangeal and interphalangeal joint"
+S63432,"Traumatic rupture of volar plate of right middle finger at metacarpophalangeal and interphalangeal joint"
+S63433,"Traumatic rupture of volar plate of left middle finger at metacarpophalangeal and interphalangeal joint"
+S63434,"Traumatic rupture of volar plate of right ring finger at metacarpophalangeal and interphalangeal joint"
+S63435,"Traumatic rupture of volar plate of left ring finger at metacarpophalangeal and interphalangeal joint"
+S63436,"Traumatic rupture of volar plate of right little finger at metacarpophalangeal and interphalangeal joint"
+S63437,"Traumatic rupture of volar plate of left little finger at metacarpophalangeal and interphalangeal joint"
+S63438,"Traumatic rupture of volar plate of other finger at metacarpophalangeal and interphalangeal joint"
+S63439,"Traumatic rupture of volar plate of unspecified finger at metacarpophalangeal and interphalangeal joint"
+S6349,"Traumatic rupture of other ligament of finger at metacarpophalangeal and interphalangeal joint"
+S63490,"Traumatic rupture of other ligament of right index finger at metacarpophalangeal and interphalangeal joint"
+S63491,"Traumatic rupture of other ligament of left index finger at metacarpophalangeal and interphalangeal joint"
+S63492,"Traumatic rupture of other ligament of right middle finger at metacarpophalangeal and interphalangeal joint"
+S63493,"Traumatic rupture of other ligament of left middle finger at metacarpophalangeal and interphalangeal joint"
+S63494,"Traumatic rupture of other ligament of right ring finger at metacarpophalangeal and interphalangeal joint"
+S63495,"Traumatic rupture of other ligament of left ring finger at metacarpophalangeal and interphalangeal joint"
+S63496,"Traumatic rupture of other ligament of right little finger at metacarpophalangeal and interphalangeal joint"
+S63497,"Traumatic rupture of other ligament of left little finger at metacarpophalangeal and interphalangeal joint"
+S63498,"Traumatic rupture of other ligament of other finger at metacarpophalangeal and interphalangeal joint"
+S63499,"Traumatic rupture of other ligament of unspecified finger at metacarpophalangeal and interphalangeal joint"
+S635,"Other and unspecified sprain of wrist"
+S6350,"Unspecified sprain of wrist"
+S63501,"Unspecified sprain of right wrist"
+S63502,"Unspecified sprain of left wrist"
+S63509,"Unspecified sprain of unspecified wrist"
+S6351,"Sprain of carpal (joint)"
+S63511,"Sprain of carpal joint of right wrist"
+S63512,"Sprain of carpal joint of left wrist"
+S63519,"Sprain of carpal joint of unspecified wrist"
+S6352,"Sprain of radiocarpal joint"
+S63521,"Sprain of radiocarpal joint of right wrist"
+S63522,"Sprain of radiocarpal joint of left wrist"
+S63529,"Sprain of radiocarpal joint of unspecified wrist"
+S6359,"Other specified sprain of wrist"
+S63591,"Other specified sprain of right wrist"
+S63592,"Other specified sprain of left wrist"
+S63599,"Other specified sprain of unspecified wrist"
+S636,"Other and unspecified sprain of finger(s)"
+S6360,"Unspecified sprain of thumb"
+S63601,"Unspecified sprain of right thumb"
+S63602,"Unspecified sprain of left thumb"
+S63609,"Unspecified sprain of unspecified thumb"
+S6361,"Unspecified sprain of other and unspecified finger(s)"
+S63610,"Unspecified sprain of right index finger"
+S63611,"Unspecified sprain of left index finger"
+S63612,"Unspecified sprain of right middle finger"
+S63613,"Unspecified sprain of left middle finger"
+S63614,"Unspecified sprain of right ring finger"
+S63615,"Unspecified sprain of left ring finger"
+S63616,"Unspecified sprain of right little finger"
+S63617,"Unspecified sprain of left little finger"
+S63618,"Unspecified sprain of other finger"
+S63619,"Unspecified sprain of unspecified finger"
+S6362,"Sprain of interphalangeal joint of thumb"
+S63621,"Sprain of interphalangeal joint of right thumb"
+S63622,"Sprain of interphalangeal joint of left thumb"
+S63629,"Sprain of interphalangeal joint of unspecified thumb"
+S6363,"Sprain of interphalangeal joint of other and unspecified finger(s)"
+S63630,"Sprain of interphalangeal joint of right index finger"
+S63631,"Sprain of interphalangeal joint of left index finger"
+S63632,"Sprain of interphalangeal joint of right middle finger"
+S63633,"Sprain of interphalangeal joint of left middle finger"
+S63634,"Sprain of interphalangeal joint of right ring finger"
+S63635,"Sprain of interphalangeal joint of left ring finger"
+S63636,"Sprain of interphalangeal joint of right little finger"
+S63637,"Sprain of interphalangeal joint of left little finger"
+S63638,"Sprain of interphalangeal joint of other finger"
+S63639,"Sprain of interphalangeal joint of unspecified finger"
+S6364,"Sprain of metacarpophalangeal joint of thumb"
+S63641,"Sprain of metacarpophalangeal joint of right thumb"
+S63642,"Sprain of metacarpophalangeal joint of left thumb"
+S63649,"Sprain of metacarpophalangeal joint of unspecified thumb"
+S6365,"Sprain of metacarpophalangeal joint of other and unspecified finger(s)"
+S63650,"Sprain of metacarpophalangeal joint of right index finger"
+S63651,"Sprain of metacarpophalangeal joint of left index finger"
+S63652,"Sprain of metacarpophalangeal joint of right middle finger"
+S63653,"Sprain of metacarpophalangeal joint of left middle finger"
+S63654,"Sprain of metacarpophalangeal joint of right ring finger"
+S63655,"Sprain of metacarpophalangeal joint of left ring finger"
+S63656,"Sprain of metacarpophalangeal joint of right little finger"
+S63657,"Sprain of metacarpophalangeal joint of left little finger"
+S63658,"Sprain of metacarpophalangeal joint of other finger"
+S63659,"Sprain of metacarpophalangeal joint of unspecified finger"
+S6368,"Other sprain of thumb"
+S63681,"Other sprain of right thumb"
+S63682,"Other sprain of left thumb"
+S63689,"Other sprain of unspecified thumb"
+S6369,"Other sprain of other and unspecified finger(s)"
+S63690,"Other sprain of right index finger"
+S63691,"Other sprain of left index finger"
+S63692,"Other sprain of right middle finger"
+S63693,"Other sprain of left middle finger"
+S63694,"Other sprain of right ring finger"
+S63695,"Other sprain of left ring finger"
+S63696,"Other sprain of right little finger"
+S63697,"Other sprain of left little finger"
+S63698,"Other sprain of other finger"
+S63699,"Other sprain of unspecified finger"
+S638,"Sprain of other part of wrist and hand"
+S638X,"Sprain of other part of wrist and hand"
+S638X1,"Sprain of other part of right wrist and hand"
+S638X2,"Sprain of other part of left wrist and hand"
+S638X9,"Sprain of other part of unspecified wrist and hand"
+S639,"Sprain of unspecified part of wrist and hand"
+S6390,"Sprain of unspecified part of unspecified wrist and hand"
+S6391,"Sprain of unspecified part of right wrist and hand"
+S6392,"Sprain of unspecified part of left wrist and hand"
+S64,"Injury of nerves at wrist and hand level"
+S640,"Injury of ulnar nerve at wrist and hand level"
+S6400,"Injury of ulnar nerve at wrist and hand level of unspecified arm"
+S6401,"Injury of ulnar nerve at wrist and hand level of right arm"
+S6402,"Injury of ulnar nerve at wrist and hand level of left arm"
+S641,"Injury of median nerve at wrist and hand level"
+S6410,"Injury of median nerve at wrist and hand level of unspecified arm"
+S6411,"Injury of median nerve at wrist and hand level of right arm"
+S6412,"Injury of median nerve at wrist and hand level of left arm"
+S642,"Injury of radial nerve at wrist and hand level"
+S6420,"Injury of radial nerve at wrist and hand level of unspecified arm"
+S6421,"Injury of radial nerve at wrist and hand level of right arm"
+S6422,"Injury of radial nerve at wrist and hand level of left arm"
+S643,"Injury of digital nerve of thumb"
+S6430,"Injury of digital nerve of unspecified thumb"
+S6431,"Injury of digital nerve of right thumb"
+S6432,"Injury of digital nerve of left thumb"
+S644,"Injury of digital nerve of other and unspecified finger"
+S6440,"Injury of digital nerve of unspecified finger"
+S6449,"Injury of digital nerve of other finger"
+S64490,"Injury of digital nerve of right index finger"
+S64491,"Injury of digital nerve of left index finger"
+S64492,"Injury of digital nerve of right middle finger"
+S64493,"Injury of digital nerve of left middle finger"
+S64494,"Injury of digital nerve of right ring finger"
+S64495,"Injury of digital nerve of left ring finger"
+S64496,"Injury of digital nerve of right little finger"
+S64497,"Injury of digital nerve of left little finger"
+S64498,"Injury of digital nerve of other finger"
+S648,"Injury of other nerves at wrist and hand level"
+S648X,"Injury of other nerves at wrist and hand level"
+S648X1,"Injury of other nerves at wrist and hand level of right arm"
+S648X2,"Injury of other nerves at wrist and hand level of left arm"
+S648X9,"Injury of other nerves at wrist and hand level of unspecified arm"
+S649,"Injury of unspecified nerve at wrist and hand level"
+S6490,"Injury of unspecified nerve at wrist and hand level of unspecified arm"
+S6491,"Injury of unspecified nerve at wrist and hand level of right arm"
+S6492,"Injury of unspecified nerve at wrist and hand level of left arm"
+S65,"Injury of blood vessels at wrist and hand level"
+S650,"Injury of ulnar artery at wrist and hand level"
+S6500,"Unspecified injury of ulnar artery at wrist and hand level"
+S65001,"Unspecified injury of ulnar artery at wrist and hand level of right arm"
+S65002,"Unspecified injury of ulnar artery at wrist and hand level of left arm"
+S65009,"Unspecified injury of ulnar artery at wrist and hand level of unspecified arm"
+S6501,"Laceration of ulnar artery at wrist and hand level"
+S65011,"Laceration of ulnar artery at wrist and hand level of right arm"
+S65012,"Laceration of ulnar artery at wrist and hand level of left arm"
+S65019,"Laceration of ulnar artery at wrist and hand level of unspecified arm"
+S6509,"Other specified injury of ulnar artery at wrist and hand level"
+S65091,"Other specified injury of ulnar artery at wrist and hand level of right arm"
+S65092,"Other specified injury of ulnar artery at wrist and hand level of left arm"
+S65099,"Other specified injury of ulnar artery at wrist and hand level of unspecified arm"
+S651,"Injury of radial artery at wrist and hand level"
+S6510,"Unspecified injury of radial artery at wrist and hand level"
+S65101,"Unspecified injury of radial artery at wrist and hand level of right arm"
+S65102,"Unspecified injury of radial artery at wrist and hand level of left arm"
+S65109,"Unspecified injury of radial artery at wrist and hand level of unspecified arm"
+S6511,"Laceration of radial artery at wrist and hand level"
+S65111,"Laceration of radial artery at wrist and hand level of right arm"
+S65112,"Laceration of radial artery at wrist and hand level of left arm"
+S65119,"Laceration of radial artery at wrist and hand level of unspecified arm"
+S6519,"Other specified injury of radial artery at wrist and hand level"
+S65191,"Other specified injury of radial artery at wrist and hand level of right arm"
+S65192,"Other specified injury of radial artery at wrist and hand level of left arm"
+S65199,"Other specified injury of radial artery at wrist and hand level of unspecified arm"
+S652,"Injury of superficial palmar arch"
+S6520,"Unspecified injury of superficial palmar arch"
+S65201,"Unspecified injury of superficial palmar arch of right hand"
+S65202,"Unspecified injury of superficial palmar arch of left hand"
+S65209,"Unspecified injury of superficial palmar arch of unspecified hand"
+S6521,"Laceration of superficial palmar arch"
+S65211,"Laceration of superficial palmar arch of right hand"
+S65212,"Laceration of superficial palmar arch of left hand"
+S65219,"Laceration of superficial palmar arch of unspecified hand"
+S6529,"Other specified injury of superficial palmar arch"
+S65291,"Other specified injury of superficial palmar arch of right hand"
+S65292,"Other specified injury of superficial palmar arch of left hand"
+S65299,"Other specified injury of superficial palmar arch of unspecified hand"
+S653,"Injury of deep palmar arch"
+S6530,"Unspecified injury of deep palmar arch"
+S65301,"Unspecified injury of deep palmar arch of right hand"
+S65302,"Unspecified injury of deep palmar arch of left hand"
+S65309,"Unspecified injury of deep palmar arch of unspecified hand"
+S6531,"Laceration of deep palmar arch"
+S65311,"Laceration of deep palmar arch of right hand"
+S65312,"Laceration of deep palmar arch of left hand"
+S65319,"Laceration of deep palmar arch of unspecified hand"
+S6539,"Other specified injury of deep palmar arch"
+S65391,"Other specified injury of deep palmar arch of right hand"
+S65392,"Other specified injury of deep palmar arch of left hand"
+S65399,"Other specified injury of deep palmar arch of unspecified hand"
+S654,"Injury of blood vessel of thumb"
+S6540,"Unspecified injury of blood vessel of thumb"
+S65401,"Unspecified injury of blood vessel of right thumb"
+S65402,"Unspecified injury of blood vessel of left thumb"
+S65409,"Unspecified injury of blood vessel of unspecified thumb"
+S6541,"Laceration of blood vessel of thumb"
+S65411,"Laceration of blood vessel of right thumb"
+S65412,"Laceration of blood vessel of left thumb"
+S65419,"Laceration of blood vessel of unspecified thumb"
+S6549,"Other specified injury of blood vessel of thumb"
+S65491,"Other specified injury of blood vessel of right thumb"
+S65492,"Other specified injury of blood vessel of left thumb"
+S65499,"Other specified injury of blood vessel of unspecified thumb"
+S655,"Injury of blood vessel of other and unspecified finger"
+S6550,"Unspecified injury of blood vessel of other and unspecified finger"
+S65500,"Unspecified injury of blood vessel of right index finger"
+S65501,"Unspecified injury of blood vessel of left index finger"
+S65502,"Unspecified injury of blood vessel of right middle finger"
+S65503,"Unspecified injury of blood vessel of left middle finger"
+S65504,"Unspecified injury of blood vessel of right ring finger"
+S65505,"Unspecified injury of blood vessel of left ring finger"
+S65506,"Unspecified injury of blood vessel of right little finger"
+S65507,"Unspecified injury of blood vessel of left little finger"
+S65508,"Unspecified injury of blood vessel of other finger"
+S65509,"Unspecified injury of blood vessel of unspecified finger"
+S6551,"Laceration of blood vessel of other and unspecified finger"
+S65510,"Laceration of blood vessel of right index finger"
+S65511,"Laceration of blood vessel of left index finger"
+S65512,"Laceration of blood vessel of right middle finger"
+S65513,"Laceration of blood vessel of left middle finger"
+S65514,"Laceration of blood vessel of right ring finger"
+S65515,"Laceration of blood vessel of left ring finger"
+S65516,"Laceration of blood vessel of right little finger"
+S65517,"Laceration of blood vessel of left little finger"
+S65518,"Laceration of blood vessel of other finger"
+S65519,"Laceration of blood vessel of unspecified finger"
+S6559,"Other specified injury of blood vessel of other and unspecified finger"
+S65590,"Other specified injury of blood vessel of right index finger"
+S65591,"Other specified injury of blood vessel of left index finger"
+S65592,"Other specified injury of blood vessel of right middle finger"
+S65593,"Other specified injury of blood vessel of left middle finger"
+S65594,"Other specified injury of blood vessel of right ring finger"
+S65595,"Other specified injury of blood vessel of left ring finger"
+S65596,"Other specified injury of blood vessel of right little finger"
+S65597,"Other specified injury of blood vessel of left little finger"
+S65598,"Other specified injury of blood vessel of other finger"
+S65599,"Other specified injury of blood vessel of unspecified finger"
+S658,"Injury of other blood vessels at wrist and hand level"
+S6580,"Unspecified injury of other blood vessels at wrist and hand level"
+S65801,"Unspecified injury of other blood vessels at wrist and hand level of right arm"
+S65802,"Unspecified injury of other blood vessels at wrist and hand level of left arm"
+S65809,"Unspecified injury of other blood vessels at wrist and hand level of unspecified arm"
+S6581,"Laceration of other blood vessels at wrist and hand level"
+S65811,"Laceration of other blood vessels at wrist and hand level of right arm"
+S65812,"Laceration of other blood vessels at wrist and hand level of left arm"
+S65819,"Laceration of other blood vessels at wrist and hand level of unspecified arm"
+S6589,"Other specified injury of other blood vessels at wrist and hand level"
+S65891,"Other specified injury of other blood vessels at wrist and hand level of right arm"
+S65892,"Other specified injury of other blood vessels at wrist and hand level of left arm"
+S65899,"Other specified injury of other blood vessels at wrist and hand level of unspecified arm"
+S659,"Injury of unspecified blood vessel at wrist and hand level"
+S6590,"Unspecified injury of unspecified blood vessel at wrist and hand level"
+S65901,"Unspecified injury of unspecified blood vessel at wrist and hand level of right arm"
+S65902,"Unspecified injury of unspecified blood vessel at wrist and hand level of left arm"
+S65909,"Unspecified injury of unspecified blood vessel at wrist and hand level of unspecified arm"
+S6591,"Laceration of unspecified blood vessel at wrist and hand level"
+S65911,"Laceration of unspecified blood vessel at wrist and hand level of right arm"
+S65912,"Laceration of unspecified blood vessel at wrist and hand level of left arm"
+S65919,"Laceration of unspecified blood vessel at wrist and hand level of unspecified arm"
+S6599,"Other specified injury of unspecified blood vessel at wrist and hand level"
+S65991,"Other specified injury of unspecified blood vessel at wrist and hand of right arm"
+S65992,"Other specified injury of unspecified blood vessel at wrist and hand of left arm"
+S65999,"Other specified injury of unspecified blood vessel at wrist and hand of unspecified arm"
+S66,"Injury of muscle, fascia and tendon at wrist and hand level"
+S660,"Injury of long flexor muscle, fascia and tendon of thumb at wrist and hand level"
+S6600,"Unspecified injury of long flexor muscle, fascia and tendon of thumb at wrist and hand level"
+S66001,"Unspecified injury of long flexor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66002,"Unspecified injury of long flexor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66009,"Unspecified injury of long flexor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6601,"Strain of long flexor muscle, fascia and tendon of thumb at wrist and hand level"
+S66011,"Strain of long flexor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66012,"Strain of long flexor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66019,"Strain of long flexor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6602,"Laceration of long flexor muscle, fascia and tendon of thumb at wrist and hand level"
+S66021,"Laceration of long flexor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66022,"Laceration of long flexor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66029,"Laceration of long flexor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6609,"Other specified injury of long flexor muscle, fascia and tendon of thumb at wrist and hand level"
+S66091,"Other specified injury of long flexor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66092,"Other specified injury of long flexor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66099,"Other specified injury of long flexor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S661,"Injury of flexor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S6610,"Unspecified injury of flexor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66100,"Unspecified injury of flexor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66101,"Unspecified injury of flexor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66102,"Unspecified injury of flexor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66103,"Unspecified injury of flexor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66104,"Unspecified injury of flexor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66105,"Unspecified injury of flexor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66106,"Unspecified injury of flexor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66107,"Unspecified injury of flexor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66108,"Unspecified injury of flexor muscle, fascia and tendon of other finger at wrist and hand level"
+S66109,"Unspecified injury of flexor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6611,"Strain of flexor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66110,"Strain of flexor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66111,"Strain of flexor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66112,"Strain of flexor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66113,"Strain of flexor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66114,"Strain of flexor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66115,"Strain of flexor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66116,"Strain of flexor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66117,"Strain of flexor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66118,"Strain of flexor muscle, fascia and tendon of other finger at wrist and hand level"
+S66119,"Strain of flexor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6612,"Laceration of flexor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66120,"Laceration of flexor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66121,"Laceration of flexor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66122,"Laceration of flexor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66123,"Laceration of flexor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66124,"Laceration of flexor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66125,"Laceration of flexor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66126,"Laceration of flexor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66127,"Laceration of flexor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66128,"Laceration of flexor muscle, fascia and tendon of other finger at wrist and hand level"
+S66129,"Laceration of flexor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6619,"Other injury of flexor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66190,"Other injury of flexor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66191,"Other injury of flexor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66192,"Other injury of flexor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66193,"Other injury of flexor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66194,"Other injury of flexor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66195,"Other injury of flexor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66196,"Other injury of flexor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66197,"Other injury of flexor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66198,"Other injury of flexor muscle, fascia and tendon of other finger at wrist and hand level"
+S66199,"Other injury of flexor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S662,"Injury of extensor muscle, fascia and tendon of thumb at wrist and hand level"
+S6620,"Unspecified injury of extensor muscle, fascia and tendon of thumb at wrist and hand level"
+S66201,"Unspecified injury of extensor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66202,"Unspecified injury of extensor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66209,"Unspecified injury of extensor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6621,"Strain of extensor muscle, fascia and tendon of thumb at wrist and hand level"
+S66211,"Strain of extensor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66212,"Strain of extensor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66219,"Strain of extensor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6622,"Laceration of extensor muscle, fascia and tendon of thumb at wrist and hand level"
+S66221,"Laceration of extensor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66222,"Laceration of extensor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66229,"Laceration of extensor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6629,"Other specified injury of extensor muscle, fascia and tendon of thumb at wrist and hand level"
+S66291,"Other specified injury of extensor muscle, fascia and tendon of right thumb at wrist and hand level"
+S66292,"Other specified injury of extensor muscle, fascia and tendon of left thumb at wrist and hand level"
+S66299,"Other specified injury of extensor muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S663,"Injury of extensor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S6630,"Unspecified injury of extensor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66300,"Unspecified injury of extensor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66301,"Unspecified injury of extensor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66302,"Unspecified injury of extensor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66303,"Unspecified injury of extensor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66304,"Unspecified injury of extensor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66305,"Unspecified injury of extensor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66306,"Unspecified injury of extensor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66307,"Unspecified injury of extensor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66308,"Unspecified injury of extensor muscle, fascia and tendon of other finger at wrist and hand level"
+S66309,"Unspecified injury of extensor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6631,"Strain of extensor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66310,"Strain of extensor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66311,"Strain of extensor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66312,"Strain of extensor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66313,"Strain of extensor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66314,"Strain of extensor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66315,"Strain of extensor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66316,"Strain of extensor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66317,"Strain of extensor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66318,"Strain of extensor muscle, fascia and tendon of other finger at wrist and hand level"
+S66319,"Strain of extensor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6632,"Laceration of extensor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66320,"Laceration of extensor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66321,"Laceration of extensor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66322,"Laceration of extensor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66323,"Laceration of extensor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66324,"Laceration of extensor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66325,"Laceration of extensor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66326,"Laceration of extensor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66327,"Laceration of extensor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66328,"Laceration of extensor muscle, fascia and tendon of other finger at wrist and hand level"
+S66329,"Laceration of extensor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6639,"Other injury of extensor muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66390,"Other injury of extensor muscle, fascia and tendon of right index finger at wrist and hand level"
+S66391,"Other injury of extensor muscle, fascia and tendon of left index finger at wrist and hand level"
+S66392,"Other injury of extensor muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66393,"Other injury of extensor muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66394,"Other injury of extensor muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66395,"Other injury of extensor muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66396,"Other injury of extensor muscle, fascia and tendon of right little finger at wrist and hand level"
+S66397,"Other injury of extensor muscle, fascia and tendon of left little finger at wrist and hand level"
+S66398,"Other injury of extensor muscle, fascia and tendon of other finger at wrist and hand level"
+S66399,"Other injury of extensor muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S664,"Injury of intrinsic muscle, fascia and tendon of thumb at wrist and hand level"
+S6640,"Unspecified injury of intrinsic muscle, fascia and tendon of thumb at wrist and hand level"
+S66401,"Unspecified injury of intrinsic muscle, fascia and tendon of right thumb at wrist and hand level"
+S66402,"Unspecified injury of intrinsic muscle, fascia and tendon of left thumb at wrist and hand level"
+S66409,"Unspecified injury of intrinsic muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6641,"Strain of intrinsic muscle, fascia and tendon of thumb at wrist and hand level"
+S66411,"Strain of intrinsic muscle, fascia and tendon of right thumb at wrist and hand level"
+S66412,"Strain of intrinsic muscle, fascia and tendon of left thumb at wrist and hand level"
+S66419,"Strain of intrinsic muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6642,"Laceration of intrinsic muscle, fascia and tendon of thumb at wrist and hand level"
+S66421,"Laceration of intrinsic muscle, fascia and tendon of right thumb at wrist and hand level"
+S66422,"Laceration of intrinsic muscle, fascia and tendon of left thumb at wrist and hand level"
+S66429,"Laceration of intrinsic muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S6649,"Other specified injury of intrinsic muscle, fascia and tendon of thumb at wrist and hand level"
+S66491,"Other specified injury of intrinsic muscle, fascia and tendon of right thumb at wrist and hand level"
+S66492,"Other specified injury of intrinsic muscle, fascia and tendon of left thumb at wrist and hand level"
+S66499,"Other specified injury of intrinsic muscle, fascia and tendon of unspecified thumb at wrist and hand level"
+S665,"Injury of intrinsic muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S6650,"Unspecified injury of intrinsic muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66500,"Unspecified injury of intrinsic muscle, fascia and tendon of right index finger at wrist and hand level"
+S66501,"Unspecified injury of intrinsic muscle, fascia and tendon of left index finger at wrist and hand level"
+S66502,"Unspecified injury of intrinsic muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66503,"Unspecified injury of intrinsic muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66504,"Unspecified injury of intrinsic muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66505,"Unspecified injury of intrinsic muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66506,"Unspecified injury of intrinsic muscle, fascia and tendon of right little finger at wrist and hand level"
+S66507,"Unspecified injury of intrinsic muscle, fascia and tendon of left little finger at wrist and hand level"
+S66508,"Unspecified injury of intrinsic muscle, fascia and tendon of other finger at wrist and hand level"
+S66509,"Unspecified injury of intrinsic muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6651,"Strain of intrinsic muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66510,"Strain of intrinsic muscle, fascia and tendon of right index finger at wrist and hand level"
+S66511,"Strain of intrinsic muscle, fascia and tendon of left index finger at wrist and hand level"
+S66512,"Strain of intrinsic muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66513,"Strain of intrinsic muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66514,"Strain of intrinsic muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66515,"Strain of intrinsic muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66516,"Strain of intrinsic muscle, fascia and tendon of right little finger at wrist and hand level"
+S66517,"Strain of intrinsic muscle, fascia and tendon of left little finger at wrist and hand level"
+S66518,"Strain of intrinsic muscle, fascia and tendon of other finger at wrist and hand level"
+S66519,"Strain of intrinsic muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6652,"Laceration of intrinsic muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66520,"Laceration of intrinsic muscle, fascia and tendon of right index finger at wrist and hand level"
+S66521,"Laceration of intrinsic muscle, fascia and tendon of left index finger at wrist and hand level"
+S66522,"Laceration of intrinsic muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66523,"Laceration of intrinsic muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66524,"Laceration of intrinsic muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66525,"Laceration of intrinsic muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66526,"Laceration of intrinsic muscle, fascia and tendon of right little finger at wrist and hand level"
+S66527,"Laceration of intrinsic muscle, fascia and tendon of left little finger at wrist and hand level"
+S66528,"Laceration of intrinsic muscle, fascia and tendon of other finger at wrist and hand level"
+S66529,"Laceration of intrinsic muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S6659,"Other injury of intrinsic muscle, fascia and tendon of other and unspecified finger at wrist and hand level"
+S66590,"Other injury of intrinsic muscle, fascia and tendon of right index finger at wrist and hand level"
+S66591,"Other injury of intrinsic muscle, fascia and tendon of left index finger at wrist and hand level"
+S66592,"Other injury of intrinsic muscle, fascia and tendon of right middle finger at wrist and hand level"
+S66593,"Other injury of intrinsic muscle, fascia and tendon of left middle finger at wrist and hand level"
+S66594,"Other injury of intrinsic muscle, fascia and tendon of right ring finger at wrist and hand level"
+S66595,"Other injury of intrinsic muscle, fascia and tendon of left ring finger at wrist and hand level"
+S66596,"Other injury of intrinsic muscle, fascia and tendon of right little finger at wrist and hand level"
+S66597,"Other injury of intrinsic muscle, fascia and tendon of left little finger at wrist and hand level"
+S66598,"Other injury of intrinsic muscle, fascia and tendon of other finger at wrist and hand level"
+S66599,"Other injury of intrinsic muscle, fascia and tendon of unspecified finger at wrist and hand level"
+S668,"Injury of other specified muscles, fascia and tendons at wrist and hand level"
+S6680,"Unspecified injury of other specified muscles, fascia and tendons at wrist and hand level"
+S66801,"Unspecified injury of other specified muscles, fascia and tendons at wrist and hand level, right hand"
+S66802,"Unspecified injury of other specified muscles, fascia and tendons at wrist and hand level, left hand"
+S66809,"Unspecified injury of other specified muscles, fascia and tendons at wrist and hand level, unspecified hand"
+S6681,"Strain of other specified muscles, fascia and tendons at wrist and hand level"
+S66811,"Strain of other specified muscles, fascia and tendons at wrist and hand level, right hand"
+S66812,"Strain of other specified muscles, fascia and tendons at wrist and hand level, left hand"
+S66819,"Strain of other specified muscles, fascia and tendons at wrist and hand level, unspecified hand"
+S6682,"Laceration of other specified muscles, fascia and tendons at wrist and hand level"
+S66821,"Laceration of other specified muscles, fascia and tendons at wrist and hand level, right hand"
+S66822,"Laceration of other specified muscles, fascia and tendons at wrist and hand level, left hand"
+S66829,"Laceration of other specified muscles, fascia and tendons at wrist and hand level, unspecified hand"
+S6689,"Other injury of other specified muscles, fascia and tendons at wrist and hand level"
+S66891,"Other injury of other specified muscles, fascia and tendons at wrist and hand level, right hand"
+S66892,"Other injury of other specified muscles, fascia and tendons at wrist and hand level, left hand"
+S66899,"Other injury of other specified muscles, fascia and tendons at wrist and hand level, unspecified hand"
+S669,"Injury of unspecified muscle, fascia and tendon at wrist and hand level"
+S6690,"Unspecified injury of unspecified muscle, fascia and tendon at wrist and hand level"
+S66901,"Unspecified injury of unspecified muscle, fascia and tendon at wrist and hand level, right hand"
+S66902,"Unspecified injury of unspecified muscle, fascia and tendon at wrist and hand level, left hand"
+S66909,"Unspecified injury of unspecified muscle, fascia and tendon at wrist and hand level, unspecified hand"
+S6691,"Strain of unspecified muscle, fascia and tendon at wrist and hand level"
+S66911,"Strain of unspecified muscle, fascia and tendon at wrist and hand level, right hand"
+S66912,"Strain of unspecified muscle, fascia and tendon at wrist and hand level, left hand"
+S66919,"Strain of unspecified muscle, fascia and tendon at wrist and hand level, unspecified hand"
+S6692,"Laceration of unspecified muscle, fascia and tendon at wrist and hand level"
+S66921,"Laceration of unspecified muscle, fascia and tendon at wrist and hand level, right hand"
+S66922,"Laceration of unspecified muscle, fascia and tendon at wrist and hand level, left hand"
+S66929,"Laceration of unspecified muscle, fascia and tendon at wrist and hand level, unspecified hand"
+S6699,"Other injury of unspecified muscle, fascia and tendon at wrist and hand level"
+S66991,"Other injury of unspecified muscle, fascia and tendon at wrist and hand level, right hand"
+S66992,"Other injury of unspecified muscle, fascia and tendon at wrist and hand level, left hand"
+S66999,"Other injury of unspecified muscle, fascia and tendon at wrist and hand level, unspecified hand"
+S67,"Crushing injury of wrist, hand and fingers"
+S670,"Crushing injury of thumb"
+S6700,"Crushing injury of unspecified thumb"
+S6701,"Crushing injury of right thumb"
+S6702,"Crushing injury of left thumb"
+S671,"Crushing injury of other and unspecified finger(s)"
+S6710,"Crushing injury of unspecified finger(s)"
+S6719,"Crushing injury of other finger(s)"
+S67190,"Crushing injury of right index finger"
+S67191,"Crushing injury of left index finger"
+S67192,"Crushing injury of right middle finger"
+S67193,"Crushing injury of left middle finger"
+S67194,"Crushing injury of right ring finger"
+S67195,"Crushing injury of left ring finger"
+S67196,"Crushing injury of right little finger"
+S67197,"Crushing injury of left little finger"
+S67198,"Crushing injury of other finger"
+S672,"Crushing injury of hand"
+S6720,"Crushing injury of unspecified hand"
+S6721,"Crushing injury of right hand"
+S6722,"Crushing injury of left hand"
+S673,"Crushing injury of wrist"
+S6730,"Crushing injury of unspecified wrist"
+S6731,"Crushing injury of right wrist"
+S6732,"Crushing injury of left wrist"
+S674,"Crushing injury of wrist and hand"
+S6740,"Crushing injury of unspecified wrist and hand"
+S6741,"Crushing injury of right wrist and hand"
+S6742,"Crushing injury of left wrist and hand"
+S679,"Crushing injury of unspecified part(s) of wrist, hand and fingers"
+S6790,"Crushing injury of unspecified part(s) of unspecified wrist, hand and fingers"
+S6791,"Crushing injury of unspecified part(s) of right wrist, hand and fingers"
+S6792,"Crushing injury of unspecified part(s) of left wrist, hand and fingers"
+S68,"Traumatic amputation of wrist, hand and fingers"
+S680,"Traumatic metacarpophalangeal amputation of thumb"
+S6801,"Complete traumatic metacarpophalangeal amputation of thumb"
+S68011,"Complete traumatic metacarpophalangeal amputation of right thumb"
+S68012,"Complete traumatic metacarpophalangeal amputation of left thumb"
+S68019,"Complete traumatic metacarpophalangeal amputation of unspecified thumb"
+S6802,"Partial traumatic metacarpophalangeal amputation of thumb"
+S68021,"Partial traumatic metacarpophalangeal amputation of right thumb"
+S68022,"Partial traumatic metacarpophalangeal amputation of left thumb"
+S68029,"Partial traumatic metacarpophalangeal amputation of unspecified thumb"
+S681,"Traumatic metacarpophalangeal amputation of other and unspecified finger"
+S6811,"Complete traumatic metacarpophalangeal amputation of other and unspecified finger"
+S68110,"Complete traumatic metacarpophalangeal amputation of right index finger"
+S68111,"Complete traumatic metacarpophalangeal amputation of left index finger"
+S68112,"Complete traumatic metacarpophalangeal amputation of right middle finger"
+S68113,"Complete traumatic metacarpophalangeal amputation of left middle finger"
+S68114,"Complete traumatic metacarpophalangeal amputation of right ring finger"
+S68115,"Complete traumatic metacarpophalangeal amputation of left ring finger"
+S68116,"Complete traumatic metacarpophalangeal amputation of right little finger"
+S68117,"Complete traumatic metacarpophalangeal amputation of left little finger"
+S68118,"Complete traumatic metacarpophalangeal amputation of other finger"
+S68119,"Complete traumatic metacarpophalangeal amputation of unspecified finger"
+S6812,"Partial traumatic metacarpophalangeal amputation of other and unspecified finger"
+S68120,"Partial traumatic metacarpophalangeal amputation of right index finger"
+S68121,"Partial traumatic metacarpophalangeal amputation of left index finger"
+S68122,"Partial traumatic metacarpophalangeal amputation of right middle finger"
+S68123,"Partial traumatic metacarpophalangeal amputation of left middle finger"
+S68124,"Partial traumatic metacarpophalangeal amputation of right ring finger"
+S68125,"Partial traumatic metacarpophalangeal amputation of left ring finger"
+S68126,"Partial traumatic metacarpophalangeal amputation of right little finger"
+S68127,"Partial traumatic metacarpophalangeal amputation of left little finger"
+S68128,"Partial traumatic metacarpophalangeal amputation of other finger"
+S68129,"Partial traumatic metacarpophalangeal amputation of unspecified finger"
+S684,"Traumatic amputation of hand at wrist level"
+S6841,"Complete traumatic amputation of hand at wrist level"
+S68411,"Complete traumatic amputation of right hand at wrist level"
+S68412,"Complete traumatic amputation of left hand at wrist level"
+S68419,"Complete traumatic amputation of unspecified hand at wrist level"
+S6842,"Partial traumatic amputation of hand at wrist level"
+S68421,"Partial traumatic amputation of right hand at wrist level"
+S68422,"Partial traumatic amputation of left hand at wrist level"
+S68429,"Partial traumatic amputation of unspecified hand at wrist level"
+S685,"Traumatic transphalangeal amputation of thumb"
+S6851,"Complete traumatic transphalangeal amputation of thumb"
+S68511,"Complete traumatic transphalangeal amputation of right thumb"
+S68512,"Complete traumatic transphalangeal amputation of left thumb"
+S68519,"Complete traumatic transphalangeal amputation of unspecified thumb"
+S6852,"Partial traumatic transphalangeal amputation of thumb"
+S68521,"Partial traumatic transphalangeal amputation of right thumb"
+S68522,"Partial traumatic transphalangeal amputation of left thumb"
+S68529,"Partial traumatic transphalangeal amputation of unspecified thumb"
+S686,"Traumatic transphalangeal amputation of other and unspecified finger"
+S6861,"Complete traumatic transphalangeal amputation of other and unspecified finger(s)"
+S68610,"Complete traumatic transphalangeal amputation of right index finger"
+S68611,"Complete traumatic transphalangeal amputation of left index finger"
+S68612,"Complete traumatic transphalangeal amputation of right middle finger"
+S68613,"Complete traumatic transphalangeal amputation of left middle finger"
+S68614,"Complete traumatic transphalangeal amputation of right ring finger"
+S68615,"Complete traumatic transphalangeal amputation of left ring finger"
+S68616,"Complete traumatic transphalangeal amputation of right little finger"
+S68617,"Complete traumatic transphalangeal amputation of left little finger"
+S68618,"Complete traumatic transphalangeal amputation of other finger"
+S68619,"Complete traumatic transphalangeal amputation of unspecified finger"
+S6862,"Partial traumatic transphalangeal amputation of other and unspecified finger"
+S68620,"Partial traumatic transphalangeal amputation of right index finger"
+S68621,"Partial traumatic transphalangeal amputation of left index finger"
+S68622,"Partial traumatic transphalangeal amputation of right middle finger"
+S68623,"Partial traumatic transphalangeal amputation of left middle finger"
+S68624,"Partial traumatic transphalangeal amputation of right ring finger"
+S68625,"Partial traumatic transphalangeal amputation of left ring finger"
+S68626,"Partial traumatic transphalangeal amputation of right little finger"
+S68627,"Partial traumatic transphalangeal amputation of left little finger"
+S68628,"Partial traumatic transphalangeal amputation of other finger"
+S68629,"Partial traumatic transphalangeal amputation of unspecified finger"
+S687,"Traumatic transmetacarpal amputation of hand"
+S6871,"Complete traumatic transmetacarpal amputation of hand"
+S68711,"Complete traumatic transmetacarpal amputation of right hand"
+S68712,"Complete traumatic transmetacarpal amputation of left hand"
+S68719,"Complete traumatic transmetacarpal amputation of unspecified hand"
+S6872,"Partial traumatic transmetacarpal amputation of hand"
+S68721,"Partial traumatic transmetacarpal amputation of right hand"
+S68722,"Partial traumatic transmetacarpal amputation of left hand"
+S68729,"Partial traumatic transmetacarpal amputation of unspecified hand"
+S69,"Other and unspecified injuries of wrist, hand and finger(s)"
+S698,"Other specified injuries of wrist, hand and finger(s)"
+S6980,"Other specified injuries of unspecified wrist, hand and finger(s)"
+S6981,"Other specified injuries of right wrist, hand and finger(s)"
+S6982,"Other specified injuries of left wrist, hand and finger(s)"
+S699,"Unspecified injury of wrist, hand and finger(s)"
+S6990,"Unspecified injury of unspecified wrist, hand and finger(s)"
+S6991,"Unspecified injury of right wrist, hand and finger(s)"
+S6992,"Unspecified injury of left wrist, hand and finger(s)"
+S70,"Superficial injury of hip and thigh"
+S700,"Contusion of hip"
+S7000,"Contusion of unspecified hip"
+S7001,"Contusion of right hip"
+S7002,"Contusion of left hip"
+S701,"Contusion of thigh"
+S7010,"Contusion of unspecified thigh"
+S7011,"Contusion of right thigh"
+S7012,"Contusion of left thigh"
+S702,"Other superficial injuries of hip"
+S7021,"Abrasion of hip"
+S70211,"Abrasion, right hip"
+S70212,"Abrasion, left hip"
+S70219,"Abrasion, unspecified hip"
+S7022,"Blister (nonthermal) of hip"
+S70221,"Blister (nonthermal), right hip"
+S70222,"Blister (nonthermal), left hip"
+S70229,"Blister (nonthermal), unspecified hip"
+S7024,"External constriction of hip"
+S70241,"External constriction, right hip"
+S70242,"External constriction, left hip"
+S70249,"External constriction, unspecified hip"
+S7025,"Superficial foreign body of hip"
+S70251,"Superficial foreign body, right hip"
+S70252,"Superficial foreign body, left hip"
+S70259,"Superficial foreign body, unspecified hip"
+S7026,"Insect bite (nonvenomous) of hip"
+S70261,"Insect bite (nonvenomous), right hip"
+S70262,"Insect bite (nonvenomous), left hip"
+S70269,"Insect bite (nonvenomous), unspecified hip"
+S7027,"Other superficial bite of hip"
+S70271,"Other superficial bite of hip, right hip"
+S70272,"Other superficial bite of hip, left hip"
+S70279,"Other superficial bite of hip, unspecified hip"
+S703,"Other superficial injuries of thigh"
+S7031,"Abrasion of thigh"
+S70311,"Abrasion, right thigh"
+S70312,"Abrasion, left thigh"
+S70319,"Abrasion, unspecified thigh"
+S7032,"Blister (nonthermal) of thigh"
+S70321,"Blister (nonthermal), right thigh"
+S70322,"Blister (nonthermal), left thigh"
+S70329,"Blister (nonthermal), unspecified thigh"
+S7034,"External constriction of thigh"
+S70341,"External constriction, right thigh"
+S70342,"External constriction, left thigh"
+S70349,"External constriction, unspecified thigh"
+S7035,"Superficial foreign body of thigh"
+S70351,"Superficial foreign body, right thigh"
+S70352,"Superficial foreign body, left thigh"
+S70359,"Superficial foreign body, unspecified thigh"
+S7036,"Insect bite (nonvenomous) of thigh"
+S70361,"Insect bite (nonvenomous), right thigh"
+S70362,"Insect bite (nonvenomous), left thigh"
+S70369,"Insect bite (nonvenomous), unspecified thigh"
+S7037,"Other superficial bite of thigh"
+S70371,"Other superficial bite of right thigh"
+S70372,"Other superficial bite of left thigh"
+S70379,"Other superficial bite of unspecified thigh"
+S709,"Unspecified superficial injury of hip and thigh"
+S7091,"Unspecified superficial injury of hip"
+S70911,"Unspecified superficial injury of right hip"
+S70912,"Unspecified superficial injury of left hip"
+S70919,"Unspecified superficial injury of unspecified hip"
+S7092,"Unspecified superficial injury of thigh"
+S70921,"Unspecified superficial injury of right thigh"
+S70922,"Unspecified superficial injury of left thigh"
+S70929,"Unspecified superficial injury of unspecified thigh"
+S71,"Open wound of hip and thigh"
+S710,"Open wound of hip"
+S7100,"Unspecified open wound of hip"
+S71001,"Unspecified open wound, right hip"
+S71002,"Unspecified open wound, left hip"
+S71009,"Unspecified open wound, unspecified hip"
+S7101,"Laceration without foreign body of hip"
+S71011,"Laceration without foreign body, right hip"
+S71012,"Laceration without foreign body, left hip"
+S71019,"Laceration without foreign body, unspecified hip"
+S7102,"Laceration with foreign body of hip"
+S71021,"Laceration with foreign body, right hip"
+S71022,"Laceration with foreign body, left hip"
+S71029,"Laceration with foreign body, unspecified hip"
+S7103,"Puncture wound without foreign body of hip"
+S71031,"Puncture wound without foreign body, right hip"
+S71032,"Puncture wound without foreign body, left hip"
+S71039,"Puncture wound without foreign body, unspecified hip"
+S7104,"Puncture wound with foreign body of hip"
+S71041,"Puncture wound with foreign body, right hip"
+S71042,"Puncture wound with foreign body, left hip"
+S71049,"Puncture wound with foreign body, unspecified hip"
+S7105,"Open bite of hip"
+S71051,"Open bite, right hip"
+S71052,"Open bite, left hip"
+S71059,"Open bite, unspecified hip"
+S711,"Open wound of thigh"
+S7110,"Unspecified open wound of thigh"
+S71101,"Unspecified open wound, right thigh"
+S71102,"Unspecified open wound, left thigh"
+S71109,"Unspecified open wound, unspecified thigh"
+S7111,"Laceration without foreign body of thigh"
+S71111,"Laceration without foreign body, right thigh"
+S71112,"Laceration without foreign body, left thigh"
+S71119,"Laceration without foreign body, unspecified thigh"
+S7112,"Laceration with foreign body of thigh"
+S71121,"Laceration with foreign body, right thigh"
+S71122,"Laceration with foreign body, left thigh"
+S71129,"Laceration with foreign body, unspecified thigh"
+S7113,"Puncture wound without foreign body of thigh"
+S71131,"Puncture wound without foreign body, right thigh"
+S71132,"Puncture wound without foreign body, left thigh"
+S71139,"Puncture wound without foreign body, unspecified thigh"
+S7114,"Puncture wound with foreign body of thigh"
+S71141,"Puncture wound with foreign body, right thigh"
+S71142,"Puncture wound with foreign body, left thigh"
+S71149,"Puncture wound with foreign body, unspecified thigh"
+S7115,"Open bite of thigh"
+S71151,"Open bite, right thigh"
+S71152,"Open bite, left thigh"
+S71159,"Open bite, unspecified thigh"
+S72,"Fracture of femur"
+S720,"Fracture of head and neck of femur"
+S7200,"Fracture of unspecified part of neck of femur"
+S72001,"Fracture of unspecified part of neck of right femur"
+S72002,"Fracture of unspecified part of neck of left femur"
+S72009,"Fracture of unspecified part of neck of unspecified femur"
+S7201,"Unspecified intracapsular fracture of femur"
+S72011,"Unspecified intracapsular fracture of right femur"
+S72012,"Unspecified intracapsular fracture of left femur"
+S72019,"Unspecified intracapsular fracture of unspecified femur"
+S7202,"Fracture of epiphysis (separation) (upper) of femur"
+S72021,"Displaced fracture of epiphysis (separation) (upper) of right femur"
+S72022,"Displaced fracture of epiphysis (separation) (upper) of left femur"
+S72023,"Displaced fracture of epiphysis (separation) (upper) of unspecified femur"
+S72024,"Nondisplaced fracture of epiphysis (separation) (upper) of right femur"
+S72025,"Nondisplaced fracture of epiphysis (separation) (upper) of left femur"
+S72026,"Nondisplaced fracture of epiphysis (separation) (upper) of unspecified femur"
+S7203,"Midcervical fracture of femur"
+S72031,"Displaced midcervical fracture of right femur"
+S72032,"Displaced midcervical fracture of left femur"
+S72033,"Displaced midcervical fracture of unspecified femur"
+S72034,"Nondisplaced midcervical fracture of right femur"
+S72035,"Nondisplaced midcervical fracture of left femur"
+S72036,"Nondisplaced midcervical fracture of unspecified femur"
+S7204,"Fracture of base of neck of femur"
+S72041,"Displaced fracture of base of neck of right femur"
+S72042,"Displaced fracture of base of neck of left femur"
+S72043,"Displaced fracture of base of neck of unspecified femur"
+S72044,"Nondisplaced fracture of base of neck of right femur"
+S72045,"Nondisplaced fracture of base of neck of left femur"
+S72046,"Nondisplaced fracture of base of neck of unspecified femur"
+S7205,"Unspecified fracture of head of femur"
+S72051,"Unspecified fracture of head of right femur"
+S72052,"Unspecified fracture of head of left femur"
+S72059,"Unspecified fracture of head of unspecified femur"
+S7206,"Articular fracture of head of femur"
+S72061,"Displaced articular fracture of head of right femur"
+S72062,"Displaced articular fracture of head of left femur"
+S72063,"Displaced articular fracture of head of unspecified femur"
+S72064,"Nondisplaced articular fracture of head of right femur"
+S72065,"Nondisplaced articular fracture of head of left femur"
+S72066,"Nondisplaced articular fracture of head of unspecified femur"
+S7209,"Other fracture of head and neck of femur"
+S72091,"Other fracture of head and neck of right femur"
+S72092,"Other fracture of head and neck of left femur"
+S72099,"Other fracture of head and neck of unspecified femur"
+S721,"Pertrochanteric fracture"
+S7210,"Unspecified trochanteric fracture of femur"
+S72101,"Unspecified trochanteric fracture of right femur"
+S72102,"Unspecified trochanteric fracture of left femur"
+S72109,"Unspecified trochanteric fracture of unspecified femur"
+S7211,"Fracture of greater trochanter of femur"
+S72111,"Displaced fracture of greater trochanter of right femur"
+S72112,"Displaced fracture of greater trochanter of left femur"
+S72113,"Displaced fracture of greater trochanter of unspecified femur"
+S72114,"Nondisplaced fracture of greater trochanter of right femur"
+S72115,"Nondisplaced fracture of greater trochanter of left femur"
+S72116,"Nondisplaced fracture of greater trochanter of unspecified femur"
+S7212,"Fracture of lesser trochanter of femur"
+S72121,"Displaced fracture of lesser trochanter of right femur"
+S72122,"Displaced fracture of lesser trochanter of left femur"
+S72123,"Displaced fracture of lesser trochanter of unspecified femur"
+S72124,"Nondisplaced fracture of lesser trochanter of right femur"
+S72125,"Nondisplaced fracture of lesser trochanter of left femur"
+S72126,"Nondisplaced fracture of lesser trochanter of unspecified femur"
+S7213,"Apophyseal fracture of femur"
+S72131,"Displaced apophyseal fracture of right femur"
+S72132,"Displaced apophyseal fracture of left femur"
+S72133,"Displaced apophyseal fracture of unspecified femur"
+S72134,"Nondisplaced apophyseal fracture of right femur"
+S72135,"Nondisplaced apophyseal fracture of left femur"
+S72136,"Nondisplaced apophyseal fracture of unspecified femur"
+S7214,"Intertrochanteric fracture of femur"
+S72141,"Displaced intertrochanteric fracture of right femur"
+S72142,"Displaced intertrochanteric fracture of left femur"
+S72143,"Displaced intertrochanteric fracture of unspecified femur"
+S72144,"Nondisplaced intertrochanteric fracture of right femur"
+S72145,"Nondisplaced intertrochanteric fracture of left femur"
+S72146,"Nondisplaced intertrochanteric fracture of unspecified femur"
+S722,"Subtrochanteric fracture of femur"
+S7221,"Displaced subtrochanteric fracture of right femur"
+S7222,"Displaced subtrochanteric fracture of left femur"
+S7223,"Displaced subtrochanteric fracture of unspecified femur"
+S7224,"Nondisplaced subtrochanteric fracture of right femur"
+S7225,"Nondisplaced subtrochanteric fracture of left femur"
+S7226,"Nondisplaced subtrochanteric fracture of unspecified femur"
+S723,"Fracture of shaft of femur"
+S7230,"Unspecified fracture of shaft of femur"
+S72301,"Unspecified fracture of shaft of right femur"
+S72302,"Unspecified fracture of shaft of left femur"
+S72309,"Unspecified fracture of shaft of unspecified femur"
+S7232,"Transverse fracture of shaft of femur"
+S72321,"Displaced transverse fracture of shaft of right femur"
+S72322,"Displaced transverse fracture of shaft of left femur"
+S72323,"Displaced transverse fracture of shaft of unspecified femur"
+S72324,"Nondisplaced transverse fracture of shaft of right femur"
+S72325,"Nondisplaced transverse fracture of shaft of left femur"
+S72326,"Nondisplaced transverse fracture of shaft of unspecified femur"
+S7233,"Oblique fracture of shaft of femur"
+S72331,"Displaced oblique fracture of shaft of right femur"
+S72332,"Displaced oblique fracture of shaft of left femur"
+S72333,"Displaced oblique fracture of shaft of unspecified femur"
+S72334,"Nondisplaced oblique fracture of shaft of right femur"
+S72335,"Nondisplaced oblique fracture of shaft of left femur"
+S72336,"Nondisplaced oblique fracture of shaft of unspecified femur"
+S7234,"Spiral fracture of shaft of femur"
+S72341,"Displaced spiral fracture of shaft of right femur"
+S72342,"Displaced spiral fracture of shaft of left femur"
+S72343,"Displaced spiral fracture of shaft of unspecified femur"
+S72344,"Nondisplaced spiral fracture of shaft of right femur"
+S72345,"Nondisplaced spiral fracture of shaft of left femur"
+S72346,"Nondisplaced spiral fracture of shaft of unspecified femur"
+S7235,"Comminuted fracture of shaft of femur"
+S72351,"Displaced comminuted fracture of shaft of right femur"
+S72352,"Displaced comminuted fracture of shaft of left femur"
+S72353,"Displaced comminuted fracture of shaft of unspecified femur"
+S72354,"Nondisplaced comminuted fracture of shaft of right femur"
+S72355,"Nondisplaced comminuted fracture of shaft of left femur"
+S72356,"Nondisplaced comminuted fracture of shaft of unspecified femur"
+S7236,"Segmental fracture of shaft of femur"
+S72361,"Displaced segmental fracture of shaft of right femur"
+S72362,"Displaced segmental fracture of shaft of left femur"
+S72363,"Displaced segmental fracture of shaft of unspecified femur"
+S72364,"Nondisplaced segmental fracture of shaft of right femur"
+S72365,"Nondisplaced segmental fracture of shaft of left femur"
+S72366,"Nondisplaced segmental fracture of shaft of unspecified femur"
+S7239,"Other fracture of shaft of femur"
+S72391,"Other fracture of shaft of right femur"
+S72392,"Other fracture of shaft of left femur"
+S72399,"Other fracture of shaft of unspecified femur"
+S724,"Fracture of lower end of femur"
+S7240,"Unspecified fracture of lower end of femur"
+S72401,"Unspecified fracture of lower end of right femur"
+S72402,"Unspecified fracture of lower end of left femur"
+S72409,"Unspecified fracture of lower end of unspecified femur"
+S7241,"Unspecified condyle fracture of lower end of femur"
+S72411,"Displaced unspecified condyle fracture of lower end of right femur"
+S72412,"Displaced unspecified condyle fracture of lower end of left femur"
+S72413,"Displaced unspecified condyle fracture of lower end of unspecified femur"
+S72414,"Nondisplaced unspecified condyle fracture of lower end of right femur"
+S72415,"Nondisplaced unspecified condyle fracture of lower end of left femur"
+S72416,"Nondisplaced unspecified condyle fracture of lower end of unspecified femur"
+S7242,"Fracture of lateral condyle of femur"
+S72421,"Displaced fracture of lateral condyle of right femur"
+S72422,"Displaced fracture of lateral condyle of left femur"
+S72423,"Displaced fracture of lateral condyle of unspecified femur"
+S72424,"Nondisplaced fracture of lateral condyle of right femur"
+S72425,"Nondisplaced fracture of lateral condyle of left femur"
+S72426,"Nondisplaced fracture of lateral condyle of unspecified femur"
+S7243,"Fracture of medial condyle of femur"
+S72431,"Displaced fracture of medial condyle of right femur"
+S72432,"Displaced fracture of medial condyle of left femur"
+S72433,"Displaced fracture of medial condyle of unspecified femur"
+S72434,"Nondisplaced fracture of medial condyle of right femur"
+S72435,"Nondisplaced fracture of medial condyle of left femur"
+S72436,"Nondisplaced fracture of medial condyle of unspecified femur"
+S7244,"Fracture of lower epiphysis (separation) of femur"
+S72441,"Displaced fracture of lower epiphysis (separation) of right femur"
+S72442,"Displaced fracture of lower epiphysis (separation) of left femur"
+S72443,"Displaced fracture of lower epiphysis (separation) of unspecified femur"
+S72444,"Nondisplaced fracture of lower epiphysis (separation) of right femur"
+S72445,"Nondisplaced fracture of lower epiphysis (separation) of left femur"
+S72446,"Nondisplaced fracture of lower epiphysis (separation) of unspecified femur"
+S7245,"Supracondylar fracture without intracondylar extension of lower end of femur"
+S72451,"Displaced supracondylar fracture without intracondylar extension of lower end of right femur"
+S72452,"Displaced supracondylar fracture without intracondylar extension of lower end of left femur"
+S72453,"Displaced supracondylar fracture without intracondylar extension of lower end of unspecified femur"
+S72454,"Nondisplaced supracondylar fracture without intracondylar extension of lower end of right femur"
+S72455,"Nondisplaced supracondylar fracture without intracondylar extension of lower end of left femur"
+S72456,"Nondisplaced supracondylar fracture without intracondylar extension of lower end of unspecified femur"
+S7246,"Supracondylar fracture with intracondylar extension of lower end of femur"
+S72461,"Displaced supracondylar fracture with intracondylar extension of lower end of right femur"
+S72462,"Displaced supracondylar fracture with intracondylar extension of lower end of left femur"
+S72463,"Displaced supracondylar fracture with intracondylar extension of lower end of unspecified femur"
+S72464,"Nondisplaced supracondylar fracture with intracondylar extension of lower end of right femur"
+S72465,"Nondisplaced supracondylar fracture with intracondylar extension of lower end of left femur"
+S72466,"Nondisplaced supracondylar fracture with intracondylar extension of lower end of unspecified femur"
+S7247,"Torus fracture of lower end of femur"
+S72471,"Torus fracture of lower end of right femur"
+S72472,"Torus fracture of lower end of left femur"
+S72479,"Torus fracture of lower end of unspecified femur"
+S7249,"Other fracture of lower end of femur"
+S72491,"Other fracture of lower end of right femur"
+S72492,"Other fracture of lower end of left femur"
+S72499,"Other fracture of lower end of unspecified femur"
+S728,"Other fracture of femur"
+S728X,"Other fracture of femur"
+S728X1,"Other fracture of right femur"
+S728X2,"Other fracture of left femur"
+S728X9,"Other fracture of unspecified femur"
+S729,"Unspecified fracture of femur"
+S7290,"Unspecified fracture of unspecified femur"
+S7291,"Unspecified fracture of right femur"
+S7292,"Unspecified fracture of left femur"
+S73,"Dislocation and sprain of joint and ligaments of hip"
+S730,"Subluxation and dislocation of hip"
+S7300,"Unspecified subluxation and dislocation of hip"
+S73001,"Unspecified subluxation of right hip"
+S73002,"Unspecified subluxation of left hip"
+S73003,"Unspecified subluxation of unspecified hip"
+S73004,"Unspecified dislocation of right hip"
+S73005,"Unspecified dislocation of left hip"
+S73006,"Unspecified dislocation of unspecified hip"
+S7301,"Posterior subluxation and dislocation of hip"
+S73011,"Posterior subluxation of right hip"
+S73012,"Posterior subluxation of left hip"
+S73013,"Posterior subluxation of unspecified hip"
+S73014,"Posterior dislocation of right hip"
+S73015,"Posterior dislocation of left hip"
+S73016,"Posterior dislocation of unspecified hip"
+S7302,"Obturator subluxation and dislocation of hip"
+S73021,"Obturator subluxation of right hip"
+S73022,"Obturator subluxation of left hip"
+S73023,"Obturator subluxation of unspecified hip"
+S73024,"Obturator dislocation of right hip"
+S73025,"Obturator dislocation of left hip"
+S73026,"Obturator dislocation of unspecified hip"
+S7303,"Other anterior subluxation and dislocation of hip"
+S73031,"Other anterior subluxation of right hip"
+S73032,"Other anterior subluxation of left hip"
+S73033,"Other anterior subluxation of unspecified hip"
+S73034,"Other anterior dislocation of right hip"
+S73035,"Other anterior dislocation of left hip"
+S73036,"Other anterior dislocation of unspecified hip"
+S7304,"Central subluxation and dislocation of hip"
+S73041,"Central subluxation of right hip"
+S73042,"Central subluxation of left hip"
+S73043,"Central subluxation of unspecified hip"
+S73044,"Central dislocation of right hip"
+S73045,"Central dislocation of left hip"
+S73046,"Central dislocation of unspecified hip"
+S731,"Sprain of hip"
+S7310,"Unspecified sprain of hip"
+S73101,"Unspecified sprain of right hip"
+S73102,"Unspecified sprain of left hip"
+S73109,"Unspecified sprain of unspecified hip"
+S7311,"Iliofemoral ligament sprain of hip"
+S73111,"Iliofemoral ligament sprain of right hip"
+S73112,"Iliofemoral ligament sprain of left hip"
+S73119,"Iliofemoral ligament sprain of unspecified hip"
+S7312,"Ischiocapsular (ligament) sprain of hip"
+S73121,"Ischiocapsular ligament sprain of right hip"
+S73122,"Ischiocapsular ligament sprain of left hip"
+S73129,"Ischiocapsular ligament sprain of unspecified hip"
+S7319,"Other sprain of hip"
+S73191,"Other sprain of right hip"
+S73192,"Other sprain of left hip"
+S73199,"Other sprain of unspecified hip"
+S74,"Injury of nerves at hip and thigh level"
+S740,"Injury of sciatic nerve at hip and thigh level"
+S7400,"Injury of sciatic nerve at hip and thigh level, unspecified leg"
+S7401,"Injury of sciatic nerve at hip and thigh level, right leg"
+S7402,"Injury of sciatic nerve at hip and thigh level, left leg"
+S741,"Injury of femoral nerve at hip and thigh level"
+S7410,"Injury of femoral nerve at hip and thigh level, unspecified leg"
+S7411,"Injury of femoral nerve at hip and thigh level, right leg"
+S7412,"Injury of femoral nerve at hip and thigh level, left leg"
+S742,"Injury of cutaneous sensory nerve at hip and thigh level"
+S7420,"Injury of cutaneous sensory nerve at hip and thigh level, unspecified leg"
+S7421,"Injury of cutaneous sensory nerve at hip and high level, right leg"
+S7422,"Injury of cutaneous sensory nerve at hip and thigh level, left leg"
+S748,"Injury of other nerves at hip and thigh level"
+S748X,"Injury of other nerves at hip and thigh level"
+S748X1,"Injury of other nerves at hip and thigh level, right leg"
+S748X2,"Injury of other nerves at hip and thigh level, left leg"
+S748X9,"Injury of other nerves at hip and thigh level, unspecified leg"
+S749,"Injury of unspecified nerve at hip and thigh level"
+S7490,"Injury of unspecified nerve at hip and thigh level, unspecified leg"
+S7491,"Injury of unspecified nerve at hip and thigh level, right leg"
+S7492,"Injury of unspecified nerve at hip and thigh level, left leg"
+S75,"Injury of blood vessels at hip and thigh level"
+S750,"Injury of femoral artery"
+S7500,"Unspecified injury of femoral artery"
+S75001,"Unspecified injury of femoral artery, right leg"
+S75002,"Unspecified injury of femoral artery, left leg"
+S75009,"Unspecified injury of femoral artery, unspecified leg"
+S7501,"Minor laceration of femoral artery"
+S75011,"Minor laceration of femoral artery, right leg"
+S75012,"Minor laceration of femoral artery, left leg"
+S75019,"Minor laceration of femoral artery, unspecified leg"
+S7502,"Major laceration of femoral artery"
+S75021,"Major laceration of femoral artery, right leg"
+S75022,"Major laceration of femoral artery, left leg"
+S75029,"Major laceration of femoral artery, unspecified leg"
+S7509,"Other specified injury of femoral artery"
+S75091,"Other specified injury of femoral artery, right leg"
+S75092,"Other specified injury of femoral artery, left leg"
+S75099,"Other specified injury of femoral artery, unspecified leg"
+S751,"Injury of femoral vein at hip and thigh level"
+S7510,"Unspecified injury of femoral vein at hip and thigh level"
+S75101,"Unspecified injury of femoral vein at hip and thigh level, right leg"
+S75102,"Unspecified injury of femoral vein at hip and thigh level, left leg"
+S75109,"Unspecified injury of femoral vein at hip and thigh level, unspecified leg"
+S7511,"Minor laceration of femoral vein at hip and thigh level"
+S75111,"Minor laceration of femoral vein at hip and thigh level, right leg"
+S75112,"Minor laceration of femoral vein at hip and thigh level, left leg"
+S75119,"Minor laceration of femoral vein at hip and thigh level, unspecified leg"
+S7512,"Major laceration of femoral vein at hip and thigh level"
+S75121,"Major laceration of femoral vein at hip and thigh level, right leg"
+S75122,"Major laceration of femoral vein at hip and thigh level, left leg"
+S75129,"Major laceration of femoral vein at hip and thigh level, unspecified leg"
+S7519,"Other specified injury of femoral vein at hip and thigh level"
+S75191,"Other specified injury of femoral vein at hip and thigh level, right leg"
+S75192,"Other specified injury of femoral vein at hip and thigh level, left leg"
+S75199,"Other specified injury of femoral vein at hip and thigh level, unspecified leg"
+S752,"Injury of greater saphenous vein at hip and thigh level"
+S7520,"Unspecified injury of greater saphenous vein at hip and thigh level"
+S75201,"Unspecified injury of greater saphenous vein at hip and thigh level, right leg"
+S75202,"Unspecified injury of greater saphenous vein at hip and thigh level, left leg"
+S75209,"Unspecified injury of greater saphenous vein at hip and thigh level, unspecified leg"
+S7521,"Minor laceration of greater saphenous vein at hip and thigh level"
+S75211,"Minor laceration of greater saphenous vein at hip and thigh level, right leg"
+S75212,"Minor laceration of greater saphenous vein at hip and thigh level, left leg"
+S75219,"Minor laceration of greater saphenous vein at hip and thigh level, unspecified leg"
+S7522,"Major laceration of greater saphenous vein at hip and thigh level"
+S75221,"Major laceration of greater saphenous vein at hip and thigh level, right leg"
+S75222,"Major laceration of greater saphenous vein at hip and thigh level, left leg"
+S75229,"Major laceration of greater saphenous vein at hip and thigh level, unspecified leg"
+S7529,"Other specified injury of greater saphenous vein at hip and thigh level"
+S75291,"Other specified injury of greater saphenous vein at hip and thigh level, right leg"
+S75292,"Other specified injury of greater saphenous vein at hip and thigh level, left leg"
+S75299,"Other specified injury of greater saphenous vein at hip and thigh level, unspecified leg"
+S758,"Injury of other blood vessels at hip and thigh level"
+S7580,"Unspecified injury of other blood vessels at hip and thigh level"
+S75801,"Unspecified injury of other blood vessels at hip and thigh level, right leg"
+S75802,"Unspecified injury of other blood vessels at hip and thigh level, left leg"
+S75809,"Unspecified injury of other blood vessels at hip and thigh level, unspecified leg"
+S7581,"Laceration of other blood vessels at hip and thigh level"
+S75811,"Laceration of other blood vessels at hip and thigh level, right leg"
+S75812,"Laceration of other blood vessels at hip and thigh level, left leg"
+S75819,"Laceration of other blood vessels at hip and thigh level, unspecified leg"
+S7589,"Other specified injury of other blood vessels at hip and thigh level"
+S75891,"Other specified injury of other blood vessels at hip and thigh level, right leg"
+S75892,"Other specified injury of other blood vessels at hip and thigh level, left leg"
+S75899,"Other specified injury of other blood vessels at hip and thigh level, unspecified leg"
+S759,"Injury of unspecified blood vessel at hip and thigh level"
+S7590,"Unspecified injury of unspecified blood vessel at hip and thigh level"
+S75901,"Unspecified injury of unspecified blood vessel at hip and thigh level, right leg"
+S75902,"Unspecified injury of unspecified blood vessel at hip and thigh level, left leg"
+S75909,"Unspecified injury of unspecified blood vessel at hip and thigh level, unspecified leg"
+S7591,"Laceration of unspecified blood vessel at hip and thigh level"
+S75911,"Laceration of unspecified blood vessel at hip and thigh level, right leg"
+S75912,"Laceration of unspecified blood vessel at hip and thigh level, left leg"
+S75919,"Laceration of unspecified blood vessel at hip and thigh level, unspecified leg"
+S7599,"Other specified injury of unspecified blood vessel at hip and thigh level"
+S75991,"Other specified injury of unspecified blood vessel at hip and thigh level, right leg"
+S75992,"Other specified injury of unspecified blood vessel at hip and thigh level, left leg"
+S75999,"Other specified injury of unspecified blood vessel at hip and thigh level, unspecified leg"
+S76,"Injury of muscle, fascia and tendon at hip and thigh level"
+S760,"Injury of muscle, fascia and tendon of hip"
+S7600,"Unspecified injury of muscle, fascia and tendon of hip"
+S76001,"Unspecified injury of muscle, fascia and tendon of right hip"
+S76002,"Unspecified injury of muscle, fascia and tendon of left hip"
+S76009,"Unspecified injury of muscle, fascia and tendon of unspecified hip"
+S7601,"Strain of muscle, fascia and tendon of hip"
+S76011,"Strain of muscle, fascia and tendon of right hip"
+S76012,"Strain of muscle, fascia and tendon of left hip"
+S76019,"Strain of muscle, fascia and tendon of unspecified hip"
+S7602,"Laceration of muscle, fascia and tendon of hip"
+S76021,"Laceration of muscle, fascia and tendon of right hip"
+S76022,"Laceration of muscle, fascia and tendon of left hip"
+S76029,"Laceration of muscle, fascia and tendon of unspecified hip"
+S7609,"Other specified injury of muscle, fascia and tendon of hip"
+S76091,"Other specified injury of muscle, fascia and tendon of right hip"
+S76092,"Other specified injury of muscle, fascia and tendon of left hip"
+S76099,"Other specified injury of muscle, fascia and tendon of unspecified hip"
+S761,"Injury of quadriceps muscle, fascia and tendon"
+S7610,"Unspecified injury of quadriceps muscle, fascia and tendon"
+S76101,"Unspecified injury of right quadriceps muscle, fascia and tendon"
+S76102,"Unspecified injury of left quadriceps muscle, fascia and tendon"
+S76109,"Unspecified injury of unspecified quadriceps muscle, fascia and tendon"
+S7611,"Strain of quadriceps muscle, fascia and tendon"
+S76111,"Strain of right quadriceps muscle, fascia and tendon"
+S76112,"Strain of left quadriceps muscle, fascia and tendon"
+S76119,"Strain of unspecified quadriceps muscle, fascia and tendon"
+S7612,"Laceration of quadriceps muscle, fascia and tendon"
+S76121,"Laceration of right quadriceps muscle, fascia and tendon"
+S76122,"Laceration of left quadriceps muscle, fascia and tendon"
+S76129,"Laceration of unspecified quadriceps muscle, fascia and tendon"
+S7619,"Other specified injury of quadriceps muscle, fascia and tendon"
+S76191,"Other specified injury of right quadriceps muscle, fascia and tendon"
+S76192,"Other specified injury of left quadriceps muscle, fascia and tendon"
+S76199,"Other specified injury of unspecified quadriceps muscle, fascia and tendon"
+S762,"Injury of adductor muscle, fascia and tendon of thigh"
+S7620,"Unspecified injury of adductor muscle, fascia and tendon of thigh"
+S76201,"Unspecified injury of adductor muscle, fascia and tendon of right thigh"
+S76202,"Unspecified injury of adductor muscle, fascia and tendon of left thigh"
+S76209,"Unspecified injury of adductor muscle, fascia and tendon of unspecified thigh"
+S7621,"Strain of adductor muscle, fascia and tendon of thigh"
+S76211,"Strain of adductor muscle, fascia and tendon of right thigh"
+S76212,"Strain of adductor muscle, fascia and tendon of left thigh"
+S76219,"Strain of adductor muscle, fascia and tendon of unspecified thigh"
+S7622,"Laceration of adductor muscle, fascia and tendon of thigh"
+S76221,"Laceration of adductor muscle, fascia and tendon of right thigh"
+S76222,"Laceration of adductor muscle, fascia and tendon of left thigh"
+S76229,"Laceration of adductor muscle, fascia and tendon of unspecified thigh"
+S7629,"Other injury of adductor muscle, fascia and tendon of thigh"
+S76291,"Other injury of adductor muscle, fascia and tendon of right thigh"
+S76292,"Other injury of adductor muscle, fascia and tendon of left thigh"
+S76299,"Other injury of adductor muscle, fascia and tendon of unspecified thigh"
+S763,"Injury of muscle, fascia and tendon of the posterior muscle group at thigh level"
+S7630,"Unspecified injury of muscle, fascia and tendon of the posterior muscle group at thigh level"
+S76301,"Unspecified injury of muscle, fascia and tendon of the posterior muscle group at thigh level, right thigh"
+S76302,"Unspecified injury of muscle, fascia and tendon of the posterior muscle group at thigh level, left thigh"
+S76309,"Unspecified injury of muscle, fascia and tendon of the posterior muscle group at thigh level, unspecified thigh"
+S7631,"Strain of muscle, fascia and tendon of the posterior muscle group at thigh level"
+S76311,"Strain of muscle, fascia and tendon of the posterior muscle group at thigh level, right thigh"
+S76312,"Strain of muscle, fascia and tendon of the posterior muscle group at thigh level, left thigh"
+S76319,"Strain of muscle, fascia and tendon of the posterior muscle group at thigh level, unspecified thigh"
+S7632,"Laceration of muscle, fascia and tendon of the posterior muscle group at thigh level"
+S76321,"Laceration of muscle, fascia and tendon of the posterior muscle group at thigh level, right thigh"
+S76322,"Laceration of muscle, fascia and tendon of the posterior muscle group at thigh level, left thigh"
+S76329,"Laceration of muscle, fascia and tendon of the posterior muscle group at thigh level, unspecified thigh"
+S7639,"Other specified injury of muscle, fascia and tendon of the posterior muscle group at thigh level"
+S76391,"Other specified injury of muscle, fascia and tendon of the posterior muscle group at thigh level, right thigh"
+S76392,"Other specified injury of muscle, fascia and tendon of the posterior muscle group at thigh level, left thigh"
+S76399,"Other specified injury of muscle, fascia and tendon of the posterior muscle group at thigh level, unspecified thigh"
+S768,"Injury of other specified muscles, fascia and tendons at thigh level"
+S7680,"Unspecified injury of other specified muscles, fascia and tendons at thigh level"
+S76801,"Unspecified injury of other specified muscles, fascia and tendons at thigh level, right thigh"
+S76802,"Unspecified injury of other specified muscles, fascia and tendons at thigh level, left thigh"
+S76809,"Unspecified injury of other specified muscles, fascia and tendons at thigh level, unspecified thigh"
+S7681,"Strain of other specified muscles, fascia and tendons at thigh level"
+S76811,"Strain of other specified muscles, fascia and tendons at thigh level, right thigh"
+S76812,"Strain of other specified muscles, fascia and tendons at thigh level, left thigh"
+S76819,"Strain of other specified muscles, fascia and tendons at thigh level, unspecified thigh"
+S7682,"Laceration of other specified muscles, fascia and tendons at thigh level"
+S76821,"Laceration of other specified muscles, fascia and tendons at thigh level, right thigh"
+S76822,"Laceration of other specified muscles, fascia and tendons at thigh level, left thigh"
+S76829,"Laceration of other specified muscles, fascia and tendons at thigh level, unspecified thigh"
+S7689,"Other injury of other specified muscles, fascia and tendons at thigh level"
+S76891,"Other injury of other specified muscles, fascia and tendons at thigh level, right thigh"
+S76892,"Other injury of other specified muscles, fascia and tendons at thigh level, left thigh"
+S76899,"Other injury of other specified muscles, fascia and tendons at thigh level, unspecified thigh"
+S769,"Injury of unspecified muscles, fascia and tendons at thigh level"
+S7690,"Unspecified injury of unspecified muscles, fascia and tendons at thigh level"
+S76901,"Unspecified injury of unspecified muscles, fascia and tendons at thigh level, right thigh"
+S76902,"Unspecified injury of unspecified muscles, fascia and tendons at thigh level, left thigh"
+S76909,"Unspecified injury of unspecified muscles, fascia and tendons at thigh level, unspecified thigh"
+S7691,"Strain of unspecified muscles, fascia and tendons at thigh level"
+S76911,"Strain of unspecified muscles, fascia and tendons at thigh level, right thigh"
+S76912,"Strain of unspecified muscles, fascia and tendons at thigh level, left thigh"
+S76919,"Strain of unspecified muscles, fascia and tendons at thigh level, unspecified thigh"
+S7692,"Laceration of unspecified muscles, fascia and tendons at thigh level"
+S76921,"Laceration of unspecified muscles, fascia and tendons at thigh level, right thigh"
+S76922,"Laceration of unspecified muscles, fascia and tendons at thigh level, left thigh"
+S76929,"Laceration of unspecified muscles, fascia and tendons at thigh level, unspecified thigh"
+S7699,"Other specified injury of unspecified muscles, fascia and tendons at thigh level"
+S76991,"Other specified injury of unspecified muscles, fascia and tendons at thigh level, right thigh"
+S76992,"Other specified injury of unspecified muscles, fascia and tendons at thigh level, left thigh"
+S76999,"Other specified injury of unspecified muscles, fascia and tendons at thigh level, unspecified thigh"
+S77,"Crushing injury of hip and thigh"
+S770,"Crushing injury of hip"
+S7700,"Crushing injury of unspecified hip"
+S7701,"Crushing injury of right hip"
+S7702,"Crushing injury of left hip"
+S771,"Crushing injury of thigh"
+S7710,"Crushing injury of unspecified thigh"
+S7711,"Crushing injury of right thigh"
+S7712,"Crushing injury of left thigh"
+S772,"Crushing injury of hip with thigh"
+S7720,"Crushing injury of unspecified hip with thigh"
+S7721,"Crushing injury of right hip with thigh"
+S7722,"Crushing injury of left hip with thigh"
+S78,"Traumatic amputation of hip and thigh"
+S780,"Traumatic amputation at hip joint"
+S7801,"Complete traumatic amputation at hip joint"
+S78011,"Complete traumatic amputation at right hip joint"
+S78012,"Complete traumatic amputation at left hip joint"
+S78019,"Complete traumatic amputation at unspecified hip joint"
+S7802,"Partial traumatic amputation at hip joint"
+S78021,"Partial traumatic amputation at right hip joint"
+S78022,"Partial traumatic amputation at left hip joint"
+S78029,"Partial traumatic amputation at unspecified hip joint"
+S781,"Traumatic amputation at level between hip and knee"
+S7811,"Complete traumatic amputation at level between hip and knee"
+S78111,"Complete traumatic amputation at level between right hip and knee"
+S78112,"Complete traumatic amputation at level between left hip and knee"
+S78119,"Complete traumatic amputation at level between unspecified hip and knee"
+S7812,"Partial traumatic amputation at level between hip and knee"
+S78121,"Partial traumatic amputation at level between right hip and knee"
+S78122,"Partial traumatic amputation at level between left hip and knee"
+S78129,"Partial traumatic amputation at level between unspecified hip and knee"
+S789,"Traumatic amputation of hip and thigh, level unspecified"
+S7891,"Complete traumatic amputation of hip and thigh, level unspecified"
+S78911,"Complete traumatic amputation of right hip and thigh, level unspecified"
+S78912,"Complete traumatic amputation of left hip and thigh, level unspecified"
+S78919,"Complete traumatic amputation of unspecified hip and thigh, level unspecified"
+S7892,"Partial traumatic amputation of hip and thigh, level unspecified"
+S78921,"Partial traumatic amputation of right hip and thigh, level unspecified"
+S78922,"Partial traumatic amputation of left hip and thigh, level unspecified"
+S78929,"Partial traumatic amputation of unspecified hip and thigh, level unspecified"
+S79,"Other and unspecified injuries of hip and thigh"
+S790,"Physeal fracture of upper end of femur"
+S7900,"Unspecified physeal fracture of upper end of femur"
+S79001,"Unspecified physeal fracture of upper end of right femur"
+S79002,"Unspecified physeal fracture of upper end of left femur"
+S79009,"Unspecified physeal fracture of upper end of unspecified femur"
+S7901,"Salter-Harris Type I physeal fracture of upper end of femur"
+S79011,"Salter-Harris Type I physeal fracture of upper end of right femur"
+S79012,"Salter-Harris Type I physeal fracture of upper end of left femur"
+S79019,"Salter-Harris Type I physeal fracture of upper end of unspecified femur"
+S7909,"Other physeal fracture of upper end of femur"
+S79091,"Other physeal fracture of upper end of right femur"
+S79092,"Other physeal fracture of upper end of left femur"
+S79099,"Other physeal fracture of upper end of unspecified femur"
+S791,"Physeal fracture of lower end of femur"
+S7910,"Unspecified physeal fracture of lower end of femur"
+S79101,"Unspecified physeal fracture of lower end of right femur"
+S79102,"Unspecified physeal fracture of lower end of left femur"
+S79109,"Unspecified physeal fracture of lower end of unspecified femur"
+S7911,"Salter-Harris Type I physeal fracture of lower end of femur"
+S79111,"Salter-Harris Type I physeal fracture of lower end of right femur"
+S79112,"Salter-Harris Type I physeal fracture of lower end of left femur"
+S79119,"Salter-Harris Type I physeal fracture of lower end of unspecified femur"
+S7912,"Salter-Harris Type II physeal fracture of lower end of femur"
+S79121,"Salter-Harris Type II physeal fracture of lower end of right femur"
+S79122,"Salter-Harris Type II physeal fracture of lower end of left femur"
+S79129,"Salter-Harris Type II physeal fracture of lower end of unspecified femur"
+S7913,"Salter-Harris Type III physeal fracture of lower end of femur"
+S79131,"Salter-Harris Type III physeal fracture of lower end of right femur"
+S79132,"Salter-Harris Type III physeal fracture of lower end of left femur"
+S79139,"Salter-Harris Type III physeal fracture of lower end of unspecified femur"
+S7914,"Salter-Harris Type IV physeal fracture of lower end of femur"
+S79141,"Salter-Harris Type IV physeal fracture of lower end of right femur"
+S79142,"Salter-Harris Type IV physeal fracture of lower end of left femur"
+S79149,"Salter-Harris Type IV physeal fracture of lower end of unspecified femur"
+S7919,"Other physeal fracture of lower end of femur"
+S79191,"Other physeal fracture of lower end of right femur"
+S79192,"Other physeal fracture of lower end of left femur"
+S79199,"Other physeal fracture of lower end of unspecified femur"
+S798,"Other specified injuries of hip and thigh"
+S7981,"Other specified injuries of hip"
+S79811,"Other specified injuries of right hip"
+S79812,"Other specified injuries of left hip"
+S79819,"Other specified injuries of unspecified hip"
+S7982,"Other specified injuries of thigh"
+S79821,"Other specified injuries of right thigh"
+S79822,"Other specified injuries of left thigh"
+S79829,"Other specified injuries of unspecified thigh"
+S799,"Unspecified injury of hip and thigh"
+S7991,"Unspecified injury of hip"
+S79911,"Unspecified injury of right hip"
+S79912,"Unspecified injury of left hip"
+S79919,"Unspecified injury of unspecified hip"
+S7992,"Unspecified injury of thigh"
+S79921,"Unspecified injury of right thigh"
+S79922,"Unspecified injury of left thigh"
+S79929,"Unspecified injury of unspecified thigh"
+S80,"Superficial injury of knee and lower leg"
+S800,"Contusion of knee"
+S8000,"Contusion of unspecified knee"
+S8001,"Contusion of right knee"
+S8002,"Contusion of left knee"
+S801,"Contusion of lower leg"
+S8010,"Contusion of unspecified lower leg"
+S8011,"Contusion of right lower leg"
+S8012,"Contusion of left lower leg"
+S802,"Other superficial injuries of knee"
+S8021,"Abrasion of knee"
+S80211,"Abrasion, right knee"
+S80212,"Abrasion, left knee"
+S80219,"Abrasion, unspecified knee"
+S8022,"Blister (nonthermal) of knee"
+S80221,"Blister (nonthermal), right knee"
+S80222,"Blister (nonthermal), left knee"
+S80229,"Blister (nonthermal), unspecified knee"
+S8024,"External constriction of knee"
+S80241,"External constriction, right knee"
+S80242,"External constriction, left knee"
+S80249,"External constriction, unspecified knee"
+S8025,"Superficial foreign body of knee"
+S80251,"Superficial foreign body, right knee"
+S80252,"Superficial foreign body, left knee"
+S80259,"Superficial foreign body, unspecified knee"
+S8026,"Insect bite (nonvenomous) of knee"
+S80261,"Insect bite (nonvenomous), right knee"
+S80262,"Insect bite (nonvenomous), left knee"
+S80269,"Insect bite (nonvenomous), unspecified knee"
+S8027,"Other superficial bite of knee"
+S80271,"Other superficial bite of right knee"
+S80272,"Other superficial bite of left knee"
+S80279,"Other superficial bite of unspecified knee"
+S808,"Other superficial injuries of lower leg"
+S8081,"Abrasion of lower leg"
+S80811,"Abrasion, right lower leg"
+S80812,"Abrasion, left lower leg"
+S80819,"Abrasion, unspecified lower leg"
+S8082,"Blister (nonthermal) of lower leg"
+S80821,"Blister (nonthermal), right lower leg"
+S80822,"Blister (nonthermal), left lower leg"
+S80829,"Blister (nonthermal), unspecified lower leg"
+S8084,"External constriction of lower leg"
+S80841,"External constriction, right lower leg"
+S80842,"External constriction, left lower leg"
+S80849,"External constriction, unspecified lower leg"
+S8085,"Superficial foreign body of lower leg"
+S80851,"Superficial foreign body, right lower leg"
+S80852,"Superficial foreign body, left lower leg"
+S80859,"Superficial foreign body, unspecified lower leg"
+S8086,"Insect bite (nonvenomous) of lower leg"
+S80861,"Insect bite (nonvenomous), right lower leg"
+S80862,"Insect bite (nonvenomous), left lower leg"
+S80869,"Insect bite (nonvenomous), unspecified lower leg"
+S8087,"Other superficial bite of lower leg"
+S80871,"Other superficial bite, right lower leg"
+S80872,"Other superficial bite, left lower leg"
+S80879,"Other superficial bite, unspecified lower leg"
+S809,"Unspecified superficial injury of knee and lower leg"
+S8091,"Unspecified superficial injury of knee"
+S80911,"Unspecified superficial injury of right knee"
+S80912,"Unspecified superficial injury of left knee"
+S80919,"Unspecified superficial injury of unspecified knee"
+S8092,"Unspecified superficial injury of lower leg"
+S80921,"Unspecified superficial injury of right lower leg"
+S80922,"Unspecified superficial injury of left lower leg"
+S80929,"Unspecified superficial injury of unspecified lower leg"
+S81,"Open wound of knee and lower leg"
+S810,"Open wound of knee"
+S8100,"Unspecified open wound of knee"
+S81001,"Unspecified open wound, right knee"
+S81002,"Unspecified open wound, left knee"
+S81009,"Unspecified open wound, unspecified knee"
+S8101,"Laceration without foreign body of knee"
+S81011,"Laceration without foreign body, right knee"
+S81012,"Laceration without foreign body, left knee"
+S81019,"Laceration without foreign body, unspecified knee"
+S8102,"Laceration with foreign body of knee"
+S81021,"Laceration with foreign body, right knee"
+S81022,"Laceration with foreign body, left knee"
+S81029,"Laceration with foreign body, unspecified knee"
+S8103,"Puncture wound without foreign body of knee"
+S81031,"Puncture wound without foreign body, right knee"
+S81032,"Puncture wound without foreign body, left knee"
+S81039,"Puncture wound without foreign body, unspecified knee"
+S8104,"Puncture wound with foreign body of knee"
+S81041,"Puncture wound with foreign body, right knee"
+S81042,"Puncture wound with foreign body, left knee"
+S81049,"Puncture wound with foreign body, unspecified knee"
+S8105,"Open bite of knee"
+S81051,"Open bite, right knee"
+S81052,"Open bite, left knee"
+S81059,"Open bite, unspecified knee"
+S818,"Open wound of lower leg"
+S8180,"Unspecified open wound of lower leg"
+S81801,"Unspecified open wound, right lower leg"
+S81802,"Unspecified open wound, left lower leg"
+S81809,"Unspecified open wound, unspecified lower leg"
+S8181,"Laceration without foreign body of lower leg"
+S81811,"Laceration without foreign body, right lower leg"
+S81812,"Laceration without foreign body, left lower leg"
+S81819,"Laceration without foreign body, unspecified lower leg"
+S8182,"Laceration with foreign body of lower leg"
+S81821,"Laceration with foreign body, right lower leg"
+S81822,"Laceration with foreign body, left lower leg"
+S81829,"Laceration with foreign body, unspecified lower leg"
+S8183,"Puncture wound without foreign body of lower leg"
+S81831,"Puncture wound without foreign body, right lower leg"
+S81832,"Puncture wound without foreign body, left lower leg"
+S81839,"Puncture wound without foreign body, unspecified lower leg"
+S8184,"Puncture wound with foreign body of lower leg"
+S81841,"Puncture wound with foreign body, right lower leg"
+S81842,"Puncture wound with foreign body, left lower leg"
+S81849,"Puncture wound with foreign body, unspecified lower leg"
+S8185,"Open bite of lower leg"
+S81851,"Open bite, right lower leg"
+S81852,"Open bite, left lower leg"
+S81859,"Open bite, unspecified lower leg"
+S82,"Fracture of lower leg, including ankle"
+S820,"Fracture of patella"
+S8200,"Unspecified fracture of patella"
+S82001,"Unspecified fracture of right patella"
+S82002,"Unspecified fracture of left patella"
+S82009,"Unspecified fracture of unspecified patella"
+S8201,"Osteochondral fracture of patella"
+S82011,"Displaced osteochondral fracture of right patella"
+S82012,"Displaced osteochondral fracture of left patella"
+S82013,"Displaced osteochondral fracture of unspecified patella"
+S82014,"Nondisplaced osteochondral fracture of right patella"
+S82015,"Nondisplaced osteochondral fracture of left patella"
+S82016,"Nondisplaced osteochondral fracture of unspecified patella"
+S8202,"Longitudinal fracture of patella"
+S82021,"Displaced longitudinal fracture of right patella"
+S82022,"Displaced longitudinal fracture of left patella"
+S82023,"Displaced longitudinal fracture of unspecified patella"
+S82024,"Nondisplaced longitudinal fracture of right patella"
+S82025,"Nondisplaced longitudinal fracture of left patella"
+S82026,"Nondisplaced longitudinal fracture of unspecified patella"
+S8203,"Transverse fracture of patella"
+S82031,"Displaced transverse fracture of right patella"
+S82032,"Displaced transverse fracture of left patella"
+S82033,"Displaced transverse fracture of unspecified patella"
+S82034,"Nondisplaced transverse fracture of right patella"
+S82035,"Nondisplaced transverse fracture of left patella"
+S82036,"Nondisplaced transverse fracture of unspecified patella"
+S8204,"Comminuted fracture of patella"
+S82041,"Displaced comminuted fracture of right patella"
+S82042,"Displaced comminuted fracture of left patella"
+S82043,"Displaced comminuted fracture of unspecified patella"
+S82044,"Nondisplaced comminuted fracture of right patella"
+S82045,"Nondisplaced comminuted fracture of left patella"
+S82046,"Nondisplaced comminuted fracture of unspecified patella"
+S8209,"Other fracture of patella"
+S82091,"Other fracture of right patella"
+S82092,"Other fracture of left patella"
+S82099,"Other fracture of unspecified patella"
+S821,"Fracture of upper end of tibia"
+S8210,"Unspecified fracture of upper end of tibia"
+S82101,"Unspecified fracture of upper end of right tibia"
+S82102,"Unspecified fracture of upper end of left tibia"
+S82109,"Unspecified fracture of upper end of unspecified tibia"
+S8211,"Fracture of tibial spine"
+S82111,"Displaced fracture of right tibial spine"
+S82112,"Displaced fracture of left tibial spine"
+S82113,"Displaced fracture of unspecified tibial spine"
+S82114,"Nondisplaced fracture of right tibial spine"
+S82115,"Nondisplaced fracture of left tibial spine"
+S82116,"Nondisplaced fracture of unspecified tibial spine"
+S8212,"Fracture of lateral condyle of tibia"
+S82121,"Displaced fracture of lateral condyle of right tibia"
+S82122,"Displaced fracture of lateral condyle of left tibia"
+S82123,"Displaced fracture of lateral condyle of unspecified tibia"
+S82124,"Nondisplaced fracture of lateral condyle of right tibia"
+S82125,"Nondisplaced fracture of lateral condyle of left tibia"
+S82126,"Nondisplaced fracture of lateral condyle of unspecified tibia"
+S8213,"Fracture of medial condyle of tibia"
+S82131,"Displaced fracture of medial condyle of right tibia"
+S82132,"Displaced fracture of medial condyle of left tibia"
+S82133,"Displaced fracture of medial condyle of unspecified tibia"
+S82134,"Nondisplaced fracture of medial condyle of right tibia"
+S82135,"Nondisplaced fracture of medial condyle of left tibia"
+S82136,"Nondisplaced fracture of medial condyle of unspecified tibia"
+S8214,"Bicondylar fracture of tibia"
+S82141,"Displaced bicondylar fracture of right tibia"
+S82142,"Displaced bicondylar fracture of left tibia"
+S82143,"Displaced bicondylar fracture of unspecified tibia"
+S82144,"Nondisplaced bicondylar fracture of right tibia"
+S82145,"Nondisplaced bicondylar fracture of left tibia"
+S82146,"Nondisplaced bicondylar fracture of unspecified tibia"
+S8215,"Fracture of tibial tuberosity"
+S82151,"Displaced fracture of right tibial tuberosity"
+S82152,"Displaced fracture of left tibial tuberosity"
+S82153,"Displaced fracture of unspecified tibial tuberosity"
+S82154,"Nondisplaced fracture of right tibial tuberosity"
+S82155,"Nondisplaced fracture of left tibial tuberosity"
+S82156,"Nondisplaced fracture of unspecified tibial tuberosity"
+S8216,"Torus fracture of upper end of tibia"
+S82161,"Torus fracture of upper end of right tibia"
+S82162,"Torus fracture of upper end of left tibia"
+S82169,"Torus fracture of upper end of unspecified tibia"
+S8219,"Other fracture of upper end of tibia"
+S82191,"Other fracture of upper end of right tibia"
+S82192,"Other fracture of upper end of left tibia"
+S82199,"Other fracture of upper end of unspecified tibia"
+S822,"Fracture of shaft of tibia"
+S8220,"Unspecified fracture of shaft of tibia"
+S82201,"Unspecified fracture of shaft of right tibia"
+S82202,"Unspecified fracture of shaft of left tibia"
+S82209,"Unspecified fracture of shaft of unspecified tibia"
+S8222,"Transverse fracture of shaft of tibia"
+S82221,"Displaced transverse fracture of shaft of right tibia"
+S82222,"Displaced transverse fracture of shaft of left tibia"
+S82223,"Displaced transverse fracture of shaft of unspecified tibia"
+S82224,"Nondisplaced transverse fracture of shaft of right tibia"
+S82225,"Nondisplaced transverse fracture of shaft of left tibia"
+S82226,"Nondisplaced transverse fracture of shaft of unspecified tibia"
+S8223,"Oblique fracture of shaft of tibia"
+S82231,"Displaced oblique fracture of shaft of right tibia"
+S82232,"Displaced oblique fracture of shaft of left tibia"
+S82233,"Displaced oblique fracture of shaft of unspecified tibia"
+S82234,"Nondisplaced oblique fracture of shaft of right tibia"
+S82235,"Nondisplaced oblique fracture of shaft of left tibia"
+S82236,"Nondisplaced oblique fracture of shaft of unspecified tibia"
+S8224,"Spiral fracture of shaft of tibia"
+S82241,"Displaced spiral fracture of shaft of right tibia"
+S82242,"Displaced spiral fracture of shaft of left tibia"
+S82243,"Displaced spiral fracture of shaft of unspecified tibia"
+S82244,"Nondisplaced spiral fracture of shaft of right tibia"
+S82245,"Nondisplaced spiral fracture of shaft of left tibia"
+S82246,"Nondisplaced spiral fracture of shaft of unspecified tibia"
+S8225,"Comminuted fracture of shaft of tibia"
+S82251,"Displaced comminuted fracture of shaft of right tibia"
+S82252,"Displaced comminuted fracture of shaft of left tibia"
+S82253,"Displaced comminuted fracture of shaft of unspecified tibia"
+S82254,"Nondisplaced comminuted fracture of shaft of right tibia"
+S82255,"Nondisplaced comminuted fracture of shaft of left tibia"
+S82256,"Nondisplaced comminuted fracture of shaft of unspecified tibia"
+S8226,"Segmental fracture of shaft of tibia"
+S82261,"Displaced segmental fracture of shaft of right tibia"
+S82262,"Displaced segmental fracture of shaft of left tibia"
+S82263,"Displaced segmental fracture of shaft of unspecified tibia"
+S82264,"Nondisplaced segmental fracture of shaft of right tibia"
+S82265,"Nondisplaced segmental fracture of shaft of left tibia"
+S82266,"Nondisplaced segmental fracture of shaft of unspecified tibia"
+S8229,"Other fracture of shaft of tibia"
+S82291,"Other fracture of shaft of right tibia"
+S82292,"Other fracture of shaft of left tibia"
+S82299,"Other fracture of shaft of unspecified tibia"
+S823,"Fracture of lower end of tibia"
+S8230,"Unspecified fracture of lower end of tibia"
+S82301,"Unspecified fracture of lower end of right tibia"
+S82302,"Unspecified fracture of lower end of left tibia"
+S82309,"Unspecified fracture of lower end of unspecified tibia"
+S8231,"Torus fracture of lower end of tibia"
+S82311,"Torus fracture of lower end of right tibia"
+S82312,"Torus fracture of lower end of left tibia"
+S82319,"Torus fracture of lower end of unspecified tibia"
+S8239,"Other fracture of lower end of tibia"
+S82391,"Other fracture of lower end of right tibia"
+S82392,"Other fracture of lower end of left tibia"
+S82399,"Other fracture of lower end of unspecified tibia"
+S824,"Fracture of shaft of fibula"
+S8240,"Unspecified fracture of shaft of fibula"
+S82401,"Unspecified fracture of shaft of right fibula"
+S82402,"Unspecified fracture of shaft of left fibula"
+S82409,"Unspecified fracture of shaft of unspecified fibula"
+S8242,"Transverse fracture of shaft of fibula"
+S82421,"Displaced transverse fracture of shaft of right fibula"
+S82422,"Displaced transverse fracture of shaft of left fibula"
+S82423,"Displaced transverse fracture of shaft of unspecified fibula"
+S82424,"Nondisplaced transverse fracture of shaft of right fibula"
+S82425,"Nondisplaced transverse fracture of shaft of left fibula"
+S82426,"Nondisplaced transverse fracture of shaft of unspecified fibula"
+S8243,"Oblique fracture of shaft of fibula"
+S82431,"Displaced oblique fracture of shaft of right fibula"
+S82432,"Displaced oblique fracture of shaft of left fibula"
+S82433,"Displaced oblique fracture of shaft of unspecified fibula"
+S82434,"Nondisplaced oblique fracture of shaft of right fibula"
+S82435,"Nondisplaced oblique fracture of shaft of left fibula"
+S82436,"Nondisplaced oblique fracture of shaft of unspecified fibula"
+S8244,"Spiral fracture of shaft of fibula"
+S82441,"Displaced spiral fracture of shaft of right fibula"
+S82442,"Displaced spiral fracture of shaft of left fibula"
+S82443,"Displaced spiral fracture of shaft of unspecified fibula"
+S82444,"Nondisplaced spiral fracture of shaft of right fibula"
+S82445,"Nondisplaced spiral fracture of shaft of left fibula"
+S82446,"Nondisplaced spiral fracture of shaft of unspecified fibula"
+S8245,"Comminuted fracture of shaft of fibula"
+S82451,"Displaced comminuted fracture of shaft of right fibula"
+S82452,"Displaced comminuted fracture of shaft of left fibula"
+S82453,"Displaced comminuted fracture of shaft of unspecified fibula"
+S82454,"Nondisplaced comminuted fracture of shaft of right fibula"
+S82455,"Nondisplaced comminuted fracture of shaft of left fibula"
+S82456,"Nondisplaced comminuted fracture of shaft of unspecified fibula"
+S8246,"Segmental fracture of shaft of fibula"
+S82461,"Displaced segmental fracture of shaft of right fibula"
+S82462,"Displaced segmental fracture of shaft of left fibula"
+S82463,"Displaced segmental fracture of shaft of unspecified fibula"
+S82464,"Nondisplaced segmental fracture of shaft of right fibula"
+S82465,"Nondisplaced segmental fracture of shaft of left fibula"
+S82466,"Nondisplaced segmental fracture of shaft of unspecified fibula"
+S8249,"Other fracture of shaft of fibula"
+S82491,"Other fracture of shaft of right fibula"
+S82492,"Other fracture of shaft of left fibula"
+S82499,"Other fracture of shaft of unspecified fibula"
+S825,"Fracture of medial malleolus"
+S8251,"Displaced fracture of medial malleolus of right tibia"
+S8252,"Displaced fracture of medial malleolus of left tibia"
+S8253,"Displaced fracture of medial malleolus of unspecified tibia"
+S8254,"Nondisplaced fracture of medial malleolus of right tibia"
+S8255,"Nondisplaced fracture of medial malleolus of left tibia"
+S8256,"Nondisplaced fracture of medial malleolus of unspecified tibia"
+S826,"Fracture of lateral malleolus"
+S8261,"Displaced fracture of lateral malleolus of right fibula"
+S8262,"Displaced fracture of lateral malleolus of left fibula"
+S8263,"Displaced fracture of lateral malleolus of unspecified fibula"
+S8264,"Nondisplaced fracture of lateral malleolus of right fibula"
+S8265,"Nondisplaced fracture of lateral malleolus of left fibula"
+S8266,"Nondisplaced fracture of lateral malleolus of unspecified fibula"
+S828,"Other fractures of lower leg"
+S8281,"Torus fracture of upper end of fibula"
+S82811,"Torus fracture of upper end of right fibula"
+S82812,"Torus fracture of upper end of left fibula"
+S82819,"Torus fracture of upper end of unspecified fibula"
+S8282,"Torus fracture of lower end of fibula"
+S82821,"Torus fracture of lower end of right fibula"
+S82822,"Torus fracture of lower end of left fibula"
+S82829,"Torus fracture of lower end of unspecified fibula"
+S8283,"Other fracture of upper and lower end of fibula"
+S82831,"Other fracture of upper and lower end of right fibula"
+S82832,"Other fracture of upper and lower end of left fibula"
+S82839,"Other fracture of upper and lower end of unspecified fibula"
+S8284,"Bimalleolar fracture of lower leg"
+S82841,"Displaced bimalleolar fracture of right lower leg"
+S82842,"Displaced bimalleolar fracture of left lower leg"
+S82843,"Displaced bimalleolar fracture of unspecified lower leg"
+S82844,"Nondisplaced bimalleolar fracture of right lower leg"
+S82845,"Nondisplaced bimalleolar fracture of left lower leg"
+S82846,"Nondisplaced bimalleolar fracture of unspecified lower leg"
+S8285,"Trimalleolar fracture of lower leg"
+S82851,"Displaced trimalleolar fracture of right lower leg"
+S82852,"Displaced trimalleolar fracture of left lower leg"
+S82853,"Displaced trimalleolar fracture of unspecified lower leg"
+S82854,"Nondisplaced trimalleolar fracture of right lower leg"
+S82855,"Nondisplaced trimalleolar fracture of left lower leg"
+S82856,"Nondisplaced trimalleolar fracture of unspecified lower leg"
+S8286,"Maisonneuve's fracture"
+S82861,"Displaced Maisonneuve's fracture of right leg"
+S82862,"Displaced Maisonneuve's fracture of left leg"
+S82863,"Displaced Maisonneuve's fracture of unspecified leg"
+S82864,"Nondisplaced Maisonneuve's fracture of right leg"
+S82865,"Nondisplaced Maisonneuve's fracture of left leg"
+S82866,"Nondisplaced Maisonneuve's fracture of unspecified leg"
+S8287,"Pilon fracture of tibia"
+S82871,"Displaced pilon fracture of right tibia"
+S82872,"Displaced pilon fracture of left tibia"
+S82873,"Displaced pilon fracture of unspecified tibia"
+S82874,"Nondisplaced pilon fracture of right tibia"
+S82875,"Nondisplaced pilon fracture of left tibia"
+S82876,"Nondisplaced pilon fracture of unspecified tibia"
+S8289,"Other fractures of lower leg"
+S82891,"Other fracture of right lower leg"
+S82892,"Other fracture of left lower leg"
+S82899,"Other fracture of unspecified lower leg"
+S829,"Unspecified fracture of lower leg"
+S8290,"Unspecified fracture of unspecified lower leg"
+S8291,"Unspecified fracture of right lower leg"
+S8292,"Unspecified fracture of left lower leg"
+S83,"Dislocation and sprain of joints and ligaments of knee"
+S830,"Subluxation and dislocation of patella"
+S8300,"Unspecified subluxation and dislocation of patella"
+S83001,"Unspecified subluxation of right patella"
+S83002,"Unspecified subluxation of left patella"
+S83003,"Unspecified subluxation of unspecified patella"
+S83004,"Unspecified dislocation of right patella"
+S83005,"Unspecified dislocation of left patella"
+S83006,"Unspecified dislocation of unspecified patella"
+S8301,"Lateral subluxation and dislocation of patella"
+S83011,"Lateral subluxation of right patella"
+S83012,"Lateral subluxation of left patella"
+S83013,"Lateral subluxation of unspecified patella"
+S83014,"Lateral dislocation of right patella"
+S83015,"Lateral dislocation of left patella"
+S83016,"Lateral dislocation of unspecified patella"
+S8309,"Other subluxation and dislocation of patella"
+S83091,"Other subluxation of right patella"
+S83092,"Other subluxation of left patella"
+S83093,"Other subluxation of unspecified patella"
+S83094,"Other dislocation of right patella"
+S83095,"Other dislocation of left patella"
+S83096,"Other dislocation of unspecified patella"
+S831,"Subluxation and dislocation of knee"
+S8310,"Unspecified subluxation and dislocation of knee"
+S83101,"Unspecified subluxation of right knee"
+S83102,"Unspecified subluxation of left knee"
+S83103,"Unspecified subluxation of unspecified knee"
+S83104,"Unspecified dislocation of right knee"
+S83105,"Unspecified dislocation of left knee"
+S83106,"Unspecified dislocation of unspecified knee"
+S8311,"Anterior subluxation and dislocation of proximal end of tibia"
+S83111,"Anterior subluxation of proximal end of tibia, right knee"
+S83112,"Anterior subluxation of proximal end of tibia, left knee"
+S83113,"Anterior subluxation of proximal end of tibia, unspecified knee"
+S83114,"Anterior dislocation of proximal end of tibia, right knee"
+S83115,"Anterior dislocation of proximal end of tibia, left knee"
+S83116,"Anterior dislocation of proximal end of tibia, unspecified knee"
+S8312,"Posterior subluxation and dislocation of proximal end of tibia"
+S83121,"Posterior subluxation of proximal end of tibia, right knee"
+S83122,"Posterior subluxation of proximal end of tibia, left knee"
+S83123,"Posterior subluxation of proximal end of tibia, unspecified knee"
+S83124,"Posterior dislocation of proximal end of tibia, right knee"
+S83125,"Posterior dislocation of proximal end of tibia, left knee"
+S83126,"Posterior dislocation of proximal end of tibia, unspecified knee"
+S8313,"Medial subluxation and dislocation of proximal end of tibia"
+S83131,"Medial subluxation of proximal end of tibia, right knee"
+S83132,"Medial subluxation of proximal end of tibia, left knee"
+S83133,"Medial subluxation of proximal end of tibia, unspecified knee"
+S83134,"Medial dislocation of proximal end of tibia, right knee"
+S83135,"Medial dislocation of proximal end of tibia, left knee"
+S83136,"Medial dislocation of proximal end of tibia, unspecified knee"
+S8314,"Lateral subluxation and dislocation of proximal end of tibia"
+S83141,"Lateral subluxation of proximal end of tibia, right knee"
+S83142,"Lateral subluxation of proximal end of tibia, left knee"
+S83143,"Lateral subluxation of proximal end of tibia, unspecified knee"
+S83144,"Lateral dislocation of proximal end of tibia, right knee"
+S83145,"Lateral dislocation of proximal end of tibia, left knee"
+S83146,"Lateral dislocation of proximal end of tibia, unspecified knee"
+S8319,"Other subluxation and dislocation of knee"
+S83191,"Other subluxation of right knee"
+S83192,"Other subluxation of left knee"
+S83193,"Other subluxation of unspecified knee"
+S83194,"Other dislocation of right knee"
+S83195,"Other dislocation of left knee"
+S83196,"Other dislocation of unspecified knee"
+S832,"Tear of meniscus, current injury"
+S8320,"Tear of unspecified meniscus, current injury"
+S83200,"Bucket-handle tear of unspecified meniscus, current injury, right knee"
+S83201,"Bucket-handle tear of unspecified meniscus, current injury, left knee"
+S83202,"Bucket-handle tear of unspecified meniscus, current injury, unspecified knee"
+S83203,"Other tear of unspecified meniscus, current injury, right knee"
+S83204,"Other tear of unspecified meniscus, current injury, left knee"
+S83205,"Other tear of unspecified meniscus, current injury, unspecified knee"
+S83206,"Unspecified tear of unspecified meniscus, current injury, right knee"
+S83207,"Unspecified tear of unspecified meniscus, current injury, left knee"
+S83209,"Unspecified tear of unspecified meniscus, current injury, unspecified knee"
+S8321,"Bucket-handle tear of medial meniscus, current injury"
+S83211,"Bucket-handle tear of medial meniscus, current injury, right knee"
+S83212,"Bucket-handle tear of medial meniscus, current injury, left knee"
+S83219,"Bucket-handle tear of medial meniscus, current injury, unspecified knee"
+S8322,"Peripheral tear of medial meniscus, current injury"
+S83221,"Peripheral tear of medial meniscus, current injury, right knee"
+S83222,"Peripheral tear of medial meniscus, current injury, left knee"
+S83229,"Peripheral tear of medial meniscus, current injury, unspecified knee"
+S8323,"Complex tear of medial meniscus, current injury"
+S83231,"Complex tear of medial meniscus, current injury, right knee"
+S83232,"Complex tear of medial meniscus, current injury, left knee"
+S83239,"Complex tear of medial meniscus, current injury, unspecified knee"
+S8324,"Other tear of medial meniscus, current injury"
+S83241,"Other tear of medial meniscus, current injury, right knee"
+S83242,"Other tear of medial meniscus, current injury, left knee"
+S83249,"Other tear of medial meniscus, current injury, unspecified knee"
+S8325,"Bucket-handle tear of lateral meniscus, current injury"
+S83251,"Bucket-handle tear of lateral meniscus, current injury, right knee"
+S83252,"Bucket-handle tear of lateral meniscus, current injury, left knee"
+S83259,"Bucket-handle tear of lateral meniscus, current injury, unspecified knee"
+S8326,"Peripheral tear of lateral meniscus, current injury"
+S83261,"Peripheral tear of lateral meniscus, current injury, right knee"
+S83262,"Peripheral tear of lateral meniscus, current injury, left knee"
+S83269,"Peripheral tear of lateral meniscus, current injury, unspecified knee"
+S8327,"Complex tear of lateral meniscus, current injury"
+S83271,"Complex tear of lateral meniscus, current injury, right knee"
+S83272,"Complex tear of lateral meniscus, current injury, left knee"
+S83279,"Complex tear of lateral meniscus, current injury, unspecified knee"
+S8328,"Other tear of lateral meniscus, current injury"
+S83281,"Other tear of lateral meniscus, current injury, right knee"
+S83282,"Other tear of lateral meniscus, current injury, left knee"
+S83289,"Other tear of lateral meniscus, current injury, unspecified knee"
+S833,"Tear of articular cartilage of knee, current"
+S8330,"Tear of articular cartilage of unspecified knee, current"
+S8331,"Tear of articular cartilage of right knee, current"
+S8332,"Tear of articular cartilage of left knee, current"
+S834,"Sprain of collateral ligament of knee"
+S8340,"Sprain of unspecified collateral ligament of knee"
+S83401,"Sprain of unspecified collateral ligament of right knee"
+S83402,"Sprain of unspecified collateral ligament of left knee"
+S83409,"Sprain of unspecified collateral ligament of unspecified knee"
+S8341,"Sprain of medial collateral ligament of knee"
+S83411,"Sprain of medial collateral ligament of right knee"
+S83412,"Sprain of medial collateral ligament of left knee"
+S83419,"Sprain of medial collateral ligament of unspecified knee"
+S8342,"Sprain of lateral collateral ligament of knee"
+S83421,"Sprain of lateral collateral ligament of right knee"
+S83422,"Sprain of lateral collateral ligament of left knee"
+S83429,"Sprain of lateral collateral ligament of unspecified knee"
+S835,"Sprain of cruciate ligament of knee"
+S8350,"Sprain of unspecified cruciate ligament of knee"
+S83501,"Sprain of unspecified cruciate ligament of right knee"
+S83502,"Sprain of unspecified cruciate ligament of left knee"
+S83509,"Sprain of unspecified cruciate ligament of unspecified knee"
+S8351,"Sprain of anterior cruciate ligament of knee"
+S83511,"Sprain of anterior cruciate ligament of right knee"
+S83512,"Sprain of anterior cruciate ligament of left knee"
+S83519,"Sprain of anterior cruciate ligament of unspecified knee"
+S8352,"Sprain of posterior cruciate ligament of knee"
+S83521,"Sprain of posterior cruciate ligament of right knee"
+S83522,"Sprain of posterior cruciate ligament of left knee"
+S83529,"Sprain of posterior cruciate ligament of unspecified knee"
+S836,"Sprain of the superior tibiofibular joint and ligament"
+S8360,"Sprain of the superior tibiofibular joint and ligament, unspecified knee"
+S8361,"Sprain of the superior tibiofibular joint and ligament, right knee"
+S8362,"Sprain of the superior tibiofibular joint and ligament, left knee"
+S838,"Sprain of other specified parts of knee"
+S838X,"Sprain of other specified parts of knee"
+S838X1,"Sprain of other specified parts of right knee"
+S838X2,"Sprain of other specified parts of left knee"
+S838X9,"Sprain of other specified parts of unspecified knee"
+S839,"Sprain of unspecified site of knee"
+S8390,"Sprain of unspecified site of unspecified knee"
+S8391,"Sprain of unspecified site of right knee"
+S8392,"Sprain of unspecified site of left knee"
+S84,"Injury of nerves at lower leg level"
+S840,"Injury of tibial nerve at lower leg level"
+S8400,"Injury of tibial nerve at lower leg level, unspecified leg"
+S8401,"Injury of tibial nerve at lower leg level, right leg"
+S8402,"Injury of tibial nerve at lower leg level, left leg"
+S841,"Injury of peroneal nerve at lower leg level"
+S8410,"Injury of peroneal nerve at lower leg level, unspecified leg"
+S8411,"Injury of peroneal nerve at lower leg level, right leg"
+S8412,"Injury of peroneal nerve at lower leg level, left leg"
+S842,"Injury of cutaneous sensory nerve at lower leg level"
+S8420,"Injury of cutaneous sensory nerve at lower leg level, unspecified leg"
+S8421,"Injury of cutaneous sensory nerve at lower leg level, right leg"
+S8422,"Injury of cutaneous sensory nerve at lower leg level, left leg"
+S848,"Injury of other nerves at lower leg level"
+S8480,"Injury of other nerves at lower leg level"
+S84801,"Injury of other nerves at lower leg level, right leg"
+S84802,"Injury of other nerves at lower leg level, left leg"
+S84809,"Injury of other nerves at lower leg level, unspecified leg"
+S849,"Injury of unspecified nerve at lower leg level"
+S8490,"Injury of unspecified nerve at lower leg level, unspecified leg"
+S8491,"Injury of unspecified nerve at lower leg level, right leg"
+S8492,"Injury of unspecified nerve at lower leg level, left leg"
+S85,"Injury of blood vessels at lower leg level"
+S850,"Injury of popliteal artery"
+S8500,"Unspecified injury of popliteal artery"
+S85001,"Unspecified injury of popliteal artery, right leg"
+S85002,"Unspecified injury of popliteal artery, left leg"
+S85009,"Unspecified injury of popliteal artery, unspecified leg"
+S8501,"Laceration of popliteal artery"
+S85011,"Laceration of popliteal artery, right leg"
+S85012,"Laceration of popliteal artery, left leg"
+S85019,"Laceration of popliteal artery, unspecified leg"
+S8509,"Other specified injury of popliteal artery"
+S85091,"Other specified injury of popliteal artery, right leg"
+S85092,"Other specified injury of popliteal artery, left leg"
+S85099,"Other specified injury of popliteal artery, unspecified leg"
+S851,"Injury of tibial artery"
+S8510,"Unspecified injury of unspecified tibial artery"
+S85101,"Unspecified injury of unspecified tibial artery, right leg"
+S85102,"Unspecified injury of unspecified tibial artery, left leg"
+S85109,"Unspecified injury of unspecified tibial artery, unspecified leg"
+S8511,"Laceration of unspecified tibial artery"
+S85111,"Laceration of unspecified tibial artery, right leg"
+S85112,"Laceration of unspecified tibial artery, left leg"
+S85119,"Laceration of unspecified tibial artery, unspecified leg"
+S8512,"Other specified injury of unspecified tibial artery"
+S85121,"Other specified injury of unspecified tibial artery, right leg"
+S85122,"Other specified injury of unspecified tibial artery, left leg"
+S85129,"Other specified injury of unspecified tibial artery, unspecified leg"
+S8513,"Unspecified injury of anterior tibial artery"
+S85131,"Unspecified injury of anterior tibial artery, right leg"
+S85132,"Unspecified injury of anterior tibial artery, left leg"
+S85139,"Unspecified injury of anterior tibial artery, unspecified leg"
+S8514,"Laceration of anterior tibial artery"
+S85141,"Laceration of anterior tibial artery, right leg"
+S85142,"Laceration of anterior tibial artery, left leg"
+S85149,"Laceration of anterior tibial artery, unspecified leg"
+S8515,"Other specified injury of anterior tibial artery"
+S85151,"Other specified injury of anterior tibial artery, right leg"
+S85152,"Other specified injury of anterior tibial artery, left leg"
+S85159,"Other specified injury of anterior tibial artery, unspecified leg"
+S8516,"Unspecified injury of posterior tibial artery"
+S85161,"Unspecified injury of posterior tibial artery, right leg"
+S85162,"Unspecified injury of posterior tibial artery, left leg"
+S85169,"Unspecified injury of posterior tibial artery, unspecified leg"
+S8517,"Laceration of posterior tibial artery"
+S85171,"Laceration of posterior tibial artery, right leg"
+S85172,"Laceration of posterior tibial artery, left leg"
+S85179,"Laceration of posterior tibial artery, unspecified leg"
+S8518,"Other specified injury of posterior tibial artery"
+S85181,"Other specified injury of posterior tibial artery, right leg"
+S85182,"Other specified injury of posterior tibial artery, left leg"
+S85189,"Other specified injury of posterior tibial artery, unspecified leg"
+S852,"Injury of peroneal artery"
+S8520,"Unspecified injury of peroneal artery"
+S85201,"Unspecified injury of peroneal artery, right leg"
+S85202,"Unspecified injury of peroneal artery, left leg"
+S85209,"Unspecified injury of peroneal artery, unspecified leg"
+S8521,"Laceration of peroneal artery"
+S85211,"Laceration of peroneal artery, right leg"
+S85212,"Laceration of peroneal artery, left leg"
+S85219,"Laceration of peroneal artery, unspecified leg"
+S8529,"Other specified injury of peroneal artery"
+S85291,"Other specified injury of peroneal artery, right leg"
+S85292,"Other specified injury of peroneal artery, left leg"
+S85299,"Other specified injury of peroneal artery, unspecified leg"
+S853,"Injury of greater saphenous vein at lower leg level"
+S8530,"Unspecified injury of greater saphenous vein at lower leg level"
+S85301,"Unspecified injury of greater saphenous vein at lower leg level, right leg"
+S85302,"Unspecified injury of greater saphenous vein at lower leg level, left leg"
+S85309,"Unspecified injury of greater saphenous vein at lower leg level, unspecified leg"
+S8531,"Laceration of greater saphenous vein at lower leg level"
+S85311,"Laceration of greater saphenous vein at lower leg level, right leg"
+S85312,"Laceration of greater saphenous vein at lower leg level, left leg"
+S85319,"Laceration of greater saphenous vein at lower leg level, unspecified leg"
+S8539,"Other specified injury of greater saphenous vein at lower leg level"
+S85391,"Other specified injury of greater saphenous vein at lower leg level, right leg"
+S85392,"Other specified injury of greater saphenous vein at lower leg level, left leg"
+S85399,"Other specified injury of greater saphenous vein at lower leg level, unspecified leg"
+S854,"Injury of lesser saphenous vein at lower leg level"
+S8540,"Unspecified injury of lesser saphenous vein at lower leg level"
+S85401,"Unspecified injury of lesser saphenous vein at lower leg level, right leg"
+S85402,"Unspecified injury of lesser saphenous vein at lower leg level, left leg"
+S85409,"Unspecified injury of lesser saphenous vein at lower leg level, unspecified leg"
+S8541,"Laceration of lesser saphenous vein at lower leg level"
+S85411,"Laceration of lesser saphenous vein at lower leg level, right leg"
+S85412,"Laceration of lesser saphenous vein at lower leg level, left leg"
+S85419,"Laceration of lesser saphenous vein at lower leg level, unspecified leg"
+S8549,"Other specified injury of lesser saphenous vein at lower leg level"
+S85491,"Other specified injury of lesser saphenous vein at lower leg level, right leg"
+S85492,"Other specified injury of lesser saphenous vein at lower leg level, left leg"
+S85499,"Other specified injury of lesser saphenous vein at lower leg level, unspecified leg"
+S855,"Injury of popliteal vein"
+S8550,"Unspecified injury of popliteal vein"
+S85501,"Unspecified injury of popliteal vein, right leg"
+S85502,"Unspecified injury of popliteal vein, left leg"
+S85509,"Unspecified injury of popliteal vein, unspecified leg"
+S8551,"Laceration of popliteal vein"
+S85511,"Laceration of popliteal vein, right leg"
+S85512,"Laceration of popliteal vein, left leg"
+S85519,"Laceration of popliteal vein, unspecified leg"
+S8559,"Other specified injury of popliteal vein"
+S85591,"Other specified injury of popliteal vein, right leg"
+S85592,"Other specified injury of popliteal vein, left leg"
+S85599,"Other specified injury of popliteal vein, unspecified leg"
+S858,"Injury of other blood vessels at lower leg level"
+S8580,"Unspecified injury of other blood vessels at lower leg level"
+S85801,"Unspecified injury of other blood vessels at lower leg level, right leg"
+S85802,"Unspecified injury of other blood vessels at lower leg level, left leg"
+S85809,"Unspecified injury of other blood vessels at lower leg level, unspecified leg"
+S8581,"Laceration of other blood vessels at lower leg level"
+S85811,"Laceration of other blood vessels at lower leg level, right leg"
+S85812,"Laceration of other blood vessels at lower leg level, left leg"
+S85819,"Laceration of other blood vessels at lower leg level, unspecified leg"
+S8589,"Other specified injury of other blood vessels at lower leg level"
+S85891,"Other specified injury of other blood vessels at lower leg level, right leg"
+S85892,"Other specified injury of other blood vessels at lower leg level, left leg"
+S85899,"Other specified injury of other blood vessels at lower leg level, unspecified leg"
+S859,"Injury of unspecified blood vessel at lower leg level"
+S8590,"Unspecified injury of unspecified blood vessel at lower leg level"
+S85901,"Unspecified injury of unspecified blood vessel at lower leg level, right leg"
+S85902,"Unspecified injury of unspecified blood vessel at lower leg level, left leg"
+S85909,"Unspecified injury of unspecified blood vessel at lower leg level, unspecified leg"
+S8591,"Laceration of unspecified blood vessel at lower leg level"
+S85911,"Laceration of unspecified blood vessel at lower leg level, right leg"
+S85912,"Laceration of unspecified blood vessel at lower leg level, left leg"
+S85919,"Laceration of unspecified blood vessel at lower leg level, unspecified leg"
+S8599,"Other specified injury of unspecified blood vessel at lower leg level"
+S85991,"Other specified injury of unspecified blood vessel at lower leg level, right leg"
+S85992,"Other specified injury of unspecified blood vessel at lower leg level, left leg"
+S85999,"Other specified injury of unspecified blood vessel at lower leg level, unspecified leg"
+S86,"Injury of muscle, fascia and tendon at lower leg level"
+S860,"Injury of Achilles tendon"
+S8600,"Unspecified injury of Achilles tendon"
+S86001,"Unspecified injury of right Achilles tendon"
+S86002,"Unspecified injury of left Achilles tendon"
+S86009,"Unspecified injury of unspecified Achilles tendon"
+S8601,"Strain of Achilles tendon"
+S86011,"Strain of right Achilles tendon"
+S86012,"Strain of left Achilles tendon"
+S86019,"Strain of unspecified Achilles tendon"
+S8602,"Laceration of Achilles tendon"
+S86021,"Laceration of right Achilles tendon"
+S86022,"Laceration of left Achilles tendon"
+S86029,"Laceration of unspecified Achilles tendon"
+S8609,"Other specified injury of Achilles tendon"
+S86091,"Other specified injury of right Achilles tendon"
+S86092,"Other specified injury of left Achilles tendon"
+S86099,"Other specified injury of unspecified Achilles tendon"
+S861,"Injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level"
+S8610,"Unspecified injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level"
+S86101,"Unspecified injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, right leg"
+S86102,"Unspecified injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, left leg"
+S86109,"Unspecified injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, unspecified leg"
+S8611,"Strain of other muscle(s) and tendon(s) of posterior muscle group at lower leg level"
+S86111,"Strain of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, right leg"
+S86112,"Strain of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, left leg"
+S86119,"Strain of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, unspecified leg"
+S8612,"Laceration of other muscle(s) and tendon(s) of posterior muscle group at lower leg level"
+S86121,"Laceration of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, right leg"
+S86122,"Laceration of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, left leg"
+S86129,"Laceration of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, unspecified leg"
+S8619,"Other injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level"
+S86191,"Other injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, right leg"
+S86192,"Other injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, left leg"
+S86199,"Other injury of other muscle(s) and tendon(s) of posterior muscle group at lower leg level, unspecified leg"
+S862,"Injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level"
+S8620,"Unspecified injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level"
+S86201,"Unspecified injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level, right leg"
+S86202,"Unspecified injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level, left leg"
+S86209,"Unspecified injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level, unspecified leg"
+S8621,"Strain of muscle(s) and tendon(s) of anterior muscle group at lower leg level"
+S86211,"Strain of muscle(s) and tendon(s) of anterior muscle group at lower leg level, right leg"
+S86212,"Strain of muscle(s) and tendon(s) of anterior muscle group at lower leg level, left leg"
+S86219,"Strain of muscle(s) and tendon(s) of anterior muscle group at lower leg level, unspecified leg"
+S8622,"Laceration of muscle(s) and tendon(s) of anterior muscle group at lower leg level"
+S86221,"Laceration of muscle(s) and tendon(s) of anterior muscle group at lower leg level, right leg"
+S86222,"Laceration of muscle(s) and tendon(s) of anterior muscle group at lower leg level, left leg"
+S86229,"Laceration of muscle(s) and tendon(s) of anterior muscle group at lower leg level, unspecified leg"
+S8629,"Other injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level"
+S86291,"Other injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level, right leg"
+S86292,"Other injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level, left leg"
+S86299,"Other injury of muscle(s) and tendon(s) of anterior muscle group at lower leg level, unspecified leg"
+S863,"Injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level"
+S8630,"Unspecified injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level"
+S86301,"Unspecified injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, right leg"
+S86302,"Unspecified injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, left leg"
+S86309,"Unspecified injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, unspecified leg"
+S8631,"Strain of muscle(s) and tendon(s) of peroneal muscle group at lower leg level"
+S86311,"Strain of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, right leg"
+S86312,"Strain of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, left leg"
+S86319,"Strain of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, unspecified leg"
+S8632,"Laceration of muscle(s) and tendon(s) of peroneal muscle group at lower leg level"
+S86321,"Laceration of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, right leg"
+S86322,"Laceration of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, left leg"
+S86329,"Laceration of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, unspecified leg"
+S8639,"Other injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level"
+S86391,"Other injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, right leg"
+S86392,"Other injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, left leg"
+S86399,"Other injury of muscle(s) and tendon(s) of peroneal muscle group at lower leg level, unspecified leg"
+S868,"Injury of other muscles and tendons at lower leg level"
+S8680,"Unspecified injury of other muscles and tendons at lower leg level"
+S86801,"Unspecified injury of other muscle(s) and tendon(s) at lower leg level, right leg"
+S86802,"Unspecified injury of other muscle(s) and tendon(s) at lower leg level, left leg"
+S86809,"Unspecified injury of other muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S8681,"Strain of other muscles and tendons at lower leg level"
+S86811,"Strain of other muscle(s) and tendon(s) at lower leg level, right leg"
+S86812,"Strain of other muscle(s) and tendon(s) at lower leg level, left leg"
+S86819,"Strain of other muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S8682,"Laceration of other muscles and tendons at lower leg level"
+S86821,"Laceration of other muscle(s) and tendon(s) at lower leg level, right leg"
+S86822,"Laceration of other muscle(s) and tendon(s) at lower leg level, left leg"
+S86829,"Laceration of other muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S8689,"Other injury of other muscles and tendons at lower leg level"
+S86891,"Other injury of other muscle(s) and tendon(s) at lower leg level, right leg"
+S86892,"Other injury of other muscle(s) and tendon(s) at lower leg level, left leg"
+S86899,"Other injury of other muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S869,"Injury of unspecified muscle and tendon at lower leg level"
+S8690,"Unspecified injury of unspecified muscle and tendon at lower leg level"
+S86901,"Unspecified injury of unspecified muscle(s) and tendon(s) at lower leg level, right leg"
+S86902,"Unspecified injury of unspecified muscle(s) and tendon(s) at lower leg level, left leg"
+S86909,"Unspecified injury of unspecified muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S8691,"Strain of unspecified muscle and tendon at lower leg level"
+S86911,"Strain of unspecified muscle(s) and tendon(s) at lower leg level, right leg"
+S86912,"Strain of unspecified muscle(s) and tendon(s) at lower leg level, left leg"
+S86919,"Strain of unspecified muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S8692,"Laceration of unspecified muscle and tendon at lower leg level"
+S86921,"Laceration of unspecified muscle(s) and tendon(s) at lower leg level, right leg"
+S86922,"Laceration of unspecified muscle(s) and tendon(s) at lower leg level, left leg"
+S86929,"Laceration of unspecified muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S8699,"Other injury of unspecified muscle and tendon at lower leg level"
+S86991,"Other injury of unspecified muscle(s) and tendon(s) at lower leg level, right leg"
+S86992,"Other injury of unspecified muscle(s) and tendon(s) at lower leg level, left leg"
+S86999,"Other injury of unspecified muscle(s) and tendon(s) at lower leg level, unspecified leg"
+S87,"Crushing injury of lower leg"
+S870,"Crushing injury of knee"
+S8700,"Crushing injury of unspecified knee"
+S8701,"Crushing injury of right knee"
+S8702,"Crushing injury of left knee"
+S878,"Crushing injury of lower leg"
+S8780,"Crushing injury of unspecified lower leg"
+S8781,"Crushing injury of right lower leg"
+S8782,"Crushing injury of left lower leg"
+S88,"Traumatic amputation of lower leg"
+S880,"Traumatic amputation at knee level"
+S8801,"Complete traumatic amputation at knee level"
+S88011,"Complete traumatic amputation at knee level, right lower leg"
+S88012,"Complete traumatic amputation at knee level, left lower leg"
+S88019,"Complete traumatic amputation at knee level, unspecified lower leg"
+S8802,"Partial traumatic amputation at knee level"
+S88021,"Partial traumatic amputation at knee level, right lower leg"
+S88022,"Partial traumatic amputation at knee level, left lower leg"
+S88029,"Partial traumatic amputation at knee level, unspecified lower leg"
+S881,"Traumatic amputation at level between knee and ankle"
+S8811,"Complete traumatic amputation at level between knee and ankle"
+S88111,"Complete traumatic amputation at level between knee and ankle, right lower leg"
+S88112,"Complete traumatic amputation at level between knee and ankle, left lower leg"
+S88119,"Complete traumatic amputation at level between knee and ankle, unspecified lower leg"
+S8812,"Partial traumatic amputation at level between knee and ankle"
+S88121,"Partial traumatic amputation at level between knee and ankle, right lower leg"
+S88122,"Partial traumatic amputation at level between knee and ankle, left lower leg"
+S88129,"Partial traumatic amputation at level between knee and ankle, unspecified lower leg"
+S889,"Traumatic amputation of lower leg, level unspecified"
+S8891,"Complete traumatic amputation of lower leg, level unspecified"
+S88911,"Complete traumatic amputation of right lower leg, level unspecified"
+S88912,"Complete traumatic amputation of left lower leg, level unspecified"
+S88919,"Complete traumatic amputation of unspecified lower leg, level unspecified"
+S8892,"Partial traumatic amputation of lower leg, level unspecified"
+S88921,"Partial traumatic amputation of right lower leg, level unspecified"
+S88922,"Partial traumatic amputation of left lower leg, level unspecified"
+S88929,"Partial traumatic amputation of unspecified lower leg, level unspecified"
+S89,"Other and unspecified injuries of lower leg"
+S890,"Physeal fracture of upper end of tibia"
+S8900,"Unspecified physeal fracture of upper end of tibia"
+S89001,"Unspecified physeal fracture of upper end of right tibia"
+S89002,"Unspecified physeal fracture of upper end of left tibia"
+S89009,"Unspecified physeal fracture of upper end of unspecified tibia"
+S8901,"Salter-Harris Type I physeal fracture of upper end of tibia"
+S89011,"Salter-Harris Type I physeal fracture of upper end of right tibia"
+S89012,"Salter-Harris Type I physeal fracture of upper end of left tibia"
+S89019,"Salter-Harris Type I physeal fracture of upper end of unspecified tibia"
+S8902,"Salter-Harris Type II physeal fracture of upper end of tibia"
+S89021,"Salter-Harris Type II physeal fracture of upper end of right tibia"
+S89022,"Salter-Harris Type II physeal fracture of upper end of left tibia"
+S89029,"Salter-Harris Type II physeal fracture of upper end of unspecified tibia"
+S8903,"Salter-Harris Type III physeal fracture of upper end of tibia"
+S89031,"Salter-Harris Type III physeal fracture of upper end of right tibia"
+S89032,"Salter-Harris Type III physeal fracture of upper end of left tibia"
+S89039,"Salter-Harris Type III physeal fracture of upper end of unspecified tibia"
+S8904,"Salter-Harris Type IV physeal fracture of upper end of tibia"
+S89041,"Salter-Harris Type IV physeal fracture of upper end of right tibia"
+S89042,"Salter-Harris Type IV physeal fracture of upper end of left tibia"
+S89049,"Salter-Harris Type IV physeal fracture of upper end of unspecified tibia"
+S8909,"Other physeal fracture of upper end of tibia"
+S89091,"Other physeal fracture of upper end of right tibia"
+S89092,"Other physeal fracture of upper end of left tibia"
+S89099,"Other physeal fracture of upper end of unspecified tibia"
+S891,"Physeal fracture of lower end of tibia"
+S8910,"Unspecified physeal fracture of lower end of tibia"
+S89101,"Unspecified physeal fracture of lower end of right tibia"
+S89102,"Unspecified physeal fracture of lower end of left tibia"
+S89109,"Unspecified physeal fracture of lower end of unspecified tibia"
+S8911,"Salter-Harris Type I physeal fracture of lower end of tibia"
+S89111,"Salter-Harris Type I physeal fracture of lower end of right tibia"
+S89112,"Salter-Harris Type I physeal fracture of lower end of left tibia"
+S89119,"Salter-Harris Type I physeal fracture of lower end of unspecified tibia"
+S8912,"Salter-Harris Type II physeal fracture of lower end of tibia"
+S89121,"Salter-Harris Type II physeal fracture of lower end of right tibia"
+S89122,"Salter-Harris Type II physeal fracture of lower end of left tibia"
+S89129,"Salter-Harris Type II physeal fracture of lower end of unspecified tibia"
+S8913,"Salter-Harris Type III physeal fracture of lower end of tibia"
+S89131,"Salter-Harris Type III physeal fracture of lower end of right tibia"
+S89132,"Salter-Harris Type III physeal fracture of lower end of left tibia"
+S89139,"Salter-Harris Type III physeal fracture of lower end of unspecified tibia"
+S8914,"Salter-Harris Type IV physeal fracture of lower end of tibia"
+S89141,"Salter-Harris Type IV physeal fracture of lower end of right tibia"
+S89142,"Salter-Harris Type IV physeal fracture of lower end of left tibia"
+S89149,"Salter-Harris Type IV physeal fracture of lower end of unspecified tibia"
+S8919,"Other physeal fracture of lower end of tibia"
+S89191,"Other physeal fracture of lower end of right tibia"
+S89192,"Other physeal fracture of lower end of left tibia"
+S89199,"Other physeal fracture of lower end of unspecified tibia"
+S892,"Physeal fracture of upper end of fibula"
+S8920,"Unspecified physeal fracture of upper end of fibula"
+S89201,"Unspecified physeal fracture of upper end of right fibula"
+S89202,"Unspecified physeal fracture of upper end of left fibula"
+S89209,"Unspecified physeal fracture of upper end of unspecified fibula"
+S8921,"Salter-Harris Type I physeal fracture of upper end of fibula"
+S89211,"Salter-Harris Type I physeal fracture of upper end of right fibula"
+S89212,"Salter-Harris Type I physeal fracture of upper end of left fibula"
+S89219,"Salter-Harris Type I physeal fracture of upper end of unspecified fibula"
+S8922,"Salter-Harris Type II physeal fracture of upper end of fibula"
+S89221,"Salter-Harris Type II physeal fracture of upper end of right fibula"
+S89222,"Salter-Harris Type II physeal fracture of upper end of left fibula"
+S89229,"Salter-Harris Type II physeal fracture of upper end of unspecified fibula"
+S8929,"Other physeal fracture of upper end of fibula"
+S89291,"Other physeal fracture of upper end of right fibula"
+S89292,"Other physeal fracture of upper end of left fibula"
+S89299,"Other physeal fracture of upper end of unspecified fibula"
+S893,"Physeal fracture of lower end of fibula"
+S8930,"Unspecified physeal fracture of lower end of fibula"
+S89301,"Unspecified physeal fracture of lower end of right fibula"
+S89302,"Unspecified physeal fracture of lower end of left fibula"
+S89309,"Unspecified physeal fracture of lower end of unspecified fibula"
+S8931,"Salter-Harris Type I physeal fracture of lower end of fibula"
+S89311,"Salter-Harris Type I physeal fracture of lower end of right fibula"
+S89312,"Salter-Harris Type I physeal fracture of lower end of left fibula"
+S89319,"Salter-Harris Type I physeal fracture of lower end of unspecified fibula"
+S8932,"Salter-Harris Type II physeal fracture of lower end of fibula"
+S89321,"Salter-Harris Type II physeal fracture of lower end of right fibula"
+S89322,"Salter-Harris Type II physeal fracture of lower end of left fibula"
+S89329,"Salter-Harris Type II physeal fracture of lower end of unspecified fibula"
+S8939,"Other physeal fracture of lower end of fibula"
+S89391,"Other physeal fracture of lower end of right fibula"
+S89392,"Other physeal fracture of lower end of left fibula"
+S89399,"Other physeal fracture of lower end of unspecified fibula"
+S898,"Other specified injuries of lower leg"
+S8980,"Other specified injuries of unspecified lower leg"
+S8981,"Other specified injuries of right lower leg"
+S8982,"Other specified injuries of left lower leg"
+S899,"Unspecified injury of lower leg"
+S8990,"Unspecified injury of unspecified lower leg"
+S8991,"Unspecified injury of right lower leg"
+S8992,"Unspecified injury of left lower leg"
+S90,"Superficial injury of ankle, foot and toes"
+S900,"Contusion of ankle"
+S9000,"Contusion of unspecified ankle"
+S9001,"Contusion of right ankle"
+S9002,"Contusion of left ankle"
+S901,"Contusion of toe without damage to nail"
+S9011,"Contusion of great toe without damage to nail"
+S90111,"Contusion of right great toe without damage to nail"
+S90112,"Contusion of left great toe without damage to nail"
+S90119,"Contusion of unspecified great toe without damage to nail"
+S9012,"Contusion of lesser toe without damage to nail"
+S90121,"Contusion of right lesser toe(s) without damage to nail"
+S90122,"Contusion of left lesser toe(s) without damage to nail"
+S90129,"Contusion of unspecified lesser toe(s) without damage to nail"
+S902,"Contusion of toe with damage to nail"
+S9021,"Contusion of great toe with damage to nail"
+S90211,"Contusion of right great toe with damage to nail"
+S90212,"Contusion of left great toe with damage to nail"
+S90219,"Contusion of unspecified great toe with damage to nail"
+S9022,"Contusion of lesser toe with damage to nail"
+S90221,"Contusion of right lesser toe(s) with damage to nail"
+S90222,"Contusion of left lesser toe(s) with damage to nail"
+S90229,"Contusion of unspecified lesser toe(s) with damage to nail"
+S903,"Contusion of foot"
+S9030,"Contusion of unspecified foot"
+S9031,"Contusion of right foot"
+S9032,"Contusion of left foot"
+S904,"Other superficial injuries of toe"
+S9041,"Abrasion of toe"
+S90411,"Abrasion, right great toe"
+S90412,"Abrasion, left great toe"
+S90413,"Abrasion, unspecified great toe"
+S90414,"Abrasion, right lesser toe(s)"
+S90415,"Abrasion, left lesser toe(s)"
+S90416,"Abrasion, unspecified lesser toe(s)"
+S9042,"Blister (nonthermal) of toe"
+S90421,"Blister (nonthermal), right great toe"
+S90422,"Blister (nonthermal), left great toe"
+S90423,"Blister (nonthermal), unspecified great toe"
+S90424,"Blister (nonthermal), right lesser toe(s)"
+S90425,"Blister (nonthermal), left lesser toe(s)"
+S90426,"Blister (nonthermal), unspecified lesser toe(s)"
+S9044,"External constriction of toe"
+S90441,"External constriction, right great toe"
+S90442,"External constriction, left great toe"
+S90443,"External constriction, unspecified great toe"
+S90444,"External constriction, right lesser toe(s)"
+S90445,"External constriction, left lesser toe(s)"
+S90446,"External constriction, unspecified lesser toe(s)"
+S9045,"Superficial foreign body of toe"
+S90451,"Superficial foreign body, right great toe"
+S90452,"Superficial foreign body, left great toe"
+S90453,"Superficial foreign body, unspecified great toe"
+S90454,"Superficial foreign body, right lesser toe(s)"
+S90455,"Superficial foreign body, left lesser toe(s)"
+S90456,"Superficial foreign body, unspecified lesser toe(s)"
+S9046,"Insect bite (nonvenomous) of toe"
+S90461,"Insect bite (nonvenomous), right great toe"
+S90462,"Insect bite (nonvenomous), left great toe"
+S90463,"Insect bite (nonvenomous), unspecified great toe"
+S90464,"Insect bite (nonvenomous), right lesser toe(s)"
+S90465,"Insect bite (nonvenomous), left lesser toe(s)"
+S90466,"Insect bite (nonvenomous), unspecified lesser toe(s)"
+S9047,"Other superficial bite of toe"
+S90471,"Other superficial bite of right great toe"
+S90472,"Other superficial bite of left great toe"
+S90473,"Other superficial bite of unspecified great toe"
+S90474,"Other superficial bite of right lesser toe(s)"
+S90475,"Other superficial bite of left lesser toe(s)"
+S90476,"Other superficial bite of unspecified lesser toe(s)"
+S905,"Other superficial injuries of ankle"
+S9051,"Abrasion of ankle"
+S90511,"Abrasion, right ankle"
+S90512,"Abrasion, left ankle"
+S90519,"Abrasion, unspecified ankle"
+S9052,"Blister (nonthermal) of ankle"
+S90521,"Blister (nonthermal), right ankle"
+S90522,"Blister (nonthermal), left ankle"
+S90529,"Blister (nonthermal), unspecified ankle"
+S9054,"External constriction of ankle"
+S90541,"External constriction, right ankle"
+S90542,"External constriction, left ankle"
+S90549,"External constriction, unspecified ankle"
+S9055,"Superficial foreign body of ankle"
+S90551,"Superficial foreign body, right ankle"
+S90552,"Superficial foreign body, left ankle"
+S90559,"Superficial foreign body, unspecified ankle"
+S9056,"Insect bite (nonvenomous) of ankle"
+S90561,"Insect bite (nonvenomous), right ankle"
+S90562,"Insect bite (nonvenomous), left ankle"
+S90569,"Insect bite (nonvenomous), unspecified ankle"
+S9057,"Other superficial bite of ankle"
+S90571,"Other superficial bite of ankle, right ankle"
+S90572,"Other superficial bite of ankle, left ankle"
+S90579,"Other superficial bite of ankle, unspecified ankle"
+S908,"Other superficial injuries of foot"
+S9081,"Abrasion of foot"
+S90811,"Abrasion, right foot"
+S90812,"Abrasion, left foot"
+S90819,"Abrasion, unspecified foot"
+S9082,"Blister (nonthermal) of foot"
+S90821,"Blister (nonthermal), right foot"
+S90822,"Blister (nonthermal), left foot"
+S90829,"Blister (nonthermal), unspecified foot"
+S9084,"External constriction of foot"
+S90841,"External constriction, right foot"
+S90842,"External constriction, left foot"
+S90849,"External constriction, unspecified foot"
+S9085,"Superficial foreign body of foot"
+S90851,"Superficial foreign body, right foot"
+S90852,"Superficial foreign body, left foot"
+S90859,"Superficial foreign body, unspecified foot"
+S9086,"Insect bite (nonvenomous) of foot"
+S90861,"Insect bite (nonvenomous), right foot"
+S90862,"Insect bite (nonvenomous), left foot"
+S90869,"Insect bite (nonvenomous), unspecified foot"
+S9087,"Other superficial bite of foot"
+S90871,"Other superficial bite of right foot"
+S90872,"Other superficial bite of left foot"
+S90879,"Other superficial bite of unspecified foot"
+S909,"Unspecified superficial injury of ankle, foot and toe"
+S9091,"Unspecified superficial injury of ankle"
+S90911,"Unspecified superficial injury of right ankle"
+S90912,"Unspecified superficial injury of left ankle"
+S90919,"Unspecified superficial injury of unspecified ankle"
+S9092,"Unspecified superficial injury of foot"
+S90921,"Unspecified superficial injury of right foot"
+S90922,"Unspecified superficial injury of left foot"
+S90929,"Unspecified superficial injury of unspecified foot"
+S9093,"Unspecified superficial injury of toes"
+S90931,"Unspecified superficial injury of right great toe"
+S90932,"Unspecified superficial injury of left great toe"
+S90933,"Unspecified superficial injury of unspecified great toe"
+S90934,"Unspecified superficial injury of right lesser toe(s)"
+S90935,"Unspecified superficial injury of left lesser toe(s)"
+S90936,"Unspecified superficial injury of unspecified lesser toe(s)"
+S91,"Open wound of ankle, foot and toes"
+S910,"Open wound of ankle"
+S9100,"Unspecified open wound of ankle"
+S91001,"Unspecified open wound, right ankle"
+S91002,"Unspecified open wound, left ankle"
+S91009,"Unspecified open wound, unspecified ankle"
+S9101,"Laceration without foreign body of ankle"
+S91011,"Laceration without foreign body, right ankle"
+S91012,"Laceration without foreign body, left ankle"
+S91019,"Laceration without foreign body, unspecified ankle"
+S9102,"Laceration with foreign body of ankle"
+S91021,"Laceration with foreign body, right ankle"
+S91022,"Laceration with foreign body, left ankle"
+S91029,"Laceration with foreign body, unspecified ankle"
+S9103,"Puncture wound without foreign body of ankle"
+S91031,"Puncture wound without foreign body, right ankle"
+S91032,"Puncture wound without foreign body, left ankle"
+S91039,"Puncture wound without foreign body, unspecified ankle"
+S9104,"Puncture wound with foreign body of ankle"
+S91041,"Puncture wound with foreign body, right ankle"
+S91042,"Puncture wound with foreign body, left ankle"
+S91049,"Puncture wound with foreign body, unspecified ankle"
+S9105,"Open bite of ankle"
+S91051,"Open bite, right ankle"
+S91052,"Open bite, left ankle"
+S91059,"Open bite, unspecified ankle"
+S911,"Open wound of toe without damage to nail"
+S9110,"Unspecified open wound of toe without damage to nail"
+S91101,"Unspecified open wound of right great toe without damage to nail"
+S91102,"Unspecified open wound of left great toe without damage to nail"
+S91103,"Unspecified open wound of unspecified great toe without damage to nail"
+S91104,"Unspecified open wound of right lesser toe(s) without damage to nail"
+S91105,"Unspecified open wound of left lesser toe(s) without damage to nail"
+S91106,"Unspecified open wound of unspecified lesser toe(s) without damage to nail"
+S91109,"Unspecified open wound of unspecified toe(s) without damage to nail"
+S9111,"Laceration without foreign body of toe without damage to nail"
+S91111,"Laceration without foreign body of right great toe without damage to nail"
+S91112,"Laceration without foreign body of left great toe without damage to nail"
+S91113,"Laceration without foreign body of unspecified great toe without damage to nail"
+S91114,"Laceration without foreign body of right lesser toe(s) without damage to nail"
+S91115,"Laceration without foreign body of left lesser toe(s) without damage to nail"
+S91116,"Laceration without foreign body of unspecified lesser toe(s) without damage to nail"
+S91119,"Laceration without foreign body of unspecified toe without damage to nail"
+S9112,"Laceration with foreign body of toe without damage to nail"
+S91121,"Laceration with foreign body of right great toe without damage to nail"
+S91122,"Laceration with foreign body of left great toe without damage to nail"
+S91123,"Laceration with foreign body of unspecified great toe without damage to nail"
+S91124,"Laceration with foreign body of right lesser toe(s) without damage to nail"
+S91125,"Laceration with foreign body of left lesser toe(s) without damage to nail"
+S91126,"Laceration with foreign body of unspecified lesser toe(s) without damage to nail"
+S91129,"Laceration with foreign body of unspecified toe(s) without damage to nail"
+S9113,"Puncture wound without foreign body of toe without damage to nail"
+S91131,"Puncture wound without foreign body of right great toe without damage to nail"
+S91132,"Puncture wound without foreign body of left great toe without damage to nail"
+S91133,"Puncture wound without foreign body of unspecified great toe without damage to nail"
+S91134,"Puncture wound without foreign body of right lesser toe(s) without damage to nail"
+S91135,"Puncture wound without foreign body of left lesser toe(s) without damage to nail"
+S91136,"Puncture wound without foreign body of unspecified lesser toe(s) without damage to nail"
+S91139,"Puncture wound without foreign body of unspecified toe(s) without damage to nail"
+S9114,"Puncture wound with foreign body of toe without damage to nail"
+S91141,"Puncture wound with foreign body of right great toe without damage to nail"
+S91142,"Puncture wound with foreign body of left great toe without damage to nail"
+S91143,"Puncture wound with foreign body of unspecified great toe without damage to nail"
+S91144,"Puncture wound with foreign body of right lesser toe(s) without damage to nail"
+S91145,"Puncture wound with foreign body of left lesser toe(s) without damage to nail"
+S91146,"Puncture wound with foreign body of unspecified lesser toe(s) without damage to nail"
+S91149,"Puncture wound with foreign body of unspecified toe(s) without damage to nail"
+S9115,"Open bite of toe without damage to nail"
+S91151,"Open bite of right great toe without damage to nail"
+S91152,"Open bite of left great toe without damage to nail"
+S91153,"Open bite of unspecified great toe without damage to nail"
+S91154,"Open bite of right lesser toe(s) without damage to nail"
+S91155,"Open bite of left lesser toe(s) without damage to nail"
+S91156,"Open bite of unspecified lesser toe(s) without damage to nail"
+S91159,"Open bite of unspecified toe(s) without damage to nail"
+S912,"Open wound of toe with damage to nail"
+S9120,"Unspecified open wound of toe with damage to nail"
+S91201,"Unspecified open wound of right great toe with damage to nail"
+S91202,"Unspecified open wound of left great toe with damage to nail"
+S91203,"Unspecified open wound of unspecified great toe with damage to nail"
+S91204,"Unspecified open wound of right lesser toe(s) with damage to nail"
+S91205,"Unspecified open wound of left lesser toe(s) with damage to nail"
+S91206,"Unspecified open wound of unspecified lesser toe(s) with damage to nail"
+S91209,"Unspecified open wound of unspecified toe(s) with damage to nail"
+S9121,"Laceration without foreign body of toe with damage to nail"
+S91211,"Laceration without foreign body of right great toe with damage to nail"
+S91212,"Laceration without foreign body of left great toe with damage to nail"
+S91213,"Laceration without foreign body of unspecified great toe with damage to nail"
+S91214,"Laceration without foreign body of right lesser toe(s) with damage to nail"
+S91215,"Laceration without foreign body of left lesser toe(s) with damage to nail"
+S91216,"Laceration without foreign body of unspecified lesser toe(s) with damage to nail"
+S91219,"Laceration without foreign body of unspecified toe(s) with damage to nail"
+S9122,"Laceration with foreign body of toe with damage to nail"
+S91221,"Laceration with foreign body of right great toe with damage to nail"
+S91222,"Laceration with foreign body of left great toe with damage to nail"
+S91223,"Laceration with foreign body of unspecified great toe with damage to nail"
+S91224,"Laceration with foreign body of right lesser toe(s) with damage to nail"
+S91225,"Laceration with foreign body of left lesser toe(s) with damage to nail"
+S91226,"Laceration with foreign body of unspecified lesser toe(s) with damage to nail"
+S91229,"Laceration with foreign body of unspecified toe(s) with damage to nail"
+S9123,"Puncture wound without foreign body of toe with damage to nail"
+S91231,"Puncture wound without foreign body of right great toe with damage to nail"
+S91232,"Puncture wound without foreign body of left great toe with damage to nail"
+S91233,"Puncture wound without foreign body of unspecified great toe with damage to nail"
+S91234,"Puncture wound without foreign body of right lesser toe(s) with damage to nail"
+S91235,"Puncture wound without foreign body of left lesser toe(s) with damage to nail"
+S91236,"Puncture wound without foreign body of unspecified lesser toe(s) with damage to nail"
+S91239,"Puncture wound without foreign body of unspecified toe(s) with damage to nail"
+S9124,"Puncture wound with foreign body of toe with damage to nail"
+S91241,"Puncture wound with foreign body of right great toe with damage to nail"
+S91242,"Puncture wound with foreign body of left great toe with damage to nail"
+S91243,"Puncture wound with foreign body of unspecified great toe with damage to nail"
+S91244,"Puncture wound with foreign body of right lesser toe(s) with damage to nail"
+S91245,"Puncture wound with foreign body of left lesser toe(s) with damage to nail"
+S91246,"Puncture wound with foreign body of unspecified lesser toe(s) with damage to nail"
+S91249,"Puncture wound with foreign body of unspecified toe(s) with damage to nail"
+S9125,"Open bite of toe with damage to nail"
+S91251,"Open bite of right great toe with damage to nail"
+S91252,"Open bite of left great toe with damage to nail"
+S91253,"Open bite of unspecified great toe with damage to nail"
+S91254,"Open bite of right lesser toe(s) with damage to nail"
+S91255,"Open bite of left lesser toe(s) with damage to nail"
+S91256,"Open bite of unspecified lesser toe(s) with damage to nail"
+S91259,"Open bite of unspecified toe(s) with damage to nail"
+S913,"Open wound of foot"
+S9130,"Unspecified open wound of foot"
+S91301,"Unspecified open wound, right foot"
+S91302,"Unspecified open wound, left foot"
+S91309,"Unspecified open wound, unspecified foot"
+S9131,"Laceration without foreign body of foot"
+S91311,"Laceration without foreign body, right foot"
+S91312,"Laceration without foreign body, left foot"
+S91319,"Laceration without foreign body, unspecified foot"
+S9132,"Laceration with foreign body of foot"
+S91321,"Laceration with foreign body, right foot"
+S91322,"Laceration with foreign body, left foot"
+S91329,"Laceration with foreign body, unspecified foot"
+S9133,"Puncture wound without foreign body of foot"
+S91331,"Puncture wound without foreign body, right foot"
+S91332,"Puncture wound without foreign body, left foot"
+S91339,"Puncture wound without foreign body, unspecified foot"
+S9134,"Puncture wound with foreign body of foot"
+S91341,"Puncture wound with foreign body, right foot"
+S91342,"Puncture wound with foreign body, left foot"
+S91349,"Puncture wound with foreign body, unspecified foot"
+S9135,"Open bite of foot"
+S91351,"Open bite, right foot"
+S91352,"Open bite, left foot"
+S91359,"Open bite, unspecified foot"
+S92,"Fracture of foot and toe, except ankle"
+S920,"Fracture of calcaneus"
+S9200,"Unspecified fracture of calcaneus"
+S92001,"Unspecified fracture of right calcaneus"
+S92002,"Unspecified fracture of left calcaneus"
+S92009,"Unspecified fracture of unspecified calcaneus"
+S9201,"Fracture of body of calcaneus"
+S92011,"Displaced fracture of body of right calcaneus"
+S92012,"Displaced fracture of body of left calcaneus"
+S92013,"Displaced fracture of body of unspecified calcaneus"
+S92014,"Nondisplaced fracture of body of right calcaneus"
+S92015,"Nondisplaced fracture of body of left calcaneus"
+S92016,"Nondisplaced fracture of body of unspecified calcaneus"
+S9202,"Fracture of anterior process of calcaneus"
+S92021,"Displaced fracture of anterior process of right calcaneus"
+S92022,"Displaced fracture of anterior process of left calcaneus"
+S92023,"Displaced fracture of anterior process of unspecified calcaneus"
+S92024,"Nondisplaced fracture of anterior process of right calcaneus"
+S92025,"Nondisplaced fracture of anterior process of left calcaneus"
+S92026,"Nondisplaced fracture of anterior process of unspecified calcaneus"
+S9203,"Avulsion fracture of tuberosity of calcaneus"
+S92031,"Displaced avulsion fracture of tuberosity of right calcaneus"
+S92032,"Displaced avulsion fracture of tuberosity of left calcaneus"
+S92033,"Displaced avulsion fracture of tuberosity of unspecified calcaneus"
+S92034,"Nondisplaced avulsion fracture of tuberosity of right calcaneus"
+S92035,"Nondisplaced avulsion fracture of tuberosity of left calcaneus"
+S92036,"Nondisplaced avulsion fracture of tuberosity of unspecified calcaneus"
+S9204,"Other fracture of tuberosity of calcaneus"
+S92041,"Displaced other fracture of tuberosity of right calcaneus"
+S92042,"Displaced other fracture of tuberosity of left calcaneus"
+S92043,"Displaced other fracture of tuberosity of unspecified calcaneus"
+S92044,"Nondisplaced other fracture of tuberosity of right calcaneus"
+S92045,"Nondisplaced other fracture of tuberosity of left calcaneus"
+S92046,"Nondisplaced other fracture of tuberosity of unspecified calcaneus"
+S9205,"Other extraarticular fracture of calcaneus"
+S92051,"Displaced other extraarticular fracture of right calcaneus"
+S92052,"Displaced other extraarticular fracture of left calcaneus"
+S92053,"Displaced other extraarticular fracture of unspecified calcaneus"
+S92054,"Nondisplaced other extraarticular fracture of right calcaneus"
+S92055,"Nondisplaced other extraarticular fracture of left calcaneus"
+S92056,"Nondisplaced other extraarticular fracture of unspecified calcaneus"
+S9206,"Intraarticular fracture of calcaneus"
+S92061,"Displaced intraarticular fracture of right calcaneus"
+S92062,"Displaced intraarticular fracture of left calcaneus"
+S92063,"Displaced intraarticular fracture of unspecified calcaneus"
+S92064,"Nondisplaced intraarticular fracture of right calcaneus"
+S92065,"Nondisplaced intraarticular fracture of left calcaneus"
+S92066,"Nondisplaced intraarticular fracture of unspecified calcaneus"
+S921,"Fracture of talus"
+S9210,"Unspecified fracture of talus"
+S92101,"Unspecified fracture of right talus"
+S92102,"Unspecified fracture of left talus"
+S92109,"Unspecified fracture of unspecified talus"
+S9211,"Fracture of neck of talus"
+S92111,"Displaced fracture of neck of right talus"
+S92112,"Displaced fracture of neck of left talus"
+S92113,"Displaced fracture of neck of unspecified talus"
+S92114,"Nondisplaced fracture of neck of right talus"
+S92115,"Nondisplaced fracture of neck of left talus"
+S92116,"Nondisplaced fracture of neck of unspecified talus"
+S9212,"Fracture of body of talus"
+S92121,"Displaced fracture of body of right talus"
+S92122,"Displaced fracture of body of left talus"
+S92123,"Displaced fracture of body of unspecified talus"
+S92124,"Nondisplaced fracture of body of right talus"
+S92125,"Nondisplaced fracture of body of left talus"
+S92126,"Nondisplaced fracture of body of unspecified talus"
+S9213,"Fracture of posterior process of talus"
+S92131,"Displaced fracture of posterior process of right talus"
+S92132,"Displaced fracture of posterior process of left talus"
+S92133,"Displaced fracture of posterior process of unspecified talus"
+S92134,"Nondisplaced fracture of posterior process of right talus"
+S92135,"Nondisplaced fracture of posterior process of left talus"
+S92136,"Nondisplaced fracture of posterior process of unspecified talus"
+S9214,"Dome fracture of talus"
+S92141,"Displaced dome fracture of right talus"
+S92142,"Displaced dome fracture of left talus"
+S92143,"Displaced dome fracture of unspecified talus"
+S92144,"Nondisplaced dome fracture of right talus"
+S92145,"Nondisplaced dome fracture of left talus"
+S92146,"Nondisplaced dome fracture of unspecified talus"
+S9215,"Avulsion fracture (chip fracture) of talus"
+S92151,"Displaced avulsion fracture (chip fracture) of right talus"
+S92152,"Displaced avulsion fracture (chip fracture) of left talus"
+S92153,"Displaced avulsion fracture (chip fracture) of unspecified talus"
+S92154,"Nondisplaced avulsion fracture (chip fracture) of right talus"
+S92155,"Nondisplaced avulsion fracture (chip fracture) of left talus"
+S92156,"Nondisplaced avulsion fracture (chip fracture) of unspecified talus"
+S9219,"Other fracture of talus"
+S92191,"Other fracture of right talus"
+S92192,"Other fracture of left talus"
+S92199,"Other fracture of unspecified talus"
+S922,"Fracture of other and unspecified tarsal bone(s)"
+S9220,"Fracture of unspecified tarsal bone(s)"
+S92201,"Fracture of unspecified tarsal bone(s) of right foot"
+S92202,"Fracture of unspecified tarsal bone(s) of left foot"
+S92209,"Fracture of unspecified tarsal bone(s) of unspecified foot"
+S9221,"Fracture of cuboid bone"
+S92211,"Displaced fracture of cuboid bone of right foot"
+S92212,"Displaced fracture of cuboid bone of left foot"
+S92213,"Displaced fracture of cuboid bone of unspecified foot"
+S92214,"Nondisplaced fracture of cuboid bone of right foot"
+S92215,"Nondisplaced fracture of cuboid bone of left foot"
+S92216,"Nondisplaced fracture of cuboid bone of unspecified foot"
+S9222,"Fracture of lateral cuneiform"
+S92221,"Displaced fracture of lateral cuneiform of right foot"
+S92222,"Displaced fracture of lateral cuneiform of left foot"
+S92223,"Displaced fracture of lateral cuneiform of unspecified foot"
+S92224,"Nondisplaced fracture of lateral cuneiform of right foot"
+S92225,"Nondisplaced fracture of lateral cuneiform of left foot"
+S92226,"Nondisplaced fracture of lateral cuneiform of unspecified foot"
+S9223,"Fracture of intermediate cuneiform"
+S92231,"Displaced fracture of intermediate cuneiform of right foot"
+S92232,"Displaced fracture of intermediate cuneiform of left foot"
+S92233,"Displaced fracture of intermediate cuneiform of unspecified foot"
+S92234,"Nondisplaced fracture of intermediate cuneiform of right foot"
+S92235,"Nondisplaced fracture of intermediate cuneiform of left foot"
+S92236,"Nondisplaced fracture of intermediate cuneiform of unspecified foot"
+S9224,"Fracture of medial cuneiform"
+S92241,"Displaced fracture of medial cuneiform of right foot"
+S92242,"Displaced fracture of medial cuneiform of left foot"
+S92243,"Displaced fracture of medial cuneiform of unspecified foot"
+S92244,"Nondisplaced fracture of medial cuneiform of right foot"
+S92245,"Nondisplaced fracture of medial cuneiform of left foot"
+S92246,"Nondisplaced fracture of medial cuneiform of unspecified foot"
+S9225,"Fracture of navicular [scaphoid] of foot"
+S92251,"Displaced fracture of navicular [scaphoid] of right foot"
+S92252,"Displaced fracture of navicular [scaphoid] of left foot"
+S92253,"Displaced fracture of navicular [scaphoid] of unspecified foot"
+S92254,"Nondisplaced fracture of navicular [scaphoid] of right foot"
+S92255,"Nondisplaced fracture of navicular [scaphoid] of left foot"
+S92256,"Nondisplaced fracture of navicular [scaphoid] of unspecified foot"
+S923,"Fracture of metatarsal bone(s)"
+S9230,"Fracture of unspecified metatarsal bone(s)"
+S92301,"Fracture of unspecified metatarsal bone(s), right foot"
+S92302,"Fracture of unspecified metatarsal bone(s), left foot"
+S92309,"Fracture of unspecified metatarsal bone(s), unspecified foot"
+S9231,"Fracture of first metatarsal bone"
+S92311,"Displaced fracture of first metatarsal bone, right foot"
+S92312,"Displaced fracture of first metatarsal bone, left foot"
+S92313,"Displaced fracture of first metatarsal bone, unspecified foot"
+S92314,"Nondisplaced fracture of first metatarsal bone, right foot"
+S92315,"Nondisplaced fracture of first metatarsal bone, left foot"
+S92316,"Nondisplaced fracture of first metatarsal bone, unspecified foot"
+S9232,"Fracture of second metatarsal bone"
+S92321,"Displaced fracture of second metatarsal bone, right foot"
+S92322,"Displaced fracture of second metatarsal bone, left foot"
+S92323,"Displaced fracture of second metatarsal bone, unspecified foot"
+S92324,"Nondisplaced fracture of second metatarsal bone, right foot"
+S92325,"Nondisplaced fracture of second metatarsal bone, left foot"
+S92326,"Nondisplaced fracture of second metatarsal bone, unspecified foot"
+S9233,"Fracture of third metatarsal bone"
+S92331,"Displaced fracture of third metatarsal bone, right foot"
+S92332,"Displaced fracture of third metatarsal bone, left foot"
+S92333,"Displaced fracture of third metatarsal bone, unspecified foot"
+S92334,"Nondisplaced fracture of third metatarsal bone, right foot"
+S92335,"Nondisplaced fracture of third metatarsal bone, left foot"
+S92336,"Nondisplaced fracture of third metatarsal bone, unspecified foot"
+S9234,"Fracture of fourth metatarsal bone"
+S92341,"Displaced fracture of fourth metatarsal bone, right foot"
+S92342,"Displaced fracture of fourth metatarsal bone, left foot"
+S92343,"Displaced fracture of fourth metatarsal bone, unspecified foot"
+S92344,"Nondisplaced fracture of fourth metatarsal bone, right foot"
+S92345,"Nondisplaced fracture of fourth metatarsal bone, left foot"
+S92346,"Nondisplaced fracture of fourth metatarsal bone, unspecified foot"
+S9235,"Fracture of fifth metatarsal bone"
+S92351,"Displaced fracture of fifth metatarsal bone, right foot"
+S92352,"Displaced fracture of fifth metatarsal bone, left foot"
+S92353,"Displaced fracture of fifth metatarsal bone, unspecified foot"
+S92354,"Nondisplaced fracture of fifth metatarsal bone, right foot"
+S92355,"Nondisplaced fracture of fifth metatarsal bone, left foot"
+S92356,"Nondisplaced fracture of fifth metatarsal bone, unspecified foot"
+S924,"Fracture of great toe"
+S9240,"Unspecified fracture of great toe"
+S92401,"Displaced unspecified fracture of right great toe"
+S92402,"Displaced unspecified fracture of left great toe"
+S92403,"Displaced unspecified fracture of unspecified great toe"
+S92404,"Nondisplaced unspecified fracture of right great toe"
+S92405,"Nondisplaced unspecified fracture of left great toe"
+S92406,"Nondisplaced unspecified fracture of unspecified great toe"
+S9241,"Fracture of proximal phalanx of great toe"
+S92411,"Displaced fracture of proximal phalanx of right great toe"
+S92412,"Displaced fracture of proximal phalanx of left great toe"
+S92413,"Displaced fracture of proximal phalanx of unspecified great toe"
+S92414,"Nondisplaced fracture of proximal phalanx of right great toe"
+S92415,"Nondisplaced fracture of proximal phalanx of left great toe"
+S92416,"Nondisplaced fracture of proximal phalanx of unspecified great toe"
+S9242,"Fracture of distal phalanx of great toe"
+S92421,"Displaced fracture of distal phalanx of right great toe"
+S92422,"Displaced fracture of distal phalanx of left great toe"
+S92423,"Displaced fracture of distal phalanx of unspecified great toe"
+S92424,"Nondisplaced fracture of distal phalanx of right great toe"
+S92425,"Nondisplaced fracture of distal phalanx of left great toe"
+S92426,"Nondisplaced fracture of distal phalanx of unspecified great toe"
+S9249,"Other fracture of great toe"
+S92491,"Other fracture of right great toe"
+S92492,"Other fracture of left great toe"
+S92499,"Other fracture of unspecified great toe"
+S925,"Fracture of lesser toe(s)"
+S9250,"Unspecified fracture of lesser toe(s)"
+S92501,"Displaced unspecified fracture of right lesser toe(s)"
+S92502,"Displaced unspecified fracture of left lesser toe(s)"
+S92503,"Displaced unspecified fracture of unspecified lesser toe(s)"
+S92504,"Nondisplaced unspecified fracture of right lesser toe(s)"
+S92505,"Nondisplaced unspecified fracture of left lesser toe(s)"
+S92506,"Nondisplaced unspecified fracture of unspecified lesser toe(s)"
+S9251,"Fracture of proximal phalanx of lesser toe(s)"
+S92511,"Displaced fracture of proximal phalanx of right lesser toe(s)"
+S92512,"Displaced fracture of proximal phalanx of left lesser toe(s)"
+S92513,"Displaced fracture of proximal phalanx of unspecified lesser toe(s)"
+S92514,"Nondisplaced fracture of proximal phalanx of right lesser toe(s)"
+S92515,"Nondisplaced fracture of proximal phalanx of left lesser toe(s)"
+S92516,"Nondisplaced fracture of proximal phalanx of unspecified lesser toe(s)"
+S9252,"Fracture of middle phalanx of lesser toe(s)"
+S92521,"Displaced fracture of middle phalanx of right lesser toe(s)"
+S92522,"Displaced fracture of middle phalanx of left lesser toe(s)"
+S92523,"Displaced fracture of middle phalanx of unspecified lesser toe(s)"
+S92524,"Nondisplaced fracture of middle phalanx of right lesser toe(s)"
+S92525,"Nondisplaced fracture of middle phalanx of left lesser toe(s)"
+S92526,"Nondisplaced fracture of middle phalanx of unspecified lesser toe(s)"
+S9253,"Fracture of distal phalanx of lesser toe(s)"
+S92531,"Displaced fracture of distal phalanx of right lesser toe(s)"
+S92532,"Displaced fracture of distal phalanx of left lesser toe(s)"
+S92533,"Displaced fracture of distal phalanx of unspecified lesser toe(s)"
+S92534,"Nondisplaced fracture of distal phalanx of right lesser toe(s)"
+S92535,"Nondisplaced fracture of distal phalanx of left lesser toe(s)"
+S92536,"Nondisplaced fracture of distal phalanx of unspecified lesser toe(s)"
+S9259,"Other fracture of lesser toe(s)"
+S92591,"Other fracture of right lesser toe(s)"
+S92592,"Other fracture of left lesser toe(s)"
+S92599,"Other fracture of unspecified lesser toe(s)"
+S928,"Other fracture of foot, except ankle"
+S9281,"Other fracture of foot"
+S92811,"Other fracture of right foot"
+S92812,"Other fracture of left foot"
+S92819,"Other fracture of unspecified foot"
+S929,"Unspecified fracture of foot and toe"
+S9290,"Unspecified fracture of foot"
+S92901,"Unspecified fracture of right foot"
+S92902,"Unspecified fracture of left foot"
+S92909,"Unspecified fracture of unspecified foot"
+S9291,"Unspecified fracture of toe"
+S92911,"Unspecified fracture of right toe(s)"
+S92912,"Unspecified fracture of left toe(s)"
+S92919,"Unspecified fracture of unspecified toe(s)"
+S93,"Dislocation and sprain of joints and ligaments at ankle, foot and toe level"
+S930,"Subluxation and dislocation of ankle joint"
+S9301,"Subluxation of right ankle joint"
+S9302,"Subluxation of left ankle joint"
+S9303,"Subluxation of unspecified ankle joint"
+S9304,"Dislocation of right ankle joint"
+S9305,"Dislocation of left ankle joint"
+S9306,"Dislocation of unspecified ankle joint"
+S931,"Subluxation and dislocation of toe"
+S9310,"Unspecified subluxation and dislocation of toe"
+S93101,"Unspecified subluxation of right toe(s)"
+S93102,"Unspecified subluxation of left toe(s)"
+S93103,"Unspecified subluxation of unspecified toe(s)"
+S93104,"Unspecified dislocation of right toe(s)"
+S93105,"Unspecified dislocation of left toe(s)"
+S93106,"Unspecified dislocation of unspecified toe(s)"
+S9311,"Dislocation of interphalangeal joint"
+S93111,"Dislocation of interphalangeal joint of right great toe"
+S93112,"Dislocation of interphalangeal joint of left great toe"
+S93113,"Dislocation of interphalangeal joint of unspecified great toe"
+S93114,"Dislocation of interphalangeal joint of right lesser toe(s)"
+S93115,"Dislocation of interphalangeal joint of left lesser toe(s)"
+S93116,"Dislocation of interphalangeal joint of unspecified lesser toe(s)"
+S93119,"Dislocation of interphalangeal joint of unspecified toe(s)"
+S9312,"Dislocation of metatarsophalangeal joint"
+S93121,"Dislocation of metatarsophalangeal joint of right great toe"
+S93122,"Dislocation of metatarsophalangeal joint of left great toe"
+S93123,"Dislocation of metatarsophalangeal joint of unspecified great toe"
+S93124,"Dislocation of metatarsophalangeal joint of right lesser toe(s)"
+S93125,"Dislocation of metatarsophalangeal joint of left lesser toe(s)"
+S93126,"Dislocation of metatarsophalangeal joint of unspecified lesser toe(s)"
+S93129,"Dislocation of metatarsophalangeal joint of unspecified toe(s)"
+S9313,"Subluxation of interphalangeal joint"
+S93131,"Subluxation of interphalangeal joint of right great toe"
+S93132,"Subluxation of interphalangeal joint of left great toe"
+S93133,"Subluxation of interphalangeal joint of unspecified great toe"
+S93134,"Subluxation of interphalangeal joint of right lesser toe(s)"
+S93135,"Subluxation of interphalangeal joint of left lesser toe(s)"
+S93136,"Subluxation of interphalangeal joint of unspecified lesser toe(s)"
+S93139,"Subluxation of interphalangeal joint of unspecified toe(s)"
+S9314,"Subluxation of metatarsophalangeal joint"
+S93141,"Subluxation of metatarsophalangeal joint of right great toe"
+S93142,"Subluxation of metatarsophalangeal joint of left great toe"
+S93143,"Subluxation of metatarsophalangeal joint of unspecified great toe"
+S93144,"Subluxation of metatarsophalangeal joint of right lesser toe(s)"
+S93145,"Subluxation of metatarsophalangeal joint of left lesser toe(s)"
+S93146,"Subluxation of metatarsophalangeal joint of unspecified lesser toe(s)"
+S93149,"Subluxation of metatarsophalangeal joint of unspecified toe(s)"
+S933,"Subluxation and dislocation of foot"
+S9330,"Unspecified subluxation and dislocation of foot"
+S93301,"Unspecified subluxation of right foot"
+S93302,"Unspecified subluxation of left foot"
+S93303,"Unspecified subluxation of unspecified foot"
+S93304,"Unspecified dislocation of right foot"
+S93305,"Unspecified dislocation of left foot"
+S93306,"Unspecified dislocation of unspecified foot"
+S9331,"Subluxation and dislocation of tarsal joint"
+S93311,"Subluxation of tarsal joint of right foot"
+S93312,"Subluxation of tarsal joint of left foot"
+S93313,"Subluxation of tarsal joint of unspecified foot"
+S93314,"Dislocation of tarsal joint of right foot"
+S93315,"Dislocation of tarsal joint of left foot"
+S93316,"Dislocation of tarsal joint of unspecified foot"
+S9332,"Subluxation and dislocation of tarsometatarsal joint"
+S93321,"Subluxation of tarsometatarsal joint of right foot"
+S93322,"Subluxation of tarsometatarsal joint of left foot"
+S93323,"Subluxation of tarsometatarsal joint of unspecified foot"
+S93324,"Dislocation of tarsometatarsal joint of right foot"
+S93325,"Dislocation of tarsometatarsal joint of left foot"
+S93326,"Dislocation of tarsometatarsal joint of unspecified foot"
+S9333,"Other subluxation and dislocation of foot"
+S93331,"Other subluxation of right foot"
+S93332,"Other subluxation of left foot"
+S93333,"Other subluxation of unspecified foot"
+S93334,"Other dislocation of right foot"
+S93335,"Other dislocation of left foot"
+S93336,"Other dislocation of unspecified foot"
+S934,"Sprain of ankle"
+S9340,"Sprain of unspecified ligament of ankle"
+S93401,"Sprain of unspecified ligament of right ankle"
+S93402,"Sprain of unspecified ligament of left ankle"
+S93409,"Sprain of unspecified ligament of unspecified ankle"
+S9341,"Sprain of calcaneofibular ligament"
+S93411,"Sprain of calcaneofibular ligament of right ankle"
+S93412,"Sprain of calcaneofibular ligament of left ankle"
+S93419,"Sprain of calcaneofibular ligament of unspecified ankle"
+S9342,"Sprain of deltoid ligament"
+S93421,"Sprain of deltoid ligament of right ankle"
+S93422,"Sprain of deltoid ligament of left ankle"
+S93429,"Sprain of deltoid ligament of unspecified ankle"
+S9343,"Sprain of tibiofibular ligament"
+S93431,"Sprain of tibiofibular ligament of right ankle"
+S93432,"Sprain of tibiofibular ligament of left ankle"
+S93439,"Sprain of tibiofibular ligament of unspecified ankle"
+S9349,"Sprain of other ligament of ankle"
+S93491,"Sprain of other ligament of right ankle"
+S93492,"Sprain of other ligament of left ankle"
+S93499,"Sprain of other ligament of unspecified ankle"
+S935,"Sprain of toe"
+S9350,"Unspecified sprain of toe"
+S93501,"Unspecified sprain of right great toe"
+S93502,"Unspecified sprain of left great toe"
+S93503,"Unspecified sprain of unspecified great toe"
+S93504,"Unspecified sprain of right lesser toe(s)"
+S93505,"Unspecified sprain of left lesser toe(s)"
+S93506,"Unspecified sprain of unspecified lesser toe(s)"
+S93509,"Unspecified sprain of unspecified toe(s)"
+S9351,"Sprain of interphalangeal joint of toe"
+S93511,"Sprain of interphalangeal joint of right great toe"
+S93512,"Sprain of interphalangeal joint of left great toe"
+S93513,"Sprain of interphalangeal joint of unspecified great toe"
+S93514,"Sprain of interphalangeal joint of right lesser toe(s)"
+S93515,"Sprain of interphalangeal joint of left lesser toe(s)"
+S93516,"Sprain of interphalangeal joint of unspecified lesser toe(s)"
+S93519,"Sprain of interphalangeal joint of unspecified toe(s)"
+S9352,"Sprain of metatarsophalangeal joint of toe"
+S93521,"Sprain of metatarsophalangeal joint of right great toe"
+S93522,"Sprain of metatarsophalangeal joint of left great toe"
+S93523,"Sprain of metatarsophalangeal joint of unspecified great toe"
+S93524,"Sprain of metatarsophalangeal joint of right lesser toe(s)"
+S93525,"Sprain of metatarsophalangeal joint of left lesser toe(s)"
+S93526,"Sprain of metatarsophalangeal joint of unspecified lesser toe(s)"
+S93529,"Sprain of metatarsophalangeal joint of unspecified toe(s)"
+S936,"Sprain of foot"
+S9360,"Unspecified sprain of foot"
+S93601,"Unspecified sprain of right foot"
+S93602,"Unspecified sprain of left foot"
+S93609,"Unspecified sprain of unspecified foot"
+S9361,"Sprain of tarsal ligament of foot"
+S93611,"Sprain of tarsal ligament of right foot"
+S93612,"Sprain of tarsal ligament of left foot"
+S93619,"Sprain of tarsal ligament of unspecified foot"
+S9362,"Sprain of tarsometatarsal ligament of foot"
+S93621,"Sprain of tarsometatarsal ligament of right foot"
+S93622,"Sprain of tarsometatarsal ligament of left foot"
+S93629,"Sprain of tarsometatarsal ligament of unspecified foot"
+S9369,"Other sprain of foot"
+S93691,"Other sprain of right foot"
+S93692,"Other sprain of left foot"
+S93699,"Other sprain of unspecified foot"
+S94,"Injury of nerves at ankle and foot level"
+S940,"Injury of lateral plantar nerve"
+S9400,"Injury of lateral plantar nerve, unspecified leg"
+S9401,"Injury of lateral plantar nerve, right leg"
+S9402,"Injury of lateral plantar nerve, left leg"
+S941,"Injury of medial plantar nerve"
+S9410,"Injury of medial plantar nerve, unspecified leg"
+S9411,"Injury of medial plantar nerve, right leg"
+S9412,"Injury of medial plantar nerve, left leg"
+S942,"Injury of deep peroneal nerve at ankle and foot level"
+S9420,"Injury of deep peroneal nerve at ankle and foot level, unspecified leg"
+S9421,"Injury of deep peroneal nerve at ankle and foot level, right leg"
+S9422,"Injury of deep peroneal nerve at ankle and foot level, left leg"
+S943,"Injury of cutaneous sensory nerve at ankle and foot level"
+S9430,"Injury of cutaneous sensory nerve at ankle and foot level, unspecified leg"
+S9431,"Injury of cutaneous sensory nerve at ankle and foot level, right leg"
+S9432,"Injury of cutaneous sensory nerve at ankle and foot level, left leg"
+S948,"Injury of other nerves at ankle and foot level"
+S948X,"Injury of other nerves at ankle and foot level"
+S948X1,"Injury of other nerves at ankle and foot level, right leg"
+S948X2,"Injury of other nerves at ankle and foot level, left leg"
+S948X9,"Injury of other nerves at ankle and foot level, unspecified leg"
+S949,"Injury of unspecified nerve at ankle and foot level"
+S9490,"Injury of unspecified nerve at ankle and foot level, unspecified leg"
+S9491,"Injury of unspecified nerve at ankle and foot level, right leg"
+S9492,"Injury of unspecified nerve at ankle and foot level, left leg"
+S95,"Injury of blood vessels at ankle and foot level"
+S950,"Injury of dorsal artery of foot"
+S9500,"Unspecified injury of dorsal artery of foot"
+S95001,"Unspecified injury of dorsal artery of right foot"
+S95002,"Unspecified injury of dorsal artery of left foot"
+S95009,"Unspecified injury of dorsal artery of unspecified foot"
+S9501,"Laceration of dorsal artery of foot"
+S95011,"Laceration of dorsal artery of right foot"
+S95012,"Laceration of dorsal artery of left foot"
+S95019,"Laceration of dorsal artery of unspecified foot"
+S9509,"Other specified injury of dorsal artery of foot"
+S95091,"Other specified injury of dorsal artery of right foot"
+S95092,"Other specified injury of dorsal artery of left foot"
+S95099,"Other specified injury of dorsal artery of unspecified foot"
+S951,"Injury of plantar artery of foot"
+S9510,"Unspecified injury of plantar artery of foot"
+S95101,"Unspecified injury of plantar artery of right foot"
+S95102,"Unspecified injury of plantar artery of left foot"
+S95109,"Unspecified injury of plantar artery of unspecified foot"
+S9511,"Laceration of plantar artery of foot"
+S95111,"Laceration of plantar artery of right foot"
+S95112,"Laceration of plantar artery of left foot"
+S95119,"Laceration of plantar artery of unspecified foot"
+S9519,"Other specified injury of plantar artery of foot"
+S95191,"Other specified injury of plantar artery of right foot"
+S95192,"Other specified injury of plantar artery of left foot"
+S95199,"Other specified injury of plantar artery of unspecified foot"
+S952,"Injury of dorsal vein of foot"
+S9520,"Unspecified injury of dorsal vein of foot"
+S95201,"Unspecified injury of dorsal vein of right foot"
+S95202,"Unspecified injury of dorsal vein of left foot"
+S95209,"Unspecified injury of dorsal vein of unspecified foot"
+S9521,"Laceration of dorsal vein of foot"
+S95211,"Laceration of dorsal vein of right foot"
+S95212,"Laceration of dorsal vein of left foot"
+S95219,"Laceration of dorsal vein of unspecified foot"
+S9529,"Other specified injury of dorsal vein of foot"
+S95291,"Other specified injury of dorsal vein of right foot"
+S95292,"Other specified injury of dorsal vein of left foot"
+S95299,"Other specified injury of dorsal vein of unspecified foot"
+S958,"Injury of other blood vessels at ankle and foot level"
+S9580,"Unspecified injury of other blood vessels at ankle and foot level"
+S95801,"Unspecified injury of other blood vessels at ankle and foot level, right leg"
+S95802,"Unspecified injury of other blood vessels at ankle and foot level, left leg"
+S95809,"Unspecified injury of other blood vessels at ankle and foot level, unspecified leg"
+S9581,"Laceration of other blood vessels at ankle and foot level"
+S95811,"Laceration of other blood vessels at ankle and foot level, right leg"
+S95812,"Laceration of other blood vessels at ankle and foot level, left leg"
+S95819,"Laceration of other blood vessels at ankle and foot level, unspecified leg"
+S9589,"Other specified injury of other blood vessels at ankle and foot level"
+S95891,"Other specified injury of other blood vessels at ankle and foot level, right leg"
+S95892,"Other specified injury of other blood vessels at ankle and foot level, left leg"
+S95899,"Other specified injury of other blood vessels at ankle and foot level, unspecified leg"
+S959,"Injury of unspecified blood vessel at ankle and foot level"
+S9590,"Unspecified injury of unspecified blood vessel at ankle and foot level"
+S95901,"Unspecified injury of unspecified blood vessel at ankle and foot level, right leg"
+S95902,"Unspecified injury of unspecified blood vessel at ankle and foot level, left leg"
+S95909,"Unspecified injury of unspecified blood vessel at ankle and foot level, unspecified leg"
+S9591,"Laceration of unspecified blood vessel at ankle and foot level"
+S95911,"Laceration of unspecified blood vessel at ankle and foot level, right leg"
+S95912,"Laceration of unspecified blood vessel at ankle and foot level, left leg"
+S95919,"Laceration of unspecified blood vessel at ankle and foot level, unspecified leg"
+S9599,"Other specified injury of unspecified blood vessel at ankle and foot level"
+S95991,"Other specified injury of unspecified blood vessel at ankle and foot level, right leg"
+S95992,"Other specified injury of unspecified blood vessel at ankle and foot level, left leg"
+S95999,"Other specified injury of unspecified blood vessel at ankle and foot level, unspecified leg"
+S96,"Injury of muscle and tendon at ankle and foot level"
+S960,"Injury of muscle and tendon of long flexor muscle of toe at ankle and foot level"
+S9600,"Unspecified injury of muscle and tendon of long flexor muscle of toe at ankle and foot level"
+S96001,"Unspecified injury of muscle and tendon of long flexor muscle of toe at ankle and foot level, right foot"
+S96002,"Unspecified injury of muscle and tendon of long flexor muscle of toe at ankle and foot level, left foot"
+S96009,"Unspecified injury of muscle and tendon of long flexor muscle of toe at ankle and foot level, unspecified foot"
+S9601,"Strain of muscle and tendon of long flexor muscle of toe at ankle and foot level"
+S96011,"Strain of muscle and tendon of long flexor muscle of toe at ankle and foot level, right foot"
+S96012,"Strain of muscle and tendon of long flexor muscle of toe at ankle and foot level, left foot"
+S96019,"Strain of muscle and tendon of long flexor muscle of toe at ankle and foot level, unspecified foot"
+S9602,"Laceration of muscle and tendon of long flexor muscle of toe at ankle and foot level"
+S96021,"Laceration of muscle and tendon of long flexor muscle of toe at ankle and foot level, right foot"
+S96022,"Laceration of muscle and tendon of long flexor muscle of toe at ankle and foot level, left foot"
+S96029,"Laceration of muscle and tendon of long flexor muscle of toe at ankle and foot level, unspecified foot"
+S9609,"Other injury of muscle and tendon of long flexor muscle of toe at ankle and foot level"
+S96091,"Other injury of muscle and tendon of long flexor muscle of toe at ankle and foot level, right foot"
+S96092,"Other injury of muscle and tendon of long flexor muscle of toe at ankle and foot level, left foot"
+S96099,"Other injury of muscle and tendon of long flexor muscle of toe at ankle and foot level, unspecified foot"
+S961,"Injury of muscle and tendon of long extensor muscle of toe at ankle and foot level"
+S9610,"Unspecified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level"
+S96101,"Unspecified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level, right foot"
+S96102,"Unspecified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level, left foot"
+S96109,"Unspecified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level, unspecified foot"
+S9611,"Strain of muscle and tendon of long extensor muscle of toe at ankle and foot level"
+S96111,"Strain of muscle and tendon of long extensor muscle of toe at ankle and foot level, right foot"
+S96112,"Strain of muscle and tendon of long extensor muscle of toe at ankle and foot level, left foot"
+S96119,"Strain of muscle and tendon of long extensor muscle of toe at ankle and foot level, unspecified foot"
+S9612,"Laceration of muscle and tendon of long extensor muscle of toe at ankle and foot level"
+S96121,"Laceration of muscle and tendon of long extensor muscle of toe at ankle and foot level, right foot"
+S96122,"Laceration of muscle and tendon of long extensor muscle of toe at ankle and foot level, left foot"
+S96129,"Laceration of muscle and tendon of long extensor muscle of toe at ankle and foot level, unspecified foot"
+S9619,"Other specified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level"
+S96191,"Other specified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level, right foot"
+S96192,"Other specified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level, left foot"
+S96199,"Other specified injury of muscle and tendon of long extensor muscle of toe at ankle and foot level, unspecified foot"
+S962,"Injury of intrinsic muscle and tendon at ankle and foot level"
+S9620,"Unspecified injury of intrinsic muscle and tendon at ankle and foot level"
+S96201,"Unspecified injury of intrinsic muscle and tendon at ankle and foot level, right foot"
+S96202,"Unspecified injury of intrinsic muscle and tendon at ankle and foot level, left foot"
+S96209,"Unspecified injury of intrinsic muscle and tendon at ankle and foot level, unspecified foot"
+S9621,"Strain of intrinsic muscle and tendon at ankle and foot level"
+S96211,"Strain of intrinsic muscle and tendon at ankle and foot level, right foot"
+S96212,"Strain of intrinsic muscle and tendon at ankle and foot level, left foot"
+S96219,"Strain of intrinsic muscle and tendon at ankle and foot level, unspecified foot"
+S9622,"Laceration of intrinsic muscle and tendon at ankle and foot level"
+S96221,"Laceration of intrinsic muscle and tendon at ankle and foot level, right foot"
+S96222,"Laceration of intrinsic muscle and tendon at ankle and foot level, left foot"
+S96229,"Laceration of intrinsic muscle and tendon at ankle and foot level, unspecified foot"
+S9629,"Other specified injury of intrinsic muscle and tendon at ankle and foot level"
+S96291,"Other specified injury of intrinsic muscle and tendon at ankle and foot level, right foot"
+S96292,"Other specified injury of intrinsic muscle and tendon at ankle and foot level, left foot"
+S96299,"Other specified injury of intrinsic muscle and tendon at ankle and foot level, unspecified foot"
+S968,"Injury of other specified muscles and tendons at ankle and foot level"
+S9680,"Unspecified injury of other specified muscles and tendons at ankle and foot level"
+S96801,"Unspecified injury of other specified muscles and tendons at ankle and foot level, right foot"
+S96802,"Unspecified injury of other specified muscles and tendons at ankle and foot level, left foot"
+S96809,"Unspecified injury of other specified muscles and tendons at ankle and foot level, unspecified foot"
+S9681,"Strain of other specified muscles and tendons at ankle and foot level"
+S96811,"Strain of other specified muscles and tendons at ankle and foot level, right foot"
+S96812,"Strain of other specified muscles and tendons at ankle and foot level, left foot"
+S96819,"Strain of other specified muscles and tendons at ankle and foot level, unspecified foot"
+S9682,"Laceration of other specified muscles and tendons at ankle and foot level"
+S96821,"Laceration of other specified muscles and tendons at ankle and foot level, right foot"
+S96822,"Laceration of other specified muscles and tendons at ankle and foot level, left foot"
+S96829,"Laceration of other specified muscles and tendons at ankle and foot level, unspecified foot"
+S9689,"Other specified injury of other specified muscles and tendons at ankle and foot level"
+S96891,"Other specified injury of other specified muscles and tendons at ankle and foot level, right foot"
+S96892,"Other specified injury of other specified muscles and tendons at ankle and foot level, left foot"
+S96899,"Other specified injury of other specified muscles and tendons at ankle and foot level, unspecified foot"
+S969,"Injury of unspecified muscle and tendon at ankle and foot level"
+S9690,"Unspecified injury of unspecified muscle and tendon at ankle and foot level"
+S96901,"Unspecified injury of unspecified muscle and tendon at ankle and foot level, right foot"
+S96902,"Unspecified injury of unspecified muscle and tendon at ankle and foot level, left foot"
+S96909,"Unspecified injury of unspecified muscle and tendon at ankle and foot level, unspecified foot"
+S9691,"Strain of unspecified muscle and tendon at ankle and foot level"
+S96911,"Strain of unspecified muscle and tendon at ankle and foot level, right foot"
+S96912,"Strain of unspecified muscle and tendon at ankle and foot level, left foot"
+S96919,"Strain of unspecified muscle and tendon at ankle and foot level, unspecified foot"
+S9692,"Laceration of unspecified muscle and tendon at ankle and foot level"
+S96921,"Laceration of unspecified muscle and tendon at ankle and foot level, right foot"
+S96922,"Laceration of unspecified muscle and tendon at ankle and foot level, left foot"
+S96929,"Laceration of unspecified muscle and tendon at ankle and foot level, unspecified foot"
+S9699,"Other specified injury of unspecified muscle and tendon at ankle and foot level"
+S96991,"Other specified injury of unspecified muscle and tendon at ankle and foot level, right foot"
+S96992,"Other specified injury of unspecified muscle and tendon at ankle and foot level, left foot"
+S96999,"Other specified injury of unspecified muscle and tendon at ankle and foot level, unspecified foot"
+S97,"Crushing injury of ankle and foot"
+S970,"Crushing injury of ankle"
+S9700,"Crushing injury of unspecified ankle"
+S9701,"Crushing injury of right ankle"
+S9702,"Crushing injury of left ankle"
+S971,"Crushing injury of toe"
+S9710,"Crushing injury of unspecified toe(s)"
+S97101,"Crushing injury of unspecified right toe(s)"
+S97102,"Crushing injury of unspecified left toe(s)"
+S97109,"Crushing injury of unspecified toe(s)"
+S9711,"Crushing injury of great toe"
+S97111,"Crushing injury of right great toe"
+S97112,"Crushing injury of left great toe"
+S97119,"Crushing injury of unspecified great toe"
+S9712,"Crushing injury of lesser toe(s)"
+S97121,"Crushing injury of right lesser toe(s)"
+S97122,"Crushing injury of left lesser toe(s)"
+S97129,"Crushing injury of unspecified lesser toe(s)"
+S978,"Crushing injury of foot"
+S9780,"Crushing injury of unspecified foot"
+S9781,"Crushing injury of right foot"
+S9782,"Crushing injury of left foot"
+S98,"Traumatic amputation of ankle and foot"
+S980,"Traumatic amputation of foot at ankle level"
+S9801,"Complete traumatic amputation of foot at ankle level"
+S98011,"Complete traumatic amputation of right foot at ankle level"
+S98012,"Complete traumatic amputation of left foot at ankle level"
+S98019,"Complete traumatic amputation of unspecified foot at ankle level"
+S9802,"Partial traumatic amputation of foot at ankle level"
+S98021,"Partial traumatic amputation of right foot at ankle level"
+S98022,"Partial traumatic amputation of left foot at ankle level"
+S98029,"Partial traumatic amputation of unspecified foot at ankle level"
+S981,"Traumatic amputation of one toe"
+S9811,"Complete traumatic amputation of great toe"
+S98111,"Complete traumatic amputation of right great toe"
+S98112,"Complete traumatic amputation of left great toe"
+S98119,"Complete traumatic amputation of unspecified great toe"
+S9812,"Partial traumatic amputation of great toe"
+S98121,"Partial traumatic amputation of right great toe"
+S98122,"Partial traumatic amputation of left great toe"
+S98129,"Partial traumatic amputation of unspecified great toe"
+S9813,"Complete traumatic amputation of one lesser toe"
+S98131,"Complete traumatic amputation of one right lesser toe"
+S98132,"Complete traumatic amputation of one left lesser toe"
+S98139,"Complete traumatic amputation of one unspecified lesser toe"
+S9814,"Partial traumatic amputation of one lesser toe"
+S98141,"Partial traumatic amputation of one right lesser toe"
+S98142,"Partial traumatic amputation of one left lesser toe"
+S98149,"Partial traumatic amputation of one unspecified lesser toe"
+S982,"Traumatic amputation of two or more lesser toes"
+S9821,"Complete traumatic amputation of two or more lesser toes"
+S98211,"Complete traumatic amputation of two or more right lesser toes"
+S98212,"Complete traumatic amputation of two or more left lesser toes"
+S98219,"Complete traumatic amputation of two or more unspecified lesser toes"
+S9822,"Partial traumatic amputation of two or more lesser toes"
+S98221,"Partial traumatic amputation of two or more right lesser toes"
+S98222,"Partial traumatic amputation of two or more left lesser toes"
+S98229,"Partial traumatic amputation of two or more unspecified lesser toes"
+S983,"Traumatic amputation of midfoot"
+S9831,"Complete traumatic amputation of midfoot"
+S98311,"Complete traumatic amputation of right midfoot"
+S98312,"Complete traumatic amputation of left midfoot"
+S98319,"Complete traumatic amputation of unspecified midfoot"
+S9832,"Partial traumatic amputation of midfoot"
+S98321,"Partial traumatic amputation of right midfoot"
+S98322,"Partial traumatic amputation of left midfoot"
+S98329,"Partial traumatic amputation of unspecified midfoot"
+S989,"Traumatic amputation of foot, level unspecified"
+S9891,"Complete traumatic amputation of foot, level unspecified"
+S98911,"Complete traumatic amputation of right foot, level unspecified"
+S98912,"Complete traumatic amputation of left foot, level unspecified"
+S98919,"Complete traumatic amputation of unspecified foot, level unspecified"
+S9892,"Partial traumatic amputation of foot, level unspecified"
+S98921,"Partial traumatic amputation of right foot, level unspecified"
+S98922,"Partial traumatic amputation of left foot, level unspecified"
+S98929,"Partial traumatic amputation of unspecified foot, level unspecified"
+S99,"Other and unspecified injuries of ankle and foot"
+S990,"Physeal fracture of calcaneus"
+S9900,"Unspecified physeal fracture of calcaneus"
+S99001,"Unspecified physeal fracture of right calcaneus"
+S99002,"Unspecified physeal fracture of left calcaneus"
+S99009,"Unspecified physeal fracture of unspecified calcaneus"
+S9901,"Salter-Harris Type I physeal fracture of calcaneus"
+S99011,"Salter-Harris Type I physeal fracture of right calcaneus"
+S99012,"Salter-Harris Type I physeal fracture of left calcaneus"
+S99019,"Salter-Harris Type I physeal fracture of unspecified calcaneus"
+S9902,"Salter-Harris Type II physeal fracture of calcaneus"
+S99021,"Salter-Harris Type II physeal fracture of right calcaneus"
+S99022,"Salter-Harris Type II physeal fracture of left calcaneus"
+S99029,"Salter-Harris Type II physeal fracture of unspecified calcaneus"
+S9903,"Salter-Harris Type III physeal fracture of calcaneus"
+S99031,"Salter-Harris Type III physeal fracture of right calcaneus"
+S99032,"Salter-Harris Type III physeal fracture of left calcaneus"
+S99039,"Salter-Harris Type III physeal fracture of unspecified calcaneus"
+S9904,"Salter-Harris Type IV physeal fracture of calcaneus"
+S99041,"Salter-Harris Type IV physeal fracture of right calcaneus"
+S99042,"Salter-Harris Type IV physeal fracture of left calcaneus"
+S99049,"Salter-Harris Type IV physeal fracture of unspecified calcaneus"
+S9909,"Other physeal fracture of calcaneus"
+S99091,"Other physeal fracture of right calcaneus"
+S99092,"Other physeal fracture of left calcaneus"
+S99099,"Other physeal fracture of unspecified calcaneus"
+S991,"Physeal fracture of metatarsal"
+S9910,"Unspecified physeal fracture of metatarsal"
+S99101,"Unspecified physeal fracture of right  metatarsal"
+S99102,"Unspecified physeal fracture of left metatarsal"
+S99109,"Unspecified physeal fracture of unspecified metatarsal"
+S9911,"Salter-Harris Type I physeal fracture of metatarsal"
+S99111,"Salter-Harris Type I physeal fracture of right metatarsal"
+S99112,"Salter-Harris Type I physeal fracture of left metatarsal"
+S99119,"Salter-Harris Type I physeal fracture of unspecified metatarsal"
+S9912,"Salter-Harris Type II physeal fracture of metatarsal"
+S99121,"Salter-Harris Type II physeal fracture of right metatarsal"
+S99122,"Salter-Harris Type II physeal fracture of left metatarsal"
+S99129,"Salter-Harris Type II physeal fracture of unspecified metatarsal"
+S9913,"Salter-Harris Type III physeal fracture of metatarsal"
+S99131,"Salter-Harris Type III physeal fracture of right metatarsal"
+S99132,"Salter-Harris Type III physeal fracture of left  metatarsal"
+S99139,"Salter-Harris Type III physeal fracture of unspecified metatarsal"
+S9914,"Salter-Harris Type IV physeal fracture of metatarsal"
+S99141,"Salter-Harris Type IV physeal fracture of right metatarsal"
+S99142,"Salter-Harris Type IV physeal fracture of left metatarsal"
+S99149,"Salter-Harris Type IV physeal fracture of unspecified metatarsal"
+S9919,"Other physeal fracture of metatarsal"
+S99191,"Other physeal fracture of right metatarsal"
+S99192,"Other physeal fracture of left metatarsal"
+S99199,"Other physeal fracture of unspecified metatarsal"
+S992,"Physeal fracture of phalanx of toe"
+S9920,"Unspecified physeal fracture of phalanx of toe"
+S99201,"Unspecified physeal fracture of phalanx of right toe"
+S99202,"Unspecified physeal fracture of phalanx of left toe"
+S99209,"Unspecified physeal fracture of phalanx of unspecified toe"
+S9921,"Salter-Harris Type I physeal fracture of phalanx of toe"
+S99211,"Salter-Harris Type I physeal fracture of phalanx of right toe"
+S99212,"Salter-Harris Type I physeal fracture of phalanx of left toe"
+S99219,"Salter-Harris Type I physeal fracture of phalanx of unspecified toe"
+S9922,"Salter-Harris Type II physeal fracture of phalanx of toe"
+S99221,"Salter-Harris Type II physeal fracture of phalanx of right toe"
+S99222,"Salter-Harris Type II physeal fracture of phalanx of left toe"
+S99229,"Salter-Harris Type II physeal fracture of phalanx of unspecified toe"
+S9923,"Salter-Harris Type III physeal fracture of phalanx of toe"
+S99231,"Salter-Harris Type III physeal fracture of phalanx of right toe"
+S99232,"Salter-Harris Type III physeal fracture of phalanx of left toe"
+S99239,"Salter-Harris Type III physeal fracture of phalanx of unspecified toe"
+S9924,"Salter-Harris Type IV physeal fracture of phalanx of toe"
+S99241,"Salter-Harris Type IV physeal fracture of phalanx of right toe"
+S99242,"Salter-Harris Type IV physeal fracture of phalanx of left toe"
+S99249,"Salter-Harris Type IV physeal fracture of phalanx of unspecified toe"
+S9929,"Other physeal fracture of phalanx of toe"
+S99291,"Other physeal fracture of phalanx of right toe"
+S99292,"Other physeal fracture of phalanx of left toe"
+S99299,"Other physeal fracture of phalanx of unspecified toe"
+S998,"Other specified injuries of ankle and foot"
+S9981,"Other specified injuries of ankle"
+S99811,"Other specified injuries of right ankle"
+S99812,"Other specified injuries of left ankle"
+S99819,"Other specified injuries of unspecified ankle"
+S9982,"Other specified injuries of foot"
+S99821,"Other specified injuries of right foot"
+S99822,"Other specified injuries of left foot"
+S99829,"Other specified injuries of unspecified foot"
+S999,"Unspecified injury of ankle and foot"
+S9991,"Unspecified injury of ankle"
+S99911,"Unspecified injury of right ankle"
+S99912,"Unspecified injury of left ankle"
+S99919,"Unspecified injury of unspecified ankle"
+S9992,"Unspecified injury of foot"
+S99921,"Unspecified injury of right foot"
+S99922,"Unspecified injury of left foot"
+S99929,"Unspecified injury of unspecified foot"
+T07,"Unspecified multiple injuries"
+T14,"Injury of unspecified body region"
+T148,"Other injury of unspecified body region"
+T149,"Unspecified injury"
+T1490,"Injury, unspecified"
+T1491,"Suicide attempt"
+T15,"Foreign body on external eye"
+T150,"Foreign body in cornea"
+T1500,"Foreign body in cornea, unspecified eye"
+T1501,"Foreign body in cornea, right eye"
+T1502,"Foreign body in cornea, left eye"
+T151,"Foreign body in conjunctival sac"
+T1510,"Foreign body in conjunctival sac, unspecified eye"
+T1511,"Foreign body in conjunctival sac, right eye"
+T1512,"Foreign body in conjunctival sac, left eye"
+T158,"Foreign body in other and multiple parts of external eye"
+T1580,"Foreign body in other and multiple parts of external eye, unspecified eye"
+T1581,"Foreign body in other and multiple parts of external eye, right eye"
+T1582,"Foreign body in other and multiple parts of external eye, left eye"
+T159,"Foreign body on external eye, part unspecified"
+T1590,"Foreign body on external eye, part unspecified, unspecified eye"
+T1591,"Foreign body on external eye, part unspecified, right eye"
+T1592,"Foreign body on external eye, part unspecified, left eye"
+T16,"Foreign body in ear"
+T161,"Foreign body in right ear"
+T162,"Foreign body in left ear"
+T169,"Foreign body in ear, unspecified ear"
+T17,"Foreign body in respiratory tract"
+T170,"Foreign body in nasal sinus"
+T171,"Foreign body in nostril"
+T172,"Foreign body in pharynx"
+T1720,"Unspecified foreign body in pharynx"
+T17200,"Unspecified foreign body in pharynx causing asphyxiation"
+T17208,"Unspecified foreign body in pharynx causing other injury"
+T1721,"Gastric contents in pharynx"
+T17210,"Gastric contents in pharynx causing asphyxiation"
+T17218,"Gastric contents in pharynx causing other injury"
+T1722,"Food in pharynx"
+T17220,"Food in pharynx causing asphyxiation"
+T17228,"Food in pharynx causing other injury"
+T1729,"Other foreign object in pharynx"
+T17290,"Other foreign object in pharynx causing asphyxiation"
+T17298,"Other foreign object in pharynx causing other injury"
+T173,"Foreign body in larynx"
+T1730,"Unspecified foreign body in larynx"
+T17300,"Unspecified foreign body in larynx causing asphyxiation"
+T17308,"Unspecified foreign body in larynx causing other injury"
+T1731,"Gastric contents in larynx"
+T17310,"Gastric contents in larynx causing asphyxiation"
+T17318,"Gastric contents in larynx causing other injury"
+T1732,"Food in larynx"
+T17320,"Food in larynx causing asphyxiation"
+T17328,"Food in larynx causing other injury"
+T1739,"Other foreign object in larynx"
+T17390,"Other foreign object in larynx causing asphyxiation"
+T17398,"Other foreign object in larynx causing other injury"
+T174,"Foreign body in trachea"
+T1740,"Unspecified foreign body in trachea"
+T17400,"Unspecified foreign body in trachea causing asphyxiation"
+T17408,"Unspecified foreign body in trachea causing other injury"
+T1741,"Gastric contents in trachea"
+T17410,"Gastric contents in trachea causing asphyxiation"
+T17418,"Gastric contents in trachea causing other injury"
+T1742,"Food in trachea"
+T17420,"Food in trachea causing asphyxiation"
+T17428,"Food in trachea causing other injury"
+T1749,"Other foreign object in trachea"
+T17490,"Other foreign object in trachea causing asphyxiation"
+T17498,"Other foreign object in trachea causing other injury"
+T175,"Foreign body in bronchus"
+T1750,"Unspecified foreign body in bronchus"
+T17500,"Unspecified foreign body in bronchus causing asphyxiation"
+T17508,"Unspecified foreign body in bronchus causing other injury"
+T1751,"Gastric contents in bronchus"
+T17510,"Gastric contents in bronchus causing asphyxiation"
+T17518,"Gastric contents in bronchus causing other injury"
+T1752,"Food in bronchus"
+T17520,"Food in bronchus causing asphyxiation"
+T17528,"Food in bronchus causing other injury"
+T1759,"Other foreign object in bronchus"
+T17590,"Other foreign object in bronchus causing asphyxiation"
+T17598,"Other foreign object in bronchus causing other injury"
+T178,"Foreign body in other parts of respiratory tract"
+T1780,"Unspecified foreign body in other parts of respiratory tract"
+T17800,"Unspecified foreign body in other parts of respiratory tract causing asphyxiation"
+T17808,"Unspecified foreign body in other parts of respiratory tract causing other injury"
+T1781,"Gastric contents in other parts of respiratory tract"
+T17810,"Gastric contents in other parts of respiratory tract causing asphyxiation"
+T17818,"Gastric contents in other parts of respiratory tract causing other injury"
+T1782,"Food in other parts of respiratory tract"
+T17820,"Food in other parts of respiratory tract causing asphyxiation"
+T17828,"Food in other parts of respiratory tract causing other injury"
+T1789,"Other foreign object in other parts of respiratory tract"
+T17890,"Other foreign object in other parts of respiratory tract causing asphyxiation"
+T17898,"Other foreign object in other parts of respiratory tract causing other injury"
+T179,"Foreign body in respiratory tract, part unspecified"
+T1790,"Unspecified foreign body in respiratory tract, part unspecified"
+T17900,"Unspecified foreign body in respiratory tract, part unspecified causing asphyxiation"
+T17908,"Unspecified foreign body in respiratory tract, part unspecified causing other injury"
+T1791,"Gastric contents in respiratory tract, part unspecified"
+T17910,"Gastric contents in respiratory tract, part unspecified causing asphyxiation"
+T17918,"Gastric contents in respiratory tract, part unspecified causing other injury"
+T1792,"Food in respiratory tract, part unspecified"
+T17920,"Food in respiratory tract, part unspecified causing asphyxiation"
+T17928,"Food in respiratory tract, part unspecified causing other injury"
+T1799,"Other foreign object in respiratory tract, part unspecified"
+T17990,"Other foreign object in respiratory tract, part unspecified in causing asphyxiation"
+T17998,"Other foreign object in respiratory tract, part unspecified causing other injury"
+T18,"Foreign body in alimentary tract"
+T180,"Foreign body in mouth"
+T181,"Foreign body in esophagus"
+T1810,"Unspecified foreign body in esophagus"
+T18100,"Unspecified foreign body in esophagus causing compression of trachea"
+T18108,"Unspecified foreign body in esophagus causing other injury"
+T1811,"Gastric contents in esophagus"
+T18110,"Gastric contents in esophagus causing compression of trachea"
+T18118,"Gastric contents in esophagus causing other injury"
+T1812,"Food in esophagus"
+T18120,"Food in esophagus causing compression of trachea"
+T18128,"Food in esophagus causing other injury"
+T1819,"Other foreign object in esophagus"
+T18190,"Other foreign object in esophagus causing compression of trachea"
+T18198,"Other foreign object in esophagus causing other injury"
+T182,"Foreign body in stomach"
+T183,"Foreign body in small intestine"
+T184,"Foreign body in colon"
+T185,"Foreign body in anus and rectum"
+T188,"Foreign body in other parts of alimentary tract"
+T189,"Foreign body of alimentary tract, part unspecified"
+T19,"Foreign body in genitourinary tract"
+T190,"Foreign body in urethra"
+T191,"Foreign body in bladder"
+T192,"Foreign body in vulva and vagina"
+T193,"Foreign body in uterus"
+T194,"Foreign body in penis"
+T198,"Foreign body in other parts of genitourinary tract"
+T199,"Foreign body in genitourinary tract, part unspecified"
+T20,"Burn and corrosion of head, face, and neck"
+T200,"Burn of unspecified degree of head, face, and neck"
+T2000,"Burn of unspecified degree of head, face, and neck, unspecified site"
+T2001,"Burn of unspecified degree of ear [any part, except ear drum]"
+T20011,"Burn of unspecified degree of right ear [any part, except ear drum]"
+T20012,"Burn of unspecified degree of left ear [any part, except ear drum]"
+T20019,"Burn of unspecified degree of unspecified ear [any part, except ear drum]"
+T2002,"Burn of unspecified degree of lip(s)"
+T2003,"Burn of unspecified degree of chin"
+T2004,"Burn of unspecified degree of nose (septum)"
+T2005,"Burn of unspecified degree of scalp [any part]"
+T2006,"Burn of unspecified degree of forehead and cheek"
+T2007,"Burn of unspecified degree of neck"
+T2009,"Burn of unspecified degree of multiple sites of head, face, and neck"
+T201,"Burn of first degree of head, face, and neck"
+T2010,"Burn of first degree of head, face, and neck, unspecified site"
+T2011,"Burn of first degree of ear [any part, except ear drum]"
+T20111,"Burn of first degree of right ear [any part, except ear drum]"
+T20112,"Burn of first degree of left ear [any part, except ear drum]"
+T20119,"Burn of first degree of unspecified ear [any part, except ear drum]"
+T2012,"Burn of first degree of lip(s)"
+T2013,"Burn of first degree of chin"
+T2014,"Burn of first degree of nose (septum)"
+T2015,"Burn of first degree of scalp [any part]"
+T2016,"Burn of first degree of forehead and cheek"
+T2017,"Burn of first degree of neck"
+T2019,"Burn of first degree of multiple sites of head, face, and neck"
+T202,"Burn of second degree of head, face, and neck"
+T2020,"Burn of second degree of head, face, and neck, unspecified site"
+T2021,"Burn of second degree of ear [any part, except ear drum]"
+T20211,"Burn of second degree of right ear [any part, except ear drum]"
+T20212,"Burn of second degree of left ear [any part, except ear drum]"
+T20219,"Burn of second degree of unspecified ear [any part, except ear drum]"
+T2022,"Burn of second degree of lip(s)"
+T2023,"Burn of second degree of chin"
+T2024,"Burn of second degree of nose (septum)"
+T2025,"Burn of second degree of scalp [any part]"
+T2026,"Burn of second degree of forehead and cheek"
+T2027,"Burn of second degree of neck"
+T2029,"Burn of second degree of multiple sites of head, face, and neck"
+T203,"Burn of third degree of head, face, and neck"
+T2030,"Burn of third degree of head, face, and neck, unspecified site"
+T2031,"Burn of third degree of ear [any part, except ear drum]"
+T20311,"Burn of third degree of right ear [any part, except ear drum]"
+T20312,"Burn of third degree of left ear [any part, except ear drum]"
+T20319,"Burn of third degree of unspecified ear [any part, except ear drum]"
+T2032,"Burn of third degree of lip(s)"
+T2033,"Burn of third degree of chin"
+T2034,"Burn of third degree of nose (septum)"
+T2035,"Burn of third degree of scalp [any part]"
+T2036,"Burn of third degree of forehead and cheek"
+T2037,"Burn of third degree of neck"
+T2039,"Burn of third degree of multiple sites of head, face, and neck"
+T204,"Corrosion of unspecified degree of head, face, and neck"
+T2040,"Corrosion of unspecified degree of head, face, and neck, unspecified site"
+T2041,"Corrosion of unspecified degree of ear [any part, except ear drum]"
+T20411,"Corrosion of unspecified degree of right ear [any part, except ear drum]"
+T20412,"Corrosion of unspecified degree of left ear [any part, except ear drum]"
+T20419,"Corrosion of unspecified degree of unspecified ear [any part, except ear drum]"
+T2042,"Corrosion of unspecified degree of lip(s)"
+T2043,"Corrosion of unspecified degree of chin"
+T2044,"Corrosion of unspecified degree of nose (septum)"
+T2045,"Corrosion of unspecified degree of scalp [any part]"
+T2046,"Corrosion of unspecified degree of forehead and cheek"
+T2047,"Corrosion of unspecified degree of neck"
+T2049,"Corrosion of unspecified degree of multiple sites of head, face, and neck"
+T205,"Corrosion of first degree of head, face, and neck"
+T2050,"Corrosion of first degree of head, face, and neck, unspecified site"
+T2051,"Corrosion of first degree of ear [any part, except ear drum]"
+T20511,"Corrosion of first degree of right ear [any part, except ear drum]"
+T20512,"Corrosion of first degree of left ear [any part, except ear drum]"
+T20519,"Corrosion of first degree of unspecified ear [any part, except ear drum]"
+T2052,"Corrosion of first degree of lip(s)"
+T2053,"Corrosion of first degree of chin"
+T2054,"Corrosion of first degree of nose (septum)"
+T2055,"Corrosion of first degree of scalp [any part]"
+T2056,"Corrosion of first degree of forehead and cheek"
+T2057,"Corrosion of first degree of neck"
+T2059,"Corrosion of first degree of multiple sites of head, face, and neck"
+T206,"Corrosion of second degree of head, face, and neck"
+T2060,"Corrosion of second degree of head, face, and neck, unspecified site"
+T2061,"Corrosion of second degree of ear [any part, except ear drum]"
+T20611,"Corrosion of second degree of right ear [any part, except ear drum]"
+T20612,"Corrosion of second degree of left ear [any part, except ear drum]"
+T20619,"Corrosion of second degree of unspecified ear [any part, except ear drum]"
+T2062,"Corrosion of second degree of lip(s)"
+T2063,"Corrosion of second degree of chin"
+T2064,"Corrosion of second degree of nose (septum)"
+T2065,"Corrosion of second degree of scalp [any part]"
+T2066,"Corrosion of second degree of forehead and cheek"
+T2067,"Corrosion of second degree of neck"
+T2069,"Corrosion of second degree of multiple sites of head, face, and neck"
+T207,"Corrosion of third degree of head, face, and neck"
+T2070,"Corrosion of third degree of head, face, and neck, unspecified site"
+T2071,"Corrosion of third degree of ear [any part, except ear drum]"
+T20711,"Corrosion of third degree of right ear [any part, except ear drum]"
+T20712,"Corrosion of third degree of left ear [any part, except ear drum]"
+T20719,"Corrosion of third degree of unspecified ear [any part, except ear drum]"
+T2072,"Corrosion of third degree of lip(s)"
+T2073,"Corrosion of third degree of chin"
+T2074,"Corrosion of third degree of nose (septum)"
+T2075,"Corrosion of third degree of scalp [any part]"
+T2076,"Corrosion of third degree of forehead and cheek"
+T2077,"Corrosion of third degree of neck"
+T2079,"Corrosion of third degree of multiple sites of head, face, and neck"
+T21,"Burn and corrosion of trunk"
+T210,"Burn of unspecified degree of trunk"
+T2100,"Burn of unspecified degree of trunk, unspecified site"
+T2101,"Burn of unspecified degree of chest wall"
+T2102,"Burn of unspecified degree of abdominal wall"
+T2103,"Burn of unspecified degree of upper back"
+T2104,"Burn of unspecified degree of lower back"
+T2105,"Burn of unspecified degree of buttock"
+T2106,"Burn of unspecified degree of male genital region"
+T2107,"Burn of unspecified degree of female genital region"
+T2109,"Burn of unspecified degree of other site of trunk"
+T211,"Burn of first degree of trunk"
+T2110,"Burn of first degree of trunk, unspecified site"
+T2111,"Burn of first degree of chest wall"
+T2112,"Burn of first degree of abdominal wall"
+T2113,"Burn of first degree of upper back"
+T2114,"Burn of first degree of lower back"
+T2115,"Burn of first degree of buttock"
+T2116,"Burn of first degree of male genital region"
+T2117,"Burn of first degree of female genital region"
+T2119,"Burn of first degree of other site of trunk"
+T212,"Burn of second degree of trunk"
+T2120,"Burn of second degree of trunk, unspecified site"
+T2121,"Burn of second degree of chest wall"
+T2122,"Burn of second degree of abdominal wall"
+T2123,"Burn of second degree of upper back"
+T2124,"Burn of second degree of lower back"
+T2125,"Burn of second degree of buttock"
+T2126,"Burn of second degree of male genital region"
+T2127,"Burn of second degree of female genital region"
+T2129,"Burn of second degree of other site of trunk"
+T213,"Burn of third degree of trunk"
+T2130,"Burn of third degree of trunk, unspecified site"
+T2131,"Burn of third degree of chest wall"
+T2132,"Burn of third degree of abdominal wall"
+T2133,"Burn of third degree of upper back"
+T2134,"Burn of third degree of lower back"
+T2135,"Burn of third degree of buttock"
+T2136,"Burn of third degree of male genital region"
+T2137,"Burn of third degree of female genital region"
+T2139,"Burn of third degree of other site of trunk"
+T214,"Corrosion of unspecified degree of trunk"
+T2140,"Corrosion of unspecified degree of trunk, unspecified site"
+T2141,"Corrosion of unspecified degree of chest wall"
+T2142,"Corrosion of unspecified degree of abdominal wall"
+T2143,"Corrosion of unspecified degree of upper back"
+T2144,"Corrosion of unspecified degree of lower back"
+T2145,"Corrosion of unspecified degree of buttock"
+T2146,"Corrosion of unspecified degree of male genital region"
+T2147,"Corrosion of unspecified degree of female genital region"
+T2149,"Corrosion of unspecified degree of other site of trunk"
+T215,"Corrosion of first degree of trunk"
+T2150,"Corrosion of first degree of trunk, unspecified site"
+T2151,"Corrosion of first degree of chest wall"
+T2152,"Corrosion of first degree of abdominal wall"
+T2153,"Corrosion of first degree of upper back"
+T2154,"Corrosion of first degree of lower back"
+T2155,"Corrosion of first degree of buttock"
+T2156,"Corrosion of first degree of male genital region"
+T2157,"Corrosion of first degree of female genital region"
+T2159,"Corrosion of first degree of other site of trunk"
+T216,"Corrosion of second degree of trunk"
+T2160,"Corrosion of second degree of trunk, unspecified site"
+T2161,"Corrosion of second degree of chest wall"
+T2162,"Corrosion of second degree of abdominal wall"
+T2163,"Corrosion of second degree of upper back"
+T2164,"Corrosion of second degree of lower back"
+T2165,"Corrosion of second degree of buttock"
+T2166,"Corrosion of second degree of male genital region"
+T2167,"Corrosion of second degree of female genital region"
+T2169,"Corrosion of second degree of other site of trunk"
+T217,"Corrosion of third degree of trunk"
+T2170,"Corrosion of third degree of trunk, unspecified site"
+T2171,"Corrosion of third degree of chest wall"
+T2172,"Corrosion of third degree of abdominal wall"
+T2173,"Corrosion of third degree of upper back"
+T2174,"Corrosion of third degree of lower back"
+T2175,"Corrosion of third degree of buttock"
+T2176,"Corrosion of third degree of male genital region"
+T2177,"Corrosion of third degree of female genital region"
+T2179,"Corrosion of third degree of other site of trunk"
+T22,"Burn and corrosion of shoulder and upper limb, except wrist and hand"
+T220,"Burn of unspecified degree of shoulder and upper limb, except wrist and hand"
+T2200,"Burn of unspecified degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2201,"Burn of unspecified degree of forearm"
+T22011,"Burn of unspecified degree of right forearm"
+T22012,"Burn of unspecified degree of left forearm"
+T22019,"Burn of unspecified degree of unspecified forearm"
+T2202,"Burn of unspecified degree of elbow"
+T22021,"Burn of unspecified degree of right elbow"
+T22022,"Burn of unspecified degree of left elbow"
+T22029,"Burn of unspecified degree of unspecified elbow"
+T2203,"Burn of unspecified degree of upper arm"
+T22031,"Burn of unspecified degree of right upper arm"
+T22032,"Burn of unspecified degree of left upper arm"
+T22039,"Burn of unspecified degree of unspecified upper arm"
+T2204,"Burn of unspecified degree of axilla"
+T22041,"Burn of unspecified degree of right axilla"
+T22042,"Burn of unspecified degree of left axilla"
+T22049,"Burn of unspecified degree of unspecified axilla"
+T2205,"Burn of unspecified degree of shoulder"
+T22051,"Burn of unspecified degree of right shoulder"
+T22052,"Burn of unspecified degree of left shoulder"
+T22059,"Burn of unspecified degree of unspecified shoulder"
+T2206,"Burn of unspecified degree of scapular region"
+T22061,"Burn of unspecified degree of right scapular region"
+T22062,"Burn of unspecified degree of left scapular region"
+T22069,"Burn of unspecified degree of unspecified scapular region"
+T2209,"Burn of unspecified degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22091,"Burn of unspecified degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22092,"Burn of unspecified degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22099,"Burn of unspecified degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T221,"Burn of first degree of shoulder and upper limb, except wrist and hand"
+T2210,"Burn of first degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2211,"Burn of first degree of forearm"
+T22111,"Burn of first degree of right forearm"
+T22112,"Burn of first degree of left forearm"
+T22119,"Burn of first degree of unspecified forearm"
+T2212,"Burn of first degree of elbow"
+T22121,"Burn of first degree of right elbow"
+T22122,"Burn of first degree of left elbow"
+T22129,"Burn of first degree of unspecified elbow"
+T2213,"Burn of first degree of upper arm"
+T22131,"Burn of first degree of right upper arm"
+T22132,"Burn of first degree of left upper arm"
+T22139,"Burn of first degree of unspecified upper arm"
+T2214,"Burn of first degree of axilla"
+T22141,"Burn of first degree of right axilla"
+T22142,"Burn of first degree of left axilla"
+T22149,"Burn of first degree of unspecified axilla"
+T2215,"Burn of first degree of shoulder"
+T22151,"Burn of first degree of right shoulder"
+T22152,"Burn of first degree of left shoulder"
+T22159,"Burn of first degree of unspecified shoulder"
+T2216,"Burn of first degree of scapular region"
+T22161,"Burn of first degree of right scapular region"
+T22162,"Burn of first degree of left scapular region"
+T22169,"Burn of first degree of unspecified scapular region"
+T2219,"Burn of first degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22191,"Burn of first degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22192,"Burn of first degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22199,"Burn of first degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T222,"Burn of second degree of shoulder and upper limb, except wrist and hand"
+T2220,"Burn of second degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2221,"Burn of second degree of forearm"
+T22211,"Burn of second degree of right forearm"
+T22212,"Burn of second degree of left forearm"
+T22219,"Burn of second degree of unspecified forearm"
+T2222,"Burn of second degree of elbow"
+T22221,"Burn of second degree of right elbow"
+T22222,"Burn of second degree of left elbow"
+T22229,"Burn of second degree of unspecified elbow"
+T2223,"Burn of second degree of upper arm"
+T22231,"Burn of second degree of right upper arm"
+T22232,"Burn of second degree of left upper arm"
+T22239,"Burn of second degree of unspecified upper arm"
+T2224,"Burn of second degree of axilla"
+T22241,"Burn of second degree of right axilla"
+T22242,"Burn of second degree of left axilla"
+T22249,"Burn of second degree of unspecified axilla"
+T2225,"Burn of second degree of shoulder"
+T22251,"Burn of second degree of right shoulder"
+T22252,"Burn of second degree of left shoulder"
+T22259,"Burn of second degree of unspecified shoulder"
+T2226,"Burn of second degree of scapular region"
+T22261,"Burn of second degree of right scapular region"
+T22262,"Burn of second degree of left scapular region"
+T22269,"Burn of second degree of unspecified scapular region"
+T2229,"Burn of second degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22291,"Burn of second degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22292,"Burn of second degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22299,"Burn of second degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T223,"Burn of third degree of shoulder and upper limb, except wrist and hand"
+T2230,"Burn of third degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2231,"Burn of third degree of forearm"
+T22311,"Burn of third degree of right forearm"
+T22312,"Burn of third degree of left forearm"
+T22319,"Burn of third degree of unspecified forearm"
+T2232,"Burn of third degree of elbow"
+T22321,"Burn of third degree of right elbow"
+T22322,"Burn of third degree of left elbow"
+T22329,"Burn of third degree of unspecified elbow"
+T2233,"Burn of third degree of upper arm"
+T22331,"Burn of third degree of right upper arm"
+T22332,"Burn of third degree of left upper arm"
+T22339,"Burn of third degree of unspecified upper arm"
+T2234,"Burn of third degree of axilla"
+T22341,"Burn of third degree of right axilla"
+T22342,"Burn of third degree of left axilla"
+T22349,"Burn of third degree of unspecified axilla"
+T2235,"Burn of third degree of shoulder"
+T22351,"Burn of third degree of right shoulder"
+T22352,"Burn of third degree of left shoulder"
+T22359,"Burn of third degree of unspecified shoulder"
+T2236,"Burn of third degree of scapular region"
+T22361,"Burn of third degree of right scapular region"
+T22362,"Burn of third degree of left scapular region"
+T22369,"Burn of third degree of unspecified scapular region"
+T2239,"Burn of third degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22391,"Burn of third degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22392,"Burn of third degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22399,"Burn of third degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T224,"Corrosion of unspecified degree of shoulder and upper limb, except wrist and hand"
+T2240,"Corrosion of unspecified degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2241,"Corrosion of unspecified degree of forearm"
+T22411,"Corrosion of unspecified degree of right forearm"
+T22412,"Corrosion of unspecified degree of left forearm"
+T22419,"Corrosion of unspecified degree of unspecified forearm"
+T2242,"Corrosion of unspecified degree of elbow"
+T22421,"Corrosion of unspecified degree of right elbow"
+T22422,"Corrosion of unspecified degree of left elbow"
+T22429,"Corrosion of unspecified degree of unspecified elbow"
+T2243,"Corrosion of unspecified degree of upper arm"
+T22431,"Corrosion of unspecified degree of right upper arm"
+T22432,"Corrosion of unspecified degree of left upper arm"
+T22439,"Corrosion of unspecified degree of unspecified upper arm"
+T2244,"Corrosion of unspecified degree of axilla"
+T22441,"Corrosion of unspecified degree of right axilla"
+T22442,"Corrosion of unspecified degree of left axilla"
+T22449,"Corrosion of unspecified degree of unspecified axilla"
+T2245,"Corrosion of unspecified degree of shoulder"
+T22451,"Corrosion of unspecified degree of right shoulder"
+T22452,"Corrosion of unspecified degree of left shoulder"
+T22459,"Corrosion of unspecified degree of unspecified shoulder"
+T2246,"Corrosion of unspecified degree of scapular region"
+T22461,"Corrosion of unspecified degree of right scapular region"
+T22462,"Corrosion of unspecified degree of left scapular region"
+T22469,"Corrosion of unspecified degree of unspecified scapular region"
+T2249,"Corrosion of unspecified degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22491,"Corrosion of unspecified degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22492,"Corrosion of unspecified degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22499,"Corrosion of unspecified degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T225,"Corrosion of first degree of shoulder and upper limb, except wrist and hand"
+T2250,"Corrosion of first degree of shoulder and upper limb, except wrist and hand unspecified site"
+T2251,"Corrosion of first degree of forearm"
+T22511,"Corrosion of first degree of right forearm"
+T22512,"Corrosion of first degree of left forearm"
+T22519,"Corrosion of first degree of unspecified forearm"
+T2252,"Corrosion of first degree of elbow"
+T22521,"Corrosion of first degree of right elbow"
+T22522,"Corrosion of first degree of left elbow"
+T22529,"Corrosion of first degree of unspecified elbow"
+T2253,"Corrosion of first degree of upper arm"
+T22531,"Corrosion of first degree of right upper arm"
+T22532,"Corrosion of first degree of left upper arm"
+T22539,"Corrosion of first degree of unspecified upper arm"
+T2254,"Corrosion of first degree of axilla"
+T22541,"Corrosion of first degree of right axilla"
+T22542,"Corrosion of first degree of left axilla"
+T22549,"Corrosion of first degree of unspecified axilla"
+T2255,"Corrosion of first degree of shoulder"
+T22551,"Corrosion of first degree of right shoulder"
+T22552,"Corrosion of first degree of left shoulder"
+T22559,"Corrosion of first degree of unspecified shoulder"
+T2256,"Corrosion of first degree of scapular region"
+T22561,"Corrosion of first degree of right scapular region"
+T22562,"Corrosion of first degree of left scapular region"
+T22569,"Corrosion of first degree of unspecified scapular region"
+T2259,"Corrosion of first degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22591,"Corrosion of first degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22592,"Corrosion of first degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22599,"Corrosion of first degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T226,"Corrosion of second degree of shoulder and upper limb, except wrist and hand"
+T2260,"Corrosion of second degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2261,"Corrosion of second degree of forearm"
+T22611,"Corrosion of second degree of right forearm"
+T22612,"Corrosion of second degree of left forearm"
+T22619,"Corrosion of second degree of unspecified forearm"
+T2262,"Corrosion of second degree of elbow"
+T22621,"Corrosion of second degree of right elbow"
+T22622,"Corrosion of second degree of left elbow"
+T22629,"Corrosion of second degree of unspecified elbow"
+T2263,"Corrosion of second degree of upper arm"
+T22631,"Corrosion of second degree of right upper arm"
+T22632,"Corrosion of second degree of left upper arm"
+T22639,"Corrosion of second degree of unspecified upper arm"
+T2264,"Corrosion of second degree of axilla"
+T22641,"Corrosion of second degree of right axilla"
+T22642,"Corrosion of second degree of left axilla"
+T22649,"Corrosion of second degree of unspecified axilla"
+T2265,"Corrosion of second degree of shoulder"
+T22651,"Corrosion of second degree of right shoulder"
+T22652,"Corrosion of second degree of left shoulder"
+T22659,"Corrosion of second degree of unspecified shoulder"
+T2266,"Corrosion of second degree of scapular region"
+T22661,"Corrosion of second degree of right scapular region"
+T22662,"Corrosion of second degree of left scapular region"
+T22669,"Corrosion of second degree of unspecified scapular region"
+T2269,"Corrosion of second degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22691,"Corrosion of second degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22692,"Corrosion of second degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22699,"Corrosion of second degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T227,"Corrosion of third degree of shoulder and upper limb, except wrist and hand"
+T2270,"Corrosion of third degree of shoulder and upper limb, except wrist and hand, unspecified site"
+T2271,"Corrosion of third degree of forearm"
+T22711,"Corrosion of third degree of right forearm"
+T22712,"Corrosion of third degree of left forearm"
+T22719,"Corrosion of third degree of unspecified forearm"
+T2272,"Corrosion of third degree of elbow"
+T22721,"Corrosion of third degree of right elbow"
+T22722,"Corrosion of third degree of left elbow"
+T22729,"Corrosion of third degree of unspecified elbow"
+T2273,"Corrosion of third degree of upper arm"
+T22731,"Corrosion of third degree of right upper arm"
+T22732,"Corrosion of third degree of left upper arm"
+T22739,"Corrosion of third degree of unspecified upper arm"
+T2274,"Corrosion of third degree of axilla"
+T22741,"Corrosion of third degree of right axilla"
+T22742,"Corrosion of third degree of left axilla"
+T22749,"Corrosion of third degree of unspecified axilla"
+T2275,"Corrosion of third degree of shoulder"
+T22751,"Corrosion of third degree of right shoulder"
+T22752,"Corrosion of third degree of left shoulder"
+T22759,"Corrosion of third degree of unspecified shoulder"
+T2276,"Corrosion of third degree of scapular region"
+T22761,"Corrosion of third degree of right scapular region"
+T22762,"Corrosion of third degree of left scapular region"
+T22769,"Corrosion of third degree of unspecified scapular region"
+T2279,"Corrosion of third degree of multiple sites of shoulder and upper limb, except wrist and hand"
+T22791,"Corrosion of third degree of multiple sites of right shoulder and upper limb, except wrist and hand"
+T22792,"Corrosion of third degree of multiple sites of left shoulder and upper limb, except wrist and hand"
+T22799,"Corrosion of third degree of multiple sites of unspecified shoulder and upper limb, except wrist and hand"
+T23,"Burn and corrosion of wrist and hand"
+T230,"Burn of unspecified degree of wrist and hand"
+T2300,"Burn of unspecified degree of hand, unspecified site"
+T23001,"Burn of unspecified degree of right hand, unspecified site"
+T23002,"Burn of unspecified degree of left hand, unspecified site"
+T23009,"Burn of unspecified degree of unspecified hand, unspecified site"
+T2301,"Burn of unspecified degree of thumb (nail)"
+T23011,"Burn of unspecified degree of right thumb (nail)"
+T23012,"Burn of unspecified degree of left thumb (nail)"
+T23019,"Burn of unspecified degree of unspecified thumb (nail)"
+T2302,"Burn of unspecified degree of single finger (nail) except thumb"
+T23021,"Burn of unspecified degree of single right finger (nail) except thumb"
+T23022,"Burn of unspecified degree of single left finger (nail) except thumb"
+T23029,"Burn of unspecified degree of unspecified single finger (nail) except thumb"
+T2303,"Burn of unspecified degree of multiple fingers (nail), not including thumb"
+T23031,"Burn of unspecified degree of multiple right fingers (nail), not including thumb"
+T23032,"Burn of unspecified degree of multiple left fingers (nail), not including thumb"
+T23039,"Burn of unspecified degree of unspecified multiple fingers (nail), not including thumb"
+T2304,"Burn of unspecified degree of multiple fingers (nail), including thumb"
+T23041,"Burn of unspecified degree of multiple right fingers (nail), including thumb"
+T23042,"Burn of unspecified degree of multiple left fingers (nail), including thumb"
+T23049,"Burn of unspecified degree of unspecified multiple fingers (nail), including thumb"
+T2305,"Burn of unspecified degree of palm"
+T23051,"Burn of unspecified degree of right palm"
+T23052,"Burn of unspecified degree of left palm"
+T23059,"Burn of unspecified degree of unspecified palm"
+T2306,"Burn of unspecified degree of back of hand"
+T23061,"Burn of unspecified degree of back of right hand"
+T23062,"Burn of unspecified degree of back of left hand"
+T23069,"Burn of unspecified degree of back of unspecified hand"
+T2307,"Burn of unspecified degree of wrist"
+T23071,"Burn of unspecified degree of right wrist"
+T23072,"Burn of unspecified degree of left wrist"
+T23079,"Burn of unspecified degree of unspecified wrist"
+T2309,"Burn of unspecified degree of multiple sites of wrist and hand"
+T23091,"Burn of unspecified degree of multiple sites of right wrist and hand"
+T23092,"Burn of unspecified degree of multiple sites of left wrist and hand"
+T23099,"Burn of unspecified degree of multiple sites of unspecified wrist and hand"
+T231,"Burn of first degree of wrist and hand"
+T2310,"Burn of first degree of hand, unspecified site"
+T23101,"Burn of first degree of right hand, unspecified site"
+T23102,"Burn of first degree of left hand, unspecified site"
+T23109,"Burn of first degree of unspecified hand, unspecified site"
+T2311,"Burn of first degree of thumb (nail)"
+T23111,"Burn of first degree of right thumb (nail)"
+T23112,"Burn of first degree of left thumb (nail)"
+T23119,"Burn of first degree of unspecified thumb (nail)"
+T2312,"Burn of first degree of single finger (nail) except thumb"
+T23121,"Burn of first degree of single right finger (nail) except thumb"
+T23122,"Burn of first degree of single left finger (nail) except thumb"
+T23129,"Burn of first degree of unspecified single finger (nail) except thumb"
+T2313,"Burn of first degree of multiple fingers (nail), not including thumb"
+T23131,"Burn of first degree of multiple right fingers (nail), not including thumb"
+T23132,"Burn of first degree of multiple left fingers (nail), not including thumb"
+T23139,"Burn of first degree of unspecified multiple fingers (nail), not including thumb"
+T2314,"Burn of first degree of multiple fingers (nail), including thumb"
+T23141,"Burn of first degree of multiple right fingers (nail), including thumb"
+T23142,"Burn of first degree of multiple left fingers (nail), including thumb"
+T23149,"Burn of first degree of unspecified multiple fingers (nail), including thumb"
+T2315,"Burn of first degree of palm"
+T23151,"Burn of first degree of right palm"
+T23152,"Burn of first degree of left palm"
+T23159,"Burn of first degree of unspecified palm"
+T2316,"Burn of first degree of back of hand"
+T23161,"Burn of first degree of back of right hand"
+T23162,"Burn of first degree of back of left hand"
+T23169,"Burn of first degree of back of unspecified hand"
+T2317,"Burn of first degree of wrist"
+T23171,"Burn of first degree of right wrist"
+T23172,"Burn of first degree of left wrist"
+T23179,"Burn of first degree of unspecified wrist"
+T2319,"Burn of first degree of multiple sites of wrist and hand"
+T23191,"Burn of first degree of multiple sites of right wrist and hand"
+T23192,"Burn of first degree of multiple sites of left wrist and hand"
+T23199,"Burn of first degree of multiple sites of unspecified wrist and hand"
+T232,"Burn of second degree of wrist and hand"
+T2320,"Burn of second degree of hand, unspecified site"
+T23201,"Burn of second degree of right hand, unspecified site"
+T23202,"Burn of second degree of left hand, unspecified site"
+T23209,"Burn of second degree of unspecified hand, unspecified site"
+T2321,"Burn of second degree of thumb (nail)"
+T23211,"Burn of second degree of right thumb (nail)"
+T23212,"Burn of second degree of left thumb (nail)"
+T23219,"Burn of second degree of unspecified thumb (nail)"
+T2322,"Burn of second degree of single finger (nail) except thumb"
+T23221,"Burn of second degree of single right finger (nail) except thumb"
+T23222,"Burn of second degree of single left finger (nail) except thumb"
+T23229,"Burn of second degree of unspecified single finger (nail) except thumb"
+T2323,"Burn of second degree of multiple fingers (nail), not including thumb"
+T23231,"Burn of second degree of multiple right fingers (nail), not including thumb"
+T23232,"Burn of second degree of multiple left fingers (nail), not including thumb"
+T23239,"Burn of second degree of unspecified multiple fingers (nail), not including thumb"
+T2324,"Burn of second degree of multiple fingers (nail), including thumb"
+T23241,"Burn of second degree of multiple right fingers (nail), including thumb"
+T23242,"Burn of second degree of multiple left fingers (nail), including thumb"
+T23249,"Burn of second degree of unspecified multiple fingers (nail), including thumb"
+T2325,"Burn of second degree of palm"
+T23251,"Burn of second degree of right palm"
+T23252,"Burn of second degree of left palm"
+T23259,"Burn of second degree of unspecified palm"
+T2326,"Burn of second degree of back of hand"
+T23261,"Burn of second degree of back of right hand"
+T23262,"Burn of second degree of back of left hand"
+T23269,"Burn of second degree of back of unspecified hand"
+T2327,"Burn of second degree of wrist"
+T23271,"Burn of second degree of right wrist"
+T23272,"Burn of second degree of left wrist"
+T23279,"Burn of second degree of unspecified wrist"
+T2329,"Burn of second degree of multiple sites of wrist and hand"
+T23291,"Burn of second degree of multiple sites of right wrist and hand"
+T23292,"Burn of second degree of multiple sites of left wrist and hand"
+T23299,"Burn of second degree of multiple sites of unspecified wrist and hand"
+T233,"Burn of third degree of wrist and hand"
+T2330,"Burn of third degree of hand, unspecified site"
+T23301,"Burn of third degree of right hand, unspecified site"
+T23302,"Burn of third degree of left hand, unspecified site"
+T23309,"Burn of third degree of unspecified hand, unspecified site"
+T2331,"Burn of third degree of thumb (nail)"
+T23311,"Burn of third degree of right thumb (nail)"
+T23312,"Burn of third degree of left thumb (nail)"
+T23319,"Burn of third degree of unspecified thumb (nail)"
+T2332,"Burn of third degree of single finger (nail) except thumb"
+T23321,"Burn of third degree of single right finger (nail) except thumb"
+T23322,"Burn of third degree of single left finger (nail) except thumb"
+T23329,"Burn of third degree of unspecified single finger (nail) except thumb"
+T2333,"Burn of third degree of multiple fingers (nail), not including thumb"
+T23331,"Burn of third degree of multiple right fingers (nail), not including thumb"
+T23332,"Burn of third degree of multiple left fingers (nail), not including thumb"
+T23339,"Burn of third degree of unspecified multiple fingers (nail), not including thumb"
+T2334,"Burn of third degree of multiple fingers (nail), including thumb"
+T23341,"Burn of third degree of multiple right fingers (nail), including thumb"
+T23342,"Burn of third degree of multiple left fingers (nail), including thumb"
+T23349,"Burn of third degree of unspecified multiple fingers (nail), including thumb"
+T2335,"Burn of third degree of palm"
+T23351,"Burn of third degree of right palm"
+T23352,"Burn of third degree of left palm"
+T23359,"Burn of third degree of unspecified palm"
+T2336,"Burn of third degree of back of hand"
+T23361,"Burn of third degree of back of right hand"
+T23362,"Burn of third degree of back of left hand"
+T23369,"Burn of third degree of back of unspecified hand"
+T2337,"Burn of third degree of wrist"
+T23371,"Burn of third degree of right wrist"
+T23372,"Burn of third degree of left wrist"
+T23379,"Burn of third degree of unspecified wrist"
+T2339,"Burn of third degree of multiple sites of wrist and hand"
+T23391,"Burn of third degree of multiple sites of right wrist and hand"
+T23392,"Burn of third degree of multiple sites of left wrist and hand"
+T23399,"Burn of third degree of multiple sites of unspecified wrist and hand"
+T234,"Corrosion of unspecified degree of wrist and hand"
+T2340,"Corrosion of unspecified degree of hand, unspecified site"
+T23401,"Corrosion of unspecified degree of right hand, unspecified site"
+T23402,"Corrosion of unspecified degree of left hand, unspecified site"
+T23409,"Corrosion of unspecified degree of unspecified hand, unspecified site"
+T2341,"Corrosion of unspecified degree of thumb (nail)"
+T23411,"Corrosion of unspecified degree of right thumb (nail)"
+T23412,"Corrosion of unspecified degree of left thumb (nail)"
+T23419,"Corrosion of unspecified degree of unspecified thumb (nail)"
+T2342,"Corrosion of unspecified degree of single finger (nail) except thumb"
+T23421,"Corrosion of unspecified degree of single right finger (nail) except thumb"
+T23422,"Corrosion of unspecified degree of single left finger (nail) except thumb"
+T23429,"Corrosion of unspecified degree of unspecified single finger (nail) except thumb"
+T2343,"Corrosion of unspecified degree of multiple fingers (nail), not including thumb"
+T23431,"Corrosion of unspecified degree of multiple right fingers (nail), not including thumb"
+T23432,"Corrosion of unspecified degree of multiple left fingers (nail), not including thumb"
+T23439,"Corrosion of unspecified degree of unspecified multiple fingers (nail), not including thumb"
+T2344,"Corrosion of unspecified degree of multiple fingers (nail), including thumb"
+T23441,"Corrosion of unspecified degree of multiple right fingers (nail), including thumb"
+T23442,"Corrosion of unspecified degree of multiple left fingers (nail), including thumb"
+T23449,"Corrosion of unspecified degree of unspecified multiple fingers (nail), including thumb"
+T2345,"Corrosion of unspecified degree of palm"
+T23451,"Corrosion of unspecified degree of right palm"
+T23452,"Corrosion of unspecified degree of left palm"
+T23459,"Corrosion of unspecified degree of unspecified palm"
+T2346,"Corrosion of unspecified degree of back of hand"
+T23461,"Corrosion of unspecified degree of back of right hand"
+T23462,"Corrosion of unspecified degree of back of left hand"
+T23469,"Corrosion of unspecified degree of back of unspecified hand"
+T2347,"Corrosion of unspecified degree of wrist"
+T23471,"Corrosion of unspecified degree of right wrist"
+T23472,"Corrosion of unspecified degree of left wrist"
+T23479,"Corrosion of unspecified degree of unspecified wrist"
+T2349,"Corrosion of unspecified degree of multiple sites of wrist and hand"
+T23491,"Corrosion of unspecified degree of multiple sites of right wrist and hand"
+T23492,"Corrosion of unspecified degree of multiple sites of left wrist and hand"
+T23499,"Corrosion of unspecified degree of multiple sites of unspecified wrist and hand"
+T235,"Corrosion of first degree of wrist and hand"
+T2350,"Corrosion of first degree of hand, unspecified site"
+T23501,"Corrosion of first degree of right hand, unspecified site"
+T23502,"Corrosion of first degree of left hand, unspecified site"
+T23509,"Corrosion of first degree of unspecified hand, unspecified site"
+T2351,"Corrosion of first degree of thumb (nail)"
+T23511,"Corrosion of first degree of right thumb (nail)"
+T23512,"Corrosion of first degree of left thumb (nail)"
+T23519,"Corrosion of first degree of unspecified thumb (nail)"
+T2352,"Corrosion of first degree of single finger (nail) except thumb"
+T23521,"Corrosion of first degree of single right finger (nail) except thumb"
+T23522,"Corrosion of first degree of single left finger (nail) except thumb"
+T23529,"Corrosion of first degree of unspecified single finger (nail) except thumb"
+T2353,"Corrosion of first degree of multiple fingers (nail), not including thumb"
+T23531,"Corrosion of first degree of multiple right fingers (nail), not including thumb"
+T23532,"Corrosion of first degree of multiple left fingers (nail), not including thumb"
+T23539,"Corrosion of first degree of unspecified multiple fingers (nail), not including thumb"
+T2354,"Corrosion of first degree of multiple fingers (nail), including thumb"
+T23541,"Corrosion of first degree of multiple right fingers (nail), including thumb"
+T23542,"Corrosion of first degree of multiple left fingers (nail), including thumb"
+T23549,"Corrosion of first degree of unspecified multiple fingers (nail), including thumb"
+T2355,"Corrosion of first degree of palm"
+T23551,"Corrosion of first degree of right palm"
+T23552,"Corrosion of first degree of left palm"
+T23559,"Corrosion of first degree of unspecified palm"
+T2356,"Corrosion of first degree of back of hand"
+T23561,"Corrosion of first degree of back of right hand"
+T23562,"Corrosion of first degree of back of left hand"
+T23569,"Corrosion of first degree of back of unspecified hand"
+T2357,"Corrosion of first degree of wrist"
+T23571,"Corrosion of first degree of right wrist"
+T23572,"Corrosion of first degree of left wrist"
+T23579,"Corrosion of first degree of unspecified wrist"
+T2359,"Corrosion of first degree of multiple sites of wrist and hand"
+T23591,"Corrosion of first degree of multiple sites of right wrist and hand"
+T23592,"Corrosion of first degree of multiple sites of left wrist and hand"
+T23599,"Corrosion of first degree of multiple sites of unspecified wrist and hand"
+T236,"Corrosion of second degree of wrist and hand"
+T2360,"Corrosion of second degree of hand, unspecified site"
+T23601,"Corrosion of second degree of right hand, unspecified site"
+T23602,"Corrosion of second degree of left hand, unspecified site"
+T23609,"Corrosion of second degree of unspecified hand, unspecified site"
+T2361,"Corrosion of second degree of thumb (nail)"
+T23611,"Corrosion of second degree of right thumb (nail)"
+T23612,"Corrosion of second degree of left thumb (nail)"
+T23619,"Corrosion of second degree of unspecified thumb (nail)"
+T2362,"Corrosion of second degree of single finger (nail) except thumb"
+T23621,"Corrosion of second degree of single right finger (nail) except thumb"
+T23622,"Corrosion of second degree of single left finger (nail) except thumb"
+T23629,"Corrosion of second degree of unspecified single finger (nail) except thumb"
+T2363,"Corrosion of second degree of multiple fingers (nail), not including thumb"
+T23631,"Corrosion of second degree of multiple right fingers (nail), not including thumb"
+T23632,"Corrosion of second degree of multiple left fingers (nail), not including thumb"
+T23639,"Corrosion of second degree of unspecified multiple fingers (nail), not including thumb"
+T2364,"Corrosion of second degree of multiple fingers (nail), including thumb"
+T23641,"Corrosion of second degree of multiple right fingers (nail), including thumb"
+T23642,"Corrosion of second degree of multiple left fingers (nail), including thumb"
+T23649,"Corrosion of second degree of unspecified multiple fingers (nail), including thumb"
+T2365,"Corrosion of second degree of palm"
+T23651,"Corrosion of second degree of right palm"
+T23652,"Corrosion of second degree of left palm"
+T23659,"Corrosion of second degree of unspecified palm"
+T2366,"Corrosion of second degree of back of hand"
+T23661,"Corrosion of second degree back of right hand"
+T23662,"Corrosion of second degree back of left hand"
+T23669,"Corrosion of second degree back of unspecified hand"
+T2367,"Corrosion of second degree of wrist"
+T23671,"Corrosion of second degree of right wrist"
+T23672,"Corrosion of second degree of left wrist"
+T23679,"Corrosion of second degree of unspecified wrist"
+T2369,"Corrosion of second degree of multiple sites of wrist and hand"
+T23691,"Corrosion of second degree of multiple sites of right wrist and hand"
+T23692,"Corrosion of second degree of multiple sites of left wrist and hand"
+T23699,"Corrosion of second degree of multiple sites of unspecified wrist and hand"
+T237,"Corrosion of third degree of wrist and hand"
+T2370,"Corrosion of third degree of hand, unspecified site"
+T23701,"Corrosion of third degree of right hand, unspecified site"
+T23702,"Corrosion of third degree of left hand, unspecified site"
+T23709,"Corrosion of third degree of unspecified hand, unspecified site"
+T2371,"Corrosion of third degree of thumb (nail)"
+T23711,"Corrosion of third degree of right thumb (nail)"
+T23712,"Corrosion of third degree of left thumb (nail)"
+T23719,"Corrosion of third degree of unspecified thumb (nail)"
+T2372,"Corrosion of third degree of single finger (nail) except thumb"
+T23721,"Corrosion of third degree of single right finger (nail) except thumb"
+T23722,"Corrosion of third degree of single left finger (nail) except thumb"
+T23729,"Corrosion of third degree of unspecified single finger (nail) except thumb"
+T2373,"Corrosion of third degree of multiple fingers (nail), not including thumb"
+T23731,"Corrosion of third degree of multiple right fingers (nail), not including thumb"
+T23732,"Corrosion of third degree of multiple left fingers (nail), not including thumb"
+T23739,"Corrosion of third degree of unspecified multiple fingers (nail), not including thumb"
+T2374,"Corrosion of third degree of multiple fingers (nail), including thumb"
+T23741,"Corrosion of third degree of multiple right fingers (nail), including thumb"
+T23742,"Corrosion of third degree of multiple left fingers (nail), including thumb"
+T23749,"Corrosion of third degree of unspecified multiple fingers (nail), including thumb"
+T2375,"Corrosion of third degree of palm"
+T23751,"Corrosion of third degree of right palm"
+T23752,"Corrosion of third degree of left palm"
+T23759,"Corrosion of third degree of unspecified palm"
+T2376,"Corrosion of third degree of back of hand"
+T23761,"Corrosion of third degree of back of right hand"
+T23762,"Corrosion of third degree of back of left hand"
+T23769,"Corrosion of third degree back of unspecified hand"
+T2377,"Corrosion of third degree of wrist"
+T23771,"Corrosion of third degree of right wrist"
+T23772,"Corrosion of third degree of left wrist"
+T23779,"Corrosion of third degree of unspecified wrist"
+T2379,"Corrosion of third degree of multiple sites of wrist and hand"
+T23791,"Corrosion of third degree of multiple sites of right wrist and hand"
+T23792,"Corrosion of third degree of multiple sites of left wrist and hand"
+T23799,"Corrosion of third degree of multiple sites of unspecified wrist and hand"
+T24,"Burn and corrosion of lower limb, except ankle and foot"
+T240,"Burn of unspecified degree of lower limb, except ankle and foot"
+T2400,"Burn of unspecified degree of unspecified site of lower limb, except ankle and foot"
+T24001,"Burn of unspecified degree of unspecified site of right lower limb, except ankle and foot"
+T24002,"Burn of unspecified degree of unspecified site of left lower limb, except ankle and foot"
+T24009,"Burn of unspecified degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2401,"Burn of unspecified degree of thigh"
+T24011,"Burn of unspecified degree of right thigh"
+T24012,"Burn of unspecified degree of left thigh"
+T24019,"Burn of unspecified degree of unspecified thigh"
+T2402,"Burn of unspecified degree of knee"
+T24021,"Burn of unspecified degree of right knee"
+T24022,"Burn of unspecified degree of left knee"
+T24029,"Burn of unspecified degree of unspecified knee"
+T2403,"Burn of unspecified degree of lower leg"
+T24031,"Burn of unspecified degree of right lower leg"
+T24032,"Burn of unspecified degree of left lower leg"
+T24039,"Burn of unspecified degree of unspecified lower leg"
+T2409,"Burn of unspecified degree of multiple sites of lower limb, except ankle and foot"
+T24091,"Burn of unspecified degree of multiple sites of right lower limb, except ankle and foot"
+T24092,"Burn of unspecified degree of multiple sites of left lower limb, except ankle and foot"
+T24099,"Burn of unspecified degree of multiple sites of unspecified lower limb, except ankle and foot"
+T241,"Burn of first degree of lower limb, except ankle and foot"
+T2410,"Burn of first degree of unspecified site of lower limb, except ankle and foot"
+T24101,"Burn of first degree of unspecified site of right lower limb, except ankle and foot"
+T24102,"Burn of first degree of unspecified site of left lower limb, except ankle and foot"
+T24109,"Burn of first degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2411,"Burn of first degree of thigh"
+T24111,"Burn of first degree of right thigh"
+T24112,"Burn of first degree of left thigh"
+T24119,"Burn of first degree of unspecified thigh"
+T2412,"Burn of first degree of knee"
+T24121,"Burn of first degree of right knee"
+T24122,"Burn of first degree of left knee"
+T24129,"Burn of first degree of unspecified knee"
+T2413,"Burn of first degree of lower leg"
+T24131,"Burn of first degree of right lower leg"
+T24132,"Burn of first degree of left lower leg"
+T24139,"Burn of first degree of unspecified lower leg"
+T2419,"Burn of first degree of multiple sites of lower limb, except ankle and foot"
+T24191,"Burn of first degree of multiple sites of right lower limb, except ankle and foot"
+T24192,"Burn of first degree of multiple sites of left lower limb, except ankle and foot"
+T24199,"Burn of first degree of multiple sites of unspecified lower limb, except ankle and foot"
+T242,"Burn of second degree of lower limb, except ankle and foot"
+T2420,"Burn of second degree of unspecified site of lower limb, except ankle and foot"
+T24201,"Burn of second degree of unspecified site of right lower limb, except ankle and foot"
+T24202,"Burn of second degree of unspecified site of left lower limb, except ankle and foot"
+T24209,"Burn of second degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2421,"Burn of second degree of thigh"
+T24211,"Burn of second degree of right thigh"
+T24212,"Burn of second degree of left thigh"
+T24219,"Burn of second degree of unspecified thigh"
+T2422,"Burn of second degree of knee"
+T24221,"Burn of second degree of right knee"
+T24222,"Burn of second degree of left knee"
+T24229,"Burn of second degree of unspecified knee"
+T2423,"Burn of second degree of lower leg"
+T24231,"Burn of second degree of right lower leg"
+T24232,"Burn of second degree of left lower leg"
+T24239,"Burn of second degree of unspecified lower leg"
+T2429,"Burn of second degree of multiple sites of lower limb, except ankle and foot"
+T24291,"Burn of second degree of multiple sites of right lower limb, except ankle and foot"
+T24292,"Burn of second degree of multiple sites of left lower limb, except ankle and foot"
+T24299,"Burn of second degree of multiple sites of unspecified lower limb, except ankle and foot"
+T243,"Burn of third degree of lower limb, except ankle and foot"
+T2430,"Burn of third degree of unspecified site of lower limb, except ankle and foot"
+T24301,"Burn of third degree of unspecified site of right lower limb, except ankle and foot"
+T24302,"Burn of third degree of unspecified site of left lower limb, except ankle and foot"
+T24309,"Burn of third degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2431,"Burn of third degree of thigh"
+T24311,"Burn of third degree of right thigh"
+T24312,"Burn of third degree of left thigh"
+T24319,"Burn of third degree of unspecified thigh"
+T2432,"Burn of third degree of knee"
+T24321,"Burn of third degree of right knee"
+T24322,"Burn of third degree of left knee"
+T24329,"Burn of third degree of unspecified knee"
+T2433,"Burn of third degree of lower leg"
+T24331,"Burn of third degree of right lower leg"
+T24332,"Burn of third degree of left lower leg"
+T24339,"Burn of third degree of unspecified lower leg"
+T2439,"Burn of third degree of multiple sites of lower limb, except ankle and foot"
+T24391,"Burn of third degree of multiple sites of right lower limb, except ankle and foot"
+T24392,"Burn of third degree of multiple sites of left lower limb, except ankle and foot"
+T24399,"Burn of third degree of multiple sites of unspecified lower limb, except ankle and foot"
+T244,"Corrosion of unspecified degree of lower limb, except ankle and foot"
+T2440,"Corrosion of unspecified degree of unspecified site of lower limb, except ankle and foot"
+T24401,"Corrosion of unspecified degree of unspecified site of right lower limb, except ankle and foot"
+T24402,"Corrosion of unspecified degree of unspecified site of left lower limb, except ankle and foot"
+T24409,"Corrosion of unspecified degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2441,"Corrosion of unspecified degree of thigh"
+T24411,"Corrosion of unspecified degree of right thigh"
+T24412,"Corrosion of unspecified degree of left thigh"
+T24419,"Corrosion of unspecified degree of unspecified thigh"
+T2442,"Corrosion of unspecified degree of knee"
+T24421,"Corrosion of unspecified degree of right knee"
+T24422,"Corrosion of unspecified degree of left knee"
+T24429,"Corrosion of unspecified degree of unspecified knee"
+T2443,"Corrosion of unspecified degree of lower leg"
+T24431,"Corrosion of unspecified degree of right lower leg"
+T24432,"Corrosion of unspecified degree of left lower leg"
+T24439,"Corrosion of unspecified degree of unspecified lower leg"
+T2449,"Corrosion of unspecified degree of multiple sites of lower limb, except ankle and foot"
+T24491,"Corrosion of unspecified degree of multiple sites of right lower limb, except ankle and foot"
+T24492,"Corrosion of unspecified degree of multiple sites of left lower limb, except ankle and foot"
+T24499,"Corrosion of unspecified degree of multiple sites of unspecified lower limb, except ankle and foot"
+T245,"Corrosion of first degree of lower limb, except ankle and foot"
+T2450,"Corrosion of first degree of unspecified site of lower limb, except ankle and foot"
+T24501,"Corrosion of first degree of unspecified site of right lower limb, except ankle and foot"
+T24502,"Corrosion of first degree of unspecified site of left lower limb, except ankle and foot"
+T24509,"Corrosion of first degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2451,"Corrosion of first degree of thigh"
+T24511,"Corrosion of first degree of right thigh"
+T24512,"Corrosion of first degree of left thigh"
+T24519,"Corrosion of first degree of unspecified thigh"
+T2452,"Corrosion of first degree of knee"
+T24521,"Corrosion of first degree of right knee"
+T24522,"Corrosion of first degree of left knee"
+T24529,"Corrosion of first degree of unspecified knee"
+T2453,"Corrosion of first degree of lower leg"
+T24531,"Corrosion of first degree of right lower leg"
+T24532,"Corrosion of first degree of left lower leg"
+T24539,"Corrosion of first degree of unspecified lower leg"
+T2459,"Corrosion of first degree of multiple sites of lower limb, except ankle and foot"
+T24591,"Corrosion of first degree of multiple sites of right lower limb, except ankle and foot"
+T24592,"Corrosion of first degree of multiple sites of left lower limb, except ankle and foot"
+T24599,"Corrosion of first degree of multiple sites of unspecified lower limb, except ankle and foot"
+T246,"Corrosion of second degree of lower limb, except ankle and foot"
+T2460,"Corrosion of second degree of unspecified site of lower limb, except ankle and foot"
+T24601,"Corrosion of second degree of unspecified site of right lower limb, except ankle and foot"
+T24602,"Corrosion of second degree of unspecified site of left lower limb, except ankle and foot"
+T24609,"Corrosion of second degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2461,"Corrosion of second degree of thigh"
+T24611,"Corrosion of second degree of right thigh"
+T24612,"Corrosion of second degree of left thigh"
+T24619,"Corrosion of second degree of unspecified thigh"
+T2462,"Corrosion of second degree of knee"
+T24621,"Corrosion of second degree of right knee"
+T24622,"Corrosion of second degree of left knee"
+T24629,"Corrosion of second degree of unspecified knee"
+T2463,"Corrosion of second degree of lower leg"
+T24631,"Corrosion of second degree of right lower leg"
+T24632,"Corrosion of second degree of left lower leg"
+T24639,"Corrosion of second degree of unspecified lower leg"
+T2469,"Corrosion of second degree of multiple sites of lower limb, except ankle and foot"
+T24691,"Corrosion of second degree of multiple sites of right lower limb, except ankle and foot"
+T24692,"Corrosion of second degree of multiple sites of left lower limb, except ankle and foot"
+T24699,"Corrosion of second degree of multiple sites of unspecified lower limb, except ankle and foot"
+T247,"Corrosion of third degree of lower limb, except ankle and foot"
+T2470,"Corrosion of third degree of unspecified site of lower limb, except ankle and foot"
+T24701,"Corrosion of third degree of unspecified site of right lower limb, except ankle and foot"
+T24702,"Corrosion of third degree of unspecified site of left lower limb, except ankle and foot"
+T24709,"Corrosion of third degree of unspecified site of unspecified lower limb, except ankle and foot"
+T2471,"Corrosion of third degree of thigh"
+T24711,"Corrosion of third degree of right thigh"
+T24712,"Corrosion of third degree of left thigh"
+T24719,"Corrosion of third degree of unspecified thigh"
+T2472,"Corrosion of third degree of knee"
+T24721,"Corrosion of third degree of right knee"
+T24722,"Corrosion of third degree of left knee"
+T24729,"Corrosion of third degree of unspecified knee"
+T2473,"Corrosion of third degree of lower leg"
+T24731,"Corrosion of third degree of right lower leg"
+T24732,"Corrosion of third degree of left lower leg"
+T24739,"Corrosion of third degree of unspecified lower leg"
+T2479,"Corrosion of third degree of multiple sites of lower limb, except ankle and foot"
+T24791,"Corrosion of third degree of multiple sites of right lower limb, except ankle and foot"
+T24792,"Corrosion of third degree of multiple sites of left lower limb, except ankle and foot"
+T24799,"Corrosion of third degree of multiple sites of unspecified lower limb, except ankle and foot"
+T25,"Burn and corrosion of ankle and foot"
+T250,"Burn of unspecified degree of ankle and foot"
+T2501,"Burn of unspecified degree of ankle"
+T25011,"Burn of unspecified degree of right ankle"
+T25012,"Burn of unspecified degree of left ankle"
+T25019,"Burn of unspecified degree of unspecified ankle"
+T2502,"Burn of unspecified degree of foot"
+T25021,"Burn of unspecified degree of right foot"
+T25022,"Burn of unspecified degree of left foot"
+T25029,"Burn of unspecified degree of unspecified foot"
+T2503,"Burn of unspecified degree of toe(s) (nail)"
+T25031,"Burn of unspecified degree of right toe(s) (nail)"
+T25032,"Burn of unspecified degree of left toe(s) (nail)"
+T25039,"Burn of unspecified degree of unspecified toe(s) (nail)"
+T2509,"Burn of unspecified degree of multiple sites of ankle and foot"
+T25091,"Burn of unspecified degree of multiple sites of right ankle and foot"
+T25092,"Burn of unspecified degree of multiple sites of left ankle and foot"
+T25099,"Burn of unspecified degree of multiple sites of unspecified ankle and foot"
+T251,"Burn of first degree of ankle and foot"
+T2511,"Burn of first degree of ankle"
+T25111,"Burn of first degree of right ankle"
+T25112,"Burn of first degree of left ankle"
+T25119,"Burn of first degree of unspecified ankle"
+T2512,"Burn of first degree of foot"
+T25121,"Burn of first degree of right foot"
+T25122,"Burn of first degree of left foot"
+T25129,"Burn of first degree of unspecified foot"
+T2513,"Burn of first degree of toe(s) (nail)"
+T25131,"Burn of first degree of right toe(s) (nail)"
+T25132,"Burn of first degree of left toe(s) (nail)"
+T25139,"Burn of first degree of unspecified toe(s) (nail)"
+T2519,"Burn of first degree of multiple sites of ankle and foot"
+T25191,"Burn of first degree of multiple sites of right ankle and foot"
+T25192,"Burn of first degree of multiple sites of left ankle and foot"
+T25199,"Burn of first degree of multiple sites of unspecified ankle and foot"
+T252,"Burn of second degree of ankle and foot"
+T2521,"Burn of second degree of ankle"
+T25211,"Burn of second degree of right ankle"
+T25212,"Burn of second degree of left ankle"
+T25219,"Burn of second degree of unspecified ankle"
+T2522,"Burn of second degree of foot"
+T25221,"Burn of second degree of right foot"
+T25222,"Burn of second degree of left foot"
+T25229,"Burn of second degree of unspecified foot"
+T2523,"Burn of second degree of toe(s) (nail)"
+T25231,"Burn of second degree of right toe(s) (nail)"
+T25232,"Burn of second degree of left toe(s) (nail)"
+T25239,"Burn of second degree of unspecified toe(s) (nail)"
+T2529,"Burn of second degree of multiple sites of ankle and foot"
+T25291,"Burn of second degree of multiple sites of right ankle and foot"
+T25292,"Burn of second degree of multiple sites of left ankle and foot"
+T25299,"Burn of second degree of multiple sites of unspecified ankle and foot"
+T253,"Burn of third degree of ankle and foot"
+T2531,"Burn of third degree of ankle"
+T25311,"Burn of third degree of right ankle"
+T25312,"Burn of third degree of left ankle"
+T25319,"Burn of third degree of unspecified ankle"
+T2532,"Burn of third degree of foot"
+T25321,"Burn of third degree of right foot"
+T25322,"Burn of third degree of left foot"
+T25329,"Burn of third degree of unspecified foot"
+T2533,"Burn of third degree of toe(s) (nail)"
+T25331,"Burn of third degree of right toe(s) (nail)"
+T25332,"Burn of third degree of left toe(s) (nail)"
+T25339,"Burn of third degree of unspecified toe(s) (nail)"
+T2539,"Burn of third degree of multiple sites of ankle and foot"
+T25391,"Burn of third degree of multiple sites of right ankle and foot"
+T25392,"Burn of third degree of multiple sites of left ankle and foot"
+T25399,"Burn of third degree of multiple sites of unspecified ankle and foot"
+T254,"Corrosion of unspecified degree of ankle and foot"
+T2541,"Corrosion of unspecified degree of ankle"
+T25411,"Corrosion of unspecified degree of right ankle"
+T25412,"Corrosion of unspecified degree of left ankle"
+T25419,"Corrosion of unspecified degree of unspecified ankle"
+T2542,"Corrosion of unspecified degree of foot"
+T25421,"Corrosion of unspecified degree of right foot"
+T25422,"Corrosion of unspecified degree of left foot"
+T25429,"Corrosion of unspecified degree of unspecified foot"
+T2543,"Corrosion of unspecified degree of toe(s) (nail)"
+T25431,"Corrosion of unspecified degree of right toe(s) (nail)"
+T25432,"Corrosion of unspecified degree of left toe(s) (nail)"
+T25439,"Corrosion of unspecified degree of unspecified toe(s) (nail)"
+T2549,"Corrosion of unspecified degree of multiple sites of ankle and foot"
+T25491,"Corrosion of unspecified degree of multiple sites of right ankle and foot"
+T25492,"Corrosion of unspecified degree of multiple sites of left ankle and foot"
+T25499,"Corrosion of unspecified degree of multiple sites of unspecified ankle and foot"
+T255,"Corrosion of first degree of ankle and foot"
+T2551,"Corrosion of first degree of ankle"
+T25511,"Corrosion of first degree of right ankle"
+T25512,"Corrosion of first degree of left ankle"
+T25519,"Corrosion of first degree of unspecified ankle"
+T2552,"Corrosion of first degree of foot"
+T25521,"Corrosion of first degree of right foot"
+T25522,"Corrosion of first degree of left foot"
+T25529,"Corrosion of first degree of unspecified foot"
+T2553,"Corrosion of first degree of toe(s) (nail)"
+T25531,"Corrosion of first degree of right toe(s) (nail)"
+T25532,"Corrosion of first degree of left toe(s) (nail)"
+T25539,"Corrosion of first degree of unspecified toe(s) (nail)"
+T2559,"Corrosion of first degree of multiple sites of ankle and foot"
+T25591,"Corrosion of first degree of multiple sites of right ankle and foot"
+T25592,"Corrosion of first degree of multiple sites of left ankle and foot"
+T25599,"Corrosion of first degree of multiple sites of unspecified ankle and foot"
+T256,"Corrosion of second degree of ankle and foot"
+T2561,"Corrosion of second degree of ankle"
+T25611,"Corrosion of second degree of right ankle"
+T25612,"Corrosion of second degree of left ankle"
+T25619,"Corrosion of second degree of unspecified ankle"
+T2562,"Corrosion of second degree of foot"
+T25621,"Corrosion of second degree of right foot"
+T25622,"Corrosion of second degree of left foot"
+T25629,"Corrosion of second degree of unspecified foot"
+T2563,"Corrosion of second degree of toe(s) (nail)"
+T25631,"Corrosion of second degree of right toe(s) (nail)"
+T25632,"Corrosion of second degree of left toe(s) (nail)"
+T25639,"Corrosion of second degree of unspecified toe(s) (nail)"
+T2569,"Corrosion of second degree of multiple sites of ankle and foot"
+T25691,"Corrosion of second degree of right ankle and foot"
+T25692,"Corrosion of second degree of left ankle and foot"
+T25699,"Corrosion of second degree of unspecified ankle and foot"
+T257,"Corrosion of third degree of ankle and foot"
+T2571,"Corrosion of third degree of ankle"
+T25711,"Corrosion of third degree of right ankle"
+T25712,"Corrosion of third degree of left ankle"
+T25719,"Corrosion of third degree of unspecified ankle"
+T2572,"Corrosion of third degree of foot"
+T25721,"Corrosion of third degree of right foot"
+T25722,"Corrosion of third degree of left foot"
+T25729,"Corrosion of third degree of unspecified foot"
+T2573,"Corrosion of third degree of toe(s) (nail)"
+T25731,"Corrosion of third degree of right toe(s) (nail)"
+T25732,"Corrosion of third degree of left toe(s) (nail)"
+T25739,"Corrosion of third degree of unspecified toe(s) (nail)"
+T2579,"Corrosion of third degree of multiple sites of ankle and foot"
+T25791,"Corrosion of third degree of multiple sites of right ankle and foot"
+T25792,"Corrosion of third degree of multiple sites of left ankle and foot"
+T25799,"Corrosion of third degree of multiple sites of unspecified ankle and foot"
+T26,"Burn and corrosion confined to eye and adnexa"
+T260,"Burn of eyelid and periocular area"
+T2600,"Burn of unspecified eyelid and periocular area"
+T2601,"Burn of right eyelid and periocular area"
+T2602,"Burn of left eyelid and periocular area"
+T261,"Burn of cornea and conjunctival sac"
+T2610,"Burn of cornea and conjunctival sac, unspecified eye"
+T2611,"Burn of cornea and conjunctival sac, right eye"
+T2612,"Burn of cornea and conjunctival sac, left eye"
+T262,"Burn with resulting rupture and destruction of eyeball"
+T2620,"Burn with resulting rupture and destruction of unspecified eyeball"
+T2621,"Burn with resulting rupture and destruction of right eyeball"
+T2622,"Burn with resulting rupture and destruction of left eyeball"
+T263,"Burns of other specified parts of eye and adnexa"
+T2630,"Burns of other specified parts of unspecified eye and adnexa"
+T2631,"Burns of other specified parts of right eye and adnexa"
+T2632,"Burns of other specified parts of left eye and adnexa"
+T264,"Burn of eye and adnexa, part unspecified"
+T2640,"Burn of unspecified eye and adnexa, part unspecified"
+T2641,"Burn of right eye and adnexa, part unspecified"
+T2642,"Burn of left eye and adnexa, part unspecified"
+T265,"Corrosion of eyelid and periocular area"
+T2650,"Corrosion of unspecified eyelid and periocular area"
+T2651,"Corrosion of right eyelid and periocular area"
+T2652,"Corrosion of left eyelid and periocular area"
+T266,"Corrosion of cornea and conjunctival sac"
+T2660,"Corrosion of cornea and conjunctival sac, unspecified eye"
+T2661,"Corrosion of cornea and conjunctival sac, right eye"
+T2662,"Corrosion of cornea and conjunctival sac, left eye"
+T267,"Corrosion with resulting rupture and destruction of eyeball"
+T2670,"Corrosion with resulting rupture and destruction of unspecified eyeball"
+T2671,"Corrosion with resulting rupture and destruction of right eyeball"
+T2672,"Corrosion with resulting rupture and destruction of left eyeball"
+T268,"Corrosions of other specified parts of eye and adnexa"
+T2680,"Corrosions of other specified parts of unspecified eye and adnexa"
+T2681,"Corrosions of other specified parts of right eye and adnexa"
+T2682,"Corrosions of other specified parts of left eye and adnexa"
+T269,"Corrosion of eye and adnexa, part unspecified"
+T2690,"Corrosion of unspecified eye and adnexa, part unspecified"
+T2691,"Corrosion of right eye and adnexa, part unspecified"
+T2692,"Corrosion of left eye and adnexa, part unspecified"
+T27,"Burn and corrosion of respiratory tract"
+T270,"Burn of larynx and trachea"
+T271,"Burn involving larynx and trachea with lung"
+T272,"Burn of other parts of respiratory tract"
+T273,"Burn of respiratory tract, part unspecified"
+T274,"Corrosion of larynx and trachea"
+T275,"Corrosion involving larynx and trachea with lung"
+T276,"Corrosion of other parts of respiratory tract"
+T277,"Corrosion of respiratory tract, part unspecified"
+T28,"Burn and corrosion of other internal organs"
+T280,"Burn of mouth and pharynx"
+T281,"Burn of esophagus"
+T282,"Burn of other parts of alimentary tract"
+T283,"Burn of internal genitourinary organs"
+T284,"Burns of other and unspecified internal organs"
+T2840,"Burn of unspecified internal organ"
+T2841,"Burn of ear drum"
+T28411,"Burn of right ear drum"
+T28412,"Burn of left ear drum"
+T28419,"Burn of unspecified ear drum"
+T2849,"Burn of other internal organ"
+T285,"Corrosion of mouth and pharynx"
+T286,"Corrosion of esophagus"
+T287,"Corrosion of other parts of alimentary tract"
+T288,"Corrosion of internal genitourinary organs"
+T289,"Corrosions of other and unspecified internal organs"
+T2890,"Corrosions of unspecified internal organs"
+T2891,"Corrosions of ear drum"
+T28911,"Corrosions of right ear drum"
+T28912,"Corrosions of left ear drum"
+T28919,"Corrosions of unspecified ear drum"
+T2899,"Corrosions of other internal organs"
+T30,"Burn and corrosion, body region unspecified"
+T31,"Burns classified according to extent of body surface involved"
+T311,"Burns involving 10-19% of body surface"
+T312,"Burns involving 20-29% of body surface"
+T313,"Burns involving 30-39% of body surface"
+T314,"Burns involving 40-49% of body surface"
+T315,"Burns involving 50-59% of body surface"
+T316,"Burns involving 60-69% of body surface"
+T317,"Burns involving 70-79% of body surface"
+T318,"Burns involving 80-89% of body surface"
+T319,"Burns involving 90% or more of body surface"
+T32,"Corrosions classified according to extent of body surface involved"
+T321,"Corrosions involving 10-19% of body surface"
+T322,"Corrosions involving 20-29% of body surface"
+T323,"Corrosions involving 30-39% of body surface"
+T324,"Corrosions involving 40-49% of body surface"
+T325,"Corrosions involving 50-59% of body surface"
+T326,"Corrosions involving 60-69% of body surface"
+T327,"Corrosions involving 70-79% of body surface"
+T328,"Corrosions involving 80-89% of body surface"
+T329,"Corrosions involving 90% or more of body surface"
+T33,"Superficial frostbite"
+T330,"Superficial frostbite of head"
+T3301,"Superficial frostbite of ear"
+T33011,"Superficial frostbite of right ear"
+T33012,"Superficial frostbite of left ear"
+T33019,"Superficial frostbite of unspecified ear"
+T3302,"Superficial frostbite of nose"
+T3309,"Superficial frostbite of other part of head"
+T331,"Superficial frostbite of neck"
+T332,"Superficial frostbite of thorax"
+T333,"Superficial frostbite of abdominal wall, lower back and pelvis"
+T334,"Superficial frostbite of arm"
+T3340,"Superficial frostbite of unspecified arm"
+T3341,"Superficial frostbite of right arm"
+T3342,"Superficial frostbite of left arm"
+T335,"Superficial frostbite of wrist, hand, and fingers"
+T3351,"Superficial frostbite of wrist"
+T33511,"Superficial frostbite of right wrist"
+T33512,"Superficial frostbite of left wrist"
+T33519,"Superficial frostbite of unspecified wrist"
+T3352,"Superficial frostbite of hand"
+T33521,"Superficial frostbite of right hand"
+T33522,"Superficial frostbite of left hand"
+T33529,"Superficial frostbite of unspecified hand"
+T3353,"Superficial frostbite of finger(s)"
+T33531,"Superficial frostbite of right finger(s)"
+T33532,"Superficial frostbite of left finger(s)"
+T33539,"Superficial frostbite of unspecified finger(s)"
+T336,"Superficial frostbite of hip and thigh"
+T3360,"Superficial frostbite of unspecified hip and thigh"
+T3361,"Superficial frostbite of right hip and thigh"
+T3362,"Superficial frostbite of left hip and thigh"
+T337,"Superficial frostbite of knee and lower leg"
+T3370,"Superficial frostbite of unspecified knee and lower leg"
+T3371,"Superficial frostbite of right knee and lower leg"
+T3372,"Superficial frostbite of left knee and lower leg"
+T338,"Superficial frostbite of ankle, foot, and toe(s)"
+T3381,"Superficial frostbite of ankle"
+T33811,"Superficial frostbite of right ankle"
+T33812,"Superficial frostbite of left ankle"
+T33819,"Superficial frostbite of unspecified ankle"
+T3382,"Superficial frostbite of foot"
+T33821,"Superficial frostbite of right foot"
+T33822,"Superficial frostbite of left foot"
+T33829,"Superficial frostbite of unspecified foot"
+T3383,"Superficial frostbite of toe(s)"
+T33831,"Superficial frostbite of right toe(s)"
+T33832,"Superficial frostbite of left toe(s)"
+T33839,"Superficial frostbite of unspecified toe(s)"
+T339,"Superficial frostbite of other and unspecified sites"
+T3390,"Superficial frostbite of unspecified sites"
+T3399,"Superficial frostbite of other sites"
+T34,"Frostbite with tissue necrosis"
+T340,"Frostbite with tissue necrosis of head"
+T3401,"Frostbite with tissue necrosis of ear"
+T34011,"Frostbite with tissue necrosis of right ear"
+T34012,"Frostbite with tissue necrosis of left ear"
+T34019,"Frostbite with tissue necrosis of unspecified ear"
+T3402,"Frostbite with tissue necrosis of nose"
+T3409,"Frostbite with tissue necrosis of other part of head"
+T341,"Frostbite with tissue necrosis of neck"
+T342,"Frostbite with tissue necrosis of thorax"
+T343,"Frostbite with tissue necrosis of abdominal wall, lower back and pelvis"
+T344,"Frostbite with tissue necrosis of arm"
+T3440,"Frostbite with tissue necrosis of unspecified arm"
+T3441,"Frostbite with tissue necrosis of right arm"
+T3442,"Frostbite with tissue necrosis of left arm"
+T345,"Frostbite with tissue necrosis of wrist, hand, and finger(s)"
+T3451,"Frostbite with tissue necrosis of wrist"
+T34511,"Frostbite with tissue necrosis of right wrist"
+T34512,"Frostbite with tissue necrosis of left wrist"
+T34519,"Frostbite with tissue necrosis of unspecified wrist"
+T3452,"Frostbite with tissue necrosis of hand"
+T34521,"Frostbite with tissue necrosis of right hand"
+T34522,"Frostbite with tissue necrosis of left hand"
+T34529,"Frostbite with tissue necrosis of unspecified hand"
+T3453,"Frostbite with tissue necrosis of finger(s)"
+T34531,"Frostbite with tissue necrosis of right finger(s)"
+T34532,"Frostbite with tissue necrosis of left finger(s)"
+T34539,"Frostbite with tissue necrosis of unspecified finger(s)"
+T346,"Frostbite with tissue necrosis of hip and thigh"
+T3460,"Frostbite with tissue necrosis of unspecified hip and thigh"
+T3461,"Frostbite with tissue necrosis of right hip and thigh"
+T3462,"Frostbite with tissue necrosis of left hip and thigh"
+T347,"Frostbite with tissue necrosis of knee and lower leg"
+T3470,"Frostbite with tissue necrosis of unspecified knee and lower leg"
+T3471,"Frostbite with tissue necrosis of right knee and lower leg"
+T3472,"Frostbite with tissue necrosis of left knee and lower leg"
+T348,"Frostbite with tissue necrosis of ankle, foot, and toe(s)"
+T3481,"Frostbite with tissue necrosis of ankle"
+T34811,"Frostbite with tissue necrosis of right ankle"
+T34812,"Frostbite with tissue necrosis of left ankle"
+T34819,"Frostbite with tissue necrosis of unspecified ankle"
+T3482,"Frostbite with tissue necrosis of foot"
+T34821,"Frostbite with tissue necrosis of right foot"
+T34822,"Frostbite with tissue necrosis of left foot"
+T34829,"Frostbite with tissue necrosis of unspecified foot"
+T3483,"Frostbite with tissue necrosis of toe(s)"
+T34831,"Frostbite with tissue necrosis of right toe(s)"
+T34832,"Frostbite with tissue necrosis of left toe(s)"
+T34839,"Frostbite with tissue necrosis of unspecified toe(s)"
+T349,"Frostbite with tissue necrosis of other and unspecified sites"
+T3490,"Frostbite with tissue necrosis of unspecified sites"
+T3499,"Frostbite with tissue necrosis of other sites"
+T36,"Poisoning by, adverse effect of and underdosing of systemic antibiotics"
+T360,"Poisoning by, adverse effect of and underdosing of penicillins"
+T360X,"Poisoning by, adverse effect of and underdosing of penicillins"
+T360X1,"Poisoning by penicillins, accidental (unintentional)"
+T360X2,"Poisoning by penicillins, intentional self-harm"
+T360X3,"Poisoning by penicillins, assault"
+T360X4,"Poisoning by penicillins, undetermined"
+T360X5,"Adverse effect of penicillins"
+T360X6,"Underdosing of penicillins"
+T361,"Poisoning by, adverse effect of and underdosing of cephalosporins and other beta-lactam antibiotics"
+T361X,"Poisoning by, adverse effect of and underdosing of cephalosporins and other beta-lactam antibiotics"
+T361X1,"Poisoning by cephalosporins and other beta-lactam antibiotics, accidental (unintentional)"
+T361X2,"Poisoning by cephalosporins and other beta-lactam antibiotics, intentional self-harm"
+T361X3,"Poisoning by cephalosporins and other beta-lactam antibiotics, assault"
+T361X4,"Poisoning by cephalosporins and other beta-lactam antibiotics, undetermined"
+T361X5,"Adverse effect of cephalosporins and other beta-lactam antibiotics"
+T361X6,"Underdosing of cephalosporins and other beta-lactam antibiotics"
+T362,"Poisoning by, adverse effect of and underdosing of chloramphenicol group"
+T362X,"Poisoning by, adverse effect of and underdosing of chloramphenicol group"
+T362X1,"Poisoning by chloramphenicol group, accidental (unintentional)"
+T362X2,"Poisoning by chloramphenicol group, intentional self-harm"
+T362X3,"Poisoning by chloramphenicol group, assault"
+T362X4,"Poisoning by chloramphenicol group, undetermined"
+T362X5,"Adverse effect of chloramphenicol group"
+T362X6,"Underdosing of chloramphenicol group"
+T363,"Poisoning by, adverse effect of and underdosing of macrolides"
+T363X,"Poisoning by, adverse effect of and underdosing of macrolides"
+T363X1,"Poisoning by macrolides, accidental (unintentional)"
+T363X2,"Poisoning by macrolides, intentional self-harm"
+T363X3,"Poisoning by macrolides, assault"
+T363X4,"Poisoning by macrolides, undetermined"
+T363X5,"Adverse effect of macrolides"
+T363X6,"Underdosing of macrolides"
+T364,"Poisoning by, adverse effect of and underdosing of tetracyclines"
+T364X,"Poisoning by, adverse effect of and underdosing of tetracyclines"
+T364X1,"Poisoning by tetracyclines, accidental (unintentional)"
+T364X2,"Poisoning by tetracyclines, intentional self-harm"
+T364X3,"Poisoning by tetracyclines, assault"
+T364X4,"Poisoning by tetracyclines, undetermined"
+T364X5,"Adverse effect of tetracyclines"
+T364X6,"Underdosing of tetracyclines"
+T365,"Poisoning by, adverse effect of and underdosing of aminoglycosides"
+T365X,"Poisoning by, adverse effect of and underdosing of aminoglycosides"
+T365X1,"Poisoning by aminoglycosides, accidental (unintentional)"
+T365X2,"Poisoning by aminoglycosides, intentional self-harm"
+T365X3,"Poisoning by aminoglycosides, assault"
+T365X4,"Poisoning by aminoglycosides, undetermined"
+T365X5,"Adverse effect of aminoglycosides"
+T365X6,"Underdosing of aminoglycosides"
+T366,"Poisoning by, adverse effect of and underdosing of rifampicins"
+T366X,"Poisoning by, adverse effect of and underdosing of rifampicins"
+T366X1,"Poisoning by rifampicins, accidental (unintentional)"
+T366X2,"Poisoning by rifampicins, intentional self-harm"
+T366X3,"Poisoning by rifampicins, assault"
+T366X4,"Poisoning by rifampicins, undetermined"
+T366X5,"Adverse effect of rifampicins"
+T366X6,"Underdosing of rifampicins"
+T367,"Poisoning by, adverse effect of and underdosing of antifungal antibiotics, systemically used"
+T367X,"Poisoning by, adverse effect of and underdosing of antifungal antibiotics, systemically used"
+T367X1,"Poisoning by antifungal antibiotics, systemically used, accidental (unintentional)"
+T367X2,"Poisoning by antifungal antibiotics, systemically used, intentional self-harm"
+T367X3,"Poisoning by antifungal antibiotics, systemically used, assault"
+T367X4,"Poisoning by antifungal antibiotics, systemically used, undetermined"
+T367X5,"Adverse effect of antifungal antibiotics, systemically used"
+T367X6,"Underdosing of antifungal antibiotics, systemically used"
+T368,"Poisoning by, adverse effect of and underdosing of other systemic antibiotics"
+T368X,"Poisoning by, adverse effect of and underdosing of other systemic antibiotics"
+T368X1,"Poisoning by other systemic antibiotics, accidental (unintentional)"
+T368X2,"Poisoning by other systemic antibiotics, intentional self-harm"
+T368X3,"Poisoning by other systemic antibiotics, assault"
+T368X4,"Poisoning by other systemic antibiotics, undetermined"
+T368X5,"Adverse effect of other systemic antibiotics"
+T368X6,"Underdosing of other systemic antibiotics"
+T369,"Poisoning by, adverse effect of and underdosing of unspecified systemic antibiotic"
+T3691,"Poisoning by unspecified systemic antibiotic, accidental (unintentional)"
+T3692,"Poisoning by unspecified systemic antibiotic, intentional self-harm"
+T3693,"Poisoning by unspecified systemic antibiotic, assault"
+T3694,"Poisoning by unspecified systemic antibiotic, undetermined"
+T3695,"Adverse effect of unspecified systemic antibiotic"
+T3696,"Underdosing of unspecified systemic antibiotic"
+T37,"Poisoning by, adverse effect of and underdosing of other systemic anti- infectives and antiparasitics"
+T370,"Poisoning by, adverse effect of and underdosing of sulfonamides"
+T370X,"Poisoning by, adverse effect of and underdosing of sulfonamides"
+T370X1,"Poisoning by sulfonamides, accidental (unintentional)"
+T370X2,"Poisoning by sulfonamides, intentional self-harm"
+T370X3,"Poisoning by sulfonamides, assault"
+T370X4,"Poisoning by sulfonamides, undetermined"
+T370X5,"Adverse effect of sulfonamides"
+T370X6,"Underdosing of sulfonamides"
+T371,"Poisoning by, adverse effect of and underdosing of antimycobacterial drugs"
+T371X,"Poisoning by, adverse effect of and underdosing of antimycobacterial drugs"
+T371X1,"Poisoning by antimycobacterial drugs, accidental (unintentional)"
+T371X2,"Poisoning by antimycobacterial drugs, intentional self-harm"
+T371X3,"Poisoning by antimycobacterial drugs, assault"
+T371X4,"Poisoning by antimycobacterial drugs, undetermined"
+T371X5,"Adverse effect of antimycobacterial drugs"
+T371X6,"Underdosing of antimycobacterial drugs"
+T372,"Poisoning by, adverse effect of and underdosing of antimalarials and drugs acting on other blood protozoa"
+T372X,"Poisoning by, adverse effect of and underdosing of antimalarials and drugs acting on other blood protozoa"
+T372X1,"Poisoning by antimalarials and drugs acting on other blood protozoa, accidental (unintentional)"
+T372X2,"Poisoning by antimalarials and drugs acting on other blood protozoa, intentional self-harm"
+T372X3,"Poisoning by antimalarials and drugs acting on other blood protozoa, assault"
+T372X4,"Poisoning by antimalarials and drugs acting on other blood protozoa, undetermined"
+T372X5,"Adverse effect of antimalarials and drugs acting on other blood protozoa"
+T372X6,"Underdosing of antimalarials and drugs acting on other blood protozoa"
+T373,"Poisoning by, adverse effect of and underdosing of other antiprotozoal drugs"
+T373X,"Poisoning by, adverse effect of and underdosing of other antiprotozoal drugs"
+T373X1,"Poisoning by other antiprotozoal drugs, accidental (unintentional)"
+T373X2,"Poisoning by other antiprotozoal drugs, intentional self-harm"
+T373X3,"Poisoning by other antiprotozoal drugs, assault"
+T373X4,"Poisoning by other antiprotozoal drugs, undetermined"
+T373X5,"Adverse effect of other antiprotozoal drugs"
+T373X6,"Underdosing of other antiprotozoal drugs"
+T374,"Poisoning by, adverse effect of and underdosing of anthelminthics"
+T374X,"Poisoning by, adverse effect of and underdosing of anthelminthics"
+T374X1,"Poisoning by anthelminthics, accidental (unintentional)"
+T374X2,"Poisoning by anthelminthics, intentional self-harm"
+T374X3,"Poisoning by anthelminthics, assault"
+T374X4,"Poisoning by anthelminthics, undetermined"
+T374X5,"Adverse effect of anthelminthics"
+T374X6,"Underdosing of anthelminthics"
+T375,"Poisoning by, adverse effect of and underdosing of antiviral drugs"
+T375X,"Poisoning by, adverse effect of and underdosing of antiviral drugs"
+T375X1,"Poisoning by antiviral drugs, accidental (unintentional)"
+T375X2,"Poisoning by antiviral drugs, intentional self-harm"
+T375X3,"Poisoning by antiviral drugs, assault"
+T375X4,"Poisoning by antiviral drugs, undetermined"
+T375X5,"Adverse effect of antiviral drugs"
+T375X6,"Underdosing of antiviral drugs"
+T378,"Poisoning by, adverse effect of and underdosing of other specified systemic anti-infectives and antiparasitics"
+T378X,"Poisoning by, adverse effect of and underdosing of other specified systemic anti-infectives and antiparasitics"
+T378X1,"Poisoning by other specified systemic anti-infectives and antiparasitics, accidental (unintentional)"
+T378X2,"Poisoning by other specified systemic anti-infectives and antiparasitics, intentional self-harm"
+T378X3,"Poisoning by other specified systemic anti-infectives and antiparasitics, assault"
+T378X4,"Poisoning by other specified systemic anti-infectives and antiparasitics, undetermined"
+T378X5,"Adverse effect of other specified systemic anti-infectives and antiparasitics"
+T378X6,"Underdosing of other specified systemic anti-infectives and antiparasitics"
+T379,"Poisoning by, adverse effect of and underdosing of unspecified systemic anti-infective and antiparasitics"
+T3791,"Poisoning by unspecified systemic anti-infective and antiparasitics, accidental (unintentional)"
+T3792,"Poisoning by unspecified systemic anti-infective and antiparasitics, intentional self-harm"
+T3793,"Poisoning by unspecified systemic anti-infective and antiparasitics, assault"
+T3794,"Poisoning by unspecified systemic anti-infective and antiparasitics, undetermined"
+T3795,"Adverse effect of unspecified systemic anti-infective and antiparasitic"
+T3796,"Underdosing of unspecified systemic anti-infectives and antiparasitics"
+T38,"Poisoning by, adverse effect of and underdosing of hormones and their synthetic substitutes and antagonists, not elsewhere classified"
+T380,"Poisoning by, adverse effect of and underdosing of glucocorticoids and synthetic analogues"
+T380X,"Poisoning by, adverse effect of and underdosing of glucocorticoids and synthetic analogues"
+T380X1,"Poisoning by glucocorticoids and synthetic analogues, accidental (unintentional)"
+T380X2,"Poisoning by glucocorticoids and synthetic analogues, intentional self-harm"
+T380X3,"Poisoning by glucocorticoids and synthetic analogues, assault"
+T380X4,"Poisoning by glucocorticoids and synthetic analogues, undetermined"
+T380X5,"Adverse effect of glucocorticoids and synthetic analogues"
+T380X6,"Underdosing of glucocorticoids and synthetic analogues"
+T381,"Poisoning by, adverse effect of and underdosing of thyroid hormones and substitutes"
+T381X,"Poisoning by, adverse effect of and underdosing of thyroid hormones and substitutes"
+T381X1,"Poisoning by thyroid hormones and substitutes, accidental (unintentional)"
+T381X2,"Poisoning by thyroid hormones and substitutes, intentional self-harm"
+T381X3,"Poisoning by thyroid hormones and substitutes, assault"
+T381X4,"Poisoning by thyroid hormones and substitutes, undetermined"
+T381X5,"Adverse effect of thyroid hormones and substitutes"
+T381X6,"Underdosing of thyroid hormones and substitutes"
+T382,"Poisoning by, adverse effect of and underdosing of antithyroid drugs"
+T382X,"Poisoning by, adverse effect of and underdosing of antithyroid drugs"
+T382X1,"Poisoning by antithyroid drugs, accidental (unintentional)"
+T382X2,"Poisoning by antithyroid drugs, intentional self-harm"
+T382X3,"Poisoning by antithyroid drugs, assault"
+T382X4,"Poisoning by antithyroid drugs, undetermined"
+T382X5,"Adverse effect of antithyroid drugs"
+T382X6,"Underdosing of antithyroid drugs"
+T383,"Poisoning by, adverse effect of and underdosing of insulin and oral hypoglycemic [antidiabetic] drugs"
+T383X,"Poisoning by, adverse effect of and underdosing of insulin and oral hypoglycemic [antidiabetic] drugs"
+T383X1,"Poisoning by insulin and oral hypoglycemic [antidiabetic] drugs, accidental (unintentional)"
+T383X2,"Poisoning by insulin and oral hypoglycemic [antidiabetic] drugs, intentional self-harm"
+T383X3,"Poisoning by insulin and oral hypoglycemic [antidiabetic] drugs, assault"
+T383X4,"Poisoning by insulin and oral hypoglycemic [antidiabetic] drugs, undetermined"
+T383X5,"Adverse effect of insulin and oral hypoglycemic [antidiabetic] drugs"
+T383X6,"Underdosing of insulin and oral hypoglycemic [antidiabetic] drugs"
+T384,"Poisoning by, adverse effect of and underdosing of oral contraceptives"
+T384X,"Poisoning by, adverse effect of and underdosing of oral contraceptives"
+T384X1,"Poisoning by oral contraceptives, accidental (unintentional)"
+T384X2,"Poisoning by oral contraceptives, intentional self-harm"
+T384X3,"Poisoning by oral contraceptives, assault"
+T384X4,"Poisoning by oral contraceptives, undetermined"
+T384X5,"Adverse effect of oral contraceptives"
+T384X6,"Underdosing of oral contraceptives"
+T385,"Poisoning by, adverse effect of and underdosing of other estrogens and progestogens"
+T385X,"Poisoning by, adverse effect of and underdosing of other estrogens and progestogens"
+T385X1,"Poisoning by other estrogens and progestogens, accidental (unintentional)"
+T385X2,"Poisoning by other estrogens and progestogens, intentional self-harm"
+T385X3,"Poisoning by other estrogens and progestogens, assault"
+T385X4,"Poisoning by other estrogens and progestogens, undetermined"
+T385X5,"Adverse effect of other estrogens and progestogens"
+T385X6,"Underdosing of other estrogens and progestogens"
+T386,"Poisoning by, adverse effect of and underdosing of antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified"
+T386X,"Poisoning by, adverse effect of and underdosing of antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified"
+T386X1,"Poisoning by antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified, accidental (unintentional)"
+T386X2,"Poisoning by antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified, intentional self-harm"
+T386X3,"Poisoning by antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified, assault"
+T386X4,"Poisoning by antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified, undetermined"
+T386X5,"Adverse effect of antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified"
+T386X6,"Underdosing of antigonadotrophins, antiestrogens, antiandrogens, not elsewhere classified"
+T387,"Poisoning by, adverse effect of and underdosing of androgens and anabolic congeners"
+T387X,"Poisoning by, adverse effect of and underdosing of androgens and anabolic congeners"
+T387X1,"Poisoning by androgens and anabolic congeners, accidental (unintentional)"
+T387X2,"Poisoning by androgens and anabolic congeners, intentional self-harm"
+T387X3,"Poisoning by androgens and anabolic congeners, assault"
+T387X4,"Poisoning by androgens and anabolic congeners, undetermined"
+T387X5,"Adverse effect of androgens and anabolic congeners"
+T387X6,"Underdosing of androgens and anabolic congeners"
+T388,"Poisoning by, adverse effect of and underdosing of other and unspecified hormones and synthetic substitutes"
+T3880,"Poisoning by, adverse effect of and underdosing of unspecified hormones and synthetic substitutes"
+T38801,"Poisoning by unspecified hormones and synthetic substitutes, accidental (unintentional)"
+T38802,"Poisoning by unspecified hormones and synthetic substitutes, intentional self-harm"
+T38803,"Poisoning by unspecified hormones and synthetic substitutes, assault"
+T38804,"Poisoning by unspecified hormones and synthetic substitutes, undetermined"
+T38805,"Adverse effect of unspecified hormones and synthetic substitutes"
+T38806,"Underdosing of unspecified hormones and synthetic substitutes"
+T3881,"Poisoning by, adverse effect of and underdosing of anterior pituitary [adenohypophyseal] hormones"
+T38811,"Poisoning by anterior pituitary [adenohypophyseal] hormones, accidental (unintentional)"
+T38812,"Poisoning by anterior pituitary [adenohypophyseal] hormones, intentional self-harm"
+T38813,"Poisoning by anterior pituitary [adenohypophyseal] hormones, assault"
+T38814,"Poisoning by anterior pituitary [adenohypophyseal] hormones, undetermined"
+T38815,"Adverse effect of anterior pituitary [adenohypophyseal] hormones"
+T38816,"Underdosing of anterior pituitary [adenohypophyseal] hormones"
+T3889,"Poisoning by, adverse effect of and underdosing of other hormones and synthetic substitutes"
+T38891,"Poisoning by other hormones and synthetic substitutes, accidental (unintentional)"
+T38892,"Poisoning by other hormones and synthetic substitutes, intentional self-harm"
+T38893,"Poisoning by other hormones and synthetic substitutes, assault"
+T38894,"Poisoning by other hormones and synthetic substitutes, undetermined"
+T38895,"Adverse effect of other hormones and synthetic substitutes"
+T38896,"Underdosing of other hormones and synthetic substitutes"
+T389,"Poisoning by, adverse effect of and underdosing of other and unspecified hormone antagonists"
+T3890,"Poisoning by, adverse effect of and underdosing of unspecified hormone antagonists"
+T38901,"Poisoning by unspecified hormone antagonists, accidental (unintentional)"
+T38902,"Poisoning by unspecified hormone antagonists, intentional self-harm"
+T38903,"Poisoning by unspecified hormone antagonists, assault"
+T38904,"Poisoning by unspecified hormone antagonists, undetermined"
+T38905,"Adverse effect of unspecified hormone antagonists"
+T38906,"Underdosing of unspecified hormone antagonists"
+T3899,"Poisoning by, adverse effect of and underdosing of other hormone antagonists"
+T38991,"Poisoning by other hormone antagonists, accidental (unintentional)"
+T38992,"Poisoning by other hormone antagonists, intentional self-harm"
+T38993,"Poisoning by other hormone antagonists, assault"
+T38994,"Poisoning by other hormone antagonists, undetermined"
+T38995,"Adverse effect of other hormone antagonists"
+T38996,"Underdosing of other hormone antagonists"
+T39,"Poisoning by, adverse effect of and underdosing of nonopioid analgesics, antipyretics and antirheumatics"
+T390,"Poisoning by, adverse effect of and underdosing of salicylates"
+T3901,"Poisoning by, adverse effect of and underdosing of aspirin"
+T39011,"Poisoning by aspirin, accidental (unintentional)"
+T39012,"Poisoning by aspirin, intentional self-harm"
+T39013,"Poisoning by aspirin, assault"
+T39014,"Poisoning by aspirin, undetermined"
+T39015,"Adverse effect of aspirin"
+T39016,"Underdosing of aspirin"
+T3909,"Poisoning by, adverse effect of and underdosing of other salicylates"
+T39091,"Poisoning by salicylates, accidental (unintentional)"
+T39092,"Poisoning by salicylates, intentional self-harm"
+T39093,"Poisoning by salicylates, assault"
+T39094,"Poisoning by salicylates, undetermined"
+T39095,"Adverse effect of salicylates"
+T39096,"Underdosing of salicylates"
+T391,"Poisoning by, adverse effect of and underdosing of 4-Aminophenol derivatives"
+T391X,"Poisoning by, adverse effect of and underdosing of 4-Aminophenol derivatives"
+T391X1,"Poisoning by 4-Aminophenol derivatives, accidental (unintentional)"
+T391X2,"Poisoning by 4-Aminophenol derivatives, intentional self-harm"
+T391X3,"Poisoning by 4-Aminophenol derivatives, assault"
+T391X4,"Poisoning by 4-Aminophenol derivatives, undetermined"
+T391X5,"Adverse effect of 4-Aminophenol derivatives"
+T391X6,"Underdosing of 4-Aminophenol derivatives"
+T392,"Poisoning by, adverse effect of and underdosing of pyrazolone derivatives"
+T392X,"Poisoning by, adverse effect of and underdosing of pyrazolone derivatives"
+T392X1,"Poisoning by pyrazolone derivatives, accidental (unintentional)"
+T392X2,"Poisoning by pyrazolone derivatives, intentional self-harm"
+T392X3,"Poisoning by pyrazolone derivatives, assault"
+T392X4,"Poisoning by pyrazolone derivatives, undetermined"
+T392X5,"Adverse effect of pyrazolone derivatives"
+T392X6,"Underdosing of pyrazolone derivatives"
+T393,"Poisoning by, adverse effect of and underdosing of other nonsteroidal anti-inflammatory drugs [NSAID]"
+T3931,"Poisoning by, adverse effect of and underdosing of propionic acid derivatives"
+T39311,"Poisoning by propionic acid derivatives, accidental (unintentional)"
+T39312,"Poisoning by propionic acid derivatives, intentional self-harm"
+T39313,"Poisoning by propionic acid derivatives, assault"
+T39314,"Poisoning by propionic acid derivatives, undetermined"
+T39315,"Adverse effect of propionic acid derivatives"
+T39316,"Underdosing of propionic acid derivatives"
+T3939,"Poisoning by, adverse effect of and underdosing of other nonsteroidal anti-inflammatory drugs [NSAID]"
+T39391,"Poisoning by other nonsteroidal anti-inflammatory drugs [NSAID], accidental (unintentional)"
+T39392,"Poisoning by other nonsteroidal anti-inflammatory drugs [NSAID], intentional self-harm"
+T39393,"Poisoning by other nonsteroidal anti-inflammatory drugs [NSAID], assault"
+T39394,"Poisoning by other nonsteroidal anti-inflammatory drugs [NSAID], undetermined"
+T39395,"Adverse effect of other nonsteroidal anti-inflammatory drugs [NSAID]"
+T39396,"Underdosing of other nonsteroidal anti-inflammatory drugs [NSAID]"
+T394,"Poisoning by, adverse effect of and underdosing of antirheumatics, not elsewhere classified"
+T394X,"Poisoning by, adverse effect of and underdosing of antirheumatics, not elsewhere classified"
+T394X1,"Poisoning by antirheumatics, not elsewhere classified, accidental (unintentional)"
+T394X2,"Poisoning by antirheumatics, not elsewhere classified, intentional self-harm"
+T394X3,"Poisoning by antirheumatics, not elsewhere classified, assault"
+T394X4,"Poisoning by antirheumatics, not elsewhere classified, undetermined"
+T394X5,"Adverse effect of antirheumatics, not elsewhere classified"
+T394X6,"Underdosing of antirheumatics, not elsewhere classified"
+T398,"Poisoning by, adverse effect of and underdosing of other nonopioid analgesics and antipyretics, not elsewhere classified"
+T398X,"Poisoning by, adverse effect of and underdosing of other nonopioid analgesics and antipyretics, not elsewhere classified"
+T398X1,"Poisoning by other nonopioid analgesics and antipyretics, not elsewhere classified, accidental (unintentional)"
+T398X2,"Poisoning by other nonopioid analgesics and antipyretics, not elsewhere classified, intentional self-harm"
+T398X3,"Poisoning by other nonopioid analgesics and antipyretics, not elsewhere classified, assault"
+T398X4,"Poisoning by other nonopioid analgesics and antipyretics, not elsewhere classified, undetermined"
+T398X5,"Adverse effect of other nonopioid analgesics and antipyretics, not elsewhere classified"
+T398X6,"Underdosing of other nonopioid analgesics and antipyretics, not elsewhere classified"
+T399,"Poisoning by, adverse effect of and underdosing of unspecified nonopioid analgesic, antipyretic and antirheumatic"
+T3991,"Poisoning by unspecified nonopioid analgesic, antipyretic and antirheumatic, accidental (unintentional)"
+T3992,"Poisoning by unspecified nonopioid analgesic, antipyretic and antirheumatic, intentional self-harm"
+T3993,"Poisoning by unspecified nonopioid analgesic, antipyretic and antirheumatic, assault"
+T3994,"Poisoning by unspecified nonopioid analgesic, antipyretic and antirheumatic, undetermined"
+T3995,"Adverse effect of unspecified nonopioid analgesic, antipyretic and antirheumatic"
+T3996,"Underdosing of unspecified nonopioid analgesic, antipyretic and antirheumatic"
+T40,"Poisoning by, adverse effect of and underdosing of narcotics and psychodysleptics [hallucinogens]"
+T400,"Poisoning by, adverse effect of and underdosing of opium"
+T400X,"Poisoning by, adverse effect of and underdosing of opium"
+T400X1,"Poisoning by opium, accidental (unintentional)"
+T400X2,"Poisoning by opium, intentional self-harm"
+T400X3,"Poisoning by opium, assault"
+T400X4,"Poisoning by opium, undetermined"
+T400X5,"Adverse effect of opium"
+T400X6,"Underdosing of opium"
+T401,"Poisoning by and adverse effect of heroin"
+T401X,"Poisoning by and adverse effect of heroin"
+T401X1,"Poisoning by heroin, accidental (unintentional)"
+T401X2,"Poisoning by heroin, intentional self-harm"
+T401X3,"Poisoning by heroin, assault"
+T401X4,"Poisoning by heroin, undetermined"
+T402,"Poisoning by, adverse effect of and underdosing of other opioids"
+T402X,"Poisoning by, adverse effect of and underdosing of other opioids"
+T402X1,"Poisoning by other opioids, accidental (unintentional)"
+T402X2,"Poisoning by other opioids, intentional self-harm"
+T402X3,"Poisoning by other opioids, assault"
+T402X4,"Poisoning by other opioids, undetermined"
+T402X5,"Adverse effect of other opioids"
+T402X6,"Underdosing of other opioids"
+T403,"Poisoning by, adverse effect of and underdosing of methadone"
+T403X,"Poisoning by, adverse effect of and underdosing of methadone"
+T403X1,"Poisoning by methadone, accidental (unintentional)"
+T403X2,"Poisoning by methadone, intentional self-harm"
+T403X3,"Poisoning by methadone, assault"
+T403X4,"Poisoning by methadone, undetermined"
+T403X5,"Adverse effect of methadone"
+T403X6,"Underdosing of methadone"
+T404,"Poisoning by, adverse effect of and underdosing of other synthetic narcotics"
+T404X,"Poisoning by, adverse effect of and underdosing of other synthetic narcotics"
+T404X1,"Poisoning by other synthetic narcotics, accidental (unintentional)"
+T404X2,"Poisoning by other synthetic narcotics, intentional self-harm"
+T404X3,"Poisoning by other synthetic narcotics, assault"
+T404X4,"Poisoning by other synthetic narcotics, undetermined"
+T404X5,"Adverse effect of other synthetic narcotics"
+T404X6,"Underdosing of other synthetic narcotics"
+T405,"Poisoning by, adverse effect of and underdosing of cocaine"
+T405X,"Poisoning by, adverse effect of and underdosing of cocaine"
+T405X1,"Poisoning by cocaine, accidental (unintentional)"
+T405X2,"Poisoning by cocaine, intentional self-harm"
+T405X3,"Poisoning by cocaine, assault"
+T405X4,"Poisoning by cocaine, undetermined"
+T405X5,"Adverse effect of cocaine"
+T405X6,"Underdosing of cocaine"
+T406,"Poisoning by, adverse effect of and underdosing of other and unspecified narcotics"
+T4060,"Poisoning by, adverse effect of and underdosing of unspecified narcotics"
+T40601,"Poisoning by unspecified narcotics, accidental (unintentional)"
+T40602,"Poisoning by unspecified narcotics, intentional self-harm"
+T40603,"Poisoning by unspecified narcotics, assault"
+T40604,"Poisoning by unspecified narcotics, undetermined"
+T40605,"Adverse effect of unspecified narcotics"
+T40606,"Underdosing of unspecified narcotics"
+T4069,"Poisoning by, adverse effect of and underdosing of other narcotics"
+T40691,"Poisoning by other narcotics, accidental (unintentional)"
+T40692,"Poisoning by other narcotics, intentional self-harm"
+T40693,"Poisoning by other narcotics, assault"
+T40694,"Poisoning by other narcotics, undetermined"
+T40695,"Adverse effect of other narcotics"
+T40696,"Underdosing of other narcotics"
+T407,"Poisoning by, adverse effect of and underdosing of cannabis (derivatives)"
+T407X,"Poisoning by, adverse effect of and underdosing of cannabis (derivatives)"
+T407X1,"Poisoning by cannabis (derivatives), accidental (unintentional)"
+T407X2,"Poisoning by cannabis (derivatives), intentional self-harm"
+T407X3,"Poisoning by cannabis (derivatives), assault"
+T407X4,"Poisoning by cannabis (derivatives), undetermined"
+T407X5,"Adverse effect of cannabis (derivatives)"
+T407X6,"Underdosing of cannabis (derivatives)"
+T408,"Poisoning by and adverse effect of lysergide [LSD]"
+T408X,"Poisoning by and adverse effect of lysergide [LSD]"
+T408X1,"Poisoning by lysergide [LSD], accidental (unintentional)"
+T408X2,"Poisoning by lysergide [LSD], intentional self-harm"
+T408X3,"Poisoning by lysergide [LSD], assault"
+T408X4,"Poisoning by lysergide [LSD], undetermined"
+T409,"Poisoning by, adverse effect of and underdosing of other and unspecified psychodysleptics [hallucinogens]"
+T4090,"Poisoning by, adverse effect of and underdosing of unspecified psychodysleptics [hallucinogens]"
+T40901,"Poisoning by unspecified psychodysleptics [hallucinogens], accidental (unintentional)"
+T40902,"Poisoning by unspecified psychodysleptics [hallucinogens], intentional self-harm"
+T40903,"Poisoning by unspecified psychodysleptics [hallucinogens], assault"
+T40904,"Poisoning by unspecified psychodysleptics [hallucinogens], undetermined"
+T40905,"Adverse effect of unspecified psychodysleptics [hallucinogens]"
+T40906,"Underdosing of unspecified psychodysleptics"
+T4099,"Poisoning by, adverse effect of and underdosing of other psychodysleptics [hallucinogens]"
+T40991,"Poisoning by other psychodysleptics [hallucinogens], accidental (unintentional)"
+T40992,"Poisoning by other psychodysleptics [hallucinogens], intentional self-harm"
+T40993,"Poisoning by other psychodysleptics [hallucinogens], assault"
+T40994,"Poisoning by other psychodysleptics [hallucinogens], undetermined"
+T40995,"Adverse effect of other psychodysleptics [hallucinogens]"
+T40996,"Underdosing of other psychodysleptics"
+T41,"Poisoning by, adverse effect of and underdosing of anesthetics and therapeutic gases"
+T410,"Poisoning by, adverse effect of and underdosing of inhaled anesthetics"
+T410X,"Poisoning by, adverse effect of and underdosing of inhaled anesthetics"
+T410X1,"Poisoning by inhaled anesthetics, accidental (unintentional)"
+T410X2,"Poisoning by inhaled anesthetics, intentional self-harm"
+T410X3,"Poisoning by inhaled anesthetics, assault"
+T410X4,"Poisoning by inhaled anesthetics, undetermined"
+T410X5,"Adverse effect of inhaled anesthetics"
+T410X6,"Underdosing of inhaled anesthetics"
+T411,"Poisoning by, adverse effect of and underdosing of intravenous anesthetics"
+T411X,"Poisoning by, adverse effect of and underdosing of intravenous anesthetics"
+T411X1,"Poisoning by intravenous anesthetics, accidental (unintentional)"
+T411X2,"Poisoning by intravenous anesthetics, intentional self-harm"
+T411X3,"Poisoning by intravenous anesthetics, assault"
+T411X4,"Poisoning by intravenous anesthetics, undetermined"
+T411X5,"Adverse effect of intravenous anesthetics"
+T411X6,"Underdosing of intravenous anesthetics"
+T412,"Poisoning by, adverse effect of and underdosing of other and unspecified general anesthetics"
+T4120,"Poisoning by, adverse effect of and underdosing of unspecified general anesthetics"
+T41201,"Poisoning by unspecified general anesthetics, accidental (unintentional)"
+T41202,"Poisoning by unspecified general anesthetics, intentional self-harm"
+T41203,"Poisoning by unspecified general anesthetics, assault"
+T41204,"Poisoning by unspecified general anesthetics, undetermined"
+T41205,"Adverse effect of unspecified general anesthetics"
+T41206,"Underdosing of unspecified general anesthetics"
+T4129,"Poisoning by, adverse effect of and underdosing of other general anesthetics"
+T41291,"Poisoning by other general anesthetics, accidental (unintentional)"
+T41292,"Poisoning by other general anesthetics, intentional self-harm"
+T41293,"Poisoning by other general anesthetics, assault"
+T41294,"Poisoning by other general anesthetics, undetermined"
+T41295,"Adverse effect of other general anesthetics"
+T41296,"Underdosing of other general anesthetics"
+T413,"Poisoning by, adverse effect of and underdosing of local anesthetics"
+T413X,"Poisoning by, adverse effect of and underdosing of local anesthetics"
+T413X1,"Poisoning by local anesthetics, accidental (unintentional)"
+T413X2,"Poisoning by local anesthetics, intentional self-harm"
+T413X3,"Poisoning by local anesthetics, assault"
+T413X4,"Poisoning by local anesthetics, undetermined"
+T413X5,"Adverse effect of local anesthetics"
+T413X6,"Underdosing of local anesthetics"
+T414,"Poisoning by, adverse effect of and underdosing of unspecified anesthetic"
+T4141,"Poisoning by unspecified anesthetic, accidental (unintentional)"
+T4142,"Poisoning by unspecified anesthetic, intentional self-harm"
+T4143,"Poisoning by unspecified anesthetic, assault"
+T4144,"Poisoning by unspecified anesthetic, undetermined"
+T4145,"Adverse effect of unspecified anesthetic"
+T4146,"Underdosing of unspecified anesthetics"
+T415,"Poisoning by, adverse effect of and underdosing of therapeutic gases"
+T415X,"Poisoning by, adverse effect of and underdosing of therapeutic gases"
+T415X1,"Poisoning by therapeutic gases, accidental (unintentional)"
+T415X2,"Poisoning by therapeutic gases, intentional self-harm"
+T415X3,"Poisoning by therapeutic gases, assault"
+T415X4,"Poisoning by therapeutic gases, undetermined"
+T415X5,"Adverse effect of therapeutic gases"
+T415X6,"Underdosing of therapeutic gases"
+T42,"Poisoning by, adverse effect of and underdosing of antiepileptic, sedative- hypnotic and antiparkinsonism drugs"
+T420,"Poisoning by, adverse effect of and underdosing of hydantoin derivatives"
+T420X,"Poisoning by, adverse effect of and underdosing of hydantoin derivatives"
+T420X1,"Poisoning by hydantoin derivatives, accidental (unintentional)"
+T420X2,"Poisoning by hydantoin derivatives, intentional self-harm"
+T420X3,"Poisoning by hydantoin derivatives, assault"
+T420X4,"Poisoning by hydantoin derivatives, undetermined"
+T420X5,"Adverse effect of hydantoin derivatives"
+T420X6,"Underdosing of hydantoin derivatives"
+T421,"Poisoning by, adverse effect of and underdosing of iminostilbenes"
+T421X,"Poisoning by, adverse effect of and underdosing of iminostilbenes"
+T421X1,"Poisoning by iminostilbenes, accidental (unintentional)"
+T421X2,"Poisoning by iminostilbenes, intentional self-harm"
+T421X3,"Poisoning by iminostilbenes, assault"
+T421X4,"Poisoning by iminostilbenes, undetermined"
+T421X5,"Adverse effect of iminostilbenes"
+T421X6,"Underdosing of iminostilbenes"
+T422,"Poisoning by, adverse effect of and underdosing of succinimides and oxazolidinediones"
+T422X,"Poisoning by, adverse effect of and underdosing of succinimides and oxazolidinediones"
+T422X1,"Poisoning by succinimides and oxazolidinediones, accidental (unintentional)"
+T422X2,"Poisoning by succinimides and oxazolidinediones, intentional self-harm"
+T422X3,"Poisoning by succinimides and oxazolidinediones, assault"
+T422X4,"Poisoning by succinimides and oxazolidinediones, undetermined"
+T422X5,"Adverse effect of succinimides and oxazolidinediones"
+T422X6,"Underdosing of succinimides and oxazolidinediones"
+T423,"Poisoning by, adverse effect of and underdosing of barbiturates"
+T423X,"Poisoning by, adverse effect of and underdosing of barbiturates"
+T423X1,"Poisoning by barbiturates, accidental (unintentional)"
+T423X2,"Poisoning by barbiturates, intentional self-harm"
+T423X3,"Poisoning by barbiturates, assault"
+T423X4,"Poisoning by barbiturates, undetermined"
+T423X5,"Adverse effect of barbiturates"
+T423X6,"Underdosing of barbiturates"
+T424,"Poisoning by, adverse effect of and underdosing of benzodiazepines"
+T424X,"Poisoning by, adverse effect of and underdosing of benzodiazepines"
+T424X1,"Poisoning by benzodiazepines, accidental (unintentional)"
+T424X2,"Poisoning by benzodiazepines, intentional self-harm"
+T424X3,"Poisoning by benzodiazepines, assault"
+T424X4,"Poisoning by benzodiazepines, undetermined"
+T424X5,"Adverse effect of benzodiazepines"
+T424X6,"Underdosing of benzodiazepines"
+T425,"Poisoning by, adverse effect of and underdosing of mixed antiepileptics"
+T425X,"Poisoning by, adverse effect of and underdosing of antiepileptics"
+T425X1,"Poisoning by mixed antiepileptics, accidental (unintentional)"
+T425X2,"Poisoning by mixed antiepileptics, intentional self-harm"
+T425X3,"Poisoning by mixed antiepileptics, assault"
+T425X4,"Poisoning by mixed antiepileptics, undetermined"
+T425X5,"Adverse effect of mixed antiepileptics"
+T425X6,"Underdosing of mixed antiepileptics"
+T426,"Poisoning by, adverse effect of and underdosing of other antiepileptic and sedative-hypnotic drugs"
+T426X,"Poisoning by, adverse effect of and underdosing of other antiepileptic and sedative-hypnotic drugs"
+T426X1,"Poisoning by other antiepileptic and sedative-hypnotic drugs, accidental (unintentional)"
+T426X2,"Poisoning by other antiepileptic and sedative-hypnotic drugs, intentional self-harm"
+T426X3,"Poisoning by other antiepileptic and sedative-hypnotic drugs, assault"
+T426X4,"Poisoning by other antiepileptic and sedative-hypnotic drugs, undetermined"
+T426X5,"Adverse effect of other antiepileptic and sedative-hypnotic drugs"
+T426X6,"Underdosing of other antiepileptic and sedative-hypnotic drugs"
+T427,"Poisoning by, adverse effect of and underdosing of unspecified antiepileptic and sedative-hypnotic drugs"
+T4271,"Poisoning by unspecified antiepileptic and sedative-hypnotic drugs, accidental (unintentional)"
+T4272,"Poisoning by unspecified antiepileptic and sedative-hypnotic drugs, intentional self-harm"
+T4273,"Poisoning by unspecified antiepileptic and sedative-hypnotic drugs, assault"
+T4274,"Poisoning by unspecified antiepileptic and sedative-hypnotic drugs, undetermined"
+T4275,"Adverse effect of unspecified antiepileptic and sedative-hypnotic drugs"
+T4276,"Underdosing of unspecified antiepileptic and sedative-hypnotic drugs"
+T428,"Poisoning by, adverse effect of and underdosing of antiparkinsonism drugs and other central muscle-tone depressants"
+T428X,"Poisoning by, adverse effect of and underdosing of antiparkinsonism drugs and other central muscle-tone depressants"
+T428X1,"Poisoning by antiparkinsonism drugs and other central muscle-tone depressants, accidental (unintentional)"
+T428X2,"Poisoning by antiparkinsonism drugs and other central muscle-tone depressants, intentional self-harm"
+T428X3,"Poisoning by antiparkinsonism drugs and other central muscle-tone depressants, assault"
+T428X4,"Poisoning by antiparkinsonism drugs and other central muscle-tone depressants, undetermined"
+T428X5,"Adverse effect of antiparkinsonism drugs and other central muscle-tone depressants"
+T428X6,"Underdosing of antiparkinsonism drugs and other central muscle-tone depressants"
+T43,"Poisoning by, adverse effect of and underdosing of psychotropic drugs, not elsewhere classified"
+T430,"Poisoning by, adverse effect of and underdosing of tricyclic and tetracyclic antidepressants"
+T4301,"Poisoning by, adverse effect of and underdosing of tricyclic antidepressants"
+T43011,"Poisoning by tricyclic antidepressants, accidental (unintentional)"
+T43012,"Poisoning by tricyclic antidepressants, intentional self-harm"
+T43013,"Poisoning by tricyclic antidepressants, assault"
+T43014,"Poisoning by tricyclic antidepressants, undetermined"
+T43015,"Adverse effect of tricyclic antidepressants"
+T43016,"Underdosing of tricyclic antidepressants"
+T4302,"Poisoning by, adverse effect of and underdosing of tetracyclic antidepressants"
+T43021,"Poisoning by tetracyclic antidepressants, accidental (unintentional)"
+T43022,"Poisoning by tetracyclic antidepressants, intentional self-harm"
+T43023,"Poisoning by tetracyclic antidepressants, assault"
+T43024,"Poisoning by tetracyclic antidepressants, undetermined"
+T43025,"Adverse effect of tetracyclic antidepressants"
+T43026,"Underdosing of tetracyclic antidepressants"
+T431,"Poisoning by, adverse effect of and underdosing of monoamine-oxidase-inhibitor antidepressants"
+T431X,"Poisoning by, adverse effect of and underdosing of monoamine-oxidase-inhibitor antidepressants"
+T431X1,"Poisoning by monoamine-oxidase-inhibitor antidepressants, accidental (unintentional)"
+T431X2,"Poisoning by monoamine-oxidase-inhibitor antidepressants, intentional self-harm"
+T431X3,"Poisoning by monoamine-oxidase-inhibitor antidepressants, assault"
+T431X4,"Poisoning by monoamine-oxidase-inhibitor antidepressants, undetermined"
+T431X5,"Adverse effect of monoamine-oxidase-inhibitor antidepressants"
+T431X6,"Underdosing of monoamine-oxidase-inhibitor antidepressants"
+T432,"Poisoning by, adverse effect of and underdosing of other and unspecified antidepressants"
+T4320,"Poisoning by, adverse effect of and underdosing of unspecified antidepressants"
+T43201,"Poisoning by unspecified antidepressants, accidental (unintentional)"
+T43202,"Poisoning by unspecified antidepressants, intentional self-harm"
+T43203,"Poisoning by unspecified antidepressants, assault"
+T43204,"Poisoning by unspecified antidepressants, undetermined"
+T43205,"Adverse effect of unspecified antidepressants"
+T43206,"Underdosing of unspecified antidepressants"
+T4321,"Poisoning by, adverse effect of and underdosing of selective serotonin and norepinephrine reuptake inhibitors"
+T43211,"Poisoning by selective serotonin and norepinephrine reuptake inhibitors, accidental (unintentional)"
+T43212,"Poisoning by selective serotonin and norepinephrine reuptake inhibitors, intentional self-harm"
+T43213,"Poisoning by selective serotonin and norepinephrine reuptake inhibitors, assault"
+T43214,"Poisoning by selective serotonin and norepinephrine reuptake inhibitors, undetermined"
+T43215,"Adverse effect of selective serotonin and norepinephrine reuptake inhibitors"
+T43216,"Underdosing of selective serotonin and norepinephrine reuptake inhibitors"
+T4322,"Poisoning by, adverse effect of and underdosing of selective serotonin reuptake inhibitors"
+T43221,"Poisoning by selective serotonin reuptake inhibitors, accidental (unintentional)"
+T43222,"Poisoning by selective serotonin reuptake inhibitors, intentional self-harm"
+T43223,"Poisoning by selective serotonin reuptake inhibitors, assault"
+T43224,"Poisoning by selective serotonin reuptake inhibitors, undetermined"
+T43225,"Adverse effect of selective serotonin reuptake inhibitors"
+T43226,"Underdosing of selective serotonin reuptake inhibitors"
+T4329,"Poisoning by, adverse effect of and underdosing of other antidepressants"
+T43291,"Poisoning by other antidepressants, accidental (unintentional)"
+T43292,"Poisoning by other antidepressants, intentional self-harm"
+T43293,"Poisoning by other antidepressants, assault"
+T43294,"Poisoning by other antidepressants, undetermined"
+T43295,"Adverse effect of other antidepressants"
+T43296,"Underdosing of other antidepressants"
+T433,"Poisoning by, adverse effect of and underdosing of phenothiazine antipsychotics and neuroleptics"
+T433X,"Poisoning by, adverse effect of and underdosing of phenothiazine antipsychotics and neuroleptics"
+T433X1,"Poisoning by phenothiazine antipsychotics and neuroleptics, accidental (unintentional)"
+T433X2,"Poisoning by phenothiazine antipsychotics and neuroleptics, intentional self-harm"
+T433X3,"Poisoning by phenothiazine antipsychotics and neuroleptics, assault"
+T433X4,"Poisoning by phenothiazine antipsychotics and neuroleptics, undetermined"
+T433X5,"Adverse effect of phenothiazine antipsychotics and neuroleptics"
+T433X6,"Underdosing of phenothiazine antipsychotics and neuroleptics"
+T434,"Poisoning by, adverse effect of and underdosing of butyrophenone and thiothixene neuroleptics"
+T434X,"Poisoning by, adverse effect of and underdosing of butyrophenone and thiothixene neuroleptics"
+T434X1,"Poisoning by butyrophenone and thiothixene neuroleptics, accidental (unintentional)"
+T434X2,"Poisoning by butyrophenone and thiothixene neuroleptics, intentional self-harm"
+T434X3,"Poisoning by butyrophenone and thiothixene neuroleptics, assault"
+T434X4,"Poisoning by butyrophenone and thiothixene neuroleptics, undetermined"
+T434X5,"Adverse effect of butyrophenone and thiothixene neuroleptics"
+T434X6,"Underdosing of butyrophenone and thiothixene neuroleptics"
+T435,"Poisoning by, adverse effect of and underdosing of other and unspecified antipsychotics and neuroleptics"
+T4350,"Poisoning by, adverse effect of and underdosing of unspecified antipsychotics and neuroleptics"
+T43501,"Poisoning by unspecified antipsychotics and neuroleptics, accidental (unintentional)"
+T43502,"Poisoning by unspecified antipsychotics and neuroleptics, intentional self-harm"
+T43503,"Poisoning by unspecified antipsychotics and neuroleptics, assault"
+T43504,"Poisoning by unspecified antipsychotics and neuroleptics, undetermined"
+T43505,"Adverse effect of unspecified antipsychotics and neuroleptics"
+T43506,"Underdosing of unspecified antipsychotics and neuroleptics"
+T4359,"Poisoning by, adverse effect of and underdosing of other antipsychotics and neuroleptics"
+T43591,"Poisoning by other antipsychotics and neuroleptics, accidental (unintentional)"
+T43592,"Poisoning by other antipsychotics and neuroleptics, intentional self-harm"
+T43593,"Poisoning by other antipsychotics and neuroleptics, assault"
+T43594,"Poisoning by other antipsychotics and neuroleptics, undetermined"
+T43595,"Adverse effect of other antipsychotics and neuroleptics"
+T43596,"Underdosing of other antipsychotics and neuroleptics"
+T436,"Poisoning by, adverse effect of and underdosing of psychostimulants"
+T4360,"Poisoning by, adverse effect of and underdosing of unspecified psychostimulant"
+T43601,"Poisoning by unspecified psychostimulants, accidental (unintentional)"
+T43602,"Poisoning by unspecified psychostimulants, intentional self-harm"
+T43603,"Poisoning by unspecified psychostimulants, assault"
+T43604,"Poisoning by unspecified psychostimulants, undetermined"
+T43605,"Adverse effect of unspecified psychostimulants"
+T43606,"Underdosing of unspecified psychostimulants"
+T4361,"Poisoning by, adverse effect of and underdosing of caffeine"
+T43611,"Poisoning by caffeine, accidental (unintentional)"
+T43612,"Poisoning by caffeine, intentional self-harm"
+T43613,"Poisoning by caffeine, assault"
+T43614,"Poisoning by caffeine, undetermined"
+T43615,"Adverse effect of caffeine"
+T43616,"Underdosing of caffeine"
+T4362,"Poisoning by, adverse effect of and underdosing of amphetamines"
+T43621,"Poisoning by amphetamines, accidental (unintentional)"
+T43622,"Poisoning by amphetamines, intentional self-harm"
+T43623,"Poisoning by amphetamines, assault"
+T43624,"Poisoning by amphetamines, undetermined"
+T43625,"Adverse effect of amphetamines"
+T43626,"Underdosing of amphetamines"
+T4363,"Poisoning by, adverse effect of and underdosing of methylphenidate"
+T43631,"Poisoning by methylphenidate, accidental (unintentional)"
+T43632,"Poisoning by methylphenidate, intentional self-harm"
+T43633,"Poisoning by methylphenidate, assault"
+T43634,"Poisoning by methylphenidate, undetermined"
+T43635,"Adverse effect of methylphenidate"
+T43636,"Underdosing of methylphenidate"
+T4369,"Poisoning by, adverse effect of and underdosing of other psychostimulants"
+T43691,"Poisoning by other psychostimulants, accidental (unintentional)"
+T43692,"Poisoning by other psychostimulants, intentional self-harm"
+T43693,"Poisoning by other psychostimulants, assault"
+T43694,"Poisoning by other psychostimulants, undetermined"
+T43695,"Adverse effect of other psychostimulants"
+T43696,"Underdosing of other psychostimulants"
+T438,"Poisoning by, adverse effect of and underdosing of other psychotropic drugs"
+T438X,"Poisoning by, adverse effect of and underdosing of other psychotropic drugs"
+T438X1,"Poisoning by other psychotropic drugs, accidental (unintentional)"
+T438X2,"Poisoning by other psychotropic drugs, intentional self-harm"
+T438X3,"Poisoning by other psychotropic drugs, assault"
+T438X4,"Poisoning by other psychotropic drugs, undetermined"
+T438X5,"Adverse effect of other psychotropic drugs"
+T438X6,"Underdosing of other psychotropic drugs"
+T439,"Poisoning by, adverse effect of and underdosing of unspecified psychotropic drug"
+T4391,"Poisoning by unspecified psychotropic drug, accidental (unintentional)"
+T4392,"Poisoning by unspecified psychotropic drug, intentional self-harm"
+T4393,"Poisoning by unspecified psychotropic drug, assault"
+T4394,"Poisoning by unspecified psychotropic drug, undetermined"
+T4395,"Adverse effect of unspecified psychotropic drug"
+T4396,"Underdosing of unspecified psychotropic drug"
+T44,"Poisoning by, adverse effect of and underdosing of drugs primarily affecting the autonomic nervous system"
+T440,"Poisoning by, adverse effect of and underdosing of anticholinesterase agents"
+T440X,"Poisoning by, adverse effect of and underdosing of anticholinesterase agents"
+T440X1,"Poisoning by anticholinesterase agents, accidental (unintentional)"
+T440X2,"Poisoning by anticholinesterase agents, intentional self-harm"
+T440X3,"Poisoning by anticholinesterase agents, assault"
+T440X4,"Poisoning by anticholinesterase agents, undetermined"
+T440X5,"Adverse effect of anticholinesterase agents"
+T440X6,"Underdosing of anticholinesterase agents"
+T441,"Poisoning by, adverse effect of and underdosing of other parasympathomimetics [cholinergics]"
+T441X,"Poisoning by, adverse effect of and underdosing of other parasympathomimetics [cholinergics]"
+T441X1,"Poisoning by other parasympathomimetics [cholinergics], accidental (unintentional)"
+T441X2,"Poisoning by other parasympathomimetics [cholinergics], intentional self-harm"
+T441X3,"Poisoning by other parasympathomimetics [cholinergics], assault"
+T441X4,"Poisoning by other parasympathomimetics [cholinergics], undetermined"
+T441X5,"Adverse effect of other parasympathomimetics [cholinergics]"
+T441X6,"Underdosing of other parasympathomimetics"
+T442,"Poisoning by, adverse effect of and underdosing of ganglionic blocking drugs"
+T442X,"Poisoning by, adverse effect of and underdosing of ganglionic blocking drugs"
+T442X1,"Poisoning by ganglionic blocking drugs, accidental (unintentional)"
+T442X2,"Poisoning by ganglionic blocking drugs, intentional self-harm"
+T442X3,"Poisoning by ganglionic blocking drugs, assault"
+T442X4,"Poisoning by ganglionic blocking drugs, undetermined"
+T442X5,"Adverse effect of ganglionic blocking drugs"
+T442X6,"Underdosing of ganglionic blocking drugs"
+T443,"Poisoning by, adverse effect of and underdosing of other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics"
+T443X,"Poisoning by, adverse effect of and underdosing of other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics"
+T443X1,"Poisoning by other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics, accidental (unintentional)"
+T443X2,"Poisoning by other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics, intentional self-harm"
+T443X3,"Poisoning by other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics, assault"
+T443X4,"Poisoning by other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics, undetermined"
+T443X5,"Adverse effect of other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics"
+T443X6,"Underdosing of other parasympatholytics [anticholinergics and antimuscarinics] and spasmolytics"
+T444,"Poisoning by, adverse effect of and underdosing of predominantly alpha-adrenoreceptor agonists"
+T444X,"Poisoning by, adverse effect of and underdosing of predominantly alpha-adrenoreceptor agonists"
+T444X1,"Poisoning by predominantly alpha-adrenoreceptor agonists, accidental (unintentional)"
+T444X2,"Poisoning by predominantly alpha-adrenoreceptor agonists, intentional self-harm"
+T444X3,"Poisoning by predominantly alpha-adrenoreceptor agonists, assault"
+T444X4,"Poisoning by predominantly alpha-adrenoreceptor agonists, undetermined"
+T444X5,"Adverse effect of predominantly alpha-adrenoreceptor agonists"
+T444X6,"Underdosing of predominantly alpha-adrenoreceptor agonists"
+T445,"Poisoning by, adverse effect of and underdosing of predominantly beta-adrenoreceptor agonists"
+T445X,"Poisoning by, adverse effect of and underdosing of predominantly beta-adrenoreceptor agonists"
+T445X1,"Poisoning by predominantly beta-adrenoreceptor agonists, accidental (unintentional)"
+T445X2,"Poisoning by predominantly beta-adrenoreceptor agonists, intentional self-harm"
+T445X3,"Poisoning by predominantly beta-adrenoreceptor agonists, assault"
+T445X4,"Poisoning by predominantly beta-adrenoreceptor agonists, undetermined"
+T445X5,"Adverse effect of predominantly beta-adrenoreceptor agonists"
+T445X6,"Underdosing of predominantly beta-adrenoreceptor agonists"
+T446,"Poisoning by, adverse effect of and underdosing of alpha-adrenoreceptor antagonists"
+T446X,"Poisoning by, adverse effect of and underdosing of alpha-adrenoreceptor antagonists"
+T446X1,"Poisoning by alpha-adrenoreceptor antagonists, accidental (unintentional)"
+T446X2,"Poisoning by alpha-adrenoreceptor antagonists, intentional self-harm"
+T446X3,"Poisoning by alpha-adrenoreceptor antagonists, assault"
+T446X4,"Poisoning by alpha-adrenoreceptor antagonists, undetermined"
+T446X5,"Adverse effect of alpha-adrenoreceptor antagonists"
+T446X6,"Underdosing of alpha-adrenoreceptor antagonists"
+T447,"Poisoning by, adverse effect of and underdosing of beta-adrenoreceptor antagonists"
+T447X,"Poisoning by, adverse effect of and underdosing of beta-adrenoreceptor antagonists"
+T447X1,"Poisoning by beta-adrenoreceptor antagonists, accidental (unintentional)"
+T447X2,"Poisoning by beta-adrenoreceptor antagonists, intentional self-harm"
+T447X3,"Poisoning by beta-adrenoreceptor antagonists, assault"
+T447X4,"Poisoning by beta-adrenoreceptor antagonists, undetermined"
+T447X5,"Adverse effect of beta-adrenoreceptor antagonists"
+T447X6,"Underdosing of beta-adrenoreceptor antagonists"
+T448,"Poisoning by, adverse effect of and underdosing of centrally-acting and adrenergic-neuron- blocking agents"
+T448X,"Poisoning by, adverse effect of and underdosing of centrally-acting and adrenergic- neuron-blocking agents"
+T448X1,"Poisoning by centrally-acting and adrenergic-neuron-blocking agents, accidental (unintentional)"
+T448X2,"Poisoning by centrally-acting and adrenergic-neuron-blocking agents, intentional self-harm"
+T448X3,"Poisoning by centrally-acting and adrenergic-neuron-blocking agents, assault"
+T448X4,"Poisoning by centrally-acting and adrenergic-neuron-blocking agents, undetermined"
+T448X5,"Adverse effect of centrally-acting and adrenergic-neuron-blocking agents"
+T448X6,"Underdosing of centrally-acting and adrenergic-neuron-blocking agents"
+T449,"Poisoning by, adverse effect of and underdosing of other and unspecified drugs primarily affecting the autonomic nervous system"
+T4490,"Poisoning by, adverse effect of and underdosing of unspecified drugs primarily affecting the autonomic nervous system"
+T44901,"Poisoning by unspecified drugs primarily affecting the autonomic nervous system, accidental (unintentional)"
+T44902,"Poisoning by unspecified drugs primarily affecting the autonomic nervous system, intentional self-harm"
+T44903,"Poisoning by unspecified drugs primarily affecting the autonomic nervous system, assault"
+T44904,"Poisoning by unspecified drugs primarily affecting the autonomic nervous system, undetermined"
+T44905,"Adverse effect of unspecified drugs primarily affecting the autonomic nervous system"
+T44906,"Underdosing of unspecified drugs primarily affecting the autonomic nervous system"
+T4499,"Poisoning by, adverse effect of and underdosing of other drugs primarily affecting the autonomic nervous system"
+T44991,"Poisoning by other drug primarily affecting the autonomic nervous system, accidental (unintentional)"
+T44992,"Poisoning by other drug primarily affecting the autonomic nervous system, intentional self-harm"
+T44993,"Poisoning by other drug primarily affecting the autonomic nervous system, assault"
+T44994,"Poisoning by other drug primarily affecting the autonomic nervous system, undetermined"
+T44995,"Adverse effect of other drug primarily affecting the autonomic nervous system"
+T44996,"Underdosing of other drug primarily affecting the autonomic nervous system"
+T45,"Poisoning by, adverse effect of and underdosing of primarily systemic and hematological agents, not elsewhere classified"
+T450,"Poisoning by, adverse effect of and underdosing of antiallergic and antiemetic drugs"
+T450X,"Poisoning by, adverse effect of and underdosing of antiallergic and antiemetic drugs"
+T450X1,"Poisoning by antiallergic and antiemetic drugs, accidental (unintentional)"
+T450X2,"Poisoning by antiallergic and antiemetic drugs, intentional self-harm"
+T450X3,"Poisoning by antiallergic and antiemetic drugs, assault"
+T450X4,"Poisoning by antiallergic and antiemetic drugs, undetermined"
+T450X5,"Adverse effect of antiallergic and antiemetic drugs"
+T450X6,"Underdosing of antiallergic and antiemetic drugs"
+T451,"Poisoning by, adverse effect of and underdosing of antineoplastic and immunosuppressive drugs"
+T451X,"Poisoning by, adverse effect of and underdosing of antineoplastic and immunosuppressive drugs"
+T451X1,"Poisoning by antineoplastic and immunosuppressive drugs, accidental (unintentional)"
+T451X2,"Poisoning by antineoplastic and immunosuppressive drugs, intentional self-harm"
+T451X3,"Poisoning by antineoplastic and immunosuppressive drugs, assault"
+T451X4,"Poisoning by antineoplastic and immunosuppressive drugs, undetermined"
+T451X5,"Adverse effect of antineoplastic and immunosuppressive drugs"
+T451X6,"Underdosing of antineoplastic and immunosuppressive drugs"
+T452,"Poisoning by, adverse effect of and underdosing of vitamins"
+T452X,"Poisoning by, adverse effect of and underdosing of vitamins"
+T452X1,"Poisoning by vitamins, accidental (unintentional)"
+T452X2,"Poisoning by vitamins, intentional self-harm"
+T452X3,"Poisoning by vitamins, assault"
+T452X4,"Poisoning by vitamins, undetermined"
+T452X5,"Adverse effect of vitamins"
+T452X6,"Underdosing of vitamins"
+T453,"Poisoning by, adverse effect of and underdosing of enzymes"
+T453X,"Poisoning by, adverse effect of and underdosing of enzymes"
+T453X1,"Poisoning by enzymes, accidental (unintentional)"
+T453X2,"Poisoning by enzymes, intentional self-harm"
+T453X3,"Poisoning by enzymes, assault"
+T453X4,"Poisoning by enzymes, undetermined"
+T453X5,"Adverse effect of enzymes"
+T453X6,"Underdosing of enzymes"
+T454,"Poisoning by, adverse effect of and underdosing of iron and its compounds"
+T454X,"Poisoning by, adverse effect of and underdosing of iron and its compounds"
+T454X1,"Poisoning by iron and its compounds, accidental (unintentional)"
+T454X2,"Poisoning by iron and its compounds, intentional self-harm"
+T454X3,"Poisoning by iron and its compounds, assault"
+T454X4,"Poisoning by iron and its compounds, undetermined"
+T454X5,"Adverse effect of iron and its compounds"
+T454X6,"Underdosing of iron and its compounds"
+T455,"Poisoning by, adverse effect of and underdosing of anticoagulants and antithrombotic drugs"
+T4551,"Poisoning by, adverse effect of and underdosing of anticoagulants"
+T45511,"Poisoning by anticoagulants, accidental (unintentional)"
+T45512,"Poisoning by anticoagulants, intentional self-harm"
+T45513,"Poisoning by anticoagulants, assault"
+T45514,"Poisoning by anticoagulants, undetermined"
+T45515,"Adverse effect of anticoagulants"
+T45516,"Underdosing of anticoagulants"
+T4552,"Poisoning by, adverse effect of and underdosing of antithrombotic drugs"
+T45521,"Poisoning by antithrombotic drugs, accidental (unintentional)"
+T45522,"Poisoning by antithrombotic drugs, intentional self-harm"
+T45523,"Poisoning by antithrombotic drugs, assault"
+T45524,"Poisoning by antithrombotic drugs, undetermined"
+T45525,"Adverse effect of antithrombotic drugs"
+T45526,"Underdosing of antithrombotic drugs"
+T456,"Poisoning by, adverse effect of and underdosing of fibrinolysis-affecting drugs"
+T4560,"Poisoning by, adverse effect of and underdosing of unspecified fibrinolysis-affecting drugs"
+T45601,"Poisoning by unspecified fibrinolysis-affecting drugs, accidental (unintentional)"
+T45602,"Poisoning by unspecified fibrinolysis-affecting drugs, intentional self-harm"
+T45603,"Poisoning by unspecified fibrinolysis-affecting drugs, assault"
+T45604,"Poisoning by unspecified fibrinolysis-affecting drugs, undetermined"
+T45605,"Adverse effect of unspecified fibrinolysis-affecting drugs"
+T45606,"Underdosing of unspecified fibrinolysis-affecting drugs"
+T4561,"Poisoning by, adverse effect of and underdosing of thrombolytic drugs"
+T45611,"Poisoning by thrombolytic drug, accidental (unintentional)"
+T45612,"Poisoning by thrombolytic drug, intentional self-harm"
+T45613,"Poisoning by thrombolytic drug, assault"
+T45614,"Poisoning by thrombolytic drug, undetermined"
+T45615,"Adverse effect of thrombolytic drugs"
+T45616,"Underdosing of thrombolytic drugs"
+T4562,"Poisoning by, adverse effect of and underdosing of hemostatic drugs"
+T45621,"Poisoning by hemostatic drug, accidental (unintentional)"
+T45622,"Poisoning by hemostatic drug, intentional self-harm"
+T45623,"Poisoning by hemostatic drug, assault"
+T45624,"Poisoning by hemostatic drug, undetermined"
+T45625,"Adverse effect of hemostatic drug"
+T45626,"Underdosing of hemostatic drugs"
+T4569,"Poisoning by, adverse effect of and underdosing of other fibrinolysis-affecting drugs"
+T45691,"Poisoning by other fibrinolysis-affecting drugs, accidental (unintentional)"
+T45692,"Poisoning by other fibrinolysis-affecting drugs, intentional self-harm"
+T45693,"Poisoning by other fibrinolysis-affecting drugs, assault"
+T45694,"Poisoning by other fibrinolysis-affecting drugs, undetermined"
+T45695,"Adverse effect of other fibrinolysis-affecting drugs"
+T45696,"Underdosing of other fibrinolysis-affecting drugs"
+T457,"Poisoning by, adverse effect of and underdosing of anticoagulant antagonists, vitamin K and other coagulants"
+T457X,"Poisoning by, adverse effect of and underdosing of anticoagulant antagonists, vitamin K and other coagulants"
+T457X1,"Poisoning by anticoagulant antagonists, vitamin K and other coagulants, accidental (unintentional)"
+T457X2,"Poisoning by anticoagulant antagonists, vitamin K and other coagulants, intentional self-harm"
+T457X3,"Poisoning by anticoagulant antagonists, vitamin K and other coagulants, assault"
+T457X4,"Poisoning by anticoagulant antagonists, vitamin K and other coagulants, undetermined"
+T457X5,"Adverse effect of anticoagulant antagonists, vitamin K and other coagulants"
+T457X6,"Underdosing of anticoagulant antagonist, vitamin K and other coagulants"
+T458,"Poisoning by, adverse effect of and underdosing of other primarily systemic and hematological agents"
+T458X,"Poisoning by, adverse effect of and underdosing of other primarily systemic and hematological agents"
+T458X1,"Poisoning by other primarily systemic and hematological agents, accidental (unintentional)"
+T458X2,"Poisoning by other primarily systemic and hematological agents, intentional self-harm"
+T458X3,"Poisoning by other primarily systemic and hematological agents, assault"
+T458X4,"Poisoning by other primarily systemic and hematological agents, undetermined"
+T458X5,"Adverse effect of other primarily systemic and hematological agents"
+T458X6,"Underdosing of other primarily systemic and hematological agents"
+T459,"Poisoning by, adverse effect of and underdosing of unspecified primarily systemic and hematological agent"
+T4591,"Poisoning by unspecified primarily systemic and hematological agent, accidental (unintentional)"
+T4592,"Poisoning by unspecified primarily systemic and hematological agent, intentional self-harm"
+T4593,"Poisoning by unspecified primarily systemic and hematological agent, assault"
+T4594,"Poisoning by unspecified primarily systemic and hematological agent, undetermined"
+T4595,"Adverse effect of unspecified primarily systemic and hematological agent"
+T4596,"Underdosing of unspecified primarily systemic and hematological agent"
+T46,"Poisoning by, adverse effect of and underdosing of agents primarily affecting the cardiovascular system"
+T460,"Poisoning by, adverse effect of and underdosing of cardiac-stimulant glycosides and drugs of similar action"
+T460X,"Poisoning by, adverse effect of and underdosing of cardiac-stimulant glycosides and drugs of similar action"
+T460X1,"Poisoning by cardiac-stimulant glycosides and drugs of similar action, accidental (unintentional)"
+T460X2,"Poisoning by cardiac-stimulant glycosides and drugs of similar action, intentional self-harm"
+T460X3,"Poisoning by cardiac-stimulant glycosides and drugs of similar action, assault"
+T460X4,"Poisoning by cardiac-stimulant glycosides and drugs of similar action, undetermined"
+T460X5,"Adverse effect of cardiac-stimulant glycosides and drugs of similar action"
+T460X6,"Underdosing of cardiac-stimulant glycosides and drugs of similar action"
+T461,"Poisoning by, adverse effect of and underdosing of calcium-channel blockers"
+T461X,"Poisoning by, adverse effect of and underdosing of calcium-channel blockers"
+T461X1,"Poisoning by calcium-channel blockers, accidental (unintentional)"
+T461X2,"Poisoning by calcium-channel blockers, intentional self-harm"
+T461X3,"Poisoning by calcium-channel blockers, assault"
+T461X4,"Poisoning by calcium-channel blockers, undetermined"
+T461X5,"Adverse effect of calcium-channel blockers"
+T461X6,"Underdosing of calcium-channel blockers"
+T462,"Poisoning by, adverse effect of and underdosing of other antidysrhythmic drugs, not elsewhere classified"
+T462X,"Poisoning by, adverse effect of and underdosing of other antidysrhythmic drugs"
+T462X1,"Poisoning by other antidysrhythmic drugs, accidental (unintentional)"
+T462X2,"Poisoning by other antidysrhythmic drugs, intentional self-harm"
+T462X3,"Poisoning by other antidysrhythmic drugs, assault"
+T462X4,"Poisoning by other antidysrhythmic drugs, undetermined"
+T462X5,"Adverse effect of other antidysrhythmic drugs"
+T462X6,"Underdosing of other antidysrhythmic drugs"
+T463,"Poisoning by, adverse effect of and underdosing of coronary vasodilators"
+T463X,"Poisoning by, adverse effect of and underdosing of coronary vasodilators"
+T463X1,"Poisoning by coronary vasodilators, accidental (unintentional)"
+T463X2,"Poisoning by coronary vasodilators, intentional self-harm"
+T463X3,"Poisoning by coronary vasodilators, assault"
+T463X4,"Poisoning by coronary vasodilators, undetermined"
+T463X5,"Adverse effect of coronary vasodilators"
+T463X6,"Underdosing of coronary vasodilators"
+T464,"Poisoning by, adverse effect of and underdosing of angiotensin-converting-enzyme inhibitors"
+T464X,"Poisoning by, adverse effect of and underdosing of  angiotensin-converting-enzyme inhibitors"
+T464X1,"Poisoning by angiotensin-converting-enzyme inhibitors, accidental (unintentional)"
+T464X2,"Poisoning by angiotensin-converting-enzyme inhibitors, intentional self-harm"
+T464X3,"Poisoning by angiotensin-converting-enzyme inhibitors, assault"
+T464X4,"Poisoning by angiotensin-converting-enzyme inhibitors, undetermined"
+T464X5,"Adverse effect of angiotensin-converting-enzyme inhibitors"
+T464X6,"Underdosing of angiotensin-converting-enzyme inhibitors"
+T465,"Poisoning by, adverse effect of and underdosing of other antihypertensive drugs"
+T465X,"Poisoning by, adverse effect of and underdosing of other antihypertensive drugs"
+T465X1,"Poisoning by other antihypertensive drugs, accidental (unintentional)"
+T465X2,"Poisoning by other antihypertensive drugs, intentional self-harm"
+T465X3,"Poisoning by other antihypertensive drugs, assault"
+T465X4,"Poisoning by other antihypertensive drugs, undetermined"
+T465X5,"Adverse effect of other antihypertensive drugs"
+T465X6,"Underdosing of other antihypertensive drugs"
+T466,"Poisoning by, adverse effect of and underdosing of antihyperlipidemic and antiarteriosclerotic drugs"
+T466X,"Poisoning by, adverse effect of and underdosing of antihyperlipidemic and antiarteriosclerotic drugs"
+T466X1,"Poisoning by antihyperlipidemic and antiarteriosclerotic drugs, accidental (unintentional)"
+T466X2,"Poisoning by antihyperlipidemic and antiarteriosclerotic drugs, intentional self-harm"
+T466X3,"Poisoning by antihyperlipidemic and antiarteriosclerotic drugs, assault"
+T466X4,"Poisoning by antihyperlipidemic and antiarteriosclerotic drugs, undetermined"
+T466X5,"Adverse effect of antihyperlipidemic and antiarteriosclerotic drugs"
+T466X6,"Underdosing of antihyperlipidemic and antiarteriosclerotic drugs"
+T467,"Poisoning by, adverse effect of and underdosing of peripheral vasodilators"
+T467X,"Poisoning by, adverse effect of and underdosing of peripheral vasodilators"
+T467X1,"Poisoning by peripheral vasodilators, accidental (unintentional)"
+T467X2,"Poisoning by peripheral vasodilators, intentional self-harm"
+T467X3,"Poisoning by peripheral vasodilators, assault"
+T467X4,"Poisoning by peripheral vasodilators, undetermined"
+T467X5,"Adverse effect of peripheral vasodilators"
+T467X6,"Underdosing of peripheral vasodilators"
+T468,"Poisoning by, adverse effect of and underdosing of antivaricose drugs, including sclerosing agents"
+T468X,"Poisoning by, adverse effect of and underdosing of antivaricose drugs, including sclerosing agents"
+T468X1,"Poisoning by antivaricose drugs, including sclerosing agents, accidental (unintentional)"
+T468X2,"Poisoning by antivaricose drugs, including sclerosing agents, intentional self-harm"
+T468X3,"Poisoning by antivaricose drugs, including sclerosing agents, assault"
+T468X4,"Poisoning by antivaricose drugs, including sclerosing agents, undetermined"
+T468X5,"Adverse effect of antivaricose drugs, including sclerosing agents"
+T468X6,"Underdosing of antivaricose drugs, including sclerosing agents"
+T469,"Poisoning by, adverse effect of and underdosing of other and unspecified agents primarily affecting the cardiovascular system"
+T4690,"Poisoning by, adverse effect of and underdosing of unspecified agents primarily affecting the cardiovascular system"
+T46901,"Poisoning by unspecified agents primarily affecting the cardiovascular system, accidental (unintentional)"
+T46902,"Poisoning by unspecified agents primarily affecting the cardiovascular system, intentional self-harm"
+T46903,"Poisoning by unspecified agents primarily affecting the cardiovascular system, assault"
+T46904,"Poisoning by unspecified agents primarily affecting the cardiovascular system, undetermined"
+T46905,"Adverse effect of unspecified agents primarily affecting the cardiovascular system"
+T46906,"Underdosing of unspecified agents primarily affecting the cardiovascular system"
+T4699,"Poisoning by, adverse effect of and underdosing of other agents primarily affecting the cardiovascular system"
+T46991,"Poisoning by other agents primarily affecting the cardiovascular system, accidental (unintentional)"
+T46992,"Poisoning by other agents primarily affecting the cardiovascular system, intentional self-harm"
+T46993,"Poisoning by other agents primarily affecting the cardiovascular system, assault"
+T46994,"Poisoning by other agents primarily affecting the cardiovascular system, undetermined"
+T46995,"Adverse effect of other agents primarily affecting the cardiovascular system"
+T46996,"Underdosing of other agents primarily affecting the cardiovascular system"
+T47,"Poisoning by, adverse effect of and underdosing of agents primarily affecting the gastrointestinal system"
+T470,"Poisoning by, adverse effect of and underdosing of histamine H2-receptor blockers"
+T470X,"Poisoning by, adverse effect of and underdosing of histamine H2-receptor blockers"
+T470X1,"Poisoning by histamine H2-receptor blockers, accidental (unintentional)"
+T470X2,"Poisoning by histamine H2-receptor blockers, intentional self-harm"
+T470X3,"Poisoning by histamine H2-receptor blockers, assault"
+T470X4,"Poisoning by histamine H2-receptor blockers, undetermined"
+T470X5,"Adverse effect of histamine H2-receptor blockers"
+T470X6,"Underdosing of histamine H2-receptor blockers"
+T471,"Poisoning by, adverse effect of and underdosing of other antacids and anti-gastric-secretion drugs"
+T471X,"Poisoning by, adverse effect of and underdosing of other antacids and anti-gastric-secretion drugs"
+T471X1,"Poisoning by other antacids and anti-gastric-secretion drugs, accidental (unintentional)"
+T471X2,"Poisoning by other antacids and anti-gastric-secretion drugs, intentional self-harm"
+T471X3,"Poisoning by other antacids and anti-gastric-secretion drugs, assault"
+T471X4,"Poisoning by other antacids and anti-gastric-secretion drugs, undetermined"
+T471X5,"Adverse effect of other antacids and anti-gastric-secretion drugs"
+T471X6,"Underdosing of other antacids and anti-gastric-secretion drugs"
+T472,"Poisoning by, adverse effect of and underdosing of stimulant laxatives"
+T472X,"Poisoning by, adverse effect of and underdosing of stimulant laxatives"
+T472X1,"Poisoning by stimulant laxatives, accidental (unintentional)"
+T472X2,"Poisoning by stimulant laxatives, intentional self-harm"
+T472X3,"Poisoning by stimulant laxatives, assault"
+T472X4,"Poisoning by stimulant laxatives, undetermined"
+T472X5,"Adverse effect of stimulant laxatives"
+T472X6,"Underdosing of stimulant laxatives"
+T473,"Poisoning by, adverse effect of and underdosing of saline and osmotic laxatives"
+T473X,"Poisoning by and adverse effect of saline and osmotic laxatives"
+T473X1,"Poisoning by saline and osmotic laxatives, accidental (unintentional)"
+T473X2,"Poisoning by saline and osmotic laxatives, intentional self-harm"
+T473X3,"Poisoning by saline and osmotic laxatives, assault"
+T473X4,"Poisoning by saline and osmotic laxatives, undetermined"
+T473X5,"Adverse effect of saline and osmotic laxatives"
+T473X6,"Underdosing of saline and osmotic laxatives"
+T474,"Poisoning by, adverse effect of and underdosing of other laxatives"
+T474X,"Poisoning by, adverse effect of and underdosing of other laxatives"
+T474X1,"Poisoning by other laxatives, accidental (unintentional)"
+T474X2,"Poisoning by other laxatives, intentional self-harm"
+T474X3,"Poisoning by other laxatives, assault"
+T474X4,"Poisoning by other laxatives, undetermined"
+T474X5,"Adverse effect of other laxatives"
+T474X6,"Underdosing of other laxatives"
+T475,"Poisoning by, adverse effect of and underdosing of digestants"
+T475X,"Poisoning by, adverse effect of and underdosing of digestants"
+T475X1,"Poisoning by digestants, accidental (unintentional)"
+T475X2,"Poisoning by digestants, intentional self-harm"
+T475X3,"Poisoning by digestants, assault"
+T475X4,"Poisoning by digestants, undetermined"
+T475X5,"Adverse effect of digestants"
+T475X6,"Underdosing of digestants"
+T476,"Poisoning by, adverse effect of and underdosing of antidiarrheal drugs"
+T476X,"Poisoning by, adverse effect of and underdosing of antidiarrheal drugs"
+T476X1,"Poisoning by antidiarrheal drugs, accidental (unintentional)"
+T476X2,"Poisoning by antidiarrheal drugs, intentional self-harm"
+T476X3,"Poisoning by antidiarrheal drugs, assault"
+T476X4,"Poisoning by antidiarrheal drugs, undetermined"
+T476X5,"Adverse effect of antidiarrheal drugs"
+T476X6,"Underdosing of antidiarrheal drugs"
+T477,"Poisoning by, adverse effect of and underdosing of emetics"
+T477X,"Poisoning by, adverse effect of and underdosing of emetics"
+T477X1,"Poisoning by emetics, accidental (unintentional)"
+T477X2,"Poisoning by emetics, intentional self-harm"
+T477X3,"Poisoning by emetics, assault"
+T477X4,"Poisoning by emetics, undetermined"
+T477X5,"Adverse effect of emetics"
+T477X6,"Underdosing of emetics"
+T478,"Poisoning by, adverse effect of and underdosing of other agents primarily affecting gastrointestinal system"
+T478X,"Poisoning by, adverse effect of and underdosing of other agents primarily affecting gastrointestinal system"
+T478X1,"Poisoning by other agents primarily affecting gastrointestinal system, accidental (unintentional)"
+T478X2,"Poisoning by other agents primarily affecting gastrointestinal system, intentional self-harm"
+T478X3,"Poisoning by other agents primarily affecting gastrointestinal system, assault"
+T478X4,"Poisoning by other agents primarily affecting gastrointestinal system, undetermined"
+T478X5,"Adverse effect of other agents primarily affecting gastrointestinal system"
+T478X6,"Underdosing of other agents primarily affecting gastrointestinal system"
+T479,"Poisoning by, adverse effect of and underdosing of unspecified agents primarily affecting the gastrointestinal system"
+T4791,"Poisoning by unspecified agents primarily affecting the gastrointestinal system, accidental (unintentional)"
+T4792,"Poisoning by unspecified agents primarily affecting the gastrointestinal system, intentional self-harm"
+T4793,"Poisoning by unspecified agents primarily affecting the gastrointestinal system, assault"
+T4794,"Poisoning by unspecified agents primarily affecting the gastrointestinal system, undetermined"
+T4795,"Adverse effect of unspecified agents primarily affecting the gastrointestinal system"
+T4796,"Underdosing of unspecified agents primarily affecting the gastrointestinal system"
+T48,"Poisoning by, adverse effect of and underdosing of agents primarily acting on smooth and skeletal muscles and the respiratory system"
+T480,"Poisoning by, adverse effect of and underdosing of oxytocic drugs"
+T480X,"Poisoning by, adverse effect of and underdosing of oxytocic drugs"
+T480X1,"Poisoning by oxytocic drugs, accidental (unintentional)"
+T480X2,"Poisoning by oxytocic drugs, intentional self-harm"
+T480X3,"Poisoning by oxytocic drugs, assault"
+T480X4,"Poisoning by oxytocic drugs, undetermined"
+T480X5,"Adverse effect of oxytocic drugs"
+T480X6,"Underdosing of oxytocic drugs"
+T481,"Poisoning by, adverse effect of and underdosing of skeletal muscle relaxants [neuromuscular blocking agents]"
+T481X,"Poisoning by, adverse effect of and underdosing of skeletal muscle relaxants [neuromuscular blocking agents]"
+T481X1,"Poisoning by skeletal muscle relaxants [neuromuscular blocking agents], accidental (unintentional)"
+T481X2,"Poisoning by skeletal muscle relaxants [neuromuscular blocking agents], intentional self-harm"
+T481X3,"Poisoning by skeletal muscle relaxants [neuromuscular blocking agents], assault"
+T481X4,"Poisoning by skeletal muscle relaxants [neuromuscular blocking agents], undetermined"
+T481X5,"Adverse effect of skeletal muscle relaxants [neuromuscular blocking agents]"
+T481X6,"Underdosing of skeletal muscle relaxants [neuromuscular blocking agents]"
+T482,"Poisoning by, adverse effect of and underdosing of other and unspecified drugs acting on muscles"
+T4820,"Poisoning by, adverse effect of and underdosing of unspecified drugs acting on muscles"
+T48201,"Poisoning by unspecified drugs acting on muscles, accidental (unintentional)"
+T48202,"Poisoning by unspecified drugs acting on muscles, intentional self-harm"
+T48203,"Poisoning by unspecified drugs acting on muscles, assault"
+T48204,"Poisoning by unspecified drugs acting on muscles, undetermined"
+T48205,"Adverse effect of unspecified drugs acting on muscles"
+T48206,"Underdosing of unspecified drugs acting on muscles"
+T4829,"Poisoning by, adverse effect of and underdosing of other drugs acting on muscles"
+T48291,"Poisoning by other drugs acting on muscles, accidental (unintentional)"
+T48292,"Poisoning by other drugs acting on muscles, intentional self-harm"
+T48293,"Poisoning by other drugs acting on muscles, assault"
+T48294,"Poisoning by other drugs acting on muscles, undetermined"
+T48295,"Adverse effect of other drugs acting on muscles"
+T48296,"Underdosing of other drugs acting on muscles"
+T483,"Poisoning by, adverse effect of and underdosing of antitussives"
+T483X,"Poisoning by, adverse effect of and underdosing of antitussives"
+T483X1,"Poisoning by antitussives, accidental (unintentional)"
+T483X2,"Poisoning by antitussives, intentional self-harm"
+T483X3,"Poisoning by antitussives, assault"
+T483X4,"Poisoning by antitussives, undetermined"
+T483X5,"Adverse effect of antitussives"
+T483X6,"Underdosing of antitussives"
+T484,"Poisoning by, adverse effect of and underdosing of expectorants"
+T484X,"Poisoning by, adverse effect of and underdosing of expectorants"
+T484X1,"Poisoning by expectorants, accidental (unintentional)"
+T484X2,"Poisoning by expectorants, intentional self-harm"
+T484X3,"Poisoning by expectorants, assault"
+T484X4,"Poisoning by expectorants, undetermined"
+T484X5,"Adverse effect of expectorants"
+T484X6,"Underdosing of expectorants"
+T485,"Poisoning by, adverse effect of and underdosing of other anti-common-cold drugs"
+T485X,"Poisoning by, adverse effect of and underdosing of other anti-common-cold drugs"
+T485X1,"Poisoning by other anti-common-cold drugs, accidental (unintentional)"
+T485X2,"Poisoning by other anti-common-cold drugs, intentional self-harm"
+T485X3,"Poisoning by other anti-common-cold drugs, assault"
+T485X4,"Poisoning by other anti-common-cold drugs, undetermined"
+T485X5,"Adverse effect of other anti-common-cold drugs"
+T485X6,"Underdosing of other anti-common-cold drugs"
+T486,"Poisoning by, adverse effect of and underdosing of antiasthmatics, not elsewhere classified"
+T486X,"Poisoning by, adverse effect of and underdosing of antiasthmatics"
+T486X1,"Poisoning by antiasthmatics, accidental (unintentional)"
+T486X2,"Poisoning by antiasthmatics, intentional self-harm"
+T486X3,"Poisoning by antiasthmatics, assault"
+T486X4,"Poisoning by antiasthmatics, undetermined"
+T486X5,"Adverse effect of antiasthmatics"
+T486X6,"Underdosing of antiasthmatics"
+T489,"Poisoning by, adverse effect of and underdosing of other and unspecified agents primarily acting on the respiratory system"
+T4890,"Poisoning by, adverse effect of and underdosing of unspecified agents primarily acting on the respiratory system"
+T48901,"Poisoning by unspecified agents primarily acting on the respiratory system, accidental (unintentional)"
+T48902,"Poisoning by unspecified agents primarily acting on the respiratory system, intentional self-harm"
+T48903,"Poisoning by unspecified agents primarily acting on the respiratory system, assault"
+T48904,"Poisoning by unspecified agents primarily acting on the respiratory system, undetermined"
+T48905,"Adverse effect of unspecified agents primarily acting on the respiratory system"
+T48906,"Underdosing of unspecified agents primarily acting on the respiratory system"
+T4899,"Poisoning by, adverse effect of and underdosing of other agents primarily acting on the respiratory system"
+T48991,"Poisoning by other agents primarily acting on the respiratory system, accidental (unintentional)"
+T48992,"Poisoning by other agents primarily acting on the respiratory system, intentional self-harm"
+T48993,"Poisoning by other agents primarily acting on the respiratory system, assault"
+T48994,"Poisoning by other agents primarily acting on the respiratory system, undetermined"
+T48995,"Adverse effect of other agents primarily acting on the respiratory system"
+T48996,"Underdosing of other agents primarily acting on the respiratory system"
+T49,"Poisoning by, adverse effect of and underdosing of topical agents primarily affecting skin and mucous membrane and by ophthalmological, otorhinorlaryngological and dental drugs"
+T490,"Poisoning by, adverse effect of and underdosing of local antifungal, anti-infective and anti-inflammatory drugs"
+T490X,"Poisoning by, adverse effect of and underdosing of local antifungal, anti-infective and anti-inflammatory drugs"
+T490X1,"Poisoning by local antifungal, anti-infective and anti-inflammatory drugs, accidental (unintentional)"
+T490X2,"Poisoning by local antifungal, anti-infective and anti-inflammatory drugs, intentional self-harm"
+T490X3,"Poisoning by local antifungal, anti-infective and anti-inflammatory drugs, assault"
+T490X4,"Poisoning by local antifungal, anti-infective and anti-inflammatory drugs, undetermined"
+T490X5,"Adverse effect of local antifungal, anti-infective and anti-inflammatory drugs"
+T490X6,"Underdosing of local antifungal, anti-infective and anti-inflammatory drugs"
+T491,"Poisoning by, adverse effect of and underdosing of antipruritics"
+T491X,"Poisoning by, adverse effect of and underdosing of antipruritics"
+T491X1,"Poisoning by antipruritics, accidental (unintentional)"
+T491X2,"Poisoning by antipruritics, intentional self-harm"
+T491X3,"Poisoning by antipruritics, assault"
+T491X4,"Poisoning by antipruritics, undetermined"
+T491X5,"Adverse effect of antipruritics"
+T491X6,"Underdosing of antipruritics"
+T492,"Poisoning by, adverse effect of and underdosing of local astringents and local detergents"
+T492X,"Poisoning by, adverse effect of and underdosing of local astringents and local detergents"
+T492X1,"Poisoning by local astringents and local detergents, accidental (unintentional)"
+T492X2,"Poisoning by local astringents and local detergents, intentional self-harm"
+T492X3,"Poisoning by local astringents and local detergents, assault"
+T492X4,"Poisoning by local astringents and local detergents, undetermined"
+T492X5,"Adverse effect of local astringents and local detergents"
+T492X6,"Underdosing of local astringents and local detergents"
+T493,"Poisoning by, adverse effect of and underdosing of emollients, demulcents and protectants"
+T493X,"Poisoning by, adverse effect of and underdosing of emollients, demulcents and protectants"
+T493X1,"Poisoning by emollients, demulcents and protectants, accidental (unintentional)"
+T493X2,"Poisoning by emollients, demulcents and protectants, intentional self-harm"
+T493X3,"Poisoning by emollients, demulcents and protectants, assault"
+T493X4,"Poisoning by emollients, demulcents and protectants, undetermined"
+T493X5,"Adverse effect of emollients, demulcents and protectants"
+T493X6,"Underdosing of emollients, demulcents and protectants"
+T494,"Poisoning by, adverse effect of and underdosing of keratolytics, keratoplastics, and other hair treatment drugs and preparations"
+T494X,"Poisoning by, adverse effect of and underdosing of keratolytics, keratoplastics, and other hair treatment drugs and preparations"
+T494X1,"Poisoning by keratolytics, keratoplastics, and other hair treatment drugs and preparations, accidental (unintentional)"
+T494X2,"Poisoning by keratolytics, keratoplastics, and other hair treatment drugs and preparations, intentional self-harm"
+T494X3,"Poisoning by keratolytics, keratoplastics, and other hair treatment drugs and preparations, assault"
+T494X4,"Poisoning by keratolytics, keratoplastics, and other hair treatment drugs and preparations, undetermined"
+T494X5,"Adverse effect of keratolytics, keratoplastics, and other hair treatment drugs and preparations"
+T494X6,"Underdosing of keratolytics, keratoplastics, and other hair treatment drugs and preparations"
+T495,"Poisoning by, adverse effect of and underdosing of ophthalmological drugs and preparations"
+T495X,"Poisoning by, adverse effect of and underdosing of ophthalmological drugs and preparations"
+T495X1,"Poisoning by ophthalmological drugs and preparations, accidental (unintentional)"
+T495X2,"Poisoning by ophthalmological drugs and preparations, intentional self-harm"
+T495X3,"Poisoning by ophthalmological drugs and preparations, assault"
+T495X4,"Poisoning by ophthalmological drugs and preparations, undetermined"
+T495X5,"Adverse effect of ophthalmological drugs and preparations"
+T495X6,"Underdosing of ophthalmological drugs and preparations"
+T496,"Poisoning by, adverse effect of and underdosing of otorhinolaryngological drugs and preparations"
+T496X,"Poisoning by, adverse effect of and underdosing of otorhinolaryngological drugs and preparations"
+T496X1,"Poisoning by otorhinolaryngological drugs and preparations, accidental (unintentional)"
+T496X2,"Poisoning by otorhinolaryngological drugs and preparations, intentional self-harm"
+T496X3,"Poisoning by otorhinolaryngological drugs and preparations, assault"
+T496X4,"Poisoning by otorhinolaryngological drugs and preparations, undetermined"
+T496X5,"Adverse effect of otorhinolaryngological drugs and preparations"
+T496X6,"Underdosing of otorhinolaryngological drugs and preparations"
+T497,"Poisoning by, adverse effect of and underdosing of dental drugs, topically applied"
+T497X,"Poisoning by, adverse effect of and underdosing of dental drugs, topically applied"
+T497X1,"Poisoning by dental drugs, topically applied, accidental (unintentional)"
+T497X2,"Poisoning by dental drugs, topically applied, intentional self-harm"
+T497X3,"Poisoning by dental drugs, topically applied, assault"
+T497X4,"Poisoning by dental drugs, topically applied, undetermined"
+T497X5,"Adverse effect of dental drugs, topically applied"
+T497X6,"Underdosing of dental drugs, topically applied"
+T498,"Poisoning by, adverse effect of and underdosing of other topical agents"
+T498X,"Poisoning by, adverse effect of and underdosing of other topical agents"
+T498X1,"Poisoning by other topical agents, accidental (unintentional)"
+T498X2,"Poisoning by other topical agents, intentional self-harm"
+T498X3,"Poisoning by other topical agents, assault"
+T498X4,"Poisoning by other topical agents, undetermined"
+T498X5,"Adverse effect of other topical agents"
+T498X6,"Underdosing of other topical agents"
+T499,"Poisoning by, adverse effect of and underdosing of unspecified topical agent"
+T4991,"Poisoning by unspecified topical agent, accidental (unintentional)"
+T4992,"Poisoning by unspecified topical agent, intentional self-harm"
+T4993,"Poisoning by unspecified topical agent, assault"
+T4994,"Poisoning by unspecified topical agent, undetermined"
+T4995,"Adverse effect of unspecified topical agent"
+T4996,"Underdosing of unspecified topical agent"
+T50,"Poisoning by, adverse effect of and underdosing of diuretics and other and unspecified drugs, medicaments and biological substances"
+T500,"Poisoning by, adverse effect of and underdosing of mineralocorticoids and their antagonists"
+T500X,"Poisoning by, adverse effect of and underdosing of mineralocorticoids and their antagonists"
+T500X1,"Poisoning by mineralocorticoids and their antagonists, accidental (unintentional)"
+T500X2,"Poisoning by mineralocorticoids and their antagonists, intentional self-harm"
+T500X3,"Poisoning by mineralocorticoids and their antagonists, assault"
+T500X4,"Poisoning by mineralocorticoids and their antagonists, undetermined"
+T500X5,"Adverse effect of mineralocorticoids and their antagonists"
+T500X6,"Underdosing of mineralocorticoids and their antagonists"
+T501,"Poisoning by, adverse effect of and underdosing of loop [high-ceiling] diuretics"
+T501X,"Poisoning by, adverse effect of and underdosing of loop [high-ceiling] diuretics"
+T501X1,"Poisoning by loop [high-ceiling] diuretics, accidental (unintentional)"
+T501X2,"Poisoning by loop [high-ceiling] diuretics, intentional self-harm"
+T501X3,"Poisoning by loop [high-ceiling] diuretics, assault"
+T501X4,"Poisoning by loop [high-ceiling] diuretics, undetermined"
+T501X5,"Adverse effect of loop [high-ceiling] diuretics"
+T501X6,"Underdosing of loop [high-ceiling] diuretics"
+T502,"Poisoning by, adverse effect of and underdosing of carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics"
+T502X,"Poisoning by, adverse effect of and underdosing of carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics"
+T502X1,"Poisoning by carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics, accidental (unintentional)"
+T502X2,"Poisoning by carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics, intentional self-harm"
+T502X3,"Poisoning by carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics, assault"
+T502X4,"Poisoning by carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics, undetermined"
+T502X5,"Adverse effect of carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics"
+T502X6,"Underdosing of carbonic-anhydrase inhibitors, benzothiadiazides and other diuretics"
+T503,"Poisoning by, adverse effect of and underdosing of electrolytic, caloric and water-balance agents"
+T503X,"Poisoning by, adverse effect of and underdosing of electrolytic, caloric and water-balance agents"
+T503X1,"Poisoning by electrolytic, caloric and water-balance agents, accidental (unintentional)"
+T503X2,"Poisoning by electrolytic, caloric and water-balance agents, intentional self-harm"
+T503X3,"Poisoning by electrolytic, caloric and water-balance agents, assault"
+T503X4,"Poisoning by electrolytic, caloric and water-balance agents, undetermined"
+T503X5,"Adverse effect of electrolytic, caloric and water-balance agents"
+T503X6,"Underdosing of electrolytic, caloric and water-balance agents"
+T504,"Poisoning by, adverse effect of and underdosing of drugs affecting uric acid metabolism"
+T504X,"Poisoning by, adverse effect of and underdosing of drugs affecting uric acid metabolism"
+T504X1,"Poisoning by drugs affecting uric acid metabolism, accidental (unintentional)"
+T504X2,"Poisoning by drugs affecting uric acid metabolism, intentional self-harm"
+T504X3,"Poisoning by drugs affecting uric acid metabolism, assault"
+T504X4,"Poisoning by drugs affecting uric acid metabolism, undetermined"
+T504X5,"Adverse effect of drugs affecting uric acid metabolism"
+T504X6,"Underdosing of drugs affecting uric acid metabolism"
+T505,"Poisoning by, adverse effect of and underdosing of appetite depressants"
+T505X,"Poisoning by, adverse effect of and underdosing of appetite depressants"
+T505X1,"Poisoning by appetite depressants, accidental (unintentional)"
+T505X2,"Poisoning by appetite depressants, intentional self-harm"
+T505X3,"Poisoning by appetite depressants, assault"
+T505X4,"Poisoning by appetite depressants, undetermined"
+T505X5,"Adverse effect of appetite depressants"
+T505X6,"Underdosing of appetite depressants"
+T506,"Poisoning by, adverse effect of and underdosing of antidotes and chelating agents"
+T506X,"Poisoning by, adverse effect of and underdosing of antidotes and chelating agents"
+T506X1,"Poisoning by antidotes and chelating agents, accidental (unintentional)"
+T506X2,"Poisoning by antidotes and chelating agents, intentional self-harm"
+T506X3,"Poisoning by antidotes and chelating agents, assault"
+T506X4,"Poisoning by antidotes and chelating agents, undetermined"
+T506X5,"Adverse effect of antidotes and chelating agents"
+T506X6,"Underdosing of antidotes and chelating agents"
+T507,"Poisoning by, adverse effect of and underdosing of analeptics and opioid receptor antagonists"
+T507X,"Poisoning by, adverse effect of and underdosing of analeptics and opioid receptor antagonists"
+T507X1,"Poisoning by analeptics and opioid receptor antagonists, accidental (unintentional)"
+T507X2,"Poisoning by analeptics and opioid receptor antagonists, intentional self-harm"
+T507X3,"Poisoning by analeptics and opioid receptor antagonists, assault"
+T507X4,"Poisoning by analeptics and opioid receptor antagonists, undetermined"
+T507X5,"Adverse effect of analeptics and opioid receptor antagonists"
+T507X6,"Underdosing of analeptics and opioid receptor antagonists"
+T508,"Poisoning by, adverse effect of and underdosing of diagnostic agents"
+T508X,"Poisoning by, adverse effect of and underdosing of diagnostic agents"
+T508X1,"Poisoning by diagnostic agents, accidental (unintentional)"
+T508X2,"Poisoning by diagnostic agents, intentional self-harm"
+T508X3,"Poisoning by diagnostic agents, assault"
+T508X4,"Poisoning by diagnostic agents, undetermined"
+T508X5,"Adverse effect of diagnostic agents"
+T508X6,"Underdosing of diagnostic agents"
+T50A,"Poisoning by, adverse effect of and underdosing of bacterial vaccines"
+T50A1,"Poisoning by, adverse effect of and underdosing of pertussis vaccine, including combinations with a pertussis component"
+T50A11,"Poisoning by pertussis vaccine, including combinations with a pertussis component, accidental (unintentional)"
+T50A12,"Poisoning by pertussis vaccine, including combinations with a pertussis component, intentional self-harm"
+T50A13,"Poisoning by pertussis vaccine, including combinations with a pertussis component, assault"
+T50A14,"Poisoning by pertussis vaccine, including combinations with a pertussis component, undetermined"
+T50A15,"Adverse effect of pertussis vaccine, including combinations with a pertussis component"
+T50A16,"Underdosing of pertussis vaccine, including combinations with a pertussis component"
+T50A2,"Poisoning by, adverse effect of and underdosing of  mixed bacterial vaccines without a pertussis component"
+T50A21,"Poisoning by mixed bacterial vaccines without a pertussis component, accidental (unintentional)"
+T50A22,"Poisoning by mixed bacterial vaccines without a pertussis component, intentional self-harm"
+T50A23,"Poisoning by mixed bacterial vaccines without a pertussis component, assault"
+T50A24,"Poisoning by mixed bacterial vaccines without a pertussis component, undetermined"
+T50A25,"Adverse effect of mixed bacterial vaccines without a pertussis component"
+T50A26,"Underdosing of mixed bacterial vaccines without a pertussis component"
+T50A9,"Poisoning by, adverse effect of and underdosing of other bacterial vaccines"
+T50A91,"Poisoning by other bacterial vaccines, accidental (unintentional)"
+T50A92,"Poisoning by other bacterial vaccines, intentional self-harm"
+T50A93,"Poisoning by other bacterial vaccines, assault"
+T50A94,"Poisoning by other bacterial vaccines, undetermined"
+T50A95,"Adverse effect of other bacterial vaccines"
+T50A96,"Underdosing of other bacterial vaccines"
+T50B,"Poisoning by, adverse effect of and underdosing of viral vaccines"
+T50B1,"Poisoning by, adverse effect of and underdosing of smallpox vaccines"
+T50B11,"Poisoning by smallpox vaccines, accidental (unintentional)"
+T50B12,"Poisoning by smallpox vaccines, intentional self-harm"
+T50B13,"Poisoning by smallpox vaccines, assault"
+T50B14,"Poisoning by smallpox vaccines, undetermined"
+T50B15,"Adverse effect of smallpox vaccines"
+T50B16,"Underdosing of smallpox vaccines"
+T50B9,"Poisoning by, adverse effect of and underdosing of other viral vaccines"
+T50B91,"Poisoning by other viral vaccines, accidental (unintentional)"
+T50B92,"Poisoning by other viral vaccines, intentional self-harm"
+T50B93,"Poisoning by other viral vaccines, assault"
+T50B94,"Poisoning by other viral vaccines, undetermined"
+T50B95,"Adverse effect of other viral vaccines"
+T50B96,"Underdosing of other viral vaccines"
+T50Z,"Poisoning by, adverse effect of and underdosing of other vaccines and biological substances"
+T50Z1,"Poisoning by, adverse effect of and underdosing of immunoglobulin"
+T50Z11,"Poisoning by immunoglobulin, accidental (unintentional)"
+T50Z12,"Poisoning by immunoglobulin, intentional self-harm"
+T50Z13,"Poisoning by immunoglobulin, assault"
+T50Z14,"Poisoning by immunoglobulin, undetermined"
+T50Z15,"Adverse effect of immunoglobulin"
+T50Z16,"Underdosing of immunoglobulin"
+T50Z9,"Poisoning by, adverse effect of and underdosing of other vaccines and biological substances"
+T50Z91,"Poisoning by other vaccines and biological substances, accidental (unintentional)"
+T50Z92,"Poisoning by other vaccines and biological substances, intentional self-harm"
+T50Z93,"Poisoning by other vaccines and biological substances, assault"
+T50Z94,"Poisoning by other vaccines and biological substances, undetermined"
+T50Z95,"Adverse effect of other vaccines and biological substances"
+T50Z96,"Underdosing of other vaccines and biological substances"
+T509,"Poisoning by, adverse effect of and underdosing of other and unspecified drugs, medicaments and biological substances"
+T5090,"Poisoning by, adverse effect of and underdosing of unspecified drugs, medicaments and biological substances"
+T50901,"Poisoning by unspecified drugs, medicaments and biological substances, accidental (unintentional)"
+T50902,"Poisoning by unspecified drugs, medicaments and biological substances, intentional self-harm"
+T50903,"Poisoning by unspecified drugs, medicaments and biological substances, assault"
+T50904,"Poisoning by unspecified drugs, medicaments and biological substances, undetermined"
+T50905,"Adverse effect of unspecified drugs, medicaments and biological substances"
+T50906,"Underdosing of unspecified drugs, medicaments and biological substances"
+T5099,"Poisoning by, adverse effect of and underdosing of other drugs, medicaments and biological substances"
+T50991,"Poisoning by other drugs, medicaments and biological substances, accidental (unintentional)"
+T50992,"Poisoning by other drugs, medicaments and biological substances, intentional self-harm"
+T50993,"Poisoning by other drugs, medicaments and biological substances, assault"
+T50994,"Poisoning by other drugs, medicaments and biological substances, undetermined"
+T50995,"Adverse effect of other drugs, medicaments and biological substances"
+T50996,"Underdosing of other drugs, medicaments and biological substances"
+T51,"Toxic effect of alcohol"
+T510,"Toxic effect of ethanol"
+T510X,"Toxic effect of ethanol"
+T510X1,"Toxic effect of ethanol, accidental (unintentional)"
+T510X2,"Toxic effect of ethanol, intentional self-harm"
+T510X3,"Toxic effect of ethanol, assault"
+T510X4,"Toxic effect of ethanol, undetermined"
+T511,"Toxic effect of methanol"
+T511X,"Toxic effect of methanol"
+T511X1,"Toxic effect of methanol, accidental (unintentional)"
+T511X2,"Toxic effect of methanol, intentional self-harm"
+T511X3,"Toxic effect of methanol, assault"
+T511X4,"Toxic effect of methanol, undetermined"
+T512,"Toxic effect of 2-Propanol"
+T512X,"Toxic effect of 2-Propanol"
+T512X1,"Toxic effect of 2-Propanol, accidental (unintentional)"
+T512X2,"Toxic effect of 2-Propanol, intentional self-harm"
+T512X3,"Toxic effect of 2-Propanol, assault"
+T512X4,"Toxic effect of 2-Propanol, undetermined"
+T513,"Toxic effect of fusel oil"
+T513X,"Toxic effect of fusel oil"
+T513X1,"Toxic effect of fusel oil, accidental (unintentional)"
+T513X2,"Toxic effect of fusel oil, intentional self-harm"
+T513X3,"Toxic effect of fusel oil, assault"
+T513X4,"Toxic effect of fusel oil, undetermined"
+T518,"Toxic effect of other alcohols"
+T518X,"Toxic effect of other alcohols"
+T518X1,"Toxic effect of other alcohols, accidental (unintentional)"
+T518X2,"Toxic effect of other alcohols, intentional self-harm"
+T518X3,"Toxic effect of other alcohols, assault"
+T518X4,"Toxic effect of other alcohols, undetermined"
+T519,"Toxic effect of unspecified alcohol"
+T5191,"Toxic effect of unspecified alcohol, accidental (unintentional)"
+T5192,"Toxic effect of unspecified alcohol, intentional self-harm"
+T5193,"Toxic effect of unspecified alcohol, assault"
+T5194,"Toxic effect of unspecified alcohol, undetermined"
+T52,"Toxic effect of organic solvents"
+T520,"Toxic effects of petroleum products"
+T520X,"Toxic effects of petroleum products"
+T520X1,"Toxic effect of petroleum products, accidental (unintentional)"
+T520X2,"Toxic effect of petroleum products, intentional self-harm"
+T520X3,"Toxic effect of petroleum products, assault"
+T520X4,"Toxic effect of petroleum products, undetermined"
+T521,"Toxic effects of benzene"
+T521X,"Toxic effects of benzene"
+T521X1,"Toxic effect of benzene, accidental (unintentional)"
+T521X2,"Toxic effect of benzene, intentional self-harm"
+T521X3,"Toxic effect of benzene, assault"
+T521X4,"Toxic effect of benzene, undetermined"
+T522,"Toxic effects of homologues of benzene"
+T522X,"Toxic effects of homologues of benzene"
+T522X1,"Toxic effect of homologues of benzene, accidental (unintentional)"
+T522X2,"Toxic effect of homologues of benzene, intentional self-harm"
+T522X3,"Toxic effect of homologues of benzene, assault"
+T522X4,"Toxic effect of homologues of benzene, undetermined"
+T523,"Toxic effects of glycols"
+T523X,"Toxic effects of glycols"
+T523X1,"Toxic effect of glycols, accidental (unintentional)"
+T523X2,"Toxic effect of glycols, intentional self-harm"
+T523X3,"Toxic effect of glycols, assault"
+T523X4,"Toxic effect of glycols, undetermined"
+T524,"Toxic effects of ketones"
+T524X,"Toxic effects of ketones"
+T524X1,"Toxic effect of ketones, accidental (unintentional)"
+T524X2,"Toxic effect of ketones, intentional self-harm"
+T524X3,"Toxic effect of ketones, assault"
+T524X4,"Toxic effect of ketones, undetermined"
+T528,"Toxic effects of other organic solvents"
+T528X,"Toxic effects of other organic solvents"
+T528X1,"Toxic effect of other organic solvents, accidental (unintentional)"
+T528X2,"Toxic effect of other organic solvents, intentional self-harm"
+T528X3,"Toxic effect of other organic solvents, assault"
+T528X4,"Toxic effect of other organic solvents, undetermined"
+T529,"Toxic effects of unspecified organic solvent"
+T5291,"Toxic effect of unspecified organic solvent, accidental (unintentional)"
+T5292,"Toxic effect of unspecified organic solvent, intentional self-harm"
+T5293,"Toxic effect of unspecified organic solvent, assault"
+T5294,"Toxic effect of unspecified organic solvent, undetermined"
+T53,"Toxic effect of halogen derivatives of aliphatic and aromatic hydrocarbons"
+T530,"Toxic effects of carbon tetrachloride"
+T530X,"Toxic effects of carbon tetrachloride"
+T530X1,"Toxic effect of carbon tetrachloride, accidental (unintentional)"
+T530X2,"Toxic effect of carbon tetrachloride, intentional self-harm"
+T530X3,"Toxic effect of carbon tetrachloride, assault"
+T530X4,"Toxic effect of carbon tetrachloride, undetermined"
+T531,"Toxic effects of chloroform"
+T531X,"Toxic effects of chloroform"
+T531X1,"Toxic effect of chloroform, accidental (unintentional)"
+T531X2,"Toxic effect of chloroform, intentional self-harm"
+T531X3,"Toxic effect of chloroform, assault"
+T531X4,"Toxic effect of chloroform, undetermined"
+T532,"Toxic effects of trichloroethylene"
+T532X,"Toxic effects of trichloroethylene"
+T532X1,"Toxic effect of trichloroethylene, accidental (unintentional)"
+T532X2,"Toxic effect of trichloroethylene, intentional self-harm"
+T532X3,"Toxic effect of trichloroethylene, assault"
+T532X4,"Toxic effect of trichloroethylene, undetermined"
+T533,"Toxic effects of tetrachloroethylene"
+T533X,"Toxic effects of tetrachloroethylene"
+T533X1,"Toxic effect of tetrachloroethylene, accidental (unintentional)"
+T533X2,"Toxic effect of tetrachloroethylene, intentional self-harm"
+T533X3,"Toxic effect of tetrachloroethylene, assault"
+T533X4,"Toxic effect of tetrachloroethylene, undetermined"
+T534,"Toxic effects of dichloromethane"
+T534X,"Toxic effects of dichloromethane"
+T534X1,"Toxic effect of dichloromethane, accidental (unintentional)"
+T534X2,"Toxic effect of dichloromethane, intentional self-harm"
+T534X3,"Toxic effect of dichloromethane, assault"
+T534X4,"Toxic effect of dichloromethane, undetermined"
+T535,"Toxic effects of chlorofluorocarbons"
+T535X,"Toxic effects of chlorofluorocarbons"
+T535X1,"Toxic effect of chlorofluorocarbons, accidental (unintentional)"
+T535X2,"Toxic effect of chlorofluorocarbons, intentional self-harm"
+T535X3,"Toxic effect of chlorofluorocarbons, assault"
+T535X4,"Toxic effect of chlorofluorocarbons, undetermined"
+T536,"Toxic effects of other halogen derivatives of aliphatic hydrocarbons"
+T536X,"Toxic effects of other halogen derivatives of aliphatic hydrocarbons"
+T536X1,"Toxic effect of other halogen derivatives of aliphatic hydrocarbons, accidental (unintentional)"
+T536X2,"Toxic effect of other halogen derivatives of aliphatic hydrocarbons, intentional self-harm"
+T536X3,"Toxic effect of other halogen derivatives of aliphatic hydrocarbons, assault"
+T536X4,"Toxic effect of other halogen derivatives of aliphatic hydrocarbons, undetermined"
+T537,"Toxic effects of other halogen derivatives of aromatic hydrocarbons"
+T537X,"Toxic effects of other halogen derivatives of aromatic hydrocarbons"
+T537X1,"Toxic effect of other halogen derivatives of aromatic hydrocarbons, accidental (unintentional)"
+T537X2,"Toxic effect of other halogen derivatives of aromatic hydrocarbons, intentional self-harm"
+T537X3,"Toxic effect of other halogen derivatives of aromatic hydrocarbons, assault"
+T537X4,"Toxic effect of other halogen derivatives of aromatic hydrocarbons, undetermined"
+T539,"Toxic effects of unspecified halogen derivatives of aliphatic and aromatic hydrocarbons"
+T5391,"Toxic effect of unspecified halogen derivatives of aliphatic and aromatic hydrocarbons, accidental (unintentional)"
+T5392,"Toxic effect of unspecified halogen derivatives of aliphatic and aromatic hydrocarbons, intentional self-harm"
+T5393,"Toxic effect of unspecified halogen derivatives of aliphatic and aromatic hydrocarbons, assault"
+T5394,"Toxic effect of unspecified halogen derivatives of aliphatic and aromatic hydrocarbons, undetermined"
+T54,"Toxic effect of corrosive substances"
+T540,"Toxic effects of phenol and phenol homologues"
+T540X,"Toxic effects of phenol and phenol homologues"
+T540X1,"Toxic effect of phenol and phenol homologues, accidental (unintentional)"
+T540X2,"Toxic effect of phenol and phenol homologues, intentional self-harm"
+T540X3,"Toxic effect of phenol and phenol homologues, assault"
+T540X4,"Toxic effect of phenol and phenol homologues, undetermined"
+T541,"Toxic effects of other corrosive organic compounds"
+T541X,"Toxic effects of other corrosive organic compounds"
+T541X1,"Toxic effect of other corrosive organic compounds, accidental (unintentional)"
+T541X2,"Toxic effect of other corrosive organic compounds, intentional self-harm"
+T541X3,"Toxic effect of other corrosive organic compounds, assault"
+T541X4,"Toxic effect of other corrosive organic compounds, undetermined"
+T542,"Toxic effects of corrosive acids and acid-like substances"
+T542X,"Toxic effects of corrosive acids and acid-like substances"
+T542X1,"Toxic effect of corrosive acids and acid-like substances, accidental (unintentional)"
+T542X2,"Toxic effect of corrosive acids and acid-like substances, intentional self-harm"
+T542X3,"Toxic effect of corrosive acids and acid-like substances, assault"
+T542X4,"Toxic effect of corrosive acids and acid-like substances, undetermined"
+T543,"Toxic effects of corrosive alkalis and alkali-like substances"
+T543X,"Toxic effects of corrosive alkalis and alkali-like substances"
+T543X1,"Toxic effect of corrosive alkalis and alkali-like substances, accidental (unintentional)"
+T543X2,"Toxic effect of corrosive alkalis and alkali-like substances, intentional self-harm"
+T543X3,"Toxic effect of corrosive alkalis and alkali-like substances, assault"
+T543X4,"Toxic effect of corrosive alkalis and alkali-like substances, undetermined"
+T549,"Toxic effects of unspecified corrosive substance"
+T5491,"Toxic effect of unspecified corrosive substance, accidental (unintentional)"
+T5492,"Toxic effect of unspecified corrosive substance, intentional self-harm"
+T5493,"Toxic effect of unspecified corrosive substance, assault"
+T5494,"Toxic effect of unspecified corrosive substance, undetermined"
+T55,"Toxic effect of soaps and detergents"
+T550,"Toxic effect of soaps"
+T550X,"Toxic effect of soaps"
+T550X1,"Toxic effect of soaps, accidental (unintentional)"
+T550X2,"Toxic effect of soaps, intentional self-harm"
+T550X3,"Toxic effect of soaps, assault"
+T550X4,"Toxic effect of soaps, undetermined"
+T551,"Toxic effect of detergents"
+T551X,"Toxic effect of detergents"
+T551X1,"Toxic effect of detergents, accidental (unintentional)"
+T551X2,"Toxic effect of detergents, intentional self-harm"
+T551X3,"Toxic effect of detergents, assault"
+T551X4,"Toxic effect of detergents, undetermined"
+T56,"Toxic effect of metals"
+T560,"Toxic effects of lead and its compounds"
+T560X,"Toxic effects of lead and its compounds"
+T560X1,"Toxic effect of lead and its compounds, accidental (unintentional)"
+T560X2,"Toxic effect of lead and its compounds, intentional self-harm"
+T560X3,"Toxic effect of lead and its compounds, assault"
+T560X4,"Toxic effect of lead and its compounds, undetermined"
+T561,"Toxic effects of mercury and its compounds"
+T561X,"Toxic effects of mercury and its compounds"
+T561X1,"Toxic effect of mercury and its compounds, accidental (unintentional)"
+T561X2,"Toxic effect of mercury and its compounds, intentional self-harm"
+T561X3,"Toxic effect of mercury and its compounds, assault"
+T561X4,"Toxic effect of mercury and its compounds, undetermined"
+T562,"Toxic effects of chromium and its compounds"
+T562X,"Toxic effects of chromium and its compounds"
+T562X1,"Toxic effect of chromium and its compounds, accidental (unintentional)"
+T562X2,"Toxic effect of chromium and its compounds, intentional self-harm"
+T562X3,"Toxic effect of chromium and its compounds, assault"
+T562X4,"Toxic effect of chromium and its compounds, undetermined"
+T563,"Toxic effects of cadmium and its compounds"
+T563X,"Toxic effects of cadmium and its compounds"
+T563X1,"Toxic effect of cadmium and its compounds, accidental (unintentional)"
+T563X2,"Toxic effect of cadmium and its compounds, intentional self-harm"
+T563X3,"Toxic effect of cadmium and its compounds, assault"
+T563X4,"Toxic effect of cadmium and its compounds, undetermined"
+T564,"Toxic effects of copper and its compounds"
+T564X,"Toxic effects of copper and its compounds"
+T564X1,"Toxic effect of copper and its compounds, accidental (unintentional)"
+T564X2,"Toxic effect of copper and its compounds, intentional self-harm"
+T564X3,"Toxic effect of copper and its compounds, assault"
+T564X4,"Toxic effect of copper and its compounds, undetermined"
+T565,"Toxic effects of zinc and its compounds"
+T565X,"Toxic effects of zinc and its compounds"
+T565X1,"Toxic effect of zinc and its compounds, accidental (unintentional)"
+T565X2,"Toxic effect of zinc and its compounds, intentional self-harm"
+T565X3,"Toxic effect of zinc and its compounds, assault"
+T565X4,"Toxic effect of zinc and its compounds, undetermined"
+T566,"Toxic effects of tin and its compounds"
+T566X,"Toxic effects of tin and its compounds"
+T566X1,"Toxic effect of tin and its compounds, accidental (unintentional)"
+T566X2,"Toxic effect of tin and its compounds, intentional self-harm"
+T566X3,"Toxic effect of tin and its compounds, assault"
+T566X4,"Toxic effect of tin and its compounds, undetermined"
+T567,"Toxic effects of beryllium and its compounds"
+T567X,"Toxic effects of beryllium and its compounds"
+T567X1,"Toxic effect of beryllium and its compounds, accidental (unintentional)"
+T567X2,"Toxic effect of beryllium and its compounds, intentional self-harm"
+T567X3,"Toxic effect of beryllium and its compounds, assault"
+T567X4,"Toxic effect of beryllium and its compounds, undetermined"
+T568,"Toxic effects of other metals"
+T5681,"Toxic effect of thallium"
+T56811,"Toxic effect of thallium, accidental (unintentional)"
+T56812,"Toxic effect of thallium, intentional self-harm"
+T56813,"Toxic effect of thallium, assault"
+T56814,"Toxic effect of thallium, undetermined"
+T5689,"Toxic effects of other metals"
+T56891,"Toxic effect of other metals, accidental (unintentional)"
+T56892,"Toxic effect of other metals, intentional self-harm"
+T56893,"Toxic effect of other metals, assault"
+T56894,"Toxic effect of other metals, undetermined"
+T569,"Toxic effects of unspecified metal"
+T5691,"Toxic effect of unspecified metal, accidental (unintentional)"
+T5692,"Toxic effect of unspecified metal, intentional self-harm"
+T5693,"Toxic effect of unspecified metal, assault"
+T5694,"Toxic effect of unspecified metal, undetermined"
+T57,"Toxic effect of other inorganic substances"
+T570,"Toxic effect of arsenic and its compounds"
+T570X,"Toxic effect of arsenic and its compounds"
+T570X1,"Toxic effect of arsenic and its compounds, accidental (unintentional)"
+T570X2,"Toxic effect of arsenic and its compounds, intentional self-harm"
+T570X3,"Toxic effect of arsenic and its compounds, assault"
+T570X4,"Toxic effect of arsenic and its compounds, undetermined"
+T571,"Toxic effect of phosphorus and its compounds"
+T571X,"Toxic effect of phosphorus and its compounds"
+T571X1,"Toxic effect of phosphorus and its compounds, accidental (unintentional)"
+T571X2,"Toxic effect of phosphorus and its compounds, intentional self-harm"
+T571X3,"Toxic effect of phosphorus and its compounds, assault"
+T571X4,"Toxic effect of phosphorus and its compounds, undetermined"
+T572,"Toxic effect of manganese and its compounds"
+T572X,"Toxic effect of manganese and its compounds"
+T572X1,"Toxic effect of manganese and its compounds, accidental (unintentional)"
+T572X2,"Toxic effect of manganese and its compounds, intentional self-harm"
+T572X3,"Toxic effect of manganese and its compounds, assault"
+T572X4,"Toxic effect of manganese and its compounds, undetermined"
+T573,"Toxic effect of hydrogen cyanide"
+T573X,"Toxic effect of hydrogen cyanide"
+T573X1,"Toxic effect of hydrogen cyanide, accidental (unintentional)"
+T573X2,"Toxic effect of hydrogen cyanide, intentional self-harm"
+T573X3,"Toxic effect of hydrogen cyanide, assault"
+T573X4,"Toxic effect of hydrogen cyanide, undetermined"
+T578,"Toxic effect of other specified inorganic substances"
+T578X,"Toxic effect of other specified inorganic substances"
+T578X1,"Toxic effect of other specified inorganic substances, accidental (unintentional)"
+T578X2,"Toxic effect of other specified inorganic substances, intentional self-harm"
+T578X3,"Toxic effect of other specified inorganic substances, assault"
+T578X4,"Toxic effect of other specified inorganic substances, undetermined"
+T579,"Toxic effect of unspecified inorganic substance"
+T5791,"Toxic effect of unspecified inorganic substance, accidental (unintentional)"
+T5792,"Toxic effect of unspecified inorganic substance, intentional self-harm"
+T5793,"Toxic effect of unspecified inorganic substance, assault"
+T5794,"Toxic effect of unspecified inorganic substance, undetermined"
+T58,"Toxic effect of carbon monoxide"
+T580,"Toxic effect of carbon monoxide from motor vehicle exhaust"
+T5801,"Toxic effect of carbon monoxide from motor vehicle exhaust, accidental (unintentional)"
+T5802,"Toxic effect of carbon monoxide from motor vehicle exhaust, intentional self-harm"
+T5803,"Toxic effect of carbon monoxide from motor vehicle exhaust, assault"
+T5804,"Toxic effect of carbon monoxide from motor vehicle exhaust, undetermined"
+T581,"Toxic effect of carbon monoxide from utility gas"
+T5811,"Toxic effect of carbon monoxide from utility gas, accidental (unintentional)"
+T5812,"Toxic effect of carbon monoxide from utility gas, intentional self-harm"
+T5813,"Toxic effect of carbon monoxide from utility gas, assault"
+T5814,"Toxic effect of carbon monoxide from utility gas, undetermined"
+T582,"Toxic effect of carbon monoxide from incomplete combustion of other domestic fuels"
+T582X,"Toxic effect of carbon monoxide from incomplete combustion of other domestic fuels"
+T582X1,"Toxic effect of carbon monoxide from incomplete combustion of other domestic fuels, accidental (unintentional)"
+T582X2,"Toxic effect of carbon monoxide from incomplete combustion of other domestic fuels, intentional self-harm"
+T582X3,"Toxic effect of carbon monoxide from incomplete combustion of other domestic fuels, assault"
+T582X4,"Toxic effect of carbon monoxide from incomplete combustion of other domestic fuels, undetermined"
+T588,"Toxic effect of carbon monoxide from other source"
+T588X,"Toxic effect of carbon monoxide from other source"
+T588X1,"Toxic effect of carbon monoxide from other source, accidental (unintentional)"
+T588X2,"Toxic effect of carbon monoxide from other source, intentional self-harm"
+T588X3,"Toxic effect of carbon monoxide from other source, assault"
+T588X4,"Toxic effect of carbon monoxide from other source, undetermined"
+T589,"Toxic effect of carbon monoxide from unspecified source"
+T5891,"Toxic effect of carbon monoxide from unspecified source, accidental (unintentional)"
+T5892,"Toxic effect of carbon monoxide from unspecified source, intentional self-harm"
+T5893,"Toxic effect of carbon monoxide from unspecified source, assault"
+T5894,"Toxic effect of carbon monoxide from unspecified source, undetermined"
+T59,"Toxic effect of other gases, fumes and vapors"
+T590,"Toxic effect of nitrogen oxides"
+T590X,"Toxic effect of nitrogen oxides"
+T590X1,"Toxic effect of nitrogen oxides, accidental (unintentional)"
+T590X2,"Toxic effect of nitrogen oxides, intentional self-harm"
+T590X3,"Toxic effect of nitrogen oxides, assault"
+T590X4,"Toxic effect of nitrogen oxides, undetermined"
+T591,"Toxic effect of sulfur dioxide"
+T591X,"Toxic effect of sulfur dioxide"
+T591X1,"Toxic effect of sulfur dioxide, accidental (unintentional)"
+T591X2,"Toxic effect of sulfur dioxide, intentional self-harm"
+T591X3,"Toxic effect of sulfur dioxide, assault"
+T591X4,"Toxic effect of sulfur dioxide, undetermined"
+T592,"Toxic effect of formaldehyde"
+T592X,"Toxic effect of formaldehyde"
+T592X1,"Toxic effect of formaldehyde, accidental (unintentional)"
+T592X2,"Toxic effect of formaldehyde, intentional self-harm"
+T592X3,"Toxic effect of formaldehyde, assault"
+T592X4,"Toxic effect of formaldehyde, undetermined"
+T593,"Toxic effect of lacrimogenic gas"
+T593X,"Toxic effect of lacrimogenic gas"
+T593X1,"Toxic effect of lacrimogenic gas, accidental (unintentional)"
+T593X2,"Toxic effect of lacrimogenic gas, intentional self-harm"
+T593X3,"Toxic effect of lacrimogenic gas, assault"
+T593X4,"Toxic effect of lacrimogenic gas, undetermined"
+T594,"Toxic effect of chlorine gas"
+T594X,"Toxic effect of chlorine gas"
+T594X1,"Toxic effect of chlorine gas, accidental (unintentional)"
+T594X2,"Toxic effect of chlorine gas, intentional self-harm"
+T594X3,"Toxic effect of chlorine gas, assault"
+T594X4,"Toxic effect of chlorine gas, undetermined"
+T595,"Toxic effect of fluorine gas and hydrogen fluoride"
+T595X,"Toxic effect of fluorine gas and hydrogen fluoride"
+T595X1,"Toxic effect of fluorine gas and hydrogen fluoride, accidental (unintentional)"
+T595X2,"Toxic effect of fluorine gas and hydrogen fluoride, intentional self-harm"
+T595X3,"Toxic effect of fluorine gas and hydrogen fluoride, assault"
+T595X4,"Toxic effect of fluorine gas and hydrogen fluoride, undetermined"
+T596,"Toxic effect of hydrogen sulfide"
+T596X,"Toxic effect of hydrogen sulfide"
+T596X1,"Toxic effect of hydrogen sulfide, accidental (unintentional)"
+T596X2,"Toxic effect of hydrogen sulfide, intentional self-harm"
+T596X3,"Toxic effect of hydrogen sulfide, assault"
+T596X4,"Toxic effect of hydrogen sulfide, undetermined"
+T597,"Toxic effect of carbon dioxide"
+T597X,"Toxic effect of carbon dioxide"
+T597X1,"Toxic effect of carbon dioxide, accidental (unintentional)"
+T597X2,"Toxic effect of carbon dioxide, intentional self-harm"
+T597X3,"Toxic effect of carbon dioxide, assault"
+T597X4,"Toxic effect of carbon dioxide, undetermined"
+T598,"Toxic effect of other specified gases, fumes and vapors"
+T5981,"Toxic effect of smoke"
+T59811,"Toxic effect of smoke, accidental (unintentional)"
+T59812,"Toxic effect of smoke, intentional self-harm"
+T59813,"Toxic effect of smoke, assault"
+T59814,"Toxic effect of smoke, undetermined"
+T5989,"Toxic effect of other specified gases, fumes and vapors"
+T59891,"Toxic effect of other specified gases, fumes and vapors, accidental (unintentional)"
+T59892,"Toxic effect of other specified gases, fumes and vapors, intentional self-harm"
+T59893,"Toxic effect of other specified gases, fumes and vapors, assault"
+T59894,"Toxic effect of other specified gases, fumes and vapors, undetermined"
+T599,"Toxic effect of unspecified gases, fumes and vapors"
+T5991,"Toxic effect of unspecified gases, fumes and vapors, accidental (unintentional)"
+T5992,"Toxic effect of unspecified gases, fumes and vapors, intentional self-harm"
+T5993,"Toxic effect of unspecified gases, fumes and vapors, assault"
+T5994,"Toxic effect of unspecified gases, fumes and vapors, undetermined"
+T60,"Toxic effect of pesticides"
+T600,"Toxic effect of organophosphate and carbamate insecticides"
+T600X,"Toxic effect of organophosphate and carbamate insecticides"
+T600X1,"Toxic effect of organophosphate and carbamate insecticides, accidental (unintentional)"
+T600X2,"Toxic effect of organophosphate and carbamate insecticides, intentional self-harm"
+T600X3,"Toxic effect of organophosphate and carbamate insecticides, assault"
+T600X4,"Toxic effect of organophosphate and carbamate insecticides, undetermined"
+T601,"Toxic effect of halogenated insecticides"
+T601X,"Toxic effect of halogenated insecticides"
+T601X1,"Toxic effect of halogenated insecticides, accidental (unintentional)"
+T601X2,"Toxic effect of halogenated insecticides, intentional self-harm"
+T601X3,"Toxic effect of halogenated insecticides, assault"
+T601X4,"Toxic effect of halogenated insecticides, undetermined"
+T602,"Toxic effect of other insecticides"
+T602X,"Toxic effect of other insecticides"
+T602X1,"Toxic effect of other insecticides, accidental (unintentional)"
+T602X2,"Toxic effect of other insecticides, intentional self-harm"
+T602X3,"Toxic effect of other insecticides, assault"
+T602X4,"Toxic effect of other insecticides, undetermined"
+T603,"Toxic effect of herbicides and fungicides"
+T603X,"Toxic effect of herbicides and fungicides"
+T603X1,"Toxic effect of herbicides and fungicides, accidental (unintentional)"
+T603X2,"Toxic effect of herbicides and fungicides, intentional self-harm"
+T603X3,"Toxic effect of herbicides and fungicides, assault"
+T603X4,"Toxic effect of herbicides and fungicides, undetermined"
+T604,"Toxic effect of rodenticides"
+T604X,"Toxic effect of rodenticides"
+T604X1,"Toxic effect of rodenticides, accidental (unintentional)"
+T604X2,"Toxic effect of rodenticides, intentional self-harm"
+T604X3,"Toxic effect of rodenticides, assault"
+T604X4,"Toxic effect of rodenticides, undetermined"
+T608,"Toxic effect of other pesticides"
+T608X,"Toxic effect of other pesticides"
+T608X1,"Toxic effect of other pesticides, accidental (unintentional)"
+T608X2,"Toxic effect of other pesticides, intentional self-harm"
+T608X3,"Toxic effect of other pesticides, assault"
+T608X4,"Toxic effect of other pesticides, undetermined"
+T609,"Toxic effect of unspecified pesticide"
+T6091,"Toxic effect of unspecified pesticide, accidental (unintentional)"
+T6092,"Toxic effect of unspecified pesticide, intentional self-harm"
+T6093,"Toxic effect of unspecified pesticide, assault"
+T6094,"Toxic effect of unspecified pesticide, undetermined"
+T61,"Toxic effect of noxious substances eaten as seafood"
+T610,"Ciguatera fish poisoning"
+T6101,"Ciguatera fish poisoning, accidental (unintentional)"
+T6102,"Ciguatera fish poisoning, intentional self-harm"
+T6103,"Ciguatera fish poisoning, assault"
+T6104,"Ciguatera fish poisoning, undetermined"
+T611,"Scombroid fish poisoning"
+T6111,"Scombroid fish poisoning, accidental (unintentional)"
+T6112,"Scombroid fish poisoning, intentional self-harm"
+T6113,"Scombroid fish poisoning, assault"
+T6114,"Scombroid fish poisoning, undetermined"
+T617,"Other fish and shellfish poisoning"
+T6177,"Other fish poisoning"
+T61771,"Other fish poisoning, accidental (unintentional)"
+T61772,"Other fish poisoning, intentional self-harm"
+T61773,"Other fish poisoning, assault"
+T61774,"Other fish poisoning, undetermined"
+T6178,"Other shellfish poisoning"
+T61781,"Other shellfish poisoning, accidental (unintentional)"
+T61782,"Other shellfish poisoning, intentional self-harm"
+T61783,"Other shellfish poisoning, assault"
+T61784,"Other shellfish poisoning, undetermined"
+T618,"Toxic effect of other seafood"
+T618X,"Toxic effect of other seafood"
+T618X1,"Toxic effect of other seafood, accidental (unintentional)"
+T618X2,"Toxic effect of other seafood, intentional self-harm"
+T618X3,"Toxic effect of other seafood, assault"
+T618X4,"Toxic effect of other seafood, undetermined"
+T619,"Toxic effect of unspecified seafood"
+T6191,"Toxic effect of unspecified seafood, accidental (unintentional)"
+T6192,"Toxic effect of unspecified seafood, intentional self-harm"
+T6193,"Toxic effect of unspecified seafood, assault"
+T6194,"Toxic effect of unspecified seafood, undetermined"
+T62,"Toxic effect of other noxious substances eaten as food"
+T620,"Toxic effect of ingested mushrooms"
+T620X,"Toxic effect of ingested mushrooms"
+T620X1,"Toxic effect of ingested mushrooms, accidental (unintentional)"
+T620X2,"Toxic effect of ingested mushrooms, intentional self-harm"
+T620X3,"Toxic effect of ingested mushrooms, assault"
+T620X4,"Toxic effect of ingested mushrooms, undetermined"
+T621,"Toxic effect of ingested berries"
+T621X,"Toxic effect of ingested berries"
+T621X1,"Toxic effect of ingested berries, accidental (unintentional)"
+T621X2,"Toxic effect of ingested berries, intentional self-harm"
+T621X3,"Toxic effect of ingested berries, assault"
+T621X4,"Toxic effect of ingested berries, undetermined"
+T622,"Toxic effect of other ingested (parts of) plant(s)"
+T622X,"Toxic effect of other ingested (parts of) plant(s)"
+T622X1,"Toxic effect of other ingested (parts of) plant(s), accidental (unintentional)"
+T622X2,"Toxic effect of other ingested (parts of) plant(s), intentional self-harm"
+T622X3,"Toxic effect of other ingested (parts of) plant(s), assault"
+T622X4,"Toxic effect of other ingested (parts of) plant(s), undetermined"
+T628,"Toxic effect of other specified noxious substances eaten as food"
+T628X,"Toxic effect of other specified noxious substances eaten as food"
+T628X1,"Toxic effect of other specified noxious substances eaten as food, accidental (unintentional)"
+T628X2,"Toxic effect of other specified noxious substances eaten as food, intentional self-harm"
+T628X3,"Toxic effect of other specified noxious substances eaten as food, assault"
+T628X4,"Toxic effect of other specified noxious substances eaten as food, undetermined"
+T629,"Toxic effect of unspecified noxious substance eaten as food"
+T6291,"Toxic effect of unspecified noxious substance eaten as food, accidental (unintentional)"
+T6292,"Toxic effect of unspecified noxious substance eaten as food, intentional self-harm"
+T6293,"Toxic effect of unspecified noxious substance eaten as food, assault"
+T6294,"Toxic effect of unspecified noxious substance eaten as food, undetermined"
+T63,"Toxic effect of contact with venomous animals and plants"
+T630,"Toxic effect of snake venom"
+T6300,"Toxic effect of unspecified snake venom"
+T63001,"Toxic effect of unspecified snake venom, accidental (unintentional)"
+T63002,"Toxic effect of unspecified snake venom, intentional self-harm"
+T63003,"Toxic effect of unspecified snake venom, assault"
+T63004,"Toxic effect of unspecified snake venom, undetermined"
+T6301,"Toxic effect of rattlesnake venom"
+T63011,"Toxic effect of rattlesnake venom, accidental (unintentional)"
+T63012,"Toxic effect of rattlesnake venom, intentional self-harm"
+T63013,"Toxic effect of rattlesnake venom, assault"
+T63014,"Toxic effect of rattlesnake venom, undetermined"
+T6302,"Toxic effect of coral snake venom"
+T63021,"Toxic effect of coral snake venom, accidental (unintentional)"
+T63022,"Toxic effect of coral snake venom, intentional self-harm"
+T63023,"Toxic effect of coral snake venom, assault"
+T63024,"Toxic effect of coral snake venom, undetermined"
+T6303,"Toxic effect of taipan venom"
+T63031,"Toxic effect of taipan venom, accidental (unintentional)"
+T63032,"Toxic effect of taipan venom, intentional self-harm"
+T63033,"Toxic effect of taipan venom, assault"
+T63034,"Toxic effect of taipan venom, undetermined"
+T6304,"Toxic effect of cobra venom"
+T63041,"Toxic effect of cobra venom, accidental (unintentional)"
+T63042,"Toxic effect of cobra venom, intentional self-harm"
+T63043,"Toxic effect of cobra venom, assault"
+T63044,"Toxic effect of cobra venom, undetermined"
+T6306,"Toxic effect of venom of other North and South American snake"
+T63061,"Toxic effect of venom of other North and South American snake, accidental (unintentional)"
+T63062,"Toxic effect of venom of other North and South American snake, intentional self-harm"
+T63063,"Toxic effect of venom of other North and South American snake, assault"
+T63064,"Toxic effect of venom of other North and South American snake, undetermined"
+T6307,"Toxic effect of venom of other Australian snake"
+T63071,"Toxic effect of venom of other Australian snake, accidental (unintentional)"
+T63072,"Toxic effect of venom of other Australian snake, intentional self-harm"
+T63073,"Toxic effect of venom of other Australian snake, assault"
+T63074,"Toxic effect of venom of other Australian snake, undetermined"
+T6308,"Toxic effect of venom of other African and Asian snake"
+T63081,"Toxic effect of venom of other African and Asian snake, accidental (unintentional)"
+T63082,"Toxic effect of venom of other African and Asian snake, intentional self-harm"
+T63083,"Toxic effect of venom of other African and Asian snake, assault"
+T63084,"Toxic effect of venom of other African and Asian snake, undetermined"
+T6309,"Toxic effect of venom of other snake"
+T63091,"Toxic effect of venom of other snake, accidental (unintentional)"
+T63092,"Toxic effect of venom of other snake, intentional self-harm"
+T63093,"Toxic effect of venom of other snake, assault"
+T63094,"Toxic effect of venom of other snake, undetermined"
+T631,"Toxic effect of venom of other reptiles"
+T6311,"Toxic effect of venom of gila monster"
+T63111,"Toxic effect of venom of gila monster, accidental (unintentional)"
+T63112,"Toxic effect of venom of gila monster, intentional self-harm"
+T63113,"Toxic effect of venom of gila monster, assault"
+T63114,"Toxic effect of venom of gila monster, undetermined"
+T6312,"Toxic effect of venom of other venomous lizard"
+T63121,"Toxic effect of venom of other venomous lizard, accidental (unintentional)"
+T63122,"Toxic effect of venom of other venomous lizard, intentional self-harm"
+T63123,"Toxic effect of venom of other venomous lizard, assault"
+T63124,"Toxic effect of venom of other venomous lizard, undetermined"
+T6319,"Toxic effect of venom of other reptiles"
+T63191,"Toxic effect of venom of other reptiles, accidental (unintentional)"
+T63192,"Toxic effect of venom of other reptiles, intentional self-harm"
+T63193,"Toxic effect of venom of other reptiles, assault"
+T63194,"Toxic effect of venom of other reptiles, undetermined"
+T632,"Toxic effect of venom of scorpion"
+T632X,"Toxic effect of venom of scorpion"
+T632X1,"Toxic effect of venom of scorpion, accidental (unintentional)"
+T632X2,"Toxic effect of venom of scorpion, intentional self-harm"
+T632X3,"Toxic effect of venom of scorpion, assault"
+T632X4,"Toxic effect of venom of scorpion, undetermined"
+T633,"Toxic effect of venom of spider"
+T6330,"Toxic effect of unspecified spider venom"
+T63301,"Toxic effect of unspecified spider venom, accidental (unintentional)"
+T63302,"Toxic effect of unspecified spider venom, intentional self-harm"
+T63303,"Toxic effect of unspecified spider venom, assault"
+T63304,"Toxic effect of unspecified spider venom, undetermined"
+T6331,"Toxic effect of venom of black widow spider"
+T63311,"Toxic effect of venom of black widow spider, accidental (unintentional)"
+T63312,"Toxic effect of venom of black widow spider, intentional self-harm"
+T63313,"Toxic effect of venom of black widow spider, assault"
+T63314,"Toxic effect of venom of black widow spider, undetermined"
+T6332,"Toxic effect of venom of tarantula"
+T63321,"Toxic effect of venom of tarantula, accidental (unintentional)"
+T63322,"Toxic effect of venom of tarantula, intentional self-harm"
+T63323,"Toxic effect of venom of tarantula, assault"
+T63324,"Toxic effect of venom of tarantula, undetermined"
+T6333,"Toxic effect of venom of brown recluse spider"
+T63331,"Toxic effect of venom of brown recluse spider, accidental (unintentional)"
+T63332,"Toxic effect of venom of brown recluse spider, intentional self-harm"
+T63333,"Toxic effect of venom of brown recluse spider, assault"
+T63334,"Toxic effect of venom of brown recluse spider, undetermined"
+T6339,"Toxic effect of venom of other spider"
+T63391,"Toxic effect of venom of other spider, accidental (unintentional)"
+T63392,"Toxic effect of venom of other spider, intentional self-harm"
+T63393,"Toxic effect of venom of other spider, assault"
+T63394,"Toxic effect of venom of other spider, undetermined"
+T634,"Toxic effect of venom of other arthropods"
+T6341,"Toxic effect of venom of centipedes and venomous millipedes"
+T63411,"Toxic effect of venom of centipedes and venomous millipedes, accidental (unintentional)"
+T63412,"Toxic effect of venom of centipedes and venomous millipedes, intentional self-harm"
+T63413,"Toxic effect of venom of centipedes and venomous millipedes, assault"
+T63414,"Toxic effect of venom of centipedes and venomous millipedes, undetermined"
+T6342,"Toxic effect of venom of ants"
+T63421,"Toxic effect of venom of ants, accidental (unintentional)"
+T63422,"Toxic effect of venom of ants, intentional self-harm"
+T63423,"Toxic effect of venom of ants, assault"
+T63424,"Toxic effect of venom of ants, undetermined"
+T6343,"Toxic effect of venom of caterpillars"
+T63431,"Toxic effect of venom of caterpillars, accidental (unintentional)"
+T63432,"Toxic effect of venom of caterpillars, intentional self-harm"
+T63433,"Toxic effect of venom of caterpillars, assault"
+T63434,"Toxic effect of venom of caterpillars, undetermined"
+T6344,"Toxic effect of venom of bees"
+T63441,"Toxic effect of venom of bees, accidental (unintentional)"
+T63442,"Toxic effect of venom of bees, intentional self-harm"
+T63443,"Toxic effect of venom of bees, assault"
+T63444,"Toxic effect of venom of bees, undetermined"
+T6345,"Toxic effect of venom of hornets"
+T63451,"Toxic effect of venom of hornets, accidental (unintentional)"
+T63452,"Toxic effect of venom of hornets, intentional self-harm"
+T63453,"Toxic effect of venom of hornets, assault"
+T63454,"Toxic effect of venom of hornets, undetermined"
+T6346,"Toxic effect of venom of wasps"
+T63461,"Toxic effect of venom of wasps, accidental (unintentional)"
+T63462,"Toxic effect of venom of wasps, intentional self-harm"
+T63463,"Toxic effect of venom of wasps, assault"
+T63464,"Toxic effect of venom of wasps, undetermined"
+T6348,"Toxic effect of venom of other arthropod"
+T63481,"Toxic effect of venom of other arthropod, accidental (unintentional)"
+T63482,"Toxic effect of venom of other arthropod, intentional self-harm"
+T63483,"Toxic effect of venom of other arthropod, assault"
+T63484,"Toxic effect of venom of other arthropod, undetermined"
+T635,"Toxic effect of contact with venomous fish"
+T6351,"Toxic effect of contact with stingray"
+T63511,"Toxic effect of contact with stingray, accidental (unintentional)"
+T63512,"Toxic effect of contact with stingray, intentional self-harm"
+T63513,"Toxic effect of contact with stingray, assault"
+T63514,"Toxic effect of contact with stingray, undetermined"
+T6359,"Toxic effect of contact with other venomous fish"
+T63591,"Toxic effect of contact with other venomous fish, accidental (unintentional)"
+T63592,"Toxic effect of contact with other venomous fish, intentional self-harm"
+T63593,"Toxic effect of contact with other venomous fish, assault"
+T63594,"Toxic effect of contact with other venomous fish, undetermined"
+T636,"Toxic effect of contact with other venomous marine animals"
+T6361,"Toxic effect of contact with Portugese Man-o-war"
+T63611,"Toxic effect of contact with Portugese Man-o-war, accidental (unintentional)"
+T63612,"Toxic effect of contact with Portugese Man-o-war, intentional self-harm"
+T63613,"Toxic effect of contact with Portugese Man-o-war, assault"
+T63614,"Toxic effect of contact with Portugese Man-o-war, undetermined"
+T6362,"Toxic effect of contact with other jellyfish"
+T63621,"Toxic effect of contact with other jellyfish, accidental (unintentional)"
+T63622,"Toxic effect of contact with other jellyfish, intentional self-harm"
+T63623,"Toxic effect of contact with other jellyfish, assault"
+T63624,"Toxic effect of contact with other jellyfish, undetermined"
+T6363,"Toxic effect of contact with sea anemone"
+T63631,"Toxic effect of contact with sea anemone, accidental (unintentional)"
+T63632,"Toxic effect of contact with sea anemone, intentional self-harm"
+T63633,"Toxic effect of contact with sea anemone, assault"
+T63634,"Toxic effect of contact with sea anemone, undetermined"
+T6369,"Toxic effect of contact with other venomous marine animals"
+T63691,"Toxic effect of contact with other venomous marine animals, accidental (unintentional)"
+T63692,"Toxic effect of contact with other venomous marine animals, intentional self-harm"
+T63693,"Toxic effect of contact with other venomous marine animals, assault"
+T63694,"Toxic effect of contact with other venomous marine animals, undetermined"
+T637,"Toxic effect of contact with venomous plant"
+T6371,"Toxic effect of contact with venomous marine plant"
+T63711,"Toxic effect of contact with venomous marine plant, accidental (unintentional)"
+T63712,"Toxic effect of contact with venomous marine plant, intentional self-harm"
+T63713,"Toxic effect of contact with venomous marine plant, assault"
+T63714,"Toxic effect of contact with venomous marine plant, undetermined"
+T6379,"Toxic effect of contact with other venomous plant"
+T63791,"Toxic effect of contact with other venomous plant, accidental (unintentional)"
+T63792,"Toxic effect of contact with other venomous plant, intentional self-harm"
+T63793,"Toxic effect of contact with other venomous plant, assault"
+T63794,"Toxic effect of contact with other venomous plant, undetermined"
+T638,"Toxic effect of contact with other venomous animals"
+T6381,"Toxic effect of contact with venomous frog"
+T63811,"Toxic effect of contact with venomous frog, accidental (unintentional)"
+T63812,"Toxic effect of contact with venomous frog, intentional self-harm"
+T63813,"Toxic effect of contact with venomous frog, assault"
+T63814,"Toxic effect of contact with venomous frog, undetermined"
+T6382,"Toxic effect of contact with venomous toad"
+T63821,"Toxic effect of contact with venomous toad, accidental (unintentional)"
+T63822,"Toxic effect of contact with venomous toad, intentional self-harm"
+T63823,"Toxic effect of contact with venomous toad, assault"
+T63824,"Toxic effect of contact with venomous toad, undetermined"
+T6383,"Toxic effect of contact with other venomous amphibian"
+T63831,"Toxic effect of contact with other venomous amphibian, accidental (unintentional)"
+T63832,"Toxic effect of contact with other venomous amphibian, intentional self-harm"
+T63833,"Toxic effect of contact with other venomous amphibian, assault"
+T63834,"Toxic effect of contact with other venomous amphibian, undetermined"
+T6389,"Toxic effect of contact with other venomous animals"
+T63891,"Toxic effect of contact with other venomous animals, accidental (unintentional)"
+T63892,"Toxic effect of contact with other venomous animals, intentional self-harm"
+T63893,"Toxic effect of contact with other venomous animals, assault"
+T63894,"Toxic effect of contact with other venomous animals, undetermined"
+T639,"Toxic effect of contact with unspecified venomous animal"
+T6391,"Toxic effect of contact with unspecified venomous animal, accidental (unintentional)"
+T6392,"Toxic effect of contact with unspecified venomous animal, intentional self-harm"
+T6393,"Toxic effect of contact with unspecified venomous animal, assault"
+T6394,"Toxic effect of contact with unspecified venomous animal, undetermined"
+T64,"Toxic effect of aflatoxin and other mycotoxin food contaminants"
+T640,"Toxic effect of aflatoxin"
+T6401,"Toxic effect of aflatoxin, accidental (unintentional)"
+T6402,"Toxic effect of aflatoxin, intentional self-harm"
+T6403,"Toxic effect of aflatoxin, assault"
+T6404,"Toxic effect of aflatoxin, undetermined"
+T648,"Toxic effect of other mycotoxin food contaminants"
+T6481,"Toxic effect of other mycotoxin food contaminants, accidental (unintentional)"
+T6482,"Toxic effect of other mycotoxin food contaminants, intentional self-harm"
+T6483,"Toxic effect of other mycotoxin food contaminants, assault"
+T6484,"Toxic effect of other mycotoxin food contaminants, undetermined"
+T65,"Toxic effect of other and unspecified substances"
+T650,"Toxic effect of cyanides"
+T650X,"Toxic effect of cyanides"
+T650X1,"Toxic effect of cyanides, accidental (unintentional)"
+T650X2,"Toxic effect of cyanides, intentional self-harm"
+T650X3,"Toxic effect of cyanides, assault"
+T650X4,"Toxic effect of cyanides, undetermined"
+T651,"Toxic effect of strychnine and its salts"
+T651X,"Toxic effect of strychnine and its salts"
+T651X1,"Toxic effect of strychnine and its salts, accidental (unintentional)"
+T651X2,"Toxic effect of strychnine and its salts, intentional self-harm"
+T651X3,"Toxic effect of strychnine and its salts, assault"
+T651X4,"Toxic effect of strychnine and its salts, undetermined"
+T652,"Toxic effect of tobacco and nicotine"
+T6521,"Toxic effect of chewing tobacco"
+T65211,"Toxic effect of chewing tobacco, accidental (unintentional)"
+T65212,"Toxic effect of chewing tobacco, intentional self-harm"
+T65213,"Toxic effect of chewing tobacco, assault"
+T65214,"Toxic effect of chewing tobacco, undetermined"
+T6522,"Toxic effect of tobacco cigarettes"
+T65221,"Toxic effect of tobacco cigarettes, accidental (unintentional)"
+T65222,"Toxic effect of tobacco cigarettes, intentional self-harm"
+T65223,"Toxic effect of tobacco cigarettes, assault"
+T65224,"Toxic effect of tobacco cigarettes, undetermined"
+T6529,"Toxic effect of other tobacco and nicotine"
+T65291,"Toxic effect of other tobacco and nicotine, accidental (unintentional)"
+T65292,"Toxic effect of other tobacco and nicotine, intentional self-harm"
+T65293,"Toxic effect of other tobacco and nicotine, assault"
+T65294,"Toxic effect of other tobacco and nicotine, undetermined"
+T653,"Toxic effect of nitroderivatives and aminoderivatives of benzene and its homologues"
+T653X,"Toxic effect of nitroderivatives and aminoderivatives of benzene and its homologues"
+T653X1,"Toxic effect of nitroderivatives and aminoderivatives of benzene and its homologues, accidental (unintentional)"
+T653X2,"Toxic effect of nitroderivatives and aminoderivatives of benzene and its homologues, intentional self-harm"
+T653X3,"Toxic effect of nitroderivatives and aminoderivatives of benzene and its homologues, assault"
+T653X4,"Toxic effect of nitroderivatives and aminoderivatives of benzene and its homologues, undetermined"
+T654,"Toxic effect of carbon disulfide"
+T654X,"Toxic effect of carbon disulfide"
+T654X1,"Toxic effect of carbon disulfide, accidental (unintentional)"
+T654X2,"Toxic effect of carbon disulfide, intentional self-harm"
+T654X3,"Toxic effect of carbon disulfide, assault"
+T654X4,"Toxic effect of carbon disulfide, undetermined"
+T655,"Toxic effect of nitroglycerin and other nitric acids and esters"
+T655X,"Toxic effect of nitroglycerin and other nitric acids and esters"
+T655X1,"Toxic effect of nitroglycerin and other nitric acids and esters, accidental (unintentional)"
+T655X2,"Toxic effect of nitroglycerin and other nitric acids and esters, intentional self-harm"
+T655X3,"Toxic effect of nitroglycerin and other nitric acids and esters, assault"
+T655X4,"Toxic effect of nitroglycerin and other nitric acids and esters, undetermined"
+T656,"Toxic effect of paints and dyes, not elsewhere classified"
+T656X,"Toxic effect of paints and dyes, not elsewhere classified"
+T656X1,"Toxic effect of paints and dyes, not elsewhere classified, accidental (unintentional)"
+T656X2,"Toxic effect of paints and dyes, not elsewhere classified, intentional self-harm"
+T656X3,"Toxic effect of paints and dyes, not elsewhere classified, assault"
+T656X4,"Toxic effect of paints and dyes, not elsewhere classified, undetermined"
+T658,"Toxic effect of other specified substances"
+T6581,"Toxic effect of latex"
+T65811,"Toxic effect of latex, accidental (unintentional)"
+T65812,"Toxic effect of latex, intentional self-harm"
+T65813,"Toxic effect of latex, assault"
+T65814,"Toxic effect of latex, undetermined"
+T6582,"Toxic effect of harmful algae and algae toxins"
+T65821,"Toxic effect of harmful algae and algae toxins, accidental (unintentional)"
+T65822,"Toxic effect of harmful algae and algae toxins, intentional self-harm"
+T65823,"Toxic effect of harmful algae and algae toxins, assault"
+T65824,"Toxic effect of harmful algae and algae toxins, undetermined"
+T6583,"Toxic effect of fiberglass"
+T65831,"Toxic effect of fiberglass, accidental (unintentional)"
+T65832,"Toxic effect of fiberglass, intentional self-harm"
+T65833,"Toxic effect of fiberglass, assault"
+T65834,"Toxic effect of fiberglass, undetermined"
+T6589,"Toxic effect of other specified substances"
+T65891,"Toxic effect of other specified substances, accidental (unintentional)"
+T65892,"Toxic effect of other specified substances, intentional self-harm"
+T65893,"Toxic effect of other specified substances, assault"
+T65894,"Toxic effect of other specified substances, undetermined"
+T659,"Toxic effect of unspecified substance"
+T6591,"Toxic effect of unspecified substance, accidental (unintentional)"
+T6592,"Toxic effect of unspecified substance, intentional self-harm"
+T6593,"Toxic effect of unspecified substance, assault"
+T6594,"Toxic effect of unspecified substance, undetermined"
+T66,"Radiation sickness, unspecified"
+T67,"Effects of heat and light"
+T670,"Heatstroke and sunstroke"
+T671,"Heat syncope"
+T672,"Heat cramp"
+T673,"Heat exhaustion, anhydrotic"
+T674,"Heat exhaustion due to salt depletion"
+T675,"Heat exhaustion, unspecified"
+T676,"Heat fatigue, transient"
+T677,"Heat edema"
+T678,"Other effects of heat and light"
+T679,"Effect of heat and light, unspecified"
+T68,"Hypothermia"
+T69,"Other effects of reduced temperature"
+T690,"Immersion hand and foot"
+T6901,"Immersion hand"
+T69011,"Immersion hand, right hand"
+T69012,"Immersion hand, left hand"
+T69019,"Immersion hand, unspecified hand"
+T6902,"Immersion foot"
+T69021,"Immersion foot, right foot"
+T69022,"Immersion foot, left foot"
+T69029,"Immersion foot, unspecified foot"
+T691,"Chilblains"
+T698,"Other specified effects of reduced temperature"
+T699,"Effect of reduced temperature, unspecified"
+T70,"Effects of air pressure and water pressure"
+T700,"Otitic barotrauma"
+T701,"Sinus barotrauma"
+T702,"Other and unspecified effects of high altitude"
+T7020,"Unspecified effects of high altitude"
+T7029,"Other effects of high altitude"
+T703,"Caisson disease [decompression sickness]"
+T704,"Effects of high-pressure fluids"
+T708,"Other effects of air pressure and water pressure"
+T709,"Effect of air pressure and water pressure, unspecified"
+T71,"Asphyxiation"
+T711,"Asphyxiation due to mechanical threat to breathing"
+T7111,"Asphyxiation due to smothering under pillow"
+T71111,"Asphyxiation due to smothering under pillow, accidental"
+T71112,"Asphyxiation due to smothering under pillow, intentional self-harm"
+T71113,"Asphyxiation due to smothering under pillow, assault"
+T71114,"Asphyxiation due to smothering under pillow, undetermined"
+T7112,"Asphyxiation due to plastic bag"
+T71121,"Asphyxiation due to plastic bag, accidental"
+T71122,"Asphyxiation due to plastic bag, intentional self-harm"
+T71123,"Asphyxiation due to plastic bag, assault"
+T71124,"Asphyxiation due to plastic bag, undetermined"
+T7113,"Asphyxiation due to being trapped in bed linens"
+T71131,"Asphyxiation due to being trapped in bed linens, accidental"
+T71132,"Asphyxiation due to being trapped in bed linens, intentional self-harm"
+T71133,"Asphyxiation due to being trapped in bed linens, assault"
+T71134,"Asphyxiation due to being trapped in bed linens, undetermined"
+T7114,"Asphyxiation due to smothering under another person's body (in bed)"
+T71141,"Asphyxiation due to smothering under another person's body (in bed), accidental"
+T71143,"Asphyxiation due to smothering under another person's body (in bed), assault"
+T71144,"Asphyxiation due to smothering under another person's body (in bed), undetermined"
+T7115,"Asphyxiation due to smothering in furniture"
+T71151,"Asphyxiation due to smothering in furniture, accidental"
+T71152,"Asphyxiation due to smothering in furniture, intentional self-harm"
+T71153,"Asphyxiation due to smothering in furniture, assault"
+T71154,"Asphyxiation due to smothering in furniture, undetermined"
+T7116,"Asphyxiation due to hanging"
+T71161,"Asphyxiation due to hanging, accidental"
+T71162,"Asphyxiation due to hanging, intentional self-harm"
+T71163,"Asphyxiation due to hanging, assault"
+T71164,"Asphyxiation due to hanging, undetermined"
+T7119,"Asphyxiation due to mechanical threat to breathing due to other causes"
+T71191,"Asphyxiation due to mechanical threat to breathing due to other causes, accidental"
+T71192,"Asphyxiation due to mechanical threat to breathing due to other causes, intentional self-harm"
+T71193,"Asphyxiation due to mechanical threat to breathing due to other causes, assault"
+T71194,"Asphyxiation due to mechanical threat to breathing due to other causes, undetermined"
+T712,"Asphyxiation due to systemic oxygen deficiency due to low oxygen content in ambient air"
+T7120,"Asphyxiation due to systemic oxygen deficiency due to low oxygen content in ambient air due to unspecified cause"
+T7121,"Asphyxiation due to cave-in or falling earth"
+T7122,"Asphyxiation due to being trapped in a car trunk"
+T71221,"Asphyxiation due to being trapped in a car trunk, accidental"
+T71222,"Asphyxiation due to being trapped in a car trunk, intentional self-harm"
+T71223,"Asphyxiation due to being trapped in a car trunk, assault"
+T71224,"Asphyxiation due to being trapped in a car trunk, undetermined"
+T7123,"Asphyxiation due to being trapped in a (discarded) refrigerator"
+T71231,"Asphyxiation due to being trapped in a (discarded) refrigerator, accidental"
+T71232,"Asphyxiation due to being trapped in a (discarded) refrigerator, intentional self-harm"
+T71233,"Asphyxiation due to being trapped in a (discarded) refrigerator, assault"
+T71234,"Asphyxiation due to being trapped in a (discarded) refrigerator, undetermined"
+T7129,"Asphyxiation due to being trapped in other low oxygen environment"
+T719,"Asphyxiation due to unspecified cause"
+T73,"Effects of other deprivation"
+T730,"Starvation"
+T731,"Deprivation of water"
+T732,"Exhaustion due to exposure"
+T733,"Exhaustion due to excessive exertion"
+T738,"Other effects of deprivation"
+T739,"Effect of deprivation, unspecified"
+T74,"Adult and child abuse, neglect and other maltreatment, confirmed"
+T740,"Neglect or abandonment, confirmed"
+T7401,"Adult neglect or abandonment, confirmed"
+T7402,"Child neglect or abandonment, confirmed"
+T741,"Physical abuse, confirmed"
+T7411,"Adult physical abuse, confirmed"
+T7412,"Child physical abuse, confirmed"
+T742,"Sexual abuse, confirmed"
+T7421,"Adult sexual abuse, confirmed"
+T7422,"Child sexual abuse, confirmed"
+T743,"Psychological abuse, confirmed"
+T7431,"Adult psychological abuse, confirmed"
+T7432,"Child psychological abuse, confirmed"
+T744,"Shaken infant syndrome"
+T749,"Unspecified maltreatment, confirmed"
+T7491,"Unspecified adult maltreatment, confirmed"
+T7492,"Unspecified child maltreatment, confirmed"
+T75,"Other and unspecified effects of other external causes"
+T750,"Effects of lightning"
+T7500,"Unspecified effects of lightning"
+T7501,"Shock due to being struck by lightning"
+T7509,"Other effects of lightning"
+T751,"Unspecified effects of drowning and nonfatal submersion"
+T752,"Effects of vibration"
+T7520,"Unspecified effects of vibration"
+T7521,"Pneumatic hammer syndrome"
+T7522,"Traumatic vasospastic syndrome"
+T7523,"Vertigo from infrasound"
+T7529,"Other effects of vibration"
+T753,"Motion sickness"
+T754,"Electrocution"
+T758,"Other specified effects of external causes"
+T7581,"Effects of abnormal gravitation [G] forces"
+T7582,"Effects of weightlessness"
+T7589,"Other specified effects of external causes"
+T76,"Adult and child abuse, neglect and other maltreatment, suspected"
+T760,"Neglect or abandonment, suspected"
+T7601,"Adult neglect or abandonment, suspected"
+T7602,"Child neglect or abandonment, suspected"
+T761,"Physical abuse, suspected"
+T7611,"Adult physical abuse, suspected"
+T7612,"Child physical abuse, suspected"
+T762,"Sexual abuse, suspected"
+T7621,"Adult sexual abuse, suspected"
+T7622,"Child sexual abuse, suspected"
+T763,"Psychological abuse, suspected"
+T7631,"Adult psychological abuse, suspected"
+T7632,"Child psychological abuse, suspected"
+T769,"Unspecified maltreatment, suspected"
+T7691,"Unspecified adult maltreatment, suspected"
+T7692,"Unspecified child maltreatment, suspected"
+T78,"Adverse effects, not elsewhere classified"
+T780,"Anaphylactic reaction due to food"
+T7800,"Anaphylactic reaction due to unspecified food"
+T7801,"Anaphylactic reaction due to peanuts"
+T7802,"Anaphylactic reaction due to shellfish (crustaceans)"
+T7803,"Anaphylactic reaction due to other fish"
+T7804,"Anaphylactic reaction due to fruits and vegetables"
+T7805,"Anaphylactic reaction due to tree nuts and seeds"
+T7806,"Anaphylactic reaction due to food additives"
+T7807,"Anaphylactic reaction due to milk and dairy products"
+T7808,"Anaphylactic reaction due to eggs"
+T7809,"Anaphylactic reaction due to other food products"
+T781,"Other adverse food reactions, not elsewhere classified"
+T782,"Anaphylactic shock, unspecified"
+T783,"Angioneurotic edema"
+T784,"Other and unspecified allergy"
+T7840,"Allergy, unspecified"
+T7841,"Arthus phenomenon"
+T7849,"Other allergy"
+T788,"Other adverse effects, not elsewhere classified"
+T79,"Certain early complications of trauma, not elsewhere classified"
+T790,"Air embolism (traumatic)"
+T791,"Fat embolism (traumatic)"
+T792,"Traumatic secondary and recurrent hemorrhage and seroma"
+T794,"Traumatic shock"
+T795,"Traumatic anuria"
+T796,"Traumatic ischemia of muscle"
+T797,"Traumatic subcutaneous emphysema"
+T79A,"Traumatic compartment syndrome"
+T79A0,"Compartment syndrome, unspecified"
+T79A1,"Traumatic compartment syndrome of upper extremity"
+T79A11,"Traumatic compartment syndrome of right upper extremity"
+T79A12,"Traumatic compartment syndrome of left upper extremity"
+T79A19,"Traumatic compartment syndrome of unspecified upper extremity"
+T79A2,"Traumatic compartment syndrome of lower extremity"
+T79A21,"Traumatic compartment syndrome of right lower extremity"
+T79A22,"Traumatic compartment syndrome of left lower extremity"
+T79A29,"Traumatic compartment syndrome of unspecified lower extremity"
+T79A3,"Traumatic compartment syndrome of abdomen"
+T79A9,"Traumatic compartment syndrome of other sites"
+T798,"Other early complications of trauma"
+T799,"Unspecified early complication of trauma"
+T80,"Complications following infusion, transfusion and therapeutic injection"
+T800,"Air embolism following infusion, transfusion and therapeutic injection"
+T801,"Vascular complications following infusion, transfusion and therapeutic injection"
+T802,"Infections following infusion, transfusion and therapeutic injection"
+T8021,"Infection due to central venous catheter"
+T80211,"Bloodstream infection due to central venous catheter"
+T80212,"Local infection due to central venous catheter"
+T80218,"Other infection due to central venous catheter"
+T80219,"Unspecified infection due to central venous catheter"
+T8022,"Acute infection following transfusion, infusion, or injection of blood and blood products"
+T8029,"Infection following other infusion, transfusion and therapeutic injection"
+T803,"ABO incompatibility reaction due to transfusion of blood or blood products"
+T8030,"ABO incompatibility reaction due to transfusion of blood or blood products, unspecified"
+T8031,"ABO incompatibility with hemolytic transfusion reaction"
+T80310,"ABO incompatibility with acute hemolytic transfusion reaction"
+T80311,"ABO incompatibility with delayed hemolytic transfusion reaction"
+T80319,"ABO incompatibility with hemolytic transfusion reaction, unspecified"
+T8039,"Other ABO incompatibility reaction due to transfusion of blood or blood products"
+T804,"Rh incompatibility reaction due to transfusion of blood or blood products"
+T8040,"Rh incompatibility reaction due to transfusion of blood or blood products, unspecified"
+T8041,"Rh incompatibility with hemolytic transfusion reaction"
+T80410,"Rh incompatibility with acute hemolytic transfusion reaction"
+T80411,"Rh incompatibility with delayed hemolytic transfusion reaction"
+T80419,"Rh incompatibility with hemolytic transfusion reaction, unspecified"
+T8049,"Other Rh incompatibility reaction due to transfusion of blood or blood products"
+T80A,"Non-ABO incompatibility reaction due to transfusion of blood or blood products"
+T80A0,"Non-ABO incompatibility reaction due to transfusion of blood or blood products, unspecified"
+T80A1,"Non-ABO incompatibility with hemolytic transfusion reaction"
+T80A10,"Non-ABO incompatibility with acute hemolytic transfusion reaction"
+T80A11,"Non-ABO incompatibility with delayed hemolytic transfusion reaction"
+T80A19,"Non-ABO incompatibility with hemolytic transfusion reaction, unspecified"
+T80A9,"Other non-ABO incompatibility reaction due to transfusion of blood or blood products"
+T805,"Anaphylactic reaction due to serum"
+T8051,"Anaphylactic reaction due to administration of blood and blood products"
+T8052,"Anaphylactic reaction due to vaccination"
+T8059,"Anaphylactic reaction due to other serum"
+T806,"Other serum reactions"
+T8061,"Other serum reaction due to administration of blood and blood products"
+T8062,"Other serum reaction due to vaccination"
+T8069,"Other serum reaction due to other serum"
+T808,"Other complications following infusion, transfusion and therapeutic injection"
+T8081,"Extravasation of vesicant agent"
+T80810,"Extravasation of vesicant antineoplastic chemotherapy"
+T80818,"Extravasation of other vesicant agent"
+T8089,"Other complications following infusion, transfusion and therapeutic injection"
+T809,"Unspecified complication following infusion, transfusion and therapeutic injection"
+T8090,"Unspecified complication following infusion and therapeutic injection"
+T8091,"Hemolytic transfusion reaction, unspecified incompatibility"
+T80910,"Acute hemolytic transfusion reaction, unspecified incompatibility"
+T80911,"Delayed hemolytic transfusion reaction, unspecified incompatibility"
+T80919,"Hemolytic transfusion reaction, unspecified incompatibility, unspecified as acute or delayed"
+T8092,"Unspecified transfusion reaction"
+T81,"Complications of procedures, not elsewhere classified"
+T811,"Postprocedural shock"
+T8110,"Postprocedural shock unspecified"
+T8111,"Postprocedural  cardiogenic shock"
+T8112,"Postprocedural septic shock"
+T8119,"Other postprocedural shock"
+T813,"Disruption of wound, not elsewhere classified"
+T8130,"Disruption of wound, unspecified"
+T8131,"Disruption of external operation (surgical) wound, not elsewhere classified"
+T8132,"Disruption of internal operation (surgical) wound, not elsewhere classified"
+T8133,"Disruption of traumatic injury wound repair"
+T814,"Infection following a procedure"
+T815,"Complications of foreign body accidentally left in body following procedure"
+T8150,"Unspecified complication of foreign body accidentally left in body following procedure"
+T81500,"Unspecified complication of foreign body accidentally left in body following surgical operation"
+T81501,"Unspecified complication of foreign body accidentally left in body following infusion or transfusion"
+T81502,"Unspecified complication of foreign body accidentally left in body following kidney dialysis"
+T81503,"Unspecified complication of foreign body accidentally left in body following injection or immunization"
+T81504,"Unspecified complication of foreign body accidentally left in body following endoscopic examination"
+T81505,"Unspecified complication of foreign body accidentally left in body following heart catheterization"
+T81506,"Unspecified complication of foreign body accidentally left in body following aspiration, puncture or other catheterization"
+T81507,"Unspecified complication of foreign body accidentally left in body following removal of catheter or packing"
+T81508,"Unspecified complication of foreign body accidentally left in body following other procedure"
+T81509,"Unspecified complication of foreign body accidentally left in body following unspecified procedure"
+T8151,"Adhesions due to foreign body accidentally left in body following procedure"
+T81510,"Adhesions due to foreign body accidentally left in body following surgical operation"
+T81511,"Adhesions due to foreign body accidentally left in body following infusion or transfusion"
+T81512,"Adhesions due to foreign body accidentally left in body following kidney dialysis"
+T81513,"Adhesions due to foreign body accidentally left in body following injection or immunization"
+T81514,"Adhesions due to foreign body accidentally left in body following endoscopic examination"
+T81515,"Adhesions due to foreign body accidentally left in body following heart catheterization"
+T81516,"Adhesions due to foreign body accidentally left in body following aspiration, puncture or other catheterization"
+T81517,"Adhesions due to foreign body accidentally left in body following removal of catheter or packing"
+T81518,"Adhesions due to foreign body accidentally left in body following other procedure"
+T81519,"Adhesions due to foreign body accidentally left in body following unspecified procedure"
+T8152,"Obstruction due to foreign body accidentally left in body following procedure"
+T81520,"Obstruction due to foreign body accidentally left in body following surgical operation"
+T81521,"Obstruction due to foreign body accidentally left in body following infusion or transfusion"
+T81522,"Obstruction due to foreign body accidentally left in body following kidney dialysis"
+T81523,"Obstruction due to foreign body accidentally left in body following injection or immunization"
+T81524,"Obstruction due to foreign body accidentally left in body following endoscopic examination"
+T81525,"Obstruction due to foreign body accidentally left in body following heart catheterization"
+T81526,"Obstruction due to foreign body accidentally left in body following aspiration, puncture or other catheterization"
+T81527,"Obstruction due to foreign body accidentally left in body following removal of catheter or packing"
+T81528,"Obstruction due to foreign body accidentally left in body following other procedure"
+T81529,"Obstruction due to foreign body accidentally left in body following unspecified procedure"
+T8153,"Perforation due to foreign body accidentally left in body following procedure"
+T81530,"Perforation due to foreign body accidentally left in body following surgical operation"
+T81531,"Perforation due to foreign body accidentally left in body following infusion or transfusion"
+T81532,"Perforation due to foreign body accidentally left in body following kidney dialysis"
+T81533,"Perforation due to foreign body accidentally left in body following injection or immunization"
+T81534,"Perforation due to foreign body accidentally left in body following endoscopic examination"
+T81535,"Perforation due to foreign body accidentally left in body following heart catheterization"
+T81536,"Perforation due to foreign body accidentally left in body following aspiration, puncture or other catheterization"
+T81537,"Perforation due to foreign body accidentally left in body following removal of catheter or packing"
+T81538,"Perforation due to foreign body accidentally left in body following other procedure"
+T81539,"Perforation due to foreign body accidentally left in body following unspecified procedure"
+T8159,"Other complications of foreign body accidentally left in body following procedure"
+T81590,"Other complications of foreign body accidentally left in body following surgical operation"
+T81591,"Other complications of foreign body accidentally left in body following infusion or transfusion"
+T81592,"Other complications of foreign body accidentally left in body following kidney dialysis"
+T81593,"Other complications of foreign body accidentally left in body following injection or immunization"
+T81594,"Other complications of foreign body accidentally left in body following endoscopic examination"
+T81595,"Other complications of foreign body accidentally left in body following heart catheterization"
+T81596,"Other complications of foreign body accidentally left in body following aspiration, puncture or other catheterization"
+T81597,"Other complications of foreign body accidentally left in body following removal of catheter or packing"
+T81598,"Other complications of foreign body accidentally left in body following other procedure"
+T81599,"Other complications of foreign body accidentally left in body following unspecified procedure"
+T816,"Acute reaction to foreign substance accidentally left during a procedure"
+T8160,"Unspecified acute reaction to foreign substance accidentally left during a procedure"
+T8161,"Aseptic peritonitis due to foreign substance accidentally left during a procedure"
+T8169,"Other acute reaction to foreign substance accidentally left during a procedure"
+T817,"Vascular complications following a procedure, not elsewhere classified"
+T8171,"Complication of artery following a procedure, not elsewhere classified"
+T81710,"Complication of mesenteric artery following a procedure, not elsewhere classified"
+T81711,"Complication of renal artery following a procedure, not elsewhere classified"
+T81718,"Complication of other artery following a procedure, not elsewhere classified"
+T81719,"Complication of unspecified artery following a procedure, not elsewhere classified"
+T8172,"Complication of vein following a procedure, not elsewhere classified"
+T818,"Other complications of procedures, not elsewhere classified"
+T8181,"Complication of inhalation therapy"
+T8182,"Emphysema (subcutaneous) resulting from a procedure"
+T8183,"Persistent postprocedural fistula"
+T8189,"Other complications of procedures, not elsewhere classified"
+T819,"Unspecified complication of procedure"
+T82,"Complications of cardiac and vascular prosthetic devices, implants and grafts"
+T820,"Mechanical complication of heart valve prosthesis"
+T8201,"Breakdown (mechanical) of heart valve prosthesis"
+T8202,"Displacement of heart valve prosthesis"
+T8203,"Leakage of heart valve prosthesis"
+T8209,"Other mechanical complication of heart valve prosthesis"
+T821,"Mechanical complication of cardiac electronic device"
+T8211,"Breakdown (mechanical) of cardiac electronic device"
+T82110,"Breakdown (mechanical) of cardiac electrode"
+T82111,"Breakdown (mechanical) of cardiac pulse generator (battery)"
+T82118,"Breakdown (mechanical) of other cardiac electronic device"
+T82119,"Breakdown (mechanical) of unspecified cardiac electronic device"
+T8212,"Displacement of cardiac electronic device"
+T82120,"Displacement of cardiac electrode"
+T82121,"Displacement of cardiac pulse generator (battery)"
+T82128,"Displacement of other cardiac electronic device"
+T82129,"Displacement of unspecified cardiac electronic device"
+T8219,"Other mechanical complication of cardiac electronic device"
+T82190,"Other mechanical complication of cardiac electrode"
+T82191,"Other mechanical complication of cardiac pulse generator (battery)"
+T82198,"Other mechanical complication of other cardiac electronic device"
+T82199,"Other mechanical complication of unspecified cardiac device"
+T822,"Mechanical complication of coronary artery bypass graft and biological heart valve graft"
+T8221,"Mechanical complication of coronary artery bypass graft"
+T82211,"Breakdown (mechanical) of coronary artery bypass graft"
+T82212,"Displacement of coronary artery bypass graft"
+T82213,"Leakage of coronary artery bypass graft"
+T82218,"Other mechanical complication of coronary artery bypass graft"
+T8222,"Mechanical complication of biological heart valve graft"
+T82221,"Breakdown (mechanical) of biological heart valve graft"
+T82222,"Displacement of biological heart valve graft"
+T82223,"Leakage of biological heart valve graft"
+T82228,"Other mechanical complication of biological heart valve graft"
+T823,"Mechanical complication of other vascular grafts"
+T8231,"Breakdown (mechanical) of other vascular grafts"
+T82310,"Breakdown (mechanical) of aortic (bifurcation) graft (replacement)"
+T82311,"Breakdown (mechanical) of carotid arterial graft (bypass)"
+T82312,"Breakdown (mechanical) of femoral arterial graft (bypass)"
+T82318,"Breakdown (mechanical) of other vascular grafts"
+T82319,"Breakdown (mechanical) of unspecified vascular grafts"
+T8232,"Displacement of other vascular grafts"
+T82320,"Displacement of aortic (bifurcation) graft (replacement)"
+T82321,"Displacement of carotid arterial graft (bypass)"
+T82322,"Displacement of femoral arterial graft (bypass)"
+T82328,"Displacement of other vascular grafts"
+T82329,"Displacement of unspecified vascular grafts"
+T8233,"Leakage of other vascular grafts"
+T82330,"Leakage of aortic (bifurcation) graft (replacement)"
+T82331,"Leakage of carotid arterial graft (bypass)"
+T82332,"Leakage of femoral arterial graft (bypass)"
+T82338,"Leakage of other vascular grafts"
+T82339,"Leakage of unspecified vascular graft"
+T8239,"Other mechanical complication of other vascular grafts"
+T82390,"Other mechanical complication of aortic (bifurcation) graft (replacement)"
+T82391,"Other mechanical complication of carotid arterial graft (bypass)"
+T82392,"Other mechanical complication of femoral arterial graft (bypass)"
+T82398,"Other mechanical complication of other vascular grafts"
+T82399,"Other mechanical complication of unspecified vascular grafts"
+T824,"Mechanical complication of vascular dialysis catheter"
+T8241,"Breakdown (mechanical) of vascular dialysis catheter"
+T8242,"Displacement of vascular dialysis catheter"
+T8243,"Leakage of vascular dialysis catheter"
+T8249,"Other complication of vascular dialysis catheter"
+T825,"Mechanical complication of other cardiac and vascular devices and implants"
+T8251,"Breakdown (mechanical) of other cardiac and vascular devices and implants"
+T82510,"Breakdown (mechanical) of surgically created arteriovenous fistula"
+T82511,"Breakdown (mechanical) of surgically created arteriovenous shunt"
+T82512,"Breakdown (mechanical) of artificial heart"
+T82513,"Breakdown (mechanical) of balloon (counterpulsation) device"
+T82514,"Breakdown (mechanical) of infusion catheter"
+T82515,"Breakdown (mechanical) of umbrella device"
+T82518,"Breakdown (mechanical) of other cardiac and vascular devices and implants"
+T82519,"Breakdown (mechanical) of unspecified cardiac and vascular devices and implants"
+T8252,"Displacement of other cardiac and vascular devices and implants"
+T82520,"Displacement of surgically created arteriovenous fistula"
+T82521,"Displacement of surgically created arteriovenous shunt"
+T82522,"Displacement of artificial heart"
+T82523,"Displacement of balloon (counterpulsation) device"
+T82524,"Displacement of infusion catheter"
+T82525,"Displacement of umbrella device"
+T82528,"Displacement of other cardiac and vascular devices and implants"
+T82529,"Displacement of unspecified cardiac and vascular devices and implants"
+T8253,"Leakage of other cardiac and vascular devices and implants"
+T82530,"Leakage of surgically created arteriovenous fistula"
+T82531,"Leakage of surgically created arteriovenous shunt"
+T82532,"Leakage of artificial heart"
+T82533,"Leakage of balloon (counterpulsation) device"
+T82534,"Leakage of infusion catheter"
+T82535,"Leakage of umbrella device"
+T82538,"Leakage of other cardiac and vascular devices and implants"
+T82539,"Leakage of unspecified cardiac and vascular devices and implants"
+T8259,"Other mechanical complication of other cardiac and vascular devices and implants"
+T82590,"Other mechanical complication of surgically created arteriovenous fistula"
+T82591,"Other mechanical complication of surgically created arteriovenous shunt"
+T82592,"Other mechanical complication of artificial heart"
+T82593,"Other mechanical complication of balloon (counterpulsation) device"
+T82594,"Other mechanical complication of infusion catheter"
+T82595,"Other mechanical complication of umbrella device"
+T82598,"Other mechanical complication of other cardiac and vascular devices and implants"
+T82599,"Other mechanical complication of unspecified cardiac and vascular devices and implants"
+T826,"Infection and inflammatory reaction due to cardiac valve prosthesis"
+T827,"Infection and inflammatory reaction due to other cardiac and vascular devices, implants and grafts"
+T828,"Other specified complications of cardiac and vascular prosthetic devices, implants and grafts"
+T8281,"Embolism due to cardiac and vascular prosthetic devices, implants and grafts"
+T82817,"Embolism due to cardiac prosthetic devices, implants and grafts"
+T82818,"Embolism due to vascular prosthetic devices, implants and grafts"
+T8282,"Fibrosis due to cardiac and vascular prosthetic devices, implants and grafts"
+T82827,"Fibrosis due to cardiac prosthetic devices, implants and grafts"
+T82828,"Fibrosis due to vascular prosthetic devices, implants and grafts"
+T8283,"Hemorrhage due to cardiac and vascular prosthetic devices, implants and grafts"
+T82837,"Hemorrhage due to cardiac prosthetic devices, implants and grafts"
+T82838,"Hemorrhage due to vascular prosthetic devices, implants and grafts"
+T8284,"Pain due to cardiac and vascular prosthetic devices, implants and grafts"
+T82847,"Pain due to cardiac prosthetic devices, implants and grafts"
+T82848,"Pain due to vascular prosthetic devices, implants and grafts"
+T8285,"Stenosis due to cardiac and vascular prosthetic devices, implants and grafts"
+T82855,"Stenosis of coronary artery stent"
+T82856,"Stenosis of peripheral vascular stent"
+T82857,"Stenosis of other cardiac prosthetic devices, implants and grafts"
+T82858,"Stenosis of other vascular prosthetic devices, implants and grafts"
+T8286,"Thrombosis of cardiac and vascular prosthetic devices, implants and grafts"
+T82867,"Thrombosis due to cardiac prosthetic devices, implants and grafts"
+T82868,"Thrombosis due to vascular prosthetic devices, implants and grafts"
+T8289,"Other specified complication of cardiac and vascular prosthetic devices, implants and grafts"
+T82897,"Other specified complication of cardiac prosthetic devices, implants and grafts"
+T82898,"Other specified complication of vascular prosthetic devices, implants and grafts"
+T829,"Unspecified complication of cardiac and vascular prosthetic device, implant and graft"
+T83,"Complications of genitourinary prosthetic devices, implants and grafts"
+T830,"Mechanical complication of urinary catheter"
+T8301,"Breakdown (mechanical) of urinary catheter"
+T83010,"Breakdown (mechanical) of cystostomy catheter"
+T83011,"Breakdown (mechanical) of indwelling urethral catheter"
+T83012,"Breakdown (mechanical) of nephrostomy catheter"
+T83018,"Breakdown (mechanical) of other urinary catheter"
+T8302,"Displacement of urinary catheter"
+T83020,"Displacement of cystostomy catheter"
+T83021,"Displacement of indwelling urethral catheter"
+T83022,"Displacement of nephrostomy catheter"
+T83028,"Displacement of other urinary catheter"
+T8303,"Leakage of urinary catheter"
+T83030,"Leakage of cystostomy catheter"
+T83031,"Leakage of indwelling urethral catheter"
+T83032,"Leakage of nephrostomy catheter"
+T83038,"Leakage of other urinary catheter"
+T8309,"Other mechanical complication of urinary catheter"
+T83090,"Other mechanical complication of cystostomy catheter"
+T83091,"Other mechanical complication of indwelling urethral catheter"
+T83092,"Other mechanical complication of nephrostomy catheter"
+T83098,"Other mechanical complication of other urinary catheter"
+T831,"Mechanical complication of other urinary devices and implants"
+T8311,"Breakdown (mechanical) of other urinary devices and implants"
+T83110,"Breakdown (mechanical) of urinary electronic stimulator device"
+T83111,"Breakdown (mechanical) of implanted urinary sphincter"
+T83112,"Breakdown (mechanical) of indwelling ureteral stent"
+T83113,"Breakdown (mechanical) of other urinary stents"
+T83118,"Breakdown (mechanical) of other urinary devices and implants"
+T8312,"Displacement of other urinary devices and implants"
+T83120,"Displacement of urinary electronic stimulator device"
+T83121,"Displacement of implanted urinary sphincter"
+T83122,"Displacement of indwelling ureteral stent"
+T83123,"Displacement of other urinary stents"
+T83128,"Displacement of other urinary devices and implants"
+T8319,"Other mechanical complication of other urinary devices and implants"
+T83190,"Other mechanical complication of urinary electronic stimulator device"
+T83191,"Other mechanical complication of implanted urinary sphincter"
+T83192,"Other mechanical complication of indwelling ureteral stent"
+T83193,"Other mechanical complication of other urinary stent"
+T83198,"Other mechanical complication of other urinary devices and implants"
+T832,"Mechanical complication of graft of urinary organ"
+T8321,"Breakdown (mechanical) of graft of urinary organ"
+T8322,"Displacement of graft of urinary organ"
+T8323,"Leakage of graft of urinary organ"
+T8324,"Erosion of graft of urinary organ"
+T8325,"Exposure of graft of urinary organ"
+T8329,"Other mechanical complication of graft of urinary organ"
+T833,"Mechanical complication of intrauterine contraceptive device"
+T8331,"Breakdown (mechanical) of intrauterine contraceptive device"
+T8332,"Displacement of intrauterine contraceptive device"
+T8339,"Other mechanical complication of intrauterine contraceptive device"
+T834,"Mechanical complication of other prosthetic devices, implants and grafts of genital tract"
+T8341,"Breakdown (mechanical) of other prosthetic devices, implants and grafts of genital tract"
+T83410,"Breakdown (mechanical) of implanted penile prosthesis"
+T83411,"Breakdown (mechanical) of implanted testicular prosthesis"
+T83418,"Breakdown (mechanical) of other prosthetic devices, implants and grafts of genital tract"
+T8342,"Displacement of other prosthetic devices, implants and grafts of genital tract"
+T83420,"Displacement of implanted penile prosthesis"
+T83421,"Displacement of implanted testicular prosthesis"
+T83428,"Displacement of other prosthetic devices, implants and grafts of genital tract"
+T8349,"Other mechanical complication of other prosthetic devices, implants and grafts of genital tract"
+T83490,"Other mechanical complication of implanted penile prosthesis"
+T83491,"Other mechanical complication of implanted testicular prosthesis"
+T83498,"Other mechanical complication of other prosthetic devices, implants and grafts of genital tract"
+T835,"Infection and inflammatory reaction due to prosthetic device, implant and graft in urinary system"
+T8351,"Infection and inflammatory reaction due to urinary catheter"
+T83510,"Infection and inflammatory reaction due to cystostomy catheter"
+T83511,"Infection and inflammatory reaction due to indwelling urethral catheter"
+T83512,"Infection and inflammatory reaction due to nephrostomy catheter"
+T83518,"Infection and inflammatory reaction due to other urinary catheter"
+T8359,"Infection and inflammatory reaction due to prosthetic device, implant and graft in urinary system"
+T83590,"Infection and inflammatory reaction due to implanted urinary neurostimulation device"
+T83591,"Infection and inflammatory reaction due to implanted urinary sphincter"
+T83592,"Infection and inflammatory reaction due to indwelling ureteral stent"
+T83593,"Infection and inflammatory reaction due to other urinary stents"
+T83598,"Infection and inflammatory reaction due to other prosthetic device, implant and graft in urinary system"
+T836,"Infection and inflammatory reaction due to prosthetic device, implant and graft in genital tract"
+T8361,"Infection and inflammatory reaction due to implanted penile prosthesis"
+T8362,"Infection and inflammatory reaction due to implanted testicular prosthesis"
+T8369,"Infection and inflammatory reaction due to other prosthetic device, implant and graft in genital tract"
+T837,"Complications due to implanted mesh and other prosthetic materials"
+T8371,"Erosion of implanted mesh and other prosthetic materials to surrounding organ or tissue"
+T83711,"Erosion of implanted vaginal mesh to surrounding organ or tissue"
+T83712,"Erosion of implanted urethral mesh to surrounding organ or tissue"
+T83713,"Erosion of implanted urethral bulking agent to surrounding organ or tissue"
+T83714,"Erosion of implanted ureteral bulking agent to surrounding organ or tissue"
+T83718,"Erosion of other implanted mesh to organ or tissue"
+T83719,"Erosion of other prosthetic materials to surrounding organ or tissue"
+T8372,"Exposure of implanted mesh and other prosthetic materials into surrounding organ or tissue"
+T83721,"Exposure of implanted vaginal mesh into vagina"
+T83722,"Exposure of implanted urethral mesh into urethra"
+T83723,"Exposure of implanted urethral bulking agent into urethra"
+T83724,"Exposure of implanted ureteral bulking agent into ureter"
+T83728,"Exposure of other implanted mesh into organ or tissue"
+T83729,"Exposure of other prosthetic materials into organ or tissue"
+T8379,"Other specified complications due to other genitourinary prosthetic materials"
+T838,"Other specified complications of genitourinary prosthetic devices, implants and grafts"
+T8381,"Embolism due to genitourinary prosthetic devices, implants and grafts"
+T8382,"Fibrosis due to genitourinary prosthetic devices, implants and grafts"
+T8383,"Hemorrhage due to genitourinary prosthetic devices, implants and grafts"
+T8384,"Pain due to genitourinary prosthetic devices, implants and grafts"
+T8385,"Stenosis due to genitourinary prosthetic devices, implants and grafts"
+T8386,"Thrombosis due to genitourinary prosthetic devices, implants and grafts"
+T8389,"Other specified complication of genitourinary prosthetic devices, implants and grafts"
+T839,"Unspecified complication of genitourinary prosthetic device, implant and graft"
+T84,"Complications of internal orthopedic prosthetic devices, implants and grafts"
+T840,"Mechanical complication of internal joint prosthesis"
+T8401,"Broken internal joint prosthesis"
+T84010,"Broken internal right hip prosthesis"
+T84011,"Broken internal left hip prosthesis"
+T84012,"Broken internal right knee prosthesis"
+T84013,"Broken internal left knee prosthesis"
+T84018,"Broken internal joint prosthesis, other site"
+T84019,"Broken internal joint prosthesis, unspecified site"
+T8402,"Dislocation of internal joint prosthesis"
+T84020,"Dislocation of internal right hip prosthesis"
+T84021,"Dislocation of internal left hip prosthesis"
+T84022,"Instability of internal right knee prosthesis"
+T84023,"Instability of internal left knee prosthesis"
+T84028,"Dislocation of other internal joint prosthesis"
+T84029,"Dislocation of unspecified internal joint prosthesis"
+T8403,"Mechanical loosening of internal prosthetic joint"
+T84030,"Mechanical loosening of internal right hip prosthetic joint"
+T84031,"Mechanical loosening of internal left hip prosthetic joint"
+T84032,"Mechanical loosening of internal right knee prosthetic joint"
+T84033,"Mechanical loosening of internal left knee prosthetic joint"
+T84038,"Mechanical loosening of other internal prosthetic joint"
+T84039,"Mechanical loosening of unspecified internal prosthetic joint"
+T8405,"Periprosthetic osteolysis of internal prosthetic joint"
+T84050,"Periprosthetic osteolysis of internal prosthetic right hip joint"
+T84051,"Periprosthetic osteolysis of internal prosthetic left hip joint"
+T84052,"Periprosthetic osteolysis of internal prosthetic right knee joint"
+T84053,"Periprosthetic osteolysis of internal prosthetic left knee joint"
+T84058,"Periprosthetic osteolysis of other internal prosthetic joint"
+T84059,"Periprosthetic osteolysis of unspecified internal prosthetic joint"
+T8406,"Wear of articular bearing surface of internal prosthetic joint"
+T84060,"Wear of articular bearing surface of internal prosthetic right hip joint"
+T84061,"Wear of articular bearing surface of internal prosthetic left hip joint"
+T84062,"Wear of articular bearing surface of internal prosthetic right knee joint"
+T84063,"Wear of articular bearing surface of internal prosthetic left knee joint"
+T84068,"Wear of articular bearing surface of other internal prosthetic joint"
+T84069,"Wear of articular bearing surface of unspecified internal prosthetic joint"
+T8409,"Other mechanical complication of internal joint prosthesis"
+T84090,"Other mechanical complication of internal right hip prosthesis"
+T84091,"Other mechanical complication of internal left hip prosthesis"
+T84092,"Other mechanical complication of internal right knee prosthesis"
+T84093,"Other mechanical complication of internal left knee prosthesis"
+T84098,"Other mechanical complication of other internal joint prosthesis"
+T84099,"Other mechanical complication of unspecified internal joint prosthesis"
+T841,"Mechanical complication of internal fixation device of bones of limb"
+T8411,"Breakdown (mechanical) of internal fixation device of bones of limb"
+T84110,"Breakdown (mechanical) of internal fixation device of right humerus"
+T84111,"Breakdown (mechanical) of internal fixation device of left humerus"
+T84112,"Breakdown (mechanical) of internal fixation device of bone of right forearm"
+T84113,"Breakdown (mechanical) of internal fixation device of bone of left forearm"
+T84114,"Breakdown (mechanical) of internal fixation device of right femur"
+T84115,"Breakdown (mechanical) of internal fixation device of left femur"
+T84116,"Breakdown (mechanical) of internal fixation device of bone of right lower leg"
+T84117,"Breakdown (mechanical) of internal fixation device of bone of left lower leg"
+T84119,"Breakdown (mechanical) of internal fixation device of unspecified bone of limb"
+T8412,"Displacement of internal fixation device of bones of limb"
+T84120,"Displacement of internal fixation device of right humerus"
+T84121,"Displacement of internal fixation device of left humerus"
+T84122,"Displacement of internal fixation device of bone of right forearm"
+T84123,"Displacement of internal fixation device of bone of left forearm"
+T84124,"Displacement of internal fixation device of right femur"
+T84125,"Displacement of internal fixation device of left femur"
+T84126,"Displacement of internal fixation device of bone of right lower leg"
+T84127,"Displacement of internal fixation device of bone of left lower leg"
+T84129,"Displacement of internal fixation device of unspecified bone of limb"
+T8419,"Other mechanical complication of internal fixation device of bones of limb"
+T84190,"Other mechanical complication of internal fixation device of right humerus"
+T84191,"Other mechanical complication of internal fixation device of left humerus"
+T84192,"Other mechanical complication of internal fixation device of bone of right forearm"
+T84193,"Other mechanical complication of internal fixation device of bone of left forearm"
+T84194,"Other mechanical complication of internal fixation device of right femur"
+T84195,"Other mechanical complication of internal fixation device of left femur"
+T84196,"Other mechanical complication of internal fixation device of bone of right lower leg"
+T84197,"Other mechanical complication of internal fixation device of bone of left lower leg"
+T84199,"Other mechanical complication of internal fixation device of unspecified bone of limb"
+T842,"Mechanical complication of internal fixation device of other bones"
+T8421,"Breakdown (mechanical) of internal fixation device of other bones"
+T84210,"Breakdown (mechanical) of internal fixation device of bones of hand and fingers"
+T84213,"Breakdown (mechanical) of internal fixation device of bones of foot and toes"
+T84216,"Breakdown (mechanical) of internal fixation device of vertebrae"
+T84218,"Breakdown (mechanical) of internal fixation device of other bones"
+T8422,"Displacement of internal fixation device of other bones"
+T84220,"Displacement of internal fixation device of bones of hand and fingers"
+T84223,"Displacement of internal fixation device of bones of foot and toes"
+T84226,"Displacement of internal fixation device of vertebrae"
+T84228,"Displacement of internal fixation device of other bones"
+T8429,"Other mechanical complication of internal fixation device of other bones"
+T84290,"Other mechanical complication of internal fixation device of bones of hand and fingers"
+T84293,"Other mechanical complication of internal fixation device of bones of foot and toes"
+T84296,"Other mechanical complication of internal fixation device of vertebrae"
+T84298,"Other mechanical complication of internal fixation device of other bones"
+T843,"Mechanical complication of other bone devices, implants and grafts"
+T8431,"Breakdown (mechanical) of other bone devices, implants and grafts"
+T84310,"Breakdown (mechanical) of electronic bone stimulator"
+T84318,"Breakdown (mechanical) of other bone devices, implants and grafts"
+T8432,"Displacement of other bone devices, implants and grafts"
+T84320,"Displacement of electronic bone stimulator"
+T84328,"Displacement of other bone devices, implants and grafts"
+T8439,"Other mechanical complication of other bone devices, implants and grafts"
+T84390,"Other mechanical complication of electronic bone stimulator"
+T84398,"Other mechanical complication of other bone devices, implants and grafts"
+T844,"Mechanical complication of other internal orthopedic devices, implants and grafts"
+T8441,"Breakdown (mechanical) of other internal orthopedic devices, implants and grafts"
+T84410,"Breakdown (mechanical) of muscle and tendon graft"
+T84418,"Breakdown (mechanical) of other internal orthopedic devices, implants and grafts"
+T8442,"Displacement of other internal orthopedic devices, implants and grafts"
+T84420,"Displacement of muscle and tendon graft"
+T84428,"Displacement of other internal orthopedic devices, implants and grafts"
+T8449,"Other mechanical complication of other internal orthopedic devices, implants and grafts"
+T84490,"Other mechanical complication of muscle and tendon graft"
+T84498,"Other mechanical complication of other internal orthopedic devices, implants and grafts"
+T845,"Infection and inflammatory reaction due to internal joint prosthesis"
+T8450,"Infection and inflammatory reaction due to unspecified internal joint prosthesis"
+T8451,"Infection and inflammatory reaction due to internal right hip prosthesis"
+T8452,"Infection and inflammatory reaction due to internal left hip prosthesis"
+T8453,"Infection and inflammatory reaction due to internal right knee prosthesis"
+T8454,"Infection and inflammatory reaction due to internal left knee prosthesis"
+T8459,"Infection and inflammatory reaction due to other internal joint prosthesis"
+T846,"Infection and inflammatory reaction due to internal fixation device"
+T8460,"Infection and inflammatory reaction due to internal fixation device of unspecified site"
+T8461,"Infection and inflammatory reaction due to internal fixation device of arm"
+T84610,"Infection and inflammatory reaction due to internal fixation device of right humerus"
+T84611,"Infection and inflammatory reaction due to internal fixation device of left humerus"
+T84612,"Infection and inflammatory reaction due to internal fixation device of right radius"
+T84613,"Infection and inflammatory reaction due to internal fixation device of left radius"
+T84614,"Infection and inflammatory reaction due to internal fixation device of right ulna"
+T84615,"Infection and inflammatory reaction due to internal fixation device of left ulna"
+T84619,"Infection and inflammatory reaction due to internal fixation device of unspecified bone of arm"
+T8462,"Infection and inflammatory reaction due to internal fixation device of leg"
+T84620,"Infection and inflammatory reaction due to internal fixation device of right femur"
+T84621,"Infection and inflammatory reaction due to internal fixation device of left femur"
+T84622,"Infection and inflammatory reaction due to internal fixation device of right tibia"
+T84623,"Infection and inflammatory reaction due to internal fixation device of left tibia"
+T84624,"Infection and inflammatory reaction due to internal fixation device of right fibula"
+T84625,"Infection and inflammatory reaction due to internal fixation device of left fibula"
+T84629,"Infection and inflammatory reaction due to internal fixation device of unspecified bone of leg"
+T8463,"Infection and inflammatory reaction due to internal fixation device of spine"
+T8469,"Infection and inflammatory reaction due to internal fixation device of other site"
+T847,"Infection and inflammatory reaction due to other internal orthopedic prosthetic devices, implants and grafts"
+T848,"Other specified complications of internal orthopedic prosthetic devices, implants and grafts"
+T8481,"Embolism due to internal orthopedic prosthetic devices, implants and grafts"
+T8482,"Fibrosis due to internal orthopedic prosthetic devices, implants and grafts"
+T8483,"Hemorrhage due to internal orthopedic prosthetic devices, implants and grafts"
+T8484,"Pain due to internal orthopedic prosthetic devices, implants and grafts"
+T8485,"Stenosis due to internal orthopedic prosthetic devices, implants and grafts"
+T8486,"Thrombosis due to internal orthopedic prosthetic devices, implants and grafts"
+T8489,"Other specified complication of internal orthopedic prosthetic devices, implants and grafts"
+T849,"Unspecified complication of internal orthopedic prosthetic device, implant and graft"
+T85,"Complications of other internal prosthetic devices, implants and grafts"
+T850,"Mechanical complication of ventricular intracranial (communicating) shunt"
+T8501,"Breakdown (mechanical) of ventricular intracranial (communicating) shunt"
+T8502,"Displacement of ventricular intracranial (communicating) shunt"
+T8503,"Leakage of ventricular intracranial (communicating) shunt"
+T8509,"Other mechanical complication of ventricular intracranial (communicating) shunt"
+T851,"Mechanical complication of implanted electronic stimulator of nervous system"
+T8511,"Breakdown (mechanical) of implanted electronic stimulator of nervous system"
+T85110,"Breakdown (mechanical) of implanted electronic neurostimulator of brain electrode (lead)"
+T85111,"Breakdown (mechanical) of implanted electronic neurostimulator of peripheral nerve electrode (lead)"
+T85112,"Breakdown (mechanical) of implanted electronic neurostimulator of spinal cord electrode (lead)"
+T85113,"Breakdown (mechanical) of implanted electronic neurostimulator, generator"
+T85118,"Breakdown (mechanical) of other implanted electronic stimulator of nervous system"
+T8512,"Displacement of implanted electronic stimulator of nervous system"
+T85120,"Displacement of implanted electronic neurostimulator of brain electrode (lead)"
+T85121,"Displacement of implanted electronic neurostimulator of peripheral nerve electrode (lead)"
+T85122,"Displacement of implanted electronic neurostimulator of spinal cord electrode (lead)"
+T85123,"Displacement of implanted electronic neurostimulator, generator"
+T85128,"Displacement of other implanted electronic stimulator of nervous system"
+T8519,"Other mechanical complication of implanted electronic stimulator of nervous system"
+T85190,"Other mechanical complication of implanted electronic neurostimulator of brain electrode (lead)"
+T85191,"Other mechanical complication of implanted electronic neurostimulator of peripheral nerve electrode (lead)"
+T85192,"Other mechanical complication of implanted electronic neurostimulator of spinal cord electrode (lead)"
+T85193,"Other mechanical complication of implanted electronic neurostimulator, generator"
+T85199,"Other mechanical complication of other implanted electronic stimulator of nervous system"
+T852,"Mechanical complication of intraocular lens"
+T8521,"Breakdown (mechanical) of intraocular lens"
+T8522,"Displacement of intraocular lens"
+T8529,"Other mechanical complication of intraocular lens"
+T853,"Mechanical complication of other ocular prosthetic devices, implants and grafts"
+T8531,"Breakdown (mechanical) of other ocular prosthetic devices, implants and grafts"
+T85310,"Breakdown (mechanical) of prosthetic orbit of right eye"
+T85311,"Breakdown (mechanical) of prosthetic orbit of left eye"
+T85318,"Breakdown (mechanical) of other ocular prosthetic devices, implants and grafts"
+T8532,"Displacement of other ocular prosthetic devices, implants and grafts"
+T85320,"Displacement of prosthetic orbit of right eye"
+T85321,"Displacement of prosthetic orbit of left eye"
+T85328,"Displacement of other ocular prosthetic devices, implants and grafts"
+T8539,"Other mechanical complication of other ocular prosthetic devices, implants and grafts"
+T85390,"Other mechanical complication of prosthetic orbit of right eye"
+T85391,"Other mechanical complication of prosthetic orbit of left eye"
+T85398,"Other mechanical complication of other ocular prosthetic devices, implants and grafts"
+T854,"Mechanical complication of breast prosthesis and implant"
+T8541,"Breakdown (mechanical) of breast prosthesis and implant"
+T8542,"Displacement of breast prosthesis and implant"
+T8543,"Leakage of breast prosthesis and implant"
+T8544,"Capsular contracture of breast implant"
+T8549,"Other mechanical complication of breast prosthesis and implant"
+T855,"Mechanical complication of gastrointestinal prosthetic devices, implants and grafts"
+T8551,"Breakdown (mechanical) of gastrointestinal prosthetic devices, implants and grafts"
+T85510,"Breakdown (mechanical) of bile duct prosthesis"
+T85511,"Breakdown (mechanical) of esophageal anti-reflux device"
+T85518,"Breakdown (mechanical) of other gastrointestinal prosthetic devices, implants and grafts"
+T8552,"Displacement of gastrointestinal prosthetic devices, implants and grafts"
+T85520,"Displacement of bile duct prosthesis"
+T85521,"Displacement of esophageal anti-reflux device"
+T85528,"Displacement of other gastrointestinal prosthetic devices, implants and grafts"
+T8559,"Other mechanical complication of gastrointestinal prosthetic devices, implants and"
+T85590,"Other mechanical complication of bile duct prosthesis"
+T85591,"Other mechanical complication of esophageal anti-reflux device"
+T85598,"Other mechanical complication of other gastrointestinal prosthetic devices, implants and grafts"
+T856,"Mechanical complication of other specified internal and external prosthetic devices, implants and grafts"
+T8561,"Breakdown (mechanical) of other specified internal prosthetic devices, implants and grafts"
+T85610,"Breakdown (mechanical) of cranial or spinal infusion catheter"
+T85611,"Breakdown (mechanical) of intraperitoneal dialysis catheter"
+T85612,"Breakdown (mechanical) of permanent sutures"
+T85613,"Breakdown (mechanical) of artificial skin graft and decellularized allodermis"
+T85614,"Breakdown (mechanical) of insulin pump"
+T85615,"Breakdown (mechanical) of other nervous system device, implant or graft"
+T85618,"Breakdown (mechanical) of other specified internal prosthetic devices, implants and grafts"
+T8562,"Displacement of other specified internal prosthetic devices, implants and grafts"
+T85620,"Displacement of cranial or spinal infusion catheter"
+T85621,"Displacement of intraperitoneal dialysis catheter"
+T85622,"Displacement of permanent sutures"
+T85623,"Displacement of artificial skin graft and decellularized allodermis"
+T85624,"Displacement of insulin pump"
+T85625,"Displacement of other nervous system device, implant or graft"
+T85628,"Displacement of other specified internal prosthetic devices, implants and grafts"
+T8563,"Leakage of other specified internal prosthetic devices, implants and grafts"
+T85630,"Leakage of cranial or spinal infusion catheter"
+T85631,"Leakage of intraperitoneal dialysis catheter"
+T85633,"Leakage of insulin pump"
+T85635,"Leakage of other nervous system device, implant or graft"
+T85638,"Leakage of other specified internal prosthetic devices, implants and grafts"
+T8569,"Other mechanical complication of other specified internal prosthetic devices, implants and grafts"
+T85690,"Other mechanical complication of cranial or spinal infusion catheter"
+T85691,"Other mechanical complication of intraperitoneal dialysis catheter"
+T85692,"Other mechanical complication of permanent sutures"
+T85693,"Other mechanical complication of artificial skin graft and decellularized allodermis"
+T85694,"Other mechanical complication of insulin pump"
+T85695,"Other mechanical complication of other nervous system device, implant or graft"
+T85698,"Other mechanical complication of other specified internal prosthetic devices, implants and grafts"
+T857,"Infection and inflammatory reaction due to other internal prosthetic devices, implants and grafts"
+T8571,"Infection and inflammatory reaction due to peritoneal dialysis catheter"
+T8572,"Infection and inflammatory reaction due to insulin pump"
+T8573,"Infection and inflammatory reaction due to nervous system devices, implants and graft"
+T85730,"Infection and inflammatory reaction due to ventricular intracranial (communicating) shunt"
+T85731,"Infection and inflammatory reaction due to implanted electronic neurostimulator of brain, electrode (lead)"
+T85732,"Infection and inflammatory reaction due to implanted electronic neurostimulator of peripheral nerve, electrode (lead)"
+T85733,"Infection and inflammatory reaction due to implanted electronic neurostimulator of spinal cord, electrode (lead)"
+T85734,"Infection and inflammatory reaction due to implanted electronic neurostimulator, generator"
+T85735,"Infection and inflammatory reaction due to cranial or spinal infusion catheter"
+T85738,"Infection and inflammatory reaction due to other nervous system device, implant or graft"
+T8579,"Infection and inflammatory reaction due to other internal prosthetic devices, implants and grafts"
+T858,"Other specified complications of internal prosthetic devices, implants and grafts, not elsewhere classified"
+T8581,"Embolism due to internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85810,"Embolism due to nervous system prosthetic devices, implants and grafts"
+T85818,"Embolism due to other internal prosthetic devices, implants and grafts"
+T8582,"Fibrosis due to internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85820,"Fibrosis due to nervous system prosthetic devices, implants and grafts"
+T85828,"Fibrosis due to other internal prosthetic devices, implants and grafts"
+T8583,"Hemorrhage due to internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85830,"Hemorrhage due to nervous system prosthetic devices, implants and grafts"
+T85838,"Hemorrhage due to other internal prosthetic devices, implants and grafts"
+T8584,"Pain due to internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85840,"Pain due to nervous system prosthetic devices, implants and grafts"
+T85848,"Pain due to other internal prosthetic devices, implants and grafts"
+T8585,"Stenosis due to internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85850,"Stenosis due to nervous system prosthetic devices, implants and grafts"
+T85858,"Stenosis due to other internal prosthetic devices, implants and grafts"
+T8586,"Thrombosis due to internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85860,"Thrombosis due to nervous system prosthetic devices, implants and grafts"
+T85868,"Thrombosis due to other internal prosthetic devices, implants and grafts"
+T8589,"Other specified complication of internal prosthetic devices, implants and grafts, not elsewhere classified"
+T85890,"Other specified complication of nervous system prosthetic devices, implants and grafts"
+T85898,"Other specified complication of other internal prosthetic devices, implants and grafts"
+T859,"Unspecified complication of internal prosthetic device, implant and graft"
+T86,"Complications of transplanted organs and tissue"
+T860,"Complications of bone marrow transplant"
+T861,"Complications of kidney transplant"
+T862,"Complications of heart transplant"
+T8629,"Other complications of heart transplant"
+T863,"Complications of heart-lung transplant"
+T864,"Complications of liver transplant"
+T865,"Complications of stem cell transplant"
+T868,"Complications of other transplanted organs and tissues"
+T8681,"Complications of lung transplant"
+T8682,"Complications of skin graft (allograft) (autograft)"
+T8683,"Complications of bone graft"
+T8684,"Complications of corneal transplant"
+T8685,"Complication of intestine transplant"
+T8689,"Complications of other transplanted tissue"
+T869,"Complication of unspecified transplanted organ and tissue"
+T87,"Complications peculiar to reattachment and amputation"
+T870,"Complications of reattached (part of) upper extremity"
+T870X,"Complications of reattached (part of) upper extremity"
+T871,"Complications of reattached (part of) lower extremity"
+T871X,"Complications of reattached (part of) lower extremity"
+T872,"Complications of other reattached body part"
+T873,"Neuroma of amputation stump"
+T874,"Infection of amputation stump"
+T875,"Necrosis of amputation stump"
+T878,"Other complications of amputation stump"
+T879,"Unspecified complications of amputation stump"
+T88,"Other complications of surgical and medical care, not elsewhere classified"
+T880,"Infection following immunization"
+T881,"Other complications following immunization, not elsewhere classified"
+T882,"Shock due to anesthesia"
+T883,"Malignant hyperthermia due to anesthesia"
+T884,"Failed or difficult intubation"
+T885,"Other complications of anesthesia"
+T8851,"Hypothermia following anesthesia"
+T8852,"Failed moderate sedation during procedure"
+T8853,"Unintended awareness under general anesthesia during procedure"
+T8859,"Other complications of anesthesia"
+T886,"Anaphylactic reaction due to adverse effect of correct drug or medicament properly administered"
+T887,"Unspecified adverse effect of drug or medicament"
+T888,"Other specified complications of surgical and medical care, not elsewhere classified"
+T889,"Complication of surgical and medical care, unspecified"
+V00,"Pedestrian conveyance accident"
+V000,"Pedestrian on foot injured in collision with pedestrian conveyance"
+V0001,"Pedestrian on foot injured in collision with roller-skater"
+V0002,"Pedestrian on foot injured in collision with skateboarder"
+V0009,"Pedestrian on foot injured in collision with other pedestrian conveyance"
+V001,"Rolling-type pedestrian conveyance accident"
+V0011,"In-line roller-skate accident"
+V00111,"Fall from in-line roller-skates"
+V00112,"In-line roller-skater colliding with stationary object"
+V00118,"Other in-line roller-skate accident"
+V0012,"Non-in- line roller-skate accident"
+V00121,"Fall from non-in-line roller-skates"
+V00122,"Non-in-line roller-skater colliding with stationary object"
+V00128,"Other non-in-line roller-skating accident"
+V0013,"Skateboard accident"
+V00131,"Fall from skateboard"
+V00132,"Skateboarder colliding with stationary object"
+V00138,"Other skateboard accident"
+V0014,"Scooter (nonmotorized) accident"
+V00141,"Fall from scooter (nonmotorized)"
+V00142,"Scooter (nonmotorized) colliding with stationary object"
+V00148,"Other scooter (nonmotorized) accident"
+V0015,"Heelies accident"
+V00151,"Fall from heelies"
+V00152,"Heelies colliding with stationary object"
+V00158,"Other heelies accident"
+V0018,"Accident on other rolling-type pedestrian conveyance"
+V00181,"Fall from other rolling-type pedestrian conveyance"
+V00182,"Pedestrian on other rolling-type pedestrian conveyance colliding with stationary object"
+V00188,"Other accident on other rolling-type pedestrian conveyance"
+V002,"Gliding-type pedestrian conveyance accident"
+V0021,"Ice-skates accident"
+V00211,"Fall from ice-skates"
+V00212,"Ice-skater colliding with stationary object"
+V00218,"Other ice-skates accident"
+V0022,"Sled accident"
+V00221,"Fall from sled"
+V00222,"Sledder colliding with stationary object"
+V00228,"Other sled accident"
+V0028,"Other gliding-type pedestrian conveyance accident"
+V00281,"Fall from other gliding-type pedestrian conveyance"
+V00282,"Pedestrian on other gliding-type pedestrian conveyance colliding with stationary object"
+V00288,"Other accident on other gliding-type pedestrian conveyance"
+V003,"Flat-bottomed pedestrian conveyance accident"
+V0031,"Snowboard accident"
+V00311,"Fall from snowboard"
+V00312,"Snowboarder colliding with stationary object"
+V00318,"Other snowboard accident"
+V0032,"Snow-ski accident"
+V00321,"Fall from snow-skis"
+V00322,"Snow-skier colliding with stationary object"
+V00328,"Other snow-ski accident"
+V0038,"Other flat-bottomed pedestrian conveyance accident"
+V00381,"Fall from other flat-bottomed pedestrian conveyance"
+V00382,"Pedestrian on other flat-bottomed pedestrian conveyance colliding with stationary object"
+V00388,"Other accident on other flat-bottomed pedestrian conveyance"
+V008,"Accident on other pedestrian conveyance"
+V0081,"Accident with wheelchair (powered)"
+V00811,"Fall from moving wheelchair (powered)"
+V00812,"Wheelchair (powered) colliding with stationary object"
+V00818,"Other accident with wheelchair (powered)"
+V0082,"Accident with babystroller"
+V00821,"Fall from babystroller"
+V00822,"Babystroller colliding with stationary object"
+V00828,"Other accident with babystroller"
+V0083,"Accident with motorized mobility scooter"
+V00831,"Fall from motorized mobility scooter"
+V00832,"Motorized mobility scooter colliding with stationary object"
+V00838,"Other accident with motorized mobility scooter"
+V0089,"Accident on other pedestrian conveyance"
+V00891,"Fall from other pedestrian conveyance"
+V00892,"Pedestrian on other pedestrian conveyance colliding with stationary object"
+V00898,"Other accident on other pedestrian conveyance"
+V01,"Pedestrian injured in collision with pedal cycle"
+V010,"Pedestrian injured in collision with pedal cycle in nontraffic accident"
+V0100,"Pedestrian on foot injured in collision with pedal cycle in nontraffic accident"
+V0101,"Pedestrian on roller-skates injured in collision with pedal cycle in nontraffic accident"
+V0102,"Pedestrian on skateboard injured in collision with pedal cycle in nontraffic accident"
+V0109,"Pedestrian with other conveyance injured in collision with pedal cycle in nontraffic accident"
+V011,"Pedestrian injured in collision with pedal cycle in traffic accident"
+V0110,"Pedestrian on foot injured in collision with pedal cycle in traffic accident"
+V0111,"Pedestrian on roller-skates injured in collision with pedal cycle in traffic accident"
+V0112,"Pedestrian on skateboard injured in collision with pedal cycle in traffic accident"
+V0119,"Pedestrian with other conveyance injured in collision with pedal cycle in traffic accident"
+V019,"Pedestrian injured in collision with pedal cycle, unspecified whether traffic or nontraffic accident"
+V0190,"Pedestrian on foot injured in collision with pedal cycle, unspecified whether traffic or nontraffic accident"
+V0191,"Pedestrian on roller-skates injured in collision with pedal cycle, unspecified whether traffic or nontraffic accident"
+V0192,"Pedestrian on skateboard injured in collision with pedal cycle, unspecified whether traffic or nontraffic accident"
+V0199,"Pedestrian with other conveyance injured in collision with pedal cycle, unspecified whether traffic or nontraffic accident"
+V02,"Pedestrian injured in collision with two- or three-wheeled motor vehicle"
+V020,"Pedestrian injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V0200,"Pedestrian on foot injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V0201,"Pedestrian on roller-skates injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V0202,"Pedestrian on skateboard injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V0209,"Pedestrian with other conveyance injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V021,"Pedestrian injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V0210,"Pedestrian on foot injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V0211,"Pedestrian on roller-skates injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V0212,"Pedestrian on skateboard injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V0219,"Pedestrian with other conveyance injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V029,"Pedestrian injured in collision with two- or three-wheeled motor vehicle, unspecified whether traffic or nontraffic accident"
+V0290,"Pedestrian on foot injured in collision with two- or three-wheeled motor vehicle, unspecified whether traffic or nontraffic accident"
+V0291,"Pedestrian on roller-skates injured in collision with two- or three-wheeled motor vehicle, unspecified whether traffic or nontraffic accident"
+V0292,"Pedestrian on skateboard injured in collision with two- or three-wheeled motor vehicle, unspecified whether traffic or nontraffic accident"
+V0299,"Pedestrian with other conveyance injured in collision with two- or three-wheeled motor vehicle, unspecified whether traffic or nontraffic accident"
+V03,"Pedestrian injured in collision with car, pick-up truck or van"
+V030,"Pedestrian injured in collision with car, pick-up truck or van in nontraffic accident"
+V0300,"Pedestrian on foot injured in collision with car, pick-up truck or van in nontraffic accident"
+V0301,"Pedestrian on roller-skates injured in collision with car, pick-up truck or van in nontraffic accident"
+V0302,"Pedestrian on skateboard injured in collision with car, pick-up truck or van in nontraffic accident"
+V0309,"Pedestrian with other conveyance injured in collision with car, pick-up truck or van in nontraffic accident"
+V031,"Pedestrian injured in collision with car, pick-up truck or van in traffic accident"
+V0310,"Pedestrian on foot injured in collision with car, pick-up truck or van in traffic accident"
+V0311,"Pedestrian on roller-skates injured in collision with car, pick-up truck or van in traffic accident"
+V0312,"Pedestrian on skateboard injured in collision with car, pick-up truck or van in traffic accident"
+V0319,"Pedestrian with other conveyance injured in collision with car, pick-up truck or van in traffic accident"
+V039,"Pedestrian injured in collision with car, pick-up truck or van, unspecified whether traffic or nontraffic accident"
+V0390,"Pedestrian on foot injured in collision with car, pick-up truck or van, unspecified whether traffic or nontraffic accident"
+V0391,"Pedestrian on roller-skates injured in collision with car, pick-up truck or van, unspecified whether traffic or nontraffic accident"
+V0392,"Pedestrian on skateboard injured in collision with car, pick-up truck or van, unspecified whether traffic or nontraffic accident"
+V0399,"Pedestrian with other conveyance injured in collision with car, pick-up truck or van, unspecified whether traffic or nontraffic accident"
+V04,"Pedestrian injured in collision with heavy transport vehicle or bus"
+V040,"Pedestrian injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V0400,"Pedestrian on foot injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V0401,"Pedestrian on roller-skates injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V0402,"Pedestrian on skateboard injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V0409,"Pedestrian with other conveyance injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V041,"Pedestrian injured in collision with heavy transport vehicle or bus in traffic accident"
+V0410,"Pedestrian on foot injured in collision with heavy transport vehicle or bus in traffic accident"
+V0411,"Pedestrian on roller-skates injured in collision with heavy transport vehicle or bus in traffic accident"
+V0412,"Pedestrian on skateboard injured in collision with heavy transport vehicle or bus in traffic accident"
+V0419,"Pedestrian with other conveyance injured in collision with heavy transport vehicle or bus in traffic accident"
+V049,"Pedestrian injured in collision with heavy transport vehicle or bus, unspecified whether traffic or nontraffic accident"
+V0490,"Pedestrian on foot injured in collision with heavy transport vehicle or bus, unspecified whether traffic or nontraffic accident"
+V0491,"Pedestrian on roller-skates injured in collision with heavy transport vehicle or bus, unspecified whether traffic or nontraffic accident"
+V0492,"Pedestrian on skateboard injured in collision with heavy transport vehicle or bus, unspecified whether traffic or nontraffic accident"
+V0499,"Pedestrian with other conveyance injured in collision with heavy transport vehicle or bus, unspecified whether traffic or nontraffic accident"
+V05,"Pedestrian injured in collision with railway train or railway vehicle"
+V050,"Pedestrian injured in collision with railway train or railway vehicle in nontraffic accident"
+V0500,"Pedestrian on foot injured in collision with railway train or railway vehicle in nontraffic accident"
+V0501,"Pedestrian on roller-skates injured in collision with railway train or railway vehicle in nontraffic accident"
+V0502,"Pedestrian on skateboard injured in collision with railway train or railway vehicle in nontraffic accident"
+V0509,"Pedestrian with other conveyance injured in collision with railway train or railway vehicle in nontraffic accident"
+V051,"Pedestrian injured in collision with railway train or railway vehicle in traffic accident"
+V0510,"Pedestrian on foot injured in collision with railway train or railway vehicle in traffic accident"
+V0511,"Pedestrian on roller-skates injured in collision with railway train or railway vehicle in traffic accident"
+V0512,"Pedestrian on skateboard injured in collision with railway train or railway vehicle in traffic accident"
+V0519,"Pedestrian with other conveyance injured in collision with railway train or railway vehicle in traffic accident"
+V059,"Pedestrian injured in collision with railway train or railway vehicle, unspecified whether traffic or nontraffic accident"
+V0590,"Pedestrian on foot injured in collision with railway train or railway vehicle, unspecified whether traffic or nontraffic accident"
+V0591,"Pedestrian on roller-skates injured in collision with railway train or railway vehicle, unspecified whether traffic or nontraffic accident"
+V0592,"Pedestrian on skateboard injured in collision with railway train or railway vehicle, unspecified whether traffic or nontraffic accident"
+V0599,"Pedestrian with other conveyance injured in collision with railway train or railway vehicle, unspecified whether traffic or nontraffic accident"
+V06,"Pedestrian injured in collision with other nonmotor vehicle"
+V060,"Pedestrian injured in collision with other nonmotor vehicle in nontraffic accident"
+V0600,"Pedestrian on foot injured in collision with other nonmotor vehicle in nontraffic accident"
+V0601,"Pedestrian on roller-skates injured in collision with other nonmotor vehicle in nontraffic accident"
+V0602,"Pedestrian on skateboard injured in collision with other nonmotor vehicle in nontraffic accident"
+V0609,"Pedestrian with other conveyance injured in collision with other nonmotor vehicle in nontraffic accident"
+V061,"Pedestrian injured in collision with other nonmotor vehicle in traffic accident"
+V0610,"Pedestrian on foot injured in collision with other nonmotor vehicle in traffic accident"
+V0611,"Pedestrian on roller-skates injured in collision with other nonmotor vehicle in traffic accident"
+V0612,"Pedestrian on skateboard injured in collision with other nonmotor vehicle in traffic accident"
+V0619,"Pedestrian with other conveyance injured in collision with other nonmotor vehicle in traffic accident"
+V069,"Pedestrian injured in collision with other nonmotor vehicle, unspecified whether traffic or nontraffic accident"
+V0690,"Pedestrian on foot injured in collision with other nonmotor vehicle, unspecified whether traffic or nontraffic accident"
+V0691,"Pedestrian on roller-skates injured in collision with other nonmotor vehicle, unspecified whether traffic or nontraffic accident"
+V0692,"Pedestrian on skateboard injured in collision with other nonmotor vehicle, unspecified whether traffic or nontraffic accident"
+V0699,"Pedestrian with other conveyance injured in collision with other nonmotor vehicle, unspecified whether traffic or nontraffic accident"
+V09,"Pedestrian injured in other and unspecified transport accidents"
+V090,"Pedestrian injured in nontraffic accident involving other and unspecified motor vehicles"
+V0900,"Pedestrian injured in nontraffic accident involving unspecified motor vehicles"
+V0901,"Pedestrian injured in nontraffic accident involving military vehicle"
+V0909,"Pedestrian injured in nontraffic accident involving other motor vehicles"
+V091,"Pedestrian injured in unspecified nontraffic accident"
+V092,"Pedestrian injured in traffic accident involving other and unspecified motor vehicles"
+V0920,"Pedestrian injured in traffic accident involving unspecified motor vehicles"
+V0921,"Pedestrian injured in traffic accident involving military vehicle"
+V0929,"Pedestrian injured in traffic accident involving other motor vehicles"
+V093,"Pedestrian injured in unspecified traffic accident"
+V099,"Pedestrian injured in unspecified transport accident"
+V10,"Pedal cycle rider injured in collision with pedestrian or animal"
+V100,"Pedal cycle driver injured in collision with pedestrian or animal in nontraffic accident"
+V101,"Pedal cycle passenger injured in collision with pedestrian or animal in nontraffic accident"
+V102,"Unspecified pedal cyclist injured in collision with pedestrian or animal in nontraffic accident"
+V103,"Person boarding or alighting a pedal cycle injured in collision with pedestrian or animal"
+V104,"Pedal cycle driver injured in collision with pedestrian or animal in traffic accident"
+V105,"Pedal cycle passenger injured in collision with pedestrian or animal in traffic accident"
+V109,"Unspecified pedal cyclist injured in collision with pedestrian or animal in traffic accident"
+V11,"Pedal cycle rider injured in collision with other pedal cycle"
+V110,"Pedal cycle driver injured in collision with other pedal cycle in nontraffic accident"
+V111,"Pedal cycle passenger injured in collision with other pedal cycle in nontraffic accident"
+V112,"Unspecified pedal cyclist injured in collision with other pedal cycle in nontraffic accident"
+V113,"Person boarding or alighting a pedal cycle injured in collision with other pedal cycle"
+V114,"Pedal cycle driver injured in collision with other pedal cycle in traffic accident"
+V115,"Pedal cycle passenger injured in collision with other pedal cycle in traffic accident"
+V119,"Unspecified pedal cyclist injured in collision with other pedal cycle in traffic accident"
+V12,"Pedal cycle rider injured in collision with two- or three-wheeled motor vehicle"
+V120,"Pedal cycle driver injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V121,"Pedal cycle passenger injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V122,"Unspecified pedal cyclist injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V123,"Person boarding or alighting a pedal cycle injured in collision with two- or three-wheeled motor vehicle"
+V124,"Pedal cycle driver injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V125,"Pedal cycle passenger injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V129,"Unspecified pedal cyclist injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V13,"Pedal cycle rider injured in collision with car, pick-up truck or van"
+V130,"Pedal cycle driver injured in collision with car, pick-up truck or van in nontraffic accident"
+V131,"Pedal cycle passenger injured in collision with car, pick-up truck or van in nontraffic accident"
+V132,"Unspecified pedal cyclist injured in collision with car, pick-up truck or van in nontraffic accident"
+V133,"Person boarding or alighting a pedal cycle injured in collision with car, pick-up truck or van"
+V134,"Pedal cycle driver injured in collision with car, pick-up truck or van in traffic accident"
+V135,"Pedal cycle passenger injured in collision with car, pick-up truck or van in traffic accident"
+V139,"Unspecified pedal cyclist injured in collision with car, pick-up truck or van in traffic accident"
+V14,"Pedal cycle rider injured in collision with heavy transport vehicle or bus"
+V140,"Pedal cycle driver injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V141,"Pedal cycle passenger injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V142,"Unspecified pedal cyclist injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V143,"Person boarding or alighting a pedal cycle injured in collision with heavy transport vehicle or bus"
+V144,"Pedal cycle driver injured in collision with heavy transport vehicle or bus in traffic accident"
+V145,"Pedal cycle passenger injured in collision with heavy transport vehicle or bus in traffic accident"
+V149,"Unspecified pedal cyclist injured in collision with heavy transport vehicle or bus in traffic accident"
+V15,"Pedal cycle rider injured in collision with railway train or railway vehicle"
+V150,"Pedal cycle driver injured in collision with railway train or railway vehicle in nontraffic accident"
+V151,"Pedal cycle passenger injured in collision with railway train or railway vehicle in nontraffic accident"
+V152,"Unspecified pedal cyclist injured in collision with railway train or railway vehicle in nontraffic accident"
+V153,"Person boarding or alighting a pedal cycle injured in collision with railway train or railway vehicle"
+V154,"Pedal cycle driver injured in collision with railway train or railway vehicle in traffic accident"
+V155,"Pedal cycle passenger injured in collision with railway train or railway vehicle in traffic accident"
+V159,"Unspecified pedal cyclist injured in collision with railway train or railway vehicle in traffic accident"
+V16,"Pedal cycle rider injured in collision with other nonmotor vehicle"
+V160,"Pedal cycle driver injured in collision with other nonmotor vehicle in nontraffic accident"
+V161,"Pedal cycle passenger injured in collision with other nonmotor vehicle in nontraffic accident"
+V162,"Unspecified pedal cyclist injured in collision with other nonmotor vehicle in nontraffic accident"
+V163,"Person boarding or alighting a pedal cycle injured in collision with other nonmotor vehicle in nontraffic accident"
+V164,"Pedal cycle driver injured in collision with other nonmotor vehicle in traffic accident"
+V165,"Pedal cycle passenger injured in collision with other nonmotor vehicle in traffic accident"
+V169,"Unspecified pedal cyclist injured in collision with other nonmotor vehicle in traffic accident"
+V17,"Pedal cycle rider injured in collision with fixed or stationary object"
+V170,"Pedal cycle driver injured in collision with fixed or stationary object in nontraffic accident"
+V171,"Pedal cycle passenger injured in collision with fixed or stationary object in nontraffic accident"
+V172,"Unspecified pedal cyclist injured in collision with fixed or stationary object in nontraffic accident"
+V173,"Person boarding or alighting a pedal cycle injured in collision with fixed or stationary object"
+V174,"Pedal cycle driver injured in collision with fixed or stationary object in traffic accident"
+V175,"Pedal cycle passenger injured in collision with fixed or stationary object in traffic accident"
+V179,"Unspecified pedal cyclist injured in collision with fixed or stationary object in traffic accident"
+V18,"Pedal cycle rider injured in noncollision transport accident"
+V180,"Pedal cycle driver injured in noncollision transport accident in nontraffic accident"
+V181,"Pedal cycle passenger injured in noncollision transport accident in nontraffic accident"
+V182,"Unspecified pedal cyclist injured in noncollision transport accident in nontraffic accident"
+V183,"Person boarding or alighting a pedal cycle injured in noncollision transport accident"
+V184,"Pedal cycle driver injured in noncollision transport accident in traffic accident"
+V185,"Pedal cycle passenger injured in noncollision transport accident in traffic accident"
+V189,"Unspecified pedal cyclist injured in noncollision transport accident in traffic accident"
+V19,"Pedal cycle rider injured in other and unspecified transport accidents"
+V190,"Pedal cycle driver injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V1900,"Pedal cycle driver injured in collision with unspecified motor vehicles in nontraffic accident"
+V1909,"Pedal cycle driver injured in collision with other motor vehicles in nontraffic accident"
+V191,"Pedal cycle passenger injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V1910,"Pedal cycle passenger injured in collision with unspecified motor vehicles in nontraffic accident"
+V1919,"Pedal cycle passenger injured in collision with other motor vehicles in nontraffic accident"
+V192,"Unspecified pedal cyclist injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V1920,"Unspecified pedal cyclist injured in collision with unspecified motor vehicles in nontraffic accident"
+V1929,"Unspecified pedal cyclist injured in collision with other motor vehicles in nontraffic accident"
+V193,"Pedal cyclist (driver) (passenger) injured in unspecified nontraffic accident"
+V194,"Pedal cycle driver injured in collision with other and unspecified motor vehicles in traffic accident"
+V1940,"Pedal cycle driver injured in collision with unspecified motor vehicles in traffic accident"
+V1949,"Pedal cycle driver injured in collision with other motor vehicles in traffic accident"
+V195,"Pedal cycle passenger injured in collision with other and unspecified motor vehicles in traffic accident"
+V1950,"Pedal cycle passenger injured in collision with unspecified motor vehicles in traffic accident"
+V1959,"Pedal cycle passenger injured in collision with other motor vehicles in traffic accident"
+V196,"Unspecified pedal cyclist injured in collision with other and unspecified motor vehicles in traffic accident"
+V1960,"Unspecified pedal cyclist injured in collision with unspecified motor vehicles in traffic accident"
+V1969,"Unspecified pedal cyclist injured in collision with other motor vehicles in traffic accident"
+V198,"Pedal cyclist (driver) (passenger) injured in other specified transport accidents"
+V1981,"Pedal cyclist (driver) (passenger) injured in transport accident with military vehicle"
+V1988,"Pedal cyclist (driver) (passenger) injured in other specified transport accidents"
+V199,"Pedal cyclist (driver) (passenger) injured in unspecified traffic accident"
+V20,"Motorcycle rider injured in collision with pedestrian or animal"
+V200,"Motorcycle driver injured in collision with pedestrian or animal in nontraffic accident"
+V201,"Motorcycle passenger injured in collision with pedestrian or animal in nontraffic accident"
+V202,"Unspecified motorcycle rider injured in collision with pedestrian or animal in nontraffic accident"
+V203,"Person boarding or alighting a motorcycle injured in collision with pedestrian or animal"
+V204,"Motorcycle driver injured in collision with pedestrian or animal in traffic accident"
+V205,"Motorcycle passenger injured in collision with pedestrian or animal in traffic accident"
+V209,"Unspecified motorcycle rider injured in collision with pedestrian or animal in traffic accident"
+V21,"Motorcycle rider injured in collision with pedal cycle"
+V210,"Motorcycle driver injured in collision with pedal cycle in nontraffic accident"
+V211,"Motorcycle passenger injured in collision with pedal cycle in nontraffic accident"
+V212,"Unspecified motorcycle rider injured in collision with pedal cycle in nontraffic accident"
+V213,"Person boarding or alighting a motorcycle injured in collision with pedal cycle"
+V214,"Motorcycle driver injured in collision with pedal cycle in traffic accident"
+V215,"Motorcycle passenger injured in collision with pedal cycle in traffic accident"
+V219,"Unspecified motorcycle rider injured in collision with pedal cycle in traffic accident"
+V22,"Motorcycle rider injured in collision with two- or three-wheeled motor vehicle"
+V220,"Motorcycle driver injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V221,"Motorcycle passenger injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V222,"Unspecified motorcycle rider injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V223,"Person boarding or alighting a motorcycle injured in collision with two- or three-wheeled motor vehicle"
+V224,"Motorcycle driver injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V225,"Motorcycle passenger injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V229,"Unspecified motorcycle rider injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V23,"Motorcycle rider injured in collision with car, pick-up truck or van"
+V230,"Motorcycle driver injured in collision with car, pick-up truck or van in nontraffic accident"
+V231,"Motorcycle passenger injured in collision with car, pick-up truck or van in nontraffic accident"
+V232,"Unspecified motorcycle rider injured in collision with car, pick-up truck or van in nontraffic accident"
+V233,"Person boarding or alighting a motorcycle injured in collision with car, pick-up truck or van"
+V234,"Motorcycle driver injured in collision with car, pick-up truck or van in traffic accident"
+V235,"Motorcycle passenger injured in collision with car, pick-up truck or van in traffic accident"
+V239,"Unspecified motorcycle rider injured in collision with car, pick-up truck or van in traffic accident"
+V24,"Motorcycle rider injured in collision with heavy transport vehicle or bus"
+V240,"Motorcycle driver injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V241,"Motorcycle passenger injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V242,"Unspecified motorcycle rider injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V243,"Person boarding or alighting a motorcycle injured in collision with heavy transport vehicle or bus"
+V244,"Motorcycle driver injured in collision with heavy transport vehicle or bus in traffic accident"
+V245,"Motorcycle passenger injured in collision with heavy transport vehicle or bus in traffic accident"
+V249,"Unspecified motorcycle rider injured in collision with heavy transport vehicle or bus in traffic accident"
+V25,"Motorcycle rider injured in collision with railway train or railway vehicle"
+V250,"Motorcycle driver injured in collision with railway train or railway vehicle in nontraffic accident"
+V251,"Motorcycle passenger injured in collision with railway train or railway vehicle in nontraffic accident"
+V252,"Unspecified motorcycle rider injured in collision with railway train or railway vehicle in nontraffic accident"
+V253,"Person boarding or alighting a motorcycle injured in collision with railway train or railway vehicle"
+V254,"Motorcycle driver injured in collision with railway train or railway vehicle in traffic accident"
+V255,"Motorcycle passenger injured in collision with railway train or railway vehicle in traffic accident"
+V259,"Unspecified motorcycle rider injured in collision with railway train or railway vehicle in traffic accident"
+V26,"Motorcycle rider injured in collision with other nonmotor vehicle"
+V260,"Motorcycle driver injured in collision with other nonmotor vehicle in nontraffic accident"
+V261,"Motorcycle passenger injured in collision with other nonmotor vehicle in nontraffic accident"
+V262,"Unspecified motorcycle rider injured in collision with other nonmotor vehicle in nontraffic accident"
+V263,"Person boarding or alighting a motorcycle injured in collision with other nonmotor vehicle"
+V264,"Motorcycle driver injured in collision with other nonmotor vehicle in traffic accident"
+V265,"Motorcycle passenger injured in collision with other nonmotor vehicle in traffic accident"
+V269,"Unspecified motorcycle rider injured in collision with other nonmotor vehicle in traffic accident"
+V27,"Motorcycle rider injured in collision with fixed or stationary object"
+V270,"Motorcycle driver injured in collision with fixed or stationary object in nontraffic accident"
+V271,"Motorcycle passenger injured in collision with fixed or stationary object in nontraffic accident"
+V272,"Unspecified motorcycle rider injured in collision with fixed or stationary object in nontraffic accident"
+V273,"Person boarding or alighting a motorcycle injured in collision with fixed or stationary object"
+V274,"Motorcycle driver injured in collision with fixed or stationary object in traffic accident"
+V275,"Motorcycle passenger injured in collision with fixed or stationary object in traffic accident"
+V279,"Unspecified motorcycle rider injured in collision with fixed or stationary object in traffic accident"
+V28,"Motorcycle rider injured in noncollision transport accident"
+V280,"Motorcycle driver injured in noncollision transport accident in nontraffic accident"
+V281,"Motorcycle passenger injured in noncollision transport accident in nontraffic accident"
+V282,"Unspecified motorcycle rider injured in noncollision transport accident in nontraffic accident"
+V283,"Person boarding or alighting a motorcycle injured in noncollision transport accident"
+V284,"Motorcycle driver injured in noncollision transport accident in traffic accident"
+V285,"Motorcycle passenger injured in noncollision transport accident in traffic accident"
+V289,"Unspecified motorcycle rider injured in noncollision transport accident in traffic accident"
+V29,"Motorcycle rider injured in other and unspecified transport accidents"
+V290,"Motorcycle driver injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V2900,"Motorcycle driver injured in collision with unspecified motor vehicles in nontraffic accident"
+V2909,"Motorcycle driver injured in collision with other motor vehicles in nontraffic accident"
+V291,"Motorcycle passenger injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V2910,"Motorcycle passenger injured in collision with unspecified motor vehicles in nontraffic accident"
+V2919,"Motorcycle passenger injured in collision with other motor vehicles in nontraffic accident"
+V292,"Unspecified motorcycle rider injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V2920,"Unspecified motorcycle rider injured in collision with unspecified motor vehicles in nontraffic accident"
+V2929,"Unspecified motorcycle rider injured in collision with other motor vehicles in nontraffic accident"
+V293,"Motorcycle rider (driver) (passenger) injured in unspecified nontraffic accident"
+V294,"Motorcycle driver injured in collision with other and unspecified motor vehicles in traffic accident"
+V2940,"Motorcycle driver injured in collision with unspecified motor vehicles in traffic accident"
+V2949,"Motorcycle driver injured in collision with other motor vehicles in traffic accident"
+V295,"Motorcycle passenger injured in collision with other and unspecified motor vehicles in traffic accident"
+V2950,"Motorcycle passenger injured in collision with unspecified motor vehicles in traffic accident"
+V2959,"Motorcycle passenger injured in collision with other motor vehicles in traffic accident"
+V296,"Unspecified motorcycle rider injured in collision with other and unspecified motor vehicles in traffic accident"
+V2960,"Unspecified motorcycle rider injured in collision with unspecified motor vehicles in traffic accident"
+V2969,"Unspecified motorcycle rider injured in collision with other motor vehicles in traffic accident"
+V298,"Motorcycle rider (driver) (passenger) injured in other specified transport accidents"
+V2981,"Motorcycle rider (driver) (passenger) injured in transport accident with military vehicle"
+V2988,"Motorcycle rider (driver) (passenger) injured in other specified transport accidents"
+V299,"Motorcycle rider (driver) (passenger) injured in unspecified traffic accident"
+V30,"Occupant of three-wheeled motor vehicle injured in collision with pedestrian or animal"
+V300,"Driver of three-wheeled motor vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V301,"Passenger in three-wheeled motor vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V302,"Person on outside of three-wheeled motor vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V303,"Unspecified occupant of three-wheeled motor vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V304,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with pedestrian or animal"
+V305,"Driver of three-wheeled motor vehicle injured in collision with pedestrian or animal in traffic accident"
+V306,"Passenger in three-wheeled motor vehicle injured in collision with pedestrian or animal in traffic accident"
+V307,"Person on outside of three-wheeled motor vehicle injured in collision with pedestrian or animal in traffic accident"
+V309,"Unspecified occupant of three-wheeled motor vehicle injured in collision with pedestrian or animal in traffic accident"
+V31,"Occupant of three-wheeled motor vehicle injured in collision with pedal cycle"
+V310,"Driver of three-wheeled motor vehicle injured in collision with pedal cycle in nontraffic accident"
+V311,"Passenger in three-wheeled motor vehicle injured in collision with pedal cycle in nontraffic accident"
+V312,"Person on outside of three-wheeled motor vehicle injured in collision with pedal cycle in nontraffic accident"
+V313,"Unspecified occupant of three-wheeled motor vehicle injured in collision with pedal cycle in nontraffic accident"
+V314,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with pedal cycle"
+V315,"Driver of three-wheeled motor vehicle injured in collision with pedal cycle in traffic accident"
+V316,"Passenger in three-wheeled motor vehicle injured in collision with pedal cycle in traffic accident"
+V317,"Person on outside of three-wheeled motor vehicle injured in collision with pedal cycle in traffic accident"
+V319,"Unspecified occupant of three-wheeled motor vehicle injured in collision with pedal cycle in traffic accident"
+V32,"Occupant of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle"
+V320,"Driver of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V321,"Passenger in three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V322,"Person on outside of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V323,"Unspecified occupant of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V324,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle"
+V325,"Driver of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V326,"Passenger in three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V327,"Person on outside of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V329,"Unspecified occupant of three-wheeled motor vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V33,"Occupant of three-wheeled motor vehicle injured in collision with car, pick-up truck or van"
+V330,"Driver of three-wheeled motor vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V331,"Passenger in three-wheeled motor vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V332,"Person on outside of three-wheeled motor vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V333,"Unspecified occupant of three-wheeled motor vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V334,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with car, pick-up truck or van"
+V335,"Driver of three-wheeled motor vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V336,"Passenger in three-wheeled motor vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V337,"Person on outside of three-wheeled motor vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V339,"Unspecified occupant of three-wheeled motor vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V34,"Occupant of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus"
+V340,"Driver of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V341,"Passenger in three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V342,"Person on outside of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V343,"Unspecified occupant of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V344,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus"
+V345,"Driver of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V346,"Passenger in three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V347,"Person on outside of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V349,"Unspecified occupant of three-wheeled motor vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V35,"Occupant of three-wheeled motor vehicle injured in collision with railway train or railway vehicle"
+V350,"Driver of three-wheeled motor vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V351,"Passenger in three-wheeled motor vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V352,"Person on outside of three-wheeled motor vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V353,"Unspecified occupant of three-wheeled motor vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V354,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with railway train or railway vehicle"
+V355,"Driver of three-wheeled motor vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V356,"Passenger in three-wheeled motor vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V357,"Person on outside of three-wheeled motor vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V359,"Unspecified occupant of three-wheeled motor vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V36,"Occupant of three-wheeled motor vehicle injured in collision with other nonmotor vehicle"
+V360,"Driver of three-wheeled motor vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V361,"Passenger in three-wheeled motor vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V362,"Person on outside of three-wheeled motor vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V363,"Unspecified occupant of three-wheeled motor vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V364,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with other nonmotor vehicle"
+V365,"Driver of three-wheeled motor vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V366,"Passenger in three-wheeled motor vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V367,"Person on outside of three-wheeled motor vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V369,"Unspecified occupant of three-wheeled motor vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V37,"Occupant of three-wheeled motor vehicle injured in collision with fixed or stationary object"
+V370,"Driver of three-wheeled motor vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V371,"Passenger in three-wheeled motor vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V372,"Person on outside of three-wheeled motor vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V373,"Unspecified occupant of three-wheeled motor vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V374,"Person boarding or alighting a three-wheeled motor vehicle injured in collision with fixed or stationary object"
+V375,"Driver of three-wheeled motor vehicle injured in collision with fixed or stationary object in traffic accident"
+V376,"Passenger in three-wheeled motor vehicle injured in collision with fixed or stationary object in traffic accident"
+V377,"Person on outside of three-wheeled motor vehicle injured in collision with fixed or stationary object in traffic accident"
+V379,"Unspecified occupant of three-wheeled motor vehicle injured in collision with fixed or stationary object in traffic accident"
+V38,"Occupant of three-wheeled motor vehicle injured in noncollision transport accident"
+V380,"Driver of three-wheeled motor vehicle injured in noncollision transport accident in nontraffic accident"
+V381,"Passenger in three-wheeled motor vehicle injured in noncollision transport accident in nontraffic accident"
+V382,"Person on outside of three-wheeled motor vehicle injured in noncollision transport accident in nontraffic accident"
+V383,"Unspecified occupant of three-wheeled motor vehicle injured in noncollision transport accident in nontraffic accident"
+V384,"Person boarding or alighting a three-wheeled motor vehicle injured in noncollision transport accident"
+V385,"Driver of three-wheeled motor vehicle injured in noncollision transport accident in traffic accident"
+V386,"Passenger in three-wheeled motor vehicle injured in noncollision transport accident in traffic accident"
+V387,"Person on outside of three-wheeled motor vehicle injured in noncollision transport accident in traffic accident"
+V389,"Unspecified occupant of three-wheeled motor vehicle injured in noncollision transport accident in traffic accident"
+V39,"Occupant of three-wheeled motor vehicle injured in other and unspecified transport accidents"
+V390,"Driver of three-wheeled motor vehicle injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V3900,"Driver of three-wheeled motor vehicle injured in collision with unspecified motor vehicles in nontraffic accident"
+V3909,"Driver of three-wheeled motor vehicle injured in collision with other motor vehicles in nontraffic accident"
+V391,"Passenger in three-wheeled motor vehicle injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V3910,"Passenger in three-wheeled motor vehicle injured in collision with unspecified motor vehicles in nontraffic accident"
+V3919,"Passenger in three-wheeled motor vehicle injured in collision with other motor vehicles in nontraffic accident"
+V392,"Unspecified occupant of three-wheeled motor vehicle injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V3920,"Unspecified occupant of three-wheeled motor vehicle injured in collision with unspecified motor vehicles in nontraffic accident"
+V3929,"Unspecified occupant of three-wheeled motor vehicle injured in collision with other motor vehicles in nontraffic accident"
+V393,"Occupant (driver) (passenger) of three-wheeled motor vehicle injured in unspecified nontraffic accident"
+V394,"Driver of three-wheeled motor vehicle injured in collision with other and unspecified motor vehicles in traffic accident"
+V3940,"Driver of three-wheeled motor vehicle injured in collision with unspecified motor vehicles in traffic accident"
+V3949,"Driver of three-wheeled motor vehicle injured in collision with other motor vehicles in traffic accident"
+V395,"Passenger in three-wheeled motor vehicle injured in collision with other and unspecified motor vehicles in traffic accident"
+V3950,"Passenger in three-wheeled motor vehicle injured in collision with unspecified motor vehicles in traffic accident"
+V3959,"Passenger in three-wheeled motor vehicle injured in collision with other motor vehicles in traffic accident"
+V396,"Unspecified occupant of three-wheeled motor vehicle injured in collision with other and unspecified motor vehicles in traffic accident"
+V3960,"Unspecified occupant of three-wheeled motor vehicle injured in collision with unspecified motor vehicles in traffic accident"
+V3969,"Unspecified occupant of three-wheeled motor vehicle injured in collision with other motor vehicles in traffic accident"
+V398,"Occupant (driver) (passenger) of three-wheeled motor vehicle injured in other specified transport accidents"
+V3981,"Occupant (driver) (passenger) of three-wheeled motor vehicle injured in transport accident with military vehicle"
+V3989,"Occupant (driver) (passenger) of three-wheeled motor vehicle injured in other specified transport accidents"
+V399,"Occupant (driver) (passenger) of three-wheeled motor vehicle injured in unspecified traffic accident"
+V40,"Car occupant injured in collision with pedestrian or animal"
+V400,"Car driver injured in collision with pedestrian or animal in nontraffic accident"
+V401,"Car passenger injured in collision with pedestrian or animal in nontraffic accident"
+V402,"Person on outside of car injured in collision with pedestrian or animal in nontraffic accident"
+V403,"Unspecified car occupant injured in collision with pedestrian or animal in nontraffic accident"
+V404,"Person boarding or alighting a car injured in collision with pedestrian or animal"
+V405,"Car driver injured in collision with pedestrian or animal in traffic accident"
+V406,"Car passenger injured in collision with pedestrian or animal in traffic accident"
+V407,"Person on outside of car injured in collision with pedestrian or animal in traffic accident"
+V409,"Unspecified car occupant injured in collision with pedestrian or animal in traffic accident"
+V41,"Car occupant injured in collision with pedal cycle"
+V410,"Car driver injured in collision with pedal cycle in nontraffic accident"
+V411,"Car passenger injured in collision with pedal cycle in nontraffic accident"
+V412,"Person on outside of car injured in collision with pedal cycle in nontraffic accident"
+V413,"Unspecified car occupant injured in collision with pedal cycle in nontraffic accident"
+V414,"Person boarding or alighting a car injured in collision with pedal cycle"
+V415,"Car driver injured in collision with pedal cycle in traffic accident"
+V416,"Car passenger injured in collision with pedal cycle in traffic accident"
+V417,"Person on outside of car injured in collision with pedal cycle in traffic accident"
+V419,"Unspecified car occupant injured in collision with pedal cycle in traffic accident"
+V42,"Car occupant injured in collision with two- or three-wheeled motor vehicle"
+V420,"Car driver injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V421,"Car passenger injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V422,"Person on outside of car injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V423,"Unspecified car occupant injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V424,"Person boarding or alighting a car injured in collision with two- or three-wheeled motor vehicle"
+V425,"Car driver injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V426,"Car passenger injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V427,"Person on outside of car injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V429,"Unspecified car occupant injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V43,"Car occupant injured in collision with car, pick-up truck or van"
+V430,"Car driver injured in collision with car, pick-up truck or van in nontraffic accident"
+V4301,"Car driver injured in collision with sport utility vehicle in nontraffic accident"
+V4302,"Car driver injured in collision with other type car in nontraffic accident"
+V4303,"Car driver injured in collision with pick-up truck in nontraffic accident"
+V4304,"Car driver injured in collision with van in nontraffic accident"
+V431,"Car passenger injured in collision with car, pick-up truck or van in nontraffic accident"
+V4311,"Car passenger injured in collision with sport utility vehicle in nontraffic accident"
+V4312,"Car passenger injured in collision with other type car in nontraffic accident"
+V4313,"Car passenger injured in collision with pick-up in nontraffic accident"
+V4314,"Car passenger injured in collision with van in nontraffic accident"
+V432,"Person on outside of car injured in collision with car, pick-up truck or van in nontraffic accident"
+V4321,"Person on outside of car injured in collision with sport utility vehicle in nontraffic accident"
+V4322,"Person on outside of car injured in collision with other type car in nontraffic accident"
+V4323,"Person on outside of car injured in collision with pick-up truck in nontraffic accident"
+V4324,"Person on outside of car injured in collision with van in nontraffic accident"
+V433,"Unspecified car occupant injured in collision with car, pick-up truck or van in nontraffic accident"
+V4331,"Unspecified car occupant injured in collision with sport utility vehicle in nontraffic accident"
+V4332,"Unspecified car occupant injured in collision with other type car in nontraffic accident"
+V4333,"Unspecified car occupant injured in collision with pick-up truck in nontraffic accident"
+V4334,"Unspecified car occupant injured in collision with van in nontraffic accident"
+V434,"Person boarding or alighting a car injured in collision with car, pick-up truck or van"
+V4341,"Person boarding or alighting a car injured in collision with sport utility vehicle"
+V4342,"Person boarding or alighting a car injured in collision with other type car"
+V4343,"Person boarding or alighting a car injured in collision with pick-up truck"
+V4344,"Person boarding or alighting a car injured in collision with van"
+V435,"Car driver injured in collision with car, pick-up truck or van in traffic accident"
+V4351,"Car driver injured in collision with sport utility vehicle in traffic accident"
+V4352,"Car driver injured in collision with other type car in traffic accident"
+V4353,"Car driver injured in collision with pick-up truck in traffic accident"
+V4354,"Car driver injured in collision with van in traffic accident"
+V436,"Car passenger injured in collision with car, pick-up truck or van in traffic accident"
+V4361,"Car passenger injured in collision with sport utility vehicle in traffic accident"
+V4362,"Car passenger injured in collision with other type car in traffic accident"
+V4363,"Car passenger injured in collision with pick-up truck in traffic accident"
+V4364,"Car passenger injured in collision with van in traffic accident"
+V437,"Person on outside of car injured in collision with car, pick-up truck or van in traffic accident"
+V4371,"Person on outside of car injured in collision with sport utility vehicle in traffic accident"
+V4372,"Person on outside of car injured in collision with other type car in traffic accident"
+V4373,"Person on outside of car injured in collision with pick-up truck in traffic accident"
+V4374,"Person on outside of car injured in collision with van in traffic accident"
+V439,"Unspecified car occupant injured in collision with car, pick-up truck or van in traffic accident"
+V4391,"Unspecified car occupant injured in collision with sport utility vehicle in traffic accident"
+V4392,"Unspecified car occupant injured in collision with other type car in traffic accident"
+V4393,"Unspecified car occupant injured in collision with pick-up truck in traffic accident"
+V4394,"Unspecified car occupant injured in collision with van in traffic accident"
+V44,"Car occupant injured in collision with heavy transport vehicle or bus"
+V440,"Car driver injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V441,"Car passenger injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V442,"Person on outside of car injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V443,"Unspecified car occupant injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V444,"Person boarding or alighting a car injured in collision with heavy transport vehicle or bus"
+V445,"Car driver injured in collision with heavy transport vehicle or bus in traffic accident"
+V446,"Car passenger injured in collision with heavy transport vehicle or bus in traffic accident"
+V447,"Person on outside of car injured in collision with heavy transport vehicle or bus in traffic accident"
+V449,"Unspecified car occupant injured in collision with heavy transport vehicle or bus in traffic accident"
+V45,"Car occupant injured in collision with railway train or railway vehicle"
+V450,"Car driver injured in collision with railway train or railway vehicle in nontraffic accident"
+V451,"Car passenger injured in collision with railway train or railway vehicle in nontraffic accident"
+V452,"Person on outside of car injured in collision with railway train or railway vehicle in nontraffic accident"
+V453,"Unspecified car occupant injured in collision with railway train or railway vehicle in nontraffic accident"
+V454,"Person boarding or alighting a car injured in collision with railway train or railway vehicle"
+V455,"Car driver injured in collision with railway train or railway vehicle in traffic accident"
+V456,"Car passenger injured in collision with railway train or railway vehicle in traffic accident"
+V457,"Person on outside of car injured in collision with railway train or railway vehicle in traffic accident"
+V459,"Unspecified car occupant injured in collision with railway train or railway vehicle in traffic accident"
+V46,"Car occupant injured in collision with other nonmotor vehicle"
+V460,"Car driver injured in collision with other nonmotor vehicle in nontraffic accident"
+V461,"Car passenger injured in collision with other nonmotor vehicle in nontraffic accident"
+V462,"Person on outside of car injured in collision with other nonmotor vehicle in nontraffic accident"
+V463,"Unspecified car occupant injured in collision with other nonmotor vehicle in nontraffic accident"
+V464,"Person boarding or alighting a car injured in collision with other nonmotor vehicle"
+V465,"Car driver injured in collision with other nonmotor vehicle in traffic accident"
+V466,"Car passenger injured in collision with other nonmotor vehicle in traffic accident"
+V467,"Person on outside of car injured in collision with other nonmotor vehicle in traffic accident"
+V469,"Unspecified car occupant injured in collision with other nonmotor vehicle in traffic accident"
+V47,"Car occupant injured in collision with fixed or stationary object"
+V470,"Car driver injured in collision with fixed or stationary object in nontraffic accident"
+V471,"Car passenger injured in collision with fixed or stationary object in nontraffic accident"
+V472,"Person on outside of car injured in collision with fixed or stationary object in nontraffic accident"
+V473,"Unspecified car occupant injured in collision with fixed or stationary object in nontraffic accident"
+V474,"Person boarding or alighting a car injured in collision with fixed or stationary object"
+V475,"Car driver injured in collision with fixed or stationary object in traffic accident"
+V476,"Car passenger injured in collision with fixed or stationary object in traffic accident"
+V477,"Person on outside of car injured in collision with fixed or stationary object in traffic accident"
+V479,"Unspecified car occupant injured in collision with fixed or stationary object in traffic accident"
+V48,"Car occupant injured in noncollision transport accident"
+V480,"Car driver injured in noncollision transport accident in nontraffic accident"
+V481,"Car passenger injured in noncollision transport accident in nontraffic accident"
+V482,"Person on outside of car injured in noncollision transport accident in nontraffic accident"
+V483,"Unspecified car occupant injured in noncollision transport accident in nontraffic accident"
+V484,"Person boarding or alighting a car injured in noncollision transport accident"
+V485,"Car driver injured in noncollision transport accident in traffic accident"
+V486,"Car passenger injured in noncollision transport accident in traffic accident"
+V487,"Person on outside of car injured in noncollision transport accident in traffic accident"
+V489,"Unspecified car occupant injured in noncollision transport accident in traffic accident"
+V49,"Car occupant injured in other and unspecified transport accidents"
+V490,"Driver injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V4900,"Driver injured in collision with unspecified motor vehicles in nontraffic accident"
+V4909,"Driver injured in collision with other motor vehicles in nontraffic accident"
+V491,"Passenger injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V4910,"Passenger injured in collision with unspecified motor vehicles in nontraffic accident"
+V4919,"Passenger injured in collision with other motor vehicles in nontraffic accident"
+V492,"Unspecified car occupant injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V4920,"Unspecified car occupant injured in collision with unspecified motor vehicles in nontraffic accident"
+V4929,"Unspecified car occupant injured in collision with other motor vehicles in nontraffic accident"
+V493,"Car occupant (driver) (passenger) injured in unspecified nontraffic accident"
+V494,"Driver injured in collision with other and unspecified motor vehicles in traffic accident"
+V4940,"Driver injured in collision with unspecified motor vehicles in traffic accident"
+V4949,"Driver injured in collision with other motor vehicles in traffic accident"
+V495,"Passenger injured in collision with other and unspecified motor vehicles in traffic accident"
+V4950,"Passenger injured in collision with unspecified motor vehicles in traffic accident"
+V4959,"Passenger injured in collision with other motor vehicles in traffic accident"
+V496,"Unspecified car occupant injured in collision with other and unspecified motor vehicles in traffic accident"
+V4960,"Unspecified car occupant injured in collision with unspecified motor vehicles in traffic accident"
+V4969,"Unspecified car occupant injured in collision with other motor vehicles in traffic accident"
+V498,"Car occupant (driver) (passenger) injured in other specified transport accidents"
+V4981,"Car occupant (driver) (passenger) injured in transport accident with military vehicle"
+V4988,"Car occupant (driver) (passenger) injured in other specified transport accidents"
+V499,"Car occupant (driver) (passenger) injured in unspecified traffic accident"
+V50,"Occupant of pick-up truck or van injured in collision with pedestrian or animal"
+V500,"Driver of pick-up truck or van injured in collision with pedestrian or animal in nontraffic accident"
+V501,"Passenger in pick-up truck or van injured in collision with pedestrian or animal in nontraffic accident"
+V502,"Person on outside of pick-up truck or van injured in collision with pedestrian or animal in nontraffic accident"
+V503,"Unspecified occupant of pick-up truck or van injured in collision with pedestrian or animal in nontraffic accident"
+V504,"Person boarding or alighting a pick-up truck or van injured in collision with pedestrian or animal"
+V505,"Driver of pick-up truck or van injured in collision with pedestrian or animal in traffic accident"
+V506,"Passenger in pick-up truck or van injured in collision with pedestrian or animal in traffic accident"
+V507,"Person on outside of pick-up truck or van injured in collision with pedestrian or animal in traffic accident"
+V509,"Unspecified occupant of pick-up truck or van injured in collision with pedestrian or animal in traffic accident"
+V51,"Occupant of pick-up truck or van injured in collision with pedal cycle"
+V510,"Driver of pick-up truck or van injured in collision with pedal cycle in nontraffic accident"
+V511,"Passenger in pick-up truck or van injured in collision with pedal cycle in nontraffic accident"
+V512,"Person on outside of pick-up truck or van injured in collision with pedal cycle in nontraffic accident"
+V513,"Unspecified occupant of pick-up truck or van injured in collision with pedal cycle in nontraffic accident"
+V514,"Person boarding or alighting a pick-up truck or van injured in collision with pedal cycle"
+V515,"Driver of pick-up truck or van injured in collision with pedal cycle in traffic accident"
+V516,"Passenger in pick-up truck or van injured in collision with pedal cycle in traffic accident"
+V517,"Person on outside of pick-up truck or van injured in collision with pedal cycle in traffic accident"
+V519,"Unspecified occupant of pick-up truck or van injured in collision with pedal cycle in traffic accident"
+V52,"Occupant of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle"
+V520,"Driver of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V521,"Passenger in pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V522,"Person on outside of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V523,"Unspecified occupant of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V524,"Person boarding or alighting a pick-up truck or van injured in collision with two- or three-wheeled motor vehicle"
+V525,"Driver of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V526,"Passenger in pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V527,"Person on outside of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V529,"Unspecified occupant of pick-up truck or van injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V53,"Occupant of pick-up truck or van injured in collision with car, pick-up truck or van"
+V530,"Driver of pick-up truck or van injured in collision with car, pick-up truck or van in nontraffic accident"
+V531,"Passenger in pick-up truck or van injured in collision with car, pick-up truck or van in nontraffic accident"
+V532,"Person on outside of pick-up truck or van injured in collision with car, pick-up truck or van in nontraffic accident"
+V533,"Unspecified occupant of pick-up truck or van injured in collision with car, pick-up truck or van in nontraffic accident"
+V534,"Person boarding or alighting a pick-up truck or van injured in collision with car, pick-up truck or van"
+V535,"Driver of pick-up truck or van injured in collision with car, pick-up truck or van in traffic accident"
+V536,"Passenger in pick-up truck or van injured in collision with car, pick-up truck or van in traffic accident"
+V537,"Person on outside of pick-up truck or van injured in collision with car, pick-up truck or van in traffic accident"
+V539,"Unspecified occupant of pick-up truck or van injured in collision with car, pick-up truck or van in traffic accident"
+V54,"Occupant of pick-up truck or van injured in collision with heavy transport vehicle or bus"
+V540,"Driver of pick-up truck or van injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V541,"Passenger in pick-up truck or van injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V542,"Person on outside of pick-up truck or van injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V543,"Unspecified occupant of pick-up truck or van injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V544,"Person boarding or alighting a pick-up truck or van injured in collision with heavy transport vehicle or bus"
+V545,"Driver of pick-up truck or van injured in collision with heavy transport vehicle or bus in traffic accident"
+V546,"Passenger in pick-up truck or van injured in collision with heavy transport vehicle or bus in traffic accident"
+V547,"Person on outside of pick-up truck or van injured in collision with heavy transport vehicle or bus in traffic accident"
+V549,"Unspecified occupant of pick-up truck or van injured in collision with heavy transport vehicle or bus in traffic accident"
+V55,"Occupant of pick-up truck or van injured in collision with railway train or railway vehicle"
+V550,"Driver of pick-up truck or van injured in collision with railway train or railway vehicle in nontraffic accident"
+V551,"Passenger in pick-up truck or van injured in collision with railway train or railway vehicle in nontraffic accident"
+V552,"Person on outside of pick-up truck or van injured in collision with railway train or railway vehicle in nontraffic accident"
+V553,"Unspecified occupant of pick-up truck or van injured in collision with railway train or railway vehicle in nontraffic accident"
+V554,"Person boarding or alighting a pick-up truck or van injured in collision with railway train or railway vehicle"
+V555,"Driver of pick-up truck or van injured in collision with railway train or railway vehicle in traffic accident"
+V556,"Passenger in pick-up truck or van injured in collision with railway train or railway vehicle in traffic accident"
+V557,"Person on outside of pick-up truck or van injured in collision with railway train or railway vehicle in traffic accident"
+V559,"Unspecified occupant of pick-up truck or van injured in collision with railway train or railway vehicle in traffic accident"
+V56,"Occupant of pick-up truck or van injured in collision with other nonmotor vehicle"
+V560,"Driver of pick-up truck or van injured in collision with other nonmotor vehicle in nontraffic accident"
+V561,"Passenger in pick-up truck or van injured in collision with other nonmotor vehicle in nontraffic accident"
+V562,"Person on outside of pick-up truck or van injured in collision with other nonmotor vehicle in nontraffic accident"
+V563,"Unspecified occupant of pick-up truck or van injured in collision with other nonmotor vehicle in nontraffic accident"
+V564,"Person boarding or alighting a pick-up truck or van injured in collision with other nonmotor vehicle"
+V565,"Driver of pick-up truck or van injured in collision with other nonmotor vehicle in traffic accident"
+V566,"Passenger in pick-up truck or van injured in collision with other nonmotor vehicle in traffic accident"
+V567,"Person on outside of pick-up truck or van injured in collision with other nonmotor vehicle in traffic accident"
+V569,"Unspecified occupant of pick-up truck or van injured in collision with other nonmotor vehicle in traffic accident"
+V57,"Occupant of pick-up truck or van injured in collision with fixed or stationary object"
+V570,"Driver of pick-up truck or van injured in collision with fixed or stationary object in nontraffic accident"
+V571,"Passenger in pick-up truck or van injured in collision with fixed or stationary object in nontraffic accident"
+V572,"Person on outside of pick-up truck or van injured in collision with fixed or stationary object in nontraffic accident"
+V573,"Unspecified occupant of pick-up truck or van injured in collision with fixed or stationary object in nontraffic accident"
+V574,"Person boarding or alighting a pick-up truck or van injured in collision with fixed or stationary object"
+V575,"Driver of pick-up truck or van injured in collision with fixed or stationary object in traffic accident"
+V576,"Passenger in pick-up truck or van injured in collision with fixed or stationary object in traffic accident"
+V577,"Person on outside of pick-up truck or van injured in collision with fixed or stationary object in traffic accident"
+V579,"Unspecified occupant of pick-up truck or van injured in collision with fixed or stationary object in traffic accident"
+V58,"Occupant of pick-up truck or van injured in noncollision transport accident"
+V580,"Driver of pick-up truck or van injured in noncollision transport accident in nontraffic accident"
+V581,"Passenger in pick-up truck or van injured in noncollision transport accident in nontraffic accident"
+V582,"Person on outside of pick-up truck or van injured in noncollision transport accident in nontraffic accident"
+V583,"Unspecified occupant of pick-up truck or van injured in noncollision transport accident in nontraffic accident"
+V584,"Person boarding or alighting a pick-up truck or van injured in noncollision transport accident"
+V585,"Driver of pick-up truck or van injured in noncollision transport accident in traffic accident"
+V586,"Passenger in pick-up truck or van injured in noncollision transport accident in traffic accident"
+V587,"Person on outside of pick-up truck or van injured in noncollision transport accident in traffic accident"
+V589,"Unspecified occupant of pick-up truck or van injured in noncollision transport accident in traffic accident"
+V59,"Occupant of pick-up truck or van injured in other and unspecified transport accidents"
+V590,"Driver of pick-up truck or van injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V5900,"Driver of pick-up truck or van injured in collision with unspecified motor vehicles in nontraffic accident"
+V5909,"Driver of pick-up truck or van injured in collision with other motor vehicles in nontraffic accident"
+V591,"Passenger in pick-up truck or van injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V5910,"Passenger in pick-up truck or van injured in collision with unspecified motor vehicles in nontraffic accident"
+V5919,"Passenger in pick-up truck or van injured in collision with other motor vehicles in nontraffic accident"
+V592,"Unspecified occupant of pick-up truck or van injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V5920,"Unspecified occupant of pick-up truck or van injured in collision with unspecified motor vehicles in nontraffic accident"
+V5929,"Unspecified occupant of pick-up truck or van injured in collision with other motor vehicles in nontraffic accident"
+V593,"Occupant (driver) (passenger) of pick-up truck or van injured in unspecified nontraffic accident"
+V594,"Driver of pick-up truck or van injured in collision with other and unspecified motor vehicles in traffic accident"
+V5940,"Driver of pick-up truck or van injured in collision with unspecified motor vehicles in traffic accident"
+V5949,"Driver of pick-up truck or van injured in collision with other motor vehicles in traffic accident"
+V595,"Passenger in pick-up truck or van injured in collision with other and unspecified motor vehicles in traffic accident"
+V5950,"Passenger in pick-up truck or van injured in collision with unspecified motor vehicles in traffic accident"
+V5959,"Passenger in pick-up truck or van injured in collision with other motor vehicles in traffic accident"
+V596,"Unspecified occupant of pick-up truck or van injured in collision with other and unspecified motor vehicles in traffic accident"
+V5960,"Unspecified occupant of pick-up truck or van injured in collision with unspecified motor vehicles in traffic accident"
+V5969,"Unspecified occupant of pick-up truck or van injured in collision with other motor vehicles in traffic accident"
+V598,"Occupant (driver) (passenger) of pick-up truck or van injured in other specified transport accidents"
+V5981,"Occupant (driver) (passenger) of pick-up truck or van injured in transport accident with military vehicle"
+V5988,"Occupant (driver) (passenger) of pick-up truck or van injured in other specified transport accidents"
+V599,"Occupant (driver) (passenger) of pick-up truck or van injured in unspecified traffic accident"
+V60,"Occupant of heavy transport vehicle injured in collision with pedestrian or animal"
+V600,"Driver of heavy transport vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V601,"Passenger in heavy transport vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V602,"Person on outside of heavy transport vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V603,"Unspecified occupant of heavy transport vehicle injured in collision with pedestrian or animal in nontraffic accident"
+V604,"Person boarding or alighting a heavy transport vehicle injured in collision with pedestrian or animal"
+V605,"Driver of heavy transport vehicle injured in collision with pedestrian or animal in traffic accident"
+V606,"Passenger in heavy transport vehicle injured in collision with pedestrian or animal in traffic accident"
+V607,"Person on outside of heavy transport vehicle injured in collision with pedestrian or animal in traffic accident"
+V609,"Unspecified occupant of heavy transport vehicle injured in collision with pedestrian or animal in traffic accident"
+V61,"Occupant of heavy transport vehicle injured in collision with pedal cycle"
+V610,"Driver of heavy transport vehicle injured in collision with pedal cycle in nontraffic accident"
+V611,"Passenger in heavy transport vehicle injured in collision with pedal cycle in nontraffic accident"
+V612,"Person on outside of heavy transport vehicle injured in collision with pedal cycle in nontraffic accident"
+V613,"Unspecified occupant of heavy transport vehicle injured in collision with pedal cycle in nontraffic accident"
+V614,"Person boarding or alighting a heavy transport vehicle injured in collision with pedal cycle while boarding or alighting"
+V615,"Driver of heavy transport vehicle injured in collision with pedal cycle in traffic accident"
+V616,"Passenger in heavy transport vehicle injured in collision with pedal cycle in traffic accident"
+V617,"Person on outside of heavy transport vehicle injured in collision with pedal cycle in traffic accident"
+V619,"Unspecified occupant of heavy transport vehicle injured in collision with pedal cycle in traffic accident"
+V62,"Occupant of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle"
+V620,"Driver of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V621,"Passenger in heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V622,"Person on outside of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V623,"Unspecified occupant of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V624,"Person boarding or alighting a heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle"
+V625,"Driver of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V626,"Passenger in heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V627,"Person on outside of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V629,"Unspecified occupant of heavy transport vehicle injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V63,"Occupant of heavy transport vehicle injured in collision with car, pick-up truck or van"
+V630,"Driver of heavy transport vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V631,"Passenger in heavy transport vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V632,"Person on outside of heavy transport vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V633,"Unspecified occupant of heavy transport vehicle injured in collision with car, pick-up truck or van in nontraffic accident"
+V634,"Person boarding or alighting a heavy transport vehicle injured in collision with car, pick-up truck or van"
+V635,"Driver of heavy transport vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V636,"Passenger in heavy transport vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V637,"Person on outside of heavy transport vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V639,"Unspecified occupant of heavy transport vehicle injured in collision with car, pick-up truck or van in traffic accident"
+V64,"Occupant of heavy transport vehicle injured in collision with heavy transport vehicle or bus"
+V640,"Driver of heavy transport vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V641,"Passenger in heavy transport vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V642,"Person on outside of heavy transport vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V643,"Unspecified occupant of heavy transport vehicle injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V644,"Person boarding or alighting a heavy transport vehicle injured in collision with heavy transport vehicle or bus while boarding or alighting"
+V645,"Driver of heavy transport vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V646,"Passenger in heavy transport vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V647,"Person on outside of heavy transport vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V649,"Unspecified occupant of heavy transport vehicle injured in collision with heavy transport vehicle or bus in traffic accident"
+V65,"Occupant of heavy transport vehicle injured in collision with railway train or railway vehicle"
+V650,"Driver of heavy transport vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V651,"Passenger in heavy transport vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V652,"Person on outside of heavy transport vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V653,"Unspecified occupant of heavy transport vehicle injured in collision with railway train or railway vehicle in nontraffic accident"
+V654,"Person boarding or alighting a heavy transport vehicle injured in collision with railway train or railway vehicle"
+V655,"Driver of heavy transport vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V656,"Passenger in heavy transport vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V657,"Person on outside of heavy transport vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V659,"Unspecified occupant of heavy transport vehicle injured in collision with railway train or railway vehicle in traffic accident"
+V66,"Occupant of heavy transport vehicle injured in collision with other nonmotor vehicle"
+V660,"Driver of heavy transport vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V661,"Passenger in heavy transport vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V662,"Person on outside of heavy transport vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V663,"Unspecified occupant of heavy transport vehicle injured in collision with other nonmotor vehicle in nontraffic accident"
+V664,"Person boarding or alighting a heavy transport vehicle injured in collision with other nonmotor vehicle"
+V665,"Driver of heavy transport vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V666,"Passenger in heavy transport vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V667,"Person on outside of heavy transport vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V669,"Unspecified occupant of heavy transport vehicle injured in collision with other nonmotor vehicle in traffic accident"
+V67,"Occupant of heavy transport vehicle injured in collision with fixed or stationary object"
+V670,"Driver of heavy transport vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V671,"Passenger in heavy transport vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V672,"Person on outside of heavy transport vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V673,"Unspecified occupant of heavy transport vehicle injured in collision with fixed or stationary object in nontraffic accident"
+V674,"Person boarding or alighting a heavy transport vehicle injured in collision with fixed or stationary object"
+V675,"Driver of heavy transport vehicle injured in collision with fixed or stationary object in traffic accident"
+V676,"Passenger in heavy transport vehicle injured in collision with fixed or stationary object in traffic accident"
+V677,"Person on outside of heavy transport vehicle injured in collision with fixed or stationary object in traffic accident"
+V679,"Unspecified occupant of heavy transport vehicle injured in collision with fixed or stationary object in traffic accident"
+V68,"Occupant of heavy transport vehicle injured in noncollision transport accident"
+V680,"Driver of heavy transport vehicle injured in noncollision transport accident in nontraffic accident"
+V681,"Passenger in heavy transport vehicle injured in noncollision transport accident in nontraffic accident"
+V682,"Person on outside of heavy transport vehicle injured in noncollision transport accident in nontraffic accident"
+V683,"Unspecified occupant of heavy transport vehicle injured in noncollision transport accident in nontraffic accident"
+V684,"Person boarding or alighting a heavy transport vehicle injured in noncollision transport accident"
+V685,"Driver of heavy transport vehicle injured in noncollision transport accident in traffic accident"
+V686,"Passenger in heavy transport vehicle injured in noncollision transport accident in traffic accident"
+V687,"Person on outside of heavy transport vehicle injured in noncollision transport accident in traffic accident"
+V689,"Unspecified occupant of heavy transport vehicle injured in noncollision transport accident in traffic accident"
+V69,"Occupant of heavy transport vehicle injured in other and unspecified transport accidents"
+V690,"Driver of heavy transport vehicle injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V6900,"Driver of heavy transport vehicle injured in collision with unspecified motor vehicles in nontraffic accident"
+V6909,"Driver of heavy transport vehicle injured in collision with other motor vehicles in nontraffic accident"
+V691,"Passenger in heavy transport vehicle injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V6910,"Passenger in heavy transport vehicle injured in collision with unspecified motor vehicles in nontraffic accident"
+V6919,"Passenger in heavy transport vehicle injured in collision with other motor vehicles in nontraffic accident"
+V692,"Unspecified occupant of heavy transport vehicle injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V6920,"Unspecified occupant of heavy transport vehicle injured in collision with unspecified motor vehicles in nontraffic accident"
+V6929,"Unspecified occupant of heavy transport vehicle injured in collision with other motor vehicles in nontraffic accident"
+V693,"Occupant (driver) (passenger) of heavy transport vehicle injured in unspecified nontraffic accident"
+V694,"Driver of heavy transport vehicle injured in collision with other and unspecified motor vehicles in traffic accident"
+V6940,"Driver of heavy transport vehicle injured in collision with unspecified motor vehicles in traffic accident"
+V6949,"Driver of heavy transport vehicle injured in collision with other motor vehicles in traffic accident"
+V695,"Passenger in heavy transport vehicle injured in collision with other and unspecified motor vehicles in traffic accident"
+V6950,"Passenger in heavy transport vehicle injured in collision with unspecified motor vehicles in traffic accident"
+V6959,"Passenger in heavy transport vehicle injured in collision with other motor vehicles in traffic accident"
+V696,"Unspecified occupant of heavy transport vehicle injured in collision with other and unspecified motor vehicles in traffic accident"
+V6960,"Unspecified occupant of heavy transport vehicle injured in collision with unspecified motor vehicles in traffic accident"
+V6969,"Unspecified occupant of heavy transport vehicle injured in collision with other motor vehicles in traffic accident"
+V698,"Occupant (driver) (passenger) of heavy transport vehicle injured in other specified transport accidents"
+V6981,"Occupant (driver) (passenger) of heavy transport vehicle injured in transport accidents with military vehicle"
+V6988,"Occupant (driver) (passenger) of heavy transport vehicle injured in other specified transport accidents"
+V699,"Occupant (driver) (passenger) of heavy transport vehicle injured in unspecified traffic accident"
+V70,"Bus occupant injured in collision with pedestrian or animal"
+V700,"Driver of bus injured in collision with pedestrian or animal in nontraffic accident"
+V701,"Passenger on bus injured in collision with pedestrian or animal in nontraffic accident"
+V702,"Person on outside of bus injured in collision with pedestrian or animal in nontraffic accident"
+V703,"Unspecified occupant of bus injured in collision with pedestrian or animal in nontraffic accident"
+V704,"Person boarding or alighting from bus injured in collision with pedestrian or animal"
+V705,"Driver of bus injured in collision with pedestrian or animal in traffic accident"
+V706,"Passenger on bus injured in collision with pedestrian or animal in traffic accident"
+V707,"Person on outside of bus injured in collision with pedestrian or animal in traffic accident"
+V709,"Unspecified occupant of bus injured in collision with pedestrian or animal in traffic accident"
+V71,"Bus occupant injured in collision with pedal cycle"
+V710,"Driver of bus injured in collision with pedal cycle in nontraffic accident"
+V711,"Passenger on bus injured in collision with pedal cycle in nontraffic accident"
+V712,"Person on outside of bus injured in collision with pedal cycle in nontraffic accident"
+V713,"Unspecified occupant of bus injured in collision with pedal cycle in nontraffic accident"
+V714,"Person boarding or alighting from bus injured in collision with pedal cycle"
+V715,"Driver of bus injured in collision with pedal cycle in traffic accident"
+V716,"Passenger on bus injured in collision with pedal cycle in traffic accident"
+V717,"Person on outside of bus injured in collision with pedal cycle in traffic accident"
+V719,"Unspecified occupant of bus injured in collision with pedal cycle in traffic accident"
+V72,"Bus occupant injured in collision with two- or three-wheeled motor vehicle"
+V720,"Driver of bus injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V721,"Passenger on bus injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V722,"Person on outside of bus injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V723,"Unspecified occupant of bus injured in collision with two- or three-wheeled motor vehicle in nontraffic accident"
+V724,"Person boarding or alighting from bus injured in collision with two- or three-wheeled motor vehicle"
+V725,"Driver of bus injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V726,"Passenger on bus injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V727,"Person on outside of bus injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V729,"Unspecified occupant of bus injured in collision with two- or three-wheeled motor vehicle in traffic accident"
+V73,"Bus occupant injured in collision with car, pick-up truck or van"
+V730,"Driver of bus injured in collision with car, pick-up truck or van in nontraffic accident"
+V731,"Passenger on bus injured in collision with car, pick-up truck or van in nontraffic accident"
+V732,"Person on outside of bus injured in collision with car, pick-up truck or van in nontraffic accident"
+V733,"Unspecified occupant of bus injured in collision with car, pick-up truck or van in nontraffic accident"
+V734,"Person boarding or alighting from bus injured in collision with car, pick-up truck or van"
+V735,"Driver of bus injured in collision with car, pick-up truck or van in traffic accident"
+V736,"Passenger on bus injured in collision with car, pick-up truck or van in traffic accident"
+V737,"Person on outside of bus injured in collision with car, pick-up truck or van in traffic accident"
+V739,"Unspecified occupant of bus injured in collision with car, pick-up truck or van in traffic accident"
+V74,"Bus occupant injured in collision with heavy transport vehicle or bus"
+V740,"Driver of bus injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V741,"Passenger on bus injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V742,"Person on outside of bus injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V743,"Unspecified occupant of bus injured in collision with heavy transport vehicle or bus in nontraffic accident"
+V744,"Person boarding or alighting from bus injured in collision with heavy transport vehicle or bus"
+V745,"Driver of bus injured in collision with heavy transport vehicle or bus in traffic accident"
+V746,"Passenger on bus injured in collision with heavy transport vehicle or bus in traffic accident"
+V747,"Person on outside of bus injured in collision with heavy transport vehicle or bus in traffic accident"
+V749,"Unspecified occupant of bus injured in collision with heavy transport vehicle or bus in traffic accident"
+V75,"Bus occupant injured in collision with railway train or railway vehicle"
+V750,"Driver of bus injured in collision with railway train or railway vehicle in nontraffic accident"
+V751,"Passenger on bus injured in collision with railway train or railway vehicle in nontraffic accident"
+V752,"Person on outside of bus injured in collision with railway train or railway vehicle in nontraffic accident"
+V753,"Unspecified occupant of bus injured in collision with railway train or railway vehicle in nontraffic accident"
+V754,"Person boarding or alighting from bus injured in collision with railway train or railway vehicle"
+V755,"Driver of bus injured in collision with railway train or railway vehicle in traffic accident"
+V756,"Passenger on bus injured in collision with railway train or railway vehicle in traffic accident"
+V757,"Person on outside of bus injured in collision with railway train or railway vehicle in traffic accident"
+V759,"Unspecified occupant of bus injured in collision with railway train or railway vehicle in traffic accident"
+V76,"Bus occupant injured in collision with other nonmotor vehicle"
+V760,"Driver of bus injured in collision with other nonmotor vehicle in nontraffic accident"
+V761,"Passenger on bus injured in collision with other nonmotor vehicle in nontraffic accident"
+V762,"Person on outside of bus injured in collision with other nonmotor vehicle in nontraffic accident"
+V763,"Unspecified occupant of bus injured in collision with other nonmotor vehicle in nontraffic accident"
+V764,"Person boarding or alighting from bus injured in collision with other nonmotor vehicle"
+V765,"Driver of bus injured in collision with other nonmotor vehicle in traffic accident"
+V766,"Passenger on bus injured in collision with other nonmotor vehicle in traffic accident"
+V767,"Person on outside of bus injured in collision with other nonmotor vehicle in traffic accident"
+V769,"Unspecified occupant of bus injured in collision with other nonmotor vehicle in traffic accident"
+V77,"Bus occupant injured in collision with fixed or stationary object"
+V770,"Driver of bus injured in collision with fixed or stationary object in nontraffic accident"
+V771,"Passenger on bus injured in collision with fixed or stationary object in nontraffic accident"
+V772,"Person on outside of bus injured in collision with fixed or stationary object in nontraffic accident"
+V773,"Unspecified occupant of bus injured in collision with fixed or stationary object in nontraffic accident"
+V774,"Person boarding or alighting from bus injured in collision with fixed or stationary object"
+V775,"Driver of bus injured in collision with fixed or stationary object in traffic accident"
+V776,"Passenger on bus injured in collision with fixed or stationary object in traffic accident"
+V777,"Person on outside of bus injured in collision with fixed or stationary object in traffic accident"
+V779,"Unspecified occupant of bus injured in collision with fixed or stationary object in traffic accident"
+V78,"Bus occupant injured in noncollision transport accident"
+V780,"Driver of bus injured in noncollision transport accident in nontraffic accident"
+V781,"Passenger on bus injured in noncollision transport accident in nontraffic accident"
+V782,"Person on outside of bus injured in noncollision transport accident in nontraffic accident"
+V783,"Unspecified occupant of bus injured in noncollision transport accident in nontraffic accident"
+V784,"Person boarding or alighting from bus injured in noncollision transport accident"
+V785,"Driver of bus injured in noncollision transport accident in traffic accident"
+V786,"Passenger on bus injured in noncollision transport accident in traffic accident"
+V787,"Person on outside of bus injured in noncollision transport accident in traffic accident"
+V789,"Unspecified occupant of bus injured in noncollision transport accident in traffic accident"
+V79,"Bus occupant injured in other and unspecified transport accidents"
+V790,"Driver of bus injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V7900,"Driver of bus injured in collision with unspecified motor vehicles in nontraffic accident"
+V7909,"Driver of bus injured in collision with other motor vehicles in nontraffic accident"
+V791,"Passenger on bus injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V7910,"Passenger on bus injured in collision with unspecified motor vehicles in nontraffic accident"
+V7919,"Passenger on bus injured in collision with other motor vehicles in nontraffic accident"
+V792,"Unspecified bus occupant injured in collision with other and unspecified motor vehicles in nontraffic accident"
+V7920,"Unspecified bus occupant injured in collision with unspecified motor vehicles in nontraffic accident"
+V7929,"Unspecified bus occupant injured in collision with other motor vehicles in nontraffic accident"
+V793,"Bus occupant (driver) (passenger) injured in unspecified nontraffic accident"
+V794,"Driver of bus injured in collision with other and unspecified motor vehicles in traffic accident"
+V7940,"Driver of bus injured in collision with unspecified motor vehicles in traffic accident"
+V7949,"Driver of bus injured in collision with other motor vehicles in traffic accident"
+V795,"Passenger on bus injured in collision with other and unspecified motor vehicles in traffic accident"
+V7950,"Passenger on bus injured in collision with unspecified motor vehicles in traffic accident"
+V7959,"Passenger on bus injured in collision with other motor vehicles in traffic accident"
+V796,"Unspecified bus occupant injured in collision with other and unspecified motor vehicles in traffic accident"
+V7960,"Unspecified bus occupant injured in collision with unspecified motor vehicles in traffic accident"
+V7969,"Unspecified bus occupant injured in collision with other motor vehicles in traffic accident"
+V798,"Bus occupant (driver) (passenger) injured in other specified transport accidents"
+V7981,"Bus occupant (driver) (passenger) injured in transport accidents with military vehicle"
+V7988,"Bus occupant (driver) (passenger) injured in other specified transport accidents"
+V799,"Bus occupant (driver) (passenger) injured in unspecified traffic accident"
+V80,"Animal-rider or occupant of animal-drawn vehicle injured in transport accident"
+V800,"Animal-rider or occupant of animal drawn vehicle injured by fall from or being thrown from animal or animal-drawn vehicle in noncollision accident"
+V8001,"Animal-rider injured by fall from or being thrown from animal in noncollision accident"
+V80010,"Animal-rider injured by fall from or being thrown from horse in noncollision accident"
+V80018,"Animal-rider injured by fall from or being thrown from other animal in noncollision accident"
+V8002,"Occupant of animal-drawn vehicle injured by fall from or being thrown from animal-drawn vehicle in noncollision accident"
+V801,"Animal-rider or occupant of animal-drawn vehicle injured in collision with pedestrian or animal"
+V8011,"Animal-rider injured in collision with pedestrian or animal"
+V8012,"Occupant of animal-drawn vehicle injured in collision with pedestrian or animal"
+V802,"Animal-rider or occupant of animal-drawn vehicle injured in collision with pedal cycle"
+V8021,"Animal-rider injured in collision with pedal cycle"
+V8022,"Occupant of animal-drawn vehicle injured in collision with pedal cycle"
+V803,"Animal-rider or occupant of animal-drawn vehicle injured in collision with two- or three-wheeled motor vehicle"
+V8031,"Animal-rider injured in collision with two- or three-wheeled motor vehicle"
+V8032,"Occupant of animal-drawn vehicle injured in collision with two- or three-wheeled motor vehicle"
+V804,"Animal-rider or occupant of animal-drawn vehicle injured in collision with car, pick-up truck, van, heavy transport vehicle or bus"
+V8041,"Animal-rider injured in collision with car, pick-up truck, van, heavy transport vehicle or bus"
+V8042,"Occupant of animal-drawn vehicle injured in collision with car, pick-up truck, van, heavy transport vehicle or bus"
+V805,"Animal-rider or occupant of animal-drawn vehicle injured in collision with other specified motor vehicle"
+V8051,"Animal-rider injured in collision with other specified motor vehicle"
+V8052,"Occupant of animal-drawn vehicle injured in collision with other specified motor vehicle"
+V806,"Animal-rider or occupant of animal-drawn vehicle injured in collision with railway train or railway vehicle"
+V8061,"Animal-rider injured in collision with railway train or railway vehicle"
+V8062,"Occupant of animal-drawn vehicle injured in collision with railway train or railway vehicle"
+V807,"Animal-rider or occupant of animal-drawn vehicle injured in collision with other nonmotor vehicles"
+V8071,"Animal-rider or occupant of animal-drawn vehicle injured in collision with animal being ridden"
+V80710,"Animal-rider injured in collision with other animal being ridden"
+V80711,"Occupant of animal-drawn vehicle injured in collision with animal being ridden"
+V8072,"Animal-rider or occupant of animal-drawn vehicle injured in collision with other animal-drawn vehicle"
+V80720,"Animal-rider injured in collision with animal-drawn vehicle"
+V80721,"Occupant of animal-drawn vehicle injured in collision with other animal-drawn vehicle"
+V8073,"Animal-rider or occupant of animal-drawn vehicle injured in collision with streetcar"
+V80730,"Animal-rider injured in collision with streetcar"
+V80731,"Occupant of animal-drawn vehicle injured in collision with streetcar"
+V8079,"Animal-rider or occupant of animal-drawn vehicle injured in collision with other nonmotor vehicles"
+V80790,"Animal-rider injured in collision with other nonmotor vehicles"
+V80791,"Occupant of animal-drawn vehicle injured in collision with other nonmotor vehicles"
+V808,"Animal-rider or occupant of animal-drawn vehicle injured in collision with fixed or stationary object"
+V8081,"Animal-rider injured in collision with fixed or stationary object"
+V8082,"Occupant of animal-drawn vehicle injured in collision with fixed or stationary object"
+V809,"Animal-rider or occupant of animal-drawn vehicle injured in other and unspecified transport accidents"
+V8091,"Animal-rider injured in other and unspecified transport accidents"
+V80910,"Animal-rider injured in transport accident with military vehicle"
+V80918,"Animal-rider injured in other transport accident"
+V80919,"Animal-rider injured in unspecified transport accident"
+V8092,"Occupant of animal-drawn vehicle injured in other and unspecified transport accidents"
+V80920,"Occupant of animal-drawn vehicle injured in transport accident with military vehicle"
+V80928,"Occupant of animal-drawn vehicle injured in other transport accident"
+V80929,"Occupant of animal-drawn vehicle injured in unspecified transport accident"
+V81,"Occupant of railway train or railway vehicle injured in transport accident"
+V810,"Occupant of railway train or railway vehicle injured in collision with motor vehicle in nontraffic accident"
+V811,"Occupant of railway train or railway vehicle injured in collision with motor vehicle in traffic accident"
+V812,"Occupant of railway train or railway vehicle injured in collision with or hit by rolling stock"
+V813,"Occupant of railway train or railway vehicle injured in collision with other object"
+V814,"Person injured while boarding or alighting from railway train or railway vehicle"
+V815,"Occupant of railway train or railway vehicle injured by fall in railway train or railway vehicle"
+V816,"Occupant of railway train or railway vehicle injured by fall from railway train or railway vehicle"
+V817,"Occupant of railway train or railway vehicle injured in derailment without antecedent collision"
+V818,"Occupant of railway train or railway vehicle injured in other specified railway accidents"
+V8181,"Occupant of railway train or railway vehicle injured due to explosion or fire on train"
+V8182,"Occupant of railway train or railway vehicle injured due to object falling onto train"
+V8183,"Occupant of railway train or railway vehicle injured due to collision with military vehicle"
+V8189,"Occupant of railway train or railway vehicle injured due to other specified railway accident"
+V819,"Occupant of railway train or railway vehicle injured in unspecified railway accident"
+V82,"Occupant of powered streetcar injured in transport accident"
+V820,"Occupant of streetcar injured in collision with motor vehicle in nontraffic accident"
+V821,"Occupant of streetcar injured in collision with motor vehicle in traffic accident"
+V822,"Occupant of streetcar injured in collision with or hit by rolling stock"
+V823,"Occupant of streetcar injured in collision with other object"
+V824,"Person injured while boarding or alighting from streetcar"
+V825,"Occupant of streetcar injured by fall in streetcar"
+V826,"Occupant of streetcar injured by fall from streetcar"
+V827,"Occupant of streetcar injured in derailment without antecedent collision"
+V828,"Occupant of streetcar injured in other specified transport accidents"
+V829,"Occupant of streetcar injured in unspecified traffic accident"
+V83,"Occupant of special vehicle mainly used on industrial premises injured in transport accident"
+V830,"Driver of special industrial vehicle injured in traffic accident"
+V831,"Passenger of special industrial vehicle injured in traffic accident"
+V832,"Person on outside of special industrial vehicle injured in traffic accident"
+V833,"Unspecified occupant of special industrial vehicle injured in traffic accident"
+V834,"Person injured while boarding or alighting from special industrial vehicle"
+V835,"Driver of special industrial vehicle injured in nontraffic accident"
+V836,"Passenger of special industrial vehicle injured in nontraffic accident"
+V837,"Person on outside of special industrial vehicle injured in nontraffic accident"
+V839,"Unspecified occupant of special industrial vehicle injured in nontraffic accident"
+V84,"Occupant of special vehicle mainly used in agriculture injured in transport accident"
+V840,"Driver of special agricultural vehicle injured in traffic accident"
+V841,"Passenger of special agricultural vehicle injured in traffic accident"
+V842,"Person on outside of special agricultural vehicle injured in traffic accident"
+V843,"Unspecified occupant of special agricultural vehicle injured in traffic accident"
+V844,"Person injured while boarding or alighting from special agricultural vehicle"
+V845,"Driver of special agricultural vehicle injured in nontraffic accident"
+V846,"Passenger of special agricultural vehicle injured in nontraffic accident"
+V847,"Person on outside of special agricultural vehicle injured in nontraffic accident"
+V849,"Unspecified occupant of special agricultural vehicle injured in nontraffic accident"
+V85,"Occupant of special construction vehicle injured in transport accident"
+V850,"Driver of special construction vehicle injured in traffic accident"
+V851,"Passenger of special construction vehicle injured in traffic accident"
+V852,"Person on outside of special construction vehicle injured in traffic accident"
+V853,"Unspecified occupant of special construction vehicle injured in traffic accident"
+V854,"Person injured while boarding or alighting from special construction vehicle"
+V855,"Driver of special construction vehicle injured in nontraffic accident"
+V856,"Passenger of special construction vehicle injured in nontraffic accident"
+V857,"Person on outside of special construction vehicle injured in nontraffic accident"
+V859,"Unspecified occupant of special construction vehicle injured in nontraffic accident"
+V86,"Occupant of special all-terrain or other off-road motor vehicle, injured in transport accident"
+V860,"Driver of special all-terrain or other off-road motor vehicle injured in traffic accident"
+V8601,"Driver of ambulance or fire engine injured in traffic accident"
+V8602,"Driver of snowmobile injured in traffic accident"
+V8603,"Driver of dune buggy injured in traffic accident"
+V8604,"Driver of military vehicle injured in traffic accident"
+V8605,"Driver of 3- or 4- wheeled all-terrain vehicle (ATV) injured in traffic accident"
+V8606,"Driver of dirt bike or motor/cross bike injured in traffic accident"
+V8609,"Driver of other special all-terrain or other off-road motor vehicle injured in traffic accident"
+V861,"Passenger of special all-terrain or other off-road motor vehicle injured in traffic accident"
+V8611,"Passenger of ambulance or fire engine injured in traffic accident"
+V8612,"Passenger of snowmobile injured in traffic accident"
+V8613,"Passenger of dune buggy injured in traffic accident"
+V8614,"Passenger of military vehicle injured in traffic accident"
+V8615,"Passenger of 3- or 4- wheeled all-terrain vehicle (ATV) injured in traffic accident"
+V8616,"Passenger of dirt bike or motor/cross bike injured in traffic accident"
+V8619,"Passenger of other special all-terrain or other off-road motor vehicle injured in traffic accident"
+V862,"Person on outside of special all-terrain or other off-road motor vehicle injured in traffic accident"
+V8621,"Person on outside of ambulance or fire engine injured in traffic accident"
+V8622,"Person on outside of snowmobile injured in traffic accident"
+V8623,"Person on outside of dune buggy injured in traffic accident"
+V8624,"Person on outside of military vehicle injured in traffic accident"
+V8625,"Person on outside of 3- or 4- wheeled all-terrain vehicle (ATV) injured in traffic accident"
+V8626,"Person on outside of dirt bike or motor/cross bike injured in traffic accident"
+V8629,"Person on outside of other special all-terrain or other off-road motor vehicle injured in traffic accident"
+V863,"Unspecified occupant of special all-terrain or other off-road motor vehicle injured in traffic accident"
+V8631,"Unspecified occupant of ambulance or fire engine injured in traffic accident"
+V8632,"Unspecified occupant of snowmobile injured in traffic accident"
+V8633,"Unspecified occupant of dune buggy injured in traffic accident"
+V8634,"Unspecified occupant of military vehicle injured in traffic accident"
+V8635,"Unspecified occupant of 3- or 4- wheeled all-terrain vehicle (ATV) injured in traffic accident"
+V8636,"Unspecified occupant of dirt bike or motor/cross bike injured in traffic accident"
+V8639,"Unspecified occupant of other special all-terrain or other off-road motor vehicle injured in traffic accident"
+V864,"Person injured while boarding or alighting from special all-terrain or other off-road motor vehicle"
+V8641,"Person injured while boarding or alighting from ambulance or fire engine"
+V8642,"Person injured while boarding or alighting from snowmobile"
+V8643,"Person injured while boarding or alighting from dune buggy"
+V8644,"Person injured while boarding or alighting from military vehicle"
+V8645,"Person injured while boarding or alighting from a 3- or 4- wheeled all-terrain vehicle (ATV)"
+V8646,"Person injured while boarding or alighting from a dirt bike or motor/cross bike"
+V8649,"Person injured while boarding or alighting from other special all-terrain or other off-road motor vehicle"
+V865,"Driver of special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V8651,"Driver of ambulance or fire engine injured in nontraffic accident"
+V8652,"Driver of snowmobile injured in nontraffic accident"
+V8653,"Driver of dune buggy injured in nontraffic accident"
+V8654,"Driver of military vehicle injured in nontraffic accident"
+V8655,"Driver of 3- or 4- wheeled all-terrain vehicle (ATV) injured in nontraffic accident"
+V8656,"Driver of dirt bike or motor/cross bike injured in nontraffic accident"
+V8659,"Driver of other special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V866,"Passenger of special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V8661,"Passenger of ambulance or fire engine injured in nontraffic accident"
+V8662,"Passenger of snowmobile injured in nontraffic accident"
+V8663,"Passenger of dune buggy injured in nontraffic accident"
+V8664,"Passenger of military vehicle injured in nontraffic accident"
+V8665,"Passenger of 3- or 4- wheeled all-terrain vehicle (ATV) injured in nontraffic accident"
+V8666,"Passenger of dirt bike or motor/cross bike injured in nontraffic accident"
+V8669,"Passenger of other special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V867,"Person on outside of special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V8671,"Person on outside of ambulance or fire engine injured in nontraffic accident"
+V8672,"Person on outside of snowmobile injured in nontraffic accident"
+V8673,"Person on outside of dune buggy injured in nontraffic accident"
+V8674,"Person on outside of military vehicle injured in nontraffic accident"
+V8675,"Person on outside of 3- or 4- wheeled all-terrain vehicle (ATV) injured in nontraffic accident"
+V8676,"Person on outside of dirt bike or motor/cross bike injured in nontraffic accident"
+V8679,"Person on outside of other special all-terrain or other off-road motor vehicles injured in nontraffic accident"
+V869,"Unspecified occupant of special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V8691,"Unspecified occupant of ambulance or fire engine injured in nontraffic accident"
+V8692,"Unspecified occupant of snowmobile injured in nontraffic accident"
+V8693,"Unspecified occupant of dune buggy injured in nontraffic accident"
+V8694,"Unspecified occupant of military vehicle injured in nontraffic accident"
+V8695,"Unspecified occupant of 3- or 4- wheeled all-terrain vehicle (ATV) injured in nontraffic accident"
+V8696,"Unspecified occupant of dirt bike or motor/cross bike injured in nontraffic accident"
+V8699,"Unspecified occupant of other special all-terrain or other off-road motor vehicle injured in nontraffic accident"
+V87,"Traffic accident of specified type but victim's mode of transport unknown"
+V870,"Person injured in collision between car and two- or three-wheeled powered vehicle (traffic)"
+V871,"Person injured in collision between other motor vehicle and two- or three-wheeled motor vehicle (traffic)"
+V872,"Person injured in collision between car and pick-up truck or van (traffic)"
+V873,"Person injured in collision between car and bus (traffic)"
+V874,"Person injured in collision between car and heavy transport vehicle (traffic)"
+V875,"Person injured in collision between heavy transport vehicle and bus (traffic)"
+V876,"Person injured in collision between railway train or railway vehicle and car (traffic)"
+V877,"Person injured in collision between other specified motor vehicles (traffic)"
+V878,"Person injured in other specified noncollision transport accidents involving motor vehicle (traffic)"
+V879,"Person injured in other specified (collision)(noncollision) transport accidents involving nonmotor vehicle (traffic)"
+V88,"Nontraffic accident of specified type but victim's mode of transport unknown"
+V880,"Person injured in collision between car and two- or three-wheeled motor vehicle, nontraffic"
+V881,"Person injured in collision between other motor vehicle and two- or three-wheeled motor vehicle, nontraffic"
+V882,"Person injured in collision between car and pick-up truck or van, nontraffic"
+V883,"Person injured in collision between car and bus, nontraffic"
+V884,"Person injured in collision between car and heavy transport vehicle, nontraffic"
+V885,"Person injured in collision between heavy transport vehicle and bus, nontraffic"
+V886,"Person injured in collision between railway train or railway vehicle and car, nontraffic"
+V887,"Person injured in collision between other specified motor vehicle, nontraffic"
+V888,"Person injured in other specified noncollision transport accidents involving motor vehicle, nontraffic"
+V889,"Person injured in other specified (collision)(noncollision) transport accidents involving nonmotor vehicle, nontraffic"
+V89,"Motor- or nonmotor-vehicle accident, type of vehicle unspecified"
+V890,"Person injured in unspecified motor-vehicle accident, nontraffic"
+V891,"Person injured in unspecified nonmotor-vehicle accident, nontraffic"
+V892,"Person injured in unspecified motor-vehicle accident, traffic"
+V893,"Person injured in unspecified nonmotor-vehicle accident, traffic"
+V899,"Person injured in unspecified vehicle accident"
+V90,"Drowning and submersion due to accident to watercraft"
+V900,"Drowning and submersion due to watercraft overturning"
+V9000,"Drowning and submersion due to merchant ship overturning"
+V9001,"Drowning and submersion due to passenger ship overturning"
+V9002,"Drowning and submersion due to fishing boat overturning"
+V9003,"Drowning and submersion due to other powered watercraft overturning"
+V9004,"Drowning and submersion due to sailboat overturning"
+V9005,"Drowning and submersion due to canoe or kayak overturning"
+V9006,"Drowning and submersion due to (nonpowered) inflatable craft overturning"
+V9008,"Drowning and submersion due to other unpowered watercraft overturning"
+V9009,"Drowning and submersion due to unspecified watercraft overturning"
+V901,"Drowning and submersion due to watercraft sinking"
+V9010,"Drowning and submersion due to merchant ship sinking"
+V9011,"Drowning and submersion due to passenger ship sinking"
+V9012,"Drowning and submersion due to fishing boat sinking"
+V9013,"Drowning and submersion due to other powered watercraft sinking"
+V9014,"Drowning and submersion due to sailboat sinking"
+V9015,"Drowning and submersion due to canoe or kayak sinking"
+V9016,"Drowning and submersion due to (nonpowered) inflatable craft sinking"
+V9018,"Drowning and submersion due to other unpowered watercraft sinking"
+V9019,"Drowning and submersion due to unspecified watercraft sinking"
+V902,"Drowning and submersion due to falling or jumping from burning watercraft"
+V9020,"Drowning and submersion due to falling or jumping from burning merchant ship"
+V9021,"Drowning and submersion due to falling or jumping from burning passenger ship"
+V9022,"Drowning and submersion due to falling or jumping from burning fishing boat"
+V9023,"Drowning and submersion due to falling or jumping from other burning powered watercraft"
+V9024,"Drowning and submersion due to falling or jumping from burning sailboat"
+V9025,"Drowning and submersion due to falling or jumping from burning canoe or kayak"
+V9026,"Drowning and submersion due to falling or jumping from burning (nonpowered) inflatable craft"
+V9027,"Drowning and submersion due to falling or jumping from burning water-skis"
+V9028,"Drowning and submersion due to falling or jumping from other burning unpowered watercraft"
+V9029,"Drowning and submersion due to falling or jumping from unspecified burning watercraft"
+V903,"Drowning and submersion due to falling or jumping from crushed watercraft"
+V9030,"Drowning and submersion due to falling or jumping from crushed merchant ship"
+V9031,"Drowning and submersion due to falling or jumping from crushed passenger ship"
+V9032,"Drowning and submersion due to falling or jumping from crushed fishing boat"
+V9033,"Drowning and submersion due to falling or jumping from other crushed powered watercraft"
+V9034,"Drowning and submersion due to falling or jumping from crushed sailboat"
+V9035,"Drowning and submersion due to falling or jumping from crushed canoe or kayak"
+V9036,"Drowning and submersion due to falling or jumping from crushed (nonpowered) inflatable craft"
+V9037,"Drowning and submersion due to falling or jumping from crushed water-skis"
+V9038,"Drowning and submersion due to falling or jumping from other crushed unpowered watercraft"
+V9039,"Drowning and submersion due to falling or jumping from crushed unspecified watercraft"
+V908,"Drowning and submersion due to other accident to watercraft"
+V9080,"Drowning and submersion due to other accident to merchant ship"
+V9081,"Drowning and submersion due to other accident to passenger ship"
+V9082,"Drowning and submersion due to other accident to fishing boat"
+V9083,"Drowning and submersion due to other accident to other powered watercraft"
+V9084,"Drowning and submersion due to other accident to sailboat"
+V9085,"Drowning and submersion due to other accident to canoe or kayak"
+V9086,"Drowning and submersion due to other accident to (nonpowered) inflatable craft"
+V9087,"Drowning and submersion due to other accident to water-skis"
+V9088,"Drowning and submersion due to other accident to other unpowered watercraft"
+V9089,"Drowning and submersion due to other accident to unspecified watercraft"
+V91,"Other injury due to accident to watercraft"
+V910,"Burn due to watercraft on fire"
+V9100,"Burn due to merchant ship on fire"
+V9101,"Burn due to passenger ship on fire"
+V9102,"Burn due to fishing boat on fire"
+V9103,"Burn due to other powered watercraft on fire"
+V9104,"Burn due to sailboat on fire"
+V9105,"Burn due to canoe or kayak on fire"
+V9106,"Burn due to (nonpowered) inflatable craft on fire"
+V9107,"Burn due to water-skis on fire"
+V9108,"Burn due to other unpowered watercraft on fire"
+V9109,"Burn due to unspecified watercraft on fire"
+V911,"Crushed between watercraft and other watercraft or other object due to collision"
+V9110,"Crushed between merchant ship and other watercraft or other object due to collision"
+V9111,"Crushed between passenger ship and other watercraft or other object due to collision"
+V9112,"Crushed between fishing boat and other watercraft or other object due to collision"
+V9113,"Crushed between other powered watercraft and other watercraft or other object due to collision"
+V9114,"Crushed between sailboat and other watercraft or other object due to collision"
+V9115,"Crushed between canoe or kayak and other watercraft or other object due to collision"
+V9116,"Crushed between (nonpowered) inflatable craft and other watercraft or other object due to collision"
+V9118,"Crushed between other unpowered watercraft and other watercraft or other object due to collision"
+V9119,"Crushed between unspecified watercraft and other watercraft or other object due to collision"
+V912,"Fall due to collision between watercraft and other watercraft or other object"
+V9120,"Fall due to collision between merchant ship and other watercraft or other object"
+V9121,"Fall due to collision between passenger ship and other watercraft or other object"
+V9122,"Fall due to collision between fishing boat and other watercraft or other object"
+V9123,"Fall due to collision between other powered watercraft and other watercraft or other object"
+V9124,"Fall due to collision between sailboat and other watercraft or other object"
+V9125,"Fall due to collision between canoe or kayak and other watercraft or other object"
+V9126,"Fall due to collision between (nonpowered) inflatable craft and other watercraft or other object"
+V9129,"Fall due to collision between unspecified watercraft and other watercraft or other object"
+V913,"Hit or struck by falling object due to accident to watercraft"
+V9130,"Hit or struck by falling object due to accident to merchant ship"
+V9131,"Hit or struck by falling object due to accident to passenger ship"
+V9132,"Hit or struck by falling object due to accident to fishing boat"
+V9133,"Hit or struck by falling object due to accident to other powered watercraft"
+V9134,"Hit or struck by falling object due to accident to sailboat"
+V9135,"Hit or struck by falling object due to accident to canoe or kayak"
+V9136,"Hit or struck by falling object due to accident to (nonpowered) inflatable craft"
+V9137,"Hit or struck by falling object due to accident to water-skis"
+V9138,"Hit or struck by falling object due to accident to other unpowered watercraft"
+V9139,"Hit or struck by falling object due to accident to unspecified watercraft"
+V918,"Other injury due to other accident to watercraft"
+V9180,"Other injury due to other accident to merchant ship"
+V9181,"Other injury due to other accident to passenger ship"
+V9182,"Other injury due to other accident to fishing boat"
+V9183,"Other injury due to other accident to other powered watercraft"
+V9184,"Other injury due to other accident to sailboat"
+V9185,"Other injury due to other accident to canoe or kayak"
+V9186,"Other injury due to other accident to (nonpowered) inflatable craft"
+V9187,"Other injury due to other accident to water-skis"
+V9188,"Other injury due to other accident to other unpowered watercraft"
+V9189,"Other injury due to other accident to unspecified watercraft"
+V92,"Drowning and submersion due to accident on board watercraft, without accident to watercraft"
+V920,"Drowning and submersion due to fall off watercraft"
+V9200,"Drowning and submersion due to fall off merchant ship"
+V9201,"Drowning and submersion due to fall off passenger ship"
+V9202,"Drowning and submersion due to fall off fishing boat"
+V9203,"Drowning and submersion due to fall off other powered watercraft"
+V9204,"Drowning and submersion due to fall off sailboat"
+V9205,"Drowning and submersion due to fall off canoe or kayak"
+V9206,"Drowning and submersion due to fall off (nonpowered) inflatable craft"
+V9207,"Drowning and submersion due to fall off water-skis"
+V9208,"Drowning and submersion due to fall off other unpowered watercraft"
+V9209,"Drowning and submersion due to fall off unspecified watercraft"
+V921,"Drowning and submersion due to being thrown overboard by motion of watercraft"
+V9210,"Drowning and submersion due to being thrown overboard by motion of merchant ship"
+V9211,"Drowning and submersion due to being thrown overboard by motion of passenger ship"
+V9212,"Drowning and submersion due to being thrown overboard by motion of fishing boat"
+V9213,"Drowning and submersion due to being thrown overboard by motion of other powered watercraft"
+V9214,"Drowning and submersion due to being thrown overboard by motion of sailboat"
+V9215,"Drowning and submersion due to being thrown overboard by motion of canoe or kayak"
+V9216,"Drowning and submersion due to being thrown overboard by motion of (nonpowered) inflatable craft"
+V9219,"Drowning and submersion due to being thrown overboard by motion of unspecified watercraft"
+V922,"Drowning and submersion due to being washed overboard from watercraft"
+V9220,"Drowning and submersion due to being washed overboard from merchant ship"
+V9221,"Drowning and submersion due to being washed overboard from passenger ship"
+V9222,"Drowning and submersion due to being washed overboard from fishing boat"
+V9223,"Drowning and submersion due to being washed overboard from other powered watercraft"
+V9224,"Drowning and submersion due to being washed overboard from sailboat"
+V9225,"Drowning and submersion due to being washed overboard from canoe or kayak"
+V9226,"Drowning and submersion due to being washed overboard from (nonpowered) inflatable craft"
+V9227,"Drowning and submersion due to being washed overboard from water-skis"
+V9228,"Drowning and submersion due to being washed overboard from other unpowered watercraft"
+V9229,"Drowning and submersion due to being washed overboard from unspecified watercraft"
+V93,"Other injury due to accident on board watercraft, without accident to watercraft"
+V930,"Burn due to localized fire on board watercraft"
+V9300,"Burn due to localized fire on board merchant vessel"
+V9301,"Burn due to localized fire on board passenger vessel"
+V9302,"Burn due to localized fire on board fishing boat"
+V9303,"Burn due to localized fire on board other powered watercraft"
+V9304,"Burn due to localized fire on board sailboat"
+V9309,"Burn due to localized fire on board unspecified watercraft"
+V931,"Other burn on board watercraft"
+V9310,"Other burn on board merchant vessel"
+V9311,"Other burn on board passenger vessel"
+V9312,"Other burn on board fishing boat"
+V9313,"Other burn on board other powered watercraft"
+V9314,"Other burn on board sailboat"
+V9319,"Other burn on board unspecified watercraft"
+V932,"Heat exposure on board watercraft"
+V9320,"Heat exposure on board merchant ship"
+V9321,"Heat exposure on board passenger ship"
+V9322,"Heat exposure on board fishing boat"
+V9323,"Heat exposure on board other powered watercraft"
+V9324,"Heat exposure on board sailboat"
+V9329,"Heat exposure on board unspecified watercraft"
+V933,"Fall on board watercraft"
+V9330,"Fall on board merchant ship"
+V9331,"Fall on board passenger ship"
+V9332,"Fall on board fishing boat"
+V9333,"Fall on board other powered watercraft"
+V9334,"Fall on board sailboat"
+V9335,"Fall on board canoe or kayak"
+V9336,"Fall on board (nonpowered) inflatable craft"
+V9338,"Fall on board other unpowered watercraft"
+V9339,"Fall on board unspecified watercraft"
+V934,"Struck by falling object on board watercraft"
+V9340,"Struck by falling object on merchant ship"
+V9341,"Struck by falling object on passenger ship"
+V9342,"Struck by falling object on fishing boat"
+V9343,"Struck by falling object on other powered watercraft"
+V9344,"Struck by falling object on sailboat"
+V9348,"Struck by falling object on other unpowered watercraft"
+V9349,"Struck by falling object on unspecified watercraft"
+V935,"Explosion on board watercraft"
+V9350,"Explosion on board merchant ship"
+V9351,"Explosion on board passenger ship"
+V9352,"Explosion on board fishing boat"
+V9353,"Explosion on board other powered watercraft"
+V9354,"Explosion on board sailboat"
+V9359,"Explosion on board unspecified watercraft"
+V936,"Machinery accident on board watercraft"
+V9360,"Machinery accident on board merchant ship"
+V9361,"Machinery accident on board passenger ship"
+V9362,"Machinery accident on board fishing boat"
+V9363,"Machinery accident on board other powered watercraft"
+V9364,"Machinery accident on board sailboat"
+V9369,"Machinery accident on board unspecified watercraft"
+V938,"Other injury due to other accident on board watercraft"
+V9380,"Other injury due to other accident on board merchant ship"
+V9381,"Other injury due to other accident on board passenger ship"
+V9382,"Other injury due to other accident on board fishing boat"
+V9383,"Other injury due to other accident on board other powered watercraft"
+V9384,"Other injury due to other accident on board sailboat"
+V9385,"Other injury due to other accident on board canoe or kayak"
+V9386,"Other injury due to other accident on board (nonpowered) inflatable craft"
+V9387,"Other injury due to other accident on board water-skis"
+V9388,"Other injury due to other accident on board other unpowered watercraft"
+V9389,"Other injury due to other accident on board unspecified watercraft"
+V94,"Other and unspecified water transport accidents"
+V940,"Hitting object or bottom of body of water due to fall from watercraft"
+V941,"Bather struck by watercraft"
+V9411,"Bather struck by powered watercraft"
+V9412,"Bather struck by nonpowered watercraft"
+V942,"Rider of nonpowered watercraft struck by other watercraft"
+V9421,"Rider of nonpowered watercraft struck by other nonpowered watercraft"
+V9422,"Rider of nonpowered watercraft struck by powered watercraft"
+V943,"Injury to rider of (inflatable) watercraft being pulled behind other watercraft"
+V9431,"Injury to rider of (inflatable) recreational watercraft being pulled behind other watercraft"
+V9432,"Injury to rider of non-recreational watercraft being pulled behind other watercraft"
+V944,"Injury to barefoot water-skier"
+V948,"Other water transport accident"
+V9481,"Water transport accident involving military watercraft"
+V94810,"Civilian watercraft involved in water transport accident with military watercraft"
+V94811,"Civilian in water injured by military watercraft"
+V94818,"Other water transport accident involving military watercraft"
+V9489,"Other water transport accident"
+V949,"Unspecified water transport accident"
+V95,"Accident to powered aircraft causing injury to occupant"
+V950,"Helicopter accident injuring occupant"
+V9500,"Unspecified helicopter accident injuring occupant"
+V9501,"Helicopter crash injuring occupant"
+V9502,"Forced landing of helicopter injuring occupant"
+V9503,"Helicopter collision injuring occupant"
+V9504,"Helicopter fire injuring occupant"
+V9505,"Helicopter explosion injuring occupant"
+V9509,"Other helicopter accident injuring occupant"
+V951,"Ultralight, microlight or powered-glider accident injuring occupant"
+V9510,"Unspecified ultralight, microlight or powered-glider accident injuring occupant"
+V9511,"Ultralight, microlight or powered-glider crash injuring occupant"
+V9512,"Forced landing of ultralight, microlight or powered-glider injuring occupant"
+V9513,"Ultralight, microlight or powered-glider collision injuring occupant"
+V9514,"Ultralight, microlight or powered-glider fire injuring occupant"
+V9515,"Ultralight, microlight or powered-glider explosion injuring occupant"
+V9519,"Other ultralight, microlight or powered-glider accident injuring occupant"
+V952,"Other private fixed-wing aircraft accident injuring occupant"
+V9520,"Unspecified accident to other private fixed-wing aircraft, injuring occupant"
+V9521,"Other private fixed-wing aircraft crash injuring occupant"
+V9522,"Forced landing of other private fixed-wing aircraft injuring occupant"
+V9523,"Other private fixed-wing aircraft collision injuring occupant"
+V9524,"Other private fixed-wing aircraft fire injuring occupant"
+V9525,"Other private fixed-wing aircraft explosion injuring occupant"
+V9529,"Other accident to other private fixed-wing aircraft injuring occupant"
+V953,"Commercial fixed-wing aircraft accident injuring occupant"
+V9530,"Unspecified accident to commercial fixed-wing aircraft injuring occupant"
+V9531,"Commercial fixed-wing aircraft crash injuring occupant"
+V9532,"Forced landing of commercial fixed-wing aircraft injuring occupant"
+V9533,"Commercial fixed-wing aircraft collision injuring occupant"
+V9534,"Commercial fixed-wing aircraft fire injuring occupant"
+V9535,"Commercial fixed-wing aircraft explosion injuring occupant"
+V9539,"Other accident to commercial fixed-wing aircraft injuring occupant"
+V954,"Spacecraft accident injuring occupant"
+V9540,"Unspecified spacecraft accident injuring occupant"
+V9541,"Spacecraft crash injuring occupant"
+V9542,"Forced landing of spacecraft injuring occupant"
+V9543,"Spacecraft collision injuring occupant"
+V9544,"Spacecraft fire injuring occupant"
+V9545,"Spacecraft explosion injuring occupant"
+V9549,"Other spacecraft accident injuring occupant"
+V958,"Other powered aircraft accidents injuring occupant"
+V959,"Unspecified aircraft accident injuring occupant"
+V96,"Accident to nonpowered aircraft causing injury to occupant"
+V960,"Balloon accident injuring occupant"
+V9600,"Unspecified balloon accident injuring occupant"
+V9601,"Balloon crash injuring occupant"
+V9602,"Forced landing of balloon injuring occupant"
+V9603,"Balloon collision injuring occupant"
+V9604,"Balloon fire injuring occupant"
+V9605,"Balloon explosion injuring occupant"
+V9609,"Other balloon accident injuring occupant"
+V961,"Hang-glider accident injuring occupant"
+V9610,"Unspecified hang-glider accident injuring occupant"
+V9611,"Hang-glider crash injuring occupant"
+V9612,"Forced landing of hang-glider injuring occupant"
+V9613,"Hang-glider collision injuring occupant"
+V9614,"Hang-glider fire injuring occupant"
+V9615,"Hang-glider explosion injuring occupant"
+V9619,"Other hang-glider accident injuring occupant"
+V962,"Glider (nonpowered) accident injuring occupant"
+V9620,"Unspecified glider (nonpowered) accident injuring occupant"
+V9621,"Glider (nonpowered) crash injuring occupant"
+V9622,"Forced landing of glider (nonpowered) injuring occupant"
+V9623,"Glider (nonpowered) collision injuring occupant"
+V9624,"Glider (nonpowered) fire injuring occupant"
+V9625,"Glider (nonpowered) explosion injuring occupant"
+V9629,"Other glider (nonpowered) accident injuring occupant"
+V968,"Other nonpowered-aircraft accidents injuring occupant"
+V969,"Unspecified nonpowered-aircraft accident injuring occupant"
+V97,"Other specified air transport accidents"
+V970,"Occupant of aircraft injured in other specified air transport accidents"
+V971,"Person injured while boarding or alighting from aircraft"
+V972,"Parachutist accident"
+V9721,"Parachutist entangled in object"
+V9722,"Parachutist injured on landing"
+V9729,"Other parachutist accident"
+V973,"Person on ground injured in air transport accident"
+V9731,"Hit by object falling from aircraft"
+V9732,"Injured by rotating propeller"
+V9733,"Sucked into jet engine"
+V9739,"Other injury to person on ground due to air transport accident"
+V978,"Other air transport accidents, not elsewhere classified"
+V9781,"Air transport accident involving military aircraft"
+V97810,"Civilian aircraft involved in air transport accident with military aircraft"
+V97811,"Civilian injured by military aircraft"
+V97818,"Other air transport accident involving military aircraft"
+V9789,"Other air transport accidents, not elsewhere classified"
+V98,"Other specified transport accidents"
+V980,"Accident to, on or involving cable-car, not on rails"
+V981,"Accident to, on or involving land-yacht"
+V982,"Accident to, on or involving ice yacht"
+V983,"Accident to, on or involving ski lift"
+V988,"Other specified transport accidents"
+V99,"Unspecified transport accident"
+W00,"Fall due to ice and snow"
+W000,"Fall on same level due to ice and snow"
+W001,"Fall from stairs and steps due to ice and snow"
+W002,"Other fall from one level to another due to ice and snow"
+W009,"Unspecified fall due to ice and snow"
+W01,"Fall on same level from slipping, tripping and stumbling"
+W010,"Fall on same level from slipping, tripping and stumbling without subsequent striking against object"
+W011,"Fall on same level from slipping, tripping and stumbling with subsequent striking against object"
+W0110,"Fall on same level from slipping, tripping and stumbling with subsequent striking against unspecified object"
+W0111,"Fall on same level from slipping, tripping and stumbling with subsequent striking against sharp object"
+W01110,"Fall on same level from slipping, tripping and stumbling with subsequent striking against sharp glass"
+W01111,"Fall on same level from slipping, tripping and stumbling with subsequent striking against power tool or machine"
+W01118,"Fall on same level from slipping, tripping and stumbling with subsequent striking against other sharp object"
+W01119,"Fall on same level from slipping, tripping and stumbling with subsequent striking against unspecified sharp object"
+W0119,"Fall on same level from slipping, tripping and stumbling with subsequent striking against other object"
+W01190,"Fall on same level from slipping, tripping and stumbling with subsequent striking against furniture"
+W01198,"Fall on same level from slipping, tripping and stumbling with subsequent striking against other object"
+W03,"Other fall on same level due to collision with another person"
+W04,"Fall while being carried or supported by other persons"
+W05,"Fall from non-moving wheelchair, nonmotorized scooter and motorized mobility scooter"
+W050,"Fall from non-moving wheelchair"
+W051,"Fall from non-moving nonmotorized scooter"
+W052,"Fall from non-moving motorized mobility scooter"
+W06,"Fall from bed"
+W07,"Fall from chair"
+W08,"Fall from other furniture"
+W09,"Fall on and from playground equipment"
+W090,"Fall on or from playground slide"
+W091,"Fall from playground swing"
+W092,"Fall on or from jungle gym"
+W098,"Fall on or from other playground equipment"
+W10,"Fall on and from stairs and steps"
+W100,"Fall (on)(from) escalator"
+W101,"Fall (on)(from) sidewalk curb"
+W102,"Fall (on)(from) incline"
+W108,"Fall (on) (from) other stairs and steps"
+W109,"Fall (on) (from) unspecified stairs and steps"
+W11,"Fall on and from ladder"
+W12,"Fall on and from scaffolding"
+W13,"Fall from, out of or through building or structure"
+W130,"Fall from, out of or through balcony"
+W131,"Fall from, out of or through bridge"
+W132,"Fall from, out of or through roof"
+W133,"Fall through floor"
+W134,"Fall from, out of or through window"
+W138,"Fall from, out of or through other building or structure"
+W139,"Fall from, out of or through building, not otherwise specified"
+W14,"Fall from tree"
+W15,"Fall from cliff"
+W16,"Fall, jump or diving into water"
+W160,"Fall into swimming pool"
+W1601,"Fall into swimming pool striking water surface"
+W16011,"Fall into swimming pool striking water surface causing drowning and submersion"
+W16012,"Fall into swimming pool striking water surface causing other injury"
+W1602,"Fall into swimming pool striking bottom"
+W16021,"Fall into swimming pool striking bottom causing drowning and submersion"
+W16022,"Fall into swimming pool striking bottom causing other injury"
+W1603,"Fall into swimming pool striking wall"
+W16031,"Fall into swimming pool striking wall causing drowning and submersion"
+W16032,"Fall into swimming pool striking wall causing other injury"
+W161,"Fall into natural body of water"
+W1611,"Fall into natural body of water striking water surface"
+W16111,"Fall into natural body of water striking water surface causing drowning and submersion"
+W16112,"Fall into natural body of water striking water surface causing other injury"
+W1612,"Fall into natural body of water striking bottom"
+W16121,"Fall into natural body of water striking bottom causing drowning and submersion"
+W16122,"Fall into natural body of water striking bottom causing other injury"
+W1613,"Fall into natural body of water striking side"
+W16131,"Fall into natural body of water striking side causing drowning and submersion"
+W16132,"Fall into natural body of water striking side causing other injury"
+W162,"Fall in (into) filled bathtub or bucket of water"
+W1621,"Fall in (into) filled bathtub"
+W16211,"Fall in (into) filled bathtub causing drowning and submersion"
+W16212,"Fall in (into) filled bathtub causing other injury"
+W1622,"Fall in (into) bucket of water"
+W16221,"Fall in (into) bucket of water causing drowning and submersion"
+W16222,"Fall in (into) bucket of water causing other injury"
+W163,"Fall into other water"
+W1631,"Fall into other water striking water surface"
+W16311,"Fall into other water striking water surface causing drowning and submersion"
+W16312,"Fall into other water striking water surface causing other injury"
+W1632,"Fall into other water striking bottom"
+W16321,"Fall into other water striking bottom causing drowning and submersion"
+W16322,"Fall into other water striking bottom causing other injury"
+W1633,"Fall into other water striking wall"
+W16331,"Fall into other water striking wall causing drowning and submersion"
+W16332,"Fall into other water striking wall causing other injury"
+W164,"Fall into unspecified water"
+W1641,"Fall into unspecified water causing drowning and submersion"
+W1642,"Fall into unspecified water causing other injury"
+W165,"Jumping or diving into swimming pool"
+W1651,"Jumping or diving into swimming pool striking water surface"
+W16511,"Jumping or diving into swimming pool striking water surface causing drowning and submersion"
+W16512,"Jumping or diving into swimming pool striking water surface causing other injury"
+W1652,"Jumping or diving into swimming pool striking bottom"
+W16521,"Jumping or diving into swimming pool striking bottom causing drowning and submersion"
+W16522,"Jumping or diving into swimming pool striking bottom causing other injury"
+W1653,"Jumping or diving into swimming pool striking wall"
+W16531,"Jumping or diving into swimming pool striking wall causing drowning and submersion"
+W16532,"Jumping or diving into swimming pool striking wall causing other injury"
+W166,"Jumping or diving into natural body of water"
+W1661,"Jumping or diving into natural body of water striking water surface"
+W16611,"Jumping or diving into natural body of water striking water surface causing drowning and submersion"
+W16612,"Jumping or diving into natural body of water striking water surface causing other injury"
+W1662,"Jumping or diving into natural body of water striking bottom"
+W16621,"Jumping or diving into natural body of water striking bottom causing drowning and submersion"
+W16622,"Jumping or diving into natural body of water striking bottom causing other injury"
+W167,"Jumping or diving from boat"
+W1671,"Jumping or diving from boat striking water surface"
+W16711,"Jumping or diving from boat striking water surface causing drowning and submersion"
+W16712,"Jumping or diving from boat striking water surface causing other injury"
+W1672,"Jumping or diving from boat striking bottom"
+W16721,"Jumping or diving from boat striking bottom causing drowning and submersion"
+W16722,"Jumping or diving from boat striking bottom causing other injury"
+W168,"Jumping or diving into other water"
+W1681,"Jumping or diving into other water striking water surface"
+W16811,"Jumping or diving into other water striking water surface causing drowning and submersion"
+W16812,"Jumping or diving into other water striking water surface causing other injury"
+W1682,"Jumping or diving into other water striking bottom"
+W16821,"Jumping or diving into other water striking bottom causing drowning and submersion"
+W16822,"Jumping or diving into other water striking bottom causing other injury"
+W1683,"Jumping or diving into other water striking wall"
+W16831,"Jumping or diving into other water striking wall causing drowning and submersion"
+W16832,"Jumping or diving into other water striking wall causing other injury"
+W169,"Jumping or diving into unspecified water"
+W1691,"Jumping or diving into unspecified water causing drowning and submersion"
+W1692,"Jumping or diving into unspecified water causing other injury"
+W17,"Other fall from one level to another"
+W170,"Fall into well"
+W171,"Fall into storm drain or manhole"
+W172,"Fall into hole"
+W173,"Fall into empty swimming pool"
+W174,"Fall from dock"
+W178,"Other fall from one level to another"
+W1781,"Fall down embankment (hill)"
+W1782,"Fall from (out of) grocery cart"
+W1789,"Other fall from one level to another"
+W18,"Other slipping, tripping and stumbling and falls"
+W180,"Fall due to bumping against object"
+W1800,"Striking against unspecified object with subsequent fall"
+W1801,"Striking against sports equipment with subsequent fall"
+W1802,"Striking against glass with subsequent fall"
+W1809,"Striking against other object with subsequent fall"
+W181,"Fall from or off toilet"
+W1811,"Fall from or off toilet without subsequent striking against object"
+W1812,"Fall from or off toilet with subsequent striking against object"
+W182,"Fall in (into) shower or empty bathtub"
+W183,"Other and unspecified fall on same level"
+W1830,"Fall on same level, unspecified"
+W1831,"Fall on same level due to stepping on an object"
+W1839,"Other fall on same level"
+W184,"Slipping, tripping and stumbling without falling"
+W1840,"Slipping, tripping and stumbling without falling, unspecified"
+W1841,"Slipping, tripping and stumbling without falling due to stepping on object"
+W1842,"Slipping, tripping and stumbling without falling due to stepping into hole or opening"
+W1843,"Slipping, tripping and stumbling without falling due to stepping from one level to another"
+W1849,"Other slipping, tripping and stumbling without falling"
+W19,"Unspecified fall"
+W20,"Struck by thrown, projected or falling object"
+W200,"Struck by falling object in cave-in"
+W201,"Struck by object due to collapse of building"
+W208,"Other cause of strike by thrown, projected or falling object"
+W21,"Striking against or struck by sports equipment"
+W210,"Struck by hit or thrown ball"
+W2100,"Struck by hit or thrown ball, unspecified type"
+W2101,"Struck by football"
+W2102,"Struck by soccer ball"
+W2103,"Struck by baseball"
+W2104,"Struck by golf ball"
+W2105,"Struck by basketball"
+W2106,"Struck by volleyball"
+W2107,"Struck by softball"
+W2109,"Struck by other hit or thrown ball"
+W211,"Struck by bat, racquet or club"
+W2111,"Struck by baseball bat"
+W2112,"Struck by tennis racquet"
+W2113,"Struck by golf club"
+W2119,"Struck by other bat, racquet or club"
+W212,"Struck by hockey stick or puck"
+W2121,"Struck by hockey stick"
+W21210,"Struck by ice hockey stick"
+W21211,"Struck by field hockey stick"
+W2122,"Struck by hockey puck"
+W21220,"Struck by ice hockey puck"
+W21221,"Struck by field hockey puck"
+W213,"Struck by sports foot wear"
+W2131,"Struck by shoe cleats"
+W2132,"Struck by skate blades"
+W2139,"Struck by other sports foot wear"
+W214,"Striking against diving board"
+W218,"Striking against or struck by other sports equipment"
+W2181,"Striking against or struck by football helmet"
+W2189,"Striking against or struck by other sports equipment"
+W219,"Striking against or struck by unspecified sports equipment"
+W22,"Striking against or struck by other objects"
+W220,"Striking against stationary object"
+W2201,"Walked into wall"
+W2202,"Walked into lamppost"
+W2203,"Walked into furniture"
+W2204,"Striking against wall of swimming pool"
+W22041,"Striking against wall of swimming pool causing drowning and submersion"
+W22042,"Striking against wall of swimming pool causing other injury"
+W2209,"Striking against other stationary object"
+W221,"Striking against or struck by automobile airbag"
+W2210,"Striking against or struck by unspecified automobile airbag"
+W2211,"Striking against or struck by driver side automobile airbag"
+W2212,"Striking against or struck by front passenger side automobile airbag"
+W2219,"Striking against or struck by other automobile airbag"
+W228,"Striking against or struck by other objects"
+W23,"Caught, crushed, jammed or pinched in or between objects"
+W230,"Caught, crushed, jammed, or pinched between moving objects"
+W231,"Caught, crushed, jammed, or pinched between stationary objects"
+W24,"Contact with lifting and transmission devices, not elsewhere classified"
+W240,"Contact with lifting devices, not elsewhere classified"
+W241,"Contact with transmission devices, not elsewhere classified"
+W25,"Contact with sharp glass"
+W26,"Contact with other sharp objects"
+W260,"Contact with knife"
+W261,"Contact with sword or dagger"
+W262,"Contact with edge of stiff paper"
+W268,"Contact with other sharp object(s), not elsewhere classified"
+W269,"Contact with unspecified sharp object(s)"
+W27,"Contact with nonpowered hand tool"
+W270,"Contact with workbench tool"
+W271,"Contact with garden tool"
+W272,"Contact with scissors"
+W273,"Contact with needle (sewing)"
+W274,"Contact with kitchen utensil"
+W275,"Contact with paper-cutter"
+W278,"Contact with other nonpowered hand tool"
+W28,"Contact with powered lawn mower"
+W29,"Contact with other powered hand tools and household machinery"
+W290,"Contact with powered kitchen appliance"
+W291,"Contact with electric knife"
+W292,"Contact with other powered household machinery"
+W293,"Contact with powered garden and outdoor hand tools and machinery"
+W294,"Contact with nail gun"
+W298,"Contact with other powered hand tools and household machinery"
+W30,"Contact with agricultural machinery"
+W300,"Contact with combine harvester"
+W301,"Contact with power take-off devices (PTO)"
+W302,"Contact with hay derrick"
+W303,"Contact with grain storage elevator"
+W308,"Contact with other specified agricultural machinery"
+W3081,"Contact with agricultural transport vehicle in stationary use"
+W3089,"Contact with other specified agricultural machinery"
+W309,"Contact with unspecified agricultural machinery"
+W31,"Contact with other and unspecified machinery"
+W310,"Contact with mining and earth-drilling machinery"
+W311,"Contact with metalworking machines"
+W312,"Contact with powered woodworking and forming machines"
+W313,"Contact with prime movers"
+W318,"Contact with other specified machinery"
+W3181,"Contact with recreational machinery"
+W3182,"Contact with other commercial machinery"
+W3183,"Contact with special construction vehicle in stationary use"
+W3189,"Contact with other specified machinery"
+W319,"Contact with unspecified machinery"
+W32,"Accidental handgun discharge and malfunction"
+W320,"Accidental handgun discharge"
+W321,"Accidental handgun malfunction"
+W33,"Accidental rifle, shotgun and larger firearm discharge and malfunction"
+W330,"Accidental rifle, shotgun and larger firearm discharge"
+W3300,"Accidental discharge of unspecified larger firearm"
+W3301,"Accidental discharge of shotgun"
+W3302,"Accidental discharge of hunting rifle"
+W3303,"Accidental discharge of machine gun"
+W3309,"Accidental discharge of other larger firearm"
+W331,"Accidental rifle, shotgun and larger firearm malfunction"
+W3310,"Accidental malfunction of unspecified larger firearm"
+W3311,"Accidental malfunction of shotgun"
+W3312,"Accidental malfunction of hunting rifle"
+W3313,"Accidental malfunction of machine gun"
+W3319,"Accidental malfunction of other larger firearm"
+W34,"Accidental discharge and malfunction from other and unspecified firearms and guns"
+W340,"Accidental discharge from other and unspecified firearms and guns"
+W3400,"Accidental discharge from unspecified firearms or gun"
+W3401,"Accidental discharge of gas, air or spring-operated guns"
+W34010,"Accidental discharge of airgun"
+W34011,"Accidental discharge of paintball gun"
+W34018,"Accidental discharge of other gas, air or spring-operated gun"
+W3409,"Accidental discharge from other specified firearms"
+W341,"Accidental malfunction from other and unspecified firearms and guns"
+W3410,"Accidental malfunction from unspecified firearms or gun"
+W3411,"Accidental malfunction of gas, air or spring-operated guns"
+W34110,"Accidental malfunction of airgun"
+W34111,"Accidental malfunction of paintball gun"
+W34118,"Accidental malfunction of other gas, air or spring-operated gun"
+W3419,"Accidental malfunction from other specified firearms"
+W35,"Explosion and rupture of boiler"
+W36,"Explosion and rupture of gas cylinder"
+W361,"Explosion and rupture of aerosol can"
+W362,"Explosion and rupture of air tank"
+W363,"Explosion and rupture of pressurized-gas tank"
+W368,"Explosion and rupture of other gas cylinder"
+W369,"Explosion and rupture of unspecified gas cylinder"
+W37,"Explosion and rupture of pressurized tire, pipe or hose"
+W370,"Explosion of bicycle tire"
+W378,"Explosion and rupture of other pressurized tire, pipe or hose"
+W38,"Explosion and rupture of other specified pressurized devices"
+W39,"Discharge of firework"
+W40,"Explosion of other materials"
+W400,"Explosion of blasting material"
+W401,"Explosion of explosive gases"
+W408,"Explosion of other specified explosive materials"
+W409,"Explosion of unspecified explosive materials"
+W42,"Exposure to noise"
+W420,"Exposure to supersonic waves"
+W429,"Exposure to other noise"
+W45,"Foreign body or object entering through skin"
+W450,"Nail entering through skin"
+W458,"Other foreign body or object entering through skin"
+W46,"Contact with hypodermic needle"
+W460,"Contact with hypodermic needle"
+W461,"Contact with contaminated hypodermic needle"
+W49,"Exposure to other inanimate mechanical forces"
+W490,"Item causing external constriction"
+W4901,"Hair causing external constriction"
+W4902,"String or thread causing external constriction"
+W4903,"Rubber band causing external constriction"
+W4904,"Ring or other jewelry causing external constriction"
+W4909,"Other specified item causing external constriction"
+W499,"Exposure to other inanimate mechanical forces"
+W50,"Accidental hit, strike, kick, twist, bite or scratch by another person"
+W500,"Accidental hit or strike by another person"
+W501,"Accidental kick by another person"
+W502,"Accidental twist by another person"
+W503,"Accidental bite by another person"
+W504,"Accidental scratch by another person"
+W51,"Accidental striking against or bumped into by another person"
+W52,"Crushed, pushed or stepped on by crowd or human stampede"
+W53,"Contact with rodent"
+W530,"Contact with mouse"
+W5301,"Bitten by mouse"
+W5309,"Other contact with mouse"
+W531,"Contact with rat"
+W5311,"Bitten by rat"
+W5319,"Other contact with rat"
+W532,"Contact with squirrel"
+W5321,"Bitten by squirrel"
+W5329,"Other contact with squirrel"
+W538,"Contact with other rodent"
+W5381,"Bitten by other rodent"
+W5389,"Other contact with other rodent"
+W54,"Contact with dog"
+W540,"Bitten by dog"
+W541,"Struck by dog"
+W548,"Other contact with dog"
+W55,"Contact with other mammals"
+W550,"Contact with cat"
+W5501,"Bitten by cat"
+W5503,"Scratched by cat"
+W5509,"Other contact with cat"
+W551,"Contact with horse"
+W5511,"Bitten by horse"
+W5512,"Struck by horse"
+W5519,"Other contact with horse"
+W552,"Contact with cow"
+W5521,"Bitten by cow"
+W5522,"Struck by cow"
+W5529,"Other contact with cow"
+W553,"Contact with other hoof stock"
+W5531,"Bitten by other hoof stock"
+W5532,"Struck by other hoof stock"
+W5539,"Other contact with other hoof stock"
+W554,"Contact with pig"
+W5541,"Bitten by pig"
+W5542,"Struck by pig"
+W5549,"Other contact with pig"
+W555,"Contact with raccoon"
+W5551,"Bitten by raccoon"
+W5552,"Struck by raccoon"
+W5559,"Other contact with raccoon"
+W558,"Contact with other mammals"
+W5581,"Bitten by other mammals"
+W5582,"Struck by other mammals"
+W5589,"Other contact with other mammals"
+W56,"Contact with nonvenomous marine animal"
+W560,"Contact with dolphin"
+W5601,"Bitten by dolphin"
+W5602,"Struck by dolphin"
+W5609,"Other contact with dolphin"
+W561,"Contact with sea lion"
+W5611,"Bitten by sea lion"
+W5612,"Struck by sea lion"
+W5619,"Other contact with sea lion"
+W562,"Contact with orca"
+W5621,"Bitten by orca"
+W5622,"Struck by orca"
+W5629,"Other contact with orca"
+W563,"Contact with other marine mammals"
+W5631,"Bitten by other marine mammals"
+W5632,"Struck by other marine mammals"
+W5639,"Other contact with other marine mammals"
+W564,"Contact with shark"
+W5641,"Bitten by shark"
+W5642,"Struck by shark"
+W5649,"Other contact with shark"
+W565,"Contact with other fish"
+W5651,"Bitten by other fish"
+W5652,"Struck by other fish"
+W5659,"Other contact with other fish"
+W568,"Contact with other nonvenomous marine animals"
+W5681,"Bitten by other nonvenomous marine animals"
+W5682,"Struck by other nonvenomous marine animals"
+W5689,"Other contact with other nonvenomous marine animals"
+W57,"Bitten or stung by nonvenomous insect and other nonvenomous arthropods"
+W58,"Contact with crocodile or alligator"
+W580,"Contact with alligator"
+W5801,"Bitten by alligator"
+W5802,"Struck by alligator"
+W5803,"Crushed by alligator"
+W5809,"Other contact with alligator"
+W581,"Contact with crocodile"
+W5811,"Bitten by crocodile"
+W5812,"Struck by crocodile"
+W5813,"Crushed by crocodile"
+W5819,"Other contact with crocodile"
+W59,"Contact with other nonvenomous reptiles"
+W590,"Contact with nonvenomous lizards"
+W5901,"Bitten by nonvenomous lizards"
+W5902,"Struck by nonvenomous lizards"
+W5909,"Other contact with nonvenomous lizards"
+W591,"Contact with nonvenomous snakes"
+W5911,"Bitten by nonvenomous snake"
+W5912,"Struck by nonvenomous snake"
+W5913,"Crushed by nonvenomous snake"
+W5919,"Other contact with nonvenomous snake"
+W592,"Contact with turtles"
+W5921,"Bitten by turtle"
+W5922,"Struck by turtle"
+W5929,"Other contact with turtle"
+W598,"Contact with other nonvenomous reptiles"
+W5981,"Bitten by other nonvenomous reptiles"
+W5982,"Struck by other nonvenomous reptiles"
+W5983,"Crushed by other nonvenomous reptiles"
+W5989,"Other contact with other nonvenomous reptiles"
+W60,"Contact with nonvenomous plant thorns and spines and sharp leaves"
+W61,"Contact with birds (domestic) (wild)"
+W610,"Contact with parrot"
+W6101,"Bitten by parrot"
+W6102,"Struck by parrot"
+W6109,"Other contact with parrot"
+W611,"Contact with macaw"
+W6111,"Bitten by macaw"
+W6112,"Struck by macaw"
+W6119,"Other contact with macaw"
+W612,"Contact with other psittacines"
+W6121,"Bitten by other psittacines"
+W6122,"Struck by other psittacines"
+W6129,"Other contact with other psittacines"
+W613,"Contact with chicken"
+W6132,"Struck by chicken"
+W6133,"Pecked by chicken"
+W6139,"Other contact with chicken"
+W614,"Contact with turkey"
+W6142,"Struck by turkey"
+W6143,"Pecked by turkey"
+W6149,"Other contact with turkey"
+W615,"Contact with goose"
+W6151,"Bitten by goose"
+W6152,"Struck by goose"
+W6159,"Other contact with goose"
+W616,"Contact with duck"
+W6161,"Bitten by duck"
+W6162,"Struck by duck"
+W6169,"Other contact with duck"
+W619,"Contact with other birds"
+W6191,"Bitten by other birds"
+W6192,"Struck by other birds"
+W6199,"Other contact with other birds"
+W62,"Contact with nonvenomous amphibians"
+W620,"Contact with nonvenomous frogs"
+W621,"Contact with nonvenomous toads"
+W629,"Contact with other nonvenomous amphibians"
+W64,"Exposure to other animate mechanical forces"
+W65,"Accidental drowning and submersion while in bath-tub"
+W67,"Accidental drowning and submersion while in swimming-pool"
+W69,"Accidental drowning and submersion while in natural water"
+W73,"Other specified cause of accidental non-transport drowning and submersion"
+W74,"Unspecified cause of accidental drowning and submersion"
+W85,"Exposure to electric transmission lines"
+W86,"Exposure to other specified electric current"
+W860,"Exposure to domestic wiring and appliances"
+W861,"Exposure to industrial wiring, appliances and electrical machinery"
+W868,"Exposure to other electric current"
+W88,"Exposure to ionizing radiation"
+W880,"Exposure to X-rays"
+W881,"Exposure to radioactive isotopes"
+W888,"Exposure to other ionizing radiation"
+W89,"Exposure to man-made visible and ultraviolet light"
+W890,"Exposure to welding light (arc)"
+W891,"Exposure to tanning bed"
+W898,"Exposure to other man-made visible and ultraviolet light"
+W899,"Exposure to unspecified man-made visible and ultraviolet light"
+W90,"Exposure to other nonionizing radiation"
+W900,"Exposure to radiofrequency"
+W901,"Exposure to infrared radiation"
+W902,"Exposure to laser radiation"
+W908,"Exposure to other nonionizing radiation"
+W92,"Exposure to excessive heat of man-made origin"
+W93,"Exposure to excessive cold of man-made origin"
+W930,"Contact with or inhalation of dry ice"
+W9301,"Contact with dry ice"
+W9302,"Inhalation of dry ice"
+W931,"Contact with or inhalation of liquid air"
+W9311,"Contact with liquid air"
+W9312,"Inhalation of liquid air"
+W932,"Prolonged exposure in deep freeze unit or refrigerator"
+W938,"Exposure to other excessive cold of man-made origin"
+W94,"Exposure to high and low air pressure and changes in air pressure"
+W940,"Exposure to prolonged high air pressure"
+W941,"Exposure to prolonged low air pressure"
+W9411,"Exposure to residence or prolonged visit at high altitude"
+W9412,"Exposure to other prolonged low air pressure"
+W942,"Exposure to rapid changes in air pressure during ascent"
+W9421,"Exposure to reduction in atmospheric pressure while surfacing from deep-water diving"
+W9422,"Exposure to reduction in atmospheric pressure while surfacing from underground"
+W9423,"Exposure to sudden change in air pressure in aircraft during ascent"
+W9429,"Exposure to other rapid changes in air pressure during ascent"
+W943,"Exposure to rapid changes in air pressure during descent"
+W9431,"Exposure to sudden change in air pressure in aircraft during descent"
+W9432,"Exposure to high air pressure from rapid descent in water"
+W9439,"Exposure to other rapid changes in air pressure during descent"
+W99,"Exposure to other man-made environmental factors"
+X00,"Exposure to uncontrolled fire in building or structure"
+X000,"Exposure to flames in uncontrolled fire in building or structure"
+X001,"Exposure to smoke in uncontrolled fire in building or structure"
+X002,"Injury due to collapse of burning building or structure in uncontrolled fire"
+X003,"Fall from burning building or structure in uncontrolled fire"
+X004,"Hit by object from burning building or structure in uncontrolled fire"
+X005,"Jump from burning building or structure in uncontrolled fire"
+X008,"Other exposure to uncontrolled fire in building or structure"
+X01,"Exposure to uncontrolled fire, not in building or structure"
+X010,"Exposure to flames in uncontrolled fire, not in building or structure"
+X011,"Exposure to smoke in uncontrolled fire, not in building or structure"
+X013,"Fall due to uncontrolled fire, not in building or structure"
+X014,"Hit by object due to uncontrolled fire, not in building or structure"
+X018,"Other exposure to uncontrolled fire, not in building or structure"
+X02,"Exposure to controlled fire in building or structure"
+X020,"Exposure to flames in controlled fire in building or structure"
+X021,"Exposure to smoke in controlled fire in building or structure"
+X022,"Injury due to collapse of burning building or structure in controlled fire"
+X023,"Fall from burning building or structure in controlled fire"
+X024,"Hit by object from burning building or structure in controlled fire"
+X025,"Jump from burning building or structure in controlled fire"
+X028,"Other exposure to controlled fire in building or structure"
+X03,"Exposure to controlled fire, not in building or structure"
+X030,"Exposure to flames in controlled fire, not in building or structure"
+X031,"Exposure to smoke in controlled fire, not in building or structure"
+X033,"Fall due to controlled fire, not in building or structure"
+X034,"Hit by object due to controlled fire, not in building or structure"
+X038,"Other exposure to controlled fire, not in building or structure"
+X04,"Exposure to ignition of highly flammable material"
+X05,"Exposure to ignition or melting of nightwear"
+X06,"Exposure to ignition or melting of other clothing and apparel"
+X060,"Exposure to ignition of plastic jewelry"
+X061,"Exposure to melting of plastic jewelry"
+X062,"Exposure to ignition of other clothing and apparel"
+X063,"Exposure to melting of other clothing and apparel"
+X08,"Exposure to other specified smoke, fire and flames"
+X080,"Exposure to bed fire"
+X0800,"Exposure to bed fire due to unspecified burning material"
+X0801,"Exposure to bed fire due to burning cigarette"
+X0809,"Exposure to bed fire due to other burning material"
+X081,"Exposure to sofa fire"
+X0810,"Exposure to sofa fire due to unspecified burning material"
+X0811,"Exposure to sofa fire due to burning cigarette"
+X0819,"Exposure to sofa fire due to other burning material"
+X082,"Exposure to other furniture fire"
+X0820,"Exposure to other furniture fire due to unspecified burning material"
+X0821,"Exposure to other furniture fire due to burning cigarette"
+X0829,"Exposure to other furniture fire due to other burning material"
+X088,"Exposure to other specified smoke, fire and flames"
+X10,"Contact with hot drinks, food, fats and cooking oils"
+X100,"Contact with hot drinks"
+X101,"Contact with hot food"
+X102,"Contact with fats and cooking oils"
+X11,"Contact with hot tap-water"
+X110,"Contact with hot water in bath or tub"
+X111,"Contact with running hot water"
+X118,"Contact with other hot tap-water"
+X12,"Contact with other hot fluids"
+X13,"Contact with steam and other hot vapors"
+X130,"Inhalation of steam and other hot vapors"
+X131,"Other contact with steam and other hot vapors"
+X14,"Contact with hot air and other hot gases"
+X140,"Inhalation of hot air and gases"
+X141,"Other contact with hot air and other hot gases"
+X15,"Contact with hot household appliances"
+X150,"Contact with hot stove (kitchen)"
+X151,"Contact with hot toaster"
+X152,"Contact with hotplate"
+X153,"Contact with hot saucepan or skillet"
+X158,"Contact with other hot household appliances"
+X16,"Contact with hot heating appliances, radiators and pipes"
+X17,"Contact with hot engines, machinery and tools"
+X18,"Contact with other hot metals"
+X19,"Contact with other heat and hot substances"
+X30,"Exposure to excessive natural heat"
+X31,"Exposure to excessive natural cold"
+X32,"Exposure to sunlight"
+X34,"Earthquake"
+X35,"Volcanic eruption"
+X36,"Avalanche, landslide and other earth movements"
+X360,"Collapse of dam or man-made structure causing earth movement"
+X361,"Avalanche, landslide, or mudslide"
+X37,"Cataclysmic storm"
+X370,"Hurricane"
+X371,"Tornado"
+X372,"Blizzard (snow)(ice)"
+X373,"Dust storm"
+X374,"Tidalwave"
+X3741,"Tidal wave due to earthquake or volcanic eruption"
+X3742,"Tidal wave due to storm"
+X3743,"Tidal wave due to landslide"
+X378,"Other cataclysmic storms"
+X379,"Unspecified cataclysmic storm"
+X38,"Flood"
+X39,"Exposure to other forces of nature"
+X390,"Exposure to natural radiation"
+X3901,"Exposure to radon"
+X3908,"Exposure to other natural radiation"
+X398,"Other exposure to forces of nature"
+X50,"Overexertion and strenuous or repetitive movements"
+X500,"Overexertion from strenuous movement or load"
+X501,"Overexertion from prolonged static or awkward postures"
+X503,"Overexertion from repetitive movements"
+X509,"Other and unspecified overexertion or strenuous movements or postures"
+X52,"Prolonged stay in weightless environment"
+X58,"Exposure to other specified factors"
+X71,"Intentional self-harm by drowning and submersion"
+X710,"Intentional self-harm by drowning and submersion while in bathtub"
+X711,"Intentional self-harm by drowning and submersion while in swimming pool"
+X712,"Intentional self-harm by drowning and submersion after jump into swimming pool"
+X713,"Intentional self-harm by drowning and submersion in natural water"
+X718,"Other intentional self-harm by drowning and submersion"
+X719,"Intentional self-harm by drowning and submersion, unspecified"
+X72,"Intentional self-harm by handgun discharge"
+X73,"Intentional self-harm by rifle, shotgun and larger firearm discharge"
+X730,"Intentional self-harm by shotgun discharge"
+X731,"Intentional self-harm by hunting rifle discharge"
+X732,"Intentional self-harm by machine gun discharge"
+X738,"Intentional self-harm by other larger firearm discharge"
+X739,"Intentional self-harm by unspecified larger firearm discharge"
+X74,"Intentional self-harm by other and unspecified firearm and gun discharge"
+X740,"Intentional self-harm by gas, air or spring-operated guns"
+X7401,"Intentional self-harm by airgun"
+X7402,"Intentional self-harm by paintball gun"
+X7409,"Intentional self-harm by other gas, air or spring-operated gun"
+X748,"Intentional self-harm by other firearm discharge"
+X749,"Intentional self-harm by unspecified firearm discharge"
+X75,"Intentional self-harm by explosive material"
+X76,"Intentional self-harm by smoke, fire and flames"
+X77,"Intentional self-harm by steam, hot vapors and hot objects"
+X770,"Intentional self-harm by steam or hot vapors"
+X771,"Intentional self-harm by hot tap water"
+X772,"Intentional self-harm by other hot fluids"
+X773,"Intentional self-harm by hot household appliances"
+X778,"Intentional self-harm by other hot objects"
+X779,"Intentional self-harm by unspecified hot objects"
+X78,"Intentional self-harm by sharp object"
+X780,"Intentional self-harm by sharp glass"
+X781,"Intentional self-harm by knife"
+X782,"Intentional self-harm by sword or dagger"
+X788,"Intentional self-harm by other sharp object"
+X789,"Intentional self-harm by unspecified sharp object"
+X79,"Intentional self-harm by blunt object"
+X80,"Intentional self-harm by jumping from a high place"
+X81,"Intentional self-harm by jumping or lying in front of moving object"
+X810,"Intentional self-harm by jumping or lying in front of motor vehicle"
+X811,"Intentional self-harm by jumping or lying in front of (subway) train"
+X818,"Intentional self-harm by jumping or lying in front of other moving object"
+X82,"Intentional self-harm by crashing of motor vehicle"
+X820,"Intentional collision of motor vehicle with other motor vehicle"
+X821,"Intentional collision of motor vehicle with train"
+X822,"Intentional collision of motor vehicle with tree"
+X828,"Other intentional self-harm by crashing of motor vehicle"
+X83,"Intentional self-harm by other specified means"
+X830,"Intentional self-harm by crashing of aircraft"
+X831,"Intentional self-harm by electrocution"
+X832,"Intentional self-harm by exposure to extremes of cold"
+X838,"Intentional self-harm by other specified means"
+X92,"Assault by drowning and submersion"
+X920,"Assault by drowning and submersion while in bathtub"
+X921,"Assault by drowning and submersion while in swimming pool"
+X922,"Assault by drowning and submersion after push into swimming pool"
+X923,"Assault by drowning and submersion in natural water"
+X928,"Other assault by drowning and submersion"
+X929,"Assault by drowning and submersion, unspecified"
+X93,"Assault by handgun discharge"
+X94,"Assault by rifle, shotgun and larger firearm discharge"
+X940,"Assault by shotgun"
+X941,"Assault by hunting rifle"
+X942,"Assault by machine gun"
+X948,"Assault by other larger firearm discharge"
+X949,"Assault by unspecified larger firearm discharge"
+X95,"Assault by other and unspecified firearm and gun discharge"
+X950,"Assault by gas, air or spring-operated guns"
+X9501,"Assault by airgun discharge"
+X9502,"Assault by paintball gun discharge"
+X9509,"Assault by other gas, air or spring-operated gun"
+X958,"Assault by other firearm discharge"
+X959,"Assault by unspecified firearm discharge"
+X96,"Assault by explosive material"
+X960,"Assault by antipersonnel bomb"
+X961,"Assault by gasoline bomb"
+X962,"Assault by letter bomb"
+X963,"Assault by fertilizer bomb"
+X964,"Assault by pipe bomb"
+X968,"Assault by other specified explosive"
+X969,"Assault by unspecified explosive"
+X97,"Assault by smoke, fire and flames"
+X98,"Assault by steam, hot vapors and hot objects"
+X980,"Assault by steam or hot vapors"
+X981,"Assault by hot tap water"
+X982,"Assault by hot fluids"
+X983,"Assault by hot household appliances"
+X988,"Assault by other hot objects"
+X989,"Assault by unspecified hot objects"
+X99,"Assault by sharp object"
+X990,"Assault by sharp glass"
+X991,"Assault by knife"
+X992,"Assault by sword or dagger"
+X998,"Assault by other sharp object"
+X999,"Assault by unspecified sharp object"
+Y00,"Assault by blunt object"
+Y01,"Assault by pushing from high place"
+Y02,"Assault by pushing or placing victim in front of moving object"
+Y020,"Assault by pushing or placing victim in front of motor vehicle"
+Y021,"Assault by pushing or placing victim in front of (subway) train"
+Y028,"Assault by pushing or placing victim in front of other moving object"
+Y03,"Assault by crashing of motor vehicle"
+Y030,"Assault by being hit or run over by motor vehicle"
+Y038,"Other assault by crashing of motor vehicle"
+Y04,"Assault by bodily force"
+Y040,"Assault by unarmed brawl or fight"
+Y041,"Assault by human bite"
+Y042,"Assault by strike against or bumped into by another person"
+Y048,"Assault by other bodily force"
+Y07,"Perpetrator of assault, maltreatment and neglect"
+Y070,"Spouse or partner, perpetrator of maltreatment and neglect"
+Y071,"Parent (adoptive) (biological), perpetrator of maltreatment and neglect"
+Y074,"Other family member, perpetrator of maltreatment and neglect"
+Y0741,"Sibling, perpetrator of maltreatment and neglect"
+Y0742,"Foster parent, perpetrator of maltreatment and neglect"
+Y0743,"Stepparent or stepsibling, perpetrator of maltreatment and neglect"
+Y0749,"Other family member, perpetrator of maltreatment and neglect"
+Y075,"Non-family member, perpetrator of maltreatment and neglect"
+Y0751,"Daycare provider, perpetrator of maltreatment and neglect"
+Y0752,"Healthcare provider, perpetrator of maltreatment and neglect"
+Y0753,"Teacher or instructor, perpetrator of maltreatment and neglect"
+Y0759,"Other non-family member, perpetrator of maltreatment and neglect"
+Y079,"Unspecified perpetrator of maltreatment and neglect"
+Y08,"Assault by other specified means"
+Y080,"Assault by strike by sport equipment"
+Y0801,"Assault by strike by hockey stick"
+Y0802,"Assault by strike by baseball bat"
+Y0809,"Assault by strike by other specified type of sport equipment"
+Y088,"Assault by other specified means"
+Y0881,"Assault by crashing of aircraft"
+Y0889,"Assault by other specified means"
+Y09,"Assault by unspecified means"
+Y21,"Drowning and submersion, undetermined intent"
+Y210,"Drowning and submersion while in bathtub, undetermined intent"
+Y211,"Drowning and submersion after fall into bathtub, undetermined intent"
+Y212,"Drowning and submersion while in swimming pool, undetermined intent"
+Y213,"Drowning and submersion after fall into swimming pool, undetermined intent"
+Y214,"Drowning and submersion in natural water, undetermined intent"
+Y218,"Other drowning and submersion, undetermined intent"
+Y219,"Unspecified drowning and submersion, undetermined intent"
+Y22,"Handgun discharge, undetermined intent"
+Y23,"Rifle, shotgun and larger firearm discharge, undetermined intent"
+Y230,"Shotgun discharge, undetermined intent"
+Y231,"Hunting rifle discharge, undetermined intent"
+Y232,"Military firearm discharge, undetermined intent"
+Y233,"Machine gun discharge, undetermined intent"
+Y238,"Other larger firearm discharge, undetermined intent"
+Y239,"Unspecified larger firearm discharge, undetermined intent"
+Y24,"Other and unspecified firearm discharge, undetermined intent"
+Y240,"Airgun discharge, undetermined intent"
+Y248,"Other firearm discharge, undetermined intent"
+Y249,"Unspecified firearm discharge, undetermined intent"
+Y25,"Contact with explosive material, undetermined intent"
+Y26,"Exposure to smoke, fire and flames, undetermined intent"
+Y27,"Contact with steam, hot vapors and hot objects, undetermined intent"
+Y270,"Contact with steam and hot vapors, undetermined intent"
+Y271,"Contact with hot tap water, undetermined intent"
+Y272,"Contact with hot fluids, undetermined intent"
+Y273,"Contact with hot household appliance, undetermined intent"
+Y278,"Contact with other hot objects, undetermined intent"
+Y279,"Contact with unspecified hot objects, undetermined intent"
+Y28,"Contact with sharp object, undetermined intent"
+Y280,"Contact with sharp glass, undetermined intent"
+Y281,"Contact with knife, undetermined intent"
+Y282,"Contact with sword or dagger, undetermined intent"
+Y288,"Contact with other sharp object, undetermined intent"
+Y289,"Contact with unspecified sharp object, undetermined intent"
+Y29,"Contact with blunt object, undetermined intent"
+Y30,"Falling, jumping or pushed from a high place, undetermined intent"
+Y31,"Falling, lying or running before or into moving object, undetermined intent"
+Y32,"Crashing of motor vehicle, undetermined intent"
+Y33,"Other specified events, undetermined intent"
+Y35,"Legal intervention"
+Y350,"Legal intervention involving firearm discharge"
+Y3500,"Legal intervention involving unspecified firearm discharge"
+Y35001,"Legal intervention involving unspecified firearm discharge, law enforcement official injured"
+Y35002,"Legal intervention involving unspecified firearm discharge, bystander injured"
+Y35003,"Legal intervention involving unspecified firearm discharge, suspect injured"
+Y3501,"Legal intervention involving injury by machine gun"
+Y35011,"Legal intervention involving injury by machine gun, law enforcement official injured"
+Y35012,"Legal intervention involving injury by machine gun, bystander injured"
+Y35013,"Legal intervention involving injury by machine gun, suspect injured"
+Y3502,"Legal intervention involving injury by handgun"
+Y35021,"Legal intervention involving injury by handgun, law enforcement official injured"
+Y35022,"Legal intervention involving injury by handgun, bystander injured"
+Y35023,"Legal intervention involving injury by handgun, suspect injured"
+Y3503,"Legal intervention involving injury by rifle pellet"
+Y35031,"Legal intervention involving injury by rifle pellet, law enforcement official injured"
+Y35032,"Legal intervention involving injury by rifle pellet, bystander injured"
+Y35033,"Legal intervention involving injury by rifle pellet, suspect injured"
+Y3504,"Legal intervention involving injury by rubber bullet"
+Y35041,"Legal intervention involving injury by rubber bullet, law enforcement official injured"
+Y35042,"Legal intervention involving injury by rubber bullet, bystander injured"
+Y35043,"Legal intervention involving injury by rubber bullet, suspect injured"
+Y3509,"Legal intervention involving other firearm discharge"
+Y35091,"Legal intervention involving other firearm discharge, law enforcement official injured"
+Y35092,"Legal intervention involving other firearm discharge, bystander injured"
+Y35093,"Legal intervention involving other firearm discharge, suspect injured"
+Y351,"Legal intervention involving explosives"
+Y3510,"Legal intervention involving unspecified explosives"
+Y35101,"Legal intervention involving unspecified explosives, law enforcement official injured"
+Y35102,"Legal intervention involving unspecified explosives, bystander injured"
+Y35103,"Legal intervention involving unspecified explosives, suspect injured"
+Y3511,"Legal intervention involving injury by dynamite"
+Y35111,"Legal intervention involving injury by dynamite, law enforcement official injured"
+Y35112,"Legal intervention involving injury by dynamite, bystander injured"
+Y35113,"Legal intervention involving injury by dynamite, suspect injured"
+Y3512,"Legal intervention involving injury by explosive shell"
+Y35121,"Legal intervention involving injury by explosive shell, law enforcement official injured"
+Y35122,"Legal intervention involving injury by explosive shell, bystander injured"
+Y35123,"Legal intervention involving injury by explosive shell, suspect injured"
+Y3519,"Legal intervention involving other explosives"
+Y35191,"Legal intervention involving other explosives, law enforcement official injured"
+Y35192,"Legal intervention involving other explosives, bystander injured"
+Y35193,"Legal intervention involving other explosives, suspect injured"
+Y352,"Legal intervention involving gas"
+Y3520,"Legal intervention involving unspecified gas"
+Y35201,"Legal intervention involving unspecified gas, law enforcement official injured"
+Y35202,"Legal intervention involving unspecified gas, bystander injured"
+Y35203,"Legal intervention involving unspecified gas, suspect injured"
+Y3521,"Legal intervention involving injury by tear gas"
+Y35211,"Legal intervention involving injury by tear gas, law enforcement official injured"
+Y35212,"Legal intervention involving injury by tear gas, bystander injured"
+Y35213,"Legal intervention involving injury by tear gas, suspect injured"
+Y3529,"Legal intervention involving other gas"
+Y35291,"Legal intervention involving other gas, law enforcement official injured"
+Y35292,"Legal intervention involving other gas, bystander injured"
+Y35293,"Legal intervention involving other gas, suspect injured"
+Y353,"Legal intervention involving blunt objects"
+Y3530,"Legal intervention involving unspecified blunt objects"
+Y35301,"Legal intervention involving unspecified blunt objects, law enforcement official injured"
+Y35302,"Legal intervention involving unspecified blunt objects, bystander injured"
+Y35303,"Legal intervention involving unspecified blunt objects, suspect injured"
+Y3531,"Legal intervention involving baton"
+Y35311,"Legal intervention involving baton, law enforcement official injured"
+Y35312,"Legal intervention involving baton, bystander injured"
+Y35313,"Legal intervention involving baton, suspect injured"
+Y3539,"Legal intervention involving other blunt objects"
+Y35391,"Legal intervention involving other blunt objects, law enforcement official injured"
+Y35392,"Legal intervention involving other blunt objects, bystander injured"
+Y35393,"Legal intervention involving other blunt objects, suspect injured"
+Y354,"Legal intervention involving sharp objects"
+Y3540,"Legal intervention involving unspecified sharp objects"
+Y35401,"Legal intervention involving unspecified sharp objects, law enforcement official injured"
+Y35402,"Legal intervention involving unspecified sharp objects, bystander injured"
+Y35403,"Legal intervention involving unspecified sharp objects, suspect injured"
+Y3541,"Legal intervention involving bayonet"
+Y35411,"Legal intervention involving bayonet, law enforcement official injured"
+Y35412,"Legal intervention involving bayonet, bystander injured"
+Y35413,"Legal intervention involving bayonet, suspect injured"
+Y3549,"Legal intervention involving other sharp objects"
+Y35491,"Legal intervention involving other sharp objects, law enforcement official injured"
+Y35492,"Legal intervention involving other sharp objects, bystander injured"
+Y35493,"Legal intervention involving other sharp objects, suspect injured"
+Y358,"Legal intervention involving other specified means"
+Y3581,"Legal intervention involving manhandling"
+Y35811,"Legal intervention involving manhandling, law enforcement official injured"
+Y35812,"Legal intervention involving manhandling, bystander injured"
+Y35813,"Legal intervention involving manhandling, suspect injured"
+Y3589,"Legal intervention involving other specified means"
+Y35891,"Legal intervention involving other specified means, law enforcement official injured"
+Y35892,"Legal intervention involving other specified means, bystander injured"
+Y35893,"Legal intervention involving other specified means, suspect injured"
+Y359,"Legal intervention, means unspecified"
+Y3591,"Legal intervention, means unspecified, law enforcement official injured"
+Y3592,"Legal intervention, means unspecified, bystander injured"
+Y3593,"Legal intervention, means unspecified, suspect injured"
+Y36,"Operations of war"
+Y360,"War operations involving explosion of marine weapons"
+Y3600,"War operations involving explosion of unspecified marine weapon"
+Y36000,"War operations involving explosion of unspecified marine weapon, military personnel"
+Y36001,"War operations involving explosion of unspecified marine weapon, civilian"
+Y3601,"War operations involving explosion of depth-charge"
+Y36010,"War operations involving explosion of depth-charge, military personnel"
+Y36011,"War operations involving explosion of depth-charge, civilian"
+Y3602,"War operations involving explosion of marine mine"
+Y36020,"War operations involving explosion of marine mine, military personnel"
+Y36021,"War operations involving explosion of marine mine, civilian"
+Y3603,"War operations involving explosion of sea-based artillery shell"
+Y36030,"War operations involving explosion of sea-based artillery shell, military personnel"
+Y36031,"War operations involving explosion of sea-based artillery shell, civilian"
+Y3604,"War operations involving explosion of torpedo"
+Y36040,"War operations involving explosion of torpedo, military personnel"
+Y36041,"War operations involving explosion of torpedo, civilian"
+Y3605,"War operations involving accidental detonation of onboard marine weapons"
+Y36050,"War operations involving accidental detonation of onboard marine weapons, military personnel"
+Y36051,"War operations involving accidental detonation of onboard marine weapons, civilian"
+Y3609,"War operations involving explosion of other marine weapons"
+Y36090,"War operations involving explosion of other marine weapons, military personnel"
+Y36091,"War operations involving explosion of other marine weapons, civilian"
+Y361,"War operations involving destruction of aircraft"
+Y3610,"War operations involving unspecified destruction of aircraft"
+Y36100,"War operations involving unspecified destruction of aircraft, military personnel"
+Y36101,"War operations involving unspecified destruction of aircraft, civilian"
+Y3611,"War operations involving destruction of aircraft due to enemy fire or explosives"
+Y36110,"War operations involving destruction of aircraft due to enemy fire or explosives, military personnel"
+Y36111,"War operations involving destruction of aircraft due to enemy fire or explosives, civilian"
+Y3612,"War operations involving destruction of aircraft due to collision with other aircraft"
+Y36120,"War operations involving destruction of aircraft due to collision with other aircraft, military personnel"
+Y36121,"War operations involving destruction of aircraft due to collision with other aircraft, civilian"
+Y3613,"War operations involving destruction of aircraft due to onboard fire"
+Y36130,"War operations involving destruction of aircraft due to onboard fire, military personnel"
+Y36131,"War operations involving destruction of aircraft due to onboard fire, civilian"
+Y3614,"War operations involving destruction of aircraft due to accidental detonation of onboard munitions and explosives"
+Y36140,"War operations involving destruction of aircraft due to accidental detonation of onboard munitions and explosives, military personnel"
+Y36141,"War operations involving destruction of aircraft due to accidental detonation of onboard munitions and explosives, civilian"
+Y3619,"War operations involving other destruction of aircraft"
+Y36190,"War operations involving other destruction of aircraft, military personnel"
+Y36191,"War operations involving other destruction of aircraft, civilian"
+Y362,"War operations involving other explosions and fragments"
+Y3620,"War operations involving unspecified explosion and fragments"
+Y36200,"War operations involving unspecified explosion and fragments, military personnel"
+Y36201,"War operations involving unspecified explosion and fragments, civilian"
+Y3621,"War operations involving explosion of aerial bomb"
+Y36210,"War operations involving explosion of aerial bomb, military personnel"
+Y36211,"War operations involving explosion of aerial bomb, civilian"
+Y3622,"War operations involving explosion of guided missile"
+Y36220,"War operations involving explosion of guided missile, military personnel"
+Y36221,"War operations involving explosion of guided missile, civilian"
+Y3623,"War operations involving explosion of improvised explosive device [IED]"
+Y36230,"War operations involving explosion of improvised explosive device [IED], military personnel"
+Y36231,"War operations involving explosion of improvised explosive device [IED], civilian"
+Y3624,"War operations involving explosion due to accidental detonation and discharge of own munitions or munitions launch device"
+Y36240,"War operations involving explosion due to accidental detonation and discharge of own munitions or munitions launch device, military personnel"
+Y36241,"War operations involving explosion due to accidental detonation and discharge of own munitions or munitions launch device, civilian"
+Y3625,"War operations involving fragments from munitions"
+Y36250,"War operations involving fragments from munitions, military personnel"
+Y36251,"War operations involving fragments from munitions, civilian"
+Y3626,"War operations involving fragments of improvised explosive device [IED]"
+Y36260,"War operations involving fragments of improvised explosive device [IED], military personnel"
+Y36261,"War operations involving fragments of improvised explosive device [IED], civilian"
+Y3627,"War operations involving fragments from weapons"
+Y36270,"War operations involving fragments from weapons, military personnel"
+Y36271,"War operations involving fragments from weapons, civilian"
+Y3629,"War operations involving other explosions and fragments"
+Y36290,"War operations involving other explosions and fragments, military personnel"
+Y36291,"War operations involving other explosions and fragments, civilian"
+Y363,"War operations involving fires, conflagrations and hot substances"
+Y3630,"War operations involving unspecified fire, conflagration and hot substance"
+Y36300,"War operations involving unspecified fire, conflagration and hot substance, military personnel"
+Y36301,"War operations involving unspecified fire, conflagration and hot substance, civilian"
+Y3631,"War operations involving gasoline bomb"
+Y36310,"War operations involving gasoline bomb, military personnel"
+Y36311,"War operations involving gasoline bomb, civilian"
+Y3632,"War operations involving incendiary bullet"
+Y36320,"War operations involving incendiary bullet, military personnel"
+Y36321,"War operations involving incendiary bullet, civilian"
+Y3633,"War operations involving flamethrower"
+Y36330,"War operations involving flamethrower, military personnel"
+Y36331,"War operations involving flamethrower, civilian"
+Y3639,"War operations involving other fires, conflagrations and hot substances"
+Y36390,"War operations involving other fires, conflagrations and hot substances, military personnel"
+Y36391,"War operations involving other fires, conflagrations and hot substances, civilian"
+Y364,"War operations involving firearm discharge and other forms of conventional warfare"
+Y3641,"War operations involving rubber bullets"
+Y36410,"War operations involving rubber bullets, military personnel"
+Y36411,"War operations involving rubber bullets, civilian"
+Y3642,"War operations involving firearms pellets"
+Y36420,"War operations involving firearms pellets, military personnel"
+Y36421,"War operations involving firearms pellets, civilian"
+Y3643,"War operations involving other firearms discharge"
+Y36430,"War operations involving other firearms discharge, military personnel"
+Y36431,"War operations involving other firearms discharge, civilian"
+Y3644,"War operations involving unarmed hand to hand combat"
+Y36440,"War operations involving unarmed hand to hand combat, military personnel"
+Y36441,"War operations involving unarmed hand to hand combat, civilian"
+Y3645,"War operations involving combat using blunt or piercing object"
+Y36450,"War operations involving combat using blunt or piercing object, military personnel"
+Y36451,"War operations involving combat using blunt or piercing object, civilian"
+Y3646,"War operations involving intentional restriction of air and airway"
+Y36460,"War operations involving intentional restriction of air and airway, military personnel"
+Y36461,"War operations involving intentional restriction of air and airway, civilian"
+Y3647,"War operations involving unintentional restriction of air and airway"
+Y36470,"War operations involving unintentional restriction of air and airway, military personnel"
+Y36471,"War operations involving unintentional restriction of air and airway, civilian"
+Y3649,"War operations involving other forms of conventional warfare"
+Y36490,"War operations involving other forms of conventional warfare, military personnel"
+Y36491,"War operations involving other forms of conventional warfare, civilian"
+Y365,"War operations involving nuclear weapons"
+Y3650,"War operations involving unspecified effect of nuclear weapon"
+Y36500,"War operations involving unspecified effect of nuclear weapon, military personnel"
+Y36501,"War operations involving unspecified effect of nuclear weapon, civilian"
+Y3651,"War operations involving direct blast effect of nuclear weapon"
+Y36510,"War operations involving direct blast effect of nuclear weapon, military personnel"
+Y36511,"War operations involving direct blast effect of nuclear weapon, civilian"
+Y3652,"War operations involving indirect blast effect of nuclear weapon"
+Y36520,"War operations involving indirect blast effect of nuclear weapon, military personnel"
+Y36521,"War operations involving indirect blast effect of nuclear weapon, civilian"
+Y3653,"War operations involving thermal radiation effect of nuclear weapon"
+Y36530,"War operations involving thermal radiation effect of nuclear weapon, military personnel"
+Y36531,"War operations involving thermal radiation effect of nuclear weapon, civilian"
+Y3654,"War operation involving nuclear radiation effects of nuclear weapon"
+Y36540,"War operation involving nuclear radiation effects of nuclear weapon, military personnel"
+Y36541,"War operation involving nuclear radiation effects of nuclear weapon, civilian"
+Y3659,"War operation involving other effects of nuclear weapons"
+Y36590,"War operation involving other effects of nuclear weapons, military personnel"
+Y36591,"War operation involving other effects of nuclear weapons, civilian"
+Y366,"War operations involving biological weapons"
+Y366X,"War operations involving biological weapons"
+Y366X0,"War operations involving biological weapons, military personnel"
+Y366X1,"War operations involving biological weapons, civilian"
+Y367,"War operations involving chemical weapons and other forms of unconventional warfare"
+Y367X,"War operations involving chemical weapons and other forms of unconventional warfare"
+Y367X0,"War operations involving chemical weapons and other forms of unconventional warfare, military personnel"
+Y367X1,"War operations involving chemical weapons and other forms of unconventional warfare, civilian"
+Y368,"War operations occurring after cessation of hostilities"
+Y3681,"Explosion of mine placed during war operations but exploding after cessation of hostilities"
+Y36810,"Explosion of mine placed during war operations but exploding after cessation of hostilities, military personnel"
+Y36811,"Explosion of mine placed during war operations but exploding after cessation of hostilities, civilian"
+Y3682,"Explosion of bomb placed during war operations but exploding after cessation of hostilities"
+Y36820,"Explosion of bomb placed during war operations but exploding after cessation of hostilities, military personnel"
+Y36821,"Explosion of bomb placed during war operations but exploding after cessation of hostilities, civilian"
+Y3688,"Other war operations occurring after cessation of hostilities"
+Y36880,"Other war operations occurring after cessation of hostilities, military personnel"
+Y36881,"Other war operations occurring after cessation of hostilities, civilian"
+Y3689,"Unspecified war operations occurring after cessation of hostilities"
+Y36890,"Unspecified war operations occurring after cessation of hostilities, military personnel"
+Y36891,"Unspecified war operations occurring after cessation of hostilities, civilian"
+Y369,"Other and unspecified war operations"
+Y3690,"War operations, unspecified"
+Y3691,"War operations involving unspecified weapon of mass destruction [WMD]"
+Y3692,"War operations involving friendly fire"
+Y37,"Military operations"
+Y370,"Military operations involving explosion of marine weapons"
+Y3700,"Military operations involving explosion of unspecified marine weapon"
+Y37000,"Military operations involving explosion of unspecified marine weapon, military personnel"
+Y37001,"Military operations involving explosion of unspecified marine weapon, civilian"
+Y3701,"Military operations involving explosion of depth-charge"
+Y37010,"Military operations involving explosion of depth-charge, military personnel"
+Y37011,"Military operations involving explosion of depth-charge, civilian"
+Y3702,"Military operations involving explosion of marine mine"
+Y37020,"Military operations involving explosion of marine mine, military personnel"
+Y37021,"Military operations involving explosion of marine mine, civilian"
+Y3703,"Military operations involving explosion of sea-based artillery shell"
+Y37030,"Military operations involving explosion of sea-based artillery shell, military personnel"
+Y37031,"Military operations involving explosion of sea-based artillery shell, civilian"
+Y3704,"Military operations involving explosion of torpedo"
+Y37040,"Military operations involving explosion of torpedo, military personnel"
+Y37041,"Military operations involving explosion of torpedo, civilian"
+Y3705,"Military operations involving accidental detonation of onboard marine weapons"
+Y37050,"Military operations involving accidental detonation of onboard marine weapons, military personnel"
+Y37051,"Military operations involving accidental detonation of onboard marine weapons, civilian"
+Y3709,"Military operations involving explosion of other marine weapons"
+Y37090,"Military operations involving explosion of other marine weapons, military personnel"
+Y37091,"Military operations involving explosion of other marine weapons, civilian"
+Y371,"Military operations involving destruction of aircraft"
+Y3710,"Military operations involving unspecified destruction of aircraft"
+Y37100,"Military operations involving unspecified destruction of aircraft, military personnel"
+Y37101,"Military operations involving unspecified destruction of aircraft, civilian"
+Y3711,"Military operations involving destruction of aircraft due to enemy fire or explosives"
+Y37110,"Military operations involving destruction of aircraft due to enemy fire or explosives, military personnel"
+Y37111,"Military operations involving destruction of aircraft due to enemy fire or explosives, civilian"
+Y3712,"Military operations involving destruction of aircraft due to collision with other aircraft"
+Y37120,"Military operations involving destruction of aircraft due to collision with other aircraft, military personnel"
+Y37121,"Military operations involving destruction of aircraft due to collision with other aircraft, civilian"
+Y3713,"Military operations involving destruction of aircraft due to onboard fire"
+Y37130,"Military operations involving destruction of aircraft due to onboard fire, military personnel"
+Y37131,"Military operations involving destruction of aircraft due to onboard fire, civilian"
+Y3714,"Military operations involving destruction of aircraft due to accidental detonation of onboard munitions and explosives"
+Y37140,"Military operations involving destruction of aircraft due to accidental detonation of onboard munitions and explosives, military personnel"
+Y37141,"Military operations involving destruction of aircraft due to accidental detonation of onboard munitions and explosives, civilian"
+Y3719,"Military operations involving other destruction of aircraft"
+Y37190,"Military operations involving other destruction of aircraft, military personnel"
+Y37191,"Military operations involving other destruction of aircraft, civilian"
+Y372,"Military operations involving other explosions and fragments"
+Y3720,"Military operations involving unspecified explosion and fragments"
+Y37200,"Military operations involving unspecified explosion and fragments, military personnel"
+Y37201,"Military operations involving unspecified explosion and fragments, civilian"
+Y3721,"Military operations involving explosion of aerial bomb"
+Y37210,"Military operations involving explosion of aerial bomb, military personnel"
+Y37211,"Military operations involving explosion of aerial bomb, civilian"
+Y3722,"Military operations involving explosion of guided missile"
+Y37220,"Military operations involving explosion of guided missile, military personnel"
+Y37221,"Military operations involving explosion of guided missile, civilian"
+Y3723,"Military operations involving explosion of improvised explosive device [IED]"
+Y37230,"Military operations involving explosion of improvised explosive device [IED], military personnel"
+Y37231,"Military operations involving explosion of improvised explosive device [IED], civilian"
+Y3724,"Military operations involving explosion due to accidental detonation and discharge of own munitions or munitions launch device"
+Y37240,"Military operations involving explosion due to accidental detonation and discharge of own munitions or munitions launch device, military personnel"
+Y37241,"Military operations involving explosion due to accidental detonation and discharge of own munitions or munitions launch device, civilian"
+Y3725,"Military operations involving fragments from munitions"
+Y37250,"Military operations involving fragments from munitions, military personnel"
+Y37251,"Military operations involving fragments from munitions, civilian"
+Y3726,"Military operations involving fragments of improvised explosive device [IED]"
+Y37260,"Military operations involving fragments of improvised explosive device [IED], military personnel"
+Y37261,"Military operations involving fragments of improvised explosive device [IED], civilian"
+Y3727,"Military operations involving fragments from weapons"
+Y37270,"Military operations involving fragments from weapons, military personnel"
+Y37271,"Military operations involving fragments from weapons, civilian"
+Y3729,"Military operations involving other explosions and fragments"
+Y37290,"Military operations involving other explosions and fragments, military personnel"
+Y37291,"Military operations involving other explosions and fragments, civilian"
+Y373,"Military operations involving fires, conflagrations and hot substances"
+Y3730,"Military operations involving unspecified fire, conflagration and hot substance"
+Y37300,"Military operations involving unspecified fire, conflagration and hot substance, military personnel"
+Y37301,"Military operations involving unspecified fire, conflagration and hot substance, civilian"
+Y3731,"Military operations involving gasoline bomb"
+Y37310,"Military operations involving gasoline bomb, military personnel"
+Y37311,"Military operations involving gasoline bomb, civilian"
+Y3732,"Military operations involving incendiary bullet"
+Y37320,"Military operations involving incendiary bullet, military personnel"
+Y37321,"Military operations involving incendiary bullet, civilian"
+Y3733,"Military operations involving flamethrower"
+Y37330,"Military operations involving flamethrower, military personnel"
+Y37331,"Military operations involving flamethrower, civilian"
+Y3739,"Military operations involving other fires, conflagrations and hot substances"
+Y37390,"Military operations involving other fires, conflagrations and hot substances, military personnel"
+Y37391,"Military operations involving other fires, conflagrations and hot substances, civilian"
+Y374,"Military operations involving firearm discharge and other forms of conventional warfare"
+Y3741,"Military operations involving rubber bullets"
+Y37410,"Military operations involving rubber bullets, military personnel"
+Y37411,"Military operations involving rubber bullets, civilian"
+Y3742,"Military operations involving firearms pellets"
+Y37420,"Military operations involving firearms pellets, military personnel"
+Y37421,"Military operations involving firearms pellets, civilian"
+Y3743,"Military operations involving other firearms discharge"
+Y37430,"Military operations involving other firearms discharge, military personnel"
+Y37431,"Military operations involving other firearms discharge, civilian"
+Y3744,"Military operations involving unarmed hand to hand combat"
+Y37440,"Military operations involving unarmed hand to hand combat, military personnel"
+Y37441,"Military operations involving unarmed hand to hand combat, civilian"
+Y3745,"Military operations involving combat using blunt or piercing object"
+Y37450,"Military operations involving combat using blunt or piercing object, military personnel"
+Y37451,"Military operations involving combat using blunt or piercing object, civilian"
+Y3746,"Military operations involving intentional restriction of air and airway"
+Y37460,"Military operations involving intentional restriction of air and airway, military personnel"
+Y37461,"Military operations involving intentional restriction of air and airway, civilian"
+Y3747,"Military operations involving unintentional restriction of air and airway"
+Y37470,"Military operations involving unintentional restriction of air and airway, military personnel"
+Y37471,"Military operations involving unintentional restriction of air and airway, civilian"
+Y3749,"Military operations involving other forms of conventional warfare"
+Y37490,"Military operations involving other forms of conventional warfare, military personnel"
+Y37491,"Military operations involving other forms of conventional warfare, civilian"
+Y375,"Military operations involving nuclear weapons"
+Y3750,"Military operations involving unspecified effect of nuclear weapon"
+Y37500,"Military operations involving unspecified effect of nuclear weapon, military personnel"
+Y37501,"Military operations involving unspecified effect of nuclear weapon, civilian"
+Y3751,"Military operations involving direct blast effect of nuclear weapon"
+Y37510,"Military operations involving direct blast effect of nuclear weapon, military personnel"
+Y37511,"Military operations involving direct blast effect of nuclear weapon, civilian"
+Y3752,"Military operations involving indirect blast effect of nuclear weapon"
+Y37520,"Military operations involving indirect blast effect of nuclear weapon, military personnel"
+Y37521,"Military operations involving indirect blast effect of nuclear weapon, civilian"
+Y3753,"Military operations involving thermal radiation effect of nuclear weapon"
+Y37530,"Military operations involving thermal radiation effect of nuclear weapon, military personnel"
+Y37531,"Military operations involving thermal radiation effect of nuclear weapon, civilian"
+Y3754,"Military operation involving nuclear radiation effects of nuclear weapon"
+Y37540,"Military operation involving nuclear radiation effects of nuclear weapon, military personnel"
+Y37541,"Military operation involving nuclear radiation effects of nuclear weapon, civilian"
+Y3759,"Military operation involving other effects of nuclear weapons"
+Y37590,"Military operation involving other effects of nuclear weapons, military personnel"
+Y37591,"Military operation involving other effects of nuclear weapons, civilian"
+Y376,"Military operations involving biological weapons"
+Y376X,"Military operations involving biological weapons"
+Y376X0,"Military operations involving biological weapons, military personnel"
+Y376X1,"Military operations involving biological weapons, civilian"
+Y377,"Military operations involving chemical weapons and other forms of unconventional warfare"
+Y377X,"Military operations involving chemical weapons and other forms of unconventional warfare"
+Y377X0,"Military operations involving chemical weapons and other forms of unconventional warfare, military personnel"
+Y377X1,"Military operations involving chemical weapons and other forms of unconventional warfare, civilian"
+Y379,"Other and unspecified military operations"
+Y3790,"Military operations, unspecified"
+Y3791,"Military operations involving unspecified weapon of mass destruction [WMD]"
+Y3792,"Military operations involving friendly fire"
+Y38,"Terrorism"
+Y380,"Terrorism involving explosion of marine weapons"
+Y380X,"Terrorism involving explosion of marine weapons"
+Y380X1,"Terrorism involving explosion of marine weapons, public safety official injured"
+Y380X2,"Terrorism involving explosion of marine weapons, civilian injured"
+Y380X3,"Terrorism involving explosion of marine weapons, terrorist injured"
+Y381,"Terrorism involving destruction of aircraft"
+Y381X,"Terrorism involving destruction of aircraft"
+Y381X1,"Terrorism involving destruction of aircraft, public safety official injured"
+Y381X2,"Terrorism involving destruction of aircraft, civilian injured"
+Y381X3,"Terrorism involving destruction of aircraft, terrorist injured"
+Y382,"Terrorism involving other explosions and fragments"
+Y382X,"Terrorism involving other explosions and fragments"
+Y382X1,"Terrorism involving other explosions and fragments, public safety official injured"
+Y382X2,"Terrorism involving other explosions and fragments, civilian injured"
+Y382X3,"Terrorism involving other explosions and fragments, terrorist injured"
+Y383,"Terrorism involving fires, conflagration and hot substances"
+Y383X,"Terrorism involving fires, conflagration and hot substances"
+Y383X1,"Terrorism involving fires, conflagration and hot substances, public safety official injured"
+Y383X2,"Terrorism involving fires, conflagration and hot substances, civilian injured"
+Y383X3,"Terrorism involving fires, conflagration and hot substances, terrorist injured"
+Y384,"Terrorism involving firearms"
+Y384X,"Terrorism involving firearms"
+Y384X1,"Terrorism involving firearms, public safety official injured"
+Y384X2,"Terrorism involving firearms, civilian injured"
+Y384X3,"Terrorism involving firearms, terrorist injured"
+Y385,"Terrorism involving nuclear weapons"
+Y385X,"Terrorism involving nuclear weapons"
+Y385X1,"Terrorism involving nuclear weapons, public safety official injured"
+Y385X2,"Terrorism involving nuclear weapons, civilian injured"
+Y385X3,"Terrorism involving nuclear weapons, terrorist injured"
+Y386,"Terrorism involving biological weapons"
+Y386X,"Terrorism involving biological weapons"
+Y386X1,"Terrorism involving biological weapons, public safety official injured"
+Y386X2,"Terrorism involving biological weapons, civilian injured"
+Y386X3,"Terrorism involving biological weapons, terrorist injured"
+Y387,"Terrorism involving chemical weapons"
+Y387X,"Terrorism involving chemical weapons"
+Y387X1,"Terrorism involving chemical weapons, public safety official injured"
+Y387X2,"Terrorism involving chemical weapons, civilian injured"
+Y387X3,"Terrorism involving chemical weapons, terrorist injured"
+Y388,"Terrorism involving other and unspecified means"
+Y3880,"Terrorism involving unspecified means"
+Y3881,"Terrorism involving suicide bomber"
+Y38811,"Terrorism involving suicide bomber, public safety official injured"
+Y38812,"Terrorism involving suicide bomber, civilian injured"
+Y3889,"Terrorism involving other means"
+Y38891,"Terrorism involving other means, public safety official injured"
+Y38892,"Terrorism involving other means, civilian injured"
+Y38893,"Terrorism involving other means, terrorist injured"
+Y389,"Terrorism, secondary effects"
+Y389X,"Terrorism, secondary effects"
+Y389X1,"Terrorism, secondary effects, public safety official injured"
+Y389X2,"Terrorism, secondary effects, civilian injured"
+Y62,"Failure of sterile precautions during surgical and medical care"
+Y63,"Failure in dosage during surgical and medical care"
+Y64,"Contaminated medical or biological substances"
+Y65,"Other misadventures during surgical and medical care"
+Y655,"Performance of wrong procedure (operation)"
+Y658,"Other specified misadventures during surgical and medical care"
+Y66,"Nonadministration of surgical and medical care"
+Y69,"Unspecified misadventure during surgical and medical care"
+Y70,"Anesthesiology devices associated with adverse incidents"
+Y71,"Cardiovascular devices associated with adverse incidents"
+Y72,"Otorhinolaryngological devices associated with adverse incidents"
+Y73,"Gastroenterology and urology devices associated with adverse incidents"
+Y74,"General hospital and personal-use devices associated with adverse incidents"
+Y75,"Neurological devices associated with adverse incidents"
+Y76,"Obstetric and gynecological devices associated with adverse incidents"
+Y77,"Ophthalmic devices associated with adverse incidents"
+Y78,"Radiological devices associated with adverse incidents"
+Y79,"Orthopedic devices associated with adverse incidents"
+Y80,"Physical medicine devices associated with adverse incidents"
+Y81,"General- and plastic-surgery devices associated with adverse incidents"
+Y82,"Other and unspecified medical devices associated with adverse incidents"
+Y83,"Surgical operation and other surgical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure"
+Y84,"Other medical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure"
+Y90,"Evidence of alcohol involvement determined by blood alcohol level"
+Y92,"Place of occurrence of the external cause"
+Y920,"Non-institutional (private) residence as the place of occurrence of the external cause"
+Y9200,"Unspecified non-institutional (private) residence as the place of occurrence of the external cause"
+Y9201,"Single-family non-institutional (private) house as the place of occurrence of the external cause"
+Y9202,"Mobile home as the place of occurrence of the external cause"
+Y9203,"Apartment as the place of occurrence of the external cause"
+Y9204,"Boarding-house as the place of occurrence of the external cause"
+Y9209,"Other non-institutional residence as the place of occurrence of the external cause"
+Y921,"Institutional (nonprivate) residence as the place of occurrence of the external cause"
+Y9211,"Children's home and orphanage as the place of occurrence of the external cause"
+Y9212,"Nursing home as the place of occurrence of the external cause"
+Y9213,"Military base as the place of occurrence of the external cause"
+Y9214,"Prison as the place of occurrence of the external cause"
+Y9215,"Reform school as the place of occurrence of the external cause"
+Y9216,"School dormitory as the place of occurrence of the external cause"
+Y9219,"Other specified residential institution as the place of occurrence of the external cause"
+Y922,"School, other institution and public administrative area as the place of occurrence of the external cause"
+Y9221,"School (private) (public) (state) as the place of occurrence of the external cause"
+Y9222,"Religious institution as the place of occurrence of the external cause"
+Y9223,"Hospital as the place of occurrence of the external cause"
+Y9224,"Public administrative building as the place of occurrence of the external cause"
+Y9225,"Cultural building as the place of occurrence of the external cause"
+Y9226,"Movie house or cinema as the place of occurrence of the external cause"
+Y9229,"Other specified public building as the place of occurrence of the external cause"
+Y923,"Sports and athletics area as the place of occurrence of the external cause"
+Y9231,"Athletic court as the place of occurrence of the external cause"
+Y9232,"Athletic field as the place of occurrence of the external cause"
+Y9233,"Skating rink as the place of occurrence of the external cause"
+Y9234,"Swimming pool (public) as the place of occurrence of the external cause"
+Y9239,"Other specified sports and athletic area as the place of occurrence of the external cause"
+Y924,"Street, highway and other paved roadways as the place of occurrence of the external cause"
+Y9241,"Street and highway as the place of occurrence of the external cause"
+Y9248,"Other paved roadways as the place of occurrence of the external cause"
+Y925,"Trade and service area as the place of occurrence of the external cause"
+Y9251,"Private commercial establishments as the place of occurrence of the external cause"
+Y9252,"Service areas as the place of occurrence of the external cause"
+Y9253,"Ambulatory health services establishments as the place of occurrence of the external cause"
+Y9259,"Other trade areas as the place of occurrence of the external cause"
+Y926,"Industrial and construction area as the place of occurrence of the external cause"
+Y927,"Farm as the place of occurrence of the external cause"
+Y928,"Other places as the place of occurrence of the external cause"
+Y9281,"Transport vehicle as the place of occurrence of the external cause"
+Y9282,"Wilderness area"
+Y9283,"Recreation area as the place of occurrence of the external cause"
+Y9284,"Military training ground as the place of occurrence of the external cause"
+Y9285,"Railroad track as the place of occurrence of the external cause"
+Y9286,"Slaughter house as the place of occurrence of the external cause"
+Y9289,"Other specified places as the place of occurrence of the external cause"
+Y929,"Unspecified place or not applicable"
+Y93,"Activity codes"
+Y930,"Activities involving walking and running"
+Y931,"Activities involving water and water craft"
+Y932,"Activities involving ice and snow"
+Y933,"Activities involving climbing, rappelling and jumping off"
+Y934,"Activities involving dancing and other rhythmic movement"
+Y935,"Activities involving other sports and athletics played individually"
+Y936,"Activities involving other sports and athletics played as a team or group"
+Y937,"Activities involving other specified sports and athletics"
+Y93A,"Activities involving other cardiorespiratory exercise"
+Y93B,"Activities involving other muscle strengthening exercises"
+Y93C,"Activities involving computer technology and electronic devices"
+Y93D,"Activities involving arts and handcrafts"
+Y93E,"Activities involving personal hygiene and interior property and clothing maintenance"
+Y93F,"Activities involving caregiving"
+Y93G,"Activities involving food preparation, cooking and grilling"
+Y93H,"Activities involving exterior property and land maintenance, building and construction"
+Y93I,"Activities involving roller coasters and other types of external motion"
+Y93J,"Activities involving playing musical instrument"
+Y93K,"Activities involving animal care"
+Y938,"Activities, other specified"
+Y939,"Activity, unspecified"
+Y95,"Nosocomial condition"
+Y99,"External cause status"
+Z00,"Encounter for general examination without complaint, suspected or reported diagnosis"
+Z000,"Encounter for general adult medical examination"
+Z001,"Encounter for newborn, infant and child health examinations"
+Z0011,"Newborn health examination"
+Z0012,"Encounter for routine child health examination"
+Z002,"Encounter for examination for period of rapid growth in childhood"
+Z003,"Encounter for examination for adolescent development state"
+Z005,"Encounter for examination of potential donor of organ and tissue"
+Z006,"Encounter for examination for normal comparison and control in clinical research program"
+Z007,"Encounter for examination for period of delayed growth in childhood"
+Z008,"Encounter for other general examination"
+Z01,"Encounter for other special examination without complaint, suspected or reported diagnosis"
+Z010,"Encounter for examination of eyes and vision"
+Z011,"Encounter for examination of ears and hearing"
+Z0111,"Encounter for examination of ears and hearing with abnormal findings"
+Z0112,"Encounter for hearing conservation and treatment"
+Z012,"Encounter for dental examination and cleaning"
+Z013,"Encounter for examination of blood pressure"
+Z014,"Encounter for gynecological examination"
+Z0141,"Encounter for routine gynecological examination"
+Z0142,"Encounter for cervical smear to confirm findings of recent normal smear following initial abnormal smear"
+Z018,"Encounter for other specified special examinations"
+Z0181,"Encounter for preprocedural examinations"
+Z0182,"Encounter for allergy testing"
+Z0183,"Encounter for blood typing"
+Z0184,"Encounter for antibody response examination"
+Z0189,"Encounter for other specified special examinations"
+Z02,"Encounter for administrative examination"
+Z027,"Encounter for issue of medical certificate"
+Z028,"Encounter for other administrative examinations"
+Z029,"Encounter for administrative examinations, unspecified"
+Z03,"Encounter for medical observation for suspected diseases and conditions ruled out"
+Z037,"Encounter for suspected maternal and fetal conditions ruled out"
+Z038,"Encounter for observation for other suspected diseases and conditions ruled out"
+Z0381,"Encounter for observation for suspected exposure to biological agents ruled out"
+Z0389,"Encounter for observation for other suspected diseases and conditions ruled out"
+Z04,"Encounter for examination and observation for other reasons"
+Z044,"Encounter for examination and observation following alleged rape"
+Z046,"Encounter for general psychiatric examination, requested by authority"
+Z047,"Encounter for examination and observation following alleged physical abuse"
+Z048,"Encounter for examination and observation for other specified reasons"
+Z049,"Encounter for examination and observation for unspecified reason"
+Z05,"Encounter for observation and evaluation of newborn for suspected diseases and conditions ruled out"
+Z054,"Observation and evaluation of newborn for suspected genetic, metabolic or immunologic condition ruled out"
+Z055,"Observation and evaluation of newborn for suspected gastrointestinal condition ruled out"
+Z056,"Observation and evaluation of newborn for suspected genitourinary condition ruled out"
+Z057,"Observation and evaluation of newborn for suspected skin, subcutaneous, musculoskeletal and connective tissue condition ruled out"
+Z058,"Observation and evaluation of newborn for other specified suspected condition ruled out"
+Z059,"Observation and evaluation of newborn for unspecified suspected condition ruled out"
+Z08,"Encounter for follow-up examination after completed treatment for malignant neoplasm"
+Z09,"Encounter for follow-up examination after completed treatment for conditions other than malignant neoplasm"
+Z11,"Encounter for screening for infectious and parasitic diseases"
+Z115,"Encounter for screening for other viral diseases"
+Z116,"Encounter for screening for other protozoal diseases and helminthiases"
+Z118,"Encounter for screening for other infectious and parasitic diseases"
+Z119,"Encounter for screening for infectious and parasitic diseases, unspecified"
+Z12,"Encounter for screening for malignant neoplasms"
+Z121,"Encounter for screening for malignant neoplasm of intestinal tract"
+Z122,"Encounter for screening for malignant neoplasm of respiratory organs"
+Z123,"Encounter for screening for malignant neoplasm of breast"
+Z124,"Encounter for screening for malignant neoplasm of cervix"
+Z125,"Encounter for screening for malignant neoplasm of prostate"
+Z126,"Encounter for screening for malignant neoplasm of bladder"
+Z127,"Encounter for screening for malignant neoplasm of other genitourinary organs"
+Z128,"Encounter for screening for malignant neoplasm of other sites"
+Z129,"Encounter for screening for malignant neoplasm, site unspecified"
+Z13,"Encounter for screening for other diseases and disorders"
+Z132,"Encounter for screening for nutritional, metabolic and other endocrine disorders"
+Z1322,"Encounter for screening for metabolic disorder"
+Z1329,"Encounter for screening for other suspected endocrine disorder"
+Z134,"Encounter for screening for certain developmental disorders in childhood"
+Z135,"Encounter for screening for eye and ear disorders"
+Z136,"Encounter for screening for cardiovascular disorders"
+Z137,"Encounter for screening for genetic and chromosomal anomalies"
+Z138,"Encounter for screening for other specified diseases and disorders"
+Z1381,"Encounter for screening for digestive system disorders"
+Z1382,"Encounter for screening for musculoskeletal disorder"
+Z1383,"Encounter for screening for respiratory disorder NEC"
+Z1384,"Encounter for screening for dental disorders"
+Z1385,"Encounter for screening for nervous system disorders"
+Z1388,"Encounter for screening for disorder due to exposure to contaminants"
+Z1389,"Encounter for screening for other disorder"
+Z139,"Encounter for screening, unspecified"
+Z14,"Genetic carrier"
+Z140,"Hemophilia A carrier"
+Z141,"Cystic fibrosis carrier"
+Z148,"Genetic carrier of other disease"
+Z15,"Genetic susceptibility to disease"
+Z150,"Genetic susceptibility to malignant neoplasm"
+Z158,"Genetic susceptibility to other disease"
+Z16,"Resistance to antimicrobial drugs"
+Z161,"Resistance to beta lactam antibiotics"
+Z162,"Resistance to other antibiotics"
+Z163,"Resistance to other antimicrobial drugs"
+Z1634,"Resistance to antimycobacterial drug(s)"
+Z1635,"Resistance to multiple antimicrobial drugs"
+Z1639,"Resistance to other specified antimicrobial drug"
+Z17,"Estrogen receptor status"
+Z18,"Retained foreign body fragments"
+Z180,"Retained radioactive fragments"
+Z181,"Retained metal fragments"
+Z182,"Retained plastic fragments"
+Z183,"Retained organic fragments"
+Z188,"Other specified retained foreign body"
+Z189,"Retained foreign body fragments, unspecified material"
+Z19,"Hormone sensitivity malignancy status"
+Z20,"Contact with and (suspected) exposure to communicable diseases"
+Z200,"Contact with and (suspected) exposure to intestinal infectious diseases"
+Z201,"Contact with and (suspected) exposure to tuberculosis"
+Z202,"Contact with and (suspected) exposure to infections with a predominantly sexual mode of transmission"
+Z203,"Contact with and (suspected) exposure to rabies"
+Z204,"Contact with and (suspected) exposure to rubella"
+Z205,"Contact with and (suspected) exposure to viral hepatitis"
+Z206,"Contact with and (suspected) exposure to human immunodeficiency virus [HIV]"
+Z207,"Contact with and (suspected) exposure to pediculosis, acariasis and other infestations"
+Z208,"Contact with and (suspected) exposure to other communicable diseases"
+Z2081,"Contact with and (suspected) exposure to other bacterial communicable diseases"
+Z2082,"Contact with and (suspected) exposure to other viral communicable diseases"
+Z2089,"Contact with and (suspected) exposure to other communicable diseases"
+Z209,"Contact with and (suspected) exposure to unspecified communicable disease"
+Z21,"Asymptomatic human immunodeficiency virus [HIV] infection status"
+Z22,"Carrier of infectious disease"
+Z223,"Carrier of other specified bacterial diseases"
+Z2232,"Carrier of bacterial disease due to staphylococci"
+Z2233,"Carrier of bacterial disease due to streptococci"
+Z2239,"Carrier of other specified bacterial diseases"
+Z224,"Carrier of infections with a predominantly sexual mode of transmission"
+Z226,"Carrier of human T-lymphotropic virus type-1 [HTLV-1] infection"
+Z228,"Carrier of other infectious diseases"
+Z229,"Carrier of infectious disease, unspecified"
+Z23,"Encounter for immunization"
+Z28,"Immunization not carried out and underimmunization status"
+Z280,"Immunization not carried out because of contraindication"
+Z281,"Immunization not carried out because of patient decision for reasons of belief or group pressure"
+Z282,"Immunization not carried out because of patient decision for other and unspecified reason"
+Z283,"Underimmunization status"
+Z288,"Immunization not carried out for other reason"
+Z289,"Immunization not carried out for unspecified reason"
+Z29,"Encounter for other prophylactic measures"
+Z291,"Encounter for prophylactic immunotherapy"
+Z293,"Encounter for prophylactic fluoride administration"
+Z298,"Encounter for other specified prophylactic measures"
+Z299,"Encounter for prophylactic measures, unspecified"
+Z30,"Encounter for contraceptive management"
+Z300,"Encounter for general counseling and advice on contraception"
+Z3001,"Encounter for initial prescription of contraceptives"
+Z3002,"Counseling and instruction in natural family planning to avoid pregnancy"
+Z3009,"Encounter for other general counseling and advice on contraception"
+Z302,"Encounter for sterilization"
+Z304,"Encounter for surveillance of contraceptives"
+Z3043,"Encounter for surveillance of intrauterine contraceptive device"
+Z3044,"Encounter for surveillance of vaginal ring hormonal contraceptive device"
+Z3045,"Encounter for surveillance of transdermal patch hormonal contraceptive device"
+Z3046,"Encounter for surveillance of implantable subdermal contraceptive"
+Z3049,"Encounter for surveillance of other contraceptives"
+Z308,"Encounter for other contraceptive management"
+Z309,"Encounter for contraceptive management, unspecified"
+Z31,"Encounter for procreative management"
+Z314,"Encounter for procreative investigation and testing"
+Z3143,"Encounter for genetic testing of female for procreative management"
+Z3144,"Encounter for genetic testing of male for procreative management"
+Z3149,"Encounter for other procreative investigation and testing"
+Z315,"Encounter for procreative genetic counseling"
+Z316,"Encounter for general counseling and advice on procreation"
+Z317,"Encounter for procreative management and counseling for gestational carrier"
+Z318,"Encounter for other procreative management"
+Z319,"Encounter for procreative management, unspecified"
+Z32,"Encounter for pregnancy test and childbirth and childcare instruction"
+Z320,"Encounter for pregnancy test"
+Z322,"Encounter for childbirth instruction"
+Z323,"Encounter for childcare instruction"
+Z33,"Pregnant state"
+Z34,"Encounter for supervision of normal pregnancy"
+Z340,"Encounter for supervision of normal first pregnancy"
+Z348,"Encounter for supervision of other normal pregnancy"
+Z349,"Encounter for supervision of normal pregnancy, unspecified"
+Z36,"Encounter for antenatal screening of mother"
+Z368,"Encounter for other antenatal screening"
+Z369,"Encounter for antenatal screening, unspecified"
+Z3A,"Weeks of gestation"
+Z3A0,"Weeks of gestation of pregnancy, unspecified or less than 10 weeks"
+Z3A1,"Weeks of gestation of pregnancy, weeks 10-19"
+Z3A2,"Weeks of gestation of pregnancy, weeks 20-29"
+Z3A3,"Weeks of gestation of pregnancy, weeks 30-39"
+Z3A4,"Weeks of gestation of pregnancy, weeks 40 or greater"
+Z37,"Outcome of delivery"
+Z375,"Other multiple births, all liveborn"
+Z376,"Other multiple births, some liveborn"
+Z377,"Other multiple births, all stillborn"
+Z379,"Outcome of delivery, unspecified"
+Z38,"Liveborn infants according to place of birth and type of delivery"
+Z380,"Single liveborn infant, born in hospital"
+Z381,"Single liveborn infant, born outside hospital"
+Z382,"Single liveborn infant, unspecified as to place of birth"
+Z383,"Twin liveborn infant, born in hospital"
+Z384,"Twin liveborn infant, born outside hospital"
+Z385,"Twin liveborn infant, unspecified as to place of birth"
+Z386,"Other multiple liveborn infant, born in hospital"
+Z387,"Other multiple liveborn infant, born outside hospital"
+Z388,"Other multiple liveborn infant, unspecified as to place of birth"
+Z39,"Encounter for maternal postpartum care and examination"
+Z40,"Encounter for prophylactic surgery"
+Z400,"Encounter for prophylactic surgery for risk factors related to malignant neoplasms"
+Z408,"Encounter for other prophylactic surgery"
+Z409,"Encounter for prophylactic surgery, unspecified"
+Z41,"Encounter for procedures for purposes other than remedying health state"
+Z42,"Encounter for plastic and reconstructive surgery following medical procedure or healed injury"
+Z43,"Encounter for attention to artificial openings"
+Z44,"Encounter for fitting and adjustment of external prosthetic device"
+Z440,"Encounter for fitting and adjustment of artificial arm"
+Z4400,"Encounter for fitting and adjustment of unspecified artificial arm"
+Z4401,"Encounter for fitting and adjustment of complete artificial arm"
+Z4402,"Encounter for fitting and adjustment of partial artificial arm"
+Z441,"Encounter for fitting and adjustment of artificial leg"
+Z4410,"Encounter for fitting and adjustment of unspecified artificial leg"
+Z4411,"Encounter for fitting and adjustment of complete artificial leg"
+Z4412,"Encounter for fitting and adjustment of partial artificial leg"
+Z442,"Encounter for fitting and adjustment of artificial eye"
+Z443,"Encounter for fitting and adjustment of external breast prosthesis"
+Z448,"Encounter for fitting and adjustment of other external prosthetic devices"
+Z449,"Encounter for fitting and adjustment of unspecified external prosthetic device"
+Z45,"Encounter for adjustment and management of implanted device"
+Z450,"Encounter for adjustment and management of cardiac device"
+Z4501,"Encounter for adjustment and management of cardiac pacemaker"
+Z4502,"Encounter for adjustment and management of automatic implantable cardiac defibrillator"
+Z4509,"Encounter for adjustment and management of other cardiac device"
+Z451,"Encounter for adjustment and management of infusion pump"
+Z452,"Encounter for adjustment and management of vascular access device"
+Z453,"Encounter for adjustment and management of implanted devices of the special senses"
+Z4532,"Encounter for adjustment and management of implanted hearing device"
+Z454,"Encounter for adjustment and management of implanted nervous system device"
+Z458,"Encounter for adjustment and management of other implanted devices"
+Z4581,"Encounter for adjustment or removal of breast implant"
+Z4582,"Encounter for adjustment or removal of myringotomy device (stent) (tube)"
+Z4589,"Encounter for adjustment and management of other implanted devices"
+Z459,"Encounter for adjustment and management of unspecified implanted device"
+Z46,"Encounter for fitting and adjustment of other devices"
+Z465,"Encounter for fitting and adjustment of other gastrointestinal appliance and device"
+Z466,"Encounter for fitting and adjustment of urinary device"
+Z468,"Encounter for fitting and adjustment of other specified devices"
+Z469,"Encounter for fitting and adjustment of unspecified device"
+Z47,"Orthopedic aftercare"
+Z473,"Aftercare following explantation of joint prosthesis"
+Z478,"Encounter for other orthopedic aftercare"
+Z48,"Encounter for other postprocedural aftercare"
+Z480,"Encounter for attention to dressings, sutures and drains"
+Z481,"Encounter for planned postprocedural wound closure"
+Z482,"Encounter for aftercare following organ transplant"
+Z4828,"Encounter for aftercare following multiple organ transplant"
+Z4829,"Encounter for aftercare following other organ transplant"
+Z483,"Aftercare following surgery for neoplasm"
+Z488,"Encounter for other specified postprocedural aftercare"
+Z4881,"Encounter for surgical aftercare following surgery on specified body systems"
+Z4889,"Encounter for other specified surgical aftercare"
+Z49,"Encounter for care involving renal dialysis"
+Z490,"Preparatory care for renal dialysis"
+Z493,"Encounter for adequacy testing for dialysis"
+Z51,"Encounter for other aftercare and medical care"
+Z511,"Encounter for antineoplastic chemotherapy and immunotherapy"
+Z515,"Encounter for palliative care"
+Z516,"Encounter for desensitization to allergens"
+Z518,"Encounter for other specified aftercare"
+Z52,"Donors of organs and tissues"
+Z520,"Blood donor"
+Z5200,"Unspecified blood donor"
+Z5201,"Autologous blood donor"
+Z5209,"Other blood donor"
+Z521,"Skin donor"
+Z522,"Bone donor"
+Z523,"Bone marrow donor"
+Z524,"Kidney donor"
+Z525,"Cornea donor"
+Z526,"Liver donor"
+Z528,"Donor of other specified organs or tissues"
+Z5281,"Egg (Oocyte) donor"
+Z5289,"Donor of other specified organs or tissues"
+Z529,"Donor of unspecified organ or tissue"
+Z53,"Persons encountering health services for specific procedures and treatment, not carried out"
+Z530,"Procedure and treatment not carried out because of contraindication"
+Z531,"Procedure and treatment not carried out because of patient's decision for reasons of belief and group pressure"
+Z532,"Procedure and treatment not carried out because of patient's decision for other and unspecified reasons"
+Z533,"Procedure converted to open procedure"
+Z538,"Procedure and treatment not carried out for other reasons"
+Z539,"Procedure and treatment not carried out, unspecified reason"
+Z55,"Problems related to education and literacy"
+Z56,"Problems related to employment and unemployment"
+Z568,"Other problems related to employment"
+Z569,"Unspecified problems related to employment"
+Z57,"Occupational exposure to risk factors"
+Z573,"Occupational exposure to other air contaminants"
+Z574,"Occupational exposure to toxic agents in agriculture"
+Z575,"Occupational exposure to toxic agents in other industries"
+Z576,"Occupational exposure to extreme temperature"
+Z577,"Occupational exposure to vibration"
+Z578,"Occupational exposure to other risk factors"
+Z579,"Occupational exposure to unspecified risk factor"
+Z59,"Problems related to housing and economic circumstances"
+Z60,"Problems related to social environment"
+Z62,"Problems related to upbringing"
+Z622,"Upbringing away from parents"
+Z623,"Hostility towards and scapegoating of child"
+Z626,"Inappropriate (excessive) parental pressure"
+Z628,"Other specified problems related to upbringing"
+Z6281,"Personal history of abuse in childhood"
+Z6282,"Parent-child conflict"
+Z6289,"Other specified problems related to upbringing"
+Z629,"Problem related to upbringing, unspecified"
+Z63,"Other problems related to primary support group, including family circumstances"
+Z633,"Absence of family member"
+Z634,"Disappearance and death of family member"
+Z635,"Disruption of family by separation and divorce"
+Z636,"Dependent relative needing care at home"
+Z637,"Other stressful life events affecting family and household"
+Z638,"Other specified problems related to primary support group"
+Z639,"Problem related to primary support group, unspecified"
+Z64,"Problems related to certain psychosocial circumstances"
+Z65,"Problems related to other psychosocial circumstances"
+Z66,"Do not resuscitate"
+Z67,"Blood type"
+Z671,"Type A blood"
+Z672,"Type B blood"
+Z673,"Type AB blood"
+Z674,"Type O blood"
+Z679,"Unspecified blood type"
+Z68,"Body mass index [BMI]"
+Z682,"Body mass index (BMI) 20-29, adult"
+Z683,"Body mass index (BMI) 30-39, adult"
+Z684,"Body mass index (BMI) 40 or greater, adult"
+Z685,"Body mass index (BMI) pediatric"
+Z69,"Encounter for mental health services for victim and perpetrator of abuse"
+Z690,"Encounter for mental health services for child abuse problems"
+Z6901,"Encounter for mental health services for parental child abuse"
+Z6902,"Encounter for mental health services for non-parental child abuse"
+Z691,"Encounter for mental health services for spousal or partner abuse problems"
+Z698,"Encounter for mental health services for victim or perpetrator of other abuse"
+Z70,"Counseling related to sexual attitude, behavior and orientation"
+Z71,"Persons encountering health services for other counseling and medical advice, not elsewhere classified"
+Z714,"Alcohol abuse counseling and surveillance"
+Z715,"Drug abuse counseling and surveillance"
+Z716,"Tobacco abuse counseling"
+Z717,"Human immunodeficiency virus [HIV] counseling"
+Z718,"Other specified counseling"
+Z719,"Counseling, unspecified"
+Z72,"Problems related to lifestyle"
+Z725,"High risk sexual behavior"
+Z726,"Gambling and betting"
+Z728,"Other problems related to lifestyle"
+Z7281,"Antisocial behavior"
+Z7282,"Problems related to sleep"
+Z7289,"Other problems related to lifestyle"
+Z729,"Problem related to lifestyle, unspecified"
+Z73,"Problems related to life management difficulty"
+Z738,"Other problems related to life management difficulty"
+Z7381,"Behavioral insomnia of childhood"
+Z7382,"Dual sensory impairment"
+Z7389,"Other problems related to life management difficulty"
+Z739,"Problem related to life management difficulty, unspecified"
+Z74,"Problems related to care provider dependency"
+Z740,"Reduced mobility"
+Z741,"Need for assistance with personal care"
+Z742,"Need for assistance at home and no other household member able to render care"
+Z743,"Need for continuous supervision"
+Z748,"Other problems related to care provider dependency"
+Z749,"Problem related to care provider dependency, unspecified"
+Z75,"Problems related to medical facilities and other health care"
+Z76,"Persons encountering health services in other circumstances"
+Z768,"Persons encountering health services in other specified circumstances"
+Z77,"Other contact with and (suspected) exposures hazardous to health"
+Z770,"Contact with and (suspected) exposure to hazardous, chiefly nonmedicinal, chemicals"
+Z7701,"Contact with and (suspected) exposure to hazardous metals"
+Z7702,"Contact with and (suspected) exposure to hazardous aromatic compounds"
+Z7709,"Contact with and (suspected) exposure to other hazardous, chiefly nonmedicinal, chemicals"
+Z771,"Contact with and (suspected) exposure to environmental pollution and hazards in the physical environment"
+Z7711,"Contact with and (suspected) exposure to environmental pollution"
+Z7712,"Contact with and (suspected) exposure to hazards in the physical environment"
+Z772,"Contact with and (suspected ) exposure to other hazardous substances"
+Z779,"Other contact with and (suspected) exposures hazardous to health"
+Z78,"Other specified health status"
+Z79,"Long term (current) drug therapy"
+Z790,"Long term (current) use of anticoagulants and antithrombotics/antiplatelets"
+Z791,"Long term (current) use of non-steroidal anti-inflammatories (NSAID)"
+Z792,"Long term (current) use of antibiotics"
+Z793,"Long term (current) use of hormonal contraceptives"
+Z794,"Long term (current) use of insulin"
+Z795,"Long term (current) use of steroids"
+Z798,"Other long term (current) drug therapy"
+Z7981,"Long term (current) use of agents affecting estrogen receptors and estrogen levels"
+Z7982,"Long term (current) use of aspirin"
+Z7983,"Long term (current) use of bisphosphonates"
+Z7984,"Long term (current) use of oral hypoglycemic drugs"
+Z7989,"Other long term (current) drug therapy"
+Z80,"Family history of primary malignant neoplasm"
+Z804,"Family history of malignant neoplasm of genital organs"
+Z805,"Family history of malignant neoplasm of urinary tract"
+Z806,"Family history of leukemia"
+Z807,"Family history of other malignant neoplasms of lymphoid, hematopoietic and related tissues"
+Z808,"Family history of malignant neoplasm of other organs or systems"
+Z809,"Family history of malignant neoplasm, unspecified"
+Z81,"Family history of mental and behavioral disorders"
+Z82,"Family history of certain disabilities and chronic diseases (leading to disablement)"
+Z824,"Family history of ischemic heart disease and other diseases of the circulatory system"
+Z825,"Family history of asthma and other chronic lower respiratory diseases"
+Z826,"Family history of arthritis and other diseases of the musculoskeletal system and connective tissue"
+Z827,"Family history of congenital malformations, deformations and chromosomal abnormalities"
+Z828,"Family history of other disabilities and chronic diseases leading to disablement, not elsewhere classified"
+Z83,"Family history of other specific disorders"
+Z834,"Family history of other endocrine, nutritional and metabolic diseases"
+Z835,"Family history of eye and ear disorders"
+Z8351,"Family history of eye disorders"
+Z8352,"Family history of ear disorders"
+Z836,"Family history of other diseases of the respiratory system"
+Z837,"Family history of diseases of the digestive system"
+Z84,"Family history of other conditions"
+Z848,"Family history of other specified conditions"
+Z85,"Personal history of malignant neoplasm"
+Z850,"Personal history of malignant neoplasm of digestive organs"
+Z8502,"Personal history of malignant neoplasm of stomach"
+Z8503,"Personal history of malignant neoplasm of large intestine"
+Z8504,"Personal history of malignant neoplasm of rectum, rectosigmoid junction, and anus"
+Z8505,"Personal history of malignant neoplasm of liver"
+Z8506,"Personal history of malignant neoplasm of small intestine"
+Z8507,"Personal history of malignant neoplasm of pancreas"
+Z8509,"Personal history of malignant neoplasm of other digestive organs"
+Z851,"Personal history of malignant neoplasm of trachea, bronchus and lung"
+Z8511,"Personal history of malignant neoplasm of bronchus and lung"
+Z8512,"Personal history of malignant neoplasm of trachea"
+Z852,"Personal history of malignant neoplasm of other respiratory and intrathoracic organs"
+Z8523,"Personal history of malignant neoplasm of thymus"
+Z8529,"Personal history of malignant neoplasm of other respiratory and intrathoracic organs"
+Z853,"Personal history of malignant neoplasm of breast"
+Z854,"Personal history of malignant neoplasm of genital organs"
+Z855,"Personal history of malignant neoplasm of urinary tract"
+Z8552,"Personal history of malignant neoplasm of kidney"
+Z8553,"Personal history of malignant neoplasm of renal pelvis"
+Z8554,"Personal history of malignant neoplasm of ureter"
+Z8559,"Personal history of malignant neoplasm of other urinary tract organ"
+Z856,"Personal history of leukemia"
+Z857,"Personal history of other malignant neoplasms of lymphoid, hematopoietic and related tissues"
+Z858,"Personal history of malignant neoplasms of other organs and systems"
+Z8581,"Personal history of malignant neoplasm of lip, oral cavity, and pharynx"
+Z8582,"Personal history of malignant neoplasm of skin"
+Z8583,"Personal history of malignant neoplasm of bone and soft tissue"
+Z8584,"Personal history of malignant neoplasm of eye and nervous tissue"
+Z8585,"Personal history of malignant neoplasm of endocrine glands"
+Z8589,"Personal history of malignant neoplasm of other organs and systems"
+Z859,"Personal history of malignant neoplasm, unspecified"
+Z86,"Personal history of certain other diseases"
+Z860,"Personal history of in-situ and benign neoplasms and neoplasms of uncertain behavior"
+Z8600,"Personal history of in-situ neoplasm"
+Z8601,"Personal history of benign neoplasm"
+Z8603,"Personal history of neoplasm of uncertain behavior"
+Z861,"Personal history of infectious and parasitic diseases"
+Z862,"Personal history of diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism"
+Z863,"Personal history of endocrine, nutritional and metabolic diseases"
+Z865,"Personal history of mental and behavioral disorders"
+Z866,"Personal history of diseases of the nervous system and sense organs"
+Z867,"Personal history of diseases of the circulatory system"
+Z8671,"Personal history of venous thrombosis and embolism"
+Z8672,"Personal history of thrombophlebitis"
+Z8673,"Personal history of transient ischemic attack (TIA), and cerebral infarction without residual deficits"
+Z8674,"Personal history of sudden cardiac arrest"
+Z8679,"Personal history of other diseases of the circulatory system"
+Z87,"Personal history of other diseases and conditions"
+Z870,"Personal history of diseases of the respiratory system"
+Z871,"Personal history of diseases of the digestive system"
+Z872,"Personal history of diseases of the skin and subcutaneous tissue"
+Z873,"Personal history of diseases of the musculoskeletal system and connective tissue"
+Z8731,"Personal history of (healed) nontraumatic fracture"
+Z8739,"Personal history of other diseases of the musculoskeletal system and connective tissue"
+Z874,"Personal history of diseases of genitourinary system"
+Z8741,"Personal history of dysplasia of the female genital tract"
+Z8742,"Personal history of other diseases of the female genital tract"
+Z8743,"Personal history of diseases of male genital organs"
+Z8744,"Personal history of diseases of urinary system"
+Z875,"Personal history of complications of pregnancy, childbirth and the puerperium"
+Z877,"Personal history of (corrected) congenital malformations"
+Z8771,"Personal history of (corrected) congenital malformations of genitourinary system"
+Z8772,"Personal history of (corrected) congenital malformations of nervous system and sense organs"
+Z8773,"Personal history of (corrected) congenital malformations of digestive system"
+Z8774,"Personal history of (corrected) congenital malformations of heart and circulatory system"
+Z8775,"Personal history of (corrected) congenital malformations of respiratory system"
+Z8776,"Personal history of (corrected) congenital malformations of integument, limbs and musculoskeletal system"
+Z8779,"Personal history of other (corrected) congenital malformations"
+Z878,"Personal history of other specified conditions"
+Z8782,"Personal history of other (healed) physical injury and trauma"
+Z8789,"Personal history of other specified conditions"
+Z88,"Allergy status to drugs, medicaments and biological substances"
+Z89,"Acquired absence of limb"
+Z890,"Acquired absence of thumb and other finger(s)"
+Z8901,"Acquired absence of thumb"
+Z8902,"Acquired absence of other finger(s)"
+Z891,"Acquired absence of hand and wrist"
+Z8911,"Acquired absence of hand"
+Z8912,"Acquired absence of wrist"
+Z892,"Acquired absence of upper limb above wrist"
+Z8920,"Acquired absence of upper limb, unspecified level"
+Z8921,"Acquired absence of upper limb below elbow"
+Z8922,"Acquired absence of upper limb above elbow"
+Z8923,"Acquired absence of shoulder"
+Z894,"Acquired absence of toe(s), foot, and ankle"
+Z8941,"Acquired absence of great toe"
+Z8942,"Acquired absence of other toe(s)"
+Z8943,"Acquired absence of foot"
+Z8944,"Acquired absence of ankle"
+Z895,"Acquired absence of leg below knee"
+Z8951,"Acquired absence of leg below knee"
+Z8952,"Acquired absence of knee"
+Z896,"Acquired absence of leg above knee"
+Z8961,"Acquired absence of leg above knee"
+Z8962,"Acquired absence of hip"
+Z899,"Acquired absence of limb, unspecified"
+Z90,"Acquired absence of organs, not elsewhere classified"
+Z900,"Acquired absence of part of head and neck"
+Z901,"Acquired absence of breast and nipple"
+Z902,"Acquired absence of lung [part of]"
+Z903,"Acquired absence of stomach [part of]"
+Z904,"Acquired absence of other specified parts of digestive tract"
+Z9041,"Acquired absence of pancreas"
+Z9049,"Acquired absence of other specified parts of digestive tract"
+Z905,"Acquired absence of kidney"
+Z906,"Acquired absence of other parts of urinary tract"
+Z907,"Acquired absence of genital organ(s)"
+Z9071,"Acquired absence of cervix and uterus"
+Z9072,"Acquired absence of ovaries"
+Z9079,"Acquired absence of other genital organ(s)"
+Z908,"Acquired absence of other organs"
+Z91,"Personal risk factors, not elsewhere classified"
+Z910,"Allergy status, other than to drugs and biological substances"
+Z9101,"Food allergy status"
+Z9102,"Food additives allergy status"
+Z9103,"Insect allergy status"
+Z9104,"Nonmedicinal substance allergy status"
+Z9109,"Other allergy status, other than to drugs and biological substances"
+Z911,"Patient's noncompliance with medical treatment and regimen"
+Z9112,"Patient's intentional underdosing of medication regimen"
+Z9113,"Patient's unintentional underdosing of medication regimen"
+Z9114,"Patient's other noncompliance with medication regimen"
+Z9115,"Patient's noncompliance with renal dialysis"
+Z9119,"Patient's noncompliance with other medical treatment and regimen"
+Z914,"Personal history of psychological trauma, not elsewhere classified"
+Z9141,"Personal history of adult abuse"
+Z9149,"Other personal history of psychological trauma, not elsewhere classified"
+Z915,"Personal history of self-harm"
+Z918,"Other specified personal risk factors, not elsewhere classified"
+Z9184,"Oral health risk factors"
+Z9189,"Other specified personal risk factors, not elsewhere classified"
+Z92,"Personal history of medical treatment"
+Z922,"Personal history of drug therapy"
+Z9224,"Personal history of steroid therapy"
+Z9225,"Personal history of immunosupression therapy"
+Z9229,"Personal history of other drug therapy"
+Z923,"Personal history of irradiation"
+Z928,"Personal history of other medical treatment"
+Z93,"Artificial opening status"
+Z935,"Cystostomy status"
+Z936,"Other artificial openings of urinary tract status"
+Z938,"Other artificial opening status"
+Z939,"Artificial opening status, unspecified"
+Z94,"Transplanted organ and tissue status"
+Z948,"Other transplanted organ and tissue status"
+Z949,"Transplanted organ and tissue status, unspecified"
+Z95,"Presence of cardiac and vascular implants and grafts"
+Z958,"Presence of other cardiac and vascular implants and grafts"
+Z9581,"Presence of other cardiac implants and grafts"
+Z9582,"Presence of other vascular implants and grafts"
+Z959,"Presence of cardiac and vascular implant and graft, unspecified"
+Z96,"Presence of other functional implants"
+Z962,"Presence of otological and audiological implants"
+Z963,"Presence of artificial larynx"
+Z964,"Presence of endocrine implants"
+Z965,"Presence of tooth-root and mandibular implants"
+Z966,"Presence of orthopedic joint implants"
+Z9661,"Presence of artificial shoulder joint"
+Z9662,"Presence of artificial elbow joint"
+Z9663,"Presence of artificial wrist joint"
+Z9664,"Presence of artificial hip joint"
+Z9665,"Presence of artificial knee joint"
+Z9666,"Presence of artificial ankle joint"
+Z9669,"Presence of other orthopedic joint implants"
+Z967,"Presence of other bone and tendon implants"
+Z968,"Presence of other specified functional implants"
+Z969,"Presence of functional implant, unspecified"
+Z97,"Presence of other devices"
+Z971,"Presence of artificial limb (complete) (partial)"
+Z972,"Presence of dental prosthetic device (complete) (partial)"
+Z973,"Presence of spectacles and contact lenses"
+Z974,"Presence of external hearing-aid"
+Z975,"Presence of (intrauterine) contraceptive device"
+Z978,"Presence of other specified devices"
+Z98,"Other postprocedural states"
+Z984,"Cataract extraction status"
+Z985,"Sterilization status"
+Z986,"Angioplasty status"
+Z988,"Other specified postprocedural states"
+Z9881,"Dental procedure status"
+Z9882,"Breast implant status"
+Z9883,"Filtering (vitreous) bleb after glaucoma surgery status"
+Z9884,"Bariatric surgery status"
+Z9885,"Transplanted organ removal status"
+Z9886,"Personal history of breast implant removal"
+Z9887,"Personal history of in utero procedure"
+Z9889,"Other specified postprocedural states"
+Z99,"Dependence on enabling machines and devices, not elsewhere classified"
+Z991,"Dependence on respirator"
+Z992,"Dependence on renal dialysis"
+Z993,"Dependence on wheelchair"
+Z998,"Dependence on other enabling machines and devices"
diff --git a/scripts/OutpatientReferral/notes.md b/scripts/OutpatientReferral/notes.md
new file mode 100644
index 00000000..1906f9fb
--- /dev/null
+++ b/scripts/OutpatientReferral/notes.md
@@ -0,0 +1,33 @@
+
+
+(1) In the 1st implementation, try to do:
+
+    referral (freq) -> specialty -> orders
+
+Noise is extremely large. 
+Even when achieving a >40% precision@10, the results do not 
+make sense. For example:
+- referral:REFERRAL TO NEUROGENETICS ONCOLOGY
+- specialty:Ophthalmology
+- actual_orders:['PROTHROMBIN TIME', 'PTT PARTIAL THROMBOPLASTIN TIME', 'CBC W/O DIFF', 'METABOLIC PANEL, BASIC', 'TYPE AND SCREEN', 'BLOOD TYPE VERIFICATION', 'MR BRAIN COMPLETE SPINE WO IV CONTRAST', 'NO SKIN PREP NEEDED', 'RISK FOR VENOUS THROMBOEMBOLISM - VTE RISK ASSESSMENT', 'NO MECHANICAL VTE PROPHYLAXIS REQUIRED']
+- predict_orders:['REFERRAL TO PHYSICAL THERAPY', 'CBC W/O DIFF', 'METABOLIC PANE', 'PROTHROMBIN TIME', 'PTT PARTIAL THROMBOPLASTIN TIME', 'RISK FOR VENOUS THROMBOEMBOLISM - VTE RISK ASSESSMENT', 'REFERRAL TO PAIN CLINIC', 'TYPE AND SCREEN', 'MR BRAIN', 'PERIPHERAL IV INSERTION CARE']
+
+(1.5) Jon Chen's advice:
+Account for orders that already happened to the patient before the specialty visit. E.g., 
+like a hyperthyroid patient will get a TSH test, so that should be predicted, but specialist 
+may skip that test if PCP already did it recently. Should not count algorithm as "wrong" for 
+predicting it in that case. Try having two different reference sets for the "correct" y labels. 
+- Everything the specialist orders on first visit and 
+- Everything ordered by ANYONE between (and including) the PCP referral visit and the first specialty 
+visit.
+
+(2) In the 2nd implementation, try to do first:
+
+    specialty -> orders
+    
+Then integrate into the logic:
+
+    referral (freq) -> specialty -> orders
+    referral (dist) -> 
+    pat_info        ->
+                                 ->
\ No newline at end of file
diff --git a/scripts/OutpatientReferral/optumQueries.sql b/scripts/OutpatientReferral/optumQueries.sql
new file mode 100644
index 00000000..7da7e51c
--- /dev/null
+++ b/scripts/OutpatientReferral/optumQueries.sql
@@ -0,0 +1,183 @@
+-- (1) Overall Claims Database size and range
+
+SELECT
+	count(distinct PATID) as nPatients,
+	min(FST_DT) as firstDate,
+	max(fst_dt) as lastDate
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.medical_claims:7 AS t0
+
+-- 1% Sample: 628,616 patients, 2003-01-01 to 2019-06-30
+-- Full Data: 62,883,828 patients, 2003-01-01 to 2019-06-30
+
+
+-- Top Visit Type / Locations
+
+SELECT
+	c.POS,
+	count(distinct PATID) as nPatients,
+	min(FST_DT) as firstDate,
+	max(fst_dt) as lastDate
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.medical_claims:7 AS c
+GROUP BY
+  c.POS
+ORDER BY nPatients DESC
+
+-- 5.8M Office Visits, 3.7M Outpatient Hospital, 3.4M Inpatient Lab...
+
+
+-- Top HCCC Type
+
+SELECT
+	c.HCCC,
+	count(distinct PATID) as nPatients,
+	min(FST_DT) as firstDate,
+	max(fst_dt) as lastDate
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.medical_claims:7 AS c
+GROUP BY
+  c.HCCC
+ORDER BY nPatients DESC
+
+-- 4.9M Primary Care (01)
+-- 4.5M Outpatient Facility (07)
+-- 4.2M Specialty Physician (02)
+-- 2.5M Allied Health Provider (03)
+...
+
+
+-- (2) Medication Prescriptions Total
+
+SELECT
+	count(distinct PATID) as nPatients,
+	count(distinct CLMID) as nClaims,
+	count(distinct gnrc_nm) as nGenericNames,
+	count(distinct ahfsclss) as nClasses
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.rx_pharmacy:9 AS rx
+
+-- 56M Patients
+-- 2.8B Claims / Prescriptions
+-- 3.8K Unique Generic Names
+-- 610 Unique Classes of Medications
+
+
+-- (3) Lab Results
+
+SELECT
+	count(distinct PATID) as nPatients,
+	count(distinct LABCLMID) as nLabClaims,
+	count(distinct LOINC_CD) as nLOINCCodes,
+	count(*) as nRows
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.laboratory_results:1 AS lr
+
+-- 26M patients
+-- 3.5B rows of data
+-- 2.0B unique lab claim IDs
+-- 12K unique LOINC codes
+
+
+-- Break down by year, relatively stable over years
+SELECT
+	extract(YEAR from fst_dt) as labYear,
+	count(distinct PATID) as nPatients,
+	count(distinct LABCLMID) as nLabClaims,
+	count(distinct LOINC_CD) as nLOINCCodes,
+	count(*) as nRows
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.laboratory_results:1 AS lr
+GROUP BY
+  labYear
+ORDER BY
+  labYear DESC
+	
+
+-- Those with a Hemoglobin A1c result based on LOINC Code
+SELECT
+	count(distinct PATID) as nPatients,
+	count(distinct LABCLMID) as nLabClaims,
+	count(distinct LOINC_CD) as nLOINCCodes,
+	count(*) as nRows
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.laboratory_results:1 AS lr
+WHERE 
+	LOINC_CD in ('4548-4H','17856-6')
+ORDER BY
+  nPatients DESC
+
+-- 400K Patients
+-- 1.5M Lab Claims
+
+
+
+
+-- (4) Diagnoses
+SELECT
+	count(distinct PATID) as nPatients,
+	count(distinct CLMID) as nClaims,
+	count(distinct DIAG) as nDiagnoses
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.medical_diagnosis:11 AS dx
+
+-- 62M patients
+-- 2.9B claims
+-- 53K unique diagnosis codes
+
+
+-- Top Diagnosis Codes
+SELECT
+  dl.DIAG_DESC,
+	dl.DIAG_CD,
+	dl.ICD_VER_CD,
+  count(distinct PATID) as nPatients,
+	count(distinct CLMID) as nClaims
+	--md.DIAG,
+	--md.ICD_FLAG
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.medical_diagnosis:11 AS md
+	INNER JOIN 
+	stanfordphs.optum_diagnosis_lookup:8:v1_1.optum_diagnosis_lookup:1 AS dl
+	  ON (md.`DIAG` = dl.`DIAG_CD`)
+	  AND (md.`ICD_FLAG` = dl.`ICD_VER_CD`)
+WHERE 
+   DIAG_DESC like '%DIABETES%' OR
+	 DIAG_DESC like '%DM%' OR
+	 DIAG_DESC like '%ANEMIA%' OR
+	 DIAG_DESC like '%THYROID%'
+GROUP BY
+  dl.DIAG_DESC,
+	dl.DIAG_CD,
+	dl.ICD_VER_CD
+ORDER BY 
+  nPatients DESC
+	
+
+
+-- Category related diagnoses
+SELECT
+  count(distinct PATID) as nPatients,
+	count(distinct CLMID) as nClaims
+FROM
+	stanfordphs.optum_ses:145:v3_0:sample.medical_diagnosis:11 AS md
+	INNER JOIN 
+	stanfordphs.optum_diagnosis_lookup:8:v1_1.optum_diagnosis_lookup:1 AS dl
+	  ON (md.`DIAG` = dl.`DIAG_CD`)
+	  AND (md.`ICD_FLAG` = dl.`ICD_VER_CD`)
+WHERE 
+	-- DIAG_CD in ('2859','D649','2809','D509','2851','2800') -- Most common Anemia related
+	-- DIAG_CD in ('E039','2449','2448','2469','7945','E038','E079') -- Most common thyroid related
+	DIAG_CD in ('E119','E1165','E1122','E1142','E1140','E1169','E118') -- Most common diabetes related
+ORDER BY 
+  nPatients DESC
+
+
+-- Anemia: 6.7M Patients, 52M claims
+-- Thyroid: 6.8M Patients, 66M claims
+-- Diabetes: 3.8M Patients, 65M claims
+				
+
+
+-- (?) Break down by provider type? Doesn't work well. Provider.Taxonomy1 is null for 5.8M out of 9.1M and then sparse distribution from there
+
diff --git a/scripts/OutpatientReferral/queries.py b/scripts/OutpatientReferral/queries.py
new file mode 100644
index 00000000..38f798b4
--- /dev/null
+++ b/scripts/OutpatientReferral/queries.py
@@ -0,0 +1,66 @@
+
+
+from medinfo.db.Model import SQLQuery
+
+ENCOUNTER_TABLE = 'datalake_47618.encounter'
+ORDERPROC_TABLE = 'datalake_47618.order_proc'
+DIAGNOSIS_TABLE = 'datalake_47618.diagnosis_code'
+DEPMAP_TABLE = 'datalake_47618.dep_map'
+ORDERMED_TABLE = 'datalake_47618.order_med'
+
+def query_sample():
+    return """
+            select patient_item_id, external_id, clinical_item_id, item_date, encounter_id, text_value, num_value, source_id 
+            from 
+            `clinical_inpatient.patient_item` 
+            where item_date >= timestamp('2014-01-01 00:00:00')
+            """
+
+def query_for_recent6months(newPatientOnly=True, referral_name=None, include_med=False):
+    query = SQLQuery()
+    query.addSelect('p1.pat_enc_csn_id_coded AS referral_enc_id')
+    query.addSelect('p1.description AS referral_name')
+    query.addSelect('e1.appt_when_jittered AS referral_time')
+    query.addSelect('e1.jc_uid AS pat_id')
+    query.addSelect('d1.icd9 AS referral_icd9')
+    query.addSelect('d1.icd10 AS referral_icd10')
+
+    query.addSelect('p2.pat_enc_csn_id_coded AS specialty_enc_id')
+    query.addSelect('e2.appt_when_jittered AS specialty_time')
+    query.addSelect('d2.department_name AS specialty_dep')
+    query.addSelect('d2.specialty AS specialty_name')
+    query.addSelect('p2.description AS specialty_order')
+    if include_med:
+        query.addSelect('o2.amb_med_disp_name as medication')
+
+    query.addFrom('%s AS e1' % ENCOUNTER_TABLE)
+    query.addFrom('%s AS p1' % ORDERPROC_TABLE)
+    query.addFrom('%s AS d1' % DIAGNOSIS_TABLE)
+
+    query.addFrom('%s AS e2' % ENCOUNTER_TABLE)
+    query.addFrom('%s AS p2' % ORDERPROC_TABLE)
+    query.addFrom('%s AS d2' % DEPMAP_TABLE)
+
+    query.addFrom('%s AS o2' % ORDERMED_TABLE)
+
+    query.addWhere("lower(p1.description) like '%referral%'")
+    query.addWhere("p1.pat_enc_csn_id_coded = e1.pat_enc_csn_id_coded")
+    query.addWhere("p1.pat_enc_csn_id_coded = d1.pat_enc_csn_id_coded")
+    query.addWhere("e1.appt_when_jittered >= '2016-01-01'")
+    query.addWhere("e1.appt_when_jittered < '2017-01-01'")
+
+    query.addWhere("e1.jc_uid = e2.jc_uid")
+    if referral_name:
+        query.addWhere("p1.description = '%s'" % referral_name)
+    query.addWhere("e1.pat_enc_csn_id_coded != e2.pat_enc_csn_id_coded")
+    query.addWhere("e1.appt_when_jittered <= e2.appt_when_jittered")
+    query.addWhere("DATE_ADD(date(timestamp(e1.appt_when_jittered)), INTERVAL 6 month) > date(timestamp(e2.appt_when_jittered))")
+
+    if newPatientOnly:
+        query.addWhere("e2.visit_type like '%NEW PATIENT%'")
+    query.addWhere("e2.department_id = d2.department_id")
+    query.addWhere("p2.pat_enc_csn_id_coded = e2.pat_enc_csn_id_coded")
+
+    if include_med:
+        query.addWhere("o2.pat_enc_csn_id_coded = e2.pat_enc_csn_id_coded")
+    return str(query)
diff --git a/scripts/OutpatientReferral/sampleQueries.sql b/scripts/OutpatientReferral/sampleQueries.sql
new file mode 100644
index 00000000..c7911652
--- /dev/null
+++ b/scripts/OutpatientReferral/sampleQueries.sql
@@ -0,0 +1,567 @@
+-- (1) Find all (outpatient) encounter referral orders for specialty (Endocrinology)
+	WITH 
+
+	referringEncountersEndocrine2017 AS
+	(
+		select op.jc_uid, op.pat_enc_csn_id_coded as referringEncounterId, 
+	      enc.appt_when_jittered as referringApptDateTime, op.order_time_jittered as referralOrderDateTime
+		from `starr_datalake2018.order_proc` as op 
+		  join `starr_datalake2018.encounter` as enc on op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded 
+		where proc_id = 34378 -- proc_code = 'REF31' -- REFERRAL TO ENDOCRINE CLINIC -- Should be the same thing, but 2020 database update seems to be missing proc_code for many records
+		and ordering_mode = 'Outpatient'
+		and EXTRACT(YEAR from order_time_jittered) = 2017
+		-- 5675 Records - Endocrine
+		-- Sometimes the Referral Order Time is days later than the appointment contact date / appt_when 
+		--  (Maybe entered later in follow-up), but usually the same 4730 times
+		--  and DATETIME_TRUNC(enc.appt_when_jittered, DAY) = DATETIME_TRUNC(op.order_time_jittered, DAY)
+		-- Many of the referring encounters are Telephone, Orders Only, BMT infusions, etc. encounters
+		--  Is okay, is still the time of referral order entry, but should use more input context from time before this encounter too
+	),
+	referringEncountersHematology2017 AS
+	(
+		select op.jc_uid, op.pat_enc_csn_id_coded as referringEncounterId, 
+	      enc.appt_when_jittered as referringApptDateTime, op.order_time_jittered as referralOrderDateTime
+		from `starr_datalake2018.order_proc` as op 
+		  join `starr_datalake2018.encounter` as enc on op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded 
+		where proc_id = 34352 -- proc_code = 'REF18' -- REFERRAL TO HEMATOLOGY CLINIC -- Should be the same thing, but 2020 database update seems to be missing proc_code for many records
+		and ordering_mode = 'Outpatient'
+		and EXTRACT(YEAR from order_time_jittered) = 2017
+		-- 2406 Records - Hematology
+	),
+
+
+
+	-- (1a) Find all (outpatient) encounters with referral orders for ANY specialty (use as reference baseline for relative risk estimates)
+
+
+	-- (1b) Find clinic encounters from Primary Care specialities
+	
+	primaryCareEncounters2017 AS
+	(
+		select enc.jc_uid, enc.pat_enc_csn_id_coded as primaryEncounterId, enc.appt_when_jittered as primaryEncounterDateTime, 
+      		dep.specialty_dep_c, dep.specialty
+		from `starr_datalake2018.encounter` as enc join `starr_datalake2018.dep_map` as dep on enc.department_id = dep.department_id 
+		where dep.specialty_dep_c in ('9','17','125','2527') -- dep.specialty in 'Family Medicine','Internal Medicine','Primary Care','Express Care'
+		and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc.
+		and appt_status = 'Completed'
+		and extract(YEAR from enc.appt_time_jittered) = 2017	-- >= So capture follow-up visits in 2018 as well
+		-- 420,964 records in 2017
+	),
+
+
+--(2) Find all (New Patient) clinic visits for the referred specialty
+
+	endocrineNewPatientEncounters2017_ AS
+	(
+		select enc.jc_uid, enc.pat_enc_csn_id_coded as specialtyEncounterId, enc.appt_when_jittered as specialtyEncounterDateTime
+		from `starr_datalake2018.encounter` as enc join `starr_datalake2018.dep_map` as dep on enc.department_id = dep.department_id 
+		where dep.specialty_dep_c = '7' -- dep.specialty like 'Endocrin%'
+		and visit_type like 'NEW PATIENT%' -- Naturally screens to only 'Office Visit' enc_type 
+		-- and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc.
+		and appt_status = 'Completed'
+		and extract(YEAR from enc.appt_time_jittered) >= 2017	-- >= So capture follow-up visits in 2018 as well
+		-- 4796 records in 2017 (10650 for 2017 and after, largely including 2018)
+	),
+	hematologyNewPatientEncounters2017_ AS
+	(
+		select enc.jc_uid, enc.pat_enc_csn_id_coded as specialtyEncounterId, enc.appt_when_jittered as specialtyEncounterDateTime
+		from `starr_datalake2018.encounter` as enc join `starr_datalake2018.dep_map` as dep on enc.department_id = dep.department_id 
+		where dep.specialty_dep_c = '14' -- dep.specialty like 'Hematology'
+		and visit_type like 'NEW PATIENT%' -- Naturally screens to only 'Office Visit' enc_type 
+		-- and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc.
+		and appt_status = 'Completed'
+		and extract(YEAR from enc.appt_time_jittered) >= 2017	-- >= So capture follow-up visits in 2018 as well
+		-- 2895 records in 2017 (5,644 for 2017 and after)
+	),
+
+
+	-- (2a) Find all NON-New Patient clinic visits for the referred specialty
+	endocrineNonNewPatientEncounters2017_ AS
+	(
+		select enc.jc_uid, enc.pat_enc_csn_id_coded as nonNewSpecialtyEncounterId, enc.appt_when_jittered as nonNewSpecialtyEncounterDateTime
+		from `starr_datalake2018.encounter` as enc join `starr_datalake2018.dep_map` as dep on enc.department_id = dep.department_id 
+		where dep.specialty_dep_c = '7' -- dep.specialty like 'Endocrin%'
+		and visit_type not like 'NEW PATIENT%' -- Wide list of different 'ESTABLISHED PATIENT...,' 'RETURN PATIENT...,' 'THYROID BIOPSY,' etc.
+		and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc. (20,489 out 21,817 are Office Visit / Appointment)
+		and appt_status = 'Completed'
+		and extract(YEAR from enc.appt_time_jittered) >= 2017
+    	-- 20,489 non-NEW PATIENT specialty encounter visits in 2017
+    ),
+    hematologyNonNewPatientEncounters2017_ AS
+	(
+		select enc.jc_uid, enc.pat_enc_csn_id_coded as nonNewSpecialtyEncounterId, enc.appt_when_jittered as nonNewSpecialtyEncounterDateTime
+		from `starr_datalake2018.encounter` as enc join `starr_datalake2018.dep_map` as dep on enc.department_id = dep.department_id 
+		where dep.specialty_dep_c = '14' -- dep.specialty like 'Hematol%'
+		and visit_type not like 'NEW PATIENT%' -- Wide list of different 'ESTABLISHED PATIENT...,' 'RETURN PATIENT...,' 'THYROID BIOPSY,' etc.
+		and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc. (20,489 out 21,817 are Office Visit / Appointment)
+		and appt_status = 'Completed'
+		and extract(YEAR from enc.appt_time_jittered) >= 2017
+    	-- 23,203 non-NEW PATIENT specialty encounter visits in 2017
+    ),
+
+
+	-- (2b) Find all (New Patient) clinic visits for ANY specialty (use as reference baseline for relative risk estimates)
+
+-- (3) Join to match referral orders to respective (first) patient visits within *6* months of referral
+
+	endocrineReferralTimes2017 AS
+	(
+		select *, DATETIME_DIFF(specEnc.specialtyEncounterDateTime, refEnc.referralOrderDateTime, DAY) as referralDelayDays
+		from referringEncountersEndocrine2017 as refEnc join endocrineNewPatientEncounters2017_ as specEnc using (jc_uid)
+		where DATETIME_DIFF(specEnc.specialtyEncounterDateTime, refEnc.referralOrderDateTime, MONTH) BETWEEN 0 AND 11
+		-- 2,640 referred New Patient visit within 6 months
+		-- 2,898 referred New Patient visit within 12 months
+			-- 67 days average, 66 days stdev, 44 days median (IQR 26-83)
+	),
+	hematologyReferralTimes2017 AS
+	(
+		select *, DATETIME_DIFF(specEnc.specialtyEncounterDateTime, refEnc.referralOrderDateTime, DAY) as referralDelayDays
+		from referringEncountersHematology2017 as refEnc join hematologyNewPatientEncounters2017_ as specEnc using (jc_uid)
+		where DATETIME_DIFF(specEnc.specialtyEncounterDateTime, refEnc.referralOrderDateTime, MONTH) BETWEEN 0 AND 11
+		-- 1,204 referred New Patient visit within 6 months
+		-- 1,238 referred New Patient visit within 12 months
+			-- 42 days average, 45 days stdev, 34 days median (IQR 19-48)
+	),
+
+    -- Outer join to count how many with no follow-up visit at all
+    -- Assess distribution of referral time by summarizing results in Excel
+
+
+--(4) Find all (sorted by prevalence)
+    -- Diagnoses from encounters in (1)
+    endocrineReferralDiagnoses AS
+    (
+		select dx.icd9, dx.icd10, dx_name, count(*)
+		from referringEncountersEndocrine2017 as refEnc 
+		  join `starr_datalake2018.diagnosis_code` as dx on refEnc.referringEncounterId = dx.pat_enc_csn_id_coded 
+		group by dx.icd9, dx.icd10, dx_name
+		order by count(*) desc
+		-- 8,240 Distinct diagnosis codes, but long tail dominated by 512 Osteoporosis, 487 Thyroid Nodule, 424 Hypothyroid...
+	),
+    hematologyReferralDiagnoses AS
+    (
+		select dx.icd9, dx.icd10, dx_name, count(*)
+		from referringEncountersHematology2017 as refEnc 
+		  join `starr_datalake2018.diagnosis_code` as dx on refEnc.referringEncounterId = dx.pat_enc_csn_id_coded 
+		group by dx.icd9, dx.icd10, dx_name
+		order by count(*) desc
+		-- 5,257 Distinct diagnosis codes, but long tail dominated by 169 Anemia, 104 Thrombocytopenia, 92 Anemia, 90 Iron Def Anemia, 72 Leukocytosis...
+	),
+    
+    -- Order Med from encounters in (2)
+    endocrineNewEncounterMeds AS
+    (
+		select medication_id, med_description, count(*)
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		group by medication_id, med_description
+		order by count(*) desc
+		-- 761 Distinct medication_id descriptions, but can likely boil down many based on active ingredient
+		-- 242 OneTouch Verio Strip, 217 OneTouch Lancet, 185 Metformin, 164 Dexamethasone, 150 Alendronate
+	),
+	endocrineNewEncounterMedClass AS
+	(
+		select pharm_class_name, count(*)
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		group by pharm_class_name
+		order by count(*) desc
+		-- 143 Distinct pharm_class_name types prescribed in the specialty visits
+		-- 817 Insulins, 803 Thyroid Hormones, 660 Blood Sugar Diagnostics, 475 Diabetic Supplies, 462 Biguanide Anti-Diabetic, 398 Bone Resorption Inhibitors...
+	),
+
+    hematologyNewEncounterMeds AS
+    (
+		select medication_id, med_description, count(*)
+		from hematologyNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		group by medication_id, med_description
+		order by count(*) desc
+		-- 304 Distinct medication_id descriptions
+		-- 463 Ondansetron IV, 462 NS IV Bolus, 460 Diphenhydramine IV, 459 Methylprednisolone IV, 459 Epinephrine IV, 118 Acetaminophen, 112 Iron IV, ... 
+		-- Looks like mostly chemotherapy pre-meds
+	),
+	hematologyNewEncounterMedClass AS
+	(
+		select pharm_class_name, count(*)
+		from hematologyNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		group by pharm_class_name
+		order by count(*) desc
+		-- 112 Distinct pharm_class_name types prescribed in the specialty visits
+		-- 593 Anti-Emetic, 521 Glucocorticoids, 484 Antihistamines, 459 Adrenergic Agent, 245 Saline Preparation, 206 Iron Replacement, ...
+	),
+
+	primaryCareMeds AS
+	(
+		select medication_id, med_description, count(*)
+		from primaryCareEncounters2017 as primEnc
+		  join `starr_datalake2018.order_med` as om on primEnc.primaryEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		group by medication_id, med_description
+		having count(*) >= 10
+		order by count(*) desc
+		-- 6416 Distinct medication_id descriptions in primary care, but can likely boil down many based on active ingredient
+		-- 2108 Distinct medication_id descriptions in primary care that were ordered at least 10 times in 2017
+	),
+	primaryCareMedClass AS
+	(
+		select pharm_class_name, count(*)
+		from primaryCareEncounters2017 as primEnc
+		  join `starr_datalake2018.order_med` as om on primEnc.primaryEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		group by pharm_class_name
+		having count(*) >= 10
+		order by count(*) desc
+		-- 503 Distinct pharm_class_name types prescribed in the primary care visits
+		-- 343 Distinct pharm_class_name types prescribed in the primary care visits that were ordered at least 10 times in 2017
+	),
+
+
+
+	-- Med Order counts per New Specialty Encounter
+	endocrineNewPatientMedCount AS
+	(
+		select avg(nDistinctOrderMed) as avgDistinctOrderMed, max(nDistinctOrderMed) as maxDistinctOrderMed
+		from
+		(
+		  select specEnc.specialtyEncounterId, count(distinct om.medication_id) as nDistinctOrderMed
+		  from endocrineNewPatientEncounters2017_ as specEnc
+		    join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		  where order_class <> 'Historical Med'
+		  group by specEnc.specialtyEncounterId 
+		)
+		-- 2.0 average distinct order_meds per specialty new visit, max 13
+	),
+	hematologyNewPatientMedCount AS
+	(
+		select avg(nDistinctOrderMed) as avgDistinctOrderMed, max(nDistinctOrderMed) as maxDistinctOrderMed
+		from
+		(
+		  select specEnc.specialtyEncounterId, count(distinct om.medication_id) as nDistinctOrderMed
+		  from hematologyNewPatientEncounters2017_ as specEnc
+		    join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		  where order_class <> 'Historical Med'
+		  group by specEnc.specialtyEncounterId 
+		)
+		-- 3.1 average distinct order_meds per specialty new visit, max 43
+	),
+
+
+    -- Order Proc from encounters in (2)
+    endocrineNewPatientProc AS
+    (
+		select op.proc_code, description, count(*)
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		group by proc_code, description
+		order by count(*) desc
+		-- 954 Distinct proc_code descriptions
+		-- 6,018 TSH, 4,655 T4, 2,841 25-OH VitD, ...
+	),
+	endocrineNewPatientProcCount AS
+	(
+		select avg(nDistinctOrderProcs) as avgDistinctOrderProcs, max(nDistinctOrderProcs) as maxDistinctOrderProcs
+		from
+		(
+		  select specEnc.specialtyEncounterId, count(distinct op.proc_code) as nDistinctOrderProcs
+		  from endocrineNewPatientEncounters2017_ as specEnc
+		    join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		  group by specEnc.specialtyEncounterId 
+		)
+		-- 5.3 average distinct order_procs per specialty new visit, max 38
+	),
+
+    hematologyNewPatientProc AS
+    (
+		select op.proc_code, description, count(*)
+		from hematologyNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		group by proc_code, description
+		order by count(*) desc
+		-- 675 Distinct proc_code descriptions
+		-- CMP, CBC w/ Diff, Ferritin, Retic Count, Type and Screen, Donor HTLV Ab, Transferrin Sat, ...
+	),
+	hematologyNewPatientProcCount AS
+	(
+		select avg(nDistinctOrderProcs) as avgDistinctOrderProcs, max(nDistinctOrderProcs) as maxDistinctOrderProcs
+		from
+		(
+		  select specEnc.specialtyEncounterId, count(distinct op.proc_code) as nDistinctOrderProcs
+		  from hematologyNewPatientEncounters2017_ as specEnc
+		    join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		  group by specEnc.specialtyEncounterId 
+		)
+		-- 6.9 average distinct order_procs per specialty new visit, max 33
+	),
+
+
+-- (5) Filter (1) by only encouters including a diagnosis for XXX
+
+-- (6) Medications from New Specialty Visits that are (rarely) ordered in Primary Care
+	endocrineSpecificMeds AS
+	(
+		select medication_id, med_description, count(*)
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		and medication_id not in
+		(
+			select medication_id
+			from primaryCareEncounters2017 as primEnc
+			  join `starr_datalake2018.order_med` as om on primEnc.primaryEncounterId = om.pat_enc_csn_id_coded 
+			where order_class <> 'Historical Med'
+			group by medication_id
+			having count(*) >= 10
+		)
+		group by medication_id, med_description
+        order by count(*) desc
+		-- 313 medications out of 761 medications ordered in New Specialty visits are rarely ordered in primary care
+		-- Freestyle Libre Sensor, Freestyle Libre Reader, Zoledronic Acid IV, Thyrotropin IM, ..
+	),
+	hematologySpecificMeds AS
+	(
+		select medication_id, med_description, count(*)
+		from hematologyNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+		and medication_id not in
+		(
+			select medication_id
+			from primaryCareEncounters2017 as primEnc
+			  join `starr_datalake2018.order_med` as om on primEnc.primaryEncounterId = om.pat_enc_csn_id_coded 
+			where order_class <> 'Historical Med'
+			group by medication_id
+			having count(*) >= 10
+		)
+		group by medication_id, med_description
+        order by count(*) desc
+        -- 145 medications rarely ordered in primary care
+        -- Epinephrine, Methylprednisolone IV, Iron IV, Filgrastim, Heparin IV, Mag oxide, KCL, ...
+	),
+
+
+-- Procedures in specialty care that are rarely encountered in primary care
+	endocrineSpecificProc AS
+	(
+		select proc_id, proc_code, description, count(*)
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		where proc_id not in
+		(
+			select proc_id
+			from primaryCareEncounters2017 as primEnc
+			  join `starr_datalake2018.order_proc` as op on primEnc.primaryEncounterId = op.pat_enc_csn_id_coded 
+			group by proc_id
+			having count(*) >= 10
+		)
+		group by proc_id, proc_code, description
+        order by count(*) desc
+		-- 368 procedures ordered in endocrine rarely done in primary care
+		-- Endo Echo Head and Neck, FNA, Chromogranin A, NM Thyroid Whole Body, ...
+	),
+	hematologySpecificProc AS
+	(
+		select proc_id, proc_code, description, count(*)
+		from hematologyNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		where proc_id not in
+		(
+			select proc_id
+			from primaryCareEncounters2017 as primEnc
+			  join `starr_datalake2018.order_proc` as op on primEnc.primaryEncounterId = op.pat_enc_csn_id_coded 
+			group by proc_id
+			having count(*) >= 10
+		)
+		group by proc_id, proc_code, description
+        order by count(*) desc
+        -- 296 proc rarely ordered by primary care
+        -- Donor virus testing, HLA order, ... doesn't look like much a primary care could not order. Just rare as cancer specific.
+	),
+
+
+
+--(7) New (Endocrine) specialty encounters in 2017 that do NOT include a specialty only procedure (e.g., biopsy, injection)
+
+endocrineNewPatientNonSpecializedEncounters2017 AS
+(
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+	from endocrineNewPatientEncounters2017_ as specEnc
+	where extract(YEAR FROM specialtyEncounterDateTime) = 2017
+ 
+    except distinct
+    
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		where op.proc_code in 
+    ('210976536', 'EIA004', '210910022', 'PATH9', 'PATH9', 'INJ190835', 'IMGNM0014', '210995251', 'IMGUSBT', 'IMGUS0033', 'IMGNM0015', '21090897', '210999358', 'INJJ0897.UHA', '210999091', '2109J0834', '230976942.UHA', '210992250') -- Injections, biopsies, etc. that would not be expected to be done by generalist
+    -- 4,479 New Patient encounters in 2017 out of 4,796 New Patient encounters
+
+    except distinct 
+
+	select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+    and om.medication_id in (90538, 2364, 25122, 199672, 210386, 199615, 86468, 199672, 210913, 126553, 94690, 115097, 7785, 541179, 2243, 84544, 115097, 2364, 213399)
+    -- 4,389 New Patient specialty encounters in 2017 that don't include an order for an (injectable) med or procedure that can likely only occur in specialty clinic
+),
+
+endocrineNewPatientNoMedsOrProcsEncounters2017 AS
+(
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+	from endocrineNewPatientEncounters2017_ as specEnc
+  	where extract(YEAR FROM specialtyEncounterDateTime) = 2017
+
+    except distinct
+    
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+		where op.proc_code in 
+    ('210976536', 'EIA004', '210910022', 'PATH9', 'PATH9', 'INJ190835', 'IMGNM0014', '210995251', 'IMGUSBT', 'IMGUS0033', 'IMGNM0015', '21090897', '210999358', 'INJJ0897.UHA', '210999091', '2109J0834', '230976942.UHA', '210992250') -- Injections, biopsies, etc. that would not be expected to be done by generalist
+    -- 4,479 New Patient encounters in 2017 out of 4,796 New Patient encounters
+
+    except distinct 
+
+	select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime
+		from endocrineNewPatientEncounters2017_ as specEnc
+		  join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+		where order_class <> 'Historical Med'
+    -- 2,874 New Patient specialty encounters in 2017 that only include non-specialized procedure orders (mostly labs, imaging, referrals). Not even any oral med / treatment prescriptions
+),
+
+
+hematologyNewPatientNonSpecializedEncounters2017 AS
+(
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+	from hematologyNewPatientEncounters2017_ as specEnc
+	where extract(YEAR FROM specialtyEncounterDateTime) = 2017
+	-- 2,895 New Patient Encounters in 2017
+ 
+    except distinct
+    
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+	from hematologyNewPatientEncounters2017_ as specEnc
+		join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+	where op.proc_code in 
+    	('LABBBPRBC','11500201','PATH16','11500202','PATH9','LABBBPPLT','NUR14606A','NUR14606B','NUR744','210936589') -- Blood transfusion, FNA, etc. that would not be expected to be done by generalist
+    -- 2,877 New Patient encounters in 2017 without the above procedure orders
+
+    except distinct 
+
+	select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime
+	from hematologyNewPatientEncounters2017_ as specEnc
+		join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+	where order_class <> 'Historical Med'	-- Exclude IV and chemotherapy related meds that a primary care would be unlikely to order
+    and om.medication_id in (213662,209509,226945,112594,126580,540302,211084,540301,540304,221562,85848,112593,230620,230501,10467,200346,206390,213297,96471,126580,10236,540145,122481,85848,540763,236322,205661,213978,94592,210518,221636,10236,540145,122481,85848,540763,236322,205661,213978,94592,210518,221636,213980,13531,237602,237606,205208,232098,213297,7319,114723,242838,114723,210379,80845,199389,210527,9062,77372,7319,115220,115220,127401,231363,10537,228717,9063,110888,7319,23204,115378,236322,94592,202044,7319,206291,237603,95354,126404,114814,88720,28921,31915,10248,3708,211638,36089,31025,870,23210,381,117152,80504,237599,237601,29481,5510,126917,121514,8679,209037,19292,7319,221638,242528,77867,127400,201699,212878,127420,91568,13733,119134,10000,2331,2364,203466,28871,237600,96053,21102,212882,110889,4871,2364,10177,127420,209888,91375,28870,9605,209878,43483,95803,234674,89428,239975,80845,4454,112403,542067,31916,112597,242207,1965,2131,241649,15861,231412,10531)
+    -- 2,732 New Patient specialty encounters in 2017 that don't include an order for an (injectable) med or procedure that can likely only occur in specialty clinic
+),
+
+hematologyNewPatientNoMedsOrProcsEncounters2017 AS
+(
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+	from hematologyNewPatientEncounters2017_ as specEnc
+	where extract(YEAR FROM specialtyEncounterDateTime) = 2017
+	-- 2,895 New Patient Encounters in 2017
+ 
+    except distinct
+    
+    select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime 
+	from hematologyNewPatientEncounters2017_ as specEnc
+		join `starr_datalake2018.order_proc` as op on specEnc.specialtyEncounterId = op.pat_enc_csn_id_coded 
+	where op.proc_code in 
+    	('LABBBPRBC','11500201','PATH16','11500202','PATH9','LABBBPPLT','NUR14606A','NUR14606B','NUR744','210936589') -- Blood transfusion, FNA, etc. that would not be expected to be done by generalist
+    -- 2,877 New Patient encounters in 2017 without the above procedure orders
+
+    except distinct 
+
+	select specEnc.jc_uid, specEnc.specialtyEncounterId, specEnc.specialtyEncounterDateTime
+	from hematologyNewPatientEncounters2017_ as specEnc
+		join `starr_datalake2018.order_med` as om on specEnc.specialtyEncounterId = om.pat_enc_csn_id_coded 
+	where order_class <> 'Historical Med'	-- Exclude IV and chemotherapy related meds that a primary care would be unlikely to order
+    -- 2,594 New Patient specialty encounters in 2017 that don't include an order for ANY meds, and not specialty specific procedurs
+),
+
+-- (8) Join to find New Patient specialty visits that did not include specialized treatment and are followed by another visit
+  	-- Implies that the actions in the New Patient visit could have been done before hand and then 
+  	--	the follow-up could have happened immediately, eliminating the need for the extra follow-up visit
+  	endocrineNonSpecialEncounterFollowup2017 AS
+  	(
+		select newEnc.*, min(nonNewSpecialtyEncounterId) AS firstFollowupEncounterId, min(nonNewSpecialtyEncounterDateTime) AS firstFollowupDateTime,
+	    	DATETIME_DIFF(min(followupEnc.nonNewSpecialtyEncounterDateTime), newEnc.specialtyEncounterDateTime, DAY) as daysUntilFollowup, 
+	    	DATETIME_DIFF(min(followupEnc.nonNewSpecialtyEncounterDateTime), newEnc.specialtyEncounterDateTime, MONTH) as monthsUntilFollowup
+		from endocrineNewPatientNoMedsOrProcsEncounters2017 as newEnc join endocrineNonNewPatientEncounters2017_ as followupEnc using (jc_uid)
+		where DATETIME_DIFF(followupEnc.nonNewSpecialtyEncounterDateTime, newEnc.specialtyEncounterDateTime, MONTH) BETWEEN 0 AND 11
+	  	group by jc_uid, specialtyEncounterId, specialtyEncounterDateTime
+	  	-- 1,167 New Patient to follow-up specialty encounters within 6 months
+	  	-- 1,451 New Patient to follow-up specialty encounters within 12 months
+	  	-- Means >1,000 didn't follow-up in specialty clinic at all within 12 months, 
+	  	--	implying many of those New Patient visits weren't even necessary
+	  	-- Follow-up: Average 96 days, StdDev 85 days
+	 ),
+
+  	hematologyNonSpecialEncounterFollowup2017 AS
+  	(
+		select newEnc.*, min(nonNewSpecialtyEncounterId) AS firstFollowupEncounterId, min(nonNewSpecialtyEncounterDateTime) AS firstFollowupDateTime,
+	    	DATETIME_DIFF(min(followupEnc.nonNewSpecialtyEncounterDateTime), newEnc.specialtyEncounterDateTime, DAY) as daysUntilFollowup, 
+	    	DATETIME_DIFF(min(followupEnc.nonNewSpecialtyEncounterDateTime), newEnc.specialtyEncounterDateTime, MONTH) as monthsUntilFollowup
+		from hematologyNewPatientNoMedsOrProcsEncounters2017 as newEnc join hematologyNonNewPatientEncounters2017_ as followupEnc using (jc_uid)
+		where DATETIME_DIFF(followupEnc.nonNewSpecialtyEncounterDateTime, newEnc.specialtyEncounterDateTime, MONTH) BETWEEN 0 AND 11
+	  	group by jc_uid, specialtyEncounterId, specialtyEncounterDateTime
+	  	-- 1,075 New Patient to follow-up specialty encounters within 6 months
+	  	-- 1,279 New Patient to follow-up specialty encounters within 12 months
+	  	-- Means >1,000 didn't follow-up in specialty clinic at all within 12 months, 
+	  	--	implying many of those New Patient visits weren't even necessary
+	  	-- Follow-up: Average 84 days, StdDev 74 days
+	 ),
+
+-- (9) New Patient specialty encouners where had a particular lab abnormality beforehand (to estimate power calculations)
+    /*
+	endocrinePriorAbnormalTSH AS
+	(
+		select specNewEnc.*, ord_num_value, DATETIME_DIFF(specNewEnc.specialtyEncounterDateTime, res.result_time_jittered, MONTH) as labToEncMonths
+		from `starr_datalake2018.lab_result` as res
+		   join endocrineNewPatientEncounters2017_ as specNewEnc
+		        on res.rit_uid = specNewEnc.jc_uid 
+		where base_name = 'TSH'
+		and ord_num_value <0.5
+		and EXTRACT(YEAR from specNewEnc.specialtyEncounterDateTime) = 2017
+		and DATETIME_DIFF(specNewEnc.specialtyEncounterDateTime, res.result_time_jittered, MONTH) BETWEEN 0 AND 11
+		-- 805 Endocrine New Patient visits in 2017 with low TSH in prior year
+	),
+
+	endocrinePriorAbnormalCalcium AS
+	(
+		select specNewEnc.*, ord_num_value, DATETIME_DIFF(specNewEnc.specialtyEncounterDateTime, res.result_time_jittered, MONTH) as labToEncMonths
+		from `starr_datalake2018.lab_result` as res
+		   join specialtyNewPatientEncounters2017 as specNewEnc
+		        on res.rit_uid = specNewEnc.jc_uid 
+		where base_name = 'CA'
+		and ord_num_value > 11.5
+		and EXTRACT(YEAR from specNewEnc.specialtyEncounterDateTime) = 2017
+		and DATETIME_DIFF(specNewEnc.specialtyEncounterDateTime, res.result_time_jittered, MONTH) BETWEEN 0 AND 11
+		-- 73 Endocrine New Patient visits in 2017 with high CA in prior year
+	),
+
+	hematologyPriorAbnormalPlt AS
+	(
+		select specNewEnc.*, ord_num_value, DATETIME_DIFF(specNewEnc.specialtyEncounterDateTime, res.result_time_jittered, MONTH) as labToEncMonths
+		from `starr_datalake2018.lab_result` as res
+		   join hematologyNewPatientEncounters2017 as specNewEnc
+		        on res.rit_uid = specNewEnc.jc_uid 
+		where base_name = 'PLT'
+		and ord_num_value < 30
+		and EXTRACT(YEAR from specNewEnc.specialtyEncounterDateTime) = 2017
+		and DATETIME_DIFF(specNewEnc.specialtyEncounterDateTime, res.result_time_jittered, MONTH) BETWEEN 0 AND 11
+		-- 2784 Hematology New Patient visits in 2017 with low PLT in prior year
+		--	(But this is likely capturing a lot of MDS, leukemia, etc., not just isolated thrombocytopenia)
+	),
+	*/
+	spacer AS (select * from primaryCareEncounters2017)
+	
\ No newline at end of file
diff --git a/scripts/OutpatientReferral/topSpecialtyOrders.sql b/scripts/OutpatientReferral/topSpecialtyOrders.sql
new file mode 100644
index 00000000..c1455c7b
--- /dev/null
+++ b/scripts/OutpatientReferral/topSpecialtyOrders.sql
@@ -0,0 +1,169 @@
+	WITH 
+
+	-- Find all (New Patient) clinic visits (with info on specialty)
+	newPatientEncounter AS
+	(
+		select enc.anon_id, enc.pat_enc_csn_id_coded, 
+			dep.specialty_dep_c, dep.specialty, -- Groups different clinics into specialty groupings. E.g,. dep.specialty_dep_c = '7' -- dep.specialty like 'Endocrin%'
+			enc.appt_when_jittered as encounterDateTime, extract(YEAR from enc.appt_time_jittered) as encounterYear
+		from `som-nero-phi-jonc101.shc_core_2021.encounter` as enc 
+		    join `som-nero-phi-jonc101.shc_core_2021.dep_map` as dep on enc.department_id = dep.department_id 
+		where visit_type like 'NEW PATIENT%' -- Naturally screens to only 'Office Visit' enc_type 
+		-- and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc.
+		and appt_status = 'Completed'
+		and extract(YEAR from enc.appt_time_jittered) = 2019	-- Avoid 2020 for now to avoid weird Covid patterns
+	),
+
+    -- Find all diagnoses associated with new patient encounters
+    newPatientEncounterDiagnosis AS
+    (
+		select newEnc.*, dx.icd9, dx.icd10, dx_name
+		from newPatientEncounter as newEnc 
+		    join `som-nero-phi-jonc101.shc_core_2021.diagnosis` as dx on newEnc.pat_enc_csn_id_coded = dx.pat_enc_csn_id_jittered 
+	),
+    -- Order Med from encounters
+    newPatientEncounterMed AS
+    (
+		select newEnc.*, medication_id, med_description, pharm_class_name
+		from newPatientEncounter as newEnc 
+		  join `som-nero-phi-jonc101.shc_core_2021.order_med` as om on newEnc.pat_enc_csn_id_coded = om.pat_enc_csn_id_coded 
+		where order_class_c <> 3 -- 'Historical Med'
+	),
+    -- Order Proc from encounters
+    newPatientEncounterProc AS
+    (
+		select newEnc.*, op.proc_code, op.description
+		from newPatientEncounter as newEnc 
+		  join `som-nero-phi-jonc101.shc_core_2021.order_proc` as op on newEnc.pat_enc_csn_id_coded = op.pat_enc_csn_id_coded 
+	),
+	-- Encounter * Diagnosis * Order Proc (will have redundancies, because one encounter can have multiple diagnoses, so later need to count unique by encounter or patient ID)
+	newPatientEncounterDiagnosisProc AS
+	(
+		select newEnc.*, 
+			dx.icd9, dx.icd10, dx_name, 
+			op.proc_code, op.description
+		from newPatientEncounter as newEnc 
+		    join `som-nero-phi-jonc101.shc_core_2021.diagnosis` as dx on newEnc.pat_enc_csn_id_coded = dx.pat_enc_csn_id_jittered 
+		  	join `som-nero-phi-jonc101.shc_core_2021.order_proc` as op on newEnc.pat_enc_csn_id_coded = op.pat_enc_csn_id_coded 
+	),
+
+	-- Start accumulating counts by different strata
+	newPatientEncounterCount AS -- For baseline prevalence numbers, get total patient encounter counts of any type
+	(
+		select
+			count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+		from newPatientEncounter
+	),
+
+	topSpecialty AS
+	(
+		select 
+			specialty_dep_c, specialty, 
+			count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+		from newPatientEncounter
+		group by
+			specialty_dep_c, specialty
+		order by nEncounters desc
+	),
+	topDiagnosisPerSpecialty AS
+	(
+		select 
+			specialty_dep_c, specialty, 
+			icd9, icd10, dx_name, 
+			count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+		from newPatientEncounterDiagnosis
+		group by
+			specialty_dep_c, specialty, 
+			icd9, icd10, dx_name
+		order by nEncounters desc
+	),
+
+	-- Break down (proc) order prevalence by different strata. Start with across all New Patient visits, any specialty, any diagnosis
+	topProc AS 
+	(
+		select
+			proc_code, description,
+			count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    	from newPatientEncounterProc
+		group by
+			proc_code, description
+		order by nEncounters desc
+	),
+	topProcPerSpecialty AS
+	(
+		select
+			specialty_dep_c, specialty,
+			proc_code, description,
+			count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    	from newPatientEncounterProc
+		group by
+			specialty_dep_c, specialty,
+			proc_code, description
+		order by nEncounters desc
+	),
+	topProcPerSpecialtyDiagnosis AS
+	(
+		select
+			specialty_dep_c, specialty,
+			icd9, icd10, dx_name, 
+			proc_code, description,
+			count(distinct anon_id) as nPatients, count(distinct pat_enc_csn_id_coded) as nEncounters
+    	from newPatientEncounterDiagnosisProc
+		group by
+			specialty_dep_c, specialty,
+			icd9, icd10, dx_name, 
+			proc_code, description
+		order by nEncounters desc
+	),
+
+	-- Compare proc order rate per specialty-diagnosis vs. overall most common rate
+	procCountsPerSpecialtyDiagnosis AS
+	(
+		select
+			specialty_dep_c, specialty,
+			icd9, icd10, dx_name, 
+			proc_code, description,
+			newEncCount.nPatients as nPatientsAny, newEncCount.nEncounters as nEncountersAny,
+			ts.nPatients as nPatientsPerSpecialty, ts.nEncounters as nEncountersPerSpecialty,			
+			tds.nPatients as nPatientsPerSpecialtyDiagnosis, tds.nEncounters as nEncountersPerSpecialtyDiagnosis,
+			tp.nPatients as nPatientsProcPerAny, tp.nEncounters as nEncountersProcPerAny,
+			tpsd.nPatients as nPatientsProcPerSpecialtyDiagnosis, tpsd.nEncounters as nEncountersProcPerSpecialtyDiagnosis
+		from 
+			topProcPerSpecialtyDiagnosis as tpsd join 
+			topProc as tp using (proc_code, description) join
+			topDiagnosisPerSpecialty as tds using (specialty_dep_c, specialty, icd9, icd10, dx_name) join
+			topSpecialty as ts using (specialty_dep_c, specialty),
+			newPatientEncounterCount as newEncCount 	-- Last one is not a joint, since no criteria. Should be a single count row product against all
+	),
+	procOrderRatePerSpecialtyDiagnosis AS
+	(
+		select
+			specialty_dep_c, specialty,
+			icd9, icd10, dx_name, 
+			proc_code, description,
+
+			(nEncountersProcPerSpecialtyDiagnosis / nEncountersPerSpecialtyDiagnosis) AS procRatePerSpecialtyDiagnosis,	-- AKA Confidence / PPV / Conditional Prevalence
+			(nEncountersProcPerAny / nEncountersAny) AS procRatePerAny, -- AKA Baseline Prevalence ~ Support
+			(nEncountersProcPerSpecialtyDiagnosis / nEncountersPerSpecialtyDiagnosis) / (nEncountersProcPerAny / nEncountersAny) AS procRateLiftPerSpecialtyDiagnosisVsAny,	-- AKA Lift / Interest (similar to relative risk)
+			-- To Do -- Add p-value / (negative log-p) value to further sort by significance of association to avoid spurious
+
+			nPatientsAny, nEncountersAny,
+			nPatientsPerSpecialty, nEncountersPerSpecialty,			
+			nPatientsPerSpecialtyDiagnosis, nEncountersPerSpecialtyDiagnosis,
+			nPatientsProcPerAny, nEncountersProcPerAny,
+			nPatientsProcPerSpecialtyDiagnosis, nEncountersProcPerSpecialtyDiagnosis,
+
+		from procCountsPerSpecialtyDiagnosis
+		where nPatientsProcPerSpecialtyDiagnosis > 10 -- Ignore small cases by only looking at items that are ordered for more than 10 different patients
+		order by
+			nEncountersPerSpecialty desc, specialty,	-- Sort by most common specialty type first
+			nEncountersPerSpecialtyDiagnosis desc, icd10, dx_name,	-- Then sort by most common diagnosis per specialty
+			procRatePerSpecialtyDiagnosis desc  -- Then sort by most common proc orders per specialty-diagnosis
+	),
+	spacer AS (select * from newPatientEncounter limit 10)
+
+    select * 
+    from procOrderRatePerSpecialtyDiagnosis
+    limit 1000
+	
+
diff --git a/scripts/PedsLabUtil/STARR-Data-Dictionary.pdf b/scripts/PedsLabUtil/STARR-Data-Dictionary.pdf
new file mode 100644
index 00000000..6518247a
Binary files /dev/null and b/scripts/PedsLabUtil/STARR-Data-Dictionary.pdf differ
diff --git a/scripts/PedsLabUtil/analysis_lradt.ipynb b/scripts/PedsLabUtil/analysis_lradt.ipynb
new file mode 100644
index 00000000..263b6302
--- /dev/null
+++ b/scripts/PedsLabUtil/analysis_lradt.ipynb
@@ -0,0 +1,2127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8d4c7e33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.dpi'] = 150\n",
+    "import seaborn as sns\n",
+    "\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 100\n",
+    "pd.options.display.min_rows = 20"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "dfc677de",
+   "metadata": {},
+   "source": [
+    "Query:\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    "    WHERE\n",
+    "        (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "        AND pat_service NOT IN (\n",
+    "            'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "            'Reproductive Endocrinology and Infertility',\n",
+    "            'Gynecology & Obstetrics', 'Gynecology',\n",
+    "            'Intensive Care', 'Neonatology', 'Cardiovascular Intensive Care')\n",
+    "        AND (pat_lv_of_care = 'Acute Care' OR pat_lv_of_care='')\n",
+    "        AND accomodation IN (\n",
+    "                'PEDS MED/SURG Level 1','PEDS MED/SURG Level 2', 'PEDS MED/SURG Level 3',\n",
+    "                'PEDS Med/Surg',\n",
+    "                'CCP Med/Surg Level 1','CCP Med/Surg Level 2','CCP Med/Surg Level 3',\n",
+    "                'PEDS TXP Med/Surg Level 1', 'PEDS TXP Med/Surg Level 2','PEDS TXP Med/Surg Level 3',\n",
+    "                'Oncology Level 1', 'Oncology Level 2', 'Oncology Level 3', 'Oncology Level 4', 'SCT', 'Onc Med/Surg', 'SCT 2 (SCT 2)',\n",
+    "                'ICN Lvl A', 'ICN Lvl B', 'ICN Lvl C', 'ICN Lvl D'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "da0a36ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the lab result - adt joined table\n",
+    "lradt = pd.read_pickle('data/lradt_20211025.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3fee0500",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_low</th>\n",
+       "      <th>reference_high</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result_flag</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13558775</td>\n",
+       "      <td>JC1027715</td>\n",
+       "      <td>318017820</td>\n",
+       "      <td>734443153</td>\n",
+       "      <td>1230100765</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB141</td>\n",
+       "      <td>Uric Acid, Ser/Plas</td>\n",
+       "      <td>Uric Acid, Ser/Plas</td>\n",
+       "      <td>URIC</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2018-02-10 00:00:00</td>\n",
+       "      <td>2018-02-10 04:19:00</td>\n",
+       "      <td>2018-02-09 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201250</td>\n",
+       "      <td>Hematology/Oncology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>Oncology Level 4</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3735168</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14489712</td>\n",
+       "      <td>JC1027715</td>\n",
+       "      <td>318017820</td>\n",
+       "      <td>736974182</td>\n",
+       "      <td>302</td>\n",
+       "      <td>Blood Bank</td>\n",
+       "      <td>LAB488</td>\n",
+       "      <td>Platelets</td>\n",
+       "      <td>Issue Date/Time</td>\n",
+       "      <td>PLTDI</td>\n",
+       "      <td>9999999.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2018-05-14 16:19:00</td>\n",
+       "      <td>2018-05-15 07:15:00</td>\n",
+       "      <td>2018-05-13 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201250</td>\n",
+       "      <td>Oncology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>Oncology Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3993263</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14611060</td>\n",
+       "      <td>JC1027715</td>\n",
+       "      <td>318017820</td>\n",
+       "      <td>737284976</td>\n",
+       "      <td>313</td>\n",
+       "      <td>Blood Bank</td>\n",
+       "      <td>LAB488</td>\n",
+       "      <td>Platelets</td>\n",
+       "      <td>Unit Product ID</td>\n",
+       "      <td>PLTPID</td>\n",
+       "      <td>9999999.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2018-05-26 09:37:00</td>\n",
+       "      <td>2018-05-27 07:15:00</td>\n",
+       "      <td>2018-05-25 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201250</td>\n",
+       "      <td>Hematology/Oncology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>Oncology Level 4</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>4028265</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        uid    anon_id pat_enc_csn_id_coded order_id_coded  component_id  \\\n",
+       "0  13558775  JC1027715            318017820      734443153    1230100765   \n",
+       "1  14489712  JC1027715            318017820      736974182           302   \n",
+       "2  14611060  JC1027715            318017820      737284976           313   \n",
+       "\n",
+       "   order_type proc_code       group_lab_name             lab_name base_name  \\\n",
+       "0         Lab    LAB141  Uric Acid, Ser/Plas  Uric Acid, Ser/Plas      URIC   \n",
+       "1  Blood Bank    LAB488            Platelets      Issue Date/Time     PLTDI   \n",
+       "2  Blood Bank    LAB488            Platelets      Unit Product ID    PLTPID   \n",
+       "\n",
+       "   ord_num_value reference_low reference_high reference_unit  \\\n",
+       "0            2.8          None           None          mg/dL   \n",
+       "1      9999999.0          None           None           None   \n",
+       "2      9999999.0          None           None           None   \n",
+       "\n",
+       "  result_in_range_yn result_flag          order_time         result_time  \\\n",
+       "0               None        None 2018-02-10 00:00:00 2018-02-10 04:19:00   \n",
+       "1               None        None 2018-05-14 16:19:00 2018-05-15 07:15:00   \n",
+       "2               None        None 2018-05-26 09:37:00 2018-05-27 07:15:00   \n",
+       "\n",
+       "  effective_time_jittered  pat_class  department_id          pat_service  \\\n",
+       "0     2018-02-09 23:59:00  Inpatient       10201250  Hematology/Oncology   \n",
+       "1     2018-05-13 23:59:00  Inpatient       10201250             Oncology   \n",
+       "2     2018-05-25 23:59:00  Inpatient       10201250  Hematology/Oncology   \n",
+       "\n",
+       "  pat_lv_of_care      accomodation from_base_class to_base_class  \\\n",
+       "0     Acute Care  Oncology Level 4       Inpatient     Inpatient   \n",
+       "1     Acute Care  Oncology Level 3       Inpatient     Inpatient   \n",
+       "2     Acute Care  Oncology Level 4       Inpatient     Inpatient   \n",
+       "\n",
+       "  in_event_type out_event_type  event_id_coded event_type  \n",
+       "0        Census         Census         3735168     Census  \n",
+       "1        Census         Census         3993263     Census  \n",
+       "2        Census         Census         4028265     Census  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4c90010c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of rows:\t 4134067\n",
+      "Total number of orders:\t 620729\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Total number of rows:\\t', len(lradt))\n",
+    "print('Total number of orders:\\t', len(lradt.order_id_coded.unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b274245",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pruning labs that occurred on the last day\t\n",
+      "Remaining number of rows:\t 4101880\n",
+      "Remaining number of orders:\t 616308\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Pruning labs that occurred on the last day\\t')\n",
+    "lradt = lradt.loc[lradt.result_time < lradt.result_time.max()]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "969bcf34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>order_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Lab</th>\n",
+       "      <td>2596230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lab Only - Beaker</th>\n",
+       "      <td>853417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Point of Care Testing</th>\n",
+       "      <td>235749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Bank</th>\n",
+       "      <td>144432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Point of Care Testing-Docked Device</th>\n",
+       "      <td>136472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Microbiology</th>\n",
+       "      <td>120373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ECG</th>\n",
+       "      <td>15091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nursing</th>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     order_type\n",
+       "Lab                                     2596230\n",
+       "Lab Only - Beaker                        853417\n",
+       "Point of Care Testing                    235749\n",
+       "Blood Bank                               144432\n",
+       "Point of Care Testing-Docked Device      136472\n",
+       "Microbiology                             120373\n",
+       "ECG                                       15091\n",
+       "Nursing                                     116"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(lradt.order_type.value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "647e2e94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Look at group_lab_names in order to prune lab orders and combine similar lab concepts\n",
+    "pd.DataFrame(lradt.group_lab_name.value_counts()).to_csv('data/group_lab_name_counts.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3de9d782",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pruning point of care labs...\n",
+      "Remaining number of rows:\t 3729659\n",
+      "Remaining number of orders:\t 544785\n",
+      "\n",
+      "Pruning ECG orders...\n",
+      "Remaining number of rows:\t 3714568\n",
+      "Remaining number of orders:\t 542264\n",
+      "\n",
+      "Pruning Blood Bank orders...\n",
+      "Remaining number of rows:\t 3570136\n",
+      "Remaining number of orders:\t 516968\n",
+      "\n",
+      "Pruning Nursing orders...\n",
+      "Remaining number of rows:\t 3570020\n",
+      "Remaining number of orders:\t 516921\n",
+      "\n",
+      "Pruning iSTAT labs...\n",
+      "Remaining number of rows:\t 3569688\n",
+      "Remaining number of orders:\t 516755\n",
+      "\n",
+      "Pruning Manual Differential/Slide Review Orders...\n",
+      "Remaining number of rows:\t 3426794\n",
+      "Remaining number of orders:\t 502338\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Pruning point of care labs...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='Point of Care Testing-Docked Device') & (lradt.order_type!='Point of Care Testing')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning ECG orders...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='ECG')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning Blood Bank orders...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='Blood Bank')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning Nursing orders...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='Nursing')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning iSTAT labs...')\n",
+    "lradt = lradt.loc[~lradt.group_lab_name.str.contains(\"ISTAT\", case=False)]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning Manual Differential/Slide Review Orders...')\n",
+    "lradt = lradt.loc[lradt.group_lab_name!='Manual Differential/Slide Review']\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9296621a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Earliest lab result datetime:\t 2014-04-04 13:28:00\n",
+      "Latest lab result datetime:\t 2019-10-30 23:23:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Earliest lab result datetime:\\t', lradt.result_time.min())\n",
+    "print('Latest lab result datetime:\\t', lradt.result_time.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cf6af603",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rename some group_lab_names that are the same\n",
+    "lradt['njr_labname'] = lradt['group_lab_name']\n",
+    "cbc =  ['CBC with Differential', 'CBC with Diff', 'CBC', 'CBC with Diff and Slide Review']\n",
+    "poct_urine = ['POCT Urine Dipstick (10)', 'POCT Urine Dipstick (9)']\n",
+    "poct_glucose = ['Glucose By Meter', 'Glucose PCX Meter']\n",
+    "lradt.loc[lradt.njr_labname.isin(cbc), 'njr_labname'] = 'CBC'\n",
+    "# lradt.loc[lradt.njr_labname.isin(poct_urine), 'njr_labname'] = 'POCT Urine'\n",
+    "# lradt.loc[lradt.njr_labname.isin(poct_glucose), 'njr_labname'] = 'POCT Glucose'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c87c451",
+   "metadata": {},
+   "source": [
+    "## Repetitive Lab Orders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "676d164d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>57600</td>\n",
+       "      <td>16055</td>\n",
+       "      <td>7751</td>\n",
+       "      <td>3.587667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>45595</td>\n",
+       "      <td>9816</td>\n",
+       "      <td>4401</td>\n",
+       "      <td>4.644967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>31070</td>\n",
+       "      <td>8219</td>\n",
+       "      <td>4427</td>\n",
+       "      <td>3.780265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>23946</td>\n",
+       "      <td>5230</td>\n",
+       "      <td>2919</td>\n",
+       "      <td>4.578585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>16806</td>\n",
+       "      <td>7689</td>\n",
+       "      <td>4602</td>\n",
+       "      <td>2.185720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>14030</td>\n",
+       "      <td>2158</td>\n",
+       "      <td>909</td>\n",
+       "      <td>6.501390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>12513</td>\n",
+       "      <td>5706</td>\n",
+       "      <td>2246</td>\n",
+       "      <td>2.192955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>12399</td>\n",
+       "      <td>5643</td>\n",
+       "      <td>2196</td>\n",
+       "      <td>2.197236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>12083</td>\n",
+       "      <td>2493</td>\n",
+       "      <td>781</td>\n",
+       "      <td>4.846771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>11164</td>\n",
+       "      <td>4480</td>\n",
+       "      <td>2851</td>\n",
+       "      <td>2.491964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>11120</td>\n",
+       "      <td>4895</td>\n",
+       "      <td>3128</td>\n",
+       "      <td>2.271706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uric Acid, Ser/Plas</th>\n",
+       "      <td>9085</td>\n",
+       "      <td>1030</td>\n",
+       "      <td>853</td>\n",
+       "      <td>8.820388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH Total, Ser/Plas</th>\n",
+       "      <td>8629</td>\n",
+       "      <td>1354</td>\n",
+       "      <td>1057</td>\n",
+       "      <td>6.372969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>7695</td>\n",
+       "      <td>2469</td>\n",
+       "      <td>1229</td>\n",
+       "      <td>3.116646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>7621</td>\n",
+       "      <td>2845</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>2.678735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>7255</td>\n",
+       "      <td>3803</td>\n",
+       "      <td>2869</td>\n",
+       "      <td>1.907704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>6847</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>1593</td>\n",
+       "      <td>2.727888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>6808</td>\n",
+       "      <td>3411</td>\n",
+       "      <td>2296</td>\n",
+       "      <td>1.995896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>6726</td>\n",
+       "      <td>4405</td>\n",
+       "      <td>2739</td>\n",
+       "      <td>1.526901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>6673</td>\n",
+       "      <td>3021</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>2.208871</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                norders   nenc  npat   per_enc\n",
+       "njr_labname                                                   \n",
+       "CBC                               57600  16055  7751  3.587667\n",
+       "Chem 10                           45595   9816  4401  4.644967\n",
+       "Magnesium, Ser/Plas               31070   8219  4427  3.780265\n",
+       "Renal Function Panel              23946   5230  2919  4.578585\n",
+       "Metabolic Panel, Comprehensive    16806   7689  4602  2.185720\n",
+       "Urinalysis Macroscopic Only       14030   2158   909  6.501390\n",
+       "Antibody Screen                   12513   5706  2246  2.192955\n",
+       "ABO Rh Typing                     12399   5643  2196  2.197236\n",
+       "Tacrolimus Level, FK506           12083   2493   781  4.846771\n",
+       "Prothrombin Time                  11164   4480  2851  2.491964\n",
+       "Phosphorus, Ser/Plas              11120   4895  3128  2.271706\n",
+       "Uric Acid, Ser/Plas                9085   1030   853  8.820388\n",
+       "LDH Total, Ser/Plas                8629   1354  1057  6.372969\n",
+       "Gamma Glutamyl Trans               7695   2469  1229  3.116646\n",
+       "Blood Culture                      7621   2845  1472  2.678735\n",
+       "C Reactive Protein                 7255   3803  2869  1.907704\n",
+       "Hepatic Function Panel             6847   2510  1593  2.727888\n",
+       "Part Thromboplastin Time           6808   3411  2296  1.995896\n",
+       "Urinalysis With Microscopic        6726   4405  2739  1.526901\n",
+       "Fibrinogen                         6673   3021  2090  2.208871"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# labcounts sorted by total number of orders\n",
+    "labcounts = lradt.groupby(by='njr_labname').agg({'order_id_coded': 'nunique', 'pat_enc_csn_id_coded': 'nunique', 'anon_id': 'nunique'}).rename(columns={'order_id_coded': 'norders', 'pat_enc_csn_id_coded': 'nenc', 'anon_id': 'npat'}).sort_values('norders', ascending=False)\n",
+    "labcounts['per_enc'] = labcounts.norders/labcounts.nenc\n",
+    "labcounts.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a67d4224",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Uric Acid, Ser/Plas</th>\n",
+       "      <td>9085</td>\n",
+       "      <td>1030</td>\n",
+       "      <td>853</td>\n",
+       "      <td>8.820388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>14030</td>\n",
+       "      <td>2158</td>\n",
+       "      <td>909</td>\n",
+       "      <td>6.501390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH Total, Ser/Plas</th>\n",
+       "      <td>8629</td>\n",
+       "      <td>1354</td>\n",
+       "      <td>1057</td>\n",
+       "      <td>6.372969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>12083</td>\n",
+       "      <td>2493</td>\n",
+       "      <td>781</td>\n",
+       "      <td>4.846771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>45595</td>\n",
+       "      <td>9816</td>\n",
+       "      <td>4401</td>\n",
+       "      <td>4.644967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>23946</td>\n",
+       "      <td>5230</td>\n",
+       "      <td>2919</td>\n",
+       "      <td>4.578585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>5554</td>\n",
+       "      <td>1388</td>\n",
+       "      <td>909</td>\n",
+       "      <td>4.001441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>31070</td>\n",
+       "      <td>8219</td>\n",
+       "      <td>4427</td>\n",
+       "      <td>3.780265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>57600</td>\n",
+       "      <td>16055</td>\n",
+       "      <td>7751</td>\n",
+       "      <td>3.587667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>7695</td>\n",
+       "      <td>2469</td>\n",
+       "      <td>1229</td>\n",
+       "      <td>3.116646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>5424</td>\n",
+       "      <td>1768</td>\n",
+       "      <td>1311</td>\n",
+       "      <td>3.067873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>5102</td>\n",
+       "      <td>1798</td>\n",
+       "      <td>1056</td>\n",
+       "      <td>2.837597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>6847</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>1593</td>\n",
+       "      <td>2.727888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>7621</td>\n",
+       "      <td>2845</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>2.678735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>11164</td>\n",
+       "      <td>4480</td>\n",
+       "      <td>2851</td>\n",
+       "      <td>2.491964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>11120</td>\n",
+       "      <td>4895</td>\n",
+       "      <td>3128</td>\n",
+       "      <td>2.271706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>6673</td>\n",
+       "      <td>3021</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>2.208871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>12399</td>\n",
+       "      <td>5643</td>\n",
+       "      <td>2196</td>\n",
+       "      <td>2.197236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>12513</td>\n",
+       "      <td>5706</td>\n",
+       "      <td>2246</td>\n",
+       "      <td>2.192955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>16806</td>\n",
+       "      <td>7689</td>\n",
+       "      <td>4602</td>\n",
+       "      <td>2.185720</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                norders   nenc  npat   per_enc\n",
+       "njr_labname                                                   \n",
+       "Uric Acid, Ser/Plas                9085   1030   853  8.820388\n",
+       "Urinalysis Macroscopic Only       14030   2158   909  6.501390\n",
+       "LDH Total, Ser/Plas                8629   1354  1057  6.372969\n",
+       "Tacrolimus Level, FK506           12083   2493   781  4.846771\n",
+       "Chem 10                           45595   9816  4401  4.644967\n",
+       "Renal Function Panel              23946   5230  2919  4.578585\n",
+       "Triglyceride, Ser/Plas             5554   1388   909  4.001441\n",
+       "Magnesium, Ser/Plas               31070   8219  4427  3.780265\n",
+       "CBC                               57600  16055  7751  3.587667\n",
+       "Gamma Glutamyl Trans               7695   2469  1229  3.116646\n",
+       "Slide Review                       5424   1768  1311  3.067873\n",
+       "Vancomycin Level, Trough           5102   1798  1056  2.837597\n",
+       "Hepatic Function Panel             6847   2510  1593  2.727888\n",
+       "Blood Culture                      7621   2845  1472  2.678735\n",
+       "Prothrombin Time                  11164   4480  2851  2.491964\n",
+       "Phosphorus, Ser/Plas              11120   4895  3128  2.271706\n",
+       "Fibrinogen                         6673   3021  2090  2.208871\n",
+       "ABO Rh Typing                     12399   5643  2196  2.197236\n",
+       "Antibody Screen                   12513   5706  2246  2.192955\n",
+       "Metabolic Panel, Comprehensive    16806   7689  4602  2.185720"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Labcounts sorted by labs per encounter for labs occurring in more than __ encounters\n",
+    "labcounts.loc[(labcounts.norders>5000) & (labcounts.nenc>500)].sort_values('per_enc', ascending=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "74bb5abb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create ordersdf dataframe for calculating the lab repetition\n",
+    "included_labs = ['Uric Acid, Ser/Plas', 'LDH Total, Ser/Plas', 'Tacrolimus Level, FK506', \\\n",
+    "                 'Chem 10', 'Renal Function Panel', 'Triglyceride, Ser/Plas', \\\n",
+    "                 'Magnesium, Ser/Plas', 'CBC', 'Gamma Glutamyl Trans', \\\n",
+    "                 'Vancomycin Level, Trough', 'Hepatic Function Panel', \\\n",
+    "                 'Prothrombin Time', 'Phosphorus, Ser/Plas']\n",
+    "# ordersdf = lradt.loc[lradt.njr_labname.isin(included_labs),['anon_id', 'pat_enc_csn_id_coded', 'order_id_coded', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'njr_labname', 'result_time', 'order_time']).groupby('order_id_coded').first()\n",
+    "ordersdf = lradt.loc[lradt.njr_labname.isin(included_labs)].groupby('order_id_coded').first()[['anon_id', 'pat_enc_csn_id_coded', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'njr_labname', 'order_time', 'result_time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5567b191",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordersdf_shift = ordersdf.shift(1)\n",
+    "ordersdf['dif'] = pd.NaT\n",
+    "ordersdf['dif'] = ordersdf.order_time - ordersdf_shift.order_time\n",
+    "newlabidx = (ordersdf.njr_labname != ordersdf_shift.njr_labname) | (ordersdf.pat_enc_csn_id_coded != ordersdf_shift.pat_enc_csn_id_coded)\n",
+    "ordersdf.loc[newlabidx, 'dif'] = pd.NaT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "39b25c8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>dif</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>701122666</th>\n",
+       "      <td>JC608450</td>\n",
+       "      <td>308903549</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-25 21:05:00</td>\n",
+       "      <td>2014-05-26 07:24:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701239393</th>\n",
+       "      <td>JC639827</td>\n",
+       "      <td>308903923</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC984330</td>\n",
+       "      <td>308904689</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-30 21:05:00</td>\n",
+       "      <td>2014-07-01 07:51:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287760</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 17:42:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701390874</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-03 21:05:00</td>\n",
+       "      <td>2014-05-04 08:24:00</td>\n",
+       "      <td>1 days 07:30:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504067</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-09 12:28:00</td>\n",
+       "      <td>2014-05-09 13:37:00</td>\n",
+       "      <td>5 days 15:23:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287759</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 18:04:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457513</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-08 21:05:00</td>\n",
+       "      <td>2014-05-09 05:49:00</td>\n",
+       "      <td>6 days 07:30:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457516</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 05:55:00</td>\n",
+       "      <td>2014-05-09 07:26:00</td>\n",
+       "      <td>0 days 08:50:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504073</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 21:05:00</td>\n",
+       "      <td>2014-05-10 05:28:00</td>\n",
+       "      <td>0 days 15:10:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded njr_labname  \\\n",
+       "order_id_coded                                               \n",
+       "701122666        JC608450            308903549         CBC   \n",
+       "701239393        JC639827            308903923         CBC   \n",
+       "701504059        JC984330            308904689         CBC   \n",
+       "701287760       JC1142284            308905542         CBC   \n",
+       "701390874       JC1142284            308905542         CBC   \n",
+       "701504067       JC1142284            308905542         CBC   \n",
+       "701287759       JC1142284            308905542     Chem 10   \n",
+       "701457513       JC1142284            308905542     Chem 10   \n",
+       "701457516       JC1142284            308905542     Chem 10   \n",
+       "701504073       JC1142284            308905542     Chem 10   \n",
+       "\n",
+       "                        order_time         result_time             dif  \n",
+       "order_id_coded                                                          \n",
+       "701122666      2014-05-25 21:05:00 2014-05-26 07:24:00             NaT  \n",
+       "701239393      2014-06-02 21:05:00 2014-06-03 08:06:00             NaT  \n",
+       "701504059      2014-06-30 21:05:00 2014-07-01 07:51:00             NaT  \n",
+       "701287760      2014-05-02 13:35:00 2014-05-02 17:42:00             NaT  \n",
+       "701390874      2014-05-03 21:05:00 2014-05-04 08:24:00 1 days 07:30:00  \n",
+       "701504067      2014-05-09 12:28:00 2014-05-09 13:37:00 5 days 15:23:00  \n",
+       "701287759      2014-05-02 13:35:00 2014-05-02 18:04:00             NaT  \n",
+       "701457513      2014-05-08 21:05:00 2014-05-09 05:49:00 6 days 07:30:00  \n",
+       "701457516      2014-05-09 05:55:00 2014-05-09 07:26:00 0 days 08:50:00  \n",
+       "701504073      2014-05-09 21:05:00 2014-05-10 05:28:00 0 days 15:10:00  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7d1774be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_quant = [pd.Timedelta('24H'), pd.Timedelta('3D'), pd.Timedelta('1W')]\n",
+    "labdtq_idx = ['<24h', '[24h, 3d)', '[3d, 7d)', '>=7d']\n",
+    "labdtq = pd.DataFrame(index=labdtq_idx)\n",
+    "for lab in included_labs:\n",
+    "    #print('Lab: ', lab)\n",
+    "    dtimes = ordersdf.loc[ordersdf.njr_labname==lab].dif\n",
+    "    p_enc = ordersdf.loc[ordersdf.njr_labname==lab, 'pat_enc_csn_id_coded'].nunique()\n",
+    "    dtq = []\n",
+    "    dtq.append(sum(dtimes < pd.Timedelta('24H')))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('3D')) & (dtimes >= pd.Timedelta('24H')) ))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('7D')) & (dtimes >= pd.Timedelta('3D')) ))\n",
+    "    dtq.append(sum((dtimes >= pd.Timedelta('7D')) | (dtimes.isnull()) ))\n",
+    "    #print('[0, 24h):\\t', dtq[0])\n",
+    "    #print('[24h, 3d)]:\\t', dtq[1])\n",
+    "    #print('[3d, 7d)]:\\t', dtq[2])\n",
+    "    #print('[7d, inf)]:\\t', dtq[3])\n",
+    "    labdtq[lab] = np.cumsum(dtq)/p_enc*1000\n",
+    "labdtq.to_csv('data/labdtq.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7d5d07ce",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# labdtq = pd.read_csv('data/labdtq.csv', index_col=0)\n",
+    "ax1 = labdtq.iloc[3].plot.barh(color='#404c54');\n",
+    "labdtq.iloc[2].plot.barh(ax=ax1, color='#f0cc94');\n",
+    "labdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "labdtq.iloc[0].plot.barh(ax=ax1, color='#807A6B');\n",
+    "ax1.legend();\n",
+    "ax1.invert_yaxis();\n",
+    "ax1.set_title('Commonly Repeated Lab Orders in Pediatric Inpatient Medicine');\n",
+    "ax1.set_xlabel('No. Lab Orders per 1000 Encounters');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce64bd6b",
+   "metadata": {},
+   "source": [
+    "## Lab Component Repitition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "64f1817e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>base_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PERFLAB</th>\n",
+       "      <td>245407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NA</th>\n",
+       "      <td>98847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>97591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GLU</th>\n",
+       "      <td>96400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CL</th>\n",
+       "      <td>96365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CR</th>\n",
+       "      <td>94426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUN</th>\n",
+       "      <td>92836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CA</th>\n",
+       "      <td>92691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2</th>\n",
+       "      <td>92282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AG</th>\n",
+       "      <td>92270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>82469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PHOS</th>\n",
+       "      <td>81814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MG</th>\n",
+       "      <td>77804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HCT</th>\n",
+       "      <td>63085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HGB</th>\n",
+       "      <td>62929</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         base_name\n",
+       "PERFLAB     245407\n",
+       "NA           98847\n",
+       "K            97591\n",
+       "GLU          96400\n",
+       "CL           96365\n",
+       "CR           94426\n",
+       "BUN          92836\n",
+       "CA           92691\n",
+       "CO2          92282\n",
+       "AG           92270\n",
+       "EGFR         82469\n",
+       "PHOS         81814\n",
+       "MG           77804\n",
+       "HCT          63085\n",
+       "HGB          62929"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(lradt.base_name.value_counts()).head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "334aea89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labdf = lradt.loc[lradt.base_name!='PERFLAB', ['anon_id', 'pat_enc_csn_id_coded', 'base_name', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'base_name', 'order_time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ef265114",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>URIC</th>\n",
+       "      <td>10288</td>\n",
+       "      <td>1520</td>\n",
+       "      <td>1127</td>\n",
+       "      <td>6.768421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH</th>\n",
+       "      <td>9826</td>\n",
+       "      <td>1813</td>\n",
+       "      <td>1289</td>\n",
+       "      <td>5.419746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FK506</th>\n",
+       "      <td>13359</td>\n",
+       "      <td>2493</td>\n",
+       "      <td>781</td>\n",
+       "      <td>5.358604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PHOS</th>\n",
+       "      <td>81814</td>\n",
+       "      <td>15443</td>\n",
+       "      <td>6760</td>\n",
+       "      <td>5.297805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MG</th>\n",
+       "      <td>77804</td>\n",
+       "      <td>15028</td>\n",
+       "      <td>6436</td>\n",
+       "      <td>5.177269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NA</th>\n",
+       "      <td>98847</td>\n",
+       "      <td>19182</td>\n",
+       "      <td>8991</td>\n",
+       "      <td>5.153112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PHCAI</th>\n",
+       "      <td>3680</td>\n",
+       "      <td>720</td>\n",
+       "      <td>467</td>\n",
+       "      <td>5.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>97591</td>\n",
+       "      <td>19123</td>\n",
+       "      <td>8956</td>\n",
+       "      <td>5.103331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MTX</th>\n",
+       "      <td>4822</td>\n",
+       "      <td>953</td>\n",
+       "      <td>205</td>\n",
+       "      <td>5.059811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CL</th>\n",
+       "      <td>96365</td>\n",
+       "      <td>19110</td>\n",
+       "      <td>8950</td>\n",
+       "      <td>5.042648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GLU</th>\n",
+       "      <td>96400</td>\n",
+       "      <td>19118</td>\n",
+       "      <td>8956</td>\n",
+       "      <td>5.042368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CR</th>\n",
+       "      <td>94426</td>\n",
+       "      <td>19281</td>\n",
+       "      <td>9062</td>\n",
+       "      <td>4.897360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUN</th>\n",
+       "      <td>92836</td>\n",
+       "      <td>19035</td>\n",
+       "      <td>8912</td>\n",
+       "      <td>4.877121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CA</th>\n",
+       "      <td>92691</td>\n",
+       "      <td>19055</td>\n",
+       "      <td>8930</td>\n",
+       "      <td>4.864393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2</th>\n",
+       "      <td>92282</td>\n",
+       "      <td>19021</td>\n",
+       "      <td>8900</td>\n",
+       "      <td>4.851585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AG</th>\n",
+       "      <td>92270</td>\n",
+       "      <td>19021</td>\n",
+       "      <td>8900</td>\n",
+       "      <td>4.850954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>82469</td>\n",
+       "      <td>17364</td>\n",
+       "      <td>8447</td>\n",
+       "      <td>4.749424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CAION</th>\n",
+       "      <td>7776</td>\n",
+       "      <td>1808</td>\n",
+       "      <td>1336</td>\n",
+       "      <td>4.300885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ALB</th>\n",
+       "      <td>50522</td>\n",
+       "      <td>12333</td>\n",
+       "      <td>6338</td>\n",
+       "      <td>4.096489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PO2A</th>\n",
+       "      <td>2409</td>\n",
+       "      <td>591</td>\n",
+       "      <td>568</td>\n",
+       "      <td>4.076142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CTO2A</th>\n",
+       "      <td>2409</td>\n",
+       "      <td>591</td>\n",
+       "      <td>568</td>\n",
+       "      <td>4.076142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PCO2A</th>\n",
+       "      <td>2409</td>\n",
+       "      <td>591</td>\n",
+       "      <td>568</td>\n",
+       "      <td>4.076142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PHA</th>\n",
+       "      <td>2416</td>\n",
+       "      <td>597</td>\n",
+       "      <td>574</td>\n",
+       "      <td>4.046901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PLTMOR</th>\n",
+       "      <td>4377</td>\n",
+       "      <td>1116</td>\n",
+       "      <td>762</td>\n",
+       "      <td>3.922043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HCT</th>\n",
+       "      <td>63085</td>\n",
+       "      <td>16493</td>\n",
+       "      <td>8051</td>\n",
+       "      <td>3.824956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HGB</th>\n",
+       "      <td>62929</td>\n",
+       "      <td>16456</td>\n",
+       "      <td>8013</td>\n",
+       "      <td>3.824076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NH3</th>\n",
+       "      <td>3344</td>\n",
+       "      <td>911</td>\n",
+       "      <td>614</td>\n",
+       "      <td>3.670692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TGL</th>\n",
+       "      <td>6950</td>\n",
+       "      <td>1921</td>\n",
+       "      <td>1218</td>\n",
+       "      <td>3.617907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WBC</th>\n",
+       "      <td>57710</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>7755</td>\n",
+       "      <td>3.593176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PLT</th>\n",
+       "      <td>57546</td>\n",
+       "      <td>16042</td>\n",
+       "      <td>7746</td>\n",
+       "      <td>3.587209</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           norders   nenc  npat   per_enc\n",
+       "base_name                                \n",
+       "URIC         10288   1520  1127  6.768421\n",
+       "LDH           9826   1813  1289  5.419746\n",
+       "FK506        13359   2493   781  5.358604\n",
+       "PHOS         81814  15443  6760  5.297805\n",
+       "MG           77804  15028  6436  5.177269\n",
+       "NA           98847  19182  8991  5.153112\n",
+       "PHCAI         3680    720   467  5.111111\n",
+       "K            97591  19123  8956  5.103331\n",
+       "MTX           4822    953   205  5.059811\n",
+       "CL           96365  19110  8950  5.042648\n",
+       "GLU          96400  19118  8956  5.042368\n",
+       "CR           94426  19281  9062  4.897360\n",
+       "BUN          92836  19035  8912  4.877121\n",
+       "CA           92691  19055  8930  4.864393\n",
+       "CO2          92282  19021  8900  4.851585\n",
+       "AG           92270  19021  8900  4.850954\n",
+       "EGFR         82469  17364  8447  4.749424\n",
+       "CAION         7776   1808  1336  4.300885\n",
+       "ALB          50522  12333  6338  4.096489\n",
+       "PO2A          2409    591   568  4.076142\n",
+       "CTO2A         2409    591   568  4.076142\n",
+       "PCO2A         2409    591   568  4.076142\n",
+       "PHA           2416    597   574  4.046901\n",
+       "PLTMOR        4377   1116   762  3.922043\n",
+       "HCT          63085  16493  8051  3.824956\n",
+       "HGB          62929  16456  8013  3.824076\n",
+       "NH3           3344    911   614  3.670692\n",
+       "TGL           6950   1921  1218  3.617907\n",
+       "WBC          57710  16061  7755  3.593176\n",
+       "PLT          57546  16042  7746  3.587209"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baselabcounts = labdf.groupby(by='base_name').agg({'base_name': 'count', 'pat_enc_csn_id_coded': 'nunique', 'anon_id': 'nunique'}).rename(columns={'base_name': 'norders', 'pat_enc_csn_id_coded': 'nenc', 'anon_id': 'npat'}).sort_values('norders', ascending=False)\n",
+    "baselabcounts['per_enc'] = baselabcounts.norders/baselabcounts.nenc\n",
+    "baselabcounts.loc[baselabcounts.nenc>500].sort_values('per_enc', ascending=False).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3ce0c692",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/qs/2zlxwwld4898377zg9fx8wqh0000gq/T/ipykernel_13872/3701289749.py:8: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df['dif'] = pd.NaT\n",
+      "/var/folders/qs/2zlxwwld4898377zg9fx8wqh0000gq/T/ipykernel_13872/3701289749.py:9: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df['dif'] = df.order_time - df_shift.order_time\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value, pi)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time_quant = [pd.Timedelta('24H'), pd.Timedelta('3D'), pd.Timedelta('1W')]\n",
+    "labdtq_idx = ['<24h', '[24h, 3d)', '[3d, 7d)', '>=7d']\n",
+    "baselabdtq = pd.DataFrame(index=labdtq_idx)\n",
+    "for basename in ['URIC', 'LDH', 'FK506', 'PHOS', 'MG', 'NA', 'K', 'CL', 'GLU', 'CR', 'BUN', 'CA', 'CO2']:\n",
+    "    #print('Lab: ', lab)\n",
+    "    df = labdf.loc[labdf.base_name==basename]\n",
+    "    df_shift = df.shift(1)\n",
+    "    df['dif'] = pd.NaT\n",
+    "    df['dif'] = df.order_time - df_shift.order_time\n",
+    "    newlabidx = (df.base_name != df_shift.base_name) | (df.pat_enc_csn_id_coded != df_shift.pat_enc_csn_id_coded)\n",
+    "    df.loc[newlabidx, 'dif'] = pd.NaT\n",
+    "    dtimes = df.dif\n",
+    "    p_enc = df['pat_enc_csn_id_coded'].nunique()\n",
+    "    dtq = []\n",
+    "    dtq.append(sum(dtimes < pd.Timedelta('24H')))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('3D')) & (dtimes >= pd.Timedelta('24H')) ))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('7D')) & (dtimes >= pd.Timedelta('3D')) ))\n",
+    "    dtq.append(sum((dtimes >= pd.Timedelta('7D')) | (dtimes.isnull()) ))\n",
+    "    #print('[0, 24h):\\t', dtq[0])\n",
+    "    #print('[24h, 3d)]:\\t', dtq[1])\n",
+    "    #print('[3d, 7d)]:\\t', dtq[2])\n",
+    "    #print('[7d, inf)]:\\t', dtq[3])\n",
+    "    baselabdtq[basename] = np.cumsum(dtq)/p_enc*1000\n",
+    "ax1 = baselabdtq.iloc[3].plot.barh(color='#404c54');\n",
+    "baselabdtq.iloc[2].plot.barh(ax=ax1, color='#f0cc94');\n",
+    "baselabdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "baselabdtq.iloc[0].plot.barh(ax=ax1, color='#807A6B');\n",
+    "ax1.legend();\n",
+    "ax1.invert_yaxis();\n",
+    "ax1.set_title('Commonly Repeated Lab Components in Pediatric Inpatient Medicine');\n",
+    "ax1.set_xlabel('No. Lab Orders per 1000 Encounters');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1764949",
+   "metadata": {},
+   "source": [
+    "## Lab Component Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ce3e2741",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def windowedLabCounts(LABNAME, LOWER, UPPER):\n",
+    "    lrdf = lradt.loc[(lradt.base_name==LABNAME) & (lradt.ord_num_value < UPPER) & (lradt.ord_num_value > LOWER)].sort_values(by=['pat_enc_csn_id_coded', 'result_time']).reset_index()\n",
+    "    lrdf['result'] = 'N'\n",
+    "    lrdf.loc[(lrdf.result_in_range_yn!='Y') & ((lrdf.result_flag=='High') | (lrdf.result_flag=='High Panic')), 'result'] = 'H'\n",
+    "    lrdf.loc[(lrdf.result_in_range_yn!='Y') & ((lrdf.result_flag=='Low') | (lrdf.result_flag=='Low Panic')), 'result'] = 'L'\n",
+    "    lrdf['normal'] = (lrdf.result=='N')\n",
+    "    print('LAB:', LABNAME)\n",
+    "    print(lrdf.normal.value_counts())\n",
+    "\n",
+    "    lrdf['preceding']=0\n",
+    "    for enc in lrdf.pat_enc_csn_id_coded.unique():\n",
+    "        enc_idx = (lrdf.pat_enc_csn_id_coded==enc)\n",
+    "        df = lrdf.loc[enc_idx, ['result_time', 'normal']]\n",
+    "        rownum = 0\n",
+    "        for (idx, row) in df.iterrows():\n",
+    "            if rownum>0:\n",
+    "                rtime = row.result_time\n",
+    "                dfslice = df.loc[(df.result_time <= rtime) & (df.result_time > rtime - pd.Timedelta('7D'))]\n",
+    "                if dfslice.shift().loc[idx, 'normal']==True:\n",
+    "                    y = dfslice.shift().normal\n",
+    "                    prec = y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)\n",
+    "                    lrdf.loc[idx, 'preceding'] = prec.values[-1]\n",
+    "            else:\n",
+    "                lrdf.loc[idx, 'preceding'] = -1\n",
+    "            rownum = rownum + 1\n",
+    "    return lrdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "2276e8f4",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAB: NA\n",
+      "True     78051\n",
+      "False    20716\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: K\n",
+      "True     79444\n",
+      "False    17936\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: MG\n",
+      "True     53318\n",
+      "False    24433\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: PHOS\n",
+      "True     55067\n",
+      "False    26681\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: CR\n",
+      "True     69513\n",
+      "False    24501\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: WBC\n",
+      "False    33309\n",
+      "True     23556\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: HGB\n",
+      "False    52284\n",
+      "True     10524\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: PLT\n",
+      "False    33207\n",
+      "True     24210\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: FK506\n",
+      "True     11103\n",
+      "False      873\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: URIC\n",
+      "True     5594\n",
+      "False    1200\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: LDH\n",
+      "False    4829\n",
+      "True     4662\n",
+      "Name: normal, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "nadf = windowedLabCounts('NA', 0, 200);\n",
+    "kdf = windowedLabCounts('K', 0, 8);\n",
+    "mgdf = windowedLabCounts('MG', 0, 10);\n",
+    "phosdf = windowedLabCounts('PHOS', 0, 50);\n",
+    "crdf = windowedLabCounts('CR', 0, 20);\n",
+    "wbcdf = windowedLabCounts('WBC', 0, 100000);\n",
+    "hgbdf = windowedLabCounts('HGB', 0, 30);\n",
+    "pltdf = windowedLabCounts('PLT', 0, 500000)\n",
+    "tacdf = windowedLabCounts('FK506', 0, 100)\n",
+    "uricdf = windowedLabCounts('URIC', 0, 1000)\n",
+    "ldhdf = windowedLabCounts('LDH', 0, 10000)\n",
+    "\n",
+    "labs_df_dict = {\n",
+    "    'NA': nadf,\n",
+    "    'K': kdf,\n",
+    "    'MG': mgdf,\n",
+    "    'PHOS': phosdf,\n",
+    "    'CR': crdf,\n",
+    "    'WBC': wbcdf,\n",
+    "    'HGB': hgbdf,\n",
+    "    'PLT': pltdf,\n",
+    "    'TAC': tacdf,\n",
+    "    'URIC': uricdf,\n",
+    "    'LDH': ldhdf\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "12d4a475",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nastab = nadf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "kstab = kdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "mgstab = mgdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "phosstab = phosdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "crstab = crdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "wbcstab = wbcdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "hgbstab = hgbdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "pltstab = pltdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "tacstab = tacdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "uricstab = uricdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "ldhstab = ldhdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8]\n",
+    "\n",
+    "labstab = pd.DataFrame()\n",
+    "labstab['Sodium'] = nastab['normal']['sum']/nastab['normal']['count'].astype('float')\n",
+    "labstab['Potassium'] = kstab['normal']['sum']/kstab['normal']['count'].astype('float')\n",
+    "labstab['Creatinine'] = crstab['normal']['sum']/crstab['normal']['count'].astype('float')\n",
+    "labstab['Magnesium']  = mgstab['normal']['sum']/mgstab['normal']['count'].astype('float')\n",
+    "labstab['Phosphorus'] = phosstab['normal']['sum']/phosstab['normal']['count'].astype('float')\n",
+    "labstab['White Cells'] = wbcstab['normal']['sum']/wbcstab['normal']['count'].astype('float')\n",
+    "labstab['Hemoglobin'] = hgbstab['normal']['sum']/hgbstab['normal']['count'].astype('float')\n",
+    "labstab['Platelets'] = pltstab['normal']['sum']/pltstab['normal']['count'].astype('float')\n",
+    "labstab['Tacrolimus'] = tacstab['normal']['sum']/tacstab['normal']['count'].astype('float')\n",
+    "labstab['Uric Acid'] = uricstab['normal']['sum']/uricstab['normal']['count'].astype('float')\n",
+    "labstab['LDH'] = ldhstab['normal']['sum']/ldhstab['normal']['count'].astype('float')\n",
+    "\n",
+    "labstabcnts = pd.DataFrame()\n",
+    "labstabcnts['Sodium'] = nastab['normal']['count']\n",
+    "labstabcnts['Potassium'] = kstab['normal']['count']\n",
+    "labstabcnts['Creatinine'] = crstab['normal']['count']\n",
+    "labstabcnts['Magnesium']  = mgstab['normal']['count']\n",
+    "labstabcnts['Phosphorus'] = phosstab['normal']['count']\n",
+    "labstabcnts['White Cells'] = wbcstab['normal']['count']\n",
+    "labstabcnts['Hemoglobin'] = hgbstab['normal']['count']\n",
+    "labstabcnts['Platelets'] = pltstab['normal']['count']\n",
+    "labstabcnts['Tacrolimus'] = tacstab['normal']['count']\n",
+    "labstabcnts['Uric Acid'] = uricstab['normal']['count']\n",
+    "labstabcnts['LDH'] = ldhstab['normal']['count']\n",
+    "labstabcnts = pd.melt(labstabcnts.transpose().reset_index(), id_vars='index').rename(columns={'index':'lab'})\n",
+    "\n",
+    "labstab.to_csv('data/labstab.csv')\n",
+    "labstabcnts.to_csv('data/labstabcnts.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "b5c7b18f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lab_name_dict = {\n",
+    "    'NA': 'Sodium',\n",
+    "    'K': 'Potassium',\n",
+    "    'MG': 'Magnesium',\n",
+    "    'PHOS': 'Phosphorus',\n",
+    "    'CR': 'Creatinine',\n",
+    "    'WBC': 'White Blood Cells',\n",
+    "    'HGB': 'Hemoglobin',\n",
+    "    'PLT': 'Platelets',\n",
+    "    'TAC': 'Tacrolimus',\n",
+    "    'URIC': 'Uric Acid',\n",
+    "    'LDH': 'LDH'\n",
+    "}\n",
+    "labstabdf = pd.DataFrame(columns=['base_name', 'lab_name', 'preceding_streak', 'normal_count', 'total_count', 'normal_prop'])\n",
+    "for base_name in labs_df_dict.keys():\n",
+    "    labdf_temp = labs_df_dict[base_name].groupby('preceding').agg({'normal' : ['sum', 'count']})[0:8].reset_index()\n",
+    "    baselabdf = pd.DataFrame()\n",
+    "    baselabdf['preceding_streak'] = labdf_temp['preceding']\n",
+    "    baselabdf['base_name'] = base_name\n",
+    "    baselabdf['lab_name'] = lab_name_dict[base_name]\n",
+    "    baselabdf['normal_count'] = labdf_temp['normal']['sum']\n",
+    "    baselabdf['total_count'] = labdf_temp['normal']['count']\n",
+    "    baselabdf['normal_prop'] = baselabdf['normal_count'].astype(float)/baselabdf['total_count'].astype(float)\n",
+    "    labstabdf = labstabdf.append(baselabdf)\n",
+    "labstabdf.to_csv('data/labstab_melted.csv')\n",
+    "# This is in melted format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "2b5ac4cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotLabStab(df, ndays, ax = None, subset=False, labs=['']):\n",
+    "    df = df[1:ndays+2]\n",
+    "    if subset:\n",
+    "        df = df[labs]\n",
+    "    sns.set_style(\"white\");\n",
+    "    if ax is None:\n",
+    "        ax = sns.lineplot(data = df, markers=True);\n",
+    "    else:\n",
+    "        sns.lineplot(data = df, markers=True, ax = ax);\n",
+    "    ax.set_xlabel('Consecutive Normal Results in Past 7 Days Preceding Lab');\n",
+    "    ax.set_xlim(-0.25, ndays+0.25)\n",
+    "    ax.set_xticks(np.arange(ndays+1))\n",
+    "    ax.set_ylabel('Proportion Normal Results');\n",
+    "    ax.set_title('Stability for Common Inpatient Laboratory Studies');\n",
+    "    ax.set_ylim(0, 1.0);\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "9ef585b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotLabStab(labstab, 6);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "29284110",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.gridspec import GridSpec\n",
+    "fig = plt.figure(constrained_layout=True)\n",
+    "gs = GridSpec(2, 3, figure=fig)\n",
+    "axes = []\n",
+    "# create sub plots as grid\n",
+    "axes.append(fig.add_subplot(gs[0, :]))\n",
+    "axes.append(fig.add_subplot(gs[1, 0]))\n",
+    "axes.append(fig.add_subplot(gs[1, 1], sharey=axes[1]))\n",
+    "axes.append(fig.add_subplot(gs[1, 2], sharey=axes[1]))\n",
+    "\n",
+    "reglabs = ['Sodium', 'Potassium', 'Creatinine', 'Magnesium', 'Phosphorus', \n",
+    "          'White Cells', 'Hemoglobin', 'Platelets']\n",
+    "chem1 = ['Sodium', 'Potassium', 'Creatinine']\n",
+    "chem2 = ['Magnesium', 'Phosphorus']\n",
+    "cbc = ['White Cells', 'Hemoglobin', 'Platelets']\n",
+    "cmap = sns.color_palette()\n",
+    "\n",
+    "STREAK = 7\n",
+    "sns.set_style(\"white\");\n",
+    "dash_list = sns._core.unique_dashes(8);\n",
+    "marker_list = sns._core.unique_markers(8)\n",
+    "sns.lineplot(data=labstab[reglabs][1:STREAK+1], ax = axes[0],\n",
+    "             palette = cmap[0:8], dashes=dash_list, markers=marker_list);\n",
+    "sns.lineplot(data=labstab[chem1][1:STREAK+1], ax=axes[1],\n",
+    "             palette = cmap[0:3], dashes=dash_list[0:3], markers=marker_list[0:3])\n",
+    "sns.lineplot(data=labstab[chem2][1:STREAK+1], ax=axes[2], \n",
+    "             palette = cmap[3:5], dashes=dash_list[3:5], markers=marker_list[3:5])\n",
+    "sns.lineplot(data=labstab[cbc][1:STREAK+1], ax=axes[3], \n",
+    "             palette = cmap[5:8], dashes=dash_list[5:8], markers=marker_list[5:8])\n",
+    "\n",
+    "\n",
+    "# plotLabStab(labstab, 6, ax=axes[0]);\n",
+    "# plotLabStab(labstab, 6, ax = axes[1], subset = True, labs = ['Sodium', 'Potassium', 'Creatinine']);\n",
+    "# plotLabStab(labstab, 6, ax = axes[2], subset = True, labs = ['Magnesium', 'Phosphorus']);\n",
+    "# plotLabStab(labstab, 6, ax = axes[3], subset = True, labs = ['White Cells', 'Hemoglobin', 'Platelets']);\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_xlim(-0.25, STREAK-0.75);\n",
+    "    ax.set_ylim(0, 1);\n",
+    "    ax.set_xticks(np.arange(STREAK));\n",
+    "    ax.set_xticklabels(labels=np.arange(STREAK), fontsize=6);\n",
+    "    ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
+    "    ax.set_yticklabels(labels=[0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=6);\n",
+    "    ax.set_ylabel('');\n",
+    "    ax.set_xlabel('');\n",
+    "    ax.set_title('');\n",
+    "    ax.legend(fontsize=5, loc=4);\n",
+    "    \n",
+    "#axes[0].set_title('All')\n",
+    "axes[1].set_title('Chem Panel', fontsize=7);\n",
+    "axes[2].set_title('Additional Chemistries', fontsize=7);\n",
+    "axes[3].set_title('Blood Count', fontsize=7);\n",
+    "plt.setp(axes[2].get_yticklabels(), visible=False)\n",
+    "plt.setp(axes[3].get_yticklabels(), visible=False)\n",
+    "plt.setp(axes[1].get_xticklabels(), visible=False)\n",
+    "plt.setp(axes[3].get_xticklabels(), visible=False)\n",
+    "axes[0].xaxis.labelpad = 10000;\n",
+    "\n",
+    "fig.supylabel('Proportion Normal Labs');\n",
+    "fig.supxlabel('Preceding Consecutive Streak of Normal Results in Prior 7 Days');\n",
+    "fig.suptitle('Stability of Common Inpatient Labs');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a1a3786",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/analysis_lradt_icu.ipynb b/scripts/PedsLabUtil/analysis_lradt_icu.ipynb
new file mode 100644
index 00000000..03fda148
--- /dev/null
+++ b/scripts/PedsLabUtil/analysis_lradt_icu.ipynb
@@ -0,0 +1,1595 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8d4c7e33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.dpi'] = 150\n",
+    "import seaborn as sns\n",
+    "\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 100\n",
+    "pd.options.display.min_rows = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "da0a36ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the lab result - adt joined table\n",
+    "lradt = pd.read_pickle('data/lradt_icu_20211027.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3fee0500",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_low</th>\n",
+       "      <th>reference_high</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result_flag</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19757473</td>\n",
+       "      <td>JC720264</td>\n",
+       "      <td>323100077</td>\n",
+       "      <td>751921985</td>\n",
+       "      <td>17181</td>\n",
+       "      <td>ECG</td>\n",
+       "      <td>ECG1</td>\n",
+       "      <td>EKG 12-Lead</td>\n",
+       "      <td>Heart Rate</td>\n",
+       "      <td>HEARTRATE</td>\n",
+       "      <td>205.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bpm</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2019-09-12 09:45:00</td>\n",
+       "      <td>2019-09-12 23:42:00</td>\n",
+       "      <td>2019-09-11 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201272</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Critical Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>5488019</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21521938</td>\n",
+       "      <td>JC720264</td>\n",
+       "      <td>323100077</td>\n",
+       "      <td>754421548</td>\n",
+       "      <td>1230100479</td>\n",
+       "      <td>Lab Only - Beaker</td>\n",
+       "      <td>LAB234B</td>\n",
+       "      <td>CBC with Differential</td>\n",
+       "      <td>Imm. Granulocyte, Abs</td>\n",
+       "      <td>IGABSWAM</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>K/uL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2019-10-31 00:00:00</td>\n",
+       "      <td>2019-10-31 00:00:00</td>\n",
+       "      <td>2019-10-30 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201273</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Critical Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>5638306</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20733890</td>\n",
+       "      <td>JC720264</td>\n",
+       "      <td>323100077</td>\n",
+       "      <td>754191949</td>\n",
+       "      <td>268</td>\n",
+       "      <td>Blood Bank</td>\n",
+       "      <td>LAB282</td>\n",
+       "      <td>Red Blood Cells</td>\n",
+       "      <td>Unit cross match</td>\n",
+       "      <td>RBCXMR</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2019-10-31 00:00:00</td>\n",
+       "      <td>2019-10-31 00:00:00</td>\n",
+       "      <td>2019-10-30 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201273</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Critical Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>5638306</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        uid   anon_id pat_enc_csn_id_coded order_id_coded  component_id  \\\n",
+       "0  19757473  JC720264            323100077      751921985         17181   \n",
+       "1  21521938  JC720264            323100077      754421548    1230100479   \n",
+       "2  20733890  JC720264            323100077      754191949           268   \n",
+       "\n",
+       "          order_type proc_code         group_lab_name               lab_name  \\\n",
+       "0                ECG      ECG1            EKG 12-Lead             Heart Rate   \n",
+       "1  Lab Only - Beaker   LAB234B  CBC with Differential  Imm. Granulocyte, Abs   \n",
+       "2         Blood Bank    LAB282        Red Blood Cells       Unit cross match   \n",
+       "\n",
+       "   base_name  ord_num_value reference_low reference_high reference_unit  \\\n",
+       "0  HEARTRATE         205.00          None           None            bpm   \n",
+       "1   IGABSWAM           0.18          0.00           0.03           K/uL   \n",
+       "2     RBCXMR     9999999.00          None           None           None   \n",
+       "\n",
+       "  result_in_range_yn result_flag          order_time         result_time  \\\n",
+       "0               None        None 2019-09-12 09:45:00 2019-09-12 23:42:00   \n",
+       "1               None        High 2019-10-31 00:00:00 2019-10-31 00:00:00   \n",
+       "2               None        None 2019-10-31 00:00:00 2019-10-31 00:00:00   \n",
+       "\n",
+       "  effective_time_jittered  pat_class  department_id pat_service  \\\n",
+       "0     2019-09-11 23:59:00  Inpatient       10201272  Cardiology   \n",
+       "1     2019-10-30 23:59:00  Inpatient       10201273  Cardiology   \n",
+       "2     2019-10-30 23:59:00  Inpatient       10201273  Cardiology   \n",
+       "\n",
+       "  pat_lv_of_care              accomodation from_base_class to_base_class  \\\n",
+       "0  Critical Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "1  Critical Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2  Critical Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "\n",
+       "  in_event_type out_event_type  event_id_coded event_type  \n",
+       "0        Census         Census         5488019     Census  \n",
+       "1        Census         Census         5638306     Census  \n",
+       "2        Census         Census         5638306     Census  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4c90010c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of rows:\t 5704209\n",
+      "Total number of orders:\t 821494\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Total number of rows:\\t', len(lradt))\n",
+    "print('Total number of orders:\\t', len(lradt.order_id_coded.unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b274245",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pruning labs that occurred on the last day\t\n",
+      "Remaining number of rows:\t 5629288\n",
+      "Remaining number of orders:\t 810413\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Pruning labs that occurred on the last day\\t')\n",
+    "lradt = lradt.loc[lradt.result_time < lradt.result_time.max()]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "647e2e94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Look at group_lab_names in order to prune lab orders and combine similar lab concepts\n",
+    "pd.DataFrame(lradt.group_lab_name.value_counts()).to_csv('data/group_lab_name_counts_icu.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b570000e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>order_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Lab</th>\n",
+       "      <td>2434193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Point of Care Testing-Docked Device</th>\n",
+       "      <td>2301793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lab Only - Beaker</th>\n",
+       "      <td>541344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Bank</th>\n",
+       "      <td>229383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Microbiology</th>\n",
+       "      <td>107669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ECG</th>\n",
+       "      <td>14730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Point of Care Testing</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nursing</th>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     order_type\n",
+       "Lab                                     2434193\n",
+       "Point of Care Testing-Docked Device     2301793\n",
+       "Lab Only - Beaker                        541344\n",
+       "Blood Bank                               229383\n",
+       "Microbiology                             107669\n",
+       "ECG                                       14730\n",
+       "Point of Care Testing                       139\n",
+       "Nursing                                      37"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(lradt.order_type.value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "65c0a43d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pruning point of care labs...\n",
+      "Remaining number of rows:\t 3327356\n",
+      "Remaining number of orders:\t 545071\n",
+      "\n",
+      "Pruning ECG orders...\n",
+      "Remaining number of rows:\t 3312626\n",
+      "Remaining number of orders:\t 542598\n",
+      "\n",
+      "Pruning Blood Bank orders...\n",
+      "Remaining number of rows:\t 3083243\n",
+      "Remaining number of orders:\t 502283\n",
+      "\n",
+      "Pruning Nursing orders...\n",
+      "Remaining number of rows:\t 3083206\n",
+      "Remaining number of orders:\t 502269\n",
+      "\n",
+      "Pruning iSTAT labs...\n",
+      "Remaining number of rows:\t 3081290\n",
+      "Remaining number of orders:\t 501311\n",
+      "\n",
+      "Pruning Manual Differential/Slide Review Orders...\n",
+      "Remaining number of rows:\t 2929249\n",
+      "Remaining number of orders:\t 488509\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Pruning point of care labs...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='Point of Care Testing-Docked Device') & (lradt.order_type!='Point of Care Testing')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning ECG orders...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='ECG')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning Blood Bank orders...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='Blood Bank')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning Nursing orders...')\n",
+    "lradt = lradt.loc[(lradt.order_type!='Nursing')]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning iSTAT labs...')\n",
+    "lradt = lradt.loc[~lradt.group_lab_name.str.contains(\"ISTAT\", case=False)]\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))\n",
+    "\n",
+    "print('\\nPruning Manual Differential/Slide Review Orders...')\n",
+    "lradt = lradt.loc[lradt.group_lab_name!='Manual Differential/Slide Review']\n",
+    "print('Remaining number of rows:\\t', len(lradt))\n",
+    "print('Remaining number of orders:\\t', len(lradt.order_id_coded.unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "daf628d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rename some group_lab_names that are the same\n",
+    "lradt['njr_labname'] = lradt['group_lab_name']\n",
+    "cbc =  ['CBC with Differential', 'CBC with Diff', 'CBC', 'CBC with Diff and Slide Review']\n",
+    "lradt.loc[lradt.njr_labname.isin(cbc), 'njr_labname'] = 'CBC'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9296621a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Earliest lab result datetime:\t 2014-04-06 00:45:00\n",
+      "Latest lab result datetime:\t 2019-10-30 22:53:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Earliest lab result datetime:\\t', lradt.result_time.min())\n",
+    "print('Latest lab result datetime:\\t', lradt.result_time.max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c87c451",
+   "metadata": {},
+   "source": [
+    "## Repetitive Lab Orders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "676d164d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>49569</td>\n",
+       "      <td>9222</td>\n",
+       "      <td>7337</td>\n",
+       "      <td>5.375081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>36633</td>\n",
+       "      <td>6683</td>\n",
+       "      <td>5495</td>\n",
+       "      <td>5.481520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>28973</td>\n",
+       "      <td>5345</td>\n",
+       "      <td>4318</td>\n",
+       "      <td>5.420580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>26975</td>\n",
+       "      <td>4700</td>\n",
+       "      <td>3988</td>\n",
+       "      <td>5.739362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>24611</td>\n",
+       "      <td>6191</td>\n",
+       "      <td>5147</td>\n",
+       "      <td>3.975287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>23540</td>\n",
+       "      <td>5888</td>\n",
+       "      <td>4956</td>\n",
+       "      <td>3.997962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>19776</td>\n",
+       "      <td>6073</td>\n",
+       "      <td>5134</td>\n",
+       "      <td>3.256381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>18027</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>3866</td>\n",
+       "      <td>4.006000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>16392</td>\n",
+       "      <td>4844</td>\n",
+       "      <td>4163</td>\n",
+       "      <td>3.383980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>10202</td>\n",
+       "      <td>3430</td>\n",
+       "      <td>3078</td>\n",
+       "      <td>2.974344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MRSA Screen</th>\n",
+       "      <td>10074</td>\n",
+       "      <td>9854</td>\n",
+       "      <td>7996</td>\n",
+       "      <td>1.022326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>9397</td>\n",
+       "      <td>1890</td>\n",
+       "      <td>1763</td>\n",
+       "      <td>4.971958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sodium, Ser/Plas</th>\n",
+       "      <td>8479</td>\n",
+       "      <td>658</td>\n",
+       "      <td>578</td>\n",
+       "      <td>12.886018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>6838</td>\n",
+       "      <td>3221</td>\n",
+       "      <td>2780</td>\n",
+       "      <td>2.122943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heparin Activity Level</th>\n",
+       "      <td>6489</td>\n",
+       "      <td>526</td>\n",
+       "      <td>490</td>\n",
+       "      <td>12.336502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>6316</td>\n",
+       "      <td>3003</td>\n",
+       "      <td>2575</td>\n",
+       "      <td>2.103230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>6254</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>1386</td>\n",
+       "      <td>3.940769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Venous</th>\n",
+       "      <td>6060</td>\n",
+       "      <td>1516</td>\n",
+       "      <td>1322</td>\n",
+       "      <td>3.997361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>5525</td>\n",
+       "      <td>698</td>\n",
+       "      <td>482</td>\n",
+       "      <td>7.915473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Procalcitonin</th>\n",
+       "      <td>5001</td>\n",
+       "      <td>1572</td>\n",
+       "      <td>1378</td>\n",
+       "      <td>3.181298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                norders  nenc  npat    per_enc\n",
+       "njr_labname                                                   \n",
+       "CBC                               49569  9222  7337   5.375081\n",
+       "Magnesium, Ser/Plas               36633  6683  5495   5.481520\n",
+       "Chem 10                           28973  5345  4318   5.420580\n",
+       "Renal Function Panel              26975  4700  3988   5.739362\n",
+       "Prothrombin Time                  24611  6191  5147   3.975287\n",
+       "Part Thromboplastin Time          23540  5888  4956   3.997962\n",
+       "Metabolic Panel, Comprehensive    19776  6073  5134   3.256381\n",
+       "Fibrinogen                        18027  4500  3866   4.006000\n",
+       "Phosphorus, Ser/Plas              16392  4844  4163   3.383980\n",
+       "C Reactive Protein                10202  3430  3078   2.974344\n",
+       "MRSA Screen                       10074  9854  7996   1.022326\n",
+       "Triglyceride, Ser/Plas             9397  1890  1763   4.971958\n",
+       "Sodium, Ser/Plas                   8479   658   578  12.886018\n",
+       "Antibody Screen                    6838  3221  2780   2.122943\n",
+       "Heparin Activity Level             6489   526   490  12.336502\n",
+       "ABO Rh Typing                      6316  3003  2575   2.103230\n",
+       "Vancomycin Level, Trough           6254  1587  1386   3.940769\n",
+       "Blood Gases, Venous                6060  1516  1322   3.997361\n",
+       "Tacrolimus Level, FK506            5525   698   482   7.915473\n",
+       "Procalcitonin                      5001  1572  1378   3.181298"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# labcounts sorted by total number of orders\n",
+    "labcounts = lradt.groupby(by='njr_labname').agg({'order_id_coded': 'nunique', 'pat_enc_csn_id_coded': 'nunique', 'anon_id': 'nunique'}).rename(columns={'order_id_coded': 'norders', 'pat_enc_csn_id_coded': 'nenc', 'anon_id': 'npat'}).sort_values('norders', ascending=False)\n",
+    "labcounts['per_enc'] = labcounts.norders/labcounts.nenc\n",
+    "labcounts.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a67d4224",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sodium, Ser/Plas</th>\n",
+       "      <td>8479</td>\n",
+       "      <td>658</td>\n",
+       "      <td>578</td>\n",
+       "      <td>12.886018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heparin Activity Level</th>\n",
+       "      <td>6489</td>\n",
+       "      <td>526</td>\n",
+       "      <td>490</td>\n",
+       "      <td>12.336502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>5525</td>\n",
+       "      <td>698</td>\n",
+       "      <td>482</td>\n",
+       "      <td>7.915473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>26975</td>\n",
+       "      <td>4700</td>\n",
+       "      <td>3988</td>\n",
+       "      <td>5.739362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>36633</td>\n",
+       "      <td>6683</td>\n",
+       "      <td>5495</td>\n",
+       "      <td>5.481520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>28973</td>\n",
+       "      <td>5345</td>\n",
+       "      <td>4318</td>\n",
+       "      <td>5.420580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>49569</td>\n",
+       "      <td>9222</td>\n",
+       "      <td>7337</td>\n",
+       "      <td>5.375081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>9397</td>\n",
+       "      <td>1890</td>\n",
+       "      <td>1763</td>\n",
+       "      <td>4.971958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>18027</td>\n",
+       "      <td>4500</td>\n",
+       "      <td>3866</td>\n",
+       "      <td>4.006000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>23540</td>\n",
+       "      <td>5888</td>\n",
+       "      <td>4956</td>\n",
+       "      <td>3.997962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Venous</th>\n",
+       "      <td>6060</td>\n",
+       "      <td>1516</td>\n",
+       "      <td>1322</td>\n",
+       "      <td>3.997361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>24611</td>\n",
+       "      <td>6191</td>\n",
+       "      <td>5147</td>\n",
+       "      <td>3.975287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>6254</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>1386</td>\n",
+       "      <td>3.940769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>16392</td>\n",
+       "      <td>4844</td>\n",
+       "      <td>4163</td>\n",
+       "      <td>3.383980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>19776</td>\n",
+       "      <td>6073</td>\n",
+       "      <td>5134</td>\n",
+       "      <td>3.256381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Procalcitonin</th>\n",
+       "      <td>5001</td>\n",
+       "      <td>1572</td>\n",
+       "      <td>1378</td>\n",
+       "      <td>3.181298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>10202</td>\n",
+       "      <td>3430</td>\n",
+       "      <td>3078</td>\n",
+       "      <td>2.974344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>6838</td>\n",
+       "      <td>3221</td>\n",
+       "      <td>2780</td>\n",
+       "      <td>2.122943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>6316</td>\n",
+       "      <td>3003</td>\n",
+       "      <td>2575</td>\n",
+       "      <td>2.103230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MRSA Screen</th>\n",
+       "      <td>10074</td>\n",
+       "      <td>9854</td>\n",
+       "      <td>7996</td>\n",
+       "      <td>1.022326</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                norders  nenc  npat    per_enc\n",
+       "njr_labname                                                   \n",
+       "Sodium, Ser/Plas                   8479   658   578  12.886018\n",
+       "Heparin Activity Level             6489   526   490  12.336502\n",
+       "Tacrolimus Level, FK506            5525   698   482   7.915473\n",
+       "Renal Function Panel              26975  4700  3988   5.739362\n",
+       "Magnesium, Ser/Plas               36633  6683  5495   5.481520\n",
+       "Chem 10                           28973  5345  4318   5.420580\n",
+       "CBC                               49569  9222  7337   5.375081\n",
+       "Triglyceride, Ser/Plas             9397  1890  1763   4.971958\n",
+       "Fibrinogen                        18027  4500  3866   4.006000\n",
+       "Part Thromboplastin Time          23540  5888  4956   3.997962\n",
+       "Blood Gases, Venous                6060  1516  1322   3.997361\n",
+       "Prothrombin Time                  24611  6191  5147   3.975287\n",
+       "Vancomycin Level, Trough           6254  1587  1386   3.940769\n",
+       "Phosphorus, Ser/Plas              16392  4844  4163   3.383980\n",
+       "Metabolic Panel, Comprehensive    19776  6073  5134   3.256381\n",
+       "Procalcitonin                      5001  1572  1378   3.181298\n",
+       "C Reactive Protein                10202  3430  3078   2.974344\n",
+       "Antibody Screen                    6838  3221  2780   2.122943\n",
+       "ABO Rh Typing                      6316  3003  2575   2.103230\n",
+       "MRSA Screen                       10074  9854  7996   1.022326"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Labcounts sorted by labs per encounter for labs occurring in more than __ encounters\n",
+    "labcounts.loc[(labcounts.norders>5000) & (labcounts.nenc>500)].sort_values('per_enc', ascending=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "74bb5abb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create ordersdf dataframe for calculating the lab repetition\n",
+    "# included_labs = ['Uric Acid, Ser/Plas', 'LDH Total, Ser/Plas', 'Tacrolimus Level, FK506', \\\n",
+    "#                  'Chem 10', 'Renal Function Panel', 'Triglyceride, Ser/Plas', \\\n",
+    "#                  'Magnesium, Ser/Plas', 'CBC', 'Gamma Glutamyl Trans', \\\n",
+    "#                  'Vancomycin Level, Trough', 'Hepatic Function Panel', \\\n",
+    "#                  'Prothrombin Time', 'Phosphorus, Ser/Plas']\n",
+    "# ordersdf = lradt.loc[lradt.njr_labname.isin(included_labs),['anon_id', 'pat_enc_csn_id_coded', 'order_id_coded', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'njr_labname', 'result_time', 'order_time']).groupby('order_id_coded').first()\n",
+    "included_labs = labcounts.loc[(labcounts.norders>5000) & (labcounts.nenc>500)].sort_values('per_enc', ascending=False).index.to_list()[0:15]\n",
+    "ordersdf = lradt.loc[lradt.njr_labname.isin(included_labs)].groupby('order_id_coded').first()[['anon_id', 'pat_enc_csn_id_coded', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'njr_labname', 'order_time', 'result_time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5567b191",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordersdf_shift = ordersdf.shift(1)\n",
+    "ordersdf['dif'] = pd.NaT\n",
+    "ordersdf['dif'] = ordersdf.order_time - ordersdf_shift.order_time\n",
+    "newlabidx = (ordersdf.njr_labname != ordersdf_shift.njr_labname) | (ordersdf.pat_enc_csn_id_coded != ordersdf_shift.pat_enc_csn_id_coded)\n",
+    "ordersdf.loc[newlabidx, 'dif'] = pd.NaT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "39b25c8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>dif</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>702910257</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Blood Gases, Venous</td>\n",
+       "      <td>2014-08-19 07:13:00</td>\n",
+       "      <td>2014-08-19 10:12:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702910258</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Blood Gases, Venous</td>\n",
+       "      <td>2014-08-19 07:13:00</td>\n",
+       "      <td>2014-08-19 11:47:00</td>\n",
+       "      <td>0 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702910259</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Blood Gases, Venous</td>\n",
+       "      <td>2014-08-19 07:13:00</td>\n",
+       "      <td>2014-08-19 12:46:00</td>\n",
+       "      <td>0 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702910256</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Blood Gases, Venous</td>\n",
+       "      <td>2014-08-19 07:13:00</td>\n",
+       "      <td>2014-08-19 12:59:00</td>\n",
+       "      <td>0 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702910260</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Blood Gases, Venous</td>\n",
+       "      <td>2014-08-19 07:13:00</td>\n",
+       "      <td>2014-08-19 14:05:00</td>\n",
+       "      <td>0 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702016227</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-11 18:16:00</td>\n",
+       "      <td>2014-07-11 18:52:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702017543</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-11 21:05:00</td>\n",
+       "      <td>2014-07-12 04:18:00</td>\n",
+       "      <td>0 days 02:49:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702040176</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-12 21:05:00</td>\n",
+       "      <td>2014-07-13 04:36:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702051294</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-13 21:05:00</td>\n",
+       "      <td>2014-07-14 05:00:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702061410</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-14 15:55:00</td>\n",
+       "      <td>2014-07-14 17:02:00</td>\n",
+       "      <td>0 days 18:50:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 anon_id pat_enc_csn_id_coded          njr_labname  \\\n",
+       "order_id_coded                                                       \n",
+       "702910257       JC605249            308913369  Blood Gases, Venous   \n",
+       "702910258       JC605249            308913369  Blood Gases, Venous   \n",
+       "702910259       JC605249            308913369  Blood Gases, Venous   \n",
+       "702910256       JC605249            308913369  Blood Gases, Venous   \n",
+       "702910260       JC605249            308913369  Blood Gases, Venous   \n",
+       "702016227       JC605249            308913369                  CBC   \n",
+       "702017543       JC605249            308913369                  CBC   \n",
+       "702040176       JC605249            308913369                  CBC   \n",
+       "702051294       JC605249            308913369                  CBC   \n",
+       "702061410       JC605249            308913369                  CBC   \n",
+       "\n",
+       "                        order_time         result_time             dif  \n",
+       "order_id_coded                                                          \n",
+       "702910257      2014-08-19 07:13:00 2014-08-19 10:12:00             NaT  \n",
+       "702910258      2014-08-19 07:13:00 2014-08-19 11:47:00 0 days 00:00:00  \n",
+       "702910259      2014-08-19 07:13:00 2014-08-19 12:46:00 0 days 00:00:00  \n",
+       "702910256      2014-08-19 07:13:00 2014-08-19 12:59:00 0 days 00:00:00  \n",
+       "702910260      2014-08-19 07:13:00 2014-08-19 14:05:00 0 days 00:00:00  \n",
+       "702016227      2014-07-11 18:16:00 2014-07-11 18:52:00             NaT  \n",
+       "702017543      2014-07-11 21:05:00 2014-07-12 04:18:00 0 days 02:49:00  \n",
+       "702040176      2014-07-12 21:05:00 2014-07-13 04:36:00 1 days 00:00:00  \n",
+       "702051294      2014-07-13 21:05:00 2014-07-14 05:00:00 1 days 00:00:00  \n",
+       "702061410      2014-07-14 15:55:00 2014-07-14 17:02:00 0 days 18:50:00  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7d1774be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_quant = [pd.Timedelta('24H'), pd.Timedelta('3D'), pd.Timedelta('1W')]\n",
+    "labdtq_idx = ['<24h', '[24h, 3d)', '[3d, 7d)', '>=7d']\n",
+    "labdtq = pd.DataFrame(index=labdtq_idx)\n",
+    "for lab in included_labs:\n",
+    "    #print('Lab: ', lab)\n",
+    "    dtimes = ordersdf.loc[ordersdf.njr_labname==lab].dif\n",
+    "    p_enc = ordersdf.loc[ordersdf.njr_labname==lab, 'pat_enc_csn_id_coded'].nunique()\n",
+    "    dtq = []\n",
+    "    dtq.append(sum(dtimes < pd.Timedelta('24H')))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('3D')) & (dtimes >= pd.Timedelta('24H')) ))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('7D')) & (dtimes >= pd.Timedelta('3D')) ))\n",
+    "    dtq.append(sum((dtimes >= pd.Timedelta('7D')) | (dtimes.isnull()) ))\n",
+    "    #print('[0, 24h):\\t', dtq[0])\n",
+    "    #print('[24h, 3d)]:\\t', dtq[1])\n",
+    "    #print('[3d, 7d)]:\\t', dtq[2])\n",
+    "    #print('[7d, inf)]:\\t', dtq[3])\n",
+    "    labdtq[lab] = np.cumsum(dtq)/p_enc*1000\n",
+    "labdtq.to_csv('data/labdtq_icu.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "7d5d07ce",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# labdtq = pd.read_csv('data/labdtq.csv', index_col=0)\n",
+    "ax1 = labdtq.iloc[3].plot.barh(color='#404c54');\n",
+    "labdtq.iloc[2].plot.barh(ax=ax1, color='#f0cc94');\n",
+    "labdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "labdtq.iloc[0].plot.barh(ax=ax1, color='#807A6B');\n",
+    "ax1.legend();\n",
+    "ax1.invert_yaxis();\n",
+    "ax1.set_title('Commonly Repeated Lab Orders in ICU Pediatric Medicine');\n",
+    "ax1.set_xlabel('No. Lab Orders per 1000 Encounters');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1764949",
+   "metadata": {},
+   "source": [
+    "## Lab Component Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ce3e2741",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def windowedLabCounts(LABNAME, LOWER, UPPER, plot_flag = False):\n",
+    "    lrdf = lradt.loc[(lradt.base_name==LABNAME) & (lradt.ord_num_value < UPPER) & (lradt.ord_num_value > LOWER)].sort_values(by=['pat_enc_csn_id_coded', 'result_time']).reset_index()\n",
+    "    lrdf['result'] = 'N'\n",
+    "    lrdf.loc[(lrdf.result_in_range_yn!='Y') & ((lrdf.result_flag=='High') | (lrdf.result_flag=='High Panic')), 'result'] = 'H'\n",
+    "    lrdf.loc[(lrdf.result_in_range_yn!='Y') & ((lrdf.result_flag=='Low') | (lrdf.result_flag=='Low Panic')), 'result'] = 'L'\n",
+    "    lrdf['normal'] = (lrdf.result=='N')\n",
+    "    print('LAB:', LABNAME)\n",
+    "    print(lrdf.normal.value_counts())\n",
+    "\n",
+    "    lrdf['preceding']=0\n",
+    "    for enc in lrdf.pat_enc_csn_id_coded.unique():\n",
+    "        enc_idx = (lrdf.pat_enc_csn_id_coded==enc)\n",
+    "        df = lrdf.loc[enc_idx, ['result_time', 'normal']]\n",
+    "        rownum = 0\n",
+    "        for (idx, row) in df.iterrows():\n",
+    "            if rownum>0:\n",
+    "                rtime = row.result_time\n",
+    "                dfslice = df.loc[(df.result_time <= rtime) & (df.result_time > rtime - pd.Timedelta('7D'))]\n",
+    "                if dfslice.shift().loc[idx, 'normal']==True:\n",
+    "                    y = dfslice.shift().normal\n",
+    "                    prec = y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)\n",
+    "                    lrdf.loc[idx, 'preceding'] = prec.values[-1]\n",
+    "            rownum = rownum + 1\n",
+    "    \n",
+    "    if plot_flag:\n",
+    "        dfstab = lrdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "        dfstab['prop'] = dfstab['normal']['sum']/dfstab['normal']['count'].astype('float')\n",
+    "        ax = dfstab.prop.plt()\n",
+    "        ax.set_xlabel('Consecutive Normal Results in Past 7 Days Preceding Lab');\n",
+    "        ax.set_ylabel('Proportion Normal Results');\n",
+    "        ax.set_title('Stability for Common Inpatient Laboratory Studies');\n",
+    "        ax.set_ylim(0, 1.0);\n",
+    "    \n",
+    "    return lrdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "2276e8f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAB: NA\n",
+      "True     66097\n",
+      "False    30706\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: K\n",
+      "True     57773\n",
+      "False    30781\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: MG\n",
+      "True     44335\n",
+      "False    21575\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: PHOS\n",
+      "True     46362\n",
+      "False    26313\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: CR\n",
+      "True     53941\n",
+      "False    23993\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: WBC\n",
+      "True     30126\n",
+      "False    19168\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: HGB\n",
+      "False    38488\n",
+      "True     20716\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: PLT\n",
+      "True     25644\n",
+      "False    24108\n",
+      "Name: normal, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "nadf = windowedLabCounts('NA', 0, 200);\n",
+    "kdf = windowedLabCounts('K', 0, 8);\n",
+    "mgdf = windowedLabCounts('MG', 0, 10);\n",
+    "phosdf = windowedLabCounts('PHOS', 0, 50);\n",
+    "crdf = windowedLabCounts('CR', 0, 20);\n",
+    "wbcdf = windowedLabCounts('WBC', 0, 100000);\n",
+    "hgbdf = windowedLabCounts('HGB', 0, 30);\n",
+    "pltdf = windowedLabCounts('PLT', 0, 500000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "12d4a475",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nastab = nadf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "kstab = kdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "mgstab = mgdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "phosstab = phosdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "crstab = crdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "wbcstab = wbcdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "hgbstab = hgbdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "pltstab = pltdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "\n",
+    "labstab = pd.DataFrame()\n",
+    "labstab['Sodium'] = nastab['normal']['sum']/nastab['normal']['count'].astype('float')\n",
+    "labstab['Potassium'] = kstab['normal']['sum']/kstab['normal']['count'].astype('float')\n",
+    "labstab['Creatinine'] = crstab['normal']['sum']/crstab['normal']['count'].astype('float')\n",
+    "labstab['Magnesium']  = mgstab['normal']['sum']/mgstab['normal']['count'].astype('float')\n",
+    "labstab['Phosphorus'] = phosstab['normal']['sum']/phosstab['normal']['count'].astype('float')\n",
+    "labstab['White Cells'] = wbcstab['normal']['sum']/wbcstab['normal']['count'].astype('float')\n",
+    "labstab['Hemoglobin'] = hgbstab['normal']['sum']/hgbstab['normal']['count'].astype('float')\n",
+    "labstab['Platelets'] = pltstab['normal']['sum']/pltstab['normal']['count'].astype('float')\n",
+    "\n",
+    "labstabcnts = pd.DataFrame()\n",
+    "labstabcnts['Sodium'] = nastab['normal']['count']\n",
+    "labstabcnts['Potassium'] = kstab['normal']['count']\n",
+    "labstabcnts['Creatinine'] = crstab['normal']['count']\n",
+    "labstabcnts['Magnesium']  = mgstab['normal']['count']\n",
+    "labstabcnts['Phosphorus'] = phosstab['normal']['count']\n",
+    "labstabcnts['White Cells'] = wbcstab['normal']['count']\n",
+    "labstabcnts['Hemoglobin'] = hgbstab['normal']['count']\n",
+    "labstabcnts['Platelets'] = pltstab['normal']['count']\n",
+    "labstabcnts = pd.melt(labstabcnts.transpose().reset_index(), id_vars='index').rename(columns={'index':'lab'})\n",
+    "\n",
+    "labstab.to_csv('data/labstab_icu.csv')\n",
+    "labstabcnts.to_csv('data/labstabcnts_icu.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "2b5ac4cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotLabStab(df, ndays, ax = None, subset=False, labs=['']):\n",
+    "    df = df[0:ndays+1]\n",
+    "    if subset:\n",
+    "        df = df[labs]\n",
+    "    sns.set_style(\"white\");\n",
+    "    if ax is None:\n",
+    "        ax = sns.lineplot(data = df, markers=True);\n",
+    "    else:\n",
+    "        sns.lineplot(data = df, markers=True, ax = ax);\n",
+    "    ax.set_xlabel('Consecutive Normal Results in Past 7 Days Preceding Lab');\n",
+    "    ax.set_xlim(-0.25, ndays+0.25)\n",
+    "    ax.set_xticks(np.arange(ndays+1))\n",
+    "    ax.set_ylabel('Proportion Normal Results');\n",
+    "    ax.set_title('Stability for Common ICU Laboratory Studies');\n",
+    "    ax.set_ylim(0, 1.0);\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9ef585b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotLabStab(labstab, 6);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "29284110",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.gridspec import GridSpec\n",
+    "fig = plt.figure(constrained_layout=True)\n",
+    "gs = GridSpec(2, 3, figure=fig)\n",
+    "axes = []\n",
+    "# create sub plots as grid\n",
+    "axes.append(fig.add_subplot(gs[0, :]))\n",
+    "axes.append(fig.add_subplot(gs[1, 0]))\n",
+    "axes.append(fig.add_subplot(gs[1, 1], sharey=axes[1]))\n",
+    "axes.append(fig.add_subplot(gs[1, 2], sharey=axes[1]))\n",
+    "\n",
+    "chem1 = ['Sodium', 'Potassium', 'Creatinine']\n",
+    "chem2 = ['Magnesium', 'Phosphorus']\n",
+    "cbc = ['White Cells', 'Hemoglobin', 'Platelets']\n",
+    "cmap = sns.color_palette()\n",
+    "\n",
+    "STREAK = 6\n",
+    "sns.set_style(\"white\");\n",
+    "dash_list = sns._core.unique_dashes(8);\n",
+    "marker_list = sns._core.unique_markers(8)\n",
+    "sns.lineplot(data=labstab[0:STREAK+1], ax = axes[0],\n",
+    "             palette = cmap[0:8], dashes=dash_list, markers=marker_list);\n",
+    "sns.lineplot(data=labstab[chem1][0:STREAK+1], ax=axes[1],\n",
+    "             palette = cmap[0:3], dashes=dash_list[0:3], markers=marker_list[0:3])\n",
+    "sns.lineplot(data=labstab[chem2][0:STREAK+1], ax=axes[2], \n",
+    "             palette = cmap[3:5], dashes=dash_list[3:5], markers=marker_list[3:5])\n",
+    "sns.lineplot(data=labstab[cbc][0:STREAK+1], ax=axes[3], \n",
+    "             palette = cmap[5:8], dashes=dash_list[5:8], markers=marker_list[5:8])\n",
+    "\n",
+    "\n",
+    "# plotLabStab(labstab, 6, ax=axes[0]);\n",
+    "# plotLabStab(labstab, 6, ax = axes[1], subset = True, labs = ['Sodium', 'Potassium', 'Creatinine']);\n",
+    "# plotLabStab(labstab, 6, ax = axes[2], subset = True, labs = ['Magnesium', 'Phosphorus']);\n",
+    "# plotLabStab(labstab, 6, ax = axes[3], subset = True, labs = ['White Cells', 'Hemoglobin', 'Platelets']);\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_xlim(-0.25, STREAK+0.25);\n",
+    "    ax.set_ylim(0, 1);\n",
+    "    ax.set_xticks(np.arange(STREAK+1));\n",
+    "    ax.set_xticklabels(labels=np.arange(STREAK+1), fontsize=6);\n",
+    "    ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
+    "    ax.set_yticklabels(labels=[0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=6);\n",
+    "    ax.set_ylabel('');\n",
+    "    ax.set_xlabel('');\n",
+    "    ax.set_title('');\n",
+    "    ax.legend(fontsize=5, loc=4);\n",
+    "    \n",
+    "#axes[0].set_title('All')\n",
+    "axes[1].set_title('Chem Panel', fontsize=7);\n",
+    "axes[2].set_title('Additional Chemistries', fontsize=7);\n",
+    "axes[3].set_title('Blood Count', fontsize=7);\n",
+    "plt.setp(axes[2].get_yticklabels(), visible=False)\n",
+    "plt.setp(axes[3].get_yticklabels(), visible=False)\n",
+    "plt.setp(axes[1].get_xticklabels(), visible=False)\n",
+    "plt.setp(axes[3].get_xticklabels(), visible=False)\n",
+    "axes[0].xaxis.labelpad = 10000;\n",
+    "\n",
+    "fig.supylabel('Proportion Normal Labs');\n",
+    "fig.supxlabel('Preceding Consecutive Streak of Normal Results in Prior 7 Days');\n",
+    "fig.suptitle('Stability of Common ICU Labs');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "031bd153",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = sns.lineplot(data=labstab[chem1][0:STREAK+1], \n",
+    "             palette = cmap[0:3], dashes=dash_list[0:3], markers=marker_list[0:3])\n",
+    "ax1.patch.set_alpha(0)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.set_zorder(-1)\n",
+    "g = sns.barplot(data=labstabcnts.loc[labstabcnts.lab.isin(chem1)], hue='lab', x='preceding', y='value',\n",
+    "           palette = cmap[0:3], ax=ax2, alpha=0.5);\n",
+    "ax2.get_legend().remove();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "21dac058",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = sns.lineplot(data=labstab[chem2][0:STREAK+1], \n",
+    "             palette = cmap[3:5], dashes=dash_list[3:5], markers=marker_list[3:5])\n",
+    "ax1.patch.set_alpha(0)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.set_zorder(-1)\n",
+    "g = sns.barplot(data=labstabcnts.loc[labstabcnts.lab.isin(chem2)], hue='lab', x='preceding', y='value',\n",
+    "           palette = cmap[3:5], ax=ax2, alpha=0.5);\n",
+    "ax2.get_legend().remove();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d5efe33d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = sns.lineplot(data=labstab[cbc][0:STREAK+1], \n",
+    "             palette = cmap[5:8], dashes=dash_list[5:8], markers=marker_list[5:8])\n",
+    "ax1.patch.set_alpha(0)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.set_zorder(-1)\n",
+    "g = sns.barplot(data=labstabcnts.loc[labstabcnts.lab.isin(cbc)], hue='lab', x='preceding', y='value',\n",
+    "           palette = cmap[5:8], ax=ax2, alpha=0.5);\n",
+    "ax2.get_legend().remove();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21e3193f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/data/group_lab_name_counts.csv b/scripts/PedsLabUtil/data/group_lab_name_counts.csv
new file mode 100644
index 00000000..e2cd5865
--- /dev/null
+++ b/scripts/PedsLabUtil/data/group_lab_name_counts.csv
@@ -0,0 +1,2383 @@
+,group_lab_name
+CBC with Differential,750464
+Chem 10,562769
+Renal Function Panel,296139
+"Metabolic Panel, Comprehensive",292846
+POCT Urine Dipstick (10),154951
+Urinalysis Macroscopic Only,150272
+Manual Differential/Slide Review,142894
+CBC with Diff,130870
+Urinalysis With Microscopic,106118
+POCT Urine Dipstick (9),79317
+Red Blood Cells,78430
+Glucose PCX Meter,76183
+Hepatic Function Panel,56051
+"Metabolic Panel, Basic",49555
+"Magnesium, Ser/Plas",47217
+CBC,45857
+"Blood Gases, Arterial",42671
+Antibody Screen,31748
+Platelets,31350
+Chem 23,30386
+"Blood Gases, Venous",29706
+Manual Differential,29560
+Prothrombin Time,27782
+Slide Review,27553
+Blood Culture,25127
+Gastroenteritis PCR Panel,24993
+"Tacrolimus Level, FK506",23605
+"iSTAT CG8, Arterial",19671
+ABO Rh Typing,19152
+Respiratory Virus PCR Panel,18104
+"Phosphorus, Ser/Plas",17095
+Respiratory Pathogen PCR Panel,16534
+"Amino Acids, Plasma",15316
+EKG 12-Lead,15046
+"Uric Acid, Ser/Plas",13903
+"LDH Total, Ser/Plas",13434
+"Cell Count, CSF",12976
+"Istat EG7, Arterial",12521
+Gamma Glutamyl Trans,11841
+"Fatty Acid Profile, Essential",11550
+"Urinalysis Screen, Culture if Positive",11290
+Urine Culture,11216
+C Reactive Protein,10781
+Fresh Frozen Plasma,10585
+Part Thromboplastin Time,9905
+"Vancomycin Level, Trough",9899
+Fibrinogen,9633
+Blood Culture (Aerobic & Anaerobic Bottles),9464
+Transfusion Svc Prod 1,9200
+"Calcium Ionized, Whole Blood",8984
+"Vitamin D 25 Hydroxy, Serum",8910
+MRSA Screen,8736
+"Triglyceride, Ser/Plas",8449
+HLA-IgG DSA,8414
+Methotrexate Level,8155
+"Differential, CSF",7866
+"C1q, DSA",7805
+Acylcarnitine Profile Quant,7506
+Glucose By Meter,7150
+HLA Typing,7046
+"IgG, Screening",6538
+"Reticulocyte Count, Automated",6485
+"ACT, Istat Kaolin Venous",5667
+"T Subsets and B Lymphs, BLD",5215
+CBC with Diff and Slide Review,5176
+"Ammonia, Plasma",4986
+Transplant Therapy Monitoring,4918
+"Bilirubin Direct, Ser/Plas",4900
+Thromboelastograph Clot Profile,4895
+"ESR, Sedimentation Rate Panel",4801
+"Creatinine, Ser/Plas",4687
+"Istat EG7,Venous",4370
+"CMV DNA PCR Quant, Plasma",4226
+"Epstein Barr PCR Quant, Plasma",4214
+"Drugs of Abuse Screen, Urine",4111
+"C1q, Screening",3895
+Ferritin Level,3750
+Transferrin Saturation,3575
+"Sodium, Ser/Plas",3472
+Blood Culture --,3468
+Lipase Ser/Plas,3440
+C1q Monitoring,3250
+Cryoprecipitate,3245
+Quantiferon Test Latent TB,3212
+Fungus Culture,3165
+TSH,2983
+Fluid Culture/GS,2981
+Anaerobic Culture,2967
+Type and Screen,2943
+"Differential, Body FLD",2907
+Blood Culture (2 Aerobic Bottles),2859
+"C Diff Toxin B, Qual Real Time PCR",2798
+"Occult bld, Guaiac Diag",2796
+"Istat CG4, Arterial",2791
+Mycophenolic Acid Level,2785
+"Protein Urine Random, w/Creat",2752
+Free T4,2735
+"Pregnancy Test, Urine",2679
+"Albumin, Ser/Plas",2583
+"Istat EC3, Arterial",2553
+"Protein Total, CSF",2524
+"Glucose, CSF",2524
+Epstein Barr AB Panel,2507
+Rituxan Panel,2392
+"iSTAT CG8, Venous",2374
+Transfusion Svc Prod 2,2296
+"Cell Count and Diff, CSF --Tube #3",2275
+"Cell Count, Body FLD",2243
+Procalcitonin,2203
+Lidocaine Level,2172
+Sirolimus Level,2089
+"IgG, Serum",2023
+AFB Culture Nonsterile,1945
+Cystic Fibrosis Respiratory Culture,1925
+IgG Monitoring,1910
+Celiac Disease Screen,1884
+"Potassium, Ser/Plas",1871
+"LMW Heparin, AntiXA",1843
+Hematocrit,1800
+"Tobramycin Level, Post Dose",1784
+Specific ANA Panel,1756
+HLA Meds/Sensitizing Event Communication (No Blood Draw),1746
+Hemoglobin,1689
+HSV DNA PCR Qual,1672
+"Cell Count and Diff, FLD",1628
+"Amylase, Ser/Plas",1551
+CSF Culture/GS --,1548
+Blood Type Verification,1548
+"Istat CG4, Venous",1495
+Adenovirus Real Time PCR Quant,1483
+HIV Antigen/Antibody Screen,1449
+"Tobramycin Level, Random",1436
+HLA ALLO B FLOW CROSSMATCH,1424
+Allo T Flow Crossmatch,1424
+Carnitine Level Free and Total,1414
+Lactic Acid Level,1396
+Wound Culture,1377
+Unlisted Lab,1370
+Blood Culture --Central catheter,1354
+AFB Culture Sterile,1342
+"TEG, Perioperative",1338
+Heparin Activity Level,1329
+Hepatitis B Surf AB Quant,1313
+"Creatine Kinase, Total",1311
+Auto T Flow Crossmatch,1305
+Auto B Flow Crossmatch,1299
+Biopsy/Tissue with Gram Stain,1273
+NT proBNP,1266
+"Chlamydia/GC, NAAT",1265
+"Amino Acids, Urine",1235
+"Sodium, Urine Random w/Creat",1219
+D Dimer,1193
+S pneumoniae Ab IgG (23 Serotypes),1150
+Cyclosporine Level,1099
+CMV IgG,1075
+T3 Total,1075
+Aspergillus Galactomannan,1067
+Prealbumin,1064
+Luteinizing Hormone Level,1021
+Hepatitis B Surf Ag,1017
+Follicle Stimulating Hormone,1010
+Specific Gravity Refractometer POC,985
+"Calcium Ionized, Ser/Plas",964
+Airborne Allergy Screen,961
+B Cell Phenotyping Profile,932
+"Bilirubin Total, Ser/Plas",923
+Lipid Panel with Direct LDL,908
+Dilute Russell Viper Venom w/Reflex,901
+"Amino Acids, CSF",883
+Flow Cytometry Order,881
+Respiratory Culture/GS,879
+"Urea Nitrogen, Ser/Plas",878
+DIC Panel,859
+"Electrolyte Panel, UR w/ Creat",856
+Lipid Panel w/ Calculated LDL,840
+"Fat Stain, Stool",839
+Haptoglobin,810
+"T Cell Phenotyping, Advanced",802
+Wound Culture/GS,796
+Hepatitis C AB IgG,793
+Theophylline Level,791
+"Organic Acids, Urine",787
+BK Virus PCR Quant,786
+"Zinc, Ser/Plas",785
+Anti Nuclear AB,782
+Anti-Platelet Therapy Response Assessment (Multiplate Method),766
+Hemoglobin A1c,759
+Immunoglobulin Panel,720
+Valproic Acid Level,720
+Herpes Simplex Virus IGG Panel,719
+Varicella Zoster AB IgG,711
+Mint Green Top,711
+"Calcium, Ser/Plas",703
+HLA DSA Screening-No Desensitiz/No IVIG,699
+Stool Culture,698
+HAV Antibody IgG,692
+Lupus Anticoagulant,688
+STR Chimerism,687
+"Istat EG7, Other",671
+Hepatitis Acute Panel,661
+"Calcium Urine, Random w/Creat",658
+"pH, Stool",657
+"Estradiol, LC-MS/MS",654
+Anticardiolipin Ab,651
+Hepatitis B Core AB Total,648
+CSF Protein & Immunofix Electrophoresis,645
+Beta HCG Quant,643
+Human Herpes Virus 6 Quantitative PCR,632
+"ALT, Ser/Plas",629
+Beta 2 Glycoprotein 1,628
+Parathyroid Hormone,627
+"AST, Ser/Plas",625
+Transfusion Svc Prod 3,625
+"Cell Count and Diff, CSF",622
+Beta Hydroxybutyrate,612
+Bilirubin Total & Fractions,609
+Direct Antiglobulin (Broad Spectrum),575
+"Osmolality, Urine",558
+"Amikacin Level, Post Dose",555
+ABO/RH,552
+Cystatin C with Estimated GFR,550
+Troponin I,536
+"Differential, BAL",535
+"Osmolality, Serum",533
+CMV IgM,527
+Calprotectin,524
+"Alpha Feto Protein, Serum",515
+"Pathologist Review, Peripheral Smear",512
+Transfusion Reaction Workup,509
+Ova and Parasites,508
+Anti DsDNA Antibody,505
+"Selenium, Ser/Plas",504
+AFB Culture Respiratory CF,496
+"M Tuberculosis, PCR",494
+"Complement C3, Serum",482
+Transfusion Svc Prod 4,482
+"Reducing Subst, Stool",479
+"Testosterone, Total/Free Pedi",476
+Lavender Top,476
+"CMV DNA PCR Qual, Non Plasma",476
+"Throat Strep Screen, PCR",473
+"Complement C4, Serum",469
+Paraneoplastic Autoantibody Eval Serum,467
+RPR,466
+VZV DNA PCR Qual,458
+Hepatitis A AB IgM,455
+S Pneumoniae Ab IgG (Prevnar 13),454
+"Blood Gases, Capillary",452
+"Cortisol, Serum",450
+Diagnostic Hepatitis B Panel,445
+"IgA, Serum",443
+Specimen Remark,438
+"Copper, Serum",436
+"Iron, Total",436
+KOH Prep,430
+"Vitamin B12, Ser/Plas",430
+Gram Stain,427
+"Albumin, Urine Random, w/Creat",406
+"IgE, Total",401
+HIV 1 RNA PCR Quant,401
+Orthopedic Aerobic Cult w/GS,397
+"Vitamin A, Ser/Plas",397
+Hemoglobin Quant / Fract,389
+"Helicob Ag, Stool",374
+"Creatinine, FLD",369
+Prolactin Level,360
+Bacterial ID by Sequencing,356
+"Electrolyte and Osmolality PNL, Feces",350
+HLA Workup at Time of Transplant,350
+Specific Gravity,345
+HLA Summaries,341
+cycloSPORINE Level,341
+Orthopedic Anaerobic Culture,339
+Voriconazole Level,335
+Factor V Leiden,335
+"IgM, Serum",334
+CMV IgG (Transfusion),334
+Urinalysis Screen/Culture,332
+"Vitamin E, Ser/Plas",332
+Unlisted Chemistry Lab Order,316
+"Vancomycin Level, Unspecified",313
+Herpes Simplex Virus Ab Panel,312
+Phosphatidylserine/prothrombin Antibodies,311
+"Istat EC3, Venous",310
+Platelet Count,307
+EBV DNA PCR Qual,305
+Meningitis/Encephalitis PCR Panel,299
+"Amikacin Level, Random",292
+Bartonella Species AB,292
+VZV IgG IgM Panel,285
+Toxoplasma IgM and IgG,282
+"Blood Gases, Circuit",280
+Prothrombin 20210A,273
+Lab Only Fluid Container,272
+Antibody Identification,270
+HLA Antibody Screening (One Time),269
+"ACT, Istat Kaolin Arterial",268
+HLA Desensitiz/AB Monitoring-Post IVIG,268
+Blood Culture --Peripheral,259
+HLA ALLO B CDC CROSSMATCH,258
+Transfusion Svc Prod 5,256
+"Cocci ID, Serum",255
+"Enterovirus RNA, Qual",255
+Unlisted Lab:,254
+HLA Desensitiz/DSA Monitoring-Post IVIG,253
+Anti Phospholipid Ab Panel,253
+HLA Desensitiz/DSA Monitoring-No IVIG,251
+"Pregnancy Test, Serum",251
+"Cell Count and Diff, CSF --Tube not numbered",251
+Hepatitis C RNA PCR Quant,250
+DAT C3 and Gamma,249
+HLA Desensitiz/AB Monitoring-Pre IVIG,247
+Bordetella Pertussis PCR,246
+HLA Transplant Event Communication,245
+Anti Streptolysin O,244
+Syphilis Treponemal Screen,243
+"Amikacin Level, Pre Dose",243
+Review by Pathologist,241
+Estradiol Level,241
+"Flow Cytometry for Leukemia/Lymphoma, BLD",240
+Von Willebrand Antigen,239
+HLA SCT Evaluation Recipient,237
+Gold Top,236
+Food Allergy Screen,234
+IgA Anti TTG,234
+HLA ALLO T CDC CROSSMATCH,232
+Epstein Barr Nuclear Ag Ab,232
+Creatine Kinase and CKMB,229
+"Histoplasma Antibody, Serum",228
+Red Cell Enzymes,226
+Bronchopulm Asperg Screen,225
+"VMA and HVA, Pediatric, Random Urine",222
+HLA Typing Add On Locus -Recip(Lab Req),221
+Phenytoin Level,220
+Measles IgG,218
+"Potassium Urine Random, w/Creat",217
+TIBC,216
+PPD Reading,214
+Paraneoplastic Autoantibody Eval CSF,214
+"Sodium, Urine Random w/o Creat",213
+Transfusion Svc Prod 6,212
+Methylmalonic Acid Level,211
+Factor VIII Assay,209
+"Istat EC4, Arterial",206
+"Cryptococcal Antigen Lateral Flow, Blood",205
+"Creatinine Clearance, Urine",204
+"Urea Nitrogen, FLD",204
+Reference Report,201
+Pyruvic Acid,199
+"Adenovirus DNA, QL PCR",198
+"POC Clinitek, Urinalysis",198
+"Creatinine, Urine Timed",197
+Epstein Barr Viral Capsid AB IgM,197
+Phenobarbital Level,195
+IgG Subclasses,193
+Kidney Stone Analysis,192
+Antithrombin III Level,192
+"Alpha 1 Antitrypsin, Stool",191
+Folic Acid Level,191
+ANCA,188
+"Urea,Urine Random,w/Creat",187
+Fungus Culture/ KOH,185
+"Cell Count and Diff, CSF --Tube #2",185
+Long Chain Fatty Acids,185
+Lead Level,184
+HLA Desensitiz/DSA Monitoring-Pre IVIG,183
+Platelet Mapping,182
+"Blood Culture, Aero 2",181
+Save CSF in Lab,180
+HLA SCT Confirmatory Recipient,180
+Pro predict TPMT,177
+Fungal ID by Sequencing,176
+"Flow Cytometry for Leuk/Lymphoma, Misc",175
+"Histoplasma AG, Serum",173
+Anti TPO Antibody,171
+"Histoplasma AG, Urine",171
+"Fungal Culture, Blood",170
+"Glucose, FLD",169
+"Parvovirus B19 Ab, IgG and IgM",168
+POCT Gastric pH Test,167
+"Mycoplasma Antibodies, IgG and IgM",166
+"Gentamicin Level, Post Dose",166
+Pneumocystis Stain,164
+Malaria Prep and Antigen Screen,161
+"Creatinine, Urine Random",160
+Unlisted Genetic Test:,159
+"Elastase, Stool",157
+Respiratory Culture,157
+"Gentamicin Level, Pre Dose",155
+HLA Typing Solid Organ Eval Recipient,155
+"Lyme Western Blot, Serum",150
+IVIG In Vitro Inhibition,146
+ANA IFA Screen with Reflex to Titer and Pattern,145
+"TRIGLYCERIDES, FLUID",143
+"WBC for IBD, Stool",143
+Slide Held for Review,143
+Alkaline Phosphatase Total Ser/Plas,142
+Pro predict Enzact,141
+Isohemaglutinin Titer,139
+Blue Top,138
+"Hepatitis B DNA, PCR Quant",137
+"PTH, Intact and Calcium",137
+"Rotavirus Antigen, Stool",137
+Hepatitis B Core AB IgM,136
+RBC Phenotyping,136
+"CD34 Panel, BLD",135
+Unlisted Lab: MRD for Day 8: RN to Draw 5 ml peripheral blood (EDTA) purple Top Tube. DRAW BLOOD PRIOR TO ADMINISTRATION OF ANY CHEMO OR IT MEDS,135
+Transfusion Svc Prod 7,133
+IgG Subclass 4,131
+"Vitamin D, 1,25-Dihydroxy",131
+HLA Deceased Donor Crossmatch/Testing,130
+"Vitamin A and Vitamin E, Ser/Plas",130
+"GAD-65, IA2, Insulin Auto-Ab",129
+AB Screen,129
+"Urea Nitrogen, Urine Timed",129
+HLA Auto Flow Crossmatch,128
+Comp Hepatitis Panel,128
+Legionella Culture,126
+Chlamydia Ab Panel,126
+Anti Sm Antibody,124
+"Protein Total, FLD",123
+"Homocysteine Total, Plasma",121
+Neutrophil Function Oxidative Burst,121
+Anti Thyroglobulin Ab,120
+"Glucose Nonfasting, Ser/Plas",119
+"Parvovirus B19 Real Time PCR, Qual",118
+levETIRAcetam Level,118
+"BK Virus PCR Quant, Plasma",117
+"Enterovirus PCR, CSF",117
+Tetanus Titer,114
+Transfusion Svc Prod 8,113
+POCT Urine Pregnancy,112
+"LDH Total, FLD",110
+VRE Screen,110
+"Protein, Urine Timed",110
+Fungitell B-D-Glucan Assay,109
+Electrolyte Panel,109
+"Cholesterol Total, Ser/Plas",109
+"Encephalopathy, Autoimmune Eval, CSF",109
+Citrate Excretion Urine Random,108
+HHV 6 DNA PCR Qual,107
+Anti-DNase B Titer,107
+"Ceruloplasmin, Serum",107
+CSF Culture/GS --Tube #2,106
+"HSV DNA PCR Qual, Non Lesion",105
+"Istat EG7, Circuit",104
+Human Insulin Antibody,103
+Smooth Muscle AB,102
+pH Gastric,102
+Myeloperoxidase Ab,101
+"Heavy Metals Screen, BLD",101
+Acetaminophen Level,100
+Epstein Barr Viral Capsid Ag IgG Ab,100
+Gastroenteritis Workup,98
+"Urea Nitrogen, Urine Random",98
+Cancer Antigen 19-9,97
+Serum Protein Immunofix  Elect,97
+Beta Strep Throat Screen,97
+CSF Culture/GS,96
+HLA Meds/Sensitizing Event Communication,96
+"Oligoclonal Banding, CSF",96
+Unlisted Lab: Liver Cytosol Auto-Ab-1,95
+"Differential, Synovial Fluid",95
+ICA 512 Antibody,95
+Immune Complex Panel,94
+"Tobramycin Level, Pre Dose",94
+"ACT, ISTAT",94
+"Oxalate Pediatric, Random Urine",92
+Helminthic Ova and Larvae,91
+lamoTRIgine Level,90
+"AFB Culture, Respiratory",90
+Sweat Chloride,90
+Infectious Mono Screen,90
+Soluble IL 2R,90
+Brucella Antibodies,89
+Cryptosporidium EIA,89
+HSV / VZV PCR Qual,89
+"1 25 Dihydroxyvitamin D, Serum (Adult)",89
+West Nile Virus AB,89
+HLA Specimen in Lab Orders - Recipient,89
+Alpha 1 Antitrypsin Phenotype,88
+"Encephalopathy, Autoimmune Evaluation",88
+Carbohydrate Def Transferrin,88
+"Porphyrins, Urine Fract",88
+"Occult Blood, Gastric",87
+"Rheumatoid Factor, Serum",87
+"TRK Flow Cytometry for B-ALL MRD, Bld",87
+"G6PD, Quantitative",86
+"Potassium, Urine Random w/o Creat",85
+M Pneumoniae DNA,84
+"Porphobilinogen Quant, Urine",84
+Proteinase 3 Antibody,84
+Protein S Activity,83
+"Bartonella DNA, Qual PCR",83
+Strongyloides IgG AB,83
+Liv Kid Microsome Ab,83
+CEA,82
+Cancer Antigen 125,82
+"Cell Count, Syn FLD",82
+"Parvovirus B19 DNA, Quant",82
+Levetiracetam Level,80
+HTLV I/II Antibody w/ Reflex,80
+Antibody Titer,80
+"Cryptococcal Antigen Lateral Flow, CSF",80
+"Metanephrines Fract, Urine Random",80
+Mitogens and Tetanus Lymphocyte Proliferation,80
+HLA STR/Chimerism WB,80
+"T Subsets and B Lymphs, FLD",79
+Isospora Cyclospora,79
+AFB Smear,78
+"Donor,HTLV AB I & II",78
+"Complement, Total",78
+PHENobarbital Level,78
+Protein C Activity,77
+"Folate, RBC",77
+Creatinine Clearance,77
+Microsporidium Screen,77
+"Aldolase, Serum",77
+"Chlamydia, NAAT",76
+"Viral Culture, Stool/Rectal",75
+"Neisseria gonorrhoeae, NAAT",75
+Toxoplasma IgG Ab,75
+IMM Neutrophil Cyto Ab Reflex Pan,74
+"Metanephrines, Urine Timed",74
+"Hepatitis E Antibody, IgM",74
+"CD34 Panel, FLD",74
+"Phosphate, Urine Random w/Creat",73
+Diphtheria Titer,73
+"Amylase, Fluid",73
+Adrenocortictropic Hormone Level,72
+TSH Reflex FT4,72
+Wound Deep Surg Asp GS,72
+Rapid HIV 1/2 Antibody,72
+Transfusion Svc Prod 9,72
+"Fecal Fat, Quant",71
+Free T3,70
+"Cell Count and Diff, CSF --Tube #1",69
+ClonoSight MRD,69
+Enterovirus Panel II,68
+Adamts 13 Profile,68
+Giardia Screen,68
+Epstein Barr Virus Early Ag (RD) IgG,68
+L-Asparaginase Activity,67
+"Aldosterone, Serum",67
+Clozapine Level,66
+"AFB Culture, Non - Respiratory",65
+Lyme Disease Ab IgG IgM w/relex,65
+Unlisted Lab: Invitae Arrhythmia and Cardiomyopathy Comprehensive Panel,64
+"BCR-ABL, Non-Blood",64
+"Chloride, Urine Random w/Creat",64
+CSF Protein Immunofix Elect,63
+Electrophoresis and Total Protein,63
+"TRK Flow Cytometry for B-ALL MRD, BM",63
+"Purine and Pyrimidine Panel, Urine",62
+Posaconazole Level,62
+Circuit Ionized Calcium,61
+"AFB Culture, BLD",61
+Platelet Function Screen,61
+Cocci ID with Reflex to CF (Initial Diagnosis),60
+Biotinidase Level,60
+"Vancomycin Level, Post Dose",60
+Procainamide Level and Napa,60
+Insulin Unspecified,60
+"Metanephrines, Fract Free, Plas",60
+Catecholamine Fractionation Free,60
+"Toxoplasma DNA PCR, Qualitative",60
+"Lactic Acid, CSF",59
+PTT Inhibitor Screen,59
+"Culture, Virus",58
+Mono Screen w/Reflex EBV Antibody,58
+Free Light Chains Ratio,57
+Transfusion Svc Prod 10,57
+"Tryptase Level, Serum",57
+Haemophilus Influ IgG,57
+Coxiella Burnetii AB,57
+"Fecal Lipids, Total",57
+"Glucose Fasting, Ser/Plas",57
+"Unlisted Lab: Send out to Mayo Labs, 1 mL royal blue-top vacutainer tube, test code PTSE",56
+"BCR-ABL, BLD",56
+"Glutathione, Whole Blood",56
+Welcome to Packard Children's Hospital,56
+"Lactate, WB",55
+HLA Desensitiz/AB Monitoring-No IVIG,55
+HTLV AB I & II,55
+pH Stool,54
+"Renin Activity, Plasma (Adult)",54
+"BK Virus PCR Quant, Urine",54
+"Istat CG4, Other",54
+GAD AB,53
+Salicylate Level,53
+"C Reactive Protein, High Sensitive",52
+Factor VII Assay,52
+Donor Hepatitis B Surf Antigen,52
+Platelet Aggregation,52
+Pediatric Food Allergy Screen,52
+Alpha Thalassemia PCR,52
+"Liver, Kid Microsome 1 Ab, IgG",51
+"West Nile Virus AB Panel,CSF",51
+IGF Binding Protein-3,51
+"Calcium Urine, Random w/o Creat",51
+ANCA IBD w/Reflex,51
+Newborn Screen,50
+"Diphtheria/Tetanus AB, IGG",50
+Anti Ro Antibody,50
+HLA Deceased Donor O.R. Specimen,49
+"Uric Acid Urine Random, w/Creat",49
+HLA Typing Summary,49
+Donor Hepatitis B Core Antibody,49
+"CD3, BLD",49
+Reverse to Coombs A Cells,49
+Lymph Mitogen and Antigen Scrn,49
+CMV Rule Out,49
+Volatile Screen,48
+Angiotensin Convert Enz Level,48
+EBV NPC PCR Panel,48
+"Magnesium, Urine Random w/o Creat",48
+Factor V Assay,47
+"Triglycerides, Pleural",47
+Insulin-Like Growth Factor 1,47
+Donor West Nile Virus NAT,47
+17a-OH Progesterone,47
+"Protein Total, Ser/Plas",47
+Unlisted Genetic Test: NUDT15/CEP72/TPMT Genotype Panel,47
+"Differential, Apheresis FLD",47
+Protein Electrophoresis,46
+HLA Request for Summary Info. Recipient,46
+Anti La Antibody,46
+Thiamin (Vitamin B1) Level,46
+Bone Marrow Culture/GS,46
+"Magnesium, Urine Random w/Creat",45
+Fungus Cult/KOH,45
+Adenovirus AG,45
+Leflunomide Metabolite Level,45
+von Willebrand Factor Activity,44
+Donor Chagas Scn w/reflex,44
+Toxoplasma IgM Ab,44
+Send Out Flow Immunophenotype,43
+"Albumin, FLD",43
+"Protein C AG, Total",43
+Mitogen Induced Lymph Prolifer.,43
+"Cell Count, Apheresis FLD",42
+Lithium Level,42
+"Coxsackie B AB, Serum",42
+Factor IX Assay,42
+Fragile X,42
+Adenosine Deaminase,42
+"Cell Count and Diff, CSF --Tube #4",42
+Unlisted Cytogenetics Lab Order,42
+Isospora Microscopic Exam,41
+"Sodium, FLD",41
+"Adenovirus PCR, Stool",41
+"Lactoferrin, Fecal",41
+"Electrolyte and Osmolatity PNL,Feces",40
+Diag Hepatitis B Panel,40
+Flecainide Level,40
+Unlisted Lab: Day 29 MRD: 2 mL of BM in EDTA  (Purple Top),40
+Transfusion Svc Prod 11,39
+"Copper, Urine",39
+Donor HIV AB,39
+Lacosamide Level,39
+Unlisted Lab: Aspergillus PCR,39
+PTH Related Protein,38
+OXcarbazepine Metabolite Level,38
+"Protein S AG, Total",38
+"Vitamin B6, Total Plas",38
+"Phosphorus, Urine Random w/o Creat",38
+Mumps Antibody IgG,38
+"Bile Acids, Total",37
+"Allergy Screen, Nuts",37
+Reptilase Time,37
+Dehydroepiandrosterone Sulfate,37
+"Cystine Quant, Urine",37
+"Flow Cytometry T Cell Development, Blood",36
+"Sodium, Urine Timed",36
+T Cell Clonality PCR,36
+"Coxsackie A AB, Serum",36
+Istat Creatinine,36
+"Borrelia Burgdorferi Panel, CSF",36
+Donor Hepatitis C Ab,36
+OR LPCH Stat Gram Stain,36
+Donor Syphilis Screen,36
+Unlisted Lab: Mayo labs PTSE,35
+HCG Tumor Marker,35
+"Calcium, Urine Timed",35
+"Acylglycines Quant, Urine",34
+Anaerobic Short Battery,34
+"Fungus Culture Skin, Nail",34
+Norovirus Antigen,33
+SCT Donor Match Grade Detail,33
+Anti GAD 65 Antibody,33
+"Cell Count and Diff, Syn FLD",33
+Lab Unlisted,32
+"Unlisted Lab: Send out to Viracor, Test Code 4900, Isavuconazole level (LC-MS/MS)",32
+NMO/AQP4-IgG CBA,32
+"AML Prognosis Assay, Non-Blood",32
+Unlisted Genetic Test: Heritable pancreatic disease testing at Cincinnati Children's - Pancreas Panel,32
+Unlisted Lab: Detection of Botulinum Neurotoxin and Culture for Neurotoxigenic Clostridium Species,32
+Unlisted Lab: Pleximmune,32
+Enterovirus PCR,32
+"Vitamin K1, Ser",32
+"Creatine, Ser/Plas",31
+C Peptide,31
+"Everolimus, Blood",31
+"Chloride, Urine Random w/o Creat",31
+SCT Donor Match Grade Summary,31
+"Erythropoietin, Serum",31
+Growth Hormone,30
+"Viral Culture, Respiratory",30
+Misc Viral Sendout,29
+Unlisted Molecular Diagnosis Lab Order,29
+Helicobacter Pylori Ab,29
+Unlisted Immunology Lab Order,29
+"Eosinophils, Urine",29
+Thiopurine Metabolites,29
+Donor CMV Antibody Living,29
+HLA Platelet Compatibility,28
+Malaria Prep,28
+"Beta 2 Microglobulin, Urine",28
+"VZV DNA PCR Qual, Non Lesion",28
+IgF Binding Protein-3,28
+"iSTAT CG8, Other",28
+Blood Culture Catheter,27
+"Soluble Liver Ag Ab, IgG",27
+ClonoSight ID,27
+"Vitamin B1, Ser/Plas",27
+Toxoplasma PCR,27
+"HTLV I/II, Confirmation Assay",27
+G-Tube Teaching (Kids Health),27
+"JC Virus DNA, PCR",27
+Lipid with Calculated LDL,27
+"Lysozyme, Plasma",27
+Genital Culture,27
+Green Top,27
+Topiramate Level,27
+Blood Culture Peripheral,26
+"Vitamin C, Plasma",26
+Alpha 1 Antitrypsin,26
+Rubella IgG Antibody,26
+Fructosamine Level,26
+"Istat EG7, Mixed Venous",26
+"Allergy Screen, Molds",26
+HLA STR/CHIMERISM (Bone Marrow),25
+Dengue Fever AB,25
+Thyroglobulin Level,25
+"Gentamicin Level, Random",25
+Strongyloides Culture,25
+Miscellaneous Processing,24
+Insulin Like Growth Factor 1,24
+"Testosterone, Total/Free Adult",24
+"Traditional Glucose Tolerance, 2 Hour",24
+Cystic Fibrosis Mutations Study,24
+HTLV 1/II DNA Qual,24
+"Fungal Culture,Blood",24
+"HSV 1/2 PCR, Non CSF",24
+HLA IVIG In Vitro Inhibition Assay,24
+G6PD Screen,24
+Unlisted Lab: ZNT8 autoantibody,24
+Unlisted Lab: Coccidioides antigen to Mayo,24
+"Oxalate, Timed Urine",24
+"Renin Activity, Plasma",23
+"Polio AB, Neutralization",23
+Sepsis Protocol Lactate,22
+Leukemia/Lymphoma Immuphentype,22
+Von Willebrand Factor Activity,22
+"Cocci ID, CSF",22
+Anti Dnase B Titer,22
+"Ribosome P Ab, IgG, Serum",22
+"Mitochondrial Ab M2, Serum",22
+Itraconazole Level,22
+"Histone Ab, IgG",22
+"Viral Culture, Tissue",21
+Zonisamide Level,21
+Mycoplasma Culture Resp,21
+Atrial Electrocardiogram,21
+Anti Centromere Antibody,21
+"ANA by IFA, IgG",21
+Mitogen-Induced Lymphocyte Proliferation,21
+Methemoglobin Level,21
+Flow Cytometry Paroxysmal Nocturnal Hemoglobinuria,21
+CFTR Diagnostic Sequencing,21
+Cardiac Risk Relative,21
+Unlisted Lab: National Jewish Center C4a,21
+"Heme-STAMP NGS Panel, Non-Blood",21
+Inhibin B,20
+Echovirus Ab,20
+Cyclic Citrullin Peptide AB,20
+"Flow Cytometry for Leukemia/Lymphoma, BM",20
+"Hgb F, Quant",20
+Unlisted Lab: Beta 2 Glucan,20
+Transferrin,20
+"Hemoglobin, Plasma",20
+"Antithrombin Antigen, Plasma",20
+"Citrate Excretion, Timed Urine",20
+"Crystal Analysis, Syn FLD",19
+"Glucose, Urine Random w/o Creat",19
+"Hemoglobin, Free",19
+Thiamine (Vitamin B1) Level,19
+CSF Culture/GS --Tube not numbered,19
+NK Function,19
+"BCR ABL Quant, BLD",19
+Specimen Processing,19
+Fibrin Degradation Products,19
+Von Willebrand Multimers,19
+Interleukin 6 Level,19
+Digoxin Level,19
+Aspergillus Ab Precipitins,18
+"Myoblobin, Urine",18
+Urine Protein Immunofix Elect,18
+inFLIXimab Qn with Reflex to Ab,18
+Reverse to Coombs B Cells,18
+EKG 12 Lead High V1/V2,18
+Donor HIV-1/HBV/HCV NAT,18
+Chlamydia GC Molec Detec,18
+HEPATITIS ACUTE PANEL,18
+"West Nile Virus RNA, RT PCR",18
+"Orotic Acid, Urine",18
+"CD3, FLD",18
+EMA for Spherocytosis,18
+"Inhibin A, Dimer",17
+"Allergy Screen, Seafood",17
+"Allergy Screen, Fish",17
+Factor X Assay,17
+"Uric Acid, Urine Random w/o Creat",17
+"Aluminum, Serum",17
+"Toxo Ab Transplant Pnl, Serum",17
+"Traditional Glucose Tolerance, 30 Minutes",17
+Hepatitis C (HCV) PCR w/Ref Genotyping,17
+Lamotrigine Level,17
+"Neurotransmitters, CSF",17
+"Uric Acid, Urine Timed",17
+Factor XI Assay,17
+"TREC, Immune Reconstitution",17
+"Cholesterol, Fluid",17
+JC Virus Antibody Index w Reflex,16
+"Traditional Glucose Tolerance, 1 Hour",16
+Unlisted Lab: anti-liver cytosol antibody,16
+Unlisted Genetic Test: Invitae epilepsy panel,16
+"Confirmed Drug Abuse Survey, Urine",16
+"Protein, Urine Random w/o Creat",16
+"Unlisted Lab: Test Name: Troponin T, Serum. Test ID: TPNT.",16
+Unlisted Lab: Send out molecular test to Hematologics for CBFA2T3-GLIS2 quant PCR - 2ml EDTA (requisition to be provided),16
+Unlisted Genetic Test: Unlisted Genetic Test: clonoSEQ; MRD for ALL,16
+pH by Meter,16
+"Traditional Glucose Tolerance, Fasting",16
+Unlisted Lab: Send out lab to Mayo Medical Laboratories,16
+Alkaline Phosphatase Isoenzymes,16
+Unlisted Lab: ZnT8 antibody,16
+Unlisted Lab: send out to Baylor - CSF metabolomics/ global MAPS,16
+"Factor VIII Inhibitor, Human",16
+"HIV 1 DNA PCR, Qual",16
+Unlisted Lab: Clobazam level,16
+Unlisted Lab: Hematologics quantitative PCR for CBFA2T3-GLIS2 fusion,16
+Unlisted Lab: urine succinylacetone,16
+"Protein S, Free",16
+Unlisted Lab: ZnT8 antibody (Zinc Transporter Antibody),16
+Unlisted Lab: Comprehensive Myositis Autoantibody Profile,16
+Unlisted Lab: Emory cholestasis panel,16
+Estradiol Level (Adult),16
+"Hepatitis E Antibody, IgG",16
+"Cocci ID, Serum (Initial Diagnosis)",15
+"Cancer Somatic Mutation Pnl, Blood",15
+HLA Crossmatch Summary,15
+HLA Miscellaneous Test,15
+Dehydroepiandrosterone,15
+Unlisted Genetic Test,15
+"Bilirubin Total, FLD",15
+Ethosuximide Level,15
+Ibuprofen Level,15
+Arbovirus AB Panel,15
+CD4 Count,15
+Giardia Screen w/ Reflex O P,15
+Unlisted Lab: C3a,14
+Unlisted Lab: Please collect 1 ml blood into a gold top tube (SST).,14
+Unlisted Lab: GeneDx Comprehensive Cardiomyopathy Panel (test #694),14
+"PML/RARA, Quantitative, Non-Blood",14
+Unlisted Lab: C4a,14
+"Porphyrins, Fecal Fract",14
+Unlisted Lab: Peritoneal fluid phosphate,14
+Neutrophil and HLA Antibody Screen,14
+"VMA and HVA, Pediatric, Urine",14
+Unlisted Lab: Cunningham panel,14
+HLA Wait List Status Change,14
+Unlisted Lab: Super Coombs,14
+"Unlisted Lab: Aspergillus real- time PCR panel from the blood to Viracor. Test code 8900, CPT 87798",14
+Unlisted Lab: P155/140 by Western Blot,14
+Unlisted Lab: Phosphorus - peritoneal fluid,14
+Unlisted Lab: whole exome sequencing,14
+Unlisted Lab: Humira (Adalimumab) level,14
+"Aspergillus Precipitins ID, Serum",14
+Unlisted Coagulation Lab Order,14
+Unlisted Lab: Gene DX Epilepsy Panel,14
+Cryoprotein,14
+IBD SGI Diagnostic,14
+Factor II Assay,14
+"Unlisted Lab: 1,3 beta D glucan",14
+Heme-STAMP NGS Panel,14
+Thyroxine Total,14
+Cholesterol HDL/Tot,14
+Unlisted Lab: Peritoneal fluid phosphorus,14
+"Unlisted Lab: Aspergillus PCR, send to FOCUS (code 87798)",14
+Unlisted Lab: Mayo Clinic Autoimmune Encephalitis Panel,14
+Unlisted Lab: aspergillus PCR,14
+"Myeloid Panel, Blood",13
+"Magnesium, Urine Timed",13
+CSF Culture/GS --Tube #1,13
+Anti RNP Antibody,13
+Direct LDL Cholesterol,13
+Immune Cell Function,13
+Unlisted Hematology Lab Order,13
+Chromogranin A,13
+"Cocci CF AB, Serum",13
+"Legionella Antigen, Urine",13
+Soluable Transferrin Receptor,13
+"Lipase, Fld",13
+Factor XIII Assay,13
+"Calcitriol (1,25-Dihydroxyvitamin D)",12
+"N Methyl D Aspartate Receptor Ab, IgG",12
+Androstenedione,12
+Pancreatitis Panel,12
+"Vitamin B3, Ser/Plas",12
+Phosphatidylserine Ab Panel,12
+Valproic Acid Level Free and Total,12
+HIV Type 1 and Type 2 Antibody,12
+Inflammatory Bowel Disease Panel,12
+"Heme-STAMP NGS Panel, Blood",12
+Miscellaneous Special Coagulation,12
+"BCR ABL Quant, Non-Blood",12
+"ALPHA FETOPROTEIN, TOTAL&L3%",12
+"Calreticulin, Blood",12
+"Zika Virus Qualitative, RT-PCR, Blood",12
+Heparin Induced Thrombocytopenia Panel,12
+"Phosphorus, Urine Timed",12
+Oxcarbazepine Metabolite Level,12
+"Copper, 24Hour, Urine",12
+"Minimal Residual Disease, BM",12
+"Adenovirus DNA, PCR Quantitative",12
+"Cortisol Level, Post Op",12
+CAH Ped Profile 6 Comp SCN,12
+"5 OH Indoleacetic AC, Urine",12
+CD4 CD8 Panel,11
+Creatin Kinase and CKMB,11
+Hemochromatosis Genotyping Analysis,11
+"Drug Screen, Prescrip/OTC",11
+ABO Discrepancy,11
+"Occult bld, Guaiac Colorectal Scn",11
+"Pyruvic Acid, CSF",11
+Worm ID,11
+Zinc Transporter 8 Antibody,11
+"Dengue Virus RT-PCR, Blood",11
+HLA Addl Specimen Recipient-Ab Scn/XM,11
+"Electrolyte Panel, UR",11
+"AFP, CSF",11
+"Fatty Acids, Free",11
+Neuron Antibody,10
+Factor XII Assay,10
+"Angiotensin Covert Enz, CSF",10
+Malaria Screen by Nucleic Acid Amplification,10
+Anti Neutrophil Antibody,10
+"Heavy Metals Panel, Urine",10
+Blood Parasite Exam (not malaria),10
+"Bicarbonate, Urine",10
+"Hgb S, Quant",10
+"Pediatric Testosterone, Total",10
+GQ1B AB,10
+"Herpes Virus 6 IgG IgM, AB",10
+"Leptospira Ab, IgM",10
+Busulfan Start of Infusion + 5 Hours,10
+"Bile Acids, Fractionated and Total",10
+Ristocetin Co-Factor,10
+HLA Prospective Crossmatch Testing,10
+HIV-1/2 Antibody Differentiation,10
+"5 Methyltetrahydrofolate Level, CSF",10
+Donor HIV1 HIV2 AB Scn w/reflex,10
+"Chloride, FLD",10
+"HCG Beta-Subunit QN, CSF",10
+"GBM Antibody, IgG",10
+Fluconazole Level,10
+PT Inhibitor Screen,10
+Anti-Mullerian Hormone Level,9
+Neuron Spec Enolase,9
+AML Prognosis Assay,9
+APC Resistance Profile,9
+"Hematocrit, Fluid",9
+"Istat CG4, Circuit",9
+Busulfan Start of Infusion + 6 Hours,9
+Cold Panel Screen,9
+"Istat CG4, Mixed Venous",9
+C4a Level,9
+Busulfan End of Infusion,9
+"Viral Culture, Fluid",9
+Humoral Immunity Panel,9
+Busulfan 15 Min Post Infusion,9
+Busulfan Start of Infusion + 4 Hours,9
+"Complement C4, Functional",9
+GC Culture Screen,9
+Mycoplasma Antibody IgG IgM,9
+"Myelin Basic Protein, CSF",9
+"Potassium, Urine Timed",9
+"Porphyrins, Serum",9
+Hypersensitivity Pneumonitis Pnl,9
+Drug Abuse Survey 9 w/Confirmation,9
+Unlisted Genetic Test: Hereditary Myeloid Malignancy and Inherited Bone Marrow Fiailure Panel (UChicago),8
+Unlisted Lab: Hold for possible confirmatory lead testing wth the state lab.  Draw in Royal blue top tube and save at 4 degrees C in lab.,8
+Unlisted Lab: 19727 quest HHV7,8
+Unlisted Lab: T cell degranulation assay to Cincinnati Children's.  See requisition.,8
+Unlisted Lab: MRD peripheral blood to UW send out. CRA to bring requisition. 3-4 mL in purple (EDTA). Draw during day shift,8
+Unlisted Genetic Test: Peripheral blood MRD day 8 Adaptive-NGS,8
+Unlisted Genetic Test: Liver gylcogen storage disorder panel (sendout to Invitae),8
+Unlisted Lab: Invitae Epilepsy panel,8
+Unlisted Lab: Pancreatic enzyme determination to KaleidaHealth Buffalo,8
+Unlisted Lab: Porphobilinogen deaminase (PBGD) to Mayo.  See attached form for instructions.,8
+"Unlisted Lab: ENS1 Encephalopathy, Autoimmune Evaluation, Serum - Mayo Clinic",8
+Unlisted Lab: Sendout MAYO Test ID: ENC1; Neurology Specialty Testing REQ (T732),8
+Unlisted Lab: Pleximmune Test,8
+"Unlisted Lab: Send-out to Cincinnati Children's: TMA Complement Panel. 1mL serum in red-top tube, spun, separated, frozen within 2 hours of collection; ship on dry ice.",8
+Unlisted Lab: Aspergillus PCR (Quest test code 18873),8
+Unlisted Lab: Please send cocci antgent to Mayo/Mirvista,8
+Unlisted Genetic Test: 3-5ml in EDTA  (provided). Deletion-duplication test SLC29A3. Order requisition in chart.,8
+Unlisted Lab: TH17 Enumeration 2-3ml sodium heparin tube,8
+Unlisted Lab: Dissacharidase Assay Intestinal biopsy,8
+Unlisted Lab: C3G Functional Panel to be sent to University of Iowa Lab,8
+Unlisted Genetic Test: Emory neonatal cholestasis panel.,8
+"Unlisted Lab: Coccidioides Quantitative Ag EIA, MiraVista Diagnostics",8
+Unlisted Lab: Ganglioside antibody panel,8
+Unlisted Lab: Perforin / Granzyme B,8
+Unlisted Lab: urine coccidiodes antigen send out to Mira Vista,8
+Unlisted Genetic Test: Machaon Diagnostics aHUS Genetic Panel. Please draw 3mLs in EDTA tube.,8
+Unlisted Lab: serum oxalate,8
+Unlisted Genetic Test: Very long chain fatty acids,8
+Unlisted Lab: Aspergillus PCR for speciation on BAL fluid,8
+Unlisted Lab: Please draw red plain or green top heparin tube and send for clobazam level,8
+"Unlisted Lab: Send-out to Cincinnati Children's: SC5b-9 (MAC Complex). 0.5mL plasma in EDTA, spun, separated, frozen within 2 hours of collection; ship on dry ice.",8
+Unlisted Lab: Urine carbohydrate sendout to Mayo Clinic,8
+Unlisted Lab: Fungal Isolator Culture,8
+"Unlisted Lab: Porphyrins, feces to Mayo.  See attached form for instructions.",8
+Unlisted Lab: 11 beta prostaglandin F2 alpha,8
+Unlisted Lab: CMV Stool PCR,8
+Unlisted Lab: Measles IgG and IgM from CSF,8
+Unlisted Lab: Cincinatti Childrens: Pancreatis Panel 10 gene,8
+Unlisted Lab: ZNT8 (Zinc transportor 8),8
+Unlisted Genetic Test: Hereditary Sarcoma Panel,8
+Unlisted Lab: Urine sample - Mayo send out test ID: CHOU,8
+Unlisted Lab: Invitae cardiomyopathy and neuromuscular genetic test,8
+Platelet Antibody ID Panel,8
+Unlisted Genetic Test: Atypical HUS genetic panel to send to Machaon Diagnostics,8
+Unlisted Lab: Pleximmune: Send 5mL NA heparin mint top tube with paperwork to lab,8
+"Unlisted Lab: Perforin/granzyme B, CD107a functional testing, to be sent to Cincinatti lab along with NK Function",8
+Unlisted Lab: 3 mL purple top,8
+Unlisted Lab: NP swab for enterovirus,8
+"Unlisted Lab: Porphobilinogen Quant, Urine RANDOM",8
+Unlisted Lab: Invitae epilepsy genetics panel. 3cc whole blood in purple top tube.,8
+"Unlisted Lab: MOGFS: Myelin Oligodendrocyte Glycoprotein (MOG IgG1) Fluorescence-Activated Cell Sorting (FACS) Assay, Serum",8
+Unlisted Lab: Cocci antigen,8
+Unlisted Lab: aHUS Genetic Panel,8
+Unlisted Lab: Autoimmune encephalopathy panel-ENS1,8
+Unlisted Lab: CD107a - Cincinnati Children's Diagnostic Immunology Lab (see accompanying lab requisition form for complete send out information,8
+Unlisted Lab: IgG index,8
+"Unlisted Lab: Adenosine Deaminase, Pleural Fluid , Order Code : 12721R",8
+Unlisted Lab: Zinc transporter 8 antibody,8
+Unlisted Genetic Test: Familial Hypertriglyceridemia NGS Panel,8
+Unlisted Genetic Test: Pancreas Panel by Next-Generation Sequencing at Cincinatti Children's,8
+"Unlisted Genetic Test: Custom Panel, send out to Prevention Genetics",8
+Unlisted Genetic Test: Hereditary cancer panel,8
+Unlisted Lab: Focus Diagnostics Rickettsial Panel,8
+"Unlisted Lab: C2 level by RID sent to Advanced Diagnostic Laboratories National Jewish Health, Complement Laboratory. Test code: C2L",8
+Unlisted Lab: anti liver cytosol antibody (LC-1),8
+Unlisted Genetic Test: Whole exome sequencing to Baylor. Test requisition form scanned and in chart.,8
+"Unlisted Lab: 10mL peripheral blood in EDTA, keep at room temperature, to be sent to send out lab with requisition form (Repeat Dx) in green binder",8
+Unlisted Lab: Clobazam serum level. ARUP test code: 2008597,8
+"Unlisted Lab: Aspergillus PCR sendout, EDTA lavender tube 3 ml of whole blood",8
+Unlisted Lab: bone marrow to UW for MRD,8
+"Unlisted Genetic Test: GeneDx send out lab test # 941 hereditary spastic paraplegia panel, please draw 2-5 mL Blood - Lavender Top Tube and send with requisiton form provided",8
+Unlisted Genetic Test: MTHFR,8
+Unlisted Lab: MRD for Day 8 (please draw on 10/14): RN to Draw 5 ml peripheral blood (EDTA) purple Top Tube. DRAW BLOOD PRIOR TO ADMINISTRATION OF ANY CHEMO OR IT MEDS,8
+Unlisted Lab: calcium,8
+Unlisted Lab: Blood sendout to Mayo - ENS1,8
+"Unlisted Lab: Aminolevulinic Acid, Urine RANDOM",8
+"Cocci CF AB, Serum (Follow-up Only)",8
+Unlisted Lab: Test ID: ENC1,8
+"Unlisted Genetic Test: Bartter Syndrome Genetic Testing through CTGT, CPT 5393.",8
+Unlisted Genetic Test: Send-out Cincinnati Children's: CD107a Mobilization (NK Cell Degran),8
+"Unlisted Lab: Urine bile acids, send out to Cincinnati Children's.",8
+Unlisted Lab: ZnT8,8
+"Unlisted Lab: Add-On TesT for GeneDx: IKBKG (exon level coverage of exons 1 and 2, the remainder of the gene is large events only",8
+"Histoplasma Antigen, CSF",8
+Unlisted Lab: Send out to University of Chicago: Comprehensive congenital hyperinsulinsim panel,8
+Unlisted Lab: CDS1 panel,8
+Unlisted Lab: ZnT8 (Zn Transporter),8
+"Unlisted Genetic Test: Prevention Genetics Custom Gene Panel CPT Codes: 81408x1, 81479x4, 81479x5.  ICD-10 code: Q04.6.",8
+Unlisted Genetic Test: CSF neopterin. Sendout to MNG NC02,8
+carBAMazepine Level,8
+Unlisted Lab: Anti-liver cytosol (anti-LC1),8
+Unlisted Lab: Invitae Congenital Heart Disease Panel,8
+Unlisted Lab: To be sent to Invitae for inherited arrhytmia and cardiomyopathy panel. Paper order to be delivered with sample,8
+"Unlisted Lab: mayo, test code FCLBZ",8
+Unlisted Lab: Send out Specialty Lab to Kaleida Health Women and Children's Hospital Buffalo Pancreatic Enzyme Analysis,8
+Unlisted Lab: ZNT8 antibody --zinc transferase antibody as part of AI diabetes workup,8
+Unlisted Lab: Mayo Medical Lab ENCEC - CSF encephalopathy autoimmune panel,8
+Unlisted Lab: Cincinatti Children's CD107a,8
+Unlisted Lab: CSF EBV Quantative PCR,8
+Unlisted Lab: breakage studies for fanconi anemia to be sent to inpatient lab,8
+Unlisted Genetic Test: GeneDx: Comprehensive Brain Malformation Panel  (Test code 691),8
+Unlisted Genetic Test: NGS peripheral blood MRD,8
+inFLIXimab Antibodies,8
+Unlisted Genetic Test: Rhabdoid tumor panel sent to Invitae,8
+Unlisted Genetic Test: TP53 Genetic Test through Invitae,8
+Unlisted Lab: Tacrolimus level,8
+Unlisted Lab: Mayo panel ENC1,8
+Unlisted Lab: Lyme PCR from CSF,8
+Unlisted Lab: Onfi serum level,8
+Unlisted Genetic Test: send out test to iowa (kidney seq),8
+Unlisted Genetic Test: S-C5B9 at Machaon Diagnostics,8
+Filaria IgG4 Antibody,8
+Unlisted Genetic Test: Invitae Gene Panel PR01101.05 and PR01106.02. ICD-10 Code: Z15.09,8
+Unlisted Lab: Mayo autoimmune encephalopathy panel (test code ENS1),8
+Unlisted Lab: Day 8 peripheral blood MRD send out to University of Washington Clinical Lab,8
+Unlisted Lab: CD33 Single Nucleotide Polymorphism Analysis for Gemtuzumab Ozogamicin,8
+Unlisted Genetic Test: Peripheral blood MRD to Adaptive,8
+Unlisted Lab: DAT NEGATIVE HEMOLYTIC ANEMIA EVALUATION,8
+Unlisted Genetic Test: Peripheral blood MRD for NGS,8
+Unlisted Lab: 5mL peripheral blood in EDTA,8
+Unlisted Genetic Test: clonoSEQ Next Generation Sequencing,8
+"Unlisted Genetic Test: Blueprint genetics. Order ID 63270, test code PU0501. Please obtain 2ml blood in EDTA tube. Order form to be with sample with DOB, name, MRN",8
+Unlisted Lab: bone marrow to UW from MRD,8
+Unlisted Lab: MMP7,8
+Unlisted Genetic Test: CSF 5- Methyltetrahydrofolate send out to MNG. NC01,8
+Unlisted Genetic Test: Craniosynostosis code 5087; Draw 2-3 ML in purple with requisition attached,8
+"Unlisted Lab: Toxoplasmosis IgM- please sent to PAMF, do not do in house test",8
+Unlisted Lab: Invitae Congenital Heart Diseease and Heterotaxy Panel,8
+Unlisted Genetic Test: hereditary cancer panel,8
+"Unlisted Lab: SIALIC ACID (CSF) SENDOUT TO MNG LABS, TEST CODE NC07",8
+"Unlisted Lab: NEUROTRANSMITTER METABOLITES (5HIAA, HVA, 3OMD) (CSF), sendout to MNG labs, test code NC04",8
+"Unlisted Lab: PYRIDOXAL 5'-PHOSPHATE (CSF), SENDOUT TO MNG LABS, TEST CODE NC05",8
+"Unlisted Lab: SUCCINYLADENOSINE (CSF), SENDOUT TO MNG LABS, TEST CODE NC06",8
+"Unlisted Lab: 5-Methyltetrahydrofolate (CSF) , sendout to MNG labs, text code NC01",8
+"Unlisted Lab: Mayo autoimmune encephalitis panel, test code ENC1. sendout to Mayo labs",8
+"Unlisted Lab: Unlisted Lab: SC5b-9 Level (MAC), sendout",8
+Unlisted Lab: Mayo panel ENS1,8
+Unlisted Lab: 3mL in EDTA x2 (total of 6mL separated into 2 tubes) - send out to Invitae,8
+Unlisted Lab: Anti Enteroctye Antibody from Children's Hospital of Philadelphia,8
+"Unlisted Genetic Test: Creatine Panel, send out to Baylor Laboratories",8
+"ACT, Istat Kaolin Other",8
+Unlisted Lab: HemePath to Univ of Washington (MRD Day 29),8
+Unlisted Lab: epilepsy genetic testing; sendout to invitae test code 03401,8
+Unlisted Lab: tracking test,8
+Unlisted Lab: coccidioidomycosis antigen from the urine to miravista laboratory,8
+Unlisted Lab: Mayo Clinic  Autoimmune Encephalitis Panel (from serum) Test ID: ENCEC,8
+Unlisted Genetic Test: Peripheral blood MRD for Adaptive NGS,8
+Unlisted Lab: onfi serum level,8
+Unlisted Lab: MTHFR mutation,8
+Unlisted Genetic Test: Cincinnati Children's Heritable Pancreatic Disease Testing,8
+Unlisted Genetic Test: Dual Genome Panel by Massively Parallel Sequencing (BCM-MitomeNGSSM)- send to Baylor Genetics with paper requisition,8
+Unlisted Lab: phenylbutyrate metabolite analysis- blood; send out to Baylor,8
+Unlisted Lab: phenylbutyrate metabolite analysis; urine; send out to Baylor,8
+Unlisted Genetic Test: C3 Glomerulopathy Genetic Panel (Machaon Diagnostics),8
+Sex Hormone Binding Globulin,8
+Unlisted Lab: SERUM - Autoimmune encephalopathy panel to Mayo Clinic.  Test ID ENS1.  NEEDS 4 mL serum in RED TOP TUBE,8
+Unlisted Genetic Test: NUD/CEP/TMPT Panel,8
+Unlisted Genetic Test: send out test to Prevention Genetics,8
+Unlisted Lab: zinc transporter ab (ZNT8),8
+Unlisted Lab: 3-5mL Na Green Top for Pleximmune,8
+Unlisted Lab: perforin sendout to cincinnati 1ml pink tube,8
+Unlisted Genetic Test: Invitae Epilepsy Panel- sendout,8
+Unlisted Lab: Rickettsia Antibody Panel,8
+Unlisted Genetic Test: Craniosynostosis NGS Panel ( Sequencing and del/dup) to CTGT,8
+Unlisted Lab: ultrasensitive DAT (super coombs),8
+Unlisted Genetic Test: Invitae Cancer panel order #01101,8
+Unlisted Lab: CSF sendout to Mayo - ENC1,8
+Unlisted Lab: ganglioside antibody panel to ARUP 0051033,8
+Unlisted Lab: anti-ganglioside ab panel: mayo GM1B,8
+Unlisted Lab: CSF send out to Mayo - ENC1,8
+Unlisted Lab: confirmatory testing for THC and Benzodiazepine,8
+Unlisted Lab: soluble CD163 1ml pink top tube sendout to Cincinnati,8
+"Unlisted Lab: Coccidioides Quantitative Ag EIA, MiraVista Diagnostics.",8
+Unlisted Lab: Creatine and Guanidinoacetate (Urine),8
+"Unlisted Lab: Lymphocytic Choriomeningitis (LCM) Virus Antibodies, IgG & IgM",8
+Unlisted Lab: Karius test,8
+Unlisted Genetic Test: Cincinnati Children's molecular genetics,8
+Unlisted Lab: calcium level on peritoneal fluid,8
+Unlisted Lab: arborvirus (Mayo ID: ABOPC),8
+Unlisted Lab: Comprehensive Myositis Autoantibody Profile to Oklahoma Medical Research Foundation.  MINIMUM 2 mL serum,8
+Unlisted Genetic Test: clonoSEQ; MRD for ALL,8
+Unlisted Lab: Dried Blood Spot- galactocerebrosidase; send out to Greenwood Genetic Center,8
+Unlisted Genetic Test: Hereditary Cancer Panel,8
+Unlisted Lab: EBV DNA PCR send out to Quest.,8
+"Unlisted Lab: Urine random Mercury level, HGRU",8
+Unlisted Lab: Urine aminolevulinic acid to Mayo clinic.  See attached form.,8
+Unlisted Lab: WashU Peripheral Neuropathy Panel,8
+Unlisted Lab: Please send to MAYO test code MYCO,8
+Unlisted Lab: dissacharidase testing,8
+Unlisted Lab: ClonoSeq,8
+Unlisted Lab: Biotin (B7) - Red top tube (full tube of blood needed)  - sendout to Mayo,8
+Unlisted Lab: Zinc Transporter 8 Ab,8
+Unlisted Lab: 3-5mL Na green top for pleximmune test,8
+Unlisted Genetic Test: foundation one heme (STAT),8
+Unlisted Lab: Guinea Pig Epithelia (e6) IgE [2606X],8
+Unlisted Lab: invitae bone marrow failure PR05301.01,8
+Unlisted Lab: ISAC panel,8
+"Unlisted Lab: Encephalopathy, Autoimmune Evaluation, Spinal Fluid",8
+"Unlisted Lab: Anti-Myelin Oligodendroccyte Glycoprotein , serum",8
+"Unlisted Lab: Aquaporin4, serum",8
+Unlisted Genetic Test: Send out tacrolimus level to confirm critically high level,8
+Unlisted Lab: Autoimmune encephalitis panel (to be sent to Mayo),8
+Unlisted Genetic Test: Developmental & Neurogenetics Laboratory,8
+Unlisted Lab: Invitae epilepsy panel; please keep Invitae requisition form with sample,8
+"Unlisted Lab: Circulating Immune Complexes (C1q-binding and C3d) sent to Advanced Diagnostic Laboratories National Jewish Health, Complement Laboratory. Test code: CIC",8
+Unlisted Genetic Test: Unlisted Genetic Test: clonoSEQ; possible new diagnosis of ALL,8
+Unlisted Lab: CD40L to Cincinnati,8
+Unlisted Lab: anti-cytosol antibody,8
+Unlisted Lab: ZnT8 send out lab,8
+Unlisted Genetic Test: Invitae Arrhythmia and Cardiomyopathy Comprehensive Panel; CPT 02101,8
+"Unlisted Lab: Folate metabolites, CSF",8
+Unlisted Lab: Clobazam Level,8
+Unlisted Lab: fk506 confirmation,8
+Unlisted Lab: GeneDx test 109 - Shwachman Diamond,8
+Unlisted Lab: serum test for enterovirus,8
+Unlisted Lab: PID panel Invitae; please send with paperwork,8
+"Unlisted Lab: UW Hematopathology - 3ml in EDTA, paper requisition provided to IR - request draw with line placement in IR",8
+Unlisted Lab: pleximmune,8
+Unlisted Genetic Test: Invitae Monogenic IBD,8
+Unlisted Lab: Coccidiodes antigen send out to Mira Vista,8
+Unlisted Lab: Oklahoma Myositis Panel,8
+Unlisted Lab: send out to Mayo labs test code ENC1,8
+Unlisted Genetic Test: Mitochondrial DNA sequencing to Baylor Genetics,8
+Unlisted Lab: sC5b-9 level. Send out test to Quest. 3ml Blood in Lavender tube.,8
+Unlisted Lab: Nocardia PCR and culture,8
+Unlisted Lab: Mayo Autoimmune encephalitis panel (unlisted ENC1),8
+Unlisted Lab: clobazam level send out,8
+Unlisted Lab: Autoimmue encephalopathy panel- MAYO send out code ENC1,8
+Unlisted Lab: TCR V-Beta Repertoire Analysis by Spectratyping (Sendout to Mayo),8
+Unlisted Genetic Test: Branchiootorenal Syndrome Sequencing Panel with Del/Dup analysis from PreventionGenetics,8
+Unlisted Lab: anti liver cytosol,8
+Unlisted Genetic Test: Hereditary Melanoma Panel,8
+"Unlisted Lab: Pleximmune, please send 5 ml whole blood in NA heparin tube. Send to lab with pleximmune paperwork (in front of chart)",8
+Unlisted Lab: Paracoccidioides titers,8
+Unlisted Lab: auto-immune epilepsy panel (Mayo ID: EPC1),8
+Unlisted Lab: Fractionated alkaline phosphatase,8
+Unlisted Lab: send out test to cincinnati childrens IL18,8
+Unlisted Lab: prostaglandin D2,8
+Unlisted Lab: send out: disaccharides,8
+"Unlisted Lab: Phospholipase A2 Receptor antibody, serum.",8
+Unlisted Lab: secretin pancreatic stimulation test,8
+Unlisted Lab: Mayo myomarker panel,8
+Unlisted Genetic Test: Send out whole exome sequencing to GeneDx,8
+Unlisted Lab: Global Metabolomic Assisted Pathway Screen to Baylor (test code 4902),8
+Unlisted Lab: Bactericidal Assay- Sendout to National Jewish Health.,8
+Unlisted Lab: Please send cocci antigen to Mayo/Mirvista,8
+Unlisted Genetic Test: 4 ml peripheral blood in EDTA (lavendar top) to send out to Developmental and Neurogenetics Laboratory (DNL) at Medical College of Wisconsin,8
+Unlisted Lab: Fluid calcium,8
+"Unlisted Lab: Serum send out for Mayo Clinic research testing. Draw 5ml SST tube at collection site-- Prefer 3 mL purified serum (fully spun down per lab standards, aliquoted off and immediately frozen serum ONLY), and shipped on dry ice.",8
+"Unlisted Lab: Send out to Mayo, test code CDS1 CNS Demyelinating Disease Evaluation, Serum",8
+"Unlisted Lab: send out lab to Mayo, autoimmune encephalitis serum, test code ENS1",8
+Unlisted Lab: AUTOIMMUNE ENCEPHALITIS PANEL,8
+Unlisted Lab: Bone Marrow Failure Panel to Invitae,8
+Unlisted Genetic Test: InVitae Behind the Seizure Epilepsy Panel send out,8
+Unlisted Lab: CSF Cocci antigen,8
+Unlisted Genetic Test: send out genetic test to U Chicago (OK to draw from central line),8
+Unlisted Lab: Encephalopathy Autoimmune Evaluation,8
+Unlisted Lab: Repeat Dx - Telomere length 6 panel assay,8
+Unlisted Genetic Test: Trio Whole Exome Sequencing through Baylor Genetics Laboratory,8
+Unlisted Lab: S100 B send out to ARUP,8
+Unlisted Lab: Repeat Dx Telomere Length Measurement  EDTA tube 5-10mL,8
+Unlisted Lab: ZnT8 - zinc transporter,8
+Unlisted Lab: Mayo clinic Test code ENC1 CSF encephalitis panel. Please send off 2mL,8
+Unlisted Lab: Genetic testing for WAS (GeneDx; test code 505),8
+Unlisted Lab: Mayo autoimmune encephalitis panel (send out) Test ID: ENCEC,8
+Rapid HIV Antigen/Antibody,8
+Unlisted Lab: SMARCB1 and SMARCA4 gene analysis,8
+Unlisted Lab: Send out to UW Hematopathology: MRD draw 3 mL in EDTA (purple) on 3/1/18. Tina Baggott to bring requisition to send to lab,8
+Unlisted Lab: Test code ENS1- send out lab to Mayo. 4mL in red top tube,8
+Unlisted Lab: zinc transporter 8 antibody,8
+Unlisted Genetic Test: Nephrotic syndrome/FSGS sequencing panel 1335- to Prevention Genetics,8
+Unlisted Lab: send out to Invitae test code 03401 -- collect 3 mL in purple top tube,8
+Unlisted Lab: send out to MNG labs - CSF pyruvate,8
+Unlisted Genetic Test: Hereditary brain tumor panel,8
+Unlisted Lab: Telomere Length Testing,8
+Unlisted Lab: Ribosomal P antibody,8
+Unlisted Lab: Aspergillus PCR send out,8
+Unlisted Lab: vigabatrin level,8
+Unlisted Genetic Test: Baylor Genetics - Trio Whole Exome Sequencing and Comprehensive mtDNA Analysis,8
+"Unlisted Lab: CDS1 - CNS Demyelinating Disease Evaluation, Serum - Mayo Send out - Red top tube 3mL",8
+Unlisted Lab: Rickettsia (LAB1264),8
+Unlisted Lab: Urine reducing substances,8
+Unlisted Lab: BK IgG  send out to Viracor,8
+"Unlisted Lab: Cytochrome P450 3A5 Genotyping, CYP3A5, 2 Variants 2012740. Sendout to ARUP Labs.",8
+Unlisted Lab: 3-5 mL NA heparin tube for Pleximmune,8
+Unlisted Lab: urine D-lactate to quest 15970X,8
+Unlisted Genetic Test: send out Invitae epilepsy panel TEST CODE: 03401,8
+Unlisted Lab: CSF glycine,8
+Unlisted Lab: Spinal Fluid Autoimmune Encephalopathy Evaluation Panel to Mayo Clinic. Test code #: ENC1,8
+Unlisted Genetic Test: Dual Genome Panel (BCM-MitomeNGS) code 2085 to be sent to Baylor with paper requisition form. Draw 3-5mlml in laventder tube EDTA tube,8
+Streptococcal Ab Profile,8
+Unlisted Lab: liver cytosol antibody send out,8
+"Mercury, Urine",8
+Unlisted Lab: Coccidiodes serum antigen,8
+TSH Receptor Ab,8
+Unlisted Lab: Zika IgM,8
+Unlisted Lab: Anti-MOG antibody,8
+"Unlisted Lab: C3a, C5a, CH50, and SC5b-9 Level",8
+"Unlisted Lab: Invitate Gata2 testing, PID panel",8
+Unlisted Lab: 5mL PB in EDTA,8
+"Unlisted Genetic Test: Comprehensive mtDNA Analysis by Massively Parallel Sequencing, Test code 2055, 81460x1, 81465x1, please send sample to Baylor Genetics Lab with test requisition form",8
+"Unlisted Lab: Viracor Lab: aGvHD Post Treatment Algorithm, Test Number 403573P",8
+"Unlisted Lab: Methylenetetrahydrofolate Reductase (MTHFR) Thermolabile Variant, DNA Analysis",8
+Unlisted Lab: Mycoplasma pneumoniae IgG IgM,8
+Unlisted Lab: Rufinamide level,8
+Unlisted Lab: Thomas Jefferson urine sulfatides  test 82542,8
+Unlisted Lab: TCR V beta spectratyping (send out to Mayo),8
+Unlisted Lab: SEND OUT INVITAE EPILEPSY PANEL PR03401.04,8
+Unlisted Lab: MICA antibody and Endothelial Cell Crossmatch,8
+Transfusion Svc Prod 12,8
+Unlisted Lab: IL-18 (send out to Cincinnati),8
+"Triglycerides, (Non Pleural)",8
+Unlisted Lab: CSF Neurochemical tests from MNG labs,8
+Unlisted Lab: Adult and neonatal cholestasis sequencing panel: mm340. Send to Emory,8
+Unlisted Lab: Mannose-Binding Lectin (MBL). Please draw 1ml (0.3 ml minimum) in a Gold or Red top tube. Do not use a glass tube.,8
+Unlisted Genetic Test: Next generation sequencing for Bartter/ Gitelman Syndromes,8
+Ictotest,8
+Unlisted Lab: beta thalassemia PCR,8
+"Unlisted Lab: Autoimmune encephalopathy panel, send to Mayo",8
+Unlisted Genetic Test: Send out to prevention genetics to eval for myopathy,8
+Unlisted Genetic Test: Neonatal and Adult Cholestasis: Sequencing Panel,8
+Unlisted Lab: INVITAE PR08100.01 Immunodeficiency Panel and PR04101.05 Primary Ciliary Dyskinesia Panel. Both requiring 3mL blood in purple top tube.,8
+Unlisted Lab: Invitae panel IBD,8
+Unlisted Lab: Invitae Comprehensive Cardiomyopathy Panel,8
+Unlisted Lab: Leflunomide level to be sent to Mayo lab,8
+Unlisted Lab: invitae congenital heart disease panel,8
+Unlisted Lab: Felbamate level,8
+Unlisted Lab: Metabolomics (plasma) send out to Baylor,8
+Unlisted Genetic Test: NUDT/CEP/TPMT Panel,8
+Unlisted Lab: NC06 SUCCINYLADENOSINE,8
+Unlisted Genetic Test: PGxome Custom Panel with reflex to TRIO PGxome.,8
+Unlisted Lab: Send Candida PCR to Quest,8
+Unlisted Genetic Test: Phenylbutyrate metabolite urine to be sent to Baylor Genetics,8
+Unlisted Lab: MNG CSF Neurotransmitter - MET11,8
+Unlisted Lab: ZnT8 insulin ab’s,8
+Unlisted Genetic Test: Epilepsy panel to Invitae,8
+"Unlisted Lab: send out to Invitae - Epilepsy Panel, test code 03401",8
+Unlisted Lab: znt8 antibody,8
+Unlisted Genetic Test: CFH Autoantibody for aHUS,8
+Unlisted Genetic Test: CFH Autoantibody testing for aHUS,8
+Unlisted Genetic Test: Trio Whole Exome Sequencing plus comprehensive mtDNA Analysis (send out to Baylor with paper requisition forms),8
+Unlisted Genetic Test: Trio Whole Exome Sequencing plus comprehensive mtDNA analysis- send out to Baylor,8
+"Unlisted Lab: random urine sample, order code YDROTC; lab 679- send out to mayo",8
+Unlisted Lab: MOG antibody; test ID MOGFS from mayo,8
+Unlisted Lab: 2 mL in EDTA (Purple top) Tube. Send to Clinical Lab with PAPER REQUISITION FORM for Clonoseq,8
+Unlisted Genetic Test: Machaon Diagnostics aHUS panel,8
+Unlisted Lab: Complex C5b-9,8
+"Unlisted Lab: Cocci antigen, please send to mira vista",8
+Unlisted Genetic Test: Invitae Primary Immunodeficiency Panel,8
+"Unlisted Lab: Ecluzimab pharmacokinetic panel (ecluzimab level, CH50, and sC5b-9/sMAC).",8
+"Unlisted Lab: toxocara IgG, IgM",8
+"Unlisted Lab: Toxoplasmosis IgG- please sent to PAMF, do not do in house test",8
+Unlisted Lab: Lymphocyte Proliferation to Mitogens (sendout to Mayo),8
+"Unlisted Lab: ALPHA AMINOADIPIC SEMIALDEHYDE (CSF), SEND OUT TO MNG LABS, TEST CODE NC08",8
+Unlisted Lab: Anti-MOG antibody (Myelin oligodendrocyte glycoprotein),8
+Unlisted Lab: duodenal biopsy for disaccharides,8
+"Unlisted Lab: Encephalopathy, Autoimmune Evaluation, Test ID: ENC1",8
+Unlisted Lab: GM1B ganglioside antibody panel serum to Mayo,8
+Unlisted Lab: Bile acid synthesis disorders by mass spectrometry. Sendout to Cincinnati. 0.5-1 mL of blood. Shipping requirements: Frozen (on dry ice). Overnight Express.,8
+Unlisted Lab: MNG CSF Neurotransmitter - MET07,8
+Unlisted Lab: MNG CSF Neurotransmitter - NC02,8
+Unlisted Lab: Tacrolimus by Mass spectrometry (to be sent to ARUP),8
+Unlisted Lab: toxocara antibody IgG,8
+Unlisted Lab: Vedolizumab Antibody. Requires 2 Gold Tops. Spin Down But Strictly Do Not Aliquot,8
+Unlisted Lab: listeria antibody to Quest. 3ml red or gold top,8
+"Unlisted Lab: Serum autoimmune encephalitis panel, send out to Mayo, test code ENS1",8
+Unlisted Lab: Autoimmune encephalitis panel to Mayo,8
+"Unlisted Lab: CSF thyroid peroxidase antibody, ARUP code 0050075",8
+Unlisted Lab: Anti-zinc transporter 8 (ZNT8),8
+Unlisted Genetic Test: Telomere length for dyskeratosis congenita.,8
+"Unlisted Lab: Aspergillus PCR, Quest EDTA lavender top 3ml of whole blood, test code 18873",8
+Unlisted Genetic Test: Russell-Silver syndrome Methylation-Specific (MLPA),8
+Unlisted Lab: anti-vedolizumab antibiody,8
+Unlisted Lab: NC08 ALPHA AMINOADIPIC,8
+"Unlisted Lab: ZnT8 antibody, Gold Tube",8
+Unlisted Lab: Mayo Autoimmune encephalitis panel Test ID: ENC1,8
+Unlisted Lab: Anti liver cytosol Ab,8
+Unlisted Lab: Chlamydia psittaci IgG Ab,8
+Unlisted Genetic Test: Machoan Diagnostics aHUS genetic panel.,8
+Unlisted Lab: TH17 enumeration 2-3 ml sodium heparin tube,8
+Unlisted Genetic Test: Biotin-Thiamine -Responsive Basal Ganglia Disease vis the SLC19A3 gene,8
+Unlisted Genetic Test: Early Onset Inflammatory Bowel Disease: Sequencing Panel to Emory Genetic Labs,8
+Unlisted Lab: Invitae combined cardiomyopathy and arrhythmia panel (saliva sample included with kit),8
+Unlisted Lab: plasma D-lactate mayo code DLAC,8
+Rickettsia AB Pnl w/Reflex Titer,8
+Unlisted Genetic Test: Invitae Lab - Arrhythmia and cardiomyopathy comprehensive panel.,8
+Unlisted Genetic Test: Emory cholestasis panel,8
+Unlisted Lab: S-100B Protein,8
+Unlisted Genetic Test: send out test to Baylor,8
+Unlisted Lab: serum oxalate level,8
+"Unlisted Lab: cocci urine ag, miravista",8
+Unlisted Lab: cocci serum ag,8
+"Unlisted Lab: Ribosome P Ab, IGG",8
+Unlisted Lab: send out to Mayo test code ENC1,8
+Hgb Quant/Fract HPLC,8
+Unlisted Lab: karius metagenomics - Collect 5 mL of whole blood in a BD Vacutainer Plasma Preparation Tube,8
+Unlisted Genetic Test: Prevention Genetics Gitelman Syndrome via SLC12A3 Gene Sequencing with CNV Detection,8
+Unlisted Genetic Test: 2-4ml in EDTA for NUD/CEP/TMPT genotyping,8
+Unlisted Lab: JC virus PCR from urine,8
+Unlisted Lab: Zinc Transportase 8 Antibody,8
+Unlisted Lab: Invitae Hereditary HLH Disorder Panel,8
+Unlisted Lab: Neuronal Antibody,8
+"Unlisted Lab: Anti-MOG: Mayo Clnical Labratories Test ID: CDS1 CNS demyelinating disease evalutation, serum",8
+"Unlisted Lab: send out to MNG lab - NC01, NC04, NC05, NC06, NC07, NC08",8
+Unlisted Genetic Test: Baylor Genetics Laboratory - Trio Whole Exome Sequencing + mtDNA,8
+Unlisted Lab: sendout to MNG labs - SIALIC ACID CSF (NC07),8
+Unlisted Lab: Cincinnati Children's Hemophagocytic Lymphohistocytosis (HLH) Panel,8
+Unlisted Lab: MTHFR,8
+Unlisted Lab: FoundationOne Heme,8
+Unlisted Genetic Test: NUD/CEP/TPMT Panel to Medical College of Wisconsin,8
+Unlisted Lab: Quest Diagnostic: Milk Precipitant Panel Test Code 15337,8
+Unlisted Lab: Liver cytosol Auto-Ab-1. Reference lab code: 10527.,8
+Unlisted Lab: Clobazam metabolite,8
+Unlisted Lab: Invitae Arrhythmia and Cardiomyopathy Comprehensive Panel Plus CHD Panel,8
+Unlisted Lab: CSF folate metabolites,8
+"Unlisted Lab: Autoantibody to C1q (C1q-CLR) sent to Advanced Diagnostic Laboratories National Jewish Health, Complement Laboratory. Test code: C1QAB",8
+"Unlisted Lab: C1q level by RID sent to Advanced Diagnostic Laboratories National Jewish Health, Complement Laboratory. Test code: C1Q",8
+Fibrospect for Liverfibrosis,8
+Unlisted Lab: Invitae Congenital Heart Defects and Heterotaxy Panel + Invitae Arrhythmia and Cardiomyopathy Comprehensive Panel,8
+Unlisted Lab: SENDOUT: Mayo Clinic autoimmune encephalitis panel (mayo code ENS1),8
+Hold Clot,8
+Transfusion Svc Prod 13,8
+Unlisted Lab: Mayo Clinic sendout lab: autoimmune encephalitis panel (Mayo Code ENC1),8
+Unlisted Lab: Emory cholestasis panel send out - EDTA purple top 5 mL,8
+"Unlisted Lab: Soluble CD 163, 1-2 ml EDTA",8
+Unlisted Lab: send out to Mayo test code ENS1,8
+Unlisted Lab: urine reducing substance,8
+Unlisted Genetic Test: send out to Invitae test code 03401,8
+Unlisted Lab: Zinc Transporter 8 (ZincT8),8
+Anti Scl70 Antibody,8
+Unlisted Lab: sendout - Repeat diagnostics test for telomere length,8
+Unlisted Genetic Test: sendout to Invitae: neurometabolic/epilepsy panel - please draw 2 lavender top tubes,8
+"Unlisted Lab: opiate confirmatory testing, send out to Mayo",8
+Unlisted Genetic Test: Baylor Trio Whole Exome,8
+"Unlisted Genetic Test: Comprehensive Diffuse Lung Disease NGS Panel, Johns Hopkins",8
+Chlamydia Molec Detec,8
+Unlisted Genetic Test: RNA Transcriptome MNG Labs,8
+Unlisted Lab: MNG CSF Neurotransmitter - MET01,8
+Unlisted Genetic Test: Send out to Invitae: Treatable neurometabolic disorders panel with reflex to whole exome sequencing,8
+Unlisted Lab: Anti liver cytosol antibody,8
+Unlisted Lab: Familial Hemophagocytic Lymphohistiocytosis (HLH) Panel,8
+Unlisted Lab: NUDT15 haplotype resolving test,8
+Unlisted Lab: NC07 SIALIC ACID,8
+Unlisted Lab: Anti-ganglioside Antibody Panel - Washington University,8
+Unlisted Genetic Test: Blood phenylbutyrate level to Baylor Genetics,8
+Unlisted Genetic Test: Hematologics sendout (CBFA2T3-GLIS2 qPCR) - 3ml heparin -- paper requisition to be given to bedside RN,8
+Unlisted Lab: Mayo Clinic Epilepsy Autoimmune Panel (CSF) Mayo Test ID: EPC1,8
+Unlisted Lab: Mycoplasma titers to Mayo,8
+Unlisted Lab: Metabolomics (CSF) send out to baylor,8
+Unlisted Lab: Liver Cytosol Auto Ab-1,8
+Unlisted Genetic Test: send out to Invitae; MUT gene testing,8
+Unlisted Genetic Test: MNG labs Plasma Creatine and Guanidinoacetate,8
+Unlisted Lab: anti-Zn T8,8
+Unlisted Lab: H pylori culture and senstivities,8
+Unlisted Lab: Hyaluronic acid serum level (sendout to ARUP),8
+Unlisted Lab: CDS1 send out to mayo,8
+Unlisted Genetic Test: NUD/CEP/TPMT Genotype Panel,8
+Unlisted Lab: parvovirus PCR qualitative from CSF,8
+Unlisted Lab: Zinc transporter antibiody,8
+Unlisted Lab: MNG labs RNA-Seq SYNGAP1,8
+"Unlisted Lab: tacrolimus level from 5/21 am, please send to santa clara valley medical center",8
+Unlisted Lab: Autoimmune Epilepsy Panel (EPS1) - send-out to Mayo Clinic lab.,8
+"Unlisted Lab: CSF thyroglobulin Ab, ARUP code 0050105",8
+"Unlisted Lab: S100B CSF (code 2013358), ARUP",8
+Unlisted Lab: Hematologics peripheral blood molecular test (FISH for CBFA2T3-GLIS2 fusion),8
+Unlisted Lab: NMOCC (NMO/AQP4-IgG plus MOG on CSF),8
+Unlisted Lab: extended drugs of abuse with mass spec to assess for synthetic marijuana,8
+Unlisted Lab: Pleximmune. Please draw 3-5 mL blood of NA heparin light green top tube.,8
+"Zika Virus RT-PCR, Non-blood",8
+Unlisted Lab: Day 29 MRD to be sent to UW hematopathology,8
+Unlisted Lab: paraneoplastic panel Mayo Lab code: PAC1,8
+Unlisted Lab: serum autoimmune epilepsy panel,8
+"Unlisted Lab: autoimmune encephalitis panel send out Mayo Lab, code: ENS1",8
+Unlisted Lab: T cell receptor V beta spectrotyping,8
+Unlisted Lab: ZnT8 transporter auto-antibody,8
+Unlisted Lab: Send out to Mayo. Mannose binding lectin. Test ID MBL.,8
+Unlisted Lab: Onfi / Clobazam level,8
+Unlisted Lab: Anti-Enterocyte Ab (Mayo Clinic Laboratories - Test ID: FAEAB),8
+Unlisted Lab: Soluble liver antigen autoantibody,8
+"Unlisted Lab: ENS1: Encephalopathy, Autoimmune Evaluation, Serum",8
+Unlisted Lab: EBV Stool PCR,8
+Unlisted Genetic Test: Familial hemophagocytic lymphocytosis (HLH) panel,8
+"Unlisted Lab: FOXP3 (Cincinnati sendout)-please attach requisition form, EDTA tube",8
+Unlisted Genetic Test: InVitae Epilepsy Gene Panel,8
+Unlisted Genetic Test: Genetic test,8
+Unlisted Genetic Test: Invitae Primary Cillary Dyskinesia Panel,8
+Unlisted Lab: IgG/IgM Toxo Panel. Send out lab to PAMF lab.,8
+"Unlisted Lab: 4-5mL of peripheral blood in EDTA, please send to lab with DNL requisition form in front of green binder",8
+Unlisted Lab: Autoimmune encephalitis panel to Mayo Clinic from CSF,8
+"Unlisted Lab: Miravista, serum antigen cocci, urine antigen cocci",8
+Unlisted Lab: ENS1 - Autoimmune encephalitis panel to be sent to mayo Clinic (Serum),8
+Unlisted Lab: IgG index (CSF),8
+Unlisted Genetic Test: sendout test to prevention genetics,8
+Unlisted Lab: aspergillus pcr,8
+"Unlisted Genetic Test: send out test to invitae, 3 ml blood in EDTA (purple top tube)",8
+Unlisted Genetic Test: send out test to prevention genetics for GNAS sequencing (3 mL purple top tube),8
+"Unlisted Lab: Phospholipase A2 Receptor Antibodies, Serum",8
+"Unlisted Genetic Test: Neonatal Respiratory Distress - Surfactant Dysfunction Panel, Blueprint genetics. Order number 61249",8
+LD Isoenzymes,8
+Unlisted Genetic Test: Nephrotic Syndrome/FSGS Sequencing Panel with CNV Detection,8
+Unlisted Genetic Test: FoundationOne Heme - bone marrow aspirate,8
+IA-2 Antibody,8
+Unlisted Lab: lab corp send out zika IgM test 163049 CPT 86794,8
+"Unlisted Lab: Serum sendout to Mayo - ENS1 (red top tube, 4ml)",8
+Unlisted Lab: stool test for enterovirus,8
+"Vitamin B2, Ser/Plas",8
+Unlisted Genetic Test: FoundationeOne HEME sendout requested on original tissue biopsy,8
+Unlisted Lab: FOXp3 Cincinnati Children's,8
+Unlisted Genetic Test: Genetic testing of the ACADVL gene through Invitae,8
+Unlisted Lab: NC05 PYRIDOXAL 5 PHOSPHATE,8
+Unlisted Lab: Mayo autoimmune encephalitis panel (unlisted ENS1),8
+"Unlisted Lab: Day 8 peripheral blood MRD, clinical send out to University of Washington",8
+Unlisted Lab: Coccidiodes urine antigen,8
+Unlisted Genetic Test: send out to Baylor: molecular testing G6PC Comprehensive sequence and deletion/duplication analysis. Please send  out with requsition form which will be uploaded under media tab.,8
+Unlisted Genetic Test: NUD/CEP/TMPT panel -- EDTA tube,8
+Unlisted Lab: Anti myelin oligodendrocyte glycoprotein (MOG) antibody,8
+"Unlisted Lab: Glucopsychosine, Blood",8
+Unlisted Genetic Test: Hereditary Myeloid Malignancy and Inherited Bone Marrow Failure Panel through University of Chicago,8
+Unlisted Lab: anti-enterocyte antibody,8
+"Unlisted Genetic Test: CPOX Gene, full gene analysis, sent to MAYO Clinic, Test ID: CPOXZ",8
+Unlisted Lab: Serum Encephalopathy Evaluation Panel to Mayo Clinic.,8
+Unlisted Lab: Vedolizumab antibody level for sendout to prometheus labs,8
+Unlisted Genetic Test: PHOX2B genetic testing: preventiongenetics,8
+Unlisted Lab: Invitae Epilepsy Panel,8
+Unlisted Lab: Hereditary PGL-PCC Panel,8
+Unlisted Lab: Legionella PCR,8
+Unlisted Lab: cocci immunodiffusion assay,8
+"N-Methylhistamine, 24 Hour, Urine",8
+"Unlisted Genetic Test: KidneySeq testing from Idaho. MUST be drawn on Thursday, 12/13",8
+Unlisted Lab: Stanford Custom ISAC panel,8
+"Unlisted Genetic Test: HMBS Gene, full gene analysis, sent to MAYO Clinic, TEST ID: HMBSZ",8
+Unlisted Lab: RT-Qulc,8
+"Unlisted Lab: Cocci antigen send out lab, red top tube 2ml blood",8
+Unlisted Genetic Test: send out test to invitae for genetic testing,8
+Unlisted Lab: Invitae Epilepsy Gene Panel,8
+Unlisted Lab: Fulgent PH and Hereditary hemorrhagic telangiectasia panel,8
+Unlisted Lab: Human Herpese Virus -7 Quantitative PCR,7
+"Unlisted Lab: 4 mls in EDTA  lavendar tube, ""for BK IgG Elisa to Viracor test code 2300""",7
+Unlisted Lab: Cincinnati Children's Familial HLH Panel - Saliva specimen,7
+"Unlisted Lab: Baylor Global MAPS Test code 4902, Send-out",7
+Unlisted Lab: perforin/granzyme B assay send out to Cincinnati,7
+"Unlisted Lab: PTSE, 1 mL royal blue top vacutainer tube. 24 hours after starting cisplatin",7
+Unlisted Lab: Myelin Oligodendrocyte Glycoprotein (MOG-IgG1) Fluorescence-Activated Cell Sorting (FACS) Assay,7
+Unlisted Lab: Autoimmune encephalitis panel . send out to Mayo clinic,7
+Unlisted Lab: Aspergillus PCR sent out to focus diagnostic. Test code 18873,7
+Unlisted Lab: Polyneuropathy panel send out to Washington University,7
+Unlisted Lab: JC VIrus PCR,7
+Unlisted Lab: trypsin (trypsinogen),7
+"Unlisted Lab: body fluid Bicarbonate from pancreatic drain, post-MRCP. Please report actual value. OK to dilute sample.",7
+"Unlisted Lab: Gene testing to be sent to Mayo Laboratory. 3mL in Lavender top (EDTA) -minimum volume 0.5mL. Please send ""HHT Gene Testing MML Supply T650"" form with specimen",7
+Unlisted Lab: Cow's milk precipitin (or cow's milk precipitating antibodies) - sent to VIracor labs.,7
+Unlisted Lab: Serum methanol,7
+Unlisted Lab: Sendout lab for familial hemophagocytic lymphohystiocytosis (cincinnati),7
+Unlisted Lab: Athena NPHS2 DNA Sequencing Test Code: 710,7
+Unlisted Lab: Unlisted Lab: LAD1 (=CD18 expression) and LAD2 (=CD15 expression) (Order #712025255) on 8/29/15,7
+"Unlisted Lab: Angelman syndrome methylation analysis - to be run at Baylor, purple top tube",7
+"Unlisted Lab: Skeletal dysplasia ciliopathy NGS panel, send to CTGT lab",7
+Unlisted Lab: Baylor congenital myopathy panel,7
+Unlisted Lab: HSV Quantitative PCR from blood,7
+Unlisted Lab: MSMD screen. Please send 10-20 cc for flow cytometry and screen. Please send with requistion form.  Please collect in a green top vacutainer tube,7
+Unlisted Lab: CD40L mutation analysis (hyper IgM syndrome),7
+"Unlisted Lab: PTSE, 1 mL royal blue top vacutainer tube. 9 hours after starting cisplatin",7
+Unlisted Lab: Telomere Length Measurement 6 panel Assay,7
+"Unlisted Lab: osteogenesis imperfecta NGS panel-dominant, test code 5099, send out to CTGT lab",7
+"Unlisted Lab: Trio Whole Exome Sequencing, sendout to Baylor Laboratories",7
+Unlisted Lab: Prometheus Celiac Serologgy Lab #1155,7
+"Unlisted Lab: Drug-Dependent Platelet Antibodies, 2 ml plain red tube, send to blood center of Wisconsin",7
+Unlisted Lab: Cytogenetics CGH,7
+Unlisted Lab: WHOLE EXOME SEQUNECING_ PLEASE send along with maternal blood sample for control to BAYLOR MIRACA LABS,7
+"Unlisted Lab: Please send Lyme Disease (Borrelia spp) DNA Qualitative Real-Time PCR, Synovial Fluid/CSF to focus diagnostics from the available synovrial fluid.",7
+Unlisted Lab: Gene Dx Infantile Epilepsy Panel,7
+Chagas Disease,7
+Unlisted Lab: MICA antibody and endothelial cell crossmatch,7
+Unlisted Lab: Oklahoma dermatomyositis panel,7
+Unlisted Lab: Gene Dx epilepsy panel per Gene Dx form. Collect in lavender tube 2-5 mL. Please refrigerate and send Monday.,7
+Unlisted Lab: genetic testing,7
+Unlisted Lab: Prometheus ANSER ADA - serum adalimumab levels and antibodies to adalimumab,7
+Unlisted Lab: Urine indicans,7
+Unlisted Lab: Cytokine Panel,7
+Unlisted Lab: CSF Pyruvate. Send out lab to MNG.,7
+"Unlisted Lab: Parecho virus PCR, send to ARUP laboratory",7
+Unlisted Lab: HLH sendout to Cincinnati Childrens,7
+"Unlisted Lab: Send-out test - for more information, please see comments below.",7
+Unlisted Lab: Autoimmune Encephalitis Panel from CSF. Lab Code: ENCEC,7
+"Unlisted Lab: GeneDX comprehensive cardiomyopathy panel, test # 694",7
+Unlisted Lab: ethambutol drug level 0091319 ARUP labs,7
+"Unlisted Lab: cytokines, intracellular, FOXP3 - send out to Cincinnati Childrens",7
+"Unlisted Lab: 3 red-top tubes: 2 ml for myositis Ab profile, 1 ml for U3RNP test, 1 ml for TH/TO test to OMRF Labs. OK TO HOLD SPECIMEN AT ROOM TEMP OR FRIDGE IN LAB AND SEND OUT WITH NEXT COURIER AS ABLE.",7
+Unlisted Lab: Please send Total Bili on Fluid from JP.,7
+Unlisted Lab: Day 8 MRD to send out to UW hematopathology - 5ml peripheral blood in EDTA (paperwork in blue chart),7
+Unlisted Genetic Test: Whole Exome sequencing to Prevention Genetics,7
+"Unlisted Lab: send out lab to Oklahoma Medical Research Foundation (Myositis antibody profile, P155, MJ, MDA-5)",7
+"Unlisted Genetic Test: Gene Dx Epilepsy. One lavendar top tube, 5-6 mL. Please send sample with paper form.",7
+"Unlisted Lab: Test code: ENCEC, autoimmune encephalitis panel send to Mayo",7
+Unlisted Lab: Gene Dx Epilepsy Panel,7
+Unlisted Lab: Autoimmune Encephalitis Panel Test ID: ENCES,7
+Unlisted Lab: Perforin/Granzyme B. Send out to Cincinnati,7
+Unlisted Lab: MJ by Western Blot,7
+Unlisted Lab: CSF Pyridoxal 5' Phosphate. Send out lab to MNG.,7
+Unlisted Lab: CSF pipecolic acid. Send out lab to ARUP.,7
+Unlisted Lab: CSF Succinyladenosine. Send out lab to MNG.,7
+Unlisted Lab: CSF Alpha-aminoadipic semialdehyde. Send out lab to MNG.,7
+"Unlisted Lab: Send out lab: 1,3 beta glucan",7
+Unlisted Lab: Parecho virus 1ml ARUP 2005731 on CSF,7
+Unlisted Lab: 5 ml EDTA to be sent to Repeat Diagnostics for telomere studies,7
+"Unlisted Lab: Please send ""Borrelia Species DNA Detection by PCR"" to ARUP labs, test code 0055570",7
+Unlisted Lab: Cysticercosis Ab IgG ELISA,7
+Unlisted Lab: 1 mL blood in royal blue top tube send out to Mayo for serum platinum level to be drawn 3.5 hours after completion of Cisplatin infusion,7
+Unlisted Lab: 1 mL blood in royal blue top tube send out to Mayo for serum platinum level to be drawn 22 hours after completion of Cisplatin infusion,7
+Unlisted Lab: 1 mL blood in royal blue top tube send out to Mayo for serum platinum level to be drawn 5.5 hours after completion of Cisplatin infusion,7
+Unlisted Lab: Moleculera-Cunningham - Requisition Required for Send Out,7
+Unlisted Genetic Test: MTHFR (Methylene tetrahydrofolate reductase) gene deletion,7
+Unlisted Lab: Sendout: To Medical College of Wisconsin. HYPER IGM.,7
+"Unlisted Lab: beta-d glucan, fungitell",7
+Unlisted Lab: Myositis antibody profile,7
+Unlisted Lab: MDA-5 (CADM-140) by Western Blot,7
+Unlisted Lab: ZnT8 (Zntransporter),7
+Unlisted Lab: azathioprine (IMURAN) level,7
+Unlisted Lab: Invitae Comprehensive Arrhythmia Panel,7
+Unlisted Lab: Send out lab: Aspergillus PCR,7
+Unlisted Lab: for perforin/granzyme B testing at Cincinnati Children's Hospital Diagnostic Immunology Lab,7
+Unlisted Lab: send-out Aspergillus PCR (5 mL in EDTA purple top tube),7
+Unlisted Lab: ZnT8 (Zn transporter),7
+"Unlisted Lab: S-100B Protein, Serum from ARUP labs (CPT code 86316)",7
+Unlisted Lab: Thyroglobulin LC/MS,7
+"Unlisted Lab: 1,3 beta-glucan serum level",7
+Unlisted Lab: Red top.  Test Code FCLBZ,7
+Unlisted Lab: autoimmune encephalopathy evaluation send out to Mayo Clinic Test ID ENCES red top tube 4 ml,7
+"Unlisted Lab: 1,3-beta glucan assay",7
+"Unlisted Lab: OTC gene sequencing and del dup, code 313E, send to Gene Dx",7
+Unlisted Lab: CD107A MOBILIZATION (NKCELL DEGRAN),7
+"Unlisted Lab: PTSE, 1 mL royal blue top vacutainer tube. 12 hours after starting cisplatin",7
+"Unlisted Lab: bile acid assay mass spectroscopy, send to Cincinatti",7
+Lipid Panel,7
+Unlisted Lab: AUTOIMMUNE ENCEPHALITIS ANTIBODY PANEL - ENCEC,7
+"Unlisted Lab: BK virus IgG ELISA, test code: 2300",7
+Unlisted Lab: GeneDx Comprehensive Epilepsy Panel,7
+Unlisted Lab: NK cell function assay send to Cincinnati,7
+"Unlisted Lab: body fluid Bicarbonate, from pancreatic drain. Pre-MRCP. Please report actual value, OK to dilute sample.",7
+"Unlisted Lab: anti-GAD antibodies, CSF sample.",7
+Unlisted Lab: Catecholamine Fractioned Random,7
+Unlisted Lab: Please send Toll Like Receptor Signaling to Wisconsin ( or Cinncinati if possible),7
+Unlisted Lab: Cunningham Panel ( Please send with send out form),7
+Unlisted Lab: Baylor test code 4902: Global Metabolomic Assisted Pathway Screen; send 1-2 cc CSF frozen,7
+"Unlisted Lab: Please send for ""Aspergillus DNA, Qualitative Real Time PCR"" to focus diagnostics. Send 2-3 ml of blood in EDTA, ACD to focus diagnostics.",7
+Unlisted Lab: Cunningham Panel ( please use send out sheet),7
+"Unlisted Lab: Please send for ""Aspergillus DNA, Qualitative Real Time PCR"" to focus diagnostics. Send 2-3 ml of BAL fluid.",7
+"Unlisted Lab: 1,3 beta D glucan assay",7
+Unlisted Genetic Test: Invitae epilepsy panel: Add on preliminary-evidence genes for epilepsy (50 genes),7
+Unlisted Lab: Chikunguna IgM and IgG - to Focus diagnostics,7
+Unlisted Lab: Japanese encephalitis virus AB (IgG+IgM) - to ARUP lab 2005689,7
+Unlisted Genetic Test: PDHA1 Comprehensive at Baylor MGL,7
+Unlisted Lab: Please draw 2 ml into a lavender top tube,7
+"Unlisted Lab: Encephalopathy, Autoimmune Evaluation (ENCES), Serum in Red Top tube, 4mL - Send out to Mayo",7
+Unlisted Lab: Ganciclovir level,7
+Unlisted Lab: Autoimmune encephalitis panel (ENCEC) send out to Mayo Clinic,7
+Unlisted Lab: neopterin,7
+Unlisted Lab: pyridoxal-5-phosphate,7
+"Unlisted Lab: mayo autoimmune encephalopathy panel, Test ID: ENCEC, 4 mL",7
+Unlisted Lab: progressive myoclonic epilepsy panel -- send out lab to gene dx. 5mL in lavender top tube.,7
+Unlisted Lab: JC virus DNA PCR,7
+Unlisted Lab: SEND OUT TO QUEST: VLCFA ( Very long chain fatty acid); Test Number: 90559 CPT Code: 82726,7
+Unlisted Lab: D-lactate 1 ml in a grey top.,7
+Thyroid Stimulating Immunoglobulin,7
+Unlisted Lab: National Jewish Center C5a,7
+Misc Processing,7
+Unlisted Lab: Mayo Clinic  Autoimmune Encephalitis Panel from serum. Order Code ENCES,7
+Unlisted Lab: urine reducing substances,7
+"Unlisted Lab: Toxoplasmosis IgG, IgM, and IgA. Send to Remington Lab at PAMF.",7
+Unlisted Lab: 5-6 ml in a lavender tube. Would like childhood-onset epilepsy panel,7
+Unlisted Lab: serum aquaporin4 IgG,7
+"Unlisted Lab: Aspergillus PCR, reference code: 48003, send-out to focus",7
+Unlisted Lab: HOUR 24 cisplatin level,7
+Unlisted Lab: HOUR 48 cisplatin level,7
+"Unlisted Lab: Please send ""Parechovirus Detection by PCR"" to ARUP labs. Test code: 2005733",7
+Unlisted Lab: Allergic Bronchopulmonary Aspergillosis Panel,7
+"Unlisted Lab: 1,3 beta D glucan. Send out lab.",7
+Unlisted Lab: ASCA (anti-saccharomyces cerevisiae antibodies),7
+Unlisted Lab: GENE DX COMPREHENSIVE EPILEPSY PANEL,7
+"Mucopolysaccharides Quant, Urine",7
+Unlisted Lab: Please perform 24 hours urine collection for 11 beta-prostaglandin F2 alpha (Test ID:  BPG2) and  N-Methylhistamine (Test ID: NMHIN):,7
+Unlisted Lab: Gene Dx Comprehensive Metabolic Epilepsy Panel,7
+Unlisted Lab: d-lactate - serum,7
+Unlisted Lab: National Jewish Center: C4a,7
+"Unlisted Lab: Anti liver cytosol 1 Ab, Test code 10527 to Quest",7
+Unlisted Lab: Sendout: To Medical College of Wisconsin. T CELL INTERLEUKIN PROLIFERATION,7
+Unlisted Lab: NATIONAL jEWISH cENTER C4a,7
+Unlisted Lab: National Jewish C4a,7
+Unlisted Lab: Please send to National Jewish Center C3A,7
+"Bone Alk Phosphatase, Serum",7
+Unlisted Lab: Inherited Focal and Segmental Glomerulosclerosis Evaluation,7
+"Unlisted Lab: Send out to focus Toxoplasma gondii DNA, Qualitative Real-Time PCR (test code 46000) on liver biopsy specimen",7
+"Unlisted Lab: Jaundice Panel (ABCB4, ABCB11, ATP8B1, JAG1, TJP2 genes), send-out to Cincinnati Children's",7
+"Beta 2 Transferrin, Fld",7
+"Unlisted Lab: Autoimmune encephalitis panel of CSF, send out to MAYO",7
+Unlisted Lab: GeneDx 541 - Infantile Epilepsy Panel,7
+Unlisted Lab: HOUR 12 cisplatin level,7
+Unlisted Lab: Cunningham Panel,7
+Unlisted Lab: National Jewish Center C3a,7
+Unlisted Lab: coccidiomycosis urine antigen to Miravista labs,7
+Unlisted Lab: toxoplasma Ab newborn panel - please send to PAMF reference laboratory,7
+Unlisted Lab: Aspergillus PCR. Send out to Focus Diagnostics. CPT code 87798X3.,7
+Unlisted Lab: Oklahoma Medical Research Foundation Myositis Panel,7
+"Unlisted Lab: Aspergillis PCR from blood. Send out lab to FOCUS Diagnostics CBT code 87798, reference lab code 48003.",7
+"Unlisted Lab: Mayo sendout test CATP (Catecholamine Fractionation, Free, Plasma)",7
+Unlisted Lab: Please send yersinia pestis serology to the CDC. CDC 10419. 500 uL of serum (red top),7
+Unlisted Lab: Murine typhus Ab IgG (Mayo - FMTAG),7
+"Unlisted Lab: Ambry Genetics, ExomeNext",7
+Unlisted Lab: TBX1 gene sequencing,7
+Unlisted Lab: aspergillus blood PCR,7
+Unlisted Lab: HHV8 PCR,7
+Unlisted Lab: Mayo Autoimmune encephalitis panel,7
+"Unlisted Lab: PTSE, 1 mL royal blue top vacutainer tube. 3 hours into cisplatin",7
+"Unlisted Lab: PTSE, 1 mL royal blue top vacutainer tube. 6 hours after starting cisplatin",7
+Research Lab Draw,7
+"Unlisted Lab: Autoimmune encephalitis, CSF sample. Send out to Mayo (Code 61509)",7
+"Unlisted Lab: Mycoplasma PCR, CSF sample, send out to Mayo (Code MPRP), 0.5 mL",7
+Unlisted Lab: Requisition in chart.,7
+Unlisted Lab: CPT: 83540.  SEND OUT 72h urine collection for Iron,7
+Unlisted Lab: Baylor Genetics Global Maps Requisition,7
+Slide Interpretation by Pathologist,7
+"Unlisted Lab: Please send for vitamin B7, Biotin",7
+"Unlisted Lab: CSF Autoimmune encephalitis panel, send to Mayo clinic (lab code: ENCEC)",7
+Unlisted Lab: Please collect 2 ml of blood into a gold top tube (SST).,7
+Unlisted Lab: whole exome sequence,7
+"Unlisted Lab: BK IgG Elisa, test code 2300 to Viracor",7
+Unlisted Lab: aHUS genetic panel send-out lab,7
+Unlisted Lab: Please collect 1 ml blood into a gold top tube (SST),7
+Unlisted Lab: Combined mito genome plus mito nuclear gene panel,7
+"Unlisted Lab: Fungitel, 1-3-beta-D Glucan. Test code 16283",7
+Unlisted Lab: Succinyladenosine (CSF) send out to MNG Laboratories. Test Code: NC 06. Requires 1mL CSF stored frozen.,7
+"Unlisted Lab: Purine and Pyrimidine Panel, Urine send out to Mayo Clinic. Test ID PUPY. Requires 3mL urine.",7
+Complement Alternate Pathway,7
+"Unlisted Lab: PHP1A / Albright Hereditary Osteodystrophy (GNAS sequence, reflexed to e1A methylation)), send out to Johns Hopkins.",7
+Unlisted Lab: Cincinnati Children's HLH lab: Perforin,7
+Unlisted Lab: Cincinnati Children's HLH lab: CD107A,7
+Unlisted Lab: alpha-aminoadipic semialdehyde,7
+"Unlisted Lab: adalimumab drug concentration and anti-adalimumab levels, to be drawn simultaneously",7
+Unlisted Lab: adalimumab antibody and level,7
+"Unlisted Lab: C3a (send out, req required, National Jewish)",7
+"Unlisted Lab: Measles IgG, Mumps IgM, Mumps IgG send out to Mayo",7
+"Unlisted Lab: Urine indican, Mayo Medical UIQL.  Ship two 4.5 mL aliquots from well-mixed random urine collection, no preservatives, shipped frozen.",7
+Unlisted Lab: ZnT8 autoantibody,7
+"Unlisted Lab: PTSE, 1 mL royal blue top vacutainer tube. 48 hours after starting cisplatin",7
+Unlisted Genetic Test: Neurological Lysosomal Storage Disorder Enzyme Panel through Greenwood Genetics,7
+"BRAF, Blood",7
+Unlisted Lab: Zinc transporter,7
+"Unlisted Genetic Test: Hyperekplexia NGS Panel, SEND OUT LAB by Molecular Diagnostics Laboratory",7
+Unlisted Lab: Thrombopoietin,7
+Unlisted Lab: Zn T8 Zinc Transporter,7
+"Unlisted Lab: Anti-aquaporin 4 antibody, from CSF",7
+Unlisted Genetic Test: Heritable Connective Tissue Gene Panel with GeneDx.,7
+Unlisted Lab: Emory IBD panel send-out test,7
+Unlisted Lab: B ALL fusion detection multiplex RT-PCR,7
+Unlisted Lab: NOONAN SPECTRUM DISORDERS/RASOPATHIES NEXTGEN SEQUENCING (NGS) PANEL,7
+Unlisted Lab: Please send aspergillus PCR from the blood,7
+Unlisted Lab: NBIA  panel,7
+Sickle Cell Prep,7
+"Unlisted Lab: autoimmune encephalitis panel, Mayo Clinic, code ENCEC",7
+Unlisted Lab: urine indicans qualitative,7
+Unlisted Lab: Trio Whole Exome Sequencing + mtDNA through Baylor Laboratories.(5cc of blood  in two separate EDTA tubes - total 10cc),7
+Unlisted Lab: Send out to Cincinnati Children's Clinical Laboratory (Thrombotic Microangiopathy Test),7
+Unlisted Lab: Please send out coccidioidomycosis urine antigen to Miravista lab,7
+Unlisted Lab: Mayo Labs PTSE,7
+Unlisted Lab: BK IgG ELISA to Viracor - test code 2300,7
+Unlisted Lab: Chikungunya antibody,7
+Unlisted Lab: JMML Associated Exon Panel,7
+Unlisted Lab: Ambry Genetics CardioNext Test #8911,7
+Unlisted Genetic Test: GeneDx infantile epilepsy panel,7
+Unlisted Lab: Molecular Genetics Lab Test sent ot St. Francis for GNAS1 testing. Requisition in Send Out,7
+Unlisted Lab: JC Virus DNA PCR from CSF fluid,7
+Unlisted Lab: LAB3413 - Cysticercus IgG Western Blot (from CSF),7
+Unlisted Lab: 2cc CSF FROZEN TUBE 3,7
+"Unlisted Lab: Hereditary angioedema type III. 1-5ml of blood in a purple tube, EDTA",7
+Unlisted Lab: Super coombs,7
+"Unlisted Lab: C3A (send out ot national jewish, requisition required)",7
+Unlisted Lab: Alpha Globin Gene - sendout lab to Mayo Clinic,7
+"Unlisted Lab: Encephalitis Antibody Panel (Test Code FENAP), Send Out to Mayo Clinic. Specimen 5ml of CSF (minimum 2ml)",7
+"Unlisted Lab: 48 hour platinum level. 1 ml in royal blue-top Vacutainer trace element tube. Send out to Mayo Lab, test code PTSE",7
+Unlisted Lab: PLA2R Autoantibodies (sendout to Cincinnati Children's),7
+Unlisted Genetic Test: Please collect 3ml sample per requisition form for pancreas panel in EDTA/lavender tube,7
+Unlisted Lab: clobazam level,7
+Unlisted Lab: Autoimmune Encephalitis panel,7
+Unlisted Lab: CSF: Mayo autoimmune encephalitis panel ENCEC,7
+Unlisted Lab: Mayo autoimmune encephalitis panel ENCEC,7
+"Unlisted Lab: 3mL in purple top, please give to sendout lab with Invitae paperwork (in green binder)",7
+Unlisted Genetic Test: Trio Whole Exome sequencing to Prevention Genetics,7
+Unlisted Lab: Plasma Global Metabolomic Assisted Pathway Screen,7
+"Unlisted Lab: CSF, pteridines. MNG Laboratories.",7
+Unlisted Lab: PRSS1 mutations,7
+"Unlisted Lab: CSF Pyruvate, MET11, MGN Lab sendout",7
+"Unlisted Lab: CSF Lactate, MET07, MNG Lab sendout",7
+"Unlisted Lab: Oklahoma Medical Research Foundation- Myositis Antibody Profile (2ml serum), P155/140 by Western Blot (1ml serum), MJ by western blot (1ml serum), MDA-5 by Wester Blot (1ml serum)",7
+"Unlisted Lab: send out to viracor IBT, 1,3 beta-d-glucan test code 1700, red top tube 1 ml",7
+Unlisted Lab: Liver cytosol AutoAb-1,7
+Unlisted Lab: CPML to be sent out to GeneDx,7
+"Unlisted Lab: Send out to focus Bartonella DNA, Qualitative Real-Time PCR  (test code 47000), on liver biopsy tissue",7
+"Unlisted Lab: Please send CSF to Mayo clinic laboratories for: Test ID: CAVPC, California Virus (La Crosse) Encephalitis Antibody Panel, IgG and IgM, Spinal Fluid",7
+"Unlisted Lab: FOXP3, Regulatory T-cells",7
+Unlisted Lab: Dilated Cardiomyopathy NGS panel,7
+"Unlisted Lab: Mycoplasma Antibodies, IgG and IgM in CSF",7
+"Unlisted Lab: C3a, C4a, C5a desArg level by RIA",7
+Unlisted Lab: 8-10 mL of blood for T helper IL 17 and toll like receptor labs to childrens hospital of wisconsin,7
+Unlisted Lab: GeneDx epilepsy panel,7
+Unlisted Lab: Botulism State Lab Send Out,7
+Unlisted Lab: FSMN: Sensory motor neuropathy evaluation complete.  Send out to Mayo,7
+"Unlisted Lab: Aspergillus DNA PCR, Quant",7
+BMT Panel II,7
+Unlisted Lab: GeneDX Panel for infantile spasms,7
+Unlisted Lab: Aspergillus PCR quantitative,7
+Unlisted Lab: D lactate,7
+Isolator Fungus,7
+t1517 PMLrar,7
+Unlisted Lab: comprehensive arrhythmia panel,7
+"Unlisted Lab: BK IgG ELISA, sendout to Viracor, 4 mL in EDTA lavender tube, 2300",7
+Unlisted Lab: MJ by western BLot,7
+"Unlisted Lab: Send out to viracor IBT, (1,3)-beta-d-glucan, test code 1700, red top tube 1ml",7
+Unlisted Lab: C4a to Mayo lab FC4a,7
+Unlisted Lab: C5a,7
+Unlisted Lab: mycoplasma PCR nasopharyngeal swab,7
+"Unlisted Lab: JC Virus DNA, Qualitative PCR from Urine.  Send out to ARUP.",7
+Unlisted Lab: Whole exome sequening,7
+Unlisted Lab: Myositis antibody panel,7
+"Unlisted Lab: 24 hour platinum level. 1 ml in royal blue-top Vacutainer trace element tube. Send out to Mayo Lab, test code PTSE",7
+Unlisted Lab: Gene testing for inherited FSGS,7
+Unlisted Lab: Hemophagocytic Lymphohistiocytosis (HLH) panel,7
+"Unlisted Lab: Encephalitis Antibody Screen from CSF collected on 4/25/15, send out to Mayo please. Thanks.",7
+Unlisted Lab: JC virus PCR from CSF sample,7
+Unlisted Lab: lysosomal enzyme assay,7
+"Unlisted Lab: Very Long Chain Fatty Acid Profile, Peroxisomal Fatty  Acid Profile (C22-C26)",7
+"Unlisted Lab: Telomere lengthening test sendout to Repeat Diagnostics in Canada, EDTA lavender top tube",7
+Unlisted Lab: Gene test ATP7B (to test for Wilson's disease),7
+Unlisted Lab: Amino Acids,7
+Unlisted Lab: Lactate,7
+Unlisted Lab: Proband Whole Exome Sequencing to be sent be Baylor Genetics,7
+Unlisted Lab: GSD ComprehensivePanel by Massively Parallel Sequencing,7
+Unlisted Lab: HOUR 6 cisplatin level,7
+Unlisted Lab: HOUR 36 cisplatin level,7
+Unlisted Lab: HOUR 18 cisplatin level,7
+Unlisted Lab: Borrelia Species by PCR (Lyme Disease),7
+Unlisted Lab: Anti Aquaporin 4 IgG,7
+"Unlisted Lab: CSF Sendout: CSF Amino Acids, Glycine, Folate Metabolites, CSF P5P, CSF succinyladenosine, Sialic Acid, Alpha-aminoadipic semialdehyde",7
+"Unlisted Lab: Send out to Wisconsin laboratory, 5 ml plain red top tube, send with paper req",7
+"Unlisted Lab: 9 hour platinum level. 1 ml in royal blue-top Vacutainer trace element tube. Send out to Mayo Lab, test code PTSE",7
+Unlisted Lab: Interleukin-18 (IL-18) Cincinnati Children's Diagnostic Immunology Laboratory,7
+"Unlisted Lab: Autoimmune Encephalitis Panel from CSF, Send out to Mayo Clinic. Lab Code: ENCEC",7
+"Unlisted Lab: send out lab: mayo clinic Test ID: ENCEC Encephalopathy, Autoimmune Evaluation, Spinal Fluid",7
+Unlisted Lab: anti-LGI1 receptor antibody,7
+"Unlisted Lab: 12 hour platinum level. 1 ml in royal blue-top Vacutainer trace element tube. Send out to Mayo Lab, test code PTSE",7
+"Unlisted Lab: Aspergillus PCR , qualititative. Serum study.",7
+VDRL CSF,7
+Unlisted Lab: CSF Measles IgG IgM antibody panel IFA  (60675 Focus laboratories) - Minimum 0.75ml,7
+Unlisted Lab: Anti Ganglioside  Antibodies. Send out to Mayo Clinic.,7
+Unlisted Lab: Cocci complement fixation antibody testing on pleural fluid sample,7
+Unlisted Lab: Proband Whole Exome Sequencing 5-10mL EDTA,7
+Unlisted Lab: Pyridoxal 5'-phosphate (CSF) send out to MNG Laboratories. Test Code: NC05. Requires 1mL CSF stored frozen.,7
+Unlisted Lab: 1/2 cc red tube,7
+"Unlisted Lab: C4A, C5A, C3A to be sent to Natinal Jewish Hospital Lab in Denver",7
+"Unlisted Lab: AH50 (per rheumatology, test for complement alternate pathway).",7
+"Unlisted Lab: Send-out: Inherited Focal and Segmental Glomerulosclerosis (FSGS) Evaluation, 10mL, Lavender top",7
+Heparin Platelet Factor 4 AB,7
+Unlisted Lab: whole exome sequencing SENDOUT to Baylor. Must arrive within 48 hours of being drawn.,7
+Unlisted Lab: anti-AMPA receptor antibody,7
+Unlisted Lab: CSF 5-methyltetrahydrofolate,7
+Unlisted Lab: see below,7
+"Trichomonas vaginalis, NAAT",7
+"Unlisted Lab: C4a (send out, req required, National Jewish)",7
+Unlisted Lab: Azithromycin drug level 2009359 ARUP labs,7
+Unlisted Lab: C4a desArg level by RIA,7
+"Unlisted Lab: Cunningham Panel (Anti-Dopamine Receptor D1, Anti-Dopamine Receptor D2L, Anti-Lysoganglioside GM1, Anti-Tubulin, CaMKII)",7
+Unlisted Lab: Send 3-10cc blood in an EDTA (purple top tube),7
+Unlisted Lab: Whole Exom Sequencing,7
+Unlisted Lab: GeneDx comprehensive cardiomyopathy panel (test #694),7
+Unlisted Lab: CSF Tetrahydrobiopterin and neopterin profile (NC03),7
+"Unlisted Lab: Mayo Clinic Mayo Medical Laboratories - Please send with attached ""Neurology Test Request Form:",7
+Unlisted Lab: mitoNGS,7
+Unlisted Lab: Infectious meningoencephalitis panel (FMEPL) send out to Mayo clinic,7
+Unlisted Lab: RT-PCR for chikunguna - to Focus diagnostics 45606,7
+Unlisted Lab: RT-PCR for dengue fever - to focus diagnostics 43400,7
+Unlisted Lab: pyruvate,7
+Unlisted Lab: Mayo Encephalopathy Panel (test id:ENCES),7
+Unlisted Lab: Oklahoma Medical Research Foundation Clinical Immunology Laboratory - myositis antibody profile,7
+Unlisted Lab: aHUS genetic panel order form.  Please draw 1 3 mL EDTA whole blood tube. Machaon diagnostics form is in paper chart.,7
+Unlisted Lab: Oklahoma Medical Researh Foundation Clinical Immunology Laboratory - MDA-5 (CADM-140),7
+Unlisted Lab: Mayo Clinic encephalopathy panel CSF (Test ID: ENCEC),7
+Unlisted Lab: CSF Methotrexate Level; Seattle Children's,7
+Unlisted Lab: Drug Dependent Platelet Antibody,7
+Unlisted Lab: Pancreatic Secretion Fluid - Enzyme Analysis with secretin stim test,7
+"Unlisted Lab: 1,3 beta D glucan: 2-3 ml red top tube",7
+MD/APP to RN Communication -- OK to move chemo up by 2 hours on 2/1/19,7
+Unlisted Lab: Alpha-aminoadipic semialdehyde (CSF) send out to MNG Laboratories. Test Code: NC 08. Requires 1mL CSF stored frozen.,7
+Unlisted Lab: Duplication and Deletion Analysis of ATP7B to Baylor. Test code 2553,7
+"Unlisted Genetic Test: Send out to invitae, comprehensive epilepsy panel, TEST CODE: 03401",7
+Unlisted Lab: Medical Neurogenetics,7
+Unlisted Lab: FoxP3,7
+Unlisted Lab: invitae 17 gene panel for long qt syndrome,7
+Unlisted Lab: interleukin-18 (IL-18) | Cincinnati Children's Diagnostic  immunology,7
+Unlisted Lab: send out lab to Mayo POXP phytanic acid plasma sample EDTA lavender,7
+Unlisted Lab: Childhood Onset Epilepsy Panel through GeneDx. Lavender Top Tube.,7
+"Unlisted Lab: 1,3 Beta D glucan",7
+Unlisted Lab: Quest qualitative aspergillious DNA PCR,7
+Unlisted Lab: Super-Coombs,7
+Unlisted Lab: Anti enterocyte,7
+Unlisted Lab: GeneDx Comprehensive Cardiomyopathy Panel,7
+Unlisted Lab: lysosomal enzyme panel,7
+"Unlisted Lab: Encephalopathy, Autoimmune Evaluation, Spinal Fluid--send to Mayo",7
+Unlisted Lab: Please draw 3-5 ml of blood to be collected into EDTA (purple top) as well as 3-5 ml to be drawn into green top to be sent via send out lab for whole exome and SNP,7
+"Unlisted Lab: body fluid pH from pancreatic drain, prior to MRCP imaging",7
+"Potassium, FLD",7
+Unlisted Lab: JaundiceChip Re-sequencing Array,7
+Unlisted Lab: Bile acid assay urine,7
+"Unlisted Lab: C4A (send out ot national jewish, requisition required)",7
+Unlisted Lab: rifabutin drug level 2009363 ARUP labs,7
+"Unlisted Lab: 1,3 beta-d-glucan",7
+Unlisted Genetic Test: Invite arrhythmia and cardiomyopathy comprehensive panel,7
+Unlisted Lab: adenosine deaminase pleural fluid,7
+Unlisted Lab: Serum free copper level,7
+HIV-1/HIV-2 Antibody Confirmation,7
+Unlisted Lab: Trio Whole Exome Sequencing PLUS comprehensive MtDNA analysis,7
+Unlisted Lab: Please draw 5 ml of blood in a lavender tube for repeat diagnostics,7
+Unlisted Lab: Aspergillus PCR.  Send out to Focus Diagnostics.  CPT code: 87798X3,7
+Unlisted Lab: Autoimmune Encephalitis Panel (Mayo Clinic) - labe code ENCEC,7
+Unlisted Lab: Aquired Neuropathy panel send out to Wash U,7
+Unlisted Lab: BK IgG Elisa to Viracor (test code 2300),7
+Prader Willi Syndrome,7
+Unlisted Lab: Send out lab to hematologics for PML-RARA testing. Please add on to bone marrow sample from 9/8.,7
+"Unlisted Lab: Machaon Diagnostics, aHUS Genetic Panel, 3 mL in a purple top tube",7
+Vasoactive Intest Polypep,7
+Unlisted Lab: Factor D level through National Jewish Health Advanced Diagnostic Laboratories,7
+Unlisted Lab: beta-1 d-glucan,7
+"Hemoglobin, Urine",6
+Unlisted Lab: SLC12A3 gene sequencing and del/dup analysis; send to Medical Neurogenetics Laboratories,6
+"Myeloid Panel, Non-Blood",6
+Unlisted Lab: Emory IBD Panel. Ship sample at room temperature with overnight delivery.,6
+"BCR-ABL1 Kinase Domain Analysis, Non-Bld",6
+Unlisted Lab: Invitae LQTS panel,6
+Transfusion Svc Prod 15,6
+"Unlisted Lab: Mayo Clinic Laboratories.  Test ID EPIES (61512) Epilepsy Autoimmunology Evaluation, serum",6
+Orthopedic Cult Mult Specimens,6
+CNS Demyelinating Disease Eval with Reflex,6
+"N Methyl D Aspartate Receptor Ab, CSF",6
+Chlamydia Serology,6
+Natural Killer Cells,6
+Unlisted Lab: 1 mL blood in royal blue top tube send out to Mayo for serum platinum level to be drawn 12.5 hours after completion of Cisplatin infusion,6
+"Unlisted Lab: POMPE Disease: Acid Maltase Activity (acid alpha-glucosidase, GAA) from Whole Blood (3-5cc EDTA tube)",6
+"Calcitonin, Serum",6
+Yersinia Enterocolitica AB,6
+Transfusion Svc Prod 14,6
+Pediatric EKG Rhythm Strip,6
+C1q Binding Assay,6
+Direct Parasite Exam,6
+"pH, Pleural Fluid",6
+"Beta 2 Microglobulin, Serum",6
+Unlisted Lab: Please draw 1 lavender top tube and 1 PAXgene tube (2.5 mL) and send to lab for send-out testing to be sent to FM.  Time sensitive specimen.  Must be sent ttoday.  Thanks!,6
+"BCR ABL, BLD",6
+Research Lab Draw: M16-106: Venetoclax PK - Hour 8,6
+Massive Transfusion Guideline,6
+T7 Thyroid Panel,6
+"21 Hydroxylase Ab, Ser",6
+Flow Cytometry Comp Hem Cases,6
+Euglobulin Clot Lysis,6
+Aminolevulinic Acid Urine Timed,6
+"Amphetamine Confirmation, Urine",6
+"Unlisted Lab: Enterovirus PCR, stool",6
+Nasal Culture,6
+"BCR ABL Quant, BM",6
+Miscellaneous Allergen 1,6
+"Herpes Viral 8 DNA, Quant",6
+Unlisted Lab: HLH genetic testing,6
+Unlisted Lab: Ambry Genetics CardioNEXT (Test #8911)- Kirsten Brown Self,6
+"Histamine Level, Urine",6
+"Gliadin (Deamidated) IgA, Serum",6
+"Unlisted Lab: Telomere length testing sendout to Repeat Diagnostics in Canada, EDTA lavender top tube (10ml)",6
+"Hemosiderin, Urine",6
+Busulfan 30 Min Post Infusion,6
+"Chikungunya Virus RT-PCR, Blood",6
+Unlisted Lab: c/f AML (please add on to blood drawn at 0917),6
+Celiac Genetics,6
+Growth Hormone Level (RIA),6
+CMV Genotype,6
+Rubeola IgM,6
+"Heavy Metal/Creatinine Ratio with Reflex, Ur",6
+Unlisted Lab: send out for FSGS genetics,6
+"Glucose Urine Random, w/Creat",6
+GC Molecular Detection,6
+Biochem Genetic Misc Test,6
+"17 Hydroxycorticosteroids, Urine",6
+Unlisted Lab: Collagen VI panel to PreventionGenetics. Please draw 6 mL whole blood in EDTA purple top tubes. Req form to accompany sample. (Order 718008305),6
+"SF3B1 Mutation Analysis, Non Blood",6
+Unlisted Lab: Send out test to the CDC through Santa Clara Public Health Department. Cysticercosis Serology. CDC-10459. Please send 0.5 ml of blood in SST Gold-top gel tube,6
+IgG Anti DGP,6
+Unlisted Lab: Epilepsy gene panel with reflex to whole exome sequencing,6
+HLA Kit Req Recipient No Donor Info,6
+Janus Kinase 2 V617F Mutation,6
+Hemoglobin A1c  with Est Mean Glucose,6
+HLA Unrelated Donor Processing,6
+"Unlisted Lab: Autoantibody to interferon gamma. Collect in a plain red top, SST or green tube top. minimum volume, 0.5 ml but 1 ml preferred volume. Please send with requistion form which is in chart.",6
+C5a Level,5
+Donath Landsteiner Test,5
+Tetrahydrobiopterin/Neopterin,5
+Human Papilloma Virus 6/11 PCR,5
+CPR for Family and Friends - Infants,5
+"Drug Screen, Plasma",5
+Osmotic Fragility,5
+"Cocci CF AB, CSF",5
+"Gastrin, Serum",5
+"Albumin, Urine (Timed)",5
+HLA Additional Specimen (Recip Typing),5
+Milk,5
+Cytokines Plasma,5
+CPR for Family and Friends-Adult/Child,5
+Unlisted Immunology: ENCEPHALOPATHY AUTOIMMUNE EVALUATION SERUM,5
+Hemoglobin Lab Only,5
+Bone Culture/GS,5
+Unlisted Genetic Test: Comprehensive mitochondrial sequencing sendout to Baylor - recquisition form is in the media tab,5
+"Manganese, Serum",5
+Donor HTLV I/II Confirmatory,5
+Dientamoeba fragilis Microscopic Exam,5
+Toxoplasma IgA,5
+Apolipoprotein A1,5
+Thiocyanate Level,5
+Strep Group B Screen by PCR,5
+Entamoeba Histolytica Ag,4
+"Myasthenia Gravis Evaluation, Adult",4
+VZV Quant,4
+Retinol Binding Protein,4
+STAMP Block/Slide Request,4
+"Oxalate, 24-Hour/Timed",4
+"Meningococcal Ab,Vaccine Response",4
+C Trachomatis / Psittaci Culture,4
+"Creatine Guanidinoacetate, Ur",4
+"Progesterone Level, Serum/Plasma",4
+"Nicotine and Metabolites Level, Urine",4
+"HVA, Timed Urine",4
+Acetylcholine Blocking Ab,4
+"Fever Syndromes Panel, Periodic",4
+Meningococcal Ab Vaccine,4
+"Carnitine, Urine",4
+"Occult bld, Immuno Colorectal Scn",4
+"VMA, Timed Urine",4
+Vedolizumab Antibodies,4
+"Oligosaccharides TLC, Urine",4
+"ACT, Istat Kaolin Circuit",4
+Citrate Excretion Urine 24 hours,4
+Lysosomal Disease Panel,4
+"Histoplasma Antigen Reflex, Urine",4
+Adenovirus Rapid Culture,4
+Blastomyces Serologies,4
+"S Sulfocysteine, Urine",4
+"Echinococcus AB, IgG w/Reflex",4
+Iodine Level,4
+Chestnut,4
+Branched Chain Amino Acids,4
+"IgD, Serum",4
+Walnut,4
+"Occult bld, Guaiac Diag Single",4
+RPR Qualitative,4
+Hazelnut,4
+Cashew,4
+"Albumin Urine, Timed",4
+"Cortisol Free, Serum",4
+Leptospirosis AB,4
+Bordetella pertussis NAAT,4
+"Estrogens, Fractionated",4
+Pecan,4
+C3a Level,4
+Toxocara AB,4
+Francisella Tularensis AB,4
+Unlisted Chemistry: HOLD one red top tube drawn on 12/7/14.,4
+Adenosine Deaminase Pleural,4
+"Occult bld, Gastric",4
+Fibrinogen Quant,4
+"Coenzyme Q10 Level, Reduced & Total",4
+Infliximab Serum Level and Ab,4
+clonazePAM Level,4
+Brazil Nut,4
+Heparin (HIT) Function Assay,4
+Pistachio,4
+Thrombin Time,4
+"Amylase Urine Random, w/Creat",4
+"Vitamin D 1 25 Dihydroxy, Serum",4
+Auto Lymph Prolif Syndrome,4
+Hematocrit Lab Only,4
+TRK Flow Cytometry for Leukemia/Lymphoma,3
+Intrinsic Ftr Blk Ab,3
+Miscellaneous Allergen 3,3
+"Uric Acid, Fluid",3
+"Nucleic Acid Isolation, DNA",3
+Adalimumab Qnt w Reflex to Ab,3
+"Trypsinogen, Serum",3
+"Unlisted Chemistry: send out to Viracor IBT, (1-3)-beta-d-glucan test code 1700, red top tube, 1ml",3
+Trichomonas Exam,3
+"Unlisted Chemistry: HLA B27, order code SHLA",3
+Handling & Storing Breastmilk,3
+"BCR ABL, BM",3
+Unlisted Molecular: Alagille syndrome (JAG1) full sequence analysis (Jaundice CHIP),3
+"Aldosterone, Serum (Adult)",3
+"Insulin,1 Hour",3
+Sputum for Eosinophils,3
+Misc Chemistry,3
+IgG Subclass 1,3
+Celiac Plus,3
+Von Willebrand Scn,3
+"Serotonin, Serum",3
+"Direct Bilirubin, Fluid",3
+Unlisted Chemistry: infliximab antibody,3
+"B Cell Clonality PCR, BM",3
+"T Cell Clonality PCR, BM",3
+Central Line the Basic Teaching,3
+IgG Anti TTG,3
+IgG Subclass 3,3
+"Mycoplasma Pneumoniae IgG, Ab",3
+"Mucopolysaccharides TLC, Urine",3
+A Phagocytophilum AB,3
+Muscle Tyrosine Kinase AB,3
+Unlisted Hematology: 2-4 mL whole blood (EDTA),3
+"Histoplasma Ag, Urine/Fluid",3
+Unlisted Cyto: Cincintti Children's Familial HLH testing,3
+Acetylcholine Modulating Ab,3
+Slide Interp/Pathologist,3
+"Acetylcholinesterase, Erythrocytes",3
+Olanzapine Level,3
+"Aminolevulinic Acid, Urine",3
+Feeding and NG Tube-Heart Center,3
+Unlisted Cyto: Array CGH cytogenetics,3
+Unlisted Immunology: Encephalopathy Autoimmune Evaluation Panel from Serum,3
+WBC Count Lab Only,3
+Angiotensin Converting Enzyme,3
+POCT pH Paper for PH Measurement,3
+"Insulin,2 Hour",3
+"Insulin, Fasting",3
+"Myoglobin Screen, Urine",3
+Creatine Guanidinoacetate,3
+Chlamydia IGG/IGA/IGM AB,3
+"T3 Reverse, LC/MS/MS",3
+"MOG-IgG1, FACS",3
+T Helper IL17,3
+"Bilirubin Indirect, Ser/Plas",3
+Encephalitis Ab Screen,3
+Soy,3
+"ANA, HEp-2, IgG IFA",3
+"Unlisted Chemistry: Sendout to Focus, test name Aspergillus antibody CF serum, test code 40150, 1ml serum.",3
+clomiPRAMINE and Metabolite Level,3
+Pain Teaching,3
+Unlisted Chemistry: JP fluid - please check for total bilirubin,3
+Miscellaneous Allergen 2,3
+Brucella Ab Agglutination,3
+Busulfan Pre Dose,3
+"Arsenic Level, WB",3
+CK Isoenzyme Electrophoresis,3
+"HIV-1 RNA, Qualitative TMA",3
+Vedolizumab with Reflex to Antibodies,3
+"Testosterone, Total Bio Free",3
+Cysticercus IgG Antibody,3
+"Insulin,30 Min.",3
+C1 Esterase Inhibitor Antigen,3
+Unlisted Chemistry: Free dopamine,3
+"Sodium, Stool Timed",3
+Unlisted Immunology: Adalimumab trough level and adalimumab antibodies,3
+Research Lab Draw: M16-106: Venetoclax PK - Hour 4,2
+Research Lab Draw: AML16 Cell Free DNA: Day -4 - Due PRIOR to start of chemotherapy,2
+"ACT, Istat Kaolin Post Oxy",2
+"Research Lab Draw: M16-106: Venetoclax PK - Day 2, Hour 6",2
+CASPR2-IgG CBA,2
+"Unlisted Micro/ Viro: mycoplasma titer IgG, IgM.",2
+Egg White,2
+Research Lab Draw: ANBL00B1,2
+Chromium Ser/Plas,2
+HLA B27 Ag,2
+SNP Microarray (Pediatric),2
+"Reverse T3, Serum",2
+Research Lab Draw: AML16 Cell Free DNA: Day 22 - Due PRIOR to start of chemotherapy,2
+"Research Lab Draw: M16-106: Venetoclax PK - Day 2, Hour 2",2
+"Copper/Creatinine Ratio, Urine (Random)",2
+Research Lab Draw: AML16 Immune Repertoire: Day -4 - Due PRIOR to start of chemotherapy,2
+Research Lab Draw: M16-106: Venetoclax PK - Hour 2,2
+Pinon Nut IgE,2
+T Cell Interleukin Prolif,2
+Felbamate Level,2
+"Research Lab Draw: M16-106: Venetoclax PK - Day 2, Hour 4",2
+Unlisted Immunology: Encephalopathy Autoimmune Evaluation Panel from CSF,2
+Macadamia Nut IgE,2
+HIV 1 Genotype and Antivir Resist,2
+Research Lab Draw: BMT 331,2
+LAB321B Antibody Differentiation,2
+"ACT, Istat Kaolin Mixed Venous",2
+"Glucose Tolerance for CF, 2 Hour",2
+Research Lab Draw: biomarkers to identify T cell mediated acute liver injury in pediatric patients,2
+Research Lab Draw: BMT 351,2
+"Cancer Somatic Mutation Pnl, BM",2
+Carboxyhemoglobin,2
+Research Lab Draw: BMT 331 (PBMTC study) ** RESEARCH BLOOD SAMPLE ** (YELLOW TOP),2
+Haloperidol Level,2
+Deoxycorticosterone,2
+Carbamazepine Level,2
+"Pbg Deaminase, RBC",2
+Thyroglobulin & Thyroid Ab,2
+"Adulterants Survey, Urine",2
+"C1 Inhibitor, Function",2
+Fam Mediterranean Fever DNA,2
+Unlisted Cyto: Prevention Genetics- KCNT1 Sanger Sequencing,2
+Blastomycosis Ab,2
+Unlisted Molecular: Comprehensive Arrhythmia Panel and Comprehensive Cardiomyopathy Panel,2
+LGI1-IgG CBA,2
+Anti Mullerian Hormone Level,2
+"17 OH Pregnenolone Level, Peds",2
+Adenovirus AB IGG and IGM,2
+IgG Subclass 2,2
+"S. Pneumoniae AG Detection, LA",2
+Thyroid Stim Immunoglobulin,2
+"PTH, Other",2
+"Schistosoma IgG AB, FMI, Serum",2
+"C1 Inhibitor, Quant",2
+C3a des Arg Fragment,2
+"Unlisted Immunology: Autoimmune Encephalopathy panel, blood sample.",2
+"Arsenic Fractionation, Random Urine",2
+Phenytoin Level Total and Free,2
+"17-OH Progesterone, Neonatal",2
+Pinworm Exam,2
+Cytokines CSF,2
+Dihydrotestosterone,2
+CK Isoenzyme with Reflex,2
+Unlisted Micro/ Viro: Chlamydophila antibody panel,2
+Individual/Proband Clinical Exome Sequencing,2
+Fanconi Mutation Analysis,2
+"Research Lab Draw: M16-106: Venetoclax PK - Day 2, Hour 8",2
+Research Lab Draw: AML16 Immune Repertoire: Due PRIOR to start of chemotherapy,2
+Research Lab Draw: AbbVie - RAD52D mutation,2
+"Arsenic, Random Urine",2
+HIV 1 Integrase Resistance,2
+Peanuts,2
+"Viscosity, Serum",2
+Hepatitis D RNA Qual PCR,2
+Research Lab Draw: CCT5007/CCT5019 - Day 13 (CyTOF and Cytokines),2
+HE4 Ovarian Cancer Monitoring,2
+GBM IgG,2
+Bartonella Henselae IgG Titer,2
+Plasminogen,2
+"Unlisted Micro/ Viro: Aspergillus Antibody, Serum",2
+Hepatitis D Virus AB Total,2
+Interleukin 5 Level,2
+"Nickel, Serum",2
+Research Lab Draw: BMT 179 ** RESEARCH BLOOD SAMPLE ** PURPLE TOP (Once),2
+Testosterone Level (Adult),2
+Glucose Check PRN,2
+Thermal Amplitude Screen,2
+Almond,2
+Insulin Fasting,2
+"Red Blood Cells, Emergency Release",2
+Unlisted Chemistry: 2-5 mL Blood - lavender top tube to gene dx for schwachman diamond testing,2
+"Glucose Tolerance for CF, 1 Hour",2
+Unlisted Chemistry: Acetazolamide quantitative level,2
+Aspergillus Mold,2
+Research Lab Draw: AbbVie M16-106: PK - Hour 2,2
+Research Lab Draw: M16-106: Venetoclax PK - Hour 1,2
+"IGF-I, LC/MS",2
+Growth Hormone Antibody,2
+"Mucopolysaccharides, Urine",2
+Research Lab Draw: BMT 179 ** RESEARCH BLOOD SAMPLE ** (PURPLE TOP:Every Monday),2
+Unlisted Immunology: ENCEPHALOPATHY AUTOIMMUNE EVALUATION CSF,2
+Misc Coagulation,2
+"Unlisted Chemistry: infantile seizure panel, STAT epilepsy panel, sendout test to gene dx",2
+Unlisted Chemistry: FUIQL Urine Indicans Qualitative Sendout to Mayo Clinical Labs,2
+Research Lab Draw: Bass Center Tissue,2
+AmniSure ROM,2
+Unlisted Molecular: Autoimmune Encephalitis Panel,2
+Research Lab Draw: M16-106: Venetoclax PK - Hour 6,2
+"Unlisted Chemistry: Lavender tube, at least 4ml; for Adaptive Technologies immunoglobulin sequencing",2
+Unlisted Chemistry: whole exome sequencing,2
+Histoplasmosis AB,2
+Research Lab Draw: AML16 Cell Free DNA: Due PRIOR to start of chemotherapy,2
+Aspirin Resistance,2
+"Research Lab Draw: M16-106: Venetoclax PK - Day 2, PRIOR to Venetoclax dose",2
+"aPTT, ACLTOP Method",2
+"Research Lab Draw: Abbvie M16-106, Biomarker sample PRE-DOSE Day 1",2
+Research Lab Draw: BMT 179,2
+Adrenal Antibody Screen with Reflex to Titer,2
+Unlisted Immunology: Anti-neuronal antibody from CSF sample,2
+7 Dehydrocholesterol,2
+"Mercury Level, WB",2
+Lipoprotein Associated Phospholipase A2,2
+Chlamydia Trachomatis Culture,2
+"Adenovirus DNA, PCR Qualitative",2
+Unlisted Chemistry: lab 3229,2
+11 Deoxycortisol,2
+Wheat,2
+C5,1
+MOG FACS Titer,1
+"Cysticercus Antibody Reflex Panel, CSF",1
+Unlisted Molecular: succinyladenosine from CSF,1
+Leishmania Antibody IgG,1
+Epilepsy Advanced Sequencing Eval,1
+Unlisted Immunology: liver cytosol autoantibody 1,1
+"Neisseria Gonorrhoeae RNA, Throat",1
+Unlisted Chemistry: Send out  to Mayo for LCMS Tacrolimus FK506,1
+Trofile DNA,1
+Trazodone Level,1
+"Unlisted Micro/ Viro: Mycoplasma Pneumoniae Antibodies (IgG, IgM) Send-out test code 34127",1
+"Progesterone Level, Pediatric",1
+Entamoeba Histolytica AB,1
+HLA Kit Processing Recipient,1
+"Neuronal Ceroid Lipofuscinosis, PPT",1
+"Histamine Level, WB",1
+"Homocysteine Quant, Urine",1
+Globulin,1
+"Cadmium Level, WB",1
+Unlisted Microbiology/ Virology Test,1
+Glutaric Aciduria Type 1 (GA1),1
+(NMO)/Aquaporin-4-IgG Cell Binding Assay,1
+PNH Sequencing/Deletion (FLNA),1
+"IgM, CSF",1
+"CO2, Ser/Plas",1
+Toll Like Receptor Function,1
+Toxoplasma Newborn Panel,1
+Hepatitis B e Antigen,1
+C1 Esterase Inhib Pl,1
+MtDNA Sequencing (MitoNGS),1
+IGF Binding Protein-1,1
+"Cocci CF AB, CSF (Follow-up Only)",1
+"HSV 1 and 2 PCR, Aqueous FLD",1
+Unlisted Chemistry: send out test to Duke biochemical genetics laboratory,1
+Cytokines Intracellular,1
+Dilated Cardiomyopathy Panel,1
+HLA Specimen in Lab Orders - Donor,1
+Mumps Rapid Culture,1
+Desipramine Level,1
+Parvovirus B19 Ab IgG,1
+"NF 1 DNA, Comp",1
+MSUD Mutation Analysis,1
+Limb Girdle Muscular Dystrophy,1
+"Creatinine Clearance, BLD",1
+Adalimumab Ab,1
+"Estrone, Serum",1
+"H pylori, Urea Breath (Pediatric)",1
+Familial Hibernian Fever/Traps,1
+"AML Prognosis Assay, Bone Marrow",1
+Unlisted Micro/ Viro: mumps,1
+Copeptin proAVP,1
+"Thyroid Stimulating Ig, Serum",1
+Free T4 By Equilibrium Dialysis,1
+"Unlisted Micro/ Viro: Mycoplasma Pneumoniae Antibodies (IgG, IgM)",1
+"Cysticercus AB Reflex Panel, Serum",1
+T Cell Activation,1
+Glucose Tolerance Fasting,1
+"Estrogen Total, Level",1
+Epstein Barr Viral Capsid AG,1
+17 Alpha Hydroxyprogesterone,1
+Alagille (JAG1) Mutation,1
+Caffeine Level,1
+Unlisted Lab: Infantile botulism treatment and prevention program stool specimen,1
+Hepatitis B e AB,1
+Congenital Cyclic Neurtopenia,1
+SPECIFIC HLA AG TYPE,1
+Von Hippel-Lindau Syndrome,1
+Vasopressin/ADH Level,1
+"Manganese, WB",1
+Fibrospect HCV,1
+ACH Receptor Binding Ab,1
+Pm 1 Antibody,1
+Wiskott Aldrich Syndrome,1
+"Chloride, Ser/Plas",1
+Prothrombin Fragment 1+2,1
+inFLIXimab Serum Level and Ab,1
+CU Index Panel,1
+"D Dimer, Elisa",1
+Unlisted Immunology: anti-ribosomal P from CSF sample,1
+"Phospholipids, Serum",1
+JCV Virus Antibody Inhibition,1
+"HDL, Serum",1
+quiNIDine Level,1
+Unlisted Molecular: Whole exome sequence (send-out lab). 10ml in purple top tube.,1
+Bartonella Henselae IgM Titer,1
+Cold Antibody Titer,1
+"Neuronal Ceroid Lipofuscinosis, TPP",1
+SRA Unfractionated Heparin Level,1
+Gastric pH Strip (POC) PRN,1
+MD/APP to RN Communication -- Please hold suction on NG for one hour following administration of Prograf,1
+"Cholesterol, FLD",1
+"IgA, CSF",1
+Pleximmune,1
+Unlisted Cyto: CLN5 Full sequence with MLPA-deletion/duplication DNA test (send-out lab),1
diff --git a/scripts/PedsLabUtil/data/group_lab_name_counts_icu.csv b/scripts/PedsLabUtil/data/group_lab_name_counts_icu.csv
new file mode 100644
index 00000000..a1fe61dd
--- /dev/null
+++ b/scripts/PedsLabUtil/data/group_lab_name_counts_icu.csv
@@ -0,0 +1,1796 @@
+,group_lab_name
+"Istat EG7, Arterial",1309776
+CBC with Differential,474952
+Chem 10,358828
+"Istat EG7,Venous",358366
+"Metabolic Panel, Comprehensive",343529
+Renal Function Panel,333090
+"Istat CG4, Arterial",295955
+Glucose PCX Meter,189242
+Manual Differential/Slide Review,152041
+CBC,126451
+Red Blood Cells,118160
+CBC with Diff,115273
+"Blood Gases, Venous",107337
+"Blood Gases, Arterial",64351
+Urinalysis With Microscopic,63948
+Prothrombin Time,60651
+"Magnesium, Ser/Plas",52360
+"Istat CG4, Venous",50535
+"iSTAT CG8, Arterial",48087
+MRSA Screen,35057
+Part Thromboplastin Time,34910
+Platelets,32155
+Hepatic Function Panel,31767
+Fresh Frozen Plasma,31423
+Manual Differential,26291
+Fibrinogen,26223
+"Metabolic Panel, Basic",24778
+"Phosphorus, Ser/Plas",24064
+Respiratory Virus PCR Panel,20406
+Slide Review,19008
+Antibody Screen,17681
+Transfusion Svc Prod 1,17510
+"Amino Acids, Plasma",16973
+Thromboelastograph Clot Profile,16272
+Blood Culture,16103
+Respiratory Pathogen PCR Panel,15191
+C Reactive Protein,14810
+EKG 12-Lead,14254
+"Blood Gases, Capillary",14069
+Urinalysis Macroscopic Only,13616
+Chem 23,13613
+"Triglyceride, Ser/Plas",13364
+Glucose By Meter,12890
+"Sodium, Ser/Plas",12250
+"Vancomycin Level, Trough",11673
+"Tacrolimus Level, FK506",10606
+"Cell Count, CSF",10447
+Acylcarnitine Profile Quant,10425
+Urine Culture,10326
+ABO Rh Typing,9885
+Cryoprecipitate,9839
+Heparin Activity Level,8954
+Procalcitonin,8378
+Transplant Therapy Monitoring,7742
+CBC with Diff and Slide Review,7697
+"Istat EG7, Other",7603
+Respiratory Culture/GS,7461
+"Differential, CSF",7038
+Gamma Glutamyl Trans,6939
+"Ammonia, Plasma",6777
+Newborn Screen,6692
+"Bilirubin Total, Ser/Plas",5932
+"Urinalysis Screen, Culture if Positive",5638
+Blood Culture (Aerobic & Anaerobic Bottles),5428
+"Bilirubin Direct, Ser/Plas",5244
+"LDH Total, Ser/Plas",5185
+"T Subsets and B Lymphs, BLD",5030
+Gastroenteritis PCR Panel,4805
+Transfusion Svc Prod 2,4670
+"Istat EG7, Post Oxy",4641
+Lactic Acid Level,4612
+"Reticulocyte Count, Automated",4611
+Methemoglobin Level,4610
+Circuit Ionized Calcium,4543
+CSF Culture/GS --,4407
+Theophylline Level,4328
+"Istat CG4, Post Oxy",4290
+"IgG, Screening",4019
+"ACT, Istat Kaolin Venous",3980
+HSV DNA PCR Qual,3891
+"Drugs of Abuse Screen, Urine",3780
+"Uric Acid, Ser/Plas",3688
+"LMW Heparin, AntiXA",3396
+"ACT, Istat Kaolin Other",3388
+Anaerobic Culture,3207
+"C1q, Screening",3185
+HLA Typing,3159
+Blood Type Verification,3134
+"TEG, Perioperative",2995
+Blood Culture --,2914
+"CMV DNA PCR Quant, Plasma",2739
+"C1q, DSA",2723
+"Calcium Ionized, Whole Blood",2617
+Procainamide Level and Napa,2604
+HLA-IgG DSA,2592
+"iSTAT CG8, Venous",2552
+"Cell Count, Body FLD",2543
+NT proBNP,2515
+Phenobarbital Level,2450
+"Albumin, Ser/Plas",2404
+Anti-Platelet Therapy Response Assessment (Multiplate Method),2397
+Lidocaine Level,2310
+"Differential, Body FLD",2302
+"Epstein Barr PCR Quant, Plasma",2188
+Lipase Ser/Plas,2172
+Fluid Culture/GS,2167
+"Istat EG7, Circuit",2127
+"Glucose, CSF",2117
+"Protein Total, CSF",2116
+DIC Panel,2108
+"Creatine Kinase, Total",2036
+Hematocrit,1964
+"Vitamin D 25 Hydroxy, Serum",1934
+"ACT, Istat Kaolin Circuit",1888
+Troponin I,1874
+ABO/RH,1864
+"Istat EC3, Arterial",1831
+Phenytoin Level,1748
+Type and Screen,1733
+Carnitine Level Free and Total,1702
+Fungus Culture,1699
+Antithrombin III Level,1662
+"Fatty Acid Profile, Essential",1648
+TSH,1633
+Rituxan Panel,1627
+"Electrolyte Panel, UR w/ Creat",1594
+Free T4,1563
+Transfusion Svc Prod 3,1528
+"Sodium, Urine Random w/Creat",1497
+Cyclosporine Level,1494
+"Istat EG7, Mixed Venous",1485
+"Occult bld, Guaiac Diag",1398
+"IgG, Serum",1394
+"Cell Count and Diff, CSF --Tube #3",1369
+D Dimer,1359
+"Osmolality, Serum",1356
+"Cortisol, Serum",1336
+Gram Stain,1312
+Transfusion Svc Prod 4,1301
+Epstein Barr AB Panel,1296
+"Istat CG4, Other",1292
+"Differential, BAL",1273
+Ferritin Level,1264
+"Amylase, Ser/Plas",1214
+"Amino Acids, Urine",1207
+Platelet Count,1138
+Transferrin Saturation,1130
+Auto B Flow Crossmatch,1124
+"Organic Acids, Urine",1118
+"ACT, Istat Kaolin Arterial",1114
+Auto T Flow Crossmatch,1112
+"Potassium, Ser/Plas",1090
+Respiratory Culture,1084
+Adenovirus Real Time PCR Quant,1070
+Quantiferon Test Latent TB,1066
+Electrolyte Panel,1046
+PHENobarbital Level,1024
+"Creatinine, Ser/Plas",1019
+Bilirubin Total & Fractions,1003
+Allo T Flow Crossmatch,988
+HLA ALLO B FLOW CROSSMATCH,987
+Airborne Allergy Screen,947
+Prealbumin,929
+B Cell Phenotyping Profile,911
+"ESR, Sedimentation Rate Panel",906
+Hepatitis B Surf AB Quant,903
+"Osmolality, Urine",892
+AFB Culture Nonsterile,889
+Transfusion Svc Prod 5,875
+"Urea Nitrogen, Ser/Plas",857
+HLA Meds/Sensitizing Event Communication (No Blood Draw),854
+"CMV DNA PCR Qual, Non Plasma",851
+"ACT, ISTAT",836
+AFB Culture Sterile,836
+Creatine Kinase and CKMB,823
+Mycophenolic Acid Level,787
+"Vancomycin Level, Post Dose",785
+"Cell Count and Diff, FLD",783
+"Gentamicin Level, Post Dose",783
+"Pregnancy Test, Urine",770
+Factor V Leiden,757
+Transfusion Svc Prod 6,756
+"Gentamicin Level, Pre Dose",737
+Blood Culture --Central catheter,732
+Valproic Acid Level,732
+"C Diff Toxin B, Qual Real Time PCR",731
+Aspergillus Galactomannan,726
+Prothrombin 20210A,725
+"T Cell Phenotyping, Advanced",725
+"Calcium Ionized, Ser/Plas",722
+STR Chimerism,695
+Hepatitis B Surf Ag,685
+"Blood Gases, Circuit",680
+Wound Culture,677
+"Cell Count and Diff, CSF --Tube not numbered",670
+"Zinc, Ser/Plas",665
+Biopsy/Tissue with Gram Stain,664
+Specific Gravity,657
+"Selenium, Ser/Plas",652
+Sirolimus Level,621
+"Cell Count and Diff, CSF",618
+KOH Prep,617
+"Copper, Serum",602
+"Amino Acids, CSF",596
+Human Herpes Virus 6 Quantitative PCR,588
+Meconium Screen w/ Reflex to Confirm,580
+Specimen Remark,542
+Meningitis/Encephalitis PCR Panel,542
+"ACT, Istat Kaolin Mixed Venous",542
+HIV Antigen/Antibody Screen,541
+Specific ANA Panel,535
+"Vancomycin Level, Unspecified",533
+Reverse to Coombs A Cells,525
+Lupus Anticoagulant,516
+"Protein Urine Random, w/Creat",515
+Unlisted Lab,507
+Dilute Russell Viper Venom w/Reflex,480
+Mint Green Top,473
+Transfusion Svc Prod 7,469
+Cystatin C with Estimated GFR,462
+Anticardiolipin Ab,459
+Celiac Disease Screen,455
+Hepatitis C AB IgG,450
+"Enterovirus RNA, Qual",447
+Lavender Top,445
+CMV IgG,428
+"Lactate, WB",425
+Atrial Electrocardiogram,418
+Haptoglobin,405
+Hemoglobin,399
+Immunoglobulin Panel,398
+Beta 2 Glycoprotein 1,396
+Transfusion Svc Prod 8,393
+HAV Antibody IgG,393
+cycloSPORINE Level,390
+Hepatitis B Core AB Total,383
+"Fat Stain, Stool",378
+"TRIGLYCERIDES, FLUID",375
+Blood Culture (2 Aerobic Bottles),373
+Platelet Mapping,366
+Save CSF in Lab,363
+Beta Hydroxybutyrate,347
+Parathyroid Hormone,344
+C1q Monitoring,340
+"Tobramycin Level, Post Dose",337
+"Pathologist Review, Peripheral Smear",331
+"Glucose Nonfasting, Ser/Plas",324
+Transfusion Svc Prod 9,323
+Hemoglobin A1c,321
+Wound Culture/GS,320
+Direct Antiglobulin (Broad Spectrum),315
+"M Tuberculosis, PCR",314
+"ALT, Ser/Plas",303
+"Calcium, Ser/Plas",298
+EBV DNA PCR Qual,297
+HIV 1 RNA PCR Quant,295
+"Cholesterol Total, Ser/Plas",294
+"AST, Ser/Plas",294
+Antibody Identification,294
+"Istat EC3, Venous",292
+Transfusion Svc Prod 10,288
+"Sodium, Urine Random w/o Creat",284
+"HSV DNA PCR Qual, Non Lesion",284
+HLA ALLO B CDC CROSSMATCH,281
+HLA Typing Solid Organ Eval Recipient,280
+Lipid Panel with Direct LDL,276
+S pneumoniae Ab IgG (23 Serotypes),276
+Hepatitis Acute Panel,275
+Flow Cytometry Order,275
+CSF Culture/GS --Tube #2,272
+"IgA, Serum",270
+Acetaminophen Level,269
+"IgM, Serum",269
+Toxoplasma IgM and IgG,268
+Reverse to Coombs B Cells,267
+HLA ALLO T CDC CROSSMATCH,264
+Diagnostic Hepatitis B Panel,261
+CMV IgM,260
+Cystic Fibrosis Respiratory Culture,259
+Hepatitis A AB IgM,258
+Alkaline Phosphatase Total Ser/Plas,254
+"Glucose Fasting, Ser/Plas",252
+Pneumocystis Stain,252
+"Urea,Urine Random,w/Creat",248
+"Protein Total, FLD",248
+"LDH Total, FLD",242
+Factor VIII Assay,242
+Anti Nuclear AB,242
+Bordetella Pertussis PCR,241
+"Tobramycin Level, Random",241
+"Istat CG4, Mixed Venous",238
+HLA Transplant Event Communication,234
+VZV IgG IgM Panel,234
+Legionella Culture,233
+Lipid Panel w/ Calculated LDL,232
+"Reducing Subst, Stool",227
+Bacterial ID by Sequencing,226
+"Homocysteine Total, Plasma",224
+Enterovirus Panel II,221
+VZV DNA PCR Qual,219
+IgG Monitoring,219
+Pyruvic Acid,219
+Isohemaglutinin Titer,218
+CSF Protein & Immunofix Electrophoresis,218
+"Flow Cytometry T Cell Development, Blood",216
+Levetiracetam Level,211
+Reference Report,210
+"Enterovirus PCR, CSF",209
+HLA Antibody Screening (One Time),207
+"Calcium Urine, Random w/Creat",207
+"iSTAT CG8, Other",206
+Herpes Simplex Virus IGG Panel,197
+Phosphatidylserine/prothrombin Antibodies,196
+"Alpha Feto Protein, Serum",195
+"Cell Count and Diff, CSF --Tube #1",193
+"Complement C3, Serum",193
+"Complement C4, Serum",192
+"pH, Stool",192
+Anti Phospholipid Ab Panel,191
+levETIRAcetam Level,190
+Blue Top,190
+BK Virus PCR Quant,185
+Carbohydrate Def Transferrin,184
+Long Chain Fatty Acids,183
+"Fungal Culture, Blood",182
+"Glucose, FLD",181
+Anti DsDNA Antibody,181
+Volatile Screen,177
+Stool Culture,176
+"G6PD, Quantitative",171
+"iSTAT CG8, Mix Venous",168
+Creatin Kinase and CKMB,166
+Unlisted Chemistry Lab Order,162
+Salicylate Level,159
+Varicella Zoster AB IgG,159
+HLA DSA Screening-No Desensitiz/No IVIG,156
+Transfusion Svc Prod 11,153
+Syphilis Treponemal Screen,152
+Protein S Activity,150
+"Adenovirus DNA, QL PCR",149
+pH Gastric,149
+RPR,148
+"Coxsackie A AB, Serum",146
+"Amikacin Level, Post Dose",146
+"Cryptococcal Antigen Lateral Flow, Blood",144
+Von Willebrand Antigen,141
+AB Screen,140
+Gold Top,137
+"Acylglycines Quant, Urine",136
+"Coxsackie B AB, Serum",135
+Urinalysis Screen/Culture,134
+Fungal ID by Sequencing,133
+Measles IgG,133
+Transfusion Reaction Workup,133
+Paraneoplastic Autoantibody Eval Serum,132
+Protein C Activity,132
+HLA Summaries,130
+"Pregnancy Test, Serum",129
+"Hepatitis B DNA, PCR Quant",128
+Blood Culture --Arterial line,127
+"iSTAT CG8, Circuit",126
+"Vitamin B12, Ser/Plas",126
+"Istat CG4, Circuit",126
+"Creatinine, Urine Random",124
+CSF Culture/GS,121
+Fungus Culture/ KOH,121
+"Parvovirus B19 Ab, IgG and IgM",119
+Toxoplasma IgG Ab,119
+Thiocyanate Level,119
+"Urea Nitrogen, Urine Random",119
+Methylmalonic Acid Level,119
+"Antithrombin Antigen, Plasma",119
+HLA Wait List Status Change,118
+"Occult Blood, Gastric",118
+"Histoplasma AG, Serum",115
+"AFB Culture, Non - Respiratory",115
+"Throat Strep Screen, PCR",114
+"Cell Count and Diff, CSF --Tube #2",114
+DAT C3 and Gamma,114
+Food Allergy Screen,114
+Reptilase Time,113
+Hepatitis B Core AB IgM,113
+"Blood Culture, Aero 2",113
+"Potassium Urine Random, w/Creat",112
+"Chlamydia/GC, NAAT",111
+Voriconazole Level,110
+"Iron, Total",108
+"Flow Cytometry for Leukemia/Lymphoma, BLD",108
+HLA Workup at Time of Transplant,107
+"Vitamin A, Ser/Plas",107
+"Triglycerides, Pleural",106
+HHV 6 DNA PCR Qual,105
+S Pneumoniae Ab IgG (Prevnar 13),104
+"Cryptococcal Antigen Lateral Flow, CSF",104
+Herpes Simplex Virus Ab Panel,104
+Fungus Cult/KOH,103
+"CD3, BLD",102
+"Cocci ID, Serum",102
+"Protein C AG, Total",101
+Heparin Induced Thrombocytopenia Panel,101
+Human Insulin Antibody,100
+HLA Desensitiz/DSA Monitoring-Post IVIG,99
+M Pneumoniae DNA,98
+Blood Culture --Peripheral,97
+Unlisted Genetic Test:,96
+Lab Only Fluid Container,96
+Hemoglobin Quant / Fract,95
+Beta HCG Quant,94
+HLA Desensitiz/DSA Monitoring-Pre IVIG,91
+"Histoplasma Antibody, Serum",91
+"Amikacin Level, Pre Dose",90
+"Protein S AG, Total",90
+"Encephalopathy, Autoimmune Evaluation",89
+"Histoplasma AG, Urine",88
+ICA 512 Antibody,87
+Ova and Parasites,86
+CMV IgG (Transfusion),86
+"AFB Culture, Respiratory",85
+Hepatitis C RNA PCR Quant,85
+Red Cell Enzymes,84
+"Vitamin E, Ser/Plas",84
+Unlisted Genetic Test,84
+HLA Deceased Donor Crossmatch/Testing,83
+"HSV PCR Qual, Neonatal",82
+Enterovirus PCR,81
+"Creatinine, FLD",81
+"Flow Cytometry for Leuk/Lymphoma, Misc",80
+"VMA and HVA, Pediatric, Random Urine",80
+"Albumin, FLD",77
+Alpha 1 Antitrypsin Phenotype,77
+"Parvovirus B19 Real Time PCR, Qual",77
+Arbovirus AB Panel,77
+Slide Held for Review,77
+HLA Meds/Sensitizing Event Communication,76
+Adamts 13 Profile,74
+Fungitell B-D-Glucan Assay,74
+Review by Pathologist,73
+ANCA,72
+"Complement, Total",72
+"Tobramycin Level, Pre Dose",72
+Antibody Titer,72
+HLA Desensitiz/DSA Monitoring-No IVIG,72
+Platelet Function Screen,71
+"Amikacin Level, Random",71
+Insulin Unspecified,70
+"Chloride, Urine Random w/Creat",70
+"IgE, Total",70
+CSF Culture/GS --Tube not numbered,69
+Cyanide Level,69
+"Albumin, Urine Random, w/Creat",68
+Transfusion Svc Prod 12,68
+"Viral Culture, Stool/Rectal",67
+Factor V Assay,67
+Toxoplasma IgM Ab,67
+"Fungal Culture,Blood",66
+"Creatinine, Urine Timed",65
+Factor VII Assay,65
+"Istat EC4, Arterial",65
+"Tryptase Level, Serum",65
+Lithium Level,65
+HLA STR/Chimerism WB,65
+Folic Acid Level,64
+AFB Culture Respiratory CF,64
+Unlisted Lab:,64
+"Glutathione, Whole Blood",64
+"Lactic Acid, CSF",64
+CSF Protein Immunofix Elect,64
+Bartonella Species AB,63
+"Alpha 1 Antitrypsin, Stool",62
+West Nile Virus AB,62
+CMV Rule Out,61
+"T Subsets and B Lymphs, FLD",61
+Free T3,61
+"Amylase, Fluid",61
+"Lyme Western Blot, Serum",60
+Misc Viral Sendout,60
+AFB Smear,60
+17a-OH Progesterone,59
+Soluble IL 2R,59
+Methotrexate Level,59
+"WBC for IBD, Stool",59
+"Rotavirus Antigen, Stool",59
+"Vitamin D, 1,25-Dihydroxy",58
+"Purine and Pyrimidine Panel, Urine",57
+"Hemoglobin, Free",57
+"Mycoplasma Antibodies, IgG and IgM",56
+Orthopedic Aerobic Cult w/GS,56
+"West Nile Virus AB Panel,CSF",56
+EBV NPC PCR Panel,55
+"Ceruloplasmin, Serum",55
+"PTH, Intact and Calcium",55
+Anti Streptolysin O,55
+"Meconium Scn w/reflex, Conf",54
+LD Isoenzymes,54
+Transfusion Svc Prod 13,54
+Epstein Barr Viral Capsid AB IgM,54
+"Hemoglobin, Plasma",54
+"Viral Culture, Respiratory",54
+Epstein Barr Nuclear Ag Ab,54
+"Encephalopathy, Autoimmune Eval, CSF",54
+Adrenocortictropic Hormone Level,53
+POCT Gastric pH Test,53
+"Drug Screen, Plasma",53
+von Willebrand Factor Activity,52
+"Potassium, Urine Random w/o Creat",52
+Factor XIII Assay,52
+"Confirmed Drug Abuse Survey, Urine",51
+T3 Total,51
+Lymph Mitogen and Antigen Scrn,51
+"Urea Nitrogen, Urine Timed",51
+"GAD-65, IA2, Insulin Auto-Ab",51
+Strongyloides IgG AB,50
+Rapid HIV 1/2 Antibody,50
+HLA Deceased Donor O.R. Specimen,50
+Unlisted Lab: Plasma thiosulfate send out  ARUP,49
+HLA Desensitiz/AB Monitoring-Post IVIG,49
+CFTR Diagnostic Sequencing,49
+Digoxin Level,49
+GAD AB,48
+OR LPCH Stat Gram Stain,48
+HSV / VZV PCR Qual,48
+Unlisted Lab: plasma thiosulfate,47
+"Phosphate, Urine Random w/Creat",47
+Mycoplasma Culture Resp,47
+"ACT, Istat Kaolin Post Oxy",46
+Lacosamide Level,46
+Hold Clot,46
+Transfusion Svc Prod 14,46
+Unlisted Cytogenetics Lab Order,46
+"Rheumatoid Factor, Serum",46
+"Magnesium, Urine Random w/Creat",45
+IgG Subclasses,45
+Citrate Excretion Urine Random,45
+Factor IX Assay,44
+Follicle Stimulating Hormone,44
+Weak D Typing,43
+Luteinizing Hormone Level,43
+Toxoplasma PCR,43
+Prolactin Level,43
+Paraneoplastic Autoantibody Eval CSF,43
+"Cell Count and Diff, CSF --Tube #4",42
+PPD Reading,42
+Flecainide Level,42
+Valproic Acid Level Free and Total,42
+"Metanephrines, Fract Free, Plas",42
+"Bicarbonate, Urine",41
+C Peptide,41
+TIBC,41
+"Gentamicin Level, Random",41
+PTT Inhibitor Screen,40
+Pediatric EKG Rhythm Strip,40
+Neutrophil Function Oxidative Burst,40
+Orthopedic Anaerobic Culture,40
+"Metanephrines Fract, Urine Random",40
+Thrombin Time,40
+Lead Level,40
+HLA Specimen in Lab Orders - Recipient,40
+Myeloperoxidase Ab,39
+"Protein Total, Ser/Plas",39
+ANA IFA Screen with Reflex to Titer and Pattern,39
+"Uric Acid Urine Random, w/Creat",39
+Von Willebrand Multimers,38
+"Vitamin A and Vitamin E, Ser/Plas",38
+"Parvovirus B19 DNA, Quant",38
+"Electrolyte Panel, UR",38
+Miscellaneous Processing,37
+Fibrin Degradation Products,37
+"1 25 Dihydroxyvitamin D, Serum (Adult)",37
+"VZV DNA PCR Qual, Non Lesion",37
+"C Reactive Protein, High Sensitive",36
+Comp Hepatitis Panel,36
+"Toxoplasma DNA PCR, Qualitative",36
+Anti TPO Antibody,36
+"Oligoclonal Banding, CSF",35
+Biotinidase Level,35
+IgF Binding Protein-3,35
+HLA Auto Flow Crossmatch,34
+"pH, Pleural Fluid",34
+Proteinase 3 Antibody,34
+Kidney Stone Analysis,34
+"Fecal Fat, Quant",34
+Transfusion Svc Prod 15,34
+"Hepatitis E Antibody, IgM",34
+Von Willebrand Factor Activity,34
+Cocci ID with Reflex to CF (Initial Diagnosis),33
+"Benzodiazepine Confirmation, Urine",33
+Growth Hormone,33
+"Urea Nitrogen, FLD",33
+pH Stool,33
+Epstein Barr Viral Capsid Ag IgG Ab,33
+Genital Culture,32
+"Aldosterone, Serum",32
+"Opiate Confirmation, Urine",32
+HLA Desensitiz/AB Monitoring-No IVIG,32
+Unlisted Lab: Invitae Arrhythmia and Cardiomyopathy Comprehensive Panel,32
+"Vitamin B6, Total Plas",31
+Unlisted Lab: Aspergillus PCR,31
+"Protein, Urine Timed",31
+"Orotic Acid, Urine",31
+Unlisted Lab: Liver Cytosol Auto-Ab-1,31
+HLA Desensitiz/AB Monitoring-Pre IVIG,30
+Echovirus Ab,30
+Catecholamine Fractionation Free,30
+"AFB Culture, BLD",30
+"Sodium, FLD",29
+Free Light Chains Ratio,29
+Serum Protein Immunofix  Elect,29
+"Cortisol Level, Post Op",29
+"Aldolase, Serum",29
+Factor II Assay,29
+Transfusion Svc Prod 16,29
+"Elastase, Stool",29
+Phenytoin Level Total and Free,28
+Beta Strep Throat Screen,28
+Unlisted Lab: HSV-1 and HSV-2 quantitative PCR to be sent to Focus Labs per Pediatric ID Team,28
+Calprotectin,28
+IGF Binding Protein-3,28
+Anti GAD 65 Antibody,28
+Transfusion Svc Prod 17,28
+Gastroenteritis Workup,27
+IgG Subclass 4,27
+"Lipase, Fld",27
+Heparin (HIT) Function Assay,27
+pH by Meter,27
+Factor XI Assay,27
+TSH Reflex FT4,27
+"Culture, Virus",26
+APC Resistance Profile,26
+"Vitamin C, Plasma",26
+"Phosphorus, Urine Random w/o Creat",26
+Prader Willi Syndrome,26
+Anti Thyroglobulin Ab,26
+"Potassium, WB",25
+"Bilirubin Total, FLD",25
+Anti Ro Antibody,25
+"Supersaturation Profile, 24 Hour Urine",25
+"Amino Acids, Blood Spot",25
+"Beta 2 Microglobulin, Serum",25
+Tetanus Titer,25
+"Viral Culture, Tissue",24
+Ureaplasma PCR,24
+Clozapine Level,24
+Smooth Muscle AB,24
+Alpha 1 Antitrypsin,24
+"Oxalate Pediatric, Random Urine",24
+POCT Urine Pregnancy,24
+Unlisted Lab: PJP PCR,24
+Infectious Mono Screen,24
+IVIG In Vitro Inhibition,24
+CEA,24
+Ictotest,24
+"Chloride, Urine Random w/o Creat",24
+"Differential, Synovial Fluid",23
+"Renin Activity, Plasma (Adult)",23
+"Cell Count, Syn FLD",23
+Topiramate Level,23
+"Bartonella DNA, Qual PCR",23
+Bronchopulm Asperg Screen,23
+"Creatinine Clearance, Urine",22
+Cholesterol HDL/Tot,22
+"Calcium Urine, Random w/o Creat",22
+Creatinine Clearance,22
+Anti-DNase B Titer,22
+Wound Deep Surg Asp GS,22
+Flow Cytometry B Cell Phenotyping,22
+"Metanephrines, Urine Timed",22
+IMM Neutrophil Cyto Ab Reflex Pan,22
+Green Top,21
+Transfusion Svc Prod 18,21
+Unlisted Coagulation Lab Order,21
+Heparin Platelet Factor 4 AB,21
+"Electrolyte and Osmolality PNL, Feces",21
+CSF Culture/GS --Tube #1,21
+Pro predict TPMT,20
+"AFP, CSF",20
+"Nucleic Acid Isolation, DNA",20
+"Cell Count and Diff, Syn FLD",20
+Factor X Assay,20
+ANCA IBD w/Reflex,20
+"Amphetamine Confirmation, Urine",20
+lamoTRIgine Level,20
+"Diphtheria/Tetanus AB, IGG",20
+HLA Typing Summary,19
+Adenovirus AG,19
+Angiotensin Convert Enz Level,19
+HLA STR/CHIMERISM (Bone Marrow),19
+Brucella Antibodies,18
+Ristocetin Co-Factor,18
+Isolator Fungus,18
+"Triglycerides, (Non Pleural)",18
+EKG 12 Lead High V1/V2,18
+PTH Related Protein,18
+Anti La Antibody,18
+"Drug Screen, Prescrip/OTC",18
+Thiamin (Vitamin B1) Level,18
+"Oxalate, Timed Urine",18
+Transfusion Svc Prod 20,18
+Transfusion Svc Prod 19,18
+"HSV 1/2 PCR, Non CSF",18
+Blood Culture Catheter,18
+"HCG Beta-Subunit QN, CSF",18
+"GBM Antibody, IgG",18
+"Fecal Lipids, Total",18
+HLA Typing Add On Locus -Recip(Lab Req),18
+Unlisted Molecular Diagnosis Lab Order,17
+Specimen Processing,17
+"Protein S, Free",17
+"Vitamin K1, Ser",17
+Mitogens and Tetanus Lymphocyte Proliferation,17
+"Helicob Ag, Stool",17
+IgA Anti TTG,17
+Blood Culture Peripheral,17
+Transfusion Svc Prod 21,17
+Transfusion Svc Prod 22,17
+"Folate, RBC",17
+"Sodium, Urine Timed",17
+"Creatine, Ser/Plas",17
+"Testosterone, Total/Free Pedi",17
+Insulin-Like Growth Factor 1,16
+Protein C and S Total Combined,16
+Malaria Prep and Antigen Screen,16
+Lyme Disease Ab IgG IgM w/relex,16
+Rubella IgG Antibody,16
+Epstein Barr Virus Early Ag (RD) IgG,16
+Mono Screen w/Reflex EBV Antibody,16
+Haemophilus Influ IgG,16
+Unlisted Lab: mayo autoimmune encephalitis panel,16
+HCG Tumor Marker,16
+Liv Kid Microsome Ab,16
+Unlisted Genetic Test: Custom gene panel with Prevention Genetics with reflex to whole exome sequencing if negative.,16
+"Cocci CF AB, Serum",16
+Unlisted Lab: Send out molecular test to Hematologics for CBFA2T3-GLIS2 quant PCR - 2ml sodium heparin green top tube (requisition to be dropped off in send-out lab),16
+Coxiella Burnetii AB,16
+Unlisted Lab: Pleximmune,16
+Unlisted Lab: Invitae Primary Immunodeficiency Panel,16
+HLA Crossmatch Summary,16
+"Cholesterol, Fluid",16
+Miscellaneous Special Coagulation,16
+"Cocci ID, CSF",15
+Oxcarbazepine Metabolite Level,15
+Zonisamide Level,15
+Platelet Aggregation,15
+Isospora Cyclospora,15
+Pro predict Enzact,15
+Cryptosporidium EIA,15
+"LYME DISEASE AB (IgG, IgM)",14
+Unlisted Lab: atypical HUS genetic panel,14
+"Myoblobin, Urine",14
+Unlisted Lab: Beta 2 Glucan,14
+Donor Hepatitis B Surf Antigen,14
+NK Function,14
+Unlisted Lab: acyclovir level,14
+OXcarbazepine Metabolite Level,14
+Unlisted Lab: Glycine,14
+Welcome to Packard Children's Hospital,14
+Unlisted Lab: clobazam level,14
+Hemochromatosis Genotyping Analysis,14
+"Borrelia Burgdorferi Panel, CSF",14
+"Unlisted Lab: 1,3 beta D glucan",14
+Apolipoprotein A1,14
+"Glucose, Urine Random w/o Creat",14
+"iSTAT CG8, Post-Ox",14
+PENTobarbital Level,14
+"Carnitine, Urine",14
+Amiodarone Level,14
+Diphtheria Titer,13
+"HIV 1 DNA PCR, Qual",13
+"Crystal Analysis, Syn FLD",13
+PT Inhibitor Screen,13
+"BCR-ABL, BLD",13
+HTLV AB I & II,13
+HLA Request for Summary Info. Recipient,13
+"Liver, Kid Microsome 1 Ab, IgG",12
+"Viral Culture, Ocular",12
+"Soluble Liver Ag Ab, IgG",12
+"Allergy Screen, Molds",12
+Neuron Spec Enolase,12
+HLA SCT Evaluation Recipient,12
+Mitogen Induced Lymph Prolifer.,12
+Unlisted Hematology Lab Order,12
+"West Nile Virus RNA, RT PCR",12
+Natural Killer Cells,12
+Adenosine Deaminase,12
+"Oligosaccharides TLC, Urine",12
+Massive Transfusion Guideline,12
+Posaconazole Level,12
+Growth Hormone Level (RIA),12
+"Myoglobin Screen, Urine",12
+Unlisted Immunology Lab Order,11
+"Vitamin B1, Ser/Plas",11
+Transfusion Svc Prod 26,11
+Transfusion Svc Prod 27,11
+Cystic Fibrosis Mutations Study,11
+Transfusion Svc Prod 24,11
+RBC Phenotyping,11
+HIV Type 1 and Type 2 Antibody,11
+"Mitochondrial Ab M2, Serum",11
+"Copper, Urine",11
+"Hematocrit, Fluid",11
+"Ribosome P Ab, IgG, Serum",11
+Cancer Antigen 19-9,11
+Transfusion Svc Prod 23,11
+"Fatty Acids, Free",11
+Thyroxine Total,11
+Transfusion Svc Prod 25,11
+"Calcium, Urine Timed",11
+Transfusion Svc Prod 28,11
+Urine Protein Immunofix Elect,11
+"Polio AB, Neutralization",10
+Mumps Antibody IgG,10
+Transfusion Svc Prod 29,10
+Ethosuximide Level,10
+"Chloride, Ser/Plas",10
+"Potassium, FLD",10
+Streptococcal Ab Profile,10
+"Viral Culture, Fluid",10
+"Pediatric Testosterone, Total",10
+Aspergillus Ab Precipitins,10
+"Bile Acids, Fractionated and Total",10
+"T3 Reverse, LC/MS/MS",10
+"Phospholipids, Serum",10
+"N Methyl D Aspartate Receptor Ab, IgG",10
+Anti Dnase B Titer,10
+Rickettsia AB Pnl w/Reflex Titer,10
+Sweat Chloride,10
+Cancer Antigen 125,10
+POCT Urine Dipstick (10),10
+"17-OH Progesterone, Neonatal",10
+"Legionella Antigen, Urine",9
+"Magnesium, Urine Random w/o Creat",9
+"BK Virus PCR Quant, Plasma",9
+HLA Prospective Crossmatch Testing,9
+"Mucopolysaccharides TLC, Urine",9
+"Renin Activity, Plasma",9
+Stat Gram Stain,9
+G6PD Screen,9
+POCT Urine Dipstick (9),9
+"Occult bld, Gastric",9
+"Allergy Screen, Nuts",9
+Tricyclic Antidepressant ID Level,9
+Phosphatidylserine Ab Panel,9
+Chlamydia Ab Panel,9
+"Heavy Metals Screen, BLD",9
+Sulfonylurea Hypoglycemics Panel,9
+"Lactoferrin, Fecal",9
+"Cortisol Free, Serum",9
+Von Willebrand Scn,9
+Helminthic Ova and Larvae,9
+Cold Panel Screen,9
+Giardia Screen,9
+Insulin Like Growth Factor 1,9
+Measles Rapid Culture,9
+Unlisted Genetic Test: Hereditary Cancer Panel,8
+Unlisted Lab: Urine Blastomycosis send out to Mira Vista,8
+GQ1B AB,8
+"Unlisted Lab: Cocci antigen, CSF",8
+Unlisted Lab: Onfi (Clobazam) trough,8
+Unlisted Lab: cocci antigen from csf,8
+Unlisted Genetic Test: Invitae Comprehensive Cardiomyopathy Panel,8
+Fluconazole Level,8
+Unlisted Genetic Test: Pompe disease testing sendout to Duke University,8
+Unlisted Lab: TPO & Thyroglobulin anitbody. Unlisted test codes: 0050105 & 0050075 please send arup,8
+"Unlisted Genetic Test: RNA sequencing, please use MNG kit. Send to Medical Neurogenetics sendout lab",8
+Unlisted Genetic Test: Send out SNP array to Baylor laboratories,8
+Unlisted Lab: Mycoplasma PCR,8
+Unlisted Lab: SCb5-9 complement (send to CCHMC),8
+"ANA by IFA, IgG",8
+Unlisted Lab: anti-GBM IgG Multiplex Bead Assay (Elisa + IFA); sendout to ARUP,8
+Unlisted Lab: granzyme,8
+Unlisted Lab: MNG send out labs,8
+"Unlisted Lab: Methylmalonic Acid Level, Urine",8
+Unlisted Lab: Rubella IgM,8
+"Unlisted Genetic Test: Trio Whole Exome Sequencing, sendout to Baylor Laboratories",8
+Unlisted Lab: Cocci antigen from urine,8
+Unlisted Lab: Serum Biotin Level,8
+Unlisted Genetic Test: Mito NGS to be sent via send out to Baylor with full paper requisition,8
+Unlisted Genetic Test: Molecular Testing,8
+Unlisted Genetic Test: Send out to University of Chicago Genetic Services Laboratories. Test: MS-MLPA FOR PRADER-WILLI SYNDROME. Send with paper requisition sheet.,8
+"Unlisted Lab: SCID immunophenotyping panel, Neonatal, flow cytometry",8
+Unlisted Genetic Test: Invitae arrhythmia and cardiomyopathy comprehensive panel.,8
+Unlisted Genetic Test: aHUS genetic panel to Machaon Diagnostics,8
+Unlisted Lab: Cocci antigen from blood,8
+"Unlisted Genetic Test: Sendout to Invitae. Test: Comprehensive Neuromuscular Disorders Panel, send with paper requisition form.",8
+Unlisted Lab: Helicobacter pylori culture in brucella broth. Tissue: stomach/antrum,8
+Unlisted Lab: SEND OUT to Mayo: TCR spectratyping (see paper form),8
+Unlisted Lab: HLH panel send-out to Cincinnati Children's,8
+Unlisted Lab: Serum Autoimmune Encephalopathy Evaluation Panel to Mayo Clinic. Test code #: ENS1,8
+"Unlisted Lab: please send Pneumocystis jirovecii,Qualitative Real-Time PCR from sputum to Quest diagnostic, test code 18835",8
+"Unlisted Genetic Test: Propionic Acidemia Testing, sendout to Prevention Genetics",8
+Unlisted Lab: D-Lactate - Mayo Medical,8
+Unlisted Lab: Invitae send out: autoinflammatory,8
+"Nicotine and Metabolites Level, Urine",8
+"Unlisted Genetic Test: SENDOUT to Rush University Medical Center: Congenital central hypoventilation Syndrome PHOX2B Gene Screening Test, Requisition in Media.",8
+Unlisted Lab: Please send Coccidial Ag from urine to Miravista lab at Mayo,8
+Unlisted Lab: serum oxalate,8
+Unlisted Lab: Pneumocystis jirovecii PCR (not stain) from BAL sample 8/17,8
+Unlisted Genetic Test: Comprehensive Diffuse Lung Disease NGS Panel (please follow instructions on attached requisition forms),8
+Unlisted Genetic Test: Whole Exome Sequencing- send out to Baylor; Proband and Maternal,8
+Unlisted Lab: MGN - Metabolic - CSF. Test ID: NC03,8
+Unlisted Genetic Test: Baylor Global MAPS sendout.,8
+Unlisted Lab: MGN - Metabolic - CSF. Test ID: NC05,8
+Unlisted Lab: Adaptive Clonoseq B and T cell clonality from fresh bone marrow in EDTA,8
+Unlisted Genetic Test: exome sequencing to Baylor,8
+Unlisted Genetic Test: Invitae RANBP2 seq and del/dup,8
+Unlisted Lab: Serum bile acid analysis to Cincinnati,8
+Unlisted Lab: Neonatal Respiratory Distress NGS Panel to John Hopkins,8
+Unlisted Lab: PCR for pneumocystis jirovecii,8
+Unlisted Lab: Please draw blood in appropriate tube for Conoseq/Adaptive testing on 6/25/19. Please see paper requisition form for instructions,8
+Unlisted Lab: ZnT8 (Zinc Transporter Antibody),8
+Unlisted Genetic Test: NUD/CEP/TMPT panel. Send-out to Medical College of Wisconsin. Collect 2-4mL venous blood in EDTA.,8
+Unlisted Lab: Urine succinylacetone,8
+Unlisted Lab: Clobazam level (aka onfi level),8
+Unlisted Lab: BALF,8
+Unlisted Lab: Invitae Comprehensive Inherrited Arrhythmia and Cardiomyopathy Panel,8
+Unlisted Lab: Coccidiodes antigen (MVista/Mayo) Sendout,8
+Unlisted Lab: peritoneal fluid for alpha fetoprotein,8
+Unlisted Lab: CSF Naegleria Fowleri,8
+Unlisted Genetic Test: Invitae Multicancer panel (resending due to sample failure) Invitae #RQ978033,8
+Unlisted Genetic Test: PCD Panel Invitae,8
+Unlisted Lab: Histoplasmosis AB,8
+Unlisted Lab: NMO/AQP4-IgG from CSF,8
+Unlisted Lab: Zinc Transporter 8,8
+Unlisted Microbiology/ Virology Test,8
+"Unlisted Genetic Test: ""Chromosomal Microarray Analysis - CytoScan HD SNP Array 8650"" to Baylor Miraca Genetics Lab",8
+Unlisted Lab: ENS1 send out to Mayo,8
+"Unlisted Lab: ENC1 send out to Mayo, please use CSF from 12/4/17",8
+Unlisted Lab: onfi level,8
+Unlisted Genetic Test: Neonatal Cholestasis panel to Emory,8
+"Unlisted Genetic Test: Connective tissue gene test (CTGT): Marfan's syndrome, Loeys-Dietz Syndrome, Familial Thoracic Aortic aneurysms and dissections, and related disorders NGS panel",8
+Unlisted Genetic Test: SLC22A5 Comprehensive sequencing and el/dup for Carnitine Transporter Deficiency,8
+"Unlisted Genetic Test: SENDOUT to Prevention Genetics: Currarino Syndrome via MNX1 Gene Sequencing with CNV Detection, Test Code: 9829, CPT; 81479,81479, Requisition faxed to Sendout Lab",8
+Unlisted Genetic Test: whole exome sequencing,8
+Unlisted Lab: MTHFR mutation analysis,8
+Unlisted Genetic Test: send out to GeneDx STAT epilepsy panel please send 2-5 mL Blood - Lavender Top Tube,8
+Unlisted Lab: Neonatal Cholestasis Sequencing Panel - NM340 - EGL Genetics,8
+Unlisted Genetic Test: INVITAE epilepsy panel,8
+Unlisted Lab: Invitae send out: HLH panel,8
+Unlisted Lab: anti liver cytosol antibody,8
+Unlisted Lab: Flow cytometry for perforin/granzyme,8
+"Unlisted Genetic Test: send out genetic test- UPD15, send to Univ of Chicago; needs to be sent with parental samples and requisition form (Order 732718564)",8
+"Adenovirus PCR, Stool",8
+Unlisted Genetic Test: send out to Invitae- CHD7 gene sequencing; please send with requsition form which will be uploaded; 3 ml purple top tube,8
+Unlisted Lab: Ehrlichia/Anaplasma sendout to Arup - Test code 2007862,8
+Unlisted Lab: Invitae arrhythmia and cardiomyopathy panel,8
+Unlisted Lab: cocci antigen please send to MiraVista,8
+Unlisted Lab: sCD163,8
+Unlisted Lab: add on to CSF from 12/16/17 - send out to Mayo labs test code: OLIGC (Mayo already has the sample),8
+Unlisted Genetic Test: EGL Genetics Neonatal cholestasis genetic panel,8
+Unlisted Lab: Parechovirus PCR Sent to Quest Code 70189X,8
+Unlisted Lab: D-Lactate,8
+Unlisted Lab: pleximmune,8
+Unlisted Lab: Autoimmune encephalitis panel send out to Mayo.,8
+Unlisted Genetic Test: invitae behind the seizure epilepsy panel RQ953453,8
+Unlisted Genetic Test: InVitae behind the seizures free comprehensive epilepsy panel,8
+"Unlisted Lab: znt8 antibody, send out to quest labs",8
+"Unlisted Lab: ENC2 for CSF, send out to Mayo, add on to CSF sample already collected",8
+"Unlisted Genetic Test: Send out lab: Critial Trio Whole Exome Sequencing #1722, Baylor Genetics",8
+"Unlisted Lab: Invitae Arrhythmia and Cardiomyopathy Comprehensive Panel, Congenital Heart Defects, and Aortopathy Panels",8
+Unlisted Lab: Karius send out lab. See test requisition form. Draw 4 mL of whole blood into a lavender top tube (K2-EDTA).,8
+Unlisted Genetic Test: Invitae Primary Immunodeficiency Panel TEST CODE: 08100,8
+Unlisted Genetic Test: Invitae Bone Marrow Failure Syndromes Panel TEST CODE: 05301,8
+Unlisted Genetic Test: Comprehensive monogenic obesity panel at PreventionGenetics,8
+Unlisted Lab: anti-GMCSF,8
+Unlisted Lab: Invitae Aortopathy Comprehensive Panel - CODE 02301,8
+Unlisted Lab: ARUP send out test code 0055233. Leptospirosis IGM,8
+Unlisted Lab: ZNT8 (Zinc Transporter 8),8
+Unlisted Genetic Test: Neonatal Diabetes Mellitus Sequencing Panel,8
+Unlisted Genetic Test: Invitae Hereditary Panel,8
+Unlisted Lab: Oklahoma medical research foundation clinical immunology laboratory,8
+Unlisted Genetic Test: Whole Exome Sequencing to Baylor Genetics,8
+Unlisted Lab: Urine GAG (glycosaminoglycans),8
+Unlisted Lab: Clobazam (onfi) level,8
+Unlisted Genetic Test: send out test to MNG labs. 3 ml in lavender top tube WES001,8
+"Unlisted Lab: 0060158. ARUP send out, leptopsprisosis culture",8
+Unlisted Genetic Test: send out to Invitae- HADHA and HADHB gene sequencing; send with requsition forms which are uploaded,8
+Unlisted Lab: BNP Plasma,8
+Unlisted Lab: Invitae Hypertrophic Cardiomyopathy Panel,8
+Unlisted Lab: Collagen Type II antibody,8
+"Unlisted Genetic Test: Custom gene panel at Prevention Genetics: Gene List: ACTG2, CHRM3",8
+"Unlisted Genetic Test: Comprehensive diffuse lung disease NGS panel, John Hopkins",8
+Unlisted Genetic Test: Invitae comprehensive epilepsy panel,8
+Unlisted Lab: Mayo Sendout Autoimmune Encephalitis Panel Serum Test code: ENS1,8
+Unlisted Lab: Aspergillus PCR QUEST send out,8
+Unlisted Lab: Send out molecular test to Hematologics for CBFA2T3-GLIS2 quant PCR - 2ml EDTA (requisition to be provided),8
+Unlisted Genetic Test: sendout to Prevention Genetics: PGXOME. Family test code 5000. Please send with maternal control specimen. lavedender top EDTA tube,8
+Unlisted Lab: sc5b-9 level,8
+Unlisted Lab: aGVHD Post-Treatment Algorithm to Viracor Eurofins,8
+Unlisted Lab: Autoimmune encephalitis panel send out to Mayo Clinic,8
+Unlisted Genetic Test: Comprehensive NGS and del/dup of the Col4A1 gene through CTGT lab.  CPT codes: 81408 x 1  81479 x 1,8
+Unlisted Lab: ZnT8 antibody,8
+Unlisted Genetic Test: Invitae Comprehensive Arrhythmia and Cardiomyopathy Panel,8
+Unlisted Genetic Test: GDX infantile epilepsy panel,8
+Unlisted Lab: Sendout Lab to ARUP - hyaluronic acid serum,8
+Unlisted Genetic Test: Invitae Epilepsy Panel,8
+Unlisted Lab: liver cytosol auto ab 1,8
+Unlisted Genetic Test: aHUS Genetic Panel by Machaon Diagnostic,8
+Unlisted Lab: Invitae Aortopathy Comprehensive Panel,8
+Unlisted Lab: aspergillus pcr,8
+CK Isoenzyme with Reflex,8
+Unlisted Lab: QUEST SENDOUT. Leptospirosis PCR (1ml),8
+Unlisted Lab: Pleximmune 5mL Na Heparin Tube,8
+Unlisted Lab: PCD (Invitae),8
+"Unlisted Genetic Test: Genetic testing for congenital HLH, sendout to Cincinnati",8
+Unlisted Lab: atypical HUS genetic panel and CFH autoantibody,8
+Unlisted Lab: MNG test code NC07,8
+"Occult bld, Guaiac Colorectal Scn",8
+Unlisted Lab: MRD/Post Therapy for AML to University of Washington,8
+"Unlisted Lab: D-lactic acid, potassium oxalate grey top tube",8
+Lamotrigine Level,8
+Lab Unlisted,8
+Unlisted Genetic Test: GeneDx sequencing: Limb abnormalities and reduction defects panel,8
+Unlisted Lab: Labref1 - send urine drugs of abuse for confirmatory opiate testing.,8
+"Unlisted Genetic Test: Urine HEX4 to Duke; CPT Codes: Urine: 82570, 83789",8
+"Unlisted Genetic Test: Mucolipidosis II/II Plasma Screen, send to Greenwood Genetic Center",8
+Unlisted Lab: please send blood for Plasma Cytokines as sendout  to Cincinnati Children's Diagnostic Lab,8
+Unlisted Lab: isavuconazole trough,8
+"Unlisted Lab: Soluble CD163 (sCD163), please send to Cincinnati",8
+Unlisted Lab: Aspergillus PCR from whole blood,8
+Unlisted Lab: TEST ID: ENCEC panel,8
+Unlisted Lab: SLA - soluble liver antigen,8
+Unlisted Lab: LC-1 (liver cytosolic antigen type 1),8
+Unlisted Lab: Clobazam (ONFI) level,8
+Unlisted Lab: vigabatrin level,8
+Unlisted Lab: MNG test code NC08,8
+Unlisted Genetic Test: Neonatal and Adult Cholestasis Panel Sequencing and Del/dup to Emory,8
+"Unlisted Genetic Test: Cardiomyopathy panel, sendout to Invitae Genetics",8
+Unlisted Lab: Sendout to Mayo Clinic ENS1 panel,8
+Unlisted Genetic Test: KidneySeq Panel to be sent to U of Iowa with paper requisition,8
+Unlisted Lab: send out to Kennedy Krieger Lab: 7-Dehydrocholesterol (7-DHC),8
+Unlisted Lab: Serum Oxalate,8
+"Plasma, Emergency Release",8
+Unlisted Lab: 3 mL in EDTA - sent with Oncology requisition form,8
+Unlisted Lab: Fulgent Pulmonary Hypertension Panel,8
+Unlisted Lab: FK506 level,8
+Unlisted Genetic Test: Invitae FoxP3 gene: 2mL EDTA.  Paperwork is at Bedside.  Contact Physician is Julia Chu,8
+Unlisted Genetic Test: send out test familial variant testing to invitae,8
+"Unlisted Lab: Clobazam level, FCLBZ (to Mayo)",8
+Unlisted Lab: Methylenetetrahydrofolate Reductase,8
+Prothrombin Fragment 1+2,8
+Unlisted Lab: KARIUS TEST (Send Out),8
+"Unlisted Lab: Cincinnati Childrens, CD107a Mobilization (NK Cell Degran)",8
+Unlisted Lab: test code fpepa,8
+Unlisted Lab: Clobazam level (send out),8
+Unlisted Lab: Plexision- Pleximmune test,8
+"Unlisted Lab: Pleximmune Test: 3-5ml whole blood, Na Green Top",8
+"Unlisted Lab: Sendout lab to Mayo Clinic, ENC1 panel",8
+Unlisted Lab: methylenetetrahydrofolate reductase (MTHFR) gene,8
+"Unlisted Lab: ENC-1 sendout to Mayo,",8
+Unlisted Lab: CSF Parvo B19,8
+Unlisted Lab: Clobazam level,8
+"Unlisted Genetic Test: SENDOUT to Rush University, PHOX2B Sequencing and Del/Dup, 3mL EDTA tube. Send with completed requisition left at bedside.",8
+Unlisted Genetic Test: SENDOUT to Baylor Genetics: Whole exome sequencing with parental control specimens.,8
+Unlisted Lab: add on to 10/15 sample: confirmatory testing (send-out) for acetaminophen level,8
+Unlisted Lab: ENS-1 sendout to Mayo,8
+Unlisted Genetic Test: send out test for glycosylation to Emory pg 22817,8
+Unlisted Lab: pyruvic acid - CSF,8
+Unlisted Lab: sendout to Baylor - metabolomics - CSF,8
+Unlisted Genetic Test: send out lysosomal storage disease test to Greenwood genetics,8
+"Unlisted Lab: Send out lab Metabolomics sendout to Baylor, Send on Ice",8
+Unlisted Genetic Test: send out to Invitae- Apert syndrome testing; FGFR2 gene sequencing and del/dup; send which req. form which is uploaded,8
+Dehydroepiandrosterone,8
+Unlisted Lab: send out for PJP PCR,8
+Unlisted Lab: Clobazam level on blood,8
+Unlisted Genetic Test: invitae epilepsy panel,8
+Unlisted Lab: ENC1 send-out to Mayo (autoimmune encephalitis panel),8
+Unlisted Genetic Test: serum bile acids,8
+"Unlisted Lab: T-Cell Subsets, Regulatory (Tregs) - MAYO lab Lavender (EDTA 3ml)",8
+Unlisted Lab: Toxoplasma Infant Panel to sendout to PAMF Toxoplasma Serology Laboratory. 1mL in red top tube,8
+Unlisted Lab: Urine bile acids to Cincinnati,8
+Unlisted Lab: Liver cytosol autoantibody 1 (LC-1),8
+Unlisted Genetic Test: Send out- Craniosynostosis panel to CTGT. Please send blood work with requsition form and attached clinical information.,8
+"Trichomonas vaginalis, NAAT",8
+Unlisted Lab: PJP PCR send out,8
+Unlisted Genetic Test: Anophthalmia/Microphthalmia Panel plus Custom Panel with reflex to Whole Exome Sequencing by Prevention Genetics,8
+Unlisted Genetic Test: send out test to invitae,8
+Unlisted Lab: MGN - Metabolic - CSF. Test ID: NC08,8
+"Unlisted Lab: Mayo Send out - inborn errors of metabolism, serum Pipecolic acid",8
+Unlisted Lab: Cocci antigen from CSF MIRAVISTA,8
+Unlisted Lab: Send-out to Mayo: 24 hour urine N-methyl histamine,8
+Unlisted Lab: Send-out to Mayo: 24 hour urine 11-beta prostaglandin F2a,8
+"Unlisted Lab: arbovirus Ab panel, IgG, IgM IFA. CPT 86651, 86652, 96653, 86654",8
+Unlisted Lab: Zinc transporter 8 antibodies (Znt8),8
+"Unlisted Lab: Pompe disease enzyme test (alpha-glucosidase, acid maltase)",8
+Unlisted Genetic Test: Invitae Cardiomyopathy Comprehensive Panel,8
+Hgb Quant/Fract HPLC,8
+"Unlisted Lab: NAME: FOXP3. 3cc EDTA. Must be sent as whole blood, room temp, same day or next day shipping to Cincinatti. Results of CBCd from today must be sent with the requisition form. Please see requisition form that is being sent to lab with...",8
+Neutrophil and HLA Antibody Screen,8
+"Unlisted Genetic Test: SENDOUT to CTGT Lab: Test Name: Spondyloepimetaphyseal dysplasias NGS & Del/Dup, Test Code: 5187",8
+Unlisted Lab: MGN - Metabolic - CSF. Test ID: NC04,8
+"Unlisted Genetic Test: SDHA Gene Sequencing, Send out to Prevention Genetics",8
+Unlisted Lab: Urine reducing substances,8
+"Uric Acid, Urine Random w/o Creat",8
+Unlisted Lab: Sendout to quest: leptospirosis urine PCR,8
+Unlisted Genetic Test: Charcot-Marie-Tooth genetic testing. Send-out to Invitae. Test code 03201. Collect 3mL venous blood in EDTA.,8
+Unlisted Genetic Test: Sendout test to Invitae; 1.5 mL in lavender top tube,8
+Unlisted Genetic Test: Factor 8 sequence analysis,8
+Unlisted Genetic Test: SENDOUT to Rush University Medical Center: Congenital Central Hypoventilation Syndrome PHOX2B Gene Screening Test,8
+Unlisted Lab: Invitae Cardiomyopathy Panel,8
+Unlisted Genetic Test: Invitae Dilated Cardiomyopathy Panel,8
+Unlisted Genetic Test: Neonatal Respiratory Distress NGS Panel,8
+Unlisted Lab: Zinc Transporter Antibody (ZnT8),8
+Unlisted Lab: Pneumocystic Jirovecii by PCR. Please send to ARUP Laboratories Test Code 2006254,8
+Unlisted Lab: Cincinnati perforin granzyme B,8
+"Unlisted Lab: Mayo autoimmune encephalitis panel, CSF. Test code ENC1.",8
+Unlisted Genetic Test: Invitae Congenital Heart Disease/Heterotaxy Panel & Primary Ciliary Dyskinesia Panel,8
+Unlisted Genetic Test: TRIO whole exome sequencing.,8
+"Unlisted Lab: Cincinnati Childrens, Lympocyte Subsets (1-3mL EDTA)",8
+Unlisted Lab: Flow for leukemia/lymphoma,8
+Unlisted Lab: MNG test code NC05,8
+Unlisted Lab: TCR V Beta Spectra Typing - Mayo sendout,8
+Unlisted Lab: Alkaline Phoshatase in CSF.  ARUP,8
+Unlisted Lab: MNG test code NC01,8
+Unlisted Lab: Miravista lab: coccidioides in urine,8
+Unlisted Lab: Aspergillus PCR (QUEST Sendout),8
+Unlisted Genetic Test: Custom Panel to Prevention Genetics,8
+Unlisted Genetic Test: Sendout: EGL Genetics Neonatal and Adult Cholestasis Panel - MM340,8
+Unlisted Lab: Hemoglobinopathy lab test,8
+"Beta 2 Microglobulin, Urine",8
+Unlisted Lab: Arbovirus Ab PCR (Send out to MAYO ARBOP),8
+Unlisted Lab: Oncology send-out lab,8
+Unlisted Genetic Test: MACHION: Atypical HUS panel,8
+Unlisted Genetic Test: mtDNA analysis,8
+Unlisted Lab: Paraechovirus PCR,8
+Unlisted Lab: MNG - Metabolic - CSF. Test ID: NC01,8
+Unlisted Lab: Invitae send out test,8
+Unlisted Lab: MNG test code NC04,8
+Unlisted Lab: Histoplasma PCR from BAL washing,8
+Unlisted Lab: Zinc transporter antibody (ZnT8),8
+"Unlisted Lab: Cincinnati Childrens, Perforin/Granzyme B (1-3mL EDTA)",8
+Unlisted Genetic Test: Invitae Hypertrophic Cardiomyopathy Panel,8
+"Unlisted Genetic Test: SENDOUT to Rush Medical, Congenital Central Hypoventilation Syndrome PHOX2B test.",8
+Unlisted Genetic Test: Alstrom Syndrome testing to Prevention Genetics,8
+Unlisted Genetic Test: Congenital Choanal Atresia,8
+Unlisted Genetic Test: Machaon Diagnostics aHUS Genetic Panel,8
+Unlisted Lab: send out to Mayo Clinic from CSF for ENC1 panel (autoimmune encephalopathy panel)  -- STAT,8
+Unlisted Lab: Anti ZnT8 anitbody for diabetes,8
+Unlisted Lab: ENC1 Send out lab to Mayo Clinic,8
+APT Test for Fetal Hgb,8
+Microsporidium Screen,8
+"Unlisted Genetic Test: Heterotaxy, Situs Inversus and Kartagener's Syndrome Sequencing Panel (Sendout to Prevention Genetics)",8
+Unlisted Genetic Test: SENDOUT to Invitae: PR04161.04 Invitae Noonan Syndrome Panel,8
+Unlisted Genetic Test: Phenylbutyrate metabolite analysis; send out 2mL green top,8
+"Unlisted Genetic Test: SENDOUT to Baylor Genetics: Trio Whole Exome, with parental controls. Signed requisition/consent at bedside.",8
+Unlisted Lab: Urine glycolate,8
+Unlisted Genetic Test: Send out to prevention genetics: primary hyperoxaluria sequencing panel,8
+"Unlisted Lab: cocci antigen, send to miravista",8
+Unlisted Lab: send out to invtae VHL sequencing,8
+NMO/AQP4-IgG CBA,8
+Unlisted Lab: aspergillus PCR from BAL,8
+Unlisted Lab: Please send comprehensive cardiomyopathy panel to In Vitae with paper requisition,8
+Unlisted Genetic Test: Cincinnati Bile Acid Synthesis Disorders,8
+"Unlisted Genetic Test: Urine Hex4, Gluc4",7
+Unlisted Lab: leptospira PCR. Send-out Quest CPT 87798,7
+"Unlisted Lab: send out to viracor IBT, (1,3)beta-d-glucan, test code 1700, red top tube, 1ml",7
+Unlisted Lab: TLREC and MSMD flow cytometry lab send out to Medical College of Wisconsin,7
+Unlisted Lab: 3mL in EDTA purple top. Surfactant Panel.,7
+Unlisted Lab: moleculera cunningham panel (2 red tops),7
+"Unlisted Lab: NKX2-1 sequence, reflex to MLPA",7
+"Unlisted Lab: Send histoplasma quantitative EIA urine to MiraVista, Test code 310, at least 0.5 mL",7
+Unlisted Lab: Stool Adenovirus,7
+Unlisted Lab: Surfactant Panel,7
+Unlisted Lab: Ureaplasma Culture - code 871 to Focus Diagnostics,7
+Unlisted Lab: Dual Genome Panel by Massively Parallel Sequencing. Please send to Baylor.,7
+Unlisted Lab: 4 specimens to be collected and sent to CA Dept of Public Health,7
+Zinc Transporter 8 Antibody,7
+Unlisted Lab: ARUP 0090055 Clonazepam,7
+"Unlisted Genetic Test: Hyperammonemia panel (665, 684) with reflex to whole exome with mtDNA if negative, send to Gene Dx with paper requsition scanned into media section of chart.",7
+Unlisted Lab: aHUS Genetic Panel - sendout,7
+"Unlisted Lab: Osteogenesis imperfecta type VII, CRTAP",7
+Unlisted Lab: Invitae Arrhythmia Panel (send out test),7
+HLA SCT Confirmatory Recipient,7
+Unlisted Lab: CHD& sequencing and del/dup (test codes 130 and 600) to be sent to Prevention Genetics. Send with paper requisition. (Requisition is uploaded to media section of chart),7
+Unlisted Lab: Please send epilepsy comprehensive panel to GENE Dx diagnostic experts. 2-5 mL Blood - Lavender Top Tube.,7
+"Unlisted Lab: Draw 3cc, lav EDTA-Whole, Blood, min 1.5cc",7
+Unlisted Lab: Send-out lab to Mayo. Urine Reducing Substances. Code: CHOU.,7
+Unlisted Lab: Adenovirus real time PCR quant from liver biopsy tissue,7
+Strongyloides Culture,7
+"Unlisted Lab: Please send CSF Aspergillus DNA, Qualitative Real-Time PCR to focus diagnostics",7
+Unlisted Lab: Unlisted Lab: Adaptive/ClonoSEQ,7
+"Unlisted Lab: Rickettsia Antibody Panel with Reflex to Titers (Order code 12050R), Gold-top gel tube or Red-top tube (Plain) no additive,",7
+Unlisted Lab: ARUP 51463 (Familial Dysautonomia test - IKBKAP),7
+Unlisted Lab: Genetic testing,7
+Blood Parasite Exam (not malaria),7
+Unlisted Lab: Invitae Comprehensive Cardiomyopathy Panel,7
+Unlisted Lab: ATM sequencing/ATM deletion/duplication,7
+Unlisted Lab: Gene DX #694 Comprehensive Cardiomyopathy Panel,7
+Unlisted Lab: Osteogenesis imperfecta NGS panel to CTGT,7
+Unlisted Lab: Atypical HUS functional testing (not genetic): Serum factor H,7
+"Unlisted Lab: ENCES Serum, red top tube",7
+Unlisted Lab: Pyruvate analysis (MET 11),7
+"Unlisted Lab: Choreoathetosis, hypothyrodism, RDS (NKX2-1 sequence reflex to MLPA",7
+Methylene THF Reductase,7
+Unlisted Lab: Invitae Congenital Heart Disease Panel,7
+Unlisted Lab: Enterovirus PCR,7
+Unlisted Lab: Autoimmune Encephalitis Panel (Test ID ENCEC) send out to Mayo Clinic (requires 4mL CSF),7
+Unlisted Lab: Neurotransmitter Metabolites (GABA NT) - Medical Neurogenetics in Atlanta,7
+Unlisted Lab: MDA 5 level,7
+Unlisted Lab: Alprazolam level,7
+Unlisted Lab: Send out Tuberous Sclerosis Complex (NGS) and Deletion/Duplication Panel,7
+"Electrolyte and Osmolatity PNL,Feces",7
+Unlisted Lab: Acylovir level after dialysis,7
+"Unlisted Lab: acyclovir level, collect in red lavander edta, at least 1mL",7
+Unlisted Lab: random urine iodine level,7
+Thyroglobulin Level,7
+"Unlisted Genetic Test: GALT 9 mutations, test number 0051176; send to ARUP labs. 1-3 ml in Lavender top EDTA tube.",7
+Unlisted Lab: MYH9 genetic testing send out.  5 cc in a lavender top.,7
+Unlisted Lab: CSF neurotransmitters,7
+"Unlisted Lab: ARUP 2007479 Pain Management High-Resolution Hybrid Drug Test Panel, Urine",7
+Unlisted Lab: jaundice chip,7
+Unlisted Lab: (ZNT8) Zinc Transporter Antibody,7
+"Unlisted Lab: 1,3 beta D glucan assay CPT code: 84311 Test code: 55310 Send to Quest Diagnostics",7
+Unlisted Lab: Eculizumab trough level (Sendout to Cincinnatti Children's). May be drawn with morning labs,7
+Unlisted Lab: POLG Comprehensive - Sequence and Deletion / Duplication Analysis (send out to Medical Genetics Laboratories at Baylor College of Medicine),7
+"Unlisted Lab: WHOLE EXOME SEQUENCING, TRIO with MITOCHONDRIAL SEQUENCING, PLEASE SEND TO BAYLOR MIRACA LABS",7
+"Unlisted Lab: 1, 3 b D glucan",7
+Unlisted Lab: Unlisted Lab: CPT4 81005. Urine reducing substances,7
+Unlisted Lab: La Crosse Encephalitis,7
+Unlisted Lab: Send out study: Gene Dx : XomeDxPlus test,7
+"Unlisted Lab: Mayo Send out - inborn errors of metabolism, urine Pipecolic acid",7
+Unlisted Lab: FOXF1 gene analysis to Baylor,7
+Unlisted Lab: confirm +TCA test,7
+Unlisted Lab: AE Panel CSF. Please send to Mayo Clinic.,7
+Unlisted Lab: National Jewish Center C4a,7
+Unlisted Lab: Succinyladenosine - Medical Neurogenetics in Atlanta,7
+Unlisted Lab: 3 ml lavendar top tube.  NKX-2 sequence; reflex to MLPA.  Send to DNA diagnostic lab at John Hopkins,7
+Unlisted Lab: MTHFRG.  Methylenetetrahydrofolate reductase gene mutation test,7
+Unlisted Lab: Aspergillus serum PCR,7
+Unlisted Lab: Lysosomal and Peroxisomal Storage Disorders Screen,7
+Unlisted Lab: MET 23: Creatinine & Guanidinoacetate,7
+"Glucose Urine Random, w/Creat",7
+Unlisted Lab: please collect 2- 4  mLand put on ice; send out to baylor cretine/guanidinoacetate,7
+Unlisted Lab: (w next lab draw) please send 2 mls in purple top EDTA tube to send out for In Vitae aortopathy panel. Paper req will be faxed to send out.,7
+Unlisted Lab: Noonan Spectrum/Rasopathies NextGen Sequencing Panel,7
+Neuron Antibody,7
+Unlisted Lab: Atypical HUS functional testing (not genetic): serum factor B,7
+Unlisted Lab: Atypical HUS functional testing (not genetic): C5b-9 level (membrane complement complex),7
+Unlisted Lab: Atypical HUS functional testing (not genetic): CD46 flow cytometry,7
+Unlisted Lab: please draw 2-5ml venou blood into one EDTA tube (lavendar top) for send out to Prevention Genetics,7
+Norovirus Antigen,7
+Unlisted Lab: National Jewish Ceneter C3a,7
+Unlisted Lab: Please send coccidioidomycosis antigen testing from the urine to MiraVista lab,7
+Unlisted Lab: Emory Cholestasis Panel,7
+Unlisted Lab: west nile PCR,7
+Unlisted Lab: Send-out labs:,7
+Unlisted Lab: Enerovirus/parechovirus RNA PCR. Send out lab to quest diagnostic (Test code 70189x),7
+Unlisted Lab: Ribosome P IgG (CSF),7
+Unlisted Lab: Antineuronal AB (CSF),7
+Unlisted Lab: ASS1 gene sequencing to Prevention Genetics.  Paper work needs to go with specimen,7
+Unlisted Lab: Paraechovirus PCR (sendout to FOCUS labs 48990) from CSF,7
+Unlisted Lab: COL7A1 gene sequencing. sendout test; please send with paper requisition to GeneDx,7
+Alpha Thalassemia PCR,7
+Unlisted Lab: CTGT EFEMP2,7
+Unlisted Lab: CTGT fibulin5,7
+"Unlisted Lab: send out to viracor IBT, test code 1700, (1,3) beta-d-glucan, 1-2 mL CSF in sterile screw top tube",7
+"Unlisted Lab: Atypical pneumonia send out to focus labs, send out 4360, requires at least 0.3 ml",7
+Unlisted Lab: St. Louis Encephalitis,7
+"Unlisted Lab: Atypical HUS Genetic Panel. Please send to Machaon Diagnostics INC, 3023 Summit St, Ste 199, Oakland, Ca 94609, phone 510 839 5600.",7
+Unlisted Lab: Pyridoxal 5'-phosphate - Medical Neurogenetics in Atlanta,7
+Unlisted Lab: NKX2-1 sequence- send out to Johns Hopkins,7
+Unlisted Lab: Can draw with next blood draw or whenever most appropriate. please send 1-2 ml in purple top edta tube to send out with requisition for whole exome sequencing and array to be sent to baylor genetics.,7
+Unlisted Lab: Glutaric and 3-hydroxyglutaric acids,7
+Unlisted Lab: C1q serum,7
+Unlisted Lab: Reducing Substances,7
+"Unlisted Lab: atypical HUS, send 3 ml in EDTA tube",7
+Unlisted Lab: Please send cocci antigen to MiraVista Labs from CSF fluid,7
+Unlisted Lab: Enterovirus quantitative PCR,7
+Unlisted Lab: MSMD,7
+Unlisted Lab: urine antigen coccidioidomycosis - send out to Mira Vista lab,7
+"Unlisted Lab: anti-C1q Antibody, IgG, sent to ARUP lab",7
+Unlisted Lab: 3ml in purple top for CYP11B1 sequence analysis to be sent to Baylor - requisition faxed to send out lab,7
+Unlisted Lab: Parechovirus Ab Panel,7
+Unlisted Lab: Please send out CSF to ARUP labs for Txoplasma gondii by PCR Test code 0055591,7
+"Unlisted Lab: send out to viracor IBT, test code 1700, (1,3) beta-d-glucan, 1 mL red top tube)",7
+Unlisted Lab: send for listeria from CSF,7
+Unlisted Lab: Send for influenza B,7
+"Unlisted Lab: Please send comprehensive epilepsy panel to GeneDx, requisition provided",7
+Unlisted Lab: Send out lab to Baylor for acid lipase deficiency,7
+ABO Discrepancy,7
+"Unlisted Lab: acyclovir level, collect in red lavendar edta, at least 1ml",7
+Unlisted Lab: Congenital Abnormalities of the Kidney and Urinary Tract (CAKUT) NextGen Sequencing Panel and Deletion/Duplication Panel,7
+Unlisted Lab: Rickettsia rickettsii -- test for rocky mountain spotted fever,7
+Unlisted Lab: Newborn toxoplasma AB panel; please send out to PAMF,7
+"Unlisted Lab: Beta 1,3 D glucan",7
+Unlisted Lab: Eculizumab trough level (Sendout to Cincinnatti Children's),7
+Unlisted Lab: HSV quantitative PCR please see ref code 43220,7
+Unlisted Lab: (1-3) - Beta - D - Glucan,7
+"Unlisted Lab: Surfactant panel, purple top EDTAs, 2ml of blood.  Send out to AmbryGenetics.",7
+"Histone Ab, IgG",7
+Unlisted Lab: California Encephalitis Panel,7
+Unlisted Lab: Urine indicans. Send-out test.,7
+Unlisted Lab: balamuthia pcr,7
+Unlisted Lab: plasma thiosulfte,7
+Unlisted Lab: Congentital Hyperinsulinism NextGen Sequencing (NGS) Panel,7
+Unlisted Lab: Mayo Autoimmune Encephalitis Panel,7
+Unlisted Lab: Surfactant protein B,7
+Unlisted Lab: Mayo Clinic sendout: Autoimmune encephalitis panel,7
+Unlisted Lab: Aspergillus PCR from blood,7
+"Unlisted Lab: Please pool all of leftover CSF from 7/25 in one tube and send to Mayo Clinic for test code ""ENCEC""",7
+Unlisted Genetic Test: GeneDx infantile epilepsy panel,7
+Unlisted Lab: Send out California Neuroscience Surveillence testing/Dept of Health; SALIVA IN STERILE CUP,7
+Unlisted Lab: Jaundice chip,7
+"Unlisted Lab: 1, 3 beta glucan",7
+Anti RNP Antibody,7
+"Unlisted Lab: ALLMS1 gene sequencing, ALMS1 deletion/duplication",7
+"Unlisted Lab: 2-3 mL in purple top tube (NaEDTA) for sendout testing - MLPA of SHANK3, Requisition will be sent to send out desk in Stanford Lab - CGC Genetics via UMDNJ",7
+Unlisted Lab: send out influenza study on spinal CSF to focus diagnostics - test code 60585,7
+Unlisted Lab: NMO antibody. Test ID NMOCC. CPT code 86255. Send out to Mayo Clinic.,7
+Unlisted Lab: Virology: CSF CMV PCR quantitative.,7
+"Unlisted Lab: Please send Pneumocystis jiroveci, molecular detection, PCR from BAL sample 1 ml in a sterile container to mayo medical laboratroies. Test ID PNRP",7
+Unlisted Lab: balamuthia PCR,7
+Unlisted Lab: GALACTOSE-1-PHOSPHATE URIDYLYLTRANSFERASE ACTIVITY,7
+Unlisted Lab: Western Equine Encephalitis,7
+Unlisted Lab: Coccidioidomycosis urine antigen to Miravista lab,7
+"Unlisted Lab: Send out for 1,3 beta D glucan",7
+Unlisted Lab: ZnT8 (Zntransporter) auto-antibody,7
+Unlisted Lab: Gene Dx Comprehensive Mitochondrial Nuclear Gene Panel,7
+Unlisted Lab: CSF for JC virus,7
+Unlisted Lab: Send out california neuroscience surveillence testing/dept of health; NP/THROAT SWAB IN VIRAL MEDIA,7
+"Unlisted Lab: Parechovirus PCR, send out to ARUP, test code 2005733 (add on to CSF from 07/25/16)",7
+"Unlisted Lab: Send out to ARUP, test code 2005685, Japanese encephalitis virus antibody, collect in serum separator tube",7
+"Unlisted Lab: HHV-6 IgG, IgM",7
+Unlisted Lab: Beta D Glucan,7
+Unlisted Lab: Two Serum Red Top tubes to be sent to California Neuroscience Surveillence and Testing Lab/Dept of Health,7
+Unlisted Lab: Please obtain sendout lab to Cincinatti Children's for CYB5R3 sequencing,7
+Unlisted Lab: INVITAE PRIMARY IMMUNODEFICIENCY PANEL,7
+Unlisted Lab: per lab,7
+Unlisted Lab: Sendout lab: ZnT8 transporter,7
+Unlisted Lab: Methylenetetrahydrofolate Reductase Mutations (MTHFR),7
+"Unlisted Lab: SLC19A3 Gene sequencing, deletion and duplication. Please send to prevention genetics.",7
+Unlisted Lab: Very long chain fatty acid.,7
+Unlisted Lab: ID: CHOU,7
+Unlisted Lab: Please send aspergillus PCR from the blood to focus diagnostics,7
+Unlisted Lab: Gene Dx Epilepsy panel,7
+Unlisted Lab: C6 Peptide (related to Lyme disease),7
+"Histoplasma Antigen, CSF",7
+Unlisted Lab: ChildLab RT-PCR for Ph like B-ALL,7
+Unlisted Lab: MET 19: Creatinien & Guanidinoacetate,7
+"Unlisted Lab: 5-methyltetrahydrofolate, neurotransmitter metabolites (5HIAA, HVA, 3OMD), pyridoxal 5'-phosphate, succinyladenosine, alpha-aminoadipic semialdehyde",7
+Unlisted Lab: Zinc transporter (ZnT8) Antibody,7
+Unlisted Lab: Interleukin 18,7
+"Red Blood Cells, Emergency Release",7
+"Unlisted Lab: Measles IgM and IgG, CSF, send out lab to Mayo, TEST ID: ROC",7
+Rapid HIV Antigen/Antibody,7
+Unlisted Lab: NMO/AQP4 on CSF,7
+Unlisted Lab: MTHFR,7
+Unlisted Lab: Proband Whole Exome Sequencing Test code 1500,7
+"Unlisted Lab: ENCEC - Encephalopathy, Autoimmune Evaluation, Spinal Fluid",7
+Unlisted Lab: 120 gene panel test to Invitae,7
+"Unlisted Lab: ENCES - Encephalopathy, Autoimmune Evaluation, Serum",7
+Unlisted Lab: pneumocystis jerovecii pneumonia PCR,7
+"Myasthenia Gravis Evaluation, Adult",7
+Unlisted Lab: aspergillus PCR,7
+Transfusion Svc Prod 30,7
+Unlisted Lab: CSF Fluid: Measles IgG and IgM - Please send to Mayo Clinic,7
+Unlisted Lab: Rett/Angelman Syndrome Sequencing Panel send out to University of Chicago. 3-10ml in purple top EDTA tube,7
+"Unlisted Lab: 1,3 beta glucan",7
+Unlisted Lab: histoplasma antibody CSF,7
+Unlisted Lab: Whole Exome Sequencing,7
+Unlisted Lab: please collect 2-4 mL for sulfocysteine determincation and put on ice. store in freezer until Baylor collection,7
+"Unlisted Lab: please collect lav EDTA whole blood, min 1.5ml for send out lab. Thank you.",7
+Unlisted Lab: D-lactate,7
+"Unlisted Lab: Congenital Myasthenia Panel, test number 1323, send to Prevention Genetics with paper requisition form",7
+Unlisted Lab: CCDC39 Deletion/Duplication Testing,7
+Unlisted Lab: Invitae Comprehensive Arrhythmia and cardiomyopathy panel,7
+Unlisted Lab: Please collect 2-5mL in lavender top tube for send out to Gene Dx for Infantile Epilepsy Panel,7
+Unlisted Lab: ARUP 0090181 Lorazepam,7
+Unlisted Lab: HH6TYP University of Washington - HHV6 subtyping,7
+Unlisted Lab: CGD: 1434 CYBB sequencing,7
+"Unlisted Genetic Test: please draw 5 mL in a red top tube whole blood. send out to prevention genetics, do not freeze. ship at room temp.",7
+Unlisted Lab: Infantile Epilepsy Panel,7
+Unlisted Lab: Atypical Pneumonia Panel Qualitative PCR. Send out to Focus labs: 4360,7
+Unlisted Lab: CSF autoimmune encephalitis panel - sendout to Mayo clinic,7
+Unlisted Genetic Test: 5mL of blood in purple top EDTA tube HOLD IN LAB UNTIL GENETICS PROVIDES FORMS.,7
+17 Alpha Hydroxyprogesterone,7
+"Chloride, FLD",7
+Unlisted Genetic Test: Comprehensive arrhythmia and cardiomyopathy panel,7
+Unlisted Lab: please send out CSF for encephalitis panel (send out to Mayo Clinic),7
+"Fungus Culture Skin, Nail",7
+Unlisted Lab: Jaundice Chip Resequencing Array (Cinncinati Children's),7
+Unlisted Lab: ENS1 send-out to Mayo (autoimmune encephalitis panel),7
+Unlisted Lab: Proband whole exome sequencing-Reflex to Comprehensive mtDNA Analysis by Massively Parallel Sequencing (MitoNGSSM,7
+"Mucopolysaccharides Quant, Urine",7
+Unlisted Lab: urine reducing substances,7
+Unlisted Lab: urine succinylacetone,7
+Unlisted Lab: rocky mountain spotted fever titers,7
+"Unlisted Lab: Please sent 1,3 beta D glucan from the blood",7
+Unlisted Genetic Test: lavender top draw of 5-6ml for GeneDx send out epilepsy panel being ordered by neurology team,7
+Direct LDL Cholesterol,7
+Unlisted Lab: Whole exome sequencing,7
+Unlisted Lab: Random Urine Calcium to Mayo: CARC1,7
+"Cell Count, Apheresis FLD",7
+Unlisted Genetic Test: 5ml in purple-top tube,7
+"Unlisted Lab: 1, 3 beta glucan level",7
+Unlisted Lab: Ureaplasma,7
+Unlisted Lab: Atypical HUS functional testing (not genetic): Factor H autoantibody,6
+Unlisted Lab: Atypical HUS functional testing (not genetic): Bb level (cleaved factor B),6
+Unlisted Lab: 3 mls EDTA tube,6
+CK Isoenzyme Electrophoresis,6
+HTLV I/II Antibody w/ Reflex,6
+Dilated Cardiomyopathy Panel,6
+Bone Marrow Culture/GS,6
+G-Tube Teaching (Kids Health),6
+"Bilirubin Indirect, Ser/Plas",6
+"Lysozyme, Plasma",6
+Slide Interp/Pathologist,6
+Unlisted Lab: 3ml blood in lavendar or yellow top tube STAT to send-out lab with paperwork,6
+"17 Hydroxycorticosteroids, Urine",6
+"Unlisted Lab: For California department of public health send-out lab, quarter-size amount of stool in sterile container",6
+VRE Screen,6
+CFTR Deletion/Duplication Analysis MLPA,6
+"PT AND INR, ISTAT",6
+Unlisted Lab: Botulinum toxin and culture,6
+HTLV 1/II DNA Qual,6
+Donor Hepatitis B Core Antibody,6
+"Amylase Urine Random, w/Creat",6
+"Erythropoietin, Serum",6
+JC Virus Antibody Index w Reflex,6
+Unlisted Lab: Marfan syndrome and aortic aneurysm panel,6
+Handling & Storing Breastmilk,6
+"Unlisted Lab: 'prevention genetics' test code 1309, noonan syndrome panel",6
+"Reverse T3, Serum",6
+"BK Virus PCR Quant, Urine",6
+Unlisted Lab: Mayo Lyme serology from CSF collected 2/22/15,6
+"Cocci ID, Serum (Initial Diagnosis)",6
+"CMV by Qualitative PCR, Saliva",6
+"CO2, Ser/Plas",6
+"Factor VIII Inhibitor, Human",6
+"JC Virus DNA, PCR",6
+"Unlisted Lab: Lysosomal enzyme panel, please send to greenwood genetics",6
+Donor Syphilis Screen,6
+"Hgb F, Quant",6
+Unlisted Lab: Atypical HUS functional testing (not genetic): serum factor I,6
+Send Out Flow Immunophenotype,6
+"Unlisted Lab: Gene Dx Comprehensive metabolic panel, lab 694",6
+Immune Complex Panel,6
+ClonoSight ID,6
+Factor XII Assay,6
+Central Line the Basic Teaching,6
+"Electrolyte II, Whole Blood",6
+"5 OH Indoleacetic AC, Urine",6
+Unlisted Lab: URINE BILE ACIDS; this is a send out test to Cincinnati Children's,6
+"Oligosaccharides, Qualitative by Mass Spectrometry, Urine",6
+Unlisted Lab: Invitae congenital heart disease panel,6
+Thiamine (Vitamin B1) Level,6
+"Herpes Virus 6 IgG IgM, AB",6
+"Donor,HTLV AB I & II",6
+"Unlisted Lab: For California department of public health send-out lab, urine in sterile collection tube",6
+Janus Kinase 2 V617F Mutation,6
+"Vitamin B3, Ser/Plas",6
+"Unlisted Lab: For California department of public health send-out lab, 2 cc blood in red top tube",6
+Unlisted Lab: Test Code 4902; Global Metabolomic  Assisted Pathway Screen (CSF),6
+Unlisted Lab: SERUM BILE ACIDS -  this is a send out test to Cincinnati Children's,6
+Unlisted Lab: 0.5 mL plasma in green top (Na or Lithium heparin) tube: for plasma acylcarnitine profile to be sent to ARUP lab (with paperwork at bedside),6
+Unlisted Lab: Sequencing/Del/Dup of MMACHC gene to Baylor genetic lab,6
+Transferrin,6
+CAH Ped Profile 6 Comp SCN,6
+"TRK Flow Cytometry for B-ALL MRD, Bld",6
+Acetylcholine Modulating Ab,5
+"Cystine Quant, Urine",5
+Caffeine Level,5
+"Aluminum, Serum",5
+Volatiles,5
+FGFR2 Craniosynostsis,5
+"Bile Acids, Total",5
+Transfusion Svc Prod 34,5
+Donor HIV1 HIV2 AB Scn w/reflex,5
+Transfusion Svc Prod 33,5
+Fibrinogen Quant,5
+Fructosamine Level,5
+"Unlisted Lab: Please add on ""Rickettsia conorii Antibody Panel, IFA, CPT 86757, Test Code 15332"" to the specimen sent to Quest on 7/28 for rickettsial AB pnl w/reflex titer",5
+CMV Genotype,5
+VZV Quant,5
+Ehrlichia Chaffeensis,5
+CPR for Family and Friends-Adult/Child,5
+Platelet Antibody ID Panel,5
+Procainamide Level,5
+Anti Sm Antibody,5
+"Manganese, Serum",5
+Peanut Component Allergy Screen,5
+Chlamydia GC Molec Detec,5
+"Hgb S, Quant",5
+VDRL CSF,5
+ClonoSight MRD,5
+"Myeloid Panel, Blood",5
+"Toxo Ab Transplant Pnl, Serum",5
+Toxoplasma Newborn Panel,5
+"Neisseria gonorrhoeae, NAAT",5
+"Chlamydia, NAAT",5
+Transfusion Svc Prod 31,5
+"S Sulfocysteine, Urine",5
+"Cortisol Free, Urine Timed",5
+"Myoglobin, Ser/Plas",5
+"Histoplasma Ag, Urine/Fluid",5
+Hepatitis D Virus AB Total,5
+Anti Scl70 Antibody,5
+Transfusion Svc Prod 32,5
+Heparin Induced Platelet AgG,5
+Unlisted Lab: CACNAIC sequencing and CACNAIC deletion/duplication,4
+"Platelet Count, Fluid",4
+STAMP Block/Slide Request,4
+Helicobacter Pylori Ab,4
+Plasminogen,4
+Inhibin B,4
+"Hepatitis E Antibody, IgG",4
+"Beta 2 Transferrin, Fld",4
+Chlamydia Trachomatis Culture,4
+Misc Chemistry,4
+"Citrate Excretion, Timed Urine",4
+Acetylcholine Blocking Ab,4
+"Vitamin B2, Ser/Plas",4
+EMA for Spherocytosis,4
+Interleukin 6 Level,4
+HLA Platelet Compatibility,4
+T Cell Interleukin Prolif,4
+HLA IVIG In Vitro Inhibition Assay,4
+"Leptospira Ab, IgM",4
+Dengue Fever AB,4
+"Zika Virus RT-PCR, Non-blood",4
+Meningococcal Ab Vaccine,4
+"D Dimer, Elisa",4
+"Herpes Viral 8 DNA, Quant",4
+"Creatine Guanidinoacetate, Ur",4
+"Estradiol, LC-MS/MS",4
+Muscle Tyrosine Kinase AB,4
+"Cocci CF AB, CSF (Follow-up Only)",4
+"Adenovirus DNA, PCR Quantitative",4
+L-Asparaginase Activity,4
+Anti Centromere Antibody,4
+Duchenne Becker Muscular Dystrophy,4
+"Glucose, Urine Timed",4
+Androstenedione,4
+"Denque Virus RT-PCR, Non-blood",4
+Chlamydia IGG/IGA/IGM AB,4
+carBAMazepine Level,4
+"Unlisted Chemistry: GAD 65 send to Mayo Test Code 11080R, GD65 S",4
+Osmotic Fragility,4
+Thyroid Stimulating Immunoglobulin,4
+Digitoxin Level,4
+"Hepatitis E Virus PCR, Quant",4
+Donor Chagas Scn w/reflex,4
+Donor HIV AB,4
+Giardia Screen w/ Reflex O P,4
+Tetrahydrocannabinol Urine,4
+"Neurotransmitters, CSF",4
+Iodine Level,4
+Mycoplasma Antibody IgG IgM,4
+"VMA and HVA, Pediatric, Urine",4
+Sepsis Protocol Lactate,4
+Sickle Cell Prep,4
+Donor Hepatitis C Ab,4
+"Amylase, Ascites",4
+Alkaline Phosphatase Isoenzymes,4
+Chlamydia Pneumoniae Culture,4
+ACH Receptor Binding Ab,4
+Ibuprofen Level,4
+Adenovirus AB IGG and IGM,4
+Anaerobic Short Battery,4
+Nasal Culture,4
+Immune Cell Function,4
+"N Methyl D Aspartate Receptor Ab, CSF",4
+Unlisted Molecular: MPS enzymes,3
+Unlisted Cyto: array CGH,3
+"Galactosemia (Gal-1-P), RBC",3
+"Testosterone, Total/Free Adult",3
+Rabies AB Titer CHK Endpoint,3
+"Vitamin D 1 25 Dihydroxy, Serum",3
+Anti Jo1 Antibody,3
+"Ethanol Confirmation, Urine",3
+"Myelin Basic Protein, CSF",3
+Pentobarbital Level,3
+Slide Interpretation by Pathologist,3
+Cyclic Citrullin Peptide AB,3
+"Gamma Hydroxybutyric Acid, Urine",3
+Filaria IgG4 Antibody,3
+"Differential, Apheresis FLD",3
+"Gliadin (Deamidated) IgA, Serum",3
+"Complement C4, Functional",3
+HLA Addl Specimen Recipient-Ab Scn/XM,3
+"Mucopolysaccharides, Urine",3
+"Platelets Apheresis, Emergency Release",3
+Entamoeba histolytica Exam,3
+Misc Processing,3
+"Cardiolipin Ab, IgA",3
+Lipoprotein Associated Phospholipase A2,3
+Unlisted Cyto: whole exome sequencing sent to Baylor,3
+Acanthamoeba Culture,3
+Unlisted Cyto: Array CGH,3
+C1 Esterase Inhibitor Antigen,3
+"Unlisted Chemistry: 1,3 Beta D glucan",3
+"Phencyclidine Confirmation, Urine",3
+Donor West Nile Virus NAT,3
+"Minimal Residual Disease, BM",3
+Chagas Disease,3
+Hepatitis B e Antigen,3
+CMV Rapid Culture,3
+"Eosinophils, Urine",3
+RPR Qualitative,3
+"Cocci CF AB, Serum (Follow-up Only)",3
+7 Dehydrocholesterol,3
+IA-2 Antibody,3
+GC Culture Screen,3
+"Unlisted Immunology: Hep B Surface Antibodies, qual",3
+"Unlisted Chemistry: Anti Ma Antibody, red top tube, send out to Mayo, send on ice.",3
+Biochem Genetic Misc Test,3
+Hepatitis B e AB,3
+"Unlisted Chemistry: whole exome sequencing, send out to Baylor",3
+Misc Microbiology,3
+"Zika Virus Qualitative, RT-PCR, Blood",3
+"NTX Telopeptide, Urine",3
+"Occult bld, Immuno Diag",2
+"Cholesterol, FLD",2
+"21 Hydroxylase Ab, Ser",2
+Unlisted Micro/ Viro: Mycoplasma pneumonia antibodies IgM and IgG,2
+Vasopressin/ADH Level,2
+Research Lab Draw: BMT 351 (Recipient Biological Specimens) ** RESEARCH BLOOD SAMPLE ** PURPLE TOP (Once),2
+Complement Component 2,2
+Lymphocytic Choriomeningiti AB,2
+Donor Hep B Core Ab Living,2
+Unlisted Chemistry: MSMD Screen: Stat1/Stat4 phosphorylation by flow. Sendout to seattle children's. Send 7 ml in green top tube at room temperature. Send along with healthy control. Draw on Monday at 11 am.,2
+Donor Hepatitis  B Core Total AB,2
+Hemoglobin Lab Only,2
+Unlisted Chemistry: Pregnenolone 500258 - to Esoterix,2
+"Mycoplasma Pneumoniae IgG, Ab",2
+Unlisted Molecular: CONGENITAL CENTRAL HYPOVENTILATION SYNDROME PHOX2B - Send to Ambry Genetics.,2
+Hypersensitivity Pneumonitis Pnl,2
+Anti Mullerian Hormone Level,2
+Dehydroepiandrosterone Sulfate,2
+Unlisted Chemistry: Progesterone 500266 - to Esoterix,2
+Unlisted Chemistry: Aldosterone 500014 - to Esoterix,2
+SRA Unfractionated Heparin Level,2
+Cytokines Plasma,2
+Aspirin Resistance,2
+ARIXTRA,2
+Exchange Transfusion Order,2
+IgG Anti TTG,2
+"Arsenic Level, WB",2
+Interleukin 5 Level,2
+Anti Neutrophil Antibody,2
+"Chikungunya Virus RT-PCR, Blood",2
+T Helper IL17,2
+SNP Microarray (Pediatric),2
+Olanzapine Level,2
+Angiotensin Converting Enzyme,2
+Histoplasmosis AB,2
+Glutaric Aciduria Type 1 (GA1),2
+Rubeola IgM,2
+Galactose 1- P Uridyl,2
+"Solid Tumor Actionable Mutation Panel (STAMP), Non-Blood",2
+Unlisted Molecular: Ambry Genetics Surfactant Panel (test code 8100),2
+Encephalitis Ab Screen,2
+Acetylcholinesterase,2
+Primidone and Metabolite Level,2
+"Unlisted Chemistry: Ribosome P Antibody, specimen CSF",2
+Unlisted Chemistry: 17-hydroxypregnenolone 500262 - to Esoterix,2
+Ganciclovir Level,2
+Unlisted Chemistry: 11 desoxycortisol aka Compound S 500132 - to Esoterix,2
+"Unlisted Cyto: Lysosomal Panel - 13 enzymes (send to Greenwood, SC",2
+"Calcitonin, Serum",2
+Direct Parasite Exam,2
+Unlisted Cyto: Prevention genetics testing for junctional EB genes. Please draw 3-5 mL in purple top tube,2
+"Bone Alk Phosphatase, Serum",2
+Unlisted Molecular: Johns Hopkins DNA Diagnostics for gene NKX2.1,2
+Leukemia/Lymphoma Immuphentype,2
+Toll Like Receptor Function,2
+"Adulterants Survey, Urine",2
+Unlisted Chemistry: 17-hydroxyprogesterone 500270 - to Esoterix,2
+Corn,2
+"ALPHA FETOPROTEIN, TOTAL&L3%",2
+Direct Viral Exam B,2
+"Cysticercus AB Reflex Panel, Serum",2
+"Specific Gravity, Fluid",2
+inFLIXimab Antibodies,2
+Hemoglobin A1c  with Est Mean Glucose,2
+"Progesterone Level, Serum/Plasma",2
+CAH Pediatric Profile 6: Comprehensive Screen,2
+"Bordetella Pertussis IgG, Ab",2
+Phenylalanine and Tyrosine Level,2
+"Dengue Virus RT-PCR, Blood",2
+Research Lab Draw: BMT 351 (Recipient Biological Specimens) ** RESEARCH BLOOD SAMPLE ** (RED TOP: Once),2
+"CEA, Spinal Fluid",2
+inFLIXimab Qn with Reflex to Ab,2
+Dihydrotestosterone,2
+"Unlisted Immunology: Autoimmune encephalitis panel. Source: CSF, minimum 2ml, ideal 4ml. CODE: ENCEC. Send out to Mayo Clinic  1800-533-1710",2
+Glucagon Level,2
+Istat Creatinine,2
+C1q Binding Assay,2
+"Calcitriol (1,25-Dihydroxyvitamin D)",2
+"5 Methyltetrahydrofolate Level, CSF",2
+"Adenovirus DNA, PCR Qualitative",2
+"Culture, Environmental",2
+"Pyruvic Acid, CSF",2
+"Sulfocysteine, Urine",2
+Hantavirus Antibodies,2
+"Occult bld, Guaiac Diag Single",2
+"ABO/Rh, Cord",2
+Clonazepam Level,2
+"Thyroid Stimulating Ig, Serum",2
+Retinol Binding Protein,2
+Blastomyces Serologies,2
+Research Lab Draw: Immunology Study for DiGeorge Syndrome,2
+"Zika Virus RT-PCR, Blood",2
+Isospora Microscopic Exam,2
+Haloperidol Level,2
+Creatine Guanidinoacetate,2
+"aPTT, ACLTOP Method",2
+"Unlisted Molecular: 3-5 mL Blood in purple top tube (NaEDTA) send out test to Baylor for Whole exome sequencing, paper requisition already in lab, parents to be drawn as controls separately at outpatient lab",2
+"Trichinella IgG, AB",2
+"Uric Acid, Fluid",2
+"Unlisted Chemistry: 1,3 beta D glucan",2
+Chromium Ser/Plas,1
+JCV Virus Antibody Inhibition,1
+Unlisted Molecular: parechovirus PCR serum,1
+"Estrogen Total, Level",1
+Chlamydia Molec Detec,1
+C4d Fragment,1
+"S. Pneumoniae AG Detection, LA",1
+T Cell Activation,1
+Thyroglobulin & Thyroid Ab,1
+"Viscosity, Serum",1
+"Cocci ID, CSF (Initial Diagnosis)",1
+Unlisted Immunology: Autoimmune encephalitis panel. Source: blood. CODE: ENCES. Send out to Mayo Clinic 1800-533-1710,1
+"Unlisted Chemistry: sendout to focus entero/parecho PCR, test code 47440",1
+Cysticercus IgG Antibody,1
+"Everolimus, Blood",1
+Lysosomal Disease Panel,1
+"Cysticercus Antibody Reflex Panel, CSF",1
+Glutaric 3 Ohglutaric Acid,1
+"Gastrin, Serum",1
+Unlisted Immunology: Donor specific antibodies (DSA),1
+C1 Esterase Inhib Pl,1
+"Intracellular Cystine Assay, WBC",1
+11 Deoxycortisol,1
+HLA Miscellaneous Test,1
+Charge (CHD7) Sequencing,1
+"Protein, Urine Random w/o Creat",1
+HTLVS Non Donor,1
+17 Hydroxypregnenolone,1
+AML Prognosis Assay,1
+"Aldosterone, Serum (Adult)",1
+Cold Antibody Titer,1
+Euglobulin Clot Lysis,1
+"ANA, HEp-2, IgG IFA",1
+Hypertrophic Cardiomyopathy Pan,1
+Unlisted Chemistry:,1
+Unlisted Molecular: Jaundice Chip send out to Cincinnati,1
+"C1 Inhibitor, Quant",1
+Sputum for PMNs,1
+Specific Gravity Refractometer POC,1
+"Inhibin A, Dimer",1
+GBM IgG,1
+Complement Alternate Pathway,1
+Unlisted Chemistry: Cortisol 500092 - to Esoterix,1
+IBD SGI Diagnostic,1
+Apolipoprotein B,1
+Unlisted Cyto: PNH clone screen flow cytometry,1
+Vasoactive Intest Polypep,1
+"Unlisted Micro/ Viro: 1,3 beta-d-glucan",1
+"Angiotensin Covert Enz, CSF",1
+Parvovirus B19 Ab IgG,1
+"Unlisted Chemistry: Neuron antibody, specimen CSF",1
+Insulin Fasting,1
+"Allergen Panel, IgE by ImmunoCap ISAC",1
+Tetrahydrobiopterin/Neopterin,1
+"17 OH Pregnenolone Level, Peds",1
+Globulin,1
+Spinal Muscular Atrophy Diagnostic,1
+Misc. Virology Test,1
+"Cysticercus AB, Serum",1
+Trazodone Level,1
+"Polyols, Ur",1
+"Mercury Level, WB",1
+Alagille (JAG1) Mutation,1
+Celiac Genetics,1
+"Progesterone Level, Pediatric",1
+Chromogranin A,1
+C3a des Arg Fragment,1
+Unlisted Chemistry: deoxycorticosterone aka DOC 500124 - to Estoerix,1
+"Unlisted Micro/ Viro: Chlamydophila Antibodies (IgG, IgA, IgM)",1
+Lorazepam Level,1
+Carboxyhemoglobin,1
+"Glucose, WB",1
+Testosterone Level (Adult),1
+"Cryoprecipitate, Emergency Release",1
+HDL,1
+Donor RPR w/reflex to Syph IgG,1
+"Pain Mgmt, Cocaine Metabolite Qn, Urine",1
+Galactose 1 Phosphate,1
+Adenosine Deaminase Pleural,1
+"Echinococcus AB, IgG w/Reflex",1
+Long QT Syndrome,1
+Cobalamin C Deficiency,1
+Gabapentin Level,1
+GC Molecular Detection,1
+"Homocysteine Quant, Urine",1
+"Chlamydia Trachomatis RNA, Throat",1
diff --git a/scripts/PedsLabUtil/data/labdtq.csv b/scripts/PedsLabUtil/data/labdtq.csv
new file mode 100644
index 00000000..b810b81a
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labdtq.csv
@@ -0,0 +1,5 @@
+,"Uric Acid, Ser/Plas","LDH Total, Ser/Plas","Tacrolimus Level, FK506",Chem 10,Renal Function Panel,"Triglyceride, Ser/Plas","Magnesium, Ser/Plas",CBC,Gamma Glutamyl Trans,"Vancomycin Level, Trough",Hepatic Function Panel,Prothrombin Time,"Phosphorus, Ser/Plas"
+<24h,6081.553398058252,3545.0516986706057,622.1419975932612,1257.3349633251833,1067.8776290630976,356.6282420749279,643.873950602263,1087.823108066023,260.42932361279867,571.7463848720801,195.6175298804781,321.875,192.84984678243106
+"[24h, 3d)",7581.553398058252,5062.038404726736,3668.271159245888,3366.3406682966584,3269.7896749521988,1899.8559077809798,2408.9305268280814,2295.920274057926,1624.5443499392468,1552.2803114571745,936.6533864541833,1109.5982142857144,924.2083758937691
+"[3d, 7d)",7707.766990291262,5256.277695716395,3811.472121941436,3577.832110839446,3488.336520076482,2770.1729106628245,2663.584377661516,2499.0968545624414,1917.7804779262858,1745.828698553949,1443.426294820717,1357.5892857142858,1110.929519918284
+>=7d,8820.388349514562,6372.968980797637,4846.770958684317,4644.967400162999,4578.585086042065,4001.440922190202,3780.26523908018,3587.6673933354095,3116.6464155528556,2837.597330367075,2727.8884462151395,2491.964285714286,2271.70582226762
diff --git a/scripts/PedsLabUtil/data/labdtq_icu.csv b/scripts/PedsLabUtil/data/labdtq_icu.csv
new file mode 100644
index 00000000..b06ef520
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labdtq_icu.csv
@@ -0,0 +1,5 @@
+,"Sodium, Ser/Plas",Heparin Activity Level,"Tacrolimus Level, FK506",Renal Function Panel,"Magnesium, Ser/Plas",Chem 10,CBC,"Triglyceride, Ser/Plas",Fibrinogen,Part Thromboplastin Time,"Blood Gases, Venous",Prothrombin Time,"Vancomycin Level, Trough","Phosphorus, Ser/Plas","Metabolic Panel, Comprehensive"
+<24h,11229.483282674773,9935.361216730038,1808.0229226361032,2104.468085106383,1778.9914708963042,1663.0495790458374,2472.5656039904575,495.76719576719574,1752.0,1979.9592391304348,1933.377308707124,1747.8597964787593,1306.2381852551985,649.6696944673823,685.3285032109336
+"[24h, 3d)",11752.27963525836,11062.737642585551,6651.862464183381,4272.340425531916,3879.2458476732004,4068.101028999065,3865.4304923010195,2466.666666666667,2623.555555555555,2671.5353260869565,2579.155672823219,2641.0919076078176,2434.152488972905,1739.0586292320395,1652.3958504857565
+"[3d, 7d)",11828.267477203648,11178.7072243346,6826.647564469914,4571.91489361702,4300.014963339818,4312.628624883068,4221.101713294296,3649.73544973545,2831.111111111111,2848.6752717391305,2756.5963060686017,2829.106767888871,2649.0233144297413,2066.6804293971923,1963.2800922114277
+>=7d,12886.018237082068,12336.501901140684,7915.4727793696275,5739.36170212766,5481.5202753254525,5420.579981290926,5375.081327260898,4971.957671957673,4006.0,3997.9619565217395,3997.3614775725596,3975.2867065094492,3940.7687460617517,3383.980181668043,3256.380701465503
diff --git a/scripts/PedsLabUtil/data/labstab.csv b/scripts/PedsLabUtil/data/labstab.csv
new file mode 100644
index 00000000..c5ce13b8
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labstab.csv
@@ -0,0 +1,9 @@
+preceding,Sodium,Potassium,Creatinine,Magnesium,Phosphorus,White Cells,Hemoglobin,Platelets,Tacrolimus,Uric Acid,LDH
+-1,0.8320471373448743,0.8404917603975935,0.7854063879511816,0.7007454739084132,0.7104734762614159,0.5931210667331298,0.327780142275187,0.62361968931312,0.8921529175050302,0.8926426426426426,0.6017748197448697
+0,0.43162913907284767,0.4667764318475832,0.17218234361890555,0.3595468177274242,0.3740016767418259,0.13414634146341464,0.045855166252143045,0.09752025010657951,0.7031055900621118,0.25203252032520324,0.13147138964577657
+1,0.8495511470686025,0.8643737109091922,0.9028381865093992,0.7777140398680625,0.7457277744530887,0.7702337414533312,0.6068575233022636,0.8444935418082937,0.949702789208962,0.9308510638297872,0.8493626882966396
+2,0.8845521774856204,0.8976474432282306,0.9464948453608247,0.8458164642375169,0.8204344503875123,0.8196022727272727,0.6938775510204082,0.8601962158374211,0.9522935779816514,0.9555555555555556,0.8983364140480592
+3,0.9045957651588066,0.9224117784529096,0.9622668174109666,0.8836499321573948,0.8476380572188955,0.8381344307270233,0.7247311827956989,0.8838199513381995,0.9636222910216719,0.957983193277311,0.9313984168865436
+4,0.907608695652174,0.9251054852320675,0.9733824733824734,0.8859696641386782,0.8619432143787444,0.8396946564885496,0.7602040816326531,0.8995983935742972,0.9637681159420289,0.9847715736040609,0.9385665529010239
+5,0.9090909090909091,0.9260442260442261,0.9747693977211069,0.8916967509025271,0.8585013313046785,0.8464730290456431,0.7916666666666666,0.9065281899109793,0.96,0.9813084112149533,0.9516908212560387
+6,0.91798810703667,0.9403690369036903,0.9778206364513018,0.9003503308680421,0.88389662027833,0.8747203579418344,0.8055555555555556,0.8990228013029316,0.9608610567514677,0.9803921568627451,0.948051948051948
diff --git a/scripts/PedsLabUtil/data/labstab_icu.csv b/scripts/PedsLabUtil/data/labstab_icu.csv
new file mode 100644
index 00000000..6aa09410
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labstab_icu.csv
@@ -0,0 +1,8 @@
+preceding,Sodium,Potassium,Creatinine,Magnesium,Phosphorus,White Cells,Hemoglobin,Platelets
+0,0.45571730745302974,0.4806838978164406,0.36885471024443833,0.5018653463965692,0.4541820785109176,0.4145318352059925,0.21107989505212565,0.31898219814241485
+1,0.7928633017178484,0.7322695035460993,0.8535170413343002,0.7427870378204586,0.7322860150619483,0.8053863027053375,0.6496918109773995,0.8091097308488613
+2,0.8408361691784152,0.7756033258973839,0.9143394588337433,0.8017656500802568,0.7777777777777778,0.8377538334024036,0.7142438452520515,0.8341369334619093
+3,0.8484150636683826,0.8110273327049953,0.9303785428951874,0.8281531983156206,0.8171265461465271,0.861579121242154,0.7744593202883625,0.8533123028391167
+4,0.8674103877103145,0.816188107619263,0.9438490214352283,0.8259880580039807,0.8308391234633885,0.8733099649474211,0.7842493847415914,0.8763837638376384
+5,0.87782173609495,0.8460579619819258,0.9559530165509877,0.8436213991769548,0.8528902734510211,0.8868613138686131,0.8048192771084337,0.8845789971617786
+6,0.8655080213903743,0.8495642288745737,0.9563567362428842,0.8569188553508428,0.852394143252869,0.8768472906403941,0.851520572450805,0.875
diff --git a/scripts/PedsLabUtil/data/labstab_melted.csv b/scripts/PedsLabUtil/data/labstab_melted.csv
new file mode 100644
index 00000000..93dc9945
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labstab_melted.csv
@@ -0,0 +1,89 @@
+,base_name,lab_name,preceding_streak,normal_count,total_count,normal_prop
+0,NA,Sodium,-1,15957,19178,0.8320471373448743
+1,NA,Sodium,0,8147,18875,0.43162913907284767
+2,NA,Sodium,1,15331,18046,0.8495511470686025
+3,NA,Sodium,2,10765,12170,0.8845521774856204
+4,NA,Sodium,3,7519,8312,0.9045957651588066
+5,NA,Sodium,4,5010,5520,0.907608695652174
+6,NA,Sodium,5,3540,3894,0.9090909090909091
+7,NA,Sodium,6,3705,4036,0.91798810703667
+0,K,Potassium,-1,16066,19115,0.8404917603975935
+1,K,Potassium,0,7938,17006,0.4667764318475832
+2,K,Potassium,1,15506,17939,0.8643737109091922
+3,K,Potassium,2,10989,12242,0.8976474432282306
+4,K,Potassium,3,7894,8558,0.9224117784529096
+5,K,Potassium,4,5262,5688,0.9251054852320675
+6,K,Potassium,5,3769,4070,0.9260442260442261
+7,K,Potassium,6,4179,4444,0.9403690369036903
+0,MG,Magnesium,-1,10528,15024,0.7007454739084132
+1,MG,Magnesium,0,7553,21007,0.3595468177274242
+2,MG,Magnesium,1,10846,13946,0.7777140398680625
+3,MG,Magnesium,2,7521,8892,0.8458164642375169
+4,MG,Magnesium,3,5210,5896,0.8836499321573948
+5,MG,Magnesium,4,3271,3692,0.8859696641386782
+6,MG,Magnesium,5,2223,2493,0.8916967509025271
+7,MG,Magnesium,6,2313,2569,0.9003503308680421
+0,PHOS,Phosphorus,-1,10969,15439,0.7104734762614159
+1,PHOS,Phosphorus,0,8476,22663,0.3740016767418259
+2,PHOS,Phosphorus,1,11215,15039,0.7457277744530887
+3,PHOS,Phosphorus,2,7516,9161,0.8204344503875123
+4,PHOS,Phosphorus,3,5096,6012,0.8476380572188955
+5,PHOS,Phosphorus,4,3309,3839,0.8619432143787444
+6,PHOS,Phosphorus,5,2257,2629,0.8585013313046785
+7,PHOS,Phosphorus,6,2223,2515,0.88389662027833
+0,CR,Creatinine,-1,15123,19255,0.7854063879511816
+1,CR,Creatinine,0,3694,21454,0.17218234361890555
+2,CR,Creatinine,1,12247,13565,0.9028381865093992
+3,CR,Creatinine,2,9181,9700,0.9464948453608247
+4,CR,Creatinine,3,6809,7076,0.9622668174109666
+5,CR,Creatinine,4,4754,4884,0.9733824733824734
+6,CR,Creatinine,5,3593,3686,0.9747693977211069
+7,CR,Creatinine,6,4056,4148,0.9778206364513018
+0,WBC,White Blood Cells,-1,9519,16049,0.5931210667331298
+1,WBC,White Blood Cells,0,3762,28044,0.13414634146341464
+2,WBC,White Blood Cells,1,4844,6289,0.7702337414533312
+3,WBC,White Blood Cells,2,2308,2816,0.8196022727272727
+4,WBC,White Blood Cells,3,1222,1458,0.8381344307270233
+5,WBC,White Blood Cells,4,660,786,0.8396946564885496
+6,WBC,White Blood Cells,5,408,482,0.8464730290456431
+7,WBC,White Blood Cells,6,391,447,0.8747203579418344
+0,HGB,Hemoglobin,-1,5391,16447,0.327780142275187
+1,HGB,Hemoglobin,0,1899,41413,0.045855166252143045
+2,HGB,Hemoglobin,1,1823,3004,0.6068575233022636
+3,HGB,Hemoglobin,2,680,980,0.6938775510204082
+4,HGB,Hemoglobin,3,337,465,0.7247311827956989
+5,HGB,Hemoglobin,4,149,196,0.7602040816326531
+6,HGB,Hemoglobin,5,95,120,0.7916666666666666
+7,HGB,Hemoglobin,6,58,72,0.8055555555555556
+0,PLT,Platelets,-1,9996,16029,0.62361968931312
+1,PLT,Platelets,0,2745,28148,0.09752025010657951
+2,PLT,Platelets,1,4969,5884,0.8444935418082937
+3,PLT,Platelets,2,2455,2854,0.8601962158374211
+4,PLT,Platelets,3,1453,1644,0.8838199513381995
+5,PLT,Platelets,4,896,996,0.8995983935742972
+6,PLT,Platelets,5,611,674,0.9065281899109793
+7,PLT,Platelets,6,552,614,0.8990228013029316
+0,TAC,Tacrolimus,-1,2217,2485,0.8921529175050302
+1,TAC,Tacrolimus,0,566,805,0.7031055900621118
+2,TAC,Tacrolimus,1,2077,2187,0.949702789208962
+3,TAC,Tacrolimus,2,1557,1635,0.9522935779816514
+4,TAC,Tacrolimus,3,1245,1292,0.9636222910216719
+5,TAC,Tacrolimus,4,931,966,0.9637681159420289
+6,TAC,Tacrolimus,5,720,750,0.96
+7,TAC,Tacrolimus,6,982,1022,0.9608610567514677
+0,URIC,Uric Acid,-1,1189,1332,0.8926426426426426
+1,URIC,Uric Acid,0,310,1230,0.25203252032520324
+2,URIC,Uric Acid,1,700,752,0.9308510638297872
+3,URIC,Uric Acid,2,559,585,0.9555555555555556
+4,URIC,Uric Acid,3,456,476,0.957983193277311
+5,URIC,Uric Acid,4,388,394,0.9847715736040609
+6,URIC,Uric Acid,5,315,321,0.9813084112149533
+7,URIC,Uric Acid,6,300,306,0.9803921568627451
+0,LDH,LDH,-1,1085,1803,0.6017748197448697
+1,LDH,LDH,0,579,4404,0.13147138964577657
+2,LDH,LDH,1,733,863,0.8493626882966396
+3,LDH,LDH,2,486,541,0.8983364140480592
+4,LDH,LDH,3,353,379,0.9313984168865436
+5,LDH,LDH,4,275,293,0.9385665529010239
+6,LDH,LDH,5,197,207,0.9516908212560387
+7,LDH,LDH,6,219,231,0.948051948051948
diff --git a/scripts/PedsLabUtil/data/labstabcnts.csv b/scripts/PedsLabUtil/data/labstabcnts.csv
new file mode 100644
index 00000000..0fd2a372
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labstabcnts.csv
@@ -0,0 +1,89 @@
+,lab,preceding,value
+0,Sodium,-1,19178
+1,Potassium,-1,19115
+2,Creatinine,-1,19255
+3,Magnesium,-1,15024
+4,Phosphorus,-1,15439
+5,White Cells,-1,16049
+6,Hemoglobin,-1,16447
+7,Platelets,-1,16029
+8,Tacrolimus,-1,2485
+9,Uric Acid,-1,1332
+10,LDH,-1,1803
+11,Sodium,0,18875
+12,Potassium,0,17006
+13,Creatinine,0,21454
+14,Magnesium,0,21007
+15,Phosphorus,0,22663
+16,White Cells,0,28044
+17,Hemoglobin,0,41413
+18,Platelets,0,28148
+19,Tacrolimus,0,805
+20,Uric Acid,0,1230
+21,LDH,0,4404
+22,Sodium,1,18046
+23,Potassium,1,17939
+24,Creatinine,1,13565
+25,Magnesium,1,13946
+26,Phosphorus,1,15039
+27,White Cells,1,6289
+28,Hemoglobin,1,3004
+29,Platelets,1,5884
+30,Tacrolimus,1,2187
+31,Uric Acid,1,752
+32,LDH,1,863
+33,Sodium,2,12170
+34,Potassium,2,12242
+35,Creatinine,2,9700
+36,Magnesium,2,8892
+37,Phosphorus,2,9161
+38,White Cells,2,2816
+39,Hemoglobin,2,980
+40,Platelets,2,2854
+41,Tacrolimus,2,1635
+42,Uric Acid,2,585
+43,LDH,2,541
+44,Sodium,3,8312
+45,Potassium,3,8558
+46,Creatinine,3,7076
+47,Magnesium,3,5896
+48,Phosphorus,3,6012
+49,White Cells,3,1458
+50,Hemoglobin,3,465
+51,Platelets,3,1644
+52,Tacrolimus,3,1292
+53,Uric Acid,3,476
+54,LDH,3,379
+55,Sodium,4,5520
+56,Potassium,4,5688
+57,Creatinine,4,4884
+58,Magnesium,4,3692
+59,Phosphorus,4,3839
+60,White Cells,4,786
+61,Hemoglobin,4,196
+62,Platelets,4,996
+63,Tacrolimus,4,966
+64,Uric Acid,4,394
+65,LDH,4,293
+66,Sodium,5,3894
+67,Potassium,5,4070
+68,Creatinine,5,3686
+69,Magnesium,5,2493
+70,Phosphorus,5,2629
+71,White Cells,5,482
+72,Hemoglobin,5,120
+73,Platelets,5,674
+74,Tacrolimus,5,750
+75,Uric Acid,5,321
+76,LDH,5,207
+77,Sodium,6,4036
+78,Potassium,6,4444
+79,Creatinine,6,4148
+80,Magnesium,6,2569
+81,Phosphorus,6,2515
+82,White Cells,6,447
+83,Hemoglobin,6,72
+84,Platelets,6,614
+85,Tacrolimus,6,1022
+86,Uric Acid,6,306
+87,LDH,6,231
diff --git a/scripts/PedsLabUtil/data/labstabcnts_icu.csv b/scripts/PedsLabUtil/data/labstabcnts_icu.csv
new file mode 100644
index 00000000..b1cd9a3a
--- /dev/null
+++ b/scripts/PedsLabUtil/data/labstabcnts_icu.csv
@@ -0,0 +1,57 @@
+,lab,preceding,value
+0,Sodium,0,40185
+1,Potassium,0,39889
+2,Creatinine,0,32769
+3,Magnesium,0,28681
+4,Phosphorus,0,33524
+5,White Cells,0,26700
+6,Hemoglobin,0,43069
+7,Platelets,0,31008
+8,Sodium,1,15077
+9,Potassium,1,15792
+10,Creatinine,1,9653
+11,Magnesium,1,11819
+12,Phosphorus,1,12349
+13,White Cells,1,8206
+14,Hemoglobin,1,6814
+15,Platelets,1,7245
+16,Sodium,2,10285
+17,Potassium,2,9862
+18,Creatinine,2,6911
+19,Magnesium,2,7476
+20,Phosphorus,2,7938
+21,White Cells,2,4826
+22,Hemoglobin,2,3412
+23,Platelets,2,4148
+24,Sodium,3,7382
+25,Potassium,3,6366
+26,Creatinine,3,5257
+27,Magnesium,3,4987
+28,Phosphorus,3,5255
+29,White Cells,3,3027
+30,Hemoglobin,3,1942
+31,Platelets,3,2536
+32,Sodium,4,5468
+33,Potassium,4,4423
+34,Creatinine,4,4292
+35,Magnesium,4,3517
+36,Phosphorus,4,3742
+37,White Cells,4,1997
+38,Hemoglobin,4,1219
+39,Platelets,4,1626
+40,Sodium,5,4297
+41,Potassium,5,3209
+42,Creatinine,5,3746
+43,Magnesium,5,2673
+44,Phosphorus,5,2889
+45,White Cells,5,1370
+46,Hemoglobin,5,830
+47,Platelets,5,1057
+48,Sodium,6,3740
+49,Potassium,6,2639
+50,Creatinine,6,4216
+51,Magnesium,6,2551
+52,Phosphorus,6,2527
+53,White Cells,6,1015
+54,Hemoglobin,6,559
+55,Platelets,6,816
diff --git a/scripts/PedsLabUtil/explore_lab_result.ipynb b/scripts/PedsLabUtil/explore_lab_result.ipynb
new file mode 100644
index 00000000..1e9585b9
--- /dev/null
+++ b/scripts/PedsLabUtil/explore_lab_result.ipynb
@@ -0,0 +1,2060 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "7f22bb85",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
+      "  %reload_ext google.cloud.bigquery\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "57cda761",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = '''\n",
+    "WITH lr_inpt as (\n",
+    "    SELECT * FROM `mining-clinical-decisions.lpch_core.lab_result`\n",
+    "    WHERE ordering_mode='Inpatient'\n",
+    "    Limit 10\n",
+    ")\n",
+    "SELECT *\n",
+    "FROM (\n",
+    "    SELECT\n",
+    "        ANY_VALUE(lr_inpt) lr_inpt,\n",
+    "        ARRAY_AGG(adt order by adt.event_time_jittered DESC LIMIT 1) adt\n",
+    "        FROM lr_inpt\n",
+    "        LEFT JOIN `mining-clinical-decisions.lpch_core.adt` adt\n",
+    "        USING (pat_enc_csn_id_coded)\n",
+    "        WHERE lr_inpt.order_time >= adt.EVENT_TIME_JITTERED\n",
+    "        GROUP BY TO_JSON_STRING(lr_inpt)\n",
+    ")\n",
+    "'''\n",
+    "\n",
+    "query_job = client.query(query)\n",
+    "df = query_job.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "c0788845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lr_inpt</th>\n",
+       "      <th>adt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>{'anon_id': 'JC799980', 'pat_enc_csn_id_coded'...</td>\n",
+       "      <td>[{'ANON_ID': 'JC799980', 'PAT_ENC_CSN_ID_CODED...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>{'anon_id': 'JC658803', 'pat_enc_csn_id_coded'...</td>\n",
+       "      <td>[{'ANON_ID': 'JC658803', 'PAT_ENC_CSN_ID_CODED...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>{'anon_id': 'JC618687', 'pat_enc_csn_id_coded'...</td>\n",
+       "      <td>[{'ANON_ID': 'JC618687', 'PAT_ENC_CSN_ID_CODED...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>{'anon_id': 'JC696156', 'pat_enc_csn_id_coded'...</td>\n",
+       "      <td>[{'ANON_ID': 'JC696156', 'PAT_ENC_CSN_ID_CODED...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>{'anon_id': 'JC711973', 'pat_enc_csn_id_coded'...</td>\n",
+       "      <td>[{'ANON_ID': 'JC711973', 'PAT_ENC_CSN_ID_CODED...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>{'anon_id': 'JC1146621', 'pat_enc_csn_id_coded...</td>\n",
+       "      <td>[{'ANON_ID': 'JC1146621', 'PAT_ENC_CSN_ID_CODE...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             lr_inpt  \\\n",
+       "0  {'anon_id': 'JC799980', 'pat_enc_csn_id_coded'...   \n",
+       "1  {'anon_id': 'JC658803', 'pat_enc_csn_id_coded'...   \n",
+       "2  {'anon_id': 'JC618687', 'pat_enc_csn_id_coded'...   \n",
+       "3  {'anon_id': 'JC696156', 'pat_enc_csn_id_coded'...   \n",
+       "4  {'anon_id': 'JC711973', 'pat_enc_csn_id_coded'...   \n",
+       "5  {'anon_id': 'JC1146621', 'pat_enc_csn_id_coded...   \n",
+       "\n",
+       "                                                 adt  \n",
+       "0  [{'ANON_ID': 'JC799980', 'PAT_ENC_CSN_ID_CODED...  \n",
+       "1  [{'ANON_ID': 'JC658803', 'PAT_ENC_CSN_ID_CODED...  \n",
+       "2  [{'ANON_ID': 'JC618687', 'PAT_ENC_CSN_ID_CODED...  \n",
+       "3  [{'ANON_ID': 'JC696156', 'PAT_ENC_CSN_ID_CODED...  \n",
+       "4  [{'ANON_ID': 'JC711973', 'PAT_ENC_CSN_ID_CODED...  \n",
+       "5  [{'ANON_ID': 'JC1146621', 'PAT_ENC_CSN_ID_CODE...  "
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "1103ee6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Critical Care</td>\n",
+       "      <td>8495061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>6762568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td></td>\n",
+       "      <td>3169406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Intermediate Intensive Care</td>\n",
+       "      <td>542719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Well Infant Care</td>\n",
+       "      <td>452042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Recovery/Post Anesthesia Care</td>\n",
+       "      <td>27211</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  pat_lv_of_care      cnt\n",
+       "0                  Critical Care  8495061\n",
+       "1                     Acute Care  6762568\n",
+       "2                                 3169406\n",
+       "3    Intermediate Intensive Care   542719\n",
+       "4               Well Infant Care   452042\n",
+       "5  Recovery/Post Anesthesia Care    27211"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT pat_lv_of_care, COUNT(pat_lv_of_care) as cnt\n",
+    "FROM njr_labutil.lradt\n",
+    "GROUP BY pat_lv_of_care\n",
+    "ORDER BY cnt desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "5340bd81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>12184370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Surgery Admit</td>\n",
+       "      <td>2691918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Newborn</td>\n",
+       "      <td>2597579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1486499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Hospital Outpatient Surgery</td>\n",
+       "      <td>425804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Observation</td>\n",
+       "      <td>60454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td></td>\n",
+       "      <td>1041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Radiation Oncology Series</td>\n",
+       "      <td>914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Nuclear Med Series</td>\n",
+       "      <td>423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>OB Series</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     pat_class       cnt\n",
+       "0                    Inpatient  12184370\n",
+       "1                Surgery Admit   2691918\n",
+       "2                      Newborn   2597579\n",
+       "3                   Outpatient   1486499\n",
+       "4  Hospital Outpatient Surgery    425804\n",
+       "5                  Observation     60454\n",
+       "6                                   1041\n",
+       "7    Radiation Oncology Series       914\n",
+       "8           Nuclear Med Series       423\n",
+       "9                    OB Series         5"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT pat_class, COUNT(pat_class) as cnt\n",
+    "FROM (\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    ")\n",
+    "GROUP BY pat_class\n",
+    "ORDER BY cnt desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "69deafb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "e3f5f7da",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Neonatology</td>\n",
+       "      <td>3252738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>2670793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Intensive Care</td>\n",
+       "      <td>2308405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Obstetrics</td>\n",
+       "      <td>1875714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Hematology/Oncology</td>\n",
+       "      <td>1847498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Cardiovascular Surgery</td>\n",
+       "      <td>1333710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>869208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td></td>\n",
+       "      <td>798017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>General Pediatrics</td>\n",
+       "      <td>680400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>560896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>General Surgery</td>\n",
+       "      <td>407952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Nephrology</td>\n",
+       "      <td>371692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Adolescent Medicine</td>\n",
+       "      <td>344174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Cardiovascular Intensive Care</td>\n",
+       "      <td>280035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Pulmonary</td>\n",
+       "      <td>264752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Oncology</td>\n",
+       "      <td>220734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Liver Transplant</td>\n",
+       "      <td>209354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Neurosurgery</td>\n",
+       "      <td>186579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Rheumatology</td>\n",
+       "      <td>184762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Transplant Surgery</td>\n",
+       "      <td>141232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Orthopedics</td>\n",
+       "      <td>89447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Otolaryngology (ENT)</td>\n",
+       "      <td>60607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Orthopedic Surgery</td>\n",
+       "      <td>55529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Anesthesia</td>\n",
+       "      <td>53755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Neurology</td>\n",
+       "      <td>53720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>GI/Liver</td>\n",
+       "      <td>46442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Interventional Radiology</td>\n",
+       "      <td>38581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Urology</td>\n",
+       "      <td>37695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Kidney Transplant</td>\n",
+       "      <td>32166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Cardiac Cath</td>\n",
+       "      <td>32158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Plastic Surgery</td>\n",
+       "      <td>21739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Cardiovascular Transplant</td>\n",
+       "      <td>17007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Endocrinology</td>\n",
+       "      <td>15536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Gynecology</td>\n",
+       "      <td>14462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>***</td>\n",
+       "      <td>11290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Immunology &amp; Allergy</td>\n",
+       "      <td>11069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Newborn Nursery</td>\n",
+       "      <td>7036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Trauma</td>\n",
+       "      <td>5519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Pulmonary &amp; Critical Care</td>\n",
+       "      <td>5203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Ophthalmology</td>\n",
+       "      <td>4676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Hematology</td>\n",
+       "      <td>3997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Genetics</td>\n",
+       "      <td>3628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Pain Management</td>\n",
+       "      <td>2902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Gynecology &amp; Obstetrics</td>\n",
+       "      <td>2431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Dentistry</td>\n",
+       "      <td>2354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Neuro Oncology</td>\n",
+       "      <td>1539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Radiology</td>\n",
+       "      <td>1467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Maternal-Fetal Medicine</td>\n",
+       "      <td>1440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>Dermatology</td>\n",
+       "      <td>1136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>Adolescent Gynecology</td>\n",
+       "      <td>954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Psychiatry</td>\n",
+       "      <td>927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Radiation Oncology</td>\n",
+       "      <td>892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>Infectious Disease</td>\n",
+       "      <td>775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Hand</td>\n",
+       "      <td>438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>Pathology</td>\n",
+       "      <td>367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>Audiology</td>\n",
+       "      <td>301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>Neuro-Oncology</td>\n",
+       "      <td>260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Development &amp; Behavior</td>\n",
+       "      <td>158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>Rehabilitation</td>\n",
+       "      <td>155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Nuclear Medicine</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>Reproductive Endocrinology and Infertility</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>Neuromuscular Medicine</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>Gastrointestinal Pathology</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>Vascular Surgery</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>OB Anesthesia</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>Radiation Therapy</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>Respiratory Care</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Addiction Medicine</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Oromaxilofacial Surgery</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   pat_service      cnt\n",
+       "0                                  Neonatology  3252738\n",
+       "1                                   Cardiology  2670793\n",
+       "2                               Intensive Care  2308405\n",
+       "3                                   Obstetrics  1875714\n",
+       "4                          Hematology/Oncology  1847498\n",
+       "5                       Cardiovascular Surgery  1333710\n",
+       "6                         Stem Cell Transplant   869208\n",
+       "7                                                798017\n",
+       "8                           General Pediatrics   680400\n",
+       "9                             Gastroenterology   560896\n",
+       "10                             General Surgery   407952\n",
+       "11                                  Nephrology   371692\n",
+       "12                         Adolescent Medicine   344174\n",
+       "13               Cardiovascular Intensive Care   280035\n",
+       "14                                   Pulmonary   264752\n",
+       "15                                    Oncology   220734\n",
+       "16                            Liver Transplant   209354\n",
+       "17                                Neurosurgery   186579\n",
+       "18                                Rheumatology   184762\n",
+       "19                          Transplant Surgery   141232\n",
+       "20                                 Orthopedics    89447\n",
+       "21                        Otolaryngology (ENT)    60607\n",
+       "22                          Orthopedic Surgery    55529\n",
+       "23                                  Anesthesia    53755\n",
+       "24                                   Neurology    53720\n",
+       "25                                    GI/Liver    46442\n",
+       "26                    Interventional Radiology    38581\n",
+       "27                                     Urology    37695\n",
+       "28                           Kidney Transplant    32166\n",
+       "29                                Cardiac Cath    32158\n",
+       "30                             Plastic Surgery    21739\n",
+       "31                   Cardiovascular Transplant    17007\n",
+       "32                               Endocrinology    15536\n",
+       "33                                  Gynecology    14462\n",
+       "34                                         ***    11290\n",
+       "35                        Immunology & Allergy    11069\n",
+       "36                             Newborn Nursery     7036\n",
+       "37                                      Trauma     5519\n",
+       "38                   Pulmonary & Critical Care     5203\n",
+       "39                               Ophthalmology     4676\n",
+       "40                                  Hematology     3997\n",
+       "41                                    Genetics     3628\n",
+       "42                             Pain Management     2902\n",
+       "43                     Gynecology & Obstetrics     2431\n",
+       "44                                   Dentistry     2354\n",
+       "45                              Neuro Oncology     1539\n",
+       "46                                   Radiology     1467\n",
+       "47                     Maternal-Fetal Medicine     1440\n",
+       "48                                 Dermatology     1136\n",
+       "49                       Adolescent Gynecology      954\n",
+       "50                                  Psychiatry      927\n",
+       "51                          Radiation Oncology      892\n",
+       "52                          Infectious Disease      775\n",
+       "53                                        Hand      438\n",
+       "54                                   Pathology      367\n",
+       "55                                   Audiology      301\n",
+       "56                              Neuro-Oncology      260\n",
+       "57                      Development & Behavior      158\n",
+       "58                              Rehabilitation      155\n",
+       "59                            Nuclear Medicine      139\n",
+       "60  Reproductive Endocrinology and Infertility      139\n",
+       "61                      Neuromuscular Medicine       87\n",
+       "62                  Gastrointestinal Pathology       84\n",
+       "63                            Vascular Surgery       47\n",
+       "64                               OB Anesthesia       37\n",
+       "65                           Radiation Therapy       31\n",
+       "66                            Respiratory Care       18\n",
+       "67                                    Diabetes       16\n",
+       "68                          Addiction Medicine        4\n",
+       "69                     Oromaxilofacial Surgery        2"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT pat_service, COUNT(pat_service) as cnt\n",
+    "FROM (\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    ")\n",
+    "GROUP BY pat_service\n",
+    "ORDER BY cnt desc\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "db150490",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td></td>\n",
+       "      <td>2216723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PEDS MED/SURG Level 2</td>\n",
+       "      <td>1346219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Critical Care Level 3</td>\n",
+       "      <td>1341010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Labor &amp; Delivery</td>\n",
+       "      <td>1289076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NICU Level 3 (174)</td>\n",
+       "      <td>1274545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CV Critical Care Level 3</td>\n",
+       "      <td>1153808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Oncology Level 3</td>\n",
+       "      <td>1089229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>908903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>837399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SCT</td>\n",
+       "      <td>774643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>NICU Level 4</td>\n",
+       "      <td>741390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>ECMO</td>\n",
+       "      <td>682946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>NITRIC</td>\n",
+       "      <td>674760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Oncology Level 2</td>\n",
+       "      <td>659299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Critical Care Level 2</td>\n",
+       "      <td>633872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Critical Care Level 1</td>\n",
+       "      <td>593674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>OB Level 4</td>\n",
+       "      <td>478394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Newborn Nursery</td>\n",
+       "      <td>472152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>CVVH</td>\n",
+       "      <td>438641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>CV Critical Care Level 1</td>\n",
+       "      <td>347799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>LVAD</td>\n",
+       "      <td>195332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>ICN Lvl C</td>\n",
+       "      <td>176834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>CCP Med/Surg Level 3</td>\n",
+       "      <td>164526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>OB Level 3</td>\n",
+       "      <td>133817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>ICN Lvl B</td>\n",
+       "      <td>118591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>PEDS MED/SURG Level 1</td>\n",
+       "      <td>93665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>92600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CCP Med/Surg Level 1</td>\n",
+       "      <td>87483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>PEDS Med/Surg</td>\n",
+       "      <td>86965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>CCP Med/Surg Level 2</td>\n",
+       "      <td>78202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Neonatal Level 2 (173)</td>\n",
+       "      <td>63834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>NICU/DIALYSIS Level 4</td>\n",
+       "      <td>40205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>NICU/NEURO Level 4</td>\n",
+       "      <td>36192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Oncology Level 4</td>\n",
+       "      <td>34042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>SCT 2 (SCT 2)</td>\n",
+       "      <td>25589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Oncology Level 1</td>\n",
+       "      <td>15316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>PEDS TXP Med/Surg Level 2</td>\n",
+       "      <td>12787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>ICN Lvl D</td>\n",
+       "      <td>11605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>IICU Level 3</td>\n",
+       "      <td>6849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>PEDS MED/SURG Level 4</td>\n",
+       "      <td>6800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>OB Level 2</td>\n",
+       "      <td>6370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Neonatal Level 3 (173)</td>\n",
+       "      <td>5299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>CARD Med/Surg</td>\n",
+       "      <td>347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Outpatient in Bed</td>\n",
+       "      <td>326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>IICU Level 1</td>\n",
+       "      <td>271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>ICN Lvl A</td>\n",
+       "      <td>239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>PEDS TXP Med/Surg Level 1</td>\n",
+       "      <td>158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>OB Level 1</td>\n",
+       "      <td>150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>IICU Level 2</td>\n",
+       "      <td>73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>Neonatal Level 1 (172)</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>PACU INPT</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Boarder Baby</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 accomodation      cnt\n",
+       "0                              2216723\n",
+       "1       PEDS MED/SURG Level 2  1346219\n",
+       "2       Critical Care Level 3  1341010\n",
+       "3            Labor & Delivery  1289076\n",
+       "4          NICU Level 3 (174)  1274545\n",
+       "5    CV Critical Care Level 3  1153808\n",
+       "6            Oncology Level 3  1089229\n",
+       "7    CV Critical Care Level 2   908903\n",
+       "8       PEDS MED/SURG Level 3   837399\n",
+       "9                        SCT    774643\n",
+       "10               NICU Level 4   741390\n",
+       "11                       ECMO   682946\n",
+       "12                     NITRIC   674760\n",
+       "13           Oncology Level 2   659299\n",
+       "14      Critical Care Level 2   633872\n",
+       "15      Critical Care Level 1   593674\n",
+       "16                 OB Level 4   478394\n",
+       "17            Newborn Nursery   472152\n",
+       "18                       CVVH   438641\n",
+       "19   CV Critical Care Level 1   347799\n",
+       "20                       LVAD   195332\n",
+       "21                  ICN Lvl C   176834\n",
+       "22       CCP Med/Surg Level 3   164526\n",
+       "23                 OB Level 3   133817\n",
+       "24                  ICN Lvl B   118591\n",
+       "25      PEDS MED/SURG Level 1    93665\n",
+       "26  PEDS TXP Med/Surg Level 3    92600\n",
+       "27       CCP Med/Surg Level 1    87483\n",
+       "28              PEDS Med/Surg    86965\n",
+       "29       CCP Med/Surg Level 2    78202\n",
+       "30     Neonatal Level 2 (173)    63834\n",
+       "31      NICU/DIALYSIS Level 4    40205\n",
+       "32         NICU/NEURO Level 4    36192\n",
+       "33           Oncology Level 4    34042\n",
+       "34              SCT 2 (SCT 2)    25589\n",
+       "35           Oncology Level 1    15316\n",
+       "36  PEDS TXP Med/Surg Level 2    12787\n",
+       "37                  ICN Lvl D    11605\n",
+       "38               IICU Level 3     6849\n",
+       "39      PEDS MED/SURG Level 4     6800\n",
+       "40                 OB Level 2     6370\n",
+       "41     Neonatal Level 3 (173)     5299\n",
+       "42              CARD Med/Surg      347\n",
+       "43          Outpatient in Bed      326\n",
+       "44               IICU Level 1      271\n",
+       "45                  ICN Lvl A      239\n",
+       "46  PEDS TXP Med/Surg Level 1      158\n",
+       "47                 OB Level 1      150\n",
+       "48               IICU Level 2       73\n",
+       "49     Neonatal Level 1 (172)       40\n",
+       "50                  PACU INPT       10\n",
+       "51               Boarder Baby        8"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT accomodation, COUNT(accomodation) as cnt\n",
+    "FROM (\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    ")\n",
+    "GROUP BY accomodation\n",
+    "ORDER BY cnt desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "0a9eb416",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1477223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Surgery Admit</td>\n",
+       "      <td>687671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>476340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hospital Outpatient Surgery</td>\n",
+       "      <td>413284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Newborn</td>\n",
+       "      <td>82455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Observation</td>\n",
+       "      <td>30050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td></td>\n",
+       "      <td>1041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Radiation Oncology Series</td>\n",
+       "      <td>914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Nuclear Med Series</td>\n",
+       "      <td>423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>OB Series</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     pat_class      cnt\n",
+       "0                   Outpatient  1477223\n",
+       "1                Surgery Admit   687671\n",
+       "2                    Inpatient   476340\n",
+       "3  Hospital Outpatient Surgery   413284\n",
+       "4                      Newborn    82455\n",
+       "5                  Observation    30050\n",
+       "6                                  1041\n",
+       "7    Radiation Oncology Series      914\n",
+       "8           Nuclear Med Series      423\n",
+       "9                    OB Series        5"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT pat_class, COUNT(pat_class) as cnt\n",
+    "FROM (\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    "    WHERE pat_lv_of_care = ''\n",
+    ")\n",
+    "GROUP BY pat_class\n",
+    "ORDER BY cnt desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "fadabaab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Labor &amp; Delivery</td>\n",
+       "      <td>100983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PEDS MED/SURG Level 2</td>\n",
+       "      <td>44914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Critical Care Level 3</td>\n",
+       "      <td>38298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NICU Level 3 (174)</td>\n",
+       "      <td>30878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OB Level 4</td>\n",
+       "      <td>27396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>23917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Oncology Level 3</td>\n",
+       "      <td>22244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td></td>\n",
+       "      <td>22062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Oncology Level 2</td>\n",
+       "      <td>21984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>ECMO</td>\n",
+       "      <td>20998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Critical Care Level 2</td>\n",
+       "      <td>20077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Critical Care Level 1</td>\n",
+       "      <td>16794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CV Critical Care Level 3</td>\n",
+       "      <td>12777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>SCT</td>\n",
+       "      <td>12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Neonatal Level 2 (173)</td>\n",
+       "      <td>9849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>NICU Level 4</td>\n",
+       "      <td>7747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>5630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>NITRIC</td>\n",
+       "      <td>5214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>CV Critical Care Level 1</td>\n",
+       "      <td>3807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>OB Level 3</td>\n",
+       "      <td>3423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>CVVH</td>\n",
+       "      <td>3213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>NICU/NEURO Level 4</td>\n",
+       "      <td>2925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Neonatal Level 3 (173)</td>\n",
+       "      <td>2651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>CCP Med/Surg Level 2</td>\n",
+       "      <td>2556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>CCP Med/Surg Level 3</td>\n",
+       "      <td>2132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>PEDS MED/SURG Level 1</td>\n",
+       "      <td>1826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>PEDS Med/Surg</td>\n",
+       "      <td>1786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CCP Med/Surg Level 1</td>\n",
+       "      <td>1644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>1499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>ICN Lvl C</td>\n",
+       "      <td>1294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Newborn Nursery</td>\n",
+       "      <td>1138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>LVAD</td>\n",
+       "      <td>593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>NICU/DIALYSIS Level 4</td>\n",
+       "      <td>530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>PEDS TXP Med/Surg Level 2</td>\n",
+       "      <td>297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>ICN Lvl B</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Oncology Level 4</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>OB Level 2</td>\n",
+       "      <td>164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Oncology Level 1</td>\n",
+       "      <td>160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>SCT 2 (SCT 2)</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Neonatal Level 1 (172)</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>IICU Level 3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 accomodation     cnt\n",
+       "0            Labor & Delivery  100983\n",
+       "1       PEDS MED/SURG Level 2   44914\n",
+       "2       Critical Care Level 3   38298\n",
+       "3          NICU Level 3 (174)   30878\n",
+       "4                  OB Level 4   27396\n",
+       "5       PEDS MED/SURG Level 3   23917\n",
+       "6            Oncology Level 3   22244\n",
+       "7                               22062\n",
+       "8            Oncology Level 2   21984\n",
+       "9                        ECMO   20998\n",
+       "10      Critical Care Level 2   20077\n",
+       "11      Critical Care Level 1   16794\n",
+       "12   CV Critical Care Level 3   12777\n",
+       "13                       SCT    12400\n",
+       "14     Neonatal Level 2 (173)    9849\n",
+       "15               NICU Level 4    7747\n",
+       "16   CV Critical Care Level 2    5630\n",
+       "17                     NITRIC    5214\n",
+       "18   CV Critical Care Level 1    3807\n",
+       "19                 OB Level 3    3423\n",
+       "20                       CVVH    3213\n",
+       "21         NICU/NEURO Level 4    2925\n",
+       "22     Neonatal Level 3 (173)    2651\n",
+       "23       CCP Med/Surg Level 2    2556\n",
+       "24       CCP Med/Surg Level 3    2132\n",
+       "25      PEDS MED/SURG Level 1    1826\n",
+       "26              PEDS Med/Surg    1786\n",
+       "27       CCP Med/Surg Level 1    1644\n",
+       "28  PEDS TXP Med/Surg Level 3    1499\n",
+       "29                  ICN Lvl C    1294\n",
+       "30            Newborn Nursery    1138\n",
+       "31                       LVAD     593\n",
+       "32      NICU/DIALYSIS Level 4     530\n",
+       "33  PEDS TXP Med/Surg Level 2     297\n",
+       "34                  ICN Lvl B     230\n",
+       "35           Oncology Level 4     182\n",
+       "36                 OB Level 2     164\n",
+       "37           Oncology Level 1     160\n",
+       "38              SCT 2 (SCT 2)      88\n",
+       "39     Neonatal Level 1 (172)      36\n",
+       "40               IICU Level 3       4"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT accomodation, COUNT(accomodation) as cnt\n",
+    "FROM (\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    "    WHERE pat_lv_of_care = '' AND pat_class = 'Inpatient'\n",
+    ")\n",
+    "GROUP BY accomodation\n",
+    "ORDER BY cnt desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "3a8ac1a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>...</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1463857</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>702849975</td>\n",
+       "      <td>797</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB15</td>\n",
+       "      <td>Metabolic Panel, Basic</td>\n",
+       "      <td>Urea Nitrogen, Ser/Plas</td>\n",
+       "      <td>BUN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>352024</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1781918</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703683058</td>\n",
+       "      <td>2507</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB347</td>\n",
+       "      <td>Urinalysis Macroscopic Only</td>\n",
+       "      <td>Blood</td>\n",
+       "      <td>UBLOOD</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>438241</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1781334</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703683066</td>\n",
+       "      <td>483</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB293</td>\n",
+       "      <td>CBC with Diff</td>\n",
+       "      <td>RDW</td>\n",
+       "      <td>RDW</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>438241</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1761220</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703644511</td>\n",
+       "      <td>6497</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB347</td>\n",
+       "      <td>Urinalysis Macroscopic Only</td>\n",
+       "      <td>pH</td>\n",
+       "      <td>UPH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>433437</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1760727</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703644516</td>\n",
+       "      <td>6046</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB293</td>\n",
+       "      <td>CBC with Diff</td>\n",
+       "      <td>MCV</td>\n",
+       "      <td>MCV</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>433437</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1811104</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703781705</td>\n",
+       "      <td>6632</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB17</td>\n",
+       "      <td>Metabolic Panel, Comprehensive</td>\n",
+       "      <td>Protein, Total</td>\n",
+       "      <td>TP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>446929</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2218369</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>704897885</td>\n",
+       "      <td>4668</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB52</td>\n",
+       "      <td>Bilirubin Direct, Ser/Plas</td>\n",
+       "      <td>Bilirubin, Direct</td>\n",
+       "      <td>DBIL</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Cardiac Cath</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Transfer In</td>\n",
+       "      <td></td>\n",
+       "      <td>553192</td>\n",
+       "      <td>Transfer In</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1587304</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703212235</td>\n",
+       "      <td>492</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB293</td>\n",
+       "      <td>CBC with Diff</td>\n",
+       "      <td>EOS, %</td>\n",
+       "      <td>EOS</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>386111</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1781703</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>703683066</td>\n",
+       "      <td>499</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB293</td>\n",
+       "      <td>CBC with Diff</td>\n",
+       "      <td>EOS, ABS</td>\n",
+       "      <td>EOSAB</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>438241</td>\n",
+       "      <td>Census</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1353054</td>\n",
+       "      <td>JC586854</td>\n",
+       "      <td>309567035</td>\n",
+       "      <td>702577412</td>\n",
+       "      <td>6046</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB293</td>\n",
+       "      <td>CBC with Diff</td>\n",
+       "      <td>MCV</td>\n",
+       "      <td>MCV</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10201101</td>\n",
+       "      <td>Stem Cell Transplant</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>SCT</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Transfer In</td>\n",
+       "      <td></td>\n",
+       "      <td>327135</td>\n",
+       "      <td>Transfer In</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       uid   anon_id pat_enc_csn_id_coded order_id_coded  component_id  \\\n",
+       "0  1463857  JC586854            309567035      702849975           797   \n",
+       "1  1781918  JC586854            309567035      703683058          2507   \n",
+       "2  1781334  JC586854            309567035      703683066           483   \n",
+       "3  1761220  JC586854            309567035      703644511          6497   \n",
+       "4  1760727  JC586854            309567035      703644516          6046   \n",
+       "5  1811104  JC586854            309567035      703781705          6632   \n",
+       "6  2218369  JC586854            309567035      704897885          4668   \n",
+       "7  1587304  JC586854            309567035      703212235           492   \n",
+       "8  1781703  JC586854            309567035      703683066           499   \n",
+       "9  1353054  JC586854            309567035      702577412          6046   \n",
+       "\n",
+       "  order_type proc_code                  group_lab_name  \\\n",
+       "0        Lab     LAB15          Metabolic Panel, Basic   \n",
+       "1        Lab    LAB347     Urinalysis Macroscopic Only   \n",
+       "2        Lab    LAB293                   CBC with Diff   \n",
+       "3        Lab    LAB347     Urinalysis Macroscopic Only   \n",
+       "4        Lab    LAB293                   CBC with Diff   \n",
+       "5        Lab     LAB17  Metabolic Panel, Comprehensive   \n",
+       "6        Lab     LAB52      Bilirubin Direct, Ser/Plas   \n",
+       "7        Lab    LAB293                   CBC with Diff   \n",
+       "8        Lab    LAB293                   CBC with Diff   \n",
+       "9        Lab    LAB293                   CBC with Diff   \n",
+       "\n",
+       "                  lab_name base_name  ...  department_id  \\\n",
+       "0  Urea Nitrogen, Ser/Plas       BUN  ...       10201101   \n",
+       "1                    Blood    UBLOOD  ...       10201101   \n",
+       "2                      RDW       RDW  ...       10201101   \n",
+       "3                       pH       UPH  ...       10201101   \n",
+       "4                      MCV       MCV  ...       10201101   \n",
+       "5           Protein, Total        TP  ...       10201101   \n",
+       "6        Bilirubin, Direct      DBIL  ...       10201101   \n",
+       "7                   EOS, %       EOS  ...       10201101   \n",
+       "8                 EOS, ABS     EOSAB  ...       10201101   \n",
+       "9                      MCV       MCV  ...       10201101   \n",
+       "\n",
+       "            pat_service pat_lv_of_care accomodation from_base_class  \\\n",
+       "0  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "1  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "2  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "3  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "4  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "5  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "6          Cardiac Cath     Acute Care         SCT        Inpatient   \n",
+       "7  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "8  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "9  Stem Cell Transplant     Acute Care         SCT        Inpatient   \n",
+       "\n",
+       "  to_base_class in_event_type out_event_type event_id_coded   event_type  \n",
+       "0     Inpatient        Census         Census         352024       Census  \n",
+       "1     Inpatient        Census         Census         438241       Census  \n",
+       "2     Inpatient        Census         Census         438241       Census  \n",
+       "3     Inpatient        Census         Census         433437       Census  \n",
+       "4     Inpatient        Census         Census         433437       Census  \n",
+       "5     Inpatient        Census         Census         446929       Census  \n",
+       "6     Inpatient   Transfer In                        553192  Transfer In  \n",
+       "7     Inpatient        Census         Census         386111       Census  \n",
+       "8     Inpatient        Census         Census         438241       Census  \n",
+       "9     Inpatient   Transfer In                        327135  Transfer In  \n",
+       "\n",
+       "[10 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%bigquery\n",
+    "SELECT * FROM njr_labutil.lradt\n",
+    "WHERE\n",
+    "    (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "    AND pat_service NOT IN (\n",
+    "        'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "        'Reproductive Endocrinology and Infertility',\n",
+    "        'Gynecology & Obstetrics', 'Gynecology',\n",
+    "        'Intensive Care', 'Neonatology', 'Cardiovascular Intensive Care')\n",
+    "    AND (pat_lv_of_care = 'Acute Care'\n",
+    "        OR (pat_lv_of_care='' AND accomodation IN (\n",
+    "            'PEDS MED/SURG Level 1','PEDS MED/SURG Level 2', 'PEDS MED/SURG Level 3',\n",
+    "            'PEDS Med/Surg',\n",
+    "            'CCP Med/Surg Level 1','CCP Med/Surg Level 2','CCP Med/Surg Level 3',\n",
+    "            'PEDS TXP Med/Surg Level 1', 'PEDS TXP Med/Surg Level 2','PEDS TXP Med/Surg Level 3',\n",
+    "            'Oncology Level 1', 'Oncology Level 2', 'Oncology Level 3', 'Oncology Level 4'\n",
+    "            )\n",
+    "        )\n",
+    "    )\n",
+    "LIMIT 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4adb83cf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/explore_lradt.ipynb b/scripts/PedsLabUtil/explore_lradt.ipynb
new file mode 100644
index 00000000..fff964e0
--- /dev/null
+++ b/scripts/PedsLabUtil/explore_lradt.ipynb
@@ -0,0 +1,16051 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9d920777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.dpi'] = 150\n",
+    "import seaborn as sns\n",
+    "\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 100\n",
+    "pd.options.display.min_rows = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d29ac95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2b6a7fcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt = pd.read_pickle('data/lradt_20210927.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "69aaa197",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before pruning last day of labs:\t 4996609\n",
+      "After pruning last day of labs:\t\t 4935345\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get rid of labs ordered on the last day, due to issues with the timing right at the edge. There are multiple\n",
+    "# labs that have the order time and result time of 2021-10-31 00:00:00 and that is messing up the repetition \n",
+    "print('Before pruning last day of labs:\\t', len(lradt))\n",
+    "lradt = lradt.loc[lradt.result_time < lradt.result_time.max()]\n",
+    "print('After pruning last day of labs:\\t\\t', len(lradt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b351e241",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt['njr_labname'] = lradt['group_lab_name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5e874b87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cbc =  ['CBC with Differential', 'CBC with Diff', 'CBC', 'CBC with Diff and Slide Review']\n",
+    "poct_urine = ['POCT Urine Dipstick (10)', 'POCT Urine Dipstick (9)']\n",
+    "poct_glucose = ['Glucose By Meter', 'Glucose PCX Meter']\n",
+    "lradt.loc[lradt.njr_labname.isin(cbc), 'njr_labname'] = 'CBC'\n",
+    "lradt.loc[lradt.njr_labname.isin(poct_urine), 'njr_labname'] = 'POCT Urine'\n",
+    "lradt.loc[lradt.njr_labname.isin(poct_glucose), 'njr_labname'] = 'POCT Glucose'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a68d3dab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>njr_labname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>1121833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>582287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>401446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>315484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>234366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>209979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>188767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>126466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>100155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>96574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>91362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>63499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>60828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Platelets</th>\n",
+       "      <td>52984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Arterial</th>\n",
+       "      <td>49383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>40775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Venous</th>\n",
+       "      <td>36851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential</th>\n",
+       "      <td>35954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Istat EG7, Arterial</th>\n",
+       "      <td>34903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>33384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>33013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 23</th>\n",
+       "      <td>32301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>31756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>30922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gastroenteritis PCR Panel</th>\n",
+       "      <td>28863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>25125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>24695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>iSTAT CG8, Arterial</th>\n",
+       "      <td>23646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Respiratory Virus PCR Panel</th>\n",
+       "      <td>21274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Respiratory Pathogen PCR Panel</th>\n",
+       "      <td>19341</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  njr_labname\n",
+       "CBC                                   1121833\n",
+       "Chem 10                                582287\n",
+       "Metabolic Panel, Comprehensive         401446\n",
+       "Renal Function Panel                   315484\n",
+       "POCT Urine                             234366\n",
+       "Urinalysis Macroscopic Only            209979\n",
+       "Manual Differential/Slide Review       188767\n",
+       "Urinalysis With Microscopic            126466\n",
+       "POCT Glucose                           100155\n",
+       "Red Blood Cells                         96574\n",
+       "Metabolic Panel, Basic                  91362\n",
+       "Magnesium, Ser/Plas                     63499\n",
+       "Hepatic Function Panel                  60828\n",
+       "Platelets                               52984\n",
+       "Blood Gases, Arterial                   49383\n",
+       "Antibody Screen                         40775\n",
+       "Blood Gases, Venous                     36851\n",
+       "Manual Differential                     35954\n",
+       "Istat EG7, Arterial                     34903\n",
+       "Slide Review                            33384\n",
+       "Prothrombin Time                        33013\n",
+       "Chem 23                                 32301\n",
+       "Phosphorus, Ser/Plas                    31756\n",
+       "Blood Culture                           30922\n",
+       "Gastroenteritis PCR Panel               28863\n",
+       "Tacrolimus Level, FK506                 25125\n",
+       "ABO Rh Typing                           24695\n",
+       "iSTAT CG8, Arterial                     23646\n",
+       "Respiratory Virus PCR Panel             21274\n",
+       "Respiratory Pathogen PCR Panel          19341"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(lradt.njr_labname.value_counts()).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "50094288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labcounts = lradt.groupby(by='njr_labname').agg({'order_id_coded': 'nunique', 'pat_enc_csn_id_coded': 'nunique', 'anon_id': 'nunique'}).rename(columns={'order_id_coded': 'norders', 'pat_enc_csn_id_coded': 'nenc', 'anon_id': 'npat'}).sort_values('norders', ascending=False).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c1c839e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>70271</td>\n",
+       "      <td>16811</td>\n",
+       "      <td>8179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>47187</td>\n",
+       "      <td>10151</td>\n",
+       "      <td>4598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>47098</td>\n",
+       "      <td>3565</td>\n",
+       "      <td>2253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>41927</td>\n",
+       "      <td>8672</td>\n",
+       "      <td>4639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>25509</td>\n",
+       "      <td>5475</td>\n",
+       "      <td>3068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>23519</td>\n",
+       "      <td>2904</td>\n",
+       "      <td>1353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>23074</td>\n",
+       "      <td>8163</td>\n",
+       "      <td>4820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>20888</td>\n",
+       "      <td>5302</td>\n",
+       "      <td>3330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>19687</td>\n",
+       "      <td>2308</td>\n",
+       "      <td>1004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>18964</td>\n",
+       "      <td>4436</td>\n",
+       "      <td>2185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>16051</td>\n",
+       "      <td>6060</td>\n",
+       "      <td>2407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>16050</td>\n",
+       "      <td>6110</td>\n",
+       "      <td>2433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>13584</td>\n",
+       "      <td>3671</td>\n",
+       "      <td>2053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>13295</td>\n",
+       "      <td>4887</td>\n",
+       "      <td>3099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>13054</td>\n",
+       "      <td>2552</td>\n",
+       "      <td>801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uric Acid, Ser/Plas</th>\n",
+       "      <td>9360</td>\n",
+       "      <td>1105</td>\n",
+       "      <td>916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>9355</td>\n",
+       "      <td>3035</td>\n",
+       "      <td>1565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH Total, Ser/Plas</th>\n",
+       "      <td>9191</td>\n",
+       "      <td>1453</td>\n",
+       "      <td>1116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>8783</td>\n",
+       "      <td>3011</td>\n",
+       "      <td>2549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>8681</td>\n",
+       "      <td>3802</td>\n",
+       "      <td>2546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>8677</td>\n",
+       "      <td>2563</td>\n",
+       "      <td>1280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Platelets</th>\n",
+       "      <td>8579</td>\n",
+       "      <td>1525</td>\n",
+       "      <td>891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>8356</td>\n",
+       "      <td>3381</td>\n",
+       "      <td>2322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>8015</td>\n",
+       "      <td>4713</td>\n",
+       "      <td>2903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>7634</td>\n",
+       "      <td>3976</td>\n",
+       "      <td>2994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>7416</td>\n",
+       "      <td>2660</td>\n",
+       "      <td>1687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>7402</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>1035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>6461</td>\n",
+       "      <td>1865</td>\n",
+       "      <td>1359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>6370</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilirubin Direct, Ser/Plas</th>\n",
+       "      <td>5146</td>\n",
+       "      <td>1640</td>\n",
+       "      <td>1181</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  norders   nenc  npat\n",
+       "njr_labname                                           \n",
+       "CBC                                 70271  16811  8179\n",
+       "Chem 10                             47187  10151  4598\n",
+       "POCT Glucose                        47098   3565  2253\n",
+       "Magnesium, Ser/Plas                 41927   8672  4639\n",
+       "Renal Function Panel                25509   5475  3068\n",
+       "POCT Urine                          23519   2904  1353\n",
+       "Metabolic Panel, Comprehensive      23074   8163  4820\n",
+       "Phosphorus, Ser/Plas                20888   5302  3330\n",
+       "Urinalysis Macroscopic Only         19687   2308  1004\n",
+       "Manual Differential/Slide Review    18964   4436  2185\n",
+       "Antibody Screen                     16051   6060  2407\n",
+       "ABO Rh Typing                       16050   6110  2433\n",
+       "Red Blood Cells                     13584   3671  2053\n",
+       "Prothrombin Time                    13295   4887  3099\n",
+       "Tacrolimus Level, FK506             13054   2552   801\n",
+       "Uric Acid, Ser/Plas                  9360   1105   916\n",
+       "Blood Culture                        9355   3035  1565\n",
+       "LDH Total, Ser/Plas                  9191   1453  1116\n",
+       "Metabolic Panel, Basic               8783   3011  2549\n",
+       "Part Thromboplastin Time             8681   3802  2546\n",
+       "Gamma Glutamyl Trans                 8677   2563  1280\n",
+       "Platelets                            8579   1525   891\n",
+       "Fibrinogen                           8356   3381  2322\n",
+       "Urinalysis With Microscopic          8015   4713  2903\n",
+       "C Reactive Protein                   7634   3976  2994\n",
+       "Hepatic Function Panel               7416   2660  1687\n",
+       "Triglyceride, Ser/Plas               7402   1565  1035\n",
+       "Slide Review                         6461   1865  1359\n",
+       "Vancomycin Level, Trough             6370   2020  1174\n",
+       "Bilirubin Direct, Ser/Plas           5146   1640  1181"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labcounts.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "604ea02c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>47098</td>\n",
+       "      <td>3565</td>\n",
+       "      <td>2253</td>\n",
+       "      <td>13.211220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>19687</td>\n",
+       "      <td>2308</td>\n",
+       "      <td>1004</td>\n",
+       "      <td>8.529896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uric Acid, Ser/Plas</th>\n",
+       "      <td>9360</td>\n",
+       "      <td>1105</td>\n",
+       "      <td>916</td>\n",
+       "      <td>8.470588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>23519</td>\n",
+       "      <td>2904</td>\n",
+       "      <td>1353</td>\n",
+       "      <td>8.098829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH Total, Ser/Plas</th>\n",
+       "      <td>9191</td>\n",
+       "      <td>1453</td>\n",
+       "      <td>1116</td>\n",
+       "      <td>6.325533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Platelets</th>\n",
+       "      <td>8579</td>\n",
+       "      <td>1525</td>\n",
+       "      <td>891</td>\n",
+       "      <td>5.625574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>13054</td>\n",
+       "      <td>2552</td>\n",
+       "      <td>801</td>\n",
+       "      <td>5.115204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>41927</td>\n",
+       "      <td>8672</td>\n",
+       "      <td>4639</td>\n",
+       "      <td>4.834756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>7402</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>1035</td>\n",
+       "      <td>4.729712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>25509</td>\n",
+       "      <td>5475</td>\n",
+       "      <td>3068</td>\n",
+       "      <td>4.659178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>47187</td>\n",
+       "      <td>10151</td>\n",
+       "      <td>4598</td>\n",
+       "      <td>4.648508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>18964</td>\n",
+       "      <td>4436</td>\n",
+       "      <td>2185</td>\n",
+       "      <td>4.275023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>70271</td>\n",
+       "      <td>16811</td>\n",
+       "      <td>8179</td>\n",
+       "      <td>4.180061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>20888</td>\n",
+       "      <td>5302</td>\n",
+       "      <td>3330</td>\n",
+       "      <td>3.939645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>13584</td>\n",
+       "      <td>3671</td>\n",
+       "      <td>2053</td>\n",
+       "      <td>3.700354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>6461</td>\n",
+       "      <td>1865</td>\n",
+       "      <td>1359</td>\n",
+       "      <td>3.464343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>8677</td>\n",
+       "      <td>2563</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>3.385486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>6370</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1174</td>\n",
+       "      <td>3.153465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilirubin Direct, Ser/Plas</th>\n",
+       "      <td>5146</td>\n",
+       "      <td>1640</td>\n",
+       "      <td>1181</td>\n",
+       "      <td>3.137805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>9355</td>\n",
+       "      <td>3035</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>3.082372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>8783</td>\n",
+       "      <td>3011</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2.916971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>23074</td>\n",
+       "      <td>8163</td>\n",
+       "      <td>4820</td>\n",
+       "      <td>2.826657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>7416</td>\n",
+       "      <td>2660</td>\n",
+       "      <td>1687</td>\n",
+       "      <td>2.787970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>13295</td>\n",
+       "      <td>4887</td>\n",
+       "      <td>3099</td>\n",
+       "      <td>2.720483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>16051</td>\n",
+       "      <td>6060</td>\n",
+       "      <td>2407</td>\n",
+       "      <td>2.648680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>16050</td>\n",
+       "      <td>6110</td>\n",
+       "      <td>2433</td>\n",
+       "      <td>2.626841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>8356</td>\n",
+       "      <td>3381</td>\n",
+       "      <td>2322</td>\n",
+       "      <td>2.471458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>8681</td>\n",
+       "      <td>3802</td>\n",
+       "      <td>2546</td>\n",
+       "      <td>2.283272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>7634</td>\n",
+       "      <td>3976</td>\n",
+       "      <td>2994</td>\n",
+       "      <td>1.920020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>8015</td>\n",
+       "      <td>4713</td>\n",
+       "      <td>2903</td>\n",
+       "      <td>1.700615</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  norders   nenc  npat    per_enc\n",
+       "njr_labname                                                      \n",
+       "POCT Glucose                        47098   3565  2253  13.211220\n",
+       "Urinalysis Macroscopic Only         19687   2308  1004   8.529896\n",
+       "Uric Acid, Ser/Plas                  9360   1105   916   8.470588\n",
+       "POCT Urine                          23519   2904  1353   8.098829\n",
+       "LDH Total, Ser/Plas                  9191   1453  1116   6.325533\n",
+       "Platelets                            8579   1525   891   5.625574\n",
+       "Tacrolimus Level, FK506             13054   2552   801   5.115204\n",
+       "Magnesium, Ser/Plas                 41927   8672  4639   4.834756\n",
+       "Triglyceride, Ser/Plas               7402   1565  1035   4.729712\n",
+       "Renal Function Panel                25509   5475  3068   4.659178\n",
+       "Chem 10                             47187  10151  4598   4.648508\n",
+       "Manual Differential/Slide Review    18964   4436  2185   4.275023\n",
+       "CBC                                 70271  16811  8179   4.180061\n",
+       "Phosphorus, Ser/Plas                20888   5302  3330   3.939645\n",
+       "Red Blood Cells                     13584   3671  2053   3.700354\n",
+       "Slide Review                         6461   1865  1359   3.464343\n",
+       "Gamma Glutamyl Trans                 8677   2563  1280   3.385486\n",
+       "Vancomycin Level, Trough             6370   2020  1174   3.153465\n",
+       "Bilirubin Direct, Ser/Plas           5146   1640  1181   3.137805\n",
+       "Blood Culture                        9355   3035  1565   3.082372\n",
+       "Metabolic Panel, Basic               8783   3011  2549   2.916971\n",
+       "Metabolic Panel, Comprehensive      23074   8163  4820   2.826657\n",
+       "Hepatic Function Panel               7416   2660  1687   2.787970\n",
+       "Prothrombin Time                    13295   4887  3099   2.720483\n",
+       "Antibody Screen                     16051   6060  2407   2.648680\n",
+       "ABO Rh Typing                       16050   6110  2433   2.626841\n",
+       "Fibrinogen                           8356   3381  2322   2.471458\n",
+       "Part Thromboplastin Time             8681   3802  2546   2.283272\n",
+       "C Reactive Protein                   7634   3976  2994   1.920020\n",
+       "Urinalysis With Microscopic          8015   4713  2903   1.700615"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labcounts['per_enc'] = labcounts.norders/labcounts.nenc\n",
+    "labcounts.sort_values(by='per_enc', ascending=False).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "45277079",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enclabcounts = lradt.groupby(by=['pat_enc_csn_id_coded', 'njr_labname']).agg({'order_id_coded': 'nunique'}).rename(columns={'order_id_coded': 'norders'}).sort_values('pat_enc_csn_id_coded', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ae3604be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enclabcounts = enclabcounts.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3b853a9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25693"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(enclabcounts.pat_enc_csn_id_coded.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a5a9c2a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>norders</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>8461</td>\n",
+       "      <td>7.318402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>6999</td>\n",
+       "      <td>6.291613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>4714</td>\n",
+       "      <td>8.054518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>3546</td>\n",
+       "      <td>6.649746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>3107</td>\n",
+       "      <td>5.799163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>2816</td>\n",
+       "      <td>8.320668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>2346</td>\n",
+       "      <td>7.192668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>2315</td>\n",
+       "      <td>19.804752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>2314</td>\n",
+       "      <td>5.295592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>2292</td>\n",
+       "      <td>5.359075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>2095</td>\n",
+       "      <td>5.731742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>2001</td>\n",
+       "      <td>8.789105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>1932</td>\n",
+       "      <td>6.435818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>1903</td>\n",
+       "      <td>5.418287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>1780</td>\n",
+       "      <td>10.763483</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  pat_enc_csn_id_coded    norders\n",
+       "njr_labname                                                      \n",
+       "CBC                                               8461   7.318402\n",
+       "Chem 10                                           6999   6.291613\n",
+       "Magnesium, Ser/Plas                               4714   8.054518\n",
+       "Renal Function Panel                              3546   6.649746\n",
+       "Metabolic Panel, Comprehensive                    3107   5.799163\n",
+       "POCT Urine                                        2816   8.320668\n",
+       "Manual Differential/Slide Review                  2346   7.192668\n",
+       "POCT Glucose                                      2315  19.804752\n",
+       "ABO Rh Typing                                     2314   5.295592\n",
+       "Antibody Screen                                   2292   5.359075\n",
+       "Red Blood Cells                                   2095   5.731742\n",
+       "Phosphorus, Ser/Plas                              2001   8.789105\n",
+       "Tacrolimus Level, FK506                           1932   6.435818\n",
+       "Prothrombin Time                                  1903   5.418287\n",
+       "Urinalysis Macroscopic Only                       1780  10.763483"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enclabcounts.loc[enclabcounts.norders>1].groupby('njr_labname').agg({'pat_enc_csn_id_coded' : 'nunique', 'norders' : 'mean'}).sort_values('pat_enc_csn_id_coded', ascending=False).head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3edc6a48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>norders</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>16811</td>\n",
+       "      <td>70271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>10151</td>\n",
+       "      <td>47187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>3565</td>\n",
+       "      <td>47098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>8672</td>\n",
+       "      <td>41927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>5475</td>\n",
+       "      <td>25509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>2904</td>\n",
+       "      <td>23519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>8163</td>\n",
+       "      <td>23074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>5302</td>\n",
+       "      <td>20888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>2308</td>\n",
+       "      <td>19687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>4436</td>\n",
+       "      <td>18964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>6060</td>\n",
+       "      <td>16051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>6110</td>\n",
+       "      <td>16050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>3671</td>\n",
+       "      <td>13584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>4887</td>\n",
+       "      <td>13295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>2552</td>\n",
+       "      <td>13054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uric Acid, Ser/Plas</th>\n",
+       "      <td>1105</td>\n",
+       "      <td>9360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>3035</td>\n",
+       "      <td>9355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH Total, Ser/Plas</th>\n",
+       "      <td>1453</td>\n",
+       "      <td>9191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>3011</td>\n",
+       "      <td>8783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>3802</td>\n",
+       "      <td>8681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>2563</td>\n",
+       "      <td>8677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Platelets</th>\n",
+       "      <td>1525</td>\n",
+       "      <td>8579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>3381</td>\n",
+       "      <td>8356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>4713</td>\n",
+       "      <td>8015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>3976</td>\n",
+       "      <td>7634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>2660</td>\n",
+       "      <td>7416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>1565</td>\n",
+       "      <td>7402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>1865</td>\n",
+       "      <td>6461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilirubin Direct, Ser/Plas</th>\n",
+       "      <td>1640</td>\n",
+       "      <td>5146</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  pat_enc_csn_id_coded  norders\n",
+       "njr_labname                                                    \n",
+       "CBC                                              16811    70271\n",
+       "Chem 10                                          10151    47187\n",
+       "POCT Glucose                                      3565    47098\n",
+       "Magnesium, Ser/Plas                               8672    41927\n",
+       "Renal Function Panel                              5475    25509\n",
+       "POCT Urine                                        2904    23519\n",
+       "Metabolic Panel, Comprehensive                    8163    23074\n",
+       "Phosphorus, Ser/Plas                              5302    20888\n",
+       "Urinalysis Macroscopic Only                       2308    19687\n",
+       "Manual Differential/Slide Review                  4436    18964\n",
+       "Antibody Screen                                   6060    16051\n",
+       "ABO Rh Typing                                     6110    16050\n",
+       "Red Blood Cells                                   3671    13584\n",
+       "Prothrombin Time                                  4887    13295\n",
+       "Tacrolimus Level, FK506                           2552    13054\n",
+       "Uric Acid, Ser/Plas                               1105     9360\n",
+       "Blood Culture                                     3035     9355\n",
+       "LDH Total, Ser/Plas                               1453     9191\n",
+       "Metabolic Panel, Basic                            3011     8783\n",
+       "Part Thromboplastin Time                          3802     8681\n",
+       "Gamma Glutamyl Trans                              2563     8677\n",
+       "Platelets                                         1525     8579\n",
+       "Fibrinogen                                        3381     8356\n",
+       "Urinalysis With Microscopic                       4713     8015\n",
+       "C Reactive Protein                                3976     7634\n",
+       "Hepatic Function Panel                            2660     7416\n",
+       "Triglyceride, Ser/Plas                            1565     7402\n",
+       "Slide Review                                      1865     6461\n",
+       "Vancomycin Level, Trough                          2020     6370\n",
+       "Bilirubin Direct, Ser/Plas                        1640     5146"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempdf2 = enclabcounts.groupby('njr_labname').agg({'pat_enc_csn_id_coded' : 'nunique', 'norders' : 'sum'}).sort_values('norders', ascending=False)\n",
+    "tempdf2.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae8205ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "533852f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>norders</th>\n",
+       "      <th>perenc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>3565</td>\n",
+       "      <td>47098</td>\n",
+       "      <td>13.211220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>2308</td>\n",
+       "      <td>19687</td>\n",
+       "      <td>8.529896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uric Acid, Ser/Plas</th>\n",
+       "      <td>1105</td>\n",
+       "      <td>9360</td>\n",
+       "      <td>8.470588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>2904</td>\n",
+       "      <td>23519</td>\n",
+       "      <td>8.098829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LDH Total, Ser/Plas</th>\n",
+       "      <td>1453</td>\n",
+       "      <td>9191</td>\n",
+       "      <td>6.325533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Calcium Ionized, Whole Blood</th>\n",
+       "      <td>702</td>\n",
+       "      <td>4264</td>\n",
+       "      <td>6.074074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sodium, Ser/Plas</th>\n",
+       "      <td>536</td>\n",
+       "      <td>3107</td>\n",
+       "      <td>5.796642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Platelets</th>\n",
+       "      <td>1525</td>\n",
+       "      <td>8579</td>\n",
+       "      <td>5.625574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>2552</td>\n",
+       "      <td>13054</td>\n",
+       "      <td>5.115204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>8672</td>\n",
+       "      <td>41927</td>\n",
+       "      <td>4.834756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>1565</td>\n",
+       "      <td>7402</td>\n",
+       "      <td>4.729712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Istat EG7, Arterial</th>\n",
+       "      <td>546</td>\n",
+       "      <td>2551</td>\n",
+       "      <td>4.672161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>5475</td>\n",
+       "      <td>25509</td>\n",
+       "      <td>4.659178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>10151</td>\n",
+       "      <td>47187</td>\n",
+       "      <td>4.648508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Methotrexate Level</th>\n",
+       "      <td>1007</td>\n",
+       "      <td>4446</td>\n",
+       "      <td>4.415094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>4436</td>\n",
+       "      <td>18964</td>\n",
+       "      <td>4.275023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>16811</td>\n",
+       "      <td>70271</td>\n",
+       "      <td>4.180061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Arterial</th>\n",
+       "      <td>679</td>\n",
+       "      <td>2789</td>\n",
+       "      <td>4.107511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transfusion Svc Prod 1</th>\n",
+       "      <td>574</td>\n",
+       "      <td>2352</td>\n",
+       "      <td>4.097561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fresh Frozen Plasma</th>\n",
+       "      <td>559</td>\n",
+       "      <td>2261</td>\n",
+       "      <td>4.044723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adenovirus Real Time PCR Quant</th>\n",
+       "      <td>507</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>3.960552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>5302</td>\n",
+       "      <td>20888</td>\n",
+       "      <td>3.939645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspergillus Galactomannan</th>\n",
+       "      <td>504</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>3.928571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ammonia, Plasma</th>\n",
+       "      <td>980</td>\n",
+       "      <td>3799</td>\n",
+       "      <td>3.876531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>3671</td>\n",
+       "      <td>13584</td>\n",
+       "      <td>3.700354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>1865</td>\n",
+       "      <td>6461</td>\n",
+       "      <td>3.464343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>2563</td>\n",
+       "      <td>8677</td>\n",
+       "      <td>3.385486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential</th>\n",
+       "      <td>977</td>\n",
+       "      <td>3207</td>\n",
+       "      <td>3.282497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6370</td>\n",
+       "      <td>3.153465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilirubin Direct, Ser/Plas</th>\n",
+       "      <td>1640</td>\n",
+       "      <td>5146</td>\n",
+       "      <td>3.137805</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  pat_enc_csn_id_coded  norders     perenc\n",
+       "njr_labname                                                               \n",
+       "POCT Glucose                                      3565    47098  13.211220\n",
+       "Urinalysis Macroscopic Only                       2308    19687   8.529896\n",
+       "Uric Acid, Ser/Plas                               1105     9360   8.470588\n",
+       "POCT Urine                                        2904    23519   8.098829\n",
+       "LDH Total, Ser/Plas                               1453     9191   6.325533\n",
+       "Calcium Ionized, Whole Blood                       702     4264   6.074074\n",
+       "Sodium, Ser/Plas                                   536     3107   5.796642\n",
+       "Platelets                                         1525     8579   5.625574\n",
+       "Tacrolimus Level, FK506                           2552    13054   5.115204\n",
+       "Magnesium, Ser/Plas                               8672    41927   4.834756\n",
+       "Triglyceride, Ser/Plas                            1565     7402   4.729712\n",
+       "Istat EG7, Arterial                                546     2551   4.672161\n",
+       "Renal Function Panel                              5475    25509   4.659178\n",
+       "Chem 10                                          10151    47187   4.648508\n",
+       "Methotrexate Level                                1007     4446   4.415094\n",
+       "Manual Differential/Slide Review                  4436    18964   4.275023\n",
+       "CBC                                              16811    70271   4.180061\n",
+       "Blood Gases, Arterial                              679     2789   4.107511\n",
+       "Transfusion Svc Prod 1                             574     2352   4.097561\n",
+       "Fresh Frozen Plasma                                559     2261   4.044723\n",
+       "Adenovirus Real Time PCR Quant                     507     2008   3.960552\n",
+       "Phosphorus, Ser/Plas                              5302    20888   3.939645\n",
+       "Aspergillus Galactomannan                          504     1980   3.928571\n",
+       "Ammonia, Plasma                                    980     3799   3.876531\n",
+       "Red Blood Cells                                   3671    13584   3.700354\n",
+       "Slide Review                                      1865     6461   3.464343\n",
+       "Gamma Glutamyl Trans                              2563     8677   3.385486\n",
+       "Manual Differential                                977     3207   3.282497\n",
+       "Vancomycin Level, Trough                          2020     6370   3.153465\n",
+       "Bilirubin Direct, Ser/Plas                        1640     5146   3.137805"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempdf2['perenc'] = tempdf2.norders / tempdf2.pat_enc_csn_id_coded\n",
+    "tempdf2 = tempdf2.loc[tempdf2.pat_enc_csn_id_coded > 500].sort_values('perenc', ascending=False)\n",
+    "# tempdf2 = tempdf2.sort_values('perenc', ascending=False)\n",
+    "tempdf2.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b562c8b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['POCT Glucose', 'Urinalysis Macroscopic Only', 'Uric Acid, Ser/Plas', 'POCT Urine', 'LDH Total, Ser/Plas', 'Platelets', 'Tacrolimus Level, FK506', 'Magnesium, Ser/Plas', 'Triglyceride, Ser/Plas', 'Renal Function Panel', 'Chem 10', 'Methotrexate Level', 'Manual Differential/Slide Review', 'CBC', 'Phosphorus, Ser/Plas']\n"
+     ]
+    }
+   ],
+   "source": [
+    "labnames = tempdf2.index.to_list()[0:15]\n",
+    "print(labnames)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "1de4a419",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt_orders = lradt.loc[lradt.njr_labname.isin(labnames)].groupby('order_id_coded').first()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a769c1cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "367082"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lradt_orders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "9bad80c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>702121786</th>\n",
+       "      <td>JC626416</td>\n",
+       "      <td>308895713</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-16 22:20:00</td>\n",
+       "      <td>2014-06-16 22:41:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702121788</th>\n",
+       "      <td>JC626416</td>\n",
+       "      <td>308895713</td>\n",
+       "      <td>Uric Acid, Ser/Plas</td>\n",
+       "      <td>2014-06-16 22:20:00</td>\n",
+       "      <td>2014-06-16 22:53:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701122666</th>\n",
+       "      <td>JC608450</td>\n",
+       "      <td>308903549</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-25 21:05:00</td>\n",
+       "      <td>2014-05-26 07:24:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701239393</th>\n",
+       "      <td>JC639827</td>\n",
+       "      <td>308903923</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC984330</td>\n",
+       "      <td>308904689</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-30 21:05:00</td>\n",
+       "      <td>2014-07-01 07:51:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 anon_id pat_enc_csn_id_coded          njr_labname  \\\n",
+       "order_id_coded                                                       \n",
+       "702121786       JC626416            308895713                  CBC   \n",
+       "702121788       JC626416            308895713  Uric Acid, Ser/Plas   \n",
+       "701122666       JC608450            308903549                  CBC   \n",
+       "701239393       JC639827            308903923                  CBC   \n",
+       "701504059       JC984330            308904689                  CBC   \n",
+       "\n",
+       "                        order_time         result_time  \n",
+       "order_id_coded                                          \n",
+       "702121786      2014-06-16 22:20:00 2014-06-16 22:41:00  \n",
+       "702121788      2014-06-16 22:20:00 2014-06-16 22:53:00  \n",
+       "701122666      2014-05-25 21:05:00 2014-05-26 07:24:00  \n",
+       "701239393      2014-06-02 21:05:00 2014-06-03 08:06:00  \n",
+       "701504059      2014-06-30 21:05:00 2014-07-01 07:51:00  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf = lradt_orders[['anon_id', 'pat_enc_csn_id_coded', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'njr_labname', 'result_time', 'order_time'])\n",
+    "ordersdf.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a873b9ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>702121786</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702121788</th>\n",
+       "      <td>JC626416</td>\n",
+       "      <td>308895713</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-16 22:20:00</td>\n",
+       "      <td>2014-06-16 22:41:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701122666</th>\n",
+       "      <td>JC626416</td>\n",
+       "      <td>308895713</td>\n",
+       "      <td>Uric Acid, Ser/Plas</td>\n",
+       "      <td>2014-06-16 22:20:00</td>\n",
+       "      <td>2014-06-16 22:53:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701239393</th>\n",
+       "      <td>JC608450</td>\n",
+       "      <td>308903549</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-25 21:05:00</td>\n",
+       "      <td>2014-05-26 07:24:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC639827</td>\n",
+       "      <td>308903923</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 anon_id pat_enc_csn_id_coded          njr_labname  \\\n",
+       "order_id_coded                                                       \n",
+       "702121786            NaN                  NaN                  NaN   \n",
+       "702121788       JC626416            308895713                  CBC   \n",
+       "701122666       JC626416            308895713  Uric Acid, Ser/Plas   \n",
+       "701239393       JC608450            308903549                  CBC   \n",
+       "701504059       JC639827            308903923                  CBC   \n",
+       "\n",
+       "                        order_time         result_time  \n",
+       "order_id_coded                                          \n",
+       "702121786                      NaT                 NaT  \n",
+       "702121788      2014-06-16 22:20:00 2014-06-16 22:41:00  \n",
+       "701122666      2014-06-16 22:20:00 2014-06-16 22:53:00  \n",
+       "701239393      2014-05-25 21:05:00 2014-05-26 07:24:00  \n",
+       "701504059      2014-06-02 21:05:00 2014-06-03 08:06:00  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf.shift(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d0949de5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordersdf_shift = ordersdf.shift(1)\n",
+    "ordersdf['dif'] = pd.NaT\n",
+    "ordersdf['dif'] = ordersdf.order_time - ordersdf_shift.order_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "59d134d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newlabidx = (ordersdf.njr_labname != ordersdf_shift.njr_labname) | (ordersdf.pat_enc_csn_id_coded != ordersdf_shift.pat_enc_csn_id_coded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "6187e17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordersdf.loc[newlabidx, 'dif'] = pd.NaT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "d6bd3cb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
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+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>702121786</th>\n",
+       "      <td>JC626416</td>\n",
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+       "      <td>JC626416</td>\n",
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+       "      <td>2014-06-16 22:53:00</td>\n",
+       "      <td>NaT</td>\n",
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+       "    <tr>\n",
+       "      <th>701122666</th>\n",
+       "      <td>JC608450</td>\n",
+       "      <td>308903549</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-25 21:05:00</td>\n",
+       "      <td>2014-05-26 07:24:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701239393</th>\n",
+       "      <td>JC639827</td>\n",
+       "      <td>308903923</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "      <td>NaT</td>\n",
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+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC984330</td>\n",
+       "      <td>308904689</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-30 21:05:00</td>\n",
+       "      <td>2014-07-01 07:51:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
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+       "      <th>701287760</th>\n",
+       "      <td>JC1142284</td>\n",
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+       "      <td>2014-05-02 17:42:00</td>\n",
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+       "      <th>701390874</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-03 21:05:00</td>\n",
+       "      <td>2014-05-04 08:24:00</td>\n",
+       "      <td>1 days 07:30:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504067</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
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+       "      <td>2014-05-09 13:37:00</td>\n",
+       "      <td>5 days 15:23:00</td>\n",
+       "    </tr>\n",
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+       "      <th>701287759</th>\n",
+       "      <td>JC1142284</td>\n",
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+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 18:04:00</td>\n",
+       "      <td>NaT</td>\n",
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+       "      <th>701457513</th>\n",
+       "      <td>JC1142284</td>\n",
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+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-08 21:05:00</td>\n",
+       "      <td>2014-05-09 05:49:00</td>\n",
+       "      <td>6 days 07:30:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457516</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 05:55:00</td>\n",
+       "      <td>2014-05-09 07:26:00</td>\n",
+       "      <td>0 days 08:50:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504073</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
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+       "      <td>2014-05-09 21:05:00</td>\n",
+       "      <td>2014-05-10 05:28:00</td>\n",
+       "      <td>0 days 15:10:00</td>\n",
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+       "      <th>701504087</th>\n",
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+       "      <td>2014-05-11 06:32:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504106</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-11 21:05:00</td>\n",
+       "      <td>2014-05-12 06:00:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
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+       "      <th>701580858</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-12 21:05:00</td>\n",
+       "      <td>2014-05-13 03:48:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702382826</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-31 07:58:00</td>\n",
+       "      <td>2014-07-31 10:59:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703408214</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-09-10 21:05:00</td>\n",
+       "      <td>2014-09-11 07:45:00</td>\n",
+       "      <td>41 days 13:07:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702343597</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-07-28 21:05:00</td>\n",
+       "      <td>2014-07-29 10:42:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282771</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-02 21:05:00</td>\n",
+       "      <td>2014-09-03 06:31:00</td>\n",
+       "      <td>36 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282797</th>\n",
+       "      <td>JC605249</td>\n",
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+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-04 21:05:00</td>\n",
+       "      <td>2014-09-05 06:58:00</td>\n",
+       "      <td>2 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282805</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-05 21:05:00</td>\n",
+       "      <td>2014-09-06 07:49:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703521782</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-10 21:05:00</td>\n",
+       "      <td>2014-09-11 08:09:00</td>\n",
+       "      <td>5 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703687965</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-18 07:02:00</td>\n",
+       "      <td>2014-09-18 07:49:00</td>\n",
+       "      <td>7 days 09:57:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703687996</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-19 21:05:00</td>\n",
+       "      <td>2014-09-20 07:35:00</td>\n",
+       "      <td>1 days 14:03:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700890706</th>\n",
+       "      <td>JC738998</td>\n",
+       "      <td>308916366</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-16 21:05:00</td>\n",
+       "      <td>2014-05-17 07:56:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701794833</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-17 11:44:00</td>\n",
+       "      <td>2014-07-17 13:11:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701839769</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-18 21:05:00</td>\n",
+       "      <td>2014-07-19 06:59:00</td>\n",
+       "      <td>1 days 09:21:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701862701</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-19 21:05:00</td>\n",
+       "      <td>2014-07-20 06:22:00</td>\n",
+       "      <td>1 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701999661</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-27 06:59:00</td>\n",
+       "      <td>2014-07-27 10:37:00</td>\n",
+       "      <td>7 days 09:54:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701845691</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>Magnesium, Ser/Plas</td>\n",
+       "      <td>2014-07-19 06:50:00</td>\n",
+       "      <td>2014-07-20 07:22:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded          njr_labname  \\\n",
+       "order_id_coded                                                        \n",
+       "702121786        JC626416            308895713                  CBC   \n",
+       "702121788        JC626416            308895713  Uric Acid, Ser/Plas   \n",
+       "701122666        JC608450            308903549                  CBC   \n",
+       "701239393        JC639827            308903923                  CBC   \n",
+       "701504059        JC984330            308904689                  CBC   \n",
+       "701287760       JC1142284            308905542                  CBC   \n",
+       "701390874       JC1142284            308905542                  CBC   \n",
+       "701504067       JC1142284            308905542                  CBC   \n",
+       "701287759       JC1142284            308905542              Chem 10   \n",
+       "701457513       JC1142284            308905542              Chem 10   \n",
+       "701457516       JC1142284            308905542              Chem 10   \n",
+       "701504073       JC1142284            308905542              Chem 10   \n",
+       "701504087       JC1142284            308905542              Chem 10   \n",
+       "701504106       JC1142284            308905542              Chem 10   \n",
+       "701580858       JC1142284            308905542              Chem 10   \n",
+       "702382826        JC605249            308913369                  CBC   \n",
+       "703408214        JC605249            308913369                  CBC   \n",
+       "702343597        JC605249            308913369              Chem 10   \n",
+       "703282771        JC605249            308913369              Chem 10   \n",
+       "703282797        JC605249            308913369              Chem 10   \n",
+       "703282805        JC605249            308913369              Chem 10   \n",
+       "703521782        JC605249            308913369              Chem 10   \n",
+       "703687965        JC605249            308913369              Chem 10   \n",
+       "703687996        JC605249            308913369              Chem 10   \n",
+       "700890706        JC738998            308916366                  CBC   \n",
+       "701794833        JC602155            308921751                  CBC   \n",
+       "701839769        JC602155            308921751                  CBC   \n",
+       "701862701        JC602155            308921751                  CBC   \n",
+       "701999661        JC602155            308921751                  CBC   \n",
+       "701845691        JC602155            308921751  Magnesium, Ser/Plas   \n",
+       "\n",
+       "                        order_time         result_time              dif  \n",
+       "order_id_coded                                                           \n",
+       "702121786      2014-06-16 22:20:00 2014-06-16 22:41:00              NaT  \n",
+       "702121788      2014-06-16 22:20:00 2014-06-16 22:53:00              NaT  \n",
+       "701122666      2014-05-25 21:05:00 2014-05-26 07:24:00              NaT  \n",
+       "701239393      2014-06-02 21:05:00 2014-06-03 08:06:00              NaT  \n",
+       "701504059      2014-06-30 21:05:00 2014-07-01 07:51:00              NaT  \n",
+       "701287760      2014-05-02 13:35:00 2014-05-02 17:42:00              NaT  \n",
+       "701390874      2014-05-03 21:05:00 2014-05-04 08:24:00  1 days 07:30:00  \n",
+       "701504067      2014-05-09 12:28:00 2014-05-09 13:37:00  5 days 15:23:00  \n",
+       "701287759      2014-05-02 13:35:00 2014-05-02 18:04:00              NaT  \n",
+       "701457513      2014-05-08 21:05:00 2014-05-09 05:49:00  6 days 07:30:00  \n",
+       "701457516      2014-05-09 05:55:00 2014-05-09 07:26:00  0 days 08:50:00  \n",
+       "701504073      2014-05-09 21:05:00 2014-05-10 05:28:00  0 days 15:10:00  \n",
+       "701504087      2014-05-10 21:05:00 2014-05-11 06:32:00  1 days 00:00:00  \n",
+       "701504106      2014-05-11 21:05:00 2014-05-12 06:00:00  1 days 00:00:00  \n",
+       "701580858      2014-05-12 21:05:00 2014-05-13 03:48:00  1 days 00:00:00  \n",
+       "702382826      2014-07-31 07:58:00 2014-07-31 10:59:00              NaT  \n",
+       "703408214      2014-09-10 21:05:00 2014-09-11 07:45:00 41 days 13:07:00  \n",
+       "702343597      2014-07-28 21:05:00 2014-07-29 10:42:00              NaT  \n",
+       "703282771      2014-09-02 21:05:00 2014-09-03 06:31:00 36 days 00:00:00  \n",
+       "703282797      2014-09-04 21:05:00 2014-09-05 06:58:00  2 days 00:00:00  \n",
+       "703282805      2014-09-05 21:05:00 2014-09-06 07:49:00  1 days 00:00:00  \n",
+       "703521782      2014-09-10 21:05:00 2014-09-11 08:09:00  5 days 00:00:00  \n",
+       "703687965      2014-09-18 07:02:00 2014-09-18 07:49:00  7 days 09:57:00  \n",
+       "703687996      2014-09-19 21:05:00 2014-09-20 07:35:00  1 days 14:03:00  \n",
+       "700890706      2014-05-16 21:05:00 2014-05-17 07:56:00              NaT  \n",
+       "701794833      2014-07-17 11:44:00 2014-07-17 13:11:00              NaT  \n",
+       "701839769      2014-07-18 21:05:00 2014-07-19 06:59:00  1 days 09:21:00  \n",
+       "701862701      2014-07-19 21:05:00 2014-07-20 06:22:00  1 days 00:00:00  \n",
+       "701999661      2014-07-27 06:59:00 2014-07-27 10:37:00  7 days 09:54:00  \n",
+       "701845691      2014-07-19 06:50:00 2014-07-20 07:22:00              NaT  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "7526ca32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>dif</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
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+       "      <td>36 days 00:00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded njr_labname  \\\n",
+       "order_id_coded                                               \n",
+       "701390874       JC1142284            308905542         CBC   \n",
+       "701504067       JC1142284            308905542         CBC   \n",
+       "701457513       JC1142284            308905542     Chem 10   \n",
+       "701457516       JC1142284            308905542     Chem 10   \n",
+       "701504073       JC1142284            308905542     Chem 10   \n",
+       "701504087       JC1142284            308905542     Chem 10   \n",
+       "701504106       JC1142284            308905542     Chem 10   \n",
+       "701580858       JC1142284            308905542     Chem 10   \n",
+       "703408214        JC605249            308913369         CBC   \n",
+       "703282771        JC605249            308913369     Chem 10   \n",
+       "\n",
+       "                        order_time         result_time              dif  \n",
+       "order_id_coded                                                           \n",
+       "701390874      2014-05-03 21:05:00 2014-05-04 08:24:00  1 days 07:30:00  \n",
+       "701504067      2014-05-09 12:28:00 2014-05-09 13:37:00  5 days 15:23:00  \n",
+       "701457513      2014-05-08 21:05:00 2014-05-09 05:49:00  6 days 07:30:00  \n",
+       "701457516      2014-05-09 05:55:00 2014-05-09 07:26:00  0 days 08:50:00  \n",
+       "701504073      2014-05-09 21:05:00 2014-05-10 05:28:00  0 days 15:10:00  \n",
+       "701504087      2014-05-10 21:05:00 2014-05-11 06:32:00  1 days 00:00:00  \n",
+       "701504106      2014-05-11 21:05:00 2014-05-12 06:00:00  1 days 00:00:00  \n",
+       "701580858      2014-05-12 21:05:00 2014-05-13 03:48:00  1 days 00:00:00  \n",
+       "703408214      2014-09-10 21:05:00 2014-09-11 07:45:00 41 days 13:07:00  \n",
+       "703282771      2014-09-02 21:05:00 2014-09-03 06:31:00 36 days 00:00:00  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repeat_orders = ordersdf.loc[ordersdf.dif.notnull()]\n",
+    "repeat_orders.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ee28f501",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_quant = [pd.Timedelta('24H'), pd.Timedelta('3D'), pd.Timedelta('1W')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "94a9ea5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lab:  POCT Glucose\n",
+      "[0, 24h):\t 42306\n",
+      "[24h, 3d)]:\t 643\n",
+      "[3d, 7d)]:\t 266\n",
+      "[7d, inf)]:\t 3883\n",
+      "Lab:  Urinalysis Macroscopic Only\n",
+      "[0, 24h):\t 11302\n",
+      "[24h, 3d)]:\t 5814\n",
+      "[3d, 7d)]:\t 171\n",
+      "[7d, inf)]:\t 2400\n",
+      "Lab:  Uric Acid, Ser/Plas\n",
+      "[0, 24h):\t 6380\n",
+      "[24h, 3d)]:\t 1624\n",
+      "[3d, 7d)]:\t 131\n",
+      "[7d, inf)]:\t 1225\n",
+      "Lab:  POCT Urine\n",
+      "[0, 24h):\t 6944\n",
+      "[24h, 3d)]:\t 13557\n",
+      "[3d, 7d)]:\t 89\n",
+      "[7d, inf)]:\t 2929\n",
+      "Lab:  LDH Total, Ser/Plas\n",
+      "[0, 24h):\t 4896\n",
+      "[24h, 3d)]:\t 2246\n",
+      "[3d, 7d)]:\t 400\n",
+      "[7d, inf)]:\t 1649\n",
+      "Lab:  Platelets\n",
+      "[0, 24h):\t 2902\n",
+      "[24h, 3d)]:\t 2429\n",
+      "[3d, 7d)]:\t 1389\n",
+      "[7d, inf)]:\t 1859\n",
+      "Lab:  Tacrolimus Level, FK506\n",
+      "[0, 24h):\t 1704\n",
+      "[24h, 3d)]:\t 8213\n",
+      "[3d, 7d)]:\t 471\n",
+      "[7d, inf)]:\t 2666\n",
+      "Lab:  Magnesium, Ser/Plas\n",
+      "[0, 24h):\t 6651\n",
+      "[24h, 3d)]:\t 23022\n",
+      "[3d, 7d)]:\t 2532\n",
+      "[7d, inf)]:\t 9722\n",
+      "Lab:  Triglyceride, Ser/Plas\n",
+      "[0, 24h):\t 577\n",
+      "[24h, 3d)]:\t 2575\n",
+      "[3d, 7d)]:\t 2207\n",
+      "[7d, inf)]:\t 2043\n",
+      "Lab:  Renal Function Panel\n",
+      "[0, 24h):\t 6123\n",
+      "[24h, 3d)]:\t 12153\n",
+      "[3d, 7d)]:\t 1220\n",
+      "[7d, inf)]:\t 6013\n",
+      "Lab:  Chem 10\n",
+      "[0, 24h):\t 12799\n",
+      "[24h, 3d)]:\t 21277\n",
+      "[3d, 7d)]:\t 2189\n",
+      "[7d, inf)]:\t 10922\n",
+      "Lab:  Methotrexate Level\n",
+      "[0, 24h):\t 2471\n",
+      "[24h, 3d)]:\t 878\n",
+      "[3d, 7d)]:\t 81\n",
+      "[7d, inf)]:\t 1016\n",
+      "Lab:  Manual Differential/Slide Review\n",
+      "[0, 24h):\t 6278\n",
+      "[24h, 3d)]:\t 6746\n",
+      "[3d, 7d)]:\t 928\n",
+      "[7d, inf)]:\t 5012\n",
+      "Lab:  CBC\n",
+      "[0, 24h):\t 23477\n",
+      "[24h, 3d)]:\t 24836\n",
+      "[3d, 7d)]:\t 3669\n",
+      "[7d, inf)]:\t 18289\n",
+      "Lab:  Phosphorus, Ser/Plas\n",
+      "[0, 24h):\t 1694\n",
+      "[24h, 3d)]:\t 11738\n",
+      "[3d, 7d)]:\t 1303\n",
+      "[7d, inf)]:\t 6153\n"
+     ]
+    }
+   ],
+   "source": [
+    "labdtq_idx = ['<24h', '[24h, 3d)', '[3d, 7d)', '>=7d']\n",
+    "labdtq = pd.DataFrame(index=labdtq_idx)\n",
+    "for lab in labnames:\n",
+    "    print('Lab: ', lab)\n",
+    "    dtimes = ordersdf.loc[ordersdf.njr_labname==lab].dif\n",
+    "    p_enc = ordersdf.loc[ordersdf.njr_labname==lab, 'pat_enc_csn_id_coded'].nunique()\n",
+    "    dtq = []\n",
+    "    dtq.append(sum(dtimes < pd.Timedelta('24H')))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('3D')) & (dtimes >= pd.Timedelta('24H')) ))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('7D')) & (dtimes >= pd.Timedelta('3D')) ))\n",
+    "    dtq.append(sum((dtimes >= pd.Timedelta('7D')) | (dtimes.isnull()) ))\n",
+    "    dtq\n",
+    "    print('[0, 24h):\\t', dtq[0])\n",
+    "    print('[24h, 3d)]:\\t', dtq[1])\n",
+    "    print('[3d, 7d)]:\\t', dtq[2])\n",
+    "    print('[7d, inf)]:\\t', dtq[3])\n",
+    "    labdtq[lab] = np.cumsum(dtq)/p_enc*1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "802d9ea3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = labdtq.iloc[3].plot.barh(color='#404c54');\n",
+    "labdtq.iloc[2].plot.barh(ax=ax1, color='#f0cc94');\n",
+    "labdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "labdtq.iloc[0].plot.barh(ax=ax1, color='#807A6B');\n",
+    "ax1.legend();\n",
+    "ax1.invert_yaxis();\n",
+    "ax1.set_title('Commonly Repeated Lab Tests in Pediatric Inpatient Medicine');\n",
+    "ax1.set_xlabel('No. Lab Orders per 1000 Encounters');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "6e79d90f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PERFLAB    309773\n",
+       "GLU        118516\n",
+       "NA         114790\n",
+       "K          113472\n",
+       "CL         110678\n",
+       "CR         108079\n",
+       "BUN        106437\n",
+       "CA         106258\n",
+       "CO2        105811\n",
+       "AG         105797\n",
+       "PHOS        94803\n",
+       "EGFR        93903\n",
+       "MG          90315\n",
+       "HCT         77788\n",
+       "HGB         77616\n",
+       "WBC         70452\n",
+       "PLT         70340\n",
+       "RBC         70176\n",
+       "MCV         70165\n",
+       "RDW         70089\n",
+       "MCHC        70078\n",
+       "MCH         70074\n",
+       "PCCOM       59757\n",
+       "ALB         59307\n",
+       "LYM         56287\n",
+       "NEUT        54897\n",
+       "LYMAB       54701\n",
+       "MONO        54355\n",
+       "MONOAB      54059\n",
+       "NITRITE     52027\n",
+       "Name: base_name, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.base_name.value_counts().head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "id": "7711e882",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def windowedLabCounts(LABNAME, LOWER, UPPER, plot_flag = False):\n",
+    "    lrdf = lradt.loc[(lradt.base_name==LABNAME) & (lradt.ord_num_value < UPPER) & (lradt.ord_num_value > LOWER)].sort_values(by=['pat_enc_csn_id_coded', 'result_time']).reset_index()\n",
+    "    lrdf['result'] = 'N'\n",
+    "    lrdf.loc[(lrdf.result_in_range_yn!='Y') & ((lrdf.result_flag=='High') | (lrdf.result_flag=='High Panic')), 'result'] = 'H'\n",
+    "    lrdf.loc[(lrdf.result_in_range_yn!='Y') & ((lrdf.result_flag=='Low') | (lrdf.result_flag=='Low Panic')), 'result'] = 'L'\n",
+    "    lrdf['normal'] = (lrdf.result=='N')\n",
+    "    print('LAB:', LABNAME)\n",
+    "    print(lrdf.normal.value_counts())\n",
+    "\n",
+    "    lrdf['preceding']=0\n",
+    "    for enc in lrdf.pat_enc_csn_id_coded.unique():\n",
+    "        enc_idx = (lrdf.pat_enc_csn_id_coded==enc)\n",
+    "        df = lrdf.loc[enc_idx, ['result_time', 'normal']]\n",
+    "        rownum = 0\n",
+    "        for (idx, row) in df.iterrows():\n",
+    "            if rownum>0:\n",
+    "                rtime = row.result_time\n",
+    "                dfslice = df.loc[(df.result_time <= rtime) & (df.result_time > rtime - pd.Timedelta('7D'))]\n",
+    "                if dfslice.shift().loc[idx, 'normal']==True:\n",
+    "                    y = dfslice.shift().normal\n",
+    "                    prec = y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)\n",
+    "                    lrdf.loc[idx, 'preceding'] = prec.values[-1]\n",
+    "            rownum = rownum + 1\n",
+    "    \n",
+    "    if plot_flag:\n",
+    "        dfstab = lrdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "        dfstab['prop'] = dfstab['normal']['sum']/dfstab['normal']['count'].astype('float')\n",
+    "        ax = dfstab.prop.plt()\n",
+    "        ax.set_xlabel('Consecutive Normal Results in Past 7 Days Preceding Lab');\n",
+    "        ax.set_ylabel('Proportion Normal Results');\n",
+    "        ax.set_title('Stability for Common Inpatient Laboratory Studies');\n",
+    "        ax.set_ylim(0, 1.0);\n",
+    "    \n",
+    "    return lrdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "76da8745",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAB: NA\n",
+      "True     90397\n",
+      "False    24298\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: K\n",
+      "True     91093\n",
+      "False    22115\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: MG\n",
+      "True     61984\n",
+      "False    28272\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: PHOS\n",
+      "True     63707\n",
+      "False    31024\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: CR\n",
+      "True     78232\n",
+      "False    29325\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: WBC\n",
+      "False    41838\n",
+      "True     26917\n",
+      "Name: normal, dtype: int64\n",
+      "LAB: HGB\n",
+      "False    64875\n",
+      "True     12583\n",
+      "Name: normal, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "nadf = windowedLabCounts('NA', 0, 200);\n",
+    "kdf = windowedLabCounts('K', 0, 8);\n",
+    "mgdf = windowedLabCounts('MG', 0, 10);\n",
+    "phosdf = windowedLabCounts('PHOS', 0, 50);\n",
+    "crdf = windowedLabCounts('CR', 0, 20);\n",
+    "wbcdf = windowedLabCounts('WBC', 0, 100000);\n",
+    "hgbdf = windowedLabCounts('HGB', 0, 30);\n",
+    "pltdf = windowedLabCounts('PLT', 0, 500000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "id": "6a5e2480",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nastab = nadf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "kstab = kdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "mgstab = mgdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "phosstab = phosdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "crstab = crdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "wbcstab = wbcdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "hgbstab = hgbdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]\n",
+    "pltstab = pltdf.groupby('preceding').agg({'normal' : ['sum', 'count']})[0:7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "id": "bf325351",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labstab = pd.DataFrame()\n",
+    "labstab['Sodium'] = nastab['normal']['sum']/nastab['normal']['count'].astype('float')\n",
+    "labstab['Potassium'] = kstab['normal']['sum']/kstab['normal']['count'].astype('float')\n",
+    "labstab['Creatinine'] = crstab['normal']['sum']/crstab['normal']['count'].astype('float')\n",
+    "labstab['Magnesium']  = mgstab['normal']['sum']/mgstab['normal']['count'].astype('float')\n",
+    "labstab['Phosphorus'] = phosstab['normal']['sum']/phosstab['normal']['count'].astype('float')\n",
+    "labstab['White Cells'] = wbcstab['normal']['sum']/wbcstab['normal']['count'].astype('float')\n",
+    "labstab['Hemoglobin'] = hgbstab['normal']['sum']/hgbstab['normal']['count'].astype('float')\n",
+    "labstab['Platelets'] = pltstab['normal']['sum']/pltstab['normal']['count'].astype('float')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "id": "00db09f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Potassium</th>\n",
+       "      <th>Creatinine</th>\n",
+       "      <th>Magnesium</th>\n",
+       "      <th>Phosphorus</th>\n",
+       "      <th>White Cells</th>\n",
+       "      <th>Hemoglobin</th>\n",
+       "      <th>Platelets</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>preceding</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.618692</td>\n",
+       "      <td>0.635123</td>\n",
+       "      <td>0.430926</td>\n",
+       "      <td>0.494435</td>\n",
+       "      <td>0.496371</td>\n",
+       "      <td>0.270960</td>\n",
+       "      <td>0.116253</td>\n",
+       "      <td>0.249895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.844057</td>\n",
+       "      <td>0.853537</td>\n",
+       "      <td>0.895200</td>\n",
+       "      <td>0.769509</td>\n",
+       "      <td>0.742317</td>\n",
+       "      <td>0.759626</td>\n",
+       "      <td>0.588298</td>\n",
+       "      <td>0.834582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.880234</td>\n",
+       "      <td>0.893681</td>\n",
+       "      <td>0.941019</td>\n",
+       "      <td>0.840809</td>\n",
+       "      <td>0.817151</td>\n",
+       "      <td>0.814815</td>\n",
+       "      <td>0.685606</td>\n",
+       "      <td>0.859774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.900209</td>\n",
+       "      <td>0.917751</td>\n",
+       "      <td>0.958936</td>\n",
+       "      <td>0.877298</td>\n",
+       "      <td>0.847630</td>\n",
+       "      <td>0.832061</td>\n",
+       "      <td>0.719764</td>\n",
+       "      <td>0.880303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.906126</td>\n",
+       "      <td>0.918838</td>\n",
+       "      <td>0.971186</td>\n",
+       "      <td>0.881874</td>\n",
+       "      <td>0.859201</td>\n",
+       "      <td>0.831041</td>\n",
+       "      <td>0.758621</td>\n",
+       "      <td>0.891269</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Sodium  Potassium  Creatinine  Magnesium  Phosphorus  \\\n",
+       "preceding                                                           \n",
+       "0          0.618692   0.635123    0.430926   0.494435    0.496371   \n",
+       "1          0.844057   0.853537    0.895200   0.769509    0.742317   \n",
+       "2          0.880234   0.893681    0.941019   0.840809    0.817151   \n",
+       "3          0.900209   0.917751    0.958936   0.877298    0.847630   \n",
+       "4          0.906126   0.918838    0.971186   0.881874    0.859201   \n",
+       "\n",
+       "           White Cells  Hemoglobin  Platelets  \n",
+       "preceding                                      \n",
+       "0             0.270960    0.116253   0.249895  \n",
+       "1             0.759626    0.588298   0.834582  \n",
+       "2             0.814815    0.685606   0.859774  \n",
+       "3             0.832061    0.719764   0.880303  \n",
+       "4             0.831041    0.758621   0.891269  "
+      ]
+     },
+     "execution_count": 414,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labstab.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "id": "fcbe8694",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotLabStab(df, ndays, ax = None, subset=False, labs=['']):\n",
+    "    df = df[0:ndays+1]\n",
+    "    if subset:\n",
+    "        df = df[labs]\n",
+    "    sns.set_style(\"white\");\n",
+    "    if ax is None:\n",
+    "        ax = sns.lineplot(data = df, markers=True);\n",
+    "    else:\n",
+    "        sns.lineplot(data = df, markers=True, ax = ax);\n",
+    "    ax.set_xlabel('Consecutive Normal Results in Past 7 Days Preceding Lab');\n",
+    "    ax.set_xlim(-0.25, ndays+0.25)\n",
+    "    ax.set_xticks(np.arange(ndays+1))\n",
+    "    ax.set_ylabel('Proportion Normal Results');\n",
+    "    ax.set_title('Stability for Common Inpatient Laboratory Studies');\n",
+    "    ax.set_ylim(0, 1.0);\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "id": "89c8e81f",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotLabStab(labstab, 6);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 466,
+   "id": "d441a8fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.gridspec import GridSpec\n",
+    "fig = plt.figure(constrained_layout=True)\n",
+    "gs = GridSpec(2, 3, figure=fig)\n",
+    "axes = []\n",
+    "# create sub plots as grid\n",
+    "axes.append(fig.add_subplot(gs[0, :]))\n",
+    "axes.append(fig.add_subplot(gs[1, 0]))\n",
+    "axes.append(fig.add_subplot(gs[1, 1], sharey=axes[1]))\n",
+    "axes.append(fig.add_subplot(gs[1, 2], sharey=axes[1]))\n",
+    "\n",
+    "chem1 = ['Sodium', 'Potassium', 'Creatinine']\n",
+    "chem2 = ['Magnesium', 'Phosphorus']\n",
+    "cbc = ['White Cells', 'Hemoglobin', 'Platelets']\n",
+    "cmap = sns.color_palette()\n",
+    "\n",
+    "STREAK = 6\n",
+    "sns.set_style(\"white\");\n",
+    "dash_list = sns._core.unique_dashes(8);\n",
+    "marker_list = sns._core.unique_markers(8)\n",
+    "sns.lineplot(data=labstab[0:STREAK+1], ax = axes[0],\n",
+    "             palette = cmap[0:8], dashes=dash_list, markers=marker_list);\n",
+    "sns.lineplot(data=labstab[chem1][0:STREAK+1], ax=axes[1],\n",
+    "             palette = cmap[0:3], dashes=dash_list[0:3], markers=marker_list[0:3])\n",
+    "sns.lineplot(data=labstab[chem2][0:STREAK+1], ax=axes[2], \n",
+    "             palette = cmap[3:5], dashes=dash_list[3:5], markers=marker_list[3:5])\n",
+    "sns.lineplot(data=labstab[cbc][0:STREAK+1], ax=axes[3], \n",
+    "             palette = cmap[5:8], dashes=dash_list[5:8], markers=marker_list[5:8])\n",
+    "\n",
+    "\n",
+    "# plotLabStab(labstab, 6, ax=axes[0]);\n",
+    "# plotLabStab(labstab, 6, ax = axes[1], subset = True, labs = ['Sodium', 'Potassium', 'Creatinine']);\n",
+    "# plotLabStab(labstab, 6, ax = axes[2], subset = True, labs = ['Magnesium', 'Phosphorus']);\n",
+    "# plotLabStab(labstab, 6, ax = axes[3], subset = True, labs = ['White Cells', 'Hemoglobin', 'Platelets']);\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_xlim(-0.25, STREAK+0.25);\n",
+    "    ax.set_ylim(0, 1);\n",
+    "    ax.set_xticks(np.arange(STREAK+1));\n",
+    "    ax.set_xticklabels(labels=np.arange(STREAK+1), fontsize=6);\n",
+    "    ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
+    "    ax.set_yticklabels(labels=[0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=6);\n",
+    "    ax.set_ylabel('');\n",
+    "    ax.set_xlabel('');\n",
+    "    ax.set_title('');\n",
+    "    ax.legend(fontsize=5, loc=4);\n",
+    "    \n",
+    "#axes[0].set_title('All')\n",
+    "axes[1].set_title('Chem Panel', fontsize=7);\n",
+    "axes[2].set_title('Additional Chemistries', fontsize=7);\n",
+    "axes[3].set_title('Blood Count', fontsize=7);\n",
+    "plt.setp(axes[2].get_yticklabels(), visible=False)\n",
+    "plt.setp(axes[3].get_yticklabels(), visible=False)\n",
+    "plt.setp(axes[1].get_xticklabels(), visible=False)\n",
+    "plt.setp(axes[3].get_xticklabels(), visible=False)\n",
+    "axes[0].xaxis.labelpad = 10000;\n",
+    "\n",
+    "fig.supylabel('Proportion Normal Labs');\n",
+    "fig.supxlabel('Preceding Consecutive Streak of Normal Results in Prior 7 Days');\n",
+    "fig.suptitle('Stability of Common Inpatient Labs');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "id": "c9f02363",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, sharey = True, sharex = True);\n",
+    "plotLabStab(labstab, 6, ax=axes[0][0]);\n",
+    "plotLabStab(labstab, 6, ax = axes[0][1], subset = True, labs = ['Sodium', 'Potassium', 'Creatinine']);\n",
+    "plotLabStab(labstab, 6, ax = axes[1][0], subset = True, labs = ['White Cells', 'Hemoglobin', 'Platelets']);\n",
+    "plotLabStab(labstab, 6, ax = axes[1][1], subset = True, labs = ['Magnesium', 'Phosphorus']);\n",
+    "\n",
+    "for ax in axes.reshape(-1):\n",
+    "    ax.set_ylabel('')\n",
+    "    ax.set_xlabel('')\n",
+    "    ax.legend(fontsize=5)\n",
+    "axes[0][0].set_title('All')\n",
+    "axes[0][1].set_title('Chem Panel')\n",
+    "axes[1][0].set_title('Blood Count')\n",
+    "axes[1][1].set_title('Additional Chemistries')\n",
+    "fig.supylabel('Proportion Normal Labs');\n",
+    "fig.supxlabel('Preceding Consecutive Streak of Normal Results in Prior 7 Days');\n",
+    "fig.suptitle('Stability of Common Inpatient Labs');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 472,
+   "id": "896a9a63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "316994052    288\n",
+       "316387272    270\n",
+       "311721758    255\n",
+       "311868608    255\n",
+       "319244154    252\n",
+       "314442120    238\n",
+       "313877087    238\n",
+       "311954171    238\n",
+       "321867423    228\n",
+       "311832770    221\n",
+       "            ... \n",
+       "314083964     17\n",
+       "312066069     17\n",
+       "314790432     17\n",
+       "315719411     17\n",
+       "312916730     17\n",
+       "313870466     17\n",
+       "313605336     17\n",
+       "314652835     17\n",
+       "312093784     17\n",
+       "314079147     17\n",
+       "Name: pat_enc_csn_id_coded, Length: 679, dtype: int64"
+      ]
+     },
+     "execution_count": 472,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.loc[lradt.group_lab_name=='Blood Gases, Arterial'].pat_enc_csn_id_coded.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 477,
+   "id": "16f0cc41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_low</th>\n",
+       "      <th>reference_high</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result_flag</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>njr_labname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>24393</th>\n",
+       "      <td>11782491</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27243</th>\n",
+       "      <td>11782526</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>25.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27244</th>\n",
+       "      <td>11782580</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>101.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27247</th>\n",
+       "      <td>11782399</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27248</th>\n",
+       "      <td>11782393</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>16.80</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27260</th>\n",
+       "      <td>11782405</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>136.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27266</th>\n",
+       "      <td>11782482</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>170.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27267</th>\n",
+       "      <td>11782431</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27268</th>\n",
+       "      <td>11782566</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>109.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27274</th>\n",
+       "      <td>11782385</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27277</th>\n",
+       "      <td>11782584</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>21.90</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18890</th>\n",
+       "      <td>11782571</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20874</th>\n",
+       "      <td>11782530</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.40</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20875</th>\n",
+       "      <td>11782568</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>313.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20883</th>\n",
+       "      <td>11782454</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>293.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20887</th>\n",
+       "      <td>11782441</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.60</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20892</th>\n",
+       "      <td>11782493</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20893</th>\n",
+       "      <td>11782560</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20896</th>\n",
+       "      <td>11782557</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.20</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20900</th>\n",
+       "      <td>11782489</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.16</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20902</th>\n",
+       "      <td>11782290</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>11.70</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20906</th>\n",
+       "      <td>11782358</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>68.20</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High Panic</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11436</th>\n",
+       "      <td>11782397</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>108.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14549</th>\n",
+       "      <td>11782473</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>137.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26664</th>\n",
+       "      <td>11782478</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>36.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15726</th>\n",
+       "      <td>11782591</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27242</th>\n",
+       "      <td>11782498</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>200.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3060</th>\n",
+       "      <td>11782300</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>109.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27236</th>\n",
+       "      <td>11782444</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.08</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27230</th>\n",
+       "      <td>11782390</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>7.90</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22073</th>\n",
+       "      <td>11782458</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>27.40</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22074</th>\n",
+       "      <td>11782453</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>35.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18618</th>\n",
+       "      <td>11782586</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24342</th>\n",
+       "      <td>11782466</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24345</th>\n",
+       "      <td>11782319</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>212.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24347</th>\n",
+       "      <td>11782544</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24349</th>\n",
+       "      <td>11782374</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461047</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>186.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:05:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24350</th>\n",
+       "      <td>11782551</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>106.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24358</th>\n",
+       "      <td>11782434</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24359</th>\n",
+       "      <td>11782519</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>12.00</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24363</th>\n",
+       "      <td>11782320</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>117.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24365</th>\n",
+       "      <td>11782359</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.80</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24366</th>\n",
+       "      <td>11782503</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-6.40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24376</th>\n",
+       "      <td>11782506</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>23.80</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24378</th>\n",
+       "      <td>11782537</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>98.30</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24382</th>\n",
+       "      <td>11782336</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>4.60</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24385</th>\n",
+       "      <td>11782464</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24388</th>\n",
+       "      <td>11782439</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24391</th>\n",
+       "      <td>11782376</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>37.30</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15502</th>\n",
+       "      <td>11782578</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>9.10</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24394</th>\n",
+       "      <td>11782311</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-4.90</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24402</th>\n",
+       "      <td>11782570</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>98.10</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24410</th>\n",
+       "      <td>11782569</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.60</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27224</th>\n",
+       "      <td>11782352</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>36.50</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27227</th>\n",
+       "      <td>11782341</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>137.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27233</th>\n",
+       "      <td>11782396</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.30</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22072</th>\n",
+       "      <td>11782455</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3070</th>\n",
+       "      <td>11782282</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461047</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:05:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3084</th>\n",
+       "      <td>11782333</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>107.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27446</th>\n",
+       "      <td>11782287</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523</th>\n",
+       "      <td>11782484</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1526</th>\n",
+       "      <td>11782408</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1534</th>\n",
+       "      <td>11782299</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1537</th>\n",
+       "      <td>11782516</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540</th>\n",
+       "      <td>11782520</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1541</th>\n",
+       "      <td>11782283</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>23.80</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1543</th>\n",
+       "      <td>11782562</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>267.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1545</th>\n",
+       "      <td>11782284</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>20.80</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1547</th>\n",
+       "      <td>11782594</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461047</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-0.40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:05:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1548</th>\n",
+       "      <td>11782308</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>63.90</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1550</th>\n",
+       "      <td>11782353</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.70</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1556</th>\n",
+       "      <td>11782356</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>27.80</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1558</th>\n",
+       "      <td>11782369</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.27</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1564</th>\n",
+       "      <td>11782527</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.42</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1566</th>\n",
+       "      <td>11782442</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>22.30</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1569</th>\n",
+       "      <td>11782360</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>17.30</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1571</th>\n",
+       "      <td>11782515</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-1.10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15775</th>\n",
+       "      <td>11782433</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20060</th>\n",
+       "      <td>11782285</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23290</th>\n",
+       "      <td>11782542</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461053</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>137.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:09:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23292</th>\n",
+       "      <td>11782553</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>140.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27514</th>\n",
+       "      <td>11782306</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>162.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7827</th>\n",
+       "      <td>11782470</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>37.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7830</th>\n",
+       "      <td>11782436</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>26.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5883</th>\n",
+       "      <td>11782349</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3076</th>\n",
+       "      <td>11782423</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>11.60</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5878</th>\n",
+       "      <td>11782449</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461052</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>19.70</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 09:46:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5871</th>\n",
+       "      <td>11782375</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>28.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3085</th>\n",
+       "      <td>11782488</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>37.60</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3088</th>\n",
+       "      <td>11782281</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454336</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 16:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3089</th>\n",
+       "      <td>11782330</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3090</th>\n",
+       "      <td>11782377</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461046</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>12.60</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:28:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3093</th>\n",
+       "      <td>11782293</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461049</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>13.20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:38:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3094</th>\n",
+       "      <td>11782556</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461048</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 11:32:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3095</th>\n",
+       "      <td>11782340</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>29.50</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3100</th>\n",
+       "      <td>11782510</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454338</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 14:39:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3103</th>\n",
+       "      <td>11782443</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454337</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>98.10</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 15:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24881</th>\n",
+       "      <td>11782332</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730454335</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 17:31:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5825</th>\n",
+       "      <td>11782312</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461047</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>15.80</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 13:05:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5826</th>\n",
+       "      <td>11782445</td>\n",
+       "      <td>JC1145044</td>\n",
+       "      <td>316994052</td>\n",
+       "      <td>730461051</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>125.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-08-16 06:18:00</td>\n",
+       "      <td>2017-08-16 10:20:00</td>\n",
+       "      <td>2017-08-15 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201258</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3290951</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            uid    anon_id pat_enc_csn_id_coded order_id_coded  component_id  \\\n",
+       "24393  11782491  JC1145044            316994052      730461052    1230100705   \n",
+       "27243  11782526  JC1145044            316994052      730461053    1230100847   \n",
+       "27244  11782580  JC1145044            316994052      730454338    1230100703   \n",
+       "27247  11782399  JC1145044            316994052      730454335    1230100705   \n",
+       "27248  11782393  JC1145044            316994052      730461048    1230100849   \n",
+       "27260  11782405  JC1145044            316994052      730461049    1230100700   \n",
+       "27266  11782482  JC1145044            316994052      730461048    1230100704   \n",
+       "27267  11782431  JC1145044            316994052      730454337    1230100838   \n",
+       "27268  11782566  JC1145044            316994052      730461052    1230100703   \n",
+       "27274  11782385  JC1145044            316994052      730461051    1230100850   \n",
+       "27277  11782584  JC1145044            316994052      730454336    1230100849   \n",
+       "18890  11782571  JC1145044            316994052      730461051         16678   \n",
+       "20874  11782530  JC1145044            316994052      730454336    1230100838   \n",
+       "20875  11782568  JC1145044            316994052      730461046    1230100840   \n",
+       "20883  11782454  JC1145044            316994052      730461049    1230100840   \n",
+       "20887  11782441  JC1145044            316994052      730461052    1230100845   \n",
+       "20892  11782493  JC1145044            316994052      730461051    1230100705   \n",
+       "20893  11782560  JC1145044            316994052      730461048    1230100850   \n",
+       "20896  11782557  JC1145044            316994052      730454336    1230100845   \n",
+       "20900  11782489  JC1145044            316994052      730454337    1230100702   \n",
+       "20902  11782290  JC1145044            316994052      730461051    1230100849   \n",
+       "20906  11782358  JC1145044            316994052      730454338    1230100839   \n",
+       "11436  11782397  JC1145044            316994052      730454336    1230100703   \n",
+       "14549  11782473  JC1145044            316994052      730461052    1230100700   \n",
+       "26664  11782478  JC1145044            316994052      730461048    1230100847   \n",
+       "15726  11782591  JC1145044            316994052      730461046         16678   \n",
+       "27242  11782498  JC1145044            316994052      730461046    1230100704   \n",
+       "3060   11782300  JC1145044            316994052      730461051    1230100703   \n",
+       "27236  11782444  JC1145044            316994052      730461048    1230100702   \n",
+       "27230  11782390  JC1145044            316994052      730461053    1230100846   \n",
+       "22073  11782458  JC1145044            316994052      730454338    1230100841   \n",
+       "22074  11782453  JC1145044            316994052      730454338    1230100847   \n",
+       "18618  11782586  JC1145044            316994052      730454337         16678   \n",
+       "24342  11782466  JC1145044            316994052      730461053    1230100850   \n",
+       "24345  11782319  JC1145044            316994052      730454338    1230100704   \n",
+       "24347  11782544  JC1145044            316994052      730454338    1230100700   \n",
+       "24349  11782374  JC1145044            316994052      730461047    1230100704   \n",
+       "24350  11782551  JC1145044            316994052      730454337    1230100703   \n",
+       "24358  11782434  JC1145044            316994052      730454338    1230100838   \n",
+       "24359  11782519  JC1145044            316994052      730454337    1230100846   \n",
+       "24363  11782320  JC1145044            316994052      730461052    1230100704   \n",
+       "24365  11782359  JC1145044            316994052      730461051    1230100701   \n",
+       "24366  11782503  JC1145044            316994052      730461049    1230100843   \n",
+       "24376  11782506  JC1145044            316994052      730454335    1230100841   \n",
+       "24378  11782537  JC1145044            316994052      730461048    1230100845   \n",
+       "24382  11782336  JC1145044            316994052      730454335    1230100701   \n",
+       "24385  11782464  JC1145044            316994052      730454338    1230100701   \n",
+       "24388  11782439  JC1145044            316994052      730461049    1230100704   \n",
+       "24391  11782376  JC1145044            316994052      730461051    1230100839   \n",
+       "15502  11782578  JC1145044            316994052      730461049    1230100846   \n",
+       "24394  11782311  JC1145044            316994052      730461052    1230100843   \n",
+       "24402  11782570  JC1145044            316994052      730454338    1230100845   \n",
+       "24410  11782569  JC1145044            316994052      730461052    1230100701   \n",
+       "27224  11782352  JC1145044            316994052      730461052    1230100839   \n",
+       "27227  11782341  JC1145044            316994052      730461051    1230100700   \n",
+       "27233  11782396  JC1145044            316994052      730461046    1230100845   \n",
+       "22072  11782455  JC1145044            316994052      730454335    1230100850   \n",
+       "3070   11782282  JC1145044            316994052      730461047    1230100705   \n",
+       "3084   11782333  JC1145044            316994052      730461049    1230100703   \n",
+       "27446  11782287  JC1145044            316994052      730461053         16678   \n",
+       "1523   11782484  JC1145044            316994052      730461048    1230100838   \n",
+       "1526   11782408  JC1145044            316994052      730454335    1230100702   \n",
+       "1534   11782299  JC1145044            316994052      730454338    1230100705   \n",
+       "1537   11782516  JC1145044            316994052      730454336    1230100850   \n",
+       "1540   11782520  JC1145044            316994052      730461049    1230100850   \n",
+       "1541   11782283  JC1145044            316994052      730454336    1230100841   \n",
+       "1543   11782562  JC1145044            316994052      730461053    1230100840   \n",
+       "1545   11782284  JC1145044            316994052      730461052    1230100842   \n",
+       "1547   11782594  JC1145044            316994052      730461047    1230100843   \n",
+       "1548   11782308  JC1145044            316994052      730454337    1230100839   \n",
+       "1550   11782353  JC1145044            316994052      730454335    1230100845   \n",
+       "1556   11782356  JC1145044            316994052      730461048    1230100842   \n",
+       "1558   11782369  JC1145044            316994052      730461052    1230100702   \n",
+       "1564   11782527  JC1145044            316994052      730454335    1230100838   \n",
+       "1566   11782442  JC1145044            316994052      730461053    1230100842   \n",
+       "1569   11782360  JC1145044            316994052      730454337    1230100849   \n",
+       "1571   11782515  JC1145044            316994052      730461046    1230100843   \n",
+       "15775  11782433  JC1145044            316994052      730461046    1230100701   \n",
+       "20060  11782285  JC1145044            316994052      730461046    1230100703   \n",
+       "23290  11782542  JC1145044            316994052      730461053    1230100700   \n",
+       "23292  11782553  JC1145044            316994052      730454337    1230100700   \n",
+       "27514  11782306  JC1145044            316994052      730454336    1230100704   \n",
+       "7827   11782470  JC1145044            316994052      730454337    1230100847   \n",
+       "7830   11782436  JC1145044            316994052      730461052    1230100847   \n",
+       "5883   11782349  JC1145044            316994052      730454336    1230100702   \n",
+       "3076   11782423  JC1145044            316994052      730461048    1230100846   \n",
+       "5878   11782449  JC1145044            316994052      730461052    1230100841   \n",
+       "5871   11782375  JC1145044            316994052      730461049    1230100847   \n",
+       "3085   11782488  JC1145044            316994052      730454335    1230100839   \n",
+       "3088   11782281  JC1145044            316994052      730454336    1230100705   \n",
+       "3089   11782330  JC1145044            316994052      730461046    1230100702   \n",
+       "3090   11782377  JC1145044            316994052      730461046    1230100846   \n",
+       "3093   11782293  JC1145044            316994052      730461049    1230100849   \n",
+       "3094   11782556  JC1145044            316994052      730461048    1230100701   \n",
+       "3095   11782340  JC1145044            316994052      730454338    1230100842   \n",
+       "3100   11782510  JC1145044            316994052      730454338    1230100702   \n",
+       "3103   11782443  JC1145044            316994052      730454337    1230100845   \n",
+       "24881  11782332  JC1145044            316994052      730454335         16678   \n",
+       "5825   11782312  JC1145044            316994052      730461047    1230100849   \n",
+       "5826   11782445  JC1145044            316994052      730461051    1230100704   \n",
+       "\n",
+       "      order_type proc_code         group_lab_name              lab_name  \\\n",
+       "24393        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "27243        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "27244        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27247        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "27248        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "27260        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "27266        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "27267        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "27268        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27274        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "27277        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "18890        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "20874        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "20875        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "20883        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "20887        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "20892        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "20893        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "20896        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "20900        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "20902        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "20906        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "11436        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "14549        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "26664        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "15726        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "27242        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "3060         Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27236        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "27230        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "22073        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "22074        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "18618        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "24342        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "24345        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "24347        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "24349        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "24350        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "24358        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "24359        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "24363        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "24365        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "24366        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "24376        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "24378        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "24382        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "24385        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "24388        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "24391        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "15502        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "24394        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "24402        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "24410        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "27224        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "27227        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "27233        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "22072        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "3070         Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "3084         Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27446        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "1523         Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "1526         Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "1534         Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "1537         Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "1540         Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "1541         Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "1543         Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "1545         Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "1547         Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "1548         Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "1550         Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "1556         Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "1558         Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "1564         Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "1566         Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "1569         Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "1571         Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "15775        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "20060        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "23290        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23292        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "27514        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "7827         Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "7830         Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "5883         Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "3076         Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "5878         Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "5871         Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "3085         Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "3088         Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "3089         Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "3090         Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "3093         Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "3094         Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "3095         Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "3100         Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "3103         Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "24881        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "5825         Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "5826         Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "\n",
+       "      base_name  ord_num_value reference_low reference_high reference_unit  \\\n",
+       "24393     METHB     9999999.00          None           None              %   \n",
+       "27243       HCT          25.00            30             42              %   \n",
+       "27244        CL         101.00            95            110         mmol/L   \n",
+       "27247     METHB           1.10          None           None              %   \n",
+       "27248     CTO2A          16.80          15.0           25.0          mL/dL   \n",
+       "27260        NA         136.00           135            145         mmol/L   \n",
+       "27266       GLU         170.00            45            200          mg/dL   \n",
+       "27267       PHA           7.23          7.35           7.45           None   \n",
+       "27268        CL         109.00            95            110         mmol/L   \n",
+       "27274      COHB           1.80          None           None              %   \n",
+       "27277     CTO2A          21.90          15.0           25.0          mL/dL   \n",
+       "18890   PERFLAB     9999999.00          None           None           None   \n",
+       "20874       PHA           7.40          7.35           7.45           None   \n",
+       "20875      PO2A         313.00          80.0          105.0           mmHg   \n",
+       "20883      PO2A         293.00          80.0          105.0           mmHg   \n",
+       "20887    O2SATA          97.60          95.0          100.0              %   \n",
+       "20892     METHB     9999999.00          None           None              %   \n",
+       "20893      COHB     9999999.00          None           None              %   \n",
+       "20896    O2SATA          97.20          95.0          100.0              %   \n",
+       "20900     CAION           1.16          1.12           1.32         mmol/L   \n",
+       "20902     CTO2A          11.70          15.0           25.0          mL/dL   \n",
+       "20906     PCO2A          68.20          35.0           45.0           mmHg   \n",
+       "11436        CL         108.00            95            110         mmol/L   \n",
+       "14549        NA         137.00           135            145         mmol/L   \n",
+       "26664       HCT          36.00            30             42              %   \n",
+       "15726   PERFLAB     9999999.00          None           None           None   \n",
+       "27242       GLU         200.00            45            200          mg/dL   \n",
+       "3060         CL         109.00            95            110         mmol/L   \n",
+       "27236     CAION           1.08          1.12           1.32         mmol/L   \n",
+       "27230       HGB           7.90          10.0           14.0           g/dL   \n",
+       "22073     HCO3A          27.40          22.0           26.0         mmol/L   \n",
+       "22074       HCT          35.00            30             42              %   \n",
+       "18618   PERFLAB     9999999.00          None           None           None   \n",
+       "24342      COHB           2.20          None           None              %   \n",
+       "24345       GLU         212.00            45            200          mg/dL   \n",
+       "24347        NA         142.00           135            145         mmol/L   \n",
+       "24349       GLU         186.00            45            200          mg/dL   \n",
+       "24350        CL         106.00            95            110         mmol/L   \n",
+       "24358       PHA           7.23          7.35           7.45           None   \n",
+       "24359       HGB          12.00          10.0           14.0           g/dL   \n",
+       "24363       GLU         117.00            45            200          mg/dL   \n",
+       "24365         K           3.80           3.5            6.0         mmol/L   \n",
+       "24366        BE          -6.40           0.0            0.0         mmol/L   \n",
+       "24376     HCO3A          23.80          22.0           26.0         mmol/L   \n",
+       "24378    O2SATA          98.30          95.0          100.0              %   \n",
+       "24382         K           4.60           3.5            6.0         mmol/L   \n",
+       "24385         K           4.10           3.5            6.0         mmol/L   \n",
+       "24388       GLU         114.00            45            200          mg/dL   \n",
+       "24391     PCO2A          37.30          35.0           45.0           mmHg   \n",
+       "15502       HGB           9.10          10.0           14.0           g/dL   \n",
+       "24394        BE          -4.90           0.0            0.0         mmol/L   \n",
+       "24402    O2SATA          98.10          95.0          100.0              %   \n",
+       "24410         K           3.60           3.5            6.0         mmol/L   \n",
+       "27224     PCO2A          36.50          35.0           45.0           mmHg   \n",
+       "27227        NA         137.00           135            145         mmol/L   \n",
+       "27233    O2SATA          97.30          95.0          100.0              %   \n",
+       "22072      COHB           1.30          None           None              %   \n",
+       "3070      METHB           1.20          None           None              %   \n",
+       "3084         CL         107.00            95            110         mmol/L   \n",
+       "27446   PERFLAB     9999999.00          None           None           None   \n",
+       "1523        PHA           7.25          7.35           7.45           None   \n",
+       "1526      CAION           0.89          1.12           1.32         mmol/L   \n",
+       "1534      METHB     9999999.00          None           None              %   \n",
+       "1537       COHB           1.10          None           None              %   \n",
+       "1540       COHB           1.50          None           None              %   \n",
+       "1541      HCO3A          23.80          22.0           26.0         mmol/L   \n",
+       "1543       PO2A         267.00          80.0          105.0           mmHg   \n",
+       "1545      TCO2A          20.80          23.0           27.0         mmol/L   \n",
+       "1547         BE          -0.40           0.0            0.0         mmol/L   \n",
+       "1548      PCO2A          63.90          35.0           45.0           mmHg   \n",
+       "1550     O2SATA          97.70          95.0          100.0              %   \n",
+       "1556      TCO2A          27.80          23.0           27.0         mmol/L   \n",
+       "1558      CAION           1.27          1.12           1.32         mmol/L   \n",
+       "1564        PHA           7.42          7.35           7.45           None   \n",
+       "1566      TCO2A          22.30          23.0           27.0         mmol/L   \n",
+       "1569      CTO2A          17.30          15.0           25.0          mL/dL   \n",
+       "1571         BE          -1.10           0.0            0.0         mmol/L   \n",
+       "15775         K           4.20           3.5            6.0         mmol/L   \n",
+       "20060        CL         105.00            95            110         mmol/L   \n",
+       "23290        NA         137.00           135            145         mmol/L   \n",
+       "23292        NA         140.00           135            145         mmol/L   \n",
+       "27514       GLU         162.00            45            200          mg/dL   \n",
+       "7827        HCT          37.00            30             42              %   \n",
+       "7830        HCT          26.00            30             42              %   \n",
+       "5883      CAION           1.01          1.12           1.32         mmol/L   \n",
+       "3076        HGB          11.60          10.0           14.0           g/dL   \n",
+       "5878      HCO3A          19.70          22.0           26.0         mmol/L   \n",
+       "5871        HCT          28.00            30             42              %   \n",
+       "3085      PCO2A          37.60          35.0           45.0           mmHg   \n",
+       "3088      METHB           1.50          None           None              %   \n",
+       "3089      CAION           1.06          1.12           1.32         mmol/L   \n",
+       "3090        HGB          12.60          10.0           14.0           g/dL   \n",
+       "3093      CTO2A          13.20          15.0           25.0          mL/dL   \n",
+       "3094          K           3.50           3.5            6.0         mmol/L   \n",
+       "3095      TCO2A          29.50          23.0           27.0         mmol/L   \n",
+       "3100      CAION           0.90          1.12           1.32         mmol/L   \n",
+       "3103     O2SATA          98.10          95.0          100.0              %   \n",
+       "24881   PERFLAB     9999999.00          None           None           None   \n",
+       "5825      CTO2A          15.80          15.0           25.0          mL/dL   \n",
+       "5826        GLU         125.00            45            200          mg/dL   \n",
+       "\n",
+       "      result_in_range_yn result_flag          order_time         result_time  \\\n",
+       "24393               None        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "27243               None         Low 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "27244                  Y        None 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "27247               None        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "27248                  Y        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "27260                  Y        None 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "27266                  Y        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "27267               None         Low 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "27268                  Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "27274               None        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "27277                  Y        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "18890               None        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "20874                  Y        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "20875               None        High 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "20883               None        High 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "20887                  Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "20892               None        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "20893               None        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "20896                  Y        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "20900                  Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "20902               None         Low 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "20906               None  High Panic 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "11436                  Y        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "14549                  Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "26664                  Y        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "15726               None        None 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "27242                  Y        None 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "3060                   Y        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "27236               None         Low 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "27230               None         Low 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "22073               None        High 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "22074                  Y        None 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "18618               None        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "24342               None        None 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "24345               None        High 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "24347                  Y        None 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "24349                  Y        None 2017-08-16 06:18:00 2017-08-16 13:05:00   \n",
+       "24350                  Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "24358               None         Low 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "24359                  Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "24363                  Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "24365                  Y        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "24366               None         Low 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "24376                  Y        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "24378                  Y        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "24382                  Y        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "24385                  Y        None 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "24388                  Y        None 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "24391                  Y        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "15502               None         Low 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "24394               None         Low 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "24402                  Y        None 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "24410                  Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "27224                  Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "27227                  Y        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "27233                  Y        None 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "22072               None        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "3070                None        None 2017-08-16 06:18:00 2017-08-16 13:05:00   \n",
+       "3084                   Y        None 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "27446               None        None 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "1523                None         Low 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "1526                None         Low 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "1534                None        None 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "1537                None        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "1540                None        None 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "1541                   Y        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "1543                None        High 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "1545                None         Low 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "1547                None         Low 2017-08-16 06:18:00 2017-08-16 13:05:00   \n",
+       "1548                None        High 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "1550                   Y        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "1556                None        High 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "1558                   Y        None 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "1564                   Y        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "1566                None         Low 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "1569                   Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "1571                None         Low 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "15775                  Y        None 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "20060                  Y        None 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "23290                  Y        None 2017-08-16 06:18:00 2017-08-16 09:09:00   \n",
+       "23292                  Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "27514                  Y        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "7827                   Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "7830                None         Low 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "5883                None         Low 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "3076                   Y        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "5878                None         Low 2017-08-16 06:18:00 2017-08-16 09:46:00   \n",
+       "5871                None         Low 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "3085                   Y        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "3088                None        None 2017-08-16 06:18:00 2017-08-16 16:31:00   \n",
+       "3089                None         Low 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "3090                   Y        None 2017-08-16 06:18:00 2017-08-16 13:28:00   \n",
+       "3093                None         Low 2017-08-16 06:18:00 2017-08-16 10:38:00   \n",
+       "3094                   Y        None 2017-08-16 06:18:00 2017-08-16 11:32:00   \n",
+       "3095                None        High 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "3100                None         Low 2017-08-16 06:18:00 2017-08-16 14:39:00   \n",
+       "3103                   Y        None 2017-08-16 06:18:00 2017-08-16 15:31:00   \n",
+       "24881               None        None 2017-08-16 06:18:00 2017-08-16 17:31:00   \n",
+       "5825                   Y        None 2017-08-16 06:18:00 2017-08-16 13:05:00   \n",
+       "5826                   Y        None 2017-08-16 06:18:00 2017-08-16 10:20:00   \n",
+       "\n",
+       "      effective_time_jittered  pat_class  department_id pat_service  \\\n",
+       "24393     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27243     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27244     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27247     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27248     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27260     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27266     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27267     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27268     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27274     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27277     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "18890     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20874     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20875     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20883     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20887     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20892     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20893     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20896     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20900     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20902     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20906     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "11436     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "14549     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "26664     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "15726     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27242     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3060      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27236     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27230     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "22073     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "22074     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "18618     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24342     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24345     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24347     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24349     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24350     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24358     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24359     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24363     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24365     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24366     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24376     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24378     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24382     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24385     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24388     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24391     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "15502     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24394     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24402     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24410     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27224     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27227     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27233     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "22072     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3070      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3084      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27446     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1523      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1526      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1534      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1537      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1540      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1541      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1543      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1545      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1547      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1548      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1550      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1556      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1558      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1564      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1566      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1569      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "1571      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "15775     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "20060     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "23290     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "23292     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "27514     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "7827      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "7830      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "5883      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3076      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "5878      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "5871      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3085      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3088      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3089      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3090      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3093      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3094      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3095      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3100      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "3103      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "24881     2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "5825      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "5826      2017-08-15 23:59:00  Inpatient       10201258  Cardiology   \n",
+       "\n",
+       "      pat_lv_of_care           accomodation from_base_class to_base_class  \\\n",
+       "24393     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27243     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27244     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27247     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27248     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27260     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27266     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27267     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27268     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27274     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27277     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "18890     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20874     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20875     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20883     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20887     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20892     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20893     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20896     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20900     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20902     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20906     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "11436     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "14549     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "26664     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "15726     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27242     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3060      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27236     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27230     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "22073     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "22074     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "18618     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24342     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24345     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24347     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24349     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24350     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24358     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24359     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24363     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24365     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24366     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24376     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24378     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24382     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24385     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24388     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24391     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "15502     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24394     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24402     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24410     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27224     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27227     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27233     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "22072     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3070      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3084      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27446     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1523      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1526      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1534      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1537      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1540      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1541      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1543      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1545      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1547      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1548      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1550      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1556      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1558      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1564      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1566      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1569      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "1571      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "15775     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "20060     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "23290     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "23292     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "27514     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "7827      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "7830      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "5883      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3076      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "5878      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "5871      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3085      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3088      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3089      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3090      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3093      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3094      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3095      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3100      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "3103      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "24881     Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "5825      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "5826      Acute Care  PEDS MED/SURG Level 3       Inpatient     Inpatient   \n",
+       "\n",
+       "      in_event_type out_event_type  event_id_coded event_type  \\\n",
+       "24393        Census         Census         3290951     Census   \n",
+       "27243        Census         Census         3290951     Census   \n",
+       "27244        Census         Census         3290951     Census   \n",
+       "27247        Census         Census         3290951     Census   \n",
+       "27248        Census         Census         3290951     Census   \n",
+       "27260        Census         Census         3290951     Census   \n",
+       "27266        Census         Census         3290951     Census   \n",
+       "27267        Census         Census         3290951     Census   \n",
+       "27268        Census         Census         3290951     Census   \n",
+       "27274        Census         Census         3290951     Census   \n",
+       "27277        Census         Census         3290951     Census   \n",
+       "18890        Census         Census         3290951     Census   \n",
+       "20874        Census         Census         3290951     Census   \n",
+       "20875        Census         Census         3290951     Census   \n",
+       "20883        Census         Census         3290951     Census   \n",
+       "20887        Census         Census         3290951     Census   \n",
+       "20892        Census         Census         3290951     Census   \n",
+       "20893        Census         Census         3290951     Census   \n",
+       "20896        Census         Census         3290951     Census   \n",
+       "20900        Census         Census         3290951     Census   \n",
+       "20902        Census         Census         3290951     Census   \n",
+       "20906        Census         Census         3290951     Census   \n",
+       "11436        Census         Census         3290951     Census   \n",
+       "14549        Census         Census         3290951     Census   \n",
+       "26664        Census         Census         3290951     Census   \n",
+       "15726        Census         Census         3290951     Census   \n",
+       "27242        Census         Census         3290951     Census   \n",
+       "3060         Census         Census         3290951     Census   \n",
+       "27236        Census         Census         3290951     Census   \n",
+       "27230        Census         Census         3290951     Census   \n",
+       "22073        Census         Census         3290951     Census   \n",
+       "22074        Census         Census         3290951     Census   \n",
+       "18618        Census         Census         3290951     Census   \n",
+       "24342        Census         Census         3290951     Census   \n",
+       "24345        Census         Census         3290951     Census   \n",
+       "24347        Census         Census         3290951     Census   \n",
+       "24349        Census         Census         3290951     Census   \n",
+       "24350        Census         Census         3290951     Census   \n",
+       "24358        Census         Census         3290951     Census   \n",
+       "24359        Census         Census         3290951     Census   \n",
+       "24363        Census         Census         3290951     Census   \n",
+       "24365        Census         Census         3290951     Census   \n",
+       "24366        Census         Census         3290951     Census   \n",
+       "24376        Census         Census         3290951     Census   \n",
+       "24378        Census         Census         3290951     Census   \n",
+       "24382        Census         Census         3290951     Census   \n",
+       "24385        Census         Census         3290951     Census   \n",
+       "24388        Census         Census         3290951     Census   \n",
+       "24391        Census         Census         3290951     Census   \n",
+       "15502        Census         Census         3290951     Census   \n",
+       "24394        Census         Census         3290951     Census   \n",
+       "24402        Census         Census         3290951     Census   \n",
+       "24410        Census         Census         3290951     Census   \n",
+       "27224        Census         Census         3290951     Census   \n",
+       "27227        Census         Census         3290951     Census   \n",
+       "27233        Census         Census         3290951     Census   \n",
+       "22072        Census         Census         3290951     Census   \n",
+       "3070         Census         Census         3290951     Census   \n",
+       "3084         Census         Census         3290951     Census   \n",
+       "27446        Census         Census         3290951     Census   \n",
+       "1523         Census         Census         3290951     Census   \n",
+       "1526         Census         Census         3290951     Census   \n",
+       "1534         Census         Census         3290951     Census   \n",
+       "1537         Census         Census         3290951     Census   \n",
+       "1540         Census         Census         3290951     Census   \n",
+       "1541         Census         Census         3290951     Census   \n",
+       "1543         Census         Census         3290951     Census   \n",
+       "1545         Census         Census         3290951     Census   \n",
+       "1547         Census         Census         3290951     Census   \n",
+       "1548         Census         Census         3290951     Census   \n",
+       "1550         Census         Census         3290951     Census   \n",
+       "1556         Census         Census         3290951     Census   \n",
+       "1558         Census         Census         3290951     Census   \n",
+       "1564         Census         Census         3290951     Census   \n",
+       "1566         Census         Census         3290951     Census   \n",
+       "1569         Census         Census         3290951     Census   \n",
+       "1571         Census         Census         3290951     Census   \n",
+       "15775        Census         Census         3290951     Census   \n",
+       "20060        Census         Census         3290951     Census   \n",
+       "23290        Census         Census         3290951     Census   \n",
+       "23292        Census         Census         3290951     Census   \n",
+       "27514        Census         Census         3290951     Census   \n",
+       "7827         Census         Census         3290951     Census   \n",
+       "7830         Census         Census         3290951     Census   \n",
+       "5883         Census         Census         3290951     Census   \n",
+       "3076         Census         Census         3290951     Census   \n",
+       "5878         Census         Census         3290951     Census   \n",
+       "5871         Census         Census         3290951     Census   \n",
+       "3085         Census         Census         3290951     Census   \n",
+       "3088         Census         Census         3290951     Census   \n",
+       "3089         Census         Census         3290951     Census   \n",
+       "3090         Census         Census         3290951     Census   \n",
+       "3093         Census         Census         3290951     Census   \n",
+       "3094         Census         Census         3290951     Census   \n",
+       "3095         Census         Census         3290951     Census   \n",
+       "3100         Census         Census         3290951     Census   \n",
+       "3103         Census         Census         3290951     Census   \n",
+       "24881        Census         Census         3290951     Census   \n",
+       "5825         Census         Census         3290951     Census   \n",
+       "5826         Census         Census         3290951     Census   \n",
+       "\n",
+       "                 njr_labname  \n",
+       "24393  Blood Gases, Arterial  \n",
+       "27243  Blood Gases, Arterial  \n",
+       "27244  Blood Gases, Arterial  \n",
+       "27247  Blood Gases, Arterial  \n",
+       "27248  Blood Gases, Arterial  \n",
+       "27260  Blood Gases, Arterial  \n",
+       "27266  Blood Gases, Arterial  \n",
+       "27267  Blood Gases, Arterial  \n",
+       "27268  Blood Gases, Arterial  \n",
+       "27274  Blood Gases, Arterial  \n",
+       "27277  Blood Gases, Arterial  \n",
+       "18890  Blood Gases, Arterial  \n",
+       "20874  Blood Gases, Arterial  \n",
+       "20875  Blood Gases, Arterial  \n",
+       "20883  Blood Gases, Arterial  \n",
+       "20887  Blood Gases, Arterial  \n",
+       "20892  Blood Gases, Arterial  \n",
+       "20893  Blood Gases, Arterial  \n",
+       "20896  Blood Gases, Arterial  \n",
+       "20900  Blood Gases, Arterial  \n",
+       "20902  Blood Gases, Arterial  \n",
+       "20906  Blood Gases, Arterial  \n",
+       "11436  Blood Gases, Arterial  \n",
+       "14549  Blood Gases, Arterial  \n",
+       "26664  Blood Gases, Arterial  \n",
+       "15726  Blood Gases, Arterial  \n",
+       "27242  Blood Gases, Arterial  \n",
+       "3060   Blood Gases, Arterial  \n",
+       "27236  Blood Gases, Arterial  \n",
+       "27230  Blood Gases, Arterial  \n",
+       "22073  Blood Gases, Arterial  \n",
+       "22074  Blood Gases, Arterial  \n",
+       "18618  Blood Gases, Arterial  \n",
+       "24342  Blood Gases, Arterial  \n",
+       "24345  Blood Gases, Arterial  \n",
+       "24347  Blood Gases, Arterial  \n",
+       "24349  Blood Gases, Arterial  \n",
+       "24350  Blood Gases, Arterial  \n",
+       "24358  Blood Gases, Arterial  \n",
+       "24359  Blood Gases, Arterial  \n",
+       "24363  Blood Gases, Arterial  \n",
+       "24365  Blood Gases, Arterial  \n",
+       "24366  Blood Gases, Arterial  \n",
+       "24376  Blood Gases, Arterial  \n",
+       "24378  Blood Gases, Arterial  \n",
+       "24382  Blood Gases, Arterial  \n",
+       "24385  Blood Gases, Arterial  \n",
+       "24388  Blood Gases, Arterial  \n",
+       "24391  Blood Gases, Arterial  \n",
+       "15502  Blood Gases, Arterial  \n",
+       "24394  Blood Gases, Arterial  \n",
+       "24402  Blood Gases, Arterial  \n",
+       "24410  Blood Gases, Arterial  \n",
+       "27224  Blood Gases, Arterial  \n",
+       "27227  Blood Gases, Arterial  \n",
+       "27233  Blood Gases, Arterial  \n",
+       "22072  Blood Gases, Arterial  \n",
+       "3070   Blood Gases, Arterial  \n",
+       "3084   Blood Gases, Arterial  \n",
+       "27446  Blood Gases, Arterial  \n",
+       "1523   Blood Gases, Arterial  \n",
+       "1526   Blood Gases, Arterial  \n",
+       "1534   Blood Gases, Arterial  \n",
+       "1537   Blood Gases, Arterial  \n",
+       "1540   Blood Gases, Arterial  \n",
+       "1541   Blood Gases, Arterial  \n",
+       "1543   Blood Gases, Arterial  \n",
+       "1545   Blood Gases, Arterial  \n",
+       "1547   Blood Gases, Arterial  \n",
+       "1548   Blood Gases, Arterial  \n",
+       "1550   Blood Gases, Arterial  \n",
+       "1556   Blood Gases, Arterial  \n",
+       "1558   Blood Gases, Arterial  \n",
+       "1564   Blood Gases, Arterial  \n",
+       "1566   Blood Gases, Arterial  \n",
+       "1569   Blood Gases, Arterial  \n",
+       "1571   Blood Gases, Arterial  \n",
+       "15775  Blood Gases, Arterial  \n",
+       "20060  Blood Gases, Arterial  \n",
+       "23290  Blood Gases, Arterial  \n",
+       "23292  Blood Gases, Arterial  \n",
+       "27514  Blood Gases, Arterial  \n",
+       "7827   Blood Gases, Arterial  \n",
+       "7830   Blood Gases, Arterial  \n",
+       "5883   Blood Gases, Arterial  \n",
+       "3076   Blood Gases, Arterial  \n",
+       "5878   Blood Gases, Arterial  \n",
+       "5871   Blood Gases, Arterial  \n",
+       "3085   Blood Gases, Arterial  \n",
+       "3088   Blood Gases, Arterial  \n",
+       "3089   Blood Gases, Arterial  \n",
+       "3090   Blood Gases, Arterial  \n",
+       "3093   Blood Gases, Arterial  \n",
+       "3094   Blood Gases, Arterial  \n",
+       "3095   Blood Gases, Arterial  \n",
+       "3100   Blood Gases, Arterial  \n",
+       "3103   Blood Gases, Arterial  \n",
+       "24881  Blood Gases, Arterial  \n",
+       "5825   Blood Gases, Arterial  \n",
+       "5826   Blood Gases, Arterial  "
+      ]
+     },
+     "execution_count": 477,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.loc[(lradt.pat_enc_csn_id_coded==316994052) & (lradt.group_lab_name=='Blood Gases, Arterial')].sort_values(by='order_time').head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 478,
+   "id": "b950d594",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_low</th>\n",
+       "      <th>reference_high</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result_flag</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>njr_labname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>10710857</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-1.90</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17788</th>\n",
+       "      <td>10710851</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>21.50</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17789</th>\n",
+       "      <td>10711009</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17793</th>\n",
+       "      <td>10711083</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17794</th>\n",
+       "      <td>10710804</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>255.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17796</th>\n",
+       "      <td>10710971</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>63.40</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17797</th>\n",
+       "      <td>10711007</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>17.20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7032</th>\n",
+       "      <td>10710921</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11215</th>\n",
+       "      <td>10710798</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>112.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11216</th>\n",
+       "      <td>10711054</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>111.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14783</th>\n",
+       "      <td>10710805</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114346</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 13:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27761</th>\n",
+       "      <td>10710966</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>18.20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27762</th>\n",
+       "      <td>10711079</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114346</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>21.00</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 13:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27763</th>\n",
+       "      <td>10710780</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>13.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27764</th>\n",
+       "      <td>10710834</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>24.80</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27766</th>\n",
+       "      <td>10710982</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>108.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27767</th>\n",
+       "      <td>10711055</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>61.20</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27771</th>\n",
+       "      <td>10711000</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>21.70</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27772</th>\n",
+       "      <td>10711068</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114346</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>110.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 13:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27773</th>\n",
+       "      <td>10710936</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27775</th>\n",
+       "      <td>10710950</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>42.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27776</th>\n",
+       "      <td>10711074</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27778</th>\n",
+       "      <td>10710852</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114347</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>16.50</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:15:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27780</th>\n",
+       "      <td>10710797</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>54.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27782</th>\n",
+       "      <td>10710891</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114357</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>110.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 08:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27783</th>\n",
+       "      <td>10710959</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>41.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17784</th>\n",
+       "      <td>10710768</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.38</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17782</th>\n",
+       "      <td>10710886</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>261.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17781</th>\n",
+       "      <td>10710887</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114347</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-6.40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:15:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17780</th>\n",
+       "      <td>10710853</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>18.30</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20838</th>\n",
+       "      <td>10710874</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114347</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:15:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20839</th>\n",
+       "      <td>10711073</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114347</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.20</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:15:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20840</th>\n",
+       "      <td>10710862</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17607</th>\n",
+       "      <td>10710949</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17609</th>\n",
+       "      <td>10711008</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114347</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:15:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23668</th>\n",
+       "      <td>10711085</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.20</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23669</th>\n",
+       "      <td>10711021</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>96.50</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23670</th>\n",
+       "      <td>10710858</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>143.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24079</th>\n",
+       "      <td>10711064</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23676</th>\n",
+       "      <td>10710840</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23678</th>\n",
+       "      <td>10710821</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23679</th>\n",
+       "      <td>10711013</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.07</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27784</th>\n",
+       "      <td>10710786</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23680</th>\n",
+       "      <td>10710910</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-8.30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23682</th>\n",
+       "      <td>10710935</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>17.00</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23683</th>\n",
+       "      <td>10710777</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>138.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23687</th>\n",
+       "      <td>10710814</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114346</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 13:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23688</th>\n",
+       "      <td>10710967</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23690</th>\n",
+       "      <td>10710987</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114357</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 08:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23691</th>\n",
+       "      <td>10710827</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>29.20</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23697</th>\n",
+       "      <td>10711052</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>37.70</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23698</th>\n",
+       "      <td>10710848</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.60</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23701</th>\n",
+       "      <td>10710841</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>194.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23702</th>\n",
+       "      <td>10711070</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>109.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17916</th>\n",
+       "      <td>10710900</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114357</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 08:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17917</th>\n",
+       "      <td>10710916</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23681</th>\n",
+       "      <td>10710894</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20834</th>\n",
+       "      <td>10710970</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.26</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27790</th>\n",
+       "      <td>10710925</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27792</th>\n",
+       "      <td>10711078</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>14.00</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2418</th>\n",
+       "      <td>10711023</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.38</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26497</th>\n",
+       "      <td>10710969</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26547</th>\n",
+       "      <td>10711038</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26548</th>\n",
+       "      <td>10711024</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114347</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:15:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26549</th>\n",
+       "      <td>10710988</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-2.80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26550</th>\n",
+       "      <td>10710964</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>64.20</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26554</th>\n",
+       "      <td>10711091</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.39</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26555</th>\n",
+       "      <td>10710809</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26556</th>\n",
+       "      <td>10710977</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114346</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 13:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26557</th>\n",
+       "      <td>10710947</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26558</th>\n",
+       "      <td>10710784</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.28</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26559</th>\n",
+       "      <td>10711063</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>28.80</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26560</th>\n",
+       "      <td>10711002</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>96.50</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26565</th>\n",
+       "      <td>10711043</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>23.60</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26567</th>\n",
+       "      <td>10711066</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-2.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26569</th>\n",
+       "      <td>10711051</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>67.20</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High Panic</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26571</th>\n",
+       "      <td>10710794</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114357</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 08:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26575</th>\n",
+       "      <td>10711058</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120600</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.50</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 09:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26580</th>\n",
+       "      <td>10710928</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15562</th>\n",
+       "      <td>10710833</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19869</th>\n",
+       "      <td>10711061</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>108.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27322</th>\n",
+       "      <td>10711041</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>115.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15351</th>\n",
+       "      <td>10710896</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>15.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24075</th>\n",
+       "      <td>10710837</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24078</th>\n",
+       "      <td>10710789</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2412</th>\n",
+       "      <td>10711057</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>26.60</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2411</th>\n",
+       "      <td>10710989</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>144.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2410</th>\n",
+       "      <td>10710930</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114346</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>111.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 13:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2409</th>\n",
+       "      <td>10710822</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>342.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27793</th>\n",
+       "      <td>10711032</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27795</th>\n",
+       "      <td>10710881</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120602</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>37.50</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 12:57:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27796</th>\n",
+       "      <td>10710898</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120603</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>107.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 18:00:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27798</th>\n",
+       "      <td>10710770</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114354</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>289.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 17:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23945</th>\n",
+       "      <td>10710961</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114348</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 21:56:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23947</th>\n",
+       "      <td>10710842</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114353</td>\n",
+       "      <td>16678</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Performing Lab</td>\n",
+       "      <td>PERFLAB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 16:11:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2372</th>\n",
+       "      <td>10711086</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114357</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>23.20</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 08:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2373</th>\n",
+       "      <td>10710912</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114355</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>19.70</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 15:16:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2375</th>\n",
+       "      <td>10710855</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>24.60</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2378</th>\n",
+       "      <td>10710761</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728114356</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 14:01:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2379</th>\n",
+       "      <td>10710905</td>\n",
+       "      <td>JC1149036</td>\n",
+       "      <td>316387272</td>\n",
+       "      <td>728120601</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>111.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2017-04-30 06:27:00</td>\n",
+       "      <td>2017-04-30 10:51:00</td>\n",
+       "      <td>2017-04-29 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201254</td>\n",
+       "      <td>Cardiology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>CV Critical Care Level 2</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>3045203</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            uid    anon_id pat_enc_csn_id_coded order_id_coded  component_id  \\\n",
+       "895    10710857  JC1149036            316387272      728120600    1230100843   \n",
+       "17788  10710851  JC1149036            316387272      728120601    1230100841   \n",
+       "17789  10711009  JC1149036            316387272      728114353    1230100838   \n",
+       "17793  10711083  JC1149036            316387272      728114353    1230100705   \n",
+       "17794  10710804  JC1149036            316387272      728114356    1230100840   \n",
+       "17796  10710971  JC1149036            316387272      728120603    1230100839   \n",
+       "17797  10711007  JC1149036            316387272      728114354    1230100849   \n",
+       "7032   10710921  JC1149036            316387272      728114355    1230100701   \n",
+       "11215  10710798  JC1149036            316387272      728120601    1230100703   \n",
+       "11216  10711054  JC1149036            316387272      728114348    1230100703   \n",
+       "14783  10710805  JC1149036            316387272      728114346         16678   \n",
+       "27761  10710966  JC1149036            316387272      728114353    1230100849   \n",
+       "27762  10711079  JC1149036            316387272      728114346    1230100841   \n",
+       "27763  10710780  JC1149036            316387272      728120601    1230100846   \n",
+       "27764  10710834  JC1149036            316387272      728114348    1230100849   \n",
+       "27766  10710982  JC1149036            316387272      728114356    1230100703   \n",
+       "27767  10711055  JC1149036            316387272      728114356    1230100839   \n",
+       "27771  10711000  JC1149036            316387272      728120602    1230100849   \n",
+       "27772  10711068  JC1149036            316387272      728114346    1230100704   \n",
+       "27773  10710936  JC1149036            316387272      728114354    1230100850   \n",
+       "27775  10710950  JC1149036            316387272      728114356    1230100847   \n",
+       "27776  10711074  JC1149036            316387272      728114354    1230100843   \n",
+       "27778  10710852  JC1149036            316387272      728114347    1230100846   \n",
+       "27780  10710797  JC1149036            316387272      728114348    1230100847   \n",
+       "27782  10710891  JC1149036            316387272      728114357    1230100703   \n",
+       "27783  10710959  JC1149036            316387272      728120601    1230100847   \n",
+       "17784  10710768  JC1149036            316387272      728120601    1230100838   \n",
+       "17782  10710886  JC1149036            316387272      728114355    1230100840   \n",
+       "17781  10710887  JC1149036            316387272      728114347    1230100843   \n",
+       "17780  10710853  JC1149036            316387272      728120602    1230100842   \n",
+       "20838  10710874  JC1149036            316387272      728114347    1230100847   \n",
+       "20839  10711073  JC1149036            316387272      728114347    1230100845   \n",
+       "20840  10710862  JC1149036            316387272      728114355    1230100843   \n",
+       "17607  10710949  JC1149036            316387272      728114354         16678   \n",
+       "17609  10711008  JC1149036            316387272      728114347         16678   \n",
+       "23668  10711085  JC1149036            316387272      728114355    1230100838   \n",
+       "23669  10711021  JC1149036            316387272      728114348    1230100845   \n",
+       "23670  10710858  JC1149036            316387272      728114356    1230100700   \n",
+       "24079  10711064  JC1149036            316387272      728114356         16678   \n",
+       "23676  10710840  JC1149036            316387272      728120602    1230100701   \n",
+       "23678  10710821  JC1149036            316387272      728120601    1230100850   \n",
+       "23679  10711013  JC1149036            316387272      728120603    1230100702   \n",
+       "27784  10710786  JC1149036            316387272      728114354    1230100703   \n",
+       "23680  10710910  JC1149036            316387272      728120602    1230100843   \n",
+       "23682  10710935  JC1149036            316387272      728120600    1230100849   \n",
+       "23683  10710777  JC1149036            316387272      728120601    1230100700   \n",
+       "23687  10710814  JC1149036            316387272      728114346    1230100705   \n",
+       "23688  10710967  JC1149036            316387272      728120601    1230100701   \n",
+       "23690  10710987  JC1149036            316387272      728114357    1230100705   \n",
+       "23691  10710827  JC1149036            316387272      728114353    1230100842   \n",
+       "23697  10711052  JC1149036            316387272      728120600    1230100839   \n",
+       "23698  10710848  JC1149036            316387272      728120602    1230100845   \n",
+       "23701  10710841  JC1149036            316387272      728114354    1230100704   \n",
+       "23702  10711070  JC1149036            316387272      728120600    1230100703   \n",
+       "17916  10710900  JC1149036            316387272      728114357         16678   \n",
+       "17917  10710916  JC1149036            316387272      728120601         16678   \n",
+       "23681  10710894  JC1149036            316387272      728120603    1230100847   \n",
+       "20834  10710970  JC1149036            316387272      728120600    1230100702   \n",
+       "27790  10710925  JC1149036            316387272      728114354    1230100701   \n",
+       "27792  10711078  JC1149036            316387272      728114355    1230100846   \n",
+       "2418   10711023  JC1149036            316387272      728114348    1230100838   \n",
+       "26497  10710969  JC1149036            316387272      728120600         16678   \n",
+       "26547  10711038  JC1149036            316387272      728120602    1230100850   \n",
+       "26548  10711024  JC1149036            316387272      728114347    1230100702   \n",
+       "26549  10710988  JC1149036            316387272      728120601    1230100843   \n",
+       "26550  10710964  JC1149036            316387272      728114354    1230100839   \n",
+       "26554  10711091  JC1149036            316387272      728120600    1230100838   \n",
+       "26555  10710809  JC1149036            316387272      728114356    1230100705   \n",
+       "26556  10710977  JC1149036            316387272      728114346    1230100702   \n",
+       "26557  10710947  JC1149036            316387272      728114354    1230100702   \n",
+       "26558  10710784  JC1149036            316387272      728120602    1230100838   \n",
+       "26559  10711063  JC1149036            316387272      728114355    1230100842   \n",
+       "26560  10711002  JC1149036            316387272      728120603    1230100845   \n",
+       "26565  10711043  JC1149036            316387272      728114348    1230100842   \n",
+       "26567  10711066  JC1149036            316387272      728114356    1230100843   \n",
+       "26569  10711051  JC1149036            316387272      728114353    1230100839   \n",
+       "26571  10710794  JC1149036            316387272      728114357    1230100702   \n",
+       "26575  10711058  JC1149036            316387272      728120600    1230100845   \n",
+       "26580  10710928  JC1149036            316387272      728120603    1230100705   \n",
+       "15562  10710833  JC1149036            316387272      728114348    1230100701   \n",
+       "19869  10711061  JC1149036            316387272      728114355    1230100703   \n",
+       "27322  10711041  JC1149036            316387272      728120602    1230100704   \n",
+       "15351  10710896  JC1149036            316387272      728120602    1230100846   \n",
+       "24075  10710837  JC1149036            316387272      728120602         16678   \n",
+       "24078  10710789  JC1149036            316387272      728114355         16678   \n",
+       "2412   10711057  JC1149036            316387272      728120603    1230100841   \n",
+       "2411   10710989  JC1149036            316387272      728114348    1230100700   \n",
+       "2410   10710930  JC1149036            316387272      728114346    1230100703   \n",
+       "2409   10710822  JC1149036            316387272      728114353    1230100840   \n",
+       "27793  10711032  JC1149036            316387272      728114354    1230100700   \n",
+       "27795  10710881  JC1149036            316387272      728120602    1230100839   \n",
+       "27796  10710898  JC1149036            316387272      728120603    1230100703   \n",
+       "27798  10710770  JC1149036            316387272      728114354    1230100840   \n",
+       "23945  10710961  JC1149036            316387272      728114348         16678   \n",
+       "23947  10710842  JC1149036            316387272      728114353         16678   \n",
+       "2372   10711086  JC1149036            316387272      728114357    1230100841   \n",
+       "2373   10710912  JC1149036            316387272      728114355    1230100849   \n",
+       "2375   10710855  JC1149036            316387272      728114356    1230100841   \n",
+       "2378   10710761  JC1149036            316387272      728114356    1230100701   \n",
+       "2379   10710905  JC1149036            316387272      728120601    1230100704   \n",
+       "\n",
+       "      order_type proc_code         group_lab_name              lab_name  \\\n",
+       "895          Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "17788        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "17789        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "17793        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "17794        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "17796        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "17797        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "7032         Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "11215        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "11216        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "14783        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "27761        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "27762        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "27763        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "27764        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "27766        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27767        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "27771        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "27772        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "27773        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "27775        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "27776        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "27778        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "27780        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "27782        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27783        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "17784        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "17782        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "17781        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "17780        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "20838        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "20839        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "20840        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "17607        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "17609        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "23668        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "23669        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "23670        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "24079        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "23676        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "23678        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "23679        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "27784        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "23680        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "23682        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "23683        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23687        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "23688        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "23690        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "23691        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "23697        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "23698        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "23701        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "23702        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "17916        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "17917        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "23681        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "20834        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "27790        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "27792        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "2418         Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "26497        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "26547        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "26548        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "26549        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "26550        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "26554        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "26555        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "26556        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "26557        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "26558        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "26559        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "26560        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "26565        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "26567        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "26569        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "26571        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "26575        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "26580        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "15562        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "19869        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27322        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "15351        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "24075        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "24078        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "2412         Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "2411         Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "2410         Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "2409         Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "27793        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "27795        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "27796        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "27798        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "23945        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "23947        Lab     LAB76  Blood Gases, Arterial        Performing Lab   \n",
+       "2372         Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "2373         Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "2375         Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "2378         Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "2379         Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "\n",
+       "      base_name  ord_num_value reference_low reference_high reference_unit  \\\n",
+       "895          BE          -1.90           0.0            0.0         mmol/L   \n",
+       "17788     HCO3A          21.50          22.0           26.0         mmol/L   \n",
+       "17789       PHA           7.23          7.35           7.45           None   \n",
+       "17793     METHB           1.30          None           None              %   \n",
+       "17794      PO2A         255.00          80.0          105.0           mmHg   \n",
+       "17796     PCO2A          63.40          35.0           45.0           mmHg   \n",
+       "17797     CTO2A          17.20          15.0           25.0          mL/dL   \n",
+       "7032          K           3.50           3.5            6.0         mmol/L   \n",
+       "11215        CL         112.00            95            110         mmol/L   \n",
+       "11216        CL         111.00            95            110         mmol/L   \n",
+       "14783   PERFLAB     9999999.00          None           None           None   \n",
+       "27761     CTO2A          18.20          15.0           25.0          mL/dL   \n",
+       "27762     HCO3A          21.00          22.0           26.0         mmol/L   \n",
+       "27763       HGB          13.40          10.0           14.0           g/dL   \n",
+       "27764     CTO2A          24.80          15.0           25.0          mL/dL   \n",
+       "27766        CL         108.00            95            110         mmol/L   \n",
+       "27767     PCO2A          61.20          35.0           45.0           mmHg   \n",
+       "27771     CTO2A          21.70          15.0           25.0          mL/dL   \n",
+       "27772       GLU         110.00            45            200          mg/dL   \n",
+       "27773      COHB           1.40          None           None              %   \n",
+       "27775       HCT          42.00            30             42              %   \n",
+       "27776        BE           0.60           0.0            0.0         mmol/L   \n",
+       "27778       HGB          16.50          10.0           14.0           g/dL   \n",
+       "27780       HCT          54.00            30             42              %   \n",
+       "27782        CL         110.00            95            110         mmol/L   \n",
+       "27783       HCT          41.00            30             42              %   \n",
+       "17784       PHA           7.38          7.35           7.45           None   \n",
+       "17782      PO2A         261.00          80.0          105.0           mmHg   \n",
+       "17781        BE          -6.40           0.0            0.0         mmol/L   \n",
+       "17780     TCO2A          18.30          23.0           27.0         mmol/L   \n",
+       "20838       HCT          50.00            30             42              %   \n",
+       "20839    O2SATA          97.20          95.0          100.0              %   \n",
+       "20840        BE          -0.50           0.0            0.0         mmol/L   \n",
+       "17607   PERFLAB     9999999.00          None           None           None   \n",
+       "17609   PERFLAB     9999999.00          None           None           None   \n",
+       "23668       PHA           7.20          7.35           7.45           None   \n",
+       "23669    O2SATA          96.50          95.0          100.0              %   \n",
+       "23670        NA         143.00           135            145         mmol/L   \n",
+       "24079   PERFLAB     9999999.00          None           None           None   \n",
+       "23676         K           3.00           3.5            6.0         mmol/L   \n",
+       "23678      COHB           1.20          None           None              %   \n",
+       "23679     CAION           1.07          1.12           1.32         mmol/L   \n",
+       "27784        CL         105.00            95            110         mmol/L   \n",
+       "23680        BE          -8.30           0.0            0.0         mmol/L   \n",
+       "23682     CTO2A          17.00          15.0           25.0          mL/dL   \n",
+       "23683        NA         138.00           135            145         mmol/L   \n",
+       "23687     METHB     9999999.00          None           None              %   \n",
+       "23688         K           3.40           3.5            6.0         mmol/L   \n",
+       "23690     METHB     9999999.00          None           None              %   \n",
+       "23691     TCO2A          29.20          23.0           27.0         mmol/L   \n",
+       "23697     PCO2A          37.70          35.0           45.0           mmHg   \n",
+       "23698    O2SATA          97.60          95.0          100.0              %   \n",
+       "23701       GLU         194.00            45            200          mg/dL   \n",
+       "23702        CL         109.00            95            110         mmol/L   \n",
+       "17916   PERFLAB     9999999.00          None           None           None   \n",
+       "17917   PERFLAB     9999999.00          None           None           None   \n",
+       "23681       HCT          40.00            30             42              %   \n",
+       "20834     CAION           1.26          1.12           1.32         mmol/L   \n",
+       "27790         K           3.20           3.5            6.0         mmol/L   \n",
+       "27792       HGB          14.00          10.0           14.0           g/dL   \n",
+       "2418        PHA           7.38          7.35           7.45           None   \n",
+       "26497   PERFLAB     9999999.00          None           None           None   \n",
+       "26547      COHB     9999999.00          None           None              %   \n",
+       "26548     CAION           1.25          1.12           1.32         mmol/L   \n",
+       "26549        BE          -2.80           0.0            0.0         mmol/L   \n",
+       "26550     PCO2A          64.20          35.0           45.0           mmHg   \n",
+       "26554       PHA           7.39          7.35           7.45           None   \n",
+       "26555     METHB           1.10          None           None              %   \n",
+       "26556     CAION           1.32          1.12           1.32         mmol/L   \n",
+       "26557     CAION           1.00          1.12           1.32         mmol/L   \n",
+       "26558       PHA           7.28          7.35           7.45           None   \n",
+       "26559     TCO2A          28.80          23.0           27.0         mmol/L   \n",
+       "26560    O2SATA          96.50          95.0          100.0              %   \n",
+       "26565     TCO2A          23.60          23.0           27.0         mmol/L   \n",
+       "26567        BE          -2.00           0.0            0.0         mmol/L   \n",
+       "26569     PCO2A          67.20          35.0           45.0           mmHg   \n",
+       "26571     CAION           1.28          1.12           1.32         mmol/L   \n",
+       "26575    O2SATA          97.50          95.0          100.0              %   \n",
+       "26580     METHB           1.10          None           None              %   \n",
+       "15562         K           3.20           3.5            6.0         mmol/L   \n",
+       "19869        CL         108.00            95            110         mmol/L   \n",
+       "27322       GLU         115.00            45            200          mg/dL   \n",
+       "15351       HGB          15.40          10.0           14.0           g/dL   \n",
+       "24075   PERFLAB     9999999.00          None           None           None   \n",
+       "24078   PERFLAB     9999999.00          None           None           None   \n",
+       "2412      HCO3A          26.60          22.0           26.0         mmol/L   \n",
+       "2411         NA         144.00           135            145         mmol/L   \n",
+       "2410         CL         111.00            95            110         mmol/L   \n",
+       "2409       PO2A         342.00          80.0          105.0           mmHg   \n",
+       "27793        NA         142.00           135            145         mmol/L   \n",
+       "27795     PCO2A          37.50          35.0           45.0           mmHg   \n",
+       "27796        CL         107.00            95            110         mmol/L   \n",
+       "27798      PO2A         289.00          80.0          105.0           mmHg   \n",
+       "23945   PERFLAB     9999999.00          None           None           None   \n",
+       "23947   PERFLAB     9999999.00          None           None           None   \n",
+       "2372      HCO3A          23.20          22.0           26.0         mmol/L   \n",
+       "2373      CTO2A          19.70          15.0           25.0          mL/dL   \n",
+       "2375      HCO3A          24.60          22.0           26.0         mmol/L   \n",
+       "2378          K           3.00           3.5            6.0         mmol/L   \n",
+       "2379        GLU         111.00            45            200          mg/dL   \n",
+       "\n",
+       "      result_in_range_yn result_flag          order_time         result_time  \\\n",
+       "895                 None         Low 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "17788               None         Low 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "17789               None         Low 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "17793               None        None 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "17794               None        High 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "17796               None        High 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "17797                  Y        None 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "7032                   Y        None 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "11215               None        High 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "11216               None        High 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "14783               None        None 2017-04-30 06:27:00 2017-04-30 13:11:00   \n",
+       "27761                  Y        None 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "27762               None         Low 2017-04-30 06:27:00 2017-04-30 13:11:00   \n",
+       "27763                  Y        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "27764                  Y        None 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "27766                  Y        None 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "27767               None        High 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "27771                  Y        None 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "27772                  Y        None 2017-04-30 06:27:00 2017-04-30 13:11:00   \n",
+       "27773               None        None 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "27775                  Y        None 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "27776               None        High 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "27778               None        High 2017-04-30 06:27:00 2017-04-30 12:15:00   \n",
+       "27780               None        High 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "27782                  Y        None 2017-04-30 06:27:00 2017-04-30 08:51:00   \n",
+       "27783                  Y        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "17784                  Y        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "17782               None        High 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "17781               None         Low 2017-04-30 06:27:00 2017-04-30 12:15:00   \n",
+       "17780               None         Low 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "20838               None        High 2017-04-30 06:27:00 2017-04-30 12:15:00   \n",
+       "20839                  Y        None 2017-04-30 06:27:00 2017-04-30 12:15:00   \n",
+       "20840               None         Low 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "17607               None        None 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "17609               None        None 2017-04-30 06:27:00 2017-04-30 12:15:00   \n",
+       "23668               None         Low 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "23669                  Y        None 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "23670                  Y        None 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "24079               None        None 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "23676               None         Low 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "23678               None        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "23679               None         Low 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "27784                  Y        None 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "23680               None         Low 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "23682                  Y        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "23683                  Y        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "23687               None        None 2017-04-30 06:27:00 2017-04-30 13:11:00   \n",
+       "23688               None         Low 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "23690               None        None 2017-04-30 06:27:00 2017-04-30 08:51:00   \n",
+       "23691               None        High 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "23697                  Y        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "23698                  Y        None 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "23701                  Y        None 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "23702                  Y        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "17916               None        None 2017-04-30 06:27:00 2017-04-30 08:51:00   \n",
+       "17917               None        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "23681                  Y        None 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "20834                  Y        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "27790               None         Low 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "27792                  Y        None 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "2418                   Y        None 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "26497               None        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "26547               None        None 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "26548                  Y        None 2017-04-30 06:27:00 2017-04-30 12:15:00   \n",
+       "26549               None         Low 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "26550               None        High 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "26554                  Y        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "26555               None        None 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "26556                  Y        None 2017-04-30 06:27:00 2017-04-30 13:11:00   \n",
+       "26557               None         Low 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "26558               None         Low 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "26559               None        High 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "26560                  Y        None 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "26565                  Y        None 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "26567               None         Low 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "26569               None  High Panic 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "26571                  Y        None 2017-04-30 06:27:00 2017-04-30 08:51:00   \n",
+       "26575                  Y        None 2017-04-30 06:27:00 2017-04-30 09:51:00   \n",
+       "26580               None        None 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "15562               None         Low 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "19869                  Y        None 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "27322                  Y        None 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "15351               None        High 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "24075               None        None 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "24078               None        None 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "2412                None        High 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "2411                   Y        None 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "2410                None        High 2017-04-30 06:27:00 2017-04-30 13:11:00   \n",
+       "2409                None        High 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "27793                  Y        None 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "27795                  Y        None 2017-04-30 06:27:00 2017-04-30 12:57:00   \n",
+       "27796                  Y        None 2017-04-30 06:27:00 2017-04-30 18:00:00   \n",
+       "27798               None        High 2017-04-30 06:27:00 2017-04-30 17:01:00   \n",
+       "23945               None        None 2017-04-30 06:27:00 2017-04-30 21:56:00   \n",
+       "23947               None        None 2017-04-30 06:27:00 2017-04-30 16:11:00   \n",
+       "2372                   Y        None 2017-04-30 06:27:00 2017-04-30 08:51:00   \n",
+       "2373                   Y        None 2017-04-30 06:27:00 2017-04-30 15:16:00   \n",
+       "2375                   Y        None 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "2378                None         Low 2017-04-30 06:27:00 2017-04-30 14:01:00   \n",
+       "2379                   Y        None 2017-04-30 06:27:00 2017-04-30 10:51:00   \n",
+       "\n",
+       "      effective_time_jittered  pat_class  department_id pat_service  \\\n",
+       "895       2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17788     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17789     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17793     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17794     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17796     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17797     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "7032      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "11215     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "11216     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "14783     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27761     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27762     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27763     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27764     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27766     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27767     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27771     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27772     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27773     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27775     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27776     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27778     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27780     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27782     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27783     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17784     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17782     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17781     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17780     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "20838     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "20839     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "20840     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17607     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17609     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23668     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23669     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23670     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "24079     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23676     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23678     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23679     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27784     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23680     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23682     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23683     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23687     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23688     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23690     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23691     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23697     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23698     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23701     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23702     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17916     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "17917     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23681     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "20834     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27790     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27792     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2418      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26497     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26547     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26548     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26549     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26550     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26554     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26555     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26556     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26557     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26558     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26559     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26560     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26565     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26567     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26569     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26571     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26575     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "26580     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "15562     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "19869     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27322     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "15351     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "24075     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "24078     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2412      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2411      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2410      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2409      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27793     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27795     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27796     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "27798     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23945     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "23947     2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2372      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2373      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2375      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2378      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "2379      2017-04-29 23:59:00  Inpatient       10201254  Cardiology   \n",
+       "\n",
+       "      pat_lv_of_care              accomodation from_base_class to_base_class  \\\n",
+       "895       Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17788     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17789     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17793     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17794     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17796     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17797     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "7032      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "11215     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "11216     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "14783     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27761     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27762     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27763     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27764     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27766     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27767     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27771     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27772     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27773     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27775     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27776     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27778     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27780     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27782     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27783     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17784     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17782     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17781     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17780     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "20838     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "20839     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "20840     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17607     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17609     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23668     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23669     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23670     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "24079     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23676     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23678     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23679     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27784     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23680     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23682     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23683     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23687     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23688     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23690     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23691     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23697     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23698     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23701     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23702     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17916     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "17917     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23681     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "20834     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27790     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27792     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2418      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26497     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26547     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26548     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26549     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26550     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26554     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26555     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26556     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26557     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26558     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26559     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26560     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26565     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26567     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26569     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26571     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26575     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "26580     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "15562     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "19869     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27322     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "15351     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "24075     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "24078     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2412      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2411      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2410      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2409      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27793     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27795     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27796     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "27798     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23945     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "23947     Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2372      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2373      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2375      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2378      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "2379      Acute Care  CV Critical Care Level 2       Inpatient     Inpatient   \n",
+       "\n",
+       "      in_event_type out_event_type  event_id_coded event_type  \\\n",
+       "895          Census         Census         3045203     Census   \n",
+       "17788        Census         Census         3045203     Census   \n",
+       "17789        Census         Census         3045203     Census   \n",
+       "17793        Census         Census         3045203     Census   \n",
+       "17794        Census         Census         3045203     Census   \n",
+       "17796        Census         Census         3045203     Census   \n",
+       "17797        Census         Census         3045203     Census   \n",
+       "7032         Census         Census         3045203     Census   \n",
+       "11215        Census         Census         3045203     Census   \n",
+       "11216        Census         Census         3045203     Census   \n",
+       "14783        Census         Census         3045203     Census   \n",
+       "27761        Census         Census         3045203     Census   \n",
+       "27762        Census         Census         3045203     Census   \n",
+       "27763        Census         Census         3045203     Census   \n",
+       "27764        Census         Census         3045203     Census   \n",
+       "27766        Census         Census         3045203     Census   \n",
+       "27767        Census         Census         3045203     Census   \n",
+       "27771        Census         Census         3045203     Census   \n",
+       "27772        Census         Census         3045203     Census   \n",
+       "27773        Census         Census         3045203     Census   \n",
+       "27775        Census         Census         3045203     Census   \n",
+       "27776        Census         Census         3045203     Census   \n",
+       "27778        Census         Census         3045203     Census   \n",
+       "27780        Census         Census         3045203     Census   \n",
+       "27782        Census         Census         3045203     Census   \n",
+       "27783        Census         Census         3045203     Census   \n",
+       "17784        Census         Census         3045203     Census   \n",
+       "17782        Census         Census         3045203     Census   \n",
+       "17781        Census         Census         3045203     Census   \n",
+       "17780        Census         Census         3045203     Census   \n",
+       "20838        Census         Census         3045203     Census   \n",
+       "20839        Census         Census         3045203     Census   \n",
+       "20840        Census         Census         3045203     Census   \n",
+       "17607        Census         Census         3045203     Census   \n",
+       "17609        Census         Census         3045203     Census   \n",
+       "23668        Census         Census         3045203     Census   \n",
+       "23669        Census         Census         3045203     Census   \n",
+       "23670        Census         Census         3045203     Census   \n",
+       "24079        Census         Census         3045203     Census   \n",
+       "23676        Census         Census         3045203     Census   \n",
+       "23678        Census         Census         3045203     Census   \n",
+       "23679        Census         Census         3045203     Census   \n",
+       "27784        Census         Census         3045203     Census   \n",
+       "23680        Census         Census         3045203     Census   \n",
+       "23682        Census         Census         3045203     Census   \n",
+       "23683        Census         Census         3045203     Census   \n",
+       "23687        Census         Census         3045203     Census   \n",
+       "23688        Census         Census         3045203     Census   \n",
+       "23690        Census         Census         3045203     Census   \n",
+       "23691        Census         Census         3045203     Census   \n",
+       "23697        Census         Census         3045203     Census   \n",
+       "23698        Census         Census         3045203     Census   \n",
+       "23701        Census         Census         3045203     Census   \n",
+       "23702        Census         Census         3045203     Census   \n",
+       "17916        Census         Census         3045203     Census   \n",
+       "17917        Census         Census         3045203     Census   \n",
+       "23681        Census         Census         3045203     Census   \n",
+       "20834        Census         Census         3045203     Census   \n",
+       "27790        Census         Census         3045203     Census   \n",
+       "27792        Census         Census         3045203     Census   \n",
+       "2418         Census         Census         3045203     Census   \n",
+       "26497        Census         Census         3045203     Census   \n",
+       "26547        Census         Census         3045203     Census   \n",
+       "26548        Census         Census         3045203     Census   \n",
+       "26549        Census         Census         3045203     Census   \n",
+       "26550        Census         Census         3045203     Census   \n",
+       "26554        Census         Census         3045203     Census   \n",
+       "26555        Census         Census         3045203     Census   \n",
+       "26556        Census         Census         3045203     Census   \n",
+       "26557        Census         Census         3045203     Census   \n",
+       "26558        Census         Census         3045203     Census   \n",
+       "26559        Census         Census         3045203     Census   \n",
+       "26560        Census         Census         3045203     Census   \n",
+       "26565        Census         Census         3045203     Census   \n",
+       "26567        Census         Census         3045203     Census   \n",
+       "26569        Census         Census         3045203     Census   \n",
+       "26571        Census         Census         3045203     Census   \n",
+       "26575        Census         Census         3045203     Census   \n",
+       "26580        Census         Census         3045203     Census   \n",
+       "15562        Census         Census         3045203     Census   \n",
+       "19869        Census         Census         3045203     Census   \n",
+       "27322        Census         Census         3045203     Census   \n",
+       "15351        Census         Census         3045203     Census   \n",
+       "24075        Census         Census         3045203     Census   \n",
+       "24078        Census         Census         3045203     Census   \n",
+       "2412         Census         Census         3045203     Census   \n",
+       "2411         Census         Census         3045203     Census   \n",
+       "2410         Census         Census         3045203     Census   \n",
+       "2409         Census         Census         3045203     Census   \n",
+       "27793        Census         Census         3045203     Census   \n",
+       "27795        Census         Census         3045203     Census   \n",
+       "27796        Census         Census         3045203     Census   \n",
+       "27798        Census         Census         3045203     Census   \n",
+       "23945        Census         Census         3045203     Census   \n",
+       "23947        Census         Census         3045203     Census   \n",
+       "2372         Census         Census         3045203     Census   \n",
+       "2373         Census         Census         3045203     Census   \n",
+       "2375         Census         Census         3045203     Census   \n",
+       "2378         Census         Census         3045203     Census   \n",
+       "2379         Census         Census         3045203     Census   \n",
+       "\n",
+       "                 njr_labname  \n",
+       "895    Blood Gases, Arterial  \n",
+       "17788  Blood Gases, Arterial  \n",
+       "17789  Blood Gases, Arterial  \n",
+       "17793  Blood Gases, Arterial  \n",
+       "17794  Blood Gases, Arterial  \n",
+       "17796  Blood Gases, Arterial  \n",
+       "17797  Blood Gases, Arterial  \n",
+       "7032   Blood Gases, Arterial  \n",
+       "11215  Blood Gases, Arterial  \n",
+       "11216  Blood Gases, Arterial  \n",
+       "14783  Blood Gases, Arterial  \n",
+       "27761  Blood Gases, Arterial  \n",
+       "27762  Blood Gases, Arterial  \n",
+       "27763  Blood Gases, Arterial  \n",
+       "27764  Blood Gases, Arterial  \n",
+       "27766  Blood Gases, Arterial  \n",
+       "27767  Blood Gases, Arterial  \n",
+       "27771  Blood Gases, Arterial  \n",
+       "27772  Blood Gases, Arterial  \n",
+       "27773  Blood Gases, Arterial  \n",
+       "27775  Blood Gases, Arterial  \n",
+       "27776  Blood Gases, Arterial  \n",
+       "27778  Blood Gases, Arterial  \n",
+       "27780  Blood Gases, Arterial  \n",
+       "27782  Blood Gases, Arterial  \n",
+       "27783  Blood Gases, Arterial  \n",
+       "17784  Blood Gases, Arterial  \n",
+       "17782  Blood Gases, Arterial  \n",
+       "17781  Blood Gases, Arterial  \n",
+       "17780  Blood Gases, Arterial  \n",
+       "20838  Blood Gases, Arterial  \n",
+       "20839  Blood Gases, Arterial  \n",
+       "20840  Blood Gases, Arterial  \n",
+       "17607  Blood Gases, Arterial  \n",
+       "17609  Blood Gases, Arterial  \n",
+       "23668  Blood Gases, Arterial  \n",
+       "23669  Blood Gases, Arterial  \n",
+       "23670  Blood Gases, Arterial  \n",
+       "24079  Blood Gases, Arterial  \n",
+       "23676  Blood Gases, Arterial  \n",
+       "23678  Blood Gases, Arterial  \n",
+       "23679  Blood Gases, Arterial  \n",
+       "27784  Blood Gases, Arterial  \n",
+       "23680  Blood Gases, Arterial  \n",
+       "23682  Blood Gases, Arterial  \n",
+       "23683  Blood Gases, Arterial  \n",
+       "23687  Blood Gases, Arterial  \n",
+       "23688  Blood Gases, Arterial  \n",
+       "23690  Blood Gases, Arterial  \n",
+       "23691  Blood Gases, Arterial  \n",
+       "23697  Blood Gases, Arterial  \n",
+       "23698  Blood Gases, Arterial  \n",
+       "23701  Blood Gases, Arterial  \n",
+       "23702  Blood Gases, Arterial  \n",
+       "17916  Blood Gases, Arterial  \n",
+       "17917  Blood Gases, Arterial  \n",
+       "23681  Blood Gases, Arterial  \n",
+       "20834  Blood Gases, Arterial  \n",
+       "27790  Blood Gases, Arterial  \n",
+       "27792  Blood Gases, Arterial  \n",
+       "2418   Blood Gases, Arterial  \n",
+       "26497  Blood Gases, Arterial  \n",
+       "26547  Blood Gases, Arterial  \n",
+       "26548  Blood Gases, Arterial  \n",
+       "26549  Blood Gases, Arterial  \n",
+       "26550  Blood Gases, Arterial  \n",
+       "26554  Blood Gases, Arterial  \n",
+       "26555  Blood Gases, Arterial  \n",
+       "26556  Blood Gases, Arterial  \n",
+       "26557  Blood Gases, Arterial  \n",
+       "26558  Blood Gases, Arterial  \n",
+       "26559  Blood Gases, Arterial  \n",
+       "26560  Blood Gases, Arterial  \n",
+       "26565  Blood Gases, Arterial  \n",
+       "26567  Blood Gases, Arterial  \n",
+       "26569  Blood Gases, Arterial  \n",
+       "26571  Blood Gases, Arterial  \n",
+       "26575  Blood Gases, Arterial  \n",
+       "26580  Blood Gases, Arterial  \n",
+       "15562  Blood Gases, Arterial  \n",
+       "19869  Blood Gases, Arterial  \n",
+       "27322  Blood Gases, Arterial  \n",
+       "15351  Blood Gases, Arterial  \n",
+       "24075  Blood Gases, Arterial  \n",
+       "24078  Blood Gases, Arterial  \n",
+       "2412   Blood Gases, Arterial  \n",
+       "2411   Blood Gases, Arterial  \n",
+       "2410   Blood Gases, Arterial  \n",
+       "2409   Blood Gases, Arterial  \n",
+       "27793  Blood Gases, Arterial  \n",
+       "27795  Blood Gases, Arterial  \n",
+       "27796  Blood Gases, Arterial  \n",
+       "27798  Blood Gases, Arterial  \n",
+       "23945  Blood Gases, Arterial  \n",
+       "23947  Blood Gases, Arterial  \n",
+       "2372   Blood Gases, Arterial  \n",
+       "2373   Blood Gases, Arterial  \n",
+       "2375   Blood Gases, Arterial  \n",
+       "2378   Blood Gases, Arterial  \n",
+       "2379   Blood Gases, Arterial  "
+      ]
+     },
+     "execution_count": 478,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.loc[(lradt.pat_enc_csn_id_coded==316387272) & (lradt.group_lab_name=='Blood Gases, Arterial')].sort_values(by='order_time').head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 479,
+   "id": "a914e218",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_low</th>\n",
+       "      <th>reference_high</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result_flag</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>njr_labname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>24937</th>\n",
+       "      <td>4783907</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964265</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:35:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13569</th>\n",
+       "      <td>4784057</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13576</th>\n",
+       "      <td>4784075</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>13.30</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13579</th>\n",
+       "      <td>4784021</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13584</th>\n",
+       "      <td>4784002</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.46</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13587</th>\n",
+       "      <td>4784153</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13588</th>\n",
+       "      <td>4783969</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13589</th>\n",
+       "      <td>4784079</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964262</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>212.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:12:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13595</th>\n",
+       "      <td>4784010</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>26.20</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13603</th>\n",
+       "      <td>4784086</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>23.90</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13605</th>\n",
+       "      <td>4784066</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964269</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>14.30</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:06:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8638</th>\n",
+       "      <td>4783948</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964270</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:26:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12824</th>\n",
+       "      <td>4783976</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12825</th>\n",
+       "      <td>4783898</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964262</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>108.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:12:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13566</th>\n",
+       "      <td>4783925</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964269</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:06:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12826</th>\n",
+       "      <td>4783923</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>109.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19481</th>\n",
+       "      <td>4784055</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964270</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>228.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:26:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19482</th>\n",
+       "      <td>4784078</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>239.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483</th>\n",
+       "      <td>4784092</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964268</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>275.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27580</th>\n",
+       "      <td>4783917</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964266</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>26.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 10:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27582</th>\n",
+       "      <td>4784158</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>27.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7657</th>\n",
+       "      <td>4784139</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>8.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7658</th>\n",
+       "      <td>4783913</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26785</th>\n",
+       "      <td>4784163</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>236.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26788</th>\n",
+       "      <td>4784067</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964268</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>20.90</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26791</th>\n",
+       "      <td>4784087</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964265</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>106.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:35:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26793</th>\n",
+       "      <td>4784100</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>21.20</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26794</th>\n",
+       "      <td>4784061</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26795</th>\n",
+       "      <td>4784033</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>12.70</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15974</th>\n",
+       "      <td>4784103</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>144.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26798</th>\n",
+       "      <td>4784032</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964275</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.34</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 18:40:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13565</th>\n",
+       "      <td>4784143</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13551</th>\n",
+       "      <td>4784042</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-3.30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23332</th>\n",
+       "      <td>4783916</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>15.70</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23333</th>\n",
+       "      <td>4784028</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>221.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23338</th>\n",
+       "      <td>4784011</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-0.30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23339</th>\n",
+       "      <td>4784096</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>29.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23341</th>\n",
+       "      <td>4784012</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964270</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:26:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23342</th>\n",
+       "      <td>4784024</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23347</th>\n",
+       "      <td>4783927</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-4.50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23349</th>\n",
+       "      <td>4783965</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>145.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23356</th>\n",
+       "      <td>4784017</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23357</th>\n",
+       "      <td>4784022</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23359</th>\n",
+       "      <td>4784156</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964265</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>146.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:35:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23360</th>\n",
+       "      <td>4784053</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.40</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23361</th>\n",
+       "      <td>4784037</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.50</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13559</th>\n",
+       "      <td>4783922</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High Panic</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23366</th>\n",
+       "      <td>4784036</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964275</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>148.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 18:40:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23374</th>\n",
+       "      <td>4784102</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964266</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>12.40</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 10:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23376</th>\n",
+       "      <td>4784093</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>145.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23382</th>\n",
+       "      <td>4783981</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>146.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23383</th>\n",
+       "      <td>4784049</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964269</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>21.80</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:06:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23385</th>\n",
+       "      <td>4784005</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.16</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low Panic</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23386</th>\n",
+       "      <td>4784162</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100703</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Chloride, Whole Bld</td>\n",
+       "      <td>CL</td>\n",
+       "      <td>107.00</td>\n",
+       "      <td>95</td>\n",
+       "      <td>110</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23387</th>\n",
+       "      <td>4784052</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964266</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 10:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23388</th>\n",
+       "      <td>4783920</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100841</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HCO3</td>\n",
+       "      <td>HCO3A</td>\n",
+       "      <td>15.60</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23389</th>\n",
+       "      <td>4784115</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964262</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>8.90</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:12:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23391</th>\n",
+       "      <td>4784107</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-4.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23392</th>\n",
+       "      <td>4783908</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low Panic</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13544</th>\n",
+       "      <td>4784131</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100705</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Methemoglobin</td>\n",
+       "      <td>METHB</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13547</th>\n",
+       "      <td>4784122</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964265</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>26.60</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:35:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23370</th>\n",
+       "      <td>4784137</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low Panic</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23329</th>\n",
+       "      <td>4784155</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964266</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 10:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26805</th>\n",
+       "      <td>4783994</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964268</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26813</th>\n",
+       "      <td>4783956</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964263</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.60</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1855</th>\n",
+       "      <td>4783959</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>39.70</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1859</th>\n",
+       "      <td>4783951</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>243.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1860</th>\n",
+       "      <td>4783943</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964268</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1862</th>\n",
+       "      <td>4784152</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>200.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1868</th>\n",
+       "      <td>4783899</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.50</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1869</th>\n",
+       "      <td>4784008</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964275</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>9999999.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 18:40:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1872</th>\n",
+       "      <td>4783957</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100700</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Sodium, Whole Blood</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>146.00</td>\n",
+       "      <td>135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1882</th>\n",
+       "      <td>4783904</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964269</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>45.70</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:06:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21957</th>\n",
+       "      <td>4783918</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100838</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pH (a)</td>\n",
+       "      <td>PHA</td>\n",
+       "      <td>7.40</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21961</th>\n",
+       "      <td>4783955</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964261</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.30</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 12:19:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21967</th>\n",
+       "      <td>4783938</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>30.60</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21969</th>\n",
+       "      <td>4784105</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.72</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High Panic</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21972</th>\n",
+       "      <td>4783944</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964266</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>292.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 10:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1854</th>\n",
+       "      <td>4784048</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-1.70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21973</th>\n",
+       "      <td>4784110</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964266</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-1.40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 10:28:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21982</th>\n",
+       "      <td>4784081</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964262</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:12:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21985</th>\n",
+       "      <td>4784050</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100850</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>CO-Hemoglobin</td>\n",
+       "      <td>COHB</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21992</th>\n",
+       "      <td>4784150</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-0.20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22013</th>\n",
+       "      <td>4784062</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964267</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>21.30</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 17:25:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22018</th>\n",
+       "      <td>4784039</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964269</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-3.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:06:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22020</th>\n",
+       "      <td>4784013</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>22.60</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22022</th>\n",
+       "      <td>4783998</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964262</td>\n",
+       "      <td>1230100842</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>tCO2</td>\n",
+       "      <td>TCO2A</td>\n",
+       "      <td>31.50</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:12:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22024</th>\n",
+       "      <td>4784040</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964275</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>252.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 18:40:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22026</th>\n",
+       "      <td>4784054</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964264</td>\n",
+       "      <td>1230100843</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Base Excess (vt)</td>\n",
+       "      <td>BE</td>\n",
+       "      <td>-9.70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 14:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22031</th>\n",
+       "      <td>4784025</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964270</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:26:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22034</th>\n",
+       "      <td>4783901</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964273</td>\n",
+       "      <td>1230100845</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>O2 Saturation (a)</td>\n",
+       "      <td>O2SATA</td>\n",
+       "      <td>97.50</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 20:04:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17130</th>\n",
+       "      <td>4783966</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>3.80</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>4784084</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964260</td>\n",
+       "      <td>1230100704</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Glucose, Whole Blood</td>\n",
+       "      <td>GLU</td>\n",
+       "      <td>83.00</td>\n",
+       "      <td>45</td>\n",
+       "      <td>200</td>\n",
+       "      <td>mg/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 11:23:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21976</th>\n",
+       "      <td>4784127</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964270</td>\n",
+       "      <td>1230100840</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pO2 (a)</td>\n",
+       "      <td>PO2A</td>\n",
+       "      <td>186.00</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:26:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26811</th>\n",
+       "      <td>4783993</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964268</td>\n",
+       "      <td>1230100849</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>ctO2 (a)</td>\n",
+       "      <td>CTO2A</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>mL/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1851</th>\n",
+       "      <td>4784140</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964271</td>\n",
+       "      <td>1230100846</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>HgB</td>\n",
+       "      <td>HGB</td>\n",
+       "      <td>10.60</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 15:11:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1842</th>\n",
+       "      <td>4783939</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964275</td>\n",
+       "      <td>1230100847</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Hct (Est)</td>\n",
+       "      <td>HCT</td>\n",
+       "      <td>18.00</td>\n",
+       "      <td>30</td>\n",
+       "      <td>42</td>\n",
+       "      <td>%</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 18:40:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26817</th>\n",
+       "      <td>4783924</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964265</td>\n",
+       "      <td>1230100702</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Calcium Ionized</td>\n",
+       "      <td>CAION</td>\n",
+       "      <td>1.07</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>1.32</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 13:35:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26818</th>\n",
+       "      <td>4783979</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964268</td>\n",
+       "      <td>1230100701</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>Potassium, Whole Bld</td>\n",
+       "      <td>K</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Low Panic</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 16:43:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26822</th>\n",
+       "      <td>4783932</td>\n",
+       "      <td>JC739583</td>\n",
+       "      <td>311721758</td>\n",
+       "      <td>710964274</td>\n",
+       "      <td>1230100839</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB76</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "      <td>pCO2 (a)</td>\n",
+       "      <td>PCO2A</td>\n",
+       "      <td>46.50</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>None</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2015-07-19 07:07:00</td>\n",
+       "      <td>2015-07-19 19:24:00</td>\n",
+       "      <td>2015-07-19 02:14:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201107</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS TXP Med/Surg Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>1215975</td>\n",
+       "      <td>Patient Update</td>\n",
+       "      <td>Blood Gases, Arterial</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           uid   anon_id pat_enc_csn_id_coded order_id_coded  component_id  \\\n",
+       "24937  4783907  JC739583            311721758      710964265    1230100850   \n",
+       "13569  4784057  JC739583            311721758      710964267    1230100702   \n",
+       "13576  4784075  JC739583            311721758      710964274    1230100849   \n",
+       "13579  4784021  JC739583            311721758      710964264    1230100705   \n",
+       "13584  4784002  JC739583            311721758      710964260    1230100838   \n",
+       "13587  4784153  JC739583            311721758      710964271    1230100850   \n",
+       "13588  4783969  JC739583            311721758      710964274    1230100705   \n",
+       "13589  4784079  JC739583            311721758      710964262    1230100840   \n",
+       "13595  4784010  JC739583            311721758      710964274    1230100842   \n",
+       "13603  4784086  JC739583            311721758      710964273    1230100841   \n",
+       "13605  4784066  JC739583            311721758      710964269    1230100849   \n",
+       "8638   4783948  JC739583            311721758      710964270    1230100701   \n",
+       "12824  4783976  JC739583            311721758      710964274    1230100703   \n",
+       "12825  4783898  JC739583            311721758      710964262    1230100703   \n",
+       "13566  4783925  JC739583            311721758      710964269    1230100838   \n",
+       "12826  4783923  JC739583            311721758      710964261    1230100703   \n",
+       "19481  4784055  JC739583            311721758      710964270    1230100704   \n",
+       "19482  4784078  JC739583            311721758      710964271    1230100704   \n",
+       "19483  4784092  JC739583            311721758      710964268    1230100704   \n",
+       "27580  4783917  JC739583            311721758      710964266    1230100847   \n",
+       "27582  4784158  JC739583            311721758      710964261    1230100847   \n",
+       "7657   4784139  JC739583            311721758      710964263    1230100846   \n",
+       "7658   4783913  JC739583            311721758      710964274    1230100846   \n",
+       "26785  4784163  JC739583            311721758      710964264    1230100840   \n",
+       "26788  4784067  JC739583            311721758      710964268    1230100841   \n",
+       "26791  4784087  JC739583            311721758      710964265    1230100703   \n",
+       "26793  4784100  JC739583            311721758      710964263    1230100841   \n",
+       "26794  4784061  JC739583            311721758      710964260    1230100850   \n",
+       "26795  4784033  JC739583            311721758      710964260    1230100849   \n",
+       "15974  4784103  JC739583            311721758      710964261    1230100700   \n",
+       "26798  4784032  JC739583            311721758      710964275    1230100838   \n",
+       "13565  4784143  JC739583            311721758      710964267    1230100705   \n",
+       "13551  4784042  JC739583            311721758      710964263    1230100843   \n",
+       "23332  4783916  JC739583            311721758      710964273    1230100849   \n",
+       "23333  4784028  JC739583            311721758      710964263    1230100704   \n",
+       "23338  4784011  JC739583            311721758      710964273    1230100843   \n",
+       "23339  4784096  JC739583            311721758      710964274    1230100847   \n",
+       "23341  4784012  JC739583            311721758      710964270    1230100839   \n",
+       "23342  4784024  JC739583            311721758      710964261    1230100841   \n",
+       "23347  4783927  JC739583            311721758      710964267    1230100843   \n",
+       "23349  4783965  JC739583            311721758      710964274    1230100700   \n",
+       "23356  4784017  JC739583            311721758      710964264    1230100842   \n",
+       "23357  4784022  JC739583            311721758      710964260    1230100702   \n",
+       "23359  4784156  JC739583            311721758      710964265    1230100700   \n",
+       "23360  4784053  JC739583            311721758      710964261    1230100838   \n",
+       "23361  4784037  JC739583            311721758      710964274    1230100845   \n",
+       "13559  4783922  JC739583            311721758      710964263    1230100702   \n",
+       "23366  4784036  JC739583            311721758      710964275    1230100700   \n",
+       "23374  4784102  JC739583            311721758      710964266    1230100849   \n",
+       "23376  4784093  JC739583            311721758      710964263    1230100700   \n",
+       "23382  4783981  JC739583            311721758      710964264    1230100700   \n",
+       "23383  4784049  JC739583            311721758      710964269    1230100841   \n",
+       "23385  4784005  JC739583            311721758      710964271    1230100838   \n",
+       "23386  4784162  JC739583            311721758      710964260    1230100703   \n",
+       "23387  4784052  JC739583            311721758      710964266    1230100705   \n",
+       "23388  4783920  JC739583            311721758      710964264    1230100841   \n",
+       "23389  4784115  JC739583            311721758      710964262    1230100846   \n",
+       "23391  4784107  JC739583            311721758      710964261    1230100843   \n",
+       "23392  4783908  JC739583            311721758      710964273    1230100701   \n",
+       "13544  4784131  JC739583            311721758      710964271    1230100705   \n",
+       "13547  4784122  JC739583            311721758      710964265    1230100842   \n",
+       "23370  4784137  JC739583            311721758      710964267    1230100701   \n",
+       "23329  4784155  JC739583            311721758      710964266    1230100846   \n",
+       "26805  4783994  JC739583            311721758      710964268    1230100838   \n",
+       "26813  4783956  JC739583            311721758      710964263    1230100701   \n",
+       "1855   4783959  JC739583            311721758      710964273    1230100839   \n",
+       "1859   4783951  JC739583            311721758      710964264    1230100704   \n",
+       "1860   4783943  JC739583            311721758      710964268    1230100850   \n",
+       "1862   4784152  JC739583            311721758      710964271    1230100840   \n",
+       "1868   4783899  JC739583            311721758      710964271    1230100845   \n",
+       "1869   4784008  JC739583            311721758      710964275    1230100850   \n",
+       "1872   4783957  JC739583            311721758      710964267    1230100700   \n",
+       "1882   4783904  JC739583            311721758      710964269    1230100839   \n",
+       "21957  4783918  JC739583            311721758      710964273    1230100838   \n",
+       "21961  4783955  JC739583            311721758      710964261    1230100845   \n",
+       "21967  4783938  JC739583            311721758      710964260    1230100839   \n",
+       "21969  4784105  JC739583            311721758      710964274    1230100702   \n",
+       "21972  4783944  JC739583            311721758      710964266    1230100840   \n",
+       "1854   4784048  JC739583            311721758      710964260    1230100843   \n",
+       "21973  4784110  JC739583            311721758      710964266    1230100843   \n",
+       "21982  4784081  JC739583            311721758      710964262    1230100850   \n",
+       "21985  4784050  JC739583            311721758      710964267    1230100850   \n",
+       "21992  4784150  JC739583            311721758      710964274    1230100843   \n",
+       "22013  4784062  JC739583            311721758      710964267    1230100842   \n",
+       "22018  4784039  JC739583            311721758      710964269    1230100843   \n",
+       "22020  4784013  JC739583            311721758      710964260    1230100842   \n",
+       "22022  4783998  JC739583            311721758      710964262    1230100842   \n",
+       "22024  4784040  JC739583            311721758      710964275    1230100840   \n",
+       "22026  4784054  JC739583            311721758      710964264    1230100843   \n",
+       "22031  4784025  JC739583            311721758      710964270    1230100702   \n",
+       "22034  4783901  JC739583            311721758      710964273    1230100845   \n",
+       "17130  4783966  JC739583            311721758      710964260    1230100701   \n",
+       "546    4784084  JC739583            311721758      710964260    1230100704   \n",
+       "21976  4784127  JC739583            311721758      710964270    1230100840   \n",
+       "26811  4783993  JC739583            311721758      710964268    1230100849   \n",
+       "1851   4784140  JC739583            311721758      710964271    1230100846   \n",
+       "1842   4783939  JC739583            311721758      710964275    1230100847   \n",
+       "26817  4783924  JC739583            311721758      710964265    1230100702   \n",
+       "26818  4783979  JC739583            311721758      710964268    1230100701   \n",
+       "26822  4783932  JC739583            311721758      710964274    1230100839   \n",
+       "\n",
+       "      order_type proc_code         group_lab_name              lab_name  \\\n",
+       "24937        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "13569        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "13576        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "13579        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "13584        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "13587        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "13588        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "13589        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "13595        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "13603        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "13605        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "8638         Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "12824        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "12825        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "13566        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "12826        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "19481        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "19482        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "19483        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "27580        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "27582        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "7657         Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "7658         Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "26785        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "26788        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "26791        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "26793        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "26794        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "26795        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "15974        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "26798        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "13565        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "13551        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "23332        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "23333        Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "23338        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "23339        Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "23341        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "23342        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "23347        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "23349        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23356        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "23357        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "23359        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23360        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "23361        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "13559        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "23366        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23374        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "23376        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23382        Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "23383        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "23385        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "23386        Lab     LAB76  Blood Gases, Arterial   Chloride, Whole Bld   \n",
+       "23387        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "23388        Lab     LAB76  Blood Gases, Arterial                  HCO3   \n",
+       "23389        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "23391        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "23392        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "13544        Lab     LAB76  Blood Gases, Arterial         Methemoglobin   \n",
+       "13547        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "23370        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "23329        Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "26805        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "26813        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "1855         Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "1859         Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "1860         Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "1862         Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "1868         Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "1869         Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "1872         Lab     LAB76  Blood Gases, Arterial   Sodium, Whole Blood   \n",
+       "1882         Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "21957        Lab     LAB76  Blood Gases, Arterial                pH (a)   \n",
+       "21961        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "21967        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "21969        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "21972        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "1854         Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "21973        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "21982        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "21985        Lab     LAB76  Blood Gases, Arterial         CO-Hemoglobin   \n",
+       "21992        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "22013        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "22018        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "22020        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "22022        Lab     LAB76  Blood Gases, Arterial                  tCO2   \n",
+       "22024        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "22026        Lab     LAB76  Blood Gases, Arterial      Base Excess (vt)   \n",
+       "22031        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "22034        Lab     LAB76  Blood Gases, Arterial     O2 Saturation (a)   \n",
+       "17130        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "546          Lab     LAB76  Blood Gases, Arterial  Glucose, Whole Blood   \n",
+       "21976        Lab     LAB76  Blood Gases, Arterial               pO2 (a)   \n",
+       "26811        Lab     LAB76  Blood Gases, Arterial              ctO2 (a)   \n",
+       "1851         Lab     LAB76  Blood Gases, Arterial                   HgB   \n",
+       "1842         Lab     LAB76  Blood Gases, Arterial             Hct (Est)   \n",
+       "26817        Lab     LAB76  Blood Gases, Arterial       Calcium Ionized   \n",
+       "26818        Lab     LAB76  Blood Gases, Arterial  Potassium, Whole Bld   \n",
+       "26822        Lab     LAB76  Blood Gases, Arterial              pCO2 (a)   \n",
+       "\n",
+       "      base_name  ord_num_value reference_low reference_high reference_unit  \\\n",
+       "24937      COHB           1.10          None           None              %   \n",
+       "13569     CAION           1.60          1.12           1.32         mmol/L   \n",
+       "13576     CTO2A          13.30          15.0           25.0          mL/dL   \n",
+       "13579     METHB     9999999.00          None           None              %   \n",
+       "13584       PHA           7.46          7.35           7.45           None   \n",
+       "13587      COHB     9999999.00          None           None              %   \n",
+       "13588     METHB           1.20          None           None              %   \n",
+       "13589      PO2A         212.00          80.0          105.0           mmHg   \n",
+       "13595     TCO2A          26.20          23.0           27.0         mmol/L   \n",
+       "13603     HCO3A          23.90          22.0           26.0         mmol/L   \n",
+       "13605     CTO2A          14.30          15.0           25.0          mL/dL   \n",
+       "8638          K           3.30           3.5            6.0         mmol/L   \n",
+       "12824        CL         105.00            95            110         mmol/L   \n",
+       "12825        CL         108.00            95            110         mmol/L   \n",
+       "13566       PHA           7.30          7.35           7.45           None   \n",
+       "12826        CL         109.00            95            110         mmol/L   \n",
+       "19481       GLU         228.00            45            200          mg/dL   \n",
+       "19482       GLU         239.00            45            200          mg/dL   \n",
+       "19483       GLU         275.00            45            200          mg/dL   \n",
+       "27580       HCT          26.00            30             42              %   \n",
+       "27582       HCT          27.00            30             42              %   \n",
+       "7657        HGB           8.40          10.0           14.0           g/dL   \n",
+       "7658        HGB           9.40          10.0           14.0           g/dL   \n",
+       "26785      PO2A         236.00          80.0          105.0           mmHg   \n",
+       "26788     HCO3A          20.90          22.0           26.0         mmol/L   \n",
+       "26791        CL         106.00            95            110         mmol/L   \n",
+       "26793     HCO3A          21.20          22.0           26.0         mmol/L   \n",
+       "26794      COHB           1.60          None           None              %   \n",
+       "26795     CTO2A          12.70          15.0           25.0          mL/dL   \n",
+       "15974        NA         144.00           135            145         mmol/L   \n",
+       "26798       PHA           7.34          7.35           7.45           None   \n",
+       "13565     METHB           1.40          None           None              %   \n",
+       "13551        BE          -3.30           0.0            0.0         mmol/L   \n",
+       "23332     CTO2A          15.70          15.0           25.0          mL/dL   \n",
+       "23333       GLU         221.00            45            200          mg/dL   \n",
+       "23338        BE          -0.30           0.0            0.0         mmol/L   \n",
+       "23339       HCT          29.00            30             42              %   \n",
+       "23341     PCO2A          40.00          35.0           45.0           mmHg   \n",
+       "23342     HCO3A          19.40          22.0           26.0         mmol/L   \n",
+       "23347        BE          -4.50           0.0            0.0         mmol/L   \n",
+       "23349        NA         145.00           135            145         mmol/L   \n",
+       "23356     TCO2A          16.60          23.0           27.0         mmol/L   \n",
+       "23357     CAION           1.20          1.12           1.32         mmol/L   \n",
+       "23359        NA         146.00           135            145         mmol/L   \n",
+       "23360       PHA           7.40          7.35           7.45           None   \n",
+       "23361    O2SATA          97.50          95.0          100.0              %   \n",
+       "13559     CAION           1.74          1.12           1.32         mmol/L   \n",
+       "23366        NA         148.00           135            145         mmol/L   \n",
+       "23374     CTO2A          12.40          15.0           25.0          mL/dL   \n",
+       "23376        NA         145.00           135            145         mmol/L   \n",
+       "23382        NA         146.00           135            145         mmol/L   \n",
+       "23383     HCO3A          21.80          22.0           26.0         mmol/L   \n",
+       "23385       PHA           7.16          7.35           7.45           None   \n",
+       "23386        CL         107.00            95            110         mmol/L   \n",
+       "23387     METHB           1.10          None           None              %   \n",
+       "23388     HCO3A          15.60          22.0           26.0         mmol/L   \n",
+       "23389       HGB           8.90          10.0           14.0           g/dL   \n",
+       "23391        BE          -4.60           0.0            0.0         mmol/L   \n",
+       "23392         K           2.80           3.5            6.0         mmol/L   \n",
+       "13544     METHB           1.20          None           None              %   \n",
+       "13547     TCO2A          26.60          23.0           27.0         mmol/L   \n",
+       "23370         K           2.80           3.5            6.0         mmol/L   \n",
+       "23329       HGB           8.50          10.0           14.0           g/dL   \n",
+       "26805       PHA           7.30          7.35           7.45           None   \n",
+       "26813         K           3.60           3.5            6.0         mmol/L   \n",
+       "1855      PCO2A          39.70          35.0           45.0           mmHg   \n",
+       "1859        GLU         243.00            45            200          mg/dL   \n",
+       "1860       COHB     9999999.00          None           None              %   \n",
+       "1862       PO2A         200.00          80.0          105.0           mmHg   \n",
+       "1868     O2SATA          97.50          95.0          100.0              %   \n",
+       "1869       COHB     9999999.00          None           None              %   \n",
+       "1872         NA         146.00           135            145         mmol/L   \n",
+       "1882      PCO2A          45.70          35.0           45.0           mmHg   \n",
+       "21957       PHA           7.40          7.35           7.45           None   \n",
+       "21961    O2SATA          97.30          95.0          100.0              %   \n",
+       "21967     PCO2A          30.60          35.0           45.0           mmHg   \n",
+       "21969     CAION           1.72          1.12           1.32         mmol/L   \n",
+       "21972      PO2A         292.00          80.0          105.0           mmHg   \n",
+       "1854         BE          -1.70           0.0            0.0         mmol/L   \n",
+       "21973        BE          -1.40           0.0            0.0         mmol/L   \n",
+       "21982      COHB           1.60          None           None              %   \n",
+       "21985      COHB           1.00          None           None              %   \n",
+       "21992        BE          -0.20           0.0            0.0         mmol/L   \n",
+       "22013     TCO2A          21.30          23.0           27.0         mmol/L   \n",
+       "22018        BE          -3.60           0.0            0.0         mmol/L   \n",
+       "22020     TCO2A          22.60          23.0           27.0         mmol/L   \n",
+       "22022     TCO2A          31.50          23.0           27.0         mmol/L   \n",
+       "22024      PO2A         252.00          80.0          105.0           mmHg   \n",
+       "22026        BE          -9.70           0.0            0.0         mmol/L   \n",
+       "22031     CAION           0.86          1.12           1.32         mmol/L   \n",
+       "22034    O2SATA          97.50          95.0          100.0              %   \n",
+       "17130         K           3.80           3.5            6.0         mmol/L   \n",
+       "546         GLU          83.00            45            200          mg/dL   \n",
+       "21976      PO2A         186.00          80.0          105.0           mmHg   \n",
+       "26811     CTO2A          15.10          15.0           25.0          mL/dL   \n",
+       "1851        HGB          10.60          10.0           14.0           g/dL   \n",
+       "1842        HCT          18.00            30             42              %   \n",
+       "26817     CAION           1.07          1.12           1.32         mmol/L   \n",
+       "26818         K           2.70           3.5            6.0         mmol/L   \n",
+       "26822     PCO2A          46.50          35.0           45.0           mmHg   \n",
+       "\n",
+       "      result_in_range_yn result_flag          order_time         result_time  \\\n",
+       "24937               None        None 2015-07-19 07:07:00 2015-07-19 13:35:00   \n",
+       "13569               None        High 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "13576               None         Low 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "13579               None        None 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "13584               None        High 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "13587               None        None 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "13588               None        None 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "13589               None        High 2015-07-19 07:07:00 2015-07-19 13:12:00   \n",
+       "13595                  Y        None 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "13603                  Y        None 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "13605               None         Low 2015-07-19 07:07:00 2015-07-19 16:06:00   \n",
+       "8638                None         Low 2015-07-19 07:07:00 2015-07-19 15:26:00   \n",
+       "12824                  Y        None 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "12825                  Y        None 2015-07-19 07:07:00 2015-07-19 13:12:00   \n",
+       "13566               None         Low 2015-07-19 07:07:00 2015-07-19 16:06:00   \n",
+       "12826                  Y        None 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "19481               None        High 2015-07-19 07:07:00 2015-07-19 15:26:00   \n",
+       "19482               None        High 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "19483               None        High 2015-07-19 07:07:00 2015-07-19 16:43:00   \n",
+       "27580               None         Low 2015-07-19 07:07:00 2015-07-19 10:28:00   \n",
+       "27582               None         Low 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "7657                None         Low 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "7658                None         Low 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "26785               None        High 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "26788               None         Low 2015-07-19 07:07:00 2015-07-19 16:43:00   \n",
+       "26791                  Y        None 2015-07-19 07:07:00 2015-07-19 13:35:00   \n",
+       "26793               None         Low 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "26794               None        None 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "26795               None         Low 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "15974                  Y        None 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "26798               None         Low 2015-07-19 07:07:00 2015-07-19 18:40:00   \n",
+       "13565               None        None 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "13551               None         Low 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "23332                  Y        None 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "23333               None        High 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "23338               None         Low 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "23339               None         Low 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "23341                  Y        None 2015-07-19 07:07:00 2015-07-19 15:26:00   \n",
+       "23342               None         Low 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "23347               None         Low 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "23349                  Y        None 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "23356               None         Low 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "23357                  Y        None 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "23359               None        High 2015-07-19 07:07:00 2015-07-19 13:35:00   \n",
+       "23360                  Y        None 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "23361                  Y        None 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "13559               None  High Panic 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "23366               None        High 2015-07-19 07:07:00 2015-07-19 18:40:00   \n",
+       "23374               None         Low 2015-07-19 07:07:00 2015-07-19 10:28:00   \n",
+       "23376                  Y        None 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "23382               None        High 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "23383               None         Low 2015-07-19 07:07:00 2015-07-19 16:06:00   \n",
+       "23385               None   Low Panic 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "23386                  Y        None 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "23387               None        None 2015-07-19 07:07:00 2015-07-19 10:28:00   \n",
+       "23388               None         Low 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "23389               None         Low 2015-07-19 07:07:00 2015-07-19 13:12:00   \n",
+       "23391               None         Low 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "23392               None   Low Panic 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "13544               None        None 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "13547                  Y        None 2015-07-19 07:07:00 2015-07-19 13:35:00   \n",
+       "23370               None   Low Panic 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "23329               None         Low 2015-07-19 07:07:00 2015-07-19 10:28:00   \n",
+       "26805               None         Low 2015-07-19 07:07:00 2015-07-19 16:43:00   \n",
+       "26813                  Y        None 2015-07-19 07:07:00 2015-07-19 14:28:00   \n",
+       "1855                   Y        None 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "1859                None        High 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "1860                None        None 2015-07-19 07:07:00 2015-07-19 16:43:00   \n",
+       "1862                None        High 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "1868                   Y        None 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "1869                None        None 2015-07-19 07:07:00 2015-07-19 18:40:00   \n",
+       "1872                None        High 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "1882                None        High 2015-07-19 07:07:00 2015-07-19 16:06:00   \n",
+       "21957                  Y        None 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "21961                  Y        None 2015-07-19 07:07:00 2015-07-19 12:19:00   \n",
+       "21967               None         Low 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "21969               None  High Panic 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "21972               None        High 2015-07-19 07:07:00 2015-07-19 10:28:00   \n",
+       "1854                None         Low 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "21973               None         Low 2015-07-19 07:07:00 2015-07-19 10:28:00   \n",
+       "21982               None        None 2015-07-19 07:07:00 2015-07-19 13:12:00   \n",
+       "21985               None        None 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "21992               None         Low 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "22013               None         Low 2015-07-19 07:07:00 2015-07-19 17:25:00   \n",
+       "22018               None         Low 2015-07-19 07:07:00 2015-07-19 16:06:00   \n",
+       "22020               None         Low 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "22022               None        High 2015-07-19 07:07:00 2015-07-19 13:12:00   \n",
+       "22024               None        High 2015-07-19 07:07:00 2015-07-19 18:40:00   \n",
+       "22026               None         Low 2015-07-19 07:07:00 2015-07-19 14:43:00   \n",
+       "22031               None         Low 2015-07-19 07:07:00 2015-07-19 15:26:00   \n",
+       "22034                  Y        None 2015-07-19 07:07:00 2015-07-19 20:04:00   \n",
+       "17130                  Y        None 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "546                    Y        None 2015-07-19 07:07:00 2015-07-19 11:23:00   \n",
+       "21976               None        High 2015-07-19 07:07:00 2015-07-19 15:26:00   \n",
+       "26811                  Y        None 2015-07-19 07:07:00 2015-07-19 16:43:00   \n",
+       "1851                   Y        None 2015-07-19 07:07:00 2015-07-19 15:11:00   \n",
+       "1842                None         Low 2015-07-19 07:07:00 2015-07-19 18:40:00   \n",
+       "26817               None         Low 2015-07-19 07:07:00 2015-07-19 13:35:00   \n",
+       "26818               None   Low Panic 2015-07-19 07:07:00 2015-07-19 16:43:00   \n",
+       "26822               None        High 2015-07-19 07:07:00 2015-07-19 19:24:00   \n",
+       "\n",
+       "      effective_time_jittered  pat_class  department_id       pat_service  \\\n",
+       "24937     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13569     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13576     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13579     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13584     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13587     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13588     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13589     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13595     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13603     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13605     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "8638      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "12824     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "12825     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13566     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "12826     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "19481     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "19482     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "19483     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "27580     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "27582     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "7657      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "7658      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26785     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26788     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26791     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26793     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26794     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26795     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "15974     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26798     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13565     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13551     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23332     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23333     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23338     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23339     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23341     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23342     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23347     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23349     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23356     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23357     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23359     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23360     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23361     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13559     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23366     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23374     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23376     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23382     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23383     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23385     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23386     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23387     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23388     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23389     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23391     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23392     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13544     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "13547     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23370     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "23329     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26805     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26813     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1855      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1859      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1860      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1862      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1868      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1869      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1872      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1882      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21957     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21961     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21967     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21969     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21972     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1854      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21973     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21982     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21985     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21992     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22013     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22018     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22020     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22022     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22024     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22026     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22031     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "22034     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "17130     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "546       2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "21976     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26811     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1851      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "1842      2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26817     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26818     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "26822     2015-07-19 02:14:00  Inpatient       10201107  Gastroenterology   \n",
+       "\n",
+       "      pat_lv_of_care               accomodation from_base_class to_base_class  \\\n",
+       "24937     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13569     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13576     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13579     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13584     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13587     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13588     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13589     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13595     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13603     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13605     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "8638      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "12824     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "12825     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13566     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "12826     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "19481     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "19482     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "19483     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "27580     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "27582     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "7657      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "7658      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26785     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26788     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26791     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26793     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26794     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26795     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "15974     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26798     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13565     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13551     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23332     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23333     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23338     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23339     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23341     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23342     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23347     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23349     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23356     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23357     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23359     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23360     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23361     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13559     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23366     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23374     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23376     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23382     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23383     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23385     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23386     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23387     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23388     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23389     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23391     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23392     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13544     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "13547     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23370     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "23329     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26805     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26813     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1855      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1859      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1860      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1862      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1868      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1869      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1872      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1882      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21957     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21961     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21967     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21969     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21972     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1854      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21973     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21982     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21985     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21992     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22013     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22018     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22020     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22022     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22024     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22026     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22031     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "22034     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "17130     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "546       Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "21976     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26811     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1851      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "1842      Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26817     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26818     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "26822     Acute Care  PEDS TXP Med/Surg Level 3       Inpatient     Inpatient   \n",
+       "\n",
+       "        in_event_type  out_event_type  event_id_coded      event_type  \\\n",
+       "24937  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13569  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13576  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13579  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13584  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13587  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13588  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13589  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13595  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13603  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13605  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "8638   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "12824  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "12825  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13566  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "12826  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "19481  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "19482  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "19483  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "27580  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "27582  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "7657   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "7658   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26785  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26788  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26791  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26793  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26794  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26795  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "15974  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26798  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13565  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13551  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23332  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23333  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23338  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23339  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23341  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23342  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23347  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23349  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23356  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23357  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23359  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23360  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23361  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13559  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23366  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23374  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23376  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23382  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23383  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23385  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23386  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23387  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23388  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23389  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23391  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23392  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13544  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "13547  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23370  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "23329  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26805  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26813  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1855   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1859   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1860   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1862   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1868   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1869   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1872   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1882   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21957  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21961  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21967  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21969  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21972  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1854   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21973  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21982  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21985  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21992  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22013  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22018  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22020  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22022  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22024  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22026  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22031  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "22034  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "17130  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "546    Patient Update  Patient Update         1215975  Patient Update   \n",
+       "21976  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26811  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1851   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "1842   Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26817  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26818  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "26822  Patient Update  Patient Update         1215975  Patient Update   \n",
+       "\n",
+       "                 njr_labname  \n",
+       "24937  Blood Gases, Arterial  \n",
+       "13569  Blood Gases, Arterial  \n",
+       "13576  Blood Gases, Arterial  \n",
+       "13579  Blood Gases, Arterial  \n",
+       "13584  Blood Gases, Arterial  \n",
+       "13587  Blood Gases, Arterial  \n",
+       "13588  Blood Gases, Arterial  \n",
+       "13589  Blood Gases, Arterial  \n",
+       "13595  Blood Gases, Arterial  \n",
+       "13603  Blood Gases, Arterial  \n",
+       "13605  Blood Gases, Arterial  \n",
+       "8638   Blood Gases, Arterial  \n",
+       "12824  Blood Gases, Arterial  \n",
+       "12825  Blood Gases, Arterial  \n",
+       "13566  Blood Gases, Arterial  \n",
+       "12826  Blood Gases, Arterial  \n",
+       "19481  Blood Gases, Arterial  \n",
+       "19482  Blood Gases, Arterial  \n",
+       "19483  Blood Gases, Arterial  \n",
+       "27580  Blood Gases, Arterial  \n",
+       "27582  Blood Gases, Arterial  \n",
+       "7657   Blood Gases, Arterial  \n",
+       "7658   Blood Gases, Arterial  \n",
+       "26785  Blood Gases, Arterial  \n",
+       "26788  Blood Gases, Arterial  \n",
+       "26791  Blood Gases, Arterial  \n",
+       "26793  Blood Gases, Arterial  \n",
+       "26794  Blood Gases, Arterial  \n",
+       "26795  Blood Gases, Arterial  \n",
+       "15974  Blood Gases, Arterial  \n",
+       "26798  Blood Gases, Arterial  \n",
+       "13565  Blood Gases, Arterial  \n",
+       "13551  Blood Gases, Arterial  \n",
+       "23332  Blood Gases, Arterial  \n",
+       "23333  Blood Gases, Arterial  \n",
+       "23338  Blood Gases, Arterial  \n",
+       "23339  Blood Gases, Arterial  \n",
+       "23341  Blood Gases, Arterial  \n",
+       "23342  Blood Gases, Arterial  \n",
+       "23347  Blood Gases, Arterial  \n",
+       "23349  Blood Gases, Arterial  \n",
+       "23356  Blood Gases, Arterial  \n",
+       "23357  Blood Gases, Arterial  \n",
+       "23359  Blood Gases, Arterial  \n",
+       "23360  Blood Gases, Arterial  \n",
+       "23361  Blood Gases, Arterial  \n",
+       "13559  Blood Gases, Arterial  \n",
+       "23366  Blood Gases, Arterial  \n",
+       "23374  Blood Gases, Arterial  \n",
+       "23376  Blood Gases, Arterial  \n",
+       "23382  Blood Gases, Arterial  \n",
+       "23383  Blood Gases, Arterial  \n",
+       "23385  Blood Gases, Arterial  \n",
+       "23386  Blood Gases, Arterial  \n",
+       "23387  Blood Gases, Arterial  \n",
+       "23388  Blood Gases, Arterial  \n",
+       "23389  Blood Gases, Arterial  \n",
+       "23391  Blood Gases, Arterial  \n",
+       "23392  Blood Gases, Arterial  \n",
+       "13544  Blood Gases, Arterial  \n",
+       "13547  Blood Gases, Arterial  \n",
+       "23370  Blood Gases, Arterial  \n",
+       "23329  Blood Gases, Arterial  \n",
+       "26805  Blood Gases, Arterial  \n",
+       "26813  Blood Gases, Arterial  \n",
+       "1855   Blood Gases, Arterial  \n",
+       "1859   Blood Gases, Arterial  \n",
+       "1860   Blood Gases, Arterial  \n",
+       "1862   Blood Gases, Arterial  \n",
+       "1868   Blood Gases, Arterial  \n",
+       "1869   Blood Gases, Arterial  \n",
+       "1872   Blood Gases, Arterial  \n",
+       "1882   Blood Gases, Arterial  \n",
+       "21957  Blood Gases, Arterial  \n",
+       "21961  Blood Gases, Arterial  \n",
+       "21967  Blood Gases, Arterial  \n",
+       "21969  Blood Gases, Arterial  \n",
+       "21972  Blood Gases, Arterial  \n",
+       "1854   Blood Gases, Arterial  \n",
+       "21973  Blood Gases, Arterial  \n",
+       "21982  Blood Gases, Arterial  \n",
+       "21985  Blood Gases, Arterial  \n",
+       "21992  Blood Gases, Arterial  \n",
+       "22013  Blood Gases, Arterial  \n",
+       "22018  Blood Gases, Arterial  \n",
+       "22020  Blood Gases, Arterial  \n",
+       "22022  Blood Gases, Arterial  \n",
+       "22024  Blood Gases, Arterial  \n",
+       "22026  Blood Gases, Arterial  \n",
+       "22031  Blood Gases, Arterial  \n",
+       "22034  Blood Gases, Arterial  \n",
+       "17130  Blood Gases, Arterial  \n",
+       "546    Blood Gases, Arterial  \n",
+       "21976  Blood Gases, Arterial  \n",
+       "26811  Blood Gases, Arterial  \n",
+       "1851   Blood Gases, Arterial  \n",
+       "1842   Blood Gases, Arterial  \n",
+       "26817  Blood Gases, Arterial  \n",
+       "26818  Blood Gases, Arterial  \n",
+       "26822  Blood Gases, Arterial  "
+      ]
+     },
+     "execution_count": 479,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt.loc[(lradt.pat_enc_csn_id_coded==311721758) & (lradt.group_lab_name=='Blood Gases, Arterial')].sort_values(by='order_time').head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbb322ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/explore_lradt_acc.ipynb b/scripts/PedsLabUtil/explore_lradt_acc.ipynb
new file mode 100644
index 00000000..85317403
--- /dev/null
+++ b/scripts/PedsLabUtil/explore_lradt_acc.ipynb
@@ -0,0 +1,2871 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9d920777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.dpi'] = 150\n",
+    "import seaborn as sns\n",
+    "\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 100\n",
+    "pd.options.display.min_rows = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d29ac95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2b6a7fcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt = pd.read_pickle('data/lradt_acc_20210927.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "69aaa197",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before pruning last day of labs:\t 2386914\n",
+      "After pruning last day of labs:\t\t 2374883\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get rid of labs ordered on the last day, due to issues with the timing right at the edge. There are multiple\n",
+    "# labs that have the order time and result time of 2021-10-31 00:00:00 and that is messing up the repetition \n",
+    "print('Before pruning last day of labs:\\t', len(lradt))\n",
+    "lradt = lradt.loc[lradt.result_time < lradt.result_time.max()]\n",
+    "print('After pruning last day of labs:\\t\\t', len(lradt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b351e241",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt['njr_labname'] = lradt['group_lab_name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5e874b87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cbc =  ['CBC with Differential', 'CBC with Diff', 'CBC', 'CBC with Diff and Slide Review']\n",
+    "poct_urine = ['POCT Urine Dipstick (10)', 'POCT Urine Dipstick (9)']\n",
+    "poct_glucose = ['Glucose By Meter', 'Glucose PCX Meter']\n",
+    "lradt.loc[lradt.njr_labname.isin(cbc), 'njr_labname'] = 'CBC'\n",
+    "lradt.loc[lradt.njr_labname.isin(poct_urine), 'njr_labname'] = 'POCT Urine'\n",
+    "lradt.loc[lradt.njr_labname.isin(poct_glucose), 'njr_labname'] = 'POCT Glucose'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a68d3dab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>njr_labname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>478822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>240101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>228085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>218235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>155812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>70365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>62027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>48188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>45320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>43813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Arterial</th>\n",
+       "      <td>38866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>35452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Venous</th>\n",
+       "      <td>24969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>22636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>21337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>19671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>iSTAT CG8, Arterial</th>\n",
+       "      <td>18915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>17845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gastroenteritis PCR Panel</th>\n",
+       "      <td>17608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 23</th>\n",
+       "      <td>14319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Amino Acids, Plasma</th>\n",
+       "      <td>14130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>13355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Respiratory Pathogen PCR Panel</th>\n",
+       "      <td>13021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Respiratory Virus PCR Panel</th>\n",
+       "      <td>12993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>12266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fatty Acid Profile, Essential</th>\n",
+       "      <td>11282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>11223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential</th>\n",
+       "      <td>10932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Istat EG7, Arterial</th>\n",
+       "      <td>10331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EKG 12-Lead</th>\n",
+       "      <td>10100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  njr_labname\n",
+       "CBC                                    478822\n",
+       "Metabolic Panel, Comprehensive         240101\n",
+       "Chem 10                                228085\n",
+       "Renal Function Panel                   218235\n",
+       "POCT Urine                             155812\n",
+       "POCT Glucose                            70365\n",
+       "Urinalysis With Microscopic             62027\n",
+       "Manual Differential/Slide Review        48188\n",
+       "Red Blood Cells                         45320\n",
+       "Metabolic Panel, Basic                  43813\n",
+       "Blood Gases, Arterial                   38866\n",
+       "Magnesium, Ser/Plas                     35452\n",
+       "Blood Gases, Venous                     24969\n",
+       "Urinalysis Macroscopic Only             22636\n",
+       "Prothrombin Time                        21337\n",
+       "Tacrolimus Level, FK506                 19671\n",
+       "iSTAT CG8, Arterial                     18915\n",
+       "Hepatic Function Panel                  17845\n",
+       "Gastroenteritis PCR Panel               17608\n",
+       "Chem 23                                 14319\n",
+       "Amino Acids, Plasma                     14130\n",
+       "Phosphorus, Ser/Plas                    13355\n",
+       "Respiratory Pathogen PCR Panel          13021\n",
+       "Respiratory Virus PCR Panel             12993\n",
+       "Slide Review                            12266\n",
+       "Fatty Acid Profile, Essential           11282\n",
+       "Blood Culture                           11223\n",
+       "Manual Differential                     10932\n",
+       "Istat EG7, Arterial                     10331\n",
+       "EKG 12-Lead                             10100"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(lradt.njr_labname.value_counts()).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "50094288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labcounts = lradt.groupby(by='njr_labname').agg({'order_id_coded': 'nunique', 'pat_enc_csn_id_coded': 'nunique', 'anon_id': 'nunique'}).rename(columns={'order_id_coded': 'norders', 'pat_enc_csn_id_coded': 'nenc', 'anon_id': 'npat'}).sort_values('norders', ascending=False).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c1c839e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>33095</td>\n",
+       "      <td>2609</td>\n",
+       "      <td>1595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>27893</td>\n",
+       "      <td>11559</td>\n",
+       "      <td>7122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>22965</td>\n",
+       "      <td>7122</td>\n",
+       "      <td>3946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>18468</td>\n",
+       "      <td>5826</td>\n",
+       "      <td>3874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>17612</td>\n",
+       "      <td>4474</td>\n",
+       "      <td>2570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>15663</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>1259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>13760</td>\n",
+       "      <td>6798</td>\n",
+       "      <td>4120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>2250</td>\n",
+       "      <td>715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>8599</td>\n",
+       "      <td>4319</td>\n",
+       "      <td>2785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>8568</td>\n",
+       "      <td>3638</td>\n",
+       "      <td>2368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>6267</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>1331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>6191</td>\n",
+       "      <td>3517</td>\n",
+       "      <td>2667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>6029</td>\n",
+       "      <td>2201</td>\n",
+       "      <td>1091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>5017</td>\n",
+       "      <td>2312</td>\n",
+       "      <td>1594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>4335</td>\n",
+       "      <td>2615</td>\n",
+       "      <td>1815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>4332</td>\n",
+       "      <td>2246</td>\n",
+       "      <td>1609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>4207</td>\n",
+       "      <td>2536</td>\n",
+       "      <td>2217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>4096</td>\n",
+       "      <td>1123</td>\n",
+       "      <td>739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>3880</td>\n",
+       "      <td>2908</td>\n",
+       "      <td>2195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>3534</td>\n",
+       "      <td>2457</td>\n",
+       "      <td>1746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>3529</td>\n",
+       "      <td>2446</td>\n",
+       "      <td>1720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>3380</td>\n",
+       "      <td>1557</td>\n",
+       "      <td>1064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ESR, Sedimentation Rate Panel</th>\n",
+       "      <td>3086</td>\n",
+       "      <td>2424</td>\n",
+       "      <td>2033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ammonia, Plasma</th>\n",
+       "      <td>2804</td>\n",
+       "      <td>800</td>\n",
+       "      <td>528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACT, Istat Kaolin Venous</th>\n",
+       "      <td>2628</td>\n",
+       "      <td>247</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>2624</td>\n",
+       "      <td>1175</td>\n",
+       "      <td>728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>2511</td>\n",
+       "      <td>1283</td>\n",
+       "      <td>1069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>2192</td>\n",
+       "      <td>1308</td>\n",
+       "      <td>1102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Arterial</th>\n",
+       "      <td>2190</td>\n",
+       "      <td>537</td>\n",
+       "      <td>517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vitamin D 25 Hydroxy, Serum</th>\n",
+       "      <td>2174</td>\n",
+       "      <td>2077</td>\n",
+       "      <td>1693</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  norders   nenc  npat\n",
+       "njr_labname                                           \n",
+       "POCT Glucose                        33095   2609  1595\n",
+       "CBC                                 27893  11559  7122\n",
+       "Magnesium, Ser/Plas                 22965   7122  3946\n",
+       "Chem 10                             18468   5826  3874\n",
+       "Renal Function Panel                17612   4474  2570\n",
+       "POCT Urine                          15663   1981  1259\n",
+       "Metabolic Panel, Comprehensive      13760   6798  4120\n",
+       "Tacrolimus Level, FK506             10009   2250   715\n",
+       "Phosphorus, Ser/Plas                 8599   4319  2785\n",
+       "Prothrombin Time                     8568   3638  2368\n",
+       "Red Blood Cells                      6267   1707  1331\n",
+       "C Reactive Protein                   6191   3517  2667\n",
+       "Gamma Glutamyl Trans                 6029   2201  1091\n",
+       "Manual Differential/Slide Review     5017   2312  1594\n",
+       "Part Thromboplastin Time             4335   2615  1815\n",
+       "Fibrinogen                           4332   2246  1609\n",
+       "Metabolic Panel, Basic               4207   2536  2217\n",
+       "Triglyceride, Ser/Plas               4096   1123   739\n",
+       "Urinalysis With Microscopic          3880   2908  2195\n",
+       "Antibody Screen                      3534   2457  1746\n",
+       "ABO Rh Typing                        3529   2446  1720\n",
+       "Blood Culture                        3380   1557  1064\n",
+       "ESR, Sedimentation Rate Panel        3086   2424  2033\n",
+       "Ammonia, Plasma                      2804    800   528\n",
+       "ACT, Istat Kaolin Venous             2628    247   124\n",
+       "Vancomycin Level, Trough             2624   1175   728\n",
+       "Slide Review                         2511   1283  1069\n",
+       "Hepatic Function Panel               2192   1308  1102\n",
+       "Blood Gases, Arterial                2190    537   517\n",
+       "Vitamin D 25 Hydroxy, Serum          2174   2077  1693"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labcounts.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "604ea02c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>npat</th>\n",
+       "      <th>per_enc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>33095</td>\n",
+       "      <td>2609</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>12.684937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACT, Istat Kaolin Venous</th>\n",
+       "      <td>2628</td>\n",
+       "      <td>247</td>\n",
+       "      <td>124</td>\n",
+       "      <td>10.639676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>15663</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>1259</td>\n",
+       "      <td>7.906613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>2250</td>\n",
+       "      <td>715</td>\n",
+       "      <td>4.448444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Arterial</th>\n",
+       "      <td>2190</td>\n",
+       "      <td>537</td>\n",
+       "      <td>517</td>\n",
+       "      <td>4.078212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>17612</td>\n",
+       "      <td>4474</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>3.936522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>6267</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>1331</td>\n",
+       "      <td>3.671353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>4096</td>\n",
+       "      <td>1123</td>\n",
+       "      <td>739</td>\n",
+       "      <td>3.647373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ammonia, Plasma</th>\n",
+       "      <td>2804</td>\n",
+       "      <td>800</td>\n",
+       "      <td>528</td>\n",
+       "      <td>3.505000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>22965</td>\n",
+       "      <td>7122</td>\n",
+       "      <td>3946</td>\n",
+       "      <td>3.224516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>18468</td>\n",
+       "      <td>5826</td>\n",
+       "      <td>3874</td>\n",
+       "      <td>3.169928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>6029</td>\n",
+       "      <td>2201</td>\n",
+       "      <td>1091</td>\n",
+       "      <td>2.739209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>27893</td>\n",
+       "      <td>11559</td>\n",
+       "      <td>7122</td>\n",
+       "      <td>2.413098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>8568</td>\n",
+       "      <td>3638</td>\n",
+       "      <td>2368</td>\n",
+       "      <td>2.355140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>2624</td>\n",
+       "      <td>1175</td>\n",
+       "      <td>728</td>\n",
+       "      <td>2.233191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>3380</td>\n",
+       "      <td>1557</td>\n",
+       "      <td>1064</td>\n",
+       "      <td>2.170841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>5017</td>\n",
+       "      <td>2312</td>\n",
+       "      <td>1594</td>\n",
+       "      <td>2.169983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>13760</td>\n",
+       "      <td>6798</td>\n",
+       "      <td>4120</td>\n",
+       "      <td>2.024125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>8599</td>\n",
+       "      <td>4319</td>\n",
+       "      <td>2785</td>\n",
+       "      <td>1.990970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>2511</td>\n",
+       "      <td>1283</td>\n",
+       "      <td>1069</td>\n",
+       "      <td>1.957132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>4332</td>\n",
+       "      <td>2246</td>\n",
+       "      <td>1609</td>\n",
+       "      <td>1.928762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>6191</td>\n",
+       "      <td>3517</td>\n",
+       "      <td>2667</td>\n",
+       "      <td>1.760307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>2192</td>\n",
+       "      <td>1308</td>\n",
+       "      <td>1102</td>\n",
+       "      <td>1.675841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>4207</td>\n",
+       "      <td>2536</td>\n",
+       "      <td>2217</td>\n",
+       "      <td>1.658912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>4335</td>\n",
+       "      <td>2615</td>\n",
+       "      <td>1815</td>\n",
+       "      <td>1.657744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>3529</td>\n",
+       "      <td>2446</td>\n",
+       "      <td>1720</td>\n",
+       "      <td>1.442764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>3534</td>\n",
+       "      <td>2457</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>1.438339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>3880</td>\n",
+       "      <td>2908</td>\n",
+       "      <td>2195</td>\n",
+       "      <td>1.334250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ESR, Sedimentation Rate Panel</th>\n",
+       "      <td>3086</td>\n",
+       "      <td>2424</td>\n",
+       "      <td>2033</td>\n",
+       "      <td>1.273102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vitamin D 25 Hydroxy, Serum</th>\n",
+       "      <td>2174</td>\n",
+       "      <td>2077</td>\n",
+       "      <td>1693</td>\n",
+       "      <td>1.046702</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  norders   nenc  npat    per_enc\n",
+       "njr_labname                                                      \n",
+       "POCT Glucose                        33095   2609  1595  12.684937\n",
+       "ACT, Istat Kaolin Venous             2628    247   124  10.639676\n",
+       "POCT Urine                          15663   1981  1259   7.906613\n",
+       "Tacrolimus Level, FK506             10009   2250   715   4.448444\n",
+       "Blood Gases, Arterial                2190    537   517   4.078212\n",
+       "Renal Function Panel                17612   4474  2570   3.936522\n",
+       "Red Blood Cells                      6267   1707  1331   3.671353\n",
+       "Triglyceride, Ser/Plas               4096   1123   739   3.647373\n",
+       "Ammonia, Plasma                      2804    800   528   3.505000\n",
+       "Magnesium, Ser/Plas                 22965   7122  3946   3.224516\n",
+       "Chem 10                             18468   5826  3874   3.169928\n",
+       "Gamma Glutamyl Trans                 6029   2201  1091   2.739209\n",
+       "CBC                                 27893  11559  7122   2.413098\n",
+       "Prothrombin Time                     8568   3638  2368   2.355140\n",
+       "Vancomycin Level, Trough             2624   1175   728   2.233191\n",
+       "Blood Culture                        3380   1557  1064   2.170841\n",
+       "Manual Differential/Slide Review     5017   2312  1594   2.169983\n",
+       "Metabolic Panel, Comprehensive      13760   6798  4120   2.024125\n",
+       "Phosphorus, Ser/Plas                 8599   4319  2785   1.990970\n",
+       "Slide Review                         2511   1283  1069   1.957132\n",
+       "Fibrinogen                           4332   2246  1609   1.928762\n",
+       "C Reactive Protein                   6191   3517  2667   1.760307\n",
+       "Hepatic Function Panel               2192   1308  1102   1.675841\n",
+       "Metabolic Panel, Basic               4207   2536  2217   1.658912\n",
+       "Part Thromboplastin Time             4335   2615  1815   1.657744\n",
+       "ABO Rh Typing                        3529   2446  1720   1.442764\n",
+       "Antibody Screen                      3534   2457  1746   1.438339\n",
+       "Urinalysis With Microscopic          3880   2908  2195   1.334250\n",
+       "ESR, Sedimentation Rate Panel        3086   2424  2033   1.273102\n",
+       "Vitamin D 25 Hydroxy, Serum          2174   2077  1693   1.046702"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labcounts['per_enc'] = labcounts.norders/labcounts.nenc\n",
+    "labcounts.sort_values(by='per_enc', ascending=False).head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "45277079",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>norders</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>324096930</th>\n",
+       "      <th>Ammonia, Plasma</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">324053387</th>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">324052544</th>\n",
+       "      <th>Hepatitis Acute Panel</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alpha 1 Antitrypsin</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alpha 1 Antitrypsin Phenotype</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Amylase, Ser/Plas</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilirubin Direct, Ser/Plas</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    norders\n",
+       "pat_enc_csn_id_coded njr_labname                           \n",
+       "324096930            Ammonia, Plasma                      1\n",
+       "324053387            Renal Function Panel                 1\n",
+       "                     POCT Glucose                         9\n",
+       "                     Magnesium, Ser/Plas                  1\n",
+       "324052544            Hepatitis Acute Panel                1\n",
+       "                     Alpha 1 Antitrypsin                  1\n",
+       "                     Alpha 1 Antitrypsin Phenotype        1\n",
+       "                     Amylase, Ser/Plas                    2\n",
+       "                     Bilirubin Direct, Ser/Plas           3\n",
+       "                     CBC                                  2"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enclabcounts = lradt.groupby(by=['pat_enc_csn_id_coded', 'njr_labname']).agg({'order_id_coded': 'nunique'}).rename(columns={'order_id_coded': 'norders'}).sort_values('pat_enc_csn_id_coded', ascending=False)\n",
+    "enclabcounts.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6b39636a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enclabcounts = enclabcounts.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "3b853a9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19822"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(enclabcounts.pat_enc_csn_id_coded.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3edc6a48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>norders</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>4943</td>\n",
+       "      <td>21277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>3569</td>\n",
+       "      <td>19412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>3445</td>\n",
+       "      <td>16087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>2815</td>\n",
+       "      <td>15953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>2320</td>\n",
+       "      <td>9282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>1853</td>\n",
+       "      <td>15535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>1781</td>\n",
+       "      <td>32267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>1601</td>\n",
+       "      <td>9360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>1362</td>\n",
+       "      <td>5642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>1192</td>\n",
+       "      <td>6122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>1174</td>\n",
+       "      <td>3848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>1073</td>\n",
+       "      <td>4901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Red Blood Cells</th>\n",
+       "      <td>965</td>\n",
+       "      <td>5525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manual Differential/Slide Review</th>\n",
+       "      <td>899</td>\n",
+       "      <td>3604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Basic</th>\n",
+       "      <td>893</td>\n",
+       "      <td>2564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Triglyceride, Ser/Plas</th>\n",
+       "      <td>689</td>\n",
+       "      <td>3662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Culture</th>\n",
+       "      <td>664</td>\n",
+       "      <td>2487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fibrinogen</th>\n",
+       "      <td>640</td>\n",
+       "      <td>2726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Part Thromboplastin Time</th>\n",
+       "      <td>638</td>\n",
+       "      <td>2358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Vancomycin Level, Trough</th>\n",
+       "      <td>565</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis With Microscopic</th>\n",
+       "      <td>520</td>\n",
+       "      <td>1492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ABO Rh Typing</th>\n",
+       "      <td>513</td>\n",
+       "      <td>1596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Antibody Screen</th>\n",
+       "      <td>507</td>\n",
+       "      <td>1584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood Gases, Arterial</th>\n",
+       "      <td>415</td>\n",
+       "      <td>2068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Slide Review</th>\n",
+       "      <td>391</td>\n",
+       "      <td>1619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilirubin Direct, Ser/Plas</th>\n",
+       "      <td>389</td>\n",
+       "      <td>1432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ammonia, Plasma</th>\n",
+       "      <td>373</td>\n",
+       "      <td>2377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hepatic Function Panel</th>\n",
+       "      <td>348</td>\n",
+       "      <td>1232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Urinalysis Macroscopic Only</th>\n",
+       "      <td>324</td>\n",
+       "      <td>1730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reticulocyte Count, Automated</th>\n",
+       "      <td>314</td>\n",
+       "      <td>1034</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  pat_enc_csn_id_coded  norders\n",
+       "njr_labname                                                    \n",
+       "CBC                                               4943    21277\n",
+       "Magnesium, Ser/Plas                               3569    19412\n",
+       "Chem 10                                           3445    16087\n",
+       "Renal Function Panel                              2815    15953\n",
+       "Metabolic Panel, Comprehensive                    2320     9282\n",
+       "POCT Urine                                        1853    15535\n",
+       "POCT Glucose                                      1781    32267\n",
+       "Tacrolimus Level, FK506                           1601     9360\n",
+       "Phosphorus, Ser/Plas                              1362     5642\n",
+       "Prothrombin Time                                  1192     6122\n",
+       "C Reactive Protein                                1174     3848\n",
+       "Gamma Glutamyl Trans                              1073     4901\n",
+       "Red Blood Cells                                    965     5525\n",
+       "Manual Differential/Slide Review                   899     3604\n",
+       "Metabolic Panel, Basic                             893     2564\n",
+       "Triglyceride, Ser/Plas                             689     3662\n",
+       "Blood Culture                                      664     2487\n",
+       "Fibrinogen                                         640     2726\n",
+       "Part Thromboplastin Time                           638     2358\n",
+       "Vancomycin Level, Trough                           565     2014\n",
+       "Urinalysis With Microscopic                        520     1492\n",
+       "ABO Rh Typing                                      513     1596\n",
+       "Antibody Screen                                    507     1584\n",
+       "Blood Gases, Arterial                              415     2068\n",
+       "Slide Review                                       391     1619\n",
+       "Bilirubin Direct, Ser/Plas                         389     1432\n",
+       "Ammonia, Plasma                                    373     2377\n",
+       "Hepatic Function Panel                             348     1232\n",
+       "Urinalysis Macroscopic Only                        324     1730\n",
+       "Reticulocyte Count, Automated                      314     1034"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempdf2 = enclabcounts.loc[enclabcounts.norders>1].groupby('njr_labname').agg({'pat_enc_csn_id_coded' : 'nunique', 'norders' : 'sum'}).sort_values('pat_enc_csn_id_coded', ascending=False)\n",
+    "tempdf2.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fa41d344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nenc</th>\n",
+       "      <th>avg_norders</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>1781</td>\n",
+       "      <td>18.117350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACT, Istat Kaolin Venous</th>\n",
+       "      <td>222</td>\n",
+       "      <td>11.725225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Clozapine Level</th>\n",
+       "      <td>2</td>\n",
+       "      <td>11.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lidocaine Level</th>\n",
+       "      <td>69</td>\n",
+       "      <td>10.869565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Specific Gravity</th>\n",
+       "      <td>18</td>\n",
+       "      <td>9.722222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Specific Gravity Refractometer POC</th>\n",
+       "      <td>94</td>\n",
+       "      <td>9.478723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heparin Activity Level</th>\n",
+       "      <td>85</td>\n",
+       "      <td>9.305882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACT, ISTAT</th>\n",
+       "      <td>5</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Istat EC3, Venous</th>\n",
+       "      <td>17</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Immune Complex Panel</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HLA Typing Solid Organ Eval Recipient</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cryptosporidium EIA</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Creatine, Ser/Plas</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B Cell Phenotyping Profile</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adenovirus AG</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Allo T Flow Crossmatch</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Angiotensin Convert Enz Level</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Anti Phospholipid Ab Panel</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Auto B Flow Crossmatch</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mycoplasma Antibody IgG IgM</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>617 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       nenc  avg_norders\n",
+       "njr_labname                                             \n",
+       "POCT Glucose                           1781    18.117350\n",
+       "ACT, Istat Kaolin Venous                222    11.725225\n",
+       "Clozapine Level                           2    11.000000\n",
+       "Lidocaine Level                          69    10.869565\n",
+       "Specific Gravity                         18     9.722222\n",
+       "Specific Gravity Refractometer POC       94     9.478723\n",
+       "Heparin Activity Level                   85     9.305882\n",
+       "ACT, ISTAT                                5     9.000000\n",
+       "Istat EC3, Venous                        17     9.000000\n",
+       "Immune Complex Panel                      1     9.000000\n",
+       "...                                     ...          ...\n",
+       "HLA Typing Solid Organ Eval Recipient     2     2.000000\n",
+       "Cryptosporidium EIA                       2     2.000000\n",
+       "Creatine, Ser/Plas                        2     2.000000\n",
+       "B Cell Phenotyping Profile                2     2.000000\n",
+       "Adenovirus AG                             2     2.000000\n",
+       "Allo T Flow Crossmatch                    2     2.000000\n",
+       "Angiotensin Convert Enz Level             2     2.000000\n",
+       "Anti Phospholipid Ab Panel                2     2.000000\n",
+       "Auto B Flow Crossmatch                    2     2.000000\n",
+       "Mycoplasma Antibody IgG IgM               1     2.000000\n",
+       "\n",
+       "[617 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempdf2.sort_values('norders', ascending=False).rename(columns={'pat_enc_csn_id_coded': 'nenc', 'norders' : 'avg_norders'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "66bdc264",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>norders</th>\n",
+       "      <th>perenc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>POCT Glucose</th>\n",
+       "      <td>1781</td>\n",
+       "      <td>32267</td>\n",
+       "      <td>18.117350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POCT Urine</th>\n",
+       "      <td>1853</td>\n",
+       "      <td>15535</td>\n",
+       "      <td>8.383702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacrolimus Level, FK506</th>\n",
+       "      <td>1601</td>\n",
+       "      <td>9360</td>\n",
+       "      <td>5.846346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renal Function Panel</th>\n",
+       "      <td>2815</td>\n",
+       "      <td>15953</td>\n",
+       "      <td>5.667140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Magnesium, Ser/Plas</th>\n",
+       "      <td>3569</td>\n",
+       "      <td>19412</td>\n",
+       "      <td>5.439059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Prothrombin Time</th>\n",
+       "      <td>1192</td>\n",
+       "      <td>6122</td>\n",
+       "      <td>5.135906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chem 10</th>\n",
+       "      <td>3445</td>\n",
+       "      <td>16087</td>\n",
+       "      <td>4.669666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gamma Glutamyl Trans</th>\n",
+       "      <td>1073</td>\n",
+       "      <td>4901</td>\n",
+       "      <td>4.567568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CBC</th>\n",
+       "      <td>4943</td>\n",
+       "      <td>21277</td>\n",
+       "      <td>4.304471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Phosphorus, Ser/Plas</th>\n",
+       "      <td>1362</td>\n",
+       "      <td>5642</td>\n",
+       "      <td>4.142438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metabolic Panel, Comprehensive</th>\n",
+       "      <td>2320</td>\n",
+       "      <td>9282</td>\n",
+       "      <td>4.000862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C Reactive Protein</th>\n",
+       "      <td>1174</td>\n",
+       "      <td>3848</td>\n",
+       "      <td>3.277683</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                pat_enc_csn_id_coded  norders     perenc\n",
+       "njr_labname                                                             \n",
+       "POCT Glucose                                    1781    32267  18.117350\n",
+       "POCT Urine                                      1853    15535   8.383702\n",
+       "Tacrolimus Level, FK506                         1601     9360   5.846346\n",
+       "Renal Function Panel                            2815    15953   5.667140\n",
+       "Magnesium, Ser/Plas                             3569    19412   5.439059\n",
+       "Prothrombin Time                                1192     6122   5.135906\n",
+       "Chem 10                                         3445    16087   4.669666\n",
+       "Gamma Glutamyl Trans                            1073     4901   4.567568\n",
+       "CBC                                             4943    21277   4.304471\n",
+       "Phosphorus, Ser/Plas                            1362     5642   4.142438\n",
+       "Metabolic Panel, Comprehensive                  2320     9282   4.000862\n",
+       "C Reactive Protein                              1174     3848   3.277683"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempdf2['perenc'] = tempdf2.norders / tempdf2.pat_enc_csn_id_coded\n",
+    "tempdf2 = tempdf2.loc[tempdf2.pat_enc_csn_id_coded > 1000].sort_values('perenc', ascending=False)\n",
+    "tempdf2.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4df7516c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labnames = [\n",
+    "    'POCT Glucose',\n",
+    "    'Tacrolimus Level, FK506',\n",
+    "    'Renal Function Panel',\n",
+    "    'Magnesium, Ser/Plas',\n",
+    "    'Prothrombin Time',\n",
+    "    'Chem 10',\n",
+    "    'Gamma Glutamyl Trans',\n",
+    "    'CBC',\n",
+    "    'Phosphorus, Ser/Plas',\n",
+    "    'Metabolic Panel, Comprehensive'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1de4a419",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt_orders = lradt.loc[lradt.njr_labname.isin(labnames)].groupby('order_id_coded').first()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a769c1cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "166998"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lradt_orders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "12e6872a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>uid</th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>component_id</th>\n",
+       "      <th>order_type</th>\n",
+       "      <th>proc_code</th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>lab_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>ord_num_value</th>\n",
+       "      <th>reference_low</th>\n",
+       "      <th>reference_high</th>\n",
+       "      <th>reference_unit</th>\n",
+       "      <th>result_in_range_yn</th>\n",
+       "      <th>result_flag</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>effective_time_jittered</th>\n",
+       "      <th>pat_class</th>\n",
+       "      <th>department_id</th>\n",
+       "      <th>pat_service</th>\n",
+       "      <th>pat_lv_of_care</th>\n",
+       "      <th>accomodation</th>\n",
+       "      <th>from_base_class</th>\n",
+       "      <th>to_base_class</th>\n",
+       "      <th>in_event_type</th>\n",
+       "      <th>out_event_type</th>\n",
+       "      <th>event_id_coded</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>njr_labname</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>700773149</th>\n",
+       "      <td>571947</td>\n",
+       "      <td>JC588552</td>\n",
+       "      <td>309013763</td>\n",
+       "      <td>5397</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB876</td>\n",
+       "      <td>Tacrolimus Level, FK506</td>\n",
+       "      <td>Dosage (incl. Units of Measure)</td>\n",
+       "      <td>TACR3</td>\n",
+       "      <td>9999999.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>ng/mL</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2014-04-13 08:06:00</td>\n",
+       "      <td>2014-04-13 10:18:00</td>\n",
+       "      <td>2014-04-12 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201108</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>123309</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Tacrolimus Level, FK506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700773150</th>\n",
+       "      <td>571959</td>\n",
+       "      <td>JC588552</td>\n",
+       "      <td>309013763</td>\n",
+       "      <td>794</td>\n",
+       "      <td>Lab</td>\n",
+       "      <td>LAB19</td>\n",
+       "      <td>Renal Function Panel</td>\n",
+       "      <td>Potassium, Ser/Plas</td>\n",
+       "      <td>K</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>High</td>\n",
+       "      <td>2014-04-13 08:06:00</td>\n",
+       "      <td>2014-04-13 09:58:00</td>\n",
+       "      <td>2014-04-12 23:59:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>10201108</td>\n",
+       "      <td>Gastroenterology</td>\n",
+       "      <td>Acute Care</td>\n",
+       "      <td>PEDS MED/SURG Level 3</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>123309</td>\n",
+       "      <td>Census</td>\n",
+       "      <td>Renal Function Panel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   uid   anon_id pat_enc_csn_id_coded  component_id  \\\n",
+       "order_id_coded                                                        \n",
+       "700773149       571947  JC588552            309013763          5397   \n",
+       "700773150       571959  JC588552            309013763           794   \n",
+       "\n",
+       "               order_type proc_code           group_lab_name  \\\n",
+       "order_id_coded                                                 \n",
+       "700773149             Lab    LAB876  Tacrolimus Level, FK506   \n",
+       "700773150             Lab     LAB19     Renal Function Panel   \n",
+       "\n",
+       "                                       lab_name base_name  ord_num_value  \\\n",
+       "order_id_coded                                                             \n",
+       "700773149       Dosage (incl. Units of Measure)     TACR3      9999999.0   \n",
+       "700773150                   Potassium, Ser/Plas         K            5.1   \n",
+       "\n",
+       "               reference_low reference_high reference_unit result_in_range_yn  \\\n",
+       "order_id_coded                                                                  \n",
+       "700773149                  3             20          ng/mL                  Y   \n",
+       "700773150                3.5            6.0         mmol/L                  Y   \n",
+       "\n",
+       "               result_flag          order_time         result_time  \\\n",
+       "order_id_coded                                                       \n",
+       "700773149             None 2014-04-13 08:06:00 2014-04-13 10:18:00   \n",
+       "700773150             High 2014-04-13 08:06:00 2014-04-13 09:58:00   \n",
+       "\n",
+       "               effective_time_jittered  pat_class  department_id  \\\n",
+       "order_id_coded                                                     \n",
+       "700773149          2014-04-12 23:59:00  Inpatient       10201108   \n",
+       "700773150          2014-04-12 23:59:00  Inpatient       10201108   \n",
+       "\n",
+       "                     pat_service pat_lv_of_care           accomodation  \\\n",
+       "order_id_coded                                                           \n",
+       "700773149       Gastroenterology     Acute Care  PEDS MED/SURG Level 3   \n",
+       "700773150       Gastroenterology     Acute Care  PEDS MED/SURG Level 3   \n",
+       "\n",
+       "               from_base_class to_base_class in_event_type out_event_type  \\\n",
+       "order_id_coded                                                              \n",
+       "700773149            Inpatient     Inpatient        Census         Census   \n",
+       "700773150            Inpatient     Inpatient        Census         Census   \n",
+       "\n",
+       "                event_id_coded event_type              njr_labname  \n",
+       "order_id_coded                                                      \n",
+       "700773149               123309     Census  Tacrolimus Level, FK506  \n",
+       "700773150               123309     Census     Renal Function Panel  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lradt_orders.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9bad80c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>701122666</th>\n",
+       "      <td>JC608450</td>\n",
+       "      <td>308903549</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-25 21:05:00</td>\n",
+       "      <td>2014-05-26 07:24:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701239393</th>\n",
+       "      <td>JC639827</td>\n",
+       "      <td>308903923</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC984330</td>\n",
+       "      <td>308904689</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-30 21:05:00</td>\n",
+       "      <td>2014-07-01 07:51:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287760</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 17:42:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701390874</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-03 21:05:00</td>\n",
+       "      <td>2014-05-04 08:24:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded njr_labname  \\\n",
+       "order_id_coded                                               \n",
+       "701122666        JC608450            308903549         CBC   \n",
+       "701239393        JC639827            308903923         CBC   \n",
+       "701504059        JC984330            308904689         CBC   \n",
+       "701287760       JC1142284            308905542         CBC   \n",
+       "701390874       JC1142284            308905542         CBC   \n",
+       "\n",
+       "                        order_time         result_time  \n",
+       "order_id_coded                                          \n",
+       "701122666      2014-05-25 21:05:00 2014-05-26 07:24:00  \n",
+       "701239393      2014-06-02 21:05:00 2014-06-03 08:06:00  \n",
+       "701504059      2014-06-30 21:05:00 2014-07-01 07:51:00  \n",
+       "701287760      2014-05-02 13:35:00 2014-05-02 17:42:00  \n",
+       "701390874      2014-05-03 21:05:00 2014-05-04 08:24:00  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf = lradt_orders[['anon_id', 'pat_enc_csn_id_coded', 'njr_labname', 'order_time', 'result_time']].sort_values(['pat_enc_csn_id_coded', 'njr_labname', 'result_time', 'order_time'])\n",
+    "ordersdf.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a873b9ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>701122666</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701239393</th>\n",
+       "      <td>JC608450</td>\n",
+       "      <td>308903549</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-25 21:05:00</td>\n",
+       "      <td>2014-05-26 07:24:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC639827</td>\n",
+       "      <td>308903923</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287760</th>\n",
+       "      <td>JC984330</td>\n",
+       "      <td>308904689</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-30 21:05:00</td>\n",
+       "      <td>2014-07-01 07:51:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701390874</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 17:42:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded njr_labname  \\\n",
+       "order_id_coded                                               \n",
+       "701122666             NaN                  NaN         NaN   \n",
+       "701239393        JC608450            308903549         CBC   \n",
+       "701504059        JC639827            308903923         CBC   \n",
+       "701287760        JC984330            308904689         CBC   \n",
+       "701390874       JC1142284            308905542         CBC   \n",
+       "\n",
+       "                        order_time         result_time  \n",
+       "order_id_coded                                          \n",
+       "701122666                      NaT                 NaT  \n",
+       "701239393      2014-05-25 21:05:00 2014-05-26 07:24:00  \n",
+       "701504059      2014-06-02 21:05:00 2014-06-03 08:06:00  \n",
+       "701287760      2014-06-30 21:05:00 2014-07-01 07:51:00  \n",
+       "701390874      2014-05-02 13:35:00 2014-05-02 17:42:00  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf.shift(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d0949de5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordersdf_shift = ordersdf.shift(1)\n",
+    "ordersdf['dif'] = pd.NaT\n",
+    "ordersdf['dif'] = ordersdf.order_time - ordersdf_shift.result_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "59d134d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newlabidx = (ordersdf.njr_labname != ordersdf_shift.njr_labname) | (ordersdf.pat_enc_csn_id_coded != ordersdf_shift.pat_enc_csn_id_coded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6187e17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordersdf.loc[newlabidx, 'dif'] = pd.NaT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d6bd3cb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>dif</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  <tbody>\n",
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+       "      <th>701239393</th>\n",
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+       "      <td>2014-06-02 21:05:00</td>\n",
+       "      <td>2014-06-03 08:06:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504059</th>\n",
+       "      <td>JC984330</td>\n",
+       "      <td>308904689</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-06-30 21:05:00</td>\n",
+       "      <td>2014-07-01 07:51:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287760</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 17:42:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701390874</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-03 21:05:00</td>\n",
+       "      <td>2014-05-04 08:24:00</td>\n",
+       "      <td>1 days 03:23:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504067</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-09 12:28:00</td>\n",
+       "      <td>2014-05-09 13:37:00</td>\n",
+       "      <td>5 days 04:04:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287759</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 18:04:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457513</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-08 21:05:00</td>\n",
+       "      <td>2014-05-09 05:49:00</td>\n",
+       "      <td>6 days 03:01:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457516</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 05:55:00</td>\n",
+       "      <td>2014-05-09 07:26:00</td>\n",
+       "      <td>0 days 00:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504073</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 21:05:00</td>\n",
+       "      <td>2014-05-10 05:28:00</td>\n",
+       "      <td>0 days 13:39:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504087</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-10 21:05:00</td>\n",
+       "      <td>2014-05-11 06:32:00</td>\n",
+       "      <td>0 days 15:37:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504106</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-11 21:05:00</td>\n",
+       "      <td>2014-05-12 06:00:00</td>\n",
+       "      <td>0 days 14:33:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701580858</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-12 21:05:00</td>\n",
+       "      <td>2014-05-13 03:48:00</td>\n",
+       "      <td>0 days 15:05:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701287761</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Prothrombin Time</td>\n",
+       "      <td>2014-05-02 13:35:00</td>\n",
+       "      <td>2014-05-02 18:03:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702382826</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-31 07:58:00</td>\n",
+       "      <td>2014-07-31 10:59:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703408214</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-09-10 21:05:00</td>\n",
+       "      <td>2014-09-11 07:45:00</td>\n",
+       "      <td>41 days 10:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702343597</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-07-28 21:05:00</td>\n",
+       "      <td>2014-07-29 10:42:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282771</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-02 21:05:00</td>\n",
+       "      <td>2014-09-03 06:31:00</td>\n",
+       "      <td>35 days 10:23:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282797</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-04 21:05:00</td>\n",
+       "      <td>2014-09-05 06:58:00</td>\n",
+       "      <td>1 days 14:34:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282805</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-05 21:05:00</td>\n",
+       "      <td>2014-09-06 07:49:00</td>\n",
+       "      <td>0 days 14:07:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703521782</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-10 21:05:00</td>\n",
+       "      <td>2014-09-11 08:09:00</td>\n",
+       "      <td>4 days 13:16:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703687965</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-18 07:02:00</td>\n",
+       "      <td>2014-09-18 07:49:00</td>\n",
+       "      <td>6 days 22:53:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703687996</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-19 21:05:00</td>\n",
+       "      <td>2014-09-20 07:35:00</td>\n",
+       "      <td>1 days 13:16:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703521779</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Prothrombin Time</td>\n",
+       "      <td>2014-09-10 21:05:00</td>\n",
+       "      <td>2014-09-11 08:01:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700890706</th>\n",
+       "      <td>JC738998</td>\n",
+       "      <td>308916366</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-05-16 21:05:00</td>\n",
+       "      <td>2014-05-17 07:56:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701794833</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-17 11:44:00</td>\n",
+       "      <td>2014-07-17 13:11:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701839769</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-18 21:05:00</td>\n",
+       "      <td>2014-07-19 06:59:00</td>\n",
+       "      <td>1 days 07:54:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701862701</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-19 21:05:00</td>\n",
+       "      <td>2014-07-20 06:22:00</td>\n",
+       "      <td>0 days 14:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701999661</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-07-27 06:59:00</td>\n",
+       "      <td>2014-07-27 10:37:00</td>\n",
+       "      <td>7 days 00:37:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701845690</th>\n",
+       "      <td>JC602155</td>\n",
+       "      <td>308921751</td>\n",
+       "      <td>Gamma Glutamyl Trans</td>\n",
+       "      <td>2014-07-19 06:50:00</td>\n",
+       "      <td>2014-07-20 07:22:00</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded           njr_labname  \\\n",
+       "order_id_coded                                                         \n",
+       "701122666        JC608450            308903549                   CBC   \n",
+       "701239393        JC639827            308903923                   CBC   \n",
+       "701504059        JC984330            308904689                   CBC   \n",
+       "701287760       JC1142284            308905542                   CBC   \n",
+       "701390874       JC1142284            308905542                   CBC   \n",
+       "701504067       JC1142284            308905542                   CBC   \n",
+       "701287759       JC1142284            308905542               Chem 10   \n",
+       "701457513       JC1142284            308905542               Chem 10   \n",
+       "701457516       JC1142284            308905542               Chem 10   \n",
+       "701504073       JC1142284            308905542               Chem 10   \n",
+       "701504087       JC1142284            308905542               Chem 10   \n",
+       "701504106       JC1142284            308905542               Chem 10   \n",
+       "701580858       JC1142284            308905542               Chem 10   \n",
+       "701287761       JC1142284            308905542      Prothrombin Time   \n",
+       "702382826        JC605249            308913369                   CBC   \n",
+       "703408214        JC605249            308913369                   CBC   \n",
+       "702343597        JC605249            308913369               Chem 10   \n",
+       "703282771        JC605249            308913369               Chem 10   \n",
+       "703282797        JC605249            308913369               Chem 10   \n",
+       "703282805        JC605249            308913369               Chem 10   \n",
+       "703521782        JC605249            308913369               Chem 10   \n",
+       "703687965        JC605249            308913369               Chem 10   \n",
+       "703687996        JC605249            308913369               Chem 10   \n",
+       "703521779        JC605249            308913369      Prothrombin Time   \n",
+       "700890706        JC738998            308916366                   CBC   \n",
+       "701794833        JC602155            308921751                   CBC   \n",
+       "701839769        JC602155            308921751                   CBC   \n",
+       "701862701        JC602155            308921751                   CBC   \n",
+       "701999661        JC602155            308921751                   CBC   \n",
+       "701845690        JC602155            308921751  Gamma Glutamyl Trans   \n",
+       "\n",
+       "                        order_time         result_time              dif  \n",
+       "order_id_coded                                                           \n",
+       "701122666      2014-05-25 21:05:00 2014-05-26 07:24:00              NaT  \n",
+       "701239393      2014-06-02 21:05:00 2014-06-03 08:06:00              NaT  \n",
+       "701504059      2014-06-30 21:05:00 2014-07-01 07:51:00              NaT  \n",
+       "701287760      2014-05-02 13:35:00 2014-05-02 17:42:00              NaT  \n",
+       "701390874      2014-05-03 21:05:00 2014-05-04 08:24:00  1 days 03:23:00  \n",
+       "701504067      2014-05-09 12:28:00 2014-05-09 13:37:00  5 days 04:04:00  \n",
+       "701287759      2014-05-02 13:35:00 2014-05-02 18:04:00              NaT  \n",
+       "701457513      2014-05-08 21:05:00 2014-05-09 05:49:00  6 days 03:01:00  \n",
+       "701457516      2014-05-09 05:55:00 2014-05-09 07:26:00  0 days 00:06:00  \n",
+       "701504073      2014-05-09 21:05:00 2014-05-10 05:28:00  0 days 13:39:00  \n",
+       "701504087      2014-05-10 21:05:00 2014-05-11 06:32:00  0 days 15:37:00  \n",
+       "701504106      2014-05-11 21:05:00 2014-05-12 06:00:00  0 days 14:33:00  \n",
+       "701580858      2014-05-12 21:05:00 2014-05-13 03:48:00  0 days 15:05:00  \n",
+       "701287761      2014-05-02 13:35:00 2014-05-02 18:03:00              NaT  \n",
+       "702382826      2014-07-31 07:58:00 2014-07-31 10:59:00              NaT  \n",
+       "703408214      2014-09-10 21:05:00 2014-09-11 07:45:00 41 days 10:06:00  \n",
+       "702343597      2014-07-28 21:05:00 2014-07-29 10:42:00              NaT  \n",
+       "703282771      2014-09-02 21:05:00 2014-09-03 06:31:00 35 days 10:23:00  \n",
+       "703282797      2014-09-04 21:05:00 2014-09-05 06:58:00  1 days 14:34:00  \n",
+       "703282805      2014-09-05 21:05:00 2014-09-06 07:49:00  0 days 14:07:00  \n",
+       "703521782      2014-09-10 21:05:00 2014-09-11 08:09:00  4 days 13:16:00  \n",
+       "703687965      2014-09-18 07:02:00 2014-09-18 07:49:00  6 days 22:53:00  \n",
+       "703687996      2014-09-19 21:05:00 2014-09-20 07:35:00  1 days 13:16:00  \n",
+       "703521779      2014-09-10 21:05:00 2014-09-11 08:01:00              NaT  \n",
+       "700890706      2014-05-16 21:05:00 2014-05-17 07:56:00              NaT  \n",
+       "701794833      2014-07-17 11:44:00 2014-07-17 13:11:00              NaT  \n",
+       "701839769      2014-07-18 21:05:00 2014-07-19 06:59:00  1 days 07:54:00  \n",
+       "701862701      2014-07-19 21:05:00 2014-07-20 06:22:00  0 days 14:06:00  \n",
+       "701999661      2014-07-27 06:59:00 2014-07-27 10:37:00  7 days 00:37:00  \n",
+       "701845690      2014-07-19 06:50:00 2014-07-20 07:22:00              NaT  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordersdf.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7526ca32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>njr_labname</th>\n",
+       "      <th>order_time</th>\n",
+       "      <th>result_time</th>\n",
+       "      <th>dif</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id_coded</th>\n",
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+       "      <th></th>\n",
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+       "  <tbody>\n",
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+       "      <th>701390874</th>\n",
+       "      <td>JC1142284</td>\n",
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+       "      <td>2014-05-03 21:05:00</td>\n",
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+       "      <td>CBC</td>\n",
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+       "      <td>2014-05-09 13:37:00</td>\n",
+       "      <td>5 days 04:04:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457513</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-08 21:05:00</td>\n",
+       "      <td>2014-05-09 05:49:00</td>\n",
+       "      <td>6 days 03:01:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701457516</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 05:55:00</td>\n",
+       "      <td>2014-05-09 07:26:00</td>\n",
+       "      <td>0 days 00:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504073</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-09 21:05:00</td>\n",
+       "      <td>2014-05-10 05:28:00</td>\n",
+       "      <td>0 days 13:39:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504087</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-10 21:05:00</td>\n",
+       "      <td>2014-05-11 06:32:00</td>\n",
+       "      <td>0 days 15:37:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701504106</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-11 21:05:00</td>\n",
+       "      <td>2014-05-12 06:00:00</td>\n",
+       "      <td>0 days 14:33:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701580858</th>\n",
+       "      <td>JC1142284</td>\n",
+       "      <td>308905542</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-05-12 21:05:00</td>\n",
+       "      <td>2014-05-13 03:48:00</td>\n",
+       "      <td>0 days 15:05:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703408214</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>CBC</td>\n",
+       "      <td>2014-09-10 21:05:00</td>\n",
+       "      <td>2014-09-11 07:45:00</td>\n",
+       "      <td>41 days 10:06:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703282771</th>\n",
+       "      <td>JC605249</td>\n",
+       "      <td>308913369</td>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>2014-09-02 21:05:00</td>\n",
+       "      <td>2014-09-03 06:31:00</td>\n",
+       "      <td>35 days 10:23:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  anon_id pat_enc_csn_id_coded njr_labname  \\\n",
+       "order_id_coded                                               \n",
+       "701390874       JC1142284            308905542         CBC   \n",
+       "701504067       JC1142284            308905542         CBC   \n",
+       "701457513       JC1142284            308905542     Chem 10   \n",
+       "701457516       JC1142284            308905542     Chem 10   \n",
+       "701504073       JC1142284            308905542     Chem 10   \n",
+       "701504087       JC1142284            308905542     Chem 10   \n",
+       "701504106       JC1142284            308905542     Chem 10   \n",
+       "701580858       JC1142284            308905542     Chem 10   \n",
+       "703408214        JC605249            308913369         CBC   \n",
+       "703282771        JC605249            308913369     Chem 10   \n",
+       "\n",
+       "                        order_time         result_time              dif  \n",
+       "order_id_coded                                                           \n",
+       "701390874      2014-05-03 21:05:00 2014-05-04 08:24:00  1 days 03:23:00  \n",
+       "701504067      2014-05-09 12:28:00 2014-05-09 13:37:00  5 days 04:04:00  \n",
+       "701457513      2014-05-08 21:05:00 2014-05-09 05:49:00  6 days 03:01:00  \n",
+       "701457516      2014-05-09 05:55:00 2014-05-09 07:26:00  0 days 00:06:00  \n",
+       "701504073      2014-05-09 21:05:00 2014-05-10 05:28:00  0 days 13:39:00  \n",
+       "701504087      2014-05-10 21:05:00 2014-05-11 06:32:00  0 days 15:37:00  \n",
+       "701504106      2014-05-11 21:05:00 2014-05-12 06:00:00  0 days 14:33:00  \n",
+       "701580858      2014-05-12 21:05:00 2014-05-13 03:48:00  0 days 15:05:00  \n",
+       "703408214      2014-09-10 21:05:00 2014-09-11 07:45:00 41 days 10:06:00  \n",
+       "703282771      2014-09-02 21:05:00 2014-09-03 06:31:00 35 days 10:23:00  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repeat_orders = ordersdf.loc[ordersdf.dif.notnull()]\n",
+    "repeat_orders.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ee28f501",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_quant = [pd.Timedelta('24H'), pd.Timedelta('3D'), pd.Timedelta('1W')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "94a9ea5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lab:  POCT Glucose\n",
+      "[0, 24h):\t 29717\n",
+      "[24h, 3d)]:\t 436\n",
+      "[3d, 7d)]:\t 171\n",
+      "[7d, inf)]:\t 162\n",
+      "Lab:  Tacrolimus Level, FK506\n",
+      "[0, 24h):\t 6559\n",
+      "[24h, 3d)]:\t 947\n",
+      "[3d, 7d)]:\t 201\n",
+      "[7d, inf)]:\t 52\n",
+      "Lab:  Renal Function Panel\n",
+      "[0, 24h):\t 10090\n",
+      "[24h, 3d)]:\t 2014\n",
+      "[3d, 7d)]:\t 816\n",
+      "[7d, inf)]:\t 218\n",
+      "Lab:  Magnesium, Ser/Plas\n",
+      "[0, 24h):\t 11307\n",
+      "[24h, 3d)]:\t 2628\n",
+      "[3d, 7d)]:\t 1397\n",
+      "[7d, inf)]:\t 511\n",
+      "Lab:  Prothrombin Time\n",
+      "[0, 24h):\t 2641\n",
+      "[24h, 3d)]:\t 1396\n",
+      "[3d, 7d)]:\t 612\n",
+      "[7d, inf)]:\t 281\n",
+      "Lab:  Chem 10\n",
+      "[0, 24h):\t 7816\n",
+      "[24h, 3d)]:\t 3475\n",
+      "[3d, 7d)]:\t 998\n",
+      "[7d, inf)]:\t 353\n",
+      "Lab:  Gamma Glutamyl Trans\n",
+      "[0, 24h):\t 2423\n",
+      "[24h, 3d)]:\t 652\n",
+      "[3d, 7d)]:\t 605\n",
+      "[7d, inf)]:\t 148\n",
+      "Lab:  CBC\n",
+      "[0, 24h):\t 8824\n",
+      "[24h, 3d)]:\t 4090\n",
+      "[3d, 7d)]:\t 2458\n",
+      "[7d, inf)]:\t 962\n",
+      "Lab:  Phosphorus, Ser/Plas\n",
+      "[0, 24h):\t 2324\n",
+      "[24h, 3d)]:\t 683\n",
+      "[3d, 7d)]:\t 922\n",
+      "[7d, inf)]:\t 351\n",
+      "Lab:  Metabolic Panel, Comprehensive\n",
+      "[0, 24h):\t 3898\n",
+      "[24h, 3d)]:\t 1214\n",
+      "[3d, 7d)]:\t 1300\n",
+      "[7d, inf)]:\t 550\n"
+     ]
+    }
+   ],
+   "source": [
+    "labdtq_idx = ['<24h', '[24h, 3d)', '[3d, 7d)', '>=7d']\n",
+    "labdtq = pd.DataFrame(index=labdtq_idx)\n",
+    "for lab in labnames:\n",
+    "    print('Lab: ', lab)\n",
+    "    dtimes = repeat_orders.loc[repeat_orders.njr_labname==lab].dif\n",
+    "    p_enc = repeat_orders.loc[repeat_orders.njr_labname==lab, 'pat_enc_csn_id_coded'].nunique()\n",
+    "    dtq = []\n",
+    "    dtq.append(sum(dtimes < pd.Timedelta('24H')))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('3D')) & (dtimes >= pd.Timedelta('24H')) ))\n",
+    "    dtq.append(sum( (dtimes < pd.Timedelta('7D')) & (dtimes >= pd.Timedelta('3D')) ))\n",
+    "    dtq.append(sum((dtimes >= pd.Timedelta('7D')) ))\n",
+    "    dtq\n",
+    "    print('[0, 24h):\\t', dtq[0])\n",
+    "    print('[24h, 3d)]:\\t', dtq[1])\n",
+    "    print('[3d, 7d)]:\\t', dtq[2])\n",
+    "    print('[7d, inf)]:\\t', dtq[3])\n",
+    "    labdtq[lab] = np.cumsum(dtq)/p_enc*1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "802d9ea3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax1 = labdtq.iloc[3].plot.barh(color='#404c54');\n",
+    "labdtq.iloc[2].plot.barh(ax=ax1, color='#f0cc94');\n",
+    "labdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "labdtq.iloc[0].plot.barh(ax=ax1, color='#807A6B');\n",
+    "ax1.legend();\n",
+    "ax1.invert_yaxis();\n",
+    "ax1.set_title('Commonly Repeated Lab Tests in Pediatric Inpatient Medicine');\n",
+    "ax1.set_xlabel('No. Lab Orders per 1000 Encounters');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e891c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e79d90f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e59dcaf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9867f691",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/figures_cic.ipynb b/scripts/PedsLabUtil/figures_cic.ipynb
new file mode 100644
index 00000000..aaf60a7d
--- /dev/null
+++ b/scripts/PedsLabUtil/figures_cic.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6e81f980",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.dpi'] = 150\n",
+    "import seaborn as sns\n",
+    "\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 100\n",
+    "pd.options.display.min_rows = 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7228fa7c",
+   "metadata": {},
+   "source": [
+    "## Figure 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "564595b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labdtq = pd.read_csv('data/labdtq.csv', index_col=0)\n",
+    "\n",
+    "# original\n",
+    "# ax1 = labdtq.iloc[3].plot.barh(color='#404c54');\n",
+    "# labdtq.iloc[2].plot.barh(ax=ax1, color='#f0cc94');\n",
+    "# labdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "# labdtq.iloc[0].plot.barh(ax=ax1, color='#807A6B');\n",
+    "\n",
+    "# blue gradient\n",
+    "ax1 = labdtq.iloc[3].plot.barh(color='#2F4858');\n",
+    "labdtq.iloc[2].plot.barh(ax=ax1, color='#F2CC88');\n",
+    "labdtq.iloc[1].plot.barh(ax=ax1, color='#c0cccc');\n",
+    "labdtq.iloc[0].plot.barh(ax=ax1, color='#918071');\n",
+    "\n",
+    "ax1.legend();\n",
+    "ax1.invert_yaxis();\n",
+    "ax1.set_title('Commonly Repeated Lab Orders in Pediatric Inpatient Medicine');\n",
+    "ax1.set_xlabel('No. Lab Orders per 1000 Encounters');\n",
+    "\n",
+    "handles, labels = ax1.get_legend_handles_labels()\n",
+    "ax1.legend(reversed(handles), reversed(labels));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4538e304",
+   "metadata": {},
+   "source": [
+    "## Figure 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dcb257bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotLabStab(df, ndays, ax = None, subset=False, labs=['']):\n",
+    "    df = df[0:ndays+1]\n",
+    "    if subset:\n",
+    "        df = df[labs]\n",
+    "    sns.set_style(\"white\");\n",
+    "    if ax is None:\n",
+    "        ax = sns.lineplot(data = df, markers=True);\n",
+    "    else:\n",
+    "        sns.lineplot(data = df, markers=True, ax = ax);\n",
+    "    ax.set_xlabel('Consecutive Normal Results in Past 7 Days Preceding Lab');\n",
+    "    ax.set_xlim(-0.25, ndays+0.25)\n",
+    "    ax.set_xticks(np.arange(ndays+1))\n",
+    "    ax.set_ylabel('Proportion Normal Results');\n",
+    "    ax.set_title('Stability for Common Inpatient Laboratory Studies');\n",
+    "    ax.set_ylim(0, 1.0);\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bd0f97ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x262.5 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labstab = pd.read_csv('data/labstab.csv', index_col=0)\n",
+    "labstabcnts = pd.read_csv('data/labstabcnts.csv', index_col=0)\n",
+    "STREAK = 6\n",
+    "\n",
+    "cmap = sns.color_palette('deep')\n",
+    "dash_list = sns._core.unique_dashes(8);\n",
+    "marker_list = sns._core.unique_markers(8)\n",
+    "\n",
+    "chem1 = ['Sodium', 'Potassium', 'Creatinine']\n",
+    "chem2 = ['Magnesium', 'Phosphorus']\n",
+    "cbc = ['White Cells', 'Hemoglobin', 'Platelets']\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(6, 1.75), constrained_layout=True)\n",
+    "\n",
+    "sns.lineplot(data=labstab[chem1][1:STREAK+2], ax=axes[0],\n",
+    "             palette = cmap[0:3], dashes=dash_list[0:3], markers=marker_list[0:3])\n",
+    "sns.lineplot(data=labstab[chem2][1:STREAK+2], ax=axes[1], \n",
+    "             palette = cmap[3:5], dashes=dash_list[3:5], markers=marker_list[3:5])\n",
+    "sns.lineplot(data=labstab[cbc][1:STREAK+2], ax=axes[2], \n",
+    "             palette = cmap[5:8], dashes=dash_list[5:8], markers=marker_list[5:8])\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_xlim(-0.25, STREAK+0.25);\n",
+    "    ax.set_ylim(-0.05, 1.05);\n",
+    "    ax.set_xticks(np.arange(STREAK+1));\n",
+    "    ax.set_xticklabels(labels=np.arange(STREAK+1), fontsize=6);\n",
+    "    ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
+    "    ax.set_yticklabels(labels=[0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=6);\n",
+    "    ax.set_ylabel('');\n",
+    "    ax.set_xlabel('');\n",
+    "    ax.set_title('');\n",
+    "    ax.legend(fontsize=5, loc=4);\n",
+    "    \n",
+    "#axes[0].set_title('Chem Panel', fontsize=7);\n",
+    "#axes[1].set_title('Additional Chemistries', fontsize=7);\n",
+    "#axes[2].set_title('Blood Count', fontsize=7);\n",
+    "\n",
+    "fig.supylabel('Proportion Normal Labs', fontsize=8);\n",
+    "fig.supxlabel('Preceding Streak of Normal Results in Past 7 Days', fontsize=8);\n",
+    "fig.suptitle('Stability of Common Inpatient Labs', fontsize=10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf1c700",
+   "metadata": {},
+   "source": [
+    "## Alternate Figure 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "af8d2c5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x262.5 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labstab = pd.read_csv('data/labstab.csv', index_col=0)\n",
+    "labstabcnts = pd.read_csv('data/labstabcnts.csv', index_col=0)\n",
+    "STREAK = 6\n",
+    "\n",
+    "cmap = sns.color_palette('deep')\n",
+    "dash_list = sns._core.unique_dashes(8);\n",
+    "marker_list = sns._core.unique_markers(8)\n",
+    "\n",
+    "tlysis = ['LDH', 'Uric Acid']\n",
+    "chem1 = ['Sodium', 'Potassium', 'Creatinine']\n",
+    "cbc = ['White Cells', 'Hemoglobin', 'Platelets']\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(6, 1.75), constrained_layout=True)\n",
+    "\n",
+    "sns.lineplot(data=labstab[tlysis][1:STREAK+2], ax=axes[0],\n",
+    "             palette = cmap[0:2], dashes=dash_list[0:2], markers=marker_list[0:2])\n",
+    "sns.lineplot(data=labstab[chem1][1:STREAK+2], ax=axes[1], \n",
+    "             palette = cmap[2:5], dashes=dash_list[2:5], markers=marker_list[2:5])\n",
+    "sns.lineplot(data=labstab[cbc][1:STREAK+2], ax=axes[2], \n",
+    "             palette = cmap[5:8], dashes=dash_list[5:8], markers=marker_list[5:8])\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_xlim(-0.25, STREAK+0.25);\n",
+    "    ax.set_ylim(-0.05, 1.05);\n",
+    "    ax.set_xticks(np.arange(STREAK+1));\n",
+    "    ax.set_xticklabels(labels=np.arange(STREAK+1), fontsize=6);\n",
+    "    ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
+    "    ax.set_yticklabels(labels=[0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=6);\n",
+    "    ax.set_ylabel('');\n",
+    "    ax.set_xlabel('');\n",
+    "    ax.set_title('');\n",
+    "    ax.legend(fontsize=5, loc=4);\n",
+    "    \n",
+    "axes[0].set_title('Tumor Lysis', fontsize=7);\n",
+    "axes[1].set_title('Chemistries', fontsize=7);\n",
+    "axes[2].set_title('CBC', fontsize=7);\n",
+    "\n",
+    "fig.supylabel('Proportion Normal Labs', fontsize=8);\n",
+    "fig.supxlabel('Preceding Streak of Normal Results in Past 7 Days', fontsize=8);\n",
+    "fig.suptitle('Stability of Commonly Repeated Inpatient Labs', fontsize=10);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f7b6066",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/query_log_2021-09-25.txt b/scripts/PedsLabUtil/query_log_2021-09-25.txt
new file mode 100644
index 00000000..2641da61
--- /dev/null
+++ b/scripts/PedsLabUtil/query_log_2021-09-25.txt
@@ -0,0 +1,59 @@
+Query 1: Create a new table at njr_labutil.lab_result_inpt that has a unique identifier column so that each lab_result row has a unique identifier called uid
+The results are saved to njr_labutil.lab_result_inpt
+
+SELECT ROW_NUMBER() OVER(ORDER BY order_time ASC) AS uid, *
+FROM `mining-clinical-decisions.lpch_core.lab_result`
+WHERE ordering_mode='Inpatient'
+--saved as njr_labutil.lab_result_inpt, includes unique ID column 'uid'
+
+
+
+Query 2: Join uid and encounter ID from the lab_result_inpt table with the nearest preceding row in adt using only the effective_time_jittered and pat_enc_csn_id_coded and seq_num_in_enc columns. In order to acehive this, we first join lab_result_inpt with adt table using the columns above, then sort by effective_time_jittered desc and seq_num_in_enc desc and then group by uid and select the most recent row as long as the adt event time is before the order time.
+The results are saved to njr_labutil.ued_time_enc
+
+WITH lr as (
+    SELECT * FROM njr_labutil.lab_result_inpt
+), 
+lradt as (
+    SELECT uid, lr.pat_enc_csn_id_coded, order_time, effective_time_jittered, seq_num_in_enc from lr
+    LEFT JOIN `mining-clinical-decisions.lpch_core.adt` as adt
+    ON lr.pat_enc_csn_id_coded = adt.pat_enc_csn_id_coded
+    ORDER BY lr.uid asc, adt.effective_time_jittered desc, adt.SEQ_NUM_IN_ENC desc
+)
+SELECT uid, MAX(lradt.pat_enc_csn_id_coded) as pat_enc_csn_id_coded, MAX(effective_time_jittered) as effective_time_jittered, MAX(seq_num_in_enc) as seq_num_in_enc
+FROM lradt
+WHERE lradt.order_time >= lradt.effective_time_jittered
+GROUP BY uid
+
+What happens here is that there are _ dropped lab_results:
+- 1,811,809 of the results do not have a corresponding encounter in adt
+-   317,807 of the results do not have an adt event that occurs before the lab_result is ordered
+
+
+
+Query 3: the ued_time_enc, and the full lab_result_inpt tables are joined together:
+Result is saved to njr_labutil.lab_result_inpt_uet_join
+
+SELECT *
+FROM `mining-clinical-decisions.njr_labutil.lab_result_inpt` as lri
+LEFT JOIN `mining-clinical-decisions.njr_labutil.uet_enc_time` as uet
+ON lri.uid = uet.uid
+ORDER BY effective_time_jittered asc
+
+
+
+Query 4: the lab_result_inpt_uet_join is joined with adt using the event time and num_seq_in_enc. Select columns selected.
+Result is saved in njr_labutil.lradt
+
+SELECT uid, lr.anon_id, lr.pat_enc_csn_id_coded, order_id_coded, component_id, order_type, proc_code,
+    group_lab_name, lab_name, base_name, ord_num_value, reference_low, reference_high, reference_unit,
+    result_in_range_yn, result_flag, order_time, result_time, lr.effective_time_jittered,
+    pat_class, department_id, pat_service, pat_lv_of_care, accomodation, from_base_class, to_base_class, in_event_type, out_event_type, 
+    event_id_coded, event_type
+FROM `mining-clinical-decisions.njr_labutil.lab_result_inpt_uet_join` as lr
+JOIN `mining-clinical-decisions.lpch_core.adt` as adt
+ON lr.pat_enc_csn_id_coded = adt.PAT_ENC_CSN_ID_CODED
+    AND lr.effective_time_jittered = adt.EFFECTIVE_TIME_JITTERED
+    AND lr.seq_num_in_enc = adt.SEQ_NUM_IN_ENC
+WHERE lr.pat_enc_csn_id_coded_1 IS NOT NULL
+ORDER BY lr.pat_enc_csn_id_coded asc
\ No newline at end of file
diff --git a/scripts/PedsLabUtil/query_lradt_20210927.ipynb b/scripts/PedsLabUtil/query_lradt_20210927.ipynb
new file mode 100644
index 00000000..458f130f
--- /dev/null
+++ b/scripts/PedsLabUtil/query_lradt_20210927.ipynb
@@ -0,0 +1,473 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "48704468",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3db83a6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = '''\n",
+    "WITH labs AS (\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    "    WHERE\n",
+    "        (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "        AND pat_service NOT IN (\n",
+    "            'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "            'Reproductive Endocrinology and Infertility',\n",
+    "            'Gynecology & Obstetrics', 'Gynecology', 'OB Anesthesia',\n",
+    "            'Intensive Care', 'Neonatology', 'Cardiovascular Intensive Care')\n",
+    "        AND (pat_lv_of_care = 'Acute Care'\n",
+    "            OR (pat_lv_of_care='' AND accomodation IN (\n",
+    "                'PEDS MED/SURG Level 1','PEDS MED/SURG Level 2', 'PEDS MED/SURG Level 3',\n",
+    "                'PEDS Med/Surg',\n",
+    "                'CCP Med/Surg Level 1','CCP Med/Surg Level 2','CCP Med/Surg Level 3',\n",
+    "                'PEDS TXP Med/Surg Level 1', 'PEDS TXP Med/Surg Level 2','PEDS TXP Med/Surg Level 3',\n",
+    "                'Oncology Level 1', 'Oncology Level 2', 'Oncology Level 3', 'Oncology Level 4'\n",
+    "                )\n",
+    "            )\n",
+    "        )\n",
+    ")\n",
+    "SELECT group_lab_name, COUNT(group_lab_name) cnt, COUNT(DISTINCT anon_id) pts FROM (\n",
+    "    SELECT DISTINCT(order_id_coded), anon_id, group_lab_name\n",
+    "    FROM labs\n",
+    ")\n",
+    "GROUP BY(group_lab_name)\n",
+    "ORDER by cnt desc\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "149475d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_job = client.query(query)\n",
+    "df = query_job.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8241f07f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group_lab_name</th>\n",
+       "      <th>cnt</th>\n",
+       "      <th>pts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CBC with Differential</td>\n",
+       "      <td>57451</td>\n",
+       "      <td>6319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Chem 10</td>\n",
+       "      <td>47573</td>\n",
+       "      <td>4601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Glucose PCX Meter</td>\n",
+       "      <td>43000</td>\n",
+       "      <td>2094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Magnesium, Ser/Plas</td>\n",
+       "      <td>42457</td>\n",
+       "      <td>4643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Renal Function Panel</td>\n",
+       "      <td>25686</td>\n",
+       "      <td>3073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Metabolic Panel, Comprehensive</td>\n",
+       "      <td>23308</td>\n",
+       "      <td>4825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Phosphorus, Ser/Plas</td>\n",
+       "      <td>21245</td>\n",
+       "      <td>3333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Urinalysis Macroscopic Only</td>\n",
+       "      <td>19734</td>\n",
+       "      <td>1004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Manual Differential/Slide Review</td>\n",
+       "      <td>18973</td>\n",
+       "      <td>2189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>ABO Rh Typing</td>\n",
+       "      <td>16377</td>\n",
+       "      <td>2544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Antibody Screen</td>\n",
+       "      <td>16273</td>\n",
+       "      <td>2413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>POCT Urine Dipstick (10)</td>\n",
+       "      <td>15510</td>\n",
+       "      <td>828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Red Blood Cells</td>\n",
+       "      <td>13814</td>\n",
+       "      <td>2059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Prothrombin Time</td>\n",
+       "      <td>13454</td>\n",
+       "      <td>3109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Tacrolimus Level, FK506</td>\n",
+       "      <td>13090</td>\n",
+       "      <td>802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Uric Acid, Ser/Plas</td>\n",
+       "      <td>9366</td>\n",
+       "      <td>917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Blood Culture</td>\n",
+       "      <td>9355</td>\n",
+       "      <td>1565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>LDH Total, Ser/Plas</td>\n",
+       "      <td>9211</td>\n",
+       "      <td>1120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Metabolic Panel, Basic</td>\n",
+       "      <td>8938</td>\n",
+       "      <td>2553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Platelets</td>\n",
+       "      <td>8823</td>\n",
+       "      <td>895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Part Thromboplastin Time</td>\n",
+       "      <td>8790</td>\n",
+       "      <td>2555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Gamma Glutamyl Trans</td>\n",
+       "      <td>8733</td>\n",
+       "      <td>1283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Fibrinogen</td>\n",
+       "      <td>8475</td>\n",
+       "      <td>2332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Urinalysis With Microscopic</td>\n",
+       "      <td>8068</td>\n",
+       "      <td>2908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>POCT Urine Dipstick (9)</td>\n",
+       "      <td>8016</td>\n",
+       "      <td>621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>CBC with Diff</td>\n",
+       "      <td>7813</td>\n",
+       "      <td>1292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>C Reactive Protein</td>\n",
+       "      <td>7670</td>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Hepatic Function Panel</td>\n",
+       "      <td>7567</td>\n",
+       "      <td>1688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Triglyceride, Ser/Plas</td>\n",
+       "      <td>7517</td>\n",
+       "      <td>1040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Slide Review</td>\n",
+       "      <td>6883</td>\n",
+       "      <td>1366</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      group_lab_name    cnt   pts\n",
+       "0              CBC with Differential  57451  6319\n",
+       "1                            Chem 10  47573  4601\n",
+       "2                  Glucose PCX Meter  43000  2094\n",
+       "3                Magnesium, Ser/Plas  42457  4643\n",
+       "4               Renal Function Panel  25686  3073\n",
+       "5     Metabolic Panel, Comprehensive  23308  4825\n",
+       "6               Phosphorus, Ser/Plas  21245  3333\n",
+       "7        Urinalysis Macroscopic Only  19734  1004\n",
+       "8   Manual Differential/Slide Review  18973  2189\n",
+       "9                      ABO Rh Typing  16377  2544\n",
+       "10                   Antibody Screen  16273  2413\n",
+       "11          POCT Urine Dipstick (10)  15510   828\n",
+       "12                   Red Blood Cells  13814  2059\n",
+       "13                  Prothrombin Time  13454  3109\n",
+       "14           Tacrolimus Level, FK506  13090   802\n",
+       "15               Uric Acid, Ser/Plas   9366   917\n",
+       "16                     Blood Culture   9355  1565\n",
+       "17               LDH Total, Ser/Plas   9211  1120\n",
+       "18            Metabolic Panel, Basic   8938  2553\n",
+       "19                         Platelets   8823   895\n",
+       "20          Part Thromboplastin Time   8790  2555\n",
+       "21              Gamma Glutamyl Trans   8733  1283\n",
+       "22                        Fibrinogen   8475  2332\n",
+       "23       Urinalysis With Microscopic   8068  2908\n",
+       "24           POCT Urine Dipstick (9)   8016   621\n",
+       "25                     CBC with Diff   7813  1292\n",
+       "26                C Reactive Protein   7670  3000\n",
+       "27            Hepatic Function Panel   7567  1688\n",
+       "28            Triglyceride, Ser/Plas   7517  1040\n",
+       "29                      Slide Review   6883  1366"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a89f12cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_all = '''\n",
+    "    SELECT * FROM njr_labutil.lradt\n",
+    "    WHERE\n",
+    "        (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "        AND pat_service NOT IN (\n",
+    "            'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "            'Reproductive Endocrinology and Infertility',\n",
+    "            'Gynecology & Obstetrics', 'Gynecology',\n",
+    "            'Intensive Care', 'Neonatology', 'Cardiovascular Intensive Care')\n",
+    "        AND (pat_lv_of_care = 'Acute Care'\n",
+    "            OR (pat_lv_of_care='' AND accomodation IN (\n",
+    "                'PEDS MED/SURG Level 1','PEDS MED/SURG Level 2', 'PEDS MED/SURG Level 3',\n",
+    "                'PEDS Med/Surg',\n",
+    "                'CCP Med/Surg Level 1','CCP Med/Surg Level 2','CCP Med/Surg Level 3',\n",
+    "                'PEDS TXP Med/Surg Level 1', 'PEDS TXP Med/Surg Level 2','PEDS TXP Med/Surg Level 3',\n",
+    "                'Oncology Level 1', 'Oncology Level 2', 'Oncology Level 3', 'Oncology Level 4'\n",
+    "                )\n",
+    "            )\n",
+    "        )\n",
+    "'''\n",
+    "query_job = client.query(query_all)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "013f12f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1353.993928194046\n"
+     ]
+    }
+   ],
+   "source": [
+    "tic = time.time()\n",
+    "lradt = query_job.to_dataframe()\n",
+    "print(time.time() - tic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7e162fdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt.loc[~lradt.ord_num_value.isnull()].to_pickle('data/lradt_20210927.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ef67270",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "query_accom = '''\n",
+    "SELECT * FROM njr_labutil.lradt\n",
+    "WHERE\n",
+    "    (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "    AND pat_service NOT IN (\n",
+    "        'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "        'Reproductive Endocrinology and Infertility',\n",
+    "        'Gynecology & Obstetrics', 'Gynecology',\n",
+    "        'Intensive Care', 'Neonatology', 'Cardiovascular Intensive Care'\n",
+    "    )\n",
+    "    AND (pat_lv_of_care = 'Acute Care' OR pat_lv_of_care = '')\n",
+    "    AND accomodation IN (\n",
+    "            'PEDS MED/SURG Level 1','PEDS MED/SURG Level 2', 'PEDS MED/SURG Level 3',\n",
+    "            'PEDS Med/Surg',\n",
+    "            'CCP Med/Surg Level 1','CCP Med/Surg Level 2','CCP Med/Surg Level 3'\n",
+    "    )\n",
+    "\n",
+    "'''\n",
+    "query_job = client.query(query_accom)\n",
+    "lradt_acc = query_job.to_dataframe()\n",
+    "lradt_acc.loc[~lradt.ord_num_value.isnull()].to_pickle('data/lradt_acc_20210927.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "50b3d95c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt_acc.loc[~lradt_acc.ord_num_value.isnull()].to_pickle('data/lradt_acc_20210927.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adf618ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/query_lradt_20211025.ipynb b/scripts/PedsLabUtil/query_lradt_20211025.ipynb
new file mode 100644
index 00000000..149fdf64
--- /dev/null
+++ b/scripts/PedsLabUtil/query_lradt_20211025.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4870d995",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "67eec892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = '''\n",
+    "SELECT * FROM njr_labutil.lradt\n",
+    "WHERE\n",
+    "    (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "    AND pat_service NOT IN (\n",
+    "        'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "        'Reproductive Endocrinology and Infertility',\n",
+    "        'Gynecology & Obstetrics', 'Gynecology',\n",
+    "        'Intensive Care', 'Neonatology', 'Cardiovascular Intensive Care')\n",
+    "    AND (pat_lv_of_care = 'Acute Care' OR pat_lv_of_care='')\n",
+    "    AND accomodation IN (\n",
+    "            'PEDS MED/SURG Level 1','PEDS MED/SURG Level 2', 'PEDS MED/SURG Level 3',\n",
+    "            'PEDS Med/Surg',\n",
+    "            'CCP Med/Surg Level 1','CCP Med/Surg Level 2','CCP Med/Surg Level 3',\n",
+    "            'PEDS TXP Med/Surg Level 1', 'PEDS TXP Med/Surg Level 2','PEDS TXP Med/Surg Level 3',\n",
+    "            'Oncology Level 1', 'Oncology Level 2', 'Oncology Level 3', 'Oncology Level 4', 'SCT', 'Onc Med/Surg', 'SCT 2 (SCT 2)',\n",
+    "            'ICN Lvl A', 'ICN Lvl B', 'ICN Lvl C', 'ICN Lvl D'\n",
+    "    )\n",
+    "'''\n",
+    "query_job = client.query(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "91368d1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "834.7488169670105\n"
+     ]
+    }
+   ],
+   "source": [
+    "tic = time.time()\n",
+    "lradt = query_job.to_dataframe()\n",
+    "print(time.time() - tic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c7c4d631",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt.loc[~lradt.ord_num_value.isnull()].to_pickle('data/lradt_20211025.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e809aaed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/PedsLabUtil/query_lradt_icu_20211027.ipynb b/scripts/PedsLabUtil/query_lradt_icu_20211027.ipynb
new file mode 100644
index 00000000..39d5ed0d
--- /dev/null
+++ b/scripts/PedsLabUtil/query_lradt_icu_20211027.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d73eecef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION+CREDENTIALS'] = '/Users/nrabbani/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'\n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8aeb0e43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Excluding accomodation ECMO and CVVH\n",
+    "query = '''\n",
+    "SELECT * FROM njr_labutil.lradt\n",
+    "    WHERE\n",
+    "        (pat_class = 'Inpatient' OR pat_class = 'Surgery Admit')\n",
+    "        AND pat_service NOT IN (\n",
+    "            'Obstetrics', 'Maternal-Fetal Medicine',\n",
+    "            'Reproductive Endocrinology and Infertility',\n",
+    "            'Gynecology & Obstetrics', 'Gynecology', 'OB Anesthesia')\n",
+    "        AND (pat_lv_of_care = 'Critical Care'\n",
+    "            OR (pat_lv_of_care='' AND accomodation IN (\n",
+    "                'CV Critical Care Level 1', 'CV Critical Care Level 2', 'CV Critical Care Level 3', \n",
+    "                'Critical Care Level 1', 'Critical Care Level 2', 'Critical Care Level 3', \n",
+    "                'NICU Level 3 (174)', 'NICU Level 4',\n",
+    "                'Neonatal Level 1 (172)', 'Neonatal Level 2 (173)', 'Neonatal Level 3 (173)'\n",
+    "                )\n",
+    "            )\n",
+    "        )\n",
+    "        AND (accomodation NOT IN ('CVVH', 'ECMO')\n",
+    "        )\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "994e3bd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_job = client.query(query)\n",
+    "df = query_job.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a464299e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query run:  1635350925.3993752\n",
+      "Query run time:  1213.1376338005066\n"
+     ]
+    }
+   ],
+   "source": [
+    "tic = time.time()\n",
+    "lradt = query_job.to_dataframe()\n",
+    "print('Query run time: ', time.time() - tic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e04e07c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lradt.loc[~lradt.ord_num_value.isnull()].to_pickle('data/lradt_icu_20211027.pkl')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/SubstanceUseRelapseNLP/sampleQueries.sql b/scripts/SubstanceUseRelapseNLP/sampleQueries.sql
new file mode 100644
index 00000000..87b6ab9a
--- /dev/null
+++ b/scripts/SubstanceUseRelapseNLP/sampleQueries.sql
@@ -0,0 +1,48 @@
+/*
+Sketch of queries against STARR-OMOP database to illustrate potential to 
+find psychiatry related visits, their notes, and keywords/concepts
+*/
+
+/*
+select cs.care_site_name, cs.care_site_source_value, count(vo.visit_occurrence_id) as nVisits
+from 
+    `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.visit_occurrence` as vo
+        join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.care_site` as cs
+            on vo.care_site_id = cs.care_site_id
+where care_site_name like '%PSYCH%'
+group by cs.care_site_name, cs.care_site_source_value
+order by nVisits desc
+*/
+
+select c.*, *
+from 
+    `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.note_nlp` as nlp
+    --`som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.visit_occurrence` as vo
+        join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.concept` as c
+            on nlp.note_nlp_source_concept_id = c.concept_id
+where note_id = 124459672
+limit 100
+
+
+
+-- Find all notes from a "PSYCH" related clinic site, and find most common note NLP
+--	source concepts (i.e., keywords) mentioned
+select cs.care_site_name, EXTRACT(YEAR FROM vo.visit_start_date) as visitYear, c.concept_name, 
+    count(distinct nlp.note_id) as nNotes, 
+    count(distinct vo.visit_occurrence_id) as nVisits, 
+    count(distinct vo.person_id) as nPatients
+from 
+    `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.visit_occurrence` as vo
+        join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.care_site` as cs
+            on vo.care_site_id = cs.care_site_id
+        join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.note` as n
+            using (visit_occurrence_id)
+        join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.note_nlp` as nlp
+            using (note_id)
+        join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.concept` as c
+            on nlp.note_nlp_source_concept_id = c.concept_id
+where care_site_name like '%PSYCH%'
+and nlp.term_exists = 'Y'
+group by cs.care_site_name, visitYear, c.concept_name
+order by nPatients desc
+limit 100
\ No newline at end of file
diff --git a/scripts/UrineCulture/notebooks/suspected-uti-cohort-eda.ipynb b/scripts/UrineCulture/notebooks/suspected-uti-cohort-eda.ipynb
new file mode 100644
index 00000000..45e24938
--- /dev/null
+++ b/scripts/UrineCulture/notebooks/suspected-uti-cohort-eda.ipynb
@@ -0,0 +1,2040 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
+      "  %reload_ext google.cloud.bigquery\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pulp import *\n",
+    "import pandas as pd\n",
+    "import os, glob\n",
+    "\n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/conorcorbin/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions' \n",
+    "%load_ext google.cloud.bigquery\n",
+    "\n",
+    "from google.cloud import bigquery\n",
+    "client=bigquery.Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UTI Labelling Cohort Project\n",
+    "\n",
+    "**Objective**: The purpose of this notebook is to define a cohort and conduct basic exploratory-data-analysis (eda) for patients with suspected UTI. We plan to manually review a subset of these cases to find instances where the urine culture was negative but the patient needed antibiotics as well as instances where the urine culture was positive but the patient did not need antibiotics. We'll then use these expert labels to train phenotyping models that attempt to address infection status in a way that is more robust than simply relying on whether the urine culture was positive. \n",
+    "\n",
+    "**Cohort Description**: We include adult patients with a urine culture ordered at Stanford hospital and clinics. The urine culture must be the first urine culture ordered in a two week time period. We only include urine culture orders that also appear in lab results.  If an eventual model is triggered by an order for a urine culture, this may lead to biased estimates of performance because not all ordered urine cultures end up on lab results. \n",
+    "\n",
+    "**Table Schema**: The SQL query below produces a table with the following schema. \n",
+    "* `anon_id`: patient identifier\n",
+    "* `pat_enc_csn_id_coded`: csn associated with urine culture order\n",
+    "* `order_proc_id_coded`: id associated with order for urine culture\n",
+    "* `order_time_jittered_utc`: time of urine culture order\n",
+    "* `ordering_mode`: either inpatient, outpatient, or null\n",
+    "* `was_positive`: 1 if urine culture was positive, 0 otherwise\n",
+    "* `had_other_pos_culture`: 1 if some other culture (from any anatomical site) ordered within a 48 hour time window centered at the urine culture order time was positive, 0 otherwise\n",
+    "* `ordered_antibiotics`: comma separated list of antibiotics ordered for the patient during the four hour window after the order of the urine culture\n",
+    "* `organisms`: comma separated list of organisms that grew from the urine culture \n",
+    "* `other_pos_sites`: comma separated list of other cultures from all anatomical sites that were positive within 48 hour time window centered at urine culture order time\n",
+    "* `other_organisms`: comma separated list of organisms that grew from these other positive cultures\n",
+    "\n",
+    "\n",
+    "#### Other columns to add\n",
+    "* `wbc_count` : closest WBC count to urine culture order time\n",
+    "* `neutra` : closest absolute neutrophil count to urine culture order time\n",
+    "* `temperature` : closest temperature to urine culture order time\n",
+    "* `dx codes related to infection` : any dx codes related to bacterial infection\n",
+    "* `urine analysis results` : leukocytes, nitrite\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executing query with job ID: f884c149-c3fd-4a1e-b935-6b45ec7dd3da\n",
+      "\r",
+      "Query executing: 0.69s"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "ERROR:\n",
+      " 400 Resources exceeded during query execution: Not enough resources for query planning - too many subqueries or query is too complex.\n",
+      "\n",
+      "(job ID: f884c149-c3fd-4a1e-b935-6b45ec7dd3da)\n",
+      "\n",
+      "                                                           -----Query Job SQL Follows-----                                                           \n",
+      "\n",
+      "    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |\n",
+      "   1:-- All urine culture orders at Stanford, order_id must be in lab_results\n",
+      "   2:WITH urine_cultures AS (\n",
+      "   3:    SELECT DISTINCT\n",
+      "   4:        op.anon_id, op.pat_enc_csn_id_coded, op.order_proc_id_coded, op.order_time_jittered_utc, op.ordering_mode\n",
+      "   5:    FROM \n",
+      "   6:        `mining-clinical-decisions.shc_core.order_proc` op\n",
+      "   7:    INNER JOIN\n",
+      "   8:        `mining-clinical-decisions.shc_core.lab_result` lr\n",
+      "   9:    ON\n",
+      "  10:        op.order_proc_id_coded = lr.order_id_coded\n",
+      "  11:    WHERE\n",
+      "  12:        op.order_type LIKE \"Microbiology%\"\n",
+      "  13:        AND op.description LIKE \"%URINE%\"\n",
+      "  14:),\n",
+      "  15:\n",
+      "  16:-- Filter to adult only\n",
+      "  17:adult_urine_cultures AS (\n",
+      "  18:    SELECT DISTINCT\n",
+      "  19:        uc.*\n",
+      "  20:    FROM \n",
+      "  21:        urine_cultures uc\n",
+      "  22:    INNER JOIN\n",
+      "  23:        `shc_core.demographic` demo\n",
+      "  24:    USING\n",
+      "  25:        (anon_id)\n",
+      "  26:    WHERE\n",
+      "  27:        DATE_DIFF(CAST(uc.order_time_jittered_utc as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18\n",
+      "  28:),\n",
+      "  29:\n",
+      "  30:-- Must not have any urine culture orders (that go on to result in lab results) in prior two weeks\n",
+      "  31:-- This finds urine cultures that do have other urine cultures orders in prior two weeks\n",
+      "  32:order_in_prior_two_weeks AS (\n",
+      "  33:    SELECT DISTINCT\n",
+      "  34:         auc.order_proc_id_coded\n",
+      "  35:    FROM \n",
+      "  36:        `mining-clinical-decisions.shc_core.order_proc` op\n",
+      "  37:    INNER JOIN\n",
+      "  38:        `mining-clinical-decisions.shc_core.lab_result` lr\n",
+      "  39:    ON\n",
+      "  40:        op.order_proc_id_coded = lr.order_id_coded\n",
+      "  41:    INNER JOIN\n",
+      "  42:        adult_urine_cultures auc \n",
+      "  43:    ON\n",
+      "  44:        op.anon_id = auc.anon_id\n",
+      "  45:    WHERE\n",
+      "  46:        op.order_type LIKE \"Microbiology%\"\n",
+      "  47:        AND op.description LIKE \"%URINE%\"\n",
+      "  48:        AND auc.order_time_jittered_utc > op.order_time_jittered_utc\n",
+      "  49:        AND TIMESTAMP_DIFF(auc.order_time_jittered_utc, op.order_time_jittered_utc, DAY) < 14\n",
+      "  50:),\n",
+      "  51:\n",
+      "  52:-- Set of antibiotics that show up in order_med (may not be totally all encompassing) \n",
+      "  53:include_abx AS (\n",
+      "  54:    SELECT\n",
+      "  55:        med_description\n",
+      "  56:    FROM\n",
+      "  57:        `mining-clinical-decisions.abx.abx_types` \n",
+      "  58:    WHERE\n",
+      "  59:        is_include_abx = 1 OR is_oral = 1\n",
+      "  60:),\n",
+      "  61:\n",
+      "  62:-- Antibiotics ordered within 48 hour window of urine culture\n",
+      "  63:ordered_abx AS (\n",
+      "  64:    SELECT \n",
+      "  65:        auc.*, om.med_description \n",
+      "  66:    FROM \n",
+      "  67:        adult_urine_cultures auc\n",
+      "  68:    INNER JOIN\n",
+      "  69:        `mining-clinical-decisions.shc_core.order_med` om\n",
+      "  70:    USING\n",
+      "  71:        (anon_id)\n",
+      "  72:    WHERE\n",
+      "  73:        om.med_description IN (SELECT med_description FROM include_abx)\n",
+      "  74:    AND \n",
+      "  75:        TIMESTAMP_DIFF(om.order_inst_utc, auc.order_time_jittered_utc, HOUR) BETWEEN -24 AND 24 \n",
+      "  76:),\n",
+      "  77:\n",
+      "  78:-- Observations where antibiotics were ordered in 14 days leading up to urine culture order\n",
+      "  79:obs_with_prior_abx AS (\n",
+      "  80:    SELECT DISTINCT\n",
+      "  81:        auc.order_proc_id_coded\n",
+      "  82:    FROM \n",
+      "  83:        adult_urine_cultures auc\n",
+      "  84:    INNER JOIN\n",
+      "  85:        `mining-clinical-decisions.shc_core.order_med` om\n",
+      "  86:    USING\n",
+      "  87:        (anon_id)\n",
+      "  88:    WHERE\n",
+      "  89:        om.med_description IN (SELECT med_description FROM include_abx)\n",
+      "  90:    AND \n",
+      "  91:        TIMESTAMP_DIFF(om.order_inst_utc, auc.order_time_jittered_utc, HOUR) BETWEEN -24*14 AND 0 \n",
+      "  92:),\n",
+      "  93:\n",
+      "  94:-- Remove urine cultures that have prior urine culture, or prior antibiotics and get append list of antibiotics ordered in first 24 hours if any\n",
+      "  95:included_urine_cultures AS (\n",
+      "  96:    SELECT DISTINCT\n",
+      "  97:        auc.*, oa.med_description\n",
+      "  98:    FROM \n",
+      "  99:        adult_urine_cultures auc\n",
+      " 100:    LEFT JOIN\n",
+      " 101:        ordered_abx oa\n",
+      " 102:    USING\n",
+      " 103:        (order_proc_id_coded)\n",
+      " 104:    WHERE \n",
+      " 105:        auc.order_proc_id_coded NOT IN (SELECT order_proc_id_coded  FROM order_in_prior_two_weeks)\n",
+      " 106:    AND\n",
+      " 107:        auc.order_proc_id_coded NOT IN (SELECT order_proc_id_coded  FROM obs_with_prior_abx)\n",
+      " 108:),\n",
+      " 109:\n",
+      " 110:-- Flag to indicated if the culture was positive, and resulting organisms\n",
+      " 111:orders_that_were_positive AS (\n",
+      " 112:    SELECT DISTINCT\n",
+      " 113:        auc.order_proc_id_coded, 1 as was_positive, organism\n",
+      " 114:    FROM\n",
+      " 115:        included_urine_cultures auc\n",
+      " 116:    INNER JOIN  \n",
+      " 117:        (SELECT DISTINCT order_proc_id_coded, organism\n",
+      " 118:        FROM `mining-clinical-decisions.shc_core.culture_sensitivity`) cs\n",
+      " 119:    USING\n",
+      " 120:        (order_proc_id_coded) \n",
+      " 121:),\n",
+      " 122:\n",
+      " 123:--Other postive culture types ordered within window of urine culture order. window = -24 to 24 hours\n",
+      " 124:orders_with_other_pos_cultures AS (\n",
+      " 125:    SELECT DISTINCT\n",
+      " 126:        auc.order_proc_id_coded, 1 as had_other_pos_culture, description other_pos_site, organism other_organism\n",
+      " 127:    FROM\n",
+      " 128:        included_urine_cultures auc\n",
+      " 129:    INNER JOIN  \n",
+      " 130:        `mining-clinical-decisions.shc_core.culture_sensitivity` cs\n",
+      " 131:    USING\n",
+      " 132:        (anon_id) \n",
+      " 133:    WHERE\n",
+      " 134:        auc.order_proc_id_coded <> cs.order_proc_id_coded\n",
+      " 135:    AND\n",
+      " 136:        -- Some urine culture orders have two order ids where only one results, make sure we're not counting these.\n",
+      " 137:        -- Likely not needed logic after forcing orders to appear in lab_results\n",
+      " 138:        (auc.order_time_jittered_utc <> cs.order_time_jittered_utc OR cs.description NOT LIKE '%URINE%')\n",
+      " 139:    AND\n",
+      " 140:        TIMESTAMP_DIFF(cs.order_time_jittered_utc, auc.order_time_jittered_utc, HOUR)\n",
+      " 141:    BETWEEN\n",
+      " 142:        -24 AND 24\n",
+      " 143:),\n",
+      " 144:\n",
+      " 145:--Temporary building block that has medications and organisms in long form\n",
+      " 146:orders_meds_and_bugs AS (\n",
+      " 147:SELECT DISTINCT\n",
+      " 148:    anon_id, pat_enc_csn_id_coded, order_proc_id_coded, order_time_jittered_utc, ordering_mode, \n",
+      " 149:    CASE WHEN was_positive = 1 THEN 1 ELSE 0 END was_positive,\n",
+      " 150:    CASE WHEN had_other_pos_culture = 1 THEN 1 ELSE 0 END had_other_pos_culture,\n",
+      " 151:    med_description,\n",
+      " 152:    organism,\n",
+      " 153:    other_pos_site,\n",
+      " 154:    other_organism\n",
+      " 155:FROM \n",
+      " 156:    included_urine_cultures\n",
+      " 157:LEFT JOIN \n",
+      " 158:    orders_with_other_pos_cultures\n",
+      " 159:USING \n",
+      " 160:    (order_proc_id_coded)\n",
+      " 161:LEFT JOIN \n",
+      " 162:    orders_that_were_positive\n",
+      " 163:USING\n",
+      " 164:    (order_proc_id_coded)\n",
+      " 165:ORDER BY\n",
+      " 166:    order_proc_id_coded\n",
+      " 167:),\n",
+      " 168:\n",
+      " 169:-- Collapse medication orders to order id of the urine culture\n",
+      " 170:concatenated_meds AS (\n",
+      " 171:    SELECT DISTINCT\n",
+      " 172:        order_proc_id_coded,\n",
+      " 173:        STRING_AGG(DISTINCT med_description ORDER BY med_description) ordered_antibiotics\n",
+      " 174:    FROM\n",
+      " 175:        orders_meds_and_bugs\n",
+      " 176:    GROUP BY\n",
+      " 177:        order_proc_id_coded\n",
+      " 178:),\n",
+      " 179:\n",
+      " 180:-- Collpase organisms to order id of the urine culture\n",
+      " 181:concatenated_bugs AS (\n",
+      " 182:    SELECT DISTINCT\n",
+      " 183:        order_proc_id_coded,\n",
+      " 184:        STRING_AGG(DISTINCT organism ORDER BY organism) organisms\n",
+      " 185:    FROM\n",
+      " 186:        orders_meds_and_bugs\n",
+      " 187:    GROUP BY\n",
+      " 188:        order_proc_id_coded\n",
+      " 189:),\n",
+      " 190:\n",
+      " 191:-- Collpase other positive sites\n",
+      " 192:concatenated_sites AS (\n",
+      " 193:    SELECT DISTINCT\n",
+      " 194:        order_proc_id_coded,\n",
+      " 195:        STRING_AGG(DISTINCT other_pos_site ORDER BY other_pos_site) other_pos_sites\n",
+      " 196:    FROM\n",
+      " 197:        orders_meds_and_bugs\n",
+      " 198:    GROUP BY\n",
+      " 199:        order_proc_id_coded\n",
+      " 200:),\n",
+      " 201:\n",
+      " 202:-- Collpase other organisms\n",
+      " 203:concatenated_other_bugs AS (\n",
+      " 204:    SELECT DISTINCT\n",
+      " 205:        order_proc_id_coded,\n",
+      " 206:        STRING_AGG(DISTINCT other_organism ORDER BY other_organism) other_organisms\n",
+      " 207:    FROM\n",
+      " 208:        orders_meds_and_bugs\n",
+      " 209:    GROUP BY\n",
+      " 210:        order_proc_id_coded\n",
+      " 211:)\n",
+      " 212:\n",
+      " 213:SELECT DISTINCT\n",
+      " 214:    anon_id, pat_enc_csn_id_coded, order_proc_id_coded, order_time_jittered_utc, ordering_mode, was_positive, had_other_pos_culture,\n",
+      " 215:    ordered_antibiotics, organisms, other_pos_sites, other_organisms\n",
+      " 216:FROM\n",
+      " 217:    orders_meds_and_bugs\n",
+      " 218:INNER JOIN \n",
+      " 219:    concatenated_meds\n",
+      " 220:USING\n",
+      " 221:    (order_proc_id_coded)\n",
+      " 222:INNER JOIN \n",
+      " 223:    concatenated_bugs \n",
+      " 224:USING\n",
+      " 225:    (order_proc_id_coded)\n",
+      " 226:INNER JOIN \n",
+      " 227:    concatenated_sites  \n",
+      " 228:USING\n",
+      " 229:    (order_proc_id_coded)\n",
+      " 230:INNER JOIN \n",
+      " 231:    concatenated_other_bugs  \n",
+      " 232:USING\n",
+      " 233:    (order_proc_id_coded)\n",
+      " 234:ORDER BY \n",
+      " 235:    order_proc_id_coded\n",
+      "    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |    .    |\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bigquery df_uc_all\n",
+    "-- All urine culture orders at Stanford, order_id must be in lab_results\n",
+    "WITH urine_cultures AS (\n",
+    "    SELECT DISTINCT\n",
+    "        op.anon_id, op.pat_enc_csn_id_coded, op.order_proc_id_coded, op.order_time_jittered_utc, op.ordering_mode\n",
+    "    FROM \n",
+    "        `mining-clinical-decisions.shc_core.order_proc` op\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.shc_core.lab_result` lr\n",
+    "    ON\n",
+    "        op.order_proc_id_coded = lr.order_id_coded\n",
+    "    WHERE\n",
+    "        op.order_type LIKE \"Microbiology%\"\n",
+    "        AND op.description LIKE \"%URINE%\"\n",
+    "),\n",
+    "\n",
+    "-- Filter to adult only\n",
+    "adult_urine_cultures AS (\n",
+    "    SELECT DISTINCT\n",
+    "        uc.*\n",
+    "    FROM \n",
+    "        urine_cultures uc\n",
+    "    INNER JOIN\n",
+    "        `shc_core.demographic` demo\n",
+    "    USING\n",
+    "        (anon_id)\n",
+    "    WHERE\n",
+    "        DATE_DIFF(CAST(uc.order_time_jittered_utc as DATE), demo.BIRTH_DATE_JITTERED, YEAR) >= 18\n",
+    "),\n",
+    "\n",
+    "-- Must not have any urine culture orders (that go on to result in lab results) in prior two weeks\n",
+    "-- This finds urine cultures that do have other urine cultures orders in prior two weeks\n",
+    "order_in_prior_two_weeks AS (\n",
+    "    SELECT DISTINCT\n",
+    "         auc.order_proc_id_coded\n",
+    "    FROM \n",
+    "        `mining-clinical-decisions.shc_core.order_proc` op\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.shc_core.lab_result` lr\n",
+    "    ON\n",
+    "        op.order_proc_id_coded = lr.order_id_coded\n",
+    "    INNER JOIN\n",
+    "        adult_urine_cultures auc \n",
+    "    ON\n",
+    "        op.anon_id = auc.anon_id\n",
+    "    WHERE\n",
+    "        op.order_type LIKE \"Microbiology%\"\n",
+    "        AND op.description LIKE \"%URINE%\"\n",
+    "        AND auc.order_time_jittered_utc > op.order_time_jittered_utc\n",
+    "        AND TIMESTAMP_DIFF(auc.order_time_jittered_utc, op.order_time_jittered_utc, DAY) < 14\n",
+    "),\n",
+    "\n",
+    "-- Set of antibiotics that show up in order_med (may not be totally all encompassing) \n",
+    "include_abx AS (\n",
+    "    SELECT\n",
+    "        med_description\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_types` \n",
+    "    WHERE\n",
+    "        is_include_abx = 1 OR is_oral = 1\n",
+    "),\n",
+    "\n",
+    "-- Antibiotics ordered within 48 hour window of urine culture\n",
+    "ordered_abx AS (\n",
+    "    SELECT \n",
+    "        auc.*, om.med_description \n",
+    "    FROM \n",
+    "        adult_urine_cultures auc\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.shc_core.order_med` om\n",
+    "    USING\n",
+    "        (anon_id)\n",
+    "    WHERE\n",
+    "        om.med_description IN (SELECT med_description FROM include_abx)\n",
+    "    AND \n",
+    "        TIMESTAMP_DIFF(om.order_inst_utc, auc.order_time_jittered_utc, HOUR) BETWEEN -24 AND 24 \n",
+    "),\n",
+    "\n",
+    "-- Remove urine cultures that have prior urine culture and get append list of antibiotics ordered in first 24 hours if any\n",
+    "included_urine_cultures AS (\n",
+    "    SELECT DISTINCT\n",
+    "        auc.*, oa.med_description\n",
+    "    FROM \n",
+    "        adult_urine_cultures auc\n",
+    "    LEFT JOIN\n",
+    "        ordered_abx oa\n",
+    "    USING\n",
+    "        (order_proc_id_coded)\n",
+    "    WHERE \n",
+    "        auc.order_proc_id_coded NOT IN (SELECT order_proc_id_coded  FROM order_in_prior_two_weeks)\n",
+    "),\n",
+    "\n",
+    "-- Flag to indicated if the culture was positive, and resulting organisms\n",
+    "orders_that_were_positive AS (\n",
+    "    SELECT DISTINCT\n",
+    "        auc.order_proc_id_coded, 1 as was_positive, organism\n",
+    "    FROM\n",
+    "        included_urine_cultures auc\n",
+    "    INNER JOIN  \n",
+    "        (SELECT DISTINCT order_proc_id_coded, organism\n",
+    "        FROM `mining-clinical-decisions.shc_core.culture_sensitivity`) cs\n",
+    "    USING\n",
+    "        (order_proc_id_coded) \n",
+    "),\n",
+    "\n",
+    "--Other postive culture types ordered within window of urine culture order. window = -24 to 24 hours\n",
+    "orders_with_other_pos_cultures AS (\n",
+    "    SELECT DISTINCT\n",
+    "        auc.order_proc_id_coded, 1 as had_other_pos_culture, description other_pos_site, organism other_organism\n",
+    "    FROM\n",
+    "        included_urine_cultures auc\n",
+    "    INNER JOIN  \n",
+    "        `mining-clinical-decisions.shc_core.culture_sensitivity` cs\n",
+    "    USING\n",
+    "        (anon_id) \n",
+    "    WHERE\n",
+    "        auc.order_proc_id_coded <> cs.order_proc_id_coded\n",
+    "    AND\n",
+    "        -- Some urine culture orders have two order ids where only one results, make sure we're not counting these.\n",
+    "        -- Likely not needed logic after forcing orders to appear in lab_results\n",
+    "        (auc.order_time_jittered_utc <> cs.order_time_jittered_utc OR cs.description NOT LIKE '%URINE%')\n",
+    "    AND\n",
+    "        TIMESTAMP_DIFF(cs.order_time_jittered_utc, auc.order_time_jittered_utc, HOUR)\n",
+    "    BETWEEN\n",
+    "        -24 AND 24\n",
+    "),\n",
+    "\n",
+    "--Temporary building block that has medications and organisms in long form\n",
+    "orders_meds_and_bugs AS (\n",
+    "SELECT DISTINCT\n",
+    "    anon_id, pat_enc_csn_id_coded, order_proc_id_coded, order_time_jittered_utc, ordering_mode, \n",
+    "    CASE WHEN was_positive = 1 THEN 1 ELSE 0 END was_positive,\n",
+    "    CASE WHEN had_other_pos_culture = 1 THEN 1 ELSE 0 END had_other_pos_culture,\n",
+    "    med_description,\n",
+    "    organism,\n",
+    "    other_pos_site,\n",
+    "    other_organism\n",
+    "FROM \n",
+    "    included_urine_cultures\n",
+    "LEFT JOIN \n",
+    "    orders_with_other_pos_cultures\n",
+    "USING \n",
+    "    (order_proc_id_coded)\n",
+    "LEFT JOIN \n",
+    "    orders_that_were_positive\n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "ORDER BY\n",
+    "    order_proc_id_coded\n",
+    "),\n",
+    "\n",
+    "-- Collapse medication orders to order id of the urine culture\n",
+    "concatenated_meds AS (\n",
+    "    SELECT DISTINCT\n",
+    "        order_proc_id_coded,\n",
+    "        STRING_AGG(DISTINCT med_description ORDER BY med_description) ordered_antibiotics\n",
+    "    FROM\n",
+    "        orders_meds_and_bugs\n",
+    "    GROUP BY\n",
+    "        order_proc_id_coded\n",
+    "),\n",
+    "\n",
+    "-- Collpase organisms to order id of the urine culture\n",
+    "concatenated_bugs AS (\n",
+    "    SELECT DISTINCT\n",
+    "        order_proc_id_coded,\n",
+    "        STRING_AGG(DISTINCT organism ORDER BY organism) organisms\n",
+    "    FROM\n",
+    "        orders_meds_and_bugs\n",
+    "    GROUP BY\n",
+    "        order_proc_id_coded\n",
+    "),\n",
+    "\n",
+    "-- Collpase other positive sites\n",
+    "concatenated_sites AS (\n",
+    "    SELECT DISTINCT\n",
+    "        order_proc_id_coded,\n",
+    "        STRING_AGG(DISTINCT other_pos_site ORDER BY other_pos_site) other_pos_sites\n",
+    "    FROM\n",
+    "        orders_meds_and_bugs\n",
+    "    GROUP BY\n",
+    "        order_proc_id_coded\n",
+    "),\n",
+    "\n",
+    "-- Collpase other organisms\n",
+    "concatenated_other_bugs AS (\n",
+    "    SELECT DISTINCT\n",
+    "        order_proc_id_coded,\n",
+    "        STRING_AGG(DISTINCT other_organism ORDER BY other_organism) other_organisms\n",
+    "    FROM\n",
+    "        orders_meds_and_bugs\n",
+    "    GROUP BY\n",
+    "        order_proc_id_coded\n",
+    ")\n",
+    "\n",
+    "SELECT DISTINCT\n",
+    "    anon_id, pat_enc_csn_id_coded, order_proc_id_coded, order_time_jittered_utc, ordering_mode, was_positive, had_other_pos_culture,\n",
+    "    ordered_antibiotics, organisms, other_pos_sites, other_organisms\n",
+    "FROM\n",
+    "    orders_meds_and_bugs\n",
+    "INNER JOIN \n",
+    "    concatenated_meds\n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "INNER JOIN \n",
+    "    concatenated_bugs \n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "INNER JOIN \n",
+    "    concatenated_sites  \n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "INNER JOIN \n",
+    "    concatenated_other_bugs  \n",
+    "USING\n",
+    "    (order_proc_id_coded)\n",
+    "ORDER BY \n",
+    "    order_proc_id_coded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(280841, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_proc_id_coded</th>\n",
+       "      <th>order_time_jittered_utc</th>\n",
+       "      <th>ordering_mode</th>\n",
+       "      <th>was_positive</th>\n",
+       "      <th>had_other_pos_culture</th>\n",
+       "      <th>ordered_antibiotics</th>\n",
+       "      <th>organisms</th>\n",
+       "      <th>other_pos_sites</th>\n",
+       "      <th>other_organisms</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC29fce55</td>\n",
+       "      <td>1952602</td>\n",
+       "      <td>303563619</td>\n",
+       "      <td>2007-12-14 16:22:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JC29fce55</td>\n",
+       "      <td>1952602</td>\n",
+       "      <td>303563620</td>\n",
+       "      <td>2007-12-14 16:22:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd247d0</td>\n",
+       "      <td>6619001</td>\n",
+       "      <td>303567177</td>\n",
+       "      <td>2003-03-14 20:13:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCddffb8</td>\n",
+       "      <td>14533332</td>\n",
+       "      <td>303568988</td>\n",
+       "      <td>2000-08-16 16:33:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd3f4b3</td>\n",
+       "      <td>6619302</td>\n",
+       "      <td>303572415</td>\n",
+       "      <td>2001-09-13 18:30:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  order_proc_id_coded  \\\n",
+       "0  JC29fce55               1952602            303563619   \n",
+       "1  JC29fce55               1952602            303563620   \n",
+       "2   JCd247d0               6619001            303567177   \n",
+       "3   JCddffb8              14533332            303568988   \n",
+       "4   JCd3f4b3               6619302            303572415   \n",
+       "\n",
+       "    order_time_jittered_utc ordering_mode  was_positive  \\\n",
+       "0 2007-12-14 16:22:00+00:00    Outpatient             1   \n",
+       "1 2007-12-14 16:22:00+00:00    Outpatient             1   \n",
+       "2 2003-03-14 20:13:00+00:00    Outpatient             0   \n",
+       "3 2000-08-16 16:33:00+00:00    Outpatient             0   \n",
+       "4 2001-09-13 18:30:00+00:00    Outpatient             0   \n",
+       "\n",
+       "   had_other_pos_culture ordered_antibiotics         organisms  \\\n",
+       "0                      0                None  ESCHERICHIA COLI   \n",
+       "1                      0                None  ESCHERICHIA COLI   \n",
+       "2                      0                None              None   \n",
+       "3                      0                None              None   \n",
+       "4                      0                None              None   \n",
+       "\n",
+       "  other_pos_sites other_organisms  \n",
+       "0            None            None  \n",
+       "1            None            None  \n",
+       "2            None            None  \n",
+       "3            None            None  \n",
+       "4            None            None  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df_uc_all.shape)\n",
+    "df_uc_all.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bigquery df_prior_abx\n",
+    "-- Assuming above query has been saved to mining-clinical-decisions.conor_db.uti_cohort\n",
+    "WITH cohort AS (\n",
+    "    SELECT * FROM mining-clinical-decisions.conor_db.uti_cohort\n",
+    "),\n",
+    "\n",
+    "-- Set of antibiotics that show up in order_med (may not be totally all encompassing) \n",
+    "include_abx AS (\n",
+    "    SELECT\n",
+    "        med_description\n",
+    "    FROM\n",
+    "        `mining-clinical-decisions.abx.abx_types` \n",
+    "    WHERE\n",
+    "        is_include_abx = 1 OR is_oral = 1\n",
+    "),\n",
+    "\n",
+    "prior_abx AS (\n",
+    "    SELECT DISTINCT\n",
+    "        cohort.order_proc_id_coded\n",
+    "    FROM \n",
+    "        cohort\n",
+    "    INNER JOIN\n",
+    "        `mining-clinical-decisions.shc_core.order_med` om\n",
+    "    USING\n",
+    "        (anon_id)\n",
+    "    WHERE\n",
+    "        om.med_description IN (SELECT med_description FROM include_abx)\n",
+    "    AND \n",
+    "        TIMESTAMP_DIFF(om.order_inst_utc, cohort.order_time_jittered_utc, HOUR) BETWEEN -24*14 AND 0 \n",
+    ")\n",
+    "\n",
+    "-- Remove observations with prior antibiotics\n",
+    "SELECT DISTINCT\n",
+    "    *\n",
+    "FROM \n",
+    "    cohort\n",
+    "WHERE \n",
+    "    order_proc_id_coded NOT IN (SELECT DISTINCT order_proc_id_coded FROM prior_abx)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(177977, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_proc_id_coded</th>\n",
+       "      <th>order_time_jittered_utc</th>\n",
+       "      <th>ordering_mode</th>\n",
+       "      <th>was_positive</th>\n",
+       "      <th>had_other_pos_culture</th>\n",
+       "      <th>ordered_antibiotics</th>\n",
+       "      <th>organisms</th>\n",
+       "      <th>other_pos_sites</th>\n",
+       "      <th>other_organisms</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC29fce55</td>\n",
+       "      <td>1952602</td>\n",
+       "      <td>303563619</td>\n",
+       "      <td>2007-12-14 16:22:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JC29fce55</td>\n",
+       "      <td>1952602</td>\n",
+       "      <td>303563620</td>\n",
+       "      <td>2007-12-14 16:22:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JCd247d0</td>\n",
+       "      <td>6619001</td>\n",
+       "      <td>303567177</td>\n",
+       "      <td>2003-03-14 20:13:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JCddffb8</td>\n",
+       "      <td>14533332</td>\n",
+       "      <td>303568988</td>\n",
+       "      <td>2000-08-16 16:33:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JCd3f4b3</td>\n",
+       "      <td>6619302</td>\n",
+       "      <td>303572415</td>\n",
+       "      <td>2001-09-13 18:30:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded  order_proc_id_coded  \\\n",
+       "0  JC29fce55               1952602            303563619   \n",
+       "1  JC29fce55               1952602            303563620   \n",
+       "2   JCd247d0               6619001            303567177   \n",
+       "3   JCddffb8              14533332            303568988   \n",
+       "4   JCd3f4b3               6619302            303572415   \n",
+       "\n",
+       "    order_time_jittered_utc ordering_mode  was_positive  \\\n",
+       "0 2007-12-14 16:22:00+00:00    Outpatient             1   \n",
+       "1 2007-12-14 16:22:00+00:00    Outpatient             1   \n",
+       "2 2003-03-14 20:13:00+00:00    Outpatient             0   \n",
+       "3 2000-08-16 16:33:00+00:00    Outpatient             0   \n",
+       "4 2001-09-13 18:30:00+00:00    Outpatient             0   \n",
+       "\n",
+       "   had_other_pos_culture ordered_antibiotics         organisms  \\\n",
+       "0                      0                None  ESCHERICHIA COLI   \n",
+       "1                      0                None  ESCHERICHIA COLI   \n",
+       "2                      0                None              None   \n",
+       "3                      0                None              None   \n",
+       "4                      0                None              None   \n",
+       "\n",
+       "  other_pos_sites other_organisms  \n",
+       "0            None            None  \n",
+       "1            None            None  \n",
+       "2            None            None  \n",
+       "3            None            None  \n",
+       "4            None            None  "
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df_prior_abx.shape)\n",
+    "df_prior_abx.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(174640, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_time_jittered_utc</th>\n",
+       "      <th>order_proc_id_coded</th>\n",
+       "      <th>ordering_mode</th>\n",
+       "      <th>was_positive</th>\n",
+       "      <th>had_other_pos_culture</th>\n",
+       "      <th>ordered_antibiotics</th>\n",
+       "      <th>organisms</th>\n",
+       "      <th>other_pos_sites</th>\n",
+       "      <th>other_organisms</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>JC29f8aa6</td>\n",
+       "      <td>131284475290</td>\n",
+       "      <td>2020-03-19 23:33:00+00:00</td>\n",
+       "      <td>659051139</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>JC29f8ae9</td>\n",
+       "      <td>131267983420</td>\n",
+       "      <td>2019-07-02 22:10:00+00:00</td>\n",
+       "      <td>615279161</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>JC29f8b0f</td>\n",
+       "      <td>131280361737</td>\n",
+       "      <td>2019-11-19 08:00:00+00:00</td>\n",
+       "      <td>646120545</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>JC29f8b1b</td>\n",
+       "      <td>131264672614</td>\n",
+       "      <td>2019-02-20 08:00:00+00:00</td>\n",
+       "      <td>601143909</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>JC29f8b1c</td>\n",
+       "      <td>131284098448</td>\n",
+       "      <td>2020-02-18 21:48:00+00:00</td>\n",
+       "      <td>657689560</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     anon_id  pat_enc_csn_id_coded   order_time_jittered_utc  \\\n",
+       "0  JC29f8aa6          131284475290 2020-03-19 23:33:00+00:00   \n",
+       "1  JC29f8ae9          131267983420 2019-07-02 22:10:00+00:00   \n",
+       "2  JC29f8b0f          131280361737 2019-11-19 08:00:00+00:00   \n",
+       "3  JC29f8b1b          131264672614 2019-02-20 08:00:00+00:00   \n",
+       "4  JC29f8b1c          131284098448 2020-02-18 21:48:00+00:00   \n",
+       "\n",
+       "   order_proc_id_coded ordering_mode  was_positive  had_other_pos_culture  \\\n",
+       "0            659051139    Outpatient             0                      0   \n",
+       "1            615279161    Outpatient             0                      0   \n",
+       "2            646120545    Outpatient             0                      0   \n",
+       "3            601143909    Outpatient             0                      0   \n",
+       "4            657689560    Outpatient             0                      0   \n",
+       "\n",
+       "  ordered_antibiotics organisms other_pos_sites other_organisms  \n",
+       "0                None      None            None            None  \n",
+       "1                None      None            None            None  \n",
+       "2                None      None            None            None  \n",
+       "3                None      None            None            None  \n",
+       "4                None      None            None            None  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def string_agg_cols(arr):\n",
+    "    final_set = []\n",
+    "    for a in arr:\n",
+    "        if a is None:\n",
+    "            continue\n",
+    "        groups = a.split(',')\n",
+    "        for g in groups:\n",
+    "            if g not in final_set:\n",
+    "                final_set.append(g)\n",
+    "    final_set.sort()\n",
+    "    if len(final_set) == 0:\n",
+    "        return None\n",
+    "    else:\n",
+    "        return ','.join([f for f in final_set])\n",
+    "    \n",
+    "groupby_cols = ['anon_id', 'pat_enc_csn_id_coded', 'order_time_jittered_utc']\n",
+    "df_final = (df_prior_abx\n",
+    "    .groupby(groupby_cols)\n",
+    "    .agg({'order_proc_id_coded' : 'first',\n",
+    "          'ordering_mode' : 'first',\n",
+    "          'was_positive' : 'max',\n",
+    "          'had_other_pos_culture' : 'max',\n",
+    "          'ordered_antibiotics' : string_agg_cols,\n",
+    "          'organisms' : string_agg_cols,\n",
+    "          'other_pos_sites' : string_agg_cols,\n",
+    "          'other_organisms' : string_agg_cols})\n",
+    "    .reset_index()\n",
+    ")\n",
+    "print(df_final.shape)\n",
+    "df_final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/conorcorbin/opt/anaconda3/lib/python3.7/site-packages/google/auth/_default.py:69: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK. We recommend that most server applications use service accounts instead. If your application continues to use end user credentials from Cloud SDK, you might receive a \"quota exceeded\" or \"API not enabled\" error. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n",
+      "1it [00:35, 35.24s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_final.to_gbq(\n",
+    "    destination_table='conor_db.uti_cohort_final',\n",
+    "    project_id='mining-clinical-decisions',\n",
+    "    if_exists='replace'\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_uc_all = df_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(174640, 11)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all = df_uc_all[~df_uc_all.duplicated(subset=['anon_id', 'pat_enc_csn_id_coded', 'order_time_jittered_utc'])]\n",
+    "df_uc_all.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inpatient vs outpatient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Outpatient    105594\n",
+       "Inpatient      65495\n",
+       "NaN             3551\n",
+       "Name: ordering_mode, dtype: int64"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all.ordering_mode.value_counts(dropna=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Positive vs negative cultures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    140620\n",
+       "1     34020\n",
+       "Name: was_positive, dtype: int64"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all.was_positive.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Positive vs negative by ordering mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outpatient positivity rate\n",
+      "0.1488626247703468\n",
+      "Inpatient positivity rate\n",
+      "0.2581113062065807\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Outpatient positivity rate\")\n",
+    "print(df_uc_all.query('ordering_mode == \"Outpatient\"', engine='python')['was_positive'].mean())\n",
+    "print(\"Inpatient positivity rate\")\n",
+    "print(df_uc_all.query('ordering_mode == \"Inpatient\"', engine='python')['was_positive'].mean())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How many had other positive cultures at different anatomical sites?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    172186\n",
+       "1      2454\n",
+       "Name: had_other_pos_culture, dtype: int64"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all.had_other_pos_culture.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Repeat within positive and negative urine cultures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "was_positive  had_other_pos_culture\n",
+       "0             0                        139100\n",
+       "1             0                         33086\n",
+       "0             1                          1520\n",
+       "1             1                           934\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all[['was_positive', 'had_other_pos_culture']].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Of all cases where urine culture was negative but another culture was positive, what were most common anatomical sites of the positive cultures?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URINE CULTURE                                                               262\n",
+       "BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES)                                 243\n",
+       "BLOOD CULTURE (2 AEROBIC BOTTLES)                                           160\n",
+       "RESPIRATORY CULTURE                                                         123\n",
+       "WOUND CULTURE                                                               107\n",
+       "                                                                           ... \n",
+       "ORTHOPEDIC AEROBIC CULT W/ GRAM STAIN                                         1\n",
+       " DEEP / SURGICAL / ASP,WOUND CULTURE,WOUND CULTURE AND GRAM STAIN             1\n",
+       " NON - RESPIRATORY,AFB CULTURE                                                1\n",
+       "BLOOD CULTURE (2 AEROBIC BOTTLES),FUNGAL CULTURE                              1\n",
+       "BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),ORTHOPEDIC AEROBIC CULT W/GS      1\n",
+       "Name: other_pos_sites, Length: 85, dtype: int64"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_uc_all\n",
+    ".query('was_positive == 0 and had_other_pos_culture == 1', engine='python')\n",
+    "['other_pos_sites']\n",
+    ").value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Most common antibiotic orders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NaN                                                                        151248\n",
+       "NITROFURANTOIN MONOHYD/M-CRYST 100 MG PO CAPS                                2653\n",
+       "CIPROFLOXACIN 500 MG PO TABS                                                 1223\n",
+       "SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS                             1181\n",
+       "CIPROFLOXACIN HCL 500 MG PO TABS                                             1148\n",
+       "CEPHALEXIN 500 MG PO CAPS                                                    1148\n",
+       "CIPROFLOXACIN HCL 250 MG PO TABS                                              371\n",
+       "CIPROFLOXACIN 250 MG PO TABS                                                  332\n",
+       "CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK                                    298\n",
+       "CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK                           248\n",
+       "PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK                       242\n",
+       "CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY)                                  238\n",
+       "LEVOFLOXACIN 500 MG PO TABS                                                   235\n",
+       "CEPHALEXIN 250 MG PO CAPS,CEPHALEXIN 500 MG PO CAPS                           235\n",
+       "MACROBID 100 MG PO CAPS                                                       224\n",
+       "NITROFURANTOIN (MACROCRYST25%) 100 MG PO CAPS                                 217\n",
+       "CEFTRIAXONE 1 GRAM/50 ML MINI-BAG PLUS                                        197\n",
+       "CIPROFLOXACIN 500 MG PO TABS,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK       192\n",
+       "AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS                                186\n",
+       "CEPHALEXIN 250 MG PO CAPS                                                     153\n",
+       "Name: ordered_antibiotics, dtype: int64"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all.ordered_antibiotics.value_counts(dropna=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "174640"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all['order_proc_id_coded'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Most common organisms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NaN                                            140620\n",
+       "ESCHERICHIA COLI                                18046\n",
+       "ENTEROCOCCUS SPECIES                             3476\n",
+       "KLEBSIELLA PNEUMONIAE                            2686\n",
+       "STREPTOCOCCUS AGALACTIAE (GROUP B)               1211\n",
+       "ENTEROCOCCUS SPECIES,ESCHERICHIA COLI            1080\n",
+       "PROTEUS MIRABILIS                                1015\n",
+       "STREPTOCOCCUS AGALACTIAE {GROUP B}                976\n",
+       "PSEUDOMONAS AERUGINOSA                            486\n",
+       "KLEBSIELLA OXYTOCA                                347\n",
+       "ESCHERICHIA COLI,KLEBSIELLA PNEUMONIAE            338\n",
+       "ZZZENTEROBACTER AEROGENES                         252\n",
+       "ENTEROBACTER CLOACAE COMPLEX                      238\n",
+       "ENTEROCOCCUS FAECALIS                             212\n",
+       "STAPHYLOCOCCUS AUREUS                             182\n",
+       "CITROBACTER KOSERI                                172\n",
+       "CITROBACTER FREUNDII COMPLEX                      158\n",
+       "ESCHERICHIA COLI,PROTEUS MIRABILIS                153\n",
+       "CITROBACTER KOSERI (CARBAPENEM RESISTANT)         150\n",
+       "ENTEROCOCCUS SPECIES,PSEUDOMONAS AERUGINOSA       136\n",
+       "Name: organisms, dtype: int64"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all.organisms.value_counts(dropna=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cultures by year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "year\n",
+       "2018    21686\n",
+       "2019    21272\n",
+       "2017    19523\n",
+       "2016    17986\n",
+       "2015    13183\n",
+       "2013     9458\n",
+       "2009     9287\n",
+       "2008     9248\n",
+       "2014     9011\n",
+       "2011     8945\n",
+       "2010     8789\n",
+       "2012     8776\n",
+       "2020     4660\n",
+       "2000     2310\n",
+       "2001     2237\n",
+       "2002     1804\n",
+       "2003     1742\n",
+       "2004     1650\n",
+       "2005     1581\n",
+       "2006     1490\n",
+       "2007        2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all.assign(year=lambda x: x.order_time_jittered_utc.dt.year)[['year']].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.duplicated of           anon_id  pat_enc_csn_id_coded   order_time_jittered_utc\n",
+       "0       JC29f8aa6          131284475290 2020-03-19 23:33:00+00:00\n",
+       "1       JC29f8ae9          131267983420 2019-07-02 22:10:00+00:00\n",
+       "2       JC29f8b0f          131280361737 2019-11-19 08:00:00+00:00\n",
+       "3       JC29f8b1b          131264672614 2019-02-20 08:00:00+00:00\n",
+       "4       JC29f8b1c          131284098448 2020-02-18 21:48:00+00:00\n",
+       "...           ...                   ...                       ...\n",
+       "174635   JCec48e9          131250306582 2018-04-07 04:42:00+00:00\n",
+       "174636   JCec48f2          131231090378 2017-05-07 07:00:00+00:00\n",
+       "174637   JCec48fd          131226848112 2017-05-05 22:24:00+00:00\n",
+       "174638   JCec48fe          131236053941 2017-07-17 23:55:00+00:00\n",
+       "174639   JCec48fe          131242938601 2018-01-15 06:07:00+00:00\n",
+       "\n",
+       "[174640 rows x 3 columns]>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all[['anon_id', 'pat_enc_csn_id_coded', 'order_time_jittered_utc']].duplicated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>anon_id</th>\n",
+       "      <th>pat_enc_csn_id_coded</th>\n",
+       "      <th>order_proc_id_coded</th>\n",
+       "      <th>order_time_jittered_utc</th>\n",
+       "      <th>ordering_mode</th>\n",
+       "      <th>was_positive</th>\n",
+       "      <th>had_other_pos_culture</th>\n",
+       "      <th>ordered_antibiotics</th>\n",
+       "      <th>organisms</th>\n",
+       "      <th>other_pos_sites</th>\n",
+       "      <th>other_organisms</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>268156</th>\n",
+       "      <td>JCd3b903</td>\n",
+       "      <td>131276079369</td>\n",
+       "      <td>640785148</td>\n",
+       "      <td>2019-10-13 19:36:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NITROFURANTOIN MONOHYD/M-CRYST 100 MG PO CAPS</td>\n",
+       "      <td>KLEBSIELLA PNEUMONIAE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268171</th>\n",
+       "      <td>JCd3b903</td>\n",
+       "      <td>131276079369</td>\n",
+       "      <td>640799200</td>\n",
+       "      <td>2019-10-13 19:36:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NITROFURANTOIN MONOHYD/M-CRYST 100 MG PO CAPS</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263832</th>\n",
+       "      <td>JCe68799</td>\n",
+       "      <td>131276290589</td>\n",
+       "      <td>633855188</td>\n",
+       "      <td>2019-08-30 17:30:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>ESCHERICHIA COLI</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263831</th>\n",
+       "      <td>JCe68799</td>\n",
+       "      <td>131276290589</td>\n",
+       "      <td>633855185</td>\n",
+       "      <td>2019-08-30 17:30:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264084</th>\n",
+       "      <td>JCe4335a</td>\n",
+       "      <td>131276471298</td>\n",
+       "      <td>634290860</td>\n",
+       "      <td>2019-08-31 18:28:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264085</th>\n",
+       "      <td>JCe4335a</td>\n",
+       "      <td>131276471298</td>\n",
+       "      <td>634290871</td>\n",
+       "      <td>2019-08-31 18:28:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267222</th>\n",
+       "      <td>JCe0c742</td>\n",
+       "      <td>131278280904</td>\n",
+       "      <td>639331707</td>\n",
+       "      <td>2019-10-27 18:16:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267223</th>\n",
+       "      <td>JCe0c742</td>\n",
+       "      <td>131278280904</td>\n",
+       "      <td>639331708</td>\n",
+       "      <td>2019-10-27 18:16:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>STAPHYLOCOCCUS AUREUS</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274705</th>\n",
+       "      <td>JCd48cc2</td>\n",
+       "      <td>131279918491</td>\n",
+       "      <td>650891865</td>\n",
+       "      <td>2020-01-27 23:54:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS</td>\n",
+       "      <td>PSEUDOMONAS AERUGINOSA</td>\n",
+       "      <td>WOUND CULTURE AND GRAM STAIN</td>\n",
+       "      <td>PSEUDOMONAS AERUGINOSA,STAPH AUREUS {MRSA}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274706</th>\n",
+       "      <td>JCd48cc2</td>\n",
+       "      <td>131279918491</td>\n",
+       "      <td>650891866</td>\n",
+       "      <td>2020-01-27 23:54:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS</td>\n",
+       "      <td>PSEUDOMONAS AERUGINOSA</td>\n",
+       "      <td>WOUND CULTURE AND GRAM STAIN</td>\n",
+       "      <td>PSEUDOMONAS AERUGINOSA,STAPH AUREUS {MRSA}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277355</th>\n",
+       "      <td>JC2a30881</td>\n",
+       "      <td>131282318278</td>\n",
+       "      <td>655247564</td>\n",
+       "      <td>2020-01-26 17:14:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277354</th>\n",
+       "      <td>JC2a30881</td>\n",
+       "      <td>131282318278</td>\n",
+       "      <td>655247563</td>\n",
+       "      <td>2020-01-26 17:14:00+00:00</td>\n",
+       "      <td>Outpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279747</th>\n",
+       "      <td>JCcd6963</td>\n",
+       "      <td>131283538823</td>\n",
+       "      <td>660051080</td>\n",
+       "      <td>2020-03-10 17:17:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>KLEBSIELLA PNEUMONIAE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279748</th>\n",
+       "      <td>JCcd6963</td>\n",
+       "      <td>131283538823</td>\n",
+       "      <td>660051081</td>\n",
+       "      <td>2020-03-10 17:17:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>KLEBSIELLA PNEUMONIAE</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280502</th>\n",
+       "      <td>JCd45e5e</td>\n",
+       "      <td>131286970061</td>\n",
+       "      <td>662513718</td>\n",
+       "      <td>2020-03-30 18:30:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AMPICILLIN 2 GRAM/100 ML MINIBAG PLUS,AMPICILL...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280503</th>\n",
+       "      <td>JCd45e5e</td>\n",
+       "      <td>131286970061</td>\n",
+       "      <td>662513779</td>\n",
+       "      <td>2020-03-30 18:30:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AMPICILLIN 2 GRAM/100 ML MINIBAG PLUS,AMPICILL...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280833</th>\n",
+       "      <td>JCe8dbee</td>\n",
+       "      <td>131288012473</td>\n",
+       "      <td>665178884</td>\n",
+       "      <td>2020-03-31 07:00:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280835</th>\n",
+       "      <td>JCe8dbee</td>\n",
+       "      <td>131288012473</td>\n",
+       "      <td>665214774</td>\n",
+       "      <td>2020-03-31 07:00:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280839</th>\n",
+       "      <td>JC2a222b6</td>\n",
+       "      <td>131288043001</td>\n",
+       "      <td>665283782</td>\n",
+       "      <td>2020-03-31 07:00:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CEPHALEXIN 250 MG/5 ML PO SUSR,CEPHALEXIN 500 ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280840</th>\n",
+       "      <td>JC2a222b6</td>\n",
+       "      <td>131288043001</td>\n",
+       "      <td>665287997</td>\n",
+       "      <td>2020-03-31 07:00:00+00:00</td>\n",
+       "      <td>Inpatient</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CEPHALEXIN 250 MG/5 ML PO SUSR,CEPHALEXIN 500 ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          anon_id  pat_enc_csn_id_coded  order_proc_id_coded  \\\n",
+       "268156   JCd3b903          131276079369            640785148   \n",
+       "268171   JCd3b903          131276079369            640799200   \n",
+       "263832   JCe68799          131276290589            633855188   \n",
+       "263831   JCe68799          131276290589            633855185   \n",
+       "264084   JCe4335a          131276471298            634290860   \n",
+       "264085   JCe4335a          131276471298            634290871   \n",
+       "267222   JCe0c742          131278280904            639331707   \n",
+       "267223   JCe0c742          131278280904            639331708   \n",
+       "274705   JCd48cc2          131279918491            650891865   \n",
+       "274706   JCd48cc2          131279918491            650891866   \n",
+       "277355  JC2a30881          131282318278            655247564   \n",
+       "277354  JC2a30881          131282318278            655247563   \n",
+       "279747   JCcd6963          131283538823            660051080   \n",
+       "279748   JCcd6963          131283538823            660051081   \n",
+       "280502   JCd45e5e          131286970061            662513718   \n",
+       "280503   JCd45e5e          131286970061            662513779   \n",
+       "280833   JCe8dbee          131288012473            665178884   \n",
+       "280835   JCe8dbee          131288012473            665214774   \n",
+       "280839  JC2a222b6          131288043001            665283782   \n",
+       "280840  JC2a222b6          131288043001            665287997   \n",
+       "\n",
+       "         order_time_jittered_utc ordering_mode  was_positive  \\\n",
+       "268156 2019-10-13 19:36:00+00:00    Outpatient             1   \n",
+       "268171 2019-10-13 19:36:00+00:00    Outpatient             0   \n",
+       "263832 2019-08-30 17:30:00+00:00     Inpatient             1   \n",
+       "263831 2019-08-30 17:30:00+00:00     Inpatient             0   \n",
+       "264084 2019-08-31 18:28:00+00:00    Outpatient             0   \n",
+       "264085 2019-08-31 18:28:00+00:00    Outpatient             0   \n",
+       "267222 2019-10-27 18:16:00+00:00    Outpatient             0   \n",
+       "267223 2019-10-27 18:16:00+00:00    Outpatient             1   \n",
+       "274705 2020-01-27 23:54:00+00:00    Outpatient             1   \n",
+       "274706 2020-01-27 23:54:00+00:00    Outpatient             1   \n",
+       "277355 2020-01-26 17:14:00+00:00    Outpatient             0   \n",
+       "277354 2020-01-26 17:14:00+00:00    Outpatient             0   \n",
+       "279747 2020-03-10 17:17:00+00:00     Inpatient             1   \n",
+       "279748 2020-03-10 17:17:00+00:00     Inpatient             1   \n",
+       "280502 2020-03-30 18:30:00+00:00     Inpatient             0   \n",
+       "280503 2020-03-30 18:30:00+00:00     Inpatient             0   \n",
+       "280833 2020-03-31 07:00:00+00:00     Inpatient             0   \n",
+       "280835 2020-03-31 07:00:00+00:00     Inpatient             0   \n",
+       "280839 2020-03-31 07:00:00+00:00     Inpatient             0   \n",
+       "280840 2020-03-31 07:00:00+00:00     Inpatient             0   \n",
+       "\n",
+       "        had_other_pos_culture  \\\n",
+       "268156                      0   \n",
+       "268171                      0   \n",
+       "263832                      0   \n",
+       "263831                      0   \n",
+       "264084                      0   \n",
+       "264085                      0   \n",
+       "267222                      0   \n",
+       "267223                      0   \n",
+       "274705                      1   \n",
+       "274706                      1   \n",
+       "277355                      0   \n",
+       "277354                      0   \n",
+       "279747                      0   \n",
+       "279748                      0   \n",
+       "280502                      0   \n",
+       "280503                      0   \n",
+       "280833                      0   \n",
+       "280835                      0   \n",
+       "280839                      0   \n",
+       "280840                      0   \n",
+       "\n",
+       "                                      ordered_antibiotics  \\\n",
+       "268156      NITROFURANTOIN MONOHYD/M-CRYST 100 MG PO CAPS   \n",
+       "268171      NITROFURANTOIN MONOHYD/M-CRYST 100 MG PO CAPS   \n",
+       "263832                                               None   \n",
+       "263831                                               None   \n",
+       "264084                                               None   \n",
+       "264085                                               None   \n",
+       "267222                                               None   \n",
+       "267223                                               None   \n",
+       "274705     AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS   \n",
+       "274706     AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS   \n",
+       "277355                                               None   \n",
+       "277354                                               None   \n",
+       "279747                                               None   \n",
+       "279748                                               None   \n",
+       "280502  AMPICILLIN 2 GRAM/100 ML MINIBAG PLUS,AMPICILL...   \n",
+       "280503  AMPICILLIN 2 GRAM/100 ML MINIBAG PLUS,AMPICILL...   \n",
+       "280833       CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY)   \n",
+       "280835       CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY)   \n",
+       "280839  CEPHALEXIN 250 MG/5 ML PO SUSR,CEPHALEXIN 500 ...   \n",
+       "280840  CEPHALEXIN 250 MG/5 ML PO SUSR,CEPHALEXIN 500 ...   \n",
+       "\n",
+       "                     organisms               other_pos_sites  \\\n",
+       "268156   KLEBSIELLA PNEUMONIAE                          None   \n",
+       "268171                    None                          None   \n",
+       "263832        ESCHERICHIA COLI                          None   \n",
+       "263831                    None                          None   \n",
+       "264084                    None                          None   \n",
+       "264085                    None                          None   \n",
+       "267222                    None                          None   \n",
+       "267223   STAPHYLOCOCCUS AUREUS                          None   \n",
+       "274705  PSEUDOMONAS AERUGINOSA  WOUND CULTURE AND GRAM STAIN   \n",
+       "274706  PSEUDOMONAS AERUGINOSA  WOUND CULTURE AND GRAM STAIN   \n",
+       "277355                    None                          None   \n",
+       "277354                    None                          None   \n",
+       "279747   KLEBSIELLA PNEUMONIAE                          None   \n",
+       "279748   KLEBSIELLA PNEUMONIAE                          None   \n",
+       "280502                    None                          None   \n",
+       "280503                    None                          None   \n",
+       "280833                    None                          None   \n",
+       "280835                    None                          None   \n",
+       "280839                    None                          None   \n",
+       "280840                    None                          None   \n",
+       "\n",
+       "                                   other_organisms  \n",
+       "268156                                        None  \n",
+       "268171                                        None  \n",
+       "263832                                        None  \n",
+       "263831                                        None  \n",
+       "264084                                        None  \n",
+       "264085                                        None  \n",
+       "267222                                        None  \n",
+       "267223                                        None  \n",
+       "274705  PSEUDOMONAS AERUGINOSA,STAPH AUREUS {MRSA}  \n",
+       "274706  PSEUDOMONAS AERUGINOSA,STAPH AUREUS {MRSA}  \n",
+       "277355                                        None  \n",
+       "277354                                        None  \n",
+       "279747                                        None  \n",
+       "279748                                        None  \n",
+       "280502                                        None  \n",
+       "280503                                        None  \n",
+       "280833                                        None  \n",
+       "280835                                        None  \n",
+       "280839                                        None  \n",
+       "280840                                        None  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uc_all[df_uc_all.duplicated(subset=['anon_id', 'pat_enc_csn_id_coded', 'order_time_jittered_utc'], keep=False)].sort_values('pat_enc_csn_id_coded').tail(20)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/UrineCulture/sql/antibiogram.sql b/scripts/UrineCulture/sql/antibiogram.sql
new file mode 100644
index 00000000..a7ad7dd0
--- /dev/null
+++ b/scripts/UrineCulture/sql/antibiogram.sql
@@ -0,0 +1,67 @@
+WITH organism_suscept_long AS (
+    SELECT DISTINCT
+        order_proc_id_coded, organism, 
+        -- Reformat name so SQL doesn't get uspet in pivot operation
+        CASE WHEN antibiotic = 'Trimethoprim/Sulfamethoxazole.' 
+        THEN 'Trimethoprim_Sulfamethoxazole' 
+        ELSE antibiotic END antibiotic,
+        CASE WHEN suscept = 'Susceptible' 
+        THEN 1 ELSE 0 END suscept
+    FROM  
+        `mining-clinical-decisions.shc_core.culture_sensitivity`
+    WHERE
+        description = "URINE CULTURE"
+    AND
+        organism in ('ESCHERICHIA COLI', 'KLEBSIELLA PNEUMONIAE',
+                     'PROTEUS MIRABILIS', 'PSEUDOMONAS AERUGINOSA')
+    AND 
+        antibiotic in ('Gentamicin', 'Ciprofloxacin', 'Ampicillin',
+                       'Nitrofurantoin', 'Trimethoprim/Sulfamethoxazole.')
+),
+
+num_tests AS (
+    SELECT 
+        *
+    FROM 
+    (
+    SELECT 
+        organism, antibiotic, suscept
+    FROM 
+        organism_suscept_long
+    )
+    PIVOT (
+        COUNT(suscept) as num_tests
+        FOR antibiotic in ('Gentamicin', 'Ciprofloxacin', 'Ampicillin', 
+                           'Nitrofurantoin', 'Trimethoprim_Sulfamethoxazole')
+    )
+),
+
+frac_suscept AS (
+    SELECT 
+        *
+    FROM 
+    (
+    SELECT 
+        organism, antibiotic, suscept
+    FROM 
+        organism_suscept_long
+    )
+    PIVOT (
+        AVG(suscept) as frac_suscept
+        FOR antibiotic in ('Gentamicin', 'Ciprofloxacin', 'Ampicillin',
+                           'Nitrofurantoin', 'Trimethoprim_Sulfamethoxazole')
+    )
+)
+
+SELECT 
+    organism, num_tests_Gentamicin, frac_suscept_Gentamicin,
+    num_tests_Ciprofloxacin, frac_suscept_Gentamicin, num_tests_Ampicillin,
+    frac_suscept_Ampicillin, num_tests_Nitrofurantoin,
+    frac_suscept_Nitrofurantoin, num_tests_Trimethoprim_Sulfamethoxazole,
+    frac_suscept_Trimethoprim_Sulfamethoxazole
+FROM 
+    num_tests
+INNER JOIN
+    frac_suscept
+USING
+    (organism)
\ No newline at end of file
diff --git a/scripts/WayfindingInfoGathering/erVisitPrimaryDiagnosisNoteConcepts.sql b/scripts/WayfindingInfoGathering/erVisitPrimaryDiagnosisNoteConcepts.sql
new file mode 100644
index 00000000..ded027a7
--- /dev/null
+++ b/scripts/WayfindingInfoGathering/erVisitPrimaryDiagnosisNoteConcepts.sql
@@ -0,0 +1,68 @@
+-- Test query for common note concepts mentioned in ER visits, organized by primary diagnosis
+-- May need further debugging, as counts seem small (even for 1% sample, most common diagnosis is Chest Pain with 161 patients, would be ~16,100 in total dataset?)
+
+WITH 
+emergencyRoomVisitPrimaryDiagnoses AS
+(
+	select 
+		co.condition_source_value, cc.concept_name as condition_concept_name, 
+		count(distinct vo.visit_occurrence_id) as nVisits,
+		count(distinct vo.person_id) as nPatients
+	from 
+			`som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.visit_occurrence` as vo
+				join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.condition_occurrence` as co
+					using (visit_occurrence_id)
+				join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.concept` as cc
+						on co.condition_concept_id = cc.concept_id
+	where 
+		vo.visit_concept_id = 9203 -- Emergency Room Visit
+		and condition_concept_id <> 0 -- Lots of 0 values
+		and condition_status_concept_id = 32902	-- Primary Diagnosis
+	group by
+		co.condition_source_value, cc.concept_name
+	having
+		nPatients > 10	 -- Avoid rare items
+	order by
+		nPatients desc
+),
+emergencyRoomVisitPrimaryDiagnosesNoteConcepts AS
+(
+	select 
+		erDx.condition_source_value, erDx.condition_concept_name,
+		erDx.nVisits as nVisitsPerERDx, erDx.nPatients as nPatientsPerERDx,
+		nc.concept_name as note_concept_name, --nc.domain_id,
+		count(distinct vo.visit_occurrence_id) as nVisits,
+		count(distinct vo.person_id) as nPatients
+	from 
+			`som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.visit_occurrence` as vo
+				join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.condition_occurrence` as co
+					using (visit_occurrence_id)
+				join emergencyRoomVisitPrimaryDiagnoses as erDx	-- Join this separately so can get patient/visit counts per diagnosis as well as per note concept
+						on co.condition_source_value = erDx.condition_source_value
+				join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.note` as n
+						using (visit_occurrence_id)
+				join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.note_nlp` as nlp
+						using (note_id)
+				join `som-rit-phi-starr-prod.starr_omop_cdm5_deid_1pcent_latest.concept` as nc
+						on nlp.note_nlp_source_concept_id = nc.concept_id
+	where 
+		vo.visit_concept_id = 9203 -- Emergency Room Visit
+		and condition_concept_id <> 0 -- Lots of 0 values
+		and condition_status_concept_id = 32902	-- Primary Diagnosis
+		and note_type_concept_id = 44814646	-- ED Provider Notes
+		--and term_exists = 'Y'	-- Interested in relevant info, so negated presence also suggests relevant
+	group by
+		erDx.condition_source_value, erDx.condition_concept_name,
+		erDx.nVisits, erDx.nPatients,
+		nc.concept_name--, nc.domain_id
+	having
+		nPatients > nPatientsPerERDx / 2 -- Only look for things that occur in at least half of cases for now
+	order by
+		nVisitsPerERDx desc,
+		erDx.condition_source_value,
+		nVisits desc
+)
+
+select *
+from emergencyRoomVisitPrimaryDiagnosesNoteConcepts
+
diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/cost_variability/SQL/sql_scripts.sql b/scripts/cost_variability/SQL/sql_scripts.sql
new file mode 100644
index 00000000..2f234909
--- /dev/null
+++ b/scripts/cost_variability/SQL/sql_scripts.sql
@@ -0,0 +1,254 @@
+-- extract anon_ids and save the result in proj_cms_sf.costUB_with_anonid
+SELECT B.mrn as mrn_in_codebook
+      , B.anon_id
+      , A.*
+FROM `som-nero-phi-jonc101-secure.shc_cost.costUB` A
+JOIN `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.codebook` B
+ON CAST(A.MRN AS STRING) = B.mrn
+
+-- Filter and save in costUB_with_anonid_filtered
+SELECT *
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid` 
+WHERE Inpatient ='Inpatient'
+AND DATE_DIFF(DischargeDate, AdmitDate, DAY) >= 3
+
+-- Find frequent base_names and save them in frequent_base_name
+WITH dist_base_names AS
+(
+(SELECT DISTINCT B.base_name
+      , A.anon_id
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+JOIN `som-nero-phi-jonc101.shc_core_2021.lab_result` B
+ON A.anon_id = B.anon_id )
+)
+SELECT base_name
+      , count(*) as num_patients
+FROM dist_base_names
+GROUP BY base_name
+ORDER BY num_patients DESC
+
+-- Find frequent base_names and save them in frequent_pharm_class_abbr
+WITH dist_phr AS
+(
+(SELECT DISTINCT B.pharm_class_abbr
+      , A.anon_id
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+JOIN `som-nero-phi-jonc101.shc_core_2021.order_med` B
+ON A.anon_id = B.anon_id )
+)
+SELECT pharm_class_abbr
+      , count(*) as num_patients
+FROM dist_phr
+GROUP BY pharm_class_abbr
+ORDER BY num_patients DESC
+
+-- Find frequent base_names and save them in frequent_proc_id
+WITH dist_proc_id AS
+(
+(SELECT DISTINCT B.proc_id
+      , A.anon_id
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+JOIN `som-nero-phi-jonc101.shc_core_2021.order_proc` B
+ON A.anon_id = B.anon_id )
+)
+SELECT proc_id
+      , count(*) as num_patients
+FROM dist_proc_id
+GROUP BY proc_id
+ORDER BY num_patients DESC
+
+
+-- icd10
+WITH dist_icd10 AS
+(
+(SELECT DISTINCT B.icd10
+      , A.anon_id
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+JOIN `som-nero-phi-jonc101.shc_core_2021.diagnosis` B
+ON A.anon_id = B.anon_id )
+)
+SELECT icd10
+      , count(*) as num_patients
+FROM dist_icd10
+GROUP BY icd10
+ORDER BY num_patients DESC
+
+
+--icd9
+WITH dist_icd9 AS
+(
+(SELECT DISTINCT B.icd9
+      , A.anon_id
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+JOIN `som-nero-phi-jonc101.shc_core_2021.diagnosis` B
+ON A.anon_id = B.anon_id )
+)
+SELECT icd9
+      , count(*) as num_patients
+FROM dist_icd9
+GROUP BY icd9
+ORDER BY num_patients DESC
+
+-- Extract lab results
+SELECT B.anon_id
+      , B.base_name
+      , B.proc_code
+      , B.result_flag
+      , B.order_time_utc
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+INNER JOIN `som-nero-phi-jonc101.shc_core_2021.lab_result` B
+ON A.anon_id = B.anon_id
+WHERE base_name IN
+(
+      SELECT A.base_name
+      FROM `som-nero-phi-jonc101-secure.proj_cms_sf.frequent_base_name` A
+      WHERE A.base_name is not NULL
+      ORDER BY num_patients DESC
+      LIMIT 100
+)
+
+
+-- Extract diagnosis
+SELECT B.anon_id
+      , B.icd9
+      , B.icd10
+      , B.start_date_utc
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+INNER JOIN `som-nero-phi-jonc101.shc_core_2021.diagnosis` B
+ON A.anon_id = B.anon_id
+WHERE icd10 IN
+(
+      SELECT A.icd10
+      FROM `som-nero-phi-jonc101-secure.proj_cms_sf.frequent_icd10` A
+      WHERE A.icd10 IS NOT NULL
+      ORDER BY num_patients DESC
+      LIMIT 100
+)
+OR icd9 IN
+(
+      SELECT A.icd9
+      FROM `som-nero-phi-jonc101-secure.proj_cms_sf.frequent_icd9` A
+      WHERE A.icd9 IS NOT NULL
+      ORDER BY num_patients DESC
+      LIMIT 100
+)
+
+
+
+-- med
+SELECT B.anon_id
+      , B.pharm_class_abbr
+      , B.order_time_jittered 
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+INNER JOIN `som-nero-phi-jonc101.shc_core_2021.order_med` B
+ON A.anon_id = B.anon_id
+WHERE pharm_class_abbr IN
+(
+      SELECT A.pharm_class_abbr
+      FROM `som-nero-phi-jonc101-secure.proj_cms_sf.frequent_pharm_class_abbr` A
+      WHERE A.pharm_class_abbr is not NULL
+      ORDER BY num_patients DESC
+      LIMIT 100
+)
+
+-- order_proc
+SELECT B.anon_id
+      , B.proc_id
+      , B.ordering_date_jittered
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+INNER JOIN `som-nero-phi-jonc101.shc_core_2021.order_proc` B
+ON A.anon_id = B.anon_id
+WHERE proc_id IN
+(
+      SELECT A.proc_id
+      FROM `som-nero-phi-jonc101-secure.proj_cms_sf.frequent_proc_id` A
+      WHERE A.proc_id is not NULL
+      ORDER BY num_patients DESC
+      LIMIT 100
+)
+
+
+SELECT B.*
+FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A
+INNER JOIN `som-nero-phi-jonc101.shc_core_2021.demographic` B
+ON A.anon_id = B.anon_id
+
+
+-- ===================
+-- Find descriptions
+
+-- This script finds most frequent med_descriptions
+WITH med_desc_freq AS
+(
+SELECT pharm_class_abbr
+        , med_description
+       , COUNT(DISTINCT anon_id) as num_patient
+FROM `som-nero-phi-jonc101.shc_core_2021.order_med` 
+WHERE pharm_class_abbr IS NOT NULL
+GROUP BY pharm_class_abbr, med_description
+)
+
+SELECT pharm_class_abbr, med_description, num_patient
+FROM med_desc_freq
+WHERE TRUE
+QUALIFY ROW_NUMBER() OVER (PARTITION BY pharm_class_abbr ORDER BY num_patient DESC) <= 5
+ORDER BY pharm_class_abbr, num_patient DESC
+
+
+
+
+-- Diagnosis
+WITH diag_records AS
+(SELECT A.*
+        , B.CCSR_CATEGORY_1
+        , B.Category_1_Description
+        , B.ICD_10_CODE_Description
+FROM `som-nero-phi-jonc101.shc_core_2021.diagnosis` A
+LEFT JOIN  `mining-clinical-decisions.mapdata.ahrq_ccsr_diagnosis` B
+ON A.icd10 = B.ICD10),
+
+diag_desc_freq AS
+(SELECT CCSR_CATEGORY_1
+        , dx_name
+       , COUNT(DISTINCT anon_id) as num_patient
+FROM diag_records
+WHERE CCSR_CATEGORY_1 IS NOT NULL
+GROUP BY CCSR_CATEGORY_1, dx_name)
+
+SELECT CCSR_CATEGORY_1, dx_name, num_patient
+FROM diag_desc_freq
+WHERE TRUE
+QUALIFY ROW_NUMBER() OVER (PARTITION BY CCSR_CATEGORY_1 ORDER BY num_patient DESC) <= 5
+ORDER BY CCSR_CATEGORY_1, num_patient DESC
+
+
+--
+WITH proc_desc_freq AS
+(SELECT proc_id
+       , description
+       , COUNT(DISTINCT anon_id) as num_patient
+FROM `som-nero-phi-jonc101.shc_core_2021.order_proc`  
+WHERE proc_id IS NOT NULL
+GROUP BY proc_id, description)
+
+SELECT proc_id, description, num_patient
+FROM proc_desc_freq
+WHERE TRUE
+QUALIFY ROW_NUMBER() OVER (PARTITION BY proc_id ORDER BY num_patient DESC) <= 5
+ORDER BY proc_id, num_patient DESC
+
+--
+WITH lab_desc_freq AS
+(SELECT base_name
+       , lab_name
+       , COUNT(DISTINCT anon_id) as num_patient
+FROM `som-nero-phi-jonc101.shc_core_2021.lab_result` 
+WHERE base_name IS NOT NULL
+GROUP BY base_name, lab_name)
+
+SELECT base_name, lab_name, num_patient
+FROM lab_desc_freq
+WHERE TRUE
+QUALIFY ROW_NUMBER() OVER (PARTITION BY base_name ORDER BY num_patient DESC) <= 5
+ORDER BY base_name, num_patient DESC
+
diff --git a/scripts/cost_variability/cost_variability.py b/scripts/cost_variability/cost_variability.py
new file mode 100644
index 00000000..3158ebbd
--- /dev/null
+++ b/scripts/cost_variability/cost_variability.py
@@ -0,0 +1,390 @@
+import pdb
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.preprocessing import OrdinalEncoder
+from sklearn.model_selection import train_test_split
+import csv
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import LinearRegression
+import xgboost as xgb
+import pickle
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+from sklearn.metrics import explained_variance_score
+from sklearn.metrics import r2_score
+from sklearn.metrics import mean_pinball_loss
+import shap
+import matplotlib.pyplot as plt
+from sklearn import linear_model
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import f_regression
+from sklearn.feature_selection import mutual_info_regression
+from numpy import arange
+from sklearn.linear_model import Lasso
+
+target_vars = ['Cost_Direct', 'Cost_Indirect', 'Cost_Total']
+target='Cost_Direct'
+
+# DISCHARGE DIET
+# DISCHARGE WOUND CARE
+# REASON TO CALL YOUR PHYSICIAN
+# WHEN TO RESUME ACTIVITIES
+# FOLLOW UP INSTRUCTIONS
+# DISCHARGE MD TO CALL FOR QUESTIONS
+# DISCHARGE INTERAGENCY REFERRAL TO HOME NURSING
+# ADMIT TO INPATIENT
+# DISCHARGE PATIENT WHEN CRITERIA MET
+# NOTIFY MD:
+# DISCHARGE PATIENT
+features_to_remove = ['PROC_118367', 'PROC_118570', 'PROC_118573', 'PROC_118575',\
+'PROC_118576', 'PROC_189185', 'PROC_189239', 'PROC_304577',\
+'PROC_46821', 'PROC_735', 'PROC_400099']
+num_selected_features = 50
+
+
+def lasso_regression_model(train_data
+						, test_data
+						, target_vars
+						, target
+						, fs_method
+						, id_name):
+	# pdb.set_trace()
+	alphas = np.array([10, 5, 1, 0.1, 0.01, 0.001, 0.0001])
+	
+	hyperparameters = {'alpha': alphas}
+	
+	print('Hyperparameters:')
+	print(hyperparameters)
+	with open('results/LASSO/LASSO_hyperparameters_'+fs_method+'.csv', 'w') as csv_file:  
+	    writer = csv.writer(csv_file)
+	    for key, value in hyperparameters.items():
+	       writer.writerow([key, value])
+	# pdb.set_trace()
+	train_data_shuffled = train_data.sample(frac=1).reset_index(drop=True)  
+	test_data_shuffled = test_data.sample(frac=1).reset_index(drop=True)  
+
+	# randomCV = RandomizedSearchCV(estimator=xgb.XGBRegressor(n_jobs=-1), param_distributions=hyperparameters, n_iter=5, cv=3,scoring="roc_auc")
+
+	randomCV = RandomizedSearchCV(estimator=Lasso(max_iter=5000), param_distributions=hyperparameters, n_iter=10, cv=3)
+	randomCV.fit(train_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False), train_data_shuffled[target])
+
+	# pdb.set_trace()
+	# === Save models
+	with open('results/LASSO/LASSO_model_'+fs_method+'.pkl','wb') as f:
+	    pickle.dump(randomCV,f)
+
+	(pd.DataFrame.from_dict(data=randomCV.best_params_, orient='index').to_csv('results/LASSO/best_params_LASSO_'+fs_method+'.csv', header=False))
+	best_model= randomCV.best_estimator_
+
+	predictions = best_model.predict(test_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False))    
+	np.savetxt('results/LASSO/predictions_LASSO_'+fs_method+'.csv', predictions, delimiter=',')
+	
+	# pdb.set_trace()
+	mse = mean_squared_error(y_true=test_data_shuffled[target], y_pred=predictions)
+	mae = mean_absolute_error(y_true=test_data_shuffled[target], y_pred=predictions)
+	evs = explained_variance_score(y_true=test_data_shuffled[target], y_pred=predictions)
+	r2 = r2_score(y_true=test_data_shuffled[target], y_pred=predictions)
+	mpl = mean_pinball_loss(y_true=test_data_shuffled[target], y_pred=predictions)
+
+	with open('results/LASSO/results_LASSO_'+fs_method+'.csv', 'w') as res_file:
+		res_file.write('mean_squared_error\n')
+		res_file.write(str(mse))
+		res_file.write('\n')
+		res_file.write('mean_absolute_error\n')
+		res_file.write(str(mae))
+		res_file.write('\n')
+		res_file.write('explained_variance_score\n')
+		res_file.write(str(evs))
+		res_file.write('\n')
+		res_file.write('r2_score\n')
+		res_file.write(str(r2))
+		res_file.write('\n')
+		res_file.write('mean_pinball_loss\n')
+		res_file.write(str(mpl))
+		res_file.write('\n')
+
+def linear_regression_model(train_data
+						, test_data
+						, target_vars
+						, target
+						, fs_method
+						, id_name):							
+	# pdb.set_trace()
+	train_data_shuffled = train_data.sample(frac=1).reset_index(drop=True)  
+	test_data_shuffled = test_data.sample(frac=1).reset_index(drop=True)  
+	model = LinearRegression()
+	model.fit(train_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False), train_data_shuffled[target])
+
+	# === Save models
+	with open('results/LR/lr_model_'+fs_method+'.pkl','wb') as f:
+	    pickle.dump(model,f)
+
+	predictions = model.predict(test_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False))    
+	np.savetxt('results/LR/predictions_lr_'+fs_method+'.csv', predictions, delimiter=',')
+	
+	# pdb.set_trace()
+	mse = mean_squared_error(y_true=test_data_shuffled[target], y_pred=predictions)
+	mae = mean_absolute_error(y_true=test_data_shuffled[target], y_pred=predictions)
+	evs = explained_variance_score(y_true=test_data_shuffled[target], y_pred=predictions)
+	r2 = r2_score(y_true=test_data_shuffled[target], y_pred=predictions)
+	mpl = mean_pinball_loss(y_true=test_data_shuffled[target], y_pred=predictions)
+
+	with open('results/LR/results_lr_'+fs_method+'.csv', 'w') as res_file:
+		res_file.write('mean_squared_error\n')
+		res_file.write(str(mse))
+		res_file.write('\n')
+		res_file.write('mean_absolute_error\n')
+		res_file.write(str(mae))
+		res_file.write('\n')
+		res_file.write('explained_variance_score\n')
+		res_file.write(str(evs))
+		res_file.write('\n')
+		res_file.write('r2_score\n')
+		res_file.write(str(r2))
+		res_file.write('\n')
+		res_file.write('mean_pinball_loss\n')
+		res_file.write(str(mpl))
+		res_file.write('\n')
+	
+def xgb_regression_model(train_data
+						, test_data
+						, target_vars
+						, target
+						, fs_method
+						, id_name):
+	# pdb.set_trace()
+	n_estimators = [100, 200, 400, 800]# [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
+	# Maximum number of levels in tree
+	max_depth = [4, 8, 16]# [int(x) for x in np.linspace(10, 110, num = 11)]
+	# gamma = [0.001, 0.01, 0.1, 1, 10]
+	learning_rate = [0.0001, 0.001, 0.01, 0.1, 1]
+	# Create the random grid
+	hyperparameters = {'n_estimators': n_estimators
+	               , 'max_depth': max_depth
+	               # , 'gamma': gamma
+	               , 'learning_rate':learning_rate
+	               }
+	print('Hyperparameters:')
+	print(hyperparameters)
+	with open('results/XGB/xgb_hyperparameters_'+fs_method+'.csv', 'w') as csv_file:  
+	    writer = csv.writer(csv_file)
+	    for key, value in hyperparameters.items():
+	       writer.writerow([key, value])
+	# pdb.set_trace()
+	train_data_shuffled = train_data.sample(frac=1).reset_index(drop=True)  
+	test_data_shuffled = test_data.sample(frac=1).reset_index(drop=True)  
+
+	# randomCV = RandomizedSearchCV(estimator=xgb.XGBRegressor(n_jobs=-1), param_distributions=hyperparameters, n_iter=5, cv=3,scoring="roc_auc")
+
+	randomCV = RandomizedSearchCV(estimator=xgb.XGBRegressor(n_jobs=-1, objectvie='reg:squarederror', booster='gbtree'), param_distributions=hyperparameters, n_iter=10, cv=3)
+
+	randomCV.fit(train_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False), train_data_shuffled[target])
+
+	# pdb.set_trace()
+	# === Save models
+	with open('results/XGB/xgb_model_'+fs_method+'.pkl','wb') as f:
+	    pickle.dump(randomCV,f)
+
+	(pd.DataFrame.from_dict(data=randomCV.best_params_, orient='index').to_csv('results/XGB/best_params_xgb_'+fs_method+'.csv', header=False))
+	best_model= randomCV.best_estimator_
+
+	predictions = best_model.predict(test_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False))    
+	np.savetxt('results/XGB/predictions_xgb_'+fs_method+'.csv', predictions, delimiter=',')
+	
+	feat_imp = pd.DataFrame(data=np.reshape(best_model.feature_importances_, (1,len(best_model.feature_importances_))), columns = train_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False).columns)
+	feat_imp.to_csv('results/XGB/xgb_'+fs_method+'_feature_importance_.csv', index=False)
+	# feat_imp.plot.bar()
+	# plt.savefig('results/XGB/feat_importance_bar_xgb.png', dpi=300)
+
+	# best_model.get_booster().get_score(importance_type='weight')
+
+	
+	# pdb.set_trace()
+	mse = mean_squared_error(y_true=test_data_shuffled[target], y_pred=predictions)
+	mae = mean_absolute_error(y_true=test_data_shuffled[target], y_pred=predictions)
+	evs = explained_variance_score(y_true=test_data_shuffled[target], y_pred=predictions)
+	r2 = r2_score(y_true=test_data_shuffled[target], y_pred=predictions)
+	mpl = mean_pinball_loss(y_true=test_data_shuffled[target], y_pred=predictions)
+
+	with open('results/XGB/results_xgb_'+fs_method+'.csv', 'w') as res_file:
+		res_file.write('mean_squared_error\n')
+		res_file.write(str(mse))
+		res_file.write('\n')
+		res_file.write('mean_absolute_error\n')
+		res_file.write(str(mae))
+		res_file.write('\n')
+		res_file.write('explained_variance_score\n')
+		res_file.write(str(evs))
+		res_file.write('\n')
+		res_file.write('r2_score\n')
+		res_file.write(str(r2))
+		res_file.write('\n')
+		res_file.write('mean_pinball_loss\n')
+		res_file.write(str(mpl))
+		res_file.write('\n')
+
+	explainer = shap.Explainer(best_model)
+	shap_values = explainer(train_data_shuffled.drop(target_vars+[id_name], axis=1, inplace=False))
+
+	shap.plots.beeswarm(shap_values, show=False)
+	plt.yticks(fontsize=5)
+	plt.savefig('results/XGB/shap_beeswarm_'+fs_method+'.png', dpi=300)
+	plt.close()
+
+def one_hot_encoding(X_train):
+	pdb.set_trace()
+	ohe = OneHotEncoder()
+	ohe.fit(X_train)
+	X_train_enc = ohe.transform(X_train)
+	column_names = ohe.get_feature_names_out(X_train.columns)
+	one_hot_encoded_frame =  pd.DataFrame(data=X_train_enc.toarray(), columns= column_names)
+	return one_hot_encoded_frame
+
+# prepare input data
+def ordinal_encoding(X_train):
+	# pdb.set_trace()
+	oe = OrdinalEncoder()
+	oe.fit(X_train)
+	X_train_enc = oe.transform(X_train)	
+	ordinal_encoded_frame =  pd.DataFrame(data=X_train_enc, columns= X_train.columns)
+	return ordinal_encoded_frame
+
+def select_features_f_regression(X_train, y_train, X_test, k_value):
+	# pdb.set_trace()
+	# configure to select all features
+	fs = SelectKBest(score_func=f_regression, k=k_value)
+	# learn relationship from training data
+	fs.fit(X_train.iloc[:,1:], y_train)
+	# transform train input data
+	X_train_fs = fs.transform(X_train.iloc[:,1:])
+	# transform test input data
+	X_test_fs = fs.transform(X_test.iloc[:,1:])
+	feature_scores = pd.DataFrame(fs.scores_, index = X_train.columns[1:], columns=['f_regression_score'])
+	return X_train_fs, X_test_fs, fs, feature_scores
+
+def select_features_mir(X_train, y_train, X_test, k_value):
+	# pdb.set_trace()
+	# configure to select all features
+	fs = SelectKBest(score_func=mutual_info_regression, k=k_value)
+	# learn relationship from training data
+	fs.fit(X_train.iloc[:,1:], y_train)
+	# transform train input data
+	X_train_fs = fs.transform(X_train.iloc[:,1:])
+	# transform test input data
+	X_test_fs = fs.transform(X_test.iloc[:,1:])
+	feature_scores = pd.DataFrame(fs.scores_, index = X_train.columns[1:], columns=['mutual_info_regression_score'])
+	return X_train_fs, X_test_fs, fs, feature_scores
+
+
+# pdb.set_trace()
+
+data = pd.read_csv('stationary_data/stationary_dataset.csv')
+data.columns = data.columns.str.strip()
+data = data.loc[:, ~data.columns.isin(features_to_remove)]
+
+# data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)
+# data.drop(features_to_remove, axis=1, inplace=True)
+
+# data_x = data.loc[:, data.columns != target_var]
+# data_y = data[target_var]
+# data_x_for_ordinal = data_x[ordinal_encoding_features]
+# data_x_for_numerical = data_x[numerical_columns]
+# data_x_for_one_hot = data_x.loc[:, ~data_x.columns.isin(ordinal_encoding_features+numerical_columns)]
+# data_x_ordinal_encoded = ordinal_encoding(data_x_for_ordinal)
+# data_x_one_hot_encoded = one_hot_encoding(data_x_for_one_hot)
+# data_encoded = pd.concat([data_x_for_numerical, data_x_ordinal_encoded, data_x_one_hot_encoded], axis=1)
+# data_encoded[target_var] = data_y
+
+# pdb.set_trace()
+train_data, test_data = train_test_split(data, test_size=0.30)
+print('Train data size is {}'.format(len(train_data)))
+print('Test data size is {}'.format(len(test_data)))
+
+# train_data= train_data.sample(200, replace=True)
+# test_data= test_data.sample(100, replace=True)
+
+X_train_fs_reg, X_test_fs_reg, fs_reg, feature_scores_reg = select_features_f_regression(train_data.drop(target_vars, axis=1, inplace=False), train_data[target], test_data.drop(target_vars, axis=1, inplace=False), num_selected_features)
+X_train_fs_mir, X_test_fs_mir, fs_mir, feature_scores_mir = select_features_mir(train_data.drop(target_vars, axis=1, inplace=False), train_data[target], test_data.drop(target_vars, axis=1, inplace=False), num_selected_features)
+
+feature_scores_reg.to_csv('results/feature_scores_reg.csv')
+feature_scores_mir.to_csv('results/feature_scores_mir.csv')
+
+
+
+
+selected_features_reg = feature_scores_reg.nlargest(num_selected_features, columns=['f_regression_score']).index.tolist()
+selected_features_mir = feature_scores_mir.nlargest(num_selected_features, columns=['mutual_info_regression_score']).index.tolist()
+
+# pdb.set_trace()
+
+lasso_regression_model(train_data
+					, test_data
+					, target_vars
+					, target
+					, 'all'
+					, 'patient_id')
+lasso_regression_model(train_data.loc[:, train_data.columns.isin(selected_features_reg + ['patient_id'] + target_vars)]
+					, test_data.loc[:, test_data.columns.isin(selected_features_reg + ['patient_id'] + target_vars)]
+					, target_vars
+					, target
+					, 'reg'
+					, 'patient_id'
+					)
+lasso_regression_model(train_data.loc[:, train_data.columns.isin(selected_features_mir + ['patient_id'] + target_vars)]
+					, test_data.loc[:, test_data.columns.isin(selected_features_mir + ['patient_id'] + target_vars )]
+					, target_vars
+					, target
+					, 'mir'
+					, 'patient_id')
+
+
+
+linear_regression_model(train_data
+					, test_data
+					, target_vars
+					, target
+					, 'all'
+					, 'patient_id')
+
+linear_regression_model(train_data.loc[:, train_data.columns.isin(selected_features_reg + ['patient_id'] + target_vars)]
+					, test_data.loc[:, test_data.columns.isin(selected_features_reg + ['patient_id'] + target_vars)]
+					, target_vars
+					, target
+					, 'reg'
+					, 'patient_id'
+					)
+linear_regression_model(train_data.loc[:, train_data.columns.isin(selected_features_mir + ['patient_id'] + target_vars)]
+					, test_data.loc[:, test_data.columns.isin(selected_features_mir + ['patient_id'] + target_vars)]
+					, target_vars
+					, target
+					, 'mir'
+					, 'patient_id'
+					)
+
+# pdb.set_trace()
+xgb_regression_model(train_data
+					, test_data
+					, target_vars
+					, target
+					, 'all'
+					, 'patient_id'
+					)
+xgb_regression_model(train_data.loc[:, train_data.columns.isin(selected_features_reg + ['patient_id'] + target_vars)]
+					, test_data.loc[:, test_data.columns.isin(selected_features_reg + ['patient_id'] + target_vars)]
+					, target_vars
+					, target
+					, 'reg'
+					, 'patient_id'
+					)
+xgb_regression_model(train_data.loc[:, train_data.columns.isin(selected_features_mir + ['patient_id'] + target_vars)]
+					, test_data.loc[:, test_data.columns.isin(selected_features_mir + ['patient_id'] + target_vars)]
+					, target_vars
+					, target
+					, 'mir'
+					, 'patient_id'
+					)
+
diff --git a/scripts/cost_variability/extract_descriptions.py b/scripts/cost_variability/extract_descriptions.py
new file mode 100644
index 00000000..f168e6fc
--- /dev/null
+++ b/scripts/cost_variability/extract_descriptions.py
@@ -0,0 +1,126 @@
+import os
+import pandas as pd
+import pdb
+from google.cloud import bigquery
+from google.cloud.bigquery import dbapi
+
+def extract_desc(features, med_desc, diag_desc, proc_desc, lab_desc):
+	features.columns = ['code', 'score']
+	features = features.sort_values(by='score', ascending=False)
+	codes_descriptions = []
+	# pdb.set_trace()
+	for idx, row in features.iterrows():
+		if row['code'].split('_')[0] == 'DIAG':
+			current_diag_code = row['code'].split('_')[1]
+			current_diag_desc = diag_desc[diag_desc['CCSR_CATEGORY_1']==current_diag_code]
+			temp_list = current_diag_desc.values.tolist()
+			codes_descriptions.extend([['Diagnosis']+x+[row['score']] for x in temp_list])
+
+		elif row['code'].split('_')[0] == 'MED':
+			current_med_code = row['code'].split('_')[1]
+			current_med_desc = med_desc[med_desc['pharm_class_abbr']==current_med_code]
+			temp_list = current_med_desc.values.tolist()
+			codes_descriptions.extend([['Medication']+x+[row['score']] for x in temp_list])
+
+		elif row['code'].split('_')[0] == 'PROC':
+			current_proc_code = row['code'].split('_')[1]				
+			current_proc_desc = proc_desc[proc_desc['proc_id']==int(current_proc_code)]		
+			temp_list = current_proc_desc.values.tolist()
+			codes_descriptions.extend([['Pocedure']+x+[row['score']] for x in temp_list])
+
+		elif row['code'].split('_')[0] == 'LAB':	
+			current_lab_code = row['code'].split('_')[1]				
+			current_lab_desc = lab_desc[lab_desc['base_name']==current_lab_code]		
+			temp_list = current_lab_desc.values.tolist()
+			codes_descriptions.extend([['Lab result']+x+[row['score']] for x in temp_list])
+
+	# pdb.set_trace()
+	codes_descriptions_df = pd.DataFrame(codes_descriptions, columns=['Table', 'Code', 'Description', 'num_patient', 'Score'])
+	return codes_descriptions_df
+
+def extract_desc_for_stat(features, med_desc, diag_desc, proc_desc, lab_desc):
+	codes_descriptions = []
+	# pdb.set_trace()
+	for i in range(len(features)):
+		current_feature = features[i]
+		if current_feature.split('_')[0] == 'DIAG':
+			current_diag_code = current_feature.split('_')[1]
+			current_diag_desc = diag_desc[diag_desc['CCSR_CATEGORY_1']==current_diag_code]
+			temp_list = current_diag_desc.values.tolist()
+			codes_descriptions.extend([['Diagnosis']+x for x in temp_list])
+
+		elif current_feature.split('_')[0] == 'MED':
+			current_med_code = current_feature.split('_')[1]
+			current_med_desc = med_desc[med_desc['pharm_class_abbr']==current_med_code]
+			temp_list = current_med_desc.values.tolist()
+			codes_descriptions.extend([['Medication']+x for x in temp_list])
+
+		elif current_feature.split('_')[0] == 'PROC':
+			current_proc_code = current_feature.split('_')[1]				
+			current_proc_desc = proc_desc[proc_desc['proc_id']==int(current_proc_code)]		
+			temp_list = current_proc_desc.values.tolist()
+			codes_descriptions.extend([['Pocedure']+x for x in temp_list])
+
+		elif current_feature.split('_')[0] == 'LAB':	
+			# pdb.set_trace()
+			current_lab_code = current_feature.split('_')[1]				
+			current_lab_desc = lab_desc[lab_desc['base_name']==current_lab_code]		
+			temp_list = current_lab_desc.values.tolist()
+			
+			temp_list = [['Lab result']+x for x in temp_list]
+			lab_result = current_feature.split('_')[-1]
+			for i in range(len(temp_list)):
+				temp_list[i][0] =  temp_list[i][0] + '_' + lab_result
+			# pdb.set_trace()	
+			codes_descriptions.extend(temp_list)
+
+	# pdb.set_trace()
+	codes_descriptions_df = pd.DataFrame(codes_descriptions, columns=['Table', 'Code', 'Description', 'num_patient'])
+	return codes_descriptions_df
+
+
+freq_med_desc = 'SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.medication_description`'
+freq_diag_desc = 'SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.diagnosis_description`'
+freq_proc_desc = 'SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.procedure_description`'
+freq_lab_desc = 'SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.lab_description`'
+
+client_name ="som-nero-phi-jonc101-secure"
+client = bigquery.Client(client_name); 
+conn = dbapi.connect(client);
+cursor = conn.cursor();
+med_desc = pd.read_sql_query(freq_med_desc, conn); 
+diag_desc = pd.read_sql_query(freq_diag_desc, conn); 
+proc_desc = pd.read_sql_query(freq_proc_desc, conn); 
+lab_desc = pd.read_sql_query(freq_lab_desc, conn); 
+
+
+stationary_data_sample = pd.read_csv('stationary_data/stationary_dataset.csv', nrows=200)
+stationary_data_sample.columns = stationary_data_sample.columns.str.strip()
+features_to_remove=['patient_id', 'age', 'DEMOG_sex_Female', 'DEMOG_sex_Male', 'DEMOG_sex_Unknown', \
+'DEMOG_canonical_race_Asian', 'DEMOG_canonical_race_Black', 'DEMOG_canonical_race_Native American', \
+'DEMOG_canonical_race_Other', 'DEMOG_canonical_race_Pacific Islander', 'DEMOG_canonical_race_Unknown', \
+'DEMOG_canonical_race_White', 'DEMOG_canonical_ethnicity_Hispanic/Latino', \
+'DEMOG_canonical_ethnicity_Non-Hispanic', 'DEMOG_canonical_ethnicity_Unknown', \
+'Cost_Direct', 'Cost_Indirect', 'Cost_Total']
+# pdb.set_trace()
+stationary_data_columns = stationary_data_sample.loc[:, ~stationary_data_sample.columns.isin(features_to_remove)].columns.tolist()
+stationary_data_columns_desc = extract_desc_for_stat(stationary_data_columns, med_desc, diag_desc, proc_desc, lab_desc)
+stationary_data_columns_desc.to_csv('stationary_data/stationary_dataset_feature_description.csv', index=False)
+
+
+
+path_to_features = 'results/feature_scores_reg.csv'
+features = pd.read_csv(path_to_features)
+features_desc = extract_desc(features, med_desc, diag_desc, proc_desc, lab_desc)
+features_desc.to_csv(path_to_features[:-4]+'_'+'descriptions.csv', index=False)
+
+path_to_features = 'results/feature_scores_mir.csv'
+features = pd.read_csv(path_to_features)
+features_desc = extract_desc(features, med_desc, diag_desc, proc_desc, lab_desc)
+features_desc.to_csv(path_to_features[:-4]+'_'+'descriptions.csv', index=False)
+
+
+print('Test')
+
+
+
diff --git a/scripts/cost_variability/main_create_metadata.py b/scripts/cost_variability/main_create_metadata.py
new file mode 100644
index 00000000..de0de6d0
--- /dev/null
+++ b/scripts/cost_variability/main_create_metadata.py
@@ -0,0 +1,17 @@
+import pdb
+import argparse
+import sys
+import os
+import utils.create_metadata as comp_pt_nums
+import logging
+
+sys.path.append(os.getcwd())
+parser = argparse.ArgumentParser()
+
+# pdb.set_trace()
+client_name ="som-nero-phi-jonc101-secure"
+patient_id = 'anon_id'
+query_demog = "select * from `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_demographic`"
+comp_pt_nums.compute_paients_demog(client_name
+									, patient_id
+									, query_demog)
diff --git a/scripts/cost_variability/main_create_stationary.py b/scripts/cost_variability/main_create_stationary.py
new file mode 100644
index 00000000..099858ad
--- /dev/null
+++ b/scripts/cost_variability/main_create_stationary.py
@@ -0,0 +1,60 @@
+'''
+1. num_features: Read frequency files for medications, diagnosis and procedure select top-n features. 
+2. For each patient in medication file:
+	a. Read their medication
+	b. Iterate through their timestamps.
+	c. Form a m by n matrix where m is the number of timestamps and n is the top-n features
+3 Repeat step 2 for diagnosis and procedure
+
+'''
+import utils.create_stationary as sta
+import argparse
+import os
+import pdb
+import sys
+import logging
+
+
+# pdb.set_trace()
+sys.path.append(os.getcwd())
+parser = argparse.ArgumentParser()
+
+parser.add_argument("--logging_milestone", type=int, default=1000)    
+# logging.basicConfig(format='Date-Time : %(asctime)s : Line No. : %(lineno)d - %(message)s', level = logging.INFO, filename = 'log/logfile_create_stationary.log', filemode = 'a')
+
+
+parser.add_argument("--top_n_med", type=int, default=100)    
+parser.add_argument("--top_n_proc", type=int, default=100)    
+parser.add_argument("--top_n_ccs", type=int, default=100)    
+parser.add_argument("--top_n_lab", type=int, default=100)    
+
+
+parser.add_argument("--diagnosis_file_path", type=str, default='intermediate_files/diagnosis_codes.csv')    
+parser.add_argument("--medication_file_path", type=str, default='intermediate_files/medication_codes.csv')    
+parser.add_argument("--procedure_file_path", type=str, default='intermediate_files/procedure_codes.csv')    
+parser.add_argument("--lab_file_path", type=str, default='intermediate_files/lab_codes.csv')    
+parser.add_argument("--demographic_file_path", type=str, default='intermediate_files/metadata.csv')    
+
+
+parser.add_argument("--ccs_frequencies_mci_path", type=str, default='intermediate_files/ccs_frequencies.csv')    
+parser.add_argument("--pharm_class_frequencies_mci_path", type=str, default='intermediate_files/pharm_class_frequencies.csv')    
+parser.add_argument("--procedure_id_frequencies_mci_path", type=str, default='intermediate_files/procedure_id_frequencies.csv')    
+parser.add_argument("--lab_id_frequencies_mci_path", type=str, default='intermediate_files/labs_id_frequencies.csv')    
+
+
+sta.create_stationary(parser.parse_args().diagnosis_file_path
+						,parser.parse_args().medication_file_path
+						,parser.parse_args().procedure_file_path
+						,parser.parse_args().lab_file_path
+						,parser.parse_args().demographic_file_path
+						,parser.parse_args().ccs_frequencies_mci_path
+						,parser.parse_args().pharm_class_frequencies_mci_path
+						,parser.parse_args().procedure_id_frequencies_mci_path
+						,parser.parse_args().lab_id_frequencies_mci_path
+						,parser.parse_args().top_n_ccs							 
+						,parser.parse_args().top_n_med
+						,parser.parse_args().top_n_proc
+						,parser.parse_args().top_n_lab							 
+						,parser.parse_args().logging_milestone
+						)
+
diff --git a/scripts/cost_variability/main_extract_codes.py b/scripts/cost_variability/main_extract_codes.py
new file mode 100644
index 00000000..66127955
--- /dev/null
+++ b/scripts/cost_variability/main_extract_codes.py
@@ -0,0 +1,89 @@
+import pdb
+import argparse
+import sys
+import os
+import utils.extract_codes as ext_cd
+import logging
+
+sys.path.append(os.getcwd())
+parser = argparse.ArgumentParser()
+parser.add_argument("--table_type", type=str, default='diagnosis', choices = ['diagnosis', 'medication', 'procedure', 'lab'])    
+
+parser.add_argument("--med_patient_id_field_name", type=str, default='anon_id')    
+parser.add_argument("--med_code_field_name", type=str, default='pharm_class_abbr')    
+parser.add_argument("--med_time_field_name", type=str, default='order_time_jittered') 
+
+parser.add_argument("--proc_patient_id_field_name", type=str, default='anon_id')    
+parser.add_argument("--proc_time_field_name", type=str, default='ordering_date_jittered')    
+parser.add_argument("--proc_code_field_name", type=str, default='proc_id')    
+
+parser.add_argument("--lab_patient_id_field_name", type=str, default='anon_id')    
+parser.add_argument("--lab_time_field_name", type=str, default='order_time_utc')    
+# parser.add_argument("--lab_code_field_name", type=str, default='proc_code')    
+parser.add_argument("--lab_code_field_name", type=str, default='base_name')    
+
+
+parser.add_argument("--icd10_field_name", type=str, default='icd10')    
+parser.add_argument("--icd9_field_name", type=str, default='icd9')    
+parser.add_argument("--diag_time_field_name", type=str, default='start_date_utc')    
+parser.add_argument("--diag_patient_id_field_name", type=str, default='anon_id')    
+
+
+
+
+parser.add_argument("--display_step", type=int, default=1000000)    
+
+client_name ="som-nero-phi-jonc101-secure"
+
+if  parser.parse_args().table_type == "diagnosis":
+    query_string = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_diagnosis` A ORDER BY A.anon_id, A.start_date_utc"
+    icd_to_ccs_table_query = "SELECT * FROM `mining-clinical-decisions.mapdata.ahrq_ccsr_diagnosis`"
+    icd9_to_icd10_query = "SELECT * FROM `mining-clinical-decisions.mapdata.icd9gem`"
+    ext_cd.extract_diagnosis(client_name
+                            , query_string
+                            , icd_to_ccs_table_query
+                            , icd9_to_icd10_query
+                            , parser.parse_args().icd10_field_name
+                            , parser.parse_args().icd9_field_name
+                            , parser.parse_args().diag_time_field_name
+                            , parser.parse_args().diag_patient_id_field_name                            
+                            , parser.parse_args().display_step)
+elif  parser.parse_args().table_type == "medication":
+    query_string = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_order_med` A ORDER BY A.anon_id, A.order_time_jittered"
+    unique_medication_id_query = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.unique_pharm_class_abbr` A WHERE A.pharm_class_abbr is not NULL"
+    ext_cd.extract_medication(client_name
+                            , query_string
+                            , unique_medication_id_query
+                            , parser.parse_args().med_code_field_name
+                            , parser.parse_args().med_time_field_name
+                            , parser.parse_args().med_patient_id_field_name                                                        
+                            , parser.parse_args().display_step)
+
+elif  parser.parse_args().table_type == "procedure":
+    query_string = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_order_proc` A ORDER BY A.anon_id, A.ordering_date_jittered"
+    unique_proc_id_query = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.unique_proc_ids` A WHERE A.proc_id is not NULL"    
+    num_lines_query = "SELECT COUNT(*) FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_order_proc`"
+    ext_cd.extract_procedure(client_name
+                            , query_string
+                            , unique_proc_id_query
+                            , num_lines_query
+                            , parser.parse_args().proc_code_field_name
+                            , parser.parse_args().proc_time_field_name
+                            , parser.parse_args().proc_patient_id_field_name                             
+                            , parser.parse_args().display_step)
+
+elif  parser.parse_args().table_type == "lab":
+    query_string = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_lab_result` A ORDER BY A.anon_id, A.order_time_utc"
+    unique_lab_id_query = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.unique_base_name` A WHERE A.base_name is not NULL"    
+    unique_lab_result_flag = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.unique_lab_result_flag` A "    
+    num_lines_query = "SELECT COUNT(*) FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered_lab_result`"
+    
+    ext_cd.extract_labs(client_name
+                            , query_string
+                            , unique_lab_id_query
+                            , unique_lab_result_flag
+                            , num_lines_query
+                            , parser.parse_args().lab_code_field_name
+                            , parser.parse_args().lab_time_field_name
+                            , parser.parse_args().lab_patient_id_field_name                             
+                            , parser.parse_args().display_step)
diff --git a/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_all.csv b/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_all.csv
new file mode 100644
index 00000000..515bdc9e
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_all.csv
@@ -0,0 +1 @@
+alpha,[1.e+01 5.e+00 1.e+00 1.e-01 1.e-02 1.e-03 1.e-04]
diff --git a/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_mir.csv b/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_mir.csv
new file mode 100644
index 00000000..515bdc9e
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_mir.csv
@@ -0,0 +1 @@
+alpha,[1.e+01 5.e+00 1.e+00 1.e-01 1.e-02 1.e-03 1.e-04]
diff --git a/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_reg.csv b/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_reg.csv
new file mode 100644
index 00000000..515bdc9e
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/LASSO_hyperparameters_reg.csv
@@ -0,0 +1 @@
+alpha,[1.e+01 5.e+00 1.e+00 1.e-01 1.e-02 1.e-03 1.e-04]
diff --git a/scripts/cost_variability/results/LASSO/best_params_LASSO_all.csv b/scripts/cost_variability/results/LASSO/best_params_LASSO_all.csv
new file mode 100644
index 00000000..c31ca76d
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/best_params_LASSO_all.csv
@@ -0,0 +1 @@
+alpha,10.0
diff --git a/scripts/cost_variability/results/LASSO/best_params_LASSO_mir.csv b/scripts/cost_variability/results/LASSO/best_params_LASSO_mir.csv
new file mode 100644
index 00000000..c31ca76d
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/best_params_LASSO_mir.csv
@@ -0,0 +1 @@
+alpha,10.0
diff --git a/scripts/cost_variability/results/LASSO/best_params_LASSO_reg.csv b/scripts/cost_variability/results/LASSO/best_params_LASSO_reg.csv
new file mode 100644
index 00000000..c31ca76d
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/best_params_LASSO_reg.csv
@@ -0,0 +1 @@
+alpha,10.0
diff --git a/scripts/cost_variability/results/LASSO/predictions_LASSO_all.csv b/scripts/cost_variability/results/LASSO/predictions_LASSO_all.csv
new file mode 100644
index 00000000..b0d07f36
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/predictions_LASSO_all.csv
@@ -0,0 +1,9449 @@
+6.710134932612410921e+04
+6.002681890949083026e+04
+3.958650922257493949e+04
+5.187487972465757048e+04
+6.801120797741018760e+04
+4.209662268267785112e+04
+3.302386901874378964e+04
+1.655460043432248494e+04
+2.421101231232934515e+04
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+1.394491063176586613e+04
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+1.713127101266624959e+04
+1.000386874228096713e+05
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+4.177770183960570284e+04
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+3.519677849681334919e+04
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diff --git a/scripts/cost_variability/results/LASSO/predictions_LASSO_mir.csv b/scripts/cost_variability/results/LASSO/predictions_LASSO_mir.csv
new file mode 100644
index 00000000..0fa0ec71
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/predictions_LASSO_mir.csv
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diff --git a/scripts/cost_variability/results/LASSO/predictions_LASSO_reg.csv b/scripts/cost_variability/results/LASSO/predictions_LASSO_reg.csv
new file mode 100644
index 00000000..9e5349fd
--- /dev/null
+++ b/scripts/cost_variability/results/LASSO/predictions_LASSO_reg.csv
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index 00000000..5f4356a5
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diff --git a/scripts/cost_variability/results/LR/predictions_lr_mir.csv b/scripts/cost_variability/results/LR/predictions_lr_mir.csv
new file mode 100644
index 00000000..147930be
--- /dev/null
+++ b/scripts/cost_variability/results/LR/predictions_lr_mir.csv
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diff --git a/scripts/cost_variability/results/LR/predictions_lr_reg.csv b/scripts/cost_variability/results/LR/predictions_lr_reg.csv
new file mode 100644
index 00000000..9ba1ad8f
--- /dev/null
+++ b/scripts/cost_variability/results/LR/predictions_lr_reg.csv
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new file mode 100644
index 00000000..5b58aabf
--- /dev/null
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diff --git a/scripts/cost_variability/results/LR/results_lr_mir.csv b/scripts/cost_variability/results/LR/results_lr_mir.csv
new file mode 100644
index 00000000..a294705c
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new file mode 100644
index 00000000..8124730e
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diff --git a/scripts/cost_variability/results/XGB/best_params_xgb_all.csv b/scripts/cost_variability/results/XGB/best_params_xgb_all.csv
new file mode 100644
index 00000000..4f7e3b8e
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diff --git a/scripts/cost_variability/results/XGB/best_params_xgb_mir.csv b/scripts/cost_variability/results/XGB/best_params_xgb_mir.csv
new file mode 100644
index 00000000..d1297b9e
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diff --git a/scripts/cost_variability/results/XGB/predictions_xgb_mir.csv b/scripts/cost_variability/results/XGB/predictions_xgb_mir.csv
new file mode 100644
index 00000000..5119acac
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/predictions_xgb_mir.csv
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diff --git a/scripts/cost_variability/results/XGB/predictions_xgb_reg.csv b/scripts/cost_variability/results/XGB/predictions_xgb_reg.csv
new file mode 100644
index 00000000..4d562ca7
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/predictions_xgb_reg.csv
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new file mode 100644
index 00000000..6c140c71
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/results_xgb_all.csv
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diff --git a/scripts/cost_variability/results/XGB/results_xgb_mir.csv b/scripts/cost_variability/results/XGB/results_xgb_mir.csv
new file mode 100644
index 00000000..37bb4a20
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/results_xgb_mir.csv
@@ -0,0 +1,10 @@
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diff --git a/scripts/cost_variability/results/XGB/results_xgb_reg.csv b/scripts/cost_variability/results/XGB/results_xgb_reg.csv
new file mode 100644
index 00000000..4a87664e
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/results_xgb_reg.csv
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diff --git a/scripts/cost_variability/results/XGB/shap_beeswarm_mir.png b/scripts/cost_variability/results/XGB/shap_beeswarm_mir.png
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index 00000000..264c2a15
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diff --git a/scripts/cost_variability/results/XGB/shap_beeswarm_reg.png b/scripts/cost_variability/results/XGB/shap_beeswarm_reg.png
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index 00000000..1e20e8e3
Binary files /dev/null and b/scripts/cost_variability/results/XGB/shap_beeswarm_reg.png differ
diff --git a/scripts/cost_variability/results/XGB/xgb_all_feature_importance_.csv b/scripts/cost_variability/results/XGB/xgb_all_feature_importance_.csv
new file mode 100644
index 00000000..59a24d9e
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/xgb_all_feature_importance_.csv
@@ -0,0 +1,2 @@
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diff --git a/scripts/cost_variability/results/XGB/xgb_hyperparameters_all.csv b/scripts/cost_variability/results/XGB/xgb_hyperparameters_all.csv
new file mode 100644
index 00000000..d00022b0
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/xgb_hyperparameters_all.csv
@@ -0,0 +1,3 @@
+n_estimators,"[100, 200, 400, 800]"
+max_depth,"[4, 8, 16]"
+learning_rate,"[0.0001, 0.001, 0.01, 0.1, 1]"
diff --git a/scripts/cost_variability/results/XGB/xgb_hyperparameters_mir.csv b/scripts/cost_variability/results/XGB/xgb_hyperparameters_mir.csv
new file mode 100644
index 00000000..d00022b0
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/xgb_hyperparameters_mir.csv
@@ -0,0 +1,3 @@
+n_estimators,"[100, 200, 400, 800]"
+max_depth,"[4, 8, 16]"
+learning_rate,"[0.0001, 0.001, 0.01, 0.1, 1]"
diff --git a/scripts/cost_variability/results/XGB/xgb_hyperparameters_reg.csv b/scripts/cost_variability/results/XGB/xgb_hyperparameters_reg.csv
new file mode 100644
index 00000000..d00022b0
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/xgb_hyperparameters_reg.csv
@@ -0,0 +1,3 @@
+n_estimators,"[100, 200, 400, 800]"
+max_depth,"[4, 8, 16]"
+learning_rate,"[0.0001, 0.001, 0.01, 0.1, 1]"
diff --git a/scripts/cost_variability/results/XGB/xgb_mir_feature_importance_.csv b/scripts/cost_variability/results/XGB/xgb_mir_feature_importance_.csv
new file mode 100644
index 00000000..7343db39
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/xgb_mir_feature_importance_.csv
@@ -0,0 +1,2 @@
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diff --git a/scripts/cost_variability/results/XGB/xgb_reg_feature_importance_.csv b/scripts/cost_variability/results/XGB/xgb_reg_feature_importance_.csv
new file mode 100644
index 00000000..2a93e5a4
--- /dev/null
+++ b/scripts/cost_variability/results/XGB/xgb_reg_feature_importance_.csv
@@ -0,0 +1,2 @@
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diff --git a/scripts/cost_variability/results/feature_scores_mir.csv b/scripts/cost_variability/results/feature_scores_mir.csv
new file mode 100644
index 00000000..55484b85
--- /dev/null
+++ b/scripts/cost_variability/results/feature_scores_mir.csv
@@ -0,0 +1,404 @@
+,mutual_info_regression_score
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diff --git a/scripts/cost_variability/results/feature_scores_mir_descriptions.csv b/scripts/cost_variability/results/feature_scores_mir_descriptions.csv
new file mode 100644
index 00000000..8f72b43e
--- /dev/null
+++ b/scripts/cost_variability/results/feature_scores_mir_descriptions.csv
@@ -0,0 +1,1407 @@
+Table,Code,Description,num_patient,Score
+Pocedure,37113,UP AD LIB,127718,0.0205329698095939
+Pocedure,474911,ABO/RH,50859,0.0180072774949093
+Medication,D1D,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,44650,0.0169458370571651
+Medication,D1D,TRIAMCINOLONE ACETONIDE 0.1 % DENTAL PSTE,2993,0.0169458370571651
+Medication,D1D,PAROEX ORAL RINSE 0.12 % MM MWSH,352,0.0169458370571651
+Medication,D1D,PERIDEX 0.12 % MM MWSH,283,0.0169458370571651
+Medication,D1D,ORALONE 0.1 % DENTAL PSTE,136,0.0169458370571651
+Medication,P2B,VASOPRESSIN 20 UNIT/ML IV SOLN,10043,0.0156485298782134
+Medication,P2B,VASOPRESSIN 20 UNIT/ML INJ SOLN,3391,0.0156485298782134
+Medication,P2B,VASOPRESSIN IN 0.9 % SOD CHLOR 60 UNIT/100 ML (0.6 UNIT/ML) IV SOLN,2718,0.0156485298782134
+Medication,P2B,DESMOPRESSIN 4 MCG/ML INJ SOLN,1374,0.0156485298782134
+Medication,P2B,DESMOPRESSIN 0.1 MG PO TABS,1158,0.0156485298782134
+Pocedure,456456,TRANSFUSE RED BLOOD CELLS,40252,0.0153146930041485
+Pocedure,900,FERRITIN,146186,0.0150856544019699
+Lab result,LYMAB,"LYM, ABS",496919,0.0149007940285614
+Lab result,LYMAB,"Lymphocyte, Absolute",386943,0.0149007940285614
+Lab result,LYMAB,"Lymphocytes, ABS (man diff)",53200,0.0149007940285614
+Lab result,LYMAB,"Lym, ABS (man diff)",43787,0.0149007940285614
+Lab result,LYMAB,"Lymphocytes, Abs.",24480,0.0149007940285614
+Lab result,RBC,RBC,975574,0.0145994287494461
+Lab result,RBC,RBC (MANUAL ENTRY) See EMR for details,10078,0.0145994287494461
+Lab result,RBC,Total Cell Count{Menlo},385,0.0145994287494461
+Lab result,RBC,Red cells,27,0.0145994287494461
+Lab result,RBC,ADULT RBC,10,0.0145994287494461
+Lab result,TP,"Protein, Total, Ser/Plas",785231,0.0144490128179033
+Lab result,TP,Protein-Total,16,0.0144490128179033
+Lab result,TBIL,Total Bilirubin,531297,0.014422169019798
+Lab result,TBIL,"Total Bilirubin, Ser/Plas",411355,0.014422169019798
+Lab result,TBIL,Total Bilirubin (Manual Entry) See EMR for details,93582,0.014422169019798
+Lab result,TBIL,"Bilirubin, Total, Neonatal",107,0.014422169019798
+Pocedure,2233,TYPE AND SCREEN,242768,0.0140860162652716
+Pocedure,473871,BLOOD TYPE VERIFICATION - FULL,28340,0.0138649461488924
+Pocedure,311,"BLOOD GASES, ARTERIAL",74180,0.0138233839858294
+Pocedure,311,ARTERIAL BLOOD GAS,46013,0.0138233839858294
+Lab result,SPECEXPDATE,Specimen Expiration Date,140639,0.0133474724566129
+Pocedure,481775,AB SCREEN (SHC),36269,0.0133033060736358
+Lab result,ALB,"Albumin, Ser/Plas",800563,0.0132559186824701
+Lab result,ALB,ALBUMIN BLD  (MANUAL ENTRY) See EMR for details,9434,0.0132559186824701
+Lab result,ALB,Albumin,7018,0.0132559186824701
+Lab result,ALB,"Albumin, Serum",50,0.0132559186824701
+Medication,M0B,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",36901,0.0125731184726547
+Medication,M0B,"ALBUMIN, HUMAN 25 % 25 % IV SOLP",10600,0.0125731184726547
+Medication,M0B,ANTITHROMBIN III (HUMAN) 500 (+/-) UNIT IV SOLR,73,0.0125731184726547
+Medication,M0B,"ALBUMIN, HUMAN 25 % 25 G/100 ML PREMIX (RWC PAV D USE ONLY)",43,0.0125731184726547
+Medication,M0B,"ALBUMIN, HUMAN 25 % 12.5 G/50 ML PREMIX (RWC PAV D USE ONLY)",16,0.0125731184726547
+Lab result,GLOB,Globulin,749063,0.0123444399868906
+Lab result,GLOB,GAMMA GLOBULIN,5347,0.0123444399868906
+Lab result,GLOB,"Globulin, Total",125,0.0123444399868906
+Pocedure,880100001,ARTERIAL LINE,49560,0.0122858322116952
+Pocedure,880100001,Arterial Line,17570,0.0122858322116952
+Lab result,ALT,"ALT (SGPT), Ser/Plas",815520,0.0120264092905502
+Lab result,ALT,ALT (Manual Entry) See EMR for details,112610,0.0120264092905502
+Lab result,ALT,ALT,8266,0.0120264092905502
+Lab result,AS,Antibody Screen,236027,0.0118363026855201
+Lab result,AS,Antibody Screen (Manual Entry) See EMR for details,4366,0.0118363026855201
+Lab result,AS,Antibody Screen Gel,3509,0.0118363026855201
+Lab result,AS,Ab Screen (Ind. Coombs),3475,0.0118363026855201
+Lab result,AST,"AST (SGOT), Ser/Plas",813935,0.011735220537056
+Lab result,AST,AST (Manual Entry) See EMR for details,111398,0.011735220537056
+Lab result,AST,AST,7194,0.011735220537056
+Medication,S2B,IBUPROFEN 600 MG PO TABS,109543,0.0116247391091381
+Medication,S2B,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,51725,0.0116247391091381
+Medication,S2B,IBUPROFEN 800 MG PO TABS,45051,0.0116247391091381
+Medication,S2B,IBUPROFEN 400 MG PO TABS,42858,0.0116247391091381
+Medication,S2B,KETOROLAC 15 MG/ML INJ SOLN,41164,0.0116247391091381
+Pocedure,100966,SURGICAL PREPARATION,109675,0.0116072056669167
+Lab result,XMUCUS,"Mucous Threads, urine",146805,0.011494868810995
+Pocedure,118571,PHYSICAL ACTIVITY,263527,0.0113006341364803
+Pocedure,100975,KEEP HEAD OF BED ELEVATED,57262,0.0111102598348844
+Lab result,BUN,"Urea Nitrogen,Ser/Plas",513379,0.0109924068467235
+Lab result,BUN,"BUN, Ser/Plas",431163,0.0109924068467235
+Lab result,BUN,BUN,144348,0.0109924068467235
+Lab result,BUN,BUN (Manual Entry) See EMR for details,87845,0.0109924068467235
+Lab result,BUN,"BUN, ISTAT",9453,0.0109924068467235
+Pocedure,419036,"LACTATE, WHOLE BLOOD",38859,0.0109533096830507
+Medication,H2C,KETAMINE 100 MG/ML INJ SOLN,46180,0.0109172775027546
+Medication,H2C,ETOMIDATE 2 MG/ML IV SOLN,12381,0.0109172775027546
+Medication,H2C,PROPOFOL 10 MG/ML IV EMUL,10433,0.0109172775027546
+Medication,H2C,PROPOFOL 10 MG/ML IV INFUSION,7689,0.0109172775027546
+Medication,H2C,KETAMINE 10 MG/ML INJ SOLN,4825,0.0109172775027546
+Lab result,CR,"Creatinine, Ser/Plas",931885,0.0105649621309555
+Lab result,CR,"Creatinine, Serum (Manual Entry) See EMR for details",122459,0.0105649621309555
+Lab result,CR,"Creatinine,ISTAT",108293,0.0105649621309555
+Lab result,CR,Creatinine,12574,0.0105649621309555
+Lab result,CR,"Creatinine, Serum",9841,0.0105649621309555
+Diagnosis,BLD006,"Thrombocytopenia, unspecified",19381,0.0105430614100765
+Diagnosis,BLD006,Thrombocytopenia (CMS-HCC),14647,0.0105430614100765
+Diagnosis,BLD006,"Thrombocytopenia, unspecified (CMS-HCC)",12938,0.0105430614100765
+Diagnosis,BLD006,Other secondary thrombocytopenia,7780,0.0105430614100765
+Diagnosis,BLD006,"Coagulation defect, unspecified",5047,0.0105430614100765
+Lab result,IGABSWAM,"Imm. Granulocyte, Abs",225266,0.0104307939461734
+Diagnosis,MUS010,Pain in limb,66008,0.0101876851575963
+Diagnosis,MUS010,Pain in right knee,54116,0.0101876851575963
+Diagnosis,MUS010,Pain in left knee,51320,0.0101876851575963
+Diagnosis,MUS010,"Pain in joint, lower leg",48101,0.0101876851575963
+Diagnosis,MUS010,"Backache, unspecified",41181,0.0101876851575963
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,12312,0.010158308369744
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL OPHTH OINT WRAPPER,7818,0.010158308369744
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL 56.8-42.5 % OPHT OINT,2208,0.010158308369744
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL 94-3 % OPHT OINT,1289,0.010158308369744
+Medication,Q6Y,WHITE PETROLATUM OPHT OINT,981,0.010158308369744
+Lab result,ALKP,"Alk P'TASE, Total, Ser/Plas",794773,0.0098146379106707
+Lab result,ALKP,"ALK P'TASE Total, Serum (Manual Entry) See EMR for details",75602,0.0098146379106707
+Lab result,ALKP,Alkaline Phosphatase,233,0.0098146379106707
+Lab result,ALKP,Total Alkaline Phosphatase,122,0.0098146379106707
+Diagnosis,RSP012,Acute respiratory failure with hypoxia,12114,0.0096405548402735
+Diagnosis,RSP012,Acute respiratory failure (CMS-HCC),10319,0.0096405548402735
+Diagnosis,RSP012,"Acute respiratory failure, unspecified whether with hypoxia or hypercapnia",4850,0.0096405548402735
+Diagnosis,RSP012,Acute and chronic respiratory failure with hypoxia,3870,0.0096405548402735
+Diagnosis,RSP012,Acute respiratory failure with hypoxia (CMS-HCC),3129,0.0096405548402735
+Lab result,IGWAM,"Imm. Granulocyte, %",225268,0.0092673270570573
+Pocedure,1746,RED BLOOD CELLS,89427,0.0092525829560814
+Lab result,ABORH,ABO/Rh (Automation),225482,0.0091273768417914
+Lab result,ABORH,ABO/RH,48626,0.0091273768417914
+Lab result,ABORH,ABO/Rh Cord,5673,0.0091273768417914
+Lab result,ABORH,ABO (Automation) (Manual Entry) See EMR for details,2995,0.0091273768417914
+Lab result,ABORH,ABO/RH Manual Entry,785,0.0091273768417914
+Diagnosis,RSP016,"Other diseases of lung, not elsewhere classified",22159,0.0090333565011457
+Diagnosis,RSP016,Chronic pulmonary edema,14987,0.0090333565011457
+Diagnosis,RSP016,Pulmonary congestion and hypostasis,11485,0.0090333565011457
+Diagnosis,RSP016,Other disorders of lung,10766,0.0090333565011457
+Diagnosis,RSP016,Other specified respiratory disorders,8171,0.0090333565011457
+Pocedure,657586,RAINBOW DRAW (FOR ED/RRT/CODE BLUE ONLY),85896,0.0090013484635471
+Pocedure,657586,RAINBOW DRAW,7980,0.0090013484635471
+Lab result,NEUTAB,"NEUT, ABS",522238,0.0089524881425453
+Lab result,NEUTAB,"Neutrophil, Absolute",384165,0.0089524881425453
+Lab result,NEUTAB,"Neut, ABS (man diff)",41867,0.0089524881425453
+Lab result,NEUTAB,"Neut, ABS (Seg+Band) (man diff)",31195,0.0089524881425453
+Lab result,NEUTAB,"NEUTS, ABS",5167,0.0089524881425453
+Lab result,EGFR,eGFR,671284,0.0089041890038172
+Lab result,EGFR,eGFR (Non African American (Manual Entry) See EMR for details,72618,0.0089041890038172
+Lab result,EGFR,EGFR African American (Manual Entry) SEE EMR for Details,23322,0.0089041890038172
+Lab result,EGFR,EGFR Mutation,1224,0.0089041890038172
+Medication,M9D,"TRANEXAMIC ACID 1,000 MG/10 ML (100 MG/ML) IV SOLN",15147,0.008689836433896
+Medication,M9D,AMINOCAPROIC ACID 250 MG/ML IV SOLN,6004,0.008689836433896
+Medication,M9D,TRANEXAMIC ACID 650 MG PO TABS,1212,0.008689836433896
+Medication,M9D,"TRANEXAMIC ACID IN NACL,ISO-OS 1,000 MG/100 ML (10 MG/ML) IV PGBK",639,0.008689836433896
+Medication,M9D,LYSTEDA 650 MG PO TABS,590,0.008689836433896
+Pocedure,1434,VITAL SIGNS PER PROTOCOL,262833,0.0085929457891424
+Pocedure,1048,HEPATIC FUNCTION PANEL A,173961,0.0085780194272959
+Pocedure,480951,"NOVEL CORONAVIRUS 2019 (SARS-COV-2), RT PCR",430381,0.0083978648493836
+Pocedure,2261,URINALYSIS WITH MICROSCOPIC,358343,0.0082449741390497
+Pocedure,2261,"URINALYSIS, COMPLETE",401,0.0082449741390497
+Medication,C1F,CALCIUM PO,36076,0.008132441804441
+Medication,C1F,VITAMIN D (WITH CALCIUM) PO,31603,0.008132441804441
+Medication,C1F,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,30407,0.008132441804441
+Medication,C1F,CALCIUM + D PO,18851,0.008132441804441
+Medication,C1F,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,18116,0.008132441804441
+Lab result,GLU,"Glucose, Ser/Plas",894413,0.0080866178323348
+Lab result,GLU,Glucose by Meter,219614,0.0080866178323348
+Lab result,GLU,"Glucose, Whole Blood",116977,0.0080866178323348
+Lab result,GLU,"Glucose, SER/PLAS (Manual Entry) See EMR for details",111348,0.0080866178323348
+Lab result,GLU,"Glucose,ISTAT",58290,0.0080866178323348
+Lab result,XURBC,"RBC, urine",218650,0.007997007076272
+Pocedure,414593,BLOOD TYPE VERIFICATION,90090,0.0078340180569014
+Pocedure,414593,BLOOD TYPE VERIFICATION - ORDER,31488,0.0078340180569014
+Lab result,XUBLOOD,"Blood, urine",220887,0.0078317675901258
+Lab result,FASTING,Fasting,354582,0.0077881250826079
+Pocedure,1735,MRSA SCREEN,66723,0.0077564954727238
+Pocedure,1735,MRSA SCREEN BY CULTURE,30403,0.0077564954727238
+Lab result,NRBCPERCENT,"nRBC, %",228738,0.0076406011312433
+Lab result,MONOAB,"MONO, ABS",515767,0.0075863689221746
+Lab result,MONOAB,"Monocyte, Absolute",386715,0.0075863689221746
+Lab result,MONOAB,"Monocytes, ABS (man diff)",52778,0.0075863689221746
+Lab result,MONOAB,"Mono, ABS (man diff)",41047,0.0075863689221746
+Lab result,MONOAB,"MONOS, ABS",237,0.0075863689221746
+Lab result,GFR,eGFR for African American,392642,0.0075093498163723
+Lab result,GFR,GFR,13115,0.0075093498163723
+Diagnosis,SYM004,Nausea,54051,0.0074620519508832
+Diagnosis,SYM004,"Nausea with vomiting, unspecified",49972,0.0074620519508832
+Diagnosis,SYM004,Nausea with vomiting,31162,0.0074620519508832
+Diagnosis,SYM004,"Vomiting, unspecified",25617,0.0074620519508832
+Diagnosis,SYM004,Nausea alone,24577,0.0074620519508832
+Lab result,HCT,Hematocrit,988518,0.0074613019009619
+Lab result,HCT,Hct (Est),99973,0.0074613019009619
+Lab result,HCT,Hematocrit (Manual Entry) See EMR for details,96497,0.0074613019009619
+Lab result,HCT,"Hct, ISTAT",44756,0.0074613019009619
+Lab result,HCT,"Hct(Calc), ISTAT",30262,0.0074613019009619
+Pocedure,17678,FOLEY RETENTION CATHETER,107867,0.0074417869216287
+Pocedure,17678,"FOLEY, RETENTION CATHETER",17502,0.0074417869216287
+Lab result,NRBCABSOLUTE,"nRBC, Abs",228741,0.0074413746313606
+Medication,R1M,FUROSEMIDE 10 MG/ML INJ SOLN,62358,0.0074376669725406
+Medication,R1M,FUROSEMIDE 20 MG PO TABS,55422,0.0074376669725406
+Medication,R1M,FUROSEMIDE 40 MG PO TABS,34906,0.0074376669725406
+Medication,R1M,LASIX 20 MG PO TABS,11992,0.0074376669725406
+Medication,R1M,LASIX 40 MG PO TABS,8573,0.0074376669725406
+Lab result,BASO,"BASO, %",506797,0.0074270742758952
+Lab result,BASO,Basophil %,386735,0.0074270742758952
+Lab result,BASO,"BASOS, %",74681,0.0074270742758952
+Lab result,BASO,Basophils,49852,0.0074270742758952
+Lab result,CL,"Chloride, Ser/Plas",909379,0.0073934028168942
+Lab result,CL,"Chloride, Whole Bld",115389,0.0073934028168942
+Lab result,CL,CHLORIDE BLD  (MANUAL ENTRY) See EMR for details,11132,0.0073934028168942
+Lab result,CL,"Chloride, ISTAT",9458,0.0073934028168942
+Lab result,CL,"CHLORIDE, SERUM/PLASMA (MANUAL ENTRY)SEE EMR FOR DETAILS",6149,0.0073934028168942
+Medication,H0A,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,176397,0.0073342692035878
+Medication,H0A,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,132838,0.0073342692035878
+Medication,H0A,LIDOCAINE HCL 10 MG/ML (1 %) INJ SOLN,57440,0.0073342692035878
+Medication,H0A,"LIDOCAINE-EPINEPHRINE 1 %-1:100,000 INJ SOLN",46048,0.0073342692035878
+Medication,H0A,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,37590,0.0073342692035878
+Pocedure,418745,ADDITIONAL INSTRUCTIONS,134480,0.007322643024862
+Pocedure,35000004791,TRANSFER PATIENT,55137,0.0073194077928548
+Pocedure,35000004791,CHANGE LEVEL OF CARE/TRANSFER PATIENT,31374,0.0073194077928548
+Pocedure,35000004791,TRANSFER PATIENT/CHANGE LEVEL OF CARE,12068,0.0073194077928548
+Pocedure,35000004791,CHANGE LEVEL OF CARE/TRANSFER INPATIENT,248,0.0073194077928548
+Lab result,BASOAB,"BASOS, ABS",570478,0.0072517249509365
+Lab result,BASOAB,"Basophil, Absolute",386712,0.0072517249509365
+Lab result,BASOAB,"Basophils, ABS (man diff)",25934,0.0072517249509365
+Lab result,BASOAB,"Baso, ABS (man diff)",20844,0.0072517249509365
+Lab result,MONO,"MONO, %",516214,0.0072437407342929
+Lab result,MONO,Monocyte %,386739,0.0072437407342929
+Lab result,MONO,Monocytes,170474,0.0072437407342929
+Lab result,MONO,Mono Titer,30,0.0072437407342929
+Lab result,MONO,Mono Antibody Titer,1,0.0072437407342929
+Lab result,AG,Anion Gap,723779,0.0072142502064054
+Lab result,AG,A/G Ratio,3789,0.0072142502064054
+Lab result,WBC,WBC,975990,0.007189485417762
+Lab result,WBC,WBC (Manual Entry) See EMR for details,104161,0.007189485417762
+Lab result,WBC,WBC count,15815,0.007189485417762
+Lab result,WBC,WBC Count,193,0.007189485417762
+Lab result,WBC,White Cells,1,0.007189485417762
+Lab result,XSQEP,Squamous Cells,158621,0.0071757336631352
+Lab result,CO2,"CO2, Ser/Plas",906147,0.0071630926501111
+Medication,C4G,INSULIN LISPRO 100 UNIT/ML SC SOLN,42013,0.0070671404873154
+Medication,C4G,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,39435,0.0070671404873154
+Medication,C4G,INSULIN GLARGINE 100 UNIT/ML SC SOLN,15498,0.0070671404873154
+Medication,C4G,INSULIN ASPART 100 UNIT/ML SC SOLN,12049,0.0070671404873154
+Medication,C4G,INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP,9163,0.0070671404873154
+Medication,Q5H,LIDOCAINE 4 % TP CREA,50103,0.0070440444712307
+Medication,Q5H,LIDOCAINE 5 % TP PTMD,44421,0.0070440444712307
+Medication,Q5H,LIDOCAINE-PRILOCAINE 2.5-2.5 % TP CREA,15900,0.0070440444712307
+Medication,Q5H,BUTAMBEN-TETRACAINE-BENZOCAINE 2 %-2 %-14 % (200 MG/SEC) TP SPRA,13857,0.0070440444712307
+Medication,Q5H,LIDOCAINE 5 %(700 MG/PATCH) TP PTMD,13442,0.0070440444712307
+Lab result,NEUT,"NEUT, %",507163,0.0070106337453521
+Lab result,NEUT,Neutrophil %,384147,0.0070106337453521
+Lab result,NEUT,Neutrophils,75228,0.0070106337453521
+Lab result,NEUT,Segmented Neutrophils,53321,0.0070106337453521
+Lab result,NEUT,Seg neutrophils,50995,0.0070106337453521
+Lab result,CAION,"Calcium, Ionized",97837,0.0070047994875952
+Lab result,CAION,Calcium Ionized,59715,0.0070047994875952
+Lab result,CAION,"Calcium,Ion, ISTAT",36786,0.0070047994875952
+Lab result,CAION,"Calcium, Ionized, Serum",706,0.0070047994875952
+Lab result,LYM,"LYM, %",512717,0.0069887063635596
+Lab result,LYM,Lymphocyte %,386962,0.0069887063635596
+Lab result,LYM,Lymphocytes,100957,0.0069887063635596
+Lab result,LYM,Lymphocytes %,24390,0.0069887063635596
+Lab result,PLT,Platelet count,975807,0.0069611115372389
+Lab result,PLT,Platelet Count (Manual Entry) See EMR for details,103123,0.0069611115372389
+Lab result,PLT,Platelets,7908,0.0069611115372389
+Lab result,PLT,"Platelet Ct, Manual",478,0.0069611115372389
+Lab result,PLT,"Platelet Count, Manual {MENLO}",125,0.0069611115372389
+Medication,W5A,VALACYCLOVIR 1 GRAM PO TABS,23451,0.0069558043695718
+Medication,W5A,ACYCLOVIR 400 MG PO TABS,23403,0.0069558043695718
+Medication,W5A,OSELTAMIVIR 75 MG PO CAPS,18189,0.0069558043695718
+Medication,W5A,VALACYCLOVIR 500 MG PO TABS,16969,0.0069558043695718
+Medication,W5A,ACYCLOVIR 800 MG PO TABS,7192,0.0069558043695718
+Pocedure,17581,NOTIFY MD:VITAL SIGNS,267085,0.0069411723385885
+Medication,J5E,EPHEDRINE SULFATE 50 MG/ML INJ SOLN,112778,0.0067544414936142
+Medication,J5E,PHENYLEPHRINE HCL 10 MG/ML INJ SOLN,107666,0.0067544414936142
+Medication,J5E,PHENYLEPHRINE HCL IN 0.9% NACL 40 MG/250 ML (160 MCG/ML) IV SOLN,7776,0.0067544414936142
+Medication,J5E,PHENYLEPHRINE IN 0.9% NACL(PF) 0.5 MG/5 ML (100 MCG/ML) IV SYRG,3399,0.0067544414936142
+Medication,J5E,PHENYLEPHRINE HCL IN 0.9% NACL 1 MG/10 ML (100 MCG/ML) IV SYRG,2543,0.0067544414936142
+Diagnosis,FAC009,Other specified postprocedural states,91771,0.0067415202951686
+Diagnosis,FAC009,Other postprocedural status(V45.89),46825,0.0067415202951686
+Diagnosis,FAC009,Fitting and adjustment of vascular catheter,25346,0.0067415202951686
+Diagnosis,FAC009,Encounter for fitting and adjustment of non-vascular catheter NEC,21907,0.0067415202951686
+Diagnosis,FAC009,Arthrodesis status,20784,0.0067415202951686
+Pocedure,433709,SLIDE REVIEW,51880,0.0066829912561507
+Lab result,HGB,Hemoglobin,982722,0.0066340530543311
+Lab result,HGB,Hemoglobin (Manual Entry) See EMR for details,114709,0.0066340530543311
+Lab result,HGB,"Hgb(Calc), ISTAT",71388,0.0066340530543311
+Lab result,HGB,HgB,46893,0.0066340530543311
+Lab result,HGB,HGB (Manual Entry) See EMR for details,3202,0.0066340530543311
+Lab result,CA,"Calcium, Ser/Plas",896056,0.0065906335036278
+Lab result,CA,"Calcium, Serum (Manual Entry) See EMR for details",91151,0.0065906335036278
+Lab result,CA,Calcium,88,0.0065906335036278
+Lab result,CA,"Calcium, Serum",25,0.0065906335036278
+Diagnosis,RSP011,Atelectasis,25228,0.006540393815601
+Diagnosis,RSP011,Pulmonary collapse,19540,0.006540393815601
+Diagnosis,RSP011,Other specified pleural conditions,2623,0.006540393815601
+Diagnosis,RSP011,Pleurisy without mention of effusion or current tuberculosis,2328,0.006540393815601
+Diagnosis,RSP011,Pleural effusion in other conditions classified elsewhere,1483,0.006540393815601
+Medication,C6D,VITAMIN D3 PO,82759,0.0064901512152468
+Medication,C6D,"ERGOCALCIFEROL (VITAMIN D2) 50,000 UNIT PO CAPS",24427,0.0064901512152468
+Medication,C6D,"CHOLECALCIFEROL (VITAMIN D3) 1,000 UNIT PO TABS",14589,0.0064901512152468
+Medication,C6D,VITAMIN D PO,11667,0.0064901512152468
+Medication,C6D,"CHOLECALCIFEROL (VITAMIN D3) 25 MCG (1,000 UNIT) PO TABS",10165,0.0064901512152468
+Medication,W1Y,CEFTRIAXONE 1 GRAM INJ SOLR,14140,0.0064602362444359
+Medication,W1Y,CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY),11238,0.0064602362444359
+Medication,W1Y,CEFDINIR 300 MG PO CAPS,6300,0.0064602362444359
+Medication,W1Y,CEFTRIAXONE 1 GRAM IV SOLR,6118,0.0064602362444359
+Medication,W1Y,CEFTRIAXONE 2 GRAM/20 ML IV PUSH (VIAL ONLY),5851,0.0064602362444359
+Lab result,K,"Potassium, Ser/Plas",918122,0.0063942569343311
+Lab result,K,"Potassium, Whole Bld",117286,0.0063942569343311
+Lab result,K,Potassium (Manual Entry) See EMR for details,113142,0.0063942569343311
+Lab result,K,"Potassium, ISTAT",71483,0.0063942569343311
+Lab result,K,"Potassium, serum/plasma",2284,0.0063942569343311
+Lab result,SARSCOV2TYPE,SPECIMEN TYPE,343807,0.0062897459418109
+Diagnosis,FAC014,Encounter for general adult medical examination without abnormal findings,278500,0.0062463244272454
+Diagnosis,FAC014,Routine general medical examination at a health care facility,184958,0.0062463244272454
+Diagnosis,FAC014,Encounter for other preprocedural examination,144380,0.0062463244272454
+Diagnosis,FAC014,Preventative health care,108293,0.0062463244272454
+Diagnosis,FAC014,Encounter for gynecological examination (general) (routine) without abnormal findings,100835,0.0062463244272454
+Lab result,EOS,"EOS, %",562503,0.0061966680766054
+Lab result,EOS,Eosinophil %,386735,0.0061966680766054
+Lab result,EOS,Eosinophils,73183,0.0061966680766054
+Pocedure,706,DIET REGULAR,226213,0.0061247111563964
+Lab result,POCTSOURCE,Sample Type,51668,0.0061079475060275
+Pocedure,1200,CONSULT TO NUTRITION,47612,0.0060771675300737
+Pocedure,880100004,SIMPLE AIRWAY,149218,0.0060551748534289
+Pocedure,880100004,Simple Airway,57279,0.0060551748534289
+Pocedure,880100004,x,1,0.0060551748534289
+Pocedure,880100004,Simple airway,1,0.0060551748534289
+Pocedure,880100004,Complex Airway,4,0.0060551748534289
+Pocedure,501391,RISK FOR VENOUS THROMBOEMBOLISM - VTE RISK ASSESSMENT,103363,0.0059445720729705
+Pocedure,482552,XR CHEST 2 VIEWS,160440,0.0059400797919204
+Medication,A2A,AMIODARONE 200 MG PO TABS,20862,0.005883180439909
+Medication,A2A,"AMIODARONE IN DEXTROSE,ISO-OSM 150 MG/100 ML (1.5 MG/ML) IV SOLN",6339,0.005883180439909
+Medication,A2A,LIDOCAINE IN 5 % DEXTROSE (PF) 8 MG/ML (0.8 %) IV SOLP,5717,0.005883180439909
+Medication,A2A,FLECAINIDE 50 MG PO TABS,3684,0.005883180439909
+Medication,A2A,FLECAINIDE 100 MG PO TABS,3540,0.005883180439909
+Lab result,GLUURN,"Glucose, urine",220885,0.0058531298914019
+Lab result,GLUURN,Glucose Urine,99481,0.0058531298914019
+Lab result,GLUURN,"Glucose, Urine POC",14235,0.0058531298914019
+Lab result,GLUURN,"Glucose, Urine",359,0.0058531298914019
+Lab result,GLUURN,Urine Glucose,328,0.0058531298914019
+Diagnosis,MBD013,Deferred diagnosis on axis I,19301,0.0058050893768868
+Diagnosis,MBD013,Other persistent mental disorders due to conditions classified elsewhere,2663,0.0058050893768868
+Diagnosis,MBD013,Unspecified nonpsychotic mental disorder,1380,0.0058050893768868
+Diagnosis,MBD013,Transsexualism,1165,0.0058050893768868
+Diagnosis,MBD013,Other mental disorders complicating the puerperium,1008,0.0058050893768868
+Lab result,RDW,RDW,974552,0.0057038333936101
+Lab result,RDW,RDW (MANUAL ENTRY) See EMR for details,4076,0.0057038333936101
+Medication,W2A,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,57065,0.0056848808379681
+Medication,W2A,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,12312,0.0056848808379681
+Medication,W2A,BACTRIM DS 800-160 MG PO TABS,7348,0.0056848808379681
+Medication,W2A,SULFAMETHOXAZOLE-TRIMETHOPRIM 200-40 MG/5 ML PO SUSP,4157,0.0056848808379681
+Medication,W2A,TRIMETHOPRIM-SULFAMETHOXAZOLE 160-800 MG PO TABS,3626,0.0056848808379681
+Medication,C1W,LACTATED RINGERS IV SOLP,198859,0.0056093685467428
+Medication,C1W,ELECTROLYTE-R (PH 7.4) IV SOLP,91300,0.0056093685467428
+Medication,C1W,ELECTROLYTE-R IV SOLP,88933,0.0056093685467428
+Medication,C1W,ARTIFICIAL SALIVA (YERBAS-LYT) MM SPRA,2212,0.0056093685467428
+Medication,C1W,ELECTROLYTE-A IV SOLP,1140,0.0056093685467428
+Pocedure,1369,"METABOLIC PANEL, COMPREHENSIVE",810054,0.0056072761975078
+Lab result,NA,"Sodium, Ser/Plas",914528,0.005558050883673
+Lab result,NA,"Sodium, Whole Blood",117026,0.005558050883673
+Lab result,NA,Sodium (Manual Entry) See EMR for details,108406,0.005558050883673
+Lab result,NA,"Sodium, ISTAT",71442,0.005558050883673
+Lab result,NA,"Sodium, whole blood, ePOC",624,0.005558050883673
+Diagnosis,CIR031,"Hypotension, unspecified",30472,0.0054754503216851
+Diagnosis,CIR031,Orthostatic hypotension,15499,0.0054754503216851
+Diagnosis,CIR031,"Hypotension, unspecified hypotension type",6053,0.0054754503216851
+Diagnosis,CIR031,Hypotension,5844,0.0054754503216851
+Diagnosis,CIR031,Other hypotension,3806,0.0054754503216851
+Diagnosis,GEN002,"Acute kidney failure, unspecified",27765,0.0054252485384791
+Diagnosis,GEN002,"Acute kidney failure, unspecified (CMS-HCC)",16552,0.0054252485384791
+Diagnosis,GEN002,Acute kidney injury (nontraumatic) (CMS-HCC),10653,0.0054252485384791
+Diagnosis,GEN002,AKI (acute kidney injury) (CMS-HCC),6117,0.0054252485384791
+Diagnosis,GEN002,Acute kidney failure with tubular necrosis,4948,0.0054252485384791
+Pocedure,68376,ADMIT TO PHASE,175723,0.0053705728278972
+Pocedure,68376,ADMIT TO PHASE (POST PROCEDURE AREA),101150,0.0053705728278972
+Lab result,SARSCOV2SRC,SPECIMEN SOURCE,379098,0.0052937828980428
+Lab result,XUPROT,"Protein, urine",220884,0.0052250677078127
+Diagnosis,FAC006,Encounter for antineoplastic chemotherapy,16361,0.0052037850996322
+Diagnosis,FAC006,Encounter for antineoplastic immunotherapy,3547,0.0052037850996322
+Diagnosis,FAC006,Encounter for antineoplastic radiation therapy,2506,0.0052037850996322
+Diagnosis,FAC006,Radiotherapy,2412,0.0052037850996322
+Diagnosis,FAC006,Admission for antineoplastic chemotherapy,401,0.0052037850996322
+Lab result,PT,Prothrombin Time,337139,0.0051630004117844
+Lab result,PT,"PT, ISTAT",53521,0.0051630004117844
+Lab result,PT,Prothrombin Time (Manual Entry) See EMR for details,14406,0.0051630004117844
+Lab result,PT,PT seconds,3497,0.0051630004117844
+Lab result,PT,PT (Manual),362,0.0051630004117844
+Pocedure,349,"CALCIUM IONIZED, SERUM/PLASMA",63372,0.0051465874746126
+Pocedure,349,"CALCIUM IONIZED, WHOLE BLOOD",33086,0.0051465874746126
+Lab result,MCH,MCH,972430,0.0051090382298626
+Pocedure,35073,DROPLET ISOLATION,53575,0.0050382858107576
+Pocedure,26702,NURSING COMMUNICATION(FOR ORDERSET ONLY),241691,0.0049267848842542
+Pocedure,26702,NURSING COMMUNICATION,46176,0.0049267848842542
+Medication,A6U,IOPAMIDOL 76 % IV SOLN (RADIOLOGY) 100 ML,33469,0.0049034569041654
+Medication,A6U,IOPAMIDOL 76 % IV SOLN (RADIOLOGY) 500 ML,25229,0.0049034569041654
+Medication,A6U,IOPAMIDOL 61 % IV SOLN (RADIOLOGY) 150 ML,16352,0.0049034569041654
+Medication,A6U,IOPAMIDOL 61 % IV SOLN (RADIOLOGY) 100 ML,15215,0.0049034569041654
+Medication,A6U,IOPAMIDOL 76 % IV SOLN (RADIOLOGY) 150 ML,14381,0.0049034569041654
+Lab result,QRSDINTERVAL,QRSD Interval,382687,0.0048852290833663
+Lab result,QTCINTERVAL,QTC Interval,382673,0.0048850767839274
+Lab result,ECGIMPRESS,ECG Impression,382686,0.004880662327225
+Lab result,QRSAXIS,QRS Axis,382694,0.0048659934531807
+Lab result,RR,RR,382675,0.0048544826678584
+Lab result,HEARTRATE,Heart Rate,382694,0.0048476096712812
+Lab result,XUPH,"pH, urine",220884,0.0048355616844129
+Lab result,QTINTERVAL,QT Interval,382675,0.0048283475994557
+Lab result,TWAVEAXIS,T Wave Axis,382269,0.0048011681861663
+Pocedure,659308,CBC WITH DIFFERENTIAL,427099,0.0047914792815639
+Medication,J5A,EPINEPHRINE 1 MG/ML (1 ML) INJ SOLN,28000,0.004763217855688
+Medication,J5A,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,11570,0.004763217855688
+Medication,J5A,EPINEPHRINE 0.1 MG/ML INJ SYRG,8234,0.004763217855688
+Medication,J5A,EPINEPHRINE 1 MG/ML INJ SOLN,8193,0.004763217855688
+Medication,J5A,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,7739,0.004763217855688
+Lab result,XUCOL,"Color, urine",220889,0.0047404275160318
+Medication,Z2Q,LORATADINE 10 MG PO TABS,37560,0.0047362002688646
+Medication,Z2Q,CETIRIZINE 10 MG PO TABS,26560,0.0047362002688646
+Medication,Z2Q,ZYRTEC 10 MG PO TABS,20412,0.0047362002688646
+Medication,Z2Q,CLARITIN PO,15566,0.0047362002688646
+Medication,Z2Q,ZYRTEC PO,15482,0.0047362002688646
+Lab result,EOSAB,"EOS, ABS",570480,0.0046586549686151
+Lab result,EOSAB,"Eosinophil, Absolute",386713,0.0046586549686151
+Lab result,EOSAB,"Eosinophils, ABS (man diff)",39330,0.0046586549686151
+Lab result,EOSAB,"Eos, ABS (man diff)",29133,0.0046586549686151
+Medication,B6W,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,123384,0.0046454205144605
+Medication,B6W,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,45075,0.0046454205144605
+Medication,B6W,PROAIR HFA 90 MCG/ACTUATION INH HFAA,38733,0.0046454205144605
+Medication,B6W,VENTOLIN HFA 90 MCG/ACTUATION INH HFAA,19639,0.0046454205144605
+Medication,B6W,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,12773,0.0046454205144605
+Diagnosis,GEN004,"Urinary tract infection, site not specified",77256,0.0045854929437041
+Diagnosis,GEN004,Acute cystitis without hematuria,24015,0.0045854929437041
+Diagnosis,GEN004,UTI (lower urinary tract infection),17607,0.0045854929437041
+Diagnosis,GEN004,"Urinary tract infection without hematuria, site unspecified",17432,0.0045854929437041
+Diagnosis,GEN004,UTI (urinary tract infection),13012,0.0045854929437041
+Pocedure,337,C - REACTIVE PROTEIN,154905,0.0045657147559221
+Medication,W3B,FLUCONAZOLE 150 MG PO TABS,32752,0.0045433726081449
+Medication,W3B,FLUCONAZOLE 200 MG PO TABS,10562,0.0045433726081449
+Medication,W3B,CLOTRIMAZOLE 10 MG MM TROC,9376,0.0045433726081449
+Medication,W3B,TERBINAFINE HCL 250 MG PO TABS,6857,0.0045433726081449
+Medication,W3B,FLUCONAZOLE 100 MG PO TABS,5284,0.0045433726081449
+Diagnosis,SYM011,Dysuria,63756,0.0045410835170565
+Diagnosis,SYM011,Urinary frequency,40161,0.0045410835170565
+Diagnosis,SYM011,Frequency of micturition,34933,0.0045410835170565
+Diagnosis,SYM011,Nocturia,25129,0.0045410835170565
+Diagnosis,SYM011,"Retention of urine, unspecified",22017,0.0045410835170565
+Lab result,XUBACT,"Bacteria, urine",218658,0.0043794966429877
+Pocedure,482536,XR CHEST 1 VIEW,124216,0.004375801286744
+Pocedure,147420,ECG PROCEDURE SCANNED REPORT,166301,0.0043012614722592
+Lab result,PRINTERVAL,P-R Interval,375625,0.0042618242767848
+Diagnosis,CIR012,"Chest pain, unspecified",144563,0.0042374788200214
+Diagnosis,CIR012,Other chest pain,91900,0.0042374788200214
+Diagnosis,CIR012,"Chest pain, unspecified type",48319,0.0042374788200214
+Diagnosis,CIR012,Chest pain,38647,0.0042374788200214
+Diagnosis,CIR012,Precordial pain,18060,0.0042374788200214
+Pocedure,2263,"URINALYSIS SCREEN, CULTURE IF POSITIVE",219072,0.004207587371551
+Pocedure,2263,"URINALYSIS, SCREEN FOR CULTURE",60277,0.004207587371551
+Pocedure,2263,URINALYSIS W/REFLEX TO CULTURE,160,0.004207587371551
+Pocedure,2263,"UA/M W/RFLX CULTURE, ROUTINE",7,0.004207587371551
+Lab result,PAXIS,P Axis,375241,0.0042055758235834
+Lab result,XUCLAR,"Clarity, urine",220885,0.0042044951797601
+Diagnosis,SYM006,Unspecified abdominal pain,79731,0.0041852247566598
+Diagnosis,SYM006,"Abdominal pain, unspecified site",56899,0.0041852247566598
+Diagnosis,SYM006,"Diarrhea, unspecified",50130,0.0041852247566598
+Diagnosis,SYM006,Epigastric pain,45748,0.0041852247566598
+Diagnosis,SYM006,Diarrhea,44535,0.0041852247566598
+Medication,P3A,LEVOTHYROXINE 50 MCG PO TABS,33238,0.0040884707067696
+Medication,P3A,LEVOTHYROXINE 75 MCG PO TABS,26404,0.0040884707067696
+Medication,P3A,LEVOTHYROXINE 25 MCG PO TABS,25685,0.0040884707067696
+Medication,P3A,LEVOTHYROXINE 100 MCG PO TABS,24784,0.0040884707067696
+Medication,P3A,LEVOTHYROXINE 125 MCG PO TABS,14043,0.0040884707067696
+Pocedure,832,ECHO - TRANSTHORACIC ECHO +DOPPLER,162284,0.0040621301166154
+Pocedure,832,ECHO - TRANSTHORACIC ECHO,9424,0.0040621301166154
+Medication,H2E,ZOLPIDEM 5 MG PO TABS,84042,0.0040000902224206
+Medication,H2E,ZOLPIDEM 10 MG PO TABS,30429,0.0040000902224206
+Medication,H2E,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,15070,0.0040000902224206
+Medication,H2E,AMBIEN 10 MG PO TABS,11316,0.0040000902224206
+Medication,H2E,DEXMEDETOMIDINE 100 MCG/ML IV SOLN,8930,0.0040000902224206
+Pocedure,1428,MONITOR INTAKE AND OUTPUT,205407,0.0039939710904846
+Medication,W1D,AZITHROMYCIN 250 MG PO TABS,65492,0.0039595125152445
+Medication,W1D,ZITHROMAX Z-PAK 250 MG PO TABS,36463,0.0039595125152445
+Medication,W1D,AZITHROMYCIN 500 MG PO TABS,22118,0.0039595125152445
+Medication,W1D,CLARITHROMYCIN 500 MG PO TABS,12221,0.0039595125152445
+Medication,W1D,AZITHROMYCIN 200 MG/5 ML PO SUSR,7554,0.0039595125152445
+Lab result,SARSCOV2RNA,SARS-CoV-2 RNA,398820,0.0038509322798849
+Lab result,SARSCOV2RNA,"SARS-COV-2,NAA",37606,0.0038509322798849
+Lab result,SARSCOV2RNA,SARS CoV 2 RNA,4958,0.0038509322798849
+Lab result,SARSCOV2RNA,"SARS CoV 2 RNA, RT PCR",1712,0.0038509322798849
+Lab result,SARSCOV2RNA,"SARS-COV-2 (COVID-19), QUALITATIVE, NAA",514,0.0038509322798849
+Diagnosis,SYM015,Hallucinations,2103,0.0037240267414491
+Diagnosis,SYM015,"Hallucinations, unspecified",1801,0.0037240267414491
+Diagnosis,SYM015,Visual hallucinations,1412,0.0037240267414491
+Diagnosis,SYM015,Auditory hallucinations,1238,0.0037240267414491
+Diagnosis,SYM015,Hallucination,461,0.0037240267414491
+Diagnosis,SYM007,Other fatigue,91834,0.0037108420659468
+Diagnosis,SYM007,Other malaise,48115,0.0037108420659468
+Diagnosis,SYM007,"Fatigue, unspecified type",40799,0.0037108420659468
+Diagnosis,SYM007,Weakness,38481,0.0037108420659468
+Diagnosis,SYM007,Fatigue,25530,0.0037108420659468
+Pocedure,153334,OXYGEN ADMINISTRATION,222581,0.0036947639769246
+Pocedure,2291,CARDIAC MONITOR,158041,0.0036646857596822
+Diagnosis,SYM014,Anesthesia of skin,28282,0.003638210635287
+Diagnosis,SYM014,Paresthesia of skin,27225,0.003638210635287
+Diagnosis,SYM014,Disturbance of skin sensation,25084,0.003638210635287
+Diagnosis,SYM014,"Localized swelling, mass and lump, neck",11108,0.003638210635287
+Diagnosis,SYM014,"Localized swelling, mass and lump, head",10788,0.003638210635287
+Pocedure,1435,VITAL SIGNS,366348,0.0036200278981319
+Medication,Q5F,KETOCONAZOLE 2 % TP CREA,18676,0.0036150003478614
+Medication,Q5F,KETOCONAZOLE 2 % TP SHAM,16161,0.0036150003478614
+Medication,Q5F,CLOTRIMAZOLE 1 % TP CREA,12803,0.0036150003478614
+Medication,Q5F,"NYSTATIN 100,000 UNIT/GRAM TP POWD",10265,0.0036150003478614
+Medication,Q5F,ECONAZOLE 1 % TP CREA,10216,0.0036150003478614
+Lab result,XUKET,"Ketone, urine",220893,0.0035882610427231
+Diagnosis,RSP002,"Pneumonia, organism unspecified(486)",26170,0.0035337043616896
+Diagnosis,RSP002,"Pneumonia, unspecified organism",24545,0.0035337043616896
+Diagnosis,RSP002,Pneumonia due to organism,9503,0.0035337043616896
+Diagnosis,RSP002,Pneumonia,8150,0.0035337043616896
+Diagnosis,RSP002,"Lobar pneumonia, unspecified organism",5214,0.0035337043616896
+Lab result,INR,INR,337518,0.0034055548768805
+Lab result,INR,"INR, ISTAT",53851,0.0034055548768805
+Lab result,INR,INR (Manual Entry) See EMR for details,17721,0.0034055548768805
+Lab result,INR,"INR, Fingerstick {Menlo}",4024,0.0034055548768805
+Lab result,INR,External INR,2084,0.0034055548768805
+Medication,A9A,AMLODIPINE 5 MG PO TABS,85557,0.0033990826551935
+Medication,A9A,AMLODIPINE 10 MG PO TABS,48605,0.0033990826551935
+Medication,A9A,AMLODIPINE 2.5 MG PO TABS,25028,0.0033990826551935
+Medication,A9A,DILTIAZEM HCL 120 MG PO CP24,9813,0.0033990826551935
+Medication,A9A,CLEVIDIPINE 50 MG/100 ML IV EMUL,9100,0.0033990826551935
+Medication,W1Q,CIPROFLOXACIN HCL 500 MG PO TABS,48509,0.0033974689371163
+Medication,W1Q,CIPROFLOXACIN 500 MG PO TABS,30962,0.0033974689371163
+Medication,W1Q,LEVOFLOXACIN 500 MG PO TABS,28370,0.0033974689371163
+Medication,W1Q,LEVOFLOXACIN 750 MG PO TABS,23663,0.0033974689371163
+Medication,W1Q,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,12871,0.0033974689371163
+Medication,H4B,GABAPENTIN 300 MG PO CAPS,114317,0.00337568637
+Medication,H4B,GABAPENTIN 100 MG PO CAPS,59419,0.00337568637
+Medication,H4B,GABAPENTIN 600 MG PO TABS,23439,0.00337568637
+Medication,H4B,LEVETIRACETAM 500 MG PO TABS,13198,0.00337568637
+Medication,H4B,TOPIRAMATE 25 MG PO TABS,9580,0.00337568637
+Diagnosis,INJ031,"Dermatitis, unspecified",48986,0.0033110554934179
+Diagnosis,INJ031,Allergic rhinitis,48771,0.0033110554934179
+Diagnosis,INJ031,Dermatitis,42692,0.0033110554934179
+Diagnosis,INJ031,"Allergic rhinitis, cause unspecified",35002,0.0033110554934179
+Diagnosis,INJ031,"Allergic rhinitis, unspecified",33707,0.0033110554934179
+Lab result,UCMT,Urine Comment,215378,0.003288524770828
+Lab result,UCMT,Urine comment,176522,0.003288524770828
+Diagnosis,INF002,"Sepsis, unspecified organism",17001,0.0032290514453299
+Diagnosis,INF002,Unspecified septicemia(038.9) (CMS-HCC),9289,0.0032290514453299
+Diagnosis,INF002,Sepsis (CMS-HCC),5718,0.0032290514453299
+Diagnosis,INF002,Severe sepsis with septic shock,5704,0.0032290514453299
+Diagnosis,INF002,Severe sepsis without septic shock,5151,0.0032290514453299
+Diagnosis,FAC008,Encounter for screening mammogram for malignant neoplasm of breast,107343,0.0031567429249175
+Diagnosis,FAC008,Encounter for screening for malignant neoplasm of colon,103907,0.0031567429249175
+Diagnosis,FAC008,Encounter for screening for malignant neoplasm of cervix,78843,0.0031567429249175
+Diagnosis,FAC008,Screen for colon cancer,54135,0.0031567429249175
+Diagnosis,FAC008,Colon cancer screening,52102,0.0031567429249175
+Medication,D4D,PROBIOTIC PO,12621,0.0031005271613626
+Medication,D4D,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP,9483,0.0031005271613626
+Medication,D4D,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,7109,0.0031005271613626
+Medication,D4D,LACTOBACILLUS COMB NO.10 20 BILLION CELL PO CAPS,4763,0.0031005271613626
+Medication,D4D,PROBIOTIC COMPLEX PO,4420,0.0031005271613626
+Pocedure,1634,"PHOSPHORUS, SERUM/PLASMA",176752,0.0030773993031603
+Lab result,XNITRITE,"Nitrite, urine",220900,0.003065670976631
+Medication,M4G,GLUCOSE 4 GRAM PO CHEW,60342,0.0030650689283056
+Medication,M4G,DEXTROSE 40 % PO GEL,15730,0.0030650689283056
+Medication,M4G,GLUCAGON (HUMAN RECOMBINANT) 1 MG INJ SOLR,8668,0.0030650689283056
+Medication,M4G,GLUCOSE 5 GRAM PO CHEW,6098,0.0030650689283056
+Medication,M4G,GLUCAGON 1 MG INJ SOLR,1865,0.0030650689283056
+Medication,J9A,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,147388,0.0030246476246447
+Medication,J9A,METOCLOPRAMIDE 5 MG/ML INJ SOLN,56656,0.0030246476246447
+Medication,J9A,METOCLOPRAMIDE HCL 10 MG PO TABS,26618,0.0030246476246447
+Medication,J9A,METOCLOPRAMIDE HCL 5 MG/ML INJ SYRG,8177,0.0030246476246447
+Medication,J9A,METOCLOPRAMIDE HCL 5 MG PO TABS,6275,0.0030246476246447
+Lab result,PCCOM,POCT Comment,313589,0.0030044314315218
+Medication,Q3S,BISACODYL 10 MG PR SUPP,140561,0.0029805562671951
+Medication,Q3S,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,33961,0.0029805562671951
+Medication,Q3S,MINERAL OIL PR ENEM,22695,0.0029805562671951
+Medication,Q3S,SODIUM PHOSPHATES 19-7 GRAM/197 ML PR ENEM,2129,0.0029805562671951
+Medication,Q3S,GLYCERIN (ADULT) PR SUPP,1801,0.0029805562671951
+Pocedure,464825,COMPREHENSIVE METABOLIC PANEL W GFR,226620,0.0028557811048295
+Pocedure,464825,LIPID PROFILE,163145,0.0028557811048295
+Pocedure,464825,TSH,159416,0.0028557811048295
+Pocedure,464825,CBC with Differential,119137,0.0028557811048295
+Pocedure,464825,BASIC METABOLIC PANEL W GFR,118526,0.0028557811048295
+Medication,Q7P,FLUTICASONE 50 MCG/ACTUATION NASAL SPSN,81341,0.0028502659546016
+Medication,Q7P,FLUTICASONE PROPIONATE 50 MCG/ACTUATION NASAL SPSN,35113,0.0028502659546016
+Medication,Q7P,FLONASE 50 MCG/ACTUATION NASAL SPSN,15207,0.0028502659546016
+Medication,Q7P,MOMETASONE 50 MCG/ACTUATION NASAL SPRY,12571,0.0028502659546016
+Medication,Q7P,FLONASE NASAL,12214,0.0028502659546016
+Medication,H21,LORAZEPAM 2 MG/ML INJ SOLN,66330,0.0028335517472735
+Medication,H21,TEMAZEPAM 15 MG PO CAPS,12074,0.0028335517472735
+Medication,H21,TEMAZEPAM 7.5 MG PO CAPS,6635,0.0028335517472735
+Medication,H21,TEMAZEPAM 30 MG PO CAPS,2719,0.0028335517472735
+Medication,H21,MIDAZOLAM 2 MG/ML PO SYRP,2180,0.0028335517472735
+Pocedure,958,FULL CODE,223542,0.0028201033405537
+Diagnosis,MUS013,Age-related osteoporosis without current pathological fracture,34876,0.0028011404350001
+Diagnosis,MUS013,Osteoporosis,25209,0.0028011404350001
+Diagnosis,MUS013,"Osteoporosis, unspecified osteoporosis type, unspecified pathological fracture presence",17289,0.0028011404350001
+Diagnosis,MUS013,"Osteoporosis, unspecified",16064,0.0028011404350001
+Diagnosis,MUS013,Other osteoporosis without current pathological fracture,4721,0.0028011404350001
+Pocedure,475,CBC WITH DIFFERENTIAL,732522,0.0027994400605844
+Pocedure,475,CBC WITH DIFF,381186,0.0027994400605844
+Diagnosis,MUS025,Other specified soft tissue disorders,37468,0.0027494230298286
+Diagnosis,MUS025,Swelling of limb,20610,0.0027494230298286
+Diagnosis,MUS025,Plantar fascial fibromatosis,14518,0.0027494230298286
+Diagnosis,MUS025,Fibromyalgia,9533,0.0027494230298286
+Diagnosis,MUS025,"Neuralgia and neuritis, unspecified",8392,0.0027494230298286
+Lab result,MCV,MCV,975106,0.0027403477833283
+Lab result,MCV,MCV (MANUAL ENTRY) See EMR for details,6487,0.0027403477833283
+Diagnosis,BLD007,"Elevated white blood cell count, unspecified",19019,0.0027290765294074
+Diagnosis,BLD007,"Leukocytosis, unspecified",10995,0.0027290765294074
+Diagnosis,BLD007,"Decreased white blood cell count, unspecified",7004,0.0027290765294074
+Diagnosis,BLD007,"Neutropenia, unspecified",6985,0.0027290765294074
+Diagnosis,BLD007,"Leukocytosis, unspecified type",6580,0.0027290765294074
+Diagnosis,CIR015,Cardiomegaly,69699,0.0027219122732016
+Diagnosis,CIR015,"Heart disease, unspecified",10156,0.0027219122732016
+Diagnosis,CIR015,LVH (left ventricular hypertrophy),3325,0.0027219122732016
+Diagnosis,CIR015,Diastolic dysfunction,2002,0.0027219122732016
+Diagnosis,CIR015,Other ill-defined heart diseases,1857,0.0027219122732016
+Pocedure,787,ECG 12-LEAD,436235,0.0027056152817199
+Lab result,XLEUKEST,"Leukocyte Esterase, urine",220888,0.0026876504582755
+Pocedure,704,DIET NUTRITIONAL SUPPLEMENTS,47646,0.0026345625145838
+Diagnosis,FAC022,Acquired absence of other specified parts of digestive tract,21778,0.0025208485319099
+Diagnosis,FAC022,Acquired absence of both cervix and uterus,18999,0.0025208485319099
+Diagnosis,FAC022,Other acquired absence of organ,7542,0.0025208485319099
+Diagnosis,FAC022,Acquired absence of other genital organ(s),5279,0.0025208485319099
+Diagnosis,FAC022,Acquired absence of breast and nipple,5224,0.0025208485319099
+Pocedure,1590,AMBULATE WITH ASSISTANCE,115301,0.0024924359726661
+Pocedure,1368,"METABOLIC PANEL, BASIC",506927,0.0024154452172167
+Diagnosis,MUS038,Low back pain,82586,0.0024093616523135
+Diagnosis,MUS038,"Low back pain, unspecified back pain laterality, unspecified chronicity, with sciatica presence unspecified",6738,0.0024093616523135
+Diagnosis,MUS038,Muscle spasm of back,4029,0.0024093616523135
+Diagnosis,MUS038,"Low back pain without sciatica, unspecified back pain laterality",2296,0.0024093616523135
+Diagnosis,MUS038,"Low back pain, unspecified back pain laterality, with sciatica presence unspecified",1610,0.0024093616523135
+Diagnosis,FAC025,Other long term (current) drug therapy,172660,0.002381656709208
+Diagnosis,FAC025,Long term (current) use of aspirin,57994,0.002381656709208
+Diagnosis,FAC025,Encounter for long-term (current) use of other medications,48742,0.002381656709208
+Diagnosis,FAC025,Long term (current) use of anticoagulants,44698,0.002381656709208
+Diagnosis,FAC025,Other specified health status,35705,0.002381656709208
+Medication,W1W,CEFAZOLIN 1 GRAM INJ SOLR,136498,0.0023717688377788
+Medication,W1W,CEPHALEXIN 500 MG PO CAPS,91544,0.0023717688377788
+Medication,W1W,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,79918,0.0023717688377788
+Medication,W1W,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,56755,0.0023717688377788
+Medication,W1W,CEFAZOLIN IV PUSH (1 GRAM VIAL ONLY),22796,0.0023717688377788
+Pocedure,1916,SURGICAL PROCEDURE,347767,0.0022815265128972
+Medication,H2S,ESCITALOPRAM OXALATE 10 MG PO TABS,25867,0.0022526578266346
+Medication,H2S,SERTRALINE 50 MG PO TABS,25390,0.0022526578266346
+Medication,H2S,SERTRALINE 100 MG PO TABS,16025,0.0022526578266346
+Medication,H2S,CITALOPRAM 20 MG PO TABS,14520,0.0022526578266346
+Medication,H2S,FLUOXETINE 20 MG PO CAPS,14084,0.0022526578266346
+Diagnosis,SYM016,Edema,32379,0.0022506413947227
+Diagnosis,SYM016,Localized edema,31644,0.0022506413947227
+Diagnosis,SYM016,Abnormal weight loss,29316,0.0022506413947227
+Diagnosis,SYM016,Localized enlarged lymph nodes,28264,0.0022506413947227
+Diagnosis,SYM016,Other general symptoms and signs,22683,0.0022506413947227
+Pocedure,474,CBC W/O DIFF,324995,0.0021928242361777
+Pocedure,474,CBC,262301,0.0021928242361777
+Pocedure,474,"CBC (H/H, RBC, INDICES, WBC, PLT)",36,0.0021928242361777
+Lab result,PT,Prothrombin Time,337139,0.0021919813331809
+Lab result,PT,"PT, ISTAT",53521,0.0021919813331809
+Lab result,PT,Prothrombin Time (Manual Entry) See EMR for details,14406,0.0021919813331809
+Lab result,PT,PT seconds,3497,0.0021919813331809
+Lab result,PT,PT (Manual),362,0.0021919813331809
+Medication,S7A,ROCURONIUM 10 MG/ML IV SOLN,127048,0.0021699841072038
+Medication,S7A,SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN,45026,0.0021699841072038
+Medication,S7A,ONABOTULINUMTOXINA 100 UNIT INJ SOLR,6013,0.0021699841072038
+Medication,S7A,VECURONIUM BROMIDE 10 MG IV SOLR,5707,0.0021699841072038
+Medication,S7A,CISATRACURIUM 2 MG/ML IV SOLN,2577,0.0021699841072038
+Pocedure,685,DIET CLEAR LIQUID,83711,0.0021644163660141
+Pocedure,412485,NURSING COMMUNICATION,214880,0.0021344349697738
+Lab result,MCHC,MCHC,974473,0.0021271979537067
+Medication,H6J,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,437292,0.0021173688857965
+Medication,H6J,ONDANSETRON 4 MG PO TBDL,197517,0.0021173688857965
+Medication,H6J,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,87264,0.0021173688857965
+Medication,H6J,ONDANSETRON HCL 4 MG PO TABS,51282,0.0021173688857965
+Medication,H6J,ONDANSETRON HCL 8 MG PO TABS,45132,0.0021173688857965
+Medication,Q5P,TRIAMCINOLONE ACETONIDE 0.1 % TP OINT,39692,0.0021173257195528
+Medication,Q5P,TRIAMCINOLONE ACETONIDE 0.1 % TP CREA,35874,0.0021173257195528
+Medication,Q5P,CLOBETASOL 0.05 % TP OINT,20720,0.0021173257195528
+Medication,Q5P,HYDROCORTISONE 2.5 % TP CREA,18978,0.0021173257195528
+Medication,Q5P,FLUOCINONIDE 0.05 % TP OINT,13942,0.0021173257195528
+Medication,H6H,CYCLOBENZAPRINE 10 MG PO TABS,43656,0.0020991061471606
+Medication,H6H,CYCLOBENZAPRINE 5 MG PO TABS,29974,0.0020991061471606
+Medication,H6H,BACLOFEN 10 MG PO TABS,23872,0.0020991061471606
+Medication,H6H,CARISOPRODOL 350 MG PO TABS,6413,0.0020991061471606
+Medication,H6H,TIZANIDINE 4 MG PO TABS,4210,0.0020991061471606
+Medication,D6D,LOPERAMIDE 2 MG PO CAPS,20419,0.0020582275650831
+Medication,D6D,DIPHENOXYLATE-ATROPINE 2.5-0.025 MG PO TABS,18317,0.0020582275650831
+Medication,D6D,LOPERAMIDE 2 MG PO TABS,3963,0.0020582275650831
+Medication,D6D,IMODIUM PO,1908,0.0020582275650831
+Medication,D6D,LOMOTIL 2.5-0.025 MG PO TABS,1456,0.0020582275650831
+Medication,H22,MIDAZOLAM 1 MG/ML INJ SOLN,245919,0.0020212255712033
+Medication,H22,MIDAZOLAM (PF) 1 MG/ML INJ SOLN,20604,0.0020212255712033
+Medication,H22,MIDAZOLAM 5 MG/ML INJ SOLN,4813,0.0020212255712033
+Medication,H22,MIDAZOLAM (PF) 2 MG/2 ML (1 MG/ML) INJ SYRG,1999,0.0020212255712033
+Medication,H22,MIDAZOLAM IN 0.9 % SOD CHLORID 1 MG/ML IV SOLN,402,0.0020212255712033
+Lab result,HCT,Hematocrit,988518,0.002007531491752
+Lab result,HCT,Hct (Est),99973,0.002007531491752
+Lab result,HCT,Hematocrit (Manual Entry) See EMR for details,96497,0.002007531491752
+Lab result,HCT,"Hct, ISTAT",44756,0.002007531491752
+Lab result,HCT,"Hct(Calc), ISTAT",30262,0.002007531491752
+Pocedure,37118,SEQUENTIAL COMPRESSION DEVICE (SCD),255080,0.0019316823680319
+Pocedure,17918,PERIPHERAL IV INSERTION CARE,365683,0.0019180612798019
+Pocedure,17918,PERIPHERAL IV INSERTION/CARE,39224,0.0019180612798019
+Diagnosis,CIR007,"Unspecified maternal hypertension, third trimester",428,0.0019062468405357
+Diagnosis,CIR007,"Unspecified pre-existing hypertension complicating pregnancy, unspecified trimester",364,0.0019062468405357
+Diagnosis,CIR007,"Unspecified maternal hypertension, complicating the puerperium",225,0.0019062468405357
+Diagnosis,CIR007,Pre-existing hypertension complicating pregnancy,198,0.0019062468405357
+Diagnosis,CIR007,"Unspecified pre-existing hypertension complicating pregnancy, third trimester",192,0.0019062468405357
+Medication,M9V,APIXABAN 5 MG PO TABS,17722,0.0018990439426986
+Medication,M9V,ELIQUIS 5 MG PO TABS,15041,0.0018990439426986
+Medication,M9V,XARELTO 20 MG PO TABS,12700,0.0018990439426986
+Medication,M9V,RIVAROXABAN 20 MG PO TABS,9162,0.0018990439426986
+Medication,M9V,APIXABAN 2.5 MG PO TABS,7443,0.0018990439426986
+Medication,A7B,NITROGLYCERIN 0.4 MG SL SUBL,48815,0.0018674294366241
+Medication,A7B,ISOSORBIDE MONONITRATE 30 MG PO TB24,10028,0.0018674294366241
+Medication,A7B,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,7927,0.0018674294366241
+Medication,A7B,NITROGLYCERIN 2 % TD OINT,6353,0.0018674294366241
+Medication,A7B,ISOSORBIDE MONONITRATE 60 MG PO TB24,4565,0.0018674294366241
+Medication,H20,LORAZEPAM 0.5 MG PO TABS,72364,0.0018642284368106
+Medication,H20,LORAZEPAM 1 MG PO TABS,72093,0.0018642284368106
+Medication,H20,DIAZEPAM 5 MG PO TABS,50209,0.0018642284368106
+Medication,H20,ALPRAZOLAM 0.25 MG PO TABS,18542,0.0018642284368106
+Medication,H20,ALPRAZOLAM 0.5 MG PO TABS,17960,0.0018642284368106
+Pocedure,1709,PROTHROMBIN TIME,354466,0.0018195971959564
+Pocedure,1423,OOB/UP IN CHAIR WITH ASSIST,59675,0.0017924872225603
+Diagnosis,MBD002,"Major depressive disorder, single episode, unspecified",76391,0.0017917060364971
+Diagnosis,MBD002,"Depressive disorder, not elsewhere classified",43564,0.0017917060364971
+Diagnosis,MBD002,Depressive disorder,12378,0.0017917060364971
+Diagnosis,MBD002,Dysthymic disorder,10713,0.0017917060364971
+Diagnosis,MBD002,"Major depressive disorder, recurrent, moderate",8078,0.0017917060364971
+Lab result,PHOS,"Phosphorus, Ser/Plas",178767,0.0017848207312707
+Lab result,PHOS,PHOSPHORUS BLD (MANUAL ENTRY) See EMR for details,5734,0.0017848207312707
+Lab result,PHOS,Phosphorus,95,0.0017848207312707
+Lab result,PHOS,"Phosphorus, Serum",53,0.0017848207312707
+Pocedure,1453,NURSING PULSE OXIMETRY,344721,0.0017735308410167
+Diagnosis,NVS019,Other chronic pain,130484,0.0017560480095299
+Diagnosis,NVS019,Other acute postprocedural pain,32050,0.0017560480095299
+Diagnosis,NVS019,Other acute postoperative pain,20496,0.0017560480095299
+Diagnosis,NVS019,Chronic pain syndrome,11165,0.0017560480095299
+Diagnosis,NVS019,Chronic pain,10927,0.0017560480095299
+Lab result,XUWBC,"WBC, urine",218658,0.0017052147398168
+Lab result,XPTT,Part. Thromboplastin Time,120024,0.0016827237250878
+Diagnosis,INF008,"Viral infection, unspecified",23913,0.0016736768959908
+Diagnosis,INF008,Other viral agents as the cause of diseases classified elsewhere,21635,0.0016736768959908
+Diagnosis,INF008,"Unspecified viral infection, in conditions classified elsewhere and of unspecified site",16255,0.0016736768959908
+Diagnosis,INF008,Viral syndrome,14784,0.0016736768959908
+Diagnosis,INF008,"Viral wart, unspecified",9905,0.0016736768959908
+Diagnosis,DIG025,"Constipation, unspecified",53129,0.001653810908698
+Diagnosis,DIG025,"Constipation, unspecified constipation type",35681,0.001653810908698
+Diagnosis,DIG025,Unspecified constipation,28743,0.001653810908698
+Diagnosis,DIG025,Constipation,28074,0.001653810908698
+Diagnosis,DIG025,Other specified diseases of intestine,17246,0.001653810908698
+Medication,H3E,ACETAMINOPHEN 325 MG PO TABS,294951,0.0016492259418769
+Medication,H3E,ACETAMINOPHEN 500 MG PO TABS,186394,0.0016492259418769
+Medication,H3E,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",121238,0.0016492259418769
+Medication,H3E,TYLENOL PO,31882,0.0016492259418769
+Medication,H3E,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,31618,0.0016492259418769
+Diagnosis,END011,Dehydration,32749,0.0016121183304365
+Diagnosis,END011,Hypokalemia,28039,0.0016121183304365
+Diagnosis,END011,Hypo-osmolality and hyponatremia,23287,0.0016121183304365
+Diagnosis,END011,Acidosis,20466,0.0016121183304365
+Diagnosis,END011,Hyponatremia,15860,0.0016121183304365
+Diagnosis,MUS011,Cervicalgia,82048,0.0015464314156568
+Diagnosis,MUS011,Neck pain,37129,0.0015464314156568
+Diagnosis,MUS011,Lumbosacral spondylosis without myelopathy,25507,0.0015464314156568
+Diagnosis,MUS011,Degeneration of lumbar or lumbosacral intervertebral disc,19778,0.0015464314156568
+Diagnosis,MUS011,"Radiculopathy, lumbar region",19649,0.0015464314156568
+Pocedure,25762,BLOOD CULTURE (2 AEROBIC BOTTLES),101072,0.001534512804217
+Lab result,BUN,"Urea Nitrogen,Ser/Plas",513379,0.0015293939741896
+Lab result,BUN,"BUN, Ser/Plas",431163,0.0015293939741896
+Lab result,BUN,BUN,144348,0.0015293939741896
+Lab result,BUN,BUN (Manual Entry) See EMR for details,87845,0.0015293939741896
+Lab result,BUN,"BUN, ISTAT",9453,0.0015293939741896
+Diagnosis,CIR011,Atherosclerotic heart disease of native coronary artery without angina pectoris,60500,0.0015159967248377
+Diagnosis,CIR011,Old myocardial infarction,33421,0.0015159967248377
+Diagnosis,CIR011,Coronary atherosclerosis of native coronary artery,31208,0.0015159967248377
+Diagnosis,CIR011,"Coronary atherosclerosis of unspecified type of vessel, native or graft",22597,0.0015159967248377
+Diagnosis,CIR011,CAD (coronary artery disease),21617,0.0015159967248377
+Medication,H3H,FENTANYL (PF) 50 MCG/ML INJECTION,226038,0.0014553911007721
+Medication,H3H,FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN (WRAPPER RECORD),123921,0.0014553911007721
+Medication,H3H,FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN,69918,0.0014553911007721
+Medication,H3H,REMIFENTANIL 2 MG IV SOLR,39830,0.0014553911007721
+Medication,H3H,ALFENTANIL 500 MCG/ML INJ SOLN,15149,0.0014553911007721
+Medication,M9F,ALTEPLASE 2 MG INTRA-CATHET SOLR,34422,0.0014396261579909
+Medication,M9F,ALTEPLASE 2 MG INJ SOLR,11557,0.0014396261579909
+Medication,M9F,ALTEPLASE 100 MG IV SOLR,599,0.0014396261579909
+Medication,M9F,ALTEPLASE IV BOLUS FOR STROKE,446,0.0014396261579909
+Medication,M9F,ALTEPLASE IV INFUSION FOR STROKE,446,0.0014396261579909
+Diagnosis,BLD008,"Disorder involving the immune mechanism, unspecified",7011,0.0014330848313701
+Diagnosis,BLD008,"Immunodeficiency, unspecified",5999,0.0014330848313701
+Diagnosis,BLD008,Immunosuppression (CMS-HCC),5680,0.0014330848313701
+Diagnosis,BLD008,"Other specified disorders involving the immune mechanism, not elsewhere classified",3600,0.0014330848313701
+Diagnosis,BLD008,Immunosuppressed status (CMS-HCC),2858,0.0014330848313701
+Diagnosis,BLD003,"Anemia, unspecified",90725,0.0014317349422989
+Diagnosis,BLD003,Anemia,38591,0.0014317349422989
+Diagnosis,BLD003,"Anemia, unspecified type",25647,0.0014317349422989
+Diagnosis,BLD003,Anemia in other chronic diseases classified elsewhere,10183,0.0014317349422989
+Diagnosis,BLD003,Anemia in chronic kidney disease,9359,0.0014317349422989
+Pocedure,3041212,NURSING COMM SERUM GLUCOSE,34170,0.0014080254686781
+Lab result,K,"Potassium, Ser/Plas",918122,0.0014003066367158
+Lab result,K,"Potassium, Whole Bld",117286,0.0014003066367158
+Lab result,K,Potassium (Manual Entry) See EMR for details,113142,0.0014003066367158
+Lab result,K,"Potassium, ISTAT",71483,0.0014003066367158
+Lab result,K,"Potassium, serum/plasma",2284,0.0014003066367158
+Lab result,AST,"AST (SGOT), Ser/Plas",813935,0.0013550773530752
+Lab result,AST,AST (Manual Entry) See EMR for details,111398,0.0013550773530752
+Lab result,AST,AST,7194,0.0013550773530752
+Diagnosis,END001,"Hypothyroidism, unspecified",73510,0.0013372572606198
+Diagnosis,END001,Hypothyroidism,48956,0.0013372572606198
+Diagnosis,END001,Unspecified hypothyroidism,45904,0.0013372572606198
+Diagnosis,END001,"Hypothyroidism, unspecified type",42272,0.0013372572606198
+Diagnosis,END001,Nontoxic single thyroid nodule,18791,0.0013372572606198
+Medication,D6S,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,156512,0.0013310982149437
+Medication,D6S,DOCUSATE SODIUM 100 MG PO CAPS,150635,0.0013310982149437
+Medication,D6S,SENNOSIDES 8.6 MG PO TABS,145474,0.0013310982149437
+Medication,D6S,DOCUSATE SODIUM 250 MG PO CAPS,108750,0.0013310982149437
+Medication,D6S,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,91861,0.0013310982149437
+Medication,C6C,VITAMIN C PO,35899,0.0013260971190978
+Medication,C6C,ASCORBIC ACID (VITAMIN C) 500 MG PO TABS,16319,0.0013260971190978
+Medication,C6C,ASCORBIC ACID 500 MG PO TABS,11741,0.0013260971190978
+Medication,C6C,"ASCORBIC ACID (VITAMIN C) 1,000 MG PO TABS",4980,0.0013260971190978
+Medication,C6C,ASCORBIC ACID 250 MG PO TABS,4047,0.0013260971190978
+Medication,Z2D,FAMOTIDINE 20 MG PO TABS,78739,0.0013147897596728
+Medication,Z2D,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,56474,0.0013147897596728
+Medication,Z2D,RANITIDINE HCL 150 MG PO TABS,16944,0.0013147897596728
+Medication,Z2D,FAMOTIDINE 40 MG PO TABS,11078,0.0013147897596728
+Medication,Z2D,RANITIDINE HCL 150 MG PO CAPS,7079,0.0013147897596728
+Medication,H3T,NALOXONE 0.4 MG/ML INJ SOLN,292371,0.0013017991875678
+Medication,H3T,NARCAN 4 MG/ACTUATION NASAL SPRY,14819,0.0013017991875678
+Medication,H3T,NALTREXONE 50 MG PO TABS,3655,0.0013017991875678
+Medication,H3T,NALOXONE 4 MG/ACTUATION NASAL SPRY,2008,0.0013017991875678
+Medication,H3T,NALOXONE 1 MG/ML INJ SYRG,1452,0.0013017991875678
+Lab result,BE,"Base Excess, ISTAT",100791,0.0012806913821818
+Lab result,BE,Base Excess (vt),82068,0.0012806913821818
+Lab result,BE,Base Excess Arterial for POC,239,0.0012806913821818
+Lab result,BE,Base Excess,7,0.0012806913821818
+Pocedure,34506,EVAL/MGMT OF EST PATIENT LEVEL 5,209691,0.0012472534201781
+Pocedure,34506,"OFFICE/OUTPATIENT VISIT, EST",418,0.0012472534201781
+Pocedure,34506,EVAL/MGMT OF EST PATIENT LEVEL FIVE,102,0.0012472534201781
+Pocedure,34506,REFERRAL TO CANCER GENETICS AND GENOMICS,1,0.0012472534201781
+Pocedure,34506,OFFICE OUTPT EST 40 MIN,13,0.0012472534201781
+Medication,J7A,LABETALOL 5 MG/ML IV SOLN,111531,0.001206229935651
+Medication,J7A,CARVEDILOL 6.25 MG PO TABS,20952,0.001206229935651
+Medication,J7A,CARVEDILOL 3.125 MG PO TABS,19546,0.001206229935651
+Medication,J7A,CARVEDILOL 12.5 MG PO TABS,16418,0.001206229935651
+Medication,J7A,CARVEDILOL 25 MG PO TABS,11980,0.001206229935651
+Diagnosis,DIG004,Gastro-esophageal reflux disease without esophagitis,107514,0.0011665870613055
+Diagnosis,DIG004,Esophageal reflux,59420,0.0011665870613055
+Diagnosis,DIG004,GERD (gastroesophageal reflux disease),49864,0.0011665870613055
+Diagnosis,DIG004,"Gastroesophageal reflux disease, esophagitis presence not specified",40948,0.0011665870613055
+Diagnosis,DIG004,Gastroesophageal reflux disease without esophagitis,23961,0.0011665870613055
+Medication,C3B,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,50472,0.0011405654228591
+Medication,C3B,IRON PO,18055,0.0011405654228591
+Medication,C3B,IRON (FERROUS SULFATE) PO,7535,0.0011405654228591
+Medication,C3B,FERROUS SULFATE 324 MG (65 MG IRON) PO TBEC,6267,0.0011405654228591
+Medication,C3B,FERROUS SULFATE PO,3927,0.0011405654228591
+Medication,S7D,SUGAMMADEX 100 MG/ML IV SOLN,57192,0.0010751112018581
+Diagnosis,SYM012,Palpitations,78284,0.0010712958866778
+Diagnosis,SYM012,"Tachycardia, unspecified",72967,0.0010712958866778
+Diagnosis,SYM012,"Bradycardia, unspecified",31184,0.0010712958866778
+Diagnosis,SYM012,"Elevated blood-pressure reading, without diagnosis of hypertension",31040,0.0010712958866778
+Diagnosis,SYM012,Elevated blood pressure reading without diagnosis of hypertension,24649,0.0010712958866778
+Lab result,GLU,"Glucose, Ser/Plas",894413,0.0010022393757149
+Lab result,GLU,Glucose by Meter,219614,0.0010022393757149
+Lab result,GLU,"Glucose, Whole Blood",116977,0.0010022393757149
+Lab result,GLU,"Glucose, SER/PLAS (Manual Entry) See EMR for details",111348,0.0010022393757149
+Lab result,GLU,"Glucose,ISTAT",58290,0.0010022393757149
+Pocedure,1429,INCENTIVE SPIROMETER (WHILE AWAKE),116631,0.000978432501723
+Lab result,MCHC,MCHC,974473,0.0009727042516474
+Diagnosis,MUS028,Osteopenia,25306,0.0009656302461618
+Diagnosis,MUS028,"Other specified disorders of bone density and structure, unspecified site",24536,0.0009656302461618
+Diagnosis,MUS028,"Osteopenia, unspecified location",15232,0.0009656302461618
+Diagnosis,MUS028,"Disorder of bone, unspecified",14122,0.0009656302461618
+Diagnosis,MUS028,"Other specified disorders of bone density and structure, other site",8258,0.0009656302461618
+Diagnosis,SYM017,Abnormal electrocardiogram (ECG) (EKG),159423,0.0009432384671324
+Diagnosis,SYM017,Other nonspecific abnormal finding of lung field,105956,0.0009432384671324
+Diagnosis,SYM017,Nonspecific abnormal electrocardiogram (ECG) (EKG),70046,0.0009432384671324
+Diagnosis,SYM017,Other specified abnormal findings of blood chemistry,45479,0.0009432384671324
+Diagnosis,SYM017,Prediabetes,43554,0.0009432384671324
+Medication,Q6T,ARTIFICIAL TEARS(HYPROMELLOSE) 0.5 % OPHT DROP,6322,0.0009319011984008
+Medication,Q6T,ARTIFICIAL TEARS OPHT,4198,0.0009319011984008
+Medication,Q6T,ARTIFICIAL TEAR (HYPROMELLOSE) 0.5 % OPHT DROP,3752,0.0009319011984008
+Medication,Q6T,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DROP,3522,0.0009319011984008
+Medication,Q6T,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DPET,3352,0.0009319011984008
+Lab result,RBC,RBC,975574,0.0009224927711388
+Lab result,RBC,RBC (MANUAL ENTRY) See EMR for details,10078,0.0009224927711388
+Lab result,RBC,Total Cell Count{Menlo},385,0.0009224927711388
+Lab result,RBC,Red cells,27,0.0009224927711388
+Lab result,RBC,ADULT RBC,10,0.0009224927711388
+Pocedure,2220,TSH,430471,0.0008560307737968
+Medication,C5K,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,30947,0.0007657272794416
+Medication,C5K,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,19253,0.0007657272794416
+Medication,C5K,DEXTROSE 5%-1/2 NORMAL SALINE IV SOLP,5457,0.0007657272794416
+Medication,C5K,D5 %-0.9 % SODIUM CHLORIDE IV SOLP,2633,0.0007657272794416
+Medication,C5K,DEXTROSE 5% IN NORMAL SALINE IV SOLP,2076,0.0007657272794416
+Medication,B62,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,52936,0.0007593892400645
+Medication,B62,IPRATROPIUM-ALBUTEROL 18-103 MCG/ACTUATION INH AERO,5795,0.0007593892400645
+Medication,B62,IPRATROPIUM-ALBUTEROL 0.5-2.5 MG/3 ML INH NEBU,5352,0.0007593892400645
+Medication,B62,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH MIST,4968,0.0007593892400645
+Medication,B62,IPRATROPIUM-ALBUTEROL 0.5MG-2.5MG BASE(3MG)/3ML INH NEB-PANEL USE ONLY,2064,0.0007593892400645
+Diagnosis,RSP009,"Unspecified asthma, uncomplicated",47317,0.00075440511229
+Diagnosis,RSP009,Unspecified asthma(493.90),35777,0.00075440511229
+Diagnosis,RSP009,Asthma,35345,0.00075440511229
+Diagnosis,RSP009,"Mild intermittent asthma, uncomplicated",22176,0.00075440511229
+Diagnosis,RSP009,Mild intermittent asthma without complication,10461,0.00075440511229
+Diagnosis,CIR019,"Heart failure, unspecified",25129,0.0007010281012718
+Diagnosis,CIR019,"Congestive heart failure, unspecified",20875,0.0007010281012718
+Diagnosis,CIR019,Chronic systolic (congestive) heart failure,12348,0.0007010281012718
+Diagnosis,CIR019,Chronic diastolic (congestive) heart failure,10801,0.0007010281012718
+Diagnosis,CIR019,CHF (congestive heart failure) (CMS-HCC),8278,0.0007010281012718
+Diagnosis,INJ028,"Adverse effect of antineoplastic and immunosuppressive drugs, initial encounter",11684,0.0006857786828839
+Diagnosis,INJ028,Antineoplastic and immunosuppressive drugs causing adverse effect in therapeutic use,5618,0.0006857786828839
+Diagnosis,INJ028,"Adverse effect of glucocorticoids and synthetic analogues, initial encounter",4725,0.0006857786828839
+Diagnosis,INJ028,"Adverse effect of unspecified drugs, medicaments and biological substances, initial encounter",4023,0.0006857786828839
+Diagnosis,INJ028,"Adverse effect of other opioids, initial encounter",3434,0.0006857786828839
+Pocedure,34544,OFFICE CONSLTJ 80 MIN,330608,0.0006785849848942
+Pocedure,34544,OFFICE CONSULTATION,728,0.0006785849848942
+Pocedure,34544,OP CONSULT/NEW OR EST PT/MOD/HIGH SEV/80 MIN,339,0.0006785849848942
+Pocedure,34544,EVAL/MGMT OF NEW PATIENT LEVEL 5,3,0.0006785849848942
+Pocedure,34544,REFERRAL TO GI ONCOLOGY,7,0.0006785849848942
+Pocedure,481797,"STAT 2019 CORONAVIRUS SARS-COV-2, RT-PCR",47517,0.0006678178985894
+Pocedure,100973,OXYGEN: NASAL CANNULA(NURSING ONLY),186937,0.0006499257523568
+Pocedure,310,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),74559,0.0006160792052851
+Pocedure,310,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLE),68870,0.0006160792052851
+Medication,Q5W,MUPIROCIN 2 % TP OINT,32942,0.0005801216629035
+Medication,Q5W,BACITRACIN ZINC 500 UNIT/GRAM TP OINT,20921,0.0005801216629035
+Medication,Q5W,CLINDAMYCIN PHOSPHATE 1 % TP LOTN,14643,0.0005801216629035
+Medication,Q5W,BACITRACIN ZINC 500 UNIT/GRAM TP PACK,10718,0.0005801216629035
+Medication,Q5W,MUPIROCIN CALCIUM 2 % TP CREA,7777,0.0005801216629035
+Pocedure,1508,WEIGHT,172781,0.0005622053225238
+Lab result,NA,"Sodium, Ser/Plas",914528,0.0005489718629971
+Lab result,NA,"Sodium, Whole Blood",117026,0.0005489718629971
+Lab result,NA,Sodium (Manual Entry) See EMR for details,108406,0.0005489718629971
+Lab result,NA,"Sodium, ISTAT",71442,0.0005489718629971
+Lab result,NA,"Sodium, whole blood, ePOC",624,0.0005489718629971
+Diagnosis,CIR017,Unspecified atrial fibrillation,47024,0.0005271749120852
+Diagnosis,CIR017,Atrial fibrillation (CMS-HCC),46861,0.0005271749120852
+Diagnosis,CIR017,Other specified cardiac dysrhythmias(427.89),43458,0.0005271749120852
+Diagnosis,CIR017,Ventricular premature depolarization,36938,0.0005271749120852
+Diagnosis,CIR017,Supraventricular tachycardia,36587,0.0005271749120852
+Medication,C4L,METFORMIN 500 MG PO TABS,54818,0.0004242370133498
+Medication,C4L,"METFORMIN 1,000 MG PO TABS",29469,0.0004242370133498
+Medication,C4L,METFORMIN 500 MG PO TB24,17322,0.0004242370133498
+Medication,C4L,METFORMIN PO,9733,0.0004242370133498
+Medication,C4L,METFORMIN 850 MG PO TABS,6989,0.0004242370133498
+Diagnosis,DIG022,"Noninfective gastroenteritis and colitis, unspecified",22303,0.0004205469161548
+Diagnosis,DIG022,Other and unspecified noninfectious gastroenteritis and colitis(558.9),12883,0.0004205469161548
+Diagnosis,DIG022,Gastroenteritis,7955,0.0004205469161548
+Diagnosis,DIG022,Chronic diarrhea,6116,0.0004205469161548
+Diagnosis,DIG022,Other specified noninfective gastroenteritis and colitis,4456,0.0004205469161548
+Pocedure,1316,"MAGNESIUM, SERUM/PLASMA",264849,0.0003983473347732
+Diagnosis,INJ030,Drug-induced polyneuropathy,2557,0.0003744886576746
+Diagnosis,INJ030,Medication overuse headache,2139,0.0003744886576746
+Diagnosis,INJ030,"Drug-induced headache, not elsewhere classified, not intractable",2119,0.0003744886576746
+Diagnosis,INJ030,Polyneuropathy due to other toxic agents,802,0.0003744886576746
+Diagnosis,INJ030,Toxic erythema,430,0.0003744886576746
+Medication,C1H,MAGNESIUM SULFATE IV SCALE ,56765,0.0003672434038213
+Medication,C1H,MAGNESIUM SULFATE IN WATER 2 GRAM/50 ML (4 %) IV PGBK,30466,0.0003672434038213
+Medication,C1H,MAGNESIUM SULFATE IN D5W 1 GRAM/100 ML IV PGBK,27374,0.0003672434038213
+Medication,C1H,MAGNESIUM PO,23947,0.0003672434038213
+Medication,C1H,MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK,20058,0.0003672434038213
+Diagnosis,NEO074,Neoplasm related pain (acute) (chronic),10444,0.0003320542498762
+Diagnosis,NEO074,Cancer related pain,3738,0.0003320542498762
+Diagnosis,NEO074,Malignant pleural effusion,3239,0.0003320542498762
+Diagnosis,NEO074,Malignant ascites,2529,0.0003320542498762
+Diagnosis,NEO074,Neoplastic (malignant) related fatigue,1762,0.0003320542498762
+Diagnosis,SKN006,Postprocedural hematoma of skin and subcutaneous tissue following other procedure,876,0.000314916116773
+Diagnosis,SKN006,Other postprocedural complications of skin and subcutaneous tissue,669,0.000314916116773
+Diagnosis,SKN006,Postprocedural seroma of skin and subcutaneous tissue following other procedure,639,0.000314916116773
+Diagnosis,SKN006,Postprocedural hemorrhage of skin and subcutaneous tissue following other procedure,416,0.000314916116773
+Diagnosis,SKN006,Incisional pain,370,0.000314916116773
+Diagnosis,RSP007,Other specified disorders of nose and nasal sinuses,30461,0.0002938178469517
+Diagnosis,RSP007,Other diseases of nasal cavity and sinuses(478.19),27513,0.0002938178469517
+Diagnosis,RSP007,Deviated nasal septum,25587,0.0002938178469517
+Diagnosis,RSP007,Hypertrophy of nasal turbinates,18305,0.0002938178469517
+Diagnosis,RSP007,Chronic rhinitis,16384,0.0002938178469517
+Medication,D4B,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,55999,0.0001631035192544
+Medication,D4B,"CALCIUM CARBONATE 400 MG CALCIUM (1,000 MG) PO CHEW",20612,0.0001631035192544
+Medication,D4B,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,17615,0.0001631035192544
+Medication,D4B,CALCIUM CARBONATE 200 MG CALCIUM (500 MG) PO CHEW,10715,0.0001631035192544
+Medication,D4B,"CALCIUM CARBONATE 400 MG (1,000 MG) PO CHEW",8543,0.0001631035192544
+Diagnosis,NVS020,Other specified disorders of brain,12338,0.0001577912481128
+Diagnosis,NVS020,Other conditions of brain(348.89),9389,0.0001577912481128
+Diagnosis,NVS020,Metabolic encephalopathy,7182,0.0001577912481128
+Diagnosis,NVS020,"Encephalopathy, unspecified",5806,0.0001577912481128
+Diagnosis,NVS020,"Disorder of brain, unspecified",3937,0.0001577912481128
+Diagnosis,EAR003,"Benign paroxysmal vertigo, unspecified ear",9282,0.0001404878997051
+Diagnosis,EAR003,"Benign paroxysmal positional vertigo, unspecified laterality",7779,0.0001404878997051
+Diagnosis,EAR003,Benign paroxysmal positional vertigo,7013,0.0001404878997051
+Diagnosis,EAR003,"Benign paroxysmal vertigo, right ear",2543,0.0001404878997051
+Diagnosis,EAR003,"Benign paroxysmal vertigo, left ear",2174,0.0001404878997051
+Diagnosis,END003,Type 2 diabetes mellitus with hyperglycemia,24117,0.0001193615512047
+Diagnosis,END003,Type 2 diabetes mellitus with diabetic chronic kidney disease,16238,0.0001193615512047
+Diagnosis,END003,Type 2 diabetes mellitus with unspecified complications,14690,0.0001193615512047
+Diagnosis,END003,Type 2 diabetes mellitus with other specified complication,9956,0.0001193615512047
+Diagnosis,END003,Type 2 diabetes mellitus with diabetic nephropathy,6389,0.0001193615512047
+Diagnosis,END007,Vitamin D deficiency,83075,0.0001192726792318
+Diagnosis,END007,"Vitamin D deficiency, unspecified",71879,0.0001192726792318
+Diagnosis,END007,Unspecified vitamin D deficiency,25190,0.0001192726792318
+Diagnosis,END007,Deficiency of other specified B group vitamins,11168,0.0001192726792318
+Diagnosis,END007,Vitamin D insufficiency,7044,0.0001192726792318
+Diagnosis,END014,Postprocedural hypoparathyroidism,409,0.0001106158739034
+Diagnosis,END014,Asymptomatic postprocedural ovarian failure,362,0.0001106158739034
+Diagnosis,END014,Symptomatic states associated with artificial menopause,264,0.0001106158739034
+Diagnosis,END014,Surgical menopause,260,0.0001106158739034
+Diagnosis,END014,Postprocedural adrenocortical (-medullary) hypofunction,174,0.0001106158739034
+Diagnosis,END016,Overweight,23032,9.473353768030268e-05
+Diagnosis,END016,Hypercalcemia,10935,9.473353768030268e-05
+Diagnosis,END016,Hypomagnesemia,9670,9.473353768030268e-05
+Diagnosis,END016,Overweight (BMI 25.0-29.9),9390,9.473353768030268e-05
+Diagnosis,END016,Hypocalcemia,8353,9.473353768030268e-05
+Diagnosis,FAC020,Tobacco use,11379,9.210659167679935e-05
+Diagnosis,FAC020,Other problems related to lifestyle,4598,9.210659167679935e-05
+Diagnosis,FAC020,Tobacco abuse,4255,9.210659167679935e-05
+Diagnosis,FAC020,"Other psychological or physical stress, not elsewhere classified(V62.89)",1980,9.210659167679935e-05
+Diagnosis,FAC020,High risk heterosexual behavior,1953,9.210659167679935e-05
+Medication,H3D,ASPIRIN 325 MG PO TABS,57934,7.292138076642196e-05
+Medication,H3D,ASPIRIN PO,16842,7.292138076642196e-05
+Medication,H3D,ASPIRIN 300 MG PR SUPP,12033,7.292138076642196e-05
+Medication,H3D,ASPIRIN 325 MG PO TBEC,6662,7.292138076642196e-05
+Medication,H3D,EXCEDRIN MIGRAINE PO,2029,7.292138076642196e-05
+Diagnosis,CIR026,"Peripheral vascular disease, unspecified",14601,5.886163231361152e-05
+Diagnosis,CIR026,"Peripheral vascular disease, unspecified (CMS-HCC)",9061,5.886163231361152e-05
+Diagnosis,CIR026,Atherosclerosis of aorta (CMS-HCC),7802,5.886163231361152e-05
+Diagnosis,CIR026,Atherosclerosis of aorta,7693,5.886163231361152e-05
+Diagnosis,CIR026,PVD (peripheral vascular disease) (CMS-HCC),7201,5.886163231361152e-05
+Diagnosis,EXT027,Unspecified place of occurrence,40908,4.748801989862272e-05
+Diagnosis,EXT027,Other specified places as the place of occurrence of the external cause,28100,4.748801989862272e-05
+Diagnosis,EXT027,"Place of occurrence, home",24394,4.748801989862272e-05
+Diagnosis,EXT027,Unspecified place in unspecified non-institutional (private) residence as the place of occurrence of the external cause,20853,4.748801989862272e-05
+Diagnosis,EXT027,"Place of occurrence, residential institution",17050,4.748801989862272e-05
+Lab result,HGB,Hemoglobin,982722,4.364163311176839e-05
+Lab result,HGB,Hemoglobin (Manual Entry) See EMR for details,114709,4.364163311176839e-05
+Lab result,HGB,"Hgb(Calc), ISTAT",71388,4.364163311176839e-05
+Lab result,HGB,HgB,46893,4.364163311176839e-05
+Lab result,HGB,HGB (Manual Entry) See EMR for details,3202,4.364163311176839e-05
+Diagnosis,FAC019,Homelessness,4175,4.247618835684364e-05
+Diagnosis,FAC019,Other specified problems related to psychosocial circumstances,2740,4.247618835684364e-05
+Diagnosis,FAC019,Other physical and mental strain related to work,2394,4.247618835684364e-05
+Diagnosis,FAC019,Stress at work,1676,4.247618835684364e-05
+Diagnosis,FAC019,Problems related to unwanted pregnancy,1628,4.247618835684364e-05
+Diagnosis,FAC011,Other sex counseling,270,2.2916663278915728e-05
+Diagnosis,FAC011,Counseling on other sexually transmitted diseases,252,2.2916663278915728e-05
+Diagnosis,FAC011,Sex counseling,167,2.2916663278915728e-05
+Diagnosis,FAC011,Sexually transmitted disease counseling,110,2.2916663278915728e-05
+Diagnosis,FAC011,"Sex counseling, unspecified",70,2.2916663278915728e-05
+Diagnosis,CIR016,"Conduction disorder, unspecified",18080,4.422842181162068e-06
+Diagnosis,CIR016,Unspecified right bundle-branch block,17334,4.422842181162068e-06
+Diagnosis,CIR016,Long QT syndrome,16149,4.422842181162068e-06
+Diagnosis,CIR016,Presence of cardiac pacemaker,14819,4.422842181162068e-06
+Diagnosis,CIR016,Cardiac pacemaker in situ,13642,4.422842181162068e-06
+Diagnosis,END008,Moderate protein-calorie malnutrition,5752,0.0
+Diagnosis,END008,Mild protein-calorie malnutrition,3014,0.0
+Diagnosis,END008,Moderate protein-calorie malnutrition (CMS-HCC),1090,0.0
+Diagnosis,END008,Malnutrition of moderate degree (CMS-HCC),739,0.0
+Diagnosis,END008,Mild protein-calorie malnutrition (CMS-HCC),558,0.0
+Pocedure,2215,TROPONIN I,117053,0.0
+Pocedure,17968,SALINE LOCK AND FLUSH,232774,0.0
+Diagnosis,CIR020,"Cerebral infarction, unspecified",16509,0.0
+Diagnosis,CIR020,Unspecified cerebral artery occlusion with cerebral infarction,9620,0.0
+Diagnosis,CIR020,"Cerebrovascular accident (CVA), unspecified mechanism (CMS-HCC)",8063,0.0
+Diagnosis,CIR020,Stroke (CMS-HCC),4886,0.0
+Diagnosis,CIR020,Other cerebral infarction,3149,0.0
+Diagnosis,CIR024,"Cerebral aneurysm, nonruptured",6231,0.0
+Diagnosis,CIR024,Cerebral atherosclerosis,3395,0.0
+Diagnosis,CIR024,Other generalized ischemic cerebrovascular disease,2529,0.0
+Diagnosis,CIR024,"Cerebrovascular disease, unspecified",2296,0.0
+Diagnosis,CIR024,"Acute, but ill-defined, cerebrovascular disease",2112,0.0
+Medication,W1A,AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS,70345,0.0
+Medication,W1A,AMOXICILLIN 500 MG PO CAPS,44727,0.0
+Medication,W1A,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,34193,0.0
+Medication,W1A,AMOXICILLIN 400 MG/5 ML PO SUSR,17573,0.0
+Medication,W1A,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,12571,0.0
+Medication,W4E,METRONIDAZOLE 500 MG PO TABS,32878,0.0
+Medication,W4E,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,26423,0.0
+Medication,W4E,METRONIDAZOLE 250 MG PO TABS,4336,0.0
+Medication,W4E,FLAGYL 500 MG PO TABS,2383,0.0
+Medication,W4E,FLAGYL 250 MG PO TABS,688,0.0
+Diagnosis,END002,Type 2 diabetes mellitus without complications,71005,0.0
+Diagnosis,END002,"Type II or unspecified type diabetes mellitus without mention of complication, not stated as uncontrolled",50212,0.0
+Diagnosis,END002,"Type 2 diabetes mellitus without complication, without long-term current use of insulin (CMS-HCC)",21953,0.0
+Diagnosis,END002,Diabetes mellitus (CMS-HCC),17960,0.0
+Diagnosis,END002,Type 2 diabetes mellitus without complication (CMS-HCC),14230,0.0
+Diagnosis,SYM010,Memory loss,16174,0.0
+Diagnosis,SYM010,"Altered mental status, unspecified",16028,0.0
+Diagnosis,SYM010,Altered mental status,14907,0.0
+Diagnosis,SYM010,Other amnesia,14327,0.0
+Diagnosis,SYM010,Other abnormalities of gait and mobility,12876,0.0
+Medication,W1J,"VANCOMYCIN 1,000 MG IV SOLR",24716,0.0
+Medication,W1J,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,21425,0.0
+Medication,W1J,VANCOMYCIN IN DEXTROSE 5 % 1 GRAM/200 ML IV PGBK,11225,0.0
+Medication,W1J,VANCOMYCIN IN DEXTROSE 1 GRAM/200 ML IV PGBK,6075,0.0
+Medication,W1J,VANCOMYCIN-WATER INJECT (PEG) 1 GRAM/200 ML IV PGBK,4964,0.0
+Diagnosis,SYM008,Restlessness and agitation,5103,0.0
+Diagnosis,SYM008,Other symptoms and signs involving appearance and behavior,3342,0.0
+Diagnosis,SYM008,Other symptoms and signs involving emotional state,2707,0.0
+Diagnosis,SYM008,Depressed mood,2057,0.0
+Diagnosis,SYM008,Agitation,1886,0.0
+Lab result,NEUTAB,"NEUT, ABS",522238,0.0
+Lab result,NEUTAB,"Neutrophil, Absolute",384165,0.0
+Lab result,NEUTAB,"Neut, ABS (man diff)",41867,0.0
+Lab result,NEUTAB,"Neut, ABS (Seg+Band) (man diff)",31195,0.0
+Lab result,NEUTAB,"NEUTS, ABS",5167,0.0
+Pocedure,37225,PT ONGOING TREATMENT,105077,0.0
+Diagnosis,SYM013,Shortness of breath,123781,0.0
+Diagnosis,SYM013,"Dyspnea, unspecified",36590,0.0
+Diagnosis,SYM013,Nasal congestion,34025,0.0
+Diagnosis,SYM013,SOB (shortness of breath),32192,0.0
+Diagnosis,SYM013,Hypoxemia,26795,0.0
+Medication,A4A,HYDRALAZINE 20 MG/ML INJ SOLN,99749,0.0
+Medication,A4A,HYDRALAZINE 25 MG PO TABS,16048,0.0
+Medication,A4A,HYDRALAZINE 10 MG PO TABS,10341,0.0
+Medication,A4A,HYDRALAZINE 50 MG PO TABS,8724,0.0
+Medication,A4A,HYDRALAZINE 20 MG/ML INJ SOLN (24 HOUR STOP TIME),4843,0.0
+Lab result,RDW,RDW,974552,0.0
+Lab result,RDW,RDW (MANUAL ENTRY) See EMR for details,4076,0.0
+Diagnosis,END009,"Obesity, unspecified",80077,0.0
+Diagnosis,END009,Morbid (severe) obesity due to excess calories,28498,0.0
+Diagnosis,END009,Morbid obesity (CMS-HCC),25684,0.0
+Diagnosis,END009,Obesity,25169,0.0
+Diagnosis,END009,Obesity (BMI 30-39.9),11212,0.0
+Medication,C1B,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,211392,0.0
+Medication,C1B,SODIUM CHLORIDE 0.9 % IV SOLP,134406,0.0
+Medication,C1B,SODIUM CHLORIDE 0.9 % (FLUSH) INJ SYRG,49739,0.0
+Medication,C1B,SODIUM CHLORIDE 0.9 % INJ SYRG,16514,0.0
+Medication,C1B,SODIUM CHLORIDE 0.9 % INJ SOLN,13936,0.0
+Medication,J2B,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,95531,0.0
+Medication,J2B,GLYCOPYRROLATE 1 MG PO TABS,1743,0.0
+Medication,J2B,CHLORDIAZEPOXIDE-CLIDINIUM 5-2.5 MG PO CAPS,299,0.0
+Medication,J2B,GLYCOPYRROLATE 2 MG PO TABS,230,0.0
+Medication,J2B,LIBRAX (WITH CLIDINIUM) 5-2.5 MG PO CAPS,193,0.0
+Medication,M4D,ATORVASTATIN 40 MG PO TABS,70294,0.0
+Medication,M4D,ATORVASTATIN 20 MG PO TABS,62829,0.0
+Medication,M4D,ATORVASTATIN 10 MG PO TABS,61005,0.0
+Medication,M4D,SIMVASTATIN 20 MG PO TABS,40287,0.0
+Medication,M4D,ATORVASTATIN 80 MG PO TABS,25555,0.0
+Medication,J2A,ATROPINE 0.1 MG/ML INJ SYRG,24962,0.0
+Medication,J2A,ATROPINE 0.4 MG/ML INJ SOLN,4501,0.0
+Medication,J2A,HYOSCYAMINE SULFATE 0.125 MG SL SUBL,3100,0.0
+Medication,J2A,HYOSCYAMINE SULFATE 0.125 MG PO TABS,3020,0.0
+Medication,J2A,ATROPINE 1 MG/ML INJ SOLN,2645,0.0
+Medication,C5J,DEXTROSE 50 % IN WATER (D50W) IV SYRG,141338,0.0
+Medication,C5J,DEXTROSE 5 % IN WATER (D5W) IV SOLP,24303,0.0
+Medication,C5J,DEXTROSE 50% IN WATER (D50W) IV SYRG,21478,0.0
+Medication,C5J,DEXTROSE 50 % IN WATER (D50W) IV SOLP,11399,0.0
+Medication,C5J,DEXTROSE 10 % IN WATER (D10W) 10 % IV SOLP,5329,0.0
+Medication,M9K,ENOXAPARIN 40 MG/0.4 ML SC SYRG,83526,0.0
+Medication,M9K,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",65177,0.0
+Medication,M9K,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",38558,0.0
+Medication,M9K,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",33588,0.0
+Medication,M9K,ENOXAPARIN 30 MG/0.3 ML SC SYRG,19047,0.0
+Lab result,XSPG,"Specific Gravity, urine",221483,0.0
+Medication,M9P,ASPIRIN 81 MG PO TBEC,172290,0.0
+Medication,M9P,ASPIRIN 81 MG PO CHEW,80379,0.0
+Medication,M9P,CLOPIDOGREL 75 MG PO TABS,35825,0.0
+Medication,M9P,PLAVIX 75 MG PO TABS,14580,0.0
+Medication,M9P,ASPIRIN 81 MG PO TABS,14063,0.0
+Medication,H7T,QUETIAPINE 25 MG PO TABS,16767,0.0
+Medication,H7T,OLANZAPINE 5 MG PO TBDL,6787,0.0
+Medication,H7T,OLANZAPINE 5 MG PO TABS,5156,0.0
+Medication,H7T,QUETIAPINE 100 MG PO TABS,4378,0.0
+Medication,H7T,QUETIAPINE 50 MG PO TABS,4372,0.0
+Medication,H7O,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,22876,0.0
+Medication,H7O,HALOPERIDOL 1 MG PO TABS,6100,0.0
+Medication,H7O,HALOPERIDOL INTRAMUSCULAR INJECTION,4022,0.0
+Medication,H7O,HALOPERIDOL 2 MG PO TABS,2630,0.0
+Medication,H7O,HALOPERIDOL 0.5 MG PO TABS,2139,0.0
+Medication,P5A,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,176739,0.0
+Medication,P5A,PREDNISONE 20 MG PO TABS,59878,0.0
+Medication,P5A,PREDNISONE 10 MG PO TABS,47737,0.0
+Medication,P5A,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,33330,0.0
+Medication,P5A,DEXAMETHASONE 4 MG PO TABS,32883,0.0
+Medication,P6A,MELATONIN 3 MG PO TABS,48005,0.0
+Medication,P6A,MELATONIN 5 MG PO TABS,36250,0.0
+Medication,P6A,MELATONIN 3 MG PO TBDL,23220,0.0
+Medication,P6A,MELATONIN PO,16022,0.0
+Medication,P6A,MELATONIN 1 MG PO TABS,5067,0.0
+Lab result,WBC,WBC,975990,0.0
+Lab result,WBC,WBC (Manual Entry) See EMR for details,104161,0.0
+Lab result,WBC,WBC count,15815,0.0
+Lab result,WBC,WBC Count,193,0.0
+Lab result,WBC,White Cells,1,0.0
+Medication,H7E,TRAZODONE 50 MG PO TABS,64606,0.0
+Medication,H7E,TRAZODONE 100 MG PO TABS,11218,0.0
+Medication,H7E,TRAZODONE PO,5211,0.0
+Medication,H7E,TRAZODONE 150 MG PO TABS,2456,0.0
+Medication,H7E,TRAZODONE 300 MG PO TABS,307,0.0
+Diagnosis,BLD001,"Iron deficiency anemia, unspecified",36070,0.0
+Diagnosis,BLD001,Iron deficiency anemia,17354,0.0
+Diagnosis,BLD001,"Iron deficiency anemia, unspecified iron deficiency anemia type",15662,0.0
+Diagnosis,BLD001,Iron deficiency anemia secondary to blood loss (chronic),15105,0.0
+Diagnosis,BLD001,Other iron deficiency anemias,9978,0.0
+Medication,A4F,LOSARTAN 50 MG PO TABS,45424,0.0
+Medication,A4F,LOSARTAN 25 MG PO TABS,32915,0.0
+Medication,A4F,LOSARTAN 100 MG PO TABS,27248,0.0
+Medication,A4F,VALSARTAN 160 MG PO TABS,8855,0.0
+Medication,A4F,VALSARTAN 80 MG PO TABS,8512,0.0
+Medication,A4D,LISINOPRIL 10 MG PO TABS,52643,0.0
+Medication,A4D,LISINOPRIL 20 MG PO TABS,41409,0.0
+Medication,A4D,LISINOPRIL 5 MG PO TABS,32374,0.0
+Medication,A4D,LISINOPRIL 40 MG PO TABS,20829,0.0
+Medication,A4D,LISINOPRIL 2.5 MG PO TABS,16168,0.0
+Medication,Q9B,TAMSULOSIN 0.4 MG PO CP24,36234,0.0
+Medication,Q9B,TAMSULOSIN 0.4 MG PO CAPS,33918,0.0
+Medication,Q9B,FINASTERIDE 5 MG PO TABS,21418,0.0
+Medication,Q9B,FLOMAX 0.4 MG PO CP24,8656,0.0
+Medication,Q9B,FLOMAX PO,3813,0.0
+Medication,R1F,HYDROCHLOROTHIAZIDE 25 MG PO TABS,56958,0.0
+Medication,R1F,HYDROCHLOROTHIAZIDE 12.5 MG PO TABS,19994,0.0
+Medication,R1F,HYDROCHLOROTHIAZIDE 12.5 MG PO CAPS,9387,0.0
+Medication,R1F,CHLORTHALIDONE 25 MG PO TABS,8240,0.0
+Medication,R1F,HYDROCHLOROTHIAZIDE PO,5775,0.0
+Diagnosis,CIR008,"Hypertensive chronic kidney disease with stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease",23383,0.0
+Diagnosis,CIR008,"Unspecified hypertensive kidney disease with chronic kidney disease stage I through stage IV, or unspecified(403.90)",13029,0.0
+Diagnosis,CIR008,Hypertensive heart disease with heart failure,12405,0.0
+Diagnosis,CIR008,"Hypertensive heart and chronic kidney disease with heart failure and stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease",10640,0.0
+Diagnosis,CIR008,"Secondary hypertension, unspecified",6725,0.0
+Medication,H3U,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,185292,0.0
+Medication,H3U,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,107101,0.0
+Medication,H3U,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,92328,0.0
+Medication,H3U,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,71846,0.0
+Medication,H3U,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,28896,0.0
+Pocedure,17587,OT ONGOING TREATMENT,89928,0.0
+Pocedure,1721,PTT PARTIAL THROMBOPLASTIN TIME,291092,0.0
+Diagnosis,END010,"Hyperlipidemia, unspecified",196135,0.0
+Diagnosis,END010,Hyperlipidemia,108416,0.0
+Diagnosis,END010,Other and unspecified hyperlipidemia,105318,0.0
+Diagnosis,END010,"Hyperlipidemia, unspecified hyperlipidemia type",101058,0.0
+Diagnosis,END010,Mixed hyperlipidemia,60657,0.0
+Medication,J7C,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,84578,0.0
+Medication,J7C,METOPROLOL TARTRATE 25 MG PO TABS,58045,0.0
+Medication,J7C,METOPROLOL SUCCINATE 25 MG PO TB24,46815,0.0
+Medication,J7C,METOPROLOL SUCCINATE 50 MG PO TB24,33636,0.0
+Medication,J7C,METOPROLOL TARTRATE 50 MG PO TABS,24930,0.0
+Lab result,CA,"Calcium, Ser/Plas",896056,0.0
+Lab result,CA,"Calcium, Serum (Manual Entry) See EMR for details",91151,0.0
+Lab result,CA,Calcium,88,0.0
+Lab result,CA,"Calcium, Serum",25,0.0
+Pocedure,119055,AB SCREEN (ASI),149182,0.0
+Pocedure,119815,POC GLUCOSE BY METER,187765,0.0
+Medication,H1U,GADOBENATE DIMEGLUMINE 529 MG/ML (0.1MMOL/0.2ML) IV SOLN,42702,0.0
+Medication,H1U,GADOPENTETATE DIMEGLUMINE 2.5 MMOL/5 ML (469.01 MG/ML) IV SOLN,726,0.0
+Medication,H1U,GADOBENATE DIMEGLUMINE 529 MG/ML IV SOLN 10 ML,294,0.0
+Medication,H1U,GADOTERATE MEGLUMINE 0.5 MMOL/ML (376.9 MG/ML) IV SOLN,158,0.0
+Medication,H1U,GADOTERATE MEGLUMINE 0.5 MMOL/ML (376.9 MG/ML) IV SOLN 15 ML,96,0.0
+Diagnosis,SKN002,Acne vulgaris,23164,0.0
+Diagnosis,SKN002,"Pruritus, unspecified",21667,0.0
+Diagnosis,SKN002,"Seborrheic dermatitis, unspecified",19108,0.0
+Diagnosis,SKN002,Other acne,17180,0.0
+Diagnosis,SKN002,Seborrheic dermatitis,16851,0.0
+Diagnosis,MBD005,"Anxiety disorder, unspecified",72665,0.0
+Diagnosis,MBD005,Anxiety,54252,0.0
+Diagnosis,MBD005,Generalized anxiety disorder,44981,0.0
+Diagnosis,MBD005,"Anxiety state, unspecified",40819,0.0
+Diagnosis,MBD005,Anxiety state,31720,0.0
+Diagnosis,MBD026,"Alcohol abuse, in remission",3180,0.0
+Diagnosis,MBD026,"Alcohol dependence, in remission",2755,0.0
+Diagnosis,MBD026,"Major depressive disorder, single episode, in full remission",2325,0.0
+Diagnosis,MBD026,"Major depressive disorder, recurrent, in full remission",1877,0.0
+Diagnosis,MBD026,"Major depressive disorder, recurrent, in remission, unspecified",1165,0.0
+Diagnosis,MUS007,Effusion of lower leg joint,10389,0.0
+Diagnosis,MUS007,"Effusion, right knee",7205,0.0
+Diagnosis,MUS007,Other specified disorders of lower leg joint,6820,0.0
+Diagnosis,MUS007,"Effusion, left knee",6546,0.0
+Diagnosis,MUS007,"Other affections of shoulder region, not elsewhere classified",5208,0.0
+Diagnosis,SKN001,Cellulitis,12342,0.0
+Diagnosis,SKN001,"Local infection of the skin and subcutaneous tissue, unspecified",9178,0.0
+Diagnosis,SKN001,"Cellulitis, unspecified",7708,0.0
+Diagnosis,SKN001,Impetigo,7483,0.0
+Diagnosis,SKN001,"Cellulitis, unspecified cellulitis site",6505,0.0
+Medication,D4N,SIMETHICONE 40 MG/0.6 ML PO DRPS,35538,0.0
+Medication,D4N,SIMETHICONE 80 MG PO CHEW,24677,0.0
+Medication,D4N,GAS-X PO,1078,0.0
+Medication,D4N,SIMETHICONE PO,441,0.0
+Medication,D4N,SIMETHICONE 125 MG PO CAPS,410,0.0
+Medication,D4J,PANTOPRAZOLE 40 MG PO TBEC,132876,0.0
+Medication,D4J,OMEPRAZOLE 20 MG PO CPDR,84307,0.0
+Medication,D4J,PANTOPRAZOLE 40 MG IV SOLR,65694,0.0
+Medication,D4J,OMEPRAZOLE 40 MG PO CPDR,32383,0.0
+Medication,D4J,PANTOPRAZOLE 20 MG PO TBEC,19654,0.0
+Pocedure,1470,NURSING PULSE OXIMETRY (SPOT CHECK),117935,0.0
+Diagnosis,MUS026,Muscle weakness (generalized),25570,0.0
+Diagnosis,MUS026,Other muscle spasm,9943,0.0
+Diagnosis,MUS026,Spasm of muscle,7285,0.0
+Diagnosis,MUS026,Other specified disorders of muscle,6549,0.0
+Diagnosis,MUS026,Muscle weakness,5701,0.0
+Lab result,ALB,"Albumin, Ser/Plas",800563,0.0
+Lab result,ALB,ALBUMIN BLD  (MANUAL ENTRY) See EMR for details,9434,0.0
+Lab result,ALB,Albumin,7018,0.0
+Lab result,ALB,"Albumin, Serum",50,0.0
+Medication,D0U,"BARIUM SULFATE 40 % (W/V), 30% (W/W) PO PSTE",5976,0.0
+Medication,D0U,BARIUM SULFATE 40 % (W/V) PO SUSP,5375,0.0
+Medication,D0U,"BARIUM SULFATE 2.1 % (W/V), 2.0 % (W/W) PO SUSP",5056,0.0
+Medication,D0U,BARIUM SULFATE 60 % (W/V) PO SUSP,4448,0.0
+Medication,D0U,BARIUM SULFATE 40 % (W/V) PO POWD,4340,0.0
+Pocedure,703,DIET NPO,186079,0.0
+Pocedure,88985,DIET NPO AFTER MIDNIGHT,58813,0.0
+Diagnosis,NVS010,Migraine,17781,0.0
+Diagnosis,NVS010,"Migraine, unspecified, not intractable, without status migrainosus",15959,0.0
+Diagnosis,NVS010,"Migraine, unspecified, without mention of intractable migraine without mention of status migrainosus",14995,0.0
+Diagnosis,NVS010,"Migraine without aura, not intractable, without status migrainosus",9723,0.0
+Diagnosis,NVS010,"Migraine with aura, not intractable, without status migrainosus",9449,0.0
+Diagnosis,NVS016,Obstructive sleep apnea (adult) (pediatric),95662,0.0
+Diagnosis,NVS016,"Insomnia, unspecified",66765,0.0
+Diagnosis,NVS016,OSA (obstructive sleep apnea),55171,0.0
+Diagnosis,NVS016,Insomnia,39038,0.0
+Diagnosis,NVS016,Obstructive sleep apnea,33252,0.0
+Pocedure,1575,OT EVALUATE AND TREAT,126795,0.0
+Pocedure,1717,PT EVALUATE AND TREAT,148648,0.0
+Diagnosis,SKN005,"Contact dermatitis and other eczema, due to unspecified cause",34510,0.0
+Diagnosis,SKN005,"Unspecified contact dermatitis, unspecified cause",5157,0.0
+Diagnosis,SKN005,Other skin changes due to chronic exposure to nonionizing radiation,4476,0.0
+Diagnosis,SKN005,Contact dermatitis,4303,0.0
+Diagnosis,SKN005,"Contact dermatitis, unspecified contact dermatitis type, unspecified trigger",3637,0.0
+Pocedure,119052,ABO/RH (ARI),163656,0.0
+Pocedure,119052,ABO/RH,15586,0.0
+Lab result,MG,"Magnesium, Ser/Plas",253937,0.0
+Lab result,MG,MAGNESIUM BLD (MANUAL ENTRY) See EMR for details,5994,0.0
+Lab result,MG,Magnesium,76,0.0
+Pocedure,1036,HEMOGLOBIN A1C,432233,0.0
+Medication,H3A,OXYCODONE 5 MG PO TABS,217621,0.0
+Medication,H3A,HYDROMORPHONE 1 MG/ML INJ SYRG,126927,0.0
+Medication,H3A,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,106201,0.0
+Medication,H3A,TRAMADOL 50 MG PO TABS,76441,0.0
+Medication,H3A,MEPERIDINE (PF) 25 MG/ML INJ SYRG,66899,0.0
+Medication,W7C,FLU VAC QS 2014-15(36MOS+)(PF) 60 MCG (15 MCG X 4)/0.5 ML IM SYRG,9767,0.0
+Medication,W7C,INFLUENZA 2009-10 VAC (PF) (FLUZONE) SYRG (FOR AGE > 36 MO),9077,0.0
+Medication,W7C,FLU VAC TS 2013-14(36MOS+)(PF) 45 MCG (15 MCG X 3)/0.5 ML IM SYRG,8921,0.0
+Medication,W7C,FLU VACC QS2019-20 6MOS UP(PF) 60 MCG (15 MCG X 4)/0.5 ML IM SYRG,4956,0.0
+Medication,W7C,FLU VACC TS2019-20(65YR UP)PF 180 MCG/0.5 ML IM SYRG,3817,0.0
+Diagnosis,SYM005,"Dysphagia, unspecified",30484,0.0
+Diagnosis,SYM005,"Dysphagia, unspecified type",16426,0.0
+Diagnosis,SYM005,"Dysphagia, unspecified(787.20)",14381,0.0
+Diagnosis,SYM005,Dysphagia,13569,0.0
+Diagnosis,SYM005,"Dysphagia, oropharyngeal phase",5457,0.0
+Lab result,LYMAB,"LYM, ABS",496919,0.0
+Lab result,LYMAB,"Lymphocyte, Absolute",386943,0.0
+Lab result,LYMAB,"Lymphocytes, ABS (man diff)",53200,0.0
+Lab result,LYMAB,"Lym, ABS (man diff)",43787,0.0
+Lab result,LYMAB,"Lymphocytes, Abs.",24480,0.0
+Diagnosis,EXT019,"Exposure to other specified factors, initial encounter",21785,0.0
+Diagnosis,EXT019,Unspecified accident,7872,0.0
+Diagnosis,EXT019,Other accident,1944,0.0
+Diagnosis,EXT019,"Unknown cause of injury, initial encounter",1321,0.0
+Diagnosis,EXT019,"Accident, initial encounter",1131,0.0
+Medication,Z2P,PROMETHAZINE 25 MG/ML INJ SOLN,138980,0.0
+Medication,Z2P,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,105573,0.0
+Medication,Z2P,DIPHENHYDRAMINE HCL 25 MG PO CAPS,89838,0.0
+Medication,Z2P,HYDROXYZINE HCL 25 MG PO TABS,37836,0.0
+Medication,Z2P,HYDROXYZINE HCL 50 MG/ML IM SOLN,23308,0.0
+Diagnosis,FAC001,Encounter for other administrative examinations,98274,0.0
+Diagnosis,FAC001,"Encounter for administrative examinations, unspecified",44697,0.0
+Diagnosis,FAC001,Encounters for other specified administrative purpose(V68.89),33024,0.0
+Diagnosis,FAC001,Encounters for administrative purposes,31929,0.0
+Diagnosis,FAC001,Encounters for unspecified administrative purpose,28199,0.0
+Medication,H2Z,FLUMAZENIL 0.1 MG/ML IV SOLN,89553,0.0
+Medication,H2Z,FLUMAZENIL IV,1,0.0
+Diagnosis,FAC010,Encounter for therapeutic drug level monitoring,22428,0.0
+Diagnosis,FAC010,Medication monitoring encounter,22415,0.0
+Diagnosis,FAC010,Encounter for other specified aftercare,21495,0.0
+Diagnosis,FAC010,Encounter for palliative care,19165,0.0
+Diagnosis,FAC010,Encounter for removal of sutures,11936,0.0
+Diagnosis,FAC012,Other specified counseling,57146,0.0
+Diagnosis,FAC012,Dietary counseling and surveillance,21910,0.0
+Diagnosis,FAC012,Persons encountering health services in other specified circumstances,21526,0.0
+Diagnosis,FAC012,Travel advice encounter,15670,0.0
+Diagnosis,FAC012,Encounter to establish care,13806,0.0
+Medication,Z9D,GADOBUTROL 1 MMOL/ML IV SOLUTION (RADIOLOGY) 7.5 ML,17358,0.0
+Medication,Z9D,GADOBUTROL 1 MMOL/ML IV SOLUTION (RADIOLOGY) 10 ML,11980,0.0
+Medication,Z9D,GLUCAGON (HUMAN RECOMBINANT) 1 MG/ML INJ SOLR,5282,0.0
+Medication,Z9D,GLUCAGON HCL 1 MG INJ SOLR,3180,0.0
+Medication,Z9D,ISOSULFAN BLUE 1 % SC SOLN,2912,0.0
+Diagnosis,FAC016,Encounter for screening for other viral diseases,193224,0.0
+Diagnosis,FAC016,Contact with and (suspected) exposure to other viral communicable diseases,129531,0.0
+Diagnosis,FAC016,Screening for viral disease,108743,0.0
+Diagnosis,FAC016,Contact with or exposure to viral disease,72009,0.0
+Diagnosis,FAC016,Need for hepatitis C screening test,68338,0.0
+Medication,C6Z,MULTIVITAMIN PO,35213,0.0
+Medication,C6Z,THERAPEUTIC MULTIVITAMIN PO TABS,33736,0.0
+Medication,C6Z,MULTIVITAMIN PO TABS,17928,0.0
+Medication,C6Z,B-COMPLEX WITH VITAMIN C PO TABS,13142,0.0
+Medication,C6Z,MULTIVITAMINS PO,10127,0.0
+Medication,C1D,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,70016,0.0
+Medication,C1D,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,50889,0.0
+Medication,C1D,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,46113,0.0
+Medication,C1D,POTASSIUM CHLORIDE ORAL SCALE,36673,0.0
+Medication,C1D,POTASSIUM CHLORIDE IV SCALE,36169,0.0
+Diagnosis,FAC021,Personal history of nicotine dependence,72828,0.0
+Diagnosis,FAC021,Family history of ischemic heart disease and other diseases of the circulatory system,40831,0.0
+Diagnosis,FAC021,"Personal history of tobacco use, presenting hazards to health",38991,0.0
+Diagnosis,FAC021,Family history of malignant neoplasm of breast,33280,0.0
+Diagnosis,FAC021,Personal history of colonic polyps,30779,0.0
+Pocedure,1001,GLUCOSE BY METER,223927,0.0
+Diagnosis,FAC024,Latent tuberculosis,2428,0.0
+Diagnosis,FAC024,Carrier or suspected carrier of methicillin resistant Staphylococcus aureus,1748,0.0
+Diagnosis,FAC024,"TB lung, latent",1152,0.0
+Diagnosis,FAC024,LTBI (latent tuberculosis infection),900,0.0
+Diagnosis,FAC024,MRSA (methicillin resistant staph aureus) culture positive,812,0.0
+Diagnosis,SYM002,"Fever, unspecified",105783,0.0
+Diagnosis,SYM002,"Fever, unspecified fever cause",28059,0.0
+Diagnosis,SYM002,Fever,28036,0.0
+Diagnosis,SYM002,Fever presenting with conditions classified elsewhere,13846,0.0
+Diagnosis,SYM002,Acute febrile illness in child,3478,0.0
+Lab result,EOSAB,"EOS, ABS",570480,0.0
+Lab result,EOSAB,"Eosinophil, Absolute",386713,0.0
+Lab result,EOSAB,"Eosinophils, ABS (man diff)",39330,0.0
+Lab result,EOSAB,"Eos, ABS (man diff)",29133,0.0
+Diagnosis,GEN003,"Chronic kidney disease, unspecified",31611,0.0
+Diagnosis,GEN003,"Chronic kidney disease, stage 3 (moderate)",23268,0.0
+Diagnosis,GEN003,"Chronic kidney disease, stage 2 (mild)",13463,0.0
+Diagnosis,GEN003,End stage renal disease (CMS-HCC),10264,0.0
+Diagnosis,GEN003,End stage renal disease,9970,0.0
+Diagnosis,GEN023,Postmenopausal atrophic vaginitis,21100,0.0
+Diagnosis,GEN023,Menopausal and female climacteric states,14023,0.0
+Diagnosis,GEN023,Symptomatic menopausal or female climacteric states,12301,0.0
+Diagnosis,GEN023,Hormone replacement therapy,11838,0.0
+Diagnosis,GEN023,Postmenopausal bleeding,11060,0.0
+Medication,W8F,SODIUM CHLORIDE 0.9 % IRRIG SOLN,70154,0.0
+Medication,W8F,"WATER FOR IRRIGATION, STERILE IRRIG SOLN",25367,0.0
+Medication,W8F,LACTATED RINGERS IRRIG SOLN,12644,0.0
+Medication,W8F,PHYSIO. IRRIGATION COMB. NO.1 140-5-3-98 MEQ/L IRRIG SOLN,848,0.0
+Medication,W8F,"NEOMYCIN-POLYMYXIN B GU 40 MG-200,000 UNIT/ML IRRIG SOLN",797,0.0
diff --git a/scripts/cost_variability/results/feature_scores_reg.csv b/scripts/cost_variability/results/feature_scores_reg.csv
new file mode 100644
index 00000000..f50be0c5
--- /dev/null
+++ b/scripts/cost_variability/results/feature_scores_reg.csv
@@ -0,0 +1,404 @@
+,f_regression_score
+age,17.42040827837709
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+DEMOG_canonical_race_Black,10.730969838093795
+DEMOG_canonical_race_Native American,0.2519240581821558
+DEMOG_canonical_race_Other,0.0013164236286685206
+DEMOG_canonical_race_Pacific Islander,1.2424037366534968
+DEMOG_canonical_race_Unknown,7.11122899729606
+DEMOG_canonical_race_White,0.07277548121790765
+DEMOG_canonical_ethnicity_Hispanic/Latino,1.5698130814203446
+DEMOG_canonical_ethnicity_Non-Hispanic,3.486200382760183
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+LAB_AG_None,85.16298153263676
+LAB_ALB_Low,9.642897634844733
+LAB_ALB_None,73.08371376028452
+LAB_ALKP_None,31.960988411680855
+LAB_ALT_None,37.80229515399018
+LAB_AST_High,10.73515381629962
+LAB_AST_None,51.173296162714806
+LAB_AS_None,53.753396939554065
+LAB_BASOAB_None,32.27435557431515
+LAB_BASO_None,30.796130621345075
+LAB_BE_None,22.668232050329554
+LAB_BUN_High,66.84980787363071
+LAB_BUN_None,158.72055757543728
+LAB_CAION_None,176.92842808285198
+LAB_CA_Low,1.302804784134666
+LAB_CA_None,89.82512411939996
+LAB_CL_None,79.46108479023448
+LAB_CO2_None,80.28627852000221
+LAB_CR_None,156.18740656453772
+LAB_ECGIMPRESS_None,0.25372956435452243
+LAB_EGFR_None,131.72216091207602
+LAB_EOSAB_Low,10.054158945195457
+LAB_EOSAB_None,20.15848468014693
+LAB_EOS_None,30.87751302395001
+LAB_FASTING_None,80.5145474573272
+LAB_GFR_None,85.29680591169429
+LAB_GLOB_None,52.81269174629786
+LAB_GLUURN_None,7.906078872673227
+LAB_GLU_High,0.01971684858175285
+LAB_GLU_None,81.2492415919112
+LAB_HCT_Low,0.6326959459299044
+LAB_HCT_None,103.44478830595187
+LAB_HEARTRATE_None,0.25372956435452243
+LAB_HGB_Low,1.1930895640301116
+LAB_HGB_None,89.40300896411156
+LAB_IGABSWAM_None,34.1949369211917
+LAB_IGWAM_None,27.938194760012664
+LAB_INR_None,1.6652990549615476
+LAB_K_Low,19.40187934632506
+LAB_K_None,81.77382792640766
+LAB_LYMAB_Low,8.780211944239687
+LAB_LYMAB_None,39.74261713443685
+LAB_LYM_None,34.56528517200469
+LAB_MCHC_Low,0.4665915186543824
+LAB_MCHC_None,37.974699644370595
+LAB_MCH_None,46.027444217886135
+LAB_MCV_None,53.78208046018094
+LAB_MG_None,2.7173645559180303e-05
+LAB_MONOAB_None,26.500377324116638
+LAB_MONO_None,30.211707939273044
+LAB_NA_Low,10.561622588671419
+LAB_NA_None,72.31926576525338
+LAB_NEUTAB_High,0.5026627267564442
+LAB_NEUTAB_None,63.45379771016022
+LAB_NEUT_None,33.56494157317013
+LAB_NRBCABSOLUTE_None,7.812520275424106
+LAB_NRBCPERCENT_None,7.826097458599933
+LAB_PAXIS_None,0.1864401264663936
+LAB_PCCOM_None,2.8416212362980575
+LAB_PHOS_None,24.66447184634755
+LAB_PLT_None,84.66075736002132
+LAB_POCTSOURCE_None,53.11909899137532
+LAB_PRINTERVAL_None,0.25497225416259706
+LAB_PT_High,109.2564187926645
+LAB_PT_None,5.1440487687026755
+LAB_QRSAXIS_None,0.25372956435452243
+LAB_QRSDINTERVAL_None,0.25372956435452243
+LAB_QTCINTERVAL_None,0.25372956435452243
+LAB_QTINTERVAL_None,0.25372956435452243
+LAB_RBC_Low,0.015838625560311525
+LAB_RBC_None,160.4150590534693
+LAB_RDW_High,10.523889629915512
+LAB_RDW_None,112.56976226686156
+LAB_RR_None,0.25372956435452243
+LAB_SARSCOV2RNA_None,0.3234267761655791
+LAB_SARSCOV2SRC_None,0.7049427644644676
+LAB_SARSCOV2TYPE_None,5.0899515245706235
+LAB_SPECEXPDATE_None,65.57803895906262
+LAB_TBIL_None,76.05468288858401
+LAB_TP_None,86.87434260904995
+LAB_TWAVEAXIS_None,0.2450019633868168
+LAB_UCMT_None,4.048922739143588
+LAB_WBC_High,0.1009169398057644
+LAB_WBC_None,99.45609690986927
+LAB_XLEUKEST_None,0.024174464375420784
+LAB_XMUCUS_None,0.184324669672767
+LAB_XNITRITE_None,1.1405201102626739
+LAB_XPTT_None,7.404946197487897
+LAB_XSPG_None,1.3838220569518893
+LAB_XSQEP_None,6.5916057944824304
+LAB_XUBACT_None,1.474152033735753
+LAB_XUBLOOD_None,18.27379715419509
+LAB_XUCLAR_None,11.71059356890461
+LAB_XUCOL_None,2.158478516320618
+LAB_XUKET_None,0.027358455966116423
+LAB_XUPH_None,1.9529547906646059
+LAB_XUPROT_None,3.1511544424000117
+LAB_XURBC_None,14.207301482995856
+LAB_XUWBC_None,0.2784266745810418
diff --git a/scripts/cost_variability/results/feature_scores_reg_descriptions.csv b/scripts/cost_variability/results/feature_scores_reg_descriptions.csv
new file mode 100644
index 00000000..d9bc7759
--- /dev/null
+++ b/scripts/cost_variability/results/feature_scores_reg_descriptions.csv
@@ -0,0 +1,1407 @@
+Table,Code,Description,num_patient,Score
+Medication,P2B,VASOPRESSIN 20 UNIT/ML IV SOLN,10043,519.794405044615
+Medication,P2B,VASOPRESSIN 20 UNIT/ML INJ SOLN,3391,519.794405044615
+Medication,P2B,VASOPRESSIN IN 0.9 % SOD CHLOR 60 UNIT/100 ML (0.6 UNIT/ML) IV SOLN,2718,519.794405044615
+Medication,P2B,DESMOPRESSIN 4 MCG/ML INJ SOLN,1374,519.794405044615
+Medication,P2B,DESMOPRESSIN 0.1 MG PO TABS,1158,519.794405044615
+Medication,D1D,CHLORHEXIDINE GLUCONATE 0.12 % MM MWSH,44650,492.3485923652703
+Medication,D1D,TRIAMCINOLONE ACETONIDE 0.1 % DENTAL PSTE,2993,492.3485923652703
+Medication,D1D,PAROEX ORAL RINSE 0.12 % MM MWSH,352,492.3485923652703
+Medication,D1D,PERIDEX 0.12 % MM MWSH,283,492.3485923652703
+Medication,D1D,ORALONE 0.1 % DENTAL PSTE,136,492.3485923652703
+Pocedure,311,"BLOOD GASES, ARTERIAL",74180,346.37401515894663
+Pocedure,311,ARTERIAL BLOOD GAS,46013,346.37401515894663
+Pocedure,349,"CALCIUM IONIZED, SERUM/PLASMA",63372,331.8942930675525
+Pocedure,349,"CALCIUM IONIZED, WHOLE BLOOD",33086,331.8942930675525
+Diagnosis,RSP012,Acute respiratory failure with hypoxia,12114,304.897102341547
+Diagnosis,RSP012,Acute respiratory failure (CMS-HCC),10319,304.897102341547
+Diagnosis,RSP012,"Acute respiratory failure, unspecified whether with hypoxia or hypercapnia",4850,304.897102341547
+Diagnosis,RSP012,Acute and chronic respiratory failure with hypoxia,3870,304.897102341547
+Diagnosis,RSP012,Acute respiratory failure with hypoxia (CMS-HCC),3129,304.897102341547
+Pocedure,456456,TRANSFUSE RED BLOOD CELLS,40252,302.705804228714
+Medication,R1M,FUROSEMIDE 10 MG/ML INJ SOLN,62358,292.44171197579794
+Medication,R1M,FUROSEMIDE 20 MG PO TABS,55422,292.44171197579794
+Medication,R1M,FUROSEMIDE 40 MG PO TABS,34906,292.44171197579794
+Medication,R1M,LASIX 20 MG PO TABS,11992,292.44171197579794
+Medication,R1M,LASIX 40 MG PO TABS,8573,292.44171197579794
+Pocedure,118571,PHYSICAL ACTIVITY,263527,284.99969191993114
+Diagnosis,RSP016,"Other diseases of lung, not elsewhere classified",22159,283.6746074981415
+Diagnosis,RSP016,Chronic pulmonary edema,14987,283.6746074981415
+Diagnosis,RSP016,Pulmonary congestion and hypostasis,11485,283.6746074981415
+Diagnosis,RSP016,Other disorders of lung,10766,283.6746074981415
+Diagnosis,RSP016,Other specified respiratory disorders,8171,283.6746074981415
+Pocedure,419036,"LACTATE, WHOLE BLOOD",38859,249.02267714911872
+Medication,M0B,"ALBUMIN, HUMAN 5 % 5 % IV SOLP",36901,244.8409128295903
+Medication,M0B,"ALBUMIN, HUMAN 25 % 25 % IV SOLP",10600,244.8409128295903
+Medication,M0B,ANTITHROMBIN III (HUMAN) 500 (+/-) UNIT IV SOLR,73,244.8409128295903
+Medication,M0B,"ALBUMIN, HUMAN 25 % 25 G/100 ML PREMIX (RWC PAV D USE ONLY)",43,244.8409128295903
+Medication,M0B,"ALBUMIN, HUMAN 25 % 12.5 G/50 ML PREMIX (RWC PAV D USE ONLY)",16,244.8409128295903
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL OPHTH OINT,12312,243.31186805431147
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL OPHTH OINT WRAPPER,7818,243.31186805431147
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL 56.8-42.5 % OPHT OINT,2208,243.31186805431147
+Medication,Q6Y,WHITE PETROLATUM-MINERAL OIL 94-3 % OPHT OINT,1289,243.31186805431147
+Medication,Q6Y,WHITE PETROLATUM OPHT OINT,981,243.31186805431147
+Pocedure,37113,UP AD LIB,127718,223.74047636072035
+Diagnosis,RSP011,Atelectasis,25228,203.2967375637827
+Diagnosis,RSP011,Pulmonary collapse,19540,203.2967375637827
+Diagnosis,RSP011,Other specified pleural conditions,2623,203.2967375637827
+Diagnosis,RSP011,Pleurisy without mention of effusion or current tuberculosis,2328,203.2967375637827
+Diagnosis,RSP011,Pleural effusion in other conditions classified elsewhere,1483,203.2967375637827
+Pocedure,1746,RED BLOOD CELLS,89427,203.2303292546636
+Diagnosis,BLD006,"Thrombocytopenia, unspecified",19381,202.2034921930646
+Diagnosis,BLD006,Thrombocytopenia (CMS-HCC),14647,202.2034921930646
+Diagnosis,BLD006,"Thrombocytopenia, unspecified (CMS-HCC)",12938,202.2034921930646
+Diagnosis,BLD006,Other secondary thrombocytopenia,7780,202.2034921930646
+Diagnosis,BLD006,"Coagulation defect, unspecified",5047,202.2034921930646
+Pocedure,418745,ADDITIONAL INSTRUCTIONS,134480,199.0257958932021
+Pocedure,100975,KEEP HEAD OF BED ELEVATED,57262,193.13784492531704
+Medication,H2E,ZOLPIDEM 5 MG PO TABS,84042,192.2228121506272
+Medication,H2E,ZOLPIDEM 10 MG PO TABS,30429,192.2228121506272
+Medication,H2E,DEXMEDETOMIDINE IN 0.9 % NACL 400 MCG/100 ML (4 MCG/ML) IV SOLN,15070,192.2228121506272
+Medication,H2E,AMBIEN 10 MG PO TABS,11316,192.2228121506272
+Medication,H2E,DEXMEDETOMIDINE 100 MCG/ML IV SOLN,8930,192.2228121506272
+Pocedure,474911,ABO/RH,50859,180.21966149469148
+Lab result,CAION,"Calcium, Ionized",97837,176.92842808285198
+Lab result,CAION,Calcium Ionized,59715,176.92842808285198
+Lab result,CAION,"Calcium,Ion, ISTAT",36786,176.92842808285198
+Lab result,CAION,"Calcium, Ionized, Serum",706,176.92842808285198
+Pocedure,832,ECHO - TRANSTHORACIC ECHO +DOPPLER,162284,173.77546200181146
+Pocedure,832,ECHO - TRANSTHORACIC ECHO,9424,173.77546200181146
+Medication,C1F,CALCIUM PO,36076,173.13211205612757
+Medication,C1F,VITAMIN D (WITH CALCIUM) PO,31603,173.13211205612757
+Medication,C1F,CALCIUM GLUCONATE IN 0.9% NACL 1 GRAM/50 ML IV SOLN,30407,173.13211205612757
+Medication,C1F,CALCIUM + D PO,18851,173.13211205612757
+Medication,C1F,CALCIUM CHLORIDE 1 GRAM (13.6 MEQ)/50 ML NS IVPB,18116,173.13211205612757
+Medication,J5A,EPINEPHRINE 1 MG/ML (1 ML) INJ SOLN,28000,172.2452069722714
+Medication,J5A,NOREPINEPHRINE BITARTRATE-NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,11570,172.2452069722714
+Medication,J5A,EPINEPHRINE 0.1 MG/ML INJ SYRG,8234,172.2452069722714
+Medication,J5A,EPINEPHRINE 1 MG/ML INJ SOLN,8193,172.2452069722714
+Medication,J5A,EPINEPHRINE HCL IN 0.9 % NACL 4 MG/250 ML (16 MCG/ML) IV SOLN,7739,172.2452069722714
+Medication,C4G,INSULIN LISPRO 100 UNIT/ML SC SOLN,42013,172.1388048619322
+Medication,C4G,INSULIN REGULAR HUMAN 100 UNIT/ML INJ SOLN,39435,172.1388048619322
+Medication,C4G,INSULIN GLARGINE 100 UNIT/ML SC SOLN,15498,172.1388048619322
+Medication,C4G,INSULIN ASPART 100 UNIT/ML SC SOLN,12049,172.1388048619322
+Medication,C4G,INSULIN NPH ISOPH U-100 HUMAN 100 UNIT/ML SC SUSP,9163,172.1388048619322
+Medication,M9D,"TRANEXAMIC ACID 1,000 MG/10 ML (100 MG/ML) IV SOLN",15147,171.44417448593032
+Medication,M9D,AMINOCAPROIC ACID 250 MG/ML IV SOLN,6004,171.44417448593032
+Medication,M9D,TRANEXAMIC ACID 650 MG PO TABS,1212,171.44417448593032
+Medication,M9D,"TRANEXAMIC ACID IN NACL,ISO-OS 1,000 MG/100 ML (10 MG/ML) IV PGBK",639,171.44417448593032
+Medication,M9D,LYSTEDA 650 MG PO TABS,590,171.44417448593032
+Medication,S2B,IBUPROFEN 600 MG PO TABS,109543,166.7726933545371
+Medication,S2B,KETOROLAC 30 MG/ML (1 ML) INJ SOLN,51725,166.7726933545371
+Medication,S2B,IBUPROFEN 800 MG PO TABS,45051,166.7726933545371
+Medication,S2B,IBUPROFEN 400 MG PO TABS,42858,166.7726933545371
+Medication,S2B,KETOROLAC 15 MG/ML INJ SOLN,41164,166.7726933545371
+Lab result,RBC,RBC,975574,160.4150590534693
+Lab result,RBC,RBC (MANUAL ENTRY) See EMR for details,10078,160.4150590534693
+Lab result,RBC,Total Cell Count{Menlo},385,160.4150590534693
+Lab result,RBC,Red cells,27,160.4150590534693
+Lab result,RBC,ADULT RBC,10,160.4150590534693
+Lab result,BUN,"Urea Nitrogen,Ser/Plas",513379,158.72055757543728
+Lab result,BUN,"BUN, Ser/Plas",431163,158.72055757543728
+Lab result,BUN,BUN,144348,158.72055757543728
+Lab result,BUN,BUN (Manual Entry) See EMR for details,87845,158.72055757543728
+Lab result,BUN,"BUN, ISTAT",9453,158.72055757543728
+Lab result,CR,"Creatinine, Ser/Plas",931885,156.18740656453772
+Lab result,CR,"Creatinine, Serum (Manual Entry) See EMR for details",122459,156.18740656453772
+Lab result,CR,"Creatinine,ISTAT",108293,156.18740656453772
+Lab result,CR,Creatinine,12574,156.18740656453772
+Lab result,CR,"Creatinine, Serum",9841,156.18740656453772
+Diagnosis,MUS010,Pain in limb,66008,152.132082205845
+Diagnosis,MUS010,Pain in right knee,54116,152.132082205845
+Diagnosis,MUS010,Pain in left knee,51320,152.132082205845
+Diagnosis,MUS010,"Pain in joint, lower leg",48101,152.132082205845
+Diagnosis,MUS010,"Backache, unspecified",41181,152.132082205845
+Pocedure,2233,TYPE AND SCREEN,242768,148.0244227619197
+Pocedure,1434,VITAL SIGNS PER PROTOCOL,262833,143.11816365648218
+Pocedure,1200,CONSULT TO NUTRITION,47612,142.65818921248157
+Pocedure,68376,ADMIT TO PHASE,175723,140.09208447400096
+Pocedure,68376,ADMIT TO PHASE (POST PROCEDURE AREA),101150,140.09208447400096
+Diagnosis,GEN002,"Acute kidney failure, unspecified",27765,140.01438005971778
+Diagnosis,GEN002,"Acute kidney failure, unspecified (CMS-HCC)",16552,140.01438005971778
+Diagnosis,GEN002,Acute kidney injury (nontraumatic) (CMS-HCC),10653,140.01438005971778
+Diagnosis,GEN002,AKI (acute kidney injury) (CMS-HCC),6117,140.01438005971778
+Diagnosis,GEN002,Acute kidney failure with tubular necrosis,4948,140.01438005971778
+Pocedure,706,DIET REGULAR,226213,139.37049292672603
+Diagnosis,FAC009,Other specified postprocedural states,91771,135.08648778367225
+Diagnosis,FAC009,Other postprocedural status(V45.89),46825,135.08648778367225
+Diagnosis,FAC009,Fitting and adjustment of vascular catheter,25346,135.08648778367225
+Diagnosis,FAC009,Encounter for fitting and adjustment of non-vascular catheter NEC,21907,135.08648778367225
+Diagnosis,FAC009,Arthrodesis status,20784,135.08648778367225
+Pocedure,17581,NOTIFY MD:VITAL SIGNS,267085,134.76510265058008
+Medication,A2A,AMIODARONE 200 MG PO TABS,20862,133.20753441769895
+Medication,A2A,"AMIODARONE IN DEXTROSE,ISO-OSM 150 MG/100 ML (1.5 MG/ML) IV SOLN",6339,133.20753441769895
+Medication,A2A,LIDOCAINE IN 5 % DEXTROSE (PF) 8 MG/ML (0.8 %) IV SOLP,5717,133.20753441769895
+Medication,A2A,FLECAINIDE 50 MG PO TABS,3684,133.20753441769895
+Medication,A2A,FLECAINIDE 100 MG PO TABS,3540,133.20753441769895
+Pocedure,153334,OXYGEN ADMINISTRATION,222581,132.8307686814681
+Lab result,EGFR,eGFR,671284,131.72216091207602
+Lab result,EGFR,eGFR (Non African American (Manual Entry) See EMR for details,72618,131.72216091207602
+Lab result,EGFR,EGFR African American (Manual Entry) SEE EMR for Details,23322,131.72216091207602
+Lab result,EGFR,EGFR Mutation,1224,131.72216091207602
+Medication,A7B,NITROGLYCERIN 0.4 MG SL SUBL,48815,122.82926903277888
+Medication,A7B,ISOSORBIDE MONONITRATE 30 MG PO TB24,10028,122.82926903277888
+Medication,A7B,NITROGLYCERIN IN 5 % DEXTROSE 50 MG/250 ML (200 MCG/ML) IV SOLN,7927,122.82926903277888
+Medication,A7B,NITROGLYCERIN 2 % TD OINT,6353,122.82926903277888
+Medication,A7B,ISOSORBIDE MONONITRATE 60 MG PO TB24,4565,122.82926903277888
+Diagnosis,CIR019,"Heart failure, unspecified",25129,117.73699591688045
+Diagnosis,CIR019,"Congestive heart failure, unspecified",20875,117.73699591688045
+Diagnosis,CIR019,Chronic systolic (congestive) heart failure,12348,117.73699591688045
+Diagnosis,CIR019,Chronic diastolic (congestive) heart failure,10801,117.73699591688045
+Diagnosis,CIR019,CHF (congestive heart failure) (CMS-HCC),8278,117.73699591688045
+Medication,W1J,"VANCOMYCIN 1,000 MG IV SOLR",24716,116.41317743401254
+Medication,W1J,VANCOMYCIN IN D5W 1 GRAM/200 ML IV PGBK,21425,116.41317743401254
+Medication,W1J,VANCOMYCIN IN DEXTROSE 5 % 1 GRAM/200 ML IV PGBK,11225,116.41317743401254
+Medication,W1J,VANCOMYCIN IN DEXTROSE 1 GRAM/200 ML IV PGBK,6075,116.41317743401254
+Medication,W1J,VANCOMYCIN-WATER INJECT (PEG) 1 GRAM/200 ML IV PGBK,4964,116.41317743401254
+Pocedure,481775,AB SCREEN (SHC),36269,113.81458363111574
+Lab result,RDW,RDW,974552,112.56976226686156
+Lab result,RDW,RDW (MANUAL ENTRY) See EMR for details,4076,112.56976226686156
+Pocedure,1735,MRSA SCREEN,66723,110.55200680520916
+Pocedure,1735,MRSA SCREEN BY CULTURE,30403,110.55200680520916
+Lab result,PT,Prothrombin Time,337139,109.2564187926645
+Lab result,PT,"PT, ISTAT",53521,109.2564187926645
+Lab result,PT,Prothrombin Time (Manual Entry) See EMR for details,14406,109.2564187926645
+Lab result,PT,PT seconds,3497,109.2564187926645
+Lab result,PT,PT (Manual),362,109.2564187926645
+Diagnosis,EXT027,Unspecified place of occurrence,40908,104.98773272500956
+Diagnosis,EXT027,Other specified places as the place of occurrence of the external cause,28100,104.98773272500956
+Diagnosis,EXT027,"Place of occurrence, home",24394,104.98773272500956
+Diagnosis,EXT027,Unspecified place in unspecified non-institutional (private) residence as the place of occurrence of the external cause,20853,104.98773272500956
+Diagnosis,EXT027,"Place of occurrence, residential institution",17050,104.98773272500956
+Lab result,HCT,Hematocrit,988518,103.44478830595187
+Lab result,HCT,Hct (Est),99973,103.44478830595187
+Lab result,HCT,Hematocrit (Manual Entry) See EMR for details,96497,103.44478830595187
+Lab result,HCT,"Hct, ISTAT",44756,103.44478830595187
+Lab result,HCT,"Hct(Calc), ISTAT",30262,103.44478830595187
+Pocedure,1470,NURSING PULSE OXIMETRY (SPOT CHECK),117935,103.30016422583968
+Medication,M4G,GLUCOSE 4 GRAM PO CHEW,60342,101.07421925299442
+Medication,M4G,DEXTROSE 40 % PO GEL,15730,101.07421925299442
+Medication,M4G,GLUCAGON (HUMAN RECOMBINANT) 1 MG INJ SOLR,8668,101.07421925299442
+Medication,M4G,GLUCOSE 5 GRAM PO CHEW,6098,101.07421925299442
+Medication,M4G,GLUCAGON 1 MG INJ SOLR,1865,101.07421925299442
+Pocedure,501391,RISK FOR VENOUS THROMBOEMBOLISM - VTE RISK ASSESSMENT,103363,100.13375181503338
+Lab result,WBC,WBC,975990,99.45609690986927
+Lab result,WBC,WBC (Manual Entry) See EMR for details,104161,99.45609690986927
+Lab result,WBC,WBC count,15815,99.45609690986927
+Lab result,WBC,WBC Count,193,99.45609690986927
+Lab result,WBC,White Cells,1,99.45609690986927
+Medication,W5A,VALACYCLOVIR 1 GRAM PO TABS,23451,99.28855028310076
+Medication,W5A,ACYCLOVIR 400 MG PO TABS,23403,99.28855028310076
+Medication,W5A,OSELTAMIVIR 75 MG PO CAPS,18189,99.28855028310076
+Medication,W5A,VALACYCLOVIR 500 MG PO TABS,16969,99.28855028310076
+Medication,W5A,ACYCLOVIR 800 MG PO TABS,7192,99.28855028310076
+Medication,H2C,KETAMINE 100 MG/ML INJ SOLN,46180,98.78045472885552
+Medication,H2C,ETOMIDATE 2 MG/ML IV SOLN,12381,98.78045472885552
+Medication,H2C,PROPOFOL 10 MG/ML IV EMUL,10433,98.78045472885552
+Medication,H2C,PROPOFOL 10 MG/ML IV INFUSION,7689,98.78045472885552
+Medication,H2C,KETAMINE 10 MG/ML INJ SOLN,4825,98.78045472885552
+Pocedure,657586,RAINBOW DRAW (FOR ED/RRT/CODE BLUE ONLY),85896,94.38463329194506
+Pocedure,657586,RAINBOW DRAW,7980,94.38463329194506
+Pocedure,1036,HEMOGLOBIN A1C,432233,90.61037500102924
+Lab result,CA,"Calcium, Ser/Plas",896056,89.82512411939996
+Lab result,CA,"Calcium, Serum (Manual Entry) See EMR for details",91151,89.82512411939996
+Lab result,CA,Calcium,88,89.82512411939996
+Lab result,CA,"Calcium, Serum",25,89.82512411939996
+Lab result,HGB,Hemoglobin,982722,89.40300896411156
+Lab result,HGB,Hemoglobin (Manual Entry) See EMR for details,114709,89.40300896411156
+Lab result,HGB,"Hgb(Calc), ISTAT",71388,89.40300896411156
+Lab result,HGB,HgB,46893,89.40300896411156
+Lab result,HGB,HGB (Manual Entry) See EMR for details,3202,89.40300896411156
+Medication,A9A,AMLODIPINE 5 MG PO TABS,85557,88.27708817981865
+Medication,A9A,AMLODIPINE 10 MG PO TABS,48605,88.27708817981865
+Medication,A9A,AMLODIPINE 2.5 MG PO TABS,25028,88.27708817981865
+Medication,A9A,DILTIAZEM HCL 120 MG PO CP24,9813,88.27708817981865
+Medication,A9A,CLEVIDIPINE 50 MG/100 ML IV EMUL,9100,88.27708817981865
+Pocedure,433709,SLIDE REVIEW,51880,87.2590645742044
+Lab result,TP,"Protein, Total, Ser/Plas",785231,86.87434260904995
+Lab result,TP,Protein-Total,16,86.87434260904995
+Lab result,GFR,eGFR for African American,392642,85.29680591169429
+Lab result,GFR,GFR,13115,85.29680591169429
+Lab result,AG,Anion Gap,723779,85.16298153263676
+Lab result,AG,A/G Ratio,3789,85.16298153263676
+Lab result,PLT,Platelet count,975807,84.66075736002132
+Lab result,PLT,Platelet Count (Manual Entry) See EMR for details,103123,84.66075736002132
+Lab result,PLT,Platelets,7908,84.66075736002132
+Lab result,PLT,"Platelet Ct, Manual",478,84.66075736002132
+Lab result,PLT,"Platelet Count, Manual {MENLO}",125,84.66075736002132
+Pocedure,880100001,ARTERIAL LINE,49560,83.89387859384648
+Pocedure,880100001,Arterial Line,17570,83.89387859384648
+Diagnosis,CIR031,"Hypotension, unspecified",30472,82.15419465897139
+Diagnosis,CIR031,Orthostatic hypotension,15499,82.15419465897139
+Diagnosis,CIR031,"Hypotension, unspecified hypotension type",6053,82.15419465897139
+Diagnosis,CIR031,Hypotension,5844,82.15419465897139
+Diagnosis,CIR031,Other hypotension,3806,82.15419465897139
+Lab result,K,"Potassium, Ser/Plas",918122,81.77382792640766
+Lab result,K,"Potassium, Whole Bld",117286,81.77382792640766
+Lab result,K,Potassium (Manual Entry) See EMR for details,113142,81.77382792640766
+Lab result,K,"Potassium, ISTAT",71483,81.77382792640766
+Lab result,K,"Potassium, serum/plasma",2284,81.77382792640766
+Medication,H3D,ASPIRIN 325 MG PO TABS,57934,81.3063848149529
+Medication,H3D,ASPIRIN PO,16842,81.3063848149529
+Medication,H3D,ASPIRIN 300 MG PR SUPP,12033,81.3063848149529
+Medication,H3D,ASPIRIN 325 MG PO TBEC,6662,81.3063848149529
+Medication,H3D,EXCEDRIN MIGRAINE PO,2029,81.3063848149529
+Lab result,GLU,"Glucose, Ser/Plas",894413,81.2492415919112
+Lab result,GLU,Glucose by Meter,219614,81.2492415919112
+Lab result,GLU,"Glucose, Whole Blood",116977,81.2492415919112
+Lab result,GLU,"Glucose, SER/PLAS (Manual Entry) See EMR for details",111348,81.2492415919112
+Lab result,GLU,"Glucose,ISTAT",58290,81.2492415919112
+Lab result,FASTING,Fasting,354582,80.5145474573272
+Lab result,CO2,"CO2, Ser/Plas",906147,80.28627852000221
+Lab result,CL,"Chloride, Ser/Plas",909379,79.46108479023448
+Lab result,CL,"Chloride, Whole Bld",115389,79.46108479023448
+Lab result,CL,CHLORIDE BLD  (MANUAL ENTRY) See EMR for details,11132,79.46108479023448
+Lab result,CL,"Chloride, ISTAT",9458,79.46108479023448
+Lab result,CL,"CHLORIDE, SERUM/PLASMA (MANUAL ENTRY)SEE EMR FOR DETAILS",6149,79.46108479023448
+Medication,W3B,FLUCONAZOLE 150 MG PO TABS,32752,76.92332973458244
+Medication,W3B,FLUCONAZOLE 200 MG PO TABS,10562,76.92332973458244
+Medication,W3B,CLOTRIMAZOLE 10 MG MM TROC,9376,76.92332973458244
+Medication,W3B,TERBINAFINE HCL 250 MG PO TABS,6857,76.92332973458244
+Medication,W3B,FLUCONAZOLE 100 MG PO TABS,5284,76.92332973458244
+Lab result,TBIL,Total Bilirubin,531297,76.05468288858401
+Lab result,TBIL,"Total Bilirubin, Ser/Plas",411355,76.05468288858401
+Lab result,TBIL,Total Bilirubin (Manual Entry) See EMR for details,93582,76.05468288858401
+Lab result,TBIL,"Bilirubin, Total, Neonatal",107,76.05468288858401
+Diagnosis,CIR011,Atherosclerotic heart disease of native coronary artery without angina pectoris,60500,74.93144693253575
+Diagnosis,CIR011,Old myocardial infarction,33421,74.93144693253575
+Diagnosis,CIR011,Coronary atherosclerosis of native coronary artery,31208,74.93144693253575
+Diagnosis,CIR011,"Coronary atherosclerosis of unspecified type of vessel, native or graft",22597,74.93144693253575
+Diagnosis,CIR011,CAD (coronary artery disease),21617,74.93144693253575
+Pocedure,1048,HEPATIC FUNCTION PANEL A,173961,73.79147532215566
+Pocedure,37118,SEQUENTIAL COMPRESSION DEVICE (SCD),255080,73.33302174278356
+Diagnosis,CIR015,Cardiomegaly,69699,73.29188213808577
+Diagnosis,CIR015,"Heart disease, unspecified",10156,73.29188213808577
+Diagnosis,CIR015,LVH (left ventricular hypertrophy),3325,73.29188213808577
+Diagnosis,CIR015,Diastolic dysfunction,2002,73.29188213808577
+Diagnosis,CIR015,Other ill-defined heart diseases,1857,73.29188213808577
+Lab result,ALB,"Albumin, Ser/Plas",800563,73.08371376028452
+Lab result,ALB,ALBUMIN BLD  (MANUAL ENTRY) See EMR for details,9434,73.08371376028452
+Lab result,ALB,Albumin,7018,73.08371376028452
+Lab result,ALB,"Albumin, Serum",50,73.08371376028452
+Medication,Z2D,FAMOTIDINE 20 MG PO TABS,78739,72.97880841108567
+Medication,Z2D,FAMOTIDINE (PF) 20 MG/2 ML IV SOLN,56474,72.97880841108567
+Medication,Z2D,RANITIDINE HCL 150 MG PO TABS,16944,72.97880841108567
+Medication,Z2D,FAMOTIDINE 40 MG PO TABS,11078,72.97880841108567
+Medication,Z2D,RANITIDINE HCL 150 MG PO CAPS,7079,72.97880841108567
+Pocedure,482536,XR CHEST 1 VIEW,124216,72.40310263128357
+Lab result,NA,"Sodium, Ser/Plas",914528,72.31926576525338
+Lab result,NA,"Sodium, Whole Blood",117026,72.31926576525338
+Lab result,NA,Sodium (Manual Entry) See EMR for details,108406,72.31926576525338
+Lab result,NA,"Sodium, ISTAT",71442,72.31926576525338
+Lab result,NA,"Sodium, whole blood, ePOC",624,72.31926576525338
+Diagnosis,SYM004,Nausea,54051,69.88935949576025
+Diagnosis,SYM004,"Nausea with vomiting, unspecified",49972,69.88935949576025
+Diagnosis,SYM004,Nausea with vomiting,31162,69.88935949576025
+Diagnosis,SYM004,"Vomiting, unspecified",25617,69.88935949576025
+Diagnosis,SYM004,Nausea alone,24577,69.88935949576025
+Diagnosis,CIR012,"Chest pain, unspecified",144563,66.96063375968433
+Diagnosis,CIR012,Other chest pain,91900,66.96063375968433
+Diagnosis,CIR012,"Chest pain, unspecified type",48319,66.96063375968433
+Diagnosis,CIR012,Chest pain,38647,66.96063375968433
+Diagnosis,CIR012,Precordial pain,18060,66.96063375968433
+Lab result,BUN,"Urea Nitrogen,Ser/Plas",513379,66.84980787363071
+Lab result,BUN,"BUN, Ser/Plas",431163,66.84980787363071
+Lab result,BUN,BUN,144348,66.84980787363071
+Lab result,BUN,BUN (Manual Entry) See EMR for details,87845,66.84980787363071
+Lab result,BUN,"BUN, ISTAT",9453,66.84980787363071
+Lab result,ABORH,ABO/Rh (Automation),225482,66.46599609797774
+Lab result,ABORH,ABO/RH,48626,66.46599609797774
+Lab result,ABORH,ABO/Rh Cord,5673,66.46599609797774
+Lab result,ABORH,ABO (Automation) (Manual Entry) See EMR for details,2995,66.46599609797774
+Lab result,ABORH,ABO/RH Manual Entry,785,66.46599609797774
+Lab result,SPECEXPDATE,Specimen Expiration Date,140639,65.57803895906262
+Pocedure,1634,"PHOSPHORUS, SERUM/PLASMA",176752,65.23798146353407
+Diagnosis,BLD008,"Disorder involving the immune mechanism, unspecified",7011,65.20026637534836
+Diagnosis,BLD008,"Immunodeficiency, unspecified",5999,65.20026637534836
+Diagnosis,BLD008,Immunosuppression (CMS-HCC),5680,65.20026637534836
+Diagnosis,BLD008,"Other specified disorders involving the immune mechanism, not elsewhere classified",3600,65.20026637534836
+Diagnosis,BLD008,Immunosuppressed status (CMS-HCC),2858,65.20026637534836
+Pocedure,17968,SALINE LOCK AND FLUSH,232774,64.95737861200836
+Pocedure,3041212,NURSING COMM SERUM GLUCOSE,34170,64.01036437168507
+Lab result,NEUTAB,"NEUT, ABS",522238,63.45379771016022
+Lab result,NEUTAB,"Neutrophil, Absolute",384165,63.45379771016022
+Lab result,NEUTAB,"Neut, ABS (man diff)",41867,63.45379771016022
+Lab result,NEUTAB,"Neut, ABS (Seg+Band) (man diff)",31195,63.45379771016022
+Lab result,NEUTAB,"NEUTS, ABS",5167,63.45379771016022
+Diagnosis,SYM015,Hallucinations,2103,63.13558375822858
+Diagnosis,SYM015,"Hallucinations, unspecified",1801,63.13558375822858
+Diagnosis,SYM015,Visual hallucinations,1412,63.13558375822858
+Diagnosis,SYM015,Auditory hallucinations,1238,63.13558375822858
+Diagnosis,SYM015,Hallucination,461,63.13558375822858
+Pocedure,473871,BLOOD TYPE VERIFICATION - FULL,28340,62.57746813364362
+Diagnosis,END008,Moderate protein-calorie malnutrition,5752,62.21813831583725
+Diagnosis,END008,Mild protein-calorie malnutrition,3014,62.21813831583725
+Diagnosis,END008,Moderate protein-calorie malnutrition (CMS-HCC),1090,62.21813831583725
+Diagnosis,END008,Malnutrition of moderate degree (CMS-HCC),739,62.21813831583725
+Diagnosis,END008,Mild protein-calorie malnutrition (CMS-HCC),558,62.21813831583725
+Diagnosis,FAC016,Encounter for screening for other viral diseases,193224,61.339658391547225
+Diagnosis,FAC016,Contact with and (suspected) exposure to other viral communicable diseases,129531,61.339658391547225
+Diagnosis,FAC016,Screening for viral disease,108743,61.339658391547225
+Diagnosis,FAC016,Contact with or exposure to viral disease,72009,61.339658391547225
+Diagnosis,FAC016,Need for hepatitis C screening test,68338,61.339658391547225
+Diagnosis,CIR017,Unspecified atrial fibrillation,47024,60.62619557608481
+Diagnosis,CIR017,Atrial fibrillation (CMS-HCC),46861,60.62619557608481
+Diagnosis,CIR017,Other specified cardiac dysrhythmias(427.89),43458,60.62619557608481
+Diagnosis,CIR017,Ventricular premature depolarization,36938,60.62619557608481
+Diagnosis,CIR017,Supraventricular tachycardia,36587,60.62619557608481
+Pocedure,414593,BLOOD TYPE VERIFICATION,90090,59.9105336351434
+Pocedure,414593,BLOOD TYPE VERIFICATION - ORDER,31488,59.9105336351434
+Medication,R1F,HYDROCHLOROTHIAZIDE 25 MG PO TABS,56958,58.95278787220208
+Medication,R1F,HYDROCHLOROTHIAZIDE 12.5 MG PO TABS,19994,58.95278787220208
+Medication,R1F,HYDROCHLOROTHIAZIDE 12.5 MG PO CAPS,9387,58.95278787220208
+Medication,R1F,CHLORTHALIDONE 25 MG PO TABS,8240,58.95278787220208
+Medication,R1F,HYDROCHLOROTHIAZIDE PO,5775,58.95278787220208
+Pocedure,119052,ABO/RH (ARI),163656,58.11146322862336
+Pocedure,119052,ABO/RH,15586,58.11146322862336
+Diagnosis,SYM011,Dysuria,63756,56.158062476958655
+Diagnosis,SYM011,Urinary frequency,40161,56.158062476958655
+Diagnosis,SYM011,Frequency of micturition,34933,56.158062476958655
+Diagnosis,SYM011,Nocturia,25129,56.158062476958655
+Diagnosis,SYM011,"Retention of urine, unspecified",22017,56.158062476958655
+Diagnosis,FAC008,Encounter for screening mammogram for malignant neoplasm of breast,107343,55.64245027535656
+Diagnosis,FAC008,Encounter for screening for malignant neoplasm of colon,103907,55.64245027535656
+Diagnosis,FAC008,Encounter for screening for malignant neoplasm of cervix,78843,55.64245027535656
+Diagnosis,FAC008,Screen for colon cancer,54135,55.64245027535656
+Diagnosis,FAC008,Colon cancer screening,52102,55.64245027535656
+Pocedure,482552,XR CHEST 2 VIEWS,160440,55.02106424231207
+Lab result,MCV,MCV,975106,53.78208046018094
+Lab result,MCV,MCV (MANUAL ENTRY) See EMR for details,6487,53.78208046018094
+Lab result,AS,Antibody Screen,236027,53.75339693955407
+Lab result,AS,Antibody Screen (Manual Entry) See EMR for details,4366,53.75339693955407
+Lab result,AS,Antibody Screen Gel,3509,53.75339693955407
+Lab result,AS,Ab Screen (Ind. Coombs),3475,53.75339693955407
+Lab result,POCTSOURCE,Sample Type,51668,53.11909899137532
+Lab result,GLOB,Globulin,749063,52.81269174629786
+Lab result,GLOB,GAMMA GLOBULIN,5347,52.81269174629786
+Lab result,GLOB,"Globulin, Total",125,52.81269174629786
+Medication,C1H,MAGNESIUM SULFATE IV SCALE ,56765,52.66607858399225
+Medication,C1H,MAGNESIUM SULFATE IN WATER 2 GRAM/50 ML (4 %) IV PGBK,30466,52.66607858399225
+Medication,C1H,MAGNESIUM SULFATE IN D5W 1 GRAM/100 ML IV PGBK,27374,52.66607858399225
+Medication,C1H,MAGNESIUM PO,23947,52.66607858399225
+Medication,C1H,MAGNESIUM SULFATE IN 0.9 %NACL 2 GRAM/50 ML IV PGBK,20058,52.66607858399225
+Lab result,AST,"AST (SGOT), Ser/Plas",813935,51.173296162714806
+Lab result,AST,AST (Manual Entry) See EMR for details,111398,51.173296162714806
+Lab result,AST,AST,7194,51.173296162714806
+Diagnosis,FAC010,Encounter for therapeutic drug level monitoring,22428,51.14478386789308
+Diagnosis,FAC010,Medication monitoring encounter,22415,51.14478386789308
+Diagnosis,FAC010,Encounter for other specified aftercare,21495,51.14478386789308
+Diagnosis,FAC010,Encounter for palliative care,19165,51.14478386789308
+Diagnosis,FAC010,Encounter for removal of sutures,11936,51.14478386789308
+Medication,B62,IPRATROPIUM-ALBUTEROL 0.5 MG-3 MG(2.5 MG BASE)/3 ML INH NEBU,52936,47.633605268543285
+Medication,B62,IPRATROPIUM-ALBUTEROL 18-103 MCG/ACTUATION INH AERO,5795,47.633605268543285
+Medication,B62,IPRATROPIUM-ALBUTEROL 0.5-2.5 MG/3 ML INH NEBU,5352,47.633605268543285
+Medication,B62,IPRATROPIUM-ALBUTEROL 20-100 MCG/ACTUATION INH MIST,4968,47.633605268543285
+Medication,B62,IPRATROPIUM-ALBUTEROL 0.5MG-2.5MG BASE(3MG)/3ML INH NEB-PANEL USE ONLY,2064,47.633605268543285
+Lab result,MCH,MCH,972430,46.027444217886135
+Pocedure,147420,ECG PROCEDURE SCANNED REPORT,166301,45.836500862478346
+Diagnosis,MUS038,Low back pain,82586,43.78143408154215
+Diagnosis,MUS038,"Low back pain, unspecified back pain laterality, unspecified chronicity, with sciatica presence unspecified",6738,43.78143408154215
+Diagnosis,MUS038,Muscle spasm of back,4029,43.78143408154215
+Diagnosis,MUS038,"Low back pain without sciatica, unspecified back pain laterality",2296,43.78143408154215
+Diagnosis,MUS038,"Low back pain, unspecified back pain laterality, with sciatica presence unspecified",1610,43.78143408154215
+Pocedure,35073,DROPLET ISOLATION,53575,42.3521716057698
+Diagnosis,MUS011,Cervicalgia,82048,40.80946865897897
+Diagnosis,MUS011,Neck pain,37129,40.80946865897897
+Diagnosis,MUS011,Lumbosacral spondylosis without myelopathy,25507,40.80946865897897
+Diagnosis,MUS011,Degeneration of lumbar or lumbosacral intervertebral disc,19778,40.80946865897897
+Diagnosis,MUS011,"Radiculopathy, lumbar region",19649,40.80946865897897
+Pocedure,17678,FOLEY RETENTION CATHETER,107867,40.78000732057437
+Pocedure,17678,"FOLEY, RETENTION CATHETER",17502,40.78000732057437
+Diagnosis,END011,Dehydration,32749,40.68870743878677
+Diagnosis,END011,Hypokalemia,28039,40.68870743878677
+Diagnosis,END011,Hypo-osmolality and hyponatremia,23287,40.68870743878677
+Diagnosis,END011,Acidosis,20466,40.68870743878677
+Diagnosis,END011,Hyponatremia,15860,40.68870743878677
+Pocedure,119055,AB SCREEN (ASI),149182,39.98758462593007
+Lab result,LYMAB,"LYM, ABS",496919,39.74261713443685
+Lab result,LYMAB,"Lymphocyte, Absolute",386943,39.74261713443685
+Lab result,LYMAB,"Lymphocytes, ABS (man diff)",53200,39.74261713443685
+Lab result,LYMAB,"Lym, ABS (man diff)",43787,39.74261713443685
+Lab result,LYMAB,"Lymphocytes, Abs.",24480,39.74261713443685
+Pocedure,25762,BLOOD CULTURE (2 AEROBIC BOTTLES),101072,38.33493239589684
+Lab result,MCHC,MCHC,974473,37.9746996443706
+Diagnosis,FAC014,Encounter for general adult medical examination without abnormal findings,278500,37.83607946733484
+Diagnosis,FAC014,Routine general medical examination at a health care facility,184958,37.83607946733484
+Diagnosis,FAC014,Encounter for other preprocedural examination,144380,37.83607946733484
+Diagnosis,FAC014,Preventative health care,108293,37.83607946733484
+Diagnosis,FAC014,Encounter for gynecological examination (general) (routine) without abnormal findings,100835,37.83607946733484
+Medication,W2A,SULFAMETHOXAZOLE-TRIMETHOPRIM 800-160 MG PO TABS,57065,37.82910834015127
+Medication,W2A,SULFAMETHOXAZOLE-TRIMETHOPRIM 400-80 MG PO TABS,12312,37.82910834015127
+Medication,W2A,BACTRIM DS 800-160 MG PO TABS,7348,37.82910834015127
+Medication,W2A,SULFAMETHOXAZOLE-TRIMETHOPRIM 200-40 MG/5 ML PO SUSP,4157,37.82910834015127
+Medication,W2A,TRIMETHOPRIM-SULFAMETHOXAZOLE 160-800 MG PO TABS,3626,37.82910834015127
+Lab result,ALT,"ALT (SGPT), Ser/Plas",815520,37.80229515399018
+Lab result,ALT,ALT (Manual Entry) See EMR for details,112610,37.80229515399018
+Lab result,ALT,ALT,8266,37.80229515399018
+Pocedure,310,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLES),74559,37.5943169511685
+Pocedure,310,BLOOD CULTURE (AEROBIC & ANAEROBIC BOTTLE),68870,37.5943169511685
+Lab result,LYM,"LYM, %",512717,34.56528517200469
+Lab result,LYM,Lymphocyte %,386962,34.56528517200469
+Lab result,LYM,Lymphocytes,100957,34.56528517200469
+Lab result,LYM,Lymphocytes %,24390,34.56528517200469
+Lab result,IGABSWAM,"Imm. Granulocyte, Abs",225266,34.1949369211917
+Pocedure,34544,OFFICE CONSLTJ 80 MIN,330608,33.80247948699804
+Pocedure,34544,OFFICE CONSULTATION,728,33.80247948699804
+Pocedure,34544,OP CONSULT/NEW OR EST PT/MOD/HIGH SEV/80 MIN,339,33.80247948699804
+Pocedure,34544,EVAL/MGMT OF NEW PATIENT LEVEL 5,3,33.80247948699804
+Pocedure,34544,REFERRAL TO GI ONCOLOGY,7,33.80247948699804
+Medication,H7O,HALOPERIDOL LACTATE 5 MG/ML INJ SOLN,22876,33.58672836764151
+Medication,H7O,HALOPERIDOL 1 MG PO TABS,6100,33.58672836764151
+Medication,H7O,HALOPERIDOL INTRAMUSCULAR INJECTION,4022,33.58672836764151
+Medication,H7O,HALOPERIDOL 2 MG PO TABS,2630,33.58672836764151
+Medication,H7O,HALOPERIDOL 0.5 MG PO TABS,2139,33.58672836764151
+Lab result,NEUT,"NEUT, %",507163,33.56494157317013
+Lab result,NEUT,Neutrophil %,384147,33.56494157317013
+Lab result,NEUT,Neutrophils,75228,33.56494157317013
+Lab result,NEUT,Segmented Neutrophils,53321,33.56494157317013
+Lab result,NEUT,Seg neutrophils,50995,33.56494157317013
+Diagnosis,MUS028,Osteopenia,25306,32.739599981856735
+Diagnosis,MUS028,"Other specified disorders of bone density and structure, unspecified site",24536,32.739599981856735
+Diagnosis,MUS028,"Osteopenia, unspecified location",15232,32.739599981856735
+Diagnosis,MUS028,"Disorder of bone, unspecified",14122,32.739599981856735
+Diagnosis,MUS028,"Other specified disorders of bone density and structure, other site",8258,32.739599981856735
+Lab result,BASOAB,"BASOS, ABS",570478,32.27435557431515
+Lab result,BASOAB,"Basophil, Absolute",386712,32.27435557431515
+Lab result,BASOAB,"Basophils, ABS (man diff)",25934,32.27435557431515
+Lab result,BASOAB,"Baso, ABS (man diff)",20844,32.27435557431515
+Lab result,ALKP,"Alk P'TASE, Total, Ser/Plas",794773,31.96098841168085
+Lab result,ALKP,"ALK P'TASE Total, Serum (Manual Entry) See EMR for details",75602,31.96098841168085
+Lab result,ALKP,Alkaline Phosphatase,233,31.96098841168085
+Lab result,ALKP,Total Alkaline Phosphatase,122,31.96098841168085
+Medication,H2S,ESCITALOPRAM OXALATE 10 MG PO TABS,25867,31.800348482155563
+Medication,H2S,SERTRALINE 50 MG PO TABS,25390,31.800348482155563
+Medication,H2S,SERTRALINE 100 MG PO TABS,16025,31.800348482155563
+Medication,H2S,CITALOPRAM 20 MG PO TABS,14520,31.800348482155563
+Medication,H2S,FLUOXETINE 20 MG PO CAPS,14084,31.800348482155563
+Diagnosis,GEN003,"Chronic kidney disease, unspecified",31611,31.645930633688245
+Diagnosis,GEN003,"Chronic kidney disease, stage 3 (moderate)",23268,31.645930633688245
+Diagnosis,GEN003,"Chronic kidney disease, stage 2 (mild)",13463,31.645930633688245
+Diagnosis,GEN003,End stage renal disease (CMS-HCC),10264,31.645930633688245
+Diagnosis,GEN003,End stage renal disease,9970,31.645930633688245
+Lab result,EOS,"EOS, %",562503,30.87751302395001
+Lab result,EOS,Eosinophil %,386735,30.87751302395001
+Lab result,EOS,Eosinophils,73183,30.87751302395001
+Lab result,BASO,"BASO, %",506797,30.796130621345075
+Lab result,BASO,Basophil %,386735,30.796130621345075
+Lab result,BASO,"BASOS, %",74681,30.796130621345075
+Lab result,BASO,Basophils,49852,30.796130621345075
+Lab result,MONO,"MONO, %",516214,30.211707939273044
+Lab result,MONO,Monocyte %,386739,30.211707939273044
+Lab result,MONO,Monocytes,170474,30.211707939273044
+Lab result,MONO,Mono Titer,30,30.211707939273044
+Lab result,MONO,Mono Antibody Titer,1,30.211707939273044
+Pocedure,1001,GLUCOSE BY METER,223927,29.517822301549643
+Pocedure,119815,POC GLUCOSE BY METER,187765,29.42063099984112
+Medication,J2A,ATROPINE 0.1 MG/ML INJ SYRG,24962,28.727763197568017
+Medication,J2A,ATROPINE 0.4 MG/ML INJ SOLN,4501,28.727763197568017
+Medication,J2A,HYOSCYAMINE SULFATE 0.125 MG SL SUBL,3100,28.727763197568017
+Medication,J2A,HYOSCYAMINE SULFATE 0.125 MG PO TABS,3020,28.727763197568017
+Medication,J2A,ATROPINE 1 MG/ML INJ SOLN,2645,28.727763197568017
+Medication,H3U,HYDROCODONE-ACETAMINOPHEN 5-325 MG PO TABS,185292,28.705894143399583
+Medication,H3U,HYDROCODONE-ACETAMINOPHEN 5-500 MG PO TABS,107101,28.705894143399583
+Medication,H3U,OXYCODONE-ACETAMINOPHEN 5-325 MG PO TABS,92328,28.705894143399583
+Medication,H3U,HYDROCODONE-ACETAMINOPHEN 10-325 MG PO TABS,71846,28.705894143399583
+Medication,H3U,ACETAMINOPHEN-CODEINE 300-30 MG PO TABS,28896,28.705894143399583
+Medication,H3T,NALOXONE 0.4 MG/ML INJ SOLN,292371,28.653095236693837
+Medication,H3T,NARCAN 4 MG/ACTUATION NASAL SPRY,14819,28.653095236693837
+Medication,H3T,NALTREXONE 50 MG PO TABS,3655,28.653095236693837
+Medication,H3T,NALOXONE 4 MG/ACTUATION NASAL SPRY,2008,28.653095236693837
+Medication,H3T,NALOXONE 1 MG/ML INJ SYRG,1452,28.653095236693837
+Pocedure,88985,DIET NPO AFTER MIDNIGHT,58813,27.99234110055478
+Lab result,IGWAM,"Imm. Granulocyte, %",225268,27.938194760012664
+Medication,H1U,GADOBENATE DIMEGLUMINE 529 MG/ML (0.1MMOL/0.2ML) IV SOLN,42702,27.92674930717823
+Medication,H1U,GADOPENTETATE DIMEGLUMINE 2.5 MMOL/5 ML (469.01 MG/ML) IV SOLN,726,27.92674930717823
+Medication,H1U,GADOBENATE DIMEGLUMINE 529 MG/ML IV SOLN 10 ML,294,27.92674930717823
+Medication,H1U,GADOTERATE MEGLUMINE 0.5 MMOL/ML (376.9 MG/ML) IV SOLN,158,27.92674930717823
+Medication,H1U,GADOTERATE MEGLUMINE 0.5 MMOL/ML (376.9 MG/ML) IV SOLN 15 ML,96,27.92674930717823
+Medication,M9F,ALTEPLASE 2 MG INTRA-CATHET SOLR,34422,27.33683621191037
+Medication,M9F,ALTEPLASE 2 MG INJ SOLR,11557,27.33683621191037
+Medication,M9F,ALTEPLASE 100 MG IV SOLR,599,27.33683621191037
+Medication,M9F,ALTEPLASE IV BOLUS FOR STROKE,446,27.33683621191037
+Medication,M9F,ALTEPLASE IV INFUSION FOR STROKE,446,27.33683621191037
+Pocedure,17918,PERIPHERAL IV INSERTION CARE,365683,27.27610082652597
+Pocedure,17918,PERIPHERAL IV INSERTION/CARE,39224,27.27610082652597
+Pocedure,2291,CARDIAC MONITOR,158041,26.64184687402991
+Medication,W8F,SODIUM CHLORIDE 0.9 % IRRIG SOLN,70154,26.533688240724505
+Medication,W8F,"WATER FOR IRRIGATION, STERILE IRRIG SOLN",25367,26.533688240724505
+Medication,W8F,LACTATED RINGERS IRRIG SOLN,12644,26.533688240724505
+Medication,W8F,PHYSIO. IRRIGATION COMB. NO.1 140-5-3-98 MEQ/L IRRIG SOLN,848,26.533688240724505
+Medication,W8F,"NEOMYCIN-POLYMYXIN B GU 40 MG-200,000 UNIT/ML IRRIG SOLN",797,26.533688240724505
+Lab result,MONOAB,"MONO, ABS",515767,26.50037732411664
+Lab result,MONOAB,"Monocyte, Absolute",386715,26.50037732411664
+Lab result,MONOAB,"Monocytes, ABS (man diff)",52778,26.50037732411664
+Lab result,MONOAB,"Mono, ABS (man diff)",41047,26.50037732411664
+Lab result,MONOAB,"MONOS, ABS",237,26.50037732411664
+Medication,C5K,D5 %-0.45 % SODIUM CHLORIDE IV SOLP,30947,26.182577213851957
+Medication,C5K,D5 % AND 0.9 % SODIUM CHLORIDE IV SOLP,19253,26.182577213851957
+Medication,C5K,DEXTROSE 5%-1/2 NORMAL SALINE IV SOLP,5457,26.182577213851957
+Medication,C5K,D5 %-0.9 % SODIUM CHLORIDE IV SOLP,2633,26.182577213851957
+Medication,C5K,DEXTROSE 5% IN NORMAL SALINE IV SOLP,2076,26.182577213851957
+Medication,D4J,PANTOPRAZOLE 40 MG PO TBEC,132876,26.131840734645504
+Medication,D4J,OMEPRAZOLE 20 MG PO CPDR,84307,26.131840734645504
+Medication,D4J,PANTOPRAZOLE 40 MG IV SOLR,65694,26.131840734645504
+Medication,D4J,OMEPRAZOLE 40 MG PO CPDR,32383,26.131840734645504
+Medication,D4J,PANTOPRAZOLE 20 MG PO TBEC,19654,26.131840734645504
+Diagnosis,INJ031,"Dermatitis, unspecified",48986,25.433771951423523
+Diagnosis,INJ031,Allergic rhinitis,48771,25.433771951423523
+Diagnosis,INJ031,Dermatitis,42692,25.433771951423523
+Diagnosis,INJ031,"Allergic rhinitis, cause unspecified",35002,25.433771951423523
+Diagnosis,INJ031,"Allergic rhinitis, unspecified",33707,25.433771951423523
+Lab result,PHOS,"Phosphorus, Ser/Plas",178767,24.66447184634755
+Lab result,PHOS,PHOSPHORUS BLD (MANUAL ENTRY) See EMR for details,5734,24.66447184634755
+Lab result,PHOS,Phosphorus,95,24.66447184634755
+Lab result,PHOS,"Phosphorus, Serum",53,24.66447184634755
+Diagnosis,INF002,"Sepsis, unspecified organism",17001,24.29250373829245
+Diagnosis,INF002,Unspecified septicemia(038.9) (CMS-HCC),9289,24.29250373829245
+Diagnosis,INF002,Sepsis (CMS-HCC),5718,24.29250373829245
+Diagnosis,INF002,Severe sepsis with septic shock,5704,24.29250373829245
+Diagnosis,INF002,Severe sepsis without septic shock,5151,24.29250373829245
+Pocedure,1435,VITAL SIGNS,366348,24.19834171386336
+Pocedure,1721,PTT PARTIAL THROMBOPLASTIN TIME,291092,23.41966204323209
+Medication,D4D,PROBIOTIC PO,12621,23.39757598313153
+Medication,D4D,LACTOBACILLUS RHAMNOSUS GG 15 BILLION CELL PO CPSP,9483,23.39757598313153
+Medication,D4D,ACIDOPHILUS-L.B-BB-S.THERMOPHL 1 BILLION-250 CELL-MG PO TABS,7109,23.39757598313153
+Medication,D4D,LACTOBACILLUS COMB NO.10 20 BILLION CELL PO CAPS,4763,23.39757598313153
+Medication,D4D,PROBIOTIC COMPLEX PO,4420,23.39757598313153
+Diagnosis,FAC022,Acquired absence of other specified parts of digestive tract,21778,23.17774765983491
+Diagnosis,FAC022,Acquired absence of both cervix and uterus,18999,23.17774765983491
+Diagnosis,FAC022,Other acquired absence of organ,7542,23.17774765983491
+Diagnosis,FAC022,Acquired absence of other genital organ(s),5279,23.17774765983491
+Diagnosis,FAC022,Acquired absence of breast and nipple,5224,23.17774765983491
+Lab result,BE,"Base Excess, ISTAT",100791,22.668232050329557
+Lab result,BE,Base Excess (vt),82068,22.668232050329557
+Lab result,BE,Base Excess Arterial for POC,239,22.668232050329557
+Lab result,BE,Base Excess,7,22.668232050329557
+Pocedure,481797,"STAT 2019 CORONAVIRUS SARS-COV-2, RT-PCR",47517,22.21252619752716
+Pocedure,37225,PT ONGOING TREATMENT,105077,22.032871332176985
+Pocedure,1590,AMBULATE WITH ASSISTANCE,115301,21.928179964233323
+Pocedure,474,CBC W/O DIFF,324995,21.89893649993972
+Pocedure,474,CBC,262301,21.89893649993972
+Pocedure,474,"CBC (H/H, RBC, INDICES, WBC, PLT)",36,21.89893649993972
+Pocedure,703,DIET NPO,186079,20.904192522717643
+Diagnosis,GEN004,"Urinary tract infection, site not specified",77256,20.86031946191417
+Diagnosis,GEN004,Acute cystitis without hematuria,24015,20.86031946191417
+Diagnosis,GEN004,UTI (lower urinary tract infection),17607,20.86031946191417
+Diagnosis,GEN004,"Urinary tract infection without hematuria, site unspecified",17432,20.86031946191417
+Diagnosis,GEN004,UTI (urinary tract infection),13012,20.86031946191417
+Diagnosis,BLD007,"Elevated white blood cell count, unspecified",19019,20.72147841983473
+Diagnosis,BLD007,"Leukocytosis, unspecified",10995,20.72147841983473
+Diagnosis,BLD007,"Decreased white blood cell count, unspecified",7004,20.72147841983473
+Diagnosis,BLD007,"Neutropenia, unspecified",6985,20.72147841983473
+Diagnosis,BLD007,"Leukocytosis, unspecified type",6580,20.72147841983473
+Pocedure,1368,"METABOLIC PANEL, BASIC",506927,20.41742023190381
+Lab result,EOSAB,"EOS, ABS",570480,20.15848468014693
+Lab result,EOSAB,"Eosinophil, Absolute",386713,20.15848468014693
+Lab result,EOSAB,"Eosinophils, ABS (man diff)",39330,20.15848468014693
+Lab result,EOSAB,"Eos, ABS (man diff)",29133,20.15848468014693
+Medication,W1Y,CEFTRIAXONE 1 GRAM INJ SOLR,14140,19.64529149634527
+Medication,W1Y,CEFTRIAXONE 1 GRAM/10 ML IV PUSH (VIAL ONLY),11238,19.64529149634527
+Medication,W1Y,CEFDINIR 300 MG PO CAPS,6300,19.64529149634527
+Medication,W1Y,CEFTRIAXONE 1 GRAM IV SOLR,6118,19.64529149634527
+Medication,W1Y,CEFTRIAXONE 2 GRAM/20 ML IV PUSH (VIAL ONLY),5851,19.64529149634527
+Pocedure,100966,SURGICAL PREPARATION,109675,19.607115480882022
+Diagnosis,FAC006,Encounter for antineoplastic chemotherapy,16361,19.60121912238451
+Diagnosis,FAC006,Encounter for antineoplastic immunotherapy,3547,19.60121912238451
+Diagnosis,FAC006,Encounter for antineoplastic radiation therapy,2506,19.60121912238451
+Diagnosis,FAC006,Radiotherapy,2412,19.60121912238451
+Diagnosis,FAC006,Admission for antineoplastic chemotherapy,401,19.60121912238451
+Pocedure,35000004791,TRANSFER PATIENT,55137,19.557681196220276
+Pocedure,35000004791,CHANGE LEVEL OF CARE/TRANSFER PATIENT,31374,19.557681196220276
+Pocedure,35000004791,TRANSFER PATIENT/CHANGE LEVEL OF CARE,12068,19.557681196220276
+Pocedure,35000004791,CHANGE LEVEL OF CARE/TRANSFER INPATIENT,248,19.557681196220276
+Lab result,K,"Potassium, Ser/Plas",918122,19.40187934632506
+Lab result,K,"Potassium, Whole Bld",117286,19.40187934632506
+Lab result,K,Potassium (Manual Entry) See EMR for details,113142,19.40187934632506
+Lab result,K,"Potassium, ISTAT",71483,19.40187934632506
+Lab result,K,"Potassium, serum/plasma",2284,19.40187934632506
+Medication,H22,MIDAZOLAM 1 MG/ML INJ SOLN,245919,18.61280079900605
+Medication,H22,MIDAZOLAM (PF) 1 MG/ML INJ SOLN,20604,18.61280079900605
+Medication,H22,MIDAZOLAM 5 MG/ML INJ SOLN,4813,18.61280079900605
+Medication,H22,MIDAZOLAM (PF) 2 MG/2 ML (1 MG/ML) INJ SYRG,1999,18.61280079900605
+Medication,H22,MIDAZOLAM IN 0.9 % SOD CHLORID 1 MG/ML IV SOLN,402,18.61280079900605
+Diagnosis,NVS010,Migraine,17781,18.306746839907152
+Diagnosis,NVS010,"Migraine, unspecified, not intractable, without status migrainosus",15959,18.306746839907152
+Diagnosis,NVS010,"Migraine, unspecified, without mention of intractable migraine without mention of status migrainosus",14995,18.306746839907152
+Diagnosis,NVS010,"Migraine without aura, not intractable, without status migrainosus",9723,18.306746839907152
+Diagnosis,NVS010,"Migraine with aura, not intractable, without status migrainosus",9449,18.306746839907152
+Lab result,XUBLOOD,"Blood, urine",220887,18.27379715419509
+Diagnosis,MUS025,Other specified soft tissue disorders,37468,18.03460615664851
+Diagnosis,MUS025,Swelling of limb,20610,18.03460615664851
+Diagnosis,MUS025,Plantar fascial fibromatosis,14518,18.03460615664851
+Diagnosis,MUS025,Fibromyalgia,9533,18.03460615664851
+Diagnosis,MUS025,"Neuralgia and neuritis, unspecified",8392,18.03460615664851
+Medication,H6J,ONDANSETRON HCL (PF) 4 MG/2 ML INJ SOLN,437292,17.45687205891732
+Medication,H6J,ONDANSETRON 4 MG PO TBDL,197517,17.45687205891732
+Medication,H6J,PROCHLORPERAZINE EDISYLATE 10 MG/2 ML (5 MG/ML) INJ SOLN,87264,17.45687205891732
+Medication,H6J,ONDANSETRON HCL 4 MG PO TABS,51282,17.45687205891732
+Medication,H6J,ONDANSETRON HCL 8 MG PO TABS,45132,17.45687205891732
+Pocedure,1709,PROTHROMBIN TIME,354466,17.30970660319963
+Diagnosis,MBD002,"Major depressive disorder, single episode, unspecified",76391,17.28009450353597
+Diagnosis,MBD002,"Depressive disorder, not elsewhere classified",43564,17.28009450353597
+Diagnosis,MBD002,Depressive disorder,12378,17.28009450353597
+Diagnosis,MBD002,Dysthymic disorder,10713,17.28009450353597
+Diagnosis,MBD002,"Major depressive disorder, recurrent, moderate",8078,17.28009450353597
+Medication,W1D,AZITHROMYCIN 250 MG PO TABS,65492,17.067299793999016
+Medication,W1D,ZITHROMAX Z-PAK 250 MG PO TABS,36463,17.067299793999016
+Medication,W1D,AZITHROMYCIN 500 MG PO TABS,22118,17.067299793999016
+Medication,W1D,CLARITHROMYCIN 500 MG PO TABS,12221,17.067299793999016
+Medication,W1D,AZITHROMYCIN 200 MG/5 ML PO SUSR,7554,17.067299793999016
+Diagnosis,SYM007,Other fatigue,91834,16.99429180787775
+Diagnosis,SYM007,Other malaise,48115,16.99429180787775
+Diagnosis,SYM007,"Fatigue, unspecified type",40799,16.99429180787775
+Diagnosis,SYM007,Weakness,38481,16.99429180787775
+Diagnosis,SYM007,Fatigue,25530,16.99429180787775
+Diagnosis,SYM005,"Dysphagia, unspecified",30484,16.965234582534283
+Diagnosis,SYM005,"Dysphagia, unspecified type",16426,16.965234582534283
+Diagnosis,SYM005,"Dysphagia, unspecified(787.20)",14381,16.965234582534283
+Diagnosis,SYM005,Dysphagia,13569,16.965234582534283
+Diagnosis,SYM005,"Dysphagia, oropharyngeal phase",5457,16.965234582534283
+Diagnosis,SYM017,Abnormal electrocardiogram (ECG) (EKG),159423,16.58482645161462
+Diagnosis,SYM017,Other nonspecific abnormal finding of lung field,105956,16.58482645161462
+Diagnosis,SYM017,Nonspecific abnormal electrocardiogram (ECG) (EKG),70046,16.58482645161462
+Diagnosis,SYM017,Other specified abnormal findings of blood chemistry,45479,16.58482645161462
+Diagnosis,SYM017,Prediabetes,43554,16.58482645161462
+Medication,H3E,ACETAMINOPHEN 325 MG PO TABS,294951,16.469858341888493
+Medication,H3E,ACETAMINOPHEN 500 MG PO TABS,186394,16.469858341888493
+Medication,H3E,"ACETAMINOPHEN 1,000 MG/100 ML (10 MG/ML) IV SOLN",121238,16.469858341888493
+Medication,H3E,TYLENOL PO,31882,16.469858341888493
+Medication,H3E,ACETAMINOPHEN 650 MG/20.3 ML PO SOLN,31618,16.469858341888493
+Diagnosis,END007,Vitamin D deficiency,83075,15.953552306341416
+Diagnosis,END007,"Vitamin D deficiency, unspecified",71879,15.953552306341416
+Diagnosis,END007,Unspecified vitamin D deficiency,25190,15.953552306341416
+Diagnosis,END007,Deficiency of other specified B group vitamins,11168,15.953552306341416
+Diagnosis,END007,Vitamin D insufficiency,7044,15.953552306341416
+Medication,W7C,FLU VAC QS 2014-15(36MOS+)(PF) 60 MCG (15 MCG X 4)/0.5 ML IM SYRG,9767,15.580612621292982
+Medication,W7C,INFLUENZA 2009-10 VAC (PF) (FLUZONE) SYRG (FOR AGE > 36 MO),9077,15.580612621292982
+Medication,W7C,FLU VAC TS 2013-14(36MOS+)(PF) 45 MCG (15 MCG X 3)/0.5 ML IM SYRG,8921,15.580612621292982
+Medication,W7C,FLU VACC QS2019-20 6MOS UP(PF) 60 MCG (15 MCG X 4)/0.5 ML IM SYRG,4956,15.580612621292982
+Medication,W7C,FLU VACC TS2019-20(65YR UP)PF 180 MCG/0.5 ML IM SYRG,3817,15.580612621292982
+Diagnosis,FAC001,Encounter for other administrative examinations,98274,15.319539891029574
+Diagnosis,FAC001,"Encounter for administrative examinations, unspecified",44697,15.319539891029574
+Diagnosis,FAC001,Encounters for other specified administrative purpose(V68.89),33024,15.319539891029574
+Diagnosis,FAC001,Encounters for administrative purposes,31929,15.319539891029574
+Diagnosis,FAC001,Encounters for unspecified administrative purpose,28199,15.319539891029574
+Diagnosis,BLD001,"Iron deficiency anemia, unspecified",36070,15.055627848226871
+Diagnosis,BLD001,Iron deficiency anemia,17354,15.055627848226871
+Diagnosis,BLD001,"Iron deficiency anemia, unspecified iron deficiency anemia type",15662,15.055627848226871
+Diagnosis,BLD001,Iron deficiency anemia secondary to blood loss (chronic),15105,15.055627848226871
+Diagnosis,BLD001,Other iron deficiency anemias,9978,15.055627848226871
+Medication,H6H,CYCLOBENZAPRINE 10 MG PO TABS,43656,14.645221471347472
+Medication,H6H,CYCLOBENZAPRINE 5 MG PO TABS,29974,14.645221471347472
+Medication,H6H,BACLOFEN 10 MG PO TABS,23872,14.645221471347472
+Medication,H6H,CARISOPRODOL 350 MG PO TABS,6413,14.645221471347472
+Medication,H6H,TIZANIDINE 4 MG PO TABS,4210,14.645221471347472
+Medication,C4L,METFORMIN 500 MG PO TABS,54818,14.264223343410864
+Medication,C4L,"METFORMIN 1,000 MG PO TABS",29469,14.264223343410864
+Medication,C4L,METFORMIN 500 MG PO TB24,17322,14.264223343410864
+Medication,C4L,METFORMIN PO,9733,14.264223343410864
+Medication,C4L,METFORMIN 850 MG PO TABS,6989,14.264223343410864
+Lab result,XURBC,"RBC, urine",218650,14.207301482995856
+Medication,S7D,SUGAMMADEX 100 MG/ML IV SOLN,57192,14.059206945269024
+Medication,H7T,QUETIAPINE 25 MG PO TABS,16767,13.787065136406977
+Medication,H7T,OLANZAPINE 5 MG PO TBDL,6787,13.787065136406977
+Medication,H7T,OLANZAPINE 5 MG PO TABS,5156,13.787065136406977
+Medication,H7T,QUETIAPINE 100 MG PO TABS,4378,13.787065136406977
+Medication,H7T,QUETIAPINE 50 MG PO TABS,4372,13.787065136406977
+Medication,C1D,POTASSIUM CHLORIDE 20 MEQ PO TBTQ,70016,13.769290144452013
+Medication,C1D,POTASSIUM CHLORID-D5-0.45%NACL 20 MEQ/L IV SOLP,50889,13.769290144452013
+Medication,C1D,POTASSIUM CHLORIDE 10 MEQ PO TBTQ,46113,13.769290144452013
+Medication,C1D,POTASSIUM CHLORIDE ORAL SCALE,36673,13.769290144452013
+Medication,C1D,POTASSIUM CHLORIDE IV SCALE,36169,13.769290144452013
+Pocedure,900,FERRITIN,146186,13.4890777382112
+Diagnosis,NVS019,Other chronic pain,130484,13.010187679617738
+Diagnosis,NVS019,Other acute postprocedural pain,32050,13.010187679617738
+Diagnosis,NVS019,Other acute postoperative pain,20496,13.010187679617738
+Diagnosis,NVS019,Chronic pain syndrome,11165,13.010187679617738
+Diagnosis,NVS019,Chronic pain,10927,13.010187679617738
+Diagnosis,NVS016,Obstructive sleep apnea (adult) (pediatric),95662,12.968705184519134
+Diagnosis,NVS016,"Insomnia, unspecified",66765,12.968705184519134
+Diagnosis,NVS016,OSA (obstructive sleep apnea),55171,12.968705184519134
+Diagnosis,NVS016,Insomnia,39038,12.968705184519134
+Diagnosis,NVS016,Obstructive sleep apnea,33252,12.968705184519134
+Pocedure,337,C - REACTIVE PROTEIN,154905,12.931617047375356
+Medication,J7C,METOPROLOL TARTRATE 5 MG/5 ML IV SOLN,84578,12.143694976360772
+Medication,J7C,METOPROLOL TARTRATE 25 MG PO TABS,58045,12.143694976360772
+Medication,J7C,METOPROLOL SUCCINATE 25 MG PO TB24,46815,12.143694976360772
+Medication,J7C,METOPROLOL SUCCINATE 50 MG PO TB24,33636,12.143694976360772
+Medication,J7C,METOPROLOL TARTRATE 50 MG PO TABS,24930,12.143694976360772
+Pocedure,1316,"MAGNESIUM, SERUM/PLASMA",264849,11.712483644420685
+Lab result,XUCLAR,"Clarity, urine",220885,11.71059356890461
+Medication,Q5P,TRIAMCINOLONE ACETONIDE 0.1 % TP OINT,39692,11.489568950669836
+Medication,Q5P,TRIAMCINOLONE ACETONIDE 0.1 % TP CREA,35874,11.489568950669836
+Medication,Q5P,CLOBETASOL 0.05 % TP OINT,20720,11.489568950669836
+Medication,Q5P,HYDROCORTISONE 2.5 % TP CREA,18978,11.489568950669836
+Medication,Q5P,FLUOCINONIDE 0.05 % TP OINT,13942,11.489568950669836
+Medication,S7A,ROCURONIUM 10 MG/ML IV SOLN,127048,11.254069452209343
+Medication,S7A,SUCCINYLCHOLINE CHLORIDE 20 MG/ML INJ SOLN,45026,11.254069452209343
+Medication,S7A,ONABOTULINUMTOXINA 100 UNIT INJ SOLR,6013,11.254069452209343
+Medication,S7A,VECURONIUM BROMIDE 10 MG IV SOLR,5707,11.254069452209343
+Medication,S7A,CISATRACURIUM 2 MG/ML IV SOLN,2577,11.254069452209343
+Pocedure,1369,"METABOLIC PANEL, COMPREHENSIVE",810054,11.226334579333798
+Medication,W4E,METRONIDAZOLE 500 MG PO TABS,32878,10.828242809127868
+Medication,W4E,METRONIDAZOLE IN NACL (ISO-OS) 500 MG/100 ML IV PGBK,26423,10.828242809127868
+Medication,W4E,METRONIDAZOLE 250 MG PO TABS,4336,10.828242809127868
+Medication,W4E,FLAGYL 500 MG PO TABS,2383,10.828242809127868
+Medication,W4E,FLAGYL 250 MG PO TABS,688,10.828242809127868
+Pocedure,17587,OT ONGOING TREATMENT,89928,10.783168703477422
+Lab result,AST,"AST (SGOT), Ser/Plas",813935,10.73515381629962
+Lab result,AST,AST (Manual Entry) See EMR for details,111398,10.73515381629962
+Lab result,AST,AST,7194,10.73515381629962
+Lab result,NA,"Sodium, Ser/Plas",914528,10.56162258867142
+Lab result,NA,"Sodium, Whole Blood",117026,10.56162258867142
+Lab result,NA,Sodium (Manual Entry) See EMR for details,108406,10.56162258867142
+Lab result,NA,"Sodium, ISTAT",71442,10.56162258867142
+Lab result,NA,"Sodium, whole blood, ePOC",624,10.56162258867142
+Lab result,RDW,RDW,974552,10.523889629915512
+Lab result,RDW,RDW (MANUAL ENTRY) See EMR for details,4076,10.523889629915512
+Pocedure,1453,NURSING PULSE OXIMETRY,344721,10.23740814485334
+Pocedure,2261,URINALYSIS WITH MICROSCOPIC,358343,10.068334124007896
+Pocedure,2261,"URINALYSIS, COMPLETE",401,10.068334124007896
+Lab result,EOSAB,"EOS, ABS",570480,10.054158945195455
+Lab result,EOSAB,"Eosinophil, Absolute",386713,10.054158945195455
+Lab result,EOSAB,"Eosinophils, ABS (man diff)",39330,10.054158945195455
+Lab result,EOSAB,"Eos, ABS (man diff)",29133,10.054158945195455
+Medication,M9V,APIXABAN 5 MG PO TABS,17722,9.902816437318688
+Medication,M9V,ELIQUIS 5 MG PO TABS,15041,9.902816437318688
+Medication,M9V,XARELTO 20 MG PO TABS,12700,9.902816437318688
+Medication,M9V,RIVAROXABAN 20 MG PO TABS,9162,9.902816437318688
+Medication,M9V,APIXABAN 2.5 MG PO TABS,7443,9.902816437318688
+Pocedure,26702,NURSING COMMUNICATION(FOR ORDERSET ONLY),241691,9.833095063477604
+Pocedure,26702,NURSING COMMUNICATION,46176,9.833095063477604
+Lab result,ALB,"Albumin, Ser/Plas",800563,9.642897634844733
+Lab result,ALB,ALBUMIN BLD  (MANUAL ENTRY) See EMR for details,9434,9.642897634844733
+Lab result,ALB,Albumin,7018,9.642897634844733
+Lab result,ALB,"Albumin, Serum",50,9.642897634844733
+Diagnosis,SYM013,Shortness of breath,123781,9.544009868342403
+Diagnosis,SYM013,"Dyspnea, unspecified",36590,9.544009868342403
+Diagnosis,SYM013,Nasal congestion,34025,9.544009868342403
+Diagnosis,SYM013,SOB (shortness of breath),32192,9.544009868342403
+Diagnosis,SYM013,Hypoxemia,26795,9.544009868342403
+Diagnosis,DIG025,"Constipation, unspecified",53129,9.400352436794869
+Diagnosis,DIG025,"Constipation, unspecified constipation type",35681,9.400352436794869
+Diagnosis,DIG025,Unspecified constipation,28743,9.400352436794869
+Diagnosis,DIG025,Constipation,28074,9.400352436794869
+Diagnosis,DIG025,Other specified diseases of intestine,17246,9.400352436794869
+Medication,J5E,EPHEDRINE SULFATE 50 MG/ML INJ SOLN,112778,9.2832552912817
+Medication,J5E,PHENYLEPHRINE HCL 10 MG/ML INJ SOLN,107666,9.2832552912817
+Medication,J5E,PHENYLEPHRINE HCL IN 0.9% NACL 40 MG/250 ML (160 MCG/ML) IV SOLN,7776,9.2832552912817
+Medication,J5E,PHENYLEPHRINE IN 0.9% NACL(PF) 0.5 MG/5 ML (100 MCG/ML) IV SYRG,3399,9.2832552912817
+Medication,J5E,PHENYLEPHRINE HCL IN 0.9% NACL 1 MG/10 ML (100 MCG/ML) IV SYRG,2543,9.2832552912817
+Medication,M9P,ASPIRIN 81 MG PO TBEC,172290,9.212650312963644
+Medication,M9P,ASPIRIN 81 MG PO CHEW,80379,9.212650312963644
+Medication,M9P,CLOPIDOGREL 75 MG PO TABS,35825,9.212650312963644
+Medication,M9P,PLAVIX 75 MG PO TABS,14580,9.212650312963644
+Medication,M9P,ASPIRIN 81 MG PO TABS,14063,9.212650312963644
+Medication,Z9D,GADOBUTROL 1 MMOL/ML IV SOLUTION (RADIOLOGY) 7.5 ML,17358,8.868808467316324
+Medication,Z9D,GADOBUTROL 1 MMOL/ML IV SOLUTION (RADIOLOGY) 10 ML,11980,8.868808467316324
+Medication,Z9D,GLUCAGON (HUMAN RECOMBINANT) 1 MG/ML INJ SOLR,5282,8.868808467316324
+Medication,Z9D,GLUCAGON HCL 1 MG INJ SOLR,3180,8.868808467316324
+Medication,Z9D,ISOSULFAN BLUE 1 % SC SOLN,2912,8.868808467316324
+Lab result,LYMAB,"LYM, ABS",496919,8.780211944239687
+Lab result,LYMAB,"Lymphocyte, Absolute",386943,8.780211944239687
+Lab result,LYMAB,"Lymphocytes, ABS (man diff)",53200,8.780211944239687
+Lab result,LYMAB,"Lym, ABS (man diff)",43787,8.780211944239687
+Lab result,LYMAB,"Lymphocytes, Abs.",24480,8.780211944239687
+Pocedure,787,ECG 12-LEAD,436235,8.643221305403301
+Diagnosis,DIG022,"Noninfective gastroenteritis and colitis, unspecified",22303,8.410318523175443
+Diagnosis,DIG022,Other and unspecified noninfectious gastroenteritis and colitis(558.9),12883,8.410318523175443
+Diagnosis,DIG022,Gastroenteritis,7955,8.410318523175443
+Diagnosis,DIG022,Chronic diarrhea,6116,8.410318523175443
+Diagnosis,DIG022,Other specified noninfective gastroenteritis and colitis,4456,8.410318523175443
+Diagnosis,RSP009,"Unspecified asthma, uncomplicated",47317,8.300600805406981
+Diagnosis,RSP009,Unspecified asthma(493.90),35777,8.300600805406981
+Diagnosis,RSP009,Asthma,35345,8.300600805406981
+Diagnosis,RSP009,"Mild intermittent asthma, uncomplicated",22176,8.300600805406981
+Diagnosis,RSP009,Mild intermittent asthma without complication,10461,8.300600805406981
+Medication,C1W,LACTATED RINGERS IV SOLP,198859,8.0750503226238
+Medication,C1W,ELECTROLYTE-R (PH 7.4) IV SOLP,91300,8.0750503226238
+Medication,C1W,ELECTROLYTE-R IV SOLP,88933,8.0750503226238
+Medication,C1W,ARTIFICIAL SALIVA (YERBAS-LYT) MM SPRA,2212,8.0750503226238
+Medication,C1W,ELECTROLYTE-A IV SOLP,1140,8.0750503226238
+Medication,D6S,POLYETHYLENE GLYCOL 3350 17 GRAM PO PWPK,156512,8.032180560197942
+Medication,D6S,DOCUSATE SODIUM 100 MG PO CAPS,150635,8.032180560197942
+Medication,D6S,SENNOSIDES 8.6 MG PO TABS,145474,8.032180560197942
+Medication,D6S,DOCUSATE SODIUM 250 MG PO CAPS,108750,8.032180560197942
+Medication,D6S,MAGNESIUM HYDROXIDE 400 MG/5 ML PO SUSP,91861,8.032180560197942
+Lab result,GLUURN,"Glucose, urine",220885,7.906078872673227
+Lab result,GLUURN,Glucose Urine,99481,7.906078872673227
+Lab result,GLUURN,"Glucose, Urine POC",14235,7.906078872673227
+Lab result,GLUURN,"Glucose, Urine",359,7.906078872673227
+Lab result,GLUURN,Urine Glucose,328,7.906078872673227
+Lab result,NRBCPERCENT,"nRBC, %",228738,7.826097458599933
+Lab result,NRBCABSOLUTE,"nRBC, Abs",228741,7.812520275424106
+Medication,H0A,LIDOCAINE (PF) 10 MG/ML (1 %) INJ SOLN,176397,7.643157939173384
+Medication,H0A,LIDOCAINE (PF) 20 MG/ML (2 %) INJ SOLN,132838,7.643157939173384
+Medication,H0A,LIDOCAINE HCL 10 MG/ML (1 %) INJ SOLN,57440,7.643157939173384
+Medication,H0A,"LIDOCAINE-EPINEPHRINE 1 %-1:100,000 INJ SOLN",46048,7.643157939173384
+Medication,H0A,BENZOCAINE-MENTHOL 15-3.6 MG MM LOZG,37590,7.643157939173384
+Medication,Q7P,FLUTICASONE 50 MCG/ACTUATION NASAL SPSN,81341,7.570648503328821
+Medication,Q7P,FLUTICASONE PROPIONATE 50 MCG/ACTUATION NASAL SPSN,35113,7.570648503328821
+Medication,Q7P,FLONASE 50 MCG/ACTUATION NASAL SPSN,15207,7.570648503328821
+Medication,Q7P,MOMETASONE 50 MCG/ACTUATION NASAL SPRY,12571,7.570648503328821
+Medication,Q7P,FLONASE NASAL,12214,7.570648503328821
+Lab result,XPTT,Part. Thromboplastin Time,120024,7.404946197487897
+Pocedure,958,FULL CODE,223542,7.404169142958365
+Diagnosis,NEO074,Neoplasm related pain (acute) (chronic),10444,7.172854124291678
+Diagnosis,NEO074,Cancer related pain,3738,7.172854124291678
+Diagnosis,NEO074,Malignant pleural effusion,3239,7.172854124291678
+Diagnosis,NEO074,Malignant ascites,2529,7.172854124291678
+Diagnosis,NEO074,Neoplastic (malignant) related fatigue,1762,7.172854124291678
+Medication,A6U,IOPAMIDOL 76 % IV SOLN (RADIOLOGY) 100 ML,33469,6.867544208621121
+Medication,A6U,IOPAMIDOL 76 % IV SOLN (RADIOLOGY) 500 ML,25229,6.867544208621121
+Medication,A6U,IOPAMIDOL 61 % IV SOLN (RADIOLOGY) 150 ML,16352,6.867544208621121
+Medication,A6U,IOPAMIDOL 61 % IV SOLN (RADIOLOGY) 100 ML,15215,6.867544208621121
+Medication,A6U,IOPAMIDOL 76 % IV SOLN (RADIOLOGY) 150 ML,14381,6.867544208621121
+Diagnosis,DIG004,Gastro-esophageal reflux disease without esophagitis,107514,6.828684038489969
+Diagnosis,DIG004,Esophageal reflux,59420,6.828684038489969
+Diagnosis,DIG004,GERD (gastroesophageal reflux disease),49864,6.828684038489969
+Diagnosis,DIG004,"Gastroesophageal reflux disease, esophagitis presence not specified",40948,6.828684038489969
+Diagnosis,DIG004,Gastroesophageal reflux disease without esophagitis,23961,6.828684038489969
+Lab result,XSQEP,Squamous Cells,158621,6.5916057944824304
+Diagnosis,SYM014,Anesthesia of skin,28282,6.529845449095936
+Diagnosis,SYM014,Paresthesia of skin,27225,6.529845449095936
+Diagnosis,SYM014,Disturbance of skin sensation,25084,6.529845449095936
+Diagnosis,SYM014,"Localized swelling, mass and lump, neck",11108,6.529845449095936
+Diagnosis,SYM014,"Localized swelling, mass and lump, head",10788,6.529845449095936
+Medication,H4B,GABAPENTIN 300 MG PO CAPS,114317,6.413566583831745
+Medication,H4B,GABAPENTIN 100 MG PO CAPS,59419,6.413566583831745
+Medication,H4B,GABAPENTIN 600 MG PO TABS,23439,6.413566583831745
+Medication,H4B,LEVETIRACETAM 500 MG PO TABS,13198,6.413566583831745
+Medication,H4B,TOPIRAMATE 25 MG PO TABS,9580,6.413566583831745
+Medication,M9K,ENOXAPARIN 40 MG/0.4 ML SC SYRG,83526,6.250238001790113
+Medication,M9K,"HEPARIN, PORCINE (PF) 5,000 UNIT/0.5 ML INJ SOLN",65177,6.250238001790113
+Medication,M9K,"HEPARIN, PORCINE (PF) 100 UNIT/ML IV SYRG",38558,6.250238001790113
+Medication,M9K,"HEPARIN (PORCINE) 1,000 UNIT/ML INJ SOLN",33588,6.250238001790113
+Medication,M9K,ENOXAPARIN 30 MG/0.3 ML SC SYRG,19047,6.250238001790113
+Medication,H3H,FENTANYL (PF) 50 MCG/ML INJECTION,226038,6.035362671701175
+Medication,H3H,FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN (WRAPPER RECORD),123921,6.035362671701175
+Medication,H3H,FENTANYL CITRATE (PF) 50 MCG/ML INJ SOLN,69918,6.035362671701175
+Medication,H3H,REMIFENTANIL 2 MG IV SOLR,39830,6.035362671701175
+Medication,H3H,ALFENTANIL 500 MCG/ML INJ SOLN,15149,6.035362671701175
+Medication,Q5H,LIDOCAINE 4 % TP CREA,50103,5.990498883372523
+Medication,Q5H,LIDOCAINE 5 % TP PTMD,44421,5.990498883372523
+Medication,Q5H,LIDOCAINE-PRILOCAINE 2.5-2.5 % TP CREA,15900,5.990498883372523
+Medication,Q5H,BUTAMBEN-TETRACAINE-BENZOCAINE 2 %-2 %-14 % (200 MG/SEC) TP SPRA,13857,5.990498883372523
+Medication,Q5H,LIDOCAINE 5 %(700 MG/PATCH) TP PTMD,13442,5.990498883372523
+Medication,D0U,"BARIUM SULFATE 40 % (W/V), 30% (W/W) PO PSTE",5976,5.974397462829918
+Medication,D0U,BARIUM SULFATE 40 % (W/V) PO SUSP,5375,5.974397462829918
+Medication,D0U,"BARIUM SULFATE 2.1 % (W/V), 2.0 % (W/W) PO SUSP",5056,5.974397462829918
+Medication,D0U,BARIUM SULFATE 60 % (W/V) PO SUSP,4448,5.974397462829918
+Medication,D0U,BARIUM SULFATE 40 % (W/V) PO POWD,4340,5.974397462829918
+Lab result,PT,Prothrombin Time,337139,5.144048768702676
+Lab result,PT,"PT, ISTAT",53521,5.144048768702676
+Lab result,PT,Prothrombin Time (Manual Entry) See EMR for details,14406,5.144048768702676
+Lab result,PT,PT seconds,3497,5.144048768702676
+Lab result,PT,PT (Manual),362,5.144048768702676
+Lab result,SARSCOV2TYPE,SPECIMEN TYPE,343807,5.089951524570624
+Pocedure,685,DIET CLEAR LIQUID,83711,4.870520245249891
+Diagnosis,SYM010,Memory loss,16174,4.857893781804293
+Diagnosis,SYM010,"Altered mental status, unspecified",16028,4.857893781804293
+Diagnosis,SYM010,Altered mental status,14907,4.857893781804293
+Diagnosis,SYM010,Other amnesia,14327,4.857893781804293
+Diagnosis,SYM010,Other abnormalities of gait and mobility,12876,4.857893781804293
+Pocedure,704,DIET NUTRITIONAL SUPPLEMENTS,47646,4.830562608405096
+Diagnosis,SYM002,"Fever, unspecified",105783,4.826974919220511
+Diagnosis,SYM002,"Fever, unspecified fever cause",28059,4.826974919220511
+Diagnosis,SYM002,Fever,28036,4.826974919220511
+Diagnosis,SYM002,Fever presenting with conditions classified elsewhere,13846,4.826974919220511
+Diagnosis,SYM002,Acute febrile illness in child,3478,4.826974919220511
+Medication,J2B,GLYCOPYRROLATE 0.2 MG/ML INJ SOLN,95531,4.746128609385658
+Medication,J2B,GLYCOPYRROLATE 1 MG PO TABS,1743,4.746128609385658
+Medication,J2B,CHLORDIAZEPOXIDE-CLIDINIUM 5-2.5 MG PO CAPS,299,4.746128609385658
+Medication,J2B,GLYCOPYRROLATE 2 MG PO TABS,230,4.746128609385658
+Medication,J2B,LIBRAX (WITH CLIDINIUM) 5-2.5 MG PO CAPS,193,4.746128609385658
+Medication,C3B,FERROUS SULFATE 325 MG (65 MG IRON) PO TABS,50472,4.4973476749558055
+Medication,C3B,IRON PO,18055,4.4973476749558055
+Medication,C3B,IRON (FERROUS SULFATE) PO,7535,4.4973476749558055
+Medication,C3B,FERROUS SULFATE 324 MG (65 MG IRON) PO TBEC,6267,4.4973476749558055
+Medication,C3B,FERROUS SULFATE PO,3927,4.4973476749558055
+Medication,D4N,SIMETHICONE 40 MG/0.6 ML PO DRPS,35538,4.4550075238576285
+Medication,D4N,SIMETHICONE 80 MG PO CHEW,24677,4.4550075238576285
+Medication,D4N,GAS-X PO,1078,4.4550075238576285
+Medication,D4N,SIMETHICONE PO,441,4.4550075238576285
+Medication,D4N,SIMETHICONE 125 MG PO CAPS,410,4.4550075238576285
+Medication,H2Z,FLUMAZENIL 0.1 MG/ML IV SOLN,89553,4.446666129792446
+Medication,H2Z,FLUMAZENIL IV,1,4.446666129792446
+Medication,Q9B,TAMSULOSIN 0.4 MG PO CP24,36234,4.360894172341035
+Medication,Q9B,TAMSULOSIN 0.4 MG PO CAPS,33918,4.360894172341035
+Medication,Q9B,FINASTERIDE 5 MG PO TABS,21418,4.360894172341035
+Medication,Q9B,FLOMAX 0.4 MG PO CP24,8656,4.360894172341035
+Medication,Q9B,FLOMAX PO,3813,4.360894172341035
+Medication,A4A,HYDRALAZINE 20 MG/ML INJ SOLN,99749,4.235028667217624
+Medication,A4A,HYDRALAZINE 25 MG PO TABS,16048,4.235028667217624
+Medication,A4A,HYDRALAZINE 10 MG PO TABS,10341,4.235028667217624
+Medication,A4A,HYDRALAZINE 50 MG PO TABS,8724,4.235028667217624
+Medication,A4A,HYDRALAZINE 20 MG/ML INJ SOLN (24 HOUR STOP TIME),4843,4.235028667217624
+Medication,Z2P,PROMETHAZINE 25 MG/ML INJ SOLN,138980,4.147393454992713
+Medication,Z2P,DIPHENHYDRAMINE HCL 50 MG/ML INJ SOLN,105573,4.147393454992713
+Medication,Z2P,DIPHENHYDRAMINE HCL 25 MG PO CAPS,89838,4.147393454992713
+Medication,Z2P,HYDROXYZINE HCL 25 MG PO TABS,37836,4.147393454992713
+Medication,Z2P,HYDROXYZINE HCL 50 MG/ML IM SOLN,23308,4.147393454992713
+Pocedure,2220,TSH,430471,4.0851583865042365
+Lab result,UCMT,Urine Comment,215378,4.048922739143588
+Lab result,UCMT,Urine comment,176522,4.048922739143588
+Medication,W1Q,CIPROFLOXACIN HCL 500 MG PO TABS,48509,3.880650807876415
+Medication,W1Q,CIPROFLOXACIN 500 MG PO TABS,30962,3.880650807876415
+Medication,W1Q,LEVOFLOXACIN 500 MG PO TABS,28370,3.880650807876415
+Medication,W1Q,LEVOFLOXACIN 750 MG PO TABS,23663,3.880650807876415
+Medication,W1Q,CIPROFLOXACIN IN D5W 400 MG/200 ML IV PGBK,12871,3.880650807876415
+Medication,C1B,SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP,211392,3.8613182003836535
+Medication,C1B,SODIUM CHLORIDE 0.9 % IV SOLP,134406,3.8613182003836535
+Medication,C1B,SODIUM CHLORIDE 0.9 % (FLUSH) INJ SYRG,49739,3.8613182003836535
+Medication,C1B,SODIUM CHLORIDE 0.9 % INJ SYRG,16514,3.8613182003836535
+Medication,C1B,SODIUM CHLORIDE 0.9 % INJ SOLN,13936,3.8613182003836535
+Pocedure,464825,COMPREHENSIVE METABOLIC PANEL W GFR,226620,3.725651930039488
+Pocedure,464825,LIPID PROFILE,163145,3.725651930039488
+Pocedure,464825,TSH,159416,3.725651930039488
+Pocedure,464825,CBC with Differential,119137,3.725651930039488
+Pocedure,464825,BASIC METABOLIC PANEL W GFR,118526,3.725651930039488
+Diagnosis,SKN005,"Contact dermatitis and other eczema, due to unspecified cause",34510,3.688230820740483
+Diagnosis,SKN005,"Unspecified contact dermatitis, unspecified cause",5157,3.688230820740483
+Diagnosis,SKN005,Other skin changes due to chronic exposure to nonionizing radiation,4476,3.688230820740483
+Diagnosis,SKN005,Contact dermatitis,4303,3.688230820740483
+Diagnosis,SKN005,"Contact dermatitis, unspecified contact dermatitis type, unspecified trigger",3637,3.688230820740483
+Diagnosis,FAC021,Personal history of nicotine dependence,72828,3.685944338399306
+Diagnosis,FAC021,Family history of ischemic heart disease and other diseases of the circulatory system,40831,3.685944338399306
+Diagnosis,FAC021,"Personal history of tobacco use, presenting hazards to health",38991,3.685944338399306
+Diagnosis,FAC021,Family history of malignant neoplasm of breast,33280,3.685944338399306
+Diagnosis,FAC021,Personal history of colonic polyps,30779,3.685944338399306
+Medication,Z2Q,LORATADINE 10 MG PO TABS,37560,3.6798413818226297
+Medication,Z2Q,CETIRIZINE 10 MG PO TABS,26560,3.6798413818226297
+Medication,Z2Q,ZYRTEC 10 MG PO TABS,20412,3.6798413818226297
+Medication,Z2Q,CLARITIN PO,15566,3.6798413818226297
+Medication,Z2Q,ZYRTEC PO,15482,3.6798413818226297
+Medication,Q5F,KETOCONAZOLE 2 % TP CREA,18676,3.6490096260754257
+Medication,Q5F,KETOCONAZOLE 2 % TP SHAM,16161,3.6490096260754257
+Medication,Q5F,CLOTRIMAZOLE 1 % TP CREA,12803,3.6490096260754257
+Medication,Q5F,"NYSTATIN 100,000 UNIT/GRAM TP POWD",10265,3.6490096260754257
+Medication,Q5F,ECONAZOLE 1 % TP CREA,10216,3.6490096260754257
+Pocedure,475,CBC WITH DIFFERENTIAL,732522,3.6296261268968952
+Pocedure,475,CBC WITH DIFF,381186,3.6296261268968952
+Diagnosis,SKN001,Cellulitis,12342,3.4643882667302384
+Diagnosis,SKN001,"Local infection of the skin and subcutaneous tissue, unspecified",9178,3.4643882667302384
+Diagnosis,SKN001,"Cellulitis, unspecified",7708,3.4643882667302384
+Diagnosis,SKN001,Impetigo,7483,3.4643882667302384
+Diagnosis,SKN001,"Cellulitis, unspecified cellulitis site",6505,3.4643882667302384
+Medication,W1W,CEFAZOLIN 1 GRAM INJ SOLR,136498,3.413563308789401
+Medication,W1W,CEPHALEXIN 500 MG PO CAPS,91544,3.413563308789401
+Medication,W1W,CEFAZOLIN IN DEXTROSE (ISO-OS) 1 GRAM/50 ML IV PGBK,79918,3.413563308789401
+Medication,W1W,CEFAZOLIN IN DEXTROSE (ISO-OS) 2 GRAM/100 ML IV PGBK,56755,3.413563308789401
+Medication,W1W,CEFAZOLIN IV PUSH (1 GRAM VIAL ONLY),22796,3.413563308789401
+Pocedure,880100004,SIMPLE AIRWAY,149218,3.3142976303923866
+Pocedure,880100004,Simple Airway,57279,3.3142976303923866
+Pocedure,880100004,x,1,3.3142976303923866
+Pocedure,880100004,Simple airway,1,3.3142976303923866
+Pocedure,880100004,Complex Airway,4,3.3142976303923866
+Diagnosis,END003,Type 2 diabetes mellitus with hyperglycemia,24117,3.2050975393332157
+Diagnosis,END003,Type 2 diabetes mellitus with diabetic chronic kidney disease,16238,3.2050975393332157
+Diagnosis,END003,Type 2 diabetes mellitus with unspecified complications,14690,3.2050975393332157
+Diagnosis,END003,Type 2 diabetes mellitus with other specified complication,9956,3.2050975393332157
+Diagnosis,END003,Type 2 diabetes mellitus with diabetic nephropathy,6389,3.2050975393332157
+Lab result,XUPROT,"Protein, urine",220884,3.151154442400012
+Diagnosis,END002,Type 2 diabetes mellitus without complications,71005,2.970679901175976
+Diagnosis,END002,"Type II or unspecified type diabetes mellitus without mention of complication, not stated as uncontrolled",50212,2.970679901175976
+Diagnosis,END002,"Type 2 diabetes mellitus without complication, without long-term current use of insulin (CMS-HCC)",21953,2.970679901175976
+Diagnosis,END002,Diabetes mellitus (CMS-HCC),17960,2.970679901175976
+Diagnosis,END002,Type 2 diabetes mellitus without complication (CMS-HCC),14230,2.970679901175976
+Diagnosis,END009,"Obesity, unspecified",80077,2.9307334328958032
+Diagnosis,END009,Morbid (severe) obesity due to excess calories,28498,2.9307334328958032
+Diagnosis,END009,Morbid obesity (CMS-HCC),25684,2.9307334328958032
+Diagnosis,END009,Obesity,25169,2.9307334328958032
+Diagnosis,END009,Obesity (BMI 30-39.9),11212,2.9307334328958032
+Diagnosis,MBD013,Deferred diagnosis on axis I,19301,2.898910502229577
+Diagnosis,MBD013,Other persistent mental disorders due to conditions classified elsewhere,2663,2.898910502229577
+Diagnosis,MBD013,Unspecified nonpsychotic mental disorder,1380,2.898910502229577
+Diagnosis,MBD013,Transsexualism,1165,2.898910502229577
+Diagnosis,MBD013,Other mental disorders complicating the puerperium,1008,2.898910502229577
+Lab result,PCCOM,POCT Comment,313589,2.841621236298057
+Diagnosis,RSP007,Other specified disorders of nose and nasal sinuses,30461,2.826110068880993
+Diagnosis,RSP007,Other diseases of nasal cavity and sinuses(478.19),27513,2.826110068880993
+Diagnosis,RSP007,Deviated nasal septum,25587,2.826110068880993
+Diagnosis,RSP007,Hypertrophy of nasal turbinates,18305,2.826110068880993
+Diagnosis,RSP007,Chronic rhinitis,16384,2.826110068880993
+Diagnosis,RSP002,"Pneumonia, organism unspecified(486)",26170,2.7597484762297024
+Diagnosis,RSP002,"Pneumonia, unspecified organism",24545,2.7597484762297024
+Diagnosis,RSP002,Pneumonia due to organism,9503,2.7597484762297024
+Diagnosis,RSP002,Pneumonia,8150,2.7597484762297024
+Diagnosis,RSP002,"Lobar pneumonia, unspecified organism",5214,2.7597484762297024
+Pocedure,1717,PT EVALUATE AND TREAT,148648,2.757113740600301
+Medication,H7E,TRAZODONE 50 MG PO TABS,64606,2.3301274436106403
+Medication,H7E,TRAZODONE 100 MG PO TABS,11218,2.3301274436106403
+Medication,H7E,TRAZODONE PO,5211,2.3301274436106403
+Medication,H7E,TRAZODONE 150 MG PO TABS,2456,2.3301274436106403
+Medication,H7E,TRAZODONE 300 MG PO TABS,307,2.3301274436106403
+Pocedure,1423,OOB/UP IN CHAIR WITH ASSIST,59675,2.272081521223585
+Lab result,XUCOL,"Color, urine",220889,2.158478516320618
+Medication,P5A,DEXAMETHASONE SODIUM PHOSPHATE 4 MG/ML INJ SOLN,176739,2.154739877533957
+Medication,P5A,PREDNISONE 20 MG PO TABS,59878,2.154739877533957
+Medication,P5A,PREDNISONE 10 MG PO TABS,47737,2.154739877533957
+Medication,P5A,METHYLPREDNISOLONE SOD SUC(PF) 125 MG/2 ML INJ SOLR,33330,2.154739877533957
+Medication,P5A,DEXAMETHASONE 4 MG PO TABS,32883,2.154739877533957
+Pocedure,100973,OXYGEN: NASAL CANNULA(NURSING ONLY),186937,2.0736803547409832
+Lab result,XUPH,"pH, urine",220884,1.9529547906646056
+Medication,D6D,LOPERAMIDE 2 MG PO CAPS,20419,1.747718735130277
+Medication,D6D,DIPHENOXYLATE-ATROPINE 2.5-0.025 MG PO TABS,18317,1.747718735130277
+Medication,D6D,LOPERAMIDE 2 MG PO TABS,3963,1.747718735130277
+Medication,D6D,IMODIUM PO,1908,1.747718735130277
+Medication,D6D,LOMOTIL 2.5-0.025 MG PO TABS,1456,1.747718735130277
+Diagnosis,END001,"Hypothyroidism, unspecified",73510,1.7411293225067257
+Diagnosis,END001,Hypothyroidism,48956,1.7411293225067257
+Diagnosis,END001,Unspecified hypothyroidism,45904,1.7411293225067257
+Diagnosis,END001,"Hypothyroidism, unspecified type",42272,1.7411293225067257
+Diagnosis,END001,Nontoxic single thyroid nodule,18791,1.7411293225067257
+Diagnosis,CIR008,"Hypertensive chronic kidney disease with stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease",23383,1.7257023536423293
+Diagnosis,CIR008,"Unspecified hypertensive kidney disease with chronic kidney disease stage I through stage IV, or unspecified(403.90)",13029,1.7257023536423293
+Diagnosis,CIR008,Hypertensive heart disease with heart failure,12405,1.7257023536423293
+Diagnosis,CIR008,"Hypertensive heart and chronic kidney disease with heart failure and stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease",10640,1.7257023536423293
+Diagnosis,CIR008,"Secondary hypertension, unspecified",6725,1.7257023536423293
+Lab result,INR,INR,337518,1.6652990549615476
+Lab result,INR,"INR, ISTAT",53851,1.6652990549615476
+Lab result,INR,INR (Manual Entry) See EMR for details,17721,1.6652990549615476
+Lab result,INR,"INR, Fingerstick {Menlo}",4024,1.6652990549615476
+Lab result,INR,External INR,2084,1.6652990549615476
+Medication,Q5W,MUPIROCIN 2 % TP OINT,32942,1.5669394209273408
+Medication,Q5W,BACITRACIN ZINC 500 UNIT/GRAM TP OINT,20921,1.5669394209273408
+Medication,Q5W,CLINDAMYCIN PHOSPHATE 1 % TP LOTN,14643,1.5669394209273408
+Medication,Q5W,BACITRACIN ZINC 500 UNIT/GRAM TP PACK,10718,1.5669394209273408
+Medication,Q5W,MUPIROCIN CALCIUM 2 % TP CREA,7777,1.5669394209273408
+Pocedure,412485,NURSING COMMUNICATION,214880,1.529983917616496
+Diagnosis,CIR026,"Peripheral vascular disease, unspecified",14601,1.5277671512590143
+Diagnosis,CIR026,"Peripheral vascular disease, unspecified (CMS-HCC)",9061,1.5277671512590143
+Diagnosis,CIR026,Atherosclerosis of aorta (CMS-HCC),7802,1.5277671512590143
+Diagnosis,CIR026,Atherosclerosis of aorta,7693,1.5277671512590143
+Diagnosis,CIR026,PVD (peripheral vascular disease) (CMS-HCC),7201,1.5277671512590143
+Lab result,XUBACT,"Bacteria, urine",218658,1.474152033735753
+Pocedure,659308,CBC WITH DIFFERENTIAL,427099,1.4676541183202478
+Lab result,XSPG,"Specific Gravity, urine",221483,1.383822056951889
+Diagnosis,INF008,"Viral infection, unspecified",23913,1.3450462342655518
+Diagnosis,INF008,Other viral agents as the cause of diseases classified elsewhere,21635,1.3450462342655518
+Diagnosis,INF008,"Unspecified viral infection, in conditions classified elsewhere and of unspecified site",16255,1.3450462342655518
+Diagnosis,INF008,Viral syndrome,14784,1.3450462342655518
+Diagnosis,INF008,"Viral wart, unspecified",9905,1.3450462342655518
+Medication,C5J,DEXTROSE 50 % IN WATER (D50W) IV SYRG,141338,1.3057174827454516
+Medication,C5J,DEXTROSE 5 % IN WATER (D5W) IV SOLP,24303,1.3057174827454516
+Medication,C5J,DEXTROSE 50% IN WATER (D50W) IV SYRG,21478,1.3057174827454516
+Medication,C5J,DEXTROSE 50 % IN WATER (D50W) IV SOLP,11399,1.3057174827454516
+Medication,C5J,DEXTROSE 10 % IN WATER (D10W) 10 % IV SOLP,5329,1.3057174827454516
+Lab result,CA,"Calcium, Ser/Plas",896056,1.302804784134666
+Lab result,CA,"Calcium, Serum (Manual Entry) See EMR for details",91151,1.302804784134666
+Lab result,CA,Calcium,88,1.302804784134666
+Lab result,CA,"Calcium, Serum",25,1.302804784134666
+Diagnosis,FAC020,Tobacco use,11379,1.201843444557289
+Diagnosis,FAC020,Other problems related to lifestyle,4598,1.201843444557289
+Diagnosis,FAC020,Tobacco abuse,4255,1.201843444557289
+Diagnosis,FAC020,"Other psychological or physical stress, not elsewhere classified(V62.89)",1980,1.201843444557289
+Diagnosis,FAC020,High risk heterosexual behavior,1953,1.201843444557289
+Medication,C6C,VITAMIN C PO,35899,1.201606537214727
+Medication,C6C,ASCORBIC ACID (VITAMIN C) 500 MG PO TABS,16319,1.201606537214727
+Medication,C6C,ASCORBIC ACID 500 MG PO TABS,11741,1.201606537214727
+Medication,C6C,"ASCORBIC ACID (VITAMIN C) 1,000 MG PO TABS",4980,1.201606537214727
+Medication,C6C,ASCORBIC ACID 250 MG PO TABS,4047,1.201606537214727
+Lab result,HGB,Hemoglobin,982722,1.1930895640301116
+Lab result,HGB,Hemoglobin (Manual Entry) See EMR for details,114709,1.1930895640301116
+Lab result,HGB,"Hgb(Calc), ISTAT",71388,1.1930895640301116
+Lab result,HGB,HgB,46893,1.1930895640301116
+Lab result,HGB,HGB (Manual Entry) See EMR for details,3202,1.1930895640301116
+Medication,A4D,LISINOPRIL 10 MG PO TABS,52643,1.1429396203582545
+Medication,A4D,LISINOPRIL 20 MG PO TABS,41409,1.1429396203582545
+Medication,A4D,LISINOPRIL 5 MG PO TABS,32374,1.1429396203582545
+Medication,A4D,LISINOPRIL 40 MG PO TABS,20829,1.1429396203582545
+Medication,A4D,LISINOPRIL 2.5 MG PO TABS,16168,1.1429396203582545
+Lab result,XNITRITE,"Nitrite, urine",220900,1.140520110262674
+Medication,A4F,LOSARTAN 50 MG PO TABS,45424,1.069638172051721
+Medication,A4F,LOSARTAN 25 MG PO TABS,32915,1.069638172051721
+Medication,A4F,LOSARTAN 100 MG PO TABS,27248,1.069638172051721
+Medication,A4F,VALSARTAN 160 MG PO TABS,8855,1.069638172051721
+Medication,A4F,VALSARTAN 80 MG PO TABS,8512,1.069638172051721
+Medication,P6A,MELATONIN 3 MG PO TABS,48005,1.0651728255319588
+Medication,P6A,MELATONIN 5 MG PO TABS,36250,1.0651728255319588
+Medication,P6A,MELATONIN 3 MG PO TBDL,23220,1.0651728255319588
+Medication,P6A,MELATONIN PO,16022,1.0651728255319588
+Medication,P6A,MELATONIN 1 MG PO TABS,5067,1.0651728255319588
+Medication,P3A,LEVOTHYROXINE 50 MCG PO TABS,33238,1.0527888530932017
+Medication,P3A,LEVOTHYROXINE 75 MCG PO TABS,26404,1.0527888530932017
+Medication,P3A,LEVOTHYROXINE 25 MCG PO TABS,25685,1.0527888530932017
+Medication,P3A,LEVOTHYROXINE 100 MCG PO TABS,24784,1.0527888530932017
+Medication,P3A,LEVOTHYROXINE 125 MCG PO TABS,14043,1.0527888530932017
+Diagnosis,MBD005,"Anxiety disorder, unspecified",72665,1.017070791004424
+Diagnosis,MBD005,Anxiety,54252,1.017070791004424
+Diagnosis,MBD005,Generalized anxiety disorder,44981,1.017070791004424
+Diagnosis,MBD005,"Anxiety state, unspecified",40819,1.017070791004424
+Diagnosis,MBD005,Anxiety state,31720,1.017070791004424
+Pocedure,1428,MONITOR INTAKE AND OUTPUT,205407,0.8610086813176581
+Medication,D4B,ALUM-MAG HYDROXIDE-SIMETH 200-200-20 MG/5 ML PO SUSP,55999,0.7887917565052549
+Medication,D4B,"CALCIUM CARBONATE 400 MG CALCIUM (1,000 MG) PO CHEW",20612,0.7887917565052549
+Medication,D4B,ALUM-MAG HYDROXIDE-SIMETH 400-400-40 MG/5 ML PO SUSP,17615,0.7887917565052549
+Medication,D4B,CALCIUM CARBONATE 200 MG CALCIUM (500 MG) PO CHEW,10715,0.7887917565052549
+Medication,D4B,"CALCIUM CARBONATE 400 MG (1,000 MG) PO CHEW",8543,0.7887917565052549
+Diagnosis,SYM008,Restlessness and agitation,5103,0.7144399074538474
+Diagnosis,SYM008,Other symptoms and signs involving appearance and behavior,3342,0.7144399074538474
+Diagnosis,SYM008,Other symptoms and signs involving emotional state,2707,0.7144399074538474
+Diagnosis,SYM008,Depressed mood,2057,0.7144399074538474
+Diagnosis,SYM008,Agitation,1886,0.7144399074538474
+Lab result,SARSCOV2SRC,SPECIMEN SOURCE,379098,0.7049427644644676
+Diagnosis,INJ030,Drug-induced polyneuropathy,2557,0.6767794981360808
+Diagnosis,INJ030,Medication overuse headache,2139,0.6767794981360808
+Diagnosis,INJ030,"Drug-induced headache, not elsewhere classified, not intractable",2119,0.6767794981360808
+Diagnosis,INJ030,Polyneuropathy due to other toxic agents,802,0.6767794981360808
+Diagnosis,INJ030,Toxic erythema,430,0.6767794981360808
+Lab result,HCT,Hematocrit,988518,0.6326959459299044
+Lab result,HCT,Hct (Est),99973,0.6326959459299044
+Lab result,HCT,Hematocrit (Manual Entry) See EMR for details,96497,0.6326959459299044
+Lab result,HCT,"Hct, ISTAT",44756,0.6326959459299044
+Lab result,HCT,"Hct(Calc), ISTAT",30262,0.6326959459299044
+Diagnosis,SKN002,Acne vulgaris,23164,0.5846744865353609
+Diagnosis,SKN002,"Pruritus, unspecified",21667,0.5846744865353609
+Diagnosis,SKN002,"Seborrheic dermatitis, unspecified",19108,0.5846744865353609
+Diagnosis,SKN002,Other acne,17180,0.5846744865353609
+Diagnosis,SKN002,Seborrheic dermatitis,16851,0.5846744865353609
+Medication,J9A,METOCLOPRAMIDE HCL 5 MG/ML INJ SOLN,147388,0.5135354792488278
+Medication,J9A,METOCLOPRAMIDE 5 MG/ML INJ SOLN,56656,0.5135354792488278
+Medication,J9A,METOCLOPRAMIDE HCL 10 MG PO TABS,26618,0.5135354792488278
+Medication,J9A,METOCLOPRAMIDE HCL 5 MG/ML INJ SYRG,8177,0.5135354792488278
+Medication,J9A,METOCLOPRAMIDE HCL 5 MG PO TABS,6275,0.5135354792488278
+Diagnosis,FAC025,Other long term (current) drug therapy,172660,0.5132878064502417
+Diagnosis,FAC025,Long term (current) use of aspirin,57994,0.5132878064502417
+Diagnosis,FAC025,Encounter for long-term (current) use of other medications,48742,0.5132878064502417
+Diagnosis,FAC025,Long term (current) use of anticoagulants,44698,0.5132878064502417
+Diagnosis,FAC025,Other specified health status,35705,0.5132878064502417
+Lab result,NEUTAB,"NEUT, ABS",522238,0.5026627267564442
+Lab result,NEUTAB,"Neutrophil, Absolute",384165,0.5026627267564442
+Lab result,NEUTAB,"Neut, ABS (man diff)",41867,0.5026627267564442
+Lab result,NEUTAB,"Neut, ABS (Seg+Band) (man diff)",31195,0.5026627267564442
+Lab result,NEUTAB,"NEUTS, ABS",5167,0.5026627267564442
+Diagnosis,FAC011,Other sex counseling,270,0.5002506666814001
+Diagnosis,FAC011,Counseling on other sexually transmitted diseases,252,0.5002506666814001
+Diagnosis,FAC011,Sex counseling,167,0.5002506666814001
+Diagnosis,FAC011,Sexually transmitted disease counseling,110,0.5002506666814001
+Diagnosis,FAC011,"Sex counseling, unspecified",70,0.5002506666814001
+Diagnosis,INJ028,"Adverse effect of antineoplastic and immunosuppressive drugs, initial encounter",11684,0.4926994351854712
+Diagnosis,INJ028,Antineoplastic and immunosuppressive drugs causing adverse effect in therapeutic use,5618,0.4926994351854712
+Diagnosis,INJ028,"Adverse effect of glucocorticoids and synthetic analogues, initial encounter",4725,0.4926994351854712
+Diagnosis,INJ028,"Adverse effect of unspecified drugs, medicaments and biological substances, initial encounter",4023,0.4926994351854712
+Diagnosis,INJ028,"Adverse effect of other opioids, initial encounter",3434,0.4926994351854712
+Lab result,MCHC,MCHC,974473,0.4665915186543824
+Diagnosis,FAC019,Homelessness,4175,0.4538071625875196
+Diagnosis,FAC019,Other specified problems related to psychosocial circumstances,2740,0.4538071625875196
+Diagnosis,FAC019,Other physical and mental strain related to work,2394,0.4538071625875196
+Diagnosis,FAC019,Stress at work,1676,0.4538071625875196
+Diagnosis,FAC019,Problems related to unwanted pregnancy,1628,0.4538071625875196
+Diagnosis,MBD026,"Alcohol abuse, in remission",3180,0.4509680754955971
+Diagnosis,MBD026,"Alcohol dependence, in remission",2755,0.4509680754955971
+Diagnosis,MBD026,"Major depressive disorder, single episode, in full remission",2325,0.4509680754955971
+Diagnosis,MBD026,"Major depressive disorder, recurrent, in full remission",1877,0.4509680754955971
+Diagnosis,MBD026,"Major depressive disorder, recurrent, in remission, unspecified",1165,0.4509680754955971
+Diagnosis,END016,Overweight,23032,0.425161415561544
+Diagnosis,END016,Hypercalcemia,10935,0.425161415561544
+Diagnosis,END016,Hypomagnesemia,9670,0.425161415561544
+Diagnosis,END016,Overweight (BMI 25.0-29.9),9390,0.425161415561544
+Diagnosis,END016,Hypocalcemia,8353,0.425161415561544
+Pocedure,1575,OT EVALUATE AND TREAT,126795,0.4114244072882275
+Medication,W1A,AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS,70345,0.3951372069987702
+Medication,W1A,AMOXICILLIN 500 MG PO CAPS,44727,0.3951372069987702
+Medication,W1A,PIPERACILLIN-TAZOBACTAM-DEXTRS 3.375 GRAM/50 ML IV PGBK,34193,0.3951372069987702
+Medication,W1A,AMOXICILLIN 400 MG/5 ML PO SUSR,17573,0.3951372069987702
+Medication,W1A,PIPERACILLIN-TAZOBACTAM-DEXTRS 4.5 GRAM/100 ML IV PGBK,12571,0.3951372069987702
+Pocedure,480951,"NOVEL CORONAVIRUS 2019 (SARS-COV-2), RT PCR",430381,0.3761668750712596
+Lab result,SARSCOV2RNA,SARS-CoV-2 RNA,398820,0.3234267761655791
+Lab result,SARSCOV2RNA,"SARS-COV-2,NAA",37606,0.3234267761655791
+Lab result,SARSCOV2RNA,SARS CoV 2 RNA,4958,0.3234267761655791
+Lab result,SARSCOV2RNA,"SARS CoV 2 RNA, RT PCR",1712,0.3234267761655791
+Lab result,SARSCOV2RNA,"SARS-COV-2 (COVID-19), QUALITATIVE, NAA",514,0.3234267761655791
+Diagnosis,FAC012,Other specified counseling,57146,0.2796513801847151
+Diagnosis,FAC012,Dietary counseling and surveillance,21910,0.2796513801847151
+Diagnosis,FAC012,Persons encountering health services in other specified circumstances,21526,0.2796513801847151
+Diagnosis,FAC012,Travel advice encounter,15670,0.2796513801847151
+Diagnosis,FAC012,Encounter to establish care,13806,0.2796513801847151
+Lab result,XUWBC,"WBC, urine",218658,0.2784266745810418
+Medication,M4D,ATORVASTATIN 40 MG PO TABS,70294,0.2689372082756002
+Medication,M4D,ATORVASTATIN 20 MG PO TABS,62829,0.2689372082756002
+Medication,M4D,ATORVASTATIN 10 MG PO TABS,61005,0.2689372082756002
+Medication,M4D,SIMVASTATIN 20 MG PO TABS,40287,0.2689372082756002
+Medication,M4D,ATORVASTATIN 80 MG PO TABS,25555,0.2689372082756002
+Diagnosis,MUS013,Age-related osteoporosis without current pathological fracture,34876,0.2643777038039338
+Diagnosis,MUS013,Osteoporosis,25209,0.2643777038039338
+Diagnosis,MUS013,"Osteoporosis, unspecified osteoporosis type, unspecified pathological fracture presence",17289,0.2643777038039338
+Diagnosis,MUS013,"Osteoporosis, unspecified",16064,0.2643777038039338
+Diagnosis,MUS013,Other osteoporosis without current pathological fracture,4721,0.2643777038039338
+Lab result,PRINTERVAL,P-R Interval,375625,0.254972254162597
+Lab result,RR,RR,382675,0.2537295643545224
+Lab result,QTINTERVAL,QT Interval,382675,0.2537295643545224
+Lab result,QRSDINTERVAL,QRSD Interval,382687,0.2537295643545224
+Lab result,QRSAXIS,QRS Axis,382694,0.2537295643545224
+Lab result,QTCINTERVAL,QTC Interval,382673,0.2537295643545224
+Lab result,HEARTRATE,Heart Rate,382694,0.2537295643545224
+Lab result,ECGIMPRESS,ECG Impression,382686,0.2537295643545224
+Diagnosis,SYM006,Unspecified abdominal pain,79731,0.2530975898531517
+Diagnosis,SYM006,"Abdominal pain, unspecified site",56899,0.2530975898531517
+Diagnosis,SYM006,"Diarrhea, unspecified",50130,0.2530975898531517
+Diagnosis,SYM006,Epigastric pain,45748,0.2530975898531517
+Diagnosis,SYM006,Diarrhea,44535,0.2530975898531517
+Diagnosis,END014,Postprocedural hypoparathyroidism,409,0.2530833930631317
+Diagnosis,END014,Asymptomatic postprocedural ovarian failure,362,0.2530833930631317
+Diagnosis,END014,Symptomatic states associated with artificial menopause,264,0.2530833930631317
+Diagnosis,END014,Surgical menopause,260,0.2530833930631317
+Diagnosis,END014,Postprocedural adrenocortical (-medullary) hypofunction,174,0.2530833930631317
+Diagnosis,MUS007,Effusion of lower leg joint,10389,0.2484838972612684
+Diagnosis,MUS007,"Effusion, right knee",7205,0.2484838972612684
+Diagnosis,MUS007,Other specified disorders of lower leg joint,6820,0.2484838972612684
+Diagnosis,MUS007,"Effusion, left knee",6546,0.2484838972612684
+Diagnosis,MUS007,"Other affections of shoulder region, not elsewhere classified",5208,0.2484838972612684
+Lab result,TWAVEAXIS,T Wave Axis,382269,0.2450019633868168
+Diagnosis,END010,"Hyperlipidemia, unspecified",196135,0.2370478444369828
+Diagnosis,END010,Hyperlipidemia,108416,0.2370478444369828
+Diagnosis,END010,Other and unspecified hyperlipidemia,105318,0.2370478444369828
+Diagnosis,END010,"Hyperlipidemia, unspecified hyperlipidemia type",101058,0.2370478444369828
+Diagnosis,END010,Mixed hyperlipidemia,60657,0.2370478444369828
+Diagnosis,CIR020,"Cerebral infarction, unspecified",16509,0.2094633869794272
+Diagnosis,CIR020,Unspecified cerebral artery occlusion with cerebral infarction,9620,0.2094633869794272
+Diagnosis,CIR020,"Cerebrovascular accident (CVA), unspecified mechanism (CMS-HCC)",8063,0.2094633869794272
+Diagnosis,CIR020,Stroke (CMS-HCC),4886,0.2094633869794272
+Diagnosis,CIR020,Other cerebral infarction,3149,0.2094633869794272
+Pocedure,34506,EVAL/MGMT OF EST PATIENT LEVEL 5,209691,0.1975707015928293
+Pocedure,34506,"OFFICE/OUTPATIENT VISIT, EST",418,0.1975707015928293
+Pocedure,34506,EVAL/MGMT OF EST PATIENT LEVEL FIVE,102,0.1975707015928293
+Pocedure,34506,REFERRAL TO CANCER GENETICS AND GENOMICS,1,0.1975707015928293
+Pocedure,34506,OFFICE OUTPT EST 40 MIN,13,0.1975707015928293
+Lab result,PAXIS,P Axis,375241,0.1864401264663936
+Diagnosis,MUS026,Muscle weakness (generalized),25570,0.184329816070172
+Diagnosis,MUS026,Other muscle spasm,9943,0.184329816070172
+Diagnosis,MUS026,Spasm of muscle,7285,0.184329816070172
+Diagnosis,MUS026,Other specified disorders of muscle,6549,0.184329816070172
+Diagnosis,MUS026,Muscle weakness,5701,0.184329816070172
+Lab result,XMUCUS,"Mucous Threads, urine",146805,0.184324669672767
+Medication,H21,LORAZEPAM 2 MG/ML INJ SOLN,66330,0.1826633676808596
+Medication,H21,TEMAZEPAM 15 MG PO CAPS,12074,0.1826633676808596
+Medication,H21,TEMAZEPAM 7.5 MG PO CAPS,6635,0.1826633676808596
+Medication,H21,TEMAZEPAM 30 MG PO CAPS,2719,0.1826633676808596
+Medication,H21,MIDAZOLAM 2 MG/ML PO SYRP,2180,0.1826633676808596
+Diagnosis,CIR007,"Unspecified maternal hypertension, third trimester",428,0.1792856843263394
+Diagnosis,CIR007,"Unspecified pre-existing hypertension complicating pregnancy, unspecified trimester",364,0.1792856843263394
+Diagnosis,CIR007,"Unspecified maternal hypertension, complicating the puerperium",225,0.1792856843263394
+Diagnosis,CIR007,Pre-existing hypertension complicating pregnancy,198,0.1792856843263394
+Diagnosis,CIR007,"Unspecified pre-existing hypertension complicating pregnancy, third trimester",192,0.1792856843263394
+Medication,Q3S,BISACODYL 10 MG PR SUPP,140561,0.1722929126991311
+Medication,Q3S,SODIUM PHOSPHATES 19-7 GRAM/118 ML PR ENEM,33961,0.1722929126991311
+Medication,Q3S,MINERAL OIL PR ENEM,22695,0.1722929126991311
+Medication,Q3S,SODIUM PHOSPHATES 19-7 GRAM/197 ML PR ENEM,2129,0.1722929126991311
+Medication,Q3S,GLYCERIN (ADULT) PR SUPP,1801,0.1722929126991311
+Pocedure,1916,SURGICAL PROCEDURE,347767,0.1700414391944952
+Medication,C6Z,MULTIVITAMIN PO,35213,0.1695182561886821
+Medication,C6Z,THERAPEUTIC MULTIVITAMIN PO TABS,33736,0.1695182561886821
+Medication,C6Z,MULTIVITAMIN PO TABS,17928,0.1695182561886821
+Medication,C6Z,B-COMPLEX WITH VITAMIN C PO TABS,13142,0.1695182561886821
+Medication,C6Z,MULTIVITAMINS PO,10127,0.1695182561886821
+Diagnosis,CIR016,"Conduction disorder, unspecified",18080,0.1549774733199503
+Diagnosis,CIR016,Unspecified right bundle-branch block,17334,0.1549774733199503
+Diagnosis,CIR016,Long QT syndrome,16149,0.1549774733199503
+Diagnosis,CIR016,Presence of cardiac pacemaker,14819,0.1549774733199503
+Diagnosis,CIR016,Cardiac pacemaker in situ,13642,0.1549774733199503
+Pocedure,2263,"URINALYSIS SCREEN, CULTURE IF POSITIVE",219072,0.1458452895921072
+Pocedure,2263,"URINALYSIS, SCREEN FOR CULTURE",60277,0.1458452895921072
+Pocedure,2263,URINALYSIS W/REFLEX TO CULTURE,160,0.1458452895921072
+Pocedure,2263,"UA/M W/RFLX CULTURE, ROUTINE",7,0.1458452895921072
+Diagnosis,CIR024,"Cerebral aneurysm, nonruptured",6231,0.1323266183711877
+Diagnosis,CIR024,Cerebral atherosclerosis,3395,0.1323266183711877
+Diagnosis,CIR024,Other generalized ischemic cerebrovascular disease,2529,0.1323266183711877
+Diagnosis,CIR024,"Cerebrovascular disease, unspecified",2296,0.1323266183711877
+Diagnosis,CIR024,"Acute, but ill-defined, cerebrovascular disease",2112,0.1323266183711877
+Diagnosis,SYM016,Edema,32379,0.1318637535474304
+Diagnosis,SYM016,Localized edema,31644,0.1318637535474304
+Diagnosis,SYM016,Abnormal weight loss,29316,0.1318637535474304
+Diagnosis,SYM016,Localized enlarged lymph nodes,28264,0.1318637535474304
+Diagnosis,SYM016,Other general symptoms and signs,22683,0.1318637535474304
+Diagnosis,BLD003,"Anemia, unspecified",90725,0.1293197600110729
+Diagnosis,BLD003,Anemia,38591,0.1293197600110729
+Diagnosis,BLD003,"Anemia, unspecified type",25647,0.1293197600110729
+Diagnosis,BLD003,Anemia in other chronic diseases classified elsewhere,10183,0.1293197600110729
+Diagnosis,BLD003,Anemia in chronic kidney disease,9359,0.1293197600110729
+Medication,H20,LORAZEPAM 0.5 MG PO TABS,72364,0.1286772555784887
+Medication,H20,LORAZEPAM 1 MG PO TABS,72093,0.1286772555784887
+Medication,H20,DIAZEPAM 5 MG PO TABS,50209,0.1286772555784887
+Medication,H20,ALPRAZOLAM 0.25 MG PO TABS,18542,0.1286772555784887
+Medication,H20,ALPRAZOLAM 0.5 MG PO TABS,17960,0.1286772555784887
+Diagnosis,NVS020,Other specified disorders of brain,12338,0.1179239056830694
+Diagnosis,NVS020,Other conditions of brain(348.89),9389,0.1179239056830694
+Diagnosis,NVS020,Metabolic encephalopathy,7182,0.1179239056830694
+Diagnosis,NVS020,"Encephalopathy, unspecified",5806,0.1179239056830694
+Diagnosis,NVS020,"Disorder of brain, unspecified",3937,0.1179239056830694
+Pocedure,1429,INCENTIVE SPIROMETER (WHILE AWAKE),116631,0.1164341712604475
+Diagnosis,EXT019,"Exposure to other specified factors, initial encounter",21785,0.1133337086993051
+Diagnosis,EXT019,Unspecified accident,7872,0.1133337086993051
+Diagnosis,EXT019,Other accident,1944,0.1133337086993051
+Diagnosis,EXT019,"Unknown cause of injury, initial encounter",1321,0.1133337086993051
+Diagnosis,EXT019,"Accident, initial encounter",1131,0.1133337086993051
+Diagnosis,EAR003,"Benign paroxysmal vertigo, unspecified ear",9282,0.1114919466225432
+Diagnosis,EAR003,"Benign paroxysmal positional vertigo, unspecified laterality",7779,0.1114919466225432
+Diagnosis,EAR003,Benign paroxysmal positional vertigo,7013,0.1114919466225432
+Diagnosis,EAR003,"Benign paroxysmal vertigo, right ear",2543,0.1114919466225432
+Diagnosis,EAR003,"Benign paroxysmal vertigo, left ear",2174,0.1114919466225432
+Lab result,WBC,WBC,975990,0.1009169398057644
+Lab result,WBC,WBC (Manual Entry) See EMR for details,104161,0.1009169398057644
+Lab result,WBC,WBC count,15815,0.1009169398057644
+Lab result,WBC,WBC Count,193,0.1009169398057644
+Lab result,WBC,White Cells,1,0.1009169398057644
+Pocedure,2215,TROPONIN I,117053,0.0900029795767736
+Pocedure,1508,WEIGHT,172781,0.0831666281346851
+Diagnosis,FAC024,Latent tuberculosis,2428,0.0498223389207656
+Diagnosis,FAC024,Carrier or suspected carrier of methicillin resistant Staphylococcus aureus,1748,0.0498223389207656
+Diagnosis,FAC024,"TB lung, latent",1152,0.0498223389207656
+Diagnosis,FAC024,LTBI (latent tuberculosis infection),900,0.0498223389207656
+Diagnosis,FAC024,MRSA (methicillin resistant staph aureus) culture positive,812,0.0498223389207656
+Medication,J7A,LABETALOL 5 MG/ML IV SOLN,111531,0.0369720556741845
+Medication,J7A,CARVEDILOL 6.25 MG PO TABS,20952,0.0369720556741845
+Medication,J7A,CARVEDILOL 3.125 MG PO TABS,19546,0.0369720556741845
+Medication,J7A,CARVEDILOL 12.5 MG PO TABS,16418,0.0369720556741845
+Medication,J7A,CARVEDILOL 25 MG PO TABS,11980,0.0369720556741845
+Medication,B6W,ALBUTEROL SULFATE 90 MCG/ACTUATION INH HFAA,123384,0.0293448582459419
+Medication,B6W,ALBUTEROL SULFATE 2.5 MG /3 ML (0.083 %) INH NEBU,45075,0.0293448582459419
+Medication,B6W,PROAIR HFA 90 MCG/ACTUATION INH HFAA,38733,0.0293448582459419
+Medication,B6W,VENTOLIN HFA 90 MCG/ACTUATION INH HFAA,19639,0.0293448582459419
+Medication,B6W,ALBUTEROL SULFATE 2.5 MG/0.5 ML INH NEBU,12773,0.0293448582459419
+Lab result,XUKET,"Ketone, urine",220893,0.0273584559661164
+Medication,H3A,OXYCODONE 5 MG PO TABS,217621,0.025005716917703
+Medication,H3A,HYDROMORPHONE 1 MG/ML INJ SYRG,126927,0.025005716917703
+Medication,H3A,HYDROMORPHONE (PF) 2 MG/ML INJ SOLN,106201,0.025005716917703
+Medication,H3A,TRAMADOL 50 MG PO TABS,76441,0.025005716917703
+Medication,H3A,MEPERIDINE (PF) 25 MG/ML INJ SYRG,66899,0.025005716917703
+Lab result,XLEUKEST,"Leukocyte Esterase, urine",220888,0.0241744643754207
+Diagnosis,SYM012,Palpitations,78284,0.0221571764995228
+Diagnosis,SYM012,"Tachycardia, unspecified",72967,0.0221571764995228
+Diagnosis,SYM012,"Bradycardia, unspecified",31184,0.0221571764995228
+Diagnosis,SYM012,"Elevated blood-pressure reading, without diagnosis of hypertension",31040,0.0221571764995228
+Diagnosis,SYM012,Elevated blood pressure reading without diagnosis of hypertension,24649,0.0221571764995228
+Medication,Q6T,ARTIFICIAL TEARS(HYPROMELLOSE) 0.5 % OPHT DROP,6322,0.0202368091289227
+Medication,Q6T,ARTIFICIAL TEARS OPHT,4198,0.0202368091289227
+Medication,Q6T,ARTIFICIAL TEAR (HYPROMELLOSE) 0.5 % OPHT DROP,3752,0.0202368091289227
+Medication,Q6T,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DROP,3522,0.0202368091289227
+Medication,Q6T,CARBOXYMETHYLCELLULOSE SODIUM 0.5 % OPHT DPET,3352,0.0202368091289227
+Lab result,GLU,"Glucose, Ser/Plas",894413,0.0197168485817528
+Lab result,GLU,Glucose by Meter,219614,0.0197168485817528
+Lab result,GLU,"Glucose, Whole Blood",116977,0.0197168485817528
+Lab result,GLU,"Glucose, SER/PLAS (Manual Entry) See EMR for details",111348,0.0197168485817528
+Lab result,GLU,"Glucose,ISTAT",58290,0.0197168485817528
+Lab result,RBC,RBC,975574,0.0158386255603115
+Lab result,RBC,RBC (MANUAL ENTRY) See EMR for details,10078,0.0158386255603115
+Lab result,RBC,Total Cell Count{Menlo},385,0.0158386255603115
+Lab result,RBC,Red cells,27,0.0158386255603115
+Lab result,RBC,ADULT RBC,10,0.0158386255603115
+Diagnosis,SKN006,Postprocedural hematoma of skin and subcutaneous tissue following other procedure,876,0.009795658038616
+Diagnosis,SKN006,Other postprocedural complications of skin and subcutaneous tissue,669,0.009795658038616
+Diagnosis,SKN006,Postprocedural seroma of skin and subcutaneous tissue following other procedure,639,0.009795658038616
+Diagnosis,SKN006,Postprocedural hemorrhage of skin and subcutaneous tissue following other procedure,416,0.009795658038616
+Diagnosis,SKN006,Incisional pain,370,0.009795658038616
+Diagnosis,GEN023,Postmenopausal atrophic vaginitis,21100,0.0059390220894074
+Diagnosis,GEN023,Menopausal and female climacteric states,14023,0.0059390220894074
+Diagnosis,GEN023,Symptomatic menopausal or female climacteric states,12301,0.0059390220894074
+Diagnosis,GEN023,Hormone replacement therapy,11838,0.0059390220894074
+Diagnosis,GEN023,Postmenopausal bleeding,11060,0.0059390220894074
+Medication,C6D,VITAMIN D3 PO,82759,0.0011478472481974
+Medication,C6D,"ERGOCALCIFEROL (VITAMIN D2) 50,000 UNIT PO CAPS",24427,0.0011478472481974
+Medication,C6D,"CHOLECALCIFEROL (VITAMIN D3) 1,000 UNIT PO TABS",14589,0.0011478472481974
+Medication,C6D,VITAMIN D PO,11667,0.0011478472481974
+Medication,C6D,"CHOLECALCIFEROL (VITAMIN D3) 25 MCG (1,000 UNIT) PO TABS",10165,0.0011478472481974
+Lab result,MG,"Magnesium, Ser/Plas",253937,2.7173645559180303e-05
+Lab result,MG,MAGNESIUM BLD (MANUAL ENTRY) See EMR for details,5994,2.7173645559180303e-05
+Lab result,MG,Magnesium,76,2.7173645559180303e-05
diff --git a/scripts/cost_variability/utils/create_metadata.py b/scripts/cost_variability/utils/create_metadata.py
new file mode 100644
index 00000000..938024ca
--- /dev/null
+++ b/scripts/cost_variability/utils/create_metadata.py
@@ -0,0 +1,39 @@
+from google.cloud import bigquery;
+from google.cloud.bigquery import dbapi;
+import pandas as pd
+import pdb
+from dateutil.relativedelta import relativedelta
+
+def compute_paients_demog(client_name, patient_id , query_demog):
+    # pdb.set_trace()
+    client = bigquery.Client(client_name); 
+    conn = dbapi.connect(client);
+    cursor = conn.cursor();
+
+    print('Executing SQL query to extract demographic records ...')
+    # cursor.execute(quenry_demog);
+    demographic_data = pd.read_sql_query(query_demog, conn);
+
+    metadata_columns=['anon_id', 'sex', 'bdate', 'canonical_race', 'canonical_ethnicity']
+    metadata_list = []
+    print('Extracting demographics and labeling the cohort ...')
+    patient_counter= 0
+    # pdb.set_trace()
+    for idx, row in demographic_data.iterrows():
+
+        patient_counter += 1
+        if patient_counter % 10000 ==0:
+            print('Finished processing {} patients'.format(patient_counter))
+
+        sex = row['gender']
+        bdate = row['birth_date_jittered']
+        canonical_race = row['canonical_race']
+        canonical_ethnicity = row['canonical_ethnicity']
+        anon_id = row['anon_id']
+
+        metadata_list.append([anon_id, sex, bdate, canonical_race, canonical_ethnicity])  
+
+    metadata_pd = pd.DataFrame(metadata_list, columns=metadata_columns)
+    metadata_pd.to_csv('intermediate_files/metadata.csv', index=False)
+    
+    
\ No newline at end of file
diff --git a/scripts/cost_variability/utils/create_stationary.py b/scripts/cost_variability/utils/create_stationary.py
new file mode 100644
index 00000000..f75eb2a1
--- /dev/null
+++ b/scripts/cost_variability/utils/create_stationary.py
@@ -0,0 +1,525 @@
+import pdb
+import pandas as pd
+import itertools
+import math
+import collections
+import numpy as np
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+import logging
+import csv 
+from google.cloud.bigquery import dbapi
+from google.cloud import bigquery
+
+logging.basicConfig(format='Date-Time : %(asctime)s : Line No. : %(lineno)d - %(message)s', level = logging.INFO, filename = 'log/logfile_create_stationary.log', filemode = 'a')
+
+def blind_data(patient_id
+				,line_med_splited
+				, line_diag_splited
+				, line_proc_splited
+				, line_lab_splited
+				, current_patient_demog
+				, blind_period
+				, admit_date):
+	# if patient_id == 'JC2872558':
+	# 	pdb.set_trace()
+	line_meds_blinded = []
+	line_diags_blinded = []
+	line_procs_blinded = []
+	line_lab_blinded = []
+
+	if (line_med_splited != [] and line_diag_splited != [] and line_proc_splited != []):
+		first_record_date = min(datetime.strptime(line_med_splited[0][0], '%Y-%m-%d'), datetime.strptime(line_diag_splited[0][0][:10], '%Y-%m-%d'), datetime.strptime(line_proc_splited[0][0][:10], '%Y-%m-%d'))
+		last_record_date = max(datetime.strptime(line_med_splited[-1][0], '%Y-%m-%d'), datetime.strptime(line_diag_splited[-1][0][:10], '%Y-%m-%d'), datetime.strptime(line_proc_splited[-1][0][:10], '%Y-%m-%d'))
+	elif (line_med_splited != [] and line_diag_splited != [] and line_proc_splited == []):
+		first_record_date = min(datetime.strptime(line_med_splited[0][0], '%Y-%m-%d'), datetime.strptime(line_diag_splited[0][0][:10], '%Y-%m-%d'))
+		last_record_date = max(datetime.strptime(line_med_splited[-1][0], '%Y-%m-%d'), datetime.strptime(line_diag_splited[-1][0][:10], '%Y-%m-%d'))
+	elif (line_med_splited == [] and line_diag_splited != [] and line_proc_splited != []):
+		first_record_date = min(datetime.strptime(line_proc_splited[0][0], '%Y-%m-%d'), datetime.strptime(line_diag_splited[0][0][:10], '%Y-%m-%d'))
+		last_record_date = max(datetime.strptime(line_proc_splited[-1][0], '%Y-%m-%d'), datetime.strptime(line_diag_splited[-1][0][:10], '%Y-%m-%d'))	
+	elif (line_med_splited == [] and line_diag_splited != [] and line_proc_splited == []):
+		# pdb.set_trace()
+		first_record_date = datetime.strptime(line_diag_splited[0][0][:10], '%Y-%m-%d')
+		last_record_date = datetime.strptime(line_diag_splited[-1][0][:10], '%Y-%m-%d')
+	else:
+		pdb.set_trace()
+	# pdb.set_trace()	
+
+	idx_date = admit_date + relativedelta(days= blind_period)
+
+	for i in range(len(line_med_splited)):
+		current_date = datetime.strptime(line_med_splited[i][0][:10], '%Y-%m-%d').date() 		
+		current_date_to_idx_date = (idx_date - current_date).days
+		if (current_date_to_idx_date >=0):
+			line_meds_blinded.append(line_med_splited[i])
+	# pdb.set_trace()		
+	for i in range(len(line_diag_splited)):
+		current_date = datetime.strptime(line_diag_splited[i][0][:10], '%Y-%m-%d').date() 		
+		current_date_to_idx_date = (idx_date - current_date).days
+		if (current_date_to_idx_date >=0):
+			line_diags_blinded.append(line_diag_splited[i])
+	# pdb.set_trace()		
+	for i in range(len(line_proc_splited)):
+		current_date = datetime.strptime(line_proc_splited[i][0][:10], '%Y-%m-%d').date() 		
+		current_date_to_idx_date = (idx_date - current_date).days
+		if (current_date_to_idx_date >=0):
+			line_procs_blinded.append(line_proc_splited[i])
+	# pdb.set_trace()	
+	for i in range(len(line_lab_splited)):
+		if line_lab_splited[i][0] == None:
+			break
+		current_date = datetime.strptime(line_lab_splited[i][0][:10], '%Y-%m-%d').date() 		
+		current_date_to_idx_date = (idx_date - current_date).days
+		if (current_date_to_idx_date >=0):
+			line_lab_blinded.append(line_lab_splited[i])
+
+	# if patient_id == 'JC2872558':
+	# 	pdb.set_trace()
+
+	return line_meds_blinded, line_diags_blinded, line_procs_blinded, line_lab_blinded	
+
+def create_stationary_ccs(patient_id, line_diag_splited, frequent_ccs_dict):
+	# pdb.set_trace()
+	epsil = 2.220446049250313e-16
+	round_num_digit = 5
+	for i in range(len(line_diag_splited)):
+		# if line_diag_splited[i][0]
+		for j in range(1, len(line_diag_splited[i])): 
+			if (line_diag_splited[i][j] in frequent_ccs_dict):           
+				frequent_ccs_dict[ line_diag_splited[i][j]] = 1
+			else:
+				continue  
+				print('test') 
+	# pdb.set_trace()
+	frequent_ccs_dict_sorted = dict(collections.OrderedDict(sorted(frequent_ccs_dict.items())))    
+	# num_records = len(line_diag_splited)
+	# if (num_records > 1):
+	# 	frequent_ccs_dict_sorted = {k: np.round(v / (math.log(num_records, 2)+ epsil), round_num_digit) for k, v in frequent_ccs_dict_sorted.items()}
+	# else:
+	# frequent_ccs_dict_sorted = {k: np.round(v , round_num_digit) for k, v in frequent_ccs_dict_sorted.items()}        
+	# pdb.set_trace()
+	return frequent_ccs_dict_sorted
+
+
+def create_stationary_med(line_med_splited, frequent_meds_dict):
+	# pdb.set_trace()
+	epsil = 2.220446049250313e-16
+	round_num_digit = 5
+	for i in range(len(line_med_splited)):
+		# if line_med_splited[i][0]
+		for j in range(1, len(line_med_splited[i])): 
+			if (line_med_splited[i][j] in frequent_meds_dict):           
+				frequent_meds_dict[ line_med_splited[i][j]] = 1
+			else:
+				continue  
+				print('test') 
+	# pdb.set_trace()
+	frequent_meds_dict_sorted = dict(collections.OrderedDict(sorted(frequent_meds_dict.items())))    
+	# num_records = len(line_med_splited)
+	# if (num_records > 1):
+	# 	frequent_meds_dict_sorted = {k: np.round(v / (math.log(num_records, 2)+ epsil), round_num_digit) for k, v in frequent_meds_dict_sorted.items()}
+	# else:
+	# 	frequent_meds_dict_sorted = {k: np.round(v , round_num_digit) for k, v in frequent_meds_dict_sorted.items()}        
+	# pdb.set_trace()
+	return frequent_meds_dict_sorted
+
+def create_stationary_proc(patient_id, line_proc_splited, frequent_procs_dict):
+	# pdb.set_trace()
+	epsil = 2.220446049250313e-16
+	round_num_digit = 5
+	for i in range(len(line_proc_splited)):
+		# if line_proc_splited[i][0]
+		for j in range(1, len(line_proc_splited[i])): 
+			if (line_proc_splited[i][j] in frequent_procs_dict):           
+				frequent_procs_dict[ line_proc_splited[i][j]] = 1
+			else:
+				continue  
+				print('test') 
+	# pdb.set_trace()
+	# if patient_id == 'JCcb6601':
+	# 	pdb.set_trace()		
+	frequent_procs_dict_sorted = dict(collections.OrderedDict(sorted(frequent_procs_dict.items())))    
+	# num_records = len(line_proc_splited)
+	# if (num_records > 1):
+	# 	frequent_procs_dict_sorted = {k: np.round(v / (math.log(num_records, 2)+ epsil), round_num_digit) for k, v in frequent_procs_dict_sorted.items()}
+	# else:
+	# 	frequent_procs_dict_sorted = {k: np.round(v , round_num_digit) for k, v in frequent_procs_dict_sorted.items()}        
+	# pdb.set_trace()
+	return frequent_procs_dict_sorted
+
+
+def create_stationary_lab(patient_id, line_lab_splited, frequent_lab_dict):
+	# pdb.set_trace()
+	epsil = 2.220446049250313e-16
+	round_num_digit = 5
+	for i in range(len(line_lab_splited)):
+		# if line_proc_splited[i][0]
+		for j in range(1, len(line_lab_splited[i])): 
+			if (line_lab_splited[i][j] in frequent_lab_dict):           
+				frequent_lab_dict[ line_lab_splited[i][j]] = 1
+			else:
+				continue  
+				print('test') 
+	frequent_labs_dict_sorted = dict(collections.OrderedDict(sorted(frequent_lab_dict.items())))    
+	
+	# num_records = len(line_lab_splited)
+	
+	# if (num_records > 1):
+	# 	frequent_labs_dict_sorted = {k: np.round(v / (math.log(num_records, 2)+ epsil), round_num_digit) for k, v in frequent_labs_dict_sorted.items()}
+	# else:
+	# 	frequent_labs_dict_sorted = {k: np.round(v , round_num_digit) for k, v in frequent_labs_dict_sorted.items()}        
+	# # pdb.set_trace()
+	return frequent_labs_dict_sorted
+
+def create_stationary(diagnosis_file_path
+					, medication_file_path
+					, procedure_file_path
+					, lab_file_path
+					, demographic_file_path
+					, ccs_frequencies_mci_path
+					, pharm_class_frequencies_mci_path
+					, procedure_id_frequencies_mci_path
+					, lab_id_frequencies_mci_path
+					, top_n_ccs							 
+					, top_n_med
+					, top_n_proc
+					, top_n_lab
+					, logging_milestone		
+					):
+	# pdb.set_trace()
+	blind_period = 1
+	client_name ="som-nero-phi-jonc101-secure"
+	query_string = "SELECT * FROM `som-nero-phi-jonc101-secure.proj_cms_sf.costUB_with_anonid_filtered` A ORDER BY A.anon_id"
+	client = bigquery.Client(client_name); 
+	conn = dbapi.connect(client);
+	cursor = conn.cursor();
+	costUB_with_anonid_filtered = pd.read_sql_query(query_string, conn); 
+
+
+	lab_data = pd.read_csv(lab_file_path, header=None, sep='\n', skiprows=1)
+	lab_data = lab_data[0].str.split(',', expand=True)
+
+	print('Reading demographic data ...')
+	demographic_data = pd.read_csv(demographic_file_path)
+	demographic_data = pd.get_dummies(demographic_data, columns=['sex', 'canonical_race', 'canonical_ethnicity'])
+
+	
+	# Rread frequent ICD codes
+	# pdb.set_trace()
+	print('Check out dtype=str')
+	print('Reading frequent codes and selecting top features')
+	frequent_ccs = pd.read_csv(ccs_frequencies_mci_path)
+	frequent_ccs.columns=frequent_ccs.columns.str.strip()
+	frequent_ccs = frequent_ccs.sort_values('num patient', ascending=False)
+	frequent_ccs_top_n = frequent_ccs['Code'].values[:top_n_ccs].tolist()
+
+
+	frequent_meds = pd.read_csv(pharm_class_frequencies_mci_path)
+	frequent_meds.columns=frequent_meds.columns.str.strip()
+	frequent_meds = frequent_meds.sort_values('num patient', ascending=False)
+	frequent_meds_top_n = frequent_meds['Code'].values[:top_n_med].tolist()
+
+	frequent_procs = pd.read_csv(procedure_id_frequencies_mci_path)
+	frequent_procs.columns = frequent_procs.columns.str.strip()
+	frequent_procs = frequent_procs.sort_values('num patient', ascending=False)	
+	frequent_procs_top_n = frequent_procs['Code'].values[:top_n_proc].tolist()
+
+	frequent_labs = pd.read_csv(lab_id_frequencies_mci_path)
+	frequent_labs.columns = frequent_labs.columns.str.strip()
+	frequent_labs = frequent_labs.sort_values('num patient', ascending=False)	
+	frequent_labs_top_n = frequent_labs['Code'].values[:top_n_proc].tolist()
+
+
+	# pdb.set_trace()
+	frequent_ccs_dict = {}
+	for i in range(len(frequent_ccs_top_n)):
+		frequent_ccs_dict[frequent_ccs_top_n[i]] = 0 
+	frequent_ccs_dict = dict(collections.OrderedDict(sorted(frequent_ccs_dict.items())))    
+
+	frequent_meds_dict = {}
+	for i in range(len(frequent_meds_top_n)):
+		frequent_meds_dict[str(frequent_meds_top_n[i])] = 0 
+	frequent_meds_dict = dict(collections.OrderedDict(sorted(frequent_meds_dict.items())))    
+
+	frequent_procs_dict = {}
+	for i in range(len(frequent_procs_top_n)):
+		frequent_procs_dict[str(frequent_procs_top_n[i])] = 0 
+	frequent_procs_dict = dict(collections.OrderedDict(sorted(frequent_procs_dict.items())))    
+
+	frequent_labs_dict = {}
+	for i in range(len(frequent_labs_top_n)):
+		frequent_labs_dict[str(frequent_labs_top_n[i])] = 0 
+	frequent_labs_dict = dict(collections.OrderedDict(sorted(frequent_labs_dict.items())))    
+
+
+	# pdb.set_trace()
+	with open(diagnosis_file_path) as diag_file, open(medication_file_path) as med_file, open(procedure_file_path) as proc_file, open('stationary_data/stationary_dataset.csv', 'w') as stationary_file, open('intermediate_files/elig_patients.csv', 'w') as elig_patients_file, open('intermediate_files/unelig_patients.csv', 'w') as unelig_patients_file:
+		header_diag = next(diag_file)
+		header_med = next(med_file)
+		header_proc = next(proc_file)
+
+		elig_patients_file.write('eligible_patients_ids\n')
+		unelig_patients_file.write('uneligible_patients_ids\n')
+
+		stationary_file.write('patient_id, age')
+		stationary_file.write(',')  
+
+		stationary_file.write(','.join('DEMOG_' + str(x) for x in demographic_data.columns[2:]))
+		stationary_file.write(',')  
+
+
+		stationary_file.write(','.join('DIAG_' + str(x) for x in [*frequent_ccs_dict.keys()]))
+		stationary_file.write(',')
+
+		stationary_file.write(','.join('MED_' + str(x) for x in [*frequent_meds_dict.keys()]))
+		stationary_file.write(',')  
+
+		stationary_file.write(','.join('PROC_' + str(x) for x in [*frequent_procs_dict.keys()]))
+		stationary_file.write(',') 
+
+		stationary_file.write(','.join('LAB_' + str(x) for x in [*frequent_labs_dict.keys()]))  
+		stationary_file.write(',')
+				
+		stationary_file.write('Cost_Direct, Cost_Indirect, Cost_Total')
+		stationary_file.write('\n')       
+		line_counter = 0
+		
+		# pdb.set_trace()
+		
+		previous_id = '0'
+		read_med = True
+		read_proc = True
+
+		# num_lines_in_diag = sum(1 for line in diag_file)
+		for line in diag_file:
+			line_counter+=1
+			if line_counter %100 ==0:
+				print('Processing line {} in the diagnosis table.'.format(line_counter))
+			# print(line_counter)
+			line_diag = line.replace('\n','').split(',')
+			line_diag_splited = [list(y) for x, y in itertools.groupby(line_diag[1:], lambda z: z == 'EOV') if not x]    
+	
+			# line_med = meds_data[ meds_data[0] == line_diag[0]]
+			if read_med == True:
+				line_med = med_file.readline().replace('\n','').split(',')
+				line_med_splited = [list(y) for x, y in itertools.groupby(line_med[1:], lambda z: z == 'EOV') if not x]    
+
+			if read_proc == True:	
+				line_proc = proc_file.readline().replace('\n','').split(',')
+				line_proc_splited = [list(y) for x, y in itertools.groupby(line_proc[1:], lambda z: z == 'EOV') if not x]    
+			
+			current_id = line_diag[0]
+			if previous_id>current_id:
+				pdb.set_trace()
+				print('Previous ID larger')
+			previous_id = current_id
+			
+			# logging.info('ID in diagnosis record is {}. Id in medication record is {}. ID in procedure record is {}.'.format(line_diag[0], line_med[0], line_proc[0]))
+
+			if not (line_diag[0] == line_med[0] == line_proc[0]):
+				# pdb.set_trace()
+				logging.info('========================================================')
+				logging.info('Patients dont match')
+				logging.info('========================================================')
+
+			if line_diag[0] == line_med[0]:
+				logging.info('line_diag[0] == line_med[0]: {} and {}. therefore read_med=True.'.format(line_diag[0], line_med[0]))
+				line_med_splited = [list(y) for x, y in itertools.groupby(line_med[1:], lambda z: z == 'EOV') if not x]    
+				read_med = True
+			elif line_diag[0] < line_med[0]:
+				# pdb.set_trace()
+				logging.info('line_diag[0] < line_med[0]: {} and {}'.format(line_diag[0], line_med[0]))
+				line_med_splited = []
+				logging.info('Setting line_med_splited=[] and read_med = False')
+				read_med = False
+			elif line_diag[0] > line_med[0]:
+				logging.info('line_diag[0] > line_med[0]: {} and {}'.format(line_diag[0], line_med[0]))
+				logging.info('start reading lines from med while line_med[0] < line_diag[0]')
+				while line_med[0] < line_diag[0]:
+					line_med = med_file.readline().replace('\n','').split(',')
+					line_med_splited = [list(y) for x, y in itertools.groupby(line_med[1:], lambda z: z == 'EOV') if not x]    
+					logging.info('Read line_med[0]: {}'.format(line_med[0]))
+					if line_med == ['']:
+						line_med_splited = []
+						# pdb.set_trace()
+						break					
+				# pdb.set_trace()
+				if line_med[0] == line_diag[0]:
+					logging.info('line_med[0] == line_diag[0] and therefore read_med = True')
+					read_med = True
+				else:
+					logging.info('line_med[0] != line_diag[0] and therefore read_med = False')
+					read_med = False	
+
+			if line_diag[0] == line_proc[0]:
+				logging.info('line_diag[0] == line_proc[0]: {} and {}. therefore read_proc=True.'.format(line_diag[0], line_proc[0]))				
+				line_proc_splited = [list(y) for x, y in itertools.groupby(line_proc[1:], lambda z: z == 'EOV') if not x]    
+				read_proc = True
+			elif line_diag[0] < line_proc[0]:
+				logging.info('line_diag[0] < line_proc[0]: {} and {}'.format(line_diag[0], line_proc[0]))
+				line_proc_splited = []
+				logging.info('Setting line_proc_splited=[] and read_proc = False')
+				read_proc = False
+			elif line_diag[0] > line_proc[0]:
+				logging.info('line_diag[0] > line_proc[0]: {} and {}'.format(line_diag[0], line_proc[0]))
+				logging.info('start reading lines from proc while line_proc[0] < line_diag[0]')
+				while line_proc[0] < line_diag[0]:
+					line_proc = proc_file.readline().replace('\n','').split(',')
+					line_proc_splited = [list(y) for x, y in itertools.groupby(line_proc[1:], lambda z: z == 'EOV') if not x]    
+					logging.info('Read line_proc[0]: {}'.format(line_proc[0]))
+					if line_proc == ['']:
+						line_proc_splited = []
+						# pdb.set_trace()
+						break					
+				# pdb.set_trace()
+				if line_proc[0] == line_diag[0]:
+					logging.info('line_proc[0] == line_diag[0] and therefore read_proc = True')
+					read_proc = True
+				else:
+					logging.info('line_med[0] != line_proc[0] and therefore read_proc = False')
+					read_proc = False	
+
+
+
+			# pdb.set_trace()
+
+			if len(line_diag_splited)==0:
+				diag_id = 'NA'
+			else:
+				diag_id = line_diag[0]
+
+			if len(line_med_splited)==0:
+				med_id = 'NA'
+			else:
+				med_id = line_med[0]
+
+			if len(line_proc_splited)==0:
+				proc_id = 'NA'
+			else:
+				proc_id = line_proc[0]
+			
+			logging.info('line_diag[0], line_med[0], line_proc[0]: {}, {}, {}'.format(diag_id, med_id, proc_id))
+
+			# pdb.set_trace()
+			current_costUB = costUB_with_anonid_filtered[costUB_with_anonid_filtered['anon_id']==line_diag[0]]
+			if len(current_costUB)<1:
+				pdb.set_trace()
+			# elif len(current_costUB) > 1:
+			# 	pdb.set_trace()	
+			current_patient_demog = demographic_data[demographic_data['anon_id']==line_diag[0]]
+
+			if current_patient_demog.empty:
+				pdb.set_trace()
+				print('Demographic data is missing ...')
+				continue
+				
+			for idx, row in  current_costUB.iterrows():
+				admit_date = row['AdmitDate']
+				dis_date = row['DischargeDate']
+				dir_cost = row['Cost_Direct']
+				ind_cost = row['Cost_Indirect']
+				tot_cost = row['Cost_Total']
+				# idx_date = admit_date + relativedelta(days= 1)
+
+				lab_line = lab_data [lab_data.iloc[:,0]==line_diag[0]]
+				if len(lab_line) == 1 :					
+					lab_line_splited = [list(y) for x, y in itertools.groupby(lab_line.values.tolist()[0][1:], lambda z: z == 'EOV') if not x]    
+					# pdb.set_trace()	
+					temp_merg = [lab_line_splited[0][0][:10]] + lab_line_splited[0][1:]
+					prev_time = lab_line_splited[0][0][:10]
+					lab_line_splited_synced = []	
+					for mrg_idx in range(1, len(lab_line_splited)):
+						if lab_line_splited[mrg_idx][0] == None:
+						   lab_line_splited_synced.append(lab_line_splited[mrg_idx])
+						   continue						   
+						if lab_line_splited[mrg_idx][0][:10] == prev_time:
+							temp_merg = temp_merg + lab_line_splited[mrg_idx][1:]														
+						else:
+						    lab_line_splited_synced.append([temp_merg[0]] + list(set(temp_merg[1:])))
+						    temp_merg = [lab_line_splited[mrg_idx][0][:10]] + lab_line_splited[mrg_idx][1:]
+						prev_time = lab_line_splited[mrg_idx][0][:10]    
+					# pdb.set_trace()	    	
+				elif len(lab_line) == 0 :
+					lab_line_splited=[]
+				else:
+					pdb.set_trace()	
+					print('Error')	
+				# pdb.set_trace()
+	
+				
+				line_med_blinded, line_diag_blinded, line_proc_blinded, line_lab_blinded = blind_data(line_med[0]
+																						, line_med_splited
+																						, line_diag_splited
+																						, line_proc_splited
+																						, lab_line_splited_synced
+																						, current_patient_demog
+																						, blind_period
+																						, admit_date)
+
+
+				frequent_ccs_dict = dict.fromkeys(frequent_ccs_dict, 0)    
+				frequent_meds_dict = dict.fromkeys(frequent_meds_dict, 0)    
+				frequent_procs_dict = dict.fromkeys(frequent_procs_dict, 0)    
+				frequent_labs_dict = dict.fromkeys(frequent_labs_dict, 0)    
+
+				if len(line_diag_blinded)==0 and len(line_med_blinded)==0 and len(line_proc_blinded)==0 and len(line_lab_blinded)==0:
+					unelig_patients_file.write(line_diag[0])
+					unelig_patients_file.write('\n')
+					continue
+				if len(line_diag_blinded)>0 or len(line_med_blinded)>0 or len(line_proc_blinded)>0 or len(line_lab_blinded)>0:
+					# pdb.set_trace() 
+					elig_patients_file.write(line_diag[0])
+					elig_patients_file.write('\n')
+				# pdb.set_trace()
+				stationary_ccs_vect = create_stationary_ccs(line_diag[0], line_diag_blinded, frequent_ccs_dict)		
+				stationary_med_vect = create_stationary_med(line_med_blinded, frequent_meds_dict)					
+				stationary_proc_vect = create_stationary_proc(line_diag[0], line_proc_blinded, frequent_procs_dict)		
+				stationary_lab_vect = create_stationary_lab(line_diag[0], line_lab_blinded, frequent_labs_dict)		
+
+							
+				# pdb.set_trace()
+				# if line_med[0] == 'JC2a00006' or line_diag[0] == 'JC2a00006':
+				# 	pdb.set_trace()	
+				current_patient_age = int(admit_date.year)-int(current_patient_demog['bdate'].values[0].split('-')[0])
+
+				sex_columns = [x for x in current_patient_demog.columns if 'sex_' in x]
+				race_columns = [x for x in current_patient_demog.columns if 'canonical_race_' in x]
+				ethnicity_columns = [x for x in current_patient_demog.columns if 'canonical_ethnicity_' in x]
+
+				
+
+				stationary_file.write((line_diag[0]))
+				stationary_file.write(',')
+
+				stationary_file.write(str(current_patient_age))
+				stationary_file.write(',')
+				stationary_file.write(','.join(map(repr, current_patient_demog[sex_columns].values.tolist()[0])))
+				stationary_file.write(',')
+				stationary_file.write(','.join(map(repr, current_patient_demog[race_columns].values.tolist()[0])))
+				stationary_file.write(',')
+				stationary_file.write(','.join(map(repr, current_patient_demog[ethnicity_columns].values.tolist()[0])))
+				stationary_file.write(',')
+
+
+				# pdb.set_trace()
+				stationary_file.write(','.join(map(repr, list(stationary_ccs_vect.values()))))
+				stationary_file.write(',')
+				stationary_file.write(','.join(map(repr, list(stationary_med_vect.values()))))
+				stationary_file.write(',')
+				stationary_file.write(','.join(map(repr, list(stationary_proc_vect.values()))))
+				stationary_file.write(',')
+				stationary_file.write(','.join(map(repr, list(stationary_lab_vect.values()))))
+				stationary_file.write(',')	
+
+				stationary_file.write(str(dir_cost))
+				stationary_file.write(',')	
+				stationary_file.write(str(ind_cost))
+				stationary_file.write(',')		
+				stationary_file.write(str(tot_cost))						
+				stationary_file.write('\n')            
+	logging.info('Finished creating stationary data')
+	logging.info('***********************************************************************')			
+	logging.info('***********************************************************************')			
+	logging.info('***********************************************************************')			
+	logging.info('***********************************************************************')			
+	logging.info('***********************************************************************')							
diff --git a/scripts/cost_variability/utils/extract_codes.py b/scripts/cost_variability/utils/extract_codes.py
new file mode 100644
index 00000000..949dfaa2
--- /dev/null
+++ b/scripts/cost_variability/utils/extract_codes.py
@@ -0,0 +1,528 @@
+from google.cloud import bigquery
+from google.cloud.bigquery import dbapi
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from dateutil.relativedelta import *
+import pdb
+import os
+import time
+import logging
+import csv
+
+def extract_diagnosis(client_name
+                    , query_diag
+                    , icd_to_ccs_table_query
+                    , icd9_to_icd10_query
+                    , icd10_field_name
+                    , icd9_field_name   
+                    , diag_time_field_name  
+                    , diag_patient_id_field_name               
+                    , display_step):
+    # pdb.set_trace()
+    # end_of_diag_file = False
+    # execute diagnosis query
+    proc_start_time = time.time()
+    client = bigquery.Client(client_name); 
+    conn = dbapi.connect(client);
+    cursor = conn.cursor();
+    cursor.execute(query_diag);    
+    results = cursor.fetchall();
+    num_fields = len(cursor.description)
+    field_names = [i[0] for i in cursor.description]
+
+    # ==== Read icd to ccs map
+    # pdb.set_trace()
+    icd_to_ccs_table = pd.read_sql_query(icd_to_ccs_table_query, conn); 
+    icd_to_ccs_table['ICD10'] = icd_to_ccs_table['ICD10'].str.strip()
+    icd_to_ccs_table['ICD10_string'] = icd_to_ccs_table['ICD10_string'].str.strip()
+    icd_to_ccs_table['CCSR_CATEGORY_1'] = icd_to_ccs_table['CCSR_CATEGORY_1'].str.strip()
+
+    icd10_to_ccs_table_dict = {}   
+    for i in range(len(icd_to_ccs_table)):
+        if icd_to_ccs_table['ICD10'].iloc[i] not in icd10_to_ccs_table_dict:
+            icd10_to_ccs_table_dict[icd_to_ccs_table['ICD10'].iloc[i]] = icd_to_ccs_table['CCSR_CATEGORY_1'].iloc[i]
+        else:
+            pdb.set_trace()    
+    
+    icd_string_to_ccs_table_dict = {}   
+    for i in range(len(icd_to_ccs_table)):
+        if icd_to_ccs_table['ICD10_string'].iloc[i] not in icd_string_to_ccs_table_dict:
+            icd_string_to_ccs_table_dict[icd_to_ccs_table['ICD10_string'].iloc[i]] = icd_to_ccs_table['CCSR_CATEGORY_1'].iloc[i]
+        else:
+            pdb.set_trace()    
+    
+
+    # pdb.set_trace()
+    # ==== Read icd9 to icd10 table
+    icd9_to_icd10 = pd.read_sql_query(icd9_to_icd10_query, conn); 
+    icd9_to_icd10['icd9_string'] = icd9_to_icd10['icd9_string'].str.strip()
+    icd9_to_icd10['icd10_string'] = icd9_to_icd10['icd10_string'].str.strip()
+
+    icd9_to_icd10_dict = {}   
+    for i in range(len(icd9_to_icd10)):
+        if icd9_to_icd10['icd9_string'].iloc[i] not in icd9_to_icd10_dict:
+            icd9_to_icd10_dict[icd9_to_icd10['icd9_string'].iloc[i]] = [icd9_to_icd10['icd10_string'].iloc[i]]
+        else:
+            # pdb.set_trace()  
+            icd9_to_icd10_dict[icd9_to_icd10['icd9_string'].iloc[i]].append(icd9_to_icd10['icd10_string'].iloc[i])
+    # pdb.set_trace()            
+    # ==== Reading unique CCS codes
+    unique_ccs_codes = icd_to_ccs_table['CCSR_CATEGORY_1'].unique()
+    unique_ccs_dict = {}
+    for i in range(len(unique_ccs_codes)):
+        if unique_ccs_codes[i] not in unique_ccs_dict:
+            unique_ccs_dict[unique_ccs_codes[i]] = 0
+
+    with open('intermediate_files/diagnosis_codes.csv', 'w') as diag_file:
+        diag_file.write('patient id, timestamp, icd10 and icd9 codes, end of visit token\n')
+        #==== While not end of the diagnoses file
+        line_counter = 0
+        patient_num = 0
+        total_num_records = len(results)
+
+        line_diag = results[line_counter]
+        while line_counter < total_num_records:
+            if line_counter%display_step==0:
+                print('Finished processing {} diagnoses records out of {} records.'.format(line_counter,total_num_records))
+            #==== Reading diagnoses visits for current patients           
+            current_id_diag = line_diag[diag_patient_id_field_name]
+            current_patient_diags = []
+
+            while current_id_diag == line_diag[diag_patient_id_field_name]:
+                current_patient_diags.append(line_diag)
+                line_counter+=1
+                if line_counter >= total_num_records:
+                    break
+
+                line_diag = results[line_counter]            
+                if line_counter%display_step==0:
+                    print('Finished processing {} diagnoses records out of {} records.'.format(line_counter,total_num_records))                
+
+            # pdb.set_trace()
+            # print(line_counter)
+            current_patient_diags_pd = pd.DataFrame(np.array(current_patient_diags), columns= field_names)
+            current_patient_diags_pd[icd10_field_name] = current_patient_diags_pd[icd10_field_name].str.strip()
+            current_patient_diags_pd[icd9_field_name] = current_patient_diags_pd[icd9_field_name].str.strip()
+
+            diagnosis_data_grouped = current_patient_diags_pd.groupby(by=diag_time_field_name)
+            # current_patient_diags_ar = np.array(current_patient_diags, dtype='U')       
+            diag_file.write(current_id_diag)
+            
+            # if current_id_diag == 'JCcb67b1':
+            # pdb.set_trace()
+            current_patient_all_ccs = []
+            for timestamp, group in diagnosis_data_grouped:
+                diag_file.write(',')
+                diag_file.write(str(timestamp))                
+                diag_file.write(',')
+                
+                current_icd10s = [x for x in group.loc[group[icd10_field_name].notna()][icd10_field_name].values.tolist()]                
+                # current_icd10s = [x.strip() for x in current_icd10s]
+                current_icd10s_strings = [x.replace('.','') for x in current_icd10s]
+                
+                temp1_ccs=[icd10_to_ccs_table_dict[x] for x in current_icd10s if x in icd10_to_ccs_table_dict]                
+                temp2_ccs=[icd_string_to_ccs_table_dict[x] for x in current_icd10s_strings if x in icd_string_to_ccs_table_dict]
+                current_ccs_codes_icd10 = temp1_ccs + temp2_ccs
+                # pdb.set_trace()
+                # current_icd_to_ccs = icd_to_ccs_table[icd_to_ccs_table['ICD10'].isin(current_icd10s) | icd_to_ccs_table['ICD10_string'].isin(current_icd10s_strings)]
+                # current_ccs_codes_icd10 = current_icd_to_ccs['CCSR_CATEGORY_1'].unique().tolist()
+                # current_ccs_codes_icd10 = list(set(current_ccs_codes_icd10))
+                
+                # pdb.set_trace()
+                current_icd9s = [x for x in group.loc[group[icd9_field_name].notna() & group[icd10_field_name].isna()][icd9_field_name].values.tolist()]    
+                # if current_icd9s:
+                #     pdb.set_trace()
+                # current_icd9s = [x.strip() for x in current_icd9s]
+                current_icd9s_strings = [x.replace('.','') for x in current_icd9s]
+                current_icd9_to_icd10 = [icd9_to_icd10_dict[x] for x in current_icd9s_strings if x in icd9_to_icd10_dict]
+                current_icd9_to_icd10 = [item for sublist in current_icd9_to_icd10 for item in sublist]
+                current_ccs_codes_icd9 = [icd_string_to_ccs_table_dict[x] for x in current_icd9_to_icd10 if x in icd_string_to_ccs_table_dict]
+
+                # current_icd9_to_icd10 = icd9_to_icd10[icd9_to_icd10['icd9_string'].isin(current_icd9s_strings)]
+                # current_icd9_ccs =  icd_to_ccs_table[icd_to_ccs_table['ICD10_string'].isin(current_icd9_to_icd10['icd10_string'].values.tolist()) ]
+                # current_ccs_codes_icd9 = current_icd9_ccs['CCSR_CATEGORY_1'].unique().tolist()
+                # current_ccs_codes_icd9 = list(set(current_ccs_codes_icd9))
+                
+                current_ccs_codes_all = current_ccs_codes_icd9 + current_ccs_codes_icd10
+                current_ccs_codes_all = list(set(current_ccs_codes_all))
+                if current_ccs_codes_all:   
+                    # pdb.set_trace()                   
+                    diag_file.write(','.join(current_ccs_codes_all))
+                    diag_file.write(',')
+                    current_patient_all_ccs.extend(current_ccs_codes_all)
+
+                diag_file.write('EOV')
+            # pdb.set_trace()
+            diag_file.write('\n')
+            
+            # Computing stats for ICD codes
+            current_patient_all_ccs = list(set(current_patient_all_ccs))
+            for icd_idx in range(len(current_patient_all_ccs)):
+                if current_patient_all_ccs[icd_idx] in unique_ccs_dict:
+                    unique_ccs_dict[current_patient_all_ccs[icd_idx]] +=1
+                else:
+                    pdb.set_trace()
+                    print('ICD does not exists')
+            # pdb.set_trace()
+            patient_num += 1
+            # if patient_num%logging_milestone==0:
+            #     logging.info('Completed extracting and writing the diagnoses stream for {} patient with ENROLID = {}.'.format(cohort, current_id_diag))
+    # pdb.set_trace()
+    print('Finished processing all {} of diagnoses records for the cohort in {}'.format(total_num_records, (time.time() - proc_start_time)))            
+    with open('intermediate_files/ccs_frequencies.csv', 'w') as csv_file:  
+        csv_file.write('Code, num patient\n')
+        writer = csv.writer(csv_file)
+        for key, value in unique_ccs_dict.items():
+           writer.writerow([key, value])    
+       
+    # logging.info('Finished processing all {} of diagnoses records for the {} cohort in {}'.format(total_num_records, cohort, (time.time() - start_time)))            
+    # logging.info('================================')    
+    # return 0      
+
+
+#========= Medications================
+def extract_medication(client_name
+                    , query_med
+                    , unique_medication_id_query
+                    , med_code_field_name
+                    , med_time_field_name
+                    , med_patient_id_field_name                      
+                    , display_step):
+    # pdb.set_trace()
+    # end_of_med_file = False
+    # execute medication query
+    proc_start_time = time.time()
+    client = bigquery.Client(client_name); 
+    conn = dbapi.connect(client);
+    cursor = conn.cursor();
+    cursor.execute(query_med);    
+    results = cursor.fetchall();
+    num_fields = len(cursor.description)
+    field_names = [i[0] for i in cursor.description]
+
+
+
+    # ==== Reading unique medication codes
+
+    cursor.execute(unique_medication_id_query);    
+    results_unique_pharm_class = cursor.fetchall();
+    unique_pharm_class = [item[0] for item in results_unique_pharm_class]
+    unique_pharm_class_dict = {}
+    for i in range(len(unique_pharm_class)):
+        if unique_pharm_class[i] not in unique_pharm_class_dict:
+            unique_pharm_class_dict[unique_pharm_class[i]] = 0
+
+
+    with open('intermediate_files/medication_codes.csv', 'w') as med_file:
+        med_file.write('patient id, order_time_jittered, pharm_class, end of visit token\n')
+        #==== While not end of the diagnoses file
+        line_counter = 0
+        patient_num = 0
+        total_num_records = len(results)
+
+        line_med = results[line_counter]
+        while line_counter < total_num_records:
+            if line_counter%display_step==0:
+                print('Finished processing {} medication records out of {} records.'.format(line_counter,total_num_records))
+            #==== Reading diagnoses visits for current patients           
+            current_id_med = line_med[med_patient_id_field_name]
+            current_patient_med = []
+
+            while current_id_med == line_med[med_patient_id_field_name]:
+                current_patient_med.append(line_med)
+                line_counter+=1
+                if line_counter >= total_num_records:
+                    break
+
+                line_med = results[line_counter]            
+                if line_counter%display_step==0:
+                    print('Finished processing {} diagnoses records out of {} records.'.format(line_counter,total_num_records))                
+
+            # pdb.set_trace()
+            # print(line_counter)
+            current_patient_med_pd = pd.DataFrame(np.array(current_patient_med), columns= field_names)
+            medication_data_grouped = current_patient_med_pd.groupby(by=med_time_field_name)
+            # current_patient_med_ar = np.array(current_patient_med, dtype='U')       
+            med_file.write(current_id_med)
+            
+            # pdb.set_trace()
+            current_patient_all_pharm_class = []
+            for timestamp, group in medication_data_grouped:
+                med_file.write(',')
+                med_file.write(str(timestamp))                
+                med_file.write(',')
+                
+                current_med_ids = group.loc[group[med_code_field_name].notna()][med_code_field_name].values.tolist()
+                current_med_ids = list(set(current_med_ids))
+                if current_med_ids:
+                    med_file.write(','.join([str(x) for x in current_med_ids]))
+                    med_file.write(',')
+                    current_patient_all_pharm_class.extend(current_med_ids)
+                med_file.write('EOV')
+            med_file.write('\n')
+            # pdb.set_trace()
+            # Computing stats for medication codes
+            current_patient_all_pharm_class = list(set(current_patient_all_pharm_class))
+            for pharm_class_idx in range(len(current_patient_all_pharm_class)):
+                if current_patient_all_pharm_class[pharm_class_idx] in unique_pharm_class_dict:
+                    unique_pharm_class_dict[current_patient_all_pharm_class[pharm_class_idx]] +=1
+                else:
+                    pdb.set_trace()
+                    print('ICD does not exists')
+            
+            patient_num += 1
+            # if patient_num%logging_milestone==0:
+            #     logging.info('Completed extracting and writing the diagnoses stream for {} patient with ENROLID = {}.'.format(cohort, current_id_diag))
+    print('Finished processing all {} of medication records for the cohort in {}'.format(total_num_records, (time.time() - proc_start_time)))            
+    with open('intermediate_files/pharm_class_frequencies.csv', 'w') as csv_file:  
+        csv_file.write('Code, num patient\n')
+        writer = csv.writer(csv_file)
+        for key, value in unique_pharm_class_dict.items():
+           writer.writerow([key, value])    
+        # logging.info('Finished processing all {} of diagnoses records for the {} cohort in {}'.format(total_num_records, cohort, (time.time() - start_time)))            
+        # logging.info('================================')    
+    # return 0      
+
+
+
+#========= procedures================
+def extract_procedure(client_name
+                    , query_proc
+                    , unique_proc_id_query
+                    , num_lines_query
+                    , proc_code_field_name
+                    , proc_time_field_name
+                    , proc_patient_id_field_name                     
+                    , display_step):
+    
+    proc_start_time = time.time()
+    client = bigquery.Client(client_name); 
+    conn = dbapi.connect(client);    
+    cursor = conn.cursor();
+    cursor.execute(query_proc);    
+    #### results = cursor.fetchall();
+    num_fields = len(cursor.description)
+    field_names = [i[0] for i in cursor.description]
+    
+
+    # ==== Reading unique procedure codes
+    cursor_unique_procs = conn.cursor();
+    cursor_unique_procs.execute(unique_proc_id_query);    
+    results_unique_proc_id = cursor_unique_procs.fetchall();
+    unique_proc_id = [item[0] for item in results_unique_proc_id]
+    unique_proc_id_dict = {}
+    for i in range(len(unique_proc_id)):
+        if unique_proc_id[i] not in unique_proc_id_dict:
+            unique_proc_id_dict[unique_proc_id[i]] = 0
+
+    # pdb.set_trace()
+    with open('intermediate_files/procedure_codes.csv', 'w') as proc_file:
+        proc_file.write('patient id, order_time_jittered_utc, proc_id, end of visit token\n')
+        #==== While not end of the diagnoses file
+        line_counter = 0
+        patient_num = 0
+        # total_num_records = len(results)
+        total_num_records = pd.read_sql_query(num_lines_query, conn).iloc[0,0]
+
+        line_proc = cursor.fetchone();
+        # line_proc = results[line_counter]
+        
+        #======================== WARNING ===========================
+        #======================== WARNING ===========================
+        #======================== WARNING ===========================
+        
+        while line_counter < total_num_records:           
+            if line_counter%display_step==0:
+                print('Finished processing {} procedure records out of {} records.'.format(line_counter,total_num_records))
+            #==== Reading diagnoses visits for current patients           
+            current_id_proc = line_proc[proc_patient_id_field_name]
+            current_patient_proc = []
+            # pdb.set_trace()
+            while current_id_proc == line_proc[proc_patient_id_field_name]:
+                current_patient_proc.append(line_proc)
+                line_counter+=1                
+                if line_counter >= total_num_records:
+                    break
+
+                # line_proc = results[line_counter]   
+                line_proc = cursor.fetchone();  
+                if line_proc == None:
+                    pdb.set_trace()
+                    break       
+                if line_counter%display_step==0:
+                    print('Finished processing {} diagnoses records out of {} records.'.format(line_counter,total_num_records))                
+            
+            # pdb.set_trace()
+            current_patient_proc_pd = pd.DataFrame(np.array(current_patient_proc), columns= field_names)
+            procedure_data_grouped = current_patient_proc_pd.groupby(by=proc_time_field_name)
+            # current_patient_proc_ar = np.array(current_patient_proc, dtype='U')       
+            proc_file.write(current_id_proc)
+            
+            # pdb.set_trace()
+            current_patient_all_proc_id = []           
+            for timestamp, group in procedure_data_grouped:
+                proc_file.write(',')
+                proc_file.write(str(timestamp))                
+                proc_file.write(',')
+                
+                current_proc_ids = group.loc[group[proc_code_field_name].notna()][proc_code_field_name].values.tolist()
+                current_proc_ids = list(set(current_proc_ids))
+                if current_proc_ids:
+                    proc_file.write(','.join([str(x) for x in current_proc_ids]))
+                    proc_file.write(',')
+                    current_patient_all_proc_id.extend(current_proc_ids)
+
+                proc_file.write('EOV')
+            # pdb.set_trace()
+            proc_file.write('\n')
+            patient_num += 1
+            # Computing stats for medication codes
+            current_patient_all_proc_id = list(set(current_patient_all_proc_id))
+            for proc_id_idx in range(len(current_patient_all_proc_id)):
+                if current_patient_all_proc_id[proc_id_idx] in unique_proc_id_dict:
+                    unique_proc_id_dict[current_patient_all_proc_id[proc_id_idx]] +=1
+                else:
+                    pdb.set_trace()
+                    print('ICD does not exists')                      
+            # if patient_num%logging_milestone==0:
+            #     logging.info('Completed extracting and writing the diagnoses stream for {} patient with ENROLID = {}.'.format(cohort, current_id_diag))
+    print('Finished processing all {} of procedure records for the cohort in {}'.format(total_num_records, (time.time() - proc_start_time)))            
+    with open('intermediate_files/procedure_id_frequencies.csv', 'w') as csv_file:  
+        csv_file.write('Code, num patient\n')
+        writer = csv.writer(csv_file)
+        for key, value in unique_proc_id_dict.items():
+           writer.writerow([key, value])    
+        
+        # logging.info('Finished processing all {} of diagnoses records for the {} cohort in {}'.format(total_num_records, cohort, (time.time() - start_time)))            
+        # logging.info('================================')    
+    # return 0      
+
+#========= labs================
+def extract_labs(client_name
+                    , query_lab
+                    , unique_lab_code_query
+                    , unique_lab_result_flag
+                    , num_lines_query
+                    , lab_code_field_name
+                    , lab_time_field_name
+                    , lab_patient_id_field_name                     
+                    , display_step):
+    
+    lab_start_time = time.time()
+    client = bigquery.Client(client_name); 
+    conn = dbapi.connect(client);    
+    cursor = conn.cursor();
+    cursor.execute(query_lab);    
+    #### results = cursor.fetchall();
+    num_fields = len(cursor.description)
+    field_names = [i[0] for i in cursor.description]
+    
+    # pdb.set_trace()
+    # ==== Reading unique labs codes
+    cursor_unique_labs = conn.cursor();
+    cursor_unique_labs.execute(unique_lab_code_query);    
+    results_unique_lab_id = cursor_unique_labs.fetchall();
+    unique_lab_id = [item[0] for item in results_unique_lab_id]
+    unique_lab_id_dict = {}
+    # pdb.set_trace()
+    unique_lab_flags = pd.read_sql_query(unique_lab_result_flag, conn)
+    unique_lab_flags[unique_lab_flags['result_flag'] == '(NONE)'] = 'None'
+    unique_lab_flags = unique_lab_flags[~unique_lab_flags['result_flag'].isin(['*'])]
+    unique_lab_flags.fillna('None', inplace=True)
+
+    for i in range(len(unique_lab_id)):
+        if unique_lab_id[i] not in unique_lab_id_dict:
+            unique_lab_id_dict[unique_lab_id[i]] = 0
+            for j in range(len(unique_lab_flags)):
+                if (unique_lab_id[i]+ '_' + unique_lab_flags.values.tolist()[j][0]) not in unique_lab_id_dict:
+                    unique_lab_id_dict[unique_lab_id[i]+ '_' + unique_lab_flags.values.tolist()[j][0]] = 0                
+
+    # pdb.set_trace()
+    with open('intermediate_files/lab_codes.csv', 'w') as lab_file:
+        lab_file.write('patient id, order_time_utc, base_name, end of visit token\n')
+        #==== While not end of the diagnoses file
+        line_counter = 0
+        patient_num = 0
+        # total_num_records = len(results)
+        total_num_records = pd.read_sql_query(num_lines_query, conn).iloc[0,0]
+
+        line_lab = cursor.fetchone();
+        # line_proc = results[line_counter]
+        #======================== WARNING ===========================
+        #======================== WARNING ===========================
+        #======================== WARNING ===========================
+        
+        while line_counter < total_num_records:            
+            if line_counter%display_step==0:
+                print('Finished processing {} labs records out of {} records.'.format(line_counter,total_num_records))
+            #==== Reading diagnoses visits for current patients           
+            current_id_lab = line_lab[lab_patient_id_field_name]
+            current_patient_lab = []
+            # pdb.set_trace()
+            while current_id_lab == line_lab[lab_patient_id_field_name]:
+                current_patient_lab.append(line_lab)
+                line_counter+=1                
+                if line_counter >= total_num_records:
+                    break
+
+                # line_proc = results[line_counter]   
+                line_lab = cursor.fetchone();  
+                if line_lab == None:
+                    pdb.set_trace()
+                    break       
+                if line_counter%display_step==0:
+                    print('Finished processing {} diagnoses records out of {} records.'.format(line_counter,total_num_records))                
+            
+            # pdb.set_trace()
+            current_patient_lab_pd = pd.DataFrame(np.array(current_patient_lab), columns= field_names)
+            labs_data_grouped = current_patient_lab_pd.groupby(by=lab_time_field_name)
+            # current_patient_proc_ar = np.array(current_patient_proc, dtype='U')       
+            lab_file.write(current_id_lab)
+
+            current_patient_all_lab_id = []           
+            for timestamp, group in labs_data_grouped:
+                lab_file.write(',')
+                lab_file.write(str(timestamp))                
+                lab_file.write(',')
+                # pdb.set_trace()
+                current_lab_ids = group.loc[group[lab_code_field_name].notna()][[lab_code_field_name, 'result_flag']]                
+                current_lab_ids = current_lab_ids[~current_lab_ids['result_flag'].isin(['*'])]
+                current_lab_ids['result_flag'] = current_lab_ids['result_flag'].str.strip('()')
+                current_lab_ids['result_flag'] = current_lab_ids['result_flag'].str.replace('NONE', 'None')                
+                current_lab_ids = (current_lab_ids['base_name'] + '_' + current_lab_ids['result_flag'].astype(str)).values.tolist() 
+                current_lab_ids = list(set(current_lab_ids))
+                if current_lab_ids:
+                    lab_file.write(','.join([str(x) for x in current_lab_ids]))
+                    lab_file.write(',')
+                    current_patient_all_lab_id.extend(current_lab_ids)
+
+                lab_file.write('EOV')
+            # pdb.set_trace()
+            lab_file.write('\n')
+            patient_num += 1
+            # Computing stats for medication codes
+            current_patient_all_lab_id = list(set(current_patient_all_lab_id))
+            for lab_id_idx in range(len(current_patient_all_lab_id)):
+                if current_patient_all_lab_id[lab_id_idx] in unique_lab_id_dict:
+                    unique_lab_id_dict[current_patient_all_lab_id[lab_id_idx]] +=1
+                else:
+                    pdb.set_trace()
+                    print('ICD does not exists')            
+            # if patient_num%logging_milestone==0:
+            #     logging.info('Completed extracting and writing the diagnoses stream for {} patient with ENROLID = {}.'.format(cohort, current_id_diag))
+    # pdb.set_trace()
+    print('Finished processing all {} of labs records for the cohort in {}'.format(total_num_records, (time.time() - lab_start_time)))            
+    with open('intermediate_files/labs_id_frequencies.csv', 'w') as csv_file:  
+        csv_file.write('Code, num patient\n')
+        writer = csv.writer(csv_file)
+        for key, value in unique_lab_id_dict.items():
+           writer.writerow([key, value])    
+        
+        # logging.info('Finished processing all {} of diagnoses records for the {} cohort in {}'.format(total_num_records, cohort, (time.time() - start_time)))            
+        # logging.info('================================')    
+    # return 0      
+
+
+
+
diff --git a/scripts/hematology-recommender/.gitignore b/scripts/hematology-recommender/.gitignore
new file mode 100644
index 00000000..2a21b1ca
--- /dev/null
+++ b/scripts/hematology-recommender/.gitignore
@@ -0,0 +1,132 @@
+# Mac files
+.DS_Store
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/scripts/hematology-recommender/README.md b/scripts/hematology-recommender/README.md
new file mode 100644
index 00000000..b1f90b6f
--- /dev/null
+++ b/scripts/hematology-recommender/README.md
@@ -0,0 +1,25 @@
+# hematology-recommender
+Implementation of a hematology recommender system for reducing treatment delays by predicting common tests a specialist would need at the first consult to diagnose the patient.
+
+### Important notation:
+- t<sub>1</sub>: the time point where the patient is referred to the hematology specialist
+- t<sub>2</sub>: the time point where the patient attends a specialist visit for the first time
+
+### Data preparation: 
+The script `data/define_cohort.sql` contains the SQL code for defining the cohort and extracting the data necessary for developing features. Each of the main tables have to be downloaded locally to be later processed in subsequent python scripts. The main tables are `baseCohort`, `demographicData`, `diagnosisT1`, `orderedProceduresT1`, `orderedMedsT1`, `labResultsT1`, `diagnosisT2`, `orderedProceduresT2`. Each table should be downloaded as a .csv file.
+
+The dataset and platform to run the querys are located in the Google Cloud Platform. To gain access follow the tutorial on the lab's github https://github.com/HealthRex/CDSS/blob/master/scripts/DevWorkshop/ReadMe.GoogleCloud-BigQuery-VPC.txt
+
+### Feature engineering and dataset division
+The functions in the `data/organize_feats.py` module prepares the data for training, they are already imported into the train.py script so the train.py script can be used directly. Currently all scripts have the data paths directly written into them but can be modified for more general use by employing `argparse` or `.config` files.
+
+- Main function: the `organize_feats()` function organizes the data from times t1 (`organize_t1_feats()`) and t2 (`organize_t2_labels()`) by creating features and targets. Then, the function splits both features and targets into training, validation and test splits (`split_cohort`).
+- Feature engineering: medicine orders, diagnosis, lab and procedure orders, and lab results are all processed individually. The topn elements are selected and the counts are binarized and used as features, that is, for example for the procedure orders the final feature would be the presence or absence of the order of a given procedure for each patient before time t1. For each table the time before t1 varies and is specified during the cohort definition.
+- Label definition: multilabel multiclass problem. Each of the selected topn features at time t2 that were ordered by the specialist and the system is aiming to recommend. Because more than one procedure can be ordered the labels are binary vectors with a 1 in each of the ordered procedures for each patient.
+- Dataset division: temporal and patient-wise division of the data. Can be redefined, but 2019 is the test year and 2018 the validation year. The remaining years are used for training. Remaining question on if 2008 should be included due to not enough data entries.
+
+### Training and evaluation
+Training is yet to be defined (`train.py`), but as mentioned before the connection is already made with the feature organization and data split functions. A tree-based method can be used for creating a baseline algorithm as it is intrinsically multiclass. The multilabel aspect of the problem should be handled by using the-per class scores directly (not converting them into probabilities by employing an argmax function) and by ensuring the evaluation is setup for a multilabel prediction. A skeleton base function for evaluation has been created in `utils/eval_utils.py` and the connection has been made with the training script.
+
+### Gold standard
+The `get_gold_standard()` function in the `data/get_gold_standard.py` script calculates the topn ordered procedures at t2 by diagnosis. We selected the top5 diagnosis at t2 and researched using upToDate the most common orders for diagnosing each condition. These orders can be used as the gold standard for evaluation. However, they should still be compared to the upToDate research and evaluated for their recall compared to the expected output at t2.
\ No newline at end of file
diff --git a/scripts/hematology-recommender/data/__init__.py b/scripts/hematology-recommender/data/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/hematology-recommender/data/define_cohort.sql b/scripts/hematology-recommender/data/define_cohort.sql
new file mode 100644
index 00000000..95382d07
--- /dev/null
+++ b/scripts/hematology-recommender/data/define_cohort.sql
@@ -0,0 +1,142 @@
+-- Script modified from https://github.com/HealthRex/CDSS/blob/master/scripts/OutpatientReferral/sampleQueries.sql
+
+-- Define Cohort
+-- 1. A) Find all patients that were referred to hematology (by their primary care physician?)
+WITH
+    referringEncounters AS
+            (
+                select op.anon_id, op.pat_enc_csn_id_coded as referringEncounterId, 
+                enc.appt_when_jittered as referringApptDateTime, op.order_time_jittered as referralOrderDateTime
+                from `mining-clinical-decisions.shc_core.order_proc` as op 
+                join `mining-clinical-decisions.shc_core.encounter` as enc on op.pat_enc_csn_id_coded = enc.pat_enc_csn_id_coded 
+                where proc_id = 34352 -- REFERRAL TO HEMATOLOGY CLINIC
+                and ordering_mode = 'Outpatient'
+                and appt_type in ('Office Visit','Appointment')
+                -- and EXTRACT(YEAR from order_time_jittered) < 2019
+                -- 16944 Historic Records - Hematology
+            ),
+    -- 2. B) Find all new patients that see a hematology specialist
+    newPatientEncounters AS
+        (
+            select enc.anon_id, enc.pat_enc_csn_id_coded as specialtyEncounterId, enc.appt_when_jittered as specialtyEncounterDateTime
+            from `mining-clinical-decisions.shc_core.encounter` as enc join `mining-clinical-decisions.shc_core.dep_map` as dep on enc.department_id = dep.department_id 
+            where dep.specialty_dep_c = '14' -- dep.specialty like 'Hematology'
+            and visit_type like 'NEW PATIENT%' -- Naturally screens to only 'Office Visit' enc_type 
+            -- LB q: what's the difference between NEW PATIENT VISIT 15 and NEW PATIENT VISIT 30?
+            -- and appt_type in ('Office Visit','Appointment') -- Otherwise Telephone, Refill, Orders Only, etc.
+            and appt_status = 'Completed'
+            -- 30592 historic records - Hematology
+        ),
+
+-- 3. A) and B), patients that were referred to hematology and actually got there
+    baseCohort AS 
+    (
+        select henc.anon_id as anon_id, href.referralOrderDateTime, href.referringEncounterId, henc.specialtyEncounterDateTime, henc.specialtyEncounterId
+        from newPatientEncounters as henc join referringEncounters as href on henc.anon_id = href.anon_id
+        -- LB: Add WHERE SP.SP_app_datetime BETWEEN PC.PC_ref_datetime AND DATETIME_ADD(PC.PC_ref_datetime, INTERVAL 4 MONTH)?
+ 
+        --  12364 patients
+
+    ),
+
+-- 4. Extract a basic table that I can manipulate to graph relevant info for understanding.
+-- Table merges baseCohort on anon_id
+-- LB: should I use anon_id or referringEncounterId for the join? anon_id, but referringEncounterId for T2 so I only get the data for that specific date (?) 
+    demographicData AS
+    (
+        select demo.anon_id, demo.gender, demo.birth_date_jittered as birthDate, demo.canonical_race as race, demo.canonical_ethnicity as ethnicity, demo.bmi, 
+        -- demo.INSURANCE_PAYOR_NAME, N_HOSPITALIZATIONS, DAYS_IN_HOSPITAL this may influence the lab results chosen
+        from baseCohort as bc join `mining-clinical-decisions.shc_core.demographic` as demo on bc.anon_id  = demo.anon_id
+    ),
+
+    diagnosisT1 AS
+    (
+        select diag.anon_id, diag.icd9, diag.icd10, diag.dx_name, diag.start_date as diagDate, diag_ccsr.CCSR_CATEGORY_1 as cssr_code, diag_ccsr.Category_1_Description as cssr_description
+        from baseCohort as bc join `mining-clinical-decisions.shc_core.diagnosis_code` as diag on bc.referringEncounterId  = diag.pat_enc_csn_id_jittered
+        join `mining-clinical-decisions.mapdata.ahrq_ccsr_diagnosis` as diag_ccsr on diag.icd10 = diag_ccsr.icd10
+        where diag.start_date <= bc.referralOrderDateTime
+        
+    ),
+    -- For later: see if including the diag of the referral date (using the encounter in the join) is helpful as feats
+    -- diagnosisReferralT1 AS
+    -- (
+    --     select diag.icd9, diag.icd10, diag.dx_name, diag.start_date as diagDate
+    --     from baseCohort as bc RIGHT JOIN `mining-clinical-decisions.shc_core.diagnosis_code` as diag on bc.referringEncounterId  = diag.pat_enc_csn_id_jittered
+    --     where diag.start_date <= bc.referralOrderDateTime
+    -- ),
+
+    -- also match on encounter id for proc and meds as extra feats
+    orderedProceduresT1 AS
+    (
+        select op.anon_id, op.proc_id, op.proc_code, op.description, op.order_time_jittered as orderedProcedureDateTime
+        from baseCohort as bc join `mining-clinical-decisions.shc_core.order_proc` as op on bc.referringEncounterId = op.pat_enc_csn_id_coded
+        where op.order_time_jittered BETWEEN DATETIME_ADD(bc.referralOrderDateTime, INTERVAL -12 MONTH) and bc.referralOrderDateTime
+        -- 1240172 observations
+    ),
+
+    orderedMedsT1 AS
+    (
+        select om.anon_id, om.pharm_class_name, om.thera_class_name, om.pharm_class_abbr, om.thera_class_abbr, om.order_start_time as medOrderDateTime
+        from baseCohort as bc join `mining-clinical-decisions.shc_core.order_med`as om on bc.referringEncounterId = om.pat_enc_csn_id_coded
+        where om.order_start_time BETWEEN DATETIME_ADD(bc.referralOrderDateTime, INTERVAL -6 MONTH) and bc.referralOrderDateTime
+        -- 165645 observations
+    ),
+
+    labResultsT1 AS
+    (
+        select lr.anon_id, lr.component_id, lr.group_lab_name, lr.base_name, lr.ord_num_value, lr.result_flag, lr.order_time as labOrderedDateTime, lr.result_time as labResultDateTime
+        -- other possible cols: lr.reference_low, lr.reference_high 
+        from baseCohort as bc join `mining-clinical-decisions.shc_core.lab_result`as lr on bc.referringEncounterId = lr.pat_enc_csn_id_coded
+        where lr.result_time BETWEEN DATETIME_ADD(bc.referralOrderDateTime, INTERVAL -6 MONTH) and bc.referralOrderDateTime
+        -- 904939 observations
+        -- lr.ordering_mode = 'Outpatient'
+    ),
+
+-- T2 information
+    diagnosisT2 AS
+    (
+        select diag.anon_id, diag.icd9, diag.icd10, diag.dx_name, diag.start_date as diagDate, diag_ccsr.CCSR_CATEGORY_1 as cssr_code, diag_ccsr.Category_1_Description as cssr_description
+        from baseCohort as bc join `mining-clinical-decisions.shc_core.diagnosis_code` as diag on bc.specialtyEncounterId  = diag.pat_enc_csn_id_jittered
+        join `mining-clinical-decisions.mapdata.ahrq_ccsr_diagnosis` as diag_ccsr on diag.icd10 = diag_ccsr.icd10
+        where diag.start_date BETWEEN bc.specialtyEncounterDateTime AND DATETIME_ADD(bc.specialtyEncounterDateTime, INTERVAL 2 MONTH)
+        -- LB: how's time here = or =>?
+    ),
+
+    orderedProceduresT2 AS
+    (
+        select op.anon_id, op.proc_id, op.proc_code, op.description, op.order_time_jittered as orderedProcedureDateTime
+        from baseCohort as bc RIGHT JOIN `mining-clinical-decisions.shc_core.order_proc` as op on bc.specialtyEncounterId  = op.pat_enc_csn_id_coded
+        where op.order_time_jittered BETWEEN bc.specialtyEncounterDateTime AND DATETIME_ADD(bc.specialtyEncounterDateTime, INTERVAL 1 DAY)
+    )
+
+    -- Example on how to download a table: run the full query and download the result
+
+    select *
+    from baseCohort
+
+    -- LB: should there be med orders in target? not for now
+    -- orderedMedsT2 AS
+    -- (
+    --     select om.anon_id, om.pharm_class_name, om.thera_class_name, om.pharm_class_abbr, om.thera_class_abbr, om.order_start_time as medOrderDateTime
+    --     from baseCohort as bc RIGHT JOIN `mining-clinical-decisions.shc_core.order_med`as om on bc.specialtyEncounterId = om.pat_enc_csn_id_coded
+    --     where om.order_start_time = bc.referralOrderDateTime
+    -- )
+--  exploratory counts
+    -- countsDiagnosisT1 AS
+    -- (
+    --     select diag.icd9, diag.icd10, diag.dx_name, count(*)
+    --     from diagnosisHistT1 as diag
+    --     group by diag.icd9, diag.icd10, diag.dx_name
+    --     order by count(*) desc
+    --     --  13920 diag codes
+    -- ),
+
+
+    --     countsOrderedProceduresT1 AS
+    -- (
+    --     select op.proc_id,	op.proc_code, op.description, count(*)
+    --     from orderedProceduresT1 as op
+    --     group by op.proc_id, op.proc_code, op.description
+    --     order by count(*) desc
+    --     -- 6750 distinct procedures
+    -- ),
diff --git a/scripts/hematology-recommender/data/get_gold_standard.py b/scripts/hematology-recommender/data/get_gold_standard.py
new file mode 100644
index 00000000..eaca3977
--- /dev/null
+++ b/scripts/hematology-recommender/data/get_gold_standard.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+import os
+import os.path as osp
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from organize_feats import explore_diag, clean_base_cohort, get_proc_labels
+
+# TODO: remove absolute paths
+data_dir = '/Users/laubravo/Documents/Rotation_Project/project/data/data_v1'
+
+base_cohort =  pd.read_csv(osp.join(data_dir, 'baseCohort.csv'))
+proc_t2 = pd.read_csv(osp.join(data_dir, 'orderedProceduresT2.csv'))
+diag_t2 = pd.read_csv(osp.join(data_dir, 'diagnosisT2.csv'))
+
+# TODO: calculate recall for gold_standard
+
+def get_gold_standard(base_cohort, diag_t2, proc_t2):
+    base_cohort = clean_base_cohort(base_cohort)
+    # find the most common diagnosis at t2
+    diag_codes, topn_diag = explore_diag(diag_t2, "", topn=5)
+    # for each diag in t2 find the most common ordered procedures
+    diag = base_cohort.merge(topn_diag, how='inner', on=['anon_id'])
+    proc = base_cohort.merge(proc_t2, how='inner', on=['anon_id'])
+    proc = proc.merge(diag, how='inner', on=['anon_id', 'referralOrderDateTime',
+    'referringEncounterId', 'specialtyEncounterDateTime', 'specialtyEncounterId'])
+    gold_standard = {code: get_proc_labels(proc[proc['cssr_code'] == code], topn_for_labs=10, topn_for_imgs=5) for code in diag_codes}
+    return gold_standard
+
+if __name__ == "__main__":
+    get_gold_standard(base_cohort, diag_t2, proc_t2)
\ No newline at end of file
diff --git a/scripts/hematology-recommender/data/organize_feats.py b/scripts/hematology-recommender/data/organize_feats.py
new file mode 100644
index 00000000..f20f2f66
--- /dev/null
+++ b/scripts/hematology-recommender/data/organize_feats.py
@@ -0,0 +1,301 @@
+# -*- coding: utf-8 -*-
+import os
+import os.path as osp
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pandas.core import base, groupby
+from pandas.core.reshape.merge import merge
+
+# TODO: remove absolute paths
+data_dir = '/Users/laubravo/Documents/Rotation_Project/project/data/data_v1'
+save_dir = '/Users/laubravo/Documents/Rotation_Project/project/data/cohort_stats'
+os.makedirs(save_dir, exist_ok=True)
+
+base_cohort =  pd.read_csv(osp.join(data_dir, 'baseCohort.csv')) # [4612 rows x 5 columns] w/ 3476 unique
+demographic = pd.read_csv(osp.join(data_dir, 'demographicData.csv')) # [4575 rows x 6 columns]
+meds_t1 = pd.read_csv(osp.join(data_dir, 'orderedMedsT1.csv')) # [165645 rows x 6 columns]
+proc_t1 = pd.read_csv(osp.join(data_dir, 'orderedProceduresT1.csv')) # hist: [1240172 rows x 6 columns] # enc: [20230 rows x 6 columns]
+diag_t1 = pd.read_csv(osp.join(data_dir, 'diagnosisT1.csv')) # [1448273 rows x 7 columns]
+labs_t1 = pd.read_csv(osp.join(data_dir, 'labResultsT1.csv')) # [904939 rows x 8 columns]
+
+proc_t2 = pd.read_csv(osp.join(data_dir, 'orderedProceduresT2.csv')) # (7179, 6)
+diag_t2 = pd.read_csv(osp.join(data_dir, 'diagnosisT2.csv')) # (7246, 7)
+
+save_hists = False
+
+
+def save_demo_hists(demographic, save_path):
+    fig = plt.figure()
+    ax1 = plt.subplot(231); ax1.hist(demographic.gender); ax1.set_title('Gender')
+    ax2 = plt.subplot(232); ax2.hist(demographic.age, bins=10); ax2.set_title('Birth Date')
+    ax3 = plt.subplot(233); ax3.hist(demographic.race); ax3.set_title('Race')
+    ax4 = plt.subplot(234); ax4.hist(demographic.ethnicity); ax4.set_title('Ethnicity')
+    ax5 = plt.subplot(235); ax5.hist(demographic.bmi); ax5.set_title('BMI')
+    fig.savefig(save_path)
+
+def save_med_hists(meds_t1, save_path):
+    fig = plt.figure()
+    ax1 = plt.subplot(121); ax1.hist(meds_t1.thera_class_name, bins=len(meds_t1.thera_class_name.unique())); ax1.set_title('thera_class_name')
+    plt.xticks(rotation=45, ha="right"); plt.xticks(size = 7); plt.tight_layout()
+    ax2 = plt.subplot(122); ax2.hist(meds_t1.pharm_class_name,  bins=len(meds_t1.pharm_class_name.unique())); ax2.set_title('pharm_class_name')
+    plt.xticks(rotation=45, ha="right"); plt.xticks(size = 7); plt.tight_layout()
+    fig.savefig(save_path)
+
+def save_diag_hists(diag_t1, save_path):
+    fig, ax1 = plt.subplots()
+    plt.hist(diag_t1.cssr_description, bins=len(diag_t1.cssr_description.unique())); ax1.set_title('CSSR description')
+    plt.xticks(rotation=45, ha="right"); plt.xticks(size = 7); plt.tight_layout()
+    fig.savefig(save_path)
+
+def get_meds_feats(meds_df, topn=10, save_dir="hists"):
+    """
+    Calculate the features for medicine orders. Uses topn of the pharma and thera classes used
+    to aggregate medicine (reduce granularity of features).
+    """
+    meds_df = filter_topn(meds_df, 'thera_class_abbr', topn=topn)
+    meds_df = filter_topn(meds_df, 'pharm_class_abbr', topn=topn)
+    pharm_feats = pivot_counts(meds_df, 'anon_id', 'pharm_class_abbr', na_fill=0)
+    thera_feats = pivot_counts(meds_df, 'anon_id', 'thera_class_abbr', na_fill=0)
+    med_feats = pharm_feats.join(thera_feats, on='anon_id', how='outer')
+    if save_hists:
+       save_med_hists(meds_df, osp.join(save_dir, "meds_t1_hists.png"))
+    return med_feats
+
+def get_diag_feats(diag_df, topn=10):
+    """
+    Calculate diagnostic features by taking topn diagnosis.
+    """
+    # TODO: remove no information feats ex: Medical examination/evaluation
+    diag_df = filter_topn(diag_df, 'cssr_code', topn=topn)
+    diag_feats = pivot_counts(diag_df, 'anon_id', 'cssr_code', na_fill=0)
+    if save_hists:
+        save_diag_hists(diag_df, osp.join(save_dir, "diag_t1_hists_enc_top{}.png".format(topn)))
+    return diag_feats
+
+def get_lab_feats(labs_df, topn=10):
+    """
+    Calculate lab results by binning according to normal and abnormal values.
+    TODO: expand features by including presence/absence and bin results by low, normal, high
+    """
+    breakpoint()
+    # TODO: update lab bining for feats
+    # remove Manual Differential/Slide Review , Slide Review (LAB230, LAB327)? 
+    labs_df = labs_df[~labs_df['proc_code'].isin(['LAB230', 'LAB327'])]
+    # TODO: merge CBCs (LABCBCD = LAB373) and merge (LABLPDC = LABLPD)
+    # labs_df.loc[labs_df['proc_code'] == 'LAB373']['proc_code'] = 'LABCBCD'
+    # result_flag [nan, 'High', 'Low', 'High Panic', 'Abnormal', '*', 'Low Panic','Low Off-Scale', 'Positive', 'High Off-Scale', 'Panic']
+    labs_df = labs_df[labs_df['order_type'].isin(['Lab', 'ECG', 'Lab Panel'])]
+    labs_df = filter_topn(labs_df, 'proc_code', topn=topn)
+    labs_df['abnormal'] = ~labs_df.result_flag.isnull()
+    labs_df = labs_df.sort_values('labResultDateTime').groupby(['anon_id', 'base_name']).head(1)
+    lab_feats = labs_df.pivot(index='anon_id', columns='base_name', values='abnormal').fillna(-1) #true for abnormal, false for normal, -1 for N/A
+    return lab_feats
+
+def get_proc_feats(proc_df, topn=10):
+    """
+    Calculate ordered procedures. Get topn for labs and non labs separately.
+    """
+    # keep only the proc types we're interested in
+    # relevant_procs = ["Lab", "Outpatient Referral", "Imaging", "Lab Panel"]
+    # proc_df[proc_df.order_type.isin relevant_procs]
+    # remove all the references to referral to hematology we already know this
+    proc_df = proc_df[proc_df.proc_id != 34352]
+    # merge all CBC synonyms into the same code (WITH DIFF AND SLIDE REVIEW, W/O DIFF, WITH DIFFERENTIAL & SLIDE REVIEW, CBC WITH DIFFERENTIAL)
+    proc_df.loc[proc_df["description"].str.contains("CBC"), "proc_id"] = 474
+    proc_df.loc[proc_df["description"].str.contains("CBC"), "description"] = "CBC"
+    # create feats for lab and non lab procedures
+    lab_proc = proc_df[proc_df.order_type.str.contains("Lab")]
+    lab_proc = filter_topn(lab_proc, 'proc_id', topn=topn)
+    lab_proc = lab_proc[['anon_id', 'proc_id']]
+    other_proc = proc_df[~proc_df.order_type.str.contains("Lab")]
+    other_proc = filter_topn(other_proc, 'proc_id', topn=topn)
+    other_proc = other_proc[['anon_id', 'proc_id']]
+    all_proc = pd.merge(lab_proc, other_proc, on=['anon_id', 'proc_id'], how='outer')
+    proc_feats = pivot_counts(all_proc, 'anon_id', 'proc_id', na_fill=0)
+    return proc_feats
+
+def get_demo_feats(demo_df, eth_race=False):
+    """
+    Calculate demographic features.
+    """
+    if not eth_race:
+        demo_df.drop(['race', 'ethnicity'], axis=1, inplace=True)
+    # change Gender column to binary
+    demo_df['gender'] = (demo_df['gender'] == 'Female')
+    demo_df['age'] = (pd.to_datetime(demo_df['referralOrderDateTime']) - pd.to_datetime(demo_df['birthDate']))/np.timedelta64(1,'Y')
+    # remove outliers
+    demo_df = remove_outliers(demo_df, 'bmi')
+    demo_df = remove_outliers(demo_df, 'age')
+    # normalize age so it's not such a big number
+    demo_df['age'] = (demo_df['age'] - demo_df['age'].mean()) / (demo_df['age'].std())
+    demo_df.drop(['birthDate'], axis=1, inplace=True)
+    # take first entry for each patient
+    demo_feats = demo_df.sort_values('referralOrderDateTime').groupby('anon_id').head(1)
+    if save_hists:
+        save_demo_hists(demo_feats, osp.join(save_dir, "demographic_hists.png"))
+    return demo_feats
+
+def explore_diag(diag_t2, save_dir, topn=5, save_fig=False):
+    """
+    Function for finding the topn diagnosis given by specialists at t2.
+    """
+    # 1672/3476 patients have diagnosis
+    diag_t2 = diag_t2.sort_values('diagDate').groupby(['anon_id', 'icd10']).head(1) # remove repeated diags
+    # remove diag that don't say anything [Medical examination/evaluation, Personal/family history of disease]
+    unimportant_diag = ['FAC014', 'FAC021'] #FAC025? Other specified status
+    diag_t2 = diag_t2[~diag_t2['cssr_code'].isin(unimportant_diag)]
+    # get topn diagnosis
+    topn_codes = list(diag_t2.groupby(['cssr_code']).size().sort_values(ascending=False)[:topn].to_dict().keys())
+    topn_diag = diag_t2[diag_t2['cssr_code'].isin(topn_codes)]
+    if save_fig:
+        fig = plt.hist(topn_diag['cssr_description'])
+        plt.xticks(rotation=45, ha="right"); plt.xticks(size = 9); plt.tight_layout()
+        fig.savefig(osp.join(save_dir, "diag_t2_{}.png".format(topn)))
+    return topn_codes, topn_diag
+
+def get_proc_labels(proc_t2, topn_for_labs=100, topn_for_imgs=10):
+    """
+    Calculate labels for time t2. Use different topn for labs and images.
+    """
+    # remove nursing orders
+    proc_t2 = proc_t2[~proc_t2['order_type'].isin(['Nursing'])]
+    # 202 people (not a lot) were referred to other specialties (192) or returned to primary care (10)
+    # top 5 referrals are 64/202 referrals
+    # proc_t2[proc_t2['order_type'].isin(['Outpatient Referral'])].groupby(['proc_id']).size().sort_values(ascending=False)
+    # proc_t2[proc_t2['proc_id'].isin([34280, 48200, 34384, 506207])].sort_values(by='description')
+    # top10 labs are 1825/7179 procedures top100 labs 5254/7179
+    topn_labs = proc_t2[proc_t2['order_type'] == 'Lab'].groupby('proc_id').size().sort_values(ascending=False)[:topn_for_labs].index.tolist()
+    # print(proc_t2[proc_t2['proc_id'].isin(topn_labs)]['description'].unique())
+    # ['LDH TOTAL, SERUM / PLASMA', 'CBC WITH DIFF', 'PROTEIN IMMUNOFIX ELECTROPHORESIS, SERUM', 'METABOLIC PANEL, COMPREHENSIVE',
+    # 'TRANSFERRIN SATURATION', 'RETICULOCYTE COUNT', 'VITAMIN B12', 'FERRITIN', 'HAPTOGLOBIN', 'ANTI CARDIOLIPIN AB']
+    # top10 imgs are 215/7179 procedures top100 imgs are 326/1000
+    topn_imgs = proc_t2[proc_t2['order_type'] == 'Imaging'].groupby('proc_id').size().sort_values(ascending=False)[:topn_for_imgs].index.tolist()
+    # print(proc_t2[proc_t2['proc_id'].isin(topn_imgs)]['description'].unique())
+     
+    # create targets
+    ordered_labs = filter_topn(proc_t2[proc_t2['order_type'] == 'Lab'], 'proc_id', topn=topn_for_labs)
+    count_labs = pivot_counts(ordered_labs, 'anon_id', 'proc_id', na_fill=0)
+    ordered_imgs = filter_topn(proc_t2[proc_t2['order_type'] == 'Imaging'], 'proc_id', topn=topn_for_imgs)
+    count_imgs = pivot_counts(ordered_imgs, 'anon_id', 'proc_id', na_fill=0)
+    targets = pd.merge(count_labs, count_imgs, on='anon_id', how='outer').fillna(0)
+    return targets
+
+def filter_topn(df, col_name, topn=10):
+    # TODO: change selection of top n by lift or interest method
+    top_n_vals = df[col_name].value_counts()[:topn].index.tolist()
+    filtered_df = df[df[col_name].isin(top_n_vals)]
+    return filtered_df
+
+def pivot_counts(df, id_col, key_col, na_fill=0):
+    count_df = df.groupby([id_col, key_col]).agg(counts=pd.NamedAgg(column=key_col, aggfunc="count")).reset_index()
+    feats = count_df.pivot(index=id_col, columns=key_col, values='counts').fillna(na_fill)
+    # binarize counts
+    feats = feats > 0
+    return feats
+
+def clean_base_cohort(base_cohort):
+    # handle repeated patient entries (692 anon_ids with repeats), take first referral
+    base_cohort = base_cohort.sort_values('referralOrderDateTime').groupby('anon_id').head(1)
+    # remove cases where speciality datetime < referral datetime (t2 <= t1)
+    base_cohort = base_cohort[base_cohort['specialtyEncounterDateTime'] > base_cohort['referralOrderDateTime']]
+    return base_cohort
+
+def remove_outliers(df, col_name):
+    limit = df[col_name].std()*3
+    df = df[df[col_name] <= limit]
+    df = df[df[col_name] >= -limit]
+    return df
+
+# prepare and return the feats
+def organize_t1_feats(base_cohort, demographic, meds_t1, proc_t1, diag_t1, labs_t1, topn = 10):
+    """
+    Organize all features for t1 and merge them to base cohort
+    """
+    # 1. Create demographic feats
+    base_cohort = clean_base_cohort(base_cohort)
+    demo_df = pd.merge(base_cohort, demographic, on='anon_id', how='left')
+    demo_feats = get_demo_feats(demo_df, eth_race=False) # don't include ethnicity/race info for training
+    # 2. Create medicine feats.
+    med_feats = get_meds_feats(meds_t1, topn=topn, save_dir=save_dir)
+    # 3. Create diagnostic feats
+    diag_feats = get_diag_feats(diag_t1, topn=topn)
+    # 4. Create Lab result feats
+    lab_feats = get_lab_feats(labs_t1, topn=topn)
+    # 5. Create procedure feats
+    proc_feats = get_proc_feats(proc_t1, topn=topn)
+    #6. join all the features together
+    # here demo_feats already has been merged w/ the base_cohort
+    all_feats = pd.merge(demo_feats, med_feats, on='anon_id', how='left')
+    all_feats.fillna(0, inplace=True)
+    all_feats = pd.merge(all_feats, diag_feats, on='anon_id', how='left')
+    all_feats.fillna(0, inplace=True)
+    all_feats = pd.merge(all_feats, lab_feats, on='anon_id', how='left')
+    all_feats.fillna(-1, inplace=True)
+    all_feats = pd.merge(all_feats, proc_feats, on='anon_id', how='left')
+    all_feats.fillna(0, inplace=True)
+    # is there any way to vis if these are ok?
+    return all_feats, base_cohort
+
+def organize_t2_labels(base_cohort, proc_t2, diag_t2, topn=10, save_hists=False):
+    """
+    Calculate labels for t2 according to the ordered procedures at t2.
+    save_hists option for exploring the diagnosis.
+    """
+    # 0. Explore diagnosis
+    if save_hists:
+       topn_diag_codes = explore_diag(diag_t2, save_dir)
+    # 1. Create targets from orders
+    targets = get_proc_labels(proc_t2)
+    # 2. join targets to cohort
+    # t = pd.merge(base_cohort['anon_id'], targets, on='anon_id', how='left').fillna(0)
+    # only 1410/3476 have a non zero target 1426 if we add ref targets
+    targets = pd.merge(base_cohort[['anon_id', 'referralOrderDateTime']], targets, on='anon_id', how='left')
+    targets.fillna(0, inplace=True)
+    return targets
+
+def split_dfs(df1, df2, keep_ids, col_name, remove_col=True):
+    df1 = df1[df1[col_name].isin(keep_ids)]
+    df2 = df2[df2[col_name].isin(keep_ids)]
+    if remove_col:
+        df1.drop(col_name, axis=1, inplace=True)
+        df2.drop(col_name, axis=1, inplace=True)
+    return df1, df2
+
+def split_cohort(base_cohort, feats, targets, test_year=2019, val_year=2018):
+    """
+    Split cohort (features and targets) into train, val and test using temporal and patient-wise splits.
+    """
+    # TODO: include 2020? only 69 pats, include 2008? only 57 pats
+    # 2019 test = 412, trainval 2008-2018+2020 = 3064, 2018 val = 474, train remaining = 2590
+    # create year col for easy comparisons
+    base_cohort['year'] = pd.DatetimeIndex(base_cohort['specialtyEncounterDateTime']).year
+    # remove zero targets
+    nonzero_ids = targets[targets.sum(axis=1) > 0]['anon_id']
+    base_cohort = base_cohort[base_cohort['anon_id'].isin(nonzero_ids)] # [1927 rows x 6 columns]
+    # LB: include 2020? only 40 pats, include 2008? only 15 pats
+    # 2019 test = 254, trainval 2008-2018+2020 = 1673, 2018 val = 252, train remaining = 1421
+
+    test_ids = base_cohort['anon_id'][base_cohort['year'] == test_year]
+    val_ids = base_cohort['anon_id'][base_cohort['year'] == val_year]
+    train_ids = base_cohort['anon_id'][~base_cohort['year'].isin([val_year, test_year])]
+
+    # remove datetime and other non-trainable columns
+    remove_cols = ['referralOrderDateTime', 'referringEncounterId', 'specialtyEncounterDateTime', 'specialtyEncounterId']
+    feats = feats[feats.columns[~feats.columns.isin(remove_cols)]]
+    targets = targets[targets.columns[~targets.columns.isin(remove_cols)]]
+    
+    train_feats, train_targets = split_dfs(feats, targets, train_ids, 'anon_id')
+    val_feats, val_targets = split_dfs(feats, targets, val_ids, 'anon_id')
+    test_feats, test_targets = split_dfs(feats, targets, test_ids, 'anon_id')
+
+    return train_feats, val_feats, test_feats, train_targets, val_targets, test_targets
+
+def organize_feats():
+    """
+    Main function for preparing the features, labels and spliting the cohort for training and evaluation.
+    """
+    feats, bc = organize_t1_feats(base_cohort, demographic, meds_t1, proc_t1, diag_t1, labs_t1)
+    targets = organize_t2_labels(bc, proc_t2, diag_t2)
+    train_feats, val_feats, test_feats, train_targets, val_targets, test_targets = split_cohort(bc, feats, targets)
+    return train_feats, val_feats, test_feats, train_targets, val_targets, test_targets
diff --git a/scripts/hematology-recommender/train.py b/scripts/hematology-recommender/train.py
new file mode 100644
index 00000000..7364a36d
--- /dev/null
+++ b/scripts/hematology-recommender/train.py
@@ -0,0 +1,58 @@
+# -*- coding: utf-8 -*-
+import os
+import os.path as osp
+import numpy as np
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.gaussian_process import GaussianProcessClassifier
+from sklearn.gaussian_process.kernels import RBF
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+from sklearn.model_selection import GridSearchCV
+
+from data.organize_feats import organize_feats
+from utils.eval_utils import evaluate
+
+np.random.seed(11111)
+
+train_feats, val_feats, test_feats, train_targets, val_targets, test_targets = organize_feats()
+
+# names = [
+#     "Nearest Neighbors",
+#     "Linear SVM",
+#     "RBF SVM",
+#     "Gaussian Process",
+#     "Decision Tree",
+#     "Random Forest",
+#     "AdaBoost",
+# ]
+
+# classifiers = [
+#     KNeighborsClassifier(3),
+#     SVC(kernel="linear", C=0.025),
+#     SVC(gamma=2, C=1),
+#     GaussianProcessClassifier(1.0 * RBF(1.0)),
+#     DecisionTreeClassifier(max_depth=5),
+#     RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
+#     AdaBoostClassifier()]
+
+# names = ["Decision Tree"]
+# classifiers = [DecisionTreeClassifier()]
+# param_grid = { 'max_features': ['auto', 'sqrt'],
+#                'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],
+#                'min_samples_split': [2, 5, 10],
+#                'min_samples_leaf': [1, 2, 4],
+#                'bootstrap': [True, False]}
+
+
+# # iterate over classifiers
+# for name, model, param_grid in zip(names, classifiers, param_grids):
+
+#     grid_model = GridSearchCV(model, param_grid, cv=4)             
+#     grid_model.fit(train_feats, train_targets)
+#     val_preds = clf.predict(val_feats)
+    
+model = DecisionTreeClassifier(max_depth= 10)
+model.fit(train_feats, train_targets)
+val_preds = model.predict_proba(val_feats)
diff --git a/scripts/hematology-recommender/utils/__init__.py b/scripts/hematology-recommender/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/hematology-recommender/utils/eval_utils.py b/scripts/hematology-recommender/utils/eval_utils.py
new file mode 100644
index 00000000..21b123d7
--- /dev/null
+++ b/scripts/hematology-recommender/utils/eval_utils.py
@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+import os
+from sklearn.metrics import precision_recall_curve
+
+def evaluate(preds, targets, k=10, thresh=0.5):
+    """
+    Metric calculating multilabel precision@k and recall@k given the threshold
+    preds: (a x N) matrix with scores for each of the N classes
+    targets: (a x N) binary matrix with expected groundtruth per class
+    """
+    # see link could be helpful, metrics are implemented
+    # https://github.com/facebookresearch/mmf/blob/322bd33d4a67f5bbe806624d9afd3c2cf921ec50/mmf/modules/metrics.py#L1020
+    # TODO: extend for multilabel case
+
+    return precision, recall
\ No newline at end of file
diff --git a/scripts/lab_prediction_pro/cbc_w_diff_pipeline.py b/scripts/lab_prediction_pro/cbc_w_diff_pipeline.py
new file mode 100644
index 00000000..cab25bf2
--- /dev/null
+++ b/scripts/lab_prediction_pro/cbc_w_diff_pipeline.py
@@ -0,0 +1,133 @@
+import os
+import sys
+import argparse
+sys.path.insert(1, '../../medinfo/dataconversion/')
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (
+    '/Users/conorcorbin/.config/gcloud/application_default_credentials.json'
+)
+os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'
+import pandas as pd
+import cohorts
+import trainers
+import featurizers
+
+
+cohort_table_id = 'mining-clinical-decisions.conor_db.20220607_cbc_diff_cohort_small'
+feature_table_id = 'mining-clinical-decisions.conor_db.20220607_cbc_features'
+project_id='som-nero-phi-jonc101'
+dataset = 'shc_core_2021'
+
+parser = argparse.ArgumentParser(description='Pipeline to train CBC models')
+parser.add_argument(
+    '--cohort',
+    action='store_true',
+    help='whether to build cohort'
+)
+parser.add_argument(
+    '--featurize',
+    action='store_true',
+    help='whether to featurize'
+)
+parser.add_argument(
+    '--train',
+    action='store_true',
+    help='whether to train'
+)
+args = parser.parse_args()
+
+
+cb = cohorts.CohortBuilder(
+    dataset_name='conor_db',
+    table_name='20220607_cbc_diff_cohort_small',
+    label_columns=['label_WBC', 'label_HCT', 'label_PLT'])
+
+query =f"""
+WITH cbcd_lab_results as (
+    SELECT DISTINCT
+        anon_id,
+        order_id_coded,
+        order_time_utc as index_time,
+        ordering_mode,
+        base_name,
+        CASE WHEN result_flag is NULL OR result_flag = "Normal" Then 0
+        ELSE 1
+        END label
+    FROM 
+        {project_id}.{dataset}.lab_result
+    WHERE 
+        # Note no inpatient results where proc code was LABCBCD
+        UPPER(group_lab_name) = 'CBC WITH DIFFERENTIAL'
+    AND
+        base_name in ('WBC', 'PLT', 'HCT')
+    AND 
+        EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2020
+),
+
+# Pivot lab result to wide
+cohort_wide as (
+    SELECT 
+        * 
+    FROM 
+        cbcd_lab_results
+    PIVOT (
+        MAX(label) as label -- should be max of one value or no value 
+        FOR base_name in ('WBC', 'PLT', 'HCT')
+    )
+    WHERE 
+        -- only keep labs where all three components we care about result
+        label_WBC is not NULL AND
+        label_PLT is not NULL AND
+        label_HCT is not NULL
+)
+
+### 2000 observations randomly sampled per year
+SELECT 
+    anon_id, order_id_coded as observation_id, index_time, ordering_mode,
+    label_WBC, label_PLT, label_HCT
+FROM 
+     (SELECT *,
+                ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                    ORDER BY RAND()) 
+                AS seqnum
+      FROM cohort_wide 
+     ) 
+WHERE 
+    seqnum <= 100
+"""
+
+# Executes query, and stores cohort in a pandas dataframe in the `df` attribute
+if args.cohort:
+    cb.build_cohort(query)
+    schema = [{'name': 'anon_id', 'type': 'STRING'},
+            {'name': 'observation_id', 'type': 'INTEGER'}]
+    # Writes the cohort to bigquery
+    cb.write_cohort_table(overwrite='True', schema=schema)
+
+if args.featurize:
+    # featurizer = featurizers.BagOfWordsFeaturizerLight(
+    #     cohort_table_id=cohort_table_id,
+    #     feature_table_id=feature_table_id,
+    #     outpath='./20220601_model_info/'
+    # )
+    # featurizer()
+    featurizer = featurizers.SequenceFeaturizer(
+        cohort_table_id=cohort_table_id,
+        feature_table_id=feature_table_id,
+        train_years=['2015', '2016', '2017', '2018'],
+        val_years=['2019'],
+        test_years=['2020'],
+        label_columns=['label_WBC', 'label_PLT', 'label_HCT'],
+        outpath='./20220607_model_info_seq/'
+    )
+    featurizer()
+
+if args.train:
+    working_dir = './20220607_model_info_seq/'
+    run_name = 'test_run'
+
+    
+    # TODO where are your feature matrices stored?
+    # trainer = trainers.BaselineModelTrainer(path)
+
+    # Component 1 - Predict Hematocrit
+    trainer(task='label_HCT')
diff --git a/scripts/lab_prediction_pro/cohort_definitions.py b/scripts/lab_prediction_pro/cohort_definitions.py
new file mode 100644
index 00000000..e2541e70
--- /dev/null
+++ b/scripts/lab_prediction_pro/cohort_definitions.py
@@ -0,0 +1,807 @@
+"""
+A place for cohort definitions (cohort + labels)
+"""
+import decimal
+import pandas as pd
+import pdb
+
+class CohortBuilder():
+    """
+    A class for constructing a cohort table and saving to a bigquery project.
+    A cohort table should have at minimum three columns:
+        1. anon_id : the patient id
+        2. observation_id : a unique identifier for each observation 
+        3. index_time : prediction time for each observation
+        4. `label` : binary or multiclass indicating class membership of the
+            observations. This may be multiple columns (if in multlilabel
+            setting) and will have column names as specified in `label_columns`
+            attribute.
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        self.client = client
+        self.project_id = working_project_id
+        self.dataset_name = dataset_name
+        self.table_name = table_name
+
+    def write_cohort_table(self, overwrite=False, schema=None):
+        """
+        Writes the cohort dataframe to specified bigquery project, dataset,
+        and table with appropriate table schema.
+
+        Args:
+            overwrite: if true overwrite existing table
+            schema: dictionary of table schema, if None detect automatically. 
+        """
+        if overwrite:
+            if_exists = 'replace'
+        else:
+            if_exists = 'fail'
+        self.df.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.project_id,
+            if_exists=if_exists,
+            table_schema=schema
+        )
+
+class InpatientMortalityCohort(CohortBuilder):
+    """
+    Defines a cohort for the task of predicting inpatient mortality.
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(InpatientMortalityCohort, self).__init__(
+            client, dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        -- Use ADT table to get inpatient admits, discharge times if they exist,
+        -- and Transfer Out times.
+        WITH inpatient_admits as (
+        SELECT 
+            anon_id, pat_enc_csn_id_coded, 
+            CASE WHEN in_event_type IS NOT NULL THEN in_event_type
+            ELSE REPLACE(out_event_type, ' ', '') END event_type,
+            event_time_jittered_utc
+        FROM
+            `som-nero-phi-jonc101.shc_core.adt` 
+        WHERE
+            pat_class = 'Inpatient' AND
+            (in_event_type = 'Admission' or out_event_type in 
+            ('Discharge', 'Transfer Out'))
+        ),
+        -- Join to demographics table to get death date for each patient
+        admissions_with_death_date as (
+        SELECT DISTINCT 
+            ia.*, d.death_date_jittered
+        FROM
+            inpatient_admits ia
+        INNER JOIN
+            `som-nero-phi-jonc101.shc_core.demographic` d
+        USING
+            (anon_id)
+        ),
+        -- Pivot so that we have one column for admission, transfer out and
+        -- discharge time (take latest of tranfer out times)
+        admissions_wide as (
+        SELECT 
+            *
+        FROM
+            admissions_with_death_date
+        PIVOT (
+            MAX(event_time_jittered_utc) as event_time
+            FOR event_type in ('Admission', 'TransferOut', 'Discharge')
+        )
+            ORDER BY anon_id, event_time_Admission
+        )
+        -- label is one when date of death exists between admission and
+        -- discharge, or between admission and transfer out if discharge
+        -- does not exist. 
+        SELECT
+            anon_id, pat_enc_csn_id_coded observation_id,
+            event_time_Admission index_time, 
+            CASE WHEN death_date_jittered
+            BETWEEN DATE(a.event_time_Admission) AND 
+            DATE(a.event_time_Discharge) THEN 1
+            WHEN a.event_time_Discharge IS NULL AND 
+            death_date_jittered BETWEEN DATE(a.event_time_Admission)
+            AND DATE(a.  event_time_TransferOut) THEN 1
+            ELSE 0 END label,
+            event_time_TransferOut, event_time_Discharge, death_date_jittered
+        FROM
+            admissions_wide a
+        WHERE
+            EXTRACT(YEAR FROM event_time_Admission) BETWEEN 2015 and 2020
+            AND NOT (event_time_TransferOut IS NULL 
+                     AND event_time_Discharge IS NULL)
+        )        
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+class LongLengthOfStayCohort(CohortBuilder):
+    """
+    Defines a cohort of patients admitted to hospital, positive label for
+    patients who stayed in inpatient setting for seven days or longer. 
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(LongLengthOfStayCohort, self).__init__(
+            client, dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        -- Use ADT table to get inpatient admits, discharge times if they exist,
+        -- and Transfer Out times.
+        WITH inpatient_admits as (
+        SELECT 
+            anon_id, pat_enc_csn_id_coded, 
+            CASE WHEN in_event_type IS NOT NULL THEN in_event_type
+            ELSE REPLACE(out_event_type, ' ', '') END event_type,
+            event_time_jittered_utc
+        FROM
+            `som-nero-phi-jonc101.shc_core.adt` 
+        WHERE
+            pat_class = 'Inpatient' AND
+            (in_event_type = 'Admission' or out_event_type in 
+            ('Discharge', 'Transfer Out'))
+        ),
+
+        admissions_wide as (
+        SELECT 
+            *
+        FROM
+            inpatient_admits
+        PIVOT (
+            MAX(event_time_jittered_utc) as event_time
+            FOR event_type in ('Admission', 'TransferOut', 'Discharge')
+        )
+            ORDER BY anon_id, event_time_Admission
+        )
+
+        SELECT
+            anon_id, pat_enc_csn_id_coded observation_id,
+            event_time_Admission index_time, 
+            CASE WHEN TIMESTAMP_DIFF(a.event_time_Discharge, 
+                                     a.event_time_Admission, DAY)< 7 AND
+            a.event_time_Discharge IS NOT NULL THEN 0
+            WHEN TIMESTAMP_DIFF(a.event_time_TransferOut,
+                                a.event_time_Admission, DAY) < 7 AND
+            a.event_time_TransferOut IS NOT NULL AND 
+            a.event_time_Discharge IS NULL THEN 0
+            ELSE 1 END label,
+            TIMESTAMP_DIFF(a.event_time_Discharge,
+                           a.event_time_Admission, DAY) time_to_discharge,
+            TIMESTAMP_DIFF(a.event_time_TransferOut, 
+                           a.event_time_Admission, DAY) time_to_transfer_out,
+            event_time_TransferOut, event_time_Discharge
+        FROM
+            admissions_wide a
+        WHERE
+            EXTRACT(YEAR FROM event_time_Admission) BETWEEN 2015 and 2020
+            AND NOT (event_time_TransferOut IS NULL AND event_time_Discharge
+            IS NULL)
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+class ThirtyDayReadmission(CohortBuilder):
+    """
+    Defines a cohort for the task of predicting inpatient mortality.
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(ThirtyDayReadmission, self).__init__(
+            client, dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        -- Use ADT table to get inpatient admits, discharge times if they exist,
+        -- and Transfer Out times.
+        WITH inpatient_admits as (
+        SELECT 
+            anon_id, pat_enc_csn_id_coded, 
+            CASE WHEN in_event_type IS NOT NULL THEN in_event_type
+            ELSE REPLACE(out_event_type, ' ', '') END event_type,
+            event_time_jittered_utc
+        FROM
+            `som-nero-phi-jonc101.shc_core.adt` 
+        WHERE
+            pat_class = 'Inpatient' AND
+            (in_event_type = 'Admission' or out_event_type = 'Discharge')
+        ),
+        -- Join to demographics table to get death date for each patient
+        admissions_with_death_date as (
+        SELECT DISTINCT 
+            ia.*, d.death_date_jittered
+        FROM
+            inpatient_admits ia
+        INNER JOIN
+            `som-nero-phi-jonc101.shc_core.demographic` d
+        USING
+            (anon_id)
+        ),
+        -- Pivot so that we have one column for admission, transfer out and
+        -- discharge time (take latest of tranfer out times)
+        admissions_wide as (
+        SELECT 
+            *
+        FROM
+            admissions_with_death_date
+        PIVOT (
+            MAX(event_time_jittered_utc) as event_time
+            FOR event_type in ('Admission', 'Discharge')
+        )
+        WHERE
+            event_time_Admission IS NOT NULL AND event_time_Discharge IS NOT
+            NULL
+        ORDER BY 
+            anon_id, event_time_Admission
+
+        )
+        -- join back to inpatient_admits to get nearest readmission
+        SELECT
+            a.anon_id, a.pat_enc_csn_id_coded observation_id,
+            event_time_Discharge index_time,
+            MAX(
+            CASE WHEN TIMESTAMP_DIFF(ia.event_time_jittered_utc, 
+            a.event_time_Discharge, DAY) > 30
+            THEN 0 WHEN ia.event_time_jittered_utc IS NULL THEN 0
+            ELSE 1 END) label,
+            MIN(ia.event_time_jittered_utc) next_admit_time,
+            MIN(TIMESTAMP_DIFF(ia.event_time_jittered_utc,
+                               a.event_time_Discharge, DAY)) days_to_next_admit
+        FROM
+            admissions_wide a
+        LEFT JOIN
+            (SELECT * FROM inpatient_admits WHERE event_type = 'Admission') ia
+        USING
+            (anon_id)
+        WHERE
+            ia.event_time_jittered_utc > a.event_time_Discharge OR 
+            ia.event_time_jittered_utc IS NULL
+        AND
+            EXTRACT(YEAR FROM event_time_Discharge) BETWEEN 2015 and 2020
+        GROUP BY
+            anon_id, observation_id, index_time
+        ORDER BY
+            anon_id, index_time, next_admit_time
+        )        
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+
+class CBCWithDifferentialCohort(CohortBuilder):
+    """
+    Defines a cohort and labels for CBC with differential models
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+        working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(CBCWithDifferentialCohort, self).__init__(client, dataset_name,
+            table_name, working_project_id)
+    
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+        """
+        query=f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        WITH cbcd_lab_results as (
+        SELECT DISTINCT
+            anon_id,
+            order_id_coded,
+            order_time_utc as index_time,
+            ordering_mode,
+            base_name,
+            CASE WHEN result_flag is NULL OR result_flag = "Normal" Then 0
+            ELSE 1
+            END label
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            # Note no inpatient results where proc code was LABCBCD
+            UPPER(group_lab_name) = 'CBC WITH DIFFERENTIAL'
+        AND
+            base_name in ('WBC', 'PLT', 'HCT', 'HGB')
+        AND 
+            EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2021
+        ),
+
+        # Pivot lab result to wide
+        cohort_wide as (
+            SELECT 
+                * 
+            FROM 
+                cbcd_lab_results
+            PIVOT (
+                MAX(label) as label -- should be max of one value or no value 
+                FOR base_name in ('WBC', 'PLT', 'HCT', 'HGB')
+            )
+            WHERE 
+                -- only keep labs where all three components result
+                label_WBC is not NULL AND
+                label_PLT is not NULL AND
+                label_HCT is not NULL AND
+                label_HGB is not NULL
+        )
+
+        ### 10000 observations randomly sampled per year from train set
+        SELECT 
+            anon_id, order_id_coded as observation_id, index_time, 
+            ordering_mode, label_WBC, label_PLT, label_HCT, label_HGB
+        FROM 
+            (SELECT *,
+                ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                    ORDER BY RAND()) 
+                        AS seqnum
+            FROM cohort_wide 
+            ) 
+        WHERE
+            seqnum <= 10000
+        )
+        
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+class MetabolicComprehensiveCohort(CohortBuilder):
+    """
+    Defines a cohort and labels for metabolic comprehensive tasks
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(MetabolicComprehensiveCohort, self).__init__(client,
+            dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        WITH metabolic_comp as (
+        SELECT DISTINCT
+            anon_id,
+            order_id_coded,
+            order_time_utc as index_time,
+            ordering_mode,
+            base_name,
+            CASE WHEN result_flag is NULL OR result_flag = "Normal" Then 0
+            ELSE 1
+            END label
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            # Note no inpatient results where proc code was LABCBCD
+            group_lab_name = 'Metabolic Panel, Comprehensive'
+        AND
+            base_name in ('NA', 'K', 'CO2', 'BUN', 'CR', 'CA', 'ALB')
+        AND 
+            EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2021
+        ),
+
+        # Pivot lab result to wide
+        cohort_wide as (
+            SELECT 
+                * 
+            FROM 
+                metabolic_comp
+            PIVOT (
+                MAX(label) as label -- should be max of one value or no value 
+                FOR base_name in ('NA', 'K', 'CO2', 'BUN', 'CR', 'CA', 'ALB')
+            )
+            WHERE 
+                -- only keep labs where all three components result
+                label_NA is not NULL AND
+                label_K is not NULL AND
+                label_CO2 is not NULL AND
+                label_BUN is not NULL AND
+                label_CR is not NULL AND
+                label_CA is not NULL AND
+                label_ALB is not NULL
+        )
+
+        ### 10000 observations randomly sampled per year from train set
+        SELECT 
+            anon_id, order_id_coded as observation_id, index_time, 
+            ordering_mode, label_NA, label_K, label_CO2, label_BUN, label_CR,
+            label_CA, label_ALB
+        FROM 
+            (SELECT *,
+                ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                    ORDER BY RAND()) 
+                        AS seqnum
+            FROM cohort_wide 
+            ) 
+        WHERE
+            seqnum <= 10000
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+class MagnesiumCohort(CohortBuilder):
+    """
+    Defines a cohort and labels for magnesium prediction models
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(MagnesiumCohort, self).__init__(client,
+            dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        WITH magnesium_results as (
+        SELECT DISTINCT
+            anon_id, order_id_coded, order_time_utc as index_time,
+            ordering_mode, base_name,
+            CASE WHEN result_flag is NULL OR result_flag = "Normal" Then 0
+            ELSE 1 END label
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            UPPER(group_lab_name) = 'MAGNESIUM, SERUM/PLASMA'
+        AND
+            base_name = 'MG'
+        AND 
+            EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2021
+        ),
+
+        # Pivot lab result to wide
+        cohort_wide as (
+            SELECT 
+                * 
+            FROM 
+                magnesium_results
+            PIVOT (
+                MAX(label) as label -- should be max of one value or no value 
+                FOR base_name in ('MG')
+            )
+            WHERE 
+                -- only keep labs where all three components result
+                label_MG is not NULL 
+        )
+
+        ### 10000 observations randomly sampled per year from train set
+        SELECT 
+            anon_id, order_id_coded as observation_id, index_time, 
+            ordering_mode, label_MG
+        FROM 
+            (SELECT *,
+                ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                    ORDER BY RAND()) 
+                        AS seqnum
+            FROM cohort_wide 
+            ) 
+        WHERE
+            seqnum <= 10000
+        )
+        
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+class BloodCultureCohort(CohortBuilder):
+    """
+    Defines a cohort and labels for blood culture prediction models
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(BloodCultureCohort, self).__init__(client,
+            dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting blood culture
+        results: positive vs negative (but say COAG NEG STAPH is neg)
+
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        WITH blood_culture_results as (
+        SELECT DISTINCT
+            anon_id, order_id_coded, order_time_utc as index_time, 
+            ordering_mode,
+            MAX(CASE WHEN result_flag is NULL OR result_flag = "Normal" Then 0
+            ELSE 1 END) label_blood
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            proc_code in ('LABBLC', 'LABBLC2')
+        AND 
+            EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2021
+        GROUP BY
+        anon_id, order_id_coded, order_time_utc, ordering_mode
+
+        )
+        ### 10000 observations randomly sampled per year from train set
+        SELECT 
+            anon_id, order_id_coded as observation_id, index_time, 
+            ordering_mode, label_blood
+        FROM 
+            (SELECT *,
+                ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                    ORDER BY RAND()) 
+                        AS seqnum
+            FROM blood_culture_results 
+            ) 
+        WHERE
+            seqnum <= 10000
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+class UrineCultureCohort(CohortBuilder):
+    """
+    Constructs a cohort table for predicting whether urine culture was positive.
+
+    What we will do is find all urine cultures (irrespective of whether
+    was triggered by urinalysis and train a model that predicts result of the
+    culture. The index time will be defined as the time of the urine culture
+    order if the urine culture was not triggered by a urinalysis, otherwise
+    will be the time of the urinalysis order. 
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(UrineCultureCohort, self).__init__(client,
+            dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting whether urine
+        culture or urinalysis was positive. This will likely need to be 
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        WITH urine_cultures AS (
+        SELECT DISTINCT 
+            anon_id, order_id_coded, order_time_utc, ordering_mode,
+            MAX(CASE WHEN result_flag is NULL OR result_flag = "Normal" 
+            Then 0 ELSE 1 END) label_urine
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            proc_code = "LABURNC"
+        GROUP BY 
+            anon_id, order_id_coded, order_time_utc, ordering_mode
+        ),
+        uas as (
+        SELECT DISTINCT 
+            anon_id, order_id_coded, order_time_utc, proc_code
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            proc_code = "LABUAPRN"
+        ),
+        matching_uas AS (
+        SELECT DISTINCT 
+            c.*, ua.proc_code ua_proc_code, ua.order_time_utc ua_order_time 
+        FROM 
+            urine_cultures c
+        LEFT JOIN 
+            uas ua
+        USING 
+            (anon_id)
+        WHERE 
+            ua.order_time_utc < c.order_time_utc OR ua.proc_code is NULL
+        ),
+
+        matching_uas_2 AS (
+        SELECT DISTINCT 
+            anon_id, order_id_coded observation_id, 
+            CASE WHEN ua_proc_code IS NULL THEN order_time_utc
+            WHEN TIMESTAMP_DIFF(order_time_utc, ua_order_time, HOUR) > 24 
+            THEN order_time_utc
+            ELSE ua_order_time END index_time, ordering_mode, label_urine,
+        FROM 
+            matching_uas
+        ),
+
+        cohort AS (
+        SELECT DISTINCT
+            anon_id, observation_id,
+            FIRST_VALUE(index_time) 
+            OVER (PARTITION BY observation_id ORDER BY index_time ASC)
+            index_time,
+            ordering_mode,
+            label_urine
+        FROM 
+            matching_uas_2
+        WHERE 
+            EXTRACT(YEAR FROM index_time) BETWEEN 2015 and 2021
+        )
+
+        ### 10000 observations max randomly sampled per year
+        SELECT 
+            anon_id, observation_id, index_time, ordering_mode, label_urine
+        FROM 
+            (SELECT 
+            *,
+            ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                ORDER BY RAND()) 
+                        AS seqnum
+            FROM cohort 
+            ) 
+        WHERE
+            seqnum <= 10000
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+
+class CBCWithDifferentialCohortSmall(CohortBuilder):
+    """
+    Defines a cohort and labels for CBC with differential models, samples 500
+    observations per year. Use to test featurizer modules
+    """
+
+    def __init__(self, client, dataset_name, table_name,
+                 working_project_id='mining-clinical-decisions'):
+        """
+        Initializes dataset_name and table_name for where cohort table will be
+        saved on bigquery
+        """
+        super(CBCWithDifferentialCohortSmall, self).__init__(client, 
+            dataset_name, table_name, working_project_id)
+
+    def __call__(self):
+        """
+        Function that constructs a cohort table for predicting cbc with
+        differential results. Done with SQL logic where possible
+        """
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.project_id}.{self.dataset_name}.{self.table_name}
+        AS (
+        WITH cbcd_lab_results as (
+        SELECT DISTINCT
+            anon_id,
+            order_id_coded,
+            order_time_utc as index_time,
+            ordering_mode,
+            base_name,
+            CASE WHEN result_flag is NULL OR result_flag = "Normal" Then 0
+            ELSE 1
+            END label
+        FROM 
+            som-nero-phi-jonc101.shc_core_2021.lab_result
+        WHERE 
+            # Note no inpatient results where proc code was LABCBCD
+            UPPER(group_lab_name) = 'CBC WITH DIFFERENTIAL'
+        AND
+            base_name in ('WBC', 'PLT', 'HCT', 'HGB')
+        AND 
+            EXTRACT(YEAR FROM order_time_utc) BETWEEN 2015 and 2021
+        ),
+
+        # Pivot lab result to wide
+        cohort_wide as (
+            SELECT 
+                * 
+            FROM 
+                cbcd_lab_results
+            PIVOT (
+                MAX(label) as label -- should be max of one value or no value 
+                FOR base_name in ('WBC', 'PLT', 'HCT', 'HGB')
+            )
+            WHERE 
+                -- only keep labs where all three components result
+                label_WBC is not NULL AND
+                label_PLT is not NULL AND
+                label_HCT is not NULL AND
+                label_HGB is not NULL
+        )
+
+        ### 10000 observations randomly sampled per year from train set
+        SELECT 
+            anon_id, order_id_coded as observation_id, index_time, 
+            ordering_mode, label_WBC, label_PLT, label_HCT, label_HGB
+        FROM 
+            (SELECT *,
+                ROW_NUMBER() OVER  (PARTITION BY EXTRACT(YEAR FROM index_time)
+                                    ORDER BY RAND()) 
+                        AS seqnum
+            FROM cohort_wide 
+            ) 
+        WHERE
+            seqnum <= 500
+        )
+        
+        """
+        query_job = self.client.query(query)
+        query_job.result()
diff --git a/scripts/lab_prediction_pro/constants.py b/scripts/lab_prediction_pro/constants.py
new file mode 100644
index 00000000..b75f1225
--- /dev/null
+++ b/scripts/lab_prediction_pro/constants.py
@@ -0,0 +1,49 @@
+# Constants to import 
+
+DEFAULT_DEPLOY_CONFIG = {
+    'Categorical': {
+        'Sex': [{'look_back' : None}],
+        'Race': [{'look_back' : None}],
+        'Diagnoses': [{'look_back' : None}],
+        'Medications': [{'look_back' : 28}]
+    },
+    'Numerical': {
+        'Age': [{'look_back': None, 'num_bins': 5}],
+        'LabResults': [{'look_back': 14, 'num_bins': 5}],
+        #'Vitals': [{'look_back': 3, 'num_bins': 5}]
+    }
+}
+
+DEFAULT_LAB_COMPONENT_IDS = [
+    'WBC',  # White Blood Cell
+    'HCT',  # Hematocrit
+    'PLT',  # Platelet Count
+    'NA',  # Sodium, Whole Blood
+    'K',  # Potassium, Whole Blood
+    'CO2',  # CO2, Serum/Plasma
+    'BUN',  # Blood Urea Nitrogen
+    'CR',  # Creatinine
+    'TBIL',  # Total Bilirubin
+    'ALB',  # Albumin
+    'CA',  # Calcium
+    'LAC',  # Lactic Acid
+    'ESR',  # Erythrocyte Sedimentation Rate
+    'CRP',  # C-Reactive Protein
+    'TNI',  # Troponin I
+    'PHA',  # Arterial pH
+    'PO2A',  # Arterial pO2
+    'PCO2A',  # Arterial pCO2
+    'PHV',  # Venous pH
+    'PO2V',  # Venous pO2
+    'PCO2V'  # Venous pCO2
+]
+
+
+DEFAULT_FLOWSHEET_FEATURES = [
+    "Heart Rate",
+    "Temp",
+    "Resp",
+    "SpO2",
+    'BP_High_Systolic',
+    "BP_Low_Diastolic"
+]
diff --git a/scripts/lab_prediction_pro/datasets.py b/scripts/lab_prediction_pro/datasets.py
new file mode 100644
index 00000000..1841b9e3
--- /dev/null
+++ b/scripts/lab_prediction_pro/datasets.py
@@ -0,0 +1,62 @@
+import numpy as np
+import torch
+from torch.nn.utils.rnn import pad_sequence 
+
+import pdb
+
+# Needed to batch data with variable length inputs
+def custom_collate(data): 
+    """
+    Data has sequences that are all variable length. Number of sequences
+    is variable per batch, and sequence length is variable across batches.
+
+    Want sequence to have shape (B, N_max, S_max) where B is batch size
+    N_max is max number of days in each batch, S_max is max number of tokens
+    within a day for each batch 
+    """
+    inputs = [d['sequence'] for d in data]
+    labels = [torch.tensor(d['labels']) for d in data]
+    patient_lens = [i.shape[0] for i in inputs]
+    inputs_unwound = [inp[i] for inp in inputs for i in range(len(inp))]
+    inputs_unwound_padded = pad_sequence(inputs_unwound, batch_first=True)
+    inputs_padded = torch.split(inputs_unwound_padded, patient_lens)
+    inputs_padded = pad_sequence(inputs_padded, batch_first=True)
+    time_deltas = [torch.exp(torch.tensor(d['time_deltas'])*-1) for d in data]
+    time_deltas = pad_sequence(time_deltas, batch_first=True)
+    return {
+        'sequence': inputs_padded, 
+        'labels': torch.tensor([lab[0][0] for lab in labels]), # refactor 
+        'time_deltas' : time_deltas,
+        'lengths' : patient_lens
+    }
+
+class SequenceDataset(torch.utils.data.Dataset):
+    """
+    Defines sequence dataset used for sequence models
+    """
+
+    def __init__(self, sequences, one_label_per_sequence=False):
+        'Initialization'
+        self.sequences = sequences
+        self.one_label = one_label_per_sequence
+
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.sequences)
+
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        data = torch.load(self.sequences[index])
+
+        # Select sample
+        sequence =  data['sequence']
+        if self.one_label:
+            labels = data['labels'][0]
+        else:
+            labels = data['labels']
+        time_deltas = data['time_deltas']
+        return {'sequence' : sequence,
+                'labels' : labels,
+                'time_deltas' : time_deltas}
+
+
diff --git a/scripts/lab_prediction_pro/driver.py b/scripts/lab_prediction_pro/driver.py
new file mode 100644
index 00000000..50813da2
--- /dev/null
+++ b/scripts/lab_prediction_pro/driver.py
@@ -0,0 +1,150 @@
+import argparse
+from google.cloud import bigquery
+import os
+import pandas as pd
+import sys
+
+from cohort_definitions import *
+from trainers import *
+from featurizers import *
+
+import pdb
+
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (
+    '/Users/conorcorbin/.config/gcloud/application_default_credentials.json'
+)
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101'
+client = bigquery.Client()
+
+parser = argparse.ArgumentParser(description='Pipeline to train CBC models')
+parser.add_argument(
+    '--run_name',
+    required=True,
+    help='Ex date of execution and cohort def'
+)
+parser.add_argument(
+    '--cohort_table_name',
+    default='cohort_table',
+    help='Bigquery cohort table name'
+)
+parser.add_argument(
+    '--feature_table_name',
+    default='feature_matrix',
+    help='Bigquery feature table name'
+)
+parser.add_argument(
+    '--outpath',
+    default='model_info',
+    help='Out directory to write files to'
+)
+parser.add_argument(
+    '--cohort', # note can add choices with choices= parameter
+    default=None,
+    help='Cohort to build'
+)
+parser.add_argument(
+    '--featurizer',
+    default=None,
+    help='Featurizer to use'
+)
+parser.add_argument(
+    '--trainer',
+    default=None,
+    help='Trainer to use'
+)
+parser.add_argument(
+    "--tasks",
+    default=[],  # set of tasks (name of label column)
+    nargs="*",
+)
+parser.add_argument(
+    '--working_dataset_name',
+    default='conor_db',
+    help='Bigquery dataset name where we store cohorts and features'
+)
+parser.add_argument(
+    '--working_project_id',
+    default='mining-clinical-decisions',
+    help='Project id for temp tables'
+)
+parser.add_argument(
+    '--project_id',
+    default='som-nero-phi-jonc101',
+    help='project id that holds dataset used to create cohort and features'
+)
+parser.add_argument(
+    '--dataset',
+    default='shc_core_2021',
+    help='dataset used to create cohort and features'
+)
+parser.add_argument(
+    '--tfidf',
+    action='store_true',
+    help='whether to transform bow with tfidf'
+)
+args = parser.parse_args()
+
+# Dictionary lookup for arguments
+cohort_builders = {
+    'CBCWithDifferentialCohortSmall': CBCWithDifferentialCohortSmall,
+    'CBCWithDifferentialCohort': CBCWithDifferentialCohort,
+    'MetabolicComprehensiveCohort': MetabolicComprehensiveCohort,
+    'MagnesiumCohort': MagnesiumCohort,
+    'BloodCultureCohort': BloodCultureCohort,
+    'UrineCultureCohort': UrineCultureCohort
+}
+featurizers = {
+    'BagOfWordsFeaturizerLight': BagOfWordsFeaturizerLight,
+    'SequenceFeaturizer': SequenceFeaturizer
+}
+trainers = {
+    'BoostingTrainer': BoostingTrainer,
+    'BaselineModelTrainer': BaselineModelTrainer
+}
+
+# Build cohort
+if args.cohort is not None:
+    cohort_builder = cohort_builders[args.cohort](
+        client=client,
+        dataset_name=args.working_dataset_name,
+        table_name=f"{args.run_name}_{args.cohort_table_name}"
+    )
+    cohort_builder() # Call to build cohort
+
+# Perform feature extraction
+cohort_table = (f"{args.working_project_id}.{args.working_dataset_name}."
+                f"{args.run_name}_{args.cohort_table_name}")
+feature_table = (f"{args.working_project_id}.{args.working_dataset_name}."
+                 f"{args.run_name}_{args.feature_table_name}")
+if args.featurizer == 'BagOfWordsFeaturizerLight':
+    featurizer = BagOfWordsFeaturizerLight(
+        cohort_table_id=cohort_table,
+        feature_table_id=feature_table,
+        outpath=f"./{args.run_name}_{args.outpath}",
+        project=args.project_id,
+        dataset=args.dataset,
+        tfidf=args.tfidf
+    )
+elif args.featurizer == 'SequenceFeaturizer':
+    featurizer = SequenceFeaturizer(
+        cohort_table_id=cohort_table,
+        feature_table_id=feature_table,
+        train_years=[2015, 2016, 2017, 2018, 2019],
+        val_years=[2020],
+        test_years=[2021],
+        label_columns=args.tasks,
+        outpath=f"./{args.run_name}_{args.outpath}",
+        project=args.project_id,
+        dataset=args.dataset,
+    )
+
+if args.featurizer is not None:
+    featurizer() # Call to featurizer
+
+# Train model
+if args.trainer is not None:
+    trainer = trainers[args.trainer](
+        working_dir=f"./{args.run_name}_{args.outpath}"
+    )
+    for task in args.tasks:
+        trainer(task)
diff --git a/scripts/lab_prediction_pro/feature_extractors.py b/scripts/lab_prediction_pro/feature_extractors.py
new file mode 100644
index 00000000..5287dc52
--- /dev/null
+++ b/scripts/lab_prediction_pro/feature_extractors.py
@@ -0,0 +1,616 @@
+"""
+Defines all feature extractors used by featurizers. A feature extractor is a
+self contained module that takes  in a cohort table and appends to or creates a
+long form feature matrix in a temporary bigquery table with the following schema
+Columns:
+    observation_id -- integer
+    index_time -- timestamp
+    feature_type -- string
+    feature_time -- timestamp
+    feature_id -- string
+    feature -- string
+    feature_value -- numeric
+"""
+from google.cloud import bigquery
+from google.cloud.exceptions import NotFound
+import pandas as pd 
+
+REPLACE_TABLE = True
+
+class FlowsheetBinsExtractor():
+    """
+    Defines logic to extract vitals from flowsheets
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 flowsheet_descriptions, look_back_days=3, bins=5,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Tokenizes flowsheets into bins and then writes or appends to temp
+        dataset. 
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            temp_dataset: name of temp dataset with cohort table
+            flowsheet_descriptions: list of row_disp_names used for flowsheet
+                descriptions
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        Returns:
+            df_lup : dataframe with bin thresholds
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.look_back_days = look_back_days
+        self.project_id = project_id
+        self.dataset = dataset
+        self.flowsheet_descriptions = "("
+        for i, flow in enumerate(flowsheet_descriptions):
+            if i == len(flowsheet_descriptions) - 1:
+                self.flowsheet_descriptions += f"'{flow}')"
+            else:
+                self.flowsheet_descriptions += f"'{flow}', "
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        WITH flowsheet_vals AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Vitals' as feature_type,
+            f.recorded_time_utc as feature_time,
+            GENERATE_UUID() as feature_id,
+            f.row_disp_name as feature,
+            CAST(f.numerical_val_1 AS float64) as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.flowsheet f
+        ON
+            labels.anon_id = f.anon_id
+        WHERE
+            f.recorded_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(f.recorded_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            f.row_disp_name in {self.flowsheet_descriptions}
+        AND
+            f.numerical_val_1 IS NOT NULL
+        ),
+
+        ranked as (
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id, feature,
+            PERCENT_RANK() OVER (PARTITION BY feature ORDER BY value) value
+        FROM 
+            flowsheet_vals
+        )
+
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id,
+            CASE WHEN value < 0.2 THEN CONCAT(feature, '_0')
+                WHEN value < 0.4 THEN CONCAT(feature, '_1')
+                WHEN value < 0.6 THEN CONCAT(feature, '_2')
+                WHEN value < 0.8 THEN CONCAT(feature, '_3')
+                ELSE CONCAT(feature, '_4')
+            END feature,
+            1 value
+        FROM
+            ranked
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+        df_lup = self.get_bin_thresholds()
+        return df_lup
+
+    def get_bin_thresholds(self):
+        query = f"""
+        WITH flowsheet_vals AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Vitals' as feature_type,
+            f.recorded_time_utc as feature_time,
+            GENERATE_UUID() as feature_id,
+            f.row_disp_name as feature,
+            CAST(f.numerical_val_1 AS float64) as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.flowsheet f
+        ON
+            labels.anon_id = f.anon_id
+        WHERE
+            f.recorded_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(f.recorded_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            f.row_disp_name in {self.flowsheet_descriptions}
+        AND
+            f.numerical_val_1 IS NOT NULL
+        )
+
+        SELECT DISTINCT
+            feature,
+            PERCENTILE_DISC(value, 0.2) OVER(PARTITION BY feature) min_bin_1,
+            PERCENTILE_DISC(value, 0.4) OVER(PARTITION BY feature) min_bin_2,
+            PERCENTILE_DISC(value, 0.6) OVER(PARTITION BY feature) min_bin_3,
+            PERCENTILE_DISC(value, 0.8) OVER(PARTITION BY feature) min_bin_4,
+        FROM 
+            flowsheet_vals
+        """
+        df = pd.read_gbq(query)
+        return df
+
+class LabResultBinsExtractor():
+    """
+    Defines logic to extract lab results orders, 
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 base_names, bins=5, look_back_days=14, 
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            temp_dataset: name of temp dataset with cohort table
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.num_bins = bins
+        self.look_back_days = look_back_days
+        self.project_id = project_id
+        self.dataset = dataset
+        self.base_name_string = "("
+        for i, base_name in enumerate(base_names):
+            if i == len(base_names) - 1:
+                self.base_name_string += f"'{base_name}')"
+            else:
+                self.base_name_string += f"'{base_name}', "
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        WITH labresults_values AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Lab Results' as feature_type,
+            lr.result_time_utc as feature_time,
+            CAST(lr.order_id_coded AS STRING) as feature_id,
+            lr.base_name as feature,
+            lr.ord_num_value as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.lab_result lr
+        ON
+            labels.anon_id = lr.anon_id
+        WHERE
+            lr.result_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(lr.result_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            lr.base_name in {self.base_name_string}
+        AND
+            lr.ord_num_value IS NOT NULL
+        ),
+
+        ranked as (
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id, feature,
+            PERCENT_RANK() OVER (PARTITION BY feature ORDER BY value) value
+        FROM 
+            labresults_values
+        )
+
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id,
+            CASE WHEN value < 0.2 THEN CONCAT(feature, '_0')
+                WHEN value < 0.4 THEN CONCAT(feature, '_1')
+                WHEN value < 0.6 THEN CONCAT(feature, '_2')
+                WHEN value < 0.8 THEN CONCAT(feature, '_3')
+                ELSE CONCAT(feature, '_4')
+            END feature,
+            1 value
+        FROM
+            ranked
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+        df_lup = self.get_bin_thresholds()
+        return df_lup
+
+    def get_bin_thresholds(self):
+        query = f"""
+        WITH labresults_values AS (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Lab Results' as feature_type,
+            lr.result_time_utc as feature_time,
+            CAST(lr.order_id_coded AS STRING) as feature_id,
+            lr.base_name as feature,
+            lr.ord_num_value as value
+        FROM
+            {self.cohort_table_id}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.lab_result lr
+        ON
+            labels.anon_id = lr.anon_id
+        WHERE
+            lr.result_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(lr.result_time_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        AND
+            lr.base_name in {self.base_name_string}
+        AND
+            lr.ord_num_value IS NOT NULL
+        )
+
+        SELECT DISTINCT
+            feature,
+            PERCENTILE_DISC(value, 0.2) OVER(PARTITION BY feature) min_bin_1,
+            PERCENTILE_DISC(value, 0.4) OVER(PARTITION BY feature) min_bin_2,
+            PERCENTILE_DISC(value, 0.6) OVER(PARTITION BY feature) min_bin_3,
+            PERCENTILE_DISC(value, 0.8) OVER(PARTITION BY feature) min_bin_4,
+        FROM 
+            labresults_values
+        """
+        df = pd.read_gbq(query)
+        return df
+
+class MedicationExtractor():
+    """
+    Defines logic to extract medication orders
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, 
+                 look_back_days=28, project_id='som-nero-phi-jonc101',
+                 dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.look_back_days = look_back_days
+        self.project_id = project_id
+        self.dataset = dataset
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Medications' as feature_type,
+            meds.order_inst_utc as feature_time,
+            CAST(meds.order_med_id_coded as STRING) as feature_id,
+            meds.med_description as feature,
+            1 as value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.order_med meds
+        ON
+            labels.anon_id = meds.anon_id
+        WHERE 
+            CAST(meds.order_inst_utc as TIMESTAMP) < labels.index_time
+        AND
+            TIMESTAMP_ADD(meds.order_inst_utc,
+                          INTERVAL 24*{self.look_back_days} HOUR)
+                          >= labels.index_time
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class PatientProblemExtractor():
+    """
+    Defines logic to extract diagnoses on the patient's problem list
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.project_id = project_id
+        self.dataset = dataset
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT
+            labels.observation_id,
+            labels.index_time,
+            'Diagnoses' as feature_type,
+            CAST(dx.start_date_utc as TIMESTAMP) as feature_time,
+            GENERATE_UUID() as feature_id,
+            dx.icd10 as feature,
+            1 value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.diagnosis dx
+        ON
+            labels.anon_id = dx.anon_id
+        WHERE 
+            CAST(dx.start_date_utc as TIMESTAMP) < labels.index_time
+        AND
+            source = 2 --problem list only
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class SexExtractor():
+    """
+    Defines logic to extract sex as feature from dataset
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.project_id = project_id
+        self.dataset = dataset
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            CASE WHEN demo.GENDER is NULL then 'sex_missing'
+            ELSE CONCAT('sex_', demo.GENDER) END feature,
+            1 value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class RaceExtractor():
+    """
+    Defines logic to extract race as feature from dataset
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.project_id = project_id
+        self.dataset = dataset
+
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            CASE WHEN demo.CANONICAL_RACE is NULL then 'race_missing'
+            ELSE CONCAT('race_', demo.CANONICAL_RACE) END feature,
+            1 value
+        FROM
+            {self.cohort_table_id}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+
+class AgeExtractor():
+    """
+    Defines logic to extract age as feature from dataset
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, bins=5,
+                 project_id='som-nero-phi-jonc101', dataset='shc_core_2021'):
+        """
+        Args:
+            cohort_table: name of cohort table -- used to join to features
+            project_id: name of project you are extracting data from
+            dataset: name of dataset you are extracting data from
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.client = bigquery.Client()
+        self.num_bins = bins
+        self.project_id = project_id
+        self.dataset = dataset
+     
+    def __call__(self):
+        """
+        Executes queries and returns all 
+        """
+        query = f"""
+        WITH age_values as (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            'Age' as feature,
+            DATE_DIFF(
+                CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR)
+            as value
+        FROM
+            {self.cohort_table_id}
+            labels
+        INNER JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        ),
+        ranked as (
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id, feature,
+            PERCENT_RANK() OVER (PARTITION BY feature ORDER BY value) value
+        FROM
+            age_values
+        )
+
+        SELECT DISTINCT
+            observation_id, index_time, feature_type,
+            feature_time, feature_id,
+            CASE WHEN value < 0.2 THEN CONCAT(feature, '_0')
+                WHEN value < 0.4 THEN CONCAT(feature, '_1')
+                WHEN value < 0.6 THEN CONCAT(feature, '_2')
+                WHEN value < 0.8 THEN CONCAT(feature, '_3')
+                ELSE CONCAT(feature, '_4')
+            END feature,
+            1 value
+        FROM
+            ranked
+        """
+        query = add_create_or_append_logic(query, self.feature_table_id)
+        query_job = self.client.query(query)
+        query_job.result()
+        df_lup = self.get_bin_thresholds()
+        return df_lup
+    
+    def get_bin_thresholds(self):
+        query = f"""
+        WITH age_values as (
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            GENERATE_UUID() as feature_id,
+            'Age' as feature,
+            DATE_DIFF(
+                CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR)
+            as value
+        FROM
+            {self.cohort_table_id}
+            labels
+        INNER JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        )
+
+        SELECT DISTINCT
+            feature,
+            PERCENTILE_DISC(value, 0.2) OVER(PARTITION BY feature) min_bin_1,
+            PERCENTILE_DISC(value, 0.4) OVER(PARTITION BY feature) min_bin_2,
+            PERCENTILE_DISC(value, 0.6) OVER(PARTITION BY feature) min_bin_3,
+            PERCENTILE_DISC(value, 0.8) OVER(PARTITION BY feature) min_bin_4,
+        FROM 
+            age_values
+        """
+        df = pd.read_gbq(query)
+        return df
+
+def table_exists(feature_table_id):
+    """
+    Check if table exists
+    """
+    client = bigquery.Client()
+    try:
+        client.get_table(f'{feature_table_id}')
+        return True
+    except NotFound:
+        return False
+
+def add_create_or_append_logic(query, feature_table_id):
+    """
+    Adds SQL logic to either append or create a new feature matrix from result
+    of user supplied SQL query. 
+    """
+    global REPLACE_TABLE
+    exists = table_exists(feature_table_id)
+    if exists and REPLACE_TABLE == False:
+        query = f"""
+        INSERT INTO
+            {feature_table_id}
+        {query}
+        """
+    else:
+        query = f"""
+        CREATE OR REPLACE TABLE {feature_table_id} AS (
+        {query}
+        )
+        """
+        REPLACE_TABLE = False
+    return query
diff --git a/scripts/lab_prediction_pro/featurizers.py b/scripts/lab_prediction_pro/featurizers.py
new file mode 100644
index 00000000..4aa3b52b
--- /dev/null
+++ b/scripts/lab_prediction_pro/featurizers.py
@@ -0,0 +1,1233 @@
+"""
+Definition of BagOfWordsFeaturizer
+TODO: Definition of SummaryStatsFeaturizer
+TODO: Definition of TimelineFeaturizer
+"""
+import json
+import os
+from re import S
+from typing_extensions import Self
+import pandas as pd
+import pickle
+from google.cloud import bigquery
+import numpy as np
+from tqdm import tqdm
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from scipy.sparse import csr_matrix
+from scipy.sparse import save_npz
+from sklearn.feature_extraction.text import TfidfTransformer
+
+from constants import DEFAULT_DEPLOY_CONFIG
+from constants import DEFAULT_LAB_COMPONENT_IDS
+from constants import DEFAULT_FLOWSHEET_FEATURES
+import feature_extractors as fextractors
+
+import pdb
+
+class SequenceFeaturizer():
+    """
+    Uses FeatureExtractors to generate a set of variable lengh sequences
+    for each observation. Saves these sequences as invididual numpy arrays in 
+    npz format. The npz contains the following data elements
+    NPZ structure:
+         sequence: array of array of tokens (continuous values binned), arrays
+            grouped by day as in Steinberg et al.
+         time_deltas: array with len equal to sequence.shape[0], days
+            from index_time for each bag of features
+         label: list of labels corresponding to sequence.  len = 1 if one label 
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, train_years,
+                 val_years, test_years, label_columns, outpath='./features',
+                 project='som-nero-phi-jonc101', dataset='shc_core_2021',
+                 feature_config=None):
+        """
+        Args:
+            cohort_table_id: ex 'mining-clinical-decisions.conor_db.table_name'
+            feature_table_id: ex
+                'mining-clinical-decisions.conor_db.feature_table'
+            train_years: list of years to include in training set
+            val_years: list of years to include in validation set
+            test_years: list of years to include in the test set
+            label_columns: list of columns desingated as labels in cohort table
+            outpath: path to dump feature matrices
+            project: bq project id to extract data from
+            dataset: bq dataset with project to extract data from
+            feature_config: dictionary with feature types, bins and look back
+                windows.
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.outpath = outpath
+        self.project = project
+        self.dataset = dataset
+        if feature_config is None:
+            self.feature_config = DEFAULT_DEPLOY_CONFIG
+        else:
+            self.feature_config = feature_config
+        self.client = bigquery.Client()
+        self.train_years = [int(y) for y in train_years]
+        self.val_years = [int(y) for y in val_years]
+        self.test_years = [int(y) for y in test_years]
+        self.label_columns = label_columns
+
+    def __call__(self):
+        """
+        Generates sequence feature vectors and saves to outpath
+        """
+        label_cols = ', '.join([f"c.{l}" for l in self.label_columns])
+        # self.construct_feature_timeline()
+        # self.collapse_timeline_to_days()
+        query = f"""
+        SELECT
+            f.*, {label_cols}, index_time
+        FROM
+            {self.feature_table_id}_days f
+        INNER JOIN
+            {self.cohort_table_id} c
+        USING
+            (observation_id)
+        ORDER BY
+            observation_id, time_deltas
+        DESC
+        """
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+
+        # Split into train, val and test and ensure only terms in train are used
+        train_seqs = df[df['index_time'].dt.year.isin(self.train_years)]
+        
+        # Build vocab dict and save
+        vocab = []
+        for feature_list in train_seqs.feature.values:
+            if feature_list is not None:
+                vocab += [v for v in feature_list.split('---')]
+        vocab = set(vocab)
+        vocab_map = {}
+        counter = 1 # reserve 0 for padding
+        for term in vocab:
+            vocab_map[term] = counter
+            counter += 1
+
+        val_seqs = df[df['index_time'].dt.year.isin(self.val_years)]
+        test_seqs = df[df['index_time'].dt.year.isin(self.test_years)]
+        seq_dict = {'train' : train_seqs, 'val' :  val_seqs, 'test' : test_seqs}
+    
+        # Create working directory if does not already exist and save features
+        for dataset, seqs in seq_dict.items():
+            os.makedirs(os.path.join(self.outpath, dataset), exist_ok=True)
+            print(f"Generating {dataset} sequences")
+            for obs in tqdm(seqs.observation_id.unique()):
+                example = seqs[seqs['observation_id']==obs]
+                sequence = self.pad_examples(example, vocab_map)
+                time_deltas = example.time_deltas.values
+                labels = example[self.label_columns].values
+                out_file = os.path.join(self.outpath, dataset, f"{obs}.pt")
+                torch.save({"sequence": sequence,
+                            "time_deltas": time_deltas,
+                            "labels": labels}, out_file)    
+                   
+        # Save feature vocab
+        with open(os.path.join(self.outpath, 'feature_vocab.npz'), 'w') as fp:
+            json.dump(vocab_map, fp)
+                            
+        # Save bin thresholds if they exist
+        self.df_lup = pd.DataFrame()
+        for lup in self.lups:
+            if lup is not None:
+                self.df_lup = pd.concat([self.df_lup, lup])
+        if not self.df_lup.empty:
+            self.df_lup.to_csv(os.path.join(self.outpath, 'bin_lup.csv'),
+                               index=None)
+
+        # Save feature_config
+        with open(os.path.join(self.outpath, 'feature_config.json'), 'w') as f:
+            json.dump(self.feature_config, f)
+
+    def pad_examples(self, example, vocab):
+        """
+        Pads days within an example with zeros so all same length. 
+        """
+        sequences = [torch.tensor(
+                    [vocab[a] for a in e.split('---') if a in vocab])
+                     for e in example.feature.values if e is not None]
+        sequences_padded = pad_sequence(sequences, batch_first=True)
+        return sequences_padded
+
+    def collapse_timeline_to_days(self):
+        """
+        Groups long form feature vector by day and collapses all feature values
+        """
+        query=f"""
+        CREATE OR REPLACE TABLE {self.feature_table_id}_days AS (
+        SELECT 
+            observation_id,
+            STRING_AGG(feature, '---') feature,
+            TIMESTAMP_DIFF(index_time, feature_time, DAY) time_deltas
+        FROM 
+            {self.feature_table_id}
+        GROUP BY
+            observation_id, time_deltas
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def construct_feature_timeline(self):
+        """
+        Executes all logic to iteratively append rows to the biq query long form
+        feature matrix destination table.  Does this by iteratively joining
+        cohort table to tables with desired features, filtering for events
+        that occur within each look up range, and then transforming into bag of
+        words style representations.  Features with numerical values are binned
+        into buckets to enable bag of words repsesentation. 
+        """
+        extractors = []
+        # Get categorical features
+        if 'Sex' in self.feature_config['Categorical']:
+            se = fextractors.SexExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(se)
+        if 'Race' in self.feature_config['Categorical']:
+            re = fextractors.RaceExtractor(self.cohort_table_id,
+                                           self.feature_table_id)
+            extractors.append(re)
+        if 'Diagnoses' in self.feature_config['Categorical']:
+            pe = fextractors.PatientProblemExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(pe)
+        if 'Medications' in self.feature_config['Categorical']:
+            me = fextractors.MedicationExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(me)
+
+        # Get numerical features
+        if 'Age' in self.feature_config['Numerical']:
+            ae = fextractors.AgeExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Age'][0]['num_bins'])
+            extractors.append(ae)
+        if 'LabResults' in self.feature_config['Numerical']:
+            lre = fextractors.LabResultBinsExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']
+                ['LabResults'][0]['num_bins'],
+                base_names=DEFAULT_LAB_COMPONENT_IDS)
+            extractors.append(lre)
+        if 'Vitals' in self.feature_config['Numerical']:
+            fbe = fextractors.FlowsheetBinsExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Vitals'][0]['num_bins'],
+                base_names=DEFAULT_FLOWSHEET_FEATURES)
+            extractors.append(fbe)
+
+        # Call extractors and collect any look up tables
+        self.lups = []
+        for extractor in tqdm(extractors):
+            self.lups.append(extractor())
+
+class BagOfWordsFeaturizerLight():
+    """
+    Bag of words but uses FeatureExtractors instead of burying all SQL logic
+    as in implementation below. 
+    """
+
+    def __init__(self, cohort_table_id, feature_table_id, outpath='./features',
+                 project='som-nero-phi-jonc101', dataset='shc_core_2021',
+                 feature_config=None, tfidf=True):
+        """
+        Args:
+            cohort_table_id: ex 'mining-clinical-decisions.conor_db.table_name'
+            feature_table_id: ex 
+                'mining-clinical-decisions.conor_db.feature_table'
+            outpath: path to dump feature matrices
+            project: bq project id to extract data from
+            dataset: bq dataset with project to extract data from
+            feature_config: dictionary with feature types, bins and look back
+                windows. 
+            tfidf: if true apply a tfidf transform, train on train apply on
+                test
+        """
+        self.cohort_table_id = cohort_table_id
+        self.feature_table_id = feature_table_id
+        self.outpath = outpath
+        self.project = project
+        self.dataset = dataset
+        self.tfidf = tfidf
+        if feature_config is None:
+            self.feature_config = DEFAULT_DEPLOY_CONFIG
+        else:
+            self.feature_config = feature_config
+        self.client = bigquery.Client()
+        # Get data splits (default last year of data held out as test set)
+        split_query = f"""
+            SELECT DISTINCT
+                EXTRACT(YEAR FROM index_time) year
+            FROM
+                {self.cohort_table_id}
+        """
+        df = pd.read_gbq(split_query).sort_values('year')
+        self.train_years = df.year.values[:-1]
+        self.test_years = [df.year.values[-1]]
+        self.replace_table = True
+
+    def __call__(self):
+        """
+        Executes all logic to construct features and labels and saves all info
+        user specified working directory.
+        """
+        self.construct_feature_timeline()
+        self.construct_bag_of_words_rep()
+        query = f"""
+        SELECT
+            *
+        FROM
+            {self.feature_table_id}_bow
+        ORDER BY
+            observation_id
+        """
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        train_features = df[df['index_time'].dt.year.isin(self.train_years)]
+        apply_features = df[~df['index_time'].dt.year.isin(self.train_years)]
+        train_csr, train_ids, train_vocab = self.construct_sparse_matrix(
+            train_features, train_features)
+        test_csr, test_ids, test_vocab = self.construct_sparse_matrix(
+            train_features, apply_features)
+        
+        # Apply tfidf transform if indicated and save
+        if self.tfidf:
+            transform = TfidfTransformer()
+            train_csr = transform.fit_transform(train_csr)
+            test_csr = transform.transform(test_csr)
+            os.makedirs(self.outpath, exist_ok=True)
+            transform_path = os.path.join(
+                self.outpath, 'tfidf_transform.pkl')
+            with open(transform_path, 'wb') as w:
+                pickle.dump(transform, w)
+
+        # Query cohort table for labels
+        q_cohort = f"""
+            SELECT
+                *
+            FROM
+               {self.cohort_table_id}
+            ORDER BY
+                observation_id
+        """
+        df_cohort = pd.read_gbq(q_cohort, progress_bar_type='tqdm')
+        train_labels = df_cohort[df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+        test_labels = df_cohort[~df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+
+        # Sanity check - make sure ids from labels and features in same order
+        for a, b in zip(train_labels['observation_id'].values, train_ids):
+            try:
+                assert a == b
+            except:
+                pdb.set_trace()
+        for a, b in zip(test_labels['observation_id'].values, test_ids):
+            assert a == b
+
+        # Create working directory if does not already exist and save features
+        os.makedirs(self.outpath, exist_ok=True)
+        save_npz(os.path.join(self.outpath, 'train_features.npz'), train_csr)
+        save_npz(os.path.join(self.outpath, 'test_features.npz'), test_csr)
+        print(f"Feature matrix generated with {train_csr.shape[1]} features")
+
+        # Save labels
+        train_labels.to_csv(os.path.join(self.outpath, 'train_labels.csv'),
+                            index=None)
+        test_labels.to_csv(os.path.join(self.outpath, 'test_labels.csv'),
+                           index=None)
+
+        # Save feature order
+        df_vocab = pd.DataFrame(data={
+            'features': [t for t in train_vocab],
+            'indices': [train_vocab[t] for t in train_vocab]
+        })
+        df_vocab.to_csv(os.path.join(self.outpath, 'feature_order.csv'),
+                        index=None)
+
+        # Save bin thresholds if they exist
+        self.df_lup = pd.DataFrame()
+        for lup in self.lups:
+            if lup is not None:
+                self.df_lup = pd.concat([self.df_lup, lup])
+        if not self.df_lup.empty:
+            self.df_lup.to_csv(os.path.join(self.outpath, 'bin_lup.csv'),
+                            index=None)
+
+        # Save feature_config
+        with open(os.path.join(self.outpath, 'feature_config.json'), 'w') as f:
+            json.dump(self.feature_config, f)
+
+    def construct_feature_timeline(self):
+        """
+        Executes all logic to iteratively append rows to the biq query long form
+        feature matrix destination table.  Does this by iteratively joining
+        cohort table to tables with desired features, filtering for events
+        that occur within each look up range, and then transforming into bag of
+        words style representations.  Features with numerical values are binned
+        into buckets to enable bag of words repsesentation. 
+        """
+        extractors = []
+        # Get categorical features
+        if 'Sex' in self.feature_config['Categorical']:
+            se = fextractors.SexExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(se)
+        if 'Race' in self.feature_config['Categorical']:
+            re = fextractors.RaceExtractor(self.cohort_table_id,
+                self.feature_table_id)
+            extractors.append(re)
+        if 'Diagnoses' in self.feature_config['Categorical']:
+            pe = fextractors.PatientProblemExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(pe)
+        if 'Medications' in self.feature_config['Categorical']:
+            me = fextractors.MedicationExtractor(
+                self.cohort_table_id, self.feature_table_id)
+            extractors.append(me)
+
+        # Get numerical features
+        if 'Age' in self.feature_config['Numerical']:
+            ae = fextractors.AgeExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Age'][0]['num_bins'])
+            extractors.append(ae)
+        if 'LabResults' in self.feature_config['Numerical']:
+            lre = fextractors.LabResultBinsExtractor(self.cohort_table_id,
+                                         self.feature_table_id,
+                                         bins=self.feature_config['Numerical']
+                                         ['LabResults'][0]['num_bins'],
+                                         base_names=DEFAULT_LAB_COMPONENT_IDS)
+            extractors.append(lre)
+        if 'Vitals' in self.feature_config['Numerical']:
+            fbe = fextractors.FlowsheetBinsExtractor(
+                self.cohort_table_id,
+                self.feature_table_id,
+                bins=self.feature_config['Numerical']['Vitals'][0]['num_bins'],
+                base_names=DEFAULT_FLOWSHEET_FEATURES)
+            extractors.append(fbe)
+
+        # Call extractors and collect any look up tables
+        self.lups = []
+        for extractor in tqdm(extractors):
+            self.lups.append(extractor())
+    
+    def construct_bag_of_words_rep(self):
+        """
+        Transforms long form feature timeline into a bag of words feature
+        matrix. Stores as a new table in the given bigquery database but also
+        constructs local sparse matrices that can be fed into various sklearn
+        style classifiers. 
+        """
+
+        # Go from timeline to counts
+        query = f"""
+        CREATE OR REPLACE TABLE {self.feature_table_id}_bow AS (
+        SELECT 
+            observation_id, index_time, feature_type, feature, COUNT(*) value 
+        FROM 
+            {self.feature_table_id}
+        WHERE 
+            feature_type IS NOT NULL
+        AND
+            feature IS NOT NULL
+        GROUP BY 
+            observation_id, index_time, feature_type, feature
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def construct_sparse_matrix(self, train_features, apply_features):
+        """
+        Takes long form feature timeline matrix and builds up a scipy csr
+        matrix without the costly pivot operation. 
+        """
+        train_features = (train_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        train_feature_names = [doc for doc in train_features.feature.values]
+        train_feature_values = [doc for doc in train_features['value'].values]
+        train_obs_id = [id_ for id_ in train_features.observation_id.values]
+
+        apply_features = (apply_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        apply_features_names = [doc for doc in apply_features.feature.values]
+        apply_features_values = [doc for doc in apply_features['value'].values]
+        apply_obs_id = [id_ for id_ in apply_features.observation_id.values]
+
+        vocabulary = self._build_vocab(train_feature_names)
+        indptr = [0]
+        indices = []
+        data = []
+        for i, d in enumerate(apply_features_names):
+            for j, term in enumerate(d):
+                if term not in vocabulary:
+                    continue
+                else:
+                    indices.append(vocabulary[term])
+                    data.append(apply_features_values[i][j])
+                if j == 0:
+                    # Add zero to data and max index in vocabulary to indices in
+                    # case max feature indice isn't in apply features.
+                    indices.append(len(vocabulary)-1)
+                    data.append(0)
+            indptr.append(len(indices))
+
+        csr_data = csr_matrix((data, indices, indptr), dtype=float)
+
+        return csr_data, apply_obs_id, vocabulary
+
+    def _build_vocab(self, data):
+        """
+        Builds vocabulary of terms from the data. Assigns each unique term
+        to a monotonically increasing integer
+        """
+        vocabulary = {}
+        for i, d in enumerate(data):
+            for j, term in enumerate(d):
+                vocabulary.setdefault(term, len(vocabulary))
+        return vocabulary
+
+
+class BagOfWordsFeaturizer(object):
+    """
+    A class containing all logic to construct a bag of words feature matrix
+    given a cohort and user provided feature configurations. 
+
+    This featurizer supports the construction of a bag of words style feature
+    matrix where each column in the resulting matrix is the number of times a 
+    certain event (ex lab order, diagnosis code, medication order) occurs
+    in the patients timeline in a given time window prior to index time. 
+
+    Categorical variables are trivial to represent in bag of words style.
+    Numerical variables (ie lab results, vital signs, age) are first binned into
+    user specified number of buckets so that they can be represented in similar 
+    fashion.
+
+    Feature matrices are constructed as csr matrices (sparse matrices) to allow
+    for feature representations that would otherwise use ungodly amounts of
+    RAM.  Sparse matrices are constructed directly from a long form feature
+    matrix representation to avoid the costly pivot operation. 
+
+    The user is able to specify various desired feature types and their look
+    back windows in a provided configuration dictionary. 
+    """
+
+    def __init__(self, cohort_table, label_columns, working_dataset_name, 
+                 table_name, project_id='mining-clinical-decisions',
+                 outpath='./features', dataset='shc_core_2021',
+                 working_project_id='mining-clinical-decisions',
+                 feature_config=None):
+        """
+        Initializes featurizer
+        Args:
+            cohort_table: location of cohort table {dataset_name}.{table_name}
+            label_columns: which columns in cohort table used as labels
+            dataset_name: big query dataset name - where to save long form
+                feature matrix.
+            table_name: big query table name - where to save long form feature
+                matrix
+            project_id: id of big query project
+            feature_config: configure which features types to extract and their
+                look back windows. If none uses default defined below
+        """
+        self.df_lup = pd.DataFrame()
+        self.outpath = outpath
+        self.cohort_table = cohort_table
+        self.label_columns = label_columns
+        self.dataset_name = working_dataset_name
+        self.dataset = dataset
+        self.table_name = table_name
+        self.project_id = project_id
+        self.working_proj_id = working_project_id
+        self.client = bigquery.Client()
+        self.create_table = True  # starts true then append
+        if feature_config is None:
+            self.feature_config = DEFAULT_DEPLOY_CONFIG
+        else:
+            self.feature_config = feature_config
+            ### TODO : write code to test whether user specified config is
+            ### adequate
+
+        # Get data splits (default last year of data held out as test set)
+        split_query = f"""
+            SELECT DISTINCT
+                EXTRACT(YEAR FROM index_time) year
+            FROM
+                {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+        """
+        df = pd.read_gbq(split_query).sort_values('year')
+        self.train_years = df.year.values[:-1]
+        self.test_years = [df.year.values[-1]]
+
+        self.feature_timeline_schema = [
+            {'name': 'observation_id', 'type': 'INTEGER'},
+            {'name': 'index_time', 'type': 'TIMESTAMP'},
+            {'name': 'feature_type', 'type': 'STRING'},
+            {'name': 'feature_time', 'type': 'TIMESTAMP'},
+            {'name': 'feature_id', 'type': 'INTEGER'},
+            {'name': 'feature', 'type': 'STRING'},
+            {'name': 'value', 'type': 'INTEGER'}
+        ]
+
+    def __call__(self):
+        """
+        Executes all logic to construct features and labels and saves all info
+        user specified working directory. 
+        """
+        self.construct_feature_timeline()
+        self.construct_bag_of_words_rep()
+        query = f"""
+        SELECT 
+            * 
+        FROM 
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name}_bow
+        ORDER BY
+            observation_id
+        """
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        train_features = df[df['index_time'].dt.year.isin(self.train_years)]
+        apply_features = df[~df['index_time'].dt.year.isin(self.train_years)]
+        train_csr, train_ids, train_vocab = self.construct_sparse_matrix(
+            train_features, train_features)
+        test_csr, test_ids, test_vocab = self.construct_sparse_matrix(
+            train_features, apply_features)
+
+        # Query cohort table for labels
+        q_cohort = f"""
+            SELECT 
+                * 
+            FROM 
+                {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            ORDER BY
+                observation_id
+        """
+        df_cohort = pd.read_gbq(q_cohort, progress_bar_type='tqdm')
+        train_labels = df_cohort[df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+        test_labels = df_cohort[~df_cohort['index_time'].dt.year.isin(
+            self.train_years)]
+
+        # Sanity check - make sure ids from labels and features in same order
+        for a, b in zip(train_labels['observation_id'].values, train_ids):
+            try:
+                assert a == b
+            except:
+                pdb.set_trace()
+        for a, b in zip(test_labels['observation_id'].values, test_ids):
+            assert a == b
+
+        # Create working directory if does not already exist and save features
+        os.makedirs(self.outpath, exist_ok=True)
+        save_npz(os.path.join(self.outpath, 'train_features.npz'), train_csr)
+        save_npz(os.path.join(self.outpath, 'test_features.npz'), test_csr)
+        print(f"Feature matrix generated with {train_csr.shape[1]} features")
+
+        # Save labels
+        train_labels.to_csv(os.path.join(self.outpath, 'train_labels.csv'),
+                            index=None)
+        test_labels.to_csv(os.path.join(self.outpath, 'test_labels.csv'),
+                           index=None)
+
+        # Save feature order
+        df_vocab = pd.DataFrame(data={
+            'features': [t for t in train_vocab],
+            'indices': [train_vocab[t] for t in train_vocab]
+        })
+        df_vocab.to_csv(os.path.join(self.outpath, 'feature_order.csv'),
+                        index=None)
+
+        # Save bin thresholds if they exist
+        if not self.df_lup.empty:
+            self.df_lup.to_csv(os.path.join(self.outpath, 'bin_lup.csv'),
+                            index=None)
+
+        # Save feature_config
+        with open(os.path.join(self.outpath, 'feature_config.json'), 'w') as f:
+            json.dump(self.feature_config, f)
+
+    def construct_feature_timeline(self):
+        """
+        Executes all logic to iteratively append rows to the biq query long form
+        feature matrix destination table.  Does this by iteratively joining
+        cohort table to tables with desired features, filtering for events
+        that occur within each look up range, and then transforming into bag of
+        words style representations.  Features with numerical values are binned
+        into buckets to enable bag of words repsesentation. 
+        """
+
+        # Get categorical features
+        if 'Sex' in self.feature_config['Categorical']:
+            self._get_sex_features()
+        if 'Race' in self.feature_config['Categorical']:
+            self._get_race_features()
+        if 'Diagnoses' in self.feature_config['Categorical']:
+            self._get_diagnoses_features()
+        if 'Medications' in self.feature_config['Categorical']:
+            self._get_medication_features()
+
+        # Get numerical features
+        if 'Age' in self.feature_config['Numerical']:
+            self._get_age_features()
+        if 'LabResults' in self.feature_config['Numerical']:
+            self._get_lab_results_features()
+        if 'Vitals' in self.feature_config['Numerical']:
+            self._get_vital_sign_features()
+
+        # Add token which is one if no features are present for a patient
+        self._get_missingness_features()
+
+    def construct_bag_of_words_rep(self):
+        """
+        Transforms long form feature timeline into a bag of words feature
+        matrix. Stores as a new table in the given bigquery database but also
+        constructs local sparse matrices that can be fed into various sklearn
+        style classifiers. 
+        """
+
+        # Go from timeline to counts
+        query = f"""
+        CREATE OR REPLACE TABLE 
+        {self.working_proj_id}.{self.dataset_name}.{self.table_name}_bow AS (
+
+        SELECT 
+            observation_id, index_time, feature_type, feature, COUNT(*) value 
+        FROM 
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name}
+        WHERE 
+            feature_type IS NOT NULL
+        AND
+            feature IS NOT NULL
+        GROUP BY 
+            observation_id, index_time, feature_type, feature
+        )
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def construct_sparse_matrix(self, train_features, apply_features):
+        """
+        Takes long form feature timeline matrix and builds up a scipy csr
+        matrix without the costly pivot operation. 
+        """
+        train_features = (train_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        train_feature_names = [doc for doc in train_features.feature.values]
+        train_feature_values = [doc for doc in train_features['value'].values]
+        train_obs_id = [id_ for id_ in train_features.observation_id.values]
+
+        apply_features = (apply_features
+                          .groupby('observation_id')
+                          .agg({'feature': lambda x: list(x),
+                                'value': lambda x: list(x)})
+                          .reset_index()
+                          )
+        apply_features_names = [doc for doc in apply_features.feature.values]
+        apply_features_values = [doc for doc in apply_features['value'].values]
+        apply_obs_id = [id_ for id_ in apply_features.observation_id.values]
+
+        vocabulary = self._build_vocab(train_feature_names)
+        indptr = [0]
+        indices = []
+        data = []
+        for i, d in enumerate(apply_features_names):
+            for j, term in enumerate(d):
+                if term not in vocabulary:
+                    continue
+                else:
+                    indices.append(vocabulary[term])
+                    data.append(apply_features_values[i][j])
+                if j == 0:
+                    # Add zero to data and max index in vocabulary to indices in
+                    # case max feature indice isn't in apply features.
+                    indices.append(len(vocabulary)-1)
+                    data.append(0)
+            indptr.append(len(indices))
+
+        csr_data = csr_matrix((data, indices, indptr), dtype=float)
+
+        return csr_data, apply_obs_id, vocabulary
+
+    def _build_vocab(self, data):
+        """
+        Builds vocabulary of terms from the data. Assigns each unique term
+        to a monotonically increasing integer
+        """
+        vocabulary = {}
+        for i, d in enumerate(data):
+            for j, term in enumerate(d):
+                vocabulary.setdefault(term, len(vocabulary))
+        return vocabulary
+
+    def get_binned_features(self, df_train, df_apply, num_bins):
+        """
+        Bins numerical features into specified number of bins based on
+        percentile values.  Uses training set to define cutoffs values across
+        each bin. 
+
+        Args:
+            df_train: dataframe of long form feature timeline for numerical
+                features for observations in the training set
+            df_apply: dataframe of long form feature timeline for numerical
+                features for osbervations in th test set
+        Returns:
+            df_binned : dataframe of long form feature timeline where the bin
+                associated with feature is specified in feature name and the
+                value is 1
+        """
+        def _get_appropriate_bin(feature, val, lut):
+            """
+            Helper to map value to bin
+            """
+            try:
+                mins = lut[feature]['min']
+            except:  # feature not found in training set
+                return 0  # removed in downstream processing
+            for i in range(len(mins)-1):
+                if i == 0 and val < mins[i]:
+                    return i
+                if val >= mins[i] and val < mins[i+1]:
+                    return i
+            return len(mins) - 1  # in last bin
+
+        ranks = df_train.groupby('feature')['value'].rank(pct=True).values
+        df_train = (df_train
+                    .assign(bins=[int(r * num_bins) for r in ranks])
+                    )
+        df_lup = (df_train
+                  .groupby(['feature', 'bins'])
+                  .agg(bin_min=('value', 'min'), bin_max=('value', 'max'))
+                  .reset_index()
+                  )
+
+        # Append to look up table we'll save later
+        self.df_lup = pd.concat([self.df_lup, df_lup])
+
+        # Convert df_lup to dictionary for faster bin look up.
+        lut_dict = {}
+        for feature in df_lup.feature.unique():
+            df_lup_feature = df_lup[df_lup['feature'] == feature]
+            for bin in df_lup_feature.bins.unique():
+                if feature not in lut_dict:
+                    lut_dict[feature] = {}
+                    lut_dict[feature]['min'] = []
+                    lut_dict[feature]['max'] = []
+                lut_dict[feature]['min'].append(df_lup_feature
+                    .query("bins == @bin", engine='python')
+                    ['bin_min'].values[0]
+                )
+                lut_dict[feature]['max'].append(df_lup_feature
+                    .query("bins == @bin", engine='python')
+                    ['bin_max'].values[0]
+                )
+
+        df_binned = (df_apply
+                     .assign(bins=lambda x:
+                             [_get_appropriate_bin(a, b, lut_dict)
+                              for a, b in zip(x.feature, x.value)]
+                             )
+                     .assign(feature=lambda x:
+                             [f"{a}_{b}" for a, b in zip(x.feature, x.bins)],
+                             value=1
+                             )
+                     .drop('bins', axis=1)
+                     )
+
+        return df_binned
+
+    def _get_missingness_features(self):
+        """
+        Finds patients that exist in cohort but not current feature matrix,
+        adds token 'missing' feature which takes value 1. 
+        """
+        query=f"""
+        INSERT INTO 
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            "Missing indicator" feature_type,
+            labels.index_time feature_time,
+            UNIX_DATE(CAST(labels.index_time as DATE)) as feature_id,
+            'no features' as feature,
+            1 as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.working_proj_id}.{self.dataset_name}.{self.table_name} feats
+        USING
+            (observation_id)
+        WHERE 
+            feats.observation_id IS NULL
+        """
+        query_job = self.client.query(query)
+        query_job.result()
+
+    def _get_sex_features(self):
+        """
+        Get's sex demographic features for patients in cohort and appends to
+        long form feature matrix on bigquery.
+        """
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            1 as feature_id,
+            CASE WHEN demo.GENDER is NULL then 'sex_missing'
+            ELSE CONCAT('sex_', demo.GENDER) END feature,
+            1 value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        {suffix}
+        """
+        print("Featurizing sex")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_race_features(self):
+        """
+        Get's race demographic features for each patient in cohort and appends
+        to long form feature matrix on bigquery. 
+        """
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            1 as feature_id,
+            CASE WHEN demo.CANONICAL_RACE is NULL then 'race_missing'
+            ELSE CONCAT('race_', demo.CANONICAL_RACE) END feature,
+            1 value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        {suffix}
+        """
+        print("Featurizing race")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_diagnoses_features(self):
+        """
+        Gets diagnosis code features for patients in cohort table and appends to
+        long form bigquery table. Only selects diagnosis codes on the patient's
+        problem list -- ie source = 2. 
+        """
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT
+            labels.observation_id,
+            labels.index_time,
+            'Diagnoses' as feature_type,
+            CAST(dx.start_date_utc as TIMESTAMP) as feature_time,
+            UNIX_DATE(CAST(dx.start_date_utc as DATE)) as feature_id,
+            dx.icd10 as feature,
+            1 value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.diagnosis dx
+        ON
+            labels.anon_id = dx.anon_id
+        WHERE 
+            CAST(dx.start_date_utc as TIMESTAMP) < labels.index_time
+        AND
+            source = 2
+        {suffix}
+        """
+        print("Featurizing diagnosis codes")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_medication_features(self):
+        """
+        Gets medication order features for patients in cohort table and appends
+        to long form bigquery table
+        """
+        max_look_back_time = 0
+        for lb in DEFAULT_DEPLOY_CONFIG['Categorical']['Medications']:
+            max_look_back_time = max(
+                lb['look_back'], max_look_back_time)
+
+        if self.create_table:
+            prefix = (
+                f"CREATE OR REPLACE TABLE {self.working_proj_id}."
+                f"{self.dataset_name}.{self.table_name} AS("
+            )
+            suffix = ")"
+        else:
+            prefix = (
+                f"INSERT INTO {self.working_proj_id}.{self.dataset_name}"
+                f".{self.table_name}"
+            )
+            suffix = ""
+
+        query = f"""
+        {prefix}
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Medications' as feature_type,
+            meds.order_inst_utc as feature_time,
+            meds.order_med_id_coded as feature_id,
+            meds.med_description as feature,
+            1 as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.order_med meds
+        ON
+            labels.anon_id = meds.anon_id
+        WHERE 
+            CAST(meds.order_inst_utc as TIMESTAMP) < labels.index_time
+        AND
+            TIMESTAMP_ADD(meds.order_inst_utc,
+                          INTERVAL 24*{max_look_back_time} HOUR)
+                          >= labels.index_time
+        {suffix}
+        """
+        print("Featurizing medication orders")
+        query_job = self.client.query(query)
+        query_job.result()
+        self.create_table = False
+
+    def _get_age_features(self):
+        """
+        Get's age features for patients in cohort table, bins them and puts
+        them in feature timeline table. 
+        """
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Demographics' as feature_type,
+            labels.index_time as feature_time,
+            1 as feature_id,
+            'Age' as feature,
+            DATE_DIFF(
+                CAST(labels.index_time AS date), demo.BIRTH_DATE_JITTERED, YEAR)
+            as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+            labels
+        INNER JOIN
+            {self.project_id}.{self.dataset}.demographic demo
+        ON
+            labels.anon_id = demo.ANON_ID
+        """
+        print("Featurizing age")
+        df = pd.read_gbq(query)
+        df_train = df[df['index_time'].dt.year.isin(self.train_years)]
+        num_bins = self.feature_config['Numerical']['Age'][0]['num_bins']
+        df_binned = self.get_binned_features(df_train, df, num_bins)
+
+        # Write feature timeline in long form to bigquery dataset.table_name
+        df_binned.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.working_proj_id,
+            if_exists='append',
+            table_schema=self.feature_timeline_schema
+        )
+        self.create_table = False
+
+    def _get_lab_results_features(self):
+        """
+        Collects lab result features for patients in cohort for select lab
+        components. 
+        """
+        # Format list of lab component ids to grab
+        base_name_string = "("
+        for i, base_name in enumerate(DEFAULT_LAB_COMPONENT_IDS):
+            if i == len(DEFAULT_LAB_COMPONENT_IDS) - 1:
+                base_name_string += f"'{base_name}')"
+            else:
+                base_name_string += f"'{base_name}', "
+
+        # Get max look back time
+        max_look_back_time = 0
+        for look_back in DEFAULT_DEPLOY_CONFIG['Numerical']['LabResults']:
+            max_look_back_time = max(
+                look_back['look_back'], max_look_back_time)
+
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Lab Results' as feature_type,
+            lr.result_time_utc as feature_time,
+            CAST(lr.order_id_coded AS INTEGER) as feature_id,
+            lr.base_name as feature,
+            lr.ord_num_value as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.lab_result lr
+        ON
+            labels.anon_id = lr.anon_id
+        WHERE
+            lr.result_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(lr.result_time_utc,
+                          INTERVAL 24*{max_look_back_time} HOUR)
+                          >= labels.index_time
+        AND
+            lr.base_name in {base_name_string}
+        AND
+            lr.ord_num_value IS NOT NULL
+        """
+        print("Featurizing lab results")
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        df = df.astype({'value': float})
+        df_train = df[df['index_time'].dt.year.isin(self.train_years)]
+        num_bins = self.feature_config['Numerical']['LabResults'][0]['num_bins']
+        df_binned = self.get_binned_features(df_train, df, num_bins)
+
+        # Write feature matrix in long form to bigquery dataset.table_name
+        df_binned.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.working_proj_id,
+            if_exists='append',
+            table_schema=self.feature_timeline_schema
+        )
+        self.create_table = False
+
+    def _get_vital_sign_features(self):
+        """
+        Collects flowsheet features for patients in cohort.
+        """
+        # Format list of lab component ids to grab
+        base_name_string = "("
+        for i, base_name in enumerate(DEFAULT_FLOWSHEET_FEATURES):
+            if i == len(DEFAULT_FLOWSHEET_FEATURES) - 1:
+                base_name_string += f"'{base_name}')"
+            else:
+                base_name_string += f"'{base_name}', "
+
+        # Get max look back time
+        max_look_back_time = 0
+        for look_back in DEFAULT_DEPLOY_CONFIG['Numerical']['Vitals']:
+            max_look_back_time = max(
+                look_back['look_back'], max_look_back_time)
+
+        query = f"""
+        SELECT DISTINCT
+            labels.observation_id,
+            labels.index_time,
+            'Vitals' as feature_type,
+            f.recorded_time_utc as feature_time,
+            UNIX_SECONDS(f.recorded_time_utc) as feature_id,
+            f.row_disp_name as feature,
+            f.numerical_val_1 as value
+        FROM
+            {self.working_proj_id}.{self.dataset_name}.{self.cohort_table}
+             labels
+        LEFT JOIN
+            {self.project_id}.{self.dataset}.flowsheet f
+        ON
+            labels.anon_id = f.anon_id
+        WHERE
+            f.recorded_time_utc < labels.index_time
+        AND
+            TIMESTAMP_ADD(f.recorded_time_utc,
+                          INTERVAL 24*{max_look_back_time} HOUR)
+                          >= labels.index_time
+        AND
+            f.row_disp_name in {base_name_string}
+        AND
+            f.numerical_val_1 IS NOT NULL
+        """
+        print("Featurizing Vitals")
+        df = pd.read_gbq(query, progress_bar_type='tqdm')
+        df = df.astype({'value': float})
+        df_train = df[df['index_time'].dt.year.isin(self.train_years)]
+        num_bins = self.feature_config['Numerical']['Vitals'][0]['num_bins']
+        df_binned = self.get_binned_features(df_train, df, num_bins)
+
+        # Write feature matrix in long form to bigquery dataset.table_name
+        df_binned.to_gbq(
+            destination_table=f"{self.dataset_name}.{self.table_name}",
+            project_id=self.working_proj_id,
+            if_exists='append',
+            table_schema=self.feature_timeline_schema
+        )
+        self.create_table = False
diff --git a/scripts/lab_prediction_pro/hemestamp_model_pipeline.py b/scripts/lab_prediction_pro/hemestamp_model_pipeline.py
new file mode 100644
index 00000000..01e94c57
--- /dev/null
+++ b/scripts/lab_prediction_pro/hemestamp_model_pipeline.py
@@ -0,0 +1,82 @@
+import os
+import sys
+import argparse
+sys.path.insert(1, '../../medinfo/dataconversion/')
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (
+    '/Users/conorcorbin/.config/gcloud/application_default_credentials.json'
+)
+os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'
+import pandas as pd
+import cohorts
+import trainers
+import featurizers
+
+
+cohort_table_id = 'mining-clinical-decisions.conor_db.20220630_hemestamp_cohort_copy'
+feature_table_id = 'mining-clinical-decisions.conor_db.20220630_hemestamp_features'
+project_id='som-nero-phi-jonc101'
+dataset = 'shc_core_2021'
+
+parser = argparse.ArgumentParser(description='Pipeline to train Hemestamp models')
+parser.add_argument(
+    '--cohort',
+    action='store_true',
+    help='whether to build cohort'
+)
+parser.add_argument(
+    '--featurize',
+    action='store_true',
+    help='whether to featurize'
+)
+parser.add_argument(
+    '--train',
+    action='store_true',
+    help='whether to train'
+)
+
+args = parser.parse_args()
+
+
+cb = cohorts.CohortBuilder(
+    dataset_name='conor_db',
+    table_name='20220630_hemestamp_cohort_copy',
+    label_columns=['label_WBC', 'label_HCT', 'label_PLT'])
+
+query =f"""
+"""
+
+# Executes query, and stores cohort in a pandas dataframe in the `df` attribute
+if args.cohort:
+    cb.build_cohort(query)
+    schema = [{'name': 'anon_id', 'type': 'STRING'},
+            {'name': 'observation_id', 'type': 'INTEGER'}]
+    # Writes the cohort to bigquery
+    cb.write_cohort_table(overwrite='True', schema=schema)
+
+if args.featurize:
+    featurizer = featurizers.BagOfWordsFeaturizerLight(
+        cohort_table_id=cohort_table_id,
+        feature_table_id=feature_table_id,
+        outpath='./20220630_hemestamp_model_info/'
+    )
+    featurizer()
+    # featurizer = featurizers.SequenceFeaturizer(
+    #     cohort_table_id=cohort_table_id,
+    #     feature_table_id=feature_table_id,
+    #     train_years=['2015', '2016', '2017', '2018'],
+    #     val_years=['2019'],
+    #     test_years=['2020'],
+    #     label_columns=['label_WBC', 'label_PLT', 'label_HCT'],
+    #     outpath='./20220607_model_info_seq/'
+    # )
+
+if args.train:
+    working_dir = './20220630_hemestamp_model_info/'
+    trainer = trainers.BaselineModelTrainer(working_dir)
+    trainer(task='label')
+    # run_name = 'test_run'
+    # TODO where are your feature matrices stored?
+    # trainer = trainers.BaselineModelTrainer(path)
+
+    # Component 1 - Predict Hematocrit
+    # trainer(task='label_HCT')
diff --git a/scripts/lab_prediction_pro/pytorch_driver.py b/scripts/lab_prediction_pro/pytorch_driver.py
new file mode 100644
index 00000000..c6504718
--- /dev/null
+++ b/scripts/lab_prediction_pro/pytorch_driver.py
@@ -0,0 +1,214 @@
+from ipaddress import collapse_addresses
+import os
+import json
+import sys
+import argparse
+from glob import glob
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+import numpy  as np
+from sklearn.metrics import roc_auc_score
+import pandas as pd
+sys.path.insert(1, '../../medinfo/dataconversion/')
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = (
+    '/Users/conorcorbin/.config/gcloud/application_default_credentials.json'
+)
+os.environ['GCLOUD_PROJECT'] = 'mining-clinical-decisions'
+from datasets import SequenceDataset, custom_collate
+from trainers import SequenceTrainer
+from pytorch_models import PatientDayGRU
+
+import pdb
+
+parser = argparse.ArgumentParser(description='Pipeline to train CBC models')
+parser.add_argument(
+    '--datapath',
+    default='./20220721_cbc_small_model_info/',
+    help='where input data is saved'
+)
+parser.add_argument(
+    '--run_name',
+    default='run_0',
+    help='name the training run'
+)
+parser.add_argument(
+    '--learning_rate',
+    default=1e-4,
+    type=float,
+    help='for adam opt'
+)
+parser.add_argument(
+    '--weight_decay',
+    default=1e-5,
+    type=float,
+    help='for adam opt'
+)
+parser.add_argument(
+    '--dropout',
+    default=0.2,
+    type=float,
+    help='regularization'
+)
+parser.add_argument(
+    '--scheduler_gamma',
+    default=0.1,
+    type=float,
+    help='learning rate scheduler'
+)
+parser.add_argument(
+    '--scheduler_step_size',
+    default=20,
+    type=int,
+    help='reduce learning rate every x epochs'
+)
+parser.add_argument(
+    '--num_epochs',
+    default=100,
+    type=int,
+    help='max number of epochs'
+)
+parser.add_argument(
+    '--stopping_tolerance',
+    default=20,
+    type=int,
+    help='exit if no improvement after x epochs'
+)
+parser.add_argument(
+    '--stopping_metric',
+    default='auc',
+    help='metric to use for early stopping'
+)
+parser.add_argument(
+    '--batch_size',
+    default=32,
+    help='batch size...'
+)
+parser.add_argument(
+    '--num_targets',
+    default=3,
+    help='batch size...'
+)
+parser.add_argument(
+    '--random_state',
+    default=42,
+    help='for randoms sake'
+)
+args = parser.parse_args()
+
+# Outpath
+outpath = os.path.join(args.datapath, 'runs', args.run_name)
+os.makedirs(outpath, exist_ok=True)
+
+# Dump a config file
+with open(os.path.join(outpath, 'config.json'), 'w') as f:
+    json.dump(args.__dict__, f)
+
+# Load data
+train_sequences = glob(os.path.join(args.datapath, 'train', '*.pt'))
+val_sequences = glob(os.path.join(args.datapath, 'val', '*.pt'))
+test_sequences = glob(os.path.join(args.datapath, 'test', '*.pt'))
+
+# Instantiate datasets
+train_dataset = SequenceDataset(train_sequences)
+val_dataset = SequenceDataset(val_sequences)
+test_dataset = SequenceDataset(test_sequences)
+
+# Instantiate dataloaders
+train_dataloader = DataLoader(
+    train_dataset,
+    batch_size=args.batch_size,
+    num_workers=0,
+    shuffle=True,
+    collate_fn=custom_collate
+)
+val_dataloader = DataLoader(
+    val_dataset,
+    batch_size=args.batch_size,
+    num_workers=0,
+    shuffle=False,
+    collate_fn=custom_collate
+)
+test_dataloader = DataLoader(
+    test_dataset,
+    batch_size=args.batch_size,
+    num_workers=0,
+    shuffle=False,
+    collate_fn=custom_collate
+)
+
+# Multi-label loss
+criterion = torch.nn.BCEWithLogitsLoss()
+
+# Load vocab
+with open(os.path.join(args.datapath, 'feature_vocab.npz'), 'r') as fp:
+    vocab = json.load(fp)
+
+# Model
+model = PatientDayGRU(vocab_size=len(vocab))
+
+# Optimizer
+optimizer = torch.optim.Adam(
+    model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
+
+# Scheduler
+scheduler = torch.optim.lr_scheduler.StepLR(
+    optimizer, step_size=args.scheduler_step_size, gamma=args.scheduler_gamma)
+
+# Device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Trainer
+trainer = SequenceTrainer(
+    outpath=outpath,
+    model=model,
+    criterion=criterion,
+    optimizer=optimizer,
+    device=device,
+    train_dataloader=train_dataloader,
+    val_dataloader=val_dataloader,
+    test_dataloader=test_dataloader,
+    stopping_metric=args.stopping_metric,
+    stopping_tolerance=args.stopping_tolerance,
+    num_epochs=args.num_epochs,
+    scheduler=scheduler
+)
+
+# Train
+trainer()
+
+# Load best model, loop through test set and save predictions, labels
+models = glob(os.path.join(outpath, "*.pt"))
+models.sort(key=os.path.getmtime)
+model = PatientDayGRU(vocab_size=len(vocab))
+model.load_state_dict(torch.load(models[-1]))
+model.to(device)
+model.eval()
+
+predictions, targets = [], []
+for batch in tqdm(test_dataloader):
+    model.zero_grad()
+    sequence = batch['sequence'].to(device)
+    seq_lengths = batch['lengths']
+    labels = batch['labels'].to(device)
+    time_deltas = batch['time_deltas'].to(device)
+    output = model(sequence, seq_lengths, time_deltas)
+    loss = criterion(output, labels.float())
+    predictions.append(output.cpu().detach().numpy())
+    targets.append(labels.cpu().detach().numpy())
+predictions = np.concatenate(predictions)
+targets = np.concatenate(targets)
+auc = roc_auc_score(targets, predictions)
+print(f"AUC on test: {auc}")
+observation_ids = [a.split('/')[-1].split('.')[0] for a in test_sequences]
+df = pd.DataFrame(data={
+    'observation_ids' : observation_ids,
+    'predictions' : predictions,
+    'labels' : 'targets'
+})
+
+df.to_csv(os.path.join(outpath, 'scores_on_test.csv'))
+
+torch.save(model.state_dict(),
+    os.path.join(outpath, f'model_final.pt'))
+
diff --git a/scripts/lab_prediction_pro/pytorch_models.py b/scripts/lab_prediction_pro/pytorch_models.py
new file mode 100644
index 00000000..60ac006b
--- /dev/null
+++ b/scripts/lab_prediction_pro/pytorch_models.py
@@ -0,0 +1,65 @@
+import torch
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pack_padded_sequence
+from torch.nn.utils.rnn import pad_packed_sequence
+
+import pdb
+
+class PatientDayGRU(torch.nn.Module):
+    def __init__(self, vocab_size, embedding_size=512, hidden_size=256,
+         output_size=1, num_layers=1, dropout=0.2):
+        super(PatientDayGRU, self).__init__()
+        self.vocab_size = vocab_size+1
+        self.embedding_size = embedding_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.output_size = output_size
+
+        # Embed collection of tokens within patient days
+        self.embedding_layer = torch.nn.EmbeddingBag(
+            num_embeddings=self.vocab_size,
+            embedding_dim=self.embedding_size-1, # because we're concating time
+            padding_idx=0
+        )
+
+        # GRU
+        self.gru = torch.nn.GRU(
+            input_size=self.embedding_size,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers
+        )
+
+        # Classification
+        self.classification_layer = torch.nn.Sequential(
+            torch.nn.Linear(self.hidden_size, int(self.hidden_size/2)),
+            torch.nn.Dropout(dropout),
+            torch.nn.ReLU(),
+            torch.nn.Linear(int(self.hidden_size/2), self.output_size),
+        )
+
+    def forward(self, x, x_lengths, time_deltas):
+        # Embed all patient days and reshape
+        x_stacked = torch.reshape(x, (x.shape[0]*x.shape[1], x.shape[2]))
+        x_embed = self.embedding_layer(x_stacked)
+        x_embed = torch.reshape(x_embed, 
+                               (x.shape[0], x.shape[1], x_embed.shape[1]))
+        time_deltas = torch.unsqueeze(time_deltas, dim=2)
+
+        try:
+            x_embed = torch.cat((x_embed, time_deltas), dim=2)
+        except:
+            pdb.set_trace()
+
+        # Pack padded days for input to GRU
+        packed_embeddings = pack_padded_sequence(x_embed,
+                                                 x_lengths,
+                                                 batch_first=True,
+                                                 enforce_sorted=False)
+        output_packed, hidden = self.gru(packed_embeddings)
+
+        output_padded, output_lengths = pad_packed_sequence(
+            output_packed, batch_first=True)
+        
+        hidden = torch.squeeze(hidden, dim=0)
+        yhat = self.classification_layer(hidden)
+        return torch.squeeze(yhat, dim=1)
diff --git a/scripts/lab_prediction_pro/train.sh b/scripts/lab_prediction_pro/train.sh
new file mode 100644
index 00000000..b57466b6
--- /dev/null
+++ b/scripts/lab_prediction_pro/train.sh
@@ -0,0 +1,45 @@
+# tasks=(label_WBC label_PLT label_HGB)
+
+# for task in ${tasks[@]}
+# do
+# python driver.py --run_name 20220705_cbc \
+#     --trainer BoostingTrainer \
+#     --tasks $task
+# done
+
+# tasks=(label_WBC label_PLT label_HGB)
+
+# tasks=(label_NA label_K label_CO2 label_BUN label_CR label_CA label_ALB)
+# for task in ${tasks[@]}
+# do
+# python driver.py --run_name 20220705_metabolic_comp \
+#     --trainer BoostingTrainer \
+#     --tasks $task
+# done
+
+
+# Magnesium
+# python driver.py --run_name 20220705_magnesium \
+#     --cohort MagnesiumCohort \
+#     --featurizer BagOfWordsFeaturizerLight \
+#     --trainer BoostingTrainer \
+#     --tasks label_MG \
+#     --tfidf
+
+# Blood cultures
+# python driver.py --run_name 20220705_blood \
+#     --cohort BloodCultureCohort \
+#     --featurizer BagOfWordsFeaturizerLight \
+#     --trainer BoostingTrainer \
+#     --tasks label_blood \
+#     --tfidf
+
+# Urine cultures
+python driver.py --run_name 20220705_urine \
+    --cohort UrineCultureCohort \
+    --featurizer BagOfWordsFeaturizerLight \
+    --trainer BoostingTrainer \
+    --tasks label_urine \
+    --tfidf
+
+
diff --git a/scripts/lab_prediction_pro/trainers.py b/scripts/lab_prediction_pro/trainers.py
new file mode 100644
index 00000000..dd90c4f6
--- /dev/null
+++ b/scripts/lab_prediction_pro/trainers.py
@@ -0,0 +1,325 @@
+"""
+Definition of ModelTrainer, SequenceTrainer
+"""
+import os
+import json
+import pandas as pd
+import pickle
+import lightgbm as lgb
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+from scipy.sparse import load_npz
+import torch
+from torch.optim import Adam    
+from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
+
+from constants import DEFAULT_LAB_COMPONENT_IDS
+from constants import DEFAULT_FLOWSHEET_FEATURES
+
+import pdb
+
+class SequenceTrainer():
+    """
+    Used to train models that leverage SequenceFeaturizer.  Example model
+    classes include GRUs, GRUs with Attention, Transformers. 
+    """
+
+    def __init__(self, outpath, model, criterion, optimizer, device,
+        train_dataloader, val_dataloader, test_dataloader, stopping_metric,
+        num_epochs=100, scheduler=None, stopping_tolerance=20):
+        self.outpath = outpath
+        self.model = model
+        self.criterion = criterion
+        self.optimizer = optimizer
+        self.device = device
+        self.train_dataloader = train_dataloader
+        self.val_dataloader = val_dataloader
+        self.test_dataloader = test_dataloader
+        self.stopping_metric = stopping_metric
+        self.stopping_tolerance = stopping_tolerance
+        self.num_epochs = num_epochs
+        self.scheduler = scheduler
+        self.writer = SummaryWriter(self.outpath)
+
+
+    def __call__(self):
+        best_stopping_metric = 0
+        tolerance_counter = 0
+        for epoch in range(self.num_epochs):
+            train_metrics = self.train()
+            print(f"Training Loss: {train_metrics['loss']} | "
+                  f"Training AUC: {train_metrics['auc']}")
+
+            val_metrics = self.evaluate()
+            print(f"Val Loss: {val_metrics['loss']} | "
+                  f"Val AUC: {val_metrics['auc']}")
+
+            self.writer.add_scalar('Loss/train', train_metrics['loss'], epoch)
+            self.writer.add_scalar('Loss/val', val_metrics['loss'], epoch)
+            self.writer.add_scalar('AUC/train', train_metrics['auc'], epoch)
+            self.writer.add_scalar('AUC/val', val_metrics['auc'], epoch)
+
+            if tolerance_counter == self.stopping_tolerance:
+                break
+
+            if epoch == 0:
+                best_stopping_metric = val_metrics[self.stopping_metric]
+                torch.save(self.model.state_dict(), 
+                    os.path.join(self.outpath, f'model_{epoch}.pt'))
+            elif val_metrics[self.stopping_metric] > best_stopping_metric:
+                best_stopping_metric = val_metrics[self.stopping_metric]
+                tolerance_counter = 0
+                torch.save(self.model.state_dict(),
+                    os.path.join(self.outpath, f'model_{epoch}.pt'))
+            else:
+                tolerance_counter += 1
+
+    def train(self):
+        self.model.train()
+        train_loss = 0
+        predictions, targets = [], []
+        for batch in tqdm(self.train_dataloader):
+            self.model.zero_grad()
+            sequence = batch['sequence'].to(self.device)
+            seq_lengths = batch['lengths']
+            labels = batch['labels'].to(self.device)
+            time_deltas = batch['time_deltas'].to(self.device)
+            output = self.model(sequence, seq_lengths, time_deltas)
+            loss = self.criterion(output, labels.float())
+            train_loss += loss.item()
+            loss.backward()
+            self.optimizer.step()
+            predictions.append(output.cpu().detach().numpy())
+            targets.append(labels.cpu().detach().numpy())
+        train_loss /= len(self.train_dataloader)
+        predictions = np.concatenate(predictions)
+        targets = np.concatenate(targets)
+        auc = roc_auc_score(targets, predictions)
+        
+        train_metrics = {
+            'loss' : train_loss,
+            'auc' : auc
+        }
+        return train_metrics
+
+    def evaluate(self):
+        self.model.eval()
+        total_loss = 0
+        predictions, targets = [], []
+        for batch in tqdm(self.val_dataloader):
+            sequence = batch['sequence'].to(self.device)
+            seq_lengths = batch['lengths']
+            labels = batch['labels'].to(self.device)
+            time_deltas = batch['time_deltas'].to(self.device)
+            output = self.model(sequence, seq_lengths, time_deltas)
+            loss = self.criterion(output, labels.float())
+            total_loss += loss.item()
+            predictions.append(output.cpu().detach().numpy())
+            targets.append(labels.cpu().detach().numpy())
+        total_loss /= len(self.val_dataloader)
+        predictions = np.concatenate(predictions)
+        targets = np.concatenate(targets)
+        auc = roc_auc_score(targets, predictions)
+        metrics = {
+            'loss' : total_loss,
+            'auc' : auc
+        }
+        return metrics
+
+class BoostingTrainer():
+    """
+    Trains a gradient boosted tree (LightGBM) and performs appropriate model
+    selection.  Currently supporting binary classification
+    """
+    def __init__(self, working_dir):
+        self.working_dir = working_dir
+
+    def __call__(self, task):
+        """
+        Trains a model against label defined by task
+        Args:
+            task: column that has label of interest
+        """
+        self.task = task
+        self.clf = lgb.LGBMClassifier(
+            objective='binary',
+            n_estimators=1000,
+            learning_rate=0.1,
+            num_leaves=64
+        )
+
+        # Read in train data
+        X_train = load_npz(os.path.join(
+            self.working_dir, 'train_features.npz'))
+        y_train = pd.read_csv(
+            os.path.join(self.working_dir, 'train_labels.csv'))
+
+        # Create val data
+        val_size = int(len(y_train) * 0.1) # 10 % of training set
+        val_inds = y_train.sort_values('index_time', ascending=False).head(
+            val_size).index.values
+        X_val = X_train[val_inds]
+        y_val = y_train.iloc[val_inds]
+
+        # Remove val inds from training set
+        train_inds = [idx for idx in y_train.index.values 
+                      if idx not in val_inds]
+        X_train = X_train[train_inds]
+        y_train = y_train.iloc[train_inds]
+
+        # Assert val observation ids not in training set
+        y_val_obs = set([a for a in y_val.observation_id.values])
+        y_train_obs = set([a for a in y_train.observation_id.values])
+        assert len(y_val_obs.intersection(y_train_obs)) == 0
+
+        # Read in test data
+        X_test = load_npz(os.path.join(self.working_dir, 'test_features.npz'))
+        y_test = pd.read_csv(
+            os.path.join(self.working_dir, 'test_labels.csv'))
+
+        # Fit model with early stopping
+        self.clf.fit(X_train,
+                     y_train[self.task].values,
+                     eval_set= [(X_val, y_val[self.task].values)],
+                     eval_metric=['binary', 'auc'],
+                     early_stopping_rounds = 25,
+                     verbose=1)
+        
+        # Predictions
+        predictions = self.clf.predict_proba(X_test)[:, 1]
+        auc = roc_auc_score(y_test[self.task], predictions)
+        print(f"{self.task} AUC: {round(auc, 2)}")
+
+        df_yhats = pd.DataFrame(data={
+            'labels': y_test[self.task].values,
+            'predictions': predictions
+        })
+        yhats_path = f"{self.task}_yhats.csv"
+        df_yhats.to_csv(os.path.join(self.working_dir, yhats_path), index=None)
+
+        # Generate config file for DEPLOYR
+        self.generate_deploy_config()
+
+    def generate_deploy_config(self):
+        """
+        Generates the config file used by the deployment module.  Contains
+        all information needed for deployment module to create feature vectors
+        compatible with the model using EPIC and FHIR APIs. This includes
+            1. model: the model itself
+            2. feature_order: order of features in feature vector
+            3. bin_map: numerical features and min value for each bin
+            4. feature_config: dictionary containing which features types used
+               in model and their corresponding look back windows. 
+            5. tfidf_transform: if tfidf used to transform feature matrix
+
+        """
+        deploy = {}
+        deploy['model'] = self.clf
+        feature_order = pd.read_csv(os.path.join(self.working_dir,
+                                                 'feature_order.csv'))
+        deploy['feature_order'] = [f for f in feature_order.features]
+        if os.path.exists(os.path.join(self.working_dir, 'bin_lup.csv')):
+            bin_map = pd.read_csv(os.path.join(self.working_dir, 'bin_lup.csv'),
+                                  na_filter=False)
+        else:
+            bin_map = None
+        deploy['bin_map'] = bin_map
+
+        # TFIDF transform 
+        transform_path = os.path.join(self.working_dir, 'tfidf_transform.pkl')
+        if os.path.exists(transform_path):
+            with open(transform_path, 'rb') as f:
+                transform = pickle.load(f)
+        else:
+            transform = None
+        deploy['transform'] = transform
+
+        with open(os.path.join(self.working_dir, 'feature_config.json'),
+                  'r') as f:
+            feature_config = json.load(f)
+        deploy['feature_config'] = feature_config
+        deploy['lab_base_names'] = DEFAULT_LAB_COMPONENT_IDS
+        deploy['vital_base_names'] = DEFAULT_FLOWSHEET_FEATURES
+
+        # Dump pickle for DEPLOYR
+        with open(os.path.join(self.working_dir, f'{self.task}_deploy.pkl'),
+                  'wb') as w:
+            pickle.dump(deploy, w)
+
+class BaselineModelTrainer():
+    """
+    Implements the most basic ML pipeline imagineable. Trains a random forest
+    with default hyperparameters using features and labels saved in working
+    directory from a Featurizer in featurizers.py
+
+    Generates a deployment config file used in deploy.py that tells us which
+    EPIC/FHIR APIs to call, how to construct and order features, and saves
+    the model itself. 
+    """
+
+    def __init__(self, working_dir):
+        self.working_dir = working_dir
+        self.task = None # useful in multilable scenario
+
+    def __call__(self, task):
+        """
+        Trains a model, saves predictions, saves a config file.
+        Args:
+            task : column for the binary label
+        """
+        self.task = task
+        self.clf = RandomForestClassifier()
+        X_train = load_npz(os.path.join(
+            self.working_dir, 'train_features.npz'))
+        X_test = load_npz(os.path.join(self.working_dir, 'test_features.npz'))
+        y_train = pd.read_csv(
+            os.path.join(self.working_dir, 'train_labels.csv'))
+        y_test = pd.read_csv(
+            os.path.join(self.working_dir, 'test_labels.csv'))
+
+        self.clf.fit(X_train, y_train[self.task])
+        predictions = self.clf.predict_proba(X_test)[:, 1]
+        auc = roc_auc_score(y_test[self.task], predictions)
+        print(f"AUC: {round(auc, 2)}")
+
+        df_yhats = pd.DataFrame(data={
+            'labels': y_test[self.task].values,
+            'predictions': predictions
+        })
+        yhats_path = f"{self.task}_yhats"
+        df_yhats.to_csv(os.path.join(self.working_dir, yhats_path), index=None)
+        self.generate_deploy_config()
+
+    def generate_deploy_config(self, tfidf=True):
+        """
+        Generates the config file used by the deployment module.  Contains
+        all information needed for deployment module to create feature vectors
+        compatible with the model using EPIC and FHIR APIs. This includes
+            1. model: the model itself
+            2. feature_order: order of features in feature vector
+            3. bin_map: numerical features and min value for each bin
+            4. feature_config: dictionary containing which features types used
+               in model and their corresponding look back windows. 
+        """
+        deploy = {}
+        deploy['model'] = self.clf
+        feature_order = pd.read_csv(os.path.join(self.working_dir,
+                                                 'feature_order.csv'))
+        deploy['feature_order'] = [f for f in feature_order.features]
+        if os.path.exists(os.path.join(self.working_dir, 'bin_lup.csv')):
+            bin_map = pd.read_csv(os.path.join(self.working_dir, 'bin_lup.csv'),
+                                na_filter=False)
+        else:
+            bin_map = None
+        deploy['bin_map'] = bin_map
+        with open(os.path.join(self.working_dir, 'feature_config.json'),
+                  'r') as f:
+            feature_config = json.load(f)
+        deploy['feature_config'] = feature_config
+        deploy['lab_base_names'] = DEFAULT_LAB_COMPONENT_IDS
+        deploy['vital_base_names'] = DEFAULT_FLOWSHEET_FEATURES
+        with open(os.path.join(self.working_dir, f'{self.task}_deploy.pkl'),
+                  'wb') as w:
+            pickle.dump(deploy, w)
diff --git a/scripts/model_performance_partitioning/driver.py b/scripts/model_performance_partitioning/driver.py
new file mode 100644
index 00000000..6d6467db
--- /dev/null
+++ b/scripts/model_performance_partitioning/driver.py
@@ -0,0 +1,226 @@
+# Main entry point for executing experiments
+import argparse
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import pandas as pd
+import pickle
+import os
+from scipy.sparse import load_npz
+from sklearn import tree
+from tqdm import tqdm
+
+sns.set_theme(style='white', font_scale=1.5)
+
+from partition import PerformancePartitioner
+
+import pdb
+
+def main():
+    parser = argparse.ArgumentParser(description='Experiment Driver')
+    parser.add_argument(
+        '--outpath',
+        default='./experiments/run_bigger_test_set/',
+        help='where to save results'
+    )
+    parser.add_argument(
+        '--model_path',
+        default='./20220629_model_info/label_HCT_deploy.pkl',
+        help='where the model lives'
+    )
+    parser.add_argument(
+        '--label_path',
+        default='./20220629_model_info/label_HCT_yhats',
+        help='where the test set labels and predictions live'
+    )
+    parser.add_argument(
+        '--feature_path',
+        default='./20220629_model_info/test_features.npz',
+        help='where the test set features live'
+    )
+    parser.add_argument(
+        '--compare_to_random_partition',
+        action='store_true',
+        help=('whether to compare PerformancePartioner to random partitioner'
+              'that permutes to which feature vector a (prediction, label) pair' 
+              'belongs')
+    )
+    parser.add_argument(
+        '--random_state',
+        default=42,
+        type=int,
+        help=('random seed')
+    )
+    parser.add_argument(
+        '--n_null',
+        default=1000,
+        type=int,
+        help=('size of null distribution')
+    )
+    args = parser.parse_args()
+    np.random.seed(args.random_state)
+
+    # Load model and model data
+    with open(args.model_path, 'rb') as f:
+        model_pkl = pickle.load(f)
+    model = model_pkl['model']
+    feature_order = model_pkl['feature_order']
+    features = load_npz(args.feature_path).toarray()
+    hats = pd.read_csv(args.label_path)
+    labels = hats['labels']
+    predictions = hats['predictions']
+
+    # Build null distribution
+    worst_metrics_null = {'auc' : [], 'accuracy' : [],
+                          'average_precision' : [], 'num_groups' : []}
+    if args.compare_to_random_partition:
+        for i in tqdm(range(args.n_null)):
+            indices = [idx for idx in range(len(labels))]
+            indices_shuffled = np.random.choice(indices, size=len(indices),
+                replace=False)
+            labels_random = [labels[idx] for idx in indices_shuffled]
+            predictions_random = [predictions[idx] for idx in indices_shuffled]
+            clf = tree.DecisionTreeRegressor(min_samples_leaf=2)
+            worst_partition = partition_performance(
+                attributes=features,
+                labels=labels_random,
+                predictions=predictions_random,
+                clf=clf,
+                random_state=args.random_state
+            )
+            worst_metrics_null['auc'].append(worst_partition['auc'])
+            worst_metrics_null['accuracy'].append(worst_partition['accuracy'])
+            worst_metrics_null['average_precision'].append(
+                worst_partition['average_precision'])
+            worst_metrics_null['num_groups'].append(
+                worst_partition['num_groups'])
+
+    # Get worst performance with with recursive partitioning
+    clf = tree.DecisionTreeRegressor(
+        min_samples_leaf=2000)
+        
+#ccp_alpha=3.329391502327633e-05)
+
+    worst_partition = partition_performance(
+        attributes=features,
+        labels=labels,
+        predictions=predictions,
+        clf=clf,
+        random_state=args.random_state,
+        tune=False
+    )
+
+    # Plot scatter
+    metrics = ['scores', 'accuracy', 'auc', 'average_precision', ]
+    fig, axs = plt.subplots(1, 4, figsize=(40, 10))
+    for i, metric in enumerate(metrics):
+        plot_partion_score_vs_metric(worst_partition['ppart'], metric, axs[i])
+    os.makedirs(args.outpath, exist_ok=True)
+    outfig = os.path.join(args.outpath, '2000_min_sample.png')
+    plt.savefig(outfig, bbox_inches='tight', dpi=300)
+
+    # Plot actual value againts null distribution
+    
+    if args.compare_to_random_partition:
+        colors = sns.color_palette()
+        fig, axs = plt.subplots(1, 4, figsize=(40, 10))
+        counter = 0
+        for ax, m in zip(axs, worst_metrics_null):
+            plot_hist_and_value(value=worst_partition[m],
+                                null_disrtibution=worst_metrics_null[m],
+                                ax=ax,
+                                metric=m,
+                                color=colors[counter])
+            counter += 1
+
+        os.makedirs(args.outpath, exist_ok=True)
+        outfig = os.path.join(args.outpath, 'compare_to_null.png')
+        plt.savefig(outfig, bbox_inches='tight', dpi=300)
+
+def plot_hist_and_value(value, null_disrtibution, ax, metric, color):
+    """
+    Plots histogram of null distribution and verticl line with actual
+    value given a metric and ax
+    """
+    n = len(null_disrtibution)
+    pvalue = len([p for p in null_disrtibution if p <= value]) / n
+    ax = sns.histplot(x=null_disrtibution,
+                      bins=20,
+                      stat='count',
+                      ax=ax,
+                      color=color)
+    ax.plot([value, value], 
+            [0, ax.get_ylim()[1]],
+            color='black',
+            linestyle='--',
+            label=f"Worst Parition auc={round(value, 2)} p < {pvalue}")
+    ax.set_title(f"Discovered Worst {metric} vs Null Distribution")
+    ax.set_xlabel(f"Worst {metric}")
+    ax.set_ylabel("Count")
+    ax.legend()
+
+def plot_partion_score_vs_metric(ppart, metric, ax):
+    """
+    Given a fit PerformancePartitioner, plots the predicted scores within
+    each partion vs the estimate scores in the parition. 
+    
+    Args:
+        ppart: a fit PerformancePartitioner
+        metric: which metric to plot againts fit score, can be 'scores', 'auc',
+        'accuracy', 'average_precision'
+    """ 
+    colors = sns.color_palette("icefire", as_cmap=True)
+    valid_inds = [i for i, val in enumerate((ppart.df_partition_scores[metric]))
+        if val != 999]
+    prevalences = [int(p * 255) for p in 
+                   ppart.df_partition_scores.prevalance[valid_inds]]
+    sizes = ppart.df_partition_scores.n_samples[valid_inds] / \
+        max(ppart.df_partition_scores.n_samples[valid_inds]) * 200
+    ax.scatter(ppart.df_partition_scores.predicted_scores[valid_inds],
+               ppart.df_partition_scores[metric][valid_inds],
+               c=[colors.colors[p] for p in prevalences],
+               s=sizes)
+    ax.set_title(f'Predicted Errors vs Actual {metric}')
+    ax.set_xlabel("Fit Scores")
+    ax.set_ylabel(f"Actual {metric}")
+
+def partition_performance(attributes, labels, predictions, clf, 
+        get_worst_path=False, random_state=42, tune=False):
+    """
+    Performs the feature partioning and returns data for logging 
+    """
+    worst_path = None
+    ppart = PerformancePartitioner(
+        attributes=attributes,
+        labels=labels,
+        predictions=predictions,
+        clf=clf,
+        random_state=random_state,
+    )
+    ppart.partition(tune=tune)
+    ppart.evaluate()
+    
+    # Get partition with worst performance
+    worst_partition = ppart.df_partition_scores.sort_values('auc').head(1)
+
+    if get_worst_path:
+        worst_parition_indices = [int(a) for a in 
+                                worst_partition['samples'].split('-')]
+        X_worst_partition = ppart.attributes[worst_parition_indices]
+        worst_path = ppart.get_paths_for_sample(
+            X_test=X_worst_partition
+        )
+    
+
+    return {
+        'auc': float(worst_partition['auc'].values[0]),
+        'accuracy': float(worst_partition['accuracy'].values[0]),
+        'average_precision': float(
+            worst_partition['average_precision'].values[0]),
+        'num_groups' : ppart.num_groups,
+        'worst_path' : worst_path,
+        'ppart' : ppart
+    }
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/model_performance_partitioning/partition.py b/scripts/model_performance_partitioning/partition.py
new file mode 100644
index 00000000..58448f3d
--- /dev/null
+++ b/scripts/model_performance_partitioning/partition.py
@@ -0,0 +1,315 @@
+### Define the PerformancePartitioner
+import numpy as np
+import pandas as pd
+from scipy.spatial.distance import euclidean
+from sklearn import tree
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import average_precision_score
+from sklearn.metrics import brier_score_loss
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import KFold
+from tqdm import tqdm
+
+import pdb
+
+"""
+Detecting Weak Points in Clinical Risk Predction Models with Recursive
+Partitioning. 
+
+Section 1 of Paper:
+    For k clincial prediction models (I'll train).
+    Can PerformancePartitioner 
+        1) find more extreme weakpoints than something that just randomly
+        partitions the data into same number of partitions
+        2) find more exteme weakpoints than an unsupervised clustering
+        method that partions the data into same number of clusters
+
+Section 2 of Paper:
+    Implications on Domain Generalization.
+        * Can use to estimate perforance on new population if you know the
+          prevalance of each partition in new population (domain adapation).
+        * Compare to inverse probability weighting
+        * Can provide semantically meaningful explanations as to why a task
+          may not be generalizing well to a new population. Ie prevalance
+          in a low performing partition is higher. 
+
+Section 3 of Paper:
+    Implications on Fairness Evaluations
+        * Can be used to evaluate fairness multi-dimensionally, alluding to
+          intersectionality.
+        * Test whether can find more extreme weakpoints that common fairness
+          evaluation that assess performance across pre-specified
+          protected subgroups.
+"""
+
+class PerformancePartitioner():
+    """
+    Definition of performance partitioner, houses logic that given attributes, 
+    predicictions, and labels on a held out test set fits a decision tree that 
+    partions the attribute space based on a particular scoring function, and
+    then estimates model peformance in these regions.
+    """
+    
+    def __init__(self, attributes, labels, predictions, clf,
+                 scoring_function=euclidean, random_state=42):
+        """
+        Args:
+            attributes: NxM matrix where N is the number of examples on test set
+                 and M is number of attributes to partition performance on.
+            labels: N length vector of labels in test set
+            predictions: N length vector of predictions in test set
+            scoring_function: a function that takes in a label and prediction 
+                and returns a score
+            performance_measure: a function that takea in labels and predicitons
+                 and returns a model performance measure
+        """
+        self.attributes = attributes
+        self.labels = np.array(labels)
+        self.predictions = np.array(predictions)
+        self.scoring_function = scoring_function
+        self.clf = clf
+        self.random_state=random_state
+    
+    def partition(self, tune=False):
+        """
+        Partions the attribute space into disjoint region that best separates
+        result of the scoring function on the test set.  Sample half of the 
+        data to fit the partions and uses the other half to estimate the 
+        performance measure within each of the paritions.
+        """
+        self.scores = np.array([self.scoring_function(l, p) for l,p in 
+                       zip(self.labels, self.predictions)])
+        indices = [i for i in range(len(self.scores))]
+        x_fit, self.x_est, s_fit, self.s_est, \
+                inds_fit, self.inds_est = train_test_split(self.attributes, 
+                                                           self.scores,
+                                                           indices,
+                                                           test_size=0.5,
+                                                           random_state=42)
+        auc_on_fit = roc_auc_score(self.labels[inds_fit], 
+                                    self.predictions[inds_fit])
+        auc_on_est = roc_auc_score(self.labels[self.inds_est],
+                                    self.predictions[self.inds_est])
+
+        if tune:
+            self.cross_val_fit(X=x_fit, y=s_fit)
+        else:
+            self.clf.fit(X=x_fit, y=s_fit)
+
+    def cross_val_fit(self, X, y):
+        """
+        Actually fit the decision tree and cross validate on the _fit set for
+        model selection. We'll maintain certain constraints on the resulting
+        partions, like a minimum number of examples required to be a leaf node.
+        This will be toyed around with in experiments that will likely go 
+        in appendix. 
+
+        Args:
+            X : attributes use generate partitions
+            y : scoring function - ex the squared error between label and
+                predicted probability of the model
+        """
+        X_train, X_val, y_train, y_val = train_test_split(
+            X, y,
+            test_size=0.2,
+            random_state=self.random_state)
+        self.clf.fit(X_train, y_train)
+        path = self.clf.cost_complexity_pruning_path(X_train, y_train)
+        ccp_alphas, impurities = path.ccp_alphas, path.impurities
+        mse_train, mse_val = [], []
+        for a in tqdm(ccp_alphas):
+            self.clf.ccp_alpha = a
+            self.clf.fit(X_train, y_train)
+            y_train_pred = self.clf.predict(X_train)
+            y_val_pred = self.clf.predict(X_val)
+            mse_train.append(mean_squared_error(y_train, y_train_pred))
+            mse_val.append(mean_squared_error(y_val, y_val_pred))
+        best_alpha = ccp_alphas[np.argmin(mse_val)]
+        self.clf.ccp_alpha = best_alpha
+        print(best_alpha)
+        self.clf.fit(X_train, y_train)
+
+        # cv = KFold(n_splits=1, random_state=self.random_state,
+        #     shuffle=True)
+        # max_alphas = 0
+        # max_path = 0
+        # for train_inds, test_inds in cv.split(X, y):
+        #     self.clf.fit(X[train_inds], y[train_inds])
+        #     path = self.clf.cost_complexity_pruning_path(
+        #         X[train_inds], y[train_inds])
+        #     ccp_alphas, impurities = path.ccp_alphas, path.impurities
+        #     if max(ccp_alphas) > max_alphas:
+        #         max_alphas = max(ccp_alphas)
+        #     if len(ccp_alphas) > max_path:
+        #         max_path = len(ccp_alphas)
+
+        #     pdb.set_trace()
+    
+        # param_grid = {
+        #     'max_depth': [3, 5, 10, 50, 100, None], 
+        #     'min_samples_leaf' : 
+        # }
+
+    def evaluate(self):
+        """
+        Given a the fit partions, estimates scores among other model performance
+        measures on evaluation set. 
+        """
+        x_est_nodes = self.clf.apply(self.x_est)
+        df = pd.DataFrame(data={
+            'leaf_node_idx': x_est_nodes,
+            'scores': self.s_est,
+            'predicted_scores' : self.clf.predict(self.x_est),
+            'predictions': self.predictions[self.inds_est],
+            'labels': self.labels[self.inds_est],
+            'est_indices' : self.inds_est
+        })
+        node_idx_map = {}
+
+        # Have to do this way because pandas does not support aggregation on 
+        # multiple columns 
+        groups = df.groupby('leaf_node_idx')
+        self.num_groups = len(groups)
+        for group in groups:
+            node_idx_map[group[0]] = {}
+            node_idx_map[group[0]]['score'] = group[1].scores.mean()
+            node_idx_map[group[0]]['predicted_scores'] = group[1
+                ].predicted_scores.mean() # will all be the same
+            node_idx_map[group[0]]['n_samples'] = len(group[1])
+            node_idx_map[group[0]]['prevalence'] = group[1].labels.mean()
+            node_idx_map[group[0]]['ex_est_index'] = '-'.join([
+                str(idx) for idx in group[1]['est_indices']])
+            node_idx_map[group[0]]['accuracy'] = accuracy_score(
+                group[1].labels, 
+                [1 if p >= 0.5 else 0 for p in group[1].predictions])
+            try:
+                node_idx_map[group[0]]['average_precision'] = \
+                    average_precision_score(group[1].labels,
+                                            group[1].predictions)
+            except:
+                node_idx_map[group[0]]['average_precision'] = 999
+            try:
+                node_idx_map[group[0]]['auc'] = roc_auc_score(group[1].labels,
+                    group[1].predictions)
+            except:
+                node_idx_map[group[0]]['auc'] = 999
+
+        # All values will be same within group, hence take first
+        self.df_partition_scores = df.assign(
+            scores=lambda x: [node_idx_map[idx]['score'] for idx 
+                              in x.leaf_node_idx],
+            predicted_scores=lambda x: [node_idx_map[idx]['predicted_scores']
+                                        for idx in x.leaf_node_idx],
+            n_samples=lambda x: [node_idx_map[idx]['n_samples']
+                                for idx in x.leaf_node_idx],
+            prevalance=lambda x: [node_idx_map[idx]['prevalence']
+                                 for idx in x.leaf_node_idx],
+            auc=lambda x: [node_idx_map[idx]['auc'] for idx in x.leaf_node_idx],
+            samples=lambda x: [node_idx_map[idx]['ex_est_index']
+                                      for idx in x.leaf_node_idx],
+            accuracy=lambda x: [node_idx_map[idx]['accuracy']
+                                for idx in x.leaf_node_idx],
+            average_precision=lambda x: [node_idx_map[idx]['average_precision']
+                                         for idx in x.leaf_node_idx]
+            ).groupby('leaf_node_idx').first().reset_index()
+
+    def get_paths_for_samples(self, X_test):
+        n_nodes = self.clf.tree_.node_count
+        children_left = self.clf.tree_.children_left
+        children_right = self.clf.tree_.children_right
+        feature = self.clf.tree_.feature
+        threshold = self.clf.tree_.threshold
+
+        node_indicator = self.clf.decision_path(X_test)
+        leaf_id = self.clf.apply(X_test)
+        
+        sample_id = 0
+        # obtain ids of the nodes `sample_id` goes through, ie, row `sample_id`
+        node_index = node_indicator.indices[
+            node_indicator.indptr[sample_id] : node_indicator.indptr[
+                sample_id + 1]
+        ]
+
+        print("Rules used to predict sample {id}:\n".format(id=sample_id))
+        for node_id in node_index:
+            # continue to the next node if it is a leaf node
+            if leaf_id[sample_id] == node_id:
+                continue
+
+            # check if value of  split feature for sample 0 is below threshold
+            if X_test[sample_id, feature[node_id]] <= threshold[node_id]:
+                threshold_sign = "<="
+            else:
+                threshold_sign = ">"
+
+            print(
+                "decision node {node} : (X_test[{sample}, {feature}] = {value})"
+                " {inequality} {threshold})".format(
+                    node=node_id,
+                    sample=sample_id,
+                    feature=feature[node_id],
+                    value=X_test[sample_id, feature[node_id]],
+                    inequality=threshold_sign,
+                    threshold=threshold[node_id],
+                )
+            )
+        return None # implement visualization later
+
+    def explain_tree(self):
+        """
+        Prints explanation of the partions geneated by the decision tree 
+        """
+        n_nodes = self.clf.tree_.node_count
+        children_left = self.clf.tree_.children_left
+        children_right = self.clf.tree_.children_right
+        feature = self.clf.tree_.feature
+        threshold = self.clf.tree_.threshold
+
+        node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
+        is_leaves = np.zeros(shape=n_nodes, dtype=bool)
+        stack = [(0, 0)]  # start with the root node id (0) and its depth (0)
+        while len(stack) > 0:
+            # `pop` ensures each node is only visited once
+            node_id, depth = stack.pop()
+            node_depth[node_id] = depth
+
+            # If the left and right child of a node is not the same we have a
+            # split node
+            is_split_node = children_left[node_id] != children_right[node_id]
+            # If a split node, append left and right children and depth to
+            # `stack` so we can loop through them
+            if is_split_node:
+                stack.append((children_left[node_id], depth + 1))
+                stack.append((children_right[node_id], depth + 1))
+            else:
+                is_leaves[node_id] = True
+
+        print(
+            "The binary tree structure has {n} nodes and has "
+            "the following tree structure:\n".format(n=n_nodes)
+        )
+        for i in range(n_nodes):
+            if is_leaves[i]:
+                print(
+                    "{space}node={node} is a leaf node.".format(
+                        space=node_depth[i] * "\t", node=i
+                    )
+                )
+            else:
+                print(
+                    "{space}node={node} is a split node: "
+                    "go to node {left} if X[:, {feature}] <= {threshold} "
+                    "else to node {right}.".format(
+                        space=node_depth[i] * "\t",
+                        node=i,
+                        left=children_left[i],
+                        feature=feature[i],
+                        threshold=threshold[i],
+                        right=children_right[i],
+                    )
+                )
+
+
+    
diff --git a/scripts/specialty_recommender/Recommender.py b/scripts/specialty_recommender/Recommender.py
new file mode 100644
index 00000000..600f07eb
--- /dev/null
+++ b/scripts/specialty_recommender/Recommender.py
@@ -0,0 +1,241 @@
+import numpy as np
+import pandas as pd
+from Util import Util
+
+class Recommender:
+
+    def __init__(self, Item, Data):
+        self.Item = Item
+        self.Data = Data
+        self.coMatrix = np.zeros((self.Item.counter,self.Item.counter))
+        self.trainInputItems = set()
+        self.trainOutputItems = set()
+        self.trainPatients = []
+        self.testPatients = []
+        self.invalidPatients = []
+        self.patient_results = {}
+        
+        
+    def trainSplitbyYear(self, patients, year):
+        for patient in patients:
+            if patient.primary_visit_time.year >= year:
+                self.testPatients.append(patient) 
+            else:
+                self.trainPatients.append(patient)
+        print("train patients = {}".format(len(self.trainPatients)))
+        print("test patients = {}".format(len(self.testPatients)))
+            
+        
+    def buildCoMatrix(self, itemsPre, itemsPost): 
+        # itemsPre and itemsPost are set
+        
+        for i in itemsPre:
+            for j in itemsPost:
+                self.coMatrix[i][j] += 1
+            
+    def buildTrainingItems(self, itemsPre, itemsPost, patient):
+        self.trainInputItems = self.trainInputItems.union(itemsPre)
+        self.trainOutputItems = self.trainOutputItems.union(itemsPost)
+        trainingitems = itemsPre.union(itemsPost)
+        if trainingitems:
+            for i in trainingitems:
+                self.Item.items[i].trainPatients.add(patient)  
+                
+    
+    def preprocessing(self):
+        print("preprocessing...")
+        self.Item.getItemPrevalence()
+        self.Item.labelRareItems()
+        self.Item.labelFilterItems()
+        
+        
+    
+    def training(self, patient_list):
+        print("building co-occurence matrix ...")
+        for patient in patient_list:
+            itemsPre, itemsPost = patient.extractItems()
+            self.buildCoMatrix(itemsPre, itemsPost)
+            self.buildTrainingItems(itemsPre, itemsPost, patient)
+        self.Item.get_group_item_patients()
+       
+    
+    def testing(self, method_list, patient_list, k = 4):
+        
+        for method in method_list:
+
+            print("testing method: {}".format(method))
+        
+            for patient in patient_list:
+                itemsPre, itemsPost = patient.extractItems()
+                inputItems = list(set(itemsPre))
+                outputItems = list(set(itemsPost))
+                    
+                if len(outputItems) != 0 and len(inputItems)!=0:
+                
+                    rankedItems, rankedScore = self.rankingItems(inputItems, method)
+                
+                    precision, recall = Util.getPrecisionRecall(rankedItems, outputItems, k = k)
+                    auc = Util.getAUC(rankedItems, rankedScore, outputItems)
+                    
+                    self.patient_results[(method, patient)] = {'precision':precision, 
+                                                                'recall': recall,
+                                                                'auc': auc}
+                
+        
+    def rankingItems(self, inputItems, method):
+        # possible candidate Items
+        candidateItems = np.array(list(self.trainOutputItems))
+        queryItems = np.array(list(self.trainInputItems.intersection(inputItems)))
+        
+        score = self.aggStats(candidateItems, queryItems, method)
+        # sort based on rankedScore
+        sortedIndex = score.argsort()[::-1]
+        rankedItems = candidateItems[sortedIndex].tolist()
+        rankedScore = score[sortedIndex].tolist()
+        return rankedItems, rankedScore
+    
+    def aggStats(self, candidateItems, queryItems, method):
+        
+        # this apply statMetrics over queryItems
+        getStats = np.vectorize(self.statMetrics) 
+                
+        def PPV(item):
+            sumStats = sum(getStats(item, queryItems, 'ppv'))
+            sumN = sum(getStats(item, queryItems, 'numA_pts'))
+            return sumStats/sumN
+        
+        def PPV_mod(item):
+            sumStats = sum(getStats(item, queryItems, 'ppv_mod'))
+            sumN = sum(getStats(item, queryItems, 'numA_pts'))
+            return sumStats/sumN
+
+        def PPV_wt(item):
+            sumStats = sum(getStats(item, queryItems, 'ppv_wt'))
+            sumN = sum(getStats(item, queryItems, 'numA_pts'))
+            return sumStats/sumN
+        
+        def PPV_mod_wt(item):
+            sumStats = sum(getStats(item, queryItems, 'ppv_mod_wt'))
+            sumN = sum(getStats(item, queryItems, 'numA_pts'))
+            return sumStats/sumN
+        
+        def RR(item):
+            prodStats = np.prod(getStats(item, queryItems, 'rr'))
+            return prodStats
+        
+        def Prevalence(item_code):
+            return self.statMetrics(item_code, item_code, 'prevalence')
+        
+        def BaselinePrevalence(item_code):
+            item_concept_id = self.Item.items[item_code].concept_id
+            if item_concept_id != '0':
+                return self.Data.itemPrevMap[item_concept_id]
+            else:
+                return 0
+        
+        def Random(item):
+            return np.random.random_sample()
+                      
+        switcher = {                   
+                    'PPV':PPV,
+                    'PPV_WT':PPV_wt,
+                    'PPV_MOD':PPV_mod,
+                    'PPV_MOD_WT':PPV_mod_wt,
+                    'RR':RR,
+                    'PREVALENCE':Prevalence,
+                    'BASELINEPREVALENCE':BaselinePrevalence,
+                    'RANDOM':Random
+                    }
+    
+        applyMethod = switcher.get(method.upper())          
+        score = np.vectorize(applyMethod)(candidateItems)
+        return score 
+    
+    
+    def statMetrics(self, itemB, itemA, metric):
+        # item B is the candidate item
+        # item A is the query item
+        AB_pts = self.coMatrix[itemA][itemB]
+        A_pts = len(self.Item.items[itemA].trainPatients)
+        N = len(self.trainPatients)
+        if itemB in self.Item.group_item_map:
+            B_pts = self.Item.group_item_map[itemB]['group_item_patients']
+        else:
+            B_pts = len(self.Item.items[itemB].trainPatients)
+            
+
+        def modifier(itemA, itemB):
+            modifierA = 1
+            modifierB = 1
+            itemA_category = self.Item.items[itemA].category
+            itemA_category = str.replace(itemA_category,'derived_','')
+            itemB_category = self.Item.items[itemB].category
+            itemB_category = str.replace(itemB_category,'derived_','')
+            
+            if itemA_category in self.Data.inputFeatures:
+                neglogP, rr = self.Item.items[itemA].getRR_Item(mode = 'input')
+                modifierA = rr
+            return modifierA, modifierB
+        
+        modifierA,modifierB = modifier(itemA, itemB)
+        
+        def numA_pts():
+            return A_pts
+        def prevalence():
+            return B_pts*100/N
+        def PPV():
+            return AB_pts
+        def PPV_wt():
+            return AB_pts/A_pts
+        def PPV_mod():
+            return modifierA * AB_pts 
+        def PPV_mod_wt():
+            return (modifierA * AB_pts) / A_pts
+        def RR():
+            if B_pts == AB_pts:
+                adj = 0.5
+            else:
+                adj = 0
+            return (AB_pts/A_pts) / ((B_pts - AB_pts + adj)/(N - A_pts + adj))
+        
+        switcher = {
+                'numA_pts': numA_pts,
+                'prevalence': prevalence,
+                'ppv': PPV,
+                'ppv_wt':PPV_wt,
+                'ppv_mod': PPV_mod,
+                'ppv_mod_wt':PPV_mod_wt,
+                'rr': RR,
+                }
+    
+        func = switcher.get(metric, lambda: 'invalid')    
+        
+        return func()
+    
+    def topItemsGivenQuery(self, query_item_codes, n=10, method = 'PPV_mod_wt', RR_threshold = 0):
+    
+        queryItemNames = list(map(Util.find_item_name, query_item_codes))
+        print("clinical items for query: {}".format(queryItemNames))
+    
+        rankedItems, rankScore = self.rankingItems(set(query_item_codes), method)
+    
+        df = pd.DataFrame(rankedItems[:n+40], columns=["Item"])
+        df["Name"] = df["Item"].map(Util.find_item_name)
+    
+        formatFunc1 = lambda x:round(x*100,1)
+        formatFunc2 = lambda x:round(x,1)
+    
+        methodMap = {'ppv': ('ppv', formatFunc1),
+                     'rr': ('RR',formatFunc2),
+                     'prevalence':('prevalence',formatFunc2),
+                     'baseline_prevalence':('baselineprevalence',formatFunc2)}
+        
+        metrics = ["PPV","RR","Prevalence","Baseline_Prevalence"]
+        for m in metrics:
+            method = m.lower()
+            df[m] = df["Item"].apply(self.aggStats, args = (query_item_codes, methodMap[method][0]))
+            df[m] = df[m].apply(methodMap[method][1])
+    
+        
+        df = df[df["RR"] >= RR_threshold][:n]
+        return df
\ No newline at end of file
diff --git a/scripts/specialty_recommender/SpecialtyReferral_Recommender.ipynb b/scripts/specialty_recommender/SpecialtyReferral_Recommender.ipynb
new file mode 100644
index 00000000..b088b0a1
--- /dev/null
+++ b/scripts/specialty_recommender/SpecialtyReferral_Recommender.ipynb
@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specialty Referral Recommender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Running the recommender pipeline \n",
+    "from schema import Data, Patient, Item, Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for index, row in Data.patientDF.iterrows():\n",
+    "    Patient(row)\n",
+    "    \n",
+    "for index, row in Data.demographicDF.iterrows():\n",
+    "    person_id = row[\"person_id\"]\n",
+    "    Patient.patients[person_id].add_demographics(row)\n",
+    "    Patient.patients[person_id].add_age(row)\n",
+    "                    \n",
+    "for category in Data.inputFeatures:\n",
+    "    instanceDF = Data.read_BQ(category)\n",
+    " \n",
+    "    for index, row in instanceDF.iterrows():   \n",
+    "        person_id = row[\"person_id\"]\n",
+    "        item_instance = Instance.add_instance(row, category)\n",
+    "        # generate a unique item code for each unique clinical item\n",
+    "        item_code = Item.addItem(item_instance, category)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Recommender import Recommender\n",
+    "from TableFigures import TableFigure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "R = Recommender(Item = Item, Data = Data)\n",
+    "R.preprocessing()\n",
+    "R.trainSplitbyYear(Patient.patients.values(), year = 2020)\n",
+    "R.training(R.trainPatients)\n",
+    "R.testing(method_list = ['ppv_mod_wt','prevalence','baselineprevalence','random'], \n",
+    "          patient_list = R.testPatients)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "TableFigure.result_table(R, method_list = ['ppv_mod_wt','prevalence', 'baselineprevalence','random'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/specialty_recommender/TableFigures.py b/scripts/specialty_recommender/TableFigures.py
new file mode 100644
index 00000000..41573331
--- /dev/null
+++ b/scripts/specialty_recommender/TableFigures.py
@@ -0,0 +1,36 @@
+from Util import Util
+import pandas as pd
+from statistics import mean
+
+class TableFigure:
+    def result_table(recommender_instance, method_list):
+        
+        R = recommender_instance
+        df_result = pd.DataFrame(columns = ['precision','precision_ci','recall','recall_ci','auc','auc_ci'], 
+                                 index = method_list)
+
+        for method in method_list:
+            precision, recall, auc = Util.get_test_results(R, method, R.testPatients, returnMean = False)
+            precision_ci, recall_ci, auc_ci = Util.get_confidence_interval(R, method, R.testPatients)
+            df_result.loc[method, 'precision'] = precision
+            df_result.loc[method, 'recall'] = recall 
+            df_result.loc[method, 'auc'] = auc
+            df_result.loc[method, 'precision_ci'] = precision_ci
+            df_result.loc[method, 'recall_ci'] = recall_ci 
+            df_result.loc[method, 'auc_ci'] = auc_ci
+        
+        """paired t-test"""
+        for m, method in enumerate(method_list): 
+            if m == 0: # using first method as reference
+                result_A = df_result.loc[method,:]
+                method_A = method
+            else:
+                result_B = df_result.loc[method,:]
+                method_B = method
+                Util.paired_t_test(method_A, result_A, method_B, result_B)
+           
+        for method in method_list:
+            df_result.loc[method, 'precision'] = round(mean(df_result.loc[method, 'precision']),2)
+            df_result.loc[method, 'recall'] = round(mean(df_result.loc[method, 'recall']),2)
+            df_result.loc[method, 'auc'] = round(mean(df_result.loc[method, 'auc']),2)
+        return df_result
\ No newline at end of file
diff --git a/scripts/specialty_recommender/Util.py b/scripts/specialty_recommender/Util.py
new file mode 100644
index 00000000..22b8e9a2
--- /dev/null
+++ b/scripts/specialty_recommender/Util.py
@@ -0,0 +1,191 @@
+import scipy.stats as stats
+import numpy as np
+from statistics import mean
+from sklearn.metrics import roc_auc_score, roc_curve, auc
+
+class Util:
+    
+    def getPrecisionRecall(predicted_items, actual_items, k = 4):
+        classPos = len(actual_items)
+        classNeg = len(predicted_items) - len(actual_items) 
+        predictPos = k 
+        predictNeg = len(predicted_items) - k
+        TruePos = 0
+        FalsePos = 0
+        
+        for item in predicted_items[:k]:
+            if item in actual_items:        
+                TruePos += 1 
+            else:
+                FalsePos += 1
+            
+        FalseNeg = classPos - TruePos
+        TrueNeg = classNeg - FalsePos
+        
+        precision = TruePos / predictPos 
+        recall = TruePos / classPos
+        
+        return precision, recall
+    
+    def getAUC(rankedItems, rankedScore, outputItems):
+        addLabel = lambda x: 1 if x in outputItems else 0
+        itemLabel = list(map(addLabel, rankedItems))
+        if sum(itemLabel) > 0:
+            auc = roc_auc_score(itemLabel, rankedScore)
+        else:
+            auc = 0.5
+        return auc
+
+    def find_item_name(item_code):
+        category = Item.items[item_code].category
+        concept_id = Item.items[item_code].concept_id
+
+        (item_code, item_name) = Data.feature_dict[category][concept_id]
+        return item_name
+    
+    def find_item_code_using_name(name):
+        import re
+        for item_code, item in enumerate(Item.items):
+            item_name = Util.find_item_name(item_code).lower()
+            match = re.search(r'{}'.format(name), item_name)
+            if match:
+                print(item_code,'     ',Item.items[item_code].concept_id,'     ', Util.find_item_name(item_code))
+    
+                    
+    def show_summary_stats(recommenderInstance):
+        ri = recommenderInstance
+        print("------------Data overview -------------")
+        print("Patients: {}".format(len(Patient.patients)))
+        print("Instances: {}".format(len(Instance.instances)))
+        print("Items: {}".format(len(Item.items)))
+        print("---------------------------------------")
+        print("Training Input items: ", len(ri.trainInputItems))
+        print("Candidate items: ", len(ri.trainOutputItems))
+        print("---------------------------------------")        
+        
+        category_count = {}
+        for item in Item.items.values():
+            if item.category in category_count:
+                category_count[item.category] += 1
+            else:
+                category_count[item.category] = 1
+                
+        print("category count: {}".format(category_count))
+        
+        InputItems = []
+        OutputItems = []
+        for p in R.trainPatients:
+                InputItems.append(len(p.itemsPre))
+                OutputItems.append(len(p.itemsPost))
+            
+        print("Avg number of query items: ", np.mean(InputItems))
+        print("Median number of query items: ", np.median(InputItems))
+        print("Avg number of output items: ", np.mean(OutputItems))
+        print("Median number of output items: ", np.median(OutputItems))
+        plt.hist(OutputItems, bins= 'auto')
+        plt.show()
+        
+    def cross_validation(method_list, patient_list, fold=10):
+        
+        patient_nparray = np.asarray(patient_list)
+
+        from sklearn.model_selection import KFold
+        kf = KFold(n_splits=fold, random_state=None, shuffle=True)
+
+        resultDF = pd.DataFrame(index = ['precision','recall','auc'], columns=method_list)
+        resultDF = resultDF.applymap(lambda x:[])
+
+        for train_index, test_index in kf.split(patient_nparray): 
+            train_patient_list = list(patient_nparray[train_index])
+            test_patient_list = list(patient_nparray[test_index])
+            R_CV = Recommender()
+            R_CV.preprocessing()
+            R_CV.training(train_patient_list)
+            R_CV.testing(method_list = method_list, patient_list = test_patient_list)
+            
+            for method in method_list:
+                
+                precision, recall, auc = Util.get_test_results(R_CV, 
+                                                               method, 
+                                                               test_patient_list, 
+                                                               returnMean = False)
+                resultDF.loc['precision', method].extend(precision*100)
+                resultDF.loc['recall', method].extend(recall*100) 
+                resultDF.loc['auc', method].extend(auc)
+                
+        """paired t-test"""
+        for m, method in enumerate(method_list):
+            if m == 0: # using first method as reference
+                result_A = resultDF.loc[:, method]
+                method_A = method
+            else:
+                result_B = resultDF.loc[:, method]
+                method_B = method
+
+                Util.paired_t_test(method_A, result_A, method_B, result_B)
+        
+        resultDF = resultDF.applymap(lambda x:mean(x))
+        return resultDF
+    
+    def paired_t_test(method_A, result_A, method_B, result_B):
+        print("paired t-test for {} and {} :".format(method_A, method_B))
+        t_precision, pval_precision = stats.ttest_rel(result_A.precision, result_B.precision)
+        t_recall, pval_recall = stats.ttest_rel(result_A.recall, result_B.recall)
+        t_auc, pval_auc = stats.ttest_rel(result_A.auc, result_B.auc)
+        print("-----------------------------------------------")
+        print("precision: ", pval_precision)
+        print("recall: ", pval_recall)
+        print("auc: ", pval_auc)
+        print("-----------------------------------------------")
+    
+    def get_test_results(recommender_instance, method, patient_list, returnMean = True):
+        precision_list = []
+        recall_list = []
+        auc_list = []
+        valid_test = 0
+                                                     
+        for patient in patient_list:
+            if (method, patient) in recommender_instance.patient_results:
+                result = recommender_instance.patient_results[(method, patient)]
+                precision_list.append(result['precision'])
+                recall_list.append(result['recall'])
+                auc_list.append(result['auc'])
+                valid_test += 1
+        
+        if valid_test > 0:
+            if returnMean:
+                return round(mean(precision_list),2), round(mean(recall_list),2), round(mean(auc_list),2)
+            else:
+                return precision_list, recall_list, auc_list
+        else:
+            pass
+        
+    def get_confidence_interval(recommender_instance, method, patient_list):
+        
+        def confidence_interval(stats):
+            alpha = 0.95
+            p = ((1.0 - alpha) / 2.0) * 100
+            lower = max(0.0, np.percentile(stats, p))
+            p = (alpha + (1.0 - alpha)/2.0) * 100
+            upper = min(1.0, np.percentile(stats, p))
+            return (round(lower,2), round(upper,2))
+        
+        from sklearn.utils import resample
+        precisions = list()
+        recalls = list()
+        aucs = list()
+        n_iteration = 1000
+        n_size = int(len(patient_list) * 1)
+        
+        for i in range(n_iteration):
+            patients = resample(patient_list, n_samples = n_size)
+            precision, recall, auc = Util.get_test_results(recommender_instance, method, patients)
+            precisions.append(precision)
+            recalls.append(recall)
+            aucs.append(auc)
+            
+        precision_ci = confidence_interval(precisions)
+        recall_ci = confidence_interval(recalls)
+        auc_ci = confidence_interval(aucs)
+        
+        return precision_ci, recall_ci, auc_ci
\ No newline at end of file
diff --git a/scripts/specialty_recommender/V3_Item_Prevalence_RR.ipynb b/scripts/specialty_recommender/V3_Item_Prevalence_RR.ipynb
new file mode 100644
index 00000000..21a5f1aa
--- /dev/null
+++ b/scripts/specialty_recommender/V3_Item_Prevalence_RR.ipynb
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### V3 OMOP Item Prevalence and RR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/google/auth/_default.py:70: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/\n",
+      "  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/wip/.config/gcloud/application_default_credentials.json'  \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "\n",
+    "import sys\n",
+    "import json\n",
+    "\n",
+    "JSON_FILE_PATH = '/Users/wip/Recommender/V3/resource'\n",
+    "sys.path.append('/Users/wip/Recommender/V3/python_scripts')\n",
+    "#from bigQueryUtil import BigQueryClient \n",
+    "from StatsUtil import getStats\n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client = bigquery.Client()\n",
+    "project_id = 'som-rit-phi-starr-prod'\n",
+    "dataset_id = 'starr_omop_cdm5_deid_latest'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining variables\n",
+    "year_start = '2014'\n",
+    "year_end = '2020'\n",
+    "prefix = 'V3'\n",
+    "\n",
+    "save_project_id = 'som-nero-phi-jonc101'\n",
+    "save_dataset_id = 'wui_omop_peds'\n",
+    "\n",
+    "table_id_outpt = prefix + '_Outpt_Cohort_' + year_start + '_' + year_end\n",
+    "table_id_cohort =  prefix + '_cohort_'+ year_start + '_' + year_end\n",
+    "\n",
+    "format_map_dict = {'project_id': project_id,\n",
+    "                   'dataset_id': dataset_id, \n",
+    "                   'save_project_id': save_project_id,\n",
+    "                   'save_dataset_id': save_dataset_id,\n",
+    "                   'table_id_outpt': table_id_outpt,\n",
+    "                   'table_id_cohort': table_id_cohort,\n",
+    "                   'prefix': prefix \n",
+    "                    }\n",
+    "\n",
+    "table_list =  [('measurement','measurement','measurement_concept_id'),\n",
+    "                 ('procedure_occurrence','procedure','procedure_concept_id'),\n",
+    "                 ('drug_exposure','drug','drug_concept_id'),\n",
+    "                 ('condition_occurrence','condition','condition_concept_id'),\n",
+    "                 ]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_query_table(sql, table_str):\n",
+    "    job_config = bigquery.QueryJobConfig(destination=table_str)\n",
+    "    client.delete_table(table_str, not_found_ok = True)\n",
+    "    query_job = client.query(sql, job_config=job_config)  \n",
+    "    query_job.result() \n",
+    "    print(\"Query results loaded to the table {}\".format(table_str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_query_row_count(sql):\n",
+    "    query_job = client.query(sql)  \n",
+    "    results = query_job.result()\n",
+    "    results_list = [row for row in results]\n",
+    "    return results_list[0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_BQ(table_id):\n",
+    "        # reading a table from BiqQuery\n",
+    "        sql = \"\"\" \n",
+    "            SELECT * FROM \n",
+    "                som-nero-phi-jonc101.wui_omop_peds.{table_id}\n",
+    "            \"\"\".format_map({'table_id':table_id})\n",
+    "        query_job = client.query(sql)\n",
+    "        dataframe = query_job.to_dataframe()\n",
+    "        return dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sqlCohort = \"\"\"SELECT COUNT(DISTINCT(person_id))  \n",
+    "                FROM `{save_project_id}.{save_dataset_id}.{table_id_cohort}` \n",
+    "                \"\"\".format_map(format_map_dict)\n",
+    "    \n",
+    "sqlAll = \"\"\"SELECT COUNT(DISTINCT(person_id))  \n",
+    "                FROM `{save_project_id}.{save_dataset_id}.{table_id_outpt}` \n",
+    "                \"\"\".format_map(format_map_dict)\n",
+    "\n",
+    "N_cohort = get_query_row_count(sqlCohort)\n",
+    "N_All = get_query_row_count(sqlAll)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query results loaded to the table som-nero-phi-jonc101.wui_omop_peds.V3_count_measurement_Outpt\n",
+      "Query results loaded to the table som-nero-phi-jonc101.wui_omop_peds.V3_count_procedure_Outpt\n",
+      "Query results loaded to the table som-nero-phi-jonc101.wui_omop_peds.V3_count_drug_Outpt\n",
+      "Query results loaded to the table som-nero-phi-jonc101.wui_omop_peds.V3_count_condition_Outpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get counts (number of patients, visits and instances) per each clinical items\n",
+    "# in all outpatient pediatric visits 2015-2019\n",
+    "\n",
+    "table_list =  [('measurement','measurement','measurement_concept_id'),\n",
+    "                 ('procedure_occurrence','procedure','procedure_concept_id'),\n",
+    "                 ('drug_exposure','drug','drug_concept_id'),\n",
+    "                 ('condition_occurrence','condition','condition_concept_id'),\n",
+    "                 ]\n",
+    "\n",
+    "\n",
+    "for t in table_list:\n",
+    "    (table, tableName, concept_id) = t\n",
+    "    \n",
+    "    feature_dict = {'table': table,\n",
+    "                    'concept_id': concept_id}\n",
+    "    \n",
+    "    feature_dict.update(format_map_dict)\n",
+    "    \n",
+    "    sql = \"\"\"\n",
+    "            WITH itemAllOutpt AS (        \n",
+    "                    SELECT \n",
+    "                        x.person_id,\n",
+    "                        x.visit_occurrence_id,\n",
+    "                        x.{concept_id} as item_concept_id\n",
+    "                    FROM \n",
+    "                        `{project_id}.{dataset_id}.{table}` x\n",
+    "                    INNER JOIN \n",
+    "                        `{save_project_id}.{save_dataset_id}.{table_id_outpt}` c \n",
+    "                        ON \n",
+    "                            (x.person_id = c.person_id) AND\n",
+    "                            (x.visit_occurrence_id = c.visit_occurrence_id)\n",
+    "                  )\n",
+    "\n",
+    "            SELECT item_concept_id,\n",
+    "                   COUNT(DISTINCT(person_id)) as num_pt, \n",
+    "                   COUNT(DISTINCT(visit_occurrence_id)) as num_visit,\n",
+    "                   COUNT(*) as instance\n",
+    "            FROM itemAllOutpt \n",
+    "            GROUP BY item_concept_id \n",
+    "            ORDER BY num_pt DESC, num_visit DESC, instance DESC\n",
+    "\n",
+    "    \"\"\".format_map(feature_dict)\n",
+    "\n",
+    "    save_table_id = prefix + \"_count_\" + tableName + \"_Outpt\"\n",
+    "    table_str = save_project_id + '.' + save_dataset_id + '.' + save_table_id\n",
+    "    save_query_table(sql, table_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reading...measurement\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/google/cloud/bigquery/client.py:444: UserWarning: Cannot create BigQuery Storage client, the dependency google-cloud-bigquery-storage is not installed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reading...procedure\n",
+      "reading...drug\n",
+      "reading...condition\n",
+      "saving baseline prevalence map as json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# getting baseline outpatient prevalence for each item \n",
+    "combined_df = pd.DataFrame()\n",
+    "features =  ['measurement','procedure','drug','condition']\n",
+    "for feature in features:\n",
+    "    print('reading...{}'.format(feature))\n",
+    "    df = read_BQ(table_id = prefix + '_count_'+ feature +'_Outpt')\n",
+    "    combined_df = pd.concat([combined_df, df])\n",
+    "\n",
+    "combined_df = combined_df[combined_df['item_concept_id']!=0]\n",
+    "combined_df[\"baseline_prevalence\"] = combined_df[\"num_pt\"].apply(lambda x : x * 100 / N_All)\n",
+    "final_df = combined_df[[\"item_concept_id\",\"baseline_prevalence\"]]\n",
+    "final_df.sort_values(by = \"baseline_prevalence\", ascending = False)\n",
+    "final_dict = final_df.set_index(\"item_concept_id\").to_dict()\n",
+    "baseline_prevalence_map = final_dict[\"baseline_prevalence\"]\n",
+    "with open(JSON_FILE_PATH + '/baseline_prevalence_map.json', 'w') as fp:\n",
+    "    print('saving baseline prevalence map as json')\n",
+    "    json.dump(baseline_prevalence_map, fp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# cohort item counts - Primary care (PC) and Specialty care (SC)\n",
+    "\n",
+    "table_list =  [('measurement','measurement_concept_id'),\n",
+    "                 ('procedure','procedure_concept_id'),\n",
+    "                 ('drug','drug_concept_id'),\n",
+    "                 ('condition','condition_concept_id'),\n",
+    "                 ]\n",
+    "\n",
+    "cohorts = [('PC','NOT'),\n",
+    "            ('SC','')]\n",
+    "\n",
+    "for c in cohorts:\n",
+    "    (cohort_suffix, cohort_str) = c \n",
+    "    feature_dict = {'cohort_str': cohort_str}\n",
+    "\n",
+    "    for t in table_list:\n",
+    "        (table, concept_id) = t\n",
+    "        feature_dict.update({'table': table,\n",
+    "                     'concept_id': concept_id})\n",
+    "    \n",
+    "        feature_dict.update(format_map_dict)\n",
+    "\n",
+    "        sql = \"\"\"\n",
+    "        WITH item AS \n",
+    "            (SELECT \n",
+    "               {concept_id} as item_concept_id,\n",
+    "               COUNT(DISTINCT(person_id)) as num_pt, \n",
+    "               COUNT(DISTINCT(visit_id)) as num_visit,\n",
+    "               COUNT(*) as instance\n",
+    "\n",
+    "        FROM  `{save_project_id}.{save_dataset_id}.{prefix}_{table}` item\n",
+    "\n",
+    "        WHERE item.visit_id {cohort_str} IN \n",
+    "                    (SELECT Specialty_visit_id \n",
+    "                     FROM `{save_project_id}.{save_dataset_id}.{table_id_cohort}`)\n",
+    "        GROUP BY \n",
+    "            {concept_id})\n",
+    "\n",
+    "        SELECT \n",
+    "            n.*, c.concept_name \n",
+    "        FROM \n",
+    "            item n \n",
+    "        LEFT JOIN \n",
+    "            `{project_id}.{dataset_id}.concept` c\n",
+    "        ON\n",
+    "            n.item_concept_id = c.concept_id\n",
+    "        ORDER BY \n",
+    "            num_pt DESC, num_visit DESC, instance DESC\n",
+    "        \"\"\".format_map(feature_dict)\n",
+    "    \n",
+    "        save_table_id = prefix + \"_count_\" + table + \"_\" + cohort_suffix + \"_Cohort\"\n",
+    "        table_str = save_project_id + '.' + save_dataset_id + '.' + save_table_id\n",
+    "        save_query_table(sql, table_str)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# get relative risk of items based on how likely it will appear in the cohort  \n",
+    "\n",
+    "def getItemRR(category, mode = 'PC', writeFile = False):\n",
+    "    \n",
+    "    item_Cohort = read_BQ(table_id = prefix + '_count_'+ category +'_' + mode + '_Cohort')\n",
+    "    item_NonCohort = read_BQ(table_id = prefix + '_count_'+ category +'_NonCohort')\n",
+    "\n",
+    "    \n",
+    "    N_noncohort = N_All - N_cohort\n",
+    "        \n",
+    "    # calculate Fisher Negative Log p-value and relative risk in the Cohort \n",
+    "    fisherMap = {}\n",
+    "    fisherList = []\n",
+    "    for index, row in item_Cohort.iterrows():\n",
+    "        concept_id = row[\"item_concept_id\"]\n",
+    "        pt_cohort = row[\"num_pt\"]\n",
+    "        concept_name = row[\"concept_name\"]\n",
+    "        if concept_id in item_NonCohort[\"item_concept_id\"].values:\n",
+    "            pt_noncohort = item_NonCohort.loc[item_NonCohort[\"item_concept_id\"] == concept_id,\"num_pt\"].iloc[0]\n",
+    "        else:\n",
+    "            pt_noncohort = 0\n",
+    "\n",
+    "        nAB = pt_cohort \n",
+    "        nA = N_cohort\n",
+    "        nB = pt_cohort + pt_noncohort \n",
+    "        N = N_cohort + N_noncohort \n",
+    "\n",
+    "        s = getStats(nAB = nAB, nA = nA, nB = nB, N = N)\n",
+    "        s.normalize()\n",
+    "        rr = s.calc('rr')\n",
+    "        neglogP = s.calc('fisher_neglog')\n",
+    "        if concept_id != 0:\n",
+    "            fisherMap[concept_id] = (neglogP, rr)\n",
+    "            fisherList.append([concept_id, concept_name, neglogP, rr])\n",
+    "    \n",
+    "    if writeFile:\n",
+    "        with open(JSON_FILE_PATH + '/' + category + '_rrMap_Cohort' + mode + '.json', 'w') as fp:\n",
+    "             json.dump(fisherMap, fp)\n",
+    "        \n",
+    "    return pd.DataFrame(fisherList, columns=[\"concept_id\",\"name\",\"neglogP\",\"rr\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "category_list =  ['measurement','procedure','drug','condition']\n",
+    "DFlist = []\n",
+    "for c in category_list:\n",
+    "    DFlist.append(getItemRR(c, writeFile=True))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/specialty_recommender/V3_OMOP_Feature_Extraction.ipynb b/scripts/specialty_recommender/V3_OMOP_Feature_Extraction.ipynb
new file mode 100644
index 00000000..773a6caa
--- /dev/null
+++ b/scripts/specialty_recommender/V3_OMOP_Feature_Extraction.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## V3 Feature Extraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/wip/.config/gcloud/application_default_credentials.json' \n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client = bigquery.Client()\n",
+    "project_id = 'som-rit-phi-starr-prod'\n",
+    "dataset_id = 'starr_omop_cdm5_deid_latest'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining variables\n",
+    "year_start = '2014'\n",
+    "year_end = '2020'\n",
+    "prefix = 'V3'\n",
+    "\n",
+    "save_project_id = 'som-nero-phi-jonc101'\n",
+    "save_dataset_id = 'wui_omop_peds'\n",
+    "\n",
+    "table_id_outpt = prefix + '_Outpt_Cohort_' + year_start + '_' + year_end\n",
+    "table_id_endorefer =  prefix + '_Endorefer_Cohort_' + year_start + '_' + year_end\n",
+    "\n",
+    "table_id_cohort = prefix + '_cohort_' + year_start + '_' + year_end\n",
+    "table_id_demographic = prefix + '_demographic'\n",
+    "\n",
+    "\n",
+    "outpt_visit_concept = \"(0,9202,581477,5083)\"\n",
+    "\n",
+    "format_map_dict = {'project_id': project_id,\n",
+    "                   'dataset_id': dataset_id, \n",
+    "                   'save_project_id': save_project_id,\n",
+    "                   'save_dataset_id': save_dataset_id,\n",
+    "                   'year_start':year_start,\n",
+    "                   'year_end':year_end,\n",
+    "                   'table_id_endorefer': table_id_endorefer,\n",
+    "                   'table_id_cohort': table_id_cohort,\n",
+    "                   'outpt_visit_concept': outpt_visit_concept \n",
+    "                    }\n",
+    "\n",
+    "def save_query_table(sql, table_str):\n",
+    "    job_config = bigquery.QueryJobConfig(destination=table_str)\n",
+    "    client.delete_table(table_str, not_found_ok = True)\n",
+    "    query_job = client.query(sql, job_config=job_config)  \n",
+    "    query_job.result() \n",
+    "    print(\"Query results loaded to the table {}\".format(table_str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cohort \n",
+    "sql = \"\"\"\n",
+    "\n",
+    "WITH rank_cohort AS \n",
+    "    (\n",
+    "    SELECT \n",
+    "        person_id, \n",
+    "        PrimaryCare_visit_id,\n",
+    "        PrimaryCare_DATETIME,\n",
+    "        Specialty_visit_id,\n",
+    "        Specialty_DATETIME,\n",
+    "        ROW_NUMBER() OVER (PARTITION BY person_id ORDER BY PrimaryCare_DATETIME) AS visit_rank\n",
+    "    FROM \n",
+    "        `{save_project_id}.{save_dataset_id}.{table_id_endorefer}`\n",
+    "    WHERE \n",
+    "        Endo_visit_rank = 1\n",
+    "    )\n",
+    "SELECT \n",
+    "    * \n",
+    "FROM \n",
+    "    rank_cohort\n",
+    "WHERE \n",
+    "    visit_rank = 1\n",
+    "ORDER BY\n",
+    "    PrimaryCare_DATETIME   \n",
+    "\"\"\".format_map(format_map_dict)\n",
+    "\n",
+    "table_str = save_project_id + '.' + save_dataset_id + '.' + table_id_cohort\n",
+    "save_query_table(sql, table_str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_cohort_feature(feature_dict, feature):\n",
+    "\n",
+    "    sql = \"\"\"\n",
+    "    WITH feature_table AS\n",
+    "        (\n",
+    "        SELECT \n",
+    "           {feature_columns}\n",
+    "        FROM \n",
+    "            `{project_id}.{dataset_id}.{feature_table_id}` f \n",
+    "        INNER JOIN \n",
+    "            `{save_project_id}.{save_dataset_id}.{table_id_cohort}` c \n",
+    "            ON \n",
+    "                f.person_id = c.person_id AND\n",
+    "                (\n",
+    "                (DATETIME_DIFF(c.PrimaryCare_DATETIME, f.{feature_datetime}, DAY) BETWEEN 0 AND 180) \n",
+    "                 OR \n",
+    "                (f.visit_occurrence_id = c.Specialty_visit_id)\n",
+    "                )\n",
+    "        LEFT JOIN \n",
+    "            `{project_id}.{dataset_id}.visit_occurrence` v\n",
+    "            ON\n",
+    "              f.visit_occurrence_id = v.visit_occurrence_id\n",
+    "        WHERE\n",
+    "            v.visit_concept_id IN {outpt_visit_concept}\n",
+    "        )\n",
+    "    \n",
+    "    SELECT\n",
+    "        ft.*,\n",
+    "        c.concept_name\n",
+    "    FROM feature_table ft\n",
+    "        LEFT JOIN \n",
+    "            `{project_id}.{dataset_id}.concept` c ON ft.{feature_concept_id} = c.concept_id\n",
+    "    ORDER BY \n",
+    "        ft.person_id,\n",
+    "        ft.visit_id\n",
+    "    \"\"\".format_map(feature_dict)\n",
+    "\n",
+    "    table_str = feature_dict['save_project_id'] + '.' + feature_dict['save_dataset_id'] + '.' + feature_dict['save_table_id']\n",
+    "    save_query_table(sql, table_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_columns_dict = {\n",
+    "    \n",
+    "            'measurement': \"\"\"\n",
+    "                        f.person_id,\n",
+    "                        f.visit_occurrence_id as visit_id,\n",
+    "                        f.measurement_DATETIME,\n",
+    "                        f.value_as_number,\n",
+    "                        f.range_low,\n",
+    "                        f.range_high,\n",
+    "                        f.measurement_source_concept_id,\n",
+    "                        f.measurement_source_value,\n",
+    "                        f.measurement_concept_id,\n",
+    "                        \"\"\",\n",
+    "    \n",
+    "    \n",
+    "            'procedure': \"\"\"\n",
+    "                        f.person_id,\n",
+    "                        f.visit_occurrence_id as visit_id,\n",
+    "                        f.procedure_DATETIME,\n",
+    "                        f.procedure_source_concept_id,\n",
+    "                        f.procedure_source_value,\n",
+    "                        f.procedure_concept_id \n",
+    "                        \"\"\",\n",
+    "            \n",
+    "            'condition': \"\"\"\n",
+    "                        f.person_id,\n",
+    "                        f.visit_occurrence_id as visit_id,\n",
+    "                        f.condition_start_DATETIME as condition_DATETIME,\n",
+    "                        f.condition_source_concept_id,\n",
+    "                        f.condition_source_value,\n",
+    "                        f.condition_concept_id\n",
+    "                        \"\"\",\n",
+    "            'drug': \"\"\"\n",
+    "                    f.person_id,\n",
+    "                    f.visit_occurrence_id as visit_id,\n",
+    "                    f.drug_exposure_start_DATETIME as drug_DATETIME,\n",
+    "                    f.drug_exposure_end_DATETIME as drug_end_DATETIME,\n",
+    "                    f.drug_exposure_id,\n",
+    "                    f.drug_source_concept_id,\n",
+    "                    f.drug_source_value,\n",
+    "                    f.drug_concept_id\n",
+    "                    \"\"\"\n",
+    "                           }\n",
+    "\n",
+    "\n",
+    "feature_table_id_dict = {'measurement': 'measurement',\n",
+    "                         'procedure': 'procedure_occurrence',\n",
+    "                         'condition': 'condition_occurrence',\n",
+    "                          'drug': 'drug_exposure'}\n",
+    "\n",
+    "feature_datetime_dict = {'measurement': 'measurement_DATETIME',\n",
+    "                        'procedure': 'procedure_DATETIME',\n",
+    "                         'condition': 'condition_start_DATETIME',\n",
+    "                         'drug': 'drug_exposure_start_DATETIME'}\n",
+    "\n",
+    "feature_concept_id_dict = {'measurement': 'measurement_concept_id',\n",
+    "                        'procedure': 'procedure_concept_id',\n",
+    "                         'condition': 'condition_concept_id',\n",
+    "                         'drug': 'drug_concept_id'}\n",
+    "\n",
+    "save_table_id_dict = {'measurement': prefix + '_measurement',\n",
+    "                         'procedure': prefix + '_procedure',\n",
+    "                         'condition': prefix + '_condition',\n",
+    "                          'drug': prefix + '_drug'}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = ['measurement','procedure','condition','drug']\n",
+    "\n",
+    "for feature in features:\n",
+    "    feature_dict = {'feature_columns': feature_columns_dict[feature],\n",
+    "                    'feature_table_id': feature_table_id_dict[feature],\n",
+    "                    'feature_datetime': feature_datetime_dict[feature],\n",
+    "                    'feature_concept_id': feature_concept_id_dict[feature],\n",
+    "                    'save_table_id': save_table_id_dict[feature]}\n",
+    "    \n",
+    "    feature_dict.update(format_map_dict)\n",
+    "    extract_cohort_feature(feature_dict, feature)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# person table for patient demographics\n",
+    "\n",
+    "sql = \"\"\"\n",
+    "\n",
+    "WITH person_table AS\n",
+    "    (\n",
+    "        SELECT \n",
+    "            p.person_id,\n",
+    "            p.birth_DATETIME,\n",
+    "            p.gender_concept_id,\n",
+    "            p.race_concept_id,\n",
+    "            p.ethnicity_concept_id\n",
+    "        FROM \n",
+    "            `{project_id}.{dataset_id}.person` p \n",
+    "        INNER JOIN \n",
+    "            (SELECT DISTINCT(person_id) FROM `{save_project_id}.{save_dataset_id}.{table_id_cohort}`) c \n",
+    "            ON \n",
+    "                p.person_id = c.person_id \n",
+    "    )\n",
+    "    \n",
+    "SELECT\n",
+    "    pt.person_id,\n",
+    "    pt.birth_DATETIME,\n",
+    "    pt.gender_concept_id,\n",
+    "    c1.concept_name as gender,\n",
+    "    pt.race_concept_id,\n",
+    "    c2.concept_name as race,\n",
+    "    pt.ethnicity_concept_id,\n",
+    "    c3.concept_name as ethnicity\n",
+    "FROM person_table pt\n",
+    "    LEFT JOIN \n",
+    "        `{project_id}.{dataset_id}.concept` c1 ON pt.gender_concept_id = c1.concept_id\n",
+    "    LEFT JOIN \n",
+    "        `{project_id}.{dataset_id}.concept` c2 ON pt.race_concept_id = c2.concept_id\n",
+    "    LEFT JOIN \n",
+    "        `{project_id}.{dataset_id}.concept` c3 ON pt.ethnicity_concept_id = c3.concept_id\n",
+    "ORDER BY \n",
+    "    pt.person_id\n",
+    "\"\"\".format_map(format_map_dict)\n",
+    "\n",
+    "table_str = save_project_id + '.' + save_dataset_id + '.' + table_id_demographic\n",
+    "save_query_table(sql, table_str)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/specialty_recommender/V3_OMOP_Pediatric_Referral.ipynb b/scripts/specialty_recommender/V3_OMOP_Pediatric_Referral.ipynb
new file mode 100644
index 00000000..4603c926
--- /dev/null
+++ b/scripts/specialty_recommender/V3_OMOP_Pediatric_Referral.ipynb
@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### V3 OMOP Pediatric Referral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "##Setting up Google sdk environment\n",
+    "import os \n",
+    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/wip/.config/gcloud/application_default_credentials.json'\n",
+    "os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' \n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "##Setting up BQ API\n",
+    "from google.cloud import bigquery\n",
+    "client = bigquery.Client()\n",
+    "project_id = 'som-rit-phi-starr-prod'\n",
+    "dataset_id = 'starr_omop_cdm5_deid_latest'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining variables\n",
+    "year_start = '2014'\n",
+    "year_end = '2020'\n",
+    "prefix = 'V3'\n",
+    "\n",
+    "save_project_id = 'som-nero-phi-jonc101'\n",
+    "save_dataset_id = 'wui_omop_peds'\n",
+    "map_dataset_id = 'starr_datalake2018'\n",
+    "\n",
+    "table_id_outpt = prefix + '_Outpt_Cohort_' + year_start + '_' + year_end\n",
+    "table_id_endorefer =  prefix + '_Endorefer_Cohort_' + year_start + '_' + year_end\n",
+    "\n",
+    "format_map_dict = {'project_id': project_id,\n",
+    "                   'dataset_id': dataset_id, \n",
+    "                   'save_project_id': save_project_id,\n",
+    "                   'save_dataset_id': save_dataset_id,\n",
+    "                   'year_start':year_start,\n",
+    "                   'year_end':year_end,\n",
+    "                   'map_dataset_id': map_dataset_id\n",
+    "                    }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_query_table(sql, table_str):\n",
+    "    job_config = bigquery.QueryJobConfig(destination=table_str)\n",
+    "    client.delete_table(table_str, not_found_ok = True)\n",
+    "    query_job = client.query(sql, job_config=job_config)  \n",
+    "    query_job.result() \n",
+    "    print(\"Query results loaded to the table {}\".format(table_str))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query results loaded to the table som-nero-phi-jonc101.wui_omop_peds.V3_Endorefer_Cohort_2014_2020\n"
+     ]
+    }
+   ],
+   "source": [
+    "sql = \"\"\"\n",
+    "WITH \n",
+    "\n",
+    "PedEndoReferral AS\n",
+    "(SELECT \n",
+    "    pr.person_id,\n",
+    "    pr.visit_occurrence_id,\n",
+    "    pr.procedure_DATETIME as referral_DATETIME,\n",
+    "    pr.procedure_source_value AS referral\n",
+    " FROM \n",
+    "     `{project_id}.{dataset_id}.procedure_occurrence` pr \n",
+    " LEFT JOIN \n",
+    "    `{project_id}.{dataset_id}.person` p \n",
+    " ON \n",
+    "     pr.person_id = p.person_id\n",
+    " WHERE \n",
+    "    REGEXP_CONTAINS(procedure_source_value, r'(?i)referral') AND\n",
+    "    REGEXP_CONTAINS(procedure_source_value, r'(?i)endo') AND\n",
+    "    (DATETIME_DIFF(pr.procedure_DATETIME, p.birth_DATETIME, DAY) BETWEEN 0 AND 6575)\n",
+    "),\n",
+    "\n",
+    "PrimaryCare AS\n",
+    "(SELECT \n",
+    "    person_id,\n",
+    "    visit_occurrence_id,\n",
+    "    visit_start_DATETIME\n",
+    "    FROM \n",
+    "        `{project_id}.{dataset_id}.visit_occurrence` v\n",
+    "    LEFT JOIN\n",
+    "        `{project_id}.{dataset_id}.care_site` c \n",
+    "    ON \n",
+    "        v.care_site_id = c.care_site_id\n",
+    "    LEFT JOIN \n",
+    "        `{save_project_id}.{map_dataset_id}.dep_map` m \n",
+    "    ON \n",
+    "        CAST(c.care_site_source_value AS NUMERIC) = m.department_id\n",
+    "    WHERE \n",
+    "        (m.specialty_dep_c NOT IN ('7','81','106')) AND \n",
+    "        v.visit_concept_id IN (0,9202,581477,5083) AND\n",
+    "        DATETIME_DIFF(v.visit_end_DATETIME, v.visit_start_DATETIME, DAY) = 0 AND\n",
+    "        EXTRACT(YEAR FROM v.visit_start_DATETIME) >= {year_start} AND\n",
+    "        EXTRACT(YEAR FROM v.visit_start_DATETIME) <= {year_end}\n",
+    "),\n",
+    "\n",
+    "Endocrine AS \n",
+    "(SELECT \n",
+    "    person_id,\n",
+    "    visit_occurrence_id,\n",
+    "    visit_start_DATETIME,\n",
+    "FROM \n",
+    "    `{project_id}.{dataset_id}.visit_occurrence` v\n",
+    "LEFT JOIN\n",
+    "    `{project_id}.{dataset_id}.care_site` c \n",
+    "ON \n",
+    "    v.care_site_id = c.care_site_id\n",
+    "LEFT JOIN \n",
+    "    `{save_project_id}.{map_dataset_id}.dep_map` m \n",
+    "ON \n",
+    "    CAST(c.care_site_source_value AS NUMERIC) = m.department_id\n",
+    "WHERE \n",
+    "    m.specialty_dep_c in ('7','81','106')\n",
+    "),\n",
+    "\n",
+    "Office_Visit AS \n",
+    "(SELECT visit_occurrence_id FROM `{project_id}.{dataset_id}.note` \n",
+    "     WHERE note_class_concept_id = 3000735)\n",
+    "\n",
+    "SELECT \n",
+    "    r.person_id,\n",
+    "    r.visit_occurrence_id AS PrimaryCare_visit_id,\n",
+    "    ec.visit_occurrence_id AS Specialty_visit_id,\n",
+    "    r.referral_DATETIME,\n",
+    "    pc.visit_start_DATETIME AS PrimaryCare_DATETIME,\n",
+    "    ec.visit_start_DATETIME AS Specialty_DATETIME, \n",
+    "    ROW_NUMBER() OVER (PARTITION BY r.visit_occurrence_id ORDER BY ec.visit_start_DATETIME) AS Endo_visit_rank\n",
+    "FROM \n",
+    "    PedEndoReferral r\n",
+    "INNER JOIN \n",
+    "    PrimaryCare pc\n",
+    "ON \n",
+    "    (r.person_id = pc.person_id) AND \n",
+    "    (r.visit_occurrence_id = pc.visit_occurrence_id)\n",
+    "INNER JOIN\n",
+    "    Endocrine ec\n",
+    "ON \n",
+    "    (r.person_id = ec.person_id) AND\n",
+    "    (DATETIME_DIFF(ec.visit_start_DATETIME, pc.visit_start_DATETIME, DAY) BETWEEN 0 AND 180)\n",
+    "WHERE\n",
+    "    ec.visit_occurrence_id IN (SELECT * FROM Office_Visit)\n",
+    "ORDER BY\n",
+    "    r.person_id, pc.visit_start_DATETIME\n",
+    "\"\"\".format_map(format_map_dict)\n",
+    "\n",
+    "table_str = save_project_id + '.' + save_dataset_id + '.' + table_id_endorefer\n",
+    "save_query_table(sql, table_str)\n",
+    "\n",
+    "# m.specialty_dep_c in ('32','72') AND "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query results loaded to the table som-nero-phi-jonc101.wui_omop_peds.V3_Outpt_Cohort_2014_2020\n"
+     ]
+    }
+   ],
+   "source": [
+    "# All outpatient visits in the same period\n",
+    "sql = \"\"\"\n",
+    "    SELECT \n",
+    "        person_id,\n",
+    "        visit_occurrence_id,\n",
+    "        visit_start_DATETIME\n",
+    "    FROM \n",
+    "        `{project_id}.{dataset_id}.visit_occurrence` v\n",
+    "    WHERE \n",
+    "        v.visit_concept_id IN (0,9202,581477,5083) AND\n",
+    "        DATETIME_DIFF(v.visit_end_DATETIME, v.visit_start_DATETIME, DAY) = 0 AND\n",
+    "        EXTRACT(YEAR FROM v.visit_start_DATETIME) >= {year_start} AND\n",
+    "        EXTRACT(YEAR FROM v.visit_start_DATETIME) <= {year_end}\n",
+    "\"\"\".format_map(format_map_dict)\n",
+    "\n",
+    "table_str = save_project_id + '.' + save_dataset_id + '.' + table_id_outpt\n",
+    "save_query_table(sql, table_str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/specialty_recommender/schema.py b/scripts/specialty_recommender/schema.py
new file mode 100644
index 00000000..ca1bcf3d
--- /dev/null
+++ b/scripts/specialty_recommender/schema.py
@@ -0,0 +1,370 @@
+import json
+import math
+import datetime
+import sys
+import getopt
+import operator
+import random
+import re
+from dateutil.relativedelta import relativedelta
+
+##Setting up Google sdk environment
+import os 
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/wip/.config/gcloud/application_default_credentials.json' 
+os.environ['GCLOUD_PROJECT'] = 'som-nero-phi-jonc101' 
+
+##Setting up BQ API
+from google.cloud import bigquery
+client = bigquery.Client()
+project_id = 'som-nero-phi-jonc101'
+dataset_id = 'wui_omop_peds'
+
+
+class Data:
+        
+    RESOURCE_PATH = ('../resource/')
+    def read_BQ(feature_name):
+        project_id = 'som-nero-phi-jonc101'
+        dataset_id = 'wui_omop_peds'
+        prefix = "V3"
+        # reading a table from BiqQuery
+        table_name = prefix + "_" + feature_name
+        print('reading...{}'.format(table_name))
+        sql = """ 
+            SELECT * FROM 
+                `{project_id}.{dataset_id}.{table_id}`
+            """.format_map(
+                {'project_id':project_id, 
+                 'dataset_id':dataset_id, 
+                 'table_id':table_name})
+        query_job = client.query(sql)
+        dataframe = query_job.to_dataframe()
+        return dataframe
+
+    def read_JSON(file_name):
+        with open(file_name,'r') as fp:
+            return json.load(fp)
+        
+    patientDF = read_BQ('cohort_2014_2020')
+    demographicDF = read_BQ('demographic')
+    filterItemList = read_BQ('concepts_tofilter')
+    
+    sameItemMap = read_JSON(RESOURCE_PATH + 'sameItemMap.json')
+    itemPrevMap = read_JSON(RESOURCE_PATH + 'baseline_prevalence_map.json') 
+    
+    outputFeatures = {'measurement','procedure'}
+    inputFeatures = {'condition','drug','measurement','procedure'}
+    feature_dict = {"age":{},
+                    "gender":{},
+                    "race":{},
+                    "condition":{},
+                    "drug":{},
+                    "measurement":{},
+                    "derived_measurement":{},
+                    "procedure":{}}
+   
+    suffixList = ['CohortPC']
+    rrMap = {}
+    for cat in inputFeatures:
+        for suffix in suffixList:
+            rrMap[(cat,suffix)] = read_JSON(RESOURCE_PATH + cat + '_rrMap_' + suffix +'.json')
+
+
+class Patient:
+    counter = 0
+    patients = {}
+
+    def __init__(self, row):
+        #if  "IndexTime_Exist" not in self.patients[person_id].keys():
+        self.person_id = row["person_id"]
+        self.primary_visit_id = row["PrimaryCare_visit_id"]
+        self.primary_visit_time = row["PrimaryCare_DATETIME"]
+        self.specialty_visit_id = row["Specialty_visit_id"]
+        self.specialty_visit_time = row["Specialty_DATETIME"]
+        self.birthdate = None
+        self.gender = None
+        self.race = None
+        self.age = None
+        self.instances = []
+        Patient.patients[self.person_id] = self
+        Patient.counter += 1
+        
+    def add_demographics(self, row):
+        self.birthdate = row["birth_DATETIME"]
+        self.gender = row["gender"]
+        self.race = row["race"]
+        race_instance = Instance.add_instance(row, 'race')
+        gender_instance = Instance.add_instance(row, 'gender')
+        Item.addItem(race_instance,'race')
+        Item.addItem(gender_instance,'gender')
+        
+    def add_age(self, row):
+        years = relativedelta(self.primary_visit_time, self.birthdate).years 
+        months = relativedelta(self.primary_visit_time, self.birthdate).months
+        self.age = years * 12 + months
+        
+        if self.age < 24:
+            age_cat = 'infant'
+        elif self.age >= 24 and self.age < 144:
+            age_cat = 'child'
+        elif self.age >= 144:
+            age_cat = 'teen'
+        age_instance = Instance.add_instance(row, 'age', age_concept = age_cat)
+        Item.addItem(age_instance,'age')
+        
+    def extractItems(self):
+        # collect items in pre-post threshold lists
+        
+        preThreshold = self.primary_visit_time
+        postThreshold = self.specialty_visit_id
+        self.itemsPre = set()
+        self.itemsPost = set()
+        
+        lookbackWindow = datetime.timedelta(days = 180)
+        lookforwardWindow = datetime.timedelta(days = 1)
+        
+        for i in self.instances:
+            instance = Instance.instances[i]
+            item_code = instance.item_code 
+            item_datetime = instance.item_datetime
+            
+            # age, race, sex
+            if item_datetime == None:
+                 self.itemsPre.add(item_code)
+            elif (item_datetime >= preThreshold - lookbackWindow) & (item_datetime < preThreshold + lookforwardWindow):
+                    self.itemsPre.add(item_code)
+            elif instance.visit_id == postThreshold:
+                    group_item_code = Item.items[item_code].groupItems()
+                    self.itemsPost.add(group_item_code)
+                    
+        self.itemsPre = Item.processItems(self.itemsPre, mode = "input")
+        self.itemsPost = Item.processItems(self.itemsPost, mode = "output") 
+        
+        return self.itemsPre, self.itemsPost
+    
+class Item:
+    counter = 0
+    items = {}
+    group_item_map = {}
+    
+    def __init__(self, item_name, category, item_code, item_concept_id, instances, patients):
+        self.name = item_name
+        self.category = category 
+        self.item_code = item_code
+        self.concept_id = item_concept_id
+        self.rare = False
+        self.filter = False
+        self.instances = instances
+        self.patients = patients
+        self.trainPatients = set()
+        self.prevalence = 0
+        self.baselinePrevalence = 0
+        Item.counter += 1
+            
+    @classmethod
+    def addItem(cls, item_instance, category):
+
+        item_concept_id = item_instance.item_concept_id
+        item_name = item_instance.item_name
+        person_id = item_instance.person_id
+        
+        # derived category for high, low, normal lab results
+        if item_instance.item_value is not None:
+            category = "derived_" + category 
+            item_concept_id = item_instance.item_value + "_" + item_concept_id
+            item_name = item_instance.item_value + " " + item_name
+
+        if item_concept_id in Data.feature_dict[category]:            
+            item_code = Data.feature_dict[category][item_concept_id][0]
+            Item.items[item_code].instances.add(Instance.counter)
+            Item.items[item_code].patients.add(person_id)
+        else:
+            item_code = Item.counter
+            Data.feature_dict[category][item_concept_id] = (item_code, item_name)
+            instances = set([Instance.counter])
+            patients = set([person_id])
+            Item.items[item_code] = cls(item_name, category, item_code, item_concept_id, instances, patients)
+        
+        item_instance.item_code = item_code
+           
+        return item_code
+    
+    
+    @classmethod
+    def labelRareItems(cls, patient_count_threshold = 10):
+        for item_code in cls.items.keys():
+            
+            item = cls.items[item_code]
+            
+            if item.category not in ["gender","race","age"]:
+                if len(item.patients) < patient_count_threshold:
+                       cls.items[item_code].rare = True
+                else:
+                       cls.items[item_code].rare = False
+    
+    @classmethod                    
+    def labelFilterItems(cls):
+        filterList = list(map(str, Data.filterItemList.concept_id))
+        filterList.append('0')
+        
+        for item_code in cls.items.keys():
+            item = cls.items[item_code]
+            
+            item_concept_id = item.concept_id.replace("high_","").replace("low_","").replace("normal_","")
+            
+            if item_concept_id == '0':
+                cls.items[item_code].filter = True
+            
+            if item.category not in ["gender","race","age"]:
+            
+                assert isinstance(item_concept_id, str)
+                assert item_concept_id.isdigit(), 'concept ID %r is not all digit' % item_concept_id
+                #print(Data.feature_dict[item.category][item_concept_id])
+
+                if item_concept_id in filterList:
+                    cls.items[item_code].filter = True
+                else: 
+                    cls.items[item_code].filter = False
+    
+    @classmethod  
+    def getItemPrevalence(cls):
+        for item_code in cls.items.keys():
+            item = cls.items[item_code]
+            cls.items[item_code].prevalence = len(item.patients)*100/Patient.counter            
+
+    def groupItems(self):
+            # group same items using sameItemMap 
+            item_category = self.category
+            if (self.concept_id in Data.sameItemMap.keys()) and (item_category == 'measurement'):
+                new_concept_id = Data.sameItemMap[self.concept_id][0]
+                item_code = Data.feature_dict[item_category][new_concept_id][0]
+                if item_code not in Item.group_item_map:
+                    # store group mapping to calculate prevalence 
+                    Item.group_item_map[item_code] = {'item_codes_set': {self.item_code},
+                                                      'group_item_patients': 0}
+                else:
+                    Item.group_item_map[item_code]['item_codes_set'].add(self.item_code)
+            else:
+                item_code = self.item_code
+            return item_code
+        
+    @classmethod
+    def get_group_item_patients(cls):
+        # calculate number of patients who have the items grouped
+        for group_item_code in cls.group_item_map:
+            group_item_patients = set()
+            group_item_patients = group_item_patients.union(cls.items[group_item_code].trainPatients)
+                   
+            for item_code in cls.group_item_map[group_item_code]['item_codes_set']:
+                group_item_patients = group_item_patients.union(cls.items[item_code].trainPatients)
+            cls.group_item_map[group_item_code]['group_item_patients'] = len(group_item_patients)
+    
+            
+    def getRR_Item(self, mode):
+        if mode == 'input':
+            tag = 'CohortPC'
+        elif mode == 'output':
+            tag = 'CohortSC'
+
+        concept_id = re.compile(r'\d+').search(self.concept_id).group(0)
+        category = str.replace(self.category,'derived_','')
+        
+        if concept_id in Data.rrMap[(category, tag)]:
+            neglogP, RR = Data.rrMap[(category, tag)][concept_id]
+            return neglogP, RR
+        else:
+            #print(concept_id, ' not found in rrMap ', category, 'with tag: ', tag)
+            return 0,0
+        
+    @staticmethod    
+    def processItems(item_codes, mode = "input"):
+        
+        def filterItems(item_codes):
+            filtered_item_codes = set()
+            for i in item_codes:
+                item = Item.items[i]
+                if not (item.rare | item.filter):
+                        filtered_item_codes.add(i)
+            return filtered_item_codes
+          
+        
+        def outputCategory(item_codes):        
+            output_item_codes = set()
+            for i in item_codes:
+                item = Item.items[i]
+                if item.category in Data.outputFeatures:
+                    output_item_codes.add(i)
+            return output_item_codes
+        
+        if item_codes:
+            processed_item_codes = filterItems(item_codes)
+            if mode == "output":
+                if processed_item_codes:
+                    processed_item_codes = outputCategory(processed_item_codes)
+            return processed_item_codes
+        else:
+            return set()
+    
+class Instance:
+    counter = 0
+    instances = []
+    def __init__(self, person_id, item_name, item_concept_id, item_datetime, visit_id, item_value):    
+        self.count = Instance.counter
+        self.person_id = person_id
+        self.item_name = item_name
+        self.item_code = None
+        self.item_concept_id = item_concept_id
+        self.item_datetime = item_datetime
+        self.visit_id = visit_id
+        self.item_value = item_value
+        Instance.instances.append(self)
+        Instance.counter += 1
+        
+    @classmethod
+    def add_instance(cls, row, feature_category, age_concept = ""):
+        person_id = row["person_id"]
+        visit_id = None
+        item_datetime = None
+              
+        if feature_category == "age":
+            item_concept_id = age_concept
+            item_name = age_concept
+        else:
+            item_concept_id = str(row[feature_category + "_concept_id"])
+            
+            if feature_category in ["gender","race"]:
+                item_name = row[feature_category]
+            else:
+                item_name = row["concept_name"]
+                visit_id = row["visit_id"]
+                item_datetime = row[feature_category + "_DATETIME"]
+                
+        if feature_category == 'measurement':
+             item_value = cls.get_item_value(row)
+        else:
+             item_value = None
+                
+        Patient.patients[person_id].instances.append(Instance.counter)
+        
+        return cls(person_id, item_name, item_concept_id, item_datetime, visit_id, item_value)
+ 
+    @staticmethod
+    def get_item_value(row):
+        def isValid(ref_h, ref_l, v):
+            if ref_h and ref_l and v and not (math.isnan(ref_h) or math.isnan(ref_l) or math.isnan(v)):
+                return True
+            else:
+                return False
+            
+        refRange_high = row["range_high"]
+        refRange_low = row["range_low"]
+        value = row["value_as_number"]
+        if isValid(refRange_high, refRange_low, value):        
+                if value >= refRange_low and value <= refRange_high:
+                    return "normal"
+                elif value > refRange_high:
+                    return "high"
+                elif value < refRange_low:
+                    return "low"
+        else:
+            return None  
\ No newline at end of file
diff --git a/setup.sh b/setup.sh
deleted file mode 100755
index 391de353..00000000
--- a/setup.sh
+++ /dev/null
@@ -1,211 +0,0 @@
-#!/bin/bash
-
-##### IDENTIFY PLATFORM #####
-echo "IDENTIFY PLATFORM"
-
-# OSX
-if [ $(uname) = "Darwin" ]
-then
-    PLATFORM="brew"
-    echo "Running setup.sh on OSX..."
-    echo ""
-fi
-# Amazon
-if [ "$(uname) | grep 'amzn'" ]
-then
-    PLATFORM="Amazon Linux"
-    echo "Running setup.sh on Amazon Linux..."
-    echo ""
-fi
-
-##### INSTALL LIBRARIES #####
-echo "INSTALL LIBRARIES"
-
-# Python (https://www.python.org/)
-if [ "$(command -v python)" ]
-then
-  PYTHON_VERSION=$(python --version 2>&1)
-  echo "Installed: $PYTHON_VERSION"
-else
-  echo "Installing python..."
-fi
-
-# PYTHONPATH
-export PYTHONPATH=${PYTHONPATH}:~/healthrex/CDSS
-printf 'export PYTHONPATH=${PYTHONPATH}:~/healthrex/CDSS\n' >> ~/.bashrc
-printf 'export PYTHONPATH=${PYTHONPATH}:~/healthrex/CDSS\n' >> ~/.bash_profile
-
-# PostgreSQL (https://www.postgresql.org/)
-# psql (http://postgresguide.com/utilities/psql.html)
-if [ "$(command -v postgres)" ]
-then
-    POSTGRES_VERSION=$(postgres --version 2>&1)
-    echo "Installed: $POSTGRES_VERSION"
-else
-    echo "Installing postgresql-server..."
-    sudo yum install postgresql-server
-fi
-
-if [ "$(command -v psql)" ]
-then
-    PSQL_VERSION=$(psql --version 2>&1)
-    echo "Installed: $PSQL_VERSION"
-else
-    echo "Installing psql..."
-    sudo yum install postgresql
-fi
-
-# psycopg2 (http://initd.org/psycopg/)
-PSYCOPG2_VERSION="$(pip list --format=legacy | grep 'psycopg' | sed 's/[)(]//g' | awk '{print $2}')"
-if [ -z "$PSYCOPG2_VERSION" ]
-then
-    echo -n "Installing psycopg2..."
-    pip install --user psycopg2
-else
-    echo -n "Installed: psycopg2 "
-    echo $PSYCOPG2_VERSION
-fi
-
-# NumPy (https://scipy.org/index.html)
-# pip install --user numpy
-
-# Pandas (http://pandas.pydata.org/)
-PANDAS_VERSION="$(pip list --format=legacy | grep 'pandas' | sed 's/[)(]//g' | awk '{print $2}')"
-if [ -z "$PANDAS_VERSION" ]
-then
-  echo -n "Installing pandas..."
-  pip install --user pandas
-  echo "OK"
-else
-  echo -n "Installed: pandas "
-  echo $PANDAS_VERSION
-fi
-
-# SciKit Learn (http://scikit-learn.org/stable/)
-# pip install --user sklearn
-
-# gensim (https://radimrehurek.com/gensim/)
-# pip install --user gensim
-
-##### INITIALIZE DATABASE #####
-echo ""
-echo "INITIALIZE DATABASE"
-
-## PostgreSQL Server ##
-sudo service postgresql initdb
-sudo service postgresql start
-
-# Set password for postgres user.
-echo ""
-echo "Use postgres command line to set password for default postgres user (run '\password postgres')." 
-echo "After setting password, quit with '\q'."
-sudo -u postgres psql postgres
-
-# Allow password-based postgres login.
-if [ PLATFORM="Amazon Linux" ]
-then
-    echo ""
-    echo "Use vi to edit line 80 of pg_hba.conf to read 'local\tall\tall\tmd5'"
-    sudo vi /var/lib/pgsql9/data/pg_hba.conf
-    sudo service postgresql restart
-fi
-
-## Production DB ##
-echo "You will now configure the database environment."
-echo "For each prompt, follow the answer recommended by the HealthRex Wiki."
-echo ""
-# Collect production DB parameters.
-echo "First, configure the production database..."
-read -p "Enter production DB hostname: " PROD_DB_HOST
-read -p "Enter production DB database source name: " PROD_DB_DSN
-read -p "Enter production DB user ID: " PROD_DB_UID
-read -s -p "Enter production DB user password: " PROD_DB_PWD
-echo
-
-# Confirm production DB already exists or create it.
-# if [ "$(psql -lqt | cut -d \| -f 1 | grep -qw $PROD_DB_DSN)" ]
-
-# Collect test DB parameters.
-echo ""
-echo "Second, configure the test database, on which unittest will run..."
-read -p "Enter test DB hostname: " TEST_DB_HOST
-read -p "Enter test DB database source name: " TEST_DB_DSN
-read -p "Enter test DB user ID: " TEST_DB_UID
-read -s -p "Enter test DB user password: " TEST_DB_PWD
-echo
-
-# Write production DB parameters to local environment file.
-echo "Writing database parameters to CDSS/LocalEnv.py, which is *not* source-controlled..."
-# Instantiate LocalEnv.py. "> filename" creates filename if it does not exist,
-# and clears it if it already does exist.
-> ~/healthrex/CDSS/LocalEnv.py
-echo '#!/usr/bin/python' >> ~/healthrex/CDSS/LocalEnv.py
-echo '' >> ~/healthrex/CDSS/LocalEnv.py
-
-# Write production DB parameters.
-echo 'LOCAL_PROD_DB_PARAM = {}' >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_PROD_DB_PARAM["HOST"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $PROD_DB_HOST >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_PROD_DB_PARAM["DSN"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $PROD_DB_DSN >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_PROD_DB_PARAM["UID"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $PROD_DB_UID >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_PROD_DB_PARAM["PWD"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $PROD_DB_PWD >> ~/healthrex/CDSS/LocalEnv.py
-echo '' >> ~/healthrex/CDSS/LocalEnv.py
-
-# Write test DB parameters.
-echo 'LOCAL_PROD_DB_PARAM = {}' >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_TEST_DB_PARAM["HOST"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $TEST_DB_HOST >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_TEST_DB_PARAM["DSN"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $TEST_DB_DSN >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_TEST_DB_PARAM["UID"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $TEST_DB_UID >> ~/healthrex/CDSS/LocalEnv.py
-echo -n 'LOCAL_TEST_DB_PARAM["PWD"] = ' >> ~/healthrex/CDSS/LocalEnv.py
-echo $TEST_DB_PWD >> ~/healthrex/CDSS/LocalEnv.py
-
-# Confirm test user already exists or create it.
-if [ "$(psql --dbname=postgres --username=postgres -tAc "SELECT rolname from pg_roles WHERE rolname='$TEST_DB_UID'" | grep $TEST_DB_UID)" ]
-then
-    echo "user exists"
-else
-    echo "user does not exist"
-fi
-
-# psql --host=$TEST_DB_HOST "CREATE USER $TEST_DB_UID PASSWORD '$TEST_DB_PWD';" 
-
-# Confirm test DSN already exists or create it.
-#if [ "$(psql -lqt | cut -d \| -f 1 | grep -qw $TEST_DB_DSN)" ]
-#then
-#    echo "Initialized: $TEST_DB_DSN"
-#else
-#    echo "Initializing $TEST_DB_DSN..."
-#    createdb -U postgres -O $TEST_DB_UID $TEST_DB_DSN
-#fi
-
-# Write test DB parameters.
-#then
-#fi
-
-# ec2-54-208-212-123.compute-1.amazonaws.com
-
-# Define schema based on medinfo/db/definition/cpoeStats.sql.
-#psql --host=$TEST_DB_HOST --username=$TEST_DB_UID --file=medinfo/db/definition/cpoeStats.sql
-
-# Test DB
-
-## CPOE Simulation Tables ##
-
-# sim_user, sim_patient, sim_state, sim_patient_state, sim_state_transition,
-# sim_note_type, sim_note, sim_result, sim_state_result, sim_order_result_map,
-# sim_patient_order
-# Definition: CDSS/medinfo/db/definition/cpoeSimultation.sql
-
-
-#####
-
-##### VERIFY TESTS PASS #####
-
-# Print success state.
-#python medinfo/db/test/TestDBUtil.py
diff --git a/setup/installPythonLibraries.py b/setup/installPythonLibraries.py
new file mode 100644
index 00000000..07671277
--- /dev/null
+++ b/setup/installPythonLibraries.py
@@ -0,0 +1,21 @@
+import pip;
+
+# If installing on Windows, May need Intel Math Kernel Library
+#	Not available through source for Windows installation, 
+#	so install from prepared binaries by downloading respective whl that have been precompiled: 
+# 	http://www.lfd.uci.edu/~gohlke/pythonlibs/
+#pip.main(["install","numpy-1.13.1+mkl-cp27-cp27m-win_amd64.whl"]); 
+#pip.main(["install","scipy-0.19.1-cp27-cp27m-win_amd64.whl"]); 
+pip.main(["install","numpy"]);
+pip.main(["install","scipy"]);
+
+pip.main(["install","psycopg2"]);
+pip.main(["install","pandas"]);
+pip.main(["install","matplotlib"]);
+pip.main(["install","scikit-learn"]);
+pip.main(["install","gensim"]);
+#pip.main(["install",""]);
+#pip.main(["install",""]);
+#pip.main(["install",""]);
+#
+
diff --git a/setup/lpch_core_2021_guide.MD b/setup/lpch_core_2021_guide.MD
new file mode 100644
index 00000000..f27182ee
--- /dev/null
+++ b/setup/lpch_core_2021_guide.MD
@@ -0,0 +1,899 @@
+<h1 style="font-size:60px;">1. Copying tables</h1>
+
+The first step is to simply copy all tables under the database ```som-nero-phi-naras-ric:Jon_Chen_data_Oct_2021``` with the prefix of ```lpch_``` in the table name to the database ```som-nero-phi-jonc101.lpch_core_2021```. Note, the database ```lpch_core_2021``` was created by me and the database ```som-nero-phi-naras-ric:Jon_Chen_data_Oct_2021``` was provided to us by the database admin. 
+
+To copy the tables: 
+1) click on the tables name under the Explorer section
+2) click on the copy button (the copy button apears on a bar on top right side after clicking on the table and it's next to other buttons QUERY, SHARE, COPY, SNAPSHOT, DELETE, EXPORT)
+3) Select the ```som-nero-phi-jonc101``` project in the project name dropbox menue. Select ```lpch_core_2021``` under the dataset name dropbox menue. And write a table name for the coppied table. I used the same name as original table names except I droped the prefix ```lpch_``` in the table names. For example, when copying ```lpch_flowsheet``` I used ```flowsheet``` as the name for the coppied table. Note, there are two tables, ```prov_map``` and ```yn_jon_chen_lpch```, that do not have ```shc``` or ```lpch``` in their name and I coppied thse two tables under both ```shc_core_2021``` and ```lpch_core_2021```.
+4) Repeat this process and coppy all lpch tables under ```lpch_core_2021```.
+
+<h1 style="font-size:60px;">2. Re-formating datetime columns</h1>
+
+Here, we convert convert string columns that nicludes date/datetime to date/datetime format. To check if a string column includes datetime values or date only values, we can use the following script and run it seperately for each column. Note, here we check the ```effective_time_jittered``` columns in table ```adt``` as an example. If this query returns more than 1 rows and each for a different time, then this means the ```effective_time_jittered``` column include multiple diiferent times and should be treated as a datetime column. For some columns, this query only returns 1 row 00:00:00(or 1 row for 00:00:00 plus another null row) and this means the current column should be converted to a date only format as all the dates are the same and do not include any extra information (but for some reason they have been save in a 'Y-M-D H:M:S' like format even though the 'H:M:S' does not include meaningful time points).  
+
+```
+select extract(time from case when effective_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', effective_time_jittered) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.adt
+group by times
+order by cnt desc;
+```
+
+For each table, visualy look into a number of rows. If you clearly see different time values in the columns that means you don't need to run the above script. However, if you don't see any different in terms of time you can run the above script to double check. 
+
+After, identifying date only and datetime columns for each table you can use the following scripts to convert the columns. Here, I provide the scripts for each table seperately. Note, after running each columns I save the results in a big query table with the same name as the original table plus a "_code" at the end of the name of this new table. 
+
+For example, for the ```adt``` tble, I first visually inspect the columns and identify that ```effective_time_jittered``` and ```event_time_jittered``` include date and time information but are saved in strings. Note, since for a sample of rows I can see they include different rows I don't need to run the above scripts. Therefore, I use the following script to convert string fields to datetime and create utc datetime:
+
+```
+select * except(effective_time_jittered, event_time_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when effective_time_jittered <> '' then effective_time_jittered else null end) as effective_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when event_time_jittered <> '' then event_time_jittered else null end) as event_time_jittered,
+    timestamp(case when effective_time_jittered <> '' then effective_time_jittered else null end, "America/Los_Angeles") as effective_time_jittered_utc,
+    timestamp(case when event_time_jittered <> '' then event_time_jittered else null end, "America/Los_Angeles") as event_time_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.adt;
+```
+ And saved the results in ```adt_code``` under ```lpch_core_2021``` manually using the SAVE RESULTS option or by setting up the query result destination settings before runnig the above query: More>Query Settings>Set a destination table for query results. 
+ 
+Here are the queries for the rest of the tables:
+
+-- alert
+```
+select * except(update_date_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when update_date_jittered <> '' then update_date_jittered else null end) as update_date_jittered,
+    timestamp(case when update_date_jittered <> '' then update_date_jittered else null end, "America/Los_Angeles") as update_date_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.alert;
+```    
+-- alert_history
+```
+select extract(time from contact_date) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.alert_history
+group by times
+order by cnt desc;
+
+select * except(update_date_jittered, alt_action_inst, contact_date),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when update_date_jittered <> '' then update_date_jittered else null end) as update_date_jittered,
+    alt_action_inst,
+    extract(date from contact_date) as contact_date,
+    timestamp(case when update_date_jittered <> '' then update_date_jittered else null end, "America/Los_Angeles") as update_date_jittered_utc,
+    timestamp(alt_action_inst, "America/Los_Angeles") as alt_action_inst_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.alert_history;
+```
+
+-- alerts_orders
+```
+select * except(update_date_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when update_date_jittered <> '' then update_date_jittered else null end) as update_date_jittered,
+    timestamp(case when update_date_jittered <> '' then update_date_jittered else null end, "America/Los_Angeles") as update_date_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.alerts_orders;
+```
+
+-- allergy
+```
+select extract(time from case when date_noted_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', date_noted_jittered) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.allergy
+group by times
+order by cnt desc;
+
+select * except(date_noted_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when date_noted_jittered <> '' then date_noted_jittered else null end)) as date_noted_jittered,
+    from som-nero-phi-jonc101.lpch_core_2021.allergy;
+```    
+
+-- alt_com_action
+```
+select extract(time from case when contact_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', contact_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.alt_com_action`
+group by times
+order by cnt desc;
+
+select * except(contact_date_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when contact_date_jittered <> '' then contact_date_jittered else null end)) as contact_date_jittered,
+    from som-nero-phi-jonc101.lpch_core_2021.alt_com_action;
+```
+
+-- clinical_doc_meta
+```
+select extract(time from case when activity_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', activity_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.clinical_doc_meta`
+group by times
+order by cnt desc;
+
+select * except(filing_date_jittered, note_date_jittered, activity_date_jittered, effective_time_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when filing_date_jittered <> '' then filing_date_jittered else null end) as filing_date_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when note_date_jittered <> '' then note_date_jittered else null end) as note_date_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when activity_date_jittered <> '' then activity_date_jittered else null end) as activity_date_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when effective_time_jittered <> '' then effective_time_jittered else null end) as effective_time_jittered,
+    timestamp(case when filing_date_jittered <> '' then filing_date_jittered else null end, "America/Los_Angeles") as filing_date_jittered_utc,
+    timestamp(case when note_date_jittered <> '' then note_date_jittered else null end, "America/Los_Angeles") as note_date_jittered_utc,
+    timestamp(case when activity_date_jittered <> '' then activity_date_jittered else null end, "America/Los_Angeles") as activity_date_jittered_utc,
+    timestamp(case when effective_time_jittered <> '' then effective_time_jittered else null end, "America/Los_Angeles") as effective_time_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.clinical_doc_meta;
+```    
+
+--culture_sensitivity
+```
+select extract(time from case when sens_obs_inst_tm_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', sens_obs_inst_tm_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.culture_sensitivity`
+group by times
+order by cnt desc;
+
+select * except(order_time_jittered, result_time_jittered, sens_obs_inst_tm_jittered, sens_anl_inst_tm_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_time_jittered <> '' then order_time_jittered else null end) as order_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when result_time_jittered <> '' then result_time_jittered else null end) as result_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when sens_obs_inst_tm_jittered <> '' then sens_obs_inst_tm_jittered else null end) as sens_obs_inst_tm_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when sens_anl_inst_tm_jittered <> '' then sens_anl_inst_tm_jittered else null end) as sens_anl_inst_tm_jittered,
+    timestamp(case when order_time_jittered <> '' then order_time_jittered else null end, "America/Los_Angeles") as order_time_jittered_utc,
+    timestamp(case when result_time_jittered <> '' then result_time_jittered else null end, "America/Los_Angeles") as result_time_jittered_utc,
+    timestamp(case when sens_obs_inst_tm_jittered <> '' then sens_obs_inst_tm_jittered else null end, "America/Los_Angeles") as sens_obs_inst_tm_jittered_utc,
+    timestamp(case when sens_anl_inst_tm_jittered <> '' then sens_anl_inst_tm_jittered else null end, "America/Los_Angeles") as sens_anl_inst_tm_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.culture_sensitivity;
+```
+
+-- demographic
+```
+select extract(time from birth_date_jittered) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.demographic`
+group by times
+order by cnt desc;
+
+select extract(time from death_date_jittered) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.demographic`
+group by times
+order by cnt desc;
+
+
+select * except(birth_date_jittered, death_date_jittered),
+    extract(date from birth_date_jittered) as birth_date_jittered,
+    extract(date from death_date_jittered) as death_date_jittered,
+    from som-nero-phi-jonc101.lpch_core_2021.demographic;
+```
+
+-- dep_map and drg_code do not include any datetime or date columns
+
+
+-- Diagnosis
+```
+select extract(time from case when start_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', start_date) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.diagnosis
+group by times
+order by cnt desc;
+
+select extract(time from case when end_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', end_date) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.diagnosis
+group by times
+order by cnt desc;
+
+select extract(time from case when noted_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', noted_date) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.diagnosis
+group by times
+order by cnt desc;
+
+select extract(time from case when hx_date_of_entry <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', hx_date_of_entry) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.diagnosis
+group by times
+order by cnt desc;
+
+select extract(time from case when resolved_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', resolved_date) else null end) times, count(*) as cnt
+from som-nero-phi-jonc101.lpch_core_2021.diagnosis
+group by times
+order by cnt desc;
+
+
+select * except(end_date, start_date, noted_date, hx_date_of_entry, resolved_date),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when end_date <> '' then end_date else null end) as end_date,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when start_date <> '' then start_date else null end) as start_date,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when noted_date <> '' then noted_date else null end)) as noted_date,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when hx_date_of_entry <> '' then hx_date_of_entry else null end)) as hx_date_of_entry,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when resolved_date <> '' then resolved_date else null end)) as resolved_date,
+    timestamp(case when end_date <> '' then end_date else null end, "America/Los_Angeles") as end_date_utc,
+    timestamp(case when start_date <> '' then start_date else null end, "America/Los_Angeles") as start_date_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.diagnosis;
+```    
+
+-- encounter
+```
+select extract(time from case when adt_arrival_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', adt_arrival_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.encounter`
+group by times
+order by cnt desc;
+
+select extract(time from case when hosp_admsn_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', hosp_admsn_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.encounter`
+group by times
+order by cnt desc;
+
+select extract(time from case when appt_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', appt_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.encounter`
+group by times
+order by cnt desc;
+
+select extract(time from case when appt_when_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', appt_when_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.encounter`
+group by times
+order by cnt desc;
+
+select extract(time from case when hosp_dischrg_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', hosp_dischrg_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.encounter`
+group by times
+order by cnt desc;
+
+select extract(time from case when contact_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', contact_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.encounter`
+group by times
+order by cnt desc;
+
+select * except(adt_arrival_time_jittered, hosp_admsn_time_jittered, appt_time_jittered, appt_when_jittered, hosp_dischrg_time_jittered, contact_date_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when contact_date_jittered <> '' then contact_date_jittered else null end)) as contact_date_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when adt_arrival_time_jittered <> '' then adt_arrival_time_jittered else null end) as adt_arrival_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when hosp_admsn_time_jittered <> '' then hosp_admsn_time_jittered else null end) as hosp_admsn_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when appt_time_jittered <> '' then appt_time_jittered else null end) as appt_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when appt_when_jittered <> '' then appt_when_jittered else null end) as appt_when_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when hosp_dischrg_time_jittered <> '' then hosp_dischrg_time_jittered else null end) as hosp_dischrg_time_jittered,
+    timestamp(case when adt_arrival_time_jittered <> '' then adt_arrival_time_jittered else null end, "America/Los_Angeles") as adt_arrival_time_jittered_utc,
+    timestamp(case when hosp_admsn_time_jittered <> '' then hosp_admsn_time_jittered else null end, "America/Los_Angeles") as hosp_admsn_time_jittered_utc,
+    timestamp(case when appt_time_jittered <> '' then appt_time_jittered else null end, "America/Los_Angeles") as appt_time_jittered_utc,
+    timestamp(case when appt_when_jittered <> '' then appt_when_jittered else null end, "America/Los_Angeles") as appt_when_jittered_utc,
+    timestamp(case when hosp_dischrg_time_jittered <> '' then hosp_dischrg_time_jittered else null end, "America/Los_Angeles") as hosp_dischrg_time_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.encounter;
+```    
+    
+-- f_ip_hsp_admission
+```
+select extract(time from case when hosp_adm_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', hosp_adm_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.f_ip_hsp_admission`
+group by times
+order by cnt desc;
+
+select extract(time from case when hosp_disch_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', hosp_disch_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.f_ip_hsp_admission`
+group by times
+order by cnt desc;
+
+select * except(hosp_adm_date_jittered, hosp_disch_date_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when hosp_adm_date_jittered <> '' then hosp_adm_date_jittered else null end)) as hosp_adm_date_jittered,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when hosp_disch_date_jittered <> '' then hosp_disch_date_jittered else null end)) as hosp_disch_date_jittered,
+    from som-nero-phi-jonc101.lpch_core_2021.f_ip_hsp_admission;
+```
+
+-- family_hx
+```
+select extract(time from case when contact_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', contact_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.family_hx`
+group by times
+order by cnt desc;
+
+select * except(contact_date_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when contact_date_jittered <> '' then contact_date_jittered else null end)) as contact_date_jittered,
+    from som-nero-phi-jonc101.lpch_core_2021.family_hx;
+```
+
+--flowsheet: NOTE: I had to set up destination because the result was too large. 
+```
+select * except(recorded_time),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when recorded_time <> '' then recorded_time else null end) as recorded_time,
+    timestamp(case when recorded_time <> '' then recorded_time else null end, "America/Los_Angeles") as recorded_time_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.flowsheet;
+```    
+    
+-- lab_result
+```
+select * except(order_time, taken_time, result_time),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_time <> '' then order_time else null end) as order_time,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when taken_time <> '' then taken_time else null end) as taken_time,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when result_time <> '' then result_time else null end) as result_time,
+    timestamp(case when order_time <> '' then order_time else null end, "America/Los_Angeles") as order_time_utc,
+    timestamp(case when taken_time <> '' then taken_time else null end, "America/Los_Angeles") as taken_time_utc,
+    timestamp(case when result_time <> '' then result_time else null end, "America/Los_Angeles") as result_time_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.lab_result;
+```    
+    
+-- lda
+```
+select * except(placement_instant_jittered, removal_instant_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when placement_instant_jittered <> '' then placement_instant_jittered else null end) as placement_instant_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when removal_instant_jittered <> '' then removal_instant_jittered else null end) as removal_instant_jittered,
+    timestamp(case when placement_instant_jittered <> '' then placement_instant_jittered else null end, "America/Los_Angeles") as placement_instant_jittered_utc,
+    timestamp(case when removal_instant_jittered <> '' then removal_instant_jittered else null end, "America/Los_Angeles") as removal_instant_jittered_utc,
+    from som-nero-phi-jonc101.lpch_core_2021.lda;
+```
+
+-- myc_mesg
+```
+select * except(created_time_jittered, final_handled_time_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when created_time_jittered <> '' then created_time_jittered else null end) as created_time_jittered,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when final_handled_time_jittered <> '' then final_handled_time_jittered else null end) as final_handled_time_jittered,
+    timestamp(case when created_time_jittered <> '' then created_time_jittered else null end, "America/Los_Angeles") as created_time_jittered_utc,
+    timestamp(case when final_handled_time_jittered <> '' then final_handled_time_jittered else null end, "America/Los_Angeles") as final_handled_time_jittered_utc,
+    from `som-nero-phi-jonc101.lpch_core_2021.myc_mesg`;
+```    
+ 
+--ndc_code does not include any datetime/date columns
+
+
+-- order_comment
+```
+select * except(order_inst_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_inst_jittered <> '' then order_inst_jittered else null end) as order_inst_jittered,
+    timestamp(case when order_inst_jittered <> '' then order_inst_jittered else null end, "America/Los_Angeles") as order_inst_jittered_utc,
+    from `som-nero-phi-jonc101.lpch_core_2021.order_comment`;
+```
+
+-- order_med
+```
+select extract(time from case when order_start_time <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', order_start_time) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select extract(time from case when order_end_time <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', order_end_time) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select extract(time from case when order_inst <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', order_inst) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select extract(time from case when discon_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', discon_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select extract(time from case when order_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', order_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select extract(time from case when start_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', start_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select extract(time from case when end_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', end_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_med`
+group by times
+order by cnt desc;
+
+select * except(order_start_time, order_end_time, order_inst, discon_time_jittered, order_time_jittered, start_time_jittered, end_time_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_start_time <> '' then order_start_time else null end) as order_start_time,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_end_time <> '' then order_end_time else null end) as order_end_time,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_inst <> '' then order_inst else null end) as order_inst,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when discon_time_jittered <> '' then discon_time_jittered else null end) as discon_time_jittered,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when order_time_jittered <> '' then order_time_jittered else null end)) as order_time_jittered,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when start_time_jittered <> '' then start_time_jittered else null end)) as start_time_jittered,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when end_time_jittered <> '' then end_time_jittered else null end)) as end_time_jittered,
+    timestamp(case when order_start_time <> '' then order_start_time else null end, "America/Los_Angeles") as order_start_time_utc,
+    timestamp(case when order_end_time <> '' then order_end_time else null end, "America/Los_Angeles") as order_end_time_utc,
+    timestamp(case when order_inst <> '' then order_inst else null end, "America/Los_Angeles") as order_inst_utc,
+    timestamp(case when discon_time_jittered <> '' then discon_time_jittered else null end, "America/Los_Angeles") as discon_time_jittered_utc,
+    from `som-nero-phi-jonc101.lpch_core_2021.order_med`;
+```    
+
+-- order_proc
+```
+select extract(time from case when ordering_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', ordering_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when standing_exp_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', standing_exp_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when proc_bgn_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', proc_bgn_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when proc_end_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', proc_end_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when order_inst_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', order_inst_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when instantiated_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', instantiated_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when order_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', order_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when result_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', result_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when proc_start_time_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', proc_start_time_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when proc_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', proc_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when last_stand_perf_dt_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', last_stand_perf_dt_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+select extract(time from case when last_stand_perf_tm_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', last_stand_perf_tm_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_proc`
+group by times
+order by cnt desc;
+
+
+select * except(ordering_date_jittered, standing_exp_date_jittered, proc_bgn_time_jittered, proc_end_time_jittered, order_inst_jittered, instantiated_time_jittered,
+                order_time_jittered, result_time_jittered, proc_start_time_jittered, proc_date_jittered, last_stand_perf_dt_jittered, last_stand_perf_tm_jittered),
+
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when ordering_date_jittered <> '' then ordering_date_jittered else null end)) as ordering_date_jittered,    
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when standing_exp_date_jittered <> '' then standing_exp_date_jittered else null end)) as standing_exp_date_jittered,    
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_bgn_time_jittered <> '' then proc_bgn_time_jittered else null end) as proc_bgn_time_jittered,
+    timestamp(case when proc_bgn_time_jittered <> '' then proc_bgn_time_jittered else null end, "America/Los_Angeles") as proc_bgn_time_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_end_time_jittered <> '' then proc_end_time_jittered else null end) as proc_end_time_jittered,
+    timestamp(case when proc_end_time_jittered <> '' then proc_end_time_jittered else null end, "America/Los_Angeles") as proc_end_time_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_inst_jittered <> '' then order_inst_jittered else null end) as order_inst_jittered,
+    timestamp(case when order_inst_jittered <> '' then order_inst_jittered else null end, "America/Los_Angeles") as order_inst_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when instantiated_time_jittered <> '' then instantiated_time_jittered else null end) as instantiated_time_jittered,
+    timestamp(case when instantiated_time_jittered <> '' then instantiated_time_jittered else null end, "America/Los_Angeles") as instantiated_time_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when order_time_jittered <> '' then order_time_jittered else null end) as order_time_jittered,
+    timestamp(case when order_time_jittered <> '' then order_time_jittered else null end, "America/Los_Angeles") as order_time_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when result_time_jittered <> '' then result_time_jittered else null end) as result_time_jittered,
+    timestamp(case when result_time_jittered <> '' then result_time_jittered else null end, "America/Los_Angeles") as result_time_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_start_time_jittered <> '' then proc_start_time_jittered else null end) as proc_start_time_jittered,
+    timestamp(case when proc_start_time_jittered <> '' then proc_start_time_jittered else null end, "America/Los_Angeles") as proc_start_time_jittered_utc,
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_date_jittered <> '' then proc_date_jittered else null end)) as proc_date_jittered,    
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when last_stand_perf_dt_jittered <> '' then last_stand_perf_dt_jittered else null end)) as last_stand_perf_dt_jittered,    
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when last_stand_perf_tm_jittered <> '' then last_stand_perf_tm_jittered else null end) as last_stand_perf_tm_jittered,
+    timestamp(case when last_stand_perf_tm_jittered <> '' then last_stand_perf_tm_jittered else null end, "America/Los_Angeles") as last_stand_perf_tm_jittered_utc,
+  
+    from `som-nero-phi-jonc101.lpch_core_2021.order_proc`;
+```    
+    
+--order_quest
+```
+select extract(time from case when ord_quest_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', ord_quest_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.order_quest`
+group by times
+order by cnt desc;
+
+select * except(ord_quest_date_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when ord_quest_date_jittered <> '' then ord_quest_date_jittered else null end)) as ord_quest_date_jittered,      
+    from `som-nero-phi-jonc101.lpch_core_2021.order_quest`;
+```    
+
+--pharmacy_mar
+```
+select * except(taken_time_jittered, scheduled_time_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when taken_time_jittered <> '' then taken_time_jittered else null end) as taken_time_jittered,
+    timestamp(case when taken_time_jittered <> '' then taken_time_jittered else null end, "America/Los_Angeles") as taken_time_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when scheduled_time_jittered <> '' then scheduled_time_jittered else null end) as scheduled_time_jittered,
+    timestamp(case when scheduled_time_jittered <> '' then scheduled_time_jittered else null end, "America/Los_Angeles") as scheduled_time_jittered_utc,
+    from `som-nero-phi-jonc101.lpch_core_2021.pharmacy_mar`;
+```
+
+--proc_note
+```
+select extract(time from case when ordering_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', ordering_date) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.proc_note`
+group by times
+order by cnt desc;
+
+select * except(ordering_date, proc_start_time, proc_end_time, result_time),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when ordering_date <> '' then ordering_date else null end)) as ordering_date,    
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_start_time <> '' then proc_start_time else null end) as proc_start_time,
+    timestamp(case when proc_start_time <> '' then proc_start_time else null end, "America/Los_Angeles") as proc_start_time_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_end_time <> '' then proc_end_time else null end) as proc_end_time,
+    timestamp(case when proc_end_time <> '' then proc_end_time else null end, "America/Los_Angeles") as proc_end_time_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when result_time <> '' then result_time else null end) as result_time,
+    timestamp(case when result_time <> '' then result_time else null end, "America/Los_Angeles") as result_time_utc,    
+    from `som-nero-phi-jonc101.lpch_core_2021.proc_note`;
+```    
+    
+-- prov_map does not include any datetime/date columns
+
+--proc_orderset does not include any datetime/date columns
+
+-- procedure
+```
+select extract(time from case when proc_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', proc_date) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.procedure`
+group by times
+order by cnt desc;
+
+select extract(time from case when start_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', start_date) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.procedure`
+group by times
+order by cnt desc;
+
+select extract(time from case when adm_date_time <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', adm_date_time) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.procedure`
+group by times
+order by cnt desc;
+
+select * except(proc_date, start_date, adm_date_time),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when proc_date <> '' then proc_date else null end)) as proc_date,    
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when start_date <> '' then start_date else null end) as start_date,
+    timestamp(case when start_date <> '' then start_date else null end, "America/Los_Angeles") as start_date_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when adm_date_time <> '' then adm_date_time else null end) as adm_date_time,
+    timestamp(case when adm_date_time <> '' then adm_date_time else null end, "America/Los_Angeles") as adm_date_time_utc,
+    from `som-nero-phi-jonc101.lpch_core_2021.procedure`;
+```
+
+-- prov_map does not include any datetime/date columns
+
+-- social_hx
+```
+select extract(time from case when contact_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', contact_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.social_hx`
+group by times
+order by cnt desc;
+
+select extract(time from case when smoking_quit_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', smoking_quit_date) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.social_hx`
+group by times
+order by cnt desc;
+
+select extract(time from case when smokeless_quit_date_jittered <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', smokeless_quit_date_jittered) else null end) times, count(*) as cnt
+from `som-nero-phi-jonc101.lpch_core_2021.social_hx`
+group by times
+order by cnt desc;
+
+
+select * except(contact_date_jittered, smoking_quit_date, smokeless_quit_date_jittered),
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when contact_date_jittered <> '' then contact_date_jittered else null end)) as contact_date_jittered,    
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when smoking_quit_date <> '' then smoking_quit_date else null end)) as smoking_quit_date,    
+    extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when smokeless_quit_date_jittered <> '' then smokeless_quit_date_jittered else null end)) as smokeless_quit_date_jittered,    
+    from `som-nero-phi-jonc101.lpch_core_2021.social_hx`;
+```
+
+-- treatment_team
+```
+select * except( trtmnt_tm_begin_dt_jittered, trtmnt_tm_end_dt_jittered),
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when trtmnt_tm_begin_dt_jittered <> '' then trtmnt_tm_begin_dt_jittered else null end) as trtmnt_tm_begin_dt_jittered,
+    timestamp(case when trtmnt_tm_begin_dt_jittered <> '' then trtmnt_tm_begin_dt_jittered else null end, "America/Los_Angeles") as trtmnt_tm_begin_dt_jittered_utc,
+    parse_datetime('%Y-%m-%d %H:%M:%S', case when trtmnt_tm_end_dt_jittered <> '' then trtmnt_tm_end_dt_jittered else null end) as trtmnt_tm_end_dt_jittered,
+    timestamp(case when trtmnt_tm_end_dt_jittered <> '' then trtmnt_tm_end_dt_jittered else null end, "America/Los_Angeles") as trtmnt_tm_end_dt_jittered_utc,
+    from `som-nero-phi-jonc101.lpch_core_2021.treatment_team`;
+```    
+
+-- yn_jon_chen_lpch does not include any datetime/date columns
+
+ Finally, I removed the ```_code``` at the end of all table.
+ 
+<h1 style="font-size:60px;">3. Extracting numerical values from meas_value in flowsheet</h1>
+
+Table ```flowsheet``` has a string column, ```meas_value```, which include numerical values. Use the following code to extract those numerical values from ```meas_value``` and store them in seperate columns:
+
+```
+select * from (
+  select A.anon_id, A.inpatient_data_id_coded , A.line, A.template, A.row_disp_name, A.meas_value, A.units, A.data_source, A.recorded_time, A.recorded_time_utc, offset + 1 as offset, num
+  from `som-nero-phi-jonc101.lpch_core_2021.flowsheet` A 
+  left join unnest(regexp_extract_all(A.meas_value, r'(-?[\d\.]+)')) num with offset
+)
+pivot (min(num) as numerical_val for offset in (1,2,3,4)) 
+```
+
+<h1 style="font-size:60px;">4. Testing SHC tables</h1>
+
+Use the following script to compare the original ```shc_clinical_doc_meta``` table with the coppied one under ```shc_core_2021```, ```clinical_doc_meta```:
+
+
+```
+SELECT
+    *
+FROM `som-nero-phi-jonc101.shc_core_2021.clinical_doc_meta` A
+FULL OUTER JOIN `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.shc_clinical_doc_meta` B
+ on FARM_FINGERPRINT(ARRAY_TO_STRING([A.anon_id 
+                                    , CAST(A.pat_enc_csn_id_coded AS STRING)
+                                    , CAST(A.filing_date_jittered AS STRING)
+                                    , CAST(A.note_date_jittered AS STRING)
+                                    , CAST(A.activity_date_jittered AS STRING)
+                                    , A.author_prov_map_id
+                                    , CAST(A.effective_dept_id AS STRING)
+                                    , A.note_status_c
+                                    , A.note_status
+                                    , A.ambulatory
+                                    , CAST(A.ltr_status_c AS STRING)
+                                    , A.letter_status
+                                    , A.note_type
+                                    , CAST(A.effective_time_jittered AS STRING)
+                                    , A.auth_lnked_prov_map_id
+                                    , A.cosign_prov_map_id
+                                    , A.data_source],"")) = 
+        FARM_FINGERPRINT(ARRAY_TO_STRING([B.anon_id 
+                                    , CAST(B.pat_enc_csn_id_coded AS STRING)
+                                    , B.filing_date_jittered
+                                    , B.note_date_jittered
+                                    , B.activity_date_jittered
+                                    , B.author_prov_map_id
+                                    , CAST(B.effective_dept_id AS STRING)
+                                    , B.note_status_c
+                                    , B.note_status
+                                    , B.ambulatory
+                                    , CAST(B.ltr_status_c AS STRING)
+                                    , B.letter_status
+                                    , B.note_type
+                                    , B.effective_time_jittered
+                                    , B.auth_lnked_prov_map_id
+                                    , B.cosign_prov_map_id
+                                    , B.data_source],"")) 
+WHERE A.anon_id IS NULL 
+OR B.anon_id IS NULL
+```
+
+For other tables similarly:
+```
+-- adt
+SELECT
+    *
+FROM `som-nero-phi-jonc101.shc_core_2021.adt` A
+FULL OUTER JOIN `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.shc_adt` B
+ on FARM_FINGERPRINT(ARRAY_TO_STRING([A.anon_id 
+                                    , CAST(A.pat_enc_csn_id_coded AS STRING)
+                                    , CAST(A.event_id_coded AS STRING)
+                                    , CAST(A.event_type_c AS STRING)
+                                    , A.event_type
+                                    , CAST(A.event_subtype_c AS STRING)
+                                    , A.event_subtype
+                                    , CAST(A.department_id AS STRING)
+                                    , A.pat_class_c
+                                    , A.pat_class
+                                    , CAST(A.base_pat_class_c AS STRING)
+                                    , A.base_pat_class
+                                    , A.pat_service_c
+                                    , A.pat_service
+                                    , A.status_of_bed_c
+                                    , A.status_of_bed
+                                    , A.pat_lvl_of_care_c
+                                    , A.pat_lv_of_care
+                                    , CAST(A.xfer_event_id_coded AS STRING)
+                                    , CAST(A.swap_event_id_coded AS STRING)
+                                    , A.reason_c
+                                    , A.accommodation_c
+                                    , A.accomodation
+                                    , CAST(A.accom_reason_c AS STRING)
+                                    , CAST(A.adm_event_id_coded AS STRING)
+                                    , CAST(A.dis_event_id_coded AS STRING)
+                                    , CAST(A.xfer_in_event_id_coded AS STRING)
+                                    , CAST(A.next_out_event_id_coded AS STRING)
+                                    , CAST(A.last_in_event_id_coded AS STRING)
+                                    , CAST(A.prev_event_id_coded AS STRING)
+                                    , CAST(A.in_event_type_c AS STRING)
+                                    , A.in_event_type
+                                    , CAST(A.out_event_type_c AS STRING)
+                                    , A.out_event_type
+                                    , CAST(A.from_base_class_c AS STRING)
+                                    , A.from_base_class
+                                    , CAST(A.to_base_class_c AS STRING)
+                                    , A.to_base_class
+                                    , CAST(A.seq_num_in_enc AS STRING)
+                                    , CAST(A.seq_num_in_bed_min AS STRING)
+                                    , CAST(A.labor_status_c AS STRING)
+                                    , A.first_ip_in_ip_yn
+                                    , CAST(A.loa_reason_c AS STRING)
+                                    , A.data_source
+                                    , CAST(A.effective_time_jittered AS STRING)
+                                    , CAST(A.event_time_jittered AS STRING)],"")) = 
+        FARM_FINGERPRINT(ARRAY_TO_STRING([B.anon_id 
+                                    , CAST(B.pat_enc_csn_id_coded AS STRING)
+                                    , CAST(B.event_id_coded AS STRING)
+                                    , CAST(B.event_type_c AS STRING)
+                                    , B.event_type
+                                    , CAST(B.event_subtype_c AS STRING)
+                                    , B.event_subtype
+                                    , CAST(B.department_id AS STRING)
+                                    , B.pat_class_c
+                                    , B.pat_class
+                                    , CAST(B.base_pat_class_c AS STRING)
+                                    , B.base_pat_class
+                                    , B.pat_service_c
+                                    , B.pat_service
+                                    , B.status_of_bed_c
+                                    , B.status_of_bed
+                                    , B.pat_lvl_of_care_c
+                                    , B.pat_lv_of_care
+                                    , CAST(B.xfer_event_id_coded AS STRING)
+                                    , CAST(B.swap_event_id_coded AS STRING)
+                                    , B.reason_c
+                                    , B.accommodation_c
+                                    , B.accomodation
+                                    , CAST(B.accom_reason_c AS STRING)
+                                    , CAST(B.adm_event_id_coded AS STRING)
+                                    , CAST(B.dis_event_id_coded AS STRING)
+                                    , CAST(B.xfer_in_event_id_coded AS STRING)
+                                    , CAST(B.next_out_event_id_coded AS STRING)
+                                    , CAST(B.last_in_event_id_coded AS STRING)
+                                    , CAST(B.prev_event_id_coded AS STRING)
+                                    , CAST(B.in_event_type_c AS STRING)
+                                    , B.in_event_type
+                                    , CAST(B.out_event_type_c AS STRING)
+                                    , B.out_event_type
+                                    , CAST(B.from_base_class_c AS STRING)
+                                    , B.from_base_class
+                                    , CAST(B.to_base_class_c AS STRING)
+                                    , B.to_base_class
+                                    , CAST(B.seq_num_in_enc AS STRING)
+                                    , CAST(B.seq_num_in_bed_min AS STRING)
+                                    , CAST(B.labor_status_c AS STRING)
+                                    , B.first_ip_in_ip_yn
+                                    , CAST(B.loa_reason_c AS STRING)
+                                    , B.data_source
+                                    , CAST(B.effective_time_jittered AS STRING)
+                                    , CAST(B.event_time_jittered AS STRING)],""))
+WHERE A.anon_id IS NULL 
+OR B.anon_id IS NULL
+
+
+-- ALERT
+SELECT
+    *
+FROM `som-nero-phi-jonc101.shc_core_2021.alert` A
+FULL OUTER JOIN `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.shc_alert` B
+ on FARM_FINGERPRINT(ARRAY_TO_STRING([A.anon_id 
+                                    , CAST(A.alt_id_coded AS STRING)
+                                    , CAST(A.pat_enc_csn_id_coded AS STRING)
+                                    , A.alert_desc
+                                    , CAST(A.general_alt_type_c AS STRING)
+                                    , A.alt_type    
+                                    , A.alt_title
+                                    , A.abbr
+                                    , CAST(A.med_alert_type_c AS STRING)
+                                    , A.med_alt_type
+                                    , A.med_alt_title
+                                    , A.med_alt_type_abbr
+                                    , CAST(A.immun_id AS STRING)
+                                    , A.immun_name
+                                    , A.immun_abbreviation
+                                    , CAST(A.immun_type AS STRING)
+                                    , CAST(A.bpa_locator_id AS STRING)
+                                    , CAST(A.update_date_jittered AS STRING)],"")) = 
+        FARM_FINGERPRINT(ARRAY_TO_STRING([B.anon_id 
+                                    , CAST(B.alt_id_coded AS STRING)
+                                    , CAST(B.pat_enc_csn_id_coded AS STRING)
+                                    , B.alert_desc
+                                    , CAST(B.general_alt_type_c AS STRING)
+                                    , B.alt_type    
+                                    , B.alt_title
+                                    , B.abbr
+                                    , CAST(B.med_alert_type_c AS STRING)
+                                    , B.med_alt_type
+                                    , B.med_alt_title
+                                    , B.med_alt_type_abbr
+                                    , CAST(B.immun_id AS STRING)
+                                    , B.immun_name
+                                    , B.immun_abbreviation
+                                    , CAST(B.immun_type AS STRING)
+                                    , CAST(B.bpa_locator_id AS STRING)
+                                    , CAST(B.update_date_jittered AS STRING)],""))
+WHERE A.anon_id IS NULL 
+OR B.anon_id IS NULL
+
+
+-- culture_sensitivity
+SELECT
+    *
+FROM `som-nero-phi-jonc101.shc_core_2021.culture_sensitivity` A
+FULL OUTER JOIN `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.shc_culture_sensitivity` B
+ on FARM_FINGERPRINT(ARRAY_TO_STRING([A.anon_id
+                                        , CAST(A.order_proc_id_coded AS STRING)
+                                        , CAST(A.line	AS STRING)
+                                        , A.description
+                                        , A.sens_organism_sid
+                                        , A.organism
+                                        , A.organism_quantity
+                                        , A.antibiotic		
+                                        , A.suscept	
+                                        , A.sensitivity_value	
+                                        , A.sensitivity_units	
+                                        , CAST(A.sens_status_c	AS STRING)	
+                                        , A.sens_ref_range		
+                                        , CAST(A.ANTIBIO_LNC_ID	AS STRING)	
+                                        , A.specimen_source		
+                                        , A.specimen_type		
+                                        , CAST(A.resulting_lab_id	AS STRING)	
+                                        , A.llb_name		
+                                        , A.data_source],"")) = 
+        FARM_FINGERPRINT(ARRAY_TO_STRING([B.anon_id
+                                        , CAST(B.order_proc_id_coded AS STRING)
+                                        , CAST(B.line	AS STRING)
+                                        , B.description
+                                        , B.sens_organism_sid
+                                        , B.organism
+                                        , B.organism_quantity
+                                        , B.antibiotic		
+                                        , B.suscept	
+                                        , B.sensitivity_value	
+                                        , B.sensitivity_units	
+                                        , CAST(B.sens_status_c	AS STRING)	
+                                        , B.sens_ref_range		
+                                        , CAST(B.ANTIBIO_LNC_ID	AS STRING)	
+                                        , B.specimen_source		
+                                        , B.specimen_type		
+                                        , CAST(B.resulting_lab_id	AS STRING)	
+                                        , B.llb_name		
+                                        , B.data_source],""))
+WHERE A.anon_id IS NULL 
+OR B.anon_id IS NULL
+
+
+-- check random records
+select *
+from `som-nero-phi-jonc101.shc_core_2021.culture_sensitivity` A
+where A.anon_id = 'JC557358'
+and A.order_proc_id_coded = 576835304
+and A.line = 12
+```
+
+Double checking myc_msg from shc_core_2021 with the original shc_myc_msg:
+```
+-- myc_mesg
+SELECT
+    *
+FROM `som-nero-phi-jonc101.shc_core_2021.myc_mesg` A
+FULL OUTER JOIN `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.shc_myc_mesg` B
+on FARM_FINGERPRINT(ARRAY_TO_STRING([A.anon_id
+                                    , CAST(A.offset_message_id	AS STRING)		
+                                    , CAST(A.offset_parent_message_id	AS STRING)		
+                                    , CAST(A.offset_inbasket_msg_id	AS STRING)		
+                                    , CAST(A.offset_pat_enc_csn_id	AS STRING)		
+                                    , CAST(A.line	AS STRING)		
+                                    , A.msg_txt
+                                    , A.message_type
+                                    , A.to_or_from_pat
+                                    , A.subject
+                                    , A.routing_department_name
+                                    , CAST(A.created_time_jittered	AS STRING)		
+                                    , CAST(A.final_handled_time_jittered	AS STRING)],"")) = 
+        FARM_FINGERPRINT(ARRAY_TO_STRING([B.anon_id
+                                    , CAST(B.offset_message_id	AS STRING)		
+                                    , CAST(B.offset_parent_message_id	AS STRING)		
+                                    , CAST(B.offset_inbasket_msg_id	AS STRING)		
+                                    , CAST(B.offset_pat_enc_csn_id	AS STRING)		
+                                    , CAST(B.line	AS STRING)		
+                                    , B.msg_txt
+                                    , B.message_type
+                                    , B.to_or_from_pat
+                                    , B.subject
+                                    , B.routing_department_name
+                                    , CAST(B.created_time_jittered	AS STRING)		
+                                    , CAST(B.final_handled_time_jittered	AS STRING)],""))
+WHERE A.anon_id IS NULL 
+OR B.anon_id IS NULL
+
+-- check random records
+
+-- select a random patient
+select *
+from `som-nero-phi-jonc101.shc_core_2021.myc_mesg` A 
+order by rand()
+limit 1
+
+-- select some of the records for that random patient in shc_core_2021.myc_msg (3396032 were selected in the previous step)
+select *
+from `som-nero-phi-jonc101.shc_core_2021.myc_mesg` A 
+where A.anon_id = 'JC1706420'
+and A.offset_message_id = 3396032
+order by A.line
+
+-- select the same records for the same patient in the original data
+select *
+from `som-nero-phi-naras-ric.Jon_Chen_data_Oct_2021.shc_myc_mesg` A 
+where A.anon_id = 'JC1706420'
+and A.offset_message_id = 3396032
+order by A.line
+
+-- compare the results: they were the same in this case.
+
+```
diff --git a/setup/setup.sh b/setup/setup.sh
new file mode 100755
index 00000000..fbbbe282
--- /dev/null
+++ b/setup/setup.sh
@@ -0,0 +1,155 @@
+#!/bin/bash
+
+# See Wiki page for general starter notes,
+#   as this script will NOT fully automate everything,
+#   and will make mistakes if you have a different environment
+#   (e.g., Windows, already installed PostgreSQL)
+#   but gives you an idea of dependencies needed.
+#   Better then to just step through this script by hand and see
+#   what it's trying to do and replicate them as needed.
+# https://github.com/HealthRex/CDSS/wiki/Dev-Environment-Setup
+# 
+# See installPythonLibraries.py for example of installing commonly needed Python dependencies through PIP
+#
+# Walking through the DevWorkshop below can be very helpful for notes on setting things up as well.
+#   https://github.com/HealthRex/CDSS/blob/master/scripts/DevWorkshop/ReadMe.AWSDevEnvironment.txt
+#
+
+##### IDENTIFY PLATFORM #####
+echo "IDENTIFY PLATFORM"
+
+# OSX
+if [ "$(uname)" = "Darwin" ]
+then
+    PLATFORM="OSX"
+
+    # Brew (https://brew.sh/)
+    if [ "$(command -v brew)" ]
+    then
+      BREW_VERSION=$(brew --version | sed -n 1p)
+      echo "Installed: $BREW_VERSION"
+    else
+      echo "Installing brew..."
+      /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+    fi
+fi
+
+# Amazon
+if [ "$(uname) | grep 'amzn')" ]
+then
+    PLATFORM="Amazon Linux"
+fi
+
+echo "Running setup.sh on $PLATFORM..."
+echo ""
+
+##### INSTALL LIBRARIES #####
+echo "INSTALL LIBRARIES"
+
+# Python (https://www.python.org/)
+if [ "$(command -v python)" ]
+then
+  PYTHON_VERSION=$(python --version 2>&1)
+  echo "Installed: $PYTHON_VERSION"
+else
+  echo "Installing python..."
+fi
+
+# PYTHONPATH environment variable, so Python knows where to find your code files
+#   If running in a Unix based environment, .bash_profile are "startup scripts" that will run
+#   whenever you start a terminal, so it will set this up for you everytime as a convenience.
+export PYTHONPATH=${PYTHONPATH}:~/healthrex/CDSS
+printf 'export PYTHONPATH=${PYTHONPATH}:~/healthrex/CDSS\n' >> ~/.bashrc
+printf 'export PYTHONPATH=${PYTHONPATH}:~/healthrex/CDSS\n' >> ~/.bash_profile
+
+# PostgreSQL (https://www.postgresql.org/)
+# psql (http://postgresguide.com/utilities/psql.html)
+# Open-source relational database platform (comparable to MySQL or commercial versions like Oracle)
+#   Includes psql command line program to faciltate communicating with PostgreSQL database servers.
+if [ "$(command -v postgres)" ]
+then
+    POSTGRES_BIN=$(command -v postgres)
+    POSTGRES_VERSION=$(postgres --version 2>&1)
+    echo "Installed: $POSTGRES_VERSION"
+else
+    echo "Installing postgresql-server..."
+    if [ "$PLATFORM" = "Amazon Linux" ]
+    then
+      sudo yum install postgresql96-server
+    fi
+    if [ "$PLATFORM" = "OSX" ]
+    then
+      sudo brew install postgresql
+    fi
+fi
+if [ "$(command -v psql)" ]
+then
+    PSQL_BIN=$(command -v psql)
+    PSQL_VERSION=$(psql --version 2>&1)
+    echo "Installed: $PSQL_VERSION"
+else
+    echo "Installing psql..."
+    if [ "$PLATFORM" = "Amazon Linux" ]
+    then
+      sudo yum install postgresql96
+    fi
+    if [ "$PLATFORM" = "OSX" ]
+    then
+      sudo brew install postgresql
+    fi
+fi
+
+# psycopg2 (http://initd.org/psycopg/)
+# Python module, allowing Python to talk to PostgreSQL databases
+PSYCOPG2_VERSION="$(pip list --format=legacy | grep 'psycopg' | sed 's/[)(]//g' | awk '{print $2}')"
+if [ -z "$PSYCOPG2_VERSION" ]
+then
+    echo -n "Installing psycopg2..."
+    pip install --user psycopg2
+else
+    echo -n "Installed: psycopg2 "
+    echo $PSYCOPG2_VERSION
+fi
+
+# NumPy (https://scipy.org/index.html)
+# Python package commonly used for scientific work, facilitating matrix algebra and numerical computations
+# pip install --user numpy
+
+# Pandas (http://pandas.pydata.org/)
+# Python package to organize data tables into dataframe objects, very similar to R, for convenient manipulation of 2D data
+PANDAS_VERSION="$(pip list --format=legacy | grep 'pandas' | sed 's/[)(]//g' | awk '{print $2}')"
+if [ -z "$PANDAS_VERSION" ]
+then
+  echo -n "Installing pandas..."
+  pip install --user pandas
+  echo "OK"
+else
+  echo -n "Installed: pandas "
+  echo $PANDAS_VERSION
+fi
+
+# SciKit Learn (http://scikit-learn.org/stable/)
+# Python package as a common platform for machine learning algorithms and evaluation
+# pip install --user sklearn
+
+# gensim (https://radimrehurek.com/gensim/)
+# Python package for topic models (latent Dirichlet allocation)
+GENSIM_VERSION="$(pip list --format=legacy | grep 'gensim' | sed 's/[)(]//g' | awk '{print $2}')"
+if [ -z "$GENSIM_VERSION" ]
+then
+  echo -n "Installing gensim..."
+  pip install --user gensim
+  echo "OK"
+else
+  echo -n "Installed: gensim "
+  echo $GENSIM_VERSION
+fi
+
+##### VERIFY TESTS PASS #####
+# Try running unit tests in the main code repository to make sure things work
+# Example below, trying to setup and connect to the database with basic functions
+#   Requires your LocalEnv.py points to a TEST database name, 
+#   and that the database login user provided has permissions to create (temporary test) databases
+python medinfo/db/test/TestDBUtil.py
+# More comprehensive test of everything that can be found in the main medinfo application code directory tree
+python TestCDSS.py
diff --git a/setup/shc_lpch_conversions.md b/setup/shc_lpch_conversions.md
new file mode 100644
index 00000000..63f92c94
--- /dev/null
+++ b/setup/shc_lpch_conversions.md
@@ -0,0 +1,111 @@
+### How the upload of LPCH/SHC was made:
+**Notes**:
+* Here we talk about copying tables already uploaded to BQ to our local project/dataset (e.g., som-nero* and shc_core/lpch_core).
+* Attention was given mostly to times conversion. Other fields may still need to be converted (e.g., integer columns might be represented as string).
+* All the time columns are assumed to be in local (Pacific) time.
+
+**PHI elimination**
+
+Drop following columns from the following tables:
+
+* Flowsheet
+    * meas_value
+* Lab_result
+    * ord_value
+    * extended_value_comment
+    * extended_comp_comment
+
+**Step 1**
+
+First, all of the columns in the table containing dates/times were analyzed to check what times they contain. I.e., if they contain only '00:00:00' times, this column is assumed to be a DATE column, if they contained different times, the it is assumed to be a DATETIME column. And according to our convention, we need to introduce additional _UTC columns for these DATETIME ones. 
+
+Below is an example of a query to check times of a single field in a given table:
+```
+select extract(time from filing_date_jittered) times, count(*) as cnt
+from lpch.clinical_note_meta
+group by times
+order by cnt desc;
+```
+
+If the times were in STRING typed column, the query changed a bit:
+```
+select extract(time from case when noted_date <> '' then parse_datetime('%Y-%m-%d %H:%M:%S', noted_date) else null end) times, count(*) as cnt
+from starr_datalake2020.shc_diagnosis
+group by times
+order by cnt desc;
+```
+
+**Step 2**
+
+Once the types were determined a `SELECT` query was executed with `EXCEPT()` and time conversion functions to convert the table.
+BigQuery allows storing the query results in a table. This functionality was used to store the `SELECT` results in a table. 
+This is done by clicking *More* button under the query window (on the same line as *Run* button) and clicking *Query Settings*.
+Here, choose *Set a destination table for query results* in *Destination* section and fill in project/dataset/table name to store the results. Also, select *Allow large results (no size limit)* in *Results section*.
+
+Here's an example `SELECT` query:
+```
+        select * except(end_date, start_date, noted_date, hx_date_of_entry, resolved_date),
+        parse_datetime('%Y-%m-%d %H:%M:%S', case when end_date <> '' then end_date else null end) as end_date,
+        parse_datetime('%Y-%m-%d %H:%M:%S', case when start_date <> '' then start_date else null end) as start_date,
+        extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when noted_date <> '' then noted_date else null end)) as noted_date,
+        extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when hx_date_of_entry <> '' then hx_date_of_entry else null end)) as hx_date_of_entry,
+        extract(date from parse_datetime('%Y-%m-%d %H:%M:%S', case when resolved_date <> '' then resolved_date else null end)) as resolved_date,
+        timestamp(case when end_date <> '' then end_date else null end, "America/Los_Angeles") as end_date_utc,
+        timestamp(case when start_date <> '' then start_date else null end, "America/Los_Angeles") as start_date_utc,
+        from starr_datalake2020.shc_diagnosis;
+```
+
+**Step 3**
+
+After the conversion, we need to check whether it went as expected. This is done by joining both tables (original and a copy) and comparing converted columns.
+Here's an example join:
+```
+        select o.anon_id, o.line, o.pat_enc_csn_id_jittered, o.dx_id, o.start_date, o.source, o.dept_id,
+        o.end_date as o1, o.noted_date as o2, o.hx_date_of_entry as o3, o.resolved_date as o4,
+        c.end_date, c.noted_date, c.hx_date_of_entry, c.resolved_date
+        from starr_datalake2020.shc_diagnosis o
+        join shc_core.diagnosis_code c
+        on o.anon_id = c.anon_id and o.line = c.line and o.pat_enc_csn_id_jittered = c.pat_enc_csn_id_jittered and o.dx_id = c.dx_id
+        and o.start_date = format_datetime('%Y-%m-%d %H:%M:%S', c.start_date)
+        and o.source = c.source
+        and o.dept_id = c.dept_id
+        where o.end_date <> format_datetime('%Y-%m-%d %H:%M:%S', c.end_date)
+        or o.noted_date <> format_date('%Y-%m-%d', c.noted_date)
+        or o.hx_date_of_entry <> format_date('%Y-%m-%d', c.hx_date_of_entry)
+        or o.resolved_date <> format_date('%Y-%m-%d', c.resolved_date);
+```
+
+In the above case, a converted field itself was a part of a unique key, so it is omitted from the check as it is used in the `JOIN` section. That field is checked separately by sorting it in both original and copy tables and ranking the sorted rows to be able to join the tables:
+```
+        select *
+        from (select row_number() over() as o_row, o1.o_start_date, o_cnt
+            from (select start_date as o_start_date, count(*) as o_cnt
+                from starr_datalake2020.shc_diagnosis
+                group by start_date
+                order by start_date
+            ) o1
+        ) o2
+        join (select row_number() over() as c_row, c1.start_date, cnt
+            from (select start_date, count(*) as cnt
+                from shc_core.diagnosis_code
+                group by start_date
+                order by start_date
+            ) c1
+        ) c2 on o2.o_row = c2.c_row
+        where o2.o_start_date <> format_datetime('%Y-%m-%d %H:%M:%S', c2.start_date);
+```
+
+Sometimes, though, the check is simpler:
+```
+        select o.anon_id,
+        o.birth_date_jittered as o1, o.death_date_jittered as o2, o.JITTER_DATE_RECENT_CONF_ENC_JITTER_MINDATE_MAXDATE_ as o3,
+        c.birth_date_jittered, c.death_date_jittered, c.recent_conf_enc_jittered
+        from starr_datalake2020.shc_demographic o
+        join shc_core.demographic c
+        using (anon_id)
+        where o.birth_date_jittered <> timestamp(c.birth_date_jittered)
+        or o.death_date_jittered <> timestamp(c.death_date_jittered)
+        or o.JITTER_DATE_RECENT_CONF_ENC_JITTER_MINDATE_MAXDATE_ <> timestamp(c.recent_conf_enc_jittered);
+```
+
+If the queries return no rows, the conversion was successful.
diff --git a/setup/stride/__init__.py b/setup/stride/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/setup/stride/box/BoxClient.py b/setup/stride/box/BoxClient.py
new file mode 100644
index 00000000..3dcd52bc
--- /dev/null
+++ b/setup/stride/box/BoxClient.py
@@ -0,0 +1,51 @@
+#!/usr/bin/python
+
+"""
+Class for writing and reading from Box.
+"""
+
+from boxsdk import Client, OAuth2
+import os
+
+from LocalEnv import BOX_CLIENT_ID, BOX_CLIENT_SECRET, BOX_ACCESS_TOKEN
+
+class BoxClient:
+    def __init__(self):
+        oauth = OAuth2(
+            client_id=BOX_CLIENT_ID,
+            client_secret=BOX_CLIENT_SECRET,
+            access_token=BOX_ACCESS_TOKEN
+        )
+        self._client = Client(oauth)
+
+    def get_file(self, remote_folder_id, file_name):
+        items = self._client.folder(remote_folder_id).get_items(1000)
+        for item in items:
+            if item.name == file_name:
+                return item
+        return None
+
+    def upload_file(self, local_file_path, remote_folder_id, file_name):
+        return self._client.folder(remote_folder_id).upload(local_file_path, file_name)
+
+    def download_file(self, remote_folder_id, file_name, local_path):
+        remote_file = self.get_file(remote_folder_id, file_name)
+        local_file = open(local_path, 'w')
+        remote_file.download_to(local_file)
+        local_file.close()
+
+    def get_folder(self, remote_folder_id):
+        return self._client.folder(remote_folder_id)
+
+    # Note that download_folder will recursively download all subdirectories.
+    def download_folder(self, remote_folder_id, local_folder_path):
+        # In case it doesn't exist, create local_folder_path.
+        if not os.path.exists(local_folder_path):
+            os.makedirs(local_folder_path)
+
+        items = self._client.folder(remote_folder_id).get_items(1000)
+        for item in items:
+            if item.type == 'file':
+                self.download_file(remote_folder_id, item.name, '/'.join([local_folder_path, item.name]))
+            elif item.type == 'folder':
+                self.download_folder(item.id, '/'.join([local_folder_path, item.name]))
diff --git a/setup/stride/box/__init__.py b/setup/stride/box/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/setup/stride/box/box-API.md b/setup/stride/box/box-API.md
new file mode 100644
index 00000000..010381a3
--- /dev/null
+++ b/setup/stride/box/box-API.md
@@ -0,0 +1,24 @@
+# Box API
+
+## Overview
+
+We use the Stanford Medicine Box to store Protected Health Information (PHI).
+To access this data, you will need the appropriate credentials. Ask Jonathan.
+Once you have credentials, you can use the Box API to query data via the
+`BoxClient`.
+
+## Box API Access
+
+Use a `stanford.edu` email address to access the Box
+![Developer Console](https://stanfordmedicine.app.box.com/developers/console/).
+
+Use the Console interface to create a new app (e.g. `HealthRex`) and set the
+authentication method to Standard OAuth 2.0.
+
+To access Box via the API, you will need:
+1. Client ID (in the console under Configuration > OAuth 2.0 Credentials)
+2. Client Secret (in the console under Configuration > OAuth 2.0 Credentials)
+3. Access Token (in the console under Configuration > Developer Token)
+4. Box STRIDE Folder ID (48947257871)
+
+Copy these values into `LocalEnv.py`.
diff --git a/setup/stride/box/test/TestBoxClient.py b/setup/stride/box/test/TestBoxClient.py
new file mode 100644
index 00000000..46458f09
--- /dev/null
+++ b/setup/stride/box/test/TestBoxClient.py
@@ -0,0 +1,77 @@
+#!/usr/bin/python
+"""
+Test suite for BoxClient.py.
+"""
+
+import filecmp
+import inspect
+import os
+import shutil
+import unittest
+
+from LocalEnv import TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from stride.box.BoxClient import BoxClient
+
+
+class TestBoxClient(MedInfoTestCase):
+    def setUp(self):
+        self.client = BoxClient()
+        self.remote_folder_id = 48912846936
+        self.upload_file_name = 'box-upload-test.txt'
+        self.download_file_name = 'box-download-test.txt'
+        module = inspect.getfile(inspect.currentframe())
+        self.package = os.path.dirname(os.path.abspath(module))
+
+    def tearDown(self):
+        uf = self.client.get_file(self.remote_folder_id, self.upload_file_name)
+        ## uf will be None after test_download_file
+        if uf: uf.delete()
+
+        df = '/'.join([self.package, 'test-' + self.download_file_name])
+        try:
+            os.remove(df)
+        except OSError:
+            pass
+
+        test_local_dir_path = '/'.join([self.package, 'box-test'])
+        try:
+            shutil.rmtree(test_local_dir_path)
+        except OSError:
+            pass
+
+    def test_download_file(self):
+        # Define file to be downloaded.
+        verify_local_file_path = '/'.join([self.package, self.download_file_name])
+        test_local_file_path = '/'.join([self.package, 'test-' + self.download_file_name])
+        self.client.download_file(self.remote_folder_id, self.download_file_name, test_local_file_path)
+        # Check file content.
+        test_file = open(test_local_file_path)
+        verify_file = open(verify_local_file_path)
+        self.assertEqualFile(test_file, verify_file)
+
+    def test_upload_file(self):
+        # Define file to be uploaded.
+        local_file_path = '/'.join([self.package, self.upload_file_name])
+        # Upload file.
+        remote_file = self.client.upload_file(local_file_path, self.remote_folder_id, self.upload_file_name)
+        # Check file content.
+        remote_content = remote_file.content()
+        local_content = open('/'.join([self.package, self.upload_file_name])).read()
+
+        self.assertEqual(remote_content, local_content)
+
+    def test_download_folder(self):
+        # Define file to be downloaded.
+        verify_local_dir_path = '/'.join([self.package, 'box-test-verify'])
+        test_local_dir_path = '/'.join([self.package, 'box-test'])
+        self.client.download_folder(self.remote_folder_id, test_local_dir_path)
+        # Check folder content.
+        # TODO(sbala): Write a proper validation of this check, which was only
+        # checked ad-hoc on the test case.
+        # filecmp.dircmp(test_local_dir_path, verify_local_dir_path).report_full_closure()
+
+
+if __name__ == "__main__":
+    suite = make_test_suite(TestBoxClient)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/setup/stride/box/test/__init__.py b/setup/stride/box/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/setup/stride/box/test/box-download-test.txt b/setup/stride/box/test/box-download-test.txt
new file mode 100644
index 00000000..ff0d4ccb
--- /dev/null
+++ b/setup/stride/box/test/box-download-test.txt
@@ -0,0 +1 @@
+This file is for testing the Box client download functionality.
diff --git a/setup/stride/box/test/box-test-verify/box-download-test.txt b/setup/stride/box/test/box-test-verify/box-download-test.txt
new file mode 100644
index 00000000..ff0d4ccb
--- /dev/null
+++ b/setup/stride/box/test/box-test-verify/box-download-test.txt
@@ -0,0 +1 @@
+This file is for testing the Box client download functionality.
diff --git a/setup/stride/box/test/box-test-verify/box-test-subdirectory/box-download-test.txt b/setup/stride/box/test/box-test-verify/box-test-subdirectory/box-download-test.txt
new file mode 100644
index 00000000..ff0d4ccb
--- /dev/null
+++ b/setup/stride/box/test/box-test-verify/box-test-subdirectory/box-download-test.txt
@@ -0,0 +1 @@
+This file is for testing the Box client download functionality.
diff --git a/setup/stride/box/test/box-test-verify/box-test-subdirectory/box-upload-test.txt b/setup/stride/box/test/box-test-verify/box-test-subdirectory/box-upload-test.txt
new file mode 100644
index 00000000..cd0fe061
--- /dev/null
+++ b/setup/stride/box/test/box-test-verify/box-test-subdirectory/box-upload-test.txt
@@ -0,0 +1 @@
+This file is for testing the Box upload functionality.
diff --git a/setup/stride/box/test/box-upload-test.txt b/setup/stride/box/test/box-upload-test.txt
new file mode 100644
index 00000000..cd0fe061
--- /dev/null
+++ b/setup/stride/box/test/box-upload-test.txt
@@ -0,0 +1 @@
+This file is for testing the Box upload functionality.
diff --git a/setup/stride/clinical_item/ClinicalItemDataLoader.py b/setup/stride/clinical_item/ClinicalItemDataLoader.py
new file mode 100644
index 00000000..cf1f4652
--- /dev/null
+++ b/setup/stride/clinical_item/ClinicalItemDataLoader.py
@@ -0,0 +1,910 @@
+#!/usr/bin/python
+"""
+Class for transforming STRIDE dataset into patient event logs.
+
+Primary result tables:
+* clinical_item_category
+* clinical_item
+* patient_item
+
+All general postprocessing on these tables should happen here.
+"""
+
+import logging
+import os
+import subprocess
+from optparse import OptionParser
+import sys
+from medinfo.db import DBUtil
+from medinfo.cpoe.DataManager import DataManager
+from medinfo.common.Util import log, ProgressDots, stdOpen
+from LocalEnv import PATH_TO_CDSS
+
+# Switch off the analysis status for any clinical item that occurs for fewer than this percent of patient records
+MINIMUM_PATIENT_SUPPORT = 0.01; # 1%
+# Likewise for any clinical item that occurs for fewer than this fraction of the total number of patient items
+MINIMUM_PATIENT_ITEM_SUPPORT = 0.000001;
+
+
+class ClinicalItemDataLoader:
+    # The order here is significant. Some tables (e.g. clinical_item),
+    # reference other tables via  FOREIGN KEY (e.g. clinical_item_category),
+    # so tables must be defined in order of their dependencies.
+    SQL_TABLES = [
+        'clinical_item_category',
+        'clinical_item',
+        'order_result_stat',
+        'patient_item',
+        'item_collection',
+        'collection_type',
+        'item_collection_item',
+        'patient_item_collection_link',
+        'clinical_item_link',
+        'backup_link_patient_item',
+        'data_cache',
+        'clinical_item_association'
+    ]
+
+    STRIDE_TABLE_TRANSFORMER_MAP = {
+        'stride_patient': 'medinfo.dataconversion.STRIDEDemographicsConversion',
+        'stride_preadmit_med': 'medinfo.dataconversion.STRIDEPreAdmitMedConversion',
+        'stride_dx_list': 'medinfo.dataconversion.STRIDEDxListConversion',
+        'stride_treatment_team': 'medinfo.dataconversion.STRIDETreatmentTeamConversion',
+        'stride_order_med': 'medinfo.dataconversion.STRIDEOrderMedConversion',
+        'stride_order_proc': 'medinfo.dataconversion.STRIDEOrderProcConversion',
+        'stride_order_results': 'medinfo.dataconversion.STRIDEOrderResultsConversion'
+    }
+
+    @staticmethod
+    def load_clinical_item_from_stride():
+        ClinicalItemDataLoader.clear_clinical_item_psql_tables()
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+        ClinicalItemDataLoader.build_clinical_item_tables()
+        # ClinicalItemDataLoader.process_stride_psql_db()
+
+    @staticmethod
+    def fetch_clinical_item_dir():
+        # CDSS/setup/stride/clinical_item
+        return os.path.join(PATH_TO_CDSS, 'setup', 'stride', 'clinical_item')
+
+    @staticmethod
+    def fetch_psql_dir():
+        # stride/clinical_item/psql/
+        return os.path.join(ClinicalItemDataLoader.fetch_clinical_item_dir(), 'psql')
+
+    @staticmethod
+    def fetch_psql_schemata_dir():
+        return os.path.join(ClinicalItemDataLoader.fetch_clinical_item_dir(), 'psql/schemata')
+
+    @staticmethod
+    def fetch_psql_indices_dir():
+        return os.path.join(ClinicalItemDataLoader.fetch_clinical_item_dir(), 'psql/indices')
+
+    @staticmethod
+    def clear_clinical_item_psql_tables():
+        log.info('Clearing clinical_item psql tables...')
+        for psql_table in ClinicalItemDataLoader.SQL_TABLES:
+            log.debug('dropping table %s...' % psql_table)
+            DBUtil.execute("DROP TABLE IF EXISTS %s CASCADE;" % psql_table)
+
+    @staticmethod
+    def build_clinical_item_psql_schemata():
+        schemata_dir = ClinicalItemDataLoader.fetch_psql_schemata_dir()
+        for psql_table in ClinicalItemDataLoader.SQL_TABLES:
+            schema_file_name = '%s.schema.sql' % psql_table
+            schema_path = os.path.join(schemata_dir, schema_file_name)
+            with open(schema_path, 'r') as schema_file:
+                DBUtil.runDBScript(schema_file)
+
+    @staticmethod
+    def transform_STRIDE_source_table(stride_source_table):
+        # Get module for doing data conversion.
+        transformer = ClinicalItemDataLoader.STRIDE_TABLE_TRANSFORMER_MAP[stride_source_table]
+
+        # Build command.
+        if stride_source_table == 'stride_patient':
+            argv = ['python', '-m', transformer]
+        elif stride_source_table == 'stride_preadmit_med':
+            argv = ['python', '-m', transformer, '-m', '5', '-s', '2008-01-01']
+        elif stride_source_table == 'stride_order_med':
+            argv = ['python', '-m', transformer, '-m', '5', '-d', '5', '-s', '2008-01-01']
+        elif stride_source_table == 'stride_treatment_team':
+            argv = ['python', '-m', transformer, '-a', '-s', '2008-01-01']
+        else:
+            argv = ['python', '-m', transformer, '-a', '-s', '2008-01-01']
+
+        # Call command.
+        log_file = stdOpen('%s.log' % ('_'.join(argv)), 'w')
+        subprocess.call(argv, stderr=log_file)
+
+    @staticmethod
+    def process_clinical_item_psql_db():
+        ClinicalItemDataLoader.selectively_deactivate_clinical_item_analysis()
+        ClinicalItemDataLoader.selectively_deactivate_clinical_item_recommendation()
+        ClinicalItemDataLoader.define_composite_clinical_items()
+        ClinicalItemDataLoader.define_virtual_clinical_items()
+        ClinicalItemDataLoader.add_clinical_item_synonyms()
+
+    @staticmethod
+    def selectively_deactivate_clinical_item_analysis():
+        log.info('Selectively deactivating clinical item analysis...')
+        # Certain clinical items are rote with low or ambiguous information value.
+        #   For example, orders for Full Code, Regular Diet, Check and Report Vital Signs, and many "Nursing Orders" that don't include relevant detail
+        # Manually prevent analysis for these items.
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE name IN (
+                'RXCUI854932', 'RXCUI854934', 'RXCUI854936', 'RXCUI854938', -- Pneumococcal vaccine components
+                'RXCUI854940', 'RXCUI854942', 'RXCUI854944', 'RXCUI854946',
+                'RXCUI854948', 'RXCUI854950', 'RXCUI854952', 'RXCUI854954',
+                'RXCUI854956', 'RXCUI854958', 'RXCUI854960', 'RXCUI854962',
+                'RXCUI854964', 'RXCUI854966', 'RXCUI854968', 'RXCUI854970',
+                'RXCUI854972', 'RXCUI854974', 'RXCUI854932', 'RXCUI854934',
+                'RXCUI854936', 'RXCUI854938', 'RXCUI854940', 'RXCUI854942',
+                'RXCUI854944', 'RXCUI854946', 'RXCUI854948', 'RXCUI854950',
+                'RXCUI854952', 'RXCUI854954', 'RXCUI854956', 'RXCUI854958',
+                'RXCUI854960', 'RXCUI854962', 'RXCUI854964', 'RXCUI854966',
+                'RXCUI854968', 'RXCUI854970', 'RXCUI854972', 'RXCUI854974',
+                '146535', 'LABSPR', 'NUR1940', 'NUR1025', 'NUR194000',
+                'NUR350', 'NURSE 1500', '146541', '146543', 'COD2', 'DIET990',
+                'TRN2', 'POC14', 'LABPOCGLU', '146537'
+            );
+            """
+        DBUtil.execute(analysis_deactivation_command)
+
+        # Remove variants of TPN that are distracting and have a bizarrely long
+        # name when amino acid ingredients are unraveled.
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE description LIKE 'Alanine-Arginine%%';
+            """
+        DBUtil.execute(analysis_deactivation_command)
+
+        # Transfusion Blood Product: Not even clear what these are orders for,
+        # since there are separate orders for RBCs, platelets, and nursing to
+        # transfuse them. By coincidence, total count appears to be ~2x sum of
+        # all other blood bank orders.
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE name IN (
+                'LABTSLIP', 'LABTBP', 'LABTBP9', 'LABTBP10', 'LABTBP11',
+                'LABTBP12', 'LABTBP13', 'LABTBP14', 'LABTBP15', 'LABTBP16',
+                'LABTBP17', 'LABTBP18', 'LABTBP1', 'LABTBP19', 'LABTBP20',
+                'LABTBP21', 'LABTBP22', 'LABTBP23', 'LABTBP24', 'LABTBP25',
+                'LABTBP26', 'LABTBP27', 'LABTBP28', 'LABTBP2', 'LABTBP29',
+                'LABTBP30', 'LABTBP31', 'LABTBP32', 'LABTBP33', 'LABTBP34',
+                'LABTBP35', 'LABTBP36', 'LABTBP37', 'LABTBP38', 'LABTBP3',
+                'LABTBP39', 'LABTBP40', 'LABTBP41', 'LABTBP42', 'LABTBP43',
+                'LABTBP44', 'LABTBP45', 'LABTBP46', 'LABTBP47', 'LABTBP48',
+                'LABTBP4', 'LABTBP49', 'LABTBP50', 'LABTBP51', 'LABTBP52',
+                'LABTBP53', 'LABTBP54', 'LABTBP55', 'LABTBP56', 'LABTBP57',
+                'LABTBP58', 'LABTBP5', 'LABTBP59', 'LABTBP60', 'LABTBP61',
+                'LABTBP62', 'LABTBP63', 'LABTBP64', 'LABTBP65', 'LABTBP66',
+                'LABTBP67', 'LABTBP68', 'LABTBP6', 'LABTBP69', 'LABTBP70',
+                'LABTBP71', 'LABTBP72', 'LABTBP73', 'LABTBP74', 'LABTBP75',
+                'LABTBP76', 'LABTBP77', 'LABTBP78', 'LABTBP7', 'LABTBP79',
+                'LABTBP80', 'LABTBP81', 'LABTBP82', 'LABTBP83', 'LABTBP84',
+                'LABTBP85', 'LABTBP86', 'LABTBP87', 'LABTBP88', 'LABTBP8',
+                'LABTBP89', 'LABTBP90', 'LABTBP91', 'LABTBP92', 'LABTBP93'
+            );
+            """
+        DBUtil.execute(analysis_deactivation_command)
+
+        # Deactivate analysis on result comments.
+        # Could try simplifying with "WHERE DESCRIPTION ~* 'comment'"
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE NAME IN (
+                'BM15CO(Result)', 'UCMT(Result)', 'DOACOM(Result)',
+                'PCCOM(Result)', 'SREQ(Result)', 'SREQ(Result)',
+                'REVUE(Result)', 'HIACOM(Result)', 'PTCMT(Result)',
+                'LEICMT(Result)', 'PF4COM(Result)', 'DIMCOM(Result)',
+                'CEPCM(Result)', 'FCMT(Result)', 'SCMT(Result)',
+                'CCMT(Result)', 'COM(Result)', 'CTRXNW(Result)',
+                '11200R(Result)', 'PLTFCM(Result)', 'QBCRCM(Result)',
+                'B2GPCM(Result)', 'DHTLV(Result)', 'ATHCT(Result)',
+                'RFCOM(Result)', 'BMBRCT(Result)', 'QBMBRC(Result)',
+                'RSVCOM(Result)', 'KITCO(Result)', 'T15CO(Result)',
+                'BCRCMT(Result)', 'BCRKCM(Result)', 'BMTCMT(Result)',
+                'MTHCMT(Result)', 'MGMCO(Result)', 'SREQ(InRange)',
+                'TEGCOM(Result)', 'BMBCMT(Result)', 'BSQCO(Result)',
+                'BM11CT(Result)', 'SREQ(Result)', 'VWFPIC(Result)',
+                'CEBCM(Result)', 'YIFB2(Result)', 'YPLAB2(Result)',
+                'BM14CT(Result)', 'YUOPCO(Result)', 'YFCHY6(Result)',
+                'YUBZCO(Result)', 'FBCMT(Result)', 'PPPCOM(Result)',
+                'CF32CM(Result)', 'YUMECO(Result)', 'BCMT(Result)',
+                'YSTNCO(Result)', 'TCMT(Result)', '11152R(Result)',
+                'TMSICO(Result)', 'UCMT(Result)', 'JAKCO(Result)',
+                'YHISS5(Result)', 'YHISU5(Result)', 'T11CT(Result)',
+                '11996R(Result)', 'KITCO(Result)', 'YVB1W2(Result)',
+                'CTSMIS(Result)', 'NJAKCO(Result)', 'T14CMT(Result)',
+                'YLCFA9(Result)', 'QFBCRC(Result)', 'TT15CO(Result)',
+                'YHISC5(Result)', 'TTCCMT(Result)', 'TBCCMT(Result)',
+                'NBTCOM(Result)', 'NBTCBC(Result)', 'FT15CO(Result)',
+                'FTCMT(Result)', 'SREQ(Abnormal)', '9078R(Result)',
+                '9260R(Result)', 'MGMCOB(Result)', '9584R(Result)',
+                '9574R(Result)', '9464R(Result)', '9450R(Result)',
+                '12291R(Result)', '9433R(Result)', '9420R(Result)',
+                '9181R(Result)', '12486R(Result)', '12473R(Result)',
+                '9280R(Result)', '12255R(Result)', '12468R(Result)',
+                '12461R(Result)', 'TBCRCT(Result)', '9233R(Result)',
+                'HITI2(Result)', '9120R(Result)', 'BGSPR(Result)',
+                'BGSPRC(Result)', 'HCHCO(Result)', '9404R(Result)',
+                '9391R(Result)', '9207R(Result)', 'ABID2E(Result)',
+                'PEDCOM(Result)', '9344R(Result)', '9586R(Result)',
+                '9582R(Result)', '9374R(Result)', '9361R(Result)',
+                '9099R(Result)', 'PMCOMMENT(Result)'
+            );
+            """
+        DBUtil.execute(analysis_deactivation_command)
+
+        # Normal / Non-Specific Results
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE name LIKE '%%(InRange)' OR name LIKE '%%(Result)';
+            """
+        DBUtil.execute(analysis_deactivation_command)
+
+        # Skip analysis for very rare orders that excessively increase
+        # association item list without adding much value. Decide the threshold
+        # for "rare" on the fly. Goal is to exclude clinical items such that
+        # 95%+ of data is still represented, but greatly reduce vocabulary size. 
+
+        # As a rough heuristic, ignore clinical_items which represent < 0.0001% of all patient items.
+        threshold_query = \
+            """
+            SELECT ROUND(%s * COUNT(patient_item_id)) AS threshold
+            FROM patient_item;
+            """ % MINIMUM_PATIENT_ITEM_SUPPORT;
+        results = DBUtil.execute(threshold_query)
+        threshold = results[0][0]
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE clinical_item_id IN (
+                SELECT clinical_item_id
+                FROM patient_item
+                GROUP BY clinical_item_id
+                HAVING COUNT(patient_item_id) < %s
+            );
+            """ % threshold
+        DBUtil.execute(analysis_deactivation_command)
+
+        # As another rough heuristic, ignore clinical_items which occur for < 1% of all patient records.
+        threshold_query = \
+            """
+            SELECT ROUND(%s * COUNT(distinct patient_id)) AS threshold
+            FROM patient_item;
+            """ % MINIMUM_PATIENT_SUPPORT;
+        results = DBUtil.execute(threshold_query)
+        threshold = results[0][0]
+        analysis_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET analysis_status = 0
+            WHERE clinical_item_id IN (
+                SELECT clinical_item_id
+                FROM patient_item
+                GROUP BY clinical_item_id
+                HAVING COUNT(distinct patient_id) < %s
+            );
+            """ % threshold
+        DBUtil.execute(analysis_deactivation_command)
+
+
+    @staticmethod
+    def selectively_deactivate_clinical_item_recommendation():
+        log.info('Selectively deactivating clinical item recommendations...')
+        # Certain categories of clinical items don't really make sense to
+        # recommend as part of clinical order recommendations.
+        # Flip the default_recommend bit for these items.
+
+        # Exclude categories including:
+        #   source_table    description
+        #   order_proc  Nursing
+        #   order_proc  Transport
+        #   order_proc  BB Call Slip
+        #   order_proc  None
+        #   order_proc  Pharmacy Supplies
+        #   order_proc  Diet Communication
+        #   order_proc  Transfer
+        #   order_proc  Admission
+        #   order_proc  Discharge
+        recommendation_deactivation_command = \
+            """
+            UPDATE clinical_item_category
+            SET default_recommend = 0
+            WHERE description IN (
+                'Nursing', 'Transfer', 'Admission', 'Transport',
+                'BB Call Slip', 'None', 'Pharmacy Supplies',
+                'Diet Communication', 'Discharge'
+            );
+            """
+        DBUtil.execute(recommendation_deactivation_command)
+
+        # Note: "recommending" diagnoses might actually be useful for a
+        # diagnostic tool, but for now exclude the following categories.
+        #   source_table    description
+        #   stride_order_results    Lab Result
+        #   stride_order_results    Point of Care Testing Result
+        #   stride_order_results    HIV Lab Restricted Result
+        #   stride_order_results    HIV Lab Non-Restricted Result
+        #   stride_preadmit_med Preadmit Med
+        #   stride_treatment_team   Treatment Team
+        #   stride_patient  Demographics
+        #	stride_dx_list    Diagnosis (PROBLEM_LIST)
+        #	stride_dx_list    Diagnosis (ADMIT_DX)
+        recommendation_deactivation_command = \
+            """
+            UPDATE clinical_item_category
+            SET default_recommend = 0
+            WHERE source_table IN (
+                'stride_order_results',
+                'stride_preadmit_med',
+                'stride_treatment_team',
+                'stride_patient',
+                'stride_dx_list'
+            );
+            """
+        DBUtil.execute(recommendation_deactivation_command)
+
+        # Minimize subsequent confusion by propagating category level default_recommend status to individual clinical_items
+        recommendation_deactivation_command = \
+            """
+            UPDATE clinical_item
+            SET default_recommend = 0
+            WHERE clinical_item_category_id IN
+            (   SELECT clinical_item_category_id
+                FROM clinical_item_category
+                WHERE default_recommend = 0
+            );
+            """
+        DBUtil.execute(recommendation_deactivation_command)
+
+
+    @staticmethod
+    def define_composite_clinical_items():
+        log.info('Defining composite clinical items...')
+        # Composite certain outcome measures, for example, ICU interventions
+        # (sub-categorization based on vasopressors).
+
+        # VASOACTIVE INFUSION - Look for by name, though may be too broad if getting single small doses for peri-procedure or other purposes
+        # Category = Med (Intravenous)
+        intravenous_med_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Med (Intravenous)';
+            """
+        results = DBUtil.execute(intravenous_med_cic_id_query)
+        intravenous_med_cic_id = results[0][0]
+        vasoactive_infusion_id_query = \
+            """
+            select clinical_item_id
+            from clinical_item
+            where clinical_item_category_id = %s
+            and 
+            ( description ~* '^Dobutamine' or
+              description ~* '^Dopamine' or
+              description ~* '^Epinephrine' or
+              description ~* '^Norepinephrine' or
+              description ~* '^Phenylephrine' or
+              description ~* '^Vasopressin'
+            )
+            order by description
+            """ % intravenous_med_cic_id
+        results = DBUtil.execute(vasoactive_infusion_id_query)
+        vasoactive_infusion_ci_ids = [result[0] for result in results]
+
+        ClinicalItemDataLoader.build_composite_clinical_item(vasoactive_infusion_ci_ids, \
+            'AnyVasoactive', 'Any Vasoactive Infusion', intravenous_med_cic_id)
+
+        # CONTINUOUS RENAL REPLACEMENT THERAPY (CRRT)
+        # CaCl infusion and IV Citrate are very closely correlated to CRRT use,
+        # but are rarely used in other scenarios.
+        # Category = Med (CRRT)
+        crrt_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Med (CRRT)';
+            """
+        results = DBUtil.execute(crrt_cic_id_query)
+        crrt_cic_id = results[0][0]
+        crrt_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE
+                (
+                    clinical_item_category_id = %s
+                ) OR
+                (
+                    name = 'NUR4084'
+                );
+            """ % crrt_cic_id
+        results = DBUtil.execute(crrt_ci_id_query)
+        crrt_ci_ids = [result[0] for result in results]
+
+        ClinicalItemDataLoader.build_composite_clinical_item(crrt_ci_ids, 'AnyCRRT', \
+            'Continuous Renal Replacement Therapy Components', crrt_cic_id)
+
+        # VENTILATOR
+        # Category = Respiratory Care
+        respiratory_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Respiratory Care';
+            """
+        results = DBUtil.execute(respiratory_cic_id_query)
+        respiratory_cic_id = results[0][0]
+        #   clinical_item_category.description   name    description
+        #   Respiratory Care  RT75    RESP - EXTUBATION
+        #   Respiratory Care    RT104   RESP - VENTILATOR SETTINGS
+        #   Respiratory Care    RT97	RESP - WEAN VENTILATOR
+        #   Respiratory Care    RT90	RESP - SPONTANEOUS BREATHING TRIAL
+        #   Respiratory Care    RT130	RESP - REPOSITION ET TUBE
+        #   Respiratory Care    RT88	RESP - HIGH FREQUENCY OSCILLATORY VENTILATION
+        #   Respiratory Care    RT997	RESP - LUNG PROTECTIVE VENTILATION PROTOCOL
+        #   Respiratory Care    RT202	RESP - MONITOR, PERFORM VENTILATOR AND PATIENT ASSESSMENT
+        #   Respiratory Care    RT91	RESP - SIMV/PRESSURE SUPPORT
+        ventilator_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE
+                clinical_item_category_id = %s AND
+                name IN (
+                'RT75', 'RT104', 'RT97', 'RT90', 'RT130', 'RT88', 'RT997',
+                'RT202', 'RT91'
+                );
+            """ % respiratory_cic_id
+        results = DBUtil.execute(ventilator_ci_id_query)
+        ventilator_ci_ids = [result[0] for result in results]
+
+        ClinicalItemDataLoader.build_composite_clinical_item(ventilator_ci_ids, \
+            'AnyVentilator', 'Ventilator Orders', respiratory_cic_id)
+
+        # ICU LIFE SUPPORT
+        # Can populate leaf linked items directly, or link to the above
+        # intermediate nodes and allow inheritance to pickup the rest.
+        # Category = Procedures
+        procedures_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Procedures';
+            """
+        results = DBUtil.execute(procedures_cic_id_query)
+        procedures_cic_id = results[0][0]
+        # Build from the previously defined composite items.
+        icu_life_support_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE name IN (
+                'AnyVasoactive',
+                'AnyCRRT',
+                'AnyVentilator'
+            );
+            """
+        results = DBUtil.execute(icu_life_support_id_query)
+        icu_life_support_ci_ids = [result[0] for result in results]
+        ClinicalItemDataLoader.build_composite_clinical_item(icu_life_support_ci_ids, \
+            'AnyICULifeSupport', \
+            'ICU Life Support (Vasoactives, Ventilator, CRRT)', \
+            procedures_cic_id)
+
+        
+        # ICU CARE
+        # Any ICU specific (or highly correlated) orders to capture events
+        # occuring in the ICU, including other drips, nursing orders, etc.
+        # Note that some medications have multiple multiple medications with
+        # the same name. Use the description to disambiguate?
+        # Beware that this set of items is not reliable to correspond to the target due to variations in naming or categorization.
+        med_none_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Med (None)';
+            """
+        results = DBUtil.execute(med_none_cic_id_query)
+        med_none_cic_id = results[0][0]
+        #   name    description
+        #   MED530008	NITROPRUSSIDE IV INFUSION
+        #   RXCUI8782	Propofol (Intravenous)
+        #   RXCUI48937	Dexmedetomidine (Intravenous)
+        #   MED530004	MIDAZOLAM 50 MG IN D5W 100 ML IV INFUSION
+        #   RXCUI8163	Phenylephrine (Intravenous)
+        #   RXCUI114200	Citrate (Intravenous)
+        #   MED530120	HYDROMORPHONE 10 MG IN 100 ML IV INFUSION (SHC)
+        #   RXCUI36676	Sodium Bicarbonate (CRRT)
+        #   MED530007	VECURONIUM IV INFUSION
+        #   RXCUI4177	Etomidate (Intravenous)
+        #   RXCUI10154	Succinylcholine (Intravenous)
+        #   MED520130	CALCIUM CHLORIDE 8 G/1000 ML IV INFUSION
+        #   MED530009	CISATRACURIUM IV INFUSION
+        #   MED530003	FENTANYL  10 MCG/ML IV INFUSION
+        #   MED530111	MIDAZOLAM 100 MG IN D5W 100 ML IV INFUSION
+        #   MED530114	MILRINONE CUSTOM IV INFUSION
+        #   MED530050	DOBUTAMINE IV INFUSION CUSTOM MIXTURE
+        #   MED530046	HYDROMORPHONE IV INFUSION
+        #   MED530112	MIDAZOLAM IV INFUSION (CUSTOM)
+        #   NUR2254	MONITOR PULMONARY ARTERY CATHETER
+        #   RT106	RESP - NITRIC OXIDE VENT
+        #   NUR2247	MONITOR CENTRAL LINE
+        #   NUR1942	EXTERNAL VENTRICULAR DRAIN CARE
+        #   NUR1528	MONITOR ARTERIAL LINE
+        #   NUR30020	RASS SEDATION ASSESSMENT
+        #   NUR1599	MONITOR CARDIAC ASSIST DEVICE
+        #   NUR1591	OROGASTRIC TUBE
+        #   NUR1529	MONITOR CO/CI
+        #   NUR2999	MONITOR SCV02
+        #   AnyICULifeSupport	ICU Life Support (Vasopressors,Ventilator,CRRT)
+        icu_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE
+                (
+                    name = 'RXCUI8163' AND
+                    description = 'Phenylephrine (Intravenous)'
+                ) OR
+                (
+                    name = 'RXCUI36676' AND
+                    description = 'Sodium Bicarbonate (CRRT)'
+                ) OR
+                (
+                    name = 'RXCUI10154' AND
+                    description = 'Succinylcholine (Intravenous)'
+                ) OR
+                (
+                    name = 'MED530008' AND
+                    description = 'NITROPRUSSIDE IV INFUSION'
+                ) OR
+                name IN (
+                    'RXCUI8782', 'RXCUI48937', 'MED530004',
+                    'RXCUI114200', 'MED530120', 'MED530007', 'RXCUI4177',
+                    'MED520130', 'MED530009', 'MED530003', 'MED530111',
+                    'MED530114', 'MED530050', 'MED530046', 'MED530112',
+                    'NUR2254', 'RT106', 'NUR2247', 'NUR1942', 'NUR1528',
+                    'NUR30020', 'NUR1599', 'NUR1591', 'NUR1529', 'NUR2999',
+                    'AnyICULifeSupport'
+                    );
+            """
+        results = DBUtil.execute(icu_ci_id_query)
+        icu_ci_ids = [result[0] for result in results]
+        ClinicalItemDataLoader.build_composite_clinical_item(icu_ci_ids, 'AnyICUOrders', \
+            'Any ICU Specific/Correlated Orders', med_none_cic_id)
+
+        # DNR / NON-FULL CODE ORDERS
+        # Category = Code Status
+        code_status_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Code Status';
+            """
+        results = DBUtil.execute(code_status_cic_id_query)
+        code_status_cic_id = results[0][0]
+        #   name    description
+        #   COD1	DNR
+        #   COD7	DNR/C
+        #   COD5	DNR/DNE
+        #   COD1	DNR/DNI
+        #   COD3    Partial Code    # Note that this is NOT being included in "AnyDNR"
+        dnr_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE name IN (
+                'COD1', 'COD5', 'COD7'
+            ) AND
+            clinical_item_category_id = %s
+            ;
+            """ % code_status_cic_id;
+        results = DBUtil.execute(dnr_ci_id_query)
+        dnr_ci_ids = [result[0] for result in results]
+        ClinicalItemDataLoader.build_composite_clinical_item(dnr_ci_ids, 'AnyDNR', \
+            'Any DNR Code Status', code_status_cic_id)
+
+        # ADMISSION
+        admission_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Admission';
+            """
+        results = DBUtil.execute(admission_cic_id_query)
+        admission_cic_id = results[0][0]
+        # Composite admission orders
+        #   name    description
+        #   ADT1    ADMIT TO INPATIENT
+        #   ADT1	ADMIT/PLACE PATIENT
+        #   ADT100	ADMIT TO INPATIENT
+        #   ADT16   ADMIT POST SURGERY    # Not sure about this one. Looks like outpatient procedure in more recent labels
+        admission_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE name IN ('ADT1', 'ADT100');
+            """
+        results = DBUtil.execute(admission_ci_id_query)
+        admission_ci_ids = [result[0] for result in results]
+        ClinicalItemDataLoader.build_composite_clinical_item(admission_ci_ids, \
+            'AnyAdmit', 'Any Admit (not observation nor outpatient surgery)',
+            admission_cic_id)
+
+    @staticmethod
+    def build_composite_clinical_item(components, name, description, category_id):
+        """
+        Simple wrapper around medinfo/cpoe/DataManager.py
+        """
+        component_str = ','.join([str(id) for id in components])
+        log.debug('(%s, %s, %s) = (%s)' % (name, description, category_id, \
+                                            component_str))
+        composite_arg = '%s|%s|%s|%s' % (component_str, name, description, \
+                                            category_id)
+        dm = DataManager()
+        dm.main(['medinfo/cpoe/DataManager.py', '--compositeRelated', composite_arg])
+
+    @staticmethod
+    def define_virtual_clinical_items():
+        log.info('Defining virtual clinical items...')
+        # READMISSION
+        # Readmission defined as a discharge --> admission.
+        # First, define a clinical item for Readmission.
+        admission_cic_id_query = \
+            """
+            SELECT clinical_item_category_id
+            FROM clinical_item_category
+            WHERE description = 'Admission';
+            """
+        results = DBUtil.execute(admission_cic_id_query)
+        admission_cic_id = results[0][0]
+        readmission_definition_command = \
+            """
+            INSERT INTO clinical_item (
+                clinical_item_category_id,
+                name,
+                description,
+                analysis_status
+            )
+            VALUES (
+                %s,
+                'READT',
+                'Readmission',
+                1
+            );
+            """ % admission_cic_id
+        DBUtil.execute(readmission_definition_command)
+        # Second, get clinical_item_id for admission, discharge, & readmission.
+        admission_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE name = 'AnyAdmit';
+            """
+        results = DBUtil.execute(admission_ci_id_query)
+        admission_ci_id = results[0][0]
+        discharge_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE name = 'ADT12';
+            """
+        results = DBUtil.execute(discharge_ci_id_query)
+        discharge_ci_id = results[0][0]
+        readmission_ci_id_query = \
+            """
+            SELECT clinical_item_id
+            FROM clinical_item
+            WHERE name = 'READT';
+            """
+        results = DBUtil.execute(readmission_ci_id_query)
+        readmission_ci_id = results[0][0]
+        # Third, use TripleAssociationAnalysis to build a new virtual item.
+        item_sequence = [discharge_ci_id, admission_ci_id]
+        ClinicalItemDataLoader.build_virtual_clinical_item(item_sequence, readmission_ci_id)
+
+    @staticmethod
+    def build_virtual_clinical_item(item_sequence, virtual_id):
+        """
+        Simple wrapper around medinfo/cpoe/TripleAssociationAnalysis.
+        """
+        sequence_str = ' -> '.join([str(id) for id in item_sequence])
+        log.debug('(%s) = (%s)' % (sequence_str, virtual_id))
+
+        build_virtual_item_command = [
+            'python', '-m', 'medinfo/cpoe/TripleAssociationAnalysis.py',
+            '-s', ','.join([str(id) for id in item_sequence]),
+            '-v', str(virtual_id)
+            ]
+
+        subprocess.call(build_virtual_item_command)
+
+    @staticmethod
+    def add_clinical_item_synonyms():
+        log.info('Adding clinical item synonyms...')
+        # Add common synonyms to major clinical items, primarily to facilitate
+        # user interface simulation order searches by name.
+
+        # Exact mappings between clinical item name and description synonym.
+        EXACT_NAME_TO_SYNONYM = {
+            'LABMETB': ' [BMP]',
+            'LABMETC': ' [CMP]',
+            'LABHFP': ' [LFT]',
+            'EKG5': ' [EKG]',
+            'LABPT': ' [PT/INR]',
+        }
+        for exact_name, synonym in EXACT_NAME_TO_SYNONYM.items():
+            synonym_command = \
+                """
+                UPDATE clinical_item
+                SET description = description || '%s'
+                WHERE name = '%s';
+                """ % (synonym, exact_name)
+            DBUtil.execute(synonym_command)
+
+        # Case insensitive regular expression matches.
+        REGEX_TO_SYNONYM = {
+            '^xr.*chest': ' [CXR]',
+            'urinalysis': ' [UA]'
+        }
+        for regex, synonym in REGEX_TO_SYNONYM.items():
+            synonym_command = \
+                """
+                UPDATE clinical_item
+                SET description = description || '%s'
+                WHERE description ~* '%s';
+                """ % (synonym, regex)
+            print(synonym_command)
+            DBUtil.execute(synonym_command)
+
+        # More complex cases that can't be easily looped.
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [NS IVF][Normal Saline]'
+            WHERE
+                name = 'RXCUI9863' AND
+                DESCRIPTION ~* 'intravenous';
+            """
+        DBUtil.execute(synonym_command)
+
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [ABG]'
+            WHERE
+                description ~* 'blood gas' AND
+                description ~* 'arteria' AND
+                name ~* '^LAB|^POC';
+            """
+        DBUtil.execute(synonym_command)
+
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [VBG]'
+            WHERE
+                description ~* 'blood gas' AND
+                description ~* 'venous' AND
+                name ~* '^LAB|^POC';
+            """
+        DBUtil.execute(synonym_command)
+
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [LR][Lactated Ringer]'
+            WHERE description ~* 'lactate.*intraven';
+            """
+        DBUtil.execute(synonym_command)
+
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [RBC]'
+            WHERE
+                description ~* 'red blood cell' AND
+                name NOT LIKE 'ICD%%';
+            """
+        DBUtil.execute(synonym_command)
+
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [PLT]'
+            WHERE description ~* 'platelet'
+            AND description NOT LIKE '%%PLT%%';
+            """
+        DBUtil.execute(synonym_command)
+
+        synonym_command = \
+            """
+            UPDATE clinical_item
+            SET description = description || ' [FFP]'
+            WHERE description ~* 'fresh frozen plasma';
+            """
+        DBUtil.execute(synonym_command)
+
+    @staticmethod
+    def build_clinical_item_indices():
+        indices_dir = ClinicalItemDataLoader.fetch_psql_indices_dir()
+        for psql_table in ClinicalItemDataLoader.SQL_TABLES:
+            indices_file_name = '%s.indices.sql' % psql_table
+            indices_path = os.path.join(indices_dir, indices_file_name)
+            if os.path.exists(indices_path):
+                with open(indices_path, 'r') as indices_file:
+                    DBUtil.runDBScript(indices_file)
+
+    @staticmethod
+    def build_clinical_item_association_table():
+        # Run Pre-Computation Association Analysis
+        # Memory intensive, but if can use large amount on 64bit Python saves a
+        # lot of DB hits / persistence. Use -a option to specify how many
+        # associations to keep in memory before commit to DB.
+        # 1,000,000 seems to just fit within 7.5 GB of RAM.
+        # Still a DB intensive I/O process that can be helped by moving more
+        # database processing to memory than disk.
+        # python medinfo/cpoe/AssociationAnalysis.py -i <patientIDFile> -u 10000 -a 900000
+        # (Find Patient IDs to work on, by query like
+        # 	select distinct patient_id
+        # 	from patient_item
+        # 	where analyze_date is null
+        # 	and patient_id % 10 = -1
+        # Use arbitrary mod parameter on patient ID and item dates to select out data subsets
+        # )
+        #
+        # # Helper driver script to break up into multiple processes (beware total memory overhead)
+        # nohup python Archive/scripts/CDSS/assocAnalysis.py &> log/assocAnalysis &
+        pass
+
+if __name__=='__main__':
+    log.level = logging.DEBUG
+
+    # Define options for command-line usage.
+    usage_str = 'usage: %prog [options]\n'
+    parser = OptionParser(usage=usage_str)
+    parser.add_option('-s', '--schemata', dest='build_schemata',
+                        action='store_true', default=False,
+                        help='build clinical_item psql schemata')
+    parser.add_option('-S', '--simSchemata', dest='build_simSchemata',
+                        action='store_true', default=False,
+                        help='build clinical provider order entry simultation psql schemata')
+    parser.add_option('-t', '--transform', dest='transform_stride_table',
+                        metavar='<transform_stride_table>', default=False,
+                        help='transform STRIDE tables to clinical_item tables')
+    parser.add_option('-b', '--backup_psql', dest='backup_psql_tables',
+                        action='store_true', default=False,
+                        help='backup psql tables to dump files')
+    parser.add_option('-d', '--delete', dest='delete_tables',
+                        action='store_true', default=False,
+                        help='delete clinical_item tables')
+    parser.add_option('-p', '--process', dest='process_tables',
+                        action='store_true', default=False,
+                        help='post-process clinical_item tables')
+    (options, args) = parser.parse_args(sys.argv[1:])
+
+    # Handle command-line usage arguments.
+    if options.build_schemata:
+        ClinicalItemDataLoader.build_clinical_item_psql_schemata()
+    if options.build_simSchemata:
+        ClinicalItemDataLoader.transform_STRIDE_source_table(options.transform_stride_table)
+    elif options.delete_tables:
+        ClinicalItemDataLoader.clear_clinical_item_psql_tables()
+    elif options.process_tables:
+        ClinicalItemDataLoader.process_clinical_item_psql_db()
diff --git a/setup/stride/clinical_item/__init__.py b/setup/stride/clinical_item/__init__.py
new file mode 100644
index 00000000..cb18a04b
--- /dev/null
+++ b/setup/stride/clinical_item/__init__.py
@@ -0,0 +1 @@
+# Simple placeholder so directory will be interpreted as a Python package
diff --git a/setup/stride/clinical_item/clinical_item_tables.md b/setup/stride/clinical_item/clinical_item_tables.md
new file mode 100644
index 00000000..5f87d289
--- /dev/null
+++ b/setup/stride/clinical_item/clinical_item_tables.md
@@ -0,0 +1,108 @@
+# Derived Clinical Item Tables
+
+## Overview
+Much of our research is based on the Stanford Clinical Data Warehouse
+aka STARR, formerly STRIDE). However, these tables are sometimes difficult
+to query because all of the information about a given patient is strewn about
+5-6 separate tables. To make day-to-day research and development easier, we
+have created derived "Clinical Item" tables from the original STRIDE data 
+that provide a simplified representation of much of the relevant clinical data.
+
+Based around a general vocabulary of clinical_items, that each correspond to 
+some type of event that can happen on a patient's timeline 
+(e.g., a medication was ordered, a diagnosis code was entered, patient died, etc.)
+
+Individual patient records are then represented as a timeline of patient_items, 
+that simply link sets of (patient_id, clinical_item, timestamp).
+
+Anything in these derived tables should be traceable back to the raw STRIDE data tables,
+but the conversion takes care of a lot of simplification and normalization
+(e.g., medications boiled down to active ingredients and route of administration, 
+rather than inconsistent medication_id or text descriptions).
+Depending on the analysis needs, it is possible to use only these derived tables, 
+and never reference the raw STRIDE tables. Unless some data type or detail you need,
+has not (yet) been converted into this simplified form.
+
+
+For more information about the raw STRIDE data, see `stride/stride.md`.
+
+## Building CDSS
+
+### Load the STRIDE tables
+
+For information on how to do this, see `stride/stride.md`.
+
+### Transform the STRIDE tables --> CDSS (runtime: 40 – 50 hours)
+
+First, build the schemata for the derived tables.
+
+**schemata (runtime: 20 – 30 seconds)**
+
+`python scripts/CDSS/ClinicalItemDataLoader.py --schemata`
+
+Second, convert all of the STRIDE tables to derived tables.
+
+**stride_patient (runtime: 5 – 10 minutes)**
+
+`python medinfo/dataconversion/STRIDEDemographicsConversion.py`
+
+**stride_treatment_team (runtime: 60 – 90 minutes)**
+
+`python medinfo/dataconversion/STRIDETreatmentTeamConversion.py -a -s 2008-01-01`
+
+**stride_dx_list (runtime: 60 - 90 minutes)**
+
+`python medinfo/dataconversion/STRIDEDxListConversion.py -s 2008-01-01`
+
+**stride_preadmit_med (runtime: 5 – 10 minutes)**
+
+`python medinfo/dataconversion/STRIDEPreAdmitMedConversion.py -m 5 -s 2008-01-01`
+
+**stride_order_med (runtime: 3 – 4 hours)**
+
+`python medinfo/dataconversion/STRIDEOrderMedConversion.py -m 5 -d 5 -s 2008-01-01`
+
+**stride_order_proc (runtime: 15 – 20 hours)**
+
+`python medinfo/dataconversion/STRIDEOrderProcConversion.py -s 2008-01-01`
+
+**stride_order_results (runtime: 20 – 25 hours)**
+
+`python medinfo/dataconversion/STRIDEOrderResultsConversion.py -s 2008-01-01`
+
+
+### Post-process the CDSS tables (runtime: 20 – 30 minutes)
+The clinical decision support system relies on an association matrix which
+illustrates how commonly two events co-occur. To perform this analysis, we need
+to do some post-processing on the CDSS tables. In particular, we need to:
+* identify clinical items we don't want in the matrix
+* identify clinical items we don't want to recommend
+* combine highly related clinical items into virtual clinical items
+* define synonyms for certain clinical item names
+* define the possible outcomes we want to track.
+
+
+`python scripts/CDSS/ClinicalItemDataLoader.py --process`
+
+### psql --> dumps (runtime: 20 – 30 minutes)
+
+Because this process only needs to be run once, we have stored a backup
+version fo these files on [Stanford Medicine Box](https://stanfordmedicine.app.box.com/folder/50484084132).
+
+First, edit the database variables in `scripts/CDSS/psql/dump_cdss.sh`.
+
+Then run the script in the same directory as the dump files.
+
+### dumps --> psql (runtime: 60 – 90 minutes)
+
+First, edit the database variables in `scripts/CDSS/psql/restore_cdss.sh`
+
+Then run the script in the same directory as the dump files.
+
+## Querying CDSS
+
+To get a better sense of the type of data contained within the STRIDE
+data set, inspect the schema definition files in `scripts/CDSS/psql/schemata`.
+
+To actually query the data, see [these SQL resources](https://github.com/HealthRex/CDSS/wiki/STRIDE-Database#postgresql)
+and [this STRIDE-specific tutorial](https://github.com/HealthRex/CDSS/wiki/STRIDE-SQL-Tutorial).
diff --git a/setup/stride/clinical_item/psql/dump_clinical_item.sh b/setup/stride/clinical_item/psql/dump_clinical_item.sh
new file mode 100644
index 00000000..e7634409
--- /dev/null
+++ b/setup/stride/clinical_item/psql/dump_clinical_item.sh
@@ -0,0 +1,27 @@
+#!/bin/sh -x
+# Define constants for connecting to database from which to dump tables.
+# For example, if dumping from local database named test_database and accessing
+# as user test_user, you would use the following lines:
+# export DB_HOST=localhost
+# export DB_DSN=test_database
+# export DB_USER=test_user
+export DB_HOST=host
+export DB_DSN=database
+export DB_USER=user
+
+# Alternatively, if just want to dump data as CSV file without schema, meta-data, etc.
+# psql -U <user> -d <database> -c "\copy clinical_item TO 'clinical_item.csv' with (format csv,header true, delimiter ',');"
+
+# pg_dump automatically dumps the contents of a database to standard output.
+# Pipe these results to gzip, which writes to a compressed file.
+pg_dump -h $DB_HOST -U $DB_USER -t  clinical_item_category $DB_DSN | gzip -c > clinical_item_category.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  clinical_item $DB_DSN | gzip -c > clinical_item.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  clinical_item_link $DB_DSN | gzip -c > clinical_item_link.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  patient_item $DB_DSN | gzip -c > patient_item.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  backup_link_patient_item $DB_DSN | gzip -c > backup_link_patient_item.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  clinical_item_association $DB_DSN | gzip -c > clinical_item_association.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  item_collection $DB_DSN | gzip -c > item_collection.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  collection_type $DB_DSN | gzip -c > collection_type.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  item_collection_item $DB_DSN | gzip -c > item_collection_item.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  patient_item_collection_link $DB_DSN | gzip -c > patient_item_collection_link.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t  order_result_stat $DB_DSN | gzip -c > order_result_stat.dump.sql.gz
diff --git a/setup/stride/clinical_item/psql/indices/build_indices.sh b/setup/stride/clinical_item/psql/indices/build_indices.sh
new file mode 100644
index 00000000..a35da584
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/build_indices.sh
@@ -0,0 +1,6 @@
+psql -U ec2-user stride-inpatient-2008-2017 -f clinical_item.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f patient_item.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f clinical_item_association.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f item_collection_item.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f clinical_item_category.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f patient_item_collection_link.indices.sql
diff --git a/setup/stride/clinical_item/psql/indices/clinical_item.indices.sql b/setup/stride/clinical_item/psql/indices/clinical_item.indices.sql
new file mode 100644
index 00000000..87ac8954
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/clinical_item.indices.sql
@@ -0,0 +1,6 @@
+-- Table: clinical_item
+
+CREATE INDEX IF NOT EXISTS index_clinical_item_clinical_item_category
+                            ON clinical_item(clinical_item_category_id);
+CREATE INDEX IF NOT EXISTS index_clinical_item_external_id
+                            ON clinical_item(external_id);
diff --git a/setup/stride/clinical_item/psql/indices/clinical_item_association.indices.sql b/setup/stride/clinical_item/psql/indices/clinical_item_association.indices.sql
new file mode 100644
index 00000000..be9e7736
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/clinical_item_association.indices.sql
@@ -0,0 +1,6 @@
+-- Table: clinical_item_association
+
+CREATE INDEX IF NOT EXISTS clinical_item_association_clinical_item_id
+                            ON clinical_item_association(clinical_item_id, subsequent_item_id);
+CREATE INDEX IF NOT EXISTS clinical_item_association_subsequent_item_id
+                            ON clinical_item_association(subsequent_item_id, clinical_item_id);
diff --git a/setup/stride/clinical_item/psql/indices/clinical_item_category.indices.sql b/setup/stride/clinical_item/psql/indices/clinical_item_category.indices.sql
new file mode 100644
index 00000000..245ed700
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/clinical_item_category.indices.sql
@@ -0,0 +1,4 @@
+-- Table: clinical_item_category
+
+CREATE INDEX IF NOT EXISTS clinical_item_category_pkey
+                            ON clinical_item_category(clinical_item_category_id);
diff --git a/setup/stride/clinical_item/psql/indices/item_collection_item.indices.sql b/setup/stride/clinical_item/psql/indices/item_collection_item.indices.sql
new file mode 100644
index 00000000..bd241e5b
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/item_collection_item.indices.sql
@@ -0,0 +1,8 @@
+-- Table: item_collection_item
+
+CREATE INDEX IF NOT EXISTS item_collection_item_collection_id
+                            ON item_collection_item(item_collection_id);
+CREATE INDEX IF NOT EXISTS item_collection_item_clinical_item_id
+                            ON item_collection_item(clinical_item_id);
+CREATE INDEX IF NOT EXISTS item_collection_item_type_id
+                            ON item_collection_item(collection_type_id);
diff --git a/setup/stride/clinical_item/psql/indices/patient_item.indices.sql b/setup/stride/clinical_item/psql/indices/patient_item.indices.sql
new file mode 100644
index 00000000..32f44aee
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/patient_item.indices.sql
@@ -0,0 +1,16 @@
+-- Table: patient_item
+
+CREATE INDEX IF NOT EXISTS index_patient_item_clinical_item_id_date
+                            ON patient_item(clinical_item_id, item_date);
+-- Natural to sort by patient, then in chronological order of clinical items
+CREATE INDEX IF NOT EXISTS index_patient_item_patient_id_date
+                            ON patient_item(patient_id, item_date);
+CREATE INDEX IF NOT EXISTS index_patient_item_external_id
+                            ON patient_item(external_id, clinical_item_id);
+-- Natural to sort by patient encounter, then chronologically
+CREATE INDEX IF NOT EXISTS index_patient_item_encounter_id_date
+                            ON patient_item(encounter_id, item_date);
+CREATE INDEX IF NOT EXISTS index_patient_item_id_num_value
+                            ON patient_item(num_value);
+CREATE INDEX IF NOT EXISTS index_patient_item_text_value
+                            ON patient_item(text_value);
diff --git a/setup/stride/clinical_item/psql/indices/patient_item_collection_link.indices.sql b/setup/stride/clinical_item/psql/indices/patient_item_collection_link.indices.sql
new file mode 100644
index 00000000..5a5e3eb1
--- /dev/null
+++ b/setup/stride/clinical_item/psql/indices/patient_item_collection_link.indices.sql
@@ -0,0 +1,6 @@
+-- Table: patient_item_collection_link
+
+CREATE INDEX IF NOT EXISTS patient_item_collection_link_patient_item_id
+                            ON patient_item_collection_link(patient_item_id);
+CREATE INDEX IF NOT EXISTS patient_item_collection_link_item_collection_item_id
+                            ON patient_item_collection_link(item_collection_item_id);
diff --git a/setup/stride/clinical_item/psql/restore-clinical_item.md b/setup/stride/clinical_item/psql/restore-clinical_item.md
new file mode 100644
index 00000000..b313faa3
--- /dev/null
+++ b/setup/stride/clinical_item/psql/restore-clinical_item.md
@@ -0,0 +1,28 @@
+# Restore derived Clinical Item tables to PostgreSQL database
+
+## Acquiring the data
+
+The data is stored in Box: https://stanfordmedicine.app.box.com/folder/48947257871
+
+The `data` subfolder contains a folder called `medinfo_2008_2017`. This folder
+contains all of the psql backup files.
+
+You can download the data in one of a few ways:
+1. Download the files via a browser 1-by-1 (manual).
+2. Download the files via `stride/box/BoxClient.py` (automated, but untested)
+3. Use the Box Sync desktop client and then select the "Sync to Desktop" option in the Box web interface "Details" section of the respective folder of data.
+
+To use the second method, follow the instructions in `stride/box/box-API.md`.
+Then call `stride.core.StrideLoader.download_stride_data()`.
+
+Either way, downloaded the data to `stride/data/*`
+
+The total download time might be several hours.
+
+## Loading the data
+
+Within `stride/data/medinfo_2008_2017`, you'll find two restore scripts.
+* `restore_stride.sql`
+* `restore_clinical_item.sql`
+
+Run them, wait for a few hours, and hope for success.
diff --git a/setup/stride/clinical_item/psql/restore_clinical_item.sh b/setup/stride/clinical_item/psql/restore_clinical_item.sh
new file mode 100644
index 00000000..e45ad7a4
--- /dev/null
+++ b/setup/stride/clinical_item/psql/restore_clinical_item.sh
@@ -0,0 +1,24 @@
+#!/bin/sh -x
+# Define constants for connecting to database from which to dump tables.
+# For example, if dumping from local database named test_database and accessing
+# as user test_user, you would use the following lines:
+# export DB_HOST=localhost
+# export DB_DSN=test_database
+# export DB_USER=test_user
+export DB_HOST=localhost
+export DB_DSN=database
+export DB_USER=user
+
+# Pipe the uncompressed data from gzip to psql to load to database.
+gzip -d -c clinical_item_category.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c clinical_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c clinical_item_link.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c patient_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c backup_link_patient_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c clinical_item_association.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c item_collection.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c collection_type.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c item_collection_item.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c patient_item_collection_link.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c order_result_stat.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c data_cache.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
diff --git a/setup/stride/clinical_item/psql/schemata/backup_link_patient_item.schema.sql b/setup/stride/clinical_item/psql/schemata/backup_link_patient_item.schema.sql
new file mode 100644
index 00000000..4bc0175a
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/backup_link_patient_item.schema.sql
@@ -0,0 +1,14 @@
+-- Table: backup_link_patient_item
+-- Description: Backup table for links overridden when merging clinical items
+--              that seem related, but don't want to destroy data completely.
+
+CREATE TABLE IF NOT EXISTS backup_link_patient_item
+(
+	backup_link_patient_item_id SERIAL NOT NULL,
+	patient_item_id BIGINT NOT NULL,
+	clinical_item_id BIGINT NOT NULL,
+  CONSTRAINT backup_link_patient_item_pkey PRIMARY KEY (backup_link_patient_item_id),
+  CONSTRAINT backup_link_patient_item_key UNIQUE (patient_item_id, clinical_item_id),
+  CONSTRAINT backup_link_patient_item_patient_item_fkey FOREIGN KEY (patient_item_id) REFERENCES patient_item(patient_item_id),
+  CONSTRAINT backup_link_patient_item_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/clinical_item.schema.sql b/setup/stride/clinical_item/psql/schemata/clinical_item.schema.sql
new file mode 100644
index 00000000..a1c62c2f
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/clinical_item.schema.sql
@@ -0,0 +1,23 @@
+-- Table: clinical_item
+-- Description: List of "clinical items" that will be included in analyses.
+--              Defines the "vocabulary" of "words" that can form part of
+--              clinical "documents."
+
+CREATE TABLE IF NOT EXISTS clinical_item
+(
+    clinical_item_id SERIAL NOT NULL,
+    clinical_item_category_id BIGINT NOT NULL,
+    external_id BIGINT,	-- ID from externally imported data (e.g. RXCUI)
+    name TEXT NOT NULL, -- e.g. MED540151
+    description TEXT,   -- e.g. Warfarin
+    default_recommend INTEGER DEFAULT 1, -- whether to recommend in CDSS
+    item_count DOUBLE PRECISION, -- summary statistic of # occurrences ()
+    patient_count DOUBLE PRECISION, -- summary statistic of # of unique patients
+    encounter_count DOUBLE PRECISION, -- summary statistic of # of unique encounters
+    analysis_status INTEGER DEFAULT 1, -- should be in assocation analysis?
+    outcome_interest INTEGER DEFAULT 0, -- could be outcome measure?
+    CONSTRAINT clinical_item_pkey PRIMARY KEY (clinical_item_id),
+    CONSTRAINT clinical_item_category_fkey FOREIGN KEY
+                (clinical_item_category_id) REFERENCES
+                clinical_item_category(clinical_item_category_id)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/clinical_item_association.schema.sql b/setup/stride/clinical_item/psql/schemata/clinical_item_association.schema.sql
new file mode 100644
index 00000000..8bec5c31
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/clinical_item_association.schema.sql
@@ -0,0 +1,93 @@
+-- Table: clinical_item_association
+-- Description: Core stat tracking table, record various association
+--              statistics mostly counting up the number of times one item /
+--              order follows another	within any patient's course.
+
+CREATE TABLE IF NOT EXISTS clinical_item_association
+(
+    clinical_item_association_id SERIAL NOT NULL,
+    clinical_item_id BIGINT NOT NULL,
+    subsequent_item_id BIGINT NOT NULL,
+    CONSTRAINT clinical_item_association_pkey PRIMARY KEY (clinical_item_association_id),
+    CONSTRAINT clinical_item_association_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id),
+    CONSTRAINT clinical_item_association_subsequent_item_fkey FOREIGN KEY (subsequent_item_id) REFERENCES clinical_item(clinical_item_id),
+    CONSTRAINT clinical_item_association_composite_key UNIQUE (clinical_item_id, subsequent_item_id)
+);
+
+-- Basic type of stats, counting up cooccurences.  Column names encoded by
+-- duration in seconds the counting timeframe represents. Use DOUBLE PRECISION
+-- / floating point values to allow for weighted pseudo-counts.
+-- Number of times the subsequent item follows the primary clinical item within
+-- _____ of event TIMESTAMP.
+ALTER TABLE clinical_item_association ADD COLUMN count_0 DOUBLE PRECISION DEFAULT 0; -- 0 seconds
+ALTER TABLE clinical_item_association ADD COLUMN count_3600 DOUBLE PRECISION DEFAULT 0;	-- 1 hour
+ALTER TABLE clinical_item_association ADD COLUMN count_7200 DOUBLE PRECISION DEFAULT 0;	-- 2 hours
+ALTER TABLE clinical_item_association ADD COLUMN count_21600 DOUBLE PRECISION DEFAULT 0;	-- 6 hours
+ALTER TABLE clinical_item_association ADD COLUMN count_43200 DOUBLE PRECISION DEFAULT 0; -- 12 hours
+ALTER TABLE clinical_item_association ADD COLUMN count_86400 DOUBLE PRECISION DEFAULT 0;  -- 1 day
+ALTER TABLE clinical_item_association ADD COLUMN count_172800 DOUBLE PRECISION DEFAULT 0; -- 2 days
+ALTER TABLE clinical_item_association ADD COLUMN count_345600 DOUBLE PRECISION DEFAULT 0; -- 4 days
+ALTER TABLE clinical_item_association ADD COLUMN count_604800 DOUBLE PRECISION DEFAULT 0; -- 1 week
+ALTER TABLE clinical_item_association ADD COLUMN count_1209600 DOUBLE PRECISION DEFAULT 0; -- 2 weeks
+ALTER TABLE clinical_item_association ADD COLUMN count_2592000 DOUBLE PRECISION DEFAULT 0; -- 1 month (30 days)
+ALTER TABLE clinical_item_association ADD COLUMN count_7776000 DOUBLE PRECISION DEFAULT 0; -- 3 months
+ALTER TABLE clinical_item_association ADD COLUMN count_15552000 DOUBLE PRECISION DEFAULT 0; -- 6 months
+ALTER TABLE clinical_item_association ADD COLUMN count_31536000 DOUBLE PRECISION DEFAULT 0; -- 1 year (365 days)
+ALTER TABLE clinical_item_association ADD COLUMN count_63072000 DOUBLE PRECISION DEFAULT 0; -- 2 years
+ALTER TABLE clinical_item_association ADD COLUMN count_126144000 DOUBLE PRECISION DEFAULT 0; -- 4 years
+
+-- Number of times the subsequent item follows within any period of recorded time
+ALTER TABLE clinical_item_association ADD COLUMN count_any DOUBLE PRECISION DEFAULT 0;
+-- Sum of differences in event times of subsequent vs. primary items, expressed
+-- in seconds.  This divided by count_any reflects the average time difference
+ALTER TABLE clinical_item_association ADD COLUMN time_diff_sum DOUBLE PRECISION DEFAULT 0;
+-- Sum of squared difference values as above.  Can be used with above to assess
+-- variance of time differences (var = (SumSquares - Sum^2) / count_any)
+ALTER TABLE clinical_item_association ADD COLUMN time_diff_sum_squares DOUBLE PRECISION DEFAULT 0;
+
+-- Similar stats, but don't count duplicates within the timeframes, only
+-- incrementing counts for the first subsequent item encountered. Expand time
+-- windows for counting. Should always meet or exceed dataset timeframe so easy
+-- to upscale without full recalc. More stats, but now count simply by the
+-- existence of a patient with the combination and only count once per patient.
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_0 DOUBLE PRECISION DEFAULT 0;
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_3600 DOUBLE PRECISION DEFAULT 0;	-- 1 hour
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_7200 DOUBLE PRECISION DEFAULT 0;	-- 2 hours
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_21600 DOUBLE PRECISION DEFAULT 0;	-- 6 hours
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_43200 DOUBLE PRECISION DEFAULT 0; -- 12 hours
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_86400 DOUBLE PRECISION DEFAULT 0;  -- 1 day
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_172800 DOUBLE PRECISION DEFAULT 0; -- 2 days
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_345600 DOUBLE PRECISION DEFAULT 0; -- 4 days
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_604800 DOUBLE PRECISION DEFAULT 0; -- 1 week
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_1209600 DOUBLE PRECISION DEFAULT 0; -- 2 weeks
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_2592000 DOUBLE PRECISION DEFAULT 0; -- 1 month (30 days)
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_7776000 DOUBLE PRECISION DEFAULT 0; -- 3 months
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_15552000 DOUBLE PRECISION DEFAULT 0; -- 6 months
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_31536000 DOUBLE PRECISION DEFAULT 0; -- 1 year (365 days)
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_63072000 DOUBLE PRECISION DEFAULT 0; -- 2 years
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_126144000 DOUBLE PRECISION DEFAULT 0; -- 4 years
+ALTER TABLE clinical_item_association ADD COLUMN patient_count_any DOUBLE PRECISION DEFAULT 0;
+ALTER TABLE clinical_item_association ADD COLUMN patient_time_diff_sum DOUBLE PRECISION DEFAULT 0;
+ALTER TABLE clinical_item_association ADD COLUMN patient_time_diff_sum_squares DOUBLE PRECISION DEFAULT 0;
+
+-- More stats, but now count by the existence of a patient encounter with the
+-- combination and only count once per encounter.
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_0 DOUBLE PRECISION DEFAULT 0;
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_3600 DOUBLE PRECISION DEFAULT 0;	-- 1 hour
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_7200 DOUBLE PRECISION DEFAULT 0;	-- 2 hours
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_21600 DOUBLE PRECISION DEFAULT 0;	-- 6 hours
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_43200 DOUBLE PRECISION DEFAULT 0; -- 12 hours
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_86400 DOUBLE PRECISION DEFAULT 0;  -- 1 day
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_172800 DOUBLE PRECISION DEFAULT 0; -- 2 days
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_345600 DOUBLE PRECISION DEFAULT 0; -- 4 days
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_604800 DOUBLE PRECISION DEFAULT 0; -- 1 week
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_1209600 DOUBLE PRECISION DEFAULT 0; -- 2 weeks
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_2592000 DOUBLE PRECISION DEFAULT 0; -- 1 month (30 days)
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_7776000 DOUBLE PRECISION DEFAULT 0; -- 3 months
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_15552000 DOUBLE PRECISION DEFAULT 0; -- 6 months
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_31536000 DOUBLE PRECISION DEFAULT 0; -- 1 year (365 days)
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_63072000 DOUBLE PRECISION DEFAULT 0; -- 2 years
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_126144000 DOUBLE PRECISION DEFAULT 0; -- 4 years
+ALTER TABLE clinical_item_association ADD COLUMN encounter_count_any DOUBLE PRECISION DEFAULT 0;
+ALTER TABLE clinical_item_association ADD COLUMN encounter_time_diff_sum DOUBLE PRECISION DEFAULT 0;
+ALTER TABLE clinical_item_association ADD COLUMN encounter_time_diff_sum_squares DOUBLE PRECISION DEFAULT 0;
diff --git a/setup/stride/clinical_item/psql/schemata/clinical_item_category.schema.sql b/setup/stride/clinical_item/psql/schemata/clinical_item_category.schema.sql
new file mode 100644
index 00000000..61effe0a
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/clinical_item_category.schema.sql
@@ -0,0 +1,11 @@
+-- Table: clinical_item_category
+-- Description: Categories of clinical items derived from STRIDE dataset.
+
+CREATE TABLE IF NOT EXISTS clinical_item_category
+(
+  clinical_item_category_id SERIAL NOT NULL,
+  source_table TEXT NOT NULL, -- source for clinical item (e.g. stride_patient)
+  description TEXT,
+  default_recommend INTEGER DEFAULT 1,
+  CONSTRAINT clinical_item_category_pkey PRIMARY KEY (clinical_item_category_id)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/clinical_item_link.schema.sql b/setup/stride/clinical_item/psql/schemata/clinical_item_link.schema.sql
new file mode 100644
index 00000000..a714253b
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/clinical_item_link.schema.sql
@@ -0,0 +1,16 @@
+-- Table: clinical_item_link
+-- Description: Track links between clinical items that should not have
+--              association stats calculated, probably because they have a
+--              specific firm relation (e.g. component clinical item of a
+--              composite one).
+
+CREATE TABLE IF NOT EXISTS clinical_item_link
+(
+	clinical_item_link_id SERIAL NOT NULL,
+	clinical_item_id BIGINT NOT NULL,
+	linked_item_id BIGINT NOT NULL,
+  CONSTRAINT clinical_item_link_pkey PRIMARY KEY (clinical_item_link_id),
+  CONSTRAINT clinical_item_link_key UNIQUE (clinical_item_id, linked_item_id),
+  CONSTRAINT clinical_item_link_clinical_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id),
+  CONSTRAINT clinical_item_link_linked_item_fkey FOREIGN KEY (linked_item_id) REFERENCES clinical_item(clinical_item_id)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/collection_type.schema.sql b/setup/stride/clinical_item/psql/schemata/collection_type.schema.sql
new file mode 100644
index 00000000..50dee1fb
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/collection_type.schema.sql
@@ -0,0 +1,45 @@
+-- Table: collection_type
+-- Description: Model different types of item_collections.
+
+CREATE TABLE IF NOT EXISTS collection_type
+(
+	collection_type_id INTEGER NOT NULL, -- SIGNED option for MySQL
+	name VARCHAR(255) NOT NULL,
+	description TEXT,
+  CONSTRAINT collection_type_pkey PRIMARY KEY (collection_type_id),
+  CONSTRAINT collection_type_key UNIQUE (name)
+);
+
+-- Insert base collection types.
+INSERT INTO collection_type
+  (
+  collection_type_id,
+  name,
+  description
+  )
+VALUES
+	(
+    1,
+    'Recommendation',
+    'Recommendation Level - For example, 1=Generally agreed recommendation, 2=Moderate or conflicting support for recommendation, 2.1=Recommendation default without quantification, 2.5=Conflicting support, favor not recommending, or highly conditional, 3=Generally agreed against'
+  ),
+	(
+    2,
+    'Evidence',
+    'Evidence Level - For example, 1=(a) Strong evidence, 2=(b) Moderate evidence, 3=(c) Weak evidence'
+  ),
+	(
+    3,
+    'Reference',
+    'Reference Level - For example, 1=Explicitly referenced in collection, 2=Implied use in collection, 2.5=Consistent with properties of collection, but may be highly conditional or secondary choice, 3=Referenced *against* use'
+  ),
+	(
+    4,
+    'OrderSet',
+    '1=Default-selected, 2=Available, 2.5=Available under sub-menu'
+  ),
+	(
+    5,
+    'DiagnosisLink',
+    'Link an (admission) diagnosis designated by clinical_item_id to the item collection (value = 3: reference guidelines or value = 4: order set collections).'
+  );
diff --git a/setup/stride/clinical_item/psql/schemata/data_cache.schema.sql b/setup/stride/clinical_item/psql/schemata/data_cache.schema.sql
new file mode 100644
index 00000000..ae769ee3
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/data_cache.schema.sql
@@ -0,0 +1,12 @@
+-- Table: data_cache
+-- Description: Store results of common queries which infrequently change.
+
+CREATE TABLE IF NOT EXISTS data_cache
+(
+	data_cache_id	SERIAL	NOT NULL,
+	data_key	VARCHAR(255)	NOT NULL,	-- VARCHAR fixed length to accomodate unique key index
+	data_value	TEXT	NOT NULL,
+	last_update	TIMESTAMP	NOT NULL,
+  CONSTRAINT data_cache_pkey PRIMARY KEY (data_cache_id),
+  CONSTRAINT data_cache_key UNIQUE (data_key)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/item_collection.schema.sql b/setup/stride/clinical_item/psql/schemata/item_collection.schema.sql
new file mode 100644
index 00000000..c47ac8d5
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/item_collection.schema.sql
@@ -0,0 +1,15 @@
+-- Table: item_collection
+-- Description: Track manually specified collections of clinical items and
+--              their relative relationships. For example, pre-existing
+--              order sets.
+
+CREATE TABLE IF NOT EXISTS item_collection
+(
+    item_collection_id SERIAL NOT NULL,
+    section VARCHAR(1024), -- Allow indexing values up to 400 characters
+    name VARCHAR(1024) NOT NULL,
+    description TEXT,
+    external_id BIGINT, -- Map to order set protocol ID
+    subgroup VARCHAR(1024), -- Map to order set "Smart Group"
+    CONSTRAINT item_collection_pkey PRIMARY KEY (item_collection_id)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/item_collection_item.schema.sql b/setup/stride/clinical_item/psql/schemata/item_collection_item.schema.sql
new file mode 100644
index 00000000..8345254e
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/item_collection_item.schema.sql
@@ -0,0 +1,17 @@
+-- Table: item_collection_item
+-- Description: Individual items in each item collection.
+
+CREATE TABLE IF NOT EXISTS item_collection_item
+(
+	item_collection_item_id SERIAL NOT NULL,
+	item_collection_id BIGINT NOT NULL,
+	clinical_item_id BIGINT,	-- Allow null for reference to non-existent items
+	collection_type_id INTEGER,
+	value DECIMAL(9,3),	-- Precision values, but allow decimals, not just integers
+	priority INTEGER,
+	comment TEXT,
+  CONSTRAINT item_collection_item_pkey PRIMARY KEY (item_collection_item_id),
+  CONSTRAINT item_collection_item_collection_fkey FOREIGN KEY (item_collection_id) REFERENCES item_collection(item_collection_id),
+  CONSTRAINT item_collection_item_item_fkey FOREIGN KEY (clinical_item_id) REFERENCES clinical_item(clinical_item_id),
+  CONSTRAINT item_collection_item_type_fkey FOREIGN KEY (collection_type_id) REFERENCES collection_type(collection_type_id)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/order_result_stat.schema.sql b/setup/stride/clinical_item/psql/schemata/order_result_stat.schema.sql
new file mode 100644
index 00000000..ba1cb69c
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/order_result_stat.schema.sql
@@ -0,0 +1,15 @@
+-- Table: order_result_stat
+-- Description: Stats on order (lab) results to help track distributions.
+
+CREATE TABLE IF NOT EXISTS order_result_stat
+(
+	order_result_stat_id SERIAL NOT NULL,
+	base_name TEXT NOT NULL,
+	value_count INTEGER DEFAULT 0,
+	value_sum FLOAT DEFAULT 0,
+	value_sum_squares FLOAT DEFAULT 0,
+	max_result_flag TEXT,
+	max_result_in_range TEXT,
+	CONSTRAINT order_result_stat_pkey PRIMARY KEY (order_result_stat_id),
+	CONSTRAINT order_result_stat_key UNIQUE (base_name)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/patient_item.schema.sql b/setup/stride/clinical_item/psql/schemata/patient_item.schema.sql
new file mode 100644
index 00000000..f825256d
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/patient_item.schema.sql
@@ -0,0 +1,26 @@
+-- Table: patient_item
+-- Description: Clinical items linked to actual applications to individual
+-- patients, additionally keyed by the date of the item event (e.g., order
+-- entry or lab result).
+
+CREATE TABLE IF NOT EXISTS patient_item
+(
+    patient_item_id SERIAL NOT NULL,
+	external_id BIGINT,	-- ID used from external source (e.g. RXCUI)
+    patient_id BIGINT NOT NULL,
+    clinical_item_id BIGINT NOT NULL,
+    item_date TIMESTAMP NOT NULL,	-- date that clinical item occurred
+    analyze_date TIMESTAMP,			-- date that item was analyzed for association
+                                -- pre-computing. Should redesign this to
+                                -- separate table to allow for multiple models
+                                -- trained on same source data.
+    encounter_id BIGINT, -- option to track at the individual encounter level
+    text_value TEXT, -- option to store text/comment data
+    num_value FLOAT, -- option to store numerical data
+    source_id BIGINT, -- ID from source table
+    item_date_utc TIMESTAMP,        -- date that clinical item occurred in UTC timezone, None if it's a date
+    CONSTRAINT patient_item_pkey PRIMARY KEY (patient_item_id),
+    CONSTRAINT patient_item_clinical_item_fkey FOREIGN KEY (clinical_item_id)
+                  REFERENCES clinical_item(clinical_item_id),
+    CONSTRAINT patient_item_composite UNIQUE (patient_id, clinical_item_id, item_date)
+);
diff --git a/setup/stride/clinical_item/psql/schemata/patient_item_collection_link.schema.sql b/setup/stride/clinical_item/psql/schemata/patient_item_collection_link.schema.sql
new file mode 100644
index 00000000..3d7c15e8
--- /dev/null
+++ b/setup/stride/clinical_item/psql/schemata/patient_item_collection_link.schema.sql
@@ -0,0 +1,14 @@
+-- Table: patient_item_collection_link
+-- Description: Link table between patient items and (order set) collections
+--              they belong to or are derived from.
+
+CREATE TABLE IF NOT EXISTS patient_item_collection_link
+(
+	patient_item_collection_link_id SERIAL NOT NULL,
+	patient_item_id BIGINT NOT NULL,
+	item_collection_item_id BIGINT NOT NULL,
+  CONSTRAINT patient_item_collection_link_pkey PRIMARY KEY (patient_item_collection_link_id),
+  CONSTRAINT patient_item_collection_link_composite UNIQUE (patient_item_id, item_collection_item_id),
+  CONSTRAINT patient_item_collection_link_patient_fkey FOREIGN KEY (patient_item_id) REFERENCES patient_item(patient_item_id),
+  CONSTRAINT patient_item_collection_link_item_fkey FOREIGN KEY (item_collection_item_id) REFERENCES item_collection_item(item_collection_item_id)
+);
diff --git a/setup/stride/core/StrideLoader.py b/setup/stride/core/StrideLoader.py
new file mode 100644
index 00000000..9d187f7d
--- /dev/null
+++ b/setup/stride/core/StrideLoader.py
@@ -0,0 +1,351 @@
+#!/usr/bin/python
+"""
+Class for extracting, transforming, and loading raw STRIDE files as delivered by
+Stanford into a proper format that can be analyzed (psql, R, etc).
+
+All knowledge about the oddities of the STRIDE dataset should be confined
+to this file. It will try to solve a number of problems, including:
+* Inconsistencies in column names (e.g. pat_id vs. pat_anon_id)
+* Inconsistencies in using quotes (e.g. pat_id vs. "pat_id" for headers)
+* Inconsistencies in capitalization
+* Quotes which prevent logical data types (e.g. "1980" vs. 1980 for timestamps)
+"""
+
+import inspect
+import logging
+import os
+import gzip
+import shutil
+import sys
+import pandas as pd
+import numpy as np
+from optparse import OptionParser
+
+from LocalEnv import BOX_STRIDE_FOLDER_ID, PATH_TO_CDSS, LOCAL_PROD_DB_PARAM
+from medinfo.db import DBUtil
+from medinfo.common.Util import log
+from stride.rxnorm.RxNormClient import RxNormClient
+from stride.core.StrideLoaderParams import TABLE_PREFIX, STRIDE_LOADER_PARAMS
+
+
+class StrideLoader:
+    @staticmethod
+    def fetch_stride_dir():
+        # CDSS/setup/stride/
+        return os.path.join(PATH_TO_CDSS, 'setup', 'stride')
+
+    @staticmethod
+    def fetch_core_dir():
+        # CDSS/setup/core/
+        return os.path.join(PATH_TO_CDSS, 'setup', 'core')
+
+    @staticmethod
+    def fetch_data_dir():
+        # CDSS/stride/data/
+        stride_dir = StrideLoader.fetch_stride_dir()
+        return os.path.join(stride_dir, 'data')
+
+    @staticmethod
+    def fetch_clean_data_dir():
+        data_dir = StrideLoader.fetch_data_dir()
+        return os.path.join(data_dir, 'clean')
+
+    @staticmethod
+    def fetch_raw_data_dir():
+        data_dir = StrideLoader.fetch_data_dir()
+        return os.path.join(data_dir, 'raw')
+
+    @staticmethod
+    def fetch_psql_dir():
+        # CDSS/stride/data/
+        stride_dir = StrideLoader.fetch_stride_dir()
+        return os.path.join(stride_dir, 'psql')
+
+    @staticmethod
+    def fetch_psql_schemata_dir():
+        # CDSS/stride/schemata/
+        psql_dir = StrideLoader.fetch_psql_dir()
+        return os.path.join(psql_dir, 'schemata')
+
+    @staticmethod
+    def fetch_psql_indices_dir():
+        # CDSS/stride/indices/
+        psql_dir = StrideLoader.fetch_psql_dir()
+        return os.path.join(psql_dir, 'indices')
+
+    @staticmethod
+    def download_stride_data():
+        data_dir = StrideLoader.fetch_data_dir()
+
+        # Remote folder ID defined in LocalEnv.
+        from stride.box.BoxClient import BoxClient  # Only import boxsdk if necessary (usually won't be)
+        box = BoxClient()
+        box.download_folder(BOX_STRIDE_FOLDER_ID, data_dir)
+
+    @staticmethod
+    def build_clean_csv_file(source_path, dest_path):
+        # Force pandas to read certain fields as an object.
+        # This both makes read_csv faster and reduces parsing errors.
+        # Fields that look like integers should be read as objects so that
+        # missing data doesn't force pandas to read as a float.
+        # http://pandas.pydata.org/pandas-docs/stable/gotchas.html#support-for-integer-na
+        raw_data = pd.read_csv(source_path, compression='gzip', \
+                                dtype=object, skipinitialspace=True,
+                                error_bad_lines=False, warn_bad_lines=True)
+
+        # Make header column all lowercase.
+        raw_data.columns = [column.lower() for column in raw_data.columns]
+
+        # Make custom patches to the data values. Any parsing errors should
+        # be fixed offline, but any data oddities should be fixed here.
+        raw_file_name = os.path.split(source_path)[-1]
+        # If generating a *_mapped_meds file, append active_ingredient.
+        if 'Chen_Mapped_Meds' in raw_file_name:
+            rxnorm = RxNormClient()
+            name_function = rxnorm.fetch_name_by_rxcui
+            raw_data['active_ingredient'] = raw_data['rxcui'].map(name_function)
+        elif 'fio2' in raw_file_name:
+            # TODO(sbala): Find a way to capture the PEEP value in our schema.
+            # FiO2 is sometimes recorded as a FiO2/PEEP value, which cannot
+            # be stored as a floating point number. Given <5% of values include
+            # a PEEP value (Positive End Expiratory-Pressure), it's OK to lose
+            # this information. e.g. "0.50/8-10" ==> "0.50"
+            float_value = raw_data['flowsheet_value'].str.split('/', \
+                                                                expand=True)[0]
+            raw_data['flowsheet_value'] = float_value
+
+
+        # Write to csv.
+        raw_data.to_csv(path_or_buf=dest_path, compression='gzip', index=False)
+
+    @staticmethod
+    def build_stride_psql_schemata():
+        schemata_dir = StrideLoader.fetch_psql_schemata_dir()
+        for params in list(STRIDE_LOADER_PARAMS.values()):
+            psql_table = params['psql_table'] % TABLE_PREFIX
+
+            log.debug('loading %s schema...' % psql_table)
+
+            # Open file, feed to DBUtil, and close file.
+            schema_file_name = '.'.join([psql_table, 'schema.sql'])
+            schema_file_path = os.path.join(schemata_dir, schema_file_name)
+            schema_file = open(schema_file_path, 'r')
+            DBUtil.runDBScript(schema_file)
+            schema_file.close()
+
+    @staticmethod
+    def build_stride_psql_indices():
+        indices_dir = StrideLoader.fetch_psql_indices_dir()
+        for params in list(STRIDE_LOADER_PARAMS.values()):
+            psql_table = params['psql_table'] % TABLE_PREFIX
+
+            # Open file, feed to DBUtil, and close file.
+            indices_file_name = '.'.join([psql_table, 'indices.sql'])
+            indices_file_path = os.path.join(indices_dir, indices_file_name)
+            if os.path.exists(indices_file_path):
+                log.debug('loading %s indices...' % psql_table)
+                indices_file = open(indices_file_path, 'r')
+                DBUtil.runDBScript(indices_file)
+                indices_file.close()
+
+    @staticmethod
+    def clear_stride_psql_tables():
+        log.info('Clearing stride psql tables...')
+        for params in list(STRIDE_LOADER_PARAMS.values()):
+            psql_table = params['psql_table'] % TABLE_PREFIX
+            log.debug('dropping table %s...' % psql_table)
+            # load_stride_to_psql is not itempotent, so in case schema already
+            # existed, clear table (avoid duplicate data).
+            DBUtil.execute("DROP TABLE IF EXISTS %s CASCADE;" % psql_table)
+
+    @staticmethod
+    def process_stride_psql_db():
+        # Unify labels in *_flowsheet. Can still recover distinctions
+        # based on flo_meas_id.
+        #   'Heart Rate' ==> 'Pulse'
+        heart_rate_label_command = \
+            """
+            UPDATE
+                %s_flowsheet
+        	SET
+                flowsheet_name = 'Pulse'
+        	WHERE
+                flowsheet_name = 'Heart Rate';
+            """ % TABLE_PREFIX
+        DBUtil.execute(heart_rate_label_command)
+        #   'Urine Output' ==> 'Urine'
+        urine_output_label_command = \
+            """
+            UPDATE
+                %s_flowsheet
+            SET
+                flowsheet_name = 'Urine'
+            WHERE
+                flowsheet_name = 'Urine Output';
+            """ % TABLE_PREFIX
+        DBUtil.execute(urine_output_label_command)
+
+        # Merge *_admit_vital and *_insurance into *_patient_encounter.
+        # Insert newly merged records with negative IDs, then discard and
+        # relabel old records.
+        patient_encounter_merge_command = \
+            """
+            INSERT INTO
+                %s_patient_encounter(
+                    pat_enc_csn_id, pat_id,
+                    payor_name, title,
+                    bp_systolic, bp_diastolic,
+                    temperature, pulse, respirations
+                )
+            SELECT
+                -spe1.pat_enc_csn_id, pat_id,
+                payor_name, title,
+          	    max_BPS, max_BPD,
+                max_temp, max_pulse, max_resp
+            FROM
+                (
+                    SELECT
+                        pat_enc_csn_id, pat_id, payor_name, title
+                    FROM
+                        %s_patient_encounter
+                    WHERE
+                        bp_systolic IS NULL
+                ) AS spe1,
+                (
+                    SELECT
+                        pat_enc_csn_id,
+                        MAX(bp_systolic) as max_BPS,
+                        MAX(bp_diastolic) as max_BPD,
+                        MAX(temperature) as max_temp,
+                        MAX(pulse) as max_pulse,
+                        MAX(respirations) as max_resp
+                    FROM
+                        %s_patient_encounter
+                    GROUP BY
+                        pat_enc_csn_id
+                ) spe2
+            WHERE
+                spe1.pat_enc_csn_id = spe2.pat_enc_csn_id;
+            """ % (TABLE_PREFIX, TABLE_PREFIX, TABLE_PREFIX)
+        DBUtil.execute(patient_encounter_merge_command)
+
+        patient_encounter_cleanup_command = \
+            """
+            DELETE FROM
+                %s_patient_encounter
+            WHERE
+                pat_enc_csn_id > 0;
+            """ % TABLE_PREFIX
+        DBUtil.execute(patient_encounter_cleanup_command)
+
+        patient_encounter_cleanup_command = \
+            """
+            UPDATE
+                %s_patient_encounter
+            SET
+                pat_enc_csn_id = -pat_enc_csn_id;
+            """ % TABLE_PREFIX
+        DBUtil.execute(patient_encounter_cleanup_command)
+
+    @staticmethod
+    def build_clean_csv_files():
+        # Build paths to clean data files.
+        raw_data_dir = StrideLoader.fetch_raw_data_dir()
+        clean_data_dir = StrideLoader.fetch_clean_data_dir()
+        for raw_file in sorted(STRIDE_LOADER_PARAMS.keys()):
+            params = STRIDE_LOADER_PARAMS[raw_file]
+            # Build clean data file path.
+            clean_file = params['clean_file'] % TABLE_PREFIX
+            raw_path = os.path.join(raw_data_dir, raw_file)
+            clean_path = os.path.join(clean_data_dir, clean_file)
+            log.debug('stride/data/[raw/%s] ==> [clean/%s]' % (raw_file, clean_file))
+            # Building the clean file is the slowest part of setup, so only
+            # do it if absolutely necessary. This means that users must be
+            # aware of issues like a stale cache.
+            if not os.path.exists(clean_path):
+                StrideLoader.build_clean_csv_file(raw_path, clean_path)
+
+    @staticmethod
+    def load_stride_to_psql():
+        # Build clean data files.
+        StrideLoader.build_clean_csv_files()
+
+        # Build psql schemata.
+        StrideLoader.build_stride_psql_schemata()
+
+        # Build paths to clean data files.
+        clean_data_dir = StrideLoader.fetch_clean_data_dir()
+        for raw_file in sorted(STRIDE_LOADER_PARAMS.keys()):
+            params = STRIDE_LOADER_PARAMS[raw_file]
+
+            # Build clean data file.
+            clean_file = params['clean_file'] % TABLE_PREFIX
+            log.info('loading %s...' % clean_file)
+            clean_path = os.path.join(clean_data_dir, clean_file)
+
+            # Uncompress data file.
+            unzipped_clean_path = clean_path[:-3]
+            with gzip.open(clean_path, 'rb') as f_in, open(unzipped_clean_path, 'wb') as f_out:
+                shutil.copyfileobj(f_in, f_out)
+
+            # psql COPY data from clean files into DB.
+            psql_table = params['psql_table'] % TABLE_PREFIX
+            log.debug('stride/data/clean/%s ==> %s' % (clean_file, psql_table))
+            # In some cases, two files going to the same table will have
+            # non-identical column names. Pass these explicitly so that
+            # psql knows which columns to try to fill from file.
+            # Strip the newline character.
+            with open(unzipped_clean_path, 'r') as f_in:
+                columns = f_in.readline()[:-1]
+            command = "COPY %s (%s) FROM '%s' WITH (FORMAT csv, HEADER);" % (psql_table, columns, unzipped_clean_path)
+            DBUtil.execute(command)
+
+            # Delete unzipped_clean_path.
+            os.remove(unzipped_clean_path)
+
+        # Run any one-off postprocessing transformations which all users
+        # of the STRIDE database should receive. Defer any application-specific
+        # transformations to other modules.
+        StrideLoader.process_stride_psql_db()
+
+        # Build indices.
+        StrideLoader.build_stride_psql_indices()
+
+    @staticmethod
+    def backup_stride_psql_tables():
+        pass
+
+if __name__=='__main__':
+    log.level = logging.DEBUG
+
+    # Define options for command-line usage.
+    usage_str = 'usage: %prog [options]\n'
+    parser = OptionParser(usage=usage_str)
+    parser.add_option('-s', '--schemata', dest='build_schemata',
+                        action='store_true', default=False,
+                        help='build STRIDE psql schemata')
+    parser.add_option('-c', '--clean', dest='clean_csv_files',
+                        action='store_true', default=False,
+                        help='clean raw CSV and build clean CSV files')
+    parser.add_option('-p', '--psql', dest='load_psql_data',
+                        action='store_true', default=False,
+                        help='load clean CSV data to psql database')
+    parser.add_option('-b', '--backup_psql', dest='backup_psql_tables',
+                        action='store_true', default=False,
+                        help='backup psql tables to dump files')
+    parser.add_option('-d', '--delete', dest='delete_stride',
+                        action='store_true', default=False,
+                        help='delete STRIDE tables')
+    (options, args) = parser.parse_args(sys.argv[1:])
+
+    # Handle command-line usage arguments.
+    if options.build_schemata:
+        StrideLoader.build_stride_psql_schemata()
+    elif options.clean_csv_files:
+        StrideLoader.build_clean_csv_files()
+    elif options.load_psql_data:
+        StrideLoader.load_stride_to_psql()
+    elif options.backup_psql_tables:
+        StrideLoader.backup_stride_psql_tables()
+    elif options.delete_stride:
+        StrideLoader.clear_stride_psql_tables()
diff --git a/setup/stride/core/StrideLoaderParams.py b/setup/stride/core/StrideLoaderParams.py
new file mode 100644
index 00000000..51788aa7
--- /dev/null
+++ b/setup/stride/core/StrideLoaderParams.py
@@ -0,0 +1,542 @@
+"""
+Parameters to be used by StrideLoader.
+
+Defines the mappings between raw_stride_file ==> clean_stride_file
+and clean_stride_file ==> psql_table. If other data is added in the future,
+(e.g. names of MySQL tables), should be added here.
+"""
+
+# For backwards compatibility with the application code throughout the
+# database which originally calls starr.
+TABLE_PREFIX = 'stride'
+
+STRIDE_LOADER_PARAMS = {
+    'Chen_Demographics.csv.gz': {
+        'clean_file': '%s_demographics_2008_2014.csv.gz',
+        'psql_table': '%s_patient'
+    },
+    'Chen_Demographics_Yr6_8.csv.gz': {
+        'clean_file': '%s_demographics_2014_2017.csv.gz',
+        'psql_table': '%s_patient'
+    },
+    'Chen_Active_Meds_At_Admit.csv.gz': {
+        'clean_file': '%s_preadmit_meds_2008_2014.csv.gz',
+        'psql_table': '%s_preadmit_med'
+    },
+    'Chen_Admit_Vitals.csv.gz': {
+        'clean_file': '%s_admit_vitals_2008_2014.csv.gz',
+        'psql_table': '%s_patient_encounter'
+    },
+    'Chen_Clinical_Notes_Yr1.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_1.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr2.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_2.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr3.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_3.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr4.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_4.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr5.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_5.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr6.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_6.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr7.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_7.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_Clinical_Notes_Yr8.csv.gz': {
+        'clean_file': '%s_clinical_notes_year_8.csv.gz',
+        'psql_table': '%s_note'
+    },
+    'Chen_DX_List_5Yr.csv.gz': {
+        'clean_file': '%s_dx_list_2008_2014.csv.gz',
+        'psql_table': '%s_dx_list'
+    },
+    'Chen_Dx_List_Yrs6_8.csv.gz': {
+        'clean_file': '%s_dx_list_2014_2017.csv.gz',
+        'psql_table': '%s_dx_list'
+    },
+    'Chen_Insurance_Info_5Yr.csv.gz': {
+        'clean_file': '%s_insurance_2008_2014.csv.gz',
+        'psql_table': '%s_patient_encounter'
+    },
+    'Chen_Insurance_Info_Yrs6_8.csv.gz': {
+        'clean_file': '%s_insurance_2014_2017.csv.gz',
+        'psql_table': '%s_patient_encounter'
+    },
+    'Chen_Mapped_Meds_5Yr.csv.gz': {
+        'clean_file': '%s_mapped_meds_2008_2014.csv.gz',
+        'psql_table': '%s_mapped_meds'
+    },
+    'Chen_Mapped_Meds_Yrs6_8.csv.gz': {
+        'clean_file': '%s_mapped_meds_2014_2017.csv.gz',
+        'psql_table': '%s_mapped_meds'
+    },
+    'Chen_MedicationID_to_MPI.csv.gz': {
+        'clean_file': '%s_medication_mpi.csv.gz',
+        'psql_table': '%s_medication_mpi'
+    },
+    'Chen_Order_MedMixInfo_5Yr.csv.gz': {
+        'clean_file': '%s_order_medmixinfo_2008_2014.csv.gz',
+        'psql_table': '%s_order_medmixinfo'
+    },
+    'Chen_MedMixInfo_Yrs6_8.patchHeader.csv.gz': {
+        'clean_file': '%s_order_medmixinfo_2014_2017.csv.gz',
+        'psql_table': '%s_order_medmixinfo'
+    },
+    'Chen_Order_Proc_Yr1.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_1.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr2.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_2.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr3.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_3.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr4.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_4.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr5.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_5.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr6.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_6.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr7.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_7.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Proc_Yr8.patchcommas.csv.gz': {
+        'clean_file': '%s_order_proc_year_8.csv.gz',
+        'psql_table': '%s_order_proc'
+    },
+    'Chen_Order_Result_Yr1.csv.gz': {
+        'clean_file': '%s_order_results_year_1.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Result_Yr2.csv.gz': {
+        'clean_file': '%s_order_results_year_2.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Result_Yr3.csv.gz': {
+        'clean_file': '%s_order_results_year_3.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Result_Yr4.csv.gz': {
+        'clean_file': '%s_order_results_year_4.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Result_Yr5.csv.gz': {
+        'clean_file': '%s_order_results_year_5.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Res_Yr6.csv.gz': {
+        'clean_file': '%s_order_results_year_6.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Res_Yr7.csv.gz': {
+        'clean_file': '%s_order_results_year_7.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_Order_Res_Yr8.csv.gz': {
+        'clean_file': '%s_order_results_year_8.csv.gz',
+        'psql_table': '%s_order_results'
+    },
+    'Chen_TreatmentTeam_Yr1.csv.gz': {
+        'clean_file': '%s_treatment_team_year_1.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_TreatmentTeam_Yr2.csv.gz': {
+        'clean_file': '%s_treatment_team_year_2.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_TreatmentTeam_Yr3.csv.gz': {
+        'clean_file': '%s_treatment_team_year_3.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_TreatmentTeam_Yr4.csv.gz': {
+        'clean_file': '%s_treatment_team_year_4.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_TreatmentTeam_Yr5.csv.gz': {
+        'clean_file': '%s_treatment_team_year_5.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_Treatment_Team_Yrs6.patchHeader.csv.gz': {
+        'clean_file': '%s_treatment_team_year_6.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_Treatment_Team_Yrs7.patchHeader.csv.gz': {
+        'clean_file': '%s_treatment_team_year_7.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_Treatment_Team_Yrs8.patchHeader.csv.gz': {
+        'clean_file': '%s_treatment_team_year_8.csv.gz',
+        'psql_table': '%s_treatment_team'
+    },
+    'Chen_income_vs_zip.csv.gz': {
+        'clean_file': '%s_income_2008_2014.csv.gz',
+        'psql_table': '%s_income'
+    },
+    'Chen_income_vs_zip_Yrs6_8.csv.gz': {
+        'clean_file': '%s_income_2014_2017.csv.gz',
+        'psql_table': '%s_income'
+    },
+    'JChenv3_BP_Table1.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_bp_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_HRate_Table2.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_hr_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Resp_Table3.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_resp_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_fio2_Table4.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_fio2_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_pulse_Table5.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_pulse_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_temp_Table6.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_temp_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_urine_Table7.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_urine_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_GCS_Table8.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_gcs_2008_2014.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_BP_Table11.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_bp_2014_2017_11.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_BP_Table12.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_bp_2014_2017_12.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_BP_Table13.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_bp_2014_2017_13.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_BP_Table14.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_bp_2014_2017_14.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_BP_Table15.patchHeader.csv.gz': {
+        'clean_file': '%s_flow_bp_2014_2017_15.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Heartrate_Table16.csv.gz': {
+        'clean_file': '%s_flow_hr_2014_2017_16.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Heartrate_Table17.csv.gz': {
+        'clean_file': '%s_flow_hr_2014_2017_17.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Heartrate_Table18.csv.gz': {
+        'clean_file': '%s_flow_hr_2014_2017_18.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Heartrate_Table19.csv.gz': {
+        'clean_file': '%s_flow_hr_2014_2017_19.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Heartrate_Table20.csv.gz': {
+        'clean_file': '%s_flow_hr_2014_2017_20.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Heartrate_Table20.csv.gz': {
+        'clean_file': '%s_flow_hr_2014_2017_20.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Resp_Table21.csv.gz': {
+        'clean_file': '%s_flow_resp_2014_2017_21.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Resp_Table22.csv.gz': {
+        'clean_file': '%s_flow_resp_2014_2017_22.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Resp_Table23.csv.gz': {
+        'clean_file': '%s_flow_resp_2014_2017_23.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Resp_Table24.csv.gz': {
+        'clean_file': '%s_flow_resp_2014_2017_24.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_Resp_Table25.csv.gz': {
+        'clean_file': '%s_flow_resp_2014_2017_25.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_temp_Table26.csv.gz': {
+        'clean_file': '%s_flow_temp_2014_2017_26.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_temp_Table27.csv.gz': {
+        'clean_file': '%s_flow_temp_2014_2017_27.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_temp_Table28.csv.gz': {
+        'clean_file': '%s_flow_temp_2014_2017_28.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_temp_Table29.csv.gz': {
+        'clean_file': '%s_flow_temp_2014_2017_29.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_temp_Table30.csv.gz': {
+        'clean_file': '%s_flow_temp_2014_2017_30.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_fio2_Table31.csv.gz': {
+        'clean_file': '%s_flow_fio2_2014_2017_31.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_fio2_Table32.csv.gz': {
+        'clean_file': '%s_flow_fio2_2014_2017_32.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_fio2_Table33.csv.gz': {
+        'clean_file': '%s_flow_fio2_2014_2017_33.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_fio2_Table34.csv.gz': {
+        'clean_file': '%s_flow_fio2_2014_2017_34.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_fio2_Table35.csv.gz': {
+        'clean_file': '%s_flow_fio2_2014_2017_35.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_pulse_Table36.csv.gz': {
+        'clean_file': '%s_flow_pulse_2014_2017_36.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_pulse_Table37.csv.gz': {
+        'clean_file': '%s_flow_pulse_2014_2017_37.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_pulse_Table38.csv.gz': {
+        'clean_file': '%s_flow_pulse_2014_2017_38.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_pulse_Table39.csv.gz': {
+        'clean_file': '%s_flow_pulse_2014_2017_39.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_pulse_Table40.csv.gz': {
+        'clean_file': '%s_flow_pulse_2014_2017_40.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_urine_Table41.csv.gz': {
+        'clean_file': '%s_flow_urine_2014_2017_41.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_urine_Table42.csv.gz': {
+        'clean_file': '%s_flow_urine_2014_2017_42.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_urine_Table43.csv.gz': {
+        'clean_file': '%s_flow_urine_2014_2017_43.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_urine_Table44.csv.gz': {
+        'clean_file': '%s_flow_urine_2014_2017_44.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_urine_Table45.csv.gz': {
+        'clean_file': '%s_flow_urine_2014_2017_45.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_GCS_Table46.csv.gz': {
+        'clean_file': '%s_flow_gcs_2014_2017_46.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_GCS_Table47.csv.gz': {
+        'clean_file': '%s_flow_gcs_2014_2017_47.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_GCS_Table48.csv.gz': {
+        'clean_file': '%s_flow_gcs_2014_2017_48.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_GCS_Table49.csv.gz': {
+        'clean_file': '%s_flow_gcs_2014_2017_49.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_GCS_Table50.csv.gz': {
+        'clean_file': '%s_flow_gcs_2014_2017_50.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_sp02_Table51.csv.gz': {
+        'clean_file': '%s_flow_spo2_2014_2017_51.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_sp02_Table52.csv.gz': {
+        'clean_file': '%s_flow_spo2_2014_2017_52.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_sp02_Table53.csv.gz': {
+        'clean_file': '%s_flow_spo2_2014_2017_53.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_sp02_Table54.csv.gz': {
+        'clean_file': '%s_flow_spo2_2014_2017_54.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChenv3_sp02_Table55.csv.gz': {
+        'clean_file': '%s_flow_spo2_2014_2017_55.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChen_Flwst_Height_Yr8.csv.gz': {
+        'clean_file': '%s_flow_height_2008_2017.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'JChen_Flwst_Weight_Yr8.csv.gz': {
+        'clean_file': '%s_flow_weight_2008_2017.csv.gz',
+        'psql_table': '%s_flowsheet'
+    },
+    'Jchen_Inpu_output_data1.csv.gz': {
+        'clean_file': '%s_io_flowsheet_1.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_data2.csv.gz': {
+        'clean_file': '%s_io_flowsheet_2.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_data3.csv.gz': {
+        'clean_file': '%s_io_flowsheet_3.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_data4.csv.gz': {
+        'clean_file': '%s_io_flowsheet_4.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_data5.csv.gz': {
+        'clean_file': '%s_io_flowsheet_5.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_11.csv.gz': {
+        'clean_file': '%s_io_flowsheet_11.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_12.csv.gz': {
+        'clean_file': '%s_io_flowsheet_12.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_13.csv.gz': {
+        'clean_file': '%s_io_flowsheet_13.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_14.csv.gz': {
+        'clean_file': '%s_io_flowsheet_14.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_15.csv.gz': {
+        'clean_file': '%s_io_flowsheet_15.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_16.csv.gz': {
+        'clean_file': '%s_io_flowsheet_16.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_17.csv.gz': {
+        'clean_file': '%s_io_flowsheet_17.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_18.csv.gz': {
+        'clean_file': '%s_io_flowsheet_18.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_19.csv.gz': {
+        'clean_file': '%s_io_flowsheet_19.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'Jchen_Inpu_output_update_20.csv.gz': {
+        'clean_file': '%s_io_flowsheet_20.csv.gz',
+        'psql_table': '%s_io_flowsheet'
+    },
+    'export_ICD-9-CM_2013.csv.gz': {
+        'clean_file': '%s_icd9_cm_2013.csv.gz',
+        'psql_table': '%s_icd9_cm'
+    },
+    'export_ICD-10-CM_2016.csv.gz': {
+        'clean_file': '%s_icd10_cm_2016.csv.gz',
+        'psql_table': '%s_icd10_cm'
+    },
+    'JChenv3_ADTTable10.csv.gz': {
+        'clean_file': '%s_adt_2008_2014.csv.gz',
+        'psql_table': '%s_adt'
+    },
+    'JChenv3_ADTTable56.csv.gz': {
+        'clean_file': '%s_adt_2014_2017.csv.gz',
+        'psql_table': '%s_adt'
+    },
+    'ChargeMaster.Stanford.2014.csv.gz': {
+        'clean_file': '%s_chargemaster_2014.csv.gz',
+        'psql_table': '%s_chargemaster'
+    },
+    'Chen_Order_Med_5Yr.patchQuotes.csv.gz': {
+        'clean_file': '%s_order_med_2008_2014.csv.gz',
+        'psql_table': '%s_order_med'
+    },
+    'Chen_Order_Med_Yrs6_8.patchHeader.csv.gz': {
+        'clean_file': '%s_order_med_2014_2017.csv.gz',
+        'psql_table': '%s_order_med'
+    },
+    'orderset_procedures.csv.gz': {
+        'clean_file': '%s_orderset_order_proc_2008_2014.csv.gz',
+        'psql_table': '%s_orderset_order_proc'
+    },
+    'Chen_OrderSets_Proc_Yrs6_8.csv.gz': {
+        'clean_file': '%s_orderset_order_proc_2014_2017.csv.gz',
+        'psql_table': '%s_orderset_order_proc'
+    },
+    'orderset_medications.csv.gz': {
+        'clean_file': '%s_orderset_order_med_2008_2014.csv.gz',
+        'psql_table': '%s_orderset_order_med'
+    },
+    'Chen_OrderSets_Med_Yrs6_8.csv.gz': {
+        'clean_file': '%s_orderset_order_med_2014_2017.csv.gz',
+        'psql_table': '%s_orderset_order_med'
+    },
+    'JChen_cult_micro_7yr.patchIds.csv.gz': {
+        'clean_file': '%s_culture_micro_7_year.csv.gz',
+        'psql_table': '%s_culture_micro'
+    },
+    'JChenv3_Admits_Table58.csv.gz': {
+        'clean_file': '%s_admit_2014_2017.csv.gz',
+        'psql_table': '%s_admit'
+    },
+    'JChenv3_DRG_Table57.csv.gz': {
+        'clean_file': '%s_drg_7_year.csv.gz',
+        'psql_table': '%s_drg'
+    }
+}
diff --git a/setup/stride/core/__init__.py b/setup/stride/core/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/setup/stride/core/test/TestStrideLoader.py b/setup/stride/core/test/TestStrideLoader.py
new file mode 100644
index 00000000..b95c6139
--- /dev/null
+++ b/setup/stride/core/test/TestStrideLoader.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python
+"""
+Test suite for respective module in application package.
+"""
+
+import os
+import unittest
+import shutil
+import gzip
+
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from LocalEnv import PATH_TO_CDSS, TEST_RUNNER_VERBOSITY
+
+from stride.core.StrideLoader import StrideLoader
+
+
+class TestStrideLoader(MedInfoTestCase):
+    def setUp(self):
+        test_dir = '/'.join([PATH_TO_CDSS, 'setup', 'stride', 'core', 'test'])
+
+        # gzip test_raw_data_file.csv
+        raw_file_path = '/'.join([test_dir, 'test_raw_data_file.csv'])
+        self.gz_raw_file_path = '.'.join([raw_file_path, 'gz'])
+        with open(raw_file_path, 'rb') as f_in, gzip.open(self.gz_raw_file_path, 'wb') as f_out:
+            shutil.copyfileobj(f_in, f_out)
+
+        # gzip test_clean_data_file.csv
+        self.clean_file_path = '/'.join([test_dir, 'test_clean_data_file.csv'])
+        self.gz_clean_file_path = '.'.join([self.clean_file_path, 'gz'])
+        self.files_to_clean = [self.gz_raw_file_path, self.gz_clean_file_path]
+
+    def tearDown(self):
+        # Delete both gzip files.
+        for file in self.files_to_clean:
+            try:
+                os.remove(file)
+            except OSError:
+                pass
+
+    def test_build_clean_data_file(self):
+        StrideLoader.build_clean_csv_file(self.gz_raw_file_path, self.gz_clean_file_path)
+        with open(self.clean_file_path, 'rt') as f_expected, gzip.open(self.gz_clean_file_path, 'rt') as f_actual:
+            content_expected = f_expected.read()
+            content_actual = f_actual.read()
+            self.assertEqual(content_expected, content_actual);
+
+if __name__=='__main__':
+    suite = make_test_suite(TestStrideLoader)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/setup/stride/core/test/__init__.py b/setup/stride/core/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/setup/stride/core/test/test_clean_data_file.csv b/setup/stride/core/test/test_clean_data_file.csv
new file mode 100644
index 00000000..4f1398f4
--- /dev/null
+++ b/setup/stride/core/test/test_clean_data_file.csv
@@ -0,0 +1,8 @@
+0_integer,1_integer,2_string,3_date,4_float,5_datetime
+-123,1950,FEMALE,10/10/2010,1.0,10/10/2010 10:10:00
+-234,1960,MALE,,2.0,
+-345,1970,FEMALE,,3.0,
+-456,1980,"DOE, JOHN",11-NOV-11,,11/11/2011 11:11:11
+,2609700,CARDIOVASCULAR,07/07/2012,5.0,
+-567,,"Asthma/COPD Therapy - Beta 2-Adrenergic Agents, Inhaled, Short Acting",06/06/2008,6.0,
+-678,40196,"URINARY TRACT ANTISPASMODIC, M(3) SELECTIVE ANTAG.",,7.0,08/18/2008 08:08:00
diff --git a/setup/stride/core/test/test_raw_data_file.csv b/setup/stride/core/test/test_raw_data_file.csv
new file mode 100644
index 00000000..1d3d301b
--- /dev/null
+++ b/setup/stride/core/test/test_raw_data_file.csv
@@ -0,0 +1,8 @@
+0_integer,1_INTEGER,"2_STRING","3_date",4_float,"5_datetime"
+-123,1950,"FEMALE",10/10/2010,1.0,10/10/2010 10:10:00
+-234,1960,"MALE",,2.0,
+"-345",1970,"FEMALE",,3.0,
+-456,1980,"DOE, JOHN",11-NOV-11,,11/11/2011 11:11:11
+,2609700,"CARDIOVASCULAR",07/07/2012,5.0,
+-567,,"Asthma/COPD Therapy - Beta 2-Adrenergic Agents, Inhaled, Short Acting",06/06/2008,6.0,
+-678,40196,"URINARY TRACT ANTISPASMODIC, M(3) SELECTIVE ANTAG.",,7.0,08/18/2008 08:08:00
diff --git a/setup/stride/psql/dump_stride.sh b/setup/stride/psql/dump_stride.sh
new file mode 100644
index 00000000..58c74e5e
--- /dev/null
+++ b/setup/stride/psql/dump_stride.sh
@@ -0,0 +1,37 @@
+#!/bin/sh -x
+# Define constants for connecting to database from which to dump tables.
+# For example, if dumping from local database named test_database and accessing
+# as user test_user, you would use the following lines:
+# export DB_HOST=localhost
+# export DB_DSN=test_database
+# export DB_USER=test_user
+export DB_HOST=database_host_name
+export DB_DSN=database_source_name
+export DB_USER=database_user_name
+
+# pg_dump automatically dumps the contents of a database to standard output.
+# Pipe these results to gzip, which writes to a compressed file.
+pg_dump -h $DB_HOST -U $DB_USER -t stride_patient_encounter $DB_DSN | gzip -c > stride_patient_encounter.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_admit $DB_DSN | gzip -c > stride_admit.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_adt $DB_DSN | gzip -c > stride_adt.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_chargemaster $DB_DSN | gzip -c > stride_chargemaster.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_culture_micro $DB_DSN | gzip -c > stride_culture_micro.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_drg $DB_DSN | gzip -c > stride_drg.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_dx_list $DB_DSN | gzip -c > stride_dx_list.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_flowsheet $DB_DSN | gzip -c > stride_flowsheet.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_icd9_cm $DB_DSN | gzip -c > stride_icd9_cm.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_icd10_cm $DB_DSN | gzip -c > stride_icd10_cm.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_income $DB_DSN | gzip -c > stride_income.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_io_flowsheet $DB_DSN | gzip -c > stride_io_flowsheet.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_mapped_meds $DB_DSN | gzip -c > stride_mapped_meds.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_medication_mpi $DB_DSN | gzip -c > stride_medication_mpi.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_note $DB_DSN | gzip -c > stride_note.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_order_med $DB_DSN | gzip -c > stride_order_med.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_order_medmixinfo $DB_DSN | gzip -c > stride_order_medmixinfo.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_order_results $DB_DSN | gzip -c > stride_order_results.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_orderset_order_med $DB_DSN | gzip -c > stride_orderset_order_med.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_orderset_order_proc $DB_DSN | gzip -c > stride_orderset_order_proc.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_patient_encounter $DB_DSN | gzip -c > stride_patient_encounter.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_patient $DB_DSN | gzip -c > stride_patient.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_preadmit_med $DB_DSN | gzip -c > stride_preadmit_med.dump.sql.gz
+pg_dump -h $DB_HOST -U $DB_USER -t stride_treatment_team $DB_DSN | gzip -c > stride_treatment_team.dump.sql.gz
diff --git a/setup/stride/psql/indices/build_indices.sh b/setup/stride/psql/indices/build_indices.sh
new file mode 100644
index 00000000..84cc587e
--- /dev/null
+++ b/setup/stride/psql/indices/build_indices.sh
@@ -0,0 +1,12 @@
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_note.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_orderset_order_proc.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_adt.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_order_med.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_patient_encouter.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_chargemaster.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_order_proc.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_patient.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_flowsheet.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_order_results.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_medication_mpi.indices.sql
+psql -U ec2-user stride-inpatient-2008-2017 -f stride_orderset_order_med.indices.sql
diff --git a/setup/stride/psql/indices/stride_adt.indices.sql b/setup/stride/psql/indices/stride_adt.indices.sql
new file mode 100644
index 00000000..6abec356
--- /dev/null
+++ b/setup/stride/psql/indices/stride_adt.indices.sql
@@ -0,0 +1,9 @@
+-- Table: stride_adt
+
+CREATE INDEX IF NOT EXISTS index_stride_adt_patient_date
+                            ON stride_adt(pat_anon_id, shifted_transf_in_dt_tm,
+                                          shifted_transf_out_dt_tm);
+CREATE INDEX IF NOT EXISTS index_stride_adt_department
+                            ON stride_adt(department_in, event_in, event_out,
+                                          shifted_transf_in_dt_tm,
+                                          shifted_transf_out_dt_tm);
diff --git a/setup/stride/psql/indices/stride_chargemaster.indices.sql b/setup/stride/psql/indices/stride_chargemaster.indices.sql
new file mode 100644
index 00000000..743b1145
--- /dev/null
+++ b/setup/stride/psql/indices/stride_chargemaster.indices.sql
@@ -0,0 +1,4 @@
+-- Table: stride_chargemaster
+
+CREATE INDEX IF NOT EXISTS index_stride_chargemaster_service_code
+                            ON stride_chargemaster(service_code);
diff --git a/setup/stride/psql/indices/stride_flowsheet.indices.sql b/setup/stride/psql/indices/stride_flowsheet.indices.sql
new file mode 100644
index 00000000..d75fa3de
--- /dev/null
+++ b/setup/stride/psql/indices/stride_flowsheet.indices.sql
@@ -0,0 +1,10 @@
+-- Table: stride_flowsheet
+
+CREATE INDEX IF NOT EXISTS index_stride_flowsheet_pat_enc
+                            ON stride_flowsheet(pat_anon_id,pat_enc_csn_anon_id);
+CREATE INDEX IF NOT EXISTS index_stride_flowsheet_measure
+                            ON stride_flowsheet(flo_meas_id,flowsheet_name);
+CREATE INDEX IF NOT EXISTS index_stride_flowsheet_pat_anon_id
+                            ON stride_flowsheet(pat_anon_id);
+CREATE INDEX IF NOT EXISTS index_stride_flowsheet_flowsheet_name
+                            ON stride_flowsheet(flowsheet_name);
diff --git a/setup/stride/psql/indices/stride_medication_mpi.indices.sql b/setup/stride/psql/indices/stride_medication_mpi.indices.sql
new file mode 100644
index 00000000..cdcaeb00
--- /dev/null
+++ b/setup/stride/psql/indices/stride_medication_mpi.indices.sql
@@ -0,0 +1,8 @@
+-- Table: stride_medication_mpi
+
+CREATE INDEX IF NOT EXISTS index_stride_medication_mpi_medication_id
+                            ON stride_medication_mpi(medication_id,mpi_id_val);
+CREATE INDEX IF NOT EXISTS index_stride_medication_mpi_med_name
+                            ON stride_medication_mpi(med_name);
+CREATE INDEX IF NOT EXISTS index_stride_medication_mpi_mpi_id
+                            ON stride_medication_mpi(mpi_id_val,medication_id);
diff --git a/setup/stride/psql/indices/stride_note.indices.sql b/setup/stride/psql/indices/stride_note.indices.sql
new file mode 100644
index 00000000..4fedf1ba
--- /dev/null
+++ b/setup/stride/psql/indices/stride_note.indices.sql
@@ -0,0 +1,8 @@
+-- Table: stride_note
+
+CREATE INDEX IF NOT EXISTS index_stride_note_pat_enc
+                            ON stride_note(pat_id,pat_enc_csn_id);
+CREATE INDEX IF NOT EXISTS index_stride_note_provider_type
+                            ON stride_note(provider_type);
+CREATE INDEX IF NOT EXISTS index_stride_note_service_type
+                            ON stride_note(hospital_service,note_type);
diff --git a/setup/stride/psql/indices/stride_order_med.indices.sql b/setup/stride/psql/indices/stride_order_med.indices.sql
new file mode 100644
index 00000000..d4342789
--- /dev/null
+++ b/setup/stride/psql/indices/stride_order_med.indices.sql
@@ -0,0 +1,4 @@
+-- Table: stride_order_med
+
+CREATE INDEX IF NOT EXISTS index_stride_order_med_pat_id
+                            ON stride_order_med(pat_id);
diff --git a/setup/stride/psql/indices/stride_order_proc.indices.sql b/setup/stride/psql/indices/stride_order_proc.indices.sql
new file mode 100644
index 00000000..d5794b72
--- /dev/null
+++ b/setup/stride/psql/indices/stride_order_proc.indices.sql
@@ -0,0 +1,12 @@
+-- Table: stride_order_proc
+
+CREATE INDEX IF NOT EXISTS index_stride_order_proc_pat_id_bigint
+                            ON stride_order_proc(CAST(pat_id AS BIGINT));
+CREATE INDEX IF NOT EXISTS index_stride_order_proc_pat_id_str
+                            ON stride_order_proc(pat_id);
+CREATE INDEX IF NOT EXISTS index_stride_order_proc_order_proc_id
+                            ON stride_order_proc(order_proc_id);
+CREATE INDEX IF NOT EXISTS index_stride_order_proc_proc_code
+                            ON stride_order_proc(proc_code);
+CREATE INDEX IF NOT EXISTS index_stride_order_proc_order_proc_id_proc_code
+                            ON stride_order_proc(proc_code, order_proc_id);
diff --git a/setup/stride/psql/indices/stride_order_results.indices.sql b/setup/stride/psql/indices/stride_order_results.indices.sql
new file mode 100644
index 00000000..7432aaeb
--- /dev/null
+++ b/setup/stride/psql/indices/stride_order_results.indices.sql
@@ -0,0 +1,6 @@
+-- Table: stride_order_results
+
+CREATE INDEX IF NOT EXISTS index_stride_order_results_order_proc_id
+                            ON stride_order_results(order_proc_id);
+CREATE INDEX IF NOT EXISTS index_stride_order_results_base_name
+                            ON stride_order_results(base_name);
diff --git a/setup/stride/psql/indices/stride_orderset_order_med.indices.sql b/setup/stride/psql/indices/stride_orderset_order_med.indices.sql
new file mode 100644
index 00000000..de05bdde
--- /dev/null
+++ b/setup/stride/psql/indices/stride_orderset_order_med.indices.sql
@@ -0,0 +1,4 @@
+-- Table: stride_orderset_order_med
+
+CREATE INDEX IF NOT EXISTS index_stride_orderset_order_med_order_med_id
+                            ON stride_orderset_order_med(order_med_id);
diff --git a/setup/stride/psql/indices/stride_orderset_order_proc.indices.sql b/setup/stride/psql/indices/stride_orderset_order_proc.indices.sql
new file mode 100644
index 00000000..12524fdc
--- /dev/null
+++ b/setup/stride/psql/indices/stride_orderset_order_proc.indices.sql
@@ -0,0 +1,4 @@
+-- Table: stride_orderset_order_proc
+
+CREATE INDEX IF NOT EXISTS index_stride_orderset_order_proc_order_proc_id
+                            ON stride_orderset_order_proc(order_proc_id);
diff --git a/setup/stride/psql/indices/stride_patient.indices.sql b/setup/stride/psql/indices/stride_patient.indices.sql
new file mode 100644
index 00000000..ae4572e6
--- /dev/null
+++ b/setup/stride/psql/indices/stride_patient.indices.sql
@@ -0,0 +1,4 @@
+-- Table: stride_patient
+
+CREATE INDEX IF NOT EXISTS index_stride_patient_pat_id
+                            ON stride_patient(SUBSTRING(pat_id, 1, 16));
diff --git a/setup/stride/psql/indices/stride_patient_encounter.indices.sql b/setup/stride/psql/indices/stride_patient_encounter.indices.sql
new file mode 100644
index 00000000..77a6c435
--- /dev/null
+++ b/setup/stride/psql/indices/stride_patient_encounter.indices.sql
@@ -0,0 +1,4 @@
+-- Table: stride_patient_encounter
+
+CREATE INDEX IF NOT EXISTS index_stride_patient_encounter_encounter_id
+                            ON stride_patient_encounter(pat_enc_csn_id);
diff --git a/setup/stride/psql/restore_stride.sh b/setup/stride/psql/restore_stride.sh
new file mode 100644
index 00000000..d6e99eaa
--- /dev/null
+++ b/setup/stride/psql/restore_stride.sh
@@ -0,0 +1,35 @@
+#!/bin/sh -x
+# Define constants for connecting to database from which to dump tables.
+# For example, if dumping from local database named test_database and accessing
+# as user test_user, you would use the following lines:
+# export DB_HOST=localhost
+# export DB_DSN=test_database
+# export DB_USER=test_user
+export DB_HOST=database_host_name
+export DB_DSN=database_source_name
+export DB_USER=database_user_name
+
+gzip -d -c stride_admit.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_adt.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_chargemaster.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_culture_micro.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_drg.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_dx_list.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_flowsheet.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_icd9_cm.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_icd10_cm.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_income.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_io_flowsheet.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_mapped_meds.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_medication_mpi.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_note.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_order_med.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_order_medmixinfo.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_order_proc.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_order_results.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_orderset_order_med.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_orderset_order_proc.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_patient_encounter.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_patient.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_preadmit_med.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
+gzip -d -c stride_treatment_team.dump.sql.gz | psql -h $DB_HOST -U $DB_USER $DB_DSN
diff --git a/setup/stride/psql/schemata/stride_admit.schema.sql b/setup/stride/psql/schemata/stride_admit.schema.sql
new file mode 100644
index 00000000..bdb83bf1
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_admit.schema.sql
@@ -0,0 +1,29 @@
+-- Table: stride_admit
+-- Description: Admission summary data.
+-- Raw Files:
+--  * JChenv3_Admits_Table58.csv.gz
+-- Clean Files:
+--  * stride_admit_2014_2017.csv.gz
+-- CSV Fields:
+--  * PAT_ENC_CSN_ANON_ID (e.g. 885413)
+--  * PAT_ANON_ID (e.g. 7046823672303)
+--  * HSP_ACC_DEID (e.g. 54767284)
+--  * SHIFTED_DEATH_DATE (e.g. 02-AUG-2015)
+--  * ALIVE_DEAD (e.g. "Deceased")
+--  * SHIFTED_ADMIT_DT_TM (e.g. 11-NOV-2015 19:00)
+--  * SHIFTED_DISCH_DT_TM (e.g. 05-OCT-2014 12:40)
+--  * LOS_DAYS (length of stay in days e.g. 2.08)
+--  * DISCH_DEST (discharge destination e.g. "Home")
+
+CREATE TABLE IF NOT EXISTS stride_admit
+(
+  pat_enc_csn_anon_id BIGINT,
+  pat_anon_id BIGINT,
+  hsp_acc_deid BIGINT,
+  shifted_death_date TIMESTAMP,
+  alive_dead TEXT,
+  shifted_admit_dt_tm TIMESTAMP,
+  shifted_disch_dt_tm TIMESTAMP,
+  los_days FLOAT,
+  disch_dest TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_admit_vital.schema.sql b/setup/stride/psql/schemata/stride_admit_vital.schema.sql
new file mode 100644
index 00000000..705d78cb
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_admit_vital.schema.sql
@@ -0,0 +1,25 @@
+-- Table: stride_admit_vitals
+-- Description: Vitals measured for pat_anon_id at time of admission.
+-- Raw Files:
+--  * Chen_Active_Vitals.csv.gz
+-- Clean Files:
+--  * stride_admit_vitals.csv.gz
+-- CSV Fields:
+--  * "PAT_ID" (e.g. 11577528280870)
+--  * "PAT_ENC_CSN_ID" (e.g. 431388)
+--  * "BP_SYSTOLIC" (e.g. 120)
+--  * "BP_DIASTOLIC" (e.g. 80)
+--  * "TEMPERATURE" (e.g. 98.6)
+--  * "PULSE" (e.g. 100)
+--  * "RESPIRATIONS" (e.g. 15)
+
+CREATE TABLE IF NOT EXISTS stride_admit_vital
+(
+  pat_id BIGINT,
+  pat_enc_csn_id BIGINT,
+  bp_systolic INTEGER,
+  bp_diastolic INTEGER,
+  temperature FLOAT,
+  pulse INTEGER,
+  RESPIRATIONS INTEGER
+);
diff --git a/setup/stride/psql/schemata/stride_adt.schema.sql b/setup/stride/psql/schemata/stride_adt.schema.sql
new file mode 100644
index 00000000..28942be9
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_adt.schema.sql
@@ -0,0 +1,39 @@
+-- Table: stride_adt
+-- Description: Admission, Discharge, and Transfer (ADT) for patient pat_anon_id.
+--  Each row is modeled as a time window in which a patient was in place X,
+--  rather than the discrete event  of each ADT.
+-- Original Files:
+--	* JChenv3_ADTTable10.csv.gz
+--  * JChenv3_ADTTable56.csv.gz
+-- Clean Files:
+--	* stride_adt_2008_2014.csv.gz
+--  * stride_adt_2014_2017.csv.gz
+-- CSV Fields:
+--  * PAT_ANON_ID (e.g. 4242249324038)
+--  * PAT_ENC_CSN_ANON_ID (e.g. 451502)
+--  * SEQ_NUM_IN_BED_MIN (e.g. 3)
+--  * SEQ_NUM_IN_ENC (e.g. 5)
+--  * PATIENT_CLASS (e.g. "OP Surgery/Procedure")
+--  * BED (e.g. "OVRFLW")
+--  * BED_STATUS (e.g. "ACTIVE")
+--  * SHIFTED_TRANSF_IN_DT_TM (e.g. "17-MAR-13 16:35")
+--  * DEPARTMENT_IN (e.g. "MOR INTRA-OP")
+--  * EVENT_IN (e.g. "Admission")
+--  * SHIFTED_TRANSF_OUT_DT_TM (e.g. "19-APR-12 17:00")
+--  * EVENT_OUT (e.g. "Discharge")
+
+CREATE TABLE IF NOT EXISTS stride_adt
+(
+  pat_anon_id BIGINT,
+  pat_enc_csn_anon_id BIGINT,
+  seq_num_in_bed_min INTEGER,
+  seq_num_in_enc INTEGER,
+  patient_class TEXT,
+  bed TEXT,
+  bed_status TEXT,
+  shifted_transf_in_dt_tm TIMESTAMP,
+  department_in TEXT,
+  event_in TEXT,
+  shifted_transf_out_dt_tm TIMESTAMP,
+  event_out TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_chargemaster.schema.sql b/setup/stride/psql/schemata/stride_chargemaster.schema.sql
new file mode 100644
index 00000000..d0cc91d2
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_chargemaster.schema.sql
@@ -0,0 +1,18 @@
+-- Table: stride_chargemaster
+-- Description: Charges in Stanford hospital (not price/cost).
+--              Publicly available at http://www.oshpd.ca.gov/chargemaster/
+-- Raw Files:
+--  * ChargeMaster.Stanford.2014.csv.gz
+-- Clean Files:
+--  * stride_chargemaster_2014.csv.gz
+-- CSV Fields:
+--  * service_code (e.g. 33178377)
+--  * description (e.g. "HC TRANSFUSION BLOOD-1 HR")
+--  * price (e.g. 2125.00)
+
+CREATE TABLE IF NOT EXISTS stride_chargemaster
+(
+	service_code INTEGER,
+	description TEXT,
+	price FLOAT
+);
diff --git a/setup/stride/psql/schemata/stride_culture_micro.schema.sql b/setup/stride/psql/schemata/stride_culture_micro.schema.sql
new file mode 100644
index 00000000..a3800817
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_culture_micro.schema.sql
@@ -0,0 +1,52 @@
+-- Table: stride_culture_micro
+-- Description: Orders and detailed results for blood culture labs.
+-- Raw Files:
+--  * JChen_cult_micro_7yr.patchIds.csv.gz
+-- Clean Files:
+--  * stride_culture_micro_7_year.csv.gz
+-- CSV Fields:
+--  * ORDER_PROC_ANON_ID (e.g. 354125129)
+--  * PAT_ANON_ID (e.g. 3512885273857)
+--  * PAT_ENC_CSN_ANON_ID (e.g. 863055)
+--  * PROC_CODE (e.g. LABFLDB)
+--  * DESCRIPTION (e.g. STOOL CULTURE)
+--  * ORGANISM_ID (e.g. 433)
+--  * ORGANISM_NAME (e.g. STREPTOCOCCUS PNEUMONIAE)
+--  * ORGANISM_GROUP_C (e.g. 123061008)
+--  * ORGANISM_GROUP (e.g. Yeast)
+--  * ANTIBIOTIC_C (e.g. 313)
+--  * ANTIBIOTIC_NAME (e.g. Ceftriaxone)
+--  * ORD_SENSITIV_LINE (e.g. 22)
+--  * SENSITIVITY_VALUE (e.g. >=64)
+--  * SENSITIVITY_UNITS (e.g. "")
+--  * SENS_REF_RANGE (e.g. "")
+--  * SHIFTED_ORDER_TIME (e.g. 12/10/2011 14:44)
+--  * SHIFTED_SPECIMN_TAKEN_TIME (e.g. 8/30/2011 21:10)
+--  * SHIFTED_RESULT_TIME (e.g. 12/6/2009 15:22)
+--  * LAB_STATUS_C (e.g. 3)
+--  * LAB_STATUS (e.g. Final result)
+
+CREATE TABLE IF NOT EXISTS stride_culture_micro
+(
+  order_proc_anon_id BIGINT,
+  pat_anon_id BIGINT,
+  pat_enc_csn_anon_id BIGINT,
+  proc_code TEXT,
+  description TEXT,
+  organism_id BIGINT,
+  organism_name TEXT,
+  organism_group_c BIGINT,
+  organism_group TEXT,
+  antibiotic_c BIGINT,
+  antibiotic_name TEXT,
+  ord_sensitiv_line INTEGER,
+  sensitivity_value TEXT,
+  sensitivity_units TEXT,
+  suseptibility TEXT,
+  sens_ref_range TEXT,
+  shifted_order_time TEXT,
+  shifted_specimn_taken_time TEXT,
+  shifted_result_time TEXT,
+  lab_status_c BIGINT,
+  lab_status TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_drg.schema.sql b/setup/stride/psql/schemata/stride_drg.schema.sql
new file mode 100644
index 00000000..07e4b8f8
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_drg.schema.sql
@@ -0,0 +1,37 @@
+-- Table: stride_drg
+-- Description: Discharge Diagnosis Related Group (DRG) summary data.
+-- Raw Files:
+--  * JChenv3_DRG_Table57.csv.gz
+-- Clean Files:
+--  * stride_drg.csv.gz
+-- CSV Fields:
+--  * HSP_ACC_DEID (e.g. 57779988)
+--  * HSP_ACCOUNT_ID (e.g. 57730295)
+--  * PAT_ENC_CSN_ANON_ID (e.g. 851217)
+--  * PAT_ANON_ID (e.g. 5212716573247)
+--  * DRG_MPI_CODE (e.g. 455)
+--  * ENC_MS_DRG_WEIGHT (e.g. 1.00)
+--  * ENC_MS_DRG_DESC (e.g. MAJOR SMALL & LARGE BOWEL PROCEDURES W CC)
+--  * ENC_PRIMARY_ICD9_ICD10 (e.g. M48.06)
+--  * ENC_PRIMARY_ICD_DESC (e.g. Malignant neoplasm of prostate)
+--  * DRG_WEIGHT (e.g. 2.5822)
+--  * CODE_TYPE (e.g. ICD-10-PCS)
+--  * ENC_BILL_CODE_PRIM_SURG (e.g. 8E09XBH)
+--  * ENC_DESC_PRIM_SURG (e.g. CORONAR ARTERIOGR-2 CATH)
+
+CREATE TABLE IF NOT EXISTS stride_drg
+(
+  hsp_acc_deid BIGINT,
+	hsp_account_id BIGINT,
+	pat_enc_csn_anon_id BIGINT,
+	pat_anon_id BIGINT,
+	drg_mpi_code INTEGER,
+	enc_ms_drg_weight FLOAT,
+	enc_ms_drg_desc TEXT,
+	enc_primary_icd9_icd10 TEXT,
+	enc_primary_icd_desc TEXT,
+	drg_weight FLOAT,
+	code_type TEXT,
+	enc_bill_code_prim_surg TEXT,
+	enc_desc_prim_surg TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_dx_list.schema.sql b/setup/stride/psql/schemata/stride_dx_list.schema.sql
new file mode 100644
index 00000000..3d2b6afa
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_dx_list.schema.sql
@@ -0,0 +1,29 @@
+-- Table: stride_dx_list
+-- Description: Diagnoses for patient pat_id in encounter pat_enc_csn_id.
+-- Original Files:
+--	* Chen_DX_List_5Yr.csv.gz
+--  * Chen_Dx_List_Yrs6_8.csv.gz
+-- Clean Files:
+-- 	* stride_dx_2008_2014.csv.gz
+--  * stride_dx_2014_2017.csv.gz
+-- CSV Fields:
+--	* pat_id (e.g. "11577528280870")
+--  * pat_enc_csn_id (e.g. 431388)
+--  * noted_date (e.g. 01-JAN-08)
+--  * resolved_date (e.g. 10-OCT-10)
+--  * dx_icd9_code (e.g. "585.3") (stride_dx_2008_2014.csv.gz only)
+--  * dx_icd9_code_list (e.g. "564.09, E980.5")
+--  * dx_icd10_code_list (e.g. "D70.9, R50.81") (stride_dx_2014_2017.csv.gz only)
+--  * data_source (e.g. "PROBLEM_LIST")
+
+CREATE TABLE IF NOT EXISTS stride_dx_list
+(
+  pat_id BIGINT,
+  pat_enc_csn_id BIGINT,
+  noted_date TIMESTAMP,
+  resolved_date TIMESTAMP,
+  dx_icd9_code TEXT,
+  dx_icd9_code_list TEXT,
+  dx_icd10_code_list TEXT,
+  data_source TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_flowsheet.schema.sql b/setup/stride/psql/schemata/stride_flowsheet.schema.sql
new file mode 100644
index 00000000..40c11d5d
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_flowsheet.schema.sql
@@ -0,0 +1,138 @@
+-- Table: stride_flowsheet
+-- Description: Flow sheet of vitals measurements and readings. Includes
+--  blood pressure, pulse, respiration rate, weight, height, temperature,
+--  fraction of inspired oxygen (fiO2), Glasgow coma scale score, urine flow,
+--  and oxygen saturation (Sp02).
+-- Original Files:
+--          (2008 – 2014)
+--  * JChenv3_BP_Table1.namepatch.csv.gz
+--  * JChenv3_HRate_Table2.csv.gz
+--  * JChenv3_Resp_Table3.csv.gz
+--  * JChenv3_fio2_Table4.csv.gz
+--  * JChenv3_pulse_Table5.csv.gz
+--  * JChenv3_temp_Table6.csv.gz
+--  * JChenv3_urine_Table7.csv.gz
+--  * JChenv3_GCS_Table8.csv.gz
+--          (2014 – 2017)
+--  * JChenv3_BP_Table11.csv.gz
+--  * JChenv3_BP_Table12.csv.gz
+--  * JChenv3_BP_Table13.csv.gz
+--  * JChenv3_BP_Table14.csv.gz
+--  * JChenv3_BP_Table15.csv.gz
+--  * JChenv3_Heartrate_Table16.csv.gz
+--  * JChenv3_Heartrate_Table17.csv.gz
+--  * JChenv3_Heartrate_Table18.csv.gz
+--  * JChenv3_Heartrate_Table19.csv.gz
+--  * JChenv3_Heartrate_Table20.csv.gz
+--  * JChenv3_Resp_Table21.csv.gz
+--  * JChenv3_Resp_Table22.csv.gz
+--  * JChenv3_Resp_Table23.csv.gz
+--  * JChenv3_Resp_Table24.csv.gz
+--  * JChenv3_Resp_Table25.csv.gz
+--  * JChenv3_temp_Table26.csv.gz
+--  * JChenv3_temp_Table27.csv.gz
+--  * JChenv3_temp_Table28.csv.gz
+--  * JChenv3_temp_Table29.csv.gz
+--  * JChenv3_temp_Table30.csv.gz
+--  * JChenv3_fio2_Table31.csv.gz
+--  * JChenv3_fio2_Table32.csv.gz
+--  * JChenv3_fio2_Table33.csv.gz
+--  * JChenv3_fio2_Table34.csv.gz
+--  * JChenv3_fio2_Table35.csv.gz
+--  * JChenv3_pulse_Table36.csv.gz
+--  * JChenv3_pulse_Table37.csv.gz
+--  * JChenv3_pulse_Table38.csv.gz
+--  * JChenv3_pulse_Table39.csv.gz
+--  * JChenv3_pulse_Table40.csv.gz
+--  * JChenv3_urine_Table41.csv.gz
+--  * JChenv3_urine_Table42.csv.gz
+--  * JChenv3_urine_Table43.csv.gz
+--  * JChenv3_urine_Table44.csv.gz
+--  * JChenv3_urine_Table45.csv.gz
+--  * JChenv3_GCS_Table46.csv.gz
+--  * JChenv3_GCS_Table47.csv.gz
+--  * JChenv3_GCS_Table48.csv.gz
+--  * JChenv3_GCS_Table49.csv.gz
+--  * JChenv3_GCS_Table50.csv.gz
+--  * JChenv3_sp02_Table51.csv.gz
+--  * JChenv3_sp02_Table52.csv.gz
+--  * JChenv3_sp02_Table53.csv.gz
+--  * JChenv3_sp02_Table54.csv.gz
+--  * JChenv3_sp02_Table55.csv.gz
+--  * JChen_Flwst_Height_Yr8.csv.gz
+--  * JChen_Flwst_Weight_Yr8.csv.gz
+-- Clean Files:
+--          (2008 – 2014)
+--  * stride_flow_bp_2008_2014.csv.gz
+--  * stride_flow_hr_2008_2014.csv.gz
+--  * stride_flow_resp_2008_2014.csv.gz
+--  * stride_flow_fio2_2008_2014.csv.gz
+--  * stride_flow_pulse_2008_2014.csv.gz
+--  * stride_flow_temp_2008_2014.csv.gz
+--  * stride_flow_urine_2008_2014.csv.gz
+--  * stride_flow_gcs_2008_2014.csv.gz
+--          (2014 – 2017)
+--  * stride_flow_bp_2014_2017_11.csv.gz
+--  * stride_flow_bp_2014_2017_12.csv.gz
+--  * stride_flow_bp_2014_2017_13.csv.gz
+--  * stride_flow_bp_2014_2017_14.csv.gz
+--  * stride_flow_bp_2014_2017_15.csv.gz
+--  * stride_flow_hr_2014_2017_16.csv.gz
+--  * stride_flow_hr_2014_2017_17.csv.gz
+--  * stride_flow_hr_2014_2017_18.csv.gz
+--  * stride_flow_hr_2014_2017_19.csv.gz
+--  * stride_flow_hr_2014_2017_20.csv.gz
+--  * stride_flow_resp_2014_2017_21.csv.gz
+--  * stride_flow_resp_2014_2017_22.csv.gz
+--  * stride_flow_resp_2014_2017_23.csv.gz
+--  * stride_flow_resp_2014_2017_24.csv.gz
+--  * stride_flow_resp_2014_2017_25.csv.gz
+--  * stride_flow_temp_2014_2017_26.csv.gz
+--  * stride_flow_temp_2014_2017_27.csv.gz
+--  * stride_flow_temp_2014_2017_28.csv.gz
+--  * stride_flow_temp_2014_2017_29.csv.gz
+--  * stride_flow_temp_2014_2017_30.csv.gz
+--  * stride_flow_fio2_2014_2017_31.csv.gz
+--  * stride_flow_fio2_2014_2017_32.csv.gz
+--  * stride_flow_fio2_2014_2017_33.csv.gz
+--  * stride_flow_fio2_2014_2017_34.csv.gz
+--  * stride_flow_fio2_2014_2017_35.csv.gz
+--  * stride_flow_pulse_2014_2017_36.csv.gz
+--  * stride_flow_pulse_2014_2017_37.csv.gz
+--  * stride_flow_pulse_2014_2017_38.csv.gz
+--  * stride_flow_pulse_2014_2017_39.csv.gz
+--  * stride_flow_pulse_2014_2017_40.csv.gz
+--  * stride_flow_urine_2014_2017_41.csv.gz
+--  * stride_flow_urine_2014_2017_42.csv.gz
+--  * stride_flow_urine_2014_2017_43.csv.gz
+--  * stride_flow_urine_2014_2017_44.csv.gz
+--  * stride_flow_urine_2014_2017_45.csv.gz
+--  * stride_flow_gcs_2014_2017_46.csv.gz
+--  * stride_flow_gcs_2014_2017_47.csv.gz
+--  * stride_flow_gcs_2014_2017_48.csv.gz
+--  * stride_flow_gcs_2014_2017_49.csv.gz
+--  * stride_flow_gcs_2014_2017_50.csv.gz
+--  * stride_flow_spo2_2014_2017_51.csv.gz
+--  * stride_flow_spo2_2014_2017_52.csv.gz
+--  * stride_flow_spo2_2014_2017_53.csv.gz
+--  * stride_flow_spo2_2014_2017_54.csv.gz
+--  * stride_flow_spo2_2014_2017_55.csv.gz
+--  * stride_flow_height_2008_2017.csv.gz
+--  * stride_flow_weight_2008_2017.csv.gz
+-- CSV Fields:
+--  * PAT_ENC_CSN_ANON_ID (e.g. 880475)
+--  * PAT_ANON_ID (e.g. -12381504547935)
+--  * FLO_MEAS_ID (e.g. 11)
+--  * FLOWSHEET_NAME (text label for FLO_MEAS_ID, e.g. 'Height_cm' for 11)
+--  * FLOWSHEET_VALUE (e.g. 162.56)
+--  * SHIFTED_DT_TM (e.g. "10/30/2013 11:00")
+
+CREATE TABLE IF NOT EXISTS stride_flowsheet
+(
+  pat_enc_csn_anon_id BIGINT,
+  pat_anon_id BIGINT,
+  flo_meas_id INTEGER,
+  flowsheet_name TEXT,
+  flowsheet_value FLOAT,
+  shifted_dt_tm TIMESTAMP
+);
diff --git a/setup/stride/psql/schemata/stride_icd10_cm.schema.sql b/setup/stride/psql/schemata/stride_icd10_cm.schema.sql
new file mode 100644
index 00000000..97e28546
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_icd10_cm.schema.sql
@@ -0,0 +1,24 @@
+-- Table: stride_icd10_cm
+-- Description: ICD-10 CM (International Classification of Diseases, Ninth
+--              Revision, Clinical Modification)
+-- Original Files:
+--  * export_ICD-10-CM_2016.csv.gz
+-- Clean Files:
+--  * stride_icd10_cm_2016.csv.gz
+-- CSV Fields:
+--  * ORDER_NUMBER (e.g. 76464)
+--  * ICD10 (e.g. M60849)
+--  * HIPAA_COVERED (e.g. 1)
+--  * SHORT_DESCRIPTION (e.g. "Acute lymphangitis of trunk, unspecified")
+--  * FULL_DESCRIPTION (e.g. "Varicella pneumonia")
+--  * ICD10_CODE (e.g. S72.355H)
+
+CREATE TABLE IF NOT EXISTS stride_icd10_cm
+(
+  order_number BIGINT,
+	icd10 TEXT,
+	hipaa_covered INTEGER,
+	short_description TEXT,
+	full_description TEXT,
+	icd10_code TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_icd9_cm.schema.sql b/setup/stride/psql/schemata/stride_icd9_cm.schema.sql
new file mode 100644
index 00000000..3811e765
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_icd9_cm.schema.sql
@@ -0,0 +1,26 @@
+-- Table: stride_icd9_cm
+-- Description: ICD-9 CM (International Classification of Diseases, Ninth
+--              Revision, Clinical Modification)
+-- Original Files:
+--  * export_ICD-9-CM_2013.csv.gz
+-- Clean Files:
+--  * stride_icd9_cm_2013.csv.gz
+-- CSV Fields:
+--  * CUI (concept unique identifier, e.g. C0005684)
+--  * ISPREF (is preferred label for disease, e.g. Y)
+--  * AUI (atom unique identifier, e.g. A8343293)
+--  * TTY (term type, e.g. PT = preferred term, HT = hierarchical term)
+--  * CODE (e.g. 010.86)
+--  * STR (e.g. "2nd deg burn upper arm")
+--  * SUPPRESS (e.g. Y)
+
+CREATE TABLE IF NOT EXISTS stride_icd9_cm
+(
+  cui TEXT,
+  ispref TEXT,
+  aui TEXT,
+  tty TEXT,
+  code TEXT,
+  str TEXT,
+  suppress TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_income.schema.sql b/setup/stride/psql/schemata/stride_income.schema.sql
new file mode 100644
index 00000000..1bbaf1e7
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_income.schema.sql
@@ -0,0 +1,17 @@
+-- Table: stride_income
+-- Description: Income range for patient pat_id.
+-- Original Files:
+--	* Chen_income_vs_zip.csv.gz
+--  * Chen_income_vs_zip_Yrs6_8.csv.gz
+-- Clean Files:
+-- 	* stride_income_2008_2014.csv.gz
+--  * stride_income_2014_2017.csv.gz
+-- CSV Fields:
+--	* PAT_ID (e.g. "11577528280870")
+--  * INCOME_RANGE (e.g. "32001-36000")
+
+CREATE TABLE IF NOT EXISTS stride_income
+(
+  PAT_ID BIGINT,
+  INCOME_RANGE TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_insurance.schema.sql b/setup/stride/psql/schemata/stride_insurance.schema.sql
new file mode 100644
index 00000000..f01fcb93
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_insurance.schema.sql
@@ -0,0 +1,21 @@
+-- Table: stride_insurance
+-- Description: Payor for patient PAT_ID on encounter PAT_ENC_CSN_ID.
+-- Original Files:
+--	* Chen_Insurance_Info_5Yr.csv.gz
+--  * Chen_Insurance_Info_Yrs6_8.csv.gz
+-- Clean Files:
+-- 	* stride_insurance_2008_2014.csv.gz
+--  * stride_insurance_2014_2017.csv.gz
+-- CSV Fields:
+--  * PAT_ID (e.g. 11577528280870)
+--  * PAT_ENC_CSN_ID (e.g. 431388)
+--  * PAYOR_NAME (e.g. "BLUE SHIELD")
+--  * TITLE (e.g. "MANAGED CARE")
+
+CREATE TABLE IF NOT EXISTS stride_insurance
+(
+  pat_id BIGINT,
+  pat_enc_csn_id BIGINT,
+  payor_name TEXT,
+  title TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_io_flowsheet.schema.sql b/setup/stride/psql/schemata/stride_io_flowsheet.schema.sql
new file mode 100644
index 00000000..933bef21
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_io_flowsheet.schema.sql
@@ -0,0 +1,59 @@
+-- Table: stride_io_flowsheet
+-- Description: Flow sheet of intake (feeding) and output (urine/bowel mvmt).
+-- Original Files:
+--            2008 – 2014
+--  * Jchen_Inpu_output_data1.csv.gz
+--  * Jchen_Inpu_output_data2.csv.gz
+--  * Jchen_Inpu_output_data3.csv.gz
+--  * Jchen_Inpu_output_data4.csv.gz
+--  * Jchen_Inpu_output_data5.csv.gz
+--            2014 – 2017
+--  * Jchen_Inpu_output_update_11.csv.gz
+--  * Jchen_Inpu_output_update_12.csv.gz
+--  * Jchen_Inpu_output_update_13.csv.gz
+--  * Jchen_Inpu_output_update_14.csv.gz
+--  * Jchen_Inpu_output_update_15.csv.gz
+--  * Jchen_Inpu_output_update_16.csv.gz
+--  * Jchen_Inpu_output_update_17.csv.gz
+--  * Jchen_Inpu_output_update_18.csv.gz
+--  * Jchen_Inpu_output_update_19.csv.gz
+--  * Jchen_Inpu_output_update_20.csv.gz
+-- Clean Files:
+--          2008 – 2014
+--  * stride_io_flowsheet_1.csv.gz
+--  * stride_io_flowsheet_2.csv.gz
+--  * stride_io_flowsheet_3.csv.gz
+--  * stride_io_flowsheet_4.csv.gz
+--  * stride_io_flowsheet_5.csv.gz
+--          2014 – 2017
+--  * stride_io_flowsheet_11.csv.gz
+--  * stride_io_flowsheet_12.csv.gz
+--  * stride_io_flowsheet_13.csv.gz
+--  * stride_io_flowsheet_14.csv.gz
+--  * stride_io_flowsheet_15.csv.gz
+--  * stride_io_flowsheet_16.csv.gz
+--  * stride_io_flowsheet_17.csv.gz
+--  * stride_io_flowsheet_18.csv.gz
+--  * stride_io_flowsheet_19.csv.gz
+--  * stride_io_flowsheet_20.csv.gz
+-- CSV Fields:
+--  * PAT_ANON_ID (e.g. 8788729772386)
+--  * PAT_ENC_CSN_ANON_ID (e.g. 377427)
+--  * FLO_MEAS_ID (e.g. 30404896)
+--  * G1_DISP_NAME (e.g. "Nutritional Intake")
+--  * G2_DISP_NAME (e.g. "Urine Output")
+--  * SHIFTED_TRANSF_IN_DT_TM (e.g. 02-NOV-13 20:00)
+--  * MEAS_VALUE (e.g. "Reg")
+--  * RN (e.g. 10205663)
+
+CREATE TABLE IF NOT EXISTS stride_io_flowsheet
+(
+  pat_anon_id BIGINT,
+	pat_enc_csn_anon_id BIGINT,
+	flo_meas_id BIGINT,
+	g1_disp_name TEXT,
+	g2_disp_name TEXT,
+	shifted_transf_in_dt_tm TIMESTAMP,
+	meas_value TEXT,
+	rn BIGINT
+);
diff --git a/setup/stride/psql/schemata/stride_mapped_meds.schema.sql b/setup/stride/psql/schemata/stride_mapped_meds.schema.sql
new file mode 100644
index 00000000..dde9b308
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_mapped_meds.schema.sql
@@ -0,0 +1,23 @@
+-- Table: stride_mapped_meds
+-- Description: Medication mapped to RxNorm Concept Unique Identifier (RxCUI).
+-- Original Files:
+--	* Chen_Mapped_Meds_5Yr.csv.gz
+--  * Chen_Mapped_Meds_Yrs6_8.patchHeader.csv.gz
+-- Clean Files:
+--	* stride_medication_2008_2014.csv.gz
+--  * stride_medication_2014_2017.patchHeader.csv.gz
+-- CSV Fields:
+--  * "MEDICATION_ID" (e.g. 127722)
+--  * "NAME" (e.g. "MAGNESIUM OXIDE 400 MG PO CAPS")
+--  * "RXCUI" (e.g. "733")
+--  * "GENERIC_NAME" (e.g. "LORazepam 1 mg tablet")
+--  * active_ingredient (e.g. azatadine) (this field imputed via RxNorm API)
+
+CREATE TABLE IF NOT EXISTS stride_mapped_meds
+(
+  medication_id INTEGER,
+  name TEXT,
+  rxcui INTEGER,
+  generic_name TEXT,
+  active_ingredient TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_medication_mpi.schema.sql b/setup/stride/psql/schemata/stride_medication_mpi.schema.sql
new file mode 100644
index 00000000..a30ae3b5
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_medication_mpi.schema.sql
@@ -0,0 +1,18 @@
+-- Table: stride_medication_mpi
+-- Description: Mapping from stride medication ID (based on Epic) to Medicare
+-- Provider Inventory (MPI).
+-- Original Files:
+--	* Chen_MedicationID_to_MPI.csv.gz
+-- Clean Files:
+--	* stride_medication_mpi.csv.gz
+-- CSV Fields:
+--  * "MEDICATION_ID" (e.g. 127722)
+--  * "MED_NAME" (e.g. "AMOXICILLIN-POT CLAVULANATE 875-125 MG PO TABS")
+--  * "MPI_ID_VAL" (e.g. "0033")
+
+CREATE TABLE IF NOT EXISTS stride_medication_mpi
+(
+	medication_id INTEGER,
+	med_name TEXT,
+	mpi_id_val TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_note.schema.sql b/setup/stride/psql/schemata/stride_note.schema.sql
new file mode 100644
index 00000000..8e22a5b9
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_note.schema.sql
@@ -0,0 +1,47 @@
+-- Table: stride_note
+-- Description: Metadata about patient notes (w/o content for deidentification).
+-- Raw Files:
+--  * Chen_Clinical_Notes_Yr1.csv.gz
+--  * Chen_Clinical_Notes_Yr2.csv.gz
+--  * Chen_Clinical_Notes_Yr3.csv.gz
+--  * Chen_Clinical_Notes_Yr4.csv.gz
+--  * Chen_Clinical_Notes_Yr5.csv.gz
+--  * Chen_Clinical_Notes_Yr6.csv.gz
+--  * Chen_Clinical_Notes_Yr7.csv.gz
+--  * Chen_Clinical_Notes_Yr8.csv.gz
+-- Clean Files:
+--  * stride_clinical_notes_year_1.csv.gz
+--  * stride_clinical_notes_year_2.csv.gz
+--  * stride_clinical_notes_year_3.csv.gz
+--  * stride_clinical_notes_year_4.csv.gz
+--  * stride_clinical_notes_year_5.csv.gz
+--  * stride_clinical_notes_year_6.csv.gz
+--  * stride_clinical_notes_year_7.csv.gz
+--  * stride_clinical_notes_year_8.csv.gz
+-- CSV Fields:
+--  * "PAT_ID" (e.g. 11577528280870)
+--  * "PAT_ENC_CSN_ID" (e.g. 431388)
+--  * "NOTE_DATE" (e.g. 12/22/2009 21:53:00)
+--  * "NOTE_TYPE" (e.g. "Progress Notes")
+--  * "AUTHOR_NAME" (e.g. "DOE, JANE")
+--  * "PROVIDER_TYPE" (e.g. "Resident")
+--  * "HOSPITAL_SERVICE" (e.g. "Interventional Radiology")
+--  * "DEPARTMENT" (e.g. "D3")
+--  * "SPECIALTY" (e.g. "Anesthesia")
+--  * "COSIGNER_NAME" (e.g. "DOE, JOHN")
+--  * "STATUS" (e.g. "Signed")
+
+CREATE TABLE IF NOT EXISTS stride_note
+(
+	pat_id BIGINT,
+	pat_enc_csn_id BIGINT,
+	note_date TIMESTAMP,
+	note_type TEXT,
+	author_name TEXT,
+	provider_type TEXT,
+	hospital_service TEXT,
+	department TEXT,
+	specialty TEXT,
+	cosigner_name TEXT,
+	status TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_order_med.schema.sql b/setup/stride/psql/schemata/stride_order_med.schema.sql
new file mode 100644
index 00000000..f6c39c89
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_order_med.schema.sql
@@ -0,0 +1,165 @@
+-- Table: stride_order_med
+-- Description: Medication orders for medication_id.
+-- Original Files:
+--	* Chen_Order_Med_5Yr.csv.gz
+--  * Chen_Order_Med_Yrs6_8.patchHeader.csv.gz
+-- Clean Files:
+--	* stride_order_med_2008_2014.csv.gz
+--  * stride_order_med_2014_2017.patchHeader.csv.gz
+-- CSV Fields:
+--  * order_med_id (e.g. 6757747)
+--  * pat_id (e.g. 10000892290139)
+--  * pat_enc_csn_id (e.g. 831773)
+--  * ordering_date (e.g. "01/19/2014 20:28")
+--  * order_class_c (e.g. 60)
+--  * order_class_name (e.g. "E-Prescribe")
+--  * medication_id (e.g. 8981)
+--  * description (e.g. "HYDROMORPHONE 1 MG/ML IV PCA")
+--  * quantity (e.g. "10 Syringe")
+--  * refills (e.g. "2")
+--  * start_taking_time (e.g. "02/23/2014 22:00")
+--  * order_end_time (e.g. "08/27/2014 13:38")
+--  * end_taking_time (e.g. "03/19/2014 21:25")
+--  * rsn_for_discon_c (e.g. 18)
+--  * rsn_for_discon (e.g. "Patient Discharge")
+--  * med_presc_prov_id (unique identifier for prescriber, e.g. 314264)
+--  * display_name (e.g. "oxyCODONE (ROXICODONE) tablet 5 mg")
+--  * order_priority_c (e.g. 2)
+--  * order_priority (e.g. "STAT")
+--  * med_route_c (e.g. 15)
+--  * med_route (e.g. "Oral")
+--  * discon_time (e.g. "03/27/2014 15:29")
+--  * chng_order_med_id (e.g. 445699906)
+--  * hv_discr_freq_id (id for medication frequency category, e.g. "200006")
+--  * freq_name (corresponds to hv_discr_freq_id, e.g. "2 TIMES DAILY" for "200006")
+--  * discrete_frequency (corresponds to hv_discr_freq_id, e.g. "Q6H PRN" for "200526")
+--  * hv_discrete_dose (e.g. "3100")
+--  * hv_dose_unit_c (e.g. 3)
+--  * hv_dose_unit (corresponds to hv_dose_unit_c, e.g. "mg" for 3)
+--  * order_status_c (e.g. 5)
+--  * order_status (e.g. "Discontinued")
+--  * authrzing_prov_id (e.g. 345324)
+--  * ord_prov_id (e.g. 403311)
+--  * min_discrete_dose (e.g. 25)
+--  * max_discrete_dose (e.g. 12)
+--  * dose_unit_c (e.g. 8)
+--  * dose_unit (e.g. "mg")
+--  * pat_loc_id (e.g. 2000232)
+--  * department_name (e.g. "D3")
+--  * modify_track_c (e.g. 2)
+--  * modify_track (e.g. "MODIFIED")
+--  * act_order_c (e.g. 3)
+--  * active_order (e.g. "Discontinued Medication")
+--  * lastdose (e.g. "Pt Unsure")
+--  * amb_med_disp_name (e.g. "acetaminophen (TYLENOL) 325 mg tablet")
+--  * refills_remaining (e.g. 3)
+--  * resume_status_c (e.g. 2)
+--  * resume_status (e.g. "Sent")
+--  * ordering_mode_c (e.g. 2)
+--  * ordering_mode (e.g. "Inpatient")
+--  * med_dis_disp_qty (e.g. 90)
+--  * med_dis_disp_unit_c (e.g. 5003)
+--  * dispense_unit (e.g. "Packet")
+--  * number_of_doses (e.g. 2)
+--  * doses_remaining (e.g. 1)
+--  * min_rate (e.g. 100)
+--  * max_rate (e.g. 200)
+--  * rate_unit_c (e.g. 41)
+--  * rate_unit (e.g. "mL/hr")
+--  * min_duration (e.g. 60)
+--  * max_duration (e.g. 120)
+--  * med_duration_unit_c (e.g. 2)
+--  * duration_unit_name (e.g. "Hours")
+--  * min_volume (e.g. 50)
+--  * max_volume (e.g. "")
+--  * volume_unit_c (e.g. 1)
+--  * volume_unit (e.g. "mL")
+--  * calc_volume_yn (e.g. "Y")
+--  * calc_min_dose (e.g. 5)
+--  * calc_max_dose (e.g. 10)
+--  * calc_dose_unit_c (e.g. 7)
+--  * calc_dose_unit (e.g. "g")
+--  * admin_min_dose (e.g. 5)
+--  * admin_max_dose (e.g. 50)
+--  * admin_dose_unit_c (e.g. 5025)
+--  * admin_dose_unit (e.g. "Units")
+
+CREATE TABLE IF NOT EXISTS stride_order_med
+(
+	order_med_id BIGINT NOT NULL,
+	pat_id BIGINT,
+	pat_enc_csn_id BIGINT,
+	ordering_date TIMESTAMP,
+	order_class_c INTEGER,
+	order_class_name TEXT,
+	medication_id INTEGER,
+	description TEXT,
+	quantity TEXT,
+	refills TEXT,
+	start_taking_time TIMESTAMP,
+	order_end_time TIMESTAMP,
+	end_taking_time TIMESTAMP,
+	rsn_for_discon_c INTEGER,
+	rsn_for_discon TEXT,
+	med_presc_prov_id INTEGER,
+	display_name TEXT,
+	order_priority_c INTEGER,
+	order_priority TEXT,
+	med_route_c INTEGER,
+	med_route TEXT,
+	discon_time TIMESTAMP,
+	chng_order_med_id BIGINT,
+	hv_discr_freq_id INTEGER,
+	freq_name TEXT,
+	discrete_frequency TEXT,
+	hv_discrete_dose TEXT,
+	hv_dose_unit_c INTEGER,
+	hv_dose_unit TEXT,
+	order_status_c INTEGER,
+	order_status TEXT,
+	authrzing_prov_id INTEGER,
+	ord_prov_id INTEGER,
+	min_discrete_dose FLOAT,
+	max_discrete_dose FLOAT,
+	dose_unit_c INTEGER,
+	dose_unit TEXT,
+	pat_loc_id INTEGER,
+	department_name TEXT,
+	modify_track_c INTEGER,
+	modify_track TEXT,
+	act_order_c INTEGER,
+	active_order TEXT,
+	lastdose TEXT,
+	amb_med_disp_name TEXT,
+	refills_remaining INTEGER,
+	resume_status_c INTEGER,
+	resume_status TEXT,
+	ordering_mode_c INTEGER,
+	ordering_mode TEXT,
+	med_dis_disp_qty FLOAT,
+	med_dis_disp_unit_c INTEGER,
+	dispense_unit TEXT,
+	number_of_doses INTEGER,
+	doses_remaining INTEGER,
+	min_rate FLOAT,
+	max_rate FLOAT,
+	rate_unit_c INTEGER,
+	rate_unit TEXT,
+	min_duration FLOAT,
+	max_duration FLOAT,
+	med_duration_unit_c INTEGER,
+	duration_unit_name TEXT,
+	min_volume FLOAT,
+	max_volume FLOAT,
+	volume_unit_c INTEGER,
+	volume_unit TEXT,
+	calc_volume_yn TEXT,
+	calc_min_dose FLOAT,
+	calc_max_dose FLOAT,
+	calc_dose_unit_c INTEGER,
+	calc_dose_unit TEXT,
+	admin_min_dose FLOAT,
+	admin_max_dose FLOAT,
+	admin_dose_unit_c INTEGER,
+	admin_dose_unit TEXT
+  );
diff --git a/setup/stride/psql/schemata/stride_order_medmixinfo.schema.sql b/setup/stride/psql/schemata/stride_order_medmixinfo.schema.sql
new file mode 100644
index 00000000..7e3e14c4
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_order_medmixinfo.schema.sql
@@ -0,0 +1,49 @@
+-- Table: stride_order_medmixinfo
+-- Description: 1-to-many mapping from order order_med_id to medication(s)
+-- in order (identified by medication_id). Note both min_dose_amount (nominal
+-- prescribed amount) vs min_calc_dose_amt (calculated amount based on
+-- concentration of ingredient).
+-- Original Files:
+--	* Chen_MedicationID_to_MPI.csv.gz
+-- Clean Files:
+--	* stride_medication_mpi.csv.gz
+-- CSV Fields:
+--  * order_med_id (e.g. 11192203)
+--  * line (1-indexed line counter of medications in order_med_id, e.g. 2)
+--  * medication_id (e.g. 210379)
+--  * medication_name (e.g. "SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP")
+--  * ingredient_type_c (ingredient category number, e.g. 1)
+--  * ingredient_type (maps to ingredient_type_c, e.g. "Base" for 1)
+--  * min_dose_amount (e.g. 1000)
+--  * max_dose_amount (e.g. 5000)
+--  * dose_unit_c (dose unit category number, e.g. 1.0)
+--  * dose_unit (maps to dose_unit_c, e.g. "mL" for 1)
+--  * nonformulary_yn (e.e. Y)
+--  * selection (e.g. 3)
+--  * min_calc_dose_amt (e.g. 10)
+--  * max_calc_dose_amt (e.g. 50)
+--  * calc_dose_unit_c (e.g. 8)
+--  * calc_dose_unit (maps to calc_dose_unit_c, e.g. "mcg" for 8)
+--  * dose_calc_info (formula for min_dose_amount -> min_calc_dose_amt, e.g.
+--                    "50 mg ? 1 mL/25 mg = 2 mL ? 25,000 mcg/mL = 50,000 mcg")
+
+CREATE TABLE IF NOT EXISTS stride_order_medmixinfo
+(
+  order_med_id BIGINT,
+  line INTEGER,
+  medication_id INTEGER,
+  medication_name TEXT,
+  ingredient_type_c INTEGER,
+  ingredient_type TEXT,
+  min_dose_amount FLOAT,
+  max_dose_amount FLOAT,
+  dose_unit_c INTEGER,
+  dose_unit TEXT,
+  nonformulary_yn TEXT,
+  selection INTEGER,
+  min_calc_dose_amt FLOAT,
+  max_calc_dose_amt FLOAT,
+  calc_dose_unit_c INTEGER,
+  calc_dose_unit TEXT,
+  dose_calc_info TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_order_proc.schema.sql b/setup/stride/psql/schemata/stride_order_proc.schema.sql
new file mode 100644
index 00000000..d5db77d3
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_order_proc.schema.sql
@@ -0,0 +1,101 @@
+-- Table: stride_order_proc
+-- Description: Procedure orders for procedure proc_code.
+-- Original Files:
+--	* Chen_Order_Proc_Yr1.patchcommas.csv.gz
+--  * Chen_Order_Proc_Yr2.patchcommascsv.gz
+--  * Chen_Order_Proc_Yr3.patchcommas.csv.gz
+--  * Chen_Order_Proc_Yr4.patchcommas.csv.gz
+--  * Chen_Order_Proc_Yr5.patchcommas.csv.gz
+--  * Chen_Order_Proc_Yr6.patchcommas.csv.gz
+--  * Chen_Order_Proc_Yr7.patchcommas.csv.gz
+--  * Chen_Order_Proc_Yr8.patchcommas.csv.gz
+-- Clean Files:
+--	* stride_order_proc_year_1.csv.gz
+--  * stride_order_proc_year_2.csv.gz
+--  * stride_order_proc_year_3.csv.gz
+--  * stride_order_proc_year_4.csv.gz
+--  * stride_order_proc_year_5.csv.gz
+--  * stride_order_proc_year_6.csv.gz
+--  * stride_order_proc_year_7.csv.gz
+--  * stride_order_proc_year_8.csv.gz
+-- CSV Fields:
+--  * order_proc_id (e.g. 41330963)
+--  * pat_id (e.g. -9799065180730)
+--  * pat_enc_csn_id (e.g. 845950)
+--  * ordering_date (e.g. "12/26/2013 00:00")
+--  * order_type (e.g. "Nursing")
+--  * proc_id (e.g. 46821)
+--  * proc_code (e.g. "IMGCTHPRW")
+--  * description (e.g. "CONSULT TO NEUROLOGY")
+--  * display_name (e.g. "SLP Evaluate and Treat")
+--  * cpt_code (e.g. "IMGMRHWWO")
+--  * proc_cat_name (e.g. "LAB BLOOD ORDERABLES")
+--  * order_class (e.g. "Normal")
+--  * authrzing_prov_id (e.g. 314264)
+--  * abnormal_yn (e.g. "Y")
+--  * lab_status (e.g. "Final result")
+--  * order_status (e.g. e.g. "Cancelled")
+--  * quantity (e.g. 1)
+--  * future_or_stand (e.g. "S")
+--  * standing_exp_date (e.g. "01/24/2014 02:00")
+--  * standing_occurs (e.g. 1)
+--  * stand_orig_occur (e.g. 11)
+--  * radiology_status (e.g. "Preliminary")
+--  * proc_bgn_time (e.g. "12/02/2009 16:30")
+--  * proc_end_time (e.g. "04/18/2009 20:01")
+--  * order_inst (e.g. "09/30/2009 00:17")
+--  * stand_interval (e.g. "CONTINUOUS")
+--  * discrete_interval (e.g. "3 Times a Week")
+--  * instantiated_time (e.g. "09/15/2009 00:17")
+--  * order_time (e.g. "09/30/2009 00:17")
+--  * result_time (e.g. "10/06/2009 07:23")
+--  * proc_start_time (e.g. "10/18/2009 00:00")
+--  * problem_list_id (e.g. 1459219)
+--  * proc_ending_time (e.g. "10/14/2009 00:00")
+--  * chng_order_proc_id (e.g. 353403435)
+--  * last_stand_perf_dt (e.g. "10/14/2009 00:00")
+--  * last_stand_perf_tm (e.g. "06/01/2011 22:00")
+--  * parent_ce_order_id (e.g. 381781414)
+--  * ordering_mode (e.g. "Inpatient")
+
+CREATE TABLE IF NOT EXISTS stride_order_proc
+(
+  order_proc_id BIGINT NOT NULL,
+  pat_id BIGINT,
+  pat_enc_csn_id BIGINT,
+  ordering_date DATE,
+  order_type TEXT,
+  proc_id BIGINT,
+  proc_code TEXT,
+  description TEXT,
+  display_name TEXT,
+  cpt_code TEXT,
+  proc_cat_name TEXT,
+  order_class TEXT,
+  authrzing_prov_id TEXT,
+  abnormal_yn TEXT,
+  lab_status TEXT,
+  order_status TEXT,
+  quantity INTEGER,
+  future_or_stand TEXT,
+  standing_exp_date DATE,
+  standing_occurs INTEGER,
+  stand_orig_occur INTEGER,
+  radiology_status TEXT,
+  proc_bgn_time TIMESTAMP,
+  proc_end_time TIMESTAMP,
+  order_inst TIMESTAMP,
+  stand_interval TEXT,
+  discrete_interval TEXT,
+  instantiated_time TIMESTAMP,
+  order_time TIMESTAMP,
+  result_time TIMESTAMP,
+  proc_start_time TIMESTAMP,
+  problem_list_id BIGINT,
+  proc_ending_time TIMESTAMP,
+  chng_order_proc_id BIGINT,
+  last_stand_perf_dt DATE,
+  last_stand_perf_tm TIMESTAMP,
+  parent_ce_order_id BIGINT,
+  ordering_mode TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_order_results.schema.sql b/setup/stride/psql/schemata/stride_order_results.schema.sql
new file mode 100644
index 00000000..d9fdcd5d
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_order_results.schema.sql
@@ -0,0 +1,54 @@
+-- Table: stride_order_result
+-- Description: Order results for procedure proc_code.
+-- Original Files:
+--	* Chen_Order_Result_Yr1.csv.gz
+--	* Chen_Order_Result_Yr2.csv.gz
+--	* Chen_Order_Result_Yr3.csv.gz
+--	* Chen_Order_Result_Yr4.csv.gz
+--	* Chen_Order_Result_Yr5.csv.gz
+--  * Chen_Order_Res_Yr6.csv.gz
+--  * Chen_Order_Res_Yr7.csv.gz
+--  * Chen_Order_Res_Yr8.csv.gz
+-- Clean Files:
+--	* stride_order_result_year_1.csv.gz
+--	* stride_order_result_year_2.csv.gz
+--	* stride_order_result_year_3.csv.gz
+--	* stride_order_result_year_4.csv.gz
+--	* stride_order_result_year_5.csv.gz
+--	* stride_order_result_year_6.csv.gz
+--	* stride_order_result_year_7.csv.gz
+--	* stride_order_result_year_8.csv.gz
+-- CSV Fields:
+--  * order_proc_id (e.g. 41330963)
+--  * line (e.g. 1)
+--  * ord_date_real (e.g. 61721.01)
+--  * result_date (e.g. "10/14/2009 00:00")
+--  * result_time (e.g. "09/24/2009 15:19")
+--  * component_name (e.g. "SOURCE")
+--  * base_name (e.g. "PCCOM")
+--  * common_name (e.g. "GLUCOSE BY METER")
+--  * ord_num_value (e.g. 1.0)
+--  * reference_unit (e.g. "mg/dL")
+--  * result_in_range_yn (e.g. "Y")
+--  * result_status (e.g. "Final")
+--  * lab_status (e.g. "Final result")
+--  * value_normalized (e.g. "")
+
+CREATE TABLE IF NOT EXISTS stride_order_results
+(
+  order_proc_id BIGINT NOT NULL,
+  line INTEGER NOT NULL,
+  ord_date_real FLOAT,
+  result_date DATE,
+  result_time TIMESTAMP,
+  component_name TEXT,
+  base_name TEXT,
+  common_name TEXT,
+  ord_num_value FLOAT,
+  reference_unit TEXT,
+  result_in_range_yn TEXT,
+  result_flag TEXT,
+  result_status TEXT,
+  lab_status TEXT,
+  value_normalized TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_orderset_order_med.schema.sql b/setup/stride/psql/schemata/stride_orderset_order_med.schema.sql
new file mode 100644
index 00000000..94765d3a
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_orderset_order_med.schema.sql
@@ -0,0 +1,29 @@
+-- Table: stride_orderset_order_med
+-- Description: Medications ordered from order sets.
+-- Raw Files:
+--  * orderset_medications.csv.gz
+-- Clean Files:
+--  * stride_orderset_order_med_2008_2014.csv.gz
+-- CSV Fields:
+--  * ORDER_MED_ID (e.g. 3000087)
+--  * DESCRIPTION (e.g. SODIUM CHLORIDE 0.9 % 0.9 % IV SOLP)
+--  * MEDICATION_ID (e.g. 2365)
+--  * SS_SG_KEY (e.g. 17968)
+--  * SECTION_NAME (e.g. HYPOGLYCEMIA CONTROL)
+--  * SMART_GROUP (e.g. Antibiotics)
+--  * ORDER_TYPE (e.g. Order Set)
+--  * PROTOCOL_ID (e.g. 1841)
+--  * PROTOCOL_NAME (e.g. IP SUR GENERAL ADMIT)
+
+CREATE TABLE IF NOT EXISTS stride_orderset_order_med
+(
+  order_med_id BIGINT,
+	description TEXT,
+	medication_id INTEGER,
+	ss_sg_key TEXT,
+	section_name TEXT,
+	smart_group TEXT,
+	order_type TEXT,
+	protocol_id BIGINT,
+	protocol_name TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_orderset_order_proc.schema.sql b/setup/stride/psql/schemata/stride_orderset_order_proc.schema.sql
new file mode 100644
index 00000000..4bc90cf8
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_orderset_order_proc.schema.sql
@@ -0,0 +1,29 @@
+-- Table: stride_orderset_order_proc
+-- Description: Procedures ordered from order sets.
+-- Raw Files:
+--  * orderset_procedures.csv.gz
+-- Clean Files:
+--  * stride_orderset_order_proc_2008_2014.csv.gz
+-- CSV Fields:
+--  * ORDER_PROC_ID (e.g. 32163106)
+--  * PROC_CODE (e.g. NUR1940)
+--  * PROC_ID (e.g. 473044)
+--  * SS_SG_KEY (e.g. 15012)
+--  * SECTION_NAME (e.g. LABORATORY)
+--  * SMART_GROUP (e.g. Chemistry)
+--  * ORDER_TYPE (e.g. Order Set)
+--  * PROTOCOL_ID (e.g. 1595)
+--  * PROTOCOL_NAME (e.g. IP INTERAGENCY DISCHARGE ORDERS)
+
+CREATE TABLE IF NOT EXISTS stride_orderset_order_proc
+(
+	order_proc_id BIGINT,
+	proc_code TEXT,
+	proc_id BIGINT,
+	ss_sg_key TEXT,
+	section_name TEXT,
+	smart_group TEXT,
+	order_type TEXT,
+	protocol_id BIGINT,
+	protocol_name TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_patient.schema.sql b/setup/stride/psql/schemata/stride_patient.schema.sql
new file mode 100644
index 00000000..07ff6fb6
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_patient.schema.sql
@@ -0,0 +1,25 @@
+-- Table: stride_patient
+-- Description: Demographics of each patient in stride dataset.
+-- Original Files:
+--	* Chen_Demographics.csv.gz
+-- 	* Chen_Demographics_Yr6_8.csv.gz
+-- Clean Files:
+-- 	* stride_demographics_2008_2014.csv.gz
+-- 	* stride_demographics_2014_2017.csv.gz
+-- CSV Fields:
+--	* "PAT_ID" (e.g. 11577528280870)
+-- 	* "BIRTH_YEAR" (e.g. 1989)
+-- 	* "GENDER" (e.g. "Male")
+-- 	* "DEATH_DATE" (e.g. 03/18/2016)
+-- 	* "RACE" (e.g. "White")
+-- 	* "ETHNICITY" (e.g. "Hispanic/Latino")
+
+CREATE TABLE IF NOT EXISTS stride_patient
+(
+	pat_id BIGINT,
+	birth_year INTEGER,
+	gender TEXT,
+	death_date TIMESTAMP,
+	race TEXT,
+	ethnicity TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_patient_encounter.schema.sql b/setup/stride/psql/schemata/stride_patient_encounter.schema.sql
new file mode 100644
index 00000000..34fa99ca
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_patient_encounter.schema.sql
@@ -0,0 +1,33 @@
+-- Table: stride_patient_encounter
+-- Description: Insurance and admit vitals for encounter pat_enc_csn_id.
+-- Original Files:
+--  * Chen_Active_Vitals.csv.gz
+--	* Chen_Insurance_Info_5Yr.csv.gz
+--  * Chen_Insurance_Info_Yrs6_8.csv.gz
+-- Clean Files:
+--  * stride_admit_vitals.csv.gz
+-- 	* stride_insurance_2008_2014.csv.gz
+--  * stride_insurance_2014_2017.csv.gz
+-- CSV Fields:
+--  * "PAT_ID" (e.g. 11577528280870)
+--  * "PAT_ENC_CSN_ID" (e.g. 431388)
+--  * PAYOR_NAME (e.g. "BLUE SHIELD")
+--  * TITLE (e.g. "MANAGED CARE")
+--  * "BP_SYSTOLIC" (e.g. 120)
+--  * "BP_DIASTOLIC" (e.g. 80)
+--  * "TEMPERATURE" (e.g. 98.6)
+--  * "PULSE" (e.g. 100)
+--  * "RESPIRATIONS" (e.g. 15)
+
+CREATE TABLE IF NOT EXISTS stride_patient_encounter
+(
+  pat_id BIGINT,
+  pat_enc_csn_id BIGINT,
+  payor_name TEXT,
+  title TEXT,
+  bp_systolic INTEGER,
+  bp_diastolic INTEGER,
+  temperature FLOAT,
+  pulse INTEGER,
+  respirations INTEGER
+);
diff --git a/setup/stride/psql/schemata/stride_preadmit_med.schema.sql b/setup/stride/psql/schemata/stride_preadmit_med.schema.sql
new file mode 100644
index 00000000..adb1335b
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_preadmit_med.schema.sql
@@ -0,0 +1,26 @@
+-- Table: stride_preadmit_med
+-- Description: Medications actively taken by pat_anon_id as of contact_date.
+-- Raw Files:
+--  * Chen_Active_Meds_At_Admit.csv.gz
+-- Clean Files:
+--  * stride_preadmit_meds_2014.csv.gz
+-- CSV Fields:
+--  * "PAT_ANON_ID" (e.g. 11577528280870)
+--  * "CONTACT_DATE" (e.g. 04/21/2009)
+--  * "MEDICATION_ID" (e.g. 1698)
+--  * "DESCRIPTION" (e.g. "PLAVIX 75 MG PO TABS")
+--  * "THERA_CLASS" (e.g. "CARDIOVASCULAR")
+--  * "PHARM_CLASS" (e.g. "PROTON-PUMP INHIBITORS")
+--  * "PHARM_SUBCLASS" (e.g. "Anticoagulants - Coumarin")
+
+CREATE TABLE IF NOT EXISTS stride_preadmit_med
+(
+  stride_preadmit_med_id SERIAL,
+  pat_anon_id BIGINT,
+  contact_date TIMESTAMP,
+  medication_id INTEGER,
+  description TEXT,
+  thera_class TEXT,
+  pharm_class TEXT,
+  pharm_subclass TEXT
+);
diff --git a/setup/stride/psql/schemata/stride_treatment_team.schema.sql b/setup/stride/psql/schemata/stride_treatment_team.schema.sql
new file mode 100644
index 00000000..5abe7b0b
--- /dev/null
+++ b/setup/stride/psql/schemata/stride_treatment_team.schema.sql
@@ -0,0 +1,40 @@
+-- Table: stride_treatment_team
+-- Description: Treatment team for patient PAT_ID in encounter PAT_ENC_CSN_ID.
+-- Original Files:
+--	* Chen_TreatmentTeam_Yr1.csv.gz
+--	* Chen_TreatmentTeam_Yr2.csv.gz
+--	* Chen_TreatmentTeam_Yr3.csv.gz
+--	* Chen_TreatmentTeam_Yr4.csv.gz
+--	* Chen_TreatmentTeam_Yr5.csv.gz
+--  * Chen_Treatment_Team_Yrs6.patchHeader.csv.gz
+--  * Chen_Treatment_Team_Yrs7.patchHeader.csv.gz
+--  * Chen_Treatment_Team_Yrs8.patchHeader.csv.gz
+-- Clean Files:
+--  * stride_treatment_team_year_1.csv.gz
+--  * stride_treatment_team_year_2.csv.gz
+--  * stride_treatment_team_year_3.csv.gz
+--  * stride_treatment_team_year_4.csv.gz
+--  * stride_treatment_team_year_5.csv.gz
+--  * stride_treatment_team_year_6.csv.gz
+--  * stride_treatment_team_year_7.csv.gz
+--  * stride_treatment_team_year_8.csv.gz
+-- CSV Fields:
+--  * PAT_ID (e.g. 4505642352918)
+--  * PAT_ENC_CSN_ID (e.g. 409048)
+--  * LINE (e.g. 3)
+--  * TRTMNT_TM_BEGIN_DATE (e.g. "10/15/2009 06:50:00")
+--  * TRTMNT_TM_END_DATE (e.g. "11/16/2009 07:30:00")
+--  * TREATMENT_TEAM (e.g. "Occupational Therapist")
+--  * PROV_NAME (e.g. "DOE, JANE")
+
+CREATE TABLE IF NOT EXISTS stride_treatment_team
+(
+  stride_treatment_team_id SERIAL, -- automatically create unique identifier
+	pat_id BIGINT,
+	pat_enc_csn_id BIGINT,
+	line INTEGER,
+	trtmnt_tm_begin_date TIMESTAMP,
+	trtmnt_tm_end_date TIMESTAMP,
+	treatment_team TEXT,
+	prov_name TEXT
+);
diff --git a/setup/stride/rxnorm/RxNormClient.py b/setup/stride/rxnorm/RxNormClient.py
new file mode 100644
index 00000000..03ac3360
--- /dev/null
+++ b/setup/stride/rxnorm/RxNormClient.py
@@ -0,0 +1,38 @@
+#!/usr/bin/python
+"""
+Simple client for calling RxNorm RxNav REST API.
+
+https://rxnav.nlm.nih.gov/RxNormAPIREST.html
+"""
+
+import json
+import urllib.request, urllib.error, urllib.parse
+
+class RxNormClient:
+    BASE_URL = 'https://rxnav.nlm.nih.gov/REST'
+
+    def __init__(self):
+        # 2/3 RxCUI's in dataset are unique, so don't make cache too fancy yet.
+        self._cache = {}
+
+    def fetch_properties_by_rxcui(self, rxcui):
+        endpoint = 'rxcui/%s/allProperties.json?prop=all' % rxcui
+        url = '/'.join([RxNormClient.BASE_URL, endpoint])
+
+        # Read from cache or query API.
+        if self._cache.get(url):
+            properties = self._cache[url]
+        else:
+            properties = json.loads(urllib.request.urlopen(url).read())
+            self._cache.update({url: properties})
+
+        return properties
+
+    def fetch_name_by_rxcui(self, rxcui):
+        properties = self.fetch_properties_by_rxcui(rxcui)
+        concepts = properties['propConceptGroup']['propConcept']
+        for concept in concepts:
+            if concept['propName'] == 'RxNorm Name':
+                return concept['propValue']
+
+        return None
diff --git a/setup/stride/rxnorm/__init__.py b/setup/stride/rxnorm/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/setup/stride/rxnorm/test/TestRxNormClient.py b/setup/stride/rxnorm/test/TestRxNormClient.py
new file mode 100644
index 00000000..3966cd5d
--- /dev/null
+++ b/setup/stride/rxnorm/test/TestRxNormClient.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python
+"""
+Test suite for respective module in application package.
+"""
+
+import unittest
+
+from LocalEnv import PATH_TO_CDSS, TEST_RUNNER_VERBOSITY
+from medinfo.common.test.Util import make_test_suite, MedInfoTestCase
+from stride.rxnorm.RxNormClient import RxNormClient
+
+
+class TestRxNormClient(MedInfoTestCase):
+    def setUp(self):
+        self.client = RxNormClient()
+        self.TEST_CASES = [
+            {
+                'rxcui': 18600,
+                'name': 'azatadine',
+                'properties': {
+                    'propConceptGroup': {
+                        'propConcept': [
+                            {
+                                'propCategory': 'ATTRIBUTES',
+                                'propName': 'TTY',
+                                'propValue': 'IN'
+                            },
+                            {
+                                'propCategory': 'ATTRIBUTES',
+                                'propName': 'GENERAL_CARDINALITY',
+                                'propValue': 'SINGLE'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'RxCUI',
+                                'propValue': '18600'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'NUI',
+                                'propValue': 'N0000007088'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'NUI',
+                                'propValue': 'N0000147714'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'UMLSCUI',
+                                'propValue': 'C0052759'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'VUID',
+                                'propValue': '4019620'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'ATC',
+                                'propValue': 'R06AX09'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'DRUGBANK',
+                                'propValue': 'DB00719'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'MESH',
+                                'propValue': 'C006656'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'SNOMEDCT',
+                                'propValue': '1594006'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'SNOMEDCT',
+                                'propValue': '372662006'
+                            },
+                            {
+                                'propCategory': 'CODES',
+                                'propName': 'MMSL_CODE',
+                                'propValue': 'GNd00791'
+                            },
+                            {
+                                'propCategory': 'NAMES',
+                                'propName': 'RxNorm Name',
+                                'propValue': 'azatadine'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'Anatomical Therapeutic Chemical'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'Drug Bank'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'Multum MediSource Lexicon'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'Medical Subject Headings'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'National Drug Data File Plus Source Vocabulary'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'National Drug File'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'US Edition of SNOMED_CT'
+                            },
+                            {
+                                'propCategory': 'SOURCES',
+                                'propName': 'Source',
+                                'propValue': 'Veterans Health Administration National Drug File'
+                            }
+                        ]
+                    }
+                }
+            }
+        ]
+
+    def tearDown(self):
+        pass
+
+    def test_fetch_properties_by_rxcui(self):
+        for test_case in self.TEST_CASES:
+            rxcui = test_case['rxcui']
+            expected_properties = test_case['properties']
+            actual_properties = self.client.fetch_properties_by_rxcui(rxcui)
+
+            self.assertEqual(expected_properties, actual_properties)
+
+    def test_fetch_name_by_rxcui(self):
+        for test_case in self.TEST_CASES:
+            rxcui = test_case['rxcui']
+            expected_name = test_case['name']
+            actual_name = self.client.fetch_name_by_rxcui(rxcui)
+
+            self.assertEqual(expected_name, actual_name)
+
+
+if __name__ == '__main__':
+    suite = make_test_suite(TestRxNormClient)
+    unittest.TextTestRunner(verbosity=TEST_RUNNER_VERBOSITY).run(suite)
diff --git a/setup/stride/stride.md b/setup/stride/stride.md
new file mode 100644
index 00000000..c6060123
--- /dev/null
+++ b/setup/stride/stride.md
@@ -0,0 +1,77 @@
+# STRIDE Inpatient Data Loading Notes
+
+## Overview
+Much of our research is based on the Stanford Clinical Data Warehouse
+aka STARR, formerly STRIDE). Our primary data source within STARR is
+[Epic "Clarity" database](https://www.med.upenn.edu/dac/epic-clarity-data-warehousing.html),
+from which STARR has provided us data for all Stanford hospital _inpatients_
+from 2008 onward. For more information about the STARR data warehouse, see [this overview](https://med.stanford.edu/researchit/services/clinical-data-warehouse.html).
+
+## Loading STRIDE
+
+### Download the data
+
+STARR delivers the data to us as compressed `.csv.gz` files.
+
+Loading the data into a queryable has the following formats:
+raw --> clean --> psql
+
+First, download all of the data from Stanford Medicine Box to
+`CDSS/stride/data/`:
+
+https://stanfordmedicine.app.box.com/folder/48947323122
+
+(For many, you can likely skip to just downloading the pre-processed PostgreSQL dumps under data/medinfo_2008_2017 and loading into PostgreSQL.
+If you have the Box Sync desktop client, you can go to the above link and pick "Sync to Desktop" under the Details options to easily download all of the files.)
+
+### raw --> clean (runtime: 1 – 1.5 hours)
+
+The CSVs as provided by STARR have some formatting issues
+(e.g. unclosed quotes, extra commas). To fix these, use StrideLoader.
+
+`python stride/core/StrideLoader.py --clean`
+
+Because this process only needs to be run once, we have stored a backup
+version of these files on
+[Stanford Medicine Box](https://stanfordmedicine.app.box.com/folder/49709022468). These files can be imported into psql, R, or any other analysis software.
+
+### clean --> psql (runtime: 1.5 – 2 hours)
+
+The clean CSVs can be easily imported into a PostgreSQL database.
+
+`python stride/core/StrideLoader.py --psql`
+
+Note that this command also builds schemata, does some post-processing, and
+builds indices. Because this database is read-only for the most part, we have
+stored a backup version of this database as table dumps in
+[Stanford Medicine Box](https://stanfordmedicine.app.box.com/folder/50484084132).
+
+### psql --> dumps (runtime: 30 – 60 minutes)
+
+First, edit the database variables in `stride/psql/dump_stride.sh`.
+
+Then run the script in the same directory as the dump files.
+
+### dumps --> psql (runtime: 60 – 90 minutes)
+
+First, edit the database variables in `stride/psql/restore_stride.sh`
+Then run the script in the same directory as the dump files. (e.g., `bash restore_stride.sh`). This will put the stride files in your psql database.
+
+Second, edit the database variables in `stride/psql/restore_clinical_item.sh`
+Then run the script in the same directory as the dump files (e.g., `bash restore_clinical_item`). This will put the clinical item files in your psql database.
+
+Tips:
+* If you don't want to have to keep entering in your PostgreSQL password at the command line, you can set an environment variable PGPASSWORD (e.g., `export PGPASSWORD=xxx` or `set PGPASSWORD=xxx` on Windows).
+
+* A small tip if you are running the restore_XXX.sh scripts on Mac Terminal: Be sure to take care of the linebreakers ^M that were generated in DOS (?), otherwise you get error message like "" to address: nodename nor servname provided, or not known."
+One way to manage this is (1) Use vim to open the .sh file and input ":e ++ff=unix" to make all ^M visible. (2) Input ":%s/^M//g" to delete ^M globally. To enter the character ^M, type CTRL-V, then CTRL-M. That is, hold down the CTRL key then press V and M in succession.
+
+* If you are in the Box folder stride_2008_2017/data/medinfo_2008_2017, there should be a copy of both `restore_stride.sh` and `restore_clinical_item.sh` in there already.
+
+## Querying STRIDE
+
+To get a better sense of the type of data contained within the STRIDE
+data set, inspect the schema definition files in `stride/psql/schemata`.
+
+To actually query the data, see [these SQL resources](https://github.com/HealthRex/CDSS/wiki/STRIDE-Database#postgresql)
+and [this STRIDE-specific tutorial](https://github.com/HealthRex/CDSS/wiki/STRIDE-SQL-Tutorial).
diff --git a/setup/webAppSetupNotes.txt b/setup/webAppSetupNotes.txt
new file mode 100644
index 00000000..44e441c8
--- /dev/null
+++ b/setup/webAppSetupNotes.txt
@@ -0,0 +1,304 @@
+======== AWS Server Setup Notes ==============
+If you're just installing on your local laptop/computer, then skip to the next section, but some tips here may still be helpful.
+
+===========================================
+May be able to reuse existing dev environments
+- Find EC2 Amazon Machine Image (AMI) SnapShot (e.g., medinfo-STRIDE-2008-2014-postgresql)
+- Launch / Restore an Instance based on one
+- Login as ec2-user as described below
+	- sudo service postgresql96 restart
+- Should now have a running Linux server with running PostgreSQL database with STRIDE 2008-2014 data already loaded
+- Working git CDSS code repo available under /home/ec2-user/CDSS 
+
+================== From Scratch Version =================
+- Sign-up for AWS account
+- Free Tier eligible EC2 Micro Instance create
+- Change Security Group for instance to allow TCP ports 22 for SSH connection any others
+- Connect as ec2-user (or ubuntu) first off using public-private key default method (requires local download)
+  ssh -i privateKey.pem ec2-user@xxx.xxx.xxx.xxx
+- Need to prefix "sudo " to most commands to act as root
+
+Can continue to just work as "ec2-user" or can create a separate user and account that can login by password:
+	- Create user to login as
+	sudo adduser jonc101
+	sudo passwd jonc101
+	- Grant user sudo privileges
+	sudo visudo
+	    jonc101 ALL=(ALL) ALL
+	- Change setting to allow SSH password login. Beware that this is less secure!
+	sudo vi /etc/ssh/sshd_config
+	PasswordAuthentication yes
+	sudo service sshd restart
+
+	Should now be able to SSH to instance using the created username and password
+
+- Install Amazon packages
+sudo yum install git
+sudo yum install postgresql96
+sudo yum install python27-psycopg2
+sudo yum install httpd
+sudo yum install mod_wsgi-python27
+sudo yum install python27-scipy
+sudo yum install python27-matplotlib
+
+# Python packages
+sudo pip install pandas
+sudo pip install sklearn
+sudo pip install gensim
+#sudo easy_install gensim
+
+(Consider general SciPy dev environment setup)
+(http://blog.yhathq.com/posts/setting-up-scientific-python.html)
+  Avoid mixing installation modalities (PIP, downloadable executables, Enthought Canopy distribution) as tend to be incompatible
+
+- Use AWS Console interface (RDService) to create a PostgreSQL relational database if want separate DB server (vs. installed on local EC2 instance)
+  Consider Provisioned IOPS on a "large' instance for computation periods to better handle high I/O cost
+
+==============================================
+Local Installation Dependencies
+
+Highly recommended that you use some standard package installers to minimize the pain of getting all of the dependencies setup right.
+(E.g., yum or apt-get for top level packages. Use pip for python packages where possible. Windows binary installers may be necessary for others. Many unofficial Windows binaries are available from https://www.lfd.uci.edu/~gohlke/pythonlibs/)
+
+- PostgreSQL
+- Python 2.7
+  - Scipy
+  - Numpy
+  - Psycopg2
+
+Only necessary if you want to develop interactive web interfaces:
+- Apache 2.2
+- Mod_WSGI
+
+For Python 3.6, the required dependencies are:
+- Apache 2.4
+- Mod_WSGI
+
+==============================================
+Download code from source control
+> git clone https://github.com/HealthRex/CDSS.git
+
+==============================================
+Download data folders
+- STRIDE-Inpatient-2008-2014
+  - dump2009-2014-5year-time
+- STRIDE-Inpatient-2004-2017
+
+# Tips for remote copying / synchronizing data files to Linux servers
+pscp * jonc101@corn.stanford.edu:private/data
+# Patching / resuming incompletely transmitted files.  Do for one file at a time to allow for proper interrupt / resume function?
+rsync --partial --progress order_results.txt jonc101@corn.stanford.edu:private/data/order_results.txt
+
+=============================================
+Update ~/.bash_profile with paths and references to make subsequent steps more convenient
+
+> vi ~/.bash_profile
+    BASE_DIR=$HOME/CDSS
+    export BASE_DIR
+
+    PYTHONPATH=$PYTHONPATH:$BASE_DIR
+    export PYTHONPATH
+
+# Quick alternative
+> echo "export PYTHONPATH=$PYTHONPATH:$HOME/CDSS" >> .bash_profile
+
+
+> source ~/.bash_profile
+
+==============================================
+# PostgreSQL Database Server Setup
+- See documentation for local setup
+- AWS RDS does much of this automatically
+- AWS EC2
+	sudo yum install postgresql96-server
+	sudo service postgresql96 initdb
+	sudo service postgresql96 restart
+	sudo -u postgres psql -U postgres 	# Connect via database superuser postgres
+
+# Create DB users and access privileges from remote connections
+# Replace "jonc101" with ec2-user or other username as per your preference
+PostgreSQL SQL Command:
+    CREATE USER "ec2-user" PASSWORD '<DBPassword>';	
+
+#   Create blank database to work with
+> createdb -U postgres -O jonc101 medinfo
+(Note that this is a command-line program. If you're already connected via psql, you can enter the CREATE DATABASE SQL command query.)
+	CREATE DATABASE medinfo OWNER "ec2-user";
+	CREATE DATABASE testmedinfo OWNER "ec2-user";
+
+# Need to configure PostgreSQL database server to allow connections by password, in this case, from any client host
+#sudo echo "host  all  all  0.0.0.0/0  md5" >> /var/lib/pgsql96/data/pg_hba.conf
+sudo vi /var/lib/pgsql96/data/pg_hba.conf
+	# Add line like this, saying allow connection to any database, by any user, from any IP address, if authenticates by an MD5 encrypted password
+	host  all  all  0.0.0.0/0  md5
+	# Comment out the line like below, so won't attempt to use alternative ident authentication method first
+	# host    all             all             127.0.0.1/32            ident
+
+# If want to allow connections from external hosts (e.g., your laptop), then need to also change / add line to configuration file to listen for external calls
+sudo vi /var/lib/pgsql96/data/postgresql.conf
+	# Add / edit this line to listen from all hosts
+	listen_addresses = '*'
+
+# Reset database service to capture configuration changes
+	sudo service postgresql96 restart
+
+# For convenience, can define database user password in environment so don't have to enter it everytime
+	export PGPASSWORD=<DBPassword>
+
+# If restoring from a previously pre-processed database, than just restore each data table's contents in turn
+> restoreCPOETables.sh (should be stored with data dump source)
+
+# See setupDataPreprocessingNotes.txt for details on reconstructing the pre-processing steps from scratch
+
+#   Create data definitions (If did direct restore from previous data dump, OK to expect many errors regarding "Relation already exists")
+# This is more for setting up a separate test database that just needs the blank schema, but not the actual data
+PostgreSQL: # Use timestamp data type
+> psql -f medinfo/db/definition/schema.strideData.sql -U jonc101 testmedinfo		# This may be in the source data directory as schema.strideData.sql
+> psql -f medinfo/db/definition/cpoeStats.sql -U jonc101 testmedinfo
+> psql -f medinfo/db/definition/cpoeSimulation.sql -U jonc101 testmedinfo
+
+# Point Python scripts to database (may want to separate your primary database from a "test" database that unit tests run against, so that the unit tests don't accidentally clobber your primary data source).
+> vi medinfo/db/Env.py
+
+# Python unit tests to verify things are functional
+# Database connection test
+> python medinfo/db/test/TestDBUtil.py
+
+# Basic stat table unit tests
+> python medinfo/cpoe/test/TestAssociationAnalysis.py
+> python medinfo/cpoe/test/TestItemRecommender.py
+
+#   Data Conversion unit test
+> python medinfo/dataconversion/test/TestSTRIDEOrderProcConversion.py
+> python medinfo/dataconversion/test/TestSTRIDEOrderMedConversion.py
+> python medinfo/dataconversion/test/TestSTRIDEOrderResultsConversion.py
+
+# Multiple Unit tests, auto discover
+> python -m unittest discover -v -p "Test*.py" -s medinfo/analysis
+
+===================================================================
+
+
+
+
+#  Setup Web Server (Apache HTTP, mod_wsgi, psycopg2 DB access).  
+# [[[Notes below are based on Python 2.7 + Apache 2.2. Several modifications are necessary for Python 3.x + Apache 2.4]]]
+
+
+# Install mod_wsgi using an install or Copy the version appropriate mod_wsgi.so file to the 
+#	apache modules directory and rename to simply mod_wsgi.so
+# 	This has a high probability of being very painful, because it requires 
+#	perfectly synced binaries / compiler versions for each component.
+#	Best if you have a package installer (e.g., yum) that does it automatically, otherwise
+#	you'll have to hunt down the right combination of pre-compiled components that are compatible.
+#   On Windows, you'll probably need precompiled binaries, so
+#      Figure out what compiler was used for your version of Python (including 32 vs. 64 bit) https://wiki.python.org/moin/WindowsCompilers
+#      Look for unofficial compiled windows binaries and select the one that matches your version of Python https://www.lfd.uci.edu/~gohlke/pythonlibs/#mod_wsgi
+#      Look for (older) versions of compiled Apache HTTP that the above references, probably off http://www.apachelounge.com/download/ 
+#      Install the mod_wsgi connector module (python3 -m pip install mod_wsgi.XXX.whl, then mod_wsgi-express module-config and copy the generated directives into the Aapche config script) https://pypi.org/project/mod-wsgi/
+# Setup the Apache webserver as per standard instructions, 
+#	Can just be a matter of downloading the respective package and running httpd -k install
+#	or using a package manager like yum / apt-get / brew, etc.,
+#	then add the extra configuration below to add the custom Python app
+# Sample Apache Config:
+sudo vi /etc/httpd/conf/httpd.conf
+
+	LoadModule wsgi_module modules/mod_wsgi.so 	# Or whatever was generated by mod_wsgi-express module-config
+	DocumentRoot "/home/jonc101/CDSS/medinfo/web"
+
+	# Medical Informatics web application configuration
+	WSGIPythonPath /home/jonc101/CDSS
+	Alias /medinfo/web/ "/home/jonc101/CDSS/medinfo/web/"
+	<Directory "/home/jonc101/CDSS/medinfo/web">
+		DirectoryIndex index.html index.htm
+
+		AllowOverride None
+		Allow from all
+
+		Order allow,deny
+	</Directory>
+	<Directory "/home/jonc101/CDSS/medinfo/web/cgibin">
+		# Handler setup for Python CGI scripts (works on files ending in .py)
+		Options +ExecCGI
+
+		# Basic CGI support
+		#AddHandler cgi-script py
+		#SetEnv PYTHONPATH /home/jonc101/CDSS
+
+		# mod_wsgi support.  # Need to define PythonPAth and other environment variables at level of user starting Apache process, or PythonPath in above WSGIPythonPath
+		AddHandler wsgi-script py
+		WSGIApplicationGroup %{GLOBAL}
+
+		AllowOverride None
+		Allow from all
+
+		Order allow,deny
+	</Directory>
+
+# Sample Apache Config for Apache 2.4 and Python 3.6 for Windows:
+
+    # CDSS HealthRex clinical recommender setup
+    LoadFile "c:/python36/python36.dll"
+    LoadModule wsgi_module "c:/python36/lib/site-packages/mod_wsgi/server/mod_wsgi.cp36-win_amd64.pyd"
+    WSGIPythonHome "c:/python36"
+    DocumentRoot "c:/CDSS/medinfo/web"
+
+    # Medical Informatics web application configuration
+    WSGIPythonPath "c:/CDSS"
+    Alias /medinfo/web/ "c:/CDSS/medinfo/web/"
+    <Directory "c:/CDSS/medinfo/web">
+        DirectoryIndex index.html index.htm
+
+        AllowOverride None
+        Require all granted
+    </Directory>
+    <Directory "c:/CDSS/medinfo/web/cgibin">
+        # Handler setup for Python CGI scripts (works on files ending in .py)
+        Options +ExecCGI
+
+        # Basic CGI support
+        #AddHandler cgi-script py
+        #SetEnv PYTHONPATH c:/CDSS
+
+        # mod_wsgi support.  # Need to define PythonPAth and other environment variables at level of user starting Apache process, or PythonPath in above WSGIPythonPath
+        AddHandler wsgi-script py
+        WSGIApplicationGroup %{GLOBAL}
+
+        AllowOverride None
+        Require all granted
+    </Directory>
+
+
+# Update medinfo/web/cgibin/Env.py, CGI_TEXT_RESPONSE depending on if using CGI or WSGI setup
+# Windows: May need to add Apache directive, so CGI knows how to run .py files
+	ScriptInterpreterSource Registry
+
+# Link with tips on setting up apache environment variable configuration if needed
+# Basically adding values to a configuration file like below, though CGI and WSGI should accept PYTHONPATH specification as above
+sudo vi /etc/sysconfig/httpd 
+	TEST_ENV_VAR=TestValue
+	export TEXT_ENV_VAR
+# If use "apachectl" rather than "service httpd" for control, can use starting user's environment as well
+# For Windows environment, add via My Computer > Advanced Settings > Environment Variables
+http://www.centos.org/docs/5/html/Deployment_Guide-en-US/s1-apache-startstop.html
+
+# Need read/execute permission on directory tree up to web files, so may need to
+chmod 755 /home/jonc101
+
+# Linux server may also need to change SELinux enforcement if running into "AH00132: file permissions deny server access"
+sudo setenforce 0
+https://wiki.apache.org/httpd/13PermissionDenied
+
+# General Tips
+- Review Apache error logs if running into server side issues: sudo tail /etc/httpd/logs/error_
+
+# Web server start / control
+sudo service httpd restart
+
+
+#  Test Pages. If these load properly, then your web setup should be in good shape
+#  	If failing with Internal Server Errors, then check the Apache /etc/httpd/logs/error.log for stderr messages
+http://localhost/medinfo/web/cgibin/admin/DebugWeb.py
+http://localhost/medinfo/web/cgibin/admin/DBUtilWeb.py
+http://localhost/medinfo/web/cgibin/cpoe/ItemRecommenderWeb.py
+
diff --git a/setup/windows.profile.bat b/setup/windows.profile.bat
new file mode 100644
index 00000000..34fe5d6f
--- /dev/null
+++ b/setup/windows.profile.bat
@@ -0,0 +1,38 @@
+@echo off
+rem Example profile script to setup environment variables need to run programs on a Windows machine.
+rem Analogous to a bash profile script on a Unix machine.
+rem Modify these to match your own computer.  
+rem Use regedit to create an entry pointing to this script to autorun on startup at
+rem		HKEY_CURRENT_USER\Software\Microsoft\Command Processor\AutoRun
+rem Enter "cmd /?" for additional info
+
+rem Application path.  Directory containing CHEM so modules can be imported by each other
+set MEDINFO_DIR=C:\HealthRex\CDSS
+set PYTHONPATH=%PYTHONPATH%;%CHEM_DIR%;%MEDINFO_DIR%
+
+rem Put Python on path for command-line convenience
+set PYTHONHOME=C:\Dev\Python36
+set PATH=%PYTHONHOME%;%PYTHONHOME%\Scripts;%PATH%
+
+rem Put R on path for command-line convenience
+set PATH=C:\Dev\R\R-3.6.1\bin;%PATH%
+
+rem PostgreSQL DLL's need to be accessible for clients to work
+set POSTGRESQL_HOME=C:\Dev\PostgreSQL\9.6
+set PATH=%POSTGRESQL_HOME%\bin;%PATH%
+
+rem Git Unix tools
+set PATH=%PATH%;"C:\Dev\Git\usr\bin"
+
+rem Google Cloud authentication
+set GOOGLE_APPLICATION_CREDENTIALS=C:\GoogleDrive\Tools\application_default_credentials.jonc101@stanford.edu.json
+
+rem Organize DIR listings
+set DIRCMD=/O:GEN /P
+
+rem Add date time to command line prompt
+set PROMPT=$D $T$_$P$G
+
+rem Starting Directory
+c:
+cd %MEDINFO_DIR%